From 47c1d5629373566df9d12fdc4ceb22f38b869482 Mon Sep 17 00:00:00 2001
From: Mike Dusenberry <dusenberrymw@gmail.com>
Date: Sun, 21 Jun 2015 18:25:36 -0700
Subject: [PATCH 0001/1454] [SPARK-7426] [MLLIB] [ML] Updated
 Attribute.fromStructField to allow any NumericType.

Updated `Attribute.fromStructField` to allow any `NumericType`, rather than just `DoubleType`, and added unit tests for a few of the other NumericTypes.

Author: Mike Dusenberry <dusenberrymw@gmail.com>

Closes #6540 from dusenberrymw/SPARK-7426_AttributeFactory.fromStructField_Should_Allow_NumericTypes and squashes the following commits:

87fecb3 [Mike Dusenberry] Updated Attribute.fromStructField to allow any NumericType, rather than just DoubleType, and added unit tests for a few of the other NumericTypes.
---
 .../scala/org/apache/spark/ml/attribute/attributes.scala     | 4 ++--
 .../scala/org/apache/spark/ml/attribute/AttributeSuite.scala | 5 +++++
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
index ce43a450daad0..e479f169021d8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.attribute
 import scala.annotation.varargs
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.types.{DoubleType, Metadata, MetadataBuilder, StructField}
+import org.apache.spark.sql.types.{DoubleType, NumericType, Metadata, MetadataBuilder, StructField}
 
 /**
  * :: DeveloperApi ::
@@ -127,7 +127,7 @@ private[attribute] trait AttributeFactory {
    * Creates an [[Attribute]] from a [[StructField]] instance.
    */
   def fromStructField(field: StructField): Attribute = {
-    require(field.dataType == DoubleType)
+    require(field.dataType.isInstanceOf[NumericType])
     val metadata = field.metadata
     val mlAttr = AttributeKeys.ML_ATTR
     if (metadata.contains(mlAttr)) {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala
index 72b575d022547..c5fd2f9d5a22a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala
@@ -215,5 +215,10 @@ class AttributeSuite extends SparkFunSuite {
     assert(Attribute.fromStructField(fldWithoutMeta) == UnresolvedAttribute)
     val fldWithMeta = new StructField("x", DoubleType, false, metadata)
     assert(Attribute.fromStructField(fldWithMeta).isNumeric)
+    // Attribute.fromStructField should accept any NumericType, not just DoubleType
+    val longFldWithMeta = new StructField("x", LongType, false, metadata)
+    assert(Attribute.fromStructField(longFldWithMeta).isNumeric)
+    val decimalFldWithMeta = new StructField("x", DecimalType(None), false, metadata)
+    assert(Attribute.fromStructField(decimalFldWithMeta).isNumeric)
   }
 }

From 0818fdec3733ec5c0a9caa48a9c0f2cd25f84d13 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 22 Jun 2015 10:03:57 -0700
Subject: [PATCH 0002/1454] [SPARK-8406] [SQL] Adding UUID to output file name
 to avoid accidental overwriting

This PR fixes a Parquet output file name collision bug which may cause data loss.  Changes made:

1.  Identify each write job issued by `InsertIntoHadoopFsRelation` with a UUID

    All concrete data sources which extend `HadoopFsRelation` (Parquet and ORC for now) must use this UUID to generate task output file path to avoid name collision.

2.  Make `TestHive` use a local mode `SparkContext` with 32 threads to increase parallelism

    The major reason for this is that, the original parallelism of 2 is too low to reproduce the data loss issue.  Also, higher concurrency may potentially caught more concurrency bugs during testing phase. (It did help us spotted SPARK-8501.)

3. `OrcSourceSuite` was updated to workaround SPARK-8501, which we detected along the way.

NOTE: This PR is made a little bit more complicated than expected because we hit two other bugs on the way and have to work them around. See [SPARK-8501] [1] and [SPARK-8513] [2].

[1]: https://github.com/liancheng/spark/tree/spark-8501
[2]: https://github.com/liancheng/spark/tree/spark-8513

----

Some background and a summary of offline discussion with yhuai about this issue for better understanding:

In 1.4.0, we added `HadoopFsRelation` to abstract partition support of all data sources that are based on Hadoop `FileSystem` interface.  Specifically, this makes partition discovery, partition pruning, and writing dynamic partitions for data sources much easier.

To support appending, the Parquet data source tries to find out the max part number of part-files in the destination directory (i.e., `<id>` in output file name `part-r-<id>.gz.parquet`) at the beginning of the write job.  In 1.3.0, this step happens on driver side before any files are written.  However, in 1.4.0, this is moved to task side.  Unfortunately, for tasks scheduled later, they may see wrong max part number generated of files newly written by other finished tasks within the same job.  This actually causes a race condition.  In most cases, this only causes nonconsecutive part numbers in output file names.  But when the DataFrame contains thousands of RDD partitions, it's likely that two tasks may choose the same part number, then one of them gets overwritten by the other.

Before `HadoopFsRelation`, Spark SQL already supports appending data to Hive tables.  From a user's perspective, these two look similar.  However, they differ a lot internally.  When data are inserted into Hive tables via Spark SQL, `InsertIntoHiveTable` simulates Hive's behaviors:

1.  Write data to a temporary location

2.  Move data in the temporary location to the final destination location using

    -   `Hive.loadTable()` for non-partitioned table
    -   `Hive.loadPartition()` for static partitions
    -   `Hive.loadDynamicPartitions()` for dynamic partitions

The important part is that, `Hive.copyFiles()` is invoked in step 2 to move the data to the destination directory (I found the name is kinda confusing since no "copying" occurs here, we are just moving and renaming stuff).  If a file in the source directory and another file in the destination directory happen to have the same name, say `part-r-00001.parquet`, the former is moved to the destination directory and renamed with a `_copy_N` postfix (`part-r-00001_copy_1.parquet`).  That's how Hive handles appending and avoids name collision between different write jobs.

Some alternatives fixes considered for this issue:

1.  Use a similar approach as Hive

    This approach is not preferred in Spark 1.4.0 mainly because file metadata operations in S3 tend to be slow, especially for tables with lots of file and/or partitions.  That's why `InsertIntoHadoopFsRelation` just inserts to destination directory directly, and is often used together with `DirectParquetOutputCommitter` to reduce latency when working with S3.  This means, we don't have the chance to do renaming, and must avoid name collision from the very beginning.

2.  Same as 1.3, just move max part number detection back to driver side

    This isn't doable because unlike 1.3, 1.4 also takes dynamic partitioning into account.  When inserting into dynamic partitions, we don't know which partition directories will be touched on driver side before issuing the write job.  Checking all partition directories is simply too expensive for tables with thousands of partitions.

3.  Add extra component to output file names to avoid name collision

    This seems to be the only reasonable solution for now.  To be more specific, we need a JOB level unique identifier to identify all write jobs issued by `InsertIntoHadoopFile`.  Notice that TASK level unique identifiers can NOT be used.  Because in this way a speculative task will write to a different output file from the original task.  If both tasks succeed, duplicate output will be left behind.  Currently, the ORC data source adds `System.currentTimeMillis` to the output file name for uniqueness.  This doesn't work because of exactly the same reason.

    That's why this PR adds a job level random UUID in `BaseWriterContainer` (which is used by `InsertIntoHadoopFsRelation` to issue write jobs).  The drawback is that record order is not preserved any more (output files of a later job may be listed before those of a earlier job).  However, we never promise to preserve record order when writing data, and Hive doesn't promise this either because the `_copy_N` trick breaks the order.

Author: Cheng Lian <lian@databricks.com>

Closes #6864 from liancheng/spark-8406 and squashes the following commits:

db7a46a [Cheng Lian] More comments
f5c1133 [Cheng Lian] Addresses comments
85c478e [Cheng Lian] Workarounds SPARK-8513
088c76c [Cheng Lian] Adds comment about SPARK-8501
99a5e7e [Cheng Lian] Uses job level UUID in SimpleTextRelation and avoids double task abortion
4088226 [Cheng Lian] Works around SPARK-8501
1d7d206 [Cheng Lian] Adds more logs
8966bbb [Cheng Lian] Fixes Scala style issue
18b7003 [Cheng Lian] Uses job level UUID to take speculative tasks into account
3806190 [Cheng Lian] Lets TestHive use all cores by default
748dbd7 [Cheng Lian] Adding UUID to output file name to avoid accidental overwriting
---
 .../apache/spark/sql/parquet/newParquet.scala | 43 ++-----------
 .../apache/spark/sql/sources/commands.scala   | 64 +++++++++++++++----
 .../spark/sql/hive/orc/OrcFileOperator.scala  |  9 +--
 .../spark/sql/hive/orc/OrcRelation.scala      |  5 +-
 .../apache/spark/sql/hive/test/TestHive.scala |  2 +-
 .../spark/sql/hive/orc/OrcSourceSuite.scala   | 28 ++++----
 .../sql/sources/SimpleTextRelation.scala      |  4 +-
 .../sql/sources/hadoopFsRelationSuites.scala  | 37 +++++++++--
 8 files changed, 120 insertions(+), 72 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index c9de45e0ddfbb..e049d54bf55dc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -42,7 +42,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{DataType, StructType}
 import org.apache.spark.util.{SerializableConfiguration, Utils}
-import org.apache.spark.{Logging, SparkException, Partition => SparkPartition}
+import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider {
   override def createRelation(
@@ -60,50 +60,21 @@ private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext
   extends OutputWriter {
 
   private val recordWriter: RecordWriter[Void, InternalRow] = {
-    val conf = context.getConfiguration
     val outputFormat = {
-      // When appending new Parquet files to an existing Parquet file directory, to avoid
-      // overwriting existing data files, we need to find out the max task ID encoded in these data
-      // file names.
-      // TODO Make this snippet a utility function for other data source developers
-      val maxExistingTaskId = {
-        // Note that `path` may point to a temporary location.  Here we retrieve the real
-        // destination path from the configuration
-        val outputPath = new Path(conf.get("spark.sql.sources.output.path"))
-        val fs = outputPath.getFileSystem(conf)
-
-        if (fs.exists(outputPath)) {
-          // Pattern used to match task ID in part file names, e.g.:
-          //
-          //   part-r-00001.gz.parquet
-          //          ^~~~~
-          val partFilePattern = """part-.-(\d{1,}).*""".r
-
-          fs.listStatus(outputPath).map(_.getPath.getName).map {
-            case partFilePattern(id) => id.toInt
-            case name if name.startsWith("_") => 0
-            case name if name.startsWith(".") => 0
-            case name => throw new AnalysisException(
-              s"Trying to write Parquet files to directory $outputPath, " +
-                s"but found items with illegal name '$name'.")
-          }.reduceOption(_ max _).getOrElse(0)
-        } else {
-          0
-        }
-      }
-
       new ParquetOutputFormat[InternalRow]() {
         // Here we override `getDefaultWorkFile` for two reasons:
         //
-        //  1. To allow appending.  We need to generate output file name based on the max available
-        //     task ID computed above.
+        //  1. To allow appending.  We need to generate unique output file names to avoid
+        //     overwriting existing files (either exist before the write job, or are just written
+        //     by other tasks within the same write job).
         //
         //  2. To allow dynamic partitioning.  Default `getDefaultWorkFile` uses
         //     `FileOutputCommitter.getWorkPath()`, which points to the base directory of all
         //     partitions in the case of dynamic partitioning.
         override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-          val split = context.getTaskAttemptID.getTaskID.getId + maxExistingTaskId + 1
-          new Path(path, f"part-r-$split%05d$extension")
+          val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
+          val split = context.getTaskAttemptID.getTaskID.getId
+          new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
         }
       }
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index c16bd9ae52c81..215e53c020849 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -17,14 +17,13 @@
 
 package org.apache.spark.sql.sources
 
-import java.util.Date
+import java.util.{Date, UUID}
 
 import scala.collection.mutable
 
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, FileOutputCommitter => MapReduceFileOutputCommitter}
-import org.apache.parquet.hadoop.util.ContextUtil
+import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
 
 import org.apache.spark._
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
@@ -59,6 +58,28 @@ private[sql] case class InsertIntoDataSource(
   }
 }
 
+/**
+ * A command for writing data to a [[HadoopFsRelation]].  Supports both overwriting and appending.
+ * Writing to dynamic partitions is also supported.  Each [[InsertIntoHadoopFsRelation]] issues a
+ * single write job, and owns a UUID that identifies this job.  Each concrete implementation of
+ * [[HadoopFsRelation]] should use this UUID together with task id to generate unique file path for
+ * each task output file.  This UUID is passed to executor side via a property named
+ * `spark.sql.sources.writeJobUUID`.
+ *
+ * Different writer containers, [[DefaultWriterContainer]] and [[DynamicPartitionWriterContainer]]
+ * are used to write to normal tables and tables with dynamic partitions.
+ *
+ * Basic work flow of this command is:
+ *
+ *   1. Driver side setup, including output committer initialization and data source specific
+ *      preparation work for the write job to be issued.
+ *   2. Issues a write job consists of one or more executor side tasks, each of which writes all
+ *      rows within an RDD partition.
+ *   3. If no exception is thrown in a task, commits that task, otherwise aborts that task;  If any
+ *      exception is thrown during task commitment, also aborts that task.
+ *   4. If all tasks are committed, commit the job, otherwise aborts the job;  If any exception is
+ *      thrown during job commitment, also aborts the job.
+ */
 private[sql] case class InsertIntoHadoopFsRelation(
     @transient relation: HadoopFsRelation,
     @transient query: LogicalPlan,
@@ -261,7 +282,14 @@ private[sql] abstract class BaseWriterContainer(
   with Logging
   with Serializable {
 
-  protected val serializableConf = new SerializableConfiguration(ContextUtil.getConfiguration(job))
+  protected val serializableConf = new SerializableConfiguration(job.getConfiguration)
+
+  // This UUID is used to avoid output file name collision between different appending write jobs.
+  // These jobs may belong to different SparkContext instances. Concrete data source implementations
+  // may use this UUID to generate unique file names (e.g., `part-r-<task-id>-<job-uuid>.parquet`).
+  //  The reason why this ID is used to identify a job rather than a single task output file is
+  // that, speculative tasks must generate the same output file name as the original task.
+  private val uniqueWriteJobId = UUID.randomUUID()
 
   // This is only used on driver side.
   @transient private val jobContext: JobContext = job
@@ -290,6 +318,11 @@ private[sql] abstract class BaseWriterContainer(
     setupIDs(0, 0, 0)
     setupConf()
 
+    // This UUID is sent to executor side together with the serialized `Configuration` object within
+    // the `Job` instance.  `OutputWriters` on the executor side should use this UUID to generate
+    // unique task output files.
+    job.getConfiguration.set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
+
     // Order of the following two lines is important.  For Hadoop 1, TaskAttemptContext constructor
     // clones the Configuration object passed in.  If we initialize the TaskAttemptContext first,
     // configurations made in prepareJobForWrite(job) are not populated into the TaskAttemptContext.
@@ -417,15 +450,16 @@ private[sql] class DefaultWriterContainer(
       assert(writer != null, "OutputWriter instance should have been initialized")
       writer.close()
       super.commitTask()
-    } catch {
-      case cause: Throwable =>
-        super.abortTask()
-        throw new RuntimeException("Failed to commit task", cause)
+    } catch { case cause: Throwable =>
+      // This exception will be handled in `InsertIntoHadoopFsRelation.insert$writeRows`, and will
+      // cause `abortTask()` to be invoked.
+      throw new RuntimeException("Failed to commit task", cause)
     }
   }
 
   override def abortTask(): Unit = {
     try {
+      // It's possible that the task fails before `writer` gets initialized
       if (writer != null) {
         writer.close()
       }
@@ -469,21 +503,25 @@ private[sql] class DynamicPartitionWriterContainer(
     })
   }
 
-  override def commitTask(): Unit = {
-    try {
+  private def clearOutputWriters(): Unit = {
+    if (outputWriters.nonEmpty) {
       outputWriters.values.foreach(_.close())
       outputWriters.clear()
+    }
+  }
+
+  override def commitTask(): Unit = {
+    try {
+      clearOutputWriters()
       super.commitTask()
     } catch { case cause: Throwable =>
-      super.abortTask()
       throw new RuntimeException("Failed to commit task", cause)
     }
   }
 
   override def abortTask(): Unit = {
     try {
-      outputWriters.values.foreach(_.close())
-      outputWriters.clear()
+      clearOutputWriters()
     } finally {
       super.abortTask()
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala
index 1e51173a19882..e3ab9442b4821 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala
@@ -27,13 +27,13 @@ import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.hive.HiveMetastoreTypes
 import org.apache.spark.sql.types.StructType
 
-private[orc] object OrcFileOperator extends Logging{
+private[orc] object OrcFileOperator extends Logging {
   def getFileReader(pathStr: String, config: Option[Configuration] = None ): Reader = {
     val conf = config.getOrElse(new Configuration)
     val fspath = new Path(pathStr)
     val fs = fspath.getFileSystem(conf)
     val orcFiles = listOrcFiles(pathStr, conf)
-
+    logDebug(s"Creating ORC Reader from ${orcFiles.head}")
     // TODO Need to consider all files when schema evolution is taken into account.
     OrcFile.createReader(fs, orcFiles.head)
   }
@@ -42,6 +42,7 @@ private[orc] object OrcFileOperator extends Logging{
     val reader = getFileReader(path, conf)
     val readerInspector = reader.getObjectInspector.asInstanceOf[StructObjectInspector]
     val schema = readerInspector.getTypeName
+    logDebug(s"Reading schema from file $path, got Hive schema string: $schema")
     HiveMetastoreTypes.toDataType(schema).asInstanceOf[StructType]
   }
 
@@ -52,14 +53,14 @@ private[orc] object OrcFileOperator extends Logging{
   def listOrcFiles(pathStr: String, conf: Configuration): Seq[Path] = {
     val origPath = new Path(pathStr)
     val fs = origPath.getFileSystem(conf)
-    val path = origPath.makeQualified(fs)
+    val path = origPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
     val paths = SparkHadoopUtil.get.listLeafStatuses(fs, origPath)
       .filterNot(_.isDir)
       .map(_.getPath)
       .filterNot(_.getName.startsWith("_"))
       .filterNot(_.getName.startsWith("."))
 
-    if (paths == null || paths.size == 0) {
+    if (paths == null || paths.isEmpty) {
       throw new IllegalArgumentException(
         s"orcFileOperator: path $path does not have valid orc files matching the pattern")
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index dbce39f21d271..705f48f1cd9f0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -31,6 +31,7 @@ import org.apache.hadoop.mapred.{InputFormat => MapRedInputFormat, JobConf, Reco
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
+import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.{HadoopRDD, RDD}
@@ -39,7 +40,6 @@ import org.apache.spark.sql.hive.{HiveContext, HiveInspectors, HiveMetastoreType
 import org.apache.spark.sql.sources.{Filter, _}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{Row, SQLContext}
-import org.apache.spark.{Logging}
 import org.apache.spark.util.SerializableConfiguration
 
 /* Implicit conversions */
@@ -105,8 +105,9 @@ private[orc] class OrcOutputWriter(
     recordWriterInstantiated = true
 
     val conf = context.getConfiguration
+    val uniqueWriteJobId = conf.get("spark.sql.sources.writeJobUUID")
     val partition = context.getTaskAttemptID.getTaskID.getId
-    val filename = f"part-r-$partition%05d-${System.currentTimeMillis}%015d.orc"
+    val filename = f"part-r-$partition%05d-$uniqueWriteJobId.orc"
 
     new OrcOutputFormat().getRecordWriter(
       new Path(path, filename).getFileSystem(conf),
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index f901bd8171508..ea325cc93cb85 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -49,7 +49,7 @@ import scala.collection.JavaConversions._
 object TestHive
   extends TestHiveContext(
     new SparkContext(
-      System.getProperty("spark.sql.test.master", "local[2]"),
+      System.getProperty("spark.sql.test.master", "local[32]"),
       "TestSQLContext",
       new SparkConf()
         .set("spark.sql.test", "")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
index 82e08caf46457..a0cdd0db42d65 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
@@ -43,8 +43,14 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
     orcTableDir.mkdir()
     import org.apache.spark.sql.hive.test.TestHive.implicits._
 
+    // Originally we were using a 10-row RDD for testing.  However, when default parallelism is
+    // greater than 10 (e.g., running on a node with 32 cores), this RDD contains empty partitions,
+    // which result in empty ORC files.  Unfortunately, ORC doesn't handle empty files properly and
+    // causes build failure on Jenkins, which happens to have 32 cores. Please refer to SPARK-8501
+    // for more details.  To workaround this issue before fixing SPARK-8501, we simply increase row
+    // number in this RDD to avoid empty partitions.
     sparkContext
-      .makeRDD(1 to 10)
+      .makeRDD(1 to 100)
       .map(i => OrcData(i, s"part-$i"))
       .toDF()
       .registerTempTable(s"orc_temp_table")
@@ -70,35 +76,35 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
   }
 
   test("create temporary orc table") {
-    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_source"), Row(10))
+    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_source"), Row(100))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source"),
-      (1 to 10).map(i => Row(i, s"part-$i")))
+      (1 to 100).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source where intField > 5"),
-      (6 to 10).map(i => Row(i, s"part-$i")))
+      (6 to 100).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT COUNT(intField), stringField FROM normal_orc_source GROUP BY stringField"),
-      (1 to 10).map(i => Row(1, s"part-$i")))
+      (1 to 100).map(i => Row(1, s"part-$i")))
   }
 
   test("create temporary orc table as") {
-    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_as_source"), Row(10))
+    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_as_source"), Row(100))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source"),
-      (1 to 10).map(i => Row(i, s"part-$i")))
+      (1 to 100).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source WHERE intField > 5"),
-      (6 to 10).map(i => Row(i, s"part-$i")))
+      (6 to 100).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT COUNT(intField), stringField FROM normal_orc_source GROUP BY stringField"),
-      (1 to 10).map(i => Row(1, s"part-$i")))
+      (1 to 100).map(i => Row(1, s"part-$i")))
   }
 
   test("appending insert") {
@@ -106,7 +112,7 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source"),
-      (1 to 5).map(i => Row(i, s"part-$i")) ++ (6 to 10).flatMap { i =>
+      (1 to 5).map(i => Row(i, s"part-$i")) ++ (6 to 100).flatMap { i =>
         Seq.fill(2)(Row(i, s"part-$i"))
       })
   }
@@ -119,7 +125,7 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_as_source"),
-      (6 to 10).map(i => Row(i, s"part-$i")))
+      (6 to 100).map(i => Row(i, s"part-$i")))
   }
 }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
index 0f959b3d0b86d..5d7cd16c129cd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
@@ -53,9 +53,10 @@ class AppendingTextOutputFormat(outputFile: Path) extends TextOutputFormat[NullW
   numberFormat.setGroupingUsed(false)
 
   override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
+    val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
     val split = context.getTaskAttemptID.getTaskID.getId
     val name = FileOutputFormat.getOutputName(context)
-    new Path(outputFile, s"$name-${numberFormat.format(split)}-${UUID.randomUUID()}")
+    new Path(outputFile, s"$name-${numberFormat.format(split)}-$uniqueWriteJobId")
   }
 }
 
@@ -156,6 +157,7 @@ class CommitFailureTestRelation(
         context: TaskAttemptContext): OutputWriter = {
       new SimpleTextOutputWriter(path, context) {
         override def close(): Unit = {
+          super.close()
           sys.error("Intentional task commitment failure for testing purpose.")
         }
       }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 76469d7a3d6a5..e0d8277a8ed3f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -35,7 +35,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
   import sqlContext.sql
   import sqlContext.implicits._
 
-  val dataSourceName = classOf[SimpleTextSource].getCanonicalName
+  val dataSourceName: String
 
   val dataSchema =
     StructType(
@@ -470,6 +470,33 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       checkAnswer(sqlContext.table("t"), df.select('b, 'c, 'a).collect())
     }
   }
+
+  // NOTE: This test suite is not super deterministic.  On nodes with only relatively few cores
+  // (4 or even 1), it's hard to reproduce the data loss issue.  But on nodes with for example 8 or
+  // more cores, the issue can be reproduced steadily.  Fortunately our Jenkins builder meets this
+  // requirement.  We probably want to move this test case to spark-integration-tests or spark-perf
+  // later.
+  test("SPARK-8406: Avoids name collision while writing Parquet files") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      sqlContext
+        .range(10000)
+        .repartition(250)
+        .write
+        .mode(SaveMode.Overwrite)
+        .format(dataSourceName)
+        .save(path)
+
+      assertResult(10000) {
+        sqlContext
+          .read
+          .format(dataSourceName)
+          .option("dataSchema", StructType(StructField("id", LongType) :: Nil).json)
+          .load(path)
+          .count()
+      }
+    }
+  }
 }
 
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
@@ -502,15 +529,17 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
 }
 
 class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils {
-  import TestHive.implicits._
-
   override val sqlContext = TestHive
 
+  // When committing a task, `CommitFailureTestSource` throws an exception for testing purpose.
   val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName
 
   test("SPARK-7684: commitTask() failure should fallback to abortTask()") {
     withTempPath { file =>
-      val df = (1 to 3).map(i => i -> s"val_$i").toDF("a", "b")
+      // Here we coalesce partition number to 1 to ensure that only a single task is issued.  This
+      // prevents race condition happened when FileOutputCommitter tries to remove the `_temporary`
+      // directory while committing/aborting the job.  See SPARK-8513 for more details.
+      val df = sqlContext.range(0, 10).coalesce(1)
       intercept[SparkException] {
         df.write.format(dataSourceName).save(file.getCanonicalPath)
       }

From 42a1f716fa35533507784be5e9117a984a03e62d Mon Sep 17 00:00:00 2001
From: Stefano Parmesan <s.parmesan@gmail.com>
Date: Mon, 22 Jun 2015 11:43:10 -0700
Subject: [PATCH 0003/1454] [SPARK-8429] [EC2] Add ability to set additional
 tags

Add the `--additional-tags` parameter that allows to set additional tags to all the created instances (masters and slaves).

The user can specify multiple tags by separating them with a comma (`,`), while each tag name and value should be separated by a colon (`:`); for example, `Task:MySparkProject,Env:production` would add two tags, `Task` and `Env`, with the given values.

Author: Stefano Parmesan <s.parmesan@gmail.com>

Closes #6857 from armisael/patch-1 and squashes the following commits:

c5ac92c [Stefano Parmesan] python style (pep8)
8e614f1 [Stefano Parmesan] Set multiple tags in a single request
bfc56af [Stefano Parmesan] Address SPARK-7900 by inceasing sleep time
daf8615 [Stefano Parmesan] Add ability to set additional tags
---
 ec2/spark_ec2.py | 28 ++++++++++++++++++++--------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 56087499464e0..103735685485b 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -289,6 +289,10 @@ def parse_args():
     parser.add_option(
         "--additional-security-group", type="string", default="",
         help="Additional security group to place the machines in")
+    parser.add_option(
+        "--additional-tags", type="string", default="",
+        help="Additional tags to set on the machines; tags are comma-separated, while name and " +
+             "value are colon separated; ex: \"Task:MySparkProject,Env:production\"")
     parser.add_option(
         "--copy-aws-credentials", action="store_true", default=False,
         help="Add AWS credentials to hadoop configuration to allow Spark to access S3")
@@ -684,16 +688,24 @@ def launch_cluster(conn, opts, cluster_name):
 
     # This wait time corresponds to SPARK-4983
     print("Waiting for AWS to propagate instance metadata...")
-    time.sleep(5)
-    # Give the instances descriptive names
+    time.sleep(15)
+
+    # Give the instances descriptive names and set additional tags
+    additional_tags = {}
+    if opts.additional_tags.strip():
+        additional_tags = dict(
+            map(str.strip, tag.split(':', 1)) for tag in opts.additional_tags.split(',')
+        )
+
     for master in master_nodes:
-        master.add_tag(
-            key='Name',
-            value='{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id))
+        master.add_tags(
+            dict(additional_tags, Name='{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id))
+        )
+
     for slave in slave_nodes:
-        slave.add_tag(
-            key='Name',
-            value='{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id))
+        slave.add_tags(
+            dict(additional_tags, Name='{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id))
+        )
 
     # Return all the instances
     return (master_nodes, slave_nodes)

From ba8a4537fee7d85f968cccf8d1c607731daae307 Mon Sep 17 00:00:00 2001
From: Pradeep Chhetri <pradeep.chhetri89@gmail.com>
Date: Mon, 22 Jun 2015 11:45:31 -0700
Subject: [PATCH 0004/1454] [SPARK-8482] Added M4 instances to the list.

AWS recently added M4 instances (https://aws.amazon.com/blogs/aws/the-new-m4-instance-type-bonus-price-reduction-on-m3-c4/).

Author: Pradeep Chhetri <pradeep.chhetri89@gmail.com>

Closes #6899 from pradeepchhetri/master and squashes the following commits:

4f4ea79 [Pradeep Chhetri] Added t2.large instance
3d2bb6c [Pradeep Chhetri] Added M4 instances to the list
---
 ec2/spark_ec2.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 103735685485b..63e2c79669763 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -362,7 +362,7 @@ def get_validate_spark_version(version, repo):
 
 
 # Source: http://aws.amazon.com/amazon-linux-ami/instance-type-matrix/
-# Last Updated: 2015-05-08
+# Last Updated: 2015-06-19
 # For easy maintainability, please keep this manually-inputted dictionary sorted by key.
 EC2_INSTANCE_TYPES = {
     "c1.medium":   "pvm",
@@ -404,6 +404,11 @@ def get_validate_spark_version(version, repo):
     "m3.large":    "hvm",
     "m3.xlarge":   "hvm",
     "m3.2xlarge":  "hvm",
+    "m4.large":    "hvm",
+    "m4.xlarge":   "hvm",
+    "m4.2xlarge":  "hvm",
+    "m4.4xlarge":  "hvm",
+    "m4.10xlarge": "hvm",
     "r3.large":    "hvm",
     "r3.xlarge":   "hvm",
     "r3.2xlarge":  "hvm",
@@ -413,6 +418,7 @@ def get_validate_spark_version(version, repo):
     "t2.micro":    "hvm",
     "t2.small":    "hvm",
     "t2.medium":   "hvm",
+    "t2.large":    "hvm",
 }
 
 
@@ -923,7 +929,7 @@ def wait_for_cluster_state(conn, opts, cluster_instances, cluster_state):
 # Get number of local disks available for a given EC2 instance type.
 def get_num_disks(instance_type):
     # Source: http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html
-    # Last Updated: 2015-05-08
+    # Last Updated: 2015-06-19
     # For easy maintainability, please keep this manually-inputted dictionary sorted by key.
     disks_by_instance = {
         "c1.medium":   1,
@@ -965,6 +971,11 @@ def get_num_disks(instance_type):
         "m3.large":    1,
         "m3.xlarge":   2,
         "m3.2xlarge":  2,
+        "m4.large":    0,
+        "m4.xlarge":   0,
+        "m4.2xlarge":  0,
+        "m4.4xlarge":  0,
+        "m4.10xlarge": 0,
         "r3.large":    1,
         "r3.xlarge":   1,
         "r3.2xlarge":  1,
@@ -974,6 +985,7 @@ def get_num_disks(instance_type):
         "t2.micro":    0,
         "t2.small":    0,
         "t2.medium":   0,
+        "t2.large":    0,
     }
     if instance_type in disks_by_instance:
         return disks_by_instance[instance_type]

From 5d89d9f00ba4d6d0767a4c4964d3af324bf6f14b Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 22 Jun 2015 11:53:11 -0700
Subject: [PATCH 0005/1454] [SPARK-8511] [PYSPARK] Modify a test to remove a
 saved model in `regression.py`

[[SPARK-8511] Modify a test to remove a saved model in `regression.py` - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8511)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6926 from yu-iskw/SPARK-8511 and squashes the following commits:

7cd0948 [Yu ISHIKAWA] Use `shutil.rmtree()` to temporary directories for saving model testings, instead of `os.removedirs()`
4a01c9e [Yu ISHIKAWA] [SPARK-8511][pyspark] Modify a test to remove a saved model in `regression.py`
---
 python/pyspark/mllib/classification.py |  9 ++++++---
 python/pyspark/mllib/clustering.py     |  3 ++-
 python/pyspark/mllib/recommendation.py |  3 ++-
 python/pyspark/mllib/regression.py     | 14 +++++++++-----
 python/pyspark/mllib/tests.py          |  3 ++-
 5 files changed, 21 insertions(+), 11 deletions(-)

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 42e41397bf4bc..758accf4b41eb 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -135,8 +135,9 @@ class LogisticRegressionModel(LinearClassificationModel):
     1
     >>> sameModel.predict(SparseVector(2, {0: 1.0}))
     0
+    >>> from shutil import rmtree
     >>> try:
-    ...    os.removedirs(path)
+    ...    rmtree(path)
     ... except:
     ...    pass
     >>> multi_class_data = [
@@ -387,8 +388,9 @@ class SVMModel(LinearClassificationModel):
     1
     >>> sameModel.predict(SparseVector(2, {0: -1.0}))
     0
+    >>> from shutil import rmtree
     >>> try:
-    ...    os.removedirs(path)
+    ...    rmtree(path)
     ... except:
     ...    pass
     """
@@ -515,8 +517,9 @@ class NaiveBayesModel(Saveable, Loader):
     >>> sameModel = NaiveBayesModel.load(sc, path)
     >>> sameModel.predict(SparseVector(2, {0: 1.0})) == model.predict(SparseVector(2, {0: 1.0}))
     True
+    >>> from shutil import rmtree
     >>> try:
-    ...     os.removedirs(path)
+    ...     rmtree(path)
     ... except OSError:
     ...     pass
     """
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index c38229864d3b4..e6ef72942ce77 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -79,8 +79,9 @@ class KMeansModel(Saveable, Loader):
     >>> sameModel = KMeansModel.load(sc, path)
     >>> sameModel.predict(sparse_data[0]) == model.predict(sparse_data[0])
     True
+    >>> from shutil import rmtree
     >>> try:
-    ...     os.removedirs(path)
+    ...     rmtree(path)
     ... except OSError:
     ...     pass
     """
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 9c4647ddfdcfd..506ca2151cce7 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -106,8 +106,9 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
     0.4...
     >>> sameModel.predictAll(testset).collect()
     [Rating(...
+    >>> from shutil import rmtree
     >>> try:
-    ...     os.removedirs(path)
+    ...     rmtree(path)
     ... except OSError:
     ...     pass
     """
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 0c4d7d3bbee02..5ddbbee4babdd 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -133,10 +133,11 @@ class LinearRegressionModel(LinearRegressionModelBase):
     True
     >>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> from shutil import rmtree
     >>> try:
-    ...    os.removedirs(path)
+    ...     rmtree(path)
     ... except:
-    ...    pass
+    ...     pass
     >>> data = [
     ...     LabeledPoint(0.0, SparseVector(1, {0: 0.0})),
     ...     LabeledPoint(1.0, SparseVector(1, {0: 1.0})),
@@ -275,8 +276,9 @@ class LassoModel(LinearRegressionModelBase):
     True
     >>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> from shutil import rmtree
     >>> try:
-    ...    os.removedirs(path)
+    ...    rmtree(path)
     ... except:
     ...    pass
     >>> data = [
@@ -389,8 +391,9 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     True
     >>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> from shutil import rmtree
     >>> try:
-    ...    os.removedirs(path)
+    ...    rmtree(path)
     ... except:
     ...    pass
     >>> data = [
@@ -500,8 +503,9 @@ class IsotonicRegressionModel(Saveable, Loader):
     2.0
     >>> sameModel.predict(5)
     16.5
+    >>> from shutil import rmtree
     >>> try:
-    ...     os.removedirs(path)
+    ...     rmtree(path)
     ... except OSError:
     ...     pass
     """
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 744dc112d9209..b13159e29d2aa 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -24,6 +24,7 @@
 import tempfile
 import array as pyarray
 from time import time, sleep
+from shutil import rmtree
 
 from numpy import array, array_equal, zeros, inf, all, random
 from numpy import sum as array_sum
@@ -398,7 +399,7 @@ def test_classification(self):
         self.assertEqual(same_gbt_model.toDebugString(), gbt_model.toDebugString())
 
         try:
-            os.removedirs(temp_dir)
+            rmtree(temp_dir)
         except OSError:
             pass
 

From da7bbb9435dae9a3bedad578599d96ea858f349e Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 22 Jun 2015 12:13:00 -0700
Subject: [PATCH 0006/1454] [SPARK-8104] [SQL] auto alias expressions in
 analyzer

Currently we auto alias expression in parser. However, during parser phase we don't have enough information to do the right alias. For example, Generator that has more than 1 kind of element need MultiAlias, ExtractValue don't need Alias if it's in middle of a ExtractValue chain.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #6647 from cloud-fan/alias and squashes the following commits:

552eba4 [Wenchen Fan] fix python
5b5786d [Wenchen Fan] fix agg
73a90cb [Wenchen Fan] fix case-preserve of ExtractValue
4cfd23c [Wenchen Fan] fix order by
d18f401 [Wenchen Fan] refine
9f07359 [Wenchen Fan] address comments
39c1aef [Wenchen Fan] small fix
33640ec [Wenchen Fan] auto alias expressions in analyzer
---
 python/pyspark/sql/context.py                 |  9 ++-
 .../apache/spark/sql/catalyst/SqlParser.scala | 11 +--
 .../sql/catalyst/analysis/Analyzer.scala      | 77 ++++++++++++-------
 .../sql/catalyst/analysis/CheckAnalysis.scala |  9 +--
 .../sql/catalyst/analysis/unresolved.scala    | 20 ++++-
 .../catalyst/expressions/ExtractValue.scala   | 36 +++++----
 .../sql/catalyst/planning/patterns.scala      |  6 +-
 .../catalyst/plans/logical/LogicalPlan.scala  | 11 ++-
 .../plans/logical/basicOperators.scala        | 20 ++++-
 .../scala/org/apache/spark/sql/Column.scala   |  1 -
 .../org/apache/spark/sql/DataFrame.scala      |  6 +-
 .../org/apache/spark/sql/GroupedData.scala    | 43 +++++------
 .../spark/sql/execution/pythonUdfs.scala      |  2 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  6 +-
 .../scala/org/apache/spark/sql/TestData.scala |  1 -
 .../org/apache/spark/sql/hive/HiveQl.scala    |  9 +--
 16 files changed, 150 insertions(+), 117 deletions(-)

diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 599c9ac5794a2..dc239226e6d3c 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -86,7 +86,8 @@ def __init__(self, sparkContext, sqlContext=None):
         >>> df.registerTempTable("allTypes")
         >>> sqlContext.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
         ...            'from allTypes where b and i > 0').collect()
-        [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
+        [Row(_c0=2, _c1=2.0, _c2=False, _c3=2, _c4=0, \
+            time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
         >>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect()
         [(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
         """
@@ -176,17 +177,17 @@ def registerFunction(self, name, f, returnType=StringType()):
 
         >>> sqlContext.registerFunction("stringLengthString", lambda x: len(x))
         >>> sqlContext.sql("SELECT stringLengthString('test')").collect()
-        [Row(c0=u'4')]
+        [Row(_c0=u'4')]
 
         >>> from pyspark.sql.types import IntegerType
         >>> sqlContext.registerFunction("stringLengthInt", lambda x: len(x), IntegerType())
         >>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
-        [Row(c0=4)]
+        [Row(_c0=4)]
 
         >>> from pyspark.sql.types import IntegerType
         >>> sqlContext.udf.register("stringLengthInt", lambda x: len(x), IntegerType())
         >>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
-        [Row(c0=4)]
+        [Row(_c0=4)]
         """
         func = lambda _, it: map(lambda x: f(*x), it)
         ser = AutoBatchedSerializer(PickleSerializer())
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index da3a717f90058..79f526e823cd4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -99,13 +99,6 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
   protected val WHERE = Keyword("WHERE")
   protected val WITH = Keyword("WITH")
 
-  protected def assignAliases(exprs: Seq[Expression]): Seq[NamedExpression] = {
-    exprs.zipWithIndex.map {
-      case (ne: NamedExpression, _) => ne
-      case (e, i) => Alias(e, s"c$i")()
-    }
-  }
-
   protected lazy val start: Parser[LogicalPlan] =
     start1 | insert | cte
 
@@ -130,8 +123,8 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
           val base = r.getOrElse(OneRowRelation)
           val withFilter = f.map(Filter(_, base)).getOrElse(base)
           val withProjection = g
-            .map(Aggregate(_, assignAliases(p), withFilter))
-            .getOrElse(Project(assignAliases(p), withFilter))
+            .map(Aggregate(_, p.map(UnresolvedAlias(_)), withFilter))
+            .getOrElse(Project(p.map(UnresolvedAlias(_)), withFilter))
           val withDistinct = d.map(_ => Distinct(withProjection)).getOrElse(withProjection)
           val withHaving = h.map(Filter(_, withDistinct)).getOrElse(withDistinct)
           val withOrder = o.map(_(withHaving)).getOrElse(withHaving)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 21b05760256b4..6311784422a91 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.{SimpleCatalystConf, CatalystConf}
 import org.apache.spark.sql.catalyst.expressions._
@@ -74,10 +72,10 @@ class Analyzer(
       ResolveSortReferences ::
       ResolveGenerate ::
       ResolveFunctions ::
+      ResolveAliases ::
       ExtractWindowExpressions ::
       GlobalAggregates ::
       UnresolvedHavingClauseAttributes ::
-      TrimGroupingAliases ::
       typeCoercionRules ++
       extendedResolutionRules : _*)
   )
@@ -132,12 +130,38 @@ class Analyzer(
   }
 
   /**
-   * Removes no-op Alias expressions from the plan.
+   * Replaces [[UnresolvedAlias]]s with concrete aliases.
    */
-  object TrimGroupingAliases extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      case Aggregate(groups, aggs, child) =>
-        Aggregate(groups.map(_.transform { case Alias(c, _) => c }), aggs, child)
+  object ResolveAliases extends Rule[LogicalPlan] {
+    private def assignAliases(exprs: Seq[NamedExpression]) = {
+      // The `UnresolvedAlias`s will appear only at root of a expression tree, we don't need
+      // to transform down the whole tree.
+      exprs.zipWithIndex.map {
+        case (u @ UnresolvedAlias(child), i) =>
+          child match {
+            case _: UnresolvedAttribute => u
+            case ne: NamedExpression => ne
+            case ev: ExtractValueWithStruct => Alias(ev, ev.field.name)()
+            case g: Generator if g.resolved && g.elementTypes.size > 1 => MultiAlias(g, Nil)
+            case e if !e.resolved => u
+            case other => Alias(other, s"_c$i")()
+          }
+        case (other, _) => other
+      }
+    }
+
+    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+      case Aggregate(groups, aggs, child)
+        if child.resolved && aggs.exists(_.isInstanceOf[UnresolvedAlias]) =>
+        Aggregate(groups, assignAliases(aggs), child)
+
+      case g: GroupingAnalytics
+        if g.child.resolved && g.aggregations.exists(_.isInstanceOf[UnresolvedAlias]) =>
+        g.withNewAggs(assignAliases(g.aggregations))
+
+      case Project(projectList, child)
+        if child.resolved && projectList.exists(_.isInstanceOf[UnresolvedAlias]) =>
+        Project(assignAliases(projectList), child)
     }
   }
 
@@ -228,7 +252,7 @@ class Analyzer(
     }
 
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      case i@InsertIntoTable(u: UnresolvedRelation, _, _, _, _) =>
+      case i @ InsertIntoTable(u: UnresolvedRelation, _, _, _, _) =>
         i.copy(table = EliminateSubQueries(getTable(u)))
       case u: UnresolvedRelation =>
         getTable(u)
@@ -248,24 +272,24 @@ class Analyzer(
         Project(
           projectList.flatMap {
             case s: Star => s.expand(child.output, resolver)
-            case Alias(f @ UnresolvedFunction(_, args), name) if containsStar(args) =>
+            case UnresolvedAlias(f @ UnresolvedFunction(_, args)) if containsStar(args) =>
               val expandedArgs = args.flatMap {
                 case s: Star => s.expand(child.output, resolver)
                 case o => o :: Nil
               }
-              Alias(child = f.copy(children = expandedArgs), name)() :: Nil
-            case Alias(c @ CreateArray(args), name) if containsStar(args) =>
+              UnresolvedAlias(child = f.copy(children = expandedArgs)) :: Nil
+            case UnresolvedAlias(c @ CreateArray(args)) if containsStar(args) =>
               val expandedArgs = args.flatMap {
                 case s: Star => s.expand(child.output, resolver)
                 case o => o :: Nil
               }
-              Alias(c.copy(children = expandedArgs), name)() :: Nil
-            case Alias(c @ CreateStruct(args), name) if containsStar(args) =>
+              UnresolvedAlias(c.copy(children = expandedArgs)) :: Nil
+            case UnresolvedAlias(c @ CreateStruct(args)) if containsStar(args) =>
               val expandedArgs = args.flatMap {
                 case s: Star => s.expand(child.output, resolver)
                 case o => o :: Nil
               }
-              Alias(c.copy(children = expandedArgs), name)() :: Nil
+              UnresolvedAlias(c.copy(children = expandedArgs)) :: Nil
             case o => o :: Nil
           },
           child)
@@ -353,7 +377,9 @@ class Analyzer(
           case u @ UnresolvedAttribute(nameParts) =>
             // Leave unchanged if resolution fails.  Hopefully will be resolved next round.
             val result =
-              withPosition(u) { q.resolveChildren(nameParts, resolver).getOrElse(u) }
+              withPosition(u) {
+                q.resolveChildren(nameParts, resolver).map(trimUnresolvedAlias).getOrElse(u)
+              }
             logDebug(s"Resolving $u to $result")
             result
           case UnresolvedExtractValue(child, fieldExpr) if child.resolved =>
@@ -379,6 +405,11 @@ class Analyzer(
       exprs.exists(_.collect { case _: Star => true }.nonEmpty)
   }
 
+  private def trimUnresolvedAlias(ne: NamedExpression) = ne match {
+    case UnresolvedAlias(child) => child
+    case other => other
+  }
+
   private def resolveSortOrders(ordering: Seq[SortOrder], plan: LogicalPlan, throws: Boolean) = {
     ordering.map { order =>
       // Resolve SortOrder in one round.
@@ -388,7 +419,7 @@ class Analyzer(
       try {
         val newOrder = order transformUp {
           case u @ UnresolvedAttribute(nameParts) =>
-            plan.resolve(nameParts, resolver).getOrElse(u)
+            plan.resolve(nameParts, resolver).map(trimUnresolvedAlias).getOrElse(u)
           case UnresolvedExtractValue(child, fieldName) if child.resolved =>
             ExtractValue(child, fieldName, resolver)
         }
@@ -586,18 +617,6 @@ class Analyzer(
     /** Extracts a [[Generator]] expression and any names assigned by aliases to their output. */
     private object AliasedGenerator {
       def unapply(e: Expression): Option[(Generator, Seq[String])] = e match {
-        case Alias(g: Generator, name)
-          if g.resolved &&
-             g.elementTypes.size > 1 &&
-             java.util.regex.Pattern.matches("_c[0-9]+", name) => {
-          // Assume the default name given by parser is "_c[0-9]+",
-          // TODO in long term, move the naming logic from Parser to Analyzer.
-          // In projection, Parser gave default name for TGF as does for normal UDF,
-          // but the TGF probably have multiple output columns/names.
-          //    e.g. SELECT explode(map(key, value)) FROM src;
-          // Let's simply ignore the default given name for this case.
-          Some((g, Nil))
-        }
         case Alias(g: Generator, name) if g.resolved && g.elementTypes.size > 1 =>
           // If not given the default names, and the TGF with multiple output columns
           failAnalysis(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 7fabd2bfc80ab..c5a1437be6d05 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -95,14 +95,7 @@ trait CheckAnalysis {
               case e => e.children.foreach(checkValidAggregateExpression)
             }
 
-            val cleaned = aggregateExprs.map(_.transform {
-              // Should trim aliases around `GetField`s. These aliases are introduced while
-              // resolving struct field accesses, because `GetField` is not a `NamedExpression`.
-              // (Should we just turn `GetField` into a `NamedExpression`?)
-              case Alias(g, _) => g
-            })
-
-            cleaned.foreach(checkValidAggregateExpression)
+            aggregateExprs.foreach(checkValidAggregateExpression)
 
           case _ => // Fallbacks to the following checks
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index c9d91425788a8..ae3adbab05108 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.{errors, trees}
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
@@ -206,3 +205,22 @@ case class UnresolvedExtractValue(child: Expression, extraction: Expression)
 
   override def toString: String = s"$child[$extraction]"
 }
+
+/**
+ * Holds the expression that has yet to be aliased.
+ */
+case class UnresolvedAlias(child: Expression) extends NamedExpression
+  with trees.UnaryNode[Expression] {
+
+  override def toAttribute: Attribute = throw new UnresolvedException(this, "toAttribute")
+  override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers")
+  override def exprId: ExprId = throw new UnresolvedException(this, "exprId")
+  override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
+  override def dataType: DataType = throw new UnresolvedException(this, "dataType")
+  override def name: String = throw new UnresolvedException(this, "name")
+
+  override lazy val resolved = false
+
+  override def eval(input: InternalRow = null): Any =
+    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
index 4aaabff15b6ee..013027b199e63 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import scala.collection.Map
 
-import org.apache.spark.sql.{catalyst, AnalysisException}
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.types._
 
@@ -41,16 +41,22 @@ object ExtractValue {
       resolver: Resolver): ExtractValue = {
 
     (child.dataType, extraction) match {
-      case (StructType(fields), Literal(fieldName, StringType)) =>
-        val ordinal = findField(fields, fieldName.toString, resolver)
-        GetStructField(child, fields(ordinal), ordinal)
-      case (ArrayType(StructType(fields), containsNull), Literal(fieldName, StringType)) =>
-        val ordinal = findField(fields, fieldName.toString, resolver)
-        GetArrayStructFields(child, fields(ordinal), ordinal, containsNull)
+      case (StructType(fields), NonNullLiteral(v, StringType)) =>
+        val fieldName = v.toString
+        val ordinal = findField(fields, fieldName, resolver)
+        GetStructField(child, fields(ordinal).copy(name = fieldName), ordinal)
+
+      case (ArrayType(StructType(fields), containsNull), NonNullLiteral(v, StringType)) =>
+        val fieldName = v.toString
+        val ordinal = findField(fields, fieldName, resolver)
+        GetArrayStructFields(child, fields(ordinal).copy(name = fieldName), ordinal, containsNull)
+
       case (_: ArrayType, _) if extraction.dataType.isInstanceOf[IntegralType] =>
         GetArrayItem(child, extraction)
+
       case (_: MapType, _) =>
         GetMapValue(child, extraction)
+
       case (otherType, _) =>
         val errorMsg = otherType match {
           case StructType(_) | ArrayType(StructType(_), _) =>
@@ -94,16 +100,21 @@ trait ExtractValue extends UnaryExpression {
   self: Product =>
 }
 
+abstract class ExtractValueWithStruct extends ExtractValue {
+  self: Product =>
+
+  def field: StructField
+  override def toString: String = s"$child.${field.name}"
+}
+
 /**
  * Returns the value of fields in the Struct `child`.
  */
 case class GetStructField(child: Expression, field: StructField, ordinal: Int)
-  extends ExtractValue {
+  extends ExtractValueWithStruct {
 
   override def dataType: DataType = field.dataType
   override def nullable: Boolean = child.nullable || field.nullable
-  override def foldable: Boolean = child.foldable
-  override def toString: String = s"$child.${field.name}"
 
   override def eval(input: InternalRow): Any = {
     val baseValue = child.eval(input).asInstanceOf[InternalRow]
@@ -118,12 +129,9 @@ case class GetArrayStructFields(
     child: Expression,
     field: StructField,
     ordinal: Int,
-    containsNull: Boolean) extends ExtractValue {
+    containsNull: Boolean) extends ExtractValueWithStruct {
 
   override def dataType: DataType = ArrayType(field.dataType, containsNull)
-  override def nullable: Boolean = child.nullable
-  override def foldable: Boolean = child.foldable
-  override def toString: String = s"$child.${field.name}"
 
   override def eval(input: InternalRow): Any = {
     val baseValue = child.eval(input).asInstanceOf[Seq[InternalRow]]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 3b6f8bfd9ff9b..179a348d5baac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -156,12 +156,8 @@ object PartialAggregation {
             partialEvaluations(new TreeNodeRef(e)).finalEvaluation
 
           case e: Expression =>
-            // Should trim aliases around `GetField`s. These aliases are introduced while
-            // resolving struct field accesses, because `GetField` is not a `NamedExpression`.
-            // (Should we just turn `GetField` into a `NamedExpression`?)
-            val trimmed = e.transform { case Alias(g: ExtractValue, _) => g }
             namedGroupingExpressions.collectFirst {
-              case (expr, ne) if expr semanticEquals trimmed => ne.toAttribute
+              case (expr, ne) if expr semanticEquals e => ne.toAttribute
             }.getOrElse(e)
         }).asInstanceOf[Seq[NamedExpression]]
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index a853e27c1212d..b009a200b920f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, EliminateSubQueries, Resolver}
+import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.trees.TreeNode
@@ -252,14 +252,13 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
       // One match, but we also need to extract the requested nested field.
       case Seq((a, nestedFields)) =>
         // The foldLeft adds ExtractValues for every remaining parts of the identifier,
-        // and aliases it with the last part of the identifier.
+        // and wrap it with UnresolvedAlias which will be removed later.
         // For example, consider "a.b.c", where "a" is resolved to an existing attribute.
-        // Then this will add ExtractValue("c", ExtractValue("b", a)), and alias
-        // the final expression as "c".
+        // Then this will add ExtractValue("c", ExtractValue("b", a)), and wrap it as
+        // UnresolvedAlias(ExtractValue("c", ExtractValue("b", a))).
         val fieldExprs = nestedFields.foldLeft(a: Expression)((expr, fieldName) =>
           ExtractValue(expr, Literal(fieldName), resolver))
-        val aliasName = nestedFields.last
-        Some(Alias(fieldExprs, aliasName)())
+        Some(UnresolvedAlias(fieldExprs))
 
       // No matches.
       case Seq() =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 963c7820914f3..f8e5916d69f9c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -242,6 +242,8 @@ trait GroupingAnalytics extends UnaryNode {
   def aggregations: Seq[NamedExpression]
 
   override def output: Seq[Attribute] = aggregations.map(_.toAttribute)
+
+  def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics
 }
 
 /**
@@ -266,7 +268,11 @@ case class GroupingSets(
     groupByExprs: Seq[Expression],
     child: LogicalPlan,
     aggregations: Seq[NamedExpression],
-    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics
+    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics {
+
+  def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics =
+    this.copy(aggregations = aggs)
+}
 
 /**
  * Cube is a syntactic sugar for GROUPING SETS, and will be transformed to GroupingSets,
@@ -284,7 +290,11 @@ case class Cube(
     groupByExprs: Seq[Expression],
     child: LogicalPlan,
     aggregations: Seq[NamedExpression],
-    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics
+    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics {
+
+  def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics =
+    this.copy(aggregations = aggs)
+}
 
 /**
  * Rollup is a syntactic sugar for GROUPING SETS, and will be transformed to GroupingSets,
@@ -303,7 +313,11 @@ case class Rollup(
     groupByExprs: Seq[Expression],
     child: LogicalPlan,
     aggregations: Seq[NamedExpression],
-    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics
+    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics {
+
+  def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics =
+    this.copy(aggregations = aggs)
+}
 
 case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index b4e008a6e8480..f201c8ea8a110 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -21,7 +21,6 @@ import scala.language.implicitConversions
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.Logging
-import org.apache.spark.sql.expressions.Window
 import org.apache.spark.sql.functions.lit
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.analysis._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 466258e76f9f6..492a3321bc0bc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -32,7 +32,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.python.SerDeUtil
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.analysis.{MultiAlias, ResolvedStar, UnresolvedAttribute}
+import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
@@ -629,6 +629,10 @@ class DataFrame private[sql](
   @scala.annotation.varargs
   def select(cols: Column*): DataFrame = {
     val namedExpressions = cols.map {
+      // Wrap UnresolvedAttribute with UnresolvedAlias, as when we resolve UnresolvedAttribute, we
+      // will remove intermediate Alias for ExtractValue chain, and we need to alias it again to
+      // make it a NamedExpression.
+      case Column(u: UnresolvedAttribute) => UnresolvedAlias(u)
       case Column(expr: NamedExpression) => expr
       // Leave an unaliased explode with an empty list of names since the analzyer will generate the
       // correct defaults after the nested expression's type has been resolved.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
index 45b3e1bc627d5..99d557b03a033 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
@@ -21,7 +21,7 @@ import scala.collection.JavaConversions._
 import scala.language.implicitConversions
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.sql.catalyst.analysis.Star
+import org.apache.spark.sql.catalyst.analysis.{UnresolvedAlias, UnresolvedAttribute, Star}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{Rollup, Cube, Aggregate}
 import org.apache.spark.sql.types.NumericType
@@ -70,27 +70,31 @@ class GroupedData protected[sql](
     groupingExprs: Seq[Expression],
     private val groupType: GroupedData.GroupType) {
 
-  private[this] def toDF(aggExprs: Seq[NamedExpression]): DataFrame = {
+  private[this] def toDF(aggExprs: Seq[Expression]): DataFrame = {
     val aggregates = if (df.sqlContext.conf.dataFrameRetainGroupColumns) {
-        val retainedExprs = groupingExprs.map {
-          case expr: NamedExpression => expr
-          case expr: Expression => Alias(expr, expr.prettyString)()
-        }
-        retainedExprs ++ aggExprs
-      } else {
-        aggExprs
-      }
+      groupingExprs ++ aggExprs
+    } else {
+      aggExprs
+    }
 
+    val aliasedAgg = aggregates.map {
+      // Wrap UnresolvedAttribute with UnresolvedAlias, as when we resolve UnresolvedAttribute, we
+      // will remove intermediate Alias for ExtractValue chain, and we need to alias it again to
+      // make it a NamedExpression.
+      case u: UnresolvedAttribute => UnresolvedAlias(u)
+      case expr: NamedExpression => expr
+      case expr: Expression => Alias(expr, expr.prettyString)()
+    }
     groupType match {
       case GroupedData.GroupByType =>
         DataFrame(
-          df.sqlContext, Aggregate(groupingExprs, aggregates, df.logicalPlan))
+          df.sqlContext, Aggregate(groupingExprs, aliasedAgg, df.logicalPlan))
       case GroupedData.RollupType =>
         DataFrame(
-          df.sqlContext, Rollup(groupingExprs, df.logicalPlan, aggregates))
+          df.sqlContext, Rollup(groupingExprs, df.logicalPlan, aliasedAgg))
       case GroupedData.CubeType =>
         DataFrame(
-          df.sqlContext, Cube(groupingExprs, df.logicalPlan, aggregates))
+          df.sqlContext, Cube(groupingExprs, df.logicalPlan, aliasedAgg))
     }
   }
 
@@ -112,10 +116,7 @@ class GroupedData protected[sql](
         namedExpr
       }
     }
-    toDF(columnExprs.map { c =>
-      val a = f(c)
-      Alias(a, a.prettyString)()
-    })
+    toDF(columnExprs.map(f))
   }
 
   private[this] def strToExpr(expr: String): (Expression => Expression) = {
@@ -169,8 +170,7 @@ class GroupedData protected[sql](
    */
   def agg(exprs: Map[String, String]): DataFrame = {
     toDF(exprs.map { case (colName, expr) =>
-      val a = strToExpr(expr)(df(colName).expr)
-      Alias(a, a.prettyString)()
+      strToExpr(expr)(df(colName).expr)
     }.toSeq)
   }
 
@@ -224,10 +224,7 @@ class GroupedData protected[sql](
    */
   @scala.annotation.varargs
   def agg(expr: Column, exprs: Column*): DataFrame = {
-    toDF((expr +: exprs).map(_.expr).map {
-      case expr: NamedExpression => expr
-      case expr: Expression => Alias(expr, expr.prettyString)()
-    })
+    toDF((expr +: exprs).map(_.expr))
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
index 1ce150ceaf5f9..c8c67ce334002 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
@@ -74,7 +74,7 @@ private[spark] object ExtractPythonUdfs extends Rule[LogicalPlan] {
     // Skip EvaluatePython nodes.
     case plan: EvaluatePython => plan
 
-    case plan: LogicalPlan =>
+    case plan: LogicalPlan if plan.resolved =>
       // Extract any PythonUDFs from the current operator.
       val udfs = plan.expressions.flatMap(_.collect { case udf: PythonUDF => udf })
       if (udfs.isEmpty) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 4441afd6bd811..73bc6c999164e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1367,9 +1367,9 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
   test("SPARK-6145: special cases") {
     sqlContext.read.json(sqlContext.sparkContext.makeRDD(
-      """{"a": {"b": [1]}, "b": [{"a": 1}], "c0": {"a": 1}}""" :: Nil)).registerTempTable("t")
-    checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY c0.a"), Row(1))
-    checkAnswer(sql("SELECT b[0].a FROM t ORDER BY c0.a"), Row(1))
+      """{"a": {"b": [1]}, "b": [{"a": 1}], "_c0": {"a": 1}}""" :: Nil)).registerTempTable("t")
+    checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY _c0.a"), Row(1))
+    checkAnswer(sql("SELECT b[0].a FROM t ORDER BY _c0.a"), Row(1))
   }
 
   test("SPARK-6898: complete support for special chars in column names") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 520a862ea0838..207d7a352c7b3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql
 
 import java.sql.Timestamp
 
-import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.test.TestSQLContext.implicits._
 import org.apache.spark.sql.test._
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index ca4b80b51b23f..7c4620952ba4b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -415,13 +415,6 @@ private[hive] object HiveQl {
       throw new NotImplementedError(s"No parse rules for StructField:\n ${dumpTree(a).toString} ")
   }
 
-  protected def nameExpressions(exprs: Seq[Expression]): Seq[NamedExpression] = {
-    exprs.zipWithIndex.map {
-      case (ne: NamedExpression, _) => ne
-      case (e, i) => Alias(e, s"_c$i")()
-    }
-  }
-
   protected def extractDbNameTableName(tableNameParts: Node): (Option[String], String) = {
     val (db, tableName) =
       tableNameParts.getChildren.map { case Token(part, Nil) => cleanIdentifier(part) } match {
@@ -942,7 +935,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         // (if there is a group by) or a script transformation.
         val withProject: LogicalPlan = transformation.getOrElse {
           val selectExpressions =
-            nameExpressions(select.getChildren.flatMap(selExprNodeToExpr).toSeq)
+            select.getChildren.flatMap(selExprNodeToExpr).map(UnresolvedAlias(_)).toSeq
           Seq(
             groupByClause.map(e => e match {
               case Token("TOK_GROUPBY", children) =>

From 5ab9fcfb01a0ad2f6c103f67c1a785d3b49e33f0 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 22 Jun 2015 13:51:23 -0700
Subject: [PATCH 0007/1454] [SPARK-8532] [SQL] In Python's DataFrameWriter,
 save/saveAsTable/json/parquet/jdbc always override mode

https://issues.apache.org/jira/browse/SPARK-8532

This PR has two changes. First, it fixes the bug that save actions (i.e. `save/saveAsTable/json/parquet/jdbc`) always override mode. Second, it adds input argument `partitionBy` to `save/saveAsTable/parquet`.

Author: Yin Huai <yhuai@databricks.com>

Closes #6937 from yhuai/SPARK-8532 and squashes the following commits:

f972d5d [Yin Huai] davies's comment.
d37abd2 [Yin Huai] style.
d21290a [Yin Huai] Python doc.
889eb25 [Yin Huai] Minor refactoring and add partitionBy to save, saveAsTable, and parquet.
7fbc24b [Yin Huai] Use None instead of "error" as the default value of mode since JVM-side already uses "error" as the default value.
d696dff [Yin Huai] Python style.
88eb6c4 [Yin Huai] If mode is "error", do not call mode method.
c40c461 [Yin Huai] Regression test.
---
 python/pyspark/sql/readwriter.py | 30 +++++++++++++++++++-----------
 python/pyspark/sql/tests.py      | 32 ++++++++++++++++++++++++++++++++
 2 files changed, 51 insertions(+), 11 deletions(-)

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index f036644acc961..1b7bc0f9a12be 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -218,7 +218,10 @@ def mode(self, saveMode):
 
         >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self._jwrite = self._jwrite.mode(saveMode)
+        # At the JVM side, the default value of mode is already set to "error".
+        # So, if the given saveMode is None, we will not call JVM-side's mode method.
+        if saveMode is not None:
+            self._jwrite = self._jwrite.mode(saveMode)
         return self
 
     @since(1.4)
@@ -253,11 +256,12 @@ def partitionBy(self, *cols):
         """
         if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
             cols = cols[0]
-        self._jwrite = self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols))
+        if len(cols) > 0:
+            self._jwrite = self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols))
         return self
 
     @since(1.4)
-    def save(self, path=None, format=None, mode="error", **options):
+    def save(self, path=None, format=None, mode=None, partitionBy=(), **options):
         """Saves the contents of the :class:`DataFrame` to a data source.
 
         The data source is specified by the ``format`` and a set of ``options``.
@@ -272,11 +276,12 @@ def save(self, path=None, format=None, mode="error", **options):
             * ``overwrite``: Overwrite existing data.
             * ``ignore``: Silently ignore this operation if data already exists.
             * ``error`` (default case): Throw an exception if data already exists.
+        :param partitionBy: names of partitioning columns
         :param options: all other string options
 
         >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self.mode(mode).options(**options)
+        self.partitionBy(partitionBy).mode(mode).options(**options)
         if format is not None:
             self.format(format)
         if path is None:
@@ -296,7 +301,7 @@ def insertInto(self, tableName, overwrite=False):
         self._jwrite.mode("overwrite" if overwrite else "append").insertInto(tableName)
 
     @since(1.4)
-    def saveAsTable(self, name, format=None, mode="error", **options):
+    def saveAsTable(self, name, format=None, mode=None, partitionBy=(), **options):
         """Saves the content of the :class:`DataFrame` as the specified table.
 
         In the case the table already exists, behavior of this function depends on the
@@ -312,15 +317,16 @@ def saveAsTable(self, name, format=None, mode="error", **options):
         :param name: the table name
         :param format: the format used to save
         :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
+        :param partitionBy: names of partitioning columns
         :param options: all other string options
         """
-        self.mode(mode).options(**options)
+        self.partitionBy(partitionBy).mode(mode).options(**options)
         if format is not None:
             self.format(format)
         self._jwrite.saveAsTable(name)
 
     @since(1.4)
-    def json(self, path, mode="error"):
+    def json(self, path, mode=None):
         """Saves the content of the :class:`DataFrame` in JSON format at the specified path.
 
         :param path: the path in any Hadoop supported file system
@@ -333,10 +339,10 @@ def json(self, path, mode="error"):
 
         >>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self._jwrite.mode(mode).json(path)
+        self.mode(mode)._jwrite.json(path)
 
     @since(1.4)
-    def parquet(self, path, mode="error"):
+    def parquet(self, path, mode=None, partitionBy=()):
         """Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
 
         :param path: the path in any Hadoop supported file system
@@ -346,13 +352,15 @@ def parquet(self, path, mode="error"):
             * ``overwrite``: Overwrite existing data.
             * ``ignore``: Silently ignore this operation if data already exists.
             * ``error`` (default case): Throw an exception if data already exists.
+        :param partitionBy: names of partitioning columns
 
         >>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self._jwrite.mode(mode).parquet(path)
+        self.partitionBy(partitionBy).mode(mode)
+        self._jwrite.parquet(path)
 
     @since(1.4)
-    def jdbc(self, url, table, mode="error", properties={}):
+    def jdbc(self, url, table, mode=None, properties={}):
         """Saves the content of the :class:`DataFrame` to a external database table via JDBC.
 
         .. note:: Don't create too many partitions in parallel on a large cluster;\
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index b5fbb7d098820..13f4556943ac8 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -539,6 +539,38 @@ def test_save_and_load(self):
 
         shutil.rmtree(tmpPath)
 
+    def test_save_and_load_builder(self):
+        df = self.df
+        tmpPath = tempfile.mkdtemp()
+        shutil.rmtree(tmpPath)
+        df.write.json(tmpPath)
+        actual = self.sqlCtx.read.json(tmpPath)
+        self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
+
+        schema = StructType([StructField("value", StringType(), True)])
+        actual = self.sqlCtx.read.json(tmpPath, schema)
+        self.assertEqual(sorted(df.select("value").collect()), sorted(actual.collect()))
+
+        df.write.mode("overwrite").json(tmpPath)
+        actual = self.sqlCtx.read.json(tmpPath)
+        self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
+
+        df.write.mode("overwrite").options(noUse="this options will not be used in save.")\
+                .format("json").save(path=tmpPath)
+        actual =\
+            self.sqlCtx.read.format("json")\
+                            .load(path=tmpPath, noUse="this options will not be used in load.")
+        self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
+
+        defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default",
+                                                    "org.apache.spark.sql.parquet")
+        self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json")
+        actual = self.sqlCtx.load(path=tmpPath)
+        self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
+        self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName)
+
+        shutil.rmtree(tmpPath)
+
     def test_help_command(self):
         # Regression test for SPARK-5464
         rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])

From afe35f0519bc7dcb85010a7eedcff854d4fc313a Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 22 Jun 2015 14:15:35 -0700
Subject: [PATCH 0008/1454] [SPARK-8455] [ML] Implement n-gram feature
 transformer

Implementation of n-gram feature transformer for ML.

Author: Feynman Liang <fliang@databricks.com>

Closes #6887 from feynmanliang/ngram-featurizer and squashes the following commits:

d2c839f [Feynman Liang] Make n > input length yield empty output
9fadd36 [Feynman Liang] Add empty and corner test cases, fix names and spaces
fe93873 [Feynman Liang] Implement n-gram feature transformer
---
 .../org/apache/spark/ml/feature/NGram.scala   | 69 ++++++++++++++
 .../apache/spark/ml/feature/NGramSuite.scala  | 94 +++++++++++++++++++
 2 files changed, 163 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
new file mode 100644
index 0000000000000..8de10eb51f923
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
+
+/**
+ * :: Experimental ::
+ * A feature transformer that converts the input array of strings into an array of n-grams. Null
+ * values in the input array are ignored.
+ * It returns an array of n-grams where each n-gram is represented by a space-separated string of
+ * words.
+ *
+ * When the input is empty, an empty array is returned.
+ * When the input array length is less than n (number of elements per n-gram), no n-grams are
+ * returned.
+ */
+@Experimental
+class NGram(override val uid: String)
+  extends UnaryTransformer[Seq[String], Seq[String], NGram] {
+
+  def this() = this(Identifiable.randomUID("ngram"))
+
+  /**
+   * Minimum n-gram length, >= 1.
+   * Default: 2, bigram features
+   * @group param
+   */
+  val n: IntParam = new IntParam(this, "n", "number elements per n-gram (>=1)",
+    ParamValidators.gtEq(1))
+
+  /** @group setParam */
+  def setN(value: Int): this.type = set(n, value)
+
+  /** @group getParam */
+  def getN: Int = $(n)
+
+  setDefault(n -> 2)
+
+  override protected def createTransformFunc: Seq[String] => Seq[String] = {
+    _.iterator.sliding($(n)).withPartial(false).map(_.mkString(" ")).toSeq
+  }
+
+  override protected def validateInputType(inputType: DataType): Unit = {
+    require(inputType.sameType(ArrayType(StringType)),
+      s"Input type must be ArrayType(StringType) but got $inputType.")
+  }
+
+  override protected def outputDataType: DataType = new ArrayType(StringType, false)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala
new file mode 100644
index 0000000000000..ab97e3dbc6ee0
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala
@@ -0,0 +1,94 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import scala.beans.BeanInfo
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, Row}
+
+@BeanInfo
+case class NGramTestData(inputTokens: Array[String], wantedNGrams: Array[String])
+
+class NGramSuite extends SparkFunSuite with MLlibTestSparkContext {
+  import org.apache.spark.ml.feature.NGramSuite._
+
+  test("default behavior yields bigram features") {
+    val nGram = new NGram()
+      .setInputCol("inputTokens")
+      .setOutputCol("nGrams")
+    val dataset = sqlContext.createDataFrame(Seq(
+      NGramTestData(
+        Array("Test", "for", "ngram", "."),
+        Array("Test for", "for ngram", "ngram .")
+    )))
+    testNGram(nGram, dataset)
+  }
+
+  test("NGramLength=4 yields length 4 n-grams") {
+    val nGram = new NGram()
+      .setInputCol("inputTokens")
+      .setOutputCol("nGrams")
+      .setN(4)
+    val dataset = sqlContext.createDataFrame(Seq(
+      NGramTestData(
+        Array("a", "b", "c", "d", "e"),
+        Array("a b c d", "b c d e")
+      )))
+    testNGram(nGram, dataset)
+  }
+
+  test("empty input yields empty output") {
+    val nGram = new NGram()
+      .setInputCol("inputTokens")
+      .setOutputCol("nGrams")
+      .setN(4)
+    val dataset = sqlContext.createDataFrame(Seq(
+      NGramTestData(
+        Array(),
+        Array()
+      )))
+    testNGram(nGram, dataset)
+  }
+
+  test("input array < n yields empty output") {
+    val nGram = new NGram()
+      .setInputCol("inputTokens")
+      .setOutputCol("nGrams")
+      .setN(6)
+    val dataset = sqlContext.createDataFrame(Seq(
+      NGramTestData(
+        Array("a", "b", "c", "d", "e"),
+        Array()
+      )))
+    testNGram(nGram, dataset)
+  }
+}
+
+object NGramSuite extends SparkFunSuite {
+
+  def testNGram(t: NGram, dataset: DataFrame): Unit = {
+    t.transform(dataset)
+      .select("nGrams", "wantedNGrams")
+      .collect()
+      .foreach { case Row(actualNGrams, wantedNGrams) =>
+        assert(actualNGrams === wantedNGrams)
+      }
+  }
+}

From b1f3a489efc6f4f9d172344c3345b9b38ae235e0 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 22 Jun 2015 14:35:38 -0700
Subject: [PATCH 0009/1454] [SPARK-8537] [SPARKR] Add a validation rule about
 the curly braces in SparkR to `.lintr`

[[SPARK-8537] Add a validation rule about the curly braces in SparkR to `.lintr` - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8537)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6940 from yu-iskw/SPARK-8537 and squashes the following commits:

7eec1a0 [Yu ISHIKAWA] [SPARK-8537][SparkR] Add a validation rule about the curly braces in SparkR to `.lintr`
---
 R/pkg/.lintr | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/pkg/.lintr b/R/pkg/.lintr
index b10ebd35c4ca7..038236fc149e6 100644
--- a/R/pkg/.lintr
+++ b/R/pkg/.lintr
@@ -1,2 +1,2 @@
-linters: with_defaults(line_length_linter(100), camel_case_linter = NULL)
+linters: with_defaults(line_length_linter(100), camel_case_linter = NULL, open_curly_linter(allow_single_line = TRUE), closed_curly_linter(allow_single_line = TRUE))
 exclusions: list("inst/profile/general.R" = 1, "inst/profile/shell.R")

From 50d3242d6a5530a51dacab249e3f3d49e2d50635 Mon Sep 17 00:00:00 2001
From: BenFradet <benjamin.fradet@gmail.com>
Date: Mon, 22 Jun 2015 15:06:47 -0700
Subject: [PATCH 0010/1454] [SPARK-8356] [SQL] Reconcile callUDF and callUdf

Deprecates ```callUdf``` in favor of ```callUDF```.

Author: BenFradet <benjamin.fradet@gmail.com>

Closes #6902 from BenFradet/SPARK-8356 and squashes the following commits:

ef4e9d8 [BenFradet] deprecated callUDF, use udf instead
9b1de4d [BenFradet] reinstated unit test for the deprecated callUdf
cbd80a5 [BenFradet] deprecated callUdf in favor of callUDF
---
 .../org/apache/spark/sql/functions.scala      | 45 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 11 ++++-
 2 files changed, 55 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 7e7a099a8318b..8cea826ae6921 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1448,7 +1448,9 @@ object functions {
      *
      * @group udf_funcs
      * @since 1.3.0
+     * @deprecated As of 1.5.0, since it's redundant with udf()
      */
+    @deprecated("Use udf", "1.5.0")
     def callUDF(f: Function$x[$fTypes], returnType: DataType${if (args.length > 0) ", " + args else ""}): Column = {
       ScalaUdf(f, returnType, Seq($argsInUdf))
     }""")
@@ -1584,7 +1586,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function0[_], returnType: DataType): Column = {
     ScalaUdf(f, returnType, Seq())
   }
@@ -1595,7 +1599,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function1[_, _], returnType: DataType, arg1: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr))
   }
@@ -1606,7 +1612,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function2[_, _, _], returnType: DataType, arg1: Column, arg2: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr))
   }
@@ -1617,7 +1625,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function3[_, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr))
   }
@@ -1628,7 +1638,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function4[_, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr))
   }
@@ -1639,7 +1651,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function5[_, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr))
   }
@@ -1650,7 +1664,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function6[_, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr))
   }
@@ -1661,7 +1677,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function7[_, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr))
   }
@@ -1672,7 +1690,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function8[_, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr))
   }
@@ -1683,7 +1703,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function9[_, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr))
   }
@@ -1694,13 +1716,34 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.3.0
+   * @deprecated As of 1.5.0, since it's redundant with udf()
    */
+  @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column): Column = {
     ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr))
   }
 
   // scalastyle:on
 
+  /**
+   * Call an user-defined function.
+   * Example:
+   * {{{
+   *  import org.apache.spark.sql._
+   *
+   *  val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value")
+   *  val sqlContext = df.sqlContext
+   *  sqlContext.udf.register("simpleUdf", (v: Int) => v * v)
+   *  df.select($"id", callUDF("simpleUdf", $"value"))
+   * }}}
+   *
+   * @group udf_funcs
+   * @since 1.5.0
+   */
+  def callUDF(udfName: String, cols: Column*): Column = {
+    UnresolvedFunction(udfName, cols.map(_.expr))
+  }
+
   /**
    * Call an user-defined function.
    * Example:
@@ -1715,7 +1758,9 @@ object functions {
    *
    * @group udf_funcs
    * @since 1.4.0
+   * @deprecated As of 1.5.0, since it was not coherent to have two functions callUdf and callUDF
    */
+  @deprecated("Use callUDF", "1.5.0")
   def callUdf(udfName: String, cols: Column*): Column = {
      UnresolvedFunction(udfName, cols.map(_.expr))
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index ba1d020f22f11..47443a917b765 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -301,7 +301,7 @@ class DataFrameSuite extends QueryTest {
     )
   }
 
-  test("call udf in SQLContext") {
+  test("deprecated callUdf in SQLContext") {
     val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value")
     val sqlctx = df.sqlContext
     sqlctx.udf.register("simpleUdf", (v: Int) => v * v)
@@ -310,6 +310,15 @@ class DataFrameSuite extends QueryTest {
       Row("id1", 1) :: Row("id2", 16) :: Row("id3", 25) :: Nil)
   }
 
+  test("callUDF in SQLContext") {
+    val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value")
+    val sqlctx = df.sqlContext
+    sqlctx.udf.register("simpleUDF", (v: Int) => v * v)
+    checkAnswer(
+      df.select($"id", callUDF("simpleUDF", $"value")),
+      Row("id1", 1) :: Row("id2", 16) :: Row("id3", 25) :: Nil)
+  }
+
   test("withColumn") {
     val df = testData.toDF().withColumn("newCol", col("key") + 1)
     checkAnswer(

From 96aa01378e3b3dbb4601d31c7312a311cb65b22e Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 22 Jun 2015 15:22:17 -0700
Subject: [PATCH 0011/1454] [SPARK-8492] [SQL] support binaryType in UnsafeRow

Support BinaryType in UnsafeRow, just like StringType.

Also change the layout of StringType and BinaryType in UnsafeRow, by combining offset and size together as Long, which will limit the size of Row to under 2G (given that fact that any single buffer can not be bigger than 2G in JVM).

Author: Davies Liu <davies@databricks.com>

Closes #6911 from davies/unsafe_bin and squashes the following commits:

d68706f [Davies Liu] update comment
519f698 [Davies Liu] address comment
98a964b [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_bin
180b49d [Davies Liu] fix zero-out
22e4c0a [Davies Liu] zero-out padding bytes
6abfe93 [Davies Liu] fix style
447dea0 [Davies Liu] support binaryType in UnsafeRow
---
 .../UnsafeFixedWidthAggregationMap.java       |  8 ---
 .../sql/catalyst/expressions/UnsafeRow.java   | 34 ++++++-----
 .../expressions/UnsafeRowConverter.scala      | 60 ++++++++++++++-----
 .../expressions/UnsafeRowConverterSuite.scala | 16 ++---
 4 files changed, 72 insertions(+), 46 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
index f7849ebebc573..83f2a312972fb 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
-import java.util.Arrays;
 import java.util.Iterator;
 
 import org.apache.spark.sql.catalyst.InternalRow;
@@ -142,14 +141,7 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     final int groupingKeySize = groupingKeyToUnsafeRowConverter.getSizeRequirement(groupingKey);
     // Make sure that the buffer is large enough to hold the key. If it's not, grow it:
     if (groupingKeySize > groupingKeyConversionScratchSpace.length) {
-      // This new array will be initially zero, so there's no need to zero it out here
       groupingKeyConversionScratchSpace = new byte[groupingKeySize];
-    } else {
-      // Zero out the buffer that's used to hold the current row. This is necessary in order
-      // to ensure that rows hash properly, since garbage data from the previous row could
-      // otherwise end up as padding in this row. As a performance optimization, we only zero out
-      // the portion of the buffer that we'll actually write to.
-      Arrays.fill(groupingKeyConversionScratchSpace, 0, groupingKeySize, (byte) 0);
     }
     final int actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow(
       groupingKey,
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index ed04d2e50ec84..bb2f2079b40f0 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -47,7 +47,8 @@
  * In the `values` region, we store one 8-byte word per field. For fields that hold fixed-length
  * primitive types, such as long, double, or int, we store the value directly in the word. For
  * fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the
- * base address of the row) that points to the beginning of the variable-length field.
+ * base address of the row) that points to the beginning of the variable-length field, and length
+ * (they are combined into a long).
  *
  * Instances of `UnsafeRow` act as pointers to row data stored in this format.
  */
@@ -92,6 +93,7 @@ public static int calculateBitSetWidthInBytes(int numFields) {
    */
   public static final Set<DataType> readableFieldTypes;
 
+  // TODO: support DecimalType
   static {
     settableFieldTypes = Collections.unmodifiableSet(
       new HashSet<DataType>(
@@ -111,7 +113,8 @@ public static int calculateBitSetWidthInBytes(int numFields) {
     // We support get() on a superset of the types for which we support set():
     final Set<DataType> _readableFieldTypes = new HashSet<DataType>(
       Arrays.asList(new DataType[]{
-        StringType
+        StringType,
+        BinaryType
       }));
     _readableFieldTypes.addAll(settableFieldTypes);
     readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes);
@@ -221,11 +224,6 @@ public void setFloat(int ordinal, float value) {
     PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
-  @Override
-  public void setString(int ordinal, String value) {
-    throw new UnsupportedOperationException();
-  }
-
   @Override
   public int size() {
     return numFields;
@@ -249,6 +247,8 @@ public Object get(int i) {
       return null;
     } else if (dataType == StringType) {
       return getUTF8String(i);
+    } else if (dataType == BinaryType) {
+      return getBinary(i);
     } else {
       throw new UnsupportedOperationException();
     }
@@ -311,19 +311,23 @@ public double getDouble(int i) {
   }
 
   public UTF8String getUTF8String(int i) {
+    return UTF8String.fromBytes(getBinary(i));
+  }
+
+  public byte[] getBinary(int i) {
     assertIndexIsValid(i);
-    final long offsetToStringSize = getLong(i);
-    final int stringSizeInBytes =
-      (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offsetToStringSize);
-    final byte[] strBytes = new byte[stringSizeInBytes];
+    final long offsetAndSize = getLong(i);
+    final int offset = (int)(offsetAndSize >> 32);
+    final int size = (int)(offsetAndSize & ((1L << 32) - 1));
+    final byte[] bytes = new byte[size];
     PlatformDependent.copyMemory(
       baseObject,
-      baseOffset + offsetToStringSize + 8,  // The `+ 8` is to skip past the size to get the data
-      strBytes,
+      baseOffset + offset,
+      bytes,
       PlatformDependent.BYTE_ARRAY_OFFSET,
-      stringSizeInBytes
+      size
     );
-    return UTF8String.fromBytes(strBytes);
+    return bytes;
   }
 
   @Override
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
index 72f740ecaead3..89adaf053b1a4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.util.DateUtils
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
@@ -72,6 +70,19 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
    */
   def writeRow(row: InternalRow, baseObject: Object, baseOffset: Long): Int = {
     unsafeRow.pointTo(baseObject, baseOffset, writers.length, null)
+
+    if (writers.length > 0) {
+      // zero-out the bitset
+      var n = writers.length / 64
+      while (n >= 0) {
+        PlatformDependent.UNSAFE.putLong(
+          unsafeRow.getBaseObject,
+          unsafeRow.getBaseOffset + n * 8,
+          0L)
+        n -= 1
+      }
+    }
+
     var fieldNumber = 0
     var appendCursor: Int = fixedLengthSize
     while (fieldNumber < writers.length) {
@@ -122,6 +133,7 @@ private object UnsafeColumnWriter {
       case FloatType => FloatUnsafeColumnWriter
       case DoubleType => DoubleUnsafeColumnWriter
       case StringType => StringUnsafeColumnWriter
+      case BinaryType => BinaryUnsafeColumnWriter
       case DateType => IntUnsafeColumnWriter
       case TimestampType => LongUnsafeColumnWriter
       case t =>
@@ -141,6 +153,7 @@ private object LongUnsafeColumnWriter extends LongUnsafeColumnWriter
 private object FloatUnsafeColumnWriter extends FloatUnsafeColumnWriter
 private object DoubleUnsafeColumnWriter extends DoubleUnsafeColumnWriter
 private object StringUnsafeColumnWriter extends StringUnsafeColumnWriter
+private object BinaryUnsafeColumnWriter extends BinaryUnsafeColumnWriter
 
 private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter {
   // Primitives don't write to the variable-length region:
@@ -235,10 +248,13 @@ private class DoubleUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWr
   }
 }
 
-private class StringUnsafeColumnWriter private() extends UnsafeColumnWriter {
+private abstract class BytesUnsafeColumnWriter extends UnsafeColumnWriter {
+
+  def getBytes(source: InternalRow, column: Int): Array[Byte]
+
   def getSize(source: InternalRow, column: Int): Int = {
-    val numBytes = source.get(column).asInstanceOf[UTF8String].getBytes.length
-    8 + ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
+    val numBytes = getBytes(source, column).length
+    ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
   }
 
   override def write(
@@ -246,19 +262,33 @@ private class StringUnsafeColumnWriter private() extends UnsafeColumnWriter {
       target: UnsafeRow,
       column: Int,
       appendCursor: Int): Int = {
-    val value = source.get(column).asInstanceOf[UTF8String]
-    val baseObject = target.getBaseObject
-    val baseOffset = target.getBaseOffset
-    val numBytes = value.getBytes.length
-    PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + appendCursor, numBytes)
+    val offset = target.getBaseOffset + appendCursor
+    val bytes = getBytes(source, column)
+    val numBytes = bytes.length
+    if ((numBytes & 0x07) > 0) {
+      // zero-out the padding bytes
+      PlatformDependent.UNSAFE.putLong(target.getBaseObject, offset + ((numBytes >> 3) << 3), 0L)
+    }
     PlatformDependent.copyMemory(
-      value.getBytes,
+      bytes,
       PlatformDependent.BYTE_ARRAY_OFFSET,
-      baseObject,
-      baseOffset + appendCursor + 8,
+      target.getBaseObject,
+      offset,
       numBytes
     )
-    target.setLong(column, appendCursor)
-    8 + ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
+    target.setLong(column, (appendCursor.toLong << 32L) | numBytes.toLong)
+    ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
+  }
+}
+
+private class StringUnsafeColumnWriter private() extends BytesUnsafeColumnWriter {
+  def getBytes(source: InternalRow, column: Int): Array[Byte] = {
+    source.getAs[UTF8String](column).getBytes
+  }
+}
+
+private class BinaryUnsafeColumnWriter private() extends BytesUnsafeColumnWriter {
+  def getBytes(source: InternalRow, column: Int): Array[Byte] = {
+    source.getAs[Array[Byte]](column)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 721ef8a22608c..d8f3351d6dff6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -23,8 +23,8 @@ import java.util.Arrays
 import org.scalatest.Matchers
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.types._
 import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
 
@@ -52,19 +52,19 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     unsafeRow.getInt(2) should be (2)
   }
 
-  test("basic conversion with primitive and string types") {
-    val fieldTypes: Array[DataType] = Array(LongType, StringType, StringType)
+  test("basic conversion with primitive, string and binary types") {
+    val fieldTypes: Array[DataType] = Array(LongType, StringType, BinaryType)
     val converter = new UnsafeRowConverter(fieldTypes)
 
     val row = new SpecificMutableRow(fieldTypes)
     row.setLong(0, 0)
     row.setString(1, "Hello")
-    row.setString(2, "World")
+    row.update(2, "World".getBytes)
 
     val sizeRequired: Int = converter.getSizeRequirement(row)
     sizeRequired should be (8 + (8 * 3) +
-      ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length + 8) +
-      ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length + 8))
+      ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length) +
+      ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET)
     numBytesWritten should be (sizeRequired)
@@ -73,7 +73,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
     unsafeRow.getLong(0) should be (0)
     unsafeRow.getString(1) should be ("Hello")
-    unsafeRow.getString(2) should be ("World")
+    unsafeRow.getBinary(2) should be ("World".getBytes)
   }
 
   test("basic conversion with primitive, string, date and timestamp types") {
@@ -88,7 +88,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
     val sizeRequired: Int = converter.getSizeRequirement(row)
     sizeRequired should be (8 + (8 * 4) +
-      ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length + 8))
+      ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET)
     numBytesWritten should be (sizeRequired)

From 1dfb0f7b2aed5ee6d07543fdeac8ff7c777b63b9 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 22 Jun 2015 16:16:26 -0700
Subject: [PATCH 0012/1454] [HOTFIX] [TESTS] Typo mqqt -> mqtt

This was introduced in #6866.
---
 dev/run-tests.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/dev/run-tests.py b/dev/run-tests.py
index 2cccfed75edee..de1b4537eda5f 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -179,14 +179,14 @@ def contains_file(self, filename):
 )
 
 
-streaming_mqqt = Module(
-    name="streaming-mqqt",
+streaming_mqtt = Module(
+    name="streaming-mqtt",
     dependencies=[streaming],
     source_file_regexes=[
-        "external/mqqt",
+        "external/mqtt",
     ],
     sbt_test_goals=[
-        "streaming-mqqt/test",
+        "streaming-mqtt/test",
     ]
 )
 

From 860a49ef20cea5711a7f54de0053ea33647e56a7 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 22 Jun 2015 17:37:35 -0700
Subject: [PATCH 0013/1454] [SPARK-7153] [SQL] support all integral type
 ordinal in GetArrayItem

first convert `ordinal` to `Number`, then convert to int type.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #5706 from cloud-fan/7153 and squashes the following commits:

915db79 [Wenchen Fan] fix 7153
---
 .../catalyst/expressions/ExtractValue.scala   |  2 +-
 ...exTypes.scala => complexTypeCreator.scala} |  1 -
 .../expressions/ComplexTypeSuite.scala        | 20 +++++++++++++++++++
 3 files changed, 21 insertions(+), 2 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{complexTypes.scala => complexTypeCreator.scala} (98%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
index 013027b199e63..4d6c1c265150d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
@@ -186,7 +186,7 @@ case class GetArrayItem(child: Expression, ordinal: Expression)
     // TODO: consider using Array[_] for ArrayType child to avoid
     // boxing of primitives
     val baseValue = value.asInstanceOf[Seq[_]]
-    val index = ordinal.asInstanceOf[Int]
+    val index = ordinal.asInstanceOf[Number].intValue()
     if (index >= baseValue.size || index < 0) {
       null
     } else {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
similarity index 98%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 72fdcebb4cbc8..e0bf07ed182f3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.types._
 
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index 2b0f4618b21e0..b80911e7257fc 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -26,6 +26,26 @@ import org.apache.spark.unsafe.types.UTF8String
 
 class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  /**
+   * Runs through the testFunc for all integral data types.
+   *
+   * @param testFunc a test function that accepts a conversion function to convert an integer
+   *                 into another data type.
+   */
+  private def testIntegralDataTypes(testFunc: (Int => Any) => Unit): Unit = {
+    testFunc(_.toByte)
+    testFunc(_.toShort)
+    testFunc(identity)
+    testFunc(_.toLong)
+  }
+
+  test("GetArrayItem") {
+    testIntegralDataTypes { convert =>
+      val array = Literal.create(Seq("a", "b"), ArrayType(StringType))
+      checkEvaluation(GetArrayItem(array, Literal(convert(1))), "b")
+    }
+  }
+
   test("CreateStruct") {
     val row = InternalRow(1, 2, 3)
     val c1 = 'a.int.at(0).as("a")

From 6b7f2ceafdcbb014791909747c2210b527305df9 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 22 Jun 2015 18:03:59 -0700
Subject: [PATCH 0014/1454] [SPARK-8307] [SQL] improve timestamp from parquet

This PR change to convert julian day to unix timestamp directly (without Calendar and Timestamp).

cc adrian-wang rxin

Author: Davies Liu <davies@databricks.com>

Closes #6759 from davies/improve_ts and squashes the following commits:

849e301 [Davies Liu] Merge branch 'master' of github.com:apache/spark into improve_ts
b0e4cad [Davies Liu] Merge branch 'master' of github.com:apache/spark into improve_ts
8e2d56f [Davies Liu] address comments
634b9f5 [Davies Liu] fix mima
4891efb [Davies Liu] address comment
bfc437c [Davies Liu] fix build
ae5979c [Davies Liu] Merge branch 'master' of github.com:apache/spark into improve_ts
602b969 [Davies Liu] remove jodd
2f2e48c [Davies Liu] fix test
8ace611 [Davies Liu] fix mima
212143b [Davies Liu] fix mina
c834108 [Davies Liu] Merge branch 'master' of github.com:apache/spark into improve_ts
a3171b8 [Davies Liu] Merge branch 'master' of github.com:apache/spark into improve_ts
5233974 [Davies Liu] fix scala style
361fd62 [Davies Liu] address comments
ea196d4 [Davies Liu] improve timestamp from parquet
---
 pom.xml                                       |  1 -
 project/MimaExcludes.scala                    | 12 ++-
 .../sql/catalyst/CatalystTypeConverters.scala | 14 +--
 .../spark/sql/catalyst/expressions/Cast.scala | 16 ++--
 .../sql/catalyst/expressions/literals.scala   |  6 +-
 .../{DateUtils.scala => DateTimeUtils.scala}  | 41 +++++++--
 .../sql/catalyst/expressions/CastSuite.scala  | 11 +--
 .../catalyst/expressions/PredicateSuite.scala |  6 +-
 .../expressions/UnsafeRowConverterSuite.scala | 10 +--
 ...lsSuite.scala => DateTimeUtilsSuite.scala} | 28 ++++--
 sql/core/pom.xml                              |  5 --
 .../spark/sql/execution/pythonUdfs.scala      | 10 +--
 .../org/apache/spark/sql/jdbc/JDBCRDD.scala   | 12 +--
 .../apache/spark/sql/json/JacksonParser.scala |  6 +-
 .../org/apache/spark/sql/json/JsonRDD.scala   |  8 +-
 .../spark/sql/parquet/ParquetConverter.scala  | 86 +++----------------
 .../sql/parquet/ParquetTableSupport.scala     | 19 ++--
 .../sql/parquet/timestamp/NanoTime.scala      | 69 ---------------
 .../org/apache/spark/sql/json/JsonSuite.scala | 20 +++--
 .../spark/sql/parquet/ParquetIOSuite.scala    |  4 +-
 .../spark/sql/sources/TableScanSuite.scala    | 11 ++-
 .../spark/sql/hive/HiveInspectors.scala       | 20 ++---
 .../apache/spark/sql/hive/TableReader.scala   |  8 +-
 .../spark/sql/hive/hiveWriterContainers.scala |  4 +-
 24 files changed, 175 insertions(+), 252 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/{DateUtils.scala => DateTimeUtils.scala} (68%)
 rename sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/{DateUtilsSuite.scala => DateTimeUtilsSuite.scala} (52%)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala

diff --git a/pom.xml b/pom.xml
index 6d4f717d4931b..80cacb5ace2d4 100644
--- a/pom.xml
+++ b/pom.xml
@@ -156,7 +156,6 @@
     <scala.binary.version>2.10</scala.binary.version>
     <jline.version>${scala.version}</jline.version>
     <jline.groupid>org.scala-lang</jline.groupid>
-    <jodd.version>3.6.3</jodd.version>
     <codehaus.jackson.version>1.9.13</codehaus.jackson.version>
     <fasterxml.jackson.version>2.4.4</fasterxml.jackson.version>
     <snappy.version>1.1.1.7</snappy.version>
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 015d0296dd369..7a748fb5e38bd 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -54,7 +54,17 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"),
             // SQL execution is considered private.
-            excludePackage("org.apache.spark.sql.execution")
+            excludePackage("org.apache.spark.sql.execution"),
+            // NanoTime and CatalystTimestampConverter is only used inside catalyst,
+            // not needed anymore
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.parquet.timestamp.NanoTime"),
+              ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.parquet.timestamp.NanoTime$"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.parquet.CatalystTimestampConverter"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.parquet.CatalystTimestampConverter$")
           )
         case v if v.startsWith("1.4") =>
           Seq(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 620e8de83a96c..429fc4077be9a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -19,15 +19,15 @@ package org.apache.spark.sql.catalyst
 
 import java.lang.{Iterable => JavaIterable}
 import java.math.{BigDecimal => JavaBigDecimal}
-import java.sql.{Timestamp, Date}
+import java.sql.{Date, Timestamp}
 import java.util.{Map => JavaMap}
 import javax.annotation.Nullable
 
 import scala.collection.mutable.HashMap
 
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateUtils
 import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -272,18 +272,18 @@ object CatalystTypeConverters {
   }
 
   private object DateConverter extends CatalystTypeConverter[Date, Date, Any] {
-    override def toCatalystImpl(scalaValue: Date): Int = DateUtils.fromJavaDate(scalaValue)
+    override def toCatalystImpl(scalaValue: Date): Int = DateTimeUtils.fromJavaDate(scalaValue)
     override def toScala(catalystValue: Any): Date =
-      if (catalystValue == null) null else DateUtils.toJavaDate(catalystValue.asInstanceOf[Int])
+      if (catalystValue == null) null else DateTimeUtils.toJavaDate(catalystValue.asInstanceOf[Int])
     override def toScalaImpl(row: InternalRow, column: Int): Date = toScala(row.getInt(column))
   }
 
   private object TimestampConverter extends CatalystTypeConverter[Timestamp, Timestamp, Any] {
     override def toCatalystImpl(scalaValue: Timestamp): Long =
-      DateUtils.fromJavaTimestamp(scalaValue)
+      DateTimeUtils.fromJavaTimestamp(scalaValue)
     override def toScala(catalystValue: Any): Timestamp =
       if (catalystValue == null) null
-      else DateUtils.toJavaTimestamp(catalystValue.asInstanceOf[Long])
+      else DateTimeUtils.toJavaTimestamp(catalystValue.asInstanceOf[Long])
     override def toScalaImpl(row: InternalRow, column: Int): Timestamp =
       toScala(row.getLong(column))
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index ad920f287820c..d271434a306dd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -24,7 +24,7 @@ import java.text.{DateFormat, SimpleDateFormat}
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -115,9 +115,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   // UDFToString
   private[this] def castToString(from: DataType): Any => Any = from match {
     case BinaryType => buildCast[Array[Byte]](_, UTF8String.fromBytes)
-    case DateType => buildCast[Int](_, d => UTF8String.fromString(DateUtils.toString(d)))
+    case DateType => buildCast[Int](_, d => UTF8String.fromString(DateTimeUtils.toString(d)))
     case TimestampType => buildCast[Long](_,
-      t => UTF8String.fromString(timestampToString(DateUtils.toJavaTimestamp(t))))
+      t => UTF8String.fromString(timestampToString(DateTimeUtils.toJavaTimestamp(t))))
     case _ => buildCast[Any](_, o => UTF8String.fromString(o.toString))
   }
 
@@ -162,7 +162,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
         if (periodIdx != -1 && n.length() - periodIdx > 9) {
           n = n.substring(0, periodIdx + 10)
         }
-        try DateUtils.fromJavaTimestamp(Timestamp.valueOf(n))
+        try DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf(n))
         catch { case _: java.lang.IllegalArgumentException => null }
       })
     case BooleanType =>
@@ -176,7 +176,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     case ByteType =>
       buildCast[Byte](_, b => longToTimestamp(b.toLong))
     case DateType =>
-      buildCast[Int](_, d => DateUtils.toMillisSinceEpoch(d) * 10000)
+      buildCast[Int](_, d => DateTimeUtils.toMillisSinceEpoch(d) * 10000)
     // TimestampWritable.decimalToTimestamp
     case DecimalType() =>
       buildCast[Decimal](_, d => decimalToTimestamp(d))
@@ -225,13 +225,13 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   private[this] def castToDate(from: DataType): Any => Any = from match {
     case StringType =>
       buildCast[UTF8String](_, s =>
-        try DateUtils.fromJavaDate(Date.valueOf(s.toString))
+        try DateTimeUtils.fromJavaDate(Date.valueOf(s.toString))
         catch { case _: java.lang.IllegalArgumentException => null }
       )
     case TimestampType =>
       // throw valid precision more than seconds, according to Hive.
       // Timestamp.nanos is in 0 to 999,999,999, no more than a second.
-      buildCast[Long](_, t => DateUtils.millisToDays(t / 10000L))
+      buildCast[Long](_, t => DateTimeUtils.millisToDays(t / 10000L))
     // Hive throws this exception as a Semantic Exception
     // It is never possible to compare result when hive return with exception,
     // so we can return null
@@ -442,7 +442,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
       case (DateType, StringType) =>
         defineCodeGen(ctx, ev, c =>
           s"""${ctx.stringType}.fromString(
-                org.apache.spark.sql.catalyst.util.DateUtils.toString($c))""")
+                org.apache.spark.sql.catalyst.util.DateTimeUtils.toString($c))""")
       // Special handling required for timestamps in hive test cases since the toString function
       // does not match the expected output.
       case (TimestampType, StringType) =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index 6c86a47ba200c..479224af5627a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -22,7 +22,7 @@ import java.sql.{Date, Timestamp}
 import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -39,8 +39,8 @@ object Literal {
     case d: BigDecimal => Literal(Decimal(d), DecimalType.Unlimited)
     case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType.Unlimited)
     case d: Decimal => Literal(d, DecimalType.Unlimited)
-    case t: Timestamp => Literal(DateUtils.fromJavaTimestamp(t), TimestampType)
-    case d: Date => Literal(DateUtils.fromJavaDate(d), DateType)
+    case t: Timestamp => Literal(DateTimeUtils.fromJavaTimestamp(t), TimestampType)
+    case d: Date => Literal(DateTimeUtils.fromJavaDate(d), DateType)
     case a: Array[Byte] => Literal(a, BinaryType)
     case null => Literal(null, NullType)
     case _ =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
similarity index 68%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateUtils.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 5cadc141af1df..ff79884a44d00 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -17,18 +17,28 @@
 
 package org.apache.spark.sql.catalyst.util
 
-import java.sql.{Timestamp, Date}
+import java.sql.{Date, Timestamp}
 import java.text.SimpleDateFormat
 import java.util.{Calendar, TimeZone}
 
 import org.apache.spark.sql.catalyst.expressions.Cast
 
 /**
- * Helper function to convert between Int value of days since 1970-01-01 and java.sql.Date
+ * Helper functions for converting between internal and external date and time representations.
+ * Dates are exposed externally as java.sql.Date and are represented internally as the number of
+ * dates since the Unix epoch (1970-01-01). Timestamps are exposed externally as java.sql.Timestamp
+ * and are stored internally as longs, which are capable of storing timestamps with 100 nanosecond
+ * precision.
  */
-object DateUtils {
-  private val MILLIS_PER_DAY = 86400000
-  private val HUNDRED_NANOS_PER_SECOND = 10000000L
+object DateTimeUtils {
+  final val MILLIS_PER_DAY = SECONDS_PER_DAY * 1000L
+
+  // see http://stackoverflow.com/questions/466321/convert-unix-timestamp-to-julian
+  final val JULIAN_DAY_OF_EPOCH = 2440587  // and .5
+  final val SECONDS_PER_DAY = 60 * 60 * 24L
+  final val HUNDRED_NANOS_PER_SECOND = 1000L * 1000L * 10L
+  final val NANOS_PER_SECOND = HUNDRED_NANOS_PER_SECOND * 100
+
 
   // Java TimeZone has no mention of thread safety. Use thread local instance to be safe.
   private val LOCAL_TIMEZONE = new ThreadLocal[TimeZone] {
@@ -117,4 +127,25 @@ object DateUtils {
       0L
     }
   }
+
+  /**
+   * Return the number of 100ns (hundred of nanoseconds) since epoch from Julian day
+   * and nanoseconds in a day
+   */
+  def fromJulianDay(day: Int, nanoseconds: Long): Long = {
+    // use Long to avoid rounding errors
+    val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY - SECONDS_PER_DAY / 2
+    seconds * HUNDRED_NANOS_PER_SECOND + nanoseconds / 100L
+  }
+
+  /**
+   * Return Julian day and nanoseconds in a day from the number of 100ns (hundred of nanoseconds)
+   */
+  def toJulianDay(num100ns: Long): (Int, Long) = {
+    val seconds = num100ns / HUNDRED_NANOS_PER_SECOND + SECONDS_PER_DAY / 2
+    val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
+    val secondsInDay = seconds % SECONDS_PER_DAY
+    val nanos = (num100ns % HUNDRED_NANOS_PER_SECOND) * 100L
+    (day.toInt, secondsInDay * NANOS_PER_SECOND + nanos)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index e407f6f166e86..f3809be722a84 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import java.sql.{Timestamp, Date}
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 
 /**
@@ -156,7 +156,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(cast(sd, DateType), StringType), sd)
     checkEvaluation(cast(cast(d, StringType), DateType), 0)
     checkEvaluation(cast(cast(nts, TimestampType), StringType), nts)
-    checkEvaluation(cast(cast(ts, StringType), TimestampType), DateUtils.fromJavaTimestamp(ts))
+    checkEvaluation(cast(cast(ts, StringType), TimestampType), DateTimeUtils.fromJavaTimestamp(ts))
 
     // all convert to string type to check
     checkEvaluation(cast(cast(cast(nts, TimestampType), DateType), StringType), sd)
@@ -301,9 +301,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(ts, LongType), 15.toLong)
     checkEvaluation(cast(ts, FloatType), 15.002f)
     checkEvaluation(cast(ts, DoubleType), 15.002)
-    checkEvaluation(cast(cast(tss, ShortType), TimestampType), DateUtils.fromJavaTimestamp(ts))
-    checkEvaluation(cast(cast(tss, IntegerType), TimestampType), DateUtils.fromJavaTimestamp(ts))
-    checkEvaluation(cast(cast(tss, LongType), TimestampType), DateUtils.fromJavaTimestamp(ts))
+    checkEvaluation(cast(cast(tss, ShortType), TimestampType), DateTimeUtils.fromJavaTimestamp(ts))
+    checkEvaluation(cast(cast(tss, IntegerType), TimestampType),
+      DateTimeUtils.fromJavaTimestamp(ts))
+    checkEvaluation(cast(cast(tss, LongType), TimestampType), DateTimeUtils.fromJavaTimestamp(ts))
     checkEvaluation(
       cast(cast(millis.toFloat / 1000, TimestampType), FloatType),
       millis.toFloat / 1000)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index b6261bfba0786..72fec3b86e5e4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -23,7 +23,7 @@ import scala.collection.immutable.HashSet
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types.{IntegerType, BooleanType}
 
 
@@ -167,8 +167,8 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal(true) <=> Literal.create(null, BooleanType), false, row)
     checkEvaluation(Literal.create(null, BooleanType) <=> Literal(true), false, row)
 
-    val d1 = DateUtils.fromJavaDate(Date.valueOf("1970-01-01"))
-    val d2 = DateUtils.fromJavaDate(Date.valueOf("1970-01-02"))
+    val d1 = DateTimeUtils.fromJavaDate(Date.valueOf("1970-01-01"))
+    val d2 = DateTimeUtils.fromJavaDate(Date.valueOf("1970-01-02"))
     checkEvaluation(Literal(d1) < Literal(d2), true)
 
     val ts1 = new Timestamp(12)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index d8f3351d6dff6..c0675f4f4dff6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -23,7 +23,7 @@ import java.util.Arrays
 import org.scalatest.Matchers
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
@@ -83,8 +83,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val row = new SpecificMutableRow(fieldTypes)
     row.setLong(0, 0)
     row.setString(1, "Hello")
-    row.update(2, DateUtils.fromJavaDate(Date.valueOf("1970-01-01")))
-    row.update(3, DateUtils.fromJavaTimestamp(Timestamp.valueOf("2015-05-08 08:10:25")))
+    row.update(2, DateTimeUtils.fromJavaDate(Date.valueOf("1970-01-01")))
+    row.update(3, DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2015-05-08 08:10:25")))
 
     val sizeRequired: Int = converter.getSizeRequirement(row)
     sizeRequired should be (8 + (8 * 4) +
@@ -98,9 +98,9 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     unsafeRow.getLong(0) should be (0)
     unsafeRow.getString(1) should be ("Hello")
     // Date is represented as Int in unsafeRow
-    DateUtils.toJavaDate(unsafeRow.getInt(2)) should be (Date.valueOf("1970-01-01"))
+    DateTimeUtils.toJavaDate(unsafeRow.getInt(2)) should be (Date.valueOf("1970-01-01"))
     // Timestamp is represented as Long in unsafeRow
-    DateUtils.toJavaTimestamp(unsafeRow.getLong(3)) should be
+    DateTimeUtils.toJavaTimestamp(unsafeRow.getLong(3)) should be
       (Timestamp.valueOf("2015-05-08 08:10:25"))
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
similarity index 52%
rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateUtilsSuite.scala
rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 4d8fe4ac5e78f..03eb64f097a37 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -21,19 +21,31 @@ import java.sql.Timestamp
 
 import org.apache.spark.SparkFunSuite
 
-class DateUtilsSuite extends SparkFunSuite {
+class DateTimeUtilsSuite extends SparkFunSuite {
 
-  test("timestamp") {
+  test("timestamp and 100ns") {
     val now = new Timestamp(System.currentTimeMillis())
     now.setNanos(100)
-    val ns = DateUtils.fromJavaTimestamp(now)
-    assert(ns % 10000000L == 1)
-    assert(DateUtils.toJavaTimestamp(ns) == now)
+    val ns = DateTimeUtils.fromJavaTimestamp(now)
+    assert(ns % 10000000L === 1)
+    assert(DateTimeUtils.toJavaTimestamp(ns) === now)
 
     List(-111111111111L, -1L, 0, 1L, 111111111111L).foreach { t =>
-      val ts = DateUtils.toJavaTimestamp(t)
-      assert(DateUtils.fromJavaTimestamp(ts) == t)
-      assert(DateUtils.toJavaTimestamp(DateUtils.fromJavaTimestamp(ts)) == ts)
+      val ts = DateTimeUtils.toJavaTimestamp(t)
+      assert(DateTimeUtils.fromJavaTimestamp(ts) === t)
+      assert(DateTimeUtils.toJavaTimestamp(DateTimeUtils.fromJavaTimestamp(ts)) === ts)
     }
   }
+
+  test("100ns and julian day") {
+    val (d, ns) = DateTimeUtils.toJulianDay(0)
+    assert(d === DateTimeUtils.JULIAN_DAY_OF_EPOCH)
+    assert(ns === DateTimeUtils.SECONDS_PER_DAY / 2 * DateTimeUtils.NANOS_PER_SECOND)
+    assert(DateTimeUtils.fromJulianDay(d, ns) == 0L)
+
+    val t = new Timestamp(61394778610000L) // (2015, 6, 11, 10, 10, 10, 100)
+    val (d1, ns1) = DateTimeUtils.toJulianDay(DateTimeUtils.fromJavaTimestamp(t))
+    val t2 = DateTimeUtils.toJavaTimestamp(DateTimeUtils.fromJulianDay(d1, ns1))
+    assert(t.equals(t2))
+  }
 }
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index ed75475a87067..8fc16928adbd9 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -73,11 +73,6 @@
       <artifactId>jackson-databind</artifactId>
       <version>${fasterxml.jackson.version}</version>
     </dependency>
-    <dependency>
-      <groupId>org.jodd</groupId>
-      <artifactId>jodd-core</artifactId>
-      <version>${jodd.version}</version>
-    </dependency>
     <dependency>
       <groupId>junit</groupId>
       <artifactId>junit</artifactId>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
index c8c67ce334002..6db551c543a9c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
@@ -34,7 +34,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -148,8 +148,8 @@ object EvaluatePython {
 
     case (ud, udt: UserDefinedType[_]) => toJava(udt.serialize(ud), udt.sqlType)
 
-    case (date: Int, DateType) => DateUtils.toJavaDate(date)
-    case (t: Long, TimestampType) => DateUtils.toJavaTimestamp(t)
+    case (date: Int, DateType) => DateTimeUtils.toJavaDate(date)
+    case (t: Long, TimestampType) => DateTimeUtils.toJavaTimestamp(t)
     case (s: UTF8String, StringType) => s.toString
 
     // Pyrolite can handle Timestamp and Decimal
@@ -188,12 +188,12 @@ object EvaluatePython {
       }): Row
 
     case (c: java.util.Calendar, DateType) =>
-      DateUtils.fromJavaDate(new java.sql.Date(c.getTimeInMillis))
+      DateTimeUtils.fromJavaDate(new java.sql.Date(c.getTimeInMillis))
 
     case (c: java.util.Calendar, TimestampType) =>
       c.getTimeInMillis * 10000L
     case (t: java.sql.Timestamp, TimestampType) =>
-      DateUtils.fromJavaTimestamp(t)
+      DateTimeUtils.fromJavaTimestamp(t)
 
     case (_, udt: UserDefinedType[_]) =>
       fromJava(obj, udt.sqlType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
index 226b143923df6..8b4276b2c364c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
@@ -22,13 +22,13 @@ import java.util.Properties
 
 import org.apache.commons.lang3.StringUtils
 
-import org.apache.spark.{Logging, Partition, SparkContext, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions.{InternalRow, SpecificMutableRow}
-import org.apache.spark.sql.catalyst.util.DateUtils
-import org.apache.spark.sql.types._
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.{Logging, Partition, SparkContext, TaskContext}
 
 /**
  * Data corresponding to one partition of a JDBCRDD.
@@ -383,10 +383,10 @@ private[sql] class JDBCRDD(
           conversions(i) match {
             case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos))
             case DateConversion =>
-              // DateUtils.fromJavaDate does not handle null value, so we need to check it.
+              // DateTimeUtils.fromJavaDate does not handle null value, so we need to check it.
               val dateVal = rs.getDate(pos)
               if (dateVal != null) {
-                mutableRow.setInt(i, DateUtils.fromJavaDate(dateVal))
+                mutableRow.setInt(i, DateTimeUtils.fromJavaDate(dateVal))
               } else {
                 mutableRow.update(i, null)
               }
@@ -421,7 +421,7 @@ private[sql] class JDBCRDD(
             case TimestampConversion =>
               val t = rs.getTimestamp(pos)
               if (t != null) {
-                mutableRow.setLong(i, DateUtils.fromJavaTimestamp(t))
+                mutableRow.setLong(i, DateTimeUtils.fromJavaTimestamp(t))
               } else {
                 mutableRow.update(i, null)
               }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index 817e8a20b34de..6222addc9aa3a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -25,7 +25,7 @@ import com.fasterxml.jackson.core._
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -63,10 +63,10 @@ private[sql] object JacksonParser {
         null
 
       case (VALUE_STRING, DateType) =>
-        DateUtils.millisToDays(DateUtils.stringToTime(parser.getText).getTime)
+        DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(parser.getText).getTime)
 
       case (VALUE_STRING, TimestampType) =>
-        DateUtils.stringToTime(parser.getText).getTime * 10000L
+        DateTimeUtils.stringToTime(parser.getText).getTime * 10000L
 
       case (VALUE_NUMBER_INT, TimestampType) =>
         parser.getLongValue * 10000L
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index 44594c5080ff4..73d9520d6f53f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -28,7 +28,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -393,8 +393,8 @@ private[sql] object JsonRDD extends Logging {
     value match {
       // only support string as date
       case value: java.lang.String =>
-        DateUtils.millisToDays(DateUtils.stringToTime(value).getTime)
-      case value: java.sql.Date => DateUtils.fromJavaDate(value)
+        DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(value).getTime)
+      case value: java.sql.Date => DateTimeUtils.fromJavaDate(value)
     }
   }
 
@@ -402,7 +402,7 @@ private[sql] object JsonRDD extends Logging {
     value match {
       case value: java.lang.Integer => value.asInstanceOf[Int].toLong * 10000L
       case value: java.lang.Long => value * 10000L
-      case value: java.lang.String => DateUtils.stringToTime(value).getTime * 10000L
+      case value: java.lang.String => DateTimeUtils.stringToTime(value).getTime * 10000L
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index 4da5e96b82e3d..cf7aa44e4cd55 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -17,21 +17,19 @@
 
 package org.apache.spark.sql.parquet
 
-import java.sql.Timestamp
-import java.util.{TimeZone, Calendar}
+import java.nio.ByteOrder
 
-import scala.collection.mutable.{Buffer, ArrayBuffer, HashMap}
+import scala.collection.mutable.{ArrayBuffer, Buffer, HashMap}
 
-import jodd.datetime.JDateTime
+import org.apache.parquet.Preconditions
 import org.apache.parquet.column.Dictionary
-import org.apache.parquet.io.api.{PrimitiveConverter, GroupConverter, Binary, Converter}
+import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
 import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.parquet.CatalystConverter.FieldType
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.parquet.timestamp.NanoTime
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -269,7 +267,12 @@ private[parquet] abstract class CatalystConverter extends GroupConverter {
    * Read a Timestamp value from a Parquet Int96Value
    */
   protected[parquet] def readTimestamp(value: Binary): Long = {
-    DateUtils.fromJavaTimestamp(CatalystTimestampConverter.convertToTimestamp(value))
+    Preconditions.checkArgument(value.length() == 12, "Must be 12 bytes")
+    val buf = value.toByteBuffer
+    buf.order(ByteOrder.LITTLE_ENDIAN)
+    val timeOfDayNanos = buf.getLong
+    val julianDay = buf.getInt
+    DateTimeUtils.fromJulianDay(julianDay, timeOfDayNanos)
   }
 }
 
@@ -498,73 +501,6 @@ private[parquet] object CatalystArrayConverter {
   val INITIAL_ARRAY_SIZE = 20
 }
 
-private[parquet] object CatalystTimestampConverter {
-  // TODO most part of this comes from Hive-0.14
-  // Hive code might have some issues, so we need to keep an eye on it.
-  // Also we use NanoTime and Int96Values from parquet-examples.
-  // We utilize jodd to convert between NanoTime and Timestamp
-  val parquetTsCalendar = new ThreadLocal[Calendar]
-  def getCalendar: Calendar = {
-    // this is a cache for the calendar instance.
-    if (parquetTsCalendar.get == null) {
-      parquetTsCalendar.set(Calendar.getInstance(TimeZone.getTimeZone("GMT")))
-    }
-    parquetTsCalendar.get
-  }
-  val NANOS_PER_SECOND: Long = 1000000000
-  val SECONDS_PER_MINUTE: Long = 60
-  val MINUTES_PER_HOUR: Long = 60
-  val NANOS_PER_MILLI: Long = 1000000
-
-  def convertToTimestamp(value: Binary): Timestamp = {
-    val nt = NanoTime.fromBinary(value)
-    val timeOfDayNanos = nt.getTimeOfDayNanos
-    val julianDay = nt.getJulianDay
-    val jDateTime = new JDateTime(julianDay.toDouble)
-    val calendar = getCalendar
-    calendar.set(Calendar.YEAR, jDateTime.getYear)
-    calendar.set(Calendar.MONTH, jDateTime.getMonth - 1)
-    calendar.set(Calendar.DAY_OF_MONTH, jDateTime.getDay)
-
-    // written in command style
-    var remainder = timeOfDayNanos
-    calendar.set(
-      Calendar.HOUR_OF_DAY,
-      (remainder / (NANOS_PER_SECOND * SECONDS_PER_MINUTE * MINUTES_PER_HOUR)).toInt)
-    remainder = remainder % (NANOS_PER_SECOND * SECONDS_PER_MINUTE * MINUTES_PER_HOUR)
-    calendar.set(
-      Calendar.MINUTE, (remainder / (NANOS_PER_SECOND * SECONDS_PER_MINUTE)).toInt)
-    remainder = remainder % (NANOS_PER_SECOND * SECONDS_PER_MINUTE)
-    calendar.set(Calendar.SECOND, (remainder / NANOS_PER_SECOND).toInt)
-    val nanos = remainder % NANOS_PER_SECOND
-    val ts = new Timestamp(calendar.getTimeInMillis)
-    ts.setNanos(nanos.toInt)
-    ts
-  }
-
-  def convertFromTimestamp(ts: Timestamp): Binary = {
-    val calendar = getCalendar
-    calendar.setTime(ts)
-    val jDateTime = new JDateTime(calendar.get(Calendar.YEAR),
-      calendar.get(Calendar.MONTH) + 1, calendar.get(Calendar.DAY_OF_MONTH))
-    // Hive-0.14 didn't set hour before get day number, while the day number should
-    // has something to do with hour, since julian day number grows at 12h GMT
-    // here we just follow what hive does.
-    val julianDay = jDateTime.getJulianDayNumber
-
-    val hour = calendar.get(Calendar.HOUR_OF_DAY)
-    val minute = calendar.get(Calendar.MINUTE)
-    val second = calendar.get(Calendar.SECOND)
-    val nanos = ts.getNanos
-    // Hive-0.14 would use hours directly, that might be wrong, since the day starts
-    // from 12h in Julian. here we just follow what hive does.
-    val nanosOfDay = nanos + second * NANOS_PER_SECOND +
-      minute * NANOS_PER_SECOND * SECONDS_PER_MINUTE +
-      hour * NANOS_PER_SECOND * SECONDS_PER_MINUTE * MINUTES_PER_HOUR
-    NanoTime(julianDay, nanosOfDay).toBinary
-  }
-}
-
 /**
  * A `parquet.io.api.GroupConverter` that converts a single-element groups that
  * match the characteristics of an array (see
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index a8775a2a8fd83..e65fa0030e179 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.parquet
 
+import java.nio.{ByteOrder, ByteBuffer}
 import java.util.{HashMap => JHashMap}
 
 import org.apache.hadoop.conf.Configuration
@@ -29,7 +30,7 @@ import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow}
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -298,7 +299,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
   }
 
   // Scratch array used to write decimals as fixed-length binary
-  private val scratchBytes = new Array[Byte](8)
+  private[this] val scratchBytes = new Array[Byte](8)
 
   private[parquet] def writeDecimal(decimal: Decimal, precision: Int): Unit = {
     val numBytes = ParquetTypesConverter.BYTES_FOR_PRECISION(precision)
@@ -313,10 +314,16 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
     writer.addBinary(Binary.fromByteArray(scratchBytes, 0, numBytes))
   }
 
+  // array used to write Timestamp as Int96 (fixed-length binary)
+  private[this] val int96buf = new Array[Byte](12)
+
   private[parquet] def writeTimestamp(ts: Long): Unit = {
-    val binaryNanoTime = CatalystTimestampConverter.convertFromTimestamp(
-      DateUtils.toJavaTimestamp(ts))
-    writer.addBinary(binaryNanoTime)
+    val (julianDay, timeOfDayNanos) = DateTimeUtils.toJulianDay(ts)
+    val buf = ByteBuffer.wrap(int96buf)
+    buf.order(ByteOrder.LITTLE_ENDIAN)
+    buf.putLong(timeOfDayNanos)
+    buf.putInt(julianDay)
+    writer.addBinary(Binary.fromByteArray(int96buf))
   }
 }
 
@@ -360,7 +367,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
       case FloatType => writer.addFloat(record.getFloat(index))
       case BooleanType => writer.addBoolean(record.getBoolean(index))
       case DateType => writer.addInteger(record.getInt(index))
-      case TimestampType => writeTimestamp(record(index).asInstanceOf[Long])
+      case TimestampType => writeTimestamp(record.getLong(index))
       case d: DecimalType =>
         if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) {
           sys.error(s"Unsupported datatype $d, cannot write to consumer")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala
deleted file mode 100644
index 4d5ed211ad0c0..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/timestamp/NanoTime.scala
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.parquet.timestamp
-
-import java.nio.{ByteBuffer, ByteOrder}
-
-import org.apache.parquet.Preconditions
-import org.apache.parquet.io.api.{Binary, RecordConsumer}
-
-private[parquet] class NanoTime extends Serializable {
-  private var julianDay = 0
-  private var timeOfDayNanos = 0L
-
-  def set(julianDay: Int, timeOfDayNanos: Long): this.type = {
-    this.julianDay = julianDay
-    this.timeOfDayNanos = timeOfDayNanos
-    this
-  }
-
-  def getJulianDay: Int = julianDay
-
-  def getTimeOfDayNanos: Long = timeOfDayNanos
-
-  def toBinary: Binary = {
-    val buf = ByteBuffer.allocate(12)
-    buf.order(ByteOrder.LITTLE_ENDIAN)
-    buf.putLong(timeOfDayNanos)
-    buf.putInt(julianDay)
-    buf.flip()
-    Binary.fromByteBuffer(buf)
-  }
-
-  def writeValue(recordConsumer: RecordConsumer): Unit = {
-    recordConsumer.addBinary(toBinary)
-  }
-
-  override def toString: String =
-    "NanoTime{julianDay=" + julianDay + ", timeOfDayNanos=" + timeOfDayNanos + "}"
-}
-
-private[sql] object NanoTime {
-  def fromBinary(bytes: Binary): NanoTime = {
-    Preconditions.checkArgument(bytes.length() == 12, "Must be 12 bytes")
-    val buf = bytes.toByteBuffer
-    buf.order(ByteOrder.LITTLE_ENDIAN)
-    val timeOfDayNanos = buf.getLong
-    val julianDay = buf.getInt
-    new NanoTime().set(julianDay, timeOfDayNanos)
-  }
-
-  def apply(julianDay: Int, timeOfDayNanos: Long): NanoTime = {
-    new NanoTime().set(julianDay, timeOfDayNanos)
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index c32d9f88dd6ee..8204a584179bb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -25,7 +25,7 @@ import org.scalactic.Tolerance._
 
 import org.apache.spark.sql.{QueryTest, Row, SQLConf}
 import org.apache.spark.sql.TestData._
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.json.InferSchema.compatibleType
 import org.apache.spark.sql.sources.LogicalRelation
 import org.apache.spark.sql.types._
@@ -76,26 +76,28 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkTypePromotion(
       Decimal(doubleNumber), enforceCorrectType(doubleNumber, DecimalType.Unlimited))
 
-    checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(intNumber)),
+    checkTypePromotion(DateTimeUtils.fromJavaTimestamp(new Timestamp(intNumber)),
         enforceCorrectType(intNumber, TimestampType))
-    checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(intNumber.toLong)),
+    checkTypePromotion(DateTimeUtils.fromJavaTimestamp(new Timestamp(intNumber.toLong)),
         enforceCorrectType(intNumber.toLong, TimestampType))
     val strTime = "2014-09-30 12:34:56"
-    checkTypePromotion(DateUtils.fromJavaTimestamp(Timestamp.valueOf(strTime)),
+    checkTypePromotion(DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf(strTime)),
         enforceCorrectType(strTime, TimestampType))
 
     val strDate = "2014-10-15"
     checkTypePromotion(
-      DateUtils.fromJavaDate(Date.valueOf(strDate)), enforceCorrectType(strDate, DateType))
+      DateTimeUtils.fromJavaDate(Date.valueOf(strDate)), enforceCorrectType(strDate, DateType))
 
     val ISO8601Time1 = "1970-01-01T01:00:01.0Z"
-    checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(3601000)),
+    checkTypePromotion(DateTimeUtils.fromJavaTimestamp(new Timestamp(3601000)),
         enforceCorrectType(ISO8601Time1, TimestampType))
-    checkTypePromotion(DateUtils.millisToDays(3601000), enforceCorrectType(ISO8601Time1, DateType))
+    checkTypePromotion(DateTimeUtils.millisToDays(3601000),
+      enforceCorrectType(ISO8601Time1, DateType))
     val ISO8601Time2 = "1970-01-01T02:00:01-01:00"
-    checkTypePromotion(DateUtils.fromJavaTimestamp(new Timestamp(10801000)),
+    checkTypePromotion(DateTimeUtils.fromJavaTimestamp(new Timestamp(10801000)),
         enforceCorrectType(ISO8601Time2, TimestampType))
-    checkTypePromotion(DateUtils.millisToDays(10801000), enforceCorrectType(ISO8601Time2, DateType))
+    checkTypePromotion(DateTimeUtils.millisToDays(10801000),
+      enforceCorrectType(ISO8601Time2, DateType))
   }
 
   test("Get compatible type") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
index 284d99d4938d1..47a7be1c6a664 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
@@ -37,7 +37,7 @@ import org.scalatest.BeforeAndAfterAll
 import org.apache.spark.SparkException
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.ScalaReflection
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 
 // Write support class for nested groups: ParquetWriter initializes GroupWriteSupport
@@ -137,7 +137,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest {
     def makeDateRDD(): DataFrame =
       sqlContext.sparkContext
         .parallelize(0 to 1000)
-        .map(i => Tuple1(DateUtils.toJavaDate(i)))
+        .map(i => Tuple1(DateTimeUtils.toJavaDate(i)))
         .toDF()
         .select($"_1")
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index 48875773224c7..79eac930e54f7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -17,13 +17,12 @@
 
 package org.apache.spark.sql.sources
 
-import java.sql.{Timestamp, Date}
-
+import java.sql.{Date, Timestamp}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.util.DateUtils
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -84,8 +83,8 @@ case class AllDataTypesScan(
         i.toDouble,
         Decimal(new java.math.BigDecimal(i)),
         Decimal(new java.math.BigDecimal(i)),
-        DateUtils.fromJavaDate(new Date(1970, 1, 1)),
-        DateUtils.fromJavaTimestamp(new Timestamp(20000 + i)),
+        DateTimeUtils.fromJavaDate(new Date(1970, 1, 1)),
+        DateTimeUtils.fromJavaTimestamp(new Timestamp(20000 + i)),
         UTF8String.fromString(s"varchar_$i"),
         Seq(i, i + 1),
         Seq(Map(UTF8String.fromString(s"str_$i") -> InternalRow(i.toLong))),
@@ -93,7 +92,7 @@ case class AllDataTypesScan(
         Map(Map(UTF8String.fromString(s"str_$i") -> i.toFloat) -> InternalRow(i.toLong)),
         Row(i, i.toString),
         Row(Seq(UTF8String.fromString(s"str_$i"), UTF8String.fromString(s"str_${i + 1}")),
-          InternalRow(Seq(DateUtils.fromJavaDate(new Date(1970, 1, i + 1))))))
+          InternalRow(Seq(DateTimeUtils.fromJavaDate(new Date(1970, 1, i + 1))))))
     }
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index d4f1ae8ee01d9..864c888ab073d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -25,7 +25,7 @@ import org.apache.hadoop.hive.serde2.{io => hiveIo}
 import org.apache.hadoop.{io => hadoopIo}
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -273,7 +273,7 @@ private[hive] trait HiveInspectors {
       System.arraycopy(writable.getBytes, 0, temp, 0, temp.length)
       temp
     case poi: WritableConstantDateObjectInspector =>
-      DateUtils.fromJavaDate(poi.getWritableConstantValue.get())
+      DateTimeUtils.fromJavaDate(poi.getWritableConstantValue.get())
     case mi: StandardConstantMapObjectInspector =>
       // take the value from the map inspector object, rather than the input data
       mi.getWritableConstantValue.map { case (k, v) =>
@@ -313,13 +313,13 @@ private[hive] trait HiveInspectors {
         System.arraycopy(bw.getBytes(), 0, result, 0, bw.getLength())
         result
       case x: DateObjectInspector if x.preferWritable() =>
-        DateUtils.fromJavaDate(x.getPrimitiveWritableObject(data).get())
-      case x: DateObjectInspector => DateUtils.fromJavaDate(x.getPrimitiveJavaObject(data))
+        DateTimeUtils.fromJavaDate(x.getPrimitiveWritableObject(data).get())
+      case x: DateObjectInspector => DateTimeUtils.fromJavaDate(x.getPrimitiveJavaObject(data))
       case x: TimestampObjectInspector if x.preferWritable() =>
         val t = x.getPrimitiveWritableObject(data)
         t.getSeconds * 10000000L + t.getNanos / 100
       case ti: TimestampObjectInspector =>
-        DateUtils.fromJavaTimestamp(ti.getPrimitiveJavaObject(data))
+        DateTimeUtils.fromJavaTimestamp(ti.getPrimitiveJavaObject(data))
       case _ => pi.getPrimitiveJavaObject(data)
     }
     case li: ListObjectInspector =>
@@ -356,10 +356,10 @@ private[hive] trait HiveInspectors {
       (o: Any) => HiveDecimal.create(o.asInstanceOf[Decimal].toJavaBigDecimal)
 
     case _: JavaDateObjectInspector =>
-      (o: Any) => DateUtils.toJavaDate(o.asInstanceOf[Int])
+      (o: Any) => DateTimeUtils.toJavaDate(o.asInstanceOf[Int])
 
     case _: JavaTimestampObjectInspector =>
-      (o: Any) => DateUtils.toJavaTimestamp(o.asInstanceOf[Long])
+      (o: Any) => DateTimeUtils.toJavaTimestamp(o.asInstanceOf[Long])
 
     case soi: StandardStructObjectInspector =>
       val wrappers = soi.getAllStructFieldRefs.map(ref => wrapperFor(ref.getFieldObjectInspector))
@@ -468,9 +468,9 @@ private[hive] trait HiveInspectors {
       case _: BinaryObjectInspector if x.preferWritable() => getBinaryWritable(a)
       case _: BinaryObjectInspector => a.asInstanceOf[Array[Byte]]
       case _: DateObjectInspector if x.preferWritable() => getDateWritable(a)
-      case _: DateObjectInspector => DateUtils.toJavaDate(a.asInstanceOf[Int])
+      case _: DateObjectInspector => DateTimeUtils.toJavaDate(a.asInstanceOf[Int])
       case _: TimestampObjectInspector if x.preferWritable() => getTimestampWritable(a)
-      case _: TimestampObjectInspector => DateUtils.toJavaTimestamp(a.asInstanceOf[Long])
+      case _: TimestampObjectInspector => DateTimeUtils.toJavaTimestamp(a.asInstanceOf[Long])
     }
     case x: SettableStructObjectInspector =>
       val fieldRefs = x.getAllStructFieldRefs
@@ -781,7 +781,7 @@ private[hive] trait HiveInspectors {
     if (value == null) {
       null
     } else {
-      new hiveIo.TimestampWritable(DateUtils.toJavaTimestamp(value.asInstanceOf[Long]))
+      new hiveIo.TimestampWritable(DateTimeUtils.toJavaTimestamp(value.asInstanceOf[Long]))
     }
 
   private def getDecimalWritable(value: Any): hiveIo.HiveDecimalWritable =
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index 439f39bafc926..00e61e35d4354 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -29,11 +29,11 @@ import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters,
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf}
 
-import org.apache.spark.{Logging}
+import org.apache.spark.Logging
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 /**
@@ -362,10 +362,10 @@ private[hive] object HadoopTableReader extends HiveInspectors with Logging {
             row.update(ordinal, HiveShim.toCatalystDecimal(oi, value))
         case oi: TimestampObjectInspector =>
           (value: Any, row: MutableRow, ordinal: Int) =>
-            row.setLong(ordinal, DateUtils.fromJavaTimestamp(oi.getPrimitiveJavaObject(value)))
+            row.setLong(ordinal, DateTimeUtils.fromJavaTimestamp(oi.getPrimitiveJavaObject(value)))
         case oi: DateObjectInspector =>
           (value: Any, row: MutableRow, ordinal: Int) =>
-            row.setInt(ordinal, DateUtils.fromJavaDate(oi.getPrimitiveJavaObject(value)))
+            row.setInt(ordinal, DateTimeUtils.fromJavaDate(oi.getPrimitiveJavaObject(value)))
         case oi: BinaryObjectInspector =>
           (value: Any, row: MutableRow, ordinal: Int) =>
             row.update(ordinal, oi.getPrimitiveJavaObject(value))
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 8b928861fcc70..ab75b12e2a2e7 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -34,7 +34,7 @@ import org.apache.hadoop.hive.common.FileUtils
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.sql.Row
 import org.apache.spark.{Logging, SerializableWritable, SparkHadoopWriter}
-import org.apache.spark.sql.catalyst.util.DateUtils
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc}
 import org.apache.spark.sql.types._
 import org.apache.spark.util.SerializableJobConf
@@ -201,7 +201,7 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
     def convertToHiveRawString(col: String, value: Any): String = {
       val raw = String.valueOf(value)
       schema(col).dataType match {
-        case DateType => DateUtils.toString(raw.toInt)
+        case DateType => DateTimeUtils.toString(raw.toInt)
         case _: DecimalType => BigDecimal(raw).toString()
         case _ => raw
       }

From 13321e65559f6354ec1287a690580fd6f498ef89 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Mon, 22 Jun 2015 20:04:49 -0700
Subject: [PATCH 0015/1454] [SPARK-7859] [SQL] Collect_set() behavior
 differences which fails the unit test under jdk8

To reproduce that:
```
JAVA_HOME=/home/hcheng/Java/jdk1.8.0_45 | build/sbt -Phadoop-2.3 -Phive  'test-only org.apache.spark.sql.hive.execution.HiveWindowFunctionQueryWithoutCodeGenSuite'
```

A simple workaround to fix that is update the original query, for getting the output size instead of the exact elements of the array (output by collect_set())

Author: Cheng Hao <hao.cheng@intel.com>

Closes #6402 from chenghao-intel/windowing and squashes the following commits:

99312ad [Cheng Hao] add order by for the select clause
edf8ce3 [Cheng Hao] update the code as suggested
7062da7 [Cheng Hao] fix the collect_set() behaviour differences under different versions of JDK
---
 .../HiveWindowFunctionQuerySuite.scala        |  8 ++
 ...estSTATs-0-6dfcd7925fb267699c4bf82737d4609 | 97 +++++++++++++++++++
 ...stSTATs-0-da0e0cca69e42118a96b8609b8fa5838 | 26 -----
 3 files changed, 105 insertions(+), 26 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-6dfcd7925fb267699c4bf82737d4609
 delete mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
index 934452fe579a1..31a49a3683338 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
@@ -526,8 +526,14 @@ abstract class HiveWindowFunctionQueryBaseSuite extends HiveComparisonTest with
       |             rows between 2 preceding and 2 following);
     """.stripMargin, reset = false)
 
+  // collect_set() output array in an arbitrary order, hence causes different result
+  // when running this test suite under Java 7 and 8.
+  // We change the original sql query a little bit for making the test suite passed
+  // under different JDK
   createQueryTest("windowing.q -- 20. testSTATs",
     """
+      |select p_mfgr,p_name, p_size, sdev, sdev_pop, uniq_data, var, cor, covarp
+      |from (
       |select  p_mfgr,p_name, p_size,
       |stddev(p_retailprice) over w1 as sdev,
       |stddev_pop(p_retailprice) over w1 as sdev_pop,
@@ -538,6 +544,8 @@ abstract class HiveWindowFunctionQueryBaseSuite extends HiveComparisonTest with
       |from part
       |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
       |             rows between 2 preceding and 2 following)
+      |) t lateral view explode(uniq_size) d as uniq_data
+      |order by p_mfgr,p_name, p_size, sdev, sdev_pop, uniq_data, var, cor, covarp
     """.stripMargin, reset = false)
 
   createQueryTest("windowing.q -- 21. testDISTs",
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-6dfcd7925fb267699c4bf82737d4609 b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-6dfcd7925fb267699c4bf82737d4609
new file mode 100644
index 0000000000000..7e5fceeddeeeb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-6dfcd7925fb267699c4bf82737d4609	
@@ -0,0 +1,97 @@
+Manufacturer#1	almond antique burnished rose metallic	2	258.10677784349235	258.10677784349235	2	66619.10876874991	0.811328754177887	2801.7074999999995
+Manufacturer#1	almond antique burnished rose metallic	2	258.10677784349235	258.10677784349235	6	66619.10876874991	0.811328754177887	2801.7074999999995
+Manufacturer#1	almond antique burnished rose metallic	2	258.10677784349235	258.10677784349235	34	66619.10876874991	0.811328754177887	2801.7074999999995
+Manufacturer#1	almond antique burnished rose metallic	2	273.70217881648074	273.70217881648074	2	74912.8826888888	1.0	4128.782222222221
+Manufacturer#1	almond antique burnished rose metallic	2	273.70217881648074	273.70217881648074	34	74912.8826888888	1.0	4128.782222222221
+Manufacturer#1	almond antique chartreuse lavender yellow	34	230.90151585470358	230.90151585470358	2	53315.51002399992	0.695639377397664	2210.7864
+Manufacturer#1	almond antique chartreuse lavender yellow	34	230.90151585470358	230.90151585470358	6	53315.51002399992	0.695639377397664	2210.7864
+Manufacturer#1	almond antique chartreuse lavender yellow	34	230.90151585470358	230.90151585470358	28	53315.51002399992	0.695639377397664	2210.7864
+Manufacturer#1	almond antique chartreuse lavender yellow	34	230.90151585470358	230.90151585470358	34	53315.51002399992	0.695639377397664	2210.7864
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	202.73109328368946	202.73109328368946	2	41099.896184	0.630785977101214	2009.9536000000007
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	202.73109328368946	202.73109328368946	6	41099.896184	0.630785977101214	2009.9536000000007
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	202.73109328368946	202.73109328368946	28	41099.896184	0.630785977101214	2009.9536000000007
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	202.73109328368946	202.73109328368946	34	41099.896184	0.630785977101214	2009.9536000000007
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	202.73109328368946	202.73109328368946	42	41099.896184	0.630785977101214	2009.9536000000007
+Manufacturer#1	almond aquamarine burnished black steel	28	121.6064517973862	121.6064517973862	6	14788.129118750014	0.2036684720435979	331.1337500000004
+Manufacturer#1	almond aquamarine burnished black steel	28	121.6064517973862	121.6064517973862	28	14788.129118750014	0.2036684720435979	331.1337500000004
+Manufacturer#1	almond aquamarine burnished black steel	28	121.6064517973862	121.6064517973862	34	14788.129118750014	0.2036684720435979	331.1337500000004
+Manufacturer#1	almond aquamarine burnished black steel	28	121.6064517973862	121.6064517973862	42	14788.129118750014	0.2036684720435979	331.1337500000004
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	96.5751586416853	96.5751586416853	6	9326.761266666683	-1.4442181184933883E-4	-0.20666666666708502
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	96.5751586416853	96.5751586416853	28	9326.761266666683	-1.4442181184933883E-4	-0.20666666666708502
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	96.5751586416853	96.5751586416853	42	9326.761266666683	-1.4442181184933883E-4	-0.20666666666708502
+Manufacturer#2	almond antique violet chocolate turquoise	14	142.2363169751898	142.2363169751898	2	20231.169866666663	-0.49369526554523185	-1113.7466666666658
+Manufacturer#2	almond antique violet chocolate turquoise	14	142.2363169751898	142.2363169751898	14	20231.169866666663	-0.49369526554523185	-1113.7466666666658
+Manufacturer#2	almond antique violet chocolate turquoise	14	142.2363169751898	142.2363169751898	40	20231.169866666663	-0.49369526554523185	-1113.7466666666658
+Manufacturer#2	almond antique violet turquoise frosted	40	137.76306498840682	137.76306498840682	2	18978.662075	-0.5205630897335946	-1004.4812499999995
+Manufacturer#2	almond antique violet turquoise frosted	40	137.76306498840682	137.76306498840682	14	18978.662075	-0.5205630897335946	-1004.4812499999995
+Manufacturer#2	almond antique violet turquoise frosted	40	137.76306498840682	137.76306498840682	25	18978.662075	-0.5205630897335946	-1004.4812499999995
+Manufacturer#2	almond antique violet turquoise frosted	40	137.76306498840682	137.76306498840682	40	18978.662075	-0.5205630897335946	-1004.4812499999995
+Manufacturer#2	almond aquamarine midnight light salmon	2	130.03972279269132	130.03972279269132	2	16910.329504000005	-0.46908967495720255	-766.1791999999995
+Manufacturer#2	almond aquamarine midnight light salmon	2	130.03972279269132	130.03972279269132	14	16910.329504000005	-0.46908967495720255	-766.1791999999995
+Manufacturer#2	almond aquamarine midnight light salmon	2	130.03972279269132	130.03972279269132	18	16910.329504000005	-0.46908967495720255	-766.1791999999995
+Manufacturer#2	almond aquamarine midnight light salmon	2	130.03972279269132	130.03972279269132	25	16910.329504000005	-0.46908967495720255	-766.1791999999995
+Manufacturer#2	almond aquamarine midnight light salmon	2	130.03972279269132	130.03972279269132	40	16910.329504000005	-0.46908967495720255	-766.1791999999995
+Manufacturer#2	almond aquamarine rose maroon antique	25	135.55100986344584	135.55100986344584	2	18374.07627499999	-0.6091405874714462	-1128.1787499999987
+Manufacturer#2	almond aquamarine rose maroon antique	25	135.55100986344584	135.55100986344584	18	18374.07627499999	-0.6091405874714462	-1128.1787499999987
+Manufacturer#2	almond aquamarine rose maroon antique	25	135.55100986344584	135.55100986344584	25	18374.07627499999	-0.6091405874714462	-1128.1787499999987
+Manufacturer#2	almond aquamarine rose maroon antique	25	135.55100986344584	135.55100986344584	40	18374.07627499999	-0.6091405874714462	-1128.1787499999987
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	156.44019460768044	156.44019460768044	2	24473.534488888927	-0.9571686373491608	-1441.4466666666676
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	156.44019460768044	156.44019460768044	18	24473.534488888927	-0.9571686373491608	-1441.4466666666676
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	156.44019460768044	156.44019460768044	25	24473.534488888927	-0.9571686373491608	-1441.4466666666676
+Manufacturer#3	almond antique chartreuse khaki white	17	196.7742266885805	196.7742266885805	14	38720.09628888887	0.5557168646224995	224.6944444444446
+Manufacturer#3	almond antique chartreuse khaki white	17	196.7742266885805	196.7742266885805	17	38720.09628888887	0.5557168646224995	224.6944444444446
+Manufacturer#3	almond antique chartreuse khaki white	17	196.7742266885805	196.7742266885805	19	38720.09628888887	0.5557168646224995	224.6944444444446
+Manufacturer#3	almond antique forest lavender goldenrod	14	275.14144189852607	275.14144189852607	1	75702.81305	-0.6720833036576083	-1296.9000000000003
+Manufacturer#3	almond antique forest lavender goldenrod	14	275.14144189852607	275.14144189852607	14	75702.81305	-0.6720833036576083	-1296.9000000000003
+Manufacturer#3	almond antique forest lavender goldenrod	14	275.14144189852607	275.14144189852607	17	75702.81305	-0.6720833036576083	-1296.9000000000003
+Manufacturer#3	almond antique forest lavender goldenrod	14	275.14144189852607	275.14144189852607	19	75702.81305	-0.6720833036576083	-1296.9000000000003
+Manufacturer#3	almond antique metallic orange dim	19	260.23473614412046	260.23473614412046	1	67722.117896	-0.5703526513979519	-2129.0664
+Manufacturer#3	almond antique metallic orange dim	19	260.23473614412046	260.23473614412046	14	67722.117896	-0.5703526513979519	-2129.0664
+Manufacturer#3	almond antique metallic orange dim	19	260.23473614412046	260.23473614412046	17	67722.117896	-0.5703526513979519	-2129.0664
+Manufacturer#3	almond antique metallic orange dim	19	260.23473614412046	260.23473614412046	19	67722.117896	-0.5703526513979519	-2129.0664
+Manufacturer#3	almond antique metallic orange dim	19	260.23473614412046	260.23473614412046	45	67722.117896	-0.5703526513979519	-2129.0664
+Manufacturer#3	almond antique misty red olive	1	275.9139962356932	275.9139962356932	1	76128.53331875012	-0.577476899644802	-2547.7868749999993
+Manufacturer#3	almond antique misty red olive	1	275.9139962356932	275.9139962356932	14	76128.53331875012	-0.577476899644802	-2547.7868749999993
+Manufacturer#3	almond antique misty red olive	1	275.9139962356932	275.9139962356932	19	76128.53331875012	-0.577476899644802	-2547.7868749999993
+Manufacturer#3	almond antique misty red olive	1	275.9139962356932	275.9139962356932	45	76128.53331875012	-0.577476899644802	-2547.7868749999993
+Manufacturer#3	almond antique olive coral navajo	45	260.5815918713796	260.5815918713796	1	67902.76602222225	-0.8710736366736884	-4099.731111111111
+Manufacturer#3	almond antique olive coral navajo	45	260.5815918713796	260.5815918713796	19	67902.76602222225	-0.8710736366736884	-4099.731111111111
+Manufacturer#3	almond antique olive coral navajo	45	260.5815918713796	260.5815918713796	45	67902.76602222225	-0.8710736366736884	-4099.731111111111
+Manufacturer#4	almond antique gainsboro frosted violet	10	170.13011889596618	170.13011889596618	10	28944.25735555559	-0.6656975320098423	-1347.4777777777779
+Manufacturer#4	almond antique gainsboro frosted violet	10	170.13011889596618	170.13011889596618	27	28944.25735555559	-0.6656975320098423	-1347.4777777777779
+Manufacturer#4	almond antique gainsboro frosted violet	10	170.13011889596618	170.13011889596618	39	28944.25735555559	-0.6656975320098423	-1347.4777777777779
+Manufacturer#4	almond antique violet mint lemon	39	242.26834609323197	242.26834609323197	7	58693.95151875002	-0.8051852719193339	-2537.328125
+Manufacturer#4	almond antique violet mint lemon	39	242.26834609323197	242.26834609323197	10	58693.95151875002	-0.8051852719193339	-2537.328125
+Manufacturer#4	almond antique violet mint lemon	39	242.26834609323197	242.26834609323197	27	58693.95151875002	-0.8051852719193339	-2537.328125
+Manufacturer#4	almond antique violet mint lemon	39	242.26834609323197	242.26834609323197	39	58693.95151875002	-0.8051852719193339	-2537.328125
+Manufacturer#4	almond aquamarine floral ivory bisque	27	234.10001662537326	234.10001662537326	7	54802.817784000035	-0.6046935574240581	-1719.8079999999995
+Manufacturer#4	almond aquamarine floral ivory bisque	27	234.10001662537326	234.10001662537326	10	54802.817784000035	-0.6046935574240581	-1719.8079999999995
+Manufacturer#4	almond aquamarine floral ivory bisque	27	234.10001662537326	234.10001662537326	12	54802.817784000035	-0.6046935574240581	-1719.8079999999995
+Manufacturer#4	almond aquamarine floral ivory bisque	27	234.10001662537326	234.10001662537326	27	54802.817784000035	-0.6046935574240581	-1719.8079999999995
+Manufacturer#4	almond aquamarine floral ivory bisque	27	234.10001662537326	234.10001662537326	39	54802.817784000035	-0.6046935574240581	-1719.8079999999995
+Manufacturer#4	almond aquamarine yellow dodger mint	7	247.3342714197732	247.3342714197732	7	61174.24181875003	-0.5508665654707869	-1719.0368749999975
+Manufacturer#4	almond aquamarine yellow dodger mint	7	247.3342714197732	247.3342714197732	12	61174.24181875003	-0.5508665654707869	-1719.0368749999975
+Manufacturer#4	almond aquamarine yellow dodger mint	7	247.3342714197732	247.3342714197732	27	61174.24181875003	-0.5508665654707869	-1719.0368749999975
+Manufacturer#4	almond aquamarine yellow dodger mint	7	247.3342714197732	247.3342714197732	39	61174.24181875003	-0.5508665654707869	-1719.0368749999975
+Manufacturer#4	almond azure aquamarine papaya violet	12	283.3344330566893	283.3344330566893	7	80278.40095555557	-0.7755740084632333	-1867.4888888888881
+Manufacturer#4	almond azure aquamarine papaya violet	12	283.3344330566893	283.3344330566893	12	80278.40095555557	-0.7755740084632333	-1867.4888888888881
+Manufacturer#4	almond azure aquamarine papaya violet	12	283.3344330566893	283.3344330566893	27	80278.40095555557	-0.7755740084632333	-1867.4888888888881
+Manufacturer#5	almond antique blue firebrick mint	31	83.69879024746363	83.69879024746363	2	7005.487488888913	0.39004303087285047	418.9233333333353
+Manufacturer#5	almond antique blue firebrick mint	31	83.69879024746363	83.69879024746363	6	7005.487488888913	0.39004303087285047	418.9233333333353
+Manufacturer#5	almond antique blue firebrick mint	31	83.69879024746363	83.69879024746363	31	7005.487488888913	0.39004303087285047	418.9233333333353
+Manufacturer#5	almond antique medium spring khaki	6	316.68049612345885	316.68049612345885	2	100286.53662500004	-0.713612911776183	-4090.853749999999
+Manufacturer#5	almond antique medium spring khaki	6	316.68049612345885	316.68049612345885	6	100286.53662500004	-0.713612911776183	-4090.853749999999
+Manufacturer#5	almond antique medium spring khaki	6	316.68049612345885	316.68049612345885	31	100286.53662500004	-0.713612911776183	-4090.853749999999
+Manufacturer#5	almond antique medium spring khaki	6	316.68049612345885	316.68049612345885	46	100286.53662500004	-0.713612911776183	-4090.853749999999
+Manufacturer#5	almond antique sky peru orange	2	285.40506298242155	285.40506298242155	2	81456.04997600002	-0.712858514567818	-3297.2011999999986
+Manufacturer#5	almond antique sky peru orange	2	285.40506298242155	285.40506298242155	6	81456.04997600002	-0.712858514567818	-3297.2011999999986
+Manufacturer#5	almond antique sky peru orange	2	285.40506298242155	285.40506298242155	23	81456.04997600002	-0.712858514567818	-3297.2011999999986
+Manufacturer#5	almond antique sky peru orange	2	285.40506298242155	285.40506298242155	31	81456.04997600002	-0.712858514567818	-3297.2011999999986
+Manufacturer#5	almond antique sky peru orange	2	285.40506298242155	285.40506298242155	46	81456.04997600002	-0.712858514567818	-3297.2011999999986
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	285.43749038756283	285.43749038756283	2	81474.56091875004	-0.984128787153391	-4871.028125000002
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	285.43749038756283	285.43749038756283	6	81474.56091875004	-0.984128787153391	-4871.028125000002
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	285.43749038756283	285.43749038756283	23	81474.56091875004	-0.984128787153391	-4871.028125000002
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	285.43749038756283	285.43749038756283	46	81474.56091875004	-0.984128787153391	-4871.028125000002
+Manufacturer#5	almond azure blanched chiffon midnight	23	315.9225931564038	315.9225931564038	2	99807.08486666664	-0.9978877469246936	-5664.856666666666
+Manufacturer#5	almond azure blanched chiffon midnight	23	315.9225931564038	315.9225931564038	23	99807.08486666664	-0.9978877469246936	-5664.856666666666
+Manufacturer#5	almond azure blanched chiffon midnight	23	315.9225931564038	315.9225931564038	46	99807.08486666664	-0.9978877469246936	-5664.856666666666
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838 b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838
deleted file mode 100644
index 1f7e8a5d67036..0000000000000
--- a/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838	
+++ /dev/null
@@ -1,26 +0,0 @@
-Manufacturer#1	almond antique burnished rose metallic	2	273.70217881648074	273.70217881648074	[34,2]	74912.8826888888	1.0	4128.782222222221
-Manufacturer#1	almond antique burnished rose metallic	2	258.10677784349235	258.10677784349235	[34,2,6]	66619.10876874991	0.811328754177887	2801.7074999999995
-Manufacturer#1	almond antique chartreuse lavender yellow	34	230.90151585470358	230.90151585470358	[34,2,6,28]	53315.51002399992	0.695639377397664	2210.7864
-Manufacturer#1	almond antique salmon chartreuse burlywood	6	202.73109328368946	202.73109328368946	[34,2,6,42,28]	41099.896184	0.630785977101214	2009.9536000000007
-Manufacturer#1	almond aquamarine burnished black steel	28	121.6064517973862	121.6064517973862	[34,6,42,28]	14788.129118750014	0.2036684720435979	331.1337500000004
-Manufacturer#1	almond aquamarine pink moccasin thistle	42	96.5751586416853	96.5751586416853	[6,42,28]	9326.761266666683	-1.4442181184933883E-4	-0.20666666666708502
-Manufacturer#2	almond antique violet chocolate turquoise	14	142.2363169751898	142.2363169751898	[2,40,14]	20231.169866666663	-0.49369526554523185	-1113.7466666666658
-Manufacturer#2	almond antique violet turquoise frosted	40	137.76306498840682	137.76306498840682	[2,25,40,14]	18978.662075	-0.5205630897335946	-1004.4812499999995
-Manufacturer#2	almond aquamarine midnight light salmon	2	130.03972279269132	130.03972279269132	[2,18,25,40,14]	16910.329504000005	-0.46908967495720255	-766.1791999999995
-Manufacturer#2	almond aquamarine rose maroon antique	25	135.55100986344584	135.55100986344584	[2,18,25,40]	18374.07627499999	-0.6091405874714462	-1128.1787499999987
-Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	156.44019460768044	156.44019460768044	[2,18,25]	24473.534488888927	-0.9571686373491608	-1441.4466666666676
-Manufacturer#3	almond antique chartreuse khaki white	17	196.7742266885805	196.7742266885805	[17,19,14]	38720.09628888887	0.5557168646224995	224.6944444444446
-Manufacturer#3	almond antique forest lavender goldenrod	14	275.14144189852607	275.14144189852607	[17,1,19,14]	75702.81305	-0.6720833036576083	-1296.9000000000003
-Manufacturer#3	almond antique metallic orange dim	19	260.23473614412046	260.23473614412046	[17,1,19,14,45]	67722.117896	-0.5703526513979519	-2129.0664
-Manufacturer#3	almond antique misty red olive	1	275.9139962356932	275.9139962356932	[1,19,14,45]	76128.53331875012	-0.577476899644802	-2547.7868749999993
-Manufacturer#3	almond antique olive coral navajo	45	260.5815918713796	260.5815918713796	[1,19,45]	67902.76602222225	-0.8710736366736884	-4099.731111111111
-Manufacturer#4	almond antique gainsboro frosted violet	10	170.13011889596618	170.13011889596618	[39,27,10]	28944.25735555559	-0.6656975320098423	-1347.4777777777779
-Manufacturer#4	almond antique violet mint lemon	39	242.26834609323197	242.26834609323197	[39,7,27,10]	58693.95151875002	-0.8051852719193339	-2537.328125
-Manufacturer#4	almond aquamarine floral ivory bisque	27	234.10001662537326	234.10001662537326	[39,7,27,10,12]	54802.817784000035	-0.6046935574240581	-1719.8079999999995
-Manufacturer#4	almond aquamarine yellow dodger mint	7	247.3342714197732	247.3342714197732	[39,7,27,12]	61174.24181875003	-0.5508665654707869	-1719.0368749999975
-Manufacturer#4	almond azure aquamarine papaya violet	12	283.3344330566893	283.3344330566893	[7,27,12]	80278.40095555557	-0.7755740084632333	-1867.4888888888881
-Manufacturer#5	almond antique blue firebrick mint	31	83.69879024746363	83.69879024746363	[2,6,31]	7005.487488888913	0.39004303087285047	418.9233333333353
-Manufacturer#5	almond antique medium spring khaki	6	316.68049612345885	316.68049612345885	[2,6,46,31]	100286.53662500004	-0.713612911776183	-4090.853749999999
-Manufacturer#5	almond antique sky peru orange	2	285.40506298242155	285.40506298242155	[2,23,6,46,31]	81456.04997600002	-0.712858514567818	-3297.2011999999986
-Manufacturer#5	almond aquamarine dodger light gainsboro	46	285.43749038756283	285.43749038756283	[2,23,6,46]	81474.56091875004	-0.984128787153391	-4871.028125000002
-Manufacturer#5	almond azure blanched chiffon midnight	23	315.9225931564038	315.9225931564038	[2,23,46]	99807.08486666664	-0.9978877469246936	-5664.856666666666

From c4d2343966cbae40a8271a2e6cad66227d2f8249 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Mon, 22 Jun 2015 20:25:32 -0700
Subject: [PATCH 0016/1454] MAINTENANCE: Automated closing of pull requests.

This commit exists to close the following pull requests on Github:

Closes #2849 (close requested by 'srowen')
Closes #2786 (close requested by 'andrewor14')
Closes #4678 (close requested by 'JoshRosen')
Closes #5457 (close requested by 'andrewor14')
Closes #3346 (close requested by 'andrewor14')
Closes #6518 (close requested by 'andrewor14')
Closes #5403 (close requested by 'pwendell')
Closes #2110 (close requested by 'srowen')

From 44fa7df64daa55bd6eb1f2c219a9701b34e1c2a3 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 22 Jun 2015 20:55:38 -0700
Subject: [PATCH 0017/1454] [SPARK-8548] [SPARKR] Remove the trailing
 whitespaces from the SparkR files

[[SPARK-8548] Remove the trailing whitespaces from the SparkR files - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8548)

- This is the result of `lint-r`
    https://gist.github.com/yu-iskw/0019b37a2c1167f33986

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6945 from yu-iskw/SPARK-8548 and squashes the following commits:

0bd567a [Yu ISHIKAWA] [SPARK-8548][SparkR] Remove the trailing whitespaces from the SparkR files
---
 R/pkg/R/DataFrame.R                     | 96 ++++++++++++-------------
 R/pkg/R/RDD.R                           | 48 ++++++-------
 R/pkg/R/SQLContext.R                    | 14 ++--
 R/pkg/R/broadcast.R                     |  6 +-
 R/pkg/R/deserialize.R                   |  2 +-
 R/pkg/R/generics.R                      | 15 ++--
 R/pkg/R/group.R                         |  1 -
 R/pkg/R/jobj.R                          |  2 +-
 R/pkg/R/pairRDD.R                       |  4 +-
 R/pkg/R/schema.R                        |  2 +-
 R/pkg/R/serialize.R                     |  2 +-
 R/pkg/R/sparkR.R                        |  6 +-
 R/pkg/R/utils.R                         | 48 ++++++-------
 R/pkg/R/zzz.R                           |  1 -
 R/pkg/inst/tests/test_binaryFile.R      |  7 +-
 R/pkg/inst/tests/test_binary_function.R | 28 ++++----
 R/pkg/inst/tests/test_rdd.R             | 12 ++--
 R/pkg/inst/tests/test_shuffle.R         | 28 ++++----
 R/pkg/inst/tests/test_sparkSQL.R        | 28 ++++----
 R/pkg/inst/tests/test_take.R            |  1 -
 R/pkg/inst/tests/test_textFile.R        |  7 +-
 R/pkg/inst/tests/test_utils.R           | 12 ++--
 22 files changed, 182 insertions(+), 188 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 0af5cb8881e35..6feabf4189c2d 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -38,7 +38,7 @@ setClass("DataFrame",
 setMethod("initialize", "DataFrame", function(.Object, sdf, isCached) {
   .Object@env <- new.env()
   .Object@env$isCached <- isCached
-  
+
   .Object@sdf <- sdf
   .Object
 })
@@ -55,11 +55,11 @@ dataFrame <- function(sdf, isCached = FALSE) {
 ############################ DataFrame Methods ##############################################
 
 #' Print Schema of a DataFrame
-#' 
+#'
 #' Prints out the schema in tree format
-#' 
+#'
 #' @param x A SparkSQL DataFrame
-#' 
+#'
 #' @rdname printSchema
 #' @export
 #' @examples
@@ -78,11 +78,11 @@ setMethod("printSchema",
           })
 
 #' Get schema object
-#' 
+#'
 #' Returns the schema of this DataFrame as a structType object.
-#' 
+#'
 #' @param x A SparkSQL DataFrame
-#' 
+#'
 #' @rdname schema
 #' @export
 #' @examples
@@ -100,9 +100,9 @@ setMethod("schema",
           })
 
 #' Explain
-#' 
+#'
 #' Print the logical and physical Catalyst plans to the console for debugging.
-#' 
+#'
 #' @param x A SparkSQL DataFrame
 #' @param extended Logical. If extended is False, explain() only prints the physical plan.
 #' @rdname explain
@@ -200,11 +200,11 @@ setMethod("show", "DataFrame",
           })
 
 #' DataTypes
-#' 
+#'
 #' Return all column names and their data types as a list
-#' 
+#'
 #' @param x A SparkSQL DataFrame
-#' 
+#'
 #' @rdname dtypes
 #' @export
 #' @examples
@@ -224,11 +224,11 @@ setMethod("dtypes",
           })
 
 #' Column names
-#' 
+#'
 #' Return all column names as a list
-#' 
+#'
 #' @param x A SparkSQL DataFrame
-#' 
+#'
 #' @rdname columns
 #' @export
 #' @examples
@@ -256,12 +256,12 @@ setMethod("names",
           })
 
 #' Register Temporary Table
-#' 
+#'
 #' Registers a DataFrame as a Temporary Table in the SQLContext
-#' 
+#'
 #' @param x A SparkSQL DataFrame
 #' @param tableName A character vector containing the name of the table
-#' 
+#'
 #' @rdname registerTempTable
 #' @export
 #' @examples
@@ -306,11 +306,11 @@ setMethod("insertInto",
           })
 
 #' Cache
-#' 
+#'
 #' Persist with the default storage level (MEMORY_ONLY).
-#' 
+#'
 #' @param x A SparkSQL DataFrame
-#' 
+#'
 #' @rdname cache-methods
 #' @export
 #' @examples
@@ -400,7 +400,7 @@ setMethod("repartition",
           signature(x = "DataFrame", numPartitions = "numeric"),
           function(x, numPartitions) {
             sdf <- callJMethod(x@sdf, "repartition", numToInt(numPartitions))
-            dataFrame(sdf)     
+            dataFrame(sdf)
           })
 
 # toJSON
@@ -489,7 +489,7 @@ setMethod("distinct",
 #' sqlContext <- sparkRSQL.init(sc)
 #' path <- "path/to/file.json"
 #' df <- jsonFile(sqlContext, path)
-#' collect(sample(df, FALSE, 0.5)) 
+#' collect(sample(df, FALSE, 0.5))
 #' collect(sample(df, TRUE, 0.5))
 #'}
 setMethod("sample",
@@ -513,11 +513,11 @@ setMethod("sample_frac",
           })
 
 #' Count
-#' 
+#'
 #' Returns the number of rows in a DataFrame
-#' 
+#'
 #' @param x A SparkSQL DataFrame
-#' 
+#'
 #' @rdname count
 #' @export
 #' @examples
@@ -568,13 +568,13 @@ setMethod("collect",
           })
 
 #' Limit
-#' 
+#'
 #' Limit the resulting DataFrame to the number of rows specified.
-#' 
+#'
 #' @param x A SparkSQL DataFrame
 #' @param num The number of rows to return
 #' @return A new DataFrame containing the number of rows specified.
-#' 
+#'
 #' @rdname limit
 #' @export
 #' @examples
@@ -593,7 +593,7 @@ setMethod("limit",
           })
 
 #' Take the first NUM rows of a DataFrame and return a the results as a data.frame
-#' 
+#'
 #' @rdname take
 #' @export
 #' @examples
@@ -613,8 +613,8 @@ setMethod("take",
 
 #' Head
 #'
-#' Return the first NUM rows of a DataFrame as a data.frame. If NUM is NULL, 
-#' then head() returns the first 6 rows in keeping with the current data.frame 
+#' Return the first NUM rows of a DataFrame as a data.frame. If NUM is NULL,
+#' then head() returns the first 6 rows in keeping with the current data.frame
 #' convention in R.
 #'
 #' @param x A SparkSQL DataFrame
@@ -659,11 +659,11 @@ setMethod("first",
           })
 
 # toRDD()
-# 
+#
 # Converts a Spark DataFrame to an RDD while preserving column names.
-# 
+#
 # @param x A Spark DataFrame
-# 
+#
 # @rdname DataFrame
 # @export
 # @examples
@@ -1167,7 +1167,7 @@ setMethod("where",
 #'
 #' @param x A Spark DataFrame
 #' @param y A Spark DataFrame
-#' @param joinExpr (Optional) The expression used to perform the join. joinExpr must be a 
+#' @param joinExpr (Optional) The expression used to perform the join. joinExpr must be a
 #' Column expression. If joinExpr is omitted, join() wil perform a Cartesian join
 #' @param joinType The type of join to perform. The following join types are available:
 #' 'inner', 'outer', 'left_outer', 'right_outer', 'semijoin'. The default joinType is "inner".
@@ -1303,7 +1303,7 @@ setMethod("except",
 #' @param source A name for external data source
 #' @param mode One of 'append', 'overwrite', 'error', 'ignore'
 #'
-#' @rdname write.df 
+#' @rdname write.df
 #' @export
 #' @examples
 #'\dontrun{
@@ -1401,7 +1401,7 @@ setMethod("saveAsTable",
 #' @param col A string of name
 #' @param ... Additional expressions
 #' @return A DataFrame
-#' @rdname describe 
+#' @rdname describe
 #' @export
 #' @examples
 #'\dontrun{
@@ -1444,7 +1444,7 @@ setMethod("describe",
 #'                    This overwrites the how parameter.
 #' @param cols Optional list of column names to consider.
 #' @return A DataFrame
-#' 
+#'
 #' @rdname nafunctions
 #' @export
 #' @examples
@@ -1465,7 +1465,7 @@ setMethod("dropna",
             if (is.null(minNonNulls)) {
               minNonNulls <- if (how == "any") { length(cols) } else { 1 }
             }
-            
+
             naFunctions <- callJMethod(x@sdf, "na")
             sdf <- callJMethod(naFunctions, "drop",
                                as.integer(minNonNulls), listToSeq(as.list(cols)))
@@ -1488,16 +1488,16 @@ setMethod("na.omit",
 #' @param value Value to replace null values with.
 #'              Should be an integer, numeric, character or named list.
 #'              If the value is a named list, then cols is ignored and
-#'              value must be a mapping from column name (character) to 
+#'              value must be a mapping from column name (character) to
 #'              replacement value. The replacement value must be an
 #'              integer, numeric or character.
 #' @param cols optional list of column names to consider.
 #'             Columns specified in cols that do not have matching data
-#'             type are ignored. For example, if value is a character, and 
+#'             type are ignored. For example, if value is a character, and
 #'             subset contains a non-character column, then the non-character
 #'             column is simply ignored.
 #' @return A DataFrame
-#' 
+#'
 #' @rdname nafunctions
 #' @export
 #' @examples
@@ -1515,14 +1515,14 @@ setMethod("fillna",
             if (!(class(value) %in% c("integer", "numeric", "character", "list"))) {
               stop("value should be an integer, numeric, charactor or named list.")
             }
-            
+
             if (class(value) == "list") {
               # Check column names in the named list
               colNames <- names(value)
               if (length(colNames) == 0 || !all(colNames != "")) {
                 stop("value should be an a named list with each name being a column name.")
               }
-              
+
               # Convert to the named list to an environment to be passed to JVM
               valueMap <- new.env()
               for (col in colNames) {
@@ -1533,19 +1533,19 @@ setMethod("fillna",
                 }
                 valueMap[[col]] <- v
               }
-              
+
               # When value is a named list, caller is expected not to pass in cols
               if (!is.null(cols)) {
                 warning("When value is a named list, cols is ignored!")
                 cols <- NULL
               }
-              
+
               value <- valueMap
             } else if (is.integer(value)) {
               # Cast an integer to a numeric
               value <- as.numeric(value)
             }
-            
+
             naFunctions <- callJMethod(x@sdf, "na")
             sdf <- if (length(cols) == 0) {
               callJMethod(naFunctions, "fill", value)
diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R
index 0513299515644..89511141d3ef7 100644
--- a/R/pkg/R/RDD.R
+++ b/R/pkg/R/RDD.R
@@ -48,7 +48,7 @@ setMethod("initialize", "RDD", function(.Object, jrdd, serializedMode,
   # byte: The RDD stores data serialized in R.
   # string: The RDD stores data as strings.
   # row: The RDD stores the serialized rows of a DataFrame.
-  
+
   # We use an environment to store mutable states inside an RDD object.
   # Note that R's call-by-value semantics makes modifying slots inside an
   # object (passed as an argument into a function, such as cache()) difficult:
@@ -363,7 +363,7 @@ setMethod("collectPartition",
 
 # @description
 # \code{collectAsMap} returns a named list as a map that contains all of the elements
-# in a key-value pair RDD. 
+# in a key-value pair RDD.
 # @examples
 #\dontrun{
 # sc <- sparkR.init()
@@ -666,7 +666,7 @@ setMethod("minimum",
 # rdd <- parallelize(sc, 1:10)
 # sumRDD(rdd) # 55
 #}
-# @rdname sumRDD 
+# @rdname sumRDD
 # @aliases sumRDD,RDD
 setMethod("sumRDD",
           signature(x = "RDD"),
@@ -1090,11 +1090,11 @@ setMethod("sortBy",
 # Return:
 #   A list of the first N elements from the RDD in the specified order.
 #
-takeOrderedElem <- function(x, num, ascending = TRUE) {          
+takeOrderedElem <- function(x, num, ascending = TRUE) {
   if (num <= 0L) {
     return(list())
   }
-  
+
   partitionFunc <- function(part) {
     if (num < length(part)) {
       # R limitation: order works only on primitive types!
@@ -1152,7 +1152,7 @@ takeOrderedElem <- function(x, num, ascending = TRUE) {
 # @aliases takeOrdered,RDD,RDD-method
 setMethod("takeOrdered",
           signature(x = "RDD", num = "integer"),
-          function(x, num) {          
+          function(x, num) {
             takeOrderedElem(x, num)
           })
 
@@ -1173,7 +1173,7 @@ setMethod("takeOrdered",
 # @aliases top,RDD,RDD-method
 setMethod("top",
           signature(x = "RDD", num = "integer"),
-          function(x, num) {          
+          function(x, num) {
             takeOrderedElem(x, num, FALSE)
           })
 
@@ -1181,7 +1181,7 @@ setMethod("top",
 #
 # Aggregate the elements of each partition, and then the results for all the
 # partitions, using a given associative function and a neutral "zero value".
-# 
+#
 # @param x An RDD.
 # @param zeroValue A neutral "zero value".
 # @param op An associative function for the folding operation.
@@ -1207,7 +1207,7 @@ setMethod("fold",
 #
 # Aggregate the elements of each partition, and then the results for all the
 # partitions, using given combine functions and a neutral "zero value".
-# 
+#
 # @param x An RDD.
 # @param zeroValue A neutral "zero value".
 # @param seqOp A function to aggregate the RDD elements. It may return a different
@@ -1230,11 +1230,11 @@ setMethod("fold",
 # @aliases aggregateRDD,RDD,RDD-method
 setMethod("aggregateRDD",
           signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", combOp = "ANY"),
-          function(x, zeroValue, seqOp, combOp) {        
+          function(x, zeroValue, seqOp, combOp) {
             partitionFunc <- function(part) {
               Reduce(seqOp, part, zeroValue)
             }
-            
+
             partitionList <- collect(lapplyPartition(x, partitionFunc),
                                      flatten = FALSE)
             Reduce(combOp, partitionList, zeroValue)
@@ -1330,7 +1330,7 @@ setMethod("setName",
 #\dontrun{
 # sc <- sparkR.init()
 # rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L)
-# collect(zipWithUniqueId(rdd)) 
+# collect(zipWithUniqueId(rdd))
 # # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2))
 #}
 # @rdname zipWithUniqueId
@@ -1426,7 +1426,7 @@ setMethod("glom",
             partitionFunc <- function(part) {
               list(part)
             }
-            
+
             lapplyPartition(x, partitionFunc)
           })
 
@@ -1498,16 +1498,16 @@ setMethod("zipRDD",
             # The jrdd's elements are of scala Tuple2 type. The serialized
             # flag here is used for the elements inside the tuples.
             rdd <- RDD(jrdd, getSerializedMode(rdds[[1]]))
-            
+
             mergePartitions(rdd, TRUE)
           })
 
 # Cartesian product of this RDD and another one.
 #
-# Return the Cartesian product of this RDD and another one, 
-# that is, the RDD of all pairs of elements (a, b) where a 
+# Return the Cartesian product of this RDD and another one,
+# that is, the RDD of all pairs of elements (a, b) where a
 # is in this and b is in other.
-# 
+#
 # @param x An RDD.
 # @param other An RDD.
 # @return A new RDD which is the Cartesian product of these two RDDs.
@@ -1515,7 +1515,7 @@ setMethod("zipRDD",
 #\dontrun{
 # sc <- sparkR.init()
 # rdd <- parallelize(sc, 1:2)
-# sortByKey(cartesian(rdd, rdd)) 
+# sortByKey(cartesian(rdd, rdd))
 # # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2))
 #}
 # @rdname cartesian
@@ -1528,7 +1528,7 @@ setMethod("cartesian",
             # The jrdd's elements are of scala Tuple2 type. The serialized
             # flag here is used for the elements inside the tuples.
             rdd <- RDD(jrdd, getSerializedMode(rdds[[1]]))
-            
+
             mergePartitions(rdd, FALSE)
           })
 
@@ -1598,11 +1598,11 @@ setMethod("intersection",
 
 # Zips an RDD's partitions with one (or more) RDD(s).
 # Same as zipPartitions in Spark.
-# 
+#
 # @param ... RDDs to be zipped.
 # @param func A function to transform zipped partitions.
-# @return A new RDD by applying a function to the zipped partitions. 
-#         Assumes that all the RDDs have the *same number of partitions*, but 
+# @return A new RDD by applying a function to the zipped partitions.
+#         Assumes that all the RDDs have the *same number of partitions*, but
 #         does *not* require them to have the same number of elements in each partition.
 # @examples
 #\dontrun{
@@ -1610,7 +1610,7 @@ setMethod("intersection",
 # rdd1 <- parallelize(sc, 1:2, 2L)  # 1, 2
 # rdd2 <- parallelize(sc, 1:4, 2L)  # 1:2, 3:4
 # rdd3 <- parallelize(sc, 1:6, 2L)  # 1:3, 4:6
-# collect(zipPartitions(rdd1, rdd2, rdd3, 
+# collect(zipPartitions(rdd1, rdd2, rdd3,
 #                       func = function(x, y, z) { list(list(x, y, z))} ))
 # # list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6)))
 #}
@@ -1627,7 +1627,7 @@ setMethod("zipPartitions",
             if (length(unique(nPart)) != 1) {
               stop("Can only zipPartitions RDDs which have the same number of partitions.")
             }
-            
+
             rrdds <- lapply(rrdds, function(rdd) {
               mapPartitionsWithIndex(rdd, function(partIndex, part) {
                 print(length(part))
diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 22a4b5bf86ebd..9a743a3411533 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -182,7 +182,7 @@ setMethod("toDF", signature(x = "RDD"),
 
 #' Create a DataFrame from a JSON file.
 #'
-#' Loads a JSON file (one object per line), returning the result as a DataFrame 
+#' Loads a JSON file (one object per line), returning the result as a DataFrame
 #' It goes through the entire dataset once to determine the schema.
 #'
 #' @param sqlContext SQLContext to use
@@ -238,7 +238,7 @@ jsonRDD <- function(sqlContext, rdd, schema = NULL, samplingRatio = 1.0) {
 
 
 #' Create a DataFrame from a Parquet file.
-#' 
+#'
 #' Loads a Parquet file, returning the result as a DataFrame.
 #'
 #' @param sqlContext SQLContext to use
@@ -278,7 +278,7 @@ sql <- function(sqlContext, sqlQuery) {
 }
 
 #' Create a DataFrame from a SparkSQL Table
-#' 
+#'
 #' Returns the specified Table as a DataFrame.  The Table must have already been registered
 #' in the SQLContext.
 #'
@@ -298,7 +298,7 @@ sql <- function(sqlContext, sqlQuery) {
 
 table <- function(sqlContext, tableName) {
   sdf <- callJMethod(sqlContext, "table", tableName)
-  dataFrame(sdf) 
+  dataFrame(sdf)
 }
 
 
@@ -352,7 +352,7 @@ tableNames <- function(sqlContext, databaseName = NULL) {
 
 
 #' Cache Table
-#' 
+#'
 #' Caches the specified table in-memory.
 #'
 #' @param sqlContext SQLContext to use
@@ -370,11 +370,11 @@ tableNames <- function(sqlContext, databaseName = NULL) {
 #' }
 
 cacheTable <- function(sqlContext, tableName) {
-  callJMethod(sqlContext, "cacheTable", tableName)  
+  callJMethod(sqlContext, "cacheTable", tableName)
 }
 
 #' Uncache Table
-#' 
+#'
 #' Removes the specified table from the in-memory cache.
 #'
 #' @param sqlContext SQLContext to use
diff --git a/R/pkg/R/broadcast.R b/R/pkg/R/broadcast.R
index 23dc38780716e..2403925b267c8 100644
--- a/R/pkg/R/broadcast.R
+++ b/R/pkg/R/broadcast.R
@@ -27,9 +27,9 @@
 # @description Broadcast variables can be created using the broadcast
 #              function from a \code{SparkContext}.
 # @rdname broadcast-class
-# @seealso broadcast 
+# @seealso broadcast
 #
-# @param id Id of the backing Spark broadcast variable 
+# @param id Id of the backing Spark broadcast variable
 # @export
 setClass("Broadcast", slots = list(id = "character"))
 
@@ -68,7 +68,7 @@ setMethod("value",
 # variable on workers. Not intended for use outside the package.
 #
 # @rdname broadcast-internal
-# @seealso broadcast, value 
+# @seealso broadcast, value
 
 # @param bcastId The id of broadcast variable to set
 # @param value The value to be set
diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 257b435607ce8..d961bbc383688 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -18,7 +18,7 @@
 # Utility functions to deserialize objects from Java.
 
 # Type mapping from Java to R
-# 
+#
 # void -> NULL
 # Int -> integer
 # String -> character
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 12e09176c9f92..79055b7f18558 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -130,7 +130,7 @@ setGeneric("maximum", function(x) { standardGeneric("maximum") })
 # @export
 setGeneric("minimum", function(x) { standardGeneric("minimum") })
 
-# @rdname sumRDD 
+# @rdname sumRDD
 # @export
 setGeneric("sumRDD", function(x) { standardGeneric("sumRDD") })
 
@@ -219,7 +219,7 @@ setGeneric("zipRDD", function(x, other) { standardGeneric("zipRDD") })
 
 # @rdname zipRDD
 # @export
-setGeneric("zipPartitions", function(..., func) { standardGeneric("zipPartitions") }, 
+setGeneric("zipPartitions", function(..., func) { standardGeneric("zipPartitions") },
            signature = "...")
 
 # @rdname zipWithIndex
@@ -364,7 +364,7 @@ setGeneric("subtract",
 
 # @rdname subtractByKey
 # @export
-setGeneric("subtractByKey", 
+setGeneric("subtractByKey",
            function(x, other, numPartitions = 1) {
              standardGeneric("subtractByKey")
            })
@@ -399,15 +399,15 @@ setGeneric("describe", function(x, col, ...) { standardGeneric("describe") })
 #' @rdname nafunctions
 #' @export
 setGeneric("dropna",
-           function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) { 
-             standardGeneric("dropna") 
+           function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
+             standardGeneric("dropna")
            })
 
 #' @rdname nafunctions
 #' @export
 setGeneric("na.omit",
-           function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) { 
-             standardGeneric("na.omit") 
+           function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
+             standardGeneric("na.omit")
            })
 
 #' @rdname schema
@@ -656,4 +656,3 @@ setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 #' @rdname column
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
-
diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R
index b758481997574..8f1c68f7c4d28 100644
--- a/R/pkg/R/group.R
+++ b/R/pkg/R/group.R
@@ -136,4 +136,3 @@ createMethods <- function() {
 }
 
 createMethods()
-
diff --git a/R/pkg/R/jobj.R b/R/pkg/R/jobj.R
index a8a25230b636d..0838a7bb35e0d 100644
--- a/R/pkg/R/jobj.R
+++ b/R/pkg/R/jobj.R
@@ -16,7 +16,7 @@
 #
 
 # References to objects that exist on the JVM backend
-# are maintained using the jobj. 
+# are maintained using the jobj.
 
 #' @include generics.R
 NULL
diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R
index 1e24286dbcae2..7f902ba8e683e 100644
--- a/R/pkg/R/pairRDD.R
+++ b/R/pkg/R/pairRDD.R
@@ -784,7 +784,7 @@ setMethod("sortByKey",
             newRDD <- partitionBy(x, numPartitions, rangePartitionFunc)
             lapplyPartition(newRDD, partitionFunc)
           })
-          
+
 # Subtract a pair RDD with another pair RDD.
 #
 # Return an RDD with the pairs from x whose keys are not in other.
@@ -820,7 +820,7 @@ setMethod("subtractByKey",
           })
 
 # Return a subset of this RDD sampled by key.
-# 
+#
 # @description
 # \code{sampleByKey} Create a sample of this RDD using variable sampling rates
 # for different keys as specified by fractions, a key to sampling rate map.
diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R
index e442119086b17..15e2bdbd55d79 100644
--- a/R/pkg/R/schema.R
+++ b/R/pkg/R/schema.R
@@ -20,7 +20,7 @@
 
 #' structType
 #'
-#' Create a structType object that contains the metadata for a DataFrame. Intended for 
+#' Create a structType object that contains the metadata for a DataFrame. Intended for
 #' use with createDataFrame and toDF.
 #'
 #' @param x a structField object (created with the field() function)
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R
index 3169d7968f8fe..78535eff0d2f6 100644
--- a/R/pkg/R/serialize.R
+++ b/R/pkg/R/serialize.R
@@ -175,7 +175,7 @@ writeGenericList <- function(con, list) {
     writeObject(con, elem)
   }
 }
-  
+
 # Used to pass in hash maps required on Java side.
 writeEnv <- function(con, env) {
   len <- length(env)
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 2efd4f0742e77..dbde0c44c55d5 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -43,7 +43,7 @@ sparkR.stop <- function() {
       callJMethod(sc, "stop")
       rm(".sparkRjsc", envir = env)
     }
-  
+
     if (exists(".backendLaunched", envir = env)) {
       callJStatic("SparkRHandler", "stopBackend")
     }
@@ -174,7 +174,7 @@ sparkR.init <- function(
   for (varname in names(sparkEnvir)) {
     sparkEnvirMap[[varname]] <- sparkEnvir[[varname]]
   }
-  
+
   sparkExecutorEnvMap <- new.env()
   if (!any(names(sparkExecutorEnv) == "LD_LIBRARY_PATH")) {
     sparkExecutorEnvMap[["LD_LIBRARY_PATH"]] <- paste0("$LD_LIBRARY_PATH:",Sys.getenv("LD_LIBRARY_PATH"))
@@ -214,7 +214,7 @@ sparkR.init <- function(
 
 #' Initialize a new SQLContext.
 #'
-#' This function creates a SparkContext from an existing JavaSparkContext and 
+#' This function creates a SparkContext from an existing JavaSparkContext and
 #' then uses it to initialize a new SQLContext
 #'
 #' @param jsc The existing JavaSparkContext created with SparkR.init()
diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 69b2700191c9a..13cec0f712fb4 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -368,21 +368,21 @@ listToSeq <- function(l) {
 }
 
 # Utility function to recursively traverse the Abstract Syntax Tree (AST) of a
-# user defined function (UDF), and to examine variables in the UDF to decide 
+# user defined function (UDF), and to examine variables in the UDF to decide
 # if their values should be included in the new function environment.
 # param
 #   node The current AST node in the traversal.
 #   oldEnv The original function environment.
 #   defVars An Accumulator of variables names defined in the function's calling environment,
 #           including function argument and local variable names.
-#   checkedFunc An environment of function objects examined during cleanClosure. It can 
+#   checkedFunc An environment of function objects examined during cleanClosure. It can
 #               be considered as a "name"-to-"list of functions" mapping.
 #   newEnv A new function environment to store necessary function dependencies, an output argument.
 processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
   nodeLen <- length(node)
-  
+
   if (nodeLen > 1 && typeof(node) == "language") {
-    # Recursive case: current AST node is an internal node, check for its children. 
+    # Recursive case: current AST node is an internal node, check for its children.
     if (length(node[[1]]) > 1) {
       for (i in 1:nodeLen) {
         processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv)
@@ -393,7 +393,7 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
         for (i in 2:nodeLen) {
           processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv)
         }
-      } else if (nodeChar == "<-" || nodeChar == "=" || 
+      } else if (nodeChar == "<-" || nodeChar == "=" ||
                    nodeChar == "<<-") { # Assignment Ops.
         defVar <- node[[2]]
         if (length(defVar) == 1 && typeof(defVar) == "symbol") {
@@ -422,21 +422,21 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
         }
       }
     }
-  } else if (nodeLen == 1 && 
+  } else if (nodeLen == 1 &&
                (typeof(node) == "symbol" || typeof(node) == "language")) {
     # Base case: current AST node is a leaf node and a symbol or a function call.
     nodeChar <- as.character(node)
     if (!nodeChar %in% defVars$data) {  # Not a function parameter or local variable.
       func.env <- oldEnv
       topEnv <- parent.env(.GlobalEnv)
-      # Search in function environment, and function's enclosing environments 
+      # Search in function environment, and function's enclosing environments
       # up to global environment. There is no need to look into package environments
-      # above the global or namespace environment that is not SparkR below the global, 
+      # above the global or namespace environment that is not SparkR below the global,
       # as they are assumed to be loaded on workers.
       while (!identical(func.env, topEnv)) {
         # Namespaces other than "SparkR" will not be searched.
-        if (!isNamespace(func.env) || 
-              (getNamespaceName(func.env) == "SparkR" && 
+        if (!isNamespace(func.env) ||
+              (getNamespaceName(func.env) == "SparkR" &&
               !(nodeChar %in% getNamespaceExports("SparkR")))) {  # Only include SparkR internals.
           # Set parameter 'inherits' to FALSE since we do not need to search in
           # attached package environments.
@@ -444,7 +444,7 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
                        error = function(e) { FALSE })) {
             obj <- get(nodeChar, envir = func.env, inherits = FALSE)
             if (is.function(obj)) {  # If the node is a function call.
-              funcList <- mget(nodeChar, envir = checkedFuncs, inherits = F, 
+              funcList <- mget(nodeChar, envir = checkedFuncs, inherits = F,
                                ifnotfound = list(list(NULL)))[[1]]
               found <- sapply(funcList, function(func) {
                 ifelse(identical(func, obj), TRUE, FALSE)
@@ -453,7 +453,7 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
                 break
               }
               # Function has not been examined, record it and recursively clean its closure.
-              assign(nodeChar, 
+              assign(nodeChar,
                      if (is.null(funcList[[1]])) {
                        list(obj)
                      } else {
@@ -466,7 +466,7 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
             break
           }
         }
-        
+
         # Continue to search in enclosure.
         func.env <- parent.env(func.env)
       }
@@ -474,8 +474,8 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
   }
 }
 
-# Utility function to get user defined function (UDF) dependencies (closure). 
-# More specifically, this function captures the values of free variables defined 
+# Utility function to get user defined function (UDF) dependencies (closure).
+# More specifically, this function captures the values of free variables defined
 # outside a UDF, and stores them in the function's environment.
 # param
 #   func A function whose closure needs to be captured.
@@ -488,7 +488,7 @@ cleanClosure <- function(func, checkedFuncs = new.env()) {
     newEnv <- new.env(parent = .GlobalEnv)
     func.body <- body(func)
     oldEnv <- environment(func)
-    # defVars is an Accumulator of variables names defined in the function's calling 
+    # defVars is an Accumulator of variables names defined in the function's calling
     # environment. First, function's arguments are added to defVars.
     defVars <- initAccumulator()
     argNames <- names(as.list(args(func)))
@@ -509,15 +509,15 @@ cleanClosure <- function(func, checkedFuncs = new.env()) {
 # return value
 #   A list of two result RDDs.
 appendPartitionLengths <- function(x, other) {
-  if (getSerializedMode(x) != getSerializedMode(other) || 
+  if (getSerializedMode(x) != getSerializedMode(other) ||
       getSerializedMode(x) == "byte") {
     # Append the number of elements in each partition to that partition so that we can later
     # know the boundary of elements from x and other.
     #
-    # Note that this appending also serves the purpose of reserialization, because even if 
+    # Note that this appending also serves the purpose of reserialization, because even if
     # any RDD is serialized, we need to reserialize it to make sure its partitions are encoded
     # as a single byte array. For example, partitions of an RDD generated from partitionBy()
-    # may be encoded as multiple byte arrays.          
+    # may be encoded as multiple byte arrays.
     appendLength <- function(part) {
       len <- length(part)
       part[[len + 1]] <- len + 1
@@ -544,23 +544,23 @@ mergePartitions <- function(rdd, zip) {
         lengthOfValues <- part[[len]]
         lengthOfKeys <- part[[len - lengthOfValues]]
         stopifnot(len == lengthOfKeys + lengthOfValues)
-        
+
         # For zip operation, check if corresponding partitions of both RDDs have the same number of elements.
         if (zip && lengthOfKeys != lengthOfValues) {
           stop("Can only zip RDDs with same number of elements in each pair of corresponding partitions.")
         }
-        
+
         if (lengthOfKeys > 1) {
           keys <- part[1 : (lengthOfKeys - 1)]
         } else {
           keys <- list()
         }
         if (lengthOfValues > 1) {
-          values <- part[(lengthOfKeys + 1) : (len - 1)]                    
+          values <- part[(lengthOfKeys + 1) : (len - 1)]
         } else {
           values <- list()
         }
-        
+
         if (!zip) {
           return(mergeCompactLists(keys, values))
         }
@@ -578,6 +578,6 @@ mergePartitions <- function(rdd, zip) {
       part
     }
   }
-  
+
   PipelinedRDD(rdd, partitionFunc)
 }
diff --git a/R/pkg/R/zzz.R b/R/pkg/R/zzz.R
index 80d796d467943..301feade65fa3 100644
--- a/R/pkg/R/zzz.R
+++ b/R/pkg/R/zzz.R
@@ -18,4 +18,3 @@
 .onLoad <- function(libname, pkgname) {
   sparkR.onLoad(libname, pkgname)
 }
-
diff --git a/R/pkg/inst/tests/test_binaryFile.R b/R/pkg/inst/tests/test_binaryFile.R
index ca4218f3819f8..4db7266abc8e2 100644
--- a/R/pkg/inst/tests/test_binaryFile.R
+++ b/R/pkg/inst/tests/test_binaryFile.R
@@ -59,15 +59,15 @@ test_that("saveAsObjectFile()/objectFile() following RDD transformations works",
   wordCount <- lapply(words, function(word) { list(word, 1L) })
 
   counts <- reduceByKey(wordCount, "+", 2L)
-  
+
   saveAsObjectFile(counts, fileName2)
   counts <- objectFile(sc, fileName2)
-    
+
   output <- collect(counts)
   expected <- list(list("awesome.", 1), list("Spark", 2), list("pretty.", 1),
                     list("is", 2))
   expect_equal(sortKeyValueList(output), sortKeyValueList(expected))
-  
+
   unlink(fileName1)
   unlink(fileName2, recursive = TRUE)
 })
@@ -87,4 +87,3 @@ test_that("saveAsObjectFile()/objectFile() works with multiple paths", {
   unlink(fileName1, recursive = TRUE)
   unlink(fileName2, recursive = TRUE)
 })
-
diff --git a/R/pkg/inst/tests/test_binary_function.R b/R/pkg/inst/tests/test_binary_function.R
index 6785a7bdae8cb..a1e354e567be5 100644
--- a/R/pkg/inst/tests/test_binary_function.R
+++ b/R/pkg/inst/tests/test_binary_function.R
@@ -30,7 +30,7 @@ mockFile <- c("Spark is pretty.", "Spark is awesome.")
 test_that("union on two RDDs", {
   actual <- collect(unionRDD(rdd, rdd))
   expect_equal(actual, as.list(rep(nums, 2)))
-  
+
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
   writeLines(mockFile, fileName)
 
@@ -52,14 +52,14 @@ test_that("union on two RDDs", {
 test_that("cogroup on two RDDs", {
   rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
   rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
-  cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L) 
+  cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L)
   actual <- collect(cogroup.rdd)
-  expect_equal(actual, 
+  expect_equal(actual,
                list(list(1, list(list(1), list(2, 3))), list(2, list(list(4), list()))))
-  
+
   rdd1 <- parallelize(sc, list(list("a", 1), list("a", 4)))
   rdd2 <- parallelize(sc, list(list("b", 2), list("a", 3)))
-  cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L) 
+  cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L)
   actual <- collect(cogroup.rdd)
 
   expected <- list(list("b", list(list(), list(2))), list("a", list(list(1, 4), list(3))))
@@ -71,31 +71,31 @@ test_that("zipPartitions() on RDDs", {
   rdd1 <- parallelize(sc, 1:2, 2L)  # 1, 2
   rdd2 <- parallelize(sc, 1:4, 2L)  # 1:2, 3:4
   rdd3 <- parallelize(sc, 1:6, 2L)  # 1:3, 4:6
-  actual <- collect(zipPartitions(rdd1, rdd2, rdd3, 
+  actual <- collect(zipPartitions(rdd1, rdd2, rdd3,
                                   func = function(x, y, z) { list(list(x, y, z))} ))
   expect_equal(actual,
                list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6))))
-  
+
   mockFile = c("Spark is pretty.", "Spark is awesome.")
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
   writeLines(mockFile, fileName)
-  
+
   rdd <- textFile(sc, fileName, 1)
-  actual <- collect(zipPartitions(rdd, rdd, 
+  actual <- collect(zipPartitions(rdd, rdd,
                                   func = function(x, y) { list(paste(x, y, sep = "\n")) }))
   expected <- list(paste(mockFile, mockFile, sep = "\n"))
   expect_equal(actual, expected)
-  
+
   rdd1 <- parallelize(sc, 0:1, 1)
-  actual <- collect(zipPartitions(rdd1, rdd, 
+  actual <- collect(zipPartitions(rdd1, rdd,
                                   func = function(x, y) { list(x + nchar(y)) }))
   expected <- list(0:1 + nchar(mockFile))
   expect_equal(actual, expected)
-  
+
   rdd <- map(rdd, function(x) { x })
-  actual <- collect(zipPartitions(rdd, rdd1, 
+  actual <- collect(zipPartitions(rdd, rdd1,
                                   func = function(x, y) { list(y + nchar(x)) }))
   expect_equal(actual, expected)
-  
+
   unlink(fileName)
 })
diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R
index 03207353c31c6..4fe653856756e 100644
--- a/R/pkg/inst/tests/test_rdd.R
+++ b/R/pkg/inst/tests/test_rdd.R
@@ -477,7 +477,7 @@ test_that("cartesian() on RDDs", {
                  list(1, 1), list(1, 2), list(1, 3),
                  list(2, 1), list(2, 2), list(2, 3),
                  list(3, 1), list(3, 2), list(3, 3)))
-  
+
   # test case where one RDD is empty
   emptyRdd <- parallelize(sc, list())
   actual <- collect(cartesian(rdd, emptyRdd))
@@ -486,7 +486,7 @@ test_that("cartesian() on RDDs", {
   mockFile = c("Spark is pretty.", "Spark is awesome.")
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
   writeLines(mockFile, fileName)
-  
+
   rdd <- textFile(sc, fileName)
   actual <- collect(cartesian(rdd, rdd))
   expected <- list(
@@ -495,7 +495,7 @@ test_that("cartesian() on RDDs", {
     list("Spark is pretty.", "Spark is pretty."),
     list("Spark is pretty.", "Spark is awesome."))
   expect_equal(sortKeyValueList(actual), expected)
-  
+
   rdd1 <- parallelize(sc, 0:1)
   actual <- collect(cartesian(rdd1, rdd))
   expect_equal(sortKeyValueList(actual),
@@ -504,11 +504,11 @@ test_that("cartesian() on RDDs", {
                  list(0, "Spark is awesome."),
                  list(1, "Spark is pretty."),
                  list(1, "Spark is awesome.")))
-  
+
   rdd1 <- map(rdd, function(x) { x })
   actual <- collect(cartesian(rdd, rdd1))
   expect_equal(sortKeyValueList(actual), expected)
-  
+
   unlink(fileName)
 })
 
@@ -760,7 +760,7 @@ test_that("collectAsMap() on a pairwise RDD", {
 })
 
 test_that("show()", {
-  rdd <- parallelize(sc, list(1:10))    
+  rdd <- parallelize(sc, list(1:10))
   expect_output(show(rdd), "ParallelCollectionRDD\\[\\d+\\] at parallelize at RRDD\\.scala:\\d+")
 })
 
diff --git a/R/pkg/inst/tests/test_shuffle.R b/R/pkg/inst/tests/test_shuffle.R
index d7dedda553c56..adf0b91d25fe9 100644
--- a/R/pkg/inst/tests/test_shuffle.R
+++ b/R/pkg/inst/tests/test_shuffle.R
@@ -106,39 +106,39 @@ test_that("aggregateByKey", {
   zeroValue <- list(0, 0)
   seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) }
   combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) }
-  aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L)   
-  
+  aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L)
+
   actual <- collect(aggregatedRDD)
-  
+
   expected <- list(list(1, list(3, 2)), list(2, list(7, 2)))
   expect_equal(sortKeyValueList(actual), sortKeyValueList(expected))
 
   # test aggregateByKey for string keys
   rdd <- parallelize(sc, list(list("a", 1), list("a", 2), list("b", 3), list("b", 4)))
-  
+
   zeroValue <- list(0, 0)
   seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) }
   combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) }
-  aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L)   
+  aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L)
 
   actual <- collect(aggregatedRDD)
-  
+
   expected <- list(list("a", list(3, 2)), list("b", list(7, 2)))
   expect_equal(sortKeyValueList(actual), sortKeyValueList(expected))
 })
 
-test_that("foldByKey", {  
+test_that("foldByKey", {
   # test foldByKey for int keys
   folded <- foldByKey(intRdd, 0, "+", 2L)
-  
+
   actual <- collect(folded)
-  
+
   expected <- list(list(2L, 101), list(1L, 199))
   expect_equal(sortKeyValueList(actual), sortKeyValueList(expected))
 
   # test foldByKey for double keys
   folded <- foldByKey(doubleRdd, 0, "+", 2L)
-  
+
   actual <- collect(folded)
 
   expected <- list(list(1.5, 199), list(2.5, 101))
@@ -146,15 +146,15 @@ test_that("foldByKey", {
 
   # test foldByKey for string keys
   stringKeyPairs <- list(list("a", -1), list("b", 100), list("b", 1), list("a", 200))
-  
+
   stringKeyRDD <- parallelize(sc, stringKeyPairs)
   folded <- foldByKey(stringKeyRDD, 0, "+", 2L)
-  
+
   actual <- collect(folded)
-  
+
   expected <- list(list("b", 101), list("a", 199))
   expect_equal(sortKeyValueList(actual), sortKeyValueList(expected))
-  
+
   # test foldByKey for empty pair RDD
   rdd <- parallelize(sc, list())
   folded <- foldByKey(rdd, 0, "+", 2L)
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 8946348ef801c..fc7f3f074b67c 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -67,7 +67,7 @@ test_that("structType and structField", {
   expect_true(inherits(testField, "structField"))
   expect_true(testField$name() == "a")
   expect_true(testField$nullable())
-  
+
   testSchema <- structType(testField, structField("b", "integer"))
   expect_true(inherits(testSchema, "structType"))
   expect_true(inherits(testSchema$fields()[[2]], "structField"))
@@ -598,7 +598,7 @@ test_that("column functions", {
   c3 <- lower(c) + upper(c) + first(c) + last(c)
   c4 <- approxCountDistinct(c) + countDistinct(c) + cast(c, "string")
   c5 <- n(c) + n_distinct(c)
-  c5 <- acos(c) + asin(c) + atan(c) + cbrt(c) 
+  c5 <- acos(c) + asin(c) + atan(c) + cbrt(c)
   c6 <- ceiling(c) + cos(c) + cosh(c) + exp(c) + expm1(c)
   c7 <- floor(c) + log(c) + log10(c) + log1p(c) + rint(c)
   c8 <- sign(c) + sin(c) + sinh(c) + tan(c) + tanh(c)
@@ -829,7 +829,7 @@ test_that("dropna() on a DataFrame", {
   rows <- collect(df)
 
   # drop with columns
-  
+
   expected <- rows[!is.na(rows$name),]
   actual <- collect(dropna(df, cols = "name"))
   expect_true(identical(expected, actual))
@@ -842,7 +842,7 @@ test_that("dropna() on a DataFrame", {
   expect_true(identical(expected$age, actual$age))
   expect_true(identical(expected$height, actual$height))
   expect_true(identical(expected$name, actual$name))
-  
+
   expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
   actual <- collect(dropna(df, cols = c("age", "height")))
   expect_true(identical(expected, actual))
@@ -850,7 +850,7 @@ test_that("dropna() on a DataFrame", {
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df))
   expect_true(identical(expected, actual))
-  
+
   # drop with how
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
@@ -860,7 +860,7 @@ test_that("dropna() on a DataFrame", {
   expected <- rows[!is.na(rows$age) | !is.na(rows$height) | !is.na(rows$name),]
   actual <- collect(dropna(df, "all"))
   expect_true(identical(expected, actual))
-  
+
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df, "any"))
   expect_true(identical(expected, actual))
@@ -872,14 +872,14 @@ test_that("dropna() on a DataFrame", {
   expected <- rows[!is.na(rows$age) | !is.na(rows$height),]
   actual <- collect(dropna(df, "all", cols = c("age", "height")))
   expect_true(identical(expected, actual))
-  
+
   # drop with threshold
-  
+
   expected <- rows[as.integer(!is.na(rows$age)) + as.integer(!is.na(rows$height)) >= 2,]
   actual <- collect(dropna(df, minNonNulls = 2, cols = c("age", "height")))
-  expect_true(identical(expected, actual))  
+  expect_true(identical(expected, actual))
 
-  expected <- rows[as.integer(!is.na(rows$age)) + 
+  expected <- rows[as.integer(!is.na(rows$age)) +
                    as.integer(!is.na(rows$height)) +
                    as.integer(!is.na(rows$name)) >= 3,]
   actual <- collect(dropna(df, minNonNulls = 3, cols = c("name", "age", "height")))
@@ -889,9 +889,9 @@ test_that("dropna() on a DataFrame", {
 test_that("fillna() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPathNa)
   rows <- collect(df)
-  
+
   # fill with value
-  
+
   expected <- rows
   expected$age[is.na(expected$age)] <- 50
   expected$height[is.na(expected$height)] <- 50.6
@@ -912,7 +912,7 @@ test_that("fillna() on a DataFrame", {
   expected$name[is.na(expected$name)] <- "unknown"
   actual <- collect(fillna(df, "unknown", c("age", "name")))
   expect_true(identical(expected, actual))
-  
+
   # fill with named list
 
   expected <- rows
@@ -920,7 +920,7 @@ test_that("fillna() on a DataFrame", {
   expected$height[is.na(expected$height)] <- 50.6
   expected$name[is.na(expected$name)] <- "unknown"
   actual <- collect(fillna(df, list("age" = 50, "height" = 50.6, "name" = "unknown")))
-  expect_true(identical(expected, actual))  
+  expect_true(identical(expected, actual))
 })
 
 unlink(parquetPath)
diff --git a/R/pkg/inst/tests/test_take.R b/R/pkg/inst/tests/test_take.R
index 7f4c7c315d787..c5eb417b40159 100644
--- a/R/pkg/inst/tests/test_take.R
+++ b/R/pkg/inst/tests/test_take.R
@@ -64,4 +64,3 @@ test_that("take() gives back the original elements in correct count and order",
   expect_true(length(take(numListRDD, 0)) == 0)
   expect_true(length(take(numVectorRDD, 0)) == 0)
 })
-
diff --git a/R/pkg/inst/tests/test_textFile.R b/R/pkg/inst/tests/test_textFile.R
index 6b87b4b3e0b08..092ad9dc10c2e 100644
--- a/R/pkg/inst/tests/test_textFile.R
+++ b/R/pkg/inst/tests/test_textFile.R
@@ -58,7 +58,7 @@ test_that("textFile() word count works as expected", {
   expected <- list(list("pretty.", 1), list("is", 2), list("awesome.", 1),
                    list("Spark", 2))
   expect_equal(sortKeyValueList(output), sortKeyValueList(expected))
-  
+
   unlink(fileName)
 })
 
@@ -115,13 +115,13 @@ test_that("textFile() and saveAsTextFile() word count works as expected", {
 
   saveAsTextFile(counts, fileName2)
   rdd <- textFile(sc, fileName2)
-   
+
   output <- collect(rdd)
   expected <- list(list("awesome.", 1), list("Spark", 2),
                    list("pretty.", 1), list("is", 2))
   expectedStr <- lapply(expected, function(x) { toString(x) })
   expect_equal(sortKeyValueList(output), sortKeyValueList(expectedStr))
-  
+
   unlink(fileName1)
   unlink(fileName2)
 })
@@ -159,4 +159,3 @@ test_that("Pipelined operations on RDDs created using textFile", {
 
   unlink(fileName)
 })
-
diff --git a/R/pkg/inst/tests/test_utils.R b/R/pkg/inst/tests/test_utils.R
index 539e3a3c19df3..15030e6f1d77e 100644
--- a/R/pkg/inst/tests/test_utils.R
+++ b/R/pkg/inst/tests/test_utils.R
@@ -43,13 +43,13 @@ test_that("serializeToBytes on RDD", {
   mockFile <- c("Spark is pretty.", "Spark is awesome.")
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
   writeLines(mockFile, fileName)
-  
+
   text.rdd <- textFile(sc, fileName)
   expect_true(getSerializedMode(text.rdd) == "string")
   ser.rdd <- serializeToBytes(text.rdd)
   expect_equal(collect(ser.rdd), as.list(mockFile))
   expect_true(getSerializedMode(ser.rdd) == "byte")
-  
+
   unlink(fileName)
 })
 
@@ -64,7 +64,7 @@ test_that("cleanClosure on R functions", {
   expect_equal(actual, y)
   actual <- get("g", envir = env, inherits = FALSE)
   expect_equal(actual, g)
-  
+
   # Test for nested enclosures and package variables.
   env2 <- new.env()
   funcEnv <- new.env(parent = env2)
@@ -106,7 +106,7 @@ test_that("cleanClosure on R functions", {
   expect_equal(length(ls(env)), 1)
   actual <- get("y", envir = env, inherits = FALSE)
   expect_equal(actual, y)
-  
+
   # Test for function (and variable) definitions.
   f <- function(x) {
     g <- function(y) { y * 2 }
@@ -115,7 +115,7 @@ test_that("cleanClosure on R functions", {
   newF <- cleanClosure(f)
   env <- environment(newF)
   expect_equal(length(ls(env)), 0)  # "y" and "g" should not be included.
-  
+
   # Test for overriding variables in base namespace (Issue: SparkR-196).
   nums <- as.list(1:10)
   rdd <- parallelize(sc, nums, 2L)
@@ -128,7 +128,7 @@ test_that("cleanClosure on R functions", {
   actual <- collect(lapply(rdd, f))
   expected <- as.list(c(rep(FALSE, 4), rep(TRUE, 6)))
   expect_equal(actual, expected)
-  
+
   # Test for broadcast variables.
   a <- matrix(nrow=10, ncol=10, data=rnorm(100))
   aBroadcast <- broadcast(sc, a)

From 164fe2aa44993da6c77af6de5efdae47a8b3958c Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Mon, 22 Jun 2015 22:40:19 -0700
Subject: [PATCH 0018/1454] [SPARK-7781] [MLLIB] gradient boosted trees.train
 regressor missing max bins

Author: Holden Karau <holden@pigscanfly.ca>

Closes #6331 from holdenk/SPARK-7781-GradientBoostedTrees.trainRegressor-missing-max-bins and squashes the following commits:

2894695 [Holden Karau] remove extra blank line
2573e8d [Holden Karau] Update the scala side of the pythonmllibapi and make the test a bit nicer too
3a09170 [Holden Karau] add maxBins to to the train method as well
af7f274 [Holden Karau] Add maxBins to GradientBoostedTrees.trainRegressor and correctly mention the default of 32 in other places where it mentioned 100
---
 .../mllib/api/python/PythonMLLibAPI.scala     |  4 +++-
 python/pyspark/mllib/tests.py                 |  7 ++++++
 python/pyspark/mllib/tree.py                  | 22 ++++++++++++-------
 3 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 634d56d08d17e..f9a271f47ee2c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -696,12 +696,14 @@ private[python] class PythonMLLibAPI extends Serializable {
       lossStr: String,
       numIterations: Int,
       learningRate: Double,
-      maxDepth: Int): GradientBoostedTreesModel = {
+      maxDepth: Int,
+      maxBins: Int): GradientBoostedTreesModel = {
     val boostingStrategy = BoostingStrategy.defaultParams(algoStr)
     boostingStrategy.setLoss(Losses.fromString(lossStr))
     boostingStrategy.setNumIterations(numIterations)
     boostingStrategy.setLearningRate(learningRate)
     boostingStrategy.treeStrategy.setMaxDepth(maxDepth)
+    boostingStrategy.treeStrategy.setMaxBins(maxBins)
     boostingStrategy.treeStrategy.categoricalFeaturesInfo = categoricalFeaturesInfo.asScala.toMap
 
     val cached = data.rdd.persist(StorageLevel.MEMORY_AND_DISK)
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index b13159e29d2aa..c8d61b9855a69 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -463,6 +463,13 @@ def test_regression(self):
         except ValueError:
             self.fail()
 
+        # Verify that maxBins is being passed through
+        GradientBoostedTrees.trainRegressor(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4, maxBins=32)
+        with self.assertRaises(Exception) as cm:
+            GradientBoostedTrees.trainRegressor(
+                rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4, maxBins=1)
+
 
 class StatTests(MLlibTestCase):
     # SPARK-4023
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index cfcbea573fd22..372b86a7c95d9 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -299,7 +299,7 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees,
                  1 internal node + 2 leaf nodes. (default: 4)
         :param maxBins: maximum number of bins used for splitting
                  features
-                 (default: 100)
+                 (default: 32)
         :param seed: Random seed for bootstrapping and choosing feature
                  subsets.
         :return: RandomForestModel that can be used for prediction
@@ -377,7 +377,7 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetSt
                  1 leaf node; depth 1 means 1 internal node + 2 leaf
                  nodes. (default: 4)
         :param maxBins: maximum number of bins used for splitting
-                 features (default: 100)
+                 features (default: 32)
         :param seed: Random seed for bootstrapping and choosing feature
                  subsets.
         :return: RandomForestModel that can be used for prediction
@@ -435,16 +435,17 @@ class GradientBoostedTrees(object):
 
     @classmethod
     def _train(cls, data, algo, categoricalFeaturesInfo,
-               loss, numIterations, learningRate, maxDepth):
+               loss, numIterations, learningRate, maxDepth, maxBins):
         first = data.first()
         assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint"
         model = callMLlibFunc("trainGradientBoostedTreesModel", data, algo, categoricalFeaturesInfo,
-                              loss, numIterations, learningRate, maxDepth)
+                              loss, numIterations, learningRate, maxDepth, maxBins)
         return GradientBoostedTreesModel(model)
 
     @classmethod
     def trainClassifier(cls, data, categoricalFeaturesInfo,
-                        loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3):
+                        loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3,
+                        maxBins=32):
         """
         Method to train a gradient-boosted trees model for
         classification.
@@ -467,6 +468,8 @@ def trainClassifier(cls, data, categoricalFeaturesInfo,
         :param maxDepth: Maximum depth of the tree. E.g., depth 0 means
                  1 leaf node; depth 1 means 1 internal node + 2 leaf
                  nodes. (default: 3)
+        :param maxBins: maximum number of bins used for splitting
+                 features (default: 32) DecisionTree requires maxBins >= max categories
         :return: GradientBoostedTreesModel that can be used for
                    prediction
 
@@ -499,11 +502,12 @@ def trainClassifier(cls, data, categoricalFeaturesInfo,
         [1.0, 0.0]
         """
         return cls._train(data, "classification", categoricalFeaturesInfo,
-                          loss, numIterations, learningRate, maxDepth)
+                          loss, numIterations, learningRate, maxDepth, maxBins)
 
     @classmethod
     def trainRegressor(cls, data, categoricalFeaturesInfo,
-                       loss="leastSquaresError", numIterations=100, learningRate=0.1, maxDepth=3):
+                       loss="leastSquaresError", numIterations=100, learningRate=0.1, maxDepth=3,
+                       maxBins=32):
         """
         Method to train a gradient-boosted trees model for regression.
 
@@ -522,6 +526,8 @@ def trainRegressor(cls, data, categoricalFeaturesInfo,
                  contribution of each estimator. The learning rate
                  should be between in the interval (0, 1].
                  (default: 0.1)
+        :param maxBins: maximum number of bins used for splitting
+                 features (default: 32) DecisionTree requires maxBins >= max categories
         :param maxDepth: Maximum depth of the tree. E.g., depth 0 means
                  1 leaf node; depth 1 means 1 internal node + 2 leaf
                  nodes.  (default: 3)
@@ -556,7 +562,7 @@ def trainRegressor(cls, data, categoricalFeaturesInfo,
         [1.0, 0.0]
         """
         return cls._train(data, "regression", categoricalFeaturesInfo,
-                          loss, numIterations, learningRate, maxDepth)
+                          loss, numIterations, learningRate, maxDepth, maxBins)
 
 
 def _test():

From d4f633514a393320c9ae64c00a75f702e6f58c67 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 22 Jun 2015 23:04:36 -0700
Subject: [PATCH 0019/1454] [SPARK-8431] [SPARKR] Add in operator to DataFrame
 Column in SparkR

[[SPARK-8431] Add in operator to DataFrame Column in SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8431)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6941 from yu-iskw/SPARK-8431 and squashes the following commits:

1f64423 [Yu ISHIKAWA] Modify the comment
f4309a7 [Yu ISHIKAWA] Make a `setMethod` for `%in%` be independent
6e37936 [Yu ISHIKAWA] Modify a variable name
c196173 [Yu ISHIKAWA] [SPARK-8431][SparkR] Add in operator to DataFrame Column in SparkR
---
 R/pkg/R/column.R                 | 16 ++++++++++++++++
 R/pkg/inst/tests/test_sparkSQL.R | 10 ++++++++++
 2 files changed, 26 insertions(+)

diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 80e92d3105a36..8e4b0f5bf1c4d 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -210,6 +210,22 @@ setMethod("cast",
             }
           })
 
+#' Match a column with given values.
+#'
+#' @rdname column
+#' @return a matched values as a result of comparing with given values.
+#' \dontrun{
+#'   filter(df, "age in (10, 30)")
+#'   where(df, df$age %in% c(10, 30))
+#' }
+setMethod("%in%",
+          signature(x = "Column"),
+          function(x, table) {
+            table <- listToSeq(as.list(table))
+            jc <- callJMethod(x@jc, "in", table)
+            return(column(jc))
+          })
+
 #' Approx Count Distinct
 #'
 #' @rdname column
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index fc7f3f074b67c..417153dc0985c 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -693,6 +693,16 @@ test_that("filter() on a DataFrame", {
   filtered2 <- where(df, df$name != "Michael")
   expect_true(count(filtered2) == 2)
   expect_true(collect(filtered2)$age[2] == 19)
+
+  # test suites for %in%
+  filtered3 <- filter(df, "age in (19)")
+  expect_equal(count(filtered3), 1)
+  filtered4 <- filter(df, "age in (19, 30)")
+  expect_equal(count(filtered4), 2)
+  filtered5 <- where(df, df$age %in% c(19))
+  expect_equal(count(filtered5), 1)
+  filtered6 <- where(df, df$age %in% c(19, 30))
+  expect_equal(count(filtered6), 2)
 })
 
 test_that("join() on a DataFrame", {

From 31bd30687bc29c0e457c37308d489ae2b6e5b72a Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Mon, 22 Jun 2015 23:11:56 -0700
Subject: [PATCH 0020/1454] [SPARK-8359] [SQL] Fix incorrect decimal precision
 after multiplication

JIRA: https://issues.apache.org/jira/browse/SPARK-8359

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #6814 from viirya/fix_decimal2 and squashes the following commits:

071a757 [Liang-Chi Hsieh] Remove maximum precision and use MathContext.UNLIMITED.
df217d4 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into fix_decimal2
a43bfc3 [Liang-Chi Hsieh] Add MathContext with maximum supported precision.
72eeb3f [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into fix_decimal2
44c9348 [Liang-Chi Hsieh] Fix incorrect decimal precision after multiplication.
---
 .../src/main/scala/org/apache/spark/sql/types/Decimal.scala | 6 ++++--
 .../org/apache/spark/sql/types/decimal/DecimalSuite.scala   | 5 +++++
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index a85af9e04aedb..bd9823bc05424 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.types
 
+import java.math.{MathContext, RoundingMode}
+
 import org.apache.spark.annotation.DeveloperApi
 
 /**
@@ -137,9 +139,9 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def toBigDecimal: BigDecimal = {
     if (decimalVal.ne(null)) {
-      decimalVal
+      decimalVal(MathContext.UNLIMITED)
     } else {
-      BigDecimal(longVal, _scale)
+      BigDecimal(longVal, _scale)(MathContext.UNLIMITED)
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
index 4c0365cf1b6f9..ccc29c0dc8c35 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
@@ -162,4 +162,9 @@ class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
     assert(new Decimal().set(100L, 10, 0).toUnscaledLong === 100L)
     assert(Decimal(Long.MaxValue, 100, 0).toUnscaledLong === Long.MaxValue)
   }
+
+  test("accurate precision after multiplication") {
+    val decimal = (Decimal(Long.MaxValue, 38, 0) * Decimal(Long.MaxValue, 38, 0)).toJavaBigDecimal
+    assert(decimal.unscaledValue.toString === "85070591730234615847396907784232501249")
+  }
 }

From 9b618fb0d2536121d2784ff5341d74723e810fc5 Mon Sep 17 00:00:00 2001
From: Hari Shreedharan <hshreedharan@apache.org>
Date: Mon, 22 Jun 2015 23:34:17 -0700
Subject: [PATCH 0021/1454] =?UTF-8?q?[SPARK-8483]=20[STREAMING]=20Remove?=
 =?UTF-8?q?=20commons-lang3=20dependency=20from=20Flume=20Si=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…nk. Also bump Flume version to 1.6.0

Author: Hari Shreedharan <hshreedharan@apache.org>

Closes #6910 from harishreedharan/remove-commons-lang3 and squashes the following commits:

9875f7d [Hari Shreedharan] Revert back to Flume 1.4.0
ca35eb0 [Hari Shreedharan] [SPARK-8483][Streaming] Remove commons-lang3 dependency from Flume Sink. Also bump Flume version to 1.6.0
---
 external/flume-sink/pom.xml                                   | 4 ----
 .../spark/streaming/flume/sink/SparkAvroCallbackHandler.scala | 4 ++--
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index 7a7dccc3d0922..0664cfb2021e1 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -35,10 +35,6 @@
   <url>http://spark.apache.org/</url>
 
   <dependencies>
-    <dependency>
-      <groupId>org.apache.commons</groupId>
-      <artifactId>commons-lang3</artifactId>
-    </dependency>
     <dependency>
       <groupId>org.apache.flume</groupId>
       <artifactId>flume-ng-sdk</artifactId>
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
index dc2a4ab138e18..719fca0938b3a 100644
--- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
@@ -16,13 +16,13 @@
  */
 package org.apache.spark.streaming.flume.sink
 
+import java.util.UUID
 import java.util.concurrent.{CountDownLatch, Executors}
 import java.util.concurrent.atomic.AtomicLong
 
 import scala.collection.mutable
 
 import org.apache.flume.Channel
-import org.apache.commons.lang3.RandomStringUtils
 
 /**
  * Class that implements the SparkFlumeProtocol, that is used by the Avro Netty Server to process
@@ -53,7 +53,7 @@ private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Cha
   // Since the new txn may not have the same sequence number we must guard against accidentally
   // committing a new transaction. To reduce the probability of that happening a random string is
   // prepended to the sequence number. Does not change for life of sink
-  private val seqBase = RandomStringUtils.randomAlphanumeric(8)
+  private val seqBase = UUID.randomUUID().toString.substring(0, 8)
   private val seqCounter = new AtomicLong(0)
 
   // Protected by `sequenceNumberToProcessor`

From f0dcbe8a7c2de510b47a21eb45cde34777638758 Mon Sep 17 00:00:00 2001
From: Scott Taylor <github@megatron.me.uk>
Date: Mon, 22 Jun 2015 23:37:56 -0700
Subject: [PATCH 0022/1454] [SPARK-8541] [PYSPARK] test the absolute error in
 approx doctests

A minor change but one which is (presumably) visible on the public api docs webpage.

Author: Scott Taylor <github@megatron.me.uk>

Closes #6942 from megatron-me-uk/patch-3 and squashes the following commits:

fbed000 [Scott Taylor] test the absolute error in approx doctests
---
 python/pyspark/rdd.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 20c0bc93f413c..1b64be23a667e 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -2198,7 +2198,7 @@ def sumApprox(self, timeout, confidence=0.95):
 
         >>> rdd = sc.parallelize(range(1000), 10)
         >>> r = sum(range(1000))
-        >>> (rdd.sumApprox(1000) - r) / r < 0.05
+        >>> abs(rdd.sumApprox(1000) - r) / r < 0.05
         True
         """
         jrdd = self.mapPartitions(lambda it: [float(sum(it))])._to_java_object_rdd()
@@ -2215,7 +2215,7 @@ def meanApprox(self, timeout, confidence=0.95):
 
         >>> rdd = sc.parallelize(range(1000), 10)
         >>> r = sum(range(1000)) / 1000.0
-        >>> (rdd.meanApprox(1000) - r) / r < 0.05
+        >>> abs(rdd.meanApprox(1000) - r) / r < 0.05
         True
         """
         jrdd = self.map(float)._to_java_object_rdd()

From 6ceb169608428a651d53c93bf73ca5ac53a6bde2 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 23 Jun 2015 01:50:31 -0700
Subject: [PATCH 0023/1454] [SPARK-8300] DataFrame hint for broadcast join.

Users can now do
```scala
left.join(broadcast(right), "joinKey")
```
to give the query planner a hint that "right" DataFrame is small and should be broadcasted.

Author: Reynold Xin <rxin@databricks.com>

Closes #6751 from rxin/broadcastjoin-hint and squashes the following commits:

953eec2 [Reynold Xin] Code review feedback.
88752d8 [Reynold Xin] Fixed import.
8187b88 [Reynold Xin] [SPARK-8300] DataFrame hint for broadcast join.
---
 .../plans/logical/basicOperators.scala        |  8 ++++++
 .../spark/sql/execution/SparkStrategies.scala | 25 +++++++++++++------
 .../org/apache/spark/sql/functions.scala      | 17 +++++++++++++
 .../apache/spark/sql/DataFrameJoinSuite.scala | 17 +++++++++++++
 4 files changed, 59 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index f8e5916d69f9c..7814e51628db6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -130,6 +130,14 @@ case class Join(
   }
 }
 
+/**
+ * A hint for the optimizer that we should broadcast the `child` if used in a join operator.
+ */
+case class BroadcastHint(child: LogicalPlan) extends UnaryNode {
+  override def output: Seq[Attribute] = child.output
+}
+
+
 case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   override def output: Seq[Attribute] = left.output
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 422992d019c7b..5c420eb9d761f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.logical.{BroadcastHint, LogicalPlan}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation}
 import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand}
@@ -52,6 +52,18 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
+  /**
+   * Matches a plan whose output should be small enough to be used in broadcast join.
+   */
+  object CanBroadcast {
+    def unapply(plan: LogicalPlan): Option[LogicalPlan] = plan match {
+      case BroadcastHint(p) => Some(p)
+      case p if sqlContext.conf.autoBroadcastJoinThreshold > 0 &&
+        p.statistics.sizeInBytes <= sqlContext.conf.autoBroadcastJoinThreshold => Some(p)
+      case _ => None
+    }
+  }
+
   /**
    * Uses the ExtractEquiJoinKeys pattern to find joins where at least some of the predicates can be
    * evaluated by matching hash keys.
@@ -80,15 +92,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
 
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-        if sqlContext.conf.autoBroadcastJoinThreshold > 0 &&
-           right.statistics.sizeInBytes <= sqlContext.conf.autoBroadcastJoinThreshold =>
+      case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildRight)
 
-      case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-        if sqlContext.conf.autoBroadcastJoinThreshold > 0 &&
-           left.statistics.sizeInBytes <= sqlContext.conf.autoBroadcastJoinThreshold =>
-          makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildLeft)
+      case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
+        makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildLeft)
 
       // If the sort merge join option is set, we want to use sort merge join prior to hashjoin
       // for now let's support inner join first, then add outer join
@@ -329,6 +337,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case e @ EvaluatePython(udf, child, _) =>
         BatchPythonEvaluation(udf, e.output, planLater(child)) :: Nil
       case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd) :: Nil
+      case BroadcastHint(child) => apply(child)
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 8cea826ae6921..38d9085a505fb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -24,6 +24,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star}
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical.BroadcastHint
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -565,6 +566,22 @@ object functions {
     array((colName +: colNames).map(col) : _*)
   }
 
+  /**
+   * Marks a DataFrame as small enough for use in broadcast joins.
+   *
+   * The following example marks the right DataFrame for broadcast hash join using `joinKey`.
+   * {{{
+   *   // left and right are DataFrames
+   *   left.join(broadcast(right), "joinKey")
+   * }}}
+   *
+   * @group normal_funcs
+   * @since 1.5.0
+   */
+  def broadcast(df: DataFrame): DataFrame = {
+    DataFrame(df.sqlContext, BroadcastHint(df.logicalPlan))
+  }
+
   /**
    * Returns the first column that is not null.
    * {{{
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
index 6165764632c29..e1c6c706242d2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.execution.joins.BroadcastHashJoin
 import org.apache.spark.sql.functions._
 
 class DataFrameJoinSuite extends QueryTest {
@@ -93,4 +94,20 @@ class DataFrameJoinSuite extends QueryTest {
       left.join(right, left("key") === right("key")),
       Row(1, 1, 1, 1) :: Row(2, 1, 2, 2) :: Nil)
   }
+
+  test("broadcast join hint") {
+    val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+    val df2 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+
+    // equijoin - should be converted into broadcast join
+    val plan1 = df1.join(broadcast(df2), "key").queryExecution.executedPlan
+    assert(plan1.collect { case p: BroadcastHashJoin => p }.size === 1)
+
+    // no join key -- should not be a broadcast join
+    val plan2 = df1.join(broadcast(df2)).queryExecution.executedPlan
+    assert(plan2.collect { case p: BroadcastHashJoin => p }.size === 0)
+
+    // planner should not crash without a join
+    broadcast(df1).queryExecution.executedPlan
+  }
 }

From 0f92be5b5f017b593bd29d4da7e89aad2b3adac2 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 23 Jun 2015 09:08:11 -0700
Subject: [PATCH 0024/1454] [SPARK-8498] [TUNGSTEN] fix npe in errorhandling
 path in unsafeshuffle writer

Author: Holden Karau <holden@pigscanfly.ca>

Closes #6918 from holdenk/SPARK-8498-fix-npe-in-errorhandling-path-in-unsafeshuffle-writer and squashes the following commits:

f807832 [Holden Karau] Log error if we can't throw it
855f9aa [Holden Karau] Spelling - not my strongest suite. Fix Propegates to Propagates.
039d620 [Holden Karau] Add missing closeandwriteoutput
30e558d [Holden Karau] go back to try/finally
e503b8c [Holden Karau] Improve the test to ensure we aren't masking the underlying exception
ae0b7a7 [Holden Karau] Fix the test
2e6abf7 [Holden Karau] Be more cautious when cleaning up during failed write and re-throw user exceptions
---
 .../shuffle/unsafe/UnsafeShuffleWriter.java    | 18 ++++++++++++++++--
 .../unsafe/UnsafeShuffleWriterSuite.java       | 17 +++++++++++++++++
 2 files changed, 33 insertions(+), 2 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index ad7eb04afcd8c..764578b181422 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -139,6 +139,9 @@ public void write(Iterator<Product2<K, V>> records) throws IOException {
 
   @Override
   public void write(scala.collection.Iterator<Product2<K, V>> records) throws IOException {
+    // Keep track of success so we know if we ecountered an exception
+    // We do this rather than a standard try/catch/re-throw to handle
+    // generic throwables.
     boolean success = false;
     try {
       while (records.hasNext()) {
@@ -147,8 +150,19 @@ public void write(scala.collection.Iterator<Product2<K, V>> records) throws IOEx
       closeAndWriteOutput();
       success = true;
     } finally {
-      if (!success) {
-        sorter.cleanupAfterError();
+      if (sorter != null) {
+        try {
+          sorter.cleanupAfterError();
+        } catch (Exception e) {
+          // Only throw this error if we won't be masking another
+          // error.
+          if (success) {
+            throw e;
+          } else {
+            logger.error("In addition to a failure during writing, we failed during " +
+                         "cleanup.", e);
+          }
+        }
       }
     }
   }
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 83d109115aa5c..10c3eedbf4b46 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -253,6 +253,23 @@ public void doNotNeedToCallWriteBeforeUnsuccessfulStop() throws IOException {
     createWriter(false).stop(false);
   }
 
+  class PandaException extends RuntimeException {
+  }
+
+  @Test(expected=PandaException.class)
+  public void writeFailurePropagates() throws Exception {
+    class BadRecords extends scala.collection.AbstractIterator<Product2<Object, Object>> {
+      @Override public boolean hasNext() {
+        throw new PandaException();
+      }
+      @Override public Product2<Object, Object> next() {
+        return null;
+      }
+    }
+    final UnsafeShuffleWriter<Object, Object> writer = createWriter(true);
+    writer.write(new BadRecords());
+  }
+
   @Test
   public void writeEmptyIterator() throws Exception {
     final UnsafeShuffleWriter<Object, Object> writer = createWriter(true);

From 4f7fbefb8db56ecaab66bb0ac2ab124416fefe58 Mon Sep 17 00:00:00 2001
From: lockwobr <lockwobr@gmail.com>
Date: Wed, 24 Jun 2015 02:48:56 +0900
Subject: [PATCH 0025/1454] [SQL] [DOCS] updated the documentation for explode

the syntax was incorrect in the example in explode

Author: lockwobr <lockwobr@gmail.com>

Closes #6943 from lockwobr/master and squashes the following commits:

3d864d1 [lockwobr] updated the documentation for explode
---
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 492a3321bc0bc..f3f0f5305318e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1049,7 +1049,7 @@ class DataFrame private[sql](
    * columns of the input row are implicitly joined with each value that is output by the function.
    *
    * {{{
-   *   df.explode("words", "word")(words: String => words.split(" "))
+   *   df.explode("words", "word"){words: String => words.split(" ")}
    * }}}
    * @group dfops
    * @since 1.3.0

From 7b1450b666f88452e7fe969a6d59e8b24842ea39 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 23 Jun 2015 10:52:17 -0700
Subject: [PATCH 0026/1454] [SPARK-7235] [SQL] Refactor the grouping sets

The logical plan `Expand` takes the `output` as constructor argument, which break the references chain. We need to refactor the code, as well as the column pruning.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #5780 from chenghao-intel/expand and squashes the following commits:

76e4aa4 [Cheng Hao] revert the change for case insenstive
7c10a83 [Cheng Hao] refactor the grouping sets
---
 .../sql/catalyst/analysis/Analyzer.scala      | 55 ++----------
 .../expressions/namedExpressions.scala        |  2 +-
 .../sql/catalyst/optimizer/Optimizer.scala    |  4 +
 .../plans/logical/basicOperators.scala        | 84 ++++++++++++++-----
 .../spark/sql/execution/SparkStrategies.scala |  4 +-
 5 files changed, 78 insertions(+), 71 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 6311784422a91..0a3f5a7b5cade 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -192,49 +192,17 @@ class Analyzer(
       Seq.tabulate(1 << c.groupByExprs.length)(i => i)
     }
 
-    /**
-     * Create an array of Projections for the child projection, and replace the projections'
-     * expressions which equal GroupBy expressions with Literal(null), if those expressions
-     * are not set for this grouping set (according to the bit mask).
-     */
-    private[this] def expand(g: GroupingSets): Seq[Seq[Expression]] = {
-      val result = new scala.collection.mutable.ArrayBuffer[Seq[Expression]]
-
-      g.bitmasks.foreach { bitmask =>
-        // get the non selected grouping attributes according to the bit mask
-        val nonSelectedGroupExprs = ArrayBuffer.empty[Expression]
-        var bit = g.groupByExprs.length - 1
-        while (bit >= 0) {
-          if (((bitmask >> bit) & 1) == 0) nonSelectedGroupExprs += g.groupByExprs(bit)
-          bit -= 1
-        }
-
-        val substitution = (g.child.output :+ g.gid).map(expr => expr transformDown {
-          case x: Expression if nonSelectedGroupExprs.find(_ semanticEquals x).isDefined =>
-            // if the input attribute in the Invalid Grouping Expression set of for this group
-            // replace it with constant null
-            Literal.create(null, expr.dataType)
-          case x if x == g.gid =>
-            // replace the groupingId with concrete value (the bit mask)
-            Literal.create(bitmask, IntegerType)
-        })
-
-        result += substitution
-      }
-
-      result.toSeq
-    }
-
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      case a: Cube if a.resolved =>
-        GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations, a.gid)
-      case a: Rollup if a.resolved =>
-        GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations, a.gid)
-      case x: GroupingSets if x.resolved =>
+      case a: Cube =>
+        GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations)
+      case a: Rollup =>
+        GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations)
+      case x: GroupingSets =>
+        val gid = AttributeReference(VirtualColumn.groupingIdName, IntegerType, false)()
         Aggregate(
-          x.groupByExprs :+ x.gid,
+          x.groupByExprs :+ VirtualColumn.groupingIdAttribute,
           x.aggregations,
-          Expand(expand(x), x.child.output :+ x.gid, x.child))
+          Expand(x.bitmasks, x.groupByExprs, gid, x.child))
     }
   }
 
@@ -368,12 +336,7 @@ class Analyzer(
 
       case q: LogicalPlan =>
         logTrace(s"Attempting to resolve ${q.simpleString}")
-        q transformExpressionsUp {
-          case u @ UnresolvedAttribute(nameParts) if nameParts.length == 1 &&
-            resolver(nameParts(0), VirtualColumn.groupingIdName) &&
-            q.isInstanceOf[GroupingAnalytics] =>
-            // Resolve the virtual column GROUPING__ID for the operator GroupingAnalytics
-            q.asInstanceOf[GroupingAnalytics].gid
+        q transformExpressionsUp  {
           case u @ UnresolvedAttribute(nameParts) =>
             // Leave unchanged if resolution fails.  Hopefully will be resolved next round.
             val result =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 58dbeaf89cad5..9cacdceb13837 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -262,5 +262,5 @@ case class PrettyAttribute(name: String) extends Attribute with trees.LeafNode[E
 
 object VirtualColumn {
   val groupingIdName: String = "grouping__id"
-  def newGroupingId: AttributeReference = AttributeReference(groupingIdName, IntegerType, false)()
+  val groupingIdAttribute: UnresolvedAttribute = UnresolvedAttribute(groupingIdName)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 9132a786f77a7..98b4476076854 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -121,6 +121,10 @@ object UnionPushdown extends Rule[LogicalPlan] {
  */
 object ColumnPruning extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    case a @ Aggregate(_, _, e @ Expand(_, groupByExprs, _, child))
+      if (child.outputSet -- AttributeSet(groupByExprs) -- a.references).nonEmpty =>
+      a.copy(child = e.copy(child = prunedChild(child, AttributeSet(groupByExprs) ++ a.references)))
+
     // Eliminate attributes that are not needed to calculate the specified aggregates.
     case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty =>
       a.copy(child = Project(a.references.toSeq, child))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 7814e51628db6..fae339808c233 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.types._
+import org.apache.spark.util.collection.OpenHashSet
 
 case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
@@ -228,24 +229,76 @@ case class Window(
 /**
  * Apply the all of the GroupExpressions to every input row, hence we will get
  * multiple output rows for a input row.
- * @param projections The group of expressions, all of the group expressions should
- *                    output the same schema specified by the parameter `output`
- * @param output      The output Schema
+ * @param bitmasks The bitmask set represents the grouping sets
+ * @param groupByExprs The grouping by expressions
  * @param child       Child operator
  */
 case class Expand(
-    projections: Seq[Seq[Expression]],
-    output: Seq[Attribute],
+    bitmasks: Seq[Int],
+    groupByExprs: Seq[Expression],
+    gid: Attribute,
     child: LogicalPlan) extends UnaryNode {
   override def statistics: Statistics = {
     val sizeInBytes = child.statistics.sizeInBytes * projections.length
     Statistics(sizeInBytes = sizeInBytes)
   }
+
+  val projections: Seq[Seq[Expression]] = expand()
+
+  /**
+   * Extract attribute set according to the grouping id
+   * @param bitmask bitmask to represent the selected of the attribute sequence
+   * @param exprs the attributes in sequence
+   * @return the attributes of non selected specified via bitmask (with the bit set to 1)
+   */
+  private def buildNonSelectExprSet(bitmask: Int, exprs: Seq[Expression])
+  : OpenHashSet[Expression] = {
+    val set = new OpenHashSet[Expression](2)
+
+    var bit = exprs.length - 1
+    while (bit >= 0) {
+      if (((bitmask >> bit) & 1) == 0) set.add(exprs(bit))
+      bit -= 1
+    }
+
+    set
+  }
+
+  /**
+   * Create an array of Projections for the child projection, and replace the projections'
+   * expressions which equal GroupBy expressions with Literal(null), if those expressions
+   * are not set for this grouping set (according to the bit mask).
+   */
+  private[this] def expand(): Seq[Seq[Expression]] = {
+    val result = new scala.collection.mutable.ArrayBuffer[Seq[Expression]]
+
+    bitmasks.foreach { bitmask =>
+      // get the non selected grouping attributes according to the bit mask
+      val nonSelectedGroupExprSet = buildNonSelectExprSet(bitmask, groupByExprs)
+
+      val substitution = (child.output :+ gid).map(expr => expr transformDown {
+        case x: Expression if nonSelectedGroupExprSet.contains(x) =>
+          // if the input attribute in the Invalid Grouping Expression set of for this group
+          // replace it with constant null
+          Literal.create(null, expr.dataType)
+        case x if x == gid =>
+          // replace the groupingId with concrete value (the bit mask)
+          Literal.create(bitmask, IntegerType)
+      })
+
+      result += substitution
+    }
+
+    result.toSeq
+  }
+
+  override def output: Seq[Attribute] = {
+    child.output :+ gid
+  }
 }
 
 trait GroupingAnalytics extends UnaryNode {
   self: Product =>
-  def gid: AttributeReference
   def groupByExprs: Seq[Expression]
   def aggregations: Seq[NamedExpression]
 
@@ -266,17 +319,12 @@ trait GroupingAnalytics extends UnaryNode {
  * @param child        Child operator
  * @param aggregations The Aggregation expressions, those non selected group by expressions
  *                     will be considered as constant null if it appears in the expressions
- * @param gid          The attribute represents the virtual column GROUPING__ID, and it's also
- *                     the bitmask indicates the selected GroupBy Expressions for each
- *                     aggregating output row.
- *                     The associated output will be one of the value in `bitmasks`
  */
 case class GroupingSets(
     bitmasks: Seq[Int],
     groupByExprs: Seq[Expression],
     child: LogicalPlan,
-    aggregations: Seq[NamedExpression],
-    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics {
+    aggregations: Seq[NamedExpression]) extends GroupingAnalytics {
 
   def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics =
     this.copy(aggregations = aggs)
@@ -290,15 +338,11 @@ case class GroupingSets(
  * @param child        Child operator
  * @param aggregations The Aggregation expressions, those non selected group by expressions
  *                     will be considered as constant null if it appears in the expressions
- * @param gid          The attribute represents the virtual column GROUPING__ID, and it's also
- *                     the bitmask indicates the selected GroupBy Expressions for each
- *                     aggregating output row.
  */
 case class Cube(
     groupByExprs: Seq[Expression],
     child: LogicalPlan,
-    aggregations: Seq[NamedExpression],
-    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics {
+    aggregations: Seq[NamedExpression]) extends GroupingAnalytics {
 
   def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics =
     this.copy(aggregations = aggs)
@@ -313,15 +357,11 @@ case class Cube(
  * @param child        Child operator
  * @param aggregations The Aggregation expressions, those non selected group by expressions
  *                     will be considered as constant null if it appears in the expressions
- * @param gid          The attribute represents the virtual column GROUPING__ID, and it's also
- *                     the bitmask indicates the selected GroupBy Expressions for each
- *                     aggregating output row.
  */
 case class Rollup(
     groupByExprs: Seq[Expression],
     child: LogicalPlan,
-    aggregations: Seq[NamedExpression],
-    gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics {
+    aggregations: Seq[NamedExpression]) extends GroupingAnalytics {
 
   def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics =
     this.copy(aggregations = aggs)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 5c420eb9d761f..1ff1cc224de8c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -308,8 +308,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Project(projectList, planLater(child)) :: Nil
       case logical.Filter(condition, child) =>
         execution.Filter(condition, planLater(child)) :: Nil
-      case logical.Expand(projections, output, child) =>
-        execution.Expand(projections, output, planLater(child)) :: Nil
+      case e @ logical.Expand(_, _, _, child) =>
+        execution.Expand(e.projections, e.output, planLater(child)) :: Nil
       case logical.Aggregate(group, agg, child) =>
         execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
       case logical.Window(projectList, windowExpressions, spec, child) =>

From 6f4cadf5ee81467d077febc53d36571dd232295d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 23 Jun 2015 11:55:47 -0700
Subject: [PATCH 0027/1454] [SPARK-8432] [SQL] fix hashCode() and equals() of
 BinaryType in Row

Also added more tests in LiteralExpressionSuite

Author: Davies Liu <davies@databricks.com>

Closes #6876 from davies/fix_hashcode and squashes the following commits:

429c2c0 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_hashcode
32d9811 [Davies Liu] fix test
a0626ed [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_hashcode
89c2432 [Davies Liu] fix style
bd20780 [Davies Liu] check with catalyst types
41caec6 [Davies Liu] change for to while
d96929b [Davies Liu] address comment
6ad2a90 [Davies Liu] fix style
5819d33 [Davies Liu] unify equals() and hashCode()
0fff25d [Davies Liu] fix style
53c38b1 [Davies Liu] fix hashCode() and equals() of BinaryType in Row
---
 .../java/org/apache/spark/sql/BaseRow.java    | 21 ------
 .../main/scala/org/apache/spark/sql/Row.scala | 32 ---------
 .../spark/sql/catalyst/InternalRow.scala      | 67 ++++++++++++++++++-
 .../codegen/GenerateProjection.scala          |  1 +
 .../spark/sql/catalyst/expressions/rows.scala | 52 --------------
 .../expressions/ExpressionEvalHelper.scala    | 27 ++++++--
 .../expressions/LiteralExpressionSuite.scala  | 61 ++++++++++++++---
 .../expressions/StringFunctionsSuite.scala    |  5 +-
 .../apache/spark/unsafe/types/UTF8String.java |  6 +-
 .../spark/unsafe/types/UTF8StringSuite.java   |  2 -
 10 files changed, 139 insertions(+), 135 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java
index 611e02d8fb666..6a2356f1f9c6f 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java
@@ -155,27 +155,6 @@ public int fieldIndex(String name) {
     throw new UnsupportedOperationException();
   }
 
-  /**
-   * A generic version of Row.equals(Row), which is used for tests.
-   */
-  @Override
-  public boolean equals(Object other) {
-    if (other instanceof Row) {
-      Row row = (Row) other;
-      int n = size();
-      if (n != row.size()) {
-        return false;
-      }
-      for (int i = 0; i < n; i ++) {
-        if (isNullAt(i) != row.isNullAt(i) || (!isNullAt(i) && !get(i).equals(row.get(i)))) {
-          return false;
-        }
-      }
-      return true;
-    }
-    return false;
-  }
-
   @Override
   public InternalRow copy() {
     final int n = size();
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 8aaf5d7d89154..e99d5c87a44fe 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql
 
-import scala.util.hashing.MurmurHash3
-
 import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.sql.types.StructType
 
@@ -365,36 +363,6 @@ trait Row extends Serializable {
     false
   }
 
-  override def equals(that: Any): Boolean = that match {
-    case null => false
-    case that: Row =>
-      if (this.length != that.length) {
-        return false
-      }
-      var i = 0
-      val len = this.length
-      while (i < len) {
-        if (apply(i) != that.apply(i)) {
-          return false
-        }
-        i += 1
-      }
-      true
-    case _ => false
-  }
-
-  override def hashCode: Int = {
-    // Using Scala's Seq hash code implementation.
-    var n = 0
-    var h = MurmurHash3.seqSeed
-    val len = length
-    while (n < len) {
-      h = MurmurHash3.mix(h, apply(n).##)
-      n += 1
-    }
-    MurmurHash3.finalizeHash(h, n)
-  }
-
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index e3c2cc243310b..d7b537a9fe3bc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.expressions.GenericRow
+import org.apache.spark.sql.catalyst.expressions._
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -26,7 +26,70 @@ import org.apache.spark.sql.catalyst.expressions.GenericRow
  */
 abstract class InternalRow extends Row {
   // A default implementation to change the return type
-  override def copy(): InternalRow = {this}
+  override def copy(): InternalRow = this
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[Row]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[Row]
+    if (length != other.length) {
+      return false
+    }
+
+    var i = 0
+    while (i < length) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = apply(i)
+        val o2 = other.apply(i)
+        if (o1.isInstanceOf[Array[Byte]]) {
+          // handle equality of Array[Byte]
+          val b1 = o1.asInstanceOf[Array[Byte]]
+          if (!o2.isInstanceOf[Array[Byte]] ||
+            !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+            return false
+          }
+        } else if (o1 != o2) {
+          return false
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    while (i < length) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          apply(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 2e20eda1a3002..e362625469e29 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -127,6 +127,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         case FloatType => s"Float.floatToIntBits($col)"
         case DoubleType =>
             s"(int)(Double.doubleToLongBits($col) ^ (Double.doubleToLongBits($col) >>> 32))"
+        case BinaryType => s"java.util.Arrays.hashCode($col)"
         case _ => s"$col.hashCode()"
       }
       s"isNullAt($i) ? 0 : ($nonNull)"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 1098962ddc018..0d4c9ace5e124 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -121,58 +121,6 @@ class GenericRow(protected[sql] val values: Array[Any]) extends InternalRow {
     }
   }
 
-  // TODO(davies): add getDate and getDecimal
-
-  // Custom hashCode function that matches the efficient code generated version.
-  override def hashCode: Int = {
-    var result: Int = 37
-
-    var i = 0
-    while (i < values.length) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          apply(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
-      i += 1
-    }
-    result
-  }
-
-  override def equals(o: Any): Boolean = o match {
-    case other: InternalRow =>
-      if (values.length != other.length) {
-        return false
-      }
-
-      var i = 0
-      while (i < values.length) {
-        if (isNullAt(i) != other.isNullAt(i)) {
-          return false
-        }
-        if (apply(i) != other.apply(i)) {
-          return false
-        }
-        i += 1
-      }
-      true
-
-    case _ => false
-  }
-
   override def copy(): InternalRow = this
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 12d2da8b33986..158f54af13802 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -38,10 +38,23 @@ trait ExpressionEvalHelper {
 
   protected def checkEvaluation(
       expression: Expression, expected: Any, inputRow: InternalRow = EmptyRow): Unit = {
-    checkEvaluationWithoutCodegen(expression, expected, inputRow)
-    checkEvaluationWithGeneratedMutableProjection(expression, expected, inputRow)
-    checkEvaluationWithGeneratedProjection(expression, expected, inputRow)
-    checkEvaluationWithOptimization(expression, expected, inputRow)
+    val catalystValue = CatalystTypeConverters.convertToCatalyst(expected)
+    checkEvaluationWithoutCodegen(expression, catalystValue, inputRow)
+    checkEvaluationWithGeneratedMutableProjection(expression, catalystValue, inputRow)
+    checkEvaluationWithGeneratedProjection(expression, catalystValue, inputRow)
+    checkEvaluationWithOptimization(expression, catalystValue, inputRow)
+  }
+
+  /**
+   * Check the equality between result of expression and expected value, it will handle
+   * Array[Byte].
+   */
+  protected def checkResult(result: Any, expected: Any): Boolean = {
+    (result, expected) match {
+      case (result: Array[Byte], expected: Array[Byte]) =>
+        java.util.Arrays.equals(result, expected)
+      case _ => result == expected
+    }
   }
 
   protected def evaluate(expression: Expression, inputRow: InternalRow = EmptyRow): Any = {
@@ -55,7 +68,7 @@ trait ExpressionEvalHelper {
     val actual = try evaluate(expression, inputRow) catch {
       case e: Exception => fail(s"Exception evaluating $expression", e)
     }
-    if (actual != expected) {
+    if (!checkResult(actual, expected)) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
       fail(s"Incorrect evaluation (codegen off): $expression, " +
         s"actual: $actual, " +
@@ -83,7 +96,7 @@ trait ExpressionEvalHelper {
     }
 
     val actual = plan(inputRow).apply(0)
-    if (actual != expected) {
+    if (!checkResult(actual, expected)) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
       fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
     }
@@ -109,7 +122,7 @@ trait ExpressionEvalHelper {
     }
 
     val actual = plan(inputRow)
-    val expectedRow = new GenericRow(Array[Any](CatalystTypeConverters.convertToCatalyst(expected)))
+    val expectedRow = new GenericRow(Array[Any](expected))
     if (actual.hashCode() != expectedRow.hashCode()) {
       fail(
         s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
index f44f55dfb92d1..d924ff7a102f6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
@@ -18,12 +18,26 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.types._
 
 
 class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
 
-  // TODO: Add tests for all data types.
+  test("null") {
+    checkEvaluation(Literal.create(null, BooleanType), null)
+    checkEvaluation(Literal.create(null, ByteType), null)
+    checkEvaluation(Literal.create(null, ShortType), null)
+    checkEvaluation(Literal.create(null, IntegerType), null)
+    checkEvaluation(Literal.create(null, LongType), null)
+    checkEvaluation(Literal.create(null, FloatType), null)
+    checkEvaluation(Literal.create(null, LongType), null)
+    checkEvaluation(Literal.create(null, StringType), null)
+    checkEvaluation(Literal.create(null, BinaryType), null)
+    checkEvaluation(Literal.create(null, DecimalType()), null)
+    checkEvaluation(Literal.create(null, ArrayType(ByteType, true)), null)
+    checkEvaluation(Literal.create(null, MapType(StringType, IntegerType)), null)
+    checkEvaluation(Literal.create(null, StructType(Seq.empty)), null)
+  }
 
   test("boolean literals") {
     checkEvaluation(Literal(true), true)
@@ -31,25 +45,52 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("int literals") {
-    checkEvaluation(Literal(1), 1)
-    checkEvaluation(Literal(0L), 0L)
+    List(0, 1, Int.MinValue, Int.MaxValue).foreach { d =>
+      checkEvaluation(Literal(d), d)
+      checkEvaluation(Literal(d.toLong), d.toLong)
+      checkEvaluation(Literal(d.toShort), d.toShort)
+      checkEvaluation(Literal(d.toByte), d.toByte)
+    }
+    checkEvaluation(Literal(Long.MinValue), Long.MinValue)
+    checkEvaluation(Literal(Long.MaxValue), Long.MaxValue)
   }
 
   test("double literals") {
-    List(0.0, -0.0, Double.NegativeInfinity, Double.PositiveInfinity).foreach {
-      d => {
-        checkEvaluation(Literal(d), d)
-        checkEvaluation(Literal(d.toFloat), d.toFloat)
-      }
+    List(0.0, -0.0, Double.NegativeInfinity, Double.PositiveInfinity).foreach { d =>
+      checkEvaluation(Literal(d), d)
+      checkEvaluation(Literal(d.toFloat), d.toFloat)
     }
+    checkEvaluation(Literal(Double.MinValue), Double.MinValue)
+    checkEvaluation(Literal(Double.MaxValue), Double.MaxValue)
+    checkEvaluation(Literal(Float.MinValue), Float.MinValue)
+    checkEvaluation(Literal(Float.MaxValue), Float.MaxValue)
+
   }
 
   test("string literals") {
+    checkEvaluation(Literal(""), "")
     checkEvaluation(Literal("test"), "test")
-    checkEvaluation(Literal.create(null, StringType), null)
+    checkEvaluation(Literal("\0"), "\0")
   }
 
   test("sum two literals") {
     checkEvaluation(Add(Literal(1), Literal(1)), 2)
   }
+
+  test("binary literals") {
+    checkEvaluation(Literal.create(new Array[Byte](0), BinaryType), new Array[Byte](0))
+    checkEvaluation(Literal.create(new Array[Byte](2), BinaryType), new Array[Byte](2))
+  }
+
+  test("decimal") {
+    List(0.0, 1.2, 1.1111, 5).foreach { d =>
+      checkEvaluation(Literal(Decimal(d)), Decimal(d))
+      checkEvaluation(Literal(Decimal(d.toInt)), Decimal(d.toInt))
+      checkEvaluation(Literal(Decimal(d.toLong)), Decimal(d.toLong))
+      checkEvaluation(Literal(Decimal((d * 1000L).toLong, 10, 1)),
+        Decimal((d * 1000L).toLong, 10, 1))
+    }
+  }
+
+  // TODO(davies): add tests for ArrayType, MapType and StructType
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
index d363e631540d8..5dbb1d562c1d9 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
@@ -222,9 +222,6 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(StringLength(regEx), 5, create_row("abdef"))
     checkEvaluation(StringLength(regEx), 0, create_row(""))
     checkEvaluation(StringLength(regEx), null, create_row(null))
-    // TODO currently bug in codegen, let's temporally disable this
-    // checkEvaluation(StringLength(Literal.create(null, StringType)), null, create_row("abdef"))
+    checkEvaluation(StringLength(Literal.create(null, StringType)), null, create_row("abdef"))
   }
-
-
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 9871a70a40e69..9302b472925ed 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -17,10 +17,10 @@
 
 package org.apache.spark.unsafe.types;
 
+import javax.annotation.Nonnull;
 import java.io.Serializable;
 import java.io.UnsupportedEncodingException;
 import java.util.Arrays;
-import javax.annotation.Nonnull;
 
 import org.apache.spark.unsafe.PlatformDependent;
 
@@ -202,10 +202,6 @@ public int compare(final UTF8String other) {
   public boolean equals(final Object other) {
     if (other instanceof UTF8String) {
       return Arrays.equals(bytes, ((UTF8String) other).getBytes());
-    } else if (other instanceof String) {
-      // Used only in unit tests.
-      String s = (String) other;
-      return bytes.length >= s.length() && length() == s.length() && toString().equals(s);
     } else {
       return false;
     }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 80c179a1b5e75..796cdc9dbebdb 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -28,8 +28,6 @@ private void checkBasic(String str, int len) throws UnsupportedEncodingException
     Assert.assertEquals(UTF8String.fromString(str).length(), len);
     Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).length(), len);
 
-    Assert.assertEquals(UTF8String.fromString(str), str);
-    Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")), str);
     Assert.assertEquals(UTF8String.fromString(str).toString(), str);
     Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).toString(), str);
     Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")), UTF8String.fromString(str));

From 2b1111dd0b8deb9ad8d43fec792e60e3d0c4de75 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 23 Jun 2015 12:42:17 -0700
Subject: [PATCH 0028/1454] [SPARK-7888] Be able to disable intercept in linear
 regression in ml package

Author: Holden Karau <holden@pigscanfly.ca>

Closes #6927 from holdenk/SPARK-7888-Be-able-to-disable-intercept-in-Linear-Regression-in-ML-package and squashes the following commits:

0ad384c [Holden Karau] Add MiMa excludes
4016fac [Holden Karau] Switch to wild card import, remove extra blank lines
ae5baa8 [Holden Karau] CR feedback, move the fitIntercept down rather than changing ymean and etc above
f34971c [Holden Karau] Fix some more long lines
319bd3f [Holden Karau] Fix long lines
3bb9ee1 [Holden Karau] Update the regression suite tests
7015b9f [Holden Karau] Our code performs the same with R, except we need more than one data point but that seems reasonable
0b0c8c0 [Holden Karau] fix the issue with the sample R code
e2140ba [Holden Karau] Add a test, it fails!
5e84a0b [Holden Karau] Write out thoughts and use the correct trait
91ffc0a [Holden Karau] more murh
006246c [Holden Karau] murp?
---
 .../ml/regression/LinearRegression.scala      |  30 +++-
 .../ml/regression/LinearRegressionSuite.scala | 149 +++++++++++++++++-
 project/MimaExcludes.scala                    |   5 +
 3 files changed, 172 insertions(+), 12 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 01306545fc7cd..1b1d7299fb496 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -26,7 +26,7 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol}
+import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS._
@@ -41,7 +41,8 @@ import org.apache.spark.util.StatCounter
  * Params for linear regression.
  */
 private[regression] trait LinearRegressionParams extends PredictorParams
-  with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
+    with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
+    with HasFitIntercept
 
 /**
  * :: Experimental ::
@@ -72,6 +73,14 @@ class LinearRegression(override val uid: String)
   def setRegParam(value: Double): this.type = set(regParam, value)
   setDefault(regParam -> 0.0)
 
+  /**
+   * Set if we should fit the intercept
+   * Default is true.
+   * @group setParam
+   */
+  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
+  setDefault(fitIntercept -> true)
+
   /**
    * Set the ElasticNet mixing parameter.
    * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
@@ -123,6 +132,7 @@ class LinearRegression(override val uid: String)
     val numFeatures = summarizer.mean.size
     val yMean = statCounter.mean
     val yStd = math.sqrt(statCounter.variance)
+    // look at glmnet5.m L761 maaaybe that has info
 
     // If the yStd is zero, then the intercept is yMean with zero weights;
     // as a result, training is not needed.
@@ -142,7 +152,7 @@ class LinearRegression(override val uid: String)
     val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam
     val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam
 
-    val costFun = new LeastSquaresCostFun(instances, yStd, yMean,
+    val costFun = new LeastSquaresCostFun(instances, yStd, yMean, $(fitIntercept),
       featuresStd, featuresMean, effectiveL2RegParam)
 
     val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
@@ -180,7 +190,7 @@ class LinearRegression(override val uid: String)
     // The intercept in R's GLMNET is computed using closed form after the coefficients are
     // converged. See the following discussion for detail.
     // http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
-    val intercept = yMean - dot(weights, Vectors.dense(featuresMean))
+    val intercept = if ($(fitIntercept)) yMean - dot(weights, Vectors.dense(featuresMean)) else 0.0
     if (handlePersistence) instances.unpersist()
 
     // TODO: Converts to sparse format based on the storage, but may base on the scoring speed.
@@ -234,6 +244,7 @@ class LinearRegressionModel private[ml] (
  * See this discussion for detail.
  * http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
  *
+ * When training with intercept enabled,
  * The objective function in the scaled space is given by
  * {{{
  * L = 1/2n ||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2,
@@ -241,6 +252,10 @@ class LinearRegressionModel private[ml] (
  * where \bar{x_i} is the mean of x_i, \hat{x_i} is the standard deviation of x_i,
  * \bar{y} is the mean of label, and \hat{y} is the standard deviation of label.
  *
+ * If we fitting the intercept disabled (that is forced through 0.0),
+ * we can use the same equation except we set \bar{y} and \bar{x_i} to 0 instead
+ * of the respective means.
+ *
  * This can be rewritten as
  * {{{
  * L = 1/2n ||\sum_i (w_i/\hat{x_i})x_i - \sum_i (w_i/\hat{x_i})\bar{x_i} - y / \hat{y}
@@ -255,6 +270,7 @@ class LinearRegressionModel private[ml] (
  * \sum_i w_i^\prime x_i - y / \hat{y} + offset
  * }}}
  *
+ *
  * Note that the effective weights and offset don't depend on training dataset,
  * so they can be precomputed.
  *
@@ -301,6 +317,7 @@ private class LeastSquaresAggregator(
     weights: Vector,
     labelStd: Double,
     labelMean: Double,
+    fitIntercept: Boolean,
     featuresStd: Array[Double],
     featuresMean: Array[Double]) extends Serializable {
 
@@ -321,7 +338,7 @@ private class LeastSquaresAggregator(
       }
       i += 1
     }
-    (weightsArray, -sum + labelMean / labelStd, weightsArray.length)
+    (weightsArray, if (fitIntercept) labelMean / labelStd - sum else 0.0, weightsArray.length)
   }
 
   private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray)
@@ -404,6 +421,7 @@ private class LeastSquaresCostFun(
     data: RDD[(Double, Vector)],
     labelStd: Double,
     labelMean: Double,
+    fitIntercept: Boolean,
     featuresStd: Array[Double],
     featuresMean: Array[Double],
     effectiveL2regParam: Double) extends DiffFunction[BDV[Double]] {
@@ -412,7 +430,7 @@ private class LeastSquaresCostFun(
     val w = Vectors.fromBreeze(weights)
 
     val leastSquaresAggregator = data.treeAggregate(new LeastSquaresAggregator(w, labelStd,
-      labelMean, featuresStd, featuresMean))(
+      labelMean, fitIntercept, featuresStd, featuresMean))(
         seqOp = (c, v) => (c, v) match {
           case (aggregator, (label, features)) => aggregator.add(label, features)
         },
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 732e2c42be144..ad1e9da692ee2 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.{DataFrame, Row}
 class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
 
   @transient var dataset: DataFrame = _
+  @transient var datasetWithoutIntercept: DataFrame = _
 
   /**
    * In `LinearRegressionSuite`, we will make sure that the model trained by SparkML
@@ -34,14 +35,24 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
    *
    * import org.apache.spark.mllib.util.LinearDataGenerator
    * val data =
-   *   sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2), 10000, 42), 2)
-   * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).saveAsTextFile("path")
+   *   sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2),
+   *     Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)
+   * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).coalesce(1)
+   *   .saveAsTextFile("path")
    */
   override def beforeAll(): Unit = {
     super.beforeAll()
     dataset = sqlContext.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
         6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2))
+    /**
+     * datasetWithoutIntercept is not needed for correctness testing but is useful for illustrating
+     * training model without intercept
+     */
+    datasetWithoutIntercept = sqlContext.createDataFrame(
+      sc.parallelize(LinearDataGenerator.generateLinearInput(
+        0.0, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2))
+
   }
 
   test("linear regression with intercept without regularization") {
@@ -78,6 +89,42 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("linear regression without intercept without regularization") {
+    val trainer = (new LinearRegression).setFitIntercept(false)
+    val model = trainer.fit(dataset)
+    val modelWithoutIntercept = trainer.fit(datasetWithoutIntercept)
+
+    /**
+     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0,
+     *   intercept = FALSE))
+     * > weights
+     *  3 x 1 sparse Matrix of class "dgCMatrix"
+     *                           s0
+     * (Intercept)         .
+     * as.numeric.data.V2. 6.995908
+     * as.numeric.data.V3. 5.275131
+     */
+    val weightsR = Array(6.995908, 5.275131)
+
+    assert(model.intercept ~== 0 relTol 1E-3)
+    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
+    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    /**
+     * Then again with the data with no intercept:
+     * > weightsWithoutIntercept
+     * 3 x 1 sparse Matrix of class "dgCMatrix"
+     *                             s0
+     * (Intercept)           .
+     * as.numeric.data3.V2. 4.70011
+     * as.numeric.data3.V3. 7.19943
+     */
+    val weightsWithoutInterceptR = Array(4.70011, 7.19943)
+
+    assert(modelWithoutIntercept.intercept ~== 0 relTol 1E-3)
+    assert(modelWithoutIntercept.weights(0) ~== weightsWithoutInterceptR(0) relTol 1E-3)
+    assert(modelWithoutIntercept.weights(1) ~== weightsWithoutInterceptR(1) relTol 1E-3)
+  }
+
   test("linear regression with intercept with L1 regularization") {
     val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
     val model = trainer.fit(dataset)
@@ -87,11 +134,11 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
      * > weights
      *  3 x 1 sparse Matrix of class "dgCMatrix"
      *                           s0
-     * (Intercept)         6.311546
-     * as.numeric.data.V2. 2.123522
-     * as.numeric.data.V3. 4.605651
+     * (Intercept)         6.24300
+     * as.numeric.data.V2. 4.024821
+     * as.numeric.data.V3. 6.679841
      */
-    val interceptR = 6.243000
+    val interceptR = 6.24300
     val weightsR = Array(4.024821, 6.679841)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
@@ -106,6 +153,36 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("linear regression without intercept with L1 regularization") {
+    val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+      .setFitIntercept(false)
+    val model = trainer.fit(dataset)
+
+    /**
+     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
+     *   intercept=FALSE))
+     * > weights
+     *  3 x 1 sparse Matrix of class "dgCMatrix"
+     *                           s0
+     * (Intercept)          .
+     * as.numeric.data.V2. 6.299752
+     * as.numeric.data.V3. 4.772913
+     */
+    val interceptR = 0.0
+    val weightsR = Array(6.299752, 4.772913)
+
+    assert(model.intercept ~== interceptR relTol 1E-3)
+    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
+    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+
+    model.transform(dataset).select("features", "prediction").collect().foreach {
+      case Row(features: DenseVector, prediction1: Double) =>
+        val prediction2 =
+          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+        assert(prediction1 ~== prediction2 relTol 1E-5)
+    }
+  }
+
   test("linear regression with intercept with L2 regularization") {
     val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
     val model = trainer.fit(dataset)
@@ -134,6 +211,36 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("linear regression without intercept with L2 regularization") {
+    val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+      .setFitIntercept(false)
+    val model = trainer.fit(dataset)
+
+    /**
+     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
+     *   intercept = FALSE))
+     * > weights
+     *  3 x 1 sparse Matrix of class "dgCMatrix"
+     *                           s0
+     * (Intercept)         .
+     * as.numeric.data.V2. 5.522875
+     * as.numeric.data.V3. 4.214502
+     */
+    val interceptR = 0.0
+    val weightsR = Array(5.522875, 4.214502)
+
+    assert(model.intercept ~== interceptR relTol 1E-3)
+    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
+    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+
+    model.transform(dataset).select("features", "prediction").collect().foreach {
+      case Row(features: DenseVector, prediction1: Double) =>
+        val prediction2 =
+          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+        assert(prediction1 ~== prediction2 relTol 1E-5)
+    }
+  }
+
   test("linear regression with intercept with ElasticNet regularization") {
     val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
     val model = trainer.fit(dataset)
@@ -161,4 +268,34 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
+
+  test("linear regression without intercept with ElasticNet regularization") {
+    val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+      .setFitIntercept(false)
+    val model = trainer.fit(dataset)
+
+    /**
+     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
+     *   intercept=FALSE))
+     * > weights
+     * 3 x 1 sparse Matrix of class "dgCMatrix"
+     * s0
+     * (Intercept)         .
+     * as.numeric.dataM.V2. 5.673348
+     * as.numeric.dataM.V3. 4.322251
+     */
+    val interceptR = 0.0
+    val weightsR = Array(5.673348, 4.322251)
+
+    assert(model.intercept ~== interceptR relTol 1E-3)
+    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
+    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+
+    model.transform(dataset).select("features", "prediction").collect().foreach {
+      case Row(features: DenseVector, prediction1: Double) =>
+        val prediction2 =
+          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+        assert(prediction1 ~== prediction2 relTol 1E-5)
+    }
+  }
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 7a748fb5e38bd..f678c69a6dfa9 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -53,6 +53,11 @@ object MimaExcludes {
             // Removing a testing method from a private class
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"),
+            // While private MiMa is still not happy about the changes,
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.ml.regression.LeastSquaresAggregator.this"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.ml.regression.LeastSquaresCostFun.this"),
             // SQL execution is considered private.
             excludePackage("org.apache.spark.sql.execution"),
             // NanoTime and CatalystTimestampConverter is only used inside catalyst,

From f2022fa0d375c804eca7803e172543b23ecbb9b7 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 23 Jun 2015 12:43:32 -0700
Subject: [PATCH 0029/1454] [SPARK-8265] [MLLIB] [PYSPARK] Add
 LinearDataGenerator to pyspark.mllib.utils

It is useful to generate linear data for easy testing of linear models and in general. Scala already has it. This is just a wrapper around the Scala code.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #6715 from MechCoder/generate_linear_input and squashes the following commits:

6182884 [MechCoder] Minor changes
8bda047 [MechCoder] Minor style fixes
0f1053c [MechCoder] [SPARK-8265] Add LinearDataGenerator to pyspark.mllib.utils
---
 .../mllib/api/python/PythonMLLibAPI.scala     | 32 ++++++++++++++++-
 python/pyspark/mllib/tests.py                 | 22 ++++++++++--
 python/pyspark/mllib/util.py                  | 35 +++++++++++++++++++
 3 files changed, 86 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index f9a271f47ee2c..c4bea7c2cad4f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -51,6 +51,7 @@ import org.apache.spark.mllib.tree.loss.Losses
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel, RandomForestModel}
 import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest}
 import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.mllib.util.LinearDataGenerator
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.storage.StorageLevel
@@ -972,7 +973,7 @@ private[python] class PythonMLLibAPI extends Serializable {
   def estimateKernelDensity(
       sample: JavaRDD[Double],
       bandwidth: Double, points: java.util.ArrayList[Double]): Array[Double] = {
-    return new KernelDensity().setSample(sample).setBandwidth(bandwidth).estimate(
+    new KernelDensity().setSample(sample).setBandwidth(bandwidth).estimate(
       points.asScala.toArray)
   }
 
@@ -991,6 +992,35 @@ private[python] class PythonMLLibAPI extends Serializable {
       List[AnyRef](model.clusterCenters, Vectors.dense(model.clusterWeights)).asJava
   }
 
+  /**
+   * Wrapper around the generateLinearInput method of LinearDataGenerator.
+   */
+  def generateLinearInputWrapper(
+      intercept: Double,
+      weights: JList[Double],
+      xMean: JList[Double],
+      xVariance: JList[Double],
+      nPoints: Int,
+      seed: Int,
+      eps: Double): Array[LabeledPoint] = {
+    LinearDataGenerator.generateLinearInput(
+      intercept, weights.asScala.toArray, xMean.asScala.toArray,
+      xVariance.asScala.toArray, nPoints, seed, eps).toArray
+  }
+
+  /**
+   * Wrapper around the generateLinearRDD method of LinearDataGenerator.
+   */
+  def generateLinearRDDWrapper(
+      sc: JavaSparkContext,
+      nexamples: Int,
+      nfeatures: Int,
+      eps: Double,
+      nparts: Int,
+      intercept: Double): JavaRDD[LabeledPoint] = {
+    LinearDataGenerator.generateLinearRDD(
+      sc, nexamples, nfeatures, eps, nparts, intercept)
+  }
 }
 
 /**
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index c8d61b9855a69..509faa11df170 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -49,8 +49,8 @@
 from pyspark.mllib.stat import Statistics
 from pyspark.mllib.feature import Word2Vec
 from pyspark.mllib.feature import IDF
-from pyspark.mllib.feature import StandardScaler
-from pyspark.mllib.feature import ElementwiseProduct
+from pyspark.mllib.feature import StandardScaler, ElementwiseProduct
+from pyspark.mllib.util import LinearDataGenerator
 from pyspark.serializers import PickleSerializer
 from pyspark.streaming import StreamingContext
 from pyspark.sql import SQLContext
@@ -1019,6 +1019,24 @@ def collect(rdd):
         self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
 
 
+class LinearDataGeneratorTests(MLlibTestCase):
+    def test_dim(self):
+        linear_data = LinearDataGenerator.generateLinearInput(
+            intercept=0.0, weights=[0.0, 0.0, 0.0],
+            xMean=[0.0, 0.0, 0.0], xVariance=[0.33, 0.33, 0.33],
+            nPoints=4, seed=0, eps=0.1)
+        self.assertEqual(len(linear_data), 4)
+        for point in linear_data:
+            self.assertEqual(len(point.features), 3)
+
+        linear_data = LinearDataGenerator.generateLinearRDD(
+            sc=sc, nexamples=6, nfeatures=2, eps=0.1,
+            nParts=2, intercept=0.0).collect()
+        self.assertEqual(len(linear_data), 6)
+        for point in linear_data:
+            self.assertEqual(len(point.features), 2)
+
+
 if __name__ == "__main__":
     if not _have_scipy:
         print("NOTE: Skipping SciPy tests as it does not seem to be installed")
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 16a90db146ef0..348238319e407 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -257,6 +257,41 @@ def load(cls, sc, path):
         return cls(java_model)
 
 
+class LinearDataGenerator(object):
+    """Utils for generating linear data"""
+
+    @staticmethod
+    def generateLinearInput(intercept, weights, xMean, xVariance,
+                            nPoints, seed, eps):
+        """
+        :param: intercept bias factor, the term c in X'w + c
+        :param: weights   feature vector, the term w in X'w + c
+        :param: xMean     Point around which the data X is centered.
+        :param: xVariance Variance of the given data
+        :param: nPoints   Number of points to be generated
+        :param: seed      Random Seed
+        :param: eps       Used to scale the noise. If eps is set high,
+                          the amount of gaussian noise added is more.
+        Returns a list of LabeledPoints of length nPoints
+        """
+        weights = [float(weight) for weight in weights]
+        xMean = [float(mean) for mean in xMean]
+        xVariance = [float(var) for var in xVariance]
+        return list(callMLlibFunc(
+            "generateLinearInputWrapper", float(intercept), weights, xMean,
+            xVariance, int(nPoints), int(seed), float(eps)))
+
+    @staticmethod
+    def generateLinearRDD(sc, nexamples, nfeatures, eps,
+                          nParts=2, intercept=0.0):
+        """
+        Generate a RDD of LabeledPoints.
+        """
+        return callMLlibFunc(
+            "generateLinearRDDWrapper", sc, int(nexamples), int(nfeatures),
+            float(eps), int(nParts), float(intercept))
+
+
 def _test():
     import doctest
     from pyspark.context import SparkContext

From f2fb0285ab6d4225c5350f109dea6c1c017bb491 Mon Sep 17 00:00:00 2001
From: Alok  Singh <singhal@Aloks-MacBook-Pro.local>
Date: Tue, 23 Jun 2015 12:47:55 -0700
Subject: [PATCH 0030/1454] [SPARK-8111] [SPARKR] SparkR shell should display
 Spark logo and version banner on startup.

spark version is taken from the environment variable SPARK_VERSION

Author: Alok  Singh <singhal@Aloks-MacBook-Pro.local>
Author: Alok  Singh <singhal@aloks-mbp.usca.ibm.com>

Closes #6944 from aloknsingh/aloknsingh_spark_jiras and squashes the following commits:

ed607bd [Alok  Singh] [SPARK-8111][SparkR] As per suggestion, 1) using the version from sparkContext rather than the Sys.env. 2) change "Welcome to SparkR!" to "Welcome to" followed by Spark logo and version
acd5b85 [Alok  Singh] fix the jira SPARK-8111 to add the spark version and logo. Currently spark version is taken from the environment variable SPARK_VERSION
---
 R/pkg/inst/profile/shell.R | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/R/pkg/inst/profile/shell.R b/R/pkg/inst/profile/shell.R
index 773b6ecf582d9..7189f1a260934 100644
--- a/R/pkg/inst/profile/shell.R
+++ b/R/pkg/inst/profile/shell.R
@@ -27,7 +27,21 @@
   sc <- SparkR::sparkR.init()
   assign("sc", sc, envir=.GlobalEnv)
   sqlContext <- SparkR::sparkRSQL.init(sc)
+  sparkVer <- SparkR:::callJMethod(sc, "version")
   assign("sqlContext", sqlContext, envir=.GlobalEnv)
-  cat("\n Welcome to SparkR!")
+  cat("\n Welcome to")
+  cat("\n")
+  cat("    ____              __", "\n")
+  cat("   / __/__  ___ _____/ /__", "\n")
+  cat("  _\\ \\/ _ \\/ _ `/ __/  '_/", "\n")
+  cat(" /___/ .__/\\_,_/_/ /_/\\_\\")
+  if (nchar(sparkVer) == 0) {
+    cat("\n")
+  } else {
+    cat("   version ", sparkVer, "\n") 
+  }
+  cat("    /_/", "\n")
+  cat("\n")
+
   cat("\n Spark context is available as sc, SQL context is available as sqlContext\n")
 }

From a8031183aff2e23de9204ddfc7e7f5edbf052a7e Mon Sep 17 00:00:00 2001
From: Oleksiy Dyagilev <oleksiy_dyagilev@epam.com>
Date: Tue, 23 Jun 2015 13:12:19 -0700
Subject: [PATCH 0031/1454] [SPARK-8525] [MLLIB] fix LabeledPoint parser when
 there is a whitespace between label and features vector

fix LabeledPoint parser when there is a whitespace between label and features vector, e.g.
(y, [x1, x2, x3])

Author: Oleksiy Dyagilev <oleksiy_dyagilev@epam.com>

Closes #6954 from fe2s/SPARK-8525 and squashes the following commits:

0755b9d [Oleksiy Dyagilev] [SPARK-8525][MLLIB] addressing comment, removing dep on commons-lang
c1abc2b [Oleksiy Dyagilev] [SPARK-8525][MLLIB] fix LabeledPoint parser when there is a whitespace on specific position
---
 .../scala/org/apache/spark/mllib/util/NumericParser.scala  | 2 ++
 .../apache/spark/mllib/regression/LabeledPointSuite.scala  | 5 +++++
 .../org/apache/spark/mllib/util/NumericParserSuite.scala   | 7 +++++++
 3 files changed, 14 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
index 308f7f3578e21..a841c5caf0142 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
@@ -98,6 +98,8 @@ private[mllib] object NumericParser {
         }
       } else if (token == ")") {
         parsing = false
+      } else if (token.trim.isEmpty){
+          // ignore whitespaces between delim chars, e.g. ", ["
       } else {
         // expecting a number
         items.append(parseDouble(token))
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
index d8364a06de4da..f8d0af8820e64 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
@@ -31,6 +31,11 @@ class LabeledPointSuite extends SparkFunSuite {
     }
   }
 
+  test("parse labeled points with whitespaces") {
+    val point = LabeledPoint.parse("(0.0, [1.0, 2.0])")
+    assert(point === LabeledPoint(0.0, Vectors.dense(1.0, 2.0)))
+  }
+
   test("parse labeled points with v0.9 format") {
     val point = LabeledPoint.parse("1.0,1.0 0.0 -2.0")
     assert(point === LabeledPoint(1.0, Vectors.dense(1.0, 0.0, -2.0)))
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
index 8dcb9ba9be108..fa4f74d71b7e7 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
@@ -37,4 +37,11 @@ class NumericParserSuite extends SparkFunSuite {
       }
     }
   }
+
+  test("parser with whitespaces") {
+    val s = "(0.0, [1.0, 2.0])"
+    val parsed = NumericParser.parse(s).asInstanceOf[Seq[_]]
+    assert(parsed(0).asInstanceOf[Double] === 0.0)
+    assert(parsed(1).asInstanceOf[Array[Double]] === Array(1.0, 2.0))
+  }
 }

From d96d7b55746cf034e3935ec4b22614a99e48c498 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 23 Jun 2015 14:19:21 -0700
Subject: [PATCH 0032/1454] [DOC] [SQL] Addes Hive metastore Parquet table
 conversion section

This PR adds a section about Hive metastore Parquet table conversion. It documents:

1. Schema reconciliation rules introduced in #5214 (see [this comment] [1] in #5188)
2. Metadata refreshing requirement introduced in #5339

[1]: https://github.com/apache/spark/pull/5188#issuecomment-86531248

Author: Cheng Lian <lian@databricks.com>

Closes #5348 from liancheng/sql-doc-parquet-conversion and squashes the following commits:

42ae0d0 [Cheng Lian] Adds Python `refreshTable` snippet
4c9847d [Cheng Lian] Resorts to SQL for Python metadata refreshing snippet
756e660 [Cheng Lian] Adds Python snippet for metadata refreshing
50675db [Cheng Lian] Addes Hive metastore Parquet table conversion section
---
 docs/sql-programming-guide.md | 94 ++++++++++++++++++++++++++++++++---
 1 file changed, 88 insertions(+), 6 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 26c036f6648da..9107c9b67681f 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -22,7 +22,7 @@ The DataFrame API is available in [Scala](api/scala/index.html#org.apache.spark.
 All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`, `pyspark` shell, or `sparkR` shell.
 
 
-## Starting Point: `SQLContext`
+## Starting Point: SQLContext
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
@@ -1036,6 +1036,15 @@ for (teenName in collect(teenNames)) {
 
 </div>
 
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+# sqlContext is an existing HiveContext
+sqlContext.sql("REFRESH TABLE my_table")
+{% endhighlight %}
+
+</div>
+
 <div data-lang="sql"  markdown="1">
 
 {% highlight sql %}
@@ -1054,7 +1063,7 @@ SELECT * FROM parquetTable
 
 </div>
 
-### Partition discovery
+### Partition Discovery
 
 Table partitioning is a common optimization approach used in systems like Hive.  In a partitioned
 table, data are usually stored in different directories, with partitioning column values encoded in
@@ -1108,7 +1117,7 @@ can be configured by `spark.sql.sources.partitionColumnTypeInference.enabled`, w
 `true`. When type inference is disabled, string type will be used for the partitioning columns.
 
 
-### Schema merging
+### Schema Merging
 
 Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution.  Users can start with
 a simple schema, and gradually add more columns to the schema as needed.  In this way, users may end
@@ -1208,6 +1217,79 @@ printSchema(df3)
 
 </div>
 
+### Hive metastore Parquet table conversion
+
+When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own
+Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the
+`spark.sql.hive.convertMetastoreParquet` configuration, and is turned on by default.
+
+#### Hive/Parquet Schema Reconciliation
+
+There are two key differences between Hive and Parquet from the perspective of table schema
+processing.
+
+1. Hive is case insensitive, while Parquet is not
+1. Hive considers all columns nullable, while nullability in Parquet is significant
+
+Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a
+Hive metastore Parquet table to a Spark SQL Parquet table.  The reconciliation rules are:
+
+1. Fields that have the same name in both schema must have the same data type regardless of
+   nullability.  The reconciled field should have the data type of the Parquet side, so that
+   nullability is respected.
+
+1. The reconciled schema contains exactly those fields defined in Hive metastore schema.
+
+   - Any fields that only appear in the Parquet schema are dropped in the reconciled schema.
+   - Any fileds that only appear in the Hive metastore schema are added as nullable field in the
+     reconciled schema.
+
+#### Metadata Refreshing
+
+Spark SQL caches Parquet metadata for better performance.  When Hive metastore Parquet table
+conversion is enabled, metadata of those converted tables are also cached.  If these tables are
+updated by Hive or other external tools, you need to refresh them manually to ensure consistent
+metadata.
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+{% highlight scala %}
+// sqlContext is an existing HiveContext
+sqlContext.refreshTable("my_table")
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+{% highlight java %}
+// sqlContext is an existing HiveContext
+sqlContext.refreshTable("my_table")
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+# sqlContext is an existing HiveContext
+sqlContext.refreshTable("my_table")
+{% endhighlight %}
+
+</div>
+
+<div data-lang="sql"  markdown="1">
+
+{% highlight sql %}
+REFRESH TABLE my_table;
+{% endhighlight %}
+
+</div>
+
+</div>
+
 ### Configuration
 
 Configuration of Parquet can be done using the `setConf` method on `SQLContext` or by running
@@ -1445,8 +1527,8 @@ This command builds a new assembly jar that includes Hive. Note that this Hive a
 on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries
 (SerDes) in order to access data stored in Hive.
 
-Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running 
-the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory 
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running
+the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory
 and `hive-site.xml` under `conf/` directory need to be available on the driver and all executors launched by the
 YARN cluster. The convenient way to do this is adding them through the `--jars` option and `--file` option of the
 `spark-submit` command.
@@ -1889,7 +1971,7 @@ options.
 #### DataFrame data reader/writer interface
 
 Based on user feedback, we created a new, more fluid API for reading data in (`SQLContext.read`)
-and writing data out (`DataFrame.write`), 
+and writing data out (`DataFrame.write`),
 and deprecated the old APIs (e.g. `SQLContext.parquetFile`, `SQLContext.jsonFile`).
 
 See the API docs for `SQLContext.read` (

From 7fb5ae5024284593204779ff463bfbdb4d1c6da5 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 23 Jun 2015 15:51:16 -0700
Subject: [PATCH 0033/1454] [SPARK-8573] [SPARK-8568] [SQL] [PYSPARK] raise
 Exception if column is used in booelan expression

It's a common mistake that user will put Column in a boolean expression (together with `and` , `or`), which does not work as expected, we should raise a exception in that case, and suggest user to use `&`, `|` instead.

Author: Davies Liu <davies@databricks.com>

Closes #6961 from davies/column_bool and squashes the following commits:

9f19beb [Davies Liu] update message
af74bd6 [Davies Liu] fix tests
07dff84 [Davies Liu] address comments, fix tests
f70c08e [Davies Liu] raise Exception if column is used in booelan expression
---
 python/pyspark/sql/column.py |  5 +++++
 python/pyspark/sql/tests.py  | 10 +++++++++-
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 1ecec5b126505..0a85da7443d3d 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -396,6 +396,11 @@ def over(self, window):
         jc = self._jc.over(window._jspec)
         return Column(jc)
 
+    def __nonzero__(self):
+        raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
+                         "'~' for 'not' when building DataFrame boolean expressions.")
+    __bool__ = __nonzero__
+
     def __repr__(self):
         return 'Column<%s>' % self._jc.toString().encode('utf8')
 
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 13f4556943ac8..e6a434e4b2dff 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -164,6 +164,14 @@ def test_explode(self):
         self.assertEqual(result[0][0], "a")
         self.assertEqual(result[0][1], "b")
 
+    def test_and_in_expression(self):
+        self.assertEqual(4, self.df.filter((self.df.key <= 10) & (self.df.value <= "2")).count())
+        self.assertRaises(ValueError, lambda: (self.df.key <= 10) and (self.df.value <= "2"))
+        self.assertEqual(14, self.df.filter((self.df.key <= 3) | (self.df.value < "2")).count())
+        self.assertRaises(ValueError, lambda: self.df.key <= 3 or self.df.value < "2")
+        self.assertEqual(99, self.df.filter(~(self.df.key == 1)).count())
+        self.assertRaises(ValueError, lambda: not self.df.key == 1)
+
     def test_udf_with_callable(self):
         d = [Row(number=i, squared=i**2) for i in range(10)]
         rdd = self.sc.parallelize(d)
@@ -408,7 +416,7 @@ def test_column_operators(self):
         self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
         rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci)
         self.assertTrue(all(isinstance(c, Column) for c in rcc))
-        cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7, ci and cs, ci or cs]
+        cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7]
         self.assertTrue(all(isinstance(c, Column) for c in cb))
         cbool = (ci & ci), (ci | ci), (~ci)
         self.assertTrue(all(isinstance(c, Column) for c in cbool))

From 111d6b9b8a584b962b6ae80c7aa8c45845ce0099 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 23 Jun 2015 17:24:26 -0700
Subject: [PATCH 0034/1454] [SPARK-8139] [SQL] Updates docs and comments of
 data sources and Parquet output committer options

This PR only applies to master branch (1.5.0-SNAPSHOT) since it references `org.apache.parquet` classes which only appear in Parquet 1.7.0.

Author: Cheng Lian <lian@databricks.com>

Closes #6683 from liancheng/output-committer-docs and squashes the following commits:

b4648b8 [Cheng Lian] Removes spark.sql.sources.outputCommitterClass as it's not a public option
ee63923 [Cheng Lian] Updates docs and comments of data sources and Parquet output committer options
---
 docs/sql-programming-guide.md                 | 30 +++++++++++++++-
 .../scala/org/apache/spark/sql/SQLConf.scala  | 30 ++++++++++++----
 .../DirectParquetOutputCommitter.scala        | 34 +++++++++++++------
 .../apache/spark/sql/parquet/newParquet.scala |  4 +--
 4 files changed, 78 insertions(+), 20 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 9107c9b67681f..2786e3d2cd6bf 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1348,6 +1348,34 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
     support.
   </td>
 </tr>
+<tr>
+  <td><code>spark.sql.parquet.output.committer.class</code></td>
+  <td><code>org.apache.parquet.hadoop.<br />ParquetOutputCommitter</code></td>
+  <td>
+    <p>
+      The output committer class used by Parquet. The specified class needs to be a subclass of
+      <code>org.apache.hadoop.<br />mapreduce.OutputCommitter</code>.  Typically, it's also a
+      subclass of <code>org.apache.parquet.hadoop.ParquetOutputCommitter</code>.
+    </p>
+    <p>
+      <b>Note:</b>
+      <ul>
+        <li>
+          This option must be set via Hadoop <code>Configuration</code> rather than Spark
+          <code>SQLConf</code>.
+        </li>
+        <li>
+          This option overrides <code>spark.sql.sources.<br />outputCommitterClass</code>.
+        </li>
+      </ul>
+    </p>
+    <p>
+      Spark SQL comes with a builtin
+      <code>org.apache.spark.sql.<br />parquet.DirectParquetOutputCommitter</code>, which can be more
+      efficient then the default Parquet output committer when writing data to S3.
+    </p>
+  </td>
+</tr>
 </table>
 
 ## JSON Datasets
@@ -1876,7 +1904,7 @@ that these options will be deprecated in future release as more optimizations ar
       Configures the number of partitions to use when shuffling data for joins or aggregations.
     </td>
   </tr>
-   <tr>
+  <tr>
     <td><code>spark.sql.planner.externalSort</code></td>
     <td>false</td>
     <td>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 16493c3d7c19c..265352647fa9f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -22,6 +22,8 @@ import java.util.Properties
 import scala.collection.immutable
 import scala.collection.JavaConversions._
 
+import org.apache.parquet.hadoop.ParquetOutputCommitter
+
 import org.apache.spark.sql.catalyst.CatalystConf
 
 private[spark] object SQLConf {
@@ -252,9 +254,9 @@ private[spark] object SQLConf {
 
   val PARQUET_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.parquet.filterPushdown",
     defaultValue = Some(false),
-    doc = "Turn on Parquet filter pushdown optimization. This feature is turned off by default" +
-      " because of a known bug in Paruet 1.6.0rc3 " +
-      "(<a href=\"https://issues.apache.org/jira/browse/PARQUET-136\">PARQUET-136</a>). However, " +
+    doc = "Turn on Parquet filter pushdown optimization. This feature is turned off by default " +
+      "because of a known bug in Parquet 1.6.0rc3 " +
+      "(PARQUET-136, https://issues.apache.org/jira/browse/PARQUET-136). However, " +
       "if your table doesn't contain any nullable string or binary columns, it's still safe to " +
       "turn this feature on.")
 
@@ -262,11 +264,21 @@ private[spark] object SQLConf {
     defaultValue = Some(true),
     doc = "<TODO>")
 
+  val PARQUET_OUTPUT_COMMITTER_CLASS = stringConf(
+    key = "spark.sql.parquet.output.committer.class",
+    defaultValue = Some(classOf[ParquetOutputCommitter].getName),
+    doc = "The output committer class used by Parquet. The specified class needs to be a " +
+      "subclass of org.apache.hadoop.mapreduce.OutputCommitter.  Typically, it's also a subclass " +
+      "of org.apache.parquet.hadoop.ParquetOutputCommitter.  NOTE: 1. Instead of SQLConf, this " +
+      "option must be set in Hadoop Configuration.  2. This option overrides " +
+      "\"spark.sql.sources.outputCommitterClass\"."
+  )
+
   val ORC_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.orc.filterPushdown",
     defaultValue = Some(false),
     doc = "<TODO>")
 
-  val HIVE_VERIFY_PARTITIONPATH = booleanConf("spark.sql.hive.verifyPartitionPath",
+  val HIVE_VERIFY_PARTITION_PATH = booleanConf("spark.sql.hive.verifyPartitionPath",
     defaultValue = Some(true),
     doc = "<TODO>")
 
@@ -325,9 +337,13 @@ private[spark] object SQLConf {
       defaultValue = Some(true),
       doc = "<TODO>")
 
-  // The output committer class used by FSBasedRelation. The specified class needs to be a
+  // The output committer class used by HadoopFsRelation. The specified class needs to be a
   // subclass of org.apache.hadoop.mapreduce.OutputCommitter.
-  // NOTE: This property should be set in Hadoop `Configuration` rather than Spark `SQLConf`
+  //
+  // NOTE:
+  //
+  //  1. Instead of SQLConf, this option *must be set in Hadoop Configuration*.
+  //  2. This option can be overriden by "spark.sql.parquet.output.committer.class".
   val OUTPUT_COMMITTER_CLASS =
     stringConf("spark.sql.sources.outputCommitterClass", isPublic = false)
 
@@ -415,7 +431,7 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
   private[spark] def orcFilterPushDown: Boolean = getConf(ORC_FILTER_PUSHDOWN_ENABLED)
 
   /** When true uses verifyPartitionPath to prune the path which is not exists. */
-  private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITIONPATH)
+  private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITION_PATH)
 
   /** When true the planner will use the external sort, which may spill to disk. */
   private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala
index 62c4e92ebec68..1551afd7b7bf2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala
@@ -17,19 +17,35 @@
 
 package org.apache.spark.sql.parquet
 
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext}
 import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
-
+import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext}
 import org.apache.parquet.Log
 import org.apache.parquet.hadoop.util.ContextUtil
 import org.apache.parquet.hadoop.{ParquetFileReader, ParquetFileWriter, ParquetOutputCommitter, ParquetOutputFormat}
 
+/**
+ * An output committer for writing Parquet files.  In stead of writing to the `_temporary` folder
+ * like what [[ParquetOutputCommitter]] does, this output committer writes data directly to the
+ * destination folder.  This can be useful for data stored in S3, where directory operations are
+ * relatively expensive.
+ *
+ * To enable this output committer, users may set the "spark.sql.parquet.output.committer.class"
+ * property via Hadoop [[Configuration]].  Not that this property overrides
+ * "spark.sql.sources.outputCommitterClass".
+ *
+ * *NOTE*
+ *
+ *   NEVER use [[DirectParquetOutputCommitter]] when appending data, because currently there's
+ *   no safe way undo a failed appending job (that's why both `abortTask()` and `abortJob()` are
+ *   left * empty).
+ */
 private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
   extends ParquetOutputCommitter(outputPath, context) {
   val LOG = Log.getLog(classOf[ParquetOutputCommitter])
 
-  override def getWorkPath(): Path = outputPath
+  override def getWorkPath: Path = outputPath
   override def abortTask(taskContext: TaskAttemptContext): Unit = {}
   override def commitTask(taskContext: TaskAttemptContext): Unit = {}
   override def needsTaskCommit(taskContext: TaskAttemptContext): Boolean = true
@@ -46,13 +62,11 @@ private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: T
         val footers = ParquetFileReader.readAllFootersInParallel(configuration, outputStatus)
         try {
           ParquetFileWriter.writeMetadataFile(configuration, outputPath, footers)
-        } catch {
-          case e: Exception => {
-            LOG.warn("could not write summary file for " + outputPath, e)
-            val metadataPath = new Path(outputPath, ParquetFileWriter.PARQUET_METADATA_FILE)
-            if (fileSystem.exists(metadataPath)) {
-              fileSystem.delete(metadataPath, true)
-            }
+        } catch { case e: Exception =>
+          LOG.warn("could not write summary file for " + outputPath, e)
+          val metadataPath = new Path(outputPath, ParquetFileWriter.PARQUET_METADATA_FILE)
+          if (fileSystem.exists(metadataPath)) {
+            fileSystem.delete(metadataPath, true)
           }
         }
       } catch {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index e049d54bf55dc..1d353bd8e1114 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -178,11 +178,11 @@ private[sql] class ParquetRelation2(
 
     val committerClass =
       conf.getClass(
-        "spark.sql.parquet.output.committer.class",
+        SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key,
         classOf[ParquetOutputCommitter],
         classOf[ParquetOutputCommitter])
 
-    if (conf.get("spark.sql.parquet.output.committer.class") == null) {
+    if (conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key) == null) {
       logInfo("Using default output committer for Parquet: " +
         classOf[ParquetOutputCommitter].getCanonicalName)
     } else {

From 0401cbaa8ee51c71f43604f338b65022a479da0a Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 23 Jun 2015 17:46:29 -0700
Subject: [PATCH 0035/1454] [SPARK-7157][SQL] add sampleBy to DataFrame

Add `sampleBy` to DataFrame. rxin

Author: Xiangrui Meng <meng@databricks.com>

Closes #6769 from mengxr/SPARK-7157 and squashes the following commits:

991f26f [Xiangrui Meng] fix seed
4a14834 [Xiangrui Meng] move sampleBy to stat
832f7cc [Xiangrui Meng] add sampleBy to DataFrame
---
 python/pyspark/sql/dataframe.py               | 40 +++++++++++++++++++
 .../spark/sql/DataFrameStatFunctions.scala    | 24 +++++++++++
 .../apache/spark/sql/DataFrameStatSuite.scala | 12 +++++-
 3 files changed, 74 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 152b87351db31..213338dfe58a4 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -448,6 +448,41 @@ def sample(self, withReplacement, fraction, seed=None):
         rdd = self._jdf.sample(withReplacement, fraction, long(seed))
         return DataFrame(rdd, self.sql_ctx)
 
+    @since(1.5)
+    def sampleBy(self, col, fractions, seed=None):
+        """
+        Returns a stratified sample without replacement based on the
+        fraction given on each stratum.
+
+        :param col: column that defines strata
+        :param fractions:
+            sampling fraction for each stratum. If a stratum is not
+            specified, we treat its fraction as zero.
+        :param seed: random seed
+        :return: a new DataFrame that represents the stratified sample
+
+        >>> from pyspark.sql.functions import col
+        >>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key"))
+        >>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
+        >>> sampled.groupBy("key").count().orderBy("key").show()
+        +---+-----+
+        |key|count|
+        +---+-----+
+        |  0|    5|
+        |  1|    8|
+        +---+-----+
+        """
+        if not isinstance(col, str):
+            raise ValueError("col must be a string, but got %r" % type(col))
+        if not isinstance(fractions, dict):
+            raise ValueError("fractions must be a dict but got %r" % type(fractions))
+        for k, v in fractions.items():
+            if not isinstance(k, (float, int, long, basestring)):
+                raise ValueError("key must be float, int, long, or string, but got %r" % type(k))
+            fractions[k] = float(v)
+        seed = seed if seed is not None else random.randint(0, sys.maxsize)
+        return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx)
+
     @since(1.4)
     def randomSplit(self, weights, seed=None):
         """Randomly splits this :class:`DataFrame` with the provided weights.
@@ -1322,6 +1357,11 @@ def freqItems(self, cols, support=None):
 
     freqItems.__doc__ = DataFrame.freqItems.__doc__
 
+    def sampleBy(self, col, fractions, seed=None):
+        return self.df.sampleBy(col, fractions, seed)
+
+    sampleBy.__doc__ = DataFrame.sampleBy.__doc__
+
 
 def _test():
     import doctest
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index edb9ed7bba56a..955d28771b4df 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import java.util.UUID
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.execution.stat._
 
@@ -163,4 +165,26 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
   def freqItems(cols: Seq[String]): DataFrame = {
     FrequentItems.singlePassFreqItems(df, cols, 0.01)
   }
+
+  /**
+   * Returns a stratified sample without replacement based on the fraction given on each stratum.
+   * @param col column that defines strata
+   * @param fractions sampling fraction for each stratum. If a stratum is not specified, we treat
+   *                  its fraction as zero.
+   * @param seed random seed
+   * @return a new [[DataFrame]] that represents the stratified sample
+   *
+   * @since 1.5.0
+   */
+  def sampleBy(col: String, fractions: Map[Any, Double], seed: Long): DataFrame = {
+    require(fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+      s"Fractions must be in [0, 1], but got $fractions.")
+    import org.apache.spark.sql.functions.rand
+    val c = Column(col)
+    val r = rand(seed).as("rand_" + UUID.randomUUID().toString.take(8))
+    val expr = fractions.toSeq.map { case (k, v) =>
+      (c === k) && (r < v)
+    }.reduce(_ || _) || false
+    df.filter(expr)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 0d3ff899dad72..3dd46889127ff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.sql
 
 import org.scalatest.Matchers._
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.functions.col
 
-class DataFrameStatSuite extends SparkFunSuite  {
+class DataFrameStatSuite extends QueryTest {
 
   private val sqlCtx = org.apache.spark.sql.test.TestSQLContext
   import sqlCtx.implicits._
@@ -98,4 +98,12 @@ class DataFrameStatSuite extends SparkFunSuite  {
     val items2 = singleColResults.collect().head
     items2.getSeq[Double](0) should contain (-1.0)
   }
+
+  test("sampleBy") {
+    val df = sqlCtx.range(0, 100).select((col("id") % 3).as("key"))
+    val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L)
+    checkAnswer(
+      sampled.groupBy("key").count().orderBy("key"),
+      Seq(Row(0, 4), Row(1, 9)))
+  }
 }

From a458efc66c31dc281af379b914bfa2b077ca6635 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 23 Jun 2015 19:30:25 -0700
Subject: [PATCH 0036/1454] Revert "[SPARK-7157][SQL] add sampleBy to
 DataFrame"

This reverts commit 0401cbaa8ee51c71f43604f338b65022a479da0a.

The new test case on Jenkins is failing.
---
 python/pyspark/sql/dataframe.py               | 40 -------------------
 .../spark/sql/DataFrameStatFunctions.scala    | 24 -----------
 .../apache/spark/sql/DataFrameStatSuite.scala | 12 +-----
 3 files changed, 2 insertions(+), 74 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 213338dfe58a4..152b87351db31 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -448,41 +448,6 @@ def sample(self, withReplacement, fraction, seed=None):
         rdd = self._jdf.sample(withReplacement, fraction, long(seed))
         return DataFrame(rdd, self.sql_ctx)
 
-    @since(1.5)
-    def sampleBy(self, col, fractions, seed=None):
-        """
-        Returns a stratified sample without replacement based on the
-        fraction given on each stratum.
-
-        :param col: column that defines strata
-        :param fractions:
-            sampling fraction for each stratum. If a stratum is not
-            specified, we treat its fraction as zero.
-        :param seed: random seed
-        :return: a new DataFrame that represents the stratified sample
-
-        >>> from pyspark.sql.functions import col
-        >>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key"))
-        >>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
-        >>> sampled.groupBy("key").count().orderBy("key").show()
-        +---+-----+
-        |key|count|
-        +---+-----+
-        |  0|    5|
-        |  1|    8|
-        +---+-----+
-        """
-        if not isinstance(col, str):
-            raise ValueError("col must be a string, but got %r" % type(col))
-        if not isinstance(fractions, dict):
-            raise ValueError("fractions must be a dict but got %r" % type(fractions))
-        for k, v in fractions.items():
-            if not isinstance(k, (float, int, long, basestring)):
-                raise ValueError("key must be float, int, long, or string, but got %r" % type(k))
-            fractions[k] = float(v)
-        seed = seed if seed is not None else random.randint(0, sys.maxsize)
-        return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx)
-
     @since(1.4)
     def randomSplit(self, weights, seed=None):
         """Randomly splits this :class:`DataFrame` with the provided weights.
@@ -1357,11 +1322,6 @@ def freqItems(self, cols, support=None):
 
     freqItems.__doc__ = DataFrame.freqItems.__doc__
 
-    def sampleBy(self, col, fractions, seed=None):
-        return self.df.sampleBy(col, fractions, seed)
-
-    sampleBy.__doc__ = DataFrame.sampleBy.__doc__
-
 
 def _test():
     import doctest
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index 955d28771b4df..edb9ed7bba56a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql
 
-import java.util.UUID
-
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.execution.stat._
 
@@ -165,26 +163,4 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
   def freqItems(cols: Seq[String]): DataFrame = {
     FrequentItems.singlePassFreqItems(df, cols, 0.01)
   }
-
-  /**
-   * Returns a stratified sample without replacement based on the fraction given on each stratum.
-   * @param col column that defines strata
-   * @param fractions sampling fraction for each stratum. If a stratum is not specified, we treat
-   *                  its fraction as zero.
-   * @param seed random seed
-   * @return a new [[DataFrame]] that represents the stratified sample
-   *
-   * @since 1.5.0
-   */
-  def sampleBy(col: String, fractions: Map[Any, Double], seed: Long): DataFrame = {
-    require(fractions.values.forall(p => p >= 0.0 && p <= 1.0),
-      s"Fractions must be in [0, 1], but got $fractions.")
-    import org.apache.spark.sql.functions.rand
-    val c = Column(col)
-    val r = rand(seed).as("rand_" + UUID.randomUUID().toString.take(8))
-    val expr = fractions.toSeq.map { case (k, v) =>
-      (c === k) && (r < v)
-    }.reduce(_ || _) || false
-    df.filter(expr)
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 3dd46889127ff..0d3ff899dad72 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.sql
 
 import org.scalatest.Matchers._
 
-import org.apache.spark.sql.functions.col
+import org.apache.spark.SparkFunSuite
 
-class DataFrameStatSuite extends QueryTest {
+class DataFrameStatSuite extends SparkFunSuite  {
 
   private val sqlCtx = org.apache.spark.sql.test.TestSQLContext
   import sqlCtx.implicits._
@@ -98,12 +98,4 @@ class DataFrameStatSuite extends QueryTest {
     val items2 = singleColResults.collect().head
     items2.getSeq[Double](0) should contain (-1.0)
   }
-
-  test("sampleBy") {
-    val df = sqlCtx.range(0, 100).select((col("id") % 3).as("key"))
-    val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L)
-    checkAnswer(
-      sampled.groupBy("key").count().orderBy("key"),
-      Seq(Row(0, 4), Row(1, 9)))
-  }
 }

From 50c3a86f42d7dfd1acbda65c1e5afbd3db1406df Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Tue, 23 Jun 2015 22:27:17 -0700
Subject: [PATCH 0037/1454] [SPARK-6749] [SQL] Make metastore client robust to
 underlying socket connection loss

This works around a bug in the underlying RetryingMetaStoreClient (HIVE-10384) by refreshing the metastore client on thrift exceptions. We attempt to emulate the proper hive behavior by retrying only as configured by hiveconf.

Author: Eric Liang <ekl@databricks.com>

Closes #6912 from ericl/spark-6749 and squashes the following commits:

2d54b55 [Eric Liang] use conf from state
0e3a74e [Eric Liang] use shim properly
980b3e5 [Eric Liang] Fix conf parsing hive 0.14 conf.
92459b6 [Eric Liang] Work around RetryingMetaStoreClient bug
---
 .../spark/sql/hive/client/ClientWrapper.scala | 55 ++++++++++++++++++-
 .../spark/sql/hive/client/HiveShim.scala      | 19 +++++++
 2 files changed, 72 insertions(+), 2 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 42c2d4c98ffb2..2f771d76793e5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.hive.client
 import java.io.{BufferedReader, InputStreamReader, File, PrintStream}
 import java.net.URI
 import java.util.{ArrayList => JArrayList, Map => JMap, List => JList, Set => JSet}
+import javax.annotation.concurrent.GuardedBy
 
 import scala.collection.JavaConversions._
 import scala.language.reflectiveCalls
@@ -136,12 +137,62 @@ private[hive] class ClientWrapper(
 
   // TODO: should be a def?s
   // When we create this val client, the HiveConf of it (conf) is the one associated with state.
-  private val client = Hive.get(conf)
+  @GuardedBy("this")
+  private var client = Hive.get(conf)
+
+  // We use hive's conf for compatibility.
+  private val retryLimit = conf.getIntVar(HiveConf.ConfVars.METASTORETHRIFTFAILURERETRIES)
+  private val retryDelayMillis = shim.getMetastoreClientConnectRetryDelayMillis(conf)
+
+  /**
+   * Runs `f` with multiple retries in case the hive metastore is temporarily unreachable.
+   */
+  private def retryLocked[A](f: => A): A = synchronized {
+    // Hive sometimes retries internally, so set a deadline to avoid compounding delays.
+    val deadline = System.nanoTime + (retryLimit * retryDelayMillis * 1e6).toLong
+    var numTries = 0
+    var caughtException: Exception = null
+    do {
+      numTries += 1
+      try {
+        return f
+      } catch {
+        case e: Exception if causedByThrift(e) =>
+          caughtException = e
+          logWarning(
+            "HiveClientWrapper got thrift exception, destroying client and retrying " +
+              s"(${retryLimit - numTries} tries remaining)", e)
+          Thread.sleep(retryDelayMillis)
+          try {
+            client = Hive.get(state.getConf, true)
+          } catch {
+            case e: Exception if causedByThrift(e) =>
+              logWarning("Failed to refresh hive client, will retry.", e)
+          }
+      }
+    } while (numTries <= retryLimit && System.nanoTime < deadline)
+    if (System.nanoTime > deadline) {
+      logWarning("Deadline exceeded")
+    }
+    throw caughtException
+  }
+
+  private def causedByThrift(e: Throwable): Boolean = {
+    var target = e
+    while (target != null) {
+      val msg = target.getMessage()
+      if (msg != null && msg.matches("(?s).*(TApplication|TProtocol|TTransport)Exception.*")) {
+        return true
+      }
+      target = target.getCause()
+    }
+    false
+  }
 
   /**
    * Runs `f` with ThreadLocal session state and classloaders configured for this version of hive.
    */
-  private def withHiveState[A](f: => A): A = synchronized {
+  private def withHiveState[A](f: => A): A = retryLocked {
     val original = Thread.currentThread().getContextClassLoader
     // Set the thread local metastore client to the client associated with this ClientWrapper.
     Hive.set(client)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 5ae2dbb50d86b..e7c1779f80ce6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -21,6 +21,7 @@ import java.lang.{Boolean => JBoolean, Integer => JInteger}
 import java.lang.reflect.{Method, Modifier}
 import java.net.URI
 import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, Set => JSet}
+import java.util.concurrent.TimeUnit
 
 import scala.collection.JavaConversions._
 
@@ -64,6 +65,8 @@ private[client] sealed abstract class Shim {
 
   def getDriverResults(driver: Driver): Seq[String]
 
+  def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long
+
   def loadPartition(
       hive: Hive,
       loadPath: Path,
@@ -192,6 +195,10 @@ private[client] class Shim_v0_12 extends Shim {
     res.toSeq
   }
 
+  override def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long = {
+    conf.getIntVar(HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY) * 1000
+  }
+
   override def loadPartition(
       hive: Hive,
       loadPath: Path,
@@ -321,6 +328,12 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       JBoolean.TYPE,
       JBoolean.TYPE,
       JBoolean.TYPE)
+  private lazy val getTimeVarMethod =
+    findMethod(
+      classOf[HiveConf],
+      "getTimeVar",
+      classOf[HiveConf.ConfVars],
+      classOf[TimeUnit])
 
   override def loadPartition(
       hive: Hive,
@@ -359,4 +372,10 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean, JBoolean.FALSE)
   }
 
+  override def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long = {
+    getTimeVarMethod.invoke(
+      conf,
+      HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY,
+      TimeUnit.MILLISECONDS).asInstanceOf[Long]
+  }
 }

From 13ae806b255cfb2bd5470b599a95c87a2cd5e978 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 23 Jun 2015 23:03:59 -0700
Subject: [PATCH 0038/1454] [HOTFIX] [BUILD] Fix MiMa checks in master branch;
 enable MiMa for launcher project

This commit changes the MiMa tests to test against the released 1.4.0 artifacts rather than 1.4.0-rc4; this change is necessary to fix a Jenkins build break since it seems that the RC4 snapshot is no longer available via Maven.

I also enabled MiMa checks for the `launcher` subproject, which we should have done right after 1.4.0 was released.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #6974 from JoshRosen/mima-hotfix and squashes the following commits:

4b4175a [Josh Rosen] [HOTFIX] [BUILD] Fix MiMa checks in master branch; enable MiMa for launcher project
---
 project/MimaBuild.scala  | 3 +--
 project/SparkBuild.scala | 3 +--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index 5812b72f0aa78..f16bf989f200b 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -91,8 +91,7 @@ object MimaBuild {
 
   def mimaSettings(sparkHome: File, projectRef: ProjectRef) = {
     val organization = "org.apache.spark"
-    // TODO: Change this once Spark 1.4.0 is released
-    val previousSparkVersion = "1.4.0-rc4"
+    val previousSparkVersion = "1.4.0"
     val fullId = "spark-" + projectRef.project + "_2.10"
     mimaDefaultSettings ++
     Seq(previousArtifact := Some(organization % fullId % previousSparkVersion),
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index e01720296fed0..f5f1c9a1a247a 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -166,9 +166,8 @@ object SparkBuild extends PomBuild {
   /* Enable tests settings for all projects except examples, assembly and tools */
   (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings))
 
-  // TODO: remove launcher from this list after 1.4.0
   allProjects.filterNot(x => Seq(spark, hive, hiveThriftServer, catalyst, repl,
-    networkCommon, networkShuffle, networkYarn, launcher, unsafe).contains(x)).foreach {
+    networkCommon, networkShuffle, networkYarn, unsafe).contains(x)).foreach {
       x => enable(MimaBuild.mimaSettings(sparkHome, x))(x)
     }
 

From 09fcf96b8f881988a4bc7fe26a3f6ed12dfb6adb Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 23 Jun 2015 23:11:42 -0700
Subject: [PATCH 0039/1454] [SPARK-8371] [SQL] improve unit test for MaxOf and
 MinOf and fix bugs

a follow up of https://github.com/apache/spark/pull/6813

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #6825 from cloud-fan/cg and squashes the following commits:

43170cc [Wenchen Fan] fix bugs in code gen
---
 .../expressions/codegen/CodeGenerator.scala   |  4 +-
 .../ArithmeticExpressionSuite.scala           | 46 +++++++++++++------
 2 files changed, 34 insertions(+), 16 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index bd5475d2066fc..47c5455435ec6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -175,8 +175,10 @@ class CodeGenContext {
    * Generate code for compare expression in Java
    */
   def genComp(dataType: DataType, c1: String, c2: String): String = dataType match {
+    // java boolean doesn't support > or < operator
+    case BooleanType => s"($c1 == $c2 ? 0 : ($c1 ? 1 : -1))"
     // use c1 - c2 may overflow
-    case dt: DataType if isPrimitiveType(dt) => s"(int)($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)"
+    case dt: DataType if isPrimitiveType(dt) => s"($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)"
     case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)"
     case other => s"$c1.compare($c2)"
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index 4bbbbe6c7f091..6c93698f8017b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{Decimal, DoubleType, IntegerType}
+import org.apache.spark.sql.types.Decimal
 
 
 class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -123,23 +123,39 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     }
   }
 
-  test("MaxOf") {
-    checkEvaluation(MaxOf(1, 2), 2)
-    checkEvaluation(MaxOf(2, 1), 2)
-    checkEvaluation(MaxOf(1L, 2L), 2L)
-    checkEvaluation(MaxOf(2L, 1L), 2L)
+  test("MaxOf basic") {
+    testNumericDataTypes { convert =>
+      val small = Literal(convert(1))
+      val large = Literal(convert(2))
+      checkEvaluation(MaxOf(small, large), convert(2))
+      checkEvaluation(MaxOf(large, small), convert(2))
+      checkEvaluation(MaxOf(Literal.create(null, small.dataType), large), convert(2))
+      checkEvaluation(MaxOf(large, Literal.create(null, small.dataType)), convert(2))
+    }
+  }
 
-    checkEvaluation(MaxOf(Literal.create(null, IntegerType), 2), 2)
-    checkEvaluation(MaxOf(2, Literal.create(null, IntegerType)), 2)
+  test("MaxOf for atomic type") {
+    checkEvaluation(MaxOf(true, false), true)
+    checkEvaluation(MaxOf("abc", "bcd"), "bcd")
+    checkEvaluation(MaxOf(Array(1.toByte, 2.toByte), Array(1.toByte, 3.toByte)),
+      Array(1.toByte, 3.toByte))
   }
 
-  test("MinOf") {
-    checkEvaluation(MinOf(1, 2), 1)
-    checkEvaluation(MinOf(2, 1), 1)
-    checkEvaluation(MinOf(1L, 2L), 1L)
-    checkEvaluation(MinOf(2L, 1L), 1L)
+  test("MinOf basic") {
+    testNumericDataTypes { convert =>
+      val small = Literal(convert(1))
+      val large = Literal(convert(2))
+      checkEvaluation(MinOf(small, large), convert(1))
+      checkEvaluation(MinOf(large, small), convert(1))
+      checkEvaluation(MinOf(Literal.create(null, small.dataType), large), convert(2))
+      checkEvaluation(MinOf(small, Literal.create(null, small.dataType)), convert(1))
+    }
+  }
 
-    checkEvaluation(MinOf(Literal.create(null, IntegerType), 1), 1)
-    checkEvaluation(MinOf(1, Literal.create(null, IntegerType)), 1)
+  test("MinOf for atomic type") {
+    checkEvaluation(MinOf(true, false), false)
+    checkEvaluation(MinOf("abc", "bcd"), "abc")
+    checkEvaluation(MinOf(Array(1.toByte, 2.toByte), Array(1.toByte, 3.toByte)),
+      Array(1.toByte, 2.toByte))
   }
 }

From cc465fd92482737c21971d82e30d4cf247acf932 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 24 Jun 2015 02:17:12 -0700
Subject: [PATCH 0040/1454] [SPARK-8138] [SQL] Improves error message when
 conflicting partition columns are found

This PR improves the error message shown when conflicting partition column names are detected.  This can be particularly annoying and confusing when there are a large number of partitions while a handful of them happened to contain unexpected temporary file(s).  Now all suspicious directories are listed as below:

```
java.lang.AssertionError: assertion failed: Conflicting partition column names detected:

        Partition column name list #0: b, c, d
        Partition column name list #1: b, c
        Partition column name list #2: b

For partitioned table directories, data files should only live in leaf directories. Please check the following directories for unexpected files:

        file:/tmp/foo/b=0
        file:/tmp/foo/b=1
        file:/tmp/foo/b=1/c=1
        file:/tmp/foo/b=0/c=0
```

Author: Cheng Lian <lian@databricks.com>

Closes #6610 from liancheng/part-errmsg and squashes the following commits:

7d05f2c [Cheng Lian] Fixes Scala style issue
a149250 [Cheng Lian] Adds test case for the error message
6b74dd8 [Cheng Lian] Also lists suspicious non-leaf partition directories
a935eb8 [Cheng Lian] Improves error message when conflicting partition columns are found
---
 .../spark/sql/sources/PartitioningUtils.scala | 47 +++++++++++++++----
 .../ParquetPartitionDiscoverySuite.scala      | 45 ++++++++++++++++++
 2 files changed, 82 insertions(+), 10 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala
index c6f535dde7676..8b2a45d8e970a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala
@@ -84,7 +84,7 @@ private[sql] object PartitioningUtils {
     } else {
       // This dataset is partitioned. We need to check whether all partitions have the same
       // partition columns and resolve potential type conflicts.
-      val resolvedPartitionValues = resolvePartitions(pathsWithPartitionValues.map(_._2))
+      val resolvedPartitionValues = resolvePartitions(pathsWithPartitionValues)
 
       // Creates the StructType which represents the partition columns.
       val fields = {
@@ -181,19 +181,18 @@ private[sql] object PartitioningUtils {
    *   StringType
    * }}}
    */
-  private[sql] def resolvePartitions(values: Seq[PartitionValues]): Seq[PartitionValues] = {
-    // Column names of all partitions must match
-    val distinctPartitionsColNames = values.map(_.columnNames).distinct
-
-    if (distinctPartitionsColNames.isEmpty) {
+  private[sql] def resolvePartitions(
+      pathsWithPartitionValues: Seq[(Path, PartitionValues)]): Seq[PartitionValues] = {
+    if (pathsWithPartitionValues.isEmpty) {
       Seq.empty
     } else {
-      assert(distinctPartitionsColNames.size == 1, {
-        val list = distinctPartitionsColNames.mkString("\t", "\n\t", "")
-        s"Conflicting partition column names detected:\n$list"
-      })
+      val distinctPartColNames = pathsWithPartitionValues.map(_._2.columnNames).distinct
+      assert(
+        distinctPartColNames.size == 1,
+        listConflictingPartitionColumns(pathsWithPartitionValues))
 
       // Resolves possible type conflicts for each column
+      val values = pathsWithPartitionValues.map(_._2)
       val columnCount = values.head.columnNames.size
       val resolvedValues = (0 until columnCount).map { i =>
         resolveTypeConflicts(values.map(_.literals(i)))
@@ -206,6 +205,34 @@ private[sql] object PartitioningUtils {
     }
   }
 
+  private[sql] def listConflictingPartitionColumns(
+      pathWithPartitionValues: Seq[(Path, PartitionValues)]): String = {
+    val distinctPartColNames = pathWithPartitionValues.map(_._2.columnNames).distinct
+
+    def groupByKey[K, V](seq: Seq[(K, V)]): Map[K, Iterable[V]] =
+      seq.groupBy { case (key, _) => key }.mapValues(_.map { case (_, value) => value })
+
+    val partColNamesToPaths = groupByKey(pathWithPartitionValues.map {
+      case (path, partValues) => partValues.columnNames -> path
+    })
+
+    val distinctPartColLists = distinctPartColNames.map(_.mkString(", ")).zipWithIndex.map {
+      case (names, index) =>
+        s"Partition column name list #$index: $names"
+    }
+
+    // Lists out those non-leaf partition directories that also contain files
+    val suspiciousPaths = distinctPartColNames.sortBy(_.length).flatMap(partColNamesToPaths)
+
+    s"Conflicting partition column names detected:\n" +
+      distinctPartColLists.mkString("\n\t", "\n\t", "\n\n") +
+      "For partitioned table directories, data files should only live in leaf directories.\n" +
+      "And directories at the same level should have the same partition column name.\n" +
+      "Please check the following directories for unexpected files or " +
+      "inconsistent partition column names:\n" +
+      suspiciousPaths.map("\t" + _).mkString("\n", "\n", "")
+  }
+
   /**
    * Converts a string to a [[Literal]] with automatic type inference.  Currently only supports
    * [[IntegerType]], [[LongType]], [[DoubleType]], [[DecimalType.Unlimited]], and
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
index 01df189d1f3be..d0ebb11b063f0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
@@ -538,4 +538,49 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest {
       checkAnswer(sqlContext.read.format("parquet").load(dir.getCanonicalPath), df)
     }
   }
+
+  test("listConflictingPartitionColumns") {
+    def makeExpectedMessage(colNameLists: Seq[String], paths: Seq[String]): String = {
+      val conflictingColNameLists = colNameLists.zipWithIndex.map { case (list, index) =>
+        s"\tPartition column name list #$index: $list"
+      }.mkString("\n", "\n", "\n")
+
+      // scalastyle:off
+      s"""Conflicting partition column names detected:
+         |$conflictingColNameLists
+         |For partitioned table directories, data files should only live in leaf directories.
+         |And directories at the same level should have the same partition column name.
+         |Please check the following directories for unexpected files or inconsistent partition column names:
+         |${paths.map("\t" + _).mkString("\n", "\n", "")}
+       """.stripMargin.trim
+      // scalastyle:on
+    }
+
+    assert(
+      listConflictingPartitionColumns(
+        Seq(
+          (new Path("file:/tmp/foo/a=1"), PartitionValues(Seq("a"), Seq(Literal(1)))),
+          (new Path("file:/tmp/foo/b=1"), PartitionValues(Seq("b"), Seq(Literal(1)))))).trim ===
+        makeExpectedMessage(Seq("a", "b"), Seq("file:/tmp/foo/a=1", "file:/tmp/foo/b=1")))
+
+    assert(
+      listConflictingPartitionColumns(
+        Seq(
+          (new Path("file:/tmp/foo/a=1/_temporary"), PartitionValues(Seq("a"), Seq(Literal(1)))),
+          (new Path("file:/tmp/foo/a=1"), PartitionValues(Seq("a"), Seq(Literal(1)))))).trim ===
+        makeExpectedMessage(
+          Seq("a"),
+          Seq("file:/tmp/foo/a=1/_temporary", "file:/tmp/foo/a=1")))
+
+    assert(
+      listConflictingPartitionColumns(
+        Seq(
+          (new Path("file:/tmp/foo/a=1"),
+            PartitionValues(Seq("a"), Seq(Literal(1)))),
+          (new Path("file:/tmp/foo/a=1/b=foo"),
+            PartitionValues(Seq("a", "b"), Seq(Literal(1), Literal("foo")))))).trim ===
+        makeExpectedMessage(
+          Seq("a", "a, b"),
+          Seq("file:/tmp/foo/a=1", "file:/tmp/foo/a=1/b=foo")))
+  }
 }

From 9d36ec24312f0a9865b4392f89e9611a5b80916d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 24 Jun 2015 09:49:20 -0700
Subject: [PATCH 0041/1454] [SPARK-8567] [SQL] Debugging flaky
 HiveSparkSubmitSuite

Using similar approach used in `HiveThriftServer2Suite` to print stdout/stderr of the spawned process instead of logging them to see what happens on Jenkins. (This test suite only fails on Jenkins and doesn't spill out any log...)

cc yhuai

Author: Cheng Lian <lian@databricks.com>

Closes #6978 from liancheng/debug-hive-spark-submit-suite and squashes the following commits:

b031647 [Cheng Lian] Prints process stdout/stderr instead of logging them
---
 .../spark/sql/hive/HiveSparkSubmitSuite.scala      | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index ab443032be20d..d85516ab0878e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
+import scala.sys.process.{ProcessLogger, Process}
+
 import org.apache.spark._
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
 import org.apache.spark.util.{ResetSystemProperties, Utils}
@@ -82,12 +84,18 @@ class HiveSparkSubmitSuite
   // This is copied from org.apache.spark.deploy.SparkSubmitSuite
   private def runSparkSubmit(args: Seq[String]): Unit = {
     val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
-    val process = Utils.executeCommand(
+    val process = Process(
       Seq("./bin/spark-submit") ++ args,
       new File(sparkHome),
-      Map("SPARK_TESTING" -> "1", "SPARK_HOME" -> sparkHome))
+      "SPARK_TESTING" -> "1",
+      "SPARK_HOME" -> sparkHome
+    ).run(ProcessLogger(
+      (line: String) => { println(s"out> $line") },
+      (line: String) => { println(s"err> $line") }
+    ))
+
     try {
-      val exitCode = failAfter(120 seconds) { process.waitFor() }
+      val exitCode = failAfter(120 seconds) { process.exitValue() }
       if (exitCode != 0) {
         fail(s"Process returned with exit code $exitCode. See the log4j logs for more detail.")
       }

From bba6699d0e9093bc041a9a33dd31992790f32174 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 24 Jun 2015 09:50:03 -0700
Subject: [PATCH 0042/1454] [SPARK-8578] [SQL] Should ignore user defined
 output committer when appending data

https://issues.apache.org/jira/browse/SPARK-8578

It is not very safe to use a custom output committer when append data to an existing dir. This changes adds the logic to check if we are appending data, and if so, we use the output committer associated with the file output format.

Author: Yin Huai <yhuai@databricks.com>

Closes #6964 from yhuai/SPARK-8578 and squashes the following commits:

43544c4 [Yin Huai] Do not use a custom output commiter when appendiing data.
---
 .../apache/spark/sql/sources/commands.scala   | 89 +++++++++++--------
 .../sql/sources/hadoopFsRelationSuites.scala  | 83 ++++++++++++++++-
 2 files changed, 136 insertions(+), 36 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index 215e53c020849..fb6173f58ece6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -96,7 +96,8 @@ private[sql] case class InsertIntoHadoopFsRelation(
     val fs = outputPath.getFileSystem(hadoopConf)
     val qualifiedOutputPath = outputPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
 
-    val doInsertion = (mode, fs.exists(qualifiedOutputPath)) match {
+    val pathExists = fs.exists(qualifiedOutputPath)
+    val doInsertion = (mode, pathExists) match {
       case (SaveMode.ErrorIfExists, true) =>
         sys.error(s"path $qualifiedOutputPath already exists.")
       case (SaveMode.Overwrite, true) =>
@@ -107,6 +108,8 @@ private[sql] case class InsertIntoHadoopFsRelation(
       case (SaveMode.Ignore, exists) =>
         !exists
     }
+    // If we are appending data to an existing dir.
+    val isAppend = (pathExists) && (mode == SaveMode.Append)
 
     if (doInsertion) {
       val job = new Job(hadoopConf)
@@ -130,10 +133,10 @@ private[sql] case class InsertIntoHadoopFsRelation(
 
       val partitionColumns = relation.partitionColumns.fieldNames
       if (partitionColumns.isEmpty) {
-        insert(new DefaultWriterContainer(relation, job), df)
+        insert(new DefaultWriterContainer(relation, job, isAppend), df)
       } else {
         val writerContainer = new DynamicPartitionWriterContainer(
-          relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME)
+          relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME, isAppend)
         insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns)
       }
     }
@@ -277,7 +280,8 @@ private[sql] case class InsertIntoHadoopFsRelation(
 
 private[sql] abstract class BaseWriterContainer(
     @transient val relation: HadoopFsRelation,
-    @transient job: Job)
+    @transient job: Job,
+    isAppend: Boolean)
   extends SparkHadoopMapReduceUtil
   with Logging
   with Serializable {
@@ -356,34 +360,47 @@ private[sql] abstract class BaseWriterContainer(
   }
 
   private def newOutputCommitter(context: TaskAttemptContext): OutputCommitter = {
-    val committerClass = context.getConfiguration.getClass(
-      SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
-
-    Option(committerClass).map { clazz =>
-      logInfo(s"Using user defined output committer class ${clazz.getCanonicalName}")
-
-      // Every output format based on org.apache.hadoop.mapreduce.lib.output.OutputFormat
-      // has an associated output committer. To override this output committer,
-      // we will first try to use the output committer set in SQLConf.OUTPUT_COMMITTER_CLASS.
-      // If a data source needs to override the output committer, it needs to set the
-      // output committer in prepareForWrite method.
-      if (classOf[MapReduceFileOutputCommitter].isAssignableFrom(clazz)) {
-        // The specified output committer is a FileOutputCommitter.
-        // So, we will use the FileOutputCommitter-specified constructor.
-        val ctor = clazz.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext])
-        ctor.newInstance(new Path(outputPath), context)
-      } else {
-        // The specified output committer is just a OutputCommitter.
-        // So, we will use the no-argument constructor.
-        val ctor = clazz.getDeclaredConstructor()
-        ctor.newInstance()
+    val defaultOutputCommitter = outputFormatClass.newInstance().getOutputCommitter(context)
+
+    if (isAppend) {
+      // If we are appending data to an existing dir, we will only use the output committer
+      // associated with the file output format since it is not safe to use a custom
+      // committer for appending. For example, in S3, direct parquet output committer may
+      // leave partial data in the destination dir when the the appending job fails.
+      logInfo(
+        s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName} " +
+        "for appending.")
+      defaultOutputCommitter
+    } else {
+      val committerClass = context.getConfiguration.getClass(
+        SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
+
+      Option(committerClass).map { clazz =>
+        logInfo(s"Using user defined output committer class ${clazz.getCanonicalName}")
+
+        // Every output format based on org.apache.hadoop.mapreduce.lib.output.OutputFormat
+        // has an associated output committer. To override this output committer,
+        // we will first try to use the output committer set in SQLConf.OUTPUT_COMMITTER_CLASS.
+        // If a data source needs to override the output committer, it needs to set the
+        // output committer in prepareForWrite method.
+        if (classOf[MapReduceFileOutputCommitter].isAssignableFrom(clazz)) {
+          // The specified output committer is a FileOutputCommitter.
+          // So, we will use the FileOutputCommitter-specified constructor.
+          val ctor = clazz.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext])
+          ctor.newInstance(new Path(outputPath), context)
+        } else {
+          // The specified output committer is just a OutputCommitter.
+          // So, we will use the no-argument constructor.
+          val ctor = clazz.getDeclaredConstructor()
+          ctor.newInstance()
+        }
+      }.getOrElse {
+        // If output committer class is not set, we will use the one associated with the
+        // file output format.
+        logInfo(
+          s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName}")
+        defaultOutputCommitter
       }
-    }.getOrElse {
-      // If output committer class is not set, we will use the one associated with the
-      // file output format.
-      val outputCommitter = outputFormatClass.newInstance().getOutputCommitter(context)
-      logInfo(s"Using output committer class ${outputCommitter.getClass.getCanonicalName}")
-      outputCommitter
     }
   }
 
@@ -433,8 +450,9 @@ private[sql] abstract class BaseWriterContainer(
 
 private[sql] class DefaultWriterContainer(
     @transient relation: HadoopFsRelation,
-    @transient job: Job)
-  extends BaseWriterContainer(relation, job) {
+    @transient job: Job,
+    isAppend: Boolean)
+  extends BaseWriterContainer(relation, job, isAppend) {
 
   @transient private var writer: OutputWriter = _
 
@@ -473,8 +491,9 @@ private[sql] class DynamicPartitionWriterContainer(
     @transient relation: HadoopFsRelation,
     @transient job: Job,
     partitionColumns: Array[String],
-    defaultPartitionName: String)
-  extends BaseWriterContainer(relation, job) {
+    defaultPartitionName: String,
+    isAppend: Boolean)
+  extends BaseWriterContainer(relation, job, isAppend) {
 
   // All output writers are created on executor side.
   @transient protected var outputWriters: mutable.Map[String, OutputWriter] = _
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index e0d8277a8ed3f..a16ab3a00ddb8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -17,10 +17,16 @@
 
 package org.apache.spark.sql.sources
 
+import scala.collection.JavaConversions._
+
 import java.io.File
 
 import com.google.common.io.Files
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext}
+import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
+import org.apache.parquet.hadoop.ParquetOutputCommitter
 
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.deploy.SparkHadoopUtil
@@ -476,7 +482,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
   // more cores, the issue can be reproduced steadily.  Fortunately our Jenkins builder meets this
   // requirement.  We probably want to move this test case to spark-integration-tests or spark-perf
   // later.
-  test("SPARK-8406: Avoids name collision while writing Parquet files") {
+  test("SPARK-8406: Avoids name collision while writing files") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
       sqlContext
@@ -497,6 +503,81 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       }
     }
   }
+
+  test("SPARK-8578 specified custom output committer will not be used to append data") {
+    val clonedConf = new Configuration(configuration)
+    try {
+      val df = sqlContext.range(1, 10).toDF("i")
+      withTempPath { dir =>
+        df.write.mode("append").format(dataSourceName).save(dir.getCanonicalPath)
+        configuration.set(
+          SQLConf.OUTPUT_COMMITTER_CLASS.key,
+          classOf[AlwaysFailOutputCommitter].getName)
+        // Since Parquet has its own output committer setting, also set it
+        // to AlwaysFailParquetOutputCommitter at here.
+        configuration.set("spark.sql.parquet.output.committer.class",
+          classOf[AlwaysFailParquetOutputCommitter].getName)
+        // Because there data already exists,
+        // this append should succeed because we will use the output committer associated
+        // with file format and AlwaysFailOutputCommitter will not be used.
+        df.write.mode("append").format(dataSourceName).save(dir.getCanonicalPath)
+        checkAnswer(
+          sqlContext.read
+            .format(dataSourceName)
+            .option("dataSchema", df.schema.json)
+            .load(dir.getCanonicalPath),
+          df.unionAll(df))
+
+        // This will fail because AlwaysFailOutputCommitter is used when we do append.
+        intercept[Exception] {
+          df.write.mode("overwrite").format(dataSourceName).save(dir.getCanonicalPath)
+        }
+      }
+      withTempPath { dir =>
+        configuration.set(
+          SQLConf.OUTPUT_COMMITTER_CLASS.key,
+          classOf[AlwaysFailOutputCommitter].getName)
+        // Since Parquet has its own output committer setting, also set it
+        // to AlwaysFailParquetOutputCommitter at here.
+        configuration.set("spark.sql.parquet.output.committer.class",
+          classOf[AlwaysFailParquetOutputCommitter].getName)
+        // Because there is no existing data,
+        // this append will fail because AlwaysFailOutputCommitter is used when we do append
+        // and there is no existing data.
+        intercept[Exception] {
+          df.write.mode("append").format(dataSourceName).save(dir.getCanonicalPath)
+        }
+      }
+    } finally {
+      // Hadoop 1 doesn't have `Configuration.unset`
+      configuration.clear()
+      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+    }
+  }
+}
+
+// This class is used to test SPARK-8578. We should not use any custom output committer when
+// we actually append data to an existing dir.
+class AlwaysFailOutputCommitter(
+    outputPath: Path,
+    context: TaskAttemptContext)
+  extends FileOutputCommitter(outputPath, context) {
+
+  override def commitJob(context: JobContext): Unit = {
+    sys.error("Intentional job commitment failure for testing purpose.")
+  }
+}
+
+// This class is used to test SPARK-8578. We should not use any custom output committer when
+// we actually append data to an existing dir.
+class AlwaysFailParquetOutputCommitter(
+    outputPath: Path,
+    context: TaskAttemptContext)
+  extends ParquetOutputCommitter(outputPath, context) {
+
+  override def commitJob(context: JobContext): Unit = {
+    sys.error("Intentional job commitment failure for testing purpose.")
+  }
 }
 
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {

From 31f48e5af887a9ccc9cea0218c36bf52bbf49d24 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Wed, 24 Jun 2015 11:20:51 -0700
Subject: [PATCH 0043/1454] [SPARK-8576] Add spark-ec2 options to set IAM roles
 and instance-initiated shutdown behavior

Both of these options are useful when spark-ec2 is being used as part of an automated pipeline and the engineers want to minimize the need to pass around AWS keys for access to things like S3 (keys are replaced by the IAM role) and to be able to launch a cluster that can terminate itself cleanly.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>

Closes #6962 from nchammas/additional-ec2-options and squashes the following commits:

fcf252e [Nicholas Chammas] PEP8 fixes
efba9ee [Nicholas Chammas] add help for --instance-initiated-shutdown-behavior
598aecf [Nicholas Chammas] option to launch instances into IAM role
2743632 [Nicholas Chammas] add option for instance initiated shutdown
---
 ec2/spark_ec2.py | 56 ++++++++++++++++++++++++++++++------------------
 1 file changed, 35 insertions(+), 21 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 63e2c79669763..e4932cfa7a4fc 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -306,6 +306,13 @@ def parse_args():
         "--private-ips", action="store_true", default=False,
         help="Use private IPs for instances rather than public if VPC/subnet " +
              "requires that.")
+    parser.add_option(
+        "--instance-initiated-shutdown-behavior", default="stop",
+        choices=["stop", "terminate"],
+        help="Whether instances should terminate when shut down or just stop")
+    parser.add_option(
+        "--instance-profile-name", default=None,
+        help="IAM profile name to launch instances under")
 
     (opts, args) = parser.parse_args()
     if len(args) != 2:
@@ -602,7 +609,8 @@ def launch_cluster(conn, opts, cluster_name):
                 block_device_map=block_map,
                 subnet_id=opts.subnet_id,
                 placement_group=opts.placement_group,
-                user_data=user_data_content)
+                user_data=user_data_content,
+                instance_profile_name=opts.instance_profile_name)
             my_req_ids += [req.id for req in slave_reqs]
             i += 1
 
@@ -647,16 +655,19 @@ def launch_cluster(conn, opts, cluster_name):
         for zone in zones:
             num_slaves_this_zone = get_partition(opts.slaves, num_zones, i)
             if num_slaves_this_zone > 0:
-                slave_res = image.run(key_name=opts.key_pair,
-                                      security_group_ids=[slave_group.id] + additional_group_ids,
-                                      instance_type=opts.instance_type,
-                                      placement=zone,
-                                      min_count=num_slaves_this_zone,
-                                      max_count=num_slaves_this_zone,
-                                      block_device_map=block_map,
-                                      subnet_id=opts.subnet_id,
-                                      placement_group=opts.placement_group,
-                                      user_data=user_data_content)
+                slave_res = image.run(
+                    key_name=opts.key_pair,
+                    security_group_ids=[slave_group.id] + additional_group_ids,
+                    instance_type=opts.instance_type,
+                    placement=zone,
+                    min_count=num_slaves_this_zone,
+                    max_count=num_slaves_this_zone,
+                    block_device_map=block_map,
+                    subnet_id=opts.subnet_id,
+                    placement_group=opts.placement_group,
+                    user_data=user_data_content,
+                    instance_initiated_shutdown_behavior=opts.instance_initiated_shutdown_behavior,
+                    instance_profile_name=opts.instance_profile_name)
                 slave_nodes += slave_res.instances
                 print("Launched {s} slave{plural_s} in {z}, regid = {r}".format(
                       s=num_slaves_this_zone,
@@ -678,16 +689,19 @@ def launch_cluster(conn, opts, cluster_name):
             master_type = opts.instance_type
         if opts.zone == 'all':
             opts.zone = random.choice(conn.get_all_zones()).name
-        master_res = image.run(key_name=opts.key_pair,
-                               security_group_ids=[master_group.id] + additional_group_ids,
-                               instance_type=master_type,
-                               placement=opts.zone,
-                               min_count=1,
-                               max_count=1,
-                               block_device_map=block_map,
-                               subnet_id=opts.subnet_id,
-                               placement_group=opts.placement_group,
-                               user_data=user_data_content)
+        master_res = image.run(
+            key_name=opts.key_pair,
+            security_group_ids=[master_group.id] + additional_group_ids,
+            instance_type=master_type,
+            placement=opts.zone,
+            min_count=1,
+            max_count=1,
+            block_device_map=block_map,
+            subnet_id=opts.subnet_id,
+            placement_group=opts.placement_group,
+            user_data=user_data_content,
+            instance_initiated_shutdown_behavior=opts.instance_initiated_shutdown_behavior,
+            instance_profile_name=opts.instance_profile_name)
 
         master_nodes = master_res.instances
         print("Launched master in %s, regid = %s" % (zone, master_res.id))

From 1173483f3f465a4c63246e83d0aaa2af521395f5 Mon Sep 17 00:00:00 2001
From: BenFradet <benjamin.fradet@gmail.com>
Date: Wed, 24 Jun 2015 11:53:03 -0700
Subject: [PATCH 0044/1454] [SPARK-8399] [STREAMING] [WEB UI] Overlap between
 histograms and axis' name in Spark Streaming UI

Moved where the X axis' name (#batches) is written in histograms in the spark streaming web ui so the histograms and the axis' name do not overlap.

Author: BenFradet <benjamin.fradet@gmail.com>

Closes #6845 from BenFradet/SPARK-8399 and squashes the following commits:

b63695f [BenFradet] adjusted inner histograms
eb610ee [BenFradet] readjusted #batches on the x axis
dd46f98 [BenFradet] aligned all unit labels and ticks
0564b62 [BenFradet] readjusted #batches placement
edd0936 [BenFradet] moved where the X axis' name (#batches) is written in histograms in the spark streaming web ui
---
 .../apache/spark/streaming/ui/static/streaming-page.js | 10 ++++++----
 .../org/apache/spark/streaming/ui/StreamingPage.scala  |  4 ++--
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.js b/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.js
index 75251f493ad22..4886b68eeaf76 100644
--- a/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.js
+++ b/streaming/src/main/resources/org/apache/spark/streaming/ui/static/streaming-page.js
@@ -31,6 +31,8 @@ var maxXForHistogram = 0;
 var histogramBinCount = 10;
 var yValueFormat = d3.format(",.2f");
 
+var unitLabelYOffset = -10;
+
 // Show a tooltip "text" for "node"
 function showBootstrapTooltip(node, text) {
     $(node).tooltip({title: text, trigger: "manual", container: "body"});
@@ -133,7 +135,7 @@ function drawTimeline(id, data, minX, maxX, minY, maxY, unitY, batchInterval) {
         .attr("class", "y axis")
         .call(yAxis)
         .append("text")
-            .attr("transform", "translate(0," + (-3) + ")")
+            .attr("transform", "translate(0," + unitLabelYOffset + ")")
             .text(unitY);
 
 
@@ -223,10 +225,10 @@ function drawHistogram(id, values, minY, maxY, unitY, batchInterval) {
         .style("border-left", "0px solid white");
 
     var margin = {top: 20, right: 30, bottom: 30, left: 10};
-    var width = 300 - margin.left - margin.right;
+    var width = 350 - margin.left - margin.right;
     var height = 150 - margin.top - margin.bottom;
 
-    var x = d3.scale.linear().domain([0, maxXForHistogram]).range([0, width]);
+    var x = d3.scale.linear().domain([0, maxXForHistogram]).range([0, width - 50]);
     var y = d3.scale.linear().domain([minY, maxY]).range([height, 0]);
 
     var xAxis = d3.svg.axis().scale(x).orient("top").ticks(5);
@@ -248,7 +250,7 @@ function drawHistogram(id, values, minY, maxY, unitY, batchInterval) {
         .attr("class", "x axis")
         .call(xAxis)
         .append("text")
-            .attr("transform", "translate(" + (margin.left + width - 40) + ", 15)")
+            .attr("transform", "translate(" + (margin.left + width - 45) + ", " + unitLabelYOffset + ")")
             .text("#batches");
 
     svg.append("g")
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
index 4ee7a486e370b..87af902428ec8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
@@ -310,7 +310,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
         <tr>
           <th style="width: 160px;"></th>
           <th style="width: 492px;">Timelines (Last {batchTimes.length} batches, {numActiveBatches} active, {numCompletedBatches} completed)</th>
-          <th style="width: 300px;">Histograms</th></tr>
+          <th style="width: 350px;">Histograms</th></tr>
       </thead>
       <tbody>
         <tr>
@@ -456,7 +456,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
       <td>{receiverActive}</td>
       <td>{receiverLocation}</td>
       <td>{receiverLastErrorTime}</td>
-      <td><div style="width: 292px;">{receiverLastError}</div></td>
+      <td><div style="width: 342px;">{receiverLastError}</div></td>
     </tr>
     <tr>
       <td colspan="3" class="timeline">

From 43e66192f45a23f7232116e9f664158862df5015 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 24 Jun 2015 11:55:20 -0700
Subject: [PATCH 0045/1454] [SPARK-8506] Add pakages to R context created
 through init.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #6928 from holdenk/SPARK-8506-sparkr-does-not-provide-an-easy-way-to-depend-on-spark-packages-when-performing-init-from-inside-of-r and squashes the following commits:

b60dd63 [Holden Karau] Add an example with the spark-csv package
fa8bc92 [Holden Karau] typo: sparm -> spark
865a90c [Holden Karau] strip spaces for comparision
c7a4471 [Holden Karau] Add some documentation
c1a9233 [Holden Karau] refactor for testing
c818556 [Holden Karau] Add pakages to R
---
 R/pkg/R/client.R               | 26 +++++++++++++++++++-------
 R/pkg/R/sparkR.R               |  7 +++++--
 R/pkg/inst/tests/test_client.R | 32 ++++++++++++++++++++++++++++++++
 docs/sparkr.md                 | 17 +++++++++++++----
 4 files changed, 69 insertions(+), 13 deletions(-)
 create mode 100644 R/pkg/inst/tests/test_client.R

diff --git a/R/pkg/R/client.R b/R/pkg/R/client.R
index 1281c41213e32..cf2e5ddeb7a9d 100644
--- a/R/pkg/R/client.R
+++ b/R/pkg/R/client.R
@@ -34,24 +34,36 @@ connectBackend <- function(hostname, port, timeout = 6000) {
   con
 }
 
-launchBackend <- function(args, sparkHome, jars, sparkSubmitOpts) {
+determineSparkSubmitBin <- function() {
   if (.Platform$OS.type == "unix") {
     sparkSubmitBinName = "spark-submit"
   } else {
     sparkSubmitBinName = "spark-submit.cmd"
   }
+  sparkSubmitBinName
+}
+
+generateSparkSubmitArgs <- function(args, sparkHome, jars, sparkSubmitOpts, packages) {
+  if (jars != "") {
+    jars <- paste("--jars", jars)
+  }
+
+  if (packages != "") {
+    packages <- paste("--packages", packages)
+  }
 
+  combinedArgs <- paste(jars, packages, sparkSubmitOpts, args, sep = " ")
+  combinedArgs
+}
+
+launchBackend <- function(args, sparkHome, jars, sparkSubmitOpts, packages) {
+  sparkSubmitBin <- determineSparkSubmitBin()
   if (sparkHome != "") {
     sparkSubmitBin <- file.path(sparkHome, "bin", sparkSubmitBinName)
   } else {
     sparkSubmitBin <- sparkSubmitBinName
   }
-
-  if (jars != "") {
-    jars <- paste("--jars", jars)
-  }
-
-  combinedArgs <- paste(jars, sparkSubmitOpts, args, sep = " ")
+  combinedArgs <- generateSparkSubmitArgs(args, sparkHome, jars, sparkSubmitOpts, packages)
   cat("Launching java with spark-submit command", sparkSubmitBin, combinedArgs, "\n")
   invisible(system2(sparkSubmitBin, combinedArgs, wait = F))
 }
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index dbde0c44c55d5..8f81d5640c1d0 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -81,6 +81,7 @@ sparkR.stop <- function() {
 #' @param sparkExecutorEnv Named list of environment variables to be used when launching executors.
 #' @param sparkJars Character string vector of jar files to pass to the worker nodes.
 #' @param sparkRLibDir The path where R is installed on the worker nodes.
+#' @param sparkPackages Character string vector of packages from spark-packages.org
 #' @export
 #' @examples
 #'\dontrun{
@@ -100,7 +101,8 @@ sparkR.init <- function(
   sparkEnvir = list(),
   sparkExecutorEnv = list(),
   sparkJars = "",
-  sparkRLibDir = "") {
+  sparkRLibDir = "",
+  sparkPackages = "") {
 
   if (exists(".sparkRjsc", envir = .sparkREnv)) {
     cat("Re-using existing Spark Context. Please stop SparkR with sparkR.stop() or restart R to create a new Spark Context\n")
@@ -129,7 +131,8 @@ sparkR.init <- function(
         args = path,
         sparkHome = sparkHome,
         jars = jars,
-        sparkSubmitOpts = Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell"))
+        sparkSubmitOpts = Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell"),
+        sparkPackages = sparkPackages)
     # wait atmost 100 seconds for JVM to launch
     wait <- 0.1
     for (i in 1:25) {
diff --git a/R/pkg/inst/tests/test_client.R b/R/pkg/inst/tests/test_client.R
new file mode 100644
index 0000000000000..30b05c1a2afcd
--- /dev/null
+++ b/R/pkg/inst/tests/test_client.R
@@ -0,0 +1,32 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+context("functions in client.R")
+
+test_that("adding spark-testing-base as a package works", {
+  args <- generateSparkSubmitArgs("", "", "", "",
+                                  "holdenk:spark-testing-base:1.3.0_0.0.5")
+  expect_equal(gsub("[[:space:]]", "", args),
+               gsub("[[:space:]]", "",
+                    "--packages holdenk:spark-testing-base:1.3.0_0.0.5"))
+})
+
+test_that("no package specified doesn't add packages flag", {
+  args <- generateSparkSubmitArgs("", "", "", "", "")
+  expect_equal(gsub("[[:space:]]", "", args),
+               "")
+})
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 4d82129921a37..095ea4308cfeb 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -27,9 +27,9 @@ All of the examples on this page use sample data included in R or the Spark dist
 <div data-lang="r"  markdown="1">
 The entry point into SparkR is the `SparkContext` which connects your R program to a Spark cluster.
 You can create a `SparkContext` using `sparkR.init` and pass in options such as the application name
-etc. Further, to work with DataFrames we will need a `SQLContext`, which can be created from the 
-SparkContext. If you are working from the SparkR shell, the `SQLContext` and `SparkContext` should
-already be created for you.
+, any spark packages depended on, etc. Further, to work with DataFrames we will need a `SQLContext`,
+which can be created from the  SparkContext. If you are working from the SparkR shell, the
+`SQLContext` and `SparkContext` should already be created for you.
 
 {% highlight r %}
 sc <- sparkR.init()
@@ -62,7 +62,16 @@ head(df)
 
 SparkR supports operating on a variety of data sources through the `DataFrame` interface. This section describes the general methods for loading and saving data using Data Sources. You can check the Spark SQL programming guide for more [specific options](sql-programming-guide.html#manually-specifying-options) that are available for the built-in data sources.
 
-The general method for creating DataFrames from data sources is `read.df`. This method takes in the `SQLContext`, the path for the file to load and the type of data source. SparkR supports reading JSON and Parquet files natively and through [Spark Packages](http://spark-packages.org/) you can find data source connectors for popular file formats like [CSV](http://spark-packages.org/package/databricks/spark-csv) and [Avro](http://spark-packages.org/package/databricks/spark-avro).
+The general method for creating DataFrames from data sources is `read.df`. This method takes in the `SQLContext`, the path for the file to load and the type of data source. SparkR supports reading JSON and Parquet files natively and through [Spark Packages](http://spark-packages.org/) you can find data source connectors for popular file formats like [CSV](http://spark-packages.org/package/databricks/spark-csv) and [Avro](http://spark-packages.org/package/databricks/spark-avro). These packages can either be added by
+specifying `--packages` with `spark-submit` or `sparkR` commands, or if creating context through `init`
+you can specify the packages with the `packages` argument.
+
+<div data-lang="r" markdown="1">
+{% highlight r %}
+sc <- sparkR.init(packages="com.databricks:spark-csv_2.11:1.0.3")
+sqlContext <- sparkRSQL.init(sc)
+{% endhighlight %}
+</div>
 
 We can see how to use data sources using an example JSON input file. Note that the file that is used here is _not_ a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail.
 

From b84d4b4dfe8ced1b96a0c74ef968a20a1bba8231 Mon Sep 17 00:00:00 2001
From: "Santiago M. Mola" <smola@stratio.com>
Date: Wed, 24 Jun 2015 12:29:07 -0700
Subject: [PATCH 0046/1454] [SPARK-7088] [SQL] Fix analysis for 3rd party
 logical plan.

ResolveReferences analysis rule now does not throw when it cannot resolve references in a self-join.

Author: Santiago M. Mola <smola@stratio.com>

Closes #6853 from smola/SPARK-7088 and squashes the following commits:

af71ac7 [Santiago M. Mola] [SPARK-7088] Fix analysis for 3rd party logical plan.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 38 ++++++++++---------
 .../sql/catalyst/analysis/CheckAnalysis.scala | 12 ++++++
 2 files changed, 32 insertions(+), 18 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 0a3f5a7b5cade..b06759f144fd9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -283,7 +283,7 @@ class Analyzer(
         val conflictingAttributes = left.outputSet.intersect(right.outputSet)
         logDebug(s"Conflicting attributes ${conflictingAttributes.mkString(",")} in $j")
 
-        val (oldRelation, newRelation) = right.collect {
+        right.collect {
           // Handle base relations that might appear more than once.
           case oldVersion: MultiInstanceRelation
               if oldVersion.outputSet.intersect(conflictingAttributes).nonEmpty =>
@@ -308,25 +308,27 @@ class Analyzer(
               if AttributeSet(windowExpressions.map(_.toAttribute)).intersect(conflictingAttributes)
                 .nonEmpty =>
             (oldVersion, oldVersion.copy(windowExpressions = newAliases(windowExpressions)))
-        }.headOption.getOrElse { // Only handle first case, others will be fixed on the next pass.
-          sys.error(
-            s"""
-              |Failure when resolving conflicting references in Join:
-              |$plan
-              |
-              |Conflicting attributes: ${conflictingAttributes.mkString(",")}
-              """.stripMargin)
         }
-
-        val attributeRewrites = AttributeMap(oldRelation.output.zip(newRelation.output))
-        val newRight = right transformUp {
-          case r if r == oldRelation => newRelation
-        } transformUp {
-          case other => other transformExpressions {
-            case a: Attribute => attributeRewrites.get(a).getOrElse(a)
-          }
+          // Only handle first case, others will be fixed on the next pass.
+          .headOption match {
+          case None =>
+            /*
+             * No result implies that there is a logical plan node that produces new references
+             * that this rule cannot handle. When that is the case, there must be another rule
+             * that resolves these conflicts. Otherwise, the analysis will fail.
+             */
+            j
+          case Some((oldRelation, newRelation)) =>
+            val attributeRewrites = AttributeMap(oldRelation.output.zip(newRelation.output))
+            val newRight = right transformUp {
+              case r if r == oldRelation => newRelation
+            } transformUp {
+              case other => other transformExpressions {
+                case a: Attribute => attributeRewrites.get(a).getOrElse(a)
+              }
+            }
+            j.copy(right = newRight)
         }
-        j.copy(right = newRight)
 
       // When resolve `SortOrder`s in Sort based on child, don't report errors as
       // we still have chance to resolve it based on grandchild
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index c5a1437be6d05..a069b4710f38c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -48,6 +48,7 @@ trait CheckAnalysis {
     // We transform up and order the rules so as to catch the first possible failure instead
     // of the result of cascading resolution failures.
     plan.foreachUp {
+
       case operator: LogicalPlan =>
         operator transformExpressionsUp {
           case a: Attribute if !a.resolved =>
@@ -121,6 +122,17 @@ trait CheckAnalysis {
 
           case _ => // Analysis successful!
         }
+
+      // Special handling for cases when self-join introduce duplicate expression ids.
+      case j @ Join(left, right, _, _) if left.outputSet.intersect(right.outputSet).nonEmpty =>
+        val conflictingAttributes = left.outputSet.intersect(right.outputSet)
+        failAnalysis(
+          s"""
+             |Failure when resolving conflicting references in Join:
+             |$plan
+             |Conflicting attributes: ${conflictingAttributes.mkString(",")}
+             |""".stripMargin)
+
     }
     extendedCheckRules.foreach(_(plan))
   }

From f04b5672c5a5562f8494df3b0df23235285c9e9e Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 24 Jun 2015 13:28:50 -0700
Subject: [PATCH 0047/1454] [SPARK-7289] handle project -> limit -> sort
 efficiently

make the `TakeOrdered` strategy and operator more general, such that it can optionally handle a projection when necessary

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #6780 from cloud-fan/limit and squashes the following commits:

34aa07b [Wenchen Fan] revert
07d5456 [Wenchen Fan] clean closure
20821ec [Wenchen Fan] fix
3676a82 [Wenchen Fan] address comments
b558549 [Wenchen Fan] address comments
214842b [Wenchen Fan] fix style
2d8be83 [Wenchen Fan] add LimitPushDown
948f740 [Wenchen Fan] fix existing
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 52 ++++++++++---------
 .../optimizer/UnionPushdownSuite.scala        |  4 +-
 .../org/apache/spark/sql/SQLContext.scala     |  2 +-
 .../spark/sql/execution/SparkPlan.scala       |  1 -
 .../spark/sql/execution/SparkStrategies.scala |  8 ++-
 .../spark/sql/execution/basicOperators.scala  | 27 +++++++---
 .../spark/sql/execution/PlannerSuite.scala    |  6 +++
 .../apache/spark/sql/hive/HiveContext.scala   |  2 +-
 8 files changed, 62 insertions(+), 40 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 98b4476076854..bfd24287c9645 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -39,19 +39,22 @@ object DefaultOptimizer extends Optimizer {
     Batch("Distinct", FixedPoint(100),
       ReplaceDistinctWithAggregate) ::
     Batch("Operator Optimizations", FixedPoint(100),
-      UnionPushdown,
-      CombineFilters,
+      // Operator push down
+      UnionPushDown,
+      PushPredicateThroughJoin,
       PushPredicateThroughProject,
       PushPredicateThroughGenerate,
       ColumnPruning,
+      // Operator combine
       ProjectCollapsing,
+      CombineFilters,
       CombineLimits,
+      // Constant folding
       NullPropagation,
       OptimizeIn,
       ConstantFolding,
       LikeSimplification,
       BooleanSimplification,
-      PushPredicateThroughJoin,
       RemovePositive,
       SimplifyFilters,
       SimplifyCasts,
@@ -63,25 +66,25 @@ object DefaultOptimizer extends Optimizer {
 }
 
 /**
-  *  Pushes operations to either side of a Union.
-  */
-object UnionPushdown extends Rule[LogicalPlan] {
+ * Pushes operations to either side of a Union.
+ */
+object UnionPushDown extends Rule[LogicalPlan] {
 
   /**
-    *  Maps Attributes from the left side to the corresponding Attribute on the right side.
-    */
-  def buildRewrites(union: Union): AttributeMap[Attribute] = {
+   * Maps Attributes from the left side to the corresponding Attribute on the right side.
+   */
+  private def buildRewrites(union: Union): AttributeMap[Attribute] = {
     assert(union.left.output.size == union.right.output.size)
 
     AttributeMap(union.left.output.zip(union.right.output))
   }
 
   /**
-    *  Rewrites an expression so that it can be pushed to the right side of a Union operator.
-    *  This method relies on the fact that the output attributes of a union are always equal
-    *  to the left child's output.
-    */
-  def pushToRight[A <: Expression](e: A, rewrites: AttributeMap[Attribute]): A = {
+   * Rewrites an expression so that it can be pushed to the right side of a Union operator.
+   * This method relies on the fact that the output attributes of a union are always equal
+   * to the left child's output.
+   */
+  private def pushToRight[A <: Expression](e: A, rewrites: AttributeMap[Attribute]) = {
     val result = e transform {
       case a: Attribute => rewrites(a)
     }
@@ -108,7 +111,6 @@ object UnionPushdown extends Rule[LogicalPlan] {
   }
 }
 
-
 /**
  * Attempts to eliminate the reading of unneeded columns from the query plan using the following
  * transformations:
@@ -117,7 +119,6 @@ object UnionPushdown extends Rule[LogicalPlan] {
  *   - Aggregate
  *   - Project <- Join
  *   - LeftSemiJoin
- *  - Performing alias substitution.
  */
 object ColumnPruning extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
@@ -159,10 +160,11 @@ object ColumnPruning extends Rule[LogicalPlan] {
 
       Join(left, prunedChild(right, allReferences), LeftSemi, condition)
 
+    // Push down project through limit, so that we may have chance to push it further.
     case Project(projectList, Limit(exp, child)) =>
       Limit(exp, Project(projectList, child))
 
-    // push down project if possible when the child is sort
+    // Push down project if possible when the child is sort
     case p @ Project(projectList, s @ Sort(_, _, grandChild))
       if s.references.subsetOf(p.outputSet) =>
       s.copy(child = Project(projectList, grandChild))
@@ -181,8 +183,8 @@ object ColumnPruning extends Rule[LogicalPlan] {
 }
 
 /**
- * Combines two adjacent [[Project]] operators into one, merging the
- * expressions into one single expression.
+ * Combines two adjacent [[Project]] operators into one and perform alias substitution,
+ * merging the expressions into one single expression.
  */
 object ProjectCollapsing extends Rule[LogicalPlan] {
 
@@ -222,10 +224,10 @@ object ProjectCollapsing extends Rule[LogicalPlan] {
 object LikeSimplification extends Rule[LogicalPlan] {
   // if guards below protect from escapes on trailing %.
   // Cases like "something\%" are not optimized, but this does not affect correctness.
-  val startsWith = "([^_%]+)%".r
-  val endsWith = "%([^_%]+)".r
-  val contains = "%([^_%]+)%".r
-  val equalTo = "([^_%]*)".r
+  private val startsWith = "([^_%]+)%".r
+  private val endsWith = "%([^_%]+)".r
+  private val contains = "%([^_%]+)%".r
+  private val equalTo = "([^_%]*)".r
 
   def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
     case Like(l, Literal(utf, StringType)) =>
@@ -497,7 +499,7 @@ object PushPredicateThroughProject extends Rule[LogicalPlan] {
         grandChild))
   }
 
-  def replaceAlias(condition: Expression, sourceAliases: Map[Attribute, Expression]): Expression = {
+  private def replaceAlias(condition: Expression, sourceAliases: Map[Attribute, Expression]) = {
     condition transform {
       case a: AttributeReference => sourceAliases.getOrElse(a, a)
     }
@@ -682,7 +684,7 @@ object DecimalAggregates extends Rule[LogicalPlan] {
   import Decimal.MAX_LONG_DIGITS
 
   /** Maximum number of decimal digits representable precisely in a Double */
-  val MAX_DOUBLE_DIGITS = 15
+  private val MAX_DOUBLE_DIGITS = 15
 
   def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
     case Sum(e @ DecimalType.Expression(prec, scale)) if prec + 10 <= MAX_LONG_DIGITS =>
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala
index 35f50be46b76f..ec379489a6d1e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala
@@ -24,13 +24,13 @@ import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.dsl.expressions._
 
-class UnionPushdownSuite extends PlanTest {
+class UnionPushDownSuite extends PlanTest {
   object Optimize extends RuleExecutor[LogicalPlan] {
     val batches =
       Batch("Subqueries", Once,
         EliminateSubQueries) ::
       Batch("Union Pushdown", Once,
-        UnionPushdown) :: Nil
+        UnionPushDown) :: Nil
   }
 
   val testRelation = LocalRelation('a.int, 'b.int, 'c.int)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 04fc798bf3738..5708df82de12f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -858,7 +858,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       experimental.extraStrategies ++ (
       DataSourceStrategy ::
       DDLStrategy ::
-      TakeOrdered ::
+      TakeOrderedAndProject ::
       HashAggregation ::
       LeftSemiJoin ::
       HashJoin ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 2b8d30294293c..47f56b2b7ebe6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -169,7 +169,6 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     log.debug(
       s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
     if(codegenEnabled && expressions.forall(_.isThreadSafe)) {
-
       GenerateMutableProjection.generate(expressions, inputSchema)
     } else {
       () => new InterpretedMutableProjection(expressions, inputSchema)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 1ff1cc224de8c..21912cf24933e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -213,10 +213,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   protected lazy val singleRowRdd =
     sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): InternalRow), 1)
 
-  object TakeOrdered extends Strategy {
+  object TakeOrderedAndProject extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Limit(IntegerLiteral(limit), logical.Sort(order, true, child)) =>
-        execution.TakeOrdered(limit, order, planLater(child)) :: Nil
+        execution.TakeOrderedAndProject(limit, order, None, planLater(child)) :: Nil
+      case logical.Limit(
+             IntegerLiteral(limit),
+             logical.Project(projectList, logical.Sort(order, true, child))) =>
+        execution.TakeOrderedAndProject(limit, order, Some(projectList), planLater(child)) :: Nil
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 7aedd630e3871..647c4ab5cb651 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -39,8 +39,8 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends
   @transient lazy val buildProjection = newMutableProjection(projectList, child.output)
 
   protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    val resuableProjection = buildProjection()
-    iter.map(resuableProjection)
+    val reusableProjection = buildProjection()
+    iter.map(reusableProjection)
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
@@ -147,12 +147,18 @@ case class Limit(limit: Int, child: SparkPlan)
 
 /**
  * :: DeveloperApi ::
- * Take the first limit elements as defined by the sortOrder. This is logically equivalent to
- * having a [[Limit]] operator after a [[Sort]] operator. This could have been named TopK, but
- * Spark's top operator does the opposite in ordering so we name it TakeOrdered to avoid confusion.
+ * Take the first limit elements as defined by the sortOrder, and do projection if needed.
+ * This is logically equivalent to having a [[Limit]] operator after a [[Sort]] operator,
+ * or having a [[Project]] operator between them.
+ * This could have been named TopK, but Spark's top operator does the opposite in ordering
+ * so we name it TakeOrdered to avoid confusion.
  */
 @DeveloperApi
-case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) extends UnaryNode {
+case class TakeOrderedAndProject(
+    limit: Int,
+    sortOrder: Seq[SortOrder],
+    projectList: Option[Seq[NamedExpression]],
+    child: SparkPlan) extends UnaryNode {
 
   override def output: Seq[Attribute] = child.output
 
@@ -160,8 +166,13 @@ case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan)
 
   private val ord: RowOrdering = new RowOrdering(sortOrder, child.output)
 
-  private def collectData(): Array[InternalRow] =
-    child.execute().map(_.copy()).takeOrdered(limit)(ord)
+  // TODO: remove @transient after figure out how to clean closure at InsertIntoHiveTable.
+  @transient private val projection = projectList.map(new InterpretedProjection(_, child.output))
+
+  private def collectData(): Array[InternalRow] = {
+    val data = child.execute().map(_.copy()).takeOrdered(limit)(ord)
+    projection.map(data.map(_)).getOrElse(data)
+  }
 
   override def executeCollect(): Array[Row] = {
     val converter = CatalystTypeConverters.createToScalaConverter(schema)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 5854ab48db552..3dd24130af81a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -141,4 +141,10 @@ class PlannerSuite extends SparkFunSuite {
 
     setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
   }
+
+  test("efficient limit -> project -> sort") {
+    val query = testData.sort('key).select('value).limit(2).logicalPlan
+    val planned = planner.TakeOrderedAndProject(query)
+    assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index cf05c6c989655..8021f915bb821 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -442,7 +442,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       HiveCommandStrategy(self),
       HiveDDLStrategy,
       DDLStrategy,
-      TakeOrdered,
+      TakeOrderedAndProject,
       ParquetOperations,
       InMemoryScans,
       ParquetConversion, // Must be before HiveTableScans

From fb32c388985ce65c1083cb435cf1f7479fecbaac Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Wed, 24 Jun 2015 14:58:43 -0700
Subject: [PATCH 0048/1454] [SPARK-7633] [MLLIB] [PYSPARK] Python bindings for
 StreamingLogisticRegressionwithSGD

Add Python bindings to StreamingLogisticRegressionwithSGD.

No Java wrappers are needed as models are updated directly using train.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #6849 from MechCoder/spark-3258 and squashes the following commits:

b4376a5 [MechCoder] minor
d7e5fc1 [MechCoder] Refactor into StreamingLinearAlgorithm Better docs
9c09d4e [MechCoder] [SPARK-7633] Python bindings for StreamingLogisticRegressionwithSGD
---
 python/pyspark/mllib/classification.py |  96 +++++++++++++++++-
 python/pyspark/mllib/tests.py          | 135 ++++++++++++++++++++++++-
 2 files changed, 229 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 758accf4b41eb..2698f10d06883 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -21,6 +21,7 @@
 from numpy import array
 
 from pyspark import RDD
+from pyspark.streaming import DStream
 from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py
 from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector
 from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper
@@ -28,7 +29,8 @@
 
 
 __all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS',
-           'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes']
+           'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes',
+           'StreamingLogisticRegressionWithSGD']
 
 
 class LinearClassificationModel(LinearModel):
@@ -583,6 +585,98 @@ def train(cls, data, lambda_=1.0):
         return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
 
 
+class StreamingLinearAlgorithm(object):
+    """
+    Base class that has to be inherited by any StreamingLinearAlgorithm.
+
+    Prevents reimplementation of methods predictOn and predictOnValues.
+    """
+    def __init__(self, model):
+        self._model = model
+
+    def latestModel(self):
+        """
+        Returns the latest model.
+        """
+        return self._model
+
+    def _validate(self, dstream):
+        if not isinstance(dstream, DStream):
+            raise TypeError(
+                "dstream should be a DStream object, got %s" % type(dstream))
+        if not self._model:
+            raise ValueError(
+                "Model must be intialized using setInitialWeights")
+
+    def predictOn(self, dstream):
+        """
+        Make predictions on a dstream.
+
+        :return: Transformed dstream object.
+        """
+        self._validate(dstream)
+        return dstream.map(lambda x: self._model.predict(x))
+
+    def predictOnValues(self, dstream):
+        """
+        Make predictions on a keyed dstream.
+
+        :return: Transformed dstream object.
+        """
+        self._validate(dstream)
+        return dstream.mapValues(lambda x: self._model.predict(x))
+
+
+@inherit_doc
+class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
+    """
+    Run LogisticRegression with SGD on a stream of data.
+
+    The weights obtained at the end of training a stream are used as initial
+    weights for the next stream.
+
+    :param stepSize: Step size for each iteration of gradient descent.
+    :param numIterations: Number of iterations run for each batch of data.
+    :param miniBatchFraction: Fraction of data on which SGD is run for each
+                              iteration.
+    :param regParam: L2 Regularization parameter.
+    """
+    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01):
+        self.stepSize = stepSize
+        self.numIterations = numIterations
+        self.regParam = regParam
+        self.miniBatchFraction = miniBatchFraction
+        self._model = None
+        super(StreamingLogisticRegressionWithSGD, self).__init__(
+            model=self._model)
+
+    def setInitialWeights(self, initialWeights):
+        """
+        Set the initial value of weights.
+
+        This must be set before running trainOn and predictOn.
+        """
+        initialWeights = _convert_to_vector(initialWeights)
+
+        # LogisticRegressionWithSGD does only binary classification.
+        self._model = LogisticRegressionModel(
+            initialWeights, 0, initialWeights.size, 2)
+        return self
+
+    def trainOn(self, dstream):
+        """Train the model on the incoming dstream."""
+        self._validate(dstream)
+
+        def update(rdd):
+            # LogisticRegressionWithSGD.train raises an error for an empty RDD.
+            if not rdd.isEmpty():
+                self._model = LogisticRegressionWithSGD.train(
+                    rdd, self.numIterations, self.stepSize,
+                    self.miniBatchFraction, self._model.weights)
+
+        dstream.foreachRDD(update)
+
+
 def _test():
     import doctest
     from pyspark import SparkContext
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 509faa11df170..cd80c3e07a4f7 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -26,7 +26,8 @@
 from time import time, sleep
 from shutil import rmtree
 
-from numpy import array, array_equal, zeros, inf, all, random
+from numpy import (
+    array, array_equal, zeros, inf, random, exp, dot, all, mean)
 from numpy import sum as array_sum
 from py4j.protocol import Py4JJavaError
 
@@ -45,6 +46,7 @@
 from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\
     DenseMatrix, SparseMatrix, Vectors, Matrices, MatrixUDT
 from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.classification import StreamingLogisticRegressionWithSGD
 from pyspark.mllib.random import RandomRDDs
 from pyspark.mllib.stat import Statistics
 from pyspark.mllib.feature import Word2Vec
@@ -1037,6 +1039,137 @@ def test_dim(self):
             self.assertEqual(len(point.features), 2)
 
 
+class StreamingLogisticRegressionWithSGDTests(MLLibStreamingTestCase):
+
+    @staticmethod
+    def generateLogisticInput(offset, scale, nPoints, seed):
+        """
+        Generate 1 / (1 + exp(-x * scale + offset))
+
+        where,
+        x is randomnly distributed and the threshold
+        and labels for each sample in x is obtained from a random uniform
+        distribution.
+        """
+        rng = random.RandomState(seed)
+        x = rng.randn(nPoints)
+        sigmoid = 1. / (1 + exp(-(dot(x, scale) + offset)))
+        y_p = rng.rand(nPoints)
+        cut_off = y_p <= sigmoid
+        y_p[cut_off] = 1.0
+        y_p[~cut_off] = 0.0
+        return [
+            LabeledPoint(y_p[i], Vectors.dense([x[i]]))
+            for i in range(nPoints)]
+
+    def test_parameter_accuracy(self):
+        """
+        Test that the final value of weights is close to the desired value.
+        """
+        input_batches = [
+            self.sc.parallelize(self.generateLogisticInput(0, 1.5, 100, 42 + i))
+            for i in range(20)]
+        input_stream = self.ssc.queueStream(input_batches)
+
+        slr = StreamingLogisticRegressionWithSGD(
+            stepSize=0.2, numIterations=25)
+        slr.setInitialWeights([0.0])
+        slr.trainOn(input_stream)
+
+        t = time()
+        self.ssc.start()
+        self._ssc_wait(t, 20.0, 0.01)
+        rel = (1.5 - slr.latestModel().weights.array[0]) / 1.5
+        self.assertAlmostEqual(rel, 0.1, 1)
+
+    def test_convergence(self):
+        """
+        Test that weights converge to the required value on toy data.
+        """
+        input_batches = [
+            self.sc.parallelize(self.generateLogisticInput(0, 1.5, 100, 42 + i))
+            for i in range(20)]
+        input_stream = self.ssc.queueStream(input_batches)
+        models = []
+
+        slr = StreamingLogisticRegressionWithSGD(
+            stepSize=0.2, numIterations=25)
+        slr.setInitialWeights([0.0])
+        slr.trainOn(input_stream)
+        input_stream.foreachRDD(
+            lambda x: models.append(slr.latestModel().weights[0]))
+
+        t = time()
+        self.ssc.start()
+        self._ssc_wait(t, 15.0, 0.01)
+        t_models = array(models)
+        diff = t_models[1:] - t_models[:-1]
+
+        # Test that weights improve with a small tolerance,
+        self.assertTrue(all(diff >= -0.1))
+        self.assertTrue(array_sum(diff > 0) > 1)
+
+    @staticmethod
+    def calculate_accuracy_error(true, predicted):
+        return sum(abs(array(true) - array(predicted))) / len(true)
+
+    def test_predictions(self):
+        """Test predicted values on a toy model."""
+        input_batches = []
+        for i in range(20):
+            batch = self.sc.parallelize(
+                self.generateLogisticInput(0, 1.5, 100, 42 + i))
+            input_batches.append(batch.map(lambda x: (x.label, x.features)))
+        input_stream = self.ssc.queueStream(input_batches)
+
+        slr = StreamingLogisticRegressionWithSGD(
+            stepSize=0.2, numIterations=25)
+        slr.setInitialWeights([1.5])
+        predict_stream = slr.predictOnValues(input_stream)
+        true_predicted = []
+        predict_stream.foreachRDD(lambda x: true_predicted.append(x.collect()))
+        t = time()
+        self.ssc.start()
+        self._ssc_wait(t, 5.0, 0.01)
+
+        # Test that the accuracy error is no more than 0.4 on each batch.
+        for batch in true_predicted:
+            true, predicted = zip(*batch)
+            self.assertTrue(
+                self.calculate_accuracy_error(true, predicted) < 0.4)
+
+    def test_training_and_prediction(self):
+        """Test that the model improves on toy data with no. of batches"""
+        input_batches = [
+            self.sc.parallelize(self.generateLogisticInput(0, 1.5, 100, 42 + i))
+            for i in range(20)]
+        predict_batches = [
+            b.map(lambda lp: (lp.label, lp.features)) for b in input_batches]
+
+        slr = StreamingLogisticRegressionWithSGD(
+            stepSize=0.01, numIterations=25)
+        slr.setInitialWeights([-0.1])
+        errors = []
+
+        def collect_errors(rdd):
+            true, predicted = zip(*rdd.collect())
+            errors.append(self.calculate_accuracy_error(true, predicted))
+
+        true_predicted = []
+        input_stream = self.ssc.queueStream(input_batches)
+        predict_stream = self.ssc.queueStream(predict_batches)
+        slr.trainOn(input_stream)
+        ps = slr.predictOnValues(predict_stream)
+        ps.foreachRDD(lambda x: collect_errors(x))
+
+        t = time()
+        self.ssc.start()
+        self._ssc_wait(t, 20.0, 0.01)
+
+        # Test that the improvement in error is atleast 0.3
+        self.assertTrue(errors[1] - errors[-1] > 0.3)
+
+
 if __name__ == "__main__":
     if not _have_scipy:
         print("NOTE: Skipping SciPy tests as it does not seem to be installed")

From 8ab50765cd793169091d983b50d87a391f6ac1f4 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 24 Jun 2015 15:03:43 -0700
Subject: [PATCH 0049/1454] [SPARK-6777] [SQL] Implements backwards
 compatibility rules in CatalystSchemaConverter

This PR introduces `CatalystSchemaConverter` for converting Parquet schema to Spark SQL schema and vice versa.  Original conversion code in `ParquetTypesConverter` is removed. Benefits of the new version are:

1. When converting Spark SQL schemas, it generates standard Parquet schemas conforming to [the most updated Parquet format spec] [1]. Converting to old style Parquet schemas is also supported via feature flag `spark.sql.parquet.followParquetFormatSpec` (which is set to `false` for now, and should be set to `true` after both read and write paths are fixed).

   Note that although this version of Parquet format spec hasn't been officially release yet, Parquet MR 1.7.0 already sticks to it. So it should be safe to follow.

1. It implements backwards-compatibility rules described in the most updated Parquet format spec. Thus can recognize more schema patterns generated by other/legacy systems/tools.
1. Code organization follows convention used in [parquet-mr] [2], which is easier to follow. (Structure of `CatalystSchemaConverter` is similar to `AvroSchemaConverter`).

To fully implement backwards-compatibility rules in both read and write path, we also need to update `CatalystRowConverter` (which is responsible for converting Parquet records to `Row`s), `RowReadSupport`, and `RowWriteSupport`. These would be done in follow-up PRs.

TODO

- [x] More schema conversion test cases for legacy schema patterns.

[1]: https://github.com/apache/parquet-format/blob/ea095226597fdbecd60c2419d96b54b2fdb4ae6c/LogicalTypes.md
[2]: https://github.com/apache/parquet-mr/

Author: Cheng Lian <lian@databricks.com>

Closes #6617 from liancheng/spark-6777 and squashes the following commits:

2a2062d [Cheng Lian] Don't convert decimals without precision information
b60979b [Cheng Lian] Adds a constructor which accepts a Configuration, and fixes default value of assumeBinaryIsString
743730f [Cheng Lian] Decimal scale shouldn't be larger than precision
a104a9e [Cheng Lian] Fixes Scala style issue
1f71d8d [Cheng Lian] Adds feature flag to allow falling back to old style Parquet schema conversion
ba84f4b [Cheng Lian] Fixes MapType schema conversion bug
13cb8d5 [Cheng Lian] Fixes MiMa failure
81de5b0 [Cheng Lian] Fixes UDT, workaround read path, and add tests
28ef95b [Cheng Lian] More AnalysisExceptions
b10c322 [Cheng Lian] Replaces require() with analysisRequire() which throws AnalysisException
cceaf3f [Cheng Lian] Implements backwards compatibility rules in CatalystSchemaConverter
---
 project/MimaExcludes.scala                    |   7 +-
 .../apache/spark/sql/types/DecimalType.scala  |   9 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  14 +
 .../sql/parquet/CatalystSchemaConverter.scala | 565 ++++++++++++++
 .../sql/parquet/ParquetTableSupport.scala     |   6 +-
 .../spark/sql/parquet/ParquetTypes.scala      | 374 +--------
 .../spark/sql/parquet/ParquetIOSuite.scala    |   6 +-
 .../sql/parquet/ParquetSchemaSuite.scala      | 736 ++++++++++++++++--
 .../sql/hive/execution/SQLQuerySuite.scala    |   2 +-
 9 files changed, 1297 insertions(+), 422 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index f678c69a6dfa9..6f86a505b3ae4 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -69,7 +69,12 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingClassProblem](
               "org.apache.spark.sql.parquet.CatalystTimestampConverter"),
             ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.CatalystTimestampConverter$")
+              "org.apache.spark.sql.parquet.CatalystTimestampConverter$"),
+            // SPARK-6777 Implements backwards compatibility rules in CatalystSchemaConverter
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.parquet.ParquetTypeInfo"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.parquet.ParquetTypeInfo$")
           )
         case v if v.startsWith("1.4") =>
           Seq(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index 407dc27326c2e..18cdfa7238f39 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -20,13 +20,18 @@ package org.apache.spark.sql.types
 import scala.reflect.runtime.universe.typeTag
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.ScalaReflectionLock
 import org.apache.spark.sql.catalyst.expressions.Expression
 
 
 /** Precision parameters for a Decimal */
-case class PrecisionInfo(precision: Int, scale: Int)
-
+case class PrecisionInfo(precision: Int, scale: Int) {
+  if (scale > precision) {
+    throw new AnalysisException(
+      s"Decimal scale ($scale) cannot be greater than precision ($precision).")
+  }
+}
 
 /**
  * :: DeveloperApi ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 265352647fa9f..9a10a23937fbb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -264,6 +264,14 @@ private[spark] object SQLConf {
     defaultValue = Some(true),
     doc = "<TODO>")
 
+  val PARQUET_FOLLOW_PARQUET_FORMAT_SPEC = booleanConf(
+    key = "spark.sql.parquet.followParquetFormatSpec",
+    defaultValue = Some(false),
+    doc = "Wether to stick to Parquet format specification when converting Parquet schema to " +
+      "Spark SQL schema and vice versa.  Sticks to the specification if set to true; falls back " +
+      "to compatible mode if set to false.",
+    isPublic = false)
+
   val PARQUET_OUTPUT_COMMITTER_CLASS = stringConf(
     key = "spark.sql.parquet.output.committer.class",
     defaultValue = Some(classOf[ParquetOutputCommitter].getName),
@@ -498,6 +506,12 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
    */
   private[spark] def isParquetINT96AsTimestamp: Boolean = getConf(PARQUET_INT96_AS_TIMESTAMP)
 
+  /**
+   * When set to true, sticks to Parquet format spec when converting Parquet schema to Spark SQL
+   * schema and vice versa.  Otherwise, falls back to compatible mode.
+   */
+  private[spark] def followParquetFormatSpec: Boolean = getConf(PARQUET_FOLLOW_PARQUET_FORMAT_SPEC)
+
   /**
    * When set to true, partition pruning for in-memory columnar tables is enabled.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
new file mode 100644
index 0000000000000..4fd3e93b70311
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -0,0 +1,565 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.parquet.schema.OriginalType._
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName._
+import org.apache.parquet.schema.Type.Repetition._
+import org.apache.parquet.schema._
+
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{AnalysisException, SQLConf}
+
+/**
+ * This converter class is used to convert Parquet [[MessageType]] to Spark SQL [[StructType]] and
+ * vice versa.
+ *
+ * Parquet format backwards-compatibility rules are respected when converting Parquet
+ * [[MessageType]] schemas.
+ *
+ * @see https://github.com/apache/parquet-format/blob/master/LogicalTypes.md
+ *
+ * @constructor
+ * @param assumeBinaryIsString Whether unannotated BINARY fields should be assumed to be Spark SQL
+ *        [[StringType]] fields when converting Parquet a [[MessageType]] to Spark SQL
+ *        [[StructType]].
+ * @param assumeInt96IsTimestamp Whether unannotated INT96 fields should be assumed to be Spark SQL
+ *        [[TimestampType]] fields when converting Parquet a [[MessageType]] to Spark SQL
+ *        [[StructType]].  Note that Spark SQL [[TimestampType]] is similar to Hive timestamp, which
+ *        has optional nanosecond precision, but different from `TIME_MILLS` and `TIMESTAMP_MILLIS`
+ *        described in Parquet format spec.
+ * @param followParquetFormatSpec Whether to generate standard DECIMAL, LIST, and MAP structure when
+ *        converting Spark SQL [[StructType]] to Parquet [[MessageType]].  For Spark 1.4.x and
+ *        prior versions, Spark SQL only supports decimals with a max precision of 18 digits, and
+ *        uses non-standard LIST and MAP structure.  Note that the current Parquet format spec is
+ *        backwards-compatible with these settings.  If this argument is set to `false`, we fallback
+ *        to old style non-standard behaviors.
+ */
+private[parquet] class CatalystSchemaConverter(
+    private val assumeBinaryIsString: Boolean,
+    private val assumeInt96IsTimestamp: Boolean,
+    private val followParquetFormatSpec: Boolean) {
+
+  // Only used when constructing converter for converting Spark SQL schema to Parquet schema, in
+  // which case `assumeInt96IsTimestamp` and `assumeBinaryIsString` are irrelevant.
+  def this() = this(
+    assumeBinaryIsString = SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get,
+    assumeInt96IsTimestamp = SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get,
+    followParquetFormatSpec = SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get)
+
+  def this(conf: SQLConf) = this(
+    assumeBinaryIsString = conf.isParquetBinaryAsString,
+    assumeInt96IsTimestamp = conf.isParquetINT96AsTimestamp,
+    followParquetFormatSpec = conf.followParquetFormatSpec)
+
+  def this(conf: Configuration) = this(
+    assumeBinaryIsString =
+      conf.getBoolean(
+        SQLConf.PARQUET_BINARY_AS_STRING.key,
+        SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get),
+    assumeInt96IsTimestamp =
+      conf.getBoolean(
+        SQLConf.PARQUET_INT96_AS_TIMESTAMP.key,
+        SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get),
+    followParquetFormatSpec =
+      conf.getBoolean(
+        SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key,
+        SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get))
+
+  /**
+   * Converts Parquet [[MessageType]] `parquetSchema` to a Spark SQL [[StructType]].
+   */
+  def convert(parquetSchema: MessageType): StructType = convert(parquetSchema.asGroupType())
+
+  private def convert(parquetSchema: GroupType): StructType = {
+    val fields = parquetSchema.getFields.map { field =>
+      field.getRepetition match {
+        case OPTIONAL =>
+          StructField(field.getName, convertField(field), nullable = true)
+
+        case REQUIRED =>
+          StructField(field.getName, convertField(field), nullable = false)
+
+        case REPEATED =>
+          throw new AnalysisException(
+            s"REPEATED not supported outside LIST or MAP. Type: $field")
+      }
+    }
+
+    StructType(fields)
+  }
+
+  /**
+   * Converts a Parquet [[Type]] to a Spark SQL [[DataType]].
+   */
+  def convertField(parquetType: Type): DataType = parquetType match {
+    case t: PrimitiveType => convertPrimitiveField(t)
+    case t: GroupType => convertGroupField(t.asGroupType())
+  }
+
+  private def convertPrimitiveField(field: PrimitiveType): DataType = {
+    val typeName = field.getPrimitiveTypeName
+    val originalType = field.getOriginalType
+
+    def typeString =
+      if (originalType == null) s"$typeName" else s"$typeName ($originalType)"
+
+    def typeNotImplemented() =
+      throw new AnalysisException(s"Parquet type not yet supported: $typeString")
+
+    def illegalType() =
+      throw new AnalysisException(s"Illegal Parquet type: $typeString")
+
+    // When maxPrecision = -1, we skip precision range check, and always respect the precision
+    // specified in field.getDecimalMetadata.  This is useful when interpreting decimal types stored
+    // as binaries with variable lengths.
+    def makeDecimalType(maxPrecision: Int = -1): DecimalType = {
+      val precision = field.getDecimalMetadata.getPrecision
+      val scale = field.getDecimalMetadata.getScale
+
+      CatalystSchemaConverter.analysisRequire(
+        maxPrecision == -1 || 1 <= precision && precision <= maxPrecision,
+        s"Invalid decimal precision: $typeName cannot store $precision digits (max $maxPrecision)")
+
+      DecimalType(precision, scale)
+    }
+
+    field.getPrimitiveTypeName match {
+      case BOOLEAN => BooleanType
+
+      case FLOAT => FloatType
+
+      case DOUBLE => DoubleType
+
+      case INT32 =>
+        field.getOriginalType match {
+          case INT_8 => ByteType
+          case INT_16 => ShortType
+          case INT_32 | null => IntegerType
+          case DATE => DateType
+          case DECIMAL => makeDecimalType(maxPrecisionForBytes(4))
+          case TIME_MILLIS => typeNotImplemented()
+          case _ => illegalType()
+        }
+
+      case INT64 =>
+        field.getOriginalType match {
+          case INT_64 | null => LongType
+          case DECIMAL => makeDecimalType(maxPrecisionForBytes(8))
+          case TIMESTAMP_MILLIS => typeNotImplemented()
+          case _ => illegalType()
+        }
+
+      case INT96 =>
+        CatalystSchemaConverter.analysisRequire(
+          assumeInt96IsTimestamp,
+          "INT96 is not supported unless it's interpreted as timestamp. " +
+            s"Please try to set ${SQLConf.PARQUET_INT96_AS_TIMESTAMP.key} to true.")
+        TimestampType
+
+      case BINARY =>
+        field.getOriginalType match {
+          case UTF8 => StringType
+          case null if assumeBinaryIsString => StringType
+          case null => BinaryType
+          case DECIMAL => makeDecimalType()
+          case _ => illegalType()
+        }
+
+      case FIXED_LEN_BYTE_ARRAY =>
+        field.getOriginalType match {
+          case DECIMAL => makeDecimalType(maxPrecisionForBytes(field.getTypeLength))
+          case INTERVAL => typeNotImplemented()
+          case _ => illegalType()
+        }
+
+      case _ => illegalType()
+    }
+  }
+
+  private def convertGroupField(field: GroupType): DataType = {
+    Option(field.getOriginalType).fold(convert(field): DataType) {
+      // A Parquet list is represented as a 3-level structure:
+      //
+      //   <list-repetition> group <name> (LIST) {
+      //     repeated group list {
+      //       <element-repetition> <element-type> element;
+      //     }
+      //   }
+      //
+      // However, according to the most recent Parquet format spec (not released yet up until
+      // writing), some 2-level structures are also recognized for backwards-compatibility.  Thus,
+      // we need to check whether the 2nd level or the 3rd level refers to list element type.
+      //
+      // See: https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#lists
+      case LIST =>
+        CatalystSchemaConverter.analysisRequire(
+          field.getFieldCount == 1, s"Invalid list type $field")
+
+        val repeatedType = field.getType(0)
+        CatalystSchemaConverter.analysisRequire(
+          repeatedType.isRepetition(REPEATED), s"Invalid list type $field")
+
+        if (isElementType(repeatedType, field.getName)) {
+          ArrayType(convertField(repeatedType), containsNull = false)
+        } else {
+          val elementType = repeatedType.asGroupType().getType(0)
+          val optional = elementType.isRepetition(OPTIONAL)
+          ArrayType(convertField(elementType), containsNull = optional)
+        }
+
+      // scalastyle:off
+      // `MAP_KEY_VALUE` is for backwards-compatibility
+      // See: https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules-1
+      // scalastyle:on
+      case MAP | MAP_KEY_VALUE =>
+        CatalystSchemaConverter.analysisRequire(
+          field.getFieldCount == 1 && !field.getType(0).isPrimitive,
+          s"Invalid map type: $field")
+
+        val keyValueType = field.getType(0).asGroupType()
+        CatalystSchemaConverter.analysisRequire(
+          keyValueType.isRepetition(REPEATED) && keyValueType.getFieldCount == 2,
+          s"Invalid map type: $field")
+
+        val keyType = keyValueType.getType(0)
+        CatalystSchemaConverter.analysisRequire(
+          keyType.isPrimitive,
+          s"Map key type is expected to be a primitive type, but found: $keyType")
+
+        val valueType = keyValueType.getType(1)
+        val valueOptional = valueType.isRepetition(OPTIONAL)
+        MapType(
+          convertField(keyType),
+          convertField(valueType),
+          valueContainsNull = valueOptional)
+
+      case _ =>
+        throw new AnalysisException(s"Unrecognized Parquet type: $field")
+    }
+  }
+
+  // scalastyle:off
+  // Here we implement Parquet LIST backwards-compatibility rules.
+  // See: https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules
+  // scalastyle:on
+  private def isElementType(repeatedType: Type, parentName: String) = {
+    {
+      // For legacy 2-level list types with primitive element type, e.g.:
+      //
+      //    // List<Integer> (nullable list, non-null elements)
+      //    optional group my_list (LIST) {
+      //      repeated int32 element;
+      //    }
+      //
+      repeatedType.isPrimitive
+    } || {
+      // For legacy 2-level list types whose element type is a group type with 2 or more fields,
+      // e.g.:
+      //
+      //    // List<Tuple<String, Integer>> (nullable list, non-null elements)
+      //    optional group my_list (LIST) {
+      //      repeated group element {
+      //        required binary str (UTF8);
+      //        required int32 num;
+      //      };
+      //    }
+      //
+      repeatedType.asGroupType().getFieldCount > 1
+    } || {
+      // For legacy 2-level list types generated by parquet-avro (Parquet version < 1.6.0), e.g.:
+      //
+      //    // List<OneTuple<String>> (nullable list, non-null elements)
+      //    optional group my_list (LIST) {
+      //      repeated group array {
+      //        required binary str (UTF8);
+      //      };
+      //    }
+      //
+      repeatedType.getName == "array"
+    } || {
+      // For Parquet data generated by parquet-thrift, e.g.:
+      //
+      //    // List<OneTuple<String>> (nullable list, non-null elements)
+      //    optional group my_list (LIST) {
+      //      repeated group my_list_tuple {
+      //        required binary str (UTF8);
+      //      };
+      //    }
+      //
+      repeatedType.getName == s"${parentName}_tuple"
+    }
+  }
+
+  /**
+   * Converts a Spark SQL [[StructType]] to a Parquet [[MessageType]].
+   */
+  def convert(catalystSchema: StructType): MessageType = {
+    Types.buildMessage().addFields(catalystSchema.map(convertField): _*).named("root")
+  }
+
+  /**
+   * Converts a Spark SQL [[StructField]] to a Parquet [[Type]].
+   */
+  def convertField(field: StructField): Type = {
+    convertField(field, if (field.nullable) OPTIONAL else REQUIRED)
+  }
+
+  private def convertField(field: StructField, repetition: Type.Repetition): Type = {
+    CatalystSchemaConverter.checkFieldName(field.name)
+
+    field.dataType match {
+      // ===================
+      // Simple atomic types
+      // ===================
+
+      case BooleanType =>
+        Types.primitive(BOOLEAN, repetition).named(field.name)
+
+      case ByteType =>
+        Types.primitive(INT32, repetition).as(INT_8).named(field.name)
+
+      case ShortType =>
+        Types.primitive(INT32, repetition).as(INT_16).named(field.name)
+
+      case IntegerType =>
+        Types.primitive(INT32, repetition).named(field.name)
+
+      case LongType =>
+        Types.primitive(INT64, repetition).named(field.name)
+
+      case FloatType =>
+        Types.primitive(FLOAT, repetition).named(field.name)
+
+      case DoubleType =>
+        Types.primitive(DOUBLE, repetition).named(field.name)
+
+      case StringType =>
+        Types.primitive(BINARY, repetition).as(UTF8).named(field.name)
+
+      case DateType =>
+        Types.primitive(INT32, repetition).as(DATE).named(field.name)
+
+      // NOTE: !! This timestamp type is not specified in Parquet format spec !!
+      // However, Impala and older versions of Spark SQL use INT96 to store timestamps with
+      // nanosecond precision (not TIME_MILLIS or TIMESTAMP_MILLIS described in the spec).
+      case TimestampType =>
+        Types.primitive(INT96, repetition).named(field.name)
+
+      case BinaryType =>
+        Types.primitive(BINARY, repetition).named(field.name)
+
+      // =====================================
+      // Decimals (for Spark version <= 1.4.x)
+      // =====================================
+
+      // Spark 1.4.x and prior versions only support decimals with a maximum precision of 18 and
+      // always store decimals in fixed-length byte arrays.
+      case DecimalType.Fixed(precision, scale)
+        if precision <= maxPrecisionForBytes(8) && !followParquetFormatSpec =>
+        Types
+          .primitive(FIXED_LEN_BYTE_ARRAY, repetition)
+          .as(DECIMAL)
+          .precision(precision)
+          .scale(scale)
+          .length(minBytesForPrecision(precision))
+          .named(field.name)
+
+      case dec @ DecimalType() if !followParquetFormatSpec =>
+        throw new AnalysisException(
+          s"Data type $dec is not supported. " +
+            s"When ${SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key} is set to false," +
+            "decimal precision and scale must be specified, " +
+            "and precision must be less than or equal to 18.")
+
+      // =====================================
+      // Decimals (follow Parquet format spec)
+      // =====================================
+
+      // Uses INT32 for 1 <= precision <= 9
+      case DecimalType.Fixed(precision, scale)
+        if precision <= maxPrecisionForBytes(4) && followParquetFormatSpec =>
+        Types
+          .primitive(INT32, repetition)
+          .as(DECIMAL)
+          .precision(precision)
+          .scale(scale)
+          .named(field.name)
+
+      // Uses INT64 for 1 <= precision <= 18
+      case DecimalType.Fixed(precision, scale)
+        if precision <= maxPrecisionForBytes(8) && followParquetFormatSpec =>
+        Types
+          .primitive(INT64, repetition)
+          .as(DECIMAL)
+          .precision(precision)
+          .scale(scale)
+          .named(field.name)
+
+      // Uses FIXED_LEN_BYTE_ARRAY for all other precisions
+      case DecimalType.Fixed(precision, scale) if followParquetFormatSpec =>
+        Types
+          .primitive(FIXED_LEN_BYTE_ARRAY, repetition)
+          .as(DECIMAL)
+          .precision(precision)
+          .scale(scale)
+          .length(minBytesForPrecision(precision))
+          .named(field.name)
+
+      case dec @ DecimalType.Unlimited if followParquetFormatSpec =>
+        throw new AnalysisException(
+          s"Data type $dec is not supported. Decimal precision and scale must be specified.")
+
+      // ===================================================
+      // ArrayType and MapType (for Spark versions <= 1.4.x)
+      // ===================================================
+
+      // Spark 1.4.x and prior versions convert ArrayType with nullable elements into a 3-level
+      // LIST structure.  This behavior mimics parquet-hive (1.6.0rc3).  Note that this case is
+      // covered by the backwards-compatibility rules implemented in `isElementType()`.
+      case ArrayType(elementType, nullable @ true) if !followParquetFormatSpec =>
+        // <list-repetition> group <name> (LIST) {
+        //   optional group bag {
+        //     repeated <element-type> element;
+        //   }
+        // }
+        ConversionPatterns.listType(
+          repetition,
+          field.name,
+          Types
+            .buildGroup(REPEATED)
+            .addField(convertField(StructField("element", elementType, nullable)))
+            .named(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME))
+
+      // Spark 1.4.x and prior versions convert ArrayType with non-nullable elements into a 2-level
+      // LIST structure.  This behavior mimics parquet-avro (1.6.0rc3).  Note that this case is
+      // covered by the backwards-compatibility rules implemented in `isElementType()`.
+      case ArrayType(elementType, nullable @ false) if !followParquetFormatSpec =>
+        // <list-repetition> group <name> (LIST) {
+        //   repeated <element-type> element;
+        // }
+        ConversionPatterns.listType(
+          repetition,
+          field.name,
+          convertField(StructField("element", elementType, nullable), REPEATED))
+
+      // Spark 1.4.x and prior versions convert MapType into a 3-level group annotated by
+      // MAP_KEY_VALUE.  This is covered by `convertGroupField(field: GroupType): DataType`.
+      case MapType(keyType, valueType, valueContainsNull) if !followParquetFormatSpec =>
+        // <map-repetition> group <name> (MAP) {
+        //   repeated group map (MAP_KEY_VALUE) {
+        //     required <key-type> key;
+        //     <value-repetition> <value-type> value;
+        //   }
+        // }
+        ConversionPatterns.mapType(
+          repetition,
+          field.name,
+          convertField(StructField("key", keyType, nullable = false)),
+          convertField(StructField("value", valueType, valueContainsNull)))
+
+      // ==================================================
+      // ArrayType and MapType (follow Parquet format spec)
+      // ==================================================
+
+      case ArrayType(elementType, containsNull) if followParquetFormatSpec =>
+        // <list-repetition> group <name> (LIST) {
+        //   repeated group list {
+        //     <element-repetition> <element-type> element;
+        //   }
+        // }
+        Types
+          .buildGroup(repetition).as(LIST)
+          .addField(
+            Types.repeatedGroup()
+              .addField(convertField(StructField("element", elementType, containsNull)))
+              .named("list"))
+          .named(field.name)
+
+      case MapType(keyType, valueType, valueContainsNull) =>
+        // <map-repetition> group <name> (MAP) {
+        //   repeated group key_value {
+        //     required <key-type> key;
+        //     <value-repetition> <value-type> value;
+        //   }
+        // }
+        Types
+          .buildGroup(repetition).as(MAP)
+          .addField(
+            Types
+              .repeatedGroup()
+              .addField(convertField(StructField("key", keyType, nullable = false)))
+              .addField(convertField(StructField("value", valueType, valueContainsNull)))
+              .named("key_value"))
+          .named(field.name)
+
+      // ===========
+      // Other types
+      // ===========
+
+      case StructType(fields) =>
+        fields.foldLeft(Types.buildGroup(repetition)) { (builder, field) =>
+          builder.addField(convertField(field))
+        }.named(field.name)
+
+      case udt: UserDefinedType[_] =>
+        convertField(field.copy(dataType = udt.sqlType))
+
+      case _ =>
+        throw new AnalysisException(s"Unsupported data type $field.dataType")
+    }
+  }
+
+  // Max precision of a decimal value stored in `numBytes` bytes
+  private def maxPrecisionForBytes(numBytes: Int): Int = {
+    Math.round(                               // convert double to long
+      Math.floor(Math.log10(                  // number of base-10 digits
+        Math.pow(2, 8 * numBytes - 1) - 1)))  // max value stored in numBytes
+      .asInstanceOf[Int]
+  }
+
+  // Min byte counts needed to store decimals with various precisions
+  private val minBytesForPrecision: Array[Int] = Array.tabulate(38) { precision =>
+    var numBytes = 1
+    while (math.pow(2.0, 8 * numBytes - 1) < math.pow(10.0, precision)) {
+      numBytes += 1
+    }
+    numBytes
+  }
+}
+
+
+private[parquet] object CatalystSchemaConverter {
+  def checkFieldName(name: String): Unit = {
+    // ,;{}()\n\t= and space are special characters in Parquet schema
+    analysisRequire(
+      !name.matches(".*[ ,;{}()\n\t=].*"),
+      s"""Attribute name "$name" contains invalid character(s) among " ,;{}()\\n\\t=".
+         |Please use alias to rename it.
+       """.stripMargin.split("\n").mkString(" "))
+  }
+
+  def analysisRequire(f: => Boolean, message: String): Unit = {
+    if (!f) {
+      throw new AnalysisException(message)
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index e65fa0030e179..0d96a1e8070b1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -86,8 +86,7 @@ private[parquet] class RowReadSupport extends ReadSupport[InternalRow] with Logg
     // TODO: Why it can be null?
     if (schema == null)  {
       log.debug("falling back to Parquet read schema")
-      schema = ParquetTypesConverter.convertToAttributes(
-        parquetSchema, false, true)
+      schema = ParquetTypesConverter.convertToAttributes(parquetSchema, false, true)
     }
     log.debug(s"list of attributes that will be read: $schema")
     new RowRecordMaterializer(parquetSchema, schema)
@@ -105,8 +104,7 @@ private[parquet] class RowReadSupport extends ReadSupport[InternalRow] with Logg
       // If the parquet file is thrift derived, there is a good chance that
       // it will have the thrift class in metadata.
       val isThriftDerived = keyValueMetaData.keySet().contains("thrift.class")
-      parquetSchema = ParquetTypesConverter
-        .convertFromAttributes(requestedAttributes, isThriftDerived)
+      parquetSchema = ParquetTypesConverter.convertFromAttributes(requestedAttributes)
       metadata.put(
         RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
         ParquetTypesConverter.convertToString(requestedAttributes))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index ba2a35b74ef82..4d5199a140344 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -29,214 +29,19 @@ import org.apache.parquet.format.converter.ParquetMetadataConverter
 import org.apache.parquet.hadoop.metadata.{FileMetaData, ParquetMetadata}
 import org.apache.parquet.hadoop.util.ContextUtil
 import org.apache.parquet.hadoop.{Footer, ParquetFileReader, ParquetFileWriter}
-import org.apache.parquet.schema.PrimitiveType.{PrimitiveTypeName => ParquetPrimitiveTypeName}
-import org.apache.parquet.schema.Type.Repetition
-import org.apache.parquet.schema.{ConversionPatterns, DecimalMetadata, GroupType => ParquetGroupType, MessageType, OriginalType => ParquetOriginalType, PrimitiveType => ParquetPrimitiveType, Type => ParquetType, Types => ParquetTypes}
+import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.types._
 
 
-/** A class representing Parquet info fields we care about, for passing back to Parquet */
-private[parquet] case class ParquetTypeInfo(
-  primitiveType: ParquetPrimitiveTypeName,
-  originalType: Option[ParquetOriginalType] = None,
-  decimalMetadata: Option[DecimalMetadata] = None,
-  length: Option[Int] = None)
-
 private[parquet] object ParquetTypesConverter extends Logging {
   def isPrimitiveType(ctype: DataType): Boolean = ctype match {
     case _: NumericType | BooleanType | StringType | BinaryType => true
     case _: DataType => false
   }
 
-  def toPrimitiveDataType(
-      parquetType: ParquetPrimitiveType,
-      binaryAsString: Boolean,
-      int96AsTimestamp: Boolean): DataType = {
-    val originalType = parquetType.getOriginalType
-    val decimalInfo = parquetType.getDecimalMetadata
-    parquetType.getPrimitiveTypeName match {
-      case ParquetPrimitiveTypeName.BINARY
-        if (originalType == ParquetOriginalType.UTF8 || binaryAsString) => StringType
-      case ParquetPrimitiveTypeName.BINARY => BinaryType
-      case ParquetPrimitiveTypeName.BOOLEAN => BooleanType
-      case ParquetPrimitiveTypeName.DOUBLE => DoubleType
-      case ParquetPrimitiveTypeName.FLOAT => FloatType
-      case ParquetPrimitiveTypeName.INT32
-        if originalType == ParquetOriginalType.DATE => DateType
-      case ParquetPrimitiveTypeName.INT32 => IntegerType
-      case ParquetPrimitiveTypeName.INT64 => LongType
-      case ParquetPrimitiveTypeName.INT96 if int96AsTimestamp => TimestampType
-      case ParquetPrimitiveTypeName.INT96 =>
-        // TODO: add BigInteger type? TODO(andre) use DecimalType instead????
-        throw new AnalysisException("Potential loss of precision: cannot convert INT96")
-      case ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY
-        if (originalType == ParquetOriginalType.DECIMAL && decimalInfo.getPrecision <= 18) =>
-          // TODO: for now, our reader only supports decimals that fit in a Long
-          DecimalType(decimalInfo.getPrecision, decimalInfo.getScale)
-      case _ => throw new AnalysisException(s"Unsupported parquet datatype $parquetType")
-    }
-  }
-
-  /**
-   * Converts a given Parquet `Type` into the corresponding
-   * [[org.apache.spark.sql.types.DataType]].
-   *
-   * We apply the following conversion rules:
-   * <ul>
-   *   <li> Primitive types are converter to the corresponding primitive type.</li>
-   *   <li> Group types that have a single field that is itself a group, which has repetition
-   *        level `REPEATED`, are treated as follows:<ul>
-   *          <li> If the nested group has name `values`, the surrounding group is converted
-   *               into an [[ArrayType]] with the corresponding field type (primitive or
-   *               complex) as element type.</li>
-   *          <li> If the nested group has name `map` and two fields (named `key` and `value`),
-   *               the surrounding group is converted into a [[MapType]]
-   *               with the corresponding key and value (value possibly complex) types.
-   *               Note that we currently assume map values are not nullable.</li>
-   *   <li> Other group types are converted into a [[StructType]] with the corresponding
-   *        field types.</li></ul></li>
-   * </ul>
-   * Note that fields are determined to be `nullable` if and only if their Parquet repetition
-   * level is not `REQUIRED`.
-   *
-   * @param parquetType The type to convert.
-   * @return The corresponding Catalyst type.
-   */
-  def toDataType(parquetType: ParquetType,
-                 isBinaryAsString: Boolean,
-                 isInt96AsTimestamp: Boolean): DataType = {
-    def correspondsToMap(groupType: ParquetGroupType): Boolean = {
-      if (groupType.getFieldCount != 1 || groupType.getFields.apply(0).isPrimitive) {
-        false
-      } else {
-        // This mostly follows the convention in ``parquet.schema.ConversionPatterns``
-        val keyValueGroup = groupType.getFields.apply(0).asGroupType()
-        keyValueGroup.getRepetition == Repetition.REPEATED &&
-          keyValueGroup.getName == CatalystConverter.MAP_SCHEMA_NAME &&
-          keyValueGroup.getFieldCount == 2 &&
-          keyValueGroup.getFields.apply(0).getName == CatalystConverter.MAP_KEY_SCHEMA_NAME &&
-          keyValueGroup.getFields.apply(1).getName == CatalystConverter.MAP_VALUE_SCHEMA_NAME
-      }
-    }
-
-    def correspondsToArray(groupType: ParquetGroupType): Boolean = {
-      groupType.getFieldCount == 1 &&
-        groupType.getFieldName(0) == CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME &&
-        groupType.getFields.apply(0).getRepetition == Repetition.REPEATED
-    }
-
-    if (parquetType.isPrimitive) {
-      toPrimitiveDataType(parquetType.asPrimitiveType, isBinaryAsString, isInt96AsTimestamp)
-    } else {
-      val groupType = parquetType.asGroupType()
-      parquetType.getOriginalType match {
-        // if the schema was constructed programmatically there may be hints how to convert
-        // it inside the metadata via the OriginalType field
-        case ParquetOriginalType.LIST => { // TODO: check enums!
-          assert(groupType.getFieldCount == 1)
-          val field = groupType.getFields.apply(0)
-          if (field.getName == CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME) {
-            val bag = field.asGroupType()
-            assert(bag.getFieldCount == 1)
-            ArrayType(
-              toDataType(bag.getFields.apply(0), isBinaryAsString, isInt96AsTimestamp),
-              containsNull = true)
-          } else {
-            ArrayType(
-              toDataType(field, isBinaryAsString, isInt96AsTimestamp), containsNull = false)
-          }
-        }
-        case ParquetOriginalType.MAP => {
-          assert(
-            !groupType.getFields.apply(0).isPrimitive,
-            "Parquet Map type malformatted: expected nested group for map!")
-          val keyValueGroup = groupType.getFields.apply(0).asGroupType()
-          assert(
-            keyValueGroup.getFieldCount == 2,
-            "Parquet Map type malformatted: nested group should have 2 (key, value) fields!")
-          assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
-
-          val keyType =
-            toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString, isInt96AsTimestamp)
-          val valueType =
-            toDataType(keyValueGroup.getFields.apply(1), isBinaryAsString, isInt96AsTimestamp)
-          MapType(keyType, valueType,
-            keyValueGroup.getFields.apply(1).getRepetition != Repetition.REQUIRED)
-        }
-        case _ => {
-          // Note: the order of these checks is important!
-          if (correspondsToMap(groupType)) { // MapType
-            val keyValueGroup = groupType.getFields.apply(0).asGroupType()
-            assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
-
-            val keyType =
-              toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString, isInt96AsTimestamp)
-            val valueType =
-              toDataType(keyValueGroup.getFields.apply(1), isBinaryAsString, isInt96AsTimestamp)
-            MapType(keyType, valueType,
-              keyValueGroup.getFields.apply(1).getRepetition != Repetition.REQUIRED)
-          } else if (correspondsToArray(groupType)) { // ArrayType
-            val field = groupType.getFields.apply(0)
-            if (field.getName == CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME) {
-              val bag = field.asGroupType()
-              assert(bag.getFieldCount == 1)
-              ArrayType(
-                toDataType(bag.getFields.apply(0), isBinaryAsString, isInt96AsTimestamp),
-                containsNull = true)
-            } else {
-              ArrayType(
-                toDataType(field, isBinaryAsString, isInt96AsTimestamp), containsNull = false)
-            }
-          } else { // everything else: StructType
-            val fields = groupType
-              .getFields
-              .map(ptype => new StructField(
-              ptype.getName,
-              toDataType(ptype, isBinaryAsString, isInt96AsTimestamp),
-              ptype.getRepetition != Repetition.REQUIRED))
-            StructType(fields)
-          }
-        }
-      }
-    }
-  }
-
-  /**
-   * For a given Catalyst [[org.apache.spark.sql.types.DataType]] return
-   * the name of the corresponding Parquet primitive type or None if the given type
-   * is not primitive.
-   *
-   * @param ctype The type to convert
-   * @return The name of the corresponding Parquet type properties
-   */
-  def fromPrimitiveDataType(ctype: DataType): Option[ParquetTypeInfo] = ctype match {
-    case StringType => Some(ParquetTypeInfo(
-      ParquetPrimitiveTypeName.BINARY, Some(ParquetOriginalType.UTF8)))
-    case BinaryType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.BINARY))
-    case BooleanType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.BOOLEAN))
-    case DoubleType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.DOUBLE))
-    case FloatType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.FLOAT))
-    case IntegerType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT32))
-    // There is no type for Byte or Short so we promote them to INT32.
-    case ShortType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT32))
-    case ByteType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT32))
-    case DateType => Some(ParquetTypeInfo(
-      ParquetPrimitiveTypeName.INT32, Some(ParquetOriginalType.DATE)))
-    case LongType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT64))
-    case TimestampType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT96))
-    case DecimalType.Fixed(precision, scale) if precision <= 18 =>
-      // TODO: for now, our writer only supports decimals that fit in a Long
-      Some(ParquetTypeInfo(ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY,
-        Some(ParquetOriginalType.DECIMAL),
-        Some(new DecimalMetadata(precision, scale)),
-        Some(BYTES_FOR_PRECISION(precision))))
-    case _ => None
-  }
-
   /**
    * Compute the FIXED_LEN_BYTE_ARRAY length needed to represent a given DECIMAL precision.
    */
@@ -248,177 +53,29 @@ private[parquet] object ParquetTypesConverter extends Logging {
     length
   }
 
-  /**
-   * Converts a given Catalyst [[org.apache.spark.sql.types.DataType]] into
-   * the corresponding Parquet `Type`.
-   *
-   * The conversion follows the rules below:
-   * <ul>
-   *   <li> Primitive types are converted into Parquet's primitive types.</li>
-   *   <li> [[org.apache.spark.sql.types.StructType]]s are converted
-   *        into Parquet's `GroupType` with the corresponding field types.</li>
-   *   <li> [[org.apache.spark.sql.types.ArrayType]]s are converted
-   *        into a 2-level nested group, where the outer group has the inner
-   *        group as sole field. The inner group has name `values` and
-   *        repetition level `REPEATED` and has the element type of
-   *        the array as schema. We use Parquet's `ConversionPatterns` for this
-   *        purpose.</li>
-   *   <li> [[org.apache.spark.sql.types.MapType]]s are converted
-   *        into a nested (2-level) Parquet `GroupType` with two fields: a key
-   *        type and a value type. The nested group has repetition level
-   *        `REPEATED` and name `map`. We use Parquet's `ConversionPatterns`
-   *        for this purpose</li>
-   * </ul>
-   * Parquet's repetition level is generally set according to the following rule:
-   * <ul>
-   *   <li> If the call to `fromDataType` is recursive inside an enclosing `ArrayType` or
-   *   `MapType`, then the repetition level is set to `REPEATED`.</li>
-   *   <li> Otherwise, if the attribute whose type is converted is `nullable`, the Parquet
-   *   type gets repetition level `OPTIONAL` and otherwise `REQUIRED`.</li>
-   * </ul>
-   *
-   *@param ctype The type to convert
-   * @param name The name of the [[org.apache.spark.sql.catalyst.expressions.Attribute]]
-   *             whose type is converted
-   * @param nullable When true indicates that the attribute is nullable
-   * @param inArray When true indicates that this is a nested attribute inside an array.
-   * @return The corresponding Parquet type.
-   */
-  def fromDataType(
-      ctype: DataType,
-      name: String,
-      nullable: Boolean = true,
-      inArray: Boolean = false,
-      toThriftSchemaNames: Boolean = false): ParquetType = {
-    val repetition =
-      if (inArray) {
-        Repetition.REPEATED
-      } else {
-        if (nullable) Repetition.OPTIONAL else Repetition.REQUIRED
-      }
-    val arraySchemaName = if (toThriftSchemaNames) {
-      name + CatalystConverter.THRIFT_ARRAY_ELEMENTS_SCHEMA_NAME_SUFFIX
-    } else {
-      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME
-    }
-    val typeInfo = fromPrimitiveDataType(ctype)
-    typeInfo.map {
-      case ParquetTypeInfo(primitiveType, originalType, decimalMetadata, length) =>
-        val builder = ParquetTypes.primitive(primitiveType, repetition).as(originalType.orNull)
-        for (len <- length) {
-          builder.length(len)
-        }
-        for (metadata <- decimalMetadata) {
-          builder.precision(metadata.getPrecision).scale(metadata.getScale)
-        }
-        builder.named(name)
-    }.getOrElse {
-      ctype match {
-        case udt: UserDefinedType[_] => {
-          fromDataType(udt.sqlType, name, nullable, inArray, toThriftSchemaNames)
-        }
-        case ArrayType(elementType, false) => {
-          val parquetElementType = fromDataType(
-            elementType,
-            arraySchemaName,
-            nullable = false,
-            inArray = true,
-            toThriftSchemaNames)
-          ConversionPatterns.listType(repetition, name, parquetElementType)
-        }
-        case ArrayType(elementType, true) => {
-          val parquetElementType = fromDataType(
-            elementType,
-            arraySchemaName,
-            nullable = true,
-            inArray = false,
-            toThriftSchemaNames)
-          ConversionPatterns.listType(
-            repetition,
-            name,
-            new ParquetGroupType(
-              Repetition.REPEATED,
-              CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME,
-              parquetElementType))
-        }
-        case StructType(structFields) => {
-          val fields = structFields.map {
-            field => fromDataType(field.dataType, field.name, field.nullable,
-                                  inArray = false, toThriftSchemaNames)
-          }
-          new ParquetGroupType(repetition, name, fields.toSeq)
-        }
-        case MapType(keyType, valueType, valueContainsNull) => {
-          val parquetKeyType =
-            fromDataType(
-              keyType,
-              CatalystConverter.MAP_KEY_SCHEMA_NAME,
-              nullable = false,
-              inArray = false,
-              toThriftSchemaNames)
-          val parquetValueType =
-            fromDataType(
-              valueType,
-              CatalystConverter.MAP_VALUE_SCHEMA_NAME,
-              nullable = valueContainsNull,
-              inArray = false,
-              toThriftSchemaNames)
-          ConversionPatterns.mapType(
-            repetition,
-            name,
-            parquetKeyType,
-            parquetValueType)
-        }
-        case _ => throw new AnalysisException(s"Unsupported datatype $ctype")
-      }
-    }
-  }
-
-  def convertToAttributes(parquetSchema: ParquetType,
-                          isBinaryAsString: Boolean,
-                          isInt96AsTimestamp: Boolean): Seq[Attribute] = {
-    parquetSchema
-      .asGroupType()
-      .getFields
-      .map(
-        field =>
-          new AttributeReference(
-            field.getName,
-            toDataType(field, isBinaryAsString, isInt96AsTimestamp),
-            field.getRepetition != Repetition.REQUIRED)())
+  def convertToAttributes(
+      parquetSchema: MessageType,
+      isBinaryAsString: Boolean,
+      isInt96AsTimestamp: Boolean): Seq[Attribute] = {
+    val converter = new CatalystSchemaConverter(
+      isBinaryAsString, isInt96AsTimestamp, followParquetFormatSpec = false)
+    converter.convert(parquetSchema).toAttributes
   }
 
-  def convertFromAttributes(attributes: Seq[Attribute],
-                            toThriftSchemaNames: Boolean = false): MessageType = {
-    checkSpecialCharacters(attributes)
-    val fields = attributes.map(
-      attribute =>
-        fromDataType(attribute.dataType, attribute.name, attribute.nullable,
-                     toThriftSchemaNames = toThriftSchemaNames))
-    new MessageType("root", fields)
+  def convertFromAttributes(attributes: Seq[Attribute]): MessageType = {
+    val converter = new CatalystSchemaConverter()
+    converter.convert(StructType.fromAttributes(attributes))
   }
 
   def convertFromString(string: String): Seq[Attribute] = {
     Try(DataType.fromJson(string)).getOrElse(DataType.fromCaseClassString(string)) match {
       case s: StructType => s.toAttributes
-      case other => throw new AnalysisException(s"Can convert $string to row")
-    }
-  }
-
-  private def checkSpecialCharacters(schema: Seq[Attribute]) = {
-    // ,;{}()\n\t= and space character are special characters in Parquet schema
-    schema.map(_.name).foreach { name =>
-      if (name.matches(".*[ ,;{}()\n\t=].*")) {
-        throw new AnalysisException(
-          s"""Attribute name "$name" contains invalid character(s) among " ,;{}()\\n\\t=".
-             |Please use alias to rename it.
-           """.stripMargin.split("\n").mkString(" "))
-      }
+      case other => sys.error(s"Can convert $string to row")
     }
   }
 
   def convertToString(schema: Seq[Attribute]): String = {
-    checkSpecialCharacters(schema)
+    schema.map(_.name).foreach(CatalystSchemaConverter.checkFieldName)
     StructType.fromAttributes(schema).json
   }
 
@@ -450,8 +107,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
       ParquetTypesConverter.convertToString(attributes))
     // TODO: add extra data, e.g., table name, date, etc.?
 
-    val parquetSchema: MessageType =
-      ParquetTypesConverter.convertFromAttributes(attributes)
+    val parquetSchema: MessageType = ParquetTypesConverter.convertFromAttributes(attributes)
     val metaData: FileMetaData = new FileMetaData(
       parquetSchema,
       extraMetadata,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
index 47a7be1c6a664..7b16eba00d6fb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
@@ -99,7 +99,6 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest {
   }
 
   test("fixed-length decimals") {
-
     def makeDecimalRDD(decimal: DecimalType): DataFrame =
       sqlContext.sparkContext
         .parallelize(0 to 1000)
@@ -158,6 +157,11 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest {
     checkParquetFile(data)
   }
 
+  test("array and double") {
+    val data = (1 to 4).map(i => (i.toDouble, Seq(i.toDouble, (i + 1).toDouble)))
+    checkParquetFile(data)
+  }
+
   test("struct") {
     val data = (1 to 4).map(i => Tuple1((i, s"val_$i")))
     withParquetDataFrame(data) { df =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
index 171a656f0e01e..d0bfcde7e032b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
@@ -24,26 +24,109 @@ import org.apache.parquet.schema.MessageTypeParser
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.ScalaReflection
+import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types._
 
-class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
+abstract class ParquetSchemaTest extends SparkFunSuite with ParquetTest {
+  val sqlContext = TestSQLContext
 
   /**
    * Checks whether the reflected Parquet message type for product type `T` conforms `messageType`.
    */
-  private def testSchema[T <: Product: ClassTag: TypeTag](
-      testName: String, messageType: String, isThriftDerived: Boolean = false): Unit = {
-    test(testName) {
-      val actual = ParquetTypesConverter.convertFromAttributes(
-        ScalaReflection.attributesFor[T], isThriftDerived)
-      val expected = MessageTypeParser.parseMessageType(messageType)
+  protected def testSchemaInference[T <: Product: ClassTag: TypeTag](
+      testName: String,
+      messageType: String,
+      binaryAsString: Boolean = true,
+      int96AsTimestamp: Boolean = true,
+      followParquetFormatSpec: Boolean = false,
+      isThriftDerived: Boolean = false): Unit = {
+    testSchema(
+      testName,
+      StructType.fromAttributes(ScalaReflection.attributesFor[T]),
+      messageType,
+      binaryAsString,
+      int96AsTimestamp,
+      followParquetFormatSpec,
+      isThriftDerived)
+  }
+
+  protected def testParquetToCatalyst(
+      testName: String,
+      sqlSchema: StructType,
+      parquetSchema: String,
+      binaryAsString: Boolean = true,
+      int96AsTimestamp: Boolean = true,
+      followParquetFormatSpec: Boolean = false,
+      isThriftDerived: Boolean = false): Unit = {
+    val converter = new CatalystSchemaConverter(
+      assumeBinaryIsString = binaryAsString,
+      assumeInt96IsTimestamp = int96AsTimestamp,
+      followParquetFormatSpec = followParquetFormatSpec)
+
+    test(s"sql <= parquet: $testName") {
+      val actual = converter.convert(MessageTypeParser.parseMessageType(parquetSchema))
+      val expected = sqlSchema
+      assert(
+        actual === expected,
+        s"""Schema mismatch.
+           |Expected schema: ${expected.json}
+           |Actual schema:   ${actual.json}
+         """.stripMargin)
+    }
+  }
+
+  protected def testCatalystToParquet(
+      testName: String,
+      sqlSchema: StructType,
+      parquetSchema: String,
+      binaryAsString: Boolean = true,
+      int96AsTimestamp: Boolean = true,
+      followParquetFormatSpec: Boolean = false,
+      isThriftDerived: Boolean = false): Unit = {
+    val converter = new CatalystSchemaConverter(
+      assumeBinaryIsString = binaryAsString,
+      assumeInt96IsTimestamp = int96AsTimestamp,
+      followParquetFormatSpec = followParquetFormatSpec)
+
+    test(s"sql => parquet: $testName") {
+      val actual = converter.convert(sqlSchema)
+      val expected = MessageTypeParser.parseMessageType(parquetSchema)
       actual.checkContains(expected)
       expected.checkContains(actual)
     }
   }
 
-  testSchema[(Boolean, Int, Long, Float, Double, Array[Byte])](
+  protected def testSchema(
+      testName: String,
+      sqlSchema: StructType,
+      parquetSchema: String,
+      binaryAsString: Boolean = true,
+      int96AsTimestamp: Boolean = true,
+      followParquetFormatSpec: Boolean = false,
+      isThriftDerived: Boolean = false): Unit = {
+
+    testCatalystToParquet(
+      testName,
+      sqlSchema,
+      parquetSchema,
+      binaryAsString,
+      int96AsTimestamp,
+      followParquetFormatSpec,
+      isThriftDerived)
+
+    testParquetToCatalyst(
+      testName,
+      sqlSchema,
+      parquetSchema,
+      binaryAsString,
+      int96AsTimestamp,
+      followParquetFormatSpec,
+      isThriftDerived)
+  }
+}
+
+class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
+  testSchemaInference[(Boolean, Int, Long, Float, Double, Array[Byte])](
     "basic types",
     """
       |message root {
@@ -54,9 +137,10 @@ class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
       |  required double  _5;
       |  optional binary  _6;
       |}
-    """.stripMargin)
+    """.stripMargin,
+    binaryAsString = false)
 
-  testSchema[(Byte, Short, Int, Long, java.sql.Date)](
+  testSchemaInference[(Byte, Short, Int, Long, java.sql.Date)](
     "logical integral types",
     """
       |message root {
@@ -68,27 +152,79 @@ class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
       |}
     """.stripMargin)
 
-  // Currently String is the only supported logical binary type.
-  testSchema[Tuple1[String]](
-    "binary logical types",
+  testSchemaInference[Tuple1[String]](
+    "string",
     """
       |message root {
       |  optional binary _1 (UTF8);
       |}
+    """.stripMargin,
+    binaryAsString = true)
+
+  testSchemaInference[Tuple1[Seq[Int]]](
+    "non-nullable array - non-standard",
+    """
+      |message root {
+      |  optional group _1 (LIST) {
+      |    repeated int32 element;
+      |  }
+      |}
     """.stripMargin)
 
-  testSchema[Tuple1[Seq[Int]]](
-    "array",
+  testSchemaInference[Tuple1[Seq[Int]]](
+    "non-nullable array - standard",
+    """
+      |message root {
+      |  optional group _1 (LIST) {
+      |    repeated group list {
+      |      required int32 element;
+      |    }
+      |  }
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchemaInference[Tuple1[Seq[Integer]]](
+    "nullable array - non-standard",
     """
       |message root {
       |  optional group _1 (LIST) {
-      |    repeated int32 array;
+      |    repeated group bag {
+      |      optional int32 element;
+      |    }
       |  }
       |}
     """.stripMargin)
 
-  testSchema[Tuple1[Map[Int, String]]](
-    "map",
+  testSchemaInference[Tuple1[Seq[Integer]]](
+    "nullable array - standard",
+    """
+      |message root {
+      |  optional group _1 (LIST) {
+      |    repeated group list {
+      |      optional int32 element;
+      |    }
+      |  }
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchemaInference[Tuple1[Map[Int, String]]](
+    "map - standard",
+    """
+      |message root {
+      |  optional group _1 (MAP) {
+      |    repeated group key_value {
+      |      required int32 key;
+      |      optional binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchemaInference[Tuple1[Map[Int, String]]](
+    "map - non-standard",
     """
       |message root {
       |  optional group _1 (MAP) {
@@ -100,7 +236,7 @@ class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
       |}
     """.stripMargin)
 
-  testSchema[Tuple1[Pair[Int, String]]](
+  testSchemaInference[Tuple1[Pair[Int, String]]](
     "struct",
     """
       |message root {
@@ -109,20 +245,21 @@ class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
       |    optional binary _2 (UTF8);
       |  }
       |}
-    """.stripMargin)
+    """.stripMargin,
+    followParquetFormatSpec = true)
 
-  testSchema[Tuple1[Map[Int, (String, Seq[(Int, Double)])]]](
-    "deeply nested type",
+  testSchemaInference[Tuple1[Map[Int, (String, Seq[(Int, Double)])]]](
+    "deeply nested type - non-standard",
     """
       |message root {
-      |  optional group _1 (MAP) {
-      |    repeated group map (MAP_KEY_VALUE) {
+      |  optional group _1 (MAP_KEY_VALUE) {
+      |    repeated group map {
       |      required int32 key;
       |      optional group value {
       |        optional binary _1 (UTF8);
       |        optional group _2 (LIST) {
       |          repeated group bag {
-      |            optional group array {
+      |            optional group element {
       |              required int32 _1;
       |              required double _2;
       |            }
@@ -134,43 +271,76 @@ class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
       |}
     """.stripMargin)
 
-  testSchema[(Option[Int], Map[Int, Option[Double]])](
-    "optional types",
+  testSchemaInference[Tuple1[Map[Int, (String, Seq[(Int, Double)])]]](
+    "deeply nested type - standard",
     """
       |message root {
-      |  optional int32 _1;
-      |  optional group _2 (MAP) {
-      |    repeated group map (MAP_KEY_VALUE) {
+      |  optional group _1 (MAP) {
+      |    repeated group key_value {
       |      required int32 key;
-      |      optional double value;
+      |      optional group value {
+      |        optional binary _1 (UTF8);
+      |        optional group _2 (LIST) {
+      |          repeated group list {
+      |            optional group element {
+      |              required int32 _1;
+      |              required double _2;
+      |            }
+      |          }
+      |        }
+      |      }
       |    }
       |  }
       |}
-    """.stripMargin)
+    """.stripMargin,
+    followParquetFormatSpec = true)
 
-  // Test for SPARK-4520 -- ensure that thrift generated parquet schema is generated
-  // as expected from attributes
-  testSchema[(Array[Byte], Array[Byte], Array[Byte], Seq[Int], Map[Array[Byte], Seq[Int]])](
-    "thrift generated parquet schema",
+  testSchemaInference[(Option[Int], Map[Int, Option[Double]])](
+    "optional types",
     """
       |message root {
-      |  optional binary _1 (UTF8);
-      |  optional binary _2 (UTF8);
-      |  optional binary _3 (UTF8);
-      |  optional group _4 (LIST) {
-      |    repeated int32 _4_tuple;
-      |  }
-      |  optional group _5 (MAP) {
-      |    repeated group map (MAP_KEY_VALUE) {
-      |      required binary key (UTF8);
-      |      optional group value (LIST) {
-      |        repeated int32 value_tuple;
-      |      }
+      |  optional int32 _1;
+      |  optional group _2 (MAP) {
+      |    repeated group key_value {
+      |      required int32 key;
+      |      optional double value;
       |    }
       |  }
       |}
-    """.stripMargin, isThriftDerived = true)
+    """.stripMargin,
+    followParquetFormatSpec = true)
 
+  // Parquet files generated by parquet-thrift are already handled by the schema converter, but
+  // let's leave this test here until both read path and write path are all updated.
+  ignore("thrift generated parquet schema") {
+    // Test for SPARK-4520 -- ensure that thrift generated parquet schema is generated
+    // as expected from attributes
+    testSchemaInference[(
+      Array[Byte], Array[Byte], Array[Byte], Seq[Int], Map[Array[Byte], Seq[Int]])](
+      "thrift generated parquet schema",
+      """
+        |message root {
+        |  optional binary _1 (UTF8);
+        |  optional binary _2 (UTF8);
+        |  optional binary _3 (UTF8);
+        |  optional group _4 (LIST) {
+        |    repeated int32 _4_tuple;
+        |  }
+        |  optional group _5 (MAP) {
+        |    repeated group map (MAP_KEY_VALUE) {
+        |      required binary key (UTF8);
+        |      optional group value (LIST) {
+        |        repeated int32 value_tuple;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+      isThriftDerived = true)
+  }
+}
+
+class ParquetSchemaSuite extends ParquetSchemaTest {
   test("DataType string parser compatibility") {
     // This is the generated string from previous versions of the Spark SQL, using the following:
     // val schema = StructType(List(
@@ -180,10 +350,7 @@ class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
       "StructType(List(StructField(c1,IntegerType,false), StructField(c2,BinaryType,true)))"
 
     // scalastyle:off
-    val jsonString =
-      """
-        |{"type":"struct","fields":[{"name":"c1","type":"integer","nullable":false,"metadata":{}},{"name":"c2","type":"binary","nullable":true,"metadata":{}}]}
-      """.stripMargin
+    val jsonString = """{"type":"struct","fields":[{"name":"c1","type":"integer","nullable":false,"metadata":{}},{"name":"c2","type":"binary","nullable":true,"metadata":{}}]}"""
     // scalastyle:on
 
     val fromCaseClassString = ParquetTypesConverter.convertFromString(caseClassString)
@@ -277,4 +444,465 @@ class ParquetSchemaSuite extends SparkFunSuite with ParquetTest {
           StructField("secondField", StringType, nullable = true))))
     }.getMessage.contains("detected conflicting schemas"))
   }
+
+  // =======================================================
+  // Tests for converting Parquet LIST to Catalyst ArrayType
+  // =======================================================
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with nullable element type - 1 - standard",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(IntegerType, containsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group list {
+      |      optional int32 element;
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with nullable element type - 2",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(IntegerType, containsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group element {
+      |      optional int32 num;
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type - 1 - standard",
+    StructType(Seq(
+      StructField("f1", ArrayType(IntegerType, containsNull = false), nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group list {
+      |      required int32 element;
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type - 2",
+    StructType(Seq(
+      StructField("f1", ArrayType(IntegerType, containsNull = false), nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group element {
+      |      required int32 num;
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type - 3",
+    StructType(Seq(
+      StructField("f1", ArrayType(IntegerType, containsNull = false), nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated int32 element;
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type - 4",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(
+          StructType(Seq(
+            StructField("str", StringType, nullable = false),
+            StructField("num", IntegerType, nullable = false))),
+          containsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group element {
+      |      required binary str (UTF8);
+      |      required int32 num;
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type - 5 - parquet-avro style",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(
+          StructType(Seq(
+            StructField("str", StringType, nullable = false))),
+          containsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group array {
+      |      required binary str (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type - 6 - parquet-thrift style",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(
+          StructType(Seq(
+            StructField("str", StringType, nullable = false))),
+          containsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group f1_tuple {
+      |      required binary str (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  // =======================================================
+  // Tests for converting Catalyst ArrayType to Parquet LIST
+  // =======================================================
+
+  testCatalystToParquet(
+    "Backwards-compatibility: LIST with nullable element type - 1 - standard",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(IntegerType, containsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group list {
+      |      optional int32 element;
+      |    }
+      |  }
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testCatalystToParquet(
+    "Backwards-compatibility: LIST with nullable element type - 2 - prior to 1.4.x",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(IntegerType, containsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group bag {
+      |      optional int32 element;
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testCatalystToParquet(
+    "Backwards-compatibility: LIST with non-nullable element type - 1 - standard",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(IntegerType, containsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated group list {
+      |      required int32 element;
+      |    }
+      |  }
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testCatalystToParquet(
+    "Backwards-compatibility: LIST with non-nullable element type - 2 - prior to 1.4.x",
+    StructType(Seq(
+      StructField(
+        "f1",
+        ArrayType(IntegerType, containsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (LIST) {
+      |    repeated int32 element;
+      |  }
+      |}
+    """.stripMargin)
+
+  // ====================================================
+  // Tests for converting Parquet Map to Catalyst MapType
+  // ====================================================
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: MAP with non-nullable value type - 1 - standard",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group key_value {
+      |      required int32 key;
+      |      required binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: MAP with non-nullable value type - 2",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP_KEY_VALUE) {
+      |    repeated group map {
+      |      required int32 num;
+      |      required binary str (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: MAP with non-nullable value type - 3 - prior to 1.4.x",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group map (MAP_KEY_VALUE) {
+      |      required int32 key;
+      |      required binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: MAP with nullable value type - 1 - standard",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group key_value {
+      |      required int32 key;
+      |      optional binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: MAP with nullable value type - 2",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP_KEY_VALUE) {
+      |    repeated group map {
+      |      required int32 num;
+      |      optional binary str (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: MAP with nullable value type - 3 - parquet-avro style",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group map (MAP_KEY_VALUE) {
+      |      required int32 key;
+      |      optional binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  // ====================================================
+  // Tests for converting Catalyst MapType to Parquet Map
+  // ====================================================
+
+  testCatalystToParquet(
+    "Backwards-compatibility: MAP with non-nullable value type - 1 - standard",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group key_value {
+      |      required int32 key;
+      |      required binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testCatalystToParquet(
+    "Backwards-compatibility: MAP with non-nullable value type - 2 - prior to 1.4.x",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = false),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group map (MAP_KEY_VALUE) {
+      |      required int32 key;
+      |      required binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  testCatalystToParquet(
+    "Backwards-compatibility: MAP with nullable value type - 1 - standard",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group key_value {
+      |      required int32 key;
+      |      optional binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testCatalystToParquet(
+    "Backwards-compatibility: MAP with nullable value type - 3 - prior to 1.4.x",
+    StructType(Seq(
+      StructField(
+        "f1",
+        MapType(IntegerType, StringType, valueContainsNull = true),
+        nullable = true))),
+    """message root {
+      |  optional group f1 (MAP) {
+      |    repeated group map (MAP_KEY_VALUE) {
+      |      required int32 key;
+      |      optional binary value (UTF8);
+      |    }
+      |  }
+      |}
+    """.stripMargin)
+
+  // =================================
+  // Tests for conversion for decimals
+  // =================================
+
+  testSchema(
+    "DECIMAL(1, 0) - standard",
+    StructType(Seq(StructField("f1", DecimalType(1, 0)))),
+    """message root {
+      |  optional int32 f1 (DECIMAL(1, 0));
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchema(
+    "DECIMAL(8, 3) - standard",
+    StructType(Seq(StructField("f1", DecimalType(8, 3)))),
+    """message root {
+      |  optional int32 f1 (DECIMAL(8, 3));
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchema(
+    "DECIMAL(9, 3) - standard",
+    StructType(Seq(StructField("f1", DecimalType(9, 3)))),
+    """message root {
+      |  optional int32 f1 (DECIMAL(9, 3));
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchema(
+    "DECIMAL(18, 3) - standard",
+    StructType(Seq(StructField("f1", DecimalType(18, 3)))),
+    """message root {
+      |  optional int64 f1 (DECIMAL(18, 3));
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchema(
+    "DECIMAL(19, 3) - standard",
+    StructType(Seq(StructField("f1", DecimalType(19, 3)))),
+    """message root {
+      |  optional fixed_len_byte_array(9) f1 (DECIMAL(19, 3));
+      |}
+    """.stripMargin,
+    followParquetFormatSpec = true)
+
+  testSchema(
+    "DECIMAL(1, 0) - prior to 1.4.x",
+    StructType(Seq(StructField("f1", DecimalType(1, 0)))),
+    """message root {
+      |  optional fixed_len_byte_array(1) f1 (DECIMAL(1, 0));
+      |}
+    """.stripMargin)
+
+  testSchema(
+    "DECIMAL(8, 3) - prior to 1.4.x",
+    StructType(Seq(StructField("f1", DecimalType(8, 3)))),
+    """message root {
+      |  optional fixed_len_byte_array(4) f1 (DECIMAL(8, 3));
+      |}
+    """.stripMargin)
+
+  testSchema(
+    "DECIMAL(9, 3) - prior to 1.4.x",
+    StructType(Seq(StructField("f1", DecimalType(9, 3)))),
+    """message root {
+      |  optional fixed_len_byte_array(5) f1 (DECIMAL(9, 3));
+      |}
+    """.stripMargin)
+
+  testSchema(
+    "DECIMAL(18, 3) - prior to 1.4.x",
+    StructType(Seq(StructField("f1", DecimalType(18, 3)))),
+    """message root {
+      |  optional fixed_len_byte_array(8) f1 (DECIMAL(18, 3));
+      |}
+    """.stripMargin)
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index a2e666586c186..f0aad8dbbe64d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -638,7 +638,7 @@ class SQLQuerySuite extends QueryTest {
   test("SPARK-5203 union with different decimal precision") {
     Seq.empty[(Decimal, Decimal)]
       .toDF("d1", "d2")
-      .select($"d1".cast(DecimalType(10, 15)).as("d"))
+      .select($"d1".cast(DecimalType(10, 5)).as("d"))
       .registerTempTable("dn")
 
     sql("select d from dn union all select d * 2 from dn")

From dca21a83ac33813dd8165acb5f20d06e4f9b9034 Mon Sep 17 00:00:00 2001
From: fe2s <aka.fe2s@gmail.com>
Date: Wed, 24 Jun 2015 15:12:23 -0700
Subject: [PATCH 0050/1454] [SPARK-8558] [BUILD] Script /dev/run-tests fails
 when _JAVA_OPTIONS env var set

Author: fe2s <aka.fe2s@gmail.com>
Author: Oleksiy Dyagilev <oleksiy_dyagilev@epam.com>

Closes #6956 from fe2s/fix-run-tests and squashes the following commits:

31b6edc [fe2s] str is a built-in function, so using it as a variable name will lead to spurious warnings in some Python linters
7d781a0 [fe2s] fixing for openjdk/IBM, seems like they have slightly different wording, but all have 'version' word. Surrounding with spaces for the case if version word appears in _JAVA_OPTIONS
cd455ef [fe2s] address comment, looking for java version string rather than expecting to have on a certain line number
ad577d7 [Oleksiy Dyagilev] [SPARK-8558][BUILD] Script /dev/run-tests fails when _JAVA_OPTIONS env var set
---
 dev/run-tests.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/dev/run-tests.py b/dev/run-tests.py
index de1b4537eda5f..e7c09b0f40cdc 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -477,7 +477,12 @@ def determine_java_version(java_exe):
 
     raw_output = subprocess.check_output([java_exe, "-version"],
                                          stderr=subprocess.STDOUT)
-    raw_version_str = raw_output.split('\n')[0]  # eg 'java version "1.8.0_25"'
+
+    raw_output_lines = raw_output.split('\n')
+
+    # find raw version string, eg 'java version "1.8.0_25"'
+    raw_version_str = next(x for x in raw_output_lines if " version " in x)
+
     version_str = raw_version_str.split()[-1].strip('"')  # eg '1.8.0_25'
     version, update = version_str.split('_')  # eg ['1.8.0', '25']
 

From 7daa70292e47be6a944351ef00c770ad4bcb0877 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 24 Jun 2015 15:52:58 -0700
Subject: [PATCH 0051/1454] [SPARK-8567] [SQL] Increase the timeout of
 HiveSparkSubmitSuite

https://issues.apache.org/jira/browse/SPARK-8567

Author: Yin Huai <yhuai@databricks.com>

Closes #6957 from yhuai/SPARK-8567 and squashes the following commits:

62dff5b [Yin Huai] Increase the timeout.
---
 .../scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index d85516ab0878e..b875e52b986ab 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -95,7 +95,7 @@ class HiveSparkSubmitSuite
     ))
 
     try {
-      val exitCode = failAfter(120 seconds) { process.exitValue() }
+      val exitCode = failAfter(180 seconds) { process.exitValue() }
       if (exitCode != 0) {
         fail(s"Process returned with exit code $exitCode. See the log4j logs for more detail.")
       }

From b71d3254e50838ccae43bdb0ff186fda25f03152 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 24 Jun 2015 16:26:00 -0700
Subject: [PATCH 0052/1454] [SPARK-8075] [SQL] apply type check interface to
 more expressions

a follow up of https://github.com/apache/spark/pull/6405.
Note: It's not a big change, a lot of changing is due to I swap some code in `aggregates.scala` to make aggregate functions right below its corresponding aggregate expressions.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #6723 from cloud-fan/type-check and squashes the following commits:

2124301 [Wenchen Fan] fix tests
5a658bb [Wenchen Fan] add tests
287d3bb [Wenchen Fan] apply type check interface to more expressions
---
 .../sql/catalyst/analysis/Analyzer.scala      |   4 +-
 .../catalyst/analysis/HiveTypeCoercion.scala  |  17 +-
 .../spark/sql/catalyst/expressions/Cast.scala |  11 +-
 .../sql/catalyst/expressions/Expression.scala |   4 +-
 .../catalyst/expressions/ExtractValue.scala   |  10 +-
 .../sql/catalyst/expressions/aggregates.scala | 420 +++++++++---------
 .../sql/catalyst/expressions/arithmetic.scala |   2 -
 .../expressions/complexTypeCreator.scala      |  30 +-
 .../expressions/decimalFunctions.scala        |  17 +-
 .../sql/catalyst/expressions/generators.scala |  13 +-
 .../spark/sql/catalyst/expressions/math.scala |   4 +-
 .../expressions/namedExpressions.scala        |   4 +-
 .../catalyst/expressions/nullFunctions.scala  |  27 +-
 .../spark/sql/catalyst/expressions/sets.scala |  10 +-
 .../expressions/stringOperations.scala        |   2 -
 .../expressions/windowExpressions.scala       |   3 +-
 .../spark/sql/catalyst/util/TypeUtils.scala   |   9 +
 .../sql/catalyst/analysis/AnalysisSuite.scala |   6 +-
 .../ExpressionTypeCheckingSuite.scala         |  26 +-
 .../spark/sql/execution/pythonUdfs.scala      |   2 +-
 .../execution/HiveTypeCoercionSuite.scala     |   6 -
 21 files changed, 337 insertions(+), 290 deletions(-)
 rename sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/{expressions => analysis}/ExpressionTypeCheckingSuite.scala (84%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index b06759f144fd9..cad2c2abe6b1a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -587,8 +587,8 @@ class Analyzer(
           failAnalysis(
             s"""Expect multiple names given for ${g.getClass.getName},
                |but only single name '${name}' specified""".stripMargin)
-        case Alias(g: Generator, name) => Some((g, name :: Nil))
-        case MultiAlias(g: Generator, names) => Some(g, names)
+        case Alias(g: Generator, name) if g.resolved => Some((g, name :: Nil))
+        case MultiAlias(g: Generator, names) if g.resolved => Some(g, names)
         case _ => None
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index d4ab1fc643c33..4ef7341a33245 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -317,6 +317,7 @@ trait HiveTypeCoercion {
         i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
 
       case Sum(e @ StringType()) => Sum(Cast(e, DoubleType))
+      case SumDistinct(e @ StringType()) => Sum(Cast(e, DoubleType))
       case Average(e @ StringType()) => Average(Cast(e, DoubleType))
     }
   }
@@ -590,11 +591,12 @@ trait HiveTypeCoercion {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      case a @ CreateArray(children) if !a.resolved =>
-        val commonType = a.childTypes.reduce(
-          (a, b) => findTightestCommonTypeOfTwo(a, b).getOrElse(StringType))
-        CreateArray(
-          children.map(c => if (c.dataType == commonType) c else Cast(c, commonType)))
+      case a @ CreateArray(children) if children.map(_.dataType).distinct.size > 1 =>
+        val types = children.map(_.dataType)
+        findTightestCommonTypeAndPromoteToString(types) match {
+          case Some(finalDataType) => CreateArray(children.map(Cast(_, finalDataType)))
+          case None => a
+        }
 
       // Promote SUM, SUM DISTINCT and AVERAGE to largest types to prevent overflows.
       case s @ Sum(e @ DecimalType()) => s // Decimal is already the biggest.
@@ -620,12 +622,11 @@ trait HiveTypeCoercion {
       // Coalesce should return the first non-null value, which could be any column
       // from the list. So we need to make sure the return type is deterministic and
       // compatible with every child column.
-      case Coalesce(es) if es.map(_.dataType).distinct.size > 1 =>
+      case c @ Coalesce(es) if es.map(_.dataType).distinct.size > 1 =>
         val types = es.map(_.dataType)
         findTightestCommonTypeAndPromoteToString(types) match {
           case Some(finalDataType) => Coalesce(es.map(Cast(_, finalDataType)))
-          case None =>
-            sys.error(s"Could not determine return type of Coalesce for ${types.mkString(",")}")
+          case None => c
         }
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index d271434a306dd..8bd7fc18a8dd4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -22,7 +22,7 @@ import java.sql.{Date, Timestamp}
 import java.text.{DateFormat, SimpleDateFormat}
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
@@ -31,7 +31,14 @@ import org.apache.spark.unsafe.types.UTF8String
 /** Cast the child expression to the target data type. */
 case class Cast(child: Expression, dataType: DataType) extends UnaryExpression with Logging {
 
-  override lazy val resolved = childrenResolved && resolve(child.dataType, dataType)
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (resolve(child.dataType, dataType)) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(
+        s"cannot cast ${child.dataType} to $dataType")
+    }
+  }
 
   override def foldable: Boolean = child.foldable
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index a10a959ae766f..f59db3d5dfc23 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -162,9 +162,7 @@ abstract class Expression extends TreeNode[Expression] {
   /**
    * Checks the input data types, returns `TypeCheckResult.success` if it's valid,
    * or returns a `TypeCheckResult` with an error message if invalid.
-   * Note: it's not valid to call this method until `childrenResolved == true`
-   * TODO: we should remove the default implementation and implement it for all
-   * expressions with proper error message.
+   * Note: it's not valid to call this method until `childrenResolved == true`.
    */
   def checkInputDataTypes(): TypeCheckResult = TypeCheckResult.TypeCheckSuccess
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
index 4d6c1c265150d..4d7c95ffd1850 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
@@ -96,6 +96,11 @@ object ExtractValue {
   }
 }
 
+/**
+ * A common interface of all kinds of extract value expressions.
+ * Note: concrete extract value expressions are created only by `ExtractValue.apply`,
+ * we don't need to do type check for them.
+ */
 trait ExtractValue extends UnaryExpression {
   self: Product =>
 }
@@ -179,9 +184,6 @@ case class GetArrayItem(child: Expression, ordinal: Expression)
 
   override def dataType: DataType = child.dataType.asInstanceOf[ArrayType].elementType
 
-  override lazy val resolved = childrenResolved &&
-    child.dataType.isInstanceOf[ArrayType] && ordinal.dataType.isInstanceOf[IntegralType]
-
   protected def evalNotNull(value: Any, ordinal: Any) = {
     // TODO: consider using Array[_] for ArrayType child to avoid
     // boxing of primitives
@@ -203,8 +205,6 @@ case class GetMapValue(child: Expression, ordinal: Expression)
 
   override def dataType: DataType = child.dataType.asInstanceOf[MapType].valueType
 
-  override lazy val resolved = childrenResolved && child.dataType.isInstanceOf[MapType]
-
   protected def evalNotNull(value: Any, ordinal: Any) = {
     val baseValue = value.asInstanceOf[Map[Any, _]]
     baseValue.get(ordinal).orNull
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 00d2e499c5890..a9fc54c548f49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -19,9 +19,10 @@ package org.apache.spark.sql.catalyst.expressions
 
 import com.clearspring.analytics.stream.cardinality.HyperLogLog
 
-import org.apache.spark.sql.catalyst
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.trees
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
 
@@ -101,6 +102,9 @@ case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[
   }
 
   override def newInstance(): MinFunction = new MinFunction(child, this)
+
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForOrderingExpr(child.dataType, "function min")
 }
 
 case class MinFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
@@ -132,6 +136,9 @@ case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[
   }
 
   override def newInstance(): MaxFunction = new MaxFunction(child, this)
+
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForOrderingExpr(child.dataType, "function max")
 }
 
 case class MaxFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
@@ -165,6 +172,21 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod
   override def newInstance(): CountFunction = new CountFunction(child, this)
 }
 
+case class CountFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+  def this() = this(null, null) // Required for serialization.
+
+  var count: Long = _
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      count += 1L
+    }
+  }
+
+  override def eval(input: InternalRow): Any = count
+}
+
 case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate {
   def this() = this(null)
 
@@ -183,6 +205,28 @@ case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate
   }
 }
 
+case class CountDistinctFunction(
+    @transient expr: Seq[Expression],
+    @transient base: AggregateExpression)
+  extends AggregateFunction {
+
+  def this() = this(null, null) // Required for serialization.
+
+  val seen = new OpenHashSet[Any]()
+
+  @transient
+  val distinctValue = new InterpretedProjection(expr)
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = distinctValue(input)
+    if (!evaluatedExpr.anyNull) {
+      seen.add(evaluatedExpr)
+    }
+  }
+
+  override def eval(input: InternalRow): Any = seen.size.toLong
+}
+
 case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpression {
   def this() = this(null)
 
@@ -278,6 +322,25 @@ case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
   }
 }
 
+case class ApproxCountDistinctPartitionFunction(
+    expr: Expression,
+    base: AggregateExpression,
+    relativeSD: Double)
+  extends AggregateFunction {
+  def this() = this(null, null, 0) // Required for serialization.
+
+  private val hyperLogLog = new HyperLogLog(relativeSD)
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      hyperLogLog.offer(evaluatedExpr)
+    }
+  }
+
+  override def eval(input: InternalRow): Any = hyperLogLog
+}
+
 case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
   extends AggregateExpression with trees.UnaryNode[Expression] {
 
@@ -289,6 +352,23 @@ case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
   }
 }
 
+case class ApproxCountDistinctMergeFunction(
+    expr: Expression,
+    base: AggregateExpression,
+    relativeSD: Double)
+  extends AggregateFunction {
+  def this() = this(null, null, 0) // Required for serialization.
+
+  private val hyperLogLog = new HyperLogLog(relativeSD)
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    hyperLogLog.addAll(evaluatedExpr.asInstanceOf[HyperLogLog])
+  }
+
+  override def eval(input: InternalRow): Any = hyperLogLog.cardinality()
+}
+
 case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
   extends PartialAggregate with trees.UnaryNode[Expression] {
 
@@ -349,159 +429,9 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN
   }
 
   override def newInstance(): AverageFunction = new AverageFunction(child, this)
-}
-
-case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-
-  override def nullable: Boolean = true
-
-  override def dataType: DataType = child.dataType match {
-    case DecimalType.Fixed(precision, scale) =>
-      DecimalType(precision + 10, scale)  // Add 10 digits left of decimal point, like Hive
-    case DecimalType.Unlimited =>
-      DecimalType.Unlimited
-    case _ =>
-      child.dataType
-  }
-
-  override def toString: String = s"SUM($child)"
-
-  override def asPartial: SplitEvaluation = {
-    child.dataType match {
-      case DecimalType.Fixed(_, _) =>
-        val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")()
-        SplitEvaluation(
-          Cast(CombineSum(partialSum.toAttribute), dataType),
-          partialSum :: Nil)
-
-      case _ =>
-        val partialSum = Alias(Sum(child), "PartialSum")()
-        SplitEvaluation(
-          CombineSum(partialSum.toAttribute),
-          partialSum :: Nil)
-    }
-  }
-
-  override def newInstance(): SumFunction = new SumFunction(child, this)
-}
-
-/**
- * Sum should satisfy 3 cases:
- * 1) sum of all null values = zero
- * 2) sum for table column with no data = null
- * 3) sum of column with null and not null values = sum of not null values
- * Require separate CombineSum Expression and function as it has to distinguish "No data" case
- * versus "data equals null" case, while aggregating results and at each partial expression.i.e.,
- * Combining    PartitionLevel   InputData
- *                           <-- null
- * Zero     <-- Zero         <-- null
- *
- *          <-- null         <-- no data
- * null     <-- null         <-- no data
- */
-case class CombineSum(child: Expression) extends AggregateExpression {
-  def this() = this(null)
-
-  override def children: Seq[Expression] = child :: Nil
-  override def nullable: Boolean = true
-  override def dataType: DataType = child.dataType
-  override def toString: String = s"CombineSum($child)"
-  override def newInstance(): CombineSumFunction = new CombineSumFunction(child, this)
-}
-
-case class SumDistinct(child: Expression)
-  extends PartialAggregate with trees.UnaryNode[Expression] {
-
-  def this() = this(null)
-  override def nullable: Boolean = true
-  override def dataType: DataType = child.dataType match {
-    case DecimalType.Fixed(precision, scale) =>
-      DecimalType(precision + 10, scale)  // Add 10 digits left of decimal point, like Hive
-    case DecimalType.Unlimited =>
-      DecimalType.Unlimited
-    case _ =>
-      child.dataType
-  }
-  override def toString: String = s"SUM(DISTINCT $child)"
-  override def newInstance(): SumDistinctFunction = new SumDistinctFunction(child, this)
-
-  override def asPartial: SplitEvaluation = {
-    val partialSet = Alias(CollectHashSet(child :: Nil), "partialSets")()
-    SplitEvaluation(
-      CombineSetsAndSum(partialSet.toAttribute, this),
-      partialSet :: Nil)
-  }
-}
 
-case class CombineSetsAndSum(inputSet: Expression, base: Expression) extends AggregateExpression {
-  def this() = this(null, null)
-
-  override def children: Seq[Expression] = inputSet :: Nil
-  override def nullable: Boolean = true
-  override def dataType: DataType = base.dataType
-  override def toString: String = s"CombineAndSum($inputSet)"
-  override def newInstance(): CombineSetsAndSumFunction = {
-    new CombineSetsAndSumFunction(inputSet, this)
-  }
-}
-
-case class CombineSetsAndSumFunction(
-    @transient inputSet: Expression,
-    @transient base: AggregateExpression)
-  extends AggregateFunction {
-
-  def this() = this(null, null) // Required for serialization.
-
-  val seen = new OpenHashSet[Any]()
-
-  override def update(input: InternalRow): Unit = {
-    val inputSetEval = inputSet.eval(input).asInstanceOf[OpenHashSet[Any]]
-    val inputIterator = inputSetEval.iterator
-    while (inputIterator.hasNext) {
-      seen.add(inputIterator.next)
-    }
-  }
-
-  override def eval(input: InternalRow): Any = {
-    val casted = seen.asInstanceOf[OpenHashSet[InternalRow]]
-    if (casted.size == 0) {
-      null
-    } else {
-      Cast(Literal(
-        casted.iterator.map(f => f.apply(0)).reduceLeft(
-          base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus)),
-        base.dataType).eval(null)
-    }
-  }
-}
-
-case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def nullable: Boolean = true
-  override def dataType: DataType = child.dataType
-  override def toString: String = s"FIRST($child)"
-
-  override def asPartial: SplitEvaluation = {
-    val partialFirst = Alias(First(child), "PartialFirst")()
-    SplitEvaluation(
-      First(partialFirst.toAttribute),
-      partialFirst :: Nil)
-  }
-  override def newInstance(): FirstFunction = new FirstFunction(child, this)
-}
-
-case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references: AttributeSet = child.references
-  override def nullable: Boolean = true
-  override def dataType: DataType = child.dataType
-  override def toString: String = s"LAST($child)"
-
-  override def asPartial: SplitEvaluation = {
-    val partialLast = Alias(Last(child), "PartialLast")()
-    SplitEvaluation(
-      Last(partialLast.toAttribute),
-      partialLast :: Nil)
-  }
-  override def newInstance(): LastFunction = new LastFunction(child, this)
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForNumericExpr(child.dataType, "function average")
 }
 
 case class AverageFunction(expr: Expression, base: AggregateExpression)
@@ -551,55 +481,41 @@ case class AverageFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
-case class CountFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
-  def this() = this(null, null) // Required for serialization.
+case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
 
-  var count: Long = _
+  override def nullable: Boolean = true
 
-  override def update(input: InternalRow): Unit = {
-    val evaluatedExpr = expr.eval(input)
-    if (evaluatedExpr != null) {
-      count += 1L
-    }
+  override def dataType: DataType = child.dataType match {
+    case DecimalType.Fixed(precision, scale) =>
+      DecimalType(precision + 10, scale)  // Add 10 digits left of decimal point, like Hive
+    case DecimalType.Unlimited =>
+      DecimalType.Unlimited
+    case _ =>
+      child.dataType
   }
 
-  override def eval(input: InternalRow): Any = count
-}
-
-case class ApproxCountDistinctPartitionFunction(
-    expr: Expression,
-    base: AggregateExpression,
-    relativeSD: Double)
-  extends AggregateFunction {
-  def this() = this(null, null, 0) // Required for serialization.
+  override def toString: String = s"SUM($child)"
 
-  private val hyperLogLog = new HyperLogLog(relativeSD)
+  override def asPartial: SplitEvaluation = {
+    child.dataType match {
+      case DecimalType.Fixed(_, _) =>
+        val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")()
+        SplitEvaluation(
+          Cast(CombineSum(partialSum.toAttribute), dataType),
+          partialSum :: Nil)
 
-  override def update(input: InternalRow): Unit = {
-    val evaluatedExpr = expr.eval(input)
-    if (evaluatedExpr != null) {
-      hyperLogLog.offer(evaluatedExpr)
+      case _ =>
+        val partialSum = Alias(Sum(child), "PartialSum")()
+        SplitEvaluation(
+          CombineSum(partialSum.toAttribute),
+          partialSum :: Nil)
     }
   }
 
-  override def eval(input: InternalRow): Any = hyperLogLog
-}
-
-case class ApproxCountDistinctMergeFunction(
-    expr: Expression,
-    base: AggregateExpression,
-    relativeSD: Double)
-  extends AggregateFunction {
-  def this() = this(null, null, 0) // Required for serialization.
-
-  private val hyperLogLog = new HyperLogLog(relativeSD)
-
-  override def update(input: InternalRow): Unit = {
-    val evaluatedExpr = expr.eval(input)
-    hyperLogLog.addAll(evaluatedExpr.asInstanceOf[HyperLogLog])
-  }
+  override def newInstance(): SumFunction = new SumFunction(child, this)
 
-  override def eval(input: InternalRow): Any = hyperLogLog.cardinality()
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForNumericExpr(child.dataType, "function sum")
 }
 
 case class SumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
@@ -633,6 +549,30 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr
   }
 }
 
+/**
+ * Sum should satisfy 3 cases:
+ * 1) sum of all null values = zero
+ * 2) sum for table column with no data = null
+ * 3) sum of column with null and not null values = sum of not null values
+ * Require separate CombineSum Expression and function as it has to distinguish "No data" case
+ * versus "data equals null" case, while aggregating results and at each partial expression.i.e.,
+ * Combining    PartitionLevel   InputData
+ *                           <-- null
+ * Zero     <-- Zero         <-- null
+ *
+ *          <-- null         <-- no data
+ * null     <-- null         <-- no data
+ */
+case class CombineSum(child: Expression) extends AggregateExpression {
+  def this() = this(null)
+
+  override def children: Seq[Expression] = child :: Nil
+  override def nullable: Boolean = true
+  override def dataType: DataType = child.dataType
+  override def toString: String = s"CombineSum($child)"
+  override def newInstance(): CombineSumFunction = new CombineSumFunction(child, this)
+}
+
 case class CombineSumFunction(expr: Expression, base: AggregateExpression)
   extends AggregateFunction {
 
@@ -670,6 +610,33 @@ case class CombineSumFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
+case class SumDistinct(child: Expression)
+  extends PartialAggregate with trees.UnaryNode[Expression] {
+
+  def this() = this(null)
+  override def nullable: Boolean = true
+  override def dataType: DataType = child.dataType match {
+    case DecimalType.Fixed(precision, scale) =>
+      DecimalType(precision + 10, scale)  // Add 10 digits left of decimal point, like Hive
+    case DecimalType.Unlimited =>
+      DecimalType.Unlimited
+    case _ =>
+      child.dataType
+  }
+  override def toString: String = s"SUM(DISTINCT $child)"
+  override def newInstance(): SumDistinctFunction = new SumDistinctFunction(child, this)
+
+  override def asPartial: SplitEvaluation = {
+    val partialSet = Alias(CollectHashSet(child :: Nil), "partialSets")()
+    SplitEvaluation(
+      CombineSetsAndSum(partialSet.toAttribute, this),
+      partialSet :: Nil)
+  }
+
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForNumericExpr(child.dataType, "function sumDistinct")
+}
+
 case class SumDistinctFunction(expr: Expression, base: AggregateExpression)
   extends AggregateFunction {
 
@@ -696,8 +663,20 @@ case class SumDistinctFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
-case class CountDistinctFunction(
-    @transient expr: Seq[Expression],
+case class CombineSetsAndSum(inputSet: Expression, base: Expression) extends AggregateExpression {
+  def this() = this(null, null)
+
+  override def children: Seq[Expression] = inputSet :: Nil
+  override def nullable: Boolean = true
+  override def dataType: DataType = base.dataType
+  override def toString: String = s"CombineAndSum($inputSet)"
+  override def newInstance(): CombineSetsAndSumFunction = {
+    new CombineSetsAndSumFunction(inputSet, this)
+  }
+}
+
+case class CombineSetsAndSumFunction(
+    @transient inputSet: Expression,
     @transient base: AggregateExpression)
   extends AggregateFunction {
 
@@ -705,17 +684,39 @@ case class CountDistinctFunction(
 
   val seen = new OpenHashSet[Any]()
 
-  @transient
-  val distinctValue = new InterpretedProjection(expr)
-
   override def update(input: InternalRow): Unit = {
-    val evaluatedExpr = distinctValue(input)
-    if (!evaluatedExpr.anyNull) {
-      seen.add(evaluatedExpr)
+    val inputSetEval = inputSet.eval(input).asInstanceOf[OpenHashSet[Any]]
+    val inputIterator = inputSetEval.iterator
+    while (inputIterator.hasNext) {
+      seen.add(inputIterator.next)
     }
   }
 
-  override def eval(input: InternalRow): Any = seen.size.toLong
+  override def eval(input: InternalRow): Any = {
+    val casted = seen.asInstanceOf[OpenHashSet[InternalRow]]
+    if (casted.size == 0) {
+      null
+    } else {
+      Cast(Literal(
+        casted.iterator.map(f => f.apply(0)).reduceLeft(
+          base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus)),
+        base.dataType).eval(null)
+    }
+  }
+}
+
+case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+  override def nullable: Boolean = true
+  override def dataType: DataType = child.dataType
+  override def toString: String = s"FIRST($child)"
+
+  override def asPartial: SplitEvaluation = {
+    val partialFirst = Alias(First(child), "PartialFirst")()
+    SplitEvaluation(
+      First(partialFirst.toAttribute),
+      partialFirst :: Nil)
+  }
+  override def newInstance(): FirstFunction = new FirstFunction(child, this)
 }
 
 case class FirstFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
@@ -732,6 +733,21 @@ case class FirstFunction(expr: Expression, base: AggregateExpression) extends Ag
   override def eval(input: InternalRow): Any = result
 }
 
+case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+  override def references: AttributeSet = child.references
+  override def nullable: Boolean = true
+  override def dataType: DataType = child.dataType
+  override def toString: String = s"LAST($child)"
+
+  override def asPartial: SplitEvaluation = {
+    val partialLast = Alias(Last(child), "PartialLast")()
+    SplitEvaluation(
+      Last(partialLast.toAttribute),
+      partialLast :: Nil)
+  }
+  override def newInstance(): LastFunction = new LastFunction(child, this)
+}
+
 case class LastFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
   def this() = this(null, null) // Required for serialization.
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index ace8427c8ddaf..3d4d9e2d798f0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -25,8 +25,6 @@ import org.apache.spark.sql.types._
 abstract class UnaryArithmetic extends UnaryExpression {
   self: Product =>
 
-  override def foldable: Boolean = child.foldable
-  override def nullable: Boolean = child.nullable
   override def dataType: DataType = child.dataType
 
   override def eval(input: InternalRow): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index e0bf07ed182f3..5def57b067424 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
-
 /**
  * Returns an Array containing the evaluation of all children expressions.
  */
@@ -27,15 +28,12 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
-  lazy val childTypes = children.map(_.dataType).distinct
-
-  override lazy val resolved =
-    childrenResolved && childTypes.size <= 1
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForSameTypeInputExpr(children.map(_.dataType), "function array")
 
   override def dataType: DataType = {
-    assert(resolved, s"Invalid dataType of mixed ArrayType ${childTypes.mkString(",")}")
     ArrayType(
-      childTypes.headOption.getOrElse(NullType),
+      children.headOption.map(_.dataType).getOrElse(NullType),
       containsNull = children.exists(_.nullable))
   }
 
@@ -56,19 +54,15 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
-  override lazy val resolved: Boolean = childrenResolved
-
   override lazy val dataType: StructType = {
-    assert(resolved,
-      s"CreateStruct contains unresolvable children: ${children.filterNot(_.resolved)}.")
-      val fields = children.zipWithIndex.map { case (child, idx) =>
-        child match {
-          case ne: NamedExpression =>
-            StructField(ne.name, ne.dataType, ne.nullable, ne.metadata)
-          case _ =>
-            StructField(s"col${idx + 1}", child.dataType, child.nullable, Metadata.empty)
-        }
+    val fields = children.zipWithIndex.map { case (child, idx) =>
+      child match {
+        case ne: NamedExpression =>
+          StructField(ne.name, ne.dataType, ne.nullable, ne.metadata)
+        case _ =>
+          StructField(s"col${idx + 1}", child.dataType, child.nullable, Metadata.empty)
       }
+    }
     StructType(fields)
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
index 2bc893af02641..f5c2dde191cf3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
@@ -17,16 +17,17 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 
-/** Return the unscaled Long value of a Decimal, assuming it fits in a Long */
+/**
+ * Return the unscaled Long value of a Decimal, assuming it fits in a Long.
+ * Note: this expression is internal and created only by the optimizer,
+ * we don't need to do type check for it.
+ */
 case class UnscaledValue(child: Expression) extends UnaryExpression {
 
   override def dataType: DataType = LongType
-  override def foldable: Boolean = child.foldable
-  override def nullable: Boolean = child.nullable
   override def toString: String = s"UnscaledValue($child)"
 
   override def eval(input: InternalRow): Any = {
@@ -43,12 +44,14 @@ case class UnscaledValue(child: Expression) extends UnaryExpression {
   }
 }
 
-/** Create a Decimal from an unscaled Long value */
+/**
+ * Create a Decimal from an unscaled Long value.
+ * Note: this expression is internal and created only by the optimizer,
+ * we don't need to do type check for it.
+ */
 case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends UnaryExpression {
 
   override def dataType: DataType = DecimalType(precision, scale)
-  override def foldable: Boolean = child.foldable
-  override def nullable: Boolean = child.nullable
   override def toString: String = s"MakeDecimal($child,$precision,$scale)"
 
   override def eval(input: InternalRow): Decimal = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index f30cb42d12b83..356560e54cae3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import scala.collection.Map
 
-import org.apache.spark.sql.catalyst
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees}
 import org.apache.spark.sql.types._
 
@@ -100,9 +100,14 @@ case class UserDefinedGenerator(
 case class Explode(child: Expression)
   extends Generator with trees.UnaryNode[Expression] {
 
-  override lazy val resolved =
-    child.resolved &&
-    (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType])
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType]) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(
+        s"input to function explode should be array or map type, not ${child.dataType}")
+    }
+  }
 
   override def elementTypes: Seq[(DataType, Boolean)] = child.dataType match {
     case ArrayType(et, containsNull) => (et, containsNull) :: Nil
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 250564dc4b818..5694afc61be05 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.lang.{Long => JLong}
 
-import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types.{DataType, DoubleType, LongType, StringType}
 import org.apache.spark.unsafe.types.UTF8String
@@ -60,7 +59,6 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
 
   override def expectedChildTypes: Seq[DataType] = Seq(DoubleType)
   override def dataType: DataType = DoubleType
-  override def foldable: Boolean = child.foldable
   override def nullable: Boolean = true
   override def toString: String = s"$name($child)"
 
@@ -224,7 +222,7 @@ case class Bin(child: Expression)
 
   def funcName: String = name.toLowerCase
 
-  override def eval(input: catalyst.InternalRow): Any = {
+  override def eval(input: InternalRow): Any = {
     val evalE = child.eval(input)
     if (evalE == null) {
       null
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 9cacdceb13837..6f56a9ec7beb5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
@@ -113,7 +112,8 @@ case class Alias(child: Expression, name: String)(
   extends NamedExpression with trees.UnaryNode[Expression] {
 
   // Alias(Generator, xx) need to be transformed into Generate(generator, ...)
-  override lazy val resolved = childrenResolved && !child.isInstanceOf[Generator]
+  override lazy val resolved =
+    childrenResolved && checkInputDataTypes().isSuccess && !child.isInstanceOf[Generator]
 
   override def eval(input: InternalRow): Any = child.eval(input)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 98acaf23c44c1..5d5911403ece1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -17,33 +17,32 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.analysis.UnresolvedException
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types.DataType
 
 case class Coalesce(children: Seq[Expression]) extends Expression {
 
   /** Coalesce is nullable if all of its children are nullable, or if it has no children. */
-  override def nullable: Boolean = !children.exists(!_.nullable)
+  override def nullable: Boolean = children.forall(_.nullable)
 
   // Coalesce is foldable if all children are foldable.
-  override def foldable: Boolean = !children.exists(!_.foldable)
+  override def foldable: Boolean = children.forall(_.foldable)
 
-  // Only resolved if all the children are of the same type.
-  override lazy val resolved = childrenResolved && (children.map(_.dataType).distinct.size == 1)
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (children == Nil) {
+      TypeCheckResult.TypeCheckFailure("input to function coalesce cannot be empty")
+    } else {
+      TypeUtils.checkForSameTypeInputExpr(children.map(_.dataType), "function coalesce")
+    }
+  }
 
   override def toString: String = s"Coalesce(${children.mkString(",")})"
 
-  override def dataType: DataType = if (resolved) {
-    children.head.dataType
-  } else {
-    val childTypes = children.map(c => s"$c: ${c.dataType}").mkString(", ")
-    throw new UnresolvedException(
-      this, s"Coalesce cannot have children of different types. $childTypes")
-  }
+  override def dataType: DataType = children.head.dataType
 
   override def eval(input: InternalRow): Any = {
-    var i = 0
     var result: Any = null
     val childIterator = children.iterator
     while (childIterator.hasNext && result == null) {
@@ -75,7 +74,6 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
 }
 
 case class IsNull(child: Expression) extends UnaryExpression with Predicate {
-  override def foldable: Boolean = child.foldable
   override def nullable: Boolean = false
 
   override def eval(input: InternalRow): Any = {
@@ -93,7 +91,6 @@ case class IsNull(child: Expression) extends UnaryExpression with Predicate {
 }
 
 case class IsNotNull(child: Expression) extends UnaryExpression with Predicate {
-  override def foldable: Boolean = child.foldable
   override def nullable: Boolean = false
   override def toString: String = s"IS NOT NULL $child"
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index 30e41677b774b..efc6f50b78943 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -78,6 +78,8 @@ case class NewSet(elementType: DataType) extends LeafExpression {
 /**
  * Adds an item to a set.
  * For performance, this expression mutates its input during evaluation.
+ * Note: this expression is internal and created only by the GeneratedAggregate,
+ * we don't need to do type check for it.
  */
 case class AddItemToSet(item: Expression, set: Expression) extends Expression {
 
@@ -85,7 +87,7 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression {
 
   override def nullable: Boolean = set.nullable
 
-  override def dataType: OpenHashSetUDT = set.dataType.asInstanceOf[OpenHashSetUDT]
+  override def dataType: DataType = set.dataType
 
   override def eval(input: InternalRow): Any = {
     val itemEval = item.eval(input)
@@ -128,12 +130,14 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression {
 /**
  * Combines the elements of two sets.
  * For performance, this expression mutates its left input set during evaluation.
+ * Note: this expression is internal and created only by the GeneratedAggregate,
+ * we don't need to do type check for it.
  */
 case class CombineSets(left: Expression, right: Expression) extends BinaryExpression {
 
   override def nullable: Boolean = left.nullable || right.nullable
 
-  override def dataType: OpenHashSetUDT = left.dataType.asInstanceOf[OpenHashSetUDT]
+  override def dataType: DataType = left.dataType
 
   override def symbol: String = "++="
 
@@ -176,6 +180,8 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres
 
 /**
  * Returns the number of elements in the input set.
+ * Note: this expression is internal and created only by the GeneratedAggregate,
+ * we don't need to do type check for it.
  */
 case class CountSet(child: Expression) extends UnaryExpression {
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 315c63e63c635..44416e79cd7aa 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -117,8 +117,6 @@ trait CaseConversionExpression extends ExpectsInputTypes {
 
   def convert(v: UTF8String): UTF8String
 
-  override def foldable: Boolean = child.foldable
-  override def nullable: Boolean = child.nullable
   override def dataType: DataType = StringType
   override def expectedChildTypes: Seq[DataType] = Seq(StringType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
index 896e383f50eac..12023ad311dc8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
@@ -68,7 +68,8 @@ case class WindowSpecDefinition(
   override def children: Seq[Expression] = partitionSpec ++ orderSpec
 
   override lazy val resolved: Boolean =
-    childrenResolved && frameSpecification.isInstanceOf[SpecifiedWindowFrame]
+    childrenResolved && checkInputDataTypes().isSuccess &&
+      frameSpecification.isInstanceOf[SpecifiedWindowFrame]
 
 
   override def toString: String = simpleString
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
index 04857a23f4c1e..8656cc334d09f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
@@ -48,6 +48,15 @@ object TypeUtils {
     }
   }
 
+  def checkForSameTypeInputExpr(types: Seq[DataType], caller: String): TypeCheckResult = {
+    if (types.distinct.size > 1) {
+      TypeCheckResult.TypeCheckFailure(
+        s"input to $caller should all be the same type, but it's ${types.mkString("[", ", ", "]")}")
+    } else {
+      TypeCheckResult.TypeCheckSuccess
+    }
+  }
+
   def getNumeric(t: DataType): Numeric[Any] =
     t.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]]
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index e09cd790a7187..77ca080f366cd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -193,7 +193,7 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
   errorTest(
     "bad casts",
     testRelation.select(Literal(1).cast(BinaryType).as('badCast)),
-    "invalid cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil)
+    "cannot cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil)
 
   errorTest(
     "non-boolean filters",
@@ -264,9 +264,9 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
     val plan =
       Aggregate(
         Nil,
-        Alias(Sum(AttributeReference("a", StringType)(exprId = ExprId(1))), "b")() :: Nil,
+        Alias(Sum(AttributeReference("a", IntegerType)(exprId = ExprId(1))), "b")() :: Nil,
         LocalRelation(
-          AttributeReference("a", StringType)(exprId = ExprId(2))))
+          AttributeReference("a", IntegerType)(exprId = ExprId(2))))
 
     assert(plan.resolved)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
similarity index 84%
rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionTypeCheckingSuite.scala
rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index 49b111989799b..bc1537b0715b5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.catalyst.expressions
+package org.apache.spark.sql.catalyst.analysis
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.analysis.SimpleAnalyzer
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
 import org.apache.spark.sql.types.StringType
 
@@ -136,6 +136,28 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertError(
       CaseWhen(Seq('booleanField, 'intField, 'intField, 'intField)),
       "WHEN expressions in CaseWhen should all be boolean type")
+  }
+
+  test("check types for aggregates") {
+    // We will cast String to Double for sum and average
+    assertSuccess(Sum('stringField))
+    assertSuccess(SumDistinct('stringField))
+    assertSuccess(Average('stringField))
+
+    assertError(Min('complexField), "function min accepts non-complex type")
+    assertError(Max('complexField), "function max accepts non-complex type")
+    assertError(Sum('booleanField), "function sum accepts numeric type")
+    assertError(SumDistinct('booleanField), "function sumDistinct accepts numeric type")
+    assertError(Average('booleanField), "function average accepts numeric type")
+  }
 
+  test("check types for others") {
+    assertError(CreateArray(Seq('intField, 'booleanField)),
+      "input to function array should all be the same type")
+    assertError(Coalesce(Seq('intField, 'booleanField)),
+      "input to function coalesce should all be the same type")
+    assertError(Coalesce(Nil), "input to function coalesce cannot be empty")
+    assertError(Explode('intField),
+      "input to function explode should be array or map type")
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
index 6db551c543a9c..f9c3fe92c2670 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
@@ -55,7 +55,7 @@ private[spark] case class PythonUDF(
 
   override def toString: String = s"PythonUDF#$name(${children.mkString(",")})"
 
-  def nullable: Boolean = true
+  override def nullable: Boolean = true
 
   override def eval(input: InternalRow): Any = {
     throw new UnsupportedOperationException("PythonUDFs can not be directly evaluated.")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
index f0f04f8c73fb4..197e9bfb02c4e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
@@ -59,10 +59,4 @@ class HiveTypeCoercionSuite extends HiveComparisonTest {
     }
     assert(numEquals === 1)
   }
-
-  test("COALESCE with different types") {
-    intercept[RuntimeException] {
-      TestHive.sql("""SELECT COALESCE(1, true, "abc") FROM src limit 1""").collect()
-    }
-  }
 }

From 82f80c1c7dc42c11bca2b6832c10f9610a43391b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 24 Jun 2015 19:34:07 -0700
Subject: [PATCH 0053/1454] Two minor SQL cleanup (compiler warning & indent).

Author: Reynold Xin <rxin@databricks.com>

Closes #7000 from rxin/minor-cleanup and squashes the following commits:

046044c [Reynold Xin] Two minor SQL cleanup (compiler warning & indent).
---
 .../org/apache/spark/sql/catalyst/analysis/Analyzer.scala     | 4 ++--
 .../apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index cad2c2abe6b1a..117c87a785fdb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -309,8 +309,8 @@ class Analyzer(
                 .nonEmpty =>
             (oldVersion, oldVersion.copy(windowExpressions = newAliases(windowExpressions)))
         }
-          // Only handle first case, others will be fixed on the next pass.
-          .headOption match {
+        // Only handle first case, others will be fixed on the next pass.
+        .headOption match {
           case None =>
             /*
              * No result implies that there is a logical plan node that produces new references
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 4ef7341a33245..976fa57cb98d5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -678,8 +678,8 @@ trait HiveTypeCoercion {
           findTightestCommonTypeAndPromoteToString((c.key +: c.whenList).map(_.dataType))
         maybeCommonType.map { commonType =>
           val castedBranches = c.branches.grouped(2).map {
-            case Seq(when, then) if when.dataType != commonType =>
-              Seq(Cast(when, commonType), then)
+            case Seq(whenExpr, thenExpr) if whenExpr.dataType != commonType =>
+              Seq(Cast(whenExpr, commonType), thenExpr)
             case other => other
           }.reduce(_ ++ _)
           CaseKeyWhen(Cast(c.key, commonType), castedBranches)

From 7bac2fe7717c0102b4875dbd95ae0bbf964536e3 Mon Sep 17 00:00:00 2001
From: Matt Massie <massie@cs.berkeley.edu>
Date: Wed, 24 Jun 2015 22:09:31 -0700
Subject: [PATCH 0054/1454] [SPARK-7884] Move block deserialization from
 BlockStoreShuffleFetcher to ShuffleReader

This commit updates the shuffle read path to enable ShuffleReader implementations more control over the deserialization process.

The BlockStoreShuffleFetcher.fetch() method has been renamed to BlockStoreShuffleFetcher.fetchBlockStreams(). Previously, this method returned a record iterator; now, it returns an iterator of (BlockId, InputStream). Deserialization of records is now handled in the ShuffleReader.read() method.

This change creates a cleaner separation of concerns and allows implementations of ShuffleReader more flexibility in how records are retrieved.

Author: Matt Massie <massie@cs.berkeley.edu>
Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #6423 from massie/shuffle-api-cleanup and squashes the following commits:

8b0632c [Matt Massie] Minor Scala style fixes
d0a1b39 [Matt Massie] Merge pull request #1 from kayousterhout/massie_shuffle-api-cleanup
290f1eb [Kay Ousterhout] Added test for HashShuffleReader.read()
5186da0 [Kay Ousterhout] Revert "Add test to ensure HashShuffleReader is freeing resources"
f98a1b9 [Matt Massie] Add test to ensure HashShuffleReader is freeing resources
a011bfa [Matt Massie] Use PrivateMethodTester on check that delegate stream is closed
4ea1712 [Matt Massie] Small code cleanup for readability
7429a98 [Matt Massie] Update tests to check that BufferReleasingStream is closing delegate InputStream
f458489 [Matt Massie] Remove unnecessary map() on return Iterator
4abb855 [Matt Massie] Consolidate metric code. Make it clear why InterrubtibleIterator is needed.
5c30405 [Matt Massie] Return visibility of BlockStoreShuffleFetcher to private[hash]
7eedd1d [Matt Massie] Small Scala import cleanup
28f8085 [Matt Massie] Small import nit
f93841e [Matt Massie] Update shuffle read metrics in ShuffleReader instead of BlockStoreShuffleFetcher.
7e8e0fe [Matt Massie] Minor Scala style fixes
01e8721 [Matt Massie] Explicitly cast iterator in branches for type clarity
7c8f73e [Matt Massie] Close Block InputStream immediately after all records are read
208b7a5 [Matt Massie] Small code style changes
b70c945 [Matt Massie] Make BlockStoreShuffleFetcher visible to shuffle package
19135f2 [Matt Massie] [SPARK-7884] Allow Spark shuffle APIs to be more customizable
---
 .../hash/BlockStoreShuffleFetcher.scala       |  59 +++----
 .../shuffle/hash/HashShuffleReader.scala      |  52 +++++-
 .../storage/ShuffleBlockFetcherIterator.scala |  90 +++++++----
 .../shuffle/hash/HashShuffleReaderSuite.scala | 150 ++++++++++++++++++
 .../ShuffleBlockFetcherIteratorSuite.scala    |  59 ++++---
 5 files changed, 314 insertions(+), 96 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
index 597d46a3d2223..9d8e7e9f03aea 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
@@ -17,29 +17,29 @@
 
 package org.apache.spark.shuffle.hash
 
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.HashMap
-import scala.util.{Failure, Success, Try}
+import java.io.InputStream
+
+import scala.collection.mutable.{ArrayBuffer, HashMap}
+import scala.util.{Failure, Success}
 
 import org.apache.spark._
-import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.FetchFailedException
-import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockFetcherIterator, ShuffleBlockId}
-import org.apache.spark.util.CompletionIterator
+import org.apache.spark.storage.{BlockId, BlockManager, BlockManagerId, ShuffleBlockFetcherIterator,
+  ShuffleBlockId}
 
 private[hash] object BlockStoreShuffleFetcher extends Logging {
-  def fetch[T](
+  def fetchBlockStreams(
       shuffleId: Int,
       reduceId: Int,
       context: TaskContext,
-      serializer: Serializer)
-    : Iterator[T] =
+      blockManager: BlockManager,
+      mapOutputTracker: MapOutputTracker)
+    : Iterator[(BlockId, InputStream)] =
   {
     logDebug("Fetching outputs for shuffle %d, reduce %d".format(shuffleId, reduceId))
-    val blockManager = SparkEnv.get.blockManager
 
     val startTime = System.currentTimeMillis
-    val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, reduceId)
+    val statuses = mapOutputTracker.getServerStatuses(shuffleId, reduceId)
     logDebug("Fetching map output location for shuffle %d, reduce %d took %d ms".format(
       shuffleId, reduceId, System.currentTimeMillis - startTime))
 
@@ -53,12 +53,21 @@ private[hash] object BlockStoreShuffleFetcher extends Logging {
         (address, splits.map(s => (ShuffleBlockId(shuffleId, s._1, reduceId), s._2)))
     }
 
-    def unpackBlock(blockPair: (BlockId, Try[Iterator[Any]])) : Iterator[T] = {
+    val blockFetcherItr = new ShuffleBlockFetcherIterator(
+      context,
+      blockManager.shuffleClient,
+      blockManager,
+      blocksByAddress,
+      // Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
+      SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024)
+
+    // Make sure that fetch failures are wrapped inside a FetchFailedException for the scheduler
+    blockFetcherItr.map { blockPair =>
       val blockId = blockPair._1
       val blockOption = blockPair._2
       blockOption match {
-        case Success(block) => {
-          block.asInstanceOf[Iterator[T]]
+        case Success(inputStream) => {
+          (blockId, inputStream)
         }
         case Failure(e) => {
           blockId match {
@@ -72,27 +81,5 @@ private[hash] object BlockStoreShuffleFetcher extends Logging {
         }
       }
     }
-
-    val blockFetcherItr = new ShuffleBlockFetcherIterator(
-      context,
-      SparkEnv.get.blockManager.shuffleClient,
-      blockManager,
-      blocksByAddress,
-      serializer,
-      // Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
-      SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024)
-    val itr = blockFetcherItr.flatMap(unpackBlock)
-
-    val completionIter = CompletionIterator[T, Iterator[T]](itr, {
-      context.taskMetrics.updateShuffleReadMetrics()
-    })
-
-    new InterruptibleIterator[T](context, completionIter) {
-      val readMetrics = context.taskMetrics.createShuffleReadMetricsForDependency()
-      override def next(): T = {
-        readMetrics.incRecordsRead(1)
-        delegate.next()
-      }
-    }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index 41bafabde05b9..d5c9880659dd3 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -17,16 +17,20 @@
 
 package org.apache.spark.shuffle.hash
 
-import org.apache.spark.{InterruptibleIterator, TaskContext}
+import org.apache.spark.{InterruptibleIterator, MapOutputTracker, SparkEnv, TaskContext}
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
+import org.apache.spark.storage.BlockManager
+import org.apache.spark.util.CompletionIterator
 import org.apache.spark.util.collection.ExternalSorter
 
 private[spark] class HashShuffleReader[K, C](
     handle: BaseShuffleHandle[K, _, C],
     startPartition: Int,
     endPartition: Int,
-    context: TaskContext)
+    context: TaskContext,
+    blockManager: BlockManager = SparkEnv.get.blockManager,
+    mapOutputTracker: MapOutputTracker = SparkEnv.get.mapOutputTracker)
   extends ShuffleReader[K, C]
 {
   require(endPartition == startPartition + 1,
@@ -36,20 +40,52 @@ private[spark] class HashShuffleReader[K, C](
 
   /** Read the combined key-values for this reduce task */
   override def read(): Iterator[Product2[K, C]] = {
+    val blockStreams = BlockStoreShuffleFetcher.fetchBlockStreams(
+      handle.shuffleId, startPartition, context, blockManager, mapOutputTracker)
+
+    // Wrap the streams for compression based on configuration
+    val wrappedStreams = blockStreams.map { case (blockId, inputStream) =>
+      blockManager.wrapForCompression(blockId, inputStream)
+    }
+
     val ser = Serializer.getSerializer(dep.serializer)
-    val iter = BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context, ser)
+    val serializerInstance = ser.newInstance()
+
+    // Create a key/value iterator for each stream
+    val recordIter = wrappedStreams.flatMap { wrappedStream =>
+      // Note: the asKeyValueIterator below wraps a key/value iterator inside of a
+      // NextIterator. The NextIterator makes sure that close() is called on the
+      // underlying InputStream when all records have been read.
+      serializerInstance.deserializeStream(wrappedStream).asKeyValueIterator
+    }
+
+    // Update the context task metrics for each record read.
+    val readMetrics = context.taskMetrics.createShuffleReadMetricsForDependency()
+    val metricIter = CompletionIterator[(Any, Any), Iterator[(Any, Any)]](
+      recordIter.map(record => {
+        readMetrics.incRecordsRead(1)
+        record
+      }),
+      context.taskMetrics().updateShuffleReadMetrics())
+
+    // An interruptible iterator must be used here in order to support task cancellation
+    val interruptibleIter = new InterruptibleIterator[(Any, Any)](context, metricIter)
 
     val aggregatedIter: Iterator[Product2[K, C]] = if (dep.aggregator.isDefined) {
       if (dep.mapSideCombine) {
-        new InterruptibleIterator(context, dep.aggregator.get.combineCombinersByKey(iter, context))
+        // We are reading values that are already combined
+        val combinedKeyValuesIterator = interruptibleIter.asInstanceOf[Iterator[(K, C)]]
+        dep.aggregator.get.combineCombinersByKey(combinedKeyValuesIterator, context)
       } else {
-        new InterruptibleIterator(context, dep.aggregator.get.combineValuesByKey(iter, context))
+        // We don't know the value type, but also don't care -- the dependency *should*
+        // have made sure its compatible w/ this aggregator, which will convert the value
+        // type to the combined type C
+        val keyValuesIterator = interruptibleIter.asInstanceOf[Iterator[(K, Nothing)]]
+        dep.aggregator.get.combineValuesByKey(keyValuesIterator, context)
       }
     } else {
       require(!dep.mapSideCombine, "Map-side combine without Aggregator specified!")
-
-      // Convert the Product2s to pairs since this is what downstream RDDs currently expect
-      iter.asInstanceOf[Iterator[Product2[K, C]]].map(pair => (pair._1, pair._2))
+      interruptibleIter.asInstanceOf[Iterator[Product2[K, C]]]
     }
 
     // Sort the output if there is a sort ordering defined.
diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
index d0faab62c9e9e..e49e39679e940 100644
--- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
@@ -17,23 +17,23 @@
 
 package org.apache.spark.storage
 
+import java.io.InputStream
 import java.util.concurrent.LinkedBlockingQueue
 
 import scala.collection.mutable.{ArrayBuffer, HashSet, Queue}
 import scala.util.{Failure, Try}
 
 import org.apache.spark.{Logging, TaskContext}
-import org.apache.spark.network.shuffle.{BlockFetchingListener, ShuffleClient}
 import org.apache.spark.network.buffer.ManagedBuffer
-import org.apache.spark.serializer.{SerializerInstance, Serializer}
-import org.apache.spark.util.{CompletionIterator, Utils}
+import org.apache.spark.network.shuffle.{BlockFetchingListener, ShuffleClient}
+import org.apache.spark.util.Utils
 
 /**
  * An iterator that fetches multiple blocks. For local blocks, it fetches from the local block
  * manager. For remote blocks, it fetches them using the provided BlockTransferService.
  *
- * This creates an iterator of (BlockID, values) tuples so the caller can handle blocks in a
- * pipelined fashion as they are received.
+ * This creates an iterator of (BlockID, Try[InputStream]) tuples so the caller can handle blocks
+ * in a pipelined fashion as they are received.
  *
  * The implementation throttles the remote fetches to they don't exceed maxBytesInFlight to avoid
  * using too much memory.
@@ -44,7 +44,6 @@ import org.apache.spark.util.{CompletionIterator, Utils}
  * @param blocksByAddress list of blocks to fetch grouped by the [[BlockManagerId]].
  *                        For each block we also require the size (in bytes as a long field) in
  *                        order to throttle the memory usage.
- * @param serializer serializer used to deserialize the data.
  * @param maxBytesInFlight max size (in bytes) of remote blocks to fetch at any given point.
  */
 private[spark]
@@ -53,9 +52,8 @@ final class ShuffleBlockFetcherIterator(
     shuffleClient: ShuffleClient,
     blockManager: BlockManager,
     blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
-    serializer: Serializer,
     maxBytesInFlight: Long)
-  extends Iterator[(BlockId, Try[Iterator[Any]])] with Logging {
+  extends Iterator[(BlockId, Try[InputStream])] with Logging {
 
   import ShuffleBlockFetcherIterator._
 
@@ -83,7 +81,7 @@ final class ShuffleBlockFetcherIterator(
 
   /**
    * A queue to hold our results. This turns the asynchronous model provided by
-   * [[BlockTransferService]] into a synchronous model (iterator).
+   * [[org.apache.spark.network.BlockTransferService]] into a synchronous model (iterator).
    */
   private[this] val results = new LinkedBlockingQueue[FetchResult]
 
@@ -102,9 +100,7 @@ final class ShuffleBlockFetcherIterator(
   /** Current bytes in flight from our requests */
   private[this] var bytesInFlight = 0L
 
-  private[this] val shuffleMetrics = context.taskMetrics.createShuffleReadMetricsForDependency()
-
-  private[this] val serializerInstance: SerializerInstance = serializer.newInstance()
+  private[this] val shuffleMetrics = context.taskMetrics().createShuffleReadMetricsForDependency()
 
   /**
    * Whether the iterator is still active. If isZombie is true, the callback interface will no
@@ -114,17 +110,23 @@ final class ShuffleBlockFetcherIterator(
 
   initialize()
 
-  /**
-   * Mark the iterator as zombie, and release all buffers that haven't been deserialized yet.
-   */
-  private[this] def cleanup() {
-    isZombie = true
+  // Decrements the buffer reference count.
+  // The currentResult is set to null to prevent releasing the buffer again on cleanup()
+  private[storage] def releaseCurrentResultBuffer(): Unit = {
     // Release the current buffer if necessary
     currentResult match {
       case SuccessFetchResult(_, _, buf) => buf.release()
       case _ =>
     }
+    currentResult = null
+  }
 
+  /**
+   * Mark the iterator as zombie, and release all buffers that haven't been deserialized yet.
+   */
+  private[this] def cleanup() {
+    isZombie = true
+    releaseCurrentResultBuffer()
     // Release buffers in the results queue
     val iter = results.iterator()
     while (iter.hasNext) {
@@ -272,7 +274,13 @@ final class ShuffleBlockFetcherIterator(
 
   override def hasNext: Boolean = numBlocksProcessed < numBlocksToFetch
 
-  override def next(): (BlockId, Try[Iterator[Any]]) = {
+  /**
+   * Fetches the next (BlockId, Try[InputStream]). If a task fails, the ManagedBuffers
+   * underlying each InputStream will be freed by the cleanup() method registered with the
+   * TaskCompletionListener. However, callers should close() these InputStreams
+   * as soon as they are no longer needed, in order to release memory as early as possible.
+   */
+  override def next(): (BlockId, Try[InputStream]) = {
     numBlocksProcessed += 1
     val startFetchWait = System.currentTimeMillis()
     currentResult = results.take()
@@ -290,22 +298,15 @@ final class ShuffleBlockFetcherIterator(
       sendRequest(fetchRequests.dequeue())
     }
 
-    val iteratorTry: Try[Iterator[Any]] = result match {
+    val iteratorTry: Try[InputStream] = result match {
       case FailureFetchResult(_, e) =>
         Failure(e)
       case SuccessFetchResult(blockId, _, buf) =>
         // There is a chance that createInputStream can fail (e.g. fetching a local file that does
         // not exist, SPARK-4085). In that case, we should propagate the right exception so
         // the scheduler gets a FetchFailedException.
-        Try(buf.createInputStream()).map { is0 =>
-          val is = blockManager.wrapForCompression(blockId, is0)
-          val iter = serializerInstance.deserializeStream(is).asKeyValueIterator
-          CompletionIterator[Any, Iterator[Any]](iter, {
-            // Once the iterator is exhausted, release the buffer and set currentResult to null
-            // so we don't release it again in cleanup.
-            currentResult = null
-            buf.release()
-          })
+        Try(buf.createInputStream()).map { inputStream =>
+          new BufferReleasingInputStream(inputStream, this)
         }
     }
 
@@ -313,6 +314,39 @@ final class ShuffleBlockFetcherIterator(
   }
 }
 
+/**
+ * Helper class that ensures a ManagedBuffer is release upon InputStream.close()
+ */
+private class BufferReleasingInputStream(
+    private val delegate: InputStream,
+    private val iterator: ShuffleBlockFetcherIterator)
+  extends InputStream {
+  private[this] var closed = false
+
+  override def read(): Int = delegate.read()
+
+  override def close(): Unit = {
+    if (!closed) {
+      delegate.close()
+      iterator.releaseCurrentResultBuffer()
+      closed = true
+    }
+  }
+
+  override def available(): Int = delegate.available()
+
+  override def mark(readlimit: Int): Unit = delegate.mark(readlimit)
+
+  override def skip(n: Long): Long = delegate.skip(n)
+
+  override def markSupported(): Boolean = delegate.markSupported()
+
+  override def read(b: Array[Byte]): Int = delegate.read(b)
+
+  override def read(b: Array[Byte], off: Int, len: Int): Int = delegate.read(b, off, len)
+
+  override def reset(): Unit = delegate.reset()
+}
 
 private[storage]
 object ShuffleBlockFetcherIterator {
diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
new file mode 100644
index 0000000000000..28ca68698e3dc
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle.hash
+
+import java.io.{ByteArrayOutputStream, InputStream}
+import java.nio.ByteBuffer
+
+import org.mockito.Matchers.{eq => meq, _}
+import org.mockito.Mockito.{mock, when}
+import org.mockito.invocation.InvocationOnMock
+import org.mockito.stubbing.Answer
+
+import org.apache.spark._
+import org.apache.spark.network.buffer.{ManagedBuffer, NioManagedBuffer}
+import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.shuffle.BaseShuffleHandle
+import org.apache.spark.storage.{BlockManager, BlockManagerId, ShuffleBlockId}
+
+/**
+ * Wrapper for a managed buffer that keeps track of how many times retain and release are called.
+ *
+ * We need to define this class ourselves instead of using a spy because the NioManagedBuffer class
+ * is final (final classes cannot be spied on).
+ */
+class RecordingManagedBuffer(underlyingBuffer: NioManagedBuffer) extends ManagedBuffer {
+  var callsToRetain = 0
+  var callsToRelease = 0
+
+  override def size(): Long = underlyingBuffer.size()
+  override def nioByteBuffer(): ByteBuffer = underlyingBuffer.nioByteBuffer()
+  override def createInputStream(): InputStream = underlyingBuffer.createInputStream()
+  override def convertToNetty(): AnyRef = underlyingBuffer.convertToNetty()
+
+  override def retain(): ManagedBuffer = {
+    callsToRetain += 1
+    underlyingBuffer.retain()
+  }
+  override def release(): ManagedBuffer = {
+    callsToRelease += 1
+    underlyingBuffer.release()
+  }
+}
+
+class HashShuffleReaderSuite extends SparkFunSuite with LocalSparkContext {
+
+  /**
+   * This test makes sure that, when data is read from a HashShuffleReader, the underlying
+   * ManagedBuffers that contain the data are eventually released.
+   */
+  test("read() releases resources on completion") {
+    val testConf = new SparkConf(false)
+    // Create a SparkContext as a convenient way of setting SparkEnv (needed because some of the
+    // shuffle code calls SparkEnv.get()).
+    sc = new SparkContext("local", "test", testConf)
+
+    val reduceId = 15
+    val shuffleId = 22
+    val numMaps = 6
+    val keyValuePairsPerMap = 10
+    val serializer = new JavaSerializer(testConf)
+
+    // Make a mock BlockManager that will return RecordingManagedByteBuffers of data, so that we
+    // can ensure retain() and release() are properly called.
+    val blockManager = mock(classOf[BlockManager])
+
+    // Create a return function to use for the mocked wrapForCompression method that just returns
+    // the original input stream.
+    val dummyCompressionFunction = new Answer[InputStream] {
+      override def answer(invocation: InvocationOnMock): InputStream =
+        invocation.getArguments()(1).asInstanceOf[InputStream]
+    }
+
+    // Create a buffer with some randomly generated key-value pairs to use as the shuffle data
+    // from each mappers (all mappers return the same shuffle data).
+    val byteOutputStream = new ByteArrayOutputStream()
+    val serializationStream = serializer.newInstance().serializeStream(byteOutputStream)
+    (0 until keyValuePairsPerMap).foreach { i =>
+      serializationStream.writeKey(i)
+      serializationStream.writeValue(2*i)
+    }
+
+    // Setup the mocked BlockManager to return RecordingManagedBuffers.
+    val localBlockManagerId = BlockManagerId("test-client", "test-client", 1)
+    when(blockManager.blockManagerId).thenReturn(localBlockManagerId)
+    val buffers = (0 until numMaps).map { mapId =>
+      // Create a ManagedBuffer with the shuffle data.
+      val nioBuffer = new NioManagedBuffer(ByteBuffer.wrap(byteOutputStream.toByteArray))
+      val managedBuffer = new RecordingManagedBuffer(nioBuffer)
+
+      // Setup the blockManager mock so the buffer gets returned when the shuffle code tries to
+      // fetch shuffle data.
+      val shuffleBlockId = ShuffleBlockId(shuffleId, mapId, reduceId)
+      when(blockManager.getBlockData(shuffleBlockId)).thenReturn(managedBuffer)
+      when(blockManager.wrapForCompression(meq(shuffleBlockId), isA(classOf[InputStream])))
+        .thenAnswer(dummyCompressionFunction)
+
+      managedBuffer
+    }
+
+    // Make a mocked MapOutputTracker for the shuffle reader to use to determine what
+    // shuffle data to read.
+    val mapOutputTracker = mock(classOf[MapOutputTracker])
+    // Test a scenario where all data is local, just to avoid creating a bunch of additional mocks
+    // for the code to read data over the network.
+    val statuses: Array[(BlockManagerId, Long)] =
+      Array.fill(numMaps)((localBlockManagerId, byteOutputStream.size().toLong))
+    when(mapOutputTracker.getServerStatuses(shuffleId, reduceId)).thenReturn(statuses)
+
+    // Create a mocked shuffle handle to pass into HashShuffleReader.
+    val shuffleHandle = {
+      val dependency = mock(classOf[ShuffleDependency[Int, Int, Int]])
+      when(dependency.serializer).thenReturn(Some(serializer))
+      when(dependency.aggregator).thenReturn(None)
+      when(dependency.keyOrdering).thenReturn(None)
+      new BaseShuffleHandle(shuffleId, numMaps, dependency)
+    }
+
+    val shuffleReader = new HashShuffleReader(
+      shuffleHandle,
+      reduceId,
+      reduceId + 1,
+      new TaskContextImpl(0, 0, 0, 0, null),
+      blockManager,
+      mapOutputTracker)
+
+    assert(shuffleReader.read().length === keyValuePairsPerMap * numMaps)
+
+    // Calling .length above will have exhausted the iterator; make sure that exhausting the
+    // iterator caused retain and release to be called on each buffer.
+    buffers.foreach { buffer =>
+      assert(buffer.callsToRetain === 1)
+      assert(buffer.callsToRelease === 1)
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
index 2a7fe67ad8585..9ced4148d7206 100644
--- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
@@ -17,23 +17,25 @@
 
 package org.apache.spark.storage
 
+import java.io.InputStream
 import java.util.concurrent.Semaphore
 
-import scala.concurrent.future
 import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent.future
 
 import org.mockito.Matchers.{any, eq => meq}
 import org.mockito.Mockito._
 import org.mockito.invocation.InvocationOnMock
 import org.mockito.stubbing.Answer
+import org.scalatest.PrivateMethodTester
 
-import org.apache.spark.{SparkConf, SparkFunSuite, TaskContextImpl}
+import org.apache.spark.{SparkFunSuite, TaskContextImpl}
 import org.apache.spark.network._
 import org.apache.spark.network.buffer.ManagedBuffer
 import org.apache.spark.network.shuffle.BlockFetchingListener
-import org.apache.spark.serializer.TestSerializer
 
-class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
+
+class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodTester {
   // Some of the tests are quite tricky because we are testing the cleanup behavior
   // in the presence of faults.
 
@@ -57,7 +59,12 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
     transfer
   }
 
-  private val conf = new SparkConf
+  // Create a mock managed buffer for testing
+  def createMockManagedBuffer(): ManagedBuffer = {
+    val mockManagedBuffer = mock(classOf[ManagedBuffer])
+    when(mockManagedBuffer.createInputStream()).thenReturn(mock(classOf[InputStream]))
+    mockManagedBuffer
+  }
 
   test("successful 3 local reads + 2 remote reads") {
     val blockManager = mock(classOf[BlockManager])
@@ -66,9 +73,9 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
 
     // Make sure blockManager.getBlockData would return the blocks
     val localBlocks = Map[BlockId, ManagedBuffer](
-      ShuffleBlockId(0, 0, 0) -> mock(classOf[ManagedBuffer]),
-      ShuffleBlockId(0, 1, 0) -> mock(classOf[ManagedBuffer]),
-      ShuffleBlockId(0, 2, 0) -> mock(classOf[ManagedBuffer]))
+      ShuffleBlockId(0, 0, 0) -> createMockManagedBuffer(),
+      ShuffleBlockId(0, 1, 0) -> createMockManagedBuffer(),
+      ShuffleBlockId(0, 2, 0) -> createMockManagedBuffer())
     localBlocks.foreach { case (blockId, buf) =>
       doReturn(buf).when(blockManager).getBlockData(meq(blockId))
     }
@@ -76,9 +83,8 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
     // Make sure remote blocks would return
     val remoteBmId = BlockManagerId("test-client-1", "test-client-1", 2)
     val remoteBlocks = Map[BlockId, ManagedBuffer](
-      ShuffleBlockId(0, 3, 0) -> mock(classOf[ManagedBuffer]),
-      ShuffleBlockId(0, 4, 0) -> mock(classOf[ManagedBuffer])
-    )
+      ShuffleBlockId(0, 3, 0) -> createMockManagedBuffer(),
+      ShuffleBlockId(0, 4, 0) -> createMockManagedBuffer())
 
     val transfer = createMockTransfer(remoteBlocks)
 
@@ -92,7 +98,6 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
       transfer,
       blockManager,
       blocksByAddress,
-      new TestSerializer,
       48 * 1024 * 1024)
 
     // 3 local blocks fetched in initialization
@@ -100,15 +105,24 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
 
     for (i <- 0 until 5) {
       assert(iterator.hasNext, s"iterator should have 5 elements but actually has $i elements")
-      val (blockId, subIterator) = iterator.next()
-      assert(subIterator.isSuccess,
+      val (blockId, inputStream) = iterator.next()
+      assert(inputStream.isSuccess,
         s"iterator should have 5 elements defined but actually has $i elements")
 
-      // Make sure we release the buffer once the iterator is exhausted.
+      // Make sure we release buffers when a wrapped input stream is closed.
       val mockBuf = localBlocks.getOrElse(blockId, remoteBlocks(blockId))
+      // Note: ShuffleBlockFetcherIterator wraps input streams in a BufferReleasingInputStream
+      val wrappedInputStream = inputStream.get.asInstanceOf[BufferReleasingInputStream]
       verify(mockBuf, times(0)).release()
-      subIterator.get.foreach(_ => Unit)  // exhaust the iterator
+      val delegateAccess = PrivateMethod[InputStream]('delegate)
+
+      verify(wrappedInputStream.invokePrivate(delegateAccess()), times(0)).close()
+      wrappedInputStream.close()
+      verify(mockBuf, times(1)).release()
+      verify(wrappedInputStream.invokePrivate(delegateAccess()), times(1)).close()
+      wrappedInputStream.close() // close should be idempotent
       verify(mockBuf, times(1)).release()
+      verify(wrappedInputStream.invokePrivate(delegateAccess()), times(1)).close()
     }
 
     // 3 local blocks, and 2 remote blocks
@@ -125,10 +139,9 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
     // Make sure remote blocks would return
     val remoteBmId = BlockManagerId("test-client-1", "test-client-1", 2)
     val blocks = Map[BlockId, ManagedBuffer](
-      ShuffleBlockId(0, 0, 0) -> mock(classOf[ManagedBuffer]),
-      ShuffleBlockId(0, 1, 0) -> mock(classOf[ManagedBuffer]),
-      ShuffleBlockId(0, 2, 0) -> mock(classOf[ManagedBuffer])
-    )
+      ShuffleBlockId(0, 0, 0) -> createMockManagedBuffer(),
+      ShuffleBlockId(0, 1, 0) -> createMockManagedBuffer(),
+      ShuffleBlockId(0, 2, 0) -> createMockManagedBuffer())
 
     // Semaphore to coordinate event sequence in two different threads.
     val sem = new Semaphore(0)
@@ -159,11 +172,10 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
       transfer,
       blockManager,
       blocksByAddress,
-      new TestSerializer,
       48 * 1024 * 1024)
 
-    // Exhaust the first block, and then it should be released.
-    iterator.next()._2.get.foreach(_ => Unit)
+    verify(blocks(ShuffleBlockId(0, 0, 0)), times(0)).release()
+    iterator.next()._2.get.close() // close() first block's input stream
     verify(blocks(ShuffleBlockId(0, 0, 0)), times(1)).release()
 
     // Get the 2nd block but do not exhaust the iterator
@@ -222,7 +234,6 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite {
       transfer,
       blockManager,
       blocksByAddress,
-      new TestSerializer,
       48 * 1024 * 1024)
 
     // Continue only after the mock calls onBlockFetchFailure

From c337844ed7f9b2cb7b217dc935183ef5e1096ca1 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 25 Jun 2015 00:06:23 -0700
Subject: [PATCH 0055/1454] [SPARK-8604] [SQL] HadoopFsRelation subclasses
 should set their output format class

`HadoopFsRelation` subclasses, especially `ParquetRelation2` should set its own output format class, so that the default output committer can be setup correctly when doing appending (where we ignore user defined output committers).

Author: Cheng Lian <lian@databricks.com>

Closes #6998 from liancheng/spark-8604 and squashes the following commits:

9be51d1 [Cheng Lian] Adds more comments
6db1368 [Cheng Lian] HadoopFsRelation subclasses should set their output format class
---
 .../apache/spark/sql/parquet/newParquet.scala |  6 ++++++
 .../spark/sql/hive/orc/OrcRelation.scala      | 12 ++++++++++-
 .../sql/sources/SimpleTextRelation.scala      |  2 ++
 .../sql/sources/hadoopFsRelationSuites.scala  | 21 +++++++++++++++++++
 4 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index 1d353bd8e1114..bc39fae2bcfde 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -194,6 +194,12 @@ private[sql] class ParquetRelation2(
       committerClass,
       classOf[ParquetOutputCommitter])
 
+    // We're not really using `ParquetOutputFormat[Row]` for writing data here, because we override
+    // it in `ParquetOutputWriter` to support appending and dynamic partitioning.  The reason why
+    // we set it here is to setup the output committer class to `ParquetOutputCommitter`, which is
+    // bundled with `ParquetOutputFormat[Row]`.
+    job.setOutputFormatClass(classOf[ParquetOutputFormat[Row]])
+
     // TODO There's no need to use two kinds of WriteSupport
     // We should unify them. `SpecificMutableRow` can process both atomic (primitive) types and
     // complex types.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 705f48f1cd9f0..0fd7b3a91d6dd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -27,7 +27,7 @@ import org.apache.hadoop.hive.ql.io.orc.{OrcInputFormat, OrcOutputFormat, OrcSer
 import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
 import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
 import org.apache.hadoop.io.{NullWritable, Writable}
-import org.apache.hadoop.mapred.{InputFormat => MapRedInputFormat, JobConf, RecordWriter, Reporter}
+import org.apache.hadoop.mapred.{InputFormat => MapRedInputFormat, JobConf, OutputFormat => MapRedOutputFormat, RecordWriter, Reporter}
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
@@ -194,6 +194,16 @@ private[sql] class OrcRelation(
   }
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
+    job.getConfiguration match {
+      case conf: JobConf =>
+        conf.setOutputFormat(classOf[OrcOutputFormat])
+      case conf =>
+        conf.setClass(
+          "mapred.output.format.class",
+          classOf[OrcOutputFormat],
+          classOf[MapRedOutputFormat[_, _]])
+    }
+
     new OutputWriterFactory {
       override def newInstance(
           path: String,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
index 5d7cd16c129cd..e8141923a9b5c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
@@ -119,6 +119,8 @@ class SimpleTextRelation(
   }
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = new OutputWriterFactory {
+    job.setOutputFormatClass(classOf[TextOutputFormat[_, _]])
+
     override def newInstance(
         path: String,
         dataSchema: StructType,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index a16ab3a00ddb8..afecf9675e11f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -719,4 +719,25 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
       }
     }
   }
+
+  test("SPARK-8604: Parquet data source should write summary file while doing appending") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(0, 5)
+      df.write.mode(SaveMode.Overwrite).parquet(path)
+
+      val summaryPath = new Path(path, "_metadata")
+      val commonSummaryPath = new Path(path, "_common_metadata")
+
+      val fs = summaryPath.getFileSystem(configuration)
+      fs.delete(summaryPath, true)
+      fs.delete(commonSummaryPath, true)
+
+      df.write.mode(SaveMode.Append).parquet(path)
+      checkAnswer(sqlContext.read.parquet(path), df.unionAll(df))
+
+      assert(fs.exists(summaryPath))
+      assert(fs.exists(commonSummaryPath))
+    }
+  }
 }

From 085a7216bf5e6c2b4f297feca4af71a751e37975 Mon Sep 17 00:00:00 2001
From: Joshi <rekhajoshm@gmail.com>
Date: Thu, 25 Jun 2015 20:21:34 +0900
Subject: [PATCH 0056/1454] [SPARK-5768] [WEB UI] Fix for incorrect memory in
 Spark UI

Fix for incorrect memory in Spark UI as per SPARK-5768

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #6972 from rekhajoshm/SPARK-5768 and squashes the following commits:

b678a91 [Joshi] Fix for incorrect memory in Spark UI
2fe53d9 [Joshi] Fix for incorrect memory in Spark UI
eb823b8 [Joshi] SPARK-5768: Fix for incorrect memory in Spark UI
0be142d [Rekha Joshi] Merge pull request #3 from apache/master
106fd8e [Rekha Joshi] Merge pull request #2 from apache/master
e3677c9 [Rekha Joshi] Merge pull request #1 from apache/master
---
 core/src/main/scala/org/apache/spark/ui/ToolTips.scala        | 4 ++++
 .../main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala   | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
index 063e2a1f8b18e..e2d25e36365fa 100644
--- a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
+++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
@@ -35,6 +35,10 @@ private[spark] object ToolTips {
 
   val OUTPUT = "Bytes and records written to Hadoop."
 
+  val STORAGE_MEMORY =
+    "Memory used / total available memory for storage of data " +
+      "like RDD partitions cached in memory. "
+
   val SHUFFLE_WRITE =
     "Bytes and records written to disk in order to be read by a shuffle in a future stage."
 
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index b247e4cdc3bd4..01cddda4c62cd 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -67,7 +67,7 @@ private[ui] class ExecutorsPage(
           <th>Executor ID</th>
           <th>Address</th>
           <th>RDD Blocks</th>
-          <th>Memory Used</th>
+          <th><span data-toggle="tooltip" title={ToolTips.STORAGE_MEMORY}>Storage Memory</span></th>
           <th>Disk Used</th>
           <th>Active Tasks</th>
           <th>Failed Tasks</th>

From e988adb58f02d06065837f3d79eee220f6558def Mon Sep 17 00:00:00 2001
From: Tom Graves <tgraves@yahoo-inc.com>
Date: Thu, 25 Jun 2015 08:27:08 -0500
Subject: [PATCH 0057/1454] =?UTF-8?q?[SPARK-8574]=20org/apache/spark/unsaf?=
 =?UTF-8?q?e=20doesn't=20honor=20the=20java=20source/ta=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…rget versions.

I basically copied the compatibility rules from the top level pom.xml into here.  Someone more familiar with all the options in the top level pom may want to make sure nothing else should be copied on down.

With this is allows me to build with jdk8 and run with lower versions.  Source shows compiled for jdk6 as its supposed to.

Author: Tom Graves <tgraves@yahoo-inc.com>
Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>

Closes #6989 from tgravescs/SPARK-8574 and squashes the following commits:

e1ea2d4 [Thomas Graves] Change to use combine.children="append"
150d645 [Tom Graves] [SPARK-8574] org/apache/spark/unsafe doesn't honor the java source/target versions
---
 unsafe/pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index 62c6354f1e203..dd2ae6457f0b9 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -80,7 +80,7 @@
           <groupId>net.alchim31.maven</groupId>
           <artifactId>scala-maven-plugin</artifactId>
           <configuration>
-            <javacArgs>
+            <javacArgs combine.children="append">
               <!-- This option is needed to suppress warnings from sun.misc.Unsafe usage -->
               <javacArg>-XDignore.symbol.file</javacArg>
             </javacArgs>

From f9b397f54d1c491680d70aba210bb8211fd249c1 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 25 Jun 2015 06:52:03 -0700
Subject: [PATCH 0058/1454] [SPARK-8567] [SQL] Add logs to record the progress
 of HiveSparkSubmitSuite.

Author: Yin Huai <yhuai@databricks.com>

Closes #7009 from yhuai/SPARK-8567 and squashes the following commits:

62fb1f9 [Yin Huai] Add sc.stop().
b22cf7d [Yin Huai] Add logs.
---
 .../org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index b875e52b986ab..a38ed23b5cf9a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -115,6 +115,7 @@ object SparkSubmitClassLoaderTest extends Logging {
     val sc = new SparkContext(conf)
     val hiveContext = new TestHiveContext(sc)
     val df = hiveContext.createDataFrame((1 to 100).map(i => (i, i))).toDF("i", "j")
+    logInfo("Testing load classes at the driver side.")
     // First, we load classes at driver side.
     try {
       Class.forName(args(0), true, Thread.currentThread().getContextClassLoader)
@@ -124,6 +125,7 @@ object SparkSubmitClassLoaderTest extends Logging {
         throw new Exception("Could not load user class from jar:\n", t)
     }
     // Second, we load classes at the executor side.
+    logInfo("Testing load classes at the executor side.")
     val result = df.mapPartitions { x =>
       var exception: String = null
       try {
@@ -141,6 +143,7 @@ object SparkSubmitClassLoaderTest extends Logging {
     }
 
     // Load a Hive UDF from the jar.
+    logInfo("Registering temporary Hive UDF provided in a jar.")
     hiveContext.sql(
       """
         |CREATE TEMPORARY FUNCTION example_max
@@ -150,18 +153,23 @@ object SparkSubmitClassLoaderTest extends Logging {
       hiveContext.createDataFrame((1 to 10).map(i => (i, s"str$i"))).toDF("key", "val")
     source.registerTempTable("sourceTable")
     // Load a Hive SerDe from the jar.
+    logInfo("Creating a Hive table with a SerDe provided in a jar.")
     hiveContext.sql(
       """
         |CREATE TABLE t1(key int, val string)
         |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'
       """.stripMargin)
     // Actually use the loaded UDF and SerDe.
+    logInfo("Writing data into the table.")
     hiveContext.sql(
       "INSERT INTO TABLE t1 SELECT example_max(key) as key, val FROM sourceTable GROUP BY val")
+    logInfo("Running a simple query on the table.")
     val count = hiveContext.table("t1").orderBy("key", "val").count()
     if (count != 10) {
       throw new Exception(s"table t1 should have 10 rows instead of $count rows")
     }
+    logInfo("Test finishes.")
+    sc.stop()
   }
 }
 
@@ -199,5 +207,6 @@ object SparkSQLConfTest extends Logging {
     val hiveContext = new TestHiveContext(sc)
     // Run a simple command to make sure all lazy vals in hiveContext get instantiated.
     hiveContext.tables().collect()
+    sc.stop()
   }
 }

From 2519dcc33bde3a6d341790d73b5d292ea7af961a Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 25 Jun 2015 08:13:17 -0700
Subject: [PATCH 0059/1454] [MINOR] [MLLIB] rename some functions of
 PythonMLLibAPI

Keep the same naming conventions for PythonMLLibAPI.
Only the following three functions is different from others
```scala
trainNaiveBayes
trainGaussianMixture
trainWord2Vec
```
So change them to
```scala
trainNaiveBayesModel
trainGaussianMixtureModel
trainWord2VecModel
```
It does not affect any users and public APIs, only to make better understand for developer and code hacker.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7011 from yanboliang/py-mllib-api-rename and squashes the following commits:

771ffec [Yanbo Liang] rename some functions of PythonMLLibAPI
---
 .../org/apache/spark/mllib/api/python/PythonMLLibAPI.scala  | 6 +++---
 python/pyspark/mllib/classification.py                      | 2 +-
 python/pyspark/mllib/clustering.py                          | 6 +++---
 python/pyspark/mllib/feature.py                             | 2 +-
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index c4bea7c2cad4f..b16903a8d515c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -278,7 +278,7 @@ private[python] class PythonMLLibAPI extends Serializable {
   /**
    * Java stub for NaiveBayes.train()
    */
-  def trainNaiveBayes(
+  def trainNaiveBayesModel(
       data: JavaRDD[LabeledPoint],
       lambda: Double): JList[Object] = {
     val model = NaiveBayes.train(data.rdd, lambda)
@@ -346,7 +346,7 @@ private[python] class PythonMLLibAPI extends Serializable {
    * Java stub for Python mllib GaussianMixture.run()
    * Returns a list containing weights, mean and covariance of each mixture component.
    */
-  def trainGaussianMixture(
+  def trainGaussianMixtureModel(
       data: JavaRDD[Vector],
       k: Int,
       convergenceTol: Double,
@@ -553,7 +553,7 @@ private[python] class PythonMLLibAPI extends Serializable {
    * @param seed initial seed for random generator
    * @return A handle to java Word2VecModelWrapper instance at python side
    */
-  def trainWord2Vec(
+  def trainWord2VecModel(
       dataJRDD: JavaRDD[java.util.ArrayList[String]],
       vectorSize: Int,
       learningRate: Double,
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 2698f10d06883..735d45ba03d27 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -581,7 +581,7 @@ def train(cls, data, lambda_=1.0):
         first = data.first()
         if not isinstance(first, LabeledPoint):
             raise ValueError("`data` should be an RDD of LabeledPoint")
-        labels, pi, theta = callMLlibFunc("trainNaiveBayes", data, lambda_)
+        labels, pi, theta = callMLlibFunc("trainNaiveBayesModel", data, lambda_)
         return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
 
 
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index e6ef72942ce77..8bc0654c76ca3 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -265,9 +265,9 @@ def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initia
             initialModelWeights = initialModel.weights
             initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
             initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
-        weight, mu, sigma = callMLlibFunc("trainGaussianMixture", rdd.map(_convert_to_vector), k,
-                                          convergenceTol, maxIterations, seed, initialModelWeights,
-                                          initialModelMu, initialModelSigma)
+        weight, mu, sigma = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
+                                          k, convergenceTol, maxIterations, seed,
+                                          initialModelWeights, initialModelMu, initialModelSigma)
         mvg_obj = [MultivariateGaussian(mu[i], sigma[i]) for i in range(k)]
         return GaussianMixtureModel(weight, mvg_obj)
 
diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index 334f5b86cd392..f00bb93b7bf40 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -549,7 +549,7 @@ def fit(self, data):
         """
         if not isinstance(data, RDD):
             raise TypeError("data should be an RDD of list of string")
-        jmodel = callMLlibFunc("trainWord2Vec", data, int(self.vectorSize),
+        jmodel = callMLlibFunc("trainWord2VecModel", data, int(self.vectorSize),
                                float(self.learningRate), int(self.numPartitions),
                                int(self.numIterations), int(self.seed),
                                int(self.minCount))

From c392a9efabcb1ec2a2c53f001ecdae33c245ba35 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Thu, 25 Jun 2015 10:56:00 -0700
Subject: [PATCH 0060/1454] [SPARK-8637] [SPARKR] [HOTFIX] Fix packages
 argument, sparkSubmitBinName

cc cafreeman

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7022 from shivaram/sparkr-init-hotfix and squashes the following commits:

9178d15 [Shivaram Venkataraman] Fix packages argument, sparkSubmitBinName
---
 R/pkg/R/client.R | 2 +-
 R/pkg/R/sparkR.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/pkg/R/client.R b/R/pkg/R/client.R
index cf2e5ddeb7a9d..78c7a3037ffac 100644
--- a/R/pkg/R/client.R
+++ b/R/pkg/R/client.R
@@ -57,7 +57,7 @@ generateSparkSubmitArgs <- function(args, sparkHome, jars, sparkSubmitOpts, pack
 }
 
 launchBackend <- function(args, sparkHome, jars, sparkSubmitOpts, packages) {
-  sparkSubmitBin <- determineSparkSubmitBin()
+  sparkSubmitBinName <- determineSparkSubmitBin()
   if (sparkHome != "") {
     sparkSubmitBin <- file.path(sparkHome, "bin", sparkSubmitBinName)
   } else {
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 8f81d5640c1d0..633b869f91784 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -132,7 +132,7 @@ sparkR.init <- function(
         sparkHome = sparkHome,
         jars = jars,
         sparkSubmitOpts = Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell"),
-        sparkPackages = sparkPackages)
+        packages = sparkPackages)
     # wait atmost 100 seconds for JVM to launch
     wait <- 0.1
     for (i in 1:25) {

From 47c874babe7779c7a2f32e0b891503ef6bebcab0 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Thu, 25 Jun 2015 22:07:37 -0700
Subject: [PATCH 0061/1454] [SPARK-8237] [SQL] Add misc function sha2

JIRA: https://issues.apache.org/jira/browse/SPARK-8237

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #6934 from viirya/expr_sha2 and squashes the following commits:

35e0bb3 [Liang-Chi Hsieh] For comments.
68b5284 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into expr_sha2
8573aff [Liang-Chi Hsieh] Remove unnecessary Product.
ee61e06 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into expr_sha2
59e41aa [Liang-Chi Hsieh] Add misc function: sha2.
---
 python/pyspark/sql/functions.py               | 19 ++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../spark/sql/catalyst/expressions/misc.scala | 98 ++++++++++++++++++-
 .../expressions/MiscFunctionsSuite.scala      | 14 ++-
 .../org/apache/spark/sql/functions.scala      | 20 ++++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 17 ++++
 6 files changed, 165 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index cfa87aeea193a..7d3d0361610b7 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -42,6 +42,7 @@
     'monotonicallyIncreasingId',
     'rand',
     'randn',
+    'sha2',
     'sparkPartitionId',
     'struct',
     'udf',
@@ -363,6 +364,24 @@ def randn(seed=None):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def sha2(col, numBits):
+    """Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,
+    and SHA-512). The numBits indicates the desired bit length of the result, which must have a
+    value of 224, 256, 384, 512, or 0 (which is equivalent to 256).
+
+    >>> digests = df.select(sha2(df.name, 256).alias('s')).collect()
+    >>> digests[0]
+    Row(s=u'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043')
+    >>> digests[1]
+    Row(s=u'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961')
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.sha2(_to_java_column(col), numBits)
+    return Column(jc)
+
+
 @since(1.4)
 def sparkPartitionId():
     """A column for partition ID of the Spark task.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 5fb3369f85d12..457948a800a17 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -135,6 +135,7 @@ object FunctionRegistry {
 
     // misc functions
     expression[Md5]("md5"),
+    expression[Sha2]("sha2"),
 
     // aggregate functions
     expression[Average]("avg"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index 4bee8cb728e5c..e80706fc65aff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -17,9 +17,12 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import java.security.MessageDigest
+import java.security.NoSuchAlgorithmException
+
 import org.apache.commons.codec.digest.DigestUtils
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.types.{BinaryType, StringType, DataType}
+import org.apache.spark.sql.types.{BinaryType, IntegerType, StringType, DataType}
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -44,7 +47,96 @@ case class Md5(child: Expression)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c =>
-      "org.apache.spark.unsafe.types.UTF8String.fromString" +
-        s"(org.apache.commons.codec.digest.DigestUtils.md5Hex($c))")
+      s"${ctx.stringType}.fromString(org.apache.commons.codec.digest.DigestUtils.md5Hex($c))")
+  }
+}
+
+/**
+ * A function that calculates the SHA-2 family of functions (SHA-224, SHA-256, SHA-384, and SHA-512)
+ * and returns it as a hex string. The first argument is the string or binary to be hashed. The
+ * second argument indicates the desired bit length of the result, which must have a value of 224,
+ * 256, 384, 512, or 0 (which is equivalent to 256). SHA-224 is supported starting from Java 8. If
+ * asking for an unsupported SHA function, the return value is NULL. If either argument is NULL or
+ * the hash length is not one of the permitted values, the return value is NULL.
+ */
+case class Sha2(left: Expression, right: Expression)
+  extends BinaryExpression with Serializable with ExpectsInputTypes {
+
+  override def dataType: DataType = StringType
+
+  override def toString: String = s"SHA2($left, $right)"
+
+  override def expectedChildTypes: Seq[DataType] = Seq(BinaryType, IntegerType)
+
+  override def eval(input: InternalRow): Any = {
+    val evalE1 = left.eval(input)
+    if (evalE1 == null) {
+      null
+    } else {
+      val evalE2 = right.eval(input)
+      if (evalE2 == null) {
+        null
+      } else {
+        val bitLength = evalE2.asInstanceOf[Int]
+        val input = evalE1.asInstanceOf[Array[Byte]]
+        bitLength match {
+          case 224 =>
+            // DigestUtils doesn't support SHA-224 now
+            try {
+              val md = MessageDigest.getInstance("SHA-224")
+              md.update(input)
+              UTF8String.fromBytes(md.digest())
+            } catch {
+              // SHA-224 is not supported on the system, return null
+              case noa: NoSuchAlgorithmException => null
+            }
+          case 256 | 0 =>
+            UTF8String.fromString(DigestUtils.sha256Hex(input))
+          case 384 =>
+            UTF8String.fromString(DigestUtils.sha384Hex(input))
+          case 512 =>
+            UTF8String.fromString(DigestUtils.sha512Hex(input))
+          case _ => null
+        }
+      }
+    }
+  }
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val eval1 = left.gen(ctx)
+    val eval2 = right.gen(ctx)
+    val digestUtils = "org.apache.commons.codec.digest.DigestUtils"
+
+    s"""
+      ${eval1.code}
+      boolean ${ev.isNull} = ${eval1.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${eval2.code}
+        if (!${eval2.isNull}) {
+          if (${eval2.primitive} == 224) {
+            try {
+              java.security.MessageDigest md = java.security.MessageDigest.getInstance("SHA-224");
+              md.update(${eval1.primitive});
+              ${ev.primitive} = ${ctx.stringType}.fromBytes(md.digest());
+            } catch (java.security.NoSuchAlgorithmException e) {
+              ${ev.isNull} = true;
+            }
+          } else if (${eval2.primitive} == 256 || ${eval2.primitive} == 0) {
+            ${ev.primitive} =
+              ${ctx.stringType}.fromString(${digestUtils}.sha256Hex(${eval1.primitive}));
+          } else if (${eval2.primitive} == 384) {
+            ${ev.primitive} =
+              ${ctx.stringType}.fromString(${digestUtils}.sha384Hex(${eval1.primitive}));
+          } else if (${eval2.primitive} == 512) {
+            ${ev.primitive} =
+              ${ctx.stringType}.fromString(${digestUtils}.sha512Hex(${eval1.primitive}));
+          } else {
+            ${ev.isNull} = true;
+          }
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+    """
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
index 48b84130b4556..38482c54c61db 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
@@ -17,8 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.commons.codec.digest.DigestUtils
+
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.types.{StringType, BinaryType}
+import org.apache.spark.sql.types.{IntegerType, StringType, BinaryType}
 
 class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -29,4 +31,14 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Md5(Literal.create(null, BinaryType)), null)
   }
 
+  test("sha2") {
+    checkEvaluation(Sha2(Literal("ABC".getBytes), Literal(256)), DigestUtils.sha256Hex("ABC"))
+    checkEvaluation(Sha2(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType), Literal(384)),
+      DigestUtils.sha384Hex(Array[Byte](1, 2, 3, 4, 5, 6)))
+    // unsupported bit length
+    checkEvaluation(Sha2(Literal.create(null, BinaryType), Literal(1024)), null)
+    checkEvaluation(Sha2(Literal.create(null, BinaryType), Literal(512)), null)
+    checkEvaluation(Sha2(Literal("ABC".getBytes), Literal.create(null, IntegerType)), null)
+    checkEvaluation(Sha2(Literal.create(null, BinaryType), Literal.create(null, IntegerType)), null)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 38d9085a505fb..355ce0e3423cf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1414,6 +1414,26 @@ object functions {
    */
   def md5(columnName: String): Column = md5(Column(columnName))
 
+  /**
+   * Calculates the SHA-2 family of hash functions and returns the value as a hex string.
+   *
+   * @group misc_funcs
+   * @since 1.5.0
+   */
+  def sha2(e: Column, numBits: Int): Column = {
+    require(Seq(0, 224, 256, 384, 512).contains(numBits),
+      s"numBits $numBits is not in the permitted values (0, 224, 256, 384, 512)")
+    Sha2(e.expr, lit(numBits).expr)
+  }
+
+  /**
+   * Calculates the SHA-2 family of hash functions and returns the value as a hex string.
+   *
+   * @group misc_funcs
+   * @since 1.5.0
+   */
+  def sha2(columnName: String, numBits: Int): Column = sha2(Column(columnName), numBits)
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   // String functions
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 8b53b384a22fd..8baed57a7f129 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -144,6 +144,23 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row("902fbdd2b1df0c4f70b4a5d23525e932", "6ac1e56bc78f031059be7be854522c4c"))
   }
 
+  test("misc sha2 function") {
+    val df = Seq(("ABC", Array[Byte](1, 2, 3, 4, 5, 6))).toDF("a", "b")
+    checkAnswer(
+      df.select(sha2($"a", 256), sha2("b", 256)),
+      Row("b5d4045c3f466fa91fe2cc6abe79232a1a57cdf104f7a26e716e0a1e2789df78",
+        "7192385c3c0605de55bb9476ce1d90748190ecb32a8eed7f5207b30cf6a1fe89"))
+
+    checkAnswer(
+      df.selectExpr("sha2(a, 256)", "sha2(b, 256)"),
+      Row("b5d4045c3f466fa91fe2cc6abe79232a1a57cdf104f7a26e716e0a1e2789df78",
+        "7192385c3c0605de55bb9476ce1d90748190ecb32a8eed7f5207b30cf6a1fe89"))
+
+    intercept[IllegalArgumentException] {
+      df.select(sha2($"a", 1024))
+    }
+  }
+
   test("string length function") {
     checkAnswer(
       nullStrings.select(strlen($"s"), strlen("s")),

From 40360112c417b5432564f4bcb8a9100f4066b55e Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 25 Jun 2015 22:16:53 -0700
Subject: [PATCH 0062/1454] [SPARK-8620] [SQL] cleanup CodeGenContext

fix docs, remove nativeTypes , use java type to get boxed type ,default value, etc. to avoid handle `DateType` and `TimestampType` as int and long again and again.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7010 from cloud-fan/cg and squashes the following commits:

aa01cf9 [Wenchen Fan] cleanup CodeGenContext
---
 .../spark/sql/catalyst/expressions/Cast.scala |   5 +-
 .../expressions/codegen/CodeGenerator.scala   | 130 +++++++++---------
 .../codegen/GenerateProjection.scala          |  34 ++---
 .../expressions/stringOperations.scala        |   1 -
 4 files changed, 82 insertions(+), 88 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 8bd7fc18a8dd4..8d66968a2fc35 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -467,11 +467,8 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
         defineCodeGen(ctx, ev, c => s"!$c.isZero()")
       case (dt: NumericType, BooleanType) =>
         defineCodeGen(ctx, ev, c => s"$c != 0")
-
-      case (_: DecimalType, IntegerType) =>
-        defineCodeGen(ctx, ev, c => s"($c).toInt()")
       case (_: DecimalType, dt: NumericType) =>
-        defineCodeGen(ctx, ev, c => s"($c).to${ctx.boxedType(dt)}()")
+        defineCodeGen(ctx, ev, c => s"($c).to${ctx.primitiveTypeName(dt)}()")
       case (_: NumericType, dt: NumericType) =>
         defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})($c)")
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 47c5455435ec6..e20e3a9dca502 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -59,6 +59,14 @@ class CodeGenContext {
   val stringType: String = classOf[UTF8String].getName
   val decimalType: String = classOf[Decimal].getName
 
+  final val JAVA_BOOLEAN = "boolean"
+  final val JAVA_BYTE = "byte"
+  final val JAVA_SHORT = "short"
+  final val JAVA_INT = "int"
+  final val JAVA_LONG = "long"
+  final val JAVA_FLOAT = "float"
+  final val JAVA_DOUBLE = "double"
+
   private val curId = new java.util.concurrent.atomic.AtomicInteger()
 
   /**
@@ -72,98 +80,94 @@ class CodeGenContext {
   }
 
   /**
-   * Return the code to access a column for given DataType
+   * Returns the code to access a column in Row for a given DataType.
    */
   def getColumn(dataType: DataType, ordinal: Int): String = {
-    if (isNativeType(dataType)) {
-      s"i.${accessorForType(dataType)}($ordinal)"
+    val jt = javaType(dataType)
+    if (isPrimitiveType(jt)) {
+      s"i.get${primitiveTypeName(jt)}($ordinal)"
     } else {
-      s"(${boxedType(dataType)})i.apply($ordinal)"
+      s"($jt)i.apply($ordinal)"
     }
   }
 
   /**
-   * Return the code to update a column in Row for given DataType
+   * Returns the code to update a column in Row for a given DataType.
    */
   def setColumn(dataType: DataType, ordinal: Int, value: String): String = {
-    if (isNativeType(dataType)) {
-      s"${mutatorForType(dataType)}($ordinal, $value)"
+    val jt = javaType(dataType)
+    if (isPrimitiveType(jt)) {
+      s"set${primitiveTypeName(jt)}($ordinal, $value)"
     } else {
       s"update($ordinal, $value)"
     }
   }
 
   /**
-   * Return the name of accessor in Row for a DataType
+   * Returns the name used in accessor and setter for a Java primitive type.
    */
-  def accessorForType(dt: DataType): String = dt match {
-    case IntegerType => "getInt"
-    case other => s"get${boxedType(dt)}"
+  def primitiveTypeName(jt: String): String = jt match {
+    case JAVA_INT => "Int"
+    case _ => boxedType(jt)
   }
 
-  /**
-   * Return the name of mutator in Row for a DataType
-   */
-  def mutatorForType(dt: DataType): String = dt match {
-    case IntegerType => "setInt"
-    case other => s"set${boxedType(dt)}"
-  }
+  def primitiveTypeName(dt: DataType): String = primitiveTypeName(javaType(dt))
 
   /**
-   * Return the Java type for a DataType
+   * Returns the Java type for a DataType.
    */
   def javaType(dt: DataType): String = dt match {
-    case IntegerType => "int"
-    case LongType => "long"
-    case ShortType => "short"
-    case ByteType => "byte"
-    case DoubleType => "double"
-    case FloatType => "float"
-    case BooleanType => "boolean"
+    case BooleanType => JAVA_BOOLEAN
+    case ByteType => JAVA_BYTE
+    case ShortType => JAVA_SHORT
+    case IntegerType => JAVA_INT
+    case LongType => JAVA_LONG
+    case FloatType => JAVA_FLOAT
+    case DoubleType => JAVA_DOUBLE
     case dt: DecimalType => decimalType
     case BinaryType => "byte[]"
     case StringType => stringType
-    case DateType => "int"
-    case TimestampType => "long"
+    case DateType => JAVA_INT
+    case TimestampType => JAVA_LONG
     case dt: OpenHashSetUDT if dt.elementType == IntegerType => classOf[IntegerHashSet].getName
     case dt: OpenHashSetUDT if dt.elementType == LongType => classOf[LongHashSet].getName
     case _ => "Object"
   }
 
   /**
-   * Return the boxed type in Java
+   * Returns the boxed type in Java.
    */
-  def boxedType(dt: DataType): String = dt match {
-    case IntegerType => "Integer"
-    case LongType => "Long"
-    case ShortType => "Short"
-    case ByteType => "Byte"
-    case DoubleType => "Double"
-    case FloatType => "Float"
-    case BooleanType => "Boolean"
-    case DateType => "Integer"
-    case TimestampType => "Long"
-    case _ => javaType(dt)
+  def boxedType(jt: String): String = jt match {
+    case JAVA_BOOLEAN => "Boolean"
+    case JAVA_BYTE => "Byte"
+    case JAVA_SHORT => "Short"
+    case JAVA_INT => "Integer"
+    case JAVA_LONG => "Long"
+    case JAVA_FLOAT => "Float"
+    case JAVA_DOUBLE => "Double"
+    case other => other
   }
 
+  def boxedType(dt: DataType): String = boxedType(javaType(dt))
+
   /**
-   * Return the representation of default value for given DataType
+   * Returns the representation of default value for a given Java Type.
    */
-  def defaultValue(dt: DataType): String = dt match {
-    case BooleanType => "false"
-    case FloatType => "-1.0f"
-    case ShortType => "(short)-1"
-    case LongType => "-1L"
-    case ByteType => "(byte)-1"
-    case DoubleType => "-1.0"
-    case IntegerType => "-1"
-    case DateType => "-1"
-    case TimestampType => "-1L"
+  def defaultValue(jt: String): String = jt match {
+    case JAVA_BOOLEAN => "false"
+    case JAVA_BYTE => "(byte)-1"
+    case JAVA_SHORT => "(short)-1"
+    case JAVA_INT => "-1"
+    case JAVA_LONG => "-1L"
+    case JAVA_FLOAT => "-1.0f"
+    case JAVA_DOUBLE => "-1.0"
     case _ => "null"
   }
 
+  def defaultValue(dt: DataType): String = defaultValue(javaType(dt))
+
   /**
-   * Generate code for equal expression in Java
+   * Generates code for equal expression in Java.
    */
   def genEqual(dataType: DataType, c1: String, c2: String): String = dataType match {
     case BinaryType => s"java.util.Arrays.equals($c1, $c2)"
@@ -172,7 +176,7 @@ class CodeGenContext {
   }
 
   /**
-   * Generate code for compare expression in Java
+   * Generates code for compare expression in Java.
    */
   def genComp(dataType: DataType, c1: String, c2: String): String = dataType match {
     // java boolean doesn't support > or < operator
@@ -184,25 +188,17 @@ class CodeGenContext {
   }
 
   /**
-   * List of data types that have special accessors and setters in [[InternalRow]].
+   * List of java data types that have special accessors and setters in [[InternalRow]].
    */
-  val nativeTypes =
-    Seq(IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType)
+  val primitiveTypes =
+    Seq(JAVA_BOOLEAN, JAVA_BYTE, JAVA_SHORT, JAVA_INT, JAVA_LONG, JAVA_FLOAT, JAVA_DOUBLE)
 
   /**
-   * Returns true if the data type has a special accessor and setter in [[InternalRow]].
+   * Returns true if the Java type has a special accessor and setter in [[InternalRow]].
    */
-  def isNativeType(dt: DataType): Boolean = nativeTypes.contains(dt)
+  def isPrimitiveType(jt: String): Boolean = primitiveTypes.contains(jt)
 
-  /**
-   * List of data types who's Java type is primitive type
-   */
-  val primitiveTypes = nativeTypes ++ Seq(DateType, TimestampType)
-
-  /**
-   * Returns true if the Java type is primitive type
-   */
-  def isPrimitiveType(dt: DataType): Boolean = primitiveTypes.contains(dt)
+  def isPrimitiveType(dt: DataType): Boolean = isPrimitiveType(javaType(dt))
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index e362625469e29..624e1cf4e201a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -72,54 +72,56 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
       s"case $i: { c$i = (${ctx.boxedType(e.dataType)})value; return;}"
     }.mkString("\n        ")
 
-    val specificAccessorFunctions = ctx.nativeTypes.map { dataType =>
+    val specificAccessorFunctions = ctx.primitiveTypes.map { jt =>
       val cases = expressions.zipWithIndex.flatMap {
-        case (e, i) if ctx.javaType(e.dataType) == ctx.javaType(dataType) =>
-          List(s"case $i: return c$i;")
-        case _ => Nil
+        case (e, i) if ctx.javaType(e.dataType) == jt =>
+          Some(s"case $i: return c$i;")
+        case _ => None
       }.mkString("\n        ")
       if (cases.length > 0) {
+        val getter = "get" + ctx.primitiveTypeName(jt)
         s"""
       @Override
-      public ${ctx.javaType(dataType)} ${ctx.accessorForType(dataType)}(int i) {
+      public $jt $getter(int i) {
         if (isNullAt(i)) {
-          return ${ctx.defaultValue(dataType)};
+          return ${ctx.defaultValue(jt)};
         }
         switch (i) {
         $cases
         }
         throw new IllegalArgumentException("Invalid index: " + i
-          + " in ${ctx.accessorForType(dataType)}");
+          + " in $getter");
       }"""
       } else {
         ""
       }
-    }.mkString("\n")
+    }.filter(_.length > 0).mkString("\n")
 
-    val specificMutatorFunctions = ctx.nativeTypes.map { dataType =>
+    val specificMutatorFunctions = ctx.primitiveTypes.map { jt =>
       val cases = expressions.zipWithIndex.flatMap {
-        case (e, i) if ctx.javaType(e.dataType) == ctx.javaType(dataType) =>
-          List(s"case $i: { c$i = value; return; }")
-        case _ => Nil
+        case (e, i) if ctx.javaType(e.dataType) == jt =>
+          Some(s"case $i: { c$i = value; return; }")
+        case _ => None
       }.mkString("\n        ")
       if (cases.length > 0) {
+        val setter = "set" + ctx.primitiveTypeName(jt)
         s"""
       @Override
-      public void ${ctx.mutatorForType(dataType)}(int i, ${ctx.javaType(dataType)} value) {
+      public void $setter(int i, $jt value) {
         nullBits[i] = false;
         switch (i) {
         $cases
         }
         throw new IllegalArgumentException("Invalid index: " + i +
-          " in ${ctx.mutatorForType(dataType)}");
+          " in $setter}");
       }"""
       } else {
         ""
       }
-    }.mkString("\n")
+    }.filter(_.length > 0).mkString("\n")
 
     val hashValues = expressions.zipWithIndex.map { case (e, i) =>
-      val col = newTermName(s"c$i")
+      val col = s"c$i"
       val nonNull = e.dataType match {
         case BooleanType => s"$col ? 0 : 1"
         case ByteType | ShortType | IntegerType | DateType => s"$col"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 44416e79cd7aa..a6225fdafedde 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import java.util.regex.Pattern
 
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
-import org.apache.spark.sql.catalyst.expressions.Substring
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String

From 1a79f0eb8da7e850c443383b3bb24e0bf8e1e7cb Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 25 Jun 2015 22:44:26 -0700
Subject: [PATCH 0063/1454] [SPARK-8635] [SQL] improve performance of
 CatalystTypeConverters

In `CatalystTypeConverters.createToCatalystConverter`, we add special handling for primitive types. We can apply this strategy to more places to improve performance.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7018 from cloud-fan/converter and squashes the following commits:

8b16630 [Wenchen Fan] another fix
326c82c [Wenchen Fan] optimize type converter
---
 .../sql/catalyst/CatalystTypeConverters.scala | 60 ++++++++++++-------
 .../sql/catalyst/expressions/ScalaUdf.scala   |  3 +-
 .../org/apache/spark/sql/DataFrame.scala      |  4 +-
 .../sql/execution/stat/FrequentItems.scala    |  2 +-
 .../sql/execution/stat/StatFunctions.scala    |  2 +-
 .../sql/sources/DataSourceStrategy.scala      |  2 +-
 .../apache/spark/sql/sources/commands.scala   |  4 +-
 .../spark/sql/sources/TableScanSuite.scala    |  4 +-
 8 files changed, 48 insertions(+), 33 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 429fc4077be9a..012f8bbecb4d3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -52,6 +52,13 @@ object CatalystTypeConverters {
     }
   }
 
+  private def isWholePrimitive(dt: DataType): Boolean = dt match {
+    case dt if isPrimitive(dt) => true
+    case ArrayType(elementType, _) => isWholePrimitive(elementType)
+    case MapType(keyType, valueType, _) => isWholePrimitive(keyType) && isWholePrimitive(valueType)
+    case _ => false
+  }
+
   private def getConverterForType(dataType: DataType): CatalystTypeConverter[Any, Any, Any] = {
     val converter = dataType match {
       case udt: UserDefinedType[_] => UDTConverter(udt)
@@ -148,6 +155,8 @@ object CatalystTypeConverters {
 
     private[this] val elementConverter = getConverterForType(elementType)
 
+    private[this] val isNoChange = isWholePrimitive(elementType)
+
     override def toCatalystImpl(scalaValue: Any): Seq[Any] = {
       scalaValue match {
         case a: Array[_] => a.toSeq.map(elementConverter.toCatalyst)
@@ -166,8 +175,10 @@ object CatalystTypeConverters {
     override def toScala(catalystValue: Seq[Any]): Seq[Any] = {
       if (catalystValue == null) {
         null
+      } else if (isNoChange) {
+        catalystValue
       } else {
-        catalystValue.asInstanceOf[Seq[_]].map(elementConverter.toScala)
+        catalystValue.map(elementConverter.toScala)
       }
     }
 
@@ -183,6 +194,8 @@ object CatalystTypeConverters {
     private[this] val keyConverter = getConverterForType(keyType)
     private[this] val valueConverter = getConverterForType(valueType)
 
+    private[this] val isNoChange = isWholePrimitive(keyType) && isWholePrimitive(valueType)
+
     override def toCatalystImpl(scalaValue: Any): Map[Any, Any] = scalaValue match {
       case m: Map[_, _] =>
         m.map { case (k, v) =>
@@ -203,6 +216,8 @@ object CatalystTypeConverters {
     override def toScala(catalystValue: Map[Any, Any]): Map[Any, Any] = {
       if (catalystValue == null) {
         null
+      } else if (isNoChange) {
+        catalystValue
       } else {
         catalystValue.map { case (k, v) =>
           keyConverter.toScala(k) -> valueConverter.toScala(v)
@@ -258,16 +273,13 @@ object CatalystTypeConverters {
       toScala(row(column).asInstanceOf[InternalRow])
   }
 
-  private object StringConverter extends CatalystTypeConverter[Any, String, Any] {
+  private object StringConverter extends CatalystTypeConverter[Any, String, UTF8String] {
     override def toCatalystImpl(scalaValue: Any): UTF8String = scalaValue match {
       case str: String => UTF8String.fromString(str)
       case utf8: UTF8String => utf8
     }
-    override def toScala(catalystValue: Any): String = catalystValue match {
-      case null => null
-      case str: String => str
-      case utf8: UTF8String => utf8.toString()
-    }
+    override def toScala(catalystValue: UTF8String): String =
+      if (catalystValue == null) null else catalystValue.toString
     override def toScalaImpl(row: InternalRow, column: Int): String = row(column).toString
   }
 
@@ -275,7 +287,8 @@ object CatalystTypeConverters {
     override def toCatalystImpl(scalaValue: Date): Int = DateTimeUtils.fromJavaDate(scalaValue)
     override def toScala(catalystValue: Any): Date =
       if (catalystValue == null) null else DateTimeUtils.toJavaDate(catalystValue.asInstanceOf[Int])
-    override def toScalaImpl(row: InternalRow, column: Int): Date = toScala(row.getInt(column))
+    override def toScalaImpl(row: InternalRow, column: Int): Date =
+      DateTimeUtils.toJavaDate(row.getInt(column))
   }
 
   private object TimestampConverter extends CatalystTypeConverter[Timestamp, Timestamp, Any] {
@@ -285,7 +298,7 @@ object CatalystTypeConverters {
       if (catalystValue == null) null
       else DateTimeUtils.toJavaTimestamp(catalystValue.asInstanceOf[Long])
     override def toScalaImpl(row: InternalRow, column: Int): Timestamp =
-      toScala(row.getLong(column))
+      DateTimeUtils.toJavaTimestamp(row.getLong(column))
   }
 
   private object BigDecimalConverter extends CatalystTypeConverter[Any, JavaBigDecimal, Decimal] {
@@ -296,10 +309,7 @@ object CatalystTypeConverters {
     }
     override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal
     override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal =
-      row.get(column) match {
-        case d: JavaBigDecimal => d
-        case d: Decimal => d.toJavaBigDecimal
-      }
+      row.get(column).asInstanceOf[Decimal].toJavaBigDecimal
   }
 
   private abstract class PrimitiveConverter[T] extends CatalystTypeConverter[T, Any, Any] {
@@ -362,6 +372,19 @@ object CatalystTypeConverters {
     }
   }
 
+  /**
+   * Creates a converter function that will convert Catalyst types to Scala type.
+   * Typical use case would be converting a collection of rows that have the same schema. You will
+   * call this function once to get a converter, and apply it to every row.
+   */
+  private[sql] def createToScalaConverter(dataType: DataType): Any => Any = {
+    if (isPrimitive(dataType)) {
+      identity
+    } else {
+      getConverterForType(dataType).toScala
+    }
+  }
+
   /**
    *  Converts Scala objects to Catalyst rows / types.
    *
@@ -389,15 +412,6 @@ object CatalystTypeConverters {
    * produced by createToScalaConverter.
    */
   def convertToScala(catalystValue: Any, dataType: DataType): Any = {
-    getConverterForType(dataType).toScala(catalystValue)
-  }
-
-  /**
-   * Creates a converter function that will convert Catalyst types to Scala type.
-   * Typical use case would be converting a collection of rows that have the same schema. You will
-   * call this function once to get a converter, and apply it to every row.
-   */
-  private[sql] def createToScalaConverter(dataType: DataType): Any => Any = {
-    getConverterForType(dataType).toScala
+    createToScalaConverter(dataType)(catalystValue)
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
index 3992f1f59dad8..55df72f102295 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.types.DataType
 
@@ -39,7 +38,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi
     (1 to 22).map { x =>
       val anys = (1 to x).map(x => "Any").reduce(_ + ", " + _)
       val childs = (0 to x - 1).map(x => s"val child$x = children($x)").reduce(_ + "\n  " + _)
-      lazy val converters = (0 to x - 1).map(x => s"lazy val converter$x = CatalystTypeConverters.createToScalaConverter(child$x.dataType)").reduce(_ + "\n  " + _)
+      val converters = (0 to x - 1).map(x => s"lazy val converter$x = CatalystTypeConverters.createToScalaConverter(child$x.dataType)").reduce(_ + "\n  " + _)
       val evals = (0 to x - 1).map(x => s"converter$x(child$x.eval(input))").reduce(_ + ",\n      " + _)
 
       s"""case $x =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index f3f0f5305318e..0db4df34f9e22 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1418,12 +1418,14 @@ class DataFrame private[sql](
   lazy val rdd: RDD[Row] = {
     // use a local variable to make sure the map closure doesn't capture the whole DataFrame
     val schema = this.schema
-    queryExecution.executedPlan.execute().mapPartitions { rows =>
+    internalRowRdd.mapPartitions { rows =>
       val converter = CatalystTypeConverters.createToScalaConverter(schema)
       rows.map(converter(_).asInstanceOf[Row])
     }
   }
 
+  private[sql] def internalRowRdd = queryExecution.executedPlan.execute()
+
   /**
    * Returns the content of the [[DataFrame]] as a [[JavaRDD]] of [[Row]]s.
    * @group rdd
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
index 8df1da037c434..3ebbf96090a55 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
@@ -90,7 +90,7 @@ private[sql] object FrequentItems extends Logging {
       (name, originalSchema.fields(index).dataType)
     }
 
-    val freqItems = df.select(cols.map(Column(_)) : _*).rdd.aggregate(countMaps)(
+    val freqItems = df.select(cols.map(Column(_)) : _*).internalRowRdd.aggregate(countMaps)(
       seqOp = (counts, row) => {
         var i = 0
         while (i < numCols) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index 93383e5a62f11..252c611d02ebc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -81,7 +81,7 @@ private[sql] object StatFunctions extends Logging {
         s"with dataType ${data.get.dataType} not supported.")
     }
     val columns = cols.map(n => Column(Cast(Column(n).expr, DoubleType)))
-    df.select(columns: _*).rdd.aggregate(new CovarianceCounter)(
+    df.select(columns: _*).internalRowRdd.aggregate(new CovarianceCounter)(
       seqOp = (counter, row) => {
         counter.add(row.getDouble(0), row.getDouble(1))
       },
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
index a8f56f4767407..ce16e050c56ed 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
@@ -313,7 +313,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       output: Seq[Attribute],
       rdd: RDD[Row]): RDD[InternalRow] = {
     if (relation.relation.needConversion) {
-      execution.RDDConversions.rowToRowRdd(rdd.asInstanceOf[RDD[Row]], output.map(_.dataType))
+      execution.RDDConversions.rowToRowRdd(rdd, output.map(_.dataType))
     } else {
       rdd.map(_.asInstanceOf[InternalRow])
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index fb6173f58ece6..dbb369cf45502 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -154,7 +154,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
     writerContainer.driverSideSetup()
 
     try {
-      df.sqlContext.sparkContext.runJob(df.queryExecution.executedPlan.execute(), writeRows _)
+      df.sqlContext.sparkContext.runJob(df.internalRowRdd, writeRows _)
       writerContainer.commitJob()
       relation.refresh()
     } catch { case cause: Throwable =>
@@ -220,7 +220,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
     writerContainer.driverSideSetup()
 
     try {
-      df.sqlContext.sparkContext.runJob(df.queryExecution.executedPlan.execute(), writeRows _)
+      df.sqlContext.sparkContext.runJob(df.internalRowRdd, writeRows _)
       writerContainer.commitJob()
       relation.refresh()
     } catch { case cause: Throwable =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index 79eac930e54f7..de0ed0c0427a6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -88,9 +88,9 @@ case class AllDataTypesScan(
         UTF8String.fromString(s"varchar_$i"),
         Seq(i, i + 1),
         Seq(Map(UTF8String.fromString(s"str_$i") -> InternalRow(i.toLong))),
-        Map(i -> i.toString),
+        Map(i -> UTF8String.fromString(i.toString)),
         Map(Map(UTF8String.fromString(s"str_$i") -> i.toFloat) -> InternalRow(i.toLong)),
-        Row(i, i.toString),
+        Row(i, UTF8String.fromString(i.toString)),
         Row(Seq(UTF8String.fromString(s"str_$i"), UTF8String.fromString(s"str_${i + 1}")),
           InternalRow(Seq(DateTimeUtils.fromJavaDate(new Date(1970, 1, i + 1))))))
     }

From 9fed6abfdcb7afcf92be56e5ccbed6599fe66bc4 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 26 Jun 2015 00:12:05 -0700
Subject: [PATCH 0064/1454] [SPARK-8344] Add message processing time metric to
 DAGScheduler

This commit adds a new metric, `messageProcessingTime`, to the DAGScheduler metrics source. This metrics tracks the time taken to process messages in the scheduler's event processing loop, which is a helpful debugging aid for diagnosing performance issues in the scheduler (such as SPARK-4961).

In order to do this, I moved the creation of the DAGSchedulerSource metrics source into DAGScheduler itself, similar to how MasterSource is created and registered in Master.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7002 from JoshRosen/SPARK-8344 and squashes the following commits:

57f914b [Josh Rosen] Fix import ordering
7d6bb83 [Josh Rosen] Add message processing time metrics to DAGScheduler
---
 .../scala/org/apache/spark/SparkContext.scala  |  1 -
 .../apache/spark/scheduler/DAGScheduler.scala  | 18 ++++++++++++++++--
 .../spark/scheduler/DAGSchedulerSource.scala   |  8 ++++++--
 3 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 141276ac901fb..c7a7436462083 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -545,7 +545,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
 
     // Post init
     _taskScheduler.postStartHook()
-    _env.metricsSystem.registerSource(new DAGSchedulerSource(dagScheduler))
     _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
     _executorAllocationManager.foreach { e =>
       _env.metricsSystem.registerSource(e.executorAllocationManagerSource)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index aea6674ed20be..b00a5fee09bf2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -81,6 +81,8 @@ class DAGScheduler(
 
   def this(sc: SparkContext) = this(sc, sc.taskScheduler)
 
+  private[scheduler] val metricsSource: DAGSchedulerSource = new DAGSchedulerSource(this)
+
   private[scheduler] val nextJobId = new AtomicInteger(0)
   private[scheduler] def numTotalJobs: Int = nextJobId.get()
   private val nextStageId = new AtomicInteger(0)
@@ -1438,17 +1440,29 @@ class DAGScheduler(
     taskScheduler.stop()
   }
 
-  // Start the event thread at the end of the constructor
+  // Start the event thread and register the metrics source at the end of the constructor
+  env.metricsSystem.registerSource(metricsSource)
   eventProcessLoop.start()
 }
 
 private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler)
   extends EventLoop[DAGSchedulerEvent]("dag-scheduler-event-loop") with Logging {
 
+  private[this] val timer = dagScheduler.metricsSource.messageProcessingTimer
+
   /**
    * The main event loop of the DAG scheduler.
    */
-  override def onReceive(event: DAGSchedulerEvent): Unit = event match {
+  override def onReceive(event: DAGSchedulerEvent): Unit = {
+    val timerContext = timer.time()
+    try {
+      doOnReceive(event)
+    } finally {
+      timerContext.stop()
+    }
+  }
+
+  private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
     case JobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite, listener, properties) =>
       dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite,
         listener, properties)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala
index 02c67073af6a0..6b667d5d7645b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.scheduler
 
-import com.codahale.metrics.{Gauge, MetricRegistry}
+import com.codahale.metrics.{Gauge, MetricRegistry, Timer}
 
 import org.apache.spark.metrics.source.Source
 
-private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler)
+private[scheduler] class DAGSchedulerSource(val dagScheduler: DAGScheduler)
     extends Source {
   override val metricRegistry = new MetricRegistry()
   override val sourceName = "DAGScheduler"
@@ -45,4 +45,8 @@ private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler)
   metricRegistry.register(MetricRegistry.name("job", "activeJobs"), new Gauge[Int] {
     override def getValue: Int = dagScheduler.activeJobs.size
   })
+
+  /** Timer that tracks the time to process messages in the DAGScheduler's event loop */
+  val messageProcessingTimer: Timer =
+    metricRegistry.timer(MetricRegistry.name("messageProcessingTime"))
 }

From c9e05a315a96fbf3026a2b3c6934dd2dec420099 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Fri, 26 Jun 2015 01:19:05 -0700
Subject: [PATCH 0065/1454] [SPARK-8613] [ML] [TRIVIAL] add param to disable
 linear feature scaling

Add a param to disable linear feature scaling (to be implemented later in linear & logistic regression). Done as a seperate PR so we can use same param & not conflict while working on the sub-tasks.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7024 from holdenk/SPARK-8522-Disable-Linear_featureScaling-Spark-8613-Add-param and squashes the following commits:

ce8931a [Holden Karau] Regenerate the sharedParams code
fa6427e [Holden Karau] update text for standardization param.
7b24a2b [Holden Karau] generate the new standardization param
3c190af [Holden Karau] Add the standardization param to sharedparamscodegen
---
 .../ml/param/shared/SharedParamsCodeGen.scala   |  3 +++
 .../spark/ml/param/shared/sharedParams.scala    | 17 +++++++++++++++++
 2 files changed, 20 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 8ffbcf0d8bc71..b0a6af171c01f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -53,6 +53,9 @@ private[shared] object SharedParamsCodeGen {
       ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)",
         isValid = "ParamValidators.gtEq(1)"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
+      ParamDesc[Boolean]("standardization", "whether to standardize the training features" +
+        " prior to fitting the model sequence. Note that the coefficients of models are" +
+        " always returned on the original scale.", Some("true")),
       ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")),
       ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." +
         " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index a0c8ccdac9ad9..bbe08939b6d75 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -233,6 +233,23 @@ private[ml] trait HasFitIntercept extends Params {
   final def getFitIntercept: Boolean = $(fitIntercept)
 }
 
+/**
+ * (private[ml]) Trait for shared param standardization (default: true).
+ */
+private[ml] trait HasStandardization extends Params {
+
+  /**
+   * Param for whether to standardize the training features prior to fitting the model sequence. Note that the coefficients of models are always returned on the original scale..
+   * @group param
+   */
+  final val standardization: BooleanParam = new BooleanParam(this, "standardization", "whether to standardize the training features prior to fitting the model sequence. Note that the coefficients of models are always returned on the original scale.")
+
+  setDefault(standardization, true)
+
+  /** @group getParam */
+  final def getStandardization: Boolean = $(standardization)
+}
+
 /**
  * (private[ml]) Trait for shared param seed (default: this.getClass.getName.hashCode.toLong).
  */

From 37bf76a2de2143ec6348a3d43b782227849520cc Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 26 Jun 2015 08:45:22 -0500
Subject: [PATCH 0066/1454] [SPARK-8302] Support heterogeneous cluster install
 paths on YARN.

Some users have Hadoop installations on different paths across
their cluster. Currently, that makes it hard to set up some
configuration in Spark since that requires hardcoding paths to
jar files or native libraries, which wouldn't work on such a cluster.

This change introduces a couple of YARN-specific configurations
that instruct the backend to replace certain paths when launching
remote processes. That way, if the configuration says the Spark
jar is in "/spark/spark.jar", and also says that "/spark" should be
replaced with "{{SPARK_INSTALL_DIR}}", YARN will start containers
in the NMs with "{{SPARK_INSTALL_DIR}}/spark.jar" as the location
of the jar.

Coupled with YARN's environment whitelist (which allows certain
env variables to be exposed to containers), this allows users to
support such heterogeneous environments, as long as a single
replacement is enough. (Otherwise, this feature would need to be
extended to support multiple path replacements.)

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #6752 from vanzin/SPARK-8302 and squashes the following commits:

4bff8d4 [Marcelo Vanzin] Add docs, rename configs.
0aa2a02 [Marcelo Vanzin] Only do replacement for paths that need it.
2e9cc9d [Marcelo Vanzin] Style.
a5e1f68 [Marcelo Vanzin] [SPARK-8302] Support heterogeneous cluster install paths on YARN.
---
 docs/running-on-yarn.md                       | 26 ++++++++++
 .../org/apache/spark/deploy/yarn/Client.scala | 47 +++++++++++++++----
 .../spark/deploy/yarn/ExecutorRunnable.scala  |  4 +-
 .../spark/deploy/yarn/ClientSuite.scala       | 19 ++++++++
 4 files changed, 84 insertions(+), 12 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 96cf612c54fdd..3f8a093bbe957 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -258,6 +258,32 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
   Principal to be used to login to KDC, while running on secure HDFS.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.config.gatewayPath</code></td>
+  <td>(none)</td>
+  <td>
+  A path that is valid on the gateway host (the host where a Spark application is started) but may
+  differ for paths for the same resource in other nodes in the cluster. Coupled with
+  <code>spark.yarn.config.replacementPath</code>, this is used to support clusters with
+  heterogeneous configurations, so that Spark can correctly launch remote processes.
+  <p/>
+  The replacement path normally will contain a reference to some environment variable exported by
+  YARN (and, thus, visible to Spark containers).
+  <p/>
+  For example, if the gateway node has Hadoop libraries installed on <code>/disk1/hadoop</code>, and
+  the location of the Hadoop install is exported by YARN as the  <code>HADOOP_HOME</code>
+  environment variable, setting this value to <code>/disk1/hadoop</code> and the replacement path to
+  <code>$HADOOP_HOME</code> will make sure that paths used to launch remote processes properly
+  reference the local YARN configuration.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.config.replacementPath</code></td>
+  <td>(none)</td>
+  <td>
+  See <code>spark.yarn.config.gatewayPath</code>.
+  </td>
+</tr>
 </table>
 
 # Launching Spark on YARN
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index da1ec2a0fe2e9..67a5c95400e53 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -676,7 +676,7 @@ private[spark] class Client(
       val libraryPaths = Seq(sys.props.get("spark.driver.extraLibraryPath"),
         sys.props.get("spark.driver.libraryPath")).flatten
       if (libraryPaths.nonEmpty) {
-        prefixEnv = Some(Utils.libraryPathEnvPrefix(libraryPaths))
+        prefixEnv = Some(getClusterPath(sparkConf, Utils.libraryPathEnvPrefix(libraryPaths)))
       }
       if (sparkConf.getOption("spark.yarn.am.extraJavaOptions").isDefined) {
         logWarning("spark.yarn.am.extraJavaOptions will not take effect in cluster mode")
@@ -698,7 +698,7 @@ private[spark] class Client(
       }
 
       sparkConf.getOption("spark.yarn.am.extraLibraryPath").foreach { paths =>
-        prefixEnv = Some(Utils.libraryPathEnvPrefix(Seq(paths)))
+        prefixEnv = Some(getClusterPath(sparkConf, Utils.libraryPathEnvPrefix(Seq(paths))))
       }
     }
 
@@ -1106,10 +1106,10 @@ object Client extends Logging {
       env: HashMap[String, String],
       isAM: Boolean,
       extraClassPath: Option[String] = None): Unit = {
-    extraClassPath.foreach(addClasspathEntry(_, env))
-    addClasspathEntry(
-      YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), env
-    )
+    extraClassPath.foreach { cp =>
+      addClasspathEntry(getClusterPath(sparkConf, cp), env)
+    }
+    addClasspathEntry(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), env)
 
     if (isAM) {
       addClasspathEntry(
@@ -1125,12 +1125,14 @@ object Client extends Logging {
           getUserClasspath(sparkConf)
         }
       userClassPath.foreach { x =>
-        addFileToClasspath(x, null, env)
+        addFileToClasspath(sparkConf, x, null, env)
       }
     }
-    addFileToClasspath(new URI(sparkJar(sparkConf)), SPARK_JAR, env)
+    addFileToClasspath(sparkConf, new URI(sparkJar(sparkConf)), SPARK_JAR, env)
     populateHadoopClasspath(conf, env)
-    sys.env.get(ENV_DIST_CLASSPATH).foreach(addClasspathEntry(_, env))
+    sys.env.get(ENV_DIST_CLASSPATH).foreach { cp =>
+      addClasspathEntry(getClusterPath(sparkConf, cp), env)
+    }
   }
 
   /**
@@ -1159,16 +1161,18 @@ object Client extends Logging {
    *
    * If not a "local:" file and no alternate name, the environment is not modified.
    *
+   * @parma conf      Spark configuration.
    * @param uri       URI to add to classpath (optional).
    * @param fileName  Alternate name for the file (optional).
    * @param env       Map holding the environment variables.
    */
   private def addFileToClasspath(
+      conf: SparkConf,
       uri: URI,
       fileName: String,
       env: HashMap[String, String]): Unit = {
     if (uri != null && uri.getScheme == LOCAL_SCHEME) {
-      addClasspathEntry(uri.getPath, env)
+      addClasspathEntry(getClusterPath(conf, uri.getPath), env)
     } else if (fileName != null) {
       addClasspathEntry(buildPath(
         YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), fileName), env)
@@ -1182,6 +1186,29 @@ object Client extends Logging {
   private def addClasspathEntry(path: String, env: HashMap[String, String]): Unit =
     YarnSparkHadoopUtil.addPathToEnvironment(env, Environment.CLASSPATH.name, path)
 
+  /**
+   * Returns the path to be sent to the NM for a path that is valid on the gateway.
+   *
+   * This method uses two configuration values:
+   *
+   * - spark.yarn.config.gatewayPath: a string that identifies a portion of the input path that may
+   *   only be valid in the gateway node.
+   * - spark.yarn.config.replacementPath: a string with which to replace the gateway path. This may
+   *   contain, for example, env variable references, which will be expanded by the NMs when
+   *   starting containers.
+   *
+   * If either config is not available, the input path is returned.
+   */
+  def getClusterPath(conf: SparkConf, path: String): String = {
+    val localPath = conf.get("spark.yarn.config.gatewayPath", null)
+    val clusterPath = conf.get("spark.yarn.config.replacementPath", null)
+    if (localPath != null && clusterPath != null) {
+      path.replace(localPath, clusterPath)
+    } else {
+      path
+    }
+  }
+
   /**
    * Obtains token for the Hive metastore and adds them to the credentials.
    */
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index b0937083bc536..78e27fb7f3337 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -146,7 +146,7 @@ class ExecutorRunnable(
       javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell)
     }
     sys.props.get("spark.executor.extraLibraryPath").foreach { p =>
-      prefixEnv = Some(Utils.libraryPathEnvPrefix(Seq(p)))
+      prefixEnv = Some(Client.getClusterPath(sparkConf, Utils.libraryPathEnvPrefix(Seq(p))))
     }
 
     javaOpts += "-Djava.io.tmpdir=" +
@@ -195,7 +195,7 @@ class ExecutorRunnable(
     val userClassPath = Client.getUserClasspath(sparkConf).flatMap { uri =>
       val absPath =
         if (new File(uri.getPath()).isAbsolute()) {
-          uri.getPath()
+          Client.getClusterPath(sparkConf, uri.getPath())
         } else {
           Client.buildPath(Environment.PWD.$(), uri.getPath())
         }
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
index 4ec976aa31387..837f8d3fa55a7 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
@@ -151,6 +151,25 @@ class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll {
     }
   }
 
+  test("Cluster path translation") {
+    val conf = new Configuration()
+    val sparkConf = new SparkConf()
+      .set(Client.CONF_SPARK_JAR, "local:/localPath/spark.jar")
+      .set("spark.yarn.config.gatewayPath", "/localPath")
+      .set("spark.yarn.config.replacementPath", "/remotePath")
+
+    Client.getClusterPath(sparkConf, "/localPath") should be ("/remotePath")
+    Client.getClusterPath(sparkConf, "/localPath/1:/localPath/2") should be (
+      "/remotePath/1:/remotePath/2")
+
+    val env = new MutableHashMap[String, String]()
+    Client.populateClasspath(null, conf, sparkConf, env, false,
+      extraClassPath = Some("/localPath/my1.jar"))
+    val cp = classpath(env)
+    cp should contain ("/remotePath/spark.jar")
+    cp should contain ("/remotePath/my1.jar")
+  }
+
   object Fixtures {
 
     val knownDefYarnAppCP: Seq[String] =

From 41afa16500e682475eaa80e31c0434b7ab66abcb Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 26 Jun 2015 08:12:22 -0700
Subject: [PATCH 0067/1454] [SPARK-8652] [PYSPARK] Check return value for all
 uses of doctest.testmod()

This patch addresses a critical issue in the PySpark tests:

Several of our Python modules' `__main__` methods call `doctest.testmod()` in order to run doctests but forget to check and handle its return value. As a result, some PySpark test failures can go unnoticed because they will not fail the build.

Fortunately, there was only one test failure which was masked by this bug: a `pyspark.profiler` doctest was failing due to changes in RDD pipelining.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7032 from JoshRosen/testmod-fix and squashes the following commits:

60dbdc0 [Josh Rosen] Account for int vs. long formatting change in Python 3
8b8d80a [Josh Rosen] Fix failing test.
e6423f9 [Josh Rosen] Check return code for all uses of doctest.testmod().
---
 dev/merge_spark_pr.py            | 4 +++-
 python/pyspark/accumulators.py   | 4 +++-
 python/pyspark/broadcast.py      | 4 +++-
 python/pyspark/heapq3.py         | 5 +++--
 python/pyspark/profiler.py       | 8 ++++++--
 python/pyspark/serializers.py    | 8 +++++---
 python/pyspark/shuffle.py        | 4 +++-
 python/pyspark/streaming/util.py | 4 +++-
 8 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index cd83b352c1bfb..cf827ce89b857 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -431,6 +431,8 @@ def main():
 
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)
     
     main()
diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py
index adca90ddaf397..6ef8cf53cc747 100644
--- a/python/pyspark/accumulators.py
+++ b/python/pyspark/accumulators.py
@@ -264,4 +264,6 @@ def _start_update_server():
 
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py
index 3de4615428bb6..663c9abe0881e 100644
--- a/python/pyspark/broadcast.py
+++ b/python/pyspark/broadcast.py
@@ -115,4 +115,6 @@ def __reduce__(self):
 
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/heapq3.py b/python/pyspark/heapq3.py
index 4ef2afe03544f..b27e91a4cc251 100644
--- a/python/pyspark/heapq3.py
+++ b/python/pyspark/heapq3.py
@@ -883,6 +883,7 @@ def nlargest(n, iterable, key=None):
 
 
 if __name__ == "__main__":
-
     import doctest
-    print(doctest.testmod())
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/profiler.py b/python/pyspark/profiler.py
index d18daaabfcb3c..44d17bd629473 100644
--- a/python/pyspark/profiler.py
+++ b/python/pyspark/profiler.py
@@ -90,9 +90,11 @@ class Profiler(object):
     >>> sc = SparkContext('local', 'test', conf=conf, profiler_cls=MyCustomProfiler)
     >>> sc.parallelize(range(1000)).map(lambda x: 2 * x).take(10)
     [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
+    >>> sc.parallelize(range(1000)).count()
+    1000
     >>> sc.show_profiles()
     My custom profiles for RDD:1
-    My custom profiles for RDD:2
+    My custom profiles for RDD:3
     >>> sc.stop()
     """
 
@@ -169,4 +171,6 @@ def stats(self):
 
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 7f9d0a338d31e..411b4dbf481f1 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -44,8 +44,8 @@
 
 >>> rdd.glom().collect()
 [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]
->>> rdd._jrdd.count()
-8L
+>>> int(rdd._jrdd.count())
+8
 >>> sc.stop()
 """
 
@@ -556,4 +556,6 @@ def write_with_length(obj, stream):
 
 if __name__ == '__main__':
     import doctest
-    doctest.testmod()
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index 67752c0d150b9..8fb71bac64a5e 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -838,4 +838,6 @@ def load_partition(j):
 
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/streaming/util.py b/python/pyspark/streaming/util.py
index 34291f30a5652..a9bfec2aab8fc 100644
--- a/python/pyspark/streaming/util.py
+++ b/python/pyspark/streaming/util.py
@@ -125,4 +125,6 @@ def rddToFileName(prefix, suffix, timestamp):
 
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+    (failure_count, test_count) = doctest.testmod()
+    if failure_count:
+        exit(-1)

From a56516fc9280724db8fdef8e7d109ed7e28e427d Mon Sep 17 00:00:00 2001
From: cafreeman <cfreeman@alteryx.com>
Date: Fri, 26 Jun 2015 10:07:35 -0700
Subject: [PATCH 0068/1454] [SPARK-8662] SparkR Update SparkSQL Test

Test `infer_type` using a more fine-grained approach rather than comparing environments. Since `all.equal`'s behavior has changed in R 3.2, the test became unpassable.

JIRA here:
https://issues.apache.org/jira/browse/SPARK-8662

Author: cafreeman <cfreeman@alteryx.com>

Closes #7045 from cafreeman/R32_Test and squashes the following commits:

b97cc52 [cafreeman] Add `checkStructField` utility
3381e5c [cafreeman] Update SparkSQL Test

(cherry picked from commit 78b31a2a630c2178987322d0221aeea183ec565f)
Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
---
 R/pkg/inst/tests/test_sparkSQL.R | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 417153dc0985c..6a08f894313c4 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -19,6 +19,14 @@ library(testthat)
 
 context("SparkSQL functions")
 
+# Utility function for easily checking the values of a StructField
+checkStructField <- function(actual, expectedName, expectedType, expectedNullable) {
+  expect_equal(class(actual), "structField")
+  expect_equal(actual$name(), expectedName)
+  expect_equal(actual$dataType.toString(), expectedType)
+  expect_equal(actual$nullable(), expectedNullable)
+}
+
 # Tests for SparkSQL functions in SparkR
 
 sc <- sparkR.init()
@@ -52,9 +60,10 @@ test_that("infer types", {
                list(type = 'array', elementType = "integer", containsNull = TRUE))
   expect_equal(infer_type(list(1L, 2L)),
                list(type = 'array', elementType = "integer", containsNull = TRUE))
-  expect_equal(infer_type(list(a = 1L, b = "2")),
-               structType(structField(x = "a", type = "integer", nullable = TRUE),
-                          structField(x = "b", type = "string", nullable = TRUE)))
+  testStruct <- infer_type(list(a = 1L, b = "2"))
+  expect_true(class(testStruct) == "structType")
+  checkStructField(testStruct$fields()[[1]], "a", "IntegerType", TRUE)
+  checkStructField(testStruct$fields()[[2]], "b", "StringType", TRUE)
   e <- new.env()
   assign("a", 1L, envir = e)
   expect_equal(infer_type(e),

From 9d11817765e2817b11b73c61bae3b32c9f119cfd Mon Sep 17 00:00:00 2001
From: cafreeman <cfreeman@alteryx.com>
Date: Fri, 26 Jun 2015 17:06:02 -0700
Subject: [PATCH 0069/1454] [SPARK-8607] SparkR -- jars not being added to
 application classpath correctly

Add `getStaticClass` method in SparkR's `RBackendHandler`

This is a fix for the problem referenced in [SPARK-5185](https://issues.apache.org/jira/browse/SPARK-5185).

cc shivaram

Author: cafreeman <cfreeman@alteryx.com>

Closes #7001 from cafreeman/branch-1.4 and squashes the following commits:

8f81194 [cafreeman] Add missing license
31aedcf [cafreeman] Refactor test to call an external R script
2c22073 [cafreeman] Merge branch 'branch-1.4' of github.com:apache/spark into branch-1.4
0bea809 [cafreeman] Fixed relative path issue and added smaller JAR
ee25e60 [cafreeman] Merge branch 'branch-1.4' of github.com:apache/spark into branch-1.4
9a5c362 [cafreeman] test for including JAR when launching sparkContext
9101223 [cafreeman] Merge branch 'branch-1.4' of github.com:apache/spark into branch-1.4
5a80844 [cafreeman] Fix style nits
7c6bd0c [cafreeman] [SPARK-8607] SparkR

(cherry picked from commit 2579948bf5d89ac2d822ace605a6a4afce5258d6)
Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
---
 .../test_support/sparktestjar_2.10-1.0.jar    | Bin 0 -> 2886 bytes
 R/pkg/inst/tests/jarTest.R                    |  32 +++++++++++++++
 R/pkg/inst/tests/test_includeJAR.R            |  37 ++++++++++++++++++
 .../apache/spark/api/r/RBackendHandler.scala  |  17 +++++++-
 4 files changed, 85 insertions(+), 1 deletion(-)
 create mode 100644 R/pkg/inst/test_support/sparktestjar_2.10-1.0.jar
 create mode 100644 R/pkg/inst/tests/jarTest.R
 create mode 100644 R/pkg/inst/tests/test_includeJAR.R

diff --git a/R/pkg/inst/test_support/sparktestjar_2.10-1.0.jar b/R/pkg/inst/test_support/sparktestjar_2.10-1.0.jar
new file mode 100644
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diff --git a/R/pkg/inst/tests/jarTest.R b/R/pkg/inst/tests/jarTest.R
new file mode 100644
index 0000000000000..d68bb20950b00
--- /dev/null
+++ b/R/pkg/inst/tests/jarTest.R
@@ -0,0 +1,32 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+library(SparkR)
+
+sc <- sparkR.init()
+
+helloTest <- SparkR:::callJStatic("sparkR.test.hello",
+                                  "helloWorld",
+                                  "Dave")
+
+basicFunction <- SparkR:::callJStatic("sparkR.test.basicFunction",
+                                      "addStuff",
+                                      2L,
+                                      2L)
+
+sparkR.stop()
+output <- c(helloTest, basicFunction)
+writeLines(output)
diff --git a/R/pkg/inst/tests/test_includeJAR.R b/R/pkg/inst/tests/test_includeJAR.R
new file mode 100644
index 0000000000000..8bc693be20c3c
--- /dev/null
+++ b/R/pkg/inst/tests/test_includeJAR.R
@@ -0,0 +1,37 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+context("include an external JAR in SparkContext")
+
+runScript <- function() {
+  sparkHome <- Sys.getenv("SPARK_HOME")
+  jarPath <- paste("--jars",
+                   shQuote(file.path(sparkHome, "R/lib/SparkR/test_support/sparktestjar_2.10-1.0.jar")))
+  scriptPath <- file.path(sparkHome, "R/lib/SparkR/tests/jarTest.R")
+  submitPath <- file.path(sparkHome, "bin/spark-submit")
+  res <- system2(command = submitPath,
+                 args = c(jarPath, scriptPath),
+                 stdout = TRUE)
+  tail(res, 2)
+}
+
+test_that("sparkJars tag in SparkContext", {
+  testOutput <- runScript()
+  helloTest <- testOutput[1]
+  expect_true(helloTest == "Hello, Dave")
+  basicFunction <- testOutput[2]
+  expect_true(basicFunction == 4L)
+})
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index 2e86984c66b3a..4b8f7fe9242e0 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -88,6 +88,21 @@ private[r] class RBackendHandler(server: RBackend)
     ctx.close()
   }
 
+  // Looks up a class given a class name. This function first checks the
+  // current class loader and if a class is not found, it looks up the class
+  // in the context class loader. Address [SPARK-5185]
+  def getStaticClass(objId: String): Class[_] = {
+    try {
+      val clsCurrent = Class.forName(objId)
+      clsCurrent
+    } catch {
+      // Use contextLoader if we can't find the JAR in the system class loader
+      case e: ClassNotFoundException =>
+        val clsContext = Class.forName(objId, true, Thread.currentThread().getContextClassLoader)
+        clsContext
+      }
+    }
+
   def handleMethodCall(
       isStatic: Boolean,
       objId: String,
@@ -98,7 +113,7 @@ private[r] class RBackendHandler(server: RBackend)
     var obj: Object = null
     try {
       val cls = if (isStatic) {
-        Class.forName(objId)
+        getStaticClass(objId)
       } else {
         JVMObjectTracker.get(objId) match {
           case None => throw new IllegalArgumentException("Object not found " + objId)

From b5a6663da28198c905df27534cd123360a9bbef1 Mon Sep 17 00:00:00 2001
From: Rosstin <asterazul@gmail.com>
Date: Sat, 27 Jun 2015 08:47:00 +0300
Subject: [PATCH 0070/1454] [SPARK-8639] [DOCS] Fixed Minor Typos in
 Documentation

Ticket: [SPARK-8639](https://issues.apache.org/jira/browse/SPARK-8639)

fixed minor typos in docs/README.md and docs/api.md

Author: Rosstin <asterazul@gmail.com>

Closes #7046 from Rosstin/SPARK-8639 and squashes the following commits:

6c18058 [Rosstin] fixed minor typos in docs/README.md and docs/api.md
---
 docs/README.md | 2 +-
 docs/api.md    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 5852f972a051d..d7652e921f7df 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -28,7 +28,7 @@ in some cases:
     $ sudo gem install jekyll
     $ sudo gem install jekyll-redirect-from
 
-Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory
+Execute `jekyll build` from the `docs/` directory to compile the site. Compiling the site with Jekyll will create a directory
 called `_site` containing index.html as well as the rest of the compiled files.
 
 You can modify the default Jekyll build as follows:
diff --git a/docs/api.md b/docs/api.md
index 45df77ac05f78..ae7d51c2aefbf 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -3,7 +3,7 @@ layout: global
 title: Spark API Documentation
 ---
 
-Here you can API docs for Spark and its submodules.
+Here you can read API docs for Spark and its submodules.
 
 - [Spark Scala API (Scaladoc)](api/scala/index.html)
 - [Spark Java API (Javadoc)](api/java/index.html)

From d48e78934a346f023bd5cf44a34320f4d5a88e12 Mon Sep 17 00:00:00 2001
From: Neelesh Srinivas Salian <nsalian@cloudera.com>
Date: Sat, 27 Jun 2015 09:07:10 +0300
Subject: [PATCH 0071/1454] [SPARK-3629] [YARN] [DOCS]: Improvement of the
 "Running Spark on YARN" document

As per the description in the JIRA, I moved the contents of the page and added a few additional content.

Author: Neelesh Srinivas Salian <nsalian@cloudera.com>

Closes #6924 from nssalian/SPARK-3629 and squashes the following commits:

944b7a0 [Neelesh Srinivas Salian] Changed the lines about deploy-mode and added backticks to all parameters
40dbc0b [Neelesh Srinivas Salian] Changed dfs to HDFS, deploy-mode in backticks and updated the master yarn line
9cbc072 [Neelesh Srinivas Salian] Updated a few lines in the Launching Spark on YARN Section
8e8db7f [Neelesh Srinivas Salian] Removed the changes in this commit to help clearly distinguish movement from update
151c298 [Neelesh Srinivas Salian] SPARK-3629: Improvement of the Spark on YARN document
---
 docs/running-on-yarn.md | 164 ++++++++++++++++++++--------------------
 1 file changed, 82 insertions(+), 82 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 3f8a093bbe957..de22ab557cacf 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -7,6 +7,51 @@ Support for running on [YARN (Hadoop
 NextGen)](http://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YARN.html)
 was added to Spark in version 0.6.0, and improved in subsequent releases.
 
+# Launching Spark on YARN
+
+Ensure that `HADOOP_CONF_DIR` or `YARN_CONF_DIR` points to the directory which contains the (client side) configuration files for the Hadoop cluster.
+These configs are used to write to HDFS and connect to the YARN ResourceManager. The
+configuration contained in this directory will be distributed to the YARN cluster so that all
+containers used by the application use the same configuration. If the configuration references
+Java system properties or environment variables not managed by YARN, they should also be set in the
+Spark application's configuration (driver, executors, and the AM when running in client mode).
+
+There are two deploy modes that can be used to launch Spark applications on YARN. In `yarn-cluster` mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In `yarn-client` mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
+
+Unlike in Spark standalone and Mesos mode, in which the master's address is specified in the `--master` parameter, in YARN mode the ResourceManager's address is picked up from the Hadoop configuration. Thus, the `--master` parameter is `yarn-client` or `yarn-cluster`. 
+To launch a Spark application in `yarn-cluster` mode:
+
+   `$ ./bin/spark-submit --class path.to.your.Class --master yarn-cluster [options] <app jar> [app options]`
+    
+For example:
+
+    $ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
+        --master yarn-cluster \
+        --num-executors 3 \
+        --driver-memory 4g \
+        --executor-memory 2g \
+        --executor-cores 1 \
+        --queue thequeue \
+        lib/spark-examples*.jar \
+        10
+
+The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Debugging your Application" section below for how to see driver and executor logs.
+
+To launch a Spark application in `yarn-client` mode, do the same, but replace `yarn-cluster` with `yarn-client`.  To run spark-shell:
+
+    $ ./bin/spark-shell --master yarn-client
+
+## Adding Other JARs
+
+In `yarn-cluster` mode, the driver runs on a different machine than the client, so `SparkContext.addJar` won't work out of the box with files that are local to the client. To make files on the client available to `SparkContext.addJar`, include them with the `--jars` option in the launch command. 
+
+    $ ./bin/spark-submit --class my.main.Class \
+        --master yarn-cluster \
+        --jars my-other-jar.jar,my-other-other-jar.jar
+        my-main-jar.jar
+        app_arg1 app_arg2
+
+
 # Preparations
 
 Running Spark-on-YARN requires a binary distribution of Spark which is built with YARN support.
@@ -17,6 +62,38 @@ To build Spark yourself, refer to [Building Spark](building-spark.html).
 
 Most of the configs are the same for Spark on YARN as for other deployment modes. See the [configuration page](configuration.html) for more information on those.  These are configs that are specific to Spark on YARN.
 
+# Debugging your Application
+
+In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the `yarn.log-aggregation-enable` config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command.
+
+    yarn logs -applicationId <app ID>
+    
+will print out the contents of all log files from all containers from the given application. You can also view the container log files directly in HDFS using the HDFS shell or API. The directory where they are located can be found by looking at your YARN configs (`yarn.nodemanager.remote-app-log-dir` and `yarn.nodemanager.remote-app-log-dir-suffix`).
+
+When log aggregation isn't turned on, logs are retained locally on each machine under `YARN_APP_LOGS_DIR`, which is usually configured to `/tmp/logs` or `$HADOOP_HOME/logs/userlogs` depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
+
+To review per-container launch environment, increase `yarn.nodemanager.delete.debug-delay-sec` to a
+large value (e.g. 36000), and then access the application cache through `yarn.nodemanager.local-dirs`
+on the nodes on which containers are launched. This directory contains the launch script, JARs, and
+all environment variables used for launching each container. This process is useful for debugging
+classpath problems in particular. (Note that enabling this requires admin privileges on cluster
+settings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
+
+To use a custom log4j configuration for the application master or executors, there are two options:
+
+- upload a custom `log4j.properties` using `spark-submit`, by adding it to the `--files` list of files
+  to be uploaded with the application.
+- add `-Dlog4j.configuration=<location of configuration file>` to `spark.driver.extraJavaOptions`
+  (for the driver) or `spark.executor.extraJavaOptions` (for executors). Note that if using a file,
+  the `file:` protocol should be explicitly provided, and the file needs to exist locally on all
+  the nodes.
+
+Note that for the first option, both executors and the application master will share the same
+log4j configuration, which may cause issues when they run on the same node (e.g. trying to write
+to the same log file).
+
+If you need a reference to the proper location to put log files in the YARN so that YARN can properly display and aggregate them, use `spark.yarn.app.container.log.dir` in your log4j.properties. For example, `log4j.appender.file_appender.File=${spark.yarn.app.container.log.dir}/spark.log`. For streaming application, configuring `RollingFileAppender` and setting file location to YARN's log directory will avoid disk overflow caused by large log file, and logs can be accessed using YARN's log utility.
+
 #### Spark Properties
 
 <table class="table">
@@ -50,8 +127,8 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
   <td><code>spark.yarn.am.waitTime</code></td>
   <td>100s</td>
   <td>
-    In yarn-cluster mode, time for the application master to wait for the
-    SparkContext to be initialized. In yarn-client mode, time for the application master to wait
+    In `yarn-cluster` mode, time for the application master to wait for the
+    SparkContext to be initialized. In `yarn-client` mode, time for the application master to wait
     for the driver to connect to it.
   </td>
 </tr>
@@ -189,8 +266,8 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
   <td>
      Add the environment variable specified by <code>EnvironmentVariableName</code> to the 
      Application Master process launched on YARN. The user can specify multiple of 
-     these and to set multiple environment variables. In yarn-cluster mode this controls 
-     the environment of the SPARK driver and in yarn-client mode it only controls 
+     these and to set multiple environment variables. In `yarn-cluster` mode this controls 
+     the environment of the SPARK driver and in `yarn-client` mode it only controls 
      the environment of the executor launcher. 
   </td>
 </tr>
@@ -206,7 +283,7 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
   <td>(none)</td>
   <td>
   A string of extra JVM options to pass to the YARN Application Master in client mode.
-  In cluster mode, use spark.driver.extraJavaOptions instead.
+  In cluster mode, use `spark.driver.extraJavaOptions` instead.
   </td>
 </tr>
 <tr>
@@ -286,83 +363,6 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
 </tr>
 </table>
 
-# Launching Spark on YARN
-
-Ensure that `HADOOP_CONF_DIR` or `YARN_CONF_DIR` points to the directory which contains the (client side) configuration files for the Hadoop cluster.
-These configs are used to write to the dfs and connect to the YARN ResourceManager. The
-configuration contained in this directory will be distributed to the YARN cluster so that all
-containers used by the application use the same configuration. If the configuration references
-Java system properties or environment variables not managed by YARN, they should also be set in the
-Spark application's configuration (driver, executors, and the AM when running in client mode).
-
-There are two deploy modes that can be used to launch Spark applications on YARN. In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In yarn-client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
-
-Unlike in Spark standalone and Mesos mode, in which the master's address is specified in the "master" parameter, in YARN mode the ResourceManager's address is picked up from the Hadoop configuration.  Thus, the master parameter is simply "yarn-client" or "yarn-cluster".
-
-To launch a Spark application in yarn-cluster mode:
-
-    ./bin/spark-submit --class path.to.your.Class --master yarn-cluster [options] <app jar> [app options]
-    
-For example:
-
-    $ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
-        --master yarn-cluster \
-        --num-executors 3 \
-        --driver-memory 4g \
-        --executor-memory 2g \
-        --executor-cores 1 \
-        --queue thequeue \
-        lib/spark-examples*.jar \
-        10
-
-The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Debugging your Application" section below for how to see driver and executor logs.
-
-To launch a Spark application in yarn-client mode, do the same, but replace "yarn-cluster" with "yarn-client".  To run spark-shell:
-
-    $ ./bin/spark-shell --master yarn-client
-
-## Adding Other JARs
-
-In yarn-cluster mode, the driver runs on a different machine than the client, so `SparkContext.addJar` won't work out of the box with files that are local to the client. To make files on the client available to `SparkContext.addJar`, include them with the `--jars` option in the launch command. 
-
-    $ ./bin/spark-submit --class my.main.Class \
-        --master yarn-cluster \
-        --jars my-other-jar.jar,my-other-other-jar.jar
-        my-main-jar.jar
-        app_arg1 app_arg2
-
-# Debugging your Application
-
-In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the `yarn.log-aggregation-enable` config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command.
-
-    yarn logs -applicationId <app ID>
-    
-will print out the contents of all log files from all containers from the given application. You can also view the container log files directly in HDFS using the HDFS shell or API. The directory where they are located can be found by looking at your YARN configs (`yarn.nodemanager.remote-app-log-dir` and `yarn.nodemanager.remote-app-log-dir-suffix`).
-
-When log aggregation isn't turned on, logs are retained locally on each machine under `YARN_APP_LOGS_DIR`, which is usually configured to `/tmp/logs` or `$HADOOP_HOME/logs/userlogs` depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
-
-To review per-container launch environment, increase `yarn.nodemanager.delete.debug-delay-sec` to a
-large value (e.g. 36000), and then access the application cache through `yarn.nodemanager.local-dirs`
-on the nodes on which containers are launched. This directory contains the launch script, JARs, and
-all environment variables used for launching each container. This process is useful for debugging
-classpath problems in particular. (Note that enabling this requires admin privileges on cluster
-settings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
-
-To use a custom log4j configuration for the application master or executors, there are two options:
-
-- upload a custom `log4j.properties` using `spark-submit`, by adding it to the `--files` list of files
-  to be uploaded with the application.
-- add `-Dlog4j.configuration=<location of configuration file>` to `spark.driver.extraJavaOptions`
-  (for the driver) or `spark.executor.extraJavaOptions` (for executors). Note that if using a file,
-  the `file:` protocol should be explicitly provided, and the file needs to exist locally on all
-  the nodes.
-
-Note that for the first option, both executors and the application master will share the same
-log4j configuration, which may cause issues when they run on the same node (e.g. trying to write
-to the same log file).
-
-If you need a reference to the proper location to put log files in the YARN so that YARN can properly display and aggregate them, use `spark.yarn.app.container.log.dir` in your log4j.properties. For example, `log4j.appender.file_appender.File=${spark.yarn.app.container.log.dir}/spark.log`. For streaming application, configuring `RollingFileAppender` and setting file location to YARN's log directory will avoid disk overflow caused by large log file, and logs can be accessed using YARN's log utility.
-
 # Important notes
 
 - Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured.

From 4153776fd840ae075e6bb608f054091b6d3ec0c4 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Sat, 27 Jun 2015 14:33:31 -0700
Subject: [PATCH 0072/1454] [SPARK-8623] Hadoop RDDs fail to properly serialize
 configuration

Author: Sandy Ryza <sandy@cloudera.com>

Closes #7050 from sryza/sandy-spark-8623 and squashes the following commits:

58a8079 [Sandy Ryza] SPARK-8623. Hadoop RDDs fail to properly serialize configuration
---
 .../scala/org/apache/spark/serializer/KryoSerializer.scala  | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index cd8a82347a1e9..ed35cffe968f8 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -36,7 +36,7 @@ import org.apache.spark.network.nio.{GetBlock, GotBlock, PutBlock}
 import org.apache.spark.network.util.ByteUnit
 import org.apache.spark.scheduler.{CompressedMapStatus, HighlyCompressedMapStatus}
 import org.apache.spark.storage._
-import org.apache.spark.util.BoundedPriorityQueue
+import org.apache.spark.util.{BoundedPriorityQueue, SerializableConfiguration, SerializableJobConf}
 import org.apache.spark.util.collection.CompactBuffer
 
 /**
@@ -94,8 +94,10 @@ class KryoSerializer(conf: SparkConf)
     // For results returned by asJavaIterable. See JavaIterableWrapperSerializer.
     kryo.register(JavaIterableWrapperSerializer.wrapperClass, new JavaIterableWrapperSerializer)
 
-    // Allow sending SerializableWritable
+    // Allow sending classes with custom Java serializers
     kryo.register(classOf[SerializableWritable[_]], new KryoJavaSerializer())
+    kryo.register(classOf[SerializableConfiguration], new KryoJavaSerializer())
+    kryo.register(classOf[SerializableJobConf], new KryoJavaSerializer())
     kryo.register(classOf[HttpBroadcast[_]], new KryoJavaSerializer())
     kryo.register(classOf[PythonBroadcast], new KryoJavaSerializer())
 

From 0b5abbf5f96a5f6bfd15a65e8788cf3fa96fe54c Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 27 Jun 2015 14:40:45 -0700
Subject: [PATCH 0073/1454] [SPARK-8606] Prevent exceptions in
 RDD.getPreferredLocations() from crashing DAGScheduler

If `RDD.getPreferredLocations()` throws an exception it may crash the DAGScheduler and SparkContext. This patch addresses this by adding a try-catch block.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7023 from JoshRosen/SPARK-8606 and squashes the following commits:

770b169 [Josh Rosen] Fix getPreferredLocations() DAGScheduler crash with try block.
44a9b55 [Josh Rosen] Add test of a buggy getPartitions() method
19aa9f7 [Josh Rosen] Add (failing) regression test for getPreferredLocations() DAGScheduler crash
---
 .../apache/spark/scheduler/DAGScheduler.scala | 37 +++++++++++--------
 .../spark/scheduler/DAGSchedulerSuite.scala   | 31 ++++++++++++++++
 2 files changed, 53 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index b00a5fee09bf2..a7cf0c23d9613 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -907,22 +907,29 @@ class DAGScheduler(
         return
     }
 
-    val tasks: Seq[Task[_]] = stage match {
-      case stage: ShuffleMapStage =>
-        partitionsToCompute.map { id =>
-          val locs = getPreferredLocs(stage.rdd, id)
-          val part = stage.rdd.partitions(id)
-          new ShuffleMapTask(stage.id, taskBinary, part, locs)
-        }
+    val tasks: Seq[Task[_]] = try {
+      stage match {
+        case stage: ShuffleMapStage =>
+          partitionsToCompute.map { id =>
+            val locs = getPreferredLocs(stage.rdd, id)
+            val part = stage.rdd.partitions(id)
+            new ShuffleMapTask(stage.id, taskBinary, part, locs)
+          }
 
-      case stage: ResultStage =>
-        val job = stage.resultOfJob.get
-        partitionsToCompute.map { id =>
-          val p: Int = job.partitions(id)
-          val part = stage.rdd.partitions(p)
-          val locs = getPreferredLocs(stage.rdd, p)
-          new ResultTask(stage.id, taskBinary, part, locs, id)
-        }
+        case stage: ResultStage =>
+          val job = stage.resultOfJob.get
+          partitionsToCompute.map { id =>
+            val p: Int = job.partitions(id)
+            val part = stage.rdd.partitions(p)
+            val locs = getPreferredLocs(stage.rdd, p)
+            new ResultTask(stage.id, taskBinary, part, locs, id)
+          }
+      }
+    } catch {
+      case NonFatal(e) =>
+        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}")
+        runningStages -= stage
+        return
     }
 
     if (tasks.size > 0) {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 833b600746e90..6bc45f249f975 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -784,6 +784,37 @@ class DAGSchedulerSuite
     assert(sc.parallelize(1 to 10, 2).first() === 1)
   }
 
+  test("getPartitions exceptions should not crash DAGScheduler and SparkContext (SPARK-8606)") {
+    val e1 = intercept[DAGSchedulerSuiteDummyException] {
+      val rdd = new MyRDD(sc, 2, Nil) {
+        override def getPartitions: Array[Partition] = {
+          throw new DAGSchedulerSuiteDummyException
+        }
+      }
+      rdd.reduceByKey(_ + _, 1).count()
+    }
+
+    // Make sure we can still run local commands as well as cluster commands.
+    assert(sc.parallelize(1 to 10, 2).count() === 10)
+    assert(sc.parallelize(1 to 10, 2).first() === 1)
+  }
+
+  test("getPreferredLocations errors should not crash DAGScheduler and SparkContext (SPARK-8606)") {
+    val e1 = intercept[SparkException] {
+      val rdd = new MyRDD(sc, 2, Nil) {
+        override def getPreferredLocations(split: Partition): Seq[String] = {
+          throw new DAGSchedulerSuiteDummyException
+        }
+      }
+      rdd.count()
+    }
+    assert(e1.getMessage.contains(classOf[DAGSchedulerSuiteDummyException].getName))
+
+    // Make sure we can still run local commands as well as cluster commands.
+    assert(sc.parallelize(1 to 10, 2).count() === 10)
+    assert(sc.parallelize(1 to 10, 2).first() === 1)
+  }
+
   test("accumulator not calculated for resubmitted result stage") {
     // just for register
     val accum = new Accumulator[Int](0, AccumulatorParam.IntAccumulatorParam)

From 40648c56cdaa52058a4771082f8f44a2d8e5a1ec Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 27 Jun 2015 20:24:34 -0700
Subject: [PATCH 0074/1454] [SPARK-8583] [SPARK-5482] [BUILD] Refactor
 python/run-tests to integrate with dev/run-tests module system

This patch refactors the `python/run-tests` script:

- It's now written in Python instead of Bash.
- The descriptions of the tests to run are now stored in `dev/run-tests`'s modules.  This allows the pull request builder to skip Python tests suites that were not affected by the pull request's changes.  For example, we can now skip the PySpark Streaming test cases when only SQL files are changed.
- `python/run-tests` now supports command-line flags to make it easier to run individual test suites (this addresses SPARK-5482):

  ```
Usage: run-tests [options]

Options:
  -h, --help            show this help message and exit
  --python-executables=PYTHON_EXECUTABLES
                        A comma-separated list of Python executables to test
                        against (default: python2.6,python3.4,pypy)
  --modules=MODULES     A comma-separated list of Python modules to test
                        (default: pyspark-core,pyspark-ml,pyspark-mllib
                        ,pyspark-sql,pyspark-streaming)
   ```
- `dev/run-tests` has been split into multiple files: the module definitions and test utility functions are now stored inside of a `dev/sparktestsupport` Python module, allowing them to be re-used from the Python test runner script.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #6967 from JoshRosen/run-tests-python-modules and squashes the following commits:

f578d6d [Josh Rosen] Fix print for Python 2.x
8233d61 [Josh Rosen] Add python/run-tests.py to Python lint checks
34c98d2 [Josh Rosen] Fix universal_newlines for Python 3
8f65ed0 [Josh Rosen] Fix handling of  module in python/run-tests
37aff00 [Josh Rosen] Python 3 fix
27a389f [Josh Rosen] Skip MLLib tests for PyPy
c364ccf [Josh Rosen] Use which() to convert PYSPARK_PYTHON to an absolute path before shelling out to run tests
568a3fd [Josh Rosen] Fix hashbang
3b852ae [Josh Rosen] Fall back to PYSPARK_PYTHON when sys.executable is None (fixes a test)
f53db55 [Josh Rosen] Remove python2 flag, since the test runner script also works fine under Python 3
9c80469 [Josh Rosen] Fix passing of PYSPARK_PYTHON
d33e525 [Josh Rosen] Merge remote-tracking branch 'origin/master' into run-tests-python-modules
4f8902c [Josh Rosen] Python lint fixes.
8f3244c [Josh Rosen] Use universal_newlines to fix dev/run-tests doctest failures on Python 3.
f542ac5 [Josh Rosen] Fix lint check for Python 3
fff4d09 [Josh Rosen] Add dev/sparktestsupport to pep8 checks
2efd594 [Josh Rosen] Update dev/run-tests to use new Python test runner flags
b2ab027 [Josh Rosen] Add command-line options for running individual suites in python/run-tests
caeb040 [Josh Rosen] Fixes to PySpark test module definitions
d6a77d3 [Josh Rosen] Fix the tests of dev/run-tests
def2d8a [Josh Rosen] Two minor fixes
aec0b8f [Josh Rosen] Actually get the Kafka stuff to run properly
04015b9 [Josh Rosen] First attempt at getting PySpark Kafka test to work in new runner script
4c97136 [Josh Rosen] PYTHONPATH fixes
dcc9c09 [Josh Rosen] Fix time division
32660fc [Josh Rosen] Initial cut at Python test runner refactoring
311c6a9 [Josh Rosen] Move shell utility functions to own module.
1bdeb87 [Josh Rosen] Move module definitions to separate file.
---
 dev/lint-python                    |   3 +-
 dev/run-tests.py                   | 435 ++++-------------------------
 dev/sparktestsupport/__init__.py   |  21 ++
 dev/sparktestsupport/modules.py    | 385 +++++++++++++++++++++++++
 dev/sparktestsupport/shellutils.py |  81 ++++++
 python/pyspark/streaming/tests.py  |  16 ++
 python/pyspark/tests.py            |   3 +-
 python/run-tests                   | 164 +----------
 python/run-tests.py                | 132 +++++++++
 9 files changed, 700 insertions(+), 540 deletions(-)
 create mode 100644 dev/sparktestsupport/__init__.py
 create mode 100644 dev/sparktestsupport/modules.py
 create mode 100644 dev/sparktestsupport/shellutils.py
 create mode 100755 python/run-tests.py

diff --git a/dev/lint-python b/dev/lint-python
index f50d149dc4d44..0c3586462cb37 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -19,7 +19,8 @@
 
 SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
 SPARK_ROOT_DIR="$(dirname "$SCRIPT_DIR")"
-PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/"
+PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/ ./dev/sparktestsupport"
+PATHS_TO_CHECK="$PATHS_TO_CHECK ./dev/run-tests.py ./python/run-tests.py"
 PYTHON_LINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/python-lint-report.txt"
 
 cd "$SPARK_ROOT_DIR"
diff --git a/dev/run-tests.py b/dev/run-tests.py
index e7c09b0f40cdc..c51b0d3010a0f 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -17,297 +17,23 @@
 # limitations under the License.
 #
 
+from __future__ import print_function
 import itertools
 import os
 import re
 import sys
-import shutil
 import subprocess
 from collections import namedtuple
 
-SPARK_HOME = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..")
-USER_HOME = os.environ.get("HOME")
-
+from sparktestsupport import SPARK_HOME, USER_HOME
+from sparktestsupport.shellutils import exit_from_command_with_retcode, run_cmd, rm_r, which
+import sparktestsupport.modules as modules
 
 # -------------------------------------------------------------------------------------------------
-# Test module definitions and functions for traversing module dependency graph
+# Functions for traversing module dependency graph
 # -------------------------------------------------------------------------------------------------
 
 
-all_modules = []
-
-
-class Module(object):
-    """
-    A module is the basic abstraction in our test runner script. Each module consists of a set of
-    source files, a set of test commands, and a set of dependencies on other modules. We use modules
-    to define a dependency graph that lets determine which tests to run based on which files have
-    changed.
-    """
-
-    def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(),
-                 sbt_test_goals=(), should_run_python_tests=False, should_run_r_tests=False):
-        """
-        Define a new module.
-
-        :param name: A short module name, for display in logging and error messages.
-        :param dependencies: A set of dependencies for this module. This should only include direct
-            dependencies; transitive dependencies are resolved automatically.
-        :param source_file_regexes: a set of regexes that match source files belonging to this
-            module. These regexes are applied by attempting to match at the beginning of the
-            filename strings.
-        :param build_profile_flags: A set of profile flags that should be passed to Maven or SBT in
-            order to build and test this module (e.g. '-PprofileName').
-        :param sbt_test_goals: A set of SBT test goals for testing this module.
-        :param should_run_python_tests: If true, changes in this module will trigger Python tests.
-            For now, this has the effect of causing _all_ Python tests to be run, although in the
-            future this should be changed to run only a subset of the Python tests that depend
-            on this module.
-        :param should_run_r_tests: If true, changes in this module will trigger all R tests.
-        """
-        self.name = name
-        self.dependencies = dependencies
-        self.source_file_prefixes = source_file_regexes
-        self.sbt_test_goals = sbt_test_goals
-        self.build_profile_flags = build_profile_flags
-        self.should_run_python_tests = should_run_python_tests
-        self.should_run_r_tests = should_run_r_tests
-
-        self.dependent_modules = set()
-        for dep in dependencies:
-            dep.dependent_modules.add(self)
-        all_modules.append(self)
-
-    def contains_file(self, filename):
-        return any(re.match(p, filename) for p in self.source_file_prefixes)
-
-
-sql = Module(
-    name="sql",
-    dependencies=[],
-    source_file_regexes=[
-        "sql/(?!hive-thriftserver)",
-        "bin/spark-sql",
-    ],
-    build_profile_flags=[
-        "-Phive",
-    ],
-    sbt_test_goals=[
-        "catalyst/test",
-        "sql/test",
-        "hive/test",
-    ]
-)
-
-
-hive_thriftserver = Module(
-    name="hive-thriftserver",
-    dependencies=[sql],
-    source_file_regexes=[
-        "sql/hive-thriftserver",
-        "sbin/start-thriftserver.sh",
-    ],
-    build_profile_flags=[
-        "-Phive-thriftserver",
-    ],
-    sbt_test_goals=[
-        "hive-thriftserver/test",
-    ]
-)
-
-
-graphx = Module(
-    name="graphx",
-    dependencies=[],
-    source_file_regexes=[
-        "graphx/",
-    ],
-    sbt_test_goals=[
-        "graphx/test"
-    ]
-)
-
-
-streaming = Module(
-    name="streaming",
-    dependencies=[],
-    source_file_regexes=[
-        "streaming",
-    ],
-    sbt_test_goals=[
-        "streaming/test",
-    ]
-)
-
-
-streaming_kinesis_asl = Module(
-    name="kinesis-asl",
-    dependencies=[streaming],
-    source_file_regexes=[
-        "extras/kinesis-asl/",
-    ],
-    build_profile_flags=[
-        "-Pkinesis-asl",
-    ],
-    sbt_test_goals=[
-        "kinesis-asl/test",
-    ]
-)
-
-
-streaming_zeromq = Module(
-    name="streaming-zeromq",
-    dependencies=[streaming],
-    source_file_regexes=[
-        "external/zeromq",
-    ],
-    sbt_test_goals=[
-        "streaming-zeromq/test",
-    ]
-)
-
-
-streaming_twitter = Module(
-    name="streaming-twitter",
-    dependencies=[streaming],
-    source_file_regexes=[
-        "external/twitter",
-    ],
-    sbt_test_goals=[
-        "streaming-twitter/test",
-    ]
-)
-
-
-streaming_mqtt = Module(
-    name="streaming-mqtt",
-    dependencies=[streaming],
-    source_file_regexes=[
-        "external/mqtt",
-    ],
-    sbt_test_goals=[
-        "streaming-mqtt/test",
-    ]
-)
-
-
-streaming_kafka = Module(
-    name="streaming-kafka",
-    dependencies=[streaming],
-    source_file_regexes=[
-        "external/kafka",
-        "external/kafka-assembly",
-    ],
-    sbt_test_goals=[
-        "streaming-kafka/test",
-    ]
-)
-
-
-streaming_flume_sink = Module(
-    name="streaming-flume-sink",
-    dependencies=[streaming],
-    source_file_regexes=[
-        "external/flume-sink",
-    ],
-    sbt_test_goals=[
-        "streaming-flume-sink/test",
-    ]
-)
-
-
-streaming_flume = Module(
-    name="streaming_flume",
-    dependencies=[streaming],
-    source_file_regexes=[
-        "external/flume",
-    ],
-    sbt_test_goals=[
-        "streaming-flume/test",
-    ]
-)
-
-
-mllib = Module(
-    name="mllib",
-    dependencies=[streaming, sql],
-    source_file_regexes=[
-        "data/mllib/",
-        "mllib/",
-    ],
-    sbt_test_goals=[
-        "mllib/test",
-    ]
-)
-
-
-examples = Module(
-    name="examples",
-    dependencies=[graphx, mllib, streaming, sql],
-    source_file_regexes=[
-        "examples/",
-    ],
-    sbt_test_goals=[
-        "examples/test",
-    ]
-)
-
-
-pyspark = Module(
-    name="pyspark",
-    dependencies=[mllib, streaming, streaming_kafka, sql],
-    source_file_regexes=[
-        "python/"
-    ],
-    should_run_python_tests=True
-)
-
-
-sparkr = Module(
-    name="sparkr",
-    dependencies=[sql, mllib],
-    source_file_regexes=[
-        "R/",
-    ],
-    should_run_r_tests=True
-)
-
-
-docs = Module(
-    name="docs",
-    dependencies=[],
-    source_file_regexes=[
-        "docs/",
-    ]
-)
-
-
-ec2 = Module(
-    name="ec2",
-    dependencies=[],
-    source_file_regexes=[
-        "ec2/",
-    ]
-)
-
-
-# The root module is a dummy module which is used to run all of the tests.
-# No other modules should directly depend on this module.
-root = Module(
-    name="root",
-    dependencies=[],
-    source_file_regexes=[],
-    # In order to run all of the tests, enable every test profile:
-    build_profile_flags=
-        list(set(itertools.chain.from_iterable(m.build_profile_flags for m in all_modules))),
-    sbt_test_goals=[
-        "test",
-    ],
-    should_run_python_tests=True,
-    should_run_r_tests=True
-)
-
-
 def determine_modules_for_files(filenames):
     """
     Given a list of filenames, return the set of modules that contain those files.
@@ -315,19 +41,19 @@ def determine_modules_for_files(filenames):
     file to belong to the 'root' module.
 
     >>> sorted(x.name for x in determine_modules_for_files(["python/pyspark/a.py", "sql/test/foo"]))
-    ['pyspark', 'sql']
+    ['pyspark-core', 'sql']
     >>> [x.name for x in determine_modules_for_files(["file_not_matched_by_any_subproject"])]
     ['root']
     """
     changed_modules = set()
     for filename in filenames:
         matched_at_least_one_module = False
-        for module in all_modules:
+        for module in modules.all_modules:
             if module.contains_file(filename):
                 changed_modules.add(module)
                 matched_at_least_one_module = True
         if not matched_at_least_one_module:
-            changed_modules.add(root)
+            changed_modules.add(modules.root)
     return changed_modules
 
 
@@ -352,7 +78,8 @@ def identify_changed_files_from_git_commits(patch_sha, target_branch=None, targe
         run_cmd(['git', 'fetch', 'origin', str(target_branch+':'+target_branch)])
     else:
         diff_target = target_ref
-    raw_output = subprocess.check_output(['git', 'diff', '--name-only', patch_sha, diff_target])
+    raw_output = subprocess.check_output(['git', 'diff', '--name-only', patch_sha, diff_target],
+                                         universal_newlines=True)
     # Remove any empty strings
     return [f for f in raw_output.split('\n') if f]
 
@@ -362,18 +89,20 @@ def determine_modules_to_test(changed_modules):
     Given a set of modules that have changed, compute the transitive closure of those modules'
     dependent modules in order to determine the set of modules that should be tested.
 
-    >>> sorted(x.name for x in determine_modules_to_test([root]))
+    >>> sorted(x.name for x in determine_modules_to_test([modules.root]))
     ['root']
-    >>> sorted(x.name for x in determine_modules_to_test([graphx]))
+    >>> sorted(x.name for x in determine_modules_to_test([modules.graphx]))
     ['examples', 'graphx']
-    >>> sorted(x.name for x in determine_modules_to_test([sql]))
-    ['examples', 'hive-thriftserver', 'mllib', 'pyspark', 'sparkr', 'sql']
+    >>> x = sorted(x.name for x in determine_modules_to_test([modules.sql]))
+    >>> x # doctest: +NORMALIZE_WHITESPACE
+    ['examples', 'hive-thriftserver', 'mllib', 'pyspark-core', 'pyspark-ml', \
+     'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming', 'sparkr', 'sql']
     """
     # If we're going to have to run all of the tests, then we can just short-circuit
     # and return 'root'. No module depends on root, so if it appears then it will be
     # in changed_modules.
-    if root in changed_modules:
-        return [root]
+    if modules.root in changed_modules:
+        return [modules.root]
     modules_to_test = set()
     for module in changed_modules:
         modules_to_test = modules_to_test.union(determine_modules_to_test(module.dependent_modules))
@@ -398,60 +127,6 @@ def get_error_codes(err_code_file):
 ERROR_CODES = get_error_codes(os.path.join(SPARK_HOME, "dev/run-tests-codes.sh"))
 
 
-def exit_from_command_with_retcode(cmd, retcode):
-    print "[error] running", ' '.join(cmd), "; received return code", retcode
-    sys.exit(int(os.environ.get("CURRENT_BLOCK", 255)))
-
-
-def rm_r(path):
-    """Given an arbitrary path properly remove it with the correct python
-    construct if it exists
-    - from: http://stackoverflow.com/a/9559881"""
-
-    if os.path.isdir(path):
-        shutil.rmtree(path)
-    elif os.path.exists(path):
-        os.remove(path)
-
-
-def run_cmd(cmd):
-    """Given a command as a list of arguments will attempt to execute the
-    command from the determined SPARK_HOME directory and, on failure, print
-    an error message"""
-
-    if not isinstance(cmd, list):
-        cmd = cmd.split()
-    try:
-        subprocess.check_call(cmd)
-    except subprocess.CalledProcessError as e:
-        exit_from_command_with_retcode(e.cmd, e.returncode)
-
-
-def is_exe(path):
-    """Check if a given path is an executable file
-    - from: http://stackoverflow.com/a/377028"""
-
-    return os.path.isfile(path) and os.access(path, os.X_OK)
-
-
-def which(program):
-    """Find and return the given program by its absolute path or 'None'
-    - from: http://stackoverflow.com/a/377028"""
-
-    fpath = os.path.split(program)[0]
-
-    if fpath:
-        if is_exe(program):
-            return program
-    else:
-        for path in os.environ.get("PATH").split(os.pathsep):
-            path = path.strip('"')
-            exe_file = os.path.join(path, program)
-            if is_exe(exe_file):
-                return exe_file
-    return None
-
-
 def determine_java_executable():
     """Will return the path of the java executable that will be used by Spark's
     tests or `None`"""
@@ -476,7 +151,8 @@ def determine_java_version(java_exe):
     with accessors '.major', '.minor', '.patch', '.update'"""
 
     raw_output = subprocess.check_output([java_exe, "-version"],
-                                         stderr=subprocess.STDOUT)
+                                         stderr=subprocess.STDOUT,
+                                         universal_newlines=True)
 
     raw_output_lines = raw_output.split('\n')
 
@@ -504,10 +180,10 @@ def set_title_and_block(title, err_block):
     os.environ["CURRENT_BLOCK"] = ERROR_CODES[err_block]
     line_str = '=' * 72
 
-    print
-    print line_str
-    print title
-    print line_str
+    print('')
+    print(line_str)
+    print(title)
+    print(line_str)
 
 
 def run_apache_rat_checks():
@@ -534,8 +210,8 @@ def build_spark_documentation():
     jekyll_bin = which("jekyll")
 
     if not jekyll_bin:
-        print "[error] Cannot find a version of `jekyll` on the system; please",
-        print "install one and retry to build documentation."
+        print("[error] Cannot find a version of `jekyll` on the system; please"
+              " install one and retry to build documentation.")
         sys.exit(int(os.environ.get("CURRENT_BLOCK", 255)))
     else:
         run_cmd([jekyll_bin, "build"])
@@ -571,7 +247,7 @@ def exec_sbt(sbt_args=()):
     echo_proc.wait()
     for line in iter(sbt_proc.stdout.readline, ''):
         if not sbt_output_filter.match(line):
-            print line,
+            print(line, end='')
     retcode = sbt_proc.wait()
 
     if retcode > 0:
@@ -594,33 +270,33 @@ def get_hadoop_profiles(hadoop_version):
     if hadoop_version in sbt_maven_hadoop_profiles:
         return sbt_maven_hadoop_profiles[hadoop_version]
     else:
-        print "[error] Could not find", hadoop_version, "in the list. Valid options",
-        print "are", sbt_maven_hadoop_profiles.keys()
+        print("[error] Could not find", hadoop_version, "in the list. Valid options"
+              " are", sbt_maven_hadoop_profiles.keys())
         sys.exit(int(os.environ.get("CURRENT_BLOCK", 255)))
 
 
 def build_spark_maven(hadoop_version):
     # Enable all of the profiles for the build:
-    build_profiles = get_hadoop_profiles(hadoop_version) + root.build_profile_flags
+    build_profiles = get_hadoop_profiles(hadoop_version) + modules.root.build_profile_flags
     mvn_goals = ["clean", "package", "-DskipTests"]
     profiles_and_goals = build_profiles + mvn_goals
 
-    print "[info] Building Spark (w/Hive 0.13.1) using Maven with these arguments:",
-    print " ".join(profiles_and_goals)
+    print("[info] Building Spark (w/Hive 0.13.1) using Maven with these arguments: "
+          " ".join(profiles_and_goals))
 
     exec_maven(profiles_and_goals)
 
 
 def build_spark_sbt(hadoop_version):
     # Enable all of the profiles for the build:
-    build_profiles = get_hadoop_profiles(hadoop_version) + root.build_profile_flags
+    build_profiles = get_hadoop_profiles(hadoop_version) + modules.root.build_profile_flags
     sbt_goals = ["package",
                  "assembly/assembly",
                  "streaming-kafka-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
-    print "[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:",
-    print " ".join(profiles_and_goals)
+    print("[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments: "
+          " ".join(profiles_and_goals))
 
     exec_sbt(profiles_and_goals)
 
@@ -648,8 +324,8 @@ def run_scala_tests_maven(test_profiles):
     mvn_test_goals = ["test", "--fail-at-end"]
     profiles_and_goals = test_profiles + mvn_test_goals
 
-    print "[info] Running Spark tests using Maven with these arguments:",
-    print " ".join(profiles_and_goals)
+    print("[info] Running Spark tests using Maven with these arguments: "
+          " ".join(profiles_and_goals))
 
     exec_maven(profiles_and_goals)
 
@@ -663,8 +339,8 @@ def run_scala_tests_sbt(test_modules, test_profiles):
 
     profiles_and_goals = test_profiles + list(sbt_test_goals)
 
-    print "[info] Running Spark tests using SBT with these arguments:",
-    print " ".join(profiles_and_goals)
+    print("[info] Running Spark tests using SBT with these arguments: "
+          " ".join(profiles_and_goals))
 
     exec_sbt(profiles_and_goals)
 
@@ -684,10 +360,13 @@ def run_scala_tests(build_tool, hadoop_version, test_modules):
         run_scala_tests_sbt(test_modules, test_profiles)
 
 
-def run_python_tests():
+def run_python_tests(test_modules):
     set_title_and_block("Running PySpark tests", "BLOCK_PYSPARK_UNIT_TESTS")
 
-    run_cmd([os.path.join(SPARK_HOME, "python", "run-tests")])
+    command = [os.path.join(SPARK_HOME, "python", "run-tests")]
+    if test_modules != [modules.root]:
+        command.append("--modules=%s" % ','.join(m.name for m in modules))
+    run_cmd(command)
 
 
 def run_sparkr_tests():
@@ -697,14 +376,14 @@ def run_sparkr_tests():
         run_cmd([os.path.join(SPARK_HOME, "R", "install-dev.sh")])
         run_cmd([os.path.join(SPARK_HOME, "R", "run-tests.sh")])
     else:
-        print "Ignoring SparkR tests as R was not found in PATH"
+        print("Ignoring SparkR tests as R was not found in PATH")
 
 
 def main():
     # Ensure the user home directory (HOME) is valid and is an absolute directory
     if not USER_HOME or not os.path.isabs(USER_HOME):
-        print "[error] Cannot determine your home directory as an absolute path;",
-        print "ensure the $HOME environment variable is set properly."
+        print("[error] Cannot determine your home directory as an absolute path;"
+              " ensure the $HOME environment variable is set properly.")
         sys.exit(1)
 
     os.chdir(SPARK_HOME)
@@ -718,14 +397,14 @@ def main():
     java_exe = determine_java_executable()
 
     if not java_exe:
-        print "[error] Cannot find a version of `java` on the system; please",
-        print "install one and retry."
+        print("[error] Cannot find a version of `java` on the system; please"
+              " install one and retry.")
         sys.exit(2)
 
     java_version = determine_java_version(java_exe)
 
     if java_version.minor < 8:
-        print "[warn] Java 8 tests will not run because JDK version is < 1.8."
+        print("[warn] Java 8 tests will not run because JDK version is < 1.8.")
 
     if os.environ.get("AMPLAB_JENKINS"):
         # if we're on the Amplab Jenkins build servers setup variables
@@ -741,8 +420,8 @@ def main():
         hadoop_version = "hadoop2.3"
         test_env = "local"
 
-    print "[info] Using build tool", build_tool, "with Hadoop profile", hadoop_version,
-    print "under environment", test_env
+    print("[info] Using build tool", build_tool, "with Hadoop profile", hadoop_version,
+          "under environment", test_env)
 
     changed_modules = None
     changed_files = None
@@ -751,8 +430,9 @@ def main():
         changed_files = identify_changed_files_from_git_commits("HEAD", target_branch=target_branch)
         changed_modules = determine_modules_for_files(changed_files)
     if not changed_modules:
-        changed_modules = [root]
-    print "[info] Found the following changed modules:", ", ".join(x.name for x in changed_modules)
+        changed_modules = [modules.root]
+    print("[info] Found the following changed modules:",
+          ", ".join(x.name for x in changed_modules))
 
     test_modules = determine_modules_to_test(changed_modules)
 
@@ -779,8 +459,9 @@ def main():
     # run the test suites
     run_scala_tests(build_tool, hadoop_version, test_modules)
 
-    if any(m.should_run_python_tests for m in test_modules):
-        run_python_tests()
+    modules_with_python_tests = [m for m in test_modules if m.python_test_goals]
+    if modules_with_python_tests:
+        run_python_tests(modules_with_python_tests)
     if any(m.should_run_r_tests for m in test_modules):
         run_sparkr_tests()
 
diff --git a/dev/sparktestsupport/__init__.py b/dev/sparktestsupport/__init__.py
new file mode 100644
index 0000000000000..12696d98fb988
--- /dev/null
+++ b/dev/sparktestsupport/__init__.py
@@ -0,0 +1,21 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+
+SPARK_HOME = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../"))
+USER_HOME = os.environ.get("HOME")
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
new file mode 100644
index 0000000000000..efe3a897e9c10
--- /dev/null
+++ b/dev/sparktestsupport/modules.py
@@ -0,0 +1,385 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import itertools
+import re
+
+all_modules = []
+
+
+class Module(object):
+    """
+    A module is the basic abstraction in our test runner script. Each module consists of a set of
+    source files, a set of test commands, and a set of dependencies on other modules. We use modules
+    to define a dependency graph that lets determine which tests to run based on which files have
+    changed.
+    """
+
+    def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(),
+                 sbt_test_goals=(), python_test_goals=(), blacklisted_python_implementations=(),
+                 should_run_r_tests=False):
+        """
+        Define a new module.
+
+        :param name: A short module name, for display in logging and error messages.
+        :param dependencies: A set of dependencies for this module. This should only include direct
+            dependencies; transitive dependencies are resolved automatically.
+        :param source_file_regexes: a set of regexes that match source files belonging to this
+            module. These regexes are applied by attempting to match at the beginning of the
+            filename strings.
+        :param build_profile_flags: A set of profile flags that should be passed to Maven or SBT in
+            order to build and test this module (e.g. '-PprofileName').
+        :param sbt_test_goals: A set of SBT test goals for testing this module.
+        :param python_test_goals: A set of Python test goals for testing this module.
+        :param blacklisted_python_implementations: A set of Python implementations that are not
+            supported by this module's Python components. The values in this set should match
+            strings returned by Python's `platform.python_implementation()`.
+        :param should_run_r_tests: If true, changes in this module will trigger all R tests.
+        """
+        self.name = name
+        self.dependencies = dependencies
+        self.source_file_prefixes = source_file_regexes
+        self.sbt_test_goals = sbt_test_goals
+        self.build_profile_flags = build_profile_flags
+        self.python_test_goals = python_test_goals
+        self.blacklisted_python_implementations = blacklisted_python_implementations
+        self.should_run_r_tests = should_run_r_tests
+
+        self.dependent_modules = set()
+        for dep in dependencies:
+            dep.dependent_modules.add(self)
+        all_modules.append(self)
+
+    def contains_file(self, filename):
+        return any(re.match(p, filename) for p in self.source_file_prefixes)
+
+
+sql = Module(
+    name="sql",
+    dependencies=[],
+    source_file_regexes=[
+        "sql/(?!hive-thriftserver)",
+        "bin/spark-sql",
+    ],
+    build_profile_flags=[
+        "-Phive",
+    ],
+    sbt_test_goals=[
+        "catalyst/test",
+        "sql/test",
+        "hive/test",
+    ]
+)
+
+
+hive_thriftserver = Module(
+    name="hive-thriftserver",
+    dependencies=[sql],
+    source_file_regexes=[
+        "sql/hive-thriftserver",
+        "sbin/start-thriftserver.sh",
+    ],
+    build_profile_flags=[
+        "-Phive-thriftserver",
+    ],
+    sbt_test_goals=[
+        "hive-thriftserver/test",
+    ]
+)
+
+
+graphx = Module(
+    name="graphx",
+    dependencies=[],
+    source_file_regexes=[
+        "graphx/",
+    ],
+    sbt_test_goals=[
+        "graphx/test"
+    ]
+)
+
+
+streaming = Module(
+    name="streaming",
+    dependencies=[],
+    source_file_regexes=[
+        "streaming",
+    ],
+    sbt_test_goals=[
+        "streaming/test",
+    ]
+)
+
+
+streaming_kinesis_asl = Module(
+    name="kinesis-asl",
+    dependencies=[streaming],
+    source_file_regexes=[
+        "extras/kinesis-asl/",
+    ],
+    build_profile_flags=[
+        "-Pkinesis-asl",
+    ],
+    sbt_test_goals=[
+        "kinesis-asl/test",
+    ]
+)
+
+
+streaming_zeromq = Module(
+    name="streaming-zeromq",
+    dependencies=[streaming],
+    source_file_regexes=[
+        "external/zeromq",
+    ],
+    sbt_test_goals=[
+        "streaming-zeromq/test",
+    ]
+)
+
+
+streaming_twitter = Module(
+    name="streaming-twitter",
+    dependencies=[streaming],
+    source_file_regexes=[
+        "external/twitter",
+    ],
+    sbt_test_goals=[
+        "streaming-twitter/test",
+    ]
+)
+
+
+streaming_mqtt = Module(
+    name="streaming-mqtt",
+    dependencies=[streaming],
+    source_file_regexes=[
+        "external/mqtt",
+    ],
+    sbt_test_goals=[
+        "streaming-mqtt/test",
+    ]
+)
+
+
+streaming_kafka = Module(
+    name="streaming-kafka",
+    dependencies=[streaming],
+    source_file_regexes=[
+        "external/kafka",
+        "external/kafka-assembly",
+    ],
+    sbt_test_goals=[
+        "streaming-kafka/test",
+    ]
+)
+
+
+streaming_flume_sink = Module(
+    name="streaming-flume-sink",
+    dependencies=[streaming],
+    source_file_regexes=[
+        "external/flume-sink",
+    ],
+    sbt_test_goals=[
+        "streaming-flume-sink/test",
+    ]
+)
+
+
+streaming_flume = Module(
+    name="streaming_flume",
+    dependencies=[streaming],
+    source_file_regexes=[
+        "external/flume",
+    ],
+    sbt_test_goals=[
+        "streaming-flume/test",
+    ]
+)
+
+
+mllib = Module(
+    name="mllib",
+    dependencies=[streaming, sql],
+    source_file_regexes=[
+        "data/mllib/",
+        "mllib/",
+    ],
+    sbt_test_goals=[
+        "mllib/test",
+    ]
+)
+
+
+examples = Module(
+    name="examples",
+    dependencies=[graphx, mllib, streaming, sql],
+    source_file_regexes=[
+        "examples/",
+    ],
+    sbt_test_goals=[
+        "examples/test",
+    ]
+)
+
+
+pyspark_core = Module(
+    name="pyspark-core",
+    dependencies=[mllib, streaming, streaming_kafka],
+    source_file_regexes=[
+        "python/(?!pyspark/(ml|mllib|sql|streaming))"
+    ],
+    python_test_goals=[
+        "pyspark.rdd",
+        "pyspark.context",
+        "pyspark.conf",
+        "pyspark.broadcast",
+        "pyspark.accumulators",
+        "pyspark.serializers",
+        "pyspark.profiler",
+        "pyspark.shuffle",
+        "pyspark.tests",
+    ]
+)
+
+
+pyspark_sql = Module(
+    name="pyspark-sql",
+    dependencies=[pyspark_core, sql],
+    source_file_regexes=[
+        "python/pyspark/sql"
+    ],
+    python_test_goals=[
+        "pyspark.sql.types",
+        "pyspark.sql.context",
+        "pyspark.sql.column",
+        "pyspark.sql.dataframe",
+        "pyspark.sql.group",
+        "pyspark.sql.functions",
+        "pyspark.sql.readwriter",
+        "pyspark.sql.window",
+        "pyspark.sql.tests",
+    ]
+)
+
+
+pyspark_streaming = Module(
+    name="pyspark-streaming",
+    dependencies=[pyspark_core, streaming, streaming_kafka],
+    source_file_regexes=[
+        "python/pyspark/streaming"
+    ],
+    python_test_goals=[
+        "pyspark.streaming.util",
+        "pyspark.streaming.tests",
+    ]
+)
+
+
+pyspark_mllib = Module(
+    name="pyspark-mllib",
+    dependencies=[pyspark_core, pyspark_streaming, pyspark_sql, mllib],
+    source_file_regexes=[
+        "python/pyspark/mllib"
+    ],
+    python_test_goals=[
+        "pyspark.mllib.classification",
+        "pyspark.mllib.clustering",
+        "pyspark.mllib.evaluation",
+        "pyspark.mllib.feature",
+        "pyspark.mllib.fpm",
+        "pyspark.mllib.linalg",
+        "pyspark.mllib.random",
+        "pyspark.mllib.recommendation",
+        "pyspark.mllib.regression",
+        "pyspark.mllib.stat._statistics",
+        "pyspark.mllib.stat.KernelDensity",
+        "pyspark.mllib.tree",
+        "pyspark.mllib.util",
+        "pyspark.mllib.tests",
+    ],
+    blacklisted_python_implementations=[
+        "PyPy"  # Skip these tests under PyPy since they require numpy and it isn't available there
+    ]
+)
+
+
+pyspark_ml = Module(
+    name="pyspark-ml",
+    dependencies=[pyspark_core, pyspark_mllib],
+    source_file_regexes=[
+        "python/pyspark/ml/"
+    ],
+    python_test_goals=[
+        "pyspark.ml.feature",
+        "pyspark.ml.classification",
+        "pyspark.ml.recommendation",
+        "pyspark.ml.regression",
+        "pyspark.ml.tuning",
+        "pyspark.ml.tests",
+        "pyspark.ml.evaluation",
+    ],
+    blacklisted_python_implementations=[
+        "PyPy"  # Skip these tests under PyPy since they require numpy and it isn't available there
+    ]
+)
+
+sparkr = Module(
+    name="sparkr",
+    dependencies=[sql, mllib],
+    source_file_regexes=[
+        "R/",
+    ],
+    should_run_r_tests=True
+)
+
+
+docs = Module(
+    name="docs",
+    dependencies=[],
+    source_file_regexes=[
+        "docs/",
+    ]
+)
+
+
+ec2 = Module(
+    name="ec2",
+    dependencies=[],
+    source_file_regexes=[
+        "ec2/",
+    ]
+)
+
+
+# The root module is a dummy module which is used to run all of the tests.
+# No other modules should directly depend on this module.
+root = Module(
+    name="root",
+    dependencies=[],
+    source_file_regexes=[],
+    # In order to run all of the tests, enable every test profile:
+    build_profile_flags=list(set(
+        itertools.chain.from_iterable(m.build_profile_flags for m in all_modules))),
+    sbt_test_goals=[
+        "test",
+    ],
+    python_test_goals=list(itertools.chain.from_iterable(m.python_test_goals for m in all_modules)),
+    should_run_r_tests=True
+)
diff --git a/dev/sparktestsupport/shellutils.py b/dev/sparktestsupport/shellutils.py
new file mode 100644
index 0000000000000..ad9b0cc89e4ab
--- /dev/null
+++ b/dev/sparktestsupport/shellutils.py
@@ -0,0 +1,81 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import shutil
+import subprocess
+import sys
+
+
+def exit_from_command_with_retcode(cmd, retcode):
+    print("[error] running", ' '.join(cmd), "; received return code", retcode)
+    sys.exit(int(os.environ.get("CURRENT_BLOCK", 255)))
+
+
+def rm_r(path):
+    """
+    Given an arbitrary path, properly remove it with the correct Python construct if it exists.
+    From: http://stackoverflow.com/a/9559881
+    """
+
+    if os.path.isdir(path):
+        shutil.rmtree(path)
+    elif os.path.exists(path):
+        os.remove(path)
+
+
+def run_cmd(cmd):
+    """
+    Given a command as a list of arguments will attempt to execute the command
+    and, on failure, print an error message and exit.
+    """
+
+    if not isinstance(cmd, list):
+        cmd = cmd.split()
+    try:
+        subprocess.check_call(cmd)
+    except subprocess.CalledProcessError as e:
+        exit_from_command_with_retcode(e.cmd, e.returncode)
+
+
+def is_exe(path):
+    """
+    Check if a given path is an executable file.
+    From: http://stackoverflow.com/a/377028
+    """
+
+    return os.path.isfile(path) and os.access(path, os.X_OK)
+
+
+def which(program):
+    """
+    Find and return the given program by its absolute path or 'None' if the program cannot be found.
+    From: http://stackoverflow.com/a/377028
+    """
+
+    fpath = os.path.split(program)[0]
+
+    if fpath:
+        if is_exe(program):
+            return program
+    else:
+        for path in os.environ.get("PATH").split(os.pathsep):
+            path = path.strip('"')
+            exe_file = os.path.join(path, program)
+            if is_exe(exe_file):
+                return exe_file
+    return None
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 57049beea4dba..91ce681fbe169 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 
+import glob
 import os
 import sys
 from itertools import chain
@@ -677,4 +678,19 @@ def test_kafka_rdd_with_leaders(self):
         self._validateRddResult(sendData, rdd)
 
 if __name__ == "__main__":
+    SPARK_HOME = os.environ["SPARK_HOME"]
+    kafka_assembly_dir = os.path.join(SPARK_HOME, "external/kafka-assembly")
+    jars = glob.glob(
+        os.path.join(kafka_assembly_dir, "target/scala-*/spark-streaming-kafka-assembly-*.jar"))
+    if not jars:
+        raise Exception(
+            ("Failed to find Spark Streaming kafka assembly jar in %s. " % kafka_assembly_dir) +
+            "You need to build Spark with "
+            "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or "
+            "'build/mvn package' before running this test")
+    elif len(jars) > 1:
+        raise Exception(("Found multiple Spark Streaming Kafka assembly JARs in %s; please "
+                         "remove all but one") % kafka_assembly_dir)
+    else:
+        os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars[0]
     unittest.main()
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 78265423682b0..17256dfc95744 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -1421,7 +1421,8 @@ def do_termination_test(self, terminator):
 
         # start daemon
         daemon_path = os.path.join(os.path.dirname(__file__), "daemon.py")
-        daemon = Popen([sys.executable, daemon_path], stdin=PIPE, stdout=PIPE)
+        python_exec = sys.executable or os.environ.get("PYSPARK_PYTHON")
+        daemon = Popen([python_exec, daemon_path], stdin=PIPE, stdout=PIPE)
 
         # read the port number
         port = read_int(daemon.stdout)
diff --git a/python/run-tests b/python/run-tests
index 4468fdb3f267e..24949657ed7ab 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -18,165 +18,7 @@
 #
 
 
-# Figure out where the Spark framework is installed
-FWDIR="$(cd "`dirname "$0"`"; cd ../; pwd)"
+FWDIR="$(cd "`dirname $0`"/..; pwd)"
+cd "$FWDIR"
 
-. "$FWDIR"/bin/load-spark-env.sh
-
-# CD into the python directory to find things on the right path
-cd "$FWDIR/python"
-
-FAILED=0
-LOG_FILE=unit-tests.log
-START=$(date +"%s")
-
-rm -f $LOG_FILE
-
-# Remove the metastore and warehouse directory created by the HiveContext tests in Spark SQL
-rm -rf metastore warehouse
-
-function run_test() {
-    echo -en "Running test: $1 ... " | tee -a $LOG_FILE
-    start=$(date +"%s")
-    SPARK_TESTING=1 time "$FWDIR"/bin/pyspark $1 > $LOG_FILE 2>&1
-
-    FAILED=$((PIPESTATUS[0]||$FAILED))
-
-    # Fail and exit on the first test failure.
-    if [[ $FAILED != 0 ]]; then
-        cat $LOG_FILE | grep -v "^[0-9][0-9]*" # filter all lines starting with a number.
-        echo -en "\033[31m"  # Red
-        echo "Had test failures; see logs."
-        echo -en "\033[0m"  # No color
-        exit -1
-    else
-        now=$(date +"%s")
-        echo "ok ($(($now - $start))s)"
-    fi
-}
-
-function run_core_tests() {
-    echo "Run core tests ..."
-    run_test "pyspark.rdd"
-    run_test "pyspark.context"
-    run_test "pyspark.conf"
-    run_test "pyspark.broadcast"
-    run_test "pyspark.accumulators"
-    run_test "pyspark.serializers"
-    run_test "pyspark.profiler"
-    run_test "pyspark.shuffle"
-    run_test "pyspark.tests"
-}
-
-function run_sql_tests() {
-    echo "Run sql tests ..."
-    run_test "pyspark.sql.types"
-    run_test "pyspark.sql.context"
-    run_test "pyspark.sql.column"
-    run_test "pyspark.sql.dataframe"
-    run_test "pyspark.sql.group"
-    run_test "pyspark.sql.functions"
-    run_test "pyspark.sql.readwriter"
-    run_test "pyspark.sql.window"
-    run_test "pyspark.sql.tests"
-}
-
-function run_mllib_tests() {
-    echo "Run mllib tests ..."
-    run_test "pyspark.mllib.classification"
-    run_test "pyspark.mllib.clustering"
-    run_test "pyspark.mllib.evaluation"
-    run_test "pyspark.mllib.feature"
-    run_test "pyspark.mllib.fpm"
-    run_test "pyspark.mllib.linalg"
-    run_test "pyspark.mllib.random"
-    run_test "pyspark.mllib.recommendation"
-    run_test "pyspark.mllib.regression"
-    run_test "pyspark.mllib.stat._statistics"
-    run_test "pyspark.mllib.stat.KernelDensity"
-    run_test "pyspark.mllib.tree"
-    run_test "pyspark.mllib.util"
-    run_test "pyspark.mllib.tests"
-}
-
-function run_ml_tests() {
-    echo "Run ml tests ..."
-    run_test "pyspark.ml.feature"
-    run_test "pyspark.ml.classification"
-    run_test "pyspark.ml.recommendation"
-    run_test "pyspark.ml.regression"
-    run_test "pyspark.ml.tuning"
-    run_test "pyspark.ml.tests"
-    run_test "pyspark.ml.evaluation"
-}
-
-function run_streaming_tests() {
-    echo "Run streaming tests ..."
-
-    KAFKA_ASSEMBLY_DIR="$FWDIR"/external/kafka-assembly
-    JAR_PATH="${KAFKA_ASSEMBLY_DIR}/target/scala-${SPARK_SCALA_VERSION}"
-    for f in "${JAR_PATH}"/spark-streaming-kafka-assembly-*.jar; do
-      if [[ ! -e "$f" ]]; then
-        echo "Failed to find Spark Streaming Kafka assembly jar in $KAFKA_ASSEMBLY_DIR" 1>&2
-        echo "You need to build Spark with " \
-             "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or" \
-             "'build/mvn package' before running this program" 1>&2
-        exit 1
-      fi
-      KAFKA_ASSEMBLY_JAR="$f"
-    done
-
-    export PYSPARK_SUBMIT_ARGS="--jars ${KAFKA_ASSEMBLY_JAR} pyspark-shell"
-    run_test "pyspark.streaming.util"
-    run_test "pyspark.streaming.tests"
-}
-
-echo "Running PySpark tests. Output is in python/$LOG_FILE."
-
-export PYSPARK_PYTHON="python"
-
-# Try to test with Python 2.6, since that's the minimum version that we support:
-if [ $(which python2.6) ]; then
-    export PYSPARK_PYTHON="python2.6"
-fi
-
-echo "Testing with Python version:"
-$PYSPARK_PYTHON --version
-
-run_core_tests
-run_sql_tests
-run_mllib_tests
-run_ml_tests
-run_streaming_tests
-
-# Try to test with Python 3
-if [ $(which python3.4) ]; then
-    export PYSPARK_PYTHON="python3.4"
-    echo "Testing with Python3.4 version:"
-    $PYSPARK_PYTHON --version
-
-    run_core_tests
-    run_sql_tests
-    run_mllib_tests
-    run_ml_tests
-    run_streaming_tests
-fi
-
-# Try to test with PyPy
-if [ $(which pypy) ]; then
-    export PYSPARK_PYTHON="pypy"
-    echo "Testing with PyPy version:"
-    $PYSPARK_PYTHON --version
-
-    run_core_tests
-    run_sql_tests
-    run_streaming_tests
-fi
-
-if [[ $FAILED == 0 ]]; then
-    now=$(date +"%s")
-    echo -e "\033[32mTests passed \033[0min $(($now - $START)) seconds"
-fi
-
-# TODO: in the long-run, it would be nice to use a test runner like `nose`.
-# The doctest fixtures are the current barrier to doing this.
+exec python -u ./python/run-tests.py "$@"
diff --git a/python/run-tests.py b/python/run-tests.py
new file mode 100755
index 0000000000000..7d485b500ee3a
--- /dev/null
+++ b/python/run-tests.py
@@ -0,0 +1,132 @@
+#!/usr/bin/env python
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+from optparse import OptionParser
+import os
+import re
+import subprocess
+import sys
+import time
+
+
+# Append `SPARK_HOME/dev` to the Python path so that we can import the sparktestsupport module
+sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../dev/"))
+
+
+from sparktestsupport import SPARK_HOME  # noqa (suppress pep8 warnings)
+from sparktestsupport.shellutils import which  # noqa
+from sparktestsupport.modules import all_modules  # noqa
+
+
+python_modules = dict((m.name, m) for m in all_modules if m.python_test_goals if m.name != 'root')
+
+
+def print_red(text):
+    print('\033[31m' + text + '\033[0m')
+
+
+LOG_FILE = os.path.join(SPARK_HOME, "python/unit-tests.log")
+
+
+def run_individual_python_test(test_name, pyspark_python):
+    env = {'SPARK_TESTING': '1', 'PYSPARK_PYTHON': which(pyspark_python)}
+    print("    Running test: %s ..." % test_name, end='')
+    start_time = time.time()
+    with open(LOG_FILE, 'a') as log_file:
+        retcode = subprocess.call(
+            [os.path.join(SPARK_HOME, "bin/pyspark"), test_name],
+            stderr=log_file, stdout=log_file, env=env)
+    duration = time.time() - start_time
+    # Exit on the first failure.
+    if retcode != 0:
+        with open(LOG_FILE, 'r') as log_file:
+            for line in log_file:
+                if not re.match('[0-9]+', line):
+                    print(line, end='')
+        print_red("\nHad test failures in %s; see logs." % test_name)
+        exit(-1)
+    else:
+        print("ok (%is)" % duration)
+
+
+def get_default_python_executables():
+    python_execs = [x for x in ["python2.6", "python3.4", "pypy"] if which(x)]
+    if "python2.6" not in python_execs:
+        print("WARNING: Not testing against `python2.6` because it could not be found; falling"
+              " back to `python` instead")
+        python_execs.insert(0, "python")
+    return python_execs
+
+
+def parse_opts():
+    parser = OptionParser(
+        prog="run-tests"
+    )
+    parser.add_option(
+        "--python-executables", type="string", default=','.join(get_default_python_executables()),
+        help="A comma-separated list of Python executables to test against (default: %default)"
+    )
+    parser.add_option(
+        "--modules", type="string",
+        default=",".join(sorted(python_modules.keys())),
+        help="A comma-separated list of Python modules to test (default: %default)"
+    )
+
+    (opts, args) = parser.parse_args()
+    if args:
+        parser.error("Unsupported arguments: %s" % ' '.join(args))
+    return opts
+
+
+def main():
+    opts = parse_opts()
+    print("Running PySpark tests. Output is in python/%s" % LOG_FILE)
+    if os.path.exists(LOG_FILE):
+        os.remove(LOG_FILE)
+    python_execs = opts.python_executables.split(',')
+    modules_to_test = []
+    for module_name in opts.modules.split(','):
+        if module_name in python_modules:
+            modules_to_test.append(python_modules[module_name])
+        else:
+            print("Error: unrecognized module %s" % module_name)
+            sys.exit(-1)
+    print("Will test against the following Python executables: %s" % python_execs)
+    print("Will test the following Python modules: %s" % [x.name for x in modules_to_test])
+
+    start_time = time.time()
+    for python_exec in python_execs:
+        python_implementation = subprocess.check_output(
+            [python_exec, "-c", "import platform; print(platform.python_implementation())"],
+            universal_newlines=True).strip()
+        print("Testing with `%s`: " % python_exec, end='')
+        subprocess.call([python_exec, "--version"])
+
+        for module in modules_to_test:
+            if python_implementation not in module.blacklisted_python_implementations:
+                print("Running %s tests ..." % module.name)
+                for test_goal in module.python_test_goals:
+                    run_individual_python_test(test_goal, python_exec)
+    total_duration = time.time() - start_time
+    print("Tests passed in %i seconds" % total_duration)
+
+
+if __name__ == "__main__":
+    main()

From 42db3a1c2fb6db61e01756be7fe88c4110ae638e Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 27 Jun 2015 23:07:20 -0700
Subject: [PATCH 0075/1454] [HOTFIX] Fix pull request builder bug in #6967

---
 dev/run-tests.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/run-tests.py b/dev/run-tests.py
index c51b0d3010a0f..3533e0c857b9b 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -365,7 +365,7 @@ def run_python_tests(test_modules):
 
     command = [os.path.join(SPARK_HOME, "python", "run-tests")]
     if test_modules != [modules.root]:
-        command.append("--modules=%s" % ','.join(m.name for m in modules))
+        command.append("--modules=%s" % ','.join(m.name for m in test_modules))
     run_cmd(command)
 
 

From f51004519c4c4915711fb9992e3aa4f05fd143ec Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 27 Jun 2015 23:27:52 -0700
Subject: [PATCH 0076/1454] [SPARK-8683] [BUILD] Depend on mockito-core instead
 of mockito-all

Spark's tests currently depend on `mockito-all`, which bundles Hamcrest and Objenesis classes. Instead, it should depend on `mockito-core`, which declares those libraries as Maven dependencies. This is necessary in order to fix a dependency conflict that leads to a NoSuchMethodError when using certain Hamcrest matchers.

See https://github.com/mockito/mockito/wiki/Declaring-mockito-dependency for more details.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7061 from JoshRosen/mockito-core-instead-of-all and squashes the following commits:

70eccbe [Josh Rosen] Depend on mockito-core instead of mockito-all.
---
 LICENSE                    | 2 +-
 core/pom.xml               | 2 +-
 extras/kinesis-asl/pom.xml | 2 +-
 launcher/pom.xml           | 2 +-
 mllib/pom.xml              | 2 +-
 network/common/pom.xml     | 2 +-
 network/shuffle/pom.xml    | 2 +-
 pom.xml                    | 2 +-
 repl/pom.xml               | 2 +-
 unsafe/pom.xml             | 2 +-
 yarn/pom.xml               | 2 +-
 11 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/LICENSE b/LICENSE
index 42010d9f5f0e6..8672be55eca3e 100644
--- a/LICENSE
+++ b/LICENSE
@@ -948,6 +948,6 @@ The following components are provided under the MIT License. See project link fo
      (MIT License) SLF4J LOG4J-12 Binding (org.slf4j:slf4j-log4j12:1.7.5 - http://www.slf4j.org)
      (MIT License) pyrolite (org.spark-project:pyrolite:2.0.1 - http://pythonhosted.org/Pyro4/)
      (MIT License) scopt (com.github.scopt:scopt_2.10:3.2.0 - https://github.com/scopt/scopt)
-     (The MIT License) Mockito (org.mockito:mockito-all:1.8.5 - http://www.mockito.org)
+     (The MIT License) Mockito (org.mockito:mockito-core:1.8.5 - http://www.mockito.org)
      (MIT License) jquery (https://jquery.org/license/)
      (MIT License) AnchorJS (https://github.com/bryanbraun/anchorjs)
diff --git a/core/pom.xml b/core/pom.xml
index 40a64beccdc24..565437c4861a4 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -354,7 +354,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index c6f60bc907438..c242e7a57b9ab 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -66,7 +66,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
diff --git a/launcher/pom.xml b/launcher/pom.xml
index 48dd0d5f9106b..a853e67f5cf78 100644
--- a/launcher/pom.xml
+++ b/launcher/pom.xml
@@ -49,7 +49,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index b16058ddc203a..a5db14407b4fc 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -106,7 +106,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
diff --git a/network/common/pom.xml b/network/common/pom.xml
index a85e0a66f4a30..7dc3068ab8cb7 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -77,7 +77,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index 4b5bfcb6f04bc..532463e96fbb7 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -79,7 +79,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
   </dependencies>
diff --git a/pom.xml b/pom.xml
index 80cacb5ace2d4..1aa70240888bc 100644
--- a/pom.xml
+++ b/pom.xml
@@ -681,7 +681,7 @@
       </dependency>
       <dependency>
         <groupId>org.mockito</groupId>
-        <artifactId>mockito-all</artifactId>
+        <artifactId>mockito-core</artifactId>
         <version>1.9.5</version>
         <scope>test</scope>
       </dependency>
diff --git a/repl/pom.xml b/repl/pom.xml
index 85f7bc8ac1024..370b2bc2fa8ed 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -93,7 +93,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
 
diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index dd2ae6457f0b9..33782c6c66f90 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -67,7 +67,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
   </dependencies>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 644def7501dc8..2aeed98285aa8 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -107,7 +107,7 @@
     </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
-      <artifactId>mockito-all</artifactId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>

From 52d128180166280af443fae84ac61386f3d6c500 Mon Sep 17 00:00:00 2001
From: Thomas Szymanski <develop@tszymanski.com>
Date: Sun, 28 Jun 2015 01:06:49 -0700
Subject: [PATCH 0077/1454] [SPARK-8649] [BUILD] Mapr repository is not defined
 properly

The previous commiter on this part was pwendell

The previous url gives 404, the new one seems to be OK.

This patch is added under the Apache License 2.0.

The JIRA link: https://issues.apache.org/jira/browse/SPARK-8649

Author: Thomas Szymanski <develop@tszymanski.com>

Closes #7054 from tszym/SPARK-8649 and squashes the following commits:

bfda9c4 [Thomas Szymanski] [SPARK-8649] [BUILD] Mapr repository is not defined properly
---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index 1aa70240888bc..00f50166b39b6 100644
--- a/pom.xml
+++ b/pom.xml
@@ -248,7 +248,7 @@
     <repository>
       <id>mapr-repo</id>
       <name>MapR Repository</name>
-      <url>http://repository.mapr.com/maven</url>
+      <url>http://repository.mapr.com/maven/</url>
       <releases>
         <enabled>true</enabled>
       </releases>

From 77da5be6f11a7e9cb1d44f7fb97b93481505afe8 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sun, 28 Jun 2015 08:03:58 -0700
Subject: [PATCH 0078/1454] [SPARK-8610] [SQL] Separate Row and InternalRow
 (part 2)

Currently, we use GenericRow both for Row and InternalRow, which is confusing because it could contain Scala type also Catalyst types.

This PR changes to use GenericInternalRow for InternalRow (contains catalyst types), GenericRow for Row (contains Scala types).

Also fixes some incorrect use of InternalRow or Row.

Author: Davies Liu <davies@databricks.com>

Closes #7003 from davies/internalrow and squashes the following commits:

d05866c [Davies Liu] fix test: rollback changes for pyspark
72878dd [Davies Liu] Merge branch 'master' of github.com:apache/spark into internalrow
efd0b25 [Davies Liu] fix copy of MutableRow
87b13cf [Davies Liu] fix test
d2ebd72 [Davies Liu] fix style
eb4b473 [Davies Liu] mark expensive API as final
bd4e99c [Davies Liu] Merge branch 'master' of github.com:apache/spark into internalrow
bdfb78f [Davies Liu] remove BaseMutableRow
6f99a97 [Davies Liu] fix catalyst test
defe931 [Davies Liu] remove BaseRow
288b31f [Davies Liu] Merge branch 'master' of github.com:apache/spark into internalrow
9d24350 [Davies Liu] separate Row and InternalRow (part 2)
---
 .../org/apache/spark/sql/BaseMutableRow.java  |  68 ------
 .../java/org/apache/spark/sql/BaseRow.java    | 197 ------------------
 .../sql/catalyst/expressions/UnsafeRow.java   |  19 +-
 .../main/scala/org/apache/spark/sql/Row.scala |  41 ++--
 .../sql/catalyst/CatalystTypeConverters.scala |   4 +-
 .../spark/sql/catalyst/InternalRow.scala      |  40 ++--
 .../sql/catalyst/expressions/Projection.scala |  50 +----
 .../expressions/SpecificMutableRow.scala      |   2 +-
 .../codegen/GenerateMutableProjection.scala   |   2 +-
 .../codegen/GenerateProjection.scala          |  16 +-
 .../sql/catalyst/expressions/generators.scala |  12 +-
 .../spark/sql/catalyst/expressions/rows.scala | 149 ++++++-------
 .../expressions/ExpressionEvalHelper.scala    |   4 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala |   6 +-
 .../org/apache/spark/sql/SQLContext.scala     |  24 ++-
 .../spark/sql/columnar/ColumnType.scala       |  70 +++----
 .../columnar/InMemoryColumnarTableScan.scala  |   3 +-
 .../sql/execution/SparkSqlSerializer.scala    |  21 +-
 .../sql/execution/SparkSqlSerializer2.scala   |   5 +-
 .../spark/sql/execution/SparkStrategies.scala |   3 +-
 .../sql/execution/joins/HashOuterJoin.scala   |   4 +-
 .../spark/sql/execution/pythonUdfs.scala      |   4 +-
 .../sql/execution/stat/StatFunctions.scala    |   3 +-
 .../org/apache/spark/sql/jdbc/JDBCRDD.scala   |   2 +-
 .../spark/sql/parquet/ParquetConverter.scala  |   8 +-
 .../apache/spark/sql/sources/commands.scala   |   6 +-
 .../sql/ScalaReflectionRelationSuite.scala    |   7 +-
 .../spark/sql/sources/DDLTestSuite.scala      |   2 +-
 .../spark/sql/sources/TableScanSuite.scala    |   4 +-
 .../spark/sql/hive/HiveInspectors.scala       |   5 +-
 .../apache/spark/sql/hive/TableReader.scala   |   3 +-
 .../hive/execution/CreateTableAsSelect.scala  |  14 +-
 .../execution/DescribeHiveTableCommand.scala  |   8 +-
 .../hive/execution/HiveNativeCommand.scala    |   8 +-
 .../sql/hive/execution/HiveTableScan.scala    |   2 +-
 .../hive/execution/ScriptTransformation.scala |   7 +-
 .../spark/sql/hive/execution/commands.scala   |  37 ++--
 .../spark/sql/hive/orc/OrcRelation.scala      |  10 +-
 .../spark/sql/hive/HiveInspectorSuite.scala   |   4 +-
 39 files changed, 299 insertions(+), 575 deletions(-)
 delete mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/BaseMutableRow.java
 delete mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseMutableRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseMutableRow.java
deleted file mode 100644
index acec2bf4520f2..0000000000000
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseMutableRow.java
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql;
-
-import org.apache.spark.sql.catalyst.expressions.MutableRow;
-
-public abstract class BaseMutableRow extends BaseRow implements MutableRow {
-
-  @Override
-  public void update(int ordinal, Object value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setInt(int ordinal, int value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setLong(int ordinal, long value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setDouble(int ordinal, double value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setBoolean(int ordinal, boolean value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setShort(int ordinal, short value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setByte(int ordinal, byte value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setFloat(int ordinal, float value) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public void setString(int ordinal, String value) {
-    throw new UnsupportedOperationException();
-  }
-}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java
deleted file mode 100644
index 6a2356f1f9c6f..0000000000000
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java
+++ /dev/null
@@ -1,197 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql;
-
-import java.math.BigDecimal;
-import java.sql.Date;
-import java.sql.Timestamp;
-import java.util.List;
-
-import scala.collection.Seq;
-import scala.collection.mutable.ArraySeq;
-
-import org.apache.spark.sql.catalyst.InternalRow;
-import org.apache.spark.sql.catalyst.expressions.GenericRow;
-import org.apache.spark.sql.types.StructType;
-
-public abstract class BaseRow extends InternalRow {
-
-  @Override
-  final public int length() {
-    return size();
-  }
-
-  @Override
-  public boolean anyNull() {
-    final int n = size();
-    for (int i=0; i < n; i++) {
-      if (isNullAt(i)) {
-        return true;
-      }
-    }
-    return false;
-  }
-
-  @Override
-  public StructType schema() { throw new UnsupportedOperationException(); }
-
-  @Override
-  final public Object apply(int i) {
-    return get(i);
-  }
-
-  @Override
-  public int getInt(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public long getLong(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public float getFloat(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public double getDouble(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public byte getByte(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public short getShort(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public boolean getBoolean(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public String getString(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public BigDecimal getDecimal(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public Date getDate(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public Timestamp getTimestamp(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public <T> Seq<T> getSeq(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public <T> List<T> getList(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public <K, V> scala.collection.Map<K, V> getMap(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public <T> scala.collection.immutable.Map<String, T> getValuesMap(Seq<String> fieldNames) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public <K, V> java.util.Map<K, V> getJavaMap(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public Row getStruct(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public <T> T getAs(int i) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public <T> T getAs(String fieldName) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public int fieldIndex(String name) {
-    throw new UnsupportedOperationException();
-  }
-
-  @Override
-  public InternalRow copy() {
-    final int n = size();
-    Object[] arr = new Object[n];
-    for (int i = 0; i < n; i++) {
-      arr[i] = get(i);
-    }
-    return new GenericRow(arr);
-  }
-
-  @Override
-  public Seq<Object> toSeq() {
-    final int n = size();
-    final ArraySeq<Object> values = new ArraySeq<Object>(n);
-    for (int i = 0; i < n; i++) {
-      values.update(i, get(i));
-    }
-    return values;
-  }
-
-  @Override
-  public String toString() {
-    return mkString("[", ",", "]");
-  }
-
-  @Override
-  public String mkString() {
-    return toSeq().mkString();
-  }
-
-  @Override
-  public String mkString(String sep) {
-    return toSeq().mkString(sep);
-  }
-
-  @Override
-  public String mkString(String start, String sep, String end) {
-    return toSeq().mkString(start, sep, end);
-  }
-}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index bb2f2079b40f0..11d51d90f1802 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -23,16 +23,12 @@
 import java.util.HashSet;
 import java.util.Set;
 
-import scala.collection.Seq;
-import scala.collection.mutable.ArraySeq;
-
 import org.apache.spark.sql.catalyst.InternalRow;
-import org.apache.spark.sql.BaseMutableRow;
 import org.apache.spark.sql.types.DataType;
 import org.apache.spark.sql.types.StructType;
-import org.apache.spark.unsafe.types.UTF8String;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
+import org.apache.spark.unsafe.types.UTF8String;
 
 import static org.apache.spark.sql.types.DataTypes.*;
 
@@ -52,7 +48,7 @@
  *
  * Instances of `UnsafeRow` act as pointers to row data stored in this format.
  */
-public final class UnsafeRow extends BaseMutableRow {
+public final class UnsafeRow extends MutableRow {
 
   private Object baseObject;
   private long baseOffset;
@@ -63,6 +59,8 @@ public final class UnsafeRow extends BaseMutableRow {
   /** The number of fields in this row, used for calculating the bitset width (and in assertions) */
   private int numFields;
 
+  public int length() { return numFields; }
+
   /** The width of the null tracking bit set, in bytes */
   private int bitSetWidthInBytes;
   /**
@@ -344,13 +342,4 @@ public InternalRow copy() {
   public boolean anyNull() {
     return BitSetMethods.anySet(baseObject, baseOffset, bitSetWidthInBytes);
   }
-
-  @Override
-  public Seq<Object> toSeq() {
-    final ArraySeq<Object> values = new ArraySeq<Object>(numFields);
-    for (int fieldNumber = 0; fieldNumber < numFields; fieldNumber++) {
-      values.update(fieldNumber, get(fieldNumber));
-    }
-    return values;
-  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index e99d5c87a44fe..0f2fd6a86d177 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -179,7 +179,7 @@ trait Row extends Serializable {
   def get(i: Int): Any = apply(i)
 
   /** Checks whether the value at position i is null. */
-  def isNullAt(i: Int): Boolean
+  def isNullAt(i: Int): Boolean = apply(i) == null
 
   /**
    * Returns the value at position i as a primitive boolean.
@@ -187,7 +187,7 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getBoolean(i: Int): Boolean
+  def getBoolean(i: Int): Boolean = getAs[Boolean](i)
 
   /**
    * Returns the value at position i as a primitive byte.
@@ -195,7 +195,7 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getByte(i: Int): Byte
+  def getByte(i: Int): Byte = getAs[Byte](i)
 
   /**
    * Returns the value at position i as a primitive short.
@@ -203,7 +203,7 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getShort(i: Int): Short
+  def getShort(i: Int): Short = getAs[Short](i)
 
   /**
    * Returns the value at position i as a primitive int.
@@ -211,7 +211,7 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getInt(i: Int): Int
+  def getInt(i: Int): Int = getAs[Int](i)
 
   /**
    * Returns the value at position i as a primitive long.
@@ -219,7 +219,7 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getLong(i: Int): Long
+  def getLong(i: Int): Long = getAs[Long](i)
 
   /**
    * Returns the value at position i as a primitive float.
@@ -228,7 +228,7 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getFloat(i: Int): Float
+  def getFloat(i: Int): Float = getAs[Float](i)
 
   /**
    * Returns the value at position i as a primitive double.
@@ -236,7 +236,7 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getDouble(i: Int): Double
+  def getDouble(i: Int): Double = getAs[Double](i)
 
   /**
    * Returns the value at position i as a String object.
@@ -244,35 +244,35 @@ trait Row extends Serializable {
    * @throws ClassCastException when data type does not match.
    * @throws NullPointerException when value is null.
    */
-  def getString(i: Int): String
+  def getString(i: Int): String = getAs[String](i)
 
   /**
    * Returns the value at position i of decimal type as java.math.BigDecimal.
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getDecimal(i: Int): java.math.BigDecimal = apply(i).asInstanceOf[java.math.BigDecimal]
+  def getDecimal(i: Int): java.math.BigDecimal = getAs[java.math.BigDecimal](i)
 
   /**
    * Returns the value at position i of date type as java.sql.Date.
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getDate(i: Int): java.sql.Date = apply(i).asInstanceOf[java.sql.Date]
+  def getDate(i: Int): java.sql.Date = getAs[java.sql.Date](i)
 
   /**
    * Returns the value at position i of date type as java.sql.Timestamp.
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getTimestamp(i: Int): java.sql.Timestamp = apply(i).asInstanceOf[java.sql.Timestamp]
+  def getTimestamp(i: Int): java.sql.Timestamp = getAs[java.sql.Timestamp](i)
 
   /**
    * Returns the value at position i of array type as a Scala Seq.
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getSeq[T](i: Int): Seq[T] = apply(i).asInstanceOf[Seq[T]]
+  def getSeq[T](i: Int): Seq[T] = getAs[Seq[T]](i)
 
   /**
    * Returns the value at position i of array type as [[java.util.List]].
@@ -288,7 +288,7 @@ trait Row extends Serializable {
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getMap[K, V](i: Int): scala.collection.Map[K, V] = apply(i).asInstanceOf[Map[K, V]]
+  def getMap[K, V](i: Int): scala.collection.Map[K, V] = getAs[Map[K, V]](i)
 
   /**
    * Returns the value at position i of array type as a [[java.util.Map]].
@@ -366,9 +366,18 @@ trait Row extends Serializable {
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
-   * Return a Scala Seq representing the row. ELements are placed in the same order in the Seq.
+   * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  def toSeq: Seq[Any]
+  def toSeq: Seq[Any] = {
+    val n = length
+    val values = new Array[Any](n)
+    var i = 0
+    while (i < n) {
+      values.update(i, get(i))
+      i += 1
+    }
+    values.toSeq
+  }
 
   /** Displays all elements of this sequence in a string (without a separator). */
   def mkString: String = toSeq.mkString
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 012f8bbecb4d3..8f63d2120ad0e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -242,7 +242,7 @@ object CatalystTypeConverters {
           ar(idx) = converters(idx).toCatalyst(row(idx))
           idx += 1
         }
-        new GenericRowWithSchema(ar, structType)
+        new GenericInternalRow(ar)
 
       case p: Product =>
         val ar = new Array[Any](structType.size)
@@ -252,7 +252,7 @@ object CatalystTypeConverters {
           ar(idx) = converters(idx).toCatalyst(iter.next())
           idx += 1
         }
-        new GenericRowWithSchema(ar, structType)
+        new GenericInternalRow(ar)
     }
 
     override def toScala(row: InternalRow): Row = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index d7b537a9fe3bc..61a29c89d8df3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -19,14 +19,38 @@ package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
  * internal types.
  */
 abstract class InternalRow extends Row {
+
+  // This is only use for test
+  override def getString(i: Int): String = getAs[UTF8String](i).toString
+
+  // These expensive API should not be used internally.
+  final override def getDecimal(i: Int): java.math.BigDecimal =
+    throw new UnsupportedOperationException
+  final override def getDate(i: Int): java.sql.Date =
+    throw new UnsupportedOperationException
+  final override def getTimestamp(i: Int): java.sql.Timestamp =
+    throw new UnsupportedOperationException
+  final override def getSeq[T](i: Int): Seq[T] = throw new UnsupportedOperationException
+  final override def getList[T](i: Int): java.util.List[T] = throw new UnsupportedOperationException
+  final override def getMap[K, V](i: Int): scala.collection.Map[K, V] =
+    throw new UnsupportedOperationException
+  final override def getJavaMap[K, V](i: Int): java.util.Map[K, V] =
+    throw new UnsupportedOperationException
+  final override def getStruct(i: Int): Row = throw new UnsupportedOperationException
+  final override def getAs[T](fieldName: String): T = throw new UnsupportedOperationException
+  final override def getValuesMap[T](fieldNames: Seq[String]): Map[String, T] =
+    throw new UnsupportedOperationException
+
   // A default implementation to change the return type
   override def copy(): InternalRow = this
+  override def apply(i: Int): Any = get(i)
 
   override def equals(o: Any): Boolean = {
     if (!o.isInstanceOf[Row]) {
@@ -93,27 +117,15 @@ abstract class InternalRow extends Row {
 }
 
 object InternalRow {
-  def unapplySeq(row: InternalRow): Some[Seq[Any]] = Some(row.toSeq)
-
   /**
    * This method can be used to construct a [[Row]] with the given values.
    */
-  def apply(values: Any*): InternalRow = new GenericRow(values.toArray)
+  def apply(values: Any*): InternalRow = new GenericInternalRow(values.toArray)
 
   /**
    * This method can be used to construct a [[Row]] from a [[Seq]] of values.
    */
-  def fromSeq(values: Seq[Any]): InternalRow = new GenericRow(values.toArray)
-
-  def fromTuple(tuple: Product): InternalRow = fromSeq(tuple.productIterator.toSeq)
-
-  /**
-   * Merge multiple rows into a single row, one after another.
-   */
-  def merge(rows: InternalRow*): InternalRow = {
-    // TODO: Improve the performance of this if used in performance critical part.
-    new GenericRow(rows.flatMap(_.toSeq).toArray)
-  }
+  def fromSeq(values: Seq[Any]): InternalRow = new GenericInternalRow(values.toArray)
 
   /** Returns an empty row. */
   val empty = apply()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index d5967438ccb5a..fcfe83ceb863a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -36,7 +36,7 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection {
       outputArray(i) = exprArray(i).eval(input)
       i += 1
     }
-    new GenericRow(outputArray)
+    new GenericInternalRow(outputArray)
   }
 
   override def toString: String = s"Row => [${exprArray.mkString(",")}]"
@@ -135,12 +135,6 @@ class JoinedRow extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
 
-  override def getString(i: Int): String =
-    if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
-
-  override def getAs[T](i: Int): T =
-    if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
-
   override def copy(): InternalRow = {
     val totalSize = row1.length + row2.length
     val copiedValues = new Array[Any](totalSize)
@@ -149,7 +143,7 @@ class JoinedRow extends InternalRow {
       copiedValues(i) = apply(i)
       i += 1
     }
-    new GenericRow(copiedValues)
+    new GenericInternalRow(copiedValues)
   }
 
   override def toString: String = {
@@ -235,12 +229,6 @@ class JoinedRow2 extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
 
-  override def getString(i: Int): String =
-    if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
-
-  override def getAs[T](i: Int): T =
-    if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
-
   override def copy(): InternalRow = {
     val totalSize = row1.length + row2.length
     val copiedValues = new Array[Any](totalSize)
@@ -249,7 +237,7 @@ class JoinedRow2 extends InternalRow {
       copiedValues(i) = apply(i)
       i += 1
     }
-    new GenericRow(copiedValues)
+    new GenericInternalRow(copiedValues)
   }
 
   override def toString: String = {
@@ -329,12 +317,6 @@ class JoinedRow3 extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
 
-  override def getString(i: Int): String =
-    if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
-
-  override def getAs[T](i: Int): T =
-    if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
-
   override def copy(): InternalRow = {
     val totalSize = row1.length + row2.length
     val copiedValues = new Array[Any](totalSize)
@@ -343,7 +325,7 @@ class JoinedRow3 extends InternalRow {
       copiedValues(i) = apply(i)
       i += 1
     }
-    new GenericRow(copiedValues)
+    new GenericInternalRow(copiedValues)
   }
 
   override def toString: String = {
@@ -423,12 +405,6 @@ class JoinedRow4 extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
 
-  override def getString(i: Int): String =
-    if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
-
-  override def getAs[T](i: Int): T =
-    if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
-
   override def copy(): InternalRow = {
     val totalSize = row1.length + row2.length
     val copiedValues = new Array[Any](totalSize)
@@ -437,7 +413,7 @@ class JoinedRow4 extends InternalRow {
       copiedValues(i) = apply(i)
       i += 1
     }
-    new GenericRow(copiedValues)
+    new GenericInternalRow(copiedValues)
   }
 
   override def toString: String = {
@@ -517,12 +493,6 @@ class JoinedRow5 extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
 
-  override def getString(i: Int): String =
-    if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
-
-  override def getAs[T](i: Int): T =
-    if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
-
   override def copy(): InternalRow = {
     val totalSize = row1.length + row2.length
     val copiedValues = new Array[Any](totalSize)
@@ -531,7 +501,7 @@ class JoinedRow5 extends InternalRow {
       copiedValues(i) = apply(i)
       i += 1
     }
-    new GenericRow(copiedValues)
+    new GenericInternalRow(copiedValues)
   }
 
   override def toString: String = {
@@ -611,12 +581,6 @@ class JoinedRow6 extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
 
-  override def getString(i: Int): String =
-    if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
-
-  override def getAs[T](i: Int): T =
-    if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
-
   override def copy(): InternalRow = {
     val totalSize = row1.length + row2.length
     val copiedValues = new Array[Any](totalSize)
@@ -625,7 +589,7 @@ class JoinedRow6 extends InternalRow {
       copiedValues(i) = apply(i)
       i += 1
     }
-    new GenericRow(copiedValues)
+    new GenericInternalRow(copiedValues)
   }
 
   override def toString: String = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 05aab34559985..53fedb531cfb2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -230,7 +230,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
       i += 1
     }
 
-    new GenericRow(newValues)
+    new GenericInternalRow(newValues)
   }
 
   override def update(ordinal: Int, value: Any) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index e75e82d380541..64ef357a4f954 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 import org.apache.spark.sql.catalyst.expressions._
 
 // MutableProjection is not accessible in Java
-abstract class BaseMutableProjection extends MutableProjection {}
+abstract class BaseMutableProjection extends MutableProjection
 
 /**
  * Generates byte code that produces a [[MutableRow]] object that can update itself based on a new
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 624e1cf4e201a..39d32b78cc14a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import org.apache.spark.sql.BaseMutableRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 
@@ -149,6 +148,10 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
       """
     }.mkString("\n")
 
+    val copyColumns = expressions.zipWithIndex.map { case (e, i) =>
+        s"""arr[$i] = c$i;"""
+    }.mkString("\n      ")
+
     val code = s"""
     public SpecificProjection generate($exprType[] expr) {
       return new SpecificProjection(expr);
@@ -167,7 +170,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
       }
     }
 
-    final class SpecificRow extends ${typeOf[BaseMutableRow]} {
+    final class SpecificRow extends ${typeOf[MutableRow]} {
 
       $columns
 
@@ -175,7 +178,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         $initColumns
       }
 
-      public int size() { return ${expressions.length};}
+      public int length() { return ${expressions.length};}
       protected boolean[] nullBits = new boolean[${expressions.length}];
       public void setNullAt(int i) { nullBits[i] = true; }
       public boolean isNullAt(int i) { return nullBits[i]; }
@@ -216,6 +219,13 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         }
         return super.equals(other);
       }
+
+      @Override
+      public InternalRow copy() {
+        Object[] arr = new Object[${expressions.length}];
+        ${copyColumns}
+        return new ${typeOf[GenericInternalRow]}(arr);
+      }
     }
     """
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index 356560e54cae3..7a42a1d310581 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import scala.collection.Map
 
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees}
 import org.apache.spark.sql.types._
@@ -68,19 +69,19 @@ abstract class Generator extends Expression {
  */
 case class UserDefinedGenerator(
     elementTypes: Seq[(DataType, Boolean)],
-    function: InternalRow => TraversableOnce[InternalRow],
+    function: Row => TraversableOnce[InternalRow],
     children: Seq[Expression])
   extends Generator {
 
   @transient private[this] var inputRow: InterpretedProjection = _
-  @transient private[this] var convertToScala: (InternalRow) => InternalRow = _
+  @transient private[this] var convertToScala: (InternalRow) => Row = _
 
   private def initializeConverters(): Unit = {
     inputRow = new InterpretedProjection(children)
     convertToScala = {
       val inputSchema = StructType(children.map(e => StructField(e.simpleString, e.dataType, true)))
       CatalystTypeConverters.createToScalaConverter(inputSchema)
-    }.asInstanceOf[(InternalRow => InternalRow)]
+    }.asInstanceOf[InternalRow => Row]
   }
 
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
@@ -118,10 +119,11 @@ case class Explode(child: Expression)
     child.dataType match {
       case ArrayType(_, _) =>
         val inputArray = child.eval(input).asInstanceOf[Seq[Any]]
-        if (inputArray == null) Nil else inputArray.map(v => new GenericRow(Array(v)))
+        if (inputArray == null) Nil else inputArray.map(v => InternalRow(v))
       case MapType(_, _, _) =>
         val inputMap = child.eval(input).asInstanceOf[Map[Any, Any]]
-        if (inputMap == null) Nil else inputMap.map { case (k, v) => new GenericRow(Array(k, v)) }
+        if (inputMap == null) Nil
+        else inputMap.map { case (k, v) => InternalRow(k, v) }
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 0d4c9ace5e124..dd5f2ed2d382e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.types.{DataType, StructType, AtomicType}
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -24,19 +25,32 @@ import org.apache.spark.unsafe.types.UTF8String
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
  * Setting a value through a primitive function implicitly marks that column as not null.
  */
-trait MutableRow extends InternalRow {
+abstract class MutableRow extends InternalRow {
   def setNullAt(i: Int): Unit
 
-  def update(ordinal: Int, value: Any)
+  def update(i: Int, value: Any)
+
+  // default implementation (slow)
+  def setInt(i: Int, value: Int): Unit = { update(i, value) }
+  def setLong(i: Int, value: Long): Unit = { update(i, value) }
+  def setDouble(i: Int, value: Double): Unit = { update(i, value) }
+  def setBoolean(i: Int, value: Boolean): Unit = { update(i, value) }
+  def setShort(i: Int, value: Short): Unit = { update(i, value) }
+  def setByte(i: Int, value: Byte): Unit = { update(i, value) }
+  def setFloat(i: Int, value: Float): Unit = { update(i, value) }
+  def setString(i: Int, value: String): Unit = {
+    update(i, UTF8String.fromString(value))
+  }
 
-  def setInt(ordinal: Int, value: Int)
-  def setLong(ordinal: Int, value: Long)
-  def setDouble(ordinal: Int, value: Double)
-  def setBoolean(ordinal: Int, value: Boolean)
-  def setShort(ordinal: Int, value: Short)
-  def setByte(ordinal: Int, value: Byte)
-  def setFloat(ordinal: Int, value: Float)
-  def setString(ordinal: Int, value: String)
+  override def copy(): InternalRow = {
+    val arr = new Array[Any](length)
+    var i = 0
+    while (i < length) {
+      arr(i) = get(i)
+      i += 1
+    }
+    new GenericInternalRow(arr)
+  }
 }
 
 /**
@@ -60,68 +74,57 @@ object EmptyRow extends InternalRow {
 }
 
 /**
- * A row implementation that uses an array of objects as the underlying storage.  Note that, while
- * the array is not copied, and thus could technically be mutated after creation, this is not
- * allowed.
+ * A row implementation that uses an array of objects as the underlying storage.
  */
-class GenericRow(protected[sql] val values: Array[Any]) extends InternalRow {
-  /** No-arg constructor for serialization. */
-  protected def this() = this(null)
+trait ArrayBackedRow {
+  self: Row =>
 
-  def this(size: Int) = this(new Array[Any](size))
+  protected val values: Array[Any]
 
   override def toSeq: Seq[Any] = values.toSeq
 
-  override def length: Int = values.length
+  def length: Int = values.length
 
   override def apply(i: Int): Any = values(i)
 
-  override def isNullAt(i: Int): Boolean = values(i) == null
-
-  override def getInt(i: Int): Int = {
-    if (values(i) == null) sys.error("Failed to check null bit for primitive int value.")
-    values(i).asInstanceOf[Int]
-  }
-
-  override def getLong(i: Int): Long = {
-    if (values(i) == null) sys.error("Failed to check null bit for primitive long value.")
-    values(i).asInstanceOf[Long]
-  }
-
-  override def getDouble(i: Int): Double = {
-    if (values(i) == null) sys.error("Failed to check null bit for primitive double value.")
-    values(i).asInstanceOf[Double]
-  }
-
-  override def getFloat(i: Int): Float = {
-    if (values(i) == null) sys.error("Failed to check null bit for primitive float value.")
-    values(i).asInstanceOf[Float]
-  }
+  def setNullAt(i: Int): Unit = { values(i) = null}
 
-  override def getBoolean(i: Int): Boolean = {
-    if (values(i) == null) sys.error("Failed to check null bit for primitive boolean value.")
-    values(i).asInstanceOf[Boolean]
-  }
+  def update(i: Int, value: Any): Unit = { values(i) = value }
+}
 
-  override def getShort(i: Int): Short = {
-    if (values(i) == null) sys.error("Failed to check null bit for primitive short value.")
-    values(i).asInstanceOf[Short]
-  }
+/**
+ * A row implementation that uses an array of objects as the underlying storage.  Note that, while
+ * the array is not copied, and thus could technically be mutated after creation, this is not
+ * allowed.
+ */
+class GenericRow(protected[sql] val values: Array[Any]) extends Row with ArrayBackedRow {
+  /** No-arg constructor for serialization. */
+  protected def this() = this(null)
 
-  override def getByte(i: Int): Byte = {
-    if (values(i) == null) sys.error("Failed to check null bit for primitive byte value.")
-    values(i).asInstanceOf[Byte]
-  }
+  def this(size: Int) = this(new Array[Any](size))
 
-  override def getString(i: Int): String = {
-    values(i) match {
-      case null => null
-      case s: String => s
-      case utf8: UTF8String => utf8.toString
-    }
+  // This is used by test or outside
+  override def equals(o: Any): Boolean = o match {
+    case other: Row if other.length == length =>
+      var i = 0
+      while (i < length) {
+        if (isNullAt(i) != other.isNullAt(i)) {
+          return false
+        }
+        val equal = (apply(i), other.apply(i)) match {
+          case (a: Array[Byte], b: Array[Byte]) => java.util.Arrays.equals(a, b)
+          case (a, b) => a == b
+        }
+        if (!equal) {
+          return false
+        }
+        i += 1
+      }
+      true
+    case _ => false
   }
 
-  override def copy(): InternalRow = this
+  override def copy(): Row = this
 }
 
 class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
@@ -133,32 +136,30 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
   override def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow {
+/**
+ * A internal row implementation that uses an array of objects as the underlying storage.
+ * Note that, while the array is not copied, and thus could technically be mutated after creation,
+ * this is not allowed.
+ */
+class GenericInternalRow(protected[sql] val values: Array[Any])
+    extends InternalRow with ArrayBackedRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
-  override def setBoolean(ordinal: Int, value: Boolean): Unit = { values(ordinal) = value }
-  override def setByte(ordinal: Int, value: Byte): Unit = { values(ordinal) = value }
-  override def setDouble(ordinal: Int, value: Double): Unit = { values(ordinal) = value }
-  override def setFloat(ordinal: Int, value: Float): Unit = { values(ordinal) = value }
-  override def setInt(ordinal: Int, value: Int): Unit = { values(ordinal) = value }
-  override def setLong(ordinal: Int, value: Long): Unit = { values(ordinal) = value }
-  override def setString(ordinal: Int, value: String): Unit = {
-    values(ordinal) = UTF8String.fromString(value)
-  }
-
-  override def setNullAt(i: Int): Unit = { values(i) = null }
+  override def copy(): InternalRow = this
+}
 
-  override def setShort(ordinal: Int, value: Short): Unit = { values(ordinal) = value }
+class GenericMutableRow(val values: Array[Any]) extends MutableRow with ArrayBackedRow {
+  /** No-arg constructor for serialization. */
+  protected def this() = this(null)
 
-  override def update(ordinal: Int, value: Any): Unit = { values(ordinal) = value }
+  def this(size: Int) = this(new Array[Any](size))
 
-  override def copy(): InternalRow = new GenericRow(values.clone())
+  override def copy(): InternalRow = new GenericInternalRow(values.clone())
 }
 
-
 class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
   def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
     this(ordering.map(BindReferences.bindReference(_, inputSchema)))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 158f54af13802..7d95ef7f710af 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -33,7 +33,7 @@ trait ExpressionEvalHelper {
   self: SparkFunSuite =>
 
   protected def create_row(values: Any*): InternalRow = {
-    new GenericRow(values.map(CatalystTypeConverters.convertToCatalyst).toArray)
+    InternalRow.fromSeq(values.map(CatalystTypeConverters.convertToCatalyst))
   }
 
   protected def checkEvaluation(
@@ -122,7 +122,7 @@ trait ExpressionEvalHelper {
     }
 
     val actual = plan(inputRow)
-    val expectedRow = new GenericRow(Array[Any](expected))
+    val expectedRow = InternalRow(expected)
     if (actual.hashCode() != expectedRow.hashCode()) {
       fail(
         s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
index 7aae2bbd8a0b8..3095ccb77761b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
@@ -37,7 +37,7 @@ class UnsafeFixedWidthAggregationMapSuite
 
   private val groupKeySchema = StructType(StructField("product", StringType) :: Nil)
   private val aggBufferSchema = StructType(StructField("salePrice", IntegerType) :: Nil)
-  private def emptyAggregationBuffer: InternalRow = new GenericRow(Array[Any](0))
+  private def emptyAggregationBuffer: InternalRow = InternalRow(0)
 
   private var memoryManager: TaskMemoryManager = null
 
@@ -84,7 +84,7 @@ class UnsafeFixedWidthAggregationMapSuite
       1024, // initial capacity
       false // disable perf metrics
     )
-    val groupKey = new GenericRow(Array[Any](UTF8String.fromString("cats")))
+    val groupKey = InternalRow(UTF8String.fromString("cats"))
 
     // Looking up a key stores a zero-entry in the map (like Python Counters or DefaultDicts)
     map.getAggregationBuffer(groupKey)
@@ -113,7 +113,7 @@ class UnsafeFixedWidthAggregationMapSuite
     val rand = new Random(42)
     val groupKeys: Set[String] = Seq.fill(512)(rand.nextString(1024)).toSet
     groupKeys.foreach { keyString =>
-      map.getAggregationBuffer(new GenericRow(Array[Any](UTF8String.fromString(keyString))))
+      map.getAggregationBuffer(InternalRow(UTF8String.fromString(keyString)))
     }
     val seenKeys: Set[String] = map.iterator().asScala.map { entry =>
       entry.key.getString(0)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 5708df82de12f..8ed44ee141be5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -42,6 +42,7 @@ import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
 import org.apache.spark.sql.execution.{Filter, _}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
 
 /**
@@ -377,10 +378,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
         val row = new SpecificMutableRow(dataType :: Nil)
         iter.map { v =>
           row.setInt(0, v)
-          row: Row
+          row: InternalRow
         }
       }
-      DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+      DataFrameHolder(
+        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
     }
 
     /**
@@ -393,10 +395,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
         val row = new SpecificMutableRow(dataType :: Nil)
         iter.map { v =>
           row.setLong(0, v)
-          row: Row
+          row: InternalRow
         }
       }
-      DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+      DataFrameHolder(
+        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
     }
 
     /**
@@ -408,11 +411,12 @@ class SQLContext(@transient val sparkContext: SparkContext)
       val rows = data.mapPartitions { iter =>
         val row = new SpecificMutableRow(dataType :: Nil)
         iter.map { v =>
-          row.setString(0, v)
-          row: Row
+          row.update(0, UTF8String.fromString(v))
+          row: InternalRow
         }
       }
-      DataFrameHolder(self.createDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+      DataFrameHolder(
+        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
     }
   }
 
@@ -559,9 +563,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
         (e, CatalystTypeConverters.createToCatalystConverter(attr.dataType))
       }
       iter.map { row =>
-        new GenericRow(
+        new GenericInternalRow(
           methodsToConverts.map { case (e, convert) => convert(e.invoke(row)) }.toArray[Any]
-        ) : InternalRow
+        ): InternalRow
       }
     }
     DataFrame(this, LogicalRDD(attributeSeq, rowRdd)(this))
@@ -1065,7 +1069,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     }
 
     val rowRdd = convertedRdd.mapPartitions { iter =>
-      iter.map { m => new GenericRow(m): InternalRow}
+      iter.map { m => new GenericInternalRow(m): InternalRow}
     }
 
     DataFrame(this, LogicalRDD(schema.toAttributes, rowRdd)(self))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 8e21020917768..8bf2151e4de68 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -21,7 +21,7 @@ import java.nio.ByteBuffer
 
 import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.MutableRow
 import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.sql.types._
@@ -63,7 +63,7 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
    * Appends `row(ordinal)` of type T into the given ByteBuffer. Subclasses should override this
    * method to avoid boxing/unboxing costs whenever possible.
    */
-  def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     append(getField(row, ordinal), buffer)
   }
 
@@ -71,13 +71,13 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
    * Returns the size of the value `row(ordinal)`. This is used to calculate the size of variable
    * length types such as byte arrays and strings.
    */
-  def actualSize(row: Row, ordinal: Int): Int = defaultSize
+  def actualSize(row: InternalRow, ordinal: Int): Int = defaultSize
 
   /**
    * Returns `row(ordinal)`. Subclasses should override this method to avoid boxing/unboxing costs
    * whenever possible.
    */
-  def getField(row: Row, ordinal: Int): JvmType
+  def getField(row: InternalRow, ordinal: Int): JvmType
 
   /**
    * Sets `row(ordinal)` to `field`. Subclasses should override this method to avoid boxing/unboxing
@@ -89,7 +89,7 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
    * Copies `from(fromOrdinal)` to `to(toOrdinal)`. Subclasses should override this method to avoid
    * boxing/unboxing costs whenever possible.
    */
-  def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
     to(toOrdinal) = from(fromOrdinal)
   }
 
@@ -118,7 +118,7 @@ private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) {
     buffer.putInt(v)
   }
 
-  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  override def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     buffer.putInt(row.getInt(ordinal))
   }
 
@@ -134,9 +134,9 @@ private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) {
     row.setInt(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): Int = row.getInt(ordinal)
+  override def getField(row: InternalRow, ordinal: Int): Int = row.getInt(ordinal)
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.setInt(toOrdinal, from.getInt(fromOrdinal))
   }
 }
@@ -146,7 +146,7 @@ private[sql] object LONG extends NativeColumnType(LongType, 1, 8) {
     buffer.putLong(v)
   }
 
-  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  override def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     buffer.putLong(row.getLong(ordinal))
   }
 
@@ -162,9 +162,9 @@ private[sql] object LONG extends NativeColumnType(LongType, 1, 8) {
     row.setLong(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): Long = row.getLong(ordinal)
+  override def getField(row: InternalRow, ordinal: Int): Long = row.getLong(ordinal)
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.setLong(toOrdinal, from.getLong(fromOrdinal))
   }
 }
@@ -174,7 +174,7 @@ private[sql] object FLOAT extends NativeColumnType(FloatType, 2, 4) {
     buffer.putFloat(v)
   }
 
-  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  override def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     buffer.putFloat(row.getFloat(ordinal))
   }
 
@@ -190,9 +190,9 @@ private[sql] object FLOAT extends NativeColumnType(FloatType, 2, 4) {
     row.setFloat(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): Float = row.getFloat(ordinal)
+  override def getField(row: InternalRow, ordinal: Int): Float = row.getFloat(ordinal)
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.setFloat(toOrdinal, from.getFloat(fromOrdinal))
   }
 }
@@ -202,7 +202,7 @@ private[sql] object DOUBLE extends NativeColumnType(DoubleType, 3, 8) {
     buffer.putDouble(v)
   }
 
-  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  override def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     buffer.putDouble(row.getDouble(ordinal))
   }
 
@@ -218,9 +218,9 @@ private[sql] object DOUBLE extends NativeColumnType(DoubleType, 3, 8) {
     row.setDouble(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): Double = row.getDouble(ordinal)
+  override def getField(row: InternalRow, ordinal: Int): Double = row.getDouble(ordinal)
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.setDouble(toOrdinal, from.getDouble(fromOrdinal))
   }
 }
@@ -230,7 +230,7 @@ private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) {
     buffer.put(if (v) 1: Byte else 0: Byte)
   }
 
-  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  override def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     buffer.put(if (row.getBoolean(ordinal)) 1: Byte else 0: Byte)
   }
 
@@ -244,9 +244,9 @@ private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) {
     row.setBoolean(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): Boolean = row.getBoolean(ordinal)
+  override def getField(row: InternalRow, ordinal: Int): Boolean = row.getBoolean(ordinal)
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.setBoolean(toOrdinal, from.getBoolean(fromOrdinal))
   }
 }
@@ -256,7 +256,7 @@ private[sql] object BYTE extends NativeColumnType(ByteType, 5, 1) {
     buffer.put(v)
   }
 
-  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  override def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     buffer.put(row.getByte(ordinal))
   }
 
@@ -272,9 +272,9 @@ private[sql] object BYTE extends NativeColumnType(ByteType, 5, 1) {
     row.setByte(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): Byte = row.getByte(ordinal)
+  override def getField(row: InternalRow, ordinal: Int): Byte = row.getByte(ordinal)
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.setByte(toOrdinal, from.getByte(fromOrdinal))
   }
 }
@@ -284,7 +284,7 @@ private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) {
     buffer.putShort(v)
   }
 
-  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+  override def append(row: InternalRow, ordinal: Int, buffer: ByteBuffer): Unit = {
     buffer.putShort(row.getShort(ordinal))
   }
 
@@ -300,15 +300,15 @@ private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) {
     row.setShort(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): Short = row.getShort(ordinal)
+  override def getField(row: InternalRow, ordinal: Int): Short = row.getShort(ordinal)
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.setShort(toOrdinal, from.getShort(fromOrdinal))
   }
 }
 
 private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
-  override def actualSize(row: Row, ordinal: Int): Int = {
+  override def actualSize(row: InternalRow, ordinal: Int): Int = {
     row.getString(ordinal).getBytes("utf-8").length + 4
   }
 
@@ -328,11 +328,11 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
     row.update(ordinal, value)
   }
 
-  override def getField(row: Row, ordinal: Int): UTF8String = {
+  override def getField(row: InternalRow, ordinal: Int): UTF8String = {
     row(ordinal).asInstanceOf[UTF8String]
   }
 
-  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     to.update(toOrdinal, from(fromOrdinal))
   }
 }
@@ -346,7 +346,7 @@ private[sql] object DATE extends NativeColumnType(DateType, 8, 4) {
     buffer.putInt(v)
   }
 
-  override def getField(row: Row, ordinal: Int): Int = {
+  override def getField(row: InternalRow, ordinal: Int): Int = {
     row(ordinal).asInstanceOf[Int]
   }
 
@@ -364,7 +364,7 @@ private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 8) {
     buffer.putLong(v)
   }
 
-  override def getField(row: Row, ordinal: Int): Long = {
+  override def getField(row: InternalRow, ordinal: Int): Long = {
     row(ordinal).asInstanceOf[Long]
   }
 
@@ -387,7 +387,7 @@ private[sql] case class FIXED_DECIMAL(precision: Int, scale: Int)
     buffer.putLong(v.toUnscaledLong)
   }
 
-  override def getField(row: Row, ordinal: Int): Decimal = {
+  override def getField(row: InternalRow, ordinal: Int): Decimal = {
     row(ordinal).asInstanceOf[Decimal]
   }
 
@@ -405,7 +405,7 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType](
     defaultSize: Int)
   extends ColumnType[T, Array[Byte]](typeId, defaultSize) {
 
-  override def actualSize(row: Row, ordinal: Int): Int = {
+  override def actualSize(row: InternalRow, ordinal: Int): Int = {
     getField(row, ordinal).length + 4
   }
 
@@ -426,7 +426,7 @@ private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](11, 16)
     row(ordinal) = value
   }
 
-  override def getField(row: Row, ordinal: Int): Array[Byte] = {
+  override def getField(row: InternalRow, ordinal: Int): Array[Byte] = {
     row(ordinal).asInstanceOf[Array[Byte]]
   }
 }
@@ -439,7 +439,7 @@ private[sql] object GENERIC extends ByteArrayColumnType[DataType](12, 16) {
     row(ordinal) = SparkSqlSerializer.deserialize[Any](value)
   }
 
-  override def getField(row: Row, ordinal: Int): Array[Byte] = {
+  override def getField(row: InternalRow, ordinal: Int): Array[Byte] = {
     SparkSqlSerializer.serialize(row(ordinal))
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 761f427b8cd0d..cb1fd4947fdbc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -146,7 +146,8 @@ private[sql] case class InMemoryRelation(
             rowCount += 1
           }
 
-          val stats = InternalRow.merge(columnBuilders.map(_.columnStats.collectedStatistics) : _*)
+          val stats = InternalRow.fromSeq(columnBuilders.map(_.columnStats.collectedStatistics)
+                        .flatMap(_.toSeq))
 
           batchStats += stats
           CachedBatch(columnBuilders.map(_.build().array()), stats)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
index eea15aff5dbcf..b19ad4f1c563e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
@@ -20,22 +20,20 @@ package org.apache.spark.sql.execution
 import java.nio.ByteBuffer
 import java.util.{HashMap => JavaHashMap}
 
-import org.apache.spark.sql.types.Decimal
-
 import scala.reflect.ClassTag
 
 import com.clearspring.analytics.stream.cardinality.HyperLogLog
 import com.esotericsoftware.kryo.io.{Input, Output}
-import com.esotericsoftware.kryo.{Serializer, Kryo}
+import com.esotericsoftware.kryo.{Kryo, Serializer}
 import com.twitter.chill.ResourcePool
 
-import org.apache.spark.{SparkEnv, SparkConf}
-import org.apache.spark.serializer.{SerializerInstance, KryoSerializer}
-import org.apache.spark.sql.catalyst.expressions.GenericRow
-import org.apache.spark.util.collection.OpenHashSet
-import org.apache.spark.util.MutablePair
-
+import org.apache.spark.serializer.{KryoSerializer, SerializerInstance}
+import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{IntegerHashSet, LongHashSet}
+import org.apache.spark.sql.types.Decimal
+import org.apache.spark.util.MutablePair
+import org.apache.spark.util.collection.OpenHashSet
+import org.apache.spark.{SparkConf, SparkEnv}
 
 private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) {
   override def newKryo(): Kryo = {
@@ -43,6 +41,7 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co
     kryo.setRegistrationRequired(false)
     kryo.register(classOf[MutablePair[_, _]])
     kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericRow])
+    kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericInternalRow])
     kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericMutableRow])
     kryo.register(classOf[com.clearspring.analytics.stream.cardinality.HyperLogLog],
                   new HyperLogLogSerializer)
@@ -139,7 +138,7 @@ private[sql] class OpenHashSetSerializer extends Serializer[OpenHashSet[_]] {
     val iterator = hs.iterator
     while(iterator.hasNext) {
       val row = iterator.next()
-      rowSerializer.write(kryo, output, row.asInstanceOf[GenericRow].values)
+      rowSerializer.write(kryo, output, row.asInstanceOf[GenericInternalRow].values)
     }
   }
 
@@ -150,7 +149,7 @@ private[sql] class OpenHashSetSerializer extends Serializer[OpenHashSet[_]] {
     var i = 0
     while (i < numItems) {
       val row =
-        new GenericRow(rowSerializer.read(
+        new GenericInternalRow(rowSerializer.read(
           kryo,
           input,
           classOf[Array[Any]].asInstanceOf[Class[Any]]).asInstanceOf[Array[Any]])
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 15b6936acd59b..74a22353b1d27 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -26,7 +26,8 @@ import scala.reflect.ClassTag
 import org.apache.spark.Logging
 import org.apache.spark.serializer._
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, MutableRow, SpecificMutableRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{MutableRow, SpecificMutableRow}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -329,7 +330,7 @@ private[sql] object SparkSqlSerializer2 {
    */
   def createDeserializationFunction(
       schema: Array[DataType],
-      in: DataInputStream): (MutableRow) => Row = {
+      in: DataInputStream): (MutableRow) => InternalRow = {
     if (schema == null) {
       (mutableRow: MutableRow) => null
     } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 21912cf24933e..5daf86d817586 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -210,8 +210,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
-  protected lazy val singleRowRdd =
-    sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): InternalRow), 1)
+  protected lazy val singleRowRdd = sparkContext.parallelize(Seq(InternalRow()), 1)
 
   object TakeOrderedAndProject extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index bce0e8d70a57b..e41538ec1fc1a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -71,8 +71,8 @@ case class HashOuterJoin(
   @transient private[this] lazy val DUMMY_LIST = Seq[InternalRow](null)
   @transient private[this] lazy val EMPTY_LIST = Seq.empty[InternalRow]
 
-  @transient private[this] lazy val leftNullRow = new GenericRow(left.output.length)
-  @transient private[this] lazy val rightNullRow = new GenericRow(right.output.length)
+  @transient private[this] lazy val leftNullRow = new GenericInternalRow(left.output.length)
+  @transient private[this] lazy val rightNullRow = new GenericInternalRow(right.output.length)
   @transient private[this] lazy val boundCondition =
     condition.map(
       newPredicate(_, left.output ++ right.output)).getOrElse((row: InternalRow) => true)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
index f9c3fe92c2670..036f5d253e385 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
@@ -183,9 +183,9 @@ object EvaluatePython {
     }.toMap
 
     case (c, StructType(fields)) if c.getClass.isArray =>
-      new GenericRow(c.asInstanceOf[Array[_]].zip(fields).map {
+      new GenericInternalRow(c.asInstanceOf[Array[_]].zip(fields).map {
         case (e, f) => fromJava(e, f.dataType)
-      }): Row
+      })
 
     case (c: java.util.Calendar, DateType) =>
       DateTimeUtils.fromJavaDate(new java.sql.Date(c.getTimeInMillis))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index 252c611d02ebc..042e2c9cbb22e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Cast}
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 private[sql] object StatFunctions extends Logging {
 
@@ -123,7 +124,7 @@ private[sql] object StatFunctions extends Logging {
         countsRow.setLong(distinctCol2.get(row.get(1)).get + 1, row.getLong(2))
       }
       // the value of col1 is the first value, the rest are the counts
-      countsRow.setString(0, col1Item.toString)
+      countsRow.update(0, UTF8String.fromString(col1Item.toString))
       countsRow
     }.toSeq
     val headerNames = distinctCol2.map(r => StructField(r._1.toString, LongType)).toSeq
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
index 8b4276b2c364c..30c5f4ca3e1b2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
@@ -417,7 +417,7 @@ private[sql] class JDBCRDD(
             case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos))
             case LongConversion => mutableRow.setLong(i, rs.getLong(pos))
             // TODO(davies): use getBytes for better performance, if the encoding is UTF-8
-            case StringConversion => mutableRow.setString(i, rs.getString(pos))
+            case StringConversion => mutableRow.update(i, UTF8String.fromString(rs.getString(pos)))
             case TimestampConversion =>
               val t = rs.getTimestamp(pos)
               if (t != null) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index cf7aa44e4cd55..ae7cbf0624dc8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -318,7 +318,7 @@ private[parquet] class CatalystGroupConverter(
     // Note: this will ever only be called in the root converter when the record has been
     // fully processed. Therefore it will be difficult to use mutable rows instead, since
     // any non-root converter never would be sure when it would be safe to re-use the buffer.
-    new GenericRow(current.toArray)
+    new GenericInternalRow(current.toArray)
   }
 
   override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
@@ -342,8 +342,8 @@ private[parquet] class CatalystGroupConverter(
   override def end(): Unit = {
     if (!isRootConverter) {
       assert(current != null) // there should be no empty groups
-      buffer.append(new GenericRow(current.toArray))
-      parent.updateField(index, new GenericRow(buffer.toArray.asInstanceOf[Array[Any]]))
+      buffer.append(new GenericInternalRow(current.toArray))
+      parent.updateField(index, new GenericInternalRow(buffer.toArray.asInstanceOf[Array[Any]]))
     }
   }
 }
@@ -788,7 +788,7 @@ private[parquet] class CatalystStructConverter(
     // here we need to make sure to use StructScalaType
     // Note: we need to actually make a copy of the array since we
     // may be in a nested field
-    parent.updateField(index, new GenericRow(current.toArray))
+    parent.updateField(index, new GenericInternalRow(current.toArray))
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index dbb369cf45502..54c8eeb41a8ea 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -44,7 +44,7 @@ private[sql] case class InsertIntoDataSource(
     overwrite: Boolean)
   extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val relation = logicalRelation.relation.asInstanceOf[InsertableRelation]
     val data = DataFrame(sqlContext, query)
     // Apply the schema of the existing table to the new data.
@@ -54,7 +54,7 @@ private[sql] case class InsertIntoDataSource(
     // Invalidate the cache.
     sqlContext.cacheManager.invalidateCache(logicalRelation)
 
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 }
 
@@ -86,7 +86,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
     mode: SaveMode)
   extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     require(
       relation.paths.length == 1,
       s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
index ece3d6fdf2af5..4cb5ba2f0d5eb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql
 import java.sql.{Date, Timestamp}
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.expressions._
 
 case class ReflectData(
     stringField: String,
@@ -128,16 +127,16 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
 
     Seq(data).toDF().registerTempTable("reflectComplexData")
     assert(ctx.sql("SELECT * FROM reflectComplexData").collect().head ===
-      new GenericRow(Array[Any](
+      Row(
         Seq(1, 2, 3),
         Seq(1, 2, null),
         Map(1 -> 10L, 2 -> 20L),
         Map(1 -> 10L, 2 -> 20L, 3 -> null),
-        new GenericRow(Array[Any](
+        Row(
           Seq(10, 20, 30),
           Seq(10, 20, null),
           Map(10 -> 100L, 20 -> 200L),
           Map(10 -> 100L, 20 -> 200L, 30 -> null),
-          new GenericRow(Array[Any](null, "abc")))))))
+          Row(null, "abc"))))
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
index 5fc53f7012994..54e1efb6e36e7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
@@ -62,7 +62,7 @@ case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlCo
 
   override def buildScan(): RDD[Row] = {
     sqlContext.sparkContext.parallelize(from to to).map { e =>
-      InternalRow(UTF8String.fromString(s"people$e"), e * 2)
+      InternalRow(UTF8String.fromString(s"people$e"), e * 2): Row
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index de0ed0c0427a6..2c916f3322b6d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -90,8 +90,8 @@ case class AllDataTypesScan(
         Seq(Map(UTF8String.fromString(s"str_$i") -> InternalRow(i.toLong))),
         Map(i -> UTF8String.fromString(i.toString)),
         Map(Map(UTF8String.fromString(s"str_$i") -> i.toFloat) -> InternalRow(i.toLong)),
-        Row(i, UTF8String.fromString(i.toString)),
-        Row(Seq(UTF8String.fromString(s"str_$i"), UTF8String.fromString(s"str_${i + 1}")),
+        InternalRow(i, UTF8String.fromString(i.toString)),
+        InternalRow(Seq(UTF8String.fromString(s"str_$i"), UTF8String.fromString(s"str_${i + 1}")),
           InternalRow(Seq(DateTimeUtils.fromJavaDate(new Date(1970, 1, i + 1))))))
     }
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 864c888ab073d..a6b8ead577fb5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -336,9 +336,8 @@ private[hive] trait HiveInspectors {
     // currently, hive doesn't provide the ConstantStructObjectInspector
     case si: StructObjectInspector =>
       val allRefs = si.getAllStructFieldRefs
-      new GenericRow(
-        allRefs.map(r =>
-          unwrap(si.getStructFieldData(data, r), r.getFieldObjectInspector)).toArray)
+      InternalRow.fromSeq(
+        allRefs.map(r => unwrap(si.getStructFieldData(data, r), r.getFieldObjectInspector)))
   }
 
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index 00e61e35d4354..b251a9523bed6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -34,6 +34,7 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 /**
@@ -356,7 +357,7 @@ private[hive] object HadoopTableReader extends HiveInspectors with Logging {
           (value: Any, row: MutableRow, ordinal: Int) => row.setDouble(ordinal, oi.get(value))
         case oi: HiveVarcharObjectInspector =>
           (value: Any, row: MutableRow, ordinal: Int) =>
-            row.setString(ordinal, oi.getPrimitiveJavaObject(value).getValue)
+            row.update(ordinal, UTF8String.fromString(oi.getPrimitiveJavaObject(value).getValue))
         case oi: HiveDecimalObjectInspector =>
           (value: Any, row: MutableRow, ordinal: Int) =>
             row.update(ordinal, HiveShim.toCatalystDecimal(oi, value))
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
index 0e4a2427a9c15..84358cb73c9e3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
@@ -17,13 +17,11 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.annotation.Experimental
-import org.apache.spark.sql.{AnalysisException, SQLContext}
-import org.apache.spark.sql.catalyst.expressions.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, LogicalPlan}
 import org.apache.spark.sql.execution.RunnableCommand
-import org.apache.spark.sql.hive.client.{HiveTable, HiveColumn}
-import org.apache.spark.sql.hive.{HiveContext, MetastoreRelation, HiveMetastoreTypes}
+import org.apache.spark.sql.hive.client.{HiveColumn, HiveTable}
+import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes, MetastoreRelation}
+import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
 
 /**
  * Create table and insert the query result into it.
@@ -42,11 +40,11 @@ case class CreateTableAsSelect(
   def database: String = tableDesc.database
   def tableName: String = tableDesc.name
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     lazy val metastoreRelation: MetastoreRelation = {
-      import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
       import org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
+      import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
       import org.apache.hadoop.io.Text
       import org.apache.hadoop.mapred.TextInputFormat
 
@@ -89,7 +87,7 @@ case class CreateTableAsSelect(
       hiveContext.executePlan(InsertIntoTable(metastoreRelation, Map(), query, true, false)).toRdd
     }
 
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 
   override def argString: String = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
index a89381000ad5f..5f0ed5393d191 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
@@ -21,10 +21,10 @@ import scala.collection.JavaConversions._
 
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.execution.RunnableCommand
 import org.apache.spark.sql.hive.MetastoreRelation
+import org.apache.spark.sql.{Row, SQLContext}
 
 /**
  * Implementation for "describe [extended] table".
@@ -35,7 +35,7 @@ case class DescribeHiveTableCommand(
     override val output: Seq[Attribute],
     isExtended: Boolean) extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     // Trying to mimic the format of Hive's output. But not exactly the same.
     var results: Seq[(String, String, String)] = Nil
 
@@ -57,7 +57,7 @@ case class DescribeHiveTableCommand(
     }
 
     results.map { case (name, dataType, comment) =>
-      InternalRow(name, dataType, comment)
+      Row(name, dataType, comment)
     }
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala
index 87f8e3f7fcfcc..41b645b2c9c93 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
 import org.apache.spark.sql.execution.RunnableCommand
 import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.{Row, SQLContext}
 
 private[hive]
 case class HiveNativeCommand(sql: String) extends RunnableCommand {
@@ -29,6 +29,6 @@ case class HiveNativeCommand(sql: String) extends RunnableCommand {
   override def output: Seq[AttributeReference] =
     Seq(AttributeReference("result", StringType, nullable = false)())
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] =
-    sqlContext.asInstanceOf[HiveContext].runSqlHive(sql).map(InternalRow(_))
+  override def run(sqlContext: SQLContext): Seq[Row] =
+    sqlContext.asInstanceOf[HiveContext].runSqlHive(sql).map(Row(_))
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index 1f5e4af2e4746..f4c8c9a7e8a68 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -123,7 +123,7 @@ case class HiveTableScan(
 
         // Only partitioned values are needed here, since the predicate has already been bound to
         // partition key attribute references.
-        val row = new GenericRow(castedValues.toArray)
+        val row = InternalRow.fromSeq(castedValues)
         shouldKeep.eval(row).asInstanceOf[Boolean]
       }
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 9d8872aa47d1f..611888055d6cf 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -129,11 +129,11 @@ case class ScriptTransformation(
             val prevLine = curLine
             curLine = reader.readLine()
             if (!ioschema.schemaLess) {
-              new GenericRow(CatalystTypeConverters.convertToCatalyst(
+              new GenericInternalRow(CatalystTypeConverters.convertToCatalyst(
                 prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD")))
                 .asInstanceOf[Array[Any]])
             } else {
-              new GenericRow(CatalystTypeConverters.convertToCatalyst(
+              new GenericInternalRow(CatalystTypeConverters.convertToCatalyst(
                 prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2))
                 .asInstanceOf[Array[Any]])
             }
@@ -167,7 +167,8 @@ case class ScriptTransformation(
 
               outputStream.write(data)
             } else {
-              val writable = inputSerde.serialize(row.asInstanceOf[GenericRow].values, inputSoi)
+              val writable = inputSerde.serialize(
+                row.asInstanceOf[GenericInternalRow].values, inputSoi)
               prepareWritable(writable).write(dataOutputStream)
             }
           }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index aad58bfa2e6e0..71fa3e9c33ad9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -17,15 +17,14 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
-import org.apache.spark.sql.catalyst.util._
-import org.apache.spark.sql.sources._
-import org.apache.spark.sql.{SaveMode, DataFrame, SQLContext}
-import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.execution.RunnableCommand
 import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -39,9 +38,9 @@ import org.apache.spark.util.Utils
 private[hive]
 case class AnalyzeTable(tableName: String) extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     sqlContext.asInstanceOf[HiveContext].analyze(tableName)
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 }
 
@@ -53,7 +52,7 @@ case class DropTable(
     tableName: String,
     ifExists: Boolean) extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     val ifExistsClause = if (ifExists) "IF EXISTS " else ""
     try {
@@ -70,7 +69,7 @@ case class DropTable(
     hiveContext.invalidateTable(tableName)
     hiveContext.runSqlHive(s"DROP TABLE $ifExistsClause$tableName")
     hiveContext.catalog.unregisterTable(Seq(tableName))
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 }
 
@@ -83,7 +82,7 @@ case class AddJar(path: String) extends RunnableCommand {
     schema.toAttributes
   }
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     val currentClassLoader = Utils.getContextOrSparkClassLoader
 
@@ -105,18 +104,18 @@ case class AddJar(path: String) extends RunnableCommand {
     // Add jar to executors
     hiveContext.sparkContext.addJar(path)
 
-    Seq(InternalRow(0))
+    Seq(Row(0))
   }
 }
 
 private[hive]
 case class AddFile(path: String) extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     hiveContext.runSqlHive(s"ADD FILE $path")
     hiveContext.sparkContext.addFile(path)
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 }
 
@@ -129,12 +128,12 @@ case class CreateMetastoreDataSource(
     allowExisting: Boolean,
     managedIfNoPath: Boolean) extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
 
     if (hiveContext.catalog.tableExists(tableName :: Nil)) {
       if (allowExisting) {
-        return Seq.empty[InternalRow]
+        return Seq.empty[Row]
       } else {
         throw new AnalysisException(s"Table $tableName already exists.")
       }
@@ -157,7 +156,7 @@ case class CreateMetastoreDataSource(
       optionsWithPath,
       isExternal)
 
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 }
 
@@ -170,7 +169,7 @@ case class CreateMetastoreDataSourceAsSelect(
     options: Map[String, String],
     query: LogicalPlan) extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     var createMetastoreTable = false
     var isExternal = true
@@ -194,7 +193,7 @@ case class CreateMetastoreDataSourceAsSelect(
             s"Or, if you are using SQL CREATE TABLE, you need to drop $tableName first.")
         case SaveMode.Ignore =>
           // Since the table already exists and the save mode is Ignore, we will just return.
-          return Seq.empty[InternalRow]
+          return Seq.empty[Row]
         case SaveMode.Append =>
           // Check if the specified data source match the data source of the existing table.
           val resolved = ResolvedDataSource(
@@ -259,6 +258,6 @@ case class CreateMetastoreDataSourceAsSelect(
 
     // Refresh the cache of the table in the catalog.
     hiveContext.refreshTable(tableName)
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 0fd7b3a91d6dd..300f83d914ea4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -190,7 +190,7 @@ private[sql] class OrcRelation(
       filters: Array[Filter],
       inputPaths: Array[FileStatus]): RDD[Row] = {
     val output = StructType(requiredColumns.map(dataSchema(_))).toAttributes
-    OrcTableScan(output, this, filters, inputPaths).execute()
+    OrcTableScan(output, this, filters, inputPaths).execute().map(_.asInstanceOf[Row])
   }
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
@@ -234,13 +234,13 @@ private[orc] case class OrcTableScan(
     HiveShim.appendReadColumns(conf, sortedIds, sortedNames)
   }
 
-  // Transform all given raw `Writable`s into `Row`s.
+  // Transform all given raw `Writable`s into `InternalRow`s.
   private def fillObject(
       path: String,
       conf: Configuration,
       iterator: Iterator[Writable],
       nonPartitionKeyAttrs: Seq[(Attribute, Int)],
-      mutableRow: MutableRow): Iterator[Row] = {
+      mutableRow: MutableRow): Iterator[InternalRow] = {
     val deserializer = new OrcSerde
     val soi = OrcFileOperator.getObjectInspector(path, Some(conf))
     val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map {
@@ -261,11 +261,11 @@ private[orc] case class OrcTableScan(
         }
         i += 1
       }
-      mutableRow: Row
+      mutableRow: InternalRow
     }
   }
 
-  def execute(): RDD[Row] = {
+  def execute(): RDD[InternalRow] = {
     val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
     val conf = job.getConfiguration
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index aff0456b37ed5..a93acb938d5fa 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -202,9 +202,9 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     val dt = StructType(dataTypes.zipWithIndex.map {
       case (t, idx) => StructField(s"c_$idx", t)
     })
-
+    val inspector = toInspector(dt)
     checkValues(row,
-      unwrap(wrap(Row.fromSeq(row), toInspector(dt)), toInspector(dt)).asInstanceOf[InternalRow])
+      unwrap(wrap(InternalRow.fromSeq(row), inspector), inspector).asInstanceOf[InternalRow])
     checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt)))
   }
 

From ec784381967506f8db4d6a357c0b72df25a0aa1b Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Sun, 28 Jun 2015 08:29:07 -0700
Subject: [PATCH 0079/1454] [SPARK-8686] [SQL] DataFrame should support `where`
 with expression represented by String

DataFrame supports `filter` function with two types of argument, `Column` and `String`. But `where` doesn't.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7063 from sarutak/SPARK-8686 and squashes the following commits:

180f9a4 [Kousuke Saruta] Added test
d61aec4 [Kousuke Saruta] Add "where" method with String argument to DataFrame
---
 .../main/scala/org/apache/spark/sql/DataFrame.scala  | 12 ++++++++++++
 .../scala/org/apache/spark/sql/DataFrameSuite.scala  |  6 ++++++
 2 files changed, 18 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 0db4df34f9e22..d75d88307562e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -714,6 +714,18 @@ class DataFrame private[sql](
    */
   def where(condition: Column): DataFrame = filter(condition)
 
+  /**
+   * Filters rows using the given SQL expression.
+   * {{{
+   *   peopleDf.where("age > 15")
+   * }}}
+   * @group dfops
+   * @since 1.5.0
+   */
+  def where(conditionExpr: String): DataFrame = {
+    filter(Column(new SqlParser().parseExpression(conditionExpr)))
+  }
+
   /**
    * Groups the [[DataFrame]] using the specified columns, so we can run aggregation on them.
    * See [[GroupedData]] for all the available aggregate functions.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 47443a917b765..d06b9c5785527 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -160,6 +160,12 @@ class DataFrameSuite extends QueryTest {
       testData.collect().filter(_.getInt(0) > 90).toSeq)
   }
 
+  test("filterExpr using where") {
+    checkAnswer(
+      testData.where("key > 50"),
+      testData.collect().filter(_.getInt(0) > 50).toSeq)
+  }
+
   test("repartition") {
     checkAnswer(
       testData.select('key).repartition(10).select('key),

From 9ce78b4343febe87c4edd650c698cc20d38f615d Mon Sep 17 00:00:00 2001
From: "Vincent D. Warmerdam" <vincentwarmerdam@gmail.com>
Date: Sun, 28 Jun 2015 13:33:33 -0700
Subject: [PATCH 0080/1454] [SPARK-8596] [EC2] Added port for Rstudio

This would otherwise need to be set manually by R users in AWS.

https://issues.apache.org/jira/browse/SPARK-8596

Author: Vincent D. Warmerdam <vincentwarmerdam@gmail.com>
Author: vincent <vincentwarmerdam@gmail.com>

Closes #7068 from koaning/rstudio-port-number and squashes the following commits:

ac8100d [vincent] Update spark_ec2.py
ce6ad88 [Vincent D. Warmerdam] added port number for rstudio
---
 ec2/spark_ec2.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index e4932cfa7a4fc..18ccbc0a3edd0 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -505,6 +505,8 @@ def launch_cluster(conn, opts, cluster_name):
         master_group.authorize('tcp', 50070, 50070, authorized_address)
         master_group.authorize('tcp', 60070, 60070, authorized_address)
         master_group.authorize('tcp', 4040, 4045, authorized_address)
+        # Rstudio (GUI for R) needs port 8787 for web access
+        master_group.authorize('tcp', 8787, 8787, authorized_address)
         # HDFS NFS gateway requires 111,2049,4242 for tcp & udp
         master_group.authorize('tcp', 111, 111, authorized_address)
         master_group.authorize('udp', 111, 111, authorized_address)

From 24fda7381171738cbbbacb5965393b660763e562 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Sun, 28 Jun 2015 14:48:44 -0700
Subject: [PATCH 0081/1454] [SPARK-8677] [SQL] Fix non-terminating decimal
 expansion for decimal divide operation

JIRA: https://issues.apache.org/jira/browse/SPARK-8677

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #7056 from viirya/fix_decimal3 and squashes the following commits:

34d7419 [Liang-Chi Hsieh] Fix Non-terminating decimal expansion for decimal divide operation.
---
 .../scala/org/apache/spark/sql/types/Decimal.scala    | 11 +++++++++--
 .../apache/spark/sql/types/decimal/DecimalSuite.scala |  5 +++++
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index bd9823bc05424..5a169488c97eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -265,8 +265,15 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def * (that: Decimal): Decimal = Decimal(toBigDecimal * that.toBigDecimal)
 
-  def / (that: Decimal): Decimal =
-    if (that.isZero) null else Decimal(toBigDecimal / that.toBigDecimal)
+  def / (that: Decimal): Decimal = {
+    if (that.isZero) {
+      null
+    } else {
+      // To avoid non-terminating decimal expansion problem, we turn to Java BigDecimal's divide
+      // with specified ROUNDING_MODE.
+      Decimal(toJavaBigDecimal.divide(that.toJavaBigDecimal, ROUNDING_MODE.id))
+    }
+  }
 
   def % (that: Decimal): Decimal =
     if (that.isZero) null else Decimal(toBigDecimal % that.toBigDecimal)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
index ccc29c0dc8c35..5f312964e5bf7 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
@@ -167,4 +167,9 @@ class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
     val decimal = (Decimal(Long.MaxValue, 38, 0) * Decimal(Long.MaxValue, 38, 0)).toJavaBigDecimal
     assert(decimal.unscaledValue.toString === "85070591730234615847396907784232501249")
   }
+
+  test("fix non-terminating decimal expansion problem") {
+    val decimal = Decimal(1.0, 10, 3) / Decimal(3.0, 10, 3)
+    assert(decimal.toString === "0.333")
+  }
 }

From 00a9d22bd6ef42c1e7d8dd936798b449bb3a9f67 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sun, 28 Jun 2015 19:34:59 -0700
Subject: [PATCH 0082/1454] [SPARK-7845] [BUILD] Bumping default Hadoop version
 used in profile hadoop-1 to 1.2.1

PR #5694 reverted PR #6384 while refactoring `dev/run-tests` to `dev/run-tests.py`. Also, PR #6384 didn't bump Hadoop 1 version defined in POM.

Author: Cheng Lian <lian@databricks.com>

Closes #7062 from liancheng/spark-7845 and squashes the following commits:

c088b72 [Cheng Lian] Bumping default Hadoop version used in profile hadoop-1 to 1.2.1
---
 dev/run-tests.py | 2 +-
 pom.xml          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/run-tests.py b/dev/run-tests.py
index 3533e0c857b9b..eb79a2a502707 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -261,7 +261,7 @@ def get_hadoop_profiles(hadoop_version):
     """
 
     sbt_maven_hadoop_profiles = {
-        "hadoop1.0": ["-Phadoop-1", "-Dhadoop.version=1.0.4"],
+        "hadoop1.0": ["-Phadoop-1", "-Dhadoop.version=1.2.1"],
         "hadoop2.0": ["-Phadoop-1", "-Dhadoop.version=2.0.0-mr1-cdh4.1.1"],
         "hadoop2.2": ["-Pyarn", "-Phadoop-2.2"],
         "hadoop2.3": ["-Pyarn", "-Phadoop-2.3", "-Dhadoop.version=2.3.0"],
diff --git a/pom.xml b/pom.xml
index 00f50166b39b6..4c18bd5e42c87 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1686,7 +1686,7 @@
     <profile>
       <id>hadoop-1</id>
       <properties>
-        <hadoop.version>1.0.4</hadoop.version>
+        <hadoop.version>1.2.1</hadoop.version>
         <protobuf.version>2.4.1</protobuf.version>
         <hbase.version>0.98.7-hadoop1</hbase.version>
         <avro.mapred.classifier>hadoop1</avro.mapred.classifier>

From 25f574eb9a3cb9b93b7d9194a8ec16e00ce2c036 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Sun, 28 Jun 2015 22:26:07 -0700
Subject: [PATCH 0083/1454] [SPARK-7212] [MLLIB] Add sequence learning flag

Support mining of ordered frequent item sequences.

Author: Feynman Liang <fliang@databricks.com>

Closes #6997 from feynmanliang/fp-sequence and squashes the following commits:

7c14e15 [Feynman Liang] Improve scalatests with R code and Seq
0d3e4b6 [Feynman Liang] Fix python test
ce987cb [Feynman Liang] Backwards compatibility aux constructor
34ef8f2 [Feynman Liang] Fix failing test due to reverse orderering
f04bd50 [Feynman Liang] Naming, add ordered to FreqItemsets, test ordering using Seq
648d4d4 [Feynman Liang] Test case for frequent item sequences
252a36a [Feynman Liang] Add sequence learning flag
---
 .../org/apache/spark/mllib/fpm/FPGrowth.scala | 38 +++++++++++---
 .../spark/mllib/fpm/FPGrowthSuite.scala       | 52 ++++++++++++++++++-
 python/pyspark/mllib/fpm.py                   |  4 +-
 3 files changed, 82 insertions(+), 12 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index efa8459d3cdba..abac08022ea47 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -36,7 +36,7 @@ import org.apache.spark.storage.StorageLevel
  * :: Experimental ::
  *
  * Model trained by [[FPGrowth]], which holds frequent itemsets.
- * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
+ * @param freqItemsets frequent itemsets, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
  */
 @Experimental
@@ -62,13 +62,14 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
 @Experimental
 class FPGrowth private (
     private var minSupport: Double,
-    private var numPartitions: Int) extends Logging with Serializable {
+    private var numPartitions: Int,
+    private var ordered: Boolean) extends Logging with Serializable {
 
   /**
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
-   * as the input data}.
+   * as the input data, ordered: `false`}.
    */
-  def this() = this(0.3, -1)
+  def this() = this(0.3, -1, false)
 
   /**
    * Sets the minimal support level (default: `0.3`).
@@ -86,6 +87,15 @@ class FPGrowth private (
     this
   }
 
+  /**
+   * Indicates whether to mine itemsets (unordered) or sequences (ordered) (default: false, mine
+   * itemsets).
+   */
+  def setOrdered(ordered: Boolean): this.type = {
+    this.ordered = ordered
+    this
+  }
+
   /**
    * Computes an FP-Growth model that contains frequent itemsets.
    * @param data input data set, each element contains a transaction
@@ -155,7 +165,7 @@ class FPGrowth private (
     .flatMap { case (part, tree) =>
       tree.extract(minCount, x => partitioner.getPartition(x) == part)
     }.map { case (ranks, count) =>
-      new FreqItemset(ranks.map(i => freqItems(i)).toArray, count)
+      new FreqItemset(ranks.map(i => freqItems(i)).reverse.toArray, count, ordered)
     }
   }
 
@@ -171,9 +181,12 @@ class FPGrowth private (
       itemToRank: Map[Item, Int],
       partitioner: Partitioner): mutable.Map[Int, Array[Int]] = {
     val output = mutable.Map.empty[Int, Array[Int]]
-    // Filter the basket by frequent items pattern and sort their ranks.
+    // Filter the basket by frequent items pattern
     val filtered = transaction.flatMap(itemToRank.get)
-    ju.Arrays.sort(filtered)
+    if (!this.ordered) {
+      ju.Arrays.sort(filtered)
+    }
+    // Generate conditional transactions
     val n = filtered.length
     var i = n - 1
     while (i >= 0) {
@@ -198,9 +211,18 @@ object FPGrowth {
    * Frequent itemset.
    * @param items items in this itemset. Java users should call [[FreqItemset#javaItems]] instead.
    * @param freq frequency
+   * @param ordered indicates if items represents an itemset (false) or sequence (true)
    * @tparam Item item type
    */
-  class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
+  class FreqItemset[Item](val items: Array[Item], val freq: Long, val ordered: Boolean)
+    extends Serializable {
+
+    /**
+     * Auxillary constructor, assumes unordered by default.
+     */
+    def this(items: Array[Item], freq: Long) {
+      this(items, freq, false)
+    }
 
     /**
      * Returns items in a Java List.
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
index 66ae3543ecc4e..1a8a1e79f2810 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
@@ -22,7 +22,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
 
 
-  test("FP-Growth using String type") {
+  test("FP-Growth frequent itemsets using String type") {
     val transactions = Seq(
       "r z h k p",
       "z y x w v u t s",
@@ -38,12 +38,14 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
+      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
+      .setOrdered(false)
       .run(rdd)
     val freqItemsets3 = model3.freqItemsets.collect().map { itemset =>
       (itemset.items.toSet, itemset.freq)
@@ -61,17 +63,59 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
+      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 54)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
+      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 625)
   }
 
-  test("FP-Growth using Int type") {
+  test("FP-Growth frequent sequences using String type"){
+    val transactions = Seq(
+      "r z h k p",
+      "z y x w v u t s",
+      "s x o n r",
+      "x z y m t s q e",
+      "z",
+      "x z y r q t p")
+      .map(_.split(" "))
+    val rdd = sc.parallelize(transactions, 2).cache()
+
+    val fpg = new FPGrowth()
+
+    val model1 = fpg
+      .setMinSupport(0.5)
+      .setNumPartitions(2)
+      .setOrdered(true)
+      .run(rdd)
+
+    /*
+      Use the following R code to verify association rules using arulesSequences package.
+
+      data = read_baskets("path", info = c("sequenceID","eventID","SIZE"))
+      freqItemSeq = cspade(data, parameter = list(support = 0.5))
+      resSeq = as(freqItemSeq, "data.frame")
+      resSeq$support = resSeq$support * length(transactions)
+      names(resSeq)[names(resSeq) == "support"] = "freq"
+      resSeq
+     */
+    val expected = Set(
+      (Seq("r"), 3L), (Seq("s"), 3L), (Seq("t"), 3L), (Seq("x"), 4L), (Seq("y"), 3L),
+      (Seq("z"), 5L), (Seq("z", "y"), 3L), (Seq("x", "t"), 3L), (Seq("y", "t"), 3L),
+      (Seq("z", "t"), 3L), (Seq("z", "y", "t"), 3L)
+    )
+    val freqItemseqs1 = model1.freqItemsets.collect().map { itemset =>
+      (itemset.items.toSeq, itemset.freq)
+    }.toSet
+    assert(freqItemseqs1 == expected)
+  }
+
+  test("FP-Growth frequent itemsets using Int type") {
     val transactions = Seq(
       "1 2 3",
       "1 2 3 4",
@@ -88,12 +132,14 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
+      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
+      .setOrdered(false)
       .run(rdd)
     assert(model3.freqItemsets.first().items.getClass === Array(1).getClass,
       "frequent itemsets should use primitive arrays")
@@ -109,12 +155,14 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
+      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 15)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
+      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 65)
   }
diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py
index bdc4a132b1b18..b7f00d60069e6 100644
--- a/python/pyspark/mllib/fpm.py
+++ b/python/pyspark/mllib/fpm.py
@@ -39,8 +39,8 @@ class FPGrowthModel(JavaModelWrapper):
     >>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]]
     >>> rdd = sc.parallelize(data, 2)
     >>> model = FPGrowth.train(rdd, 0.6, 2)
-    >>> sorted(model.freqItemsets().collect())
-    [FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'c'], freq=3), ...
+    >>> sorted(model.freqItemsets().collect(), key=lambda x: x.items)
+    [FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'a', u'c'], freq=3), ...
     """
 
     def freqItemsets(self):

From dfde31da5ce30e0d44cad4fb6618b44d5353d946 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Sun, 28 Jun 2015 22:38:04 -0700
Subject: [PATCH 0084/1454] [SPARK-5962] [MLLIB] Python support for Power
 Iteration Clustering

Python support for Power Iteration Clustering
https://issues.apache.org/jira/browse/SPARK-5962

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #6992 from yanboliang/pyspark-pic and squashes the following commits:

6b03d82 [Yanbo Liang] address comments
4be4423 [Yanbo Liang] Python support for Power Iteration Clustering
---
 ...PowerIterationClusteringModelWrapper.scala | 32 ++++++
 .../mllib/api/python/PythonMLLibAPI.scala     | 27 +++++
 python/pyspark/mllib/clustering.py            | 98 ++++++++++++++++++-
 3 files changed, 154 insertions(+), 3 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/api/python/PowerIterationClusteringModelWrapper.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PowerIterationClusteringModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PowerIterationClusteringModelWrapper.scala
new file mode 100644
index 0000000000000..bc6041b221732
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PowerIterationClusteringModelWrapper.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.api.python
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.clustering.PowerIterationClusteringModel
+
+/**
+ * A Wrapper of PowerIterationClusteringModel to provide helper method for Python
+ */
+private[python] class PowerIterationClusteringModelWrapper(model: PowerIterationClusteringModel)
+  extends PowerIterationClusteringModel(model.k, model.assignments) {
+
+  def getAssignments: RDD[Array[Any]] = {
+    model.assignments.map(x => Array(x.id, x.cluster))
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index b16903a8d515c..a66a404d5c846 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -406,6 +406,33 @@ private[python] class PythonMLLibAPI extends Serializable {
       model.predictSoft(data).map(Vectors.dense)
   }
 
+  /**
+   * Java stub for Python mllib PowerIterationClustering.run(). This stub returns a
+   * handle to the Java object instead of the content of the Java object.  Extra care
+   * needs to be taken in the Python code to ensure it gets freed on exit; see the
+   * Py4J documentation.
+   * @param data an RDD of (i, j, s,,ij,,) tuples representing the affinity matrix.
+   * @param k number of clusters.
+   * @param maxIterations maximum number of iterations of the power iteration loop.
+   * @param initMode the initialization mode. This can be either "random" to use
+   *                 a random vector as vertex properties, or "degree" to use
+   *                 normalized sum similarities. Default: random.
+   */
+  def trainPowerIterationClusteringModel(
+      data: JavaRDD[Vector],
+      k: Int,
+      maxIterations: Int,
+      initMode: String): PowerIterationClusteringModel = {
+
+    val pic = new PowerIterationClustering()
+      .setK(k)
+      .setMaxIterations(maxIterations)
+      .setInitializationMode(initMode)
+
+    val model = pic.run(data.rdd.map(v => (v(0).toLong, v(1).toLong, v(2))))
+    new PowerIterationClusteringModelWrapper(model)
+  }
+
   /**
    * Java stub for Python mllib ALS.train().  This stub returns a handle
    * to the Java object instead of the content of the Java object.  Extra care
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index 8bc0654c76ca3..e3c8a24c4a751 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -25,15 +25,18 @@
 
 from numpy import array, random, tile
 
+from collections import namedtuple
+
 from pyspark import SparkContext
 from pyspark.rdd import RDD, ignore_unicode_prefix
-from pyspark.mllib.common import callMLlibFunc, callJavaFunc, _py2java, _java2py
+from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, callJavaFunc, _py2java, _java2py
 from pyspark.mllib.linalg import SparseVector, _convert_to_vector, DenseVector
 from pyspark.mllib.stat.distribution import MultivariateGaussian
-from pyspark.mllib.util import Saveable, Loader, inherit_doc
+from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader, JavaSaveable
 from pyspark.streaming import DStream
 
 __all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture',
+           'PowerIterationClusteringModel', 'PowerIterationClustering',
            'StreamingKMeans', 'StreamingKMeansModel']
 
 
@@ -272,6 +275,94 @@ def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initia
         return GaussianMixtureModel(weight, mvg_obj)
 
 
+class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader):
+
+    """
+    .. note:: Experimental
+
+    Model produced by [[PowerIterationClustering]].
+
+    >>> data = [(0, 1, 1.0), (0, 2, 1.0), (1, 3, 1.0), (2, 3, 1.0),
+    ...     (0, 3, 1.0), (1, 2, 1.0), (0, 4, 0.1)]
+    >>> rdd = sc.parallelize(data, 2)
+    >>> model = PowerIterationClustering.train(rdd, 2, 100)
+    >>> model.k
+    2
+    >>> sorted(model.assignments().collect())
+    [Assignment(id=0, cluster=1), Assignment(id=1, cluster=0), ...
+    >>> import os, tempfile
+    >>> path = tempfile.mkdtemp()
+    >>> model.save(sc, path)
+    >>> sameModel = PowerIterationClusteringModel.load(sc, path)
+    >>> sameModel.k
+    2
+    >>> sorted(sameModel.assignments().collect())
+    [Assignment(id=0, cluster=1), Assignment(id=1, cluster=0), ...
+    >>> from shutil import rmtree
+    >>> try:
+    ...     rmtree(path)
+    ... except OSError:
+    ...     pass
+    """
+
+    @property
+    def k(self):
+        """
+        Returns the number of clusters.
+        """
+        return self.call("k")
+
+    def assignments(self):
+        """
+        Returns the cluster assignments of this model.
+        """
+        return self.call("getAssignments").map(
+            lambda x: (PowerIterationClustering.Assignment(*x)))
+
+    @classmethod
+    def load(cls, sc, path):
+        model = cls._load_java(sc, path)
+        wrapper = sc._jvm.PowerIterationClusteringModelWrapper(model)
+        return PowerIterationClusteringModel(wrapper)
+
+
+class PowerIterationClustering(object):
+    """
+    .. note:: Experimental
+
+    Power Iteration Clustering (PIC), a scalable graph clustering algorithm
+    developed by [[http://www.icml2010.org/papers/387.pdf Lin and Cohen]].
+    From the abstract: PIC finds a very low-dimensional embedding of a
+    dataset using truncated power iteration on a normalized pair-wise
+    similarity matrix of the data.
+    """
+
+    @classmethod
+    def train(cls, rdd, k, maxIterations=100, initMode="random"):
+        """
+        :param rdd: an RDD of (i, j, s,,ij,,) tuples representing the
+            affinity matrix, which is the matrix A in the PIC paper.
+            The similarity s,,ij,, must be nonnegative.
+            This is a symmetric matrix and hence s,,ij,, = s,,ji,,.
+            For any (i, j) with nonzero similarity, there should be
+            either (i, j, s,,ij,,) or (j, i, s,,ji,,) in the input.
+            Tuples with i = j are ignored, because we assume
+            s,,ij,, = 0.0.
+        :param k: Number of clusters.
+        :param maxIterations: Maximum number of iterations of the
+            PIC algorithm.
+        :param initMode: Initialization mode.
+        """
+        model = callMLlibFunc("trainPowerIterationClusteringModel",
+                              rdd.map(_convert_to_vector), int(k), int(maxIterations), initMode)
+        return PowerIterationClusteringModel(model)
+
+    class Assignment(namedtuple("Assignment", ["id", "cluster"])):
+        """
+        Represents an (id, cluster) tuple.
+        """
+
+
 class StreamingKMeansModel(KMeansModel):
     """
     .. note:: Experimental
@@ -466,7 +557,8 @@ def predictOnValues(self, dstream):
 
 def _test():
     import doctest
-    globs = globals().copy()
+    import pyspark.mllib.clustering
+    globs = pyspark.mllib.clustering.__dict__.copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()

From 0b10662fef11a56f82144b4953d457738e6961ae Mon Sep 17 00:00:00 2001
From: BenFradet <benjamin.fradet@gmail.com>
Date: Sun, 28 Jun 2015 22:43:47 -0700
Subject: [PATCH 0085/1454] [SPARK-8575] [SQL] Deprecate callUDF in favor of
 udf

Follow up of [SPARK-8356](https://issues.apache.org/jira/browse/SPARK-8356) and #6902.
Removes the unit test for the now deprecated ```callUdf```
Unit test in SQLQuerySuite now uses ```udf``` instead of ```callUDF```
Replaced ```callUDF``` by ```udf``` where possible in mllib

Author: BenFradet <benjamin.fradet@gmail.com>

Closes #6993 from BenFradet/SPARK-8575 and squashes the following commits:

26f5a7a [BenFradet] 2 spaces instead of 1
1ddb452 [BenFradet] renamed initUDF in order to be consistent in OneVsRest
48ca15e [BenFradet] used vector type tag for udf call in VectorIndexer
0ebd0da [BenFradet] replace the now deprecated callUDF by udf in VectorIndexer
8013409 [BenFradet] replaced the now deprecated callUDF by udf in Predictor
94345b5 [BenFradet] unifomized udf calls in ProbabilisticClassifier
1305492 [BenFradet] uniformized udf calls in Classifier
a672228 [BenFradet] uniformized udf calls in OneVsRest
49e4904 [BenFradet] Revert "removal of the unit test for the now deprecated callUdf"
bbdeaf3 [BenFradet] fixed syntax for init udf in OneVsRest
fe2a10b [BenFradet] callUDF => udf in ProbabilisticClassifier
0ea30b3 [BenFradet] callUDF => udf in Classifier where possible
197ec82 [BenFradet] callUDF => udf in OneVsRest
84d6780 [BenFradet] modified unit test in SQLQuerySuite to use udf instead of callUDF
477709f [BenFradet] removal of the unit test for the now deprecated callUdf
---
 .../scala/org/apache/spark/ml/Predictor.scala |  9 ++++---
 .../spark/ml/classification/Classifier.scala  | 13 ++++++---
 .../spark/ml/classification/OneVsRest.scala   | 27 +++++++++----------
 .../ProbabilisticClassifier.scala             | 22 ++++++++++-----
 .../spark/ml/feature/VectorIndexer.scala      |  5 ++--
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  5 ++--
 6 files changed, 46 insertions(+), 35 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
index edaa2afb790e6..333b42711ec52 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -122,9 +122,7 @@ abstract class Predictor[
    */
   protected def extractLabeledPoints(dataset: DataFrame): RDD[LabeledPoint] = {
     dataset.select($(labelCol), $(featuresCol))
-      .map { case Row(label: Double, features: Vector) =>
-      LabeledPoint(label, features)
-    }
+      .map { case Row(label: Double, features: Vector) => LabeledPoint(label, features) }
   }
 }
 
@@ -171,7 +169,10 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema, logging = true)
     if ($(predictionCol).nonEmpty) {
-      dataset.withColumn($(predictionCol), callUDF(predict _, DoubleType, col($(featuresCol))))
+      val predictUDF = udf { (features: Any) =>
+        predict(features.asInstanceOf[FeaturesType])
+      }
+      dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
     } else {
       this.logWarning(s"$uid: Predictor.transform() was called as NOOP" +
         " since no output columns were set.")
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index 14c285dbfc54a..85c097bc64a4f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -102,15 +102,20 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
     var outputData = dataset
     var numColsOutput = 0
     if (getRawPredictionCol != "") {
-      outputData = outputData.withColumn(getRawPredictionCol,
-        callUDF(predictRaw _, new VectorUDT, col(getFeaturesCol)))
+      val predictRawUDF = udf { (features: Any) =>
+        predictRaw(features.asInstanceOf[FeaturesType])
+      }
+      outputData = outputData.withColumn(getRawPredictionCol, predictRawUDF(col(getFeaturesCol)))
       numColsOutput += 1
     }
     if (getPredictionCol != "") {
       val predUDF = if (getRawPredictionCol != "") {
-        callUDF(raw2prediction _, DoubleType, col(getRawPredictionCol))
+        udf(raw2prediction _).apply(col(getRawPredictionCol))
       } else {
-        callUDF(predict _, DoubleType, col(getFeaturesCol))
+        val predictUDF = udf { (features: Any) =>
+          predict(features.asInstanceOf[FeaturesType])
+        }
+        predictUDF(col(getFeaturesCol))
       }
       outputData = outputData.withColumn(getPredictionCol, predUDF)
       numColsOutput += 1
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index b657882f8ad3f..ea757c5e40c76 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -88,9 +88,9 @@ final class OneVsRestModel private[ml] (
 
     // add an accumulator column to store predictions of all the models
     val accColName = "mbc$acc" + UUID.randomUUID().toString
-    val init: () => Map[Int, Double] = () => {Map()}
+    val initUDF = udf { () => Map[Int, Double]() }
     val mapType = MapType(IntegerType, DoubleType, valueContainsNull = false)
-    val newDataset = dataset.withColumn(accColName, callUDF(init, mapType))
+    val newDataset = dataset.withColumn(accColName, initUDF())
 
     // persist if underlying dataset is not persistent.
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
@@ -106,13 +106,12 @@ final class OneVsRestModel private[ml] (
 
         // add temporary column to store intermediate scores and update
         val tmpColName = "mbc$tmp" + UUID.randomUUID().toString
-        val update: (Map[Int, Double], Vector) => Map[Int, Double] =
-          (predictions: Map[Int, Double], prediction: Vector) => {
-            predictions + ((index, prediction(1)))
-          }
-        val updateUdf = callUDF(update, mapType, col(accColName), col(rawPredictionCol))
+        val updateUDF = udf { (predictions: Map[Int, Double], prediction: Vector) =>
+          predictions + ((index, prediction(1)))
+        }
         val transformedDataset = model.transform(df).select(columns : _*)
-        val updatedDataset = transformedDataset.withColumn(tmpColName, updateUdf)
+        val updatedDataset = transformedDataset
+          .withColumn(tmpColName, updateUDF(col(accColName), col(rawPredictionCol)))
         val newColumns = origCols ++ List(col(tmpColName))
 
         // switch out the intermediate column with the accumulator column
@@ -124,13 +123,13 @@ final class OneVsRestModel private[ml] (
     }
 
     // output the index of the classifier with highest confidence as prediction
-    val label: Map[Int, Double] => Double = (predictions: Map[Int, Double]) => {
+    val labelUDF = udf { (predictions: Map[Int, Double]) =>
       predictions.maxBy(_._2)._1.toDouble
     }
 
     // output label and label metadata as prediction
-    val labelUdf = callUDF(label, DoubleType, col(accColName))
-    aggregatedDataset.withColumn($(predictionCol), labelUdf.as($(predictionCol), labelMetadata))
+    aggregatedDataset
+      .withColumn($(predictionCol), labelUDF(col(accColName)).as($(predictionCol), labelMetadata))
       .drop(accColName)
   }
 
@@ -185,17 +184,15 @@ final class OneVsRest(override val uid: String)
 
     // create k columns, one for each binary classifier.
     val models = Range(0, numClasses).par.map { index =>
-
-      val label: Double => Double = (label: Double) => {
+      val labelUDF = udf { (label: Double) =>
         if (label.toInt == index) 1.0 else 0.0
       }
 
       // generate new label metadata for the binary problem.
       // TODO: use when ... otherwise after SPARK-7321 is merged
-      val labelUDF = callUDF(label, DoubleType, col($(labelCol)))
       val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
       val labelColName = "mc2b$" + index
-      val labelUDFWithNewMeta = labelUDF.as(labelColName, newLabelMeta)
+      val labelUDFWithNewMeta = labelUDF(col($(labelCol))).as(labelColName, newLabelMeta)
       val trainingDataset = multiclassLabeled.withColumn(labelColName, labelUDFWithNewMeta)
       val classifier = getClassifier
       classifier.fit(trainingDataset, classifier.labelCol -> labelColName)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index 330ae2938f4e0..38e832372698c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -98,26 +98,34 @@ private[spark] abstract class ProbabilisticClassificationModel[
     var outputData = dataset
     var numColsOutput = 0
     if ($(rawPredictionCol).nonEmpty) {
-      outputData = outputData.withColumn(getRawPredictionCol,
-        callUDF(predictRaw _, new VectorUDT, col(getFeaturesCol)))
+      val predictRawUDF = udf { (features: Any) =>
+        predictRaw(features.asInstanceOf[FeaturesType])
+      }
+      outputData = outputData.withColumn(getRawPredictionCol, predictRawUDF(col(getFeaturesCol)))
       numColsOutput += 1
     }
     if ($(probabilityCol).nonEmpty) {
       val probUDF = if ($(rawPredictionCol).nonEmpty) {
-        callUDF(raw2probability _, new VectorUDT, col($(rawPredictionCol)))
+        udf(raw2probability _).apply(col($(rawPredictionCol)))
       } else {
-        callUDF(predictProbability _, new VectorUDT, col($(featuresCol)))
+        val probabilityUDF = udf { (features: Any) =>
+          predictProbability(features.asInstanceOf[FeaturesType])
+        }
+        probabilityUDF(col($(featuresCol)))
       }
       outputData = outputData.withColumn($(probabilityCol), probUDF)
       numColsOutput += 1
     }
     if ($(predictionCol).nonEmpty) {
       val predUDF = if ($(rawPredictionCol).nonEmpty) {
-        callUDF(raw2prediction _, DoubleType, col($(rawPredictionCol)))
+        udf(raw2prediction _).apply(col($(rawPredictionCol)))
       } else if ($(probabilityCol).nonEmpty) {
-        callUDF(probability2prediction _, DoubleType, col($(probabilityCol)))
+        udf(probability2prediction _).apply(col($(probabilityCol)))
       } else {
-        callUDF(predict _, DoubleType, col($(featuresCol)))
+        val predictUDF = udf { (features: Any) =>
+          predict(features.asInstanceOf[FeaturesType])
+        }
+        predictUDF(col($(featuresCol)))
       }
       outputData = outputData.withColumn($(predictionCol), predUDF)
       numColsOutput += 1
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index f4854a5e4b7b7..c73bdccdef5fa 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -30,7 +30,7 @@ import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, VectorUDT}
 import org.apache.spark.sql.{DataFrame, Row}
-import org.apache.spark.sql.functions.callUDF
+import org.apache.spark.sql.functions.udf
 import org.apache.spark.sql.types.{StructField, StructType}
 import org.apache.spark.util.collection.OpenHashSet
 
@@ -339,7 +339,8 @@ class VectorIndexerModel private[ml] (
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema, logging = true)
     val newField = prepOutputField(dataset.schema)
-    val newCol = callUDF(transformFunc, new VectorUDT, dataset($(inputCol)))
+    val transformUDF = udf { (vector: Vector) => transformFunc(vector) }
+    val newCol = transformUDF(dataset($(inputCol)))
     dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 73bc6c999164e..22c54e43c1d16 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -137,13 +137,12 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
   test("SPARK-7158 collect and take return different results") {
     import java.util.UUID
-    import org.apache.spark.sql.types._
 
     val df = Seq(Tuple1(1), Tuple1(2), Tuple1(3)).toDF("index")
     // we except the id is materialized once
-    def id: () => String = () => { UUID.randomUUID().toString() }
+    val idUdf = udf(() => UUID.randomUUID().toString)
 
-    val dfWithId = df.withColumn("id", callUDF(id, StringType))
+    val dfWithId = df.withColumn("id", idUdf())
     // Make a new DataFrame (actually the same reference to the old one)
     val cached = dfWithId.cache()
     // Trigger the cache

From ac2e17b01c0843d928a363d2cc4faf57ec8c8b47 Mon Sep 17 00:00:00 2001
From: Cheolsoo Park <cheolsoop@netflix.com>
Date: Mon, 29 Jun 2015 00:13:39 -0700
Subject: [PATCH 0086/1454] [SPARK-8355] [SQL] Python DataFrameReader/Writer
 should mirror Scala

I compared PySpark DataFrameReader/Writer against Scala ones. `Option` function is missing in both reader and writer, but the rest seems to all match.

I added `Option` to reader and writer and updated the `pyspark-sql` test.

Author: Cheolsoo Park <cheolsoop@netflix.com>

Closes #7078 from piaozhexiu/SPARK-8355 and squashes the following commits:

c63d419 [Cheolsoo Park] Fix version
524e0aa [Cheolsoo Park] Add option function to df reader and writer
---
 python/pyspark/sql/readwriter.py | 14 ++++++++++++++
 python/pyspark/sql/tests.py      |  1 +
 2 files changed, 15 insertions(+)

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 1b7bc0f9a12be..c4cc62e82a160 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -73,6 +73,13 @@ def schema(self, schema):
         self._jreader = self._jreader.schema(jschema)
         return self
 
+    @since(1.5)
+    def option(self, key, value):
+        """Adds an input option for the underlying data source.
+        """
+        self._jreader = self._jreader.option(key, value)
+        return self
+
     @since(1.4)
     def options(self, **options):
         """Adds input options for the underlying data source.
@@ -235,6 +242,13 @@ def format(self, source):
         self._jwrite = self._jwrite.format(source)
         return self
 
+    @since(1.5)
+    def option(self, key, value):
+        """Adds an output option for the underlying data source.
+        """
+        self._jwrite = self._jwrite.option(key, value)
+        return self
+
     @since(1.4)
     def options(self, **options):
         """Adds output options for the underlying data source.
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index e6a434e4b2dff..ffee43a94baba 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -564,6 +564,7 @@ def test_save_and_load_builder(self):
         self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
 
         df.write.mode("overwrite").options(noUse="this options will not be used in save.")\
+                .option("noUse", "this option will not be used in save.")\
                 .format("json").save(path=tmpPath)
         actual =\
             self.sqlCtx.read.format("json")\

From 660c6cec75dc165cf5d62cdc1b0951bdb93df365 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 29 Jun 2015 00:22:44 -0700
Subject: [PATCH 0087/1454] [SPARK-8698] partitionBy in Python DataFrame
 reader/writer interface should not default to empty tuple.

Author: Reynold Xin <rxin@databricks.com>

Closes #7079 from rxin/SPARK-8698 and squashes the following commits:

8513e1c [Reynold Xin] [SPARK-8698] partitionBy in Python DataFrame reader/writer interface should not default to empty tuple.
---
 python/pyspark/sql/readwriter.py | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index c4cc62e82a160..882a03090ec13 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -270,12 +270,11 @@ def partitionBy(self, *cols):
         """
         if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
             cols = cols[0]
-        if len(cols) > 0:
-            self._jwrite = self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols))
+        self._jwrite = self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols))
         return self
 
     @since(1.4)
-    def save(self, path=None, format=None, mode=None, partitionBy=(), **options):
+    def save(self, path=None, format=None, mode=None, partitionBy=None, **options):
         """Saves the contents of the :class:`DataFrame` to a data source.
 
         The data source is specified by the ``format`` and a set of ``options``.
@@ -295,7 +294,9 @@ def save(self, path=None, format=None, mode=None, partitionBy=(), **options):
 
         >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self.partitionBy(partitionBy).mode(mode).options(**options)
+        self.mode(mode).options(**options)
+        if partitionBy is not None:
+            self.partitionBy(partitionBy)
         if format is not None:
             self.format(format)
         if path is None:
@@ -315,7 +316,7 @@ def insertInto(self, tableName, overwrite=False):
         self._jwrite.mode("overwrite" if overwrite else "append").insertInto(tableName)
 
     @since(1.4)
-    def saveAsTable(self, name, format=None, mode=None, partitionBy=(), **options):
+    def saveAsTable(self, name, format=None, mode=None, partitionBy=None, **options):
         """Saves the content of the :class:`DataFrame` as the specified table.
 
         In the case the table already exists, behavior of this function depends on the
@@ -334,7 +335,9 @@ def saveAsTable(self, name, format=None, mode=None, partitionBy=(), **options):
         :param partitionBy: names of partitioning columns
         :param options: all other string options
         """
-        self.partitionBy(partitionBy).mode(mode).options(**options)
+        self.mode(mode).options(**options)
+        if partitionBy is not None:
+            self.partitionBy(partitionBy)
         if format is not None:
             self.format(format)
         self._jwrite.saveAsTable(name)
@@ -356,7 +359,7 @@ def json(self, path, mode=None):
         self.mode(mode)._jwrite.json(path)
 
     @since(1.4)
-    def parquet(self, path, mode=None, partitionBy=()):
+    def parquet(self, path, mode=None, partitionBy=None):
         """Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
 
         :param path: the path in any Hadoop supported file system
@@ -370,7 +373,9 @@ def parquet(self, path, mode=None, partitionBy=()):
 
         >>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self.partitionBy(partitionBy).mode(mode)
+        self.mode(mode)
+        if partitionBy is not None:
+            self.partitionBy(partitionBy)
         self._jwrite.parquet(path)
 
     @since(1.4)

From 630bd5fd80193ab6dc6ad0e7bcc13ee0dadabd38 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 30 Jun 2015 00:46:55 +0900
Subject: [PATCH 0088/1454] [SPARK-8702] [WEBUI] Avoid massive concating
 strings in Javascript

When there are massive tasks, such as `sc.parallelize(1 to 100000, 10000).count()`, the generated JS codes have a lot of string concatenations in the stage page, nearly 40 string concatenations for one task.

We can generate the whole string for a task instead of execution string concatenations in the browser.

Before this patch, the load time of the page is about 21 seconds.
![screen shot 2015-06-29 at 6 44 04 pm](https://cloud.githubusercontent.com/assets/1000778/8406644/eb55ed18-1e90-11e5-9ad5-50d27ad1dff1.png)

After this patch, it reduces to about 17 seconds.

![screen shot 2015-06-29 at 6 47 34 pm](https://cloud.githubusercontent.com/assets/1000778/8406665/087003ca-1e91-11e5-80a8-3485aa9adafa.png)

One disadvantage is that the generated JS codes become hard to read.

Author: zsxwing <zsxwing@gmail.com>

Closes #7082 from zsxwing/js-string and squashes the following commits:

b29231d [zsxwing] Avoid massive concating strings in Javascript
---
 .../org/apache/spark/ui/jobs/StagePage.scala  | 88 +++++++++----------
 1 file changed, 44 insertions(+), 44 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index b83a49f79c8a8..e96bf49d0dd14 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -572,55 +572,55 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         val attempt = taskInfo.attempt
         val timelineObject =
           s"""
-             {
-               'className': 'task task-assignment-timeline-object',
-               'group': '$executorId',
-               'content': '<div class="task-assignment-timeline-content"' +
-                 'data-toggle="tooltip" data-placement="top"' +
-                 'data-html="true" data-container="body"' +
-                 'data-title="${s"Task " + index + " (attempt " + attempt + ")"}<br>' +
-                 'Status: ${taskInfo.status}<br>' +
-                 'Launch Time: ${UIUtils.formatDate(new Date(launchTime))}' +
-                 '${
+             |{
+               |'className': 'task task-assignment-timeline-object',
+               |'group': '$executorId',
+               |'content': '<div class="task-assignment-timeline-content"
+                 |data-toggle="tooltip" data-placement="top"
+                 |data-html="true" data-container="body"
+                 |data-title="${s"Task " + index + " (attempt " + attempt + ")"}<br>
+                 |Status: ${taskInfo.status}<br>
+                 |Launch Time: ${UIUtils.formatDate(new Date(launchTime))}
+                 |${
                      if (!taskInfo.running) {
                        s"""<br>Finish Time: ${UIUtils.formatDate(new Date(finishTime))}"""
                      } else {
                         ""
                       }
-                   }' +
-                 '<br>Scheduler Delay: $schedulerDelay ms' +
-                 '<br>Task Deserialization Time: ${UIUtils.formatDuration(deserializationTime)}' +
-                 '<br>Shuffle Read Time: ${UIUtils.formatDuration(shuffleReadTime)}' +
-                 '<br>Executor Computing Time: ${UIUtils.formatDuration(executorComputingTime)}' +
-                 '<br>Shuffle Write Time: ${UIUtils.formatDuration(shuffleWriteTime)}' +
-                 '<br>Result Serialization Time: ${UIUtils.formatDuration(serializationTime)}' +
-                 '<br>Getting Result Time: ${UIUtils.formatDuration(gettingResultTime)}">' +
-                 '<svg class="task-assignment-timeline-duration-bar">' +
-                 '<rect class="scheduler-delay-proportion" ' +
-                   'x="$schedulerDelayProportionPos%" y="0px" height="26px"' +
-                   'width="$schedulerDelayProportion%""></rect>' +
-                 '<rect class="deserialization-time-proportion" '+
-                   'x="$deserializationTimeProportionPos%" y="0px" height="26px"' +
-                   'width="$deserializationTimeProportion%"></rect>' +
-                 '<rect class="shuffle-read-time-proportion" ' +
-                   'x="$shuffleReadTimeProportionPos%" y="0px" height="26px"' +
-                   'width="$shuffleReadTimeProportion%"></rect>' +
-                 '<rect class="executor-runtime-proportion" ' +
-                   'x="$executorRuntimeProportionPos%" y="0px" height="26px"' +
-                   'width="$executorComputingTimeProportion%"></rect>' +
-                 '<rect class="shuffle-write-time-proportion" ' +
-                   'x="$shuffleWriteTimeProportionPos%" y="0px" height="26px"' +
-                   'width="$shuffleWriteTimeProportion%"></rect>' +
-                 '<rect class="serialization-time-proportion" ' +
-                   'x="$serializationTimeProportionPos%" y="0px" height="26px"' +
-                   'width="$serializationTimeProportion%"></rect>' +
-                 '<rect class="getting-result-time-proportion" ' +
-                   'x="$gettingResultTimeProportionPos%" y="0px" height="26px"' +
-                   'width="$gettingResultTimeProportion%"></rect></svg>',
-               'start': new Date($launchTime),
-               'end': new Date($finishTime)
-             }
-           """
+                   }
+                 |<br>Scheduler Delay: $schedulerDelay ms
+                 |<br>Task Deserialization Time: ${UIUtils.formatDuration(deserializationTime)}
+                 |<br>Shuffle Read Time: ${UIUtils.formatDuration(shuffleReadTime)}
+                 |<br>Executor Computing Time: ${UIUtils.formatDuration(executorComputingTime)}
+                 |<br>Shuffle Write Time: ${UIUtils.formatDuration(shuffleWriteTime)}
+                 |<br>Result Serialization Time: ${UIUtils.formatDuration(serializationTime)}
+                 |<br>Getting Result Time: ${UIUtils.formatDuration(gettingResultTime)}">
+                 |<svg class="task-assignment-timeline-duration-bar">
+                 |<rect class="scheduler-delay-proportion"
+                   |x="$schedulerDelayProportionPos%" y="0px" height="26px"
+                   |width="$schedulerDelayProportion%""></rect>
+                 |<rect class="deserialization-time-proportion"
+                   |x="$deserializationTimeProportionPos%" y="0px" height="26px"
+                   |width="$deserializationTimeProportion%"></rect>
+                 |<rect class="shuffle-read-time-proportion"
+                   |x="$shuffleReadTimeProportionPos%" y="0px" height="26px"
+                   |width="$shuffleReadTimeProportion%"></rect>
+                 |<rect class="executor-runtime-proportion"
+                   |x="$executorRuntimeProportionPos%" y="0px" height="26px"
+                   |width="$executorComputingTimeProportion%"></rect>
+                 |<rect class="shuffle-write-time-proportion"
+                   |x="$shuffleWriteTimeProportionPos%" y="0px" height="26px"
+                   |width="$shuffleWriteTimeProportion%"></rect>
+                 |<rect class="serialization-time-proportion"
+                   |x="$serializationTimeProportionPos%" y="0px" height="26px"
+                   |width="$serializationTimeProportion%"></rect>
+                 |<rect class="getting-result-time-proportion"
+                   |x="$gettingResultTimeProportionPos%" y="0px" height="26px"
+                   |width="$gettingResultTimeProportion%"></rect></svg>',
+               |'start': new Date($launchTime),
+               |'end': new Date($finishTime)
+             |}
+           |""".stripMargin.replaceAll("\n", " ")
         timelineObject
       }.mkString("[", ",", "]")
 

From 5c796d576ec2de96bf72dbf6ccd0e85480a6e3b1 Mon Sep 17 00:00:00 2001
From: Brennon York <brennon.york@capitalone.com>
Date: Mon, 29 Jun 2015 08:55:06 -0700
Subject: [PATCH 0089/1454] [SPARK-8693] [PROJECT INFRA] profiles and goals are
 not printed in a nice way

Hotfix to correct formatting errors of print statements within the dev and jenkins builds. Error looks like:

```
-Phadoop-1[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:  -Dhadoop.version=1.0.4[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:  -Pkinesis-asl[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:  -Phive-thriftserver[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:  -Phive[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:  package[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:  assembly/assembly[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments:  streaming-kafka-assembly/assembly
```

Author: Brennon York <brennon.york@capitalone.com>

Closes #7085 from brennonyork/SPARK-8693 and squashes the following commits:

c5575f1 [Brennon York] added commas to end of print statements for proper printing
---
 dev/run-tests.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/dev/run-tests.py b/dev/run-tests.py
index eb79a2a502707..e5c897b94d167 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -210,7 +210,7 @@ def build_spark_documentation():
     jekyll_bin = which("jekyll")
 
     if not jekyll_bin:
-        print("[error] Cannot find a version of `jekyll` on the system; please"
+        print("[error] Cannot find a version of `jekyll` on the system; please",
               " install one and retry to build documentation.")
         sys.exit(int(os.environ.get("CURRENT_BLOCK", 255)))
     else:
@@ -270,7 +270,7 @@ def get_hadoop_profiles(hadoop_version):
     if hadoop_version in sbt_maven_hadoop_profiles:
         return sbt_maven_hadoop_profiles[hadoop_version]
     else:
-        print("[error] Could not find", hadoop_version, "in the list. Valid options"
+        print("[error] Could not find", hadoop_version, "in the list. Valid options",
               " are", sbt_maven_hadoop_profiles.keys())
         sys.exit(int(os.environ.get("CURRENT_BLOCK", 255)))
 
@@ -281,7 +281,7 @@ def build_spark_maven(hadoop_version):
     mvn_goals = ["clean", "package", "-DskipTests"]
     profiles_and_goals = build_profiles + mvn_goals
 
-    print("[info] Building Spark (w/Hive 0.13.1) using Maven with these arguments: "
+    print("[info] Building Spark (w/Hive 0.13.1) using Maven with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_maven(profiles_and_goals)
@@ -295,7 +295,7 @@ def build_spark_sbt(hadoop_version):
                  "streaming-kafka-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
-    print("[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments: "
+    print("[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_sbt(profiles_and_goals)
@@ -324,7 +324,7 @@ def run_scala_tests_maven(test_profiles):
     mvn_test_goals = ["test", "--fail-at-end"]
     profiles_and_goals = test_profiles + mvn_test_goals
 
-    print("[info] Running Spark tests using Maven with these arguments: "
+    print("[info] Running Spark tests using Maven with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_maven(profiles_and_goals)
@@ -339,7 +339,7 @@ def run_scala_tests_sbt(test_modules, test_profiles):
 
     profiles_and_goals = test_profiles + list(sbt_test_goals)
 
-    print("[info] Running Spark tests using SBT with these arguments: "
+    print("[info] Running Spark tests using SBT with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_sbt(profiles_and_goals)
@@ -382,7 +382,7 @@ def run_sparkr_tests():
 def main():
     # Ensure the user home directory (HOME) is valid and is an absolute directory
     if not USER_HOME or not os.path.isabs(USER_HOME):
-        print("[error] Cannot determine your home directory as an absolute path;"
+        print("[error] Cannot determine your home directory as an absolute path;",
               " ensure the $HOME environment variable is set properly.")
         sys.exit(1)
 
@@ -397,7 +397,7 @@ def main():
     java_exe = determine_java_executable()
 
     if not java_exe:
-        print("[error] Cannot find a version of `java` on the system; please"
+        print("[error] Cannot find a version of `java` on the system; please",
               " install one and retry.")
         sys.exit(2)
 

From 715f084ca08ad48174ab19a699a0ac77f80b68cd Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 29 Jun 2015 09:22:55 -0700
Subject: [PATCH 0090/1454] [SPARK-8554] Add the SparkR document files to
 `.rat-excludes` for `./dev/check-license`

[[SPARK-8554] Add the SparkR document files to `.rat-excludes` for `./dev/check-license` - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8554)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6947 from yu-iskw/SPARK-8554 and squashes the following commits:

5ca240c [Yu ISHIKAWA] [SPARK-8554] Add the SparkR document files to `.rat-excludes` for `./dev/check-license`
---
 .rat-excludes | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.rat-excludes b/.rat-excludes
index c24667c18dbda..0240e81c45ea2 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -86,4 +86,8 @@ local-1430917381535_2
 DESCRIPTION
 NAMESPACE
 test_support/*
+.*Rd
+help/*
+html/*
+INDEX
 .lintr

From ea88b1a5077e6ba980b0de6d3bc508c62285ba4c Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 29 Jun 2015 10:52:05 -0700
Subject: [PATCH 0091/1454] Revert "[SPARK-8372] History server shows incorrect
 information for application not started"

This reverts commit 2837e067099921dd4ab6639ac5f6e89f789d4ff4.
---
 .../deploy/history/FsHistoryProvider.scala    | 38 +++++++---------
 .../history/FsHistoryProviderSuite.scala      | 43 ++++++-------------
 2 files changed, 28 insertions(+), 53 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index db383b9823d3c..5427a88f32ffd 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -160,7 +160,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
           replayBus.addListener(appListener)
           val appInfo = replay(fs.getFileStatus(new Path(logDir, attempt.logPath)), replayBus)
 
-          appInfo.foreach { app => ui.setAppName(s"${app.name} ($appId)") }
+          ui.setAppName(s"${appInfo.name} ($appId)")
 
           val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false)
           ui.getSecurityManager.setAcls(uiAclsEnabled)
@@ -282,12 +282,8 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
     val newAttempts = logs.flatMap { fileStatus =>
       try {
         val res = replay(fileStatus, bus)
-        res match {
-          case Some(r) => logDebug(s"Application log ${r.logPath} loaded successfully.")
-          case None => logWarning(s"Failed to load application log ${fileStatus.getPath}. " +
-            "The application may have not started.")
-        }
-        res
+        logInfo(s"Application log ${res.logPath} loaded successfully.")
+        Some(res)
       } catch {
         case e: Exception =>
           logError(
@@ -433,11 +429,9 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
 
   /**
    * Replays the events in the specified log file and returns information about the associated
-   * application. Return `None` if the application ID cannot be located.
+   * application.
    */
-  private def replay(
-      eventLog: FileStatus,
-      bus: ReplayListenerBus): Option[FsApplicationAttemptInfo] = {
+  private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationAttemptInfo = {
     val logPath = eventLog.getPath()
     logInfo(s"Replaying log path: $logPath")
     val logInput =
@@ -451,18 +445,16 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
       val appCompleted = isApplicationCompleted(eventLog)
       bus.addListener(appListener)
       bus.replay(logInput, logPath.toString, !appCompleted)
-      appListener.appId.map { appId =>
-        new FsApplicationAttemptInfo(
-          logPath.getName(),
-          appListener.appName.getOrElse(NOT_STARTED),
-          appId,
-          appListener.appAttemptId,
-          appListener.startTime.getOrElse(-1L),
-          appListener.endTime.getOrElse(-1L),
-          getModificationTime(eventLog).get,
-          appListener.sparkUser.getOrElse(NOT_STARTED),
-          appCompleted)
-      }
+      new FsApplicationAttemptInfo(
+        logPath.getName(),
+        appListener.appName.getOrElse(NOT_STARTED),
+        appListener.appId.getOrElse(logPath.getName()),
+        appListener.appAttemptId,
+        appListener.startTime.getOrElse(-1L),
+        appListener.endTime.getOrElse(-1L),
+        getModificationTime(eventLog).get,
+        appListener.sparkUser.getOrElse(NOT_STARTED),
+        appCompleted)
     } finally {
       logInput.close()
     }
diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
index d3a6db5f260d6..09075eeb539aa 100644
--- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
@@ -67,8 +67,7 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     // Write a new-style application log.
     val newAppComplete = newLogFile("new1", None, inProgress = false)
     writeFile(newAppComplete, true, None,
-      SparkListenerApplicationStart(
-        "new-app-complete", Some("new-app-complete"), 1L, "test", None),
+      SparkListenerApplicationStart("new-app-complete", None, 1L, "test", None),
       SparkListenerApplicationEnd(5L)
       )
 
@@ -76,15 +75,13 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     val newAppCompressedComplete = newLogFile("new1compressed", None, inProgress = false,
       Some("lzf"))
     writeFile(newAppCompressedComplete, true, None,
-      SparkListenerApplicationStart(
-        "new-app-compressed-complete", Some("new-app-compressed-complete"), 1L, "test", None),
+      SparkListenerApplicationStart("new-app-compressed-complete", None, 1L, "test", None),
       SparkListenerApplicationEnd(4L))
 
     // Write an unfinished app, new-style.
     val newAppIncomplete = newLogFile("new2", None, inProgress = true)
     writeFile(newAppIncomplete, true, None,
-      SparkListenerApplicationStart(
-        "new-app-incomplete", Some("new-app-incomplete"), 1L, "test", None)
+      SparkListenerApplicationStart("new-app-incomplete", None, 1L, "test", None)
       )
 
     // Write an old-style application log.
@@ -92,8 +89,7 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     oldAppComplete.mkdir()
     createEmptyFile(new File(oldAppComplete, provider.SPARK_VERSION_PREFIX + "1.0"))
     writeFile(new File(oldAppComplete, provider.LOG_PREFIX + "1"), false, None,
-      SparkListenerApplicationStart(
-        "old-app-complete", Some("old-app-complete"), 2L, "test", None),
+      SparkListenerApplicationStart("old-app-complete", None, 2L, "test", None),
       SparkListenerApplicationEnd(3L)
       )
     createEmptyFile(new File(oldAppComplete, provider.APPLICATION_COMPLETE))
@@ -107,8 +103,7 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     oldAppIncomplete.mkdir()
     createEmptyFile(new File(oldAppIncomplete, provider.SPARK_VERSION_PREFIX + "1.0"))
     writeFile(new File(oldAppIncomplete, provider.LOG_PREFIX + "1"), false, None,
-      SparkListenerApplicationStart(
-        "old-app-incomplete", Some("old-app-incomplete"), 2L, "test", None)
+      SparkListenerApplicationStart("old-app-incomplete", None, 2L, "test", None)
       )
 
     // Force a reload of data from the log directory, and check that both logs are loaded.
@@ -129,16 +124,16 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
           List(ApplicationAttemptInfo(None, start, end, lastMod, user, completed)))
       }
 
-      list(0) should be (makeAppInfo("new-app-complete", "new-app-complete", 1L, 5L,
+      list(0) should be (makeAppInfo(newAppComplete.getName(), "new-app-complete", 1L, 5L,
         newAppComplete.lastModified(), "test", true))
-      list(1) should be (makeAppInfo("new-app-compressed-complete",
+      list(1) should be (makeAppInfo(newAppCompressedComplete.getName(),
         "new-app-compressed-complete", 1L, 4L, newAppCompressedComplete.lastModified(), "test",
         true))
-      list(2) should be (makeAppInfo("old-app-complete", "old-app-complete", 2L, 3L,
+      list(2) should be (makeAppInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L,
         oldAppComplete.lastModified(), "test", true))
-      list(3) should be (makeAppInfo("old-app-incomplete", "old-app-incomplete", 2L, -1L,
+      list(3) should be (makeAppInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, -1L,
         oldAppIncomplete.lastModified(), "test", false))
-      list(4) should be (makeAppInfo("new-app-incomplete", "new-app-incomplete", 1L, -1L,
+      list(4) should be (makeAppInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, -1L,
         newAppIncomplete.lastModified(), "test", false))
 
       // Make sure the UI can be rendered.
@@ -162,7 +157,7 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
       logDir.mkdir()
       createEmptyFile(new File(logDir, provider.SPARK_VERSION_PREFIX + "1.0"))
       writeFile(new File(logDir, provider.LOG_PREFIX + "1"), false, Option(codec),
-        SparkListenerApplicationStart("app2", Some("app2"), 2L, "test", None),
+        SparkListenerApplicationStart("app2", None, 2L, "test", None),
         SparkListenerApplicationEnd(3L)
         )
       createEmptyFile(new File(logDir, provider.COMPRESSION_CODEC_PREFIX + codecName))
@@ -185,12 +180,12 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
   test("SPARK-3697: ignore directories that cannot be read.") {
     val logFile1 = newLogFile("new1", None, inProgress = false)
     writeFile(logFile1, true, None,
-      SparkListenerApplicationStart("app1-1", Some("app1-1"), 1L, "test", None),
+      SparkListenerApplicationStart("app1-1", None, 1L, "test", None),
       SparkListenerApplicationEnd(2L)
       )
     val logFile2 = newLogFile("new2", None, inProgress = false)
     writeFile(logFile2, true, None,
-      SparkListenerApplicationStart("app1-2", Some("app1-2"), 1L, "test", None),
+      SparkListenerApplicationStart("app1-2", None, 1L, "test", None),
       SparkListenerApplicationEnd(2L)
       )
     logFile2.setReadable(false, false)
@@ -223,18 +218,6 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     }
   }
 
-  test("Parse logs that application is not started") {
-    val provider = new FsHistoryProvider((createTestConf()))
-
-    val logFile1 = newLogFile("app1", None, inProgress = true)
-    writeFile(logFile1, true, None,
-      SparkListenerLogStart("1.4")
-    )
-    updateAndCheck(provider) { list =>
-      list.size should be (0)
-    }
-  }
-
   test("SPARK-5582: empty log directory") {
     val provider = new FsHistoryProvider(createTestConf())
 

From ed413bcc78d8d97a1a0cd0871d7a20f7170476d0 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 29 Jun 2015 11:41:26 -0700
Subject: [PATCH 0092/1454] [SPARK-8692] [SQL] re-order the case statements
 that handling catalyst data types

use same order: boolean, byte, short, int, date, long, timestamp, float, double, string, binary, decimal.

Then we can easily check whether some data types are missing by just one glance, and make sure we handle data/timestamp just as int/long.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7073 from cloud-fan/fix-date and squashes the following commits:

463044d [Wenchen Fan] fix style
51cd347 [Wenchen Fan] refactor handling of date and timestmap
---
 .../expressions/SpecificMutableRow.scala      | 12 +--
 .../expressions/UnsafeRowConverter.scala      |  6 +-
 .../expressions/codegen/CodeGenerator.scala   |  6 +-
 .../spark/sql/columnar/ColumnAccessor.scala   | 42 +++++-----
 .../spark/sql/columnar/ColumnBuilder.scala    | 30 +++----
 .../spark/sql/columnar/ColumnStats.scala      | 74 ++++++++---------
 .../spark/sql/columnar/ColumnType.scala       | 10 +--
 .../sql/execution/SparkSqlSerializer2.scala   | 82 ++++++-------------
 .../sql/parquet/ParquetTableSupport.scala     | 34 ++++----
 .../spark/sql/parquet/ParquetTypes.scala      |  4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  9 +-
 .../spark/sql/columnar/ColumnTypeSuite.scala  | 54 ++++++------
 .../sql/columnar/ColumnarTestUtils.scala      |  8 +-
 .../NullableColumnAccessorSuite.scala         |  6 +-
 .../columnar/NullableColumnBuilderSuite.scala |  6 +-
 15 files changed, 174 insertions(+), 209 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 53fedb531cfb2..3928c0f2ffdaf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -196,15 +196,15 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
   def this(dataTypes: Seq[DataType]) =
     this(
       dataTypes.map {
-        case IntegerType => new MutableInt
+        case BooleanType => new MutableBoolean
         case ByteType => new MutableByte
-        case FloatType => new MutableFloat
         case ShortType => new MutableShort
+        // We use INT for DATE internally
+        case IntegerType | DateType => new MutableInt
+        // We use Long for Timestamp internally
+        case LongType | TimestampType => new MutableLong
+        case FloatType => new MutableFloat
         case DoubleType => new MutableDouble
-        case BooleanType => new MutableBoolean
-        case LongType => new MutableLong
-        case DateType => new MutableInt // We use INT for DATE internally
-        case TimestampType => new MutableLong  // We use Long for Timestamp internally
         case _ => new MutableAny
       }.toArray)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
index 89adaf053b1a4..b61d490429e4f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
@@ -128,14 +128,12 @@ private object UnsafeColumnWriter {
       case BooleanType => BooleanUnsafeColumnWriter
       case ByteType => ByteUnsafeColumnWriter
       case ShortType => ShortUnsafeColumnWriter
-      case IntegerType => IntUnsafeColumnWriter
-      case LongType => LongUnsafeColumnWriter
+      case IntegerType | DateType => IntUnsafeColumnWriter
+      case LongType | TimestampType => LongUnsafeColumnWriter
       case FloatType => FloatUnsafeColumnWriter
       case DoubleType => DoubleUnsafeColumnWriter
       case StringType => StringUnsafeColumnWriter
       case BinaryType => BinaryUnsafeColumnWriter
-      case DateType => IntUnsafeColumnWriter
-      case TimestampType => LongUnsafeColumnWriter
       case t =>
         throw new UnsupportedOperationException(s"Do not know how to write columns of type $t")
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index e20e3a9dca502..57e0bede5db20 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -120,15 +120,13 @@ class CodeGenContext {
     case BooleanType => JAVA_BOOLEAN
     case ByteType => JAVA_BYTE
     case ShortType => JAVA_SHORT
-    case IntegerType => JAVA_INT
-    case LongType => JAVA_LONG
+    case IntegerType | DateType => JAVA_INT
+    case LongType | TimestampType => JAVA_LONG
     case FloatType => JAVA_FLOAT
     case DoubleType => JAVA_DOUBLE
     case dt: DecimalType => decimalType
     case BinaryType => "byte[]"
     case StringType => stringType
-    case DateType => JAVA_INT
-    case TimestampType => JAVA_LONG
     case dt: OpenHashSetUDT if dt.elementType == IntegerType => classOf[IntegerHashSet].getName
     case dt: OpenHashSetUDT if dt.elementType == LongType => classOf[LongHashSet].getName
     case _ => "Object"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
index 64449b2659b4b..931469bed634a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
@@ -71,44 +71,44 @@ private[sql] abstract class NativeColumnAccessor[T <: AtomicType](
 private[sql] class BooleanColumnAccessor(buffer: ByteBuffer)
   extends NativeColumnAccessor(buffer, BOOLEAN)
 
-private[sql] class IntColumnAccessor(buffer: ByteBuffer)
-  extends NativeColumnAccessor(buffer, INT)
+private[sql] class ByteColumnAccessor(buffer: ByteBuffer)
+  extends NativeColumnAccessor(buffer, BYTE)
 
 private[sql] class ShortColumnAccessor(buffer: ByteBuffer)
   extends NativeColumnAccessor(buffer, SHORT)
 
+private[sql] class IntColumnAccessor(buffer: ByteBuffer)
+  extends NativeColumnAccessor(buffer, INT)
+
 private[sql] class LongColumnAccessor(buffer: ByteBuffer)
   extends NativeColumnAccessor(buffer, LONG)
 
-private[sql] class ByteColumnAccessor(buffer: ByteBuffer)
-  extends NativeColumnAccessor(buffer, BYTE)
-
-private[sql] class DoubleColumnAccessor(buffer: ByteBuffer)
-  extends NativeColumnAccessor(buffer, DOUBLE)
-
 private[sql] class FloatColumnAccessor(buffer: ByteBuffer)
   extends NativeColumnAccessor(buffer, FLOAT)
 
-private[sql] class FixedDecimalColumnAccessor(buffer: ByteBuffer, precision: Int, scale: Int)
-  extends NativeColumnAccessor(buffer, FIXED_DECIMAL(precision, scale))
+private[sql] class DoubleColumnAccessor(buffer: ByteBuffer)
+  extends NativeColumnAccessor(buffer, DOUBLE)
 
 private[sql] class StringColumnAccessor(buffer: ByteBuffer)
   extends NativeColumnAccessor(buffer, STRING)
 
-private[sql] class DateColumnAccessor(buffer: ByteBuffer)
-  extends NativeColumnAccessor(buffer, DATE)
-
-private[sql] class TimestampColumnAccessor(buffer: ByteBuffer)
-  extends NativeColumnAccessor(buffer, TIMESTAMP)
-
 private[sql] class BinaryColumnAccessor(buffer: ByteBuffer)
   extends BasicColumnAccessor[BinaryType.type, Array[Byte]](buffer, BINARY)
   with NullableColumnAccessor
 
+private[sql] class FixedDecimalColumnAccessor(buffer: ByteBuffer, precision: Int, scale: Int)
+  extends NativeColumnAccessor(buffer, FIXED_DECIMAL(precision, scale))
+
 private[sql] class GenericColumnAccessor(buffer: ByteBuffer)
   extends BasicColumnAccessor[DataType, Array[Byte]](buffer, GENERIC)
   with NullableColumnAccessor
 
+private[sql] class DateColumnAccessor(buffer: ByteBuffer)
+  extends NativeColumnAccessor(buffer, DATE)
+
+private[sql] class TimestampColumnAccessor(buffer: ByteBuffer)
+  extends NativeColumnAccessor(buffer, TIMESTAMP)
+
 private[sql] object ColumnAccessor {
   def apply(dataType: DataType, buffer: ByteBuffer): ColumnAccessor = {
     val dup = buffer.duplicate().order(ByteOrder.nativeOrder)
@@ -118,17 +118,17 @@ private[sql] object ColumnAccessor {
     dup.getInt()
 
     dataType match {
+      case BooleanType => new BooleanColumnAccessor(dup)
+      case ByteType => new ByteColumnAccessor(dup)
+      case ShortType => new ShortColumnAccessor(dup)
       case IntegerType => new IntColumnAccessor(dup)
+      case DateType => new DateColumnAccessor(dup)
       case LongType => new LongColumnAccessor(dup)
+      case TimestampType => new TimestampColumnAccessor(dup)
       case FloatType => new FloatColumnAccessor(dup)
       case DoubleType => new DoubleColumnAccessor(dup)
-      case BooleanType => new BooleanColumnAccessor(dup)
-      case ByteType => new ByteColumnAccessor(dup)
-      case ShortType => new ShortColumnAccessor(dup)
       case StringType => new StringColumnAccessor(dup)
       case BinaryType => new BinaryColumnAccessor(dup)
-      case DateType => new DateColumnAccessor(dup)
-      case TimestampType => new TimestampColumnAccessor(dup)
       case DecimalType.Fixed(precision, scale) if precision < 19 =>
         new FixedDecimalColumnAccessor(dup, precision, scale)
       case _ => new GenericColumnAccessor(dup)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index 1949625699ca8..087c52239713d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -94,17 +94,21 @@ private[sql] abstract class NativeColumnBuilder[T <: AtomicType](
 
 private[sql] class BooleanColumnBuilder extends NativeColumnBuilder(new BooleanColumnStats, BOOLEAN)
 
-private[sql] class IntColumnBuilder extends NativeColumnBuilder(new IntColumnStats, INT)
+private[sql] class ByteColumnBuilder extends NativeColumnBuilder(new ByteColumnStats, BYTE)
 
 private[sql] class ShortColumnBuilder extends NativeColumnBuilder(new ShortColumnStats, SHORT)
 
+private[sql] class IntColumnBuilder extends NativeColumnBuilder(new IntColumnStats, INT)
+
 private[sql] class LongColumnBuilder extends NativeColumnBuilder(new LongColumnStats, LONG)
 
-private[sql] class ByteColumnBuilder extends NativeColumnBuilder(new ByteColumnStats, BYTE)
+private[sql] class FloatColumnBuilder extends NativeColumnBuilder(new FloatColumnStats, FLOAT)
 
 private[sql] class DoubleColumnBuilder extends NativeColumnBuilder(new DoubleColumnStats, DOUBLE)
 
-private[sql] class FloatColumnBuilder extends NativeColumnBuilder(new FloatColumnStats, FLOAT)
+private[sql] class StringColumnBuilder extends NativeColumnBuilder(new StringColumnStats, STRING)
+
+private[sql] class BinaryColumnBuilder extends ComplexColumnBuilder(new BinaryColumnStats, BINARY)
 
 private[sql] class FixedDecimalColumnBuilder(
     precision: Int,
@@ -113,19 +117,15 @@ private[sql] class FixedDecimalColumnBuilder(
     new FixedDecimalColumnStats,
     FIXED_DECIMAL(precision, scale))
 
-private[sql] class StringColumnBuilder extends NativeColumnBuilder(new StringColumnStats, STRING)
+// TODO (lian) Add support for array, struct and map
+private[sql] class GenericColumnBuilder
+  extends ComplexColumnBuilder(new GenericColumnStats, GENERIC)
 
 private[sql] class DateColumnBuilder extends NativeColumnBuilder(new DateColumnStats, DATE)
 
 private[sql] class TimestampColumnBuilder
   extends NativeColumnBuilder(new TimestampColumnStats, TIMESTAMP)
 
-private[sql] class BinaryColumnBuilder extends ComplexColumnBuilder(new BinaryColumnStats, BINARY)
-
-// TODO (lian) Add support for array, struct and map
-private[sql] class GenericColumnBuilder
-  extends ComplexColumnBuilder(new GenericColumnStats, GENERIC)
-
 private[sql] object ColumnBuilder {
   val DEFAULT_INITIAL_BUFFER_SIZE = 1024 * 1024
 
@@ -151,17 +151,17 @@ private[sql] object ColumnBuilder {
       columnName: String = "",
       useCompression: Boolean = false): ColumnBuilder = {
     val builder: ColumnBuilder = dataType match {
+      case BooleanType => new BooleanColumnBuilder
+      case ByteType => new ByteColumnBuilder
+      case ShortType => new ShortColumnBuilder
       case IntegerType => new IntColumnBuilder
+      case DateType => new DateColumnBuilder
       case LongType => new LongColumnBuilder
+      case TimestampType => new TimestampColumnBuilder
       case FloatType => new FloatColumnBuilder
       case DoubleType => new DoubleColumnBuilder
-      case BooleanType => new BooleanColumnBuilder
-      case ByteType => new ByteColumnBuilder
-      case ShortType => new ShortColumnBuilder
       case StringType => new StringColumnBuilder
       case BinaryType => new BinaryColumnBuilder
-      case DateType => new DateColumnBuilder
-      case TimestampType => new TimestampColumnBuilder
       case DecimalType.Fixed(precision, scale) if precision < 19 =>
         new FixedDecimalColumnBuilder(precision, scale)
       case _ => new GenericColumnBuilder
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 1bce214d1d6c3..00374d1fa3ef1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -132,17 +132,17 @@ private[sql] class ShortColumnStats extends ColumnStats {
     InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
-private[sql] class LongColumnStats extends ColumnStats {
-  protected var upper = Long.MinValue
-  protected var lower = Long.MaxValue
+private[sql] class IntColumnStats extends ColumnStats {
+  protected var upper = Int.MinValue
+  protected var lower = Int.MaxValue
 
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
     if (!row.isNullAt(ordinal)) {
-      val value = row.getLong(ordinal)
+      val value = row.getInt(ordinal)
       if (value > upper) upper = value
       if (value < lower) lower = value
-      sizeInBytes += LONG.defaultSize
+      sizeInBytes += INT.defaultSize
     }
   }
 
@@ -150,17 +150,17 @@ private[sql] class LongColumnStats extends ColumnStats {
     InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
-private[sql] class DoubleColumnStats extends ColumnStats {
-  protected var upper = Double.MinValue
-  protected var lower = Double.MaxValue
+private[sql] class LongColumnStats extends ColumnStats {
+  protected var upper = Long.MinValue
+  protected var lower = Long.MaxValue
 
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
     if (!row.isNullAt(ordinal)) {
-      val value = row.getDouble(ordinal)
+      val value = row.getLong(ordinal)
       if (value > upper) upper = value
       if (value < lower) lower = value
-      sizeInBytes += DOUBLE.defaultSize
+      sizeInBytes += LONG.defaultSize
     }
   }
 
@@ -186,35 +186,17 @@ private[sql] class FloatColumnStats extends ColumnStats {
     InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
-private[sql] class FixedDecimalColumnStats extends ColumnStats {
-  protected var upper: Decimal = null
-  protected var lower: Decimal = null
-
-  override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
-    super.gatherStats(row, ordinal)
-    if (!row.isNullAt(ordinal)) {
-      val value = row(ordinal).asInstanceOf[Decimal]
-      if (upper == null || value.compareTo(upper) > 0) upper = value
-      if (lower == null || value.compareTo(lower) < 0) lower = value
-      sizeInBytes += FIXED_DECIMAL.defaultSize
-    }
-  }
-
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
-}
-
-private[sql] class IntColumnStats extends ColumnStats {
-  protected var upper = Int.MinValue
-  protected var lower = Int.MaxValue
+private[sql] class DoubleColumnStats extends ColumnStats {
+  protected var upper = Double.MinValue
+  protected var lower = Double.MaxValue
 
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
     if (!row.isNullAt(ordinal)) {
-      val value = row.getInt(ordinal)
+      val value = row.getDouble(ordinal)
       if (value > upper) upper = value
       if (value < lower) lower = value
-      sizeInBytes += INT.defaultSize
+      sizeInBytes += DOUBLE.defaultSize
     }
   }
 
@@ -240,10 +222,6 @@ private[sql] class StringColumnStats extends ColumnStats {
     InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
-private[sql] class DateColumnStats extends IntColumnStats
-
-private[sql] class TimestampColumnStats extends LongColumnStats
-
 private[sql] class BinaryColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
@@ -256,6 +234,24 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     InternalRow(null, null, nullCount, count, sizeInBytes)
 }
 
+private[sql] class FixedDecimalColumnStats extends ColumnStats {
+  protected var upper: Decimal = null
+  protected var lower: Decimal = null
+
+  override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
+    super.gatherStats(row, ordinal)
+    if (!row.isNullAt(ordinal)) {
+      val value = row(ordinal).asInstanceOf[Decimal]
+      if (upper == null || value.compareTo(upper) > 0) upper = value
+      if (lower == null || value.compareTo(lower) < 0) lower = value
+      sizeInBytes += FIXED_DECIMAL.defaultSize
+    }
+  }
+
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+}
+
 private[sql] class GenericColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
@@ -267,3 +263,7 @@ private[sql] class GenericColumnStats extends ColumnStats {
   override def collectedStatistics: InternalRow =
     InternalRow(null, null, nullCount, count, sizeInBytes)
 }
+
+private[sql] class DateColumnStats extends IntColumnStats
+
+private[sql] class TimestampColumnStats extends LongColumnStats
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 8bf2151e4de68..fc72360c88fe1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -447,17 +447,17 @@ private[sql] object GENERIC extends ByteArrayColumnType[DataType](12, 16) {
 private[sql] object ColumnType {
   def apply(dataType: DataType): ColumnType[_, _] = {
     dataType match {
+      case BooleanType => BOOLEAN
+      case ByteType => BYTE
+      case ShortType => SHORT
       case IntegerType => INT
+      case DateType => DATE
       case LongType => LONG
+      case TimestampType => TIMESTAMP
       case FloatType => FLOAT
       case DoubleType => DOUBLE
-      case BooleanType => BOOLEAN
-      case ByteType => BYTE
-      case ShortType => SHORT
       case StringType => STRING
       case BinaryType => BINARY
-      case DateType => DATE
-      case TimestampType => TIMESTAMP
       case DecimalType.Fixed(precision, scale) if precision < 19 =>
         FIXED_DECIMAL(precision, scale)
       case _ => GENERIC
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 74a22353b1d27..056d435eecd23 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -237,7 +237,7 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeShort(row.getShort(i))
               }
 
-            case IntegerType =>
+            case IntegerType | DateType =>
               if (row.isNullAt(i)) {
                 out.writeByte(NULL)
               } else {
@@ -245,7 +245,7 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeInt(row.getInt(i))
               }
 
-            case LongType =>
+            case LongType | TimestampType =>
               if (row.isNullAt(i)) {
                 out.writeByte(NULL)
               } else {
@@ -269,55 +269,39 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeDouble(row.getDouble(i))
               }
 
-            case decimal: DecimalType =>
+            case StringType =>
               if (row.isNullAt(i)) {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val value = row.apply(i).asInstanceOf[Decimal]
-                val javaBigDecimal = value.toJavaBigDecimal
-                // First, write out the unscaled value.
-                val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray
+                val bytes = row.getAs[UTF8String](i).getBytes
                 out.writeInt(bytes.length)
                 out.write(bytes)
-                // Then, write out the scale.
-                out.writeInt(javaBigDecimal.scale())
               }
 
-            case DateType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeInt(row.getAs[Int](i))
-              }
-
-            case TimestampType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeLong(row.getAs[Long](i))
-              }
-
-            case StringType =>
+            case BinaryType =>
               if (row.isNullAt(i)) {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val bytes = row.getAs[UTF8String](i).getBytes
+                val bytes = row.getAs[Array[Byte]](i)
                 out.writeInt(bytes.length)
                 out.write(bytes)
               }
 
-            case BinaryType =>
+            case decimal: DecimalType =>
               if (row.isNullAt(i)) {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val bytes = row.getAs[Array[Byte]](i)
+                val value = row.apply(i).asInstanceOf[Decimal]
+                val javaBigDecimal = value.toJavaBigDecimal
+                // First, write out the unscaled value.
+                val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray
                 out.writeInt(bytes.length)
                 out.write(bytes)
+                // Then, write out the scale.
+                out.writeInt(javaBigDecimal.scale())
               }
           }
           i += 1
@@ -364,14 +348,14 @@ private[sql] object SparkSqlSerializer2 {
                 mutableRow.setShort(i, in.readShort())
               }
 
-            case IntegerType =>
+            case IntegerType | DateType =>
               if (in.readByte() == NULL) {
                 mutableRow.setNullAt(i)
               } else {
                 mutableRow.setInt(i, in.readInt())
               }
 
-            case LongType =>
+            case LongType | TimestampType =>
               if (in.readByte() == NULL) {
                 mutableRow.setNullAt(i)
               } else {
@@ -392,53 +376,39 @@ private[sql] object SparkSqlSerializer2 {
                 mutableRow.setDouble(i, in.readDouble())
               }
 
-            case decimal: DecimalType =>
+            case StringType =>
               if (in.readByte() == NULL) {
                 mutableRow.setNullAt(i)
               } else {
-                // First, read in the unscaled value.
                 val length = in.readInt()
                 val bytes = new Array[Byte](length)
                 in.readFully(bytes)
-                val unscaledVal = new BigInteger(bytes)
-                // Then, read the scale.
-                val scale = in.readInt()
-                // Finally, create the Decimal object and set it in the row.
-                mutableRow.update(i, Decimal(new BigDecimal(unscaledVal, scale)))
-              }
-
-            case DateType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.update(i, in.readInt())
-              }
-
-            case TimestampType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.update(i, in.readLong())
+                mutableRow.update(i, UTF8String.fromBytes(bytes))
               }
 
-            case StringType =>
+            case BinaryType =>
               if (in.readByte() == NULL) {
                 mutableRow.setNullAt(i)
               } else {
                 val length = in.readInt()
                 val bytes = new Array[Byte](length)
                 in.readFully(bytes)
-                mutableRow.update(i, UTF8String.fromBytes(bytes))
+                mutableRow.update(i, bytes)
               }
 
-            case BinaryType =>
+            case decimal: DecimalType =>
               if (in.readByte() == NULL) {
                 mutableRow.setNullAt(i)
               } else {
+                // First, read in the unscaled value.
                 val length = in.readInt()
                 val bytes = new Array[Byte](length)
                 in.readFully(bytes)
-                mutableRow.update(i, bytes)
+                val unscaledVal = new BigInteger(bytes)
+                // Then, read the scale.
+                val scale = in.readInt()
+                // Finally, create the Decimal object and set it in the row.
+                mutableRow.update(i, Decimal(new BigDecimal(unscaledVal, scale)))
               }
           }
           i += 1
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 0d96a1e8070b1..df2a96dfeb619 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -198,19 +198,18 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
   private[parquet] def writePrimitive(schema: DataType, value: Any): Unit = {
     if (value != null) {
       schema match {
-        case StringType => writer.addBinary(
-          Binary.fromByteArray(value.asInstanceOf[UTF8String].getBytes))
-        case BinaryType => writer.addBinary(
-          Binary.fromByteArray(value.asInstanceOf[Array[Byte]]))
-        case IntegerType => writer.addInteger(value.asInstanceOf[Int])
+        case BooleanType => writer.addBoolean(value.asInstanceOf[Boolean])
+        case ByteType => writer.addInteger(value.asInstanceOf[Byte])
         case ShortType => writer.addInteger(value.asInstanceOf[Short])
+        case IntegerType | DateType => writer.addInteger(value.asInstanceOf[Int])
         case LongType => writer.addLong(value.asInstanceOf[Long])
         case TimestampType => writeTimestamp(value.asInstanceOf[Long])
-        case ByteType => writer.addInteger(value.asInstanceOf[Byte])
-        case DoubleType => writer.addDouble(value.asInstanceOf[Double])
         case FloatType => writer.addFloat(value.asInstanceOf[Float])
-        case BooleanType => writer.addBoolean(value.asInstanceOf[Boolean])
-        case DateType => writer.addInteger(value.asInstanceOf[Int])
+        case DoubleType => writer.addDouble(value.asInstanceOf[Double])
+        case StringType => writer.addBinary(
+          Binary.fromByteArray(value.asInstanceOf[UTF8String].getBytes))
+        case BinaryType => writer.addBinary(
+          Binary.fromByteArray(value.asInstanceOf[Array[Byte]]))
         case d: DecimalType =>
           if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) {
             sys.error(s"Unsupported datatype $d, cannot write to consumer")
@@ -353,19 +352,18 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
       record: InternalRow,
       index: Int): Unit = {
     ctype match {
+      case BooleanType => writer.addBoolean(record.getBoolean(index))
+      case ByteType => writer.addInteger(record.getByte(index))
+      case ShortType => writer.addInteger(record.getShort(index))
+      case IntegerType | DateType => writer.addInteger(record.getInt(index))
+      case LongType => writer.addLong(record.getLong(index))
+      case TimestampType => writeTimestamp(record.getLong(index))
+      case FloatType => writer.addFloat(record.getFloat(index))
+      case DoubleType => writer.addDouble(record.getDouble(index))
       case StringType => writer.addBinary(
         Binary.fromByteArray(record(index).asInstanceOf[UTF8String].getBytes))
       case BinaryType => writer.addBinary(
         Binary.fromByteArray(record(index).asInstanceOf[Array[Byte]]))
-      case IntegerType => writer.addInteger(record.getInt(index))
-      case ShortType => writer.addInteger(record.getShort(index))
-      case LongType => writer.addLong(record.getLong(index))
-      case ByteType => writer.addInteger(record.getByte(index))
-      case DoubleType => writer.addDouble(record.getDouble(index))
-      case FloatType => writer.addFloat(record.getFloat(index))
-      case BooleanType => writer.addBoolean(record.getBoolean(index))
-      case DateType => writer.addInteger(record.getInt(index))
-      case TimestampType => writeTimestamp(record.getLong(index))
       case d: DecimalType =>
         if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) {
           sys.error(s"Unsupported datatype $d, cannot write to consumer")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index 4d5199a140344..e748bd7857bd8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -38,8 +38,8 @@ import org.apache.spark.sql.types._
 
 private[parquet] object ParquetTypesConverter extends Logging {
   def isPrimitiveType(ctype: DataType): Boolean = ctype match {
-    case _: NumericType | BooleanType | StringType | BinaryType => true
-    case _: DataType => false
+    case _: NumericType | BooleanType | DateType | TimestampType | StringType | BinaryType => true
+    case _ => false
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 1f37455dd0bc4..9bd7b221e93f8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -22,19 +22,20 @@ import org.apache.spark.sql.catalyst.expressions.InternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
+  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, InternalRow(true, false, 0))
   testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0))
   testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0))
   testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
   testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
+    InternalRow(Long.MaxValue, Long.MinValue, 0))
   testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0))
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
     InternalRow(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
   testColumnStats(classOf[FixedDecimalColumnStats],
     FIXED_DECIMAL(15, 10), InternalRow(null, null, 0))
-  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
-  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
-    InternalRow(Long.MaxValue, Long.MinValue, 0))
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
index 6daddfb2c4804..4d46a657056e0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
@@ -36,9 +36,9 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
 
   test("defaultSize") {
     val checks = Map(
-      INT -> 4, SHORT -> 2, LONG -> 8, BYTE -> 1, DOUBLE -> 8, FLOAT -> 4,
-      FIXED_DECIMAL(15, 10) -> 8, BOOLEAN -> 1, STRING -> 8, DATE -> 4, TIMESTAMP -> 8,
-      BINARY -> 16, GENERIC -> 16)
+      BOOLEAN -> 1, BYTE -> 1, SHORT -> 2, INT -> 4, DATE -> 4,
+      LONG -> 8, TIMESTAMP -> 8, FLOAT -> 4, DOUBLE -> 8,
+      STRING -> 8, BINARY -> 16, FIXED_DECIMAL(15, 10) -> 8, GENERIC -> 16)
 
     checks.foreach { case (columnType, expectedSize) =>
       assertResult(expectedSize, s"Wrong defaultSize for $columnType") {
@@ -60,27 +60,24 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
       }
     }
 
-    checkActualSize(INT, Int.MaxValue, 4)
+    checkActualSize(BOOLEAN, true, 1)
+    checkActualSize(BYTE, Byte.MaxValue, 1)
     checkActualSize(SHORT, Short.MaxValue, 2)
+    checkActualSize(INT, Int.MaxValue, 4)
+    checkActualSize(DATE, Int.MaxValue, 4)
     checkActualSize(LONG, Long.MaxValue, 8)
-    checkActualSize(BYTE, Byte.MaxValue, 1)
-    checkActualSize(DOUBLE, Double.MaxValue, 8)
+    checkActualSize(TIMESTAMP, Long.MaxValue, 8)
     checkActualSize(FLOAT, Float.MaxValue, 4)
-    checkActualSize(FIXED_DECIMAL(15, 10), Decimal(0, 15, 10), 8)
-    checkActualSize(BOOLEAN, true, 1)
+    checkActualSize(DOUBLE, Double.MaxValue, 8)
     checkActualSize(STRING, UTF8String.fromString("hello"), 4 + "hello".getBytes("utf-8").length)
-    checkActualSize(DATE, 0, 4)
-    checkActualSize(TIMESTAMP, 0L, 8)
-
-    val binary = Array.fill[Byte](4)(0: Byte)
-    checkActualSize(BINARY, binary, 4 + 4)
+    checkActualSize(BINARY, Array.fill[Byte](4)(0.toByte), 4 + 4)
+    checkActualSize(FIXED_DECIMAL(15, 10), Decimal(0, 15, 10), 8)
 
     val generic = Map(1 -> "a")
     checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 8)
   }
 
-  testNativeColumnType[BooleanType.type](
-    BOOLEAN,
+  testNativeColumnType(BOOLEAN)(
     (buffer: ByteBuffer, v: Boolean) => {
       buffer.put((if (v) 1 else 0).toByte)
     },
@@ -88,18 +85,23 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
       buffer.get() == 1
     })
 
-  testNativeColumnType[IntegerType.type](INT, _.putInt(_), _.getInt)
+  testNativeColumnType(BYTE)(_.put(_), _.get)
+
+  testNativeColumnType(SHORT)(_.putShort(_), _.getShort)
+
+  testNativeColumnType(INT)(_.putInt(_), _.getInt)
+
+  testNativeColumnType(DATE)(_.putInt(_), _.getInt)
 
-  testNativeColumnType[ShortType.type](SHORT, _.putShort(_), _.getShort)
+  testNativeColumnType(LONG)(_.putLong(_), _.getLong)
 
-  testNativeColumnType[LongType.type](LONG, _.putLong(_), _.getLong)
+  testNativeColumnType(TIMESTAMP)(_.putLong(_), _.getLong)
 
-  testNativeColumnType[ByteType.type](BYTE, _.put(_), _.get)
+  testNativeColumnType(FLOAT)(_.putFloat(_), _.getFloat)
 
-  testNativeColumnType[DoubleType.type](DOUBLE, _.putDouble(_), _.getDouble)
+  testNativeColumnType(DOUBLE)(_.putDouble(_), _.getDouble)
 
-  testNativeColumnType[DecimalType](
-    FIXED_DECIMAL(15, 10),
+  testNativeColumnType(FIXED_DECIMAL(15, 10))(
     (buffer: ByteBuffer, decimal: Decimal) => {
       buffer.putLong(decimal.toUnscaledLong)
     },
@@ -107,10 +109,8 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
       Decimal(buffer.getLong(), 15, 10)
     })
 
-  testNativeColumnType[FloatType.type](FLOAT, _.putFloat(_), _.getFloat)
 
-  testNativeColumnType[StringType.type](
-    STRING,
+  testNativeColumnType(STRING)(
     (buffer: ByteBuffer, string: UTF8String) => {
       val bytes = string.getBytes
       buffer.putInt(bytes.length)
@@ -197,8 +197,8 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
   }
 
   def testNativeColumnType[T <: AtomicType](
-      columnType: NativeColumnType[T],
-      putter: (ByteBuffer, T#InternalType) => Unit,
+      columnType: NativeColumnType[T])
+      (putter: (ByteBuffer, T#InternalType) => Unit,
       getter: (ByteBuffer) => T#InternalType): Unit = {
 
     testColumnType[T, T#InternalType](columnType, putter, getter)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
index 7c86eae3f77fd..d9861339739c9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
@@ -39,18 +39,18 @@ object ColumnarTestUtils {
     }
 
     (columnType match {
+      case BOOLEAN => Random.nextBoolean()
       case BYTE => (Random.nextInt(Byte.MaxValue * 2) - Byte.MaxValue).toByte
       case SHORT => (Random.nextInt(Short.MaxValue * 2) - Short.MaxValue).toShort
       case INT => Random.nextInt()
+      case DATE => Random.nextInt()
       case LONG => Random.nextLong()
+      case TIMESTAMP => Random.nextLong()
       case FLOAT => Random.nextFloat()
       case DOUBLE => Random.nextDouble()
-      case FIXED_DECIMAL(precision, scale) => Decimal(Random.nextLong() % 100, precision, scale)
       case STRING => UTF8String.fromString(Random.nextString(Random.nextInt(32)))
-      case BOOLEAN => Random.nextBoolean()
       case BINARY => randomBytes(Random.nextInt(32))
-      case DATE => Random.nextInt()
-      case TIMESTAMP => Random.nextLong()
+      case FIXED_DECIMAL(precision, scale) => Decimal(Random.nextLong() % 100, precision, scale)
       case _ =>
         // Using a random one-element map instead of an arbitrary object
         Map(Random.nextInt() -> Random.nextString(Random.nextInt(32)))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
index 2a6e0c376551a..9eaa769846088 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
@@ -42,9 +42,9 @@ class NullableColumnAccessorSuite extends SparkFunSuite {
   import ColumnarTestUtils._
 
   Seq(
-    INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, FIXED_DECIMAL(15, 10), BINARY, GENERIC,
-    DATE, TIMESTAMP
-  ).foreach {
+    BOOLEAN, BYTE, SHORT, INT, DATE, LONG, TIMESTAMP, FLOAT, DOUBLE,
+    STRING, BINARY, FIXED_DECIMAL(15, 10), GENERIC)
+    .foreach {
     testNullableColumnAccessor(_)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
index cb4e9f1eb7f46..17e9ae464bcc0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
@@ -38,9 +38,9 @@ class NullableColumnBuilderSuite extends SparkFunSuite {
   import ColumnarTestUtils._
 
   Seq(
-    INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, FIXED_DECIMAL(15, 10), BINARY, GENERIC,
-    DATE, TIMESTAMP
-  ).foreach {
+    BOOLEAN, BYTE, SHORT, INT, DATE, LONG, TIMESTAMP, FLOAT, DOUBLE,
+    STRING, BINARY, FIXED_DECIMAL(15, 10), GENERIC)
+    .foreach {
     testNullableColumnBuilder(_)
   }
 

From 3664ee25f0a67de5ba76e9487a55a55216ae589f Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 29 Jun 2015 11:53:17 -0700
Subject: [PATCH 0093/1454] [SPARK-8066, SPARK-8067] [hive] Add support for
 Hive 1.0, 1.1 and 1.2.

Allow HiveContext to connect to metastores of those versions; some new shims
had to be added to account for changing internal APIs.

A new test was added to exercise the "reset()" path which now also requires
a shim; and the test code was changed to use a directory under the build's
target to store ivy dependencies. Without that, at least I consistently run
into issues with Ivy messing up (or being confused) by my existing caches.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7026 from vanzin/SPARK-8067 and squashes the following commits:

3e2e67b [Marcelo Vanzin] [SPARK-8066, SPARK-8067] [hive] Add support for Hive 1.0, 1.1 and 1.2.
---
 .../spark/sql/hive/client/ClientWrapper.scala |  5 +-
 .../spark/sql/hive/client/HiveShim.scala      | 70 ++++++++++++++++++-
 .../hive/client/IsolatedClientLoader.scala    | 13 ++--
 .../spark/sql/hive/client/package.scala       | 33 +++++++--
 .../spark/sql/hive/client/VersionsSuite.scala | 25 +++++--
 5 files changed, 131 insertions(+), 15 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 2f771d76793e5..4c708cec572ae 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -97,6 +97,9 @@ private[hive] class ClientWrapper(
     case hive.v12 => new Shim_v0_12()
     case hive.v13 => new Shim_v0_13()
     case hive.v14 => new Shim_v0_14()
+    case hive.v1_0 => new Shim_v1_0()
+    case hive.v1_1 => new Shim_v1_1()
+    case hive.v1_2 => new Shim_v1_2()
   }
 
   // Create an internal session state for this ClientWrapper.
@@ -456,7 +459,7 @@ private[hive] class ClientWrapper(
         logDebug(s"Deleting table $t")
         val table = client.getTable("default", t)
         client.getIndexes("default", t, 255).foreach { index =>
-          client.dropIndex("default", t, index.getIndexName, true)
+          shim.dropIndex(client, "default", t, index.getIndexName)
         }
         if (!table.isIndexTable) {
           client.dropTable("default", t)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index e7c1779f80ce6..1fa9d278e2a57 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.client
 
-import java.lang.{Boolean => JBoolean, Integer => JInteger}
+import java.lang.{Boolean => JBoolean, Integer => JInteger, Long => JLong}
 import java.lang.reflect.{Method, Modifier}
 import java.net.URI
 import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, Set => JSet}
@@ -94,6 +94,8 @@ private[client] sealed abstract class Shim {
       holdDDLTime: Boolean,
       listBucketingEnabled: Boolean): Unit
 
+  def dropIndex(hive: Hive, dbName: String, tableName: String, indexName: String): Unit
+
   protected def findStaticMethod(klass: Class[_], name: String, args: Class[_]*): Method = {
     val method = findMethod(klass, name, args: _*)
     require(Modifier.isStatic(method.getModifiers()),
@@ -166,6 +168,14 @@ private[client] class Shim_v0_12 extends Shim {
       JInteger.TYPE,
       JBoolean.TYPE,
       JBoolean.TYPE)
+  private lazy val dropIndexMethod =
+    findMethod(
+      classOf[Hive],
+      "dropIndex",
+      classOf[String],
+      classOf[String],
+      classOf[String],
+      JBoolean.TYPE)
 
   override def setCurrentSessionState(state: SessionState): Unit = {
     // Starting from Hive 0.13, setCurrentSessionState will internally override
@@ -234,6 +244,10 @@ private[client] class Shim_v0_12 extends Shim {
       numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean)
   }
 
+  override def dropIndex(hive: Hive, dbName: String, tableName: String, indexName: String): Unit = {
+    dropIndexMethod.invoke(hive, dbName, tableName, indexName, true: JBoolean)
+  }
+
 }
 
 private[client] class Shim_v0_13 extends Shim_v0_12 {
@@ -379,3 +393,57 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       TimeUnit.MILLISECONDS).asInstanceOf[Long]
   }
 }
+
+private[client] class Shim_v1_0 extends Shim_v0_14 {
+
+}
+
+private[client] class Shim_v1_1 extends Shim_v1_0 {
+
+  private lazy val dropIndexMethod =
+    findMethod(
+      classOf[Hive],
+      "dropIndex",
+      classOf[String],
+      classOf[String],
+      classOf[String],
+      JBoolean.TYPE,
+      JBoolean.TYPE)
+
+  override def dropIndex(hive: Hive, dbName: String, tableName: String, indexName: String): Unit = {
+    dropIndexMethod.invoke(hive, dbName, tableName, indexName, true: JBoolean, true: JBoolean)
+  }
+
+}
+
+private[client] class Shim_v1_2 extends Shim_v1_1 {
+
+  private lazy val loadDynamicPartitionsMethod =
+    findMethod(
+      classOf[Hive],
+      "loadDynamicPartitions",
+      classOf[Path],
+      classOf[String],
+      classOf[JMap[String, String]],
+      JBoolean.TYPE,
+      JInteger.TYPE,
+      JBoolean.TYPE,
+      JBoolean.TYPE,
+      JBoolean.TYPE,
+      JLong.TYPE)
+
+  override def loadDynamicPartitions(
+      hive: Hive,
+      loadPath: Path,
+      tableName: String,
+      partSpec: JMap[String, String],
+      replace: Boolean,
+      numDP: Int,
+      holdDDLTime: Boolean,
+      listBucketingEnabled: Boolean): Unit = {
+    loadDynamicPartitionsMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean,
+      numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean, JBoolean.FALSE,
+      0: JLong)
+  }
+
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index 0934ad5034671..3d609a66f3664 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -41,9 +41,11 @@ private[hive] object IsolatedClientLoader {
    */
   def forVersion(
       version: String,
-      config: Map[String, String] = Map.empty): IsolatedClientLoader = synchronized {
+      config: Map[String, String] = Map.empty,
+      ivyPath: Option[String] = None): IsolatedClientLoader = synchronized {
     val resolvedVersion = hiveVersion(version)
-    val files = resolvedVersions.getOrElseUpdate(resolvedVersion, downloadVersion(resolvedVersion))
+    val files = resolvedVersions.getOrElseUpdate(resolvedVersion,
+      downloadVersion(resolvedVersion, ivyPath))
     new IsolatedClientLoader(hiveVersion(version), files, config)
   }
 
@@ -51,9 +53,12 @@ private[hive] object IsolatedClientLoader {
     case "12" | "0.12" | "0.12.0" => hive.v12
     case "13" | "0.13" | "0.13.0" | "0.13.1" => hive.v13
     case "14" | "0.14" | "0.14.0" => hive.v14
+    case "1.0" | "1.0.0" => hive.v1_0
+    case "1.1" | "1.1.0" => hive.v1_1
+    case "1.2" | "1.2.0" => hive.v1_2
   }
 
-  private def downloadVersion(version: HiveVersion): Seq[URL] = {
+  private def downloadVersion(version: HiveVersion, ivyPath: Option[String]): Seq[URL] = {
     val hiveArtifacts = version.extraDeps ++
       Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde")
         .map(a => s"org.apache.hive:$a:${version.fullVersion}") ++
@@ -64,7 +69,7 @@ private[hive] object IsolatedClientLoader {
       SparkSubmitUtils.resolveMavenCoordinates(
         hiveArtifacts.mkString(","),
         Some("http://www.datanucleus.org/downloads/maven2"),
-        None,
+        ivyPath,
         exclusions = version.exclusions)
     }
     val allFiles = classpath.split(",").map(new File(_)).toSet
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
index 27a3d8f5896cc..b48082fe4b363 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
@@ -32,13 +32,36 @@ package object client {
     // Hive 0.14 depends on calcite 0.9.2-incubating-SNAPSHOT which does not exist in
     // maven central anymore, so override those with a version that exists.
     //
-    // org.pentaho:pentaho-aggdesigner-algorithm is also nowhere to be found, so exclude
-    // it explicitly. If it's needed by the metastore client, users will have to dig it
-    // out of somewhere and use configuration to point Spark at the correct jars.
+    // The other excluded dependencies are also nowhere to be found, so exclude them explicitly. If
+    // they're needed by the metastore client, users will have to dig them out of somewhere and use
+    // configuration to point Spark at the correct jars.
     case object v14 extends HiveVersion("0.14.0",
-      Seq("org.apache.calcite:calcite-core:1.3.0-incubating",
+      extraDeps = Seq("org.apache.calcite:calcite-core:1.3.0-incubating",
         "org.apache.calcite:calcite-avatica:1.3.0-incubating"),
-      Seq("org.pentaho:pentaho-aggdesigner-algorithm"))
+      exclusions = Seq("org.pentaho:pentaho-aggdesigner-algorithm"))
+
+    case object v1_0 extends HiveVersion("1.0.0",
+      exclusions = Seq("eigenbase:eigenbase-properties",
+        "org.pentaho:pentaho-aggdesigner-algorithm",
+        "net.hydromatic:linq4j",
+        "net.hydromatic:quidem"))
+
+    // The curator dependency was added to the exclusions here because it seems to confuse the ivy
+    // library. org.apache.curator:curator is a pom dependency but ivy tries to find the jar for it,
+    // and fails.
+    case object v1_1 extends HiveVersion("1.1.0",
+      exclusions = Seq("eigenbase:eigenbase-properties",
+        "org.apache.curator:*",
+        "org.pentaho:pentaho-aggdesigner-algorithm",
+        "net.hydromatic:linq4j",
+        "net.hydromatic:quidem"))
+
+    case object v1_2 extends HiveVersion("1.2.0",
+      exclusions = Seq("eigenbase:eigenbase-properties",
+        "org.apache.curator:*",
+        "org.pentaho:pentaho-aggdesigner-algorithm",
+        "net.hydromatic:linq4j",
+        "net.hydromatic:quidem"))
   }
   // scalastyle:on
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index 9a571650b6e25..d52e162acbd04 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive.client
 
+import java.io.File
+
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.catalyst.util.quietly
 import org.apache.spark.util.Utils
@@ -28,6 +30,12 @@ import org.apache.spark.util.Utils
  * is not fully tested.
  */
 class VersionsSuite extends SparkFunSuite with Logging {
+
+  // Do not use a temp path here to speed up subsequent executions of the unit test during
+  // development.
+  private val ivyPath = Some(
+    new File(sys.props("java.io.tmpdir"), "hive-ivy-cache").getAbsolutePath())
+
   private def buildConf() = {
     lazy val warehousePath = Utils.createTempDir()
     lazy val metastorePath = Utils.createTempDir()
@@ -38,7 +46,7 @@ class VersionsSuite extends SparkFunSuite with Logging {
   }
 
   test("success sanity check") {
-    val badClient = IsolatedClientLoader.forVersion("13", buildConf()).client
+    val badClient = IsolatedClientLoader.forVersion("13", buildConf(), ivyPath).client
     val db = new HiveDatabase("default", "")
     badClient.createDatabase(db)
   }
@@ -67,19 +75,21 @@ class VersionsSuite extends SparkFunSuite with Logging {
   // TODO: currently only works on mysql where we manually create the schema...
   ignore("failure sanity check") {
     val e = intercept[Throwable] {
-      val badClient = quietly { IsolatedClientLoader.forVersion("13", buildConf()).client }
+      val badClient = quietly {
+        IsolatedClientLoader.forVersion("13", buildConf(), ivyPath).client
+      }
     }
     assert(getNestedMessages(e) contains "Unknown column 'A0.OWNER_NAME' in 'field list'")
   }
 
-  private val versions = Seq("12", "13", "14")
+  private val versions = Seq("12", "13", "14", "1.0.0", "1.1.0", "1.2.0")
 
   private var client: ClientInterface = null
 
   versions.foreach { version =>
     test(s"$version: create client") {
       client = null
-      client = IsolatedClientLoader.forVersion(version, buildConf()).client
+      client = IsolatedClientLoader.forVersion(version, buildConf(), ivyPath).client
     }
 
     test(s"$version: createDatabase") {
@@ -170,5 +180,12 @@ class VersionsSuite extends SparkFunSuite with Logging {
         false,
         false)
     }
+
+    test(s"$version: create index and reset") {
+      client.runSqlHive("CREATE TABLE indexed_table (key INT)")
+      client.runSqlHive("CREATE INDEX index_1 ON TABLE indexed_table(key) " +
+        "as 'COMPACT' WITH DEFERRED REBUILD")
+      client.reset()
+    }
   }
 }

From a5c2961caaafd751f11bdd406bb6885443d7572e Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@gmail.com>
Date: Mon, 29 Jun 2015 11:57:19 -0700
Subject: [PATCH 0094/1454] [SPARK-8235] [SQL] misc function sha / sha1

Jira: https://issues.apache.org/jira/browse/SPARK-8235

I added the support for sha1. If I understood rxin correctly, sha and sha1 should execute the same algorithm, shouldn't they?

Please take a close look on the Python part. This is adopted from #6934

Author: Tarek Auel <tarek.auel@gmail.com>
Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #6963 from tarekauel/SPARK-8235 and squashes the following commits:

f064563 [Tarek Auel] change to shaHex
7ce3cdc [Tarek Auel] rely on automatic cast
a1251d6 [Tarek Auel] Merge remote-tracking branch 'upstream/master' into SPARK-8235
68eb043 [Tarek Auel] added docstring
be5aff1 [Tarek Auel] improved error message
7336c96 [Tarek Auel] added type check
cf23a80 [Tarek Auel] simplified example
ebf75ef [Tarek Auel] [SPARK-8301] updated the python documentation. Removed sha in python and scala
6d6ff0d [Tarek Auel] [SPARK-8233] added docstring
ea191a9 [Tarek Auel] [SPARK-8233] fixed signatureof python function. Added expected type to misc
e3fd7c3 [Tarek Auel] SPARK[8235] added sha to the list of __all__
e5dad4e [Tarek Auel] SPARK[8235] sha / sha1
---
 python/pyspark/sql/functions.py               | 14 +++++++++
 .../catalyst/analysis/FunctionRegistry.scala  |  2 ++
 .../spark/sql/catalyst/expressions/misc.scala | 30 ++++++++++++++++++-
 .../expressions/MiscFunctionsSuite.scala      |  8 +++++
 .../org/apache/spark/sql/functions.scala      | 16 ++++++++++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 12 ++++++++
 6 files changed, 81 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 7d3d0361610b7..45ecd826bd3bd 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -42,6 +42,7 @@
     'monotonicallyIncreasingId',
     'rand',
     'randn',
+    'sha1',
     'sha2',
     'sparkPartitionId',
     'struct',
@@ -382,6 +383,19 @@ def sha2(col, numBits):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def sha1(col):
+    """Returns the hex string result of SHA-1.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
+    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.sha1(_to_java_column(col))
+    return Column(jc)
+
+
 @since(1.4)
 def sparkPartitionId():
     """A column for partition ID of the Spark task.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 457948a800a17..b24064d061533 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -136,6 +136,8 @@ object FunctionRegistry {
     // misc functions
     expression[Md5]("md5"),
     expression[Sha2]("sha2"),
+    expression[Sha1]("sha1"),
+    expression[Sha1]("sha"),
 
     // aggregate functions
     expression[Average]("avg"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index e80706fc65aff..9a39165a1ff05 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -21,8 +21,9 @@ import java.security.MessageDigest
 import java.security.NoSuchAlgorithmException
 
 import org.apache.commons.codec.digest.DigestUtils
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.types.{BinaryType, IntegerType, StringType, DataType}
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -140,3 +141,30 @@ case class Sha2(left: Expression, right: Expression)
     """
   }
 }
+
+/**
+ * A function that calculates a sha1 hash value and returns it as a hex string
+ * For input of type [[BinaryType]] or [[StringType]]
+ */
+case class Sha1(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+
+  override def dataType: DataType = StringType
+
+  override def expectedChildTypes: Seq[DataType] = Seq(BinaryType)
+
+  override def eval(input: InternalRow): Any = {
+    val value = child.eval(input)
+    if (value == null) {
+      null
+    } else {
+      UTF8String.fromString(DigestUtils.shaHex(value.asInstanceOf[Array[Byte]]))
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, c =>
+      "org.apache.spark.unsafe.types.UTF8String.fromString" +
+        s"(org.apache.commons.codec.digest.DigestUtils.shaHex($c))"
+    )
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
index 38482c54c61db..36e636b5da6b8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
@@ -31,6 +31,14 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Md5(Literal.create(null, BinaryType)), null)
   }
 
+  test("sha1") {
+    checkEvaluation(Sha1(Literal("ABC".getBytes)), "3c01bdbb26f358bab27f267924aa2c9a03fcfdb8")
+    checkEvaluation(Sha1(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType)),
+      "5d211bad8f4ee70e16c7d343a838fc344a1ed961")
+    checkEvaluation(Sha1(Literal.create(null, BinaryType)), null)
+    checkEvaluation(Sha1(Literal("".getBytes)), "da39a3ee5e6b4b0d3255bfef95601890afd80709")
+  }
+
   test("sha2") {
     checkEvaluation(Sha2(Literal("ABC".getBytes), Literal(256)), DigestUtils.sha256Hex("ABC"))
     checkEvaluation(Sha2(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType), Literal(384)),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 355ce0e3423cf..ef92801548a13 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1414,6 +1414,22 @@ object functions {
    */
   def md5(columnName: String): Column = md5(Column(columnName))
 
+  /**
+   * Calculates the SHA-1 digest and returns the value as a 40 character hex string.
+   *
+   * @group misc_funcs
+   * @since 1.5.0
+   */
+  def sha1(e: Column): Column = Sha1(e.expr)
+
+  /**
+   * Calculates the SHA-1 digest and returns the value as a 40 character hex string.
+   *
+   * @group misc_funcs
+   * @since 1.5.0
+   */
+  def sha1(columnName: String): Column = sha1(Column(columnName))
+
   /**
    * Calculates the SHA-2 family of hash functions and returns the value as a hex string.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 8baed57a7f129..abfd47c811ed9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -144,6 +144,18 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row("902fbdd2b1df0c4f70b4a5d23525e932", "6ac1e56bc78f031059be7be854522c4c"))
   }
 
+  test("misc sha1 function") {
+    val df = Seq(("ABC", "ABC".getBytes)).toDF("a", "b")
+    checkAnswer(
+      df.select(sha1($"a"), sha1("b")),
+      Row("3c01bdbb26f358bab27f267924aa2c9a03fcfdb8", "3c01bdbb26f358bab27f267924aa2c9a03fcfdb8"))
+
+    val dfEmpty = Seq(("", "".getBytes)).toDF("a", "b")
+    checkAnswer(
+      dfEmpty.selectExpr("sha1(a)", "sha1(b)"),
+      Row("da39a3ee5e6b4b0d3255bfef95601890afd80709", "da39a3ee5e6b4b0d3255bfef95601890afd80709"))
+  }
+
   test("misc sha2 function") {
     val df = Seq(("ABC", Array[Byte](1, 2, 3, 4, 5, 6))).toDF("a", "b")
     checkAnswer(

From 492dca3a73e70705b5d5639e8fe4640b80e78d31 Mon Sep 17 00:00:00 2001
From: Vladimir Vladimirov <vladimir.vladimirov@magnetic.com>
Date: Mon, 29 Jun 2015 12:03:41 -0700
Subject: [PATCH 0095/1454] [SPARK-8528] Expose SparkContext.applicationId in
 PySpark

Use case - we want to log applicationId (YARN in hour case) to request help with troubleshooting from the DevOps

Author: Vladimir Vladimirov <vladimir.vladimirov@magnetic.com>

Closes #6936 from smartkiwi/master and squashes the following commits:

870338b [Vladimir Vladimirov] this would make doctest to run in python3
0eae619 [Vladimir Vladimirov] Scala doesn't use u'...' for unicode literals
14d77a8 [Vladimir Vladimirov] stop using ELLIPSIS
b4ebfc5 [Vladimir Vladimirov] addressed PR feedback - updated docstring
223a32f [Vladimir Vladimirov] fixed test - applicationId is property that returns the string
3221f5a [Vladimir Vladimirov] [SPARK-8528] added documentation for Scala
2cff090 [Vladimir Vladimirov] [SPARK-8528] add applicationId property for SparkContext object in pyspark
---
 .../scala/org/apache/spark/SparkContext.scala     |  8 ++++++++
 python/pyspark/context.py                         | 15 +++++++++++++++
 2 files changed, 23 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index c7a7436462083..b3c3bf3746e18 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -315,6 +315,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _dagScheduler = ds
   }
 
+  /**
+   * A unique identifier for the Spark application.
+   * Its format depends on the scheduler implementation.
+   * (i.e.
+   *  in case of local spark app something like 'local-1433865536131'
+   *  in case of YARN something like 'application_1433865536131_34483'
+   * )
+   */
   def applicationId: String = _applicationId
   def applicationAttemptId: Option[String] = _applicationAttemptId
 
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 90b2fffbb9c7c..d7466729b8f36 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -291,6 +291,21 @@ def version(self):
         """
         return self._jsc.version()
 
+    @property
+    @ignore_unicode_prefix
+    def applicationId(self):
+        """
+        A unique identifier for the Spark application.
+        Its format depends on the scheduler implementation.
+        (i.e.
+            in case of local spark app something like 'local-1433865536131'
+            in case of YARN something like 'application_1433865536131_34483'
+        )
+        >>> sc.applicationId  # doctest: +ELLIPSIS
+        u'local-...'
+        """
+        return self._jsc.sc().applicationId()
+
     @property
     def startTime(self):
         """Return the epoch time when the Spark Context was started."""

From 94e040d05996111b2b448bcdee1cda184c6d039b Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 29 Jun 2015 12:16:12 -0700
Subject: [PATCH 0096/1454] [SQL][DOCS] Remove wrong example from
 DataFrame.scala

In DataFrame.scala, there are examples like as follows.

```
 * // The following are equivalent:
 * peopleDf.filter($"age" > 15)
 * peopleDf.where($"age" > 15)
 * peopleDf($"age" > 15)
```

But, I think the last example doesn't work.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #6977 from sarutak/fix-dataframe-example and squashes the following commits:

46efbd7 [Kousuke Saruta] Removed wrong example
---
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 --
 1 file changed, 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index d75d88307562e..986e59133919f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -682,7 +682,6 @@ class DataFrame private[sql](
    *   // The following are equivalent:
    *   peopleDf.filter($"age" > 15)
    *   peopleDf.where($"age" > 15)
-   *   peopleDf($"age" > 15)
    * }}}
    * @group dfops
    * @since 1.3.0
@@ -707,7 +706,6 @@ class DataFrame private[sql](
    *   // The following are equivalent:
    *   peopleDf.filter($"age" > 15)
    *   peopleDf.where($"age" > 15)
-   *   peopleDf($"age" > 15)
    * }}}
    * @group dfops
    * @since 1.3.0

From 637b4eedad84dcff1769454137a64ac70c7f2397 Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Mon, 29 Jun 2015 12:25:16 -0700
Subject: [PATCH 0097/1454] [SPARK-8214] [SQL] Add function hex

cc chenghao-intel  adrian-wang

Author: zhichao.li <zhichao.li@intel.com>

Closes #6976 from zhichao-li/hex and squashes the following commits:

e218d1b [zhichao.li] turn off scalastyle for non-ascii
de3f5ea [zhichao.li] non-ascii char
cf9c936 [zhichao.li] give separated buffer for each hex method
967ec90 [zhichao.li] Make 'value' as a feild of Hex
3b2fa13 [zhichao.li] tiny fix
a647641 [zhichao.li] remove duplicate null check
7cab020 [zhichao.li] tiny refactoring
35ecfe5 [zhichao.li] add function hex
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../spark/sql/catalyst/expressions/math.scala | 86 ++++++++++++++++++-
 .../expressions/MathFunctionsSuite.scala      | 14 ++-
 .../org/apache/spark/sql/functions.scala      | 16 ++++
 .../spark/sql/MathExpressionsSuite.scala      | 13 +++
 5 files changed, 125 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index b24064d061533..b17457d3094c2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -113,6 +113,7 @@ object FunctionRegistry {
     expression[Expm1]("expm1"),
     expression[Floor]("floor"),
     expression[Hypot]("hypot"),
+    expression[Hex]("hex"),
     expression[Logarithm]("log"),
     expression[Log]("ln"),
     expression[Log10]("log10"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 5694afc61be05..4b57ddd9c5768 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -18,9 +18,11 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.lang.{Long => JLong}
+import java.util.Arrays
 
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.types.{DataType, DoubleType, LongType, StringType}
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -273,9 +275,6 @@ case class Atan2(left: Expression, right: Expression)
   }
 }
 
-case class Hypot(left: Expression, right: Expression)
-  extends BinaryMathExpression(math.hypot, "HYPOT")
-
 case class Pow(left: Expression, right: Expression)
   extends BinaryMathExpression(math.pow, "POWER") {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -287,6 +286,85 @@ case class Pow(left: Expression, right: Expression)
   }
 }
 
+/**
+ * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
+ * Otherwise if the number is a STRING,
+ * it converts each character into its hexadecimal representation and returns the resulting STRING.
+ * Negative numbers would be treated as two's complement.
+ */
+case class Hex(child: Expression)
+  extends UnaryExpression with Serializable  {
+
+  override def dataType: DataType = StringType
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (child.dataType.isInstanceOf[StringType]
+      || child.dataType.isInstanceOf[IntegerType]
+      || child.dataType.isInstanceOf[LongType]
+      || child.dataType.isInstanceOf[BinaryType]
+      || child.dataType == NullType) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(s"hex doesn't accepts ${child.dataType} type")
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val num = child.eval(input)
+    if (num == null) {
+      null
+    } else {
+      child.dataType match {
+        case LongType => hex(num.asInstanceOf[Long])
+        case IntegerType => hex(num.asInstanceOf[Integer].toLong)
+        case BinaryType => hex(num.asInstanceOf[Array[Byte]])
+        case StringType => hex(num.asInstanceOf[UTF8String])
+      }
+    }
+  }
+
+  /**
+   * Converts every character in s to two hex digits.
+   */
+  private def hex(str: UTF8String): UTF8String = {
+    hex(str.getBytes)
+  }
+
+  private def hex(bytes: Array[Byte]): UTF8String = {
+    doHex(bytes, bytes.length)
+  }
+
+  private def doHex(bytes: Array[Byte], length: Int): UTF8String = {
+    val value = new Array[Byte](length * 2)
+    var i = 0
+    while(i < length) {
+      value(i * 2) = Character.toUpperCase(Character.forDigit(
+        (bytes(i) & 0xF0) >>> 4, 16)).toByte
+      value(i * 2 + 1) = Character.toUpperCase(Character.forDigit(
+        bytes(i) & 0x0F, 16)).toByte
+      i += 1
+    }
+    UTF8String.fromBytes(value)
+  }
+
+  private def hex(num: Long): UTF8String = {
+    // Extract the hex digits of num into value[] from right to left
+    val value = new Array[Byte](16)
+    var numBuf = num
+    var len = 0
+    do {
+      len += 1
+      value(value.length - len) = Character.toUpperCase(Character
+        .forDigit((numBuf & 0xF).toInt, 16)).toByte
+      numBuf >>>= 4
+    } while (numBuf != 0)
+    UTF8String.fromBytes(Arrays.copyOfRange(value, value.length - len, value.length))
+  }
+}
+
+case class Hypot(left: Expression, right: Expression)
+  extends BinaryMathExpression(math.hypot, "HYPOT")
+
 case class Logarithm(left: Expression, right: Expression)
   extends BinaryMathExpression((c1, c2) => math.log(c2) / math.log(c1), "LOG") {
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 0d1d5ebdff2d5..b932d4ab850c7 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types.{DataType, DoubleType, LongType}
@@ -226,6 +225,19 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     testBinary(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), expectNull = true)
   }
 
+  test("hex") {
+    checkEvaluation(Hex(Literal(28)), "1C")
+    checkEvaluation(Hex(Literal(-28)), "FFFFFFFFFFFFFFE4")
+    checkEvaluation(Hex(Literal(100800200404L)), "177828FED4")
+    checkEvaluation(Hex(Literal(-100800200404L)), "FFFFFFE887D7012C")
+    checkEvaluation(Hex(Literal("helloHex")), "68656C6C6F486578")
+    checkEvaluation(Hex(Literal("helloHex".getBytes())), "68656C6C6F486578")
+    // scalastyle:off
+    // Turn off scala style for non-ascii chars
+    checkEvaluation(Hex(Literal("三重的")), "E4B889E9878DE79A84")
+    // scalastyle:on
+  }
+
   test("hypot") {
     testBinary(Hypot, math.hypot)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index ef92801548a13..5422e066afcb1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1046,6 +1046,22 @@ object functions {
    */
   def floor(columnName: String): Column = floor(Column(columnName))
 
+  /**
+   * Computes hex value of the given column
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def hex(column: Column): Column = Hex(column.expr)
+
+  /**
+   * Computes hex value of the given input
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def hex(colName: String): Column = hex(Column(colName))
+
   /**
    * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 2768d7dfc8030..d6331aa4ff09e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -212,6 +212,19 @@ class MathExpressionsSuite extends QueryTest {
     )
   }
 
+  test("hex") {
+    val data = Seq((28, -28, 100800200404L, "hello")).toDF("a", "b", "c", "d")
+    checkAnswer(data.select(hex('a)), Seq(Row("1C")))
+    checkAnswer(data.select(hex('b)), Seq(Row("FFFFFFFFFFFFFFE4")))
+    checkAnswer(data.select(hex('c)), Seq(Row("177828FED4")))
+    checkAnswer(data.select(hex('d)), Seq(Row("68656C6C6F")))
+    checkAnswer(data.selectExpr("hex(a)"), Seq(Row("1C")))
+    checkAnswer(data.selectExpr("hex(b)"), Seq(Row("FFFFFFFFFFFFFFE4")))
+    checkAnswer(data.selectExpr("hex(c)"), Seq(Row("177828FED4")))
+    checkAnswer(data.selectExpr("hex(d)"), Seq(Row("68656C6C6F")))
+    checkAnswer(data.selectExpr("hex(cast(d as binary))"), Seq(Row("68656C6C6F")))
+  }
+
   test("hypot") {
     testTwoToOneMathFunction(hypot, hypot, math.hypot)
   }

From c6ba2ea341ad23de265d870669b25e6a41f461e5 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Mon, 29 Jun 2015 12:46:33 -0700
Subject: [PATCH 0098/1454] [SPARK-7862] [SQL] Disable the error message
 redirect to stderr

This is a follow up of #6404, the ScriptTransformation prints the error msg into stderr directly, probably be a disaster for application log.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #6882 from chenghao-intel/verbose and squashes the following commits:

bfedd77 [Cheng Hao] revert the write
76ff46b [Cheng Hao] update the CircularBuffer
692b19e [Cheng Hao] check the process exitValue for ScriptTransform
47e0970 [Cheng Hao] Use the RedirectThread instead
1de771d [Cheng Hao] naming the threads in ScriptTransformation
8536e81 [Cheng Hao] disable the error message redirection for stderr
---
 .../scala/org/apache/spark/util/Utils.scala   | 33 ++++++++++++
 .../org/apache/spark/util/UtilsSuite.scala    |  8 +++
 .../spark/sql/hive/client/ClientWrapper.scala | 29 ++---------
 .../hive/execution/ScriptTransformation.scala | 51 ++++++++++++-------
 .../sql/hive/execution/SQLQuerySuite.scala    |  2 +-
 5 files changed, 77 insertions(+), 46 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 19157af5b6f4d..a7fc749a2b0c6 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -2333,3 +2333,36 @@ private[spark] class RedirectThread(
     }
   }
 }
+
+/**
+ * An [[OutputStream]] that will store the last 10 kilobytes (by default) written to it
+ * in a circular buffer. The current contents of the buffer can be accessed using
+ * the toString method.
+ */
+private[spark] class CircularBuffer(sizeInBytes: Int = 10240) extends java.io.OutputStream {
+  var pos: Int = 0
+  var buffer = new Array[Int](sizeInBytes)
+
+  def write(i: Int): Unit = {
+    buffer(pos) = i
+    pos = (pos + 1) % buffer.length
+  }
+
+  override def toString: String = {
+    val (end, start) = buffer.splitAt(pos)
+    val input = new java.io.InputStream {
+      val iterator = (start ++ end).iterator
+
+      def read(): Int = if (iterator.hasNext) iterator.next() else -1
+    }
+    val reader = new BufferedReader(new InputStreamReader(input))
+    val stringBuilder = new StringBuilder
+    var line = reader.readLine()
+    while (line != null) {
+      stringBuilder.append(line)
+      stringBuilder.append("\n")
+      line = reader.readLine()
+    }
+    stringBuilder.toString()
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index a61ea3918f46a..baa4c661cc21e 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -673,4 +673,12 @@ class UtilsSuite extends SparkFunSuite with ResetSystemProperties with Logging {
     assert(!Utils.isInDirectory(nullFile, parentDir))
     assert(!Utils.isInDirectory(nullFile, childFile3))
   }
+
+  test("circular buffer") {
+    val buffer = new CircularBuffer(25)
+    val stream = new java.io.PrintStream(buffer, true, "UTF-8")
+
+    stream.println("test circular test circular test circular test circular test circular")
+    assert(buffer.toString === "t circular test circular\n")
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 4c708cec572ae..cbd2bf6b5eede 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -22,6 +22,8 @@ import java.net.URI
 import java.util.{ArrayList => JArrayList, Map => JMap, List => JList, Set => JSet}
 import javax.annotation.concurrent.GuardedBy
 
+import org.apache.spark.util.CircularBuffer
+
 import scala.collection.JavaConversions._
 import scala.language.reflectiveCalls
 
@@ -66,32 +68,7 @@ private[hive] class ClientWrapper(
   with Logging {
 
   // Circular buffer to hold what hive prints to STDOUT and ERR.  Only printed when failures occur.
-  private val outputBuffer = new java.io.OutputStream {
-    var pos: Int = 0
-    var buffer = new Array[Int](10240)
-    def write(i: Int): Unit = {
-      buffer(pos) = i
-      pos = (pos + 1) % buffer.size
-    }
-
-    override def toString: String = {
-      val (end, start) = buffer.splitAt(pos)
-      val input = new java.io.InputStream {
-        val iterator = (start ++ end).iterator
-
-        def read(): Int = if (iterator.hasNext) iterator.next() else -1
-      }
-      val reader = new BufferedReader(new InputStreamReader(input))
-      val stringBuilder = new StringBuilder
-      var line = reader.readLine()
-      while(line != null) {
-        stringBuilder.append(line)
-        stringBuilder.append("\n")
-        line = reader.readLine()
-      }
-      stringBuilder.toString()
-    }
-  }
+  private val outputBuffer = new CircularBuffer()
 
   private val shim = version match {
     case hive.v12 => new Shim_v0_12()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 611888055d6cf..b967e191c5855 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -35,7 +35,7 @@ import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive.HiveShim._
 import org.apache.spark.sql.hive.{HiveContext, HiveInspectors}
 import org.apache.spark.sql.types.DataType
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{CircularBuffer, RedirectThread, Utils}
 
 /**
  * Transforms the input by forking and running the specified script.
@@ -59,15 +59,13 @@ case class ScriptTransformation(
     child.execute().mapPartitions { iter =>
       val cmd = List("/bin/bash", "-c", script)
       val builder = new ProcessBuilder(cmd)
-      // redirectError(Redirect.INHERIT) would consume the error output from buffer and
-      // then print it to stderr (inherit the target from the current Scala process).
-      // If without this there would be 2 issues:
+      // We need to start threads connected to the process pipeline:
       // 1) The error msg generated by the script process would be hidden.
       // 2) If the error msg is too big to chock up the buffer, the input logic would be hung
-      builder.redirectError(Redirect.INHERIT)
       val proc = builder.start()
       val inputStream = proc.getInputStream
       val outputStream = proc.getOutputStream
+      val errorStream = proc.getErrorStream
       val reader = new BufferedReader(new InputStreamReader(inputStream))
 
       val (outputSerde, outputSoi) = ioschema.initOutputSerDe(output)
@@ -152,29 +150,43 @@ case class ScriptTransformation(
       val dataOutputStream = new DataOutputStream(outputStream)
       val outputProjection = new InterpretedProjection(input, child.output)
 
+      // TODO make the 2048 configurable?
+      val stderrBuffer = new CircularBuffer(2048)
+      // Consume the error stream from the pipeline, otherwise it will be blocked if
+      // the pipeline is full.
+      new RedirectThread(errorStream, // input stream from the pipeline
+        stderrBuffer,                 // output to a circular buffer
+        "Thread-ScriptTransformation-STDERR-Consumer").start()
+
       // Put the write(output to the pipeline) into a single thread
       // and keep the collector as remain in the main thread.
       // otherwise it will causes deadlock if the data size greater than
       // the pipeline / buffer capacity.
       new Thread(new Runnable() {
         override def run(): Unit = {
-          iter
-            .map(outputProjection)
-            .foreach { row =>
-            if (inputSerde == null) {
-              val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
-                ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
-
-              outputStream.write(data)
-            } else {
-              val writable = inputSerde.serialize(
-                row.asInstanceOf[GenericInternalRow].values, inputSoi)
-              prepareWritable(writable).write(dataOutputStream)
+          Utils.tryWithSafeFinally {
+            iter
+              .map(outputProjection)
+              .foreach { row =>
+              if (inputSerde == null) {
+                val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
+                  ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
+
+                outputStream.write(data)
+              } else {
+                val writable = inputSerde.serialize(
+                  row.asInstanceOf[GenericInternalRow].values, inputSoi)
+                prepareWritable(writable).write(dataOutputStream)
+              }
+            }
+            outputStream.close()
+          } {
+            if (proc.waitFor() != 0) {
+              logError(stderrBuffer.toString) // log the stderr circular buffer
             }
           }
-          outputStream.close()
         }
-      }).start()
+      }, "Thread-ScriptTransformation-Feed").start()
 
       iterator
     }
@@ -278,3 +290,4 @@ case class HiveScriptIOSchema (
     }
   }
 }
+
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index f0aad8dbbe64d..9f7e58f890241 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -653,7 +653,7 @@ class SQLQuerySuite extends QueryTest {
         .queryExecution.toRdd.count())
   }
 
-  ignore("test script transform for stderr") {
+  test("test script transform for stderr") {
     val data = (1 to 100000).map { i => (i, i, i) }
     data.toDF("d1", "d2", "d3").registerTempTable("script_trans")
     assert(0 ===

From be7ef067620408859144e0244b0f1b8eb56faa86 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Mon, 29 Jun 2015 13:15:04 -0700
Subject: [PATCH 0099/1454] [SPARK-8681] fixed wrong ordering of columns in
 crosstab

I specifically randomized the test. What crosstab does is equivalent to a countByKey, therefore if this test fails again for any reason, we will know that we hit a corner case or something.

cc rxin marmbrus

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7060 from brkyvz/crosstab-fixes and squashes the following commits:

0a65234 [Burak Yavuz] addressed comments v1
d96da7e [Burak Yavuz] fixed wrong ordering of columns in crosstab
---
 .../sql/execution/stat/StatFunctions.scala    |  8 ++++--
 .../apache/spark/sql/DataFrameStatSuite.scala | 28 ++++++++++---------
 2 files changed, 20 insertions(+), 16 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index 042e2c9cbb22e..b624ef7e8fa1a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -111,7 +111,7 @@ private[sql] object StatFunctions extends Logging {
         "the pairs. Please try reducing the amount of distinct items in your columns.")
     }
     // get the distinct values of column 2, so that we can make them the column names
-    val distinctCol2 = counts.map(_.get(1)).distinct.zipWithIndex.toMap
+    val distinctCol2: Map[Any, Int] = counts.map(_.get(1)).distinct.zipWithIndex.toMap
     val columnSize = distinctCol2.size
     require(columnSize < 1e4, s"The number of distinct values for $col2, can't " +
       s"exceed 1e4. Currently $columnSize")
@@ -120,14 +120,16 @@ private[sql] object StatFunctions extends Logging {
       rows.foreach { (row: Row) =>
         // row.get(0) is column 1
         // row.get(1) is column 2
-        // row.get(3) is the frequency
+        // row.get(2) is the frequency
         countsRow.setLong(distinctCol2.get(row.get(1)).get + 1, row.getLong(2))
       }
       // the value of col1 is the first value, the rest are the counts
       countsRow.update(0, UTF8String.fromString(col1Item.toString))
       countsRow
     }.toSeq
-    val headerNames = distinctCol2.map(r => StructField(r._1.toString, LongType)).toSeq
+    // In the map, the column names (._1) are not ordered by the index (._2). This was the bug in
+    // SPARK-8681. We need to explicitly sort by the column index and assign the column names.
+    val headerNames = distinctCol2.toSeq.sortBy(_._2).map(r => StructField(r._1.toString, LongType))
     val schema = StructType(StructField(tableName, StringType) +: headerNames)
 
     new DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table)).na.fill(0.0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 0d3ff899dad72..64ec1a70c47e6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import java.util.Random
+
 import org.scalatest.Matchers._
 
 import org.apache.spark.SparkFunSuite
@@ -65,22 +67,22 @@ class DataFrameStatSuite extends SparkFunSuite  {
   }
 
   test("crosstab") {
-    val df = Seq((0, 0), (2, 1), (1, 0), (2, 0), (0, 0), (2, 0)).toDF("a", "b")
+    val rng = new Random()
+    val data = Seq.tabulate(25)(i => (rng.nextInt(5), rng.nextInt(10)))
+    val df = data.toDF("a", "b")
     val crosstab = df.stat.crosstab("a", "b")
     val columnNames = crosstab.schema.fieldNames
     assert(columnNames(0) === "a_b")
-    assert(columnNames(1) === "0")
-    assert(columnNames(2) === "1")
-    val rows: Array[Row] = crosstab.collect().sortBy(_.getString(0))
-    assert(rows(0).get(0).toString === "0")
-    assert(rows(0).getLong(1) === 2L)
-    assert(rows(0).get(2) === 0L)
-    assert(rows(1).get(0).toString === "1")
-    assert(rows(1).getLong(1) === 1L)
-    assert(rows(1).get(2) === 0L)
-    assert(rows(2).get(0).toString === "2")
-    assert(rows(2).getLong(1) === 2L)
-    assert(rows(2).getLong(2) === 1L)
+    // reduce by key
+    val expected = data.map(t => (t, 1)).groupBy(_._1).mapValues(_.length)
+    val rows = crosstab.collect()
+    rows.foreach { row =>
+      val i = row.getString(0).toInt
+      for (col <- 1 to 9) {
+        val j = columnNames(col).toInt
+        assert(row.getLong(col) === expected.getOrElse((i, j), 0).toLong)
+      }
+    }
   }
 
   test("Frequent Items") {

From afae9766f28d2e58297405c39862d20a04267b62 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 29 Jun 2015 13:20:55 -0700
Subject: [PATCH 0100/1454] [SPARK-8070] [SQL] [PYSPARK] avoid spark jobs in
 createDataFrame

Avoid the unnecessary jobs when infer schema from list.

cc yhuai mengxr

Author: Davies Liu <davies@databricks.com>

Closes #6606 from davies/improve_create and squashes the following commits:

a5928bf [Davies Liu] Update MimaExcludes.scala
62da911 [Davies Liu] fix mima
bab4d7d [Davies Liu] Merge branch 'improve_create' of github.com:davies/spark into improve_create
eee44a8 [Davies Liu] Merge branch 'master' of github.com:apache/spark into improve_create
8d9292d [Davies Liu] Update context.py
eb24531 [Davies Liu] Update context.py
c969997 [Davies Liu] bug fix
d5a8ab0 [Davies Liu] fix tests
8c3f10d [Davies Liu] Merge branch 'master' of github.com:apache/spark into improve_create
6ea5925 [Davies Liu] address comments
6ceaeff [Davies Liu] avoid spark jobs in createDataFrame
---
 python/pyspark/sql/context.py | 64 +++++++++++++++++++++++++----------
 python/pyspark/sql/types.py   | 48 +++++++++++++++-----------
 2 files changed, 75 insertions(+), 37 deletions(-)

diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index dc239226e6d3c..4dda3b430cfbf 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -203,7 +203,37 @@ def registerFunction(self, name, f, returnType=StringType()):
                                             self._sc._javaAccumulator,
                                             returnType.json())
 
+    def _inferSchemaFromList(self, data):
+        """
+        Infer schema from list of Row or tuple.
+
+        :param data: list of Row or tuple
+        :return: StructType
+        """
+        if not data:
+            raise ValueError("can not infer schema from empty dataset")
+        first = data[0]
+        if type(first) is dict:
+            warnings.warn("inferring schema from dict is deprecated,"
+                          "please use pyspark.sql.Row instead")
+        schema = _infer_schema(first)
+        if _has_nulltype(schema):
+            for r in data:
+                schema = _merge_type(schema, _infer_schema(r))
+                if not _has_nulltype(schema):
+                    break
+            else:
+                raise ValueError("Some of types cannot be determined after inferring")
+        return schema
+
     def _inferSchema(self, rdd, samplingRatio=None):
+        """
+        Infer schema from an RDD of Row or tuple.
+
+        :param rdd: an RDD of Row or tuple
+        :param samplingRatio: sampling ratio, or no sampling (default)
+        :return: StructType
+        """
         first = rdd.first()
         if not first:
             raise ValueError("The first row in RDD is empty, "
@@ -322,6 +352,8 @@ def createDataFrame(self, data, schema=None, samplingRatio=None):
             data = [r.tolist() for r in data.to_records(index=False)]
 
         if not isinstance(data, RDD):
+            if not isinstance(data, list):
+                data = list(data)
             try:
                 # data could be list, tuple, generator ...
                 rdd = self._sc.parallelize(data)
@@ -330,28 +362,26 @@ def createDataFrame(self, data, schema=None, samplingRatio=None):
         else:
             rdd = data
 
-        if schema is None:
-            schema = self._inferSchema(rdd, samplingRatio)
+        if schema is None or isinstance(schema, (list, tuple)):
+            if isinstance(data, RDD):
+                struct = self._inferSchema(rdd, samplingRatio)
+            else:
+                struct = self._inferSchemaFromList(data)
+            if isinstance(schema, (list, tuple)):
+                for i, name in enumerate(schema):
+                    struct.fields[i].name = name
+            schema = struct
             converter = _create_converter(schema)
             rdd = rdd.map(converter)
 
-        if isinstance(schema, (list, tuple)):
-            first = rdd.first()
-            if not isinstance(first, (list, tuple)):
-                raise TypeError("each row in `rdd` should be list or tuple, "
-                                "but got %r" % type(first))
-            row_cls = Row(*schema)
-            schema = self._inferSchema(rdd.map(lambda r: row_cls(*r)), samplingRatio)
-
-        # take the first few rows to verify schema
-        rows = rdd.take(10)
-        # Row() cannot been deserialized by Pyrolite
-        if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row':
-            rdd = rdd.map(tuple)
+        elif isinstance(schema, StructType):
+            # take the first few rows to verify schema
             rows = rdd.take(10)
+            for row in rows:
+                _verify_type(row, schema)
 
-        for row in rows:
-            _verify_type(row, schema)
+        else:
+            raise TypeError("schema should be StructType or list or None")
 
         # convert python objects to sql data
         converter = _python_to_sql_converter(schema)
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 23d9adb0daea1..932686e5e4b01 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -635,7 +635,7 @@ def _need_python_to_sql_conversion(dataType):
     >>> schema0 = StructType([StructField("indices", ArrayType(IntegerType(), False), False),
     ...                       StructField("values", ArrayType(DoubleType(), False), False)])
     >>> _need_python_to_sql_conversion(schema0)
-    False
+    True
     >>> _need_python_to_sql_conversion(ExamplePointUDT())
     True
     >>> schema1 = ArrayType(ExamplePointUDT(), False)
@@ -647,7 +647,8 @@ def _need_python_to_sql_conversion(dataType):
     True
     """
     if isinstance(dataType, StructType):
-        return any([_need_python_to_sql_conversion(f.dataType) for f in dataType.fields])
+        # convert namedtuple or Row into tuple
+        return True
     elif isinstance(dataType, ArrayType):
         return _need_python_to_sql_conversion(dataType.elementType)
     elif isinstance(dataType, MapType):
@@ -688,21 +689,25 @@ def _python_to_sql_converter(dataType):
 
     if isinstance(dataType, StructType):
         names, types = zip(*[(f.name, f.dataType) for f in dataType.fields])
-        converters = [_python_to_sql_converter(t) for t in types]
-
-        def converter(obj):
-            if isinstance(obj, dict):
-                return tuple(c(obj.get(n)) for n, c in zip(names, converters))
-            elif isinstance(obj, tuple):
-                if hasattr(obj, "__fields__") or hasattr(obj, "_fields"):
-                    return tuple(c(v) for c, v in zip(converters, obj))
-                elif all(isinstance(x, tuple) and len(x) == 2 for x in obj):  # k-v pairs
-                    d = dict(obj)
-                    return tuple(c(d.get(n)) for n, c in zip(names, converters))
+        if any(_need_python_to_sql_conversion(t) for t in types):
+            converters = [_python_to_sql_converter(t) for t in types]
+
+            def converter(obj):
+                if isinstance(obj, dict):
+                    return tuple(c(obj.get(n)) for n, c in zip(names, converters))
+                elif isinstance(obj, tuple):
+                    if hasattr(obj, "__fields__") or hasattr(obj, "_fields"):
+                        return tuple(c(v) for c, v in zip(converters, obj))
+                    else:
+                        return tuple(c(v) for c, v in zip(converters, obj))
+                elif obj is not None:
+                    raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType))
+        else:
+            def converter(obj):
+                if isinstance(obj, dict):
+                    return tuple(obj.get(n) for n in names)
                 else:
-                    return tuple(c(v) for c, v in zip(converters, obj))
-            elif obj is not None:
-                raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType))
+                    return tuple(obj)
         return converter
     elif isinstance(dataType, ArrayType):
         element_converter = _python_to_sql_converter(dataType.elementType)
@@ -1027,10 +1032,13 @@ def _verify_type(obj, dataType):
     _type = type(dataType)
     assert _type in _acceptable_types, "unknown datatype: %s" % dataType
 
-    # subclass of them can not be deserialized in JVM
-    if type(obj) not in _acceptable_types[_type]:
-        raise TypeError("%s can not accept object in type %s"
-                        % (dataType, type(obj)))
+    if _type is StructType:
+        if not isinstance(obj, (tuple, list)):
+            raise TypeError("StructType can not accept object in type %s" % type(obj))
+    else:
+        # subclass of them can not be deserialized in JVM
+        if type(obj) not in _acceptable_types[_type]:
+            raise TypeError("%s can not accept object in type %s" % (dataType, type(obj)))
 
     if isinstance(dataType, ArrayType):
         for i in obj:

From 27ef85451cd237caa7016baa69957a35ab365aa8 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 29 Jun 2015 14:07:55 -0700
Subject: [PATCH 0101/1454] [SPARK-8709] Exclude hadoop-client's mockito-all
 dependency

This patch excludes `hadoop-client`'s dependency on `mockito-all`.  As of #7061, Spark depends on `mockito-core` instead of `mockito-all`, so the dependency from Hadoop was leading to test compilation failures for some of the Hadoop 2 SBT builds.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7090 from JoshRosen/SPARK-8709 and squashes the following commits:

e190122 [Josh Rosen] [SPARK-8709] Exclude hadoop-client's mockito-all dependency.
---
 LICENSE          |  2 +-
 core/pom.xml     | 10 ----------
 launcher/pom.xml |  6 ------
 pom.xml          |  8 ++++++++
 4 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/LICENSE b/LICENSE
index 8672be55eca3e..f9e412cade345 100644
--- a/LICENSE
+++ b/LICENSE
@@ -948,6 +948,6 @@ The following components are provided under the MIT License. See project link fo
      (MIT License) SLF4J LOG4J-12 Binding (org.slf4j:slf4j-log4j12:1.7.5 - http://www.slf4j.org)
      (MIT License) pyrolite (org.spark-project:pyrolite:2.0.1 - http://pythonhosted.org/Pyro4/)
      (MIT License) scopt (com.github.scopt:scopt_2.10:3.2.0 - https://github.com/scopt/scopt)
-     (The MIT License) Mockito (org.mockito:mockito-core:1.8.5 - http://www.mockito.org)
+     (The MIT License) Mockito (org.mockito:mockito-core:1.9.5 - http://www.mockito.org)
      (MIT License) jquery (https://jquery.org/license/)
      (MIT License) AnchorJS (https://github.com/bryanbraun/anchorjs)
diff --git a/core/pom.xml b/core/pom.xml
index 565437c4861a4..aee0d92620606 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -69,16 +69,6 @@
     <dependency>
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-client</artifactId>
-      <exclusions>
-        <exclusion>
-          <groupId>javax.servlet</groupId>
-          <artifactId>servlet-api</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.codehaus.jackson</groupId>
-          <artifactId>jackson-mapper-asl</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
diff --git a/launcher/pom.xml b/launcher/pom.xml
index a853e67f5cf78..2fd768d8119c4 100644
--- a/launcher/pom.xml
+++ b/launcher/pom.xml
@@ -68,12 +68,6 @@
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-client</artifactId>
       <scope>test</scope>
-      <exclusions>
-        <exclusion>
-          <groupId>org.codehaus.jackson</groupId>
-          <artifactId>jackson-mapper-asl</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
   </dependencies>
 
diff --git a/pom.xml b/pom.xml
index 4c18bd5e42c87..94dd512cfb618 100644
--- a/pom.xml
+++ b/pom.xml
@@ -747,6 +747,10 @@
             <groupId>asm</groupId>
             <artifactId>asm</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>org.codehaus.jackson</groupId>
+            <artifactId>jackson-mapper-asl</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>org.ow2.asm</groupId>
             <artifactId>asm</artifactId>
@@ -759,6 +763,10 @@
             <groupId>commons-logging</groupId>
             <artifactId>commons-logging</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>org.mockito</groupId>
+            <artifactId>mockito-all</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>org.mortbay.jetty</groupId>
             <artifactId>servlet-api-2.5</artifactId>

From f6fc254ec4ce5f103d45da6d007b4066ce751236 Mon Sep 17 00:00:00 2001
From: Ilya Ganelin <ilya.ganelin@capitalone.com>
Date: Mon, 29 Jun 2015 14:15:15 -0700
Subject: [PATCH 0102/1454] [SPARK-8056][SQL] Design an easier way to construct
 schema for both Scala and Python

I've added functionality to create new StructType similar to how we add parameters to a new SparkContext.

I've also added tests for this type of creation.

Author: Ilya Ganelin <ilya.ganelin@capitalone.com>

Closes #6686 from ilganeli/SPARK-8056B and squashes the following commits:

27c1de1 [Ilya Ganelin] Rename
467d836 [Ilya Ganelin] Removed from_string in favor of _parse_Datatype_json_value
5fef5a4 [Ilya Ganelin] Updates for type parsing
4085489 [Ilya Ganelin] Style errors
3670cf5 [Ilya Ganelin] added string to DataType conversion
8109e00 [Ilya Ganelin] Fixed error in tests
41ab686 [Ilya Ganelin] Fixed style errors
e7ba7e0 [Ilya Ganelin] Moved some python tests to tests.py. Added cleaner handling of null data type and added test for correctness of input format
15868fa [Ilya Ganelin] Fixed python errors
b79b992 [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-8056B
a3369fc [Ilya Ganelin] Fixing space errors
e240040 [Ilya Ganelin] Style
bab7823 [Ilya Ganelin] Constructor error
73d4677 [Ilya Ganelin] Style
4ed00d9 [Ilya Ganelin] Fixed default arg
67df57a [Ilya Ganelin] Removed Foo
04cbf0c [Ilya Ganelin] Added comments for single object
0484d7a [Ilya Ganelin] Restored second method
6aeb740 [Ilya Ganelin] Style
689e54d [Ilya Ganelin] Style
f497e9e [Ilya Ganelin] Got rid of old code
e3c7a88 [Ilya Ganelin] Fixed doctest failure
a62ccde [Ilya Ganelin] Style
966ac06 [Ilya Ganelin] style checks
dabb7e6 [Ilya Ganelin] Added Python tests
a3f4152 [Ilya Ganelin] added python bindings and better comments
e6e536c [Ilya Ganelin] Added extra space
7529a2e [Ilya Ganelin] Fixed formatting
d388f86 [Ilya Ganelin] Fixed small bug
c4e3bf5 [Ilya Ganelin] Reverted to using parse. Updated parse to support long
d7634b6 [Ilya Ganelin] Reverted to fromString to properly support types
22c39d5 [Ilya Ganelin] replaced FromString with DataTypeParser.parse. Replaced empty constructor initializing a null to have it instead create a new array to allow appends to it.
faca398 [Ilya Ganelin] [SPARK-8056] Replaced default argument usage. Updated usage and code for DataType.fromString
1acf76e [Ilya Ganelin] Scala style
e31c674 [Ilya Ganelin] Fixed bug in test
8dc0795 [Ilya Ganelin] Added tests for creation of StructType object with new methods
fdf7e9f [Ilya Ganelin] [SPARK-8056] Created add methods to facilitate building new StructType objects.
---
 python/pyspark/sql/tests.py                   |  29 +++++
 python/pyspark/sql/types.py                   |  52 ++++++++-
 .../spark/sql/types/DataTypeParser.scala      |   2 +-
 .../apache/spark/sql/types/StructType.scala   | 104 +++++++++++++++++-
 .../spark/sql/types/DataTypeSuite.scala       |  31 ++++++
 5 files changed, 212 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index ffee43a94baba..34f397d0ffef0 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -516,6 +516,35 @@ def test_between_function(self):
         self.assertEqual([Row(a=2, b=1, c=3), Row(a=4, b=1, c=4)],
                          df.filter(df.a.between(df.b, df.c)).collect())
 
+    def test_struct_type(self):
+        from pyspark.sql.types import StructType, StringType, StructField
+        struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None)
+        struct2 = StructType([StructField("f1", StringType(), True),
+                              StructField("f2", StringType(), True, None)])
+        self.assertEqual(struct1, struct2)
+
+        struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None)
+        struct2 = StructType([StructField("f1", StringType(), True)])
+        self.assertNotEqual(struct1, struct2)
+
+        struct1 = (StructType().add(StructField("f1", StringType(), True))
+                   .add(StructField("f2", StringType(), True, None)))
+        struct2 = StructType([StructField("f1", StringType(), True),
+                              StructField("f2", StringType(), True, None)])
+        self.assertEqual(struct1, struct2)
+
+        struct1 = (StructType().add(StructField("f1", StringType(), True))
+                   .add(StructField("f2", StringType(), True, None)))
+        struct2 = StructType([StructField("f1", StringType(), True)])
+        self.assertNotEqual(struct1, struct2)
+
+        # Catch exception raised during improper construction
+        try:
+            struct1 = StructType().add("name")
+            self.assertEqual(1, 0)
+        except ValueError:
+            self.assertEqual(1, 1)
+
     def test_save_and_load(self):
         df = self.df
         tmpPath = tempfile.mkdtemp()
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 932686e5e4b01..ae9344e6106a4 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -355,8 +355,7 @@ class StructType(DataType):
 
     This is the data type representing a :class:`Row`.
     """
-
-    def __init__(self, fields):
+    def __init__(self, fields=None):
         """
         >>> struct1 = StructType([StructField("f1", StringType(), True)])
         >>> struct2 = StructType([StructField("f1", StringType(), True)])
@@ -368,8 +367,53 @@ def __init__(self, fields):
         >>> struct1 == struct2
         False
         """
-        assert all(isinstance(f, DataType) for f in fields), "fields should be a list of DataType"
-        self.fields = fields
+        if not fields:
+            self.fields = []
+        else:
+            self.fields = fields
+            assert all(isinstance(f, StructField) for f in fields),\
+                "fields should be a list of StructField"
+
+    def add(self, field, data_type=None, nullable=True, metadata=None):
+        """
+        Construct a StructType by adding new elements to it to define the schema. The method accepts
+        either:
+            a) A single parameter which is a StructField object.
+            b) Between 2 and 4 parameters as (name, data_type, nullable (optional),
+             metadata(optional). The data_type parameter may be either a String or a DataType object
+
+        >>> struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None)
+        >>> struct2 = StructType([StructField("f1", StringType(), True),\
+         StructField("f2", StringType(), True, None)])
+        >>> struct1 == struct2
+        True
+        >>> struct1 = StructType().add(StructField("f1", StringType(), True))
+        >>> struct2 = StructType([StructField("f1", StringType(), True)])
+        >>> struct1 == struct2
+        True
+        >>> struct1 = StructType().add("f1", "string", True)
+        >>> struct2 = StructType([StructField("f1", StringType(), True)])
+        >>> struct1 == struct2
+        True
+
+        :param field: Either the name of the field or a StructField object
+        :param data_type: If present, the DataType of the StructField to create
+        :param nullable: Whether the field to add should be nullable (default True)
+        :param metadata: Any additional metadata (default None)
+        :return: a new updated StructType
+        """
+        if isinstance(field, StructField):
+            self.fields.append(field)
+        else:
+            if isinstance(field, str) and data_type is None:
+                raise ValueError("Must specify DataType if passing name of struct_field to create.")
+
+            if isinstance(data_type, str):
+                data_type_f = _parse_datatype_json_value(data_type)
+            else:
+                data_type_f = data_type
+            self.fields.append(StructField(field, data_type_f, nullable, metadata))
+        return self
 
     def simpleString(self):
         return 'struct<%s>' % (','.join(f.simpleString() for f in self.fields))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala
index 04f3379afb38d..6b43224feb1f2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala
@@ -44,7 +44,7 @@ private[sql] trait DataTypeParser extends StandardTokenParsers {
     "(?i)tinyint".r ^^^ ByteType |
     "(?i)smallint".r ^^^ ShortType |
     "(?i)double".r ^^^ DoubleType |
-    "(?i)bigint".r ^^^ LongType |
+    "(?i)(?:bigint|long)".r ^^^ LongType |
     "(?i)binary".r ^^^ BinaryType |
     "(?i)boolean".r ^^^ BooleanType |
     fixedDecimalType |
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 193c08a4d0df7..2db0a359e9db5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -94,7 +94,7 @@ import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute}
 case class StructType(fields: Array[StructField]) extends DataType with Seq[StructField] {
 
   /** No-arg constructor for kryo. */
-  protected def this() = this(null)
+  def this() = this(Array.empty[StructField])
 
   /** Returns all field names in an array. */
   def fieldNames: Array[String] = fields.map(_.name)
@@ -103,6 +103,108 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
   private lazy val nameToField: Map[String, StructField] = fields.map(f => f.name -> f).toMap
   private lazy val nameToIndex: Map[String, Int] = fieldNames.zipWithIndex.toMap
 
+  /**
+   * Creates a new [[StructType]] by adding a new field.
+   * {{{
+   * val struct = (new StructType)
+   *   .add(StructField("a", IntegerType, true))
+   *   .add(StructField("b", LongType, false))
+   *   .add(StructField("c", StringType, true))
+   *}}}
+   */
+  def add(field: StructField): StructType = {
+    StructType(fields :+ field)
+  }
+
+  /**
+   * Creates a new [[StructType]] by adding a new nullable field with no metadata.
+   *
+   * val struct = (new StructType)
+   *   .add("a", IntegerType)
+   *   .add("b", LongType)
+   *   .add("c", StringType)
+   */
+  def add(name: String, dataType: DataType): StructType = {
+    StructType(fields :+ new StructField(name, dataType, nullable = true, Metadata.empty))
+  }
+
+  /**
+   * Creates a new [[StructType]] by adding a new field with no metadata.
+   *
+   * val struct = (new StructType)
+   *   .add("a", IntegerType, true)
+   *   .add("b", LongType, false)
+   *   .add("c", StringType, true)
+   */
+  def add(name: String, dataType: DataType, nullable: Boolean): StructType = {
+    StructType(fields :+ new StructField(name, dataType, nullable, Metadata.empty))
+  }
+
+  /**
+   * Creates a new [[StructType]] by adding a new field and specifying metadata.
+   * {{{
+   * val struct = (new StructType)
+   *   .add("a", IntegerType, true, Metadata.empty)
+   *   .add("b", LongType, false, Metadata.empty)
+   *   .add("c", StringType, true, Metadata.empty)
+   * }}}
+   */
+  def add(
+      name: String,
+      dataType: DataType,
+      nullable: Boolean,
+      metadata: Metadata): StructType = {
+    StructType(fields :+ new StructField(name, dataType, nullable, metadata))
+  }
+
+  /**
+   * Creates a new [[StructType]] by adding a new nullable field with no metadata where the
+   * dataType is specified as a String.
+   *
+   * {{{
+   * val struct = (new StructType)
+   *   .add("a", "int")
+   *   .add("b", "long")
+   *   .add("c", "string")
+   * }}}
+   */
+  def add(name: String, dataType: String): StructType = {
+    add(name, DataTypeParser.parse(dataType), nullable = true, Metadata.empty)
+  }
+
+  /**
+   * Creates a new [[StructType]] by adding a new field with no metadata where the
+   * dataType is specified as a String.
+   *
+   * {{{
+   * val struct = (new StructType)
+   *   .add("a", "int", true)
+   *   .add("b", "long", false)
+   *   .add("c", "string", true)
+   * }}}
+   */
+  def add(name: String, dataType: String, nullable: Boolean): StructType = {
+    add(name, DataTypeParser.parse(dataType), nullable, Metadata.empty)
+  }
+
+  /**
+   * Creates a new [[StructType]] by adding a new field and specifying metadata where the
+   * dataType is specified as a String.
+   * {{{
+   * val struct = (new StructType)
+   *   .add("a", "int", true, Metadata.empty)
+   *   .add("b", "long", false, Metadata.empty)
+   *   .add("c", "string", true, Metadata.empty)
+   * }}}
+   */
+  def add(
+      name: String,
+      dataType: String,
+      nullable: Boolean,
+      metadata: Metadata): StructType = {
+    add(name, DataTypeParser.parse(dataType), nullable, metadata)
+  }
+
   /**
    * Extracts a [[StructField]] of the given name. If the [[StructType]] object does not
    * have a name matching the given name, `null` will be returned.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
index 077c0ad70ac4f..14e7b4a9561b6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
@@ -33,6 +33,37 @@ class DataTypeSuite extends SparkFunSuite {
     assert(MapType(StringType, IntegerType, true) === map)
   }
 
+  test("construct with add") {
+    val struct = (new StructType)
+      .add("a", IntegerType, true)
+      .add("b", LongType, false)
+      .add("c", StringType, true)
+
+    assert(StructField("b", LongType, false) === struct("b"))
+  }
+
+  test("construct with add from StructField") {
+    // Test creation from StructField type
+    val struct = (new StructType)
+      .add(StructField("a", IntegerType, true))
+      .add(StructField("b", LongType, false))
+      .add(StructField("c", StringType, true))
+
+    assert(StructField("b", LongType, false) === struct("b"))
+  }
+
+  test("construct with String DataType") {
+    // Test creation with DataType as String
+    val struct = (new StructType)
+      .add("a", "int", true)
+      .add("b", "long", false)
+      .add("c", "string", true)
+
+    assert(StructField("a", IntegerType, true) === struct("a"))
+    assert(StructField("b", LongType, false) === struct("b"))
+    assert(StructField("c", StringType, true) === struct("c"))
+  }
+
   test("extract fields from a StructType") {
     val struct = StructType(
       StructField("a", IntegerType, true) ::

From ecd3aacf2805bb231cfb44bab079319cfe73c3f1 Mon Sep 17 00:00:00 2001
From: Ai He <ai.he@ussuning.com>
Date: Mon, 29 Jun 2015 14:36:26 -0700
Subject: [PATCH 0103/1454] [SPARK-7810] [PYSPARK] solve python rdd socket
 connection problem

Method "_load_from_socket" in rdd.py cannot load data from jvm socket when ipv6 is used. The current method only works well with ipv4. New modification should work around both two protocols.

Author: Ai He <ai.he@ussuning.com>
Author: AiHe <ai.he@ussuning.com>

Closes #6338 from AiHe/pyspark-networking-issue and squashes the following commits:

d4fc9c4 [Ai He] handle code review 2
e75c5c8 [Ai He] handle code review
5644953 [AiHe] solve python rdd socket connection problem to jvm
---
 python/pyspark/rdd.py | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 1b64be23a667e..cb20bc8b54027 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -121,10 +121,22 @@ def _parse_memory(s):
 
 
 def _load_from_socket(port, serializer):
-    sock = socket.socket()
-    sock.settimeout(3)
+    sock = None
+    # Support for both IPv4 and IPv6.
+    # On most of IPv6-ready systems, IPv6 will take precedence.
+    for res in socket.getaddrinfo("localhost", port, socket.AF_UNSPEC, socket.SOCK_STREAM):
+        af, socktype, proto, canonname, sa = res
+        try:
+            sock = socket.socket(af, socktype, proto)
+            sock.settimeout(3)
+            sock.connect(sa)
+        except socket.error:
+            sock = None
+            continue
+        break
+    if not sock:
+        raise Exception("could not open socket")
     try:
-        sock.connect(("localhost", port))
         rf = sock.makefile("rb", 65536)
         for item in serializer.load_stream(rf):
             yield item

From c8ae887ef02b8f7e2ad06841719fb12eacf1f7f9 Mon Sep 17 00:00:00 2001
From: Rosstin <asterazul@gmail.com>
Date: Mon, 29 Jun 2015 14:45:08 -0700
Subject: [PATCH 0104/1454] [SPARK-8660][ML] Convert JavaDoc style comments
 inLogisticRegressionSuite.scala to regular multiline comments, to make
 copy-pasting R commands easier

Converted JavaDoc style comments in mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala to regular multiline comments, to make copy-pasting R commands easier.

Author: Rosstin <asterazul@gmail.com>

Closes #7096 from Rosstin/SPARK-8660 and squashes the following commits:

242aedd [Rosstin] SPARK-8660, changed comment style from JavaDoc style to normal multiline comment in order to make copypaste into R easier, in file classification/LogisticRegressionSuite.scala
2cd2985 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
21ac1e5 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
6c18058 [Rosstin] fixed minor typos in docs/README.md and docs/api.md
---
 .../LogisticRegressionSuite.scala             | 342 +++++++++---------
 1 file changed, 171 insertions(+), 171 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 5a6265ea992c6..bc6eeac1db5da 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -36,19 +36,19 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     dataset = sqlContext.createDataFrame(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42))
 
-    /**
-     * Here is the instruction describing how to export the test data into CSV format
-     * so we can validate the training accuracy compared with R's glmnet package.
-     *
-     * import org.apache.spark.mllib.classification.LogisticRegressionSuite
-     * val nPoints = 10000
-     * val weights = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191)
-     * val xMean = Array(5.843, 3.057, 3.758, 1.199)
-     * val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
-     * val data = sc.parallelize(LogisticRegressionSuite.generateMultinomialLogisticInput(
-     *   weights, xMean, xVariance, true, nPoints, 42), 1)
-     * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1) + ", "
-     *   + x.features(2) + ", " + x.features(3)).saveAsTextFile("path")
+    /*
+       Here is the instruction describing how to export the test data into CSV format
+       so we can validate the training accuracy compared with R's glmnet package.
+
+       import org.apache.spark.mllib.classification.LogisticRegressionSuite
+       val nPoints = 10000
+       val weights = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191)
+       val xMean = Array(5.843, 3.057, 3.758, 1.199)
+       val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
+       val data = sc.parallelize(LogisticRegressionSuite.generateMultinomialLogisticInput(
+         weights, xMean, xVariance, true, nPoints, 42), 1)
+       data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1) + ", "
+         + x.features(2) + ", " + x.features(3)).saveAsTextFile("path")
      */
     binaryDataset = {
       val nPoints = 10000
@@ -211,22 +211,22 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LogisticRegression).setFitIntercept(true)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                     s0
-     * (Intercept)  2.8366423
-     * data.V2     -0.5895848
-     * data.V3      0.8931147
-     * data.V4     -0.3925051
-     * data.V5     -0.7996864
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                           s0
+       (Intercept)  2.8366423
+       data.V2     -0.5895848
+       data.V3      0.8931147
+       data.V4     -0.3925051
+       data.V5     -0.7996864
      */
     val interceptR = 2.8366423
     val weightsR = Array(-0.5895848, 0.8931147, -0.3925051, -0.7996864)
@@ -242,23 +242,23 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LogisticRegression).setFitIntercept(false)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights =
-     *     coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                     s0
-     * (Intercept)   .
-     * data.V2     -0.3534996
-     * data.V3      1.2964482
-     * data.V4     -0.3571741
-     * data.V5     -0.7407946
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights =
+           coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                           s0
+       (Intercept)   .
+       data.V2     -0.3534996
+       data.V3      1.2964482
+       data.V4     -0.3571741
+       data.V5     -0.7407946
      */
     val interceptR = 0.0
     val weightsR = Array(-0.3534996, 1.2964482, -0.3571741, -0.7407946)
@@ -275,22 +275,22 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setElasticNetParam(1.0).setRegParam(0.12)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                      s0
-     * (Intercept) -0.05627428
-     * data.V2       .
-     * data.V3       .
-     * data.V4     -0.04325749
-     * data.V5     -0.02481551
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept) -0.05627428
+       data.V2       .
+       data.V3       .
+       data.V4     -0.04325749
+       data.V5     -0.02481551
      */
     val interceptR = -0.05627428
     val weightsR = Array(0.0, 0.0, -0.04325749, -0.02481551)
@@ -307,23 +307,23 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setElasticNetParam(1.0).setRegParam(0.12)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
-     *     intercept=FALSE))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                      s0
-     * (Intercept)   .
-     * data.V2       .
-     * data.V3       .
-     * data.V4     -0.05189203
-     * data.V5     -0.03891782
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
+           intercept=FALSE))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)   .
+       data.V2       .
+       data.V3       .
+       data.V4     -0.05189203
+       data.V5     -0.03891782
      */
     val interceptR = 0.0
     val weightsR = Array(0.0, 0.0, -0.05189203, -0.03891782)
@@ -340,22 +340,22 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setElasticNetParam(0.0).setRegParam(1.37)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                      s0
-     * (Intercept)  0.15021751
-     * data.V2     -0.07251837
-     * data.V3      0.10724191
-     * data.V4     -0.04865309
-     * data.V5     -0.10062872
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)  0.15021751
+       data.V2     -0.07251837
+       data.V3      0.10724191
+       data.V4     -0.04865309
+       data.V5     -0.10062872
      */
     val interceptR = 0.15021751
     val weightsR = Array(-0.07251837, 0.10724191, -0.04865309, -0.10062872)
@@ -372,23 +372,23 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setElasticNetParam(0.0).setRegParam(1.37)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
-     *     intercept=FALSE))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                      s0
-     * (Intercept)   .
-     * data.V2     -0.06099165
-     * data.V3      0.12857058
-     * data.V4     -0.04708770
-     * data.V5     -0.09799775
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
+           intercept=FALSE))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)   .
+       data.V2     -0.06099165
+       data.V3      0.12857058
+       data.V4     -0.04708770
+       data.V5     -0.09799775
      */
     val interceptR = 0.0
     val weightsR = Array(-0.06099165, 0.12857058, -0.04708770, -0.09799775)
@@ -405,22 +405,22 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setElasticNetParam(0.38).setRegParam(0.21)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                      s0
-     * (Intercept)  0.57734851
-     * data.V2     -0.05310287
-     * data.V3       .
-     * data.V4     -0.08849250
-     * data.V5     -0.15458796
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)  0.57734851
+       data.V2     -0.05310287
+       data.V3       .
+       data.V4     -0.08849250
+       data.V5     -0.15458796
      */
     val interceptR = 0.57734851
     val weightsR = Array(-0.05310287, 0.0, -0.08849250, -0.15458796)
@@ -437,23 +437,23 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setElasticNetParam(0.38).setRegParam(0.21)
     val model = trainer.fit(binaryDataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
-     *     intercept=FALSE))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                      s0
-     * (Intercept)   .
-     * data.V2     -0.001005743
-     * data.V3      0.072577857
-     * data.V4     -0.081203769
-     * data.V5     -0.142534158
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
+           intercept=FALSE))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)   .
+       data.V2     -0.001005743
+       data.V3      0.072577857
+       data.V4     -0.081203769
+       data.V5     -0.142534158
      */
     val interceptR = 0.0
     val weightsR = Array(-0.001005743, 0.072577857, -0.081203769, -0.142534158)
@@ -480,16 +480,16 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
             classSummarizer1.merge(classSummarizer2)
         }).histogram
 
-    /**
-     * For binary logistic regression with strong L1 regularization, all the weights will be zeros.
-     * As a result,
-     * {{{
-     * P(0) = 1 / (1 + \exp(b)), and
-     * P(1) = \exp(b) / (1 + \exp(b))
-     * }}}, hence
-     * {{{
-     * b = \log{P(1) / P(0)} = \log{count_1 / count_0}
-     * }}}
+    /*
+       For binary logistic regression with strong L1 regularization, all the weights will be zeros.
+       As a result,
+       {{{
+       P(0) = 1 / (1 + \exp(b)), and
+       P(1) = \exp(b) / (1 + \exp(b))
+       }}}, hence
+       {{{
+       b = \log{P(1) / P(0)} = \log{count_1 / count_0}
+       }}}
      */
     val interceptTheory = math.log(histogram(1).toDouble / histogram(0).toDouble)
     val weightsTheory = Array(0.0, 0.0, 0.0, 0.0)
@@ -500,22 +500,22 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(model.weights(2) ~== weightsTheory(2) absTol 1E-6)
     assert(model.weights(3) ~== weightsTheory(3) absTol 1E-6)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * > library("glmnet")
-     * > data <- read.csv("path", header=FALSE)
-     * > label = factor(data$V1)
-     * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-     * > weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
-     * > weights
-     * 5 x 1 sparse Matrix of class "dgCMatrix"
-     *                      s0
-     * (Intercept) -0.2480643
-     * data.V2      0.0000000
-     * data.V3       .
-     * data.V4       .
-     * data.V5       .
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       > library("glmnet")
+       > data <- read.csv("path", header=FALSE)
+       > label = factor(data$V1)
+       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       > weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
+       > weights
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept) -0.2480643
+       data.V2      0.0000000
+       data.V3       .
+       data.V4       .
+       data.V5       .
      */
     val interceptR = -0.248065
     val weightsR = Array(0.0, 0.0, 0.0, 0.0)

From 931da5c8ab271ff2ee04419c7e3c6b0012459694 Mon Sep 17 00:00:00 2001
From: BenFradet <benjamin.fradet@gmail.com>
Date: Mon, 29 Jun 2015 15:27:13 -0700
Subject: [PATCH 0105/1454] [SPARK-8478] [SQL] Harmonize UDF-related code to
 use uniformly UDF instead of Udf

Follow-up of #6902 for being coherent between ```Udf``` and ```UDF```

Author: BenFradet <benjamin.fradet@gmail.com>

Closes #6920 from BenFradet/SPARK-8478 and squashes the following commits:

c500f29 [BenFradet] renamed a few variables in functions to use UDF
8ab0f2d [BenFradet] renamed idUdf to idUDF in SQLQuerySuite
98696c2 [BenFradet] renamed originalUdfs in TestHive to originalUDFs
7738f74 [BenFradet] modified HiveUDFSuite to use only UDF
c52608d [BenFradet] renamed HiveUdfSuite to HiveUDFSuite
e51b9ac [BenFradet] renamed ExtractPythonUdfs to ExtractPythonUDFs
8c756f1 [BenFradet] renamed Hive UDF related code
2a1ca76 [BenFradet] renamed pythonUdfs to pythonUDFs
261e6fb [BenFradet] renamed ScalaUdf to ScalaUDF
---
 .../{ScalaUdf.scala => ScalaUDF.scala}        |   4 +-
 .../org/apache/spark/sql/SQLContext.scala     |   4 +-
 .../apache/spark/sql/UDFRegistration.scala    |  96 +++++++++---------
 .../spark/sql/UserDefinedFunction.scala       |   4 +-
 .../{pythonUdfs.scala => pythonUDFs.scala}    |   2 +-
 .../org/apache/spark/sql/functions.scala      |  34 +++----
 .../org/apache/spark/sql/SQLQuerySuite.scala  |   4 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   4 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |   2 +-
 .../hive/{hiveUdfs.scala => hiveUDFs.scala}   |  26 ++---
 .../apache/spark/sql/hive/test/TestHive.scala |   4 +-
 .../files/{testUdf => testUDF}/part-00000     | Bin
 ...{HiveUdfSuite.scala => HiveUDFSuite.scala} |  24 ++---
 13 files changed, 104 insertions(+), 104 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{ScalaUdf.scala => ScalaUDF.scala} (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/execution/{pythonUdfs.scala => pythonUDFs.scala} (99%)
 rename sql/hive/src/main/scala/org/apache/spark/sql/hive/{hiveUdfs.scala => hiveUDFs.scala} (96%)
 rename sql/hive/src/test/resources/data/files/{testUdf => testUDF}/part-00000 (100%)
 rename sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/{HiveUdfSuite.scala => HiveUDFSuite.scala} (93%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
similarity index 99%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index 55df72f102295..dbb4381d54c4f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.types.DataType
  * User-defined function.
  * @param dataType  Return type of function.
  */
-case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expression])
+case class ScalaUDF(function: AnyRef, dataType: DataType, children: Seq[Expression])
   extends Expression {
 
   override def nullable: Boolean = true
@@ -957,6 +957,6 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi
   private[this] val converter = CatalystTypeConverters.createToCatalystConverter(dataType)
   override def eval(input: InternalRow): Any = converter(f(input))
 
-  // TODO(davies): make ScalaUdf work with codegen
+  // TODO(davies): make ScalaUDF work with codegen
   override def isThreadSafe: Boolean = false
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 8ed44ee141be5..fc14a77538ef1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -146,7 +146,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
   protected[sql] lazy val analyzer: Analyzer =
     new Analyzer(catalog, functionRegistry, conf) {
       override val extendedResolutionRules =
-        ExtractPythonUdfs ::
+        ExtractPythonUDFs ::
         sources.PreInsertCastAndRename ::
         Nil
 
@@ -257,7 +257,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    *
    * The following example registers a Scala closure as UDF:
    * {{{
-   *   sqlContext.udf.register("myUdf", (arg1: Int, arg2: String) => arg2 + arg1)
+   *   sqlContext.udf.register("myUDF", (arg1: Int, arg2: String) => arg2 + arg1)
    * }}}
    *
    * The following example registers a UDF in Java:
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
index 3cc5c2441d8a5..03dc37aa73f0c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
@@ -26,7 +26,7 @@ import org.apache.spark.api.python.PythonBroadcast
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.sql.api.java._
 import org.apache.spark.sql.catalyst.ScalaReflection
-import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUdf}
+import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
 import org.apache.spark.sql.execution.PythonUDF
 import org.apache.spark.sql.types.DataType
 
@@ -95,7 +95,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
          */
         def register[$typeTags](name: String, func: Function$x[$types]): UserDefinedFunction = {
           val dataType = ScalaReflection.schemaFor[RT].dataType
-          def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+          def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
           functionRegistry.registerFunction(name, builder)
           UserDefinedFunction(func, dataType)
         }""")
@@ -114,7 +114,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
          |def register(name: String, f: UDF$i[$extTypeArgs, _], returnType: DataType) = {
          |  functionRegistry.registerFunction(
          |    name,
-         |    (e: Seq[Expression]) => ScalaUdf(f$anyCast.call($anyParams), returnType, e))
+         |    (e: Seq[Expression]) => ScalaUDF(f$anyCast.call($anyParams), returnType, e))
          |}""".stripMargin)
     }
     */
@@ -126,7 +126,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag](name: String, func: Function0[RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -138,7 +138,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag](name: String, func: Function1[A1, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -150,7 +150,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag](name: String, func: Function2[A1, A2, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -162,7 +162,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag](name: String, func: Function3[A1, A2, A3, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -174,7 +174,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag](name: String, func: Function4[A1, A2, A3, A4, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -186,7 +186,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag](name: String, func: Function5[A1, A2, A3, A4, A5, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -198,7 +198,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag](name: String, func: Function6[A1, A2, A3, A4, A5, A6, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -210,7 +210,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag](name: String, func: Function7[A1, A2, A3, A4, A5, A6, A7, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -222,7 +222,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag](name: String, func: Function8[A1, A2, A3, A4, A5, A6, A7, A8, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -234,7 +234,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag](name: String, func: Function9[A1, A2, A3, A4, A5, A6, A7, A8, A9, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -246,7 +246,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](name: String, func: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -258,7 +258,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag](name: String, func: Function11[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -270,7 +270,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag](name: String, func: Function12[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -282,7 +282,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag](name: String, func: Function13[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -294,7 +294,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag](name: String, func: Function14[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -306,7 +306,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag](name: String, func: Function15[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -318,7 +318,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag](name: String, func: Function16[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -330,7 +330,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag](name: String, func: Function17[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -342,7 +342,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag](name: String, func: Function18[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -354,7 +354,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag](name: String, func: Function19[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -366,7 +366,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag](name: String, func: Function20[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -378,7 +378,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag](name: String, func: Function21[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -390,7 +390,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag, A22: TypeTag](name: String, func: Function22[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, A22, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUdf(func, dataType, e)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -405,7 +405,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF1[_, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF1[Any, Any]].call(_: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF1[Any, Any]].call(_: Any), returnType, e))
   }
 
   /**
@@ -415,7 +415,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF2[_, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF2[Any, Any, Any]].call(_: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF2[Any, Any, Any]].call(_: Any, _: Any), returnType, e))
   }
 
   /**
@@ -425,7 +425,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF3[_, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF3[Any, Any, Any, Any]].call(_: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF3[Any, Any, Any, Any]].call(_: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -435,7 +435,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF4[_, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF4[Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF4[Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -445,7 +445,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF5[_, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF5[Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF5[Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -455,7 +455,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF6[_, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF6[Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF6[Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -465,7 +465,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF7[_, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF7[Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF7[Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -475,7 +475,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF8[_, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF8[Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF8[Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -485,7 +485,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF9[_, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF9[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF9[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -495,7 +495,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF10[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF10[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -505,7 +505,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF11[_, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF11[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF11[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -515,7 +515,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF12[_, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF12[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF12[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -525,7 +525,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF13[_, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF13[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF13[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -535,7 +535,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF14[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF14[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF14[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -545,7 +545,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF15[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF15[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF15[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -555,7 +555,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF16[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF16[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF16[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -565,7 +565,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF17[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF17[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF17[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -575,7 +575,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF18[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF18[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF18[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -585,7 +585,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF19[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF19[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF19[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -595,7 +595,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF20[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF20[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF20[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -605,7 +605,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF21[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF21[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF21[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   /**
@@ -615,7 +615,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
   def register(name: String, f: UDF22[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType) = {
     functionRegistry.registerFunction(
       name,
-      (e: Seq[Expression]) => ScalaUdf(f.asInstanceOf[UDF22[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
+      (e: Seq[Expression]) => ScalaUDF(f.asInstanceOf[UDF22[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]].call(_: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any, _: Any), returnType, e))
   }
 
   // scalastyle:on
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
index a02e202d2eebc..831eb7eb0fae9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
@@ -23,7 +23,7 @@ import org.apache.spark.Accumulator
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.python.PythonBroadcast
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.sql.catalyst.expressions.ScalaUdf
+import org.apache.spark.sql.catalyst.expressions.ScalaUDF
 import org.apache.spark.sql.execution.PythonUDF
 import org.apache.spark.sql.types.DataType
 
@@ -44,7 +44,7 @@ import org.apache.spark.sql.types.DataType
 case class UserDefinedFunction protected[sql] (f: AnyRef, dataType: DataType) {
 
   def apply(exprs: Column*): Column = {
-    Column(ScalaUdf(f, dataType, exprs.map(_.expr)))
+    Column(ScalaUDF(f, dataType, exprs.map(_.expr)))
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 036f5d253e385..9e1cff06c7eea 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -69,7 +69,7 @@ private[spark] case class PythonUDF(
  * This has the limitation that the input to the Python UDF is not allowed include attributes from
  * multiple child operators.
  */
-private[spark] object ExtractPythonUdfs extends Rule[LogicalPlan] {
+private[spark] object ExtractPythonUDFs extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     // Skip EvaluatePython nodes.
     case plan: EvaluatePython => plan
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 5422e066afcb1..4d9a019058228 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1509,7 +1509,7 @@ object functions {
   (0 to 10).map { x =>
     val args = (1 to x).map(i => s"arg$i: Column").mkString(", ")
     val fTypes = Seq.fill(x + 1)("_").mkString(", ")
-    val argsInUdf = (1 to x).map(i => s"arg$i.expr").mkString(", ")
+    val argsInUDF = (1 to x).map(i => s"arg$i.expr").mkString(", ")
     println(s"""
     /**
      * Call a Scala function of ${x} arguments as user-defined function (UDF). This requires
@@ -1521,7 +1521,7 @@ object functions {
      */
     @deprecated("Use udf", "1.5.0")
     def callUDF(f: Function$x[$fTypes], returnType: DataType${if (args.length > 0) ", " + args else ""}): Column = {
-      ScalaUdf(f, returnType, Seq($argsInUdf))
+      ScalaUDF(f, returnType, Seq($argsInUDF))
     }""")
   }
   }
@@ -1659,7 +1659,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function0[_], returnType: DataType): Column = {
-    ScalaUdf(f, returnType, Seq())
+    ScalaUDF(f, returnType, Seq())
   }
 
   /**
@@ -1672,7 +1672,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function1[_, _], returnType: DataType, arg1: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr))
   }
 
   /**
@@ -1685,7 +1685,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function2[_, _, _], returnType: DataType, arg1: Column, arg2: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr))
   }
 
   /**
@@ -1698,7 +1698,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function3[_, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr))
   }
 
   /**
@@ -1711,7 +1711,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function4[_, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr))
   }
 
   /**
@@ -1724,7 +1724,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function5[_, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr))
   }
 
   /**
@@ -1737,7 +1737,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function6[_, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr))
   }
 
   /**
@@ -1750,7 +1750,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function7[_, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr))
   }
 
   /**
@@ -1763,7 +1763,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function8[_, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr))
   }
 
   /**
@@ -1776,7 +1776,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function9[_, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr))
   }
 
   /**
@@ -1789,7 +1789,7 @@ object functions {
    */
   @deprecated("Use udf", "1.5.0")
   def callUDF(f: Function10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column): Column = {
-    ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr))
+    ScalaUDF(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr))
   }
 
   // scalastyle:on
@@ -1802,8 +1802,8 @@ object functions {
    *
    *  val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value")
    *  val sqlContext = df.sqlContext
-   *  sqlContext.udf.register("simpleUdf", (v: Int) => v * v)
-   *  df.select($"id", callUDF("simpleUdf", $"value"))
+   *  sqlContext.udf.register("simpleUDF", (v: Int) => v * v)
+   *  df.select($"id", callUDF("simpleUDF", $"value"))
    * }}}
    *
    * @group udf_funcs
@@ -1821,8 +1821,8 @@ object functions {
    *
    *  val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value")
    *  val sqlContext = df.sqlContext
-   *  sqlContext.udf.register("simpleUdf", (v: Int) => v * v)
-   *  df.select($"id", callUdf("simpleUdf", $"value"))
+   *  sqlContext.udf.register("simpleUDF", (v: Int) => v * v)
+   *  df.select($"id", callUdf("simpleUDF", $"value"))
    * }}}
    *
    * @group udf_funcs
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 22c54e43c1d16..82dc0e9ce5132 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -140,9 +140,9 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
     val df = Seq(Tuple1(1), Tuple1(2), Tuple1(3)).toDF("index")
     // we except the id is materialized once
-    val idUdf = udf(() => UUID.randomUUID().toString)
+    val idUDF = udf(() => UUID.randomUUID().toString)
 
-    val dfWithId = df.withColumn("id", idUdf())
+    val dfWithId = df.withColumn("id", idUDF())
     // Make a new DataFrame (actually the same reference to the old one)
     val cached = dfWithId.cache()
     // Trigger the cache
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 8021f915bb821..b91242af2d155 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -42,7 +42,7 @@ import org.apache.spark.sql.SQLConf.SQLConfEntry._
 import org.apache.spark.sql.catalyst.ParserDialect
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUdfs, SetCommand}
+import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUDFs, SetCommand}
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNativeCommand}
 import org.apache.spark.sql.sources.DataSourceStrategy
@@ -381,7 +381,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         catalog.ParquetConversions ::
         catalog.CreateTables ::
         catalog.PreInsertionCasts ::
-        ExtractPythonUdfs ::
+        ExtractPythonUDFs ::
         ResolveHiveWindowFunction ::
         sources.PreInsertCastAndRename ::
         Nil
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 7c4620952ba4b..2de7a99c122fd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -1638,7 +1638,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             sys.error(s"Couldn't find function $functionName"))
         val functionClassName = functionInfo.getFunctionClass.getName
 
-        (HiveGenericUdtf(
+        (HiveGenericUDTF(
           new HiveFunctionWrapper(functionClassName),
           children.map(nodeToExpr)), attributes)
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
similarity index 96%
rename from sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
rename to sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 4986b1ea9d906..d7827d56ca8c5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -59,16 +59,16 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
       val functionClassName = functionInfo.getFunctionClass.getName
 
       if (classOf[UDF].isAssignableFrom(functionInfo.getFunctionClass)) {
-        HiveSimpleUdf(new HiveFunctionWrapper(functionClassName), children)
+        HiveSimpleUDF(new HiveFunctionWrapper(functionClassName), children)
       } else if (classOf[GenericUDF].isAssignableFrom(functionInfo.getFunctionClass)) {
-        HiveGenericUdf(new HiveFunctionWrapper(functionClassName), children)
+        HiveGenericUDF(new HiveFunctionWrapper(functionClassName), children)
       } else if (
         classOf[AbstractGenericUDAFResolver].isAssignableFrom(functionInfo.getFunctionClass)) {
-        HiveGenericUdaf(new HiveFunctionWrapper(functionClassName), children)
+        HiveGenericUDAF(new HiveFunctionWrapper(functionClassName), children)
       } else if (classOf[UDAF].isAssignableFrom(functionInfo.getFunctionClass)) {
-        HiveUdaf(new HiveFunctionWrapper(functionClassName), children)
+        HiveUDAF(new HiveFunctionWrapper(functionClassName), children)
       } else if (classOf[GenericUDTF].isAssignableFrom(functionInfo.getFunctionClass)) {
-        HiveGenericUdtf(new HiveFunctionWrapper(functionClassName), children)
+        HiveGenericUDTF(new HiveFunctionWrapper(functionClassName), children)
       } else {
         sys.error(s"No handler for udf ${functionInfo.getFunctionClass}")
       }
@@ -79,7 +79,7 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
     throw new UnsupportedOperationException
 }
 
-private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
+private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
   extends Expression with HiveInspectors with Logging {
 
   type UDFType = UDF
@@ -146,7 +146,7 @@ private[hive] class DeferredObjectAdapter(oi: ObjectInspector)
   override def get(): AnyRef = wrap(func(), oi)
 }
 
-private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
+private[hive] case class HiveGenericUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
   extends Expression with HiveInspectors with Logging {
   type UDFType = GenericUDF
 
@@ -413,7 +413,7 @@ private[hive] case class HiveWindowFunction(
     new HiveWindowFunction(funcWrapper, pivotResult, isUDAFBridgeRequired, children)
 }
 
-private[hive] case class HiveGenericUdaf(
+private[hive] case class HiveGenericUDAF(
     funcWrapper: HiveFunctionWrapper,
     children: Seq[Expression]) extends AggregateExpression
   with HiveInspectors {
@@ -441,11 +441,11 @@ private[hive] case class HiveGenericUdaf(
     s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})"
   }
 
-  def newInstance(): HiveUdafFunction = new HiveUdafFunction(funcWrapper, children, this)
+  def newInstance(): HiveUDAFFunction = new HiveUDAFFunction(funcWrapper, children, this)
 }
 
 /** It is used as a wrapper for the hive functions which uses UDAF interface */
-private[hive] case class HiveUdaf(
+private[hive] case class HiveUDAF(
     funcWrapper: HiveFunctionWrapper,
     children: Seq[Expression]) extends AggregateExpression
   with HiveInspectors {
@@ -474,7 +474,7 @@ private[hive] case class HiveUdaf(
     s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})"
   }
 
-  def newInstance(): HiveUdafFunction = new HiveUdafFunction(funcWrapper, children, this, true)
+  def newInstance(): HiveUDAFFunction = new HiveUDAFFunction(funcWrapper, children, this, true)
 }
 
 /**
@@ -488,7 +488,7 @@ private[hive] case class HiveUdaf(
  * Operators that require maintaining state in between input rows should instead be implemented as
  * user defined aggregations, which have clean semantics even in a partitioned execution.
  */
-private[hive] case class HiveGenericUdtf(
+private[hive] case class HiveGenericUDTF(
     funcWrapper: HiveFunctionWrapper,
     children: Seq[Expression])
   extends Generator with HiveInspectors {
@@ -553,7 +553,7 @@ private[hive] case class HiveGenericUdtf(
   }
 }
 
-private[hive] case class HiveUdafFunction(
+private[hive] case class HiveUDAFFunction(
     funcWrapper: HiveFunctionWrapper,
     exprs: Seq[Expression],
     base: AggregateExpression,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index ea325cc93cb85..7978fdacaedba 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -391,7 +391,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
    * Records the UDFs present when the server starts, so we can delete ones that are created by
    * tests.
    */
-  protected val originalUdfs: JavaSet[String] = FunctionRegistry.getFunctionNames
+  protected val originalUDFs: JavaSet[String] = FunctionRegistry.getFunctionNames
 
   /**
    * Resets the test instance by deleting any tables that have been created.
@@ -410,7 +410,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       catalog.client.reset()
       catalog.unregisterAllTables()
 
-      FunctionRegistry.getFunctionNames.filterNot(originalUdfs.contains(_)).foreach { udfName =>
+      FunctionRegistry.getFunctionNames.filterNot(originalUDFs.contains(_)).foreach { udfName =>
         FunctionRegistry.unregisterTemporaryUDF(udfName)
       }
 
diff --git a/sql/hive/src/test/resources/data/files/testUdf/part-00000 b/sql/hive/src/test/resources/data/files/testUDF/part-00000
similarity index 100%
rename from sql/hive/src/test/resources/data/files/testUdf/part-00000
rename to sql/hive/src/test/resources/data/files/testUDF/part-00000
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
similarity index 93%
rename from sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
rename to sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index ce5985888f540..56b0bef1d0571 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -46,7 +46,7 @@ case class ListStringCaseClass(l: Seq[String])
 /**
  * A test suite for Hive custom UDFs.
  */
-class HiveUdfSuite extends QueryTest {
+class HiveUDFSuite extends QueryTest {
 
   import TestHive.{udf, sql}
   import TestHive.implicits._
@@ -73,7 +73,7 @@ class HiveUdfSuite extends QueryTest {
   test("hive struct udf") {
     sql(
       """
-      |CREATE EXTERNAL TABLE hiveUdfTestTable (
+      |CREATE EXTERNAL TABLE hiveUDFTestTable (
       |   pair STRUCT<id: INT, value: INT>
       |)
       |PARTITIONED BY (partition STRING)
@@ -82,15 +82,15 @@ class HiveUdfSuite extends QueryTest {
     """.
         stripMargin.format(classOf[PairSerDe].getName))
 
-    val location = Utils.getSparkClassLoader.getResource("data/files/testUdf").getFile
+    val location = Utils.getSparkClassLoader.getResource("data/files/testUDF").getFile
     sql(s"""
-      ALTER TABLE hiveUdfTestTable
-      ADD IF NOT EXISTS PARTITION(partition='testUdf')
+      ALTER TABLE hiveUDFTestTable
+      ADD IF NOT EXISTS PARTITION(partition='testUDF')
       LOCATION '$location'""")
 
-    sql(s"CREATE TEMPORARY FUNCTION testUdf AS '${classOf[PairUdf].getName}'")
-    sql("SELECT testUdf(pair) FROM hiveUdfTestTable")
-    sql("DROP TEMPORARY FUNCTION IF EXISTS testUdf")
+    sql(s"CREATE TEMPORARY FUNCTION testUDF AS '${classOf[PairUDF].getName}'")
+    sql("SELECT testUDF(pair) FROM hiveUDFTestTable")
+    sql("DROP TEMPORARY FUNCTION IF EXISTS testUDF")
   }
 
   test("SPARK-6409 UDAFAverage test") {
@@ -169,11 +169,11 @@ class HiveUdfSuite extends QueryTest {
       StringCaseClass("world") :: StringCaseClass("goodbye") :: Nil).toDF()
     testData.registerTempTable("stringTable")
 
-    sql(s"CREATE TEMPORARY FUNCTION testStringStringUdf AS '${classOf[UDFStringString].getName}'")
+    sql(s"CREATE TEMPORARY FUNCTION testStringStringUDF AS '${classOf[UDFStringString].getName}'")
     checkAnswer(
-      sql("SELECT testStringStringUdf(\"hello\", s) FROM stringTable"),
+      sql("SELECT testStringStringUDF(\"hello\", s) FROM stringTable"),
       Seq(Row("hello world"), Row("hello goodbye")))
-    sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUdf")
+    sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUDF")
 
     TestHive.reset()
   }
@@ -244,7 +244,7 @@ class PairSerDe extends AbstractSerDe {
   }
 }
 
-class PairUdf extends GenericUDF {
+class PairUDF extends GenericUDF {
   override def initialize(p1: Array[ObjectInspector]): ObjectInspector =
     ObjectInspectorFactory.getStandardStructObjectInspector(
       Seq("id", "value"),

From ed359de595d5dd67b666660eddf092eaf89041c8 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 29 Jun 2015 15:59:20 -0700
Subject: [PATCH 0106/1454] [SPARK-8579] [SQL] support arbitrary object in
 UnsafeRow

This PR brings arbitrary object support in UnsafeRow (both in grouping key and aggregation buffer).

Two object pools will be created to hold those non-primitive objects, and put the index of them into UnsafeRow. In order to compare the grouping key as bytes, the objects in key will be stored in a unique object pool, to make sure same objects will have same index (used as hashCode).

For StringType and BinaryType, we still put them as var-length in UnsafeRow when initializing for better performance. But for update, they will be an object inside object pools (there will be some garbages left in the buffer).

BTW: Will create a JIRA once issue.apache.org is available.

cc JoshRosen rxin

Author: Davies Liu <davies@databricks.com>

Closes #6959 from davies/unsafe_obj and squashes the following commits:

5ce39da [Davies Liu] fix comment
5e797bf [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_obj
5803d64 [Davies Liu] fix conflict
461d304 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_obj
2f41c90 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_obj
b04d69c [Davies Liu] address comments
4859b80 [Davies Liu] fix comments
f38011c [Davies Liu] add a test for grouping by decimal
d2cf7ab [Davies Liu] add more tests for null checking
71983c5 [Davies Liu] add test for timestamp
e8a1649 [Davies Liu] reuse buffer for string
39f09ca [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_obj
035501e [Davies Liu] fix style
236d6de [Davies Liu] support arbitrary object in UnsafeRow
---
 .../UnsafeFixedWidthAggregationMap.java       | 144 ++++++------
 .../sql/catalyst/expressions/UnsafeRow.java   | 218 +++++++++---------
 .../spark/sql/catalyst/util/ObjectPool.java   |  78 +++++++
 .../sql/catalyst/util/UniqueObjectPool.java   |  59 +++++
 .../spark/sql/catalyst/InternalRow.scala      |   5 +-
 .../expressions/UnsafeRowConverter.scala      |  94 +++-----
 .../UnsafeFixedWidthAggregationMapSuite.scala |  65 ++++--
 .../expressions/UnsafeRowConverterSuite.scala | 190 +++++++++++----
 .../sql/catalyst/util/ObjectPoolSuite.scala   |  57 +++++
 .../sql/execution/GeneratedAggregate.scala    |  16 +-
 10 files changed, 615 insertions(+), 311 deletions(-)
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
index 83f2a312972fb..1e79f4b2e88e5 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
@@ -19,9 +19,11 @@
 
 import java.util.Iterator;
 
+import scala.Function1;
+
 import org.apache.spark.sql.catalyst.InternalRow;
-import org.apache.spark.sql.types.StructField;
-import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.catalyst.util.ObjectPool;
+import org.apache.spark.sql.catalyst.util.UniqueObjectPool;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
 import org.apache.spark.unsafe.memory.MemoryLocation;
@@ -38,26 +40,48 @@ public final class UnsafeFixedWidthAggregationMap {
    * An empty aggregation buffer, encoded in UnsafeRow format. When inserting a new key into the
    * map, we copy this buffer and use it as the value.
    */
-  private final byte[] emptyAggregationBuffer;
+  private final byte[] emptyBuffer;
 
-  private final StructType aggregationBufferSchema;
+  /**
+   * An empty row used by `initProjection`
+   */
+  private static final InternalRow emptyRow = new GenericInternalRow();
 
-  private final StructType groupingKeySchema;
+  /**
+   * Whether can the empty aggregation buffer be reuse without calling `initProjection` or not.
+   */
+  private final boolean reuseEmptyBuffer;
 
   /**
-   * Encodes grouping keys as UnsafeRows.
+   * The projection used to initialize the emptyBuffer
    */
-  private final UnsafeRowConverter groupingKeyToUnsafeRowConverter;
+  private final Function1<InternalRow, InternalRow> initProjection;
+
+  /**
+   * Encodes grouping keys or buffers as UnsafeRows.
+   */
+  private final UnsafeRowConverter keyConverter;
+  private final UnsafeRowConverter bufferConverter;
 
   /**
    * A hashmap which maps from opaque bytearray keys to bytearray values.
    */
   private final BytesToBytesMap map;
 
+  /**
+   * An object pool for objects that are used in grouping keys.
+   */
+  private final UniqueObjectPool keyPool;
+
+  /**
+   * An object pool for objects that are used in aggregation buffers.
+   */
+  private final ObjectPool bufferPool;
+
   /**
    * Re-used pointer to the current aggregation buffer
    */
-  private final UnsafeRow currentAggregationBuffer = new UnsafeRow();
+  private final UnsafeRow currentBuffer = new UnsafeRow();
 
   /**
    * Scratch space that is used when encoding grouping keys into UnsafeRow format.
@@ -69,68 +93,39 @@ public final class UnsafeFixedWidthAggregationMap {
 
   private final boolean enablePerfMetrics;
 
-  /**
-   * @return true if UnsafeFixedWidthAggregationMap supports grouping keys with the given schema,
-   *         false otherwise.
-   */
-  public static boolean supportsGroupKeySchema(StructType schema) {
-    for (StructField field: schema.fields()) {
-      if (!UnsafeRow.readableFieldTypes.contains(field.dataType())) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  /**
-   * @return true if UnsafeFixedWidthAggregationMap supports aggregation buffers with the given
-   *         schema, false otherwise.
-   */
-  public static boolean supportsAggregationBufferSchema(StructType schema) {
-    for (StructField field: schema.fields()) {
-      if (!UnsafeRow.settableFieldTypes.contains(field.dataType())) {
-        return false;
-      }
-    }
-    return true;
-  }
-
   /**
    * Create a new UnsafeFixedWidthAggregationMap.
    *
-   * @param emptyAggregationBuffer the default value for new keys (a "zero" of the agg. function)
-   * @param aggregationBufferSchema the schema of the aggregation buffer, used for row conversion.
-   * @param groupingKeySchema the schema of the grouping key, used for row conversion.
+   * @param initProjection the default value for new keys (a "zero" of the agg. function)
+   * @param keyConverter the converter of the grouping key, used for row conversion.
+   * @param bufferConverter the converter of the aggregation buffer, used for row conversion.
    * @param memoryManager the memory manager used to allocate our Unsafe memory structures.
    * @param initialCapacity the initial capacity of the map (a sizing hint to avoid re-hashing).
    * @param enablePerfMetrics if true, performance metrics will be recorded (has minor perf impact)
    */
   public UnsafeFixedWidthAggregationMap(
-      InternalRow emptyAggregationBuffer,
-      StructType aggregationBufferSchema,
-      StructType groupingKeySchema,
+      Function1<InternalRow, InternalRow> initProjection,
+      UnsafeRowConverter keyConverter,
+      UnsafeRowConverter bufferConverter,
       TaskMemoryManager memoryManager,
       int initialCapacity,
       boolean enablePerfMetrics) {
-    this.emptyAggregationBuffer =
-      convertToUnsafeRow(emptyAggregationBuffer, aggregationBufferSchema);
-    this.aggregationBufferSchema = aggregationBufferSchema;
-    this.groupingKeyToUnsafeRowConverter = new UnsafeRowConverter(groupingKeySchema);
-    this.groupingKeySchema = groupingKeySchema;
-    this.map = new BytesToBytesMap(memoryManager, initialCapacity, enablePerfMetrics);
+    this.initProjection = initProjection;
+    this.keyConverter = keyConverter;
+    this.bufferConverter = bufferConverter;
     this.enablePerfMetrics = enablePerfMetrics;
-  }
 
-  /**
-   * Convert a Java object row into an UnsafeRow, allocating it into a new byte array.
-   */
-  private static byte[] convertToUnsafeRow(InternalRow javaRow, StructType schema) {
-    final UnsafeRowConverter converter = new UnsafeRowConverter(schema);
-    final byte[] unsafeRow = new byte[converter.getSizeRequirement(javaRow)];
-    final int writtenLength =
-      converter.writeRow(javaRow, unsafeRow, PlatformDependent.BYTE_ARRAY_OFFSET);
-    assert (writtenLength == unsafeRow.length): "Size requirement calculation was wrong!";
-    return unsafeRow;
+    this.map = new BytesToBytesMap(memoryManager, initialCapacity, enablePerfMetrics);
+    this.keyPool = new UniqueObjectPool(100);
+    this.bufferPool = new ObjectPool(initialCapacity);
+
+    InternalRow initRow = initProjection.apply(emptyRow);
+    this.emptyBuffer = new byte[bufferConverter.getSizeRequirement(initRow)];
+    int writtenLength = bufferConverter.writeRow(
+      initRow, emptyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, bufferPool);
+    assert (writtenLength == emptyBuffer.length): "Size requirement calculation was wrong!";
+    // re-use the empty buffer only when there is no object saved in pool.
+    reuseEmptyBuffer = bufferPool.size() == 0;
   }
 
   /**
@@ -138,15 +133,16 @@ private static byte[] convertToUnsafeRow(InternalRow javaRow, StructType schema)
    * return the same object.
    */
   public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
-    final int groupingKeySize = groupingKeyToUnsafeRowConverter.getSizeRequirement(groupingKey);
+    final int groupingKeySize = keyConverter.getSizeRequirement(groupingKey);
     // Make sure that the buffer is large enough to hold the key. If it's not, grow it:
     if (groupingKeySize > groupingKeyConversionScratchSpace.length) {
       groupingKeyConversionScratchSpace = new byte[groupingKeySize];
     }
-    final int actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow(
+    final int actualGroupingKeySize = keyConverter.writeRow(
       groupingKey,
       groupingKeyConversionScratchSpace,
-      PlatformDependent.BYTE_ARRAY_OFFSET);
+      PlatformDependent.BYTE_ARRAY_OFFSET,
+      keyPool);
     assert (groupingKeySize == actualGroupingKeySize) : "Size requirement calculation was wrong!";
 
     // Probe our map using the serialized key
@@ -157,25 +153,31 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     if (!loc.isDefined()) {
       // This is the first time that we've seen this grouping key, so we'll insert a copy of the
       // empty aggregation buffer into the map:
+      if (!reuseEmptyBuffer) {
+        // There is some objects referenced by emptyBuffer, so generate a new one
+        InternalRow initRow = initProjection.apply(emptyRow);
+        bufferConverter.writeRow(initRow, emptyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET,
+          bufferPool);
+      }
       loc.putNewKey(
         groupingKeyConversionScratchSpace,
         PlatformDependent.BYTE_ARRAY_OFFSET,
         groupingKeySize,
-        emptyAggregationBuffer,
+        emptyBuffer,
         PlatformDependent.BYTE_ARRAY_OFFSET,
-        emptyAggregationBuffer.length
+        emptyBuffer.length
       );
     }
 
     // Reset the pointer to point to the value that we just stored or looked up:
     final MemoryLocation address = loc.getValueAddress();
-    currentAggregationBuffer.pointTo(
+    currentBuffer.pointTo(
       address.getBaseObject(),
       address.getBaseOffset(),
-      aggregationBufferSchema.length(),
-      aggregationBufferSchema
+      bufferConverter.numFields(),
+      bufferPool
     );
-    return currentAggregationBuffer;
+    return currentBuffer;
   }
 
   /**
@@ -211,14 +213,14 @@ public MapEntry next() {
         entry.key.pointTo(
           keyAddress.getBaseObject(),
           keyAddress.getBaseOffset(),
-          groupingKeySchema.length(),
-          groupingKeySchema
+          keyConverter.numFields(),
+          keyPool
         );
         entry.value.pointTo(
           valueAddress.getBaseObject(),
           valueAddress.getBaseOffset(),
-          aggregationBufferSchema.length(),
-          aggregationBufferSchema
+          bufferConverter.numFields(),
+          bufferPool
         );
         return entry;
       }
@@ -246,6 +248,8 @@ public void printPerfMetrics() {
     System.out.println("Number of hash collisions: " + map.getNumHashCollisions());
     System.out.println("Time spent resizing (ns): " + map.getTimeSpentResizingNs());
     System.out.println("Total memory consumption (bytes): " + map.getTotalMemoryConsumption());
+    System.out.println("Number of unique objects in keys: " + keyPool.size());
+    System.out.println("Number of objects in buffers: " + bufferPool.size());
   }
 
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 11d51d90f1802..f077064a02ec0 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -17,20 +17,12 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
-import javax.annotation.Nullable;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.HashSet;
-import java.util.Set;
-
 import org.apache.spark.sql.catalyst.InternalRow;
-import org.apache.spark.sql.types.DataType;
-import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.catalyst.util.ObjectPool;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
 import org.apache.spark.unsafe.types.UTF8String;
 
-import static org.apache.spark.sql.types.DataTypes.*;
 
 /**
  * An Unsafe implementation of Row which is backed by raw memory instead of Java objects.
@@ -44,7 +36,20 @@
  * primitive types, such as long, double, or int, we store the value directly in the word. For
  * fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the
  * base address of the row) that points to the beginning of the variable-length field, and length
- * (they are combined into a long).
+ * (they are combined into a long). For other objects, they are stored in a pool, the indexes of
+ * them are hold in the the word.
+ *
+ * In order to support fast hashing and equality checks for UnsafeRows that contain objects
+ * when used as grouping key in BytesToBytesMap, we put the objects in an UniqueObjectPool to make
+ * sure all the key have the same index for same object, then we can hash/compare the objects by
+ * hash/compare the index.
+ *
+ * For non-primitive types, the word of a field could be:
+ *   UNION {
+ *     [1] [offset: 31bits] [length: 31bits]  // StringType
+ *     [0] [offset: 31bits] [length: 31bits]  // BinaryType
+ *     - [index: 63bits]                      // StringType, Binary, index to object in pool
+ *   }
  *
  * Instances of `UnsafeRow` act as pointers to row data stored in this format.
  */
@@ -53,8 +58,12 @@ public final class UnsafeRow extends MutableRow {
   private Object baseObject;
   private long baseOffset;
 
+  /** A pool to hold non-primitive objects */
+  private ObjectPool pool;
+
   Object getBaseObject() { return baseObject; }
   long getBaseOffset() { return baseOffset; }
+  ObjectPool getPool() { return pool; }
 
   /** The number of fields in this row, used for calculating the bitset width (and in assertions) */
   private int numFields;
@@ -63,15 +72,6 @@ public final class UnsafeRow extends MutableRow {
 
   /** The width of the null tracking bit set, in bytes */
   private int bitSetWidthInBytes;
-  /**
-   * This optional schema is required if you want to call generic get() and set() methods on
-   * this UnsafeRow, but is optional if callers will only use type-specific getTYPE() and setTYPE()
-   * methods. This should be removed after the planned InternalRow / Row split; right now, it's only
-   * needed by the generic get() method, which is only called internally by code that accesses
-   * UTF8String-typed columns.
-   */
-  @Nullable
-  private StructType schema;
 
   private long getFieldOffset(int ordinal) {
    return baseOffset + bitSetWidthInBytes + ordinal * 8L;
@@ -81,42 +81,7 @@ public static int calculateBitSetWidthInBytes(int numFields) {
     return ((numFields / 64) + (numFields % 64 == 0 ? 0 : 1)) * 8;
   }
 
-  /**
-   * Field types that can be updated in place in UnsafeRows (e.g. we support set() for these types)
-   */
-  public static final Set<DataType> settableFieldTypes;
-
-  /**
-   * Fields types can be read(but not set (e.g. set() will throw UnsupportedOperationException).
-   */
-  public static final Set<DataType> readableFieldTypes;
-
-  // TODO: support DecimalType
-  static {
-    settableFieldTypes = Collections.unmodifiableSet(
-      new HashSet<DataType>(
-        Arrays.asList(new DataType[] {
-          NullType,
-          BooleanType,
-          ByteType,
-          ShortType,
-          IntegerType,
-          LongType,
-          FloatType,
-          DoubleType,
-          DateType,
-          TimestampType
-    })));
-
-    // We support get() on a superset of the types for which we support set():
-    final Set<DataType> _readableFieldTypes = new HashSet<DataType>(
-      Arrays.asList(new DataType[]{
-        StringType,
-        BinaryType
-      }));
-    _readableFieldTypes.addAll(settableFieldTypes);
-    readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes);
-  }
+  public static final long OFFSET_BITS = 31L;
 
   /**
    * Construct a new UnsafeRow. The resulting row won't be usable until `pointTo()` has been called,
@@ -130,22 +95,15 @@ public UnsafeRow() { }
    * @param baseObject the base object
    * @param baseOffset the offset within the base object
    * @param numFields the number of fields in this row
-   * @param schema an optional schema; this is necessary if you want to call generic get() or set()
-   *               methods on this row, but is optional if the caller will only use type-specific
-   *               getTYPE() and setTYPE() methods.
+   * @param pool the object pool to hold arbitrary objects
    */
-  public void pointTo(
-      Object baseObject,
-      long baseOffset,
-      int numFields,
-      @Nullable StructType schema) {
+  public void pointTo(Object baseObject, long baseOffset, int numFields, ObjectPool pool) {
     assert numFields >= 0 : "numFields should >= 0";
-    assert schema == null || schema.fields().length == numFields;
     this.bitSetWidthInBytes = calculateBitSetWidthInBytes(numFields);
     this.baseObject = baseObject;
     this.baseOffset = baseOffset;
     this.numFields = numFields;
-    this.schema = schema;
+    this.pool = pool;
   }
 
   private void assertIndexIsValid(int index) {
@@ -168,9 +126,68 @@ private void setNotNullAt(int i) {
     BitSetMethods.unset(baseObject, baseOffset, i);
   }
 
+  /**
+   * Updates the column `i` as Object `value`, which cannot be primitive types.
+   */
   @Override
-  public void update(int ordinal, Object value) {
-    throw new UnsupportedOperationException();
+  public void update(int i, Object value) {
+    if (value == null) {
+      if (!isNullAt(i)) {
+        // remove the old value from pool
+        long idx = getLong(i);
+        if (idx <= 0) {
+          // this is the index of old value in pool, remove it
+          pool.replace((int)-idx, null);
+        } else {
+          // there will be some garbage left (UTF8String or byte[])
+        }
+        setNullAt(i);
+      }
+      return;
+    }
+
+    if (isNullAt(i)) {
+      // there is not an old value, put the new value into pool
+      int idx = pool.put(value);
+      setLong(i, (long)-idx);
+    } else {
+      // there is an old value, check the type, then replace it or update it
+      long v = getLong(i);
+      if (v <= 0) {
+        // it's the index in the pool, replace old value with new one
+        int idx = (int)-v;
+        pool.replace(idx, value);
+      } else {
+        // old value is UTF8String or byte[], try to reuse the space
+        boolean isString;
+        byte[] newBytes;
+        if (value instanceof UTF8String) {
+          newBytes = ((UTF8String) value).getBytes();
+          isString = true;
+        } else {
+          newBytes = (byte[]) value;
+          isString = false;
+        }
+        int offset = (int) ((v >> OFFSET_BITS) & Integer.MAX_VALUE);
+        int oldLength = (int) (v & Integer.MAX_VALUE);
+        if (newBytes.length <= oldLength) {
+          // the new value can fit in the old buffer, re-use it
+          PlatformDependent.copyMemory(
+            newBytes,
+            PlatformDependent.BYTE_ARRAY_OFFSET,
+            baseObject,
+            baseOffset + offset,
+            newBytes.length);
+          long flag = isString ? 1L << (OFFSET_BITS * 2) : 0L;
+          setLong(i, flag | (((long) offset) << OFFSET_BITS) | (long) newBytes.length);
+        } else {
+          // Cannot fit in the buffer
+          int idx = pool.put(value);
+          setLong(i, (long) -idx);
+        }
+      }
+    }
+    setNotNullAt(i);
   }
 
   @Override
@@ -227,28 +244,38 @@ public int size() {
     return numFields;
   }
 
-  @Override
-  public StructType schema() {
-    return schema;
-  }
-
+  /**
+   * Returns the object for column `i`, which should not be primitive type.
+   */
   @Override
   public Object get(int i) {
     assertIndexIsValid(i);
-    assert (schema != null) : "Schema must be defined when calling generic get() method";
-    final DataType dataType = schema.fields()[i].dataType();
-    // UnsafeRow is only designed to be invoked by internal code, which only invokes this generic
-    // get() method when trying to access UTF8String-typed columns. If we refactor the codebase to
-    // separate the internal and external row interfaces, then internal code can fetch strings via
-    // a new getUTF8String() method and we'll be able to remove this method.
     if (isNullAt(i)) {
       return null;
-    } else if (dataType == StringType) {
-      return getUTF8String(i);
-    } else if (dataType == BinaryType) {
-      return getBinary(i);
+    }
+    long v = PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(i));
+    if (v <= 0) {
+      // It's an index to object in the pool.
+      int idx = (int)-v;
+      return pool.get(idx);
     } else {
-      throw new UnsupportedOperationException();
+      // The column could be StingType or BinaryType
+      boolean isString = (v >> (OFFSET_BITS * 2)) > 0;
+      int offset = (int) ((v >> OFFSET_BITS) & Integer.MAX_VALUE);
+      int size = (int) (v & Integer.MAX_VALUE);
+      final byte[] bytes = new byte[size];
+      PlatformDependent.copyMemory(
+        baseObject,
+        baseOffset + offset,
+        bytes,
+        PlatformDependent.BYTE_ARRAY_OFFSET,
+        size
+      );
+      if (isString) {
+        return UTF8String.fromBytes(bytes);
+      } else {
+        return bytes;
+      }
     }
   }
 
@@ -308,31 +335,6 @@ public double getDouble(int i) {
     }
   }
 
-  public UTF8String getUTF8String(int i) {
-    return UTF8String.fromBytes(getBinary(i));
-  }
-
-  public byte[] getBinary(int i) {
-    assertIndexIsValid(i);
-    final long offsetAndSize = getLong(i);
-    final int offset = (int)(offsetAndSize >> 32);
-    final int size = (int)(offsetAndSize & ((1L << 32) - 1));
-    final byte[] bytes = new byte[size];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset + offset,
-      bytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      size
-    );
-    return bytes;
-  }
-
-  @Override
-  public String getString(int i) {
-    return getUTF8String(i).toString();
-  }
-
   @Override
   public InternalRow copy() {
     throw new UnsupportedOperationException();
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java
new file mode 100644
index 0000000000000..97f89a7d0b758
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util;
+
+/**
+ * A object pool stores a collection of objects in array, then they can be referenced by the
+ * pool plus an index.
+ */
+public class ObjectPool {
+
+  /**
+   * An array to hold objects, which will grow as needed.
+   */
+  private Object[] objects;
+
+  /**
+   * How many objects in the pool.
+   */
+  private int numObj;
+
+  public ObjectPool(int capacity) {
+    objects = new Object[capacity];
+    numObj = 0;
+  }
+
+  /**
+   * Returns how many objects in the pool.
+   */
+  public int size() {
+    return numObj;
+  }
+
+  /**
+   * Returns the object at position `idx` in the array.
+   */
+  public Object get(int idx) {
+    assert (idx < numObj);
+    return objects[idx];
+  }
+
+  /**
+   * Puts an object `obj` at the end of array, returns the index of it.
+   * <p/>
+   * The array will grow as needed.
+   */
+  public int put(Object obj) {
+    if (numObj >= objects.length) {
+      Object[] tmp = new Object[objects.length * 2];
+      System.arraycopy(objects, 0, tmp, 0, objects.length);
+      objects = tmp;
+    }
+    objects[numObj++] = obj;
+    return numObj - 1;
+  }
+
+  /**
+   * Replaces the object at `idx` with new one `obj`.
+   */
+  public void replace(int idx, Object obj) {
+    assert (idx < numObj);
+    objects[idx] = obj;
+  }
+}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java
new file mode 100644
index 0000000000000..d512392dcaacc
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util;
+
+import java.util.HashMap;
+
+/**
+ * An unique object pool stores a collection of unique objects in it.
+ */
+public class UniqueObjectPool extends ObjectPool {
+
+  /**
+   * A hash map from objects to their indexes in the array.
+   */
+  private HashMap<Object, Integer> objIndex;
+
+  public UniqueObjectPool(int capacity) {
+    super(capacity);
+    objIndex = new HashMap<Object, Integer>();
+  }
+
+  /**
+   * Put an object `obj` into the pool. If there is an existing object equals to `obj`, it will
+   * return the index of the existing one.
+   */
+  @Override
+  public int put(Object obj) {
+    if (objIndex.containsKey(obj)) {
+      return objIndex.get(obj);
+    } else {
+      int idx = super.put(obj);
+      objIndex.put(obj, idx);
+      return idx;
+    }
+  }
+
+  /**
+   * The objects can not be replaced.
+   */
+  @Override
+  public void replace(int idx, Object obj) {
+    throw new UnsupportedOperationException();
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 61a29c89d8df3..57de0f26a9720 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -28,7 +28,10 @@ import org.apache.spark.unsafe.types.UTF8String
 abstract class InternalRow extends Row {
 
   // This is only use for test
-  override def getString(i: Int): String = getAs[UTF8String](i).toString
+  override def getString(i: Int): String = {
+    val str = getAs[UTF8String](i)
+    if (str != null) str.toString else null
+  }
 
   // These expensive API should not be used internally.
   final override def getDecimal(i: Int): java.math.BigDecimal =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
index b61d490429e4f..b11fc245c4af9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.util.ObjectPool
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
@@ -33,6 +34,8 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
     this(schema.fields.map(_.dataType))
   }
 
+  def numFields: Int = fieldTypes.length
+
   /** Re-used pointer to the unsafe row being written */
   private[this] val unsafeRow = new UnsafeRow()
 
@@ -68,8 +71,8 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
    * @param baseOffset the base offset of the destination address
    * @return the number of bytes written. This should be equal to `getSizeRequirement(row)`.
    */
-  def writeRow(row: InternalRow, baseObject: Object, baseOffset: Long): Int = {
-    unsafeRow.pointTo(baseObject, baseOffset, writers.length, null)
+  def writeRow(row: InternalRow, baseObject: Object, baseOffset: Long, pool: ObjectPool): Int = {
+    unsafeRow.pointTo(baseObject, baseOffset, writers.length, pool)
 
     if (writers.length > 0) {
       // zero-out the bitset
@@ -84,16 +87,16 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
     }
 
     var fieldNumber = 0
-    var appendCursor: Int = fixedLengthSize
+    var cursor: Int = fixedLengthSize
     while (fieldNumber < writers.length) {
       if (row.isNullAt(fieldNumber)) {
         unsafeRow.setNullAt(fieldNumber)
       } else {
-        appendCursor += writers(fieldNumber).write(row, unsafeRow, fieldNumber, appendCursor)
+        cursor += writers(fieldNumber).write(row, unsafeRow, fieldNumber, cursor)
       }
       fieldNumber += 1
     }
-    appendCursor
+    cursor
   }
 
 }
@@ -108,11 +111,11 @@ private abstract class UnsafeColumnWriter {
    * @param source the row being converted
    * @param target a pointer to the converted unsafe row
    * @param column the column to write
-   * @param appendCursor the offset from the start of the unsafe row to the end of the row;
+   * @param cursor the offset from the start of the unsafe row to the end of the row;
    *                     used for calculating where variable-length data should be written
    * @return the number of variable-length bytes written
    */
-  def write(source: InternalRow, target: UnsafeRow, column: Int, appendCursor: Int): Int
+  def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int
 
   /**
    * Return the number of bytes that are needed to write this variable-length value.
@@ -134,8 +137,7 @@ private object UnsafeColumnWriter {
       case DoubleType => DoubleUnsafeColumnWriter
       case StringType => StringUnsafeColumnWriter
       case BinaryType => BinaryUnsafeColumnWriter
-      case t =>
-        throw new UnsupportedOperationException(s"Do not know how to write columns of type $t")
+      case t => ObjectUnsafeColumnWriter
     }
   }
 }
@@ -152,6 +154,7 @@ private object FloatUnsafeColumnWriter extends FloatUnsafeColumnWriter
 private object DoubleUnsafeColumnWriter extends DoubleUnsafeColumnWriter
 private object StringUnsafeColumnWriter extends StringUnsafeColumnWriter
 private object BinaryUnsafeColumnWriter extends BinaryUnsafeColumnWriter
+private object ObjectUnsafeColumnWriter extends ObjectUnsafeColumnWriter
 
 private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter {
   // Primitives don't write to the variable-length region:
@@ -159,88 +162,56 @@ private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter {
 }
 
 private class NullUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setNullAt(column)
     0
   }
 }
 
 private class BooleanUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setBoolean(column, source.getBoolean(column))
     0
   }
 }
 
 private class ByteUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setByte(column, source.getByte(column))
     0
   }
 }
 
 private class ShortUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setShort(column, source.getShort(column))
     0
   }
 }
 
 private class IntUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setInt(column, source.getInt(column))
     0
   }
 }
 
 private class LongUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setLong(column, source.getLong(column))
     0
   }
 }
 
 private class FloatUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setFloat(column, source.getFloat(column))
     0
   }
 }
 
 private class DoubleUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setDouble(column, source.getDouble(column))
     0
   }
@@ -255,12 +226,10 @@ private abstract class BytesUnsafeColumnWriter extends UnsafeColumnWriter {
     ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
   }
 
-  override def write(
-      source: InternalRow,
-      target: UnsafeRow,
-      column: Int,
-      appendCursor: Int): Int = {
-    val offset = target.getBaseOffset + appendCursor
+  protected[this] def isString: Boolean
+
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
+    val offset = target.getBaseOffset + cursor
     val bytes = getBytes(source, column)
     val numBytes = bytes.length
     if ((numBytes & 0x07) > 0) {
@@ -274,19 +243,32 @@ private abstract class BytesUnsafeColumnWriter extends UnsafeColumnWriter {
       offset,
       numBytes
     )
-    target.setLong(column, (appendCursor.toLong << 32L) | numBytes.toLong)
+    val flag = if (isString) 1L << (UnsafeRow.OFFSET_BITS * 2) else 0
+    target.setLong(column, flag | (cursor.toLong << UnsafeRow.OFFSET_BITS) | numBytes.toLong)
     ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
   }
 }
 
 private class StringUnsafeColumnWriter private() extends BytesUnsafeColumnWriter {
+  protected[this] def isString: Boolean = true
   def getBytes(source: InternalRow, column: Int): Array[Byte] = {
     source.getAs[UTF8String](column).getBytes
   }
 }
 
 private class BinaryUnsafeColumnWriter private() extends BytesUnsafeColumnWriter {
+  protected[this] def isString: Boolean = false
   def getBytes(source: InternalRow, column: Int): Array[Byte] = {
     source.getAs[Array[Byte]](column)
   }
 }
+
+private class ObjectUnsafeColumnWriter private() extends UnsafeColumnWriter {
+  def getSize(sourceRow: InternalRow, column: Int): Int = 0
+  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
+    val obj = source.get(column)
+    val idx = target.getPool.put(obj)
+    target.setLong(column, - idx)
+    0
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
index 3095ccb77761b..6fafc2f86684c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
@@ -23,8 +23,9 @@ import scala.util.Random
 import org.scalatest.{BeforeAndAfterEach, Matchers}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, TaskMemoryManager, MemoryAllocator}
+import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.unsafe.types.UTF8String
 
 
@@ -33,10 +34,10 @@ class UnsafeFixedWidthAggregationMapSuite
   with Matchers
   with BeforeAndAfterEach {
 
-  import UnsafeFixedWidthAggregationMap._
-
   private val groupKeySchema = StructType(StructField("product", StringType) :: Nil)
   private val aggBufferSchema = StructType(StructField("salePrice", IntegerType) :: Nil)
+  private def emptyProjection: Projection =
+    GenerateProjection.generate(Seq(Literal(0)), Seq(AttributeReference("price", IntegerType)()))
   private def emptyAggregationBuffer: InternalRow = InternalRow(0)
 
   private var memoryManager: TaskMemoryManager = null
@@ -52,21 +53,11 @@ class UnsafeFixedWidthAggregationMapSuite
     }
   }
 
-  test("supported schemas") {
-    assert(!supportsAggregationBufferSchema(StructType(StructField("x", StringType) :: Nil)))
-    assert(supportsGroupKeySchema(StructType(StructField("x", StringType) :: Nil)))
-
-    assert(
-      !supportsAggregationBufferSchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil)))
-    assert(
-      !supportsGroupKeySchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil)))
-  }
-
   test("empty map") {
     val map = new UnsafeFixedWidthAggregationMap(
-      emptyAggregationBuffer,
-      aggBufferSchema,
-      groupKeySchema,
+      emptyProjection,
+      new UnsafeRowConverter(groupKeySchema),
+      new UnsafeRowConverter(aggBufferSchema),
       memoryManager,
       1024, // initial capacity
       false // disable perf metrics
@@ -77,9 +68,9 @@ class UnsafeFixedWidthAggregationMapSuite
 
   test("updating values for a single key") {
     val map = new UnsafeFixedWidthAggregationMap(
-      emptyAggregationBuffer,
-      aggBufferSchema,
-      groupKeySchema,
+      emptyProjection,
+      new UnsafeRowConverter(groupKeySchema),
+      new UnsafeRowConverter(aggBufferSchema),
       memoryManager,
       1024, // initial capacity
       false // disable perf metrics
@@ -103,9 +94,9 @@ class UnsafeFixedWidthAggregationMapSuite
 
   test("inserting large random keys") {
     val map = new UnsafeFixedWidthAggregationMap(
-      emptyAggregationBuffer,
-      aggBufferSchema,
-      groupKeySchema,
+      emptyProjection,
+      new UnsafeRowConverter(groupKeySchema),
+      new UnsafeRowConverter(aggBufferSchema),
       memoryManager,
       128, // initial capacity
       false // disable perf metrics
@@ -120,6 +111,36 @@ class UnsafeFixedWidthAggregationMapSuite
     }.toSet
     seenKeys.size should be (groupKeys.size)
     seenKeys should be (groupKeys)
+
+    map.free()
+  }
+
+  test("with decimal in the key and values") {
+    val groupKeySchema = StructType(StructField("price", DecimalType(10, 0)) :: Nil)
+    val aggBufferSchema = StructType(StructField("amount", DecimalType.Unlimited) :: Nil)
+    val emptyProjection = GenerateProjection.generate(Seq(Literal(Decimal(0))),
+      Seq(AttributeReference("price", DecimalType.Unlimited)()))
+    val map = new UnsafeFixedWidthAggregationMap(
+      emptyProjection,
+      new UnsafeRowConverter(groupKeySchema),
+      new UnsafeRowConverter(aggBufferSchema),
+      memoryManager,
+      1, // initial capacity
+      false // disable perf metrics
+    )
+
+    (0 until 100).foreach { i =>
+      val groupKey = InternalRow(Decimal(i % 10))
+      val row = map.getAggregationBuffer(groupKey)
+      row.update(0, Decimal(i))
+    }
+    val seenKeys: Set[Int] = map.iterator().asScala.map { entry =>
+      entry.key.getAs[Decimal](0).toInt
+    }.toSet
+    seenKeys.size should be (10)
+    seenKeys should be ((0 until 10).toSet)
+
+    map.free()
   }
 
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index c0675f4f4dff6..94c2f3242b122 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -23,10 +23,11 @@ import java.util.Arrays
 import org.scalatest.Matchers
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.catalyst.util.{ObjectPool, DateTimeUtils}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
+import org.apache.spark.unsafe.types.UTF8String
 
 class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
@@ -40,16 +41,21 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     row.setInt(2, 2)
 
     val sizeRequired: Int = converter.getSizeRequirement(row)
-    sizeRequired should be (8 + (3 * 8))
+    assert(sizeRequired === 8 + (3 * 8))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET)
-    numBytesWritten should be (sizeRequired)
+    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+    assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
     unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
-    unsafeRow.getLong(0) should be (0)
-    unsafeRow.getLong(1) should be (1)
-    unsafeRow.getInt(2) should be (2)
+    assert(unsafeRow.getLong(0) === 0)
+    assert(unsafeRow.getLong(1) === 1)
+    assert(unsafeRow.getInt(2) === 2)
+
+    unsafeRow.setLong(1, 3)
+    assert(unsafeRow.getLong(1) === 3)
+    unsafeRow.setInt(2, 4)
+    assert(unsafeRow.getInt(2) === 4)
   }
 
   test("basic conversion with primitive, string and binary types") {
@@ -58,22 +64,67 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
     val row = new SpecificMutableRow(fieldTypes)
     row.setLong(0, 0)
-    row.setString(1, "Hello")
+    row.update(1, UTF8String.fromString("Hello"))
     row.update(2, "World".getBytes)
 
     val sizeRequired: Int = converter.getSizeRequirement(row)
-    sizeRequired should be (8 + (8 * 3) +
+    assert(sizeRequired === 8 + (8 * 3) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET)
-    numBytesWritten should be (sizeRequired)
+    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+    assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
-    unsafeRow.getLong(0) should be (0)
-    unsafeRow.getString(1) should be ("Hello")
-    unsafeRow.getBinary(2) should be ("World".getBytes)
+    val pool = new ObjectPool(10)
+    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, pool)
+    assert(unsafeRow.getLong(0) === 0)
+    assert(unsafeRow.getString(1) === "Hello")
+    assert(unsafeRow.get(2) === "World".getBytes)
+
+    unsafeRow.update(1, UTF8String.fromString("World"))
+    assert(unsafeRow.getString(1) === "World")
+    assert(pool.size === 0)
+    unsafeRow.update(1, UTF8String.fromString("Hello World"))
+    assert(unsafeRow.getString(1) === "Hello World")
+    assert(pool.size === 1)
+
+    unsafeRow.update(2, "World".getBytes)
+    assert(unsafeRow.get(2) === "World".getBytes)
+    assert(pool.size === 1)
+    unsafeRow.update(2, "Hello World".getBytes)
+    assert(unsafeRow.get(2) === "Hello World".getBytes)
+    assert(pool.size === 2)
+  }
+
+  test("basic conversion with primitive, decimal and array") {
+    val fieldTypes: Array[DataType] = Array(LongType, DecimalType(10, 0), ArrayType(StringType))
+    val converter = new UnsafeRowConverter(fieldTypes)
+
+    val row = new SpecificMutableRow(fieldTypes)
+    row.setLong(0, 0)
+    row.update(1, Decimal(1))
+    row.update(2, Array(2))
+
+    val pool = new ObjectPool(10)
+    val sizeRequired: Int = converter.getSizeRequirement(row)
+    assert(sizeRequired === 8 + (8 * 3))
+    val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
+    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, pool)
+    assert(numBytesWritten === sizeRequired)
+    assert(pool.size === 2)
+
+    val unsafeRow = new UnsafeRow()
+    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, pool)
+    assert(unsafeRow.getLong(0) === 0)
+    assert(unsafeRow.get(1) === Decimal(1))
+    assert(unsafeRow.get(2) === Array(2))
+
+    unsafeRow.update(1, Decimal(2))
+    assert(unsafeRow.get(1) === Decimal(2))
+    unsafeRow.update(2, Array(3, 4))
+    assert(unsafeRow.get(2) === Array(3, 4))
+    assert(pool.size === 2)
   }
 
   test("basic conversion with primitive, string, date and timestamp types") {
@@ -87,21 +138,27 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     row.update(3, DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2015-05-08 08:10:25")))
 
     val sizeRequired: Int = converter.getSizeRequirement(row)
-    sizeRequired should be (8 + (8 * 4) +
+    assert(sizeRequired === 8 + (8 * 4) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET)
-    numBytesWritten should be (sizeRequired)
+    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+    assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
     unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
-    unsafeRow.getLong(0) should be (0)
-    unsafeRow.getString(1) should be ("Hello")
+    assert(unsafeRow.getLong(0) === 0)
+    assert(unsafeRow.getString(1) === "Hello")
     // Date is represented as Int in unsafeRow
-    DateTimeUtils.toJavaDate(unsafeRow.getInt(2)) should be (Date.valueOf("1970-01-01"))
+    assert(DateTimeUtils.toJavaDate(unsafeRow.getInt(2)) === Date.valueOf("1970-01-01"))
     // Timestamp is represented as Long in unsafeRow
     DateTimeUtils.toJavaTimestamp(unsafeRow.getLong(3)) should be
       (Timestamp.valueOf("2015-05-08 08:10:25"))
+
+    unsafeRow.setInt(2, DateTimeUtils.fromJavaDate(Date.valueOf("2015-06-22")))
+    assert(DateTimeUtils.toJavaDate(unsafeRow.getInt(2)) === Date.valueOf("2015-06-22"))
+    unsafeRow.setLong(3, DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2015-06-22 08:10:25")))
+    DateTimeUtils.toJavaTimestamp(unsafeRow.getLong(3)) should be
+      (Timestamp.valueOf("2015-06-22 08:10:25"))
   }
 
   test("null handling") {
@@ -113,7 +170,12 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       IntegerType,
       LongType,
       FloatType,
-      DoubleType)
+      DoubleType,
+      StringType,
+      BinaryType,
+      DecimalType.Unlimited,
+      ArrayType(IntegerType)
+    )
     val converter = new UnsafeRowConverter(fieldTypes)
 
     val rowWithAllNullColumns: InternalRow = {
@@ -127,8 +189,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val sizeRequired: Int = converter.getSizeRequirement(rowWithAllNullColumns)
     val createdFromNullBuffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten = converter.writeRow(
-      rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET)
-    numBytesWritten should be (sizeRequired)
+      rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+    assert(numBytesWritten === sizeRequired)
 
     val createdFromNull = new UnsafeRow()
     createdFromNull.pointTo(
@@ -136,13 +198,17 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     for (i <- 0 to fieldTypes.length - 1) {
       assert(createdFromNull.isNullAt(i))
     }
-    createdFromNull.getBoolean(1) should be (false)
-    createdFromNull.getByte(2) should be (0)
-    createdFromNull.getShort(3) should be (0)
-    createdFromNull.getInt(4) should be (0)
-    createdFromNull.getLong(5) should be (0)
+    assert(createdFromNull.getBoolean(1) === false)
+    assert(createdFromNull.getByte(2) === 0)
+    assert(createdFromNull.getShort(3) === 0)
+    assert(createdFromNull.getInt(4) === 0)
+    assert(createdFromNull.getLong(5) === 0)
     assert(java.lang.Float.isNaN(createdFromNull.getFloat(6)))
-    assert(java.lang.Double.isNaN(createdFromNull.getFloat(7)))
+    assert(java.lang.Double.isNaN(createdFromNull.getDouble(7)))
+    assert(createdFromNull.getString(8) === null)
+    assert(createdFromNull.get(9) === null)
+    assert(createdFromNull.get(10) === null)
+    assert(createdFromNull.get(11) === null)
 
     // If we have an UnsafeRow with columns that are initially non-null and we null out those
     // columns, then the serialized row representation should be identical to what we would get by
@@ -157,28 +223,68 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       r.setLong(5, 500)
       r.setFloat(6, 600)
       r.setDouble(7, 700)
+      r.update(8, UTF8String.fromString("hello"))
+      r.update(9, "world".getBytes)
+      r.update(10, Decimal(10))
+      r.update(11, Array(11))
       r
     }
-    val setToNullAfterCreationBuffer: Array[Long] = new Array[Long](sizeRequired / 8)
+    val pool = new ObjectPool(1)
+    val setToNullAfterCreationBuffer: Array[Long] = new Array[Long](sizeRequired / 8 + 2)
     converter.writeRow(
-      rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET)
+      rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, pool)
     val setToNullAfterCreation = new UnsafeRow()
     setToNullAfterCreation.pointTo(
-      setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
+      setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, pool)
 
-    setToNullAfterCreation.isNullAt(0) should be (rowWithNoNullColumns.isNullAt(0))
-    setToNullAfterCreation.getBoolean(1) should be (rowWithNoNullColumns.getBoolean(1))
-    setToNullAfterCreation.getByte(2) should be (rowWithNoNullColumns.getByte(2))
-    setToNullAfterCreation.getShort(3) should be (rowWithNoNullColumns.getShort(3))
-    setToNullAfterCreation.getInt(4) should be (rowWithNoNullColumns.getInt(4))
-    setToNullAfterCreation.getLong(5) should be (rowWithNoNullColumns.getLong(5))
-    setToNullAfterCreation.getFloat(6) should be (rowWithNoNullColumns.getFloat(6))
-    setToNullAfterCreation.getDouble(7) should be (rowWithNoNullColumns.getDouble(7))
+    assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
+    assert(setToNullAfterCreation.getBoolean(1) === rowWithNoNullColumns.getBoolean(1))
+    assert(setToNullAfterCreation.getByte(2) === rowWithNoNullColumns.getByte(2))
+    assert(setToNullAfterCreation.getShort(3) === rowWithNoNullColumns.getShort(3))
+    assert(setToNullAfterCreation.getInt(4) === rowWithNoNullColumns.getInt(4))
+    assert(setToNullAfterCreation.getLong(5) === rowWithNoNullColumns.getLong(5))
+    assert(setToNullAfterCreation.getFloat(6) === rowWithNoNullColumns.getFloat(6))
+    assert(setToNullAfterCreation.getDouble(7) === rowWithNoNullColumns.getDouble(7))
+    assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
+    assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
+    assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
+    assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
 
     for (i <- 0 to fieldTypes.length - 1) {
+      if (i >= 8) {
+        setToNullAfterCreation.update(i, null)
+      }
       setToNullAfterCreation.setNullAt(i)
     }
-    assert(Arrays.equals(createdFromNullBuffer, setToNullAfterCreationBuffer))
+    // There are some garbage left in the var-length area
+    assert(Arrays.equals(createdFromNullBuffer,
+      java.util.Arrays.copyOf(setToNullAfterCreationBuffer, sizeRequired / 8)))
+
+    setToNullAfterCreation.setNullAt(0)
+    setToNullAfterCreation.setBoolean(1, false)
+    setToNullAfterCreation.setByte(2, 20)
+    setToNullAfterCreation.setShort(3, 30)
+    setToNullAfterCreation.setInt(4, 400)
+    setToNullAfterCreation.setLong(5, 500)
+    setToNullAfterCreation.setFloat(6, 600)
+    setToNullAfterCreation.setDouble(7, 700)
+    setToNullAfterCreation.update(8, UTF8String.fromString("hello"))
+    setToNullAfterCreation.update(9, "world".getBytes)
+    setToNullAfterCreation.update(10, Decimal(10))
+    setToNullAfterCreation.update(11, Array(11))
+
+    assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
+    assert(setToNullAfterCreation.getBoolean(1) === rowWithNoNullColumns.getBoolean(1))
+    assert(setToNullAfterCreation.getByte(2) === rowWithNoNullColumns.getByte(2))
+    assert(setToNullAfterCreation.getShort(3) === rowWithNoNullColumns.getShort(3))
+    assert(setToNullAfterCreation.getInt(4) === rowWithNoNullColumns.getInt(4))
+    assert(setToNullAfterCreation.getLong(5) === rowWithNoNullColumns.getLong(5))
+    assert(setToNullAfterCreation.getFloat(6) === rowWithNoNullColumns.getFloat(6))
+    assert(setToNullAfterCreation.getDouble(7) === rowWithNoNullColumns.getDouble(7))
+    assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
+    assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
+    assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
+    assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
   }
 
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala
new file mode 100644
index 0000000000000..94764df4b9cdb
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util
+
+import org.scalatest.Matchers
+
+import org.apache.spark.SparkFunSuite
+
+class ObjectPoolSuite extends SparkFunSuite with Matchers {
+
+  test("pool") {
+    val pool = new ObjectPool(1)
+    assert(pool.put(1) === 0)
+    assert(pool.put("hello") === 1)
+    assert(pool.put(false) === 2)
+
+    assert(pool.get(0) === 1)
+    assert(pool.get(1) === "hello")
+    assert(pool.get(2) === false)
+    assert(pool.size() === 3)
+
+    pool.replace(1, "world")
+    assert(pool.get(1) === "world")
+    assert(pool.size() === 3)
+  }
+
+  test("unique pool") {
+    val pool = new UniqueObjectPool(1)
+    assert(pool.put(1) === 0)
+    assert(pool.put("hello") === 1)
+    assert(pool.put(1) === 0)
+    assert(pool.put("hello") === 1)
+
+    assert(pool.get(0) === 1)
+    assert(pool.get(1) === "hello")
+    assert(pool.size() === 2)
+
+    intercept[UnsupportedOperationException] {
+      pool.replace(1, "world")
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index ba2c8f53d702d..44930f82b53a0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -238,11 +238,6 @@ case class GeneratedAggregate(
       StructType(fields)
     }
 
-    val schemaSupportsUnsafe: Boolean = {
-      UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
-        UnsafeFixedWidthAggregationMap.supportsGroupKeySchema(groupKeySchema)
-    }
-
     child.execute().mapPartitions { iter =>
       // Builds a new custom class for holding the results of aggregation for a group.
       val initialValues = computeFunctions.flatMap(_.initialValues)
@@ -283,12 +278,12 @@ case class GeneratedAggregate(
 
         val resultProjection = resultProjectionBuilder()
         Iterator(resultProjection(buffer))
-      } else if (unsafeEnabled && schemaSupportsUnsafe) {
+      } else if (unsafeEnabled) {
         log.info("Using Unsafe-based aggregator")
         val aggregationMap = new UnsafeFixedWidthAggregationMap(
-          newAggregationBuffer(EmptyRow),
-          aggregationBufferSchema,
-          groupKeySchema,
+          newAggregationBuffer,
+          new UnsafeRowConverter(groupKeySchema),
+          new UnsafeRowConverter(aggregationBufferSchema),
           TaskContext.get.taskMemoryManager(),
           1024 * 16, // initial capacity
           false // disable tracking of performance metrics
@@ -323,9 +318,6 @@ case class GeneratedAggregate(
           }
         }
       } else {
-        if (unsafeEnabled) {
-          log.info("Not using Unsafe-based aggregator because it is not supported for this schema")
-        }
         val buffers = new java.util.HashMap[InternalRow, MutableRow]()
 
         var currentRow: InternalRow = null

From 4e880cf5967c0933e1d098a1d1f7db34b23ca8f8 Mon Sep 17 00:00:00 2001
From: Rosstin <asterazul@gmail.com>
Date: Mon, 29 Jun 2015 16:09:29 -0700
Subject: [PATCH 0107/1454] [SPARK-8661][ML] for LinearRegressionSuite.scala,
 changed javadoc-style comments to regular multiline comments, to make
 copy-pasting R code more simple

for mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala, changed javadoc-style comments to regular multiline comments, to make copy-pasting R code more simple

Author: Rosstin <asterazul@gmail.com>

Closes #7098 from Rosstin/SPARK-8661 and squashes the following commits:

5a05dee [Rosstin] SPARK-8661 for LinearRegressionSuite.scala, changed javadoc-style comments to regular multiline comments to make it easier to copy-paste the R code.
bb9a4b1 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8660
242aedd [Rosstin] SPARK-8660, changed comment style from JavaDoc style to normal multiline comment in order to make copypaste into R easier, in file classification/LogisticRegressionSuite.scala
2cd2985 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
21ac1e5 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
6c18058 [Rosstin] fixed minor typos in docs/README.md and docs/api.md
---
 .../ml/regression/LinearRegressionSuite.scala | 192 +++++++++---------
 1 file changed, 96 insertions(+), 96 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index ad1e9da692ee2..5f39d44f37352 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -28,26 +28,26 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   @transient var dataset: DataFrame = _
   @transient var datasetWithoutIntercept: DataFrame = _
 
-  /**
-   * In `LinearRegressionSuite`, we will make sure that the model trained by SparkML
-   * is the same as the one trained by R's glmnet package. The following instruction
-   * describes how to reproduce the data in R.
-   *
-   * import org.apache.spark.mllib.util.LinearDataGenerator
-   * val data =
-   *   sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2),
-   *     Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)
-   * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).coalesce(1)
-   *   .saveAsTextFile("path")
+  /*
+     In `LinearRegressionSuite`, we will make sure that the model trained by SparkML
+     is the same as the one trained by R's glmnet package. The following instruction
+     describes how to reproduce the data in R.
+
+     import org.apache.spark.mllib.util.LinearDataGenerator
+     val data =
+       sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2),
+         Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)
+     data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).coalesce(1)
+       .saveAsTextFile("path")
    */
   override def beforeAll(): Unit = {
     super.beforeAll()
     dataset = sqlContext.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
         6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2))
-    /**
-     * datasetWithoutIntercept is not needed for correctness testing but is useful for illustrating
-     * training model without intercept
+    /*
+       datasetWithoutIntercept is not needed for correctness testing but is useful for illustrating
+       training model without intercept
      */
     datasetWithoutIntercept = sqlContext.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
@@ -59,20 +59,20 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = new LinearRegression
     val model = trainer.fit(dataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * library("glmnet")
-     * data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
-     * features <- as.matrix(data.frame(as.numeric(data$V2), as.numeric(data$V3)))
-     * label <- as.numeric(data$V1)
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         6.300528
-     * as.numeric.data.V2. 4.701024
-     * as.numeric.data.V3. 7.198257
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
+       features <- as.matrix(data.frame(as.numeric(data$V2), as.numeric(data$V3)))
+       label <- as.numeric(data$V1)
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         6.300528
+       as.numeric.data.V2. 4.701024
+       as.numeric.data.V3. 7.198257
      */
     val interceptR = 6.298698
     val weightsR = Array(4.700706, 7.199082)
@@ -94,29 +94,29 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model = trainer.fit(dataset)
     val modelWithoutIntercept = trainer.fit(datasetWithoutIntercept)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0,
-     *   intercept = FALSE))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         .
-     * as.numeric.data.V2. 6.995908
-     * as.numeric.data.V3. 5.275131
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0,
+         intercept = FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 6.995908
+       as.numeric.data.V3. 5.275131
      */
     val weightsR = Array(6.995908, 5.275131)
 
     assert(model.intercept ~== 0 relTol 1E-3)
     assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
     assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
-    /**
-     * Then again with the data with no intercept:
-     * > weightsWithoutIntercept
-     * 3 x 1 sparse Matrix of class "dgCMatrix"
-     *                             s0
-     * (Intercept)           .
-     * as.numeric.data3.V2. 4.70011
-     * as.numeric.data3.V3. 7.19943
+    /*
+       Then again with the data with no intercept:
+       > weightsWithoutIntercept
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                   s0
+       (Intercept)           .
+       as.numeric.data3.V2. 4.70011
+       as.numeric.data3.V3. 7.19943
      */
     val weightsWithoutInterceptR = Array(4.70011, 7.19943)
 
@@ -129,14 +129,14 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         6.24300
-     * as.numeric.data.V2. 4.024821
-     * as.numeric.data.V3. 6.679841
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         6.24300
+       as.numeric.data.V2. 4.024821
+       as.numeric.data.V3. 6.679841
      */
     val interceptR = 6.24300
     val weightsR = Array(4.024821, 6.679841)
@@ -158,15 +158,15 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFitIntercept(false)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
-     *   intercept=FALSE))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)          .
-     * as.numeric.data.V2. 6.299752
-     * as.numeric.data.V3. 4.772913
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
+         intercept=FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)          .
+       as.numeric.data.V2. 6.299752
+       as.numeric.data.V3. 4.772913
      */
     val interceptR = 0.0
     val weightsR = Array(6.299752, 4.772913)
@@ -187,14 +187,14 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         6.328062
-     * as.numeric.data.V2. 3.222034
-     * as.numeric.data.V3. 4.926260
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         6.328062
+       as.numeric.data.V2. 3.222034
+       as.numeric.data.V3. 4.926260
      */
     val interceptR = 5.269376
     val weightsR = Array(3.736216, 5.712356)
@@ -216,15 +216,15 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFitIntercept(false)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
-     *   intercept = FALSE))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         .
-     * as.numeric.data.V2. 5.522875
-     * as.numeric.data.V3. 4.214502
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
+         intercept = FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 5.522875
+       as.numeric.data.V3. 4.214502
      */
     val interceptR = 0.0
     val weightsR = Array(5.522875, 4.214502)
@@ -245,14 +245,14 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6))
-     * > weights
-     * 3 x 1 sparse Matrix of class "dgCMatrix"
-     * s0
-     * (Intercept)         6.324108
-     * as.numeric.data.V2. 3.168435
-     * as.numeric.data.V3. 5.200403
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+       s0
+       (Intercept)         6.324108
+       as.numeric.data.V2. 3.168435
+       as.numeric.data.V3. 5.200403
      */
     val interceptR = 5.696056
     val weightsR = Array(3.670489, 6.001122)
@@ -274,15 +274,15 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFitIntercept(false)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
-     *   intercept=FALSE))
-     * > weights
-     * 3 x 1 sparse Matrix of class "dgCMatrix"
-     * s0
-     * (Intercept)         .
-     * as.numeric.dataM.V2. 5.673348
-     * as.numeric.dataM.V3. 4.322251
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
+         intercept=FALSE))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+       s0
+       (Intercept)         .
+       as.numeric.dataM.V2. 5.673348
+       as.numeric.dataM.V3. 4.322251
      */
     val interceptR = 0.0
     val weightsR = Array(5.673348, 4.322251)

From 4b497a724a87ef24702c2df9ec6863ee57a87c1c Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 29 Jun 2015 16:26:05 -0700
Subject: [PATCH 0108/1454] [SPARK-8710] [SQL] Change ScalaReflection.mirror
 from a val to a def.

jira: https://issues.apache.org/jira/browse/SPARK-8710

Author: Yin Huai <yhuai@databricks.com>

Closes #7094 from yhuai/SPARK-8710 and squashes the following commits:

c854baa [Yin Huai] Change ScalaReflection.mirror from a val to a def.
---
 .../org/apache/spark/sql/catalyst/ScalaReflection.scala   | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 90698cd572de4..21b1de1ab9cb1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -28,7 +28,11 @@ import org.apache.spark.sql.types._
  */
 object ScalaReflection extends ScalaReflection {
   val universe: scala.reflect.runtime.universe.type = scala.reflect.runtime.universe
-  val mirror: universe.Mirror = universe.runtimeMirror(Thread.currentThread().getContextClassLoader)
+  // Since we are creating a runtime mirror usign the class loader of current thread,
+  // we need to use def at here. So, every time we call mirror, it is using the
+  // class loader of the current thread.
+  override def mirror: universe.Mirror =
+    universe.runtimeMirror(Thread.currentThread().getContextClassLoader)
 }
 
 /**
@@ -39,7 +43,7 @@ trait ScalaReflection {
   val universe: scala.reflect.api.Universe
 
   /** The mirror used to access types in the universe */
-  val mirror: universe.Mirror
+  def mirror: universe.Mirror
 
   import universe._
 

From 881662e9c93893430756320f51cef0fc6643f681 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 29 Jun 2015 16:34:50 -0700
Subject: [PATCH 0109/1454] [SPARK-8589] [SQL] cleanup DateTimeUtils

move date time related operations into `DateTimeUtils` and rename some methods to make it more clear.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #6980 from cloud-fan/datetime and squashes the following commits:

9373a9d [Wenchen Fan] cleanup DateTimeUtil
---
 .../spark/sql/catalyst/expressions/Cast.scala | 43 ++----------
 .../sql/catalyst/util/DateTimeUtils.scala     | 70 +++++++++++++------
 .../spark/sql/hive/hiveWriterContainers.scala |  2 +-
 3 files changed, 58 insertions(+), 57 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 8d66968a2fc35..d69d490ad666a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.math.{BigDecimal => JavaBigDecimal}
 import java.sql.{Date, Timestamp}
-import java.text.{DateFormat, SimpleDateFormat}
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
@@ -122,9 +121,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   // UDFToString
   private[this] def castToString(from: DataType): Any => Any = from match {
     case BinaryType => buildCast[Array[Byte]](_, UTF8String.fromBytes)
-    case DateType => buildCast[Int](_, d => UTF8String.fromString(DateTimeUtils.toString(d)))
+    case DateType => buildCast[Int](_, d => UTF8String.fromString(DateTimeUtils.dateToString(d)))
     case TimestampType => buildCast[Long](_,
-      t => UTF8String.fromString(timestampToString(DateTimeUtils.toJavaTimestamp(t))))
+      t => UTF8String.fromString(DateTimeUtils.timestampToString(t)))
     case _ => buildCast[Any](_, o => UTF8String.fromString(o.toString))
   }
 
@@ -183,7 +182,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     case ByteType =>
       buildCast[Byte](_, b => longToTimestamp(b.toLong))
     case DateType =>
-      buildCast[Int](_, d => DateTimeUtils.toMillisSinceEpoch(d) * 10000)
+      buildCast[Int](_, d => DateTimeUtils.daysToMillis(d) * 10000)
     // TimestampWritable.decimalToTimestamp
     case DecimalType() =>
       buildCast[Decimal](_, d => decimalToTimestamp(d))
@@ -216,18 +215,6 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     ts / 10000000.0
   }
 
-  // Converts Timestamp to string according to Hive TimestampWritable convention
-  private[this] def timestampToString(ts: Timestamp): String = {
-    val timestampString = ts.toString
-    val formatted = Cast.threadLocalTimestampFormat.get.format(ts)
-
-    if (timestampString.length > 19 && timestampString.substring(19) != ".0") {
-      formatted + timestampString.substring(19)
-    } else {
-      formatted
-    }
-  }
-
   // DateConverter
   private[this] def castToDate(from: DataType): Any => Any = from match {
     case StringType =>
@@ -449,11 +436,11 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
       case (DateType, StringType) =>
         defineCodeGen(ctx, ev, c =>
           s"""${ctx.stringType}.fromString(
-                org.apache.spark.sql.catalyst.util.DateTimeUtils.toString($c))""")
-      // Special handling required for timestamps in hive test cases since the toString function
-      // does not match the expected output.
+                org.apache.spark.sql.catalyst.util.DateTimeUtils.dateToString($c))""")
       case (TimestampType, StringType) =>
-        super.genCode(ctx, ev)
+        defineCodeGen(ctx, ev, c =>
+          s"""${ctx.stringType}.fromString(
+                org.apache.spark.sql.catalyst.util.DateTimeUtils.timestampToString($c))""")
       case (_, StringType) =>
         defineCodeGen(ctx, ev, c => s"${ctx.stringType}.fromString(String.valueOf($c))")
 
@@ -477,19 +464,3 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     }
   }
 }
-
-object Cast {
-  // `SimpleDateFormat` is not thread-safe.
-  private[sql] val threadLocalTimestampFormat = new ThreadLocal[DateFormat] {
-    override def initialValue(): SimpleDateFormat = {
-      new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
-    }
-  }
-
-  // `SimpleDateFormat` is not thread-safe.
-  private[sql] val threadLocalDateFormat = new ThreadLocal[DateFormat] {
-    override def initialValue(): SimpleDateFormat = {
-      new SimpleDateFormat("yyyy-MM-dd")
-    }
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index ff79884a44d00..640e67e2ecd76 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -18,11 +18,9 @@
 package org.apache.spark.sql.catalyst.util
 
 import java.sql.{Date, Timestamp}
-import java.text.SimpleDateFormat
+import java.text.{DateFormat, SimpleDateFormat}
 import java.util.{Calendar, TimeZone}
 
-import org.apache.spark.sql.catalyst.expressions.Cast
-
 /**
  * Helper functions for converting between internal and external date and time representations.
  * Dates are exposed externally as java.sql.Date and are represented internally as the number of
@@ -41,35 +39,53 @@ object DateTimeUtils {
 
 
   // Java TimeZone has no mention of thread safety. Use thread local instance to be safe.
-  private val LOCAL_TIMEZONE = new ThreadLocal[TimeZone] {
+  private val threadLocalLocalTimeZone = new ThreadLocal[TimeZone] {
     override protected def initialValue: TimeZone = {
       Calendar.getInstance.getTimeZone
     }
   }
 
-  private def javaDateToDays(d: Date): Int = {
-    millisToDays(d.getTime)
+  // `SimpleDateFormat` is not thread-safe.
+  private val threadLocalTimestampFormat = new ThreadLocal[DateFormat] {
+    override def initialValue(): SimpleDateFormat = {
+      new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+    }
+  }
+
+  // `SimpleDateFormat` is not thread-safe.
+  private val threadLocalDateFormat = new ThreadLocal[DateFormat] {
+    override def initialValue(): SimpleDateFormat = {
+      new SimpleDateFormat("yyyy-MM-dd")
+    }
   }
 
+
   // we should use the exact day as Int, for example, (year, month, day) -> day
   def millisToDays(millisLocal: Long): Int = {
-    ((millisLocal + LOCAL_TIMEZONE.get().getOffset(millisLocal)) / MILLIS_PER_DAY).toInt
+    ((millisLocal + threadLocalLocalTimeZone.get().getOffset(millisLocal)) / MILLIS_PER_DAY).toInt
   }
 
-  def toMillisSinceEpoch(days: Int): Long = {
+  // reverse of millisToDays
+  def daysToMillis(days: Int): Long = {
     val millisUtc = days.toLong * MILLIS_PER_DAY
-    millisUtc - LOCAL_TIMEZONE.get().getOffset(millisUtc)
+    millisUtc - threadLocalLocalTimeZone.get().getOffset(millisUtc)
   }
 
-  def fromJavaDate(date: Date): Int = {
-    javaDateToDays(date)
-  }
+  def dateToString(days: Int): String =
+    threadLocalDateFormat.get.format(toJavaDate(days))
 
-  def toJavaDate(daysSinceEpoch: Int): Date = {
-    new Date(toMillisSinceEpoch(daysSinceEpoch))
-  }
+  // Converts Timestamp to string according to Hive TimestampWritable convention.
+  def timestampToString(num100ns: Long): String = {
+    val ts = toJavaTimestamp(num100ns)
+    val timestampString = ts.toString
+    val formatted = threadLocalTimestampFormat.get.format(ts)
 
-  def toString(days: Int): String = Cast.threadLocalDateFormat.get.format(toJavaDate(days))
+    if (timestampString.length > 19 && timestampString.substring(19) != ".0") {
+      formatted + timestampString.substring(19)
+    } else {
+      formatted
+    }
+  }
 
   def stringToTime(s: String): java.util.Date = {
     if (!s.contains('T')) {
@@ -100,7 +116,21 @@ object DateTimeUtils {
   }
 
   /**
-   * Return a java.sql.Timestamp from number of 100ns since epoch
+   * Returns the number of days since epoch from from java.sql.Date.
+   */
+  def fromJavaDate(date: Date): Int = {
+    millisToDays(date.getTime)
+  }
+
+  /**
+   * Returns a java.sql.Date from number of days since epoch.
+   */
+  def toJavaDate(daysSinceEpoch: Int): Date = {
+    new Date(daysToMillis(daysSinceEpoch))
+  }
+
+  /**
+   * Returns a java.sql.Timestamp from number of 100ns since epoch.
    */
   def toJavaTimestamp(num100ns: Long): Timestamp = {
     // setNanos() will overwrite the millisecond part, so the milliseconds should be
@@ -118,7 +148,7 @@ object DateTimeUtils {
   }
 
   /**
-   * Return the number of 100ns since epoch from java.sql.Timestamp.
+   * Returns the number of 100ns since epoch from java.sql.Timestamp.
    */
   def fromJavaTimestamp(t: Timestamp): Long = {
     if (t != null) {
@@ -129,7 +159,7 @@ object DateTimeUtils {
   }
 
   /**
-   * Return the number of 100ns (hundred of nanoseconds) since epoch from Julian day
+   * Returns the number of 100ns (hundred of nanoseconds) since epoch from Julian day
    * and nanoseconds in a day
    */
   def fromJulianDay(day: Int, nanoseconds: Long): Long = {
@@ -139,7 +169,7 @@ object DateTimeUtils {
   }
 
   /**
-   * Return Julian day and nanoseconds in a day from the number of 100ns (hundred of nanoseconds)
+   * Returns Julian day and nanoseconds in a day from the number of 100ns (hundred of nanoseconds)
    */
   def toJulianDay(num100ns: Long): (Int, Long) = {
     val seconds = num100ns / HUNDRED_NANOS_PER_SECOND + SECONDS_PER_DAY / 2
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index ab75b12e2a2e7..ecc78a5f8d321 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -201,7 +201,7 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
     def convertToHiveRawString(col: String, value: Any): String = {
       val raw = String.valueOf(value)
       schema(col).dataType match {
-        case DateType => DateTimeUtils.toString(raw.toInt)
+        case DateType => DateTimeUtils.dateToString(raw.toInt)
         case _: DecimalType => BigDecimal(raw).toString()
         case _ => raw
       }

From cec98525fd2b731cb78935bf7bc6c7963411744e Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 29 Jun 2015 17:19:05 -0700
Subject: [PATCH 0110/1454] [SPARK-8634] [STREAMING] [TESTS] Fix flaky test
 StreamingListenerSuite "receiver info reporting"

As per the unit test log in https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/35754/

```
15/06/24 23:09:10.210 Thread-3495 INFO ReceiverTracker: Starting 1 receivers
15/06/24 23:09:10.270 Thread-3495 INFO SparkContext: Starting job: apply at Transformer.scala:22
...
15/06/24 23:09:14.259 ForkJoinPool-4-worker-29 INFO StreamingListenerSuiteReceiver: Started receiver and sleeping
15/06/24 23:09:14.270 ForkJoinPool-4-worker-29 INFO StreamingListenerSuiteReceiver: Reporting error and sleeping
```

it needs at least 4 seconds to receive all receiver events in this slow machine, but `timeout` for `eventually` is only 2 seconds.
This PR increases `timeout` to make this test stable.

Author: zsxwing <zsxwing@gmail.com>

Closes #7017 from zsxwing/SPARK-8634 and squashes the following commits:

719cae4 [zsxwing] Fix flaky test StreamingListenerSuite "receiver info reporting"
---
 .../org/apache/spark/streaming/StreamingListenerSuite.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
index 1dc8960d60528..7bc7727a9fbe4 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
@@ -116,7 +116,7 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers {
 
     ssc.start()
     try {
-      eventually(timeout(2000 millis), interval(20 millis)) {
+      eventually(timeout(30 seconds), interval(20 millis)) {
         collector.startedReceiverStreamIds.size should equal (1)
         collector.startedReceiverStreamIds(0) should equal (0)
         collector.stoppedReceiverStreamIds should have size 1

From fbf75738feddebb352d5cedf503b573105d4b7a7 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 29 Jun 2015 17:20:05 -0700
Subject: [PATCH 0111/1454] [SPARK-7287] [SPARK-8567] [TEST] Add sc.stop to
 applications in SparkSubmitSuite

Hopefully, this suite will not be flaky anymore.

Author: Yin Huai <yhuai@databricks.com>

Closes #7027 from yhuai/SPARK-8567 and squashes the following commits:

c0167e2 [Yin Huai] Add sc.stop().
---
 .../spark/deploy/SparkSubmitSuite.scala       |   2 ++
 .../regression-test-SPARK-8489/Main.scala     |   1 +
 .../regression-test-SPARK-8489/test.jar       | Bin 6811 -> 6828 bytes
 3 files changed, 3 insertions(+)

diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 357ed90be3f5c..2e05dec99b6bf 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -548,6 +548,7 @@ object JarCreationTest extends Logging {
     if (result.nonEmpty) {
       throw new Exception("Could not load user class from jar:\n" + result(0))
     }
+    sc.stop()
   }
 }
 
@@ -573,6 +574,7 @@ object SimpleApplicationTest {
           s"Master had $config=$masterValue but executor had $config=$executorValue")
       }
     }
+    sc.stop()
   }
 }
 
diff --git a/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala b/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala
index e1715177e3f1b..0e428ba1d7456 100644
--- a/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala
+++ b/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala
@@ -38,6 +38,7 @@ object Main {
     val df = hc.createDataFrame(Seq(MyCoolClass("1", "2", "3")))
     df.collect()
     println("Regression test for SPARK-8489 success!")
+    sc.stop()
   }
 }
 
diff --git a/sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar b/sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar
index 4f59fba9eab558131b2587e51b7c2e2d54348bd1..5944aa6076a5fe7a8188c947fd6847c046614101 100644
GIT binary patch
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From 5d30eae56051c563a8427f330b09ef66db0a0d21 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 29 Jun 2015 17:21:35 -0700
Subject: [PATCH 0112/1454] [SPARK-8437] [DOCS] Using directory path without
 wildcard for filename slow for large number of files with wholeTextFiles and
 binaryFiles

Note that 'dir/*' can be more efficient in some Hadoop FS implementations that 'dir/'

Author: Sean Owen <sowen@cloudera.com>

Closes #7036 from srowen/SPARK-8437 and squashes the following commits:

0e813ae [Sean Owen] Note that 'dir/*' can be more efficient in some Hadoop FS implementations that 'dir/'
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index b3c3bf3746e18..cb7e24c374152 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -831,6 +831,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * }}}
    *
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
+   * @note On some filesystems, `.../path/*` can be a more efficient way to read all files in a directory
+   *       rather than `.../path/` or `.../path`
    *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
@@ -878,9 +880,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    *   (a-hdfs-path/part-nnnnn, its content)
    * }}}
    *
-   * @param minPartitions A suggestion value of the minimal splitting number for input data.
-   *
    * @note Small files are preferred; very large files may cause bad performance.
+   * @note On some filesystems, `.../path/*` can be a more efficient way to read all files in a directory
+   *       rather than `.../path/` or `.../path`
+   *
+   * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
   @Experimental
   def binaryFiles(

From d7f796da45d9a7c76ee4c29a9e0661ef76d8028a Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Mon, 29 Jun 2015 17:27:02 -0700
Subject: [PATCH 0113/1454] [SPARK-8410] [SPARK-8475] remove previous ivy
 resolution when using spark-submit

This PR also includes re-ordering the order that repositories are used when resolving packages. User provided repositories will be prioritized.

cc andrewor14

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7089 from brkyvz/delete-prev-ivy-resolution and squashes the following commits:

a21f95a [Burak Yavuz] remove previous ivy resolution when using spark-submit
---
 .../org/apache/spark/deploy/SparkSubmit.scala | 37 ++++++++++++-------
 .../spark/deploy/SparkSubmitUtilsSuite.scala  |  6 +--
 2 files changed, 26 insertions(+), 17 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index abf222757a95b..b1d6ec209d62b 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -756,6 +756,20 @@ private[spark] object SparkSubmitUtils {
     val cr = new ChainResolver
     cr.setName("list")
 
+    val repositoryList = remoteRepos.getOrElse("")
+    // add any other remote repositories other than maven central
+    if (repositoryList.trim.nonEmpty) {
+      repositoryList.split(",").zipWithIndex.foreach { case (repo, i) =>
+        val brr: IBiblioResolver = new IBiblioResolver
+        brr.setM2compatible(true)
+        brr.setUsepoms(true)
+        brr.setRoot(repo)
+        brr.setName(s"repo-${i + 1}")
+        cr.add(brr)
+        printStream.println(s"$repo added as a remote repository with the name: ${brr.getName}")
+      }
+    }
+
     val localM2 = new IBiblioResolver
     localM2.setM2compatible(true)
     localM2.setRoot(m2Path.toURI.toString)
@@ -786,20 +800,6 @@ private[spark] object SparkSubmitUtils {
     sp.setRoot("http://dl.bintray.com/spark-packages/maven")
     sp.setName("spark-packages")
     cr.add(sp)
-
-    val repositoryList = remoteRepos.getOrElse("")
-    // add any other remote repositories other than maven central
-    if (repositoryList.trim.nonEmpty) {
-      repositoryList.split(",").zipWithIndex.foreach { case (repo, i) =>
-        val brr: IBiblioResolver = new IBiblioResolver
-        brr.setM2compatible(true)
-        brr.setUsepoms(true)
-        brr.setRoot(repo)
-        brr.setName(s"repo-${i + 1}")
-        cr.add(brr)
-        printStream.println(s"$repo added as a remote repository with the name: ${brr.getName}")
-      }
-    }
     cr
   }
 
@@ -922,6 +922,15 @@ private[spark] object SparkSubmitUtils {
 
         // A Module descriptor must be specified. Entries are dummy strings
         val md = getModuleDescriptor
+        // clear ivy resolution from previous launches. The resolution file is usually at
+        // ~/.ivy2/org.apache.spark-spark-submit-parent-default.xml. In between runs, this file
+        // leads to confusion with Ivy when the files can no longer be found at the repository
+        // declared in that file/
+        val mdId = md.getModuleRevisionId
+        val previousResolution = new File(ivySettings.getDefaultCache,
+          s"${mdId.getOrganisation}-${mdId.getName}-$ivyConfName.xml")
+        if (previousResolution.exists) previousResolution.delete
+
         md.setDefaultConf(ivyConfName)
 
         // Add exclusion rules for Spark and Scala Library
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
index 12c40f0b7d658..c9b435a9228d3 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
@@ -77,9 +77,9 @@ class SparkSubmitUtilsSuite extends SparkFunSuite with BeforeAndAfterAll {
     assert(resolver2.getResolvers.size() === 7)
     val expected = repos.split(",").map(r => s"$r/")
     resolver2.getResolvers.toArray.zipWithIndex.foreach { case (resolver: AbstractResolver, i) =>
-      if (i > 3) {
-        assert(resolver.getName === s"repo-${i - 3}")
-        assert(resolver.asInstanceOf[IBiblioResolver].getRoot === expected(i - 4))
+      if (i < 3) {
+        assert(resolver.getName === s"repo-${i + 1}")
+        assert(resolver.asInstanceOf[IBiblioResolver].getRoot === expected(i))
       }
     }
   }

From 4a9e03fa850af9e4ee56d011671faa04fb601170 Mon Sep 17 00:00:00 2001
From: Michael Sannella x268 <msannell@tibco.com>
Date: Mon, 29 Jun 2015 17:28:28 -0700
Subject: [PATCH 0114/1454] [SPARK-8019] [SPARKR] Support SparkR spawning
 worker R processes with a command other then Rscript

This is a simple change to add a new environment variable
"spark.sparkr.r.command" that specifies the command that SparkR will
use when creating an R engine process.  If this is not specified,
"Rscript" will be used by default.

I did not add any documentation, since I couldn't find any place where
environment variables (such as "spark.sparkr.use.daemon") are
documented.

I also did not add a unit test.  The only test that would work
generally would be one starting SparkR with
sparkR.init(sparkEnvir=list(spark.sparkr.r.command="Rscript")), just
using the default value.  I think that this is a low-risk change.

Likely committers: shivaram

Author: Michael Sannella x268 <msannell@tibco.com>

Closes #6557 from msannell/altR and squashes the following commits:

7eac142 [Michael Sannella x268] add spark.sparkr.r.command config parameter
---
 core/src/main/scala/org/apache/spark/api/r/RRDD.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
index 4dfa7325934ff..524676544d6f5 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
@@ -391,7 +391,7 @@ private[r] object RRDD {
   }
 
   private def createRProcess(rLibDir: String, port: Int, script: String): BufferedStreamThread = {
-    val rCommand = "Rscript"
+    val rCommand = SparkEnv.get.conf.get("spark.sparkr.r.command", "Rscript")
     val rOptions = "--vanilla"
     val rExecScript = rLibDir + "/SparkR/worker/" + script
     val pb = new ProcessBuilder(List(rCommand, rOptions, rExecScript))

From 4c1808be4d3aaa37a5a878892e91ca73ea405ffa Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 29 Jun 2015 18:32:31 -0700
Subject: [PATCH 0115/1454] Revert "[SPARK-8437] [DOCS] Using directory path
 without wildcard for filename slow for large number of files with
 wholeTextFiles and binaryFiles"

This reverts commit 5d30eae56051c563a8427f330b09ef66db0a0d21.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index cb7e24c374152..b3c3bf3746e18 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -831,8 +831,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * }}}
    *
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
-   * @note On some filesystems, `.../path/*` can be a more efficient way to read all files in a directory
-   *       rather than `.../path/` or `.../path`
    *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
@@ -880,11 +878,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    *   (a-hdfs-path/part-nnnnn, its content)
    * }}}
    *
-   * @note Small files are preferred; very large files may cause bad performance.
-   * @note On some filesystems, `.../path/*` can be a more efficient way to read all files in a directory
-   *       rather than `.../path/` or `.../path`
-   *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
+   *
+   * @note Small files are preferred; very large files may cause bad performance.
    */
   @Experimental
   def binaryFiles(

From 620605a4a1123afaab2674e38251f1231dea17ce Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 29 Jun 2015 18:40:30 -0700
Subject: [PATCH 0116/1454] [SPARK-8456] [ML] Ngram featurizer python

Python API for N-gram feature transformer

Author: Feynman Liang <fliang@databricks.com>

Closes #6960 from feynmanliang/ngram-featurizer-python and squashes the following commits:

f9e37c9 [Feynman Liang] Remove debugging code
4dd81f4 [Feynman Liang] Fix typo and doctest
06c79ac [Feynman Liang] Style guide
26c1175 [Feynman Liang] Add python NGram API
---
 python/pyspark/ml/feature.py | 71 +++++++++++++++++++++++++++++++++++-
 python/pyspark/ml/tests.py   | 11 ++++++
 2 files changed, 81 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index ddb33f427ac64..8804dace849b3 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -21,7 +21,7 @@
 from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer
 from pyspark.mllib.common import inherit_doc
 
-__all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'Normalizer', 'OneHotEncoder',
+__all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
            'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
            'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
            'Word2Vec', 'Word2VecModel']
@@ -265,6 +265,75 @@ class IDFModel(JavaModel):
     """
 
 
+@inherit_doc
+@ignore_unicode_prefix
+class NGram(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    A feature transformer that converts the input array of strings into an array of n-grams. Null
+    values in the input array are ignored.
+    It returns an array of n-grams where each n-gram is represented by a space-separated string of
+    words.
+    When the input is empty, an empty array is returned.
+    When the input array length is less than n (number of elements per n-gram), no n-grams are
+    returned.
+
+    >>> df = sqlContext.createDataFrame([Row(inputTokens=["a", "b", "c", "d", "e"])])
+    >>> ngram = NGram(n=2, inputCol="inputTokens", outputCol="nGrams")
+    >>> ngram.transform(df).head()
+    Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b', u'b c', u'c d', u'd e'])
+    >>> # Change n-gram length
+    >>> ngram.setParams(n=4).transform(df).head()
+    Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b c d', u'b c d e'])
+    >>> # Temporarily modify output column.
+    >>> ngram.transform(df, {ngram.outputCol: "output"}).head()
+    Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], output=[u'a b c d', u'b c d e'])
+    >>> ngram.transform(df).head()
+    Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b c d', u'b c d e'])
+    >>> # Must use keyword arguments to specify params.
+    >>> ngram.setParams("text")
+    Traceback (most recent call last):
+        ...
+    TypeError: Method setParams forces keyword arguments.
+    """
+
+    # a placeholder to make it appear in the generated doc
+    n = Param(Params._dummy(), "n", "number of elements per n-gram (>=1)")
+
+    @keyword_only
+    def __init__(self, n=2, inputCol=None, outputCol=None):
+        """
+        __init__(self, n=2, inputCol=None, outputCol=None)
+        """
+        super(NGram, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.NGram", self.uid)
+        self.n = Param(self, "n", "number of elements per n-gram (>=1)")
+        self._setDefault(n=2)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, n=2, inputCol=None, outputCol=None):
+        """
+        setParams(self, n=2, inputCol=None, outputCol=None)
+        Sets params for this NGram.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setN(self, value):
+        """
+        Sets the value of :py:attr:`n`.
+        """
+        self._paramMap[self.n] = value
+        return self
+
+    def getN(self):
+        """
+        Gets the value of n or its default value.
+        """
+        return self.getOrDefault(self.n)
+
+
 @inherit_doc
 class Normalizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 6adbf166f34a8..c151d21fd661a 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -252,6 +252,17 @@ def test_idf(self):
         output = idf0m.transform(dataset)
         self.assertIsNotNone(output.head().idf)
 
+    def test_ngram(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([
+            ([["a", "b", "c", "d", "e"]])], ["input"])
+        ngram0 = NGram(n=4, inputCol="input", outputCol="output")
+        self.assertEqual(ngram0.getN(), 4)
+        self.assertEqual(ngram0.getInputCol(), "input")
+        self.assertEqual(ngram0.getOutputCol(), "output")
+        transformedDF = ngram0.transform(dataset)
+        self.assertEquals(transformedDF.head().output, ["a b c d", "b c d e"])
+
 
 if __name__ == "__main__":
     unittest.main()

From ecacb1e88a135c802e253793e7c863d6ca8d2408 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Mon, 29 Jun 2015 18:48:28 -0700
Subject: [PATCH 0117/1454] [SPARK-8715] ArrayOutOfBoundsException fixed for
 DataFrameStatSuite.crosstab

cc yhuai

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7100 from brkyvz/ct-flakiness-fix and squashes the following commits:

abc299a [Burak Yavuz] change 'to' to until
7e96d7c [Burak Yavuz] ArrayOutOfBoundsException fixed for DataFrameStatSuite.crosstab
---
 .../test/scala/org/apache/spark/sql/DataFrameStatSuite.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 64ec1a70c47e6..765094da6bda7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -78,7 +78,7 @@ class DataFrameStatSuite extends SparkFunSuite  {
     val rows = crosstab.collect()
     rows.foreach { row =>
       val i = row.getString(0).toInt
-      for (col <- 1 to 9) {
+      for (col <- 1 until columnNames.length) {
         val j = columnNames(col).toInt
         assert(row.getLong(col) === expected.getOrElse((i, j), 0).toLong)
       }

From 4915e9e3bffb57eac319ef2173b4a6ae4073d25e Mon Sep 17 00:00:00 2001
From: Steven She <steven@canopylabs.com>
Date: Mon, 29 Jun 2015 18:50:09 -0700
Subject: [PATCH 0118/1454] [SPARK-8669] [SQL] Fix crash with BINARY (ENUM)
 fields with Parquet 1.7

Patch to fix crash with BINARY fields with ENUM original types.

Author: Steven She <steven@canopylabs.com>

Closes #7048 from stevencanopy/SPARK-8669 and squashes the following commits:

2e72979 [Steven She] [SPARK-8669] [SQL] Fix crash with BINARY (ENUM) fields with Parquet 1.7
---
 .../spark/sql/parquet/CatalystSchemaConverter.scala       | 2 +-
 .../org/apache/spark/sql/parquet/ParquetSchemaSuite.scala | 8 ++++++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index 4fd3e93b70311..2be7c64612cd2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -177,7 +177,7 @@ private[parquet] class CatalystSchemaConverter(
 
       case BINARY =>
         field.getOriginalType match {
-          case UTF8 => StringType
+          case UTF8 | ENUM => StringType
           case null if assumeBinaryIsString => StringType
           case null => BinaryType
           case DECIMAL => makeDecimalType()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
index d0bfcde7e032b..35d3c33f99a06 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
@@ -161,6 +161,14 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
     """.stripMargin,
     binaryAsString = true)
 
+  testSchemaInference[Tuple1[String]](
+    "binary enum as string",
+    """
+      |message root {
+      |  optional binary _1 (ENUM);
+      |}
+    """.stripMargin)
+
   testSchemaInference[Tuple1[Seq[Int]]](
     "non-nullable array - non-standard",
     """

From f9b6bf2f83d9dad273aa36d65d0560d35b941cc2 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 29 Jun 2015 18:50:23 -0700
Subject: [PATCH 0119/1454] [SPARK-7667] [MLLIB] MLlib Python API consistency
 check

MLlib Python API consistency check

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #6856 from yanboliang/spark-7667 and squashes the following commits:

21bae35 [Yanbo Liang] remove duplicate code
eb12f95 [Yanbo Liang] fix doc inherit problem
9e7ec3c [Yanbo Liang] address comments
e763d32 [Yanbo Liang] MLlib Python API consistency check
---
 python/pyspark/mllib/feature.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index f00bb93b7bf40..b5138773fd61b 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -111,6 +111,15 @@ class JavaVectorTransformer(JavaModelWrapper, VectorTransformer):
     """
 
     def transform(self, vector):
+        """
+        Applies transformation on a vector or an RDD[Vector].
+
+        Note: In Python, transform cannot currently be used within
+              an RDD transformation or action.
+              Call transform directly on the RDD instead.
+
+        :param vector: Vector or RDD of Vector to be transformed.
+        """
         if isinstance(vector, RDD):
             vector = vector.map(_convert_to_vector)
         else:
@@ -191,7 +200,7 @@ def fit(self, dataset):
         Computes the mean and variance and stores as a model to be used
         for later scaling.
 
-        :param data: The data used to compute the mean and variance
+        :param dataset: The data used to compute the mean and variance
                      to build the transformation model.
         :return: a StandardScalarModel
         """
@@ -346,10 +355,6 @@ def transform(self, x):
                   vector
         :return: an RDD of TF-IDF vectors or a TF-IDF vector
         """
-        if isinstance(x, RDD):
-            return JavaVectorTransformer.transform(self, x)
-
-        x = _convert_to_vector(x)
         return JavaVectorTransformer.transform(self, x)
 
     def idf(self):

From 7bbbe380c52419cd580d1c99c10131184e4ad440 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 29 Jun 2015 21:32:40 -0700
Subject: [PATCH 0120/1454] [SPARK-5161] Parallelize Python test execution

This commit parallelizes the Python unit test execution, significantly reducing Jenkins build times.  Parallelism is now configurable by passing the `-p` or `--parallelism` flags to either `dev/run-tests` or `python/run-tests` (the default parallelism is 4, but I've successfully tested with higher parallelism).

To avoid flakiness, I've disabled the Spark Web UI for the Python tests, similar to what we've done for the JVM tests.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7031 from JoshRosen/parallelize-python-tests and squashes the following commits:

feb3763 [Josh Rosen] Re-enable other tests
f87ea81 [Josh Rosen] Only log output from failed tests
d4ded73 [Josh Rosen] Logging improvements
a2717e1 [Josh Rosen] Make parallelism configurable via dev/run-tests
1bacf1b [Josh Rosen] Merge remote-tracking branch 'origin/master' into parallelize-python-tests
110cd9d [Josh Rosen] Fix universal_newlines for Python 3
cd13db8 [Josh Rosen] Also log python_implementation
9e31127 [Josh Rosen] Log Python --version output for each executable.
a2b9094 [Josh Rosen] Bump up parallelism.
5552380 [Josh Rosen] Python 3 fix
866b5b9 [Josh Rosen] Fix lazy logging warnings in Prospector checks
87cb988 [Josh Rosen] Skip MLLib tests for PyPy
8309bfe [Josh Rosen] Temporarily disable parallelism to debug a failure
9129027 [Josh Rosen] Disable Spark UI in Python tests
037b686 [Josh Rosen] Temporarily disable JVM tests so we can test Python speedup in Jenkins.
af4cef4 [Josh Rosen] Initial attempt at parallelizing Python test execution
---
 dev/run-tests                      |  2 +-
 dev/run-tests.py                   | 24 +++++++-
 dev/sparktestsupport/shellutils.py |  1 +
 python/pyspark/java_gateway.py     |  2 +
 python/run-tests.py                | 97 +++++++++++++++++++++++-------
 5 files changed, 101 insertions(+), 25 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index a00d9f0c27639..257d1e8d50bb4 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -20,4 +20,4 @@
 FWDIR="$(cd "`dirname $0`"/..; pwd)"
 cd "$FWDIR"
 
-exec python -u ./dev/run-tests.py
+exec python -u ./dev/run-tests.py "$@"
diff --git a/dev/run-tests.py b/dev/run-tests.py
index e5c897b94d167..4596e07014733 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -19,6 +19,7 @@
 
 from __future__ import print_function
 import itertools
+from optparse import OptionParser
 import os
 import re
 import sys
@@ -360,12 +361,13 @@ def run_scala_tests(build_tool, hadoop_version, test_modules):
         run_scala_tests_sbt(test_modules, test_profiles)
 
 
-def run_python_tests(test_modules):
+def run_python_tests(test_modules, parallelism):
     set_title_and_block("Running PySpark tests", "BLOCK_PYSPARK_UNIT_TESTS")
 
     command = [os.path.join(SPARK_HOME, "python", "run-tests")]
     if test_modules != [modules.root]:
         command.append("--modules=%s" % ','.join(m.name for m in test_modules))
+    command.append("--parallelism=%i" % parallelism)
     run_cmd(command)
 
 
@@ -379,7 +381,25 @@ def run_sparkr_tests():
         print("Ignoring SparkR tests as R was not found in PATH")
 
 
+def parse_opts():
+    parser = OptionParser(
+        prog="run-tests"
+    )
+    parser.add_option(
+        "-p", "--parallelism", type="int", default=4,
+        help="The number of suites to test in parallel (default %default)"
+    )
+
+    (opts, args) = parser.parse_args()
+    if args:
+        parser.error("Unsupported arguments: %s" % ' '.join(args))
+    if opts.parallelism < 1:
+        parser.error("Parallelism cannot be less than 1")
+    return opts
+
+
 def main():
+    opts = parse_opts()
     # Ensure the user home directory (HOME) is valid and is an absolute directory
     if not USER_HOME or not os.path.isabs(USER_HOME):
         print("[error] Cannot determine your home directory as an absolute path;",
@@ -461,7 +481,7 @@ def main():
 
     modules_with_python_tests = [m for m in test_modules if m.python_test_goals]
     if modules_with_python_tests:
-        run_python_tests(modules_with_python_tests)
+        run_python_tests(modules_with_python_tests, opts.parallelism)
     if any(m.should_run_r_tests for m in test_modules):
         run_sparkr_tests()
 
diff --git a/dev/sparktestsupport/shellutils.py b/dev/sparktestsupport/shellutils.py
index ad9b0cc89e4ab..12bd0bf3a4fe9 100644
--- a/dev/sparktestsupport/shellutils.py
+++ b/dev/sparktestsupport/shellutils.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 
+from __future__ import print_function
 import os
 import shutil
 import subprocess
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 3cee4ea6e3a35..90cd342a6cf7f 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -51,6 +51,8 @@ def launch_gateway():
         on_windows = platform.system() == "Windows"
         script = "./bin/spark-submit.cmd" if on_windows else "./bin/spark-submit"
         submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "pyspark-shell")
+        if os.environ.get("SPARK_TESTING"):
+            submit_args = "--conf spark.ui.enabled=false " + submit_args
         command = [os.path.join(SPARK_HOME, script)] + shlex.split(submit_args)
 
         # Start a socket that will be used by PythonGatewayServer to communicate its port to us
diff --git a/python/run-tests.py b/python/run-tests.py
index 7d485b500ee3a..aaa35e936a806 100755
--- a/python/run-tests.py
+++ b/python/run-tests.py
@@ -18,12 +18,19 @@
 #
 
 from __future__ import print_function
+import logging
 from optparse import OptionParser
 import os
 import re
 import subprocess
 import sys
+import tempfile
+from threading import Thread, Lock
 import time
+if sys.version < '3':
+    import Queue
+else:
+    import queue as Queue
 
 
 # Append `SPARK_HOME/dev` to the Python path so that we can import the sparktestsupport module
@@ -43,34 +50,44 @@ def print_red(text):
 
 
 LOG_FILE = os.path.join(SPARK_HOME, "python/unit-tests.log")
+FAILURE_REPORTING_LOCK = Lock()
+LOGGER = logging.getLogger()
 
 
 def run_individual_python_test(test_name, pyspark_python):
     env = {'SPARK_TESTING': '1', 'PYSPARK_PYTHON': which(pyspark_python)}
-    print("    Running test: %s ..." % test_name, end='')
+    LOGGER.debug("Starting test(%s): %s", pyspark_python, test_name)
     start_time = time.time()
-    with open(LOG_FILE, 'a') as log_file:
-        retcode = subprocess.call(
-            [os.path.join(SPARK_HOME, "bin/pyspark"), test_name],
-            stderr=log_file, stdout=log_file, env=env)
+    per_test_output = tempfile.TemporaryFile()
+    retcode = subprocess.Popen(
+        [os.path.join(SPARK_HOME, "bin/pyspark"), test_name],
+        stderr=per_test_output, stdout=per_test_output, env=env).wait()
     duration = time.time() - start_time
     # Exit on the first failure.
     if retcode != 0:
-        with open(LOG_FILE, 'r') as log_file:
-            for line in log_file:
+        with FAILURE_REPORTING_LOCK:
+            with open(LOG_FILE, 'ab') as log_file:
+                per_test_output.seek(0)
+                log_file.writelines(per_test_output.readlines())
+            per_test_output.seek(0)
+            for line in per_test_output:
                 if not re.match('[0-9]+', line):
                     print(line, end='')
-        print_red("\nHad test failures in %s; see logs." % test_name)
-        exit(-1)
+            per_test_output.close()
+            print_red("\nHad test failures in %s with %s; see logs." % (test_name, pyspark_python))
+            # Here, we use os._exit() instead of sys.exit() in order to force Python to exit even if
+            # this code is invoked from a thread other than the main thread.
+            os._exit(-1)
     else:
-        print("ok (%is)" % duration)
+        per_test_output.close()
+        LOGGER.info("Finished test(%s): %s (%is)", pyspark_python, test_name, duration)
 
 
 def get_default_python_executables():
     python_execs = [x for x in ["python2.6", "python3.4", "pypy"] if which(x)]
     if "python2.6" not in python_execs:
-        print("WARNING: Not testing against `python2.6` because it could not be found; falling"
-              " back to `python` instead")
+        LOGGER.warning("Not testing against `python2.6` because it could not be found; falling"
+                       " back to `python` instead")
         python_execs.insert(0, "python")
     return python_execs
 
@@ -88,16 +105,31 @@ def parse_opts():
         default=",".join(sorted(python_modules.keys())),
         help="A comma-separated list of Python modules to test (default: %default)"
     )
+    parser.add_option(
+        "-p", "--parallelism", type="int", default=4,
+        help="The number of suites to test in parallel (default %default)"
+    )
+    parser.add_option(
+        "--verbose", action="store_true",
+        help="Enable additional debug logging"
+    )
 
     (opts, args) = parser.parse_args()
     if args:
         parser.error("Unsupported arguments: %s" % ' '.join(args))
+    if opts.parallelism < 1:
+        parser.error("Parallelism cannot be less than 1")
     return opts
 
 
 def main():
     opts = parse_opts()
-    print("Running PySpark tests. Output is in python/%s" % LOG_FILE)
+    if (opts.verbose):
+        log_level = logging.DEBUG
+    else:
+        log_level = logging.INFO
+    logging.basicConfig(stream=sys.stdout, level=log_level, format="%(message)s")
+    LOGGER.info("Running PySpark tests. Output is in python/%s", LOG_FILE)
     if os.path.exists(LOG_FILE):
         os.remove(LOG_FILE)
     python_execs = opts.python_executables.split(',')
@@ -108,24 +140,45 @@ def main():
         else:
             print("Error: unrecognized module %s" % module_name)
             sys.exit(-1)
-    print("Will test against the following Python executables: %s" % python_execs)
-    print("Will test the following Python modules: %s" % [x.name for x in modules_to_test])
+    LOGGER.info("Will test against the following Python executables: %s", python_execs)
+    LOGGER.info("Will test the following Python modules: %s", [x.name for x in modules_to_test])
 
-    start_time = time.time()
+    task_queue = Queue.Queue()
     for python_exec in python_execs:
         python_implementation = subprocess.check_output(
             [python_exec, "-c", "import platform; print(platform.python_implementation())"],
             universal_newlines=True).strip()
-        print("Testing with `%s`: " % python_exec, end='')
-        subprocess.call([python_exec, "--version"])
-
+        LOGGER.debug("%s python_implementation is %s", python_exec, python_implementation)
+        LOGGER.debug("%s version is: %s", python_exec, subprocess.check_output(
+            [python_exec, "--version"], stderr=subprocess.STDOUT, universal_newlines=True).strip())
         for module in modules_to_test:
             if python_implementation not in module.blacklisted_python_implementations:
-                print("Running %s tests ..." % module.name)
                 for test_goal in module.python_test_goals:
-                    run_individual_python_test(test_goal, python_exec)
+                    task_queue.put((python_exec, test_goal))
+
+    def process_queue(task_queue):
+        while True:
+            try:
+                (python_exec, test_goal) = task_queue.get_nowait()
+            except Queue.Empty:
+                break
+            try:
+                run_individual_python_test(test_goal, python_exec)
+            finally:
+                task_queue.task_done()
+
+    start_time = time.time()
+    for _ in range(opts.parallelism):
+        worker = Thread(target=process_queue, args=(task_queue,))
+        worker.daemon = True
+        worker.start()
+    try:
+        task_queue.join()
+    except (KeyboardInterrupt, SystemExit):
+        print_red("Exiting due to interrupt")
+        sys.exit(-1)
     total_duration = time.time() - start_time
-    print("Tests passed in %i seconds" % total_duration)
+    LOGGER.info("Tests passed in %i seconds", total_duration)
 
 
 if __name__ == "__main__":

From ea775b0662b952849ac7fe2026fc3fd4714c37e3 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Mon, 29 Jun 2015 21:41:59 -0700
Subject: [PATCH 0121/1454] MAINTENANCE: Automated closing of pull requests.

This commit exists to close the following pull requests on Github:

Closes #1767 (close requested by 'andrewor14')
Closes #6952 (close requested by 'andrewor14')
Closes #7051 (close requested by 'andrewor14')
Closes #5357 (close requested by 'marmbrus')
Closes #5233 (close requested by 'andrewor14')
Closes #6930 (close requested by 'JoshRosen')
Closes #5502 (close requested by 'andrewor14')
Closes #6778 (close requested by 'andrewor14')
Closes #7006 (close requested by 'andrewor14')

From f79410c49b2225b2acdc58293574860230987775 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 29 Jun 2015 22:32:43 -0700
Subject: [PATCH 0122/1454] [SPARK-8721][SQL] Rename ExpectsInputTypes =>
 AutoCastInputTypes.

Author: Reynold Xin <rxin@databricks.com>

Closes #7109 from rxin/auto-cast and squashes the following commits:

a914cc3 [Reynold Xin] [SPARK-8721][SQL] Rename ExpectsInputTypes => AutoCastInputTypes.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |   8 +-
 .../sql/catalyst/expressions/Expression.scala |   2 +-
 .../spark/sql/catalyst/expressions/math.scala | 118 ++++++++----------
 .../spark/sql/catalyst/expressions/misc.scala |   6 +-
 .../sql/catalyst/expressions/predicates.scala |   6 +-
 .../expressions/stringOperations.scala        |  10 +-
 6 files changed, 71 insertions(+), 79 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 976fa57cb98d5..c3d68197d64ac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -116,7 +116,7 @@ trait HiveTypeCoercion {
     IfCoercion ::
     Division ::
     PropagateTypes ::
-    ExpectedInputConversion ::
+    AddCastForAutoCastInputTypes ::
     Nil
 
   /**
@@ -709,15 +709,15 @@ trait HiveTypeCoercion {
 
   /**
    * Casts types according to the expected input types for Expressions that have the trait
-   * `ExpectsInputTypes`.
+   * [[AutoCastInputTypes]].
    */
-  object ExpectedInputConversion extends Rule[LogicalPlan] {
+  object AddCastForAutoCastInputTypes extends Rule[LogicalPlan] {
 
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      case e: ExpectsInputTypes if e.children.map(_.dataType) != e.expectedChildTypes =>
+      case e: AutoCastInputTypes if e.children.map(_.dataType) != e.expectedChildTypes =>
         val newC = (e.children, e.children.map(_.dataType), e.expectedChildTypes).zipped.map {
           case (child, actual, expected) =>
             if (actual == expected) child else Cast(child, expected)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index f59db3d5dfc23..e5dc7b9b5c884 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -261,7 +261,7 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
  * Expressions that require a specific `DataType` as input should implement this trait
  * so that the proper type conversions can be performed in the analyzer.
  */
-trait ExpectsInputTypes {
+trait AutoCastInputTypes {
   self: Expression =>
 
   def expectedChildTypes: Seq[DataType]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 4b57ddd9c5768..a022f3727bd58 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -56,7 +56,7 @@ abstract class LeafMathExpression(c: Double, name: String)
  * @param name The short name of the function
  */
 abstract class UnaryMathExpression(f: Double => Double, name: String)
-  extends UnaryExpression with Serializable with ExpectsInputTypes {
+  extends UnaryExpression with Serializable with AutoCastInputTypes {
   self: Product =>
 
   override def expectedChildTypes: Seq[DataType] = Seq(DoubleType)
@@ -99,7 +99,7 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
  * @param name The short name of the function
  */
 abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
-  extends BinaryExpression with Serializable with ExpectsInputTypes { self: Product =>
+  extends BinaryExpression with Serializable with AutoCastInputTypes { self: Product =>
 
   override def expectedChildTypes: Seq[DataType] = Seq(DoubleType, DoubleType)
 
@@ -211,19 +211,11 @@ case class ToRadians(child: Expression) extends UnaryMathExpression(math.toRadia
 }
 
 case class Bin(child: Expression)
-  extends UnaryExpression with Serializable with ExpectsInputTypes {
-
-  val name: String = "BIN"
-
-  override def foldable: Boolean = child.foldable
-  override def nullable: Boolean = true
-  override def toString: String = s"$name($child)"
+  extends UnaryExpression with Serializable with AutoCastInputTypes {
 
   override def expectedChildTypes: Seq[DataType] = Seq(LongType)
   override def dataType: DataType = StringType
 
-  def funcName: String = name.toLowerCase
-
   override def eval(input: InternalRow): Any = {
     val evalE = child.eval(input)
     if (evalE == null) {
@@ -239,61 +231,13 @@ case class Bin(child: Expression)
   }
 }
 
-////////////////////////////////////////////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////////////////////////////////////////////
-// Binary math functions
-////////////////////////////////////////////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-
-case class Atan2(left: Expression, right: Expression)
-  extends BinaryMathExpression(math.atan2, "ATAN2") {
-
-  override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    if (evalE1 == null) {
-      null
-    } else {
-      val evalE2 = right.eval(input)
-      if (evalE2 == null) {
-        null
-      } else {
-        // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0
-        val result = math.atan2(evalE1.asInstanceOf[Double] + 0.0,
-          evalE2.asInstanceOf[Double] + 0.0)
-        if (result.isNaN) null else result
-      }
-    }
-  }
-
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.atan2($c1 + 0.0, $c2 + 0.0)") + s"""
-      if (Double.valueOf(${ev.primitive}).isNaN()) {
-        ${ev.isNull} = true;
-      }
-      """
-  }
-}
-
-case class Pow(left: Expression, right: Expression)
-  extends BinaryMathExpression(math.pow, "POWER") {
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.pow($c1, $c2)") + s"""
-      if (Double.valueOf(${ev.primitive}).isNaN()) {
-        ${ev.isNull} = true;
-      }
-      """
-  }
-}
 
 /**
  * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
- * Otherwise if the number is a STRING,
- * it converts each character into its hexadecimal representation and returns the resulting STRING.
- * Negative numbers would be treated as two's complement.
+ * Otherwise if the number is a STRING, it converts each character into its hex representation
+ * and returns the resulting STRING. Negative numbers would be treated as two's complement.
  */
-case class Hex(child: Expression)
-  extends UnaryExpression with Serializable  {
+case class Hex(child: Expression) extends UnaryExpression with Serializable  {
 
   override def dataType: DataType = StringType
 
@@ -337,7 +281,7 @@ case class Hex(child: Expression)
   private def doHex(bytes: Array[Byte], length: Int): UTF8String = {
     val value = new Array[Byte](length * 2)
     var i = 0
-    while(i < length) {
+    while (i < length) {
       value(i * 2) = Character.toUpperCase(Character.forDigit(
         (bytes(i) & 0xF0) >>> 4, 16)).toByte
       value(i * 2 + 1) = Character.toUpperCase(Character.forDigit(
@@ -362,6 +306,54 @@ case class Hex(child: Expression)
   }
 }
 
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// Binary math functions
+////////////////////////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+case class Atan2(left: Expression, right: Expression)
+  extends BinaryMathExpression(math.atan2, "ATAN2") {
+
+  override def eval(input: InternalRow): Any = {
+    val evalE1 = left.eval(input)
+    if (evalE1 == null) {
+      null
+    } else {
+      val evalE2 = right.eval(input)
+      if (evalE2 == null) {
+        null
+      } else {
+        // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0
+        val result = math.atan2(evalE1.asInstanceOf[Double] + 0.0,
+          evalE2.asInstanceOf[Double] + 0.0)
+        if (result.isNaN) null else result
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.atan2($c1 + 0.0, $c2 + 0.0)") + s"""
+      if (Double.valueOf(${ev.primitive}).isNaN()) {
+        ${ev.isNull} = true;
+      }
+      """
+  }
+}
+
+case class Pow(left: Expression, right: Expression)
+  extends BinaryMathExpression(math.pow, "POWER") {
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.pow($c1, $c2)") + s"""
+      if (Double.valueOf(${ev.primitive}).isNaN()) {
+        ${ev.isNull} = true;
+      }
+      """
+  }
+}
+
 case class Hypot(left: Expression, right: Expression)
   extends BinaryMathExpression(math.hypot, "HYPOT")
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index 9a39165a1ff05..27805bff293f4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -31,7 +31,7 @@ import org.apache.spark.unsafe.types.UTF8String
  * For input of type [[BinaryType]]
  */
 case class Md5(child: Expression)
-  extends UnaryExpression with ExpectsInputTypes {
+  extends UnaryExpression with AutoCastInputTypes {
 
   override def dataType: DataType = StringType
 
@@ -61,7 +61,7 @@ case class Md5(child: Expression)
  * the hash length is not one of the permitted values, the return value is NULL.
  */
 case class Sha2(left: Expression, right: Expression)
-  extends BinaryExpression with Serializable with ExpectsInputTypes {
+  extends BinaryExpression with Serializable with AutoCastInputTypes {
 
   override def dataType: DataType = StringType
 
@@ -146,7 +146,7 @@ case class Sha2(left: Expression, right: Expression)
  * A function that calculates a sha1 hash value and returns it as a hex string
  * For input of type [[BinaryType]] or [[StringType]]
  */
-case class Sha1(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Sha1(child: Expression) extends UnaryExpression with AutoCastInputTypes {
 
   override def dataType: DataType = StringType
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 3a12d03ba6bb9..386cf6a8df6df 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -70,7 +70,7 @@ trait PredicateHelper {
 }
 
 
-case class Not(child: Expression) extends UnaryExpression with Predicate with ExpectsInputTypes {
+case class Not(child: Expression) extends UnaryExpression with Predicate with AutoCastInputTypes {
   override def foldable: Boolean = child.foldable
   override def nullable: Boolean = child.nullable
   override def toString: String = s"NOT $child"
@@ -123,7 +123,7 @@ case class InSet(value: Expression, hset: Set[Any])
 }
 
 case class And(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with ExpectsInputTypes {
+  extends BinaryExpression with Predicate with AutoCastInputTypes {
 
   override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
@@ -172,7 +172,7 @@ case class And(left: Expression, right: Expression)
 }
 
 case class Or(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with ExpectsInputTypes {
+  extends BinaryExpression with Predicate with AutoCastInputTypes {
 
   override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index a6225fdafedde..ce184e4f32f18 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
-trait StringRegexExpression extends ExpectsInputTypes {
+trait StringRegexExpression extends AutoCastInputTypes {
   self: BinaryExpression =>
 
   def escape(v: String): String
@@ -111,7 +111,7 @@ case class RLike(left: Expression, right: Expression)
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
 }
 
-trait CaseConversionExpression extends ExpectsInputTypes {
+trait CaseConversionExpression extends AutoCastInputTypes {
   self: UnaryExpression =>
 
   def convert(v: UTF8String): UTF8String
@@ -158,7 +158,7 @@ case class Lower(child: Expression) extends UnaryExpression with CaseConversionE
 }
 
 /** A base trait for functions that compare two strings, returning a boolean. */
-trait StringComparison extends ExpectsInputTypes {
+trait StringComparison extends AutoCastInputTypes {
   self: BinaryExpression =>
 
   def compare(l: UTF8String, r: UTF8String): Boolean
@@ -221,7 +221,7 @@ case class EndsWith(left: Expression, right: Expression)
  * Defined for String and Binary types.
  */
 case class Substring(str: Expression, pos: Expression, len: Expression)
-  extends Expression with ExpectsInputTypes {
+  extends Expression with AutoCastInputTypes {
 
   def this(str: Expression, pos: Expression) = {
     this(str, pos, Literal(Integer.MAX_VALUE))
@@ -295,7 +295,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
 /**
  * A function that return the length of the given string expression.
  */
-case class StringLength(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class StringLength(child: Expression) extends UnaryExpression with AutoCastInputTypes {
   override def dataType: DataType = IntegerType
   override def expectedChildTypes: Seq[DataType] = Seq(StringType)
 

From e6c3f7462b3fde220ec0084b52388dd4dabb75b9 Mon Sep 17 00:00:00 2001
From: Yadong Qi <qiyadong2010@gmail.com>
Date: Mon, 29 Jun 2015 22:34:38 -0700
Subject: [PATCH 0123/1454] [SPARK-8650] [SQL] Use the user-specified app name
 priority in SparkSQLCLIDriver or HiveThriftServer2

When run `./bin/spark-sql --name query1.sql`
[Before]
![before](https://cloud.githubusercontent.com/assets/1400819/8370336/fa20b75a-1bf8-11e5-9171-040049a53240.png)

[After]
![after](https://cloud.githubusercontent.com/assets/1400819/8370189/dcc35cb4-1bf6-11e5-8796-a0694140bffb.png)

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #7030 from watermen/SPARK-8650 and squashes the following commits:

51b5134 [Yadong Qi] Improve code and add comment.
e3d7647 [Yadong Qi] use spark.app.name priority.
---
 .../apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala   | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
index 79eda1f5123bf..1d41c46131828 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
@@ -38,9 +38,14 @@ private[hive] object SparkSQLEnv extends Logging {
       val sparkConf = new SparkConf(loadDefaults = true)
       val maybeSerializer = sparkConf.getOption("spark.serializer")
       val maybeKryoReferenceTracking = sparkConf.getOption("spark.kryo.referenceTracking")
+      // If user doesn't specify the appName, we want to get [SparkSQL::localHostName] instead of
+      // the default appName [SparkSQLCLIDriver] in cli or beeline.
+      val maybeAppName = sparkConf
+        .getOption("spark.app.name")
+        .filterNot(_ == classOf[SparkSQLCLIDriver].getName)
 
       sparkConf
-        .setAppName(s"SparkSQL::${Utils.localHostName()}")
+        .setAppName(maybeAppName.getOrElse(s"SparkSQL::${Utils.localHostName()}"))
         .set(
           "spark.serializer",
           maybeSerializer.getOrElse("org.apache.spark.serializer.KryoSerializer"))

From 6c5a6db4d53d6db8aa3464ea6713cf0d3a3bdfb5 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 29 Jun 2015 23:08:51 -0700
Subject: [PATCH 0124/1454] [SPARK-5161] [HOTFIX] Fix bug in Python test
 failure reporting

This patch fixes a bug introduced in #7031 which can cause Jenkins to incorrectly report a build with failed Python tests as passing if an error occurred while printing the test failure message.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7112 from JoshRosen/python-tests-hotfix and squashes the following commits:

c3f2961 [Josh Rosen] Hotfix for bug in Python test failure reporting
---
 python/run-tests.py | 35 +++++++++++++++++++++++------------
 1 file changed, 23 insertions(+), 12 deletions(-)

diff --git a/python/run-tests.py b/python/run-tests.py
index aaa35e936a806..b7737650daa54 100755
--- a/python/run-tests.py
+++ b/python/run-tests.py
@@ -58,22 +58,33 @@ def run_individual_python_test(test_name, pyspark_python):
     env = {'SPARK_TESTING': '1', 'PYSPARK_PYTHON': which(pyspark_python)}
     LOGGER.debug("Starting test(%s): %s", pyspark_python, test_name)
     start_time = time.time()
-    per_test_output = tempfile.TemporaryFile()
-    retcode = subprocess.Popen(
-        [os.path.join(SPARK_HOME, "bin/pyspark"), test_name],
-        stderr=per_test_output, stdout=per_test_output, env=env).wait()
+    try:
+        per_test_output = tempfile.TemporaryFile()
+        retcode = subprocess.Popen(
+            [os.path.join(SPARK_HOME, "bin/pyspark"), test_name],
+            stderr=per_test_output, stdout=per_test_output, env=env).wait()
+    except:
+        LOGGER.exception("Got exception while running %s with %s", test_name, pyspark_python)
+        # Here, we use os._exit() instead of sys.exit() in order to force Python to exit even if
+        # this code is invoked from a thread other than the main thread.
+        os._exit(1)
     duration = time.time() - start_time
     # Exit on the first failure.
     if retcode != 0:
-        with FAILURE_REPORTING_LOCK:
-            with open(LOG_FILE, 'ab') as log_file:
+        try:
+            with FAILURE_REPORTING_LOCK:
+                with open(LOG_FILE, 'ab') as log_file:
+                    per_test_output.seek(0)
+                    log_file.writelines(per_test_output)
                 per_test_output.seek(0)
-                log_file.writelines(per_test_output.readlines())
-            per_test_output.seek(0)
-            for line in per_test_output:
-                if not re.match('[0-9]+', line):
-                    print(line, end='')
-            per_test_output.close()
+                for line in per_test_output:
+                    decoded_line = line.decode()
+                    if not re.match('[0-9]+', decoded_line):
+                        print(decoded_line, end='')
+                per_test_output.close()
+        except:
+            LOGGER.exception("Got an exception while trying to print failed test output")
+        finally:
             print_red("\nHad test failures in %s with %s; see logs." % (test_name, pyspark_python))
             # Here, we use os._exit() instead of sys.exit() in order to force Python to exit even if
             # this code is invoked from a thread other than the main thread.

From 12671dd5e468beedc2681ff2bdf95fba81f8f29c Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 29 Jun 2015 23:44:11 -0700
Subject: [PATCH 0125/1454] [SPARK-8434][SQL]Add a "pretty" parameter to the
 "show" method to display long strings

Sometimes the user may want to show the complete content of cells. Now `sql("set -v").show()` displays:

![screen shot 2015-06-18 at 4 34 51 pm](https://cloud.githubusercontent.com/assets/1000778/8227339/14d3c5ea-15d9-11e5-99b9-f00b7e93beef.png)

The user needs to use something like `sql("set -v").collect().foreach(r => r.toSeq.mkString("\t"))` to show the complete content.

This PR adds a `pretty` parameter to show. If `pretty` is false, `show` won't truncate strings or align cells right.

![screen shot 2015-06-18 at 4 21 44 pm](https://cloud.githubusercontent.com/assets/1000778/8227407/b6f8dcac-15d9-11e5-8219-8079280d76fc.png)

Author: zsxwing <zsxwing@gmail.com>

Closes #6877 from zsxwing/show and squashes the following commits:

22e28e9 [zsxwing] pretty -> truncate
e582628 [zsxwing] Add pretty parameter to the show method in R
a3cd55b [zsxwing] Fix calling showString in R
923cee4 [zsxwing] Add a "pretty" parameter to show to display long strings
---
 R/pkg/R/DataFrame.R                           |  4 +-
 python/pyspark/sql/dataframe.py               |  7 ++-
 .../org/apache/spark/sql/DataFrame.scala      | 55 ++++++++++++++++---
 .../org/apache/spark/sql/DataFrameSuite.scala | 21 +++++++
 4 files changed, 76 insertions(+), 11 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 6feabf4189c2d..60702824acb46 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -169,8 +169,8 @@ setMethod("isLocal",
 #'}
 setMethod("showDF",
           signature(x = "DataFrame"),
-          function(x, numRows = 20) {
-            s <- callJMethod(x@sdf, "showString", numToInt(numRows))
+          function(x, numRows = 20, truncate = TRUE) {
+            s <- callJMethod(x@sdf, "showString", numToInt(numRows), truncate)
             cat(s)
           })
 
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 152b87351db31..4b9efa0a210fb 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -247,9 +247,12 @@ def isLocal(self):
         return self._jdf.isLocal()
 
     @since(1.3)
-    def show(self, n=20):
+    def show(self, n=20, truncate=True):
         """Prints the first ``n`` rows to the console.
 
+        :param n: Number of rows to show.
+        :param truncate: Whether truncate long strings and align cells right.
+
         >>> df
         DataFrame[age: int, name: string]
         >>> df.show()
@@ -260,7 +263,7 @@ def show(self, n=20):
         |  5|  Bob|
         +---+-----+
         """
-        print(self._jdf.showString(n))
+        print(self._jdf.showString(n, truncate))
 
     def __repr__(self):
         return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 986e59133919f..8fe1f7e34cb5e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -169,8 +169,9 @@ class DataFrame private[sql](
   /**
    * Internal API for Python
    * @param _numRows Number of rows to show
+   * @param truncate Whether truncate long strings and align cells right
    */
-  private[sql] def showString(_numRows: Int): String = {
+  private[sql] def showString(_numRows: Int, truncate: Boolean = true): String = {
     val numRows = _numRows.max(0)
     val sb = new StringBuilder
     val takeResult = take(numRows + 1)
@@ -188,7 +189,7 @@ class DataFrame private[sql](
           case seq: Seq[_] => seq.mkString("[", ", ", "]")
           case _ => cell.toString
         }
-        if (str.length > 20) str.substring(0, 17) + "..." else str
+        if (truncate && str.length > 20) str.substring(0, 17) + "..." else str
       }: Seq[String]
     }
 
@@ -207,7 +208,11 @@ class DataFrame private[sql](
 
     // column names
     rows.head.zipWithIndex.map { case (cell, i) =>
-      StringUtils.leftPad(cell, colWidths(i))
+      if (truncate) {
+        StringUtils.leftPad(cell, colWidths(i))
+      } else {
+        StringUtils.rightPad(cell, colWidths(i))
+      }
     }.addString(sb, "|", "|", "|\n")
 
     sb.append(sep)
@@ -215,7 +220,11 @@ class DataFrame private[sql](
     // data
     rows.tail.map {
       _.zipWithIndex.map { case (cell, i) =>
-        StringUtils.leftPad(cell.toString, colWidths(i))
+        if (truncate) {
+          StringUtils.leftPad(cell.toString, colWidths(i))
+        } else {
+          StringUtils.rightPad(cell.toString, colWidths(i))
+        }
       }.addString(sb, "|", "|", "|\n")
     }
 
@@ -331,7 +340,8 @@ class DataFrame private[sql](
   def isLocal: Boolean = logicalPlan.isInstanceOf[LocalRelation]
 
   /**
-   * Displays the [[DataFrame]] in a tabular form. For example:
+   * Displays the [[DataFrame]] in a tabular form. Strings more than 20 characters will be
+   * truncated, and all cells will be aligned right. For example:
    * {{{
    *   year  month AVG('Adj Close) MAX('Adj Close)
    *   1980  12    0.503218        0.595103
@@ -345,15 +355,46 @@ class DataFrame private[sql](
    * @group action
    * @since 1.3.0
    */
-  def show(numRows: Int): Unit = println(showString(numRows))
+  def show(numRows: Int): Unit = show(numRows, true)
 
   /**
-   * Displays the top 20 rows of [[DataFrame]] in a tabular form.
+   * Displays the top 20 rows of [[DataFrame]] in a tabular form. Strings more than 20 characters
+   * will be truncated, and all cells will be aligned right.
    * @group action
    * @since 1.3.0
    */
   def show(): Unit = show(20)
 
+  /**
+   * Displays the top 20 rows of [[DataFrame]] in a tabular form.
+   *
+   * @param truncate Whether truncate long strings. If true, strings more than 20 characters will
+   *              be truncated and all cells will be aligned right
+   *
+   * @group action
+   * @since 1.5.0
+   */
+  def show(truncate: Boolean): Unit = show(20, truncate)
+
+  /**
+   * Displays the [[DataFrame]] in a tabular form. For example:
+   * {{{
+   *   year  month AVG('Adj Close) MAX('Adj Close)
+   *   1980  12    0.503218        0.595103
+   *   1981  01    0.523289        0.570307
+   *   1982  02    0.436504        0.475256
+   *   1983  03    0.410516        0.442194
+   *   1984  04    0.450090        0.483521
+   * }}}
+   * @param numRows Number of rows to show
+   * @param truncate Whether truncate long strings. If true, strings more than 20 characters will
+   *              be truncated and all cells will be aligned right
+   *
+   * @group action
+   * @since 1.5.0
+   */
+  def show(numRows: Int, truncate: Boolean): Unit = println(showString(numRows, truncate))
+
   /**
    * Returns a [[DataFrameNaFunctions]] for working with missing data.
    * {{{
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index d06b9c5785527..50d324c0686fa 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -492,6 +492,27 @@ class DataFrameSuite extends QueryTest {
     testData.select($"*").show(1000)
   }
 
+  test("showString: truncate = [true, false]") {
+    val longString = Array.fill(21)("1").mkString
+    val df = ctx.sparkContext.parallelize(Seq("1", longString)).toDF()
+    val expectedAnswerForFalse = """+---------------------+
+                                   ||_1                   |
+                                   |+---------------------+
+                                   ||1                    |
+                                   ||111111111111111111111|
+                                   |+---------------------+
+                                   |""".stripMargin
+    assert(df.showString(10, false) === expectedAnswerForFalse)
+    val expectedAnswerForTrue = """+--------------------+
+                                  ||                  _1|
+                                  |+--------------------+
+                                  ||                   1|
+                                  ||11111111111111111...|
+                                  |+--------------------+
+                                  |""".stripMargin
+    assert(df.showString(10, true) === expectedAnswerForTrue)
+  }
+
   test("showString(negative)") {
     val expectedAnswer = """+---+-----+
                            ||key|value|

From 5452457410ffe881773f2f2cdcdc752467b19720 Mon Sep 17 00:00:00 2001
From: Shuo Xiang <shuoxiangpub@gmail.com>
Date: Mon, 29 Jun 2015 23:50:34 -0700
Subject: [PATCH 0126/1454] [SPARK-8551] [ML] Elastic net python code example

Author: Shuo Xiang <shuoxiangpub@gmail.com>

Closes #6946 from coderxiang/en-java-code-example and squashes the following commits:

7a4bdf8 [Shuo Xiang] address comments
cddb02b [Shuo Xiang] add elastic net python example code
f4fa534 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
6ad4865 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
180b496 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
aa0717d [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
5f109b4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
c5c5bfe [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
98804c9 [Shuo Xiang] fix bug in topBykey and update test
---
 .../src/main/python/ml/logistic_regression.py | 67 +++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 examples/src/main/python/ml/logistic_regression.py

diff --git a/examples/src/main/python/ml/logistic_regression.py b/examples/src/main/python/ml/logistic_regression.py
new file mode 100644
index 0000000000000..55afe1b207fe0
--- /dev/null
+++ b/examples/src/main/python/ml/logistic_regression.py
@@ -0,0 +1,67 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.ml.classification import LogisticRegression
+from pyspark.mllib.evaluation import MulticlassMetrics
+from pyspark.ml.feature import StringIndexer
+from pyspark.mllib.util import MLUtils
+from pyspark.sql import SQLContext
+
+"""
+A simple example demonstrating a logistic regression with elastic net regularization Pipeline.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/logistic_regression.py
+"""
+
+if __name__ == "__main__":
+
+    if len(sys.argv) > 1:
+        print("Usage: logistic_regression", file=sys.stderr)
+        exit(-1)
+
+    sc = SparkContext(appName="PythonLogisticRegressionExample")
+    sqlContext = SQLContext(sc)
+
+    # Load and parse the data file into a dataframe.
+    df = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+    # Map labels into an indexed column of labels in [0, numLabels)
+    stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel")
+    si_model = stringIndexer.fit(df)
+    td = si_model.transform(df)
+    [training, test] = td.randomSplit([0.7, 0.3])
+
+    lr = LogisticRegression(maxIter=100, regParam=0.3).setLabelCol("indexedLabel")
+    lr.setElasticNetParam(0.8)
+
+    # Fit the model
+    lrModel = lr.fit(training)
+
+    predictionAndLabels = lrModel.transform(test).select("prediction", "indexedLabel") \
+        .map(lambda x: (x.prediction, x.indexedLabel))
+
+    metrics = MulticlassMetrics(predictionAndLabels)
+    print("weighted f-measure %.3f" % metrics.weightedFMeasure())
+    print("precision %s" % metrics.precision())
+    print("recall %s" % metrics.recall())
+
+    sc.stop()

From 2ed0c0ac4686ea779f98713978e37b97094edc1c Mon Sep 17 00:00:00 2001
From: Tim Ellison <t.p.ellison@gmail.com>
Date: Tue, 30 Jun 2015 13:49:52 +0100
Subject: [PATCH 0127/1454] [SPARK-7756] [CORE] More robust SSL options
 processing.

Subset the enabled algorithms in an SSLOptions to the elements that are supported by the protocol provider.

Update the list of ciphers in the sample config to include modern algorithms, and specify both Oracle and IBM names.  In practice the user would either specify their own chosen cipher suites, or specify none, and delegate the decision to the provider.

Author: Tim Ellison <t.p.ellison@gmail.com>

Closes #7043 from tellison/SSLEnhancements and squashes the following commits:

034efa5 [Tim Ellison] Ensure Java imports are grouped and ordered by package.
3797f8b [Tim Ellison] Remove unnecessary use of Option to improve clarity, and fix import style ordering.
4b5c89f [Tim Ellison] More robust SSL options processing.
---
 .../scala/org/apache/spark/SSLOptions.scala   | 43 ++++++++++++++++---
 .../org/apache/spark/SSLOptionsSuite.scala    | 20 ++++++---
 .../org/apache/spark/SSLSampleConfigs.scala   | 24 ++++++++---
 .../apache/spark/SecurityManagerSuite.scala   | 21 ++++++---
 4 files changed, 85 insertions(+), 23 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SSLOptions.scala b/core/src/main/scala/org/apache/spark/SSLOptions.scala
index 2cdc167f85af0..32df42d57dbd6 100644
--- a/core/src/main/scala/org/apache/spark/SSLOptions.scala
+++ b/core/src/main/scala/org/apache/spark/SSLOptions.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark
 
-import java.io.File
+import java.io.{File, FileInputStream}
+import java.security.{KeyStore, NoSuchAlgorithmException}
+import javax.net.ssl.{KeyManager, KeyManagerFactory, SSLContext, TrustManager, TrustManagerFactory}
 
 import com.typesafe.config.{Config, ConfigFactory, ConfigValueFactory}
 import org.eclipse.jetty.util.ssl.SslContextFactory
@@ -38,7 +40,7 @@ import org.eclipse.jetty.util.ssl.SslContextFactory
  * @param trustStore          a path to the trust-store file
  * @param trustStorePassword  a password to access the trust-store file
  * @param protocol            SSL protocol (remember that SSLv3 was compromised) supported by Java
- * @param enabledAlgorithms   a set of encryption algorithms to use
+ * @param enabledAlgorithms   a set of encryption algorithms that may be used
  */
 private[spark] case class SSLOptions(
     enabled: Boolean = false,
@@ -48,7 +50,8 @@ private[spark] case class SSLOptions(
     trustStore: Option[File] = None,
     trustStorePassword: Option[String] = None,
     protocol: Option[String] = None,
-    enabledAlgorithms: Set[String] = Set.empty) {
+    enabledAlgorithms: Set[String] = Set.empty)
+    extends Logging {
 
   /**
    * Creates a Jetty SSL context factory according to the SSL settings represented by this object.
@@ -63,7 +66,7 @@ private[spark] case class SSLOptions(
       trustStorePassword.foreach(sslContextFactory.setTrustStorePassword)
       keyPassword.foreach(sslContextFactory.setKeyManagerPassword)
       protocol.foreach(sslContextFactory.setProtocol)
-      sslContextFactory.setIncludeCipherSuites(enabledAlgorithms.toSeq: _*)
+      sslContextFactory.setIncludeCipherSuites(supportedAlgorithms.toSeq: _*)
 
       Some(sslContextFactory)
     } else {
@@ -94,7 +97,7 @@ private[spark] case class SSLOptions(
         .withValue("akka.remote.netty.tcp.security.protocol",
           ConfigValueFactory.fromAnyRef(protocol.getOrElse("")))
         .withValue("akka.remote.netty.tcp.security.enabled-algorithms",
-          ConfigValueFactory.fromIterable(enabledAlgorithms.toSeq))
+          ConfigValueFactory.fromIterable(supportedAlgorithms.toSeq))
         .withValue("akka.remote.netty.tcp.enable-ssl",
           ConfigValueFactory.fromAnyRef(true)))
     } else {
@@ -102,6 +105,36 @@ private[spark] case class SSLOptions(
     }
   }
 
+  /*
+   * The supportedAlgorithms set is a subset of the enabledAlgorithms that
+   * are supported by the current Java security provider for this protocol.
+   */
+  private val supportedAlgorithms: Set[String] = {
+    var context: SSLContext = null
+    try {
+      context = SSLContext.getInstance(protocol.orNull)
+      /* The set of supported algorithms does not depend upon the keys, trust, or
+         rng, although they will influence which algorithms are eventually used. */
+      context.init(null, null, null)
+    } catch {
+      case npe: NullPointerException =>
+        logDebug("No SSL protocol specified")
+        context = SSLContext.getDefault
+      case nsa: NoSuchAlgorithmException =>
+        logDebug(s"No support for requested SSL protocol ${protocol.get}")
+        context = SSLContext.getDefault
+    }
+
+    val providerAlgorithms = context.getServerSocketFactory.getSupportedCipherSuites.toSet
+
+    // Log which algorithms we are discarding
+    (enabledAlgorithms &~ providerAlgorithms).foreach { cipher =>
+      logDebug(s"Discarding unsupported cipher $cipher")
+    }
+
+    enabledAlgorithms & providerAlgorithms
+  }
+
   /** Returns a string representation of this SSLOptions with all the passwords masked. */
   override def toString: String = s"SSLOptions{enabled=$enabled, " +
       s"keyStore=$keyStore, keyStorePassword=${keyStorePassword.map(_ => "xxx")}, " +
diff --git a/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala b/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala
index 376481ba541fa..25b79bce6ab98 100644
--- a/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark
 
 import java.io.File
+import javax.net.ssl.SSLContext
 
 import com.google.common.io.Files
 import org.apache.spark.util.Utils
@@ -29,6 +30,15 @@ class SSLOptionsSuite extends SparkFunSuite with BeforeAndAfterAll {
     val keyStorePath = new File(this.getClass.getResource("/keystore").toURI).getAbsolutePath
     val trustStorePath = new File(this.getClass.getResource("/truststore").toURI).getAbsolutePath
 
+    // Pick two cipher suites that the provider knows about
+    val sslContext = SSLContext.getInstance("TLSv1.2")
+    sslContext.init(null, null, null)
+    val algorithms = sslContext
+      .getServerSocketFactory
+      .getDefaultCipherSuites
+      .take(2)
+      .toSet
+
     val conf = new SparkConf
     conf.set("spark.ssl.enabled", "true")
     conf.set("spark.ssl.keyStore", keyStorePath)
@@ -36,9 +46,8 @@ class SSLOptionsSuite extends SparkFunSuite with BeforeAndAfterAll {
     conf.set("spark.ssl.keyPassword", "password")
     conf.set("spark.ssl.trustStore", trustStorePath)
     conf.set("spark.ssl.trustStorePassword", "password")
-    conf.set("spark.ssl.enabledAlgorithms",
-      "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA")
-    conf.set("spark.ssl.protocol", "SSLv3")
+    conf.set("spark.ssl.enabledAlgorithms", algorithms.mkString(","))
+    conf.set("spark.ssl.protocol", "TLSv1.2")
 
     val opts = SSLOptions.parse(conf, "spark.ssl")
 
@@ -52,9 +61,8 @@ class SSLOptionsSuite extends SparkFunSuite with BeforeAndAfterAll {
     assert(opts.trustStorePassword === Some("password"))
     assert(opts.keyStorePassword === Some("password"))
     assert(opts.keyPassword === Some("password"))
-    assert(opts.protocol === Some("SSLv3"))
-    assert(opts.enabledAlgorithms ===
-      Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA"))
+    assert(opts.protocol === Some("TLSv1.2"))
+    assert(opts.enabledAlgorithms === algorithms)
   }
 
   test("test resolving property with defaults specified ") {
diff --git a/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala b/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala
index 1a099da2c6c8e..33270bec6247c 100644
--- a/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala
+++ b/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala
@@ -25,6 +25,20 @@ object SSLSampleConfigs {
     this.getClass.getResource("/untrusted-keystore").toURI).getAbsolutePath
   val trustStorePath = new File(this.getClass.getResource("/truststore").toURI).getAbsolutePath
 
+  val enabledAlgorithms =
+    // A reasonable set of TLSv1.2 Oracle security provider suites
+    "TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384, " +
+    "TLS_RSA_WITH_AES_256_CBC_SHA256, " +
+    "TLS_DHE_RSA_WITH_AES_256_CBC_SHA256, " +
+    "TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256, " +
+    "TLS_DHE_RSA_WITH_AES_128_CBC_SHA256, " +
+    // and their equivalent names in the IBM Security provider
+    "SSL_ECDHE_RSA_WITH_AES_256_CBC_SHA384, " +
+    "SSL_RSA_WITH_AES_256_CBC_SHA256, " +
+    "SSL_DHE_RSA_WITH_AES_256_CBC_SHA256, " +
+    "SSL_ECDHE_RSA_WITH_AES_128_CBC_SHA256, " +
+    "SSL_DHE_RSA_WITH_AES_128_CBC_SHA256"
+
   def sparkSSLConfig(): SparkConf = {
     val conf = new SparkConf(loadDefaults = false)
     conf.set("spark.ssl.enabled", "true")
@@ -33,9 +47,8 @@ object SSLSampleConfigs {
     conf.set("spark.ssl.keyPassword", "password")
     conf.set("spark.ssl.trustStore", trustStorePath)
     conf.set("spark.ssl.trustStorePassword", "password")
-    conf.set("spark.ssl.enabledAlgorithms",
-      "SSL_RSA_WITH_RC4_128_SHA, SSL_RSA_WITH_DES_CBC_SHA")
-    conf.set("spark.ssl.protocol", "TLSv1")
+    conf.set("spark.ssl.enabledAlgorithms", enabledAlgorithms)
+    conf.set("spark.ssl.protocol", "TLSv1.2")
     conf
   }
 
@@ -47,9 +60,8 @@ object SSLSampleConfigs {
     conf.set("spark.ssl.keyPassword", "password")
     conf.set("spark.ssl.trustStore", trustStorePath)
     conf.set("spark.ssl.trustStorePassword", "password")
-    conf.set("spark.ssl.enabledAlgorithms",
-      "SSL_RSA_WITH_RC4_128_SHA, SSL_RSA_WITH_DES_CBC_SHA")
-    conf.set("spark.ssl.protocol", "TLSv1")
+    conf.set("spark.ssl.enabledAlgorithms", enabledAlgorithms)
+    conf.set("spark.ssl.protocol", "TLSv1.2")
     conf
   }
 
diff --git a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
index e9b64aa82a17a..f34aefca4eb18 100644
--- a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
@@ -127,6 +127,17 @@ class SecurityManagerSuite extends SparkFunSuite {
 
   test("ssl on setup") {
     val conf = SSLSampleConfigs.sparkSSLConfig()
+    val expectedAlgorithms = Set(
+    "TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384",
+    "TLS_RSA_WITH_AES_256_CBC_SHA256",
+    "TLS_DHE_RSA_WITH_AES_256_CBC_SHA256",
+    "TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256",
+    "TLS_DHE_RSA_WITH_AES_128_CBC_SHA256",
+    "SSL_ECDHE_RSA_WITH_AES_256_CBC_SHA384",
+    "SSL_RSA_WITH_AES_256_CBC_SHA256",
+    "SSL_DHE_RSA_WITH_AES_256_CBC_SHA256",
+    "SSL_ECDHE_RSA_WITH_AES_128_CBC_SHA256",
+    "SSL_DHE_RSA_WITH_AES_128_CBC_SHA256")
 
     val securityManager = new SecurityManager(conf)
 
@@ -143,9 +154,8 @@ class SecurityManagerSuite extends SparkFunSuite {
     assert(securityManager.fileServerSSLOptions.trustStorePassword === Some("password"))
     assert(securityManager.fileServerSSLOptions.keyStorePassword === Some("password"))
     assert(securityManager.fileServerSSLOptions.keyPassword === Some("password"))
-    assert(securityManager.fileServerSSLOptions.protocol === Some("TLSv1"))
-    assert(securityManager.fileServerSSLOptions.enabledAlgorithms ===
-        Set("SSL_RSA_WITH_RC4_128_SHA", "SSL_RSA_WITH_DES_CBC_SHA"))
+    assert(securityManager.fileServerSSLOptions.protocol === Some("TLSv1.2"))
+    assert(securityManager.fileServerSSLOptions.enabledAlgorithms === expectedAlgorithms)
 
     assert(securityManager.akkaSSLOptions.trustStore.isDefined === true)
     assert(securityManager.akkaSSLOptions.trustStore.get.getName === "truststore")
@@ -154,9 +164,8 @@ class SecurityManagerSuite extends SparkFunSuite {
     assert(securityManager.akkaSSLOptions.trustStorePassword === Some("password"))
     assert(securityManager.akkaSSLOptions.keyStorePassword === Some("password"))
     assert(securityManager.akkaSSLOptions.keyPassword === Some("password"))
-    assert(securityManager.akkaSSLOptions.protocol === Some("TLSv1"))
-    assert(securityManager.akkaSSLOptions.enabledAlgorithms ===
-        Set("SSL_RSA_WITH_RC4_128_SHA", "SSL_RSA_WITH_DES_CBC_SHA"))
+    assert(securityManager.akkaSSLOptions.protocol === Some("TLSv1.2"))
+    assert(securityManager.akkaSSLOptions.enabledAlgorithms === expectedAlgorithms)
   }
 
   test("ssl off setup") {

From 08fab4843845136358f3a7251e8d90135126b419 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 30 Jun 2015 07:58:49 -0700
Subject: [PATCH 0128/1454] [SPARK-8590] [SQL] add code gen for ExtractValue

TODO:  use array instead of Seq as internal representation for `ArrayType`

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #6982 from cloud-fan/extract-value and squashes the following commits:

e203bc1 [Wenchen Fan] address comments
4da0f0b [Wenchen Fan] some clean up
f679969 [Wenchen Fan] fix bug
e64f942 [Wenchen Fan] remove generic
e3f8427 [Wenchen Fan] fix style and address comments
fc694e8 [Wenchen Fan] add code gen for extract value
---
 .../catalyst/expressions/BoundAttribute.scala |   2 +-
 .../sql/catalyst/expressions/Expression.scala |  46 ++++--
 .../catalyst/expressions/ExtractValue.scala   |  76 ++++++++--
 .../sql/catalyst/expressions/arithmetic.scala |   6 +-
 .../expressions/codegen/CodeGenerator.scala   |  15 +-
 .../codegen/GenerateMutableProjection.scala   |   2 +-
 .../spark/sql/catalyst/expressions/math.scala |  13 +-
 .../sql/catalyst/expressions/predicates.scala |   3 -
 .../spark/sql/catalyst/expressions/sets.scala |   4 -
 .../spark/sql/catalyst/util/TypeUtils.scala   |   2 +-
 .../expressions/ComplexTypeSuite.scala        | 131 ++++++++++--------
 11 files changed, 199 insertions(+), 101 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 5db2fcfcb267b..dc0b4ac5cd9bb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -47,7 +47,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
     s"""
         boolean ${ev.isNull} = i.isNullAt($ordinal);
         ${ctx.javaType(dataType)} ${ev.primitive} = ${ev.isNull} ?
-            ${ctx.defaultValue(dataType)} : (${ctx.getColumn(dataType, ordinal)});
+            ${ctx.defaultValue(dataType)} : (${ctx.getColumn("i", dataType, ordinal)});
     """
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index e5dc7b9b5c884..aed48921bdeb5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -179,9 +179,10 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
   override def toString: String = s"($left $symbol $right)"
 
   override def isThreadSafe: Boolean = left.isThreadSafe && right.isThreadSafe
+
   /**
-   * Short hand for generating binary evaluation code, which depends on two sub-evaluations of
-   * the same type.  If either of the sub-expressions is null, the result of this computation
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
    * is assumed to be null.
    *
    * @param f accepts two variable names and returns Java code to compute the output.
@@ -190,15 +191,23 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
       ctx: CodeGenContext,
       ev: GeneratedExpressionCode,
       f: (String, String) => String): String = {
-    // TODO: Right now some timestamp tests fail if we enforce this...
-    if (left.dataType != right.dataType) {
-      // log.warn(s"${left.dataType} != ${right.dataType}")
-    }
+    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+      s"$result = ${f(eval1, eval2)};"
+    })
+  }
 
+  /**
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
+   */
+  protected def nullSafeCodeGen(
+      ctx: CodeGenContext,
+      ev: GeneratedExpressionCode,
+      f: (String, String, String) => String): String = {
     val eval1 = left.gen(ctx)
     val eval2 = right.gen(ctx)
-    val resultCode = f(eval1.primitive, eval2.primitive)
-
+    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
     s"""
       ${eval1.code}
       boolean ${ev.isNull} = ${eval1.isNull};
@@ -206,7 +215,7 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
       if (!${ev.isNull}) {
         ${eval2.code}
         if (!${eval2.isNull}) {
-          ${ev.primitive} = $resultCode;
+          $resultCode
         } else {
           ${ev.isNull} = true;
         }
@@ -245,13 +254,26 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
       ctx: CodeGenContext,
       ev: GeneratedExpressionCode,
       f: String => String): String = {
+    nullSafeCodeGen(ctx, ev, (result, eval) => {
+      s"$result = ${f(eval)};"
+    })
+  }
+
+  /**
+   * Called by unary expressions to generate a code block that returns null if its parent returns
+   * null, and if not not null, use `f` to generate the expression.
+   */
+  protected def nullSafeCodeGen(
+      ctx: CodeGenContext,
+      ev: GeneratedExpressionCode,
+      f: (String, String) => String): String = {
     val eval = child.gen(ctx)
-    // reuse the previous isNull
-    ev.isNull = eval.isNull
+    val resultCode = f(ev.primitive, eval.primitive)
     eval.code + s"""
+      boolean ${ev.isNull} = ${eval.isNull};
       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
       if (!${ev.isNull}) {
-        ${ev.primitive} = ${f(eval.primitive)};
+        $resultCode
       }
     """
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
index 4d7c95ffd1850..3020e7fc967f2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
@@ -21,6 +21,7 @@ import scala.collection.Map
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.analysis._
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types._
 
 object ExtractValue {
@@ -38,7 +39,7 @@ object ExtractValue {
   def apply(
       child: Expression,
       extraction: Expression,
-      resolver: Resolver): ExtractValue = {
+      resolver: Resolver): Expression = {
 
     (child.dataType, extraction) match {
       case (StructType(fields), NonNullLiteral(v, StringType)) =>
@@ -73,7 +74,7 @@ object ExtractValue {
   def unapply(g: ExtractValue): Option[(Expression, Expression)] = {
     g match {
       case o: ExtractValueWithOrdinal => Some((o.child, o.ordinal))
-      case _ => Some((g.child, null))
+      case s: ExtractValueWithStruct => Some((s.child, null))
     }
   }
 
@@ -101,11 +102,11 @@ object ExtractValue {
  * Note: concrete extract value expressions are created only by `ExtractValue.apply`,
  * we don't need to do type check for them.
  */
-trait ExtractValue extends UnaryExpression {
-  self: Product =>
+trait ExtractValue {
+  self: Expression =>
 }
 
-abstract class ExtractValueWithStruct extends ExtractValue {
+abstract class ExtractValueWithStruct extends UnaryExpression with ExtractValue {
   self: Product =>
 
   def field: StructField
@@ -125,6 +126,18 @@ case class GetStructField(child: Expression, field: StructField, ordinal: Int)
     val baseValue = child.eval(input).asInstanceOf[InternalRow]
     if (baseValue == null) null else baseValue(ordinal)
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (result, eval) => {
+      s"""
+        if ($eval.isNullAt($ordinal)) {
+          ${ev.isNull} = true;
+        } else {
+          $result = ${ctx.getColumn(eval, dataType, ordinal)};
+        }
+      """
+    })
+  }
 }
 
 /**
@@ -137,6 +150,7 @@ case class GetArrayStructFields(
     containsNull: Boolean) extends ExtractValueWithStruct {
 
   override def dataType: DataType = ArrayType(field.dataType, containsNull)
+  override def nullable: Boolean = child.nullable || containsNull || field.nullable
 
   override def eval(input: InternalRow): Any = {
     val baseValue = child.eval(input).asInstanceOf[Seq[InternalRow]]
@@ -146,18 +160,39 @@ case class GetArrayStructFields(
       }
     }
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val arraySeqClass = "scala.collection.mutable.ArraySeq"
+    // TODO: consider using Array[_] for ArrayType child to avoid
+    // boxing of primitives
+    nullSafeCodeGen(ctx, ev, (result, eval) => {
+      s"""
+        final int n = $eval.size();
+        final $arraySeqClass<Object> values = new $arraySeqClass<Object>(n);
+        for (int j = 0; j < n; j++) {
+          InternalRow row = (InternalRow) $eval.apply(j);
+          if (row != null && !row.isNullAt($ordinal)) {
+            values.update(j, ${ctx.getColumn("row", field.dataType, ordinal)});
+          }
+        }
+        $result = (${ctx.javaType(dataType)}) values;
+      """
+    })
+  }
 }
 
-abstract class ExtractValueWithOrdinal extends ExtractValue {
+abstract class ExtractValueWithOrdinal extends BinaryExpression with ExtractValue {
   self: Product =>
 
   def ordinal: Expression
+  def child: Expression
+
+  override def left: Expression = child
+  override def right: Expression = ordinal
 
   /** `Null` is returned for invalid ordinals. */
   override def nullable: Boolean = true
-  override def foldable: Boolean = child.foldable && ordinal.foldable
   override def toString: String = s"$child[$ordinal]"
-  override def children: Seq[Expression] = child :: ordinal :: Nil
 
   override def eval(input: InternalRow): Any = {
     val value = child.eval(input)
@@ -195,6 +230,19 @@ case class GetArrayItem(child: Expression, ordinal: Expression)
       baseValue(index)
     }
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+      s"""
+        final int index = (int)$eval2;
+        if (index >= $eval1.size() || index < 0) {
+          ${ev.isNull} = true;
+        } else {
+          $result = (${ctx.boxedType(dataType)})$eval1.apply(index);
+        }
+      """
+    })
+  }
 }
 
 /**
@@ -209,4 +257,16 @@ case class GetMapValue(child: Expression, ordinal: Expression)
     val baseValue = value.asInstanceOf[Map[Any, _]]
     baseValue.get(ordinal).orNull
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+      s"""
+        if ($eval1.contains($eval2)) {
+          $result = (${ctx.boxedType(dataType)})$eval1.apply($eval2);
+        } else {
+          ${ev.isNull} = true;
+        }
+      """
+    })
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 3d4d9e2d798f0..ae765c1653203 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -82,8 +82,6 @@ case class Abs(child: Expression) extends UnaryArithmetic {
 abstract class BinaryArithmetic extends BinaryExpression {
   self: Product =>
 
-  /** Name of the function for this expression on a [[Decimal]] type. */
-  def decimalMethod: String = ""
 
   override def dataType: DataType = left.dataType
 
@@ -113,6 +111,10 @@ abstract class BinaryArithmetic extends BinaryExpression {
     }
   }
 
+  /** Name of the function for this expression on a [[Decimal]] type. */
+  def decimalMethod: String =
+    sys.error("BinaryArithmetics must override either decimalMethod or genCode")
+
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
     case dt: DecimalType =>
       defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.$decimalMethod($eval2)")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 57e0bede5db20..bf6a6a124088e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -82,24 +82,24 @@ class CodeGenContext {
   /**
    * Returns the code to access a column in Row for a given DataType.
    */
-  def getColumn(dataType: DataType, ordinal: Int): String = {
+  def getColumn(row: String, dataType: DataType, ordinal: Int): String = {
     val jt = javaType(dataType)
     if (isPrimitiveType(jt)) {
-      s"i.get${primitiveTypeName(jt)}($ordinal)"
+      s"$row.get${primitiveTypeName(jt)}($ordinal)"
     } else {
-      s"($jt)i.apply($ordinal)"
+      s"($jt)$row.apply($ordinal)"
     }
   }
 
   /**
    * Returns the code to update a column in Row for a given DataType.
    */
-  def setColumn(dataType: DataType, ordinal: Int, value: String): String = {
+  def setColumn(row: String, dataType: DataType, ordinal: Int, value: String): String = {
     val jt = javaType(dataType)
     if (isPrimitiveType(jt)) {
-      s"set${primitiveTypeName(jt)}($ordinal, $value)"
+      s"$row.set${primitiveTypeName(jt)}($ordinal, $value)"
     } else {
-      s"update($ordinal, $value)"
+      s"$row.update($ordinal, $value)"
     }
   }
 
@@ -127,6 +127,9 @@ class CodeGenContext {
     case dt: DecimalType => decimalType
     case BinaryType => "byte[]"
     case StringType => stringType
+    case _: StructType => "InternalRow"
+    case _: ArrayType => s"scala.collection.Seq"
+    case _: MapType => s"scala.collection.Map"
     case dt: OpenHashSetUDT if dt.elementType == IntegerType => classOf[IntegerHashSet].getName
     case dt: OpenHashSetUDT if dt.elementType == LongType => classOf[LongHashSet].getName
     case _ => "Object"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index 64ef357a4f954..addb8023d9c0b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -43,7 +43,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
           if(${evaluationCode.isNull})
             mutableRow.setNullAt($i);
           else
-            mutableRow.${ctx.setColumn(e.dataType, i, evaluationCode.primitive)};
+            ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
         """
     }.mkString("\n")
     val code = s"""
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index a022f3727bd58..da63f2fa970cf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -78,17 +78,14 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
   def funcName: String = name.toLowerCase
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val eval = child.gen(ctx)
-    eval.code + s"""
-      boolean ${ev.isNull} = ${eval.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${ev.primitive} = java.lang.Math.${funcName}(${eval.primitive});
+    nullSafeCodeGen(ctx, ev, (result, eval) => {
+      s"""
+        ${ev.primitive} = java.lang.Math.${funcName}($eval);
         if (Double.valueOf(${ev.primitive}).isNaN()) {
           ${ev.isNull} = true;
         }
-      }
-    """
+      """
+    })
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 386cf6a8df6df..98cd5aa8148c4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -69,10 +69,7 @@ trait PredicateHelper {
     expr.references.subsetOf(plan.outputSet)
 }
 
-
 case class Not(child: Expression) extends UnaryExpression with Predicate with AutoCastInputTypes {
-  override def foldable: Boolean = child.foldable
-  override def nullable: Boolean = child.nullable
   override def toString: String = s"NOT $child"
 
   override def expectedChildTypes: Seq[DataType] = Seq(BooleanType)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index efc6f50b78943..daa9f4403ffab 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -135,8 +135,6 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression {
  */
 case class CombineSets(left: Expression, right: Expression) extends BinaryExpression {
 
-  override def nullable: Boolean = left.nullable || right.nullable
-
   override def dataType: DataType = left.dataType
 
   override def symbol: String = "++="
@@ -185,8 +183,6 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres
  */
 case class CountSet(child: Expression) extends UnaryExpression {
 
-  override def nullable: Boolean = child.nullable
-
   override def dataType: DataType = LongType
 
   override def eval(input: InternalRow): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
index 8656cc334d09f..3148309a2166f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
@@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.types._
 
 /**
- * Helper function to check for valid data types
+ * Helper functions to check for valid data types.
  */
 object TypeUtils {
   def checkForNumericExpr(t: DataType, caller: String): TypeCheckResult = {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index b80911e7257fc..3515d044b2f7e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -40,51 +40,42 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("GetArrayItem") {
+    val typeA = ArrayType(StringType)
+    val array = Literal.create(Seq("a", "b"), typeA)
     testIntegralDataTypes { convert =>
-      val array = Literal.create(Seq("a", "b"), ArrayType(StringType))
       checkEvaluation(GetArrayItem(array, Literal(convert(1))), "b")
     }
+    val nullArray = Literal.create(null, typeA)
+    val nullInt = Literal.create(null, IntegerType)
+    checkEvaluation(GetArrayItem(nullArray, Literal(1)), null)
+    checkEvaluation(GetArrayItem(array, nullInt), null)
+    checkEvaluation(GetArrayItem(nullArray, nullInt), null)
+
+    val nestedArray = Literal.create(Seq(Seq(1)), ArrayType(ArrayType(IntegerType)))
+    checkEvaluation(GetArrayItem(nestedArray, Literal(0)), Seq(1))
   }
 
-  test("CreateStruct") {
-    val row = InternalRow(1, 2, 3)
-    val c1 = 'a.int.at(0).as("a")
-    val c3 = 'c.int.at(2).as("c")
-    checkEvaluation(CreateStruct(Seq(c1, c3)), InternalRow(1, 3), row)
+  test("GetMapValue") {
+    val typeM = MapType(StringType, StringType)
+    val map = Literal.create(Map("a" -> "b"), typeM)
+    val nullMap = Literal.create(null, typeM)
+    val nullString = Literal.create(null, StringType)
+
+    checkEvaluation(GetMapValue(map, Literal("a")), "b")
+    checkEvaluation(GetMapValue(map, nullString), null)
+    checkEvaluation(GetMapValue(nullMap, nullString), null)
+    checkEvaluation(GetMapValue(map, nullString), null)
+
+    val nestedMap = Literal.create(Map("a" -> Map("b" -> "c")), MapType(StringType, typeM))
+    checkEvaluation(GetMapValue(nestedMap, Literal("a")), Map("b" -> "c"))
   }
 
-  test("complex type") {
-    val row = create_row(
-      "^Ba*n",                                // 0
-      null.asInstanceOf[UTF8String],          // 1
-      create_row("aa", "bb"),                 // 2
-      Map("aa"->"bb"),                        // 3
-      Seq("aa", "bb")                         // 4
-    )
-
-    val typeS = StructType(
-      StructField("a", StringType, true) :: StructField("b", StringType, true) :: Nil
-    )
-    val typeMap = MapType(StringType, StringType)
-    val typeArray = ArrayType(StringType)
-
-    checkEvaluation(GetMapValue(BoundReference(3, typeMap, true),
-      Literal("aa")), "bb", row)
-    checkEvaluation(GetMapValue(Literal.create(null, typeMap), Literal("aa")), null, row)
-    checkEvaluation(
-      GetMapValue(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row)
-    checkEvaluation(GetMapValue(BoundReference(3, typeMap, true),
-      Literal.create(null, StringType)), null, row)
-
-    checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true),
-      Literal(1)), "bb", row)
-    checkEvaluation(GetArrayItem(Literal.create(null, typeArray), Literal(1)), null, row)
-    checkEvaluation(
-      GetArrayItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row)
-    checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true),
-      Literal.create(null, IntegerType)), null, row)
-
-    def getStructField(expr: Expression, fieldName: String): ExtractValue = {
+  test("GetStructField") {
+    val typeS = StructType(StructField("a", IntegerType) :: Nil)
+    val struct = Literal.create(create_row(1), typeS)
+    val nullStruct = Literal.create(null, typeS)
+
+    def getStructField(expr: Expression, fieldName: String): GetStructField = {
       expr.dataType match {
         case StructType(fields) =>
           val field = fields.find(_.name == fieldName).get
@@ -92,28 +83,58 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
       }
     }
 
-    def quickResolve(u: UnresolvedExtractValue): ExtractValue = {
-      ExtractValue(u.child, u.extraction, _ == _)
-    }
+    checkEvaluation(getStructField(struct, "a"), 1)
+    checkEvaluation(getStructField(nullStruct, "a"), null)
+
+    val nestedStruct = Literal.create(create_row(create_row(1)),
+      StructType(StructField("a", typeS) :: Nil))
+    checkEvaluation(getStructField(nestedStruct, "a"), create_row(1))
+
+    val typeS_fieldNotNullable = StructType(StructField("a", IntegerType, false) :: Nil)
+    val struct_fieldNotNullable = Literal.create(create_row(1), typeS_fieldNotNullable)
+    val nullStruct_fieldNotNullable = Literal.create(null, typeS_fieldNotNullable)
+
+    assert(getStructField(struct_fieldNotNullable, "a").nullable === false)
+    assert(getStructField(struct, "a").nullable === true)
+    assert(getStructField(nullStruct_fieldNotNullable, "a").nullable === true)
+    assert(getStructField(nullStruct, "a").nullable === true)
+  }
 
-    checkEvaluation(getStructField(BoundReference(2, typeS, nullable = true), "a"), "aa", row)
-    checkEvaluation(getStructField(Literal.create(null, typeS), "a"), null, row)
+  test("GetArrayStructFields") {
+    val typeAS = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
+    val arrayStruct = Literal.create(Seq(create_row(1)), typeAS)
+    val nullArrayStruct = Literal.create(null, typeAS)
 
-    val typeS_notNullable = StructType(
-      StructField("a", StringType, nullable = false)
-        :: StructField("b", StringType, nullable = false) :: Nil
-    )
+    def getArrayStructFields(expr: Expression, fieldName: String): GetArrayStructFields = {
+      expr.dataType match {
+        case ArrayType(StructType(fields), containsNull) =>
+          val field = fields.find(_.name == fieldName).get
+          GetArrayStructFields(expr, field, fields.indexOf(field), containsNull)
+      }
+    }
+
+    checkEvaluation(getArrayStructFields(arrayStruct, "a"), Seq(1))
+    checkEvaluation(getArrayStructFields(nullArrayStruct, "a"), null)
+  }
 
-    assert(getStructField(BoundReference(2, typeS, nullable = true), "a").nullable === true)
-    assert(getStructField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable
-      === false)
+  test("CreateStruct") {
+    val row = create_row(1, 2, 3)
+    val c1 = 'a.int.at(0).as("a")
+    val c3 = 'c.int.at(2).as("c")
+    checkEvaluation(CreateStruct(Seq(c1, c3)), create_row(1, 3), row)
+  }
 
-    assert(getStructField(Literal.create(null, typeS), "a").nullable === true)
-    assert(getStructField(Literal.create(null, typeS_notNullable), "a").nullable === true)
+  test("test dsl for complex type") {
+    def quickResolve(u: UnresolvedExtractValue): Expression = {
+      ExtractValue(u.child, u.extraction, _ == _)
+    }
 
-    checkEvaluation(quickResolve('c.map(typeMap).at(3).getItem("aa")), "bb", row)
-    checkEvaluation(quickResolve('c.array(typeArray.elementType).at(4).getItem(1)), "bb", row)
-    checkEvaluation(quickResolve('c.struct(typeS).at(2).getField("a")), "aa", row)
+    checkEvaluation(quickResolve('c.map(MapType(StringType, StringType)).at(0).getItem("a")),
+      "b", create_row(Map("a" -> "b")))
+    checkEvaluation(quickResolve('c.array(StringType).at(0).getItem(1)),
+      "b", create_row(Seq("a", "b")))
+    checkEvaluation(quickResolve('c.struct(StructField("a", IntegerType)).at(0).getField("a")),
+      1, create_row(create_row(1)))
   }
 
   test("error message of ExtractValue") {

From 865a834e51ac3074811a11fd99a36d942f7f7de8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 30 Jun 2015 08:08:15 -0700
Subject: [PATCH 0129/1454] [SPARK-8723] [SQL] improve divide and remainder
 code gen

We can avoid execution of both left and right expression by null and zero check.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7111 from cloud-fan/cg and squashes the following commits:

d6b12ef [Wenchen Fan] improve divide and remainder code gen
---
 .../sql/catalyst/expressions/arithmetic.scala | 54 ++++++++++++-------
 1 file changed, 36 insertions(+), 18 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index ae765c1653203..5363b3556886a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -216,23 +216,32 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val eval1 = left.gen(ctx)
     val eval2 = right.gen(ctx)
-    val test = if (left.dataType.isInstanceOf[DecimalType]) {
+    val isZero = if (dataType.isInstanceOf[DecimalType]) {
       s"${eval2.primitive}.isZero()"
     } else {
       s"${eval2.primitive} == 0"
     }
-    val method = if (left.dataType.isInstanceOf[DecimalType]) s".$decimalMethod" else s" $symbol "
-    val javaType = ctx.javaType(left.dataType)
-    eval1.code + eval2.code +
-      s"""
+    val javaType = ctx.javaType(dataType)
+    val divide = if (dataType.isInstanceOf[DecimalType]) {
+      s"${eval1.primitive}.$decimalMethod(${eval2.primitive})"
+    } else {
+      s"($javaType)(${eval1.primitive} $symbol ${eval2.primitive})"
+    }
+    s"""
+      ${eval2.code}
       boolean ${ev.isNull} = false;
-      ${ctx.javaType(left.dataType)} ${ev.primitive} = ${ctx.defaultValue(left.dataType)};
-      if (${eval1.isNull} || ${eval2.isNull} || $test) {
+      $javaType ${ev.primitive} = ${ctx.defaultValue(javaType)};
+      if (${eval2.isNull} || $isZero) {
         ${ev.isNull} = true;
       } else {
-        ${ev.primitive} = ($javaType) (${eval1.primitive}$method(${eval2.primitive}));
+        ${eval1.code}
+        if (${eval1.isNull}) {
+          ${ev.isNull} = true;
+        } else {
+          ${ev.primitive} = $divide;
+        }
       }
-      """
+    """
   }
 }
 
@@ -273,23 +282,32 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val eval1 = left.gen(ctx)
     val eval2 = right.gen(ctx)
-    val test = if (left.dataType.isInstanceOf[DecimalType]) {
+    val isZero = if (dataType.isInstanceOf[DecimalType]) {
       s"${eval2.primitive}.isZero()"
     } else {
       s"${eval2.primitive} == 0"
     }
-    val method = if (left.dataType.isInstanceOf[DecimalType]) s".$decimalMethod" else s" $symbol "
-    val javaType = ctx.javaType(left.dataType)
-    eval1.code + eval2.code +
-      s"""
+    val javaType = ctx.javaType(dataType)
+    val remainder = if (dataType.isInstanceOf[DecimalType]) {
+      s"${eval1.primitive}.$decimalMethod(${eval2.primitive})"
+    } else {
+      s"($javaType)(${eval1.primitive} $symbol ${eval2.primitive})"
+    }
+    s"""
+      ${eval2.code}
       boolean ${ev.isNull} = false;
-      ${ctx.javaType(left.dataType)} ${ev.primitive} = ${ctx.defaultValue(left.dataType)};
-      if (${eval1.isNull} || ${eval2.isNull} || $test) {
+      $javaType ${ev.primitive} = ${ctx.defaultValue(javaType)};
+      if (${eval2.isNull} || $isZero) {
         ${ev.isNull} = true;
       } else {
-        ${ev.primitive} = ($javaType) (${eval1.primitive}$method(${eval2.primitive}));
+        ${eval1.code}
+        if (${eval1.isNull}) {
+          ${ev.isNull} = true;
+        } else {
+          ${ev.primitive} = $remainder;
+        }
       }
-      """
+    """
   }
 }
 

From a48e61915354d33fb98944a8eb5a5d48dd102041 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Tue, 30 Jun 2015 08:17:24 -0700
Subject: [PATCH 0130/1454] [SPARK-8680] [SQL] Slightly improve PropagateTypes

JIRA: https://issues.apache.org/jira/browse/SPARK-8680

This PR slightly improve `PropagateTypes` in `HiveTypeCoercion`. It moves `q.inputSet` outside `q transformExpressions` instead calling `inputSet` multiple times. It also builds a map of attributes for looking attribute easily.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #7087 from viirya/improve_propagatetypes and squashes the following commits:

5c314c1 [Liang-Chi Hsieh] For comments.
913f6ad [Liang-Chi Hsieh] Slightly improve PropagateTypes.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 30 ++++++++++---------
 1 file changed, 16 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index c3d68197d64ac..e525ad623ff12 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -131,20 +131,22 @@ trait HiveTypeCoercion {
       // Don't propagate types from unresolved children.
       case q: LogicalPlan if !q.childrenResolved => q
 
-      case q: LogicalPlan => q transformExpressions {
-        case a: AttributeReference =>
-          q.inputSet.find(_.exprId == a.exprId) match {
-            // This can happen when a Attribute reference is born in a non-leaf node, for example
-            // due to a call to an external script like in the Transform operator.
-            // TODO: Perhaps those should actually be aliases?
-            case None => a
-            // Leave the same if the dataTypes match.
-            case Some(newType) if a.dataType == newType.dataType => a
-            case Some(newType) =>
-              logDebug(s"Promoting $a to $newType in ${q.simpleString}}")
-              newType
-          }
-      }
+      case q: LogicalPlan =>
+        val inputMap = q.inputSet.toSeq.map(a => (a.exprId, a)).toMap
+        q transformExpressions {
+          case a: AttributeReference =>
+            inputMap.get(a.exprId) match {
+              // This can happen when a Attribute reference is born in a non-leaf node, for example
+              // due to a call to an external script like in the Transform operator.
+              // TODO: Perhaps those should actually be aliases?
+              case None => a
+              // Leave the same if the dataTypes match.
+              case Some(newType) if a.dataType == newType.dataType => a
+              case Some(newType) =>
+                logDebug(s"Promoting $a to $newType in ${q.simpleString}}")
+                newType
+            }
+        }
     }
   }
 

From 722aa5f48ec105bf23eee2361adddfe3a0cd6fc4 Mon Sep 17 00:00:00 2001
From: Shilei <shilei.qian@intel.com>
Date: Tue, 30 Jun 2015 09:49:58 -0700
Subject: [PATCH 0131/1454] [SPARK-8236] [SQL] misc functions: crc32

https://issues.apache.org/jira/browse/SPARK-8236

Author: Shilei <shilei.qian@intel.com>

Closes #7108 from qiansl127/Crc32 and squashes the following commits:

5477352 [Shilei] Change to AutoCastInputTypes
5f16e5d [Shilei] Add misc function crc32
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../spark/sql/catalyst/expressions/misc.scala | 40 +++++++++++++++++++
 .../expressions/MiscFunctionsSuite.scala      |  8 ++++
 .../org/apache/spark/sql/functions.scala      | 16 ++++++++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 11 +++++
 5 files changed, 76 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index b17457d3094c2..d53eaedda56b0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -139,6 +139,7 @@ object FunctionRegistry {
     expression[Sha2]("sha2"),
     expression[Sha1]("sha1"),
     expression[Sha1]("sha"),
+    expression[Crc32]("crc32"),
 
     // aggregate functions
     expression[Average]("avg"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index 27805bff293f4..a7bcbe46c339a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.security.MessageDigest
 import java.security.NoSuchAlgorithmException
+import java.util.zip.CRC32
 
 import org.apache.commons.codec.digest.DigestUtils
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
@@ -168,3 +169,42 @@ case class Sha1(child: Expression) extends UnaryExpression with AutoCastInputTyp
     )
   }
 }
+
+/**
+ * A function that computes a cyclic redundancy check value and returns it as a bigint
+ * For input of type [[BinaryType]]
+ */
+case class Crc32(child: Expression)
+  extends UnaryExpression with AutoCastInputTypes {
+
+  override def dataType: DataType = LongType
+
+  override def expectedChildTypes: Seq[DataType] = Seq(BinaryType)
+
+  override def eval(input: InternalRow): Any = {
+    val value = child.eval(input)
+    if (value == null) {
+      null
+    } else {
+      val checksum = new CRC32
+      checksum.update(value.asInstanceOf[Array[Byte]], 0, value.asInstanceOf[Array[Byte]].length)
+      checksum.getValue
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val value = child.gen(ctx)
+    val CRC32 = "java.util.zip.CRC32"
+    s"""
+      ${value.code}
+      boolean ${ev.isNull} = ${value.isNull};
+      long ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${CRC32} checksum = new ${CRC32}();
+        checksum.update(${value.primitive}, 0, ${value.primitive}.length);
+        ${ev.primitive} = checksum.getValue();
+      }
+    """
+  }
+
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
index 36e636b5da6b8..b524d0af14a67 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
@@ -49,4 +49,12 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Sha2(Literal("ABC".getBytes), Literal.create(null, IntegerType)), null)
     checkEvaluation(Sha2(Literal.create(null, BinaryType), Literal.create(null, IntegerType)), null)
   }
+
+  test("crc32") {
+    checkEvaluation(Crc32(Literal("ABC".getBytes)), 2743272264L)
+    checkEvaluation(Crc32(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType)),
+      2180413220L)
+    checkEvaluation(Crc32(Literal.create(null, BinaryType)), null)
+  }
+
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4d9a019058228..6331fe61052ab 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1466,6 +1466,22 @@ object functions {
    */
   def sha2(columnName: String, numBits: Int): Column = sha2(Column(columnName), numBits)
 
+  /**
+   * Calculates the cyclic redundancy check value and returns the value as a bigint.
+   *
+   * @group misc_funcs
+   * @since 1.5.0
+   */
+  def crc32(e: Column): Column = Crc32(e.expr)
+
+  /**
+   * Calculates the cyclic redundancy check value and returns the value as a bigint.
+   *
+   * @group misc_funcs
+   * @since 1.5.0
+   */
+  def crc32(columnName: String): Column = crc32(Column(columnName))
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   // String functions
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index abfd47c811ed9..11a8767ead96c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -173,6 +173,17 @@ class DataFrameFunctionsSuite extends QueryTest {
     }
   }
 
+  test("misc crc32 function") {
+    val df = Seq(("ABC", Array[Byte](1, 2, 3, 4, 5, 6))).toDF("a", "b")
+    checkAnswer(
+      df.select(crc32($"a"), crc32("b")),
+      Row(2743272264L, 2180413220L))
+
+    checkAnswer(
+      df.selectExpr("crc32(a)", "crc32(b)"),
+      Row(2743272264L, 2180413220L))
+  }
+
   test("string length function") {
     checkAnswer(
       nullStrings.select(strlen($"s"), strlen("s")),

From 689da28a53cf720ae607a1a935093612a7001615 Mon Sep 17 00:00:00 2001
From: xuchenCN <chenxu198511@gmail.com>
Date: Tue, 30 Jun 2015 10:05:51 -0700
Subject: [PATCH 0132/1454] [SPARK-8592] [CORE] CoarseGrainedExecutorBackend:
 Cannot register with driver => NPE

Look detail of this issue at [SPARK-8592](https://issues.apache.org/jira/browse/SPARK-8592)

**CoarseGrainedExecutorBackend** should exit when **RegisterExecutor** failed

Author: xuchenCN <chenxu198511@gmail.com>

Closes #7110 from xuchenCN/SPARK-8592 and squashes the following commits:

71e0077 [xuchenCN] [SPARK-8592] [CORE] CoarseGrainedExecutorBackend: Cannot register with driver => NPE
---
 .../apache/spark/executor/CoarseGrainedExecutorBackend.scala | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index f3a26f54a81fb..34d4cfdca7732 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -66,7 +66,10 @@ private[spark] class CoarseGrainedExecutorBackend(
       case Success(msg) => Utils.tryLogNonFatalError {
         Option(self).foreach(_.send(msg)) // msg must be RegisteredExecutor
       }
-      case Failure(e) => logError(s"Cannot register with driver: $driverUrl", e)
+      case Failure(e) => {
+        logError(s"Cannot register with driver: $driverUrl", e)
+        System.exit(1)
+      }
     }(ThreadUtils.sameThread)
   }
 

From ada384b785c663392a0b69fad5bfe7a0a0584ee0 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 30 Jun 2015 10:07:26 -0700
Subject: [PATCH 0133/1454] [SPARK-8437] [DOCS] Corrected: Using directory path
 without wildcard for filename slow for large number of files with
 wholeTextFiles and binaryFiles

Note that 'dir/*' can be more efficient in some Hadoop FS implementations that 'dir/' (now fixed scaladoc by using HTML entity for *)

Author: Sean Owen <sowen@cloudera.com>

Closes #7126 from srowen/SPARK-8437.2 and squashes the following commits:

7bb45da [Sean Owen] Note that 'dir/*' can be more efficient in some Hadoop FS implementations that 'dir/' (now fixed scaladoc by using HTML entity for *)
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index b3c3bf3746e18..0e5a86f44e410 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -831,7 +831,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * }}}
    *
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
-   *
+   * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
+   *       in a directory rather than `.../path/` or `.../path`
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
   def wholeTextFiles(
@@ -878,9 +879,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    *   (a-hdfs-path/part-nnnnn, its content)
    * }}}
    *
-   * @param minPartitions A suggestion value of the minimal splitting number for input data.
-   *
    * @note Small files are preferred; very large files may cause bad performance.
+   * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
+   *       in a directory rather than `.../path/` or `.../path`
+   * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
   @Experimental
   def binaryFiles(

From 45281664e0d3b22cd63660ca8ad6dd574f10e21f Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 30 Jun 2015 10:25:59 -0700
Subject: [PATCH 0134/1454] [SPARK-4127] [MLLIB] [PYSPARK] Python bindings for
 StreamingLinearRegressionWithSGD

Python bindings for StreamingLinearRegressionWithSGD

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #6744 from MechCoder/spark-4127 and squashes the following commits:

d8f6457 [MechCoder] Moved StreamingLinearAlgorithm to pyspark.mllib.regression
d47cc24 [MechCoder] Inherit from StreamingLinearAlgorithm
1b4ddd6 [MechCoder] minor
4de6c68 [MechCoder] Minor refactor
5e85a3b [MechCoder] Add tests for simultaneous training and prediction
fb27889 [MechCoder] Add example and docs
505380b [MechCoder] Add tests
d42bdae [MechCoder] [SPARK-4127] Python bindings for StreamingLinearRegressionWithSGD
---
 docs/mllib-linear-methods.md           |  52 +++++++++++
 python/pyspark/mllib/classification.py |  50 +---------
 python/pyspark/mllib/regression.py     |  90 ++++++++++++++++++
 python/pyspark/mllib/tests.py          | 124 ++++++++++++++++++++++++-
 4 files changed, 269 insertions(+), 47 deletions(-)

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 3dc8cc902fa72..2a2a7c13186d8 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -768,6 +768,58 @@ will get better!
 
 </div>
 
+<div data-lang="python" markdown="1">
+
+First, we import the necessary classes for parsing our input data and creating the model.
+
+{% highlight python %}
+from pyspark.mllib.linalg import Vectors
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.regression import StreamingLinearRegressionWithSGD
+{% endhighlight %}
+
+Then we make input streams for training and testing data. We assume a StreamingContext `ssc`
+has already been created, see [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing)
+for more info. For this example, we use labeled points in training and testing streams,
+but in practice you will likely want to use unlabeled vectors for test data.
+
+{% highlight python %}
+def parse(lp):
+    label = float(lp[lp.find('(') + 1: lp.find(',')])
+    vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(','))
+    return LabeledPoint(label, vec)
+
+trainingData = ssc.textFileStream("/training/data/dir").map(parse).cache()
+testData = ssc.textFileStream("/testing/data/dir").map(parse)
+{% endhighlight %}
+
+We create our model by initializing the weights to 0
+
+{% highlight python %}
+numFeatures = 3
+model = StreamingLinearRegressionWithSGD()
+model.setInitialWeights([0.0, 0.0, 0.0])
+{% endhighlight %}
+
+Now we register the streams for training and testing and start the job.
+
+{% highlight python %}
+model.trainOn(trainingData)
+print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features))))
+
+ssc.start()
+ssc.awaitTermination()
+{% endhighlight %}
+
+We can now save text files with data to the training or testing folders.
+Each line should be a data point formatted as `(y,[x1,x2,x3])` where `y` is the label
+and `x1,x2,x3` are the features. Anytime a text file is placed in `/training/data/dir`
+the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions.
+As you feed more data to the training directory, the predictions
+will get better!
+
+</div>
+
 </div>
 
 
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 735d45ba03d27..8f27c446a66e8 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -24,7 +24,9 @@
 from pyspark.streaming import DStream
 from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py
 from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector
-from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper
+from pyspark.mllib.regression import (
+    LabeledPoint, LinearModel, _regression_train_wrapper,
+    StreamingLinearAlgorithm)
 from pyspark.mllib.util import Saveable, Loader, inherit_doc
 
 
@@ -585,55 +587,13 @@ def train(cls, data, lambda_=1.0):
         return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
 
 
-class StreamingLinearAlgorithm(object):
-    """
-    Base class that has to be inherited by any StreamingLinearAlgorithm.
-
-    Prevents reimplementation of methods predictOn and predictOnValues.
-    """
-    def __init__(self, model):
-        self._model = model
-
-    def latestModel(self):
-        """
-        Returns the latest model.
-        """
-        return self._model
-
-    def _validate(self, dstream):
-        if not isinstance(dstream, DStream):
-            raise TypeError(
-                "dstream should be a DStream object, got %s" % type(dstream))
-        if not self._model:
-            raise ValueError(
-                "Model must be intialized using setInitialWeights")
-
-    def predictOn(self, dstream):
-        """
-        Make predictions on a dstream.
-
-        :return: Transformed dstream object.
-        """
-        self._validate(dstream)
-        return dstream.map(lambda x: self._model.predict(x))
-
-    def predictOnValues(self, dstream):
-        """
-        Make predictions on a keyed dstream.
-
-        :return: Transformed dstream object.
-        """
-        self._validate(dstream)
-        return dstream.mapValues(lambda x: self._model.predict(x))
-
-
 @inherit_doc
 class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
     """
-    Run LogisticRegression with SGD on a stream of data.
+    Run LogisticRegression with SGD on a batch of data.
 
     The weights obtained at the end of training a stream are used as initial
-    weights for the next stream.
+    weights for the next batch.
 
     :param stepSize: Step size for each iteration of gradient descent.
     :param numIterations: Number of iterations run for each batch of data.
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 5ddbbee4babdd..8e90adee5f4c2 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -19,6 +19,7 @@
 from numpy import array
 
 from pyspark import RDD
+from pyspark.streaming.dstream import DStream
 from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py, inherit_doc
 from pyspark.mllib.linalg import SparseVector, Vectors, _convert_to_vector
 from pyspark.mllib.util import Saveable, Loader
@@ -570,6 +571,95 @@ def train(cls, data, isotonic=True):
         return IsotonicRegressionModel(boundaries.toArray(), predictions.toArray(), isotonic)
 
 
+class StreamingLinearAlgorithm(object):
+    """
+    Base class that has to be inherited by any StreamingLinearAlgorithm.
+
+    Prevents reimplementation of methods predictOn and predictOnValues.
+    """
+    def __init__(self, model):
+        self._model = model
+
+    def latestModel(self):
+        """
+        Returns the latest model.
+        """
+        return self._model
+
+    def _validate(self, dstream):
+        if not isinstance(dstream, DStream):
+            raise TypeError(
+                "dstream should be a DStream object, got %s" % type(dstream))
+        if not self._model:
+            raise ValueError(
+                "Model must be intialized using setInitialWeights")
+
+    def predictOn(self, dstream):
+        """
+        Make predictions on a dstream.
+
+        :return: Transformed dstream object.
+        """
+        self._validate(dstream)
+        return dstream.map(lambda x: self._model.predict(x))
+
+    def predictOnValues(self, dstream):
+        """
+        Make predictions on a keyed dstream.
+
+        :return: Transformed dstream object.
+        """
+        self._validate(dstream)
+        return dstream.mapValues(lambda x: self._model.predict(x))
+
+
+@inherit_doc
+class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm):
+    """
+    Run LinearRegression with SGD on a batch of data.
+
+    The problem minimized is (1 / n_samples) * (y - weights'X)**2.
+    After training on a batch of data, the weights obtained at the end of
+    training are used as initial weights for the next batch.
+
+    :param: stepSize Step size for each iteration of gradient descent.
+    :param: numIterations Total number of iterations run.
+    :param: miniBatchFraction Fraction of data on which SGD is run for each
+                              iteration.
+    """
+    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0):
+        self.stepSize = stepSize
+        self.numIterations = numIterations
+        self.miniBatchFraction = miniBatchFraction
+        self._model = None
+        super(StreamingLinearRegressionWithSGD, self).__init__(
+            model=self._model)
+
+    def setInitialWeights(self, initialWeights):
+        """
+        Set the initial value of weights.
+
+        This must be set before running trainOn and predictOn
+        """
+        initialWeights = _convert_to_vector(initialWeights)
+        self._model = LinearRegressionModel(initialWeights, 0)
+        return self
+
+    def trainOn(self, dstream):
+        """Train the model on the incoming dstream."""
+        self._validate(dstream)
+
+        def update(rdd):
+            # LinearRegressionWithSGD.train raises an error for an empty RDD.
+            if not rdd.isEmpty():
+                self._model = LinearRegressionWithSGD.train(
+                    rdd, self.numIterations, self.stepSize,
+                    self.miniBatchFraction, self._model.weights,
+                    self._model.intercept)
+
+        dstream.foreachRDD(update)
+
+
 def _test():
     import doctest
     from pyspark import SparkContext
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index cd80c3e07a4f7..f0091d6faccce 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -27,8 +27,9 @@
 from shutil import rmtree
 
 from numpy import (
-    array, array_equal, zeros, inf, random, exp, dot, all, mean)
+    array, array_equal, zeros, inf, random, exp, dot, all, mean, abs)
 from numpy import sum as array_sum
+
 from py4j.protocol import Py4JJavaError
 
 if sys.version_info[:2] <= (2, 6):
@@ -45,8 +46,8 @@
 from pyspark.mllib.clustering import StreamingKMeans, StreamingKMeansModel
 from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\
     DenseMatrix, SparseMatrix, Vectors, Matrices, MatrixUDT
-from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.classification import StreamingLogisticRegressionWithSGD
+from pyspark.mllib.regression import LabeledPoint, StreamingLinearRegressionWithSGD
 from pyspark.mllib.random import RandomRDDs
 from pyspark.mllib.stat import Statistics
 from pyspark.mllib.feature import Word2Vec
@@ -56,6 +57,7 @@
 from pyspark.serializers import PickleSerializer
 from pyspark.streaming import StreamingContext
 from pyspark.sql import SQLContext
+from pyspark.streaming import StreamingContext
 
 _have_scipy = False
 try:
@@ -1170,6 +1172,124 @@ def collect_errors(rdd):
         self.assertTrue(errors[1] - errors[-1] > 0.3)
 
 
+class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
+
+    def assertArrayAlmostEqual(self, array1, array2, dec):
+        for i, j in array1, array2:
+            self.assertAlmostEqual(i, j, dec)
+
+    def test_parameter_accuracy(self):
+        """Test that coefs are predicted accurately by fitting on toy data."""
+
+        # Test that fitting (10*X1 + 10*X2), (X1, X2) gives coefficients
+        # (10, 10)
+        slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
+        slr.setInitialWeights([0.0, 0.0])
+        xMean = [0.0, 0.0]
+        xVariance = [1.0 / 3.0, 1.0 / 3.0]
+
+        # Create ten batches with 100 sample points in each.
+        batches = []
+        for i in range(10):
+            batch = LinearDataGenerator.generateLinearInput(
+                0.0, [10.0, 10.0], xMean, xVariance, 100, 42 + i, 0.1)
+            batches.append(sc.parallelize(batch))
+
+        input_stream = self.ssc.queueStream(batches)
+        t = time()
+        slr.trainOn(input_stream)
+        self.ssc.start()
+        self._ssc_wait(t, 10, 0.01)
+        self.assertArrayAlmostEqual(
+            slr.latestModel().weights.array, [10., 10.], 1)
+        self.assertAlmostEqual(slr.latestModel().intercept, 0.0, 1)
+
+    def test_parameter_convergence(self):
+        """Test that the model parameters improve with streaming data."""
+        slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
+        slr.setInitialWeights([0.0])
+
+        # Create ten batches with 100 sample points in each.
+        batches = []
+        for i in range(10):
+            batch = LinearDataGenerator.generateLinearInput(
+                0.0, [10.0], [0.0], [1.0 / 3.0], 100, 42 + i, 0.1)
+            batches.append(sc.parallelize(batch))
+
+        model_weights = []
+        input_stream = self.ssc.queueStream(batches)
+        input_stream.foreachRDD(
+            lambda x: model_weights.append(slr.latestModel().weights[0]))
+        t = time()
+        slr.trainOn(input_stream)
+        self.ssc.start()
+        self._ssc_wait(t, 10, 0.01)
+
+        model_weights = array(model_weights)
+        diff = model_weights[1:] - model_weights[:-1]
+        self.assertTrue(all(diff >= -0.1))
+
+    def test_prediction(self):
+        """Test prediction on a model with weights already set."""
+        # Create a model with initial Weights equal to coefs
+        slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
+        slr.setInitialWeights([10.0, 10.0])
+
+        # Create ten batches with 100 sample points in each.
+        batches = []
+        for i in range(10):
+            batch = LinearDataGenerator.generateLinearInput(
+                0.0, [10.0, 10.0], [0.0, 0.0], [1.0 / 3.0, 1.0 / 3.0],
+                100, 42 + i, 0.1)
+            batches.append(
+                sc.parallelize(batch).map(lambda lp: (lp.label, lp.features)))
+
+        input_stream = self.ssc.queueStream(batches)
+        t = time()
+        output_stream = slr.predictOnValues(input_stream)
+        samples = []
+        output_stream.foreachRDD(lambda x: samples.append(x.collect()))
+
+        self.ssc.start()
+        self._ssc_wait(t, 5, 0.01)
+
+        # Test that mean absolute error on each batch is less than 0.1
+        for batch in samples:
+            true, predicted = zip(*batch)
+            self.assertTrue(mean(abs(array(true) - array(predicted))) < 0.1)
+
+    def test_train_prediction(self):
+        """Test that error on test data improves as model is trained."""
+        slr = StreamingLinearRegressionWithSGD(stepSize=0.2, numIterations=25)
+        slr.setInitialWeights([0.0])
+
+        # Create ten batches with 100 sample points in each.
+        batches = []
+        for i in range(10):
+            batch = LinearDataGenerator.generateLinearInput(
+                0.0, [10.0], [0.0], [1.0 / 3.0], 100, 42 + i, 0.1)
+            batches.append(sc.parallelize(batch))
+
+        predict_batches = [
+            b.map(lambda lp: (lp.label, lp.features)) for b in batches]
+        mean_absolute_errors = []
+
+        def func(rdd):
+            true, predicted = zip(*rdd.collect())
+            mean_absolute_errors.append(mean(abs(true) - abs(predicted)))
+
+        model_weights = []
+        input_stream = self.ssc.queueStream(batches)
+        output_stream = self.ssc.queueStream(predict_batches)
+        t = time()
+        slr.trainOn(input_stream)
+        output_stream = slr.predictOnValues(output_stream)
+        output_stream.foreachRDD(func)
+        self.ssc.start()
+        self._ssc_wait(t, 10, 0.01)
+        self.assertTrue(mean_absolute_errors[1] - mean_absolute_errors[-1] > 2)
+
+
 if __name__ == "__main__":
     if not _have_scipy:
         print("NOTE: Skipping SciPy tests as it does not seem to be installed")

From 5fa0863626aaf5a9a41756a0b1ec82bddccbf067 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 30 Jun 2015 10:27:29 -0700
Subject: [PATCH 0135/1454] [SPARK-8679] [PYSPARK] [MLLIB] Default values in
 Pipeline API should be immutable

It might be dangerous to have a mutable as value for default param. (http://stackoverflow.com/a/11416002/1170730)

e.g

    def func(example, f={}):
        f[example] = 1
        return f

    func(2)

    {2: 1}
    func(3)
    {2:1, 3:1}

mengxr

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7058 from MechCoder/pipeline_api_playground and squashes the following commits:

40a5eb2 [MechCoder] copy
95f7ff2 [MechCoder] [SPARK-8679] [PySpark] [MLlib] Default values in Pipeline API should be immutable
---
 python/pyspark/ml/pipeline.py | 24 ++++++++++++++++++------
 python/pyspark/ml/wrapper.py  |  4 +++-
 2 files changed, 21 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
index a563024b2cdcb..9889f56cac9e4 100644
--- a/python/pyspark/ml/pipeline.py
+++ b/python/pyspark/ml/pipeline.py
@@ -42,7 +42,7 @@ def _fit(self, dataset):
         """
         raise NotImplementedError()
 
-    def fit(self, dataset, params={}):
+    def fit(self, dataset, params=None):
         """
         Fits a model to the input dataset with optional parameters.
 
@@ -54,6 +54,8 @@ def fit(self, dataset, params={}):
                        list of models.
         :returns: fitted model(s)
         """
+        if params is None:
+            params = dict()
         if isinstance(params, (list, tuple)):
             return [self.fit(dataset, paramMap) for paramMap in params]
         elif isinstance(params, dict):
@@ -86,7 +88,7 @@ def _transform(self, dataset):
         """
         raise NotImplementedError()
 
-    def transform(self, dataset, params={}):
+    def transform(self, dataset, params=None):
         """
         Transforms the input dataset with optional parameters.
 
@@ -96,6 +98,8 @@ def transform(self, dataset, params={}):
                        params.
         :returns: transformed dataset
         """
+        if params is None:
+            params = dict()
         if isinstance(params, dict):
             if params:
                 return self.copy(params,)._transform(dataset)
@@ -135,10 +139,12 @@ class Pipeline(Estimator):
     """
 
     @keyword_only
-    def __init__(self, stages=[]):
+    def __init__(self, stages=None):
         """
         __init__(self, stages=[])
         """
+        if stages is None:
+            stages = []
         super(Pipeline, self).__init__()
         #: Param for pipeline stages.
         self.stages = Param(self, "stages", "pipeline stages")
@@ -162,11 +168,13 @@ def getStages(self):
             return self._paramMap[self.stages]
 
     @keyword_only
-    def setParams(self, stages=[]):
+    def setParams(self, stages=None):
         """
         setParams(self, stages=[])
         Sets params for Pipeline.
         """
+        if stages is None:
+            stages = []
         kwargs = self.setParams._input_kwargs
         return self._set(**kwargs)
 
@@ -195,7 +203,9 @@ def _fit(self, dataset):
                 transformers.append(stage)
         return PipelineModel(transformers)
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
+        if extra is None:
+            extra = dict()
         that = Params.copy(self, extra)
         stages = [stage.copy(extra) for stage in that.getStages()]
         return that.setStages(stages)
@@ -216,6 +226,8 @@ def _transform(self, dataset):
             dataset = t.transform(dataset)
         return dataset
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
+        if extra is None:
+            extra = dict()
         stages = [stage.copy(extra) for stage in self.stages]
         return PipelineModel(stages)
diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index 7b0893e2cdadc..253705bde913e 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -166,7 +166,7 @@ def __init__(self, java_model):
         self._java_obj = java_model
         self.uid = java_model.uid()
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
         """
         Creates a copy of this instance with the same uid and some
         extra params. This implementation first calls Params.copy and
@@ -175,6 +175,8 @@ def copy(self, extra={}):
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
         """
+        if extra is None:
+            extra = dict()
         that = super(JavaModel, self).copy(extra)
         that._java_obj = self._java_obj.copy(self._empty_java_param_map())
         that._transfer_params_to_java()

From fbb267ed6fe799a58f88c2fba2d41e954e5f1547 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 30 Jun 2015 10:48:49 -0700
Subject: [PATCH 0136/1454] [SPARK-8713] Make codegen thread safe

Codegen takes three steps:

1. Take a list of expressions, convert them into Java source code and a list of expressions that don't not support codegen (fallback to interpret mode).
2. Compile the Java source into Java class (bytecode)
3. Using the Java class and the list of expression to build a Projection.

Currently, we cache the whole three steps, the key is a list of expression, result is projection. Because some of expressions (which may not thread-safe, for example, Random) will be hold by the Projection, the projection maybe not thread safe.

This PR change to only cache the second step, then we can build projection using codegen even some expressions are not thread-safe, because the cache will not hold any expression anymore.

cc marmbrus rxin JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #7101 from davies/codegen_safe and squashes the following commits:

7dd41f1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into codegen_safe
847bd08 [Davies Liu] don't use scala.refect
4ddaaed [Davies Liu] Merge branch 'master' of github.com:apache/spark into codegen_safe
1793cf1 [Davies Liu] make codegen thread safe
---
 .../sql/catalyst/expressions/Expression.scala | 14 -----------
 .../sql/catalyst/expressions/ScalaUDF.scala   |  3 ---
 .../expressions/codegen/CodeGenerator.scala   | 25 ++++++++++---------
 .../codegen/GenerateOrdering.scala            |  9 +++----
 .../codegen/GenerateProjection.scala          |  7 +++---
 .../expressions/namedExpressions.scala        |  2 --
 .../catalyst/expressions/nullFunctions.scala  |  2 --
 .../spark/sql/execution/SparkPlan.scala       |  6 ++---
 .../MonotonicallyIncreasingID.scala           |  2 --
 .../apache/spark/sql/sources/commands.scala   |  2 +-
 .../apache/spark/sql/sources/interfaces.scala |  2 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |  4 ---
 12 files changed, 24 insertions(+), 54 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index aed48921bdeb5..b5063f32fa529 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -60,14 +60,6 @@ abstract class Expression extends TreeNode[Expression] {
   /** Returns the result of evaluating this expression on a given input Row */
   def eval(input: InternalRow = null): Any
 
-  /**
-   * Return true if this expression is thread-safe, which means it could be used by multiple
-   * threads in the same time.
-   *
-   * An expression that is not thread-safe can not be cached and re-used, especially for codegen.
-   */
-  def isThreadSafe: Boolean = true
-
   /**
    * Returns an [[GeneratedExpressionCode]], which contains Java source code that
    * can be used to generate the result of evaluating the expression on an input row.
@@ -76,9 +68,6 @@ abstract class Expression extends TreeNode[Expression] {
    * @return [[GeneratedExpressionCode]]
    */
   def gen(ctx: CodeGenContext): GeneratedExpressionCode = {
-    if (!isThreadSafe) {
-      throw new Exception(s"$this is not thread-safe, can not be used in codegen")
-    }
     val isNull = ctx.freshName("isNull")
     val primitive = ctx.freshName("primitive")
     val ve = GeneratedExpressionCode("", isNull, primitive)
@@ -178,8 +167,6 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
 
   override def toString: String = s"($left $symbol $right)"
 
-  override def isThreadSafe: Boolean = left.isThreadSafe && right.isThreadSafe
-
   /**
    * Short hand for generating binary evaluation code.
    * If either of the sub-expressions is null, the result of this computation
@@ -237,7 +224,6 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
 
   override def foldable: Boolean = child.foldable
   override def nullable: Boolean = child.nullable
-  override def isThreadSafe: Boolean = child.isThreadSafe
 
   /**
    * Called by unary expressions to generate a code block that returns null if its parent returns
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index dbb4381d54c4f..ebabb6f117851 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -956,7 +956,4 @@ case class ScalaUDF(function: AnyRef, dataType: DataType, children: Seq[Expressi
   // scalastyle:on
   private[this] val converter = CatalystTypeConverters.createToCatalystConverter(dataType)
   override def eval(input: InternalRow): Any = converter(f(input))
-
-  // TODO(davies): make ScalaUDF work with codegen
-  override def isThreadSafe: Boolean = false
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index bf6a6a124088e..a64027e48a00b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -235,11 +235,15 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
 
   /**
    * Compile the Java source code into a Java class, using Janino.
-   *
-   * It will track the time used to compile
    */
   protected def compile(code: String): GeneratedClass = {
-    val startTime = System.nanoTime()
+    cache.get(code)
+  }
+
+  /**
+   * Compile the Java source code into a Java class, using Janino.
+   */
+  private[this] def doCompile(code: String): GeneratedClass = {
     val evaluator = new ClassBodyEvaluator()
     evaluator.setParentClassLoader(getClass.getClassLoader)
     evaluator.setDefaultImports(Array("org.apache.spark.sql.catalyst.InternalRow"))
@@ -251,9 +255,6 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
         logError(s"failed to compile:\n $code", e)
         throw e
     }
-    val endTime = System.nanoTime()
-    def timeMs: Double = (endTime - startTime).toDouble / 1000000
-    logDebug(s"Code (${code.size} bytes) compiled in $timeMs ms")
     evaluator.getClazz().newInstance().asInstanceOf[GeneratedClass]
   }
 
@@ -266,16 +267,16 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
    * automatically, in order to constrain its memory footprint.  Note that this cache does not use
    * weak keys/values and thus does not respond to memory pressure.
    */
-  protected val cache = CacheBuilder.newBuilder()
+  private val cache = CacheBuilder.newBuilder()
     .maximumSize(100)
     .build(
-      new CacheLoader[InType, OutType]() {
-        override def load(in: InType): OutType = {
+      new CacheLoader[String, GeneratedClass]() {
+        override def load(code: String): GeneratedClass = {
           val startTime = System.nanoTime()
-          val result = create(in)
+          val result = doCompile(code)
           val endTime = System.nanoTime()
           def timeMs: Double = (endTime - startTime).toDouble / 1000000
-          logInfo(s"Code generated expression $in in $timeMs ms")
+          logInfo(s"Code generated in $timeMs ms")
           result
         }
       })
@@ -285,7 +286,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
     generate(bind(expressions, inputSchema))
 
   /** Generates the requested evaluator given already bound expression(s). */
-  def generate(expressions: InType): OutType = cache.get(canonicalize(expressions))
+  def generate(expressions: InType): OutType = create(canonicalize(expressions))
 
   /**
    * Create a new codegen context for expression evaluator, used to store those
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index 7ed2c5addec9b..97cb16045ae4a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -38,7 +38,6 @@ class BaseOrdering extends Ordering[InternalRow] {
  */
 object GenerateOrdering
     extends CodeGenerator[Seq[SortOrder], Ordering[InternalRow]] with Logging {
-  import scala.reflect.runtime.universe._
 
   protected def canonicalize(in: Seq[SortOrder]): Seq[SortOrder] =
     in.map(ExpressionCanonicalizer.execute(_).asInstanceOf[SortOrder])
@@ -47,8 +46,6 @@ object GenerateOrdering
     in.map(BindReferences.bindReference(_, inputSchema))
 
   protected def create(ordering: Seq[SortOrder]): Ordering[InternalRow] = {
-    val a = newTermName("a")
-    val b = newTermName("b")
     val ctx = newCodeGenContext()
 
     val comparisons = ordering.zipWithIndex.map { case (order, i) =>
@@ -56,9 +53,9 @@ object GenerateOrdering
       val evalB = order.child.gen(ctx)
       val asc = order.direction == Ascending
       s"""
-          i = $a;
+          i = a;
           ${evalA.code}
-          i = $b;
+          i = b;
           ${evalB.code}
           if (${evalA.isNull} && ${evalB.isNull}) {
             // Nothing
@@ -80,7 +77,7 @@ object GenerateOrdering
         return new SpecificOrdering(expr);
       }
 
-      class SpecificOrdering extends ${typeOf[BaseOrdering]} {
+      class SpecificOrdering extends ${classOf[BaseOrdering].getName} {
 
         private $exprType[] expressions = null;
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 39d32b78cc14a..5be47175fa7f1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -32,7 +32,6 @@ abstract class BaseProject extends Projection {}
  * primitive values.
  */
 object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
-  import scala.reflect.runtime.universe._
 
   protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
     in.map(ExpressionCanonicalizer.execute)
@@ -157,7 +156,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
       return new SpecificProjection(expr);
     }
 
-    class SpecificProjection extends ${typeOf[BaseProject]} {
+    class SpecificProjection extends ${classOf[BaseProject].getName} {
       private $exprType[] expressions = null;
 
       public SpecificProjection($exprType[] expr) {
@@ -170,7 +169,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
       }
     }
 
-    final class SpecificRow extends ${typeOf[MutableRow]} {
+    final class SpecificRow extends ${classOf[MutableRow].getName} {
 
       $columns
 
@@ -224,7 +223,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
       public InternalRow copy() {
         Object[] arr = new Object[${expressions.length}];
         ${copyColumns}
-        return new ${typeOf[GenericInternalRow]}(arr);
+        return new ${classOf[GenericInternalRow].getName}(arr);
       }
     }
     """
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 6f56a9ec7beb5..81ebda3060c51 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -117,8 +117,6 @@ case class Alias(child: Expression, name: String)(
 
   override def eval(input: InternalRow): Any = child.eval(input)
 
-  override def isThreadSafe: Boolean = child.isThreadSafe
-
   override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx)
 
   override def dataType: DataType = child.dataType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 5d5911403ece1..78be2824347d7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -51,8 +51,6 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
     result
   }
 
-  override def isThreadSafe: Boolean = children.forall(_.isThreadSafe)
-
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     s"""
       boolean ${ev.isNull} = true;
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 47f56b2b7ebe6..7739a9f949c77 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -156,7 +156,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
       expressions: Seq[Expression], inputSchema: Seq[Attribute]): Projection = {
     log.debug(
       s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
-    if (codegenEnabled && expressions.forall(_.isThreadSafe)) {
+    if (codegenEnabled) {
       GenerateProjection.generate(expressions, inputSchema)
     } else {
       new InterpretedProjection(expressions, inputSchema)
@@ -168,7 +168,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
       inputSchema: Seq[Attribute]): () => MutableProjection = {
     log.debug(
       s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
-    if(codegenEnabled && expressions.forall(_.isThreadSafe)) {
+    if(codegenEnabled) {
       GenerateMutableProjection.generate(expressions, inputSchema)
     } else {
       () => new InterpretedMutableProjection(expressions, inputSchema)
@@ -178,7 +178,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
 
   protected def newPredicate(
       expression: Expression, inputSchema: Seq[Attribute]): (InternalRow) => Boolean = {
-    if (codegenEnabled && expression.isThreadSafe) {
+    if (codegenEnabled) {
       GeneratePredicate.generate(expression, inputSchema)
     } else {
       InterpretedPredicate.create(expression, inputSchema)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
index 3b217348b7b7a..68914cf85cb50 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
@@ -48,6 +48,4 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression {
     count += 1
     (TaskContext.get().partitionId().toLong << 33) + currentCount
   }
-
-  override def isThreadSafe: Boolean = false
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index 54c8eeb41a8ea..42b51caab5ce9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -270,7 +270,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
       inputSchema: Seq[Attribute]): Projection = {
     log.debug(
       s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
-    if (codegenEnabled && expressions.forall(_.isThreadSafe)) {
+    if (codegenEnabled) {
       GenerateProjection.generate(expressions, inputSchema)
     } else {
       new InterpretedProjection(expressions, inputSchema)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 7005c7079af91..0b875304f9b0e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -591,7 +591,7 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
         rdd.map(_.asInstanceOf[InternalRow])
       }
     converted.mapPartitions { rows =>
-      val buildProjection = if (codegenEnabled && requiredOutput.forall(_.isThreadSafe)) {
+      val buildProjection = if (codegenEnabled) {
         GenerateMutableProjection.generate(requiredOutput, dataSchema.toAttributes)
       } else {
         () => new InterpretedMutableProjection(requiredOutput, dataSchema.toAttributes)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index d7827d56ca8c5..4dea561ae5f60 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -120,8 +120,6 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
   @transient
   protected lazy val cached: Array[AnyRef] = new Array[AnyRef](children.length)
 
-  override def isThreadSafe: Boolean = false
-
   // TODO: Finish input output types.
   override def eval(input: InternalRow): Any = {
     unwrap(
@@ -180,8 +178,6 @@ private[hive] case class HiveGenericUDF(funcWrapper: HiveFunctionWrapper, childr
 
   lazy val dataType: DataType = inspectorToDataType(returnInspector)
 
-  override def isThreadSafe: Boolean = false
-
   override def eval(input: InternalRow): Any = {
     returnInspector // Make sure initialized.
 

From 9213f73a8ea09ae343af825a6b576c212cf4a0c7 Mon Sep 17 00:00:00 2001
From: Tijo Thomas <tijoparacka@gmail.com>
Date: Tue, 30 Jun 2015 10:50:45 -0700
Subject: [PATCH 0137/1454] [SPARK-8615] [DOCUMENTATION] Fixed Sample
 deprecated code

Modified the deprecated jdbc api in the documentation.

Author: Tijo Thomas <tijoparacka@gmail.com>

Closes #7039 from tijoparacka/JIRA_8615 and squashes the following commits:

6e73b8a [Tijo Thomas] Reverted new lines
4042fcf [Tijo Thomas] updated to sql documentation
a27949c [Tijo Thomas] Fixed Sample deprecated code
---
 docs/sql-programming-guide.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 2786e3d2cd6bf..88c96a9a095b3 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1773,9 +1773,9 @@ the Data Sources API.  The following options are supported:
 <div data-lang="scala"  markdown="1">
 
 {% highlight scala %}
-val jdbcDF = sqlContext.load("jdbc", Map(
-  "url" -> "jdbc:postgresql:dbserver",
-  "dbtable" -> "schema.tablename"))
+val jdbcDF = sqlContext.read.format("jdbc").options( 
+  Map("url" -> "jdbc:postgresql:dbserver",
+  "dbtable" -> "schema.tablename")).load()
 {% endhighlight %}
 
 </div>
@@ -1788,7 +1788,7 @@ Map<String, String> options = new HashMap<String, String>();
 options.put("url", "jdbc:postgresql:dbserver");
 options.put("dbtable", "schema.tablename");
 
-DataFrame jdbcDF = sqlContext.load("jdbc", options)
+DataFrame jdbcDF = sqlContext.read().format("jdbc"). options(options).load();
 {% endhighlight %}
 
 
@@ -1798,7 +1798,7 @@ DataFrame jdbcDF = sqlContext.load("jdbc", options)
 
 {% highlight python %}
 
-df = sqlContext.load(source="jdbc", url="jdbc:postgresql:dbserver", dbtable="schema.tablename")
+df = sqlContext.read.format('jdbc').options(url = 'jdbc:postgresql:dbserver', dbtable='schema.tablename').load()
 
 {% endhighlight %}
 

From ca7e460f7d6fb898dc29236a85520bbe954c8a13 Mon Sep 17 00:00:00 2001
From: nishkamravi2 <nishkamravi@gmail.com>
Date: Tue, 30 Jun 2015 11:12:15 -0700
Subject: [PATCH 0138/1454] [SPARK-7988] [STREAMING] Round-robin scheduling of
 receivers by default

Minimal PR for round-robin scheduling of receivers. Dense scheduling can be enabled by setting preferredLocation, so a new config parameter isn't really needed. Tested this on a cluster of 6 nodes and noticed 20-25% gain in throughput compared to random scheduling.

tdas pwendell

Author: nishkamravi2 <nishkamravi@gmail.com>
Author: Nishkam Ravi <nravi@cloudera.com>

Closes #6607 from nishkamravi2/master_nravi and squashes the following commits:

1918819 [Nishkam Ravi] Update ReceiverTrackerSuite.scala
f747739 [Nishkam Ravi] Update ReceiverTrackerSuite.scala
6127e58 [Nishkam Ravi] Update ReceiverTracker and ReceiverTrackerSuite
9f1abc2 [nishkamravi2] Update ReceiverTrackerSuite.scala
ae29152 [Nishkam Ravi] Update test suite with TD's suggestions
48a4a97 [nishkamravi2] Update ReceiverTracker.scala
bc23907 [nishkamravi2] Update ReceiverTracker.scala
68e8540 [nishkamravi2] Update SchedulerSuite.scala
4604f28 [nishkamravi2] Update SchedulerSuite.scala
179b90f [nishkamravi2] Update ReceiverTracker.scala
242e677 [nishkamravi2] Update SchedulerSuite.scala
7f3e028 [Nishkam Ravi] Update ReceiverTracker.scala, add unit test cases in SchedulerSuite
f8a3e05 [nishkamravi2] Update ReceiverTracker.scala
4cf97b6 [nishkamravi2] Update ReceiverTracker.scala
16e84ec [Nishkam Ravi] Update ReceiverTracker.scala
45e3a99 [Nishkam Ravi] Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi
02dbdb8 [Nishkam Ravi] Update ReceiverTracker.scala
07b9dfa [nishkamravi2] Update ReceiverTracker.scala
6caeefe [nishkamravi2] Update ReceiverTracker.scala
7888257 [nishkamravi2] Update ReceiverTracker.scala
6e3515c [Nishkam Ravi] Minor changes
975b8d8 [Nishkam Ravi] Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi
3cac21b [Nishkam Ravi] Generalize the scheduling algorithm
b05ee2f [nishkamravi2] Update ReceiverTracker.scala
bb5e09b [Nishkam Ravi] Add a new var in receiver to store location information for round-robin scheduling
41705de [nishkamravi2] Update ReceiverTracker.scala
fff1b2e [Nishkam Ravi] Round-robin scheduling of streaming receivers
---
 .../streaming/scheduler/ReceiverTracker.scala | 64 ++++++++++---
 .../scheduler/ReceiverTrackerSuite.scala      | 90 +++++++++++++++++++
 2 files changed, 141 insertions(+), 13 deletions(-)
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index e6cdbec11e94c..644e581cd8279 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -17,8 +17,10 @@
 
 package org.apache.spark.streaming.scheduler
 
-import scala.collection.mutable.{HashMap, SynchronizedMap}
+import scala.collection.mutable.{ArrayBuffer, HashMap, SynchronizedMap}
 import scala.language.existentials
+import scala.math.max
+import org.apache.spark.rdd._
 
 import org.apache.spark.streaming.util.WriteAheadLogUtils
 import org.apache.spark.{Logging, SparkEnv, SparkException}
@@ -272,6 +274,41 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
       }
     }
 
+    /**
+     * Get the list of executors excluding driver
+     */
+    private def getExecutors(ssc: StreamingContext): List[String] = {
+      val executors = ssc.sparkContext.getExecutorMemoryStatus.map(_._1.split(":")(0)).toList
+      val driver = ssc.sparkContext.getConf.get("spark.driver.host")
+      executors.diff(List(driver))
+    }
+
+    /** Set host location(s) for each receiver so as to distribute them over
+     * executors in a round-robin fashion taking into account preferredLocation if set
+     */
+    private[streaming] def scheduleReceivers(receivers: Seq[Receiver[_]],
+      executors: List[String]): Array[ArrayBuffer[String]] = {
+      val locations = new Array[ArrayBuffer[String]](receivers.length)
+      var i = 0
+      for (i <- 0 until receivers.length) {
+        locations(i) = new ArrayBuffer[String]()
+        if (receivers(i).preferredLocation.isDefined) {
+          locations(i) += receivers(i).preferredLocation.get
+        }
+      }
+      var count = 0
+      for (i <- 0 until max(receivers.length, executors.length)) {
+        if (!receivers(i % receivers.length).preferredLocation.isDefined) {
+          locations(i % receivers.length) += executors(count)
+          count += 1
+          if (count == executors.length) {
+            count = 0
+          }
+        }
+      }
+      locations
+    }
+
     /**
      * Get the receivers from the ReceiverInputDStreams, distributes them to the
      * worker nodes as a parallel collection, and runs them.
@@ -283,18 +320,6 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         rcvr
       })
 
-      // Right now, we only honor preferences if all receivers have them
-      val hasLocationPreferences = receivers.map(_.preferredLocation.isDefined).reduce(_ && _)
-
-      // Create the parallel collection of receivers to distributed them on the worker nodes
-      val tempRDD =
-        if (hasLocationPreferences) {
-          val receiversWithPreferences = receivers.map(r => (r, Seq(r.preferredLocation.get)))
-          ssc.sc.makeRDD[Receiver[_]](receiversWithPreferences)
-        } else {
-          ssc.sc.makeRDD(receivers, receivers.size)
-        }
-
       val checkpointDirOption = Option(ssc.checkpointDir)
       val serializableHadoopConf =
         new SerializableConfiguration(ssc.sparkContext.hadoopConfiguration)
@@ -311,12 +336,25 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         supervisor.start()
         supervisor.awaitTermination()
       }
+
       // Run the dummy Spark job to ensure that all slaves have registered.
       // This avoids all the receivers to be scheduled on the same node.
       if (!ssc.sparkContext.isLocal) {
         ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
       }
 
+      // Get the list of executors and schedule receivers
+      val executors = getExecutors(ssc)
+      val tempRDD =
+        if (!executors.isEmpty) {
+          val locations = scheduleReceivers(receivers, executors)
+          val roundRobinReceivers = (0 until receivers.length).map(i =>
+            (receivers(i), locations(i)))
+          ssc.sc.makeRDD[Receiver[_]](roundRobinReceivers)
+        } else {
+          ssc.sc.makeRDD(receivers, receivers.size)
+        }
+
       // Distribute the receivers and start them
       logInfo("Starting " + receivers.length + " receivers")
       running = true
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
new file mode 100644
index 0000000000000..a6e783861dbe6
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import org.apache.spark.streaming._
+import org.apache.spark.SparkConf
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.receiver._
+import org.apache.spark.util.Utils
+
+/** Testsuite for receiver scheduling */
+class ReceiverTrackerSuite extends TestSuiteBase {
+  val sparkConf = new SparkConf().setMaster("local[8]").setAppName("test")
+  val ssc = new StreamingContext(sparkConf, Milliseconds(100))
+  val tracker = new ReceiverTracker(ssc)
+  val launcher = new tracker.ReceiverLauncher()
+  val executors: List[String] = List("0", "1", "2", "3")
+
+  test("receiver scheduling - all or none have preferred location") {
+
+    def parse(s: String): Array[Array[String]] = {
+      val outerSplit = s.split("\\|")
+      val loc = new Array[Array[String]](outerSplit.length)
+      var i = 0
+      for (i <- 0 until outerSplit.length) {
+        loc(i) = outerSplit(i).split("\\,")
+      }
+      loc
+    }
+
+    def testScheduler(numReceivers: Int, preferredLocation: Boolean, allocation: String) {
+      val receivers =
+        if (preferredLocation) {
+          Array.tabulate(numReceivers)(i => new DummyReceiver(host =
+            Some(((i + 1) % executors.length).toString)))
+        } else {
+          Array.tabulate(numReceivers)(_ => new DummyReceiver)
+        }
+      val locations = launcher.scheduleReceivers(receivers, executors)
+      val expectedLocations = parse(allocation)
+      assert(locations.deep === expectedLocations.deep)
+    }
+
+    testScheduler(numReceivers = 5, preferredLocation = false, allocation = "0|1|2|3|0")
+    testScheduler(numReceivers = 3, preferredLocation = false, allocation = "0,3|1|2")
+    testScheduler(numReceivers = 4, preferredLocation = true, allocation = "1|2|3|0")
+  }
+
+  test("receiver scheduling - some have preferred location") {
+    val numReceivers = 4;
+    val receivers: Seq[Receiver[_]] = Seq(new DummyReceiver(host = Some("1")),
+      new DummyReceiver, new DummyReceiver, new DummyReceiver)
+    val locations = launcher.scheduleReceivers(receivers, executors)
+    assert(locations(0)(0) === "1")
+    assert(locations(1)(0) === "0")
+    assert(locations(2)(0) === "1")
+    assert(locations(0).length === 1)
+    assert(locations(3).length === 1)
+  }
+}
+
+/**
+ * Dummy receiver implementation
+ */
+private class DummyReceiver(host: Option[String] = None)
+  extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
+
+  def onStart() {
+  }
+
+  def onStop() {
+  }
+
+  override def preferredLocation: Option[String] = host
+}

From 57264400ac7d9f9c59c387c252a9ed8d93fed4fa Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 30 Jun 2015 11:14:38 -0700
Subject: [PATCH 0139/1454] [SPARK-8630] [STREAMING] Prevent from checkpointing
 QueueInputDStream

This PR throws an exception in `QueueInputDStream.writeObject` so that it can fail the application when calling `StreamingContext.start` rather than failing it during recovering QueueInputDStream.

Author: zsxwing <zsxwing@gmail.com>

Closes #7016 from zsxwing/queueStream-checkpoint and squashes the following commits:

89a3d73 [zsxwing] Fix JavaAPISuite.testQueueStream
cc40fd7 [zsxwing] Prevent from checkpointing QueueInputDStream
---
 .../spark/streaming/StreamingContext.scala     |  8 ++++++++
 .../api/java/JavaStreamingContext.scala        | 18 +++++++++++++++---
 .../streaming/dstream/QueueInputDStream.scala  | 15 ++++++++++-----
 .../apache/spark/streaming/JavaAPISuite.java   |  8 ++++++++
 .../streaming/StreamingContextSuite.scala      | 15 +++++++++++++++
 5 files changed, 56 insertions(+), 8 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 1708f309fc002..ec49d0f42d122 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -477,6 +477,10 @@ class StreamingContext private[streaming] (
   /**
    * Create an input stream from a queue of RDDs. In each batch,
    * it will process either one or all of the RDDs returned by the queue.
+   *
+   * NOTE: Arbitrary RDDs can be added to `queueStream`, there is no way to recover data of
+   * those RDDs, so `queueStream` doesn't support checkpointing.
+   *
    * @param queue      Queue of RDDs
    * @param oneAtATime Whether only one RDD should be consumed from the queue in every interval
    * @tparam T         Type of objects in the RDD
@@ -491,6 +495,10 @@ class StreamingContext private[streaming] (
   /**
    * Create an input stream from a queue of RDDs. In each batch,
    * it will process either one or all of the RDDs returned by the queue.
+   *
+   * NOTE: Arbitrary RDDs can be added to `queueStream`, there is no way to recover data of
+   * those RDDs, so `queueStream` doesn't support checkpointing.
+   *
    * @param queue      Queue of RDDs
    * @param oneAtATime Whether only one RDD should be consumed from the queue in every interval
    * @param defaultRDD Default RDD is returned by the DStream when the queue is empty.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 989e3a729ebc2..40deb6d7ea79a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -419,7 +419,11 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
    * Create an input stream from an queue of RDDs. In each batch,
    * it will process either one or all of the RDDs returned by the queue.
    *
-   * NOTE: changes to the queue after the stream is created will not be recognized.
+   * NOTE:
+   * 1. Changes to the queue after the stream is created will not be recognized.
+   * 2. Arbitrary RDDs can be added to `queueStream`, there is no way to recover data of
+   * those RDDs, so `queueStream` doesn't support checkpointing.
+   *
    * @param queue      Queue of RDDs
    * @tparam T         Type of objects in the RDD
    */
@@ -435,7 +439,11 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
    * Create an input stream from an queue of RDDs. In each batch,
    * it will process either one or all of the RDDs returned by the queue.
    *
-   * NOTE: changes to the queue after the stream is created will not be recognized.
+   * NOTE:
+   * 1. Changes to the queue after the stream is created will not be recognized.
+   * 2. Arbitrary RDDs can be added to `queueStream`, there is no way to recover data of
+   * those RDDs, so `queueStream` doesn't support checkpointing.
+   *
    * @param queue      Queue of RDDs
    * @param oneAtATime Whether only one RDD should be consumed from the queue in every interval
    * @tparam T         Type of objects in the RDD
@@ -455,7 +463,11 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
    * Create an input stream from an queue of RDDs. In each batch,
    * it will process either one or all of the RDDs returned by the queue.
    *
-   * NOTE: changes to the queue after the stream is created will not be recognized.
+   * NOTE:
+   * 1. Changes to the queue after the stream is created will not be recognized.
+   * 2. Arbitrary RDDs can be added to `queueStream`, there is no way to recover data of
+   * those RDDs, so `queueStream` doesn't support checkpointing.
+   *
    * @param queue      Queue of RDDs
    * @param oneAtATime Whether only one RDD should be consumed from the queue in every interval
    * @param defaultRDD Default RDD is returned by the DStream when the queue is empty
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
index ed7da6dc1315e..a2f5d82a79bd3 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
@@ -17,13 +17,14 @@
 
 package org.apache.spark.streaming.dstream
 
-import org.apache.spark.rdd.RDD
-import org.apache.spark.rdd.UnionRDD
-import scala.collection.mutable.Queue
-import scala.collection.mutable.ArrayBuffer
-import org.apache.spark.streaming.{Time, StreamingContext}
+import java.io.{NotSerializableException, ObjectOutputStream}
+
+import scala.collection.mutable.{ArrayBuffer, Queue}
 import scala.reflect.ClassTag
 
+import org.apache.spark.rdd.{RDD, UnionRDD}
+import org.apache.spark.streaming.{Time, StreamingContext}
+
 private[streaming]
 class QueueInputDStream[T: ClassTag](
     @transient ssc: StreamingContext,
@@ -36,6 +37,10 @@ class QueueInputDStream[T: ClassTag](
 
   override def stop() { }
 
+  private def writeObject(oos: ObjectOutputStream): Unit = {
+    throw new NotSerializableException("queueStream doesn't support checkpointing")
+  }
+
   override def compute(validTime: Time): Option[RDD[T]] = {
     val buffer = new ArrayBuffer[RDD[T]]()
     if (oneAtATime && queue.size > 0) {
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
index 1077b1b2cb7e3..a34f23475804a 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
@@ -364,6 +364,14 @@ private void testReduceByWindow(boolean withInverse) {
   @SuppressWarnings("unchecked")
   @Test
   public void testQueueStream() {
+    ssc.stop();
+    // Create a new JavaStreamingContext without checkpointing
+    SparkConf conf = new SparkConf()
+        .setMaster("local[2]")
+        .setAppName("test")
+        .set("spark.streaming.clock", "org.apache.spark.util.ManualClock");
+    ssc = new JavaStreamingContext(conf, new Duration(1000));
+
     List<List<Integer>> expected = Arrays.asList(
         Arrays.asList(1,2,3),
         Arrays.asList(4,5,6),
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 819dd2ccfe915..56b4ce5638a51 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -20,6 +20,8 @@ package org.apache.spark.streaming
 import java.io.{File, NotSerializableException}
 import java.util.concurrent.atomic.AtomicInteger
 
+import scala.collection.mutable.Queue
+
 import org.apache.commons.io.FileUtils
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.concurrent.Timeouts
@@ -665,6 +667,19 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
       transformed.foreachRDD { rdd => rdd.collect() } }
   }
 
+  test("queueStream doesn't support checkpointing") {
+    val checkpointDir = Utils.createTempDir()
+    ssc = new StreamingContext(master, appName, batchDuration)
+    val rdd = ssc.sparkContext.parallelize(1 to 10)
+    ssc.queueStream[Int](Queue(rdd)).print()
+    ssc.checkpoint(checkpointDir.getAbsolutePath)
+    val e = intercept[NotSerializableException] {
+      ssc.start()
+    }
+    // StreamingContext.validate changes the message, so use "contains" here
+    assert(e.getMessage.contains("queueStream doesn't support checkpointing"))
+  }
+
   def addInputStream(s: StreamingContext): DStream[Int] = {
     val input = (1 to 100).map(i => 1 to i)
     val inputStream = new TestInputStream(s, input, 1)

From d16a9443750eebb7a3d7688d4b98a2ac39cc0da7 Mon Sep 17 00:00:00 2001
From: huangzhaowei <carlmartinmax@gmail.com>
Date: Tue, 30 Jun 2015 11:46:22 -0700
Subject: [PATCH 0140/1454] [SPARK-8619] [STREAMING] Don't recover keytab and
 principal configuration within Streaming checkpoint

[Client.scala](https://github.com/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L786) will change these configurations, so this would cause the problem that the Streaming recover logic can't find the local keytab file(since configuration was changed)
```scala
      sparkConf.set("spark.yarn.keytab", keytabFileName)
      sparkConf.set("spark.yarn.principal", args.principal)
```

Problem described at [Jira](https://issues.apache.org/jira/browse/SPARK-8619)

Author: huangzhaowei <carlmartinmax@gmail.com>

Closes #7008 from SaintBacchus/SPARK-8619 and squashes the following commits:

d50dbdf [huangzhaowei] Delect one blank space
9b8e92c [huangzhaowei] Fix code style and add a short comment.
0d8f800 [huangzhaowei] Don't recover keytab and principal configuration within Streaming checkpoint.
---
 .../org/apache/spark/streaming/Checkpoint.scala  | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index d8dc4e4101664..5279331c9e122 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -44,11 +44,23 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
   val sparkConfPairs = ssc.conf.getAll
 
   def createSparkConf(): SparkConf = {
+
+    // Reload properties for the checkpoint application since user wants to set a reload property
+    // or spark had changed its value and user wants to set it back.
+    val propertiesToReload = List(
+      "spark.master",
+      "spark.yarn.keytab",
+      "spark.yarn.principal")
+
     val newSparkConf = new SparkConf(loadDefaults = false).setAll(sparkConfPairs)
       .remove("spark.driver.host")
       .remove("spark.driver.port")
-    val newMasterOption = new SparkConf(loadDefaults = true).getOption("spark.master")
-    newMasterOption.foreach { newMaster => newSparkConf.setMaster(newMaster) }
+    val newReloadConf = new SparkConf(loadDefaults = true)
+    propertiesToReload.foreach { prop =>
+      newReloadConf.getOption(prop).foreach { value =>
+        newSparkConf.set(prop, value)
+      }
+    }
     newSparkConf
   }
 

From 1e1f339976641af4cc87d4010db57c3b600f91af Mon Sep 17 00:00:00 2001
From: Christian Kadner <ckadner@us.ibm.com>
Date: Tue, 30 Jun 2015 12:22:34 -0700
Subject: [PATCH 0141/1454] [SPARK-6785] [SQL] fix DateTimeUtils for dates
 before 1970

Hi Michael,
this Pull-Request is a follow-up to [PR-6242](https://github.com/apache/spark/pull/6242). I removed the two obsolete test cases from the HiveQuerySuite and deleted the corresponding golden answer files.
Thanks for your review!

Author: Christian Kadner <ckadner@us.ibm.com>

Closes #6983 from ckadner/SPARK-6785 and squashes the following commits:

ab1e79b [Christian Kadner] Merge remote-tracking branch 'origin/SPARK-6785' into SPARK-6785
1fed877 [Christian Kadner] [SPARK-6785][SQL] failed Scala style test, remove spaces on empty line DateTimeUtils.scala:61
9d8021d [Christian Kadner] [SPARK-6785][SQL] merge recent changes in DateTimeUtils & MiscFunctionsSuite
b97c3fb [Christian Kadner] [SPARK-6785][SQL] move test case for DateTimeUtils to DateTimeUtilsSuite
a451184 [Christian Kadner] [SPARK-6785][SQL] fix DateTimeUtils.fromJavaDate(java.util.Date) for Dates before 1970
---
 .../sql/catalyst/util/DateTimeUtils.scala     |  8 ++--
 .../catalyst/util/DateTimeUtilsSuite.scala    | 40 ++++++++++++++++++-
 .../sql/ScalaReflectionRelationSuite.scala    |  2 +-
 ...te cast-0-a7cd69b80c77a771a2c955db666be53d |  1 -
 ... test 2-0-dc1b267f1d79d49e6675afe4fd2a34a5 |  1 -
 .../sql/hive/execution/HiveQuerySuite.scala   | 14 -------
 .../sql/hive/execution/SQLQuerySuite.scala    | 31 +++++++++++++-
 7 files changed, 75 insertions(+), 22 deletions(-)
 delete mode 100644 sql/hive/src/test/resources/golden/Date cast-0-a7cd69b80c77a771a2c955db666be53d
 delete mode 100644 sql/hive/src/test/resources/golden/Date comparison test 2-0-dc1b267f1d79d49e6675afe4fd2a34a5

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 640e67e2ecd76..4269ad5d56737 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -59,10 +59,12 @@ object DateTimeUtils {
     }
   }
 
-
   // we should use the exact day as Int, for example, (year, month, day) -> day
-  def millisToDays(millisLocal: Long): Int = {
-    ((millisLocal + threadLocalLocalTimeZone.get().getOffset(millisLocal)) / MILLIS_PER_DAY).toInt
+  def millisToDays(millisUtc: Long): Int = {
+    // SPARK-6785: use Math.floor so negative number of days (dates before 1970)
+    // will correctly work as input for function toJavaDate(Int)
+    val millisLocal = millisUtc.toDouble + threadLocalLocalTimeZone.get().getOffset(millisUtc)
+    Math.floor(millisLocal / MILLIS_PER_DAY).toInt
   }
 
   // reverse of millisToDays
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 03eb64f097a37..1d4a60c81efc5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -17,7 +17,8 @@
 
 package org.apache.spark.sql.catalyst.util
 
-import java.sql.Timestamp
+import java.sql.{Date, Timestamp}
+import java.text.SimpleDateFormat
 
 import org.apache.spark.SparkFunSuite
 
@@ -48,4 +49,41 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     val t2 = DateTimeUtils.toJavaTimestamp(DateTimeUtils.fromJulianDay(d1, ns1))
     assert(t.equals(t2))
   }
+
+  test("SPARK-6785: java date conversion before and after epoch") {
+    def checkFromToJavaDate(d1: Date): Unit = {
+      val d2 = DateTimeUtils.toJavaDate(DateTimeUtils.fromJavaDate(d1))
+      assert(d2.toString === d1.toString)
+    }
+
+    val df1 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+    val df2 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss z")
+
+    checkFromToJavaDate(new Date(100))
+
+    checkFromToJavaDate(Date.valueOf("1970-01-01"))
+
+    checkFromToJavaDate(new Date(df1.parse("1970-01-01 00:00:00").getTime))
+    checkFromToJavaDate(new Date(df2.parse("1970-01-01 00:00:00 UTC").getTime))
+
+    checkFromToJavaDate(new Date(df1.parse("1970-01-01 00:00:01").getTime))
+    checkFromToJavaDate(new Date(df2.parse("1970-01-01 00:00:01 UTC").getTime))
+
+    checkFromToJavaDate(new Date(df1.parse("1969-12-31 23:59:59").getTime))
+    checkFromToJavaDate(new Date(df2.parse("1969-12-31 23:59:59 UTC").getTime))
+
+    checkFromToJavaDate(Date.valueOf("1969-01-01"))
+
+    checkFromToJavaDate(new Date(df1.parse("1969-01-01 00:00:00").getTime))
+    checkFromToJavaDate(new Date(df2.parse("1969-01-01 00:00:00 UTC").getTime))
+
+    checkFromToJavaDate(new Date(df1.parse("1969-01-01 00:00:01").getTime))
+    checkFromToJavaDate(new Date(df2.parse("1969-01-01 00:00:01 UTC").getTime))
+
+    checkFromToJavaDate(new Date(df1.parse("1989-11-09 11:59:59").getTime))
+    checkFromToJavaDate(new Date(df2.parse("1989-11-09 19:59:59 UTC").getTime))
+
+    checkFromToJavaDate(new Date(df1.parse("1776-07-04 10:30:00").getTime))
+    checkFromToJavaDate(new Date(df2.parse("1776-07-04 18:30:00 UTC").getTime))
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
index 4cb5ba2f0d5eb..ab6d3dd96d271 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
@@ -78,7 +78,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
 
   test("query case class RDD") {
     val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true,
-      new java.math.BigDecimal(1), new Date(12345), new Timestamp(12345), Seq(1, 2, 3))
+      new java.math.BigDecimal(1), Date.valueOf("1970-01-01"), new Timestamp(12345), Seq(1, 2, 3))
     Seq(data).toDF().registerTempTable("reflectData")
 
     assert(ctx.sql("SELECT * FROM reflectData").collect().head ===
diff --git a/sql/hive/src/test/resources/golden/Date cast-0-a7cd69b80c77a771a2c955db666be53d b/sql/hive/src/test/resources/golden/Date cast-0-a7cd69b80c77a771a2c955db666be53d
deleted file mode 100644
index 98da82fa89386..0000000000000
--- a/sql/hive/src/test/resources/golden/Date cast-0-a7cd69b80c77a771a2c955db666be53d	
+++ /dev/null
@@ -1 +0,0 @@
-1970-01-01	1970-01-01	1969-12-31 16:00:00	1969-12-31 16:00:00	1970-01-01 00:00:00
diff --git a/sql/hive/src/test/resources/golden/Date comparison test 2-0-dc1b267f1d79d49e6675afe4fd2a34a5 b/sql/hive/src/test/resources/golden/Date comparison test 2-0-dc1b267f1d79d49e6675afe4fd2a34a5
deleted file mode 100644
index 27ba77ddaf615..0000000000000
--- a/sql/hive/src/test/resources/golden/Date comparison test 2-0-dc1b267f1d79d49e6675afe4fd2a34a5	
+++ /dev/null
@@ -1 +0,0 @@
-true
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 51dabc67fa7c1..4cdba03b27022 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -324,20 +324,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       | FROM src LIMIT 1
     """.stripMargin)
 
-  createQueryTest("Date comparison test 2",
-    "SELECT CAST(CAST(0 AS timestamp) AS date) > CAST(0 AS timestamp) FROM src LIMIT 1")
-
-  createQueryTest("Date cast",
-    """
-      | SELECT
-      | CAST(CAST(0 AS timestamp) AS date),
-      | CAST(CAST(CAST(0 AS timestamp) AS date) AS string),
-      | CAST(0 AS timestamp),
-      | CAST(CAST(0 AS timestamp) AS string),
-      | CAST(CAST(CAST('1970-01-01 23:00:00' AS timestamp) AS date) AS timestamp)
-      | FROM src LIMIT 1
-    """.stripMargin)
-
   createQueryTest("Simple Average",
     "SELECT AVG(key) FROM src")
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 9f7e58f890241..6d645393a6da1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -17,10 +17,12 @@
 
 package org.apache.spark.sql.hive.execution
 
+import java.sql.{Date, Timestamp}
+
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
 import org.apache.spark.sql.catalyst.errors.DialectException
-import org.apache.spark.sql._
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
@@ -962,4 +964,31 @@ class SQLQuerySuite extends QueryTest {
       case None => // OK
     }
   }
+
+  test("SPARK-6785: HiveQuerySuite - Date comparison test 2") {
+    checkAnswer(
+      sql("SELECT CAST(CAST(0 AS timestamp) AS date) > CAST(0 AS timestamp) FROM src LIMIT 1"),
+      Row(false))
+  }
+
+  test("SPARK-6785: HiveQuerySuite - Date cast") {
+    // new Date(0) == 1970-01-01 00:00:00.0 GMT == 1969-12-31 16:00:00.0 PST
+    checkAnswer(
+      sql(
+        """
+          | SELECT
+          | CAST(CAST(0 AS timestamp) AS date),
+          | CAST(CAST(CAST(0 AS timestamp) AS date) AS string),
+          | CAST(0 AS timestamp),
+          | CAST(CAST(0 AS timestamp) AS string),
+          | CAST(CAST(CAST('1970-01-01 23:00:00' AS timestamp) AS date) AS timestamp)
+          | FROM src LIMIT 1
+        """.stripMargin),
+      Row(
+        Date.valueOf("1969-12-31"),
+        String.valueOf("1969-12-31"),
+        Timestamp.valueOf("1969-12-31 16:00:00"),
+        String.valueOf("1969-12-31 16:00:00"),
+        Timestamp.valueOf("1970-01-01 00:00:00")))
+  }
 }

From c1befd780c3defc843baa75097de7ec427d3f8ca Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 30 Jun 2015 12:23:48 -0700
Subject: [PATCH 0142/1454] [SPARK-8664] [ML] Add PCA transformer

Add PCA transformer for ML pipeline

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7065 from yanboliang/spark-8664 and squashes the following commits:

4afae45 [Yanbo Liang] address comments
e9effd7 [Yanbo Liang] Add PCA transformer
---
 .../org/apache/spark/ml/feature/PCA.scala     | 130 ++++++++++++++++++
 .../org/apache/spark/mllib/feature/PCA.scala  |   2 +-
 .../apache/spark/ml/feature/PCASuite.scala    |  64 +++++++++
 3 files changed, 195 insertions(+), 1 deletion(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
new file mode 100644
index 0000000000000..2d3bb680cf309
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
@@ -0,0 +1,130 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.feature
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{StructField, StructType}
+
+/**
+ * Params for [[PCA]] and [[PCAModel]].
+ */
+private[feature] trait PCAParams extends Params with HasInputCol with HasOutputCol {
+
+  /**
+   * The number of principal components.
+   * @group param
+   */
+  final val k: IntParam = new IntParam(this, "k", "the number of principal components")
+
+  /** @group getParam */
+  def getK: Int = $(k)
+
+}
+
+/**
+ * :: Experimental ::
+ * PCA trains a model to project vectors to a low-dimensional space using PCA.
+ */
+@Experimental
+class PCA (override val uid: String) extends Estimator[PCAModel] with PCAParams {
+
+  def this() = this(Identifiable.randomUID("pca"))
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setK(value: Int): this.type = set(k, value)
+
+  /**
+   * Computes a [[PCAModel]] that contains the principal components of the input vectors.
+   */
+  override def fit(dataset: DataFrame): PCAModel = {
+    transformSchema(dataset.schema, logging = true)
+    val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v}
+    val pca = new feature.PCA(k = $(k))
+    val pcaModel = pca.fit(input)
+    copyValues(new PCAModel(uid, pcaModel).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    val inputType = schema($(inputCol)).dataType
+    require(inputType.isInstanceOf[VectorUDT],
+      s"Input column ${$(inputCol)} must be a vector column")
+    require(!schema.fieldNames.contains($(outputCol)),
+      s"Output column ${$(outputCol)} already exists.")
+    val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
+    StructType(outputFields)
+  }
+
+  override def copy(extra: ParamMap): PCA = defaultCopy(extra)
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by [[PCA]].
+ */
+@Experimental
+class PCAModel private[ml] (
+    override val uid: String,
+    pcaModel: feature.PCAModel)
+  extends Model[PCAModel] with PCAParams {
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /**
+   * Transform a vector by computed Principal Components.
+   * NOTE: Vectors to be transformed must be the same length
+   * as the source vectors given to [[PCA.fit()]].
+   */
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    val pcaOp = udf { pcaModel.transform _ }
+    dataset.withColumn($(outputCol), pcaOp(col($(inputCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    val inputType = schema($(inputCol)).dataType
+    require(inputType.isInstanceOf[VectorUDT],
+      s"Input column ${$(inputCol)} must be a vector column")
+    require(!schema.fieldNames.contains($(outputCol)),
+      s"Output column ${$(outputCol)} already exists.")
+    val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
+    StructType(outputFields)
+  }
+
+  override def copy(extra: ParamMap): PCAModel = {
+    val copied = new PCAModel(uid, pcaModel)
+    copyValues(copied, extra)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
index 4e01e402b4283..2a66263d8b7d6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
@@ -68,7 +68,7 @@ class PCA(val k: Int) {
  * @param k number of principal components.
  * @param pc a principal components Matrix. Each column is one principal component.
  */
-class PCAModel private[mllib] (val k: Int, val pc: DenseMatrix) extends VectorTransformer {
+class PCAModel private[spark] (val k: Int, val pc: DenseMatrix) extends VectorTransformer {
   /**
    * Transform a vector by computed Principal Components.
    *
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
new file mode 100644
index 0000000000000..d0ae36b28c7a9
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.mllib.linalg.{Vector, Vectors, DenseMatrix, Matrices}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.mllib.feature.{PCAModel => OldPCAModel}
+import org.apache.spark.sql.Row
+
+class PCASuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    ParamsSuite.checkParams(new PCA)
+    val mat = Matrices.dense(2, 2, Array(0.0, 1.0, 2.0, 3.0)).asInstanceOf[DenseMatrix]
+    val model = new PCAModel("pca", new OldPCAModel(2, mat))
+    ParamsSuite.checkParams(model)
+  }
+
+  test("pca") {
+    val data = Array(
+      Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
+      Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
+      Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
+    )
+
+    val dataRDD = sc.parallelize(data, 2)
+
+    val mat = new RowMatrix(dataRDD)
+    val pc = mat.computePrincipalComponents(3)
+    val expected = mat.multiply(pc).rows
+
+    val df = sqlContext.createDataFrame(dataRDD.zip(expected)).toDF("features", "expected")
+
+    val pca = new PCA()
+      .setInputCol("features")
+      .setOutputCol("pca_features")
+      .setK(3)
+      .fit(df)
+
+    pca.transform(df).select("pca_features", "expected").collect().foreach {
+      case Row(x: Vector, y: Vector) =>
+        assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.")
+    }
+  }
+}

From b8e5bb6fc1553256e950fdad9cb5acc6b296816e Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Tue, 30 Jun 2015 12:24:47 -0700
Subject: [PATCH 0143/1454] [SPARK-8628] [SQL] Race condition in
 AbstractSparkSQLParser.parse

Made lexical iniatialization as lazy val

Author: Vinod K C <vinod.kc@huawei.com>

Closes #7015 from vinodkc/handle_lexical_initialize_schronization and squashes the following commits:

b6d1c74 [Vinod K C] Avoided repeated lexical  initialization
5863cf7 [Vinod K C] Removed space
e27c66c [Vinod K C] Avoid reinitialization of lexical in parse method
ef4f60f [Vinod K C] Reverted import order
e9fc49a [Vinod K C] handle  synchronization in SqlLexical.initialize
---
 .../apache/spark/sql/catalyst/AbstractSparkSQLParser.scala  | 6 ++++--
 .../scala/org/apache/spark/sql/catalyst/SqlParser.scala     | 2 +-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
index ef7b3ad9432cf..d494ae7b71d16 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst
 import scala.language.implicitConversions
 import scala.util.parsing.combinator.lexical.StdLexical
 import scala.util.parsing.combinator.syntactical.StandardTokenParsers
-import scala.util.parsing.combinator.{PackratParsers, RegexParsers}
+import scala.util.parsing.combinator.PackratParsers
 import scala.util.parsing.input.CharArrayReader.EofCh
 
 import org.apache.spark.sql.catalyst.plans.logical._
@@ -30,12 +30,14 @@ private[sql] abstract class AbstractSparkSQLParser
 
   def parse(input: String): LogicalPlan = {
     // Initialize the Keywords.
-    lexical.initialize(reservedWords)
+    initLexical
     phrase(start)(new lexical.Scanner(input)) match {
       case Success(plan, _) => plan
       case failureOrError => sys.error(failureOrError.toString)
     }
   }
+  /* One time initialization of lexical.This avoid reinitialization of  lexical in parse method */
+  protected lazy val initLexical: Unit = lexical.initialize(reservedWords)
 
   protected case class Keyword(str: String) {
     def normalize: String = lexical.normalizeKeyword(str)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 79f526e823cd4..8d02fbf4f92c4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -40,7 +40,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
 
   def parseExpression(input: String): Expression = {
     // Initialize the Keywords.
-    lexical.initialize(reservedWords)
+    initLexical
     phrase(projection)(new lexical.Scanner(input)) match {
       case Success(plan, _) => plan
       case failureOrError => sys.error(failureOrError.toString)

From 74cc16dbc35e35fd5cd5542239dcb6e5e7f92d18 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 30 Jun 2015 12:31:33 -0700
Subject: [PATCH 0144/1454] [SPARK-8471] [ML] Discrete Cosine Transform Feature
 Transformer

Implementation and tests for Discrete Cosine Transformer.

Author: Feynman Liang <fliang@databricks.com>

Closes #6894 from feynmanliang/dct-features and squashes the following commits:

433dbc7 [Feynman Liang] Test refactoring
91e9636 [Feynman Liang] Style guide and test helper refactor
b5ac19c [Feynman Liang] Use Vector types, add Java test
530983a [Feynman Liang] Tests for other numeric datatypes
195d7aa [Feynman Liang] Implement support for arbitrary numeric types
95d4939 [Feynman Liang] Working DCT for 1D Doubles
---
 .../feature/DiscreteCosineTransformer.scala   | 72 +++++++++++++++++
 .../JavaDiscreteCosineTransformerSuite.java   | 78 +++++++++++++++++++
 .../DiscreteCosineTransformerSuite.scala      | 73 +++++++++++++++++
 3 files changed, 223 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/DiscreteCosineTransformer.scala
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaDiscreteCosineTransformerSuite.java
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/DiscreteCosineTransformerSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/DiscreteCosineTransformer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/DiscreteCosineTransformer.scala
new file mode 100644
index 0000000000000..a2f4d59f81c44
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/DiscreteCosineTransformer.scala
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import edu.emory.mathcs.jtransforms.dct._
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.param.BooleanParam
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
+import org.apache.spark.sql.types.DataType
+
+/**
+ * :: Experimental ::
+ * A feature transformer that takes the 1D discrete cosine transform of a real vector. No zero
+ * padding is performed on the input vector.
+ * It returns a real vector of the same length representing the DCT. The return vector is scaled
+ * such that the transform matrix is unitary (aka scaled DCT-II).
+ *
+ * More information on [[https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia]].
+ */
+@Experimental
+class DiscreteCosineTransformer(override val uid: String)
+  extends UnaryTransformer[Vector, Vector, DiscreteCosineTransformer] {
+
+  def this() = this(Identifiable.randomUID("dct"))
+
+  /**
+   * Indicates whether to perform the inverse DCT (true) or forward DCT (false).
+   * Default: false
+   * @group param
+   */
+  def inverse: BooleanParam = new BooleanParam(
+    this, "inverse", "Set transformer to perform inverse DCT")
+
+  /** @group setParam */
+  def setInverse(value: Boolean): this.type = set(inverse, value)
+
+  /** @group getParam */
+  def getInverse: Boolean = $(inverse)
+
+  setDefault(inverse -> false)
+
+  override protected def createTransformFunc: Vector => Vector = { vec =>
+    val result = vec.toArray
+    val jTransformer = new DoubleDCT_1D(result.length)
+    if ($(inverse)) jTransformer.inverse(result, true) else jTransformer.forward(result, true)
+    Vectors.dense(result)
+  }
+
+  override protected def validateInputType(inputType: DataType): Unit = {
+    require(inputType.isInstanceOf[VectorUDT], s"Input type must be VectorUDT but got $inputType.")
+  }
+
+  override protected def outputDataType: DataType = new VectorUDT
+}
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDiscreteCosineTransformerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDiscreteCosineTransformerSuite.java
new file mode 100644
index 0000000000000..28bc5f65e0532
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDiscreteCosineTransformerSuite.java
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature;
+
+import com.google.common.collect.Lists;
+import edu.emory.mathcs.jtransforms.dct.DoubleDCT_1D;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+public class JavaDiscreteCosineTransformerSuite {
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaDiscreteCosineTransformerSuite");
+    jsql = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  @Test
+  public void javaCompatibilityTest() {
+    double[] input = new double[] {1D, 2D, 3D, 4D};
+    JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+      RowFactory.create(Vectors.dense(input))
+    ));
+    DataFrame dataset = jsql.createDataFrame(data, new StructType(new StructField[]{
+      new StructField("vec", (new VectorUDT()), false, Metadata.empty())
+    }));
+
+    double[] expectedResult = input.clone();
+    (new DoubleDCT_1D(input.length)).forward(expectedResult, true);
+
+    DiscreteCosineTransformer DCT = new DiscreteCosineTransformer()
+      .setInputCol("vec")
+      .setOutputCol("resultVec");
+
+    Row[] result = DCT.transform(dataset).select("resultVec").collect();
+    Vector resultVec = result[0].getAs("resultVec");
+
+    Assert.assertArrayEquals(expectedResult, resultVec.toArray(), 1e-6);
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/DiscreteCosineTransformerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/DiscreteCosineTransformerSuite.scala
new file mode 100644
index 0000000000000..ed0fc11f78f69
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/DiscreteCosineTransformerSuite.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import scala.beans.BeanInfo
+
+import edu.emory.mathcs.jtransforms.dct.DoubleDCT_1D
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, Row}
+
+@BeanInfo
+case class DCTTestData(vec: Vector, wantedVec: Vector)
+
+class DiscreteCosineTransformerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("forward transform of discrete cosine matches jTransforms result") {
+    val data = Vectors.dense((0 until 128).map(_ => 2D * math.random - 1D).toArray)
+    val inverse = false
+
+    testDCT(data, inverse)
+  }
+
+  test("inverse transform of discrete cosine matches jTransforms result") {
+    val data = Vectors.dense((0 until 128).map(_ => 2D * math.random - 1D).toArray)
+    val inverse = true
+
+    testDCT(data, inverse)
+  }
+
+  private def testDCT(data: Vector, inverse: Boolean): Unit = {
+    val expectedResultBuffer = data.toArray.clone()
+    if (inverse) {
+      (new DoubleDCT_1D(data.size)).inverse(expectedResultBuffer, true)
+    } else {
+      (new DoubleDCT_1D(data.size)).forward(expectedResultBuffer, true)
+    }
+    val expectedResult = Vectors.dense(expectedResultBuffer)
+
+    val dataset = sqlContext.createDataFrame(Seq(
+      DCTTestData(data, expectedResult)
+    ))
+
+    val transformer = new DiscreteCosineTransformer()
+      .setInputCol("vec")
+      .setOutputCol("resultVec")
+      .setInverse(inverse)
+
+    transformer.transform(dataset)
+      .select("resultVec", "wantedVec")
+      .collect()
+      .foreach { case Row(resultVec: Vector, wantedVec: Vector) =>
+      assert(Vectors.sqdist(resultVec, wantedVec) < 1e-6)
+    }
+  }
+}

From 61d7b533dd50bfac2162b4edcea94724bbd8fcb1 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 30 Jun 2015 12:44:43 -0700
Subject: [PATCH 0145/1454] [SPARK-7514] [MLLIB] Add MinMaxScaler to feature
 transformation

jira: https://issues.apache.org/jira/browse/SPARK-7514
Add a popular scaling method to feature component, which is commonly known as min-max normalization or Rescaling.

Core function is,
Normalized(x) = (x - min) / (max - min) * scale + newBase

where `newBase` and `scale` are parameters (type Double) of the `VectorTransformer`. `newBase` is the new minimum number for the features, and `scale` controls the ranges after transformation. This is a little complicated than the basic MinMax normalization, yet it provides flexibility so that users can control the range more specifically. like [0.1, 0.9] in some NN application.

For case that `max == min`, 0.5 is used as the raw value. (0.5 * scale + newBase)
I'll add UT once the design got settled ( and this is not considered as too naive)

reference:
 http://en.wikipedia.org/wiki/Feature_scaling
http://stn.spotfire.com/spotfire_client_help/index.htm#norm/norm_scale_between_0_and_1.htm

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #6039 from hhbyyh/minMaxNorm and squashes the following commits:

f942e9f [Yuhao Yang] add todo for metadata
8b37bbc [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into minMaxNorm
4894dbc [Yuhao Yang] add copy
fa2989f [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into minMaxNorm
29db415 [Yuhao Yang] add clue and minor adjustment
5b8f7cc [Yuhao Yang] style fix
9b133d0 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into minMaxNorm
22f20f2 [Yuhao Yang] style change and bug fix
747c9bb [Yuhao Yang] add ut and remove mllib version
a5ba0aa [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into minMaxNorm
585cc07 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into minMaxNorm
1c6dcb1 [Yuhao Yang] minor change
0f1bc80 [Yuhao Yang] add MinMaxScaler to ml
8e7436e [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into minMaxNorm
3663165 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into minMaxNorm
1247c27 [Yuhao Yang] some comments improvement
d285a19 [Yuhao Yang] initial checkin for minMaxNorm
---
 .../spark/ml/feature/MinMaxScaler.scala       | 170 ++++++++++++++++++
 .../spark/ml/feature/MinMaxScalerSuite.scala  |  68 +++++++
 2 files changed, 238 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
new file mode 100644
index 0000000000000..b30adf3df48d2
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -0,0 +1,170 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.param.{ParamMap, DoubleParam, Params}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
+import org.apache.spark.mllib.stat.Statistics
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{StructField, StructType}
+
+/**
+ * Params for [[MinMaxScaler]] and [[MinMaxScalerModel]].
+ */
+private[feature] trait MinMaxScalerParams extends Params with HasInputCol with HasOutputCol {
+
+  /**
+   * lower bound after transformation, shared by all features
+   * Default: 0.0
+   * @group param
+   */
+  val min: DoubleParam = new DoubleParam(this, "min",
+    "lower bound of the output feature range")
+
+  /**
+   * upper bound after transformation, shared by all features
+   * Default: 1.0
+   * @group param
+   */
+  val max: DoubleParam = new DoubleParam(this, "max",
+    "upper bound of the output feature range")
+
+  /** Validates and transforms the input schema. */
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    val inputType = schema($(inputCol)).dataType
+    require(inputType.isInstanceOf[VectorUDT],
+      s"Input column ${$(inputCol)} must be a vector column")
+    require(!schema.fieldNames.contains($(outputCol)),
+      s"Output column ${$(outputCol)} already exists.")
+    val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
+    StructType(outputFields)
+  }
+
+  override def validateParams(): Unit = {
+    require($(min) < $(max), s"The specified min(${$(min)}) is larger or equal to max(${$(max)})")
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Rescale each feature individually to a common range [min, max] linearly using column summary
+ * statistics, which is also known as min-max normalization or Rescaling. The rescaled value for
+ * feature E is calculated as,
+ *
+ * Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
+ *
+ * For the case E_{max} == E_{min}, Rescaled(e_i) = 0.5 * (max + min)
+ * Note that since zero values will probably be transformed to non-zero values, output of the
+ * transformer will be DenseVector even for sparse input.
+ */
+@Experimental
+class MinMaxScaler(override val uid: String)
+  extends Estimator[MinMaxScalerModel] with MinMaxScalerParams {
+
+  def this() = this(Identifiable.randomUID("minMaxScal"))
+
+  setDefault(min -> 0.0, max -> 1.0)
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setMin(value: Double): this.type = set(min, value)
+
+  /** @group setParam */
+  def setMax(value: Double): this.type = set(max, value)
+
+  override def fit(dataset: DataFrame): MinMaxScalerModel = {
+    transformSchema(dataset.schema, logging = true)
+    val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v }
+    val summary = Statistics.colStats(input)
+    copyValues(new MinMaxScalerModel(uid, summary.min, summary.max).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): MinMaxScaler = defaultCopy(extra)
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by [[MinMaxScaler]].
+ *
+ * TODO: The transformer does not yet set the metadata in the output column (SPARK-8529).
+ */
+@Experimental
+class MinMaxScalerModel private[ml] (
+    override val uid: String,
+    val originalMin: Vector,
+    val originalMax: Vector)
+  extends Model[MinMaxScalerModel] with MinMaxScalerParams {
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setMin(value: Double): this.type = set(min, value)
+
+  /** @group setParam */
+  def setMax(value: Double): this.type = set(max, value)
+
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val originalRange = (originalMax.toBreeze - originalMin.toBreeze).toArray
+    val minArray = originalMin.toArray
+
+    val reScale = udf { (vector: Vector) =>
+      val scale = $(max) - $(min)
+
+      // 0 in sparse vector will probably be rescaled to non-zero
+      val values = vector.toArray
+      val size = values.size
+      var i = 0
+      while (i < size) {
+        val raw = if (originalRange(i) != 0) (values(i) - minArray(i)) / originalRange(i) else 0.5
+        values(i) = raw * scale + $(min)
+        i += 1
+      }
+      Vectors.dense(values)
+    }
+
+    dataset.withColumn($(outputCol), reScale(col($(inputCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): MinMaxScalerModel = {
+    val copied = new MinMaxScalerModel(uid, originalMin, originalMax)
+    copyValues(copied, extra)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
new file mode 100644
index 0000000000000..c452054bec92f
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{Row, SQLContext}
+
+class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("MinMaxScaler fit basic case") {
+    val sqlContext = new SQLContext(sc)
+
+    val data = Array(
+      Vectors.dense(1, 0, Long.MinValue),
+      Vectors.dense(2, 0, 0),
+      Vectors.sparse(3, Array(0, 2), Array(3, Long.MaxValue)),
+      Vectors.sparse(3, Array(0), Array(1.5)))
+
+    val expected: Array[Vector] = Array(
+      Vectors.dense(-5, 0, -5),
+      Vectors.dense(0, 0, 0),
+      Vectors.sparse(3, Array(0, 2), Array(5, 5)),
+      Vectors.sparse(3, Array(0), Array(-2.5)))
+
+    val df = sqlContext.createDataFrame(data.zip(expected)).toDF("features", "expected")
+    val scaler = new MinMaxScaler()
+      .setInputCol("features")
+      .setOutputCol("scaled")
+      .setMin(-5)
+      .setMax(5)
+
+    val model = scaler.fit(df)
+    model.transform(df).select("expected", "scaled").collect()
+      .foreach { case Row(vector1: Vector, vector2: Vector) =>
+        assert(vector1.equals(vector2), "Transformed vector is different with expected.")
+    }
+  }
+
+  test("MinMaxScaler arguments max must be larger than min") {
+    withClue("arguments max must be larger than min") {
+      intercept[IllegalArgumentException] {
+        val scaler = new MinMaxScaler().setMin(10).setMax(0)
+        scaler.validateParams()
+      }
+      intercept[IllegalArgumentException] {
+        val scaler = new MinMaxScaler().setMin(0).setMax(0)
+        scaler.validateParams()
+      }
+    }
+  }
+}

From 79f0b371a36560a009c1b0943c928adc5a1bdd8f Mon Sep 17 00:00:00 2001
From: xutingjun <xutingjun@huawei.com>
Date: Tue, 30 Jun 2015 13:56:59 -0700
Subject: [PATCH 0146/1454] [SPARK-8560] [UI] The Executors page will have
 negative if having resubmitted tasks

 when the ```taskEnd.reason``` is ```Resubmitted```, it shouldn't  do statistics. Because this tasks has a ```SUCCESS``` taskEnd before.

Author: xutingjun <xutingjun@huawei.com>

Closes #6950 from XuTingjun/pageError and squashes the following commits:

af35dc3 [xutingjun] When taskEnd is Resubmitted, don't do statistics
---
 .../org/apache/spark/ui/exec/ExecutorsTab.scala     | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
index 39583af14390d..a88fc4c37d3c9 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ui.exec
 
 import scala.collection.mutable.HashMap
 
-import org.apache.spark.{ExceptionFailure, SparkContext}
+import org.apache.spark.{Resubmitted, ExceptionFailure, SparkContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.{StorageStatus, StorageStatusListener}
@@ -92,15 +92,22 @@ class ExecutorsListener(storageStatusListener: StorageStatusListener) extends Sp
     val info = taskEnd.taskInfo
     if (info != null) {
       val eid = info.executorId
-      executorToTasksActive(eid) = executorToTasksActive.getOrElse(eid, 1) - 1
-      executorToDuration(eid) = executorToDuration.getOrElse(eid, 0L) + info.duration
       taskEnd.reason match {
+        case Resubmitted =>
+          // Note: For resubmitted tasks, we continue to use the metrics that belong to the
+          // first attempt of this task. This may not be 100% accurate because the first attempt
+          // could have failed half-way through. The correct fix would be to keep track of the
+          // metrics added by each attempt, but this is much more complicated.
+          return
         case e: ExceptionFailure =>
           executorToTasksFailed(eid) = executorToTasksFailed.getOrElse(eid, 0) + 1
         case _ =>
           executorToTasksComplete(eid) = executorToTasksComplete.getOrElse(eid, 0) + 1
       }
 
+      executorToTasksActive(eid) = executorToTasksActive.getOrElse(eid, 1) - 1
+      executorToDuration(eid) = executorToDuration.getOrElse(eid, 0L) + info.duration
+
       // Update shuffle read/write
       val metrics = taskEnd.taskMetrics
       if (metrics != null) {

From 7dda0844e1eb6df7455af68592751806b3b92251 Mon Sep 17 00:00:00 2001
From: Joshi <rekhajoshm@gmail.com>
Date: Tue, 30 Jun 2015 14:00:35 -0700
Subject: [PATCH 0147/1454] [SPARK-2645] [CORE] Allow SparkEnv.stop() to be
 called multiple times without side effects.

Fix for SparkContext stop behavior - Allow sc.stop() to be called multiple times without side effects.

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #6973 from rekhajoshm/SPARK-2645 and squashes the following commits:

277043e [Joshi] Fix for SparkContext stop behavior
446b0a4 [Joshi] Fix for SparkContext stop behavior
2ce5760 [Joshi] Fix for SparkContext stop behavior
c97839a [Joshi] Fix for SparkContext stop behavior
1aff39c [Joshi] Fix for SparkContext stop behavior
12f66b5 [Joshi] Fix for SparkContext stop behavior
72bb484 [Joshi] Fix for SparkContext stop behavior
a5a7d7f [Joshi] Fix for SparkContext stop behavior
9193a0c [Joshi] Fix for SparkContext stop behavior
58dba70 [Joshi] SPARK-2645: Fix for SparkContext stop behavior
380c5b0 [Joshi] SPARK-2645: Fix for SparkContext stop behavior
b566b66 [Joshi] SPARK-2645: Fix for SparkContext stop behavior
0be142d [Rekha Joshi] Merge pull request #3 from apache/master
106fd8e [Rekha Joshi] Merge pull request #2 from apache/master
e3677c9 [Rekha Joshi] Merge pull request #1 from apache/master
---
 .../scala/org/apache/spark/SparkEnv.scala     | 66 ++++++++++---------
 .../org/apache/spark/SparkContextSuite.scala  | 13 ++++
 2 files changed, 47 insertions(+), 32 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index b0665570e2681..1b133fbdfaf59 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -22,7 +22,6 @@ import java.net.Socket
 
 import akka.actor.ActorSystem
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.util.Properties
 
@@ -90,39 +89,42 @@ class SparkEnv (
   private var driverTmpDirToDelete: Option[String] = None
 
   private[spark] def stop() {
-    isStopped = true
-    pythonWorkers.foreach { case(key, worker) => worker.stop() }
-    Option(httpFileServer).foreach(_.stop())
-    mapOutputTracker.stop()
-    shuffleManager.stop()
-    broadcastManager.stop()
-    blockManager.stop()
-    blockManager.master.stop()
-    metricsSystem.stop()
-    outputCommitCoordinator.stop()
-    rpcEnv.shutdown()
-
-    // Unfortunately Akka's awaitTermination doesn't actually wait for the Netty server to shut
-    // down, but let's call it anyway in case it gets fixed in a later release
-    // UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it.
-    // actorSystem.awaitTermination()
-
-    // Note that blockTransferService is stopped by BlockManager since it is started by it.
-
-    // If we only stop sc, but the driver process still run as a services then we need to delete
-    // the tmp dir, if not, it will create too many tmp dirs.
-    // We only need to delete the tmp dir create by driver, because sparkFilesDir is point to the
-    // current working dir in executor which we do not need to delete.
-    driverTmpDirToDelete match {
-      case Some(path) => {
-        try {
-          Utils.deleteRecursively(new File(path))
-        } catch {
-          case e: Exception =>
-            logWarning(s"Exception while deleting Spark temp dir: $path", e)
+
+    if (!isStopped) {
+      isStopped = true
+      pythonWorkers.values.foreach(_.stop())
+      Option(httpFileServer).foreach(_.stop())
+      mapOutputTracker.stop()
+      shuffleManager.stop()
+      broadcastManager.stop()
+      blockManager.stop()
+      blockManager.master.stop()
+      metricsSystem.stop()
+      outputCommitCoordinator.stop()
+      rpcEnv.shutdown()
+
+      // Unfortunately Akka's awaitTermination doesn't actually wait for the Netty server to shut
+      // down, but let's call it anyway in case it gets fixed in a later release
+      // UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it.
+      // actorSystem.awaitTermination()
+
+      // Note that blockTransferService is stopped by BlockManager since it is started by it.
+
+      // If we only stop sc, but the driver process still run as a services then we need to delete
+      // the tmp dir, if not, it will create too many tmp dirs.
+      // We only need to delete the tmp dir create by driver, because sparkFilesDir is point to the
+      // current working dir in executor which we do not need to delete.
+      driverTmpDirToDelete match {
+        case Some(path) => {
+          try {
+            Utils.deleteRecursively(new File(path))
+          } catch {
+            case e: Exception =>
+              logWarning(s"Exception while deleting Spark temp dir: $path", e)
+          }
         }
+        case None => // We just need to delete tmp dir created by driver, so do nothing on executor
       }
-      case None => // We just need to delete tmp dir created by driver, so do nothing on executor
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
index 6838b35ab4cc8..5c57940fa5f77 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
@@ -30,6 +30,7 @@ import org.apache.spark.util.Utils
 
 import scala.concurrent.Await
 import scala.concurrent.duration.Duration
+import org.scalatest.Matchers._
 
 class SparkContextSuite extends SparkFunSuite with LocalSparkContext {
 
@@ -272,4 +273,16 @@ class SparkContextSuite extends SparkFunSuite with LocalSparkContext {
       sc.stop()
     }
   }
+
+  test("calling multiple sc.stop() must not throw any exception") {
+    noException should be thrownBy {
+      sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local"))
+      val cnt = sc.parallelize(1 to 4).count()
+      sc.cancelAllJobs()
+      sc.stop()
+      // call stop second time
+      sc.stop()
+    }
+  }
+
 }

From 4bb8375fc2c6aa8342df03c3617aa97e7d01de3f Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 30 Jun 2015 14:01:52 -0700
Subject: [PATCH 0148/1454] [SPARK-8372] Do not show applications that haven't
 recorded their app ID yet.

Showing these applications may lead to weird behavior in the History Server. For old logs, if
the app ID is recorded later, you may end up with a duplicate entry. For new logs, the app might
be listed with a ".inprogress" suffix.

So ignore those, but still allow old applications that don't record app IDs at all (1.0 and 1.1) to be shown.

Author: Marcelo Vanzin <vanzin@cloudera.com>
Author: Carson Wang <carson.wang@intel.com>

Closes #7097 from vanzin/SPARK-8372 and squashes the following commits:

a24eab2 [Marcelo Vanzin] Feedback.
112ae8f [Marcelo Vanzin] Merge branch 'master' into SPARK-8372
7b91b74 [Marcelo Vanzin] Handle logs generated by 1.0 and 1.1.
1eca3fe [Carson Wang] [SPARK-8372] History server shows incorrect information for application not started
---
 .../deploy/history/FsHistoryProvider.scala    |  98 ++++++++++------
 .../history/FsHistoryProviderSuite.scala      | 109 +++++++++++++-----
 2 files changed, 147 insertions(+), 60 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index 5427a88f32ffd..2cc465e55fceb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -83,12 +83,6 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
   // List of application logs to be deleted by event log cleaner.
   private var attemptsToClean = new mutable.ListBuffer[FsApplicationAttemptInfo]
 
-  // Constants used to parse Spark 1.0.0 log directories.
-  private[history] val LOG_PREFIX = "EVENT_LOG_"
-  private[history] val SPARK_VERSION_PREFIX = EventLoggingListener.SPARK_VERSION_KEY + "_"
-  private[history] val COMPRESSION_CODEC_PREFIX = EventLoggingListener.COMPRESSION_CODEC_KEY + "_"
-  private[history] val APPLICATION_COMPLETE = "APPLICATION_COMPLETE"
-
   /**
    * Return a runnable that performs the given operation on the event logs.
    * This operation is expected to be executed periodically.
@@ -146,7 +140,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
   override def getAppUI(appId: String, attemptId: Option[String]): Option[SparkUI] = {
     try {
       applications.get(appId).flatMap { appInfo =>
-        appInfo.attempts.find(_.attemptId == attemptId).map { attempt =>
+        appInfo.attempts.find(_.attemptId == attemptId).flatMap { attempt =>
           val replayBus = new ReplayListenerBus()
           val ui = {
             val conf = this.conf.clone()
@@ -155,20 +149,20 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
               HistoryServer.getAttemptURI(appId, attempt.attemptId), attempt.startTime)
             // Do not call ui.bind() to avoid creating a new server for each application
           }
-
           val appListener = new ApplicationEventListener()
           replayBus.addListener(appListener)
           val appInfo = replay(fs.getFileStatus(new Path(logDir, attempt.logPath)), replayBus)
-
-          ui.setAppName(s"${appInfo.name} ($appId)")
-
-          val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false)
-          ui.getSecurityManager.setAcls(uiAclsEnabled)
-          // make sure to set admin acls before view acls so they are properly picked up
-          ui.getSecurityManager.setAdminAcls(appListener.adminAcls.getOrElse(""))
-          ui.getSecurityManager.setViewAcls(attempt.sparkUser,
-            appListener.viewAcls.getOrElse(""))
-          ui
+          appInfo.map { info =>
+            ui.setAppName(s"${info.name} ($appId)")
+
+            val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false)
+            ui.getSecurityManager.setAcls(uiAclsEnabled)
+            // make sure to set admin acls before view acls so they are properly picked up
+            ui.getSecurityManager.setAdminAcls(appListener.adminAcls.getOrElse(""))
+            ui.getSecurityManager.setViewAcls(attempt.sparkUser,
+              appListener.viewAcls.getOrElse(""))
+            ui
+          }
         }
       }
     } catch {
@@ -282,8 +276,12 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
     val newAttempts = logs.flatMap { fileStatus =>
       try {
         val res = replay(fileStatus, bus)
-        logInfo(s"Application log ${res.logPath} loaded successfully.")
-        Some(res)
+        res match {
+          case Some(r) => logDebug(s"Application log ${r.logPath} loaded successfully.")
+          case None => logWarning(s"Failed to load application log ${fileStatus.getPath}. " +
+            "The application may have not started.")
+        }
+        res
       } catch {
         case e: Exception =>
           logError(
@@ -429,9 +427,11 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
 
   /**
    * Replays the events in the specified log file and returns information about the associated
-   * application.
+   * application. Return `None` if the application ID cannot be located.
    */
-  private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationAttemptInfo = {
+  private def replay(
+      eventLog: FileStatus,
+      bus: ReplayListenerBus): Option[FsApplicationAttemptInfo] = {
     val logPath = eventLog.getPath()
     logInfo(s"Replaying log path: $logPath")
     val logInput =
@@ -445,16 +445,24 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
       val appCompleted = isApplicationCompleted(eventLog)
       bus.addListener(appListener)
       bus.replay(logInput, logPath.toString, !appCompleted)
-      new FsApplicationAttemptInfo(
-        logPath.getName(),
-        appListener.appName.getOrElse(NOT_STARTED),
-        appListener.appId.getOrElse(logPath.getName()),
-        appListener.appAttemptId,
-        appListener.startTime.getOrElse(-1L),
-        appListener.endTime.getOrElse(-1L),
-        getModificationTime(eventLog).get,
-        appListener.sparkUser.getOrElse(NOT_STARTED),
-        appCompleted)
+
+      // Without an app ID, new logs will render incorrectly in the listing page, so do not list or
+      // try to show their UI. Some old versions of Spark generate logs without an app ID, so let
+      // logs generated by those versions go through.
+      if (appListener.appId.isDefined || !sparkVersionHasAppId(eventLog)) {
+        Some(new FsApplicationAttemptInfo(
+          logPath.getName(),
+          appListener.appName.getOrElse(NOT_STARTED),
+          appListener.appId.getOrElse(logPath.getName()),
+          appListener.appAttemptId,
+          appListener.startTime.getOrElse(-1L),
+          appListener.endTime.getOrElse(-1L),
+          getModificationTime(eventLog).get,
+          appListener.sparkUser.getOrElse(NOT_STARTED),
+          appCompleted))
+      } else {
+        None
+      }
     } finally {
       logInput.close()
     }
@@ -529,10 +537,34 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
     }
   }
 
+  /**
+   * Returns whether the version of Spark that generated logs records app IDs. App IDs were added
+   * in Spark 1.1.
+   */
+  private def sparkVersionHasAppId(entry: FileStatus): Boolean = {
+    if (isLegacyLogDirectory(entry)) {
+      fs.listStatus(entry.getPath())
+        .find { status => status.getPath().getName().startsWith(SPARK_VERSION_PREFIX) }
+        .map { status =>
+          val version = status.getPath().getName().substring(SPARK_VERSION_PREFIX.length())
+          version != "1.0" && version != "1.1"
+        }
+        .getOrElse(true)
+    } else {
+      true
+    }
+  }
+
 }
 
-private object FsHistoryProvider {
+private[history] object FsHistoryProvider {
   val DEFAULT_LOG_DIR = "file:/tmp/spark-events"
+
+  // Constants used to parse Spark 1.0.0 log directories.
+  val LOG_PREFIX = "EVENT_LOG_"
+  val SPARK_VERSION_PREFIX = EventLoggingListener.SPARK_VERSION_KEY + "_"
+  val COMPRESSION_CODEC_PREFIX = EventLoggingListener.COMPRESSION_CODEC_KEY + "_"
+  val APPLICATION_COMPLETE = "APPLICATION_COMPLETE"
 }
 
 private class FsApplicationAttemptInfo(
diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
index 09075eeb539aa..2a62450bcdbad 100644
--- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
@@ -39,6 +39,8 @@ import org.apache.spark.util.{JsonProtocol, ManualClock, Utils}
 
 class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matchers with Logging {
 
+  import FsHistoryProvider._
+
   private var testDir: File = null
 
   before {
@@ -67,7 +69,8 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     // Write a new-style application log.
     val newAppComplete = newLogFile("new1", None, inProgress = false)
     writeFile(newAppComplete, true, None,
-      SparkListenerApplicationStart("new-app-complete", None, 1L, "test", None),
+      SparkListenerApplicationStart(newAppComplete.getName(), Some("new-app-complete"), 1L, "test",
+        None),
       SparkListenerApplicationEnd(5L)
       )
 
@@ -75,35 +78,30 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     val newAppCompressedComplete = newLogFile("new1compressed", None, inProgress = false,
       Some("lzf"))
     writeFile(newAppCompressedComplete, true, None,
-      SparkListenerApplicationStart("new-app-compressed-complete", None, 1L, "test", None),
+      SparkListenerApplicationStart(newAppCompressedComplete.getName(), Some("new-complete-lzf"),
+        1L, "test", None),
       SparkListenerApplicationEnd(4L))
 
     // Write an unfinished app, new-style.
     val newAppIncomplete = newLogFile("new2", None, inProgress = true)
     writeFile(newAppIncomplete, true, None,
-      SparkListenerApplicationStart("new-app-incomplete", None, 1L, "test", None)
+      SparkListenerApplicationStart(newAppIncomplete.getName(), Some("new-incomplete"), 1L, "test",
+        None)
       )
 
     // Write an old-style application log.
-    val oldAppComplete = new File(testDir, "old1")
-    oldAppComplete.mkdir()
-    createEmptyFile(new File(oldAppComplete, provider.SPARK_VERSION_PREFIX + "1.0"))
-    writeFile(new File(oldAppComplete, provider.LOG_PREFIX + "1"), false, None,
-      SparkListenerApplicationStart("old-app-complete", None, 2L, "test", None),
+    val oldAppComplete = writeOldLog("old1", "1.0", None, true,
+      SparkListenerApplicationStart("old1", Some("old-app-complete"), 2L, "test", None),
       SparkListenerApplicationEnd(3L)
       )
-    createEmptyFile(new File(oldAppComplete, provider.APPLICATION_COMPLETE))
 
     // Check for logs so that we force the older unfinished app to be loaded, to make
     // sure unfinished apps are also sorted correctly.
     provider.checkForLogs()
 
     // Write an unfinished app, old-style.
-    val oldAppIncomplete = new File(testDir, "old2")
-    oldAppIncomplete.mkdir()
-    createEmptyFile(new File(oldAppIncomplete, provider.SPARK_VERSION_PREFIX + "1.0"))
-    writeFile(new File(oldAppIncomplete, provider.LOG_PREFIX + "1"), false, None,
-      SparkListenerApplicationStart("old-app-incomplete", None, 2L, "test", None)
+    val oldAppIncomplete = writeOldLog("old2", "1.0", None, false,
+      SparkListenerApplicationStart("old2", None, 2L, "test", None)
       )
 
     // Force a reload of data from the log directory, and check that both logs are loaded.
@@ -124,16 +122,15 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
           List(ApplicationAttemptInfo(None, start, end, lastMod, user, completed)))
       }
 
-      list(0) should be (makeAppInfo(newAppComplete.getName(), "new-app-complete", 1L, 5L,
+      list(0) should be (makeAppInfo("new-app-complete", newAppComplete.getName(), 1L, 5L,
         newAppComplete.lastModified(), "test", true))
-      list(1) should be (makeAppInfo(newAppCompressedComplete.getName(),
-        "new-app-compressed-complete", 1L, 4L, newAppCompressedComplete.lastModified(), "test",
-        true))
-      list(2) should be (makeAppInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L,
+      list(1) should be (makeAppInfo("new-complete-lzf", newAppCompressedComplete.getName(),
+        1L, 4L, newAppCompressedComplete.lastModified(), "test", true))
+      list(2) should be (makeAppInfo("old-app-complete", oldAppComplete.getName(), 2L, 3L,
         oldAppComplete.lastModified(), "test", true))
-      list(3) should be (makeAppInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, -1L,
-        oldAppIncomplete.lastModified(), "test", false))
-      list(4) should be (makeAppInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, -1L,
+      list(3) should be (makeAppInfo(oldAppIncomplete.getName(), oldAppIncomplete.getName(), 2L,
+        -1L, oldAppIncomplete.lastModified(), "test", false))
+      list(4) should be (makeAppInfo("new-incomplete", newAppIncomplete.getName(), 1L, -1L,
         newAppIncomplete.lastModified(), "test", false))
 
       // Make sure the UI can be rendered.
@@ -155,12 +152,12 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
       val codec = if (valid) CompressionCodec.createCodec(new SparkConf(), codecName) else null
       val logDir = new File(testDir, codecName)
       logDir.mkdir()
-      createEmptyFile(new File(logDir, provider.SPARK_VERSION_PREFIX + "1.0"))
-      writeFile(new File(logDir, provider.LOG_PREFIX + "1"), false, Option(codec),
+      createEmptyFile(new File(logDir, SPARK_VERSION_PREFIX + "1.0"))
+      writeFile(new File(logDir, LOG_PREFIX + "1"), false, Option(codec),
         SparkListenerApplicationStart("app2", None, 2L, "test", None),
         SparkListenerApplicationEnd(3L)
         )
-      createEmptyFile(new File(logDir, provider.COMPRESSION_CODEC_PREFIX + codecName))
+      createEmptyFile(new File(logDir, COMPRESSION_CODEC_PREFIX + codecName))
 
       val logPath = new Path(logDir.getAbsolutePath())
       try {
@@ -180,12 +177,12 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
   test("SPARK-3697: ignore directories that cannot be read.") {
     val logFile1 = newLogFile("new1", None, inProgress = false)
     writeFile(logFile1, true, None,
-      SparkListenerApplicationStart("app1-1", None, 1L, "test", None),
+      SparkListenerApplicationStart("app1-1", Some("app1-1"), 1L, "test", None),
       SparkListenerApplicationEnd(2L)
       )
     val logFile2 = newLogFile("new2", None, inProgress = false)
     writeFile(logFile2, true, None,
-      SparkListenerApplicationStart("app1-2", None, 1L, "test", None),
+      SparkListenerApplicationStart("app1-2", Some("app1-2"), 1L, "test", None),
       SparkListenerApplicationEnd(2L)
       )
     logFile2.setReadable(false, false)
@@ -218,6 +215,18 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     }
   }
 
+  test("Parse logs that application is not started") {
+    val provider = new FsHistoryProvider((createTestConf()))
+
+    val logFile1 = newLogFile("app1", None, inProgress = true)
+    writeFile(logFile1, true, None,
+      SparkListenerLogStart("1.4")
+    )
+    updateAndCheck(provider) { list =>
+      list.size should be (0)
+    }
+  }
+
   test("SPARK-5582: empty log directory") {
     val provider = new FsHistoryProvider(createTestConf())
 
@@ -373,6 +382,33 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     }
   }
 
+  test("SPARK-8372: new logs with no app ID are ignored") {
+    val provider = new FsHistoryProvider(createTestConf())
+
+    // Write a new log file without an app id, to make sure it's ignored.
+    val logFile1 = newLogFile("app1", None, inProgress = true)
+    writeFile(logFile1, true, None,
+      SparkListenerLogStart("1.4")
+    )
+
+    // Write a 1.2 log file with no start event (= no app id), it should be ignored.
+    writeOldLog("v12Log", "1.2", None, false)
+
+    // Write 1.0 and 1.1 logs, which don't have app ids.
+    writeOldLog("v11Log", "1.1", None, true,
+      SparkListenerApplicationStart("v11Log", None, 2L, "test", None),
+      SparkListenerApplicationEnd(3L))
+    writeOldLog("v10Log", "1.0", None, true,
+      SparkListenerApplicationStart("v10Log", None, 2L, "test", None),
+      SparkListenerApplicationEnd(4L))
+
+    updateAndCheck(provider) { list =>
+      list.size should be (2)
+      list(0).id should be ("v10Log")
+      list(1).id should be ("v11Log")
+    }
+  }
+
   /**
    * Asks the provider to check for logs and calls a function to perform checks on the updated
    * app list. Example:
@@ -412,4 +448,23 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     new SparkConf().set("spark.history.fs.logDirectory", testDir.getAbsolutePath())
   }
 
+  private def writeOldLog(
+      fname: String,
+      sparkVersion: String,
+      codec: Option[CompressionCodec],
+      completed: Boolean,
+      events: SparkListenerEvent*): File = {
+    val log = new File(testDir, fname)
+    log.mkdir()
+
+    val oldEventLog = new File(log, LOG_PREFIX + "1")
+    createEmptyFile(new File(log, SPARK_VERSION_PREFIX + sparkVersion))
+    writeFile(new File(log, LOG_PREFIX + "1"), false, codec, events: _*)
+    if (completed) {
+      createEmptyFile(new File(log, APPLICATION_COMPLETE))
+    }
+
+    log
+  }
+
 }

From 3ba23ffd377d12383d923d1550ac8e2b916090fc Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 30 Jun 2015 14:02:50 -0700
Subject: [PATCH 0149/1454] [SPARK-8736] [ML] GBTRegressor should not threshold
 prediction

Changed GBTRegressor so it does NOT threshold the prediction.  Added test which fails with bug but works after fix.

CC: feynmanliang  mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7134 from jkbradley/gbrt-fix and squashes the following commits:

613b90e [Joseph K. Bradley] Changed GBTRegressor so it does NOT threshold the prediction
---
 .../spark/ml/regression/GBTRegressor.scala    |  3 +--
 .../ml/regression/GBTRegressorSuite.scala     | 23 ++++++++++++++++++-
 2 files changed, 23 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 036e3acb07412..47c110d027d67 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -172,8 +172,7 @@ final class GBTRegressionModel(
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
     val treePredictions = _trees.map(_.rootNode.predict(features))
-    val prediction = blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
-    if (prediction > 0.0) 1.0 else 0.0
+    blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
   }
 
   override def copy(extra: ParamMap): GBTRegressionModel = {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index 98fb3d3f5f22c..9682edcd9ba84 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -19,12 +19,13 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, Row}
 
 
 /**
@@ -67,6 +68,26 @@ class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("GBTRegressor behaves reasonably on toy data") {
+    val df = sqlContext.createDataFrame(Seq(
+      LabeledPoint(10, Vectors.dense(1, 2, 3, 4)),
+      LabeledPoint(-5, Vectors.dense(6, 3, 2, 1)),
+      LabeledPoint(11, Vectors.dense(2, 2, 3, 4)),
+      LabeledPoint(-6, Vectors.dense(6, 4, 2, 1)),
+      LabeledPoint(9, Vectors.dense(1, 2, 6, 4)),
+      LabeledPoint(-4, Vectors.dense(6, 3, 2, 2))
+    ))
+    val gbt = new GBTRegressor()
+      .setMaxDepth(2)
+      .setMaxIter(2)
+    val model = gbt.fit(df)
+    val preds = model.transform(df)
+    val predictions = preds.select("prediction").map(_.getDouble(0))
+    // Checks based on SPARK-8736 (to ensure it is not doing classification)
+    assert(predictions.max() > 2)
+    assert(predictions.min() < -1)
+  }
+
   // TODO: Reinstate test once runWithValidation is implemented  SPARK-7132
   /*
   test("runWithValidation stops early and performs better on a validation dataset") {

From 8c898964f095fcb5bb1c9212e1e484b1eb55c296 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 30 Jun 2015 14:06:50 -0700
Subject: [PATCH 0150/1454] [SPARK-8705] [WEBUI] Don't display rects when
 totalExecutionTime is 0

Because `System.currentTimeMillis()` is not accurate for tasks that only need several milliseconds, sometimes `totalExecutionTime` in `makeTimeline` will be 0. If `totalExecutionTime` is 0, there will the following error in the console.

![screen shot 2015-06-29 at 7 08 55 pm](https://cloud.githubusercontent.com/assets/1000778/8406776/5cd38e04-1e92-11e5-89f2-0c5134fe4b6b.png)

This PR fixes it by using an empty svg tag when `totalExecutionTime` is 0. This is a screenshot for a task that its totalExecutionTime is 0 after fixing it.

![screen shot 2015-06-30 at 12 26 52 am](https://cloud.githubusercontent.com/assets/1000778/8412896/7b33b4be-1ebf-11e5-9100-d6d656af3747.png)

Author: zsxwing <zsxwing@gmail.com>

Closes #7088 from zsxwing/SPARK-8705 and squashes the following commits:

9ee4ef5 [zsxwing] Address comments
ef2ecfa [zsxwing] Don't display rects when totalExecutionTime is 0
---
 .../org/apache/spark/ui/jobs/StagePage.scala  | 52 +++++++++++--------
 1 file changed, 30 insertions(+), 22 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index e96bf49d0dd14..17e7519ddd01c 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -570,6 +570,35 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
 
         val index = taskInfo.index
         val attempt = taskInfo.attempt
+
+        val svgTag =
+          if (totalExecutionTime == 0) {
+            // SPARK-8705: Avoid invalid attribute error in JavaScript if execution time is 0
+            """<svg class="task-assignment-timeline-duration-bar"></svg>"""
+          } else {
+           s"""<svg class="task-assignment-timeline-duration-bar">
+                 |<rect class="scheduler-delay-proportion"
+                   |x="$schedulerDelayProportionPos%" y="0px" height="26px"
+                   |width="$schedulerDelayProportion%"></rect>
+                 |<rect class="deserialization-time-proportion"
+                   |x="$deserializationTimeProportionPos%" y="0px" height="26px"
+                   |width="$deserializationTimeProportion%"></rect>
+                 |<rect class="shuffle-read-time-proportion"
+                   |x="$shuffleReadTimeProportionPos%" y="0px" height="26px"
+                   |width="$shuffleReadTimeProportion%"></rect>
+                 |<rect class="executor-runtime-proportion"
+                   |x="$executorRuntimeProportionPos%" y="0px" height="26px"
+                   |width="$executorComputingTimeProportion%"></rect>
+                 |<rect class="shuffle-write-time-proportion"
+                   |x="$shuffleWriteTimeProportionPos%" y="0px" height="26px"
+                   |width="$shuffleWriteTimeProportion%"></rect>
+                 |<rect class="serialization-time-proportion"
+                   |x="$serializationTimeProportionPos%" y="0px" height="26px"
+                   |width="$serializationTimeProportion%"></rect>
+                 |<rect class="getting-result-time-proportion"
+                   |x="$gettingResultTimeProportionPos%" y="0px" height="26px"
+                   |width="$gettingResultTimeProportion%"></rect></svg>""".stripMargin
+          }
         val timelineObject =
           s"""
              |{
@@ -595,28 +624,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
                  |<br>Shuffle Write Time: ${UIUtils.formatDuration(shuffleWriteTime)}
                  |<br>Result Serialization Time: ${UIUtils.formatDuration(serializationTime)}
                  |<br>Getting Result Time: ${UIUtils.formatDuration(gettingResultTime)}">
-                 |<svg class="task-assignment-timeline-duration-bar">
-                 |<rect class="scheduler-delay-proportion"
-                   |x="$schedulerDelayProportionPos%" y="0px" height="26px"
-                   |width="$schedulerDelayProportion%""></rect>
-                 |<rect class="deserialization-time-proportion"
-                   |x="$deserializationTimeProportionPos%" y="0px" height="26px"
-                   |width="$deserializationTimeProportion%"></rect>
-                 |<rect class="shuffle-read-time-proportion"
-                   |x="$shuffleReadTimeProportionPos%" y="0px" height="26px"
-                   |width="$shuffleReadTimeProportion%"></rect>
-                 |<rect class="executor-runtime-proportion"
-                   |x="$executorRuntimeProportionPos%" y="0px" height="26px"
-                   |width="$executorComputingTimeProportion%"></rect>
-                 |<rect class="shuffle-write-time-proportion"
-                   |x="$shuffleWriteTimeProportionPos%" y="0px" height="26px"
-                   |width="$shuffleWriteTimeProportion%"></rect>
-                 |<rect class="serialization-time-proportion"
-                   |x="$serializationTimeProportionPos%" y="0px" height="26px"
-                   |width="$serializationTimeProportion%"></rect>
-                 |<rect class="getting-result-time-proportion"
-                   |x="$gettingResultTimeProportionPos%" y="0px" height="26px"
-                   |width="$gettingResultTimeProportion%"></rect></svg>',
+                 |$svgTag',
                |'start': new Date($launchTime),
                |'end': new Date($finishTime)
              |}

From e72526227fdcf93b7a33375ef954746ac08753f5 Mon Sep 17 00:00:00 2001
From: lee19 <lee19@live.co.kr>
Date: Tue, 30 Jun 2015 14:08:00 -0700
Subject: [PATCH 0151/1454] [SPARK-8563] [MLLIB] Fixed a bug so that
 IndexedRowMatrix.computeSVD().U.numCols = k

I'm sorry that I made https://github.com/apache/spark/pull/6949 closed by mistake.
I pushed codes again.

And, I added a test code.

>
There is a bug that `U.numCols() = self.nCols` in `IndexedRowMatrix.computeSVD()`
It should have been `U.numCols() = k = svd.U.numCols()`

>
```
self = U * sigma * V.transpose
(m x n) = (m x n) * (k x k) * (k x n) //ASIS
-->
(m x n) = (m x k) * (k x k) * (k x n) //TOBE
```

Author: lee19 <lee19@live.co.kr>

Closes #6953 from lee19/MLlibBugfix and squashes the following commits:

c1812a0 [lee19] [SPARK-8563] [MLlib] Used nRows instead of numRows() to reduce a burden.
4b9803b [lee19] [SPARK-8563] [MLlib] Fixed a build error.
c2ccd89 [lee19] Added a unit test that validates matrix sizes of svd for [SPARK-8563][MLlib]
8373424 [lee19] [SPARK-8563][MLlib] Fixed a bug so that IndexedRowMatrix.computeSVD().U.numCols = k
---
 .../mllib/linalg/distributed/IndexedRowMatrix.scala   |  2 +-
 .../linalg/distributed/IndexedRowMatrixSuite.scala    | 11 +++++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index 3be530fa07537..1c33b43ea7a8a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -146,7 +146,7 @@ class IndexedRowMatrix(
       val indexedRows = indices.zip(svd.U.rows).map { case (i, v) =>
         IndexedRow(i, v)
       }
-      new IndexedRowMatrix(indexedRows, nRows, nCols)
+      new IndexedRowMatrix(indexedRows, nRows, svd.U.numCols().toInt)
     } else {
       null
     }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala
index 4a7b99a976f0a..0ecb7a221a503 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala
@@ -135,6 +135,17 @@ class IndexedRowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(closeToZero(U * brzDiag(s) * V.t - localA))
   }
 
+  test("validate matrix sizes of svd") {
+    val k = 2
+    val A = new IndexedRowMatrix(indexedRows)
+    val svd = A.computeSVD(k, computeU = true)
+    assert(svd.U.numRows() === m)
+    assert(svd.U.numCols() === k)
+    assert(svd.s.size === k)
+    assert(svd.V.numRows === n)
+    assert(svd.V.numCols === k)
+  }
+
   test("validate k in svd") {
     val A = new IndexedRowMatrix(indexedRows)
     intercept[IllegalArgumentException] {

From d2495f7cc7d7caaa50d122d2969ddb693e6ecebd Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Tue, 30 Jun 2015 14:09:29 -0700
Subject: [PATCH 0152/1454] [SPARK-8739] [WEB UI] [WINDOWS] A illegal character
 `\r` can be contained in StagePage.

This issue was reported by saurfang. Thanks!

There is a following code in StagePage.scala.

```
                   |width="$serializationTimeProportion%"></rect>
                 |<rect class="getting-result-time-proportion"
                   |x="$gettingResultTimeProportionPos%" y="0px" height="26px"
                   |width="$gettingResultTimeProportion%"></rect></svg>',
               |'start': new Date($launchTime),
               |'end': new Date($finishTime)
             |}
           |""".stripMargin.replaceAll("\n", " ")
```

The last `replaceAll("\n", "")` doesn't work when we checkout and build source code on Windows and deploy on Linux.
It's because when we checkout the source code on Windows, new-line-code is replaced with `"\r\n"` and `replaceAll("\n", "")` replaces only `"\n"`.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7133 from sarutak/SPARK-8739 and squashes the following commits:

17fb044 [Kousuke Saruta] Fixed a new-line-code issue
---
 core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 17e7519ddd01c..60e3c6343122c 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -628,7 +628,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
                |'start': new Date($launchTime),
                |'end': new Date($finishTime)
              |}
-           |""".stripMargin.replaceAll("\n", " ")
+           |""".stripMargin.replaceAll("""[\r\n]+""", " ")
         timelineObject
       }.mkString("[", ",", "]")
 

From 58ee2a2e47948a895e557fbcabbeadb31f0a1022 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 30 Jun 2015 16:17:46 -0700
Subject: [PATCH 0153/1454] [SPARK-8738] [SQL] [PYSPARK] capture SQL
 AnalysisException in Python API

Capture the AnalysisException in SQL, hide the long java stack trace, only show the error message.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7135 from davies/ananylis and squashes the following commits:

dad7ae7 [Davies Liu] add comment
ec0c0e8 [Davies Liu] Update utils.py
cdd7edd [Davies Liu] add doc
7b044c2 [Davies Liu] fix python 3
f84d3bd [Davies Liu] capture SQL AnalysisException in Python API
---
 python/pyspark/rdd.py         |  3 +-
 python/pyspark/sql/context.py |  2 ++
 python/pyspark/sql/tests.py   |  7 +++++
 python/pyspark/sql/utils.py   | 54 +++++++++++++++++++++++++++++++++++
 4 files changed, 65 insertions(+), 1 deletion(-)
 create mode 100644 python/pyspark/sql/utils.py

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index cb20bc8b54027..79dafb0a4ef27 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -126,11 +126,12 @@ def _load_from_socket(port, serializer):
     # On most of IPv6-ready systems, IPv6 will take precedence.
     for res in socket.getaddrinfo("localhost", port, socket.AF_UNSPEC, socket.SOCK_STREAM):
         af, socktype, proto, canonname, sa = res
+        sock = socket.socket(af, socktype, proto)
         try:
-            sock = socket.socket(af, socktype, proto)
             sock.settimeout(3)
             sock.connect(sa)
         except socket.error:
+            sock.close()
             sock = None
             continue
         break
diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 4dda3b430cfbf..4bf232111c496 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -33,6 +33,7 @@
     _infer_schema, _has_nulltype, _merge_type, _create_converter, _python_to_sql_converter
 from pyspark.sql.dataframe import DataFrame
 from pyspark.sql.readwriter import DataFrameReader
+from pyspark.sql.utils import install_exception_handler
 
 try:
     import pandas
@@ -96,6 +97,7 @@ def __init__(self, sparkContext, sqlContext=None):
         self._jvm = self._sc._jvm
         self._scala_SQLContext = sqlContext
         _monkey_patch_RDD(self)
+        install_exception_handler()
 
     @property
     def _ssql_ctx(self):
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 34f397d0ffef0..5af2ce09bc122 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -46,6 +46,7 @@
 from pyspark.tests import ReusedPySparkTestCase
 from pyspark.sql.functions import UserDefinedFunction
 from pyspark.sql.window import Window
+from pyspark.sql.utils import AnalysisException
 
 
 class UTC(datetime.tzinfo):
@@ -847,6 +848,12 @@ def test_replace(self):
         self.assertEqual(row.age, 10)
         self.assertEqual(row.height, None)
 
+    def test_capture_analysis_exception(self):
+        self.assertRaises(AnalysisException, lambda: self.sqlCtx.sql("select abc"))
+        self.assertRaises(AnalysisException, lambda: self.df.selectExpr("a + b"))
+        # RuntimeException should not be captured
+        self.assertRaises(py4j.protocol.Py4JJavaError, lambda: self.sqlCtx.sql("abc"))
+
 
 class HiveContextSQLTests(ReusedPySparkTestCase):
 
diff --git a/python/pyspark/sql/utils.py b/python/pyspark/sql/utils.py
new file mode 100644
index 0000000000000..8096802e7302f
--- /dev/null
+++ b/python/pyspark/sql/utils.py
@@ -0,0 +1,54 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import py4j
+
+
+class AnalysisException(Exception):
+    """
+    Failed to analyze a SQL query plan.
+    """
+
+
+def capture_sql_exception(f):
+    def deco(*a, **kw):
+        try:
+            return f(*a, **kw)
+        except py4j.protocol.Py4JJavaError as e:
+            cls, msg = e.java_exception.toString().split(': ', 1)
+            if cls == 'org.apache.spark.sql.AnalysisException':
+                raise AnalysisException(msg)
+            raise
+    return deco
+
+
+def install_exception_handler():
+    """
+    Hook an exception handler into Py4j, which could capture some SQL exceptions in Java.
+
+    When calling Java API, it will call `get_return_value` to parse the returned object.
+    If any exception happened in JVM, the result will be Java exception object, it raise
+    py4j.protocol.Py4JJavaError. We replace the original `get_return_value` with one that
+    could capture the Java exception and throw a Python one (with the same error message).
+
+    It's idempotent, could be called multiple times.
+    """
+    original = py4j.protocol.get_return_value
+    # The original `get_return_value` is not patched, it's idempotent.
+    patched = capture_sql_exception(original)
+    # only patch the one used in in py4j.java_gateway (call Java API)
+    py4j.java_gateway.get_return_value = patched

From 8d23587f1d285e93983b4b7d1decea01c2fe2e9e Mon Sep 17 00:00:00 2001
From: sethah <seth.hendrickson16@gmail.com>
Date: Tue, 30 Jun 2015 16:28:25 -0700
Subject: [PATCH 0154/1454] [SPARK-7739] [MLLIB] Improve ChiSqSelector example
 code in user guide

Author: sethah <seth.hendrickson16@gmail.com>

Closes #7029 from sethah/working_on_SPARK-7739 and squashes the following commits:

ef96916 [sethah] Fixing some style issues
efea1f8 [sethah] adding clarification to ChiSqSelector example
---
 docs/mllib-feature-extraction.md | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 83e937635a55b..a69e41e2a1936 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -384,7 +384,7 @@ data2 = labels.zip(normalizer2.transform(features))
 [Feature selection](http://en.wikipedia.org/wiki/Feature_selection) allows selecting the most relevant features for use in model construction. Feature selection reduces the size of the vector space and, in turn, the complexity of any subsequent operation with vectors. The number of features to select can be tuned using a held-out validation set.
 
 ### ChiSqSelector
-[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) stands for Chi-Squared feature selection. It operates on labeled data with categorical features. `ChiSqSelector` orders features based on a Chi-Squared test of independence from the class, and then filters (selects) the top features which are most closely related to the label.
+[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) stands for Chi-Squared feature selection. It operates on labeled data with categorical features. `ChiSqSelector` orders features based on a Chi-Squared test of independence from the class, and then filters (selects) the top features which the class label depends on the most. This is akin to yielding the features with the most predictive power.
 
 #### Model Fitting
 
@@ -405,7 +405,7 @@ Note that the user can also construct a `ChiSqSelectorModel` by hand by providin
 
 #### Example
 
-The following example shows the basic use of ChiSqSelector.
+The following example shows the basic use of ChiSqSelector. The data set used has a feature matrix consisting of greyscale values that vary from 0 to 255 for each feature.
 
 <div class="codetabs">
 <div data-lang="scala">
@@ -419,10 +419,11 @@ import org.apache.spark.mllib.feature.ChiSqSelector
 // Load some data in libsvm format
 val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 // Discretize data in 16 equal bins since ChiSqSelector requires categorical features
+// Even though features are doubles, the ChiSqSelector treats each unique value as a category
 val discretizedData = data.map { lp =>
-  LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => x / 16 } ) )
+  LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => (x / 16).floor } ) )
 }
-// Create ChiSqSelector that will select 50 features
+// Create ChiSqSelector that will select top 50 of 692 features
 val selector = new ChiSqSelector(50)
 // Create ChiSqSelector model (selecting features)
 val transformer = selector.fit(discretizedData)
@@ -451,19 +452,20 @@ JavaRDD<LabeledPoint> points = MLUtils.loadLibSVMFile(sc.sc(),
     "data/mllib/sample_libsvm_data.txt").toJavaRDD().cache();
 
 // Discretize data in 16 equal bins since ChiSqSelector requires categorical features
+// Even though features are doubles, the ChiSqSelector treats each unique value as a category
 JavaRDD<LabeledPoint> discretizedData = points.map(
     new Function<LabeledPoint, LabeledPoint>() {
       @Override
       public LabeledPoint call(LabeledPoint lp) {
         final double[] discretizedFeatures = new double[lp.features().size()];
         for (int i = 0; i < lp.features().size(); ++i) {
-          discretizedFeatures[i] = lp.features().apply(i) / 16;
+          discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
         }
         return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures));
       }
     });
 
-// Create ChiSqSelector that will select 50 features
+// Create ChiSqSelector that will select top 50 of 692 features
 ChiSqSelector selector = new ChiSqSelector(50);
 // Create ChiSqSelector model (selecting features)
 final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());

From 8133125ca0b83985e0c2aa2a6ad477556867e412 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 30 Jun 2015 16:54:51 -0700
Subject: [PATCH 0155/1454] [SPARK-8741] [SQL] Remove e and pi from DataFrame
 functions.

Author: Reynold Xin <rxin@databricks.com>

Closes #7137 from rxin/SPARK-8741 and squashes the following commits:

32c7e75 [Reynold Xin] [SPARK-8741][SQL] Remove e and pi from DataFrame functions.
---
 .../scala/org/apache/spark/sql/functions.scala | 18 ------------------
 .../spark/sql/DataFrameFunctionsSuite.scala    |  8 --------
 2 files changed, 26 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 6331fe61052ab..5767668dd339b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -989,15 +989,6 @@ object functions {
    */
   def cosh(columnName: String): Column = cosh(Column(columnName))
 
-  /**
-   * Returns the double value that is closer than any other to e, the base of the natural
-   * logarithms.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def e(): Column = EulerNumber()
-
   /**
    * Computes the exponential of the given value.
    *
@@ -1191,15 +1182,6 @@ object functions {
    */
   def log1p(columnName: String): Column = log1p(Column(columnName))
 
-  /**
-   * Returns the double value that is closer than any other to pi, the ratio of the circumference
-   * of a circle to its diameter.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def pi(): Column = Pi()
-
   /**
    * Computes the logarithm of the given column in base 2.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 11a8767ead96c..7ae89bcb1b9cf 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -86,14 +86,6 @@ class DataFrameFunctionsSuite extends QueryTest {
   }
 
   test("constant functions") {
-    checkAnswer(
-      testData2.select(e()).limit(1),
-      Row(scala.math.E)
-    )
-    checkAnswer(
-      testData2.select(pi()).limit(1),
-      Row(scala.math.Pi)
-    )
     checkAnswer(
       ctx.sql("SELECT E()"),
       Row(scala.math.E)

From ccdb05222a223187199183fd48e3a3313d536965 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@gmail.com>
Date: Tue, 30 Jun 2015 16:59:44 -0700
Subject: [PATCH 0156/1454] [SPARK-8727] [SQL] Missing python api; md5, log2

Jira: https://issues.apache.org/jira/browse/SPARK-8727

Author: Tarek Auel <tarek.auel@gmail.com>
Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7114 from tarekauel/missing-python and squashes the following commits:

ef4c61b [Tarek Auel] [SPARK-8727] revert dataframe change
4029d4d [Tarek Auel] removed dataframe pi and e unit test
66f0d2b [Tarek Auel] removed pi and e from python api and dataframe api; added _to_java_column(col) for strlen
4d07318 [Tarek Auel] fixed python unit test
45f2bee [Tarek Auel] fixed result of pi and e
c39f47b [Tarek Auel] add python api
bd50a3a [Tarek Auel] add missing python functions
---
 python/pyspark/sql/functions.py | 65 ++++++++++++++++++++++++++-------
 1 file changed, 52 insertions(+), 13 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 45ecd826bd3bd..4e2be88e9e3b9 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -39,12 +39,15 @@
     'coalesce',
     'countDistinct',
     'explode',
+    'log2',
+    'md5',
     'monotonicallyIncreasingId',
     'rand',
     'randn',
     'sha1',
     'sha2',
     'sparkPartitionId',
+    'strlen',
     'struct',
     'udf',
     'when']
@@ -320,6 +323,19 @@ def explode(col):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def md5(col):
+    """Calculates the MD5 digest and returns the value as a 32 character hex string.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(md5('a').alias('hash')).collect()
+    [Row(hash=u'902fbdd2b1df0c4f70b4a5d23525e932')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.md5(_to_java_column(col))
+    return Column(jc)
+
+
 @since(1.4)
 def monotonicallyIncreasingId():
     """A column that generates monotonically increasing 64-bit integers.
@@ -365,6 +381,19 @@ def randn(seed=None):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def sha1(col):
+    """Returns the hex string result of SHA-1.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
+    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.sha1(_to_java_column(col))
+    return Column(jc)
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def sha2(col, numBits):
@@ -383,19 +412,6 @@ def sha2(col, numBits):
     return Column(jc)
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def sha1(col):
-    """Returns the hex string result of SHA-1.
-
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
-    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.sha1(_to_java_column(col))
-    return Column(jc)
-
-
 @since(1.4)
 def sparkPartitionId():
     """A column for partition ID of the Spark task.
@@ -409,6 +425,18 @@ def sparkPartitionId():
     return Column(sc._jvm.functions.sparkPartitionId())
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def strlen(col):
+    """Calculates the length of a string expression.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(strlen('a').alias('length')).collect()
+    [Row(length=3)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.strlen(_to_java_column(col)))
+
+
 @ignore_unicode_prefix
 @since(1.4)
 def struct(*cols):
@@ -471,6 +499,17 @@ def log(arg1, arg2=None):
     return Column(jc)
 
 
+@since(1.5)
+def log2(col):
+    """Returns the base-2 logarithm of the argument.
+
+    >>> sqlContext.createDataFrame([(4,)], ['a']).select(log2('a').alias('log2')).collect()
+    [Row(log2=2.0)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.log2(_to_java_column(col)))
+
+
 @since(1.4)
 def lag(col, count=1, default=None):
     """

From 3bee0f1466ddd69f26e95297b5e0d2398b6c6268 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 30 Jun 2015 17:39:55 -0700
Subject: [PATCH 0157/1454] [SPARK-6602][Core] Update Master, Worker, Client,
 AppClient and related classes to use RpcEndpoint

This PR updates the rest Actors in core to RpcEndpoint.

Because there is no `ActorSelection` in RpcEnv, I changes the logic of `registerWithMaster` in Worker and AppClient to avoid blocking the message loop. These changes need to be reviewed carefully.

Author: zsxwing <zsxwing@gmail.com>

Closes #5392 from zsxwing/rpc-rewrite-part3 and squashes the following commits:

2de7bed [zsxwing] Merge branch 'master' into rpc-rewrite-part3
f12d943 [zsxwing] Address comments
9137b82 [zsxwing] Fix the code style
e734c71 [zsxwing] Merge branch 'master' into rpc-rewrite-part3
2d24fb5 [zsxwing] Fix the code style
5a82374 [zsxwing] Merge branch 'master' into rpc-rewrite-part3
fa47110 [zsxwing] Merge branch 'master' into rpc-rewrite-part3
72304f0 [zsxwing] Update the error strategy for AkkaRpcEnv
e56cb16 [zsxwing] Always send failure back to the sender
a7b86e6 [zsxwing] Use JFuture for java.util.concurrent.Future
aa34b9b [zsxwing] Fix the code style
bd541e7 [zsxwing] Merge branch 'master' into rpc-rewrite-part3
25a84d8 [zsxwing] Use ThreadUtils
060ff31 [zsxwing] Merge branch 'master' into rpc-rewrite-part3
dbfc916 [zsxwing] Improve the docs and comments
837927e [zsxwing] Merge branch 'master' into rpc-rewrite-part3
5c27f97 [zsxwing] Merge branch 'master' into rpc-rewrite-part3
fadbb9e [zsxwing] Fix the code style
6637e3c [zsxwing] Merge remote-tracking branch 'origin/master' into rpc-rewrite-part3
7fdee0e [zsxwing] Fix the return type to ExecutorService and ScheduledExecutorService
e8ad0a5 [zsxwing] Fix the code style
6b2a104 [zsxwing] Log error and use SparkExitCode.UNCAUGHT_EXCEPTION exit code
fbf3194 [zsxwing] Add Utils.newDaemonSingleThreadExecutor and newDaemonSingleThreadScheduledExecutor
b776817 [zsxwing] Update Master, Worker, Client, AppClient and related classes to use RpcEndpoint
---
 .../org/apache/spark/deploy/Client.scala      | 156 ++++---
 .../apache/spark/deploy/DeployMessage.scala   |  22 +-
 .../spark/deploy/LocalSparkCluster.scala      |  26 +-
 .../spark/deploy/client/AppClient.scala       | 199 +++++----
 .../spark/deploy/client/TestClient.scala      |  10 +-
 .../spark/deploy/master/ApplicationInfo.scala |   5 +-
 .../apache/spark/deploy/master/Master.scala   | 392 +++++++++---------
 .../spark/deploy/master/MasterMessages.scala  |   2 +-
 .../spark/deploy/master/WorkerInfo.scala      |   6 +-
 .../master/ZooKeeperLeaderElectionAgent.scala |   3 -
 .../deploy/master/ui/ApplicationPage.scala    |   9 +-
 .../spark/deploy/master/ui/MasterPage.scala   |  14 +-
 .../spark/deploy/master/ui/MasterWebUI.scala  |   4 +-
 .../deploy/rest/StandaloneRestServer.scala    |  35 +-
 .../spark/deploy/worker/DriverRunner.scala    |   6 +-
 .../spark/deploy/worker/ExecutorRunner.scala  |   8 +-
 .../apache/spark/deploy/worker/Worker.scala   | 318 +++++++++-----
 .../spark/deploy/worker/WorkerWatcher.scala   |   1 -
 .../spark/deploy/worker/ui/WorkerPage.scala   |  11 +-
 .../scala/org/apache/spark/rpc/RpcEnv.scala   |   2 +
 .../apache/spark/rpc/akka/AkkaRpcEnv.scala    |   8 +-
 .../cluster/SparkDeploySchedulerBackend.scala |   2 +-
 .../spark/deploy/master/MasterSuite.scala     |  56 +--
 .../rest/StandaloneRestSubmitSuite.scala      |  54 +--
 .../deploy/worker/WorkerWatcherSuite.scala    |  15 +-
 .../apache/spark/rpc/RpcAddressSuite.scala    |  55 +++
 .../spark/rpc/akka/AkkaRpcEnvSuite.scala      |  20 +-
 27 files changed, 806 insertions(+), 633 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/rpc/RpcAddressSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala
index 848b62f9de71b..71f7e2129116f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/Client.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala
@@ -18,17 +18,17 @@
 package org.apache.spark.deploy
 
 import scala.collection.mutable.HashSet
-import scala.concurrent._
+import scala.concurrent.ExecutionContext
+import scala.reflect.ClassTag
+import scala.util.{Failure, Success}
 
-import akka.actor._
-import akka.pattern.ask
-import akka.remote.{AssociationErrorEvent, DisassociatedEvent, RemotingLifecycleEvent}
 import org.apache.log4j.{Level, Logger}
 
+import org.apache.spark.rpc.{RpcEndpointRef, RpcAddress, RpcEnv, ThreadSafeRpcEndpoint}
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.master.{DriverState, Master}
-import org.apache.spark.util.{ActorLogReceive, AkkaUtils, RpcUtils, Utils}
+import org.apache.spark.util.{ThreadUtils, SparkExitCode, Utils}
 
 /**
  * Proxy that relays messages to the driver.
@@ -36,20 +36,30 @@ import org.apache.spark.util.{ActorLogReceive, AkkaUtils, RpcUtils, Utils}
  * We currently don't support retry if submission fails. In HA mode, client will submit request to
  * all masters and see which one could handle it.
  */
-private class ClientActor(driverArgs: ClientArguments, conf: SparkConf)
-  extends Actor with ActorLogReceive with Logging {
-
-  private val masterActors = driverArgs.masters.map { m =>
-    context.actorSelection(Master.toAkkaUrl(m, AkkaUtils.protocol(context.system)))
-  }
-  private val lostMasters = new HashSet[Address]
-  private var activeMasterActor: ActorSelection = null
-
-  val timeout = RpcUtils.askTimeout(conf)
-
-  override def preStart(): Unit = {
-    context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
-
+private class ClientEndpoint(
+    override val rpcEnv: RpcEnv,
+    driverArgs: ClientArguments,
+    masterEndpoints: Seq[RpcEndpointRef],
+    conf: SparkConf)
+  extends ThreadSafeRpcEndpoint with Logging {
+
+  // A scheduled executor used to send messages at the specified time.
+  private val forwardMessageThread =
+    ThreadUtils.newDaemonSingleThreadScheduledExecutor("client-forward-message")
+  // Used to provide the implicit parameter of `Future` methods.
+  private val forwardMessageExecutionContext =
+    ExecutionContext.fromExecutor(forwardMessageThread,
+      t => t match {
+        case ie: InterruptedException => // Exit normally
+        case e: Throwable =>
+          logError(e.getMessage, e)
+          System.exit(SparkExitCode.UNCAUGHT_EXCEPTION)
+      })
+
+   private val lostMasters = new HashSet[RpcAddress]
+   private var activeMasterEndpoint: RpcEndpointRef = null
+
+  override def onStart(): Unit = {
     driverArgs.cmd match {
       case "launch" =>
         // TODO: We could add an env variable here and intercept it in `sc.addJar` that would
@@ -82,29 +92,37 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf)
           driverArgs.cores,
           driverArgs.supervise,
           command)
-
-        // This assumes only one Master is active at a time
-        for (masterActor <- masterActors) {
-          masterActor ! RequestSubmitDriver(driverDescription)
-        }
+        ayncSendToMasterAndForwardReply[SubmitDriverResponse](
+          RequestSubmitDriver(driverDescription))
 
       case "kill" =>
         val driverId = driverArgs.driverId
-        // This assumes only one Master is active at a time
-        for (masterActor <- masterActors) {
-          masterActor ! RequestKillDriver(driverId)
-        }
+        ayncSendToMasterAndForwardReply[KillDriverResponse](RequestKillDriver(driverId))
+    }
+  }
+
+  /**
+   * Send the message to master and forward the reply to self asynchronously.
+   */
+  private def ayncSendToMasterAndForwardReply[T: ClassTag](message: Any): Unit = {
+    for (masterEndpoint <- masterEndpoints) {
+      masterEndpoint.ask[T](message).onComplete {
+        case Success(v) => self.send(v)
+        case Failure(e) =>
+          logWarning(s"Error sending messages to master $masterEndpoint", e)
+      }(forwardMessageExecutionContext)
     }
   }
 
   /* Find out driver status then exit the JVM */
   def pollAndReportStatus(driverId: String) {
+    // Since ClientEndpoint is the only RpcEndpoint in the process, blocking the event loop thread
+    // is fine.
     println("... waiting before polling master for driver state")
     Thread.sleep(5000)
     println("... polling master for driver state")
-    val statusFuture = (activeMasterActor ? RequestDriverStatus(driverId))(timeout)
-      .mapTo[DriverStatusResponse]
-    val statusResponse = Await.result(statusFuture, timeout)
+    val statusResponse =
+      activeMasterEndpoint.askWithRetry[DriverStatusResponse](RequestDriverStatus(driverId))
     statusResponse.found match {
       case false =>
         println(s"ERROR: Cluster master did not recognize $driverId")
@@ -127,50 +145,62 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf)
     }
   }
 
-  override def receiveWithLogging: PartialFunction[Any, Unit] = {
+  override def receive: PartialFunction[Any, Unit] = {
 
-    case SubmitDriverResponse(success, driverId, message) =>
+    case SubmitDriverResponse(master, success, driverId, message) =>
       println(message)
       if (success) {
-        activeMasterActor = context.actorSelection(sender.path)
+        activeMasterEndpoint = master
         pollAndReportStatus(driverId.get)
       } else if (!Utils.responseFromBackup(message)) {
         System.exit(-1)
       }
 
 
-    case KillDriverResponse(driverId, success, message) =>
+    case KillDriverResponse(master, driverId, success, message) =>
       println(message)
       if (success) {
-        activeMasterActor = context.actorSelection(sender.path)
+        activeMasterEndpoint = master
         pollAndReportStatus(driverId)
       } else if (!Utils.responseFromBackup(message)) {
         System.exit(-1)
       }
+  }
 
-    case DisassociatedEvent(_, remoteAddress, _) =>
-      if (!lostMasters.contains(remoteAddress)) {
-        println(s"Error connecting to master $remoteAddress.")
-        lostMasters += remoteAddress
-        // Note that this heuristic does not account for the fact that a Master can recover within
-        // the lifetime of this client. Thus, once a Master is lost it is lost to us forever. This
-        // is not currently a concern, however, because this client does not retry submissions.
-        if (lostMasters.size >= masterActors.size) {
-          println("No master is available, exiting.")
-          System.exit(-1)
-        }
+  override def onDisconnected(remoteAddress: RpcAddress): Unit = {
+    if (!lostMasters.contains(remoteAddress)) {
+      println(s"Error connecting to master $remoteAddress.")
+      lostMasters += remoteAddress
+      // Note that this heuristic does not account for the fact that a Master can recover within
+      // the lifetime of this client. Thus, once a Master is lost it is lost to us forever. This
+      // is not currently a concern, however, because this client does not retry submissions.
+      if (lostMasters.size >= masterEndpoints.size) {
+        println("No master is available, exiting.")
+        System.exit(-1)
       }
+    }
+  }
 
-    case AssociationErrorEvent(cause, _, remoteAddress, _, _) =>
-      if (!lostMasters.contains(remoteAddress)) {
-        println(s"Error connecting to master ($remoteAddress).")
-        println(s"Cause was: $cause")
-        lostMasters += remoteAddress
-        if (lostMasters.size >= masterActors.size) {
-          println("No master is available, exiting.")
-          System.exit(-1)
-        }
+  override def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = {
+    if (!lostMasters.contains(remoteAddress)) {
+      println(s"Error connecting to master ($remoteAddress).")
+      println(s"Cause was: $cause")
+      lostMasters += remoteAddress
+      if (lostMasters.size >= masterEndpoints.size) {
+        println("No master is available, exiting.")
+        System.exit(-1)
       }
+    }
+  }
+
+  override def onError(cause: Throwable): Unit = {
+    println(s"Error processing messages, exiting.")
+    cause.printStackTrace()
+    System.exit(-1)
+  }
+
+  override def onStop(): Unit = {
+    forwardMessageThread.shutdownNow()
   }
 }
 
@@ -194,15 +224,13 @@ object Client {
     conf.set("akka.loglevel", driverArgs.logLevel.toString.replace("WARN", "WARNING"))
     Logger.getRootLogger.setLevel(driverArgs.logLevel)
 
-    val (actorSystem, _) = AkkaUtils.createActorSystem(
-      "driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))
+    val rpcEnv =
+      RpcEnv.create("driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))
 
-    // Verify driverArgs.master is a valid url so that we can use it in ClientActor safely
-    for (m <- driverArgs.masters) {
-      Master.toAkkaUrl(m, AkkaUtils.protocol(actorSystem))
-    }
-    actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf))
+    val masterEndpoints = driverArgs.masters.map(RpcAddress.fromSparkURL).
+      map(rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, _, Master.ENDPOINT_NAME))
+    rpcEnv.setupEndpoint("client", new ClientEndpoint(rpcEnv, driverArgs, masterEndpoints, conf))
 
-    actorSystem.awaitTermination()
+    rpcEnv.awaitTermination()
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala
index 9db6fd1ac4dbe..12727de9b4cf3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala
@@ -24,11 +24,12 @@ import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, WorkerInfo}
 import org.apache.spark.deploy.master.DriverState.DriverState
 import org.apache.spark.deploy.master.RecoveryState.MasterState
 import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner}
+import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.util.Utils
 
 private[deploy] sealed trait DeployMessage extends Serializable
 
-/** Contains messages sent between Scheduler actor nodes. */
+/** Contains messages sent between Scheduler endpoint nodes. */
 private[deploy] object DeployMessages {
 
   // Worker to Master
@@ -37,6 +38,7 @@ private[deploy] object DeployMessages {
       id: String,
       host: String,
       port: Int,
+      worker: RpcEndpointRef,
       cores: Int,
       memory: Int,
       webUiPort: Int,
@@ -63,11 +65,11 @@ private[deploy] object DeployMessages {
   case class WorkerSchedulerStateResponse(id: String, executors: List[ExecutorDescription],
      driverIds: Seq[String])
 
-  case class Heartbeat(workerId: String) extends DeployMessage
+  case class Heartbeat(workerId: String, worker: RpcEndpointRef) extends DeployMessage
 
   // Master to Worker
 
-  case class RegisteredWorker(masterUrl: String, masterWebUiUrl: String) extends DeployMessage
+  case class RegisteredWorker(master: RpcEndpointRef, masterWebUiUrl: String) extends DeployMessage
 
   case class RegisterWorkerFailed(message: String) extends DeployMessage
 
@@ -92,13 +94,13 @@ private[deploy] object DeployMessages {
 
   // Worker internal
 
-  case object WorkDirCleanup      // Sent to Worker actor periodically for cleaning up app folders
+  case object WorkDirCleanup // Sent to Worker endpoint periodically for cleaning up app folders
 
   case object ReregisterWithMaster // used when a worker attempts to reconnect to a master
 
   // AppClient to Master
 
-  case class RegisterApplication(appDescription: ApplicationDescription)
+  case class RegisterApplication(appDescription: ApplicationDescription, driver: RpcEndpointRef)
     extends DeployMessage
 
   case class UnregisterApplication(appId: String)
@@ -107,7 +109,7 @@ private[deploy] object DeployMessages {
 
   // Master to AppClient
 
-  case class RegisteredApplication(appId: String, masterUrl: String) extends DeployMessage
+  case class RegisteredApplication(appId: String, master: RpcEndpointRef) extends DeployMessage
 
   // TODO(matei): replace hostPort with host
   case class ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) {
@@ -123,12 +125,14 @@ private[deploy] object DeployMessages {
 
   case class RequestSubmitDriver(driverDescription: DriverDescription) extends DeployMessage
 
-  case class SubmitDriverResponse(success: Boolean, driverId: Option[String], message: String)
+  case class SubmitDriverResponse(
+      master: RpcEndpointRef, success: Boolean, driverId: Option[String], message: String)
     extends DeployMessage
 
   case class RequestKillDriver(driverId: String) extends DeployMessage
 
-  case class KillDriverResponse(driverId: String, success: Boolean, message: String)
+  case class KillDriverResponse(
+      master: RpcEndpointRef, driverId: String, success: Boolean, message: String)
     extends DeployMessage
 
   case class RequestDriverStatus(driverId: String) extends DeployMessage
@@ -142,7 +146,7 @@ private[deploy] object DeployMessages {
 
   // Master to Worker & AppClient
 
-  case class MasterChanged(masterUrl: String, masterWebUiUrl: String)
+  case class MasterChanged(master: RpcEndpointRef, masterWebUiUrl: String)
 
   // MasterWebUI To Master
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
index 0550f00a172ab..53356addf6edb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
@@ -19,8 +19,7 @@ package org.apache.spark.deploy
 
 import scala.collection.mutable.ArrayBuffer
 
-import akka.actor.ActorSystem
-
+import org.apache.spark.rpc.RpcEnv
 import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.deploy.worker.Worker
 import org.apache.spark.deploy.master.Master
@@ -41,8 +40,8 @@ class LocalSparkCluster(
   extends Logging {
 
   private val localHostname = Utils.localHostName()
-  private val masterActorSystems = ArrayBuffer[ActorSystem]()
-  private val workerActorSystems = ArrayBuffer[ActorSystem]()
+  private val masterRpcEnvs = ArrayBuffer[RpcEnv]()
+  private val workerRpcEnvs = ArrayBuffer[RpcEnv]()
   // exposed for testing
   var masterWebUIPort = -1
 
@@ -55,18 +54,17 @@ class LocalSparkCluster(
       .set("spark.shuffle.service.enabled", "false")
 
     /* Start the Master */
-    val (masterSystem, masterPort, webUiPort, _) =
-      Master.startSystemAndActor(localHostname, 0, 0, _conf)
+    val (rpcEnv, webUiPort, _) = Master.startRpcEnvAndEndpoint(localHostname, 0, 0, _conf)
     masterWebUIPort = webUiPort
-    masterActorSystems += masterSystem
-    val masterUrl = "spark://" + Utils.localHostNameForURI() + ":" + masterPort
+    masterRpcEnvs += rpcEnv
+    val masterUrl = "spark://" + Utils.localHostNameForURI() + ":" + rpcEnv.address.port
     val masters = Array(masterUrl)
 
     /* Start the Workers */
     for (workerNum <- 1 to numWorkers) {
-      val (workerSystem, _) = Worker.startSystemAndActor(localHostname, 0, 0, coresPerWorker,
+      val workerEnv = Worker.startRpcEnvAndEndpoint(localHostname, 0, 0, coresPerWorker,
         memoryPerWorker, masters, null, Some(workerNum), _conf)
-      workerActorSystems += workerSystem
+      workerRpcEnvs += workerEnv
     }
 
     masters
@@ -77,11 +75,11 @@ class LocalSparkCluster(
     // Stop the workers before the master so they don't get upset that it disconnected
     // TODO: In Akka 2.1.x, ActorSystem.awaitTermination hangs when you have remote actors!
     //       This is unfortunate, but for now we just comment it out.
-    workerActorSystems.foreach(_.shutdown())
+    workerRpcEnvs.foreach(_.shutdown())
     // workerActorSystems.foreach(_.awaitTermination())
-    masterActorSystems.foreach(_.shutdown())
+    masterRpcEnvs.foreach(_.shutdown())
     // masterActorSystems.foreach(_.awaitTermination())
-    masterActorSystems.clear()
-    workerActorSystems.clear()
+    masterRpcEnvs.clear()
+    workerRpcEnvs.clear()
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index 43c8a934c311a..79b251e7e62fe 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -17,20 +17,17 @@
 
 package org.apache.spark.deploy.client
 
-import java.util.concurrent.TimeoutException
+import java.util.concurrent._
+import java.util.concurrent.{Future => JFuture, ScheduledFuture => JScheduledFuture}
 
-import scala.concurrent.Await
-import scala.concurrent.duration._
-
-import akka.actor._
-import akka.pattern.ask
-import akka.remote.{AssociationErrorEvent, DisassociatedEvent, RemotingLifecycleEvent}
+import scala.util.control.NonFatal
 
 import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.deploy.{ApplicationDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.master.Master
-import org.apache.spark.util.{ActorLogReceive, RpcUtils, Utils, AkkaUtils}
+import org.apache.spark.rpc._
+import org.apache.spark.util.{ThreadUtils, Utils}
 
 /**
  * Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL,
@@ -40,98 +37,143 @@ import org.apache.spark.util.{ActorLogReceive, RpcUtils, Utils, AkkaUtils}
  * @param masterUrls Each url should look like spark://host:port.
  */
 private[spark] class AppClient(
-    actorSystem: ActorSystem,
+    rpcEnv: RpcEnv,
     masterUrls: Array[String],
     appDescription: ApplicationDescription,
     listener: AppClientListener,
     conf: SparkConf)
   extends Logging {
 
-  private val masterAkkaUrls = masterUrls.map(Master.toAkkaUrl(_, AkkaUtils.protocol(actorSystem)))
+  private val masterRpcAddresses = masterUrls.map(RpcAddress.fromSparkURL(_))
 
-  private val REGISTRATION_TIMEOUT = 20.seconds
+  private val REGISTRATION_TIMEOUT_SECONDS = 20
   private val REGISTRATION_RETRIES = 3
 
-  private var masterAddress: Address = null
-  private var actor: ActorRef = null
+  private var endpoint: RpcEndpointRef = null
   private var appId: String = null
-  private var registered = false
-  private var activeMasterUrl: String = null
+  @volatile private var registered = false
+
+  private class ClientEndpoint(override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint
+    with Logging {
+
+    private var master: Option[RpcEndpointRef] = None
+    // To avoid calling listener.disconnected() multiple times
+    private var alreadyDisconnected = false
+    @volatile private var alreadyDead = false // To avoid calling listener.dead() multiple times
+    @volatile private var registerMasterFutures: Array[JFuture[_]] = null
+    @volatile private var registrationRetryTimer: JScheduledFuture[_] = null
+
+    // A thread pool for registering with masters. Because registering with a master is a blocking
+    // action, this thread pool must be able to create "masterRpcAddresses.size" threads at the same
+    // time so that we can register with all masters.
+    private val registerMasterThreadPool = new ThreadPoolExecutor(
+      0,
+      masterRpcAddresses.size, // Make sure we can register with all masters at the same time
+      60L, TimeUnit.SECONDS,
+      new SynchronousQueue[Runnable](),
+      ThreadUtils.namedThreadFactory("appclient-register-master-threadpool"))
 
-  private class ClientActor extends Actor with ActorLogReceive with Logging {
-    var master: ActorSelection = null
-    var alreadyDisconnected = false  // To avoid calling listener.disconnected() multiple times
-    var alreadyDead = false  // To avoid calling listener.dead() multiple times
-    var registrationRetryTimer: Option[Cancellable] = None
+    // A scheduled executor for scheduling the registration actions
+    private val registrationRetryThread =
+      ThreadUtils.newDaemonSingleThreadScheduledExecutor("appclient-registration-retry-thread")
 
-    override def preStart() {
-      context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
+    override def onStart(): Unit = {
       try {
-        registerWithMaster()
+        registerWithMaster(1)
       } catch {
         case e: Exception =>
           logWarning("Failed to connect to master", e)
           markDisconnected()
-          context.stop(self)
+          stop()
       }
     }
 
-    def tryRegisterAllMasters() {
-      for (masterAkkaUrl <- masterAkkaUrls) {
-        logInfo("Connecting to master " + masterAkkaUrl + "...")
-        val actor = context.actorSelection(masterAkkaUrl)
-        actor ! RegisterApplication(appDescription)
+    /**
+     *  Register with all masters asynchronously and returns an array `Future`s for cancellation.
+     */
+    private def tryRegisterAllMasters(): Array[JFuture[_]] = {
+      for (masterAddress <- masterRpcAddresses) yield {
+        registerMasterThreadPool.submit(new Runnable {
+          override def run(): Unit = try {
+            if (registered) {
+              return
+            }
+            logInfo("Connecting to master " + masterAddress.toSparkURL + "...")
+            val masterRef =
+              rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, masterAddress, Master.ENDPOINT_NAME)
+            masterRef.send(RegisterApplication(appDescription, self))
+          } catch {
+            case ie: InterruptedException => // Cancelled
+            case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
+          }
+        })
       }
     }
 
-    def registerWithMaster() {
-      tryRegisterAllMasters()
-      import context.dispatcher
-      var retries = 0
-      registrationRetryTimer = Some {
-        context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
+    /**
+     * Register with all masters asynchronously. It will call `registerWithMaster` every
+     * REGISTRATION_TIMEOUT_SECONDS seconds until exceeding REGISTRATION_RETRIES times.
+     * Once we connect to a master successfully, all scheduling work and Futures will be cancelled.
+     *
+     * nthRetry means this is the nth attempt to register with master.
+     */
+    private def registerWithMaster(nthRetry: Int) {
+      registerMasterFutures = tryRegisterAllMasters()
+      registrationRetryTimer = registrationRetryThread.scheduleAtFixedRate(new Runnable {
+        override def run(): Unit = {
           Utils.tryOrExit {
-            retries += 1
             if (registered) {
-              registrationRetryTimer.foreach(_.cancel())
-            } else if (retries >= REGISTRATION_RETRIES) {
+              registerMasterFutures.foreach(_.cancel(true))
+              registerMasterThreadPool.shutdownNow()
+            } else if (nthRetry >= REGISTRATION_RETRIES) {
               markDead("All masters are unresponsive! Giving up.")
             } else {
-              tryRegisterAllMasters()
+              registerMasterFutures.foreach(_.cancel(true))
+              registerWithMaster(nthRetry + 1)
             }
           }
         }
-      }
+      }, REGISTRATION_TIMEOUT_SECONDS, REGISTRATION_TIMEOUT_SECONDS, TimeUnit.SECONDS)
     }
 
-    def changeMaster(url: String) {
-      // activeMasterUrl is a valid Spark url since we receive it from master.
-      activeMasterUrl = url
-      master = context.actorSelection(
-        Master.toAkkaUrl(activeMasterUrl, AkkaUtils.protocol(actorSystem)))
-      masterAddress = Master.toAkkaAddress(activeMasterUrl, AkkaUtils.protocol(actorSystem))
+    /**
+     * Send a message to the current master. If we have not yet registered successfully with any
+     * master, the message will be dropped.
+     */
+    private def sendToMaster(message: Any): Unit = {
+      master match {
+        case Some(masterRef) => masterRef.send(message)
+        case None => logWarning(s"Drop $message because has not yet connected to master")
+      }
     }
 
-    private def isPossibleMaster(remoteUrl: Address) = {
-      masterAkkaUrls.map(AddressFromURIString(_).hostPort).contains(remoteUrl.hostPort)
+    private def isPossibleMaster(remoteAddress: RpcAddress): Boolean = {
+      masterRpcAddresses.contains(remoteAddress)
     }
 
-    override def receiveWithLogging: PartialFunction[Any, Unit] = {
-      case RegisteredApplication(appId_, masterUrl) =>
+    override def receive: PartialFunction[Any, Unit] = {
+      case RegisteredApplication(appId_, masterRef) =>
+        // FIXME How to handle the following cases?
+        // 1. A master receives multiple registrations and sends back multiple
+        // RegisteredApplications due to an unstable network.
+        // 2. Receive multiple RegisteredApplication from different masters because the master is
+        // changing.
         appId = appId_
         registered = true
-        changeMaster(masterUrl)
+        master = Some(masterRef)
         listener.connected(appId)
 
       case ApplicationRemoved(message) =>
         markDead("Master removed our application: %s".format(message))
-        context.stop(self)
+        stop()
 
       case ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) =>
         val fullId = appId + "/" + id
         logInfo("Executor added: %s on %s (%s) with %d cores".format(fullId, workerId, hostPort,
           cores))
-        master ! ExecutorStateChanged(appId, id, ExecutorState.RUNNING, None, None)
+        // FIXME if changing master and `ExecutorAdded` happen at the same time (the order is not
+        // guaranteed), `ExecutorStateChanged` may be sent to a dead master.
+        sendToMaster(ExecutorStateChanged(appId, id, ExecutorState.RUNNING, None, None))
         listener.executorAdded(fullId, workerId, hostPort, cores, memory)
 
       case ExecutorUpdated(id, state, message, exitStatus) =>
@@ -142,24 +184,32 @@ private[spark] class AppClient(
           listener.executorRemoved(fullId, message.getOrElse(""), exitStatus)
         }
 
-      case MasterChanged(masterUrl, masterWebUiUrl) =>
-        logInfo("Master has changed, new master is at " + masterUrl)
-        changeMaster(masterUrl)
+      case MasterChanged(masterRef, masterWebUiUrl) =>
+        logInfo("Master has changed, new master is at " + masterRef.address.toSparkURL)
+        master = Some(masterRef)
         alreadyDisconnected = false
-        sender ! MasterChangeAcknowledged(appId)
+        masterRef.send(MasterChangeAcknowledged(appId))
+    }
 
-      case DisassociatedEvent(_, address, _) if address == masterAddress =>
+    override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+      case StopAppClient =>
+        markDead("Application has been stopped.")
+        sendToMaster(UnregisterApplication(appId))
+        context.reply(true)
+        stop()
+    }
+
+    override def onDisconnected(address: RpcAddress): Unit = {
+      if (master.exists(_.address == address)) {
         logWarning(s"Connection to $address failed; waiting for master to reconnect...")
         markDisconnected()
+      }
+    }
 
-      case AssociationErrorEvent(cause, _, address, _, _) if isPossibleMaster(address) =>
+    override def onNetworkError(cause: Throwable, address: RpcAddress): Unit = {
+      if (isPossibleMaster(address)) {
         logWarning(s"Could not connect to $address: $cause")
-
-      case StopAppClient =>
-        markDead("Application has been stopped.")
-        master ! UnregisterApplication(appId)
-        sender ! true
-        context.stop(self)
+      }
     }
 
     /**
@@ -179,28 +229,31 @@ private[spark] class AppClient(
       }
     }
 
-    override def postStop() {
-      registrationRetryTimer.foreach(_.cancel())
+    override def onStop(): Unit = {
+      if (registrationRetryTimer != null) {
+        registrationRetryTimer.cancel(true)
+      }
+      registrationRetryThread.shutdownNow()
+      registerMasterFutures.foreach(_.cancel(true))
+      registerMasterThreadPool.shutdownNow()
     }
 
   }
 
   def start() {
     // Just launch an actor; it will call back into the listener.
-    actor = actorSystem.actorOf(Props(new ClientActor))
+    endpoint = rpcEnv.setupEndpoint("AppClient", new ClientEndpoint(rpcEnv))
   }
 
   def stop() {
-    if (actor != null) {
+    if (endpoint != null) {
       try {
-        val timeout = RpcUtils.askTimeout(conf)
-        val future = actor.ask(StopAppClient)(timeout)
-        Await.result(future, timeout)
+        endpoint.askWithRetry[Boolean](StopAppClient)
       } catch {
         case e: TimeoutException =>
           logInfo("Stop request to Master timed out; it may already be shut down.")
       }
-      actor = null
+      endpoint = null
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
index 40835b9550586..1c79089303e3d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.deploy.client
 
+import org.apache.spark.rpc.RpcEnv
 import org.apache.spark.{SecurityManager, SparkConf, Logging}
 import org.apache.spark.deploy.{ApplicationDescription, Command}
-import org.apache.spark.util.{AkkaUtils, Utils}
+import org.apache.spark.util.Utils
 
 private[spark] object TestClient {
 
@@ -46,13 +47,12 @@ private[spark] object TestClient {
   def main(args: Array[String]) {
     val url = args(0)
     val conf = new SparkConf
-    val (actorSystem, _) = AkkaUtils.createActorSystem("spark", Utils.localHostName(), 0,
-      conf = conf, securityManager = new SecurityManager(conf))
+    val rpcEnv = RpcEnv.create("spark", Utils.localHostName(), 0, conf, new SecurityManager(conf))
     val desc = new ApplicationDescription("TestClient", Some(1), 512,
       Command("spark.deploy.client.TestExecutor", Seq(), Map(), Seq(), Seq(), Seq()), "ignored")
     val listener = new TestListener
-    val client = new AppClient(actorSystem, Array(url), desc, listener, new SparkConf)
+    val client = new AppClient(rpcEnv, Array(url), desc, listener, new SparkConf)
     client.start()
-    actorSystem.awaitTermination()
+    rpcEnv.awaitTermination()
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
index 1620e95bea218..aa54ed9360f36 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
@@ -22,10 +22,9 @@ import java.util.Date
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
-import akka.actor.ActorRef
-
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.deploy.ApplicationDescription
+import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.util.Utils
 
 private[spark] class ApplicationInfo(
@@ -33,7 +32,7 @@ private[spark] class ApplicationInfo(
     val id: String,
     val desc: ApplicationDescription,
     val submitDate: Date,
-    val driver: ActorRef,
+    val driver: RpcEndpointRef,
     defaultCores: Int)
   extends Serializable {
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index fccceb3ea528b..3e7c16722805e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -21,20 +21,18 @@ import java.io.FileNotFoundException
 import java.net.URLEncoder
 import java.text.SimpleDateFormat
 import java.util.Date
+import java.util.concurrent.{ScheduledFuture, TimeUnit}
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
-import scala.concurrent.Await
-import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.util.Random
 
-import akka.actor._
-import akka.pattern.ask
-import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent}
 import akka.serialization.Serialization
 import akka.serialization.SerializationExtension
 import org.apache.hadoop.fs.Path
 
+import org.apache.spark.rpc.akka.AkkaRpcEnv
+import org.apache.spark.rpc._
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.deploy.{ApplicationDescription, DriverDescription,
   ExecutorState, SparkHadoopUtil}
@@ -47,23 +45,27 @@ import org.apache.spark.deploy.rest.StandaloneRestServer
 import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.scheduler.{EventLoggingListener, ReplayListenerBus}
 import org.apache.spark.ui.SparkUI
-import org.apache.spark.util.{ActorLogReceive, AkkaUtils, RpcUtils, SignalLogger, Utils}
+import org.apache.spark.util.{ThreadUtils, SignalLogger, Utils}
 
 private[master] class Master(
-    host: String,
-    port: Int,
+    override val rpcEnv: RpcEnv,
+    address: RpcAddress,
     webUiPort: Int,
     val securityMgr: SecurityManager,
     val conf: SparkConf)
-  extends Actor with ActorLogReceive with Logging with LeaderElectable {
+  extends ThreadSafeRpcEndpoint with Logging with LeaderElectable {
 
-  import context.dispatcher   // to use Akka's scheduler.schedule()
+  private val forwardMessageThread =
+    ThreadUtils.newDaemonSingleThreadScheduledExecutor("master-forward-message-thread")
+
+  // TODO Remove it once we don't use akka.serialization.Serialization
+  private val actorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem
 
   private val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
 
-  private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss")  // For application IDs
+  private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs
 
-  private val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000
+  private val WORKER_TIMEOUT_MS = conf.getLong("spark.worker.timeout", 60) * 1000
   private val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200)
   private val RETAINED_DRIVERS = conf.getInt("spark.deploy.retainedDrivers", 200)
   private val REAPER_ITERATIONS = conf.getInt("spark.dead.worker.persistence", 15)
@@ -75,10 +77,10 @@ private[master] class Master(
   val apps = new HashSet[ApplicationInfo]
 
   private val idToWorker = new HashMap[String, WorkerInfo]
-  private val addressToWorker = new HashMap[Address, WorkerInfo]
+  private val addressToWorker = new HashMap[RpcAddress, WorkerInfo]
 
-  private val actorToApp = new HashMap[ActorRef, ApplicationInfo]
-  private val addressToApp = new HashMap[Address, ApplicationInfo]
+  private val endpointToApp = new HashMap[RpcEndpointRef, ApplicationInfo]
+  private val addressToApp = new HashMap[RpcAddress, ApplicationInfo]
   private val completedApps = new ArrayBuffer[ApplicationInfo]
   private var nextAppNumber = 0
   private val appIdToUI = new HashMap[String, SparkUI]
@@ -89,21 +91,22 @@ private[master] class Master(
   private val waitingDrivers = new ArrayBuffer[DriverInfo]
   private var nextDriverNumber = 0
 
-  Utils.checkHost(host, "Expected hostname")
+  Utils.checkHost(address.host, "Expected hostname")
 
   private val masterMetricsSystem = MetricsSystem.createMetricsSystem("master", conf, securityMgr)
   private val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications", conf,
     securityMgr)
   private val masterSource = new MasterSource(this)
 
-  private val webUi = new MasterWebUI(this, webUiPort)
+  // After onStart, webUi will be set
+  private var webUi: MasterWebUI = null
 
   private val masterPublicAddress = {
     val envVar = conf.getenv("SPARK_PUBLIC_DNS")
-    if (envVar != null) envVar else host
+    if (envVar != null) envVar else address.host
   }
 
-  private val masterUrl = "spark://" + host + ":" + port
+  private val masterUrl = address.toSparkURL
   private var masterWebUiUrl: String = _
 
   private var state = RecoveryState.STANDBY
@@ -112,7 +115,9 @@ private[master] class Master(
 
   private var leaderElectionAgent: LeaderElectionAgent = _
 
-  private var recoveryCompletionTask: Cancellable = _
+  private var recoveryCompletionTask: ScheduledFuture[_] = _
+
+  private var checkForWorkerTimeOutTask: ScheduledFuture[_] = _
 
   // As a temporary workaround before better ways of configuring memory, we allow users to set
   // a flag that will perform round-robin scheduling across the nodes (spreading out each app
@@ -130,20 +135,23 @@ private[master] class Master(
   private val restServer =
     if (restServerEnabled) {
       val port = conf.getInt("spark.master.rest.port", 6066)
-      Some(new StandaloneRestServer(host, port, conf, self, masterUrl))
+      Some(new StandaloneRestServer(address.host, port, conf, self, masterUrl))
     } else {
       None
     }
   private val restServerBoundPort = restServer.map(_.start())
 
-  override def preStart() {
+  override def onStart(): Unit = {
     logInfo("Starting Spark master at " + masterUrl)
     logInfo(s"Running Spark version ${org.apache.spark.SPARK_VERSION}")
-    // Listen for remote client disconnection events, since they don't go through Akka's watch()
-    context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
+    webUi = new MasterWebUI(this, webUiPort)
     webUi.bind()
     masterWebUiUrl = "http://" + masterPublicAddress + ":" + webUi.boundPort
-    context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis, self, CheckForWorkerTimeOut)
+    checkForWorkerTimeOutTask = forwardMessageThread.scheduleAtFixedRate(new Runnable {
+      override def run(): Unit = Utils.tryLogNonFatalError {
+        self.send(CheckForWorkerTimeOut)
+      }
+    }, 0, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS)
 
     masterMetricsSystem.registerSource(masterSource)
     masterMetricsSystem.start()
@@ -157,16 +165,16 @@ private[master] class Master(
       case "ZOOKEEPER" =>
         logInfo("Persisting recovery state to ZooKeeper")
         val zkFactory =
-          new ZooKeeperRecoveryModeFactory(conf, SerializationExtension(context.system))
+          new ZooKeeperRecoveryModeFactory(conf, SerializationExtension(actorSystem))
         (zkFactory.createPersistenceEngine(), zkFactory.createLeaderElectionAgent(this))
       case "FILESYSTEM" =>
         val fsFactory =
-          new FileSystemRecoveryModeFactory(conf, SerializationExtension(context.system))
+          new FileSystemRecoveryModeFactory(conf, SerializationExtension(actorSystem))
         (fsFactory.createPersistenceEngine(), fsFactory.createLeaderElectionAgent(this))
       case "CUSTOM" =>
         val clazz = Class.forName(conf.get("spark.deploy.recoveryMode.factory"))
         val factory = clazz.getConstructor(classOf[SparkConf], classOf[Serialization])
-          .newInstance(conf, SerializationExtension(context.system))
+          .newInstance(conf, SerializationExtension(actorSystem))
           .asInstanceOf[StandaloneRecoveryModeFactory]
         (factory.createPersistenceEngine(), factory.createLeaderElectionAgent(this))
       case _ =>
@@ -176,18 +184,17 @@ private[master] class Master(
     leaderElectionAgent = leaderElectionAgent_
   }
 
-  override def preRestart(reason: Throwable, message: Option[Any]) {
-    super.preRestart(reason, message) // calls postStop()!
-    logError("Master actor restarted due to exception", reason)
-  }
-
-  override def postStop() {
+  override def onStop() {
     masterMetricsSystem.report()
     applicationMetricsSystem.report()
     // prevent the CompleteRecovery message sending to restarted master
     if (recoveryCompletionTask != null) {
-      recoveryCompletionTask.cancel()
+      recoveryCompletionTask.cancel(true)
     }
+    if (checkForWorkerTimeOutTask != null) {
+      checkForWorkerTimeOutTask.cancel(true)
+    }
+    forwardMessageThread.shutdownNow()
     webUi.stop()
     restServer.foreach(_.stop())
     masterMetricsSystem.stop()
@@ -197,14 +204,14 @@ private[master] class Master(
   }
 
   override def electedLeader() {
-    self ! ElectedLeader
+    self.send(ElectedLeader)
   }
 
   override def revokedLeadership() {
-    self ! RevokedLeadership
+    self.send(RevokedLeadership)
   }
 
-  override def receiveWithLogging: PartialFunction[Any, Unit] = {
+  override def receive: PartialFunction[Any, Unit] = {
     case ElectedLeader => {
       val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData()
       state = if (storedApps.isEmpty && storedDrivers.isEmpty && storedWorkers.isEmpty) {
@@ -215,8 +222,11 @@ private[master] class Master(
       logInfo("I have been elected leader! New state: " + state)
       if (state == RecoveryState.RECOVERING) {
         beginRecovery(storedApps, storedDrivers, storedWorkers)
-        recoveryCompletionTask = context.system.scheduler.scheduleOnce(WORKER_TIMEOUT millis, self,
-          CompleteRecovery)
+        recoveryCompletionTask = forwardMessageThread.schedule(new Runnable {
+          override def run(): Unit = Utils.tryLogNonFatalError {
+            self.send(CompleteRecovery)
+          }
+        }, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS)
       }
     }
 
@@ -227,111 +237,42 @@ private[master] class Master(
       System.exit(0)
     }
 
-    case RegisterWorker(id, workerHost, workerPort, cores, memory, workerUiPort, publicAddress) =>
-    {
+    case RegisterWorker(
+        id, workerHost, workerPort, workerRef, cores, memory, workerUiPort, publicAddress) => {
       logInfo("Registering worker %s:%d with %d cores, %s RAM".format(
         workerHost, workerPort, cores, Utils.megabytesToString(memory)))
       if (state == RecoveryState.STANDBY) {
         // ignore, don't send response
       } else if (idToWorker.contains(id)) {
-        sender ! RegisterWorkerFailed("Duplicate worker ID")
+        workerRef.send(RegisterWorkerFailed("Duplicate worker ID"))
       } else {
         val worker = new WorkerInfo(id, workerHost, workerPort, cores, memory,
-          sender, workerUiPort, publicAddress)
+          workerRef, workerUiPort, publicAddress)
         if (registerWorker(worker)) {
           persistenceEngine.addWorker(worker)
-          sender ! RegisteredWorker(masterUrl, masterWebUiUrl)
+          workerRef.send(RegisteredWorker(self, masterWebUiUrl))
           schedule()
         } else {
-          val workerAddress = worker.actor.path.address
+          val workerAddress = worker.endpoint.address
           logWarning("Worker registration failed. Attempted to re-register worker at same " +
             "address: " + workerAddress)
-          sender ! RegisterWorkerFailed("Attempted to re-register worker at same address: "
-            + workerAddress)
+          workerRef.send(RegisterWorkerFailed("Attempted to re-register worker at same address: "
+            + workerAddress))
         }
       }
     }
 
-    case RequestSubmitDriver(description) => {
-      if (state != RecoveryState.ALIVE) {
-        val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " +
-          "Can only accept driver submissions in ALIVE state."
-        sender ! SubmitDriverResponse(false, None, msg)
-      } else {
-        logInfo("Driver submitted " + description.command.mainClass)
-        val driver = createDriver(description)
-        persistenceEngine.addDriver(driver)
-        waitingDrivers += driver
-        drivers.add(driver)
-        schedule()
-
-        // TODO: It might be good to instead have the submission client poll the master to determine
-        //       the current status of the driver. For now it's simply "fire and forget".
-
-        sender ! SubmitDriverResponse(true, Some(driver.id),
-          s"Driver successfully submitted as ${driver.id}")
-      }
-    }
-
-    case RequestKillDriver(driverId) => {
-      if (state != RecoveryState.ALIVE) {
-        val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " +
-          s"Can only kill drivers in ALIVE state."
-        sender ! KillDriverResponse(driverId, success = false, msg)
-      } else {
-        logInfo("Asked to kill driver " + driverId)
-        val driver = drivers.find(_.id == driverId)
-        driver match {
-          case Some(d) =>
-            if (waitingDrivers.contains(d)) {
-              waitingDrivers -= d
-              self ! DriverStateChanged(driverId, DriverState.KILLED, None)
-            } else {
-              // We just notify the worker to kill the driver here. The final bookkeeping occurs
-              // on the return path when the worker submits a state change back to the master
-              // to notify it that the driver was successfully killed.
-              d.worker.foreach { w =>
-                w.actor ! KillDriver(driverId)
-              }
-            }
-            // TODO: It would be nice for this to be a synchronous response
-            val msg = s"Kill request for $driverId submitted"
-            logInfo(msg)
-            sender ! KillDriverResponse(driverId, success = true, msg)
-          case None =>
-            val msg = s"Driver $driverId has already finished or does not exist"
-            logWarning(msg)
-            sender ! KillDriverResponse(driverId, success = false, msg)
-        }
-      }
-    }
-
-    case RequestDriverStatus(driverId) => {
-      if (state != RecoveryState.ALIVE) {
-        val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " +
-          "Can only request driver status in ALIVE state."
-        sender ! DriverStatusResponse(found = false, None, None, None, Some(new Exception(msg)))
-      } else {
-        (drivers ++ completedDrivers).find(_.id == driverId) match {
-          case Some(driver) =>
-            sender ! DriverStatusResponse(found = true, Some(driver.state),
-              driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception)
-          case None =>
-            sender ! DriverStatusResponse(found = false, None, None, None, None)
-        }
-      }
-    }
-
-    case RegisterApplication(description) => {
+    case RegisterApplication(description, driver) => {
+      // TODO Prevent repeated registrations from some driver
       if (state == RecoveryState.STANDBY) {
         // ignore, don't send response
       } else {
         logInfo("Registering app " + description.name)
-        val app = createApplication(description, sender)
+        val app = createApplication(description, driver)
         registerApplication(app)
         logInfo("Registered app " + description.name + " with ID " + app.id)
         persistenceEngine.addApplication(app)
-        sender ! RegisteredApplication(app.id, masterUrl)
+        driver.send(RegisteredApplication(app.id, self))
         schedule()
       }
     }
@@ -343,7 +284,7 @@ private[master] class Master(
           val appInfo = idToApp(appId)
           exec.state = state
           if (state == ExecutorState.RUNNING) { appInfo.resetRetryCount() }
-          exec.application.driver ! ExecutorUpdated(execId, state, message, exitStatus)
+          exec.application.driver.send(ExecutorUpdated(execId, state, message, exitStatus))
           if (ExecutorState.isFinished(state)) {
             // Remove this executor from the worker and app
             logInfo(s"Removing executor ${exec.fullId} because it is $state")
@@ -384,7 +325,7 @@ private[master] class Master(
       }
     }
 
-    case Heartbeat(workerId) => {
+    case Heartbeat(workerId, worker) => {
       idToWorker.get(workerId) match {
         case Some(workerInfo) =>
           workerInfo.lastHeartbeat = System.currentTimeMillis()
@@ -392,7 +333,7 @@ private[master] class Master(
           if (workers.map(_.id).contains(workerId)) {
             logWarning(s"Got heartbeat from unregistered worker $workerId." +
               " Asking it to re-register.")
-            sender ! ReconnectWorker(masterUrl)
+            worker.send(ReconnectWorker(masterUrl))
           } else {
             logWarning(s"Got heartbeat from unregistered worker $workerId." +
               " This worker was never registered, so ignoring the heartbeat.")
@@ -444,30 +385,103 @@ private[master] class Master(
       logInfo(s"Received unregister request from application $applicationId")
       idToApp.get(applicationId).foreach(finishApplication)
 
-    case DisassociatedEvent(_, address, _) => {
-      // The disconnected client could've been either a worker or an app; remove whichever it was
-      logInfo(s"$address got disassociated, removing it.")
-      addressToWorker.get(address).foreach(removeWorker)
-      addressToApp.get(address).foreach(finishApplication)
-      if (state == RecoveryState.RECOVERING && canCompleteRecovery) { completeRecovery() }
+    case CheckForWorkerTimeOut => {
+      timeOutDeadWorkers()
     }
+  }
 
-    case RequestMasterState => {
-      sender ! MasterStateResponse(
-        host, port, restServerBoundPort,
-        workers.toArray, apps.toArray, completedApps.toArray,
-        drivers.toArray, completedDrivers.toArray, state)
+  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+    case RequestSubmitDriver(description) => {
+      if (state != RecoveryState.ALIVE) {
+        val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " +
+          "Can only accept driver submissions in ALIVE state."
+        context.reply(SubmitDriverResponse(self, false, None, msg))
+      } else {
+        logInfo("Driver submitted " + description.command.mainClass)
+        val driver = createDriver(description)
+        persistenceEngine.addDriver(driver)
+        waitingDrivers += driver
+        drivers.add(driver)
+        schedule()
+
+        // TODO: It might be good to instead have the submission client poll the master to determine
+        //       the current status of the driver. For now it's simply "fire and forget".
+
+        context.reply(SubmitDriverResponse(self, true, Some(driver.id),
+          s"Driver successfully submitted as ${driver.id}"))
+      }
     }
 
-    case CheckForWorkerTimeOut => {
-      timeOutDeadWorkers()
+    case RequestKillDriver(driverId) => {
+      if (state != RecoveryState.ALIVE) {
+        val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " +
+          s"Can only kill drivers in ALIVE state."
+        context.reply(KillDriverResponse(self, driverId, success = false, msg))
+      } else {
+        logInfo("Asked to kill driver " + driverId)
+        val driver = drivers.find(_.id == driverId)
+        driver match {
+          case Some(d) =>
+            if (waitingDrivers.contains(d)) {
+              waitingDrivers -= d
+              self.send(DriverStateChanged(driverId, DriverState.KILLED, None))
+            } else {
+              // We just notify the worker to kill the driver here. The final bookkeeping occurs
+              // on the return path when the worker submits a state change back to the master
+              // to notify it that the driver was successfully killed.
+              d.worker.foreach { w =>
+                w.endpoint.send(KillDriver(driverId))
+              }
+            }
+            // TODO: It would be nice for this to be a synchronous response
+            val msg = s"Kill request for $driverId submitted"
+            logInfo(msg)
+            context.reply(KillDriverResponse(self, driverId, success = true, msg))
+          case None =>
+            val msg = s"Driver $driverId has already finished or does not exist"
+            logWarning(msg)
+            context.reply(KillDriverResponse(self, driverId, success = false, msg))
+        }
+      }
+    }
+
+    case RequestDriverStatus(driverId) => {
+      if (state != RecoveryState.ALIVE) {
+        val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " +
+          "Can only request driver status in ALIVE state."
+        context.reply(
+          DriverStatusResponse(found = false, None, None, None, Some(new Exception(msg))))
+      } else {
+        (drivers ++ completedDrivers).find(_.id == driverId) match {
+          case Some(driver) =>
+            context.reply(DriverStatusResponse(found = true, Some(driver.state),
+              driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception))
+          case None =>
+            context.reply(DriverStatusResponse(found = false, None, None, None, None))
+        }
+      }
+    }
+
+    case RequestMasterState => {
+      context.reply(MasterStateResponse(
+        address.host, address.port, restServerBoundPort,
+        workers.toArray, apps.toArray, completedApps.toArray,
+        drivers.toArray, completedDrivers.toArray, state))
     }
 
     case BoundPortsRequest => {
-      sender ! BoundPortsResponse(port, webUi.boundPort, restServerBoundPort)
+      context.reply(BoundPortsResponse(address.port, webUi.boundPort, restServerBoundPort))
     }
   }
 
+  override def onDisconnected(address: RpcAddress): Unit = {
+    // The disconnected client could've been either a worker or an app; remove whichever it was
+    logInfo(s"$address got disassociated, removing it.")
+    addressToWorker.get(address).foreach(removeWorker)
+    addressToApp.get(address).foreach(finishApplication)
+    if (state == RecoveryState.RECOVERING && canCompleteRecovery) { completeRecovery() }
+  }
+
   private def canCompleteRecovery =
     workers.count(_.state == WorkerState.UNKNOWN) == 0 &&
       apps.count(_.state == ApplicationState.UNKNOWN) == 0
@@ -479,7 +493,7 @@ private[master] class Master(
       try {
         registerApplication(app)
         app.state = ApplicationState.UNKNOWN
-        app.driver ! MasterChanged(masterUrl, masterWebUiUrl)
+        app.driver.send(MasterChanged(self, masterWebUiUrl))
       } catch {
         case e: Exception => logInfo("App " + app.id + " had exception on reconnect")
       }
@@ -496,7 +510,7 @@ private[master] class Master(
       try {
         registerWorker(worker)
         worker.state = WorkerState.UNKNOWN
-        worker.actor ! MasterChanged(masterUrl, masterWebUiUrl)
+        worker.endpoint.send(MasterChanged(self, masterWebUiUrl))
       } catch {
         case e: Exception => logInfo("Worker " + worker.id + " had exception on reconnect")
       }
@@ -504,6 +518,7 @@ private[master] class Master(
   }
 
   private def completeRecovery() {
+    // TODO Why synchronized
     // Ensure "only-once" recovery semantics using a short synchronization period.
     synchronized {
       if (state != RecoveryState.RECOVERING) { return }
@@ -623,10 +638,10 @@ private[master] class Master(
   private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc): Unit = {
     logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
     worker.addExecutor(exec)
-    worker.actor ! LaunchExecutor(masterUrl,
-      exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)
-    exec.application.driver ! ExecutorAdded(
-      exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
+    worker.endpoint.send(LaunchExecutor(masterUrl,
+      exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory))
+    exec.application.driver.send(ExecutorAdded(
+      exec.id, worker.id, worker.hostPort, exec.cores, exec.memory))
   }
 
   private def registerWorker(worker: WorkerInfo): Boolean = {
@@ -638,7 +653,7 @@ private[master] class Master(
       workers -= w
     }
 
-    val workerAddress = worker.actor.path.address
+    val workerAddress = worker.endpoint.address
     if (addressToWorker.contains(workerAddress)) {
       val oldWorker = addressToWorker(workerAddress)
       if (oldWorker.state == WorkerState.UNKNOWN) {
@@ -661,11 +676,11 @@ private[master] class Master(
     logInfo("Removing worker " + worker.id + " on " + worker.host + ":" + worker.port)
     worker.setState(WorkerState.DEAD)
     idToWorker -= worker.id
-    addressToWorker -= worker.actor.path.address
+    addressToWorker -= worker.endpoint.address
     for (exec <- worker.executors.values) {
       logInfo("Telling app of lost executor: " + exec.id)
-      exec.application.driver ! ExecutorUpdated(
-        exec.id, ExecutorState.LOST, Some("worker lost"), None)
+      exec.application.driver.send(ExecutorUpdated(
+        exec.id, ExecutorState.LOST, Some("worker lost"), None))
       exec.application.removeExecutor(exec)
     }
     for (driver <- worker.drivers.values) {
@@ -687,14 +702,15 @@ private[master] class Master(
     schedule()
   }
 
-  private def createApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = {
+  private def createApplication(desc: ApplicationDescription, driver: RpcEndpointRef):
+      ApplicationInfo = {
     val now = System.currentTimeMillis()
     val date = new Date(now)
     new ApplicationInfo(now, newApplicationId(date), desc, date, driver, defaultCores)
   }
 
   private def registerApplication(app: ApplicationInfo): Unit = {
-    val appAddress = app.driver.path.address
+    val appAddress = app.driver.address
     if (addressToApp.contains(appAddress)) {
       logInfo("Attempted to re-register application at same address: " + appAddress)
       return
@@ -703,7 +719,7 @@ private[master] class Master(
     applicationMetricsSystem.registerSource(app.appSource)
     apps += app
     idToApp(app.id) = app
-    actorToApp(app.driver) = app
+    endpointToApp(app.driver) = app
     addressToApp(appAddress) = app
     waitingApps += app
   }
@@ -717,8 +733,8 @@ private[master] class Master(
       logInfo("Removing app " + app.id)
       apps -= app
       idToApp -= app.id
-      actorToApp -= app.driver
-      addressToApp -= app.driver.path.address
+      endpointToApp -= app.driver
+      addressToApp -= app.driver.address
       if (completedApps.size >= RETAINED_APPLICATIONS) {
         val toRemove = math.max(RETAINED_APPLICATIONS / 10, 1)
         completedApps.take(toRemove).foreach( a => {
@@ -735,19 +751,19 @@ private[master] class Master(
 
       for (exec <- app.executors.values) {
         exec.worker.removeExecutor(exec)
-        exec.worker.actor ! KillExecutor(masterUrl, exec.application.id, exec.id)
+        exec.worker.endpoint.send(KillExecutor(masterUrl, exec.application.id, exec.id))
         exec.state = ExecutorState.KILLED
       }
       app.markFinished(state)
       if (state != ApplicationState.FINISHED) {
-        app.driver ! ApplicationRemoved(state.toString)
+        app.driver.send(ApplicationRemoved(state.toString))
       }
       persistenceEngine.removeApplication(app)
       schedule()
 
       // Tell all workers that the application has finished, so they can clean up any app state.
       workers.foreach { w =>
-        w.actor ! ApplicationFinished(app.id)
+        w.endpoint.send(ApplicationFinished(app.id))
       }
     }
   }
@@ -768,7 +784,7 @@ private[master] class Master(
         }
 
       val eventLogFilePrefix = EventLoggingListener.getLogPath(
-          eventLogDir, app.id, None, app.desc.eventLogCodec)
+          eventLogDir, app.id, app.desc.eventLogCodec)
       val fs = Utils.getHadoopFileSystem(eventLogDir, hadoopConf)
       val inProgressExists = fs.exists(new Path(eventLogFilePrefix +
           EventLoggingListener.IN_PROGRESS))
@@ -832,14 +848,14 @@ private[master] class Master(
   private def timeOutDeadWorkers() {
     // Copy the workers into an array so we don't modify the hashset while iterating through it
     val currentTime = System.currentTimeMillis()
-    val toRemove = workers.filter(_.lastHeartbeat < currentTime - WORKER_TIMEOUT).toArray
+    val toRemove = workers.filter(_.lastHeartbeat < currentTime - WORKER_TIMEOUT_MS).toArray
     for (worker <- toRemove) {
       if (worker.state != WorkerState.DEAD) {
         logWarning("Removing %s because we got no heartbeat in %d seconds".format(
-          worker.id, WORKER_TIMEOUT/1000))
+          worker.id, WORKER_TIMEOUT_MS / 1000))
         removeWorker(worker)
       } else {
-        if (worker.lastHeartbeat < currentTime - ((REAPER_ITERATIONS + 1) * WORKER_TIMEOUT)) {
+        if (worker.lastHeartbeat < currentTime - ((REAPER_ITERATIONS + 1) * WORKER_TIMEOUT_MS)) {
           workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it
         }
       }
@@ -862,7 +878,7 @@ private[master] class Master(
     logInfo("Launching driver " + driver.id + " on worker " + worker.id)
     worker.addDriver(driver)
     driver.worker = Some(worker)
-    worker.actor ! LaunchDriver(driver.id, driver.desc)
+    worker.endpoint.send(LaunchDriver(driver.id, driver.desc))
     driver.state = DriverState.RUNNING
   }
 
@@ -891,57 +907,33 @@ private[master] class Master(
 }
 
 private[deploy] object Master extends Logging {
-  val systemName = "sparkMaster"
-  private val actorName = "Master"
+  val SYSTEM_NAME = "sparkMaster"
+  val ENDPOINT_NAME = "Master"
 
   def main(argStrings: Array[String]) {
     SignalLogger.register(log)
     val conf = new SparkConf
     val args = new MasterArguments(argStrings, conf)
-    val (actorSystem, _, _, _) = startSystemAndActor(args.host, args.port, args.webUiPort, conf)
-    actorSystem.awaitTermination()
-  }
-
-  /**
-   * Returns an `akka.tcp://...` URL for the Master actor given a sparkUrl `spark://host:port`.
-   *
-   * @throws SparkException if the url is invalid
-   */
-  def toAkkaUrl(sparkUrl: String, protocol: String): String = {
-    val (host, port) = Utils.extractHostPortFromSparkUrl(sparkUrl)
-    AkkaUtils.address(protocol, systemName, host, port, actorName)
-  }
-
-  /**
-   * Returns an akka `Address` for the Master actor given a sparkUrl `spark://host:port`.
-   *
-   * @throws SparkException if the url is invalid
-   */
-  def toAkkaAddress(sparkUrl: String, protocol: String): Address = {
-    val (host, port) = Utils.extractHostPortFromSparkUrl(sparkUrl)
-    Address(protocol, systemName, host, port)
+    val (rpcEnv, _, _) = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, conf)
+    rpcEnv.awaitTermination()
   }
 
   /**
-   * Start the Master and return a four tuple of:
-   *   (1) The Master actor system
-   *   (2) The bound port
-   *   (3) The web UI bound port
-   *   (4) The REST server bound port, if any
+   * Start the Master and return a three tuple of:
+   *   (1) The Master RpcEnv
+   *   (2) The web UI bound port
+   *   (3) The REST server bound port, if any
    */
-  def startSystemAndActor(
+  def startRpcEnvAndEndpoint(
       host: String,
       port: Int,
       webUiPort: Int,
-      conf: SparkConf): (ActorSystem, Int, Int, Option[Int]) = {
+      conf: SparkConf): (RpcEnv, Int, Option[Int]) = {
     val securityMgr = new SecurityManager(conf)
-    val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, conf = conf,
-      securityManager = securityMgr)
-    val actor = actorSystem.actorOf(
-      Props(classOf[Master], host, boundPort, webUiPort, securityMgr, conf), actorName)
-    val timeout = RpcUtils.askTimeout(conf)
-    val portsRequest = actor.ask(BoundPortsRequest)(timeout)
-    val portsResponse = Await.result(portsRequest, timeout).asInstanceOf[BoundPortsResponse]
-    (actorSystem, boundPort, portsResponse.webUIPort, portsResponse.restPort)
+    val rpcEnv = RpcEnv.create(SYSTEM_NAME, host, port, conf, securityMgr)
+    val masterEndpoint = rpcEnv.setupEndpoint(ENDPOINT_NAME,
+      new Master(rpcEnv, rpcEnv.address, webUiPort, securityMgr, conf))
+    val portsResponse = masterEndpoint.askWithRetry[BoundPortsResponse](BoundPortsRequest)
+    (rpcEnv, portsResponse.webUIPort, portsResponse.restPort)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
index 15c6296888f70..68c937188b333 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
@@ -28,7 +28,7 @@ private[master] object MasterMessages {
 
   case object RevokedLeadership
 
-  // Actor System to Master
+  // Master to itself
 
   case object CheckForWorkerTimeOut
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
index 9b3d48c6edc84..471811037e5e2 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
@@ -19,9 +19,7 @@ package org.apache.spark.deploy.master
 
 import scala.collection.mutable
 
-import akka.actor.ActorRef
-
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.util.Utils
 
 private[spark] class WorkerInfo(
@@ -30,7 +28,7 @@ private[spark] class WorkerInfo(
     val port: Int,
     val cores: Int,
     val memory: Int,
-    val actor: ActorRef,
+    val endpoint: RpcEndpointRef,
     val webUiPort: Int,
     val publicAddress: String)
   extends Serializable {
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
index 52758d6a7c4be..6fdff86f66e01 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
@@ -17,10 +17,7 @@
 
 package org.apache.spark.deploy.master
 
-import akka.actor.ActorRef
-
 import org.apache.spark.{Logging, SparkConf}
-import org.apache.spark.deploy.master.MasterMessages._
 import org.apache.curator.framework.CuratorFramework
 import org.apache.curator.framework.recipes.leader.{LeaderLatchListener, LeaderLatch}
 import org.apache.spark.deploy.SparkCuratorUtil
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
index 06e265f99e231..e28e7e379ac91 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
@@ -19,11 +19,8 @@ package org.apache.spark.deploy.master.ui
 
 import javax.servlet.http.HttpServletRequest
 
-import scala.concurrent.Await
 import scala.xml.Node
 
-import akka.pattern.ask
-
 import org.apache.spark.deploy.ExecutorState
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState}
 import org.apache.spark.deploy.master.ExecutorDesc
@@ -32,14 +29,12 @@ import org.apache.spark.util.Utils
 
 private[ui] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app") {
 
-  private val master = parent.masterActorRef
-  private val timeout = parent.timeout
+  private val master = parent.masterEndpointRef
 
   /** Executor details for a particular application */
   def render(request: HttpServletRequest): Seq[Node] = {
     val appId = request.getParameter("appId")
-    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
-    val state = Await.result(stateFuture, timeout)
+    val state = master.askWithRetry[MasterStateResponse](RequestMasterState)
     val app = state.activeApps.find(_.id == appId).getOrElse({
       state.completedApps.find(_.id == appId).getOrElse(null)
     })
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
index 6a7c74020bace..c3e20ebf8d6eb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
@@ -19,25 +19,21 @@ package org.apache.spark.deploy.master.ui
 
 import javax.servlet.http.HttpServletRequest
 
-import scala.concurrent.Await
 import scala.xml.Node
 
-import akka.pattern.ask
 import org.json4s.JValue
 
 import org.apache.spark.deploy.JsonProtocol
-import org.apache.spark.deploy.DeployMessages.{RequestKillDriver, MasterStateResponse, RequestMasterState}
+import org.apache.spark.deploy.DeployMessages.{KillDriverResponse, RequestKillDriver, MasterStateResponse, RequestMasterState}
 import org.apache.spark.deploy.master._
 import org.apache.spark.ui.{WebUIPage, UIUtils}
 import org.apache.spark.util.Utils
 
 private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
-  private val master = parent.masterActorRef
-  private val timeout = parent.timeout
+  private val master = parent.masterEndpointRef
 
   def getMasterState: MasterStateResponse = {
-    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
-    Await.result(stateFuture, timeout)
+    master.askWithRetry[MasterStateResponse](RequestMasterState)
   }
 
   override def renderJson(request: HttpServletRequest): JValue = {
@@ -53,7 +49,9 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
   }
 
   def handleDriverKillRequest(request: HttpServletRequest): Unit = {
-    handleKillRequest(request, id => { master ! RequestKillDriver(id) })
+    handleKillRequest(request, id => {
+      master.ask[KillDriverResponse](RequestKillDriver(id))
+    })
   }
 
   private def handleKillRequest(request: HttpServletRequest, action: String => Unit): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
index 2111a8581f2e4..6174fc11f83d8 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
@@ -23,7 +23,6 @@ import org.apache.spark.status.api.v1.{ApiRootResource, ApplicationsListResource
   UIRoot}
 import org.apache.spark.ui.{SparkUI, WebUI}
 import org.apache.spark.ui.JettyUtils._
-import org.apache.spark.util.RpcUtils
 
 /**
  * Web UI server for the standalone master.
@@ -33,8 +32,7 @@ class MasterWebUI(val master: Master, requestedPort: Int)
   extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging
   with UIRoot {
 
-  val masterActorRef = master.self
-  val timeout = RpcUtils.askTimeout(master.conf)
+  val masterEndpointRef = master.self
   val killEnabled = master.conf.getBoolean("spark.ui.killEnabled", true)
 
   val masterPage = new MasterPage(this)
diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala
index 502b9bb701ccf..d5b9bcab1423f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala
@@ -20,10 +20,10 @@ package org.apache.spark.deploy.rest
 import java.io.File
 import javax.servlet.http.HttpServletResponse
 
-import akka.actor.ActorRef
 import org.apache.spark.deploy.ClientArguments._
 import org.apache.spark.deploy.{Command, DeployMessages, DriverDescription}
-import org.apache.spark.util.{AkkaUtils, RpcUtils, Utils}
+import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.util.Utils
 import org.apache.spark.{SPARK_VERSION => sparkVersion, SparkConf}
 
 /**
@@ -45,35 +45,34 @@ import org.apache.spark.{SPARK_VERSION => sparkVersion, SparkConf}
  * @param host the address this server should bind to
  * @param requestedPort the port this server will attempt to bind to
  * @param masterConf the conf used by the Master
- * @param masterActor reference to the Master actor to which requests can be sent
+ * @param masterEndpoint reference to the Master endpoint to which requests can be sent
  * @param masterUrl the URL of the Master new drivers will attempt to connect to
  */
 private[deploy] class StandaloneRestServer(
     host: String,
     requestedPort: Int,
     masterConf: SparkConf,
-    masterActor: ActorRef,
+    masterEndpoint: RpcEndpointRef,
     masterUrl: String)
   extends RestSubmissionServer(host, requestedPort, masterConf) {
 
   protected override val submitRequestServlet =
-    new StandaloneSubmitRequestServlet(masterActor, masterUrl, masterConf)
+    new StandaloneSubmitRequestServlet(masterEndpoint, masterUrl, masterConf)
   protected override val killRequestServlet =
-    new StandaloneKillRequestServlet(masterActor, masterConf)
+    new StandaloneKillRequestServlet(masterEndpoint, masterConf)
   protected override val statusRequestServlet =
-    new StandaloneStatusRequestServlet(masterActor, masterConf)
+    new StandaloneStatusRequestServlet(masterEndpoint, masterConf)
 }
 
 /**
  * A servlet for handling kill requests passed to the [[StandaloneRestServer]].
  */
-private[rest] class StandaloneKillRequestServlet(masterActor: ActorRef, conf: SparkConf)
+private[rest] class StandaloneKillRequestServlet(masterEndpoint: RpcEndpointRef, conf: SparkConf)
   extends KillRequestServlet {
 
   protected def handleKill(submissionId: String): KillSubmissionResponse = {
-    val askTimeout = RpcUtils.askTimeout(conf)
-    val response = AkkaUtils.askWithReply[DeployMessages.KillDriverResponse](
-      DeployMessages.RequestKillDriver(submissionId), masterActor, askTimeout)
+    val response = masterEndpoint.askWithRetry[DeployMessages.KillDriverResponse](
+      DeployMessages.RequestKillDriver(submissionId))
     val k = new KillSubmissionResponse
     k.serverSparkVersion = sparkVersion
     k.message = response.message
@@ -86,13 +85,12 @@ private[rest] class StandaloneKillRequestServlet(masterActor: ActorRef, conf: Sp
 /**
  * A servlet for handling status requests passed to the [[StandaloneRestServer]].
  */
-private[rest] class StandaloneStatusRequestServlet(masterActor: ActorRef, conf: SparkConf)
+private[rest] class StandaloneStatusRequestServlet(masterEndpoint: RpcEndpointRef, conf: SparkConf)
   extends StatusRequestServlet {
 
   protected def handleStatus(submissionId: String): SubmissionStatusResponse = {
-    val askTimeout = RpcUtils.askTimeout(conf)
-    val response = AkkaUtils.askWithReply[DeployMessages.DriverStatusResponse](
-      DeployMessages.RequestDriverStatus(submissionId), masterActor, askTimeout)
+    val response = masterEndpoint.askWithRetry[DeployMessages.DriverStatusResponse](
+      DeployMessages.RequestDriverStatus(submissionId))
     val message = response.exception.map { s"Exception from the cluster:\n" + formatException(_) }
     val d = new SubmissionStatusResponse
     d.serverSparkVersion = sparkVersion
@@ -110,7 +108,7 @@ private[rest] class StandaloneStatusRequestServlet(masterActor: ActorRef, conf:
  * A servlet for handling submit requests passed to the [[StandaloneRestServer]].
  */
 private[rest] class StandaloneSubmitRequestServlet(
-    masterActor: ActorRef,
+    masterEndpoint: RpcEndpointRef,
     masterUrl: String,
     conf: SparkConf)
   extends SubmitRequestServlet {
@@ -175,10 +173,9 @@ private[rest] class StandaloneSubmitRequestServlet(
       responseServlet: HttpServletResponse): SubmitRestProtocolResponse = {
     requestMessage match {
       case submitRequest: CreateSubmissionRequest =>
-        val askTimeout = RpcUtils.askTimeout(conf)
         val driverDescription = buildDriverDescription(submitRequest)
-        val response = AkkaUtils.askWithReply[DeployMessages.SubmitDriverResponse](
-          DeployMessages.RequestSubmitDriver(driverDescription), masterActor, askTimeout)
+        val response = masterEndpoint.askWithRetry[DeployMessages.SubmitDriverResponse](
+          DeployMessages.RequestSubmitDriver(driverDescription))
         val submitResponse = new CreateSubmissionResponse
         submitResponse.serverSparkVersion = sparkVersion
         submitResponse.message = response.message
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
index 1386055eb8c48..ec51c3d935d8e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
@@ -21,7 +21,6 @@ import java.io._
 
 import scala.collection.JavaConversions._
 
-import akka.actor.ActorRef
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
@@ -31,6 +30,7 @@ import org.apache.spark.deploy.{DriverDescription, SparkHadoopUtil}
 import org.apache.spark.deploy.DeployMessages.DriverStateChanged
 import org.apache.spark.deploy.master.DriverState
 import org.apache.spark.deploy.master.DriverState.DriverState
+import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.util.{Utils, Clock, SystemClock}
 
 /**
@@ -43,7 +43,7 @@ private[deploy] class DriverRunner(
     val workDir: File,
     val sparkHome: File,
     val driverDesc: DriverDescription,
-    val worker: ActorRef,
+    val worker: RpcEndpointRef,
     val workerUrl: String,
     val securityManager: SecurityManager)
   extends Logging {
@@ -107,7 +107,7 @@ private[deploy] class DriverRunner(
 
         finalState = Some(state)
 
-        worker ! DriverStateChanged(driverId, state, finalException)
+        worker.send(DriverStateChanged(driverId, state, finalException))
       }
     }.start()
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index fff17e1095042..29a5042285578 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -21,10 +21,10 @@ import java.io._
 
 import scala.collection.JavaConversions._
 
-import akka.actor.ActorRef
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
 
+import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.{SecurityManager, SparkConf, Logging}
 import org.apache.spark.deploy.{ApplicationDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged
@@ -41,7 +41,7 @@ private[deploy] class ExecutorRunner(
     val appDesc: ApplicationDescription,
     val cores: Int,
     val memory: Int,
-    val worker: ActorRef,
+    val worker: RpcEndpointRef,
     val workerId: String,
     val host: String,
     val webUiPort: Int,
@@ -91,7 +91,7 @@ private[deploy] class ExecutorRunner(
       process.destroy()
       exitCode = Some(process.waitFor())
     }
-    worker ! ExecutorStateChanged(appId, execId, state, message, exitCode)
+    worker.send(ExecutorStateChanged(appId, execId, state, message, exitCode))
   }
 
   /** Stop this executor runner, including killing the process it launched */
@@ -159,7 +159,7 @@ private[deploy] class ExecutorRunner(
       val exitCode = process.waitFor()
       state = ExecutorState.EXITED
       val message = "Command exited with code " + exitCode
-      worker ! ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode))
+      worker.send(ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode)))
     } catch {
       case interrupted: InterruptedException => {
         logInfo("Runner thread for executor " + fullId + " interrupted")
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index ebc6cd76c6afd..82e9578bbcba5 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -21,15 +21,14 @@ import java.io.File
 import java.io.IOException
 import java.text.SimpleDateFormat
 import java.util.{UUID, Date}
+import java.util.concurrent._
+import java.util.concurrent.{Future => JFuture, ScheduledFuture => JScheduledFuture}
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, HashSet}
-import scala.concurrent.duration._
-import scala.language.postfixOps
+import scala.concurrent.ExecutionContext
 import scala.util.Random
-
-import akka.actor._
-import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent}
+import scala.util.control.NonFatal
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.{Command, ExecutorDescription, ExecutorState}
@@ -38,32 +37,39 @@ import org.apache.spark.deploy.ExternalShuffleService
 import org.apache.spark.deploy.master.{DriverState, Master}
 import org.apache.spark.deploy.worker.ui.WorkerWebUI
 import org.apache.spark.metrics.MetricsSystem
-import org.apache.spark.util.{ActorLogReceive, AkkaUtils, SignalLogger, Utils}
+import org.apache.spark.rpc._
+import org.apache.spark.util.{ThreadUtils, SignalLogger, Utils}
 
-/**
-  * @param masterAkkaUrls Each url should be a valid akka url.
-  */
 private[worker] class Worker(
-    host: String,
-    port: Int,
+    override val rpcEnv: RpcEnv,
     webUiPort: Int,
     cores: Int,
     memory: Int,
-    masterAkkaUrls: Array[String],
-    actorSystemName: String,
-    actorName: String,
+    masterRpcAddresses: Array[RpcAddress],
+    systemName: String,
+    endpointName: String,
     workDirPath: String = null,
     val conf: SparkConf,
     val securityMgr: SecurityManager)
-  extends Actor with ActorLogReceive with Logging {
-  import context.dispatcher
+  extends ThreadSafeRpcEndpoint with Logging {
+
+  private val host = rpcEnv.address.host
+  private val port = rpcEnv.address.port
 
   Utils.checkHost(host, "Expected hostname")
   assert (port > 0)
 
+  // A scheduled executor used to send messages at the specified time.
+  private val forwordMessageScheduler =
+    ThreadUtils.newDaemonSingleThreadScheduledExecutor("worker-forward-message-scheduler")
+
+  // A separated thread to clean up the workDir. Used to provide the implicit parameter of `Future`
+  // methods.
+  private val cleanupThreadExecutor = ExecutionContext.fromExecutorService(
+    ThreadUtils.newDaemonSingleThreadExecutor("worker-cleanup-thread"))
+
   // For worker and executor IDs
   private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss")
-
   // Send a heartbeat every (heartbeat timeout) / 4 milliseconds
   private val HEARTBEAT_MILLIS = conf.getLong("spark.worker.timeout", 60) * 1000 / 4
 
@@ -79,32 +85,26 @@ private[worker] class Worker(
     val randomNumberGenerator = new Random(UUID.randomUUID.getMostSignificantBits)
     randomNumberGenerator.nextDouble + FUZZ_MULTIPLIER_INTERVAL_LOWER_BOUND
   }
-  private val INITIAL_REGISTRATION_RETRY_INTERVAL = (math.round(10 *
-    REGISTRATION_RETRY_FUZZ_MULTIPLIER)).seconds
-  private val PROLONGED_REGISTRATION_RETRY_INTERVAL = (math.round(60
-    * REGISTRATION_RETRY_FUZZ_MULTIPLIER)).seconds
+  private val INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS = (math.round(10 *
+    REGISTRATION_RETRY_FUZZ_MULTIPLIER))
+  private val PROLONGED_REGISTRATION_RETRY_INTERVAL_SECONDS = (math.round(60
+    * REGISTRATION_RETRY_FUZZ_MULTIPLIER))
 
   private val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", false)
   // How often worker will clean up old app folders
   private val CLEANUP_INTERVAL_MILLIS =
     conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000
   // TTL for app folders/data;  after TTL expires it will be cleaned up
-  private val APP_DATA_RETENTION_SECS =
+  private val APP_DATA_RETENTION_SECONDS =
     conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600)
 
   private val testing: Boolean = sys.props.contains("spark.testing")
-  private var master: ActorSelection = null
-  private var masterAddress: Address = null
+  private var master: Option[RpcEndpointRef] = None
   private var activeMasterUrl: String = ""
   private[worker] var activeMasterWebUiUrl : String = ""
-  private val akkaUrl = AkkaUtils.address(
-    AkkaUtils.protocol(context.system),
-    actorSystemName,
-    host,
-    port,
-    actorName)
-  @volatile private var registered = false
-  @volatile private var connected = false
+  private val workerUri = rpcEnv.uriOf(systemName, rpcEnv.address, endpointName)
+  private var registered = false
+  private var connected = false
   private val workerId = generateWorkerId()
   private val sparkHome =
     if (testing) {
@@ -136,7 +136,18 @@ private[worker] class Worker(
   private val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf, securityMgr)
   private val workerSource = new WorkerSource(this)
 
-  private var registrationRetryTimer: Option[Cancellable] = None
+  private var registerMasterFutures: Array[JFuture[_]] = null
+  private var registrationRetryTimer: Option[JScheduledFuture[_]] = None
+
+  // A thread pool for registering with masters. Because registering with a master is a blocking
+  // action, this thread pool must be able to create "masterRpcAddresses.size" threads at the same
+  // time so that we can register with all masters.
+  private val registerMasterThreadPool = new ThreadPoolExecutor(
+    0,
+    masterRpcAddresses.size, // Make sure we can register with all masters at the same time
+    60L, TimeUnit.SECONDS,
+    new SynchronousQueue[Runnable](),
+    ThreadUtils.namedThreadFactory("worker-register-master-threadpool"))
 
   var coresUsed = 0
   var memoryUsed = 0
@@ -162,14 +173,13 @@ private[worker] class Worker(
     }
   }
 
-  override def preStart() {
+  override def onStart() {
     assert(!registered)
     logInfo("Starting Spark worker %s:%d with %d cores, %s RAM".format(
       host, port, cores, Utils.megabytesToString(memory)))
     logInfo(s"Running Spark version ${org.apache.spark.SPARK_VERSION}")
     logInfo("Spark home: " + sparkHome)
     createWorkDir()
-    context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
     shuffleService.startIfEnabled()
     webUi = new WorkerWebUI(this, workDir, webUiPort)
     webUi.bind()
@@ -181,24 +191,32 @@ private[worker] class Worker(
     metricsSystem.getServletHandlers.foreach(webUi.attachHandler)
   }
 
-  private def changeMaster(url: String, uiUrl: String) {
+  private def changeMaster(masterRef: RpcEndpointRef, uiUrl: String) {
     // activeMasterUrl it's a valid Spark url since we receive it from master.
-    activeMasterUrl = url
+    activeMasterUrl = masterRef.address.toSparkURL
     activeMasterWebUiUrl = uiUrl
-    master = context.actorSelection(
-      Master.toAkkaUrl(activeMasterUrl, AkkaUtils.protocol(context.system)))
-    masterAddress = Master.toAkkaAddress(activeMasterUrl, AkkaUtils.protocol(context.system))
+    master = Some(masterRef)
     connected = true
     // Cancel any outstanding re-registration attempts because we found a new master
-    registrationRetryTimer.foreach(_.cancel())
-    registrationRetryTimer = None
+    cancelLastRegistrationRetry()
   }
 
-  private def tryRegisterAllMasters() {
-    for (masterAkkaUrl <- masterAkkaUrls) {
-      logInfo("Connecting to master " + masterAkkaUrl + "...")
-      val actor = context.actorSelection(masterAkkaUrl)
-      actor ! RegisterWorker(workerId, host, port, cores, memory, webUi.boundPort, publicAddress)
+  private def tryRegisterAllMasters(): Array[JFuture[_]] = {
+    masterRpcAddresses.map { masterAddress =>
+      registerMasterThreadPool.submit(new Runnable {
+        override def run(): Unit = {
+          try {
+            logInfo("Connecting to master " + masterAddress + "...")
+            val masterEndpoint =
+              rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, masterAddress, Master.ENDPOINT_NAME)
+            masterEndpoint.send(RegisterWorker(
+              workerId, host, port, self, cores, memory, webUi.boundPort, publicAddress))
+          } catch {
+            case ie: InterruptedException => // Cancelled
+            case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
+          }
+        }
+      })
     }
   }
 
@@ -211,8 +229,7 @@ private[worker] class Worker(
     Utils.tryOrExit {
       connectionAttemptCount += 1
       if (registered) {
-        registrationRetryTimer.foreach(_.cancel())
-        registrationRetryTimer = None
+        cancelLastRegistrationRetry()
       } else if (connectionAttemptCount <= TOTAL_REGISTRATION_RETRIES) {
         logInfo(s"Retrying connection to master (attempt # $connectionAttemptCount)")
         /**
@@ -235,21 +252,48 @@ private[worker] class Worker(
          * still not safe if the old master recovers within this interval, but this is a much
          * less likely scenario.
          */
-        if (master != null) {
-          master ! RegisterWorker(
-            workerId, host, port, cores, memory, webUi.boundPort, publicAddress)
-        } else {
-          // We are retrying the initial registration
-          tryRegisterAllMasters()
+        master match {
+          case Some(masterRef) =>
+            // registered == false && master != None means we lost the connection to master, so
+            // masterRef cannot be used and we need to recreate it again. Note: we must not set
+            // master to None due to the above comments.
+            if (registerMasterFutures != null) {
+              registerMasterFutures.foreach(_.cancel(true))
+            }
+            val masterAddress = masterRef.address
+            registerMasterFutures = Array(registerMasterThreadPool.submit(new Runnable {
+              override def run(): Unit = {
+                try {
+                  logInfo("Connecting to master " + masterAddress + "...")
+                  val masterEndpoint =
+                    rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, masterAddress, Master.ENDPOINT_NAME)
+                  masterEndpoint.send(RegisterWorker(
+                    workerId, host, port, self, cores, memory, webUi.boundPort, publicAddress))
+                } catch {
+                  case ie: InterruptedException => // Cancelled
+                  case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
+                }
+              }
+            }))
+          case None =>
+            if (registerMasterFutures != null) {
+              registerMasterFutures.foreach(_.cancel(true))
+            }
+            // We are retrying the initial registration
+            registerMasterFutures = tryRegisterAllMasters()
         }
         // We have exceeded the initial registration retry threshold
         // All retries from now on should use a higher interval
         if (connectionAttemptCount == INITIAL_REGISTRATION_RETRIES) {
-          registrationRetryTimer.foreach(_.cancel())
-          registrationRetryTimer = Some {
-            context.system.scheduler.schedule(PROLONGED_REGISTRATION_RETRY_INTERVAL,
-              PROLONGED_REGISTRATION_RETRY_INTERVAL, self, ReregisterWithMaster)
-          }
+          registrationRetryTimer.foreach(_.cancel(true))
+          registrationRetryTimer = Some(
+            forwordMessageScheduler.scheduleAtFixedRate(new Runnable {
+              override def run(): Unit = Utils.tryLogNonFatalError {
+                self.send(ReregisterWithMaster)
+              }
+            }, PROLONGED_REGISTRATION_RETRY_INTERVAL_SECONDS,
+              PROLONGED_REGISTRATION_RETRY_INTERVAL_SECONDS,
+              TimeUnit.SECONDS))
         }
       } else {
         logError("All masters are unresponsive! Giving up.")
@@ -258,41 +302,67 @@ private[worker] class Worker(
     }
   }
 
+  /**
+   * Cancel last registeration retry, or do nothing if no retry
+   */
+  private def cancelLastRegistrationRetry(): Unit = {
+    if (registerMasterFutures != null) {
+      registerMasterFutures.foreach(_.cancel(true))
+      registerMasterFutures = null
+    }
+    registrationRetryTimer.foreach(_.cancel(true))
+    registrationRetryTimer = None
+  }
+
   private def registerWithMaster() {
-    // DisassociatedEvent may be triggered multiple times, so don't attempt registration
+    // onDisconnected may be triggered multiple times, so don't attempt registration
     // if there are outstanding registration attempts scheduled.
     registrationRetryTimer match {
       case None =>
         registered = false
-        tryRegisterAllMasters()
+        registerMasterFutures = tryRegisterAllMasters()
         connectionAttemptCount = 0
-        registrationRetryTimer = Some {
-          context.system.scheduler.schedule(INITIAL_REGISTRATION_RETRY_INTERVAL,
-            INITIAL_REGISTRATION_RETRY_INTERVAL, self, ReregisterWithMaster)
-        }
+        registrationRetryTimer = Some(forwordMessageScheduler.scheduleAtFixedRate(
+          new Runnable {
+            override def run(): Unit = Utils.tryLogNonFatalError {
+              self.send(ReregisterWithMaster)
+            }
+          },
+          INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS,
+          INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS,
+          TimeUnit.SECONDS))
       case Some(_) =>
         logInfo("Not spawning another attempt to register with the master, since there is an" +
           " attempt scheduled already.")
     }
   }
 
-  override def receiveWithLogging: PartialFunction[Any, Unit] = {
-    case RegisteredWorker(masterUrl, masterWebUiUrl) =>
-      logInfo("Successfully registered with master " + masterUrl)
+  override def receive: PartialFunction[Any, Unit] = {
+    case RegisteredWorker(masterRef, masterWebUiUrl) =>
+      logInfo("Successfully registered with master " + masterRef.address.toSparkURL)
       registered = true
-      changeMaster(masterUrl, masterWebUiUrl)
-      context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis, self, SendHeartbeat)
+      changeMaster(masterRef, masterWebUiUrl)
+      forwordMessageScheduler.scheduleAtFixedRate(new Runnable {
+        override def run(): Unit = Utils.tryLogNonFatalError {
+          self.send(SendHeartbeat)
+        }
+      }, 0, HEARTBEAT_MILLIS, TimeUnit.MILLISECONDS)
       if (CLEANUP_ENABLED) {
         logInfo(s"Worker cleanup enabled; old application directories will be deleted in: $workDir")
-        context.system.scheduler.schedule(CLEANUP_INTERVAL_MILLIS millis,
-          CLEANUP_INTERVAL_MILLIS millis, self, WorkDirCleanup)
+        forwordMessageScheduler.scheduleAtFixedRate(new Runnable {
+          override def run(): Unit = Utils.tryLogNonFatalError {
+            self.send(WorkDirCleanup)
+          }
+        }, CLEANUP_INTERVAL_MILLIS, CLEANUP_INTERVAL_MILLIS, TimeUnit.MILLISECONDS)
       }
 
     case SendHeartbeat =>
-      if (connected) { master ! Heartbeat(workerId) }
+      if (connected) { sendToMaster(Heartbeat(workerId, self)) }
 
     case WorkDirCleanup =>
       // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker actor
+      // Copy ids so that it can be used in the cleanup thread.
+      val appIds = executors.values.map(_.appId).toSet
       val cleanupFuture = concurrent.future {
         val appDirs = workDir.listFiles()
         if (appDirs == null) {
@@ -302,27 +372,27 @@ private[worker] class Worker(
           // the directory is used by an application - check that the application is not running
           // when cleaning up
           val appIdFromDir = dir.getName
-          val isAppStillRunning = executors.values.map(_.appId).contains(appIdFromDir)
+          val isAppStillRunning = appIds.contains(appIdFromDir)
           dir.isDirectory && !isAppStillRunning &&
-          !Utils.doesDirectoryContainAnyNewFiles(dir, APP_DATA_RETENTION_SECS)
+          !Utils.doesDirectoryContainAnyNewFiles(dir, APP_DATA_RETENTION_SECONDS)
         }.foreach { dir =>
           logInfo(s"Removing directory: ${dir.getPath}")
           Utils.deleteRecursively(dir)
         }
-      }
+      }(cleanupThreadExecutor)
 
-      cleanupFuture onFailure {
+      cleanupFuture.onFailure {
         case e: Throwable =>
           logError("App dir cleanup failed: " + e.getMessage, e)
-      }
+      }(cleanupThreadExecutor)
 
-    case MasterChanged(masterUrl, masterWebUiUrl) =>
-      logInfo("Master has changed, new master is at " + masterUrl)
-      changeMaster(masterUrl, masterWebUiUrl)
+    case MasterChanged(masterRef, masterWebUiUrl) =>
+      logInfo("Master has changed, new master is at " + masterRef.address.toSparkURL)
+      changeMaster(masterRef, masterWebUiUrl)
 
       val execs = executors.values.
         map(e => new ExecutorDescription(e.appId, e.execId, e.cores, e.state))
-      sender ! WorkerSchedulerStateResponse(workerId, execs.toList, drivers.keys.toSeq)
+      masterRef.send(WorkerSchedulerStateResponse(workerId, execs.toList, drivers.keys.toSeq))
 
     case RegisterWorkerFailed(message) =>
       if (!registered) {
@@ -369,14 +439,14 @@ private[worker] class Worker(
             publicAddress,
             sparkHome,
             executorDir,
-            akkaUrl,
+            workerUri,
             conf,
             appLocalDirs, ExecutorState.LOADING)
           executors(appId + "/" + execId) = manager
           manager.start()
           coresUsed += cores_
           memoryUsed += memory_
-          master ! ExecutorStateChanged(appId, execId, manager.state, None, None)
+          sendToMaster(ExecutorStateChanged(appId, execId, manager.state, None, None))
         } catch {
           case e: Exception => {
             logError(s"Failed to launch executor $appId/$execId for ${appDesc.name}.", e)
@@ -384,14 +454,14 @@ private[worker] class Worker(
               executors(appId + "/" + execId).kill()
               executors -= appId + "/" + execId
             }
-            master ! ExecutorStateChanged(appId, execId, ExecutorState.FAILED,
-              Some(e.toString), None)
+            sendToMaster(ExecutorStateChanged(appId, execId, ExecutorState.FAILED,
+              Some(e.toString), None))
           }
         }
       }
 
-    case ExecutorStateChanged(appId, execId, state, message, exitStatus) =>
-      master ! ExecutorStateChanged(appId, execId, state, message, exitStatus)
+    case executorStateChanged @ ExecutorStateChanged(appId, execId, state, message, exitStatus) =>
+      sendToMaster(executorStateChanged)
       val fullId = appId + "/" + execId
       if (ExecutorState.isFinished(state)) {
         executors.get(fullId) match {
@@ -434,7 +504,7 @@ private[worker] class Worker(
         sparkHome,
         driverDesc.copy(command = Worker.maybeUpdateSSLSettings(driverDesc.command, conf)),
         self,
-        akkaUrl,
+        workerUri,
         securityMgr)
       drivers(driverId) = driver
       driver.start()
@@ -453,7 +523,7 @@ private[worker] class Worker(
       }
     }
 
-    case DriverStateChanged(driverId, state, exception) => {
+    case driverStageChanged @ DriverStateChanged(driverId, state, exception) => {
       state match {
         case DriverState.ERROR =>
           logWarning(s"Driver $driverId failed with unrecoverable exception: ${exception.get}")
@@ -466,23 +536,13 @@ private[worker] class Worker(
         case _ =>
           logDebug(s"Driver $driverId changed state to $state")
       }
-      master ! DriverStateChanged(driverId, state, exception)
+      sendToMaster(driverStageChanged)
       val driver = drivers.remove(driverId).get
       finishedDrivers(driverId) = driver
       memoryUsed -= driver.driverDesc.mem
       coresUsed -= driver.driverDesc.cores
     }
 
-    case x: DisassociatedEvent if x.remoteAddress == masterAddress =>
-      logInfo(s"$x Disassociated !")
-      masterDisconnected()
-
-    case RequestWorkerState =>
-      sender ! WorkerStateResponse(host, port, workerId, executors.values.toList,
-        finishedExecutors.values.toList, drivers.values.toList,
-        finishedDrivers.values.toList, activeMasterUrl, cores, memory,
-        coresUsed, memoryUsed, activeMasterWebUiUrl)
-
     case ReregisterWithMaster =>
       reregisterWithMaster()
 
@@ -491,6 +551,21 @@ private[worker] class Worker(
       maybeCleanupApplication(id)
   }
 
+  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+    case RequestWorkerState =>
+      context.reply(WorkerStateResponse(host, port, workerId, executors.values.toList,
+        finishedExecutors.values.toList, drivers.values.toList,
+        finishedDrivers.values.toList, activeMasterUrl, cores, memory,
+        coresUsed, memoryUsed, activeMasterWebUiUrl))
+  }
+
+  override def onDisconnected(remoteAddress: RpcAddress): Unit = {
+    if (master.exists(_.address == remoteAddress)) {
+      logInfo(s"$remoteAddress Disassociated !")
+      masterDisconnected()
+    }
+  }
+
   private def masterDisconnected() {
     logError("Connection to master failed! Waiting for master to reconnect...")
     connected = false
@@ -510,13 +585,29 @@ private[worker] class Worker(
     }
   }
 
+  /**
+   * Send a message to the current master. If we have not yet registered successfully with any
+   * master, the message will be dropped.
+   */
+  private def sendToMaster(message: Any): Unit = {
+    master match {
+      case Some(masterRef) => masterRef.send(message)
+      case None =>
+        logWarning(
+          s"Dropping $message because the connection to master has not yet been established")
+    }
+  }
+
   private def generateWorkerId(): String = {
     "worker-%s-%s-%d".format(createDateFormat.format(new Date), host, port)
   }
 
-  override def postStop() {
+  override def onStop() {
+    cleanupThreadExecutor.shutdownNow()
     metricsSystem.report()
-    registrationRetryTimer.foreach(_.cancel())
+    cancelLastRegistrationRetry()
+    forwordMessageScheduler.shutdownNow()
+    registerMasterThreadPool.shutdownNow()
     executors.values.foreach(_.kill())
     drivers.values.foreach(_.kill())
     shuffleService.stop()
@@ -530,12 +621,12 @@ private[deploy] object Worker extends Logging {
     SignalLogger.register(log)
     val conf = new SparkConf
     val args = new WorkerArguments(argStrings, conf)
-    val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores,
+    val rpcEnv = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, args.cores,
       args.memory, args.masters, args.workDir)
-    actorSystem.awaitTermination()
+    rpcEnv.awaitTermination()
   }
 
-  def startSystemAndActor(
+  def startRpcEnvAndEndpoint(
       host: String,
       port: Int,
       webUiPort: Int,
@@ -544,18 +635,17 @@ private[deploy] object Worker extends Logging {
       masterUrls: Array[String],
       workDir: String,
       workerNumber: Option[Int] = None,
-      conf: SparkConf = new SparkConf): (ActorSystem, Int) = {
+      conf: SparkConf = new SparkConf): RpcEnv = {
 
     // The LocalSparkCluster runs multiple local sparkWorkerX actor systems
     val systemName = "sparkWorker" + workerNumber.map(_.toString).getOrElse("")
     val actorName = "Worker"
     val securityMgr = new SecurityManager(conf)
-    val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port,
-      conf = conf, securityManager = securityMgr)
-    val masterAkkaUrls = masterUrls.map(Master.toAkkaUrl(_, AkkaUtils.protocol(actorSystem)))
-    actorSystem.actorOf(Props(classOf[Worker], host, boundPort, webUiPort, cores, memory,
-      masterAkkaUrls, systemName, actorName, workDir, conf, securityMgr), name = actorName)
-    (actorSystem, boundPort)
+    val rpcEnv = RpcEnv.create(systemName, host, port, conf, securityMgr)
+    val masterAddresses = masterUrls.map(RpcAddress.fromSparkURL(_))
+    rpcEnv.setupEndpoint(actorName, new Worker(rpcEnv, webUiPort, cores, memory, masterAddresses,
+      systemName, actorName, workDir, conf, securityMgr))
+    rpcEnv
   }
 
   def isUseLocalNodeSSLConfig(cmd: Command): Boolean = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
index 83fb991891a41..fae5640b9a213 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.deploy.worker
 
 import org.apache.spark.Logging
-import org.apache.spark.deploy.DeployMessages.SendHeartbeat
 import org.apache.spark.rpc._
 
 /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala
index 9f9f27d71e1ae..fd905feb97e92 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerPage.scala
@@ -17,10 +17,8 @@
 
 package org.apache.spark.deploy.worker.ui
 
-import scala.concurrent.Await
 import scala.xml.Node
 
-import akka.pattern.ask
 import javax.servlet.http.HttpServletRequest
 import org.json4s.JValue
 
@@ -32,18 +30,15 @@ import org.apache.spark.ui.{WebUIPage, UIUtils}
 import org.apache.spark.util.Utils
 
 private[ui] class WorkerPage(parent: WorkerWebUI) extends WebUIPage("") {
-  private val workerActor = parent.worker.self
-  private val timeout = parent.timeout
+  private val workerEndpoint = parent.worker.self
 
   override def renderJson(request: HttpServletRequest): JValue = {
-    val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse]
-    val workerState = Await.result(stateFuture, timeout)
+    val workerState = workerEndpoint.askWithRetry[WorkerStateResponse](RequestWorkerState)
     JsonProtocol.writeWorkerState(workerState)
   }
 
   def render(request: HttpServletRequest): Seq[Node] = {
-    val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse]
-    val workerState = Await.result(stateFuture, timeout)
+    val workerState = workerEndpoint.askWithRetry[WorkerStateResponse](RequestWorkerState)
 
     val executorHeaders = Seq("ExecutorID", "Cores", "State", "Memory", "Job Details", "Logs")
     val runningExecutors = workerState.executors
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
index 12b6b28d4d7ec..3b6938ec639c3 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
@@ -158,6 +158,8 @@ private[spark] case class RpcAddress(host: String, port: Int) {
   val hostPort: String = host + ":" + port
 
   override val toString: String = hostPort
+
+  def toSparkURL: String = "spark://" + hostPort
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
index 0161962cde073..31ebe5ac5bca3 100644
--- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
@@ -180,10 +180,10 @@ private[spark] class AkkaRpcEnv private[akka] (
       })
     } catch {
       case NonFatal(e) =>
-        if (needReply) {
-          // If the sender asks a reply, we should send the error back to the sender
-          _sender ! AkkaFailure(e)
-        } else {
+        _sender ! AkkaFailure(e)
+        if (!needReply) {
+          // If the sender does not require a reply, it may not handle the exception. So we rethrow
+          // "e" to make sure it will be processed.
           throw e
         }
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index ccf1dc5af6120..687ae9620460f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -85,7 +85,7 @@ private[spark] class SparkDeploySchedulerBackend(
     val coresPerExecutor = conf.getOption("spark.executor.cores").map(_.toInt)
     val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory,
       command, appUIAddress, sc.eventLogDir, sc.eventLogCodec, coresPerExecutor)
-    client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
+    client = new AppClient(sc.env.rpcEnv, masters, appDesc, this, conf)
     client.start()
     waitForRegistration()
   }
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 014e87bb40254..9cb6dd43bac47 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -19,63 +19,21 @@ package org.apache.spark.deploy.master
 
 import java.util.Date
 
-import scala.concurrent.Await
 import scala.concurrent.duration._
 import scala.io.Source
 import scala.language.postfixOps
 
-import akka.actor.Address
 import org.json4s._
 import org.json4s.jackson.JsonMethods._
 import org.scalatest.Matchers
 import org.scalatest.concurrent.Eventually
 import other.supplier.{CustomPersistenceEngine, CustomRecoveryModeFactory}
 
-import org.apache.spark.{SparkConf, SparkException, SparkFunSuite}
+import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.deploy._
 
 class MasterSuite extends SparkFunSuite with Matchers with Eventually {
 
-  test("toAkkaUrl") {
-    val conf = new SparkConf(loadDefaults = false)
-    val akkaUrl = Master.toAkkaUrl("spark://1.2.3.4:1234", "akka.tcp")
-    assert("akka.tcp://sparkMaster@1.2.3.4:1234/user/Master" === akkaUrl)
-  }
-
-  test("toAkkaUrl with SSL") {
-    val conf = new SparkConf(loadDefaults = false)
-    val akkaUrl = Master.toAkkaUrl("spark://1.2.3.4:1234", "akka.ssl.tcp")
-    assert("akka.ssl.tcp://sparkMaster@1.2.3.4:1234/user/Master" === akkaUrl)
-  }
-
-  test("toAkkaUrl: a typo url") {
-    val conf = new SparkConf(loadDefaults = false)
-    val e = intercept[SparkException] {
-      Master.toAkkaUrl("spark://1.2. 3.4:1234", "akka.tcp")
-    }
-    assert("Invalid master URL: spark://1.2. 3.4:1234" === e.getMessage)
-  }
-
-  test("toAkkaAddress") {
-    val conf = new SparkConf(loadDefaults = false)
-    val address = Master.toAkkaAddress("spark://1.2.3.4:1234", "akka.tcp")
-    assert(Address("akka.tcp", "sparkMaster", "1.2.3.4", 1234) === address)
-  }
-
-  test("toAkkaAddress with SSL") {
-    val conf = new SparkConf(loadDefaults = false)
-    val address = Master.toAkkaAddress("spark://1.2.3.4:1234", "akka.ssl.tcp")
-    assert(Address("akka.ssl.tcp", "sparkMaster", "1.2.3.4", 1234) === address)
-  }
-
-  test("toAkkaAddress: a typo url") {
-    val conf = new SparkConf(loadDefaults = false)
-    val e = intercept[SparkException] {
-      Master.toAkkaAddress("spark://1.2. 3.4:1234", "akka.tcp")
-    }
-    assert("Invalid master URL: spark://1.2. 3.4:1234" === e.getMessage)
-  }
-
   test("can use a custom recovery mode factory") {
     val conf = new SparkConf(loadDefaults = false)
     conf.set("spark.deploy.recoveryMode", "CUSTOM")
@@ -129,16 +87,16 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually {
       port = 10000,
       cores = 0,
       memory = 0,
-      actor = null,
+      endpoint = null,
       webUiPort = 0,
       publicAddress = ""
     )
 
-    val (actorSystem, port, uiPort, restPort) =
-      Master.startSystemAndActor("127.0.0.1", 7077, 8080, conf)
+    val (rpcEnv, uiPort, restPort) =
+      Master.startRpcEnvAndEndpoint("127.0.0.1", 7077, 8080, conf)
 
     try {
-      Await.result(actorSystem.actorSelection("/user/Master").resolveOne(10 seconds), 10 seconds)
+      rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, rpcEnv.address, Master.ENDPOINT_NAME)
 
       CustomPersistenceEngine.lastInstance.isDefined shouldBe true
       val persistenceEngine = CustomPersistenceEngine.lastInstance.get
@@ -154,8 +112,8 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually {
       workers.map(_.id) should contain(workerToPersist.id)
 
     } finally {
-      actorSystem.shutdown()
-      actorSystem.awaitTermination()
+      rpcEnv.shutdown()
+      rpcEnv.awaitTermination()
     }
 
     CustomRecoveryModeFactory.instantiationAttempts should be > instantiationAttempts
diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
index 197f68e7ec5ed..96e456d889ac3 100644
--- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
@@ -23,14 +23,14 @@ import javax.servlet.http.HttpServletResponse
 
 import scala.collection.mutable
 
-import akka.actor.{Actor, ActorRef, ActorSystem, Props}
 import com.google.common.base.Charsets
 import org.scalatest.BeforeAndAfterEach
 import org.json4s.JsonAST._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark._
-import org.apache.spark.util.{AkkaUtils, Utils}
+import org.apache.spark.rpc._
+import org.apache.spark.util.Utils
 import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.{SparkSubmit, SparkSubmitArguments}
 import org.apache.spark.deploy.master.DriverState._
@@ -39,11 +39,11 @@ import org.apache.spark.deploy.master.DriverState._
  * Tests for the REST application submission protocol used in standalone cluster mode.
  */
 class StandaloneRestSubmitSuite extends SparkFunSuite with BeforeAndAfterEach {
-  private var actorSystem: Option[ActorSystem] = None
+  private var rpcEnv: Option[RpcEnv] = None
   private var server: Option[RestSubmissionServer] = None
 
   override def afterEach() {
-    actorSystem.foreach(_.shutdown())
+    rpcEnv.foreach(_.shutdown())
     server.foreach(_.stop())
   }
 
@@ -377,31 +377,32 @@ class StandaloneRestSubmitSuite extends SparkFunSuite with BeforeAndAfterEach {
       killMessage: String = "driver is killed",
       state: DriverState = FINISHED,
       exception: Option[Exception] = None): String = {
-    startServer(new DummyMaster(submitId, submitMessage, killMessage, state, exception))
+    startServer(new DummyMaster(_, submitId, submitMessage, killMessage, state, exception))
   }
 
   /** Start a smarter dummy server that keeps track of submitted driver states. */
   private def startSmartServer(): String = {
-    startServer(new SmarterMaster)
+    startServer(new SmarterMaster(_))
   }
 
   /** Start a dummy server that is faulty in many ways... */
   private def startFaultyServer(): String = {
-    startServer(new DummyMaster, faulty = true)
+    startServer(new DummyMaster(_), faulty = true)
   }
 
   /**
-   * Start a [[StandaloneRestServer]] that communicates with the given actor.
+   * Start a [[StandaloneRestServer]] that communicates with the given endpoint.
    * If `faulty` is true, start an [[FaultyStandaloneRestServer]] instead.
    * Return the master URL that corresponds to the address of this server.
    */
-  private def startServer(makeFakeMaster: => Actor, faulty: Boolean = false): String = {
+  private def startServer(
+      makeFakeMaster: RpcEnv => RpcEndpoint, faulty: Boolean = false): String = {
     val name = "test-standalone-rest-protocol"
     val conf = new SparkConf
     val localhost = Utils.localHostName()
     val securityManager = new SecurityManager(conf)
-    val (_actorSystem, _) = AkkaUtils.createActorSystem(name, localhost, 0, conf, securityManager)
-    val fakeMasterRef = _actorSystem.actorOf(Props(makeFakeMaster))
+    val _rpcEnv = RpcEnv.create(name, localhost, 0, conf, securityManager)
+    val fakeMasterRef = _rpcEnv.setupEndpoint("fake-master", makeFakeMaster(_rpcEnv))
     val _server =
       if (faulty) {
         new FaultyStandaloneRestServer(localhost, 0, conf, fakeMasterRef, "spark://fake:7077")
@@ -410,7 +411,7 @@ class StandaloneRestSubmitSuite extends SparkFunSuite with BeforeAndAfterEach {
       }
     val port = _server.start()
     // set these to clean them up after every test
-    actorSystem = Some(_actorSystem)
+    rpcEnv = Some(_rpcEnv)
     server = Some(_server)
     s"spark://$localhost:$port"
   }
@@ -505,20 +506,21 @@ class StandaloneRestSubmitSuite extends SparkFunSuite with BeforeAndAfterEach {
  * In all responses, the success parameter is always true.
  */
 private class DummyMaster(
+    override val rpcEnv: RpcEnv,
     submitId: String = "fake-driver-id",
     submitMessage: String = "submitted",
     killMessage: String = "killed",
     state: DriverState = FINISHED,
     exception: Option[Exception] = None)
-  extends Actor {
+  extends RpcEndpoint {
 
-  override def receive: PartialFunction[Any, Unit] = {
+  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
     case RequestSubmitDriver(driverDesc) =>
-      sender ! SubmitDriverResponse(success = true, Some(submitId), submitMessage)
+      context.reply(SubmitDriverResponse(self, success = true, Some(submitId), submitMessage))
     case RequestKillDriver(driverId) =>
-      sender ! KillDriverResponse(driverId, success = true, killMessage)
+      context.reply(KillDriverResponse(self, driverId, success = true, killMessage))
     case RequestDriverStatus(driverId) =>
-      sender ! DriverStatusResponse(found = true, Some(state), None, None, exception)
+      context.reply(DriverStatusResponse(found = true, Some(state), None, None, exception))
   }
 }
 
@@ -531,28 +533,28 @@ private class DummyMaster(
  * Submits are always successful while kills and status requests are successful only
  * if the driver was submitted in the past.
  */
-private class SmarterMaster extends Actor {
+private class SmarterMaster(override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint {
   private var counter: Int = 0
   private val submittedDrivers = new mutable.HashMap[String, DriverState]
 
-  override def receive: PartialFunction[Any, Unit] = {
+  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
     case RequestSubmitDriver(driverDesc) =>
       val driverId = s"driver-$counter"
       submittedDrivers(driverId) = RUNNING
       counter += 1
-      sender ! SubmitDriverResponse(success = true, Some(driverId), "submitted")
+      context.reply(SubmitDriverResponse(self, success = true, Some(driverId), "submitted"))
 
     case RequestKillDriver(driverId) =>
       val success = submittedDrivers.contains(driverId)
       if (success) {
         submittedDrivers(driverId) = KILLED
       }
-      sender ! KillDriverResponse(driverId, success, "killed")
+      context.reply(KillDriverResponse(self, driverId, success, "killed"))
 
     case RequestDriverStatus(driverId) =>
       val found = submittedDrivers.contains(driverId)
       val state = submittedDrivers.get(driverId)
-      sender ! DriverStatusResponse(found, state, None, None, None)
+      context.reply(DriverStatusResponse(found, state, None, None, None))
   }
 }
 
@@ -568,7 +570,7 @@ private class FaultyStandaloneRestServer(
     host: String,
     requestedPort: Int,
     masterConf: SparkConf,
-    masterActor: ActorRef,
+    masterEndpoint: RpcEndpointRef,
     masterUrl: String)
   extends RestSubmissionServer(host, requestedPort, masterConf) {
 
@@ -578,7 +580,7 @@ private class FaultyStandaloneRestServer(
 
   /** A faulty servlet that produces malformed responses. */
   class MalformedSubmitServlet
-    extends StandaloneSubmitRequestServlet(masterActor, masterUrl, masterConf) {
+    extends StandaloneSubmitRequestServlet(masterEndpoint, masterUrl, masterConf) {
     protected override def sendResponse(
         responseMessage: SubmitRestProtocolResponse,
         responseServlet: HttpServletResponse): Unit = {
@@ -588,7 +590,7 @@ private class FaultyStandaloneRestServer(
   }
 
   /** A faulty servlet that produces invalid responses. */
-  class InvalidKillServlet extends StandaloneKillRequestServlet(masterActor, masterConf) {
+  class InvalidKillServlet extends StandaloneKillRequestServlet(masterEndpoint, masterConf) {
     protected override def handleKill(submissionId: String): KillSubmissionResponse = {
       val k = super.handleKill(submissionId)
       k.submissionId = null
@@ -597,7 +599,7 @@ private class FaultyStandaloneRestServer(
   }
 
   /** A faulty status servlet that explodes. */
-  class ExplodingStatusServlet extends StandaloneStatusRequestServlet(masterActor, masterConf) {
+  class ExplodingStatusServlet extends StandaloneStatusRequestServlet(masterEndpoint, masterConf) {
     private def explode: Int = 1 / 0
     protected override def handleStatus(submissionId: String): SubmissionStatusResponse = {
       val s = super.handleStatus(submissionId)
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
index ac18f04a11475..cd24d79423316 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.deploy.worker
 
-import akka.actor.AddressFromURIString
 import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.SecurityManager
 import org.apache.spark.rpc.{RpcAddress, RpcEnv}
@@ -26,13 +25,11 @@ class WorkerWatcherSuite extends SparkFunSuite {
   test("WorkerWatcher shuts down on valid disassociation") {
     val conf = new SparkConf()
     val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
-    val targetWorkerUrl = "akka://test@1.2.3.4:1234/user/Worker"
-    val targetWorkerAddress = AddressFromURIString(targetWorkerUrl)
+    val targetWorkerUrl = rpcEnv.uriOf("test", RpcAddress("1.2.3.4", 1234), "Worker")
     val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl)
     workerWatcher.setTesting(testing = true)
     rpcEnv.setupEndpoint("worker-watcher", workerWatcher)
-    workerWatcher.onDisconnected(
-      RpcAddress(targetWorkerAddress.host.get, targetWorkerAddress.port.get))
+    workerWatcher.onDisconnected(RpcAddress("1.2.3.4", 1234))
     assert(workerWatcher.isShutDown)
     rpcEnv.shutdown()
   }
@@ -40,13 +37,13 @@ class WorkerWatcherSuite extends SparkFunSuite {
   test("WorkerWatcher stays alive on invalid disassociation") {
     val conf = new SparkConf()
     val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
-    val targetWorkerUrl = "akka://test@1.2.3.4:1234/user/Worker"
-    val otherAkkaURL = "akka://test@4.3.2.1:1234/user/OtherActor"
-    val otherAkkaAddress = AddressFromURIString(otherAkkaURL)
+    val targetWorkerUrl = rpcEnv.uriOf("test", RpcAddress("1.2.3.4", 1234), "Worker")
+    val otherAddress = "akka://test@4.3.2.1:1234/user/OtherActor"
+    val otherAkkaAddress = RpcAddress("4.3.2.1", 1234)
     val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl)
     workerWatcher.setTesting(testing = true)
     rpcEnv.setupEndpoint("worker-watcher", workerWatcher)
-    workerWatcher.onDisconnected(RpcAddress(otherAkkaAddress.host.get, otherAkkaAddress.port.get))
+    workerWatcher.onDisconnected(otherAkkaAddress)
     assert(!workerWatcher.isShutDown)
     rpcEnv.shutdown()
   }
diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcAddressSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcAddressSuite.scala
new file mode 100644
index 0000000000000..b3223ec61bf79
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/rpc/RpcAddressSuite.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rpc
+
+import org.apache.spark.{SparkException, SparkFunSuite}
+
+class RpcAddressSuite extends SparkFunSuite {
+
+  test("hostPort") {
+    val address = RpcAddress("1.2.3.4", 1234)
+    assert(address.host == "1.2.3.4")
+    assert(address.port == 1234)
+    assert(address.hostPort == "1.2.3.4:1234")
+  }
+
+  test("fromSparkURL") {
+    val address = RpcAddress.fromSparkURL("spark://1.2.3.4:1234")
+    assert(address.host == "1.2.3.4")
+    assert(address.port == 1234)
+  }
+
+  test("fromSparkURL: a typo url") {
+    val e = intercept[SparkException] {
+      RpcAddress.fromSparkURL("spark://1.2. 3.4:1234")
+    }
+    assert("Invalid master URL: spark://1.2. 3.4:1234" === e.getMessage)
+  }
+
+  test("fromSparkURL: invalid scheme") {
+    val e = intercept[SparkException] {
+      RpcAddress.fromSparkURL("invalid://1.2.3.4:1234")
+    }
+    assert("Invalid master URL: invalid://1.2.3.4:1234" === e.getMessage)
+  }
+
+  test("toSparkURL") {
+    val address = RpcAddress("1.2.3.4", 1234)
+    assert(address.toSparkURL == "spark://1.2.3.4:1234")
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala
index a33a83db7bc9e..4aa75c9230b2c 100644
--- a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.rpc.akka
 
 import org.apache.spark.rpc._
-import org.apache.spark.{SecurityManager, SparkConf}
+import org.apache.spark.{SSLSampleConfigs, SecurityManager, SparkConf}
 
 class AkkaRpcEnvSuite extends RpcEnvSuite {
 
@@ -47,4 +47,22 @@ class AkkaRpcEnvSuite extends RpcEnvSuite {
     }
   }
 
+  test("uriOf") {
+    val uri = env.uriOf("local", RpcAddress("1.2.3.4", 12345), "test_endpoint")
+    assert("akka.tcp://local@1.2.3.4:12345/user/test_endpoint" === uri)
+  }
+
+  test("uriOf: ssl") {
+    val conf = SSLSampleConfigs.sparkSSLConfig()
+    val securityManager = new SecurityManager(conf)
+    val rpcEnv = new AkkaRpcEnvFactory().create(
+      RpcEnvConfig(conf, "test", "localhost", 12346, securityManager))
+    try {
+      val uri = rpcEnv.uriOf("local", RpcAddress("1.2.3.4", 12345), "test_endpoint")
+      assert("akka.ssl.tcp://local@1.2.3.4:12345/user/test_endpoint" === uri)
+    } finally {
+      rpcEnv.shutdown()
+    }
+  }
+
 }

From f457569886e9de9256ad269cb4a3d73a8918766d Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 30 Jun 2015 20:19:43 -0700
Subject: [PATCH 0158/1454] [SPARK-8471] [ML] Rename DiscreteCosineTransformer
 to DCT

Rename DiscreteCosineTransformer and related classes to DCT.

Author: Feynman Liang <fliang@databricks.com>

Closes #7138 from feynmanliang/dct-features and squashes the following commits:

e547b3e [Feynman Liang] Fix renaming bug
9d5c9e4 [Feynman Liang] Lowercase JavaDCTSuite variable
f9a8958 [Feynman Liang] Remove old files
f8fe794 [Feynman Liang] Merge branch 'master' into dct-features
894d0b2 [Feynman Liang] Rename DiscreteCosineTransformer to DCT
433dbc7 [Feynman Liang] Test refactoring
91e9636 [Feynman Liang] Style guide and test helper refactor
b5ac19c [Feynman Liang] Use Vector types, add Java test
530983a [Feynman Liang] Tests for other numeric datatypes
195d7aa [Feynman Liang] Implement support for arbitrary numeric types
95d4939 [Feynman Liang] Working DCT for 1D Doubles
---
 .../{DiscreteCosineTransformer.scala => DCT.scala}        | 4 ++--
 ...creteCosineTransformerSuite.java => JavaDCTSuite.java} | 8 ++++----
 ...iscreteCosineTransformerSuite.scala => DCTSuite.scala} | 4 ++--
 3 files changed, 8 insertions(+), 8 deletions(-)
 rename mllib/src/main/scala/org/apache/spark/ml/feature/{DiscreteCosineTransformer.scala => DCT.scala} (95%)
 rename mllib/src/test/java/org/apache/spark/ml/feature/{JavaDiscreteCosineTransformerSuite.java => JavaDCTSuite.java} (90%)
 rename mllib/src/test/scala/org/apache/spark/ml/feature/{DiscreteCosineTransformerSuite.scala => DCTSuite.scala} (94%)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/DiscreteCosineTransformer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
similarity index 95%
rename from mllib/src/main/scala/org/apache/spark/ml/feature/DiscreteCosineTransformer.scala
rename to mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
index a2f4d59f81c44..228347635c92b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/DiscreteCosineTransformer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
@@ -36,8 +36,8 @@ import org.apache.spark.sql.types.DataType
  * More information on [[https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia]].
  */
 @Experimental
-class DiscreteCosineTransformer(override val uid: String)
-  extends UnaryTransformer[Vector, Vector, DiscreteCosineTransformer] {
+class DCT(override val uid: String)
+  extends UnaryTransformer[Vector, Vector, DCT] {
 
   def this() = this(Identifiable.randomUID("dct"))
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDiscreteCosineTransformerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
similarity index 90%
rename from mllib/src/test/java/org/apache/spark/ml/feature/JavaDiscreteCosineTransformerSuite.java
rename to mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
index 28bc5f65e0532..845eed61c45c6 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDiscreteCosineTransformerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
@@ -37,13 +37,13 @@
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-public class JavaDiscreteCosineTransformerSuite {
+public class JavaDCTSuite {
   private transient JavaSparkContext jsc;
   private transient SQLContext jsql;
 
   @Before
   public void setUp() {
-    jsc = new JavaSparkContext("local", "JavaDiscreteCosineTransformerSuite");
+    jsc = new JavaSparkContext("local", "JavaDCTSuite");
     jsql = new SQLContext(jsc);
   }
 
@@ -66,11 +66,11 @@ public void javaCompatibilityTest() {
     double[] expectedResult = input.clone();
     (new DoubleDCT_1D(input.length)).forward(expectedResult, true);
 
-    DiscreteCosineTransformer DCT = new DiscreteCosineTransformer()
+    DCT dct = new DCT()
       .setInputCol("vec")
       .setOutputCol("resultVec");
 
-    Row[] result = DCT.transform(dataset).select("resultVec").collect();
+    Row[] result = dct.transform(dataset).select("resultVec").collect();
     Vector resultVec = result[0].getAs("resultVec");
 
     Assert.assertArrayEquals(expectedResult, resultVec.toArray(), 1e-6);
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/DiscreteCosineTransformerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala
similarity index 94%
rename from mllib/src/test/scala/org/apache/spark/ml/feature/DiscreteCosineTransformerSuite.scala
rename to mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala
index ed0fc11f78f69..37ed2367c33f7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/DiscreteCosineTransformerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala
@@ -29,7 +29,7 @@ import org.apache.spark.sql.{DataFrame, Row}
 @BeanInfo
 case class DCTTestData(vec: Vector, wantedVec: Vector)
 
-class DiscreteCosineTransformerSuite extends SparkFunSuite with MLlibTestSparkContext {
+class DCTSuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("forward transform of discrete cosine matches jTransforms result") {
     val data = Vectors.dense((0 until 128).map(_ => 2D * math.random - 1D).toArray)
@@ -58,7 +58,7 @@ class DiscreteCosineTransformerSuite extends SparkFunSuite with MLlibTestSparkCo
       DCTTestData(data, expectedResult)
     ))
 
-    val transformer = new DiscreteCosineTransformer()
+    val transformer = new DCT()
       .setInputCol("vec")
       .setOutputCol("resultVec")
       .setInverse(inverse)

From b6e76edf3005c078b407f63b0a05d3a28c18c742 Mon Sep 17 00:00:00 2001
From: x1- <viva008@gmail.com>
Date: Tue, 30 Jun 2015 20:35:46 -0700
Subject: [PATCH 0159/1454] [SPARK-8535] [PYSPARK] PySpark : Can't create
 DataFrame from Pandas dataframe with no explicit column name

Because implicit name of `pandas.columns` are Int, but `StructField` json expect `String`.
So I think `pandas.columns` are should be convert to `String`.

### issue

* [SPARK-8535 PySpark : Can't create DataFrame from Pandas dataframe with no explicit column name](https://issues.apache.org/jira/browse/SPARK-8535)

Author: x1- <viva008@gmail.com>

Closes #7124 from x1-/SPARK-8535 and squashes the following commits:

d68fd38 [x1-] modify unit-test using pandas.
ea1897d [x1-] For implicit name of pandas.columns are Int, so should be convert to String.
---
 python/pyspark/sql/context.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 4bf232111c496..309c11faf9319 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -344,13 +344,15 @@ def createDataFrame(self, data, schema=None, samplingRatio=None):
 
         >>> sqlContext.createDataFrame(df.toPandas()).collect()  # doctest: +SKIP
         [Row(name=u'Alice', age=1)]
+        >>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]]).collect())  # doctest: +SKIP
+        [Row(0=1, 1=2)]
         """
         if isinstance(data, DataFrame):
             raise TypeError("data is already a DataFrame")
 
         if has_pandas and isinstance(data, pandas.DataFrame):
             if schema is None:
-                schema = list(data.columns)
+                schema = [str(x) for x in data.columns]
             data = [r.tolist() for r in data.to_records(index=False)]
 
         if not isinstance(data, RDD):

From 64c14618d3f4ede042bd3f6a542bc17a730afb0e Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 30 Jun 2015 21:57:07 -0700
Subject: [PATCH 0160/1454] [SPARK-6602][Core]Remove unnecessary synchronized

A follow-up pr to address https://github.com/apache/spark/pull/5392#discussion_r33627528

Author: zsxwing <zsxwing@gmail.com>

Closes #7141 from zsxwing/pr5392-follow-up and squashes the following commits:

fcf7b50 [zsxwing] Remove unnecessary synchronized
---
 .../main/scala/org/apache/spark/deploy/master/Master.scala | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 3e7c16722805e..48070768f6edb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -518,12 +518,9 @@ private[master] class Master(
   }
 
   private def completeRecovery() {
-    // TODO Why synchronized
     // Ensure "only-once" recovery semantics using a short synchronization period.
-    synchronized {
-      if (state != RecoveryState.RECOVERING) { return }
-      state = RecoveryState.COMPLETING_RECOVERY
-    }
+    if (state != RecoveryState.RECOVERING) { return }
+    state = RecoveryState.COMPLETING_RECOVERY
 
     // Kill off any workers and apps that didn't respond to us.
     workers.filter(_.state == WorkerState.UNKNOWN).foreach(removeWorker)

From 365c14055e90db5ea4b25afec03022be81c8a704 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 30 Jun 2015 23:04:54 -0700
Subject: [PATCH 0161/1454] [SPARK-8748][SQL] Move castability test out from
 Cast case class into Cast object.

This patch moved resolve function in Cast case class into the companion object, and renamed it canCast. We can then use this in the analyzer without a Cast expr.

Author: Reynold Xin <rxin@databricks.com>

Closes #7145 from rxin/cast and squashes the following commits:

cd086a9 [Reynold Xin] Whitespace changes.
4d2d989 [Reynold Xin] [SPARK-8748][SQL] Move castability test out from Cast case class into Cast object.
---
 .../spark/sql/catalyst/expressions/Cast.scala | 144 ++++++++++--------
 1 file changed, 78 insertions(+), 66 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index d69d490ad666a..2d99d1a3fe8dc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -27,23 +27,65 @@ import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
-/** Cast the child expression to the target data type. */
-case class Cast(child: Expression, dataType: DataType) extends UnaryExpression with Logging {
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    if (resolve(child.dataType, dataType)) {
-      TypeCheckResult.TypeCheckSuccess
-    } else {
-      TypeCheckResult.TypeCheckFailure(
-        s"cannot cast ${child.dataType} to $dataType")
-    }
-  }
+object Cast {
 
-  override def foldable: Boolean = child.foldable
+  /**
+   * Returns true iff we can cast `from` type to `to` type.
+   */
+  def canCast(from: DataType, to: DataType): Boolean = (from, to) match {
+    case (fromType, toType) if fromType == toType => true
+
+    case (NullType, _) => true
+
+    case (_, StringType) => true
 
-  override def nullable: Boolean = forceNullable(child.dataType, dataType) || child.nullable
+    case (StringType, BinaryType) => true
 
-  private[this] def forceNullable(from: DataType, to: DataType) = (from, to) match {
+    case (StringType, BooleanType) => true
+    case (DateType, BooleanType) => true
+    case (TimestampType, BooleanType) => true
+    case (_: NumericType, BooleanType) => true
+
+    case (StringType, TimestampType) => true
+    case (BooleanType, TimestampType) => true
+    case (DateType, TimestampType) => true
+    case (_: NumericType, TimestampType) => true
+
+    case (_, DateType) => true
+
+    case (StringType, _: NumericType) => true
+    case (BooleanType, _: NumericType) => true
+    case (DateType, _: NumericType) => true
+    case (TimestampType, _: NumericType) => true
+    case (_: NumericType, _: NumericType) => true
+
+    case (ArrayType(fromType, fn), ArrayType(toType, tn)) =>
+      canCast(fromType, toType) &&
+        resolvableNullability(fn || forceNullable(fromType, toType), tn)
+
+    case (MapType(fromKey, fromValue, fn), MapType(toKey, toValue, tn)) =>
+      canCast(fromKey, toKey) &&
+        (!forceNullable(fromKey, toKey)) &&
+        canCast(fromValue, toValue) &&
+        resolvableNullability(fn || forceNullable(fromValue, toValue), tn)
+
+    case (StructType(fromFields), StructType(toFields)) =>
+      fromFields.length == toFields.length &&
+        fromFields.zip(toFields).forall {
+          case (fromField, toField) =>
+            canCast(fromField.dataType, toField.dataType) &&
+              resolvableNullability(
+                fromField.nullable || forceNullable(fromField.dataType, toField.dataType),
+                toField.nullable)
+        }
+
+    case _ => false
+  }
+
+  private def resolvableNullability(from: Boolean, to: Boolean) = !from || to
+
+  private def forceNullable(from: DataType, to: DataType) = (from, to) match {
     case (StringType, _: NumericType) => true
     case (StringType, TimestampType) => true
     case (DoubleType, TimestampType) => true
@@ -58,61 +100,24 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     case (_, DecimalType.Fixed(_, _)) => true // TODO: not all upcasts here can really give null
     case _ => false
   }
+}
 
-  private[this] def resolvableNullability(from: Boolean, to: Boolean) = !from || to
-
-  private[this] def resolve(from: DataType, to: DataType): Boolean = {
-    (from, to) match {
-      case (from, to) if from == to => true
-
-      case (NullType, _) => true
-
-      case (_, StringType) => true
-
-      case (StringType, BinaryType) => true
-
-      case (StringType, BooleanType) => true
-      case (DateType, BooleanType) => true
-      case (TimestampType, BooleanType) => true
-      case (_: NumericType, BooleanType) => true
-
-      case (StringType, TimestampType) => true
-      case (BooleanType, TimestampType) => true
-      case (DateType, TimestampType) => true
-      case (_: NumericType, TimestampType) => true
-
-      case (_, DateType) => true
-
-      case (StringType, _: NumericType) => true
-      case (BooleanType, _: NumericType) => true
-      case (DateType, _: NumericType) => true
-      case (TimestampType, _: NumericType) => true
-      case (_: NumericType, _: NumericType) => true
-
-      case (ArrayType(from, fn), ArrayType(to, tn)) =>
-        resolve(from, to) &&
-          resolvableNullability(fn || forceNullable(from, to), tn)
-
-      case (MapType(fromKey, fromValue, fn), MapType(toKey, toValue, tn)) =>
-        resolve(fromKey, toKey) &&
-          (!forceNullable(fromKey, toKey)) &&
-          resolve(fromValue, toValue) &&
-          resolvableNullability(fn || forceNullable(fromValue, toValue), tn)
-
-      case (StructType(fromFields), StructType(toFields)) =>
-        fromFields.size == toFields.size &&
-          fromFields.zip(toFields).forall {
-            case (fromField, toField) =>
-              resolve(fromField.dataType, toField.dataType) &&
-                resolvableNullability(
-                  fromField.nullable || forceNullable(fromField.dataType, toField.dataType),
-                  toField.nullable)
-          }
+/** Cast the child expression to the target data type. */
+case class Cast(child: Expression, dataType: DataType) extends UnaryExpression with Logging {
 
-      case _ => false
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (Cast.canCast(child.dataType, dataType)) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(
+        s"cannot cast ${child.dataType} to $dataType")
     }
   }
 
+  override def foldable: Boolean = child.foldable
+
+  override def nullable: Boolean = Cast.forceNullable(child.dataType, dataType) || child.nullable
+
   override def toString: String = s"CAST($child, $dataType)"
 
   // [[func]] assumes the input is no longer null because eval already does the null check.
@@ -172,7 +177,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
         catch { case _: java.lang.IllegalArgumentException => null }
       })
     case BooleanType =>
-      buildCast[Boolean](_, b => (if (b) 1L else 0))
+      buildCast[Boolean](_, b => if (b) 1L else 0)
     case LongType =>
       buildCast[Long](_, l => longToTimestamp(l))
     case IntegerType =>
@@ -388,7 +393,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
       case (fromField, toField) => cast(fromField.dataType, toField.dataType)
     }
     // TODO: Could be faster?
-    val newRow = new GenericMutableRow(from.fields.size)
+    val newRow = new GenericMutableRow(from.fields.length)
     buildCast[InternalRow](_, row => {
       var i = 0
       while (i < row.length) {
@@ -427,20 +432,23 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    // TODO(cg): Add support for more data types.
+    // TODO: Add support for more data types.
     (child.dataType, dataType) match {
 
       case (BinaryType, StringType) =>
         defineCodeGen (ctx, ev, c =>
           s"${ctx.stringType}.fromBytes($c)")
+
       case (DateType, StringType) =>
         defineCodeGen(ctx, ev, c =>
           s"""${ctx.stringType}.fromString(
                 org.apache.spark.sql.catalyst.util.DateTimeUtils.dateToString($c))""")
+
       case (TimestampType, StringType) =>
         defineCodeGen(ctx, ev, c =>
           s"""${ctx.stringType}.fromString(
                 org.apache.spark.sql.catalyst.util.DateTimeUtils.timestampToString($c))""")
+
       case (_, StringType) =>
         defineCodeGen(ctx, ev, c => s"${ctx.stringType}.fromString(String.valueOf($c))")
 
@@ -450,12 +458,16 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
 
       case (BooleanType, dt: NumericType) =>
         defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})($c ? 1 : 0)")
+
       case (dt: DecimalType, BooleanType) =>
         defineCodeGen(ctx, ev, c => s"!$c.isZero()")
+
       case (dt: NumericType, BooleanType) =>
         defineCodeGen(ctx, ev, c => s"$c != 0")
+
       case (_: DecimalType, dt: NumericType) =>
         defineCodeGen(ctx, ev, c => s"($c).to${ctx.primitiveTypeName(dt)}()")
+
       case (_: NumericType, dt: NumericType) =>
         defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})($c)")
 

From fc3a6fe67f5aeda2443958c31f097daeba8549e5 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 1 Jul 2015 00:08:16 -0700
Subject: [PATCH 0162/1454] [SPARK-8749][SQL] Remove HiveTypeCoercion trait.

Moved all the rules into the companion object.

Author: Reynold Xin <rxin@databricks.com>

Closes #7147 from rxin/SPARK-8749 and squashes the following commits:

c1c6dc0 [Reynold Xin] [SPARK-8749][SQL] Remove HiveTypeCoercion trait.
---
 .../sql/catalyst/analysis/Analyzer.scala      |  4 +-
 .../catalyst/analysis/HiveTypeCoercion.scala  | 59 ++++++++-----------
 .../analysis/HiveTypeCoercionSuite.scala      | 14 ++---
 3 files changed, 33 insertions(+), 44 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 117c87a785fdb..15e84e68b9881 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -43,7 +43,7 @@ class Analyzer(
     registry: FunctionRegistry,
     conf: CatalystConf,
     maxIterations: Int = 100)
-  extends RuleExecutor[LogicalPlan] with HiveTypeCoercion with CheckAnalysis {
+  extends RuleExecutor[LogicalPlan] with CheckAnalysis {
 
   def resolver: Resolver = {
     if (conf.caseSensitiveAnalysis) {
@@ -76,7 +76,7 @@ class Analyzer(
       ExtractWindowExpressions ::
       GlobalAggregates ::
       UnresolvedHavingClauseAttributes ::
-      typeCoercionRules ++
+      HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*)
   )
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index e525ad623ff12..a9d396d1faeeb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -22,7 +22,32 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project, Union}
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
 
+
+/**
+ * A collection of [[Rule Rules]] that can be used to coerce differing types that
+ * participate in operations into compatible ones.  Most of these rules are based on Hive semantics,
+ * but they do not introduce any dependencies on the hive codebase.  For this reason they remain in
+ * Catalyst until we have a more standard set of coercions.
+ */
 object HiveTypeCoercion {
+
+  val typeCoercionRules =
+    PropagateTypes ::
+      ConvertNaNs ::
+      InConversion ::
+      WidenTypes ::
+      PromoteStrings ::
+      DecimalPrecision ::
+      BooleanEquality ::
+      StringToIntegralCasts ::
+      FunctionArgumentConversion ::
+      CaseWhenCoercion ::
+      IfCoercion ::
+      Division ::
+      PropagateTypes ::
+      AddCastForAutoCastInputTypes ::
+      Nil
+
   // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
   // The conversion for integral and floating point types have a linear widening hierarchy:
   private val numericPrecedence =
@@ -79,7 +104,6 @@ object HiveTypeCoercion {
     })
   }
 
-
   /**
    * Find the tightest common type of a set of types by continuously applying
    * `findTightestCommonTypeOfTwo` on these types.
@@ -90,34 +114,6 @@ object HiveTypeCoercion {
       case Some(d) => findTightestCommonTypeOfTwo(d, c)
     })
   }
-}
-
-/**
- * A collection of [[Rule Rules]] that can be used to coerce differing types that
- * participate in operations into compatible ones.  Most of these rules are based on Hive semantics,
- * but they do not introduce any dependencies on the hive codebase.  For this reason they remain in
- * Catalyst until we have a more standard set of coercions.
- */
-trait HiveTypeCoercion {
-
-  import HiveTypeCoercion._
-
-  val typeCoercionRules =
-    PropagateTypes ::
-    ConvertNaNs ::
-    InConversion ::
-    WidenTypes ::
-    PromoteStrings ::
-    DecimalPrecision ::
-    BooleanEquality ::
-    StringToIntegralCasts ::
-    FunctionArgumentConversion ::
-    CaseWhenCoercion ::
-    IfCoercion ::
-    Division ::
-    PropagateTypes ::
-    AddCastForAutoCastInputTypes ::
-    Nil
 
   /**
    * Applies any changes to [[AttributeReference]] data types that are made by other rules to
@@ -202,8 +198,6 @@ trait HiveTypeCoercion {
    * - LongType to DoubleType
    */
   object WidenTypes extends Rule[LogicalPlan] {
-    import HiveTypeCoercion._
-
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
       // TODO: unions with fixed-precision decimals
       case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
@@ -655,8 +649,6 @@ trait HiveTypeCoercion {
    * Coerces the type of different branches of a CASE WHEN statement to a common type.
    */
   object CaseWhenCoercion extends Rule[LogicalPlan] {
-    import HiveTypeCoercion._
-
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       case c: CaseWhenLike if c.childrenResolved && !c.valueTypesEqual =>
         logDebug(s"Input values for null casting ${c.valueTypes.mkString(",")}")
@@ -714,7 +706,6 @@ trait HiveTypeCoercion {
    * [[AutoCastInputTypes]].
    */
   object AddCastForAutoCastInputTypes extends Rule[LogicalPlan] {
-
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index f7b8e21bed490..eae3666595a38 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -113,8 +113,7 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("coalesce casts") {
-    val fac = new HiveTypeCoercion { }.FunctionArgumentConversion
-    ruleTest(fac,
+    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
       Coalesce(Literal(1.0)
         :: Literal(1)
         :: Literal.create(1.0, FloatType)
@@ -123,7 +122,7 @@ class HiveTypeCoercionSuite extends PlanTest {
         :: Cast(Literal(1), DoubleType)
         :: Cast(Literal.create(1.0, FloatType), DoubleType)
         :: Nil))
-    ruleTest(fac,
+    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
       Coalesce(Literal(1L)
         :: Literal(1)
         :: Literal(new java.math.BigDecimal("1000000000000000000000"))
@@ -135,7 +134,7 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("type coercion for If") {
-    val rule = new HiveTypeCoercion { }.IfCoercion
+    val rule = HiveTypeCoercion.IfCoercion
     ruleTest(rule,
       If(Literal(true), Literal(1), Literal(1L)),
       If(Literal(true), Cast(Literal(1), LongType), Literal(1L))
@@ -148,19 +147,18 @@ class HiveTypeCoercionSuite extends PlanTest {
   }
 
   test("type coercion for CaseKeyWhen") {
-    val cwc = new HiveTypeCoercion {}.CaseWhenCoercion
-    ruleTest(cwc,
+    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
       CaseKeyWhen(Literal(1.toShort), Seq(Literal(1), Literal("a"))),
       CaseKeyWhen(Cast(Literal(1.toShort), IntegerType), Seq(Literal(1), Literal("a")))
     )
-    ruleTest(cwc,
+    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a"))),
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a")))
     )
   }
 
   test("type coercion simplification for equal to") {
-    val be = new HiveTypeCoercion {}.BooleanEquality
+    val be = HiveTypeCoercion.BooleanEquality
 
     ruleTest(be,
       EqualTo(Literal(true), Literal(1)),

From 0eee0615894cda8ae1b2c8e61b8bda0ff648a219 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 1 Jul 2015 01:02:33 -0700
Subject: [PATCH 0163/1454] [SQL] [MINOR] remove internalRowRDD in DataFrame

Developers have already familiar with `queryExecution.toRDD` as internal row RDD, and we should not add new concept.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7116 from cloud-fan/internal-rdd and squashes the following commits:

24756ca [Wenchen Fan] remove internalRowRDD
---
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala  | 4 +---
 .../org/apache/spark/sql/execution/stat/FrequentItems.scala   | 2 +-
 .../org/apache/spark/sql/execution/stat/StatFunctions.scala   | 2 +-
 .../main/scala/org/apache/spark/sql/sources/commands.scala    | 4 ++--
 4 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 8fe1f7e34cb5e..caad2da80b1eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1469,14 +1469,12 @@ class DataFrame private[sql](
   lazy val rdd: RDD[Row] = {
     // use a local variable to make sure the map closure doesn't capture the whole DataFrame
     val schema = this.schema
-    internalRowRdd.mapPartitions { rows =>
+    queryExecution.toRdd.mapPartitions { rows =>
       val converter = CatalystTypeConverters.createToScalaConverter(schema)
       rows.map(converter(_).asInstanceOf[Row])
     }
   }
 
-  private[sql] def internalRowRdd = queryExecution.executedPlan.execute()
-
   /**
    * Returns the content of the [[DataFrame]] as a [[JavaRDD]] of [[Row]]s.
    * @group rdd
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
index 3ebbf96090a55..4e2e2c210d5a9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
@@ -90,7 +90,7 @@ private[sql] object FrequentItems extends Logging {
       (name, originalSchema.fields(index).dataType)
     }
 
-    val freqItems = df.select(cols.map(Column(_)) : _*).internalRowRdd.aggregate(countMaps)(
+    val freqItems = df.select(cols.map(Column(_)) : _*).queryExecution.toRdd.aggregate(countMaps)(
       seqOp = (counts, row) => {
         var i = 0
         while (i < numCols) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index b624ef7e8fa1a..23ddfa9839e5e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -82,7 +82,7 @@ private[sql] object StatFunctions extends Logging {
         s"with dataType ${data.get.dataType} not supported.")
     }
     val columns = cols.map(n => Column(Cast(Column(n).expr, DoubleType)))
-    df.select(columns: _*).internalRowRdd.aggregate(new CovarianceCounter)(
+    df.select(columns: _*).queryExecution.toRdd.aggregate(new CovarianceCounter)(
       seqOp = (counter, row) => {
         counter.add(row.getDouble(0), row.getDouble(1))
       },
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index 42b51caab5ce9..7214eb0b4169a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -154,7 +154,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
     writerContainer.driverSideSetup()
 
     try {
-      df.sqlContext.sparkContext.runJob(df.internalRowRdd, writeRows _)
+      df.sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writeRows _)
       writerContainer.commitJob()
       relation.refresh()
     } catch { case cause: Throwable =>
@@ -220,7 +220,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
     writerContainer.driverSideSetup()
 
     try {
-      df.sqlContext.sparkContext.runJob(df.internalRowRdd, writeRows _)
+      df.sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writeRows _)
       writerContainer.commitJob()
       relation.refresh()
     } catch { case cause: Throwable =>

From 97652416e22ae7d4c471178377a7dda61afb1f7a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 1 Jul 2015 01:08:20 -0700
Subject: [PATCH 0164/1454] [SPARK-8750][SQL] Remove the closure in
 functions.callUdf.

Author: Reynold Xin <rxin@databricks.com>

Closes #7148 from rxin/calludf-closure and squashes the following commits:

00df372 [Reynold Xin] Fixed index out of bound exception.
4beba76 [Reynold Xin] [SPARK-8750][SQL] Remove the closure in functions.callUdf.
---
 .../main/scala/org/apache/spark/sql/functions.scala    | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 5767668dd339b..4e8f3f96bf4db 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1829,7 +1829,15 @@ object functions {
    */
   @deprecated("Use callUDF", "1.5.0")
   def callUdf(udfName: String, cols: Column*): Column = {
-     UnresolvedFunction(udfName, cols.map(_.expr))
+    // Note: we avoid using closures here because on file systems that are case-insensitive, the
+    // compiled class file for the closure here will conflict with the one in callUDF (upper case).
+    val exprs = new Array[Expression](cols.size)
+    var i = 0
+    while (i < cols.size) {
+      exprs(i) = cols(i).expr
+      i += 1
+    }
+    UnresolvedFunction(udfName, exprs)
   }
 
 }

From fdcad6ef48a9e790776c316124bd6478ab6bd5c8 Mon Sep 17 00:00:00 2001
From: cocoatomo <cocoatomo77@gmail.com>
Date: Wed, 1 Jul 2015 09:37:09 -0700
Subject: [PATCH 0165/1454] [SPARK-8763] [PYSPARK] executing run-tests.py with
 Python 2.6 fails with absence of subprocess.check_output function

Running run-tests.py with Python 2.6 cause following error:

```
Running PySpark tests. Output is in python//Users/tomohiko/.jenkins/jobs/pyspark_test/workspace/python/unit-tests.log
Will test against the following Python executables: ['python2.6', 'python3.4', 'pypy']
Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming']
Traceback (most recent call last):
  File "./python/run-tests.py", line 196, in <module>
    main()
  File "./python/run-tests.py", line 159, in main
    python_implementation = subprocess.check_output(
AttributeError: 'module' object has no attribute 'check_output'
...
```

The cause of this error is using subprocess.check_output function, which exists since Python 2.7.
(ref. https://docs.python.org/2.7/library/subprocess.html#subprocess.check_output)

Author: cocoatomo <cocoatomo77@gmail.com>

Closes #7161 from cocoatomo/issues/8763-test-fails-py26 and squashes the following commits:

cf4f901 [cocoatomo] [SPARK-8763] backport process.check_output function from Python 2.7
---
 python/run-tests.py | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/python/run-tests.py b/python/run-tests.py
index b7737650daa54..7638854def2e8 100755
--- a/python/run-tests.py
+++ b/python/run-tests.py
@@ -31,6 +31,23 @@
     import Queue
 else:
     import queue as Queue
+if sys.version_info >= (2, 7):
+    subprocess_check_output = subprocess.check_output
+else:
+    # SPARK-8763
+    # backported from subprocess module in Python 2.7
+    def subprocess_check_output(*popenargs, **kwargs):
+        if 'stdout' in kwargs:
+            raise ValueError('stdout argument not allowed, it will be overridden.')
+        process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)
+        output, unused_err = process.communicate()
+        retcode = process.poll()
+        if retcode:
+            cmd = kwargs.get("args")
+            if cmd is None:
+                cmd = popenargs[0]
+            raise subprocess.CalledProcessError(retcode, cmd, output=output)
+        return output
 
 
 # Append `SPARK_HOME/dev` to the Python path so that we can import the sparktestsupport module
@@ -156,11 +173,11 @@ def main():
 
     task_queue = Queue.Queue()
     for python_exec in python_execs:
-        python_implementation = subprocess.check_output(
+        python_implementation = subprocess_check_output(
             [python_exec, "-c", "import platform; print(platform.python_implementation())"],
             universal_newlines=True).strip()
         LOGGER.debug("%s python_implementation is %s", python_exec, python_implementation)
-        LOGGER.debug("%s version is: %s", python_exec, subprocess.check_output(
+        LOGGER.debug("%s version is: %s", python_exec, subprocess_check_output(
             [python_exec, "--version"], stderr=subprocess.STDOUT, universal_newlines=True).strip())
         for module in modules_to_test:
             if python_implementation not in module.blacklisted_python_implementations:

From 69c5dee2f01b1ae35bd813d31d46429a32cb475d Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Wed, 1 Jul 2015 09:50:12 -0700
Subject: [PATCH 0166/1454] [SPARK-7714] [SPARKR] SparkR tests should use more
 specific expectations than expect_true

1. Update the pattern 'expect_true(a == b)' to 'expect_equal(a, b)'.
2. Update the pattern 'expect_true(inherits(a, b))' to 'expect_is(a, b)'.
3. Update the pattern 'expect_true(identical(a, b))' to 'expect_identical(a, b)'.

Author: Sun Rui <rui.sun@intel.com>

Closes #7152 from sun-rui/SPARK-7714 and squashes the following commits:

8ad2440 [Sun Rui] Fix test case errors.
8fe9f0c [Sun Rui] Update the pattern 'expect_true(identical(a, b))' to 'expect_identical(a, b)'.
f1b8005 [Sun Rui] Update the pattern 'expect_true(inherits(a, b))' to 'expect_is(a, b)'.
f631e94 [Sun Rui] Update the pattern 'expect_true(a == b)' to 'expect_equal(a, b)'.
---
 R/pkg/inst/tests/test_binaryFile.R          |   2 +-
 R/pkg/inst/tests/test_binary_function.R     |   4 +-
 R/pkg/inst/tests/test_includeJAR.R          |   4 +-
 R/pkg/inst/tests/test_parallelize_collect.R |   2 +-
 R/pkg/inst/tests/test_rdd.R                 |   4 +-
 R/pkg/inst/tests/test_sparkSQL.R            | 354 ++++++++++----------
 R/pkg/inst/tests/test_take.R                |   8 +-
 R/pkg/inst/tests/test_textFile.R            |   6 +-
 R/pkg/inst/tests/test_utils.R               |   4 +-
 9 files changed, 194 insertions(+), 194 deletions(-)

diff --git a/R/pkg/inst/tests/test_binaryFile.R b/R/pkg/inst/tests/test_binaryFile.R
index 4db7266abc8e2..ccaea18ecab2a 100644
--- a/R/pkg/inst/tests/test_binaryFile.R
+++ b/R/pkg/inst/tests/test_binaryFile.R
@@ -82,7 +82,7 @@ test_that("saveAsObjectFile()/objectFile() works with multiple paths", {
   saveAsObjectFile(rdd2, fileName2)
 
   rdd <- objectFile(sc, c(fileName1, fileName2))
-  expect_true(count(rdd) == 2)
+  expect_equal(count(rdd), 2)
 
   unlink(fileName1, recursive = TRUE)
   unlink(fileName2, recursive = TRUE)
diff --git a/R/pkg/inst/tests/test_binary_function.R b/R/pkg/inst/tests/test_binary_function.R
index a1e354e567be5..3be8c65a6c1a0 100644
--- a/R/pkg/inst/tests/test_binary_function.R
+++ b/R/pkg/inst/tests/test_binary_function.R
@@ -38,13 +38,13 @@ test_that("union on two RDDs", {
   union.rdd <- unionRDD(rdd, text.rdd)
   actual <- collect(union.rdd)
   expect_equal(actual, c(as.list(nums), mockFile))
-  expect_true(getSerializedMode(union.rdd) == "byte")
+  expect_equal(getSerializedMode(union.rdd), "byte")
 
   rdd<- map(text.rdd, function(x) {x})
   union.rdd <- unionRDD(rdd, text.rdd)
   actual <- collect(union.rdd)
   expect_equal(actual, as.list(c(mockFile, mockFile)))
-  expect_true(getSerializedMode(union.rdd) == "byte")
+  expect_equal(getSerializedMode(union.rdd), "byte")
 
   unlink(fileName)
 })
diff --git a/R/pkg/inst/tests/test_includeJAR.R b/R/pkg/inst/tests/test_includeJAR.R
index 8bc693be20c3c..844d86f3cc97f 100644
--- a/R/pkg/inst/tests/test_includeJAR.R
+++ b/R/pkg/inst/tests/test_includeJAR.R
@@ -31,7 +31,7 @@ runScript <- function() {
 test_that("sparkJars tag in SparkContext", {
   testOutput <- runScript()
   helloTest <- testOutput[1]
-  expect_true(helloTest == "Hello, Dave")
+  expect_equal(helloTest, "Hello, Dave")
   basicFunction <- testOutput[2]
-  expect_true(basicFunction == 4L)
+  expect_equal(basicFunction, "4")
 })
diff --git a/R/pkg/inst/tests/test_parallelize_collect.R b/R/pkg/inst/tests/test_parallelize_collect.R
index fff028657db37..2552127cc547f 100644
--- a/R/pkg/inst/tests/test_parallelize_collect.R
+++ b/R/pkg/inst/tests/test_parallelize_collect.R
@@ -57,7 +57,7 @@ test_that("parallelize() on simple vectors and lists returns an RDD", {
              strListRDD2)
 
   for (rdd in rdds) {
-    expect_true(inherits(rdd, "RDD"))
+    expect_is(rdd, "RDD")
     expect_true(.hasSlot(rdd, "jrdd")
                 && inherits(rdd@jrdd, "jobj")
                 && isInstanceOf(rdd@jrdd, "org.apache.spark.api.java.JavaRDD"))
diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R
index 4fe653856756e..fc3c01d837de4 100644
--- a/R/pkg/inst/tests/test_rdd.R
+++ b/R/pkg/inst/tests/test_rdd.R
@@ -33,9 +33,9 @@ test_that("get number of partitions in RDD", {
 })
 
 test_that("first on RDD", {
-  expect_true(first(rdd) == 1)
+  expect_equal(first(rdd), 1)
   newrdd <- lapply(rdd, function(x) x + 1)
-  expect_true(first(newrdd) == 2)
+  expect_equal(first(newrdd), 2)
 })
 
 test_that("count and length on RDD", {
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 6a08f894313c4..0e4235ea8b4b3 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -61,7 +61,7 @@ test_that("infer types", {
   expect_equal(infer_type(list(1L, 2L)),
                list(type = 'array', elementType = "integer", containsNull = TRUE))
   testStruct <- infer_type(list(a = 1L, b = "2"))
-  expect_true(class(testStruct) == "structType")
+  expect_equal(class(testStruct), "structType")
   checkStructField(testStruct$fields()[[1]], "a", "IntegerType", TRUE)
   checkStructField(testStruct$fields()[[2]], "b", "StringType", TRUE)
   e <- new.env()
@@ -73,39 +73,39 @@ test_that("infer types", {
 
 test_that("structType and structField", {
   testField <- structField("a", "string")
-  expect_true(inherits(testField, "structField"))
-  expect_true(testField$name() == "a")
+  expect_is(testField, "structField")
+  expect_equal(testField$name(), "a")
   expect_true(testField$nullable())
 
   testSchema <- structType(testField, structField("b", "integer"))
-  expect_true(inherits(testSchema, "structType"))
-  expect_true(inherits(testSchema$fields()[[2]], "structField"))
-  expect_true(testSchema$fields()[[1]]$dataType.toString() == "StringType")
+  expect_is(testSchema, "structType")
+  expect_is(testSchema$fields()[[2]], "structField")
+  expect_equal(testSchema$fields()[[1]]$dataType.toString(), "StringType")
 })
 
 test_that("create DataFrame from RDD", {
   rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
   df <- createDataFrame(sqlContext, rdd, list("a", "b"))
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 10)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 10)
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
 
   df <- createDataFrame(sqlContext, rdd)
-  expect_true(inherits(df, "DataFrame"))
+  expect_is(df, "DataFrame")
   expect_equal(columns(df), c("_1", "_2"))
 
   schema <- structType(structField(x = "a", type = "integer", nullable = TRUE),
                         structField(x = "b", type = "string", nullable = TRUE))
   df <- createDataFrame(sqlContext, rdd, schema)
-  expect_true(inherits(df, "DataFrame"))
+  expect_is(df, "DataFrame")
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
 
   rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) })
   df <- createDataFrame(sqlContext, rdd)
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 10)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 10)
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
 })
@@ -150,26 +150,26 @@ test_that("convert NAs to null type in DataFrames", {
 test_that("toDF", {
   rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
   df <- toDF(rdd, list("a", "b"))
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 10)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 10)
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
 
   df <- toDF(rdd)
-  expect_true(inherits(df, "DataFrame"))
+  expect_is(df, "DataFrame")
   expect_equal(columns(df), c("_1", "_2"))
 
   schema <- structType(structField(x = "a", type = "integer", nullable = TRUE),
                         structField(x = "b", type = "string", nullable = TRUE))
   df <- toDF(rdd, schema)
-  expect_true(inherits(df, "DataFrame"))
+  expect_is(df, "DataFrame")
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
 
   rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) })
   df <- toDF(rdd)
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 10)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 10)
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
 })
@@ -219,21 +219,21 @@ test_that("create DataFrame with different data types", {
 
 test_that("jsonFile() on a local file returns a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 3)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 3)
 })
 
 test_that("jsonRDD() on a RDD with json string", {
   rdd <- parallelize(sc, mockLines)
-  expect_true(count(rdd) == 3)
+  expect_equal(count(rdd), 3)
   df <- jsonRDD(sqlContext, rdd)
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 3)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 3)
 
   rdd2 <- flatMap(rdd, function(x) c(x, x))
   df <- jsonRDD(sqlContext, rdd2)
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 6)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 6)
 })
 
 test_that("test cache, uncache and clearCache", {
@@ -248,9 +248,9 @@ test_that("test cache, uncache and clearCache", {
 test_that("test tableNames and tables", {
   df <- jsonFile(sqlContext, jsonPath)
   registerTempTable(df, "table1")
-  expect_true(length(tableNames(sqlContext)) == 1)
+  expect_equal(length(tableNames(sqlContext)), 1)
   df <- tables(sqlContext)
-  expect_true(count(df) == 1)
+  expect_equal(count(df), 1)
   dropTempTable(sqlContext, "table1")
 })
 
@@ -258,8 +258,8 @@ test_that("registerTempTable() results in a queryable table and sql() results in
   df <- jsonFile(sqlContext, jsonPath)
   registerTempTable(df, "table1")
   newdf <- sql(sqlContext, "SELECT * FROM table1 where name = 'Michael'")
-  expect_true(inherits(newdf, "DataFrame"))
-  expect_true(count(newdf) == 1)
+  expect_is(newdf, "DataFrame")
+  expect_equal(count(newdf), 1)
   dropTempTable(sqlContext, "table1")
 })
 
@@ -279,14 +279,14 @@ test_that("insertInto() on a registered table", {
 
   registerTempTable(dfParquet, "table1")
   insertInto(dfParquet2, "table1")
-  expect_true(count(sql(sqlContext, "select * from table1")) == 5)
-  expect_true(first(sql(sqlContext, "select * from table1 order by age"))$name == "Michael")
+  expect_equal(count(sql(sqlContext, "select * from table1")), 5)
+  expect_equal(first(sql(sqlContext, "select * from table1 order by age"))$name, "Michael")
   dropTempTable(sqlContext, "table1")
 
   registerTempTable(dfParquet, "table1")
   insertInto(dfParquet2, "table1", overwrite = TRUE)
-  expect_true(count(sql(sqlContext, "select * from table1")) == 2)
-  expect_true(first(sql(sqlContext, "select * from table1 order by age"))$name == "Bob")
+  expect_equal(count(sql(sqlContext, "select * from table1")), 2)
+  expect_equal(first(sql(sqlContext, "select * from table1 order by age"))$name, "Bob")
   dropTempTable(sqlContext, "table1")
 })
 
@@ -294,16 +294,16 @@ test_that("table() returns a new DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   registerTempTable(df, "table1")
   tabledf <- table(sqlContext, "table1")
-  expect_true(inherits(tabledf, "DataFrame"))
-  expect_true(count(tabledf) == 3)
+  expect_is(tabledf, "DataFrame")
+  expect_equal(count(tabledf), 3)
   dropTempTable(sqlContext, "table1")
 })
 
 test_that("toRDD() returns an RRDD", {
   df <- jsonFile(sqlContext, jsonPath)
   testRDD <- toRDD(df)
-  expect_true(inherits(testRDD, "RDD"))
-  expect_true(count(testRDD) == 3)
+  expect_is(testRDD, "RDD")
+  expect_equal(count(testRDD), 3)
 })
 
 test_that("union on two RDDs created from DataFrames returns an RRDD", {
@@ -311,9 +311,9 @@ test_that("union on two RDDs created from DataFrames returns an RRDD", {
   RDD1 <- toRDD(df)
   RDD2 <- toRDD(df)
   unioned <- unionRDD(RDD1, RDD2)
-  expect_true(inherits(unioned, "RDD"))
-  expect_true(SparkR:::getSerializedMode(unioned) == "byte")
-  expect_true(collect(unioned)[[2]]$name == "Andy")
+  expect_is(unioned, "RDD")
+  expect_equal(SparkR:::getSerializedMode(unioned), "byte")
+  expect_equal(collect(unioned)[[2]]$name, "Andy")
 })
 
 test_that("union on mixed serialization types correctly returns a byte RRDD", {
@@ -333,16 +333,16 @@ test_that("union on mixed serialization types correctly returns a byte RRDD", {
   dfRDD <- toRDD(df)
 
   unionByte <- unionRDD(rdd, dfRDD)
-  expect_true(inherits(unionByte, "RDD"))
-  expect_true(SparkR:::getSerializedMode(unionByte) == "byte")
-  expect_true(collect(unionByte)[[1]] == 1)
-  expect_true(collect(unionByte)[[12]]$name == "Andy")
+  expect_is(unionByte, "RDD")
+  expect_equal(SparkR:::getSerializedMode(unionByte), "byte")
+  expect_equal(collect(unionByte)[[1]], 1)
+  expect_equal(collect(unionByte)[[12]]$name, "Andy")
 
   unionString <- unionRDD(textRDD, dfRDD)
-  expect_true(inherits(unionString, "RDD"))
-  expect_true(SparkR:::getSerializedMode(unionString) == "byte")
-  expect_true(collect(unionString)[[1]] == "Michael")
-  expect_true(collect(unionString)[[5]]$name == "Andy")
+  expect_is(unionString, "RDD")
+  expect_equal(SparkR:::getSerializedMode(unionString), "byte")
+  expect_equal(collect(unionString)[[1]], "Michael")
+  expect_equal(collect(unionString)[[5]]$name, "Andy")
 })
 
 test_that("objectFile() works with row serialization", {
@@ -352,7 +352,7 @@ test_that("objectFile() works with row serialization", {
   saveAsObjectFile(coalesce(dfRDD, 1L), objectPath)
   objectIn <- objectFile(sc, objectPath)
 
-  expect_true(inherits(objectIn, "RDD"))
+  expect_is(objectIn, "RDD")
   expect_equal(SparkR:::getSerializedMode(objectIn), "byte")
   expect_equal(collect(objectIn)[[2]]$age, 30)
 })
@@ -363,32 +363,32 @@ test_that("lapply() on a DataFrame returns an RDD with the correct columns", {
     row$newCol <- row$age + 5
     row
     })
-  expect_true(inherits(testRDD, "RDD"))
+  expect_is(testRDD, "RDD")
   collected <- collect(testRDD)
-  expect_true(collected[[1]]$name == "Michael")
-  expect_true(collected[[2]]$newCol == "35")
+  expect_equal(collected[[1]]$name, "Michael")
+  expect_equal(collected[[2]]$newCol, 35)
 })
 
 test_that("collect() returns a data.frame", {
   df <- jsonFile(sqlContext, jsonPath)
   rdf <- collect(df)
   expect_true(is.data.frame(rdf))
-  expect_true(names(rdf)[1] == "age")
-  expect_true(nrow(rdf) == 3)
-  expect_true(ncol(rdf) == 2)
+  expect_equal(names(rdf)[1], "age")
+  expect_equal(nrow(rdf), 3)
+  expect_equal(ncol(rdf), 2)
 })
 
 test_that("limit() returns DataFrame with the correct number of rows", {
   df <- jsonFile(sqlContext, jsonPath)
   dfLimited <- limit(df, 2)
-  expect_true(inherits(dfLimited, "DataFrame"))
-  expect_true(count(dfLimited) == 2)
+  expect_is(dfLimited, "DataFrame")
+  expect_equal(count(dfLimited), 2)
 })
 
 test_that("collect() and take() on a DataFrame return the same number of rows and columns", {
   df <- jsonFile(sqlContext, jsonPath)
-  expect_true(nrow(collect(df)) == nrow(take(df, 10)))
-  expect_true(ncol(collect(df)) == ncol(take(df, 10)))
+  expect_equal(nrow(collect(df)), nrow(take(df, 10)))
+  expect_equal(ncol(collect(df)), ncol(take(df, 10)))
 })
 
 test_that("multiple pipeline transformations starting with a DataFrame result in an RDD with the correct values", {
@@ -401,9 +401,9 @@ test_that("multiple pipeline transformations starting with a DataFrame result in
     row$testCol <- if (row$age == 35 && !is.na(row$age)) TRUE else FALSE
     row
   })
-  expect_true(inherits(second, "RDD"))
-  expect_true(count(second) == 3)
-  expect_true(collect(second)[[2]]$age == 35)
+  expect_is(second, "RDD")
+  expect_equal(count(second), 3)
+  expect_equal(collect(second)[[2]]$age, 35)
   expect_true(collect(second)[[2]]$testCol)
   expect_false(collect(second)[[3]]$testCol)
 })
@@ -430,36 +430,36 @@ test_that("cache(), persist(), and unpersist() on a DataFrame", {
 test_that("schema(), dtypes(), columns(), names() return the correct values/format", {
   df <- jsonFile(sqlContext, jsonPath)
   testSchema <- schema(df)
-  expect_true(length(testSchema$fields()) == 2)
-  expect_true(testSchema$fields()[[1]]$dataType.toString() == "LongType")
-  expect_true(testSchema$fields()[[2]]$dataType.simpleString() == "string")
-  expect_true(testSchema$fields()[[1]]$name() == "age")
+  expect_equal(length(testSchema$fields()), 2)
+  expect_equal(testSchema$fields()[[1]]$dataType.toString(), "LongType")
+  expect_equal(testSchema$fields()[[2]]$dataType.simpleString(), "string")
+  expect_equal(testSchema$fields()[[1]]$name(), "age")
 
   testTypes <- dtypes(df)
-  expect_true(length(testTypes[[1]]) == 2)
-  expect_true(testTypes[[1]][1] == "age")
+  expect_equal(length(testTypes[[1]]), 2)
+  expect_equal(testTypes[[1]][1], "age")
 
   testCols <- columns(df)
-  expect_true(length(testCols) == 2)
-  expect_true(testCols[2] == "name")
+  expect_equal(length(testCols), 2)
+  expect_equal(testCols[2], "name")
 
   testNames <- names(df)
-  expect_true(length(testNames) == 2)
-  expect_true(testNames[2] == "name")
+  expect_equal(length(testNames), 2)
+  expect_equal(testNames[2], "name")
 })
 
 test_that("head() and first() return the correct data", {
   df <- jsonFile(sqlContext, jsonPath)
   testHead <- head(df)
-  expect_true(nrow(testHead) == 3)
-  expect_true(ncol(testHead) == 2)
+  expect_equal(nrow(testHead), 3)
+  expect_equal(ncol(testHead), 2)
 
   testHead2 <- head(df, 2)
-  expect_true(nrow(testHead2) == 2)
-  expect_true(ncol(testHead2) == 2)
+  expect_equal(nrow(testHead2), 2)
+  expect_equal(ncol(testHead2), 2)
 
   testFirst <- first(df)
-  expect_true(nrow(testFirst) == 1)
+  expect_equal(nrow(testFirst), 1)
 })
 
 test_that("distinct() on DataFrames", {
@@ -472,15 +472,15 @@ test_that("distinct() on DataFrames", {
 
   df <- jsonFile(sqlContext, jsonPathWithDup)
   uniques <- distinct(df)
-  expect_true(inherits(uniques, "DataFrame"))
-  expect_true(count(uniques) == 3)
+  expect_is(uniques, "DataFrame")
+  expect_equal(count(uniques), 3)
 })
 
 test_that("sample on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   sampled <- sample(df, FALSE, 1.0)
   expect_equal(nrow(collect(sampled)), count(df))
-  expect_true(inherits(sampled, "DataFrame"))
+  expect_is(sampled, "DataFrame")
   sampled2 <- sample(df, FALSE, 0.1)
   expect_true(count(sampled2) < 3)
 
@@ -491,15 +491,15 @@ test_that("sample on a DataFrame", {
 
 test_that("select operators", {
   df <- select(jsonFile(sqlContext, jsonPath), "name", "age")
-  expect_true(inherits(df$name, "Column"))
-  expect_true(inherits(df[[2]], "Column"))
-  expect_true(inherits(df[["age"]], "Column"))
+  expect_is(df$name, "Column")
+  expect_is(df[[2]], "Column")
+  expect_is(df[["age"]], "Column")
 
-  expect_true(inherits(df[,1], "DataFrame"))
+  expect_is(df[,1], "DataFrame")
   expect_equal(columns(df[,1]), c("name"))
   expect_equal(columns(df[,"age"]), c("age"))
   df2 <- df[,c("age", "name")]
-  expect_true(inherits(df2, "DataFrame"))
+  expect_is(df2, "DataFrame")
   expect_equal(columns(df2), c("age", "name"))
 
   df$age2 <- df$age
@@ -518,50 +518,50 @@ test_that("select operators", {
 test_that("select with column", {
   df <- jsonFile(sqlContext, jsonPath)
   df1 <- select(df, "name")
-  expect_true(columns(df1) == c("name"))
-  expect_true(count(df1) == 3)
+  expect_equal(columns(df1), c("name"))
+  expect_equal(count(df1), 3)
 
   df2 <- select(df, df$age)
-  expect_true(columns(df2) == c("age"))
-  expect_true(count(df2) == 3)
+  expect_equal(columns(df2), c("age"))
+  expect_equal(count(df2), 3)
 })
 
 test_that("selectExpr() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   selected <- selectExpr(df, "age * 2")
-  expect_true(names(selected) == "(age * 2)")
+  expect_equal(names(selected), "(age * 2)")
   expect_equal(collect(selected), collect(select(df, df$age * 2L)))
 
   selected2 <- selectExpr(df, "name as newName", "abs(age) as age")
   expect_equal(names(selected2), c("newName", "age"))
-  expect_true(count(selected2) == 3)
+  expect_equal(count(selected2), 3)
 })
 
 test_that("column calculation", {
   df <- jsonFile(sqlContext, jsonPath)
   d <- collect(select(df, alias(df$age + 1, "age2")))
-  expect_true(names(d) == c("age2"))
+  expect_equal(names(d), c("age2"))
   df2 <- select(df, lower(df$name), abs(df$age))
-  expect_true(inherits(df2, "DataFrame"))
-  expect_true(count(df2) == 3)
+  expect_is(df2, "DataFrame")
+  expect_equal(count(df2), 3)
 })
 
 test_that("read.df() from json file", {
   df <- read.df(sqlContext, jsonPath, "json")
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 3)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 3)
 
   # Check if we can apply a user defined schema
   schema <- structType(structField("name", type = "string"),
                        structField("age", type = "double"))
 
   df1 <- read.df(sqlContext, jsonPath, "json", schema)
-  expect_true(inherits(df1, "DataFrame"))
+  expect_is(df1, "DataFrame")
   expect_equal(dtypes(df1), list(c("name", "string"), c("age", "double")))
 
   # Run the same with loadDF
   df2 <- loadDF(sqlContext, jsonPath, "json", schema)
-  expect_true(inherits(df2, "DataFrame"))
+  expect_is(df2, "DataFrame")
   expect_equal(dtypes(df2), list(c("name", "string"), c("age", "double")))
 })
 
@@ -569,8 +569,8 @@ test_that("write.df() as parquet file", {
   df <- read.df(sqlContext, jsonPath, "json")
   write.df(df, parquetPath, "parquet", mode="overwrite")
   df2 <- read.df(sqlContext, parquetPath, "parquet")
-  expect_true(inherits(df2, "DataFrame"))
-  expect_true(count(df2) == 3)
+  expect_is(df2, "DataFrame")
+  expect_equal(count(df2), 3)
 })
 
 test_that("test HiveContext", {
@@ -580,17 +580,17 @@ test_that("test HiveContext", {
     skip("Hive is not build with SparkSQL, skipped")
   })
   df <- createExternalTable(hiveCtx, "json", jsonPath, "json")
-  expect_true(inherits(df, "DataFrame"))
-  expect_true(count(df) == 3)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 3)
   df2 <- sql(hiveCtx, "select * from json")
-  expect_true(inherits(df2, "DataFrame"))
-  expect_true(count(df2) == 3)
+  expect_is(df2, "DataFrame")
+  expect_equal(count(df2), 3)
 
   jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp")
   saveAsTable(df, "json", "json", "append", path = jsonPath2)
   df3 <- sql(hiveCtx, "select * from json")
-  expect_true(inherits(df3, "DataFrame"))
-  expect_true(count(df3) == 6)
+  expect_is(df3, "DataFrame")
+  expect_equal(count(df3), 6)
 })
 
 test_that("column operators", {
@@ -643,65 +643,65 @@ test_that("string operators", {
 test_that("group by", {
   df <- jsonFile(sqlContext, jsonPath)
   df1 <- agg(df, name = "max", age = "sum")
-  expect_true(1 == count(df1))
+  expect_equal(1, count(df1))
   df1 <- agg(df, age2 = max(df$age))
-  expect_true(1 == count(df1))
+  expect_equal(1, count(df1))
   expect_equal(columns(df1), c("age2"))
 
   gd <- groupBy(df, "name")
-  expect_true(inherits(gd, "GroupedData"))
+  expect_is(gd, "GroupedData")
   df2 <- count(gd)
-  expect_true(inherits(df2, "DataFrame"))
-  expect_true(3 == count(df2))
+  expect_is(df2, "DataFrame")
+  expect_equal(3, count(df2))
 
   # Also test group_by, summarize, mean
   gd1 <- group_by(df, "name")
-  expect_true(inherits(gd1, "GroupedData"))
+  expect_is(gd1, "GroupedData")
   df_summarized <- summarize(gd, mean_age = mean(df$age))
-  expect_true(inherits(df_summarized, "DataFrame"))
-  expect_true(3 == count(df_summarized))
+  expect_is(df_summarized, "DataFrame")
+  expect_equal(3, count(df_summarized))
 
   df3 <- agg(gd, age = "sum")
-  expect_true(inherits(df3, "DataFrame"))
-  expect_true(3 == count(df3))
+  expect_is(df3, "DataFrame")
+  expect_equal(3, count(df3))
 
   df3 <- agg(gd, age = sum(df$age))
-  expect_true(inherits(df3, "DataFrame"))
-  expect_true(3 == count(df3))
+  expect_is(df3, "DataFrame")
+  expect_equal(3, count(df3))
   expect_equal(columns(df3), c("name", "age"))
 
   df4 <- sum(gd, "age")
-  expect_true(inherits(df4, "DataFrame"))
-  expect_true(3 == count(df4))
-  expect_true(3 == count(mean(gd, "age")))
-  expect_true(3 == count(max(gd, "age")))
+  expect_is(df4, "DataFrame")
+  expect_equal(3, count(df4))
+  expect_equal(3, count(mean(gd, "age")))
+  expect_equal(3, count(max(gd, "age")))
 })
 
 test_that("arrange() and orderBy() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   sorted <- arrange(df, df$age)
-  expect_true(collect(sorted)[1,2] == "Michael")
+  expect_equal(collect(sorted)[1,2], "Michael")
 
   sorted2 <- arrange(df, "name")
-  expect_true(collect(sorted2)[2,"age"] == 19)
+  expect_equal(collect(sorted2)[2,"age"], 19)
 
   sorted3 <- orderBy(df, asc(df$age))
   expect_true(is.na(first(sorted3)$age))
-  expect_true(collect(sorted3)[2, "age"] == 19)
+  expect_equal(collect(sorted3)[2, "age"], 19)
 
   sorted4 <- orderBy(df, desc(df$name))
-  expect_true(first(sorted4)$name == "Michael")
-  expect_true(collect(sorted4)[3,"name"] == "Andy")
+  expect_equal(first(sorted4)$name, "Michael")
+  expect_equal(collect(sorted4)[3,"name"], "Andy")
 })
 
 test_that("filter() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   filtered <- filter(df, "age > 20")
-  expect_true(count(filtered) == 1)
-  expect_true(collect(filtered)$name == "Andy")
+  expect_equal(count(filtered), 1)
+  expect_equal(collect(filtered)$name, "Andy")
   filtered2 <- where(df, df$name != "Michael")
-  expect_true(count(filtered2) == 2)
-  expect_true(collect(filtered2)$age[2] == 19)
+  expect_equal(count(filtered2), 2)
+  expect_equal(collect(filtered2)$age[2], 19)
 
   # test suites for %in%
   filtered3 <- filter(df, "age in (19)")
@@ -727,29 +727,29 @@ test_that("join() on a DataFrame", {
 
   joined <- join(df, df2)
   expect_equal(names(joined), c("age", "name", "name", "test"))
-  expect_true(count(joined) == 12)
+  expect_equal(count(joined), 12)
 
   joined2 <- join(df, df2, df$name == df2$name)
   expect_equal(names(joined2), c("age", "name", "name", "test"))
-  expect_true(count(joined2) == 3)
+  expect_equal(count(joined2), 3)
 
   joined3 <- join(df, df2, df$name == df2$name, "right_outer")
   expect_equal(names(joined3), c("age", "name", "name", "test"))
-  expect_true(count(joined3) == 4)
+  expect_equal(count(joined3), 4)
   expect_true(is.na(collect(orderBy(joined3, joined3$age))$age[2]))
 
   joined4 <- select(join(df, df2, df$name == df2$name, "outer"),
                     alias(df$age + 5, "newAge"), df$name, df2$test)
   expect_equal(names(joined4), c("newAge", "name", "test"))
-  expect_true(count(joined4) == 4)
+  expect_equal(count(joined4), 4)
   expect_equal(collect(orderBy(joined4, joined4$name))$newAge[3], 24)
 })
 
 test_that("toJSON() returns an RDD of the correct values", {
   df <- jsonFile(sqlContext, jsonPath)
   testRDD <- toJSON(df)
-  expect_true(inherits(testRDD, "RDD"))
-  expect_true(SparkR:::getSerializedMode(testRDD) == "string")
+  expect_is(testRDD, "RDD")
+  expect_equal(SparkR:::getSerializedMode(testRDD), "string")
   expect_equal(collect(testRDD)[[1]], mockLines[1])
 })
 
@@ -775,50 +775,50 @@ test_that("unionAll(), except(), and intersect() on a DataFrame", {
   df2 <- read.df(sqlContext, jsonPath2, "json")
 
   unioned <- arrange(unionAll(df, df2), df$age)
-  expect_true(inherits(unioned, "DataFrame"))
-  expect_true(count(unioned) == 6)
-  expect_true(first(unioned)$name == "Michael")
+  expect_is(unioned, "DataFrame")
+  expect_equal(count(unioned), 6)
+  expect_equal(first(unioned)$name, "Michael")
 
   excepted <- arrange(except(df, df2), desc(df$age))
-  expect_true(inherits(unioned, "DataFrame"))
-  expect_true(count(excepted) == 2)
-  expect_true(first(excepted)$name == "Justin")
+  expect_is(unioned, "DataFrame")
+  expect_equal(count(excepted), 2)
+  expect_equal(first(excepted)$name, "Justin")
 
   intersected <- arrange(intersect(df, df2), df$age)
-  expect_true(inherits(unioned, "DataFrame"))
-  expect_true(count(intersected) == 1)
-  expect_true(first(intersected)$name == "Andy")
+  expect_is(unioned, "DataFrame")
+  expect_equal(count(intersected), 1)
+  expect_equal(first(intersected)$name, "Andy")
 })
 
 test_that("withColumn() and withColumnRenamed()", {
   df <- jsonFile(sqlContext, jsonPath)
   newDF <- withColumn(df, "newAge", df$age + 2)
-  expect_true(length(columns(newDF)) == 3)
-  expect_true(columns(newDF)[3] == "newAge")
-  expect_true(first(filter(newDF, df$name != "Michael"))$newAge == 32)
+  expect_equal(length(columns(newDF)), 3)
+  expect_equal(columns(newDF)[3], "newAge")
+  expect_equal(first(filter(newDF, df$name != "Michael"))$newAge, 32)
 
   newDF2 <- withColumnRenamed(df, "age", "newerAge")
-  expect_true(length(columns(newDF2)) == 2)
-  expect_true(columns(newDF2)[1] == "newerAge")
+  expect_equal(length(columns(newDF2)), 2)
+  expect_equal(columns(newDF2)[1], "newerAge")
 })
 
 test_that("mutate() and rename()", {
   df <- jsonFile(sqlContext, jsonPath)
   newDF <- mutate(df, newAge = df$age + 2)
-  expect_true(length(columns(newDF)) == 3)
-  expect_true(columns(newDF)[3] == "newAge")
-  expect_true(first(filter(newDF, df$name != "Michael"))$newAge == 32)
+  expect_equal(length(columns(newDF)), 3)
+  expect_equal(columns(newDF)[3], "newAge")
+  expect_equal(first(filter(newDF, df$name != "Michael"))$newAge, 32)
 
   newDF2 <- rename(df, newerAge = df$age)
-  expect_true(length(columns(newDF2)) == 2)
-  expect_true(columns(newDF2)[1] == "newerAge")
+  expect_equal(length(columns(newDF2)), 2)
+  expect_equal(columns(newDF2)[1], "newerAge")
 })
 
 test_that("write.df() on DataFrame and works with parquetFile", {
   df <- jsonFile(sqlContext, jsonPath)
   write.df(df, parquetPath, "parquet", mode="overwrite")
   parquetDF <- parquetFile(sqlContext, parquetPath)
-  expect_true(inherits(parquetDF, "DataFrame"))
+  expect_is(parquetDF, "DataFrame")
   expect_equal(count(df), count(parquetDF))
 })
 
@@ -828,8 +828,8 @@ test_that("parquetFile works with multiple input paths", {
   parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet")
   write.df(df, parquetPath2, "parquet", mode="overwrite")
   parquetDF <- parquetFile(sqlContext, parquetPath, parquetPath2)
-  expect_true(inherits(parquetDF, "DataFrame"))
-  expect_true(count(parquetDF) == count(df)*2)
+  expect_is(parquetDF, "DataFrame")
+  expect_equal(count(parquetDF), count(df)*2)
 })
 
 test_that("describe() on a DataFrame", {
@@ -851,58 +851,58 @@ test_that("dropna() on a DataFrame", {
 
   expected <- rows[!is.na(rows$name),]
   actual <- collect(dropna(df, cols = "name"))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age),]
   actual <- collect(dropna(df, cols = "age"))
   row.names(expected) <- row.names(actual)
   # identical on two dataframes does not work here. Don't know why.
   # use identical on all columns as a workaround.
-  expect_true(identical(expected$age, actual$age))
-  expect_true(identical(expected$height, actual$height))
-  expect_true(identical(expected$name, actual$name))
+  expect_identical(expected$age, actual$age)
+  expect_identical(expected$height, actual$height)
+  expect_identical(expected$name, actual$name)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
   actual <- collect(dropna(df, cols = c("age", "height")))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   # drop with how
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) | !is.na(rows$height) | !is.na(rows$name),]
   actual <- collect(dropna(df, "all"))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df, "any"))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
   actual <- collect(dropna(df, "any", cols = c("age", "height")))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) | !is.na(rows$height),]
   actual <- collect(dropna(df, "all", cols = c("age", "height")))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   # drop with threshold
 
   expected <- rows[as.integer(!is.na(rows$age)) + as.integer(!is.na(rows$height)) >= 2,]
   actual <- collect(dropna(df, minNonNulls = 2, cols = c("age", "height")))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows[as.integer(!is.na(rows$age)) +
                    as.integer(!is.na(rows$height)) +
                    as.integer(!is.na(rows$name)) >= 3,]
   actual <- collect(dropna(df, minNonNulls = 3, cols = c("name", "age", "height")))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 })
 
 test_that("fillna() on a DataFrame", {
@@ -915,22 +915,22 @@ test_that("fillna() on a DataFrame", {
   expected$age[is.na(expected$age)] <- 50
   expected$height[is.na(expected$height)] <- 50.6
   actual <- collect(fillna(df, 50.6))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows
   expected$name[is.na(expected$name)] <- "unknown"
   actual <- collect(fillna(df, "unknown"))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows
   expected$age[is.na(expected$age)] <- 50
   actual <- collect(fillna(df, 50.6, "age"))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   expected <- rows
   expected$name[is.na(expected$name)] <- "unknown"
   actual <- collect(fillna(df, "unknown", c("age", "name")))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 
   # fill with named list
 
@@ -939,7 +939,7 @@ test_that("fillna() on a DataFrame", {
   expected$height[is.na(expected$height)] <- 50.6
   expected$name[is.na(expected$name)] <- "unknown"
   actual <- collect(fillna(df, list("age" = 50, "height" = 50.6, "name" = "unknown")))
-  expect_true(identical(expected, actual))
+  expect_identical(expected, actual)
 })
 
 unlink(parquetPath)
diff --git a/R/pkg/inst/tests/test_take.R b/R/pkg/inst/tests/test_take.R
index c5eb417b40159..c2c724cdc762f 100644
--- a/R/pkg/inst/tests/test_take.R
+++ b/R/pkg/inst/tests/test_take.R
@@ -59,8 +59,8 @@ test_that("take() gives back the original elements in correct count and order",
   expect_equal(take(strListRDD, 3), as.list(head(strList, n = 3)))
   expect_equal(take(strListRDD2, 1), as.list(head(strList, n = 1)))
 
-  expect_true(length(take(strListRDD, 0)) == 0)
-  expect_true(length(take(strVectorRDD, 0)) == 0)
-  expect_true(length(take(numListRDD, 0)) == 0)
-  expect_true(length(take(numVectorRDD, 0)) == 0)
+  expect_equal(length(take(strListRDD, 0)), 0)
+  expect_equal(length(take(strVectorRDD, 0)), 0)
+  expect_equal(length(take(numListRDD, 0)), 0)
+  expect_equal(length(take(numVectorRDD, 0)), 0)
 })
diff --git a/R/pkg/inst/tests/test_textFile.R b/R/pkg/inst/tests/test_textFile.R
index 092ad9dc10c2e..58318dfef71ab 100644
--- a/R/pkg/inst/tests/test_textFile.R
+++ b/R/pkg/inst/tests/test_textFile.R
@@ -27,9 +27,9 @@ test_that("textFile() on a local file returns an RDD", {
   writeLines(mockFile, fileName)
 
   rdd <- textFile(sc, fileName)
-  expect_true(inherits(rdd, "RDD"))
+  expect_is(rdd, "RDD")
   expect_true(count(rdd) > 0)
-  expect_true(count(rdd) == 2)
+  expect_equal(count(rdd), 2)
 
   unlink(fileName)
 })
@@ -133,7 +133,7 @@ test_that("textFile() on multiple paths", {
   writeLines("Spark is awesome.", fileName2)
 
   rdd <- textFile(sc, c(fileName1, fileName2))
-  expect_true(count(rdd) == 2)
+  expect_equal(count(rdd), 2)
 
   unlink(fileName1)
   unlink(fileName2)
diff --git a/R/pkg/inst/tests/test_utils.R b/R/pkg/inst/tests/test_utils.R
index 15030e6f1d77e..aa0d2a66b9082 100644
--- a/R/pkg/inst/tests/test_utils.R
+++ b/R/pkg/inst/tests/test_utils.R
@@ -45,10 +45,10 @@ test_that("serializeToBytes on RDD", {
   writeLines(mockFile, fileName)
 
   text.rdd <- textFile(sc, fileName)
-  expect_true(getSerializedMode(text.rdd) == "string")
+  expect_equal(getSerializedMode(text.rdd), "string")
   ser.rdd <- serializeToBytes(text.rdd)
   expect_equal(collect(ser.rdd), as.list(mockFile))
-  expect_true(getSerializedMode(ser.rdd) == "byte")
+  expect_equal(getSerializedMode(ser.rdd), "byte")
 
   unlink(fileName)
 })

From 4137f769b84300648ad933b0b3054d69a7316745 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 1 Jul 2015 10:30:54 -0700
Subject: [PATCH 0167/1454] [SPARK-8752][SQL] Add ExpectsInputTypes trait for
 defining expected input types.

This patch doesn't actually introduce any code that uses the new ExpectsInputTypes. It just adds the trait so others can use it. Also renamed the old expectsInputTypes function to just inputTypes.

We should add implicit type casting also in the future.

Author: Reynold Xin <rxin@databricks.com>

Closes #7151 from rxin/expects-input-types and squashes the following commits:

16cf07b [Reynold Xin] [SPARK-8752][SQL] Add ExpectsInputTypes trait for defining expected input types.
---
 .../sql/catalyst/analysis/CheckAnalysis.scala |  1 -
 .../catalyst/analysis/HiveTypeCoercion.scala  |  8 ++---
 .../sql/catalyst/expressions/Expression.scala | 29 ++++++++++++++++---
 .../spark/sql/catalyst/expressions/math.scala |  6 ++--
 .../spark/sql/catalyst/expressions/misc.scala |  8 ++---
 .../sql/catalyst/expressions/predicates.scala |  6 ++--
 .../expressions/stringOperations.scala        | 10 +++----
 7 files changed, 44 insertions(+), 24 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index a069b4710f38c..583338da57117 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -26,7 +26,6 @@ import org.apache.spark.sql.types._
  * Throws user facing errors when passed invalid queries that fail to analyze.
  */
 trait CheckAnalysis {
-  self: Analyzer =>
 
   /**
    * Override to provide additional checks for correct analysis.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index a9d396d1faeeb..2ab5cb666fbcd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -45,7 +45,7 @@ object HiveTypeCoercion {
       IfCoercion ::
       Division ::
       PropagateTypes ::
-      AddCastForAutoCastInputTypes ::
+      ImplicitTypeCasts ::
       Nil
 
   // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
@@ -705,13 +705,13 @@ object HiveTypeCoercion {
    * Casts types according to the expected input types for Expressions that have the trait
    * [[AutoCastInputTypes]].
    */
-  object AddCastForAutoCastInputTypes extends Rule[LogicalPlan] {
+  object ImplicitTypeCasts extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      case e: AutoCastInputTypes if e.children.map(_.dataType) != e.expectedChildTypes =>
-        val newC = (e.children, e.children.map(_.dataType), e.expectedChildTypes).zipped.map {
+      case e: AutoCastInputTypes if e.children.map(_.dataType) != e.inputTypes =>
+        val newC = (e.children, e.children.map(_.dataType), e.inputTypes).zipped.map {
           case (child, actual, expected) =>
             if (actual == expected) child else Cast(child, expected)
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index b5063f32fa529..e18a3118945e8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -265,17 +265,38 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
   }
 }
 
+/**
+ * An trait that gets mixin to define the expected input types of an expression.
+ */
+trait ExpectsInputTypes { self: Expression =>
+
+  /**
+   * Expected input types from child expressions. The i-th position in the returned seq indicates
+   * the type requirement for the i-th child.
+   *
+   * The possible values at each position are:
+   * 1. a specific data type, e.g. LongType, StringType.
+   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
+   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
+   */
+  def inputTypes: Seq[Any]
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
+    TypeCheckResult.TypeCheckSuccess
+  }
+}
+
 /**
  * Expressions that require a specific `DataType` as input should implement this trait
  * so that the proper type conversions can be performed in the analyzer.
  */
-trait AutoCastInputTypes {
-  self: Expression =>
+trait AutoCastInputTypes { self: Expression =>
 
-  def expectedChildTypes: Seq[DataType]
+  def inputTypes: Seq[DataType]
 
   override def checkInputDataTypes(): TypeCheckResult = {
-    // We will always do type casting for `ExpectsInputTypes` in `HiveTypeCoercion`,
+    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
     // so type mismatch error won't be reported here, but for underling `Cast`s.
     TypeCheckResult.TypeCheckSuccess
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index da63f2fa970cf..b51318dd5044c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -59,7 +59,7 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
   extends UnaryExpression with Serializable with AutoCastInputTypes {
   self: Product =>
 
-  override def expectedChildTypes: Seq[DataType] = Seq(DoubleType)
+  override def inputTypes: Seq[DataType] = Seq(DoubleType)
   override def dataType: DataType = DoubleType
   override def nullable: Boolean = true
   override def toString: String = s"$name($child)"
@@ -98,7 +98,7 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
 abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
   extends BinaryExpression with Serializable with AutoCastInputTypes { self: Product =>
 
-  override def expectedChildTypes: Seq[DataType] = Seq(DoubleType, DoubleType)
+  override def inputTypes: Seq[DataType] = Seq(DoubleType, DoubleType)
 
   override def toString: String = s"$name($left, $right)"
 
@@ -210,7 +210,7 @@ case class ToRadians(child: Expression) extends UnaryMathExpression(math.toRadia
 case class Bin(child: Expression)
   extends UnaryExpression with Serializable with AutoCastInputTypes {
 
-  override def expectedChildTypes: Seq[DataType] = Seq(LongType)
+  override def inputTypes: Seq[DataType] = Seq(LongType)
   override def dataType: DataType = StringType
 
   override def eval(input: InternalRow): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index a7bcbe46c339a..407023e472081 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -36,7 +36,7 @@ case class Md5(child: Expression)
 
   override def dataType: DataType = StringType
 
-  override def expectedChildTypes: Seq[DataType] = Seq(BinaryType)
+  override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
   override def eval(input: InternalRow): Any = {
     val value = child.eval(input)
@@ -68,7 +68,7 @@ case class Sha2(left: Expression, right: Expression)
 
   override def toString: String = s"SHA2($left, $right)"
 
-  override def expectedChildTypes: Seq[DataType] = Seq(BinaryType, IntegerType)
+  override def inputTypes: Seq[DataType] = Seq(BinaryType, IntegerType)
 
   override def eval(input: InternalRow): Any = {
     val evalE1 = left.eval(input)
@@ -151,7 +151,7 @@ case class Sha1(child: Expression) extends UnaryExpression with AutoCastInputTyp
 
   override def dataType: DataType = StringType
 
-  override def expectedChildTypes: Seq[DataType] = Seq(BinaryType)
+  override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
   override def eval(input: InternalRow): Any = {
     val value = child.eval(input)
@@ -179,7 +179,7 @@ case class Crc32(child: Expression)
 
   override def dataType: DataType = LongType
 
-  override def expectedChildTypes: Seq[DataType] = Seq(BinaryType)
+  override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
   override def eval(input: InternalRow): Any = {
     val value = child.eval(input)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 98cd5aa8148c4..a777f77add2db 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -72,7 +72,7 @@ trait PredicateHelper {
 case class Not(child: Expression) extends UnaryExpression with Predicate with AutoCastInputTypes {
   override def toString: String = s"NOT $child"
 
-  override def expectedChildTypes: Seq[DataType] = Seq(BooleanType)
+  override def inputTypes: Seq[DataType] = Seq(BooleanType)
 
   override def eval(input: InternalRow): Any = {
     child.eval(input) match {
@@ -122,7 +122,7 @@ case class InSet(value: Expression, hset: Set[Any])
 case class And(left: Expression, right: Expression)
   extends BinaryExpression with Predicate with AutoCastInputTypes {
 
-  override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
   override def symbol: String = "&&"
 
@@ -171,7 +171,7 @@ case class And(left: Expression, right: Expression)
 case class Or(left: Expression, right: Expression)
   extends BinaryExpression with Predicate with AutoCastInputTypes {
 
-  override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
   override def symbol: String = "||"
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index ce184e4f32f18..4cbfc4e084948 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -32,7 +32,7 @@ trait StringRegexExpression extends AutoCastInputTypes {
 
   override def nullable: Boolean = left.nullable || right.nullable
   override def dataType: DataType = BooleanType
-  override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType)
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
   // try cache the pattern for Literal
   private lazy val cache: Pattern = right match {
@@ -117,7 +117,7 @@ trait CaseConversionExpression extends AutoCastInputTypes {
   def convert(v: UTF8String): UTF8String
 
   override def dataType: DataType = StringType
-  override def expectedChildTypes: Seq[DataType] = Seq(StringType)
+  override def inputTypes: Seq[DataType] = Seq(StringType)
 
   override def eval(input: InternalRow): Any = {
     val evaluated = child.eval(input)
@@ -165,7 +165,7 @@ trait StringComparison extends AutoCastInputTypes {
 
   override def nullable: Boolean = left.nullable || right.nullable
 
-  override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType)
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
   override def eval(input: InternalRow): Any = {
     val leftEval = left.eval(input)
@@ -238,7 +238,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
     if (str.dataType == BinaryType) str.dataType else StringType
   }
 
-  override def expectedChildTypes: Seq[DataType] = Seq(StringType, IntegerType, IntegerType)
+  override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, IntegerType)
 
   override def children: Seq[Expression] = str :: pos :: len :: Nil
 
@@ -297,7 +297,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
  */
 case class StringLength(child: Expression) extends UnaryExpression with AutoCastInputTypes {
   override def dataType: DataType = IntegerType
-  override def expectedChildTypes: Seq[DataType] = Seq(StringType)
+  override def inputTypes: Seq[DataType] = Seq(StringType)
 
   override def eval(input: InternalRow): Any = {
     val string = child.eval(input)

From 31b4a3d7f2be9053a041e5ae67418562a93d80d8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 1 Jul 2015 10:31:35 -0700
Subject: [PATCH 0168/1454] [SPARK-8621] [SQL] support empty string as column
 name

improve the empty check in `parseAttributeName` so that we can allow empty string as column name.
Close https://github.com/apache/spark/pull/7117

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7149 from cloud-fan/8621 and squashes the following commits:

efa9e3e [Wenchen Fan] support empty string
---
 .../spark/sql/catalyst/plans/logical/LogicalPlan.scala     | 4 ++--
 .../test/scala/org/apache/spark/sql/DataFrameSuite.scala   | 7 +++++++
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index b009a200b920f..e911b907e8536 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -161,7 +161,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
           if (tmp.nonEmpty) throw e
           inBacktick = true
         } else if (char == '.') {
-          if (tmp.isEmpty) throw e
+          if (name(i - 1) == '.' || i == name.length - 1) throw e
           nameParts += tmp.mkString
           tmp.clear()
         } else {
@@ -170,7 +170,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
       }
       i += 1
     }
-    if (tmp.isEmpty || inBacktick) throw e
+    if (inBacktick) throw e
     nameParts += tmp.mkString
     nameParts.toSeq
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 50d324c0686fa..afb1cf5f8d1cb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -730,4 +730,11 @@ class DataFrameSuite extends QueryTest {
     val res11 = ctx.range(-1).select("id")
     assert(res11.count == 0)
   }
+
+  test("SPARK-8621: support empty string column name") {
+    val df = Seq(Tuple1(1)).toDF("").as("t")
+    // We should allow empty string as column name
+    df.col("")
+    df.col("t.``")
+  }
 }

From 184de91d15a4bfc5c014e8cf86211874bba4593f Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Wed, 1 Jul 2015 11:14:07 -0700
Subject: [PATCH 0169/1454] [SPARK-6263] [MLLIB] Python MLlib API missing
 items: Utils

Implement missing API in pyspark.

MLUtils
* appendBias
* loadVectors

`kFold` is also missing however I am not sure `ClassTag` can be passed or restored through python.

Author: lewuathe <lewuathe@me.com>

Closes #5707 from Lewuathe/SPARK-6263 and squashes the following commits:

16863ea [lewuathe] Merge master
3fc27e7 [lewuathe] Merge branch 'master' into SPARK-6263
6084e9c [lewuathe] Resolv conflict
d2aa2a0 [lewuathe] Resolv conflict
9c329d8 [lewuathe] Fix efficiency
3a12a2d [lewuathe] Merge branch 'master' into SPARK-6263
1d4714b [lewuathe] Fix style
b29e2bc [lewuathe] Remove scipy dependencies
e32eb40 [lewuathe] Merge branch 'master' into SPARK-6263
25d3c9d [lewuathe] Remove unnecessary imports
7ec04db [lewuathe] Resolv conflict
1502d13 [lewuathe] Resolv conflict
d6bd416 [lewuathe] Check existence of scipy.sparse
5d555b1 [lewuathe] Construct scipy.sparse matrix
c345a44 [lewuathe] Merge branch 'master' into SPARK-6263
b8b5ef7 [lewuathe] Fix unnecessary sort method
d254be7 [lewuathe] Merge branch 'master' into SPARK-6263
62a9c7e [lewuathe] Fix appendBias return type
454c73d [lewuathe] Merge branch 'master' into SPARK-6263
a353354 [lewuathe] Remove unnecessary appendBias implementation
44295c2 [lewuathe] Merge branch 'master' into SPARK-6263
64f72ad [lewuathe] Merge branch 'master' into SPARK-6263
c728046 [lewuathe] Fix style
2980569 [lewuathe] [SPARK-6263] Python MLlib API missing items: Utils
---
 .../mllib/api/python/PythonMLLibAPI.scala     |  9 ++++
 python/pyspark/mllib/tests.py                 | 43 +++++++++++++++++++
 python/pyspark/mllib/util.py                  | 22 ++++++++++
 3 files changed, 74 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index a66a404d5c846..458fab48fef5a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -75,6 +75,15 @@ private[python] class PythonMLLibAPI extends Serializable {
       minPartitions: Int): JavaRDD[LabeledPoint] =
     MLUtils.loadLabeledPoints(jsc.sc, path, minPartitions)
 
+  /**
+   * Loads and serializes vectors saved with `RDD#saveAsTextFile`.
+   * @param jsc Java SparkContext
+   * @param path file or directory path in any Hadoop-supported file system URI
+   * @return serialized vectors in a RDD
+   */
+  def loadVectors(jsc: JavaSparkContext, path: String): RDD[Vector] =
+    MLUtils.loadVectors(jsc.sc, path)
+
   private def trainRegressionModel(
       learner: GeneralizedLinearAlgorithm[_ <: GeneralizedLinearModel],
       data: JavaRDD[LabeledPoint],
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index f0091d6faccce..49ce125de7e78 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -54,6 +54,7 @@
 from pyspark.mllib.feature import IDF
 from pyspark.mllib.feature import StandardScaler, ElementwiseProduct
 from pyspark.mllib.util import LinearDataGenerator
+from pyspark.mllib.util import MLUtils
 from pyspark.serializers import PickleSerializer
 from pyspark.streaming import StreamingContext
 from pyspark.sql import SQLContext
@@ -1290,6 +1291,48 @@ def func(rdd):
         self.assertTrue(mean_absolute_errors[1] - mean_absolute_errors[-1] > 2)
 
 
+class MLUtilsTests(MLlibTestCase):
+    def test_append_bias(self):
+        data = [2.0, 2.0, 2.0]
+        ret = MLUtils.appendBias(data)
+        self.assertEqual(ret[3], 1.0)
+        self.assertEqual(type(ret), DenseVector)
+
+    def test_append_bias_with_vector(self):
+        data = Vectors.dense([2.0, 2.0, 2.0])
+        ret = MLUtils.appendBias(data)
+        self.assertEqual(ret[3], 1.0)
+        self.assertEqual(type(ret), DenseVector)
+
+    def test_append_bias_with_sp_vector(self):
+        data = Vectors.sparse(3, {0: 2.0, 2: 2.0})
+        expected = Vectors.sparse(4, {0: 2.0, 2: 2.0, 3: 1.0})
+        # Returned value must be SparseVector
+        ret = MLUtils.appendBias(data)
+        self.assertEqual(ret, expected)
+        self.assertEqual(type(ret), SparseVector)
+
+    def test_load_vectors(self):
+        import shutil
+        data = [
+            [1.0, 2.0, 3.0],
+            [1.0, 2.0, 3.0]
+        ]
+        temp_dir = tempfile.mkdtemp()
+        load_vectors_path = os.path.join(temp_dir, "test_load_vectors")
+        try:
+            self.sc.parallelize(data).saveAsTextFile(load_vectors_path)
+            ret_rdd = MLUtils.loadVectors(self.sc, load_vectors_path)
+            ret = ret_rdd.collect()
+            self.assertEqual(len(ret), 2)
+            self.assertEqual(ret[0], DenseVector([1.0, 2.0, 3.0]))
+            self.assertEqual(ret[1], DenseVector([1.0, 2.0, 3.0]))
+        except:
+            self.fail()
+        finally:
+            shutil.rmtree(load_vectors_path)
+
+
 if __name__ == "__main__":
     if not _have_scipy:
         print("NOTE: Skipping SciPy tests as it does not seem to be installed")
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 348238319e407..875d3b2d642c6 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -169,6 +169,28 @@ def loadLabeledPoints(sc, path, minPartitions=None):
         minPartitions = minPartitions or min(sc.defaultParallelism, 2)
         return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
 
+    @staticmethod
+    def appendBias(data):
+        """
+        Returns a new vector with `1.0` (bias) appended to
+        the end of the input vector.
+        """
+        vec = _convert_to_vector(data)
+        if isinstance(vec, SparseVector):
+            newIndices = np.append(vec.indices, len(vec))
+            newValues = np.append(vec.values, 1.0)
+            return SparseVector(len(vec) + 1, newIndices, newValues)
+        else:
+            return _convert_to_vector(np.append(vec.toArray(), 1.0))
+
+    @staticmethod
+    def loadVectors(sc, path):
+        """
+        Loads vectors saved using `RDD[Vector].saveAsTextFile`
+        with the default number of partitions.
+        """
+        return callMLlibFunc("loadVectors", sc, path)
+
 
 class Saveable(object):
     """

From 2012913355993e6516e4c81dbc92e579977131da Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Wed, 1 Jul 2015 11:17:56 -0700
Subject: [PATCH 0170/1454] [SPARK-8308] [MLLIB] add missing save load for
 python example

jira: https://issues.apache.org/jira/browse/SPARK-8308

1. add some missing save/load in python examples. , LogisticRegression, LinearRegression and NaiveBayes
2. tune down iterations for MatrixFactorization, since current number will trigger StackOverflow for default java configuration (>1M)

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #6760 from hhbyyh/docUpdate and squashes the following commits:

9bd3383 [Yuhao Yang] update scala example
8a44692 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into docUpdate
077cbb8 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into docUpdate
3e948dc [Yuhao Yang] add missing save load for python example
---
 docs/mllib-collaborative-filtering.md |  6 +++---
 docs/mllib-linear-methods.md          | 12 ++++++++++--
 docs/mllib-naive-bayes.md             |  6 +++++-
 3 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index dfdf6216b270c..eedc23424ad54 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -77,7 +77,7 @@ val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
 
 // Build the recommendation model using ALS
 val rank = 10
-val numIterations = 20
+val numIterations = 10
 val model = ALS.train(ratings, rank, numIterations, 0.01)
 
 // Evaluate the model on rating data
@@ -149,7 +149,7 @@ public class CollaborativeFiltering {
 
     // Build the recommendation model using ALS
     int rank = 10;
-    int numIterations = 20;
+    int numIterations = 10;
     MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01); 
 
     // Evaluate the model on rating data
@@ -210,7 +210,7 @@ ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l
 
 # Build the recommendation model using Alternating Least Squares
 rank = 10
-numIterations = 20
+numIterations = 10
 model = ALS.train(ratings, rank, numIterations)
 
 # Evaluate the model on training data
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 2a2a7c13186d8..3927d65fbf8fb 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -499,7 +499,7 @@ Note that the Python API does not yet support multiclass classification and mode
 will in the future.
 
 {% highlight python %}
-from pyspark.mllib.classification import LogisticRegressionWithLBFGS
+from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
 from pyspark.mllib.regression import LabeledPoint
 from numpy import array
 
@@ -518,6 +518,10 @@ model = LogisticRegressionWithLBFGS.train(parsedData)
 labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
 trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
 print("Training Error = " + str(trainErr))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LogisticRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 </div>
@@ -668,7 +672,7 @@ values. We compute the mean squared error at the end to evaluate
 Note that the Python API does not yet support model save/load but will in the future.
 
 {% highlight python %}
-from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD
+from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel
 from numpy import array
 
 # Load and parse the data
@@ -686,6 +690,10 @@ model = LinearRegressionWithSGD.train(parsedData)
 valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
 MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y) / valuesAndPreds.count()
 print("Mean Squared Error = " + str(MSE))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LinearRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 </div>
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index bf6d124fd5d8d..e73bd30f3a90a 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -119,7 +119,7 @@ used for evaluation and prediction.
 Note that the Python API does not yet support model save/load but will in the future.
 
 {% highlight python %}
-from pyspark.mllib.classification import NaiveBayes
+from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
 from pyspark.mllib.linalg import Vectors
 from pyspark.mllib.regression import LabeledPoint
 
@@ -140,6 +140,10 @@ model = NaiveBayes.train(training, 1.0)
 # Make prediction and test accuracy.
 predictionAndLabel = test.map(lambda p : (model.predict(p.features), p.label))
 accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = NaiveBayesModel.load(sc, "myModelPath")
 {% endhighlight %}
 
 </div>

From b8faa32875aa560cdce340266d898902a920418d Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 1 Jul 2015 11:57:52 -0700
Subject: [PATCH 0171/1454] [SPARK-8765] [MLLIB] [PYTHON] removed flaky python
 PIC test

See failure: [https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/36133/console]

CC yanboliang  mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7164 from jkbradley/pic-python-test and squashes the following commits:

156d55b [Joseph K. Bradley] removed flaky python PIC test
---
 python/pyspark/mllib/clustering.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index e3c8a24c4a751..a3eab635282f6 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -288,16 +288,12 @@ class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader):
     >>> model = PowerIterationClustering.train(rdd, 2, 100)
     >>> model.k
     2
-    >>> sorted(model.assignments().collect())
-    [Assignment(id=0, cluster=1), Assignment(id=1, cluster=0), ...
     >>> import os, tempfile
     >>> path = tempfile.mkdtemp()
     >>> model.save(sc, path)
     >>> sameModel = PowerIterationClusteringModel.load(sc, path)
     >>> sameModel.k
     2
-    >>> sorted(sameModel.assignments().collect())
-    [Assignment(id=0, cluster=1), Assignment(id=1, cluster=0), ...
     >>> from shutil import rmtree
     >>> try:
     ...     rmtree(path)

From 75b9fe4c5ff6f206c6fc9100563d625b39f142ba Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 1 Jul 2015 11:59:24 -0700
Subject: [PATCH 0172/1454] [SPARK-8378] [STREAMING] Add the Python API for
 Flume

Author: zsxwing <zsxwing@gmail.com>

Closes #6830 from zsxwing/flume-python and squashes the following commits:

78dfdac [zsxwing] Fix the compile error in the test code
f1bf3c0 [zsxwing] Address TD's comments
0449723 [zsxwing] Add sbt goal streaming-flume-assembly/assembly
e93736b [zsxwing] Fix the test case for determine_modules_to_test
9d5821e [zsxwing] Fix pyspark_core dependencies
f9ee681 [zsxwing] Merge branch 'master' into flume-python
7a55837 [zsxwing] Add streaming_flume_assembly to run-tests.py
b96b0de [zsxwing] Merge branch 'master' into flume-python
ce85e83 [zsxwing] Fix incompatible issues for Python 3
01cbb3d [zsxwing] Add import sys
152364c [zsxwing] Fix the issue that StringIO doesn't work in Python 3
14ba0ff [zsxwing] Add flume-assembly for sbt building
b8d5551 [zsxwing] Merge branch 'master' into flume-python
4762c34 [zsxwing] Fix the doc
0336579 [zsxwing] Refactor Flume unit tests and also add tests for Python API
9f33873 [zsxwing] Add the Python API for Flume
---
 dev/run-tests.py                              |   7 +-
 dev/sparktestsupport/modules.py               |  15 +-
 docs/streaming-flume-integration.md           |  18 ++
 docs/streaming-programming-guide.md           |   2 +-
 .../main/python/streaming/flume_wordcount.py  |  55 +++++
 external/flume-assembly/pom.xml               | 135 +++++++++++
 .../streaming/flume/FlumeTestUtils.scala      | 116 ++++++++++
 .../spark/streaming/flume/FlumeUtils.scala    |  76 ++++++-
 .../flume/PollingFlumeTestUtils.scala         | 209 ++++++++++++++++++
 .../flume/FlumePollingStreamSuite.scala       | 173 +++------------
 .../streaming/flume/FlumeStreamSuite.scala    | 106 ++-------
 pom.xml                                       |   1 +
 project/SparkBuild.scala                      |   6 +-
 python/pyspark/streaming/flume.py             | 147 ++++++++++++
 python/pyspark/streaming/tests.py             | 179 ++++++++++++++-
 15 files changed, 1009 insertions(+), 236 deletions(-)
 create mode 100644 examples/src/main/python/streaming/flume_wordcount.py
 create mode 100644 external/flume-assembly/pom.xml
 create mode 100644 external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
 create mode 100644 external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
 create mode 100644 python/pyspark/streaming/flume.py

diff --git a/dev/run-tests.py b/dev/run-tests.py
index 4596e07014733..1f0d218514f92 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -96,8 +96,8 @@ def determine_modules_to_test(changed_modules):
     ['examples', 'graphx']
     >>> x = sorted(x.name for x in determine_modules_to_test([modules.sql]))
     >>> x # doctest: +NORMALIZE_WHITESPACE
-    ['examples', 'hive-thriftserver', 'mllib', 'pyspark-core', 'pyspark-ml', \
-     'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming', 'sparkr', 'sql']
+    ['examples', 'hive-thriftserver', 'mllib', 'pyspark-ml', \
+     'pyspark-mllib', 'pyspark-sql', 'sparkr', 'sql']
     """
     # If we're going to have to run all of the tests, then we can just short-circuit
     # and return 'root'. No module depends on root, so if it appears then it will be
@@ -293,7 +293,8 @@ def build_spark_sbt(hadoop_version):
     build_profiles = get_hadoop_profiles(hadoop_version) + modules.root.build_profile_flags
     sbt_goals = ["package",
                  "assembly/assembly",
-                 "streaming-kafka-assembly/assembly"]
+                 "streaming-kafka-assembly/assembly",
+                 "streaming-flume-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
     print("[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments: ",
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index efe3a897e9c10..993583e2f4119 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -203,7 +203,7 @@ def contains_file(self, filename):
 
 
 streaming_flume = Module(
-    name="streaming_flume",
+    name="streaming-flume",
     dependencies=[streaming],
     source_file_regexes=[
         "external/flume",
@@ -214,6 +214,15 @@ def contains_file(self, filename):
 )
 
 
+streaming_flume_assembly = Module(
+    name="streaming-flume-assembly",
+    dependencies=[streaming_flume, streaming_flume_sink],
+    source_file_regexes=[
+        "external/flume-assembly",
+    ]
+)
+
+
 mllib = Module(
     name="mllib",
     dependencies=[streaming, sql],
@@ -241,7 +250,7 @@ def contains_file(self, filename):
 
 pyspark_core = Module(
     name="pyspark-core",
-    dependencies=[mllib, streaming, streaming_kafka],
+    dependencies=[],
     source_file_regexes=[
         "python/(?!pyspark/(ml|mllib|sql|streaming))"
     ],
@@ -281,7 +290,7 @@ def contains_file(self, filename):
 
 pyspark_streaming = Module(
     name="pyspark-streaming",
-    dependencies=[pyspark_core, streaming, streaming_kafka],
+    dependencies=[pyspark_core, streaming, streaming_kafka, streaming_flume_assembly],
     source_file_regexes=[
         "python/pyspark/streaming"
     ],
diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md
index 8d6e74370918f..de0461010daec 100644
--- a/docs/streaming-flume-integration.md
+++ b/docs/streaming-flume-integration.md
@@ -58,6 +58,15 @@ configuring Flume agents.
 	See the [API docs](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html)
 	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java).
 	</div>
+	<div data-lang="python" markdown="1">
+		from pyspark.streaming.flume import FlumeUtils
+
+		flumeStream = FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port])
+
+	By default, the Python API will decode Flume event body as UTF8 encoded strings. You can specify your custom decoding function to decode the body byte arrays in Flume events to any arbitrary data type. 
+	See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.flume.FlumeUtils)
+	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/flume_wordcount.py).
+	</div>
 	</div>
 
 	Note that the hostname should be the same as the one used by the resource manager in the
@@ -135,6 +144,15 @@ configuring Flume agents.
 		JavaReceiverInputDStream<SparkFlumeEvent>flumeStream =
 			FlumeUtils.createPollingStream(streamingContext, [sink machine hostname], [sink port]);
 	</div>
+	<div data-lang="python" markdown="1">
+		from pyspark.streaming.flume import FlumeUtils
+
+		addresses = [([sink machine hostname 1], [sink port 1]), ([sink machine hostname 2], [sink port 2])]
+		flumeStream = FlumeUtils.createPollingStream(streamingContext, addresses)
+
+	By default, the Python API will decode Flume event body as UTF8 encoded strings. You can specify your custom decoding function to decode the body byte arrays in Flume events to any arbitrary data type.
+	See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.flume.FlumeUtils).
+	</div>
 	</div>
 
 	See the Scala example [FlumePollingEventCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala).
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index b784d59666fec..e72d5580dae55 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -683,7 +683,7 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea
 {:.no_toc}
 
 <span class="badge" style="background-color: grey">Python API</span> As of Spark {{site.SPARK_VERSION_SHORT}},
-out of these sources, *only* Kafka is available in the Python API. We will add more advanced sources in the Python API in future.
+out of these sources, *only* Kafka and Flume are available in the Python API. We will add more advanced sources in the Python API in future.
 
 This category of sources require interfacing with external non-Spark libraries, some of them with
 complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts
diff --git a/examples/src/main/python/streaming/flume_wordcount.py b/examples/src/main/python/streaming/flume_wordcount.py
new file mode 100644
index 0000000000000..091b64d8c4af4
--- /dev/null
+++ b/examples/src/main/python/streaming/flume_wordcount.py
@@ -0,0 +1,55 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+ Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
+ Usage: flume_wordcount.py <hostname> <port>
+
+ To run this on your local machine, you need to setup Flume first, see
+ https://flume.apache.org/documentation.html
+
+ and then run the example
+    `$ bin/spark-submit --jars external/flume-assembly/target/scala-*/\
+      spark-streaming-flume-assembly-*.jar examples/src/main/python/streaming/flume_wordcount.py \
+      localhost 12345
+"""
+from __future__ import print_function
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+from pyspark.streaming.flume import FlumeUtils
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("Usage: flume_wordcount.py <hostname> <port>", file=sys.stderr)
+        exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingFlumeWordCount")
+    ssc = StreamingContext(sc, 1)
+
+    hostname, port = sys.argv[1:]
+    kvs = FlumeUtils.createStream(ssc, hostname, int(port))
+    lines = kvs.map(lambda x: x[1])
+    counts = lines.flatMap(lambda line: line.split(" ")) \
+        .map(lambda word: (word, 1)) \
+        .reduceByKey(lambda a, b: a+b)
+    counts.pprint()
+
+    ssc.start()
+    ssc.awaitTermination()
diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
new file mode 100644
index 0000000000000..8565cd83edfa2
--- /dev/null
+++ b/external/flume-assembly/pom.xml
@@ -0,0 +1,135 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent_2.10</artifactId>
+    <version>1.5.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-flume-assembly_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External Flume Assembly</name>
+  <url>http://spark.apache.org/</url>
+
+  <properties>
+    <sbt.project.name>streaming-flume-assembly</sbt.project.name>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-flume_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro</artifactId>
+      <version>${avro.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-ipc</artifactId>
+      <version>${avro.version}</version>
+      <exclusions>
+        <exclusion>
+          <groupId>io.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.mortbay.jetty</groupId>
+          <artifactId>jetty</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.mortbay.jetty</groupId>
+          <artifactId>jetty-util</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.mortbay.jetty</groupId>
+          <artifactId>servlet-api</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.apache.velocity</groupId>
+          <artifactId>velocity</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
+  </dependencies>
+
+  <build>
+  <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+  <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+  <plugins>
+    <plugin>
+      <groupId>org.apache.maven.plugins</groupId>
+      <artifactId>maven-shade-plugin</artifactId>
+      <configuration>
+        <shadedArtifactAttached>false</shadedArtifactAttached>
+        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-flume-assembly-${project.version}.jar</outputFile>
+        <artifactSet>
+          <includes>
+            <include>*:*</include>
+          </includes>
+        </artifactSet>
+        <filters>
+          <filter>
+            <artifact>*:*</artifact>
+            <excludes>
+              <exclude>META-INF/*.SF</exclude>
+              <exclude>META-INF/*.DSA</exclude>
+              <exclude>META-INF/*.RSA</exclude>
+            </excludes>
+          </filter>
+        </filters>
+      </configuration>
+      <executions>
+        <execution>
+          <phase>package</phase>
+          <goals>
+            <goal>shade</goal>
+          </goals>
+          <configuration>
+            <transformers>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                <resource>reference.conf</resource>
+              </transformer>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
+                <resource>log4j.properties</resource>
+              </transformer>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer"/>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/>
+            </transformers>
+          </configuration>
+        </execution>
+      </executions>
+    </plugin>
+  </plugins>
+</build>
+</project>
+
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
new file mode 100644
index 0000000000000..9d9c3b189415f
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
@@ -0,0 +1,116 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.flume
+
+import java.net.{InetSocketAddress, ServerSocket}
+import java.nio.ByteBuffer
+import java.util.{List => JList}
+
+import scala.collection.JavaConversions._
+
+import com.google.common.base.Charsets.UTF_8
+import org.apache.avro.ipc.NettyTransceiver
+import org.apache.avro.ipc.specific.SpecificRequestor
+import org.apache.commons.lang3.RandomUtils
+import org.apache.flume.source.avro
+import org.apache.flume.source.avro.{AvroSourceProtocol, AvroFlumeEvent}
+import org.jboss.netty.channel.ChannelPipeline
+import org.jboss.netty.channel.socket.SocketChannel
+import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory
+import org.jboss.netty.handler.codec.compression.{ZlibDecoder, ZlibEncoder}
+
+import org.apache.spark.util.Utils
+import org.apache.spark.SparkConf
+
+/**
+ * Share codes for Scala and Python unit tests
+ */
+private[flume] class FlumeTestUtils {
+
+  private var transceiver: NettyTransceiver = null
+
+  private val testPort: Int = findFreePort()
+
+  def getTestPort(): Int = testPort
+
+  /** Find a free port */
+  private def findFreePort(): Int = {
+    val candidatePort = RandomUtils.nextInt(1024, 65536)
+    Utils.startServiceOnPort(candidatePort, (trialPort: Int) => {
+      val socket = new ServerSocket(trialPort)
+      socket.close()
+      (null, trialPort)
+    }, new SparkConf())._2
+  }
+
+  /** Send data to the flume receiver */
+  def writeInput(input: JList[String], enableCompression: Boolean): Unit = {
+    val testAddress = new InetSocketAddress("localhost", testPort)
+
+    val inputEvents = input.map { item =>
+      val event = new AvroFlumeEvent
+      event.setBody(ByteBuffer.wrap(item.getBytes(UTF_8)))
+      event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header"))
+      event
+    }
+
+    // if last attempted transceiver had succeeded, close it
+    close()
+
+    // Create transceiver
+    transceiver = {
+      if (enableCompression) {
+        new NettyTransceiver(testAddress, new CompressionChannelFactory(6))
+      } else {
+        new NettyTransceiver(testAddress)
+      }
+    }
+
+    // Create Avro client with the transceiver
+    val client = SpecificRequestor.getClient(classOf[AvroSourceProtocol], transceiver)
+    if (client == null) {
+      throw new AssertionError("Cannot create client")
+    }
+
+    // Send data
+    val status = client.appendBatch(inputEvents.toList)
+    if (status != avro.Status.OK) {
+      throw new AssertionError("Sent events unsuccessfully")
+    }
+  }
+
+  def close(): Unit = {
+    if (transceiver != null) {
+      transceiver.close()
+      transceiver = null
+    }
+  }
+
+  /** Class to create socket channel with compression */
+  private class CompressionChannelFactory(compressionLevel: Int)
+    extends NioClientSocketChannelFactory {
+
+    override def newChannel(pipeline: ChannelPipeline): SocketChannel = {
+      val encoder = new ZlibEncoder(compressionLevel)
+      pipeline.addFirst("deflater", encoder)
+      pipeline.addFirst("inflater", new ZlibDecoder())
+      super.newChannel(pipeline)
+    }
+  }
+
+}
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
index 44dec45c227ca..095bfb0c73a9a 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -18,10 +18,16 @@
 package org.apache.spark.streaming.flume
 
 import java.net.InetSocketAddress
+import java.io.{DataOutputStream, ByteArrayOutputStream}
+import java.util.{List => JList, Map => JMap}
 
+import scala.collection.JavaConversions._
+
+import org.apache.spark.api.java.function.PairFunction
+import org.apache.spark.api.python.PythonRDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext}
+import org.apache.spark.streaming.api.java.{JavaPairDStream, JavaReceiverInputDStream, JavaStreamingContext}
 import org.apache.spark.streaming.dstream.ReceiverInputDStream
 
 
@@ -236,3 +242,71 @@ object FlumeUtils {
     createPollingStream(jssc.ssc, addresses, storageLevel, maxBatchSize, parallelism)
   }
 }
+
+/**
+ * This is a helper class that wraps the methods in FlumeUtils into more Python-friendly class and
+ * function so that it can be easily instantiated and called from Python's FlumeUtils.
+ */
+private class FlumeUtilsPythonHelper {
+
+  def createStream(
+      jssc: JavaStreamingContext,
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel,
+      enableDecompression: Boolean
+    ): JavaPairDStream[Array[Byte], Array[Byte]] = {
+    val dstream = FlumeUtils.createStream(jssc, hostname, port, storageLevel, enableDecompression)
+    FlumeUtilsPythonHelper.toByteArrayPairDStream(dstream)
+  }
+
+  def createPollingStream(
+      jssc: JavaStreamingContext,
+      hosts: JList[String],
+      ports: JList[Int],
+      storageLevel: StorageLevel,
+      maxBatchSize: Int,
+      parallelism: Int
+    ): JavaPairDStream[Array[Byte], Array[Byte]] = {
+    assert(hosts.length == ports.length)
+    val addresses = hosts.zip(ports).map {
+      case (host, port) => new InetSocketAddress(host, port)
+    }
+    val dstream = FlumeUtils.createPollingStream(
+      jssc.ssc, addresses, storageLevel, maxBatchSize, parallelism)
+    FlumeUtilsPythonHelper.toByteArrayPairDStream(dstream)
+  }
+
+}
+
+private object FlumeUtilsPythonHelper {
+
+  private def stringMapToByteArray(map: JMap[CharSequence, CharSequence]): Array[Byte] = {
+    val byteStream = new ByteArrayOutputStream()
+    val output = new DataOutputStream(byteStream)
+    try {
+      output.writeInt(map.size)
+      map.foreach { kv =>
+        PythonRDD.writeUTF(kv._1.toString, output)
+        PythonRDD.writeUTF(kv._2.toString, output)
+      }
+      byteStream.toByteArray
+    }
+    finally {
+      output.close()
+    }
+  }
+
+  private def toByteArrayPairDStream(dstream: JavaReceiverInputDStream[SparkFlumeEvent]):
+    JavaPairDStream[Array[Byte], Array[Byte]] = {
+    dstream.mapToPair(new PairFunction[SparkFlumeEvent, Array[Byte], Array[Byte]] {
+      override def call(sparkEvent: SparkFlumeEvent): (Array[Byte], Array[Byte]) = {
+        val event = sparkEvent.event
+        val byteBuffer = event.getBody
+        val body = new Array[Byte](byteBuffer.remaining())
+        byteBuffer.get(body)
+        (stringMapToByteArray(event.getHeaders), body)
+      }
+    })
+  }
+}
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
new file mode 100644
index 0000000000000..91d63d49dbec3
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
@@ -0,0 +1,209 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.flume
+
+import java.util.concurrent._
+import java.util.{List => JList, Map => JMap}
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+import com.google.common.base.Charsets.UTF_8
+import org.apache.flume.event.EventBuilder
+import org.apache.flume.Context
+import org.apache.flume.channel.MemoryChannel
+import org.apache.flume.conf.Configurables
+
+import org.apache.spark.streaming.flume.sink.{SparkSinkConfig, SparkSink}
+
+/**
+ * Share codes for Scala and Python unit tests
+ */
+private[flume] class PollingFlumeTestUtils {
+
+  private val batchCount = 5
+  val eventsPerBatch = 100
+  private val totalEventsPerChannel = batchCount * eventsPerBatch
+  private val channelCapacity = 5000
+
+  def getTotalEvents: Int = totalEventsPerChannel * channels.size
+
+  private val channels = new ArrayBuffer[MemoryChannel]
+  private val sinks = new ArrayBuffer[SparkSink]
+
+  /**
+   * Start a sink and return the port of this sink
+   */
+  def startSingleSink(): Int = {
+    channels.clear()
+    sinks.clear()
+
+    // Start the channel and sink.
+    val context = new Context()
+    context.put("capacity", channelCapacity.toString)
+    context.put("transactionCapacity", "1000")
+    context.put("keep-alive", "0")
+    val channel = new MemoryChannel()
+    Configurables.configure(channel, context)
+
+    val sink = new SparkSink()
+    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
+    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(0))
+    Configurables.configure(sink, context)
+    sink.setChannel(channel)
+    sink.start()
+
+    channels += (channel)
+    sinks += sink
+
+    sink.getPort()
+  }
+
+  /**
+   * Start 2 sinks and return the ports
+   */
+  def startMultipleSinks(): JList[Int] = {
+    channels.clear()
+    sinks.clear()
+
+    // Start the channel and sink.
+    val context = new Context()
+    context.put("capacity", channelCapacity.toString)
+    context.put("transactionCapacity", "1000")
+    context.put("keep-alive", "0")
+    val channel = new MemoryChannel()
+    Configurables.configure(channel, context)
+
+    val channel2 = new MemoryChannel()
+    Configurables.configure(channel2, context)
+
+    val sink = new SparkSink()
+    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
+    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(0))
+    Configurables.configure(sink, context)
+    sink.setChannel(channel)
+    sink.start()
+
+    val sink2 = new SparkSink()
+    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
+    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(0))
+    Configurables.configure(sink2, context)
+    sink2.setChannel(channel2)
+    sink2.start()
+
+    sinks += sink
+    sinks += sink2
+    channels += channel
+    channels += channel2
+
+    sinks.map(_.getPort())
+  }
+
+  /**
+   * Send data and wait until all data has been received
+   */
+  def sendDatAndEnsureAllDataHasBeenReceived(): Unit = {
+    val executor = Executors.newCachedThreadPool()
+    val executorCompletion = new ExecutorCompletionService[Void](executor)
+
+    val latch = new CountDownLatch(batchCount * channels.size)
+    sinks.foreach(_.countdownWhenBatchReceived(latch))
+
+    channels.foreach(channel => {
+      executorCompletion.submit(new TxnSubmitter(channel))
+    })
+
+    for (i <- 0 until channels.size) {
+      executorCompletion.take()
+    }
+
+    latch.await(15, TimeUnit.SECONDS) // Ensure all data has been received.
+  }
+
+  /**
+   * A Python-friendly method to assert the output
+   */
+  def assertOutput(
+      outputHeaders: JList[JMap[String, String]], outputBodies: JList[String]): Unit = {
+    require(outputHeaders.size == outputBodies.size)
+    val eventSize = outputHeaders.size
+    if (eventSize != totalEventsPerChannel * channels.size) {
+      throw new AssertionError(
+        s"Expected ${totalEventsPerChannel * channels.size} events, but was $eventSize")
+    }
+    var counter = 0
+    for (k <- 0 until channels.size; i <- 0 until totalEventsPerChannel) {
+      val eventBodyToVerify = s"${channels(k).getName}-$i"
+      val eventHeaderToVerify: JMap[String, String] = Map[String, String](s"test-$i" -> "header")
+      var found = false
+      var j = 0
+      while (j < eventSize && !found) {
+        if (eventBodyToVerify == outputBodies.get(j) &&
+          eventHeaderToVerify == outputHeaders.get(j)) {
+          found = true
+          counter += 1
+        }
+        j += 1
+      }
+    }
+    if (counter != totalEventsPerChannel * channels.size) {
+      throw new AssertionError(
+        s"111 Expected ${totalEventsPerChannel * channels.size} events, but was $counter")
+    }
+  }
+
+  def assertChannelsAreEmpty(): Unit = {
+    channels.foreach(assertChannelIsEmpty)
+  }
+
+  private def assertChannelIsEmpty(channel: MemoryChannel): Unit = {
+    val queueRemaining = channel.getClass.getDeclaredField("queueRemaining")
+    queueRemaining.setAccessible(true)
+    val m = queueRemaining.get(channel).getClass.getDeclaredMethod("availablePermits")
+    if (m.invoke(queueRemaining.get(channel)).asInstanceOf[Int] != 5000) {
+      throw new AssertionError(s"Channel ${channel.getName} is not empty")
+    }
+  }
+
+  def close(): Unit = {
+    sinks.foreach(_.stop())
+    sinks.clear()
+    channels.foreach(_.stop())
+    channels.clear()
+  }
+
+  private class TxnSubmitter(channel: MemoryChannel) extends Callable[Void] {
+    override def call(): Void = {
+      var t = 0
+      for (i <- 0 until batchCount) {
+        val tx = channel.getTransaction
+        tx.begin()
+        for (j <- 0 until eventsPerBatch) {
+          channel.put(EventBuilder.withBody(s"${channel.getName}-$t".getBytes(UTF_8),
+            Map[String, String](s"test-$t" -> "header")))
+          t += 1
+        }
+        tx.commit()
+        tx.close()
+        Thread.sleep(500) // Allow some time for the events to reach
+      }
+      null
+    }
+  }
+
+}
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
index d772b9ca9b570..d5f9a0aa38f9f 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
@@ -18,47 +18,33 @@
 package org.apache.spark.streaming.flume
 
 import java.net.InetSocketAddress
-import java.util.concurrent._
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
 import scala.concurrent.duration._
 import scala.language.postfixOps
 
-import org.apache.flume.Context
-import org.apache.flume.channel.MemoryChannel
-import org.apache.flume.conf.Configurables
-import org.apache.flume.event.EventBuilder
-import org.scalatest.concurrent.Eventually._
-
+import com.google.common.base.Charsets.UTF_8
 import org.scalatest.BeforeAndAfter
+import org.scalatest.concurrent.Eventually._
 
 import org.apache.spark.{Logging, SparkConf, SparkFunSuite}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.dstream.ReceiverInputDStream
 import org.apache.spark.streaming.{Seconds, TestOutputStream, StreamingContext}
-import org.apache.spark.streaming.flume.sink._
 import org.apache.spark.util.{ManualClock, Utils}
 
 class FlumePollingStreamSuite extends SparkFunSuite with BeforeAndAfter with Logging {
 
-  val batchCount = 5
-  val eventsPerBatch = 100
-  val totalEventsPerChannel = batchCount * eventsPerBatch
-  val channelCapacity = 5000
   val maxAttempts = 5
   val batchDuration = Seconds(1)
 
   val conf = new SparkConf()
     .setMaster("local[2]")
     .setAppName(this.getClass.getSimpleName)
+    .set("spark.streaming.clock", "org.apache.spark.util.ManualClock")
 
-  def beforeFunction() {
-    logInfo("Using manual clock")
-    conf.set("spark.streaming.clock", "org.apache.spark.util.ManualClock")
-  }
-
-  before(beforeFunction())
+  val utils = new PollingFlumeTestUtils
 
   test("flume polling test") {
     testMultipleTimes(testFlumePolling)
@@ -89,146 +75,55 @@ class FlumePollingStreamSuite extends SparkFunSuite with BeforeAndAfter with Log
   }
 
   private def testFlumePolling(): Unit = {
-    // Start the channel and sink.
-    val context = new Context()
-    context.put("capacity", channelCapacity.toString)
-    context.put("transactionCapacity", "1000")
-    context.put("keep-alive", "0")
-    val channel = new MemoryChannel()
-    Configurables.configure(channel, context)
-
-    val sink = new SparkSink()
-    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
-    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(0))
-    Configurables.configure(sink, context)
-    sink.setChannel(channel)
-    sink.start()
-
-    writeAndVerify(Seq(sink), Seq(channel))
-    assertChannelIsEmpty(channel)
-    sink.stop()
-    channel.stop()
+    try {
+      val port = utils.startSingleSink()
+
+      writeAndVerify(Seq(port))
+      utils.assertChannelsAreEmpty()
+    } finally {
+      utils.close()
+    }
   }
 
   private def testFlumePollingMultipleHost(): Unit = {
-    // Start the channel and sink.
-    val context = new Context()
-    context.put("capacity", channelCapacity.toString)
-    context.put("transactionCapacity", "1000")
-    context.put("keep-alive", "0")
-    val channel = new MemoryChannel()
-    Configurables.configure(channel, context)
-
-    val channel2 = new MemoryChannel()
-    Configurables.configure(channel2, context)
-
-    val sink = new SparkSink()
-    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
-    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(0))
-    Configurables.configure(sink, context)
-    sink.setChannel(channel)
-    sink.start()
-
-    val sink2 = new SparkSink()
-    context.put(SparkSinkConfig.CONF_HOSTNAME, "localhost")
-    context.put(SparkSinkConfig.CONF_PORT, String.valueOf(0))
-    Configurables.configure(sink2, context)
-    sink2.setChannel(channel2)
-    sink2.start()
     try {
-      writeAndVerify(Seq(sink, sink2), Seq(channel, channel2))
-      assertChannelIsEmpty(channel)
-      assertChannelIsEmpty(channel2)
+      val ports = utils.startMultipleSinks()
+      writeAndVerify(ports)
+      utils.assertChannelsAreEmpty()
     } finally {
-      sink.stop()
-      sink2.stop()
-      channel.stop()
-      channel2.stop()
+      utils.close()
     }
   }
 
-  def writeAndVerify(sinks: Seq[SparkSink], channels: Seq[MemoryChannel]) {
+  def writeAndVerify(sinkPorts: Seq[Int]): Unit = {
     // Set up the streaming context and input streams
     val ssc = new StreamingContext(conf, batchDuration)
-    val addresses = sinks.map(sink => new InetSocketAddress("localhost", sink.getPort()))
+    val addresses = sinkPorts.map(port => new InetSocketAddress("localhost", port))
     val flumeStream: ReceiverInputDStream[SparkFlumeEvent] =
       FlumeUtils.createPollingStream(ssc, addresses, StorageLevel.MEMORY_AND_DISK,
-        eventsPerBatch, 5)
+        utils.eventsPerBatch, 5)
     val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]]
       with SynchronizedBuffer[Seq[SparkFlumeEvent]]
     val outputStream = new TestOutputStream(flumeStream, outputBuffer)
     outputStream.register()
 
     ssc.start()
-    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
-    val executor = Executors.newCachedThreadPool()
-    val executorCompletion = new ExecutorCompletionService[Void](executor)
-
-    val latch = new CountDownLatch(batchCount * channels.size)
-    sinks.foreach(_.countdownWhenBatchReceived(latch))
-
-    channels.foreach(channel => {
-      executorCompletion.submit(new TxnSubmitter(channel, clock))
-    })
-
-    for (i <- 0 until channels.size) {
-      executorCompletion.take()
-    }
-
-    latch.await(15, TimeUnit.SECONDS) // Ensure all data has been received.
-    clock.advance(batchDuration.milliseconds)
-
-    // The eventually is required to ensure that all data in the batch has been processed.
-    eventually(timeout(10 seconds), interval(100 milliseconds)) {
-      val flattenedBuffer = outputBuffer.flatten
-      assert(flattenedBuffer.size === totalEventsPerChannel * channels.size)
-      var counter = 0
-      for (k <- 0 until channels.size; i <- 0 until totalEventsPerChannel) {
-        val eventToVerify = EventBuilder.withBody((channels(k).getName + " - " +
-          String.valueOf(i)).getBytes("utf-8"),
-          Map[String, String]("test-" + i.toString -> "header"))
-        var found = false
-        var j = 0
-        while (j < flattenedBuffer.size && !found) {
-          val strToCompare = new String(flattenedBuffer(j).event.getBody.array(), "utf-8")
-          if (new String(eventToVerify.getBody, "utf-8") == strToCompare &&
-            eventToVerify.getHeaders.get("test-" + i.toString)
-              .equals(flattenedBuffer(j).event.getHeaders.get("test-" + i.toString))) {
-            found = true
-            counter += 1
-          }
-          j += 1
-        }
-      }
-      assert(counter === totalEventsPerChannel * channels.size)
-    }
-    ssc.stop()
-  }
-
-  def assertChannelIsEmpty(channel: MemoryChannel): Unit = {
-    val queueRemaining = channel.getClass.getDeclaredField("queueRemaining")
-    queueRemaining.setAccessible(true)
-    val m = queueRemaining.get(channel).getClass.getDeclaredMethod("availablePermits")
-    assert(m.invoke(queueRemaining.get(channel)).asInstanceOf[Int] === 5000)
-  }
-
-  private class TxnSubmitter(channel: MemoryChannel, clock: ManualClock) extends Callable[Void] {
-    override def call(): Void = {
-      var t = 0
-      for (i <- 0 until batchCount) {
-        val tx = channel.getTransaction
-        tx.begin()
-        for (j <- 0 until eventsPerBatch) {
-          channel.put(EventBuilder.withBody((channel.getName + " - " + String.valueOf(t)).getBytes(
-            "utf-8"),
-            Map[String, String]("test-" + t.toString -> "header")))
-          t += 1
-        }
-        tx.commit()
-        tx.close()
-        Thread.sleep(500) // Allow some time for the events to reach
+    try {
+      utils.sendDatAndEnsureAllDataHasBeenReceived()
+      val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
+      clock.advance(batchDuration.milliseconds)
+
+      // The eventually is required to ensure that all data in the batch has been processed.
+      eventually(timeout(10 seconds), interval(100 milliseconds)) {
+        val flattenOutputBuffer = outputBuffer.flatten
+        val headers = flattenOutputBuffer.map(_.event.getHeaders.map {
+          case kv => (kv._1.toString, kv._2.toString)
+        }).map(mapAsJavaMap)
+        val bodies = flattenOutputBuffer.map(e => new String(e.event.getBody.array(), UTF_8))
+        utils.assertOutput(headers, bodies)
       }
-      null
+    } finally {
+      ssc.stop()
     }
   }
 
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
index c926359987d89..5bc4cdf65306c 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -17,20 +17,12 @@
 
 package org.apache.spark.streaming.flume
 
-import java.net.{InetSocketAddress, ServerSocket}
-import java.nio.ByteBuffer
-
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
 import scala.concurrent.duration._
 import scala.language.postfixOps
 
 import com.google.common.base.Charsets
-import org.apache.avro.ipc.NettyTransceiver
-import org.apache.avro.ipc.specific.SpecificRequestor
-import org.apache.commons.lang3.RandomUtils
-import org.apache.flume.source.avro
-import org.apache.flume.source.avro.{AvroFlumeEvent, AvroSourceProtocol}
 import org.jboss.netty.channel.ChannelPipeline
 import org.jboss.netty.channel.socket.SocketChannel
 import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory
@@ -41,22 +33,10 @@ import org.scalatest.concurrent.Eventually._
 import org.apache.spark.{Logging, SparkConf, SparkFunSuite}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Milliseconds, StreamingContext, TestOutputStream}
-import org.apache.spark.util.Utils
 
 class FlumeStreamSuite extends SparkFunSuite with BeforeAndAfter with Matchers with Logging {
   val conf = new SparkConf().setMaster("local[4]").setAppName("FlumeStreamSuite")
-
   var ssc: StreamingContext = null
-  var transceiver: NettyTransceiver = null
-
-  after {
-    if (ssc != null) {
-      ssc.stop()
-    }
-    if (transceiver != null) {
-      transceiver.close()
-    }
-  }
 
   test("flume input stream") {
     testFlumeStream(testCompression = false)
@@ -69,19 +49,29 @@ class FlumeStreamSuite extends SparkFunSuite with BeforeAndAfter with Matchers w
   /** Run test on flume stream */
   private def testFlumeStream(testCompression: Boolean): Unit = {
     val input = (1 to 100).map { _.toString }
-    val testPort = findFreePort()
-    val outputBuffer = startContext(testPort, testCompression)
-    writeAndVerify(input, testPort, outputBuffer, testCompression)
-  }
+    val utils = new FlumeTestUtils
+    try {
+      val outputBuffer = startContext(utils.getTestPort(), testCompression)
 
-  /** Find a free port */
-  private def findFreePort(): Int = {
-    val candidatePort = RandomUtils.nextInt(1024, 65536)
-    Utils.startServiceOnPort(candidatePort, (trialPort: Int) => {
-      val socket = new ServerSocket(trialPort)
-      socket.close()
-      (null, trialPort)
-    }, conf)._2
+      eventually(timeout(10 seconds), interval(100 milliseconds)) {
+        utils.writeInput(input, testCompression)
+      }
+
+      eventually(timeout(10 seconds), interval(100 milliseconds)) {
+        val outputEvents = outputBuffer.flatten.map { _.event }
+        outputEvents.foreach {
+          event =>
+            event.getHeaders.get("test") should be("header")
+        }
+        val output = outputEvents.map(event => new String(event.getBody.array(), Charsets.UTF_8))
+        output should be (input)
+      }
+    } finally {
+      if (ssc != null) {
+        ssc.stop()
+      }
+      utils.close()
+    }
   }
 
   /** Setup and start the streaming context */
@@ -98,58 +88,6 @@ class FlumeStreamSuite extends SparkFunSuite with BeforeAndAfter with Matchers w
     outputBuffer
   }
 
-  /** Send data to the flume receiver and verify whether the data was received */
-  private def writeAndVerify(
-      input: Seq[String],
-      testPort: Int,
-      outputBuffer: ArrayBuffer[Seq[SparkFlumeEvent]],
-      enableCompression: Boolean
-    ) {
-    val testAddress = new InetSocketAddress("localhost", testPort)
-
-    val inputEvents = input.map { item =>
-      val event = new AvroFlumeEvent
-      event.setBody(ByteBuffer.wrap(item.getBytes(Charsets.UTF_8)))
-      event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header"))
-      event
-    }
-
-    eventually(timeout(10 seconds), interval(100 milliseconds)) {
-      // if last attempted transceiver had succeeded, close it
-      if (transceiver != null) {
-        transceiver.close()
-        transceiver = null
-      }
-
-      // Create transceiver
-      transceiver = {
-        if (enableCompression) {
-          new NettyTransceiver(testAddress, new CompressionChannelFactory(6))
-        } else {
-          new NettyTransceiver(testAddress)
-        }
-      }
-
-      // Create Avro client with the transceiver
-      val client = SpecificRequestor.getClient(classOf[AvroSourceProtocol], transceiver)
-      client should not be null
-
-      // Send data
-      val status = client.appendBatch(inputEvents.toList)
-      status should be (avro.Status.OK)
-    }
-
-    eventually(timeout(10 seconds), interval(100 milliseconds)) {
-      val outputEvents = outputBuffer.flatten.map { _.event }
-      outputEvents.foreach {
-        event =>
-          event.getHeaders.get("test") should be("header")
-      }
-      val output = outputEvents.map(event => new String(event.getBody.array(), Charsets.UTF_8))
-      output should be (input)
-    }
-  }
-
   /** Class to create socket channel with compression */
   private class CompressionChannelFactory(compressionLevel: Int)
     extends NioClientSocketChannelFactory {
diff --git a/pom.xml b/pom.xml
index 94dd512cfb618..211da9ee74a3f 100644
--- a/pom.xml
+++ b/pom.xml
@@ -102,6 +102,7 @@
     <module>external/twitter</module>
     <module>external/flume</module>
     <module>external/flume-sink</module>
+    <module>external/flume-assembly</module>
     <module>external/mqtt</module>
     <module>external/zeromq</module>
     <module>examples</module>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index f5f1c9a1a247a..4ef4dc8bdc039 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -45,8 +45,8 @@ object BuildCommons {
     sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
     "kinesis-asl").map(ProjectRef(buildLocation, _))
 
-  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingKafkaAssembly) =
-    Seq("assembly", "examples", "network-yarn", "streaming-kafka-assembly")
+  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly) =
+    Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly")
       .map(ProjectRef(buildLocation, _))
 
   val tools = ProjectRef(buildLocation, "tools")
@@ -347,7 +347,7 @@ object Assembly {
         .getOrElse(SbtPomKeys.effectivePom.value.getProperties.get("hadoop.version").asInstanceOf[String])
     },
     jarName in assembly <<= (version, moduleName, hadoopVersion) map { (v, mName, hv) =>
-      if (mName.contains("streaming-kafka-assembly")) {
+      if (mName.contains("streaming-flume-assembly") || mName.contains("streaming-kafka-assembly")) {
         // This must match the same name used in maven (see external/kafka-assembly/pom.xml)
         s"${mName}-${v}.jar"
       } else {
diff --git a/python/pyspark/streaming/flume.py b/python/pyspark/streaming/flume.py
new file mode 100644
index 0000000000000..cbb573f226bbe
--- /dev/null
+++ b/python/pyspark/streaming/flume.py
@@ -0,0 +1,147 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import sys
+if sys.version >= "3":
+    from io import BytesIO
+else:
+    from StringIO import StringIO
+from py4j.java_gateway import Py4JJavaError
+
+from pyspark.storagelevel import StorageLevel
+from pyspark.serializers import PairDeserializer, NoOpSerializer, UTF8Deserializer, read_int
+from pyspark.streaming import DStream
+
+__all__ = ['FlumeUtils', 'utf8_decoder']
+
+
+def utf8_decoder(s):
+    """ Decode the unicode as UTF-8 """
+    return s and s.decode('utf-8')
+
+
+class FlumeUtils(object):
+
+    @staticmethod
+    def createStream(ssc, hostname, port,
+                     storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
+                     enableDecompression=False,
+                     bodyDecoder=utf8_decoder):
+        """
+        Create an input stream that pulls events from Flume.
+
+        :param ssc:  StreamingContext object
+        :param hostname:  Hostname of the slave machine to which the flume data will be sent
+        :param port:  Port of the slave machine to which the flume data will be sent
+        :param storageLevel:  Storage level to use for storing the received objects
+        :param enableDecompression:  Should netty server decompress input stream
+        :param bodyDecoder:  A function used to decode body (default is utf8_decoder)
+        :return: A DStream object
+        """
+        jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
+
+        try:
+            helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\
+                .loadClass("org.apache.spark.streaming.flume.FlumeUtilsPythonHelper")
+            helper = helperClass.newInstance()
+            jstream = helper.createStream(ssc._jssc, hostname, port, jlevel, enableDecompression)
+        except Py4JJavaError as e:
+            if 'ClassNotFoundException' in str(e.java_exception):
+                FlumeUtils._printErrorMsg(ssc.sparkContext)
+            raise e
+
+        return FlumeUtils._toPythonDStream(ssc, jstream, bodyDecoder)
+
+    @staticmethod
+    def createPollingStream(ssc, addresses,
+                            storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
+                            maxBatchSize=1000,
+                            parallelism=5,
+                            bodyDecoder=utf8_decoder):
+        """
+        Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
+        This stream will poll the sink for data and will pull events as they are available.
+
+        :param ssc:  StreamingContext object
+        :param addresses:  List of (host, port)s on which the Spark Sink is running.
+        :param storageLevel:  Storage level to use for storing the received objects
+        :param maxBatchSize:  The maximum number of events to be pulled from the Spark sink
+                              in a single RPC call
+        :param parallelism:  Number of concurrent requests this stream should send to the sink.
+                             Note that having a higher number of requests concurrently being pulled
+                             will result in this stream using more threads
+        :param bodyDecoder:  A function used to decode body (default is utf8_decoder)
+        :return: A DStream object
+        """
+        jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
+        hosts = []
+        ports = []
+        for (host, port) in addresses:
+            hosts.append(host)
+            ports.append(port)
+
+        try:
+            helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+                .loadClass("org.apache.spark.streaming.flume.FlumeUtilsPythonHelper")
+            helper = helperClass.newInstance()
+            jstream = helper.createPollingStream(
+                ssc._jssc, hosts, ports, jlevel, maxBatchSize, parallelism)
+        except Py4JJavaError as e:
+            if 'ClassNotFoundException' in str(e.java_exception):
+                FlumeUtils._printErrorMsg(ssc.sparkContext)
+            raise e
+
+        return FlumeUtils._toPythonDStream(ssc, jstream, bodyDecoder)
+
+    @staticmethod
+    def _toPythonDStream(ssc, jstream, bodyDecoder):
+        ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
+        stream = DStream(jstream, ssc, ser)
+
+        def func(event):
+            headersBytes = BytesIO(event[0]) if sys.version >= "3" else StringIO(event[0])
+            headers = {}
+            strSer = UTF8Deserializer()
+            for i in range(0, read_int(headersBytes)):
+                key = strSer.loads(headersBytes)
+                value = strSer.loads(headersBytes)
+                headers[key] = value
+            body = bodyDecoder(event[1])
+            return (headers, body)
+        return stream.map(func)
+
+    @staticmethod
+    def _printErrorMsg(sc):
+        print("""
+________________________________________________________________________________________________
+
+  Spark Streaming's Flume libraries not found in class path. Try one of the following.
+
+  1. Include the Flume library and its dependencies with in the
+     spark-submit command as
+
+     $ bin/spark-submit --packages org.apache.spark:spark-streaming-flume:%s ...
+
+  2. Download the JAR of the artifact from Maven Central http://search.maven.org/,
+     Group Id = org.apache.spark, Artifact Id = spark-streaming-flume-assembly, Version = %s.
+     Then, include the jar in the spark-submit command as
+
+     $ bin/spark-submit --jars <spark-streaming-flume-assembly.jar> ...
+
+________________________________________________________________________________________________
+
+""" % (sc.version, sc.version))
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 91ce681fbe169..188c8ff12067e 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -38,6 +38,7 @@
 from pyspark.context import SparkConf, SparkContext, RDD
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.kafka import Broker, KafkaUtils, OffsetRange, TopicAndPartition
+from pyspark.streaming.flume import FlumeUtils
 
 
 class PySparkStreamingTestCase(unittest.TestCase):
@@ -677,7 +678,156 @@ def test_kafka_rdd_with_leaders(self):
         rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, leaders)
         self._validateRddResult(sendData, rdd)
 
-if __name__ == "__main__":
+
+class FlumeStreamTests(PySparkStreamingTestCase):
+    timeout = 20  # seconds
+    duration = 1
+
+    def setUp(self):
+        super(FlumeStreamTests, self).setUp()
+
+        utilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+            .loadClass("org.apache.spark.streaming.flume.FlumeTestUtils")
+        self._utils = utilsClz.newInstance()
+
+    def tearDown(self):
+        if self._utils is not None:
+            self._utils.close()
+            self._utils = None
+
+        super(FlumeStreamTests, self).tearDown()
+
+    def _startContext(self, n, compressed):
+        # Start the StreamingContext and also collect the result
+        dstream = FlumeUtils.createStream(self.ssc, "localhost", self._utils.getTestPort(),
+                                          enableDecompression=compressed)
+        result = []
+
+        def get_output(_, rdd):
+            for event in rdd.collect():
+                if len(result) < n:
+                    result.append(event)
+        dstream.foreachRDD(get_output)
+        self.ssc.start()
+        return result
+
+    def _validateResult(self, input, result):
+        # Validate both the header and the body
+        header = {"test": "header"}
+        self.assertEqual(len(input), len(result))
+        for i in range(0, len(input)):
+            self.assertEqual(header, result[i][0])
+            self.assertEqual(input[i], result[i][1])
+
+    def _writeInput(self, input, compressed):
+        # Try to write input to the receiver until success or timeout
+        start_time = time.time()
+        while True:
+            try:
+                self._utils.writeInput(input, compressed)
+                break
+            except:
+                if time.time() - start_time < self.timeout:
+                    time.sleep(0.01)
+                else:
+                    raise
+
+    def test_flume_stream(self):
+        input = [str(i) for i in range(1, 101)]
+        result = self._startContext(len(input), False)
+        self._writeInput(input, False)
+        self.wait_for(result, len(input))
+        self._validateResult(input, result)
+
+    def test_compressed_flume_stream(self):
+        input = [str(i) for i in range(1, 101)]
+        result = self._startContext(len(input), True)
+        self._writeInput(input, True)
+        self.wait_for(result, len(input))
+        self._validateResult(input, result)
+
+
+class FlumePollingStreamTests(PySparkStreamingTestCase):
+    timeout = 20  # seconds
+    duration = 1
+    maxAttempts = 5
+
+    def setUp(self):
+        utilsClz = \
+            self.sc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+                .loadClass("org.apache.spark.streaming.flume.PollingFlumeTestUtils")
+        self._utils = utilsClz.newInstance()
+
+    def tearDown(self):
+        if self._utils is not None:
+            self._utils.close()
+            self._utils = None
+
+    def _writeAndVerify(self, ports):
+        # Set up the streaming context and input streams
+        ssc = StreamingContext(self.sc, self.duration)
+        try:
+            addresses = [("localhost", port) for port in ports]
+            dstream = FlumeUtils.createPollingStream(
+                ssc,
+                addresses,
+                maxBatchSize=self._utils.eventsPerBatch(),
+                parallelism=5)
+            outputBuffer = []
+
+            def get_output(_, rdd):
+                for e in rdd.collect():
+                    outputBuffer.append(e)
+
+            dstream.foreachRDD(get_output)
+            ssc.start()
+            self._utils.sendDatAndEnsureAllDataHasBeenReceived()
+
+            self.wait_for(outputBuffer, self._utils.getTotalEvents())
+            outputHeaders = [event[0] for event in outputBuffer]
+            outputBodies = [event[1] for event in outputBuffer]
+            self._utils.assertOutput(outputHeaders, outputBodies)
+        finally:
+            ssc.stop(False)
+
+    def _testMultipleTimes(self, f):
+        attempt = 0
+        while True:
+            try:
+                f()
+                break
+            except:
+                attempt += 1
+                if attempt >= self.maxAttempts:
+                    raise
+                else:
+                    import traceback
+                    traceback.print_exc()
+
+    def _testFlumePolling(self):
+        try:
+            port = self._utils.startSingleSink()
+            self._writeAndVerify([port])
+            self._utils.assertChannelsAreEmpty()
+        finally:
+            self._utils.close()
+
+    def _testFlumePollingMultipleHosts(self):
+        try:
+            port = self._utils.startSingleSink()
+            self._writeAndVerify([port])
+            self._utils.assertChannelsAreEmpty()
+        finally:
+            self._utils.close()
+
+    def test_flume_polling(self):
+        self._testMultipleTimes(self._testFlumePolling)
+
+    def test_flume_polling_multiple_hosts(self):
+        self._testMultipleTimes(self._testFlumePollingMultipleHosts)
+
+
+def search_kafka_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     kafka_assembly_dir = os.path.join(SPARK_HOME, "external/kafka-assembly")
     jars = glob.glob(
@@ -692,5 +842,30 @@ def test_kafka_rdd_with_leaders(self):
         raise Exception(("Found multiple Spark Streaming Kafka assembly JARs in %s; please "
                          "remove all but one") % kafka_assembly_dir)
     else:
-        os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars[0]
+        return jars[0]
+
+
+def search_flume_assembly_jar():
+    SPARK_HOME = os.environ["SPARK_HOME"]
+    flume_assembly_dir = os.path.join(SPARK_HOME, "external/flume-assembly")
+    jars = glob.glob(
+        os.path.join(flume_assembly_dir, "target/scala-*/spark-streaming-flume-assembly-*.jar"))
+    if not jars:
+        raise Exception(
+            ("Failed to find Spark Streaming Flume assembly jar in %s. " % flume_assembly_dir) +
+            "You need to build Spark with "
+            "'build/sbt assembly/assembly streaming-flume-assembly/assembly' or "
+            "'build/mvn package' before running this test")
+    elif len(jars) > 1:
+        raise Exception(("Found multiple Spark Streaming Flume assembly JARs in %s; please "
+                         "remove all but one") % flume_assembly_dir)
+    else:
+        return jars[0]
+
+if __name__ == "__main__":
+    kafka_assembly_jar = search_kafka_assembly_jar()
+    flume_assembly_jar = search_flume_assembly_jar()
+    jars = "%s,%s" % (kafka_assembly_jar, flume_assembly_jar)
+
+    os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
     unittest.main()

From 9f7db3486fcb403cae8da9dfce8978373c3f47b7 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Wed, 1 Jul 2015 12:33:24 -0700
Subject: [PATCH 0173/1454] [SPARK-7820] [BUILD] Fix Java8-tests suite compile
 and test error under sbt

Author: jerryshao <saisai.shao@intel.com>

Closes #7120 from jerryshao/SPARK-7820 and squashes the following commits:

6902439 [jerryshao] fix Java8-tests suite compile error under sbt
---
 extras/java8-tests/pom.xml | 8 ++++++++
 project/SparkBuild.scala   | 4 ++--
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index f138251748c9e..3636a9037d43f 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -39,6 +39,13 @@
       <artifactId>spark-core_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-core_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
@@ -49,6 +56,7 @@
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
       <type>test-jar</type>
+      <scope>test</scope>
     </dependency>
     <dependency>
       <groupId>junit</groupId>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 4ef4dc8bdc039..5f389bcc9ceeb 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -161,7 +161,7 @@ object SparkBuild extends PomBuild {
   // Note ordering of these settings matter.
   /* Enable shared settings on all projects */
   (allProjects ++ optionallyEnabledProjects ++ assemblyProjects ++ Seq(spark, tools))
-    .foreach(enable(sharedSettings ++ ExludedDependencies.settings ++ Revolver.settings))
+    .foreach(enable(sharedSettings ++ ExcludedDependencies.settings ++ Revolver.settings))
 
   /* Enable tests settings for all projects except examples, assembly and tools */
   (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings))
@@ -246,7 +246,7 @@ object Flume {
   This excludes library dependencies in sbt, which are specified in maven but are
   not needed by sbt build.
   */
-object ExludedDependencies {
+object ExcludedDependencies {
   lazy val settings = Seq(
     libraryDependencies ~= { libs => libs.filterNot(_.name == "groovy-all") }
   )

From 3083e17645e4b707646fe48e406e02c156a0f37b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 1 Jul 2015 12:39:57 -0700
Subject: [PATCH 0174/1454] [QUICKFIX] [SQL] fix copy of generated row

copy() of generated Row doesn't check nullability of columns

Author: Davies Liu <davies@databricks.com>

Closes #7163 from davies/fix_copy and squashes the following commits:

661a206 [Davies Liu] fix copy of generated row
---
 .../sql/catalyst/expressions/codegen/GenerateProjection.scala  | 2 +-
 .../spark/sql/catalyst/expressions/ExpressionEvalHelper.scala  | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 5be47175fa7f1..3c7ee9cc16599 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -148,7 +148,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
     }.mkString("\n")
 
     val copyColumns = expressions.zipWithIndex.map { case (e, i) =>
-        s"""arr[$i] = c$i;"""
+        s"""if (!nullBits[$i]) arr[$i] = c$i;"""
     }.mkString("\n      ")
 
     val code = s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 7d95ef7f710af..3171caf6ad77f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -136,6 +136,9 @@ trait ExpressionEvalHelper {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
       fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
     }
+    if (actual.copy() != expectedRow) {
+      fail(s"Copy of generated Row is wrong: actual: ${actual.copy()}, expected: $expectedRow")
+    }
   }
 
   protected def checkEvaluationWithOptimization(

From 1ce6428907b4ddcf52dbf0c86196d82ab7392442 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 1 Jul 2015 20:40:47 +0100
Subject: [PATCH 0175/1454] [SPARK-3444] [CORE] Restore INFO level after log4j
 test.

Otherwise other tests don't log anything useful...

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7140 from vanzin/SPARK-3444 and squashes the following commits:

de14836 [Marcelo Vanzin] Better fix.
6cff13a [Marcelo Vanzin] [SPARK-3444] [core] Restore INFO level after log4j test.
---
 .../scala/org/apache/spark/util/UtilsSuite.scala | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index baa4c661cc21e..251a797dc28a2 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -486,11 +486,17 @@ class UtilsSuite extends SparkFunSuite with ResetSystemProperties with Logging {
 
   // Test for using the util function to change our log levels.
   test("log4j log level change") {
-    Utils.setLogLevel(org.apache.log4j.Level.ALL)
-    assert(log.isInfoEnabled())
-    Utils.setLogLevel(org.apache.log4j.Level.ERROR)
-    assert(!log.isInfoEnabled())
-    assert(log.isErrorEnabled())
+    val current = org.apache.log4j.Logger.getRootLogger().getLevel()
+    try {
+      Utils.setLogLevel(org.apache.log4j.Level.ALL)
+      assert(log.isInfoEnabled())
+      Utils.setLogLevel(org.apache.log4j.Level.ERROR)
+      assert(!log.isInfoEnabled())
+      assert(log.isErrorEnabled())
+    } finally {
+      // Best effort at undoing changes this test made.
+      Utils.setLogLevel(current)
+    }
   }
 
   test("deleteRecursively") {

From f958f27e2056f9e380373c2807d8bb5977ecf269 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 1 Jul 2015 16:43:18 -0700
Subject: [PATCH 0176/1454] [SPARK-8766] support non-ascii character in column
 names

Use UTF-8 to encode the name of column in Python 2, or it may failed to encode with default encoding ('ascii').

This PR also fix a bug when there is Java exception without error message.

Author: Davies Liu <davies@databricks.com>

Closes #7165 from davies/non_ascii and squashes the following commits:

02cb61a [Davies Liu] fix tests
3b09d31 [Davies Liu] add encoding in header
867754a [Davies Liu] support non-ascii character in column names
---
 python/pyspark/sql/dataframe.py | 3 +--
 python/pyspark/sql/tests.py     | 9 +++++++++
 python/pyspark/sql/types.py     | 2 ++
 python/pyspark/sql/utils.py     | 6 +++---
 4 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 4b9efa0a210fb..273a40dd526cf 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -484,13 +484,12 @@ def dtypes(self):
         return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields]
 
     @property
-    @ignore_unicode_prefix
     @since(1.3)
     def columns(self):
         """Returns all column names as a list.
 
         >>> df.columns
-        [u'age', u'name']
+        ['age', 'name']
         """
         return [f.name for f in self.schema.fields]
 
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 5af2ce09bc122..333378c7f1854 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1,3 +1,4 @@
+# -*- encoding: utf-8 -*-
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -628,6 +629,14 @@ def test_access_column(self):
         self.assertRaises(IndexError, lambda: df["bad_key"])
         self.assertRaises(TypeError, lambda: df[{}])
 
+    def test_column_name_with_non_ascii(self):
+        df = self.sqlCtx.createDataFrame([(1,)], ["数量"])
+        self.assertEqual(StructType([StructField("数量", LongType(), True)]), df.schema)
+        self.assertEqual("DataFrame[数量: bigint]", str(df))
+        self.assertEqual([("数量", 'bigint')], df.dtypes)
+        self.assertEqual(1, df.select("数量").first()[0])
+        self.assertEqual(1, df.select(df["数量"]).first()[0])
+
     def test_access_nested_types(self):
         df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
         self.assertEqual(1, df.select(df.l[0]).first()[0])
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index ae9344e6106a4..160df40d65cc1 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -324,6 +324,8 @@ def __init__(self, name, dataType, nullable=True, metadata=None):
         False
         """
         assert isinstance(dataType, DataType), "dataType should be DataType"
+        if not isinstance(name, str):
+            name = name.encode('utf-8')
         self.name = name
         self.dataType = dataType
         self.nullable = nullable
diff --git a/python/pyspark/sql/utils.py b/python/pyspark/sql/utils.py
index 8096802e7302f..cc5b2c088b7cc 100644
--- a/python/pyspark/sql/utils.py
+++ b/python/pyspark/sql/utils.py
@@ -29,9 +29,9 @@ def deco(*a, **kw):
         try:
             return f(*a, **kw)
         except py4j.protocol.Py4JJavaError as e:
-            cls, msg = e.java_exception.toString().split(': ', 1)
-            if cls == 'org.apache.spark.sql.AnalysisException':
-                raise AnalysisException(msg)
+            s = e.java_exception.toString()
+            if s.startswith('org.apache.spark.sql.AnalysisException: '):
+                raise AnalysisException(s.split(': ', 1)[1])
             raise
     return deco
 

From 272778999823ed79af92280350c5869a87a21f29 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 1 Jul 2015 16:56:48 -0700
Subject: [PATCH 0177/1454] [SPARK-8770][SQL] Create BinaryOperator abstract
 class.

Our current BinaryExpression abstract class is not for generic binary expressions, i.e. it requires left/right children to have the same type. However, due to its name, contributors build new binary expressions that don't have that assumption (e.g. Sha) and still extend BinaryExpression.

This patch creates a new BinaryOperator abstract class, and update the analyzer o only apply type casting rule there. This patch also adds the notion of "prettyName" to expressions, which defines the user-facing name for the expression.

Author: Reynold Xin <rxin@databricks.com>

Closes #7170 from rxin/binaryoperator and squashes the following commits:

51264a5 [Reynold Xin] [SPARK-8770][SQL] Create BinaryOperator abstract class.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  17 +-
 .../expressions/ExpectsInputTypes.scala       |  59 +++++++
 .../sql/catalyst/expressions/Expression.scala | 161 +++++++++---------
 .../sql/catalyst/expressions/ScalaUDF.scala   |   2 +-
 .../sql/catalyst/expressions/aggregates.scala |   6 -
 .../sql/catalyst/expressions/arithmetic.scala |  14 +-
 .../expressions/complexTypeCreator.scala      |   4 +-
 .../catalyst/expressions/nullFunctions.scala  |   2 -
 .../sql/catalyst/expressions/predicates.scala |   6 +-
 .../spark/sql/catalyst/expressions/sets.scala |   2 -
 .../expressions/stringOperations.scala        |  26 +--
 .../sql/catalyst/trees/TreeNodeSuite.scala    |   6 +-
 12 files changed, 170 insertions(+), 135 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 2ab5cb666fbcd..8420c54f7c335 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -150,6 +150,7 @@ object HiveTypeCoercion {
    * Converts string "NaN"s that are in binary operators with a NaN-able types (Float / Double) to
    * the appropriate numeric equivalent.
    */
+  // TODO: remove this rule and make Cast handle Nan.
   object ConvertNaNs extends Rule[LogicalPlan] {
     private val StringNaN = Literal("NaN")
 
@@ -159,19 +160,19 @@ object HiveTypeCoercion {
         case e if !e.childrenResolved => e
 
         /* Double Conversions */
-        case b @ BinaryExpression(StringNaN, right @ DoubleType()) =>
+        case b @ BinaryOperator(StringNaN, right @ DoubleType()) =>
           b.makeCopy(Array(Literal(Double.NaN), right))
-        case b @ BinaryExpression(left @ DoubleType(), StringNaN) =>
+        case b @ BinaryOperator(left @ DoubleType(), StringNaN) =>
           b.makeCopy(Array(left, Literal(Double.NaN)))
 
         /* Float Conversions */
-        case b @ BinaryExpression(StringNaN, right @ FloatType()) =>
+        case b @ BinaryOperator(StringNaN, right @ FloatType()) =>
           b.makeCopy(Array(Literal(Float.NaN), right))
-        case b @ BinaryExpression(left @ FloatType(), StringNaN) =>
+        case b @ BinaryOperator(left @ FloatType(), StringNaN) =>
           b.makeCopy(Array(left, Literal(Float.NaN)))
 
         /* Use float NaN by default to avoid unnecessary type widening */
-        case b @ BinaryExpression(left @ StringNaN, StringNaN) =>
+        case b @ BinaryOperator(left @ StringNaN, StringNaN) =>
           b.makeCopy(Array(left, Literal(Float.NaN)))
       }
     }
@@ -245,12 +246,12 @@ object HiveTypeCoercion {
 
         Union(newLeft, newRight)
 
-      // Also widen types for BinaryExpressions.
+      // Also widen types for BinaryOperator.
       case q: LogicalPlan => q transformExpressions {
         // Skip nodes who's children have not been resolved yet.
         case e if !e.childrenResolved => e
 
-        case b @ BinaryExpression(left, right) if left.dataType != right.dataType =>
+        case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
           findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { widestType =>
             val newLeft = if (left.dataType == widestType) left else Cast(left, widestType)
             val newRight = if (right.dataType == widestType) right else Cast(right, widestType)
@@ -478,7 +479,7 @@ object HiveTypeCoercion {
 
         // Promote integers inside a binary expression with fixed-precision decimals to decimals,
         // and fixed-precision decimals in an expression with floats / doubles to doubles
-        case b @ BinaryExpression(left, right) if left.dataType != right.dataType =>
+        case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
           (left.dataType, right.dataType) match {
             case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) =>
               b.makeCopy(Array(Cast(left, intTypeToFixed(t)), right))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
new file mode 100644
index 0000000000000..450fc4165f93b
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.types.DataType
+
+
+/**
+ * An trait that gets mixin to define the expected input types of an expression.
+ */
+trait ExpectsInputTypes { self: Expression =>
+
+  /**
+   * Expected input types from child expressions. The i-th position in the returned seq indicates
+   * the type requirement for the i-th child.
+   *
+   * The possible values at each position are:
+   * 1. a specific data type, e.g. LongType, StringType.
+   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
+   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
+   */
+  def inputTypes: Seq[Any]
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
+    TypeCheckResult.TypeCheckSuccess
+  }
+}
+
+/**
+ * Expressions that require a specific `DataType` as input should implement this trait
+ * so that the proper type conversions can be performed in the analyzer.
+ */
+trait AutoCastInputTypes { self: Expression =>
+
+  def inputTypes: Seq[DataType]
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
+    // so type mismatch error won't be reported here, but for underling `Cast`s.
+    TypeCheckResult.TypeCheckSuccess
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index e18a3118945e8..cafbbafdca207 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -119,17 +119,6 @@ abstract class Expression extends TreeNode[Expression] {
    */
   def childrenResolved: Boolean = children.forall(_.resolved)
 
-  /**
-   * Returns a string representation of this expression that does not have developer centric
-   * debugging information like the expression id.
-   */
-  def prettyString: String = {
-    transform {
-      case a: AttributeReference => PrettyAttribute(a.name)
-      case u: UnresolvedAttribute => PrettyAttribute(u.name)
-    }.toString
-  }
-
   /**
    * Returns true when two expressions will always compute the same result, even if they differ
    * cosmetically (i.e. capitalization of names in attributes may be different).
@@ -154,71 +143,40 @@ abstract class Expression extends TreeNode[Expression] {
    * Note: it's not valid to call this method until `childrenResolved == true`.
    */
   def checkInputDataTypes(): TypeCheckResult = TypeCheckResult.TypeCheckSuccess
-}
-
-abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] {
-  self: Product =>
-
-  def symbol: String = sys.error(s"BinaryExpressions must override either toString or symbol")
-
-  override def foldable: Boolean = left.foldable && right.foldable
-
-  override def nullable: Boolean = left.nullable || right.nullable
-
-  override def toString: String = s"($left $symbol $right)"
 
   /**
-   * Short hand for generating binary evaluation code.
-   * If either of the sub-expressions is null, the result of this computation
-   * is assumed to be null.
-   *
-   * @param f accepts two variable names and returns Java code to compute the output.
+   * Returns a user-facing string representation of this expression's name.
+   * This should usually match the name of the function in SQL.
    */
-  protected def defineCodeGen(
-      ctx: CodeGenContext,
-      ev: GeneratedExpressionCode,
-      f: (String, String) => String): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
-      s"$result = ${f(eval1, eval2)};"
-    })
-  }
+  def prettyName: String = getClass.getSimpleName.toLowerCase
 
   /**
-   * Short hand for generating binary evaluation code.
-   * If either of the sub-expressions is null, the result of this computation
-   * is assumed to be null.
+   * Returns a user-facing string representation of this expression, i.e. does not have developer
+   * centric debugging information like the expression id.
    */
-  protected def nullSafeCodeGen(
-      ctx: CodeGenContext,
-      ev: GeneratedExpressionCode,
-      f: (String, String, String) => String): String = {
-    val eval1 = left.gen(ctx)
-    val eval2 = right.gen(ctx)
-    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
-    s"""
-      ${eval1.code}
-      boolean ${ev.isNull} = ${eval1.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${eval2.code}
-        if (!${eval2.isNull}) {
-          $resultCode
-        } else {
-          ${ev.isNull} = true;
-        }
-      }
-    """
+  def prettyString: String = {
+    transform {
+      case a: AttributeReference => PrettyAttribute(a.name)
+      case u: UnresolvedAttribute => PrettyAttribute(u.name)
+    }.toString
   }
-}
 
-private[sql] object BinaryExpression {
-  def unapply(e: BinaryExpression): Option[(Expression, Expression)] = Some((e.left, e.right))
+  override def toString: String = prettyName + children.mkString("(", ",", ")")
 }
 
+
+/**
+ * A leaf expression, i.e. one without any child expressions.
+ */
 abstract class LeafExpression extends Expression with trees.LeafNode[Expression] {
   self: Product =>
 }
 
+
+/**
+ * An expression with one input and one output. The output is by default evaluated to null
+ * if the input is evaluated to null.
+ */
 abstract class UnaryExpression extends Expression with trees.UnaryNode[Expression] {
   self: Product =>
 
@@ -265,39 +223,76 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
   }
 }
 
+
 /**
- * An trait that gets mixin to define the expected input types of an expression.
+ * An expression with two inputs and one output. The output is by default evaluated to null
+ * if any input is evaluated to null.
  */
-trait ExpectsInputTypes { self: Expression =>
+abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] {
+  self: Product =>
+
+  override def foldable: Boolean = left.foldable && right.foldable
+
+  override def nullable: Boolean = left.nullable || right.nullable
 
   /**
-   * Expected input types from child expressions. The i-th position in the returned seq indicates
-   * the type requirement for the i-th child.
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
    *
-   * The possible values at each position are:
-   * 1. a specific data type, e.g. LongType, StringType.
-   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
-   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
+   * @param f accepts two variable names and returns Java code to compute the output.
    */
-  def inputTypes: Seq[Any]
+  protected def defineCodeGen(
+    ctx: CodeGenContext,
+    ev: GeneratedExpressionCode,
+    f: (String, String) => String): String = {
+    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+      s"$result = ${f(eval1, eval2)};"
+    })
+  }
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
-    TypeCheckResult.TypeCheckSuccess
+  /**
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
+   */
+  protected def nullSafeCodeGen(
+    ctx: CodeGenContext,
+    ev: GeneratedExpressionCode,
+    f: (String, String, String) => String): String = {
+    val eval1 = left.gen(ctx)
+    val eval2 = right.gen(ctx)
+    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
+    s"""
+      ${eval1.code}
+      boolean ${ev.isNull} = ${eval1.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${eval2.code}
+        if (!${eval2.isNull}) {
+          $resultCode
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+    """
   }
 }
 
+
 /**
- * Expressions that require a specific `DataType` as input should implement this trait
- * so that the proper type conversions can be performed in the analyzer.
+ * An expression that has two inputs that are expected to the be same type. If the two inputs have
+ * different types, the analyzer will find the tightest common type and do the proper type casting.
  */
-trait AutoCastInputTypes { self: Expression =>
+abstract class BinaryOperator extends BinaryExpression {
+  self: Product =>
 
-  def inputTypes: Seq[DataType]
+  def symbol: String
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
-    // so type mismatch error won't be reported here, but for underling `Cast`s.
-    TypeCheckResult.TypeCheckSuccess
-  }
+  override def toString: String = s"($left $symbol $right)"
+}
+
+
+private[sql] object BinaryOperator {
+  def unapply(e: BinaryOperator): Option[(Expression, Expression)] = Some((e.left, e.right))
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index ebabb6f117851..caf021b016a41 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -29,7 +29,7 @@ case class ScalaUDF(function: AnyRef, dataType: DataType, children: Seq[Expressi
 
   override def nullable: Boolean = true
 
-  override def toString: String = s"scalaUDF(${children.mkString(",")})"
+  override def toString: String = s"UDF(${children.mkString(",")})"
 
   // scalastyle:off
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index a9fc54c548f49..da520f56b430e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -128,7 +128,6 @@ case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
-  override def toString: String = s"MAX($child)"
 
   override def asPartial: SplitEvaluation = {
     val partialMax = Alias(Max(child), "PartialMax")()
@@ -162,7 +161,6 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
-  override def toString: String = s"COUNT($child)"
 
   override def asPartial: SplitEvaluation = {
     val partialCount = Alias(Count(child), "PartialCount")()
@@ -401,8 +399,6 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN
       DoubleType
   }
 
-  override def toString: String = s"AVG($child)"
-
   override def asPartial: SplitEvaluation = {
     child.dataType match {
       case DecimalType.Fixed(_, _) | DecimalType.Unlimited =>
@@ -494,8 +490,6 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[
       child.dataType
   }
 
-  override def toString: String = s"SUM($child)"
-
   override def asPartial: SplitEvaluation = {
     child.dataType match {
       case DecimalType.Fixed(_, _) =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 5363b3556886a..4fbf4c87009c2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -57,7 +57,7 @@ case class UnaryMinus(child: Expression) extends UnaryArithmetic {
 }
 
 case class UnaryPositive(child: Expression) extends UnaryArithmetic {
-  override def toString: String = s"positive($child)"
+  override def prettyName: String = "positive"
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
     defineCodeGen(ctx, ev, c => c)
@@ -69,8 +69,6 @@ case class UnaryPositive(child: Expression) extends UnaryArithmetic {
  * A function that get the absolute value of the numeric value.
  */
 case class Abs(child: Expression) extends UnaryArithmetic {
-  override def toString: String = s"Abs($child)"
-
   override def checkInputDataTypes(): TypeCheckResult =
     TypeUtils.checkForNumericExpr(child.dataType, "function abs")
 
@@ -79,10 +77,9 @@ case class Abs(child: Expression) extends UnaryArithmetic {
   protected override def evalInternal(evalE: Any) = numeric.abs(evalE)
 }
 
-abstract class BinaryArithmetic extends BinaryExpression {
+abstract class BinaryArithmetic extends BinaryOperator {
   self: Product =>
 
-
   override def dataType: DataType = left.dataType
 
   override def checkInputDataTypes(): TypeCheckResult = {
@@ -360,7 +357,9 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
       }
     """
   }
-  override def toString: String = s"MaxOf($left, $right)"
+
+  override def symbol: String = "max"
+  override def prettyName: String = symbol
 }
 
 case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
@@ -413,5 +412,6 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
     """
   }
 
-  override def toString: String = s"MinOf($left, $right)"
+  override def symbol: String = "min"
+  override def prettyName: String = symbol
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 5def57b067424..67e7dc4ec8b14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -43,7 +43,7 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
     children.map(_.eval(input))
   }
 
-  override def toString: String = s"Array(${children.mkString(",")})"
+  override def prettyName: String = "array"
 }
 
 /**
@@ -71,4 +71,6 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
   override def eval(input: InternalRow): Any = {
     InternalRow(children.map(_.eval(input)): _*)
   }
+
+  override def prettyName: String = "struct"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 78be2824347d7..145d323a9f0bb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -38,8 +38,6 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
     }
   }
 
-  override def toString: String = s"Coalesce(${children.mkString(",")})"
-
   override def dataType: DataType = children.head.dataType
 
   override def eval(input: InternalRow): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index a777f77add2db..34df89a163895 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -120,7 +120,7 @@ case class InSet(value: Expression, hset: Set[Any])
 }
 
 case class And(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with AutoCastInputTypes {
+  extends BinaryOperator with Predicate with AutoCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
@@ -169,7 +169,7 @@ case class And(left: Expression, right: Expression)
 }
 
 case class Or(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with AutoCastInputTypes {
+  extends BinaryOperator with Predicate with AutoCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
@@ -217,7 +217,7 @@ case class Or(left: Expression, right: Expression)
   }
 }
 
-abstract class BinaryComparison extends BinaryExpression with Predicate {
+abstract class BinaryComparison extends BinaryOperator with Predicate {
   self: Product =>
 
   override def checkInputDataTypes(): TypeCheckResult = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index daa9f4403ffab..5d51a4ca65332 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -137,8 +137,6 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres
 
   override def dataType: DataType = left.dataType
 
-  override def symbol: String = "++="
-
   override def eval(input: InternalRow): Any = {
     val leftEval = left.eval(input).asInstanceOf[OpenHashSet[Any]]
     if(leftEval != null) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 4cbfc4e084948..b020f2bbc5818 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -75,8 +75,6 @@ trait StringRegexExpression extends AutoCastInputTypes {
 case class Like(left: Expression, right: Expression)
   extends BinaryExpression with StringRegexExpression {
 
-  override def symbol: String = "LIKE"
-
   // replace the _ with .{1} exactly match 1 time of any character
   // replace the % with .*, match 0 or more times with any character
   override def escape(v: String): String =
@@ -101,14 +99,16 @@ case class Like(left: Expression, right: Expression)
     }
 
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
+
+  override def toString: String = s"$left LIKE $right"
 }
 
 case class RLike(left: Expression, right: Expression)
   extends BinaryExpression with StringRegexExpression {
 
-  override def symbol: String = "RLIKE"
   override def escape(v: String): String = v
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
+  override def toString: String = s"$left RLIKE $right"
 }
 
 trait CaseConversionExpression extends AutoCastInputTypes {
@@ -134,9 +134,7 @@ trait CaseConversionExpression extends AutoCastInputTypes {
  */
 case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
 
-  override def convert(v: UTF8String): UTF8String = v.toUpperCase()
-
-  override def toString: String = s"Upper($child)"
+  override def convert(v: UTF8String): UTF8String = v.toUpperCase
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).toUpperCase()")
@@ -148,9 +146,7 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE
  */
 case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
 
-  override def convert(v: UTF8String): UTF8String = v.toLowerCase()
-
-  override def toString: String = s"Lower($child)"
+  override def convert(v: UTF8String): UTF8String = v.toLowerCase
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).toLowerCase()")
@@ -178,8 +174,6 @@ trait StringComparison extends AutoCastInputTypes {
     }
   }
 
-  override def symbol: String = nodeName
-
   override def toString: String = s"$nodeName($left, $right)"
 }
 
@@ -284,12 +278,6 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
       }
     }
   }
-
-  override def toString: String = len match {
-    // TODO: This is broken because max is not an integer value.
-    case max if max == Integer.MAX_VALUE => s"SUBSTR($str, $pos)"
-    case _ => s"SUBSTR($str, $pos, $len)"
-  }
 }
 
 /**
@@ -304,9 +292,9 @@ case class StringLength(child: Expression) extends UnaryExpression with AutoCast
     if (string == null) null else string.asInstanceOf[UTF8String].length
   }
 
-  override def toString: String = s"length($child)"
-
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).length()")
   }
+
+  override def prettyName: String = "length"
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index bda217935cb05..86792f0217572 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -73,7 +73,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("+", "1", "*", "2", "-", "3", "4")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression transformDown {
-      case b: BinaryExpression => actual.append(b.symbol); b
+      case b: BinaryOperator => actual.append(b.symbol); b
       case l: Literal => actual.append(l.toString); l
     }
 
@@ -85,7 +85,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("1", "2", "3", "4", "-", "*", "+")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression transformUp {
-      case b: BinaryExpression => actual.append(b.symbol); b
+      case b: BinaryOperator => actual.append(b.symbol); b
       case l: Literal => actual.append(l.toString); l
     }
 
@@ -125,7 +125,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("1", "2", "3", "4", "-", "*", "+")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression foreachUp {
-      case b: BinaryExpression => actual.append(b.symbol);
+      case b: BinaryOperator => actual.append(b.symbol);
       case l: Literal => actual.append(l.toString);
     }
 

From 3a342dedc04799948bf6da69843bd1a91202ffe5 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 1 Jul 2015 16:59:39 -0700
Subject: [PATCH 0178/1454] Revert "[SPARK-8770][SQL] Create BinaryOperator
 abstract class."

This reverts commit 272778999823ed79af92280350c5869a87a21f29.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  17 +-
 .../expressions/ExpectsInputTypes.scala       |  59 -------
 .../sql/catalyst/expressions/Expression.scala | 161 +++++++++---------
 .../sql/catalyst/expressions/ScalaUDF.scala   |   2 +-
 .../sql/catalyst/expressions/aggregates.scala |   6 +
 .../sql/catalyst/expressions/arithmetic.scala |  14 +-
 .../expressions/complexTypeCreator.scala      |   4 +-
 .../catalyst/expressions/nullFunctions.scala  |   2 +
 .../sql/catalyst/expressions/predicates.scala |   6 +-
 .../spark/sql/catalyst/expressions/sets.scala |   2 +
 .../expressions/stringOperations.scala        |  26 ++-
 .../sql/catalyst/trees/TreeNodeSuite.scala    |   6 +-
 12 files changed, 135 insertions(+), 170 deletions(-)
 delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 8420c54f7c335..2ab5cb666fbcd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -150,7 +150,6 @@ object HiveTypeCoercion {
    * Converts string "NaN"s that are in binary operators with a NaN-able types (Float / Double) to
    * the appropriate numeric equivalent.
    */
-  // TODO: remove this rule and make Cast handle Nan.
   object ConvertNaNs extends Rule[LogicalPlan] {
     private val StringNaN = Literal("NaN")
 
@@ -160,19 +159,19 @@ object HiveTypeCoercion {
         case e if !e.childrenResolved => e
 
         /* Double Conversions */
-        case b @ BinaryOperator(StringNaN, right @ DoubleType()) =>
+        case b @ BinaryExpression(StringNaN, right @ DoubleType()) =>
           b.makeCopy(Array(Literal(Double.NaN), right))
-        case b @ BinaryOperator(left @ DoubleType(), StringNaN) =>
+        case b @ BinaryExpression(left @ DoubleType(), StringNaN) =>
           b.makeCopy(Array(left, Literal(Double.NaN)))
 
         /* Float Conversions */
-        case b @ BinaryOperator(StringNaN, right @ FloatType()) =>
+        case b @ BinaryExpression(StringNaN, right @ FloatType()) =>
           b.makeCopy(Array(Literal(Float.NaN), right))
-        case b @ BinaryOperator(left @ FloatType(), StringNaN) =>
+        case b @ BinaryExpression(left @ FloatType(), StringNaN) =>
           b.makeCopy(Array(left, Literal(Float.NaN)))
 
         /* Use float NaN by default to avoid unnecessary type widening */
-        case b @ BinaryOperator(left @ StringNaN, StringNaN) =>
+        case b @ BinaryExpression(left @ StringNaN, StringNaN) =>
           b.makeCopy(Array(left, Literal(Float.NaN)))
       }
     }
@@ -246,12 +245,12 @@ object HiveTypeCoercion {
 
         Union(newLeft, newRight)
 
-      // Also widen types for BinaryOperator.
+      // Also widen types for BinaryExpressions.
       case q: LogicalPlan => q transformExpressions {
         // Skip nodes who's children have not been resolved yet.
         case e if !e.childrenResolved => e
 
-        case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
+        case b @ BinaryExpression(left, right) if left.dataType != right.dataType =>
           findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { widestType =>
             val newLeft = if (left.dataType == widestType) left else Cast(left, widestType)
             val newRight = if (right.dataType == widestType) right else Cast(right, widestType)
@@ -479,7 +478,7 @@ object HiveTypeCoercion {
 
         // Promote integers inside a binary expression with fixed-precision decimals to decimals,
         // and fixed-precision decimals in an expression with floats / doubles to doubles
-        case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
+        case b @ BinaryExpression(left, right) if left.dataType != right.dataType =>
           (left.dataType, right.dataType) match {
             case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) =>
               b.makeCopy(Array(Cast(left, intTypeToFixed(t)), right))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
deleted file mode 100644
index 450fc4165f93b..0000000000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.expressions
-
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.types.DataType
-
-
-/**
- * An trait that gets mixin to define the expected input types of an expression.
- */
-trait ExpectsInputTypes { self: Expression =>
-
-  /**
-   * Expected input types from child expressions. The i-th position in the returned seq indicates
-   * the type requirement for the i-th child.
-   *
-   * The possible values at each position are:
-   * 1. a specific data type, e.g. LongType, StringType.
-   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
-   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
-   */
-  def inputTypes: Seq[Any]
-
-  override def checkInputDataTypes(): TypeCheckResult = {
-    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
-    TypeCheckResult.TypeCheckSuccess
-  }
-}
-
-/**
- * Expressions that require a specific `DataType` as input should implement this trait
- * so that the proper type conversions can be performed in the analyzer.
- */
-trait AutoCastInputTypes { self: Expression =>
-
-  def inputTypes: Seq[DataType]
-
-  override def checkInputDataTypes(): TypeCheckResult = {
-    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
-    // so type mismatch error won't be reported here, but for underling `Cast`s.
-    TypeCheckResult.TypeCheckSuccess
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index cafbbafdca207..e18a3118945e8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -119,6 +119,17 @@ abstract class Expression extends TreeNode[Expression] {
    */
   def childrenResolved: Boolean = children.forall(_.resolved)
 
+  /**
+   * Returns a string representation of this expression that does not have developer centric
+   * debugging information like the expression id.
+   */
+  def prettyString: String = {
+    transform {
+      case a: AttributeReference => PrettyAttribute(a.name)
+      case u: UnresolvedAttribute => PrettyAttribute(u.name)
+    }.toString
+  }
+
   /**
    * Returns true when two expressions will always compute the same result, even if they differ
    * cosmetically (i.e. capitalization of names in attributes may be different).
@@ -143,40 +154,71 @@ abstract class Expression extends TreeNode[Expression] {
    * Note: it's not valid to call this method until `childrenResolved == true`.
    */
   def checkInputDataTypes(): TypeCheckResult = TypeCheckResult.TypeCheckSuccess
+}
+
+abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] {
+  self: Product =>
+
+  def symbol: String = sys.error(s"BinaryExpressions must override either toString or symbol")
+
+  override def foldable: Boolean = left.foldable && right.foldable
+
+  override def nullable: Boolean = left.nullable || right.nullable
+
+  override def toString: String = s"($left $symbol $right)"
 
   /**
-   * Returns a user-facing string representation of this expression's name.
-   * This should usually match the name of the function in SQL.
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
+   *
+   * @param f accepts two variable names and returns Java code to compute the output.
    */
-  def prettyName: String = getClass.getSimpleName.toLowerCase
+  protected def defineCodeGen(
+      ctx: CodeGenContext,
+      ev: GeneratedExpressionCode,
+      f: (String, String) => String): String = {
+    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+      s"$result = ${f(eval1, eval2)};"
+    })
+  }
 
   /**
-   * Returns a user-facing string representation of this expression, i.e. does not have developer
-   * centric debugging information like the expression id.
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
    */
-  def prettyString: String = {
-    transform {
-      case a: AttributeReference => PrettyAttribute(a.name)
-      case u: UnresolvedAttribute => PrettyAttribute(u.name)
-    }.toString
+  protected def nullSafeCodeGen(
+      ctx: CodeGenContext,
+      ev: GeneratedExpressionCode,
+      f: (String, String, String) => String): String = {
+    val eval1 = left.gen(ctx)
+    val eval2 = right.gen(ctx)
+    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
+    s"""
+      ${eval1.code}
+      boolean ${ev.isNull} = ${eval1.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${eval2.code}
+        if (!${eval2.isNull}) {
+          $resultCode
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+    """
   }
-
-  override def toString: String = prettyName + children.mkString("(", ",", ")")
 }
 
+private[sql] object BinaryExpression {
+  def unapply(e: BinaryExpression): Option[(Expression, Expression)] = Some((e.left, e.right))
+}
 
-/**
- * A leaf expression, i.e. one without any child expressions.
- */
 abstract class LeafExpression extends Expression with trees.LeafNode[Expression] {
   self: Product =>
 }
 
-
-/**
- * An expression with one input and one output. The output is by default evaluated to null
- * if the input is evaluated to null.
- */
 abstract class UnaryExpression extends Expression with trees.UnaryNode[Expression] {
   self: Product =>
 
@@ -223,76 +265,39 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
   }
 }
 
-
 /**
- * An expression with two inputs and one output. The output is by default evaluated to null
- * if any input is evaluated to null.
+ * An trait that gets mixin to define the expected input types of an expression.
  */
-abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] {
-  self: Product =>
-
-  override def foldable: Boolean = left.foldable && right.foldable
-
-  override def nullable: Boolean = left.nullable || right.nullable
+trait ExpectsInputTypes { self: Expression =>
 
   /**
-   * Short hand for generating binary evaluation code.
-   * If either of the sub-expressions is null, the result of this computation
-   * is assumed to be null.
+   * Expected input types from child expressions. The i-th position in the returned seq indicates
+   * the type requirement for the i-th child.
    *
-   * @param f accepts two variable names and returns Java code to compute the output.
+   * The possible values at each position are:
+   * 1. a specific data type, e.g. LongType, StringType.
+   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
+   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
    */
-  protected def defineCodeGen(
-    ctx: CodeGenContext,
-    ev: GeneratedExpressionCode,
-    f: (String, String) => String): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
-      s"$result = ${f(eval1, eval2)};"
-    })
-  }
+  def inputTypes: Seq[Any]
 
-  /**
-   * Short hand for generating binary evaluation code.
-   * If either of the sub-expressions is null, the result of this computation
-   * is assumed to be null.
-   */
-  protected def nullSafeCodeGen(
-    ctx: CodeGenContext,
-    ev: GeneratedExpressionCode,
-    f: (String, String, String) => String): String = {
-    val eval1 = left.gen(ctx)
-    val eval2 = right.gen(ctx)
-    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
-    s"""
-      ${eval1.code}
-      boolean ${ev.isNull} = ${eval1.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${eval2.code}
-        if (!${eval2.isNull}) {
-          $resultCode
-        } else {
-          ${ev.isNull} = true;
-        }
-      }
-    """
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
+    TypeCheckResult.TypeCheckSuccess
   }
 }
 
-
 /**
- * An expression that has two inputs that are expected to the be same type. If the two inputs have
- * different types, the analyzer will find the tightest common type and do the proper type casting.
+ * Expressions that require a specific `DataType` as input should implement this trait
+ * so that the proper type conversions can be performed in the analyzer.
  */
-abstract class BinaryOperator extends BinaryExpression {
-  self: Product =>
+trait AutoCastInputTypes { self: Expression =>
 
-  def symbol: String
+  def inputTypes: Seq[DataType]
 
-  override def toString: String = s"($left $symbol $right)"
-}
-
-
-private[sql] object BinaryOperator {
-  def unapply(e: BinaryOperator): Option[(Expression, Expression)] = Some((e.left, e.right))
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
+    // so type mismatch error won't be reported here, but for underling `Cast`s.
+    TypeCheckResult.TypeCheckSuccess
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index caf021b016a41..ebabb6f117851 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -29,7 +29,7 @@ case class ScalaUDF(function: AnyRef, dataType: DataType, children: Seq[Expressi
 
   override def nullable: Boolean = true
 
-  override def toString: String = s"UDF(${children.mkString(",")})"
+  override def toString: String = s"scalaUDF(${children.mkString(",")})"
 
   // scalastyle:off
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index da520f56b430e..a9fc54c548f49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -128,6 +128,7 @@ case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
+  override def toString: String = s"MAX($child)"
 
   override def asPartial: SplitEvaluation = {
     val partialMax = Alias(Max(child), "PartialMax")()
@@ -161,6 +162,7 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
+  override def toString: String = s"COUNT($child)"
 
   override def asPartial: SplitEvaluation = {
     val partialCount = Alias(Count(child), "PartialCount")()
@@ -399,6 +401,8 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN
       DoubleType
   }
 
+  override def toString: String = s"AVG($child)"
+
   override def asPartial: SplitEvaluation = {
     child.dataType match {
       case DecimalType.Fixed(_, _) | DecimalType.Unlimited =>
@@ -490,6 +494,8 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[
       child.dataType
   }
 
+  override def toString: String = s"SUM($child)"
+
   override def asPartial: SplitEvaluation = {
     child.dataType match {
       case DecimalType.Fixed(_, _) =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 4fbf4c87009c2..5363b3556886a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -57,7 +57,7 @@ case class UnaryMinus(child: Expression) extends UnaryArithmetic {
 }
 
 case class UnaryPositive(child: Expression) extends UnaryArithmetic {
-  override def prettyName: String = "positive"
+  override def toString: String = s"positive($child)"
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
     defineCodeGen(ctx, ev, c => c)
@@ -69,6 +69,8 @@ case class UnaryPositive(child: Expression) extends UnaryArithmetic {
  * A function that get the absolute value of the numeric value.
  */
 case class Abs(child: Expression) extends UnaryArithmetic {
+  override def toString: String = s"Abs($child)"
+
   override def checkInputDataTypes(): TypeCheckResult =
     TypeUtils.checkForNumericExpr(child.dataType, "function abs")
 
@@ -77,9 +79,10 @@ case class Abs(child: Expression) extends UnaryArithmetic {
   protected override def evalInternal(evalE: Any) = numeric.abs(evalE)
 }
 
-abstract class BinaryArithmetic extends BinaryOperator {
+abstract class BinaryArithmetic extends BinaryExpression {
   self: Product =>
 
+
   override def dataType: DataType = left.dataType
 
   override def checkInputDataTypes(): TypeCheckResult = {
@@ -357,9 +360,7 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
       }
     """
   }
-
-  override def symbol: String = "max"
-  override def prettyName: String = symbol
+  override def toString: String = s"MaxOf($left, $right)"
 }
 
 case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
@@ -412,6 +413,5 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
     """
   }
 
-  override def symbol: String = "min"
-  override def prettyName: String = symbol
+  override def toString: String = s"MinOf($left, $right)"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 67e7dc4ec8b14..5def57b067424 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -43,7 +43,7 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
     children.map(_.eval(input))
   }
 
-  override def prettyName: String = "array"
+  override def toString: String = s"Array(${children.mkString(",")})"
 }
 
 /**
@@ -71,6 +71,4 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
   override def eval(input: InternalRow): Any = {
     InternalRow(children.map(_.eval(input)): _*)
   }
-
-  override def prettyName: String = "struct"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 145d323a9f0bb..78be2824347d7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -38,6 +38,8 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
     }
   }
 
+  override def toString: String = s"Coalesce(${children.mkString(",")})"
+
   override def dataType: DataType = children.head.dataType
 
   override def eval(input: InternalRow): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 34df89a163895..a777f77add2db 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -120,7 +120,7 @@ case class InSet(value: Expression, hset: Set[Any])
 }
 
 case class And(left: Expression, right: Expression)
-  extends BinaryOperator with Predicate with AutoCastInputTypes {
+  extends BinaryExpression with Predicate with AutoCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
@@ -169,7 +169,7 @@ case class And(left: Expression, right: Expression)
 }
 
 case class Or(left: Expression, right: Expression)
-  extends BinaryOperator with Predicate with AutoCastInputTypes {
+  extends BinaryExpression with Predicate with AutoCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
@@ -217,7 +217,7 @@ case class Or(left: Expression, right: Expression)
   }
 }
 
-abstract class BinaryComparison extends BinaryOperator with Predicate {
+abstract class BinaryComparison extends BinaryExpression with Predicate {
   self: Product =>
 
   override def checkInputDataTypes(): TypeCheckResult = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index 5d51a4ca65332..daa9f4403ffab 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -137,6 +137,8 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres
 
   override def dataType: DataType = left.dataType
 
+  override def symbol: String = "++="
+
   override def eval(input: InternalRow): Any = {
     val leftEval = left.eval(input).asInstanceOf[OpenHashSet[Any]]
     if(leftEval != null) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index b020f2bbc5818..4cbfc4e084948 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -75,6 +75,8 @@ trait StringRegexExpression extends AutoCastInputTypes {
 case class Like(left: Expression, right: Expression)
   extends BinaryExpression with StringRegexExpression {
 
+  override def symbol: String = "LIKE"
+
   // replace the _ with .{1} exactly match 1 time of any character
   // replace the % with .*, match 0 or more times with any character
   override def escape(v: String): String =
@@ -99,16 +101,14 @@ case class Like(left: Expression, right: Expression)
     }
 
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
-
-  override def toString: String = s"$left LIKE $right"
 }
 
 case class RLike(left: Expression, right: Expression)
   extends BinaryExpression with StringRegexExpression {
 
+  override def symbol: String = "RLIKE"
   override def escape(v: String): String = v
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
-  override def toString: String = s"$left RLIKE $right"
 }
 
 trait CaseConversionExpression extends AutoCastInputTypes {
@@ -134,7 +134,9 @@ trait CaseConversionExpression extends AutoCastInputTypes {
  */
 case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
 
-  override def convert(v: UTF8String): UTF8String = v.toUpperCase
+  override def convert(v: UTF8String): UTF8String = v.toUpperCase()
+
+  override def toString: String = s"Upper($child)"
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).toUpperCase()")
@@ -146,7 +148,9 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE
  */
 case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
 
-  override def convert(v: UTF8String): UTF8String = v.toLowerCase
+  override def convert(v: UTF8String): UTF8String = v.toLowerCase()
+
+  override def toString: String = s"Lower($child)"
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).toLowerCase()")
@@ -174,6 +178,8 @@ trait StringComparison extends AutoCastInputTypes {
     }
   }
 
+  override def symbol: String = nodeName
+
   override def toString: String = s"$nodeName($left, $right)"
 }
 
@@ -278,6 +284,12 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
       }
     }
   }
+
+  override def toString: String = len match {
+    // TODO: This is broken because max is not an integer value.
+    case max if max == Integer.MAX_VALUE => s"SUBSTR($str, $pos)"
+    case _ => s"SUBSTR($str, $pos, $len)"
+  }
 }
 
 /**
@@ -292,9 +304,9 @@ case class StringLength(child: Expression) extends UnaryExpression with AutoCast
     if (string == null) null else string.asInstanceOf[UTF8String].length
   }
 
+  override def toString: String = s"length($child)"
+
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).length()")
   }
-
-  override def prettyName: String = "length"
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index 86792f0217572..bda217935cb05 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -73,7 +73,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("+", "1", "*", "2", "-", "3", "4")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression transformDown {
-      case b: BinaryOperator => actual.append(b.symbol); b
+      case b: BinaryExpression => actual.append(b.symbol); b
       case l: Literal => actual.append(l.toString); l
     }
 
@@ -85,7 +85,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("1", "2", "3", "4", "-", "*", "+")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression transformUp {
-      case b: BinaryOperator => actual.append(b.symbol); b
+      case b: BinaryExpression => actual.append(b.symbol); b
       case l: Literal => actual.append(l.toString); l
     }
 
@@ -125,7 +125,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("1", "2", "3", "4", "-", "*", "+")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression foreachUp {
-      case b: BinaryOperator => actual.append(b.symbol);
+      case b: BinaryExpression => actual.append(b.symbol);
       case l: Literal => actual.append(l.toString);
     }
 

From 9fd13d5613b6d16a78d97d4798f085b56107d343 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 1 Jul 2015 21:14:13 -0700
Subject: [PATCH 0179/1454] [SPARK-8770][SQL] Create BinaryOperator abstract
 class.

Our current BinaryExpression abstract class is not for generic binary expressions, i.e. it requires left/right children to have the same type. However, due to its name, contributors build new binary expressions that don't have that assumption (e.g. Sha) and still extend BinaryExpression.

This patch creates a new BinaryOperator abstract class, and update the analyzer o only apply type casting rule there. This patch also adds the notion of "prettyName" to expressions, which defines the user-facing name for the expression.

Author: Reynold Xin <rxin@databricks.com>

Closes #7174 from rxin/binary-opterator and squashes the following commits:

f31900d [Reynold Xin] [SPARK-8770][SQL] Create BinaryOperator abstract class.
fceb216 [Reynold Xin] Merge branch 'master' of github.com:apache/spark into binary-opterator
d8518cf [Reynold Xin] Updated Python tests.
---
 python/pyspark/sql/dataframe.py               |  10 +-
 python/pyspark/sql/functions.py               |   4 +-
 python/pyspark/sql/group.py                   |  24 +--
 .../catalyst/analysis/HiveTypeCoercion.scala  |  17 +-
 .../expressions/ExpectsInputTypes.scala       |  59 +++++++
 .../sql/catalyst/expressions/Expression.scala | 161 +++++++++---------
 .../sql/catalyst/expressions/ScalaUDF.scala   |   2 +-
 .../sql/catalyst/expressions/aggregates.scala |   9 +-
 .../sql/catalyst/expressions/arithmetic.scala |  14 +-
 .../expressions/complexTypeCreator.scala      |   4 +-
 .../catalyst/expressions/nullFunctions.scala  |   2 -
 .../sql/catalyst/expressions/predicates.scala |   6 +-
 .../spark/sql/catalyst/expressions/sets.scala |   2 -
 .../expressions/stringOperations.scala        |  26 +--
 .../sql/catalyst/trees/TreeNodeSuite.scala    |   6 +-
 15 files changed, 191 insertions(+), 155 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 273a40dd526cf..1e9c657cf81b3 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -802,11 +802,11 @@ def groupBy(self, *cols):
             Each element should be a column name (string) or an expression (:class:`Column`).
 
         >>> df.groupBy().avg().collect()
-        [Row(AVG(age)=3.5)]
+        [Row(avg(age)=3.5)]
         >>> df.groupBy('name').agg({'age': 'mean'}).collect()
-        [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)]
+        [Row(name=u'Alice', avg(age)=2.0), Row(name=u'Bob', avg(age)=5.0)]
         >>> df.groupBy(df.name).avg().collect()
-        [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)]
+        [Row(name=u'Alice', avg(age)=2.0), Row(name=u'Bob', avg(age)=5.0)]
         >>> df.groupBy(['name', df.age]).count().collect()
         [Row(name=u'Bob', age=5, count=1), Row(name=u'Alice', age=2, count=1)]
         """
@@ -864,10 +864,10 @@ def agg(self, *exprs):
         (shorthand for ``df.groupBy.agg()``).
 
         >>> df.agg({"age": "max"}).collect()
-        [Row(MAX(age)=5)]
+        [Row(max(age)=5)]
         >>> from pyspark.sql import functions as F
         >>> df.agg(F.min(df.age)).collect()
-        [Row(MIN(age)=2)]
+        [Row(min(age)=2)]
         """
         return self.groupBy().agg(*exprs)
 
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 4e2be88e9e3b9..f9a15d4a66309 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -266,7 +266,7 @@ def coalesce(*cols):
 
     >>> cDf.select(coalesce(cDf["a"], cDf["b"])).show()
     +-------------+
-    |Coalesce(a,b)|
+    |coalesce(a,b)|
     +-------------+
     |         null|
     |            1|
@@ -275,7 +275,7 @@ def coalesce(*cols):
 
     >>> cDf.select('*', coalesce(cDf["a"], lit(0.0))).show()
     +----+----+---------------+
-    |   a|   b|Coalesce(a,0.0)|
+    |   a|   b|coalesce(a,0.0)|
     +----+----+---------------+
     |null|null|            0.0|
     |   1|null|            1.0|
diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py
index 5a37a673ee80c..04594d5a836ce 100644
--- a/python/pyspark/sql/group.py
+++ b/python/pyspark/sql/group.py
@@ -75,11 +75,11 @@ def agg(self, *exprs):
 
         >>> gdf = df.groupBy(df.name)
         >>> gdf.agg({"*": "count"}).collect()
-        [Row(name=u'Alice', COUNT(1)=1), Row(name=u'Bob', COUNT(1)=1)]
+        [Row(name=u'Alice', count(1)=1), Row(name=u'Bob', count(1)=1)]
 
         >>> from pyspark.sql import functions as F
         >>> gdf.agg(F.min(df.age)).collect()
-        [Row(name=u'Alice', MIN(age)=2), Row(name=u'Bob', MIN(age)=5)]
+        [Row(name=u'Alice', min(age)=2), Row(name=u'Bob', min(age)=5)]
         """
         assert exprs, "exprs should not be empty"
         if len(exprs) == 1 and isinstance(exprs[0], dict):
@@ -110,9 +110,9 @@ def mean(self, *cols):
         :param cols: list of column names (string). Non-numeric columns are ignored.
 
         >>> df.groupBy().mean('age').collect()
-        [Row(AVG(age)=3.5)]
+        [Row(avg(age)=3.5)]
         >>> df3.groupBy().mean('age', 'height').collect()
-        [Row(AVG(age)=3.5, AVG(height)=82.5)]
+        [Row(avg(age)=3.5, avg(height)=82.5)]
         """
 
     @df_varargs_api
@@ -125,9 +125,9 @@ def avg(self, *cols):
         :param cols: list of column names (string). Non-numeric columns are ignored.
 
         >>> df.groupBy().avg('age').collect()
-        [Row(AVG(age)=3.5)]
+        [Row(avg(age)=3.5)]
         >>> df3.groupBy().avg('age', 'height').collect()
-        [Row(AVG(age)=3.5, AVG(height)=82.5)]
+        [Row(avg(age)=3.5, avg(height)=82.5)]
         """
 
     @df_varargs_api
@@ -136,9 +136,9 @@ def max(self, *cols):
         """Computes the max value for each numeric columns for each group.
 
         >>> df.groupBy().max('age').collect()
-        [Row(MAX(age)=5)]
+        [Row(max(age)=5)]
         >>> df3.groupBy().max('age', 'height').collect()
-        [Row(MAX(age)=5, MAX(height)=85)]
+        [Row(max(age)=5, max(height)=85)]
         """
 
     @df_varargs_api
@@ -149,9 +149,9 @@ def min(self, *cols):
         :param cols: list of column names (string). Non-numeric columns are ignored.
 
         >>> df.groupBy().min('age').collect()
-        [Row(MIN(age)=2)]
+        [Row(min(age)=2)]
         >>> df3.groupBy().min('age', 'height').collect()
-        [Row(MIN(age)=2, MIN(height)=80)]
+        [Row(min(age)=2, min(height)=80)]
         """
 
     @df_varargs_api
@@ -162,9 +162,9 @@ def sum(self, *cols):
         :param cols: list of column names (string). Non-numeric columns are ignored.
 
         >>> df.groupBy().sum('age').collect()
-        [Row(SUM(age)=7)]
+        [Row(sum(age)=7)]
         >>> df3.groupBy().sum('age', 'height').collect()
-        [Row(SUM(age)=7, SUM(height)=165)]
+        [Row(sum(age)=7, sum(height)=165)]
         """
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 2ab5cb666fbcd..8420c54f7c335 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -150,6 +150,7 @@ object HiveTypeCoercion {
    * Converts string "NaN"s that are in binary operators with a NaN-able types (Float / Double) to
    * the appropriate numeric equivalent.
    */
+  // TODO: remove this rule and make Cast handle Nan.
   object ConvertNaNs extends Rule[LogicalPlan] {
     private val StringNaN = Literal("NaN")
 
@@ -159,19 +160,19 @@ object HiveTypeCoercion {
         case e if !e.childrenResolved => e
 
         /* Double Conversions */
-        case b @ BinaryExpression(StringNaN, right @ DoubleType()) =>
+        case b @ BinaryOperator(StringNaN, right @ DoubleType()) =>
           b.makeCopy(Array(Literal(Double.NaN), right))
-        case b @ BinaryExpression(left @ DoubleType(), StringNaN) =>
+        case b @ BinaryOperator(left @ DoubleType(), StringNaN) =>
           b.makeCopy(Array(left, Literal(Double.NaN)))
 
         /* Float Conversions */
-        case b @ BinaryExpression(StringNaN, right @ FloatType()) =>
+        case b @ BinaryOperator(StringNaN, right @ FloatType()) =>
           b.makeCopy(Array(Literal(Float.NaN), right))
-        case b @ BinaryExpression(left @ FloatType(), StringNaN) =>
+        case b @ BinaryOperator(left @ FloatType(), StringNaN) =>
           b.makeCopy(Array(left, Literal(Float.NaN)))
 
         /* Use float NaN by default to avoid unnecessary type widening */
-        case b @ BinaryExpression(left @ StringNaN, StringNaN) =>
+        case b @ BinaryOperator(left @ StringNaN, StringNaN) =>
           b.makeCopy(Array(left, Literal(Float.NaN)))
       }
     }
@@ -245,12 +246,12 @@ object HiveTypeCoercion {
 
         Union(newLeft, newRight)
 
-      // Also widen types for BinaryExpressions.
+      // Also widen types for BinaryOperator.
       case q: LogicalPlan => q transformExpressions {
         // Skip nodes who's children have not been resolved yet.
         case e if !e.childrenResolved => e
 
-        case b @ BinaryExpression(left, right) if left.dataType != right.dataType =>
+        case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
           findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { widestType =>
             val newLeft = if (left.dataType == widestType) left else Cast(left, widestType)
             val newRight = if (right.dataType == widestType) right else Cast(right, widestType)
@@ -478,7 +479,7 @@ object HiveTypeCoercion {
 
         // Promote integers inside a binary expression with fixed-precision decimals to decimals,
         // and fixed-precision decimals in an expression with floats / doubles to doubles
-        case b @ BinaryExpression(left, right) if left.dataType != right.dataType =>
+        case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
           (left.dataType, right.dataType) match {
             case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) =>
               b.makeCopy(Array(Cast(left, intTypeToFixed(t)), right))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
new file mode 100644
index 0000000000000..450fc4165f93b
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.types.DataType
+
+
+/**
+ * An trait that gets mixin to define the expected input types of an expression.
+ */
+trait ExpectsInputTypes { self: Expression =>
+
+  /**
+   * Expected input types from child expressions. The i-th position in the returned seq indicates
+   * the type requirement for the i-th child.
+   *
+   * The possible values at each position are:
+   * 1. a specific data type, e.g. LongType, StringType.
+   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
+   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
+   */
+  def inputTypes: Seq[Any]
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
+    TypeCheckResult.TypeCheckSuccess
+  }
+}
+
+/**
+ * Expressions that require a specific `DataType` as input should implement this trait
+ * so that the proper type conversions can be performed in the analyzer.
+ */
+trait AutoCastInputTypes { self: Expression =>
+
+  def inputTypes: Seq[DataType]
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
+    // so type mismatch error won't be reported here, but for underling `Cast`s.
+    TypeCheckResult.TypeCheckSuccess
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index e18a3118945e8..cafbbafdca207 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -119,17 +119,6 @@ abstract class Expression extends TreeNode[Expression] {
    */
   def childrenResolved: Boolean = children.forall(_.resolved)
 
-  /**
-   * Returns a string representation of this expression that does not have developer centric
-   * debugging information like the expression id.
-   */
-  def prettyString: String = {
-    transform {
-      case a: AttributeReference => PrettyAttribute(a.name)
-      case u: UnresolvedAttribute => PrettyAttribute(u.name)
-    }.toString
-  }
-
   /**
    * Returns true when two expressions will always compute the same result, even if they differ
    * cosmetically (i.e. capitalization of names in attributes may be different).
@@ -154,71 +143,40 @@ abstract class Expression extends TreeNode[Expression] {
    * Note: it's not valid to call this method until `childrenResolved == true`.
    */
   def checkInputDataTypes(): TypeCheckResult = TypeCheckResult.TypeCheckSuccess
-}
-
-abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] {
-  self: Product =>
-
-  def symbol: String = sys.error(s"BinaryExpressions must override either toString or symbol")
-
-  override def foldable: Boolean = left.foldable && right.foldable
-
-  override def nullable: Boolean = left.nullable || right.nullable
-
-  override def toString: String = s"($left $symbol $right)"
 
   /**
-   * Short hand for generating binary evaluation code.
-   * If either of the sub-expressions is null, the result of this computation
-   * is assumed to be null.
-   *
-   * @param f accepts two variable names and returns Java code to compute the output.
+   * Returns a user-facing string representation of this expression's name.
+   * This should usually match the name of the function in SQL.
    */
-  protected def defineCodeGen(
-      ctx: CodeGenContext,
-      ev: GeneratedExpressionCode,
-      f: (String, String) => String): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
-      s"$result = ${f(eval1, eval2)};"
-    })
-  }
+  def prettyName: String = getClass.getSimpleName.toLowerCase
 
   /**
-   * Short hand for generating binary evaluation code.
-   * If either of the sub-expressions is null, the result of this computation
-   * is assumed to be null.
+   * Returns a user-facing string representation of this expression, i.e. does not have developer
+   * centric debugging information like the expression id.
    */
-  protected def nullSafeCodeGen(
-      ctx: CodeGenContext,
-      ev: GeneratedExpressionCode,
-      f: (String, String, String) => String): String = {
-    val eval1 = left.gen(ctx)
-    val eval2 = right.gen(ctx)
-    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
-    s"""
-      ${eval1.code}
-      boolean ${ev.isNull} = ${eval1.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${eval2.code}
-        if (!${eval2.isNull}) {
-          $resultCode
-        } else {
-          ${ev.isNull} = true;
-        }
-      }
-    """
+  def prettyString: String = {
+    transform {
+      case a: AttributeReference => PrettyAttribute(a.name)
+      case u: UnresolvedAttribute => PrettyAttribute(u.name)
+    }.toString
   }
-}
 
-private[sql] object BinaryExpression {
-  def unapply(e: BinaryExpression): Option[(Expression, Expression)] = Some((e.left, e.right))
+  override def toString: String = prettyName + children.mkString("(", ",", ")")
 }
 
+
+/**
+ * A leaf expression, i.e. one without any child expressions.
+ */
 abstract class LeafExpression extends Expression with trees.LeafNode[Expression] {
   self: Product =>
 }
 
+
+/**
+ * An expression with one input and one output. The output is by default evaluated to null
+ * if the input is evaluated to null.
+ */
 abstract class UnaryExpression extends Expression with trees.UnaryNode[Expression] {
   self: Product =>
 
@@ -265,39 +223,76 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
   }
 }
 
+
 /**
- * An trait that gets mixin to define the expected input types of an expression.
+ * An expression with two inputs and one output. The output is by default evaluated to null
+ * if any input is evaluated to null.
  */
-trait ExpectsInputTypes { self: Expression =>
+abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] {
+  self: Product =>
+
+  override def foldable: Boolean = left.foldable && right.foldable
+
+  override def nullable: Boolean = left.nullable || right.nullable
 
   /**
-   * Expected input types from child expressions. The i-th position in the returned seq indicates
-   * the type requirement for the i-th child.
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
    *
-   * The possible values at each position are:
-   * 1. a specific data type, e.g. LongType, StringType.
-   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
-   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
+   * @param f accepts two variable names and returns Java code to compute the output.
    */
-  def inputTypes: Seq[Any]
+  protected def defineCodeGen(
+    ctx: CodeGenContext,
+    ev: GeneratedExpressionCode,
+    f: (String, String) => String): String = {
+    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+      s"$result = ${f(eval1, eval2)};"
+    })
+  }
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
-    TypeCheckResult.TypeCheckSuccess
+  /**
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
+   */
+  protected def nullSafeCodeGen(
+    ctx: CodeGenContext,
+    ev: GeneratedExpressionCode,
+    f: (String, String, String) => String): String = {
+    val eval1 = left.gen(ctx)
+    val eval2 = right.gen(ctx)
+    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
+    s"""
+      ${eval1.code}
+      boolean ${ev.isNull} = ${eval1.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${eval2.code}
+        if (!${eval2.isNull}) {
+          $resultCode
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+    """
   }
 }
 
+
 /**
- * Expressions that require a specific `DataType` as input should implement this trait
- * so that the proper type conversions can be performed in the analyzer.
+ * An expression that has two inputs that are expected to the be same type. If the two inputs have
+ * different types, the analyzer will find the tightest common type and do the proper type casting.
  */
-trait AutoCastInputTypes { self: Expression =>
+abstract class BinaryOperator extends BinaryExpression {
+  self: Product =>
 
-  def inputTypes: Seq[DataType]
+  def symbol: String
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
-    // so type mismatch error won't be reported here, but for underling `Cast`s.
-    TypeCheckResult.TypeCheckSuccess
-  }
+  override def toString: String = s"($left $symbol $right)"
+}
+
+
+private[sql] object BinaryOperator {
+  def unapply(e: BinaryOperator): Option[(Expression, Expression)] = Some((e.left, e.right))
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index ebabb6f117851..caf021b016a41 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -29,7 +29,7 @@ case class ScalaUDF(function: AnyRef, dataType: DataType, children: Seq[Expressi
 
   override def nullable: Boolean = true
 
-  override def toString: String = s"scalaUDF(${children.mkString(",")})"
+  override def toString: String = s"UDF(${children.mkString(",")})"
 
   // scalastyle:off
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index a9fc54c548f49..64e07bd2a17db 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -94,7 +94,6 @@ case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
-  override def toString: String = s"MIN($child)"
 
   override def asPartial: SplitEvaluation = {
     val partialMin = Alias(Min(child), "PartialMin")()
@@ -128,7 +127,6 @@ case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
-  override def toString: String = s"MAX($child)"
 
   override def asPartial: SplitEvaluation = {
     val partialMax = Alias(Max(child), "PartialMax")()
@@ -162,7 +160,6 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
-  override def toString: String = s"COUNT($child)"
 
   override def asPartial: SplitEvaluation = {
     val partialCount = Alias(Count(child), "PartialCount")()
@@ -390,6 +387,8 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
 
 case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
 
+  override def prettyName: String = "avg"
+
   override def nullable: Boolean = true
 
   override def dataType: DataType = child.dataType match {
@@ -401,8 +400,6 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN
       DoubleType
   }
 
-  override def toString: String = s"AVG($child)"
-
   override def asPartial: SplitEvaluation = {
     child.dataType match {
       case DecimalType.Fixed(_, _) | DecimalType.Unlimited =>
@@ -494,8 +491,6 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[
       child.dataType
   }
 
-  override def toString: String = s"SUM($child)"
-
   override def asPartial: SplitEvaluation = {
     child.dataType match {
       case DecimalType.Fixed(_, _) =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 5363b3556886a..4fbf4c87009c2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -57,7 +57,7 @@ case class UnaryMinus(child: Expression) extends UnaryArithmetic {
 }
 
 case class UnaryPositive(child: Expression) extends UnaryArithmetic {
-  override def toString: String = s"positive($child)"
+  override def prettyName: String = "positive"
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
     defineCodeGen(ctx, ev, c => c)
@@ -69,8 +69,6 @@ case class UnaryPositive(child: Expression) extends UnaryArithmetic {
  * A function that get the absolute value of the numeric value.
  */
 case class Abs(child: Expression) extends UnaryArithmetic {
-  override def toString: String = s"Abs($child)"
-
   override def checkInputDataTypes(): TypeCheckResult =
     TypeUtils.checkForNumericExpr(child.dataType, "function abs")
 
@@ -79,10 +77,9 @@ case class Abs(child: Expression) extends UnaryArithmetic {
   protected override def evalInternal(evalE: Any) = numeric.abs(evalE)
 }
 
-abstract class BinaryArithmetic extends BinaryExpression {
+abstract class BinaryArithmetic extends BinaryOperator {
   self: Product =>
 
-
   override def dataType: DataType = left.dataType
 
   override def checkInputDataTypes(): TypeCheckResult = {
@@ -360,7 +357,9 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
       }
     """
   }
-  override def toString: String = s"MaxOf($left, $right)"
+
+  override def symbol: String = "max"
+  override def prettyName: String = symbol
 }
 
 case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
@@ -413,5 +412,6 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
     """
   }
 
-  override def toString: String = s"MinOf($left, $right)"
+  override def symbol: String = "min"
+  override def prettyName: String = symbol
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 5def57b067424..67e7dc4ec8b14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -43,7 +43,7 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
     children.map(_.eval(input))
   }
 
-  override def toString: String = s"Array(${children.mkString(",")})"
+  override def prettyName: String = "array"
 }
 
 /**
@@ -71,4 +71,6 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
   override def eval(input: InternalRow): Any = {
     InternalRow(children.map(_.eval(input)): _*)
   }
+
+  override def prettyName: String = "struct"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 78be2824347d7..145d323a9f0bb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -38,8 +38,6 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
     }
   }
 
-  override def toString: String = s"Coalesce(${children.mkString(",")})"
-
   override def dataType: DataType = children.head.dataType
 
   override def eval(input: InternalRow): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index a777f77add2db..34df89a163895 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -120,7 +120,7 @@ case class InSet(value: Expression, hset: Set[Any])
 }
 
 case class And(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with AutoCastInputTypes {
+  extends BinaryOperator with Predicate with AutoCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
@@ -169,7 +169,7 @@ case class And(left: Expression, right: Expression)
 }
 
 case class Or(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with AutoCastInputTypes {
+  extends BinaryOperator with Predicate with AutoCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
@@ -217,7 +217,7 @@ case class Or(left: Expression, right: Expression)
   }
 }
 
-abstract class BinaryComparison extends BinaryExpression with Predicate {
+abstract class BinaryComparison extends BinaryOperator with Predicate {
   self: Product =>
 
   override def checkInputDataTypes(): TypeCheckResult = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index daa9f4403ffab..5d51a4ca65332 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -137,8 +137,6 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres
 
   override def dataType: DataType = left.dataType
 
-  override def symbol: String = "++="
-
   override def eval(input: InternalRow): Any = {
     val leftEval = left.eval(input).asInstanceOf[OpenHashSet[Any]]
     if(leftEval != null) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 4cbfc4e084948..b020f2bbc5818 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -75,8 +75,6 @@ trait StringRegexExpression extends AutoCastInputTypes {
 case class Like(left: Expression, right: Expression)
   extends BinaryExpression with StringRegexExpression {
 
-  override def symbol: String = "LIKE"
-
   // replace the _ with .{1} exactly match 1 time of any character
   // replace the % with .*, match 0 or more times with any character
   override def escape(v: String): String =
@@ -101,14 +99,16 @@ case class Like(left: Expression, right: Expression)
     }
 
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
+
+  override def toString: String = s"$left LIKE $right"
 }
 
 case class RLike(left: Expression, right: Expression)
   extends BinaryExpression with StringRegexExpression {
 
-  override def symbol: String = "RLIKE"
   override def escape(v: String): String = v
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
+  override def toString: String = s"$left RLIKE $right"
 }
 
 trait CaseConversionExpression extends AutoCastInputTypes {
@@ -134,9 +134,7 @@ trait CaseConversionExpression extends AutoCastInputTypes {
  */
 case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
 
-  override def convert(v: UTF8String): UTF8String = v.toUpperCase()
-
-  override def toString: String = s"Upper($child)"
+  override def convert(v: UTF8String): UTF8String = v.toUpperCase
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).toUpperCase()")
@@ -148,9 +146,7 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE
  */
 case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
 
-  override def convert(v: UTF8String): UTF8String = v.toLowerCase()
-
-  override def toString: String = s"Lower($child)"
+  override def convert(v: UTF8String): UTF8String = v.toLowerCase
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).toLowerCase()")
@@ -178,8 +174,6 @@ trait StringComparison extends AutoCastInputTypes {
     }
   }
 
-  override def symbol: String = nodeName
-
   override def toString: String = s"$nodeName($left, $right)"
 }
 
@@ -284,12 +278,6 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
       }
     }
   }
-
-  override def toString: String = len match {
-    // TODO: This is broken because max is not an integer value.
-    case max if max == Integer.MAX_VALUE => s"SUBSTR($str, $pos)"
-    case _ => s"SUBSTR($str, $pos, $len)"
-  }
 }
 
 /**
@@ -304,9 +292,9 @@ case class StringLength(child: Expression) extends UnaryExpression with AutoCast
     if (string == null) null else string.asInstanceOf[UTF8String].length
   }
 
-  override def toString: String = s"length($child)"
-
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).length()")
   }
+
+  override def prettyName: String = "length"
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index bda217935cb05..86792f0217572 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -73,7 +73,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("+", "1", "*", "2", "-", "3", "4")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression transformDown {
-      case b: BinaryExpression => actual.append(b.symbol); b
+      case b: BinaryOperator => actual.append(b.symbol); b
       case l: Literal => actual.append(l.toString); l
     }
 
@@ -85,7 +85,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("1", "2", "3", "4", "-", "*", "+")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression transformUp {
-      case b: BinaryExpression => actual.append(b.symbol); b
+      case b: BinaryOperator => actual.append(b.symbol); b
       case l: Literal => actual.append(l.toString); l
     }
 
@@ -125,7 +125,7 @@ class TreeNodeSuite extends SparkFunSuite {
     val expected = Seq("1", "2", "3", "4", "-", "*", "+")
     val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
     expression foreachUp {
-      case b: BinaryExpression => actual.append(b.symbol);
+      case b: BinaryOperator => actual.append(b.symbol);
       case l: Literal => actual.append(l.toString);
     }
 

From 4e4f74b5e1267d1ada4a8f57b86aee0d9c17d90a Mon Sep 17 00:00:00 2001
From: Rosstin <asterazul@gmail.com>
Date: Wed, 1 Jul 2015 21:42:06 -0700
Subject: [PATCH 0180/1454] [SPARK-8660] [MLLIB] removed > symbols from
 comments in LogisticRegressionSuite.scala for ease of copypaste

'>' symbols removed from comments in LogisticRegressionSuite.scala, for ease of copypaste

also single-lined the multiline commands (is this desirable, or does it violate style?)

Author: Rosstin <asterazul@gmail.com>

Closes #7167 from Rosstin/SPARK-8660-2 and squashes the following commits:

f4b9bc8 [Rosstin] SPARK-8660 restored character limit on multiline comments in LogisticRegressionSuite.scala
fe6b112 [Rosstin] SPARK-8660 > symbols removed from LogisticRegressionSuite.scala for easy of copypaste
39ddd50 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8661
5a05dee [Rosstin] SPARK-8661 for LinearRegressionSuite.scala, changed javadoc-style comments to regular multiline comments to make it easier to copy-paste the R code.
bb9a4b1 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8660
242aedd [Rosstin] SPARK-8660, changed comment style from JavaDoc style to normal multiline comment in order to make copypaste into R easier, in file classification/LogisticRegressionSuite.scala
2cd2985 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
21ac1e5 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
6c18058 [Rosstin] fixed minor typos in docs/README.md and docs/api.md
---
 .../LogisticRegressionSuite.scala             | 117 ++++++++++--------
 1 file changed, 63 insertions(+), 54 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index bc6eeac1db5da..ba8fbee84197c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -214,12 +214,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
        (Intercept)  2.8366423
@@ -245,13 +246,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights =
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights =
            coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
        (Intercept)   .
@@ -278,12 +280,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept) -0.05627428
@@ -310,13 +313,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
            intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -343,12 +347,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)  0.15021751
@@ -375,13 +380,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
            intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -408,12 +414,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)  0.57734851
@@ -440,13 +447,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
            intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -503,12 +511,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept) -0.2480643

From b285ac5ba85fe0b32b00726ad7d3a2efb602e885 Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Wed, 1 Jul 2015 22:19:51 -0700
Subject: [PATCH 0181/1454] [SPARK-8227] [SQL] Add function unhex

cc chenghao-intel  adrian-wang

Author: zhichao.li <zhichao.li@intel.com>

Closes #7113 from zhichao-li/unhex and squashes the following commits:

379356e [zhichao.li] remove exception checking
a4ae6dc [zhichao.li] add udf_unhex to whitelist
fe5c14a [zhichao.li] add todigit
607d7a3 [zhichao.li] use checkInputTypes
bffd37f [zhichao.li] change to use Hex in apache common package
cde73f5 [zhichao.li] update to use AutoCastInputTypes
11945c7 [zhichao.li] style
c852d46 [zhichao.li] Add function unhex
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../spark/sql/catalyst/expressions/math.scala | 52 +++++++++++++++++++
 .../expressions/MathFunctionsSuite.scala      |  6 +++
 .../org/apache/spark/sql/functions.scala      | 18 +++++++
 .../spark/sql/MathExpressionsSuite.scala      | 10 ++++
 .../execution/HiveCompatibilitySuite.scala    |  1 +
 6 files changed, 88 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index d53eaedda56b0..6f04298d4711b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -157,6 +157,7 @@ object FunctionRegistry {
     expression[Substring]("substr"),
     expression[Substring]("substring"),
     expression[Upper]("ucase"),
+    expression[UnHex]("unhex"),
     expression[Upper]("upper")
   )
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index b51318dd5044c..8633eb06ffee4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -351,6 +351,58 @@ case class Pow(left: Expression, right: Expression)
   }
 }
 
+/**
+ * Performs the inverse operation of HEX.
+ * Resulting characters are returned as a byte array.
+ */
+case class UnHex(child: Expression) extends UnaryExpression with Serializable {
+
+  override def dataType: DataType = BinaryType
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (child.dataType.isInstanceOf[StringType] || child.dataType == NullType) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(s"unHex accepts String type, not ${child.dataType}")
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val num = child.eval(input)
+    if (num == null) {
+      null
+    } else {
+      unhex(num.asInstanceOf[UTF8String].getBytes)
+    }
+  }
+
+  private val unhexDigits = {
+    val array = Array.fill[Byte](128)(-1)
+    (0 to 9).foreach(i => array('0' + i) = i.toByte)
+    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
+    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
+    array
+  }
+
+  private def unhex(inputBytes: Array[Byte]): Array[Byte] = {
+    var bytes = inputBytes
+    if ((bytes.length & 0x01) != 0) {
+      bytes = '0'.toByte +: bytes
+    }
+    val out = new Array[Byte](bytes.length >> 1)
+    // two characters form the hex value.
+    var i = 0
+    while (i < bytes.length) {
+        val first = unhexDigits(bytes(i))
+        val second = unhexDigits(bytes(i + 1))
+        if (first == -1 || second == -1) { return null}
+        out(i / 2) = (((first << 4) | second) & 0xFF).toByte
+        i += 2
+    }
+    out
+  }
+}
+
 case class Hypot(left: Expression, right: Expression)
   extends BinaryMathExpression(math.hypot, "HYPOT")
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index b932d4ab850c7..b3345d7069159 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -238,6 +238,12 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     // scalastyle:on
   }
 
+  test("unhex") {
+    checkEvaluation(UnHex(Literal("737472696E67")), "string".getBytes)
+    checkEvaluation(UnHex(Literal("")), new Array[Byte](0))
+    checkEvaluation(UnHex(Literal("0")), Array[Byte](0))
+  }
+
   test("hypot") {
     testBinary(Hypot, math.hypot)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4e8f3f96bf4db..e6f623bdf39eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1053,6 +1053,24 @@ object functions {
    */
   def hex(colName: String): Column = hex(Column(colName))
 
+  /**
+   * Inverse of hex. Interprets each pair of characters as a hexadecimal number
+   * and converts to the byte representation of number.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def unhex(column: Column): Column = UnHex(column.expr)
+
+  /**
+   * Inverse of hex. Interprets each pair of characters as a hexadecimal number
+   * and converts to the byte representation of number.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def unhex(colName: String): Column = unhex(Column(colName))
+
   /**
    * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index d6331aa4ff09e..c03cde38d75d0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -225,6 +225,16 @@ class MathExpressionsSuite extends QueryTest {
     checkAnswer(data.selectExpr("hex(cast(d as binary))"), Seq(Row("68656C6C6F")))
   }
 
+  test("unhex") {
+    val data = Seq(("1C", "737472696E67")).toDF("a", "b")
+    checkAnswer(data.select(unhex('a)), Row(Array[Byte](28.toByte)))
+    checkAnswer(data.select(unhex('b)), Row("string".getBytes))
+    checkAnswer(data.selectExpr("unhex(a)"), Row(Array[Byte](28.toByte)))
+    checkAnswer(data.selectExpr("unhex(b)"), Row("string".getBytes))
+    checkAnswer(data.selectExpr("""unhex("##")"""), Row(null))
+    checkAnswer(data.selectExpr("""unhex("G123")"""), Row(null))
+  }
+
   test("hypot") {
     testTwoToOneMathFunction(hypot, hypot, math.hypot)
   }
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index f88e62763ca70..415a81644c58f 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -949,6 +949,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_trim",
     "udf_ucase",
     "udf_unix_timestamp",
+    "udf_unhex",
     "udf_upper",
     "udf_var_pop",
     "udf_var_samp",

From 792fcd802c99a0aef2b67d54f0e6e58710e65956 Mon Sep 17 00:00:00 2001
From: Devaraj K <devaraj@apache.org>
Date: Wed, 1 Jul 2015 22:59:04 -0700
Subject: [PATCH 0182/1454] [SPARK-8754] [YARN] YarnClientSchedulerBackend
 doesn't stop gracefully in failure conditions

In YarnClientSchedulerBackend.stop(), added a check for monitorThread.

Author: Devaraj K <devaraj@apache.org>

Closes #7153 from devaraj-kavali/master and squashes the following commits:

66be9ad [Devaraj K] https://issues.apache.org/jira/browse/SPARK-8754 YarnClientSchedulerBackend doesn't stop gracefully in failure conditions
---
 .../spark/scheduler/cluster/YarnClientSchedulerBackend.scala  | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 1c8d7ec57635f..dd8c4fdb549ed 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -148,7 +148,9 @@ private[spark] class YarnClientSchedulerBackend(
    */
   override def stop() {
     assert(client != null, "Attempted to stop this scheduler before starting it!")
-    monitorThread.interrupt()
+    if (monitorThread != null) {
+      monitorThread.interrupt()
+    }
     super.stop()
     client.stop()
     logInfo("Stopped")

From 646366b5d2f12e42f8e7287672ba29a8c918a17d Mon Sep 17 00:00:00 2001
From: huangzhaowei <carlmartinmax@gmail.com>
Date: Wed, 1 Jul 2015 23:01:44 -0700
Subject: [PATCH 0183/1454] [SPARK-8688] [YARN] Bug fix: disable the cache fs
 to gain the HDFS connection.

If `fs.hdfs.impl.disable.cache` was `false`(default), `FileSystem` will use the cached `DFSClient` which use old token.
[AMDelegationTokenRenewer](https://github.com/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala#L196)
```scala
    val credentials = UserGroupInformation.getCurrentUser.getCredentials
    credentials.writeTokenStorageFile(tempTokenPath, discachedConfiguration)
```
Although the `credentials` had the new Token, but it still use the cached client and old token.
So It's better to set the `fs.hdfs.impl.disable.cache`  as `true` to avoid token expired.

[Jira](https://issues.apache.org/jira/browse/SPARK-8688)

Author: huangzhaowei <carlmartinmax@gmail.com>

Closes #7069 from SaintBacchus/SPARK-8688 and squashes the following commits:

f94cd0b [huangzhaowei] modify function parameter
8fb9eb9 [huangzhaowei] explicit  the comment
0cd55c9 [huangzhaowei] Rename function name to be an accurate one
cf776a1 [huangzhaowei] [SPARK-8688][YARN]Bug fix: disable the cache fs to gain the HDFS connection.
---
 .../org/apache/spark/deploy/SparkHadoopUtil.scala   | 13 +++++++++++++
 .../deploy/yarn/AMDelegationTokenRenewer.scala      | 10 ++++++----
 .../yarn/ExecutorDelegationTokenUpdater.scala       |  5 ++++-
 3 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 7fa75ac8c2b54..6d14590a1d192 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -334,6 +334,19 @@ class SparkHadoopUtil extends Logging {
    * Stop the thread that does the delegation token updates.
    */
   private[spark] def stopExecutorDelegationTokenRenewer() {}
+
+  /**
+   * Return a fresh Hadoop configuration, bypassing the HDFS cache mechanism.
+   * This is to prevent the DFSClient from using an old cached token to connect to the NameNode.
+   */
+  private[spark] def getConfBypassingFSCache(
+      hadoopConf: Configuration,
+      scheme: String): Configuration = {
+    val newConf = new Configuration(hadoopConf)
+    val confKey = s"fs.${scheme}.impl.disable.cache"
+    newConf.setBoolean(confKey, true)
+    newConf
+  }
 }
 
 object SparkHadoopUtil {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala
index 77af46c192cc2..56e4741b93873 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala
@@ -65,6 +65,8 @@ private[yarn] class AMDelegationTokenRenewer(
     sparkConf.getInt("spark.yarn.credentials.file.retention.days", 5)
   private val numFilesToKeep =
     sparkConf.getInt("spark.yarn.credentials.file.retention.count", 5)
+  private val freshHadoopConf =
+    hadoopUtil.getConfBypassingFSCache(hadoopConf, new Path(credentialsFile).toUri.getScheme)
 
   /**
    * Schedule a login from the keytab and principal set using the --principal and --keytab
@@ -123,7 +125,7 @@ private[yarn] class AMDelegationTokenRenewer(
   private def cleanupOldFiles(): Unit = {
     import scala.concurrent.duration._
     try {
-      val remoteFs = FileSystem.get(hadoopConf)
+      val remoteFs = FileSystem.get(freshHadoopConf)
       val credentialsPath = new Path(credentialsFile)
       val thresholdTime = System.currentTimeMillis() - (daysToKeepFiles days).toMillis
       hadoopUtil.listFilesSorted(
@@ -169,13 +171,13 @@ private[yarn] class AMDelegationTokenRenewer(
       // Get a copy of the credentials
       override def run(): Void = {
         val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst
-        hadoopUtil.obtainTokensForNamenodes(nns, hadoopConf, tempCreds)
+        hadoopUtil.obtainTokensForNamenodes(nns, freshHadoopConf, tempCreds)
         null
       }
     })
     // Add the temp credentials back to the original ones.
     UserGroupInformation.getCurrentUser.addCredentials(tempCreds)
-    val remoteFs = FileSystem.get(hadoopConf)
+    val remoteFs = FileSystem.get(freshHadoopConf)
     // If lastCredentialsFileSuffix is 0, then the AM is either started or restarted. If the AM
     // was restarted, then the lastCredentialsFileSuffix might be > 0, so find the newest file
     // and update the lastCredentialsFileSuffix.
@@ -194,7 +196,7 @@ private[yarn] class AMDelegationTokenRenewer(
     val tempTokenPath = new Path(tokenPathStr + SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION)
     logInfo("Writing out delegation tokens to " + tempTokenPath.toString)
     val credentials = UserGroupInformation.getCurrentUser.getCredentials
-    credentials.writeTokenStorageFile(tempTokenPath, hadoopConf)
+    credentials.writeTokenStorageFile(tempTokenPath, freshHadoopConf)
     logInfo(s"Delegation Tokens written out successfully. Renaming file to $tokenPathStr")
     remoteFs.rename(tempTokenPath, tokenPath)
     logInfo("Delegation token file rename complete.")
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala
index 229c2c4d5eb36..94feb6393fd69 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala
@@ -35,6 +35,9 @@ private[spark] class ExecutorDelegationTokenUpdater(
   @volatile private var lastCredentialsFileSuffix = 0
 
   private val credentialsFile = sparkConf.get("spark.yarn.credentials.file")
+  private val freshHadoopConf =
+    SparkHadoopUtil.get.getConfBypassingFSCache(
+      hadoopConf, new Path(credentialsFile).toUri.getScheme)
 
   private val delegationTokenRenewer =
     Executors.newSingleThreadScheduledExecutor(
@@ -49,7 +52,7 @@ private[spark] class ExecutorDelegationTokenUpdater(
   def updateCredentialsIfRequired(): Unit = {
     try {
       val credentialsFilePath = new Path(credentialsFile)
-      val remoteFs = FileSystem.get(hadoopConf)
+      val remoteFs = FileSystem.get(freshHadoopConf)
       SparkHadoopUtil.get.listFilesSorted(
         remoteFs, credentialsFilePath.getParent,
         credentialsFilePath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION)

From d14338eafc5d633f766bd52ba610fd7c4fe90581 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 1 Jul 2015 23:04:05 -0700
Subject: [PATCH 0184/1454] [SPARK-8771] [TRIVIAL] Add a version to the
 deprecated annotation for the actorSystem

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7172 from holdenk/SPARK-8771-actor-system-deprecation-tag-uses-deprecated-deprecation-tag and squashes the following commits:

7f1455b [Holden Karau] Add .0s to the versions for the derpecated anotations in SparkEnv.scala
ca13c9d [Holden Karau] Add a version to the deprecated annotation for the actorSystem in SparkEnv
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 1b133fbdfaf59..d18fc599e9890 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -76,7 +76,7 @@ class SparkEnv (
     val conf: SparkConf) extends Logging {
 
   // TODO Remove actorSystem
-  @deprecated("Actor system is no longer supported as of 1.4")
+  @deprecated("Actor system is no longer supported as of 1.4.0", "1.4.0")
   val actorSystem: ActorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem
 
   private[spark] var isStopped = false
@@ -173,7 +173,7 @@ object SparkEnv extends Logging {
   /**
    * Returns the ThreadLocal SparkEnv.
    */
-  @deprecated("Use SparkEnv.get instead", "1.2")
+  @deprecated("Use SparkEnv.get instead", "1.2.0")
   def getThreadLocal: SparkEnv = {
     env
   }

From 15d41cc501f5fa7ac82c4a6741e416bb557f610a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 1 Jul 2015 23:05:45 -0700
Subject: [PATCH 0185/1454] [SPARK-8769] [TRIVIAL] [DOCS] toLocalIterator
 should mention it results in many jobs

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7171 from holdenk/SPARK-8769-toLocalIterator-documentation-improvement and squashes the following commits:

97ddd99 [Holden Karau] Add note
---
 core/src/main/scala/org/apache/spark/rdd/RDD.scala | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 10610f4b6f1ff..cac6e3b477e16 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -890,6 +890,10 @@ abstract class RDD[T: ClassTag](
    * Return an iterator that contains all of the elements in this RDD.
    *
    * The iterator will consume as much memory as the largest partition in this RDD.
+   *
+   * Note: this results in multiple Spark jobs, and if the input RDD is the result
+   * of a wide transformation (e.g. join with different partitioners), to avoid
+   * recomputing the input RDD should be cached first.
    */
   def toLocalIterator: Iterator[T] = withScope {
     def collectPartition(p: Int): Array[T] = {

From 377ff4c9e8942882183d94698684824e9dc9f391 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 1 Jul 2015 23:06:52 -0700
Subject: [PATCH 0186/1454] [SPARK-8740] [PROJECT INFRA] Support GitHub OAuth
 tokens in dev/merge_spark_pr.py

This commit allows `dev/merge_spark_pr.py` to use personal GitHub OAuth tokens in order to make authenticated requests. This is necessary to work around per-IP rate limiting issues.

To use a token, just set the `GITHUB_OAUTH_KEY` environment variable.  You can create a personal token at https://github.com/settings/tokens; we only require `public_repo` scope.

If the script fails due to a rate-limit issue, it now logs a useful message directing the user to the OAuth token instructions.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7136 from JoshRosen/pr-merge-script-oauth-authentication and squashes the following commits:

4d011bd [Josh Rosen] Fix error message
23d92ff [Josh Rosen] Support GitHub OAuth tokens in dev/merge_spark_pr.py
---
 dev/merge_spark_pr.py | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index cf827ce89b857..4a17d48d8171d 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -47,6 +47,12 @@
 JIRA_USERNAME = os.environ.get("JIRA_USERNAME", "")
 # ASF JIRA password
 JIRA_PASSWORD = os.environ.get("JIRA_PASSWORD", "")
+# OAuth key used for issuing requests against the GitHub API. If this is not defined, then requests
+# will be unauthenticated. You should only need to configure this if you find yourself regularly
+# exceeding your IP's unauthenticated request rate limit. You can create an OAuth key at
+# https://github.com/settings/tokens. This script only requires the "public_repo" scope.
+GITHUB_OAUTH_KEY = os.environ.get("GITHUB_OAUTH_KEY")
+
 
 GITHUB_BASE = "https://github.com/apache/spark/pull"
 GITHUB_API_BASE = "https://api.github.com/repos/apache/spark"
@@ -58,9 +64,17 @@
 
 def get_json(url):
     try:
-        return json.load(urllib2.urlopen(url))
+        request = urllib2.Request(url)
+        if GITHUB_OAUTH_KEY:
+            request.add_header('Authorization', 'token %s' % GITHUB_OAUTH_KEY)
+        return json.load(urllib2.urlopen(request))
     except urllib2.HTTPError as e:
-        print "Unable to fetch URL, exiting: %s" % url
+        if "X-RateLimit-Remaining" in e.headers and e.headers["X-RateLimit-Remaining"] == '0':
+            print "Exceeded the GitHub API rate limit; see the instructions in " + \
+                  "dev/merge_spark_pr.py to configure an OAuth token for making authenticated " + \
+                  "GitHub requests."
+        else:
+            print "Unable to fetch URL, exiting: %s" % url
         sys.exit(-1)
 
 

From 3697232b7d438979cc119b2a364296b0eec4a16a Mon Sep 17 00:00:00 2001
From: Ilya Ganelin <ilya.ganelin@capitalone.com>
Date: Wed, 1 Jul 2015 23:11:02 -0700
Subject: [PATCH 0187/1454] [SPARK-3071] Increase default driver memory

I've updated default values in comments, documentation, and in the command line builder to be 1g based on comments in the JIRA. I've also updated most usages to point at a single variable defined in the Utils.scala and JavaUtils.java files. This wasn't possible in all cases (R, shell scripts etc.) but usage in most code is now pointing at the same place.

Please let me know if I've missed anything.

Will the spark-shell use the value within the command line builder during instantiation?

Author: Ilya Ganelin <ilya.ganelin@capitalone.com>

Closes #7132 from ilganeli/SPARK-3071 and squashes the following commits:

4074164 [Ilya Ganelin] String fix
271610b [Ilya Ganelin] Merge branch 'SPARK-3071' of github.com:ilganeli/spark into SPARK-3071
273b6e9 [Ilya Ganelin] Test fix
fd67721 [Ilya Ganelin] Update JavaUtils.java
26cc177 [Ilya Ganelin] test fix
e5db35d [Ilya Ganelin] Fixed test failure
39732a1 [Ilya Ganelin] merge fix
a6f7deb [Ilya Ganelin] Created default value for DRIVER MEM in Utils that's now used in almost all locations instead of setting manually in each
09ad698 [Ilya Ganelin] Update SubmitRestProtocolSuite.scala
19b6f25 [Ilya Ganelin] Missed one doc update
2698a3d [Ilya Ganelin] Updated default value for driver memory
---
 R/pkg/R/sparkR.R                                       |  2 +-
 conf/spark-env.sh.template                             |  2 +-
 .../org/apache/spark/deploy/ClientArguments.scala      |  2 +-
 .../org/apache/spark/deploy/SparkSubmitArguments.scala |  5 +++--
 .../spark/deploy/rest/mesos/MesosRestServer.scala      |  2 +-
 .../apache/spark/deploy/worker/WorkerArguments.scala   |  2 +-
 core/src/main/scala/org/apache/spark/util/Utils.scala  |  6 ++++++
 .../spark/deploy/rest/SubmitRestProtocolSuite.scala    | 10 +++++-----
 docs/configuration.md                                  |  4 ++--
 .../org/apache/spark/launcher/CommandBuilderUtils.java |  2 +-
 .../spark/launcher/SparkSubmitCommandBuilder.java      |  2 +-
 .../spark/mllib/tree/model/DecisionTreeModel.scala     |  2 +-
 .../spark/mllib/tree/model/treeEnsembleModels.scala    |  2 +-
 .../java/org/apache/spark/network/util/JavaUtils.java  |  6 ++++++
 .../org/apache/spark/deploy/yarn/ClientArguments.scala |  7 ++++---
 15 files changed, 35 insertions(+), 21 deletions(-)

diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 633b869f91784..86233e01db365 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -109,7 +109,7 @@ sparkR.init <- function(
     return(get(".sparkRjsc", envir = .sparkREnv))
   }
 
-  sparkMem <- Sys.getenv("SPARK_MEM", "512m")
+  sparkMem <- Sys.getenv("SPARK_MEM", "1024m")
   jars <- suppressWarnings(normalizePath(as.character(sparkJars)))
 
   # Classpath separator is ";" on Windows
diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index 43c4288912b18..192d3ae091134 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -22,7 +22,7 @@
 # - SPARK_EXECUTOR_INSTANCES, Number of workers to start (Default: 2)
 # - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1).
 # - SPARK_EXECUTOR_MEMORY, Memory per Worker (e.g. 1000M, 2G) (Default: 1G)
-# - SPARK_DRIVER_MEMORY, Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)
+# - SPARK_DRIVER_MEMORY, Memory for Master (e.g. 1000M, 2G) (Default: 1G)
 # - SPARK_YARN_APP_NAME, The name of your application (Default: Spark)
 # - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: ‘default’)
 # - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job.
diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
index 316e2d59f01b8..42d3296062e6d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
@@ -117,7 +117,7 @@ private[deploy] class ClientArguments(args: Array[String]) {
 
 private[deploy] object ClientArguments {
   val DEFAULT_CORES = 1
-  val DEFAULT_MEMORY = 512 // MB
+  val DEFAULT_MEMORY = Utils.DEFAULT_DRIVER_MEM_MB // MB
   val DEFAULT_SUPERVISE = false
 
   def isValidJarUrl(s: String): Boolean = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index b7429a901e162..73ab18332feb4 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -461,8 +461,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
         |Usage: spark-submit --status [submission ID] --master [spark://...]""".stripMargin)
     outStream.println(command)
 
+    val mem_mb = Utils.DEFAULT_DRIVER_MEM_MB
     outStream.println(
-      """
+      s"""
         |Options:
         |  --master MASTER_URL         spark://host:port, mesos://host:port, yarn, or local.
         |  --deploy-mode DEPLOY_MODE   Whether to launch the driver program locally ("client") or
@@ -488,7 +489,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
         |  --properties-file FILE      Path to a file from which to load extra properties. If not
         |                              specified, this will look for conf/spark-defaults.conf.
         |
-        |  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 512M).
+        |  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: ${mem_mb}M).
         |  --driver-java-options       Extra Java options to pass to the driver.
         |  --driver-library-path       Extra library path entries to pass to the driver.
         |  --driver-class-path         Extra class path entries to pass to the driver. Note that
diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala
index 8198296eeb341..868cc35d06ef3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala
@@ -59,7 +59,7 @@ private[mesos] class MesosSubmitRequestServlet(
   extends SubmitRequestServlet {
 
   private val DEFAULT_SUPERVISE = false
-  private val DEFAULT_MEMORY = 512 // mb
+  private val DEFAULT_MEMORY = Utils.DEFAULT_DRIVER_MEM_MB // mb
   private val DEFAULT_CORES = 1.0
 
   private val nextDriverNumber = new AtomicLong(0)
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
index 9678631da9f6f..1d2ecab517613 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
@@ -164,7 +164,7 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) {
       }
     }
     // Leave out 1 GB for the operating system, but don't return a negative memory size
-    math.max(totalMb - 1024, 512)
+    math.max(totalMb - 1024, Utils.DEFAULT_DRIVER_MEM_MB)
   }
 
   def checkWorkerMemory(): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index a7fc749a2b0c6..944560a91354a 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -80,6 +80,12 @@ private[spark] object Utils extends Logging {
    */
   val TEMP_DIR_SHUTDOWN_PRIORITY = 25
 
+  /**
+   * Define a default value for driver memory here since this value is referenced across the code
+   * base and nearly all files already use Utils.scala
+   */
+  val DEFAULT_DRIVER_MEM_MB = JavaUtils.DEFAULT_DRIVER_MEM_MB.toInt
+
   private val MAX_DIR_CREATION_ATTEMPTS: Int = 10
   @volatile private var localRootDirs: Array[String] = null
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala
index 115ac0534a1b4..725b8848bc052 100644
--- a/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.deploy.rest
 
 import java.lang.Boolean
-import java.lang.Integer
 
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{SparkConf, SparkFunSuite}
+import org.apache.spark.util.Utils
 
 /**
  * Tests for the REST application submission protocol.
@@ -93,7 +93,7 @@ class SubmitRestProtocolSuite extends SparkFunSuite {
     // optional fields
     conf.set("spark.jars", "mayonnaise.jar,ketchup.jar")
     conf.set("spark.files", "fireball.png")
-    conf.set("spark.driver.memory", "512m")
+    conf.set("spark.driver.memory", s"${Utils.DEFAULT_DRIVER_MEM_MB}m")
     conf.set("spark.driver.cores", "180")
     conf.set("spark.driver.extraJavaOptions", " -Dslices=5 -Dcolor=mostly_red")
     conf.set("spark.driver.extraClassPath", "food-coloring.jar")
@@ -126,7 +126,7 @@ class SubmitRestProtocolSuite extends SparkFunSuite {
     assert(newMessage.sparkProperties("spark.app.name") === "SparkPie")
     assert(newMessage.sparkProperties("spark.jars") === "mayonnaise.jar,ketchup.jar")
     assert(newMessage.sparkProperties("spark.files") === "fireball.png")
-    assert(newMessage.sparkProperties("spark.driver.memory") === "512m")
+    assert(newMessage.sparkProperties("spark.driver.memory") === s"${Utils.DEFAULT_DRIVER_MEM_MB}m")
     assert(newMessage.sparkProperties("spark.driver.cores") === "180")
     assert(newMessage.sparkProperties("spark.driver.extraJavaOptions") ===
       " -Dslices=5 -Dcolor=mostly_red")
@@ -230,7 +230,7 @@ class SubmitRestProtocolSuite extends SparkFunSuite {
     """.stripMargin
 
   private val submitDriverRequestJson =
-    """
+    s"""
       |{
       |  "action" : "CreateSubmissionRequest",
       |  "appArgs" : [ "two slices", "a hint of cinnamon" ],
@@ -246,7 +246,7 @@ class SubmitRestProtocolSuite extends SparkFunSuite {
       |    "spark.driver.supervise" : "false",
       |    "spark.app.name" : "SparkPie",
       |    "spark.cores.max" : "10000",
-      |    "spark.driver.memory" : "512m",
+      |    "spark.driver.memory" : "${Utils.DEFAULT_DRIVER_MEM_MB}m",
       |    "spark.files" : "fireball.png",
       |    "spark.driver.cores" : "180",
       |    "spark.driver.extraJavaOptions" : " -Dslices=5 -Dcolor=mostly_red",
diff --git a/docs/configuration.md b/docs/configuration.md
index affcd21514d88..bebaf6f62e90a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -137,10 +137,10 @@ of the most common options to set are:
 </tr>
 <tr>
   <td><code>spark.driver.memory</code></td>
-  <td>512m</td>
+  <td>1g</td>
   <td>
     Amount of memory to use for the driver process, i.e. where SparkContext is initialized.
-    (e.g. <code>512m</code>, <code>2g</code>).
+    (e.g. <code>1g</code>, <code>2g</code>).
     
     <br /><em>Note:</em> In client mode, this config must not be set through the <code>SparkConf</code>
     directly in your application, because the driver JVM has already started at that point.
diff --git a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java
index 2665a700fe1f5..a16c0d2b5ca0b 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java
@@ -27,7 +27,7 @@
  */
 class CommandBuilderUtils {
 
-  static final String DEFAULT_MEM = "512m";
+  static final String DEFAULT_MEM = "1g";
   static final String DEFAULT_PROPERTIES_FILE = "spark-defaults.conf";
   static final String ENV_SPARK_HOME = "SPARK_HOME";
   static final String ENV_SPARK_ASSEMBLY = "_SPARK_ASSEMBLY";
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
index 3e5a2820b6c11..87c43aa9980e1 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
@@ -208,7 +208,7 @@ private List<String> buildSparkSubmitCommand(Map<String, String> env) throws IOE
       // - properties file.
       // - SPARK_DRIVER_MEMORY env variable
       // - SPARK_MEM env variable
-      // - default value (512m)
+      // - default value (1g)
       // Take Thrift Server as daemon
       String tsMemory =
         isThriftServer(mainClass) ? System.getenv("SPARK_DAEMON_MEMORY") : null;
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index 25bb1453db404..f2c78bbabff0b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -198,7 +198,7 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
         val driverMemory = sc.getConf.getOption("spark.driver.memory")
           .orElse(Option(System.getenv("SPARK_DRIVER_MEMORY")))
           .map(Utils.memoryStringToMb)
-          .getOrElse(512)
+          .getOrElse(Utils.DEFAULT_DRIVER_MEM_MB)
         if (driverMemory <= memThreshold) {
           logWarning(s"$thisClassName.save() was called, but it may fail because of too little" +
             s" driver memory (${driverMemory}m)." +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 1e3333d8d81d0..905c5fb42bd44 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -387,7 +387,7 @@ private[tree] object TreeEnsembleModel extends Logging {
         val driverMemory = sc.getConf.getOption("spark.driver.memory")
           .orElse(Option(System.getenv("SPARK_DRIVER_MEMORY")))
           .map(Utils.memoryStringToMb)
-          .getOrElse(512)
+          .getOrElse(Utils.DEFAULT_DRIVER_MEM_MB)
         if (driverMemory <= memThreshold) {
           logWarning(s"$className.save() was called, but it may fail because of too little" +
             s" driver memory (${driverMemory}m)." +
diff --git a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java
index 6b514aaa1290d..7d27439cfde7a 100644
--- a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java
+++ b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java
@@ -39,6 +39,12 @@
 public class JavaUtils {
   private static final Logger logger = LoggerFactory.getLogger(JavaUtils.class);
 
+  /**
+   * Define a default value for driver memory here since this value is referenced across the code
+   * base and nearly all files already use Utils.scala
+   */
+  public static final long DEFAULT_DRIVER_MEM_MB = 1024;
+
   /** Closes the given object, ignoring IOExceptions. */
   public static void closeQuietly(Closeable closeable) {
     try {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 35e990602a6cf..19d1bbff9993f 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -46,7 +46,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
   var keytab: String = null
   def isClusterMode: Boolean = userClass != null
 
-  private var driverMemory: Int = 512 // MB
+  private var driverMemory: Int = Utils.DEFAULT_DRIVER_MEM_MB // MB
   private var driverCores: Int = 1
   private val driverMemOverheadKey = "spark.yarn.driver.memoryOverhead"
   private val amMemKey = "spark.yarn.am.memory"
@@ -262,8 +262,9 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
 
   private def getUsageMessage(unknownParam: List[String] = null): String = {
     val message = if (unknownParam != null) s"Unknown/unsupported param $unknownParam\n" else ""
+    val mem_mb = Utils.DEFAULT_DRIVER_MEM_MB
     message +
-      """
+      s"""
       |Usage: org.apache.spark.deploy.yarn.Client [options]
       |Options:
       |  --jar JAR_PATH           Path to your application's JAR file (required in yarn-cluster
@@ -275,7 +276,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
       |                           Multiple invocations are possible, each will be passed in order.
       |  --num-executors NUM      Number of executors to start (Default: 2)
       |  --executor-cores NUM     Number of cores per executor (Default: 1).
-      |  --driver-memory MEM      Memory for driver (e.g. 1000M, 2G) (Default: 512 Mb)
+      |  --driver-memory MEM      Memory for driver (e.g. 1000M, 2G) (Default: $mem_mb Mb)
       |  --driver-cores NUM       Number of cores used by the driver (Default: 1).
       |  --executor-memory MEM    Memory per executor (e.g. 1000M, 2G) (Default: 1G)
       |  --name NAME              The name of your application (Default: Spark)

From 1b0c8e61040bf06213f9758f775679dcc41b0cce Mon Sep 17 00:00:00 2001
From: huangzhaowei <carlmartinmax@gmail.com>
Date: Wed, 1 Jul 2015 23:14:13 -0700
Subject: [PATCH 0188/1454] [SPARK-8687] [YARN] Fix bug: Executor can't fetch
 the new set configuration in yarn-client

Spark initi the properties CoarseGrainedSchedulerBackend.start
```scala
    // TODO (prashant) send conf instead of properties
    driverEndpoint = rpcEnv.setupEndpoint(
      CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties))
```
Then the yarn logic will set some configuration but not update in this `properties`.
So `Executor` won't gain the `properties`.

[Jira](https://issues.apache.org/jira/browse/SPARK-8687)

Author: huangzhaowei <carlmartinmax@gmail.com>

Closes #7066 from SaintBacchus/SPARK-8687 and squashes the following commits:

1de4f48 [huangzhaowei] Ensure all necessary properties have already been set before startup ExecutorLaucher
---
 .../scheduler/cluster/YarnClientSchedulerBackend.scala     | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index dd8c4fdb549ed..3a0b9443d2d7b 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -41,7 +41,6 @@ private[spark] class YarnClientSchedulerBackend(
    * This waits until the application is running.
    */
   override def start() {
-    super.start()
     val driverHost = conf.get("spark.driver.host")
     val driverPort = conf.get("spark.driver.port")
     val hostport = driverHost + ":" + driverPort
@@ -56,6 +55,12 @@ private[spark] class YarnClientSchedulerBackend(
     totalExpectedExecutors = args.numExecutors
     client = new Client(args, conf)
     appId = client.submitApplication()
+
+    // SPARK-8687: Ensure all necessary properties have already been set before
+    // we initialize our driver scheduler backend, which serves these properties
+    // to the executors
+    super.start()
+
     waitForApplication()
     monitorThread = asyncMonitorApplication()
     monitorThread.start()

From 41588365ad29408ccabd216b411e9c43f0053151 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Thu, 2 Jul 2015 21:16:35 +0900
Subject: [PATCH 0189/1454] [DOCS] Fix minor wrong lambda expression example.

It's a really minor issue but there is an example with wrong lambda-expression usage in `SQLContext.scala` like as follows.

```
sqlContext.udf().register("myUDF",
       (Integer arg1, String arg2) -> arg2 + arg1),  <- We have an extra `)` here.
       DataTypes.StringType);
```

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7187 from sarutak/fix-minor-wrong-lambda-expression and squashes the following commits:

a13196d [Kousuke Saruta] Fixed minor wrong lambda expression example.
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index fc14a77538ef1..e81371e7b0e83 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -274,7 +274,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * Or, to use Java 8 lambda syntax:
    * {{{
    *   sqlContext.udf().register("myUDF",
-   *       (Integer arg1, String arg2) -> arg2 + arg1),
+   *       (Integer arg1, String arg2) -> arg2 + arg1,
    *       DataTypes.StringType);
    * }}}
    *

From c572e25617f993c6b2e7d5f15f0fbf4426f89fab Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Thu, 2 Jul 2015 13:42:48 +0100
Subject: [PATCH 0190/1454] [SPARK-8787] [SQL] Changed parameter order of
 @deprecated in package object sql

Parameter order of deprecated annotation in package object sql is wrong
>>deprecated("1.3.0", "use DataFrame") .

This has to be changed to deprecated("use DataFrame", "1.3.0")

Author: Vinod K C <vinod.kc@huawei.com>

Closes #7183 from vinodkc/fix_deprecated_param_order and squashes the following commits:

1cbdbe8 [Vinod K C] Modified the message
700911c [Vinod K C] Changed order of parameters
---
 sql/core/src/main/scala/org/apache/spark/sql/package.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/package.scala
index 4e94fd07a8771..a9c600b139b18 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/package.scala
@@ -46,6 +46,6 @@ package object sql {
    * Type alias for [[DataFrame]]. Kept here for backward source compatibility for Scala.
    * @deprecated As of 1.3.0, replaced by `DataFrame`.
    */
-  @deprecated("1.3.0", "use DataFrame")
+  @deprecated("use DataFrame", "1.3.0")
   type SchemaRDD = DataFrame
 }

From 1bbdf9ead9e912f60dccbb23029b7de4948ebee3 Mon Sep 17 00:00:00 2001
From: Christian Kadner <ckadner@us.ibm.com>
Date: Thu, 2 Jul 2015 13:45:19 +0100
Subject: [PATCH 0191/1454] [SPARK-8746] [SQL] update download link for Hive
 0.13.1

updated the [Hive 0.13.1](https://archive.apache.org/dist/hive/hive-0.13.1) download link in `sql/README.md`

Author: Christian Kadner <ckadner@us.ibm.com>

Closes #7144 from ckadner/SPARK-8746 and squashes the following commits:

65d80f7 [Christian Kadner] [SPARK-8746][SQL] update download link for Hive 0.13.1
---
 sql/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/README.md b/sql/README.md
index 46aec7cef7984..63d4dac9829e0 100644
--- a/sql/README.md
+++ b/sql/README.md
@@ -25,7 +25,7 @@ export HADOOP_HOME="<path to>/hadoop-1.0.4"
 
 If you are working with Hive 0.13.1, the following steps are needed:
 
-1. Download Hive's [0.13.1](https://hive.apache.org/downloads.html) and set `HIVE_HOME` with `export HIVE_HOME="<path to hive>"`. Please do not set `HIVE_DEV_HOME` (See [SPARK-4119](https://issues.apache.org/jira/browse/SPARK-4119)).
+1. Download Hive's [0.13.1](https://archive.apache.org/dist/hive/hive-0.13.1) and set `HIVE_HOME` with `export HIVE_HOME="<path to hive>"`. Please do not set `HIVE_DEV_HOME` (See [SPARK-4119](https://issues.apache.org/jira/browse/SPARK-4119)).
 2. Set `HADOOP_HOME` with `export HADOOP_HOME="<path to hadoop>"`
 3. Download all Hive 0.13.1a jars (Hive jars actually used by Spark) from [here](http://mvnrepository.com/artifact/org.spark-project.hive) and replace corresponding original 0.13.1 jars in `$HIVE_HOME/lib`.
 4. Download [Kryo 2.21 jar](http://mvnrepository.com/artifact/com.esotericsoftware.kryo/kryo/2.21) (Note: 2.22 jar does not work) and [Javolution 5.5.1 jar](http://mvnrepository.com/artifact/javolution/javolution/5.5.1) to `$HIVE_HOME/lib`.

From 246265f2bb056d5e9011d3331b809471a24ff8d7 Mon Sep 17 00:00:00 2001
From: Wisely Chen <wiselychen@appier.com>
Date: Thu, 2 Jul 2015 09:58:12 -0700
Subject: [PATCH 0192/1454] [SPARK-8690] [SQL] Add a setting to disable
 SparkSQL parquet schema merge by using datasource API

The detail problem story is in https://issues.apache.org/jira/browse/SPARK-8690

General speaking, I add a config spark.sql.parquet.mergeSchema to achieve the  sqlContext.load("parquet" , Map( "path" -> "..." , "mergeSchema" -> "false" ))

It will become a simple flag and without any side affect.

Author: Wisely Chen <wiselychen@appier.com>

Closes #7070 from thegiive/SPARK8690 and squashes the following commits:

c6f3e86 [Wisely Chen] Refactor some code style and merge the test case to ParquetSchemaMergeConfigSuite
94c9307 [Wisely Chen] Remove some style problem
db8ef1b [Wisely Chen] Change config to SQLConf and add test case
b6806fb [Wisely Chen] remove text
c0edb8c [Wisely Chen] [SPARK-8690] add a config spark.sql.parquet.mergeSchema to disable datasource API schema merge feature.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  6 ++++++
 .../apache/spark/sql/parquet/newParquet.scala |  5 ++++-
 .../spark/sql/parquet/ParquetQuerySuite.scala | 20 +++++++++++++++++++
 3 files changed, 30 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 9a10a23937fbb..2c258b6ee399c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -227,6 +227,12 @@ private[spark] object SQLConf {
     defaultValue = Some(true),
     doc = "<TODO>")
 
+  val PARQUET_SCHEMA_MERGING_ENABLED = booleanConf("spark.sql.parquet.mergeSchema",
+    defaultValue = Some(true),
+    doc = "When true, the Parquet data source merges schemas collected from all data files, " +
+          "otherwise the schema is picked from the summary file or a random data file " +
+          "if no summary file is available.")
+
   val PARQUET_BINARY_AS_STRING = booleanConf("spark.sql.parquet.binaryAsString",
     defaultValue = Some(false),
     doc = "Some other Parquet-producing systems, in particular Impala and older versions of " +
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index bc39fae2bcfde..5ac3e9a44e6fe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -114,7 +114,10 @@ private[sql] class ParquetRelation2(
 
   // Should we merge schemas from all Parquet part-files?
   private val shouldMergeSchemas =
-    parameters.getOrElse(ParquetRelation2.MERGE_SCHEMA, "true").toBoolean
+    parameters
+      .get(ParquetRelation2.MERGE_SCHEMA)
+      .map(_.toBoolean)
+      .getOrElse(sqlContext.conf.getConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED))
 
   private val maybeMetastoreSchema = parameters
     .get(ParquetRelation2.METASTORE_SCHEMA)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index fafad67dde3a7..a0a81c4309c0f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.parquet
 
+import org.apache.hadoop.fs.Path
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.types._
@@ -122,6 +123,25 @@ class ParquetQuerySuiteBase extends QueryTest with ParquetTest {
       checkAnswer(df2, df.collect().toSeq)
     }
   }
+
+  test("Enabling/disabling schema merging") {
+    def testSchemaMerging(expectedColumnNumber: Int): Unit = {
+      withTempDir { dir =>
+        val basePath = dir.getCanonicalPath
+        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
+      }
+    }
+
+    withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "true") {
+      testSchemaMerging(3)
+    }
+
+    withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "false") {
+      testSchemaMerging(2)
+    }
+  }
 }
 
 class ParquetDataSourceOnQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll {

From 99c40cd0d8465525cac34dfa373b81532ef3d719 Mon Sep 17 00:00:00 2001
From: Alok  Singh <singhal@Aloks-MacBook-Pro.local>
Date: Thu, 2 Jul 2015 09:58:57 -0700
Subject: [PATCH 0193/1454] [SPARK-8647] [MLLIB] Potential issue with constant
 hashCode

I added the code,
  // see [SPARK-8647], this achieves the needed constant hash code without constant no.
  override def hashCode(): Int = this.getClass.getName.hashCode()

does getting the constant hash code as per jira

Author: Alok  Singh <singhal@Aloks-MacBook-Pro.local>

Closes #7146 from aloknsingh/aloknsingh_SPARK-8647 and squashes the following commits:

e58bccf [Alok  Singh] [SPARK-8647][MLlib] to avoid the class derivation issues, change the constant hashCode to override def hashCode(): Int = classOf[MatrixUDT].getName.hashCode()
43cdb89 [Alok  Singh] [SPARK-8647][MLlib] Potential issue with constant hashCode
---
 .../main/scala/org/apache/spark/mllib/linalg/Matrices.scala    | 3 ++-
 .../src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 85e63b1382b5e..0a615494bb2d1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -193,7 +193,8 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
     }
   }
 
-  override def hashCode(): Int = 1994
+  // see [SPARK-8647], this achieves the needed constant hash code without constant no.
+  override def hashCode(): Int = classOf[MatrixUDT].getName.hashCode()
 
   override def typeName: String = "matrix"
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 2ffa497a99d93..c9c27425d2877 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -234,7 +234,8 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
     }
   }
 
-  override def hashCode: Int = 7919
+  // see [SPARK-8647], this achieves the needed constant hash code without constant no.
+  override def hashCode(): Int = classOf[VectorUDT].getName.hashCode()
 
   override def typeName: String = "vector"
 

From 0a468a46bf5b905e9b0205e98b862570b2ac556e Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 2 Jul 2015 09:59:54 -0700
Subject: [PATCH 0194/1454] [SPARK-8758] [MLLIB] Add Python user guide for
 PowerIterationClustering

Add Python user guide for PowerIterationClustering

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7155 from yanboliang/spark-8758 and squashes the following commits:

18d803b [Yanbo Liang] address comments
dd29577 [Yanbo Liang] Add Python user guide for PowerIterationClustering
---
 data/mllib/pic_data.txt  | 19 ++++++++++++++
 docs/mllib-clustering.md | 54 +++++++++++++++++++++++++++++++++++++---
 2 files changed, 69 insertions(+), 4 deletions(-)
 create mode 100644 data/mllib/pic_data.txt

diff --git a/data/mllib/pic_data.txt b/data/mllib/pic_data.txt
new file mode 100644
index 0000000000000..fcfef8cd19131
--- /dev/null
+++ b/data/mllib/pic_data.txt
@@ -0,0 +1,19 @@
+0 1 1.0
+0 2 1.0
+0 3 1.0
+1 2 1.0
+1 3 1.0
+2 3 1.0
+3 4 0.1
+4 5 1.0
+4 15 1.0
+5 6 1.0
+6 7 1.0
+7 8 1.0
+8 9 1.0
+9 10 1.0
+10 11 1.0
+11 12 1.0
+12 13 1.0
+13 14 1.0
+14 15 1.0
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index dcaa3784be874..3aad4149f99db 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -327,11 +327,17 @@ which contains the computed clustering assignments.
 import org.apache.spark.mllib.clustering.{PowerIterationClustering, PowerIterationClusteringModel}
 import org.apache.spark.mllib.linalg.Vectors
 
-val similarities: RDD[(Long, Long, Double)] = ...
+// Load and parse the data
+val data = sc.textFile("data/mllib/pic_data.txt")
+val similarities = data.map { line =>
+  val parts = line.split(' ')
+  (parts(0).toLong, parts(1).toLong, parts(2).toDouble)
+}
 
+// Cluster the data into two classes using PowerIterationClustering
 val pic = new PowerIterationClustering()
-  .setK(3)
-  .setMaxIterations(20)
+  .setK(2)
+  .setMaxIterations(10)
 val model = pic.run(similarities)
 
 model.assignments.foreach { a =>
@@ -363,11 +369,22 @@ import scala.Tuple2;
 import scala.Tuple3;
 
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
 import org.apache.spark.mllib.clustering.PowerIterationClustering;
 import org.apache.spark.mllib.clustering.PowerIterationClusteringModel;
 
-JavaRDD<Tuple3<Long, Long, Double>> similarities = ...
+// Load and parse the data
+JavaRDD<String> data = sc.textFile("data/mllib/pic_data.txt");
+JavaRDD<Tuple3<Long, Long, Double>> similarities = data.map(
+  new Function<String, Tuple3<Long, Long, Double>>() {
+    public Tuple3<Long, Long, Double> call(String line) {
+      String[] parts = line.split(" ");
+      return new Tuple3<>(new Long(parts[0]), new Long(parts[1]), new Double(parts[2]));
+    }
+  }
+);
 
+// Cluster the data into two classes using PowerIterationClustering
 PowerIterationClustering pic = new PowerIterationClustering()
   .setK(2)
   .setMaxIterations(10);
@@ -383,6 +400,35 @@ PowerIterationClusteringModel sameModel = PowerIterationClusteringModel.load(sc.
 {% endhighlight %}
 </div>
 
+<div data-lang="python" markdown="1">
+
+[`PowerIterationClustering`](api/python/pyspark.mllib.html#pyspark.mllib.clustering.PowerIterationClustering)
+implements the PIC algorithm.
+It takes an `RDD` of `(srcId: Long, dstId: Long, similarity: Double)` tuples representing the
+affinity matrix.
+Calling `PowerIterationClustering.run` returns a
+[`PowerIterationClusteringModel`](api/python/pyspark.mllib.html#pyspark.mllib.clustering.PowerIterationClustering),
+which contains the computed clustering assignments.
+
+{% highlight python %}
+from __future__ import print_function
+from pyspark.mllib.clustering import PowerIterationClustering, PowerIterationClusteringModel
+
+# Load and parse the data
+data = sc.textFile("data/mllib/pic_data.txt")
+similarities = data.map(lambda line: tuple([float(x) for x in line.split(' ')]))
+
+# Cluster the data into two classes using PowerIterationClustering
+model = PowerIterationClustering.train(similarities, 2, 10)
+
+model.assignments().foreach(lambda x: print(str(x.id) + " -> " + str(x.cluster)))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = PowerIterationClusteringModel.load(sc, "myModelPath")
+{% endhighlight %}
+</div>
+
 </div>
 
 ## Latent Dirichlet allocation (LDA)

From 5b3338130dfd9db92c4894a348839a62ebb57ef3 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Thu, 2 Jul 2015 10:02:19 -0700
Subject: [PATCH 0195/1454] [SPARK-8223] [SPARK-8224] [SQL] shift left and
 shift right

Jira:
https://issues.apache.org/jira/browse/SPARK-8223
https://issues.apache.org/jira/browse/SPARK-8224

~~I am aware of #7174 and will update this pr, if it's merged.~~ Done
I don't know if #7034 can simplify this, but we can have a look on it, if it gets merged

rxin In the Jira ticket the function as no second argument. I added a `numBits` argument that allows to specify the number of bits. I guess this improves the usability. I wanted to add `shiftleft(value)` as well, but the `selectExpr` dataframe tests crashes, if I have both. I order to do this, I added the following to the functions.scala `def shiftRight(e: Column): Column = ShiftRight(e.expr, lit(1).expr)`, but as I mentioned this doesn't pass tests like `df.selectExpr("shiftRight(a)", ...` (not enough arguments exception).

If we need the bitwise shift in order to be hive compatible, I suggest to add `shiftLeft` and something like `shiftLeftX`

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7178 from tarekauel/8223 and squashes the following commits:

8023bb5 [Tarek Auel] [SPARK-8223][SPARK-8224] fixed test
f3f64e6 [Tarek Auel] [SPARK-8223][SPARK-8224] Integer -> Int
f628706 [Tarek Auel] [SPARK-8223][SPARK-8224] removed toString; updated function description
3b56f2a [Tarek Auel] Merge remote-tracking branch 'origin/master' into 8223
5189690 [Tarek Auel] [SPARK-8223][SPARK-8224] minor fix and style fix
9434a28 [Tarek Auel] Merge remote-tracking branch 'origin/master' into 8223
44ee324 [Tarek Auel] [SPARK-8223][SPARK-8224] docu fix
ac7fe9d [Tarek Auel] [SPARK-8223][SPARK-8224] right and left bit shift
---
 python/pyspark/sql/functions.py               | 24 +++++
 .../catalyst/analysis/FunctionRegistry.scala  |  2 +
 .../spark/sql/catalyst/expressions/math.scala | 98 +++++++++++++++++++
 .../expressions/MathFunctionsSuite.scala      | 28 +++++-
 .../org/apache/spark/sql/functions.scala      | 38 +++++++
 .../spark/sql/MathExpressionsSuite.scala      | 34 +++++++
 6 files changed, 223 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index f9a15d4a66309..bccde6083ca3c 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -412,6 +412,30 @@ def sha2(col, numBits):
     return Column(jc)
 
 
+@since(1.5)
+def shiftLeft(col, numBits):
+    """Shift the the given value numBits left.
+
+    >>> sqlContext.createDataFrame([(21,)], ['a']).select(shiftLeft('a', 1).alias('r')).collect()
+    [Row(r=42)]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.shiftLeft(_to_java_column(col), numBits)
+    return Column(jc)
+
+
+@since(1.5)
+def shiftRight(col, numBits):
+    """Shift the the given value numBits right.
+
+    >>> sqlContext.createDataFrame([(42,)], ['a']).select(shiftRight('a', 1).alias('r')).collect()
+    [Row(r=21)]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.shiftRight(_to_java_column(col), numBits)
+    return Column(jc)
+
+
 @since(1.4)
 def sparkPartitionId():
     """A column for partition ID of the Spark task.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 6f04298d4711b..aa051b163363a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -125,6 +125,8 @@ object FunctionRegistry {
     expression[Pow]("power"),
     expression[UnaryPositive]("positive"),
     expression[Rint]("rint"),
+    expression[ShiftLeft]("shiftleft"),
+    expression[ShiftRight]("shiftright"),
     expression[Signum]("sign"),
     expression[Signum]("signum"),
     expression[Sin]("sin"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 8633eb06ffee4..7504c6a066657 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -351,6 +351,104 @@ case class Pow(left: Expression, right: Expression)
   }
 }
 
+case class ShiftLeft(left: Expression, right: Expression) extends BinaryExpression {
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    (left.dataType, right.dataType) match {
+      case (NullType, _) | (_, NullType) => return TypeCheckResult.TypeCheckSuccess
+      case (_, IntegerType) => left.dataType match {
+        case LongType | IntegerType | ShortType | ByteType =>
+          return TypeCheckResult.TypeCheckSuccess
+        case _ => // failed
+      }
+      case _ => // failed
+    }
+    TypeCheckResult.TypeCheckFailure(
+        s"ShiftLeft expects long, integer, short or byte value as first argument and an " +
+          s"integer value as second argument, not (${left.dataType}, ${right.dataType})")
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val valueLeft = left.eval(input)
+    if (valueLeft != null) {
+      val valueRight = right.eval(input)
+      if (valueRight != null) {
+        valueLeft match {
+          case l: Long => l << valueRight.asInstanceOf[Integer]
+          case i: Integer => i << valueRight.asInstanceOf[Integer]
+          case s: Short => s << valueRight.asInstanceOf[Integer]
+          case b: Byte => b << valueRight.asInstanceOf[Integer]
+        }
+      } else {
+        null
+      }
+    } else {
+      null
+    }
+  }
+
+  override def dataType: DataType = {
+    left.dataType match {
+      case LongType => LongType
+      case IntegerType | ShortType | ByteType => IntegerType
+      case _ => NullType
+    }
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left << $right;")
+  }
+}
+
+case class ShiftRight(left: Expression, right: Expression) extends BinaryExpression {
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    (left.dataType, right.dataType) match {
+      case (NullType, _) | (_, NullType) => return TypeCheckResult.TypeCheckSuccess
+      case (_, IntegerType) => left.dataType match {
+        case LongType | IntegerType | ShortType | ByteType =>
+          return TypeCheckResult.TypeCheckSuccess
+        case _ => // failed
+      }
+      case _ => // failed
+    }
+    TypeCheckResult.TypeCheckFailure(
+          s"ShiftRight expects long, integer, short or byte value as first argument and an " +
+            s"integer value as second argument, not (${left.dataType}, ${right.dataType})")
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val valueLeft = left.eval(input)
+    if (valueLeft != null) {
+      val valueRight = right.eval(input)
+      if (valueRight != null) {
+        valueLeft match {
+          case l: Long => l >> valueRight.asInstanceOf[Integer]
+          case i: Integer => i >> valueRight.asInstanceOf[Integer]
+          case s: Short => s >> valueRight.asInstanceOf[Integer]
+          case b: Byte => b >> valueRight.asInstanceOf[Integer]
+        }
+      } else {
+        null
+      }
+    } else {
+      null
+    }
+  }
+
+  override def dataType: DataType = {
+    left.dataType match {
+      case LongType => LongType
+      case IntegerType | ShortType | ByteType => IntegerType
+      case _ => NullType
+    }
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left >> $right;")
+  }
+}
+
 /**
  * Performs the inverse operation of HEX.
  * Resulting characters are returned as a byte array.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index b3345d7069159..aa27fe3cd5564 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{DataType, DoubleType, LongType}
+import org.apache.spark.sql.types.{IntegerType, DataType, DoubleType, LongType}
 
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -225,6 +225,32 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     testBinary(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), expectNull = true)
   }
 
+  test("shift left") {
+    checkEvaluation(ShiftLeft(Literal.create(null, IntegerType), Literal(1)), null)
+    checkEvaluation(ShiftLeft(Literal(21), Literal.create(null, IntegerType)), null)
+    checkEvaluation(
+      ShiftLeft(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null)
+    checkEvaluation(ShiftLeft(Literal(21), Literal(1)), 42)
+    checkEvaluation(ShiftLeft(Literal(21.toByte), Literal(1)), 42)
+    checkEvaluation(ShiftLeft(Literal(21.toShort), Literal(1)), 42)
+    checkEvaluation(ShiftLeft(Literal(21.toLong), Literal(1)), 42.toLong)
+
+    checkEvaluation(ShiftLeft(Literal(-21.toLong), Literal(1)), -42.toLong)
+  }
+
+  test("shift right") {
+    checkEvaluation(ShiftRight(Literal.create(null, IntegerType), Literal(1)), null)
+    checkEvaluation(ShiftRight(Literal(42), Literal.create(null, IntegerType)), null)
+    checkEvaluation(
+      ShiftRight(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null)
+    checkEvaluation(ShiftRight(Literal(42), Literal(1)), 21)
+    checkEvaluation(ShiftRight(Literal(42.toByte), Literal(1)), 21)
+    checkEvaluation(ShiftRight(Literal(42.toShort), Literal(1)), 21)
+    checkEvaluation(ShiftRight(Literal(42.toLong), Literal(1)), 21.toLong)
+
+    checkEvaluation(ShiftRight(Literal(-42.toLong), Literal(1)), -21.toLong)
+  }
+
   test("hex") {
     checkEvaluation(Hex(Literal(28)), "1C")
     checkEvaluation(Hex(Literal(-28)), "FFFFFFFFFFFFFFE4")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index e6f623bdf39eb..a5b68286853ed 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1298,6 +1298,44 @@ object functions {
    */
   def rint(columnName: String): Column = rint(Column(columnName))
 
+  /**
+   * Shift the the given value numBits left. If the given value is a long value, this function
+   * will return a long value else it will return an integer value.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def shiftLeft(e: Column, numBits: Int): Column = ShiftLeft(e.expr, lit(numBits).expr)
+
+  /**
+   * Shift the the given value numBits left. If the given value is a long value, this function
+   * will return a long value else it will return an integer value.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def shiftLeft(columnName: String, numBits: Int): Column =
+    shiftLeft(Column(columnName), numBits)
+
+  /**
+   * Shift the the given value numBits right. If the given value is a long value, it will return
+   * a long value else it will return an integer value.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def shiftRight(e: Column, numBits: Int): Column = ShiftRight(e.expr, lit(numBits).expr)
+
+  /**
+   * Shift the the given value numBits right. If the given value is a long value, it will return
+   * a long value else it will return an integer value.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def shiftRight(columnName: String, numBits: Int): Column =
+    shiftRight(Column(columnName), numBits)
+
   /**
    * Computes the signum of the given value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index c03cde38d75d0..4c5696deaff81 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -259,6 +259,40 @@ class MathExpressionsSuite extends QueryTest {
     testOneToOneNonNegativeMathFunction(log1p, math.log1p)
   }
 
+  test("shift left") {
+    val df = Seq[(Long, Integer, Short, Byte, Integer, Integer)]((21, 21, 21, 21, 21, null))
+      .toDF("a", "b", "c", "d", "e", "f")
+
+    checkAnswer(
+      df.select(
+        shiftLeft('a, 1), shiftLeft('b, 1), shiftLeft('c, 1), shiftLeft('d, 1),
+        shiftLeft('f, 1)),
+        Row(42.toLong, 42, 42.toShort, 42.toByte, null))
+
+    checkAnswer(
+      df.selectExpr(
+        "shiftLeft(a, 1)", "shiftLeft(b, 1)", "shiftLeft(b, 1)", "shiftLeft(d, 1)",
+        "shiftLeft(f, 1)"),
+      Row(42.toLong, 42, 42.toShort, 42.toByte, null))
+  }
+
+  test("shift right") {
+    val df = Seq[(Long, Integer, Short, Byte, Integer, Integer)]((42, 42, 42, 42, 42, null))
+      .toDF("a", "b", "c", "d", "e", "f")
+
+    checkAnswer(
+      df.select(
+        shiftRight('a, 1), shiftRight('b, 1), shiftRight('c, 1), shiftRight('d, 1),
+        shiftRight('f, 1)),
+      Row(21.toLong, 21, 21.toShort, 21.toByte, null))
+
+    checkAnswer(
+      df.selectExpr(
+        "shiftRight(a, 1)", "shiftRight(b, 1)", "shiftRight(c, 1)", "shiftRight(d, 1)",
+        "shiftRight(f, 1)"),
+      Row(21.toLong, 21, 21.toShort, 21.toByte, null))
+  }
+
   test("binary log") {
     val df = Seq[(Integer, Integer)]((123, null)).toDF("a", "b")
     checkAnswer(

From afa021e03f0a1a326be2ed742332845b77f94c55 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 2 Jul 2015 10:06:38 -0700
Subject: [PATCH 0196/1454] [SPARK-8747] [SQL] fix EqualNullSafe for binary
 type

also improve tests for binary comparison.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7143 from cloud-fan/binary and squashes the following commits:

28a5b76 [Wenchen Fan] improve test
04ef4b0 [Wenchen Fan] fix equalNullSafe
---
 .../sql/catalyst/expressions/predicates.scala |   3 +-
 .../catalyst/expressions/PredicateSuite.scala | 122 +++++++++++-------
 2 files changed, 78 insertions(+), 47 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 34df89a163895..d4569241e7364 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -302,7 +302,8 @@ case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComp
     } else if (l == null || r == null) {
       false
     } else {
-      l == r
+      if (left.dataType != BinaryType) l == r
+      else java.util.Arrays.equals(l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]])
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 72fec3b86e5e4..188ecef9e7679 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -17,14 +17,11 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import java.sql.{Date, Timestamp}
-
 import scala.collection.immutable.HashSet
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.types.{IntegerType, BooleanType}
+import org.apache.spark.sql.types.{Decimal, IntegerType, BooleanType}
 
 
 class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -66,12 +63,12 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
    * Unknown Unknown
    */
   // scalastyle:on
-  val notTrueTable =
-    (true, false) ::
-      (false, true) ::
-      (null, null) :: Nil
 
   test("3VL Not") {
+    val notTrueTable =
+      (true, false) ::
+        (false, true) ::
+        (null, null) :: Nil
     notTrueTable.foreach { case (v, answer) =>
       checkEvaluation(Not(Literal.create(v, BooleanType)), answer)
     }
@@ -126,8 +123,6 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     val two = Literal(2)
     val three = Literal(3)
     val nl = Literal(null)
-    val s = Seq(one, two)
-    val nullS = Seq(one, two, null)
     checkEvaluation(InSet(one, hS), true)
     checkEvaluation(InSet(two, hS), true)
     checkEvaluation(InSet(two, nS), true)
@@ -137,43 +132,78 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true)
   }
 
+  private val smallValues = Seq(1, Decimal(1), Array(1.toByte), "a").map(Literal(_))
+  private val largeValues = Seq(2, Decimal(2), Array(2.toByte), "b").map(Literal(_))
 
-  test("BinaryComparison") {
-    val row = create_row(1, 2, 3, null, 3, null)
-    val c1 = 'a.int.at(0)
-    val c2 = 'a.int.at(1)
-    val c3 = 'a.int.at(2)
-    val c4 = 'a.int.at(3)
-    val c5 = 'a.int.at(4)
-    val c6 = 'a.int.at(5)
+  private val equalValues1 = smallValues
+  private val equalValues2 = Seq(1, Decimal(1), Array(1.toByte), "a").map(Literal(_))
 
-    checkEvaluation(LessThan(c1, c4), null, row)
-    checkEvaluation(LessThan(c1, c2), true, row)
-    checkEvaluation(LessThan(c1, Literal.create(null, IntegerType)), null, row)
-    checkEvaluation(LessThan(Literal.create(null, IntegerType), c2), null, row)
-    checkEvaluation(
-      LessThan(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row)
-
-    checkEvaluation(c1 < c2, true, row)
-    checkEvaluation(c1 <= c2, true, row)
-    checkEvaluation(c1 > c2, false, row)
-    checkEvaluation(c1 >= c2, false, row)
-    checkEvaluation(c1 === c2, false, row)
-    checkEvaluation(c1 !== c2, true, row)
-    checkEvaluation(c4 <=> c1, false, row)
-    checkEvaluation(c1 <=> c4, false, row)
-    checkEvaluation(c4 <=> c6, true, row)
-    checkEvaluation(c3 <=> c5, true, row)
-    checkEvaluation(Literal(true) <=> Literal.create(null, BooleanType), false, row)
-    checkEvaluation(Literal.create(null, BooleanType) <=> Literal(true), false, row)
-
-    val d1 = DateTimeUtils.fromJavaDate(Date.valueOf("1970-01-01"))
-    val d2 = DateTimeUtils.fromJavaDate(Date.valueOf("1970-01-02"))
-    checkEvaluation(Literal(d1) < Literal(d2), true)
-
-    val ts1 = new Timestamp(12)
-    val ts2 = new Timestamp(123)
-    checkEvaluation(Literal("ab") < Literal("abc"), true)
-    checkEvaluation(Literal(ts1) < Literal(ts2), true)
+  test("BinaryComparison: <") {
+    for (i <- 0 until smallValues.length) {
+      checkEvaluation(smallValues(i) < largeValues(i), true)
+      checkEvaluation(equalValues1(i) < equalValues2(i), false)
+      checkEvaluation(largeValues(i) < smallValues(i), false)
+    }
+  }
+
+  test("BinaryComparison: <=") {
+    for (i <- 0 until smallValues.length) {
+      checkEvaluation(smallValues(i) <= largeValues(i), true)
+      checkEvaluation(equalValues1(i) <= equalValues2(i), true)
+      checkEvaluation(largeValues(i) <= smallValues(i), false)
+    }
+  }
+
+  test("BinaryComparison: >") {
+    for (i <- 0 until smallValues.length) {
+      checkEvaluation(smallValues(i) > largeValues(i), false)
+      checkEvaluation(equalValues1(i) > equalValues2(i), false)
+      checkEvaluation(largeValues(i) > smallValues(i), true)
+    }
+  }
+
+  test("BinaryComparison: >=") {
+    for (i <- 0 until smallValues.length) {
+      checkEvaluation(smallValues(i) >= largeValues(i), false)
+      checkEvaluation(equalValues1(i) >= equalValues2(i), true)
+      checkEvaluation(largeValues(i) >= smallValues(i), true)
+    }
+  }
+
+  test("BinaryComparison: ===") {
+    for (i <- 0 until smallValues.length) {
+      checkEvaluation(smallValues(i) === largeValues(i), false)
+      checkEvaluation(equalValues1(i) === equalValues2(i), true)
+      checkEvaluation(largeValues(i) === smallValues(i), false)
+    }
+  }
+
+  test("BinaryComparison: <=>") {
+    for (i <- 0 until smallValues.length) {
+      checkEvaluation(smallValues(i) <=> largeValues(i), false)
+      checkEvaluation(equalValues1(i) <=> equalValues2(i), true)
+      checkEvaluation(largeValues(i) <=> smallValues(i), false)
+    }
+  }
+
+  test("BinaryComparison: null test") {
+    val normalInt = Literal(1)
+    val nullInt = Literal.create(null, IntegerType)
+
+    def nullTest(op: (Expression, Expression) => Expression): Unit = {
+      checkEvaluation(op(normalInt, nullInt), null)
+      checkEvaluation(op(nullInt, normalInt), null)
+      checkEvaluation(op(nullInt, nullInt), null)
+    }
+
+    nullTest(LessThan)
+    nullTest(LessThanOrEqual)
+    nullTest(GreaterThan)
+    nullTest(GreaterThanOrEqual)
+    nullTest(EqualTo)
+
+    checkEvaluation(normalInt <=> nullInt, false)
+    checkEvaluation(nullInt <=> normalInt, false)
+    checkEvaluation(nullInt <=> nullInt, true)
   }
 }

From 52302a803967114b29a8bf6b74459477364c5b88 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Thu, 2 Jul 2015 10:12:25 -0700
Subject: [PATCH 0197/1454] [SPARK-8407] [SQL] complex type constructors:
 struct and named_struct

This is a follow up of [SPARK-8283](https://issues.apache.org/jira/browse/SPARK-8283) ([PR-6828](https://github.com/apache/spark/pull/6828)), to support both `struct` and `named_struct` in Spark SQL.

After [#6725](https://github.com/apache/spark/pull/6828), the semantic of [`CreateStruct`](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala#L56) methods have changed a little and do not limited to cols of `NamedExpressions`, it will name non-NamedExpression fields following the hive convention, col1, col2 ...

This PR would both loosen [`struct`](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/functions.scala#L723) to take children of `Expression` type and add `named_struct` support.

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #6874 from yijieshen/SPARK-8283 and squashes the following commits:

4cd3375ac [Yijie Shen] change struct documentation
d599d0b [Yijie Shen] rebase code
9a7039e [Yijie Shen] fix reviews and regenerate golden answers
b487354 [Yijie Shen] replace assert using checkAnswer
f07e114 [Yijie Shen] tiny fix
9613be9 [Yijie Shen] review fix
7fef712 [Yijie Shen] Fix checkInputTypes' implementation using foldable and nullable
60812a7 [Yijie Shen] Fix type check
828d694 [Yijie Shen] remove unnecessary resolved assertion inside dataType method
fd3cd8e [Yijie Shen] remove type check from eval
7a71255 [Yijie Shen] tiny fix
ccbbd86 [Yijie Shen] Fix reviews
47da332 [Yijie Shen] remove nameStruct API from DataFrame
917e680 [Yijie Shen] Fix reviews
4bd75ad [Yijie Shen] loosen struct method in functions.scala to take Expression children
0acb7be [Yijie Shen] Add CreateNamedStruct in both DataFrame function API and FunctionRegistery
---
 python/pyspark/sql/functions.py               |  1 -
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/complexTypeCreator.scala      | 49 +++++++++++++++++++
 .../ExpressionTypeCheckingSuite.scala         | 11 +++++
 .../expressions/ComplexTypeSuite.scala        | 24 ++++++++-
 .../org/apache/spark/sql/functions.scala      | 11 +++--
 .../spark/sql/DataFrameFunctionsSuite.scala   | 40 +++++++++++++--
 ...ic udf-0-638f81ad9077c7d0c5c735c6e73742ad} |  0
 .../sql/hive/execution/HiveQuerySuite.scala   |  2 +-
 9 files changed, 126 insertions(+), 13 deletions(-)
 rename sql/hive/src/test/resources/golden/{constant object inspector for generic udf-0-cc120a2331158f570a073599985d3f55 => constant object inspector for generic udf-0-638f81ad9077c7d0c5c735c6e73742ad} (100%)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index bccde6083ca3c..12263e6a75af8 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -467,7 +467,6 @@ def struct(*cols):
     """Creates a new struct column.
 
     :param cols: list of column names (string) or list of :class:`Column` expressions
-        that are named or aliased.
 
     >>> df.select(struct('age', 'name').alias("struct")).collect()
     [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index aa051b163363a..e7e4d1c4efe18 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -96,6 +96,7 @@ object FunctionRegistry {
     expression[Rand]("rand"),
     expression[Randn]("randn"),
     expression[CreateStruct]("struct"),
+    expression[CreateNamedStruct]("named_struct"),
     expression[Sqrt]("sqrt"),
 
     // math functions
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 67e7dc4ec8b14..fa70409353e79 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -17,9 +17,12 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.util.TypeUtils
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.TypeCheckFailure
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * Returns an Array containing the evaluation of all children expressions.
@@ -54,6 +57,8 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
+  override lazy val resolved: Boolean = childrenResolved
+
   override lazy val dataType: StructType = {
     val fields = children.zipWithIndex.map { case (child, idx) =>
       child match {
@@ -74,3 +79,47 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
 
   override def prettyName: String = "struct"
 }
+
+/**
+ * Creates a struct with the given field names and values
+ *
+ * @param children Seq(name1, val1, name2, val2, ...)
+ */
+case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
+
+  private lazy val (nameExprs, valExprs) =
+    children.grouped(2).map { case Seq(name, value) => (name, value) }.toList.unzip
+
+  private lazy val names = nameExprs.map(_.eval(EmptyRow).toString)
+
+  override lazy val dataType: StructType = {
+    val fields = names.zip(valExprs).map { case (name, valExpr) =>
+      StructField(name, valExpr.dataType, valExpr.nullable, Metadata.empty)
+    }
+    StructType(fields)
+  }
+
+  override def foldable: Boolean = valExprs.forall(_.foldable)
+
+  override def nullable: Boolean = false
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (children.size % 2 != 0) {
+      TypeCheckResult.TypeCheckFailure("CreateNamedStruct expects an even number of arguments.")
+    } else {
+      val invalidNames =
+        nameExprs.filterNot(e => e.foldable && e.dataType == StringType && !nullable)
+      if (invalidNames.size != 0) {
+        TypeCheckResult.TypeCheckFailure(
+          s"Odd position only allow foldable and not-null StringType expressions, got :" +
+            s" ${invalidNames.mkString(",")}")
+      } else {
+        TypeCheckResult.TypeCheckSuccess
+      }
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    InternalRow(valExprs.map(_.eval(input)): _*)
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index bc1537b0715b5..8e0551b23eea6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -160,4 +160,15 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertError(Explode('intField),
       "input to function explode should be array or map type")
   }
+
+  test("check types for CreateNamedStruct") {
+    assertError(
+      CreateNamedStruct(Seq("a", "b", 2.0)), "even number of arguments")
+    assertError(
+      CreateNamedStruct(Seq(1, "a", "b", 2.0)),
+        "Odd position only allow foldable and not-null StringType expressions")
+    assertError(
+      CreateNamedStruct(Seq('a.string.at(0), "a", "b", 2.0)),
+        "Odd position only allow foldable and not-null StringType expressions")
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index 3515d044b2f7e..a09014e1ffc15 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.scalatest.exceptions.TestFailedException
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.analysis.UnresolvedExtractValue
 import org.apache.spark.sql.catalyst.dsl.expressions._
@@ -119,11 +121,29 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("CreateStruct") {
     val row = create_row(1, 2, 3)
-    val c1 = 'a.int.at(0).as("a")
-    val c3 = 'c.int.at(2).as("c")
+    val c1 = 'a.int.at(0)
+    val c3 = 'c.int.at(2)
     checkEvaluation(CreateStruct(Seq(c1, c3)), create_row(1, 3), row)
   }
 
+  test("CreateNamedStruct") {
+    val row = InternalRow(1, 2, 3)
+    val c1 = 'a.int.at(0)
+    val c3 = 'c.int.at(2)
+    checkEvaluation(CreateNamedStruct(Seq("a", c1, "b", c3)), InternalRow(1, 3), row)
+  }
+
+  test("CreateNamedStruct with literal field") {
+    val row = InternalRow(1, 2, 3)
+    val c1 = 'a.int.at(0)
+    checkEvaluation(CreateNamedStruct(Seq("a", c1, "b", "y")), InternalRow(1, "y"), row)
+  }
+
+  test("CreateNamedStruct from all literal fields") {
+    checkEvaluation(
+      CreateNamedStruct(Seq("a", "x", "b", 2.0)), InternalRow("x", 2.0), InternalRow.empty)
+  }
+
   test("test dsl for complex type") {
     def quickResolve(u: UnresolvedExtractValue): Expression = {
       ExtractValue(u.child, u.extraction, _ == _)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index a5b68286853ed..4ee1fb8374b07 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -739,17 +739,18 @@ object functions {
   def sqrt(colName: String): Column = sqrt(Column(colName))
 
   /**
-   * Creates a new struct column. The input column must be a column in a [[DataFrame]], or
-   * a derived column expression that is named (i.e. aliased).
+   * Creates a new struct column.
+   * If the input column is a column in a [[DataFrame]], or a derived column expression
+   * that is named (i.e. aliased), its name would be remained as the StructField's name,
+   * otherwise, the newly generated StructField's name would be auto generated as col${index + 1},
+   * i.e. col1, col2, col3, ...
    *
    * @group normal_funcs
    * @since 1.4.0
    */
   @scala.annotation.varargs
   def struct(cols: Column*): Column = {
-    require(cols.forall(_.expr.isInstanceOf[NamedExpression]),
-      s"struct input columns must all be named or aliased ($cols)")
-    CreateStruct(cols.map(_.expr.asInstanceOf[NamedExpression]))
+    CreateStruct(cols.map(_.expr))
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 7ae89bcb1b9cf..0d43aca877f68 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -79,10 +79,42 @@ class DataFrameFunctionsSuite extends QueryTest {
     assert(row.getAs[Row](0) === Row(2, "str"))
   }
 
-  test("struct: must use named column expression") {
-    intercept[IllegalArgumentException] {
-      struct(col("a") * 2)
-    }
+  test("struct with column expression to be automatically named") {
+    val df = Seq((1, "str")).toDF("a", "b")
+    val result = df.select(struct((col("a") * 2), col("b")))
+
+    val expectedType = StructType(Seq(
+      StructField("col1", IntegerType, nullable = false),
+      StructField("b", StringType)
+    ))
+    assert(result.first.schema(0).dataType === expectedType)
+    checkAnswer(result, Row(Row(2, "str")))
+  }
+
+  test("struct with literal columns") {
+    val df = Seq((1, "str1"), (2, "str2")).toDF("a", "b")
+    val result = df.select(struct((col("a") * 2), lit(5.0)))
+
+    val expectedType = StructType(Seq(
+      StructField("col1", IntegerType, nullable = false),
+      StructField("col2", DoubleType, nullable = false)
+    ))
+
+    assert(result.first.schema(0).dataType === expectedType)
+    checkAnswer(result, Seq(Row(Row(2, 5.0)), Row(Row(4, 5.0))))
+  }
+
+  test("struct with all literal columns") {
+    val df = Seq((1, "str1"), (2, "str2")).toDF("a", "b")
+    val result = df.select(struct(lit("v"), lit(5.0)))
+
+    val expectedType = StructType(Seq(
+      StructField("col1", StringType, nullable = false),
+      StructField("col2", DoubleType, nullable = false)
+    ))
+
+    assert(result.first.schema(0).dataType === expectedType)
+    checkAnswer(result, Seq(Row(Row("v", 5.0)), Row(Row("v", 5.0))))
   }
 
   test("constant functions") {
diff --git a/sql/hive/src/test/resources/golden/constant object inspector for generic udf-0-cc120a2331158f570a073599985d3f55 b/sql/hive/src/test/resources/golden/constant object inspector for generic udf-0-638f81ad9077c7d0c5c735c6e73742ad
similarity index 100%
rename from sql/hive/src/test/resources/golden/constant object inspector for generic udf-0-cc120a2331158f570a073599985d3f55
rename to sql/hive/src/test/resources/golden/constant object inspector for generic udf-0-638f81ad9077c7d0c5c735c6e73742ad
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 4cdba03b27022..991da2f829ae5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -132,7 +132,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       lower("AA"), "10",
       repeat(lower("AA"), 3), "11",
       lower(repeat("AA", 3)), "12",
-      printf("Bb%d", 12), "13",
+      printf("bb%d", 12), "13",
       repeat(printf("s%d", 14), 2), "14") FROM src LIMIT 1""")
 
   createQueryTest("NaN to Decimal",

From 0e553a3e9360a736920e2214d634373fef0dbcf7 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Thu, 2 Jul 2015 10:18:23 -0700
Subject: [PATCH 0198/1454] [SPARK-8708] [MLLIB] Paritition ALS ratings based
 on both users and products

JIRA: https://issues.apache.org/jira/browse/SPARK-8708

Previously the partitions of ratings are only based on the given products. So if the `usersProducts` given for prediction contains only few products or even one product, the generated ratings will be pushed into few or single partition and can't use high parallelism.

The following codes are the example reported in the JIRA. Because it asks the predictions for users on product 2. There is only one partition in the result.

    >>> r1 = (1, 1, 1.0)
    >>> r2 = (1, 2, 2.0)
    >>> r3 = (2, 1, 2.0)
    >>> r4 = (2, 2, 2.0)
    >>> r5 = (3, 1, 1.0)
    >>> ratings = sc.parallelize([r1, r2, r3, r4, r5], 5)
    >>> users = ratings.map(itemgetter(0)).distinct()
    >>> model = ALS.trainImplicit(ratings, 1, seed=10)
    >>> predictions_for_2 = model.predictAll(users.map(lambda u: (u, 2)))
    >>> predictions_for_2.glom().map(len).collect()
    [0, 0, 3, 0, 0]

This PR uses user and product instead of only product to partition the ratings.

Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7121 from viirya/mfm_fix_partition and squashes the following commits:

779946d [Liang-Chi Hsieh] Calculate approximate numbers of users and products in one pass.
4336dc2 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into mfm_fix_partition
83e56c1 [Liang-Chi Hsieh] Instead of additional join, use the numbers of users and products to decide how to perform join.
b534dc8 [Liang-Chi Hsieh] Paritition ratings based on both users and products.
---
 .../MatrixFactorizationModel.scala            | 55 +++++++++++++++++--
 1 file changed, 49 insertions(+), 6 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 93aa41e49961e..43d219a49cf4e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -22,6 +22,7 @@ import java.lang.{Integer => JavaInteger}
 
 import scala.collection.mutable
 
+import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 import org.apache.hadoop.fs.Path
 import org.json4s._
@@ -79,6 +80,30 @@ class MatrixFactorizationModel(
     blas.ddot(rank, userVector, 1, productVector, 1)
   }
 
+  /**
+   * Return approximate numbers of users and products in the given usersProducts tuples.
+   * This method is based on `countApproxDistinct` in class `RDD`.
+   *
+   * @param usersProducts  RDD of (user, product) pairs.
+   * @return approximate numbers of users and products.
+   */
+  private[this] def countApproxDistinctUserProduct(usersProducts: RDD[(Int, Int)]): (Long, Long) = {
+    val zeroCounterUser = new HyperLogLogPlus(4, 0)
+    val zeroCounterProduct = new HyperLogLogPlus(4, 0)
+    val aggregated = usersProducts.aggregate((zeroCounterUser, zeroCounterProduct))(
+      (hllTuple: (HyperLogLogPlus, HyperLogLogPlus), v: (Int, Int)) => {
+        hllTuple._1.offer(v._1)
+        hllTuple._2.offer(v._2)
+        hllTuple
+      },
+      (h1: (HyperLogLogPlus, HyperLogLogPlus), h2: (HyperLogLogPlus, HyperLogLogPlus)) => {
+        h1._1.addAll(h2._1)
+        h1._2.addAll(h2._2)
+        h1
+      })
+    (aggregated._1.cardinality(), aggregated._2.cardinality())
+  }
+
   /**
    * Predict the rating of many users for many products.
    * The output RDD has an element per each element in the input RDD (including all duplicates)
@@ -88,12 +113,30 @@ class MatrixFactorizationModel(
    * @return RDD of Ratings.
    */
   def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
-    val users = userFeatures.join(usersProducts).map {
-      case (user, (uFeatures, product)) => (product, (user, uFeatures))
-    }
-    users.join(productFeatures).map {
-      case (product, ((user, uFeatures), pFeatures)) =>
-        Rating(user, product, blas.ddot(uFeatures.length, uFeatures, 1, pFeatures, 1))
+    // Previously the partitions of ratings are only based on the given products.
+    // So if the usersProducts given for prediction contains only few products or
+    // even one product, the generated ratings will be pushed into few or single partition
+    // and can't use high parallelism.
+    // Here we calculate approximate numbers of users and products. Then we decide the
+    // partitions should be based on users or products.
+    val (usersCount, productsCount) = countApproxDistinctUserProduct(usersProducts)
+
+    if (usersCount < productsCount) {
+      val users = userFeatures.join(usersProducts).map {
+        case (user, (uFeatures, product)) => (product, (user, uFeatures))
+      }
+      users.join(productFeatures).map {
+        case (product, ((user, uFeatures), pFeatures)) =>
+          Rating(user, product, blas.ddot(uFeatures.length, uFeatures, 1, pFeatures, 1))
+      }
+    } else {
+      val products = productFeatures.join(usersProducts.map(_.swap)).map {
+        case (product, (pFeatures, user)) => (user, (product, pFeatures))
+      }
+      products.join(userFeatures).map {
+        case (user, ((product, pFeatures), uFeatures)) =>
+          Rating(user, product, blas.ddot(uFeatures.length, uFeatures, 1, pFeatures, 1))
+      }
     }
   }
 

From 2e2f32603c110b9c6ddfbb836f63882eacf0a8cc Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 2 Jul 2015 10:57:02 -0700
Subject: [PATCH 0199/1454] [SPARK-8581] [SPARK-8584] Simplify checkpointing
 code + better error message

This patch rewrites the old checkpointing code in a way that is easier to understand. It also adds a guard against an invalid specification of checkpoint directory to provide a clearer error message. Most of the changes here are relatively minor.

Author: Andrew Or <andrew@databricks.com>

Closes #6968 from andrewor14/checkpoint-cleanup and squashes the following commits:

4ef8263 [Andrew Or] Use global synchronized instead
6f6fd84 [Andrew Or] Merge branch 'master' of github.com:apache/spark into checkpoint-cleanup
b1437ad [Andrew Or] Warn instead of throw
5484293 [Andrew Or] Merge branch 'master' of github.com:apache/spark into checkpoint-cleanup
7fb4af5 [Andrew Or] Guard against bad settings of checkpoint directory
691da98 [Andrew Or] Simplify checkpoint code / code style / comments
---
 .../scala/org/apache/spark/SparkContext.scala | 10 +++
 .../org/apache/spark/rdd/CheckpointRDD.scala  | 17 +++--
 .../main/scala/org/apache/spark/rdd/RDD.scala | 14 ++--
 .../apache/spark/rdd/RDDCheckpointData.scala  | 71 +++++++++----------
 .../org/apache/spark/CheckpointSuite.scala    |  2 +-
 5 files changed, 60 insertions(+), 54 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 0e5a86f44e410..8eed46759f340 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1906,6 +1906,16 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * be a HDFS path if running on a cluster.
    */
   def setCheckpointDir(directory: String) {
+
+    // If we are running on a cluster, log a warning if the directory is local.
+    // Otherwise, the driver may attempt to reconstruct the checkpointed RDD from
+    // its own local file system, which is incorrect because the checkpoint files
+    // are actually on the executor machines.
+    if (!isLocal && Utils.nonLocalPaths(directory).isEmpty) {
+      logWarning("Checkpoint directory must be non-local " +
+        "if Spark is running on a cluster: " + directory)
+    }
+
     checkpointDir = Option(directory).map { dir =>
       val path = new Path(dir, UUID.randomUUID().toString)
       val fs = path.getFileSystem(hadoopConfiguration)
diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
index 33e6998b2cb10..e17bd47905d7a 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
@@ -28,7 +28,7 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
-private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {}
+private[spark] class CheckpointRDDPartition(val index: Int) extends Partition
 
 /**
  * This RDD represents a RDD checkpoint file (similar to HadoopRDD).
@@ -37,9 +37,11 @@ private[spark]
 class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String)
   extends RDD[T](sc, Nil) {
 
-  val broadcastedConf = sc.broadcast(new SerializableConfiguration(sc.hadoopConfiguration))
+  private val broadcastedConf = sc.broadcast(new SerializableConfiguration(sc.hadoopConfiguration))
 
-  @transient val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration)
+  @transient private val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration)
+
+  override def getCheckpointFile: Option[String] = Some(checkpointPath)
 
   override def getPartitions: Array[Partition] = {
     val cpath = new Path(checkpointPath)
@@ -59,9 +61,6 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String)
     Array.tabulate(numPartitions)(i => new CheckpointRDDPartition(i))
   }
 
-  checkpointData = Some(new RDDCheckpointData[T](this))
-  checkpointData.get.cpFile = Some(checkpointPath)
-
   override def getPreferredLocations(split: Partition): Seq[String] = {
     val status = fs.getFileStatus(new Path(checkpointPath,
       CheckpointRDD.splitIdToFile(split.index)))
@@ -74,9 +73,9 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String)
     CheckpointRDD.readFromFile(file, broadcastedConf, context)
   }
 
-  override def checkpoint() {
-    // Do nothing. CheckpointRDD should not be checkpointed.
-  }
+  // CheckpointRDD should not be checkpointed again
+  override def checkpoint(): Unit = { }
+  override def doCheckpoint(): Unit = { }
 }
 
 private[spark] object CheckpointRDD extends Logging {
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index cac6e3b477e16..9f7ebae3e9af3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -194,7 +194,7 @@ abstract class RDD[T: ClassTag](
   @transient private var partitions_ : Array[Partition] = null
 
   /** An Option holding our checkpoint RDD, if we are checkpointed */
-  private def checkpointRDD: Option[RDD[T]] = checkpointData.flatMap(_.checkpointRDD)
+  private def checkpointRDD: Option[CheckpointRDD[T]] = checkpointData.flatMap(_.checkpointRDD)
 
   /**
    * Get the list of dependencies of this RDD, taking into account whether the
@@ -1451,12 +1451,16 @@ abstract class RDD[T: ClassTag](
    * executed on this RDD. It is strongly recommended that this RDD is persisted in
    * memory, otherwise saving it on a file will require recomputation.
    */
-  def checkpoint() {
+  def checkpoint(): Unit = {
     if (context.checkpointDir.isEmpty) {
       throw new SparkException("Checkpoint directory has not been set in the SparkContext")
     } else if (checkpointData.isEmpty) {
-      checkpointData = Some(new RDDCheckpointData(this))
-      checkpointData.get.markForCheckpoint()
+      // NOTE: we use a global lock here due to complexities downstream with ensuring
+      // children RDD partitions point to the correct parent partitions. In the future
+      // we should revisit this consideration.
+      RDDCheckpointData.synchronized {
+        checkpointData = Some(new RDDCheckpointData(this))
+      }
     }
   }
 
@@ -1497,7 +1501,7 @@ abstract class RDD[T: ClassTag](
   private[spark] var checkpointData: Option[RDDCheckpointData[T]] = None
 
   /** Returns the first parent RDD */
-  protected[spark] def firstParent[U: ClassTag] = {
+  protected[spark] def firstParent[U: ClassTag]: RDD[U] = {
     dependencies.head.rdd.asInstanceOf[RDD[U]]
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index acbd31aacdf59..4f954363bed8e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -22,16 +22,15 @@ import scala.reflect.ClassTag
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark._
-import org.apache.spark.scheduler.{ResultTask, ShuffleMapTask}
 import org.apache.spark.util.SerializableConfiguration
 
 /**
  * Enumeration to manage state transitions of an RDD through checkpointing
- * [ Initialized --> marked for checkpointing --> checkpointing in progress --> checkpointed ]
+ * [ Initialized --> checkpointing in progress --> checkpointed ].
  */
 private[spark] object CheckpointState extends Enumeration {
   type CheckpointState = Value
-  val Initialized, MarkedForCheckpoint, CheckpointingInProgress, Checkpointed = Value
+  val Initialized, CheckpointingInProgress, Checkpointed = Value
 }
 
 /**
@@ -46,37 +45,37 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
   import CheckpointState._
 
   // The checkpoint state of the associated RDD.
-  var cpState = Initialized
+  private var cpState = Initialized
 
   // The file to which the associated RDD has been checkpointed to
-  @transient var cpFile: Option[String] = None
+  private var cpFile: Option[String] = None
 
   // The CheckpointRDD created from the checkpoint file, that is, the new parent the associated RDD.
-  var cpRDD: Option[RDD[T]] = None
+  // This is defined if and only if `cpState` is `Checkpointed`.
+  private var cpRDD: Option[CheckpointRDD[T]] = None
 
-  // Mark the RDD for checkpointing
-  def markForCheckpoint() {
-    RDDCheckpointData.synchronized {
-      if (cpState == Initialized) cpState = MarkedForCheckpoint
-    }
-  }
+  // TODO: are we sure we need to use a global lock in the following methods?
 
   // Is the RDD already checkpointed
-  def isCheckpointed: Boolean = {
-    RDDCheckpointData.synchronized { cpState == Checkpointed }
+  def isCheckpointed: Boolean = RDDCheckpointData.synchronized {
+    cpState == Checkpointed
   }
 
   // Get the file to which this RDD was checkpointed to as an Option
-  def getCheckpointFile: Option[String] = {
-    RDDCheckpointData.synchronized { cpFile }
+  def getCheckpointFile: Option[String] = RDDCheckpointData.synchronized {
+    cpFile
   }
 
-  // Do the checkpointing of the RDD. Called after the first job using that RDD is over.
-  def doCheckpoint() {
-    // If it is marked for checkpointing AND checkpointing is not already in progress,
-    // then set it to be in progress, else return
+  /**
+   * Materialize this RDD and write its content to a reliable DFS.
+   * This is called immediately after the first action invoked on this RDD has completed.
+   */
+  def doCheckpoint(): Unit = {
+
+    // Guard against multiple threads checkpointing the same RDD by
+    // atomically flipping the state of this RDDCheckpointData
     RDDCheckpointData.synchronized {
-      if (cpState == MarkedForCheckpoint) {
+      if (cpState == Initialized) {
         cpState = CheckpointingInProgress
       } else {
         return
@@ -87,7 +86,7 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
     val path = RDDCheckpointData.rddCheckpointDataPath(rdd.context, rdd.id).get
     val fs = path.getFileSystem(rdd.context.hadoopConfiguration)
     if (!fs.mkdirs(path)) {
-      throw new SparkException("Failed to create checkpoint path " + path)
+      throw new SparkException(s"Failed to create checkpoint path $path")
     }
 
     // Save to file, and reload it as an RDD
@@ -99,6 +98,8 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
         cleaner.registerRDDCheckpointDataForCleanup(newRDD, rdd.id)
       }
     }
+
+    // TODO: This is expensive because it computes the RDD again unnecessarily (SPARK-8582)
     rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _)
     if (newRDD.partitions.length != rdd.partitions.length) {
       throw new SparkException(
@@ -113,34 +114,26 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
       rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and partitions
       cpState = Checkpointed
     }
-    logInfo("Done checkpointing RDD " + rdd.id + " to " + path + ", new parent is RDD " + newRDD.id)
-  }
-
-  // Get preferred location of a split after checkpointing
-  def getPreferredLocations(split: Partition): Seq[String] = {
-    RDDCheckpointData.synchronized {
-      cpRDD.get.preferredLocations(split)
-    }
+    logInfo(s"Done checkpointing RDD ${rdd.id} to $path, new parent is RDD ${newRDD.id}")
   }
 
-  def getPartitions: Array[Partition] = {
-    RDDCheckpointData.synchronized {
-      cpRDD.get.partitions
-    }
+  def getPartitions: Array[Partition] = RDDCheckpointData.synchronized {
+    cpRDD.get.partitions
   }
 
-  def checkpointRDD: Option[RDD[T]] = {
-    RDDCheckpointData.synchronized {
-      cpRDD
-    }
+  def checkpointRDD: Option[CheckpointRDD[T]] = RDDCheckpointData.synchronized {
+    cpRDD
   }
 }
 
 private[spark] object RDDCheckpointData {
+
+  /** Return the path of the directory to which this RDD's checkpoint data is written. */
   def rddCheckpointDataPath(sc: SparkContext, rddId: Int): Option[Path] = {
-    sc.checkpointDir.map { dir => new Path(dir, "rdd-" + rddId) }
+    sc.checkpointDir.map { dir => new Path(dir, s"rdd-$rddId") }
   }
 
+  /** Clean up the files associated with the checkpoint data for this RDD. */
   def clearRDDCheckpointData(sc: SparkContext, rddId: Int): Unit = {
     rddCheckpointDataPath(sc, rddId).foreach { path =>
       val fs = path.getFileSystem(sc.hadoopConfiguration)
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index d1761a48babbc..cc50e6d79a3e2 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -46,7 +46,7 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     val parCollection = sc.makeRDD(1 to 4)
     val flatMappedRDD = parCollection.flatMap(x => 1 to x)
     flatMappedRDD.checkpoint()
-    assert(flatMappedRDD.dependencies.head.rdd == parCollection)
+    assert(flatMappedRDD.dependencies.head.rdd === parCollection)
     val result = flatMappedRDD.collect()
     assert(flatMappedRDD.dependencies.head.rdd != parCollection)
     assert(flatMappedRDD.collect() === result)

From 34d448dbe1d7bd5bf9a8d6ef473878e570ca6161 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 2 Jul 2015 11:28:14 -0700
Subject: [PATCH 0200/1454] [SPARK-8479] [MLLIB] Add numNonzeros and numActives
 to linalg.Matrices

Matrices allow zeros to be stored in values. Sometimes a method is handy to check if the numNonZeros are same as number of Active values.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #6904 from MechCoder/nnz_matrix and squashes the following commits:

252c6b7 [MechCoder] Add to MiMa excludes
e2390f5 [MechCoder] Use count instead of foreach
2f62b2f [MechCoder] Add to MiMa excludes
d6e96ef [MechCoder] [SPARK-8479] Add numNonzeros and numActives to linalg.Matrices
---
 .../apache/spark/mllib/linalg/Matrices.scala  | 19 +++++++++++++++++++
 .../spark/mllib/linalg/MatricesSuite.scala    | 10 ++++++++++
 project/MimaExcludes.scala                    |  6 ++++++
 3 files changed, 35 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 0a615494bb2d1..75e7004464af9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -114,6 +114,16 @@ sealed trait Matrix extends Serializable {
    *          corresponding value in the matrix with type `Double`.
    */
   private[spark] def foreachActive(f: (Int, Int, Double) => Unit)
+
+  /**
+   * Find the number of non-zero active values.
+   */
+  def numNonzeros: Int
+
+  /**
+   * Find the number of values stored explicitly. These values can be zero as well.
+   */
+  def numActives: Int
 }
 
 @DeveloperApi
@@ -324,6 +334,10 @@ class DenseMatrix(
     }
   }
 
+  override def numNonzeros: Int = values.count(_ != 0)
+
+  override def numActives: Int = values.length
+
   /**
    * Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed
    * set to false.
@@ -593,6 +607,11 @@ class SparseMatrix(
   def toDense: DenseMatrix = {
     new DenseMatrix(numRows, numCols, toArray)
   }
+
+  override def numNonzeros: Int = values.count(_ != 0)
+
+  override def numActives: Int = values.length
+
 }
 
 /**
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
index 8dbb70f5d1c4c..a270ba2562db9 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
@@ -455,4 +455,14 @@ class MatricesSuite extends SparkFunSuite {
     lines = mat.toString(5, 100).lines.toArray
     assert(lines.size == 5 && lines.forall(_.size <= 100))
   }
+
+  test("numNonzeros and numActives") {
+    val dm1 = Matrices.dense(3, 2, Array(0, 0, -1, 1, 0, 1))
+    assert(dm1.numNonzeros === 3)
+    assert(dm1.numActives === 6)
+
+    val sm1 = Matrices.sparse(3, 2, Array(0, 2, 3), Array(0, 2, 1), Array(0.0, -1.2, 0.0))
+    assert(sm1.numNonzeros === 1)
+    assert(sm1.numActives === 3)
+  }
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 6f86a505b3ae4..680b699e9e4a1 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -75,6 +75,12 @@ object MimaExcludes {
               "org.apache.spark.sql.parquet.ParquetTypeInfo"),
             ProblemFilters.exclude[MissingClassProblem](
               "org.apache.spark.sql.parquet.ParquetTypeInfo$")
+          ) ++ Seq(
+            // SPARK-8479 Add numNonzeros and numActives to Matrix.
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.linalg.Matrix.numNonzeros"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.linalg.Matrix.numActives")
           )
         case v if v.startsWith("1.4") =>
           Seq(

From 82cf3315e690f4ac15b50edea6a3d673aa5be4c0 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 2 Jul 2015 13:49:45 -0700
Subject: [PATCH 0201/1454] [SPARK-8781] Fix variables in published pom.xml are
 not resolved

The issue is summarized in the JIRA and is caused by this commit: 984ad60147c933f2d5a2040c87ae687c14eb1724.

This patch reverts that commit and fixes the maven build in a different way. We limit the dependencies of `KinesisReceiverSuite` to avoid having to deal with the complexities in how maven deals with transitive test dependencies.

Author: Andrew Or <andrew@databricks.com>

Closes #7193 from andrewor14/fix-kinesis-pom and squashes the following commits:

ca3d5d4 [Andrew Or] Limit kinesis test dependencies
f24e09c [Andrew Or] Revert "[BUILD] Fix Maven build for Kinesis"
---
 extras/kinesis-asl/pom.xml                    |  7 -------
 .../kinesis/KinesisReceiverSuite.scala        | 20 +++++++++++--------
 pom.xml                                       |  2 --
 3 files changed, 12 insertions(+), 17 deletions(-)

diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index c242e7a57b9ab..5289073eb457a 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -40,13 +40,6 @@
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
-    <dependency>
-      <groupId>org.apache.spark</groupId>
-      <artifactId>spark-core_${scala.binary.version}</artifactId>
-      <version>${project.version}</version>
-      <type>test-jar</type>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
index 2103dca6b766f..6c262624833cd 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
@@ -26,18 +26,23 @@ import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionIn
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
 import org.mockito.Mockito._
-import org.scalatest.{BeforeAndAfter, Matchers}
+// scalastyle:off
+// To avoid introducing a dependency on Spark core tests, simply use scalatest's FunSuite
+// here instead of our own SparkFunSuite. Introducing the dependency has caused problems
+// in the past (SPARK-8781) that are complicated by bugs in the maven shade plugin (MSHADE-148).
+import org.scalatest.{BeforeAndAfter, FunSuite, Matchers}
 import org.scalatest.mock.MockitoSugar
 
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext, TestSuiteBase}
+import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext}
 import org.apache.spark.util.{Clock, ManualClock, Utils}
 
 /**
  * Suite of Kinesis streaming receiver tests focusing mostly on the KinesisRecordProcessor
  */
-class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAfter
-    with MockitoSugar {
+class KinesisReceiverSuite extends FunSuite with Matchers with BeforeAndAfter
+  with MockitoSugar {
+// scalastyle:on
 
   val app = "TestKinesisReceiver"
   val stream = "mySparkStream"
@@ -57,7 +62,7 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   var checkpointStateMock: KinesisCheckpointState = _
   var currentClockMock: Clock = _
 
-  override def beforeFunction(): Unit = {
+  before {
     receiverMock = mock[KinesisReceiver]
     checkpointerMock = mock[IRecordProcessorCheckpointer]
     checkpointClockMock = mock[ManualClock]
@@ -65,8 +70,7 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
     currentClockMock = mock[Clock]
   }
 
-  override def afterFunction(): Unit = {
-    super.afterFunction()
+  after {
     // Since this suite was originally written using EasyMock, add this to preserve the old
     // mocking semantics (see SPARK-5735 for more details)
     verifyNoMoreInteractions(receiverMock, checkpointerMock, checkpointClockMock,
@@ -74,7 +78,7 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   }
 
   test("KinesisUtils API") {
-    val ssc = new StreamingContext(master, framework, batchDuration)
+    val ssc = new StreamingContext("local[2]", getClass.getSimpleName, Seconds(1))
     // Tests the API, does not actually test data receiving
     val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
       "https://kinesis.us-west-2.amazonaws.com", Seconds(2),
diff --git a/pom.xml b/pom.xml
index 211da9ee74a3f..ffa96128a3d61 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1440,8 +1440,6 @@
         <version>2.3</version>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <!-- Work around MSHADE-148 -->
-          <createDependencyReducedPom>false</createDependencyReducedPom>
           <artifactSet>
             <includes>
               <!-- At a minimum we must include this to force effective pom generation -->

From fcbcba66c92871fe3936e5ca605017e9c2a2eb95 Mon Sep 17 00:00:00 2001
From: Deron Eriksson <deron@us.ibm.com>
Date: Thu, 2 Jul 2015 13:55:53 -0700
Subject: [PATCH 0202/1454] [SPARK-1564] [DOCS] Added Javascript to Javadocs to
 create badges for tags like :: Experimental ::

Modified copy_api_dirs.rb and created api-javadocs.js and api-javadocs.css files in order to add badges to javadoc files for :: Experimental ::, :: DeveloperApi ::, and :: AlphaComponent :: tags

Author: Deron Eriksson <deron@us.ibm.com>

Closes #7169 from deroneriksson/SPARK-1564_JavaDocs_badges and squashes the following commits:

a8353db [Deron Eriksson] added license headers to api-docs.css and api-javadocs.css
07feb07 [Deron Eriksson] added linebreaks to make jquery more readable when adding html badge tags
65b4930 [Deron Eriksson] Modified copy_api_dirs.rb and created api-javadocs.js and api-javadocs.css files in order to add badges to javadoc files for :: Experimental ::, :: DeveloperApi ::, and :: AlphaComponent :: tags
---
 docs/_plugins/copy_api_dirs.rb | 45 +++++++++++++++++++++++++
 docs/css/api-docs.css          | 17 ++++++++++
 docs/css/api-javadocs.css      | 52 +++++++++++++++++++++++++++++
 docs/js/api-javadocs.js        | 60 ++++++++++++++++++++++++++++++++++
 4 files changed, 174 insertions(+)
 create mode 100644 docs/css/api-javadocs.css
 create mode 100644 docs/js/api-javadocs.js

diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index 6073b3626c45b..15ceda11a8a80 100644
--- a/docs/_plugins/copy_api_dirs.rb
+++ b/docs/_plugins/copy_api_dirs.rb
@@ -63,6 +63,51 @@
 
     puts "cp -r " + source + "/. " + dest
     cp_r(source + "/.", dest)
+
+    # Begin updating JavaDoc files for badge post-processing
+    puts "Updating JavaDoc files for badge post-processing"
+    js_script_start = '<script defer="defer" type="text/javascript" src="'
+    js_script_end = '.js"></script>'
+
+    javadoc_files = Dir["./" + dest + "/**/*.html"]
+    javadoc_files.each do |javadoc_file|
+      # Determine file depths to reference js files
+      slash_count = javadoc_file.count "/"
+      i = 3
+      path_to_js_file = ""
+      while (i < slash_count) do
+        path_to_js_file = path_to_js_file + "../"
+        i += 1
+      end
+
+      # Create script elements to reference js files
+      javadoc_jquery_script = js_script_start + path_to_js_file + "lib/jquery" + js_script_end;
+      javadoc_api_docs_script = js_script_start + path_to_js_file + "lib/api-javadocs" + js_script_end;
+      javadoc_script_elements = javadoc_jquery_script + javadoc_api_docs_script
+
+      # Add script elements to JavaDoc files
+      javadoc_file_content = File.open(javadoc_file, "r") { |f| f.read }
+      javadoc_file_content = javadoc_file_content.sub("</body>", javadoc_script_elements + "</body>")
+      File.open(javadoc_file, "w") { |f| f.puts(javadoc_file_content) }
+
+    end
+    # End updating JavaDoc files for badge post-processing
+
+    puts "Copying jquery.js from Scala API to Java API for page post-processing of badges"
+    jquery_src_file = "./api/scala/lib/jquery.js"
+    jquery_dest_file = "./api/java/lib/jquery.js"
+    mkdir_p("./api/java/lib")
+    cp(jquery_src_file, jquery_dest_file)
+
+    puts "Copying api_javadocs.js to Java API for page post-processing of badges"
+    api_javadocs_src_file = "./js/api-javadocs.js"
+    api_javadocs_dest_file = "./api/java/lib/api-javadocs.js"
+    cp(api_javadocs_src_file, api_javadocs_dest_file)
+
+    puts "Appending content of api-javadocs.css to JavaDoc stylesheet.css for badge styles"
+    css = File.readlines("./css/api-javadocs.css")
+    css_file = dest + "/stylesheet.css"
+    File.open(css_file, 'a') { |f| f.write("\n" + css.join()) }
   end
 
   # Build Sphinx docs for Python
diff --git a/docs/css/api-docs.css b/docs/css/api-docs.css
index b2d1d7f869790..7cf222aad24f6 100644
--- a/docs/css/api-docs.css
+++ b/docs/css/api-docs.css
@@ -1,3 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 /* Dynamically injected style for the API docs */
 
 .developer {
diff --git a/docs/css/api-javadocs.css b/docs/css/api-javadocs.css
new file mode 100644
index 0000000000000..832e92609e011
--- /dev/null
+++ b/docs/css/api-javadocs.css
@@ -0,0 +1,52 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* Dynamically injected style for the API docs */
+
+.badge {
+  font-family: Arial, san-serif;
+  float: right;
+  margin: 4px;
+  /* The following declarations are taken from the ScalaDoc template.css */
+  display: inline-block;
+  padding: 2px 4px;
+  font-size: 11.844px;
+  font-weight: bold;
+  line-height: 14px;
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  white-space: nowrap;
+  vertical-align: baseline;
+  background-color: #999999;
+  padding-right: 9px;
+  padding-left: 9px;
+  -webkit-border-radius: 9px;
+     -moz-border-radius: 9px;
+          border-radius: 9px;
+}
+
+.developer {
+  background-color: #44751E;
+}
+
+.experimental {
+  background-color: #257080;
+}
+
+.alphaComponent {
+  background-color: #bb0000;
+}
diff --git a/docs/js/api-javadocs.js b/docs/js/api-javadocs.js
new file mode 100644
index 0000000000000..ead13d6e5fa7c
--- /dev/null
+++ b/docs/js/api-javadocs.js
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* Dynamically injected post-processing code for the API docs */
+
+$(document).ready(function() {
+  addBadges(":: AlphaComponent ::", '<span class="alphaComponent badge">Alpha Component</span>');
+  addBadges(":: DeveloperApi ::", '<span class="developer badge">Developer API</span>');
+  addBadges(":: Experimental ::", '<span class="experimental badge">Experimental</span>');
+});
+
+function addBadges(tag, html) {
+  var tags = $(".block:contains(" + tag + ")")
+
+  // Remove identifier tags
+  tags.each(function(index) {
+    var oldHTML = $(this).html();
+    var newHTML = oldHTML.replace(tag, "");
+    $(this).html(newHTML);
+  });
+
+  // Add html badge tags
+  tags.each(function(index) {
+    if ($(this).parent().is('td.colLast')) {
+      $(this).parent().prepend(html);
+    } else if ($(this).parent('li.blockList')
+                      .parent('ul.blockList')
+                      .parent('div.description')
+                      .parent().is('div.contentContainer')) {
+      var contentContainer = $(this).parent('li.blockList')
+                                    .parent('ul.blockList')
+                                    .parent('div.description')
+                                    .parent('div.contentContainer')
+      var header = contentContainer.prev('div.header');
+      if (header.length > 0) {
+        header.prepend(html);
+      } else {
+        contentContainer.prepend(html);
+      }
+    } else if ($(this).parent().is('li.blockList')) {
+      $(this).parent().prepend(html);
+    } else {
+      $(this).prepend(html);
+    }
+  });
+}

From cd2035507891a7f426f6f45902d3b5f4fdbe88cf Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 2 Jul 2015 13:59:56 -0700
Subject: [PATCH 0203/1454] [SPARK-7835] Refactor HeartbeatReceiverSuite for
 coverage + cleanup

The existing test suite has a lot of duplicate code and doesn't even cover the most fundamental feature of the HeartbeatReceiver, which is expiring hosts that have not responded in a while.

This introduces manual clocks in `HeartbeatReceiver` and makes it respond to heartbeats only for registered executors. A few internal messages are moved to `receiveAndReply` to increase determinism of the tests so we don't have to rely on flaky constructs like `eventually`.

Author: Andrew Or <andrew@databricks.com>

Closes #7173 from andrewor14/heartbeat-receiver-tests and squashes the following commits:

4a903d6 [Andrew Or] Increase HeartReceiverSuite coverage and clean up
---
 .../org/apache/spark/HeartbeatReceiver.scala  |  89 +++++++---
 .../scala/org/apache/spark/SparkContext.scala |   2 +-
 .../apache/spark/HeartbeatReceiverSuite.scala | 161 +++++++++++++-----
 3 files changed, 191 insertions(+), 61 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
index 6909015ff66e6..221b1dab43278 100644
--- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
+++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
@@ -24,8 +24,8 @@ import scala.collection.mutable
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcEnv, RpcCallContext}
 import org.apache.spark.storage.BlockManagerId
-import org.apache.spark.scheduler.{SlaveLost, TaskScheduler}
-import org.apache.spark.util.{ThreadUtils, Utils}
+import org.apache.spark.scheduler._
+import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils}
 
 /**
  * A heartbeat from executors to the driver. This is a shared message used by several internal
@@ -45,13 +45,23 @@ private[spark] case object TaskSchedulerIsSet
 
 private[spark] case object ExpireDeadHosts
 
+private case class ExecutorRegistered(executorId: String)
+
+private case class ExecutorRemoved(executorId: String)
+
 private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean)
 
 /**
  * Lives in the driver to receive heartbeats from executors..
  */
-private[spark] class HeartbeatReceiver(sc: SparkContext)
-  extends ThreadSafeRpcEndpoint with Logging {
+private[spark] class HeartbeatReceiver(sc: SparkContext, clock: Clock)
+  extends ThreadSafeRpcEndpoint with SparkListener with Logging {
+
+  def this(sc: SparkContext) {
+    this(sc, new SystemClock)
+  }
+
+  sc.addSparkListener(this)
 
   override val rpcEnv: RpcEnv = sc.env.rpcEnv
 
@@ -86,30 +96,48 @@ private[spark] class HeartbeatReceiver(sc: SparkContext)
   override def onStart(): Unit = {
     timeoutCheckingTask = eventLoopThread.scheduleAtFixedRate(new Runnable {
       override def run(): Unit = Utils.tryLogNonFatalError {
-        Option(self).foreach(_.send(ExpireDeadHosts))
+        Option(self).foreach(_.ask[Boolean](ExpireDeadHosts))
       }
     }, 0, checkTimeoutIntervalMs, TimeUnit.MILLISECONDS)
   }
 
-  override def receive: PartialFunction[Any, Unit] = {
-    case ExpireDeadHosts =>
-      expireDeadHosts()
+  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+
+    // Messages sent and received locally
+    case ExecutorRegistered(executorId) =>
+      executorLastSeen(executorId) = clock.getTimeMillis()
+      context.reply(true)
+    case ExecutorRemoved(executorId) =>
+      executorLastSeen.remove(executorId)
+      context.reply(true)
     case TaskSchedulerIsSet =>
       scheduler = sc.taskScheduler
-  }
+      context.reply(true)
+    case ExpireDeadHosts =>
+      expireDeadHosts()
+      context.reply(true)
 
-  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+    // Messages received from executors
     case heartbeat @ Heartbeat(executorId, taskMetrics, blockManagerId) =>
       if (scheduler != null) {
-        executorLastSeen(executorId) = System.currentTimeMillis()
-        eventLoopThread.submit(new Runnable {
-          override def run(): Unit = Utils.tryLogNonFatalError {
-            val unknownExecutor = !scheduler.executorHeartbeatReceived(
-              executorId, taskMetrics, blockManagerId)
-            val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor)
-            context.reply(response)
-          }
-        })
+        if (executorLastSeen.contains(executorId)) {
+          executorLastSeen(executorId) = clock.getTimeMillis()
+          eventLoopThread.submit(new Runnable {
+            override def run(): Unit = Utils.tryLogNonFatalError {
+              val unknownExecutor = !scheduler.executorHeartbeatReceived(
+                executorId, taskMetrics, blockManagerId)
+              val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor)
+              context.reply(response)
+            }
+          })
+        } else {
+          // This may happen if we get an executor's in-flight heartbeat immediately
+          // after we just removed it. It's not really an error condition so we should
+          // not log warning here. Otherwise there may be a lot of noise especially if
+          // we explicitly remove executors (SPARK-4134).
+          logDebug(s"Received heartbeat from unknown executor $executorId")
+          context.reply(HeartbeatResponse(reregisterBlockManager = true))
+        }
       } else {
         // Because Executor will sleep several seconds before sending the first "Heartbeat", this
         // case rarely happens. However, if it really happens, log it and ask the executor to
@@ -119,9 +147,30 @@ private[spark] class HeartbeatReceiver(sc: SparkContext)
       }
   }
 
+  /**
+   * If the heartbeat receiver is not stopped, notify it of executor registrations.
+   */
+  override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = {
+    Option(self).foreach(_.ask[Boolean](ExecutorRegistered(executorAdded.executorId)))
+  }
+
+  /**
+   * If the heartbeat receiver is not stopped, notify it of executor removals so it doesn't
+   * log superfluous errors.
+   *
+   * Note that we must do this after the executor is actually removed to guard against the
+   * following race condition: if we remove an executor's metadata from our data structure
+   * prematurely, we may get an in-flight heartbeat from the executor before the executor is
+   * actually removed, in which case we will still mark the executor as a dead host later
+   * and expire it with loud error messages.
+   */
+  override def onExecutorRemoved(executorRemoved: SparkListenerExecutorRemoved): Unit = {
+    Option(self).foreach(_.ask[Boolean](ExecutorRemoved(executorRemoved.executorId)))
+  }
+
   private def expireDeadHosts(): Unit = {
     logTrace("Checking for hosts with no recent heartbeats in HeartbeatReceiver.")
-    val now = System.currentTimeMillis()
+    val now = clock.getTimeMillis()
     for ((executorId, lastSeenMs) <- executorLastSeen) {
       if (now - lastSeenMs > executorTimeoutMs) {
         logWarning(s"Removing executor $executorId with no recent heartbeats: " +
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 8eed46759f340..d2547eeff2b4e 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -498,7 +498,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _schedulerBackend = sched
     _taskScheduler = ts
     _dagScheduler = new DAGScheduler(this)
-    _heartbeatReceiver.send(TaskSchedulerIsSet)
+    _heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)
 
     // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
     // constructor
diff --git a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
index 911b3bddd1836..b31b09196608f 100644
--- a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
+++ b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
@@ -17,64 +17,145 @@
 
 package org.apache.spark
 
-import scala.concurrent.duration._
 import scala.language.postfixOps
 
-import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.storage.BlockManagerId
+import org.scalatest.{BeforeAndAfterEach, PrivateMethodTester}
 import org.mockito.Mockito.{mock, spy, verify, when}
 import org.mockito.Matchers
 import org.mockito.Matchers._
 
-import org.apache.spark.scheduler.TaskScheduler
-import org.apache.spark.util.RpcUtils
-import org.scalatest.concurrent.Eventually._
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.scheduler._
+import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.util.ManualClock
 
-class HeartbeatReceiverSuite extends SparkFunSuite with LocalSparkContext {
+class HeartbeatReceiverSuite
+  extends SparkFunSuite
+  with BeforeAndAfterEach
+  with PrivateMethodTester
+  with LocalSparkContext {
 
-  test("HeartbeatReceiver") {
+  private val executorId1 = "executor-1"
+  private val executorId2 = "executor-2"
+
+  // Shared state that must be reset before and after each test
+  private var scheduler: TaskScheduler = null
+  private var heartbeatReceiver: HeartbeatReceiver = null
+  private var heartbeatReceiverRef: RpcEndpointRef = null
+  private var heartbeatReceiverClock: ManualClock = null
+
+  override def beforeEach(): Unit = {
     sc = spy(new SparkContext("local[2]", "test"))
-    val scheduler = mock(classOf[TaskScheduler])
-    when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(true)
+    scheduler = mock(classOf[TaskScheduler])
     when(sc.taskScheduler).thenReturn(scheduler)
+    heartbeatReceiverClock = new ManualClock
+    heartbeatReceiver = new HeartbeatReceiver(sc, heartbeatReceiverClock)
+    heartbeatReceiverRef = sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver)
+    when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(true)
+  }
 
-    val heartbeatReceiver = new HeartbeatReceiver(sc)
-    sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver).send(TaskSchedulerIsSet)
-    eventually(timeout(5 seconds), interval(5 millis)) {
-      assert(heartbeatReceiver.scheduler != null)
-    }
-    val receiverRef = RpcUtils.makeDriverRef("heartbeat", sc.conf, sc.env.rpcEnv)
+  override def afterEach(): Unit = {
+    resetSparkContext()
+    scheduler = null
+    heartbeatReceiver = null
+    heartbeatReceiverRef = null
+    heartbeatReceiverClock = null
+  }
 
-    val metrics = new TaskMetrics
-    val blockManagerId = BlockManagerId("executor-1", "localhost", 12345)
-    val response = receiverRef.askWithRetry[HeartbeatResponse](
-      Heartbeat("executor-1", Array(1L -> metrics), blockManagerId))
+  test("task scheduler is set correctly") {
+    assert(heartbeatReceiver.scheduler === null)
+    heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
+    assert(heartbeatReceiver.scheduler !== null)
+  }
 
-    verify(scheduler).executorHeartbeatReceived(
-      Matchers.eq("executor-1"), Matchers.eq(Array(1L -> metrics)), Matchers.eq(blockManagerId))
-    assert(false === response.reregisterBlockManager)
+  test("normal heartbeat") {
+    heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId1, null))
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId2, null))
+    triggerHeartbeat(executorId1, executorShouldReregister = false)
+    triggerHeartbeat(executorId2, executorShouldReregister = false)
+    val trackedExecutors = executorLastSeen(heartbeatReceiver)
+    assert(trackedExecutors.size === 2)
+    assert(trackedExecutors.contains(executorId1))
+    assert(trackedExecutors.contains(executorId2))
   }
 
-  test("HeartbeatReceiver re-register") {
-    sc = spy(new SparkContext("local[2]", "test"))
-    val scheduler = mock(classOf[TaskScheduler])
-    when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(false)
-    when(sc.taskScheduler).thenReturn(scheduler)
+  test("reregister if scheduler is not ready yet") {
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId1, null))
+    // Task scheduler not set in HeartbeatReceiver
+    triggerHeartbeat(executorId1, executorShouldReregister = true)
+  }
 
-    val heartbeatReceiver = new HeartbeatReceiver(sc)
-    sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver).send(TaskSchedulerIsSet)
-    eventually(timeout(5 seconds), interval(5 millis)) {
-      assert(heartbeatReceiver.scheduler != null)
-    }
-    val receiverRef = RpcUtils.makeDriverRef("heartbeat", sc.conf, sc.env.rpcEnv)
+  test("reregister if heartbeat from unregistered executor") {
+    heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
+    // Received heartbeat from unknown receiver, so we ask it to re-register
+    triggerHeartbeat(executorId1, executorShouldReregister = true)
+    assert(executorLastSeen(heartbeatReceiver).isEmpty)
+  }
+
+  test("reregister if heartbeat from removed executor") {
+    heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId1, null))
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId2, null))
+    // Remove the second executor but not the first
+    heartbeatReceiver.onExecutorRemoved(SparkListenerExecutorRemoved(0, executorId2, "bad boy"))
+    // Now trigger the heartbeats
+    // A heartbeat from the second executor should require reregistering
+    triggerHeartbeat(executorId1, executorShouldReregister = false)
+    triggerHeartbeat(executorId2, executorShouldReregister = true)
+    val trackedExecutors = executorLastSeen(heartbeatReceiver)
+    assert(trackedExecutors.size === 1)
+    assert(trackedExecutors.contains(executorId1))
+    assert(!trackedExecutors.contains(executorId2))
+  }
 
+  test("expire dead hosts") {
+    val executorTimeout = executorTimeoutMs(heartbeatReceiver)
+    heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId1, null))
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId2, null))
+    triggerHeartbeat(executorId1, executorShouldReregister = false)
+    triggerHeartbeat(executorId2, executorShouldReregister = false)
+    // Advance the clock and only trigger a heartbeat for the first executor
+    heartbeatReceiverClock.advance(executorTimeout / 2)
+    triggerHeartbeat(executorId1, executorShouldReregister = false)
+    heartbeatReceiverClock.advance(executorTimeout)
+    heartbeatReceiverRef.askWithRetry[Boolean](ExpireDeadHosts)
+    // Only the second executor should be expired as a dead host
+    verify(scheduler).executorLost(Matchers.eq(executorId2), any())
+    val trackedExecutors = executorLastSeen(heartbeatReceiver)
+    assert(trackedExecutors.size === 1)
+    assert(trackedExecutors.contains(executorId1))
+    assert(!trackedExecutors.contains(executorId2))
+  }
+
+  /** Manually send a heartbeat and return the response. */
+  private def triggerHeartbeat(
+      executorId: String,
+      executorShouldReregister: Boolean): Unit = {
     val metrics = new TaskMetrics
-    val blockManagerId = BlockManagerId("executor-1", "localhost", 12345)
-    val response = receiverRef.askWithRetry[HeartbeatResponse](
-      Heartbeat("executor-1", Array(1L -> metrics), blockManagerId))
+    val blockManagerId = BlockManagerId(executorId, "localhost", 12345)
+    val response = heartbeatReceiverRef.askWithRetry[HeartbeatResponse](
+      Heartbeat(executorId, Array(1L -> metrics), blockManagerId))
+    if (executorShouldReregister) {
+      assert(response.reregisterBlockManager)
+    } else {
+      assert(!response.reregisterBlockManager)
+      // Additionally verify that the scheduler callback is called with the correct parameters
+      verify(scheduler).executorHeartbeatReceived(
+        Matchers.eq(executorId), Matchers.eq(Array(1L -> metrics)), Matchers.eq(blockManagerId))
+    }
+  }
 
-    verify(scheduler).executorHeartbeatReceived(
-      Matchers.eq("executor-1"), Matchers.eq(Array(1L -> metrics)), Matchers.eq(blockManagerId))
-    assert(true === response.reregisterBlockManager)
+  // Helper methods to access private fields in HeartbeatReceiver
+  private val _executorLastSeen = PrivateMethod[collection.Map[String, Long]]('executorLastSeen)
+  private val _executorTimeoutMs = PrivateMethod[Long]('executorTimeoutMs)
+  private def executorLastSeen(receiver: HeartbeatReceiver): collection.Map[String, Long] = {
+    receiver invokePrivate _executorLastSeen()
+  }
+  private def executorTimeoutMs(receiver: HeartbeatReceiver): Long = {
+    receiver invokePrivate _executorTimeoutMs()
   }
+
 }

From 52508beb650a863ed5c89384414b3b7675cac11e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 2 Jul 2015 14:16:14 -0700
Subject: [PATCH 0204/1454] [SPARK-8772][SQL] Implement implicit type cast for
 expressions that define input types.

Author: Reynold Xin <rxin@databricks.com>

Closes #7175 from rxin/implicitCast and squashes the following commits:

88080a2 [Reynold Xin] Clearer definition of implicit type cast.
f0ff97f [Reynold Xin] Added missing file.
c65e532 [Reynold Xin] [SPARK-8772][SQL] Implement implicit type cast for expressions that defines input types.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  41 ++++++-
 .../expressions/ExpectsInputTypes.scala       |  24 +---
 .../spark/sql/catalyst/expressions/math.scala |   7 +-
 .../spark/sql/catalyst/expressions/misc.scala |  12 +-
 .../sql/catalyst/expressions/predicates.scala |  14 +--
 .../expressions/stringOperations.scala        |  10 +-
 .../spark/sql/types/AbstractDataType.scala    | 114 ++++++++++++++++++
 .../apache/spark/sql/types/ArrayType.scala    |   6 +-
 .../apache/spark/sql/types/BinaryType.scala   |   2 -
 .../apache/spark/sql/types/BooleanType.scala  |   2 -
 .../org/apache/spark/sql/types/ByteType.scala |   2 -
 .../org/apache/spark/sql/types/DataType.scala |  86 +------------
 .../org/apache/spark/sql/types/DateType.scala |   5 +-
 .../apache/spark/sql/types/DecimalType.scala  |   7 +-
 .../apache/spark/sql/types/DoubleType.scala   |   2 -
 .../apache/spark/sql/types/FloatType.scala    |   2 -
 .../apache/spark/sql/types/IntegerType.scala  |   2 -
 .../org/apache/spark/sql/types/LongType.scala |   2 -
 .../org/apache/spark/sql/types/MapType.scala  |   7 +-
 .../org/apache/spark/sql/types/NullType.scala |   2 -
 .../apache/spark/sql/types/ShortType.scala    |   2 -
 .../apache/spark/sql/types/StringType.scala   |   2 -
 .../apache/spark/sql/types/StructType.scala   |   2 -
 .../spark/sql/types/TimestampType.scala       |   2 -
 .../analysis/HiveTypeCoercionSuite.scala      |  25 ++++
 25 files changed, 213 insertions(+), 169 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 8420c54f7c335..0bc893224026e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -704,19 +704,48 @@ object HiveTypeCoercion {
 
   /**
    * Casts types according to the expected input types for Expressions that have the trait
-   * [[AutoCastInputTypes]].
+   * [[ExpectsInputTypes]].
    */
   object ImplicitTypeCasts extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      case e: AutoCastInputTypes if e.children.map(_.dataType) != e.inputTypes =>
-        val newC = (e.children, e.children.map(_.dataType), e.inputTypes).zipped.map {
-          case (child, actual, expected) =>
-            if (actual == expected) child else Cast(child, expected)
+      case e: ExpectsInputTypes =>
+        val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
+          implicitCast(in, expected)
         }
-        e.withNewChildren(newC)
+        e.withNewChildren(children)
+    }
+
+    /**
+     * If needed, cast the expression into the expected type.
+     * If the implicit cast is not allowed, return the expression itself.
+     */
+    def implicitCast(e: Expression, expectedType: AbstractDataType): Expression = {
+      val inType = e.dataType
+      (inType, expectedType) match {
+        // Cast null type (usually from null literals) into target types
+        case (NullType, target: DataType) => Cast(e, target.defaultConcreteType)
+
+        // Implicit cast among numeric types
+        case (_: NumericType, target: NumericType) if e.dataType != target => Cast(e, target)
+
+        // Implicit cast between date time types
+        case (DateType, TimestampType) => Cast(e, TimestampType)
+        case (TimestampType, DateType) => Cast(e, DateType)
+
+        // Implicit cast from/to string
+        case (StringType, NumericType) => Cast(e, DoubleType)
+        case (StringType, target: NumericType) => Cast(e, target)
+        case (StringType, DateType) => Cast(e, DateType)
+        case (StringType, TimestampType) => Cast(e, TimestampType)
+        case (StringType, BinaryType) => Cast(e, BinaryType)
+        case (any, StringType) if any != StringType => Cast(e, StringType)
+
+        // Else, just return the same input expression
+        case _ => e
+      }
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
index 450fc4165f93b..916e30154d4f1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.types.AbstractDataType
 
 
 /**
@@ -32,28 +32,12 @@ trait ExpectsInputTypes { self: Expression =>
    *
    * The possible values at each position are:
    * 1. a specific data type, e.g. LongType, StringType.
-   * 2. a non-leaf data type, e.g. NumericType, IntegralType, FractionalType.
-   * 3. a list of specific data types, e.g. Seq(StringType, BinaryType).
+   * 2. a non-leaf abstract data type, e.g. NumericType, IntegralType, FractionalType.
    */
-  def inputTypes: Seq[Any]
+  def inputTypes: Seq[AbstractDataType]
 
   override def checkInputDataTypes(): TypeCheckResult = {
-    // We will do the type checking in `HiveTypeCoercion`, so always returning success here.
-    TypeCheckResult.TypeCheckSuccess
-  }
-}
-
-/**
- * Expressions that require a specific `DataType` as input should implement this trait
- * so that the proper type conversions can be performed in the analyzer.
- */
-trait AutoCastInputTypes { self: Expression =>
-
-  def inputTypes: Seq[DataType]
-
-  override def checkInputDataTypes(): TypeCheckResult = {
-    // We will always do type casting for `AutoCastInputTypes` in `HiveTypeCoercion`,
-    // so type mismatch error won't be reported here, but for underling `Cast`s.
+    // TODO: implement proper type checking.
     TypeCheckResult.TypeCheckSuccess
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 7504c6a066657..035980da568d3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -56,8 +56,7 @@ abstract class LeafMathExpression(c: Double, name: String)
  * @param name The short name of the function
  */
 abstract class UnaryMathExpression(f: Double => Double, name: String)
-  extends UnaryExpression with Serializable with AutoCastInputTypes {
-  self: Product =>
+  extends UnaryExpression with Serializable with ExpectsInputTypes { self: Product =>
 
   override def inputTypes: Seq[DataType] = Seq(DoubleType)
   override def dataType: DataType = DoubleType
@@ -96,7 +95,7 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
  * @param name The short name of the function
  */
 abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
-  extends BinaryExpression with Serializable with AutoCastInputTypes { self: Product =>
+  extends BinaryExpression with Serializable with ExpectsInputTypes { self: Product =>
 
   override def inputTypes: Seq[DataType] = Seq(DoubleType, DoubleType)
 
@@ -208,7 +207,7 @@ case class ToRadians(child: Expression) extends UnaryMathExpression(math.toRadia
 }
 
 case class Bin(child: Expression)
-  extends UnaryExpression with Serializable with AutoCastInputTypes {
+  extends UnaryExpression with Serializable with ExpectsInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(LongType)
   override def dataType: DataType = StringType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index 407023e472081..e008af3966941 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -31,8 +31,7 @@ import org.apache.spark.unsafe.types.UTF8String
  * A function that calculates an MD5 128-bit checksum and returns it as a hex string
  * For input of type [[BinaryType]]
  */
-case class Md5(child: Expression)
-  extends UnaryExpression with AutoCastInputTypes {
+case class Md5(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
   override def dataType: DataType = StringType
 
@@ -62,12 +61,10 @@ case class Md5(child: Expression)
  * the hash length is not one of the permitted values, the return value is NULL.
  */
 case class Sha2(left: Expression, right: Expression)
-  extends BinaryExpression with Serializable with AutoCastInputTypes {
+  extends BinaryExpression with Serializable with ExpectsInputTypes {
 
   override def dataType: DataType = StringType
 
-  override def toString: String = s"SHA2($left, $right)"
-
   override def inputTypes: Seq[DataType] = Seq(BinaryType, IntegerType)
 
   override def eval(input: InternalRow): Any = {
@@ -147,7 +144,7 @@ case class Sha2(left: Expression, right: Expression)
  * A function that calculates a sha1 hash value and returns it as a hex string
  * For input of type [[BinaryType]] or [[StringType]]
  */
-case class Sha1(child: Expression) extends UnaryExpression with AutoCastInputTypes {
+case class Sha1(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
   override def dataType: DataType = StringType
 
@@ -174,8 +171,7 @@ case class Sha1(child: Expression) extends UnaryExpression with AutoCastInputTyp
  * A function that computes a cyclic redundancy check value and returns it as a bigint
  * For input of type [[BinaryType]]
  */
-case class Crc32(child: Expression)
-  extends UnaryExpression with AutoCastInputTypes {
+case class Crc32(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
   override def dataType: DataType = LongType
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index d4569241e7364..0b479f466c63c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -69,7 +69,7 @@ trait PredicateHelper {
     expr.references.subsetOf(plan.outputSet)
 }
 
-case class Not(child: Expression) extends UnaryExpression with Predicate with AutoCastInputTypes {
+case class Not(child: Expression) extends UnaryExpression with Predicate with ExpectsInputTypes {
   override def toString: String = s"NOT $child"
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType)
@@ -120,11 +120,11 @@ case class InSet(value: Expression, hset: Set[Any])
 }
 
 case class And(left: Expression, right: Expression)
-  extends BinaryOperator with Predicate with AutoCastInputTypes {
+  extends BinaryExpression with Predicate with ExpectsInputTypes {
 
-  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+  override def toString: String = s"($left && $right)"
 
-  override def symbol: String = "&&"
+  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
   override def eval(input: InternalRow): Any = {
     val l = left.eval(input)
@@ -169,11 +169,11 @@ case class And(left: Expression, right: Expression)
 }
 
 case class Or(left: Expression, right: Expression)
-  extends BinaryOperator with Predicate with AutoCastInputTypes {
+  extends BinaryExpression with Predicate with ExpectsInputTypes {
 
-  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+  override def toString: String = s"($left || $right)"
 
-  override def symbol: String = "||"
+  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
   override def eval(input: InternalRow): Any = {
     val l = left.eval(input)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index b020f2bbc5818..57918b32f8a47 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
-trait StringRegexExpression extends AutoCastInputTypes {
+trait StringRegexExpression extends ExpectsInputTypes {
   self: BinaryExpression =>
 
   def escape(v: String): String
@@ -111,7 +111,7 @@ case class RLike(left: Expression, right: Expression)
   override def toString: String = s"$left RLIKE $right"
 }
 
-trait CaseConversionExpression extends AutoCastInputTypes {
+trait CaseConversionExpression extends ExpectsInputTypes {
   self: UnaryExpression =>
 
   def convert(v: UTF8String): UTF8String
@@ -154,7 +154,7 @@ case class Lower(child: Expression) extends UnaryExpression with CaseConversionE
 }
 
 /** A base trait for functions that compare two strings, returning a boolean. */
-trait StringComparison extends AutoCastInputTypes {
+trait StringComparison extends ExpectsInputTypes {
   self: BinaryExpression =>
 
   def compare(l: UTF8String, r: UTF8String): Boolean
@@ -215,7 +215,7 @@ case class EndsWith(left: Expression, right: Expression)
  * Defined for String and Binary types.
  */
 case class Substring(str: Expression, pos: Expression, len: Expression)
-  extends Expression with AutoCastInputTypes {
+  extends Expression with ExpectsInputTypes {
 
   def this(str: Expression, pos: Expression) = {
     this(str, pos, Literal(Integer.MAX_VALUE))
@@ -283,7 +283,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
 /**
  * A function that return the length of the given string expression.
  */
-case class StringLength(child: Expression) extends UnaryExpression with AutoCastInputTypes {
+case class StringLength(child: Expression) extends UnaryExpression with ExpectsInputTypes {
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
new file mode 100644
index 0000000000000..43e2f8a46e62e
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -0,0 +1,114 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types
+
+import scala.reflect.ClassTag
+import scala.reflect.runtime.universe.{TypeTag, runtimeMirror}
+
+import org.apache.spark.sql.catalyst.ScalaReflectionLock
+import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.util.Utils
+
+/**
+ * A non-concrete data type, reserved for internal uses.
+ */
+private[sql] abstract class AbstractDataType {
+  private[sql] def defaultConcreteType: DataType
+}
+
+
+/**
+ * An internal type used to represent everything that is not null, UDTs, arrays, structs, and maps.
+ */
+protected[sql] abstract class AtomicType extends DataType {
+  private[sql] type InternalType
+  @transient private[sql] val tag: TypeTag[InternalType]
+  private[sql] val ordering: Ordering[InternalType]
+
+  @transient private[sql] val classTag = ScalaReflectionLock.synchronized {
+    val mirror = runtimeMirror(Utils.getSparkClassLoader)
+    ClassTag[InternalType](mirror.runtimeClass(tag.tpe))
+  }
+}
+
+
+/**
+ * :: DeveloperApi ::
+ * Numeric data types.
+ */
+abstract class NumericType extends AtomicType {
+  // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for
+  // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a
+  // type parameter and add a numeric annotation (i.e., [JvmType : Numeric]). This gets
+  // desugared by the compiler into an argument to the objects constructor. This means there is no
+  // longer an no argument constructor and thus the JVM cannot serialize the object anymore.
+  private[sql] val numeric: Numeric[InternalType]
+}
+
+
+private[sql] object NumericType extends AbstractDataType {
+  /**
+   * Enables matching against NumericType for expressions:
+   * {{{
+   *   case Cast(child @ NumericType(), StringType) =>
+   *     ...
+   * }}}
+   */
+  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType]
+
+  private[sql] override def defaultConcreteType: DataType = IntegerType
+}
+
+
+private[sql] object IntegralType extends AbstractDataType {
+  /**
+   * Enables matching against IntegralType for expressions:
+   * {{{
+   *   case Cast(child @ IntegralType(), StringType) =>
+   *     ...
+   * }}}
+   */
+  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[IntegralType]
+
+  private[sql] override def defaultConcreteType: DataType = IntegerType
+}
+
+
+private[sql] abstract class IntegralType extends NumericType {
+  private[sql] val integral: Integral[InternalType]
+}
+
+
+private[sql] object FractionalType extends AbstractDataType {
+  /**
+   * Enables matching against FractionalType for expressions:
+   * {{{
+   *   case Cast(child @ FractionalType(), StringType) =>
+   *     ...
+   * }}}
+   */
+  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[FractionalType]
+
+  private[sql] override def defaultConcreteType: DataType = DoubleType
+}
+
+
+private[sql] abstract class FractionalType extends NumericType {
+  private[sql] val fractional: Fractional[InternalType]
+  private[sql] val asIntegral: Integral[InternalType]
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index b116163faccad..81553e7fc91a8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -22,9 +22,11 @@ import org.json4s.JsonDSL._
 import org.apache.spark.annotation.DeveloperApi
 
 
-object ArrayType {
+object ArrayType extends AbstractDataType {
   /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is true. */
   def apply(elementType: DataType): ArrayType = ArrayType(elementType, containsNull = true)
+
+  override def defaultConcreteType: DataType = ArrayType(NullType, containsNull = true)
 }
 
 
@@ -41,8 +43,6 @@ object ArrayType {
  *
  * @param elementType The data type of values.
  * @param containsNull Indicates if values have `null` values
- *
- * @group dataType
  */
 @DeveloperApi
 case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala
index 9b58601e5e6ec..f2c6f34ea51c7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala
@@ -29,8 +29,6 @@ import org.apache.spark.sql.catalyst.util.TypeUtils
  * :: DeveloperApi ::
  * The data type representing `Array[Byte]` values.
  * Please use the singleton [[DataTypes.BinaryType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class BinaryType private() extends AtomicType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala
index a7f228cefa57a..2d8ee3d9bc286 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala
@@ -27,8 +27,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 /**
  * :: DeveloperApi ::
  * The data type representing `Boolean` values. Please use the singleton [[DataTypes.BooleanType]].
- *
- *@group dataType
  */
 @DeveloperApi
 class BooleanType private() extends AtomicType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala
index 4d8685796ec76..2ca427975a1cf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala
@@ -27,8 +27,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 /**
  * :: DeveloperApi ::
  * The data type representing `Byte` values. Please use the singleton [[DataTypes.ByteType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class ByteType private() extends IntegralType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index 74677ddfcad65..c333fa70d1ef4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.types
 
-import scala.reflect.ClassTag
-import scala.reflect.runtime.universe.{TypeTag, runtimeMirror}
 import scala.util.parsing.combinator.RegexParsers
 
 import org.json4s._
@@ -27,19 +25,15 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.ScalaReflectionLock
 import org.apache.spark.sql.catalyst.expressions.Expression
-import org.apache.spark.util.Utils
 
 
 /**
  * :: DeveloperApi ::
  * The base type of all Spark SQL data types.
- *
- * @group dataType
  */
 @DeveloperApi
-abstract class DataType {
+abstract class DataType extends AbstractDataType {
   /**
    * Enables matching against DataType for expressions:
    * {{{
@@ -80,84 +74,8 @@ abstract class DataType {
    * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`).
    */
   private[spark] def asNullable: DataType
-}
-
-
-/**
- * An internal type used to represent everything that is not null, UDTs, arrays, structs, and maps.
- */
-protected[sql] abstract class AtomicType extends DataType {
-  private[sql] type InternalType
-  @transient private[sql] val tag: TypeTag[InternalType]
-  private[sql] val ordering: Ordering[InternalType]
-
-  @transient private[sql] val classTag = ScalaReflectionLock.synchronized {
-    val mirror = runtimeMirror(Utils.getSparkClassLoader)
-    ClassTag[InternalType](mirror.runtimeClass(tag.tpe))
-  }
-}
-
-
-/**
- * :: DeveloperApi ::
- * Numeric data types.
- *
- * @group dataType
- */
-abstract class NumericType extends AtomicType {
-  // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for
-  // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a
-  // type parameter and add a numeric annotation (i.e., [JvmType : Numeric]). This gets
-  // desugared by the compiler into an argument to the objects constructor. This means there is no
-  // longer an no argument constructor and thus the JVM cannot serialize the object anymore.
-  private[sql] val numeric: Numeric[InternalType]
-}
-
-
-private[sql] object NumericType {
-  /**
-   * Enables matching against NumericType for expressions:
-   * {{{
-   *   case Cast(child @ NumericType(), StringType) =>
-   *     ...
-   * }}}
-   */
-  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType]
-}
-
-
-private[sql] object IntegralType {
-  /**
-   * Enables matching against IntegralType for expressions:
-   * {{{
-   *   case Cast(child @ IntegralType(), StringType) =>
-   *     ...
-   * }}}
-   */
-  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[IntegralType]
-}
-
-
-private[sql] abstract class IntegralType extends NumericType {
-  private[sql] val integral: Integral[InternalType]
-}
-
-
-private[sql] object FractionalType {
-  /**
-   * Enables matching against FractionalType for expressions:
-   * {{{
-   *   case Cast(child @ FractionalType(), StringType) =>
-   *     ...
-   * }}}
-   */
-  def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[FractionalType]
-}
-
 
-private[sql] abstract class FractionalType extends NumericType {
-  private[sql] val fractional: Fractional[InternalType]
-  private[sql] val asIntegral: Integral[InternalType]
+  override def defaultConcreteType: DataType = this
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala
index 03f0644bc784c..1d73e40ffcd36 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala
@@ -26,10 +26,11 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 
 /**
  * :: DeveloperApi ::
- * The data type representing `java.sql.Date` values.
+ * A date type, supporting "0001-01-01" through "9999-12-31".
+ *
  * Please use the singleton [[DataTypes.DateType]].
  *
- * @group dataType
+ * Internally, this is represented as the number of days from epoch (1970-01-01 00:00:00 UTC).
  */
 @DeveloperApi
 class DateType private() extends AtomicType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index 18cdfa7238f39..06373a095b1b0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -39,8 +39,6 @@ case class PrecisionInfo(precision: Int, scale: Int) {
  * A Decimal that might have fixed precision and scale, or unlimited values for these.
  *
  * Please use [[DataTypes.createDecimalType()]] to create a specific instance.
- *
- * @group dataType
  */
 @DeveloperApi
 case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalType {
@@ -84,7 +82,10 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT
 
 
 /** Extra factory methods and pattern matchers for Decimals */
-object DecimalType {
+object DecimalType extends AbstractDataType {
+
+  private[sql] override def defaultConcreteType: DataType = Unlimited
+
   val Unlimited: DecimalType = DecimalType(None)
 
   private[sql] object Fixed {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala
index 66766623213c9..986c2ab055386 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala
@@ -27,8 +27,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 /**
  * :: DeveloperApi ::
  * The data type representing `Double` values. Please use the singleton [[DataTypes.DoubleType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class DoubleType private() extends FractionalType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala
index 1d5a2f4f6f86c..9bd48ece83a1c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala
@@ -27,8 +27,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 /**
  * :: DeveloperApi ::
  * The data type representing `Float` values. Please use the singleton [[DataTypes.FloatType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class FloatType private() extends FractionalType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala
index 74e464c082873..a2c6e19b05b3c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala
@@ -27,8 +27,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 /**
  * :: DeveloperApi ::
  * The data type representing `Int` values. Please use the singleton [[DataTypes.IntegerType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class IntegerType private() extends IntegralType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala
index 390675782e5fd..2b3adf6ade83b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala
@@ -26,8 +26,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 /**
  * :: DeveloperApi ::
  * The data type representing `Long` values. Please use the singleton [[DataTypes.LongType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class LongType private() extends IntegralType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
index cfdf493074415..69c2119e23436 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
@@ -30,8 +30,6 @@ import org.json4s.JsonDSL._
  * @param keyType The data type of map keys.
  * @param valueType The data type of map values.
  * @param valueContainsNull Indicates if map values have `null` values.
- *
- * @group dataType
  */
 case class MapType(
   keyType: DataType,
@@ -69,7 +67,10 @@ case class MapType(
 }
 
 
-object MapType {
+object MapType extends AbstractDataType {
+
+  private[sql] override def defaultConcreteType: DataType = apply(NullType, NullType)
+
   /**
    * Construct a [[MapType]] object with the given key type and value type.
    * The `valueContainsNull` is true.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala
index b64b07431fa96..aa84115c2e42c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala
@@ -23,8 +23,6 @@ import org.apache.spark.annotation.DeveloperApi
 /**
  * :: DeveloperApi ::
  * The data type representing `NULL` values. Please use the singleton [[DataTypes.NullType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class NullType private() extends DataType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala
index 73e9ec780b0af..a13119e659064 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala
@@ -26,8 +26,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
 /**
  * :: DeveloperApi ::
  * The data type representing `Short` values. Please use the singleton [[DataTypes.ShortType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class ShortType private() extends IntegralType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala
index 1e9476ad06656..a7627a2de1611 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala
@@ -27,8 +27,6 @@ import org.apache.spark.unsafe.types.UTF8String
 /**
  * :: DeveloperApi ::
  * The data type representing `String` values. Please use the singleton [[DataTypes.StringType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class StringType private() extends AtomicType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 2db0a359e9db5..6fedeabf23203 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -87,8 +87,6 @@ import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute}
  * val row = Row(Row(1, 2, true))
  * // row: Row = [[1,2,true]]
  * }}}
- *
- * @group dataType
  */
 @DeveloperApi
 case class StructType(fields: Array[StructField]) extends DataType with Seq[StructField] {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala
index a558641fcfed7..de4b511edccd9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala
@@ -28,8 +28,6 @@ import org.apache.spark.sql.catalyst.ScalaReflectionLock
  * :: DeveloperApi ::
  * The data type representing `java.sql.Timestamp` values.
  * Please use the singleton [[DataTypes.TimestampType]].
- *
- * @group dataType
  */
 @DeveloperApi
 class TimestampType private() extends AtomicType {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index eae3666595a38..498fd86a06fd9 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -26,6 +26,31 @@ import org.apache.spark.sql.types._
 
 class HiveTypeCoercionSuite extends PlanTest {
 
+  test("implicit type cast") {
+    def shouldCast(from: DataType, to: AbstractDataType): Unit = {
+      val got = HiveTypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
+      assert(got.dataType === to.defaultConcreteType)
+    }
+
+    // TODO: write the entire implicit cast table out for test cases.
+    shouldCast(ByteType, IntegerType)
+    shouldCast(IntegerType, IntegerType)
+    shouldCast(IntegerType, LongType)
+    shouldCast(IntegerType, DecimalType.Unlimited)
+    shouldCast(LongType, IntegerType)
+    shouldCast(LongType, DecimalType.Unlimited)
+
+    shouldCast(DateType, TimestampType)
+    shouldCast(TimestampType, DateType)
+
+    shouldCast(StringType, IntegerType)
+    shouldCast(StringType, DateType)
+    shouldCast(StringType, TimestampType)
+    shouldCast(IntegerType, StringType)
+    shouldCast(DateType, StringType)
+    shouldCast(TimestampType, StringType)
+  }
+
   test("tightest common bound for types") {
     def widenTest(t1: DataType, t2: DataType, tightestCommon: Option[DataType]) {
       var found = HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2)

From 7d9cc9673e47227f58411ca1f4e647cd8233a219 Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Thu, 2 Jul 2015 15:00:13 -0700
Subject: [PATCH 0205/1454] [SPARK-3382] [MLLIB] GradientDescent convergence
 tolerance

GrandientDescent can receive convergence tolerance value. Default value is 0.0.
When loss value becomes less than the tolerance which is set by user, iteration is terminated.

Author: lewuathe <lewuathe@me.com>

Closes #3636 from Lewuathe/gd-convergence-tolerance and squashes the following commits:

0b8a9a8 [lewuathe] Update doc
ce91b15 [lewuathe] Merge branch 'master' into gd-convergence-tolerance
4f22c2b [lewuathe] Modify based on SPARK-1503
5e47b82 [lewuathe] Merge branch 'master' into gd-convergence-tolerance
abadb7e [lewuathe] Fix LassoSuite
8fadebd [lewuathe] Fix failed unit tests
ee5de46 [lewuathe] Merge branch 'master' into gd-convergence-tolerance
8313ba2 [lewuathe] Fix styles
0ead94c [lewuathe] Merge branch 'master' into gd-convergence-tolerance
a94cfd5 [lewuathe] Modify some styles
3aef0a2 [lewuathe] Modify converged logic to do relative comparison
f7b19d5 [lewuathe] [SPARK-3382] Clarify comparison logic
e6c9cd2 [lewuathe] [SPARK-3382] Compare with the diff of solution vector
4b125d2 [lewuathe] [SPARK3382] Fix scala style
e7c10dd [lewuathe] [SPARK-3382] format improvements
f867eea [lewuathe] [SPARK-3382] Modify warning message statements
b9d5e61 [lewuathe] [SPARK-3382] should compare diff inside loss history and convergence tolerance
5433f71 [lewuathe] [SPARK-3382] GradientDescent convergence tolerance
---
 .../mllib/optimization/GradientDescent.scala  | 105 +++++++++++++++---
 .../StreamingLinearRegressionWithSGD.scala    |   6 +
 .../LogisticRegressionSuite.scala             |   1 +
 .../optimization/GradientDescentSuite.scala   |  45 +++++++-
 .../spark/mllib/optimization/LBFGSSuite.scala |   6 +-
 .../spark/mllib/regression/LassoSuite.scala   |   2 +-
 .../StreamingLinearRegressionSuite.scala      |   1 +
 7 files changed, 144 insertions(+), 22 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index 06e45e10c5bf4..ab7611fd077ef 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -19,13 +19,14 @@ package org.apache.spark.mllib.optimization
 
 import scala.collection.mutable.ArrayBuffer
 
-import breeze.linalg.{DenseVector => BDV}
+import breeze.linalg.{DenseVector => BDV, norm}
 
 import org.apache.spark.annotation.{Experimental, DeveloperApi}
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
+
 /**
  * Class used to solve an optimization problem using Gradient Descent.
  * @param gradient Gradient function to be used.
@@ -38,6 +39,7 @@ class GradientDescent private[mllib] (private var gradient: Gradient, private va
   private var numIterations: Int = 100
   private var regParam: Double = 0.0
   private var miniBatchFraction: Double = 1.0
+  private var convergenceTol: Double = 0.001
 
   /**
    * Set the initial step size of SGD for the first step. Default 1.0.
@@ -75,6 +77,23 @@ class GradientDescent private[mllib] (private var gradient: Gradient, private va
     this
   }
 
+  /**
+   * Set the convergence tolerance. Default 0.001
+   * convergenceTol is a condition which decides iteration termination.
+   * The end of iteration is decided based on below logic.
+   * - If the norm of the new solution vector is >1, the diff of solution vectors
+   *   is compared to relative tolerance which means normalizing by the norm of
+   *   the new solution vector.
+   * - If the norm of the new solution vector is <=1, the diff of solution vectors
+   *   is compared to absolute tolerance which is not normalizing.
+   * Must be between 0.0 and 1.0 inclusively.
+   */
+  def setConvergenceTol(tolerance: Double): this.type = {
+    require(0.0 <= tolerance && tolerance <= 1.0)
+    this.convergenceTol = tolerance
+    this
+  }
+
   /**
    * Set the gradient function (of the loss function of one single data example)
    * to be used for SGD.
@@ -112,7 +131,8 @@ class GradientDescent private[mllib] (private var gradient: Gradient, private va
       numIterations,
       regParam,
       miniBatchFraction,
-      initialWeights)
+      initialWeights,
+      convergenceTol)
     weights
   }
 
@@ -131,17 +151,20 @@ object GradientDescent extends Logging {
    * Sampling, and averaging the subgradients over this subset is performed using one standard
    * spark map-reduce in each iteration.
    *
-   * @param data - Input data for SGD. RDD of the set of data examples, each of
-   *               the form (label, [feature values]).
-   * @param gradient - Gradient object (used to compute the gradient of the loss function of
-   *                   one single data example)
-   * @param updater - Updater function to actually perform a gradient step in a given direction.
-   * @param stepSize - initial step size for the first step
-   * @param numIterations - number of iterations that SGD should be run.
-   * @param regParam - regularization parameter
-   * @param miniBatchFraction - fraction of the input data set that should be used for
-   *                            one iteration of SGD. Default value 1.0.
-   *
+   * @param data Input data for SGD. RDD of the set of data examples, each of
+   *             the form (label, [feature values]).
+   * @param gradient Gradient object (used to compute the gradient of the loss function of
+   *                 one single data example)
+   * @param updater Updater function to actually perform a gradient step in a given direction.
+   * @param stepSize initial step size for the first step
+   * @param numIterations number of iterations that SGD should be run.
+   * @param regParam regularization parameter
+   * @param miniBatchFraction fraction of the input data set that should be used for
+   *                          one iteration of SGD. Default value 1.0.
+   * @param convergenceTol Minibatch iteration will end before numIterations if the relative
+   *                       difference between the current weight and the previous weight is less
+   *                       than this value. In measuring convergence, L2 norm is calculated.
+   *                       Default value 0.001. Must be between 0.0 and 1.0 inclusively.
    * @return A tuple containing two elements. The first element is a column matrix containing
    *         weights for every feature, and the second element is an array containing the
    *         stochastic loss computed for every iteration.
@@ -154,9 +177,20 @@ object GradientDescent extends Logging {
       numIterations: Int,
       regParam: Double,
       miniBatchFraction: Double,
-      initialWeights: Vector): (Vector, Array[Double]) = {
+      initialWeights: Vector,
+      convergenceTol: Double): (Vector, Array[Double]) = {
+
+    // convergenceTol should be set with non minibatch settings
+    if (miniBatchFraction < 1.0 && convergenceTol > 0.0) {
+      logWarning("Testing against a convergenceTol when using miniBatchFraction " +
+        "< 1.0 can be unstable because of the stochasticity in sampling.")
+    }
 
     val stochasticLossHistory = new ArrayBuffer[Double](numIterations)
+    // Record previous weight and current one to calculate solution vector difference
+
+    var previousWeights: Option[Vector] = None
+    var currentWeights: Option[Vector] = None
 
     val numExamples = data.count()
 
@@ -181,7 +215,9 @@ object GradientDescent extends Logging {
     var regVal = updater.compute(
       weights, Vectors.zeros(weights.size), 0, 1, regParam)._2
 
-    for (i <- 1 to numIterations) {
+    var converged = false // indicates whether converged based on convergenceTol
+    var i = 1
+    while (!converged && i <= numIterations) {
       val bcWeights = data.context.broadcast(weights)
       // Sample a subset (fraction miniBatchFraction) of the total data
       // compute and sum up the subgradients on this subset (this is one map-reduce)
@@ -204,12 +240,21 @@ object GradientDescent extends Logging {
          */
         stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
         val update = updater.compute(
-          weights, Vectors.fromBreeze(gradientSum / miniBatchSize.toDouble), stepSize, i, regParam)
+          weights, Vectors.fromBreeze(gradientSum / miniBatchSize.toDouble),
+          stepSize, i, regParam)
         weights = update._1
         regVal = update._2
+
+        previousWeights = currentWeights
+        currentWeights = Some(weights)
+        if (previousWeights != None && currentWeights != None) {
+          converged = isConverged(previousWeights.get,
+            currentWeights.get, convergenceTol)
+        }
       } else {
         logWarning(s"Iteration ($i/$numIterations). The size of sampled batch is zero")
       }
+      i += 1
     }
 
     logInfo("GradientDescent.runMiniBatchSGD finished. Last 10 stochastic losses %s".format(
@@ -218,4 +263,32 @@ object GradientDescent extends Logging {
     (weights, stochasticLossHistory.toArray)
 
   }
+
+  def runMiniBatchSGD(
+      data: RDD[(Double, Vector)],
+      gradient: Gradient,
+      updater: Updater,
+      stepSize: Double,
+      numIterations: Int,
+      regParam: Double,
+      miniBatchFraction: Double,
+      initialWeights: Vector): (Vector, Array[Double]) =
+    GradientDescent.runMiniBatchSGD(data, gradient, updater, stepSize, numIterations,
+                                    regParam, miniBatchFraction, initialWeights, 0.001)
+
+
+  private def isConverged(
+      previousWeights: Vector,
+      currentWeights: Vector,
+      convergenceTol: Double): Boolean = {
+    // To compare with convergence tolerance.
+    val previousBDV = previousWeights.toBreeze.toDenseVector
+    val currentBDV = currentWeights.toBreeze.toDenseVector
+
+    // This represents the difference of updated weights in the iteration.
+    val solutionVecDiff: Double = norm(previousBDV - currentBDV)
+
+    solutionVecDiff < convergenceTol * Math.max(norm(currentBDV), 1.0)
+  }
+
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index 235e043c7754b..c6d04464a12ba 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -85,4 +85,10 @@ class StreamingLinearRegressionWithSGD private[mllib] (
     this
   }
 
+  /** Set the convergence tolerance. */
+  def setConvergenceTol(tolerance: Double): this.type = {
+    this.algorithm.optimizer.setConvergenceTol(tolerance)
+    this
+  }
+
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index e8f3d0c4db20a..2473510e13514 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -196,6 +196,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w
       .setStepSize(10.0)
       .setRegParam(0.0)
       .setNumIterations(20)
+      .setConvergenceTol(0.0005)
 
     val model = lr.run(testRDD)
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
index a5a59e9fad5ae..13b754a03943a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -25,7 +25,7 @@ import org.scalatest.Matchers
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext}
+import org.apache.spark.mllib.util.{MLUtils, LocalClusterSparkContext, MLlibTestSparkContext}
 import org.apache.spark.mllib.util.TestingUtils._
 
 object GradientDescentSuite {
@@ -82,11 +82,11 @@ class GradientDescentSuite extends SparkFunSuite with MLlibTestSparkContext with
     // Add a extra variable consisting of all 1.0's for the intercept.
     val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42)
     val data = testData.map { case LabeledPoint(label, features) =>
-      label -> Vectors.dense(1.0 +: features.toArray)
+      label -> MLUtils.appendBias(features)
     }
 
     val dataRDD = sc.parallelize(data, 2).cache()
-    val initialWeightsWithIntercept = Vectors.dense(1.0 +: initialWeights.toArray)
+    val initialWeightsWithIntercept = Vectors.dense(initialWeights.toArray :+ 1.0)
 
     val (_, loss) = GradientDescent.runMiniBatchSGD(
       dataRDD,
@@ -139,6 +139,45 @@ class GradientDescentSuite extends SparkFunSuite with MLlibTestSparkContext with
       "The different between newWeights with/without regularization " +
         "should be initialWeightsWithIntercept.")
   }
+
+  test("iteration should end with convergence tolerance") {
+    val nPoints = 10000
+    val A = 2.0
+    val B = -1.5
+
+    val initialB = -1.0
+    val initialWeights = Array(initialB)
+
+    val gradient = new LogisticGradient()
+    val updater = new SimpleUpdater()
+    val stepSize = 1.0
+    val numIterations = 10
+    val regParam = 0
+    val miniBatchFrac = 1.0
+    val convergenceTolerance = 5.0e-1
+
+    // Add a extra variable consisting of all 1.0's for the intercept.
+    val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42)
+    val data = testData.map { case LabeledPoint(label, features) =>
+      label -> MLUtils.appendBias(features)
+    }
+
+    val dataRDD = sc.parallelize(data, 2).cache()
+    val initialWeightsWithIntercept = Vectors.dense(initialWeights.toArray :+ 1.0)
+
+    val (_, loss) = GradientDescent.runMiniBatchSGD(
+      dataRDD,
+      gradient,
+      updater,
+      stepSize,
+      numIterations,
+      regParam,
+      miniBatchFrac,
+      initialWeightsWithIntercept,
+      convergenceTolerance)
+
+    assert(loss.length < numIterations, "convergenceTolerance failed to stop optimization early")
+  }
 }
 
 class GradientDescentClusterSuite extends SparkFunSuite with LocalClusterSparkContext {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index d07b9d5b89227..75ae0eb32fb7b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -122,7 +122,8 @@ class LBFGSSuite extends SparkFunSuite with MLlibTestSparkContext with Matchers
       numGDIterations,
       regParam,
       miniBatchFrac,
-      initialWeightsWithIntercept)
+      initialWeightsWithIntercept,
+      convergenceTol)
 
     assert(lossGD(0) ~= lossLBFGS(0) absTol 1E-5,
       "The first losses of LBFGS and GD should be the same.")
@@ -221,7 +222,8 @@ class LBFGSSuite extends SparkFunSuite with MLlibTestSparkContext with Matchers
       numGDIterations,
       regParam,
       miniBatchFrac,
-      initialWeightsWithIntercept)
+      initialWeightsWithIntercept,
+      convergenceTol)
 
     // for class LBFGS and the optimize method, we only look at the weights
     assert(
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
index 08a152ffc7a23..39537e7bb4c72 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
@@ -100,7 +100,7 @@ class LassoSuite extends SparkFunSuite with MLlibTestSparkContext {
     val testRDD = sc.parallelize(testData, 2).cache()
 
     val ls = new LassoWithSGD()
-    ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(40)
+    ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(40).setConvergenceTol(0.0005)
 
     val model = ls.run(testRDD, initialWeights)
     val weight0 = model.weights(0)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
index f5e2d31056cbd..a2a4c5f6b8b70 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
@@ -53,6 +53,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase {
       .setInitialWeights(Vectors.dense(0.0, 0.0))
       .setStepSize(0.2)
       .setNumIterations(25)
+      .setConvergenceTol(0.0001)
 
     // generate sequence of simulated data
     val numBatches = 10

From fc7aebd94a3c09657fc4dbded0997ed068304e0a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 2 Jul 2015 15:43:02 -0700
Subject: [PATCH 0206/1454] [SPARK-8784] [SQL] Add Python API for hex and unhex

Also improve the performance of hex/unhex

Author: Davies Liu <davies@databricks.com>

Closes #7181 from davies/hex and squashes the following commits:

f032fbb [Davies Liu] Merge branch 'hex' of github.com:davies/spark into hex
49e325f [Davies Liu] Merge branch 'master' of github.com:apache/spark into hex
b31fc9a [Davies Liu] Update math.scala
25156b7 [Davies Liu] address comments and fix test
c3af78c [Davies Liu] address commments
1a24082 [Davies Liu] Add Python API for hex and unhex
---
 python/pyspark/sql/functions.py               |  28 ++++
 .../catalyst/analysis/FunctionRegistry.scala  |   2 +-
 .../spark/sql/catalyst/expressions/math.scala | 142 +++++++++---------
 .../expressions/MathFunctionsSuite.scala      |  18 ++-
 .../org/apache/spark/sql/functions.scala      |   2 +-
 5 files changed, 115 insertions(+), 77 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 12263e6a75af8..8a470ce19bc30 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -381,6 +381,34 @@ def randn(seed=None):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def hex(col):
+    """Computes hex value of the given column, which could be StringType,
+    BinaryType, IntegerType or LongType.
+
+    >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
+    [Row(hex(a)=u'414243', hex(b)=u'3')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.hex(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def unhex(col):
+    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
+    and converts to the byte representation of number.
+
+    >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
+    [Row(unhex(a)=bytearray(b'ABC'))]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.unhex(_to_java_column(col))
+    return Column(jc)
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def sha1(col):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index e7e4d1c4efe18..ca87bcc4c4aab 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -160,7 +160,7 @@ object FunctionRegistry {
     expression[Substring]("substr"),
     expression[Substring]("substring"),
     expression[Upper]("ucase"),
-    expression[UnHex]("unhex"),
+    expression[Unhex]("unhex"),
     expression[Upper]("upper")
   )
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 035980da568d3..1e095149f1166 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -227,6 +227,20 @@ case class Bin(child: Expression)
   }
 }
 
+object Hex {
+  val hexDigits = Array[Char](
+    '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F'
+  ).map(_.toByte)
+
+  // lookup table to translate '0' -> 0 ... 'F'/'f' -> 15
+  val unhexDigits = {
+    val array = Array.fill[Byte](128)(-1)
+    (0 to 9).foreach(i => array('0' + i) = i.toByte)
+    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
+    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
+    array
+  }
+}
 
 /**
  * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
@@ -258,30 +272,18 @@ case class Hex(child: Expression) extends UnaryExpression with Serializable  {
         case LongType => hex(num.asInstanceOf[Long])
         case IntegerType => hex(num.asInstanceOf[Integer].toLong)
         case BinaryType => hex(num.asInstanceOf[Array[Byte]])
-        case StringType => hex(num.asInstanceOf[UTF8String])
+        case StringType => hex(num.asInstanceOf[UTF8String].getBytes)
       }
     }
   }
 
-  /**
-   * Converts every character in s to two hex digits.
-   */
-  private def hex(str: UTF8String): UTF8String = {
-    hex(str.getBytes)
-  }
-
-  private def hex(bytes: Array[Byte]): UTF8String = {
-    doHex(bytes, bytes.length)
-  }
-
-  private def doHex(bytes: Array[Byte], length: Int): UTF8String = {
+  private[this] def hex(bytes: Array[Byte]): UTF8String = {
+    val length = bytes.length
     val value = new Array[Byte](length * 2)
     var i = 0
     while (i < length) {
-      value(i * 2) = Character.toUpperCase(Character.forDigit(
-        (bytes(i) & 0xF0) >>> 4, 16)).toByte
-      value(i * 2 + 1) = Character.toUpperCase(Character.forDigit(
-        bytes(i) & 0x0F, 16)).toByte
+      value(i * 2) = Hex.hexDigits((bytes(i) & 0xF0) >> 4)
+      value(i * 2 + 1) = Hex.hexDigits(bytes(i) & 0x0F)
       i += 1
     }
     UTF8String.fromBytes(value)
@@ -294,14 +296,64 @@ case class Hex(child: Expression) extends UnaryExpression with Serializable  {
     var len = 0
     do {
       len += 1
-      value(value.length - len) = Character.toUpperCase(Character
-        .forDigit((numBuf & 0xF).toInt, 16)).toByte
+      value(value.length - len) = Hex.hexDigits((numBuf & 0xF).toInt)
       numBuf >>>= 4
     } while (numBuf != 0)
     UTF8String.fromBytes(Arrays.copyOfRange(value, value.length - len, value.length))
   }
 }
 
+/**
+ * Performs the inverse operation of HEX.
+ * Resulting characters are returned as a byte array.
+ */
+case class Unhex(child: Expression)
+  extends UnaryExpression with ExpectsInputTypes with Serializable {
+
+  override def nullable: Boolean = true
+  override def dataType: DataType = BinaryType
+  override def inputTypes: Seq[DataType] = Seq(BinaryType)
+
+  override def eval(input: InternalRow): Any = {
+    val num = child.eval(input)
+    if (num == null) {
+      null
+    } else {
+      unhex(num.asInstanceOf[UTF8String].getBytes)
+    }
+  }
+
+  private[this] def unhex(bytes: Array[Byte]): Array[Byte] = {
+    val out = new Array[Byte]((bytes.length + 1) >> 1)
+    var i = 0
+    if ((bytes.length & 0x01) != 0) {
+      // padding with '0'
+      if (bytes(0) < 0) {
+        return null
+      }
+      val v = Hex.unhexDigits(bytes(0))
+      if (v == -1) {
+        return null
+      }
+      out(0) = v
+      i += 1
+    }
+    // two characters form the hex value.
+    while (i < bytes.length) {
+      if (bytes(i) < 0 || bytes(i + 1) < 0) {
+        return null
+      }
+      val first = Hex.unhexDigits(bytes(i))
+      val second = Hex.unhexDigits(bytes(i + 1))
+      if (first == -1 || second == -1) {
+        return null
+      }
+      out(i / 2) = (((first << 4) | second) & 0xFF).toByte
+      i += 2
+    }
+    out
+  }
+}
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 ////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -448,58 +500,6 @@ case class ShiftRight(left: Expression, right: Expression) extends BinaryExpress
   }
 }
 
-/**
- * Performs the inverse operation of HEX.
- * Resulting characters are returned as a byte array.
- */
-case class UnHex(child: Expression) extends UnaryExpression with Serializable {
-
-  override def dataType: DataType = BinaryType
-
-  override def checkInputDataTypes(): TypeCheckResult = {
-    if (child.dataType.isInstanceOf[StringType] || child.dataType == NullType) {
-      TypeCheckResult.TypeCheckSuccess
-    } else {
-      TypeCheckResult.TypeCheckFailure(s"unHex accepts String type, not ${child.dataType}")
-    }
-  }
-
-  override def eval(input: InternalRow): Any = {
-    val num = child.eval(input)
-    if (num == null) {
-      null
-    } else {
-      unhex(num.asInstanceOf[UTF8String].getBytes)
-    }
-  }
-
-  private val unhexDigits = {
-    val array = Array.fill[Byte](128)(-1)
-    (0 to 9).foreach(i => array('0' + i) = i.toByte)
-    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
-    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
-    array
-  }
-
-  private def unhex(inputBytes: Array[Byte]): Array[Byte] = {
-    var bytes = inputBytes
-    if ((bytes.length & 0x01) != 0) {
-      bytes = '0'.toByte +: bytes
-    }
-    val out = new Array[Byte](bytes.length >> 1)
-    // two characters form the hex value.
-    var i = 0
-    while (i < bytes.length) {
-        val first = unhexDigits(bytes(i))
-        val second = unhexDigits(bytes(i + 1))
-        if (first == -1 || second == -1) { return null}
-        out(i / 2) = (((first << 4) | second) & 0xFF).toByte
-        i += 2
-    }
-    out
-  }
-}
-
 case class Hypot(left: Expression, right: Expression)
   extends BinaryMathExpression(math.hypot, "HYPOT")
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index aa27fe3cd5564..550c6e3cc9f0b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{IntegerType, DataType, DoubleType, LongType}
+import org.apache.spark.sql.types._
 
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -252,11 +252,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("hex") {
+    checkEvaluation(Hex(Literal.create(null, IntegerType)), null)
     checkEvaluation(Hex(Literal(28)), "1C")
     checkEvaluation(Hex(Literal(-28)), "FFFFFFFFFFFFFFE4")
+    checkEvaluation(Hex(Literal.create(null, LongType)), null)
     checkEvaluation(Hex(Literal(100800200404L)), "177828FED4")
     checkEvaluation(Hex(Literal(-100800200404L)), "FFFFFFE887D7012C")
+    checkEvaluation(Hex(Literal.create(null, StringType)), null)
     checkEvaluation(Hex(Literal("helloHex")), "68656C6C6F486578")
+    checkEvaluation(Hex(Literal.create(null, BinaryType)), null)
     checkEvaluation(Hex(Literal("helloHex".getBytes())), "68656C6C6F486578")
     // scalastyle:off
     // Turn off scala style for non-ascii chars
@@ -265,9 +269,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("unhex") {
-    checkEvaluation(UnHex(Literal("737472696E67")), "string".getBytes)
-    checkEvaluation(UnHex(Literal("")), new Array[Byte](0))
-    checkEvaluation(UnHex(Literal("0")), Array[Byte](0))
+    checkEvaluation(Unhex(Literal.create(null, StringType)), null)
+    checkEvaluation(Unhex(Literal("737472696E67")), "string".getBytes)
+    checkEvaluation(Unhex(Literal("")), new Array[Byte](0))
+    checkEvaluation(Unhex(Literal("F")), Array[Byte](15))
+    checkEvaluation(Unhex(Literal("ff")), Array[Byte](-1))
+    // scalastyle:off
+    // Turn off scala style for non-ascii chars
+    checkEvaluation(Unhex(Literal("E4B889E9878DE79A84")), "三重的".getBytes("UTF-8"))
+    // scalastyle:on
   }
 
   test("hypot") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4ee1fb8374b07..4b1353fc32c35 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1061,7 +1061,7 @@ object functions {
    * @group math_funcs
    * @since 1.5.0
    */
-  def unhex(column: Column): Column = UnHex(column.expr)
+  def unhex(column: Column): Column = Unhex(column.expr)
 
   /**
    * Inverse of hex. Interprets each pair of characters as a hexadecimal number

From 488bad319a70975733e83c83490240a70beb0c90 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Thu, 2 Jul 2015 15:55:16 -0700
Subject: [PATCH 0207/1454] [SPARK-7104] [MLLIB] Support model save/load in
 Python's Word2Vec

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6821 from yu-iskw/SPARK-7104 and squashes the following commits:

975136b [Yu ISHIKAWA] Organize import
0ef58b6 [Yu ISHIKAWA] Use rmtree, instead of removedirs
cb21653 [Yu ISHIKAWA] Add an explicit type for `Word2VecModelWrapper.save`
1d468ef [Yu ISHIKAWA] [SPARK-7104][MLlib] Support model save/load in Python's Word2Vec
---
 .../mllib/api/python/PythonMLLibAPI.scala     |  3 +++
 python/pyspark/mllib/feature.py               | 21 ++++++++++++++++++-
 2 files changed, 23 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 458fab48fef5a..e628059c4af8e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -28,6 +28,7 @@ import scala.reflect.ClassTag
 
 import net.razorvine.pickle._
 
+import org.apache.spark.SparkContext
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.api.python.SerDeUtil
 import org.apache.spark.mllib.classification._
@@ -641,6 +642,8 @@ private[python] class PythonMLLibAPI extends Serializable {
     def getVectors: JMap[String, JList[Float]] = {
       model.getVectors.map({case (k, v) => (k, v.toList.asJava)}).asJava
     }
+
+    def save(sc: SparkContext, path: String): Unit = model.save(sc, path)
   }
 
   /**
diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index b5138773fd61b..f921e3ad1a314 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -36,6 +36,7 @@
 from pyspark.mllib.linalg import (
     Vector, Vectors, DenseVector, SparseVector, _convert_to_vector)
 from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.util import JavaLoader, JavaSaveable
 
 __all__ = ['Normalizer', 'StandardScalerModel', 'StandardScaler',
            'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel',
@@ -416,7 +417,7 @@ def fit(self, dataset):
         return IDFModel(jmodel)
 
 
-class Word2VecModel(JavaVectorTransformer):
+class Word2VecModel(JavaVectorTransformer, JavaSaveable, JavaLoader):
     """
     class for Word2Vec model
     """
@@ -455,6 +456,12 @@ def getVectors(self):
         """
         return self.call("getVectors")
 
+    @classmethod
+    def load(cls, sc, path):
+        jmodel = sc._jvm.org.apache.spark.mllib.feature \
+            .Word2VecModel.load(sc._jsc.sc(), path)
+        return Word2VecModel(jmodel)
+
 
 @ignore_unicode_prefix
 class Word2Vec(object):
@@ -488,6 +495,18 @@ class Word2Vec(object):
     >>> syms = model.findSynonyms(vec, 2)
     >>> [s[0] for s in syms]
     [u'b', u'c']
+
+    >>> import os, tempfile
+    >>> path = tempfile.mkdtemp()
+    >>> model.save(sc, path)
+    >>> sameModel = Word2VecModel.load(sc, path)
+    >>> model.transform("a") == sameModel.transform("a")
+    True
+    >>> from shutil import rmtree
+    >>> try:
+    ...     rmtree(path)
+    ... except OSError:
+    ...     pass
     """
     def __init__(self):
         """

From e589e71a2914588985eaea799b52e2f6b4f1e9ae Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 2 Jul 2015 16:25:10 -0700
Subject: [PATCH 0208/1454] Revert "[SPARK-8784] [SQL] Add Python API for hex
 and unhex"

This reverts commit fc7aebd94a3c09657fc4dbded0997ed068304e0a.
---
 python/pyspark/sql/functions.py               |  28 ----
 .../catalyst/analysis/FunctionRegistry.scala  |   2 +-
 .../spark/sql/catalyst/expressions/math.scala | 142 +++++++++---------
 .../expressions/MathFunctionsSuite.scala      |  18 +--
 .../org/apache/spark/sql/functions.scala      |   2 +-
 5 files changed, 77 insertions(+), 115 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 8a470ce19bc30..12263e6a75af8 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -381,34 +381,6 @@ def randn(seed=None):
     return Column(jc)
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def hex(col):
-    """Computes hex value of the given column, which could be StringType,
-    BinaryType, IntegerType or LongType.
-
-    >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
-    [Row(hex(a)=u'414243', hex(b)=u'3')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.hex(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def unhex(col):
-    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
-    and converts to the byte representation of number.
-
-    >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
-    [Row(unhex(a)=bytearray(b'ABC'))]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.unhex(_to_java_column(col))
-    return Column(jc)
-
-
 @ignore_unicode_prefix
 @since(1.5)
 def sha1(col):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index ca87bcc4c4aab..e7e4d1c4efe18 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -160,7 +160,7 @@ object FunctionRegistry {
     expression[Substring]("substr"),
     expression[Substring]("substring"),
     expression[Upper]("ucase"),
-    expression[Unhex]("unhex"),
+    expression[UnHex]("unhex"),
     expression[Upper]("upper")
   )
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 1e095149f1166..035980da568d3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -227,20 +227,6 @@ case class Bin(child: Expression)
   }
 }
 
-object Hex {
-  val hexDigits = Array[Char](
-    '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F'
-  ).map(_.toByte)
-
-  // lookup table to translate '0' -> 0 ... 'F'/'f' -> 15
-  val unhexDigits = {
-    val array = Array.fill[Byte](128)(-1)
-    (0 to 9).foreach(i => array('0' + i) = i.toByte)
-    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
-    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
-    array
-  }
-}
 
 /**
  * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
@@ -272,18 +258,30 @@ case class Hex(child: Expression) extends UnaryExpression with Serializable  {
         case LongType => hex(num.asInstanceOf[Long])
         case IntegerType => hex(num.asInstanceOf[Integer].toLong)
         case BinaryType => hex(num.asInstanceOf[Array[Byte]])
-        case StringType => hex(num.asInstanceOf[UTF8String].getBytes)
+        case StringType => hex(num.asInstanceOf[UTF8String])
       }
     }
   }
 
-  private[this] def hex(bytes: Array[Byte]): UTF8String = {
-    val length = bytes.length
+  /**
+   * Converts every character in s to two hex digits.
+   */
+  private def hex(str: UTF8String): UTF8String = {
+    hex(str.getBytes)
+  }
+
+  private def hex(bytes: Array[Byte]): UTF8String = {
+    doHex(bytes, bytes.length)
+  }
+
+  private def doHex(bytes: Array[Byte], length: Int): UTF8String = {
     val value = new Array[Byte](length * 2)
     var i = 0
     while (i < length) {
-      value(i * 2) = Hex.hexDigits((bytes(i) & 0xF0) >> 4)
-      value(i * 2 + 1) = Hex.hexDigits(bytes(i) & 0x0F)
+      value(i * 2) = Character.toUpperCase(Character.forDigit(
+        (bytes(i) & 0xF0) >>> 4, 16)).toByte
+      value(i * 2 + 1) = Character.toUpperCase(Character.forDigit(
+        bytes(i) & 0x0F, 16)).toByte
       i += 1
     }
     UTF8String.fromBytes(value)
@@ -296,64 +294,14 @@ case class Hex(child: Expression) extends UnaryExpression with Serializable  {
     var len = 0
     do {
       len += 1
-      value(value.length - len) = Hex.hexDigits((numBuf & 0xF).toInt)
+      value(value.length - len) = Character.toUpperCase(Character
+        .forDigit((numBuf & 0xF).toInt, 16)).toByte
       numBuf >>>= 4
     } while (numBuf != 0)
     UTF8String.fromBytes(Arrays.copyOfRange(value, value.length - len, value.length))
   }
 }
 
-/**
- * Performs the inverse operation of HEX.
- * Resulting characters are returned as a byte array.
- */
-case class Unhex(child: Expression)
-  extends UnaryExpression with ExpectsInputTypes with Serializable {
-
-  override def nullable: Boolean = true
-  override def dataType: DataType = BinaryType
-  override def inputTypes: Seq[DataType] = Seq(BinaryType)
-
-  override def eval(input: InternalRow): Any = {
-    val num = child.eval(input)
-    if (num == null) {
-      null
-    } else {
-      unhex(num.asInstanceOf[UTF8String].getBytes)
-    }
-  }
-
-  private[this] def unhex(bytes: Array[Byte]): Array[Byte] = {
-    val out = new Array[Byte]((bytes.length + 1) >> 1)
-    var i = 0
-    if ((bytes.length & 0x01) != 0) {
-      // padding with '0'
-      if (bytes(0) < 0) {
-        return null
-      }
-      val v = Hex.unhexDigits(bytes(0))
-      if (v == -1) {
-        return null
-      }
-      out(0) = v
-      i += 1
-    }
-    // two characters form the hex value.
-    while (i < bytes.length) {
-      if (bytes(i) < 0 || bytes(i + 1) < 0) {
-        return null
-      }
-      val first = Hex.unhexDigits(bytes(i))
-      val second = Hex.unhexDigits(bytes(i + 1))
-      if (first == -1 || second == -1) {
-        return null
-      }
-      out(i / 2) = (((first << 4) | second) & 0xFF).toByte
-      i += 2
-    }
-    out
-  }
-}
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 ////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -500,6 +448,58 @@ case class ShiftRight(left: Expression, right: Expression) extends BinaryExpress
   }
 }
 
+/**
+ * Performs the inverse operation of HEX.
+ * Resulting characters are returned as a byte array.
+ */
+case class UnHex(child: Expression) extends UnaryExpression with Serializable {
+
+  override def dataType: DataType = BinaryType
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (child.dataType.isInstanceOf[StringType] || child.dataType == NullType) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(s"unHex accepts String type, not ${child.dataType}")
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val num = child.eval(input)
+    if (num == null) {
+      null
+    } else {
+      unhex(num.asInstanceOf[UTF8String].getBytes)
+    }
+  }
+
+  private val unhexDigits = {
+    val array = Array.fill[Byte](128)(-1)
+    (0 to 9).foreach(i => array('0' + i) = i.toByte)
+    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
+    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
+    array
+  }
+
+  private def unhex(inputBytes: Array[Byte]): Array[Byte] = {
+    var bytes = inputBytes
+    if ((bytes.length & 0x01) != 0) {
+      bytes = '0'.toByte +: bytes
+    }
+    val out = new Array[Byte](bytes.length >> 1)
+    // two characters form the hex value.
+    var i = 0
+    while (i < bytes.length) {
+        val first = unhexDigits(bytes(i))
+        val second = unhexDigits(bytes(i + 1))
+        if (first == -1 || second == -1) { return null}
+        out(i / 2) = (((first << 4) | second) & 0xFF).toByte
+        i += 2
+    }
+    out
+  }
+}
+
 case class Hypot(left: Expression, right: Expression)
   extends BinaryMathExpression(math.hypot, "HYPOT")
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 550c6e3cc9f0b..aa27fe3cd5564 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types._
+import org.apache.spark.sql.types.{IntegerType, DataType, DoubleType, LongType}
 
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -252,15 +252,11 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("hex") {
-    checkEvaluation(Hex(Literal.create(null, IntegerType)), null)
     checkEvaluation(Hex(Literal(28)), "1C")
     checkEvaluation(Hex(Literal(-28)), "FFFFFFFFFFFFFFE4")
-    checkEvaluation(Hex(Literal.create(null, LongType)), null)
     checkEvaluation(Hex(Literal(100800200404L)), "177828FED4")
     checkEvaluation(Hex(Literal(-100800200404L)), "FFFFFFE887D7012C")
-    checkEvaluation(Hex(Literal.create(null, StringType)), null)
     checkEvaluation(Hex(Literal("helloHex")), "68656C6C6F486578")
-    checkEvaluation(Hex(Literal.create(null, BinaryType)), null)
     checkEvaluation(Hex(Literal("helloHex".getBytes())), "68656C6C6F486578")
     // scalastyle:off
     // Turn off scala style for non-ascii chars
@@ -269,15 +265,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("unhex") {
-    checkEvaluation(Unhex(Literal.create(null, StringType)), null)
-    checkEvaluation(Unhex(Literal("737472696E67")), "string".getBytes)
-    checkEvaluation(Unhex(Literal("")), new Array[Byte](0))
-    checkEvaluation(Unhex(Literal("F")), Array[Byte](15))
-    checkEvaluation(Unhex(Literal("ff")), Array[Byte](-1))
-    // scalastyle:off
-    // Turn off scala style for non-ascii chars
-    checkEvaluation(Unhex(Literal("E4B889E9878DE79A84")), "三重的".getBytes("UTF-8"))
-    // scalastyle:on
+    checkEvaluation(UnHex(Literal("737472696E67")), "string".getBytes)
+    checkEvaluation(UnHex(Literal("")), new Array[Byte](0))
+    checkEvaluation(UnHex(Literal("0")), Array[Byte](0))
   }
 
   test("hypot") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4b1353fc32c35..4ee1fb8374b07 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1061,7 +1061,7 @@ object functions {
    * @group math_funcs
    * @since 1.5.0
    */
-  def unhex(column: Column): Column = Unhex(column.expr)
+  def unhex(column: Column): Column = UnHex(column.expr)
 
   /**
    * Inverse of hex. Interprets each pair of characters as a hexadecimal number

From d9838196ff48faeac19756852a7f695129c08047 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Thu, 2 Jul 2015 18:07:09 -0700
Subject: [PATCH 0209/1454] [SPARK-8782] [SQL] Fix code generation for ORDER BY
 NULL

This fixes code generation for queries containing `ORDER BY NULL`.  Previously, the generated code would fail to compile.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7179 from JoshRosen/generate-order-fixes and squashes the following commits:

6ef49a6 [Josh Rosen] Fix ORDER BY NULL
0036696 [Josh Rosen] Add regression test for SPARK-8782 (ORDER BY NULL)
---
 .../sql/catalyst/expressions/codegen/CodeGenerator.scala   | 1 +
 .../test/scala/org/apache/spark/sql/SQLQuerySuite.scala    | 7 +++++++
 2 files changed, 8 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index a64027e48a00b..9f6329bbda4ec 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -185,6 +185,7 @@ class CodeGenContext {
     // use c1 - c2 may overflow
     case dt: DataType if isPrimitiveType(dt) => s"($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)"
     case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)"
+    case NullType => "0"
     case other => s"$c1.compare($c2)"
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 82dc0e9ce5132..cc6af1ccc1cce 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1451,4 +1451,11 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       checkAnswer(sql("SELECT a.b FROM t ORDER BY b[0].d"), Row(Seq(Row(1))))
     }
   }
+
+  test("SPARK-8782: ORDER BY NULL") {
+    withTempTable("t") {
+      Seq((1, 2), (1, 2)).toDF("a", "b").registerTempTable("t")
+      checkAnswer(sql("SELECT * FROM t ORDER BY NULL"), Seq(Row(1, 2), Row(1, 2)))
+    }
+  }
 }

From aa7bbc143844020e4711b3aa4ce75c1b7733a80d Mon Sep 17 00:00:00 2001
From: Bryan Cutler <bjcutler@us.ibm.com>
Date: Thu, 2 Jul 2015 21:38:21 -0500
Subject: [PATCH 0210/1454] [SPARK-6980] [CORE] Akka timeout exceptions
 indicate which conf controls them (RPC Layer)

Latest changes after refactoring to the RPC layer.  I rebased against trunk to make sure to get any recent changes since it had been a while.  I wasn't crazy about the name `ConfigureTimeout` and `RpcTimeout` seemed to fit better, but I'm open to suggestions!

I ran most of the tests and they pass, but others would get stuck with "WARN TaskSchedulerImpl: Initial job has not accepted any resources".  I think its just my machine, so I'd though I would push what I have anyway.

Still left to do:
* I only added a couple unit tests so far, there are probably some more cases to test
* Make sure all uses require a `RpcTimeout`
* Right now, both the `ask` and `Await.result` use the same timeout, should we differentiate between these in the TimeoutException message?
* I wrapped `Await.result` in `RpcTimeout`, should we also wrap `Await.ready`?
* Proper scoping of classes and methods

hardmettle, feel free to help out with any of these!

Author: Bryan Cutler <bjcutler@us.ibm.com>
Author: Harsh Gupta <harsh@Harshs-MacBook-Pro.local>
Author: BryanCutler <cutlerb@gmail.com>

Closes #6205 from BryanCutler/configTimeout-6980 and squashes the following commits:

46c8d48 [Bryan Cutler] [SPARK-6980] Changed RpcEnvSuite test to never reply instead of just sleeping, to avoid possible sync issues
06afa53 [Bryan Cutler] [SPARK-6980] RpcTimeout class extends Serializable, was causing error in MasterSuite
7bb70f1 [Bryan Cutler] Merge branch 'master' into configTimeout-6980
dbd5f73 [Bryan Cutler] [SPARK-6980] Changed RpcUtils askRpcTimeout and lookupRpcTimeout scope to private[spark] and improved deprecation warning msg
4e89c75 [Bryan Cutler] [SPARK-6980] Missed one usage of deprecated RpcUtils.askTimeout in YarnSchedulerBackend although it is not being used, and fixed SparkConfSuite UT to not use deprecated RpcUtils functions
6a1c50d [Bryan Cutler] [SPARK-6980] Minor cleanup of test case
7f4d78e [Bryan Cutler] [SPARK-6980] Fixed scala style checks
287059a [Bryan Cutler] [SPARK-6980] Removed extra import in AkkaRpcEnvSuite
3d8b1ff [Bryan Cutler] [SPARK-6980] Cleaned up imports in AkkaRpcEnvSuite
3a168c7 [Bryan Cutler] [SPARK-6980] Rewrote Akka RpcTimeout UTs in RpcEnvSuite
7636189 [Bryan Cutler] [SPARK-6980] Fixed call to askWithReply in DAGScheduler to use RpcTimeout - this was being compiled by auto-tupling and changing the message type of BlockManagerHeartbeat
be11c4e [Bryan Cutler] Merge branch 'master' into configTimeout-6980
039afed [Bryan Cutler] [SPARK-6980] Corrected import organization
218aa50 [Bryan Cutler] [SPARK-6980] Corrected issues from feedback
fadaf6f [Bryan Cutler] [SPARK-6980] Put back in deprecated RpcUtils askTimeout and lookupTimout to fix MiMa errors
fa6ed82 [Bryan Cutler] [SPARK-6980] Had to increase timeout on positive test case because a processor slowdown could trigger an Future TimeoutException
b05d449 [Bryan Cutler] [SPARK-6980] Changed constructor to use val duration instead of getter function, changed name of string property from conf to timeoutProp for consistency
c6cfd33 [Bryan Cutler] [SPARK-6980] Changed UT ask message timeout to explicitly intercept a SparkException
1394de6 [Bryan Cutler] [SPARK-6980] Moved MessagePrefix to createRpcTimeoutException directly
1517721 [Bryan Cutler] [SPARK-6980] RpcTimeout object scope should be private[spark]
2206b4d [Bryan Cutler] [SPARK-6980] Added unit test for ask then immediat awaitReply
1b9beab [Bryan Cutler] [SPARK-6980] Cleaned up import ordering
08f5afc [Bryan Cutler] [SPARK-6980] Added UT for constructing RpcTimeout with default value
d3754d1 [Bryan Cutler] [SPARK-6980] Added akkaConf to prevent dead letter logging
995d196 [Bryan Cutler] [SPARK-6980] Cleaned up import ordering, comments, spacing from PR feedback
7774d56 [Bryan Cutler] [SPARK-6980] Cleaned up UT imports
4351c48 [Bryan Cutler] [SPARK-6980] Added UT for addMessageIfTimeout, cleaned up UTs
1607a5f [Bryan Cutler] [SPARK-6980] Changed addMessageIfTimeout to PartialFunction, cleanup from PR comments
2f94095 [Bryan Cutler] [SPARK-6980] Added addMessageIfTimeout for when a Future is completed with TimeoutException
235919b [Bryan Cutler] [SPARK-6980] Resolved conflicts after master merge
c07d05c [Bryan Cutler] Merge branch 'master' into configTimeout-6980-tmp
b7fb99f [BryanCutler] Merge pull request #2 from hardmettle/configTimeoutUpdates_6980
4be3a8d [Harsh Gupta] Modifying loop condition to find property match
0ee5642 [Harsh Gupta] Changing the loop condition to halt at the first match in the property list for RpcEnv exception catch
f74064d [Harsh Gupta] Retrieving properties from property list using iterator and while loop instead of chained functions
a294569 [Bryan Cutler] [SPARK-6980] Added creation of RpcTimeout with Seq of property keys
23d2f26 [Bryan Cutler] [SPARK-6980] Fixed await result not being handled by RpcTimeout
49f9f04 [Bryan Cutler] [SPARK-6980] Minor cleanup and scala style fix
5b59a44 [Bryan Cutler] [SPARK-6980] Added some RpcTimeout unit tests
78a2c0a [Bryan Cutler] [SPARK-6980] Using RpcTimeout.awaitResult for future in AppClient now
97523e0 [Bryan Cutler] [SPARK-6980] Akka ask timeout description refactored to RPC layer
---
 .../spark/deploy/worker/ui/WorkerWebUI.scala  |   2 +-
 .../org/apache/spark/rpc/RpcEndpointRef.scala |  17 +--
 .../scala/org/apache/spark/rpc/RpcEnv.scala   | 112 +++++++++++++++++-
 .../apache/spark/rpc/akka/AkkaRpcEnv.scala    |  15 ++-
 .../apache/spark/scheduler/DAGScheduler.scala |   3 +-
 .../cluster/YarnSchedulerBackend.scala        |   2 +-
 .../spark/storage/BlockManagerMaster.scala    |  14 +--
 .../org/apache/spark/util/AkkaUtils.scala     |  19 ++-
 .../org/apache/spark/util/RpcUtils.scala      |  20 +++-
 .../org/apache/spark/SparkConfSuite.scala     |   4 +-
 .../org/apache/spark/rpc/RpcEnvSuite.scala    |  97 ++++++++++++++-
 11 files changed, 258 insertions(+), 47 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
index b3bb5f911dbd7..334a5b10142aa 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
@@ -38,7 +38,7 @@ class WorkerWebUI(
   extends WebUI(worker.securityMgr, requestedPort, worker.conf, name = "WorkerUI")
   with Logging {
 
-  private[ui] val timeout = RpcUtils.askTimeout(worker.conf)
+  private[ui] val timeout = RpcUtils.askRpcTimeout(worker.conf)
 
   initialize()
 
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
index 69181edb9ad44..6ae47894598be 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
@@ -17,8 +17,7 @@
 
 package org.apache.spark.rpc
 
-import scala.concurrent.{Await, Future}
-import scala.concurrent.duration.FiniteDuration
+import scala.concurrent.Future
 import scala.reflect.ClassTag
 
 import org.apache.spark.util.RpcUtils
@@ -32,7 +31,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
 
   private[this] val maxRetries = RpcUtils.numRetries(conf)
   private[this] val retryWaitMs = RpcUtils.retryWaitMs(conf)
-  private[this] val defaultAskTimeout = RpcUtils.askTimeout(conf)
+  private[this] val defaultAskTimeout = RpcUtils.askRpcTimeout(conf)
 
   /**
    * return the address for the [[RpcEndpointRef]]
@@ -52,7 +51,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
    *
    * This method only sends the message once and never retries.
    */
-  def ask[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T]
+  def ask[T: ClassTag](message: Any, timeout: RpcTimeout): Future[T]
 
   /**
    * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a [[Future]] to
@@ -91,7 +90,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
    * @tparam T type of the reply message
    * @return the reply message from the corresponding [[RpcEndpoint]]
    */
-  def askWithRetry[T: ClassTag](message: Any, timeout: FiniteDuration): T = {
+  def askWithRetry[T: ClassTag](message: Any, timeout: RpcTimeout): T = {
     // TODO: Consider removing multiple attempts
     var attempts = 0
     var lastException: Exception = null
@@ -99,7 +98,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
       attempts += 1
       try {
         val future = ask[T](message, timeout)
-        val result = Await.result(future, timeout)
+        val result = timeout.awaitResult(future)
         if (result == null) {
           throw new SparkException("Actor returned null")
         }
@@ -110,10 +109,14 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
           lastException = e
           logWarning(s"Error sending message [message = $message] in $attempts attempts", e)
       }
-      Thread.sleep(retryWaitMs)
+
+      if (attempts < maxRetries) {
+        Thread.sleep(retryWaitMs)
+      }
     }
 
     throw new SparkException(
       s"Error sending message [message = $message]", lastException)
   }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
index 3b6938ec639c3..1709bdf560b6f 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
@@ -18,8 +18,10 @@
 package org.apache.spark.rpc
 
 import java.net.URI
+import java.util.concurrent.TimeoutException
 
-import scala.concurrent.{Await, Future}
+import scala.concurrent.{Awaitable, Await, Future}
+import scala.concurrent.duration._
 import scala.language.postfixOps
 
 import org.apache.spark.{SecurityManager, SparkConf}
@@ -66,7 +68,7 @@ private[spark] object RpcEnv {
  */
 private[spark] abstract class RpcEnv(conf: SparkConf) {
 
-  private[spark] val defaultLookupTimeout = RpcUtils.lookupTimeout(conf)
+  private[spark] val defaultLookupTimeout = RpcUtils.lookupRpcTimeout(conf)
 
   /**
    * Return RpcEndpointRef of the registered [[RpcEndpoint]]. Will be used to implement
@@ -94,7 +96,7 @@ private[spark] abstract class RpcEnv(conf: SparkConf) {
    * Retrieve the [[RpcEndpointRef]] represented by `uri`. This is a blocking action.
    */
   def setupEndpointRefByURI(uri: String): RpcEndpointRef = {
-    Await.result(asyncSetupEndpointRefByURI(uri), defaultLookupTimeout)
+    defaultLookupTimeout.awaitResult(asyncSetupEndpointRefByURI(uri))
   }
 
   /**
@@ -184,3 +186,107 @@ private[spark] object RpcAddress {
     RpcAddress(host, port)
   }
 }
+
+
+/**
+ * An exception thrown if RpcTimeout modifies a [[TimeoutException]].
+ */
+private[rpc] class RpcTimeoutException(message: String, cause: TimeoutException)
+  extends TimeoutException(message) { initCause(cause) }
+
+
+/**
+ * Associates a timeout with a description so that a when a TimeoutException occurs, additional
+ * context about the timeout can be amended to the exception message.
+ * @param duration timeout duration in seconds
+ * @param timeoutProp the configuration property that controls this timeout
+ */
+private[spark] class RpcTimeout(val duration: FiniteDuration, val timeoutProp: String)
+  extends Serializable {
+
+  /** Amends the standard message of TimeoutException to include the description */
+  private def createRpcTimeoutException(te: TimeoutException): RpcTimeoutException = {
+    new RpcTimeoutException(te.getMessage() + ". This timeout is controlled by " + timeoutProp, te)
+  }
+
+  /**
+   * PartialFunction to match a TimeoutException and add the timeout description to the message
+   *
+   * @note This can be used in the recover callback of a Future to add to a TimeoutException
+   * Example:
+   *    val timeout = new RpcTimeout(5 millis, "short timeout")
+   *    Future(throw new TimeoutException).recover(timeout.addMessageIfTimeout)
+   */
+  def addMessageIfTimeout[T]: PartialFunction[Throwable, T] = {
+    // The exception has already been converted to a RpcTimeoutException so just raise it
+    case rte: RpcTimeoutException => throw rte
+    // Any other TimeoutException get converted to a RpcTimeoutException with modified message
+    case te: TimeoutException => throw createRpcTimeoutException(te)
+  }
+
+  /**
+   * Wait for the completed result and return it. If the result is not available within this
+   * timeout, throw a [[RpcTimeoutException]] to indicate which configuration controls the timeout.
+   * @param  awaitable  the `Awaitable` to be awaited
+   * @throws RpcTimeoutException if after waiting for the specified time `awaitable`
+   *         is still not ready
+   */
+  def awaitResult[T](awaitable: Awaitable[T]): T = {
+    try {
+      Await.result(awaitable, duration)
+    } catch addMessageIfTimeout
+  }
+}
+
+
+private[spark] object RpcTimeout {
+
+  /**
+   * Lookup the timeout property in the configuration and create
+   * a RpcTimeout with the property key in the description.
+   * @param conf configuration properties containing the timeout
+   * @param timeoutProp property key for the timeout in seconds
+   * @throws NoSuchElementException if property is not set
+   */
+  def apply(conf: SparkConf, timeoutProp: String): RpcTimeout = {
+    val timeout = { conf.getTimeAsSeconds(timeoutProp) seconds }
+    new RpcTimeout(timeout, timeoutProp)
+  }
+
+  /**
+   * Lookup the timeout property in the configuration and create
+   * a RpcTimeout with the property key in the description.
+   * Uses the given default value if property is not set
+   * @param conf configuration properties containing the timeout
+   * @param timeoutProp property key for the timeout in seconds
+   * @param defaultValue default timeout value in seconds if property not found
+   */
+  def apply(conf: SparkConf, timeoutProp: String, defaultValue: String): RpcTimeout = {
+    val timeout = { conf.getTimeAsSeconds(timeoutProp, defaultValue) seconds }
+    new RpcTimeout(timeout, timeoutProp)
+  }
+
+  /**
+   * Lookup prioritized list of timeout properties in the configuration
+   * and create a RpcTimeout with the first set property key in the
+   * description.
+   * Uses the given default value if property is not set
+   * @param conf configuration properties containing the timeout
+   * @param timeoutPropList prioritized list of property keys for the timeout in seconds
+   * @param defaultValue default timeout value in seconds if no properties found
+   */
+  def apply(conf: SparkConf, timeoutPropList: Seq[String], defaultValue: String): RpcTimeout = {
+    require(timeoutPropList.nonEmpty)
+
+    // Find the first set property or use the default value with the first property
+    val itr = timeoutPropList.iterator
+    var foundProp: Option[(String, String)] = None
+    while (itr.hasNext && foundProp.isEmpty){
+      val propKey = itr.next()
+      conf.getOption(propKey).foreach { prop => foundProp = Some(propKey, prop) }
+    }
+    val finalProp = foundProp.getOrElse(timeoutPropList.head, defaultValue)
+    val timeout = { Utils.timeStringAsSeconds(finalProp._2) seconds }
+    new RpcTimeout(timeout, finalProp._1)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
index 31ebe5ac5bca3..f2d87f68341af 100644
--- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
@@ -20,7 +20,6 @@ package org.apache.spark.rpc.akka
 import java.util.concurrent.ConcurrentHashMap
 
 import scala.concurrent.Future
-import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.reflect.ClassTag
 import scala.util.control.NonFatal
@@ -214,8 +213,11 @@ private[spark] class AkkaRpcEnv private[akka] (
 
   override def asyncSetupEndpointRefByURI(uri: String): Future[RpcEndpointRef] = {
     import actorSystem.dispatcher
-    actorSystem.actorSelection(uri).resolveOne(defaultLookupTimeout).
-      map(new AkkaRpcEndpointRef(defaultAddress, _, conf))
+    actorSystem.actorSelection(uri).resolveOne(defaultLookupTimeout.duration).
+      map(new AkkaRpcEndpointRef(defaultAddress, _, conf)).
+      // this is just in case there is a timeout from creating the future in resolveOne, we want the
+      // exception to indicate the conf that determines the timeout
+      recover(defaultLookupTimeout.addMessageIfTimeout)
   }
 
   override def uriOf(systemName: String, address: RpcAddress, endpointName: String): String = {
@@ -295,8 +297,8 @@ private[akka] class AkkaRpcEndpointRef(
     actorRef ! AkkaMessage(message, false)
   }
 
-  override def ask[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = {
-    actorRef.ask(AkkaMessage(message, true))(timeout).flatMap {
+  override def ask[T: ClassTag](message: Any, timeout: RpcTimeout): Future[T] = {
+    actorRef.ask(AkkaMessage(message, true))(timeout.duration).flatMap {
       // The function will run in the calling thread, so it should be short and never block.
       case msg @ AkkaMessage(message, reply) =>
         if (reply) {
@@ -307,7 +309,8 @@ private[akka] class AkkaRpcEndpointRef(
         }
       case AkkaFailure(e) =>
         Future.failed(e)
-    }(ThreadUtils.sameThread).mapTo[T]
+    }(ThreadUtils.sameThread).mapTo[T].
+    recover(timeout.addMessageIfTimeout)(ThreadUtils.sameThread)
   }
 
   override def toString: String = s"${getClass.getSimpleName}($actorRef)"
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index a7cf0c23d9613..6841fa835747f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -35,6 +35,7 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.rpc.RpcTimeout
 import org.apache.spark.storage._
 import org.apache.spark.unsafe.memory.TaskMemoryManager
 import org.apache.spark.util._
@@ -188,7 +189,7 @@ class DAGScheduler(
       blockManagerId: BlockManagerId): Boolean = {
     listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics))
     blockManagerMaster.driverEndpoint.askWithRetry[Boolean](
-      BlockManagerHeartbeat(blockManagerId), 600 seconds)
+      BlockManagerHeartbeat(blockManagerId), new RpcTimeout(600 seconds, "BlockManagerHeartbeat"))
   }
 
   // Called by TaskScheduler when an executor fails.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
index 190ff61d689d1..bc67abb5df446 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
@@ -46,7 +46,7 @@ private[spark] abstract class YarnSchedulerBackend(
   private val yarnSchedulerEndpoint = rpcEnv.setupEndpoint(
     YarnSchedulerBackend.ENDPOINT_NAME, new YarnSchedulerEndpoint(rpcEnv))
 
-  private implicit val askTimeout = RpcUtils.askTimeout(sc.conf)
+  private implicit val askTimeout = RpcUtils.askRpcTimeout(sc.conf)
 
   /**
    * Request executors from the ApplicationMaster by specifying the total number desired.
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index 7cdae22b0e253..f70f701494dbf 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -33,7 +33,7 @@ class BlockManagerMaster(
     isDriver: Boolean)
   extends Logging {
 
-  val timeout = RpcUtils.askTimeout(conf)
+  val timeout = RpcUtils.askRpcTimeout(conf)
 
   /** Remove a dead executor from the driver endpoint. This is only called on the driver side. */
   def removeExecutor(execId: String) {
@@ -106,7 +106,7 @@ class BlockManagerMaster(
         logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
-      Await.result(future, timeout)
+      timeout.awaitResult(future)
     }
   }
 
@@ -118,7 +118,7 @@ class BlockManagerMaster(
         logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
-      Await.result(future, timeout)
+      timeout.awaitResult(future)
     }
   }
 
@@ -132,7 +132,7 @@ class BlockManagerMaster(
           s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
-      Await.result(future, timeout)
+      timeout.awaitResult(future)
     }
   }
 
@@ -176,8 +176,8 @@ class BlockManagerMaster(
         CanBuildFrom[Iterable[Future[Option[BlockStatus]]],
         Option[BlockStatus],
         Iterable[Option[BlockStatus]]]]
-    val blockStatus = Await.result(
-      Future.sequence[Option[BlockStatus], Iterable](futures)(cbf, ThreadUtils.sameThread), timeout)
+    val blockStatus = timeout.awaitResult(
+      Future.sequence[Option[BlockStatus], Iterable](futures)(cbf, ThreadUtils.sameThread))
     if (blockStatus == null) {
       throw new SparkException("BlockManager returned null for BlockStatus query: " + blockId)
     }
@@ -199,7 +199,7 @@ class BlockManagerMaster(
       askSlaves: Boolean): Seq[BlockId] = {
     val msg = GetMatchingBlockIds(filter, askSlaves)
     val future = driverEndpoint.askWithRetry[Future[Seq[BlockId]]](msg)
-    Await.result(future, timeout)
+    timeout.awaitResult(future)
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index 96aa2fe164703..c179833e5b06a 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -18,8 +18,6 @@
 package org.apache.spark.util
 
 import scala.collection.JavaConversions.mapAsJavaMap
-import scala.concurrent.Await
-import scala.concurrent.duration.FiniteDuration
 
 import akka.actor.{ActorRef, ActorSystem, ExtendedActorSystem}
 import akka.pattern.ask
@@ -28,6 +26,7 @@ import com.typesafe.config.ConfigFactory
 import org.apache.log4j.{Level, Logger}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv, SparkException}
+import org.apache.spark.rpc.RpcTimeout
 
 /**
  * Various utility classes for working with Akka.
@@ -147,7 +146,7 @@ private[spark] object AkkaUtils extends Logging {
   def askWithReply[T](
       message: Any,
       actor: ActorRef,
-      timeout: FiniteDuration): T = {
+      timeout: RpcTimeout): T = {
     askWithReply[T](message, actor, maxAttempts = 1, retryInterval = Int.MaxValue, timeout)
   }
 
@@ -160,7 +159,7 @@ private[spark] object AkkaUtils extends Logging {
       actor: ActorRef,
       maxAttempts: Int,
       retryInterval: Long,
-      timeout: FiniteDuration): T = {
+      timeout: RpcTimeout): T = {
     // TODO: Consider removing multiple attempts
     if (actor == null) {
       throw new SparkException(s"Error sending message [message = $message]" +
@@ -171,8 +170,8 @@ private[spark] object AkkaUtils extends Logging {
     while (attempts < maxAttempts) {
       attempts += 1
       try {
-        val future = actor.ask(message)(timeout)
-        val result = Await.result(future, timeout)
+        val future = actor.ask(message)(timeout.duration)
+        val result = timeout.awaitResult(future)
         if (result == null) {
           throw new SparkException("Actor returned null")
         }
@@ -198,9 +197,9 @@ private[spark] object AkkaUtils extends Logging {
     val driverPort: Int = conf.getInt("spark.driver.port", 7077)
     Utils.checkHost(driverHost, "Expected hostname")
     val url = address(protocol(actorSystem), driverActorSystemName, driverHost, driverPort, name)
-    val timeout = RpcUtils.lookupTimeout(conf)
+    val timeout = RpcUtils.lookupRpcTimeout(conf)
     logInfo(s"Connecting to $name: $url")
-    Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout)
+    timeout.awaitResult(actorSystem.actorSelection(url).resolveOne(timeout.duration))
   }
 
   def makeExecutorRef(
@@ -212,9 +211,9 @@ private[spark] object AkkaUtils extends Logging {
     val executorActorSystemName = SparkEnv.executorActorSystemName
     Utils.checkHost(host, "Expected hostname")
     val url = address(protocol(actorSystem), executorActorSystemName, host, port, name)
-    val timeout = RpcUtils.lookupTimeout(conf)
+    val timeout = RpcUtils.lookupRpcTimeout(conf)
     logInfo(s"Connecting to $name: $url")
-    Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout)
+    timeout.awaitResult(actorSystem.actorSelection(url).resolveOne(timeout.duration))
   }
 
   def protocol(actorSystem: ActorSystem): String = {
diff --git a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala
index f16cc8e7e42c6..7578a3b1d85f2 100644
--- a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.util
 
-import scala.concurrent.duration._
+import scala.concurrent.duration.FiniteDuration
 import scala.language.postfixOps
 
 import org.apache.spark.{SparkEnv, SparkConf}
-import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcEnv}
+import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcEnv, RpcTimeout}
 
 object RpcUtils {
 
@@ -47,14 +47,22 @@ object RpcUtils {
   }
 
   /** Returns the default Spark timeout to use for RPC ask operations. */
+  private[spark] def askRpcTimeout(conf: SparkConf): RpcTimeout = {
+    RpcTimeout(conf, Seq("spark.rpc.askTimeout", "spark.network.timeout"), "120s")
+  }
+
+  @deprecated("use askRpcTimeout instead, this method was not intended to be public", "1.5.0")
   def askTimeout(conf: SparkConf): FiniteDuration = {
-    conf.getTimeAsSeconds("spark.rpc.askTimeout",
-      conf.get("spark.network.timeout", "120s")) seconds
+    askRpcTimeout(conf).duration
   }
 
   /** Returns the default Spark timeout to use for RPC remote endpoint lookup. */
+  private[spark] def lookupRpcTimeout(conf: SparkConf): RpcTimeout = {
+    RpcTimeout(conf, Seq("spark.rpc.lookupTimeout", "spark.network.timeout"), "120s")
+  }
+
+  @deprecated("use lookupRpcTimeout instead, this method was not intended to be public", "1.5.0")
   def lookupTimeout(conf: SparkConf): FiniteDuration = {
-    conf.getTimeAsSeconds("spark.rpc.lookupTimeout",
-      conf.get("spark.network.timeout", "120s")) seconds
+    lookupRpcTimeout(conf).duration
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
index 9fbaeb33f97cd..90cb7da94e88a 100644
--- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
@@ -260,10 +260,10 @@ class SparkConfSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
     assert(RpcUtils.retryWaitMs(conf) === 2L)
 
     conf.set("spark.akka.askTimeout", "3")
-    assert(RpcUtils.askTimeout(conf) === (3 seconds))
+    assert(RpcUtils.askRpcTimeout(conf).duration === (3 seconds))
 
     conf.set("spark.akka.lookupTimeout", "4")
-    assert(RpcUtils.lookupTimeout(conf) === (4 seconds))
+    assert(RpcUtils.lookupRpcTimeout(conf).duration === (4 seconds))
   }
 }
 
diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala
index 1f0aa759b08da..6ceafe4337747 100644
--- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala
@@ -155,16 +155,21 @@ abstract class RpcEnvSuite extends SparkFunSuite with BeforeAndAfterAll {
     })
 
     val conf = new SparkConf()
+    val shortProp = "spark.rpc.short.timeout"
     conf.set("spark.rpc.retry.wait", "0")
     conf.set("spark.rpc.numRetries", "1")
     val anotherEnv = createRpcEnv(conf, "remote", 13345)
     // Use anotherEnv to find out the RpcEndpointRef
     val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-timeout")
     try {
-      val e = intercept[Exception] {
-        rpcEndpointRef.askWithRetry[String]("hello", 1 millis)
+      // Any exception thrown in askWithRetry is wrapped with a SparkException and set as the cause
+      val e = intercept[SparkException] {
+        rpcEndpointRef.askWithRetry[String]("hello", new RpcTimeout(1 millis, shortProp))
       }
-      assert(e.isInstanceOf[TimeoutException] || e.getCause.isInstanceOf[TimeoutException])
+      // The SparkException cause should be a RpcTimeoutException with message indicating the
+      // controlling timeout property
+      assert(e.getCause.isInstanceOf[RpcTimeoutException])
+      assert(e.getCause.getMessage.contains(shortProp))
     } finally {
       anotherEnv.shutdown()
       anotherEnv.awaitTermination()
@@ -539,6 +544,92 @@ abstract class RpcEnvSuite extends SparkFunSuite with BeforeAndAfterAll {
     }
   }
 
+  test("construct RpcTimeout with conf property") {
+    val conf = new SparkConf
+
+    val testProp = "spark.ask.test.timeout"
+    val testDurationSeconds = 30
+    val secondaryProp = "spark.ask.secondary.timeout"
+
+    conf.set(testProp, s"${testDurationSeconds}s")
+    conf.set(secondaryProp, "100s")
+
+    // Construct RpcTimeout with a single property
+    val rt1 = RpcTimeout(conf, testProp)
+    assert( testDurationSeconds === rt1.duration.toSeconds )
+
+    // Construct RpcTimeout with prioritized list of properties
+    val rt2 = RpcTimeout(conf, Seq("spark.ask.invalid.timeout", testProp, secondaryProp), "1s")
+    assert( testDurationSeconds === rt2.duration.toSeconds )
+
+    // Construct RpcTimeout with default value,
+    val defaultProp = "spark.ask.default.timeout"
+    val defaultDurationSeconds = 1
+    val rt3 = RpcTimeout(conf, Seq(defaultProp), defaultDurationSeconds.toString + "s")
+    assert( defaultDurationSeconds === rt3.duration.toSeconds )
+    assert( rt3.timeoutProp.contains(defaultProp) )
+
+    // Try to construct RpcTimeout with an unconfigured property
+    intercept[NoSuchElementException] {
+      RpcTimeout(conf, "spark.ask.invalid.timeout")
+    }
+  }
+
+  test("ask a message timeout on Future using RpcTimeout") {
+    case class NeverReply(msg: String)
+
+    val rpcEndpointRef = env.setupEndpoint("ask-future", new RpcEndpoint {
+      override val rpcEnv = env
+
+      override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+        case msg: String => context.reply(msg)
+        case _: NeverReply =>
+      }
+    })
+
+    val longTimeout = new RpcTimeout(1 second, "spark.rpc.long.timeout")
+    val shortTimeout = new RpcTimeout(10 millis, "spark.rpc.short.timeout")
+
+    // Ask with immediate response, should complete successfully
+    val fut1 = rpcEndpointRef.ask[String]("hello", longTimeout)
+    val reply1 = longTimeout.awaitResult(fut1)
+    assert("hello" === reply1)
+
+    // Ask with a delayed response and wait for response immediately that should timeout
+    val fut2 = rpcEndpointRef.ask[String](NeverReply("doh"), shortTimeout)
+    val reply2 =
+      intercept[RpcTimeoutException] {
+        shortTimeout.awaitResult(fut2)
+      }.getMessage
+
+    // RpcTimeout.awaitResult should have added the property to the TimeoutException message
+    assert(reply2.contains(shortTimeout.timeoutProp))
+
+    // Ask with delayed response and allow the Future to timeout before Await.result
+    val fut3 = rpcEndpointRef.ask[String](NeverReply("goodbye"), shortTimeout)
+
+    // Allow future to complete with failure using plain Await.result, this will return
+    // once the future is complete to verify addMessageIfTimeout was invoked
+    val reply3 =
+      intercept[RpcTimeoutException] {
+        Await.result(fut3, 200 millis)
+      }.getMessage
+
+    // When the future timed out, the recover callback should have used
+    // RpcTimeout.addMessageIfTimeout to add the property to the TimeoutException message
+    assert(reply3.contains(shortTimeout.timeoutProp))
+
+    // Use RpcTimeout.awaitResult to process Future, since it has already failed with
+    // RpcTimeoutException, the same RpcTimeoutException should be thrown
+    val reply4 =
+      intercept[RpcTimeoutException] {
+        shortTimeout.awaitResult(fut3)
+      }.getMessage
+
+    // Ensure description is not in message twice after addMessageIfTimeout and awaitResult
+    assert(shortTimeout.timeoutProp.r.findAllIn(reply4).length === 1)
+  }
+
 }
 
 class UnserializableClass

From 1a7a7d7d579c5cba104daffbda977915802bf9b9 Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Thu, 2 Jul 2015 20:37:31 -0700
Subject: [PATCH 0211/1454] [SPARK-8213][SQL]Add function factorial

Author: zhichao.li <zhichao.li@intel.com>

Closes #6822 from zhichao-li/factorial and squashes the following commits:

26edf4f [zhichao.li] add factorial
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../spark/sql/catalyst/expressions/math.scala | 80 ++++++++++++++++++-
 .../expressions/MathFunctionsSuite.scala      | 15 +++-
 .../org/apache/spark/sql/functions.scala      | 16 ++++
 .../spark/sql/MathExpressionsSuite.scala      | 13 ++-
 5 files changed, 122 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index e7e4d1c4efe18..9163b032adee4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -113,6 +113,7 @@ object FunctionRegistry {
     expression[Exp]("exp"),
     expression[Expm1]("expm1"),
     expression[Floor]("floor"),
+    expression[Factorial]("factorial"),
     expression[Hypot]("hypot"),
     expression[Hex]("hex"),
     expression[Logarithm]("log"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 035980da568d3..701ab9912adba 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -21,8 +21,10 @@ import java.lang.{Long => JLong}
 import java.util.Arrays
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.types.{StringType}
+import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.types.{DataType, DoubleType, LongType, IntegerType}
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -159,6 +161,82 @@ case class Expm1(child: Expression) extends UnaryMathExpression(math.expm1, "EXP
 
 case class Floor(child: Expression) extends UnaryMathExpression(math.floor, "FLOOR")
 
+object Factorial {
+
+  def factorial(n: Int): Long = {
+    if (n < factorials.length) factorials(n) else Long.MaxValue
+  }
+
+  private val factorials: Array[Long] = Array[Long](
+    1,
+    1,
+    2,
+    6,
+    24,
+    120,
+    720,
+    5040,
+    40320,
+    362880,
+    3628800,
+    39916800,
+    479001600,
+    6227020800L,
+    87178291200L,
+    1307674368000L,
+    20922789888000L,
+    355687428096000L,
+    6402373705728000L,
+    121645100408832000L,
+    2432902008176640000L
+  )
+}
+
+case class Factorial(child: Expression)
+  extends UnaryExpression with ExpectsInputTypes {
+
+  override def inputTypes: Seq[DataType] = Seq(IntegerType)
+
+  override def dataType: DataType = LongType
+
+  override def foldable: Boolean = child.foldable
+
+  // If the value not in the range of [0, 20], it still will be null, so set it to be true here.
+  override def nullable: Boolean = true
+
+  override def toString: String = s"factorial($child)"
+
+  override def eval(input: InternalRow): Any = {
+    val evalE = child.eval(input)
+    if (evalE == null) {
+      null
+    } else {
+      val input = evalE.asInstanceOf[Integer]
+      if (input > 20 || input < 0) {
+        null
+      } else {
+        Factorial.factorial(input)
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val eval = child.gen(ctx)
+    eval.code + s"""
+      boolean ${ev.isNull} = ${eval.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        if (${eval.primitive} > 20 || ${eval.primitive} < 0) {
+          ${ev.isNull} = true;
+        } else {
+          ${ev.primitive} =
+            org.apache.spark.sql.catalyst.expressions.Factorial.factorial(${eval.primitive});
+        }
+      }
+    """
+  }
+}
+
 case class Log(child: Expression) extends UnaryMathExpression(math.log, "LOG")
 
 case class Log2(child: Expression)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index aa27fe3cd5564..8457864d1782d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -17,9 +17,12 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import com.google.common.math.LongMath
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{IntegerType, DataType, DoubleType, LongType}
+import org.apache.spark.sql.types.{DataType, LongType}
+import org.apache.spark.sql.types.{IntegerType, DoubleType}
 
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -157,6 +160,16 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     testUnary(Floor, math.floor)
   }
 
+  test("factorial") {
+    val dataLong = (0 to 20)
+    dataLong.foreach { value =>
+      checkEvaluation(Factorial(Literal(value)), LongMath.factorial(value), EmptyRow)
+    }
+    checkEvaluation((Literal.create(null, IntegerType)), null, create_row(null))
+    checkEvaluation(Factorial(Literal(20)), 2432902008176640000L, EmptyRow)
+    checkEvaluation(Factorial(Literal(21)), null, EmptyRow)
+  }
+
   test("rint") {
     testUnary(Rint, math.rint)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4ee1fb8374b07..0d5d49c3dd1d7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1022,6 +1022,22 @@ object functions {
    */
   def expm1(columnName: String): Column = expm1(Column(columnName))
 
+  /**
+   * Computes the factorial of the given value.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def factorial(e: Column): Column = Factorial(e.expr)
+
+  /**
+   * Computes the factorial of the given column.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def factorial(columnName: String): Column = factorial(Column(columnName))
+
   /**
    * Computes the floor of the given value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 4c5696deaff81..dc8f994adbd39 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.functions.{log => logarithm}
 
-
 private object MathExpressionsTestData {
   case class DoubleData(a: java.lang.Double, b: java.lang.Double)
   case class NullDoubles(a: java.lang.Double)
@@ -183,6 +182,18 @@ class MathExpressionsSuite extends QueryTest {
     testOneToOneMathFunction(floor, math.floor)
   }
 
+  test("factorial") {
+    val df = (0 to 5).map(i => (i, i)).toDF("a", "b")
+    checkAnswer(
+      df.select(factorial('a)),
+      Seq(Row(1), Row(1), Row(2), Row(6), Row(24), Row(120))
+    )
+    checkAnswer(
+      df.selectExpr("factorial(a)"),
+      Seq(Row(1), Row(1), Row(2), Row(6), Row(24), Row(120))
+    )
+  }
+
   test("rint") {
     testOneToOneMathFunction(rint, math.rint)
   }

From dfd8bac8f5b4f2b733c1ddd58e53ee0ba431e6b3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 2 Jul 2015 20:47:04 -0700
Subject: [PATCH 0212/1454] Minor style fix for the previous commit.

---
 .../spark/sql/catalyst/expressions/math.scala       | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 701ab9912adba..273a6c5016577 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -21,10 +21,8 @@ import java.lang.{Long => JLong}
 import java.util.Arrays
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.types._
-import org.apache.spark.sql.types.{StringType}
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.types.{DataType, DoubleType, LongType, IntegerType}
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -192,8 +190,7 @@ object Factorial {
   )
 }
 
-case class Factorial(child: Expression)
-  extends UnaryExpression with ExpectsInputTypes {
+case class Factorial(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(IntegerType)
 
@@ -204,8 +201,6 @@ case class Factorial(child: Expression)
   // If the value not in the range of [0, 20], it still will be null, so set it to be true here.
   override def nullable: Boolean = true
 
-  override def toString: String = s"factorial($child)"
-
   override def eval(input: InternalRow): Any = {
     val evalE = child.eval(input)
     if (evalE == null) {
@@ -372,8 +367,8 @@ case class Hex(child: Expression) extends UnaryExpression with Serializable  {
     var len = 0
     do {
       len += 1
-      value(value.length - len) = Character.toUpperCase(Character
-        .forDigit((numBuf & 0xF).toInt, 16)).toByte
+      value(value.length - len) =
+        Character.toUpperCase(Character.forDigit((numBuf & 0xF).toInt, 16)).toByte
       numBuf >>>= 4
     } while (numBuf != 0)
     UTF8String.fromBytes(Arrays.copyOfRange(value, value.length - len, value.length))

From 20a4d7dbd18fd4d1e3fb9324749453123714f99f Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 2 Jul 2015 21:30:57 -0700
Subject: [PATCH 0213/1454] [SPARK-8501] [SQL] Avoids reading schema from empty
 ORC files

ORC writes empty schema (`struct<>`) to ORC files containing zero rows.  This is OK for Hive since the table schema is managed by the metastore. But it causes trouble when reading raw ORC files via Spark SQL since we have to discover the schema from the files.

Notice that the ORC data source always avoids writing empty ORC files, but it's still problematic when reading Hive tables which contain empty part-files.

Author: Cheng Lian <lian@databricks.com>

Closes #7199 from liancheng/spark-8501 and squashes the following commits:

bb8cd95 [Cheng Lian] Addresses comments
a290221 [Cheng Lian] Avoids reading schema from empty ORC files
---
 .../spark/sql/hive/orc/OrcFileOperator.scala  | 60 +++++++++++++++----
 .../spark/sql/hive/orc/OrcRelation.scala      | 44 ++++++++------
 .../spark/sql/hive/orc/OrcQuerySuite.scala    | 55 ++++++++++++++---
 .../spark/sql/hive/orc/OrcSourceSuite.scala   | 28 ++++-----
 4 files changed, 135 insertions(+), 52 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala
index e3ab9442b4821..0f9a1a6ef3b27 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala
@@ -24,30 +24,70 @@ import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
 
 import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.hive.HiveMetastoreTypes
 import org.apache.spark.sql.types.StructType
 
 private[orc] object OrcFileOperator extends Logging {
-  def getFileReader(pathStr: String, config: Option[Configuration] = None ): Reader = {
+  /**
+   * Retrieves a ORC file reader from a given path.  The path can point to either a directory or a
+   * single ORC file.  If it points to an directory, it picks any non-empty ORC file within that
+   * directory.
+   *
+   * The reader returned by this method is mainly used for two purposes:
+   *
+   * 1. Retrieving file metadata (schema and compression codecs, etc.)
+   * 2. Read the actual file content (in this case, the given path should point to the target file)
+   *
+   * @note As recorded by SPARK-8501, ORC writes an empty schema (<code>struct&lt;&gt;</code) to an
+   *       ORC file if the file contains zero rows. This is OK for Hive since the schema of the
+   *       table is managed by metastore.  But this becomes a problem when reading ORC files
+   *       directly from HDFS via Spark SQL, because we have to discover the schema from raw ORC
+   *       files.  So this method always tries to find a ORC file whose schema is non-empty, and
+   *       create the result reader from that file.  If no such file is found, it returns `None`.
+   *
+   * @todo Needs to consider all files when schema evolution is taken into account.
+   */
+  def getFileReader(basePath: String, config: Option[Configuration] = None): Option[Reader] = {
+    def isWithNonEmptySchema(path: Path, reader: Reader): Boolean = {
+      reader.getObjectInspector match {
+        case oi: StructObjectInspector if oi.getAllStructFieldRefs.size() == 0 =>
+          logInfo(
+            s"ORC file $path has empty schema, it probably contains no rows. " +
+              "Trying to read another ORC file to figure out the schema.")
+          false
+        case _ => true
+      }
+    }
+
     val conf = config.getOrElse(new Configuration)
-    val fspath = new Path(pathStr)
-    val fs = fspath.getFileSystem(conf)
-    val orcFiles = listOrcFiles(pathStr, conf)
-    logDebug(s"Creating ORC Reader from ${orcFiles.head}")
-    // TODO Need to consider all files when schema evolution is taken into account.
-    OrcFile.createReader(fs, orcFiles.head)
+    val fs = {
+      val hdfsPath = new Path(basePath)
+      hdfsPath.getFileSystem(conf)
+    }
+
+    listOrcFiles(basePath, conf).iterator.map { path =>
+      path -> OrcFile.createReader(fs, path)
+    }.collectFirst {
+      case (path, reader) if isWithNonEmptySchema(path, reader) => reader
+    }
   }
 
   def readSchema(path: String, conf: Option[Configuration]): StructType = {
-    val reader = getFileReader(path, conf)
+    val reader = getFileReader(path, conf).getOrElse {
+      throw new AnalysisException(
+        s"Failed to discover schema from ORC files stored in $path. " +
+          "Probably there are either no ORC files or only empty ORC files.")
+    }
     val readerInspector = reader.getObjectInspector.asInstanceOf[StructObjectInspector]
     val schema = readerInspector.getTypeName
     logDebug(s"Reading schema from file $path, got Hive schema string: $schema")
     HiveMetastoreTypes.toDataType(schema).asInstanceOf[StructType]
   }
 
-  def getObjectInspector(path: String, conf: Option[Configuration]): StructObjectInspector = {
-    getFileReader(path, conf).getObjectInspector.asInstanceOf[StructObjectInspector]
+  def getObjectInspector(
+      path: String, conf: Option[Configuration]): Option[StructObjectInspector] = {
+    getFileReader(path, conf).map(_.getObjectInspector.asInstanceOf[StructObjectInspector])
   }
 
   def listOrcFiles(pathStr: String, conf: Configuration): Seq[Path] = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 300f83d914ea4..9dc9fbb78e01f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -242,26 +242,34 @@ private[orc] case class OrcTableScan(
       nonPartitionKeyAttrs: Seq[(Attribute, Int)],
       mutableRow: MutableRow): Iterator[InternalRow] = {
     val deserializer = new OrcSerde
-    val soi = OrcFileOperator.getObjectInspector(path, Some(conf))
-    val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map {
-      case (attr, ordinal) =>
-        soi.getStructFieldRef(attr.name.toLowerCase) -> ordinal
-    }.unzip
-    val unwrappers = fieldRefs.map(unwrapperFor)
-    // Map each tuple to a row object
-    iterator.map { value =>
-      val raw = deserializer.deserialize(value)
-      var i = 0
-      while (i < fieldRefs.length) {
-        val fieldValue = soi.getStructFieldData(raw, fieldRefs(i))
-        if (fieldValue == null) {
-          mutableRow.setNullAt(fieldOrdinals(i))
-        } else {
-          unwrappers(i)(fieldValue, mutableRow, fieldOrdinals(i))
+    val maybeStructOI = OrcFileOperator.getObjectInspector(path, Some(conf))
+
+    // SPARK-8501: ORC writes an empty schema ("struct<>") to an ORC file if the file contains zero
+    // rows, and thus couldn't give a proper ObjectInspector.  In this case we just return an empty
+    // partition since we know that this file is empty.
+    maybeStructOI.map { soi =>
+      val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map {
+        case (attr, ordinal) =>
+          soi.getStructFieldRef(attr.name.toLowerCase) -> ordinal
+      }.unzip
+      val unwrappers = fieldRefs.map(unwrapperFor)
+      // Map each tuple to a row object
+      iterator.map { value =>
+        val raw = deserializer.deserialize(value)
+        var i = 0
+        while (i < fieldRefs.length) {
+          val fieldValue = soi.getStructFieldData(raw, fieldRefs(i))
+          if (fieldValue == null) {
+            mutableRow.setNullAt(fieldOrdinals(i))
+          } else {
+            unwrappers(i)(fieldValue, mutableRow, fieldOrdinals(i))
+          }
+          i += 1
         }
-        i += 1
+        mutableRow: InternalRow
       }
-      mutableRow: InternalRow
+    }.getOrElse {
+      Iterator.empty
     }
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
index 267d22c6b5f1e..ca131faaeef05 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
@@ -23,10 +23,7 @@ import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.io.orc.CompressionKind
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.expressions.InternalRow
-import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 
@@ -170,7 +167,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
   test("Default compression options for writing to an ORC file") {
     withOrcFile((1 to 100).map(i => (i, s"val_$i"))) { file =>
       assertResult(CompressionKind.ZLIB) {
-        OrcFileOperator.getFileReader(file).getCompression
+        OrcFileOperator.getFileReader(file).get.getCompression
       }
     }
   }
@@ -183,21 +180,21 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
     conf.set(ConfVars.HIVE_ORC_DEFAULT_COMPRESS.varname, "SNAPPY")
     withOrcFile(data) { file =>
       assertResult(CompressionKind.SNAPPY) {
-        OrcFileOperator.getFileReader(file).getCompression
+        OrcFileOperator.getFileReader(file).get.getCompression
       }
     }
 
     conf.set(ConfVars.HIVE_ORC_DEFAULT_COMPRESS.varname, "NONE")
     withOrcFile(data) { file =>
       assertResult(CompressionKind.NONE) {
-        OrcFileOperator.getFileReader(file).getCompression
+        OrcFileOperator.getFileReader(file).get.getCompression
       }
     }
 
     conf.set(ConfVars.HIVE_ORC_DEFAULT_COMPRESS.varname, "LZO")
     withOrcFile(data) { file =>
       assertResult(CompressionKind.LZO) {
-        OrcFileOperator.getFileReader(file).getCompression
+        OrcFileOperator.getFileReader(file).get.getCompression
       }
     }
   }
@@ -289,4 +286,48 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
         List(Row("same", "run_5", 100)))
     }
   }
+
+  test("SPARK-8501: Avoids discovery schema from empty ORC files") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withTable("empty_orc") {
+        withTempTable("empty", "single") {
+          sqlContext.sql(
+            s"""CREATE TABLE empty_orc(key INT, value STRING)
+               |STORED AS ORC
+               |LOCATION '$path'
+             """.stripMargin)
+
+          val emptyDF = Seq.empty[(Int, String)].toDF("key", "value").coalesce(1)
+          emptyDF.registerTempTable("empty")
+
+          // This creates 1 empty ORC file with Hive ORC SerDe.  We are using this trick because
+          // Spark SQL ORC data source always avoids write empty ORC files.
+          sqlContext.sql(
+            s"""INSERT INTO TABLE empty_orc
+               |SELECT key, value FROM empty
+             """.stripMargin)
+
+          val errorMessage = intercept[AnalysisException] {
+            sqlContext.read.format("orc").load(path)
+          }.getMessage
+
+          assert(errorMessage.contains("Failed to discover schema from ORC files"))
+
+          val singleRowDF = Seq((0, "foo")).toDF("key", "value").coalesce(1)
+          singleRowDF.registerTempTable("single")
+
+          sqlContext.sql(
+            s"""INSERT INTO TABLE empty_orc
+               |SELECT key, value FROM single
+             """.stripMargin)
+
+          val df = sqlContext.read.format("orc").load(path)
+          assert(df.schema === singleRowDF.schema.asNullable)
+          checkAnswer(df, singleRowDF)
+        }
+      }
+    }
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
index a0cdd0db42d65..82e08caf46457 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
@@ -43,14 +43,8 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
     orcTableDir.mkdir()
     import org.apache.spark.sql.hive.test.TestHive.implicits._
 
-    // Originally we were using a 10-row RDD for testing.  However, when default parallelism is
-    // greater than 10 (e.g., running on a node with 32 cores), this RDD contains empty partitions,
-    // which result in empty ORC files.  Unfortunately, ORC doesn't handle empty files properly and
-    // causes build failure on Jenkins, which happens to have 32 cores. Please refer to SPARK-8501
-    // for more details.  To workaround this issue before fixing SPARK-8501, we simply increase row
-    // number in this RDD to avoid empty partitions.
     sparkContext
-      .makeRDD(1 to 100)
+      .makeRDD(1 to 10)
       .map(i => OrcData(i, s"part-$i"))
       .toDF()
       .registerTempTable(s"orc_temp_table")
@@ -76,35 +70,35 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
   }
 
   test("create temporary orc table") {
-    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_source"), Row(100))
+    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_source"), Row(10))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source"),
-      (1 to 100).map(i => Row(i, s"part-$i")))
+      (1 to 10).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source where intField > 5"),
-      (6 to 100).map(i => Row(i, s"part-$i")))
+      (6 to 10).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT COUNT(intField), stringField FROM normal_orc_source GROUP BY stringField"),
-      (1 to 100).map(i => Row(1, s"part-$i")))
+      (1 to 10).map(i => Row(1, s"part-$i")))
   }
 
   test("create temporary orc table as") {
-    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_as_source"), Row(100))
+    checkAnswer(sql("SELECT COUNT(*) FROM normal_orc_as_source"), Row(10))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source"),
-      (1 to 100).map(i => Row(i, s"part-$i")))
+      (1 to 10).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source WHERE intField > 5"),
-      (6 to 100).map(i => Row(i, s"part-$i")))
+      (6 to 10).map(i => Row(i, s"part-$i")))
 
     checkAnswer(
       sql("SELECT COUNT(intField), stringField FROM normal_orc_source GROUP BY stringField"),
-      (1 to 100).map(i => Row(1, s"part-$i")))
+      (1 to 10).map(i => Row(1, s"part-$i")))
   }
 
   test("appending insert") {
@@ -112,7 +106,7 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_source"),
-      (1 to 5).map(i => Row(i, s"part-$i")) ++ (6 to 100).flatMap { i =>
+      (1 to 5).map(i => Row(i, s"part-$i")) ++ (6 to 10).flatMap { i =>
         Seq.fill(2)(Row(i, s"part-$i"))
       })
   }
@@ -125,7 +119,7 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
 
     checkAnswer(
       sql("SELECT * FROM normal_orc_as_source"),
-      (6 to 100).map(i => Row(i, s"part-$i")))
+      (6 to 10).map(i => Row(i, s"part-$i")))
   }
 }
 

From a59d14f623633c7aef97991341b587c11ca42328 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 2 Jul 2015 21:45:25 -0700
Subject: [PATCH 0214/1454] [SPARK-8801][SQL] Support TypeCollection in
 ExpectsInputTypes

This patch adds a new TypeCollection AbstractDataType that can be used by expressions to specify more than one expected input types.

Author: Reynold Xin <rxin@databricks.com>

Closes #7202 from rxin/type-collection and squashes the following commits:

c714ca1 [Reynold Xin] Fixed style.
a0c0d12 [Reynold Xin] Fixed bugs and unit tests.
d8b8ae7 [Reynold Xin] Added TypeCollection.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 47 +++++++++++++---
 .../spark/sql/types/AbstractDataType.scala    | 50 ++++++++++++++---
 .../apache/spark/sql/types/ArrayType.scala    |  6 +-
 .../org/apache/spark/sql/types/DataType.scala |  4 +-
 .../apache/spark/sql/types/DecimalType.scala  |  4 ++
 .../org/apache/spark/sql/types/MapType.scala  |  4 ++
 .../apache/spark/sql/types/StructType.scala   |  8 ++-
 .../analysis/HiveTypeCoercionSuite.scala      | 55 +++++++++++++------
 8 files changed, 140 insertions(+), 38 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 0bc893224026e..6006e7bf00c13 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import javax.annotation.Nullable
+
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project, Union}
 import org.apache.spark.sql.catalyst.rules.Rule
@@ -713,39 +715,68 @@ object HiveTypeCoercion {
 
       case e: ExpectsInputTypes =>
         val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
-          implicitCast(in, expected)
+          // If we cannot do the implicit cast, just use the original input.
+          implicitCast(in, expected).getOrElse(in)
         }
         e.withNewChildren(children)
     }
 
     /**
-     * If needed, cast the expression into the expected type.
-     * If the implicit cast is not allowed, return the expression itself.
+     * Given an expected data type, try to cast the expression and return the cast expression.
+     *
+     * If the expression already fits the input type, we simply return the expression itself.
+     * If the expression has an incompatible type that cannot be implicitly cast, return None.
      */
-    def implicitCast(e: Expression, expectedType: AbstractDataType): Expression = {
+    def implicitCast(e: Expression, expectedType: AbstractDataType): Option[Expression] = {
       val inType = e.dataType
-      (inType, expectedType) match {
+
+      // Note that ret is nullable to avoid typing a lot of Some(...) in this local scope.
+      // We wrap immediately an Option after this.
+      @Nullable val ret: Expression = (inType, expectedType) match {
+
+        // If the expected type is already a parent of the input type, no need to cast.
+        case _ if expectedType.isParentOf(inType) => e
+
         // Cast null type (usually from null literals) into target types
-        case (NullType, target: DataType) => Cast(e, target.defaultConcreteType)
+        case (NullType, target) => Cast(e, target.defaultConcreteType)
 
         // Implicit cast among numeric types
+        // If input is decimal, and we expect a decimal type, just use the input.
+        case (_: DecimalType, DecimalType) => e
+        // If input is a numeric type but not decimal, and we expect a decimal type,
+        // cast the input to unlimited precision decimal.
+        case (_: NumericType, DecimalType) if !inType.isInstanceOf[DecimalType] =>
+          Cast(e, DecimalType.Unlimited)
+        // For any other numeric types, implicitly cast to each other, e.g. long -> int, int -> long
         case (_: NumericType, target: NumericType) if e.dataType != target => Cast(e, target)
+        case (_: NumericType, target: NumericType) => e
 
         // Implicit cast between date time types
         case (DateType, TimestampType) => Cast(e, TimestampType)
         case (TimestampType, DateType) => Cast(e, DateType)
 
         // Implicit cast from/to string
-        case (StringType, NumericType) => Cast(e, DoubleType)
+        case (StringType, DecimalType) => Cast(e, DecimalType.Unlimited)
         case (StringType, target: NumericType) => Cast(e, target)
         case (StringType, DateType) => Cast(e, DateType)
         case (StringType, TimestampType) => Cast(e, TimestampType)
         case (StringType, BinaryType) => Cast(e, BinaryType)
         case (any, StringType) if any != StringType => Cast(e, StringType)
 
+        // Type collection.
+        // First see if we can find our input type in the type collection. If we can, then just
+        // use the current expression; otherwise, find the first one we can implicitly cast.
+        case (_, TypeCollection(types)) =>
+          if (types.exists(_.isParentOf(inType))) {
+            e
+          } else {
+            types.flatMap(implicitCast(e, _)).headOption.orNull
+          }
+
         // Else, just return the same input expression
-        case _ => e
+        case _ => null
       }
+      Option(ret)
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index 43e2f8a46e62e..e5dc99fb625d8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -28,7 +28,45 @@ import org.apache.spark.util.Utils
  * A non-concrete data type, reserved for internal uses.
  */
 private[sql] abstract class AbstractDataType {
+  /**
+   * The default concrete type to use if we want to cast a null literal into this type.
+   */
   private[sql] def defaultConcreteType: DataType
+
+  /**
+   * Returns true if this data type is a parent of the `childCandidate`.
+   */
+  private[sql] def isParentOf(childCandidate: DataType): Boolean
+}
+
+
+/**
+ * A collection of types that can be used to specify type constraints. The sequence also specifies
+ * precedence: an earlier type takes precedence over a latter type.
+ *
+ * {{{
+ *   TypeCollection(StringType, BinaryType)
+ * }}}
+ *
+ * This means that we prefer StringType over BinaryType if it is possible to cast to StringType.
+ */
+private[sql] class TypeCollection(private val types: Seq[DataType]) extends AbstractDataType {
+  require(types.nonEmpty, s"TypeCollection ($types) cannot be empty")
+
+  private[sql] override def defaultConcreteType: DataType = types.head
+
+  private[sql] override def isParentOf(childCandidate: DataType): Boolean = false
+}
+
+
+private[sql] object TypeCollection {
+
+  def apply(types: DataType*): TypeCollection = new TypeCollection(types)
+
+  def unapply(typ: AbstractDataType): Option[Seq[DataType]] = typ match {
+    case typ: TypeCollection => Some(typ.types)
+    case _ => None
+  }
 }
 
 
@@ -61,7 +99,7 @@ abstract class NumericType extends AtomicType {
 }
 
 
-private[sql] object NumericType extends AbstractDataType {
+private[sql] object NumericType {
   /**
    * Enables matching against NumericType for expressions:
    * {{{
@@ -70,12 +108,10 @@ private[sql] object NumericType extends AbstractDataType {
    * }}}
    */
   def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType]
-
-  private[sql] override def defaultConcreteType: DataType = IntegerType
 }
 
 
-private[sql] object IntegralType extends AbstractDataType {
+private[sql] object IntegralType {
   /**
    * Enables matching against IntegralType for expressions:
    * {{{
@@ -84,8 +120,6 @@ private[sql] object IntegralType extends AbstractDataType {
    * }}}
    */
   def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[IntegralType]
-
-  private[sql] override def defaultConcreteType: DataType = IntegerType
 }
 
 
@@ -94,7 +128,7 @@ private[sql] abstract class IntegralType extends NumericType {
 }
 
 
-private[sql] object FractionalType extends AbstractDataType {
+private[sql] object FractionalType {
   /**
    * Enables matching against FractionalType for expressions:
    * {{{
@@ -103,8 +137,6 @@ private[sql] object FractionalType extends AbstractDataType {
    * }}}
    */
   def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[FractionalType]
-
-  private[sql] override def defaultConcreteType: DataType = DoubleType
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index 81553e7fc91a8..8ea6cb14c360e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -26,7 +26,11 @@ object ArrayType extends AbstractDataType {
   /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is true. */
   def apply(elementType: DataType): ArrayType = ArrayType(elementType, containsNull = true)
 
-  override def defaultConcreteType: DataType = ArrayType(NullType, containsNull = true)
+  private[sql] override def defaultConcreteType: DataType = ArrayType(NullType, containsNull = true)
+
+  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
+    childCandidate.isInstanceOf[ArrayType]
+  }
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index c333fa70d1ef4..7d00047d08d74 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -75,7 +75,9 @@ abstract class DataType extends AbstractDataType {
    */
   private[spark] def asNullable: DataType
 
-  override def defaultConcreteType: DataType = this
+  private[sql] override def defaultConcreteType: DataType = this
+
+  private[sql] override def isParentOf(childCandidate: DataType): Boolean = this == childCandidate
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index 06373a095b1b0..434fc037aad4f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -86,6 +86,10 @@ object DecimalType extends AbstractDataType {
 
   private[sql] override def defaultConcreteType: DataType = Unlimited
 
+  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
+    childCandidate.isInstanceOf[DecimalType]
+  }
+
   val Unlimited: DecimalType = DecimalType(None)
 
   private[sql] object Fixed {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
index 69c2119e23436..2b25617ec6655 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
@@ -71,6 +71,10 @@ object MapType extends AbstractDataType {
 
   private[sql] override def defaultConcreteType: DataType = apply(NullType, NullType)
 
+  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
+    childCandidate.isInstanceOf[MapType]
+  }
+
   /**
    * Construct a [[MapType]] object with the given key type and value type.
    * The `valueContainsNull` is true.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 6fedeabf23203..7e77b77e73940 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -301,7 +301,13 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
 }
 
 
-object StructType {
+object StructType extends AbstractDataType {
+
+  private[sql] override def defaultConcreteType: DataType = new StructType
+
+  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
+    childCandidate.isInstanceOf[StructType]
+  }
 
   def apply(fields: Seq[StructField]): StructType = StructType(fields.toArray)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 498fd86a06fd9..60e727c6c7d4d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -27,28 +27,47 @@ import org.apache.spark.sql.types._
 class HiveTypeCoercionSuite extends PlanTest {
 
   test("implicit type cast") {
-    def shouldCast(from: DataType, to: AbstractDataType): Unit = {
+    def shouldCast(from: DataType, to: AbstractDataType, expected: DataType): Unit = {
       val got = HiveTypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
-      assert(got.dataType === to.defaultConcreteType)
+      assert(got.map(_.dataType) == Option(expected),
+        s"Failed to cast $from to $to")
     }
 
+    shouldCast(NullType, NullType, NullType)
+    shouldCast(NullType, IntegerType, IntegerType)
+    shouldCast(NullType, DecimalType, DecimalType.Unlimited)
+
     // TODO: write the entire implicit cast table out for test cases.
-    shouldCast(ByteType, IntegerType)
-    shouldCast(IntegerType, IntegerType)
-    shouldCast(IntegerType, LongType)
-    shouldCast(IntegerType, DecimalType.Unlimited)
-    shouldCast(LongType, IntegerType)
-    shouldCast(LongType, DecimalType.Unlimited)
-
-    shouldCast(DateType, TimestampType)
-    shouldCast(TimestampType, DateType)
-
-    shouldCast(StringType, IntegerType)
-    shouldCast(StringType, DateType)
-    shouldCast(StringType, TimestampType)
-    shouldCast(IntegerType, StringType)
-    shouldCast(DateType, StringType)
-    shouldCast(TimestampType, StringType)
+    shouldCast(ByteType, IntegerType, IntegerType)
+    shouldCast(IntegerType, IntegerType, IntegerType)
+    shouldCast(IntegerType, LongType, LongType)
+    shouldCast(IntegerType, DecimalType, DecimalType.Unlimited)
+    shouldCast(LongType, IntegerType, IntegerType)
+    shouldCast(LongType, DecimalType, DecimalType.Unlimited)
+
+    shouldCast(DateType, TimestampType, TimestampType)
+    shouldCast(TimestampType, DateType, DateType)
+
+    shouldCast(StringType, IntegerType, IntegerType)
+    shouldCast(StringType, DateType, DateType)
+    shouldCast(StringType, TimestampType, TimestampType)
+    shouldCast(IntegerType, StringType, StringType)
+    shouldCast(DateType, StringType, StringType)
+    shouldCast(TimestampType, StringType, StringType)
+
+    shouldCast(StringType, BinaryType, BinaryType)
+    shouldCast(BinaryType, StringType, StringType)
+
+    shouldCast(NullType, TypeCollection(StringType, BinaryType), StringType)
+
+    shouldCast(StringType, TypeCollection(StringType, BinaryType), StringType)
+    shouldCast(BinaryType, TypeCollection(StringType, BinaryType), BinaryType)
+    shouldCast(StringType, TypeCollection(BinaryType, StringType), StringType)
+
+    shouldCast(IntegerType, TypeCollection(IntegerType, BinaryType), IntegerType)
+    shouldCast(IntegerType, TypeCollection(BinaryType, IntegerType), IntegerType)
+    shouldCast(BinaryType, TypeCollection(BinaryType, IntegerType), BinaryType)
+    shouldCast(BinaryType, TypeCollection(IntegerType, BinaryType), BinaryType)
   }
 
   test("tightest common bound for types") {

From f743c79abe5a2fb66be32a896ea47e858569b0c7 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 2 Jul 2015 22:09:07 -0700
Subject: [PATCH 0215/1454] [SPARK-8776] Increase the default MaxPermSize

I am increasing the perm gen size to 256m.

https://issues.apache.org/jira/browse/SPARK-8776

Author: Yin Huai <yhuai@databricks.com>

Closes #7196 from yhuai/SPARK-8776 and squashes the following commits:

60901b4 [Yin Huai] Fix test.
d44b713 [Yin Huai] Make sparkShell and hiveConsole use 256m PermGen size.
30aaf8e [Yin Huai] Increase the default PermGen size to 256m.
---
 .../org/apache/spark/launcher/AbstractCommandBuilder.java     | 2 +-
 .../apache/spark/launcher/SparkSubmitCommandBuilderSuite.java | 2 +-
 project/SparkBuild.scala                                      | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
index 33d65d13f0d25..5e793a5c48775 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
@@ -136,7 +136,7 @@ void addPermGenSizeOpt(List<String> cmd) {
       }
     }
 
-    cmd.add("-XX:MaxPermSize=128m");
+    cmd.add("-XX:MaxPermSize=256m");
   }
 
   void addOptionString(List<String> cmd, String options) {
diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java
index 97043a76cc612..7329ac9f7fb8c 100644
--- a/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java
+++ b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java
@@ -194,7 +194,7 @@ private void testCmdBuilder(boolean isDriver) throws Exception {
         if (isDriver) {
           assertEquals("-XX:MaxPermSize=256m", arg);
         } else {
-          assertEquals("-XX:MaxPermSize=128m", arg);
+          assertEquals("-XX:MaxPermSize=256m", arg);
         }
       }
     }
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 5f389bcc9ceeb..3408c6d51ed4c 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -206,7 +206,7 @@ object SparkBuild extends PomBuild {
     fork := true,
     outputStrategy in run := Some (StdoutOutput),
 
-    javaOptions ++= Seq("-Xmx2G", "-XX:MaxPermSize=1g"),
+    javaOptions ++= Seq("-Xmx2G", "-XX:MaxPermSize=256m"),
 
     sparkShell := {
       (runMain in Compile).toTask(" org.apache.spark.repl.Main -usejavacp").value
@@ -299,7 +299,7 @@ object SQL {
 object Hive {
 
   lazy val settings = Seq(
-    javaOptions += "-XX:MaxPermSize=1g",
+    javaOptions += "-XX:MaxPermSize=256m",
     // Specially disable assertions since some Hive tests fail them
     javaOptions in Test := (javaOptions in Test).value.filterNot(_ == "-ea"),
     // Multiple queries rely on the TestHive singleton. See comments there for more details.

From 9b23e92c727881ff9038b4fe9643c49b96914159 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Thu, 2 Jul 2015 22:10:24 -0700
Subject: [PATCH 0216/1454] [SPARK-8803] handle special characters in elements
 in crosstab

cc rxin

Having back ticks or null as elements causes problems.
Since elements become column names, we have to drop them from the element as back ticks are special characters.
Having null throws exceptions, we could replace them with empty strings.

Handling back ticks should be improved for 1.5

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7201 from brkyvz/weird-ct-elements and squashes the following commits:

e06b840 [Burak Yavuz] fix scalastyle
93a0d3f [Burak Yavuz] added tests for NaN and Infinity
9dba6ce [Burak Yavuz] address cr1
db71dbd [Burak Yavuz] handle special characters in elements in crosstab
---
 .../spark/sql/DataFrameNaFunctions.scala      |  2 +-
 .../spark/sql/DataFrameStatFunctions.scala    |  3 ++
 .../sql/execution/stat/StatFunctions.scala    | 20 ++++++++++---
 .../apache/spark/sql/DataFrameStatSuite.scala | 30 +++++++++++++++++++
 4 files changed, 50 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index b4c2daa055868..8681a56c82f1e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -391,7 +391,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * Returns a [[Column]] expression that replaces null value in `col` with `replacement`.
    */
   private def fillCol[T](col: StructField, replacement: T): Column = {
-    coalesce(df.col(col.name), lit(replacement).cast(col.dataType)).as(col.name)
+    coalesce(df.col("`" + col.name + "`"), lit(replacement).cast(col.dataType)).as(col.name)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index edb9ed7bba56a..587869e57f96e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -78,6 +78,9 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * The first column of each row will be the distinct values of `col1` and the column names will
    * be the distinct values of `col2`. The name of the first column will be `$col1_$col2`. Counts
    * will be returned as `Long`s. Pairs that have no occurrences will have `null` as their counts.
+   * Null elements will be replaced by "null", and back ticks will be dropped from elements if they
+   * exist.
+   *
    *
    * @param col1 The name of the first column. Distinct items will make the first item of
    *             each row.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index 23ddfa9839e5e..00231d65a7d54 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -110,8 +110,12 @@ private[sql] object StatFunctions extends Logging {
       logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " +
         "the pairs. Please try reducing the amount of distinct items in your columns.")
     }
+    def cleanElement(element: Any): String = {
+      if (element == null) "null" else element.toString
+    }
     // get the distinct values of column 2, so that we can make them the column names
-    val distinctCol2: Map[Any, Int] = counts.map(_.get(1)).distinct.zipWithIndex.toMap
+    val distinctCol2: Map[Any, Int] =
+      counts.map(e => cleanElement(e.get(1))).distinct.zipWithIndex.toMap
     val columnSize = distinctCol2.size
     require(columnSize < 1e4, s"The number of distinct values for $col2, can't " +
       s"exceed 1e4. Currently $columnSize")
@@ -121,15 +125,23 @@ private[sql] object StatFunctions extends Logging {
         // row.get(0) is column 1
         // row.get(1) is column 2
         // row.get(2) is the frequency
-        countsRow.setLong(distinctCol2.get(row.get(1)).get + 1, row.getLong(2))
+        val columnIndex = distinctCol2.get(cleanElement(row.get(1))).get
+        countsRow.setLong(columnIndex + 1, row.getLong(2))
       }
       // the value of col1 is the first value, the rest are the counts
-      countsRow.update(0, UTF8String.fromString(col1Item.toString))
+      countsRow.update(0, UTF8String.fromString(cleanElement(col1Item)))
       countsRow
     }.toSeq
+    // Back ticks can't exist in DataFrame column names, therefore drop them. To be able to accept
+    // special keywords and `.`, wrap the column names in ``.
+    def cleanColumnName(name: String): String = {
+      name.replace("`", "")
+    }
     // In the map, the column names (._1) are not ordered by the index (._2). This was the bug in
     // SPARK-8681. We need to explicitly sort by the column index and assign the column names.
-    val headerNames = distinctCol2.toSeq.sortBy(_._2).map(r => StructField(r._1.toString, LongType))
+    val headerNames = distinctCol2.toSeq.sortBy(_._2).map { r =>
+      StructField(cleanColumnName(r._1.toString), LongType)
+    }
     val schema = StructType(StructField(tableName, StringType) +: headerNames)
 
     new DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table)).na.fill(0.0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 765094da6bda7..7ba4ba73e0cc9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -85,6 +85,36 @@ class DataFrameStatSuite extends SparkFunSuite  {
     }
   }
 
+  test("special crosstab elements (., '', null, ``)") {
+    val data = Seq(
+      ("a", Double.NaN, "ho"),
+      (null, 2.0, "ho"),
+      ("a.b", Double.NegativeInfinity, ""),
+      ("b", Double.PositiveInfinity, "`ha`"),
+      ("a", 1.0, null)
+    )
+    val df = data.toDF("1", "2", "3")
+    val ct1 = df.stat.crosstab("1", "2")
+    // column fields should be 1 + distinct elements of second column
+    assert(ct1.schema.fields.length === 6)
+    assert(ct1.collect().length === 4)
+    val ct2 = df.stat.crosstab("1", "3")
+    assert(ct2.schema.fields.length === 5)
+    assert(ct2.schema.fieldNames.contains("ha"))
+    assert(ct2.collect().length === 4)
+    val ct3 = df.stat.crosstab("3", "2")
+    assert(ct3.schema.fields.length === 6)
+    assert(ct3.schema.fieldNames.contains("NaN"))
+    assert(ct3.schema.fieldNames.contains("Infinity"))
+    assert(ct3.schema.fieldNames.contains("-Infinity"))
+    assert(ct3.collect().length === 4)
+    val ct4 = df.stat.crosstab("3", "1")
+    assert(ct4.schema.fields.length === 5)
+    assert(ct4.schema.fieldNames.contains("null"))
+    assert(ct4.schema.fieldNames.contains("a.b"))
+    assert(ct4.collect().length === 4)
+  }
+
   test("Frequent Items") {
     val rows = Seq.tabulate(1000) { i =>
       if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0)

From 2848f4da47d5c395de93ab9960bd905edfbd3439 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 3 Jul 2015 00:25:02 -0700
Subject: [PATCH 0217/1454] [SPARK-8809][SQL] Remove ConvertNaNs analyzer rule.

"NaN" from string to double is already handled by Cast expression itself.

Author: Reynold Xin <rxin@databricks.com>

Closes #7206 from rxin/convertnans and squashes the following commits:

3d99c33 [Reynold Xin] [SPARK-8809][SQL] Remove ConvertNaNs analyzer rule.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 33 -------------------
 1 file changed, 33 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 6006e7bf00c13..38eb8322c854f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -35,7 +35,6 @@ object HiveTypeCoercion {
 
   val typeCoercionRules =
     PropagateTypes ::
-      ConvertNaNs ::
       InConversion ::
       WidenTypes ::
       PromoteStrings ::
@@ -148,38 +147,6 @@ object HiveTypeCoercion {
     }
   }
 
-  /**
-   * Converts string "NaN"s that are in binary operators with a NaN-able types (Float / Double) to
-   * the appropriate numeric equivalent.
-   */
-  // TODO: remove this rule and make Cast handle Nan.
-  object ConvertNaNs extends Rule[LogicalPlan] {
-    private val StringNaN = Literal("NaN")
-
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      case q: LogicalPlan => q transformExpressions {
-        // Skip nodes who's children have not been resolved yet.
-        case e if !e.childrenResolved => e
-
-        /* Double Conversions */
-        case b @ BinaryOperator(StringNaN, right @ DoubleType()) =>
-          b.makeCopy(Array(Literal(Double.NaN), right))
-        case b @ BinaryOperator(left @ DoubleType(), StringNaN) =>
-          b.makeCopy(Array(left, Literal(Double.NaN)))
-
-        /* Float Conversions */
-        case b @ BinaryOperator(StringNaN, right @ FloatType()) =>
-          b.makeCopy(Array(Literal(Float.NaN), right))
-        case b @ BinaryOperator(left @ FloatType(), StringNaN) =>
-          b.makeCopy(Array(left, Literal(Float.NaN)))
-
-        /* Use float NaN by default to avoid unnecessary type widening */
-        case b @ BinaryOperator(left @ StringNaN, StringNaN) =>
-          b.makeCopy(Array(left, Literal(Float.NaN)))
-      }
-    }
-  }
-
   /**
    * Widens numeric types and converts strings to numbers when appropriate.
    *

From ab535b9a1dab40ea7335ff9abb9b522fc2b5ed66 Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Fri, 3 Jul 2015 15:39:16 -0700
Subject: [PATCH 0218/1454] [SPARK-8226] [SQL] Add function shiftrightunsigned

Author: zhichao.li <zhichao.li@intel.com>

Closes #7035 from zhichao-li/shiftRightUnsigned and squashes the following commits:

6bcca5a [zhichao.li] change coding style
3e9f5ae [zhichao.li] python style
d85ae0b [zhichao.li] add shiftrightunsigned
---
 python/pyspark/sql/functions.py               | 13 +++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../spark/sql/catalyst/expressions/math.scala | 49 +++++++++++++++++++
 .../expressions/MathFunctionsSuite.scala      | 13 +++++
 .../org/apache/spark/sql/functions.scala      | 20 ++++++++
 .../spark/sql/MathExpressionsSuite.scala      | 17 +++++++
 6 files changed, 113 insertions(+)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 12263e6a75af8..69e563ef36e87 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -436,6 +436,19 @@ def shiftRight(col, numBits):
     return Column(jc)
 
 
+@since(1.5)
+def shiftRightUnsigned(col, numBits):
+    """Unsigned shift the the given value numBits right.
+
+    >>> sqlContext.createDataFrame([(-42,)], ['a']).select(shiftRightUnsigned('a', 1).alias('r'))\
+    .collect()
+    [Row(r=9223372036854775787)]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.shiftRightUnsigned(_to_java_column(col), numBits)
+    return Column(jc)
+
+
 @since(1.4)
 def sparkPartitionId():
     """A column for partition ID of the Spark task.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 9163b032adee4..cd5ba1217ccc0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -129,6 +129,7 @@ object FunctionRegistry {
     expression[Rint]("rint"),
     expression[ShiftLeft]("shiftleft"),
     expression[ShiftRight]("shiftright"),
+    expression[ShiftRightUnsigned]("shiftrightunsigned"),
     expression[Signum]("sign"),
     expression[Signum]("signum"),
     expression[Sin]("sin"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 273a6c5016577..0fc320fb08876 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -521,6 +521,55 @@ case class ShiftRight(left: Expression, right: Expression) extends BinaryExpress
   }
 }
 
+case class ShiftRightUnsigned(left: Expression, right: Expression) extends BinaryExpression {
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    (left.dataType, right.dataType) match {
+      case (NullType, _) | (_, NullType) => return TypeCheckResult.TypeCheckSuccess
+      case (_, IntegerType) => left.dataType match {
+        case LongType | IntegerType | ShortType | ByteType =>
+          return TypeCheckResult.TypeCheckSuccess
+        case _ => // failed
+      }
+      case _ => // failed
+    }
+    TypeCheckResult.TypeCheckFailure(
+      s"ShiftRightUnsigned expects long, integer, short or byte value as first argument and an " +
+        s"integer value as second argument, not (${left.dataType}, ${right.dataType})")
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val valueLeft = left.eval(input)
+    if (valueLeft != null) {
+      val valueRight = right.eval(input)
+      if (valueRight != null) {
+        valueLeft match {
+          case l: Long => l >>> valueRight.asInstanceOf[Integer]
+          case i: Integer => i >>> valueRight.asInstanceOf[Integer]
+          case s: Short => s >>> valueRight.asInstanceOf[Integer]
+          case b: Byte => b >>> valueRight.asInstanceOf[Integer]
+        }
+      } else {
+        null
+      }
+    } else {
+      null
+    }
+  }
+
+  override def dataType: DataType = {
+    left.dataType match {
+      case LongType => LongType
+      case IntegerType | ShortType | ByteType => IntegerType
+      case _ => NullType
+    }
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left >>> $right;")
+  }
+}
+
 /**
  * Performs the inverse operation of HEX.
  * Resulting characters are returned as a byte array.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 8457864d1782d..20839c83d4fd0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -264,6 +264,19 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(ShiftRight(Literal(-42.toLong), Literal(1)), -21.toLong)
   }
 
+  test("shift right unsigned") {
+    checkEvaluation(ShiftRightUnsigned(Literal.create(null, IntegerType), Literal(1)), null)
+    checkEvaluation(ShiftRightUnsigned(Literal(42), Literal.create(null, IntegerType)), null)
+    checkEvaluation(
+      ShiftRight(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null)
+    checkEvaluation(ShiftRightUnsigned(Literal(42), Literal(1)), 21)
+    checkEvaluation(ShiftRightUnsigned(Literal(42.toByte), Literal(1)), 21)
+    checkEvaluation(ShiftRightUnsigned(Literal(42.toShort), Literal(1)), 21)
+    checkEvaluation(ShiftRightUnsigned(Literal(42.toLong), Literal(1)), 21.toLong)
+
+    checkEvaluation(ShiftRightUnsigned(Literal(-42.toLong), Literal(1)), 9223372036854775787L)
+  }
+
   test("hex") {
     checkEvaluation(Hex(Literal(28)), "1C")
     checkEvaluation(Hex(Literal(-28)), "FFFFFFFFFFFFFFE4")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 0d5d49c3dd1d7..4b70dc5fdde8d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1343,6 +1343,26 @@ object functions {
    */
   def shiftRight(e: Column, numBits: Int): Column = ShiftRight(e.expr, lit(numBits).expr)
 
+  /**
+   * Unsigned shift the the given value numBits right. If the given value is a long value,
+   * it will return a long value else it will return an integer value.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def shiftRightUnsigned(columnName: String, numBits: Int): Column =
+    shiftRightUnsigned(Column(columnName), numBits)
+
+  /**
+   * Unsigned shift the the given value numBits right. If the given value is a long value,
+   * it will return a long value else it will return an integer value.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def shiftRightUnsigned(e: Column, numBits: Int): Column =
+    ShiftRightUnsigned(e.expr, lit(numBits).expr)
+
   /**
    * Shift the the given value numBits right. If the given value is a long value, it will return
    * a long value else it will return an integer value.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index dc8f994adbd39..24bef21b999ea 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -304,6 +304,23 @@ class MathExpressionsSuite extends QueryTest {
       Row(21.toLong, 21, 21.toShort, 21.toByte, null))
   }
 
+  test("shift right unsigned") {
+    val df = Seq[(Long, Integer, Short, Byte, Integer, Integer)]((-42, 42, 42, 42, 42, null))
+      .toDF("a", "b", "c", "d", "e", "f")
+
+    checkAnswer(
+      df.select(
+        shiftRightUnsigned('a, 1), shiftRightUnsigned('b, 1), shiftRightUnsigned('c, 1),
+        shiftRightUnsigned('d, 1), shiftRightUnsigned('f, 1)),
+      Row(9223372036854775787L, 21, 21.toShort, 21.toByte, null))
+
+    checkAnswer(
+      df.selectExpr(
+        "shiftRightUnsigned(a, 1)", "shiftRightUnsigned(b, 1)", "shiftRightUnsigned(c, 1)",
+        "shiftRightUnsigned(d, 1)", "shiftRightUnsigned(f, 1)"),
+      Row(9223372036854775787L, 21, 21.toShort, 21.toByte, null))
+  }
+
   test("binary log") {
     val df = Seq[(Integer, Integer)]((123, null)).toDF("a", "b")
     checkAnswer(

From f0fac2aa80da7c739b88043571e5d49ba40f9413 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Fri, 3 Jul 2015 15:49:32 -0700
Subject: [PATCH 0219/1454] [SPARK-7401] [MLLIB] [PYSPARK] Vectorize dot
 product and sq_dist between SparseVector and DenseVector

Currently we iterate over indices which can be vectorized.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #5946 from MechCoder/spark-7203 and squashes the following commits:

034d086 [MechCoder] Vectorize dot calculation for numpy arrays for ndim=2
bce2b07 [MechCoder] fix doctest
fcad0a3 [MechCoder] Remove type checks for list, pyarray etc
0ee5dd4 [MechCoder] Add tests and other isinstance changes
e5f1de0 [MechCoder] [SPARK-7401] Vectorize dot product and sq_dist
---
 python/pyspark/mllib/linalg.py | 44 ++++++++++++++++------------------
 python/pyspark/mllib/tests.py  |  8 +++++++
 2 files changed, 29 insertions(+), 23 deletions(-)

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index e96c5ef87df86..9959a01cce7e0 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -577,22 +577,19 @@ def dot(self, other):
             ...
         AssertionError: dimension mismatch
         """
-        if type(other) == np.ndarray:
-            if other.ndim == 2:
-                results = [self.dot(other[:, i]) for i in xrange(other.shape[1])]
-                return np.array(results)
-            elif other.ndim > 2:
+
+        if isinstance(other, np.ndarray):
+            if other.ndim not in [2, 1]:
                 raise ValueError("Cannot call dot with %d-dimensional array" % other.ndim)
+            assert len(self) == other.shape[0], "dimension mismatch"
+            return np.dot(self.values, other[self.indices])
 
         assert len(self) == _vector_size(other), "dimension mismatch"
 
-        if type(other) in (np.ndarray, array.array, DenseVector):
-            result = 0.0
-            for i in xrange(len(self.indices)):
-                result += self.values[i] * other[self.indices[i]]
-            return result
+        if isinstance(other, DenseVector):
+            return np.dot(other.array[self.indices], self.values)
 
-        elif type(other) is SparseVector:
+        elif isinstance(other, SparseVector):
             result = 0.0
             i, j = 0, 0
             while i < len(self.indices) and j < len(other.indices):
@@ -635,22 +632,23 @@ def squared_distance(self, other):
         AssertionError: dimension mismatch
         """
         assert len(self) == _vector_size(other), "dimension mismatch"
-        if type(other) in (list, array.array, DenseVector, np.array, np.ndarray):
-            if type(other) is np.array and other.ndim != 1:
+
+        if isinstance(other, np.ndarray) or isinstance(other, DenseVector):
+            if isinstance(other, np.ndarray) and other.ndim != 1:
                 raise Exception("Cannot call squared_distance with %d-dimensional array" %
                                 other.ndim)
-            result = 0.0
-            j = 0   # index into our own array
-            for i in xrange(len(other)):
-                if j < len(self.indices) and self.indices[j] == i:
-                    diff = self.values[j] - other[i]
-                    result += diff * diff
-                    j += 1
-                else:
-                    result += other[i] * other[i]
+            if isinstance(other, DenseVector):
+                other = other.array
+            sparse_ind = np.zeros(other.size, dtype=bool)
+            sparse_ind[self.indices] = True
+            dist = other[sparse_ind] - self.values
+            result = np.dot(dist, dist)
+
+            other_ind = other[~sparse_ind]
+            result += np.dot(other_ind, other_ind)
             return result
 
-        elif type(other) is SparseVector:
+        elif isinstance(other, SparseVector):
             result = 0.0
             i, j = 0, 0
             while i < len(self.indices) and j < len(other.indices):
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 49ce125de7e78..d9f9874d50c1a 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -129,17 +129,22 @@ def test_dot(self):
                      [1., 2., 3., 4.],
                      [1., 2., 3., 4.],
                      [1., 2., 3., 4.]])
+        arr = pyarray.array('d', [0, 1, 2, 3])
         self.assertEquals(10.0, sv.dot(dv))
         self.assertTrue(array_equal(array([3., 6., 9., 12.]), sv.dot(mat)))
         self.assertEquals(30.0, dv.dot(dv))
         self.assertTrue(array_equal(array([10., 20., 30., 40.]), dv.dot(mat)))
         self.assertEquals(30.0, lst.dot(dv))
         self.assertTrue(array_equal(array([10., 20., 30., 40.]), lst.dot(mat)))
+        self.assertEquals(7.0, sv.dot(arr))
 
     def test_squared_distance(self):
         sv = SparseVector(4, {1: 1, 3: 2})
         dv = DenseVector(array([1., 2., 3., 4.]))
         lst = DenseVector([4, 3, 2, 1])
+        lst1 = [4, 3, 2, 1]
+        arr = pyarray.array('d', [0, 2, 1, 3])
+        narr = array([0, 2, 1, 3])
         self.assertEquals(15.0, _squared_distance(sv, dv))
         self.assertEquals(25.0, _squared_distance(sv, lst))
         self.assertEquals(20.0, _squared_distance(dv, lst))
@@ -149,6 +154,9 @@ def test_squared_distance(self):
         self.assertEquals(0.0, _squared_distance(sv, sv))
         self.assertEquals(0.0, _squared_distance(dv, dv))
         self.assertEquals(0.0, _squared_distance(lst, lst))
+        self.assertEquals(25.0, _squared_distance(sv, lst1))
+        self.assertEquals(3.0, _squared_distance(sv, arr))
+        self.assertEquals(3.0, _squared_distance(sv, narr))
 
     def test_conversion(self):
         # numpy arrays should be automatically upcast to float64

From e92c24d37cae54634e7af20cbfe313d023786f87 Mon Sep 17 00:00:00 2001
From: Spiro Michaylov <spiro@michaylov.com>
Date: Fri, 3 Jul 2015 20:15:58 -0700
Subject: [PATCH 0220/1454] [SPARK-8810] [SQL] Added several UDF unit tests for
 Spark SQL

One test for each of the GROUP BY, WHERE and HAVING clauses, and one that combines all three with an additional UDF in the SELECT.

(Since this is my first attempt at contributing to SPARK, meta-level guidance on anything I've screwed up would be greatly appreciated, whether important or minor.)

Author: Spiro Michaylov <spiro@michaylov.com>

Closes #7207 from spirom/udf-test-branch and squashes the following commits:

6bbba9e [Spiro Michaylov] Responded to review comments on UDF unit tests
1a3c5ff [Spiro Michaylov] Added several UDF unit tests for Spark SQL
---
 .../scala/org/apache/spark/sql/UDFSuite.scala | 70 +++++++++++++++++++
 1 file changed, 70 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index 703a34c47ec20..8e5da3ac14da6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -82,6 +82,76 @@ class UDFSuite extends QueryTest {
     assert(ctx.sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5)
   }
 
+  test("UDF in a WHERE") {
+    ctx.udf.register("oneArgFilter", (n: Int) => { n > 80 })
+
+    val df = ctx.sparkContext.parallelize(
+      (1 to 100).map(i => TestData(i, i.toString))).toDF()
+    df.registerTempTable("integerData")
+
+    val result =
+      ctx.sql("SELECT * FROM integerData WHERE oneArgFilter(key)")
+    assert(result.count() === 20)
+  }
+
+  test("UDF in a HAVING") {
+    ctx.udf.register("havingFilter", (n: Long) => { n > 5 })
+
+    val df = Seq(("red", 1), ("red", 2), ("blue", 10),
+      ("green", 100), ("green", 200)).toDF("g", "v")
+    df.registerTempTable("groupData")
+
+    val result =
+      ctx.sql(
+        """
+         | SELECT g, SUM(v) as s
+         | FROM groupData
+         | GROUP BY g
+         | HAVING havingFilter(s)
+        """.stripMargin)
+
+    assert(result.count() === 2)
+  }
+
+  test("UDF in a GROUP BY") {
+    ctx.udf.register("groupFunction", (n: Int) => { n > 10 })
+
+    val df = Seq(("red", 1), ("red", 2), ("blue", 10),
+      ("green", 100), ("green", 200)).toDF("g", "v")
+    df.registerTempTable("groupData")
+
+    val result =
+      ctx.sql(
+        """
+         | SELECT SUM(v)
+         | FROM groupData
+         | GROUP BY groupFunction(v)
+        """.stripMargin)
+    assert(result.count() === 2)
+  }
+
+  test("UDFs everywhere") {
+    ctx.udf.register("groupFunction", (n: Int) => { n > 10 })
+    ctx.udf.register("havingFilter", (n: Long) => { n > 2000 })
+    ctx.udf.register("whereFilter", (n: Int) => { n < 150 })
+    ctx.udf.register("timesHundred", (n: Long) => { n * 100 })
+
+    val df = Seq(("red", 1), ("red", 2), ("blue", 10),
+      ("green", 100), ("green", 200)).toDF("g", "v")
+    df.registerTempTable("groupData")
+
+    val result =
+      ctx.sql(
+        """
+         | SELECT timesHundred(SUM(v)) as v100
+         | FROM groupData
+         | WHERE whereFilter(v)
+         | GROUP BY groupFunction(v)
+         | HAVING havingFilter(v100)
+        """.stripMargin)
+    assert(result.count() === 1)
+  }
+
   test("struct UDF") {
     ctx.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
 

From 4a22bce8fce30f86f364467a8ba51d2e744ff379 Mon Sep 17 00:00:00 2001
From: Cheolsoo Park <cheolsoop@netflix.com>
Date: Fri, 3 Jul 2015 22:14:21 -0700
Subject: [PATCH 0221/1454] [SPARK-8572] [SQL] Type coercion for ScalaUDFs

Implemented type coercion for udf arguments in Scala. The changes include-
* Add `with ExpectsInputTypes ` to `ScalaUDF` class.
* Pass down argument types info from `UDFRegistration` and `functions`.

With this patch, the example query in [SPARK-8572](https://issues.apache.org/jira/browse/SPARK-8572) no longer throws a type cast error at runtime.

Also added a unit test to `UDFSuite` in which a decimal type is passed to a udf that expects an int.

Author: Cheolsoo Park <cheolsoop@netflix.com>

Closes #7203 from piaozhexiu/SPARK-8572 and squashes the following commits:

2d0ed15 [Cheolsoo Park] Incorporate comments
dce1efd [Cheolsoo Park] Fix unit tests and update the codegen script
066deed [Cheolsoo Park] Type coercion for udf inputs
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  2 +-
 .../sql/catalyst/expressions/ScalaUDF.scala   |  7 +-
 .../apache/spark/sql/UDFRegistration.scala    | 75 ++++++++++++-------
 .../spark/sql/UserDefinedFunction.scala       |  7 +-
 .../org/apache/spark/sql/functions.scala      | 38 +++++++---
 .../scala/org/apache/spark/sql/UDFSuite.scala |  6 ++
 6 files changed, 93 insertions(+), 42 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 38eb8322c854f..84acc0e7e90ec 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -680,7 +680,7 @@ object HiveTypeCoercion {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      case e: ExpectsInputTypes =>
+      case e: ExpectsInputTypes if (e.inputTypes.nonEmpty) =>
         val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
           // If we cannot do the implicit cast, just use the original input.
           implicitCast(in, expected).getOrElse(in)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index caf021b016a41..fc055c97a179f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -24,8 +24,11 @@ import org.apache.spark.sql.types.DataType
  * User-defined function.
  * @param dataType  Return type of function.
  */
-case class ScalaUDF(function: AnyRef, dataType: DataType, children: Seq[Expression])
-  extends Expression {
+case class ScalaUDF(
+    function: AnyRef,
+    dataType: DataType,
+    children: Seq[Expression],
+    inputTypes: Seq[DataType] = Nil) extends Expression with ExpectsInputTypes {
 
   override def nullable: Boolean = true
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
index 03dc37aa73f0c..d35d37d017198 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.util.{List => JList, Map => JMap}
 
 import scala.reflect.runtime.universe.TypeTag
+import scala.util.Try
 
 import org.apache.spark.{Accumulator, Logging}
 import org.apache.spark.api.python.PythonBroadcast
@@ -30,7 +31,6 @@ import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
 import org.apache.spark.sql.execution.PythonUDF
 import org.apache.spark.sql.types.DataType
 
-
 /**
  * Functions for registering user-defined functions. Use [[SQLContext.udf]] to access this.
  *
@@ -87,6 +87,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
     (0 to 22).map { x =>
       val types = (1 to x).foldRight("RT")((i, s) => {s"A$i, $s"})
       val typeTags = (1 to x).map(i => s"A${i}: TypeTag").foldLeft("RT: TypeTag")(_ + ", " + _)
+      val inputTypes = (1 to x).foldRight("Nil")((i, s) => {s"ScalaReflection.schemaFor[A$i].dataType :: $s"})
       println(s"""
         /**
          * Register a Scala closure of ${x} arguments as user-defined function (UDF).
@@ -95,7 +96,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
          */
         def register[$typeTags](name: String, func: Function$x[$types]): UserDefinedFunction = {
           val dataType = ScalaReflection.schemaFor[RT].dataType
-          def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+          val inputTypes = Try($inputTypes).getOrElse(Nil)
+          def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
           functionRegistry.registerFunction(name, builder)
           UserDefinedFunction(func, dataType)
         }""")
@@ -126,7 +128,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag](name: String, func: Function0[RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -138,7 +141,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag](name: String, func: Function1[A1, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -150,7 +154,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag](name: String, func: Function2[A1, A2, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -162,7 +167,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag](name: String, func: Function3[A1, A2, A3, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -174,7 +180,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag](name: String, func: Function4[A1, A2, A3, A4, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -186,7 +193,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag](name: String, func: Function5[A1, A2, A3, A4, A5, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -198,7 +206,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag](name: String, func: Function6[A1, A2, A3, A4, A5, A6, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -210,7 +219,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag](name: String, func: Function7[A1, A2, A3, A4, A5, A6, A7, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -222,7 +232,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag](name: String, func: Function8[A1, A2, A3, A4, A5, A6, A7, A8, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -234,7 +245,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag](name: String, func: Function9[A1, A2, A3, A4, A5, A6, A7, A8, A9, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -246,7 +258,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](name: String, func: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -258,7 +271,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag](name: String, func: Function11[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -270,7 +284,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag](name: String, func: Function12[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -282,7 +297,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag](name: String, func: Function13[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -294,7 +310,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag](name: String, func: Function14[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -306,7 +323,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag](name: String, func: Function15[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -318,7 +336,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag](name: String, func: Function16[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: ScalaReflection.schemaFor[A16].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -330,7 +349,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag](name: String, func: Function17[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: ScalaReflection.schemaFor[A16].dataType :: ScalaReflection.schemaFor[A17].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -342,7 +362,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag](name: String, func: Function18[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: ScalaReflection.schemaFor[A16].dataType :: ScalaReflection.schemaFor[A17].dataType :: ScalaReflection.schemaFor[A18].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -354,7 +375,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag](name: String, func: Function19[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: ScalaReflection.schemaFor[A16].dataType :: ScalaReflection.schemaFor[A17].dataType :: ScalaReflection.schemaFor[A18].dataType :: ScalaReflection.schemaFor[A19].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -366,7 +388,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag](name: String, func: Function20[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: ScalaReflection.schemaFor[A16].dataType :: ScalaReflection.schemaFor[A17].dataType :: ScalaReflection.schemaFor[A18].dataType :: ScalaReflection.schemaFor[A19].dataType :: ScalaReflection.schemaFor[A20].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -378,7 +401,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag](name: String, func: Function21[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: ScalaReflection.schemaFor[A16].dataType :: ScalaReflection.schemaFor[A17].dataType :: ScalaReflection.schemaFor[A18].dataType :: ScalaReflection.schemaFor[A19].dataType :: ScalaReflection.schemaFor[A20].dataType :: ScalaReflection.schemaFor[A21].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
@@ -390,7 +414,8 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
    */
   def register[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag, A22: TypeTag](name: String, func: Function22[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, A22, RT]): UserDefinedFunction = {
     val dataType = ScalaReflection.schemaFor[RT].dataType
-    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e)
+    val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType :: ScalaReflection.schemaFor[A2].dataType :: ScalaReflection.schemaFor[A3].dataType :: ScalaReflection.schemaFor[A4].dataType :: ScalaReflection.schemaFor[A5].dataType :: ScalaReflection.schemaFor[A6].dataType :: ScalaReflection.schemaFor[A7].dataType :: ScalaReflection.schemaFor[A8].dataType :: ScalaReflection.schemaFor[A9].dataType :: ScalaReflection.schemaFor[A10].dataType :: ScalaReflection.schemaFor[A11].dataType :: ScalaReflection.schemaFor[A12].dataType :: ScalaReflection.schemaFor[A13].dataType :: ScalaReflection.schemaFor[A14].dataType :: ScalaReflection.schemaFor[A15].dataType :: ScalaReflection.schemaFor[A16].dataType :: ScalaReflection.schemaFor[A17].dataType :: ScalaReflection.schemaFor[A18].dataType :: ScalaReflection.schemaFor[A19].dataType :: ScalaReflection.schemaFor[A20].dataType :: ScalaReflection.schemaFor[A21].dataType :: ScalaReflection.schemaFor[A22].dataType :: Nil).getOrElse(Nil)
+    def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e, inputTypes)
     functionRegistry.registerFunction(name, builder)
     UserDefinedFunction(func, dataType)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
index 831eb7eb0fae9..b14e00ab9b163 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
@@ -41,10 +41,13 @@ import org.apache.spark.sql.types.DataType
  * @since 1.3.0
  */
 @Experimental
-case class UserDefinedFunction protected[sql] (f: AnyRef, dataType: DataType) {
+case class UserDefinedFunction protected[sql] (
+    f: AnyRef,
+    dataType: DataType,
+    inputTypes: Seq[DataType] = Nil) {
 
   def apply(exprs: Column*): Column = {
-    Column(ScalaUDF(f, dataType, exprs.map(_.expr)))
+    Column(ScalaUDF(f, dataType, exprs.map(_.expr), inputTypes))
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4b70dc5fdde8d..d261baf920c0c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql
 
 import scala.language.implicitConversions
 import scala.reflect.runtime.universe.{TypeTag, typeTag}
+import scala.util.Try
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.ScalaReflection
@@ -1584,6 +1585,7 @@ object functions {
   (0 to 10).map { x =>
     val types = (1 to x).foldRight("RT")((i, s) => {s"A$i, $s"})
     val typeTags = (1 to x).map(i => s"A$i: TypeTag").foldLeft("RT: TypeTag")(_ + ", " + _)
+    val inputTypes = (1 to x).foldRight("Nil")((i, s) => {s"ScalaReflection.schemaFor(typeTag[A$i]).dataType :: $s"})
     println(s"""
     /**
      * Defines a user-defined function of ${x} arguments as user-defined function (UDF).
@@ -1593,7 +1595,8 @@ object functions {
      * @since 1.3.0
      */
     def udf[$typeTags](f: Function$x[$types]): UserDefinedFunction = {
-      UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+      val inputTypes = Try($inputTypes).getOrElse(Nil)
+      UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
     }""")
   }
 
@@ -1625,7 +1628,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag](f: Function0[RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1636,7 +1640,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag](f: Function1[A1, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1647,7 +1652,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1658,7 +1664,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag](f: Function3[A1, A2, A3, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1669,7 +1676,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag](f: Function4[A1, A2, A3, A4, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: ScalaReflection.schemaFor(typeTag[A4]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1680,7 +1688,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag](f: Function5[A1, A2, A3, A4, A5, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: ScalaReflection.schemaFor(typeTag[A4]).dataType :: ScalaReflection.schemaFor(typeTag[A5]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1691,7 +1700,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag](f: Function6[A1, A2, A3, A4, A5, A6, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: ScalaReflection.schemaFor(typeTag[A4]).dataType :: ScalaReflection.schemaFor(typeTag[A5]).dataType :: ScalaReflection.schemaFor(typeTag[A6]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1702,7 +1712,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag](f: Function7[A1, A2, A3, A4, A5, A6, A7, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: ScalaReflection.schemaFor(typeTag[A4]).dataType :: ScalaReflection.schemaFor(typeTag[A5]).dataType :: ScalaReflection.schemaFor(typeTag[A6]).dataType :: ScalaReflection.schemaFor(typeTag[A7]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1713,7 +1724,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag](f: Function8[A1, A2, A3, A4, A5, A6, A7, A8, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: ScalaReflection.schemaFor(typeTag[A4]).dataType :: ScalaReflection.schemaFor(typeTag[A5]).dataType :: ScalaReflection.schemaFor(typeTag[A6]).dataType :: ScalaReflection.schemaFor(typeTag[A7]).dataType :: ScalaReflection.schemaFor(typeTag[A8]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1724,7 +1736,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag](f: Function9[A1, A2, A3, A4, A5, A6, A7, A8, A9, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: ScalaReflection.schemaFor(typeTag[A4]).dataType :: ScalaReflection.schemaFor(typeTag[A5]).dataType :: ScalaReflection.schemaFor(typeTag[A6]).dataType :: ScalaReflection.schemaFor(typeTag[A7]).dataType :: ScalaReflection.schemaFor(typeTag[A8]).dataType :: ScalaReflection.schemaFor(typeTag[A9]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   /**
@@ -1735,7 +1748,8 @@ object functions {
    * @since 1.3.0
    */
   def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](f: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT]): UserDefinedFunction = {
-    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType)
+    val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: ScalaReflection.schemaFor(typeTag[A3]).dataType :: ScalaReflection.schemaFor(typeTag[A4]).dataType :: ScalaReflection.schemaFor(typeTag[A5]).dataType :: ScalaReflection.schemaFor(typeTag[A6]).dataType :: ScalaReflection.schemaFor(typeTag[A7]).dataType :: ScalaReflection.schemaFor(typeTag[A8]).dataType :: ScalaReflection.schemaFor(typeTag[A9]).dataType :: ScalaReflection.schemaFor(typeTag[A10]).dataType :: Nil).getOrElse(Nil)
+    UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
   }
 
   //////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index 8e5da3ac14da6..c1516b450cbd4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -166,4 +166,10 @@ class UDFSuite extends QueryTest {
     // 1 + 1 is constant folded causing a transformation.
     assert(ctx.sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2))
   }
+
+  test("type coercion for udf inputs") {
+    ctx.udf.register("intExpected", (x: Int) => x)
+    // pass a decimal to intExpected.
+    assert(ctx.sql("SELECT intExpected(1.0)").head().getInt(0) === 1)
+  }
 }

From 9fb6b832bcc2556aa9db2981106cbd09f2959031 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Fri, 3 Jul 2015 22:19:43 -0700
Subject: [PATCH 0222/1454] [SPARK-8192] [SPARK-8193] [SQL] udf current_date,
 current_timestamp

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #6985 from adrian-wang/udfcurrent and squashes the following commits:

6a20b64 [Daoyuan Wang] remove codegen and add lazy in testsuite
27c9f95 [Daoyuan Wang] refine tests..
e11ae75 [Daoyuan Wang] refine tests
61ed3d5 [Daoyuan Wang] add in functions
98e8550 [Daoyuan Wang] fix sytle
427d9dc [Daoyuan Wang] add tests and codegen
0b69a1f [Daoyuan Wang] udf current
---
 .../catalyst/analysis/FunctionRegistry.scala  |  6 ++-
 .../expressions/datetimeFunctions.scala       | 52 +++++++++++++++++++
 .../expressions/DatetimeFunctionsSuite.scala  | 37 +++++++++++++
 .../org/apache/spark/sql/functions.scala      | 17 ++++++
 .../spark/sql/DatetimeExpressionsSuite.scala  | 48 +++++++++++++++++
 5 files changed, 159 insertions(+), 1 deletion(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index cd5ba1217ccc0..a1299aed555c1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -163,7 +163,11 @@ object FunctionRegistry {
     expression[Substring]("substring"),
     expression[Upper]("ucase"),
     expression[UnHex]("unhex"),
-    expression[Upper]("upper")
+    expression[Upper]("upper"),
+
+    // datetime functions
+    expression[CurrentDate]("current_date"),
+    expression[CurrentTimestamp]("current_timestamp")
   )
 
   val builtin: FunctionRegistry = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
new file mode 100644
index 0000000000000..13ba2f2e5d62d
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -0,0 +1,52 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types._
+
+/**
+ * Returns the current date at the start of query evaluation.
+ * All calls of current_date within the same query return the same value.
+ */
+case class CurrentDate() extends LeafExpression {
+  override def foldable: Boolean = true
+  override def nullable: Boolean = false
+
+  override def dataType: DataType = DateType
+
+  override def eval(input: InternalRow): Any = {
+    DateTimeUtils.millisToDays(System.currentTimeMillis())
+  }
+}
+
+/**
+ * Returns the current timestamp at the start of query evaluation.
+ * All calls of current_timestamp within the same query return the same value.
+ */
+case class CurrentTimestamp() extends LeafExpression {
+  override def foldable: Boolean = true
+  override def nullable: Boolean = false
+
+  override def dataType: DataType = TimestampType
+
+  override def eval(input: InternalRow): Any = {
+    System.currentTimeMillis() * 10000L
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala
new file mode 100644
index 0000000000000..1618c24871c60
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+
+class DatetimeFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+  test("datetime function current_date") {
+    val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    val cd = CurrentDate().eval(EmptyRow).asInstanceOf[Int]
+    val d1 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    assert(d0 <= cd && cd <= d1 && d1 - d0 <= 1)
+  }
+
+  test("datetime function current_timestamp") {
+    val ct = DateTimeUtils.toJavaTimestamp(CurrentTimestamp().eval(EmptyRow).asInstanceOf[Long])
+    val t1 = System.currentTimeMillis()
+    assert(math.abs(t1 - ct.getTime) < 5000)
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index d261baf920c0c..25e37ff67aa00 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -35,6 +35,7 @@ import org.apache.spark.util.Utils
  *
  * @groupname udf_funcs UDF functions
  * @groupname agg_funcs Aggregate functions
+ * @groupname datetime_funcs Date time functions
  * @groupname sort_funcs Sorting functions
  * @groupname normal_funcs Non-aggregate functions
  * @groupname math_funcs Math functions
@@ -991,6 +992,22 @@ object functions {
    */
   def cosh(columnName: String): Column = cosh(Column(columnName))
 
+  /**
+   * Returns the current date.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def current_date(): Column = CurrentDate()
+
+  /**
+   * Returns the current timestamp.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def current_timestamp(): Column = CurrentTimestamp()
+
   /**
    * Computes the exponential of the given value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala
new file mode 100644
index 0000000000000..44b915304533c
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.functions._
+
+class DatetimeExpressionsSuite extends QueryTest {
+  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+
+  import ctx.implicits._
+
+  lazy val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
+
+  test("function current_date") {
+    val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    val d1 = DateTimeUtils.fromJavaDate(df1.select(current_date()).collect().head.getDate(0))
+    val d2 = DateTimeUtils.fromJavaDate(
+      ctx.sql("""SELECT CURRENT_DATE()""").collect().head.getDate(0))
+    val d3 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    assert(d0 <= d1 && d1 <= d2 && d2 <= d3 && d3 - d0 <= 1)
+  }
+
+  test("function current_timestamp") {
+    checkAnswer(df1.select(countDistinct(current_timestamp())), Row(1))
+    // Execution in one query should return the same value
+    checkAnswer(ctx.sql("""SELECT CURRENT_TIMESTAMP() = CURRENT_TIMESTAMP()"""),
+      Row(true))
+    assert(math.abs(ctx.sql("""SELECT CURRENT_TIMESTAMP()""").collect().head.getTimestamp(
+      0).getTime - System.currentTimeMillis()) < 5000)
+  }
+
+}

From f32487b7ca86f768336a7c9b173f7c610fcde86f Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 3 Jul 2015 23:05:17 -0700
Subject: [PATCH 0223/1454] [SPARK-8777] [SQL] Add random data generator test
 utilities to Spark SQL

This commit adds a set of random data generation utilities to Spark SQL, for use in its own unit tests.

- `RandomDataGenerator.forType(DataType)` returns an `Option[() => Any]` that, if defined, contains a function for generating random values for the given DataType.  The random values use the external representations for the given DataType (for example, for DateType we return `java.sql.Date` instances instead of longs).
- `DateTypeTestUtilities` defines some convenience fields for looping over instances of data types.  For example, `numericTypes` holds `DataType` instances for all supported numeric types.  These constants will help us to raise the level of abstraction in our tests.  For example, it's now very easy to write a test which is parameterized by all common data types.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7176 from JoshRosen/sql-random-data-generators and squashes the following commits:

f71634d [Josh Rosen] Roll back ScalaCheck usage
e0d7d49 [Josh Rosen] Bump ScalaCheck version in LICENSE
89d86b1 [Josh Rosen] Bump ScalaCheck version.
0c20905 [Josh Rosen] Initial attempt at using ScalaCheck.
b55875a [Josh Rosen] Generate doubles and floats over entire possible range.
5acdd5c [Josh Rosen] Infinity and NaN are interesting.
ab76cbd [Josh Rosen] Move code to Catalyst package.
d2b4a4a [Josh Rosen] Add random data generator test utilities to Spark SQL.
---
 .../spark/sql/RandomDataGenerator.scala       | 158 ++++++++++++++++++
 .../spark/sql/RandomDataGeneratorSuite.scala  |  98 +++++++++++
 .../spark/sql/types/DataTypeTestUtils.scala   |  63 +++++++
 3 files changed, 319 insertions(+)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
new file mode 100644
index 0000000000000..13aad467fa578
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -0,0 +1,158 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.lang.Double.longBitsToDouble
+import java.lang.Float.intBitsToFloat
+import java.math.MathContext
+
+import scala.util.Random
+
+import org.apache.spark.sql.types._
+
+/**
+ * Random data generators for Spark SQL DataTypes. These generators do not generate uniformly random
+ * values; instead, they're biased to return "interesting" values (such as maximum / minimum values)
+ * with higher probability.
+ */
+object RandomDataGenerator {
+
+  /**
+   * The conditional probability of a non-null value being drawn from a set of "interesting" values
+   * instead of being chosen uniformly at random.
+   */
+  private val PROBABILITY_OF_INTERESTING_VALUE: Float = 0.5f
+
+  /**
+   * The probability of the generated value being null
+   */
+  private val PROBABILITY_OF_NULL: Float = 0.1f
+
+  private val MAX_STR_LEN: Int = 1024
+  private val MAX_ARR_SIZE: Int = 128
+  private val MAX_MAP_SIZE: Int = 128
+
+  /**
+   * Helper function for constructing a biased random number generator which returns "interesting"
+   * values with a higher probability.
+   */
+  private def randomNumeric[T](
+      rand: Random,
+      uniformRand: Random => T,
+      interestingValues: Seq[T]): Some[() => T] = {
+    val f = () => {
+      if (rand.nextFloat() <= PROBABILITY_OF_INTERESTING_VALUE) {
+        interestingValues(rand.nextInt(interestingValues.length))
+      } else {
+        uniformRand(rand)
+      }
+    }
+    Some(f)
+  }
+
+  /**
+   * Returns a function which generates random values for the given [[DataType]], or `None` if no
+   * random data generator is defined for that data type. The generated values will use an external
+   * representation of the data type; for example, the random generator for [[DateType]] will return
+   * instances of [[java.sql.Date]] and the generator for [[StructType]] will return a
+   * [[org.apache.spark.Row]].
+   *
+   * @param dataType the type to generate values for
+   * @param nullable whether null values should be generated
+   * @param seed an optional seed for the random number generator
+   * @return a function which can be called to generate random values.
+   */
+  def forType(
+      dataType: DataType,
+      nullable: Boolean = true,
+      seed: Option[Long] = None): Option[() => Any] = {
+    val rand = new Random()
+    seed.foreach(rand.setSeed)
+
+    val valueGenerator: Option[() => Any] = dataType match {
+      case StringType => Some(() => rand.nextString(rand.nextInt(MAX_STR_LEN)))
+      case BinaryType => Some(() => {
+        val arr = new Array[Byte](rand.nextInt(MAX_STR_LEN))
+        rand.nextBytes(arr)
+        arr
+      })
+      case BooleanType => Some(() => rand.nextBoolean())
+      case DateType => Some(() => new java.sql.Date(rand.nextInt()))
+      case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
+      case DecimalType.Unlimited => Some(
+        () => BigDecimal.apply(rand.nextLong, rand.nextInt, MathContext.UNLIMITED))
+      case DoubleType => randomNumeric[Double](
+        rand, r => longBitsToDouble(r.nextLong()), Seq(Double.MinValue, Double.MinPositiveValue,
+          Double.MaxValue, Double.PositiveInfinity, Double.NegativeInfinity, Double.NaN, 0.0))
+      case FloatType => randomNumeric[Float](
+        rand, r => intBitsToFloat(r.nextInt()), Seq(Float.MinValue, Float.MinPositiveValue,
+          Float.MaxValue, Float.PositiveInfinity, Float.NegativeInfinity, Float.NaN, 0.0f))
+      case ByteType => randomNumeric[Byte](
+        rand, _.nextInt().toByte, Seq(Byte.MinValue, Byte.MaxValue, 0.toByte))
+      case IntegerType => randomNumeric[Int](
+        rand, _.nextInt(), Seq(Int.MinValue, Int.MaxValue, 0))
+      case LongType => randomNumeric[Long](
+        rand, _.nextLong(), Seq(Long.MinValue, Long.MaxValue, 0L))
+      case ShortType => randomNumeric[Short](
+        rand, _.nextInt().toShort, Seq(Short.MinValue, Short.MaxValue, 0.toShort))
+      case NullType => Some(() => null)
+      case ArrayType(elementType, containsNull) => {
+        forType(elementType, nullable = containsNull, seed = Some(rand.nextLong())).map {
+          elementGenerator => () => Array.fill(rand.nextInt(MAX_ARR_SIZE))(elementGenerator())
+        }
+      }
+      case MapType(keyType, valueType, valueContainsNull) => {
+        for (
+          keyGenerator <- forType(keyType, nullable = false, seed = Some(rand.nextLong()));
+          valueGenerator <-
+            forType(valueType, nullable = valueContainsNull, seed = Some(rand.nextLong()))
+        ) yield {
+          () => {
+            Seq.fill(rand.nextInt(MAX_MAP_SIZE))((keyGenerator(), valueGenerator())).toMap
+          }
+        }
+      }
+      case StructType(fields) => {
+        val maybeFieldGenerators: Seq[Option[() => Any]] = fields.map { field =>
+          forType(field.dataType, nullable = field.nullable, seed = Some(rand.nextLong()))
+        }
+        if (maybeFieldGenerators.forall(_.isDefined)) {
+          val fieldGenerators: Seq[() => Any] = maybeFieldGenerators.map(_.get)
+          Some(() => Row.fromSeq(fieldGenerators.map(_.apply())))
+        } else {
+          None
+        }
+      }
+      case unsupportedType => None
+    }
+    // Handle nullability by wrapping the non-null value generator:
+    valueGenerator.map { valueGenerator =>
+      if (nullable) {
+        () => {
+          if (rand.nextFloat() <= PROBABILITY_OF_NULL) {
+            null
+          } else {
+            valueGenerator()
+          }
+        }
+      } else {
+        valueGenerator
+      }
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
new file mode 100644
index 0000000000000..dbba93dba668e
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.types._
+
+/**
+ * Tests of [[RandomDataGenerator]].
+ */
+class RandomDataGeneratorSuite extends SparkFunSuite {
+
+  /**
+   * Tests random data generation for the given type by using it to generate random values then
+   * converting those values into their Catalyst equivalents using CatalystTypeConverters.
+   */
+  def testRandomDataGeneration(dataType: DataType, nullable: Boolean = true): Unit = {
+    val toCatalyst = CatalystTypeConverters.createToCatalystConverter(dataType)
+    val generator = RandomDataGenerator.forType(dataType, nullable).getOrElse {
+      fail(s"Random data generator was not defined for $dataType")
+    }
+    if (nullable) {
+      assert(Iterator.fill(100)(generator()).contains(null))
+    } else {
+      assert(Iterator.fill(100)(generator()).forall(_ != null))
+    }
+    for (_ <- 1 to 10) {
+      val generatedValue = generator()
+      toCatalyst(generatedValue)
+    }
+  }
+
+  // Basic types:
+  for (
+    dataType <- DataTypeTestUtils.atomicTypes;
+    nullable <- Seq(true, false)
+    if !dataType.isInstanceOf[DecimalType] ||
+      dataType.asInstanceOf[DecimalType].precisionInfo.isEmpty
+  ) {
+    test(s"$dataType (nullable=$nullable)") {
+      testRandomDataGeneration(dataType)
+    }
+  }
+
+  for (
+    arrayType <- DataTypeTestUtils.atomicArrayTypes
+    if RandomDataGenerator.forType(arrayType.elementType, arrayType.containsNull).isDefined
+  ) {
+    test(s"$arrayType") {
+      testRandomDataGeneration(arrayType)
+    }
+  }
+
+  val atomicTypesWithDataGenerators =
+    DataTypeTestUtils.atomicTypes.filter(RandomDataGenerator.forType(_).isDefined)
+
+  // Complex types:
+  for (
+    keyType <- atomicTypesWithDataGenerators;
+    valueType <- atomicTypesWithDataGenerators
+    // Scala's BigDecimal.hashCode can lead to OutOfMemoryError on Scala 2.10 (see SI-6173) and
+    // Spark can hit NumberFormatException errors when converting certain BigDecimals (SPARK-8802).
+    // For these reasons, we don't support generation of maps with decimal keys.
+    if !keyType.isInstanceOf[DecimalType]
+  ) {
+    val mapType = MapType(keyType, valueType)
+    test(s"$mapType") {
+      testRandomDataGeneration(mapType)
+    }
+  }
+
+  for (
+    colOneType <- atomicTypesWithDataGenerators;
+    colTwoType <- atomicTypesWithDataGenerators
+  ) {
+    val structType = StructType(StructField("a", colOneType) :: StructField("b", colTwoType) :: Nil)
+    test(s"$structType") {
+      testRandomDataGeneration(structType)
+    }
+  }
+
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
new file mode 100644
index 0000000000000..32632b5d6e342
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types
+
+/**
+ * Utility functions for working with DataTypes in tests.
+ */
+object DataTypeTestUtils {
+
+  /**
+   * Instances of all [[IntegralType]]s.
+   */
+  val integralType: Set[IntegralType] = Set(
+    ByteType, ShortType, IntegerType, LongType
+  )
+
+  /**
+   * Instances of all [[FractionalType]]s, including both fixed- and unlimited-precision
+   * decimal types.
+   */
+  val fractionalTypes: Set[FractionalType] = Set(
+    DecimalType(precisionInfo = None),
+    DecimalType(2, 1),
+    DoubleType,
+    FloatType
+  )
+
+  /**
+   * Instances of all [[NumericType]]s.
+   */
+  val numericTypes: Set[NumericType] = integralType ++ fractionalTypes
+
+  /**
+   * Instances of all [[AtomicType]]s.
+   */
+  val atomicTypes: Set[DataType] = numericTypes ++ Set(
+    BinaryType,
+    BooleanType,
+    DateType,
+    StringType,
+    TimestampType
+  )
+
+  /**
+   * Instances of [[ArrayType]] for all [[AtomicType]]s. Arrays of these types may contain null.
+   */
+  val atomicArrayTypes: Set[ArrayType] = atomicTypes.map(ArrayType(_, containsNull = true))
+}

From f35b0c3436898f22860d2c6c1d12f3a661005201 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 3 Jul 2015 23:45:21 -0700
Subject: [PATCH 0224/1454] 
 [SPARK-8238][SPARK-8239][SPARK-8242][SPARK-8243][SPARK-8268][SQL]Add
 ascii/base64/unbase64/encode/decode functions

Add `ascii`,`base64`,`unbase64`,`encode` and `decode` expressions.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #6843 from chenghao-intel/str_funcs2 and squashes the following commits:

78dee7d [Cheng Hao] base 64 -> base64
9d6f9f4 [Cheng Hao] remove the toString method for expressions
ed5c19c [Cheng Hao] update code as comments
96170fc [Cheng Hao] scalastyle issues
e2df768 [Cheng Hao] remove the unused import
491ce7b [Cheng Hao] add ascii/base64/unbase64/encode/decode functions
---
 .../catalyst/analysis/FunctionRegistry.scala  |   5 +
 .../expressions/stringOperations.scala        | 117 ++++++++++++++++++
 .../expressions/StringFunctionsSuite.scala    |  60 ++++++++-
 .../org/apache/spark/sql/functions.scala      |  93 ++++++++++++++
 .../spark/sql/DataFrameFunctionsSuite.scala   |  38 ++++++
 5 files changed, 308 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index a1299aed555c1..e249b58927cc4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -156,11 +156,16 @@ object FunctionRegistry {
     expression[Sum]("sum"),
 
     // string functions
+    expression[Ascii]("ascii"),
+    expression[Base64]("base64"),
+    expression[Encode]("encode"),
+    expression[Decode]("decode"),
     expression[Lower]("lcase"),
     expression[Lower]("lower"),
     expression[StringLength]("length"),
     expression[Substring]("substr"),
     expression[Substring]("substring"),
+    expression[UnBase64]("unbase64"),
     expression[Upper]("ucase"),
     expression[UnHex]("unhex"),
     expression[Upper]("upper"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 57918b32f8a47..154ac3508c0c5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -298,3 +298,120 @@ case class StringLength(child: Expression) extends UnaryExpression with ExpectsI
 
   override def prettyName: String = "length"
 }
+
+/**
+ * Returns the numeric value of the first character of str.
+ */
+case class Ascii(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+  override def dataType: DataType = IntegerType
+  override def inputTypes: Seq[DataType] = Seq(StringType)
+
+  override def eval(input: InternalRow): Any = {
+    val string = child.eval(input)
+    if (string == null) {
+      null
+    } else {
+      val bytes = string.asInstanceOf[UTF8String].getBytes
+      if (bytes.length > 0) {
+        bytes(0).asInstanceOf[Int]
+      } else {
+        0
+      }
+    }
+  }
+}
+
+/**
+ * Converts the argument from binary to a base 64 string.
+ */
+case class Base64(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(BinaryType)
+
+  override def eval(input: InternalRow): Any = {
+    val bytes = child.eval(input)
+    if (bytes == null) {
+      null
+    } else {
+      UTF8String.fromBytes(
+        org.apache.commons.codec.binary.Base64.encodeBase64(
+          bytes.asInstanceOf[Array[Byte]]))
+    }
+  }
+}
+
+/**
+ * Converts the argument from a base 64 string to BINARY.
+ */
+case class UnBase64(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+  override def dataType: DataType = BinaryType
+  override def inputTypes: Seq[DataType] = Seq(StringType)
+
+  override def eval(input: InternalRow): Any = {
+    val string = child.eval(input)
+    if (string == null) {
+      null
+    } else {
+      org.apache.commons.codec.binary.Base64.decodeBase64(string.asInstanceOf[UTF8String].toString)
+    }
+  }
+}
+
+/**
+ * Decodes the first argument into a String using the provided character set
+ * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+ * If either argument is null, the result will also be null. (As of Hive 0.12.0.).
+ */
+case class Decode(bin: Expression, charset: Expression) extends Expression with ExpectsInputTypes {
+  override def children: Seq[Expression] = bin :: charset :: Nil
+  override def foldable: Boolean = bin.foldable && charset.foldable
+  override def nullable: Boolean = bin.nullable || charset.nullable
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(BinaryType, StringType)
+
+  override def eval(input: InternalRow): Any = {
+    val l = bin.eval(input)
+    if (l == null) {
+      null
+    } else {
+      val r = charset.eval(input)
+      if (r == null) {
+        null
+      } else {
+        val fromCharset = r.asInstanceOf[UTF8String].toString
+        UTF8String.fromString(new String(l.asInstanceOf[Array[Byte]], fromCharset))
+      }
+    }
+  }
+}
+
+/**
+ * Encodes the first argument into a BINARY using the provided character set
+ * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+ * If either argument is null, the result will also be null. (As of Hive 0.12.0.)
+*/
+case class Encode(value: Expression, charset: Expression)
+  extends Expression with ExpectsInputTypes {
+  override def children: Seq[Expression] = value :: charset :: Nil
+  override def foldable: Boolean = value.foldable && charset.foldable
+  override def nullable: Boolean = value.nullable || charset.nullable
+  override def dataType: DataType = BinaryType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+
+  override def eval(input: InternalRow): Any = {
+    val l = value.eval(input)
+    if (l == null) {
+      null
+    } else {
+      val r = charset.eval(input)
+      if (r == null) {
+        null
+      } else {
+        val toCharset = r.asInstanceOf[UTF8String].toString
+        l.asInstanceOf[UTF8String].toString.getBytes(toCharset)
+      }
+    }
+  }
+}
+
+
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
index 5dbb1d562c1d9..468df20442d38 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{IntegerType, StringType}
+import org.apache.spark.sql.types.{BinaryType, IntegerType, StringType}
 
 
 class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -217,11 +217,61 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("length for string") {
-    val regEx = 'a.string.at(0)
+    val a = 'a.string.at(0)
     checkEvaluation(StringLength(Literal("abc")), 3, create_row("abdef"))
-    checkEvaluation(StringLength(regEx), 5, create_row("abdef"))
-    checkEvaluation(StringLength(regEx), 0, create_row(""))
-    checkEvaluation(StringLength(regEx), null, create_row(null))
+    checkEvaluation(StringLength(a), 5, create_row("abdef"))
+    checkEvaluation(StringLength(a), 0, create_row(""))
+    checkEvaluation(StringLength(a), null, create_row(null))
     checkEvaluation(StringLength(Literal.create(null, StringType)), null, create_row("abdef"))
   }
+
+  test("ascii for string") {
+    val a = 'a.string.at(0)
+    checkEvaluation(Ascii(Literal("efg")), 101, create_row("abdef"))
+    checkEvaluation(Ascii(a), 97, create_row("abdef"))
+    checkEvaluation(Ascii(a), 0, create_row(""))
+    checkEvaluation(Ascii(a), null, create_row(null))
+    checkEvaluation(Ascii(Literal.create(null, StringType)), null, create_row("abdef"))
+  }
+
+  test("base64/unbase64 for string") {
+    val a = 'a.string.at(0)
+    val b = 'b.binary.at(0)
+    val bytes = Array[Byte](1, 2, 3, 4)
+
+    checkEvaluation(Base64(Literal(bytes)), "AQIDBA==", create_row("abdef"))
+    checkEvaluation(Base64(UnBase64(Literal("AQIDBA=="))), "AQIDBA==", create_row("abdef"))
+    checkEvaluation(Base64(UnBase64(Literal(""))), "", create_row("abdef"))
+    checkEvaluation(Base64(UnBase64(Literal.create(null, StringType))), null, create_row("abdef"))
+    checkEvaluation(Base64(UnBase64(a)), "AQIDBA==", create_row("AQIDBA=="))
+
+    checkEvaluation(Base64(b), "AQIDBA==", create_row(bytes))
+    checkEvaluation(Base64(b), "", create_row(Array[Byte]()))
+    checkEvaluation(Base64(b), null, create_row(null))
+    checkEvaluation(Base64(Literal.create(null, StringType)), null, create_row("abdef"))
+
+    checkEvaluation(UnBase64(a), null, create_row(null))
+    checkEvaluation(UnBase64(Literal.create(null, StringType)), null, create_row("abdef"))
+  }
+
+  test("encode/decode for string") {
+    val a = 'a.string.at(0)
+    val b = 'b.binary.at(0)
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    checkEvaluation(
+      Decode(Encode(Literal("大千世界"), Literal("UTF-16LE")), Literal("UTF-16LE")), "大千世界")
+    checkEvaluation(
+      Decode(Encode(a, Literal("utf-8")), Literal("utf-8")), "大千世界", create_row("大千世界"))
+    checkEvaluation(
+      Decode(Encode(a, Literal("utf-8")), Literal("utf-8")), "", create_row(""))
+    // scalastyle:on
+    checkEvaluation(Encode(a, Literal("utf-8")), null, create_row(null))
+    checkEvaluation(Encode(Literal.create(null, StringType), Literal("utf-8")), null)
+    checkEvaluation(Encode(a, Literal.create(null, StringType)), null, create_row(""))
+
+    checkEvaluation(Decode(b, Literal("utf-8")), null, create_row(null))
+    checkEvaluation(Decode(Literal.create(null, BinaryType), Literal("utf-8")), null)
+    checkEvaluation(Decode(b, Literal.create(null, StringType)), null, create_row(null))
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 25e37ff67aa00..b63c6ee8aba4b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1581,6 +1581,7 @@ object functions {
 
   /**
    * Computes the length of a given string value
+   *
    * @group string_funcs
    * @since 1.5.0
    */
@@ -1588,11 +1589,103 @@ object functions {
 
   /**
    * Computes the length of a given string column
+   *
    * @group string_funcs
    * @since 1.5.0
    */
   def strlen(columnName: String): Column = strlen(Column(columnName))
 
+  /**
+   * Computes the numeric value of the first character of the specified string value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def ascii(e: Column): Column = Ascii(e.expr)
+
+  /**
+   * Computes the numeric value of the first character of the specified string column.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def ascii(columnName: String): Column = ascii(Column(columnName))
+
+  /**
+   * Computes the specified value from binary to a base64 string.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def base64(e: Column): Column = Base64(e.expr)
+
+  /**
+   * Computes the specified column from binary to a base64 string.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def base64(columnName: String): Column = base64(Column(columnName))
+
+  /**
+   * Computes the specified value from a base64 string to binary.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def unbase64(e: Column): Column = UnBase64(e.expr)
+
+  /**
+   * Computes the specified column from a base64 string to binary.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def unbase64(columnName: String): Column = unbase64(Column(columnName))
+
+  /**
+   * Computes the first argument into a binary from a string using the provided character set
+   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+   * If either argument is null, the result will also be null.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def encode(value: Column, charset: Column): Column = Encode(value.expr, charset.expr)
+
+  /**
+   * Computes the first argument into a binary from a string using the provided character set
+   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+   * If either argument is null, the result will also be null.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def encode(columnName: String, charsetColumnName: String): Column =
+    encode(Column(columnName), Column(charsetColumnName))
+
+  /**
+   * Computes the first argument into a string from a binary using the provided character set
+   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+   * If either argument is null, the result will also be null.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def decode(value: Column, charset: Column): Column = Decode(value.expr, charset.expr)
+
+  /**
+   * Computes the first argument into a string from a binary using the provided character set
+   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+   * If either argument is null, the result will also be null.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def decode(columnName: String, charsetColumnName: String): Column =
+    decode(Column(columnName), Column(charsetColumnName))
+
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 0d43aca877f68..bd9fa400e5b34 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -225,4 +225,42 @@ class DataFrameFunctionsSuite extends QueryTest {
         Row(l)
       })
   }
+
+  test("string ascii function") {
+    val df = Seq(("abc", "")).toDF("a", "b")
+    checkAnswer(
+      df.select(ascii($"a"), ascii("b")),
+      Row(97, 0))
+
+    checkAnswer(
+      df.selectExpr("ascii(a)", "ascii(b)"),
+      Row(97, 0))
+  }
+
+  test("string base64/unbase64 function") {
+    val bytes = Array[Byte](1, 2, 3, 4)
+    val df = Seq((bytes, "AQIDBA==")).toDF("a", "b")
+    checkAnswer(
+      df.select(base64("a"), base64($"a"), unbase64("b"), unbase64($"b")),
+      Row("AQIDBA==", "AQIDBA==", bytes, bytes))
+
+    checkAnswer(
+      df.selectExpr("base64(a)", "unbase64(b)"),
+      Row("AQIDBA==", bytes))
+  }
+
+  test("string encode/decode function") {
+    val bytes = Array[Byte](-27, -92, -89, -27, -115, -125, -28, -72, -106, -25, -107, -116)
+    // scalastyle:off  
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    val df = Seq(("大千世界", "utf-8", bytes)).toDF("a", "b", "c")
+    checkAnswer(
+      df.select(encode($"a", $"b"), encode("a", "b"), decode($"c", $"b"), decode("c", "b")),
+      Row(bytes, bytes, "大千世界", "大千世界"))
+
+    checkAnswer(
+      df.selectExpr("encode(a, b)", "decode(c, b)"),
+      Row(bytes, "大千世界"))
+    // scalastyle:on
+  }
 }

From 6b3574e68704d58ba41efe0ea4fe928cc166afcd Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Sat, 4 Jul 2015 01:10:52 -0700
Subject: [PATCH 0225/1454] [SPARK-8270][SQL] levenshtein distance

Jira: https://issues.apache.org/jira/browse/SPARK-8270

Info: I can not build the latest master, it stucks during the build process: `[INFO] Dependency-reduced POM written at: /Users/tarek/test/spark/bagel/dependency-reduced-pom.xml`

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7214 from tarekauel/SPARK-8270 and squashes the following commits:

ab348b9 [Tarek Auel] Merge branch 'master' into SPARK-8270
a2ad318 [Tarek Auel] [SPARK-8270] changed order of fields
d91b12c [Tarek Auel] [SPARK-8270] python fix
adbd075 [Tarek Auel] [SPARK-8270] fixed typo
23185c9 [Tarek Auel] [SPARK-8270] levenshtein distance
---
 python/pyspark/sql/functions.py               | 14 ++++++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/stringOperations.scala        | 32 +++++++++++++++++++
 .../expressions/StringFunctionsSuite.scala    |  9 ++++++
 .../org/apache/spark/sql/functions.scala      | 23 ++++++++++---
 .../spark/sql/DataFrameFunctionsSuite.scala   |  6 ++++
 6 files changed, 81 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 69e563ef36e87..49dd0332afe74 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -323,6 +323,20 @@ def explode(col):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def levenshtein(left, right):
+    """Computes the Levenshtein distance of the two given strings.
+
+    >>> df0 = sqlContext.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])
+    >>> df0.select(levenshtein('l', 'r').alias('d')).collect()
+    [Row(d=3)]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.levenshtein(_to_java_column(left), _to_java_column(right))
+    return Column(jc)
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def md5(col):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index e249b58927cc4..92a50e7092317 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -163,6 +163,7 @@ object FunctionRegistry {
     expression[Lower]("lcase"),
     expression[Lower]("lower"),
     expression[StringLength]("length"),
+    expression[Levenshtein]("levenshtein"),
     expression[Substring]("substr"),
     expression[Substring]("substring"),
     expression[UnBase64]("unbase64"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 154ac3508c0c5..6de40629ff27e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.util.regex.Pattern
 
+import org.apache.commons.lang3.StringUtils
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
@@ -299,6 +300,37 @@ case class StringLength(child: Expression) extends UnaryExpression with ExpectsI
   override def prettyName: String = "length"
 }
 
+/**
+ * A function that return the Levenshtein distance between the two given strings.
+ */
+case class Levenshtein(left: Expression, right: Expression) extends BinaryExpression
+    with ExpectsInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType)
+
+  override def dataType: DataType = IntegerType
+
+  override def eval(input: InternalRow): Any = {
+    val leftValue = left.eval(input)
+    if (leftValue == null) {
+      null
+    } else {
+      val rightValue = right.eval(input)
+      if(rightValue == null) {
+        null
+      } else {
+        StringUtils.getLevenshteinDistance(leftValue.toString, rightValue.toString)
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val stringUtils = classOf[StringUtils].getName
+    nullSafeCodeGen(ctx, ev, (res, left, right) =>
+      s"$res = $stringUtils.getLevenshteinDistance($left.toString(), $right.toString());")
+  }
+}
+
 /**
  * Returns the numeric value of the first character of str.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
index 468df20442d38..1efbe1a245e83 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
@@ -274,4 +274,13 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Decode(Literal.create(null, BinaryType), Literal("utf-8")), null)
     checkEvaluation(Decode(b, Literal.create(null, StringType)), null, create_row(null))
   }
+
+  test("Levenshtein distance") {
+    checkEvaluation(Levenshtein(Literal.create(null, StringType), Literal("")), null)
+    checkEvaluation(Levenshtein(Literal(""), Literal.create(null, StringType)), null)
+    checkEvaluation(Levenshtein(Literal(""), Literal("")), 0)
+    checkEvaluation(Levenshtein(Literal("abc"), Literal("abc")), 0)
+    checkEvaluation(Levenshtein(Literal("kitten"), Literal("sitting")), 3)
+    checkEvaluation(Levenshtein(Literal("frog"), Literal("fog")), 1)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index b63c6ee8aba4b..e4109da08e0a4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1580,21 +1580,36 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Computes the length of a given string value
-   *
+   * Computes the length of a given string value.
+   * 
    * @group string_funcs
    * @since 1.5.0
    */
   def strlen(e: Column): Column = StringLength(e.expr)
 
   /**
-   * Computes the length of a given string column
-   *
+   * Computes the length of a given string column.
+   * 
    * @group string_funcs
    * @since 1.5.0
    */
   def strlen(columnName: String): Column = strlen(Column(columnName))
 
+  /**
+   * Computes the Levenshtein distance of the two given strings.
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def levenshtein(l: Column, r: Column): Column = Levenshtein(l.expr, r.expr)
+
+  /**
+   * Computes the Levenshtein distance of the two given strings.
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def levenshtein(leftColumnName: String, rightColumnName: String): Column =
+    levenshtein(Column(leftColumnName), Column(rightColumnName))
+
   /**
    * Computes the numeric value of the first character of the specified string value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index bd9fa400e5b34..bc455a922d154 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -226,6 +226,12 @@ class DataFrameFunctionsSuite extends QueryTest {
       })
   }
 
+  test("Levenshtein distance") {
+    val df = Seq(("kitten", "sitting"), ("frog", "fog")).toDF("l", "r")
+    checkAnswer(df.select(levenshtein("l", "r")), Seq(Row(3), Row(1)))
+    checkAnswer(df.selectExpr("levenshtein(l, r)"), Seq(Row(3), Row(1)))
+  }
+
   test("string ascii function") {
     val df = Seq(("abc", "")).toDF("a", "b")
     checkAnswer(

From 48f7aed686afde70a6f0802c6cb37b0cad0509f1 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 4 Jul 2015 01:11:35 -0700
Subject: [PATCH 0226/1454] Fixed minor style issue with the previous merge.

---
 sql/core/src/main/scala/org/apache/spark/sql/functions.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index e4109da08e0a4..abcfc0b65020c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1581,7 +1581,7 @@ object functions {
 
   /**
    * Computes the length of a given string value.
-   * 
+   *
    * @group string_funcs
    * @since 1.5.0
    */
@@ -1589,7 +1589,7 @@ object functions {
 
   /**
    * Computes the length of a given string column.
-   * 
+   *
    * @group string_funcs
    * @since 1.5.0
    */

From 347cab85cd924ffd326f3d1367b3b156ee08052d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 4 Jul 2015 11:55:04 -0700
Subject: [PATCH 0227/1454] [SQL] More unit tests for implicit type cast & add
 simpleString to AbstractDataType.

Author: Reynold Xin <rxin@databricks.com>

Closes #7221 from rxin/implicit-cast-tests and squashes the following commits:

64b13bd [Reynold Xin] Fixed a bug ..
489b732 [Reynold Xin] [SQL] More unit tests for implicit type cast & add simpleString to AbstractDataType.
---
 .../sql/catalyst/analysis/CheckAnalysis.scala |  6 ++---
 .../spark/sql/types/AbstractDataType.scala    |  7 ++++++
 .../apache/spark/sql/types/ArrayType.scala    |  2 ++
 .../apache/spark/sql/types/DecimalType.scala  |  2 ++
 .../org/apache/spark/sql/types/MapType.scala  |  2 ++
 .../apache/spark/sql/types/StructType.scala   |  2 ++
 .../analysis/HiveTypeCoercionSuite.scala      | 25 ++++++++++++++++++-
 7 files changed, 42 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 583338da57117..476ac2b7cb474 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -40,7 +40,7 @@ trait CheckAnalysis {
   def containsMultipleGenerators(exprs: Seq[Expression]): Boolean = {
     exprs.flatMap(_.collect {
       case e: Generator => true
-    }).length >= 1
+    }).nonEmpty
   }
 
   def checkAnalysis(plan: LogicalPlan): Unit = {
@@ -85,12 +85,12 @@ trait CheckAnalysis {
           case Aggregate(groupingExprs, aggregateExprs, child) =>
             def checkValidAggregateExpression(expr: Expression): Unit = expr match {
               case _: AggregateExpression => // OK
-              case e: Attribute if groupingExprs.find(_ semanticEquals e).isEmpty =>
+              case e: Attribute if !groupingExprs.exists(_.semanticEquals(e)) =>
                 failAnalysis(
                   s"expression '${e.prettyString}' is neither present in the group by, " +
                     s"nor is it an aggregate function. " +
                     "Add to group by or wrap in first() if you don't care which value you get.")
-              case e if groupingExprs.find(_ semanticEquals e).isDefined => // OK
+              case e if groupingExprs.exists(_.semanticEquals(e)) => // OK
               case e if e.references.isEmpty => // OK
               case e => e.children.foreach(checkValidAggregateExpression)
             }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index e5dc99fb625d8..ffefb0e7837e9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -37,6 +37,9 @@ private[sql] abstract class AbstractDataType {
    * Returns true if this data type is a parent of the `childCandidate`.
    */
   private[sql] def isParentOf(childCandidate: DataType): Boolean
+
+  /** Readable string representation for the type. */
+  private[sql] def simpleString: String
 }
 
 
@@ -56,6 +59,10 @@ private[sql] class TypeCollection(private val types: Seq[DataType]) extends Abst
   private[sql] override def defaultConcreteType: DataType = types.head
 
   private[sql] override def isParentOf(childCandidate: DataType): Boolean = false
+
+  private[sql] override def simpleString: String = {
+    types.map(_.simpleString).mkString("(", " or ", ")")
+  }
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index 8ea6cb14c360e..43413ec761e6b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -31,6 +31,8 @@ object ArrayType extends AbstractDataType {
   private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
     childCandidate.isInstanceOf[ArrayType]
   }
+
+  private[sql] override def simpleString: String = "array"
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index 434fc037aad4f..127b16ff85bed 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -90,6 +90,8 @@ object DecimalType extends AbstractDataType {
     childCandidate.isInstanceOf[DecimalType]
   }
 
+  private[sql] override def simpleString: String = "decimal"
+
   val Unlimited: DecimalType = DecimalType(None)
 
   private[sql] object Fixed {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
index 2b25617ec6655..868dea13d971e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
@@ -75,6 +75,8 @@ object MapType extends AbstractDataType {
     childCandidate.isInstanceOf[MapType]
   }
 
+  private[sql] override def simpleString: String = "map"
+
   /**
    * Construct a [[MapType]] object with the given key type and value type.
    * The `valueContainsNull` is true.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 7e77b77e73940..3b17566d54d9b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -309,6 +309,8 @@ object StructType extends AbstractDataType {
     childCandidate.isInstanceOf[StructType]
   }
 
+  private[sql] override def simpleString: String = "struct"
+
   def apply(fields: Seq[StructField]): StructType = StructType(fields.toArray)
 
   def apply(fields: java.util.List[StructField]): StructType = {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 60e727c6c7d4d..67d05ab536b7f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.types._
 
 class HiveTypeCoercionSuite extends PlanTest {
 
-  test("implicit type cast") {
+  test("eligible implicit type cast") {
     def shouldCast(from: DataType, to: AbstractDataType, expected: DataType): Unit = {
       val got = HiveTypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
       assert(got.map(_.dataType) == Option(expected),
@@ -68,6 +68,29 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldCast(IntegerType, TypeCollection(BinaryType, IntegerType), IntegerType)
     shouldCast(BinaryType, TypeCollection(BinaryType, IntegerType), BinaryType)
     shouldCast(BinaryType, TypeCollection(IntegerType, BinaryType), BinaryType)
+
+    shouldCast(IntegerType, TypeCollection(StringType, BinaryType), StringType)
+    shouldCast(IntegerType, TypeCollection(BinaryType, StringType), StringType)
+  }
+
+  test("ineligible implicit type cast") {
+    def shouldNotCast(from: DataType, to: AbstractDataType): Unit = {
+      val got = HiveTypeCoercion.ImplicitTypeCasts.implicitCast(Literal.create(null, from), to)
+      assert(got.isEmpty, s"Should not be able to cast $from to $to, but got $got")
+    }
+
+    shouldNotCast(IntegerType, DateType)
+    shouldNotCast(IntegerType, TimestampType)
+    shouldNotCast(LongType, DateType)
+    shouldNotCast(LongType, TimestampType)
+    shouldNotCast(DecimalType.Unlimited, DateType)
+    shouldNotCast(DecimalType.Unlimited, TimestampType)
+
+    shouldNotCast(IntegerType, TypeCollection(DateType, TimestampType))
+
+    shouldNotCast(IntegerType, ArrayType)
+    shouldNotCast(IntegerType, MapType)
+    shouldNotCast(IntegerType, StructType)
   }
 
   test("tightest common bound for types") {

From c991ef5abbb501933b2a68eea1987cf8d88794a5 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 4 Jul 2015 11:55:20 -0700
Subject: [PATCH 0228/1454] [SPARK-8822][SQL] clean up type checking in
 math.scala.

Author: Reynold Xin <rxin@databricks.com>

Closes #7220 from rxin/SPARK-8822 and squashes the following commits:

0cda076 [Reynold Xin] Test cases.
22d0463 [Reynold Xin] Fixed type precedence.
beb2a97 [Reynold Xin] [SPARK-8822][SQL] clean up type checking in math.scala.
---
 .../spark/sql/catalyst/expressions/math.scala | 260 +++++++-----------
 .../expressions/MathFunctionsSuite.scala      |  31 ++-
 2 files changed, 123 insertions(+), 168 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 0fc320fb08876..45b7e4d3405c8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -17,10 +17,8 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import java.lang.{Long => JLong}
-import java.util.Arrays
+import java.{lang => jl}
 
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -206,7 +204,7 @@ case class Factorial(child: Expression) extends UnaryExpression with ExpectsInpu
     if (evalE == null) {
       null
     } else {
-      val input = evalE.asInstanceOf[Integer]
+      val input = evalE.asInstanceOf[jl.Integer]
       if (input > 20 || input < 0) {
         null
       } else {
@@ -290,7 +288,7 @@ case class Bin(child: Expression)
     if (evalE == null) {
       null
     } else {
-      UTF8String.fromString(JLong.toBinaryString(evalE.asInstanceOf[Long]))
+      UTF8String.fromString(jl.Long.toBinaryString(evalE.asInstanceOf[Long]))
     }
   }
 
@@ -300,27 +298,18 @@ case class Bin(child: Expression)
   }
 }
 
-
 /**
  * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
  * Otherwise if the number is a STRING, it converts each character into its hex representation
  * and returns the resulting STRING. Negative numbers would be treated as two's complement.
  */
-case class Hex(child: Expression) extends UnaryExpression with Serializable  {
+case class Hex(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+  // TODO: Create code-gen version.
 
-  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(LongType, StringType, BinaryType))
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    if (child.dataType.isInstanceOf[StringType]
-      || child.dataType.isInstanceOf[IntegerType]
-      || child.dataType.isInstanceOf[LongType]
-      || child.dataType.isInstanceOf[BinaryType]
-      || child.dataType == NullType) {
-      TypeCheckResult.TypeCheckSuccess
-    } else {
-      TypeCheckResult.TypeCheckFailure(s"hex doesn't accepts ${child.dataType} type")
-    }
-  }
+  override def dataType: DataType = StringType
 
   override def eval(input: InternalRow): Any = {
     val num = child.eval(input)
@@ -329,7 +318,6 @@ case class Hex(child: Expression) extends UnaryExpression with Serializable  {
     } else {
       child.dataType match {
         case LongType => hex(num.asInstanceOf[Long])
-        case IntegerType => hex(num.asInstanceOf[Integer].toLong)
         case BinaryType => hex(num.asInstanceOf[Array[Byte]])
         case StringType => hex(num.asInstanceOf[UTF8String])
       }
@@ -371,7 +359,55 @@ case class Hex(child: Expression) extends UnaryExpression with Serializable  {
         Character.toUpperCase(Character.forDigit((numBuf & 0xF).toInt, 16)).toByte
       numBuf >>>= 4
     } while (numBuf != 0)
-    UTF8String.fromBytes(Arrays.copyOfRange(value, value.length - len, value.length))
+    UTF8String.fromBytes(java.util.Arrays.copyOfRange(value, value.length - len, value.length))
+  }
+}
+
+
+/**
+ * Performs the inverse operation of HEX.
+ * Resulting characters are returned as a byte array.
+ */
+case class UnHex(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+  // TODO: Create code-gen version.
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType)
+
+  override def dataType: DataType = BinaryType
+
+  override def eval(input: InternalRow): Any = {
+    val num = child.eval(input)
+    if (num == null) {
+      null
+    } else {
+      unhex(num.asInstanceOf[UTF8String].getBytes)
+    }
+  }
+
+  private val unhexDigits = {
+    val array = Array.fill[Byte](128)(-1)
+    (0 to 9).foreach(i => array('0' + i) = i.toByte)
+    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
+    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
+    array
+  }
+
+  private def unhex(inputBytes: Array[Byte]): Array[Byte] = {
+    var bytes = inputBytes
+    if ((bytes.length & 0x01) != 0) {
+      bytes = '0'.toByte +: bytes
+    }
+    val out = new Array[Byte](bytes.length >> 1)
+    // two characters form the hex value.
+    var i = 0
+    while (i < bytes.length) {
+      val first = unhexDigits(bytes(i))
+      val second = unhexDigits(bytes(i + 1))
+      if (first == -1 || second == -1) { return null}
+      out(i / 2) = (((first << 4) | second) & 0xFF).toByte
+      i += 2
+    }
+    out
   }
 }
 
@@ -423,22 +459,19 @@ case class Pow(left: Expression, right: Expression)
   }
 }
 
-case class ShiftLeft(left: Expression, right: Expression) extends BinaryExpression {
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    (left.dataType, right.dataType) match {
-      case (NullType, _) | (_, NullType) => return TypeCheckResult.TypeCheckSuccess
-      case (_, IntegerType) => left.dataType match {
-        case LongType | IntegerType | ShortType | ByteType =>
-          return TypeCheckResult.TypeCheckSuccess
-        case _ => // failed
-      }
-      case _ => // failed
-    }
-    TypeCheckResult.TypeCheckFailure(
-        s"ShiftLeft expects long, integer, short or byte value as first argument and an " +
-          s"integer value as second argument, not (${left.dataType}, ${right.dataType})")
-  }
+/**
+ * Bitwise unsigned left shift.
+ * @param left the base number to shift.
+ * @param right number of bits to left shift.
+ */
+case class ShiftLeft(left: Expression, right: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(IntegerType, LongType), IntegerType)
+
+  override def dataType: DataType = left.dataType
 
   override def eval(input: InternalRow): Any = {
     val valueLeft = left.eval(input)
@@ -446,10 +479,8 @@ case class ShiftLeft(left: Expression, right: Expression) extends BinaryExpressi
       val valueRight = right.eval(input)
       if (valueRight != null) {
         valueLeft match {
-          case l: Long => l << valueRight.asInstanceOf[Integer]
-          case i: Integer => i << valueRight.asInstanceOf[Integer]
-          case s: Short => s << valueRight.asInstanceOf[Integer]
-          case b: Byte => b << valueRight.asInstanceOf[Integer]
+          case l: jl.Long => l << valueRight.asInstanceOf[jl.Integer]
+          case i: jl.Integer => i << valueRight.asInstanceOf[jl.Integer]
         }
       } else {
         null
@@ -459,35 +490,24 @@ case class ShiftLeft(left: Expression, right: Expression) extends BinaryExpressi
     }
   }
 
-  override def dataType: DataType = {
-    left.dataType match {
-      case LongType => LongType
-      case IntegerType | ShortType | ByteType => IntegerType
-      case _ => NullType
-    }
-  }
-
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left << $right;")
   }
 }
 
-case class ShiftRight(left: Expression, right: Expression) extends BinaryExpression {
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    (left.dataType, right.dataType) match {
-      case (NullType, _) | (_, NullType) => return TypeCheckResult.TypeCheckSuccess
-      case (_, IntegerType) => left.dataType match {
-        case LongType | IntegerType | ShortType | ByteType =>
-          return TypeCheckResult.TypeCheckSuccess
-        case _ => // failed
-      }
-      case _ => // failed
-    }
-    TypeCheckResult.TypeCheckFailure(
-          s"ShiftRight expects long, integer, short or byte value as first argument and an " +
-            s"integer value as second argument, not (${left.dataType}, ${right.dataType})")
-  }
+/**
+ * Bitwise unsigned left shift.
+ * @param left the base number to shift.
+ * @param right number of bits to left shift.
+ */
+case class ShiftRight(left: Expression, right: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(IntegerType, LongType), IntegerType)
+
+  override def dataType: DataType = left.dataType
 
   override def eval(input: InternalRow): Any = {
     val valueLeft = left.eval(input)
@@ -495,10 +515,8 @@ case class ShiftRight(left: Expression, right: Expression) extends BinaryExpress
       val valueRight = right.eval(input)
       if (valueRight != null) {
         valueLeft match {
-          case l: Long => l >> valueRight.asInstanceOf[Integer]
-          case i: Integer => i >> valueRight.asInstanceOf[Integer]
-          case s: Short => s >> valueRight.asInstanceOf[Integer]
-          case b: Byte => b >> valueRight.asInstanceOf[Integer]
+          case l: jl.Long => l >> valueRight.asInstanceOf[jl.Integer]
+          case i: jl.Integer => i >> valueRight.asInstanceOf[jl.Integer]
         }
       } else {
         null
@@ -508,35 +526,24 @@ case class ShiftRight(left: Expression, right: Expression) extends BinaryExpress
     }
   }
 
-  override def dataType: DataType = {
-    left.dataType match {
-      case LongType => LongType
-      case IntegerType | ShortType | ByteType => IntegerType
-      case _ => NullType
-    }
-  }
-
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left >> $right;")
   }
 }
 
-case class ShiftRightUnsigned(left: Expression, right: Expression) extends BinaryExpression {
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    (left.dataType, right.dataType) match {
-      case (NullType, _) | (_, NullType) => return TypeCheckResult.TypeCheckSuccess
-      case (_, IntegerType) => left.dataType match {
-        case LongType | IntegerType | ShortType | ByteType =>
-          return TypeCheckResult.TypeCheckSuccess
-        case _ => // failed
-      }
-      case _ => // failed
-    }
-    TypeCheckResult.TypeCheckFailure(
-      s"ShiftRightUnsigned expects long, integer, short or byte value as first argument and an " +
-        s"integer value as second argument, not (${left.dataType}, ${right.dataType})")
-  }
+/**
+ * Bitwise unsigned right shift, for integer and long data type.
+ * @param left the base number.
+ * @param right the number of bits to right shift.
+ */
+case class ShiftRightUnsigned(left: Expression, right: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(IntegerType, LongType), IntegerType)
+
+  override def dataType: DataType = left.dataType
 
   override def eval(input: InternalRow): Any = {
     val valueLeft = left.eval(input)
@@ -544,10 +551,8 @@ case class ShiftRightUnsigned(left: Expression, right: Expression) extends Binar
       val valueRight = right.eval(input)
       if (valueRight != null) {
         valueLeft match {
-          case l: Long => l >>> valueRight.asInstanceOf[Integer]
-          case i: Integer => i >>> valueRight.asInstanceOf[Integer]
-          case s: Short => s >>> valueRight.asInstanceOf[Integer]
-          case b: Byte => b >>> valueRight.asInstanceOf[Integer]
+          case l: jl.Long => l >>> valueRight.asInstanceOf[jl.Integer]
+          case i: jl.Integer => i >>> valueRight.asInstanceOf[jl.Integer]
         }
       } else {
         null
@@ -557,74 +562,21 @@ case class ShiftRightUnsigned(left: Expression, right: Expression) extends Binar
     }
   }
 
-  override def dataType: DataType = {
-    left.dataType match {
-      case LongType => LongType
-      case IntegerType | ShortType | ByteType => IntegerType
-      case _ => NullType
-    }
-  }
-
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left >>> $right;")
   }
 }
 
-/**
- * Performs the inverse operation of HEX.
- * Resulting characters are returned as a byte array.
- */
-case class UnHex(child: Expression) extends UnaryExpression with Serializable {
-
-  override def dataType: DataType = BinaryType
-
-  override def checkInputDataTypes(): TypeCheckResult = {
-    if (child.dataType.isInstanceOf[StringType] || child.dataType == NullType) {
-      TypeCheckResult.TypeCheckSuccess
-    } else {
-      TypeCheckResult.TypeCheckFailure(s"unHex accepts String type, not ${child.dataType}")
-    }
-  }
-
-  override def eval(input: InternalRow): Any = {
-    val num = child.eval(input)
-    if (num == null) {
-      null
-    } else {
-      unhex(num.asInstanceOf[UTF8String].getBytes)
-    }
-  }
-
-  private val unhexDigits = {
-    val array = Array.fill[Byte](128)(-1)
-    (0 to 9).foreach(i => array('0' + i) = i.toByte)
-    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
-    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
-    array
-  }
-
-  private def unhex(inputBytes: Array[Byte]): Array[Byte] = {
-    var bytes = inputBytes
-    if ((bytes.length & 0x01) != 0) {
-      bytes = '0'.toByte +: bytes
-    }
-    val out = new Array[Byte](bytes.length >> 1)
-    // two characters form the hex value.
-    var i = 0
-    while (i < bytes.length) {
-        val first = unhexDigits(bytes(i))
-        val second = unhexDigits(bytes(i + 1))
-        if (first == -1 || second == -1) { return null}
-        out(i / 2) = (((first << 4) | second) & 0xFF).toByte
-        i += 2
-    }
-    out
-  }
-}
 
 case class Hypot(left: Expression, right: Expression)
   extends BinaryMathExpression(math.hypot, "HYPOT")
 
+
+/**
+ * Computes the logarithm of a number.
+ * @param left the logarithm base, default to e.
+ * @param right the number to compute the logarithm of.
+ */
 case class Logarithm(left: Expression, right: Expression)
   extends BinaryMathExpression((c1, c2) => math.log(c2) / math.log(c1), "LOG") {
 
@@ -642,7 +594,7 @@ case class Logarithm(left: Expression, right: Expression)
       defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.log($c2) / java.lang.Math.log($c1)")
     }
     logCode + s"""
-      if (Double.valueOf(${ev.primitive}).isNaN()) {
+      if (Double.isNaN(${ev.primitive})) {
         ${ev.isNull} = true;
       }
     """
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 20839c83d4fd0..03d8400cf356b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -161,11 +161,10 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("factorial") {
-    val dataLong = (0 to 20)
-    dataLong.foreach { value =>
+    (0 to 20).foreach { value =>
       checkEvaluation(Factorial(Literal(value)), LongMath.factorial(value), EmptyRow)
     }
-    checkEvaluation((Literal.create(null, IntegerType)), null, create_row(null))
+    checkEvaluation(Literal.create(null, IntegerType), null, create_row(null))
     checkEvaluation(Factorial(Literal(20)), 2432902008176640000L, EmptyRow)
     checkEvaluation(Factorial(Literal(21)), null, EmptyRow)
   }
@@ -244,10 +243,8 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       ShiftLeft(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null)
     checkEvaluation(ShiftLeft(Literal(21), Literal(1)), 42)
-    checkEvaluation(ShiftLeft(Literal(21.toByte), Literal(1)), 42)
-    checkEvaluation(ShiftLeft(Literal(21.toShort), Literal(1)), 42)
-    checkEvaluation(ShiftLeft(Literal(21.toLong), Literal(1)), 42.toLong)
 
+    checkEvaluation(ShiftLeft(Literal(21.toLong), Literal(1)), 42.toLong)
     checkEvaluation(ShiftLeft(Literal(-21.toLong), Literal(1)), -42.toLong)
   }
 
@@ -257,10 +254,8 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       ShiftRight(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null)
     checkEvaluation(ShiftRight(Literal(42), Literal(1)), 21)
-    checkEvaluation(ShiftRight(Literal(42.toByte), Literal(1)), 21)
-    checkEvaluation(ShiftRight(Literal(42.toShort), Literal(1)), 21)
-    checkEvaluation(ShiftRight(Literal(42.toLong), Literal(1)), 21.toLong)
 
+    checkEvaluation(ShiftRight(Literal(42.toLong), Literal(1)), 21.toLong)
     checkEvaluation(ShiftRight(Literal(-42.toLong), Literal(1)), -21.toLong)
   }
 
@@ -270,16 +265,12 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       ShiftRight(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null)
     checkEvaluation(ShiftRightUnsigned(Literal(42), Literal(1)), 21)
-    checkEvaluation(ShiftRightUnsigned(Literal(42.toByte), Literal(1)), 21)
-    checkEvaluation(ShiftRightUnsigned(Literal(42.toShort), Literal(1)), 21)
-    checkEvaluation(ShiftRightUnsigned(Literal(42.toLong), Literal(1)), 21.toLong)
 
+    checkEvaluation(ShiftRightUnsigned(Literal(42.toLong), Literal(1)), 21.toLong)
     checkEvaluation(ShiftRightUnsigned(Literal(-42.toLong), Literal(1)), 9223372036854775787L)
   }
 
   test("hex") {
-    checkEvaluation(Hex(Literal(28)), "1C")
-    checkEvaluation(Hex(Literal(-28)), "FFFFFFFFFFFFFFE4")
     checkEvaluation(Hex(Literal(100800200404L)), "177828FED4")
     checkEvaluation(Hex(Literal(-100800200404L)), "FFFFFFE887D7012C")
     checkEvaluation(Hex(Literal("helloHex")), "68656C6C6F486578")
@@ -313,6 +304,8 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(Logarithm(Literal(v2), Literal(v1)), f(v2 + 0.0, v1 + 0.0), EmptyRow)
       checkEvaluation(new Logarithm(Literal(v1)), f(math.E, v1 + 0.0), EmptyRow)
     }
+
+    // null input should yield null output
     checkEvaluation(
       Logarithm(Literal.create(null, DoubleType), Literal(1.0)),
       null,
@@ -321,5 +314,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       Logarithm(Literal(1.0), Literal.create(null, DoubleType)),
       null,
       create_row(null))
+
+    // negative input should yield null output
+    checkEvaluation(
+      Logarithm(Literal(-1.0), Literal(1.0)),
+      null,
+      create_row(null))
+    checkEvaluation(
+      Logarithm(Literal(1.0), Literal(-1.0)),
+      null,
+      create_row(null))
   }
 }

From 2b820f2a4bf9b154762e7516a5b0485322799da9 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Sat, 4 Jul 2015 22:52:50 -0700
Subject: [PATCH 0229/1454] [MINOR] [SQL] Minor fix for CatalystSchemaConverter

ping liancheng

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #7224 from viirya/few_fix_catalystschema and squashes the following commits:

d994330 [Liang-Chi Hsieh] Minor fix for CatalystSchemaConverter.
---
 .../main/scala/org/apache/spark/sql/SQLConf.scala    |  2 +-
 .../spark/sql/parquet/CatalystSchemaConverter.scala  | 12 ++++++------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2c258b6ee399c..6005d35f015a9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -273,7 +273,7 @@ private[spark] object SQLConf {
   val PARQUET_FOLLOW_PARQUET_FORMAT_SPEC = booleanConf(
     key = "spark.sql.parquet.followParquetFormatSpec",
     defaultValue = Some(false),
-    doc = "Wether to stick to Parquet format specification when converting Parquet schema to " +
+    doc = "Whether to stick to Parquet format specification when converting Parquet schema to " +
       "Spark SQL schema and vice versa.  Sticks to the specification if set to true; falls back " +
       "to compatible mode if set to false.",
     isPublic = false)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index 2be7c64612cd2..4ab274ec17a02 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -142,7 +142,7 @@ private[parquet] class CatalystSchemaConverter(
       DecimalType(precision, scale)
     }
 
-    field.getPrimitiveTypeName match {
+    typeName match {
       case BOOLEAN => BooleanType
 
       case FLOAT => FloatType
@@ -150,7 +150,7 @@ private[parquet] class CatalystSchemaConverter(
       case DOUBLE => DoubleType
 
       case INT32 =>
-        field.getOriginalType match {
+        originalType match {
           case INT_8 => ByteType
           case INT_16 => ShortType
           case INT_32 | null => IntegerType
@@ -161,7 +161,7 @@ private[parquet] class CatalystSchemaConverter(
         }
 
       case INT64 =>
-        field.getOriginalType match {
+        originalType match {
           case INT_64 | null => LongType
           case DECIMAL => makeDecimalType(maxPrecisionForBytes(8))
           case TIMESTAMP_MILLIS => typeNotImplemented()
@@ -176,7 +176,7 @@ private[parquet] class CatalystSchemaConverter(
         TimestampType
 
       case BINARY =>
-        field.getOriginalType match {
+        originalType match {
           case UTF8 | ENUM => StringType
           case null if assumeBinaryIsString => StringType
           case null => BinaryType
@@ -185,7 +185,7 @@ private[parquet] class CatalystSchemaConverter(
         }
 
       case FIXED_LEN_BYTE_ARRAY =>
-        field.getOriginalType match {
+        originalType match {
           case DECIMAL => makeDecimalType(maxPrecisionForBytes(field.getTypeLength))
           case INTERVAL => typeNotImplemented()
           case _ => illegalType()
@@ -261,7 +261,7 @@ private[parquet] class CatalystSchemaConverter(
   // Here we implement Parquet LIST backwards-compatibility rules.
   // See: https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules
   // scalastyle:on
-  private def isElementType(repeatedType: Type, parentName: String) = {
+  private def isElementType(repeatedType: Type, parentName: String): Boolean = {
     {
       // For legacy 2-level list types with primitive element type, e.g.:
       //

From f9c448dce8139e85ac564daa0f7e0325e778cffe Mon Sep 17 00:00:00 2001
From: Joshi <rekhajoshm@gmail.com>
Date: Sun, 5 Jul 2015 12:58:03 -0700
Subject: [PATCH 0230/1454] [SPARK-7137] [ML] Update SchemaUtils
 checkInputColumn to print more info if needed

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #5992 from rekhajoshm/fix/SPARK-7137 and squashes the following commits:

8c42b57 [Joshi] update checkInputColumn to print more info if needed
33ddd2e [Joshi] update checkInputColumn to print more info if needed
acf3e17 [Joshi] update checkInputColumn to print more info if needed
8993c0e [Joshi] SPARK-7137: Add checkInputColumn back to Params and print more info
e3677c9 [Rekha Joshi] Merge pull request #1 from apache/master
---
 .../scala/org/apache/spark/ml/util/SchemaUtils.scala     | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala
index 7cd53c6d7ef79..76f651488aef9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala
@@ -32,10 +32,15 @@ private[spark] object SchemaUtils {
    * @param colName  column name
    * @param dataType  required column data type
    */
-  def checkColumnType(schema: StructType, colName: String, dataType: DataType): Unit = {
+  def checkColumnType(
+      schema: StructType,
+      colName: String,
+      dataType: DataType,
+      msg: String = ""): Unit = {
     val actualDataType = schema(colName).dataType
+    val message = if (msg != null && msg.trim.length > 0) " " + msg else ""
     require(actualDataType.equals(dataType),
-      s"Column $colName must be of type $dataType but was actually $actualDataType.")
+      s"Column $colName must be of type $dataType but was actually $actualDataType.$message")
   }
 
   /**

From a0cb111b22cb093e86b0daeecb3dcc41d095df40 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Sun, 5 Jul 2015 20:50:02 -0700
Subject: [PATCH 0231/1454] [SPARK-8549] [SPARKR] Fix the line length of SparkR

[[SPARK-8549] Fix the line length of SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8549)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7204 from yu-iskw/SPARK-8549 and squashes the following commits:

6fb131a [Yu ISHIKAWA] Fix the typo
1737598 [Yu ISHIKAWA] [SPARK-8549][SparkR] Fix the line length of SparkR
---
 R/pkg/R/generics.R                 |  3 ++-
 R/pkg/R/pairRDD.R                  | 12 ++++++------
 R/pkg/R/sparkR.R                   |  9 ++++++---
 R/pkg/R/utils.R                    | 31 +++++++++++++++++-------------
 R/pkg/inst/tests/test_includeJAR.R |  4 ++--
 R/pkg/inst/tests/test_rdd.R        | 12 ++++++++----
 R/pkg/inst/tests/test_sparkSQL.R   | 11 +++++++++--
 7 files changed, 51 insertions(+), 31 deletions(-)

diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 79055b7f18558..fad9d71158c51 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -20,7 +20,8 @@
 # @rdname aggregateRDD
 # @seealso reduce
 # @export
-setGeneric("aggregateRDD", function(x, zeroValue, seqOp, combOp) { standardGeneric("aggregateRDD") })
+setGeneric("aggregateRDD",
+           function(x, zeroValue, seqOp, combOp) { standardGeneric("aggregateRDD") })
 
 # @rdname cache-methods
 # @export
diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R
index 7f902ba8e683e..0f1179e0aa51a 100644
--- a/R/pkg/R/pairRDD.R
+++ b/R/pkg/R/pairRDD.R
@@ -560,8 +560,8 @@ setMethod("join",
 # Left outer join two RDDs
 #
 # @description
-# \code{leftouterjoin} This function left-outer-joins two RDDs where every element is of the form list(K, V).
-# The key types of the two RDDs should be the same.
+# \code{leftouterjoin} This function left-outer-joins two RDDs where every element is of
+# the form list(K, V). The key types of the two RDDs should be the same.
 #
 # @param x An RDD to be joined. Should be an RDD where each element is
 #             list(K, V).
@@ -597,8 +597,8 @@ setMethod("leftOuterJoin",
 # Right outer join two RDDs
 #
 # @description
-# \code{rightouterjoin} This function right-outer-joins two RDDs where every element is of the form list(K, V).
-# The key types of the two RDDs should be the same.
+# \code{rightouterjoin} This function right-outer-joins two RDDs where every element is of
+# the form list(K, V). The key types of the two RDDs should be the same.
 #
 # @param x An RDD to be joined. Should be an RDD where each element is
 #             list(K, V).
@@ -634,8 +634,8 @@ setMethod("rightOuterJoin",
 # Full outer join two RDDs
 #
 # @description
-# \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of the form list(K, V).
-# The key types of the two RDDs should be the same.
+# \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of
+# the form list(K, V). The key types of the two RDDs should be the same.
 #
 # @param x An RDD to be joined. Should be an RDD where each element is
 #             list(K, V).
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 86233e01db365..048eb8ed541e4 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -105,7 +105,8 @@ sparkR.init <- function(
   sparkPackages = "") {
 
   if (exists(".sparkRjsc", envir = .sparkREnv)) {
-    cat("Re-using existing Spark Context. Please stop SparkR with sparkR.stop() or restart R to create a new Spark Context\n")
+    cat(paste("Re-using existing Spark Context.",
+              "Please stop SparkR with sparkR.stop() or restart R to create a new Spark Context\n"))
     return(get(".sparkRjsc", envir = .sparkREnv))
   }
 
@@ -180,14 +181,16 @@ sparkR.init <- function(
 
   sparkExecutorEnvMap <- new.env()
   if (!any(names(sparkExecutorEnv) == "LD_LIBRARY_PATH")) {
-    sparkExecutorEnvMap[["LD_LIBRARY_PATH"]] <- paste0("$LD_LIBRARY_PATH:",Sys.getenv("LD_LIBRARY_PATH"))
+    sparkExecutorEnvMap[["LD_LIBRARY_PATH"]] <-
+      paste0("$LD_LIBRARY_PATH:",Sys.getenv("LD_LIBRARY_PATH"))
   }
   for (varname in names(sparkExecutorEnv)) {
     sparkExecutorEnvMap[[varname]] <- sparkExecutorEnv[[varname]]
   }
 
   nonEmptyJars <- Filter(function(x) { x != "" }, jars)
-  localJarPaths <- sapply(nonEmptyJars, function(j) { utils::URLencode(paste("file:", uriSep, j, sep = "")) })
+  localJarPaths <- sapply(nonEmptyJars,
+                          function(j) { utils::URLencode(paste("file:", uriSep, j, sep = "")) })
 
   # Set the start time to identify jobjs
   # Seconds resolution is good enough for this purpose, so use ints
diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 13cec0f712fb4..ea629a64f7158 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -334,18 +334,21 @@ getStorageLevel <- function(newLevel = c("DISK_ONLY",
                                          "MEMORY_ONLY_SER_2",
                                          "OFF_HEAP")) {
   match.arg(newLevel)
+  storageLevelClass <- "org.apache.spark.storage.StorageLevel"
   storageLevel <- switch(newLevel,
-                         "DISK_ONLY" = callJStatic("org.apache.spark.storage.StorageLevel", "DISK_ONLY"),
-                         "DISK_ONLY_2" = callJStatic("org.apache.spark.storage.StorageLevel", "DISK_ONLY_2"),
-                         "MEMORY_AND_DISK" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK"),
-                         "MEMORY_AND_DISK_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_2"),
-                         "MEMORY_AND_DISK_SER" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_SER"),
-                         "MEMORY_AND_DISK_SER_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_SER_2"),
-                         "MEMORY_ONLY" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY"),
-                         "MEMORY_ONLY_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_2"),
-                         "MEMORY_ONLY_SER" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_SER"),
-                         "MEMORY_ONLY_SER_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_SER_2"),
-                         "OFF_HEAP" = callJStatic("org.apache.spark.storage.StorageLevel", "OFF_HEAP"))
+                         "DISK_ONLY" = callJStatic(storageLevelClass, "DISK_ONLY"),
+                         "DISK_ONLY_2" = callJStatic(storageLevelClass, "DISK_ONLY_2"),
+                         "MEMORY_AND_DISK" = callJStatic(storageLevelClass, "MEMORY_AND_DISK"),
+                         "MEMORY_AND_DISK_2" = callJStatic(storageLevelClass, "MEMORY_AND_DISK_2"),
+                         "MEMORY_AND_DISK_SER" = callJStatic(storageLevelClass,
+                                                             "MEMORY_AND_DISK_SER"),
+                         "MEMORY_AND_DISK_SER_2" = callJStatic(storageLevelClass,
+                                                               "MEMORY_AND_DISK_SER_2"),
+                         "MEMORY_ONLY" = callJStatic(storageLevelClass, "MEMORY_ONLY"),
+                         "MEMORY_ONLY_2" = callJStatic(storageLevelClass, "MEMORY_ONLY_2"),
+                         "MEMORY_ONLY_SER" = callJStatic(storageLevelClass, "MEMORY_ONLY_SER"),
+                         "MEMORY_ONLY_SER_2" = callJStatic(storageLevelClass, "MEMORY_ONLY_SER_2"),
+                         "OFF_HEAP" = callJStatic(storageLevelClass, "OFF_HEAP"))
 }
 
 # Utility function for functions where an argument needs to be integer but we want to allow
@@ -545,9 +548,11 @@ mergePartitions <- function(rdd, zip) {
         lengthOfKeys <- part[[len - lengthOfValues]]
         stopifnot(len == lengthOfKeys + lengthOfValues)
 
-        # For zip operation, check if corresponding partitions of both RDDs have the same number of elements.
+        # For zip operation, check if corresponding partitions
+        # of both RDDs have the same number of elements.
         if (zip && lengthOfKeys != lengthOfValues) {
-          stop("Can only zip RDDs with same number of elements in each pair of corresponding partitions.")
+          stop(paste("Can only zip RDDs with same number of elements",
+                     "in each pair of corresponding partitions."))
         }
 
         if (lengthOfKeys > 1) {
diff --git a/R/pkg/inst/tests/test_includeJAR.R b/R/pkg/inst/tests/test_includeJAR.R
index 844d86f3cc97f..cc1faeabffe30 100644
--- a/R/pkg/inst/tests/test_includeJAR.R
+++ b/R/pkg/inst/tests/test_includeJAR.R
@@ -18,8 +18,8 @@ context("include an external JAR in SparkContext")
 
 runScript <- function() {
   sparkHome <- Sys.getenv("SPARK_HOME")
-  jarPath <- paste("--jars",
-                   shQuote(file.path(sparkHome, "R/lib/SparkR/test_support/sparktestjar_2.10-1.0.jar")))
+  sparkTestJarPath <- "R/lib/SparkR/test_support/sparktestjar_2.10-1.0.jar"
+  jarPath <- paste("--jars", shQuote(file.path(sparkHome, sparkTestJarPath)))
   scriptPath <- file.path(sparkHome, "R/lib/SparkR/tests/jarTest.R")
   submitPath <- file.path(sparkHome, "bin/spark-submit")
   res <- system2(command = submitPath,
diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R
index fc3c01d837de4..b79692873cec3 100644
--- a/R/pkg/inst/tests/test_rdd.R
+++ b/R/pkg/inst/tests/test_rdd.R
@@ -669,13 +669,15 @@ test_that("fullOuterJoin() on pairwise RDDs", {
   rdd1 <- parallelize(sc, list(list(1,2), list(1,3), list(3,3)))
   rdd2 <- parallelize(sc, list(list(1,1), list(2,4)))
   actual <- collect(fullOuterJoin(rdd1, rdd2, 2L))
-  expected <- list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4)), list(3, list(3, NULL)))
+  expected <- list(list(1, list(2, 1)), list(1, list(3, 1)),
+                   list(2, list(NULL, 4)), list(3, list(3, NULL)))
   expect_equal(sortKeyValueList(actual), sortKeyValueList(expected))
 
   rdd1 <- parallelize(sc, list(list("a",2), list("a",3), list("c", 1)))
   rdd2 <- parallelize(sc, list(list("a",1), list("b",4)))
   actual <- collect(fullOuterJoin(rdd1, rdd2, 2L))
-  expected <- list(list("b", list(NULL, 4)), list("a", list(2, 1)), list("a", list(3, 1)), list("c", list(1, NULL)))
+  expected <- list(list("b", list(NULL, 4)), list("a", list(2, 1)),
+                   list("a", list(3, 1)), list("c", list(1, NULL)))
   expect_equal(sortKeyValueList(actual),
                sortKeyValueList(expected))
 
@@ -683,13 +685,15 @@ test_that("fullOuterJoin() on pairwise RDDs", {
   rdd2 <- parallelize(sc, list(list(3,3), list(4,4)))
   actual <- collect(fullOuterJoin(rdd1, rdd2, 2L))
   expect_equal(sortKeyValueList(actual),
-               sortKeyValueList(list(list(1, list(1, NULL)), list(2, list(2, NULL)), list(3, list(NULL, 3)), list(4, list(NULL, 4)))))
+               sortKeyValueList(list(list(1, list(1, NULL)), list(2, list(2, NULL)),
+                                     list(3, list(NULL, 3)), list(4, list(NULL, 4)))))
 
   rdd1 <- parallelize(sc, list(list("a",1), list("b",2)))
   rdd2 <- parallelize(sc, list(list("c",3), list("d",4)))
   actual <- collect(fullOuterJoin(rdd1, rdd2, 2L))
   expect_equal(sortKeyValueList(actual),
-               sortKeyValueList(list(list("a", list(1, NULL)), list("b", list(2, NULL)), list("d", list(NULL, 4)), list("c", list(NULL, 3)))))
+               sortKeyValueList(list(list("a", list(1, NULL)), list("b", list(2, NULL)),
+                                     list("d", list(NULL, 4)), list("c", list(NULL, 3)))))
 })
 
 test_that("sortByKey() on pairwise RDDs", {
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 0e4235ea8b4b3..b0ea38854304e 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -391,7 +391,7 @@ test_that("collect() and take() on a DataFrame return the same number of rows an
   expect_equal(ncol(collect(df)), ncol(take(df, 10)))
 })
 
-test_that("multiple pipeline transformations starting with a DataFrame result in an RDD with the correct values", {
+test_that("multiple pipeline transformations result in an RDD with the correct values", {
   df <- jsonFile(sqlContext, jsonPath)
   first <- lapply(df, function(row) {
     row$age <- row$age + 5
@@ -756,7 +756,14 @@ test_that("toJSON() returns an RDD of the correct values", {
 test_that("showDF()", {
   df <- jsonFile(sqlContext, jsonPath)
   s <- capture.output(showDF(df))
-  expect_output(s , "+----+-------+\n| age|   name|\n+----+-------+\n|null|Michael|\n|  30|   Andy|\n|  19| Justin|\n+----+-------+\n")
+  expected <- paste("+----+-------+\n",
+                    "| age|   name|\n",
+                    "+----+-------+\n",
+                    "|null|Michael|\n",
+                    "|  30|   Andy|\n",
+                    "|  19| Justin|\n",
+                    "+----+-------+\n", sep="")
+  expect_output(s , expected)
 })
 
 test_that("isLocal()", {

From 6d0411b4f3a202cfb53f638ee5fd49072b42d3a6 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Sun, 5 Jul 2015 21:50:52 -0700
Subject: [PATCH 0232/1454] [SQL][Minor] Update the DataFrame API for
 encode/decode

This is a the follow up of #6843.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7230 from chenghao-intel/str_funcs2_followup and squashes the following commits:

52cc553 [Cheng Hao] update the code as comment
---
 .../expressions/stringOperations.scala        | 21 ++++++++++---------
 .../org/apache/spark/sql/functions.scala      | 14 +++++++------
 .../spark/sql/DataFrameFunctionsSuite.scala   |  8 +++++--
 3 files changed, 25 insertions(+), 18 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 6de40629ff27e..1a14a7a449342 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -392,12 +392,13 @@ case class UnBase64(child: Expression) extends UnaryExpression with ExpectsInput
 /**
  * Decodes the first argument into a String using the provided character set
  * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
- * If either argument is null, the result will also be null. (As of Hive 0.12.0.).
+ * If either argument is null, the result will also be null.
  */
-case class Decode(bin: Expression, charset: Expression) extends Expression with ExpectsInputTypes {
-  override def children: Seq[Expression] = bin :: charset :: Nil
-  override def foldable: Boolean = bin.foldable && charset.foldable
-  override def nullable: Boolean = bin.nullable || charset.nullable
+case class Decode(bin: Expression, charset: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def left: Expression = bin
+  override def right: Expression = charset
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(BinaryType, StringType)
 
@@ -420,13 +421,13 @@ case class Decode(bin: Expression, charset: Expression) extends Expression with
 /**
  * Encodes the first argument into a BINARY using the provided character set
  * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
- * If either argument is null, the result will also be null. (As of Hive 0.12.0.)
+ * If either argument is null, the result will also be null.
 */
 case class Encode(value: Expression, charset: Expression)
-  extends Expression with ExpectsInputTypes {
-  override def children: Seq[Expression] = value :: charset :: Nil
-  override def foldable: Boolean = value.foldable && charset.foldable
-  override def nullable: Boolean = value.nullable || charset.nullable
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def left: Expression = value
+  override def right: Expression = charset
   override def dataType: DataType = BinaryType
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index abcfc0b65020c..f80291776f335 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1666,18 +1666,19 @@ object functions {
    * @group string_funcs
    * @since 1.5.0
    */
-  def encode(value: Column, charset: Column): Column = Encode(value.expr, charset.expr)
+  def encode(value: Column, charset: String): Column = Encode(value.expr, lit(charset).expr)
 
   /**
    * Computes the first argument into a binary from a string using the provided character set
    * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
    * If either argument is null, the result will also be null.
+   * NOTE: charset represents the string value of the character set, not the column name.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def encode(columnName: String, charsetColumnName: String): Column =
-    encode(Column(columnName), Column(charsetColumnName))
+  def encode(columnName: String, charset: String): Column =
+    encode(Column(columnName), charset)
 
   /**
    * Computes the first argument into a string from a binary using the provided character set
@@ -1687,18 +1688,19 @@ object functions {
    * @group string_funcs
    * @since 1.5.0
    */
-  def decode(value: Column, charset: Column): Column = Decode(value.expr, charset.expr)
+  def decode(value: Column, charset: String): Column = Decode(value.expr, lit(charset).expr)
 
   /**
    * Computes the first argument into a string from a binary using the provided character set
    * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
    * If either argument is null, the result will also be null.
+   * NOTE: charset represents the string value of the character set, not the column name.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def decode(columnName: String, charsetColumnName: String): Column =
-    decode(Column(columnName), Column(charsetColumnName))
+  def decode(columnName: String, charset: String): Column =
+    decode(Column(columnName), charset)
 
 
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index bc455a922d154..afba28515e032 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -261,11 +261,15 @@ class DataFrameFunctionsSuite extends QueryTest {
     // non ascii characters are not allowed in the code, so we disable the scalastyle here.
     val df = Seq(("大千世界", "utf-8", bytes)).toDF("a", "b", "c")
     checkAnswer(
-      df.select(encode($"a", $"b"), encode("a", "b"), decode($"c", $"b"), decode("c", "b")),
+      df.select(
+        encode($"a", "utf-8"),
+        encode("a", "utf-8"),
+        decode($"c", "utf-8"),
+        decode("c", "utf-8")),
       Row(bytes, bytes, "大千世界", "大千世界"))
 
     checkAnswer(
-      df.selectExpr("encode(a, b)", "decode(c, b)"),
+      df.selectExpr("encode(a, 'utf-8')", "decode(c, 'utf-8')"),
       Row(bytes, "大千世界"))
     // scalastyle:on
   }

From 86768b7b3b0c2964e744bc491bc20a1d3140ce93 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 5 Jul 2015 23:54:25 -0700
Subject: [PATCH 0233/1454] [SPARK-8831][SQL] Support AbstractDataType in
 TypeCollection.

Otherwise it is impossible to declare an expression supporting DecimalType.

Author: Reynold Xin <rxin@databricks.com>

Closes #7232 from rxin/typecollection-adt and squashes the following commits:

934d3d1 [Reynold Xin] [SPARK-8831][SQL] Support AbstractDataType in TypeCollection.
---
 .../spark/sql/catalyst/analysis/HiveTypeCoercion.scala |  2 --
 .../org/apache/spark/sql/types/AbstractDataType.scala  | 10 ++++++----
 .../sql/catalyst/analysis/HiveTypeCoercionSuite.scala  |  6 ++++++
 3 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 84acc0e7e90ec..5367b7f3308ee 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -708,8 +708,6 @@ object HiveTypeCoercion {
         case (NullType, target) => Cast(e, target.defaultConcreteType)
 
         // Implicit cast among numeric types
-        // If input is decimal, and we expect a decimal type, just use the input.
-        case (_: DecimalType, DecimalType) => e
         // If input is a numeric type but not decimal, and we expect a decimal type,
         // cast the input to unlimited precision decimal.
         case (_: NumericType, DecimalType) if !inType.isInstanceOf[DecimalType] =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index ffefb0e7837e9..fb1b47e946214 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -53,10 +53,12 @@ private[sql] abstract class AbstractDataType {
  *
  * This means that we prefer StringType over BinaryType if it is possible to cast to StringType.
  */
-private[sql] class TypeCollection(private val types: Seq[DataType]) extends AbstractDataType {
+private[sql] class TypeCollection(private val types: Seq[AbstractDataType])
+  extends AbstractDataType {
+
   require(types.nonEmpty, s"TypeCollection ($types) cannot be empty")
 
-  private[sql] override def defaultConcreteType: DataType = types.head
+  private[sql] override def defaultConcreteType: DataType = types.head.defaultConcreteType
 
   private[sql] override def isParentOf(childCandidate: DataType): Boolean = false
 
@@ -68,9 +70,9 @@ private[sql] class TypeCollection(private val types: Seq[DataType]) extends Abst
 
 private[sql] object TypeCollection {
 
-  def apply(types: DataType*): TypeCollection = new TypeCollection(types)
+  def apply(types: AbstractDataType*): TypeCollection = new TypeCollection(types)
 
-  def unapply(typ: AbstractDataType): Option[Seq[DataType]] = typ match {
+  def unapply(typ: AbstractDataType): Option[Seq[AbstractDataType]] = typ match {
     case typ: TypeCollection => Some(typ.types)
     case _ => None
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 67d05ab536b7f..b56426617789e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -71,6 +71,12 @@ class HiveTypeCoercionSuite extends PlanTest {
 
     shouldCast(IntegerType, TypeCollection(StringType, BinaryType), StringType)
     shouldCast(IntegerType, TypeCollection(BinaryType, StringType), StringType)
+
+    shouldCast(
+      DecimalType.Unlimited, TypeCollection(IntegerType, DecimalType), DecimalType.Unlimited)
+    shouldCast(DecimalType(10, 2), TypeCollection(IntegerType, DecimalType), DecimalType(10, 2))
+    shouldCast(DecimalType(10, 2), TypeCollection(DecimalType, IntegerType), DecimalType(10, 2))
+    shouldCast(IntegerType, TypeCollection(DecimalType(10, 2), StringType), DecimalType(10, 2))
   }
 
   test("ineligible implicit type cast") {

From 39e4e7e4d89077a637c4cad3a986e0e3447d1ae7 Mon Sep 17 00:00:00 2001
From: Steve Lindemann <steve.lindemann@engineersgatelp.com>
Date: Mon, 6 Jul 2015 10:17:05 -0700
Subject: [PATCH 0234/1454] [SPARK-8841] [SQL] Fix partition pruning percentage
 log message

When pruning partitions for a query plan, a message is logged indicating what how many partitions were selected based on predicate criteria, and what percent were pruned.

The current release erroneously uses `1 - total/selected` to compute this quantity, leading to nonsense messages like "pruned -1000% partitions". The fix is simple and obvious.

Author: Steve Lindemann <steve.lindemann@engineersgatelp.com>

Closes #7227 from srlindemann/master and squashes the following commits:

c788061 [Steve Lindemann] fix percentPruned log message
---
 .../scala/org/apache/spark/sql/sources/DataSourceStrategy.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
index ce16e050c56ed..66f7ba90140b5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
@@ -65,7 +65,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       logInfo {
         val total = t.partitionSpec.partitions.length
         val selected = selectedPartitions.length
-        val percentPruned = (1 - total.toDouble / selected.toDouble) * 100
+        val percentPruned = (1 - selected.toDouble / total.toDouble) * 100
         s"Selected $selected partitions out of $total, pruned $percentPruned% partitions."
       }
 

From 293225e0cd9318ad368dde30ac6a17725d33ebb6 Mon Sep 17 00:00:00 2001
From: "Daniel Emaasit (PhD Student)" <daniel.emaasit@gmail.com>
Date: Mon, 6 Jul 2015 10:36:02 -0700
Subject: [PATCH 0235/1454] [SPARK-8124] [SPARKR] Created more examples on
 SparkR DataFrames

Here are more examples on SparkR DataFrames including creating a Spark Contect and a SQL
context, loading data and simple data manipulation.

Author: Daniel Emaasit (PhD Student) <daniel.emaasit@gmail.com>

Closes #6668 from Emaasit/dan-dev and squashes the following commits:

3a97867 [Daniel Emaasit (PhD Student)] Used fewer rows for createDataFrame
f7227f9 [Daniel Emaasit (PhD Student)] Using command line arguments
a550f70 [Daniel Emaasit (PhD Student)] Used base R functions
33f9882 [Daniel Emaasit (PhD Student)] Renamed file
b6603e3 [Daniel Emaasit (PhD Student)] changed "Describe" function to "describe"
90565dd [Daniel Emaasit (PhD Student)] Deleted the getting-started file
b95a103 [Daniel Emaasit (PhD Student)] Deleted this file
cc55cd8 [Daniel Emaasit (PhD Student)] combined all the code into one .R file
c6933af [Daniel Emaasit (PhD Student)] changed variable name to SQLContext
8e0fe14 [Daniel Emaasit (PhD Student)] provided two options for creating DataFrames
2653573 [Daniel Emaasit (PhD Student)] Updates to a comment and variable name
275b787 [Daniel Emaasit (PhD Student)] Added the Apache License at the top of the file
2e8f724 [Daniel Emaasit (PhD Student)] Added the Apache License at the top of the file
486f44e [Daniel Emaasit (PhD Student)] Added the Apache License at the file
d705112 [Daniel Emaasit (PhD Student)] Created more examples on SparkR DataFrames
---
 examples/src/main/r/data-manipulation.R | 107 ++++++++++++++++++++++++
 1 file changed, 107 insertions(+)
 create mode 100644 examples/src/main/r/data-manipulation.R

diff --git a/examples/src/main/r/data-manipulation.R b/examples/src/main/r/data-manipulation.R
new file mode 100644
index 0000000000000..aa2336e300a91
--- /dev/null
+++ b/examples/src/main/r/data-manipulation.R
@@ -0,0 +1,107 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# For this example, we shall use the "flights" dataset
+# The dataset consists of every flight departing Houston in 2011.
+# The data set is made up of 227,496 rows x 14 columns. 
+
+# To run this example use
+# ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3
+#     examples/src/main/r/data-manipulation.R <path_to_csv>
+
+# Load SparkR library into your R session
+library(SparkR)
+
+args <- commandArgs(trailing = TRUE)
+
+if (length(args) != 1) {
+  print("Usage: data-manipulation.R <path-to-flights.csv")
+  print("The data can be downloaded from: http://s3-us-west-2.amazonaws.com/sparkr-data/flights.csv ")
+  q("no")
+}
+
+## Initialize SparkContext
+sc <- sparkR.init(appName = "SparkR-data-manipulation-example")
+
+## Initialize SQLContext
+sqlContext <- sparkRSQL.init(sc)
+
+flightsCsvPath <- args[[1]]
+
+# Create a local R dataframe
+flights_df <- read.csv(flightsCsvPath, header = TRUE)
+flights_df$date <- as.Date(flights_df$date)
+
+## Filter flights whose destination is San Francisco and write to a local data frame
+SFO_df <- flights_df[flights_df$dest == "SFO", ] 
+
+# Convert the local data frame into a SparkR DataFrame
+SFO_DF <- createDataFrame(sqlContext, SFO_df)
+
+#  Directly create a SparkR DataFrame from the source data
+flightsDF <- read.df(sqlContext, flightsCsvPath, source = "com.databricks.spark.csv", header = "true")
+
+# Print the schema of this Spark DataFrame
+printSchema(flightsDF)
+
+# Cache the DataFrame
+cache(flightsDF)
+
+# Print the first 6 rows of the DataFrame
+showDF(flightsDF, numRows = 6) ## Or
+head(flightsDF)
+
+# Show the column names in the DataFrame
+columns(flightsDF)
+
+# Show the number of rows in the DataFrame
+count(flightsDF)
+
+# Select specific columns
+destDF <- select(flightsDF, "dest", "cancelled")
+
+# Using SQL to select columns of data
+# First, register the flights DataFrame as a table
+registerTempTable(flightsDF, "flightsTable")
+destDF <- sql(sqlContext, "SELECT dest, cancelled FROM flightsTable")
+
+# Use collect to create a local R data frame
+local_df <- collect(destDF)
+
+# Print the newly created local data frame
+head(local_df)
+
+# Filter flights whose destination is JFK
+jfkDF <- filter(flightsDF, "dest = \"JFK\"") ##OR
+jfkDF <- filter(flightsDF, flightsDF$dest == "JFK")
+
+# If the magrittr library is available, we can use it to
+# chain data frame operations
+if("magrittr" %in% rownames(installed.packages())) {
+  library(magrittr)
+
+  # Group the flights by date and then find the average daily delay
+  # Write the result into a DataFrame
+  groupBy(flightsDF, flightsDF$date) %>%
+    summarize(avg(flightsDF$dep_delay), avg(flightsDF$arr_delay)) -> dailyDelayDF
+
+  # Print the computed data frame
+  head(dailyDelayDF)
+}
+
+# Stop the SparkContext now
+sparkR.stop()

From 0e194645f42be0d6ac9b5a712f8fc1798418736d Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 6 Jul 2015 13:26:46 -0700
Subject: [PATCH 0236/1454] [SPARK-8837][SPARK-7114][SQL] support using keyword
 in column name

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7237 from cloud-fan/parser and squashes the following commits:

e7b49bb [Wenchen Fan] support using keyword in column name
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 28 ++++++++++++-------
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  9 ++++++
 2 files changed, 27 insertions(+), 10 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 8d02fbf4f92c4..e8e9b9802e94b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -287,15 +287,18 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
           throw new AnalysisException(s"invalid function approximate($floatLit) $udfName")
         }
       }
-    | CASE ~> expression.? ~ rep1(WHEN ~> expression ~ (THEN ~> expression)) ~
-        (ELSE ~> expression).? <~ END ^^ {
-          case casePart ~ altPart ~ elsePart =>
-            val branches = altPart.flatMap { case whenExpr ~ thenExpr =>
-              Seq(whenExpr, thenExpr)
-            } ++ elsePart
-            casePart.map(CaseKeyWhen(_, branches)).getOrElse(CaseWhen(branches))
-        }
-      )
+    | CASE ~> whenThenElse ^^ CaseWhen
+    | CASE ~> expression ~ whenThenElse ^^
+      { case keyPart ~ branches => CaseKeyWhen(keyPart, branches) }
+    )
+
+  protected lazy val whenThenElse: Parser[List[Expression]] =
+    rep1(WHEN ~> expression ~ (THEN ~> expression)) ~ (ELSE ~> expression).? <~ END ^^ {
+      case altPart ~ elsePart =>
+        altPart.flatMap { case whenExpr ~ thenExpr =>
+          Seq(whenExpr, thenExpr)
+        } ++ elsePart
+    }
 
   protected lazy val cast: Parser[Expression] =
     CAST ~ "(" ~> expression ~ (AS ~> dataType) <~ ")" ^^ {
@@ -354,6 +357,11 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
   protected lazy val signedPrimary: Parser[Expression] =
     sign ~ primary ^^ { case s ~ e => if (s == "-") UnaryMinus(e) else e}
 
+  protected lazy val attributeName: Parser[String] = acceptMatch("attribute name", {
+    case lexical.Identifier(str) => str
+    case lexical.Keyword(str) if !lexical.delimiters.contains(str) => str
+  })
+
   protected lazy val primary: PackratParser[Expression] =
     ( literal
     | expression ~ ("[" ~> expression <~ "]") ^^
@@ -364,9 +372,9 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     | "(" ~> expression <~ ")"
     | function
     | dotExpressionHeader
-    | ident ^^ {case i => UnresolvedAttribute.quoted(i)}
     | signedPrimary
     | "~" ~> expression ^^ BitwiseNot
+    | attributeName ^^ UnresolvedAttribute.quoted
     )
 
   protected lazy val dotExpressionHeader: Parser[Expression] =
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index cc6af1ccc1cce..12ad019e8b473 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1458,4 +1458,13 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       checkAnswer(sql("SELECT * FROM t ORDER BY NULL"), Seq(Row(1, 2), Row(1, 2)))
     }
   }
+
+  test("SPARK-8837: use keyword in column name") {
+    withTempTable("t") {
+      val df = Seq(1 -> "a").toDF("count", "sort")
+      checkAnswer(df.filter("count > 0"), Row(1, "a"))
+      df.registerTempTable("t")
+      checkAnswer(sql("select count, sort from t"), Row(1, "a"))
+    }
+  }
 }

From 57c72fcce75907c08a1ae53a0d85447176fc3c69 Mon Sep 17 00:00:00 2001
From: Dirceu Semighini Filho <dirceu.semighini@gmail.com>
Date: Mon, 6 Jul 2015 13:28:07 -0700
Subject: [PATCH 0237/1454] Small update in the readme file

Just change the attribute from -PsparkR to -Psparkr

Author: Dirceu Semighini Filho <dirceu.semighini@gmail.com>

Closes #7242 from dirceusemighini/patch-1 and squashes the following commits:

fad5991 [Dirceu Semighini Filho] Small update in the readme file
---
 R/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/README.md b/R/README.md
index d7d65b4f0eca5..005f56da1670c 100644
--- a/R/README.md
+++ b/R/README.md
@@ -6,7 +6,7 @@ SparkR is an R package that provides a light-weight frontend to use Spark from R
 
 #### Build Spark
 
-Build Spark with [Maven](http://spark.apache.org/docs/latest/building-spark.html#building-with-buildmvn) and include the `-PsparkR` profile to build the R package. For example to use the default Hadoop versions you can run
+Build Spark with [Maven](http://spark.apache.org/docs/latest/building-spark.html#building-with-buildmvn) and include the `-Psparkr` profile to build the R package. For example to use the default Hadoop versions you can run
 ```
   build/mvn -DskipTests -Psparkr package
 ```

From 37e4d92142a6309e2df7d36883e0c7892c3d792d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 6 Jul 2015 13:31:31 -0700
Subject: [PATCH 0238/1454] [SPARK-8784] [SQL] Add Python API for hex and unhex

Add Python API for hex/unhex,  also cleanup Hex/Unhex

Author: Davies Liu <davies@databricks.com>

Closes #7223 from davies/hex and squashes the following commits:

6f1249d [Davies Liu] no explicit rule to cast string into binary
711a6ed [Davies Liu] fix test
f9fe5a3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into hex
f032fbb [Davies Liu] Merge branch 'hex' of github.com:davies/spark into hex
49e325f [Davies Liu] Merge branch 'master' of github.com:apache/spark into hex
b31fc9a [Davies Liu] Update math.scala
25156b7 [Davies Liu] address comments and fix test
c3af78c [Davies Liu] address commments
1a24082 [Davies Liu] Add Python API for hex and unhex
---
 python/pyspark/sql/functions.py               | 28 +++++++
 .../catalyst/analysis/FunctionRegistry.scala  |  2 +-
 .../spark/sql/catalyst/expressions/math.scala | 83 ++++++++++---------
 .../expressions/MathFunctionsSuite.scala      | 25 ++++--
 .../org/apache/spark/sql/functions.scala      |  2 +-
 5 files changed, 93 insertions(+), 47 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 49dd0332afe74..dca39fa833435 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -395,6 +395,34 @@ def randn(seed=None):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def hex(col):
+    """Computes hex value of the given column, which could be StringType,
+    BinaryType, IntegerType or LongType.
+
+    >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
+    [Row(hex(a)=u'414243', hex(b)=u'3')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.hex(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def unhex(col):
+    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
+    and converts to the byte representation of number.
+
+    >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
+    [Row(unhex(a)=bytearray(b'ABC'))]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.unhex(_to_java_column(col))
+    return Column(jc)
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def sha1(col):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 92a50e7092317..fef276353022c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -168,7 +168,7 @@ object FunctionRegistry {
     expression[Substring]("substring"),
     expression[UnBase64]("unbase64"),
     expression[Upper]("ucase"),
-    expression[UnHex]("unhex"),
+    expression[Unhex]("unhex"),
     expression[Upper]("upper"),
 
     // datetime functions
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 45b7e4d3405c8..92500453980f6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -298,6 +298,21 @@ case class Bin(child: Expression)
   }
 }
 
+object Hex {
+  val hexDigits = Array[Char](
+    '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F'
+  ).map(_.toByte)
+
+  // lookup table to translate '0' -> 0 ... 'F'/'f' -> 15
+  val unhexDigits = {
+    val array = Array.fill[Byte](128)(-1)
+    (0 to 9).foreach(i => array('0' + i) = i.toByte)
+    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
+    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
+    array
+  }
+}
+
 /**
  * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
  * Otherwise if the number is a STRING, it converts each character into its hex representation
@@ -307,7 +322,7 @@ case class Hex(child: Expression) extends UnaryExpression with ExpectsInputTypes
   // TODO: Create code-gen version.
 
   override def inputTypes: Seq[AbstractDataType] =
-    Seq(TypeCollection(LongType, StringType, BinaryType))
+    Seq(TypeCollection(LongType, BinaryType, StringType))
 
   override def dataType: DataType = StringType
 
@@ -319,30 +334,18 @@ case class Hex(child: Expression) extends UnaryExpression with ExpectsInputTypes
       child.dataType match {
         case LongType => hex(num.asInstanceOf[Long])
         case BinaryType => hex(num.asInstanceOf[Array[Byte]])
-        case StringType => hex(num.asInstanceOf[UTF8String])
+        case StringType => hex(num.asInstanceOf[UTF8String].getBytes)
       }
     }
   }
 
-  /**
-   * Converts every character in s to two hex digits.
-   */
-  private def hex(str: UTF8String): UTF8String = {
-    hex(str.getBytes)
-  }
-
-  private def hex(bytes: Array[Byte]): UTF8String = {
-    doHex(bytes, bytes.length)
-  }
-
-  private def doHex(bytes: Array[Byte], length: Int): UTF8String = {
+  private[this] def hex(bytes: Array[Byte]): UTF8String = {
+    val length = bytes.length
     val value = new Array[Byte](length * 2)
     var i = 0
     while (i < length) {
-      value(i * 2) = Character.toUpperCase(Character.forDigit(
-        (bytes(i) & 0xF0) >>> 4, 16)).toByte
-      value(i * 2 + 1) = Character.toUpperCase(Character.forDigit(
-        bytes(i) & 0x0F, 16)).toByte
+      value(i * 2) = Hex.hexDigits((bytes(i) & 0xF0) >> 4)
+      value(i * 2 + 1) = Hex.hexDigits(bytes(i) & 0x0F)
       i += 1
     }
     UTF8String.fromBytes(value)
@@ -355,24 +358,23 @@ case class Hex(child: Expression) extends UnaryExpression with ExpectsInputTypes
     var len = 0
     do {
       len += 1
-      value(value.length - len) =
-        Character.toUpperCase(Character.forDigit((numBuf & 0xF).toInt, 16)).toByte
+      value(value.length - len) = Hex.hexDigits((numBuf & 0xF).toInt)
       numBuf >>>= 4
     } while (numBuf != 0)
     UTF8String.fromBytes(java.util.Arrays.copyOfRange(value, value.length - len, value.length))
   }
 }
 
-
 /**
  * Performs the inverse operation of HEX.
  * Resulting characters are returned as a byte array.
  */
-case class UnHex(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Unhex(child: Expression) extends UnaryExpression with ExpectsInputTypes {
   // TODO: Create code-gen version.
 
   override def inputTypes: Seq[AbstractDataType] = Seq(StringType)
 
+  override def nullable: Boolean = true
   override def dataType: DataType = BinaryType
 
   override def eval(input: InternalRow): Any = {
@@ -384,26 +386,31 @@ case class UnHex(child: Expression) extends UnaryExpression with ExpectsInputTyp
     }
   }
 
-  private val unhexDigits = {
-    val array = Array.fill[Byte](128)(-1)
-    (0 to 9).foreach(i => array('0' + i) = i.toByte)
-    (0 to 5).foreach(i => array('A' + i) = (i + 10).toByte)
-    (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
-    array
-  }
-
-  private def unhex(inputBytes: Array[Byte]): Array[Byte] = {
-    var bytes = inputBytes
+  private[this] def unhex(bytes: Array[Byte]): Array[Byte] = {
+    val out = new Array[Byte]((bytes.length + 1) >> 1)
+    var i = 0
     if ((bytes.length & 0x01) != 0) {
-      bytes = '0'.toByte +: bytes
+      // padding with '0'
+      if (bytes(0) < 0) {
+        return null
+      }
+      val v = Hex.unhexDigits(bytes(0))
+      if (v == -1) {
+        return null
+      }
+      out(0) = v
+      i += 1
     }
-    val out = new Array[Byte](bytes.length >> 1)
     // two characters form the hex value.
-    var i = 0
     while (i < bytes.length) {
-      val first = unhexDigits(bytes(i))
-      val second = unhexDigits(bytes(i + 1))
-      if (first == -1 || second == -1) { return null}
+      if (bytes(i) < 0 || bytes(i + 1) < 0) {
+        return null
+      }
+      val first = Hex.unhexDigits(bytes(i))
+      val second = Hex.unhexDigits(bytes(i + 1))
+      if (first == -1 || second == -1) {
+        return null
+      }
       out(i / 2) = (((first << 4) | second) & 0xFF).toByte
       i += 2
     }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 03d8400cf356b..7ca9e30b2bcd5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -21,8 +21,7 @@ import com.google.common.math.LongMath
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{DataType, LongType}
-import org.apache.spark.sql.types.{IntegerType, DoubleType}
+import org.apache.spark.sql.types._
 
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -271,20 +270,32 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("hex") {
+    checkEvaluation(Hex(Literal.create(null, LongType)), null)
+    checkEvaluation(Hex(Literal(28L)), "1C")
+    checkEvaluation(Hex(Literal(-28L)), "FFFFFFFFFFFFFFE4")
     checkEvaluation(Hex(Literal(100800200404L)), "177828FED4")
     checkEvaluation(Hex(Literal(-100800200404L)), "FFFFFFE887D7012C")
-    checkEvaluation(Hex(Literal("helloHex")), "68656C6C6F486578")
+    checkEvaluation(Hex(Literal.create(null, BinaryType)), null)
     checkEvaluation(Hex(Literal("helloHex".getBytes())), "68656C6C6F486578")
     // scalastyle:off
     // Turn off scala style for non-ascii chars
-    checkEvaluation(Hex(Literal("三重的")), "E4B889E9878DE79A84")
+    checkEvaluation(Hex(Literal("三重的".getBytes("UTF8"))), "E4B889E9878DE79A84")
     // scalastyle:on
   }
 
   test("unhex") {
-    checkEvaluation(UnHex(Literal("737472696E67")), "string".getBytes)
-    checkEvaluation(UnHex(Literal("")), new Array[Byte](0))
-    checkEvaluation(UnHex(Literal("0")), Array[Byte](0))
+    checkEvaluation(Unhex(Literal.create(null, StringType)), null)
+    checkEvaluation(Unhex(Literal("737472696E67")), "string".getBytes)
+    checkEvaluation(Unhex(Literal("")), new Array[Byte](0))
+    checkEvaluation(Unhex(Literal("F")), Array[Byte](15))
+    checkEvaluation(Unhex(Literal("ff")), Array[Byte](-1))
+    checkEvaluation(Unhex(Literal("GG")), null)
+    // scalastyle:off
+    // Turn off scala style for non-ascii chars
+    checkEvaluation(Unhex(Literal("E4B889E9878DE79A84")), "三重的".getBytes("UTF-8"))
+    checkEvaluation(Unhex(Literal("三重的")), null)
+
+    // scalastyle:on
   }
 
   test("hypot") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index f80291776f335..4da9ffc495e17 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1095,7 +1095,7 @@ object functions {
    * @group math_funcs
    * @since 1.5.0
    */
-  def unhex(column: Column): Column = UnHex(column.expr)
+  def unhex(column: Column): Column = Unhex(column.expr)
 
   /**
    * Inverse of hex. Interprets each pair of characters as a hexadecimal number

From 2471c0bf7f463bb144b44a2e51c0f363e71e099d Mon Sep 17 00:00:00 2001
From: kai <kaizeng@eecs.berkeley.edu>
Date: Mon, 6 Jul 2015 14:33:30 -0700
Subject: [PATCH 0239/1454] [SPARK-4485] [SQL] 1) Add broadcast hash outer
 join, (2) Fix SparkPlanTest

This pull request
(1) extracts common functions used by hash outer joins and put it in interface HashOuterJoin
(2) adds ShuffledHashOuterJoin and BroadcastHashOuterJoin
(3) adds test cases for shuffled and broadcast hash outer join
(3) makes SparkPlanTest to support binary or more complex operators, and fixes bugs in plan composition in SparkPlanTest

Author: kai <kaizeng@eecs.berkeley.edu>

Closes #7162 from kai-zeng/outer and squashes the following commits:

3742359 [kai] Fix not-serializable exception for code-generated keys in broadcasted relations
14e4bf8 [kai] Use CanBroadcast in broadcast outer join planning
dc5127e [kai] code style fixes
b5a4efa [kai] (1) Add broadcast hash outer join, (2) Fix SparkPlanTest
---
 .../spark/sql/execution/SparkStrategies.scala |  12 +-
 .../joins/BroadcastHashOuterJoin.scala        | 121 ++++++++++++++++++
 .../sql/execution/joins/HashOuterJoin.scala   |  95 ++++----------
 .../joins/ShuffledHashOuterJoin.scala         |  85 ++++++++++++
 .../org/apache/spark/sql/JoinSuite.scala      |  40 +++++-
 .../spark/sql/execution/SparkPlanTest.scala   |  99 +++++++++++---
 .../sql/execution/joins/OuterJoinSuite.scala  |  88 +++++++++++++
 7 files changed, 441 insertions(+), 99 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 5daf86d817586..32044989044a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -117,8 +117,18 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           leftKeys, rightKeys, buildSide, planLater(left), planLater(right))
         condition.map(Filter(_, hashJoin)).getOrElse(hashJoin) :: Nil
 
+      case ExtractEquiJoinKeys(
+             LeftOuter, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
+        joins.BroadcastHashOuterJoin(
+          leftKeys, rightKeys, LeftOuter, condition, planLater(left), planLater(right)) :: Nil
+
+      case ExtractEquiJoinKeys(
+             RightOuter, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
+        joins.BroadcastHashOuterJoin(
+          leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
+
       case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right) =>
-        joins.HashOuterJoin(
+        joins.ShuffledHashOuterJoin(
           leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil
 
       case _ => Nil
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
new file mode 100644
index 0000000000000..5da04c78744d9
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -0,0 +1,121 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{Distribution, UnspecifiedDistribution}
+import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
+import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.util.ThreadUtils
+
+import scala.collection.JavaConversions._
+import scala.concurrent._
+import scala.concurrent.duration._
+
+/**
+ * :: DeveloperApi ::
+ * Performs a outer hash join for two child relations.  When the output RDD of this operator is
+ * being constructed, a Spark job is asynchronously started to calculate the values for the
+ * broadcasted relation.  This data is then placed in a Spark broadcast variable.  The streamed
+ * relation is not shuffled.
+ */
+@DeveloperApi
+case class BroadcastHashOuterJoin(
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    joinType: JoinType,
+    condition: Option[Expression],
+    left: SparkPlan,
+    right: SparkPlan) extends BinaryNode with HashOuterJoin {
+
+  val timeout = {
+    val timeoutValue = sqlContext.conf.broadcastTimeout
+    if (timeoutValue < 0) {
+      Duration.Inf
+    } else {
+      timeoutValue.seconds
+    }
+  }
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
+
+  private[this] lazy val (buildPlan, streamedPlan) = joinType match {
+    case RightOuter => (left, right)
+    case LeftOuter => (right, left)
+    case x =>
+      throw new IllegalArgumentException(
+        s"BroadcastHashOuterJoin should not take $x as the JoinType")
+  }
+
+  private[this] lazy val (buildKeys, streamedKeys) = joinType match {
+    case RightOuter => (leftKeys, rightKeys)
+    case LeftOuter => (rightKeys, leftKeys)
+    case x =>
+      throw new IllegalArgumentException(
+        s"BroadcastHashOuterJoin should not take $x as the JoinType")
+  }
+
+  @transient
+  private val broadcastFuture = future {
+    // Note that we use .execute().collect() because we don't want to convert data to Scala types
+    val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
+    // buildHashTable uses code-generated rows as keys, which are not serializable
+    val hashed =
+      buildHashTable(input.iterator, new InterpretedProjection(buildKeys, buildPlan.output))
+    sparkContext.broadcast(hashed)
+  }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
+
+  override def doExecute(): RDD[InternalRow] = {
+    val broadcastRelation = Await.result(broadcastFuture, timeout)
+
+    streamedPlan.execute().mapPartitions { streamedIter =>
+      val joinedRow = new JoinedRow()
+      val hashTable = broadcastRelation.value
+      val keyGenerator = newProjection(streamedKeys, streamedPlan.output)
+
+      joinType match {
+        case LeftOuter =>
+          streamedIter.flatMap(currentRow => {
+            val rowKey = keyGenerator(currentRow)
+            joinedRow.withLeft(currentRow)
+            leftOuterIterator(rowKey, joinedRow, hashTable.getOrElse(rowKey, EMPTY_LIST))
+          })
+
+        case RightOuter =>
+          streamedIter.flatMap(currentRow => {
+            val rowKey = keyGenerator(currentRow)
+            joinedRow.withRight(currentRow)
+            rightOuterIterator(rowKey, hashTable.getOrElse(rowKey, EMPTY_LIST), joinedRow)
+          })
+
+        case x =>
+          throw new IllegalArgumentException(
+            s"BroadcastHashOuterJoin should not take $x as the JoinType")
+      }
+    }
+  }
+}
+
+object BroadcastHashOuterJoin {
+
+  private val broadcastHashOuterJoinExecutionContext = ExecutionContext.fromExecutorService(
+    ThreadUtils.newDaemonCachedThreadPool("broadcast-hash-outer-join", 128))
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index e41538ec1fc1a..886b5fa0c5103 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -19,32 +19,25 @@ package org.apache.spark.sql.execution.joins
 
 import java.util.{HashMap => JavaHashMap}
 
-import org.apache.spark.rdd.RDD
-
-import scala.collection.JavaConversions._
-
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning, UnknownPartitioning}
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning}
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.util.collection.CompactBuffer
 
-/**
- * :: DeveloperApi ::
- * Performs a hash based outer join for two child relations by shuffling the data using
- * the join keys. This operator requires loading the associated partition in both side into memory.
- */
 @DeveloperApi
-case class HashOuterJoin(
-    leftKeys: Seq[Expression],
-    rightKeys: Seq[Expression],
-    joinType: JoinType,
-    condition: Option[Expression],
-    left: SparkPlan,
-    right: SparkPlan) extends BinaryNode {
-
-  override def outputPartitioning: Partitioning = joinType match {
+trait HashOuterJoin {
+  self: SparkPlan =>
+
+  val leftKeys: Seq[Expression]
+  val rightKeys: Seq[Expression]
+  val joinType: JoinType
+  val condition: Option[Expression]
+  val left: SparkPlan
+  val right: SparkPlan
+
+override def outputPartitioning: Partitioning = joinType match {
     case LeftOuter => left.outputPartitioning
     case RightOuter => right.outputPartitioning
     case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
@@ -52,9 +45,6 @@ case class HashOuterJoin(
       throw new IllegalArgumentException(s"HashOuterJoin should not take $x as the JoinType")
   }
 
-  override def requiredChildDistribution: Seq[ClusteredDistribution] =
-    ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
-
   override def output: Seq[Attribute] = {
     joinType match {
       case LeftOuter =>
@@ -68,8 +58,8 @@ case class HashOuterJoin(
     }
   }
 
-  @transient private[this] lazy val DUMMY_LIST = Seq[InternalRow](null)
-  @transient private[this] lazy val EMPTY_LIST = Seq.empty[InternalRow]
+  @transient private[this] lazy val DUMMY_LIST = CompactBuffer[InternalRow](null)
+  @transient protected[this] lazy val EMPTY_LIST = CompactBuffer[InternalRow]()
 
   @transient private[this] lazy val leftNullRow = new GenericInternalRow(left.output.length)
   @transient private[this] lazy val rightNullRow = new GenericInternalRow(right.output.length)
@@ -80,7 +70,7 @@ case class HashOuterJoin(
   // TODO we need to rewrite all of the iterators with our own implementation instead of the Scala
   // iterator for performance purpose.
 
-  private[this] def leftOuterIterator(
+  protected[this] def leftOuterIterator(
       key: InternalRow,
       joinedRow: JoinedRow,
       rightIter: Iterable[InternalRow]): Iterator[InternalRow] = {
@@ -89,7 +79,7 @@ case class HashOuterJoin(
         val temp = rightIter.collect {
           case r if boundCondition(joinedRow.withRight(r)) => joinedRow.copy()
         }
-        if (temp.size == 0) {
+        if (temp.isEmpty) {
           joinedRow.withRight(rightNullRow).copy :: Nil
         } else {
           temp
@@ -101,18 +91,17 @@ case class HashOuterJoin(
     ret.iterator
   }
 
-  private[this] def rightOuterIterator(
+  protected[this] def rightOuterIterator(
       key: InternalRow,
       leftIter: Iterable[InternalRow],
       joinedRow: JoinedRow): Iterator[InternalRow] = {
-
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = leftIter.collect {
           case l if boundCondition(joinedRow.withLeft(l)) =>
-            joinedRow.copy
+            joinedRow.copy()
         }
-        if (temp.size == 0) {
+        if (temp.isEmpty) {
           joinedRow.withLeft(leftNullRow).copy :: Nil
         } else {
           temp
@@ -124,10 +113,9 @@ case class HashOuterJoin(
     ret.iterator
   }
 
-  private[this] def fullOuterIterator(
+  protected[this] def fullOuterIterator(
       key: InternalRow, leftIter: Iterable[InternalRow], rightIter: Iterable[InternalRow],
       joinedRow: JoinedRow): Iterator[InternalRow] = {
-
     if (!key.anyNull) {
       // Store the positions of records in right, if one of its associated row satisfy
       // the join condition.
@@ -171,7 +159,7 @@ case class HashOuterJoin(
     }
   }
 
-  private[this] def buildHashTable(
+  protected[this] def buildHashTable(
       iter: Iterator[InternalRow],
       keyGenerator: Projection): JavaHashMap[InternalRow, CompactBuffer[InternalRow]] = {
     val hashTable = new JavaHashMap[InternalRow, CompactBuffer[InternalRow]]()
@@ -190,43 +178,4 @@ case class HashOuterJoin(
 
     hashTable
   }
-
-  protected override def doExecute(): RDD[InternalRow] = {
-    val joinedRow = new JoinedRow()
-    left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
-      // TODO this probably can be replaced by external sort (sort merged join?)
-
-      joinType match {
-        case LeftOuter =>
-          val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
-          val keyGenerator = newProjection(leftKeys, left.output)
-          leftIter.flatMap( currentRow => {
-            val rowKey = keyGenerator(currentRow)
-            joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, rightHashTable.getOrElse(rowKey, EMPTY_LIST))
-          })
-
-        case RightOuter =>
-          val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
-          val keyGenerator = newProjection(rightKeys, right.output)
-          rightIter.flatMap ( currentRow => {
-            val rowKey = keyGenerator(currentRow)
-            joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, leftHashTable.getOrElse(rowKey, EMPTY_LIST), joinedRow)
-          })
-
-        case FullOuter =>
-          val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
-          val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
-          (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
-            fullOuterIterator(key,
-              leftHashTable.getOrElse(key, EMPTY_LIST),
-              rightHashTable.getOrElse(key, EMPTY_LIST), joinedRow)
-          }
-
-        case x =>
-          throw new IllegalArgumentException(s"HashOuterJoin should not take $x as the JoinType")
-      }
-    }
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
new file mode 100644
index 0000000000000..cfc9c14aaa363
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{Distribution, ClusteredDistribution}
+import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
+import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+
+import scala.collection.JavaConversions._
+
+/**
+ * :: DeveloperApi ::
+ * Performs a hash based outer join for two child relations by shuffling the data using
+ * the join keys. This operator requires loading the associated partition in both side into memory.
+ */
+@DeveloperApi
+case class ShuffledHashOuterJoin(
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    joinType: JoinType,
+    condition: Option[Expression],
+    left: SparkPlan,
+    right: SparkPlan) extends BinaryNode with HashOuterJoin {
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
+
+  protected override def doExecute(): RDD[InternalRow] = {
+    val joinedRow = new JoinedRow()
+    left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
+      // TODO this probably can be replaced by external sort (sort merged join?)
+      joinType match {
+        case LeftOuter =>
+          val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
+          val keyGenerator = newProjection(leftKeys, left.output)
+          leftIter.flatMap( currentRow => {
+            val rowKey = keyGenerator(currentRow)
+            joinedRow.withLeft(currentRow)
+            leftOuterIterator(rowKey, joinedRow, rightHashTable.getOrElse(rowKey, EMPTY_LIST))
+          })
+
+        case RightOuter =>
+          val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
+          val keyGenerator = newProjection(rightKeys, right.output)
+          rightIter.flatMap ( currentRow => {
+            val rowKey = keyGenerator(currentRow)
+            joinedRow.withRight(currentRow)
+            rightOuterIterator(rowKey, leftHashTable.getOrElse(rowKey, EMPTY_LIST), joinedRow)
+          })
+
+        case FullOuter =>
+          val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
+          val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
+          (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
+            fullOuterIterator(key,
+              leftHashTable.getOrElse(key, EMPTY_LIST),
+              rightHashTable.getOrElse(key, EMPTY_LIST),
+              joinedRow)
+          }
+
+        case x =>
+          throw new IllegalArgumentException(
+            s"ShuffledHashOuterJoin should not take $x as the JoinType")
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 20390a5544304..8953889d1fae9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -45,9 +45,10 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
     val physical = df.queryExecution.sparkPlan
     val operators = physical.collect {
       case j: ShuffledHashJoin => j
-      case j: HashOuterJoin => j
+      case j: ShuffledHashOuterJoin => j
       case j: LeftSemiJoinHash => j
       case j: BroadcastHashJoin => j
+      case j: BroadcastHashOuterJoin => j
       case j: LeftSemiJoinBNL => j
       case j: CartesianProduct => j
       case j: BroadcastNestedLoopJoin => j
@@ -81,12 +82,13 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[ShuffledHashJoin]),
       ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[ShuffledHashJoin]),
       ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[ShuffledHashJoin]),
-      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[HashOuterJoin]),
+      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
       ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
-        classOf[HashOuterJoin]),
+        classOf[ShuffledHashOuterJoin]),
       ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
-        classOf[HashOuterJoin]),
-      ("SELECT * FROM testData full outer join testData2 ON key = a", classOf[HashOuterJoin]),
+        classOf[ShuffledHashOuterJoin]),
+      ("SELECT * FROM testData full outer join testData2 ON key = a",
+        classOf[ShuffledHashOuterJoin]),
       ("SELECT * FROM testData left JOIN testData2 ON (key * a != key + a)",
         classOf[BroadcastNestedLoopJoin]),
       ("SELECT * FROM testData right JOIN testData2 ON (key * a != key + a)",
@@ -133,6 +135,34 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
     ctx.sql("UNCACHE TABLE testData")
   }
 
+  test("broadcasted hash outer join operator selection") {
+    ctx.cacheManager.clearCache()
+    ctx.sql("CACHE TABLE testData")
+
+    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
+    Seq(
+      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+      ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
+        classOf[BroadcastHashOuterJoin]),
+      ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
+        classOf[BroadcastHashOuterJoin])
+    ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
+    try {
+      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+      Seq(
+        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
+          classOf[BroadcastHashOuterJoin]),
+        ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
+          classOf[BroadcastHashOuterJoin])
+      ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
+    } finally {
+      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
+    }
+
+    ctx.sql("UNCACHE TABLE testData")
+  }
+
   test("multiple-key equi-join is hash-join") {
     val x = testData2.as("x")
     val y = testData2.as("y")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index 13f3be8ca28d6..108b1122f7bff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -54,6 +54,37 @@ class SparkPlanTest extends SparkFunSuite {
       input: DataFrame,
       planFunction: SparkPlan => SparkPlan,
       expectedAnswer: Seq[Row]): Unit = {
+    checkAnswer(input :: Nil, (plans: Seq[SparkPlan]) => planFunction(plans.head), expectedAnswer)
+  }
+
+  /**
+   * Runs the plan and makes sure the answer matches the expected result.
+   * @param left the left input data to be used.
+   * @param right the right input data to be used.
+   * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
+   *                     the physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   */
+  protected def checkAnswer(
+      left: DataFrame,
+      right: DataFrame,
+      planFunction: (SparkPlan, SparkPlan) => SparkPlan,
+      expectedAnswer: Seq[Row]): Unit = {
+    checkAnswer(left :: right :: Nil,
+      (plans: Seq[SparkPlan]) => planFunction(plans(0), plans(1)), expectedAnswer)
+  }
+
+  /**
+   * Runs the plan and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
+   *                     the physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   */
+  protected def checkAnswer(
+      input: Seq[DataFrame],
+      planFunction: Seq[SparkPlan] => SparkPlan,
+      expectedAnswer: Seq[Row]): Unit = {
     SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
@@ -72,11 +103,41 @@ class SparkPlanTest extends SparkFunSuite {
       planFunction: SparkPlan => SparkPlan,
       expectedAnswer: Seq[A]): Unit = {
     val expectedRows = expectedAnswer.map(Row.fromTuple)
-    SparkPlanTest.checkAnswer(input, planFunction, expectedRows) match {
-      case Some(errorMessage) => fail(errorMessage)
-      case None =>
-    }
+    checkAnswer(input, planFunction, expectedRows)
+  }
+
+  /**
+   * Runs the plan and makes sure the answer matches the expected result.
+   * @param left the left input data to be used.
+   * @param right the right input data to be used.
+   * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
+   *                     the physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s.
+   */
+  protected def checkAnswer[A <: Product : TypeTag](
+      left: DataFrame,
+      right: DataFrame,
+      planFunction: (SparkPlan, SparkPlan) => SparkPlan,
+      expectedAnswer: Seq[A]): Unit = {
+    val expectedRows = expectedAnswer.map(Row.fromTuple)
+    checkAnswer(left, right, planFunction, expectedRows)
+  }
+
+  /**
+   * Runs the plan and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
+   *                     the physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s.
+   */
+  protected def checkAnswer[A <: Product : TypeTag](
+      input: Seq[DataFrame],
+      planFunction: Seq[SparkPlan] => SparkPlan,
+      expectedAnswer: Seq[A]): Unit = {
+    val expectedRows = expectedAnswer.map(Row.fromTuple)
+    checkAnswer(input, planFunction, expectedRows)
   }
+
 }
 
 /**
@@ -92,27 +153,25 @@ object SparkPlanTest {
    * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
    */
   def checkAnswer(
-      input: DataFrame,
-      planFunction: SparkPlan => SparkPlan,
+      input: Seq[DataFrame],
+      planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row]): Option[String] = {
 
-    val outputPlan = planFunction(input.queryExecution.sparkPlan)
+    val outputPlan = planFunction(input.map(_.queryExecution.sparkPlan))
 
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
-    val resolvedPlan = outputPlan transform {
-      case plan: SparkPlan =>
-        val inputMap = plan.children.flatMap(_.output).zipWithIndex.map {
-          case (a, i) =>
-            (a.name, BoundReference(i, a.dataType, a.nullable))
-        }.toMap
-
-        plan.transformExpressions {
-          case UnresolvedAttribute(Seq(u)) =>
-            inputMap.getOrElse(u,
-              sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap"))
-        }
-    }
+    val resolvedPlan = TestSQLContext.prepareForExecution.execute(
+      outputPlan transform {
+        case plan: SparkPlan =>
+          val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
+          plan.transformExpressions {
+            case UnresolvedAttribute(Seq(u)) =>
+              inputMap.getOrElse(u,
+                sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap"))
+          }
+      }
+    )
 
     def prepareAnswer(answer: Seq[Row]): Seq[Row] = {
       // Converts data to types that we can do equality comparison using Scala collections.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
new file mode 100644
index 0000000000000..5707d2fb300ae
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -0,0 +1,88 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions.{Expression, LessThan}
+import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, RightOuter}
+import org.apache.spark.sql.execution.{SparkPlan, SparkPlanTest}
+
+class OuterJoinSuite extends SparkPlanTest {
+
+  val left = Seq(
+    (1, 2.0),
+    (2, 1.0),
+    (3, 3.0)
+  ).toDF("a", "b")
+
+  val right = Seq(
+    (2, 3.0),
+    (3, 2.0),
+    (4, 1.0)
+  ).toDF("c", "d")
+
+  val leftKeys: List[Expression] = 'a :: Nil
+  val rightKeys: List[Expression] = 'c :: Nil
+  val condition = Some(LessThan('b, 'd))
+
+  test("shuffled hash outer join") {
+    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+      ShuffledHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
+      Seq(
+        (1, 2.0, null, null),
+        (2, 1.0, 2, 3.0),
+        (3, 3.0, null, null)
+      ))
+
+    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+      ShuffledHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
+      Seq(
+        (2, 1.0, 2, 3.0),
+        (null, null, 3, 2.0),
+        (null, null, 4, 1.0)
+      ))
+
+    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+      ShuffledHashOuterJoin(leftKeys, rightKeys, FullOuter, condition, left, right),
+      Seq(
+        (1, 2.0, null, null),
+        (2, 1.0, 2, 3.0),
+        (3, 3.0, null, null),
+        (null, null, 3, 2.0),
+        (null, null, 4, 1.0)
+      ))
+  }
+
+  test("broadcast hash outer join") {
+    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+      BroadcastHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
+      Seq(
+        (1, 2.0, null, null),
+        (2, 1.0, 2, 3.0),
+        (3, 3.0, null, null)
+      ))
+
+    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+      BroadcastHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
+      Seq(
+        (2, 1.0, 2, 3.0),
+        (null, null, 3, 2.0),
+        (null, null, 4, 1.0)
+      ))
+  }
+}

From 132e7fca129be8f00ba429a51bcef60abb2eed6d Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Mon, 6 Jul 2015 15:54:43 -0700
Subject: [PATCH 0240/1454] [MINOR] [SQL] remove unused code in Exchange

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #7234 from adrian-wang/exchangeclean and squashes the following commits:

b093ec9 [Daoyuan Wang] remove unused code
---
 .../org/apache/spark/sql/execution/Exchange.scala  | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index edc64a03335d6..e054c1d144e34 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -117,20 +117,6 @@ case class Exchange(
     }
   }
 
-  private val keyOrdering = {
-    if (newOrdering.nonEmpty) {
-      val key = newPartitioning.keyExpressions
-      val boundOrdering = newOrdering.map { o =>
-        val ordinal = key.indexOf(o.child)
-        if (ordinal == -1) sys.error(s"Invalid ordering on $o requested for $newPartitioning")
-        o.copy(child = BoundReference(ordinal, o.child.dataType, o.child.nullable))
-      }
-      new RowOrdering(boundOrdering)
-    } else {
-      null // Ordering will not be used
-    }
-  }
-
   @transient private lazy val sparkConf = child.sqlContext.sparkContext.getConf
 
   private def getSerializer(

From 9ff203346ca4decf2999e33bfb8c400ec75313e6 Mon Sep 17 00:00:00 2001
From: Wisely Chen <wiselychen@appier.com>
Date: Mon, 6 Jul 2015 16:04:01 -0700
Subject: [PATCH 0241/1454] [SPARK-8656] [WEBUI] Fix the webUI and JSON API
 number is not synced

Spark standalone master web UI show "Alive Workers" total core, total used cores and "Alive workers" total memory, memory used.
But the JSON API page "http://MASTERURL:8088/json" shows "ALL workers"  core, memory number.
This webUI data is not sync with the JSON API.
The proper way is to sync the number with webUI and JSON API.

Author: Wisely Chen <wiselychen@appier.com>

Closes #7038 from thegiive/SPARK-8656 and squashes the following commits:

9e54bf0 [Wisely Chen] Change variable name to camel case
2c8ea89 [Wisely Chen] Change some styling and add local variable
431d2b0 [Wisely Chen] Worker List should contain DEAD node also
8b3b8e8 [Wisely Chen] [SPARK-8656] Fix the webUI and JSON API number is not synced
---
 .../scala/org/apache/spark/deploy/JsonProtocol.scala     | 9 +++++----
 .../org/apache/spark/deploy/master/WorkerInfo.scala      | 2 ++
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala
index 2954f932b4f41..ccffb36652988 100644
--- a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala
@@ -76,12 +76,13 @@ private[deploy] object JsonProtocol {
   }
 
   def writeMasterState(obj: MasterStateResponse): JObject = {
+    val aliveWorkers = obj.workers.filter(_.isAlive())
     ("url" -> obj.uri) ~
     ("workers" -> obj.workers.toList.map(writeWorkerInfo)) ~
-    ("cores" -> obj.workers.map(_.cores).sum) ~
-    ("coresused" -> obj.workers.map(_.coresUsed).sum) ~
-    ("memory" -> obj.workers.map(_.memory).sum) ~
-    ("memoryused" -> obj.workers.map(_.memoryUsed).sum) ~
+    ("cores" -> aliveWorkers.map(_.cores).sum) ~
+    ("coresused" -> aliveWorkers.map(_.coresUsed).sum) ~
+    ("memory" -> aliveWorkers.map(_.memory).sum) ~
+    ("memoryused" -> aliveWorkers.map(_.memoryUsed).sum) ~
     ("activeapps" -> obj.activeApps.toList.map(writeApplicationInfo)) ~
     ("completedapps" -> obj.completedApps.toList.map(writeApplicationInfo)) ~
     ("activedrivers" -> obj.activeDrivers.toList.map(writeDriverInfo)) ~
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
index 471811037e5e2..f751966605206 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
@@ -105,4 +105,6 @@ private[spark] class WorkerInfo(
   def setState(state: WorkerState.Value): Unit = {
     this.state = state
   }
+
+  def isAlive(): Boolean = this.state == WorkerState.ALIVE
 }

From 1165b17d24cdf1dbebb2faca14308dfe5c2a652c Mon Sep 17 00:00:00 2001
From: Ankur Chauhan <achauhan@brightcove.com>
Date: Mon, 6 Jul 2015 16:04:57 -0700
Subject: [PATCH 0242/1454] [SPARK-6707] [CORE] [MESOS] Mesos Scheduler should
 allow the user to specify constraints based on slave attributes

Currently, the mesos scheduler only looks at the 'cpu' and 'mem' resources when trying to determine the usablility of a resource offer from a mesos slave node. It may be preferable for the user to be able to ensure that the spark jobs are only started on a certain set of nodes (based on attributes).

For example, If the user sets a property, let's say `spark.mesos.constraints` is set to `tachyon=true;us-east-1=false`, then the resource offers will be checked to see if they meet both these constraints and only then will be accepted to start new executors.

Author: Ankur Chauhan <achauhan@brightcove.com>

Closes #5563 from ankurcha/mesos_attribs and squashes the following commits:

902535b [Ankur Chauhan] Fix line length
d83801c [Ankur Chauhan] Update code as per code review comments
8b73f2d [Ankur Chauhan] Fix imports
c3523e7 [Ankur Chauhan] Added docs
1a24d0b [Ankur Chauhan] Expand scope of attributes matching to include all data types
482fd71 [Ankur Chauhan] Update access modifier to private[this] for offer constraints
5ccc32d [Ankur Chauhan] Fix nit pick whitespace
1bce782 [Ankur Chauhan] Fix nit pick whitespace
c0cbc75 [Ankur Chauhan] Use offer id value for debug message
7fee0ea [Ankur Chauhan] Add debug statements
fc7eb5b [Ankur Chauhan] Fix import codestyle
00be252 [Ankur Chauhan] Style changes as per code review comments
662535f [Ankur Chauhan] Incorporate code review comments + use SparkFunSuite
fdc0937 [Ankur Chauhan] Decline offers that did not meet criteria
67b58a0 [Ankur Chauhan] Add documentation for spark.mesos.constraints
63f53f4 [Ankur Chauhan] Update codestyle - uniform style for config values
02031e4 [Ankur Chauhan] Fix scalastyle warnings in tests
c09ed84 [Ankur Chauhan] Fixed the access modifier on offerConstraints val to private[mesos]
0c64df6 [Ankur Chauhan] Rename overhead fractions to memory_*, fix spacing
8cc1e8f [Ankur Chauhan] Make exception message more explicit about the source of the error
addedba [Ankur Chauhan] Added test case for malformed constraint string
ec9d9a6 [Ankur Chauhan] Add tests for parse constraint string
72fe88a [Ankur Chauhan] Fix up tests + remove redundant method override, combine utility class into new mesos scheduler util trait
92b47fd [Ankur Chauhan] Add attributes based constraints support to MesosScheduler
---
 .../mesos/CoarseMesosSchedulerBackend.scala   |  43 +++--
 .../scheduler/cluster/mesos/MemoryUtils.scala |  31 ----
 .../cluster/mesos/MesosClusterScheduler.scala |   1 +
 .../cluster/mesos/MesosSchedulerBackend.scala |  62 ++++---
 .../cluster/mesos/MesosSchedulerUtils.scala   | 153 +++++++++++++++++-
 .../cluster/mesos/MemoryUtilsSuite.scala      |  46 ------
 .../mesos/MesosSchedulerBackendSuite.scala    |   6 +-
 .../mesos/MesosSchedulerUtilsSuite.scala      | 140 ++++++++++++++++
 docs/running-on-mesos.md                      |  22 +++
 9 files changed, 376 insertions(+), 128 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala
 delete mode 100644 core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtilsSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 6b8edca5aa485..b68f8c7685eba 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -18,18 +18,18 @@
 package org.apache.spark.scheduler.cluster.mesos
 
 import java.io.File
-import java.util.{Collections, List => JList}
+import java.util.{List => JList}
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, HashSet}
 
-import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _}
 import org.apache.mesos.{Scheduler => MScheduler, _}
+import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _}
+import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState}
 import org.apache.spark.rpc.RpcAddress
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
-import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState}
 
 /**
  * A SchedulerBackend that runs tasks on Mesos, but uses "coarse-grained" tasks, where it holds
@@ -66,6 +66,10 @@ private[spark] class CoarseMesosSchedulerBackend(
 
   val extraCoresPerSlave = conf.getInt("spark.mesos.extra.cores", 0)
 
+  // Offer constraints
+  private val slaveOfferConstraints =
+    parseConstraintString(sc.conf.get("spark.mesos.constraints", ""))
+
   var nextMesosTaskId = 0
 
   @volatile var appId: String = _
@@ -170,13 +174,16 @@ private[spark] class CoarseMesosSchedulerBackend(
   override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) {
     synchronized {
       val filters = Filters.newBuilder().setRefuseSeconds(5).build()
-
       for (offer <- offers) {
+        val offerAttributes = toAttributeMap(offer.getAttributesList)
+        val meetsConstraints = matchesAttributeRequirements(slaveOfferConstraints, offerAttributes)
         val slaveId = offer.getSlaveId.toString
         val mem = getResource(offer.getResourcesList, "mem")
         val cpus = getResource(offer.getResourcesList, "cpus").toInt
-        if (totalCoresAcquired < maxCores &&
-            mem >= MemoryUtils.calculateTotalMemory(sc) &&
+        val id = offer.getId.getValue
+        if (meetsConstraints &&
+            totalCoresAcquired < maxCores &&
+            mem >= calculateTotalMemory(sc) &&
             cpus >= 1 &&
             failuresBySlaveId.getOrElse(slaveId, 0) < MAX_SLAVE_FAILURES &&
             !slaveIdsWithExecutors.contains(slaveId)) {
@@ -193,33 +200,25 @@ private[spark] class CoarseMesosSchedulerBackend(
             .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave))
             .setName("Task " + taskId)
             .addResources(createResource("cpus", cpusToUse))
-            .addResources(createResource("mem",
-              MemoryUtils.calculateTotalMemory(sc)))
+            .addResources(createResource("mem", calculateTotalMemory(sc)))
 
           sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image =>
             MesosSchedulerBackendUtil
-              .setupContainerBuilderDockerInfo(image, sc.conf, task.getContainerBuilder())
+              .setupContainerBuilderDockerInfo(image, sc.conf, task.getContainerBuilder)
           }
 
-          d.launchTasks(
-            Collections.singleton(offer.getId), Collections.singletonList(task.build()), filters)
+          // accept the offer and launch the task
+          logDebug(s"Accepting offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
+          d.launchTasks(List(offer.getId), List(task.build()), filters)
         } else {
-          // Filter it out
-          d.launchTasks(
-            Collections.singleton(offer.getId), Collections.emptyList[MesosTaskInfo](), filters)
+          // Decline the offer
+          logDebug(s"Declining offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
+          d.declineOffer(offer.getId)
         }
       }
     }
   }
 
-  /** Build a Mesos resource protobuf object */
-  private def createResource(resourceName: String, quantity: Double): Protos.Resource = {
-    Resource.newBuilder()
-      .setName(resourceName)
-      .setType(Value.Type.SCALAR)
-      .setScalar(Value.Scalar.newBuilder().setValue(quantity).build())
-      .build()
-  }
 
   override def statusUpdate(d: SchedulerDriver, status: TaskStatus) {
     val taskId = status.getTaskId.getValue.toInt
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala
deleted file mode 100644
index 8df4f3b554c41..0000000000000
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.scheduler.cluster.mesos
-
-import org.apache.spark.SparkContext
-
-private[spark] object MemoryUtils {
-  // These defaults copied from YARN
-  val OVERHEAD_FRACTION = 0.10
-  val OVERHEAD_MINIMUM = 384
-
-  def calculateTotalMemory(sc: SparkContext): Int = {
-    sc.conf.getInt("spark.mesos.executor.memoryOverhead",
-      math.max(OVERHEAD_FRACTION * sc.executorMemory, OVERHEAD_MINIMUM).toInt) + sc.executorMemory
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 1067a7f1caf4c..d3a20f822176e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -29,6 +29,7 @@ import org.apache.mesos.Protos.Environment.Variable
 import org.apache.mesos.Protos.TaskStatus.Reason
 import org.apache.mesos.Protos.{TaskState => MesosTaskState, _}
 import org.apache.mesos.{Scheduler, SchedulerDriver}
+
 import org.apache.spark.deploy.mesos.MesosDriverDescription
 import org.apache.spark.deploy.rest.{CreateSubmissionResponse, KillSubmissionResponse, SubmissionStatusResponse}
 import org.apache.spark.metrics.MetricsSystem
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 49de85ef48ada..d72e2af456e15 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -23,14 +23,14 @@ import java.util.{ArrayList => JArrayList, Collections, List => JList}
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, HashSet}
 
+import org.apache.mesos.{Scheduler => MScheduler, _}
 import org.apache.mesos.Protos.{ExecutorInfo => MesosExecutorInfo, TaskInfo => MesosTaskInfo, _}
 import org.apache.mesos.protobuf.ByteString
-import org.apache.mesos.{Scheduler => MScheduler, _}
+import org.apache.spark.{SparkContext, SparkException, TaskState}
 import org.apache.spark.executor.MesosExecutorBackend
 import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.cluster.ExecutorInfo
 import org.apache.spark.util.Utils
-import org.apache.spark.{SparkContext, SparkException, TaskState}
 
 /**
  * A SchedulerBackend for running fine-grained tasks on Mesos. Each Spark task is mapped to a
@@ -59,6 +59,10 @@ private[spark] class MesosSchedulerBackend(
 
   private[mesos] val mesosExecutorCores = sc.conf.getDouble("spark.mesos.mesosExecutor.cores", 1)
 
+  // Offer constraints
+  private[this] val slaveOfferConstraints =
+    parseConstraintString(sc.conf.get("spark.mesos.constraints", ""))
+
   @volatile var appId: String = _
 
   override def start() {
@@ -71,8 +75,8 @@ private[spark] class MesosSchedulerBackend(
     val executorSparkHome = sc.conf.getOption("spark.mesos.executor.home")
       .orElse(sc.getSparkHome()) // Fall back to driver Spark home for backward compatibility
       .getOrElse {
-        throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
-      }
+      throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
+    }
     val environment = Environment.newBuilder()
     sc.conf.getOption("spark.executor.extraClassPath").foreach { cp =>
       environment.addVariables(
@@ -115,14 +119,14 @@ private[spark] class MesosSchedulerBackend(
       .setName("cpus")
       .setType(Value.Type.SCALAR)
       .setScalar(Value.Scalar.newBuilder()
-        .setValue(mesosExecutorCores).build())
+      .setValue(mesosExecutorCores).build())
       .build()
     val memory = Resource.newBuilder()
       .setName("mem")
       .setType(Value.Type.SCALAR)
       .setScalar(
         Value.Scalar.newBuilder()
-          .setValue(MemoryUtils.calculateTotalMemory(sc)).build())
+          .setValue(calculateTotalMemory(sc)).build())
       .build()
     val executorInfo = MesosExecutorInfo.newBuilder()
       .setExecutorId(ExecutorID.newBuilder().setValue(execId).build())
@@ -191,13 +195,31 @@ private[spark] class MesosSchedulerBackend(
         val mem = getResource(o.getResourcesList, "mem")
         val cpus = getResource(o.getResourcesList, "cpus")
         val slaveId = o.getSlaveId.getValue
-        (mem >= MemoryUtils.calculateTotalMemory(sc) &&
-          // need at least 1 for executor, 1 for task
-          cpus >= (mesosExecutorCores + scheduler.CPUS_PER_TASK)) ||
-          (slaveIdsWithExecutors.contains(slaveId) &&
-            cpus >= scheduler.CPUS_PER_TASK)
+        val offerAttributes = toAttributeMap(o.getAttributesList)
+
+        // check if all constraints are satisfield
+        //  1. Attribute constraints
+        //  2. Memory requirements
+        //  3. CPU requirements - need at least 1 for executor, 1 for task
+        val meetsConstraints = matchesAttributeRequirements(slaveOfferConstraints, offerAttributes)
+        val meetsMemoryRequirements = mem >= calculateTotalMemory(sc)
+        val meetsCPURequirements = cpus >= (mesosExecutorCores + scheduler.CPUS_PER_TASK)
+
+        val meetsRequirements =
+          (meetsConstraints && meetsMemoryRequirements && meetsCPURequirements) ||
+          (slaveIdsWithExecutors.contains(slaveId) && cpus >= scheduler.CPUS_PER_TASK)
+
+        // add some debug messaging
+        val debugstr = if (meetsRequirements) "Accepting" else "Declining"
+        val id = o.getId.getValue
+        logDebug(s"$debugstr offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
+
+        meetsRequirements
       }
 
+      // Decline offers we ruled out immediately
+      unUsableOffers.foreach(o => d.declineOffer(o.getId))
+
       val workerOffers = usableOffers.map { o =>
         val cpus = if (slaveIdsWithExecutors.contains(o.getSlaveId.getValue)) {
           getResource(o.getResourcesList, "cpus").toInt
@@ -223,15 +245,15 @@ private[spark] class MesosSchedulerBackend(
       val acceptedOffers = scheduler.resourceOffers(workerOffers).filter(!_.isEmpty)
       acceptedOffers
         .foreach { offer =>
-          offer.foreach { taskDesc =>
-            val slaveId = taskDesc.executorId
-            slaveIdsWithExecutors += slaveId
-            slavesIdsOfAcceptedOffers += slaveId
-            taskIdToSlaveId(taskDesc.taskId) = slaveId
-            mesosTasks.getOrElseUpdate(slaveId, new JArrayList[MesosTaskInfo])
-              .add(createMesosTask(taskDesc, slaveId))
-          }
+        offer.foreach { taskDesc =>
+          val slaveId = taskDesc.executorId
+          slaveIdsWithExecutors += slaveId
+          slavesIdsOfAcceptedOffers += slaveId
+          taskIdToSlaveId(taskDesc.taskId) = slaveId
+          mesosTasks.getOrElseUpdate(slaveId, new JArrayList[MesosTaskInfo])
+            .add(createMesosTask(taskDesc, slaveId))
         }
+      }
 
       // Reply to the offers
       val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout?
@@ -251,8 +273,6 @@ private[spark] class MesosSchedulerBackend(
         d.declineOffer(o.getId)
       }
 
-      // Decline offers we ruled out immediately
-      unUsableOffers.foreach(o => d.declineOffer(o.getId))
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
index d11228f3d016a..d8a8c848bb4d1 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
@@ -17,14 +17,17 @@
 
 package org.apache.spark.scheduler.cluster.mesos
 
-import java.util.List
+import java.util.{List => JList}
 import java.util.concurrent.CountDownLatch
 
 import scala.collection.JavaConversions._
+import scala.util.control.NonFatal
 
-import org.apache.mesos.Protos.{FrameworkInfo, Resource, Status}
-import org.apache.mesos.{MesosSchedulerDriver, Scheduler}
-import org.apache.spark.Logging
+import com.google.common.base.Splitter
+import org.apache.mesos.{MesosSchedulerDriver, Protos, Scheduler}
+import org.apache.mesos.Protos._
+import org.apache.mesos.protobuf.GeneratedMessage
+import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.util.Utils
 
 /**
@@ -86,10 +89,150 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
   /**
    * Get the amount of resources for the specified type from the resource list
    */
-  protected def getResource(res: List[Resource], name: String): Double = {
+  protected def getResource(res: JList[Resource], name: String): Double = {
     for (r <- res if r.getName == name) {
       return r.getScalar.getValue
     }
     0.0
   }
+
+  /** Helper method to get the key,value-set pair for a Mesos Attribute protobuf */
+  protected def getAttribute(attr: Attribute): (String, Set[String]) = {
+    (attr.getName, attr.getText.getValue.split(',').toSet)
+  }
+
+
+  /** Build a Mesos resource protobuf object */
+  protected def createResource(resourceName: String, quantity: Double): Protos.Resource = {
+    Resource.newBuilder()
+      .setName(resourceName)
+      .setType(Value.Type.SCALAR)
+      .setScalar(Value.Scalar.newBuilder().setValue(quantity).build())
+      .build()
+  }
+
+  /**
+   * Converts the attributes from the resource offer into a Map of name -> Attribute Value
+   * The attribute values are the mesos attribute types and they are
+   * @param offerAttributes
+   * @return
+   */
+  protected def toAttributeMap(offerAttributes: JList[Attribute]): Map[String, GeneratedMessage] = {
+    offerAttributes.map(attr => {
+      val attrValue = attr.getType match {
+        case Value.Type.SCALAR => attr.getScalar
+        case Value.Type.RANGES => attr.getRanges
+        case Value.Type.SET => attr.getSet
+        case Value.Type.TEXT => attr.getText
+      }
+      (attr.getName, attrValue)
+    }).toMap
+  }
+
+
+  /**
+   * Match the requirements (if any) to the offer attributes.
+   * if attribute requirements are not specified - return true
+   * else if attribute is defined and no values are given, simple attribute presence is performed
+   * else if attribute name and value is specified, subset match is performed on slave attributes
+   */
+  def matchesAttributeRequirements(
+      slaveOfferConstraints: Map[String, Set[String]],
+      offerAttributes: Map[String, GeneratedMessage]): Boolean = {
+    slaveOfferConstraints.forall {
+      // offer has the required attribute and subsumes the required values for that attribute
+      case (name, requiredValues) =>
+        offerAttributes.get(name) match {
+          case None => false
+          case Some(_) if requiredValues.isEmpty => true // empty value matches presence
+          case Some(scalarValue: Value.Scalar) =>
+            // check if provided values is less than equal to the offered values
+            requiredValues.map(_.toDouble).exists(_ <= scalarValue.getValue)
+          case Some(rangeValue: Value.Range) =>
+            val offerRange = rangeValue.getBegin to rangeValue.getEnd
+            // Check if there is some required value that is between the ranges specified
+            // Note: We only support the ability to specify discrete values, in the future
+            // we may expand it to subsume ranges specified with a XX..YY value or something
+            // similar to that.
+            requiredValues.map(_.toLong).exists(offerRange.contains(_))
+          case Some(offeredValue: Value.Set) =>
+            // check if the specified required values is a subset of offered set
+            requiredValues.subsetOf(offeredValue.getItemList.toSet)
+          case Some(textValue: Value.Text) =>
+            // check if the specified value is equal, if multiple values are specified
+            // we succeed if any of them match.
+            requiredValues.contains(textValue.getValue)
+        }
+    }
+  }
+
+  /**
+   * Parses the attributes constraints provided to spark and build a matching data struct:
+   *  Map[<attribute-name>, Set[values-to-match]]
+   *  The constraints are specified as ';' separated key-value pairs where keys and values
+   *  are separated by ':'. The ':' implies equality (for singular values) and "is one of" for
+   *  multiple values (comma separated). For example:
+   *  {{{
+   *  parseConstraintString("tachyon:true;zone:us-east-1a,us-east-1b")
+   *  // would result in
+   *  <code>
+   *  Map(
+   *    "tachyon" -> Set("true"),
+   *    "zone":   -> Set("us-east-1a", "us-east-1b")
+   *  )
+   *  }}}
+   *
+   *  Mesos documentation: http://mesos.apache.org/documentation/attributes-resources/
+   *                       https://github.com/apache/mesos/blob/master/src/common/values.cpp
+   *                       https://github.com/apache/mesos/blob/master/src/common/attributes.cpp
+   *
+   * @param constraintsVal constaints string consisting of ';' separated key-value pairs (separated
+   *                       by ':')
+   * @return  Map of constraints to match resources offers.
+   */
+  def parseConstraintString(constraintsVal: String): Map[String, Set[String]] = {
+    /*
+      Based on mesos docs:
+      attributes : attribute ( ";" attribute )*
+      attribute : labelString ":" ( labelString | "," )+
+      labelString : [a-zA-Z0-9_/.-]
+    */
+    val splitter = Splitter.on(';').trimResults().withKeyValueSeparator(':')
+    // kv splitter
+    if (constraintsVal.isEmpty) {
+      Map()
+    } else {
+      try {
+        Map() ++ mapAsScalaMap(splitter.split(constraintsVal)).map {
+          case (k, v) =>
+            if (v == null || v.isEmpty) {
+              (k, Set[String]())
+            } else {
+              (k, v.split(',').toSet)
+            }
+        }
+      } catch {
+        case NonFatal(e) =>
+          throw new IllegalArgumentException(s"Bad constraint string: $constraintsVal", e)
+      }
+    }
+  }
+
+  // These defaults copied from YARN
+  private val MEMORY_OVERHEAD_FRACTION = 0.10
+  private val MEMORY_OVERHEAD_MINIMUM = 384
+
+  /**
+   * Return the amount of memory to allocate to each executor, taking into account
+   * container overheads.
+   * @param sc SparkContext to use to get `spark.mesos.executor.memoryOverhead` value
+   * @return memory requirement as (0.1 * <memoryOverhead>) or MEMORY_OVERHEAD_MINIMUM
+   *         (whichever is larger)
+   */
+  def calculateTotalMemory(sc: SparkContext): Int = {
+    sc.conf.getInt("spark.mesos.executor.memoryOverhead",
+      math.max(MEMORY_OVERHEAD_FRACTION * sc.executorMemory, MEMORY_OVERHEAD_MINIMUM).toInt) +
+      sc.executorMemory
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtilsSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtilsSuite.scala
deleted file mode 100644
index e72285d03d3ee..0000000000000
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtilsSuite.scala
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.scheduler.cluster.mesos
-
-import org.mockito.Mockito._
-import org.scalatest.mock.MockitoSugar
-
-import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
-
-class MemoryUtilsSuite extends SparkFunSuite with MockitoSugar {
-  test("MesosMemoryUtils should always override memoryOverhead when it's set") {
-    val sparkConf = new SparkConf
-
-    val sc = mock[SparkContext]
-    when(sc.conf).thenReturn(sparkConf)
-
-    // 384 > sc.executorMemory * 0.1 => 512 + 384 = 896
-    when(sc.executorMemory).thenReturn(512)
-    assert(MemoryUtils.calculateTotalMemory(sc) === 896)
-
-    // 384 < sc.executorMemory * 0.1 => 4096 + (4096 * 0.1) = 4505.6
-    when(sc.executorMemory).thenReturn(4096)
-    assert(MemoryUtils.calculateTotalMemory(sc) === 4505)
-
-    // set memoryOverhead
-    sparkConf.set("spark.mesos.executor.memoryOverhead", "100")
-    assert(MemoryUtils.calculateTotalMemory(sc) === 4196)
-    sparkConf.set("spark.mesos.executor.memoryOverhead", "400")
-    assert(MemoryUtils.calculateTotalMemory(sc) === 4496)
-  }
-}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
index 68df46a41ddc8..d01837fe78957 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
@@ -149,7 +149,9 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     when(sc.conf).thenReturn(new SparkConf)
     when(sc.listenerBus).thenReturn(listenerBus)
 
-    val minMem = MemoryUtils.calculateTotalMemory(sc).toInt
+    val backend = new MesosSchedulerBackend(taskScheduler, sc, "master")
+
+    val minMem = backend.calculateTotalMemory(sc)
     val minCpu = 4
 
     val mesosOffers = new java.util.ArrayList[Offer]
@@ -157,8 +159,6 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     mesosOffers.add(createOffer(2, minMem - 1, minCpu))
     mesosOffers.add(createOffer(3, minMem, minCpu))
 
-    val backend = new MesosSchedulerBackend(taskScheduler, sc, "master")
-
     val expectedWorkerOffers = new ArrayBuffer[WorkerOffer](2)
     expectedWorkerOffers.append(new WorkerOffer(
       mesosOffers.get(0).getSlaveId.getValue,
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
new file mode 100644
index 0000000000000..b354914b6ffd0
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
@@ -0,0 +1,140 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler.cluster.mesos
+
+import org.apache.mesos.Protos.Value
+import org.mockito.Mockito._
+import org.scalatest._
+import org.scalatest.mock.MockitoSugar
+import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
+
+class MesosSchedulerUtilsSuite extends SparkFunSuite with Matchers with MockitoSugar {
+
+  // scalastyle:off structural.type
+  // this is the documented way of generating fixtures in scalatest
+  def fixture: Object {val sc: SparkContext; val sparkConf: SparkConf} = new {
+    val sparkConf = new SparkConf
+    val sc = mock[SparkContext]
+    when(sc.conf).thenReturn(sparkConf)
+  }
+  val utils = new MesosSchedulerUtils { }
+  // scalastyle:on structural.type
+
+  test("use at-least minimum overhead") {
+    val f = fixture
+    when(f.sc.executorMemory).thenReturn(512)
+    utils.calculateTotalMemory(f.sc) shouldBe 896
+  }
+
+  test("use overhead if it is greater than minimum value") {
+    val f = fixture
+    when(f.sc.executorMemory).thenReturn(4096)
+    utils.calculateTotalMemory(f.sc) shouldBe 4505
+  }
+
+  test("use spark.mesos.executor.memoryOverhead (if set)") {
+    val f = fixture
+    when(f.sc.executorMemory).thenReturn(1024)
+    f.sparkConf.set("spark.mesos.executor.memoryOverhead", "512")
+    utils.calculateTotalMemory(f.sc) shouldBe 1536
+  }
+
+  test("parse a non-empty constraint string correctly") {
+    val expectedMap = Map(
+      "tachyon" -> Set("true"),
+      "zone" -> Set("us-east-1a", "us-east-1b")
+    )
+    utils.parseConstraintString("tachyon:true;zone:us-east-1a,us-east-1b") should be (expectedMap)
+  }
+
+  test("parse an empty constraint string correctly") {
+    utils.parseConstraintString("") shouldBe Map()
+  }
+
+  test("throw an exception when the input is malformed") {
+    an[IllegalArgumentException] should be thrownBy
+      utils.parseConstraintString("tachyon;zone:us-east")
+  }
+
+  test("empty values for attributes' constraints matches all values") {
+    val constraintsStr = "tachyon:"
+    val parsedConstraints = utils.parseConstraintString(constraintsStr)
+
+    parsedConstraints shouldBe Map("tachyon" -> Set())
+
+    val zoneSet = Value.Set.newBuilder().addItem("us-east-1a").addItem("us-east-1b").build()
+    val noTachyonOffer = Map("zone" -> zoneSet)
+    val tachyonTrueOffer = Map("tachyon" -> Value.Text.newBuilder().setValue("true").build())
+    val tachyonFalseOffer = Map("tachyon" -> Value.Text.newBuilder().setValue("false").build())
+
+    utils.matchesAttributeRequirements(parsedConstraints, noTachyonOffer) shouldBe false
+    utils.matchesAttributeRequirements(parsedConstraints, tachyonTrueOffer) shouldBe true
+    utils.matchesAttributeRequirements(parsedConstraints, tachyonFalseOffer) shouldBe true
+  }
+
+  test("subset match is performed for set attributes") {
+    val supersetConstraint = Map(
+      "tachyon" -> Value.Text.newBuilder().setValue("true").build(),
+      "zone" -> Value.Set.newBuilder()
+        .addItem("us-east-1a")
+        .addItem("us-east-1b")
+        .addItem("us-east-1c")
+        .build())
+
+    val zoneConstraintStr = "tachyon:;zone:us-east-1a,us-east-1c"
+    val parsedConstraints = utils.parseConstraintString(zoneConstraintStr)
+
+    utils.matchesAttributeRequirements(parsedConstraints, supersetConstraint) shouldBe true
+  }
+
+  test("less than equal match is performed on scalar attributes") {
+    val offerAttribs = Map("gpus" -> Value.Scalar.newBuilder().setValue(3).build())
+
+    val ltConstraint = utils.parseConstraintString("gpus:2")
+    val eqConstraint = utils.parseConstraintString("gpus:3")
+    val gtConstraint = utils.parseConstraintString("gpus:4")
+
+    utils.matchesAttributeRequirements(ltConstraint, offerAttribs) shouldBe true
+    utils.matchesAttributeRequirements(eqConstraint, offerAttribs) shouldBe true
+    utils.matchesAttributeRequirements(gtConstraint, offerAttribs) shouldBe false
+  }
+
+  test("contains match is performed for range attributes") {
+    val offerAttribs = Map("ports" -> Value.Range.newBuilder().setBegin(7000).setEnd(8000).build())
+    val ltConstraint = utils.parseConstraintString("ports:6000")
+    val eqConstraint = utils.parseConstraintString("ports:7500")
+    val gtConstraint = utils.parseConstraintString("ports:8002")
+    val multiConstraint = utils.parseConstraintString("ports:5000,7500,8300")
+
+    utils.matchesAttributeRequirements(ltConstraint, offerAttribs) shouldBe false
+    utils.matchesAttributeRequirements(eqConstraint, offerAttribs) shouldBe true
+    utils.matchesAttributeRequirements(gtConstraint, offerAttribs) shouldBe false
+    utils.matchesAttributeRequirements(multiConstraint, offerAttribs) shouldBe true
+  }
+
+  test("equality match is performed for text attributes") {
+    val offerAttribs = Map("tachyon" -> Value.Text.newBuilder().setValue("true").build())
+
+    val trueConstraint = utils.parseConstraintString("tachyon:true")
+    val falseConstraint = utils.parseConstraintString("tachyon:false")
+
+    utils.matchesAttributeRequirements(trueConstraint, offerAttribs) shouldBe true
+    utils.matchesAttributeRequirements(falseConstraint, offerAttribs) shouldBe false
+  }
+
+}
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 5f1d6daeb27f0..1f915d8ea1d73 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -184,6 +184,14 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere
 only makes sense if you run just one application at a time. You can cap the maximum number of cores
 using `conf.set("spark.cores.max", "10")` (for example).
 
+You may also make use of `spark.mesos.constraints` to set attribute based constraints on mesos resource offers. By default, all resource offers will be accepted.
+
+{% highlight scala %}
+conf.set("spark.mesos.constraints", "tachyon=true;us-east-1=false")
+{% endhighlight %}
+
+For example, Let's say `spark.mesos.constraints` is set to `tachyon=true;us-east-1=false`, then the resource offers will be checked to see if they meet both these constraints and only then will be accepted to start new executors.
+
 # Mesos Docker Support
 
 Spark can make use of a Mesos Docker containerizer by setting the property `spark.mesos.executor.docker.image`
@@ -298,6 +306,20 @@ See the [configuration page](configuration.html) for information on Spark config
     the final overhead will be this value.
   </td>
 </tr>
+<tr>
+  <td><code>spark.mesos.constraints</code></td>
+  <td>Attribute based constraints to be matched against when accepting resource offers.</td>
+  <td>
+    Attribute based constraints on mesos resource offers. By default, all resource offers will be accepted. Refer to <a href="http://mesos.apache.org/documentation/attributes-resources/">Mesos Attributes & Resources</a> for more information on attributes.
+    <ul>
+      <li>Scalar constraints are matched with "less than equal" semantics i.e. value in the constraint must be less than or equal to the value in the resource offer.</li>
+      <li>Range constraints are matched with "contains" semantics i.e. value in the constraint must be within the resource offer's value.</li>
+      <li>Set constraints are matched with "subset of" semantics i.e. value in the constraint must be a subset of the resource offer's value.</li>
+      <li>Text constraints are metched with "equality" semantics i.e. value in the constraint must be exactly equal to the resource offer's value.</li>
+      <li>In case there is no value present as a part of the constraint any offer with the corresponding attribute will be accepted (without value check).</li>
+    </ul>
+  </td>
+</tr>
 </table>
 
 # Troubleshooting and Debugging

From 96c5eeec3970e8b1ebc6ddf5c97a7acc47f539dc Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 6 Jul 2015 16:11:22 -0700
Subject: [PATCH 0243/1454] Revert "[SPARK-7212] [MLLIB] Add sequence learning
 flag"

This reverts commit 25f574eb9a3cb9b93b7d9194a8ec16e00ce2c036. After speaking to some users and developers, we realized that FP-growth doesn't meet the requirement for frequent sequence mining. PrefixSpan (SPARK-6487) would be the correct algorithm for it. feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #7240 from mengxr/SPARK-7212.revert and squashes the following commits:

2b3d66b [Xiangrui Meng] Revert "[SPARK-7212] [MLLIB] Add sequence learning flag"
---
 .../org/apache/spark/mllib/fpm/FPGrowth.scala | 38 +++-----------
 .../spark/mllib/fpm/FPGrowthSuite.scala       | 52 +------------------
 python/pyspark/mllib/fpm.py                   |  4 +-
 3 files changed, 12 insertions(+), 82 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index abac08022ea47..efa8459d3cdba 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -36,7 +36,7 @@ import org.apache.spark.storage.StorageLevel
  * :: Experimental ::
  *
  * Model trained by [[FPGrowth]], which holds frequent itemsets.
- * @param freqItemsets frequent itemsets, which is an RDD of [[FreqItemset]]
+ * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
  */
 @Experimental
@@ -62,14 +62,13 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
 @Experimental
 class FPGrowth private (
     private var minSupport: Double,
-    private var numPartitions: Int,
-    private var ordered: Boolean) extends Logging with Serializable {
+    private var numPartitions: Int) extends Logging with Serializable {
 
   /**
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
-   * as the input data, ordered: `false`}.
+   * as the input data}.
    */
-  def this() = this(0.3, -1, false)
+  def this() = this(0.3, -1)
 
   /**
    * Sets the minimal support level (default: `0.3`).
@@ -87,15 +86,6 @@ class FPGrowth private (
     this
   }
 
-  /**
-   * Indicates whether to mine itemsets (unordered) or sequences (ordered) (default: false, mine
-   * itemsets).
-   */
-  def setOrdered(ordered: Boolean): this.type = {
-    this.ordered = ordered
-    this
-  }
-
   /**
    * Computes an FP-Growth model that contains frequent itemsets.
    * @param data input data set, each element contains a transaction
@@ -165,7 +155,7 @@ class FPGrowth private (
     .flatMap { case (part, tree) =>
       tree.extract(minCount, x => partitioner.getPartition(x) == part)
     }.map { case (ranks, count) =>
-      new FreqItemset(ranks.map(i => freqItems(i)).reverse.toArray, count, ordered)
+      new FreqItemset(ranks.map(i => freqItems(i)).toArray, count)
     }
   }
 
@@ -181,12 +171,9 @@ class FPGrowth private (
       itemToRank: Map[Item, Int],
       partitioner: Partitioner): mutable.Map[Int, Array[Int]] = {
     val output = mutable.Map.empty[Int, Array[Int]]
-    // Filter the basket by frequent items pattern
+    // Filter the basket by frequent items pattern and sort their ranks.
     val filtered = transaction.flatMap(itemToRank.get)
-    if (!this.ordered) {
-      ju.Arrays.sort(filtered)
-    }
-    // Generate conditional transactions
+    ju.Arrays.sort(filtered)
     val n = filtered.length
     var i = n - 1
     while (i >= 0) {
@@ -211,18 +198,9 @@ object FPGrowth {
    * Frequent itemset.
    * @param items items in this itemset. Java users should call [[FreqItemset#javaItems]] instead.
    * @param freq frequency
-   * @param ordered indicates if items represents an itemset (false) or sequence (true)
    * @tparam Item item type
    */
-  class FreqItemset[Item](val items: Array[Item], val freq: Long, val ordered: Boolean)
-    extends Serializable {
-
-    /**
-     * Auxillary constructor, assumes unordered by default.
-     */
-    def this(items: Array[Item], freq: Long) {
-      this(items, freq, false)
-    }
+  class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
index 1a8a1e79f2810..66ae3543ecc4e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
@@ -22,7 +22,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
 
 
-  test("FP-Growth frequent itemsets using String type") {
+  test("FP-Growth using String type") {
     val transactions = Seq(
       "r z h k p",
       "z y x w v u t s",
@@ -38,14 +38,12 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
-      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .setOrdered(false)
       .run(rdd)
     val freqItemsets3 = model3.freqItemsets.collect().map { itemset =>
       (itemset.items.toSet, itemset.freq)
@@ -63,59 +61,17 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
-      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 54)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
-      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 625)
   }
 
-  test("FP-Growth frequent sequences using String type"){
-    val transactions = Seq(
-      "r z h k p",
-      "z y x w v u t s",
-      "s x o n r",
-      "x z y m t s q e",
-      "z",
-      "x z y r q t p")
-      .map(_.split(" "))
-    val rdd = sc.parallelize(transactions, 2).cache()
-
-    val fpg = new FPGrowth()
-
-    val model1 = fpg
-      .setMinSupport(0.5)
-      .setNumPartitions(2)
-      .setOrdered(true)
-      .run(rdd)
-
-    /*
-      Use the following R code to verify association rules using arulesSequences package.
-
-      data = read_baskets("path", info = c("sequenceID","eventID","SIZE"))
-      freqItemSeq = cspade(data, parameter = list(support = 0.5))
-      resSeq = as(freqItemSeq, "data.frame")
-      resSeq$support = resSeq$support * length(transactions)
-      names(resSeq)[names(resSeq) == "support"] = "freq"
-      resSeq
-     */
-    val expected = Set(
-      (Seq("r"), 3L), (Seq("s"), 3L), (Seq("t"), 3L), (Seq("x"), 4L), (Seq("y"), 3L),
-      (Seq("z"), 5L), (Seq("z", "y"), 3L), (Seq("x", "t"), 3L), (Seq("y", "t"), 3L),
-      (Seq("z", "t"), 3L), (Seq("z", "y", "t"), 3L)
-    )
-    val freqItemseqs1 = model1.freqItemsets.collect().map { itemset =>
-      (itemset.items.toSeq, itemset.freq)
-    }.toSet
-    assert(freqItemseqs1 == expected)
-  }
-
-  test("FP-Growth frequent itemsets using Int type") {
+  test("FP-Growth using Int type") {
     val transactions = Seq(
       "1 2 3",
       "1 2 3 4",
@@ -132,14 +88,12 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
-      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .setOrdered(false)
       .run(rdd)
     assert(model3.freqItemsets.first().items.getClass === Array(1).getClass,
       "frequent itemsets should use primitive arrays")
@@ -155,14 +109,12 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
-      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 15)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
-      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 65)
   }
diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py
index b7f00d60069e6..bdc4a132b1b18 100644
--- a/python/pyspark/mllib/fpm.py
+++ b/python/pyspark/mllib/fpm.py
@@ -39,8 +39,8 @@ class FPGrowthModel(JavaModelWrapper):
     >>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]]
     >>> rdd = sc.parallelize(data, 2)
     >>> model = FPGrowth.train(rdd, 0.6, 2)
-    >>> sorted(model.freqItemsets().collect(), key=lambda x: x.items)
-    [FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'a', u'c'], freq=3), ...
+    >>> sorted(model.freqItemsets().collect())
+    [FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'c'], freq=3), ...
     """
 
     def freqItemsets(self):

From 0effe180f4c2cf37af1012b33b43912bdecaf756 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 6 Jul 2015 16:15:12 -0700
Subject: [PATCH 0244/1454] [SPARK-8765] [MLLIB] Fix PySpark
 PowerIterationClustering test issue

PySpark PowerIterationClustering test failure due to bad demo data.
If the data is small,  PowerIterationClustering will behavior indeterministic.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7177 from yanboliang/spark-8765 and squashes the following commits:

392ae54 [Yanbo Liang] fix model.assignments output
5ec3f1e [Yanbo Liang] fix PySpark PowerIterationClustering test issue
---
 python/pyspark/mllib/clustering.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index a3eab635282f6..ed4d78a2c6788 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -282,18 +282,30 @@ class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader):
 
     Model produced by [[PowerIterationClustering]].
 
-    >>> data = [(0, 1, 1.0), (0, 2, 1.0), (1, 3, 1.0), (2, 3, 1.0),
-    ...     (0, 3, 1.0), (1, 2, 1.0), (0, 4, 0.1)]
+    >>> data = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (1, 3, 1.0),
+    ... (2, 3, 1.0), (3, 4, 0.1), (4, 5, 1.0), (4, 15, 1.0), (5, 6, 1.0),
+    ... (6, 7, 1.0), (7, 8, 1.0), (8, 9, 1.0), (9, 10, 1.0), (10, 11, 1.0),
+    ... (11, 12, 1.0), (12, 13, 1.0), (13, 14, 1.0), (14, 15, 1.0)]
     >>> rdd = sc.parallelize(data, 2)
     >>> model = PowerIterationClustering.train(rdd, 2, 100)
     >>> model.k
     2
+    >>> result = sorted(model.assignments().collect(), key=lambda x: x.id)
+    >>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster
+    True
+    >>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster
+    True
     >>> import os, tempfile
     >>> path = tempfile.mkdtemp()
     >>> model.save(sc, path)
     >>> sameModel = PowerIterationClusteringModel.load(sc, path)
     >>> sameModel.k
     2
+    >>> result = sorted(model.assignments().collect(), key=lambda x: x.id)
+    >>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster
+    True
+    >>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster
+    True
     >>> from shutil import rmtree
     >>> try:
     ...     rmtree(path)

From 7b467cc9348fa910e445ad08914a72f8ed4fc249 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 6 Jul 2015 16:26:31 -0700
Subject: [PATCH 0245/1454] [SPARK-8588] [SQL] Regression test

This PR adds regression test for https://issues.apache.org/jira/browse/SPARK-8588 (fixed by https://github.com/apache/spark/commit/457d07eaa023b44b75344110508f629925eb6247).

Author: Yin Huai <yhuai@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>

Closes #7103 from yhuai/SPARK-8588-test and squashes the following commits:

eb5f418 [Yin Huai] Add a query test.
c61a173 [Yin Huai] Regression test for SPARK-8588.
---
 .../analysis/HiveTypeCoercionSuite.scala      | 21 +++++++++++++++++++
 .../sql/hive/execution/SQLQuerySuite.scala    | 16 ++++++++++++++
 2 files changed, 37 insertions(+)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index b56426617789e..93db33d44eb25 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -271,4 +271,25 @@ class HiveTypeCoercionSuite extends PlanTest {
       Literal(true)
     )
   }
+
+  /**
+   * There are rules that need to not fire before child expressions get resolved.
+   * We use this test to make sure those rules do not fire early.
+   */
+  test("make sure rules do not fire early") {
+    // InConversion
+    val inConversion = HiveTypeCoercion.InConversion
+    ruleTest(inConversion,
+      In(UnresolvedAttribute("a"), Seq(Literal(1))),
+      In(UnresolvedAttribute("a"), Seq(Literal(1)))
+    )
+    ruleTest(inConversion,
+      In(Literal("test"), Seq(UnresolvedAttribute("a"), Literal(1))),
+      In(Literal("test"), Seq(UnresolvedAttribute("a"), Literal(1)))
+    )
+    ruleTest(inConversion,
+      In(Literal("a"), Seq(Literal(1), Literal("b"))),
+      In(Literal("a"), Seq(Cast(Literal(1), StringType), Cast(Literal("b"), StringType)))
+    )
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 6d645393a6da1..bf9f2ecd51793 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -990,5 +990,21 @@ class SQLQuerySuite extends QueryTest {
         Timestamp.valueOf("1969-12-31 16:00:00"),
         String.valueOf("1969-12-31 16:00:00"),
         Timestamp.valueOf("1970-01-01 00:00:00")))
+
+  }
+
+  test("SPARK-8588 HiveTypeCoercion.inConversion fires too early") {
+    val df =
+      TestHive.createDataFrame(Seq((1, "2014-01-01"), (2, "2015-01-01"), (3, "2016-01-01")))
+    df.toDF("id", "date").registerTempTable("test_SPARK8588")
+    checkAnswer(
+      TestHive.sql(
+        """
+          |select id, concat(year(date))
+          |from test_SPARK8588 where concat(year(date), ' year') in ('2015 year', '2014 year')
+        """.stripMargin),
+      Row(1, "2014") :: Row(2, "2015") :: Nil
+    )
+    TestHive.dropTempTable("test_SPARK8588")
   }
 }

From 09a06418debc25da0191d98798f7c5016d39be91 Mon Sep 17 00:00:00 2001
From: animesh <animesh@apache.spark>
Date: Mon, 6 Jul 2015 16:39:49 -0700
Subject: [PATCH 0246/1454] [SPARK-8072] [SQL] Better AnalysisException for
 writing DataFrame with identically named columns

Adding a function checkConstraints which will check for the constraints to be applied on the dataframe / dataframe schema. Function called before storing the dataframe to an external storage. Function added in the corresponding datasource API.
cc rxin marmbrus

Author: animesh <animesh@apache.spark>

This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>

Closes #7013 from animeshbaranawal/8072 and squashes the following commits:

f70dd0e [animesh] Change IO exception to Analysis Exception
fd45e1b [animesh] 8072: Fix Style Issues
a8a964f [animesh] 8072: Improving on previous commits
3cc4d2c [animesh] Fix Style Issues
1a89115 [animesh] Fix Style Issues
98b4399 [animesh] 8072 : Moved the exception handling to ResolvedDataSource specific to parquet format
7c3d928 [animesh] 8072: Adding check to DataFrameWriter.scala
---
 .../apache/spark/sql/json/JSONRelation.scala  | 31 +++++++++++++++++++
 .../apache/spark/sql/parquet/newParquet.scala | 19 +++++++++++-
 .../org/apache/spark/sql/DataFrameSuite.scala | 24 ++++++++++++++
 3 files changed, 73 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 69bf13e1e5a6a..2361d3bf52d2b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -22,6 +22,7 @@ import java.io.IOException
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
@@ -37,6 +38,17 @@ private[sql] class DefaultSource
     parameters.getOrElse("path", sys.error("'path' must be specified for json data."))
   }
 
+  /** Constraints to be imposed on dataframe to be stored. */
+  private def checkConstraints(data: DataFrame): Unit = {
+    if (data.schema.fieldNames.length != data.schema.fieldNames.distinct.length) {
+      val duplicateColumns = data.schema.fieldNames.groupBy(identity).collect {
+        case (x, ys) if ys.length > 1 => "\"" + x + "\""
+      }.mkString(", ")
+      throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
+        s"cannot save to JSON format")
+    }
+  }
+
   /** Returns a new base relation with the parameters. */
   override def createRelation(
       sqlContext: SQLContext,
@@ -63,6 +75,10 @@ private[sql] class DefaultSource
       mode: SaveMode,
       parameters: Map[String, String],
       data: DataFrame): BaseRelation = {
+    // check if dataframe satisfies the constraints
+    // before moving forward
+    checkConstraints(data)
+
     val path = checkPath(parameters)
     val filesystemPath = new Path(path)
     val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
@@ -130,6 +146,17 @@ private[sql] class JSONRelation(
       samplingRatio,
       userSpecifiedSchema)(sqlContext)
 
+  /** Constraints to be imposed on dataframe to be stored. */
+  private def checkConstraints(data: DataFrame): Unit = {
+    if (data.schema.fieldNames.length != data.schema.fieldNames.distinct.length) {
+      val duplicateColumns = data.schema.fieldNames.groupBy(identity).collect {
+        case (x, ys) if ys.length > 1 => "\"" + x + "\""
+      }.mkString(", ")
+      throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
+        s"cannot save to JSON format")
+    }
+  }
+
   private val useJacksonStreamingAPI: Boolean = sqlContext.conf.useJacksonStreamingAPI
 
   override val needConversion: Boolean = false
@@ -178,6 +205,10 @@ private[sql] class JSONRelation(
   }
 
   override def insert(data: DataFrame, overwrite: Boolean): Unit = {
+    // check if dataframe satisfies constraints
+    // before moving forward
+    checkConstraints(data)
+
     val filesystemPath = path match {
       case Some(p) => new Path(p)
       case None =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index 5ac3e9a44e6fe..6bc69c6ad0847 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -164,7 +164,24 @@ private[sql] class ParquetRelation2(
     }
   }
 
-  override def dataSchema: StructType = maybeDataSchema.getOrElse(metadataCache.dataSchema)
+  /** Constraints on schema of dataframe to be stored. */
+  private def checkConstraints(schema: StructType): Unit = {
+    if (schema.fieldNames.length != schema.fieldNames.distinct.length) {
+      val duplicateColumns = schema.fieldNames.groupBy(identity).collect {
+        case (x, ys) if ys.length > 1 => "\"" + x + "\""
+      }.mkString(", ")
+      throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
+        s"cannot save to parquet format")
+    }
+  }
+
+  override def dataSchema: StructType = {
+    val schema = maybeDataSchema.getOrElse(metadataCache.dataSchema)
+    // check if schema satisfies the constraints
+    // before moving forward
+    checkConstraints(schema)
+    schema
+  }
 
   override private[sql] def refresh(): Unit = {
     super.refresh()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index afb1cf5f8d1cb..f592a9934d0e6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -737,4 +737,28 @@ class DataFrameSuite extends QueryTest {
     df.col("")
     df.col("t.``")
   }
+
+  test("SPARK-8072: Better Exception for Duplicate Columns") {
+    // only one duplicate column present
+    val e = intercept[org.apache.spark.sql.AnalysisException] {
+      val df1 = Seq((1, 2, 3), (2, 3, 4), (3, 4, 5)).toDF("column1", "column2", "column1")
+                .write.format("parquet").save("temp")
+    }
+    assert(e.getMessage.contains("Duplicate column(s)"))
+    assert(e.getMessage.contains("parquet"))
+    assert(e.getMessage.contains("column1"))
+    assert(!e.getMessage.contains("column2"))
+
+    // multiple duplicate columns present
+    val f = intercept[org.apache.spark.sql.AnalysisException] {
+      val df2 = Seq((1, 2, 3, 4, 5), (2, 3, 4, 5, 6), (3, 4, 5, 6, 7))
+                .toDF("column1", "column2", "column3", "column1", "column3")
+                .write.format("json").save("temp")
+    }
+    assert(f.getMessage.contains("Duplicate column(s)"))
+    assert(f.getMessage.contains("JSON"))
+    assert(f.getMessage.contains("column1"))
+    assert(f.getMessage.contains("column3"))
+    assert(!f.getMessage.contains("column2"))
+  }
 }

From d4d6d31db5cc5c69ac369f754b7489f444c9ba2f Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Mon, 6 Jul 2015 17:16:44 -0700
Subject: [PATCH 0247/1454] [SPARK-8463][SQL] Use DriverRegistry to load jdbc
 driver at writing path

JIRA: https://issues.apache.org/jira/browse/SPARK-8463

Currently, at the reading path, `DriverRegistry` is used to load needed jdbc driver at executors. However, at the writing path, we also need `DriverRegistry` to load jdbc driver.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #6900 from viirya/jdbc_write_driver and squashes the following commits:

16cd04b [Liang-Chi Hsieh] Use DriverRegistry to load jdbc driver at writing path.
---
 .../main/scala/org/apache/spark/sql/jdbc/jdbc.scala   | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
index dd8aaf6474895..f7ea852fe7f58 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
@@ -58,13 +58,12 @@ package object jdbc {
      * are used.
      */
     def savePartition(
-        url: String,
+        getConnection: () => Connection,
         table: String,
         iterator: Iterator[Row],
         rddSchema: StructType,
-        nullTypes: Array[Int],
-        properties: Properties): Iterator[Byte] = {
-      val conn = DriverManager.getConnection(url, properties)
+        nullTypes: Array[Int]): Iterator[Byte] = {
+      val conn = getConnection()
       var committed = false
       try {
         conn.setAutoCommit(false) // Everything in the same db transaction.
@@ -185,8 +184,10 @@ package object jdbc {
       }
 
       val rddSchema = df.schema
+      val driver: String = DriverRegistry.getDriverClassName(url)
+      val getConnection: () => Connection = JDBCRDD.getConnector(driver, url, properties)
       df.foreachPartition { iterator =>
-        JDBCWriteDetails.savePartition(url, table, iterator, rddSchema, nullTypes, properties)
+        JDBCWriteDetails.savePartition(getConnection, table, iterator, rddSchema, nullTypes)
       }
     }
 

From 9eae5fa642317dd11fc783d832d4cbb7e62db471 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 6 Jul 2015 19:22:30 -0700
Subject: [PATCH 0248/1454] [SPARK-8819] Fix build for maven 3.3.x

This is a workaround for MSHADE-148, which leads to an infinite loop when building Spark with maven 3.3.x. This was originally caused by #6441, which added a bunch of test dependencies on the spark-core test module. Recently, it was revealed by #7193.

This patch adds a `-Prelease` profile. If present, it will set `createDependencyReducedPom` to true. The consequences are:
- If you are releasing Spark with this profile, you are fine as long as you use maven 3.2.x or before.
- If you are releasing Spark without this profile, you will run into SPARK-8781.
- If you are not releasing Spark but you are using this profile, you may run into SPARK-8819.
- If you are not releasing Spark and you did not include this profile, you are fine.

This is all documented in `pom.xml` and tested locally with both versions of maven.

Author: Andrew Or <andrew@databricks.com>

Closes #7219 from andrewor14/fix-maven-build and squashes the following commits:

1d37e87 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-maven-build
3574ae4 [Andrew Or] Review comments
f39199c [Andrew Or] Create a -Prelease profile that flags `createDependencyReducedPom`
---
 dev/create-release/create-release.sh |  4 ++--
 pom.xml                              | 24 ++++++++++++++++++++++++
 2 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 54274a83f6d66..cfe2cd4752b3f 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -118,13 +118,13 @@ if [[ ! "$@" =~ --skip-publish ]]; then
 
   rm -rf $SPARK_REPO
 
-  build/mvn -DskipTests -Pyarn -Phive \
+  build/mvn -DskipTests -Pyarn -Phive -Prelease-profile\
     -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
   ./dev/change-version-to-2.11.sh
   
-  build/mvn -DskipTests -Pyarn -Phive \
+  build/mvn -DskipTests -Pyarn -Phive -Prelease-profile\
     -Dscala-2.11 -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
diff --git a/pom.xml b/pom.xml
index ffa96128a3d61..fbcc9152765cf 100644
--- a/pom.xml
+++ b/pom.xml
@@ -161,6 +161,8 @@
     <fasterxml.jackson.version>2.4.4</fasterxml.jackson.version>
     <snappy.version>1.1.1.7</snappy.version>
     <netlib.java.version>1.1.2</netlib.java.version>
+    <!-- For maven shade plugin (see SPARK-8819) -->
+    <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
 
     <test.java.home>${java.home}</test.java.home>
 
@@ -1440,6 +1442,8 @@
         <version>2.3</version>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
+          <!-- Work around MSHADE-148. See SPARK-8819. -->
+          <createDependencyReducedPom>${create.dependency.reduced.pom}</createDependencyReducedPom>
           <artifactSet>
             <includes>
               <!-- At a minimum we must include this to force effective pom generation -->
@@ -1826,6 +1830,26 @@
       </properties>
     </profile>
 
+    <profile>
+      <!--
+          Use this profile only for making Spark releases. Note that due to SPARK-8819,
+          you must use maven version 3.2.x or before to avoid running into MSHADE-148.
+      -->
+      <id>release-profile</id>
+      <properties>
+        <!--
+            The maven shade plugin has a bug where enabling the `createDependencyReducedPom`
+            property causes maven to go into an infinite loop (MSHADE-148). This is only an
+            issue for the Spark build if the maven version is 3.3.x or newer (SPARK-8819).
+
+            However, since disabling this property has the side effect of not resolving
+            variables in the released pom files (SPARK-8781), we need to enable this during
+            releases.
+        -->
+        <create.dependency.reduced.pom>true</create.dependency.reduced.pom>
+      </properties>
+    </profile>
+
     <!--
       These empty profiles are available in some sub-modules. Declare them here so that
       maven does not complain when they're provided on the command line for a sub-module

From 929dfa24b13b8d1bd08aa8a3b256766a9765f985 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 6 Jul 2015 19:27:04 -0700
Subject: [PATCH 0249/1454] Revert "[SPARK-8781] Fix variables in published
 pom.xml are not resolved"

This reverts commit 82cf3315e690f4ac15b50edea6a3d673aa5be4c0.

Conflicts:
	pom.xml
---
 extras/kinesis-asl/pom.xml                    |  7 +++++++
 .../kinesis/KinesisReceiverSuite.scala        | 20 ++++++++-----------
 2 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index 5289073eb457a..c242e7a57b9ab 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -40,6 +40,13 @@
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-core_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
index 6c262624833cd..2103dca6b766f 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
@@ -26,23 +26,18 @@ import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionIn
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
 import org.mockito.Mockito._
-// scalastyle:off
-// To avoid introducing a dependency on Spark core tests, simply use scalatest's FunSuite
-// here instead of our own SparkFunSuite. Introducing the dependency has caused problems
-// in the past (SPARK-8781) that are complicated by bugs in the maven shade plugin (MSHADE-148).
-import org.scalatest.{BeforeAndAfter, FunSuite, Matchers}
+import org.scalatest.{BeforeAndAfter, Matchers}
 import org.scalatest.mock.MockitoSugar
 
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext}
+import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext, TestSuiteBase}
 import org.apache.spark.util.{Clock, ManualClock, Utils}
 
 /**
  * Suite of Kinesis streaming receiver tests focusing mostly on the KinesisRecordProcessor
  */
-class KinesisReceiverSuite extends FunSuite with Matchers with BeforeAndAfter
-  with MockitoSugar {
-// scalastyle:on
+class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAfter
+    with MockitoSugar {
 
   val app = "TestKinesisReceiver"
   val stream = "mySparkStream"
@@ -62,7 +57,7 @@ class KinesisReceiverSuite extends FunSuite with Matchers with BeforeAndAfter
   var checkpointStateMock: KinesisCheckpointState = _
   var currentClockMock: Clock = _
 
-  before {
+  override def beforeFunction(): Unit = {
     receiverMock = mock[KinesisReceiver]
     checkpointerMock = mock[IRecordProcessorCheckpointer]
     checkpointClockMock = mock[ManualClock]
@@ -70,7 +65,8 @@ class KinesisReceiverSuite extends FunSuite with Matchers with BeforeAndAfter
     currentClockMock = mock[Clock]
   }
 
-  after {
+  override def afterFunction(): Unit = {
+    super.afterFunction()
     // Since this suite was originally written using EasyMock, add this to preserve the old
     // mocking semantics (see SPARK-5735 for more details)
     verifyNoMoreInteractions(receiverMock, checkpointerMock, checkpointClockMock,
@@ -78,7 +74,7 @@ class KinesisReceiverSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("KinesisUtils API") {
-    val ssc = new StreamingContext("local[2]", getClass.getSimpleName, Seconds(1))
+    val ssc = new StreamingContext(master, framework, batchDuration)
     // Tests the API, does not actually test data receiving
     val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
       "https://kinesis.us-west-2.amazonaws.com", Seconds(2),

From 1821fc165808143e98b3d9626141b1a55bde90ac Mon Sep 17 00:00:00 2001
From: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Date: Mon, 6 Jul 2015 19:44:31 -0700
Subject: [PATCH 0250/1454] [SPARK-6747] [SQL] Throw an AnalysisException when
 unsupported Java list types used in Hive UDF

The current implementation can't handle List<> as a return type in Hive UDF and
throws meaningless Match Error.
We assume an UDF below;
public class UDFToListString extends UDF {
public List<String> evaluate(Object o)
{ return Arrays.asList("xxx", "yyy", "zzz"); }
}
An exception of scala.MatchError is thrown as follows when the UDF used;
scala.MatchError: interface java.util.List (of class java.lang.Class)
at org.apache.spark.sql.hive.HiveInspectors$class.javaClassToDataType(HiveInspectors.scala:174)
at org.apache.spark.sql.hive.HiveSimpleUdf.javaClassToDataType(hiveUdfs.scala:76)
at org.apache.spark.sql.hive.HiveSimpleUdf.dataType$lzycompute(hiveUdfs.scala:106)
at org.apache.spark.sql.hive.HiveSimpleUdf.dataType(hiveUdfs.scala:106)
at org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:131)
at org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$collectAliases$1.applyOrElse(patterns.scala:95)
at org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$collectAliases$1.applyOrElse(patterns.scala:94)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278)
...
To make udf developers more understood, we need to throw a more suitable exception.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #7248 from maropu/FixBugInHiveInspectors and squashes the following commits:

1c3df2a [Takeshi YAMAMURO] Fix comments
56305de [Takeshi YAMAMURO] Fix conflicts
92ed7a6 [Takeshi YAMAMURO] Throw an exception when java list type used
2844a8e [Takeshi YAMAMURO] Apply comments
7114a47 [Takeshi YAMAMURO] Add TODO comments in UDFToListString of HiveUdfSuite
fdb2ae4 [Takeshi YAMAMURO] Add StringToUtf8 to comvert String into UTF8String
af61f2e [Takeshi YAMAMURO] Remove a new type
7f812fd [Takeshi YAMAMURO] Fix code-style errors
6984bf4 [Takeshi YAMAMURO] Apply review comments
93e3d4e [Takeshi YAMAMURO] Add a blank line at the end of UDFToListString
ee232db [Takeshi YAMAMURO] Support List as a return type in Hive UDF
1e82316 [Takeshi YAMAMURO] Apply comments
21e8763 [Takeshi YAMAMURO] Add TODO comments in UDFToListString of HiveUdfSuite
a488712 [Takeshi YAMAMURO] Add StringToUtf8 to comvert String into UTF8String
1c7b9d1 [Takeshi YAMAMURO] Remove a new type
f965c34 [Takeshi YAMAMURO] Fix code-style errors
9406416 [Takeshi YAMAMURO] Apply review comments
e21ce7e [Takeshi YAMAMURO] Add a blank line at the end of UDFToListString
e553f10 [Takeshi YAMAMURO] Support List as a return type in Hive UDF
---
 .../spark/sql/hive/HiveInspectors.scala       | 10 +++++-
 .../sql/hive/execution/UDFToListInt.java      | 29 +++++++++++++++++
 .../sql/hive/execution/UDFToListString.java   | 29 +++++++++++++++++
 .../sql/hive/execution/HiveUDFSuite.scala     | 32 ++++++++++++++++++-
 4 files changed, 98 insertions(+), 2 deletions(-)
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListInt.java
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListString.java

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index a6b8ead577fb5..7423d8030f2b5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -26,8 +26,8 @@ import org.apache.hadoop.{io => hadoopIo}
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.types
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.{AnalysisException, types}
 import org.apache.spark.unsafe.types.UTF8String
 
 /* Implicit conversions */
@@ -218,6 +218,14 @@ private[hive] trait HiveInspectors {
 
     // Hive seems to return this for struct types?
     case c: Class[_] if c == classOf[java.lang.Object] => NullType
+
+    // java list type unsupported
+    case c: Class[_] if c == classOf[java.util.List[_]] =>
+      throw new AnalysisException(
+        "List type in java is unsupported because " +
+        "JVM type erasure makes spark fail to catch a component type in List<>")
+
+    case c => throw new AnalysisException(s"Unsupported java type $c")
   }
 
   /**
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListInt.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListInt.java
new file mode 100644
index 0000000000000..67576a72f1980
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListInt.java
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution;
+
+import org.apache.hadoop.hive.ql.exec.UDF;
+
+import java.util.Arrays;
+import java.util.List;
+
+public class UDFToListInt extends UDF {
+    public List<Integer> evaluate(Object o) {
+        return Arrays.asList(1, 2, 3);
+    }
+}
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListString.java
new file mode 100644
index 0000000000000..f02395cbba88b
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToListString.java
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution;
+
+import org.apache.hadoop.hive.ql.exec.UDF;
+
+import java.util.Arrays;
+import java.util.List;
+
+public class UDFToListString extends UDF {
+    public List<String> evaluate(Object o) {
+        return Arrays.asList("data1", "data2", "data3");
+    }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 56b0bef1d0571..44686204c2af7 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectIn
 import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectInspectorFactory}
 import org.apache.hadoop.hive.serde2.{AbstractSerDe, SerDeStats}
 import org.apache.hadoop.io.Writable
-import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.{AnalysisException, QueryTest, Row}
 import org.apache.spark.sql.hive.test.TestHive
 
 import org.apache.spark.util.Utils
@@ -133,6 +133,36 @@ class HiveUDFSuite extends QueryTest {
     TestHive.reset()
   }
 
+  test("UDFToListString") {
+    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    testData.registerTempTable("inputTable")
+
+    sql(s"CREATE TEMPORARY FUNCTION testUDFToListString AS '${classOf[UDFToListString].getName}'")
+    val errMsg = intercept[AnalysisException] {
+      sql("SELECT testUDFToListString(s) FROM inputTable")
+    }
+    assert(errMsg.getMessage === "List type in java is unsupported because " +
+      "JVM type erasure makes spark fail to catch a component type in List<>;")
+
+    sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListString")
+    TestHive.reset()
+  }
+
+  test("UDFToListInt") {
+    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    testData.registerTempTable("inputTable")
+
+    sql(s"CREATE TEMPORARY FUNCTION testUDFToListInt AS '${classOf[UDFToListInt].getName}'")
+    val errMsg = intercept[AnalysisException] {
+      sql("SELECT testUDFToListInt(s) FROM inputTable")
+    }
+    assert(errMsg.getMessage === "List type in java is unsupported because " +
+      "JVM type erasure makes spark fail to catch a component type in List<>;")
+
+    sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListInt")
+    TestHive.reset()
+  }
+
   test("UDFListListInt") {
     val testData = TestHive.sparkContext.parallelize(
       ListListIntCaseClass(Nil) ::

From 6718c1eb671faaf5c1d865ad5d01dbf78dae9cd2 Mon Sep 17 00:00:00 2001
From: Alok  Singh <singhal@Aloks-MacBook-Pro.local>
Date: Mon, 6 Jul 2015 21:53:55 -0700
Subject: [PATCH 0251/1454] [SPARK-5562] [MLLIB] LDA should handle empty
 document.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

See the jira https://issues.apache.org/jira/browse/SPARK-5562

Author: Alok  Singh <singhal@Aloks-MacBook-Pro.local>
Author: Alok  Singh <singhal@aloks-mbp.usca.ibm.com>
Author: Alok Singh <“singhal@us.ibm.com”>

Closes #7064 from aloknsingh/aloknsingh_SPARK-5562 and squashes the following commits:

259a0a7 [Alok Singh] change as per the comments by @jkbradley
be48491 [Alok  Singh] [SPARK-5562][MLlib] re-order import in alphabhetical order
c01311b [Alok  Singh] [SPARK-5562][MLlib] fix the newline typo
b271c8a [Alok  Singh] [SPARK-5562][Mllib] As per github discussion with jkbradley. We would like to simply things.
7c06251 [Alok  Singh] [SPARK-5562][MLlib] modified the JavaLDASuite for test passing
c710cb6 [Alok  Singh] fix the scala code style to have space after :
2572a08 [Alok  Singh] [SPARK-5562][MLlib] change the import xyz._ to the import xyz.{c1, c2} ..
ab55fbf [Alok  Singh] [SPARK-5562][MLlib] Change as per Sean Owen's comments https://github.com/apache/spark/pull/7064/files#diff-9236d23975e6f5a5608ffc81dfd79146
9f4f9ea [Alok  Singh] [SPARK-5562][MLlib] LDA should handle empty document.
---
 docs/mllib-clustering.md                            |  2 +-
 .../apache/spark/mllib/clustering/JavaLDASuite.java | 13 +++++++++++--
 .../apache/spark/mllib/clustering/LDASuite.scala    | 13 +++++++++++--
 3 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 3aad4149f99db..d72dc20a5ad6e 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -447,7 +447,7 @@ It supports different inference algorithms via `setOptimizer` function. EMLDAOpt
 on the likelihood function and yields comprehensive results, while OnlineLDAOptimizer uses iterative mini-batch sampling for [online variational inference](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf) and is generally memory friendly. After fitting on the documents, LDA provides:
 
 * Topics: Inferred topics, each of which is a probability distribution over terms (words).
-* Topic distributions for documents: For each document in the training set, LDA gives a probability distribution over topics. (EM only)
+* Topic distributions for documents: For each non empty document in the training set, LDA gives a probability distribution over topics. (EM only). Note that for empty documents, we don't create the topic distributions. (EM only)
 
 LDA takes the following parameters:
 
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index 581c033f08ebe..b48f190f599a2 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -28,12 +28,13 @@
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.linalg.Matrix;
 import org.apache.spark.mllib.linalg.Vector;
-
+import org.apache.spark.mllib.linalg.Vectors;
 
 public class JavaLDASuite implements Serializable {
   private transient JavaSparkContext sc;
@@ -110,7 +111,15 @@ public void distributedLDAModel() {
 
     // Check: topic distributions
     JavaPairRDD<Long, Vector> topicDistributions = model.javaTopicDistributions();
-    assertEquals(topicDistributions.count(), corpus.count());
+    // SPARK-5562. since the topicDistribution returns the distribution of the non empty docs
+    // over topics. Compare it against nonEmptyCorpus instead of corpus
+    JavaPairRDD<Long, Vector> nonEmptyCorpus = corpus.filter(
+      new Function<Tuple2<Long, Vector>, Boolean>() {
+        public Boolean call(Tuple2<Long, Vector> tuple2) {
+          return Vectors.norm(tuple2._2(), 1.0) != 0.0;
+        }
+    });
+    assertEquals(topicDistributions.count(), nonEmptyCorpus.count());
   }
 
   @Test
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 406affa25539d..03a8a2538b464 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -99,9 +99,13 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
     // Check: per-doc topic distributions
     val topicDistributions = model.topicDistributions.collect()
+
     //  Ensure all documents are covered.
-    assert(topicDistributions.length === tinyCorpus.length)
-    assert(tinyCorpus.map(_._1).toSet === topicDistributions.map(_._1).toSet)
+    // SPARK-5562. since the topicDistribution returns the distribution of the non empty docs
+    // over topics. Compare it against nonEmptyTinyCorpus instead of tinyCorpus
+    val nonEmptyTinyCorpus = getNonEmptyDoc(tinyCorpus)
+    assert(topicDistributions.length === nonEmptyTinyCorpus.length)
+    assert(nonEmptyTinyCorpus.map(_._1).toSet === topicDistributions.map(_._1).toSet)
     //  Ensure we have proper distributions
     topicDistributions.foreach { case (docId, topicDistribution) =>
       assert(topicDistribution.size === tinyK)
@@ -232,12 +236,17 @@ private[clustering] object LDASuite {
   }
 
   def tinyCorpus: Array[(Long, Vector)] = Array(
+    Vectors.dense(0, 0, 0, 0, 0), // empty doc
     Vectors.dense(1, 3, 0, 2, 8),
     Vectors.dense(0, 2, 1, 0, 4),
     Vectors.dense(2, 3, 12, 3, 1),
+    Vectors.dense(0, 0, 0, 0, 0), // empty doc
     Vectors.dense(0, 3, 1, 9, 8),
     Vectors.dense(1, 1, 4, 2, 6)
   ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
   assert(tinyCorpus.forall(_._2.size == tinyVocabSize)) // sanity check for test data
 
+  def getNonEmptyDoc(corpus: Array[(Long, Vector)]): Array[(Long, Vector)] = corpus.filter {
+    case (_, wc: Vector) => Vectors.norm(wc, p = 1.0) != 0.0
+  }
 }

From c46aaf47f38163e9c7be671d7b8398512df34e62 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 6 Jul 2015 22:13:50 -0700
Subject: [PATCH 0252/1454] [SPARK-8759][SQL] add default eval to binary and
 unary expression according to default behavior of nullable

We have `nullSafeCodeGen` to provide default code generation for binary and unary expression, and we can do the same thing for `eval`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7157 from cloud-fan/refactor and squashes the following commits:

f3987c6 [Wenchen Fan] refactor Expression
---
 .../spark/sql/catalyst/expressions/Cast.scala |   7 +-
 .../sql/catalyst/expressions/Expression.scala |  69 ++++++-
 .../catalyst/expressions/ExtractValue.scala   |  51 ++---
 .../sql/catalyst/expressions/arithmetic.scala |  97 ++++-----
 .../sql/catalyst/expressions/bitwise.scala    |   8 +-
 .../expressions/decimalFunctions.scala        |  33 +---
 .../spark/sql/catalyst/expressions/math.scala | 186 +++++-------------
 .../spark/sql/catalyst/expressions/misc.scala | 153 ++++++--------
 .../sql/catalyst/expressions/predicates.scala |  84 +++-----
 .../spark/sql/catalyst/expressions/sets.scala |   9 +-
 .../expressions/stringOperations.scala        | 138 +++----------
 11 files changed, 292 insertions(+), 543 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 2d99d1a3fe8dc..4f73ba40b5a18 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -114,8 +114,6 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     }
   }
 
-  override def foldable: Boolean = child.foldable
-
   override def nullable: Boolean = Cast.forceNullable(child.dataType, dataType) || child.nullable
 
   override def toString: String = s"CAST($child, $dataType)"
@@ -426,10 +424,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
 
   private[this] lazy val cast: Any => Any = cast(child.dataType, dataType)
 
-  override def eval(input: InternalRow): Any = {
-    val evaluated = child.eval(input)
-    if (evaluated == null) null else cast(evaluated)
-  }
+  protected override def nullSafeEval(input: Any): Any = cast(input)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     // TODO: Add support for more data types.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index cafbbafdca207..386feb95b4ac8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -183,6 +183,27 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
   override def foldable: Boolean = child.foldable
   override def nullable: Boolean = child.nullable
 
+  /**
+   * Default behavior of evaluation according to the default nullability of UnaryExpression.
+   * If subclass of UnaryExpression override nullable, probably should also override this.
+   */
+  override def eval(input: InternalRow): Any = {
+    val value = child.eval(input)
+    if (value == null) {
+      null
+    } else {
+      nullSafeEval(value)
+    }
+  }
+
+  /**
+   * Called by default [[eval]] implementation.  If subclass of UnaryExpression keep the default
+   * nullability, they can override this method to save null-check code.  If we need full control
+   * of evaluation process, we should override [[eval]].
+   */
+  protected def nullSafeEval(input: Any): Any =
+    sys.error(s"UnaryExpressions must override either eval or nullSafeEval")
+
   /**
    * Called by unary expressions to generate a code block that returns null if its parent returns
    * null, and if not not null, use `f` to generate the expression.
@@ -198,21 +219,24 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
       ctx: CodeGenContext,
       ev: GeneratedExpressionCode,
       f: String => String): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval) => {
-      s"$result = ${f(eval)};"
+    nullSafeCodeGen(ctx, ev, eval => {
+      s"${ev.primitive} = ${f(eval)};"
     })
   }
 
   /**
    * Called by unary expressions to generate a code block that returns null if its parent returns
    * null, and if not not null, use `f` to generate the expression.
+   *
+   * @param f function that accepts the non-null evaluation result name of child and returns Java
+   *          code to compute the output.
    */
   protected def nullSafeCodeGen(
       ctx: CodeGenContext,
       ev: GeneratedExpressionCode,
-      f: (String, String) => String): String = {
+      f: String => String): String = {
     val eval = child.gen(ctx)
-    val resultCode = f(ev.primitive, eval.primitive)
+    val resultCode = f(eval.primitive)
     eval.code + s"""
       boolean ${ev.isNull} = ${eval.isNull};
       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
@@ -235,6 +259,32 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
 
   override def nullable: Boolean = left.nullable || right.nullable
 
+  /**
+   * Default behavior of evaluation according to the default nullability of BinaryExpression.
+   * If subclass of BinaryExpression override nullable, probably should also override this.
+   */
+  override def eval(input: InternalRow): Any = {
+    val value1 = left.eval(input)
+    if (value1 == null) {
+      null
+    } else {
+      val value2 = right.eval(input)
+      if (value2 == null) {
+        null
+      } else {
+        nullSafeEval(value1, value2)
+      }
+    }
+  }
+
+  /**
+   * Called by default [[eval]] implementation.  If subclass of BinaryExpression keep the default
+   * nullability, they can override this method to save null-check code.  If we need full control
+   * of evaluation process, we should override [[eval]].
+   */
+  protected def nullSafeEval(input1: Any, input2: Any): Any =
+    sys.error(s"BinaryExpressions must override either eval or nullSafeEval")
+
   /**
    * Short hand for generating binary evaluation code.
    * If either of the sub-expressions is null, the result of this computation
@@ -246,8 +296,8 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
     ctx: CodeGenContext,
     ev: GeneratedExpressionCode,
     f: (String, String) => String): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
-      s"$result = ${f(eval1, eval2)};"
+    nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+      s"${ev.primitive} = ${f(eval1, eval2)};"
     })
   }
 
@@ -255,14 +305,17 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
    * Short hand for generating binary evaluation code.
    * If either of the sub-expressions is null, the result of this computation
    * is assumed to be null.
+   *
+   * @param f function that accepts the 2 non-null evaluation result names of children
+   *          and returns Java code to compute the output.
    */
   protected def nullSafeCodeGen(
     ctx: CodeGenContext,
     ev: GeneratedExpressionCode,
-    f: (String, String, String) => String): String = {
+    f: (String, String) => String): String = {
     val eval1 = left.gen(ctx)
     val eval2 = right.gen(ctx)
-    val resultCode = f(ev.primitive, eval1.primitive, eval2.primitive)
+    val resultCode = f(eval1.primitive, eval2.primitive)
     s"""
       ${eval1.code}
       boolean ${ev.isNull} = ${eval1.isNull};
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
index 3020e7fc967f2..e451c7ffbddc1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
@@ -122,18 +122,16 @@ case class GetStructField(child: Expression, field: StructField, ordinal: Int)
   override def dataType: DataType = field.dataType
   override def nullable: Boolean = child.nullable || field.nullable
 
-  override def eval(input: InternalRow): Any = {
-    val baseValue = child.eval(input).asInstanceOf[InternalRow]
-    if (baseValue == null) null else baseValue(ordinal)
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    input.asInstanceOf[InternalRow](ordinal)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval) => {
+    nullSafeCodeGen(ctx, ev, eval => {
       s"""
         if ($eval.isNullAt($ordinal)) {
           ${ev.isNull} = true;
         } else {
-          $result = ${ctx.getColumn(eval, dataType, ordinal)};
+          ${ev.primitive} = ${ctx.getColumn(eval, dataType, ordinal)};
         }
       """
     })
@@ -152,12 +150,9 @@ case class GetArrayStructFields(
   override def dataType: DataType = ArrayType(field.dataType, containsNull)
   override def nullable: Boolean = child.nullable || containsNull || field.nullable
 
-  override def eval(input: InternalRow): Any = {
-    val baseValue = child.eval(input).asInstanceOf[Seq[InternalRow]]
-    if (baseValue == null) null else {
-      baseValue.map { row =>
-        if (row == null) null else row(ordinal)
-      }
+  protected override def nullSafeEval(input: Any): Any = {
+    input.asInstanceOf[Seq[InternalRow]].map { row =>
+      if (row == null) null else row(ordinal)
     }
   }
 
@@ -165,7 +160,7 @@ case class GetArrayStructFields(
     val arraySeqClass = "scala.collection.mutable.ArraySeq"
     // TODO: consider using Array[_] for ArrayType child to avoid
     // boxing of primitives
-    nullSafeCodeGen(ctx, ev, (result, eval) => {
+    nullSafeCodeGen(ctx, ev, eval => {
       s"""
         final int n = $eval.size();
         final $arraySeqClass<Object> values = new $arraySeqClass<Object>(n);
@@ -175,7 +170,7 @@ case class GetArrayStructFields(
             values.update(j, ${ctx.getColumn("row", field.dataType, ordinal)});
           }
         }
-        $result = (${ctx.javaType(dataType)}) values;
+        ${ev.primitive} = (${ctx.javaType(dataType)}) values;
       """
     })
   }
@@ -193,22 +188,6 @@ abstract class ExtractValueWithOrdinal extends BinaryExpression with ExtractValu
   /** `Null` is returned for invalid ordinals. */
   override def nullable: Boolean = true
   override def toString: String = s"$child[$ordinal]"
-
-  override def eval(input: InternalRow): Any = {
-    val value = child.eval(input)
-    if (value == null) {
-      null
-    } else {
-      val o = ordinal.eval(input)
-      if (o == null) {
-        null
-      } else {
-        evalNotNull(value, o)
-      }
-    }
-  }
-
-  protected def evalNotNull(value: Any, ordinal: Any): Any
 }
 
 /**
@@ -219,7 +198,7 @@ case class GetArrayItem(child: Expression, ordinal: Expression)
 
   override def dataType: DataType = child.dataType.asInstanceOf[ArrayType].elementType
 
-  protected def evalNotNull(value: Any, ordinal: Any) = {
+  protected override def nullSafeEval(value: Any, ordinal: Any): Any = {
     // TODO: consider using Array[_] for ArrayType child to avoid
     // boxing of primitives
     val baseValue = value.asInstanceOf[Seq[_]]
@@ -232,13 +211,13 @@ case class GetArrayItem(child: Expression, ordinal: Expression)
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+    nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
       s"""
         final int index = (int)$eval2;
         if (index >= $eval1.size() || index < 0) {
           ${ev.isNull} = true;
         } else {
-          $result = (${ctx.boxedType(dataType)})$eval1.apply(index);
+          ${ev.primitive} = (${ctx.boxedType(dataType)})$eval1.apply(index);
         }
       """
     })
@@ -253,16 +232,16 @@ case class GetMapValue(child: Expression, ordinal: Expression)
 
   override def dataType: DataType = child.dataType.asInstanceOf[MapType].valueType
 
-  protected def evalNotNull(value: Any, ordinal: Any) = {
+  protected override def nullSafeEval(value: Any, ordinal: Any): Any = {
     val baseValue = value.asInstanceOf[Map[Any, _]]
     baseValue.get(ordinal).orNull
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval1, eval2) => {
+    nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
       s"""
         if ($eval1.contains($eval2)) {
-          $result = (${ctx.boxedType(dataType)})$eval1.apply($eval2);
+          ${ev.primitive} = (${ctx.boxedType(dataType)})$eval1.apply($eval2);
         } else {
           ${ev.isNull} = true;
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 4fbf4c87009c2..dca6642665741 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -26,18 +26,6 @@ abstract class UnaryArithmetic extends UnaryExpression {
   self: Product =>
 
   override def dataType: DataType = child.dataType
-
-  override def eval(input: InternalRow): Any = {
-    val evalE = child.eval(input)
-    if (evalE == null) {
-      null
-    } else {
-      evalInternal(evalE)
-    }
-  }
-
-  protected def evalInternal(evalE: Any): Any =
-    sys.error(s"UnaryArithmetics must override either eval or evalInternal")
 }
 
 case class UnaryMinus(child: Expression) extends UnaryArithmetic {
@@ -53,7 +41,7 @@ case class UnaryMinus(child: Expression) extends UnaryArithmetic {
     case dt: NumericType => defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})(-($c))")
   }
 
-  protected override def evalInternal(evalE: Any) = numeric.negate(evalE)
+  protected override def nullSafeEval(input: Any): Any = numeric.negate(input)
 }
 
 case class UnaryPositive(child: Expression) extends UnaryArithmetic {
@@ -62,7 +50,7 @@ case class UnaryPositive(child: Expression) extends UnaryArithmetic {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
     defineCodeGen(ctx, ev, c => c)
 
-  protected override def evalInternal(evalE: Any) = evalE
+  protected override def nullSafeEval(input: Any): Any = input
 }
 
 /**
@@ -74,7 +62,7 @@ case class Abs(child: Expression) extends UnaryArithmetic {
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
-  protected override def evalInternal(evalE: Any) = numeric.abs(evalE)
+  protected override def nullSafeEval(input: Any): Any = numeric.abs(input)
 }
 
 abstract class BinaryArithmetic extends BinaryOperator {
@@ -94,20 +82,6 @@ abstract class BinaryArithmetic extends BinaryOperator {
 
   protected def checkTypesInternal(t: DataType): TypeCheckResult
 
-  override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    if(evalE1 == null) {
-      null
-    } else {
-      val evalE2 = right.eval(input)
-      if (evalE2 == null) {
-        null
-      } else {
-        evalInternal(evalE1, evalE2)
-      }
-    }
-  }
-
   /** Name of the function for this expression on a [[Decimal]] type. */
   def decimalMethod: String =
     sys.error("BinaryArithmetics must override either decimalMethod or genCode")
@@ -122,9 +96,6 @@ abstract class BinaryArithmetic extends BinaryOperator {
     case _ =>
       defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1 $symbol $eval2")
   }
-
-  protected def evalInternal(evalE1: Any, evalE2: Any): Any =
-    sys.error(s"BinaryArithmetics must override either eval or evalInternal")
 }
 
 private[sql] object BinaryArithmetic {
@@ -143,7 +114,7 @@ case class Add(left: Expression, right: Expression) extends BinaryArithmetic {
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = numeric.plus(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.plus(input1, input2)
 }
 
 case class Subtract(left: Expression, right: Expression) extends BinaryArithmetic {
@@ -158,7 +129,7 @@ case class Subtract(left: Expression, right: Expression) extends BinaryArithmeti
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = numeric.minus(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.minus(input1, input2)
 }
 
 case class Multiply(left: Expression, right: Expression) extends BinaryArithmetic {
@@ -173,7 +144,7 @@ case class Multiply(left: Expression, right: Expression) extends BinaryArithmeti
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = numeric.times(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.times(input1, input2)
 }
 
 case class Divide(left: Expression, right: Expression) extends BinaryArithmetic {
@@ -194,15 +165,15 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic
   }
 
   override def eval(input: InternalRow): Any = {
-    val evalE2 = right.eval(input)
-    if (evalE2 == null || evalE2 == 0) {
+    val input2 = right.eval(input)
+    if (input2 == null || input2 == 0) {
       null
     } else {
-      val evalE1 = left.eval(input)
-      if (evalE1 == null) {
+      val input1 = left.eval(input)
+      if (input1 == null) {
         null
       } else {
-        div(evalE1, evalE2)
+        div(input1, input2)
       }
     }
   }
@@ -260,15 +231,15 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet
   }
 
   override def eval(input: InternalRow): Any = {
-    val evalE2 = right.eval(input)
-    if (evalE2 == null || evalE2 == 0) {
+    val input2 = right.eval(input)
+    if (input2 == null || input2 == 0) {
       null
     } else {
-      val evalE1 = left.eval(input)
-      if (evalE1 == null) {
+      val input1 = left.eval(input)
+      if (input1 == null) {
         null
       } else {
-        integral.rem(evalE1, evalE2)
+        integral.rem(input1, input2)
       }
     }
   }
@@ -317,17 +288,17 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
   private lazy val ordering = TypeUtils.getOrdering(dataType)
 
   override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    val evalE2 = right.eval(input)
-    if (evalE1 == null) {
-      evalE2
-    } else if (evalE2 == null) {
-      evalE1
+    val input1 = left.eval(input)
+    val input2 = right.eval(input)
+    if (input1 == null) {
+      input2
+    } else if (input2 == null) {
+      input1
     } else {
-      if (ordering.compare(evalE1, evalE2) < 0) {
-        evalE2
+      if (ordering.compare(input1, input2) < 0) {
+        input2
       } else {
-        evalE1
+        input1
       }
     }
   }
@@ -371,17 +342,17 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
   private lazy val ordering = TypeUtils.getOrdering(dataType)
 
   override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    val evalE2 = right.eval(input)
-    if (evalE1 == null) {
-      evalE2
-    } else if (evalE2 == null) {
-      evalE1
+    val input1 = left.eval(input)
+    val input2 = right.eval(input)
+    if (input1 == null) {
+      input2
+    } else if (input2 == null) {
+      input1
     } else {
-      if (ordering.compare(evalE1, evalE2) < 0) {
-        evalE1
+      if (ordering.compare(input1, input2) < 0) {
+        input1
       } else {
-        evalE2
+        input2
       }
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
index 9002dda7bf4d0..2d47124d247e7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
@@ -45,7 +45,7 @@ case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithme
       ((evalE1: Long, evalE2: Long) => evalE1 & evalE2).asInstanceOf[(Any, Any) => Any]
   }
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = and(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = and(input1, input2)
 }
 
 /**
@@ -70,7 +70,7 @@ case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmet
       ((evalE1: Long, evalE2: Long) => evalE1 | evalE2).asInstanceOf[(Any, Any) => Any]
   }
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = or(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = or(input1, input2)
 }
 
 /**
@@ -95,7 +95,7 @@ case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithme
       ((evalE1: Long, evalE2: Long) => evalE1 ^ evalE2).asInstanceOf[(Any, Any) => Any]
   }
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any): Any = xor(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = xor(input1, input2)
 }
 
 /**
@@ -122,5 +122,5 @@ case class BitwiseNot(child: Expression) extends UnaryArithmetic {
     defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dataType)}) ~($c)")
   }
 
-  protected override def evalInternal(evalE: Any) = not(evalE)
+  protected override def nullSafeEval(input: Any): Any = not(input)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
index f5c2dde191cf3..2fa74b4ffc5da 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
@@ -30,14 +30,8 @@ case class UnscaledValue(child: Expression) extends UnaryExpression {
   override def dataType: DataType = LongType
   override def toString: String = s"UnscaledValue($child)"
 
-  override def eval(input: InternalRow): Any = {
-    val childResult = child.eval(input)
-    if (childResult == null) {
-      null
-    } else {
-      childResult.asInstanceOf[Decimal].toUnscaledLong
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    input.asInstanceOf[Decimal].toUnscaledLong
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"$c.toUnscaledLong()")
@@ -54,26 +48,15 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un
   override def dataType: DataType = DecimalType(precision, scale)
   override def toString: String = s"MakeDecimal($child,$precision,$scale)"
 
-  override def eval(input: InternalRow): Decimal = {
-    val childResult = child.eval(input)
-    if (childResult == null) {
-      null
-    } else {
-      new Decimal().setOrNull(childResult.asInstanceOf[Long], precision, scale)
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    Decimal(input.asInstanceOf[Long], precision, scale)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val eval = child.gen(ctx)
-    eval.code + s"""
-      boolean ${ev.isNull} = ${eval.isNull};
-      ${ctx.decimalType} ${ev.primitive} = null;
-
-      if (!${ev.isNull}) {
-        ${ev.primitive} = (new ${ctx.decimalType}()).setOrNull(
-          ${eval.primitive}, $precision, $scale);
+    nullSafeCodeGen(ctx, ev, eval => {
+      s"""
+        ${ev.primitive} = (new ${ctx.decimalType}()).setOrNull($eval, $precision, $scale);
         ${ev.isNull} = ${ev.primitive} == null;
-      }
       """
+    })
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 92500453980f6..9dca8513c0d9c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -61,21 +61,16 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
   override def nullable: Boolean = true
   override def toString: String = s"$name($child)"
 
-  override def eval(input: InternalRow): Any = {
-    val evalE = child.eval(input)
-    if (evalE == null) {
-      null
-    } else {
-      val result = f(evalE.asInstanceOf[Double])
-      if (result.isNaN) null else result
-    }
+  protected override def nullSafeEval(input: Any): Any = {
+    val result = f(input.asInstanceOf[Double])
+    if (result.isNaN) null else result
   }
 
   // name of function in java.lang.Math
   def funcName: String = name.toLowerCase
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (result, eval) => {
+    nullSafeCodeGen(ctx, ev, eval => {
       s"""
         ${ev.primitive} = java.lang.Math.${funcName}($eval);
         if (Double.valueOf(${ev.primitive}).isNaN()) {
@@ -101,19 +96,9 @@ abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
 
   override def dataType: DataType = DoubleType
 
-  override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    if (evalE1 == null) {
-      null
-    } else {
-      val evalE2 = right.eval(input)
-      if (evalE2 == null) {
-        null
-      } else {
-        val result = f(evalE1.asInstanceOf[Double], evalE2.asInstanceOf[Double])
-        if (result.isNaN) null else result
-      }
-    }
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val result = f(input1.asInstanceOf[Double], input2.asInstanceOf[Double])
+    if (result.isNaN) null else result
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -194,39 +179,29 @@ case class Factorial(child: Expression) extends UnaryExpression with ExpectsInpu
 
   override def dataType: DataType = LongType
 
-  override def foldable: Boolean = child.foldable
-
   // If the value not in the range of [0, 20], it still will be null, so set it to be true here.
   override def nullable: Boolean = true
 
-  override def eval(input: InternalRow): Any = {
-    val evalE = child.eval(input)
-    if (evalE == null) {
+  protected override def nullSafeEval(input: Any): Any = {
+    val value = input.asInstanceOf[jl.Integer]
+    if (value > 20 || value < 0) {
       null
     } else {
-      val input = evalE.asInstanceOf[jl.Integer]
-      if (input > 20 || input < 0) {
-        null
-      } else {
-        Factorial.factorial(input)
-      }
+      Factorial.factorial(value)
     }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val eval = child.gen(ctx)
-    eval.code + s"""
-      boolean ${ev.isNull} = ${eval.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        if (${eval.primitive} > 20 || ${eval.primitive} < 0) {
+    nullSafeCodeGen(ctx, ev, eval => {
+      s"""
+        if ($eval > 20 || $eval < 0) {
           ${ev.isNull} = true;
         } else {
           ${ev.primitive} =
-            org.apache.spark.sql.catalyst.expressions.Factorial.factorial(${eval.primitive});
+            org.apache.spark.sql.catalyst.expressions.Factorial.factorial($eval);
         }
-      }
-    """
+      """
+    })
   }
 }
 
@@ -235,17 +210,14 @@ case class Log(child: Expression) extends UnaryMathExpression(math.log, "LOG")
 case class Log2(child: Expression)
   extends UnaryMathExpression((x: Double) => math.log(x) / math.log(2), "LOG2") {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val eval = child.gen(ctx)
-    eval.code + s"""
-      boolean ${ev.isNull} = ${eval.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${ev.primitive} = java.lang.Math.log(${eval.primitive}) / java.lang.Math.log(2);
+    nullSafeCodeGen(ctx, ev, eval => {
+      s"""
+        ${ev.primitive} = java.lang.Math.log($eval) / java.lang.Math.log(2);
         if (Double.valueOf(${ev.primitive}).isNaN()) {
           ${ev.isNull} = true;
         }
-      }
-    """
+      """
+    })
   }
 }
 
@@ -283,14 +255,8 @@ case class Bin(child: Expression)
   override def inputTypes: Seq[DataType] = Seq(LongType)
   override def dataType: DataType = StringType
 
-  override def eval(input: InternalRow): Any = {
-    val evalE = child.eval(input)
-    if (evalE == null) {
-      null
-    } else {
-      UTF8String.fromString(jl.Long.toBinaryString(evalE.asInstanceOf[Long]))
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    UTF8String.fromString(jl.Long.toBinaryString(input.asInstanceOf[Long]))
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c) =>
@@ -326,17 +292,10 @@ case class Hex(child: Expression) extends UnaryExpression with ExpectsInputTypes
 
   override def dataType: DataType = StringType
 
-  override def eval(input: InternalRow): Any = {
-    val num = child.eval(input)
-    if (num == null) {
-      null
-    } else {
-      child.dataType match {
-        case LongType => hex(num.asInstanceOf[Long])
-        case BinaryType => hex(num.asInstanceOf[Array[Byte]])
-        case StringType => hex(num.asInstanceOf[UTF8String].getBytes)
-      }
-    }
+  protected override def nullSafeEval(num: Any): Any = child.dataType match {
+    case LongType => hex(num.asInstanceOf[Long])
+    case BinaryType => hex(num.asInstanceOf[Array[Byte]])
+    case StringType => hex(num.asInstanceOf[UTF8String].getBytes)
   }
 
   private[this] def hex(bytes: Array[Byte]): UTF8String = {
@@ -377,14 +336,8 @@ case class Unhex(child: Expression) extends UnaryExpression with ExpectsInputTyp
   override def nullable: Boolean = true
   override def dataType: DataType = BinaryType
 
-  override def eval(input: InternalRow): Any = {
-    val num = child.eval(input)
-    if (num == null) {
-      null
-    } else {
-      unhex(num.asInstanceOf[UTF8String].getBytes)
-    }
-  }
+  protected override def nullSafeEval(num: Any): Any =
+    unhex(num.asInstanceOf[UTF8String].getBytes)
 
   private[this] def unhex(bytes: Array[Byte]): Array[Byte] = {
     val out = new Array[Byte]((bytes.length + 1) >> 1)
@@ -429,21 +382,10 @@ case class Unhex(child: Expression) extends UnaryExpression with ExpectsInputTyp
 case class Atan2(left: Expression, right: Expression)
   extends BinaryMathExpression(math.atan2, "ATAN2") {
 
-  override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    if (evalE1 == null) {
-      null
-    } else {
-      val evalE2 = right.eval(input)
-      if (evalE2 == null) {
-        null
-      } else {
-        // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0
-        val result = math.atan2(evalE1.asInstanceOf[Double] + 0.0,
-          evalE2.asInstanceOf[Double] + 0.0)
-        if (result.isNaN) null else result
-      }
-    }
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0
+    val result = math.atan2(input1.asInstanceOf[Double] + 0.0, input2.asInstanceOf[Double] + 0.0)
+    if (result.isNaN) null else result
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -480,25 +422,15 @@ case class ShiftLeft(left: Expression, right: Expression)
 
   override def dataType: DataType = left.dataType
 
-  override def eval(input: InternalRow): Any = {
-    val valueLeft = left.eval(input)
-    if (valueLeft != null) {
-      val valueRight = right.eval(input)
-      if (valueRight != null) {
-        valueLeft match {
-          case l: jl.Long => l << valueRight.asInstanceOf[jl.Integer]
-          case i: jl.Integer => i << valueRight.asInstanceOf[jl.Integer]
-        }
-      } else {
-        null
-      }
-    } else {
-      null
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    input1 match {
+      case l: jl.Long => l << input2.asInstanceOf[jl.Integer]
+      case i: jl.Integer => i << input2.asInstanceOf[jl.Integer]
     }
   }
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left << $right;")
+    defineCodeGen(ctx, ev, (left, right) => s"$left << $right")
   }
 }
 
@@ -516,25 +448,15 @@ case class ShiftRight(left: Expression, right: Expression)
 
   override def dataType: DataType = left.dataType
 
-  override def eval(input: InternalRow): Any = {
-    val valueLeft = left.eval(input)
-    if (valueLeft != null) {
-      val valueRight = right.eval(input)
-      if (valueRight != null) {
-        valueLeft match {
-          case l: jl.Long => l >> valueRight.asInstanceOf[jl.Integer]
-          case i: jl.Integer => i >> valueRight.asInstanceOf[jl.Integer]
-        }
-      } else {
-        null
-      }
-    } else {
-      null
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    input1 match {
+      case l: jl.Long => l >> input2.asInstanceOf[jl.Integer]
+      case i: jl.Integer => i >> input2.asInstanceOf[jl.Integer]
     }
   }
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left >> $right;")
+    defineCodeGen(ctx, ev, (left, right) => s"$left >> $right")
   }
 }
 
@@ -552,25 +474,15 @@ case class ShiftRightUnsigned(left: Expression, right: Expression)
 
   override def dataType: DataType = left.dataType
 
-  override def eval(input: InternalRow): Any = {
-    val valueLeft = left.eval(input)
-    if (valueLeft != null) {
-      val valueRight = right.eval(input)
-      if (valueRight != null) {
-        valueLeft match {
-          case l: jl.Long => l >>> valueRight.asInstanceOf[jl.Integer]
-          case i: jl.Integer => i >>> valueRight.asInstanceOf[jl.Integer]
-        }
-      } else {
-        null
-      }
-    } else {
-      null
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    input1 match {
+      case l: jl.Long => l >>> input2.asInstanceOf[jl.Integer]
+      case i: jl.Integer => i >>> input2.asInstanceOf[jl.Integer]
     }
   }
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (result, left, right) => s"$result = $left >>> $right;")
+    defineCodeGen(ctx, ev, (left, right) => s"$left >>> $right")
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index e008af3966941..3b59cd431b871 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -37,14 +37,8 @@ case class Md5(child: Expression) extends UnaryExpression with ExpectsInputTypes
 
   override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
-  override def eval(input: InternalRow): Any = {
-    val value = child.eval(input)
-    if (value == null) {
-      null
-    } else {
-      UTF8String.fromString(DigestUtils.md5Hex(value.asInstanceOf[Array[Byte]]))
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    UTF8String.fromString(DigestUtils.md5Hex(input.asInstanceOf[Array[Byte]]))
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c =>
@@ -67,76 +61,56 @@ case class Sha2(left: Expression, right: Expression)
 
   override def inputTypes: Seq[DataType] = Seq(BinaryType, IntegerType)
 
-  override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    if (evalE1 == null) {
-      null
-    } else {
-      val evalE2 = right.eval(input)
-      if (evalE2 == null) {
-        null
-      } else {
-        val bitLength = evalE2.asInstanceOf[Int]
-        val input = evalE1.asInstanceOf[Array[Byte]]
-        bitLength match {
-          case 224 =>
-            // DigestUtils doesn't support SHA-224 now
-            try {
-              val md = MessageDigest.getInstance("SHA-224")
-              md.update(input)
-              UTF8String.fromBytes(md.digest())
-            } catch {
-              // SHA-224 is not supported on the system, return null
-              case noa: NoSuchAlgorithmException => null
-            }
-          case 256 | 0 =>
-            UTF8String.fromString(DigestUtils.sha256Hex(input))
-          case 384 =>
-            UTF8String.fromString(DigestUtils.sha384Hex(input))
-          case 512 =>
-            UTF8String.fromString(DigestUtils.sha512Hex(input))
-          case _ => null
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val bitLength = input2.asInstanceOf[Int]
+    val input = input1.asInstanceOf[Array[Byte]]
+    bitLength match {
+      case 224 =>
+        // DigestUtils doesn't support SHA-224 now
+        try {
+          val md = MessageDigest.getInstance("SHA-224")
+          md.update(input)
+          UTF8String.fromBytes(md.digest())
+        } catch {
+          // SHA-224 is not supported on the system, return null
+          case noa: NoSuchAlgorithmException => null
         }
-      }
+      case 256 | 0 =>
+        UTF8String.fromString(DigestUtils.sha256Hex(input))
+      case 384 =>
+        UTF8String.fromString(DigestUtils.sha384Hex(input))
+      case 512 =>
+        UTF8String.fromString(DigestUtils.sha512Hex(input))
+      case _ => null
     }
   }
+
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val eval1 = left.gen(ctx)
-    val eval2 = right.gen(ctx)
     val digestUtils = "org.apache.commons.codec.digest.DigestUtils"
-
-    s"""
-      ${eval1.code}
-      boolean ${ev.isNull} = ${eval1.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${eval2.code}
-        if (!${eval2.isNull}) {
-          if (${eval2.primitive} == 224) {
-            try {
-              java.security.MessageDigest md = java.security.MessageDigest.getInstance("SHA-224");
-              md.update(${eval1.primitive});
-              ${ev.primitive} = ${ctx.stringType}.fromBytes(md.digest());
-            } catch (java.security.NoSuchAlgorithmException e) {
-              ${ev.isNull} = true;
-            }
-          } else if (${eval2.primitive} == 256 || ${eval2.primitive} == 0) {
-            ${ev.primitive} =
-              ${ctx.stringType}.fromString(${digestUtils}.sha256Hex(${eval1.primitive}));
-          } else if (${eval2.primitive} == 384) {
-            ${ev.primitive} =
-              ${ctx.stringType}.fromString(${digestUtils}.sha384Hex(${eval1.primitive}));
-          } else if (${eval2.primitive} == 512) {
-            ${ev.primitive} =
-              ${ctx.stringType}.fromString(${digestUtils}.sha512Hex(${eval1.primitive}));
-          } else {
+    nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+      s"""
+        if ($eval2 == 224) {
+          try {
+            java.security.MessageDigest md = java.security.MessageDigest.getInstance("SHA-224");
+            md.update($eval1);
+            ${ev.primitive} = ${ctx.stringType}.fromBytes(md.digest());
+          } catch (java.security.NoSuchAlgorithmException e) {
             ${ev.isNull} = true;
           }
+        } else if ($eval2 == 256 || $eval2 == 0) {
+          ${ev.primitive} =
+            ${ctx.stringType}.fromString($digestUtils.sha256Hex($eval1));
+        } else if ($eval2 == 384) {
+          ${ev.primitive} =
+            ${ctx.stringType}.fromString($digestUtils.sha384Hex($eval1));
+        } else if ($eval2 == 512) {
+          ${ev.primitive} =
+            ${ctx.stringType}.fromString($digestUtils.sha512Hex($eval1));
         } else {
           ${ev.isNull} = true;
         }
-      }
-    """
+      """
+    })
   }
 }
 
@@ -150,19 +124,12 @@ case class Sha1(child: Expression) extends UnaryExpression with ExpectsInputType
 
   override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
-  override def eval(input: InternalRow): Any = {
-    val value = child.eval(input)
-    if (value == null) {
-      null
-    } else {
-      UTF8String.fromString(DigestUtils.shaHex(value.asInstanceOf[Array[Byte]]))
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    UTF8String.fromString(DigestUtils.shaHex(input.asInstanceOf[Array[Byte]]))
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c =>
-      "org.apache.spark.unsafe.types.UTF8String.fromString" +
-        s"(org.apache.commons.codec.digest.DigestUtils.shaHex($c))"
+      s"${ctx.stringType}.fromString(org.apache.commons.codec.digest.DigestUtils.shaHex($c))"
     )
   }
 }
@@ -177,30 +144,20 @@ case class Crc32(child: Expression) extends UnaryExpression with ExpectsInputTyp
 
   override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
-  override def eval(input: InternalRow): Any = {
-    val value = child.eval(input)
-    if (value == null) {
-      null
-    } else {
-      val checksum = new CRC32
-      checksum.update(value.asInstanceOf[Array[Byte]], 0, value.asInstanceOf[Array[Byte]].length)
-      checksum.getValue
-    }
+  protected override def nullSafeEval(input: Any): Any = {
+    val checksum = new CRC32
+    checksum.update(input.asInstanceOf[Array[Byte]], 0, input.asInstanceOf[Array[Byte]].length)
+    checksum.getValue
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val value = child.gen(ctx)
     val CRC32 = "java.util.zip.CRC32"
-    s"""
-      ${value.code}
-      boolean ${ev.isNull} = ${value.isNull};
-      long ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${CRC32} checksum = new ${CRC32}();
-        checksum.update(${value.primitive}, 0, ${value.primitive}.length);
+    nullSafeCodeGen(ctx, ev, value => {
+      s"""
+        $CRC32 checksum = new $CRC32();
+        checksum.update($value, 0, $value.length);
         ${ev.primitive} = checksum.getValue();
-      }
-    """
+      """
+    })
   }
-
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 0b479f466c63c..402a0aa232493 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -74,12 +74,7 @@ case class Not(child: Expression) extends UnaryExpression with Predicate with Ex
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType)
 
-  override def eval(input: InternalRow): Any = {
-    child.eval(input) match {
-      case null => null
-      case b: Boolean => !b
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any = !input.asInstanceOf[Boolean]
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"!($c)")
@@ -105,17 +100,14 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate {
  * Optimized version of In clause, when all filter values of In clause are
  * static.
  */
-case class InSet(value: Expression, hset: Set[Any])
-  extends Predicate {
-
-  override def children: Seq[Expression] = value :: Nil
+case class InSet(child: Expression, hset: Set[Any])
+  extends UnaryExpression with Predicate {
 
-  override def foldable: Boolean = value.foldable
   override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN.
-  override def toString: String = s"$value INSET ${hset.mkString("(", ",", ")")}"
+  override def toString: String = s"$child INSET ${hset.mkString("(", ",", ")")}"
 
   override def eval(input: InternalRow): Any = {
-    hset.contains(value.eval(input))
+    hset.contains(child.eval(input))
   }
 }
 
@@ -127,15 +119,15 @@ case class And(left: Expression, right: Expression)
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
   override def eval(input: InternalRow): Any = {
-    val l = left.eval(input)
-    if (l == false) {
+    val input1 = left.eval(input)
+    if (input1 == false) {
        false
     } else {
-      val r = right.eval(input)
-      if (r == false) {
+      val input2 = right.eval(input)
+      if (input2 == false) {
         false
       } else {
-        if (l != null && r != null) {
+        if (input1 != null && input2 != null) {
           true
         } else {
           null
@@ -176,15 +168,15 @@ case class Or(left: Expression, right: Expression)
   override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
 
   override def eval(input: InternalRow): Any = {
-    val l = left.eval(input)
-    if (l == true) {
+    val input1 = left.eval(input)
+    if (input1 == true) {
       true
     } else {
-      val r = right.eval(input)
-      if (r == true) {
+      val input2 = right.eval(input)
+      if (input2 == true) {
         true
       } else {
-        if (l != null && r != null) {
+        if (input1 != null && input2 != null) {
           false
         } else {
           null
@@ -232,20 +224,6 @@ abstract class BinaryComparison extends BinaryOperator with Predicate {
 
   protected def checkTypesInternal(t: DataType): TypeCheckResult
 
-  override def eval(input: InternalRow): Any = {
-    val evalE1 = left.eval(input)
-    if (evalE1 == null) {
-      null
-    } else {
-      val evalE2 = right.eval(input)
-      if (evalE2 == null) {
-        null
-      } else {
-        evalInternal(evalE1, evalE2)
-      }
-    }
-  }
-
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     if (ctx.isPrimitiveType(left.dataType)) {
       // faster version
@@ -254,9 +232,6 @@ abstract class BinaryComparison extends BinaryOperator with Predicate {
       defineCodeGen(ctx, ev, (c1, c2) => s"${ctx.genComp(left.dataType, c1, c2)} $symbol 0")
     }
   }
-
-  protected def evalInternal(evalE1: Any, evalE2: Any): Any =
-    sys.error(s"BinaryComparisons must override either eval or evalInternal")
 }
 
 private[sql] object BinaryComparison {
@@ -277,9 +252,9 @@ case class EqualTo(left: Expression, right: Expression) extends BinaryComparison
 
   override protected def checkTypesInternal(t: DataType) = TypeCheckResult.TypeCheckSuccess
 
-  protected override def evalInternal(l: Any, r: Any) = {
-    if (left.dataType != BinaryType) l == r
-    else java.util.Arrays.equals(l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]])
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    if (left.dataType != BinaryType) input1 == input2
+    else java.util.Arrays.equals(input1.asInstanceOf[Array[Byte]], input2.asInstanceOf[Array[Byte]])
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -295,15 +270,18 @@ case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComp
   override protected def checkTypesInternal(t: DataType) = TypeCheckResult.TypeCheckSuccess
 
   override def eval(input: InternalRow): Any = {
-    val l = left.eval(input)
-    val r = right.eval(input)
-    if (l == null && r == null) {
+    val input1 = left.eval(input)
+    val input2 = right.eval(input)
+    if (input1 == null && input2 == null) {
       true
-    } else if (l == null || r == null) {
+    } else if (input1 == null || input2 == null) {
       false
     } else {
-      if (left.dataType != BinaryType) l == r
-      else java.util.Arrays.equals(l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]])
+      if (left.dataType != BinaryType) {
+        input1 == input2
+      } else {
+        java.util.Arrays.equals(input1.asInstanceOf[Array[Byte]], input2.asInstanceOf[Array[Byte]])
+      }
     }
   }
 
@@ -327,7 +305,7 @@ case class LessThan(left: Expression, right: Expression) extends BinaryCompariso
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.lt(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lt(input1, input2)
 }
 
 case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison {
@@ -338,7 +316,7 @@ case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryCo
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.lteq(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lteq(input1, input2)
 }
 
 case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison {
@@ -349,7 +327,7 @@ case class GreaterThan(left: Expression, right: Expression) extends BinaryCompar
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.gt(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.gt(input1, input2)
 }
 
 case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison {
@@ -360,5 +338,5 @@ case class GreaterThanOrEqual(left: Expression, right: Expression) extends Binar
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
-  protected override def evalInternal(evalE1: Any, evalE2: Any) = ordering.gteq(evalE1, evalE2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.gteq(input1, input2)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index 5d51a4ca65332..9b44fb1ed5b3f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -135,6 +135,7 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression {
  */
 case class CombineSets(left: Expression, right: Expression) extends BinaryExpression {
 
+  override def nullable: Boolean = left.nullable
   override def dataType: DataType = left.dataType
 
   override def eval(input: InternalRow): Any = {
@@ -183,12 +184,8 @@ case class CountSet(child: Expression) extends UnaryExpression {
 
   override def dataType: DataType = LongType
 
-  override def eval(input: InternalRow): Any = {
-    val childEval = child.eval(input).asInstanceOf[OpenHashSet[Any]]
-    if (childEval != null) {
-      childEval.size.toLong
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    input.asInstanceOf[OpenHashSet[Any]].size.toLong
 
   override def toString: String = s"$child.count()"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 1a14a7a449342..6e6a7fb171214 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -31,7 +31,6 @@ trait StringRegexExpression extends ExpectsInputTypes {
   def escape(v: String): String
   def matches(regex: Pattern, str: String): Boolean
 
-  override def nullable: Boolean = left.nullable || right.nullable
   override def dataType: DataType = BooleanType
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
@@ -50,22 +49,12 @@ trait StringRegexExpression extends ExpectsInputTypes {
 
   protected def pattern(str: String) = if (cache == null) compile(str) else cache
 
-  override def eval(input: InternalRow): Any = {
-    val l = left.eval(input)
-    if (l == null) {
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val regex = pattern(input2.asInstanceOf[UTF8String].toString())
+    if(regex == null) {
       null
     } else {
-      val r = right.eval(input)
-      if(r == null) {
-        null
-      } else {
-        val regex = pattern(r.asInstanceOf[UTF8String].toString())
-        if(regex == null) {
-          null
-        } else {
-          matches(regex, l.asInstanceOf[UTF8String].toString())
-        }
-      }
+      matches(regex, input1.asInstanceOf[UTF8String].toString())
     }
   }
 }
@@ -120,14 +109,8 @@ trait CaseConversionExpression extends ExpectsInputTypes {
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val evaluated = child.eval(input)
-    if (evaluated == null) {
-      null
-    } else {
-      convert(evaluated.asInstanceOf[UTF8String])
-    }
-  }
+  protected override def nullSafeEval(input: Any): Any =
+    convert(input.asInstanceOf[UTF8String])
 }
 
 /**
@@ -160,20 +143,10 @@ trait StringComparison extends ExpectsInputTypes {
 
   def compare(l: UTF8String, r: UTF8String): Boolean
 
-  override def nullable: Boolean = left.nullable || right.nullable
-
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val leftEval = left.eval(input)
-    if(leftEval == null) {
-      null
-    } else {
-      val rightEval = right.eval(input)
-      if (rightEval == null) null
-      else compare(leftEval.asInstanceOf[UTF8String], rightEval.asInstanceOf[UTF8String])
-    }
-  }
+  protected override def nullSafeEval(input1: Any, input2: Any): Any =
+    compare(input1.asInstanceOf[UTF8String], input2.asInstanceOf[UTF8String])
 
   override def toString: String = s"$nodeName($left, $right)"
 }
@@ -288,10 +261,8 @@ case class StringLength(child: Expression) extends UnaryExpression with ExpectsI
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val string = child.eval(input)
-    if (string == null) null else string.asInstanceOf[UTF8String].length
-  }
+  protected override def nullSafeEval(string: Any): Any =
+    string.asInstanceOf[UTF8String].length
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c => s"($c).length()")
@@ -310,24 +281,13 @@ case class Levenshtein(left: Expression, right: Expression) extends BinaryExpres
 
   override def dataType: DataType = IntegerType
 
-  override def eval(input: InternalRow): Any = {
-    val leftValue = left.eval(input)
-    if (leftValue == null) {
-      null
-    } else {
-      val rightValue = right.eval(input)
-      if(rightValue == null) {
-        null
-      } else {
-        StringUtils.getLevenshteinDistance(leftValue.toString, rightValue.toString)
-      }
-    }
-  }
+  protected override def nullSafeEval(input1: Any, input2: Any): Any =
+    StringUtils.getLevenshteinDistance(input1.toString, input2.toString)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val stringUtils = classOf[StringUtils].getName
-    nullSafeCodeGen(ctx, ev, (res, left, right) =>
-      s"$res = $stringUtils.getLevenshteinDistance($left.toString(), $right.toString());")
+    defineCodeGen(ctx, ev, (left, right) =>
+      s"$stringUtils.getLevenshteinDistance($left.toString(), $right.toString())")
   }
 }
 
@@ -338,17 +298,12 @@ case class Ascii(child: Expression) extends UnaryExpression with ExpectsInputTyp
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val string = child.eval(input)
-    if (string == null) {
-      null
+  protected override def nullSafeEval(string: Any): Any = {
+    val bytes = string.asInstanceOf[UTF8String].getBytes
+    if (bytes.length > 0) {
+      bytes(0).asInstanceOf[Int]
     } else {
-      val bytes = string.asInstanceOf[UTF8String].getBytes
-      if (bytes.length > 0) {
-        bytes(0).asInstanceOf[Int]
-      } else {
-        0
-      }
+      0
     }
   }
 }
@@ -360,15 +315,10 @@ case class Base64(child: Expression) extends UnaryExpression with ExpectsInputTy
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
-  override def eval(input: InternalRow): Any = {
-    val bytes = child.eval(input)
-    if (bytes == null) {
-      null
-    } else {
-      UTF8String.fromBytes(
-        org.apache.commons.codec.binary.Base64.encodeBase64(
-          bytes.asInstanceOf[Array[Byte]]))
-    }
+  protected override def nullSafeEval(bytes: Any): Any = {
+    UTF8String.fromBytes(
+      org.apache.commons.codec.binary.Base64.encodeBase64(
+        bytes.asInstanceOf[Array[Byte]]))
   }
 }
 
@@ -379,14 +329,8 @@ case class UnBase64(child: Expression) extends UnaryExpression with ExpectsInput
   override def dataType: DataType = BinaryType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val string = child.eval(input)
-    if (string == null) {
-      null
-    } else {
-      org.apache.commons.codec.binary.Base64.decodeBase64(string.asInstanceOf[UTF8String].toString)
-    }
-  }
+  protected override def nullSafeEval(string: Any): Any =
+    org.apache.commons.codec.binary.Base64.decodeBase64(string.asInstanceOf[UTF8String].toString)
 }
 
 /**
@@ -402,19 +346,9 @@ case class Decode(bin: Expression, charset: Expression)
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(BinaryType, StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val l = bin.eval(input)
-    if (l == null) {
-      null
-    } else {
-      val r = charset.eval(input)
-      if (r == null) {
-        null
-      } else {
-        val fromCharset = r.asInstanceOf[UTF8String].toString
-        UTF8String.fromString(new String(l.asInstanceOf[Array[Byte]], fromCharset))
-      }
-    }
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val fromCharset = input2.asInstanceOf[UTF8String].toString
+    UTF8String.fromString(new String(input1.asInstanceOf[Array[Byte]], fromCharset))
   }
 }
 
@@ -431,19 +365,9 @@ case class Encode(value: Expression, charset: Expression)
   override def dataType: DataType = BinaryType
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val l = value.eval(input)
-    if (l == null) {
-      null
-    } else {
-      val r = charset.eval(input)
-      if (r == null) {
-        null
-      } else {
-        val toCharset = r.asInstanceOf[UTF8String].toString
-        l.asInstanceOf[UTF8String].toString.getBytes(toCharset)
-      }
-    }
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val toCharset = input2.asInstanceOf[UTF8String].toString
+    input1.asInstanceOf[UTF8String].toString.getBytes(toCharset)
   }
 }
 

From 1cb2629f1aa466f92246828c562ea6f35c89ab87 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Mon, 6 Jul 2015 22:14:24 -0700
Subject: [PATCH 0253/1454] [HOTFIX] Rename release-profile to release

when publishing releases. We named it as 'release-profile' because that is
the Maven convention. However, it turns out this special name causes several
other things to kick-in when we are creating releases that are not desirable.
For instance, it triggers the javadoc plugin to run, which actually fails
in our current build set-up.

The fix is just to rename this to a different profile to have no
collateral damage associated with its use.
---
 dev/create-release/create-release.sh | 4 ++--
 pom.xml                              | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index cfe2cd4752b3f..30190dcd41ec5 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -118,13 +118,13 @@ if [[ ! "$@" =~ --skip-publish ]]; then
 
   rm -rf $SPARK_REPO
 
-  build/mvn -DskipTests -Pyarn -Phive -Prelease-profile\
+  build/mvn -DskipTests -Pyarn -Phive -Prelease\
     -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
   ./dev/change-version-to-2.11.sh
   
-  build/mvn -DskipTests -Pyarn -Phive -Prelease-profile\
+  build/mvn -DskipTests -Pyarn -Phive -Prelease\
     -Dscala-2.11 -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
diff --git a/pom.xml b/pom.xml
index fbcc9152765cf..27dd4f750a3f6 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1835,7 +1835,7 @@
           Use this profile only for making Spark releases. Note that due to SPARK-8819,
           you must use maven version 3.2.x or before to avoid running into MSHADE-148.
       -->
-      <id>release-profile</id>
+      <id>release</id>
       <properties>
         <!--
             The maven shade plugin has a bug where enabling the `createDependencyReducedPom`

From dcbd85b70f026fbc0b7e77fcc364513581007c8d Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 7 Jul 2015 08:09:56 -0700
Subject: [PATCH 0254/1454] [SPARK-6731] [CORE] Addendum: Upgrade Apache
 commons-math3 to 3.4.1

(This finishes the job by removing the version overridden by Hadoop profiles.)

See discussion at https://github.com/apache/spark/pull/6994#issuecomment-119113167

Author: Sean Owen <sowen@cloudera.com>

Closes #7261 from srowen/SPARK-6731.2 and squashes the following commits:

5a3f59e [Sean Owen] Finish updating Commons Math3 to 3.4.1 from 3.1.1
---
 pom.xml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pom.xml b/pom.xml
index 27dd4f750a3f6..bece526a2a3e5 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1717,7 +1717,6 @@
       <properties>
         <hadoop.version>2.3.0</hadoop.version>
         <jets3t.version>0.9.3</jets3t.version>
-        <commons.math3.version>3.1.1</commons.math3.version>
       </properties>
     </profile>
 
@@ -1726,7 +1725,6 @@
       <properties>
         <hadoop.version>2.4.0</hadoop.version>
         <jets3t.version>0.9.3</jets3t.version>
-        <commons.math3.version>3.1.1</commons.math3.version>
       </properties>
     </profile>
 
@@ -1735,7 +1733,6 @@
       <properties>
         <hadoop.version>2.6.0</hadoop.version>
         <jets3t.version>0.9.3</jets3t.version>
-        <commons.math3.version>3.1.1</commons.math3.version>
         <zookeeper.version>3.4.6</zookeeper.version>
         <curator.version>2.6.0</curator.version>
       </properties>

From d73bc08d98e803889ef6215eab81d7bb0049e941 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 7 Jul 2015 08:19:17 -0700
Subject: [PATCH 0255/1454] [SPARK-8788] [ML] Add Java unit test for PCA
 transformer

Add Java unit test for PCA transformer

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7184 from yanboliang/spark-8788 and squashes the following commits:

9d1a2af [Yanbo Liang] address comments
b34451f [Yanbo Liang] Add Java unit test for PCA transformer
---
 .../apache/spark/ml/feature/JavaPCASuite.java | 114 ++++++++++++++++++
 1 file changed, 114 insertions(+)
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java

diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
new file mode 100644
index 0000000000000..5cf43fec6f29e
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
@@ -0,0 +1,114 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature;
+
+import java.io.Serializable;
+import java.util.List;
+
+import scala.Tuple2;
+
+import com.google.common.collect.Lists;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.distributed.RowMatrix;
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+
+public class JavaPCASuite implements Serializable {
+  private transient JavaSparkContext jsc;
+  private transient SQLContext sqlContext;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaPCASuite");
+    sqlContext = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  public static class VectorPair implements Serializable {
+    private Vector features = Vectors.dense(0.0);
+    private Vector expected = Vectors.dense(0.0);
+
+    public void setFeatures(Vector features) {
+      this.features = features;
+    }
+
+    public Vector getFeatures() {
+      return this.features;
+    }
+
+    public void setExpected(Vector expected) {
+      this.expected = expected;
+    }
+
+    public Vector getExpected() {
+      return this.expected;
+    }
+  }
+
+  @Test
+  public void testPCA() {
+    List<Vector> points = Lists.newArrayList(
+      Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0}),
+      Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
+      Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
+    );
+    JavaRDD<Vector> dataRDD = jsc.parallelize(points, 2);
+
+    RowMatrix mat = new RowMatrix(dataRDD.rdd());
+    Matrix pc = mat.computePrincipalComponents(3);
+    JavaRDD<Vector> expected = mat.multiply(pc).rows().toJavaRDD();
+
+    JavaRDD<VectorPair> featuresExpected = dataRDD.zip(expected).map(
+      new Function<Tuple2<Vector, Vector>, VectorPair>() {
+        public VectorPair call(Tuple2<Vector, Vector> pair) {
+          VectorPair featuresExpected = new VectorPair();
+          featuresExpected.setFeatures(pair._1());
+          featuresExpected.setExpected(pair._2());
+          return featuresExpected;
+        }
+      }
+    );
+
+    DataFrame df = sqlContext.createDataFrame(featuresExpected, VectorPair.class);
+    PCAModel pca = new PCA()
+      .setInputCol("features")
+      .setOutputCol("pca_features")
+      .setK(3)
+      .fit(df);
+    List<Row> result = pca.transform(df).select("pca_features", "expected").toJavaRDD().collect();
+    for (Row r : result) {
+      Assert.assertEquals(r.get(1), r.get(0));
+    }
+  }
+}

From 0a63d7ab8a58d3e48d01740729a7832f1834efe8 Mon Sep 17 00:00:00 2001
From: Mike Dusenberry <mwdusenb@us.ibm.com>
Date: Tue, 7 Jul 2015 08:24:52 -0700
Subject: [PATCH 0256/1454] [SPARK-8570] [MLLIB] [DOCS] Improve MLlib Local
 Matrix Documentation.

Updated MLlib Data Types Local Matrix section to include information on sparse matrices, added sparse matrix examples to the Scala and Java examples, and added Python examples for both dense and sparse matrices.

Author: Mike Dusenberry <mwdusenb@us.ibm.com>

Closes #6958 from dusenberrymw/Improve_MLlib_Local_Matrix_Documentation and squashes the following commits:

ceae407 [Mike Dusenberry] Updated MLlib Data Types Local Matrix section to include information on sparse matrices, added sparse matrix examples to the Scala and Java examples, and added Python examples for both dense and sparse matrices.
---
 docs/mllib-data-types.md | 44 +++++++++++++++++++++++++++++++++-------
 1 file changed, 37 insertions(+), 7 deletions(-)

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index d824dab1d7f7b..3aa040046fca5 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -226,7 +226,8 @@ examples = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 
 A local matrix has integer-typed row and column indices and double-typed values, stored on a single
 machine.  MLlib supports dense matrices, whose entry values are stored in a single double array in
-column major.  For example, the following matrix `\[ \begin{pmatrix}
+column-major order, and sparse matrices, whose non-zero entry values are stored in the Compressed Sparse
+Column (CSC) format in column-major order.  For example, the following dense matrix `\[ \begin{pmatrix}
 1.0 & 2.0 \\
 3.0 & 4.0 \\
 5.0 & 6.0
@@ -238,28 +239,33 @@ is stored in a one-dimensional array `[1.0, 3.0, 5.0, 2.0, 4.0, 6.0]` with the m
 <div data-lang="scala" markdown="1">
 
 The base class of local matrices is
-[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
-implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
+[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide two
+implementations: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix),
+and [`SparseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseMatrix).
 We recommend using the factory methods implemented
 in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$) to create local
-matrices.
+matrices. Remember, local matrices in MLlib are stored in column-major order.
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.{Matrix, Matrices}
 
 // Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
 val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+
+// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
+val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8))
 {% endhighlight %}
 </div>
 
 <div data-lang="java" markdown="1">
 
 The base class of local matrices is
-[`Matrix`](api/java/org/apache/spark/mllib/linalg/Matrix.html), and we provide one
-implementation: [`DenseMatrix`](api/java/org/apache/spark/mllib/linalg/DenseMatrix.html).
+[`Matrix`](api/java/org/apache/spark/mllib/linalg/Matrix.html), and we provide two
+implementations: [`DenseMatrix`](api/java/org/apache/spark/mllib/linalg/DenseMatrix.html),
+and [`SparseMatrix`](api/java/org/apache/spark/mllib/linalg/SparseMatrix.html).
 We recommend using the factory methods implemented
 in [`Matrices`](api/java/org/apache/spark/mllib/linalg/Matrices.html) to create local
-matrices.
+matrices. Remember, local matrices in MLlib are stored in column-major order.
 
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Matrix;
@@ -267,6 +273,30 @@ import org.apache.spark.mllib.linalg.Matrices;
 
 // Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
 Matrix dm = Matrices.dense(3, 2, new double[] {1.0, 3.0, 5.0, 2.0, 4.0, 6.0});
+
+// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
+Matrix sm = Matrices.sparse(3, 2, new int[] {0, 1, 3}, new int[] {0, 2, 1}, new double[] {9, 6, 8});
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+The base class of local matrices is
+[`Matrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrix), and we provide two
+implementations: [`DenseMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseMatrix),
+and [`SparseMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.SparseMatrix).
+We recommend using the factory methods implemented
+in [`Matrices`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrices) to create local
+matrices. Remember, local matrices in MLlib are stored in column-major order.
+
+{% highlight python %}
+import org.apache.spark.mllib.linalg.{Matrix, Matrices}
+
+// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
+dm2 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])
+
+// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
+sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8])
 {% endhighlight %}
 </div>
 

From 1dbc4a155f3697a3973909806be42a1be6017d12 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 7 Jul 2015 08:58:08 -0700
Subject: [PATCH 0257/1454] [SPARK-8711] [ML] Add additional methods to PySpark
 ML tree models

Add numNodes and depth to treeModels, add treeWeights to ensemble Models.
Add __repr__ to all models.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7095 from MechCoder/missing_methods_tree and squashes the following commits:

23b08be [MechCoder] private [spark]
38a0860 [MechCoder] rename pyTreeWeights to javaTreeWeights
6d16ad8 [MechCoder] Fix Python 3 Error
47d7023 [MechCoder] Use np.allclose and treeEnsembleModel -> TreeEnsembleMethods
819098c [MechCoder] [SPARK-8711] [ML] Add additional methods ot PySpark ML tree models
---
 .../org/apache/spark/ml/tree/treeModels.scala |  5 ++
 python/pyspark/ml/classification.py           | 20 ++++++--
 python/pyspark/ml/regression.py               | 46 +++++++++++++++++--
 3 files changed, 64 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
index 1929f9d02156e..22873909c33fa 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.tree
 
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
  * Abstraction for Decision Tree models.
@@ -70,6 +71,10 @@ private[ml] trait TreeEnsembleModel {
   /** Weights for each tree, zippable with [[trees]] */
   def treeWeights: Array[Double]
 
+  /** Weights used by the python wrappers. */
+  // Note: An array cannot be returned directly due to serialization problems.
+  private[spark] def javaTreeWeights: Vector = Vectors.dense(treeWeights)
+
   /** Summary of the model */
   override def toString: String = {
     // Implementing classes should generally override this method to be more descriptive.
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 7abbde8b260eb..89117e492846b 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -18,7 +18,8 @@
 from pyspark.ml.util import keyword_only
 from pyspark.ml.wrapper import JavaEstimator, JavaModel
 from pyspark.ml.param.shared import *
-from pyspark.ml.regression import RandomForestParams
+from pyspark.ml.regression import (
+    RandomForestParams, DecisionTreeModel, TreeEnsembleModels)
 from pyspark.mllib.common import inherit_doc
 
 
@@ -202,6 +203,10 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> td = si_model.transform(df)
     >>> dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed")
     >>> model = dt.fit(td)
+    >>> model.numNodes
+    3
+    >>> model.depth
+    1
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -269,7 +274,8 @@ def getImpurity(self):
         return self.getOrDefault(self.impurity)
 
 
-class DecisionTreeClassificationModel(JavaModel):
+@inherit_doc
+class DecisionTreeClassificationModel(DecisionTreeModel):
     """
     Model fitted by DecisionTreeClassifier.
     """
@@ -284,6 +290,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     It supports both binary and multiclass labels, as well as both continuous and categorical
     features.
 
+    >>> from numpy import allclose
     >>> from pyspark.mllib.linalg import Vectors
     >>> from pyspark.ml.feature import StringIndexer
     >>> df = sqlContext.createDataFrame([
@@ -294,6 +301,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> td = si_model.transform(df)
     >>> rf = RandomForestClassifier(numTrees=2, maxDepth=2, labelCol="indexed", seed=42)
     >>> model = rf.fit(td)
+    >>> allclose(model.treeWeights, [1.0, 1.0])
+    True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -423,7 +432,7 @@ def getFeatureSubsetStrategy(self):
         return self.getOrDefault(self.featureSubsetStrategy)
 
 
-class RandomForestClassificationModel(JavaModel):
+class RandomForestClassificationModel(TreeEnsembleModels):
     """
     Model fitted by RandomForestClassifier.
     """
@@ -438,6 +447,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
     It supports binary labels, as well as both continuous and categorical features.
     Note: Multiclass labels are not currently supported.
 
+    >>> from numpy import allclose
     >>> from pyspark.mllib.linalg import Vectors
     >>> from pyspark.ml.feature import StringIndexer
     >>> df = sqlContext.createDataFrame([
@@ -448,6 +458,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
     >>> td = si_model.transform(df)
     >>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed")
     >>> model = gbt.fit(td)
+    >>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
+    True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -558,7 +570,7 @@ def getStepSize(self):
         return self.getOrDefault(self.stepSize)
 
 
-class GBTClassificationModel(JavaModel):
+class GBTClassificationModel(TreeEnsembleModels):
     """
     Model fitted by GBTClassifier.
     """
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index b139e27372d80..44f60a769566d 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -172,6 +172,10 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
     >>> dt = DecisionTreeRegressor(maxDepth=2)
     >>> model = dt.fit(df)
+    >>> model.depth
+    1
+    >>> model.numNodes
+    3
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -239,7 +243,37 @@ def getImpurity(self):
         return self.getOrDefault(self.impurity)
 
 
-class DecisionTreeRegressionModel(JavaModel):
+@inherit_doc
+class DecisionTreeModel(JavaModel):
+
+    @property
+    def numNodes(self):
+        """Return number of nodes of the decision tree."""
+        return self._call_java("numNodes")
+
+    @property
+    def depth(self):
+        """Return depth of the decision tree."""
+        return self._call_java("depth")
+
+    def __repr__(self):
+        return self._call_java("toString")
+
+
+@inherit_doc
+class TreeEnsembleModels(JavaModel):
+
+    @property
+    def treeWeights(self):
+        """Return the weights for each tree"""
+        return list(self._call_java("javaTreeWeights"))
+
+    def __repr__(self):
+        return self._call_java("toString")
+
+
+@inherit_doc
+class DecisionTreeRegressionModel(DecisionTreeModel):
     """
     Model fitted by DecisionTreeRegressor.
     """
@@ -253,12 +287,15 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     learning algorithm for regression.
     It supports both continuous and categorical features.
 
+    >>> from numpy import allclose
     >>> from pyspark.mllib.linalg import Vectors
     >>> df = sqlContext.createDataFrame([
     ...     (1.0, Vectors.dense(1.0)),
     ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
     >>> rf = RandomForestRegressor(numTrees=2, maxDepth=2, seed=42)
     >>> model = rf.fit(df)
+    >>> allclose(model.treeWeights, [1.0, 1.0])
+    True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -389,7 +426,7 @@ def getFeatureSubsetStrategy(self):
         return self.getOrDefault(self.featureSubsetStrategy)
 
 
-class RandomForestRegressionModel(JavaModel):
+class RandomForestRegressionModel(TreeEnsembleModels):
     """
     Model fitted by RandomForestRegressor.
     """
@@ -403,12 +440,15 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
     learning algorithm for regression.
     It supports both continuous and categorical features.
 
+    >>> from numpy import allclose
     >>> from pyspark.mllib.linalg import Vectors
     >>> df = sqlContext.createDataFrame([
     ...     (1.0, Vectors.dense(1.0)),
     ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
     >>> gbt = GBTRegressor(maxIter=5, maxDepth=2)
     >>> model = gbt.fit(df)
+    >>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
+    True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -518,7 +558,7 @@ def getStepSize(self):
         return self.getOrDefault(self.stepSize)
 
 
-class GBTRegressionModel(JavaModel):
+class GBTRegressionModel(TreeEnsembleModels):
     """
     Model fitted by GBTRegressor.
     """

From 738c10748b49eb8a475d1fd26c6a271ca36497cf Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 7 Jul 2015 08:59:52 -0700
Subject: [PATCH 0258/1454] [SPARK-8823] [MLLIB] [PYSPARK] Optimizations for
 SparseVector dot products

Follow up for https://github.com/apache/spark/pull/5946

Currently we iterate over indices and values in SparseVector and can be vectorized.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7222 from MechCoder/sparse_optim and squashes the following commits:

dcb51d3 [MechCoder] [SPARK-8823] [MLlib] [PySpark] Optimizations for SparseVector dot product
---
 python/pyspark/mllib/linalg.py | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 9959a01cce7e0..12d8dbbb92c56 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -590,18 +590,14 @@ def dot(self, other):
             return np.dot(other.array[self.indices], self.values)
 
         elif isinstance(other, SparseVector):
-            result = 0.0
-            i, j = 0, 0
-            while i < len(self.indices) and j < len(other.indices):
-                if self.indices[i] == other.indices[j]:
-                    result += self.values[i] * other.values[j]
-                    i += 1
-                    j += 1
-                elif self.indices[i] < other.indices[j]:
-                    i += 1
-                else:
-                    j += 1
-            return result
+            # Find out common indices.
+            self_cmind = np.in1d(self.indices, other.indices, assume_unique=True)
+            self_values = self.values[self_cmind]
+            if self_values.size == 0:
+                return 0.0
+            else:
+                other_cmind = np.in1d(other.indices, self.indices, assume_unique=True)
+                return np.dot(self_values, other.values[other_cmind])
 
         else:
             return self.dot(_convert_to_vector(other))

From 70beb808e13f6371968ac87f7cf625ed110375e6 Mon Sep 17 00:00:00 2001
From: Simon Hafner <hafnersimon@gmail.com>
Date: Tue, 7 Jul 2015 09:42:59 -0700
Subject: [PATCH 0259/1454] [SPARK-8821] [EC2] Switched to binary mode for file
 reading

Otherwise the script will crash with

    - Downloading boto...
    Traceback (most recent call last):
      File "ec2/spark_ec2.py", line 148, in <module>
        setup_external_libs(external_libs)
      File "ec2/spark_ec2.py", line 128, in setup_external_libs
        if hashlib.md5(tar.read()).hexdigest() != lib["md5"]:
      File "/usr/lib/python3.4/codecs.py", line 319, in decode
        (result, consumed) = self._buffer_decode(data, self.errors, final)
    UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte

In case of an utf8 env setting.

Author: Simon Hafner <hafnersimon@gmail.com>

Closes #7215 from reactormonk/branch-1.4 and squashes the following commits:

e86957a [Simon Hafner] [SPARK-8821] [EC2] Switched to binary mode

(cherry picked from commit 83a621a5a8f8a2991c4cfa687279589e5c623d46)
Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
---
 ec2/spark_ec2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 18ccbc0a3edd0..8582d434a8c55 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -125,7 +125,7 @@ def setup_external_libs(libs):
             )
             with open(tgz_file_path, "wb") as tgz_file:
                 tgz_file.write(download_stream.read())
-            with open(tgz_file_path) as tar:
+            with open(tgz_file_path, "rb") as tar:
                 if hashlib.md5(tar.read()).hexdigest() != lib["md5"]:
                     print("ERROR: Got wrong md5sum for {lib}.".format(lib=lib["name"]), file=stderr)
                     sys.exit(1)

From 3336c7b148ad543d1f9b64ca2b559ea04930f5be Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 7 Jul 2015 11:34:30 -0700
Subject: [PATCH 0260/1454] [SPARK-8559] [MLLIB] Support Association Rule
 Generation

Distributed generation of single-consequent association rules from a RDD of frequent itemsets. Tests referenced against `R`'s implementation of A Priori in [arules](http://cran.r-project.org/web/packages/arules/index.html).

Author: Feynman Liang <fliang@databricks.com>

Closes #7005 from feynmanliang/fp-association-rules-distributed and squashes the following commits:

466ced0 [Feynman Liang] Refactor AR generation impl
73c1cff [Feynman Liang] Make rule attributes public, remove numTransactions from FreqItemset
80f63ff [Feynman Liang] Change default confidence and optimize imports
04cf5b5 [Feynman Liang] Code review with @mengxr, add R to tests
0cc1a6a [Feynman Liang] Java compatibility test
f3c14b5 [Feynman Liang] Fix MiMa test
764375e [Feynman Liang] Fix tests
1187307 [Feynman Liang] Almost working tests
b20779b [Feynman Liang] Working implementation
5395c4e [Feynman Liang] Fix imports
2d34405 [Feynman Liang] Partial implementation of distributed ar
83ace4b [Feynman Liang] Local rule generation without pruning complete
69c2c87 [Feynman Liang] Working local implementation, now to parallelize../..
4e1ec9a [Feynman Liang] Pull FreqItemsets out, refactor type param, tests
69ccedc [Feynman Liang] First implementation of association rule generation
---
 .../spark/mllib/fpm/AssociationRules.scala    | 108 ++++++++++++++++++
 .../org/apache/spark/mllib/fpm/FPGrowth.scala |   2 +-
 .../mllib/fpm/JavaAssociationRulesSuite.java  |  58 ++++++++++
 .../spark/mllib/fpm/JavaFPGrowthSuite.java    |   5 +-
 .../mllib/fpm/AssociationRulesSuite.scala     |  89 +++++++++++++++
 5 files changed, 258 insertions(+), 4 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
 create mode 100644 mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/fpm/AssociationRulesSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
new file mode 100644
index 0000000000000..4a0f842f3338d
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.mllib.fpm
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
+import org.apache.spark.mllib.fpm.AssociationRules.Rule
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
+import org.apache.spark.rdd.RDD
+
+/**
+ * :: Experimental ::
+ *
+ * Generates association rules from a [[RDD[FreqItemset[Item]]]. This method only generates
+ * association rules which have a single item as the consequent.
+ */
+@Experimental
+class AssociationRules private (
+    private var minConfidence: Double) extends Logging with Serializable {
+
+  /**
+   * Constructs a default instance with default parameters {minConfidence = 0.8}.
+   */
+  def this() = this(0.8)
+
+  /**
+   * Sets the minimal confidence (default: `0.8`).
+   */
+  def setMinConfidence(minConfidence: Double): this.type = {
+    this.minConfidence = minConfidence
+    this
+  }
+
+  /**
+   * Computes the association rules with confidence above [[minConfidence]].
+   * @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
+   * @return a [[Set[Rule[Item]]] containing the assocation rules.
+   */
+  def run[Item: ClassTag](freqItemsets: RDD[FreqItemset[Item]]): RDD[Rule[Item]] = {
+    // For candidate rule X => Y, generate (X, (Y, freq(X union Y)))
+    val candidates = freqItemsets.flatMap { itemset =>
+      val items = itemset.items
+      items.flatMap { item =>
+        items.partition(_ == item) match {
+          case (consequent, antecedent) if !antecedent.isEmpty =>
+            Some((antecedent.toSeq, (consequent.toSeq, itemset.freq)))
+          case _ => None
+        }
+      }
+    }
+
+    // Join to get (X, ((Y, freq(X union Y)), freq(X))), generate rules, and filter by confidence
+    candidates.join(freqItemsets.map(x => (x.items.toSeq, x.freq)))
+      .map { case (antecendent, ((consequent, freqUnion), freqAntecedent)) =>
+      new Rule(antecendent.toArray, consequent.toArray, freqUnion, freqAntecedent)
+    }.filter(_.confidence >= minConfidence)
+  }
+
+  def run[Item](freqItemsets: JavaRDD[FreqItemset[Item]]): JavaRDD[Rule[Item]] = {
+    val tag = fakeClassTag[Item]
+    run(freqItemsets.rdd)(tag)
+  }
+}
+
+object AssociationRules {
+
+  /**
+   * :: Experimental ::
+   *
+   * An association rule between sets of items.
+   * @param antecedent hypotheses of the rule
+   * @param consequent conclusion of the rule
+   * @tparam Item item type
+   */
+  @Experimental
+  class Rule[Item] private[mllib] (
+      val antecedent: Array[Item],
+      val consequent: Array[Item],
+      freqUnion: Double,
+      freqAntecedent: Double) extends Serializable {
+
+    def confidence: Double = freqUnion.toDouble / freqAntecedent
+
+    require(antecedent.toSet.intersect(consequent.toSet).isEmpty, {
+      val sharedItems = antecedent.toSet.intersect(consequent.toSet)
+      s"A valid association rule must have disjoint antecedent and " +
+        s"consequent but ${sharedItems} is present in both."
+    })
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index efa8459d3cdba..0da59e812d5f9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -28,7 +28,7 @@ import org.apache.spark.{HashPartitioner, Logging, Partitioner, SparkException}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
-import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
+import org.apache.spark.mllib.fpm.FPGrowth._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
new file mode 100644
index 0000000000000..b3815ae6039c0
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.mllib.fpm;
+
+import java.io.Serializable;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import com.google.common.collect.Lists;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset;
+
+
+public class JavaAssociationRulesSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaFPGrowth");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void runAssociationRules() {
+
+    @SuppressWarnings("unchecked")
+    JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Lists.newArrayList(
+      new FreqItemset<String>(new String[] {"a"}, 15L),
+      new FreqItemset<String>(new String[] {"b"}, 35L),
+      new FreqItemset<String>(new String[] {"a", "b"}, 18L)
+    ));
+
+    JavaRDD<AssociationRules.Rule<String>> results = (new AssociationRules()).run(freqItemsets);
+  }
+}
+
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
index bd0edf2b9ea62..9ce2c52dca8b6 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
@@ -29,7 +29,6 @@
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset;
 
 public class JavaFPGrowthSuite implements Serializable {
   private transient JavaSparkContext sc;
@@ -62,10 +61,10 @@ public void runFPGrowth() {
       .setNumPartitions(2)
       .run(rdd);
 
-    List<FreqItemset<String>> freqItemsets = model.freqItemsets().toJavaRDD().collect();
+    List<FPGrowth.FreqItemset<String>> freqItemsets = model.freqItemsets().toJavaRDD().collect();
     assertEquals(18, freqItemsets.size());
 
-    for (FreqItemset<String> itemset: freqItemsets) {
+    for (FPGrowth.FreqItemset<String> itemset: freqItemsets) {
       // Test return types.
       List<String> items = itemset.javaItems();
       long freq = itemset.freq();
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/AssociationRulesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/AssociationRulesSuite.scala
new file mode 100644
index 0000000000000..77a2773c36f56
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/AssociationRulesSuite.scala
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.mllib.fpm
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class AssociationRulesSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("association rules using String type") {
+    val freqItemsets = sc.parallelize(Seq(
+      (Set("s"), 3L), (Set("z"), 5L), (Set("x"), 4L), (Set("t"), 3L), (Set("y"), 3L),
+      (Set("r"), 3L),
+      (Set("x", "z"), 3L), (Set("t", "y"), 3L), (Set("t", "x"), 3L), (Set("s", "x"), 3L),
+      (Set("y", "x"), 3L), (Set("y", "z"), 3L), (Set("t", "z"), 3L),
+      (Set("y", "x", "z"), 3L), (Set("t", "x", "z"), 3L), (Set("t", "y", "z"), 3L),
+      (Set("t", "y", "x"), 3L),
+      (Set("t", "y", "x", "z"), 3L)
+    ).map {
+      case (items, freq) => new FPGrowth.FreqItemset(items.toArray, freq)
+    })
+
+    val ar = new AssociationRules()
+
+    val results1 = ar
+      .setMinConfidence(0.9)
+      .run(freqItemsets)
+      .collect()
+
+    /* Verify results using the `R` code:
+       transactions = as(sapply(
+         list("r z h k p",
+              "z y x w v u t s",
+              "s x o n r",
+              "x z y m t s q e",
+              "z",
+              "x z y r q t p"),
+         FUN=function(x) strsplit(x," ",fixed=TRUE)),
+         "transactions")
+       ars = apriori(transactions,
+                     parameter = list(support = 0.0, confidence = 0.5, target="rules", minlen=2))
+       arsDF = as(ars, "data.frame")
+       arsDF$support = arsDF$support * length(transactions)
+       names(arsDF)[names(arsDF) == "support"] = "freq"
+       > nrow(arsDF)
+       [1] 23
+       > sum(arsDF$confidence == 1)
+       [1] 23
+     */
+    assert(results1.size === 23)
+    assert(results1.count(rule => math.abs(rule.confidence - 1.0D) < 1e-6) == 23)
+
+    val results2 = ar
+      .setMinConfidence(0)
+      .run(freqItemsets)
+      .collect()
+
+    /* Verify results using the `R` code:
+       ars = apriori(transactions,
+                  parameter = list(support = 0.5, confidence = 0.5, target="rules", minlen=2))
+       arsDF = as(ars, "data.frame")
+       arsDF$support = arsDF$support * length(transactions)
+       names(arsDF)[names(arsDF) == "support"] = "freq"
+       nrow(arsDF)
+       sum(arsDF$confidence == 1)
+       > nrow(arsDF)
+       [1] 30
+       > sum(arsDF$confidence == 1)
+       [1] 23
+     */
+    assert(results2.size === 30)
+    assert(results2.count(rule => math.abs(rule.confidence - 1.0D) < 1e-6) == 23)
+  }
+}
+

From 35d781e71b68eb6da7f49fdae40fa6c4f8e27060 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 7 Jul 2015 12:35:40 -0700
Subject: [PATCH 0261/1454] [SPARK-8704] [ML] [PySpark] Add missing methods in
 StandardScaler

Add std, mean to StandardScalerModel
getVectors, findSynonyms to Word2Vec Model
setFeatures and getFeatures to hashingTF

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7086 from MechCoder/missing_model_methods and squashes the following commits:

9fbae90 [MechCoder] Add type
6e3d6b2 [MechCoder] [SPARK-8704] Add missing methods in StandardScaler (ML and PySpark)
---
 .../spark/ml/feature/StandardScaler.scala      |  6 ++++++
 python/pyspark/ml/feature.py                   | 18 ++++++++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
index ca3c1cfb56b7f..72b545e5db3e4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -106,6 +106,12 @@ class StandardScalerModel private[ml] (
     scaler: feature.StandardScalerModel)
   extends Model[StandardScalerModel] with StandardScalerParams {
 
+  /** Standard deviation of the StandardScalerModel */
+  val std: Vector = scaler.std
+
+  /** Mean of the StandardScalerModel */
+  val mean: Vector = scaler.mean
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 8804dace849b3..9bca7cc000aa5 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -627,6 +627,10 @@ class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol):
     >>> df = sqlContext.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
     >>> standardScaler = StandardScaler(inputCol="a", outputCol="scaled")
     >>> model = standardScaler.fit(df)
+    >>> model.mean
+    DenseVector([1.0])
+    >>> model.std
+    DenseVector([1.4142])
     >>> model.transform(df).collect()[1].scaled
     DenseVector([1.4142])
     """
@@ -692,6 +696,20 @@ class StandardScalerModel(JavaModel):
     Model fitted by StandardScaler.
     """
 
+    @property
+    def std(self):
+        """
+        Standard deviation of the StandardScalerModel.
+        """
+        return self._call_java("std")
+
+    @property
+    def mean(self):
+        """
+        Mean of the StandardScalerModel.
+        """
+        return self._call_java("mean")
+
 
 @inherit_doc
 class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):

From 3bf20c27ff3cb3a32bfc3a44e08a57865957c117 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbt@netflix.com>
Date: Tue, 7 Jul 2015 15:46:44 -0700
Subject: [PATCH 0262/1454] [SPARK-8845] [ML] ML use of Breeze optimization:
 use adjustedValue  instead of value

In LinearRegression and LogisticRegression, we use Breeze's optimizers (LBFGS and OWLQN). We check the State.value to see the current objective. However, Breeze's documentation makes it sound like value and adjustedValue differ for some optimizers, possibly including OWLQN: https://github.com/scalanlp/breeze/blob/26faf622862e8d7a42a401aef601347aac655f2b/math/src/main/scala/breeze/optimize/FirstOrderMinimizer.scala#L36
If that is the case, then we should use adjustedValue instead of value. This is relevant to SPARK-8538 and SPARK-8539, where we will provide the objective trace to the user.

Author: DB Tsai <dbt@netflix.com>

Closes #7245 from dbtsai/SPARK-8845 and squashes the following commits:

fa4c91e [DB Tsai] address feedback
e6caac1 [DB Tsai] java style multiline comment
b10c574 [DB Tsai] address feedback
c9ff81e [DB Tsai] first commit
---
 .../classification/LogisticRegression.scala   | 83 ++++++++++---------
 .../ml/regression/LinearRegression.scala      | 57 ++++++++-----
 2 files changed, 80 insertions(+), 60 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 2e6eedd45ab07..3967151f76c7c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -116,7 +116,7 @@ class LogisticRegression(override val uid: String)
           case ((summarizer: MultivariateOnlineSummarizer, labelSummarizer: MultiClassSummarizer),
           (label: Double, features: Vector)) =>
             (summarizer.add(features), labelSummarizer.add(label))
-      },
+        },
         combOp = (c1, c2) => (c1, c2) match {
           case ((summarizer1: MultivariateOnlineSummarizer,
           classSummarizer1: MultiClassSummarizer), (summarizer2: MultivariateOnlineSummarizer,
@@ -166,18 +166,18 @@ class LogisticRegression(override val uid: String)
       Vectors.zeros(if ($(fitIntercept)) numFeatures + 1 else numFeatures)
 
     if ($(fitIntercept)) {
-      /**
-       * For binary logistic regression, when we initialize the weights as zeros,
-       * it will converge faster if we initialize the intercept such that
-       * it follows the distribution of the labels.
-       *
-       * {{{
-       * P(0) = 1 / (1 + \exp(b)), and
-       * P(1) = \exp(b) / (1 + \exp(b))
-       * }}}, hence
-       * {{{
-       * b = \log{P(1) / P(0)} = \log{count_1 / count_0}
-       * }}}
+      /*
+         For binary logistic regression, when we initialize the weights as zeros,
+         it will converge faster if we initialize the intercept such that
+         it follows the distribution of the labels.
+
+         {{{
+         P(0) = 1 / (1 + \exp(b)), and
+         P(1) = \exp(b) / (1 + \exp(b))
+         }}}, hence
+         {{{
+         b = \log{P(1) / P(0)} = \log{count_1 / count_0}
+         }}}
        */
       initialWeightsWithIntercept.toArray(numFeatures)
         = math.log(histogram(1).toDouble / histogram(0).toDouble)
@@ -186,39 +186,48 @@ class LogisticRegression(override val uid: String)
     val states = optimizer.iterations(new CachedDiffFunction(costFun),
       initialWeightsWithIntercept.toBreeze.toDenseVector)
 
-    var state = states.next()
-    val lossHistory = mutable.ArrayBuilder.make[Double]
+    val (weights, intercept, objectiveHistory) = {
+      /*
+         Note that in Logistic Regression, the objective history (loss + regularization)
+         is log-likelihood which is invariance under feature standardization. As a result,
+         the objective history from optimizer is the same as the one in the original space.
+       */
+      val arrayBuilder = mutable.ArrayBuilder.make[Double]
+      var state: optimizer.State = null
+      while (states.hasNext) {
+        state = states.next()
+        arrayBuilder += state.adjustedValue
+      }
 
-    while (states.hasNext) {
-      lossHistory += state.value
-      state = states.next()
-    }
-    lossHistory += state.value
+      if (state == null) {
+        val msg = s"${optimizer.getClass.getName} failed."
+        logError(msg)
+        throw new SparkException(msg)
+      }
 
-    // The weights are trained in the scaled space; we're converting them back to
-    // the original space.
-    val weightsWithIntercept = {
+      /*
+         The weights are trained in the scaled space; we're converting them back to
+         the original space.
+         Note that the intercept in scaled space and original space is the same;
+         as a result, no scaling is needed.
+       */
       val rawWeights = state.x.toArray.clone()
       var i = 0
-      // Note that the intercept in scaled space and original space is the same;
-      // as a result, no scaling is needed.
       while (i < numFeatures) {
         rawWeights(i) *= { if (featuresStd(i) != 0.0) 1.0 / featuresStd(i) else 0.0 }
         i += 1
       }
-      Vectors.dense(rawWeights)
+
+      if ($(fitIntercept)) {
+        (Vectors.dense(rawWeights.dropRight(1)).compressed, rawWeights.last, arrayBuilder.result())
+      } else {
+        (Vectors.dense(rawWeights).compressed, 0.0, arrayBuilder.result())
+      }
     }
 
     if (handlePersistence) instances.unpersist()
 
-    val (weights, intercept) = if ($(fitIntercept)) {
-      (Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)),
-        weightsWithIntercept(weightsWithIntercept.size - 1))
-    } else {
-      (weightsWithIntercept, 0.0)
-    }
-
-    new LogisticRegressionModel(uid, weights.compressed, intercept)
+    copyValues(new LogisticRegressionModel(uid, weights, intercept))
   }
 
   override def copy(extra: ParamMap): LogisticRegression = defaultCopy(extra)
@@ -423,16 +432,12 @@ private class LogisticAggregator(
     require(dim == data.size, s"Dimensions mismatch when adding new sample." +
       s" Expecting $dim but got ${data.size}.")
 
-    val dataSize = data.size
-
     val localWeightsArray = weightsArray
     val localGradientSumArray = gradientSumArray
 
     numClasses match {
       case 2 =>
-        /**
-         * For Binary Logistic Regression.
-         */
+        // For Binary Logistic Regression.
         val margin = - {
           var sum = 0.0
           data.foreachActive { (index, value) =>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 1b1d7299fb496..f672c96576a33 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable
 import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
 import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkException, Logging}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
@@ -132,7 +132,6 @@ class LinearRegression(override val uid: String)
     val numFeatures = summarizer.mean.size
     val yMean = statCounter.mean
     val yStd = math.sqrt(statCounter.variance)
-    // look at glmnet5.m L761 maaaybe that has info
 
     // If the yStd is zero, then the intercept is yMean with zero weights;
     // as a result, training is not needed.
@@ -162,21 +161,34 @@ class LinearRegression(override val uid: String)
     }
 
     val initialWeights = Vectors.zeros(numFeatures)
-    val states =
-      optimizer.iterations(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector)
-
-    var state = states.next()
-    val lossHistory = mutable.ArrayBuilder.make[Double]
+    val states = optimizer.iterations(new CachedDiffFunction(costFun),
+      initialWeights.toBreeze.toDenseVector)
+
+    val (weights, objectiveHistory) = {
+      /*
+         Note that in Linear Regression, the objective history (loss + regularization) returned
+         from optimizer is computed in the scaled space given by the following formula.
+         {{{
+         L = 1/2n||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2 + regTerms
+         }}}
+       */
+      val arrayBuilder = mutable.ArrayBuilder.make[Double]
+      var state: optimizer.State = null
+      while (states.hasNext) {
+        state = states.next()
+        arrayBuilder += state.adjustedValue
+      }
 
-    while (states.hasNext) {
-      lossHistory += state.value
-      state = states.next()
-    }
-    lossHistory += state.value
+      if (state == null) {
+        val msg = s"${optimizer.getClass.getName} failed."
+        logError(msg)
+        throw new SparkException(msg)
+      }
 
-    // The weights are trained in the scaled space; we're converting them back to
-    // the original space.
-    val weights = {
+      /*
+         The weights are trained in the scaled space; we're converting them back to
+         the original space.
+       */
       val rawWeights = state.x.toArray.clone()
       var i = 0
       val len = rawWeights.length
@@ -184,17 +196,20 @@ class LinearRegression(override val uid: String)
         rawWeights(i) *= { if (featuresStd(i) != 0.0) yStd / featuresStd(i) else 0.0 }
         i += 1
       }
-      Vectors.dense(rawWeights)
+
+      (Vectors.dense(rawWeights).compressed, arrayBuilder.result())
     }
 
-    // The intercept in R's GLMNET is computed using closed form after the coefficients are
-    // converged. See the following discussion for detail.
-    // http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
+    /*
+       The intercept in R's GLMNET is computed using closed form after the coefficients are
+       converged. See the following discussion for detail.
+       http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
+     */
     val intercept = if ($(fitIntercept)) yMean - dot(weights, Vectors.dense(featuresMean)) else 0.0
+
     if (handlePersistence) instances.unpersist()
 
-    // TODO: Converts to sparse format based on the storage, but may base on the scoring speed.
-    copyValues(new LinearRegressionModel(uid, weights.compressed, intercept))
+    copyValues(new LinearRegressionModel(uid, weights, intercept))
   }
 
   override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra)

From da56c4e728d6bdb6d5559ec7fd8a3f7ddd1e2319 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Tue, 7 Jul 2015 15:49:22 -0700
Subject: [PATCH 0263/1454] [SPARK-8794] [SQL] Make PrunedScan work for Sample

JIRA: https://issues.apache.org/jira/browse/SPARK-8794

Currently `PrunedScan` works only when followed by project or filter operations. However, even if there is a `Sample` between these operations and `PrunedScan`, `PrunedScan` should work too.

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #7228 from viirya/sample_prunedscan and squashes the following commits:

ede7cd8 [Liang-Chi Hsieh] Keep PrunedScanSuite untouched.
6f05d30 [Liang-Chi Hsieh] Move unit test to FilterPushdownSuite.
5f32473 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into sample_prunedscan
7e4ba76 [Liang-Chi Hsieh] Use Optimzier for push down projection and filter.
0686830 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into sample_prunedscan
df82785 [Liang-Chi Hsieh] Make PrunedScan work on Sample.
---
 .../sql/catalyst/optimizer/Optimizer.scala     | 18 ++++++++++++++++++
 .../optimizer/FilterPushdownSuite.scala        | 16 ++++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index bfd24287c9645..7d41ef9aafa44 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -41,6 +41,7 @@ object DefaultOptimizer extends Optimizer {
     Batch("Operator Optimizations", FixedPoint(100),
       // Operator push down
       UnionPushDown,
+      SamplePushDown,
       PushPredicateThroughJoin,
       PushPredicateThroughProject,
       PushPredicateThroughGenerate,
@@ -65,6 +66,23 @@ object DefaultOptimizer extends Optimizer {
       ConvertToLocalRelation) :: Nil
 }
 
+/**
+ * Pushes operations down into a Sample.
+ */
+object SamplePushDown extends Rule[LogicalPlan] {
+
+  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    // Push down filter into sample
+    case Filter(condition, s @ Sample(lb, up, replace, seed, child)) =>
+      Sample(lb, up, replace, seed,
+        Filter(condition, child))
+    // Push down projection into sample
+    case Project(projectList, s @ Sample(lb, up, replace, seed, child)) =>
+      Sample(lb, up, replace, seed,
+        Project(projectList, child))
+  }
+}
+
 /**
  * Pushes operations to either side of a Union.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
index ffdc673cdc455..dc28b3ffb59ee 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
@@ -34,6 +34,7 @@ class FilterPushdownSuite extends PlanTest {
       Batch("Subqueries", Once,
         EliminateSubQueries) ::
       Batch("Filter Pushdown", Once,
+        SamplePushDown,
         CombineFilters,
         PushPredicateThroughProject,
         BooleanSimplification,
@@ -593,4 +594,19 @@ class FilterPushdownSuite extends PlanTest {
 
     comparePlans(optimized1, analysis.EliminateSubQueries(correctAnswer1))
   }
+
+  test("push project and filter down into sample") {
+    val x = testRelation.subquery('x)
+    val originalQuery =
+      Sample(0.0, 0.6, false, 11L, x).select('a)
+
+    val originalQueryAnalyzed = EliminateSubQueries(analysis.SimpleAnalyzer.execute(originalQuery))
+
+    val optimized = Optimize.execute(originalQueryAnalyzed)
+
+    val correctAnswer =
+      Sample(0.0, 0.6, false, 11L, x.select('a))
+
+    comparePlans(optimized, correctAnswer.analyze)
+  }
 }

From 770ff1025e751ad139b6284c1d7cc3de778b1d87 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 7 Jul 2015 17:40:14 -0700
Subject: [PATCH 0264/1454] [SPARK-8876][SQL] Remove InternalRow type alias in
 expressions package.

The type alias was there because initially when I moved Row around, I didn't want to do massive changes to the expression code. But now it should be pretty easy to just remove it. One less concept to worry about.

Author: Reynold Xin <rxin@databricks.com>

Closes #7270 from rxin/internalrow and squashes the following commits:

72fc842 [Reynold Xin] [SPARK-8876][SQL] Remove InternalRow type alias in expressions package.
---
 .../spark/sql/catalyst/analysis/unresolved.scala   |  1 +
 .../sql/catalyst/expressions/BoundAttribute.scala  |  1 +
 .../spark/sql/catalyst/expressions/Cast.scala      |  1 +
 .../sql/catalyst/expressions/Expression.scala      |  1 +
 .../sql/catalyst/expressions/ExtractValue.scala    |  2 ++
 .../sql/catalyst/expressions/Projection.scala      |  2 ++
 .../spark/sql/catalyst/expressions/ScalaUDF.scala  |  1 +
 .../spark/sql/catalyst/expressions/SortOrder.scala |  1 +
 .../catalyst/expressions/SpecificMutableRow.scala  |  1 +
 .../catalyst/expressions/UnsafeRowConverter.scala  |  1 +
 .../sql/catalyst/expressions/aggregates.scala      |  1 +
 .../sql/catalyst/expressions/arithmetic.scala      |  1 +
 .../expressions/codegen/GenerateOrdering.scala     |  8 +++-----
 .../expressions/codegen/GeneratePredicate.scala    |  1 +
 .../catalyst/expressions/complexTypeCreator.scala  |  4 +---
 .../sql/catalyst/expressions/conditionals.scala    |  2 +-
 .../catalyst/expressions/datetimeFunctions.scala   |  6 +++++-
 .../sql/catalyst/expressions/generators.scala      |  1 +
 .../spark/sql/catalyst/expressions/literals.scala  |  2 +-
 .../spark/sql/catalyst/expressions/math.scala      |  1 +
 .../catalyst/expressions/namedExpressions.scala    |  1 +
 .../sql/catalyst/expressions/nullFunctions.scala   |  1 +
 .../spark/sql/catalyst/expressions/package.scala   |  4 ----
 .../sql/catalyst/expressions/predicates.scala      |  1 +
 .../spark/sql/catalyst/expressions/random.scala    |  1 +
 .../spark/sql/catalyst/expressions/rows.scala      |  1 +
 .../spark/sql/catalyst/expressions/sets.scala      |  1 +
 .../catalyst/expressions/stringOperations.scala    |  3 +++
 .../catalyst/expressions/windowExpressions.scala   |  1 +
 .../spark/sql/catalyst/expressions/CastSuite.scala |  1 +
 .../catalyst/expressions/ComplexTypeSuite.scala    |  4 +---
 .../expressions/ExpressionEvalHelper.scala         |  1 +
 .../UnsafeFixedWidthAggregationMapSuite.scala      |  1 +
 .../expressions/UnsafeRowConverterSuite.scala      |  1 +
 .../optimizer/ConvertToLocalRelationSuite.scala    |  2 +-
 .../spark/sql/catalyst/trees/TreeNodeSuite.scala   |  1 +
 .../scala/org/apache/spark/sql/DataFrame.scala     |  1 +
 .../sql/columnar/InMemoryColumnarTableScan.scala   |  1 +
 .../org/apache/spark/sql/execution/Aggregate.scala |  1 +
 .../org/apache/spark/sql/execution/Exchange.scala  |  1 +
 .../org/apache/spark/sql/execution/Expand.scala    |  1 +
 .../org/apache/spark/sql/execution/Generate.scala  |  3 ++-
 .../spark/sql/execution/GeneratedAggregate.scala   |  1 +
 .../org/apache/spark/sql/execution/SparkPlan.scala |  1 +
 .../spark/sql/execution/SparkStrategies.scala      |  1 +
 .../org/apache/spark/sql/execution/Window.scala    |  1 +
 .../spark/sql/execution/basicOperators.scala       |  1 +
 .../expressions/MonotonicallyIncreasingID.scala    |  3 ++-
 .../execution/expressions/SparkPartitionID.scala   |  3 ++-
 .../sql/execution/joins/BroadcastHashJoin.scala    |  3 ++-
 .../execution/joins/BroadcastHashOuterJoin.scala   |  1 +
 .../joins/BroadcastLeftSemiJoinHash.scala          |  1 +
 .../execution/joins/BroadcastNestedLoopJoin.scala  |  1 +
 .../spark/sql/execution/joins/HashJoin.scala       |  1 +
 .../spark/sql/execution/joins/HashOuterJoin.scala  |  1 +
 .../spark/sql/execution/joins/HashedRelation.scala |  3 ++-
 .../sql/execution/joins/LeftSemiJoinBNL.scala      |  1 +
 .../sql/execution/joins/LeftSemiJoinHash.scala     |  3 ++-
 .../execution/joins/ShuffledHashOuterJoin.scala    |  5 +++--
 .../spark/sql/execution/joins/SortMergeJoin.scala  |  1 +
 .../apache/spark/sql/execution/pythonUDFs.scala    |  1 +
 .../spark/sql/execution/stat/FrequentItems.scala   |  2 +-
 .../scala/org/apache/spark/sql/jdbc/JDBCRDD.scala  |  3 ++-
 .../org/apache/spark/sql/json/JacksonParser.scala  |  1 +
 .../scala/org/apache/spark/sql/json/JsonRDD.scala  |  1 +
 .../spark/sql/parquet/ParquetConverter.scala       |  1 +
 .../spark/sql/parquet/ParquetTableOperations.scala | 14 +++++---------
 .../spark/sql/parquet/ParquetTableSupport.scala    |  3 ++-
 .../spark/sql/sources/DataSourceStrategy.scala     |  1 +
 .../org/apache/spark/sql/sources/commands.scala    |  1 +
 .../scala/org/apache/spark/sql/sources/ddl.scala   |  3 ++-
 .../org/apache/spark/sql/sources/interfaces.scala  |  3 ++-
 .../spark/sql/columnar/ColumnStatsSuite.scala      |  2 +-
 .../sql/execution/joins/HashedRelationSuite.scala  |  3 ++-
 .../org/apache/spark/sql/hive/HiveInspectors.scala |  1 +
 .../spark/sql/hive/HiveMetastoreCatalog.scala      |  1 +
 .../org/apache/spark/sql/hive/HiveStrategies.scala |  3 ++-
 .../org/apache/spark/sql/hive/TableReader.scala    |  1 +
 .../spark/sql/hive/execution/HiveTableScan.scala   |  1 +
 .../sql/hive/execution/InsertIntoHiveTable.scala   |  3 ++-
 .../sql/hive/execution/ScriptTransformation.scala  |  2 +-
 .../scala/org/apache/spark/sql/hive/hiveUDFs.scala |  1 +
 .../apache/spark/sql/hive/orc/OrcRelation.scala    |  1 +
 .../apache/spark/sql/hive/HiveInspectorSuite.scala |  3 ++-
 .../sql/hive/orc/OrcPartitionDiscoverySuite.scala  |  2 +-
 85 files changed, 114 insertions(+), 48 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index ae3adbab05108..f2e579afe833a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.{errors, trees}
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index dc0b4ac5cd9bb..3f0d7b803125f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.trees
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 4f73ba40b5a18..cfe201bf83070 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -21,6 +21,7 @@ import java.math.{BigDecimal => JavaBigDecimal}
 import java.sql.{Date, Timestamp}
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 386feb95b4ac8..54ec10444c4f3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.trees
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
index e451c7ffbddc1..2b25ba03579ec 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
@@ -20,10 +20,12 @@ package org.apache.spark.sql.catalyst.expressions
 import scala.collection.Map
 
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types._
 
+
 object ExtractValue {
   /**
    * Returns the resolved `ExtractValue`. It will return one kind of concrete `ExtractValue`,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index fcfe83ceb863a..886a486bf5ee0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
+
 /**
  * A [[Projection]] that is calculated by calling the `eval` of each of the specified expressions.
  * @param expressions a sequence of expressions that determine the value of each column of the
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index fc055c97a179f..6fb3343bb63f2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.types.DataType
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index 4baae03b3a224..8ab4ef060b68c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.types.DataType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 3928c0f2ffdaf..efa24710a5a67 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
index b11fc245c4af9..1f395497a9839 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.ObjectPool
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 64e07bd2a17db..c0e17f97e9b3c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import com.clearspring.analytics.stream.cardinality.HyperLogLog
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index dca6642665741..8476af4a5d8d6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.TypeUtils
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index 97cb16045ae4a..d05dfc108e63a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Private
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 
 /**
@@ -33,11 +33,9 @@ class BaseOrdering extends Ordering[InternalRow] {
 }
 
 /**
- * Generates bytecode for an [[Ordering]] of [[Row Rows]] for a given set of
- * [[Expression Expressions]].
+ * Generates bytecode for an [[Ordering]] of rows for a given set of expressions.
  */
-object GenerateOrdering
-    extends CodeGenerator[Seq[SortOrder], Ordering[InternalRow]] with Logging {
+object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalRow]] with Logging {
 
   protected def canonicalize(in: Seq[SortOrder]): Seq[SortOrder] =
     in.map(ExpressionCanonicalizer.execute(_).asInstanceOf[SortOrder])
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
index 3ebc2c147579b..274a42cb69087 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index fa70409353e79..65b9d7e608320 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -17,12 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.util.TypeUtils
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.TypeCheckFailure
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * Returns an Array containing the evaluation of all children expressions.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index 1d7393d3d91f9..395e84f089e41 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types.{BooleanType, DataType}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index 13ba2f2e5d62d..a492b966a5e31 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -17,13 +17,15 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 
 /**
  * Returns the current date at the start of query evaluation.
  * All calls of current_date within the same query return the same value.
+ *
+ * There is no code generation since this expression should get constant folded by the optimizer.
  */
 case class CurrentDate() extends LeafExpression {
   override def foldable: Boolean = true
@@ -39,6 +41,8 @@ case class CurrentDate() extends LeafExpression {
 /**
  * Returns the current timestamp at the start of query evaluation.
  * All calls of current_timestamp within the same query return the same value.
+ *
+ * There is no code generation since this expression should get constant folded by the optimizer.
  */
 case class CurrentTimestamp() extends LeafExpression {
   override def foldable: Boolean = true
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index 7a42a1d310581..b68d30a26abd8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import scala.collection.Map
 
 import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees}
 import org.apache.spark.sql.types._
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index 479224af5627a..3a7a7ae440036 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.sql.{Date, Timestamp}
 
-import org.apache.spark.sql.catalyst
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 9dca8513c0d9c..c31890e27fb54 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.{lang => jl}
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 81ebda3060c51..6181c60c0e453 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 145d323a9f0bb..1522bcae08d17 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.TypeUtils
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
index d24d74e7b82ad..5155bfe41ba66 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
@@ -49,10 +49,6 @@ package org.apache.spark.sql.catalyst
  */
 package object expressions  {
 
-  type InternalRow = org.apache.spark.sql.catalyst.InternalRow
-
-  val InternalRow = org.apache.spark.sql.catalyst.InternalRow
-
   /**
    * Converts a [[InternalRow]] to another Row given a sequence of expression that define each
    * column of the new row. If the schema of the input row is specified, then the given expression
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 402a0aa232493..f74fd04619714 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
index 45588bacd2e45..6cdc3000382e2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.{DataType, DoubleType}
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index dd5f2ed2d382e..1363884c201bc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.{DataType, StructType, AtomicType}
 import org.apache.spark.unsafe.types.UTF8String
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index 9b44fb1ed5b3f..49b2026364cd6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 6e6a7fb171214..2e51cbb28c10a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -20,11 +20,14 @@ package org.apache.spark.sql.catalyst.expressions
 import java.util.regex.Pattern
 
 import org.apache.commons.lang3.StringUtils
+
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
+
 trait StringRegexExpression extends ExpectsInputTypes {
   self: BinaryExpression =>
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
index 12023ad311dc8..344361685853f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.types.{DataType, NumericType}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index f3809be722a84..518961e38396f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import java.sql.{Timestamp, Date}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index a09014e1ffc15..e3042143632aa 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -17,13 +17,11 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.scalatest.exceptions.TestFailedException
-
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedExtractValue
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 
 
 class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 3171caf6ad77f..43392df4bec2e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -21,6 +21,7 @@ import org.scalactic.TripleEqualsSupport.Spread
 import org.scalatest.Matchers._
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateProjection, GenerateMutableProjection}
 import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
index 6fafc2f86684c..c9667e90a0aaa 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
@@ -23,6 +23,7 @@ import scala.util.Random
 import org.scalatest.{BeforeAndAfterEach, Matchers}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 94c2f3242b122..96d4e64ea344a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -23,6 +23,7 @@ import java.util.Arrays
 import org.scalatest.Matchers
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.{ObjectPool, DateTimeUtils}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala
index 54e8c6462e962..049a19b86f7cd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.sql.catalyst.optimizer
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
-import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index 86792f0217572..1bd7d4e5cdf0f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.trees
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types.{IntegerType, StringType, NullType}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index caad2da80b1eb..60142291eaf30 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -32,6 +32,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.python.SerDeUtil
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index cb1fd4947fdbc..38720968c1313 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -22,6 +22,7 @@ import java.nio.ByteBuffer
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index 6e8a5ef18ab62..3cd60a2aa55ed 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -21,6 +21,7 @@ import java.util.HashMap
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index e054c1d144e34..74d933404551c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -24,6 +24,7 @@ import org.apache.spark.shuffle.hash.HashShuffleManager
 import org.apache.spark.shuffle.sort.SortShuffleManager
 import org.apache.spark.shuffle.unsafe.UnsafeShuffleManager
 import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala
index 42a0c1be4f694..d90cae1c4c060 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Expand.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
index c1665f78a960e..c3c0dc441c928 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 
 /**
@@ -35,7 +36,7 @@ private[execution] sealed case class LazyIterator(func: () => TraversableOnce[In
 
 /**
  * :: DeveloperApi ::
- * Applies a [[catalyst.expressions.Generator Generator]] to a stream of input rows, combining the
+ * Applies a [[Generator]] to a stream of input rows, combining the
  * output of each into a new stream of rows.  This operation is similar to a `flatMap` in functional
  * programming with one important additional feature, which allows the input rows to be joined with
  * their output.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 44930f82b53a0..c069da016f9f0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution
 import org.apache.spark.TaskContext
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.trees._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 7739a9f949c77..ca53186383237 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -21,6 +21,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.Logging
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.Row
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 32044989044a6..59b9b553a7ae5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
index fd6f1d7ae1255..6e127e548a120 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution
 import java.util
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, Partitioning}
 import org.apache.spark.util.collection.CompactBuffer
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 647c4ab5cb651..de14e6ad79ad6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, ShuffledRDD}
 import org.apache.spark.shuffle.sort.SortShuffleManager
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
index 68914cf85cb50..437d143e53f3f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
@@ -18,7 +18,8 @@
 package org.apache.spark.sql.execution.expressions
 
 import org.apache.spark.TaskContext
-import org.apache.spark.sql.catalyst.expressions.{InternalRow, LeafExpression}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.LeafExpression
 import org.apache.spark.sql.types.{LongType, DataType}
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
index 12c2eed0d6b7c..822d3d8c9108d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
@@ -18,7 +18,8 @@
 package org.apache.spark.sql.execution.expressions
 
 import org.apache.spark.TaskContext
-import org.apache.spark.sql.catalyst.expressions.{LeafExpression, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.LeafExpression
 import org.apache.spark.sql.types.{IntegerType, DataType}
 
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index 2d2e1b92b86be..7ffdce60d2955 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -22,7 +22,8 @@ import scala.concurrent.duration._
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.expressions.{Expression, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 import org.apache.spark.util.ThreadUtils
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index 5da04c78744d9..06c244f211aba 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index 412a3d4178e12..f7b46d6888d7d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 0b2cf8e12a6c4..60b4266fad8b1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 3a4196a90d14a..ff85ea3f6a410 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.util.collection.CompactBuffer
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 886b5fa0c5103..333745187728e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import java.util.{HashMap => JavaHashMap}
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning}
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index e18c817975134..de062c791f5fb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -20,7 +20,8 @@ package org.apache.spark.sql.execution.joins
 import java.io.{ObjectInput, ObjectOutput, Externalizable}
 import java.util.{HashMap => JavaHashMap}
 
-import org.apache.spark.sql.catalyst.expressions.{Projection, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Projection
 import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.util.collection.CompactBuffer
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
index 2a6d4d1ab08bb..db5be9f453674 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
index 20d74270afb48..611ba928a16ec 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
@@ -19,7 +19,8 @@ package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
 import org.apache.spark.sql.catalyst.plans.physical.ClusteredDistribution
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index cfc9c14aaa363..ab0a6ad56acde 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -17,15 +17,16 @@
 
 package org.apache.spark.sql.execution.joins
 
+import scala.collection.JavaConversions._
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, ClusteredDistribution}
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 
-import scala.collection.JavaConversions._
-
 /**
  * :: DeveloperApi ::
  * Performs a hash based outer join for two child relations by shuffling the data using
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 2abe65a71813d..981447eacad74 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -21,6 +21,7 @@ import java.util.NoSuchElementException
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 9e1cff06c7eea..6946e798b71b0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -29,6 +29,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.api.python.{PythonBroadcast, PythonRDD}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.plans.logical
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
index 4e2e2c210d5a9..ec5c6950f37ad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.stat
 import scala.collection.mutable.{Map => MutableMap}
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
 import org.apache.spark.sql.types.{ArrayType, StructField, StructType}
 import org.apache.spark.sql.{Column, DataFrame}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
index 30c5f4ca3e1b2..7a27fba1780b9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
@@ -23,7 +23,8 @@ import java.util.Properties
 import org.apache.commons.lang3.StringUtils
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.expressions.{InternalRow, SpecificMutableRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index 6222addc9aa3a..4b8ab63b5ab39 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -24,6 +24,7 @@ import scala.collection.Map
 import com.fasterxml.jackson.core._
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.json.JacksonUtils.nextUntil
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index 73d9520d6f53f..01ba05cbd14f1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -25,6 +25,7 @@ import com.fasterxml.jackson.databind.ObjectMapper
 
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
 import org.apache.spark.sql.catalyst.expressions._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index ae7cbf0624dc8..86a77bf965daa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -26,6 +26,7 @@ import org.apache.parquet.column.Dictionary
 import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
 import org.apache.parquet.schema.MessageType
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.parquet.CatalystConverter.FieldType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index b30fc171c0af1..9058b09375291 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -18,13 +18,11 @@
 package org.apache.spark.sql.parquet
 
 import java.io.IOException
-import java.lang.{Long => JLong}
 import java.text.{NumberFormat, SimpleDateFormat}
-import java.util.concurrent.{Callable, TimeUnit}
-import java.util.{Date, List => JList}
+import java.util.concurrent.TimeUnit
+import java.util.Date
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable
 import scala.util.Try
 
 import com.google.common.cache.CacheBuilder
@@ -34,11 +32,8 @@ import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter, FileOutputFormat => NewFileOutputFormat}
 import org.apache.parquet.hadoop._
-import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
-import org.apache.parquet.hadoop.api.{InitContext, ReadSupport}
-import org.apache.parquet.hadoop.metadata.GlobalMetaData
+import org.apache.parquet.hadoop.api.ReadSupport
 import org.apache.parquet.hadoop.util.ContextUtil
-import org.apache.parquet.io.ParquetDecodingException
 import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.annotation.DeveloperApi
@@ -46,7 +41,8 @@ import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, InternalRow, _}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, _}
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.{Logging, TaskContext}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index df2a96dfeb619..8402cd756140d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -29,7 +29,8 @@ import org.apache.parquet.io.api._
 import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
index 66f7ba90140b5..70c9e06927582 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
@@ -21,6 +21,7 @@ import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index 7214eb0b4169a..a97142d4a1142 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -28,6 +28,7 @@ import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceF
 import org.apache.spark._
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.expressions._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
index b7095c8ead797..1f0b93e2850ba 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
@@ -26,7 +26,8 @@ import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.catalyst.AbstractSparkSQLParser
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.RunnableCommand
 import org.apache.spark.sql.types._
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 0b875304f9b0e..b13c5313b25c9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -27,8 +27,9 @@ import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.execution.RDDConversions
 import org.apache.spark.sql.{DataFrame, Row, SaveMode, SQLContext}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.RDDConversions
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
 import org.apache.spark.sql.types.StructType
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 9bd7b221e93f8..3333fee6711c0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.expressions.InternalRow
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 71db6a2159857..9d9858b1c6151 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -18,7 +18,8 @@
 package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.expressions.{Projection, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Projection
 import org.apache.spark.util.collection.CompactBuffer
 
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 7423d8030f2b5..924e2ba3ba442 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -24,6 +24,7 @@ import org.apache.hadoop.hive.serde2.typeinfo.{DecimalTypeInfo, TypeInfoFactory}
 import org.apache.hadoop.hive.serde2.{io => hiveIo}
 import org.apache.hadoop.{io => hadoopIo}
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index f35ae96ee0b50..4b7a782c805a0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -28,6 +28,7 @@ import org.apache.hadoop.hive.ql.metadata._
 import org.apache.hadoop.hive.ql.plan.TableDesc
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{Catalog, MultiInstanceRelation, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 452b7f0bcc749..ed359620a5f7f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -22,9 +22,10 @@ import scala.collection.JavaConversions._
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
-import org.apache.spark.sql.catalyst.expressions.{InternalRow, _}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index b251a9523bed6..d65d29daacf31 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -32,6 +32,7 @@ import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf}
 import org.apache.spark.Logging
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.unsafe.types.UTF8String
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index f4c8c9a7e8a68..d33da8242cc1d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -27,6 +27,7 @@ import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.Object
 import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive._
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 05f425f2b65f3..8202e553afbfe 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -31,7 +31,8 @@ import org.apache.hadoop.mapred.{FileOutputFormat, JobConf}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.expressions.{Attribute, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
 import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc}
 import org.apache.spark.sql.hive._
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index b967e191c5855..205e622195f09 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.hive.execution
 
 import java.io.{BufferedReader, DataInputStream, DataOutputStream, EOFException, InputStreamReader}
-import java.lang.ProcessBuilder.Redirect
 import java.util.Properties
 
 import scala.collection.JavaConversions._
@@ -28,6 +27,7 @@ import org.apache.hadoop.hive.serde2.AbstractSerDe
 import org.apache.hadoop.hive.serde2.objectinspector._
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 4dea561ae5f60..1deef6b37eeac 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -33,6 +33,7 @@ import org.apache.hadoop.hive.ql.udf.generic.GenericUDFUtils.ConversionHelper
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.FunctionBuilder
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 9dc9fbb78e01f..48d35a60a759b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -35,6 +35,7 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.{HadoopRDD, RDD}
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.hive.{HiveContext, HiveInspectors, HiveMetastoreTypes, HiveShim}
 import org.apache.spark.sql.sources.{Filter, _}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index a93acb938d5fa..8bb498a06fc9e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -28,7 +28,8 @@ import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectIn
 import org.apache.hadoop.io.LongWritable
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.expressions.{Literal, InternalRow}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Literal
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.Row
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
index 8707f9f936be6..3c2efe329bfd5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
@@ -21,7 +21,7 @@ import java.io.File
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.expressions.InternalRow
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._

From 4ca90935c5ff15ccca13452572e317b86814f238 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 7 Jul 2015 17:57:17 -0700
Subject: [PATCH 0265/1454] [SPARK-7190] [SPARK-8804] [SPARK-7815] [SQL] unsafe
 UTF8String

Let UTF8String work with binary buffer. Before we have better idea on manage the lifecycle of UTF8String in Row, we still do the copy when calling `UnsafeRow.get()` for StringType.

cc rxin JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #7197 from davies/unsafe_string and squashes the following commits:

51b0ea0 [Davies Liu] fix test
50c1ebf [Davies Liu] remove optimization for upper/lower case
315d491 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_string
93fce17 [Davies Liu] address comment
e9ff7ba [Davies Liu] clean up
67ec266 [Davies Liu] fix bug
7b74b1f [Davies Liu] fallback to String if local dependent
ab7857c [Davies Liu] address comments
7da92f5 [Davies Liu] handle local in toUpperCase/toLowerCase
59dbb23 [Davies Liu] revert python change
d1e0716 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_string
002e35f [Davies Liu] rollback hashCode change
a87b7a8 [Davies Liu] improve toLowerCase and toUpperCase
76e794a [Davies Liu] fix test
8b2d5ce [Davies Liu] fix tests
fd3f0a6 [Davies Liu] bug fix
c4e9c88 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_string
c45d921 [Davies Liu] address comments
175405f [Davies Liu] unsafe UTF8String
---
 .../sql/catalyst/expressions/UnsafeRow.java   |   1 +
 .../spark/sql/catalyst/expressions/Cast.scala |   2 +-
 .../expressions/stringOperations.scala        |   6 +-
 .../spark/unsafe/array/ByteArrayMethods.java  |  34 ++--
 .../spark/unsafe/map/BytesToBytesMap.java     |   2 +-
 .../apache/spark/unsafe/types/UTF8String.java | 155 ++++++++++++------
 .../map/AbstractBytesToBytesMapSuite.java     |   2 +-
 .../spark/unsafe/types/UTF8StringSuite.java   | 120 +++++++++-----
 8 files changed, 210 insertions(+), 112 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index f077064a02ec0..aeb64b045812f 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -264,6 +264,7 @@ public Object get(int i) {
       int offset = (int) ((v >> OFFSET_BITS) & Integer.MAX_VALUE);
       int size = (int) (v & Integer.MAX_VALUE);
       final byte[] bytes = new byte[size];
+      // TODO(davies): Avoid the copy once we can manage the life cycle of Row well.
       PlatformDependent.copyMemory(
         baseObject,
         baseOffset + offset,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index cfe201bf83070..662ceeca7782d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -139,7 +139,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   // UDFToBoolean
   private[this] def castToBoolean(from: DataType): Any => Any = from match {
     case StringType =>
-      buildCast[UTF8String](_, _.length() != 0)
+      buildCast[UTF8String](_, _.numBytes() != 0)
     case TimestampType =>
       buildCast[Long](_, t => t != 0)
     case DateType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 2e51cbb28c10a..47fc7cdaa826c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -250,7 +250,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
           val (st, end) = slicePos(start, length, () => ba.length)
           ba.slice(st, end)
         case s: UTF8String =>
-          val (st, end) = slicePos(start, length, () => s.length())
+          val (st, end) = slicePos(start, length, () => s.numChars())
           s.substring(st, end)
       }
     }
@@ -265,10 +265,10 @@ case class StringLength(child: Expression) extends UnaryExpression with ExpectsI
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
   protected override def nullSafeEval(string: Any): Any =
-    string.asInstanceOf[UTF8String].length
+    string.asInstanceOf[UTF8String].numChars
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    defineCodeGen(ctx, ev, c => s"($c).length()")
+    defineCodeGen(ctx, ev, c => s"($c).numChars()")
   }
 
   override def prettyName: String = "length"
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
index 53eadf96a6b52..cf693d01a4f5b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.array;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import static org.apache.spark.unsafe.PlatformDependent.*;
 
 public class ByteArrayMethods {
 
@@ -35,21 +35,27 @@ public static int roundNumberOfBytesToNearestWord(int numBytes) {
   }
 
   /**
-   * Optimized byte array equality check for 8-byte-word-aligned byte arrays.
+   * Optimized byte array equality check for byte arrays.
    * @return true if the arrays are equal, false otherwise
    */
-  public static boolean wordAlignedArrayEquals(
-      Object leftBaseObject,
-      long leftBaseOffset,
-      Object rightBaseObject,
-      long rightBaseOffset,
-      long arrayLengthInBytes) {
-    for (int i = 0; i < arrayLengthInBytes; i += 8) {
-      final long left =
-        PlatformDependent.UNSAFE.getLong(leftBaseObject, leftBaseOffset + i);
-      final long right =
-        PlatformDependent.UNSAFE.getLong(rightBaseObject, rightBaseOffset + i);
-      if (left != right) return false;
+  public static boolean arrayEquals(
+      Object leftBase,
+      long leftOffset,
+      Object rightBase,
+      long rightOffset,
+      final long length) {
+    int i = 0;
+    while (i <= length - 8) {
+      if (UNSAFE.getLong(leftBase, leftOffset + i) != UNSAFE.getLong(rightBase, rightOffset + i)) {
+        return false;
+      }
+      i += 8;
+    }
+    while (i < length) {
+      if (UNSAFE.getByte(leftBase, leftOffset + i) != UNSAFE.getByte(rightBase, rightOffset + i)) {
+        return false;
+      }
+      i += 1;
     }
     return true;
   }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 0b4d8d286f5f9..8d8c08de52b84 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -277,7 +277,7 @@ public Location lookup(
             final MemoryLocation keyAddress = loc.getKeyAddress();
             final Object storedKeyBaseObject = keyAddress.getBaseObject();
             final long storedKeyBaseOffset = keyAddress.getBaseOffset();
-            final boolean areEqual = ByteArrayMethods.wordAlignedArrayEquals(
+            final boolean areEqual = ByteArrayMethods.arrayEquals(
               keyBaseObject,
               keyBaseOffset,
               storedKeyBaseObject,
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 9302b472925ed..d2a25096a5e7a 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -20,9 +20,10 @@
 import javax.annotation.Nonnull;
 import java.io.Serializable;
 import java.io.UnsupportedEncodingException;
-import java.util.Arrays;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
+
+import static org.apache.spark.unsafe.PlatformDependent.*;
 
 /**
  * A UTF-8 String for internal Spark use.
@@ -35,7 +36,9 @@
 public final class UTF8String implements Comparable<UTF8String>, Serializable {
 
   @Nonnull
-  private byte[] bytes;
+  private final Object base;
+  private final long offset;
+  private final int numBytes;
 
   private static int[] bytesOfCodePointInUTF8 = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
@@ -44,60 +47,82 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
     5, 5, 5, 5,
     6, 6, 6, 6};
 
+  /**
+   * Creates an UTF8String from byte array, which should be encoded in UTF-8.
+   *
+   * Note: `bytes` will be hold by returned UTF8String.
+   */
   public static UTF8String fromBytes(byte[] bytes) {
-    return (bytes != null) ? new UTF8String().set(bytes) : null;
-  }
-
-  public static UTF8String fromString(String str) {
-    return (str != null) ? new UTF8String().set(str) : null;
+    if (bytes != null) {
+      return new UTF8String(bytes, BYTE_ARRAY_OFFSET, bytes.length);
+    } else {
+      return null;
+    }
   }
 
   /**
-   * Updates the UTF8String with String.
+   * Creates an UTF8String from String.
    */
-  protected UTF8String set(final String str) {
+  public static UTF8String fromString(String str) {
+    if (str == null) return null;
     try {
-      bytes = str.getBytes("utf-8");
+      return fromBytes(str.getBytes("utf-8"));
     } catch (UnsupportedEncodingException e) {
       // Turn the exception into unchecked so we can find out about it at runtime, but
       // don't need to add lots of boilerplate code everywhere.
-      PlatformDependent.throwException(e);
+      throwException(e);
+      return null;
     }
-    return this;
   }
 
-  /**
-   * Updates the UTF8String with byte[], which should be encoded in UTF-8.
-   */
-  protected UTF8String set(final byte[] bytes) {
-    this.bytes = bytes;
-    return this;
+  protected UTF8String(Object base, long offset, int size) {
+    this.base = base;
+    this.offset = offset;
+    this.numBytes = size;
   }
 
   /**
    * Returns the number of bytes for a code point with the first byte as `b`
    * @param b The first byte of a code point
    */
-  public int numBytes(final byte b) {
+  private static int numBytesForFirstByte(final byte b) {
     final int offset = (b & 0xFF) - 192;
     return (offset >= 0) ? bytesOfCodePointInUTF8[offset] : 1;
   }
 
+  /**
+   * Returns the number of bytes
+   */
+  public int numBytes() {
+    return numBytes;
+  }
+
   /**
    * Returns the number of code points in it.
    *
    * This is only used by Substring() when `start` is negative.
    */
-  public int length() {
+  public int numChars() {
     int len = 0;
-    for (int i = 0; i < bytes.length; i+= numBytes(bytes[i])) {
+    for (int i = 0; i < numBytes; i += numBytesForFirstByte(getByte(i))) {
       len += 1;
     }
     return len;
   }
 
+  /**
+   * Returns the underline bytes, will be a copy of it if it's part of another array.
+   */
   public byte[] getBytes() {
-    return bytes;
+    // avoid copy if `base` is `byte[]`
+    if (offset == BYTE_ARRAY_OFFSET && base instanceof byte[]
+      && ((byte[]) base).length == numBytes) {
+      return (byte[]) base;
+    } else {
+      byte[] bytes = new byte[numBytes];
+      copyMemory(base, offset, bytes, BYTE_ARRAY_OFFSET, numBytes);
+      return bytes;
+    }
   }
 
   /**
@@ -106,92 +131,110 @@ public byte[] getBytes() {
    * @param until the position after last code point, exclusive.
    */
   public UTF8String substring(final int start, final int until) {
-    if (until <= start || start >= bytes.length) {
-      return UTF8String.fromBytes(new byte[0]);
+    if (until <= start || start >= numBytes) {
+      return fromBytes(new byte[0]);
     }
 
     int i = 0;
     int c = 0;
-    for (; i < bytes.length && c < start; i += numBytes(bytes[i])) {
+    while (i < numBytes && c < start) {
+      i += numBytesForFirstByte(getByte(i));
       c += 1;
     }
 
     int j = i;
-    for (; j < bytes.length && c < until; j += numBytes(bytes[i])) {
+    while (i < numBytes && c < until) {
+      i += numBytesForFirstByte(getByte(i));
       c += 1;
     }
 
-    return UTF8String.fromBytes(Arrays.copyOfRange(bytes, i, j));
+    byte[] bytes = new byte[i - j];
+    copyMemory(base, offset + j, bytes, BYTE_ARRAY_OFFSET, i - j);
+    return fromBytes(bytes);
   }
 
+  /**
+   * Returns whether this contains `substring` or not.
+   */
   public boolean contains(final UTF8String substring) {
-    final byte[] b = substring.getBytes();
-    if (b.length == 0) {
+    if (substring.numBytes == 0) {
       return true;
     }
 
-    for (int i = 0; i <= bytes.length - b.length; i++) {
-      if (bytes[i] == b[0] && startsWith(b, i)) {
+    byte first = substring.getByte(0);
+    for (int i = 0; i <= numBytes - substring.numBytes; i++) {
+      if (getByte(i) == first && matchAt(substring, i)) {
         return true;
       }
     }
     return false;
   }
 
-  private boolean startsWith(final byte[] prefix, int offsetInBytes) {
-    if (prefix.length + offsetInBytes > bytes.length || offsetInBytes < 0) {
+  /**
+   * Returns the byte at position `i`.
+   */
+  private byte getByte(int i) {
+    return UNSAFE.getByte(base, offset + i);
+  }
+
+  private boolean matchAt(final UTF8String s, int pos) {
+    if (s.numBytes + pos > numBytes || pos < 0) {
       return false;
     }
-    int i = 0;
-    while (i < prefix.length && prefix[i] == bytes[i + offsetInBytes]) {
-      i++;
-    }
-    return i == prefix.length;
+    return ByteArrayMethods.arrayEquals(base, offset + pos, s.base, s.offset, s.numBytes);
   }
 
   public boolean startsWith(final UTF8String prefix) {
-    return startsWith(prefix.getBytes(), 0);
+    return matchAt(prefix, 0);
   }
 
   public boolean endsWith(final UTF8String suffix) {
-    return startsWith(suffix.getBytes(), bytes.length - suffix.getBytes().length);
+    return matchAt(suffix, numBytes - suffix.numBytes);
   }
 
+  /**
+   * Returns the upper case of this string
+   */
   public UTF8String toUpperCase() {
-    return UTF8String.fromString(toString().toUpperCase());
+    return fromString(toString().toUpperCase());
   }
 
+  /**
+   * Returns the lower case of this string
+   */
   public UTF8String toLowerCase() {
-    return UTF8String.fromString(toString().toLowerCase());
+    return fromString(toString().toLowerCase());
   }
 
   @Override
   public String toString() {
     try {
-      return new String(bytes, "utf-8");
+      return new String(getBytes(), "utf-8");
     } catch (UnsupportedEncodingException e) {
       // Turn the exception into unchecked so we can find out about it at runtime, but
       // don't need to add lots of boilerplate code everywhere.
-      PlatformDependent.throwException(e);
+      throwException(e);
       return "unknown";  // we will never reach here.
     }
   }
 
   @Override
   public UTF8String clone() {
-    return new UTF8String().set(bytes);
+    return fromBytes(getBytes());
   }
 
   @Override
   public int compareTo(final UTF8String other) {
-    final byte[] b = other.getBytes();
-    for (int i = 0; i < bytes.length && i < b.length; i++) {
-      int res = bytes[i] - b[i];
+    int len = Math.min(numBytes, other.numBytes);
+    // TODO: compare 8 bytes as unsigned long
+    for (int i = 0; i < len; i ++) {
+      // In UTF-8, the byte should be unsigned, so we should compare them as unsigned int.
+      int res = (getByte(i) & 0xFF) - (other.getByte(i) & 0xFF);
       if (res != 0) {
         return res;
       }
     }
-    return bytes.length - b.length;
+    return numBytes - other.numBytes;
   }
 
   public int compare(final UTF8String other) {
@@ -201,7 +244,11 @@ public int compare(final UTF8String other) {
   @Override
   public boolean equals(final Object other) {
     if (other instanceof UTF8String) {
-      return Arrays.equals(bytes, ((UTF8String) other).getBytes());
+      UTF8String o = (UTF8String) other;
+      if (numBytes != o.numBytes){
+        return false;
+      }
+      return ByteArrayMethods.arrayEquals(base, offset, o.base, o.offset, numBytes);
     } else {
       return false;
     }
@@ -209,6 +256,10 @@ public boolean equals(final Object other) {
 
   @Override
   public int hashCode() {
-    return Arrays.hashCode(bytes);
+    int result = 1;
+    for (int i = 0; i < numBytes; i ++) {
+      result = 31 * result + getByte(i);
+    }
+    return result;
   }
 }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 81315f7c94645..dae47e4bab0cb 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -99,7 +99,7 @@ private static boolean arrayEquals(
       byte[] expected,
       MemoryLocation actualAddr,
       long actualLengthBytes) {
-    return (actualLengthBytes == expected.length) && ByteArrayMethods.wordAlignedArrayEquals(
+    return (actualLengthBytes == expected.length) && ByteArrayMethods.arrayEquals(
       expected,
       BYTE_ARRAY_OFFSET,
       actualAddr.getBaseObject(),
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 796cdc9dbebdb..8ec69ebac8b37 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -19,73 +19,113 @@
 
 import java.io.UnsupportedEncodingException;
 
-import junit.framework.Assert;
 import org.junit.Test;
 
+import static junit.framework.Assert.*;
+
+import static org.apache.spark.unsafe.types.UTF8String.*;
+
 public class UTF8StringSuite {
 
   private void checkBasic(String str, int len) throws UnsupportedEncodingException {
-    Assert.assertEquals(UTF8String.fromString(str).length(), len);
-    Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).length(), len);
+    UTF8String s1 = fromString(str);
+    UTF8String s2 = fromBytes(str.getBytes("utf8"));
+    assertEquals(s1.numChars(), len);
+    assertEquals(s2.numChars(), len);
+
+    assertEquals(s1.toString(), str);
+    assertEquals(s2.toString(), str);
+    assertEquals(s1, s2);
+
+    assertEquals(s1.hashCode(), s2.hashCode());
 
-    Assert.assertEquals(UTF8String.fromString(str).toString(), str);
-    Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).toString(), str);
-    Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")), UTF8String.fromString(str));
+    assertEquals(s1.compareTo(s2), 0);
 
-    Assert.assertEquals(UTF8String.fromString(str).hashCode(),
-      UTF8String.fromBytes(str.getBytes("utf8")).hashCode());
+    assertEquals(s1.contains(s2), true);
+    assertEquals(s2.contains(s1), true);
+    assertEquals(s1.startsWith(s1), true);
+    assertEquals(s1.endsWith(s1), true);
   }
 
   @Test
   public void basicTest() throws UnsupportedEncodingException {
+    checkBasic("", 0);
     checkBasic("hello", 5);
-    checkBasic("世 界", 3);
+    checkBasic("大 千 世 界", 7);
+  }
+
+  @Test
+  public void compareTo() {
+    assertTrue(fromString("abc").compareTo(fromString("ABC")) > 0);
+    assertTrue(fromString("abc0").compareTo(fromString("abc")) > 0);
+    assertTrue(fromString("abcabcabc").compareTo(fromString("abcabcabc")) == 0);
+    assertTrue(fromString("aBcabcabc").compareTo(fromString("Abcabcabc")) > 0);
+    assertTrue(fromString("Abcabcabc").compareTo(fromString("abcabcabC")) < 0);
+    assertTrue(fromString("abcabcabc").compareTo(fromString("abcabcabC")) > 0);
+
+    assertTrue(fromString("abc").compareTo(fromString("世界")) < 0);
+    assertTrue(fromString("你好").compareTo(fromString("世界")) > 0);
+    assertTrue(fromString("你好123").compareTo(fromString("你好122")) > 0);
+  }
+
+  protected void testUpperandLower(String upper, String lower) {
+    UTF8String us = fromString(upper);
+    UTF8String ls = fromString(lower);
+    assertEquals(ls, us.toLowerCase());
+    assertEquals(us, ls.toUpperCase());
+    assertEquals(us, us.toUpperCase());
+    assertEquals(ls, ls.toLowerCase());
+  }
+
+  @Test
+  public void upperAndLower() {
+    testUpperandLower("", "");
+    testUpperandLower("0123456", "0123456");
+    testUpperandLower("ABCXYZ", "abcxyz");
+    testUpperandLower("ЀЁЂѺΏỀ", "ѐёђѻώề");
+    testUpperandLower("大千世界 数据砖头", "大千世界 数据砖头");
   }
 
   @Test
   public void contains() {
-    Assert.assertTrue(UTF8String.fromString("hello").contains(UTF8String.fromString("ello")));
-    Assert.assertFalse(UTF8String.fromString("hello").contains(UTF8String.fromString("vello")));
-    Assert.assertFalse(UTF8String.fromString("hello").contains(UTF8String.fromString("hellooo")));
-    Assert.assertTrue(UTF8String.fromString("大千世界").contains(UTF8String.fromString("千世")));
-    Assert.assertFalse(UTF8String.fromString("大千世界").contains(UTF8String.fromString("世千")));
-    Assert.assertFalse(
-      UTF8String.fromString("大千世界").contains(UTF8String.fromString("大千世界好")));
+    assertTrue(fromString("").contains(fromString("")));
+    assertTrue(fromString("hello").contains(fromString("ello")));
+    assertFalse(fromString("hello").contains(fromString("vello")));
+    assertFalse(fromString("hello").contains(fromString("hellooo")));
+    assertTrue(fromString("大千世界").contains(fromString("千世界")));
+    assertFalse(fromString("大千世界").contains(fromString("世千")));
+    assertFalse(fromString("大千世界").contains(fromString("大千世界好")));
   }
 
   @Test
   public void startsWith() {
-    Assert.assertTrue(UTF8String.fromString("hello").startsWith(UTF8String.fromString("hell")));
-    Assert.assertFalse(UTF8String.fromString("hello").startsWith(UTF8String.fromString("ell")));
-    Assert.assertFalse(UTF8String.fromString("hello").startsWith(UTF8String.fromString("hellooo")));
-    Assert.assertTrue(UTF8String.fromString("数据砖头").startsWith(UTF8String.fromString("数据")));
-    Assert.assertFalse(UTF8String.fromString("大千世界").startsWith(UTF8String.fromString("千")));
-    Assert.assertFalse(
-      UTF8String.fromString("大千世界").startsWith(UTF8String.fromString("大千世界好")));
+    assertTrue(fromString("").startsWith(fromString("")));
+    assertTrue(fromString("hello").startsWith(fromString("hell")));
+    assertFalse(fromString("hello").startsWith(fromString("ell")));
+    assertFalse(fromString("hello").startsWith(fromString("hellooo")));
+    assertTrue(fromString("数据砖头").startsWith(fromString("数据")));
+    assertFalse(fromString("大千世界").startsWith(fromString("千")));
+    assertFalse(fromString("大千世界").startsWith(fromString("大千世界好")));
   }
 
   @Test
   public void endsWith() {
-    Assert.assertTrue(UTF8String.fromString("hello").endsWith(UTF8String.fromString("ello")));
-    Assert.assertFalse(UTF8String.fromString("hello").endsWith(UTF8String.fromString("ellov")));
-    Assert.assertFalse(UTF8String.fromString("hello").endsWith(UTF8String.fromString("hhhello")));
-    Assert.assertTrue(UTF8String.fromString("大千世界").endsWith(UTF8String.fromString("世界")));
-    Assert.assertFalse(UTF8String.fromString("大千世界").endsWith(UTF8String.fromString("世")));
-    Assert.assertFalse(
-      UTF8String.fromString("数据砖头").endsWith(UTF8String.fromString("我的数据砖头")));
+    assertTrue(fromString("").endsWith(fromString("")));
+    assertTrue(fromString("hello").endsWith(fromString("ello")));
+    assertFalse(fromString("hello").endsWith(fromString("ellov")));
+    assertFalse(fromString("hello").endsWith(fromString("hhhello")));
+    assertTrue(fromString("大千世界").endsWith(fromString("世界")));
+    assertFalse(fromString("大千世界").endsWith(fromString("世")));
+    assertFalse(fromString("数据砖头").endsWith(fromString("我的数据砖头")));
   }
 
   @Test
   public void substring() {
-    Assert.assertEquals(
-      UTF8String.fromString("hello").substring(0, 0), UTF8String.fromString(""));
-    Assert.assertEquals(
-      UTF8String.fromString("hello").substring(1, 3), UTF8String.fromString("el"));
-    Assert.assertEquals(
-      UTF8String.fromString("数据砖头").substring(0, 1), UTF8String.fromString("数"));
-    Assert.assertEquals(
-      UTF8String.fromString("数据砖头").substring(1, 3), UTF8String.fromString("据砖"));
-    Assert.assertEquals(
-      UTF8String.fromString("数据砖头").substring(3, 5), UTF8String.fromString("头"));
+    assertEquals(fromString("hello").substring(0, 0), fromString(""));
+    assertEquals(fromString("hello").substring(1, 3), fromString("el"));
+    assertEquals(fromString("数据砖头").substring(0, 1), fromString("数"));
+    assertEquals(fromString("数据砖头").substring(1, 3), fromString("据砖"));
+    assertEquals(fromString("数据砖头").substring(3, 5), fromString("头"));
+    assertEquals(fromString("ߵ梷").substring(0, 2), fromString("ߵ梷"));
   }
 }

From 68a4a169714e11d8c537ad9431ae9974f6b7e8d3 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 7 Jul 2015 18:36:35 -0700
Subject: [PATCH 0266/1454] [SPARK-8868] SqlSerializer2 can go into infinite
 loop when row consists only of NullType columns

https://issues.apache.org/jira/browse/SPARK-8868

Author: Yin Huai <yhuai@databricks.com>

Closes #7262 from yhuai/SPARK-8868 and squashes the following commits:

cb58780 [Yin Huai] Andrew's comment.
e456857 [Yin Huai] Josh's comments.
5122e65 [Yin Huai] If types of all columns are NullTypes, do not use serializer2.
---
 .../sql/execution/SparkSqlSerializer2.scala   | 25 +++++++++++++++----
 .../execution/SparkSqlSerializer2Suite.scala  | 20 ++++++++++++++-
 2 files changed, 39 insertions(+), 6 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 056d435eecd23..6ed822dc70d68 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -179,23 +179,38 @@ private[sql] object SparkSqlSerializer2 {
 
   /**
    * Check if rows with the given schema can be serialized with ShuffleSerializer.
+   * Right now, we do not support a schema having complex types or UDTs, or all data types
+   * of fields are NullTypes.
    */
   def support(schema: Array[DataType]): Boolean = {
     if (schema == null) return true
 
+    var allNullTypes = true
     var i = 0
     while (i < schema.length) {
       schema(i) match {
-        case udt: UserDefinedType[_] => return false
-        case array: ArrayType => return false
-        case map: MapType => return false
-        case struct: StructType => return false
+        case NullType => // Do nothing
+        case udt: UserDefinedType[_] =>
+          allNullTypes = false
+          return false
+        case array: ArrayType =>
+          allNullTypes = false
+          return false
+        case map: MapType =>
+          allNullTypes = false
+          return false
+        case struct: StructType =>
+          allNullTypes = false
+          return false
         case _ =>
+          allNullTypes = false
       }
       i += 1
     }
 
-    return true
+    // If types of fields are all NullTypes, we return false.
+    // Otherwise, we return true.
+    return !allNullTypes
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
index 8631e247c6c05..71f6b26bcd01a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
@@ -42,7 +42,6 @@ class SparkSqlSerializer2DataTypeSuite extends SparkFunSuite {
   }
 
   checkSupported(null, isSupported = true)
-  checkSupported(NullType, isSupported = true)
   checkSupported(BooleanType, isSupported = true)
   checkSupported(ByteType, isSupported = true)
   checkSupported(ShortType, isSupported = true)
@@ -57,6 +56,8 @@ class SparkSqlSerializer2DataTypeSuite extends SparkFunSuite {
   checkSupported(DecimalType(10, 5), isSupported = true)
   checkSupported(DecimalType.Unlimited, isSupported = true)
 
+  // If NullType is the only data type in the schema, we do not support it.
+  checkSupported(NullType, isSupported = false)
   // For now, ArrayType, MapType, and StructType are not supported.
   checkSupported(ArrayType(DoubleType, true), isSupported = false)
   checkSupported(ArrayType(StringType, false), isSupported = false)
@@ -170,6 +171,23 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll
     val df = ctx.sql(s"SELECT 1 + 1 FROM shuffle")
     checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer])
   }
+
+  test("types of fields are all NullTypes") {
+    // Test range partitioning code path.
+    val nulls = ctx.sql(s"SELECT null as a, null as b, null as c")
+    val df = nulls.unionAll(nulls).sort("a")
+    checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer])
+    checkAnswer(
+      df,
+      Row(null, null, null) :: Row(null, null, null) :: Nil)
+
+    // Test hash partitioning code path.
+    val oneRow = ctx.sql(s"SELECT DISTINCT null, null, null FROM shuffle")
+    checkSerializer(oneRow.queryExecution.executedPlan, classOf[SparkSqlSerializer])
+    checkAnswer(
+      oneRow,
+      Row(null, null, null))
+  }
 }
 
 /** Tests SparkSqlSerializer2 with sort based shuffle without sort merge. */

From 5d603dfe497ded78d8f09711578f1e764e6e4e91 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 7 Jul 2015 19:12:40 -0700
Subject: [PATCH 0267/1454] [SPARK-8878][SQL] Improve unit test coverage for
 bitwise expressions.

Author: Reynold Xin <rxin@databricks.com>

Closes #7273 from rxin/bitwise-unittest and squashes the following commits:

60c5667 [Reynold Xin] [SPARK-8878][SQL] Improve unit test coverage for bitwise expressions.
---
 .../expressions/BitwiseFunctionsSuite.scala   | 108 ++++++++++--------
 1 file changed, 61 insertions(+), 47 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
index c9bbc7a8b8c14..648fbf5a4c30b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
@@ -18,63 +18,77 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
 
 
 class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
-  test("Bitwise operations") {
-    val row = create_row(1, 2, 3, null)
-    val c1 = 'a.int.at(0)
-    val c2 = 'a.int.at(1)
-    val c3 = 'a.int.at(2)
-    val c4 = 'a.int.at(3)
-
-    checkEvaluation(BitwiseAnd(c1, c4), null, row)
-    checkEvaluation(BitwiseAnd(c1, c2), 0, row)
-    checkEvaluation(BitwiseAnd(c1, Literal.create(null, IntegerType)), null, row)
-    checkEvaluation(
-      BitwiseAnd(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row)
-
-    checkEvaluation(BitwiseOr(c1, c4), null, row)
-    checkEvaluation(BitwiseOr(c1, c2), 3, row)
-    checkEvaluation(BitwiseOr(c1, Literal.create(null, IntegerType)), null, row)
-    checkEvaluation(
-      BitwiseOr(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row)
-
-    checkEvaluation(BitwiseXor(c1, c4), null, row)
-    checkEvaluation(BitwiseXor(c1, c2), 3, row)
-    checkEvaluation(BitwiseXor(c1, Literal.create(null, IntegerType)), null, row)
-    checkEvaluation(
-      BitwiseXor(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row)
-
-    checkEvaluation(BitwiseNot(c4), null, row)
-    checkEvaluation(BitwiseNot(c1), -2, row)
-    checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null, row)
-
-    checkEvaluation(c1 & c2, 0, row)
-    checkEvaluation(c1 | c2, 3, row)
-    checkEvaluation(c1 ^ c2, 3, row)
-    checkEvaluation(~c1, -2, row)
+  test("BitwiseNOT") {
+    def check(input: Any, expected: Any): Unit = {
+      val expr = BitwiseNot(Literal(input))
+      assert(expr.dataType === Literal(input).dataType)
+      checkEvaluation(expr, expected)
+    }
+
+    check(1.toByte, ~1.toByte)
+    check(1000.toShort, ~1000.toShort)
+    check(1000000, ~1000000)
+    check(123456789123L, ~123456789123L)
+
+    checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null)
   }
 
-  test("unary BitwiseNOT") {
-    checkEvaluation(BitwiseNot(1), -2)
-    assert(BitwiseNot(1).dataType === IntegerType)
-    assert(BitwiseNot(1).eval(EmptyRow).isInstanceOf[Int])
+  test("BitwiseAnd") {
+    def check(input1: Any, input2: Any, expected: Any): Unit = {
+      val expr = BitwiseAnd(Literal(input1), Literal(input2))
+      assert(expr.dataType === Literal(input1).dataType)
+      checkEvaluation(expr, expected)
+    }
+
+    check(1.toByte, 2.toByte, 1.toByte & 2.toByte)
+    check(1000.toShort, 2.toShort, 1000.toShort & 2.toShort)
+    check(1000000, 4, 1000000 & 4)
+    check(123456789123L, 5L, 123456789123L & 5L)
+
+    val nullLit = Literal.create(null, IntegerType)
+    checkEvaluation(BitwiseAnd(nullLit, Literal(1)), null)
+    checkEvaluation(BitwiseAnd(Literal(1), nullLit), null)
+    checkEvaluation(BitwiseAnd(nullLit, nullLit), null)
+  }
 
-    checkEvaluation(BitwiseNot(1.toLong), -2.toLong)
-    assert(BitwiseNot(1.toLong).dataType === LongType)
-    assert(BitwiseNot(1.toLong).eval(EmptyRow).isInstanceOf[Long])
+  test("BitwiseOr") {
+    def check(input1: Any, input2: Any, expected: Any): Unit = {
+      val expr = BitwiseOr(Literal(input1), Literal(input2))
+      assert(expr.dataType === Literal(input1).dataType)
+      checkEvaluation(expr, expected)
+    }
 
-    checkEvaluation(BitwiseNot(1.toShort), -2.toShort)
-    assert(BitwiseNot(1.toShort).dataType === ShortType)
-    assert(BitwiseNot(1.toShort).eval(EmptyRow).isInstanceOf[Short])
+    check(1.toByte, 2.toByte, 1.toByte | 2.toByte)
+    check(1000.toShort, 2.toShort, 1000.toShort | 2.toShort)
+    check(1000000, 4, 1000000 | 4)
+    check(123456789123L, 5L, 123456789123L | 5L)
 
-    checkEvaluation(BitwiseNot(1.toByte), -2.toByte)
-    assert(BitwiseNot(1.toByte).dataType === ByteType)
-    assert(BitwiseNot(1.toByte).eval(EmptyRow).isInstanceOf[Byte])
+    val nullLit = Literal.create(null, IntegerType)
+    checkEvaluation(BitwiseOr(nullLit, Literal(1)), null)
+    checkEvaluation(BitwiseOr(Literal(1), nullLit), null)
+    checkEvaluation(BitwiseOr(nullLit, nullLit), null)
   }
 
+  test("BitwiseXor") {
+    def check(input1: Any, input2: Any, expected: Any): Unit = {
+      val expr = BitwiseXor(Literal(input1), Literal(input2))
+      assert(expr.dataType === Literal(input1).dataType)
+      checkEvaluation(expr, expected)
+    }
+
+    check(1.toByte, 2.toByte, 1.toByte ^ 2.toByte)
+    check(1000.toShort, 2.toShort, 1000.toShort ^ 2.toShort)
+    check(1000000, 4, 1000000 ^ 4)
+    check(123456789123L, 5L, 123456789123L ^ 5L)
+
+    val nullLit = Literal.create(null, IntegerType)
+    checkEvaluation(BitwiseXor(nullLit, Literal(1)), null)
+    checkEvaluation(BitwiseXor(Literal(1), nullLit), null)
+    checkEvaluation(BitwiseXor(nullLit, nullLit), null)
+  }
 }

From 61c3cf793d82f3e8da85bb918d36e95986244786 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 7 Jul 2015 22:12:46 -0700
Subject: [PATCH 0268/1454] [SPARK-8879][SQL] Remove EmptyRow class.

As a baby step towards no megamorphic InternalRow.

Author: Reynold Xin <rxin@databricks.com>

Closes #7277 from rxin/remove-empty-row and squashes the following commits:

594100e [Reynold Xin] [SPARK-8879][SQL] Remove EmptyRow class.
---
 .../expressions/complexTypeCreator.scala      |  2 ++
 .../sql/catalyst/expressions/package.scala    |  7 +++++++
 .../spark/sql/catalyst/expressions/rows.scala | 20 -------------------
 3 files changed, 9 insertions(+), 20 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 65b9d7e608320..d1e4c458864f1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -120,4 +120,6 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
   override def eval(input: InternalRow): Any = {
     InternalRow(valExprs.map(_.eval(input)): _*)
   }
+
+  override def prettyName: String = "named_struct"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
index 5155bfe41ba66..30b7f8d3766a5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst
 
+import org.apache.spark.sql.catalyst.expressions._
+
 /**
  * A set of classes that can be used to represent trees of relational expressions.  A key goal of
  * the expression library is to hide the details of naming and scoping from developers who want to
@@ -49,6 +51,11 @@ package org.apache.spark.sql.catalyst
  */
 package object expressions  {
 
+  /**
+   * Used as input into expressions whose output does not depend on any input value.
+   */
+  val EmptyRow: InternalRow = null
+
   /**
    * Converts a [[InternalRow]] to another Row given a sequence of expression that define each
    * column of the new row. If the schema of the input row is specified, then the given expression
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 1363884c201bc..8b472a529e5c9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -54,26 +54,6 @@ abstract class MutableRow extends InternalRow {
   }
 }
 
-/**
- * A row with no data.  Calling any methods will result in an error.  Can be used as a placeholder.
- */
-object EmptyRow extends InternalRow {
-  override def apply(i: Int): Any = throw new UnsupportedOperationException
-  override def toSeq: Seq[Any] = Seq.empty
-  override def length: Int = 0
-  override def isNullAt(i: Int): Boolean = throw new UnsupportedOperationException
-  override def getInt(i: Int): Int = throw new UnsupportedOperationException
-  override def getLong(i: Int): Long = throw new UnsupportedOperationException
-  override def getDouble(i: Int): Double = throw new UnsupportedOperationException
-  override def getFloat(i: Int): Float = throw new UnsupportedOperationException
-  override def getBoolean(i: Int): Boolean = throw new UnsupportedOperationException
-  override def getShort(i: Int): Short = throw new UnsupportedOperationException
-  override def getByte(i: Int): Byte = throw new UnsupportedOperationException
-  override def getString(i: Int): String = throw new UnsupportedOperationException
-  override def getAs[T](i: Int): T = throw new UnsupportedOperationException
-  override def copy(): InternalRow = this
-}
-
 /**
  * A row implementation that uses an array of objects as the underlying storage.
  */

From 08192a1b8a375f29fbd852bbaed3a50021737475 Mon Sep 17 00:00:00 2001
From: Tijo Thomas <tijoparacka@gmail.com>
Date: Tue, 7 Jul 2015 22:35:39 -0700
Subject: [PATCH 0269/1454] [SPARK-8886][Documentation]python Style update

Fixed comment given by rxin

Author: Tijo Thomas <tijoparacka@gmail.com>

Closes #7281 from tijoparacka/modification_for_python_style and squashes the following commits:

6334e21 [Tijo Thomas] removed space
3de4cd8 [Tijo Thomas] python Style update
---
 docs/sql-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 88c96a9a095b3..7c255789790ed 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1798,7 +1798,7 @@ DataFrame jdbcDF = sqlContext.read().format("jdbc"). options(options).load();
 
 {% highlight python %}
 
-df = sqlContext.read.format('jdbc').options(url = 'jdbc:postgresql:dbserver', dbtable='schema.tablename').load()
+df = sqlContext.read.format('jdbc').options(url='jdbc:postgresql:dbserver', dbtable='schema.tablename').load()
 
 {% endhighlight %}
 

From 351a36d0c54d2f995df956ffb0a4236e12f89aad Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 8 Jul 2015 00:10:24 -0700
Subject: [PATCH 0270/1454] [SPARK-8883][SQL]Remove the
 OverrideFunctionRegistry

Remove the `OverrideFunctionRegistry` from the Spark SQL, as the subclasses of `FunctionRegistry` have their own way to the delegate to the right underlying `FunctionRegistry`.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7260 from chenghao-intel/override and squashes the following commits:

164d093 [Cheng Hao] enable the function registry
2ca8459 [Cheng Hao] remove the OverrideFunctionRegistry
---
 .../sql/catalyst/analysis/FunctionRegistry.scala    | 13 -------------
 .../scala/org/apache/spark/sql/SQLContext.scala     |  3 +--
 .../org/apache/spark/sql/hive/HiveContext.scala     |  2 +-
 .../scala/org/apache/spark/sql/hive/hiveUDFs.scala  |  2 +-
 4 files changed, 3 insertions(+), 17 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index fef276353022c..5c25181e1cf50 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -35,19 +35,6 @@ trait FunctionRegistry {
   def lookupFunction(name: String, children: Seq[Expression]): Expression
 }
 
-class OverrideFunctionRegistry(underlying: FunctionRegistry) extends FunctionRegistry {
-
-  private val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive = false)
-
-  override def registerFunction(name: String, builder: FunctionBuilder): Unit = {
-    functionBuilders.put(name, builder)
-  }
-
-  override def lookupFunction(name: String, children: Seq[Expression]): Expression = {
-    functionBuilders.get(name).map(_(children)).getOrElse(underlying.lookupFunction(name, children))
-  }
-}
-
 class SimpleFunctionRegistry extends FunctionRegistry {
 
   private val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive = false)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index e81371e7b0e83..079f31ab8fe6d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -139,8 +139,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   // TODO how to handle the temp function per user session?
   @transient
-  protected[sql] lazy val functionRegistry: FunctionRegistry =
-    new OverrideFunctionRegistry(FunctionRegistry.builtin)
+  protected[sql] lazy val functionRegistry: FunctionRegistry = FunctionRegistry.builtin
 
   @transient
   protected[sql] lazy val analyzer: Analyzer =
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index b91242af2d155..439d8cab5f257 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -371,7 +371,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   // Note that HiveUDFs will be overridden by functions registered in this context.
   @transient
   override protected[sql] lazy val functionRegistry: FunctionRegistry =
-    new OverrideFunctionRegistry(new HiveFunctionRegistry(FunctionRegistry.builtin))
+    new HiveFunctionRegistry(FunctionRegistry.builtin)
 
   /* An analyzer that uses the Hive metastore. */
   @transient
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 1deef6b37eeac..0bc8adb16afc0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -77,7 +77,7 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
   }
 
   override def registerFunction(name: String, builder: FunctionBuilder): Unit =
-    throw new UnsupportedOperationException
+    underlying.registerFunction(name, builder)
 }
 
 private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])

From 8a9d9cc1561cf157793c90db6700ffa6f1f00a69 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Wed, 8 Jul 2015 12:23:32 +0100
Subject: [PATCH 0271/1454] [SPARK-7050] [BUILD] Fix Python Kafka test assembly
 jar not found issue under Maven build

 To fix Spark Streaming unit test with maven build. Previously the name and path of maven generated jar is different from sbt, which will lead to following exception. This fix keep the same behavior with both Maven and sbt build.

```
Failed to find Spark Streaming Kafka assembly jar in /home/xyz/spark/external/kafka-assembly
You need to build Spark with  'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or 'build/mvn package' before running this program
```

Author: jerryshao <saisai.shao@intel.com>

Closes #5632 from jerryshao/SPARK-7050 and squashes the following commits:

74b068d [jerryshao] Fix mvn build issue
---
 external/kafka-assembly/pom.xml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml
index 8059c443827ef..977514fa5a1ec 100644
--- a/external/kafka-assembly/pom.xml
+++ b/external/kafka-assembly/pom.xml
@@ -58,6 +58,7 @@
       <artifactId>maven-shade-plugin</artifactId>
       <configuration>
         <shadedArtifactAttached>false</shadedArtifactAttached>
+        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-kafka-assembly-${project.version}.jar</outputFile>
         <artifactSet>
           <includes>
             <include>*:*</include>

From 3bb217750ada18a49c40d974ac57050ef2abfd2c Mon Sep 17 00:00:00 2001
From: Kashif Rasul <kashif.rasul@gmail.com>
Date: Wed, 8 Jul 2015 08:44:58 -0700
Subject: [PATCH 0272/1454] [SPARK-8872] [MLLIB] added verification results
 from R for FPGrowthSuite

Author: Kashif Rasul <kashif.rasul@gmail.com>

Closes #7269 from kashif/SPARK-8872 and squashes the following commits:

2d5457f [Kashif Rasul] added R code for FP Int type
3de6808 [Kashif Rasul] added verification results from R for FPGrowthSuite
---
 .../spark/mllib/fpm/FPGrowthSuite.scala       | 114 ++++++++++++++++++
 1 file changed, 114 insertions(+)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
index 66ae3543ecc4e..ddc296a428907 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
@@ -39,6 +39,22 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setMinSupport(0.9)
       .setNumPartitions(1)
       .run(rdd)
+
+    /* Verify results using the `R` code:
+       transactions = as(sapply(
+         list("r z h k p",
+              "z y x w v u t s",
+              "s x o n r",
+              "x z y m t s q e",
+              "z",
+              "x z y r q t p"),
+         FUN=function(x) strsplit(x," ",fixed=TRUE)),
+         "transactions")
+       > eclat(transactions, parameter = list(support = 0.9))
+       ...
+       eclat - zero frequent items
+       set of 0 itemsets
+     */
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
@@ -48,6 +64,33 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val freqItemsets3 = model3.freqItemsets.collect().map { itemset =>
       (itemset.items.toSet, itemset.freq)
     }
+
+    /* Verify results using the `R` code:
+       fp = eclat(transactions, parameter = list(support = 0.5))
+       fpDF = as(sort(fp), "data.frame")
+       fpDF$support = fpDF$support * length(transactions)
+       names(fpDF)[names(fpDF) == "support"] = "freq"
+       > fpDF
+              items freq
+       13       {z}    5
+       14       {x}    4
+       1      {s,x}    3
+       2  {t,x,y,z}    3
+       3    {t,y,z}    3
+       4    {t,x,y}    3
+       5    {x,y,z}    3
+       6      {y,z}    3
+       7      {x,y}    3
+       8      {t,y}    3
+       9    {t,x,z}    3
+       10     {t,z}    3
+       11     {t,x}    3
+       12     {x,z}    3
+       15       {t}    3
+       16       {y}    3
+       17       {s}    3
+       18       {r}    3
+     */
     val expected = Set(
       (Set("s"), 3L), (Set("z"), 5L), (Set("x"), 4L), (Set("t"), 3L), (Set("y"), 3L),
       (Set("r"), 3L),
@@ -62,12 +105,30 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setMinSupport(0.3)
       .setNumPartitions(4)
       .run(rdd)
+
+    /* Verify results using the `R` code:
+       fp = eclat(transactions, parameter = list(support = 0.3))
+       fpDF = as(fp, "data.frame")
+       fpDF$support = fpDF$support * length(transactions)
+       names(fpDF)[names(fpDF) == "support"] = "freq"
+       > nrow(fpDF)
+       [1] 54
+     */
     assert(model2.freqItemsets.count() === 54)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
       .run(rdd)
+
+    /* Verify results using the `R` code:
+       fp = eclat(transactions, parameter = list(support = 0.1))
+       fpDF = as(fp, "data.frame")
+       fpDF$support = fpDF$support * length(transactions)
+       names(fpDF)[names(fpDF) == "support"] = "freq"
+       > nrow(fpDF)
+       [1] 625
+     */
     assert(model1.freqItemsets.count() === 625)
   }
 
@@ -89,6 +150,23 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setMinSupport(0.9)
       .setNumPartitions(1)
       .run(rdd)
+
+    /* Verify results using the `R` code:
+       transactions = as(sapply(
+         list("1 2 3",
+              "1 2 3 4",
+              "5 4 3 2 1",
+              "6 5 4 3 2 1",
+              "2 4",
+              "1 3",
+              "1 7"),
+         FUN=function(x) strsplit(x," ",fixed=TRUE)),
+         "transactions")
+       > eclat(transactions, parameter = list(support = 0.9))
+       ...
+       eclat - zero frequent items
+       set of 0 itemsets
+     */
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
@@ -100,6 +178,24 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val freqItemsets3 = model3.freqItemsets.collect().map { itemset =>
       (itemset.items.toSet, itemset.freq)
     }
+
+    /* Verify results using the `R` code:
+       fp = eclat(transactions, parameter = list(support = 0.5))
+       fpDF = as(sort(fp), "data.frame")
+       fpDF$support = fpDF$support * length(transactions)
+       names(fpDF)[names(fpDF) == "support"] = "freq"
+       > fpDF
+          items freq
+      6     {1}    6
+      3   {1,3}    5
+      7     {2}    5
+      8     {3}    5
+      1   {2,4}    4
+      2 {1,2,3}    4
+      4   {2,3}    4
+      5   {1,2}    4
+      9     {4}    4
+     */
     val expected = Set(
       (Set(1), 6L), (Set(2), 5L), (Set(3), 5L), (Set(4), 4L),
       (Set(1, 2), 4L), (Set(1, 3), 5L), (Set(2, 3), 4L),
@@ -110,12 +206,30 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setMinSupport(0.3)
       .setNumPartitions(4)
       .run(rdd)
+
+    /* Verify results using the `R` code:
+       fp = eclat(transactions, parameter = list(support = 0.3))
+       fpDF = as(fp, "data.frame")
+       fpDF$support = fpDF$support * length(transactions)
+       names(fpDF)[names(fpDF) == "support"] = "freq"
+       > nrow(fpDF)
+       [1] 15
+     */
     assert(model2.freqItemsets.count() === 15)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
       .run(rdd)
+
+    /* Verify results using the `R` code:
+       fp = eclat(transactions, parameter = list(support = 0.1))
+       fpDF = as(fp, "data.frame")
+       fpDF$support = fpDF$support * length(transactions)
+       names(fpDF)[names(fpDF) == "support"] = "freq"
+       > nrow(fpDF)
+       [1] 65
+     */
     assert(model1.freqItemsets.count() === 65)
   }
 }

From bf02e377168f39459d5c216e939097ae5705f573 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Wed, 8 Jul 2015 09:48:16 -0700
Subject: [PATCH 0273/1454] [SPARK-8894] [SPARKR] [DOC] Example code errors in
 SparkR documentation.

Author: Sun Rui <rui.sun@intel.com>

Closes #7287 from sun-rui/SPARK-8894 and squashes the following commits:

da63898 [Sun Rui] [SPARK-8894][SPARKR][DOC] Example code errors in SparkR documentation.
---
 docs/sparkr.md                | 2 +-
 docs/sql-programming-guide.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/sparkr.md b/docs/sparkr.md
index 095ea4308cfeb..1197c0d601ebd 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -116,7 +116,7 @@ sql(hiveContext, "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
 sql(hiveContext, "LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
 
 # Queries can be expressed in HiveQL.
-results <- hiveContext.sql("FROM src SELECT key, value")
+results <- sql(hiveContext, "FROM src SELECT key, value")
 
 # results is now a DataFrame
 head(results)
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 7c255789790ed..26f6ba84b91b2 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1637,7 +1637,7 @@ sql(sqlContext, "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
 sql(sqlContext, "LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
 
 # Queries can be expressed in HiveQL.
-results = sqlContext.sql("FROM src SELECT key, value").collect()
+results <- collect(sql(sqlContext, "FROM src SELECT key, value"))
 
 {% endhighlight %}
 

From 6722aca809ddc28aa20abf3bbb2e0de8629a9903 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Wed, 8 Jul 2015 10:09:50 -0700
Subject: [PATCH 0274/1454] [SPARK-8785] [SQL] Improve Parquet schema merging

JIRA: https://issues.apache.org/jira/browse/SPARK-8785

Currently, the parquet schema merging (`ParquetRelation2.readSchema`) may spend much time to merge duplicate schema. We can select only non duplicate schema and merge them later.

Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7182 from viirya/improve_parquet_merging and squashes the following commits:

5cf934f [Liang-Chi Hsieh] Refactor it to make it faster.
f3411ea [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into improve_parquet_merging
a63c3ff [Liang-Chi Hsieh] Improve Parquet schema merging.
---
 .../apache/spark/sql/parquet/newParquet.scala | 82 +++++++++++--------
 1 file changed, 48 insertions(+), 34 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index 6bc69c6ad0847..ce456e7fbe17e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -21,6 +21,7 @@ import java.net.URI
 import java.util.{List => JList}
 
 import scala.collection.JavaConversions._
+import scala.collection.mutable
 import scala.util.Try
 
 import com.google.common.base.Objects
@@ -30,8 +31,9 @@ import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.parquet.filter2.predicate.FilterApi
 import org.apache.parquet.hadoop._
-import org.apache.parquet.hadoop.metadata.CompressionCodecName
+import org.apache.parquet.hadoop.metadata.{FileMetaData, CompressionCodecName}
 import org.apache.parquet.hadoop.util.ContextUtil
+import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
@@ -508,44 +510,56 @@ private[sql] object ParquetRelation2 extends Logging {
 
   private[parquet] def readSchema(
       footers: Seq[Footer], sqlContext: SQLContext): Option[StructType] = {
-    footers.map { footer =>
+
+    def parseParquetSchema(schema: MessageType): StructType = {
+      StructType.fromAttributes(
+        // TODO Really no need to use `Attribute` here, we only need to know the data type.
+        ParquetTypesConverter.convertToAttributes(
+          schema,
+          sqlContext.conf.isParquetBinaryAsString,
+          sqlContext.conf.isParquetINT96AsTimestamp))
+    }
+
+    val seen = mutable.HashSet[String]()
+    val finalSchemas: Seq[StructType] = footers.flatMap { footer =>
       val metadata = footer.getParquetMetadata.getFileMetaData
-      val parquetSchema = metadata.getSchema
-      val maybeSparkSchema = metadata
+      val serializedSchema = metadata
         .getKeyValueMetaData
         .toMap
         .get(RowReadSupport.SPARK_METADATA_KEY)
-        .flatMap { serializedSchema =>
-          // Don't throw even if we failed to parse the serialized Spark schema. Just fallback to
-          // whatever is available.
-          Try(DataType.fromJson(serializedSchema))
-            .recover { case _: Throwable =>
-              logInfo(
-                s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " +
-                  "falling back to the deprecated DataType.fromCaseClassString parser.")
-              DataType.fromCaseClassString(serializedSchema)
-            }
-            .recover { case cause: Throwable =>
-              logWarning(
-                s"""Failed to parse serialized Spark schema in Parquet key-value metadata:
-                   |\t$serializedSchema
-                 """.stripMargin,
-                cause)
-            }
-            .map(_.asInstanceOf[StructType])
-            .toOption
-        }
-
-      maybeSparkSchema.getOrElse {
-        // Falls back to Parquet schema if Spark SQL schema is absent.
-        StructType.fromAttributes(
-          // TODO Really no need to use `Attribute` here, we only need to know the data type.
-          ParquetTypesConverter.convertToAttributes(
-            parquetSchema,
-            sqlContext.conf.isParquetBinaryAsString,
-            sqlContext.conf.isParquetINT96AsTimestamp))
+      if (serializedSchema == None) {
+        // Falls back to Parquet schema if no Spark SQL schema found.
+        Some(parseParquetSchema(metadata.getSchema))
+      } else if (!seen.contains(serializedSchema.get)) {
+        seen += serializedSchema.get
+
+        // Don't throw even if we failed to parse the serialized Spark schema. Just fallback to
+        // whatever is available.
+        Some(Try(DataType.fromJson(serializedSchema.get))
+          .recover { case _: Throwable =>
+            logInfo(
+              s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " +
+                "falling back to the deprecated DataType.fromCaseClassString parser.")
+            DataType.fromCaseClassString(serializedSchema.get)
+          }
+          .recover { case cause: Throwable =>
+            logWarning(
+              s"""Failed to parse serialized Spark schema in Parquet key-value metadata:
+                 |\t$serializedSchema
+               """.stripMargin,
+              cause)
+          }
+          .map(_.asInstanceOf[StructType])
+          .getOrElse {
+            // Falls back to Parquet schema if Spark SQL schema can't be parsed.
+            parseParquetSchema(metadata.getSchema)
+          })
+      } else {
+        None
       }
-    }.reduceOption { (left, right) =>
+    }
+
+    finalSchemas.reduceOption { (left, right) =>
       try left.merge(right) catch { case e: Throwable =>
         throw new SparkException(s"Failed to merge incompatible schemas $left and $right", e)
       }

From 3e831a26965a5e92210431f9ad6935f70aa01b48 Mon Sep 17 00:00:00 2001
From: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Date: Wed, 8 Jul 2015 10:33:27 -0700
Subject: [PATCH 0275/1454] [SPARK-6912] [SQL] Throw an AnalysisException when
 unsupported Java Map<K,V> types used in Hive UDF

To make UDF developers understood, throw an exception when unsupported Map<K,V> types used in Hive UDF. This fix is the same with #7248.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #7257 from maropu/ThrowExceptionWhenMapUsed and squashes the following commits:

916099a [Takeshi YAMAMURO] Fix style errors
7886dcc [Takeshi YAMAMURO] Throw an exception when Map<> used in Hive UDF
---
 .../spark/sql/hive/HiveInspectors.scala       |  6 ++++
 .../sql/hive/execution/UDFToIntIntMap.java    | 35 +++++++++++++++++++
 .../sql/hive/execution/UDFToStringIntMap.java | 35 +++++++++++++++++++
 .../sql/hive/execution/HiveUDFSuite.scala     | 32 +++++++++++++++++
 4 files changed, 108 insertions(+)
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToIntIntMap.java
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToStringIntMap.java

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 924e2ba3ba442..4cba17524af6c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -226,6 +226,12 @@ private[hive] trait HiveInspectors {
         "List type in java is unsupported because " +
         "JVM type erasure makes spark fail to catch a component type in List<>")
 
+    // java map type unsupported
+    case c: Class[_] if c == classOf[java.util.Map[_, _]] =>
+      throw new AnalysisException(
+        "Map type in java is unsupported because " +
+        "JVM type erasure makes spark fail to catch key and value types in Map<>")
+
     case c => throw new AnalysisException(s"Unsupported java type $c")
   }
 
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToIntIntMap.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToIntIntMap.java
new file mode 100644
index 0000000000000..b3e8bcbbd8221
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToIntIntMap.java
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution;
+
+import org.apache.hadoop.hive.ql.exec.UDF;
+
+import java.util.HashMap;
+import java.util.Map;
+
+public class UDFToIntIntMap extends UDF {
+    public Map<Integer, Integer> evaluate(Object o) {
+        return new HashMap<Integer, Integer>() {
+            {
+                put(1, 1);
+                put(2, 1);
+                put(3, 1);
+            }
+        };
+    }
+}
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToStringIntMap.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToStringIntMap.java
new file mode 100644
index 0000000000000..9eea5c9a881fd
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFToStringIntMap.java
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution;
+
+import org.apache.hadoop.hive.ql.exec.UDF;
+
+import java.util.HashMap;
+import java.util.Map;
+
+public class UDFToStringIntMap extends UDF {
+    public Map<String, Integer> evaluate(Object o) {
+        return new HashMap<String, Integer>() {
+            {
+                put("key1", 1);
+                put("key2", 2);
+                put("key3", 3);
+            }
+        };
+    }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 44686204c2af7..eaaa88e17002b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -163,6 +163,38 @@ class HiveUDFSuite extends QueryTest {
     TestHive.reset()
   }
 
+  test("UDFToStringIntMap") {
+    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    testData.registerTempTable("inputTable")
+
+    sql(s"CREATE TEMPORARY FUNCTION testUDFToStringIntMap " +
+      s"AS '${classOf[UDFToStringIntMap].getName}'")
+    val errMsg = intercept[AnalysisException] {
+      sql("SELECT testUDFToStringIntMap(s) FROM inputTable")
+    }
+    assert(errMsg.getMessage === "Map type in java is unsupported because " +
+      "JVM type erasure makes spark fail to catch key and value types in Map<>;")
+
+    sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToStringIntMap")
+    TestHive.reset()
+  }
+
+  test("UDFToIntIntMap") {
+    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    testData.registerTempTable("inputTable")
+
+    sql(s"CREATE TEMPORARY FUNCTION testUDFToIntIntMap " +
+      s"AS '${classOf[UDFToIntIntMap].getName}'")
+    val errMsg = intercept[AnalysisException] {
+      sql("SELECT testUDFToIntIntMap(s) FROM inputTable")
+    }
+    assert(errMsg.getMessage === "Map type in java is unsupported because " +
+      "JVM type erasure makes spark fail to catch key and value types in Map<>;")
+
+    sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToIntIntMap")
+    TestHive.reset()
+  }
+
   test("UDFListListInt") {
     val testData = TestHive.sparkContext.parallelize(
       ListListIntCaseClass(Nil) ::

From 74335b31072951244967f878d8b766cd1bfc2ac6 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 8 Jul 2015 10:43:00 -0700
Subject: [PATCH 0276/1454] [SPARK-5707] [SQL] fix serialization of generated
 projection

Author: Davies Liu <davies@databricks.com>

Closes #7272 from davies/fix_projection and squashes the following commits:

075ef76 [Davies Liu] fix codegen with BroadcastHashJion
---
 .../spark/sql/execution/joins/BroadcastHashOuterJoin.scala     | 3 +--
 .../org/apache/spark/sql/execution/joins/HashOuterJoin.scala   | 2 +-
 .../org/apache/spark/sql/execution/joins/HashedRelation.scala  | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index 06c244f211aba..ab757fc7de6cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -79,8 +79,7 @@ case class BroadcastHashOuterJoin(
     // Note that we use .execute().collect() because we don't want to convert data to Scala types
     val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
     // buildHashTable uses code-generated rows as keys, which are not serializable
-    val hashed =
-      buildHashTable(input.iterator, new InterpretedProjection(buildKeys, buildPlan.output))
+    val hashed = buildHashTable(input.iterator, newProjection(buildKeys, buildPlan.output))
     sparkContext.broadcast(hashed)
   }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 333745187728e..0522ee85eeb8a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -171,7 +171,7 @@ override def outputPartitioning: Partitioning = joinType match {
       var existingMatchList = hashTable.get(rowKey)
       if (existingMatchList == null) {
         existingMatchList = new CompactBuffer[InternalRow]()
-        hashTable.put(rowKey, existingMatchList)
+        hashTable.put(rowKey.copy(), existingMatchList)
       }
 
       existingMatchList += currentRow.copy()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index de062c791f5fb..6b51f5d4151d3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -125,7 +125,7 @@ private[joins] object HashedRelation {
         val existingMatchList = hashTable.get(rowKey)
         val matchList = if (existingMatchList == null) {
           val newMatchList = new CompactBuffer[InternalRow]()
-          hashTable.put(rowKey, newMatchList)
+          hashTable.put(rowKey.copy(), newMatchList)
           newMatchList
         } else {
           keyIsUnique = false

From 0ba98c04c726a827df8cb19b0db17c352a647960 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 8 Jul 2015 10:51:32 -0700
Subject: [PATCH 0277/1454] [SPARK-8753][SQL] Create an IntervalType data type

We need a new data type to represent time intervals. Because we can't determine how many days in a month, so we need 2 values for interval: a int `months`, a long `microseconds`.

The interval literal syntax looks like:
`interval 3 years -4 month 4 weeks 3 second`

Because we use number of 100ns as value of `TimestampType`, so it may not makes sense to support nano second unit.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7226 from cloud-fan/interval and squashes the following commits:

632062d [Wenchen Fan] address comments
ac348c3 [Wenchen Fan] use case class
0342d2e [Wenchen Fan] use array byte
df9256c [Wenchen Fan] fix style
fd6f18a [Wenchen Fan] address comments
1856af3 [Wenchen Fan] support interval type
---
 .../org/apache/spark/sql/types/DataTypes.java |  5 ++
 .../apache/spark/sql/catalyst/SqlParser.scala | 86 +++++++++++++++----
 .../apache/spark/sql/types/IntervalType.scala | 37 ++++++++
 .../spark/sql/types/TimestampType.scala       |  2 +-
 .../org/apache/spark/sql/sources/ddl.scala    |  3 +
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 25 ++++++
 .../apache/spark/unsafe/types/Interval.java   | 47 ++++++++++
 7 files changed, 185 insertions(+), 20 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntervalType.scala
 create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java b/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
index e457542c647e7..d22ad6794d608 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
@@ -49,6 +49,11 @@ public class DataTypes {
    */
   public static final DataType TimestampType = TimestampType$.MODULE$;
 
+  /**
+   * Gets the IntervalType object.
+   */
+  public static final DataType IntervalType = IntervalType$.MODULE$;
+
   /**
    * Gets the DoubleType object.
    */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index e8e9b9802e94b..dedd8c8fa3620 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.Interval
 
 /**
  * A very simple SQL parser.  Based loosely on:
@@ -72,6 +73,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
   protected val INNER = Keyword("INNER")
   protected val INSERT = Keyword("INSERT")
   protected val INTERSECT = Keyword("INTERSECT")
+  protected val INTERVAL = Keyword("INTERVAL")
   protected val INTO = Keyword("INTO")
   protected val IS = Keyword("IS")
   protected val JOIN = Keyword("JOIN")
@@ -279,12 +281,12 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
         throw new AnalysisException(s"invalid function approximate $udfName")
       }
     }
-    | APPROXIMATE ~> "(" ~> floatLit ~ ")" ~ ident ~ "(" ~ DISTINCT ~ expression <~ ")" ^^
+    | APPROXIMATE ~> "(" ~> unsignedFloat ~ ")" ~ ident ~ "(" ~ DISTINCT ~ expression <~ ")" ^^
       { case s ~ _ ~ udfName ~ _ ~ _ ~ exp =>
         if (lexical.normalizeKeyword(udfName) == "count") {
           ApproxCountDistinct(exp, s.toDouble)
         } else {
-          throw new AnalysisException(s"invalid function approximate($floatLit) $udfName")
+          throw new AnalysisException(s"invalid function approximate($s) $udfName")
         }
       }
     | CASE ~> whenThenElse ^^ CaseWhen
@@ -309,6 +311,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     ( numericLiteral
     | booleanLiteral
     | stringLit ^^ {case s => Literal.create(s, StringType) }
+    | intervalLiteral
     | NULL ^^^ Literal.create(null, NullType)
     )
 
@@ -318,21 +321,71 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     )
 
   protected lazy val numericLiteral: Parser[Literal] =
-    signedNumericLiteral | unsignedNumericLiteral
-
-  protected lazy val sign: Parser[String] =
-    "+" | "-"
-
-  protected lazy val signedNumericLiteral: Parser[Literal] =
-    ( sign ~ numericLit  ^^ { case s ~ l => Literal(toNarrowestIntegerType(s + l)) }
-    | sign ~ floatLit ^^ { case s ~ f => Literal((s + f).toDouble) }
+    ( integral  ^^ { case i => Literal(toNarrowestIntegerType(i)) }
+    | sign.? ~ unsignedFloat ^^ { case s ~ f => Literal((s.getOrElse("") + f).toDouble) }
     )
 
-  protected lazy val unsignedNumericLiteral: Parser[Literal] =
-    ( numericLit ^^ { n => Literal(toNarrowestIntegerType(n)) }
-    | floatLit ^^ { f => Literal(f.toDouble) }
+  protected lazy val unsignedFloat: Parser[String] =
+    ( "." ~> numericLit ^^ { u => "0." + u }
+    | elem("decimal", _.isInstanceOf[lexical.FloatLit]) ^^ (_.chars)
     )
 
+  protected lazy val sign: Parser[String] = ("+" | "-")
+
+  protected lazy val integral: Parser[String] =
+    sign.? ~ numericLit ^^ { case s ~ n => s.getOrElse("") + n }
+
+  private def intervalUnit(unitName: String) =
+    acceptIf {
+      case lexical.Identifier(str) =>
+        val normalized = lexical.normalizeKeyword(str)
+        normalized == unitName || normalized == unitName + "s"
+      case _ => false
+    } {_ => "wrong interval unit"}
+
+  protected lazy val month: Parser[Int] =
+    integral <~ intervalUnit("month") ^^ { case num => num.toInt }
+
+  protected lazy val year: Parser[Int] =
+    integral <~ intervalUnit("year") ^^ { case num => num.toInt * 12 }
+
+  protected lazy val microsecond: Parser[Long] =
+    integral <~ intervalUnit("microsecond") ^^ { case num => num.toLong }
+
+  protected lazy val millisecond: Parser[Long] =
+    integral <~ intervalUnit("millisecond") ^^ { case num => num.toLong * 1000 }
+
+  protected lazy val second: Parser[Long] =
+    integral <~ intervalUnit("second") ^^ { case num => num.toLong * 1000 * 1000 }
+
+  protected lazy val minute: Parser[Long] =
+    integral <~ intervalUnit("minute") ^^ { case num => num.toLong * 1000 * 1000 * 60 }
+
+  protected lazy val hour: Parser[Long] =
+    integral <~ intervalUnit("hour") ^^ { case num => num.toLong * 1000 * 1000 * 3600 }
+
+  protected lazy val day: Parser[Long] =
+    integral <~ intervalUnit("day") ^^ { case num => num.toLong * 1000 * 1000 * 3600 * 24 }
+
+  protected lazy val week: Parser[Long] =
+    integral <~ intervalUnit("week") ^^ { case num => num.toLong * 1000 * 1000 * 3600 * 24 * 7 }
+
+  protected lazy val intervalLiteral: Parser[Literal] =
+    INTERVAL ~> year.? ~ month.? ~ week.? ~ day.? ~ hour.? ~ minute.? ~ second.? ~
+      millisecond.? ~ microsecond.? ^^ {
+        case year ~ month ~ week ~ day ~ hour ~ minute ~ second ~
+          millisecond ~ microsecond =>
+          if (!Seq(year, month, week, day, hour, minute, second,
+            millisecond, microsecond).exists(_.isDefined)) {
+            throw new AnalysisException(
+              "at least one time unit should be given for interval literal")
+          }
+          val months = Seq(year, month).map(_.getOrElse(0)).sum
+          val microseconds = Seq(week, day, hour, minute, second, millisecond, microsecond)
+            .map(_.getOrElse(0L)).sum
+          Literal.create(new Interval(months, microseconds), IntervalType)
+      }
+
   private def toNarrowestIntegerType(value: String): Any = {
     val bigIntValue = BigDecimal(value)
 
@@ -343,11 +396,6 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     }
   }
 
-  protected lazy val floatLit: Parser[String] =
-    ( "." ~> unsignedNumericLiteral ^^ { u => "0." + u }
-    | elem("decimal", _.isInstanceOf[lexical.FloatLit]) ^^ (_.chars)
-    )
-
   protected lazy val baseExpression: Parser[Expression] =
     ( "*" ^^^ UnresolvedStar(None)
     | ident <~ "." ~ "*" ^^ { case tableName => UnresolvedStar(Option(tableName)) }
@@ -355,7 +403,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     )
 
   protected lazy val signedPrimary: Parser[Expression] =
-    sign ~ primary ^^ { case s ~ e => if (s == "-") UnaryMinus(e) else e}
+    sign ~ primary ^^ { case s ~ e => if (s == "-") UnaryMinus(e) else e }
 
   protected lazy val attributeName: Parser[String] = acceptMatch("attribute name", {
     case lexical.Identifier(str) => str
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntervalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntervalType.scala
new file mode 100644
index 0000000000000..87c6e9e6e5e2c
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntervalType.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types
+
+import org.apache.spark.annotation.DeveloperApi
+
+
+/**
+ * :: DeveloperApi ::
+ * The data type representing time intervals.
+ *
+ * Please use the singleton [[DataTypes.IntervalType]].
+ */
+@DeveloperApi
+class IntervalType private() extends DataType {
+
+  override def defaultSize: Int = 4096
+
+  private[spark] override def asNullable: IntervalType = this
+}
+
+case object IntervalType extends IntervalType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala
index de4b511edccd9..2be9b2d76c9fe 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala
@@ -41,7 +41,7 @@ class TimestampType private() extends AtomicType {
   private[sql] val ordering = implicitly[Ordering[InternalType]]
 
   /**
-   * The default size of a value of the TimestampType is 12 bytes.
+   * The default size of a value of the TimestampType is 8 bytes.
    */
   override def defaultSize: Int = 8
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
index 1f0b93e2850ba..d7440c55bd4a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
@@ -304,6 +304,9 @@ private[sql] object ResolvedDataSource {
       mode: SaveMode,
       options: Map[String, String],
       data: DataFrame): ResolvedDataSource = {
+    if (data.schema.map(_.dataType).exists(_.isInstanceOf[IntervalType])) {
+      throw new AnalysisException("Cannot save interval data type into external storage.")
+    }
     val clazz: Class[_] = lookupDataSource(provider)
     val relation = clazz.newInstance() match {
       case dataSource: CreatableRelationProvider =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 12ad019e8b473..231440892bf0b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1467,4 +1467,29 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       checkAnswer(sql("select count, sort from t"), Row(1, "a"))
     }
   }
+
+  test("SPARK-8753: add interval type") {
+    import org.apache.spark.unsafe.types.Interval
+
+    val df = sql("select interval 3 years -3 month 7 week 123 microseconds")
+    checkAnswer(df, Row(new Interval(12 * 3 - 3, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123 )))
+    withTempPath(f => {
+      // Currently we don't yet support saving out values of interval data type.
+      val e = intercept[AnalysisException] {
+        df.write.json(f.getCanonicalPath)
+      }
+      e.message.contains("Cannot save interval data type into external storage")
+    })
+
+    def checkIntervalParseError(s: String): Unit = {
+      val e = intercept[AnalysisException] {
+        sql(s)
+      }
+      e.message.contains("at least one time unit should be given for interval literal")
+    }
+
+    checkIntervalParseError("select interval")
+    // Currently we don't yet support nanosecond
+    checkIntervalParseError("select interval 23 nanosecond")
+  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
new file mode 100644
index 0000000000000..3eb67ede062d9
--- /dev/null
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.unsafe.types;
+
+import java.io.Serializable;
+
+/**
+ * The internal representation of interval type.
+ */
+public final class Interval implements Serializable {
+  public final int months;
+  public final long microseconds;
+
+  public Interval(int months, long microseconds) {
+    this.months = months;
+    this.microseconds = microseconds;
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (this == other) return true;
+    if (other == null || !(other instanceof Interval)) return false;
+
+    Interval o = (Interval) other;
+    return this.months == o.months && this.microseconds == o.microseconds;
+  }
+
+  @Override
+  public int hashCode() {
+    return 31 * months + (int) microseconds;
+  }
+}

From f61c989b404808f79a58b6503cf3835cf602528a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 8 Jul 2015 10:56:31 -0700
Subject: [PATCH 0278/1454] [SPARK-8888][SQL] Use java.util.HashMap in
 DynamicPartitionWriterContainer.

Just a baby step towards making it more efficient.

Author: Reynold Xin <rxin@databricks.com>

Closes #7282 from rxin/SPARK-8888 and squashes the following commits:

3da51ae [Reynold Xin] [SPARK-8888][SQL] Use java.util.HashMap in DynamicPartitionWriterContainer.
---
 .../apache/spark/sql/sources/commands.scala   | 36 ++++++++++++-------
 1 file changed, 23 insertions(+), 13 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index a97142d4a1142..ecbc889770625 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -19,8 +19,6 @@ package org.apache.spark.sql.sources
 
 import java.util.{Date, UUID}
 
-import scala.collection.mutable
-
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
@@ -110,7 +108,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
         !exists
     }
     // If we are appending data to an existing dir.
-    val isAppend = (pathExists) && (mode == SaveMode.Append)
+    val isAppend = pathExists && (mode == SaveMode.Append)
 
     if (doInsertion) {
       val job = new Job(hadoopConf)
@@ -142,9 +140,12 @@ private[sql] case class InsertIntoHadoopFsRelation(
       }
     }
 
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 
+  /**
+   * Inserts the content of the [[DataFrame]] into a table without any partitioning columns.
+   */
   private def insert(writerContainer: BaseWriterContainer, df: DataFrame): Unit = {
     // Uses local vals for serialization
     val needsConversion = relation.needConversion
@@ -188,6 +189,9 @@ private[sql] case class InsertIntoHadoopFsRelation(
     }
   }
 
+  /**
+   * Inserts the content of the [[DataFrame]] into a table with partitioning columns.
+   */
   private def insertWithDynamicPartitions(
       sqlContext: SQLContext,
       writerContainer: BaseWriterContainer,
@@ -497,13 +501,14 @@ private[sql] class DynamicPartitionWriterContainer(
   extends BaseWriterContainer(relation, job, isAppend) {
 
   // All output writers are created on executor side.
-  @transient protected var outputWriters: mutable.Map[String, OutputWriter] = _
+  @transient protected var outputWriters: java.util.HashMap[String, OutputWriter] = _
 
   override protected def initWriters(): Unit = {
-    outputWriters = mutable.Map.empty[String, OutputWriter]
+    outputWriters = new java.util.HashMap[String, OutputWriter]
   }
 
   override def outputWriterForRow(row: Row): OutputWriter = {
+    // TODO (SPARK-8888): zip and all the stuff happening here is very inefficient.
     val partitionPath = partitionColumns.zip(row.toSeq).map { case (col, rawValue) =>
       val string = if (rawValue == null) null else String.valueOf(rawValue)
       val valueString = if (string == null || string.isEmpty) {
@@ -514,18 +519,23 @@ private[sql] class DynamicPartitionWriterContainer(
       s"/$col=$valueString"
     }.mkString.stripPrefix(Path.SEPARATOR)
 
-    outputWriters.getOrElseUpdate(partitionPath, {
+    val writer = outputWriters.get(partitionPath)
+    if (writer.eq(null)) {
       val path = new Path(getWorkPath, partitionPath)
-      taskAttemptContext.getConfiguration.set(
-        "spark.sql.sources.output.path",
+      taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path",
         new Path(outputPath, partitionPath).toString)
-      outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
-    })
+      val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
+      outputWriters.put(partitionPath, newWriter)
+      newWriter
+    } else {
+      writer
+    }
   }
 
   private def clearOutputWriters(): Unit = {
-    if (outputWriters.nonEmpty) {
-      outputWriters.values.foreach(_.close())
+    if (!outputWriters.isEmpty) {
+      val iter = scala.collection.JavaConversions.asScalaIterator(outputWriters.values().iterator())
+      iter.foreach(_.close())
       outputWriters.clear()
     }
   }

From 26d9b6b8cae9ac6593f78ab98dd45a25d03cf71c Mon Sep 17 00:00:00 2001
From: Tao Li <litao@sogou-inc.com>
Date: Wed, 8 Jul 2015 19:02:24 +0100
Subject: [PATCH 0279/1454] [SPARK-8657] [YARN] Fail to upload resource to
 viewfs

Fail to upload resource to viewfs in spark-1.4
JIRA Link: https://issues.apache.org/jira/browse/SPARK-8657

Author: Tao Li <litao@sogou-inc.com>

Closes #7125 from litao-buptsse/SPARK-8657-for-master and squashes the following commits:

65b13f4 [Tao Li] [SPARK-8657] [YARN] Fail to upload resource to viewfs
---
 yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 67a5c95400e53..4d52ae774ea00 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -321,8 +321,9 @@ private[spark] class Client(
           val linkname = targetDir.map(_ + "/").getOrElse("") +
             destName.orElse(Option(localURI.getFragment())).getOrElse(localPath.getName())
           val destPath = copyFileToRemote(dst, localPath, replication)
+          val destFs = FileSystem.get(destPath.toUri(), hadoopConf)
           distCacheMgr.addResource(
-            fs, hadoopConf, destPath, localResources, resType, linkname, statCache,
+            destFs, hadoopConf, destPath, localResources, resType, linkname, statCache,
             appMasterOnly = appMasterOnly)
           (false, linkname)
         } else {

From 374c8a8a4a8ac4171d312a6c31080a6724e55c60 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Wed, 8 Jul 2015 12:39:32 -0700
Subject: [PATCH 0280/1454] [SPARK-8900] [SPARKR] Fix sparkPackages in init
 documentation

cc pwendell

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7293 from shivaram/sparkr-packages-doc and squashes the following commits:

c91471d [Shivaram Venkataraman] Fix sparkPackages in init documentation
---
 docs/sparkr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sparkr.md b/docs/sparkr.md
index 1197c0d601ebd..4385a4eeacd5c 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -68,7 +68,7 @@ you can specify the packages with the `packages` argument.
 
 <div data-lang="r" markdown="1">
 {% highlight r %}
-sc <- sparkR.init(packages="com.databricks:spark-csv_2.11:1.0.3")
+sc <- sparkR.init(sparkPackages="com.databricks:spark-csv_2.11:1.0.3")
 sqlContext <- sparkRSQL.init(sc)
 {% endhighlight %}
 </div>

From 2b40365d76b7d9d382ad5077cdf979906bca17f2 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Wed, 8 Jul 2015 13:19:27 -0700
Subject: [PATCH 0281/1454] [SPARK-7785] [MLLIB] [PYSPARK] Add __str__ and
 __repr__ to Matrices

Adding __str__ and  __repr__ to DenseMatrix and SparseMatrix

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #6342 from MechCoder/spark-7785 and squashes the following commits:

7b9a82c [MechCoder] Add tests for greater than 16 elements
b88e9dd [MechCoder] Increment limit to 16
1425a01 [MechCoder] Change tests
36bd166 [MechCoder] Change str and repr representation
97f0da9 [MechCoder] zip is same as izip in python3
94ca4b2 [MechCoder] Added doctests and iterate over values instead of colPtrs
b26fa89 [MechCoder] minor
394dde9 [MechCoder] [SPARK-7785] Add __str__ and __repr__ to Matrices
---
 python/pyspark/mllib/linalg.py | 127 +++++++++++++++++++++++++++++++++
 python/pyspark/mllib/tests.py  |  52 +++++++++++++-
 2 files changed, 178 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 12d8dbbb92c56..51ac198305711 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -31,6 +31,7 @@
     xrange = range
     import copyreg as copy_reg
 else:
+    from itertools import izip as zip
     import copy_reg
 
 import numpy as np
@@ -116,6 +117,10 @@ def _format_float(f, digits=4):
     return s
 
 
+def _format_float_list(l):
+    return [_format_float(x) for x in l]
+
+
 class VectorUDT(UserDefinedType):
     """
     SQL user-defined type (UDT) for Vector.
@@ -870,6 +875,50 @@ def __reduce__(self):
             self.numRows, self.numCols, self.values.tostring(),
             int(self.isTransposed))
 
+    def __str__(self):
+        """
+        Pretty printing of a DenseMatrix
+
+        >>> dm = DenseMatrix(2, 2, range(4))
+        >>> print(dm)
+        DenseMatrix([[ 0.,  2.],
+                     [ 1.,  3.]])
+        >>> dm = DenseMatrix(2, 2, range(4), isTransposed=True)
+        >>> print(dm)
+        DenseMatrix([[ 0.,  1.],
+                     [ 2.,  3.]])
+        """
+        # Inspired by __repr__ in scipy matrices.
+        array_lines = repr(self.toArray()).splitlines()
+
+        # We need to adjust six spaces which is the difference in number
+        # of letters between "DenseMatrix" and "array"
+        x = '\n'.join([(" " * 6 + line) for line in array_lines[1:]])
+        return array_lines[0].replace("array", "DenseMatrix") + "\n" + x
+
+    def __repr__(self):
+        """
+        Representation of a DenseMatrix
+
+        >>> dm = DenseMatrix(2, 2, range(4))
+        >>> dm
+        DenseMatrix(2, 2, [0.0, 1.0, 2.0, 3.0], False)
+        """
+        # If the number of values are less than seventeen then return as it is.
+        # Else return first eight values and last eight values.
+        if len(self.values) < 17:
+            entries = _format_float_list(self.values)
+        else:
+            entries = (
+                _format_float_list(self.values[:8]) +
+                ["..."] +
+                _format_float_list(self.values[-8:])
+            )
+
+        entries = ", ".join(entries)
+        return "DenseMatrix({0}, {1}, [{2}], {3})".format(
+            self.numRows, self.numCols, entries, self.isTransposed)
+
     def toArray(self):
         """
         Return an numpy.ndarray
@@ -946,6 +995,84 @@ def __init__(self, numRows, numCols, colPtrs, rowIndices, values,
             raise ValueError("Expected rowIndices of length %d, got %d."
                              % (self.rowIndices.size, self.values.size))
 
+    def __str__(self):
+        """
+        Pretty printing of a SparseMatrix
+
+        >>> sm1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
+        >>> print(sm1)
+        2 X 2 CSCMatrix
+        (0,0) 2.0
+        (1,0) 3.0
+        (1,1) 4.0
+        >>> sm1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
+        >>> print(sm1)
+        2 X 2 CSRMatrix
+        (0,0) 2.0
+        (0,1) 3.0
+        (1,1) 4.0
+        """
+        spstr = "{0} X {1} ".format(self.numRows, self.numCols)
+        if self.isTransposed:
+            spstr += "CSRMatrix\n"
+        else:
+            spstr += "CSCMatrix\n"
+
+        cur_col = 0
+        smlist = []
+
+        # Display first 16 values.
+        if len(self.values) <= 16:
+            zipindval = zip(self.rowIndices, self.values)
+        else:
+            zipindval = zip(self.rowIndices[:16], self.values[:16])
+        for i, (rowInd, value) in enumerate(zipindval):
+            if self.colPtrs[cur_col + 1] <= i:
+                cur_col += 1
+            if self.isTransposed:
+                smlist.append('({0},{1}) {2}'.format(
+                    cur_col, rowInd, _format_float(value)))
+            else:
+                smlist.append('({0},{1}) {2}'.format(
+                    rowInd, cur_col, _format_float(value)))
+        spstr += "\n".join(smlist)
+
+        if len(self.values) > 16:
+            spstr += "\n.." * 2
+        return spstr
+
+    def __repr__(self):
+        """
+        Representation of a SparseMatrix
+
+        >>> sm1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
+        >>> sm1
+        SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2.0, 3.0, 4.0], False)
+        """
+        rowIndices = list(self.rowIndices)
+        colPtrs = list(self.colPtrs)
+
+        if len(self.values) <= 16:
+            values = _format_float_list(self.values)
+
+        else:
+            values = (
+                _format_float_list(self.values[:8]) +
+                ["..."] +
+                _format_float_list(self.values[-8:])
+            )
+            rowIndices = rowIndices[:8] + ["..."] + rowIndices[-8:]
+
+        if len(self.colPtrs) > 16:
+            colPtrs = colPtrs[:8] + ["..."] + colPtrs[-8:]
+
+        values = ", ".join(values)
+        rowIndices = ", ".join([str(ind) for ind in rowIndices])
+        colPtrs = ", ".join([str(ptr) for ptr in colPtrs])
+        return "SparseMatrix({0}, {1}, [{2}], [{3}], [{4}], {5})".format(
+            self.numRows, self.numCols, colPtrs, rowIndices,
+            values, self.isTransposed)
+
     def __reduce__(self):
         return SparseMatrix, (
             self.numRows, self.numCols, self.colPtrs.tostring(),
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index d9f9874d50c1a..f2eab5b18f077 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -27,7 +27,7 @@
 from shutil import rmtree
 
 from numpy import (
-    array, array_equal, zeros, inf, random, exp, dot, all, mean, abs)
+    array, array_equal, zeros, inf, random, exp, dot, all, mean, abs, arange, tile, ones)
 from numpy import sum as array_sum
 
 from py4j.protocol import Py4JJavaError
@@ -189,6 +189,53 @@ def test_matrix_indexing(self):
             for j in range(2):
                 self.assertEquals(mat[i, j], expected[i][j])
 
+    def test_repr_dense_matrix(self):
+        mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10])
+        self.assertTrue(
+            repr(mat),
+            'DenseMatrix(3, 2, [0.0, 1.0, 4.0, 6.0, 8.0, 10.0], False)')
+
+        mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10], True)
+        self.assertTrue(
+            repr(mat),
+            'DenseMatrix(3, 2, [0.0, 1.0, 4.0, 6.0, 8.0, 10.0], False)')
+
+        mat = DenseMatrix(6, 3, zeros(18))
+        self.assertTrue(
+            repr(mat),
+            'DenseMatrix(6, 3, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ..., \
+                0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], False)')
+
+    def test_repr_sparse_matrix(self):
+        sm1t = SparseMatrix(
+            3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0],
+            isTransposed=True)
+        self.assertTrue(
+            repr(sm1t),
+            'SparseMatrix(3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0], True)')
+
+        indices = tile(arange(6), 3)
+        values = ones(18)
+        sm = SparseMatrix(6, 3, [0, 6, 12, 18], indices, values)
+        self.assertTrue(
+            repr(sm), "SparseMatrix(6, 3, [0, 6, 12, 18], \
+                [0, 1, 2, 3, 4, 5, 0, 1, ..., 4, 5, 0, 1, 2, 3, 4, 5], \
+                [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ..., \
+                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], False)")
+
+        self.assertTrue(
+            str(sm),
+            "6 X 3 CSCMatrix\n\
+            (0,0) 1.0\n(1,0) 1.0\n(2,0) 1.0\n(3,0) 1.0\n(4,0) 1.0\n(5,0) 1.0\n\
+            (0,1) 1.0\n(1,1) 1.0\n(2,1) 1.0\n(3,1) 1.0\n(4,1) 1.0\n(5,1) 1.0\n\
+            (0,2) 1.0\n(1,2) 1.0\n(2,2) 1.0\n(3,2) 1.0\n..\n..")
+
+        sm = SparseMatrix(1, 18, zeros(19), [], [])
+        self.assertTrue(
+            repr(sm),
+            'SparseMatrix(1, 18, \
+                [0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0], [], [], False)')
+
     def test_sparse_matrix(self):
         # Test sparse matrix creation.
         sm1 = SparseMatrix(
@@ -198,6 +245,9 @@ def test_sparse_matrix(self):
         self.assertEquals(sm1.colPtrs.tolist(), [0, 2, 2, 4, 4])
         self.assertEquals(sm1.rowIndices.tolist(), [1, 2, 1, 2])
         self.assertEquals(sm1.values.tolist(), [1.0, 2.0, 4.0, 5.0])
+        self.assertTrue(
+            repr(sm1),
+            'SparseMatrix(3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0], False)')
 
         # Test indexing
         expected = [

From f031543782e8f0f5b6a4471ba1c1d5c53efbe5cd Mon Sep 17 00:00:00 2001
From: Keuntae Park <sirpkt@apache.org>
Date: Wed, 8 Jul 2015 14:29:52 -0700
Subject: [PATCH 0282/1454] [SPARK-8783] [SQL] CTAS with WITH clause does not
 work

Currently, CTESubstitution only handles the case that WITH is on the top of the plan.
I think it SHOULD handle the case that WITH is child of CTAS.
This patch simply changes 'match' to 'transform' for recursive search of WITH in the plan.

Author: Keuntae Park <sirpkt@apache.org>

Closes #7180 from sirpkt/SPARK-8783 and squashes the following commits:

e4428f0 [Keuntae Park] Merge remote-tracking branch 'upstream/master' into CTASwithWITH
1671c77 [Keuntae Park] WITH clause can be inside CTAS
---
 .../spark/sql/catalyst/analysis/Analyzer.scala |  2 +-
 .../sql/hive/execution/SQLQuerySuite.scala     | 18 ++++++++++++++++++
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 15e84e68b9881..3fdc6d62bc786 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -85,7 +85,7 @@ class Analyzer(
    */
   object CTESubstitution extends Rule[LogicalPlan] {
     // TODO allow subquery to define CTE
-    def apply(plan: LogicalPlan): LogicalPlan = plan match {
+    def apply(plan: LogicalPlan): LogicalPlan = plan transform  {
       case With(child, relations) => substituteCTE(child, relations)
       case other => other
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index bf9f2ecd51793..05a1f0094e5e1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -159,6 +159,24 @@ class SQLQuerySuite extends QueryTest {
     checkAnswer(query, Row(1, 1) :: Nil)
   }
 
+  test("CTAS with WITH clause") {
+    val df = Seq((1, 1)).toDF("c1", "c2")
+    df.registerTempTable("table1")
+
+    sql(
+      """
+        |CREATE TABLE with_table1 AS
+        |WITH T AS (
+        |  SELECT *
+        |  FROM table1
+        |)
+        |SELECT *
+        |FROM T
+      """.stripMargin)
+    val query = sql("SELECT * FROM with_table1")
+    checkAnswer(query, Row(1, 1) :: Nil)
+  }
+
   test("explode nested Field") {
     Seq(NestedArray1(NestedArray2(Seq(1, 2, 3)))).toDF.registerTempTable("nestedArray")
     checkAnswer(

From c5532e2fe700978da4bdfdb54a522f5934c3db55 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 8 Jul 2015 14:49:52 -0700
Subject: [PATCH 0283/1454] [SPARK-8457] [ML] NGram Documentation

Add documentation for NGram feature transformer.

Author: Feynman Liang <fliang@databricks.com>

Closes #7244 from feynmanliang/SPARK-8457 and squashes the following commits:

5aface9 [Feynman Liang] Pretty print Scala output and add API doc to each codetab
60d5ac0 [Feynman Liang] Inline API doc and fix indentation
736ccbc [Feynman Liang] NGram feature transformer documentation
---
 docs/ml-features.md | 88 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 88 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index f88c0248c1a8a..54068debe2159 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -288,6 +288,94 @@ for words_label in wordsDataFrame.select("words", "label").take(3):
 </div>
 
 
+## $n$-gram
+
+An [n-gram](https://en.wikipedia.org/wiki/N-gram) is a sequence of $n$ tokens (typically words) for some integer $n$. The `NGram` class can be used to transform input features into $n$-grams.
+
+`NGram` takes as input a sequence of strings (e.g. the output of a [Tokenizer](ml-features.html#tokenizer).  The parameter `n` is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of $n$ consecutive words.  If the input sequence contains fewer than `n` strings, no output is produced.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+[`NGram`](api/scala/index.html#org.apache.spark.ml.feature.NGram) takes an input column name, an output column name, and an optional length parameter n (n=2 by default).
+
+{% highlight scala %}
+import org.apache.spark.ml.feature.NGram
+
+val wordDataFrame = sqlContext.createDataFrame(Seq(
+  (0, Array("Hi", "I", "heard", "about", "Spark")),
+  (1, Array("I", "wish", "Java", "could", "use", "case", "classes")),
+  (2, Array("Logistic", "regression", "models", "are", "neat"))
+)).toDF("label", "words")
+
+val ngram = new NGram().setInputCol("words").setOutputCol("ngrams")
+val ngramDataFrame = ngram.transform(wordDataFrame)
+ngramDataFrame.take(3).map(_.getAs[Stream[String]]("ngrams").toList).foreach(println)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`NGram`](api/java/org/apache/spark/ml/feature/NGram.html) takes an input column name, an output column name, and an optional length parameter n (n=2 by default).
+
+{% highlight java %}
+import com.google.common.collect.Lists;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.NGram;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+  RowFactory.create(0D, Lists.newArrayList("Hi", "I", "heard", "about", "Spark")),
+  RowFactory.create(1D, Lists.newArrayList("I", "wish", "Java", "could", "use", "case", "classes")),
+  RowFactory.create(2D, Lists.newArrayList("Logistic", "regression", "models", "are", "neat"))
+));
+StructType schema = new StructType(new StructField[]{
+  new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
+  new StructField("words", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
+});
+DataFrame wordDataFrame = sqlContext.createDataFrame(jrdd, schema);
+NGram ngramTransformer = new NGram().setInputCol("words").setOutputCol("ngrams");
+DataFrame ngramDataFrame = ngramTransformer.transform(wordDataFrame);
+for (Row r : ngramDataFrame.select("ngrams", "label").take(3)) {
+  java.util.List<String> ngrams = r.getList(0);
+  for (String ngram : ngrams) System.out.print(ngram + " --- ");
+  System.out.println();
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+[`NGram`](api/python/pyspark.ml.html#pyspark.ml.feature.NGram) takes an input column name, an output column name, and an optional length parameter n (n=2 by default).
+
+{% highlight python %}
+from pyspark.ml.feature import NGram
+
+wordDataFrame = sqlContext.createDataFrame([
+  (0, ["Hi", "I", "heard", "about", "Spark"]),
+  (1, ["I", "wish", "Java", "could", "use", "case", "classes"]),
+  (2, ["Logistic", "regression", "models", "are", "neat"])
+], ["label", "words"])
+ngram = NGram(inputCol="words", outputCol="ngrams")
+ngramDataFrame = ngram.transform(wordDataFrame)
+for ngrams_label in ngramDataFrame.select("ngrams", "label").take(3):
+  print(ngrams_label)
+{% endhighlight %}
+</div>
+</div>
+
+
 ## Binarizer
 
 Binarization is the process of thresholding numerical features to binary features. As some probabilistic estimators make assumption that the input data is distributed according to [Bernoulli distribution](http://en.wikipedia.org/wiki/Bernoulli_distribution), a binarizer is useful for pre-processing the input data with continuous numerical features.

From 8f3cd93278337dc10b9dd3a344d6f7b51ba9960d Mon Sep 17 00:00:00 2001
From: Alok Singh <“singhal@us.ibm.com”>
Date: Wed, 8 Jul 2015 14:51:18 -0700
Subject: [PATCH 0284/1454] =?UTF-8?q?[SPARK-8909][Documentation]=20Change?=
 =?UTF-8?q?=20the=20scala=20example=20in=20sql-programmi=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…ng-guide#Manually Specifying Options to be in sync with java,python, R version

Author: Alok Singh <“singhal@us.ibm.com”>

Closes #7299 from aloknsingh/aloknsingh_SPARK-8909 and squashes the following commits:

d3c20ba [Alok Singh] fix the file to .parquet from .json
d476140 [Alok Singh] [SPARK-8909][Documentation] Change the scala example in sql-programming-guide#Manually Specifying Options to be in sync with java,python, R version
---
 docs/sql-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 26f6ba84b91b2..5838bc172fe86 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -828,7 +828,7 @@ using this syntax.
 
 {% highlight scala %}
 val df = sqlContext.read.format("json").load("examples/src/main/resources/people.json")
-df.select("name", "age").write.format("json").save("namesAndAges.json")
+df.select("name", "age").write.format("parquet").save("namesAndAges.parquet")
 {% endhighlight %}
 
 </div>

From 00b265f12c0f0271b7036f831fee09b694908b29 Mon Sep 17 00:00:00 2001
From: Cheolsoo Park <cheolsoop@netflix.com>
Date: Wed, 8 Jul 2015 15:18:24 -0700
Subject: [PATCH 0285/1454] [SPARK-8908] [SQL] Add () to distinct definition in
 dataframe

Adding `()` to the definition of `distinct` in DataFrame allows distinct to be called with parentheses, which is consistent with `dropDuplicates`.

Author: Cheolsoo Park <cheolsoop@netflix.com>

Closes #7298 from piaozhexiu/SPARK-8908 and squashes the following commits:

7f0d923 [Cheolsoo Park] Add () to distinct definition in dataframe
---
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 60142291eaf30..f33e19a0cb7dd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1415,7 +1415,7 @@ class DataFrame private[sql](
    * @group dfops
    * @since 1.3.0
    */
-  override def distinct: DataFrame = dropDuplicates()
+  override def distinct(): DataFrame = dropDuplicates()
 
   /**
    * @group basic

From 57221934e0376e5bb8421dc35d4bf91db4deeca1 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbt@netflix.com>
Date: Wed, 8 Jul 2015 15:21:58 -0700
Subject: [PATCH 0286/1454] [SPARK-8700][ML] Disable feature scaling in
 Logistic Regression

All compressed sensing applications, and some of the regression use-cases will have better result by turning the feature scaling off. However, if we implement this naively by training the dataset without doing any standardization, the rate of convergency will not be good. This can be implemented by still standardizing the training dataset but we penalize each component differently to get effectively the same objective function but a better numerical problem. As a result, for those columns with high variances, they will be penalized less, and vice versa. Without this, since all the features are standardized, so they will be penalized the same.

In R, there is an option for this.
`standardize`
Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".

+cc holdenk mengxr jkbradley

Author: DB Tsai <dbt@netflix.com>

Closes #7080 from dbtsai/lors and squashes the following commits:

877e6c7 [DB Tsai] repahse the doc
7cf45f2 [DB Tsai] address feedback
78d75c9 [DB Tsai] small change
c2c9e60 [DB Tsai] style
6e1a8e0 [DB Tsai] first commit
---
 .../classification/LogisticRegression.scala   |  89 +++-
 .../ml/param/shared/SharedParamsCodeGen.scala |   3 +-
 .../spark/ml/param/shared/sharedParams.scala  |   4 +-
 .../LogisticRegressionSuite.scala             | 403 ++++++++++++++----
 project/MimaExcludes.scala                    |   2 +
 5 files changed, 384 insertions(+), 117 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 3967151f76c7c..8fc9199fb4602 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ml.classification
 
 import scala.collection.mutable
 
-import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
+import breeze.linalg.{DenseVector => BDV}
 import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
 import org.apache.spark.{Logging, SparkException}
@@ -41,7 +41,7 @@ import org.apache.spark.storage.StorageLevel
  */
 private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol
-  with HasThreshold
+  with HasThreshold with HasStandardization
 
 /**
  * :: Experimental ::
@@ -98,6 +98,18 @@ class LogisticRegression(override val uid: String)
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
   setDefault(fitIntercept -> true)
 
+  /**
+   * Whether to standardize the training features before fitting the model.
+   * The coefficients of models will be always returned on the original scale,
+   * so it will be transparent for users. Note that when no regularization,
+   * with or without standardization, the models should be always converged to
+   * the same solution.
+   * Default is true.
+   * @group setParam
+   * */
+  def setStandardization(value: Boolean): this.type = set(standardization, value)
+  setDefault(standardization -> true)
+
   /** @group setParam */
   def setThreshold(value: Double): this.type = set(threshold, value)
   setDefault(threshold -> 0.5)
@@ -149,15 +161,28 @@ class LogisticRegression(override val uid: String)
     val regParamL1 = $(elasticNetParam) * $(regParam)
     val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)
 
-    val costFun = new LogisticCostFun(instances, numClasses, $(fitIntercept),
+    val costFun = new LogisticCostFun(instances, numClasses, $(fitIntercept), $(standardization),
       featuresStd, featuresMean, regParamL2)
 
     val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) == 0.0) {
       new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
     } else {
-      // Remove the L1 penalization on the intercept
       def regParamL1Fun = (index: Int) => {
-        if (index == numFeatures) 0.0 else regParamL1
+        // Remove the L1 penalization on the intercept
+        if (index == numFeatures) {
+          0.0
+        } else {
+          if ($(standardization)) {
+            regParamL1
+          } else {
+            // If `standardization` is false, we still standardize the data
+            // to improve the rate of convergence; as a result, we have to
+            // perform this reverse standardization by penalizing each component
+            // differently to get effectively the same objective function when
+            // the training dataset is not standardized.
+            if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else 0.0
+          }
+        }
       }
       new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun, $(tol))
     }
@@ -523,11 +548,13 @@ private class LogisticCostFun(
     data: RDD[(Double, Vector)],
     numClasses: Int,
     fitIntercept: Boolean,
+    standardization: Boolean,
     featuresStd: Array[Double],
     featuresMean: Array[Double],
     regParamL2: Double) extends DiffFunction[BDV[Double]] {
 
   override def calculate(weights: BDV[Double]): (Double, BDV[Double]) = {
+    val numFeatures = featuresStd.length
     val w = Vectors.fromBreeze(weights)
 
     val logisticAggregator = data.treeAggregate(new LogisticAggregator(w, numClasses, fitIntercept,
@@ -539,27 +566,43 @@ private class LogisticCostFun(
           case (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
         })
 
-    // regVal is the sum of weight squares for L2 regularization
-    val norm = if (regParamL2 == 0.0) {
-      0.0
-    } else if (fitIntercept) {
-      brzNorm(Vectors.dense(weights.toArray.slice(0, weights.size -1)).toBreeze, 2.0)
-    } else {
-      brzNorm(weights, 2.0)
-    }
-    val regVal = 0.5 * regParamL2 * norm * norm
+    val totalGradientArray = logisticAggregator.gradient.toArray
 
-    val loss = logisticAggregator.loss + regVal
-    val gradient = logisticAggregator.gradient
-
-    if (fitIntercept) {
-      val wArray = w.toArray.clone()
-      wArray(wArray.length - 1) = 0.0
-      axpy(regParamL2, Vectors.dense(wArray), gradient)
+    // regVal is the sum of weight squares excluding intercept for L2 regularization.
+    val regVal = if (regParamL2 == 0.0) {
+      0.0
     } else {
-      axpy(regParamL2, w, gradient)
+      var sum = 0.0
+      w.foreachActive { (index, value) =>
+        // If `fitIntercept` is true, the last term which is intercept doesn't
+        // contribute to the regularization.
+        if (index != numFeatures) {
+          // The following code will compute the loss of the regularization; also
+          // the gradient of the regularization, and add back to totalGradientArray.
+          sum += {
+            if (standardization) {
+              totalGradientArray(index) += regParamL2 * value
+              value * value
+            } else {
+              if (featuresStd(index) != 0.0) {
+                // If `standardization` is false, we still standardize the data
+                // to improve the rate of convergence; as a result, we have to
+                // perform this reverse standardization by penalizing each component
+                // differently to get effectively the same objective function when
+                // the training dataset is not standardized.
+                val temp = value / (featuresStd(index) * featuresStd(index))
+                totalGradientArray(index) += regParamL2 * temp
+                value * temp
+              } else {
+                0.0
+              }
+            }
+          }
+        }
+      }
+      0.5 * regParamL2 * sum
     }
 
-    (loss, gradient.toBreeze.asInstanceOf[BDV[Double]])
+    (logisticAggregator.loss + regVal, new BDV(totalGradientArray))
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index b0a6af171c01f..66b751a1b02ee 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -54,8 +54,7 @@ private[shared] object SharedParamsCodeGen {
         isValid = "ParamValidators.gtEq(1)"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
       ParamDesc[Boolean]("standardization", "whether to standardize the training features" +
-        " prior to fitting the model sequence. Note that the coefficients of models are" +
-        " always returned on the original scale.", Some("true")),
+        " before fitting the model.", Some("true")),
       ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")),
       ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." +
         " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index bbe08939b6d75..f81bd76c22376 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -239,10 +239,10 @@ private[ml] trait HasFitIntercept extends Params {
 private[ml] trait HasStandardization extends Params {
 
   /**
-   * Param for whether to standardize the training features prior to fitting the model sequence. Note that the coefficients of models are always returned on the original scale..
+   * Param for whether to standardize the training features before fitting the model..
    * @group param
    */
-  final val standardization: BooleanParam = new BooleanParam(this, "standardization", "whether to standardize the training features prior to fitting the model sequence. Note that the coefficients of models are always returned on the original scale.")
+  final val standardization: BooleanParam = new BooleanParam(this, "standardization", "whether to standardize the training features before fitting the model.")
 
   setDefault(standardization, true)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index ba8fbee84197c..27253c1db2fff 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -77,6 +77,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lr.getRawPredictionCol === "rawPrediction")
     assert(lr.getProbabilityCol === "probability")
     assert(lr.getFitIntercept)
+    assert(lr.getStandardization)
     val model = lr.fit(dataset)
     model.transform(dataset)
       .select("label", "probability", "prediction", "rawPrediction")
@@ -208,8 +209,11 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("binary logistic regression with intercept without regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(true)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(true).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(true).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -232,16 +236,26 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val interceptR = 2.8366423
     val weightsR = Array(-0.5895848, 0.8931147, -0.3925051, -0.7996864)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
-    assert(model.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model.weights(3) ~== weightsR(3) relTol 1E-3)
+    assert(model1.intercept ~== interceptR relTol 1E-3)
+    assert(model1.weights(0) ~== weightsR(0) relTol 1E-3)
+    assert(model1.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model1.weights(2) ~== weightsR(2) relTol 1E-3)
+    assert(model1.weights(3) ~== weightsR(3) relTol 1E-3)
+
+    // Without regularization, with or without standardization will converge to the same solution.
+    assert(model2.intercept ~== interceptR relTol 1E-3)
+    assert(model2.weights(0) ~== weightsR(0) relTol 1E-3)
+    assert(model2.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model2.weights(2) ~== weightsR(2) relTol 1E-3)
+    assert(model2.weights(3) ~== weightsR(3) relTol 1E-3)
   }
 
   test("binary logistic regression without intercept without regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(false)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(false).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(false).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -265,17 +279,28 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val interceptR = 0.0
     val weightsR = Array(-0.3534996, 1.2964482, -0.3571741, -0.7407946)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-2)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-2)
-    assert(model.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model.weights(3) ~== weightsR(3) relTol 1E-3)
+    assert(model1.intercept ~== interceptR relTol 1E-3)
+    assert(model1.weights(0) ~== weightsR(0) relTol 1E-2)
+    assert(model1.weights(1) ~== weightsR(1) relTol 1E-2)
+    assert(model1.weights(2) ~== weightsR(2) relTol 1E-3)
+    assert(model1.weights(3) ~== weightsR(3) relTol 1E-3)
+
+    // Without regularization, with or without standardization should converge to the same solution.
+    assert(model2.intercept ~== interceptR relTol 1E-3)
+    assert(model2.weights(0) ~== weightsR(0) relTol 1E-2)
+    assert(model2.weights(1) ~== weightsR(1) relTol 1E-2)
+    assert(model2.weights(2) ~== weightsR(2) relTol 1E-3)
+    assert(model2.weights(3) ~== weightsR(3) relTol 1E-3)
   }
 
   test("binary logistic regression with intercept with L1 regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(true)
-      .setElasticNetParam(1.0).setRegParam(0.12)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -295,20 +320,52 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V4     -0.04325749
        data.V5     -0.02481551
      */
-    val interceptR = -0.05627428
-    val weightsR = Array(0.0, 0.0, -0.04325749, -0.02481551)
-
-    assert(model.intercept ~== interceptR relTol 1E-2)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
-    assert(model.weights(2) ~== weightsR(2) relTol 1E-2)
-    assert(model.weights(3) ~== weightsR(3) relTol 2E-2)
+    val interceptR1 = -0.05627428
+    val weightsR1 = Array(0.0, 0.0, -0.04325749, -0.02481551)
+
+    assert(model1.intercept ~== interceptR1 relTol 1E-2)
+    assert(model1.weights(0) ~== weightsR1(0) absTol 1E-3)
+    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-3)
+    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-2)
+    assert(model1.weights(3) ~== weightsR1(3) relTol 2E-2)
+
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
+           standardize=FALSE))
+       weights
+
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                           s0
+       (Intercept)  0.3722152
+       data.V2       .
+       data.V3       .
+       data.V4     -0.1665453
+       data.V5       .
+     */
+    val interceptR2 = 0.3722152
+    val weightsR2 = Array(0.0, 0.0, -0.1665453, 0.0)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-2)
+    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
+    assert(model2.weights(1) ~== weightsR2(1) absTol 1E-3)
+    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-2)
+    assert(model2.weights(3) ~== weightsR2(3) absTol 1E-3)
   }
 
   test("binary logistic regression without intercept with L1 regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(false)
-      .setElasticNetParam(1.0).setRegParam(0.12)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -329,20 +386,52 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V4     -0.05189203
        data.V5     -0.03891782
      */
-    val interceptR = 0.0
-    val weightsR = Array(0.0, 0.0, -0.05189203, -0.03891782)
+    val interceptR1 = 0.0
+    val weightsR1 = Array(0.0, 0.0, -0.05189203, -0.03891782)
+
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights(0) ~== weightsR1(0) absTol 1E-3)
+    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-3)
+    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-2)
+    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-2)
+
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
+           intercept=FALSE, standardize=FALSE))
+       weights
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
-    assert(model.weights(2) ~== weightsR(2) relTol 1E-2)
-    assert(model.weights(3) ~== weightsR(3) relTol 1E-2)
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)   .
+       data.V2       .
+       data.V3       .
+       data.V4     -0.08420782
+       data.V5       .
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Array(0.0, 0.0, -0.08420782, 0.0)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
+    assert(model2.weights(1) ~== weightsR2(1) absTol 1E-3)
+    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-2)
+    assert(model2.weights(3) ~== weightsR2(3) absTol 1E-3)
   }
 
   test("binary logistic regression with intercept with L2 regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(true)
-      .setElasticNetParam(0.0).setRegParam(1.37)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -362,20 +451,52 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V4     -0.04865309
        data.V5     -0.10062872
      */
-    val interceptR = 0.15021751
-    val weightsR = Array(-0.07251837, 0.10724191, -0.04865309, -0.10062872)
-
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
-    assert(model.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model.weights(3) ~== weightsR(3) relTol 1E-3)
+    val interceptR1 = 0.15021751
+    val weightsR1 = Array(-0.07251837, 0.10724191, -0.04865309, -0.10062872)
+
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights(0) ~== weightsR1(0) relTol 1E-3)
+    assert(model1.weights(1) ~== weightsR1(1) relTol 1E-3)
+    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-3)
+    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-3)
+
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
+           standardize=FALSE))
+       weights
+
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)  0.48657516
+       data.V2     -0.05155371
+       data.V3      0.02301057
+       data.V4     -0.11482896
+       data.V5     -0.06266838
+     */
+    val interceptR2 = 0.48657516
+    val weightsR2 = Array(-0.05155371, 0.02301057, -0.11482896, -0.06266838)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights(0) ~== weightsR2(0) relTol 1E-3)
+    assert(model2.weights(1) ~== weightsR2(1) relTol 1E-3)
+    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-3)
+    assert(model2.weights(3) ~== weightsR2(3) relTol 1E-3)
   }
 
   test("binary logistic regression without intercept with L2 regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(false)
-      .setElasticNetParam(0.0).setRegParam(1.37)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -396,20 +517,52 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V4     -0.04708770
        data.V5     -0.09799775
      */
-    val interceptR = 0.0
-    val weightsR = Array(-0.06099165, 0.12857058, -0.04708770, -0.09799775)
+    val interceptR1 = 0.0
+    val weightsR1 = Array(-0.06099165, 0.12857058, -0.04708770, -0.09799775)
+
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights(0) ~== weightsR1(0) relTol 1E-2)
+    assert(model1.weights(1) ~== weightsR1(1) relTol 1E-2)
+    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-3)
+    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-3)
+
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
+           intercept=FALSE, standardize=FALSE))
+       weights
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-2)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-2)
-    assert(model.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model.weights(3) ~== weightsR(3) relTol 1E-3)
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                             s0
+       (Intercept)   .
+       data.V2     -0.005679651
+       data.V3      0.048967094
+       data.V4     -0.093714016
+       data.V5     -0.053314311
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Array(-0.005679651, 0.048967094, -0.093714016, -0.053314311)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights(0) ~== weightsR2(0) relTol 1E-2)
+    assert(model2.weights(1) ~== weightsR2(1) relTol 1E-2)
+    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-3)
+    assert(model2.weights(3) ~== weightsR2(3) relTol 1E-3)
   }
 
   test("binary logistic regression with intercept with ElasticNet regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(true)
-      .setElasticNetParam(0.38).setRegParam(0.21)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -429,20 +582,52 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V4     -0.08849250
        data.V5     -0.15458796
      */
-    val interceptR = 0.57734851
-    val weightsR = Array(-0.05310287, 0.0, -0.08849250, -0.15458796)
-
-    assert(model.intercept ~== interceptR relTol 6E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 5E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
-    assert(model.weights(2) ~== weightsR(2) relTol 5E-3)
-    assert(model.weights(3) ~== weightsR(3) relTol 1E-3)
+    val interceptR1 = 0.57734851
+    val weightsR1 = Array(-0.05310287, 0.0, -0.08849250, -0.15458796)
+
+    assert(model1.intercept ~== interceptR1 relTol 6E-3)
+    assert(model1.weights(0) ~== weightsR1(0) relTol 5E-3)
+    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-3)
+    assert(model1.weights(2) ~== weightsR1(2) relTol 5E-3)
+    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-3)
+
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
+           standardize=FALSE))
+       weights
+
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)  0.51555993
+       data.V2       .
+       data.V3       .
+       data.V4     -0.18807395
+       data.V5     -0.05350074
+     */
+    val interceptR2 = 0.51555993
+    val weightsR2 = Array(0.0, 0.0, -0.18807395, -0.05350074)
+
+    assert(model2.intercept ~== interceptR2 relTol 6E-3)
+    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
+    assert(model2.weights(1) ~== weightsR2(1) absTol 1E-3)
+    assert(model2.weights(2) ~== weightsR2(2) relTol 5E-3)
+    assert(model2.weights(3) ~== weightsR2(3) relTol 1E-2)
   }
 
   test("binary logistic regression without intercept with ElasticNet regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(false)
-      .setElasticNetParam(0.38).setRegParam(0.21)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -463,20 +648,52 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V4     -0.081203769
        data.V5     -0.142534158
      */
-    val interceptR = 0.0
-    val weightsR = Array(-0.001005743, 0.072577857, -0.081203769, -0.142534158)
+    val interceptR1 = 0.0
+    val weightsR1 = Array(-0.001005743, 0.072577857, -0.081203769, -0.142534158)
+
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights(0) ~== weightsR1(0) absTol 1E-2)
+    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-2)
+    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-3)
+    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-2)
+
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
+           intercept=FALSE, standardize=FALSE))
+       weights
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) absTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) absTol 1E-2)
-    assert(model.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model.weights(3) ~== weightsR(3) relTol 1E-2)
+       5 x 1 sparse Matrix of class "dgCMatrix"
+                            s0
+       (Intercept)   .
+       data.V2       .
+       data.V3      0.03345223
+       data.V4     -0.11304532
+       data.V5       .
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Array(0.0, 0.03345223, -0.11304532, 0.0)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
+    assert(model2.weights(1) ~== weightsR2(1) relTol 1E-2)
+    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-2)
+    assert(model2.weights(3) ~== weightsR2(3) absTol 1E-3)
   }
 
   test("binary logistic regression with intercept with strong L1 regularization") {
-    val trainer = (new LogisticRegression).setFitIntercept(true)
-      .setElasticNetParam(1.0).setRegParam(6.0)
-    val model = trainer.fit(binaryDataset)
+    val trainer1 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(1.0).setRegParam(6.0).setStandardization(true)
+    val trainer2 = (new LogisticRegression).setFitIntercept(true)
+      .setElasticNetParam(1.0).setRegParam(6.0).setStandardization(false)
+
+    val model1 = trainer1.fit(binaryDataset)
+    val model2 = trainer2.fit(binaryDataset)
 
     val histogram = binaryDataset.map { case Row(label: Double, features: Vector) => label }
       .treeAggregate(new MultiClassSummarizer)(
@@ -502,11 +719,17 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val interceptTheory = math.log(histogram(1).toDouble / histogram(0).toDouble)
     val weightsTheory = Array(0.0, 0.0, 0.0, 0.0)
 
-    assert(model.intercept ~== interceptTheory relTol 1E-5)
-    assert(model.weights(0) ~== weightsTheory(0) absTol 1E-6)
-    assert(model.weights(1) ~== weightsTheory(1) absTol 1E-6)
-    assert(model.weights(2) ~== weightsTheory(2) absTol 1E-6)
-    assert(model.weights(3) ~== weightsTheory(3) absTol 1E-6)
+    assert(model1.intercept ~== interceptTheory relTol 1E-5)
+    assert(model1.weights(0) ~== weightsTheory(0) absTol 1E-6)
+    assert(model1.weights(1) ~== weightsTheory(1) absTol 1E-6)
+    assert(model1.weights(2) ~== weightsTheory(2) absTol 1E-6)
+    assert(model1.weights(3) ~== weightsTheory(3) absTol 1E-6)
+
+    assert(model2.intercept ~== interceptTheory relTol 1E-5)
+    assert(model2.weights(0) ~== weightsTheory(0) absTol 1E-6)
+    assert(model2.weights(1) ~== weightsTheory(1) absTol 1E-6)
+    assert(model2.weights(2) ~== weightsTheory(2) absTol 1E-6)
+    assert(model2.weights(3) ~== weightsTheory(3) absTol 1E-6)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -529,10 +752,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val interceptR = -0.248065
     val weightsR = Array(0.0, 0.0, 0.0, 0.0)
 
-    assert(model.intercept ~== interceptR relTol 1E-5)
-    assert(model.weights(0) ~== weightsR(0) absTol 1E-6)
-    assert(model.weights(1) ~== weightsR(1) absTol 1E-6)
-    assert(model.weights(2) ~== weightsR(2) absTol 1E-6)
-    assert(model.weights(3) ~== weightsR(3) absTol 1E-6)
+    assert(model1.intercept ~== interceptR relTol 1E-5)
+    assert(model1.weights(0) ~== weightsR(0) absTol 1E-6)
+    assert(model1.weights(1) ~== weightsR(1) absTol 1E-6)
+    assert(model1.weights(2) ~== weightsR(2) absTol 1E-6)
+    assert(model1.weights(3) ~== weightsR(3) absTol 1E-6)
   }
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 680b699e9e4a1..41e19fd9cc11e 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -58,6 +58,8 @@ object MimaExcludes {
               "org.apache.spark.ml.regression.LeastSquaresAggregator.this"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.ml.regression.LeastSquaresCostFun.this"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.ml.classification.LogisticCostFun.this"),
             // SQL execution is considered private.
             excludePackage("org.apache.spark.sql.execution"),
             // NanoTime and CatalystTimestampConverter is only used inside catalyst,

From 5687f76552369fa20b3a4385eab4810214653aa7 Mon Sep 17 00:00:00 2001
From: Daniel Darabos <darabos.daniel@gmail.com>
Date: Thu, 9 Jul 2015 07:34:02 +0900
Subject: [PATCH 0287/1454] [SPARK-8902] Correctly print hostname in error

With "+" the strings are separate expressions, and format() is called on the last string before concatenation. (So substitution does not happen.) Without "+" the string literals are merged first by the parser, so format() is called on the complete string.

Should I make a JIRA for this?

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #7288 from darabos/patch-2 and squashes the following commits:

be0d3b7 [Daniel Darabos] Correctly print hostname in error
---
 ec2/spark_ec2.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 8582d434a8c55..dd0c12d25980b 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -1153,8 +1153,8 @@ def ssh(host, opts, command):
                 # If this was an ssh failure, provide the user with hints.
                 if e.returncode == 255:
                     raise UsageError(
-                        "Failed to SSH to remote host {0}.\n" +
-                        "Please check that you have provided the correct --identity-file and " +
+                        "Failed to SSH to remote host {0}.\n"
+                        "Please check that you have provided the correct --identity-file and "
                         "--key-pair parameters and try again.".format(host))
                 else:
                     raise e

From 4ffc27caaf46ffac56c3c0b3e928f1aff227a184 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 8 Jul 2015 15:51:01 -0700
Subject: [PATCH 0288/1454] [SPARK-6123] [SPARK-6775] [SPARK-6776] [SQL]
 Refactors Parquet read path for interoperability and backwards-compatibility

This PR is a follow-up of #6617 and is part of [SPARK-6774] [2], which aims to ensure interoperability and backwards-compatibility for Spark SQL Parquet support.  And this one fixes the read path.  Now Spark SQL is expected to be able to read legacy Parquet data files generated by most (if not all) common libraries/tools like parquet-thrift, parquet-avro, and parquet-hive. However, we still need to refactor the write path to write standard Parquet LISTs and MAPs ([SPARK-8848] [4]).

### Major changes

1. `CatalystConverter` class hierarchy refactoring

   - Replaces `CatalystConverter` trait with a much simpler `ParentContainerUpdater`.

     Now instead of extending the original `CatalystConverter` trait, every converter class accepts an updater which is responsible for propagating the converted value to some parent container. For example, appending array elements to a parent array buffer, appending a key-value pairs to a parent mutable map, or setting a converted value to some specific field of a parent row. Root converter doesn't have a parent and thus uses a `NoopUpdater`.

     This simplifies the design since converters don't need to care about details of their parent converters anymore.

   - Unifies `CatalystRootConverter`, `CatalystGroupConverter` and `CatalystPrimitiveRowConverter` into `CatalystRowConverter`

     Specifically, now all row objects are represented by `SpecificMutableRow` during conversion.

   - Refactors `CatalystArrayConverter`, and removes `CatalystArrayContainsNullConverter` and `CatalystNativeArrayConverter`

     `CatalystNativeArrayConverter` was probably designed with the intention of avoiding boxing costs. However, the way it uses Scala generics actually doesn't achieve this goal.

     The new `CatalystArrayConverter` handles both nullable and non-nullable array elements in a consistent way.

   - Implements backwards-compatibility rules in `CatalystArrayConverter`

     When Parquet records are being converted, schema of Parquet files should have already been verified. So we only need to care about the structure rather than field names in the Parquet schema. Since all map objects represented in legacy systems have the same structure as the standard one (see [backwards-compatibility rules for MAP] [1]), we only need to deal with LIST (namely array) in `CatalystArrayConverter`.

2. Requested columns handling

   When specifying requested columns in `RowReadSupport`, we used to use a Parquet `MessageType` converted from a Catalyst `StructType` which contains all requested columns.  This is not preferable when taking compatibility and interoperability into consideration.  Because the actual Parquet file may have different physical structure from the converted schema.

   In this PR, the schema for requested columns is constructed using the following method:

   - For a column that exists in the target Parquet file, we extract the column type by name from the full file schema, and construct a single-field `MessageType` for that column.
   - For a column that doesn't exist in the target Parquet file, we create a single-field `StructType` and convert it to a `MessageType` using `CatalystSchemaConverter`.
   - Unions all single-field `MessageType`s into a full schema containing all requested fields

   With this change, we also fix [SPARK-6123] [3] by validating the global schema against each individual Parquet part-files.

### Testing

This PR also adds compatibility tests for parquet-avro, parquet-thrift, and parquet-hive. Please refer to `README.md` under `sql/core/src/test` for more information about these tests. To avoid build time code generation and adding extra complexity to the build system, Java code generated from testing Thrift schema and Avro IDL is also checked in.

[1]: https://github.com/apache/incubator-parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules-1
[2]: https://issues.apache.org/jira/browse/SPARK-6774
[3]: https://issues.apache.org/jira/browse/SPARK-6123
[4]: https://issues.apache.org/jira/browse/SPARK-8848

Author: Cheng Lian <lian@databricks.com>

Closes #7231 from liancheng/spark-6776 and squashes the following commits:

360fe18 [Cheng Lian] Adds ParquetHiveCompatibilitySuite
c6fbc06 [Cheng Lian] Removes WIP file committed by mistake
b8c1295 [Cheng Lian] Excludes the whole parquet package from MiMa
598c3e8 [Cheng Lian] Adds extra Maven repo for hadoop-lzo, which is a transitive dependency of parquet-thrift
926af87 [Cheng Lian] Simplifies Parquet compatibility test suites
7946ee1 [Cheng Lian] Fixes Scala styling issues
3d7ab36 [Cheng Lian] Fixes .rat-excludes
a8f13bb [Cheng Lian] Using Parquet writer API to do compatibility tests
f2208cd [Cheng Lian] Adds README.md for Thrift/Avro code generation
1d390aa [Cheng Lian] Adds parquet-thrift compatibility test
440f7b3 [Cheng Lian] Adds generated files to .rat-excludes
13b9121 [Cheng Lian] Adds ParquetAvroCompatibilitySuite
06cfe9d [Cheng Lian] Adds comments about TimestampType handling
a099d3e [Cheng Lian] More comments
0cc1b37 [Cheng Lian] Fixes MiMa checks
884d3e6 [Cheng Lian] Fixes styling issue and reverts unnecessary changes
802cbd7 [Cheng Lian] Fixes bugs related to schema merging and empty requested columns
38fe1e7 [Cheng Lian] Adds explicit return type
7fb21f1 [Cheng Lian] Reverts an unnecessary debugging change
1781dff [Cheng Lian] Adds test case for SPARK-8811
6437d4b [Cheng Lian] Assembles requested schema from Parquet file schema
bcac49f [Cheng Lian] Removes the 16-byte restriction of decimals
a74fb2c [Cheng Lian] More comments
0525346 [Cheng Lian] Removes old Parquet record converters
03c3bd9 [Cheng Lian] Refactors Parquet read path to implement backwards-compatibility rules
---
 .rat-excludes                                 |    2 +
 pom.xml                                       |   33 +
 project/MimaExcludes.scala                    |   17 +-
 .../org/apache/spark/sql/types/DataType.scala |    6 +-
 .../apache/spark/sql/types/StructType.scala   |    5 +
 sql/core/pom.xml                              |   36 +
 .../sql/parquet/CatalystRowConverter.scala    |  434 +++
 .../sql/parquet/CatalystSchemaConverter.scala |   21 +-
 .../spark/sql/parquet/ParquetConverter.scala  |  831 +----
 .../sql/parquet/ParquetTableSupport.scala     |  179 +-
 .../apache/spark/sql/parquet/newParquet.scala |   22 +-
 sql/core/src/test/README.md                   |   33 +
 sql/core/src/test/avro/parquet-compat.avdl    |   47 +
 sql/core/src/test/avro/parquet-compat.avpr    |   86 +
 .../parquet/test/avro/CompatibilityTest.java  |   17 +
 .../spark/sql/parquet/test/avro/Nested.java   |  196 ++
 .../parquet/test/avro/ParquetAvroCompat.java  | 1001 ++++++
 .../spark/sql/parquet/test/thrift/Nested.java |  541 ++++
 .../test/thrift/ParquetThriftCompat.java      | 2808 +++++++++++++++++
 .../spark/sql/parquet/test/thrift/Suit.java   |   51 +
 .../ParquetAvroCompatibilitySuite.scala       |  125 +
 .../parquet/ParquetCompatibilityTest.scala    |   56 +
 .../ParquetThriftCompatibilitySuite.scala     |  140 +
 sql/core/src/test/scripts/gen-code.sh         |   31 +
 .../src/test/thrift/parquet-compat.thrift     |   60 +
 .../hive/ParquetHiveCompatibilitySuite.scala  |   92 +
 .../apache/spark/sql/hive/parquetSuites.scala |   33 +-
 27 files changed, 5984 insertions(+), 919 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
 create mode 100644 sql/core/src/test/README.md
 create mode 100644 sql/core/src/test/avro/parquet-compat.avdl
 create mode 100644 sql/core/src/test/avro/parquet-compat.avpr
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
 create mode 100755 sql/core/src/test/scripts/gen-code.sh
 create mode 100644 sql/core/src/test/thrift/parquet-compat.thrift
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala

diff --git a/.rat-excludes b/.rat-excludes
index 0240e81c45ea2..236c2db05367c 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -91,3 +91,5 @@ help/*
 html/*
 INDEX
 .lintr
+gen-java.*
+.*avpr
diff --git a/pom.xml b/pom.xml
index bece526a2a3e5..9cf2471b51304 100644
--- a/pom.xml
+++ b/pom.xml
@@ -161,6 +161,7 @@
     <fasterxml.jackson.version>2.4.4</fasterxml.jackson.version>
     <snappy.version>1.1.1.7</snappy.version>
     <netlib.java.version>1.1.2</netlib.java.version>
+    <thrift.version>0.9.2</thrift.version>
     <!-- For maven shade plugin (see SPARK-8819) -->
     <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
 
@@ -179,6 +180,8 @@
     <hbase.deps.scope>compile</hbase.deps.scope>
     <hive.deps.scope>compile</hive.deps.scope>
     <parquet.deps.scope>compile</parquet.deps.scope>
+    <parquet.test.deps.scope>test</parquet.test.deps.scope>
+    <thrift.test.deps.scope>test</thrift.test.deps.scope>
 
     <!--
       Overridable test home. So that you can call individual pom files directly without
@@ -270,6 +273,18 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <!-- For transitive dependencies brougt by parquet-thrift -->
+    <repository>
+      <id>twttr-repo</id>
+      <name>Twttr Repository</name>
+      <url>http://maven.twttr.com</url>
+      <releases>
+        <enabled>true</enabled>
+      </releases>
+      <snapshots>
+        <enabled>false</enabled>
+      </snapshots>
+    </repository>
     <!-- TODO: This can be deleted after Spark 1.4 is posted -->
     <repository>
       <id>spark-1.4-staging</id>
@@ -1101,6 +1116,24 @@
         <version>${parquet.version}</version>
         <scope>${parquet.deps.scope}</scope>
       </dependency>
+      <dependency>
+        <groupId>org.apache.parquet</groupId>
+        <artifactId>parquet-avro</artifactId>
+        <version>${parquet.version}</version>
+        <scope>${parquet.test.deps.scope}</scope>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.parquet</groupId>
+        <artifactId>parquet-thrift</artifactId>
+        <version>${parquet.version}</version>
+        <scope>${parquet.test.deps.scope}</scope>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.thrift</groupId>
+        <artifactId>libthrift</artifactId>
+        <version>${thrift.version}</version>
+        <scope>${thrift.test.deps.scope}</scope>
+      </dependency>
       <dependency>
         <groupId>org.apache.flume</groupId>
         <artifactId>flume-ng-core</artifactId>
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 41e19fd9cc11e..7346d804632bc 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -62,21 +62,8 @@ object MimaExcludes {
               "org.apache.spark.ml.classification.LogisticCostFun.this"),
             // SQL execution is considered private.
             excludePackage("org.apache.spark.sql.execution"),
-            // NanoTime and CatalystTimestampConverter is only used inside catalyst,
-            // not needed anymore
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.timestamp.NanoTime"),
-              ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.timestamp.NanoTime$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.CatalystTimestampConverter"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.CatalystTimestampConverter$"),
-            // SPARK-6777 Implements backwards compatibility rules in CatalystSchemaConverter
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTypeInfo"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTypeInfo$")
+            // Parquet support is considered private.
+            excludePackage("org.apache.spark.sql.parquet")
           ) ++ Seq(
             // SPARK-8479 Add numNonzeros and numActives to Matrix.
             ProblemFilters.exclude[MissingMethodProblem](
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index 7d00047d08d74..a4c2da8e05f5d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -17,11 +17,12 @@
 
 package org.apache.spark.sql.types
 
+import scala.util.Try
 import scala.util.parsing.combinator.RegexParsers
 
-import org.json4s._
 import org.json4s.JsonAST.JValue
 import org.json4s.JsonDSL._
+import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.annotation.DeveloperApi
@@ -82,6 +83,9 @@ abstract class DataType extends AbstractDataType {
 
 
 object DataType {
+  private[sql] def fromString(raw: String): DataType = {
+    Try(DataType.fromJson(raw)).getOrElse(DataType.fromCaseClassString(raw))
+  }
 
   def fromJson(json: String): DataType = parseDataType(parse(json))
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 3b17566d54d9b..e2d3f53f7d978 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -311,6 +311,11 @@ object StructType extends AbstractDataType {
 
   private[sql] override def simpleString: String = "struct"
 
+  private[sql] def fromString(raw: String): StructType = DataType.fromString(raw) match {
+    case t: StructType => t
+    case _ => throw new RuntimeException(s"Failed parsing StructType: $raw")
+  }
+
   def apply(fields: Seq[StructField]): StructType = StructType(fields.toArray)
 
   def apply(fields: java.util.List[StructField]): StructType = {
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 8fc16928adbd9..f90099f22d4bd 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -101,9 +101,45 @@
       <version>9.3-1102-jdbc41</version>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.parquet</groupId>
+      <artifactId>parquet-avro</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.parquet</groupId>
+      <artifactId>parquet-thrift</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.thrift</groupId>
+      <artifactId>libthrift</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.codehaus.mojo</groupId>
+        <artifactId>build-helper-maven-plugin</artifactId>
+        <executions>
+          <execution>
+            <id>add-scala-test-sources</id>
+            <phase>generate-test-sources</phase>
+            <goals>
+              <goal>add-test-source</goal>
+            </goals>
+            <configuration>
+              <sources>
+                <source>src/test/scala</source>
+                <source>src/test/gen-java</source>
+              </sources>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
   </build>
 </project>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
new file mode 100644
index 0000000000000..0c3d8fdab6bd2
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
@@ -0,0 +1,434 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import java.nio.ByteOrder
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.parquet.column.Dictionary
+import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
+import org.apache.parquet.schema.Type.Repetition
+import org.apache.parquet.schema.{GroupType, PrimitiveType, Type}
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * A [[ParentContainerUpdater]] is used by a Parquet converter to set converted values to some
+ * corresponding parent container. For example, a converter for a `StructType` field may set
+ * converted values to a [[MutableRow]]; or a converter for array elements may append converted
+ * values to an [[ArrayBuffer]].
+ */
+private[parquet] trait ParentContainerUpdater {
+  def set(value: Any): Unit = ()
+  def setBoolean(value: Boolean): Unit = set(value)
+  def setByte(value: Byte): Unit = set(value)
+  def setShort(value: Short): Unit = set(value)
+  def setInt(value: Int): Unit = set(value)
+  def setLong(value: Long): Unit = set(value)
+  def setFloat(value: Float): Unit = set(value)
+  def setDouble(value: Double): Unit = set(value)
+}
+
+/** A no-op updater used for root converter (who doesn't have a parent). */
+private[parquet] object NoopUpdater extends ParentContainerUpdater
+
+/**
+ * A [[CatalystRowConverter]] is used to convert Parquet "structs" into Spark SQL [[Row]]s.  Since
+ * any Parquet record is also a struct, this converter can also be used as root converter.
+ *
+ * When used as a root converter, [[NoopUpdater]] should be used since root converters don't have
+ * any "parent" container.
+ *
+ * @param parquetType Parquet schema of Parquet records
+ * @param catalystType Spark SQL schema that corresponds to the Parquet record type
+ * @param updater An updater which propagates converted field values to the parent container
+ */
+private[parquet] class CatalystRowConverter(
+    parquetType: GroupType,
+    catalystType: StructType,
+    updater: ParentContainerUpdater)
+  extends GroupConverter {
+
+  /**
+   * Updater used together with field converters within a [[CatalystRowConverter]].  It propagates
+   * converted filed values to the `ordinal`-th cell in `currentRow`.
+   */
+  private final class RowUpdater(row: MutableRow, ordinal: Int) extends ParentContainerUpdater {
+    override def set(value: Any): Unit = row(ordinal) = value
+    override def setBoolean(value: Boolean): Unit = row.setBoolean(ordinal, value)
+    override def setByte(value: Byte): Unit = row.setByte(ordinal, value)
+    override def setShort(value: Short): Unit = row.setShort(ordinal, value)
+    override def setInt(value: Int): Unit = row.setInt(ordinal, value)
+    override def setLong(value: Long): Unit = row.setLong(ordinal, value)
+    override def setDouble(value: Double): Unit = row.setDouble(ordinal, value)
+    override def setFloat(value: Float): Unit = row.setFloat(ordinal, value)
+  }
+
+  /**
+   * Represents the converted row object once an entire Parquet record is converted.
+   *
+   * @todo Uses [[UnsafeRow]] for better performance.
+   */
+  val currentRow = new SpecificMutableRow(catalystType.map(_.dataType))
+
+  // Converters for each field.
+  private val fieldConverters: Array[Converter] = {
+    parquetType.getFields.zip(catalystType).zipWithIndex.map {
+      case ((parquetFieldType, catalystField), ordinal) =>
+        // Converted field value should be set to the `ordinal`-th cell of `currentRow`
+        newConverter(parquetFieldType, catalystField.dataType, new RowUpdater(currentRow, ordinal))
+    }.toArray
+  }
+
+  override def getConverter(fieldIndex: Int): Converter = fieldConverters(fieldIndex)
+
+  override def end(): Unit = updater.set(currentRow)
+
+  override def start(): Unit = {
+    var i = 0
+    while (i < currentRow.length) {
+      currentRow.setNullAt(i)
+      i += 1
+    }
+  }
+
+  /**
+   * Creates a converter for the given Parquet type `parquetType` and Spark SQL data type
+   * `catalystType`. Converted values are handled by `updater`.
+   */
+  private def newConverter(
+      parquetType: Type,
+      catalystType: DataType,
+      updater: ParentContainerUpdater): Converter = {
+
+    catalystType match {
+      case BooleanType | IntegerType | LongType | FloatType | DoubleType | BinaryType =>
+        new CatalystPrimitiveConverter(updater)
+
+      case ByteType =>
+        new PrimitiveConverter {
+          override def addInt(value: Int): Unit =
+            updater.setByte(value.asInstanceOf[ByteType#InternalType])
+        }
+
+      case ShortType =>
+        new PrimitiveConverter {
+          override def addInt(value: Int): Unit =
+            updater.setShort(value.asInstanceOf[ShortType#InternalType])
+        }
+
+      case t: DecimalType =>
+        new CatalystDecimalConverter(t, updater)
+
+      case StringType =>
+        new CatalystStringConverter(updater)
+
+      case TimestampType =>
+        // TODO Implements `TIMESTAMP_MICROS` once parquet-mr has that.
+        new PrimitiveConverter {
+          // Converts nanosecond timestamps stored as INT96
+          override def addBinary(value: Binary): Unit = {
+            assert(
+              value.length() == 12,
+              "Timestamps (with nanoseconds) are expected to be stored in 12-byte long binaries, " +
+              s"but got a ${value.length()}-byte binary.")
+
+            val buf = value.toByteBuffer.order(ByteOrder.LITTLE_ENDIAN)
+            val timeOfDayNanos = buf.getLong
+            val julianDay = buf.getInt
+            updater.setLong(DateTimeUtils.fromJulianDay(julianDay, timeOfDayNanos))
+          }
+        }
+
+      case DateType =>
+        new PrimitiveConverter {
+          override def addInt(value: Int): Unit = {
+            // DateType is not specialized in `SpecificMutableRow`, have to box it here.
+            updater.set(value.asInstanceOf[DateType#InternalType])
+          }
+        }
+
+      case t: ArrayType =>
+        new CatalystArrayConverter(parquetType.asGroupType(), t, updater)
+
+      case t: MapType =>
+        new CatalystMapConverter(parquetType.asGroupType(), t, updater)
+
+      case t: StructType =>
+        new CatalystRowConverter(parquetType.asGroupType(), t, new ParentContainerUpdater {
+          override def set(value: Any): Unit = updater.set(value.asInstanceOf[Row].copy())
+        })
+
+      case t: UserDefinedType[_] =>
+        val catalystTypeForUDT = t.sqlType
+        val nullable = parquetType.isRepetition(Repetition.OPTIONAL)
+        val field = StructField("udt", catalystTypeForUDT, nullable)
+        val parquetTypeForUDT = new CatalystSchemaConverter().convertField(field)
+        newConverter(parquetTypeForUDT, catalystTypeForUDT, updater)
+
+      case _ =>
+        throw new RuntimeException(
+          s"Unable to create Parquet converter for data type ${catalystType.json}")
+    }
+  }
+
+  /**
+   * Parquet converter for Parquet primitive types.  Note that not all Spark SQL atomic types
+   * are handled by this converter.  Parquet primitive types are only a subset of those of Spark
+   * SQL.  For example, BYTE, SHORT, and INT in Spark SQL are all covered by INT32 in Parquet.
+   */
+  private final class CatalystPrimitiveConverter(updater: ParentContainerUpdater)
+    extends PrimitiveConverter {
+
+    override def addBoolean(value: Boolean): Unit = updater.setBoolean(value)
+    override def addInt(value: Int): Unit = updater.setInt(value)
+    override def addLong(value: Long): Unit = updater.setLong(value)
+    override def addFloat(value: Float): Unit = updater.setFloat(value)
+    override def addDouble(value: Double): Unit = updater.setDouble(value)
+    override def addBinary(value: Binary): Unit = updater.set(value.getBytes)
+  }
+
+  /**
+   * Parquet converter for strings. A dictionary is used to minimize string decoding cost.
+   */
+  private final class CatalystStringConverter(updater: ParentContainerUpdater)
+    extends PrimitiveConverter {
+
+    private var expandedDictionary: Array[UTF8String] = null
+
+    override def hasDictionarySupport: Boolean = true
+
+    override def setDictionary(dictionary: Dictionary): Unit = {
+      this.expandedDictionary = Array.tabulate(dictionary.getMaxId + 1) { i =>
+        UTF8String.fromBytes(dictionary.decodeToBinary(i).getBytes)
+      }
+    }
+
+    override def addValueFromDictionary(dictionaryId: Int): Unit = {
+      updater.set(expandedDictionary(dictionaryId))
+    }
+
+    override def addBinary(value: Binary): Unit = {
+      updater.set(UTF8String.fromBytes(value.getBytes))
+    }
+  }
+
+  /**
+   * Parquet converter for fixed-precision decimals.
+   */
+  private final class CatalystDecimalConverter(
+      decimalType: DecimalType,
+      updater: ParentContainerUpdater)
+    extends PrimitiveConverter {
+
+    // Converts decimals stored as INT32
+    override def addInt(value: Int): Unit = {
+      addLong(value: Long)
+    }
+
+    // Converts decimals stored as INT64
+    override def addLong(value: Long): Unit = {
+      updater.set(Decimal(value, decimalType.precision, decimalType.scale))
+    }
+
+    // Converts decimals stored as either FIXED_LENGTH_BYTE_ARRAY or BINARY
+    override def addBinary(value: Binary): Unit = {
+      updater.set(toDecimal(value))
+    }
+
+    private def toDecimal(value: Binary): Decimal = {
+      val precision = decimalType.precision
+      val scale = decimalType.scale
+      val bytes = value.getBytes
+
+      var unscaled = 0L
+      var i = 0
+
+      while (i < bytes.length) {
+        unscaled = (unscaled << 8) | (bytes(i) & 0xff)
+        i += 1
+      }
+
+      val bits = 8 * bytes.length
+      unscaled = (unscaled << (64 - bits)) >> (64 - bits)
+      Decimal(unscaled, precision, scale)
+    }
+  }
+
+  /**
+   * Parquet converter for arrays.  Spark SQL arrays are represented as Parquet lists.  Standard
+   * Parquet lists are represented as a 3-level group annotated by `LIST`:
+   * {{{
+   *   <list-repetition> group <name> (LIST) {            <-- parquetSchema points here
+   *     repeated group list {
+   *       <element-repetition> <element-type> element;
+   *     }
+   *   }
+   * }}}
+   * The `parquetSchema` constructor argument points to the outermost group.
+   *
+   * However, before this representation is standardized, some Parquet libraries/tools also use some
+   * non-standard formats to represent list-like structures.  Backwards-compatibility rules for
+   * handling these cases are described in Parquet format spec.
+   *
+   * @see https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#lists
+   */
+  private final class CatalystArrayConverter(
+      parquetSchema: GroupType,
+      catalystSchema: ArrayType,
+      updater: ParentContainerUpdater)
+    extends GroupConverter {
+
+    private var currentArray: ArrayBuffer[Any] = _
+
+    private val elementConverter: Converter = {
+      val repeatedType = parquetSchema.getType(0)
+      val elementType = catalystSchema.elementType
+
+      if (isElementType(repeatedType, elementType)) {
+        newConverter(repeatedType, elementType, new ParentContainerUpdater {
+          override def set(value: Any): Unit = currentArray += value
+        })
+      } else {
+        new ElementConverter(repeatedType.asGroupType().getType(0), elementType)
+      }
+    }
+
+    override def getConverter(fieldIndex: Int): Converter = elementConverter
+
+    override def end(): Unit = updater.set(currentArray)
+
+    // NOTE: We can't reuse the mutable `ArrayBuffer` here and must instantiate a new buffer for the
+    // next value.  `Row.copy()` only copies row cells, it doesn't do deep copy to objects stored
+    // in row cells.
+    override def start(): Unit = currentArray = ArrayBuffer.empty[Any]
+
+    // scalastyle:off
+    /**
+     * Returns whether the given type is the element type of a list or is a syntactic group with
+     * one field that is the element type.  This is determined by checking whether the type can be
+     * a syntactic group and by checking whether a potential syntactic group matches the expected
+     * schema.
+     * {{{
+     *   <list-repetition> group <name> (LIST) {
+     *     repeated group list {                          <-- repeatedType points here
+     *       <element-repetition> <element-type> element;
+     *     }
+     *   }
+     * }}}
+     * In short, here we handle Parquet list backwards-compatibility rules on the read path.  This
+     * method is based on `AvroIndexedRecordConverter.isElementType`.
+     *
+     * @see https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules
+     */
+    // scalastyle:on
+    private def isElementType(parquetRepeatedType: Type, catalystElementType: DataType): Boolean = {
+      (parquetRepeatedType, catalystElementType) match {
+        case (t: PrimitiveType, _) => true
+        case (t: GroupType, _) if t.getFieldCount > 1 => true
+        case (t: GroupType, StructType(Array(f))) if f.name == t.getFieldName(0) => true
+        case _ => false
+      }
+    }
+
+    /** Array element converter */
+    private final class ElementConverter(parquetType: Type, catalystType: DataType)
+      extends GroupConverter {
+
+      private var currentElement: Any = _
+
+      private val converter = newConverter(parquetType, catalystType, new ParentContainerUpdater {
+        override def set(value: Any): Unit = currentElement = value
+      })
+
+      override def getConverter(fieldIndex: Int): Converter = converter
+
+      override def end(): Unit = currentArray += currentElement
+
+      override def start(): Unit = currentElement = null
+    }
+  }
+
+  /** Parquet converter for maps */
+  private final class CatalystMapConverter(
+      parquetType: GroupType,
+      catalystType: MapType,
+      updater: ParentContainerUpdater)
+    extends GroupConverter {
+
+    private var currentMap: mutable.Map[Any, Any] = _
+
+    private val keyValueConverter = {
+      val repeatedType = parquetType.getType(0).asGroupType()
+      new KeyValueConverter(
+        repeatedType.getType(0),
+        repeatedType.getType(1),
+        catalystType.keyType,
+        catalystType.valueType)
+    }
+
+    override def getConverter(fieldIndex: Int): Converter = keyValueConverter
+
+    override def end(): Unit = updater.set(currentMap)
+
+    // NOTE: We can't reuse the mutable Map here and must instantiate a new `Map` for the next
+    // value.  `Row.copy()` only copies row cells, it doesn't do deep copy to objects stored in row
+    // cells.
+    override def start(): Unit = currentMap = mutable.Map.empty[Any, Any]
+
+    /** Parquet converter for key-value pairs within the map. */
+    private final class KeyValueConverter(
+        parquetKeyType: Type,
+        parquetValueType: Type,
+        catalystKeyType: DataType,
+        catalystValueType: DataType)
+      extends GroupConverter {
+
+      private var currentKey: Any = _
+
+      private var currentValue: Any = _
+
+      private val converters = Array(
+        // Converter for keys
+        newConverter(parquetKeyType, catalystKeyType, new ParentContainerUpdater {
+          override def set(value: Any): Unit = currentKey = value
+        }),
+
+        // Converter for values
+        newConverter(parquetValueType, catalystValueType, new ParentContainerUpdater {
+          override def set(value: Any): Unit = currentValue = value
+        }))
+
+      override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
+
+      override def end(): Unit = currentMap(currentKey) = currentValue
+
+      override def start(): Unit = {
+        currentKey = null
+        currentValue = null
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index 4ab274ec17a02..de3a72d8146c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -358,9 +358,24 @@ private[parquet] class CatalystSchemaConverter(
       case DateType =>
         Types.primitive(INT32, repetition).as(DATE).named(field.name)
 
-      // NOTE: !! This timestamp type is not specified in Parquet format spec !!
-      // However, Impala and older versions of Spark SQL use INT96 to store timestamps with
-      // nanosecond precision (not TIME_MILLIS or TIMESTAMP_MILLIS described in the spec).
+      // NOTE: Spark SQL TimestampType is NOT a well defined type in Parquet format spec.
+      //
+      // As stated in PARQUET-323, Parquet `INT96` was originally introduced to represent nanosecond
+      // timestamp in Impala for some historical reasons, it's not recommended to be used for any
+      // other types and will probably be deprecated in future Parquet format spec.  That's the
+      // reason why Parquet format spec only defines `TIMESTAMP_MILLIS` and `TIMESTAMP_MICROS` which
+      // are both logical types annotating `INT64`.
+      //
+      // Originally, Spark SQL uses the same nanosecond timestamp type as Impala and Hive.  Starting
+      // from Spark 1.5.0, we resort to a timestamp type with 100 ns precision so that we can store
+      // a timestamp into a `Long`.  This design decision is subject to change though, for example,
+      // we may resort to microsecond precision in the future.
+      //
+      // For Parquet, we plan to write all `TimestampType` value as `TIMESTAMP_MICROS`, but it's
+      // currently not implemented yet because parquet-mr 1.7.0 (the version we're currently using)
+      // hasn't implemented `TIMESTAMP_MICROS` yet.
+      //
+      // TODO Implements `TIMESTAMP_MICROS` once parquet-mr has that.
       case TimestampType =>
         Types.primitive(INT96, repetition).named(field.name)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index 86a77bf965daa..be0a2029d233b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -17,61 +17,15 @@
 
 package org.apache.spark.sql.parquet
 
-import java.nio.ByteOrder
-
-import scala.collection.mutable.{ArrayBuffer, Buffer, HashMap}
-
-import org.apache.parquet.Preconditions
-import org.apache.parquet.column.Dictionary
-import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
-import org.apache.parquet.schema.MessageType
-
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.parquet.CatalystConverter.FieldType
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
-
-/**
- * Collection of converters of Parquet types (group and primitive types) that
- * model arrays and maps. The conversions are partly based on the AvroParquet
- * converters that are part of Parquet in order to be able to process these
- * types.
- *
- * There are several types of converters:
- * <ul>
- *   <li>[[org.apache.spark.sql.parquet.CatalystPrimitiveConverter]] for primitive
- *   (numeric, boolean and String) types</li>
- *   <li>[[org.apache.spark.sql.parquet.CatalystNativeArrayConverter]] for arrays
- *   of native JVM element types; note: currently null values are not supported!</li>
- *   <li>[[org.apache.spark.sql.parquet.CatalystArrayConverter]] for arrays of
- *   arbitrary element types (including nested element types); note: currently
- *   null values are not supported!</li>
- *   <li>[[org.apache.spark.sql.parquet.CatalystStructConverter]] for structs</li>
- *   <li>[[org.apache.spark.sql.parquet.CatalystMapConverter]] for maps; note:
- *   currently null values are not supported!</li>
- *   <li>[[org.apache.spark.sql.parquet.CatalystPrimitiveRowConverter]] for rows
- *   of only primitive element types</li>
- *   <li>[[org.apache.spark.sql.parquet.CatalystGroupConverter]] for other nested
- *   records, including the top-level row record</li>
- * </ul>
- */
 
 private[sql] object CatalystConverter {
-  // The type internally used for fields
-  type FieldType = StructField
-
   // This is mostly Parquet convention (see, e.g., `ConversionPatterns`).
   // Note that "array" for the array elements is chosen by ParquetAvro.
   // Using a different value will result in Parquet silently dropping columns.
   val ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME = "bag"
   val ARRAY_ELEMENTS_SCHEMA_NAME = "array"
-  // SPARK-4520: Thrift generated parquet files have different array element
-  // schema names than avro. Thrift parquet uses array_schema_name + "_tuple"
-  // as opposed to "array" used by default. For more information, check
-  // TestThriftSchemaConverter.java in parquet.thrift.
-  val THRIFT_ARRAY_ELEMENTS_SCHEMA_NAME_SUFFIX = "_tuple"
+
   val MAP_KEY_SCHEMA_NAME = "key"
   val MAP_VALUE_SCHEMA_NAME = "value"
   val MAP_SCHEMA_NAME = "map"
@@ -80,787 +34,4 @@ private[sql] object CatalystConverter {
   type ArrayScalaType[T] = Seq[T]
   type StructScalaType[T] = InternalRow
   type MapScalaType[K, V] = Map[K, V]
-
-  protected[parquet] def createConverter(
-      field: FieldType,
-      fieldIndex: Int,
-      parent: CatalystConverter): Converter = {
-    val fieldType: DataType = field.dataType
-    fieldType match {
-      case udt: UserDefinedType[_] => {
-        createConverter(field.copy(dataType = udt.sqlType), fieldIndex, parent)
-      }
-      // For native JVM types we use a converter with native arrays
-      case ArrayType(elementType: AtomicType, false) => {
-        new CatalystNativeArrayConverter(elementType, fieldIndex, parent)
-      }
-      // This is for other types of arrays, including those with nested fields
-      case ArrayType(elementType: DataType, false) => {
-        new CatalystArrayConverter(elementType, fieldIndex, parent)
-      }
-      case ArrayType(elementType: DataType, true) => {
-        new CatalystArrayContainsNullConverter(elementType, fieldIndex, parent)
-      }
-      case StructType(fields: Array[StructField]) => {
-        new CatalystStructConverter(fields, fieldIndex, parent)
-      }
-      case MapType(keyType: DataType, valueType: DataType, valueContainsNull: Boolean) => {
-        new CatalystMapConverter(
-          Array(
-            new FieldType(MAP_KEY_SCHEMA_NAME, keyType, false),
-            new FieldType(MAP_VALUE_SCHEMA_NAME, valueType, valueContainsNull)),
-          fieldIndex,
-          parent)
-      }
-      // Strings, Shorts and Bytes do not have a corresponding type in Parquet
-      // so we need to treat them separately
-      case StringType =>
-        new CatalystPrimitiveStringConverter(parent, fieldIndex)
-      case ShortType => {
-        new CatalystPrimitiveConverter(parent, fieldIndex) {
-          override def addInt(value: Int): Unit =
-            parent.updateShort(fieldIndex, value.asInstanceOf[ShortType.InternalType])
-        }
-      }
-      case ByteType => {
-        new CatalystPrimitiveConverter(parent, fieldIndex) {
-          override def addInt(value: Int): Unit =
-            parent.updateByte(fieldIndex, value.asInstanceOf[ByteType.InternalType])
-        }
-      }
-      case DateType => {
-        new CatalystPrimitiveConverter(parent, fieldIndex) {
-          override def addInt(value: Int): Unit =
-            parent.updateDate(fieldIndex, value.asInstanceOf[DateType.InternalType])
-        }
-      }
-      case d: DecimalType => {
-        new CatalystPrimitiveConverter(parent, fieldIndex) {
-          override def addBinary(value: Binary): Unit =
-            parent.updateDecimal(fieldIndex, value, d)
-        }
-      }
-      case TimestampType => {
-        new CatalystPrimitiveConverter(parent, fieldIndex) {
-          override def addBinary(value: Binary): Unit =
-            parent.updateTimestamp(fieldIndex, value)
-        }
-      }
-      // All other primitive types use the default converter
-      case ctype: DataType if ParquetTypesConverter.isPrimitiveType(ctype) => {
-        // note: need the type tag here!
-        new CatalystPrimitiveConverter(parent, fieldIndex)
-      }
-      case _ => throw new RuntimeException(
-        s"unable to convert datatype ${field.dataType.toString} in CatalystConverter")
-    }
-  }
-
-  protected[parquet] def createRootConverter(
-      parquetSchema: MessageType,
-      attributes: Seq[Attribute]): CatalystConverter = {
-    // For non-nested types we use the optimized Row converter
-    if (attributes.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType))) {
-      new CatalystPrimitiveRowConverter(attributes.toArray)
-    } else {
-      new CatalystGroupConverter(attributes.toArray)
-    }
-  }
-}
-
-private[parquet] abstract class CatalystConverter extends GroupConverter {
-  /**
-   * The number of fields this group has
-   */
-  protected[parquet] val size: Int
-
-  /**
-   * The index of this converter in the parent
-   */
-  protected[parquet] val index: Int
-
-  /**
-   * The parent converter
-   */
-  protected[parquet] val parent: CatalystConverter
-
-  /**
-   * Called by child converters to update their value in its parent (this).
-   * Note that if possible the more specific update methods below should be used
-   * to avoid auto-boxing of native JVM types.
-   *
-   * @param fieldIndex
-   * @param value
-   */
-  protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit
-
-  protected[parquet] def updateBoolean(fieldIndex: Int, value: Boolean): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateDate(fieldIndex: Int, value: Int): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateShort(fieldIndex: Int, value: Short): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateByte(fieldIndex: Int, value: Byte): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateDouble(fieldIndex: Int, value: Double): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateFloat(fieldIndex: Int, value: Float): Unit =
-    updateField(fieldIndex, value)
-
-  protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit =
-    updateField(fieldIndex, value.getBytes)
-
-  protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit =
-    updateField(fieldIndex, UTF8String.fromBytes(value))
-
-  protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit =
-    updateField(fieldIndex, readTimestamp(value))
-
-  protected[parquet] def updateDecimal(fieldIndex: Int, value: Binary, ctype: DecimalType): Unit =
-    updateField(fieldIndex, readDecimal(new Decimal(), value, ctype))
-
-  protected[parquet] def isRootConverter: Boolean = parent == null
-
-  protected[parquet] def clearBuffer(): Unit
-
-  /**
-   * Should only be called in the root (group) converter!
-   *
-   * @return
-   */
-  def getCurrentRecord: InternalRow = throw new UnsupportedOperationException
-
-  /**
-   * Read a decimal value from a Parquet Binary into "dest". Only supports decimals that fit in
-   * a long (i.e. precision <= 18)
-   *
-   * Returned value is needed by CatalystConverter, which doesn't reuse the Decimal object.
-   */
-  protected[parquet] def readDecimal(dest: Decimal, value: Binary, ctype: DecimalType): Decimal = {
-    val precision = ctype.precisionInfo.get.precision
-    val scale = ctype.precisionInfo.get.scale
-    val bytes = value.getBytes
-    require(bytes.length <= 16, "Decimal field too large to read")
-    var unscaled = 0L
-    var i = 0
-    while (i < bytes.length) {
-      unscaled = (unscaled << 8) | (bytes(i) & 0xFF)
-      i += 1
-    }
-    // Make sure unscaled has the right sign, by sign-extending the first bit
-    val numBits = 8 * bytes.length
-    unscaled = (unscaled << (64 - numBits)) >> (64 - numBits)
-    dest.set(unscaled, precision, scale)
-  }
-
-  /**
-   * Read a Timestamp value from a Parquet Int96Value
-   */
-  protected[parquet] def readTimestamp(value: Binary): Long = {
-    Preconditions.checkArgument(value.length() == 12, "Must be 12 bytes")
-    val buf = value.toByteBuffer
-    buf.order(ByteOrder.LITTLE_ENDIAN)
-    val timeOfDayNanos = buf.getLong
-    val julianDay = buf.getInt
-    DateTimeUtils.fromJulianDay(julianDay, timeOfDayNanos)
-  }
-}
-
-/**
- * A `parquet.io.api.GroupConverter` that is able to convert a Parquet record
- * to a [[org.apache.spark.sql.catalyst.expressions.InternalRow]] object.
- *
- * @param schema The corresponding Catalyst schema in the form of a list of attributes.
- */
-private[parquet] class CatalystGroupConverter(
-    protected[parquet] val schema: Array[FieldType],
-    protected[parquet] val index: Int,
-    protected[parquet] val parent: CatalystConverter,
-    protected[parquet] var current: ArrayBuffer[Any],
-    protected[parquet] var buffer: ArrayBuffer[InternalRow])
-  extends CatalystConverter {
-
-  def this(schema: Array[FieldType], index: Int, parent: CatalystConverter) =
-    this(
-      schema,
-      index,
-      parent,
-      current = null,
-      buffer = new ArrayBuffer[InternalRow](
-        CatalystArrayConverter.INITIAL_ARRAY_SIZE))
-
-  /**
-   * This constructor is used for the root converter only!
-   */
-  def this(attributes: Array[Attribute]) =
-    this(attributes.map(a => new FieldType(a.name, a.dataType, a.nullable)), 0, null)
-
-  protected [parquet] val converters: Array[Converter] =
-    schema.zipWithIndex.map {
-      case (field, idx) => CatalystConverter.createConverter(field, idx, this)
-    }.toArray
-
-  override val size = schema.size
-
-  override def getCurrentRecord: InternalRow = {
-    assert(isRootConverter, "getCurrentRecord should only be called in root group converter!")
-    // TODO: use iterators if possible
-    // Note: this will ever only be called in the root converter when the record has been
-    // fully processed. Therefore it will be difficult to use mutable rows instead, since
-    // any non-root converter never would be sure when it would be safe to re-use the buffer.
-    new GenericInternalRow(current.toArray)
-  }
-
-  override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
-
-  // for child converters to update upstream values
-  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
-    current.update(fieldIndex, value)
-  }
-
-  override protected[parquet] def clearBuffer(): Unit = buffer.clear()
-
-  override def start(): Unit = {
-    current = ArrayBuffer.fill(size)(null)
-    converters.foreach { converter =>
-      if (!converter.isPrimitive) {
-        converter.asInstanceOf[CatalystConverter].clearBuffer()
-      }
-    }
-  }
-
-  override def end(): Unit = {
-    if (!isRootConverter) {
-      assert(current != null) // there should be no empty groups
-      buffer.append(new GenericInternalRow(current.toArray))
-      parent.updateField(index, new GenericInternalRow(buffer.toArray.asInstanceOf[Array[Any]]))
-    }
-  }
-}
-
-/**
- * A `parquet.io.api.GroupConverter` that is able to convert a Parquet record
- * to a [[org.apache.spark.sql.catalyst.expressions.InternalRow]] object. Note that his
- * converter is optimized for rows of primitive types (non-nested records).
- */
-private[parquet] class CatalystPrimitiveRowConverter(
-    protected[parquet] val schema: Array[FieldType],
-    protected[parquet] var current: MutableRow)
-  extends CatalystConverter {
-
-  // This constructor is used for the root converter only
-  def this(attributes: Array[Attribute]) =
-    this(
-      attributes.map(a => new FieldType(a.name, a.dataType, a.nullable)),
-      new SpecificMutableRow(attributes.map(_.dataType)))
-
-  protected [parquet] val converters: Array[Converter] =
-    schema.zipWithIndex.map {
-      case (field, idx) => CatalystConverter.createConverter(field, idx, this)
-    }.toArray
-
-  override val size = schema.size
-
-  override val index = 0
-
-  override val parent = null
-
-  // Should be only called in root group converter!
-  override def getCurrentRecord: InternalRow = current
-
-  override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
-
-  // for child converters to update upstream values
-  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
-    throw new UnsupportedOperationException // child converters should use the
-    // specific update methods below
-  }
-
-  override protected[parquet] def clearBuffer(): Unit = {}
-
-  override def start(): Unit = {
-    var i = 0
-    while (i < size) {
-      current.setNullAt(i)
-      i = i + 1
-    }
-  }
-
-  override def end(): Unit = {}
-
-  // Overridden here to avoid auto-boxing for primitive types
-  override protected[parquet] def updateBoolean(fieldIndex: Int, value: Boolean): Unit =
-    current.setBoolean(fieldIndex, value)
-
-  override protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit =
-    current.setInt(fieldIndex, value)
-
-  override protected[parquet] def updateDate(fieldIndex: Int, value: Int): Unit =
-    current.setInt(fieldIndex, value)
-
-  override protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit =
-    current.setLong(fieldIndex, value)
-
-  override protected[parquet] def updateShort(fieldIndex: Int, value: Short): Unit =
-    current.setShort(fieldIndex, value)
-
-  override protected[parquet] def updateByte(fieldIndex: Int, value: Byte): Unit =
-    current.setByte(fieldIndex, value)
-
-  override protected[parquet] def updateDouble(fieldIndex: Int, value: Double): Unit =
-    current.setDouble(fieldIndex, value)
-
-  override protected[parquet] def updateFloat(fieldIndex: Int, value: Float): Unit =
-    current.setFloat(fieldIndex, value)
-
-  override protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit =
-    current.update(fieldIndex, value.getBytes)
-
-  override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit =
-    current.update(fieldIndex, UTF8String.fromBytes(value))
-
-  override protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit =
-    current.setLong(fieldIndex, readTimestamp(value))
-
-  override protected[parquet] def updateDecimal(
-      fieldIndex: Int, value: Binary, ctype: DecimalType): Unit = {
-    var decimal = current(fieldIndex).asInstanceOf[Decimal]
-    if (decimal == null) {
-      decimal = new Decimal
-      current(fieldIndex) = decimal
-    }
-    readDecimal(decimal, value, ctype)
-  }
-}
-
-/**
- * A `parquet.io.api.PrimitiveConverter` that converts Parquet types to Catalyst types.
- *
- * @param parent The parent group converter.
- * @param fieldIndex The index inside the record.
- */
-private[parquet] class CatalystPrimitiveConverter(
-    parent: CatalystConverter,
-    fieldIndex: Int) extends PrimitiveConverter {
-  override def addBinary(value: Binary): Unit =
-    parent.updateBinary(fieldIndex, value)
-
-  override def addBoolean(value: Boolean): Unit =
-    parent.updateBoolean(fieldIndex, value)
-
-  override def addDouble(value: Double): Unit =
-    parent.updateDouble(fieldIndex, value)
-
-  override def addFloat(value: Float): Unit =
-    parent.updateFloat(fieldIndex, value)
-
-  override def addInt(value: Int): Unit =
-    parent.updateInt(fieldIndex, value)
-
-  override def addLong(value: Long): Unit =
-    parent.updateLong(fieldIndex, value)
-}
-
-/**
- * A `parquet.io.api.PrimitiveConverter` that converts Parquet Binary to Catalyst String.
- * Supports dictionaries to reduce Binary to String conversion overhead.
- *
- * Follows pattern in Parquet of using dictionaries, where supported, for String conversion.
- *
- * @param parent The parent group converter.
- * @param fieldIndex The index inside the record.
- */
-private[parquet] class CatalystPrimitiveStringConverter(parent: CatalystConverter, fieldIndex: Int)
-  extends CatalystPrimitiveConverter(parent, fieldIndex) {
-
-  private[this] var dict: Array[Array[Byte]] = null
-
-  override def hasDictionarySupport: Boolean = true
-
-  override def setDictionary(dictionary: Dictionary): Unit =
-    dict = Array.tabulate(dictionary.getMaxId + 1) { dictionary.decodeToBinary(_).getBytes }
-
-  override def addValueFromDictionary(dictionaryId: Int): Unit =
-    parent.updateString(fieldIndex, dict(dictionaryId))
-
-  override def addBinary(value: Binary): Unit =
-    parent.updateString(fieldIndex, value.getBytes)
-}
-
-private[parquet] object CatalystArrayConverter {
-  val INITIAL_ARRAY_SIZE = 20
-}
-
-/**
- * A `parquet.io.api.GroupConverter` that converts a single-element groups that
- * match the characteristics of an array (see
- * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
- * [[org.apache.spark.sql.types.ArrayType]].
- *
- * @param elementType The type of the array elements (complex or primitive)
- * @param index The position of this (array) field inside its parent converter
- * @param parent The parent converter
- * @param buffer A data buffer
- */
-private[parquet] class CatalystArrayConverter(
-    val elementType: DataType,
-    val index: Int,
-    protected[parquet] val parent: CatalystConverter,
-    protected[parquet] var buffer: Buffer[Any])
-  extends CatalystConverter {
-
-  def this(elementType: DataType, index: Int, parent: CatalystConverter) =
-    this(
-      elementType,
-      index,
-      parent,
-      new ArrayBuffer[Any](CatalystArrayConverter.INITIAL_ARRAY_SIZE))
-
-  protected[parquet] val converter: Converter = CatalystConverter.createConverter(
-    new CatalystConverter.FieldType(
-      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
-      elementType,
-      false),
-    fieldIndex = 0,
-    parent = this)
-
-  override def getConverter(fieldIndex: Int): Converter = converter
-
-  // arrays have only one (repeated) field, which is its elements
-  override val size = 1
-
-  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
-    // fieldIndex is ignored (assumed to be zero but not checked)
-    if (value == null) {
-      throw new IllegalArgumentException("Null values inside Parquet arrays are not supported!")
-    }
-    buffer += value
-  }
-
-  override protected[parquet] def clearBuffer(): Unit = {
-    buffer.clear()
-  }
-
-  override def start(): Unit = {
-    if (!converter.isPrimitive) {
-      converter.asInstanceOf[CatalystConverter].clearBuffer()
-    }
-  }
-
-  override def end(): Unit = {
-    assert(parent != null)
-    // here we need to make sure to use ArrayScalaType
-    parent.updateField(index, buffer.toArray.toSeq)
-    clearBuffer()
-  }
-}
-
-/**
- * A `parquet.io.api.GroupConverter` that converts a single-element groups that
- * match the characteristics of an array (see
- * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
- * [[org.apache.spark.sql.types.ArrayType]].
- *
- * @param elementType The type of the array elements (native)
- * @param index The position of this (array) field inside its parent converter
- * @param parent The parent converter
- * @param capacity The (initial) capacity of the buffer
- */
-private[parquet] class CatalystNativeArrayConverter(
-    val elementType: AtomicType,
-    val index: Int,
-    protected[parquet] val parent: CatalystConverter,
-    protected[parquet] var capacity: Int = CatalystArrayConverter.INITIAL_ARRAY_SIZE)
-  extends CatalystConverter {
-
-  type NativeType = elementType.InternalType
-
-  private var buffer: Array[NativeType] = elementType.classTag.newArray(capacity)
-
-  private var elements: Int = 0
-
-  protected[parquet] val converter: Converter = CatalystConverter.createConverter(
-    new CatalystConverter.FieldType(
-      CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
-      elementType,
-      false),
-    fieldIndex = 0,
-    parent = this)
-
-  override def getConverter(fieldIndex: Int): Converter = converter
-
-  // arrays have only one (repeated) field, which is its elements
-  override val size = 1
-
-  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit =
-    throw new UnsupportedOperationException
-
-  // Overridden here to avoid auto-boxing for primitive types
-  override protected[parquet] def updateBoolean(fieldIndex: Int, value: Boolean): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateShort(fieldIndex: Int, value: Short): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateByte(fieldIndex: Int, value: Byte): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateDouble(fieldIndex: Int, value: Double): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateFloat(fieldIndex: Int, value: Float): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = value.getBytes.asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = {
-    checkGrowBuffer()
-    buffer(elements) = UTF8String.fromBytes(value).asInstanceOf[NativeType]
-    elements += 1
-  }
-
-  override protected[parquet] def clearBuffer(): Unit = {
-    elements = 0
-  }
-
-  override def start(): Unit = {}
-
-  override def end(): Unit = {
-    assert(parent != null)
-    // here we need to make sure to use ArrayScalaType
-    parent.updateField(
-      index,
-      buffer.slice(0, elements).toSeq)
-    clearBuffer()
-  }
-
-  private def checkGrowBuffer(): Unit = {
-    if (elements >= capacity) {
-      val newCapacity = 2 * capacity
-      val tmp: Array[NativeType] = elementType.classTag.newArray(newCapacity)
-      Array.copy(buffer, 0, tmp, 0, capacity)
-      buffer = tmp
-      capacity = newCapacity
-    }
-  }
-}
-
-/**
- * A `parquet.io.api.GroupConverter` that converts a single-element groups that
- * match the characteristics of an array contains null (see
- * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
- * [[org.apache.spark.sql.types.ArrayType]].
- *
- * @param elementType The type of the array elements (complex or primitive)
- * @param index The position of this (array) field inside its parent converter
- * @param parent The parent converter
- * @param buffer A data buffer
- */
-private[parquet] class CatalystArrayContainsNullConverter(
-    val elementType: DataType,
-    val index: Int,
-    protected[parquet] val parent: CatalystConverter,
-    protected[parquet] var buffer: Buffer[Any])
-  extends CatalystConverter {
-
-  def this(elementType: DataType, index: Int, parent: CatalystConverter) =
-    this(
-      elementType,
-      index,
-      parent,
-      new ArrayBuffer[Any](CatalystArrayConverter.INITIAL_ARRAY_SIZE))
-
-  protected[parquet] val converter: Converter = new CatalystConverter {
-
-    private var current: Any = null
-
-    val converter = CatalystConverter.createConverter(
-      new CatalystConverter.FieldType(
-        CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
-        elementType,
-        false),
-      fieldIndex = 0,
-      parent = this)
-
-    override def getConverter(fieldIndex: Int): Converter = converter
-
-    override def end(): Unit = parent.updateField(index, current)
-
-    override def start(): Unit = {
-      current = null
-    }
-
-    override protected[parquet] val size: Int = 1
-    override protected[parquet] val index: Int = 0
-    override protected[parquet] val parent = CatalystArrayContainsNullConverter.this
-
-    override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
-      current = value
-    }
-
-    override protected[parquet] def clearBuffer(): Unit = {}
-  }
-
-  override def getConverter(fieldIndex: Int): Converter = converter
-
-  // arrays have only one (repeated) field, which is its elements
-  override val size = 1
-
-  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
-    buffer += value
-  }
-
-  override protected[parquet] def clearBuffer(): Unit = {
-    buffer.clear()
-  }
-
-  override def start(): Unit = {}
-
-  override def end(): Unit = {
-    assert(parent != null)
-    // here we need to make sure to use ArrayScalaType
-    parent.updateField(index, buffer.toArray.toSeq)
-    clearBuffer()
-  }
-}
-
-/**
- * This converter is for multi-element groups of primitive or complex types
- * that have repetition level optional or required (so struct fields).
- *
- * @param schema The corresponding Catalyst schema in the form of a list of
- *               attributes.
- * @param index
- * @param parent
- */
-private[parquet] class CatalystStructConverter(
-    override protected[parquet] val schema: Array[FieldType],
-    override protected[parquet] val index: Int,
-    override protected[parquet] val parent: CatalystConverter)
-  extends CatalystGroupConverter(schema, index, parent) {
-
-  override protected[parquet] def clearBuffer(): Unit = {}
-
-  // TODO: think about reusing the buffer
-  override def end(): Unit = {
-    assert(!isRootConverter)
-    // here we need to make sure to use StructScalaType
-    // Note: we need to actually make a copy of the array since we
-    // may be in a nested field
-    parent.updateField(index, new GenericInternalRow(current.toArray))
-  }
-}
-
-/**
- * A `parquet.io.api.GroupConverter` that converts two-element groups that
- * match the characteristics of a map (see
- * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
- * [[org.apache.spark.sql.types.MapType]].
- *
- * @param schema
- * @param index
- * @param parent
- */
-private[parquet] class CatalystMapConverter(
-    protected[parquet] val schema: Array[FieldType],
-    override protected[parquet] val index: Int,
-    override protected[parquet] val parent: CatalystConverter)
-  extends CatalystConverter {
-
-  private val map = new HashMap[Any, Any]()
-
-  private val keyValueConverter = new CatalystConverter {
-    private var currentKey: Any = null
-    private var currentValue: Any = null
-    val keyConverter = CatalystConverter.createConverter(schema(0), 0, this)
-    val valueConverter = CatalystConverter.createConverter(schema(1), 1, this)
-
-    override def getConverter(fieldIndex: Int): Converter = {
-      if (fieldIndex == 0) keyConverter else valueConverter
-    }
-
-    override def end(): Unit = CatalystMapConverter.this.map += currentKey -> currentValue
-
-    override def start(): Unit = {
-      currentKey = null
-      currentValue = null
-    }
-
-    override protected[parquet] val size: Int = 2
-    override protected[parquet] val index: Int = 0
-    override protected[parquet] val parent: CatalystConverter = CatalystMapConverter.this
-
-    override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
-      fieldIndex match {
-        case 0 =>
-          currentKey = value
-        case 1 =>
-          currentValue = value
-        case _ =>
-          new RuntimePermission(s"trying to update Map with fieldIndex $fieldIndex")
-      }
-    }
-
-    override protected[parquet] def clearBuffer(): Unit = {}
-  }
-
-  override protected[parquet] val size: Int = 1
-
-  override protected[parquet] def clearBuffer(): Unit = {}
-
-  override def start(): Unit = {
-    map.clear()
-  }
-
-  override def end(): Unit = {
-    // here we need to make sure to use MapScalaType
-    parent.updateField(index, map.toMap)
-  }
-
-  override def getConverter(fieldIndex: Int): Converter = keyValueConverter
-
-  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit =
-    throw new UnsupportedOperationException
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 8402cd756140d..e8851ddb68026 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -17,14 +17,17 @@
 
 package org.apache.spark.sql.parquet
 
-import java.nio.{ByteOrder, ByteBuffer}
+import java.nio.{ByteBuffer, ByteOrder}
+import java.util
 import java.util.{HashMap => JHashMap}
 
+import scala.collection.JavaConversions._
+
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.column.ParquetProperties
 import org.apache.parquet.hadoop.ParquetOutputFormat
 import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
-import org.apache.parquet.hadoop.api.{ReadSupport, WriteSupport}
+import org.apache.parquet.hadoop.api.{InitContext, ReadSupport, WriteSupport}
 import org.apache.parquet.io.api._
 import org.apache.parquet.schema.MessageType
 
@@ -36,87 +39,133 @@ import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
- * A `parquet.io.api.RecordMaterializer` for Rows.
+ * A [[RecordMaterializer]] for Catalyst rows.
  *
- *@param root The root group converter for the record.
+ * @param parquetSchema Parquet schema of the records to be read
+ * @param catalystSchema Catalyst schema of the rows to be constructed
  */
-private[parquet] class RowRecordMaterializer(root: CatalystConverter)
+private[parquet] class RowRecordMaterializer(parquetSchema: MessageType, catalystSchema: StructType)
   extends RecordMaterializer[InternalRow] {
 
-  def this(parquetSchema: MessageType, attributes: Seq[Attribute]) =
-    this(CatalystConverter.createRootConverter(parquetSchema, attributes))
+  private val rootConverter = new CatalystRowConverter(parquetSchema, catalystSchema, NoopUpdater)
 
-  override def getCurrentRecord: InternalRow = root.getCurrentRecord
+  override def getCurrentRecord: InternalRow = rootConverter.currentRow
 
-  override def getRootConverter: GroupConverter = root.asInstanceOf[GroupConverter]
+  override def getRootConverter: GroupConverter = rootConverter
 }
 
-/**
- * A `parquet.hadoop.api.ReadSupport` for Row objects.
- */
 private[parquet] class RowReadSupport extends ReadSupport[InternalRow] with Logging {
-
   override def prepareForRead(
       conf: Configuration,
-      stringMap: java.util.Map[String, String],
+      keyValueMetaData: util.Map[String, String],
       fileSchema: MessageType,
       readContext: ReadContext): RecordMaterializer[InternalRow] = {
-    log.debug(s"preparing for read with Parquet file schema $fileSchema")
-    // Note: this very much imitates AvroParquet
-    val parquetSchema = readContext.getRequestedSchema
-    var schema: Seq[Attribute] = null
-
-    if (readContext.getReadSupportMetadata != null) {
-      // first try to find the read schema inside the metadata (can result from projections)
-      if (
-        readContext
-          .getReadSupportMetadata
-          .get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA) != null) {
-        schema = ParquetTypesConverter.convertFromString(
-          readContext.getReadSupportMetadata.get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA))
-      } else {
-        // if unavailable, try the schema that was read originally from the file or provided
-        // during the creation of the Parquet relation
-        if (readContext.getReadSupportMetadata.get(RowReadSupport.SPARK_METADATA_KEY) != null) {
-          schema = ParquetTypesConverter.convertFromString(
-            readContext.getReadSupportMetadata.get(RowReadSupport.SPARK_METADATA_KEY))
-        }
+    log.debug(s"Preparing for read Parquet file with message type: $fileSchema")
+
+    val toCatalyst = new CatalystSchemaConverter(conf)
+    val parquetRequestedSchema = readContext.getRequestedSchema
+
+    val catalystRequestedSchema =
+      Option(readContext.getReadSupportMetadata).map(_.toMap).flatMap { metadata =>
+        metadata
+          // First tries to read requested schema, which may result from projections
+          .get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
+          // If not available, tries to read Catalyst schema from file metadata.  It's only
+          // available if the target file is written by Spark SQL.
+          .orElse(metadata.get(RowReadSupport.SPARK_METADATA_KEY))
+      }.map(StructType.fromString).getOrElse {
+        logDebug("Catalyst schema not available, falling back to Parquet schema")
+        toCatalyst.convert(parquetRequestedSchema)
       }
-    }
-    // if both unavailable, fall back to deducing the schema from the given Parquet schema
-    // TODO: Why it can be null?
-    if (schema == null)  {
-      log.debug("falling back to Parquet read schema")
-      schema = ParquetTypesConverter.convertToAttributes(parquetSchema, false, true)
-    }
-    log.debug(s"list of attributes that will be read: $schema")
-    new RowRecordMaterializer(parquetSchema, schema)
+
+    logDebug(s"Catalyst schema used to read Parquet files: $catalystRequestedSchema")
+    new RowRecordMaterializer(parquetRequestedSchema, catalystRequestedSchema)
   }
 
-  override def init(
-      configuration: Configuration,
-      keyValueMetaData: java.util.Map[String, String],
-      fileSchema: MessageType): ReadContext = {
-    var parquetSchema = fileSchema
-    val metadata = new JHashMap[String, String]()
-    val requestedAttributes = RowReadSupport.getRequestedSchema(configuration)
-
-    if (requestedAttributes != null) {
-      // If the parquet file is thrift derived, there is a good chance that
-      // it will have the thrift class in metadata.
-      val isThriftDerived = keyValueMetaData.keySet().contains("thrift.class")
-      parquetSchema = ParquetTypesConverter.convertFromAttributes(requestedAttributes)
-      metadata.put(
-        RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
-        ParquetTypesConverter.convertToString(requestedAttributes))
-    }
+  override def init(context: InitContext): ReadContext = {
+    val conf = context.getConfiguration
+
+    // If the target file was written by Spark SQL, we should be able to find a serialized Catalyst
+    // schema of this file from its the metadata.
+    val maybeRowSchema = Option(conf.get(RowWriteSupport.SPARK_ROW_SCHEMA))
+
+    // Optional schema of requested columns, in the form of a string serialized from a Catalyst
+    // `StructType` containing all requested columns.
+    val maybeRequestedSchema = Option(conf.get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA))
+
+    // Below we construct a Parquet schema containing all requested columns.  This schema tells
+    // Parquet which columns to read.
+    //
+    // If `maybeRequestedSchema` is defined, we assemble an equivalent Parquet schema.  Otherwise,
+    // we have to fallback to the full file schema which contains all columns in the file.
+    // Obviously this may waste IO bandwidth since it may read more columns than requested.
+    //
+    // Two things to note:
+    //
+    // 1. It's possible that some requested columns don't exist in the target Parquet file.  For
+    //    example, in the case of schema merging, the globally merged schema may contain extra
+    //    columns gathered from other Parquet files.  These columns will be simply filled with nulls
+    //    when actually reading the target Parquet file.
+    //
+    // 2. When `maybeRequestedSchema` is available, we can't simply convert the Catalyst schema to
+    //    Parquet schema using `CatalystSchemaConverter`, because the mapping is not unique due to
+    //    non-standard behaviors of some Parquet libraries/tools.  For example, a Parquet file
+    //    containing a single integer array field `f1` may have the following legacy 2-level
+    //    structure:
+    //
+    //      message root {
+    //        optional group f1 (LIST) {
+    //          required INT32 element;
+    //        }
+    //      }
+    //
+    //    while `CatalystSchemaConverter` may generate a standard 3-level structure:
+    //
+    //      message root {
+    //        optional group f1 (LIST) {
+    //          repeated group list {
+    //            required INT32 element;
+    //          }
+    //        }
+    //      }
+    //
+    //    Apparently, we can't use the 2nd schema to read the target Parquet file as they have
+    //    different physical structures.
+    val parquetRequestedSchema =
+      maybeRequestedSchema.fold(context.getFileSchema) { schemaString =>
+        val toParquet = new CatalystSchemaConverter(conf)
+        val fileSchema = context.getFileSchema.asGroupType()
+        val fileFieldNames = fileSchema.getFields.map(_.getName).toSet
+
+        StructType
+          // Deserializes the Catalyst schema of requested columns
+          .fromString(schemaString)
+          .map { field =>
+            if (fileFieldNames.contains(field.name)) {
+              // If the field exists in the target Parquet file, extracts the field type from the
+              // full file schema and makes a single-field Parquet schema
+              new MessageType("root", fileSchema.getType(field.name))
+            } else {
+              // Otherwise, just resorts to `CatalystSchemaConverter`
+              toParquet.convert(StructType(Array(field)))
+            }
+          }
+          // Merges all single-field Parquet schemas to form a complete schema for all requested
+          // columns.  Note that it's possible that no columns are requested at all (e.g., count
+          // some partition column of a partitioned Parquet table). That's why `fold` is used here
+          // and always fallback to an empty Parquet schema.
+          .fold(new MessageType("root")) {
+            _ union _
+          }
+      }
 
-    val origAttributesStr: String = configuration.get(RowWriteSupport.SPARK_ROW_SCHEMA)
-    if (origAttributesStr != null) {
-      metadata.put(RowReadSupport.SPARK_METADATA_KEY, origAttributesStr)
-    }
+    val metadata =
+      Map.empty[String, String] ++
+        maybeRequestedSchema.map(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA -> _) ++
+        maybeRowSchema.map(RowWriteSupport.SPARK_ROW_SCHEMA -> _)
 
-    new ReadSupport.ReadContext(parquetSchema, metadata)
+    logInfo(s"Going to read Parquet file with these requested columns: $parquetRequestedSchema")
+    new ReadContext(parquetRequestedSchema, metadata)
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index ce456e7fbe17e..01dd6f471bd7c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -259,6 +259,10 @@ private[sql] class ParquetRelation2(
       broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = {
     val useMetadataCache = sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA)
     val parquetFilterPushDown = sqlContext.conf.parquetFilterPushDown
+    val assumeBinaryIsString = sqlContext.conf.isParquetBinaryAsString
+    val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
+    val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
+
     // Create the function to set variable Parquet confs at both driver and executor side.
     val initLocalJobFuncOpt =
       ParquetRelation2.initializeLocalJobFunc(
@@ -266,7 +270,11 @@ private[sql] class ParquetRelation2(
         filters,
         dataSchema,
         useMetadataCache,
-        parquetFilterPushDown) _
+        parquetFilterPushDown,
+        assumeBinaryIsString,
+        assumeInt96IsTimestamp,
+        followParquetFormatSpec) _
+
     // Create the function to set input paths at the driver side.
     val setInputPaths = ParquetRelation2.initializeDriverSideJobFunc(inputFiles) _
 
@@ -471,9 +479,12 @@ private[sql] object ParquetRelation2 extends Logging {
       filters: Array[Filter],
       dataSchema: StructType,
       useMetadataCache: Boolean,
-      parquetFilterPushDown: Boolean)(job: Job): Unit = {
+      parquetFilterPushDown: Boolean,
+      assumeBinaryIsString: Boolean,
+      assumeInt96IsTimestamp: Boolean,
+      followParquetFormatSpec: Boolean)(job: Job): Unit = {
     val conf = job.getConfiguration
-    conf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[RowReadSupport].getName())
+    conf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[RowReadSupport].getName)
 
     // Try to push down filters when filter push-down is enabled.
     if (parquetFilterPushDown) {
@@ -497,6 +508,11 @@ private[sql] object ParquetRelation2 extends Logging {
 
     // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata
     conf.setBoolean(SQLConf.PARQUET_CACHE_METADATA.key, useMetadataCache)
+
+    // Sets flags for Parquet schema conversion
+    conf.setBoolean(SQLConf.PARQUET_BINARY_AS_STRING.key, assumeBinaryIsString)
+    conf.setBoolean(SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, assumeInt96IsTimestamp)
+    conf.setBoolean(SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key, followParquetFormatSpec)
   }
 
   /** This closure sets input paths at the driver side. */
diff --git a/sql/core/src/test/README.md b/sql/core/src/test/README.md
new file mode 100644
index 0000000000000..3dd9861b4896d
--- /dev/null
+++ b/sql/core/src/test/README.md
@@ -0,0 +1,33 @@
+# Notes for Parquet compatibility tests
+
+The following directories and files are used for Parquet compatibility tests:
+
+```
+.
+├── README.md                   # This file
+├── avro
+│   ├── parquet-compat.avdl     # Testing Avro IDL
+│   └── parquet-compat.avpr     # !! NO TOUCH !! Protocol file generated from parquet-compat.avdl
+├── gen-java                    # !! NO TOUCH !! Generated Java code
+├── scripts
+│   └── gen-code.sh             # Script used to generate Java code for Thrift and Avro
+└── thrift
+    └── parquet-compat.thrift   # Testing Thrift schema
+```
+
+Generated Java code are used in the following test suites:
+
+- `org.apache.spark.sql.parquet.ParquetAvroCompatibilitySuite`
+- `org.apache.spark.sql.parquet.ParquetThriftCompatibilitySuite`
+
+To avoid code generation during build time, Java code generated from testing Thrift schema and Avro IDL are also checked in.
+
+When updating the testing Thrift schema and Avro IDL, please run `gen-code.sh` to update all the generated Java code.
+
+## Prerequisites
+
+Please ensure `avro-tools` and `thrift` are installed.  You may install these two on Mac OS X via:
+
+```bash
+$ brew install thrift avro-tools
+```
diff --git a/sql/core/src/test/avro/parquet-compat.avdl b/sql/core/src/test/avro/parquet-compat.avdl
new file mode 100644
index 0000000000000..24729f6143e6c
--- /dev/null
+++ b/sql/core/src/test/avro/parquet-compat.avdl
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This is a test protocol for testing parquet-avro compatibility.
+@namespace("org.apache.spark.sql.parquet.test.avro")
+protocol CompatibilityTest {
+    record Nested {
+        array<int> nested_ints_column;
+        string nested_string_column;
+    }
+
+    record ParquetAvroCompat {
+        boolean bool_column;
+        int int_column;
+        long long_column;
+        float float_column;
+        double double_column;
+        bytes binary_column;
+        string string_column;
+
+        union { null, boolean } maybe_bool_column;
+        union { null, int } maybe_int_column;
+        union { null, long } maybe_long_column;
+        union { null, float } maybe_float_column;
+        union { null, double } maybe_double_column;
+        union { null, bytes } maybe_binary_column;
+        union { null, string } maybe_string_column;
+
+        array<string> strings_column;
+        map<int> string_to_int_column;
+        map<array<Nested>> complex_column;
+    }
+}
diff --git a/sql/core/src/test/avro/parquet-compat.avpr b/sql/core/src/test/avro/parquet-compat.avpr
new file mode 100644
index 0000000000000..a83b7c990dd2e
--- /dev/null
+++ b/sql/core/src/test/avro/parquet-compat.avpr
@@ -0,0 +1,86 @@
+{
+  "protocol" : "CompatibilityTest",
+  "namespace" : "org.apache.spark.sql.parquet.test.avro",
+  "types" : [ {
+    "type" : "record",
+    "name" : "Nested",
+    "fields" : [ {
+      "name" : "nested_ints_column",
+      "type" : {
+        "type" : "array",
+        "items" : "int"
+      }
+    }, {
+      "name" : "nested_string_column",
+      "type" : "string"
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "ParquetAvroCompat",
+    "fields" : [ {
+      "name" : "bool_column",
+      "type" : "boolean"
+    }, {
+      "name" : "int_column",
+      "type" : "int"
+    }, {
+      "name" : "long_column",
+      "type" : "long"
+    }, {
+      "name" : "float_column",
+      "type" : "float"
+    }, {
+      "name" : "double_column",
+      "type" : "double"
+    }, {
+      "name" : "binary_column",
+      "type" : "bytes"
+    }, {
+      "name" : "string_column",
+      "type" : "string"
+    }, {
+      "name" : "maybe_bool_column",
+      "type" : [ "null", "boolean" ]
+    }, {
+      "name" : "maybe_int_column",
+      "type" : [ "null", "int" ]
+    }, {
+      "name" : "maybe_long_column",
+      "type" : [ "null", "long" ]
+    }, {
+      "name" : "maybe_float_column",
+      "type" : [ "null", "float" ]
+    }, {
+      "name" : "maybe_double_column",
+      "type" : [ "null", "double" ]
+    }, {
+      "name" : "maybe_binary_column",
+      "type" : [ "null", "bytes" ]
+    }, {
+      "name" : "maybe_string_column",
+      "type" : [ "null", "string" ]
+    }, {
+      "name" : "strings_column",
+      "type" : {
+        "type" : "array",
+        "items" : "string"
+      }
+    }, {
+      "name" : "string_to_int_column",
+      "type" : {
+        "type" : "map",
+        "values" : "int"
+      }
+    }, {
+      "name" : "complex_column",
+      "type" : {
+        "type" : "map",
+        "values" : {
+          "type" : "array",
+          "items" : "Nested"
+        }
+      }
+    } ]
+  } ],
+  "messages" : { }
+}
\ No newline at end of file
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java
new file mode 100644
index 0000000000000..daec65a5bbe57
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java
@@ -0,0 +1,17 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.parquet.test.avro;
+
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public interface CompatibilityTest {
+  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"types\":[{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
+
+  @SuppressWarnings("all")
+  public interface Callback extends CompatibilityTest {
+    public static final org.apache.avro.Protocol PROTOCOL = org.apache.spark.sql.parquet.test.avro.CompatibilityTest.PROTOCOL;
+  }
+}
\ No newline at end of file
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java
new file mode 100644
index 0000000000000..051f1ee903863
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java
@@ -0,0 +1,196 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class Nested extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"Nested\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.util.List<java.lang.Integer> nested_ints_column;
+  @Deprecated public java.lang.String nested_string_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public Nested() {}
+
+  /**
+   * All-args constructor.
+   */
+  public Nested(java.util.List<java.lang.Integer> nested_ints_column, java.lang.String nested_string_column) {
+    this.nested_ints_column = nested_ints_column;
+    this.nested_string_column = nested_string_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return nested_ints_column;
+    case 1: return nested_string_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: nested_ints_column = (java.util.List<java.lang.Integer>)value$; break;
+    case 1: nested_string_column = (java.lang.String)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'nested_ints_column' field.
+   */
+  public java.util.List<java.lang.Integer> getNestedIntsColumn() {
+    return nested_ints_column;
+  }
+
+  /**
+   * Sets the value of the 'nested_ints_column' field.
+   * @param value the value to set.
+   */
+  public void setNestedIntsColumn(java.util.List<java.lang.Integer> value) {
+    this.nested_ints_column = value;
+  }
+
+  /**
+   * Gets the value of the 'nested_string_column' field.
+   */
+  public java.lang.String getNestedStringColumn() {
+    return nested_string_column;
+  }
+
+  /**
+   * Sets the value of the 'nested_string_column' field.
+   * @param value the value to set.
+   */
+  public void setNestedStringColumn(java.lang.String value) {
+    this.nested_string_column = value;
+  }
+
+  /** Creates a new Nested RecordBuilder */
+  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder() {
+    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder();
+  }
+  
+  /** Creates a new Nested RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
+    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder(other);
+  }
+  
+  /** Creates a new Nested RecordBuilder by copying an existing Nested instance */
+  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.Nested other) {
+    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for Nested instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<Nested>
+    implements org.apache.avro.data.RecordBuilder<Nested> {
+
+    private java.util.List<java.lang.Integer> nested_ints_column;
+    private java.lang.String nested_string_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.parquet.test.avro.Nested.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.nested_ints_column)) {
+        this.nested_ints_column = data().deepCopy(fields()[0].schema(), other.nested_ints_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.nested_string_column)) {
+        this.nested_string_column = data().deepCopy(fields()[1].schema(), other.nested_string_column);
+        fieldSetFlags()[1] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing Nested instance */
+    private Builder(org.apache.spark.sql.parquet.test.avro.Nested other) {
+            super(org.apache.spark.sql.parquet.test.avro.Nested.SCHEMA$);
+      if (isValidValue(fields()[0], other.nested_ints_column)) {
+        this.nested_ints_column = data().deepCopy(fields()[0].schema(), other.nested_ints_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.nested_string_column)) {
+        this.nested_string_column = data().deepCopy(fields()[1].schema(), other.nested_string_column);
+        fieldSetFlags()[1] = true;
+      }
+    }
+
+    /** Gets the value of the 'nested_ints_column' field */
+    public java.util.List<java.lang.Integer> getNestedIntsColumn() {
+      return nested_ints_column;
+    }
+    
+    /** Sets the value of the 'nested_ints_column' field */
+    public org.apache.spark.sql.parquet.test.avro.Nested.Builder setNestedIntsColumn(java.util.List<java.lang.Integer> value) {
+      validate(fields()[0], value);
+      this.nested_ints_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'nested_ints_column' field has been set */
+    public boolean hasNestedIntsColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'nested_ints_column' field */
+    public org.apache.spark.sql.parquet.test.avro.Nested.Builder clearNestedIntsColumn() {
+      nested_ints_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'nested_string_column' field */
+    public java.lang.String getNestedStringColumn() {
+      return nested_string_column;
+    }
+    
+    /** Sets the value of the 'nested_string_column' field */
+    public org.apache.spark.sql.parquet.test.avro.Nested.Builder setNestedStringColumn(java.lang.String value) {
+      validate(fields()[1], value);
+      this.nested_string_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'nested_string_column' field has been set */
+    public boolean hasNestedStringColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'nested_string_column' field */
+    public org.apache.spark.sql.parquet.test.avro.Nested.Builder clearNestedStringColumn() {
+      nested_string_column = null;
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    @Override
+    public Nested build() {
+      try {
+        Nested record = new Nested();
+        record.nested_ints_column = fieldSetFlags()[0] ? this.nested_ints_column : (java.util.List<java.lang.Integer>) defaultValue(fields()[0]);
+        record.nested_string_column = fieldSetFlags()[1] ? this.nested_string_column : (java.lang.String) defaultValue(fields()[1]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java
new file mode 100644
index 0000000000000..354c9d73cca31
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java
@@ -0,0 +1,1001 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public boolean bool_column;
+  @Deprecated public int int_column;
+  @Deprecated public long long_column;
+  @Deprecated public float float_column;
+  @Deprecated public double double_column;
+  @Deprecated public java.nio.ByteBuffer binary_column;
+  @Deprecated public java.lang.String string_column;
+  @Deprecated public java.lang.Boolean maybe_bool_column;
+  @Deprecated public java.lang.Integer maybe_int_column;
+  @Deprecated public java.lang.Long maybe_long_column;
+  @Deprecated public java.lang.Float maybe_float_column;
+  @Deprecated public java.lang.Double maybe_double_column;
+  @Deprecated public java.nio.ByteBuffer maybe_binary_column;
+  @Deprecated public java.lang.String maybe_string_column;
+  @Deprecated public java.util.List<java.lang.String> strings_column;
+  @Deprecated public java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
+  @Deprecated public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public ParquetAvroCompat() {}
+
+  /**
+   * All-args constructor.
+   */
+  public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column, java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column, java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column) {
+    this.bool_column = bool_column;
+    this.int_column = int_column;
+    this.long_column = long_column;
+    this.float_column = float_column;
+    this.double_column = double_column;
+    this.binary_column = binary_column;
+    this.string_column = string_column;
+    this.maybe_bool_column = maybe_bool_column;
+    this.maybe_int_column = maybe_int_column;
+    this.maybe_long_column = maybe_long_column;
+    this.maybe_float_column = maybe_float_column;
+    this.maybe_double_column = maybe_double_column;
+    this.maybe_binary_column = maybe_binary_column;
+    this.maybe_string_column = maybe_string_column;
+    this.strings_column = strings_column;
+    this.string_to_int_column = string_to_int_column;
+    this.complex_column = complex_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return bool_column;
+    case 1: return int_column;
+    case 2: return long_column;
+    case 3: return float_column;
+    case 4: return double_column;
+    case 5: return binary_column;
+    case 6: return string_column;
+    case 7: return maybe_bool_column;
+    case 8: return maybe_int_column;
+    case 9: return maybe_long_column;
+    case 10: return maybe_float_column;
+    case 11: return maybe_double_column;
+    case 12: return maybe_binary_column;
+    case 13: return maybe_string_column;
+    case 14: return strings_column;
+    case 15: return string_to_int_column;
+    case 16: return complex_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: bool_column = (java.lang.Boolean)value$; break;
+    case 1: int_column = (java.lang.Integer)value$; break;
+    case 2: long_column = (java.lang.Long)value$; break;
+    case 3: float_column = (java.lang.Float)value$; break;
+    case 4: double_column = (java.lang.Double)value$; break;
+    case 5: binary_column = (java.nio.ByteBuffer)value$; break;
+    case 6: string_column = (java.lang.String)value$; break;
+    case 7: maybe_bool_column = (java.lang.Boolean)value$; break;
+    case 8: maybe_int_column = (java.lang.Integer)value$; break;
+    case 9: maybe_long_column = (java.lang.Long)value$; break;
+    case 10: maybe_float_column = (java.lang.Float)value$; break;
+    case 11: maybe_double_column = (java.lang.Double)value$; break;
+    case 12: maybe_binary_column = (java.nio.ByteBuffer)value$; break;
+    case 13: maybe_string_column = (java.lang.String)value$; break;
+    case 14: strings_column = (java.util.List<java.lang.String>)value$; break;
+    case 15: string_to_int_column = (java.util.Map<java.lang.String,java.lang.Integer>)value$; break;
+    case 16: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>>)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'bool_column' field.
+   */
+  public java.lang.Boolean getBoolColumn() {
+    return bool_column;
+  }
+
+  /**
+   * Sets the value of the 'bool_column' field.
+   * @param value the value to set.
+   */
+  public void setBoolColumn(java.lang.Boolean value) {
+    this.bool_column = value;
+  }
+
+  /**
+   * Gets the value of the 'int_column' field.
+   */
+  public java.lang.Integer getIntColumn() {
+    return int_column;
+  }
+
+  /**
+   * Sets the value of the 'int_column' field.
+   * @param value the value to set.
+   */
+  public void setIntColumn(java.lang.Integer value) {
+    this.int_column = value;
+  }
+
+  /**
+   * Gets the value of the 'long_column' field.
+   */
+  public java.lang.Long getLongColumn() {
+    return long_column;
+  }
+
+  /**
+   * Sets the value of the 'long_column' field.
+   * @param value the value to set.
+   */
+  public void setLongColumn(java.lang.Long value) {
+    this.long_column = value;
+  }
+
+  /**
+   * Gets the value of the 'float_column' field.
+   */
+  public java.lang.Float getFloatColumn() {
+    return float_column;
+  }
+
+  /**
+   * Sets the value of the 'float_column' field.
+   * @param value the value to set.
+   */
+  public void setFloatColumn(java.lang.Float value) {
+    this.float_column = value;
+  }
+
+  /**
+   * Gets the value of the 'double_column' field.
+   */
+  public java.lang.Double getDoubleColumn() {
+    return double_column;
+  }
+
+  /**
+   * Sets the value of the 'double_column' field.
+   * @param value the value to set.
+   */
+  public void setDoubleColumn(java.lang.Double value) {
+    this.double_column = value;
+  }
+
+  /**
+   * Gets the value of the 'binary_column' field.
+   */
+  public java.nio.ByteBuffer getBinaryColumn() {
+    return binary_column;
+  }
+
+  /**
+   * Sets the value of the 'binary_column' field.
+   * @param value the value to set.
+   */
+  public void setBinaryColumn(java.nio.ByteBuffer value) {
+    this.binary_column = value;
+  }
+
+  /**
+   * Gets the value of the 'string_column' field.
+   */
+  public java.lang.String getStringColumn() {
+    return string_column;
+  }
+
+  /**
+   * Sets the value of the 'string_column' field.
+   * @param value the value to set.
+   */
+  public void setStringColumn(java.lang.String value) {
+    this.string_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_bool_column' field.
+   */
+  public java.lang.Boolean getMaybeBoolColumn() {
+    return maybe_bool_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_bool_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeBoolColumn(java.lang.Boolean value) {
+    this.maybe_bool_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_int_column' field.
+   */
+  public java.lang.Integer getMaybeIntColumn() {
+    return maybe_int_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_int_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeIntColumn(java.lang.Integer value) {
+    this.maybe_int_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_long_column' field.
+   */
+  public java.lang.Long getMaybeLongColumn() {
+    return maybe_long_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_long_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeLongColumn(java.lang.Long value) {
+    this.maybe_long_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_float_column' field.
+   */
+  public java.lang.Float getMaybeFloatColumn() {
+    return maybe_float_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_float_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeFloatColumn(java.lang.Float value) {
+    this.maybe_float_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_double_column' field.
+   */
+  public java.lang.Double getMaybeDoubleColumn() {
+    return maybe_double_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_double_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeDoubleColumn(java.lang.Double value) {
+    this.maybe_double_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_binary_column' field.
+   */
+  public java.nio.ByteBuffer getMaybeBinaryColumn() {
+    return maybe_binary_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_binary_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+    this.maybe_binary_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_string_column' field.
+   */
+  public java.lang.String getMaybeStringColumn() {
+    return maybe_string_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_string_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeStringColumn(java.lang.String value) {
+    this.maybe_string_column = value;
+  }
+
+  /**
+   * Gets the value of the 'strings_column' field.
+   */
+  public java.util.List<java.lang.String> getStringsColumn() {
+    return strings_column;
+  }
+
+  /**
+   * Sets the value of the 'strings_column' field.
+   * @param value the value to set.
+   */
+  public void setStringsColumn(java.util.List<java.lang.String> value) {
+    this.strings_column = value;
+  }
+
+  /**
+   * Gets the value of the 'string_to_int_column' field.
+   */
+  public java.util.Map<java.lang.String,java.lang.Integer> getStringToIntColumn() {
+    return string_to_int_column;
+  }
+
+  /**
+   * Sets the value of the 'string_to_int_column' field.
+   * @param value the value to set.
+   */
+  public void setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
+    this.string_to_int_column = value;
+  }
+
+  /**
+   * Gets the value of the 'complex_column' field.
+   */
+  public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> getComplexColumn() {
+    return complex_column;
+  }
+
+  /**
+   * Sets the value of the 'complex_column' field.
+   * @param value the value to set.
+   */
+  public void setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> value) {
+    this.complex_column = value;
+  }
+
+  /** Creates a new ParquetAvroCompat RecordBuilder */
+  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder() {
+    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder();
+  }
+  
+  /** Creates a new ParquetAvroCompat RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder other) {
+    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder(other);
+  }
+  
+  /** Creates a new ParquetAvroCompat RecordBuilder by copying an existing ParquetAvroCompat instance */
+  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat other) {
+    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for ParquetAvroCompat instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<ParquetAvroCompat>
+    implements org.apache.avro.data.RecordBuilder<ParquetAvroCompat> {
+
+    private boolean bool_column;
+    private int int_column;
+    private long long_column;
+    private float float_column;
+    private double double_column;
+    private java.nio.ByteBuffer binary_column;
+    private java.lang.String string_column;
+    private java.lang.Boolean maybe_bool_column;
+    private java.lang.Integer maybe_int_column;
+    private java.lang.Long maybe_long_column;
+    private java.lang.Float maybe_float_column;
+    private java.lang.Double maybe_double_column;
+    private java.nio.ByteBuffer maybe_binary_column;
+    private java.lang.String maybe_string_column;
+    private java.util.List<java.lang.String> strings_column;
+    private java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
+    private java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.bool_column)) {
+        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.int_column)) {
+        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.long_column)) {
+        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.float_column)) {
+        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.double_column)) {
+        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.binary_column)) {
+        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.string_column)) {
+        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
+        fieldSetFlags()[6] = true;
+      }
+      if (isValidValue(fields()[7], other.maybe_bool_column)) {
+        this.maybe_bool_column = data().deepCopy(fields()[7].schema(), other.maybe_bool_column);
+        fieldSetFlags()[7] = true;
+      }
+      if (isValidValue(fields()[8], other.maybe_int_column)) {
+        this.maybe_int_column = data().deepCopy(fields()[8].schema(), other.maybe_int_column);
+        fieldSetFlags()[8] = true;
+      }
+      if (isValidValue(fields()[9], other.maybe_long_column)) {
+        this.maybe_long_column = data().deepCopy(fields()[9].schema(), other.maybe_long_column);
+        fieldSetFlags()[9] = true;
+      }
+      if (isValidValue(fields()[10], other.maybe_float_column)) {
+        this.maybe_float_column = data().deepCopy(fields()[10].schema(), other.maybe_float_column);
+        fieldSetFlags()[10] = true;
+      }
+      if (isValidValue(fields()[11], other.maybe_double_column)) {
+        this.maybe_double_column = data().deepCopy(fields()[11].schema(), other.maybe_double_column);
+        fieldSetFlags()[11] = true;
+      }
+      if (isValidValue(fields()[12], other.maybe_binary_column)) {
+        this.maybe_binary_column = data().deepCopy(fields()[12].schema(), other.maybe_binary_column);
+        fieldSetFlags()[12] = true;
+      }
+      if (isValidValue(fields()[13], other.maybe_string_column)) {
+        this.maybe_string_column = data().deepCopy(fields()[13].schema(), other.maybe_string_column);
+        fieldSetFlags()[13] = true;
+      }
+      if (isValidValue(fields()[14], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[14].schema(), other.strings_column);
+        fieldSetFlags()[14] = true;
+      }
+      if (isValidValue(fields()[15], other.string_to_int_column)) {
+        this.string_to_int_column = data().deepCopy(fields()[15].schema(), other.string_to_int_column);
+        fieldSetFlags()[15] = true;
+      }
+      if (isValidValue(fields()[16], other.complex_column)) {
+        this.complex_column = data().deepCopy(fields()[16].schema(), other.complex_column);
+        fieldSetFlags()[16] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing ParquetAvroCompat instance */
+    private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat other) {
+            super(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
+      if (isValidValue(fields()[0], other.bool_column)) {
+        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.int_column)) {
+        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.long_column)) {
+        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.float_column)) {
+        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.double_column)) {
+        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.binary_column)) {
+        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.string_column)) {
+        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
+        fieldSetFlags()[6] = true;
+      }
+      if (isValidValue(fields()[7], other.maybe_bool_column)) {
+        this.maybe_bool_column = data().deepCopy(fields()[7].schema(), other.maybe_bool_column);
+        fieldSetFlags()[7] = true;
+      }
+      if (isValidValue(fields()[8], other.maybe_int_column)) {
+        this.maybe_int_column = data().deepCopy(fields()[8].schema(), other.maybe_int_column);
+        fieldSetFlags()[8] = true;
+      }
+      if (isValidValue(fields()[9], other.maybe_long_column)) {
+        this.maybe_long_column = data().deepCopy(fields()[9].schema(), other.maybe_long_column);
+        fieldSetFlags()[9] = true;
+      }
+      if (isValidValue(fields()[10], other.maybe_float_column)) {
+        this.maybe_float_column = data().deepCopy(fields()[10].schema(), other.maybe_float_column);
+        fieldSetFlags()[10] = true;
+      }
+      if (isValidValue(fields()[11], other.maybe_double_column)) {
+        this.maybe_double_column = data().deepCopy(fields()[11].schema(), other.maybe_double_column);
+        fieldSetFlags()[11] = true;
+      }
+      if (isValidValue(fields()[12], other.maybe_binary_column)) {
+        this.maybe_binary_column = data().deepCopy(fields()[12].schema(), other.maybe_binary_column);
+        fieldSetFlags()[12] = true;
+      }
+      if (isValidValue(fields()[13], other.maybe_string_column)) {
+        this.maybe_string_column = data().deepCopy(fields()[13].schema(), other.maybe_string_column);
+        fieldSetFlags()[13] = true;
+      }
+      if (isValidValue(fields()[14], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[14].schema(), other.strings_column);
+        fieldSetFlags()[14] = true;
+      }
+      if (isValidValue(fields()[15], other.string_to_int_column)) {
+        this.string_to_int_column = data().deepCopy(fields()[15].schema(), other.string_to_int_column);
+        fieldSetFlags()[15] = true;
+      }
+      if (isValidValue(fields()[16], other.complex_column)) {
+        this.complex_column = data().deepCopy(fields()[16].schema(), other.complex_column);
+        fieldSetFlags()[16] = true;
+      }
+    }
+
+    /** Gets the value of the 'bool_column' field */
+    public java.lang.Boolean getBoolColumn() {
+      return bool_column;
+    }
+    
+    /** Sets the value of the 'bool_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setBoolColumn(boolean value) {
+      validate(fields()[0], value);
+      this.bool_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'bool_column' field has been set */
+    public boolean hasBoolColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'bool_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearBoolColumn() {
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'int_column' field */
+    public java.lang.Integer getIntColumn() {
+      return int_column;
+    }
+    
+    /** Sets the value of the 'int_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setIntColumn(int value) {
+      validate(fields()[1], value);
+      this.int_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'int_column' field has been set */
+    public boolean hasIntColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'int_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearIntColumn() {
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'long_column' field */
+    public java.lang.Long getLongColumn() {
+      return long_column;
+    }
+    
+    /** Sets the value of the 'long_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setLongColumn(long value) {
+      validate(fields()[2], value);
+      this.long_column = value;
+      fieldSetFlags()[2] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'long_column' field has been set */
+    public boolean hasLongColumn() {
+      return fieldSetFlags()[2];
+    }
+    
+    /** Clears the value of the 'long_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearLongColumn() {
+      fieldSetFlags()[2] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'float_column' field */
+    public java.lang.Float getFloatColumn() {
+      return float_column;
+    }
+    
+    /** Sets the value of the 'float_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setFloatColumn(float value) {
+      validate(fields()[3], value);
+      this.float_column = value;
+      fieldSetFlags()[3] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'float_column' field has been set */
+    public boolean hasFloatColumn() {
+      return fieldSetFlags()[3];
+    }
+    
+    /** Clears the value of the 'float_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearFloatColumn() {
+      fieldSetFlags()[3] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'double_column' field */
+    public java.lang.Double getDoubleColumn() {
+      return double_column;
+    }
+    
+    /** Sets the value of the 'double_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setDoubleColumn(double value) {
+      validate(fields()[4], value);
+      this.double_column = value;
+      fieldSetFlags()[4] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'double_column' field has been set */
+    public boolean hasDoubleColumn() {
+      return fieldSetFlags()[4];
+    }
+    
+    /** Clears the value of the 'double_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearDoubleColumn() {
+      fieldSetFlags()[4] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'binary_column' field */
+    public java.nio.ByteBuffer getBinaryColumn() {
+      return binary_column;
+    }
+    
+    /** Sets the value of the 'binary_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setBinaryColumn(java.nio.ByteBuffer value) {
+      validate(fields()[5], value);
+      this.binary_column = value;
+      fieldSetFlags()[5] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'binary_column' field has been set */
+    public boolean hasBinaryColumn() {
+      return fieldSetFlags()[5];
+    }
+    
+    /** Clears the value of the 'binary_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearBinaryColumn() {
+      binary_column = null;
+      fieldSetFlags()[5] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'string_column' field */
+    public java.lang.String getStringColumn() {
+      return string_column;
+    }
+    
+    /** Sets the value of the 'string_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringColumn(java.lang.String value) {
+      validate(fields()[6], value);
+      this.string_column = value;
+      fieldSetFlags()[6] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'string_column' field has been set */
+    public boolean hasStringColumn() {
+      return fieldSetFlags()[6];
+    }
+    
+    /** Clears the value of the 'string_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringColumn() {
+      string_column = null;
+      fieldSetFlags()[6] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_bool_column' field */
+    public java.lang.Boolean getMaybeBoolColumn() {
+      return maybe_bool_column;
+    }
+    
+    /** Sets the value of the 'maybe_bool_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBoolColumn(java.lang.Boolean value) {
+      validate(fields()[7], value);
+      this.maybe_bool_column = value;
+      fieldSetFlags()[7] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_bool_column' field has been set */
+    public boolean hasMaybeBoolColumn() {
+      return fieldSetFlags()[7];
+    }
+    
+    /** Clears the value of the 'maybe_bool_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBoolColumn() {
+      maybe_bool_column = null;
+      fieldSetFlags()[7] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_int_column' field */
+    public java.lang.Integer getMaybeIntColumn() {
+      return maybe_int_column;
+    }
+    
+    /** Sets the value of the 'maybe_int_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeIntColumn(java.lang.Integer value) {
+      validate(fields()[8], value);
+      this.maybe_int_column = value;
+      fieldSetFlags()[8] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_int_column' field has been set */
+    public boolean hasMaybeIntColumn() {
+      return fieldSetFlags()[8];
+    }
+    
+    /** Clears the value of the 'maybe_int_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeIntColumn() {
+      maybe_int_column = null;
+      fieldSetFlags()[8] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_long_column' field */
+    public java.lang.Long getMaybeLongColumn() {
+      return maybe_long_column;
+    }
+    
+    /** Sets the value of the 'maybe_long_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeLongColumn(java.lang.Long value) {
+      validate(fields()[9], value);
+      this.maybe_long_column = value;
+      fieldSetFlags()[9] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_long_column' field has been set */
+    public boolean hasMaybeLongColumn() {
+      return fieldSetFlags()[9];
+    }
+    
+    /** Clears the value of the 'maybe_long_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeLongColumn() {
+      maybe_long_column = null;
+      fieldSetFlags()[9] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_float_column' field */
+    public java.lang.Float getMaybeFloatColumn() {
+      return maybe_float_column;
+    }
+    
+    /** Sets the value of the 'maybe_float_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeFloatColumn(java.lang.Float value) {
+      validate(fields()[10], value);
+      this.maybe_float_column = value;
+      fieldSetFlags()[10] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_float_column' field has been set */
+    public boolean hasMaybeFloatColumn() {
+      return fieldSetFlags()[10];
+    }
+    
+    /** Clears the value of the 'maybe_float_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeFloatColumn() {
+      maybe_float_column = null;
+      fieldSetFlags()[10] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_double_column' field */
+    public java.lang.Double getMaybeDoubleColumn() {
+      return maybe_double_column;
+    }
+    
+    /** Sets the value of the 'maybe_double_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeDoubleColumn(java.lang.Double value) {
+      validate(fields()[11], value);
+      this.maybe_double_column = value;
+      fieldSetFlags()[11] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_double_column' field has been set */
+    public boolean hasMaybeDoubleColumn() {
+      return fieldSetFlags()[11];
+    }
+    
+    /** Clears the value of the 'maybe_double_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeDoubleColumn() {
+      maybe_double_column = null;
+      fieldSetFlags()[11] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_binary_column' field */
+    public java.nio.ByteBuffer getMaybeBinaryColumn() {
+      return maybe_binary_column;
+    }
+    
+    /** Sets the value of the 'maybe_binary_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+      validate(fields()[12], value);
+      this.maybe_binary_column = value;
+      fieldSetFlags()[12] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_binary_column' field has been set */
+    public boolean hasMaybeBinaryColumn() {
+      return fieldSetFlags()[12];
+    }
+    
+    /** Clears the value of the 'maybe_binary_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBinaryColumn() {
+      maybe_binary_column = null;
+      fieldSetFlags()[12] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_string_column' field */
+    public java.lang.String getMaybeStringColumn() {
+      return maybe_string_column;
+    }
+    
+    /** Sets the value of the 'maybe_string_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeStringColumn(java.lang.String value) {
+      validate(fields()[13], value);
+      this.maybe_string_column = value;
+      fieldSetFlags()[13] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_string_column' field has been set */
+    public boolean hasMaybeStringColumn() {
+      return fieldSetFlags()[13];
+    }
+    
+    /** Clears the value of the 'maybe_string_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeStringColumn() {
+      maybe_string_column = null;
+      fieldSetFlags()[13] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'strings_column' field */
+    public java.util.List<java.lang.String> getStringsColumn() {
+      return strings_column;
+    }
+    
+    /** Sets the value of the 'strings_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringsColumn(java.util.List<java.lang.String> value) {
+      validate(fields()[14], value);
+      this.strings_column = value;
+      fieldSetFlags()[14] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'strings_column' field has been set */
+    public boolean hasStringsColumn() {
+      return fieldSetFlags()[14];
+    }
+    
+    /** Clears the value of the 'strings_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringsColumn() {
+      strings_column = null;
+      fieldSetFlags()[14] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'string_to_int_column' field */
+    public java.util.Map<java.lang.String,java.lang.Integer> getStringToIntColumn() {
+      return string_to_int_column;
+    }
+    
+    /** Sets the value of the 'string_to_int_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
+      validate(fields()[15], value);
+      this.string_to_int_column = value;
+      fieldSetFlags()[15] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'string_to_int_column' field has been set */
+    public boolean hasStringToIntColumn() {
+      return fieldSetFlags()[15];
+    }
+    
+    /** Clears the value of the 'string_to_int_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringToIntColumn() {
+      string_to_int_column = null;
+      fieldSetFlags()[15] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'complex_column' field */
+    public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> getComplexColumn() {
+      return complex_column;
+    }
+    
+    /** Sets the value of the 'complex_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> value) {
+      validate(fields()[16], value);
+      this.complex_column = value;
+      fieldSetFlags()[16] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'complex_column' field has been set */
+    public boolean hasComplexColumn() {
+      return fieldSetFlags()[16];
+    }
+    
+    /** Clears the value of the 'complex_column' field */
+    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearComplexColumn() {
+      complex_column = null;
+      fieldSetFlags()[16] = false;
+      return this;
+    }
+
+    @Override
+    public ParquetAvroCompat build() {
+      try {
+        ParquetAvroCompat record = new ParquetAvroCompat();
+        record.bool_column = fieldSetFlags()[0] ? this.bool_column : (java.lang.Boolean) defaultValue(fields()[0]);
+        record.int_column = fieldSetFlags()[1] ? this.int_column : (java.lang.Integer) defaultValue(fields()[1]);
+        record.long_column = fieldSetFlags()[2] ? this.long_column : (java.lang.Long) defaultValue(fields()[2]);
+        record.float_column = fieldSetFlags()[3] ? this.float_column : (java.lang.Float) defaultValue(fields()[3]);
+        record.double_column = fieldSetFlags()[4] ? this.double_column : (java.lang.Double) defaultValue(fields()[4]);
+        record.binary_column = fieldSetFlags()[5] ? this.binary_column : (java.nio.ByteBuffer) defaultValue(fields()[5]);
+        record.string_column = fieldSetFlags()[6] ? this.string_column : (java.lang.String) defaultValue(fields()[6]);
+        record.maybe_bool_column = fieldSetFlags()[7] ? this.maybe_bool_column : (java.lang.Boolean) defaultValue(fields()[7]);
+        record.maybe_int_column = fieldSetFlags()[8] ? this.maybe_int_column : (java.lang.Integer) defaultValue(fields()[8]);
+        record.maybe_long_column = fieldSetFlags()[9] ? this.maybe_long_column : (java.lang.Long) defaultValue(fields()[9]);
+        record.maybe_float_column = fieldSetFlags()[10] ? this.maybe_float_column : (java.lang.Float) defaultValue(fields()[10]);
+        record.maybe_double_column = fieldSetFlags()[11] ? this.maybe_double_column : (java.lang.Double) defaultValue(fields()[11]);
+        record.maybe_binary_column = fieldSetFlags()[12] ? this.maybe_binary_column : (java.nio.ByteBuffer) defaultValue(fields()[12]);
+        record.maybe_string_column = fieldSetFlags()[13] ? this.maybe_string_column : (java.lang.String) defaultValue(fields()[13]);
+        record.strings_column = fieldSetFlags()[14] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[14]);
+        record.string_to_int_column = fieldSetFlags()[15] ? this.string_to_int_column : (java.util.Map<java.lang.String,java.lang.Integer>) defaultValue(fields()[15]);
+        record.complex_column = fieldSetFlags()[16] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>>) defaultValue(fields()[16]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java
new file mode 100644
index 0000000000000..281e60cc3ae34
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java
@@ -0,0 +1,541 @@
+/**
+ * Autogenerated by Thrift Compiler (0.9.2)
+ *
+ * DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
+ *  @generated
+ */
+package org.apache.spark.sql.parquet.test.thrift;
+
+import org.apache.thrift.scheme.IScheme;
+import org.apache.thrift.scheme.SchemeFactory;
+import org.apache.thrift.scheme.StandardScheme;
+
+import org.apache.thrift.scheme.TupleScheme;
+import org.apache.thrift.protocol.TTupleProtocol;
+import org.apache.thrift.protocol.TProtocolException;
+import org.apache.thrift.EncodingUtils;
+import org.apache.thrift.TException;
+import org.apache.thrift.async.AsyncMethodCallback;
+import org.apache.thrift.server.AbstractNonblockingServer.*;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Map;
+import java.util.HashMap;
+import java.util.EnumMap;
+import java.util.Set;
+import java.util.HashSet;
+import java.util.EnumSet;
+import java.util.Collections;
+import java.util.BitSet;
+import java.nio.ByteBuffer;
+import java.util.Arrays;
+import javax.annotation.Generated;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+@SuppressWarnings({"cast", "rawtypes", "serial", "unchecked"})
+@Generated(value = "Autogenerated by Thrift Compiler (0.9.2)", date = "2015-7-7")
+public class Nested implements org.apache.thrift.TBase<Nested, Nested._Fields>, java.io.Serializable, Cloneable, Comparable<Nested> {
+  private static final org.apache.thrift.protocol.TStruct STRUCT_DESC = new org.apache.thrift.protocol.TStruct("Nested");
+
+  private static final org.apache.thrift.protocol.TField NESTED_INTS_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("nestedIntsColumn", org.apache.thrift.protocol.TType.LIST, (short)1);
+  private static final org.apache.thrift.protocol.TField NESTED_STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("nestedStringColumn", org.apache.thrift.protocol.TType.STRING, (short)2);
+
+  private static final Map<Class<? extends IScheme>, SchemeFactory> schemes = new HashMap<Class<? extends IScheme>, SchemeFactory>();
+  static {
+    schemes.put(StandardScheme.class, new NestedStandardSchemeFactory());
+    schemes.put(TupleScheme.class, new NestedTupleSchemeFactory());
+  }
+
+  public List<Integer> nestedIntsColumn; // required
+  public String nestedStringColumn; // required
+
+  /** The set of fields this struct contains, along with convenience methods for finding and manipulating them. */
+  public enum _Fields implements org.apache.thrift.TFieldIdEnum {
+    NESTED_INTS_COLUMN((short)1, "nestedIntsColumn"),
+    NESTED_STRING_COLUMN((short)2, "nestedStringColumn");
+
+    private static final Map<String, _Fields> byName = new HashMap<String, _Fields>();
+
+    static {
+      for (_Fields field : EnumSet.allOf(_Fields.class)) {
+        byName.put(field.getFieldName(), field);
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, or null if its not found.
+     */
+    public static _Fields findByThriftId(int fieldId) {
+      switch(fieldId) {
+        case 1: // NESTED_INTS_COLUMN
+          return NESTED_INTS_COLUMN;
+        case 2: // NESTED_STRING_COLUMN
+          return NESTED_STRING_COLUMN;
+        default:
+          return null;
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, throwing an exception
+     * if it is not found.
+     */
+    public static _Fields findByThriftIdOrThrow(int fieldId) {
+      _Fields fields = findByThriftId(fieldId);
+      if (fields == null) throw new IllegalArgumentException("Field " + fieldId + " doesn't exist!");
+      return fields;
+    }
+
+    /**
+     * Find the _Fields constant that matches name, or null if its not found.
+     */
+    public static _Fields findByName(String name) {
+      return byName.get(name);
+    }
+
+    private final short _thriftId;
+    private final String _fieldName;
+
+    _Fields(short thriftId, String fieldName) {
+      _thriftId = thriftId;
+      _fieldName = fieldName;
+    }
+
+    public short getThriftFieldId() {
+      return _thriftId;
+    }
+
+    public String getFieldName() {
+      return _fieldName;
+    }
+  }
+
+  // isset id assignments
+  public static final Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> metaDataMap;
+  static {
+    Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> tmpMap = new EnumMap<_Fields, org.apache.thrift.meta_data.FieldMetaData>(_Fields.class);
+    tmpMap.put(_Fields.NESTED_INTS_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("nestedIntsColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST, 
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32))));
+    tmpMap.put(_Fields.NESTED_STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("nestedStringColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
+    metaDataMap = Collections.unmodifiableMap(tmpMap);
+    org.apache.thrift.meta_data.FieldMetaData.addStructMetaDataMap(Nested.class, metaDataMap);
+  }
+
+  public Nested() {
+  }
+
+  public Nested(
+    List<Integer> nestedIntsColumn,
+    String nestedStringColumn)
+  {
+    this();
+    this.nestedIntsColumn = nestedIntsColumn;
+    this.nestedStringColumn = nestedStringColumn;
+  }
+
+  /**
+   * Performs a deep copy on <i>other</i>.
+   */
+  public Nested(Nested other) {
+    if (other.isSetNestedIntsColumn()) {
+      List<Integer> __this__nestedIntsColumn = new ArrayList<Integer>(other.nestedIntsColumn);
+      this.nestedIntsColumn = __this__nestedIntsColumn;
+    }
+    if (other.isSetNestedStringColumn()) {
+      this.nestedStringColumn = other.nestedStringColumn;
+    }
+  }
+
+  public Nested deepCopy() {
+    return new Nested(this);
+  }
+
+  @Override
+  public void clear() {
+    this.nestedIntsColumn = null;
+    this.nestedStringColumn = null;
+  }
+
+  public int getNestedIntsColumnSize() {
+    return (this.nestedIntsColumn == null) ? 0 : this.nestedIntsColumn.size();
+  }
+
+  public java.util.Iterator<Integer> getNestedIntsColumnIterator() {
+    return (this.nestedIntsColumn == null) ? null : this.nestedIntsColumn.iterator();
+  }
+
+  public void addToNestedIntsColumn(int elem) {
+    if (this.nestedIntsColumn == null) {
+      this.nestedIntsColumn = new ArrayList<Integer>();
+    }
+    this.nestedIntsColumn.add(elem);
+  }
+
+  public List<Integer> getNestedIntsColumn() {
+    return this.nestedIntsColumn;
+  }
+
+  public Nested setNestedIntsColumn(List<Integer> nestedIntsColumn) {
+    this.nestedIntsColumn = nestedIntsColumn;
+    return this;
+  }
+
+  public void unsetNestedIntsColumn() {
+    this.nestedIntsColumn = null;
+  }
+
+  /** Returns true if field nestedIntsColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetNestedIntsColumn() {
+    return this.nestedIntsColumn != null;
+  }
+
+  public void setNestedIntsColumnIsSet(boolean value) {
+    if (!value) {
+      this.nestedIntsColumn = null;
+    }
+  }
+
+  public String getNestedStringColumn() {
+    return this.nestedStringColumn;
+  }
+
+  public Nested setNestedStringColumn(String nestedStringColumn) {
+    this.nestedStringColumn = nestedStringColumn;
+    return this;
+  }
+
+  public void unsetNestedStringColumn() {
+    this.nestedStringColumn = null;
+  }
+
+  /** Returns true if field nestedStringColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetNestedStringColumn() {
+    return this.nestedStringColumn != null;
+  }
+
+  public void setNestedStringColumnIsSet(boolean value) {
+    if (!value) {
+      this.nestedStringColumn = null;
+    }
+  }
+
+  public void setFieldValue(_Fields field, Object value) {
+    switch (field) {
+    case NESTED_INTS_COLUMN:
+      if (value == null) {
+        unsetNestedIntsColumn();
+      } else {
+        setNestedIntsColumn((List<Integer>)value);
+      }
+      break;
+
+    case NESTED_STRING_COLUMN:
+      if (value == null) {
+        unsetNestedStringColumn();
+      } else {
+        setNestedStringColumn((String)value);
+      }
+      break;
+
+    }
+  }
+
+  public Object getFieldValue(_Fields field) {
+    switch (field) {
+    case NESTED_INTS_COLUMN:
+      return getNestedIntsColumn();
+
+    case NESTED_STRING_COLUMN:
+      return getNestedStringColumn();
+
+    }
+    throw new IllegalStateException();
+  }
+
+  /** Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise */
+  public boolean isSet(_Fields field) {
+    if (field == null) {
+      throw new IllegalArgumentException();
+    }
+
+    switch (field) {
+    case NESTED_INTS_COLUMN:
+      return isSetNestedIntsColumn();
+    case NESTED_STRING_COLUMN:
+      return isSetNestedStringColumn();
+    }
+    throw new IllegalStateException();
+  }
+
+  @Override
+  public boolean equals(Object that) {
+    if (that == null)
+      return false;
+    if (that instanceof Nested)
+      return this.equals((Nested)that);
+    return false;
+  }
+
+  public boolean equals(Nested that) {
+    if (that == null)
+      return false;
+
+    boolean this_present_nestedIntsColumn = true && this.isSetNestedIntsColumn();
+    boolean that_present_nestedIntsColumn = true && that.isSetNestedIntsColumn();
+    if (this_present_nestedIntsColumn || that_present_nestedIntsColumn) {
+      if (!(this_present_nestedIntsColumn && that_present_nestedIntsColumn))
+        return false;
+      if (!this.nestedIntsColumn.equals(that.nestedIntsColumn))
+        return false;
+    }
+
+    boolean this_present_nestedStringColumn = true && this.isSetNestedStringColumn();
+    boolean that_present_nestedStringColumn = true && that.isSetNestedStringColumn();
+    if (this_present_nestedStringColumn || that_present_nestedStringColumn) {
+      if (!(this_present_nestedStringColumn && that_present_nestedStringColumn))
+        return false;
+      if (!this.nestedStringColumn.equals(that.nestedStringColumn))
+        return false;
+    }
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    List<Object> list = new ArrayList<Object>();
+
+    boolean present_nestedIntsColumn = true && (isSetNestedIntsColumn());
+    list.add(present_nestedIntsColumn);
+    if (present_nestedIntsColumn)
+      list.add(nestedIntsColumn);
+
+    boolean present_nestedStringColumn = true && (isSetNestedStringColumn());
+    list.add(present_nestedStringColumn);
+    if (present_nestedStringColumn)
+      list.add(nestedStringColumn);
+
+    return list.hashCode();
+  }
+
+  @Override
+  public int compareTo(Nested other) {
+    if (!getClass().equals(other.getClass())) {
+      return getClass().getName().compareTo(other.getClass().getName());
+    }
+
+    int lastComparison = 0;
+
+    lastComparison = Boolean.valueOf(isSetNestedIntsColumn()).compareTo(other.isSetNestedIntsColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetNestedIntsColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.nestedIntsColumn, other.nestedIntsColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetNestedStringColumn()).compareTo(other.isSetNestedStringColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetNestedStringColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.nestedStringColumn, other.nestedStringColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    return 0;
+  }
+
+  public _Fields fieldForId(int fieldId) {
+    return _Fields.findByThriftId(fieldId);
+  }
+
+  public void read(org.apache.thrift.protocol.TProtocol iprot) throws org.apache.thrift.TException {
+    schemes.get(iprot.getScheme()).getScheme().read(iprot, this);
+  }
+
+  public void write(org.apache.thrift.protocol.TProtocol oprot) throws org.apache.thrift.TException {
+    schemes.get(oprot.getScheme()).getScheme().write(oprot, this);
+  }
+
+  @Override
+  public String toString() {
+    StringBuilder sb = new StringBuilder("Nested(");
+    boolean first = true;
+
+    sb.append("nestedIntsColumn:");
+    if (this.nestedIntsColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.nestedIntsColumn);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("nestedStringColumn:");
+    if (this.nestedStringColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.nestedStringColumn);
+    }
+    first = false;
+    sb.append(")");
+    return sb.toString();
+  }
+
+  public void validate() throws org.apache.thrift.TException {
+    // check for required fields
+    if (nestedIntsColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'nestedIntsColumn' was not present! Struct: " + toString());
+    }
+    if (nestedStringColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'nestedStringColumn' was not present! Struct: " + toString());
+    }
+    // check for sub-struct validity
+  }
+
+  private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException {
+    try {
+      write(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(out)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, ClassNotFoundException {
+    try {
+      read(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(in)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private static class NestedStandardSchemeFactory implements SchemeFactory {
+    public NestedStandardScheme getScheme() {
+      return new NestedStandardScheme();
+    }
+  }
+
+  private static class NestedStandardScheme extends StandardScheme<Nested> {
+
+    public void read(org.apache.thrift.protocol.TProtocol iprot, Nested struct) throws org.apache.thrift.TException {
+      org.apache.thrift.protocol.TField schemeField;
+      iprot.readStructBegin();
+      while (true)
+      {
+        schemeField = iprot.readFieldBegin();
+        if (schemeField.type == org.apache.thrift.protocol.TType.STOP) { 
+          break;
+        }
+        switch (schemeField.id) {
+          case 1: // NESTED_INTS_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list0 = iprot.readListBegin();
+                struct.nestedIntsColumn = new ArrayList<Integer>(_list0.size);
+                int _elem1;
+                for (int _i2 = 0; _i2 < _list0.size; ++_i2)
+                {
+                  _elem1 = iprot.readI32();
+                  struct.nestedIntsColumn.add(_elem1);
+                }
+                iprot.readListEnd();
+              }
+              struct.setNestedIntsColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 2: // NESTED_STRING_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
+              struct.nestedStringColumn = iprot.readString();
+              struct.setNestedStringColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          default:
+            org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+        }
+        iprot.readFieldEnd();
+      }
+      iprot.readStructEnd();
+
+      // check for required fields of primitive type, which can't be checked in the validate method
+      struct.validate();
+    }
+
+    public void write(org.apache.thrift.protocol.TProtocol oprot, Nested struct) throws org.apache.thrift.TException {
+      struct.validate();
+
+      oprot.writeStructBegin(STRUCT_DESC);
+      if (struct.nestedIntsColumn != null) {
+        oprot.writeFieldBegin(NESTED_INTS_COLUMN_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, struct.nestedIntsColumn.size()));
+          for (int _iter3 : struct.nestedIntsColumn)
+          {
+            oprot.writeI32(_iter3);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.nestedStringColumn != null) {
+        oprot.writeFieldBegin(NESTED_STRING_COLUMN_FIELD_DESC);
+        oprot.writeString(struct.nestedStringColumn);
+        oprot.writeFieldEnd();
+      }
+      oprot.writeFieldStop();
+      oprot.writeStructEnd();
+    }
+
+  }
+
+  private static class NestedTupleSchemeFactory implements SchemeFactory {
+    public NestedTupleScheme getScheme() {
+      return new NestedTupleScheme();
+    }
+  }
+
+  private static class NestedTupleScheme extends TupleScheme<Nested> {
+
+    @Override
+    public void write(org.apache.thrift.protocol.TProtocol prot, Nested struct) throws org.apache.thrift.TException {
+      TTupleProtocol oprot = (TTupleProtocol) prot;
+      {
+        oprot.writeI32(struct.nestedIntsColumn.size());
+        for (int _iter4 : struct.nestedIntsColumn)
+        {
+          oprot.writeI32(_iter4);
+        }
+      }
+      oprot.writeString(struct.nestedStringColumn);
+    }
+
+    @Override
+    public void read(org.apache.thrift.protocol.TProtocol prot, Nested struct) throws org.apache.thrift.TException {
+      TTupleProtocol iprot = (TTupleProtocol) prot;
+      {
+        org.apache.thrift.protocol.TList _list5 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, iprot.readI32());
+        struct.nestedIntsColumn = new ArrayList<Integer>(_list5.size);
+        int _elem6;
+        for (int _i7 = 0; _i7 < _list5.size; ++_i7)
+        {
+          _elem6 = iprot.readI32();
+          struct.nestedIntsColumn.add(_elem6);
+        }
+      }
+      struct.setNestedIntsColumnIsSet(true);
+      struct.nestedStringColumn = iprot.readString();
+      struct.setNestedStringColumnIsSet(true);
+    }
+  }
+
+}
+
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java
new file mode 100644
index 0000000000000..326ae9dbaa0d1
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java
@@ -0,0 +1,2808 @@
+/**
+ * Autogenerated by Thrift Compiler (0.9.2)
+ *
+ * DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
+ *  @generated
+ */
+package org.apache.spark.sql.parquet.test.thrift;
+
+import org.apache.thrift.scheme.IScheme;
+import org.apache.thrift.scheme.SchemeFactory;
+import org.apache.thrift.scheme.StandardScheme;
+
+import org.apache.thrift.scheme.TupleScheme;
+import org.apache.thrift.protocol.TTupleProtocol;
+import org.apache.thrift.protocol.TProtocolException;
+import org.apache.thrift.EncodingUtils;
+import org.apache.thrift.TException;
+import org.apache.thrift.async.AsyncMethodCallback;
+import org.apache.thrift.server.AbstractNonblockingServer.*;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Map;
+import java.util.HashMap;
+import java.util.EnumMap;
+import java.util.Set;
+import java.util.HashSet;
+import java.util.EnumSet;
+import java.util.Collections;
+import java.util.BitSet;
+import java.nio.ByteBuffer;
+import java.util.Arrays;
+import javax.annotation.Generated;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+@SuppressWarnings({"cast", "rawtypes", "serial", "unchecked"})
+/**
+ * This is a test struct for testing parquet-thrift compatibility.
+ */
+@Generated(value = "Autogenerated by Thrift Compiler (0.9.2)", date = "2015-7-7")
+public class ParquetThriftCompat implements org.apache.thrift.TBase<ParquetThriftCompat, ParquetThriftCompat._Fields>, java.io.Serializable, Cloneable, Comparable<ParquetThriftCompat> {
+  private static final org.apache.thrift.protocol.TStruct STRUCT_DESC = new org.apache.thrift.protocol.TStruct("ParquetThriftCompat");
+
+  private static final org.apache.thrift.protocol.TField BOOL_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("boolColumn", org.apache.thrift.protocol.TType.BOOL, (short)1);
+  private static final org.apache.thrift.protocol.TField BYTE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("byteColumn", org.apache.thrift.protocol.TType.BYTE, (short)2);
+  private static final org.apache.thrift.protocol.TField SHORT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("shortColumn", org.apache.thrift.protocol.TType.I16, (short)3);
+  private static final org.apache.thrift.protocol.TField INT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("intColumn", org.apache.thrift.protocol.TType.I32, (short)4);
+  private static final org.apache.thrift.protocol.TField LONG_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("longColumn", org.apache.thrift.protocol.TType.I64, (short)5);
+  private static final org.apache.thrift.protocol.TField DOUBLE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("doubleColumn", org.apache.thrift.protocol.TType.DOUBLE, (short)6);
+  private static final org.apache.thrift.protocol.TField BINARY_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("binaryColumn", org.apache.thrift.protocol.TType.STRING, (short)7);
+  private static final org.apache.thrift.protocol.TField STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("stringColumn", org.apache.thrift.protocol.TType.STRING, (short)8);
+  private static final org.apache.thrift.protocol.TField ENUM_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("enumColumn", org.apache.thrift.protocol.TType.I32, (short)9);
+  private static final org.apache.thrift.protocol.TField MAYBE_BOOL_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeBoolColumn", org.apache.thrift.protocol.TType.BOOL, (short)10);
+  private static final org.apache.thrift.protocol.TField MAYBE_BYTE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeByteColumn", org.apache.thrift.protocol.TType.BYTE, (short)11);
+  private static final org.apache.thrift.protocol.TField MAYBE_SHORT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeShortColumn", org.apache.thrift.protocol.TType.I16, (short)12);
+  private static final org.apache.thrift.protocol.TField MAYBE_INT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeIntColumn", org.apache.thrift.protocol.TType.I32, (short)13);
+  private static final org.apache.thrift.protocol.TField MAYBE_LONG_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeLongColumn", org.apache.thrift.protocol.TType.I64, (short)14);
+  private static final org.apache.thrift.protocol.TField MAYBE_DOUBLE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeDoubleColumn", org.apache.thrift.protocol.TType.DOUBLE, (short)15);
+  private static final org.apache.thrift.protocol.TField MAYBE_BINARY_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeBinaryColumn", org.apache.thrift.protocol.TType.STRING, (short)16);
+  private static final org.apache.thrift.protocol.TField MAYBE_STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeStringColumn", org.apache.thrift.protocol.TType.STRING, (short)17);
+  private static final org.apache.thrift.protocol.TField MAYBE_ENUM_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeEnumColumn", org.apache.thrift.protocol.TType.I32, (short)18);
+  private static final org.apache.thrift.protocol.TField STRINGS_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("stringsColumn", org.apache.thrift.protocol.TType.LIST, (short)19);
+  private static final org.apache.thrift.protocol.TField INT_SET_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("intSetColumn", org.apache.thrift.protocol.TType.SET, (short)20);
+  private static final org.apache.thrift.protocol.TField INT_TO_STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("intToStringColumn", org.apache.thrift.protocol.TType.MAP, (short)21);
+  private static final org.apache.thrift.protocol.TField COMPLEX_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("complexColumn", org.apache.thrift.protocol.TType.MAP, (short)22);
+
+  private static final Map<Class<? extends IScheme>, SchemeFactory> schemes = new HashMap<Class<? extends IScheme>, SchemeFactory>();
+  static {
+    schemes.put(StandardScheme.class, new ParquetThriftCompatStandardSchemeFactory());
+    schemes.put(TupleScheme.class, new ParquetThriftCompatTupleSchemeFactory());
+  }
+
+  public boolean boolColumn; // required
+  public byte byteColumn; // required
+  public short shortColumn; // required
+  public int intColumn; // required
+  public long longColumn; // required
+  public double doubleColumn; // required
+  public ByteBuffer binaryColumn; // required
+  public String stringColumn; // required
+  /**
+   * 
+   * @see Suit
+   */
+  public Suit enumColumn; // required
+  public boolean maybeBoolColumn; // optional
+  public byte maybeByteColumn; // optional
+  public short maybeShortColumn; // optional
+  public int maybeIntColumn; // optional
+  public long maybeLongColumn; // optional
+  public double maybeDoubleColumn; // optional
+  public ByteBuffer maybeBinaryColumn; // optional
+  public String maybeStringColumn; // optional
+  /**
+   * 
+   * @see Suit
+   */
+  public Suit maybeEnumColumn; // optional
+  public List<String> stringsColumn; // required
+  public Set<Integer> intSetColumn; // required
+  public Map<Integer,String> intToStringColumn; // required
+  public Map<Integer,List<Nested>> complexColumn; // required
+
+  /** The set of fields this struct contains, along with convenience methods for finding and manipulating them. */
+  public enum _Fields implements org.apache.thrift.TFieldIdEnum {
+    BOOL_COLUMN((short)1, "boolColumn"),
+    BYTE_COLUMN((short)2, "byteColumn"),
+    SHORT_COLUMN((short)3, "shortColumn"),
+    INT_COLUMN((short)4, "intColumn"),
+    LONG_COLUMN((short)5, "longColumn"),
+    DOUBLE_COLUMN((short)6, "doubleColumn"),
+    BINARY_COLUMN((short)7, "binaryColumn"),
+    STRING_COLUMN((short)8, "stringColumn"),
+    /**
+     * 
+     * @see Suit
+     */
+    ENUM_COLUMN((short)9, "enumColumn"),
+    MAYBE_BOOL_COLUMN((short)10, "maybeBoolColumn"),
+    MAYBE_BYTE_COLUMN((short)11, "maybeByteColumn"),
+    MAYBE_SHORT_COLUMN((short)12, "maybeShortColumn"),
+    MAYBE_INT_COLUMN((short)13, "maybeIntColumn"),
+    MAYBE_LONG_COLUMN((short)14, "maybeLongColumn"),
+    MAYBE_DOUBLE_COLUMN((short)15, "maybeDoubleColumn"),
+    MAYBE_BINARY_COLUMN((short)16, "maybeBinaryColumn"),
+    MAYBE_STRING_COLUMN((short)17, "maybeStringColumn"),
+    /**
+     * 
+     * @see Suit
+     */
+    MAYBE_ENUM_COLUMN((short)18, "maybeEnumColumn"),
+    STRINGS_COLUMN((short)19, "stringsColumn"),
+    INT_SET_COLUMN((short)20, "intSetColumn"),
+    INT_TO_STRING_COLUMN((short)21, "intToStringColumn"),
+    COMPLEX_COLUMN((short)22, "complexColumn");
+
+    private static final Map<String, _Fields> byName = new HashMap<String, _Fields>();
+
+    static {
+      for (_Fields field : EnumSet.allOf(_Fields.class)) {
+        byName.put(field.getFieldName(), field);
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, or null if its not found.
+     */
+    public static _Fields findByThriftId(int fieldId) {
+      switch(fieldId) {
+        case 1: // BOOL_COLUMN
+          return BOOL_COLUMN;
+        case 2: // BYTE_COLUMN
+          return BYTE_COLUMN;
+        case 3: // SHORT_COLUMN
+          return SHORT_COLUMN;
+        case 4: // INT_COLUMN
+          return INT_COLUMN;
+        case 5: // LONG_COLUMN
+          return LONG_COLUMN;
+        case 6: // DOUBLE_COLUMN
+          return DOUBLE_COLUMN;
+        case 7: // BINARY_COLUMN
+          return BINARY_COLUMN;
+        case 8: // STRING_COLUMN
+          return STRING_COLUMN;
+        case 9: // ENUM_COLUMN
+          return ENUM_COLUMN;
+        case 10: // MAYBE_BOOL_COLUMN
+          return MAYBE_BOOL_COLUMN;
+        case 11: // MAYBE_BYTE_COLUMN
+          return MAYBE_BYTE_COLUMN;
+        case 12: // MAYBE_SHORT_COLUMN
+          return MAYBE_SHORT_COLUMN;
+        case 13: // MAYBE_INT_COLUMN
+          return MAYBE_INT_COLUMN;
+        case 14: // MAYBE_LONG_COLUMN
+          return MAYBE_LONG_COLUMN;
+        case 15: // MAYBE_DOUBLE_COLUMN
+          return MAYBE_DOUBLE_COLUMN;
+        case 16: // MAYBE_BINARY_COLUMN
+          return MAYBE_BINARY_COLUMN;
+        case 17: // MAYBE_STRING_COLUMN
+          return MAYBE_STRING_COLUMN;
+        case 18: // MAYBE_ENUM_COLUMN
+          return MAYBE_ENUM_COLUMN;
+        case 19: // STRINGS_COLUMN
+          return STRINGS_COLUMN;
+        case 20: // INT_SET_COLUMN
+          return INT_SET_COLUMN;
+        case 21: // INT_TO_STRING_COLUMN
+          return INT_TO_STRING_COLUMN;
+        case 22: // COMPLEX_COLUMN
+          return COMPLEX_COLUMN;
+        default:
+          return null;
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, throwing an exception
+     * if it is not found.
+     */
+    public static _Fields findByThriftIdOrThrow(int fieldId) {
+      _Fields fields = findByThriftId(fieldId);
+      if (fields == null) throw new IllegalArgumentException("Field " + fieldId + " doesn't exist!");
+      return fields;
+    }
+
+    /**
+     * Find the _Fields constant that matches name, or null if its not found.
+     */
+    public static _Fields findByName(String name) {
+      return byName.get(name);
+    }
+
+    private final short _thriftId;
+    private final String _fieldName;
+
+    _Fields(short thriftId, String fieldName) {
+      _thriftId = thriftId;
+      _fieldName = fieldName;
+    }
+
+    public short getThriftFieldId() {
+      return _thriftId;
+    }
+
+    public String getFieldName() {
+      return _fieldName;
+    }
+  }
+
+  // isset id assignments
+  private static final int __BOOLCOLUMN_ISSET_ID = 0;
+  private static final int __BYTECOLUMN_ISSET_ID = 1;
+  private static final int __SHORTCOLUMN_ISSET_ID = 2;
+  private static final int __INTCOLUMN_ISSET_ID = 3;
+  private static final int __LONGCOLUMN_ISSET_ID = 4;
+  private static final int __DOUBLECOLUMN_ISSET_ID = 5;
+  private static final int __MAYBEBOOLCOLUMN_ISSET_ID = 6;
+  private static final int __MAYBEBYTECOLUMN_ISSET_ID = 7;
+  private static final int __MAYBESHORTCOLUMN_ISSET_ID = 8;
+  private static final int __MAYBEINTCOLUMN_ISSET_ID = 9;
+  private static final int __MAYBELONGCOLUMN_ISSET_ID = 10;
+  private static final int __MAYBEDOUBLECOLUMN_ISSET_ID = 11;
+  private short __isset_bitfield = 0;
+  private static final _Fields optionals[] = {_Fields.MAYBE_BOOL_COLUMN,_Fields.MAYBE_BYTE_COLUMN,_Fields.MAYBE_SHORT_COLUMN,_Fields.MAYBE_INT_COLUMN,_Fields.MAYBE_LONG_COLUMN,_Fields.MAYBE_DOUBLE_COLUMN,_Fields.MAYBE_BINARY_COLUMN,_Fields.MAYBE_STRING_COLUMN,_Fields.MAYBE_ENUM_COLUMN};
+  public static final Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> metaDataMap;
+  static {
+    Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> tmpMap = new EnumMap<_Fields, org.apache.thrift.meta_data.FieldMetaData>(_Fields.class);
+    tmpMap.put(_Fields.BOOL_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("boolColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BOOL)));
+    tmpMap.put(_Fields.BYTE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("byteColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BYTE)));
+    tmpMap.put(_Fields.SHORT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("shortColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I16)));
+    tmpMap.put(_Fields.INT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("intColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32)));
+    tmpMap.put(_Fields.LONG_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("longColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I64)));
+    tmpMap.put(_Fields.DOUBLE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("doubleColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.DOUBLE)));
+    tmpMap.put(_Fields.BINARY_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("binaryColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING        , true)));
+    tmpMap.put(_Fields.STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("stringColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
+    tmpMap.put(_Fields.ENUM_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("enumColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.EnumMetaData(org.apache.thrift.protocol.TType.ENUM, Suit.class)));
+    tmpMap.put(_Fields.MAYBE_BOOL_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeBoolColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BOOL)));
+    tmpMap.put(_Fields.MAYBE_BYTE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeByteColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BYTE)));
+    tmpMap.put(_Fields.MAYBE_SHORT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeShortColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I16)));
+    tmpMap.put(_Fields.MAYBE_INT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeIntColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32)));
+    tmpMap.put(_Fields.MAYBE_LONG_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeLongColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I64)));
+    tmpMap.put(_Fields.MAYBE_DOUBLE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeDoubleColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.DOUBLE)));
+    tmpMap.put(_Fields.MAYBE_BINARY_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeBinaryColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING        , true)));
+    tmpMap.put(_Fields.MAYBE_STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeStringColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
+    tmpMap.put(_Fields.MAYBE_ENUM_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeEnumColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
+        new org.apache.thrift.meta_data.EnumMetaData(org.apache.thrift.protocol.TType.ENUM, Suit.class)));
+    tmpMap.put(_Fields.STRINGS_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("stringsColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST, 
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
+    tmpMap.put(_Fields.INT_SET_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("intSetColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.SetMetaData(org.apache.thrift.protocol.TType.SET, 
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32))));
+    tmpMap.put(_Fields.INT_TO_STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("intToStringColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.MapMetaData(org.apache.thrift.protocol.TType.MAP, 
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32), 
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
+    tmpMap.put(_Fields.COMPLEX_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("complexColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
+        new org.apache.thrift.meta_data.MapMetaData(org.apache.thrift.protocol.TType.MAP, 
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32), 
+            new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST, 
+                new org.apache.thrift.meta_data.StructMetaData(org.apache.thrift.protocol.TType.STRUCT, Nested.class)))));
+    metaDataMap = Collections.unmodifiableMap(tmpMap);
+    org.apache.thrift.meta_data.FieldMetaData.addStructMetaDataMap(ParquetThriftCompat.class, metaDataMap);
+  }
+
+  public ParquetThriftCompat() {
+  }
+
+  public ParquetThriftCompat(
+    boolean boolColumn,
+    byte byteColumn,
+    short shortColumn,
+    int intColumn,
+    long longColumn,
+    double doubleColumn,
+    ByteBuffer binaryColumn,
+    String stringColumn,
+    Suit enumColumn,
+    List<String> stringsColumn,
+    Set<Integer> intSetColumn,
+    Map<Integer,String> intToStringColumn,
+    Map<Integer,List<Nested>> complexColumn)
+  {
+    this();
+    this.boolColumn = boolColumn;
+    setBoolColumnIsSet(true);
+    this.byteColumn = byteColumn;
+    setByteColumnIsSet(true);
+    this.shortColumn = shortColumn;
+    setShortColumnIsSet(true);
+    this.intColumn = intColumn;
+    setIntColumnIsSet(true);
+    this.longColumn = longColumn;
+    setLongColumnIsSet(true);
+    this.doubleColumn = doubleColumn;
+    setDoubleColumnIsSet(true);
+    this.binaryColumn = org.apache.thrift.TBaseHelper.copyBinary(binaryColumn);
+    this.stringColumn = stringColumn;
+    this.enumColumn = enumColumn;
+    this.stringsColumn = stringsColumn;
+    this.intSetColumn = intSetColumn;
+    this.intToStringColumn = intToStringColumn;
+    this.complexColumn = complexColumn;
+  }
+
+  /**
+   * Performs a deep copy on <i>other</i>.
+   */
+  public ParquetThriftCompat(ParquetThriftCompat other) {
+    __isset_bitfield = other.__isset_bitfield;
+    this.boolColumn = other.boolColumn;
+    this.byteColumn = other.byteColumn;
+    this.shortColumn = other.shortColumn;
+    this.intColumn = other.intColumn;
+    this.longColumn = other.longColumn;
+    this.doubleColumn = other.doubleColumn;
+    if (other.isSetBinaryColumn()) {
+      this.binaryColumn = org.apache.thrift.TBaseHelper.copyBinary(other.binaryColumn);
+    }
+    if (other.isSetStringColumn()) {
+      this.stringColumn = other.stringColumn;
+    }
+    if (other.isSetEnumColumn()) {
+      this.enumColumn = other.enumColumn;
+    }
+    this.maybeBoolColumn = other.maybeBoolColumn;
+    this.maybeByteColumn = other.maybeByteColumn;
+    this.maybeShortColumn = other.maybeShortColumn;
+    this.maybeIntColumn = other.maybeIntColumn;
+    this.maybeLongColumn = other.maybeLongColumn;
+    this.maybeDoubleColumn = other.maybeDoubleColumn;
+    if (other.isSetMaybeBinaryColumn()) {
+      this.maybeBinaryColumn = org.apache.thrift.TBaseHelper.copyBinary(other.maybeBinaryColumn);
+    }
+    if (other.isSetMaybeStringColumn()) {
+      this.maybeStringColumn = other.maybeStringColumn;
+    }
+    if (other.isSetMaybeEnumColumn()) {
+      this.maybeEnumColumn = other.maybeEnumColumn;
+    }
+    if (other.isSetStringsColumn()) {
+      List<String> __this__stringsColumn = new ArrayList<String>(other.stringsColumn);
+      this.stringsColumn = __this__stringsColumn;
+    }
+    if (other.isSetIntSetColumn()) {
+      Set<Integer> __this__intSetColumn = new HashSet<Integer>(other.intSetColumn);
+      this.intSetColumn = __this__intSetColumn;
+    }
+    if (other.isSetIntToStringColumn()) {
+      Map<Integer,String> __this__intToStringColumn = new HashMap<Integer,String>(other.intToStringColumn);
+      this.intToStringColumn = __this__intToStringColumn;
+    }
+    if (other.isSetComplexColumn()) {
+      Map<Integer,List<Nested>> __this__complexColumn = new HashMap<Integer,List<Nested>>(other.complexColumn.size());
+      for (Map.Entry<Integer, List<Nested>> other_element : other.complexColumn.entrySet()) {
+
+        Integer other_element_key = other_element.getKey();
+        List<Nested> other_element_value = other_element.getValue();
+
+        Integer __this__complexColumn_copy_key = other_element_key;
+
+        List<Nested> __this__complexColumn_copy_value = new ArrayList<Nested>(other_element_value.size());
+        for (Nested other_element_value_element : other_element_value) {
+          __this__complexColumn_copy_value.add(new Nested(other_element_value_element));
+        }
+
+        __this__complexColumn.put(__this__complexColumn_copy_key, __this__complexColumn_copy_value);
+      }
+      this.complexColumn = __this__complexColumn;
+    }
+  }
+
+  public ParquetThriftCompat deepCopy() {
+    return new ParquetThriftCompat(this);
+  }
+
+  @Override
+  public void clear() {
+    setBoolColumnIsSet(false);
+    this.boolColumn = false;
+    setByteColumnIsSet(false);
+    this.byteColumn = 0;
+    setShortColumnIsSet(false);
+    this.shortColumn = 0;
+    setIntColumnIsSet(false);
+    this.intColumn = 0;
+    setLongColumnIsSet(false);
+    this.longColumn = 0;
+    setDoubleColumnIsSet(false);
+    this.doubleColumn = 0.0;
+    this.binaryColumn = null;
+    this.stringColumn = null;
+    this.enumColumn = null;
+    setMaybeBoolColumnIsSet(false);
+    this.maybeBoolColumn = false;
+    setMaybeByteColumnIsSet(false);
+    this.maybeByteColumn = 0;
+    setMaybeShortColumnIsSet(false);
+    this.maybeShortColumn = 0;
+    setMaybeIntColumnIsSet(false);
+    this.maybeIntColumn = 0;
+    setMaybeLongColumnIsSet(false);
+    this.maybeLongColumn = 0;
+    setMaybeDoubleColumnIsSet(false);
+    this.maybeDoubleColumn = 0.0;
+    this.maybeBinaryColumn = null;
+    this.maybeStringColumn = null;
+    this.maybeEnumColumn = null;
+    this.stringsColumn = null;
+    this.intSetColumn = null;
+    this.intToStringColumn = null;
+    this.complexColumn = null;
+  }
+
+  public boolean isBoolColumn() {
+    return this.boolColumn;
+  }
+
+  public ParquetThriftCompat setBoolColumn(boolean boolColumn) {
+    this.boolColumn = boolColumn;
+    setBoolColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetBoolColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __BOOLCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field boolColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetBoolColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __BOOLCOLUMN_ISSET_ID);
+  }
+
+  public void setBoolColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __BOOLCOLUMN_ISSET_ID, value);
+  }
+
+  public byte getByteColumn() {
+    return this.byteColumn;
+  }
+
+  public ParquetThriftCompat setByteColumn(byte byteColumn) {
+    this.byteColumn = byteColumn;
+    setByteColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetByteColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __BYTECOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field byteColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetByteColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __BYTECOLUMN_ISSET_ID);
+  }
+
+  public void setByteColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __BYTECOLUMN_ISSET_ID, value);
+  }
+
+  public short getShortColumn() {
+    return this.shortColumn;
+  }
+
+  public ParquetThriftCompat setShortColumn(short shortColumn) {
+    this.shortColumn = shortColumn;
+    setShortColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetShortColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __SHORTCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field shortColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetShortColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __SHORTCOLUMN_ISSET_ID);
+  }
+
+  public void setShortColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __SHORTCOLUMN_ISSET_ID, value);
+  }
+
+  public int getIntColumn() {
+    return this.intColumn;
+  }
+
+  public ParquetThriftCompat setIntColumn(int intColumn) {
+    this.intColumn = intColumn;
+    setIntColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetIntColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __INTCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field intColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetIntColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __INTCOLUMN_ISSET_ID);
+  }
+
+  public void setIntColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __INTCOLUMN_ISSET_ID, value);
+  }
+
+  public long getLongColumn() {
+    return this.longColumn;
+  }
+
+  public ParquetThriftCompat setLongColumn(long longColumn) {
+    this.longColumn = longColumn;
+    setLongColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetLongColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __LONGCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field longColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetLongColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __LONGCOLUMN_ISSET_ID);
+  }
+
+  public void setLongColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __LONGCOLUMN_ISSET_ID, value);
+  }
+
+  public double getDoubleColumn() {
+    return this.doubleColumn;
+  }
+
+  public ParquetThriftCompat setDoubleColumn(double doubleColumn) {
+    this.doubleColumn = doubleColumn;
+    setDoubleColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetDoubleColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __DOUBLECOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field doubleColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetDoubleColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __DOUBLECOLUMN_ISSET_ID);
+  }
+
+  public void setDoubleColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __DOUBLECOLUMN_ISSET_ID, value);
+  }
+
+  public byte[] getBinaryColumn() {
+    setBinaryColumn(org.apache.thrift.TBaseHelper.rightSize(binaryColumn));
+    return binaryColumn == null ? null : binaryColumn.array();
+  }
+
+  public ByteBuffer bufferForBinaryColumn() {
+    return org.apache.thrift.TBaseHelper.copyBinary(binaryColumn);
+  }
+
+  public ParquetThriftCompat setBinaryColumn(byte[] binaryColumn) {
+    this.binaryColumn = binaryColumn == null ? (ByteBuffer)null : ByteBuffer.wrap(Arrays.copyOf(binaryColumn, binaryColumn.length));
+    return this;
+  }
+
+  public ParquetThriftCompat setBinaryColumn(ByteBuffer binaryColumn) {
+    this.binaryColumn = org.apache.thrift.TBaseHelper.copyBinary(binaryColumn);
+    return this;
+  }
+
+  public void unsetBinaryColumn() {
+    this.binaryColumn = null;
+  }
+
+  /** Returns true if field binaryColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetBinaryColumn() {
+    return this.binaryColumn != null;
+  }
+
+  public void setBinaryColumnIsSet(boolean value) {
+    if (!value) {
+      this.binaryColumn = null;
+    }
+  }
+
+  public String getStringColumn() {
+    return this.stringColumn;
+  }
+
+  public ParquetThriftCompat setStringColumn(String stringColumn) {
+    this.stringColumn = stringColumn;
+    return this;
+  }
+
+  public void unsetStringColumn() {
+    this.stringColumn = null;
+  }
+
+  /** Returns true if field stringColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetStringColumn() {
+    return this.stringColumn != null;
+  }
+
+  public void setStringColumnIsSet(boolean value) {
+    if (!value) {
+      this.stringColumn = null;
+    }
+  }
+
+  /**
+   * 
+   * @see Suit
+   */
+  public Suit getEnumColumn() {
+    return this.enumColumn;
+  }
+
+  /**
+   * 
+   * @see Suit
+   */
+  public ParquetThriftCompat setEnumColumn(Suit enumColumn) {
+    this.enumColumn = enumColumn;
+    return this;
+  }
+
+  public void unsetEnumColumn() {
+    this.enumColumn = null;
+  }
+
+  /** Returns true if field enumColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetEnumColumn() {
+    return this.enumColumn != null;
+  }
+
+  public void setEnumColumnIsSet(boolean value) {
+    if (!value) {
+      this.enumColumn = null;
+    }
+  }
+
+  public boolean isMaybeBoolColumn() {
+    return this.maybeBoolColumn;
+  }
+
+  public ParquetThriftCompat setMaybeBoolColumn(boolean maybeBoolColumn) {
+    this.maybeBoolColumn = maybeBoolColumn;
+    setMaybeBoolColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetMaybeBoolColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEBOOLCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field maybeBoolColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeBoolColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __MAYBEBOOLCOLUMN_ISSET_ID);
+  }
+
+  public void setMaybeBoolColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEBOOLCOLUMN_ISSET_ID, value);
+  }
+
+  public byte getMaybeByteColumn() {
+    return this.maybeByteColumn;
+  }
+
+  public ParquetThriftCompat setMaybeByteColumn(byte maybeByteColumn) {
+    this.maybeByteColumn = maybeByteColumn;
+    setMaybeByteColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetMaybeByteColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEBYTECOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field maybeByteColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeByteColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __MAYBEBYTECOLUMN_ISSET_ID);
+  }
+
+  public void setMaybeByteColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEBYTECOLUMN_ISSET_ID, value);
+  }
+
+  public short getMaybeShortColumn() {
+    return this.maybeShortColumn;
+  }
+
+  public ParquetThriftCompat setMaybeShortColumn(short maybeShortColumn) {
+    this.maybeShortColumn = maybeShortColumn;
+    setMaybeShortColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetMaybeShortColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBESHORTCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field maybeShortColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeShortColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __MAYBESHORTCOLUMN_ISSET_ID);
+  }
+
+  public void setMaybeShortColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBESHORTCOLUMN_ISSET_ID, value);
+  }
+
+  public int getMaybeIntColumn() {
+    return this.maybeIntColumn;
+  }
+
+  public ParquetThriftCompat setMaybeIntColumn(int maybeIntColumn) {
+    this.maybeIntColumn = maybeIntColumn;
+    setMaybeIntColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetMaybeIntColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEINTCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field maybeIntColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeIntColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __MAYBEINTCOLUMN_ISSET_ID);
+  }
+
+  public void setMaybeIntColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEINTCOLUMN_ISSET_ID, value);
+  }
+
+  public long getMaybeLongColumn() {
+    return this.maybeLongColumn;
+  }
+
+  public ParquetThriftCompat setMaybeLongColumn(long maybeLongColumn) {
+    this.maybeLongColumn = maybeLongColumn;
+    setMaybeLongColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetMaybeLongColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBELONGCOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field maybeLongColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeLongColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __MAYBELONGCOLUMN_ISSET_ID);
+  }
+
+  public void setMaybeLongColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBELONGCOLUMN_ISSET_ID, value);
+  }
+
+  public double getMaybeDoubleColumn() {
+    return this.maybeDoubleColumn;
+  }
+
+  public ParquetThriftCompat setMaybeDoubleColumn(double maybeDoubleColumn) {
+    this.maybeDoubleColumn = maybeDoubleColumn;
+    setMaybeDoubleColumnIsSet(true);
+    return this;
+  }
+
+  public void unsetMaybeDoubleColumn() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEDOUBLECOLUMN_ISSET_ID);
+  }
+
+  /** Returns true if field maybeDoubleColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeDoubleColumn() {
+    return EncodingUtils.testBit(__isset_bitfield, __MAYBEDOUBLECOLUMN_ISSET_ID);
+  }
+
+  public void setMaybeDoubleColumnIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEDOUBLECOLUMN_ISSET_ID, value);
+  }
+
+  public byte[] getMaybeBinaryColumn() {
+    setMaybeBinaryColumn(org.apache.thrift.TBaseHelper.rightSize(maybeBinaryColumn));
+    return maybeBinaryColumn == null ? null : maybeBinaryColumn.array();
+  }
+
+  public ByteBuffer bufferForMaybeBinaryColumn() {
+    return org.apache.thrift.TBaseHelper.copyBinary(maybeBinaryColumn);
+  }
+
+  public ParquetThriftCompat setMaybeBinaryColumn(byte[] maybeBinaryColumn) {
+    this.maybeBinaryColumn = maybeBinaryColumn == null ? (ByteBuffer)null : ByteBuffer.wrap(Arrays.copyOf(maybeBinaryColumn, maybeBinaryColumn.length));
+    return this;
+  }
+
+  public ParquetThriftCompat setMaybeBinaryColumn(ByteBuffer maybeBinaryColumn) {
+    this.maybeBinaryColumn = org.apache.thrift.TBaseHelper.copyBinary(maybeBinaryColumn);
+    return this;
+  }
+
+  public void unsetMaybeBinaryColumn() {
+    this.maybeBinaryColumn = null;
+  }
+
+  /** Returns true if field maybeBinaryColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeBinaryColumn() {
+    return this.maybeBinaryColumn != null;
+  }
+
+  public void setMaybeBinaryColumnIsSet(boolean value) {
+    if (!value) {
+      this.maybeBinaryColumn = null;
+    }
+  }
+
+  public String getMaybeStringColumn() {
+    return this.maybeStringColumn;
+  }
+
+  public ParquetThriftCompat setMaybeStringColumn(String maybeStringColumn) {
+    this.maybeStringColumn = maybeStringColumn;
+    return this;
+  }
+
+  public void unsetMaybeStringColumn() {
+    this.maybeStringColumn = null;
+  }
+
+  /** Returns true if field maybeStringColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeStringColumn() {
+    return this.maybeStringColumn != null;
+  }
+
+  public void setMaybeStringColumnIsSet(boolean value) {
+    if (!value) {
+      this.maybeStringColumn = null;
+    }
+  }
+
+  /**
+   * 
+   * @see Suit
+   */
+  public Suit getMaybeEnumColumn() {
+    return this.maybeEnumColumn;
+  }
+
+  /**
+   * 
+   * @see Suit
+   */
+  public ParquetThriftCompat setMaybeEnumColumn(Suit maybeEnumColumn) {
+    this.maybeEnumColumn = maybeEnumColumn;
+    return this;
+  }
+
+  public void unsetMaybeEnumColumn() {
+    this.maybeEnumColumn = null;
+  }
+
+  /** Returns true if field maybeEnumColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetMaybeEnumColumn() {
+    return this.maybeEnumColumn != null;
+  }
+
+  public void setMaybeEnumColumnIsSet(boolean value) {
+    if (!value) {
+      this.maybeEnumColumn = null;
+    }
+  }
+
+  public int getStringsColumnSize() {
+    return (this.stringsColumn == null) ? 0 : this.stringsColumn.size();
+  }
+
+  public java.util.Iterator<String> getStringsColumnIterator() {
+    return (this.stringsColumn == null) ? null : this.stringsColumn.iterator();
+  }
+
+  public void addToStringsColumn(String elem) {
+    if (this.stringsColumn == null) {
+      this.stringsColumn = new ArrayList<String>();
+    }
+    this.stringsColumn.add(elem);
+  }
+
+  public List<String> getStringsColumn() {
+    return this.stringsColumn;
+  }
+
+  public ParquetThriftCompat setStringsColumn(List<String> stringsColumn) {
+    this.stringsColumn = stringsColumn;
+    return this;
+  }
+
+  public void unsetStringsColumn() {
+    this.stringsColumn = null;
+  }
+
+  /** Returns true if field stringsColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetStringsColumn() {
+    return this.stringsColumn != null;
+  }
+
+  public void setStringsColumnIsSet(boolean value) {
+    if (!value) {
+      this.stringsColumn = null;
+    }
+  }
+
+  public int getIntSetColumnSize() {
+    return (this.intSetColumn == null) ? 0 : this.intSetColumn.size();
+  }
+
+  public java.util.Iterator<Integer> getIntSetColumnIterator() {
+    return (this.intSetColumn == null) ? null : this.intSetColumn.iterator();
+  }
+
+  public void addToIntSetColumn(int elem) {
+    if (this.intSetColumn == null) {
+      this.intSetColumn = new HashSet<Integer>();
+    }
+    this.intSetColumn.add(elem);
+  }
+
+  public Set<Integer> getIntSetColumn() {
+    return this.intSetColumn;
+  }
+
+  public ParquetThriftCompat setIntSetColumn(Set<Integer> intSetColumn) {
+    this.intSetColumn = intSetColumn;
+    return this;
+  }
+
+  public void unsetIntSetColumn() {
+    this.intSetColumn = null;
+  }
+
+  /** Returns true if field intSetColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetIntSetColumn() {
+    return this.intSetColumn != null;
+  }
+
+  public void setIntSetColumnIsSet(boolean value) {
+    if (!value) {
+      this.intSetColumn = null;
+    }
+  }
+
+  public int getIntToStringColumnSize() {
+    return (this.intToStringColumn == null) ? 0 : this.intToStringColumn.size();
+  }
+
+  public void putToIntToStringColumn(int key, String val) {
+    if (this.intToStringColumn == null) {
+      this.intToStringColumn = new HashMap<Integer,String>();
+    }
+    this.intToStringColumn.put(key, val);
+  }
+
+  public Map<Integer,String> getIntToStringColumn() {
+    return this.intToStringColumn;
+  }
+
+  public ParquetThriftCompat setIntToStringColumn(Map<Integer,String> intToStringColumn) {
+    this.intToStringColumn = intToStringColumn;
+    return this;
+  }
+
+  public void unsetIntToStringColumn() {
+    this.intToStringColumn = null;
+  }
+
+  /** Returns true if field intToStringColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetIntToStringColumn() {
+    return this.intToStringColumn != null;
+  }
+
+  public void setIntToStringColumnIsSet(boolean value) {
+    if (!value) {
+      this.intToStringColumn = null;
+    }
+  }
+
+  public int getComplexColumnSize() {
+    return (this.complexColumn == null) ? 0 : this.complexColumn.size();
+  }
+
+  public void putToComplexColumn(int key, List<Nested> val) {
+    if (this.complexColumn == null) {
+      this.complexColumn = new HashMap<Integer,List<Nested>>();
+    }
+    this.complexColumn.put(key, val);
+  }
+
+  public Map<Integer,List<Nested>> getComplexColumn() {
+    return this.complexColumn;
+  }
+
+  public ParquetThriftCompat setComplexColumn(Map<Integer,List<Nested>> complexColumn) {
+    this.complexColumn = complexColumn;
+    return this;
+  }
+
+  public void unsetComplexColumn() {
+    this.complexColumn = null;
+  }
+
+  /** Returns true if field complexColumn is set (has been assigned a value) and false otherwise */
+  public boolean isSetComplexColumn() {
+    return this.complexColumn != null;
+  }
+
+  public void setComplexColumnIsSet(boolean value) {
+    if (!value) {
+      this.complexColumn = null;
+    }
+  }
+
+  public void setFieldValue(_Fields field, Object value) {
+    switch (field) {
+    case BOOL_COLUMN:
+      if (value == null) {
+        unsetBoolColumn();
+      } else {
+        setBoolColumn((Boolean)value);
+      }
+      break;
+
+    case BYTE_COLUMN:
+      if (value == null) {
+        unsetByteColumn();
+      } else {
+        setByteColumn((Byte)value);
+      }
+      break;
+
+    case SHORT_COLUMN:
+      if (value == null) {
+        unsetShortColumn();
+      } else {
+        setShortColumn((Short)value);
+      }
+      break;
+
+    case INT_COLUMN:
+      if (value == null) {
+        unsetIntColumn();
+      } else {
+        setIntColumn((Integer)value);
+      }
+      break;
+
+    case LONG_COLUMN:
+      if (value == null) {
+        unsetLongColumn();
+      } else {
+        setLongColumn((Long)value);
+      }
+      break;
+
+    case DOUBLE_COLUMN:
+      if (value == null) {
+        unsetDoubleColumn();
+      } else {
+        setDoubleColumn((Double)value);
+      }
+      break;
+
+    case BINARY_COLUMN:
+      if (value == null) {
+        unsetBinaryColumn();
+      } else {
+        setBinaryColumn((ByteBuffer)value);
+      }
+      break;
+
+    case STRING_COLUMN:
+      if (value == null) {
+        unsetStringColumn();
+      } else {
+        setStringColumn((String)value);
+      }
+      break;
+
+    case ENUM_COLUMN:
+      if (value == null) {
+        unsetEnumColumn();
+      } else {
+        setEnumColumn((Suit)value);
+      }
+      break;
+
+    case MAYBE_BOOL_COLUMN:
+      if (value == null) {
+        unsetMaybeBoolColumn();
+      } else {
+        setMaybeBoolColumn((Boolean)value);
+      }
+      break;
+
+    case MAYBE_BYTE_COLUMN:
+      if (value == null) {
+        unsetMaybeByteColumn();
+      } else {
+        setMaybeByteColumn((Byte)value);
+      }
+      break;
+
+    case MAYBE_SHORT_COLUMN:
+      if (value == null) {
+        unsetMaybeShortColumn();
+      } else {
+        setMaybeShortColumn((Short)value);
+      }
+      break;
+
+    case MAYBE_INT_COLUMN:
+      if (value == null) {
+        unsetMaybeIntColumn();
+      } else {
+        setMaybeIntColumn((Integer)value);
+      }
+      break;
+
+    case MAYBE_LONG_COLUMN:
+      if (value == null) {
+        unsetMaybeLongColumn();
+      } else {
+        setMaybeLongColumn((Long)value);
+      }
+      break;
+
+    case MAYBE_DOUBLE_COLUMN:
+      if (value == null) {
+        unsetMaybeDoubleColumn();
+      } else {
+        setMaybeDoubleColumn((Double)value);
+      }
+      break;
+
+    case MAYBE_BINARY_COLUMN:
+      if (value == null) {
+        unsetMaybeBinaryColumn();
+      } else {
+        setMaybeBinaryColumn((ByteBuffer)value);
+      }
+      break;
+
+    case MAYBE_STRING_COLUMN:
+      if (value == null) {
+        unsetMaybeStringColumn();
+      } else {
+        setMaybeStringColumn((String)value);
+      }
+      break;
+
+    case MAYBE_ENUM_COLUMN:
+      if (value == null) {
+        unsetMaybeEnumColumn();
+      } else {
+        setMaybeEnumColumn((Suit)value);
+      }
+      break;
+
+    case STRINGS_COLUMN:
+      if (value == null) {
+        unsetStringsColumn();
+      } else {
+        setStringsColumn((List<String>)value);
+      }
+      break;
+
+    case INT_SET_COLUMN:
+      if (value == null) {
+        unsetIntSetColumn();
+      } else {
+        setIntSetColumn((Set<Integer>)value);
+      }
+      break;
+
+    case INT_TO_STRING_COLUMN:
+      if (value == null) {
+        unsetIntToStringColumn();
+      } else {
+        setIntToStringColumn((Map<Integer,String>)value);
+      }
+      break;
+
+    case COMPLEX_COLUMN:
+      if (value == null) {
+        unsetComplexColumn();
+      } else {
+        setComplexColumn((Map<Integer,List<Nested>>)value);
+      }
+      break;
+
+    }
+  }
+
+  public Object getFieldValue(_Fields field) {
+    switch (field) {
+    case BOOL_COLUMN:
+      return Boolean.valueOf(isBoolColumn());
+
+    case BYTE_COLUMN:
+      return Byte.valueOf(getByteColumn());
+
+    case SHORT_COLUMN:
+      return Short.valueOf(getShortColumn());
+
+    case INT_COLUMN:
+      return Integer.valueOf(getIntColumn());
+
+    case LONG_COLUMN:
+      return Long.valueOf(getLongColumn());
+
+    case DOUBLE_COLUMN:
+      return Double.valueOf(getDoubleColumn());
+
+    case BINARY_COLUMN:
+      return getBinaryColumn();
+
+    case STRING_COLUMN:
+      return getStringColumn();
+
+    case ENUM_COLUMN:
+      return getEnumColumn();
+
+    case MAYBE_BOOL_COLUMN:
+      return Boolean.valueOf(isMaybeBoolColumn());
+
+    case MAYBE_BYTE_COLUMN:
+      return Byte.valueOf(getMaybeByteColumn());
+
+    case MAYBE_SHORT_COLUMN:
+      return Short.valueOf(getMaybeShortColumn());
+
+    case MAYBE_INT_COLUMN:
+      return Integer.valueOf(getMaybeIntColumn());
+
+    case MAYBE_LONG_COLUMN:
+      return Long.valueOf(getMaybeLongColumn());
+
+    case MAYBE_DOUBLE_COLUMN:
+      return Double.valueOf(getMaybeDoubleColumn());
+
+    case MAYBE_BINARY_COLUMN:
+      return getMaybeBinaryColumn();
+
+    case MAYBE_STRING_COLUMN:
+      return getMaybeStringColumn();
+
+    case MAYBE_ENUM_COLUMN:
+      return getMaybeEnumColumn();
+
+    case STRINGS_COLUMN:
+      return getStringsColumn();
+
+    case INT_SET_COLUMN:
+      return getIntSetColumn();
+
+    case INT_TO_STRING_COLUMN:
+      return getIntToStringColumn();
+
+    case COMPLEX_COLUMN:
+      return getComplexColumn();
+
+    }
+    throw new IllegalStateException();
+  }
+
+  /** Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise */
+  public boolean isSet(_Fields field) {
+    if (field == null) {
+      throw new IllegalArgumentException();
+    }
+
+    switch (field) {
+    case BOOL_COLUMN:
+      return isSetBoolColumn();
+    case BYTE_COLUMN:
+      return isSetByteColumn();
+    case SHORT_COLUMN:
+      return isSetShortColumn();
+    case INT_COLUMN:
+      return isSetIntColumn();
+    case LONG_COLUMN:
+      return isSetLongColumn();
+    case DOUBLE_COLUMN:
+      return isSetDoubleColumn();
+    case BINARY_COLUMN:
+      return isSetBinaryColumn();
+    case STRING_COLUMN:
+      return isSetStringColumn();
+    case ENUM_COLUMN:
+      return isSetEnumColumn();
+    case MAYBE_BOOL_COLUMN:
+      return isSetMaybeBoolColumn();
+    case MAYBE_BYTE_COLUMN:
+      return isSetMaybeByteColumn();
+    case MAYBE_SHORT_COLUMN:
+      return isSetMaybeShortColumn();
+    case MAYBE_INT_COLUMN:
+      return isSetMaybeIntColumn();
+    case MAYBE_LONG_COLUMN:
+      return isSetMaybeLongColumn();
+    case MAYBE_DOUBLE_COLUMN:
+      return isSetMaybeDoubleColumn();
+    case MAYBE_BINARY_COLUMN:
+      return isSetMaybeBinaryColumn();
+    case MAYBE_STRING_COLUMN:
+      return isSetMaybeStringColumn();
+    case MAYBE_ENUM_COLUMN:
+      return isSetMaybeEnumColumn();
+    case STRINGS_COLUMN:
+      return isSetStringsColumn();
+    case INT_SET_COLUMN:
+      return isSetIntSetColumn();
+    case INT_TO_STRING_COLUMN:
+      return isSetIntToStringColumn();
+    case COMPLEX_COLUMN:
+      return isSetComplexColumn();
+    }
+    throw new IllegalStateException();
+  }
+
+  @Override
+  public boolean equals(Object that) {
+    if (that == null)
+      return false;
+    if (that instanceof ParquetThriftCompat)
+      return this.equals((ParquetThriftCompat)that);
+    return false;
+  }
+
+  public boolean equals(ParquetThriftCompat that) {
+    if (that == null)
+      return false;
+
+    boolean this_present_boolColumn = true;
+    boolean that_present_boolColumn = true;
+    if (this_present_boolColumn || that_present_boolColumn) {
+      if (!(this_present_boolColumn && that_present_boolColumn))
+        return false;
+      if (this.boolColumn != that.boolColumn)
+        return false;
+    }
+
+    boolean this_present_byteColumn = true;
+    boolean that_present_byteColumn = true;
+    if (this_present_byteColumn || that_present_byteColumn) {
+      if (!(this_present_byteColumn && that_present_byteColumn))
+        return false;
+      if (this.byteColumn != that.byteColumn)
+        return false;
+    }
+
+    boolean this_present_shortColumn = true;
+    boolean that_present_shortColumn = true;
+    if (this_present_shortColumn || that_present_shortColumn) {
+      if (!(this_present_shortColumn && that_present_shortColumn))
+        return false;
+      if (this.shortColumn != that.shortColumn)
+        return false;
+    }
+
+    boolean this_present_intColumn = true;
+    boolean that_present_intColumn = true;
+    if (this_present_intColumn || that_present_intColumn) {
+      if (!(this_present_intColumn && that_present_intColumn))
+        return false;
+      if (this.intColumn != that.intColumn)
+        return false;
+    }
+
+    boolean this_present_longColumn = true;
+    boolean that_present_longColumn = true;
+    if (this_present_longColumn || that_present_longColumn) {
+      if (!(this_present_longColumn && that_present_longColumn))
+        return false;
+      if (this.longColumn != that.longColumn)
+        return false;
+    }
+
+    boolean this_present_doubleColumn = true;
+    boolean that_present_doubleColumn = true;
+    if (this_present_doubleColumn || that_present_doubleColumn) {
+      if (!(this_present_doubleColumn && that_present_doubleColumn))
+        return false;
+      if (this.doubleColumn != that.doubleColumn)
+        return false;
+    }
+
+    boolean this_present_binaryColumn = true && this.isSetBinaryColumn();
+    boolean that_present_binaryColumn = true && that.isSetBinaryColumn();
+    if (this_present_binaryColumn || that_present_binaryColumn) {
+      if (!(this_present_binaryColumn && that_present_binaryColumn))
+        return false;
+      if (!this.binaryColumn.equals(that.binaryColumn))
+        return false;
+    }
+
+    boolean this_present_stringColumn = true && this.isSetStringColumn();
+    boolean that_present_stringColumn = true && that.isSetStringColumn();
+    if (this_present_stringColumn || that_present_stringColumn) {
+      if (!(this_present_stringColumn && that_present_stringColumn))
+        return false;
+      if (!this.stringColumn.equals(that.stringColumn))
+        return false;
+    }
+
+    boolean this_present_enumColumn = true && this.isSetEnumColumn();
+    boolean that_present_enumColumn = true && that.isSetEnumColumn();
+    if (this_present_enumColumn || that_present_enumColumn) {
+      if (!(this_present_enumColumn && that_present_enumColumn))
+        return false;
+      if (!this.enumColumn.equals(that.enumColumn))
+        return false;
+    }
+
+    boolean this_present_maybeBoolColumn = true && this.isSetMaybeBoolColumn();
+    boolean that_present_maybeBoolColumn = true && that.isSetMaybeBoolColumn();
+    if (this_present_maybeBoolColumn || that_present_maybeBoolColumn) {
+      if (!(this_present_maybeBoolColumn && that_present_maybeBoolColumn))
+        return false;
+      if (this.maybeBoolColumn != that.maybeBoolColumn)
+        return false;
+    }
+
+    boolean this_present_maybeByteColumn = true && this.isSetMaybeByteColumn();
+    boolean that_present_maybeByteColumn = true && that.isSetMaybeByteColumn();
+    if (this_present_maybeByteColumn || that_present_maybeByteColumn) {
+      if (!(this_present_maybeByteColumn && that_present_maybeByteColumn))
+        return false;
+      if (this.maybeByteColumn != that.maybeByteColumn)
+        return false;
+    }
+
+    boolean this_present_maybeShortColumn = true && this.isSetMaybeShortColumn();
+    boolean that_present_maybeShortColumn = true && that.isSetMaybeShortColumn();
+    if (this_present_maybeShortColumn || that_present_maybeShortColumn) {
+      if (!(this_present_maybeShortColumn && that_present_maybeShortColumn))
+        return false;
+      if (this.maybeShortColumn != that.maybeShortColumn)
+        return false;
+    }
+
+    boolean this_present_maybeIntColumn = true && this.isSetMaybeIntColumn();
+    boolean that_present_maybeIntColumn = true && that.isSetMaybeIntColumn();
+    if (this_present_maybeIntColumn || that_present_maybeIntColumn) {
+      if (!(this_present_maybeIntColumn && that_present_maybeIntColumn))
+        return false;
+      if (this.maybeIntColumn != that.maybeIntColumn)
+        return false;
+    }
+
+    boolean this_present_maybeLongColumn = true && this.isSetMaybeLongColumn();
+    boolean that_present_maybeLongColumn = true && that.isSetMaybeLongColumn();
+    if (this_present_maybeLongColumn || that_present_maybeLongColumn) {
+      if (!(this_present_maybeLongColumn && that_present_maybeLongColumn))
+        return false;
+      if (this.maybeLongColumn != that.maybeLongColumn)
+        return false;
+    }
+
+    boolean this_present_maybeDoubleColumn = true && this.isSetMaybeDoubleColumn();
+    boolean that_present_maybeDoubleColumn = true && that.isSetMaybeDoubleColumn();
+    if (this_present_maybeDoubleColumn || that_present_maybeDoubleColumn) {
+      if (!(this_present_maybeDoubleColumn && that_present_maybeDoubleColumn))
+        return false;
+      if (this.maybeDoubleColumn != that.maybeDoubleColumn)
+        return false;
+    }
+
+    boolean this_present_maybeBinaryColumn = true && this.isSetMaybeBinaryColumn();
+    boolean that_present_maybeBinaryColumn = true && that.isSetMaybeBinaryColumn();
+    if (this_present_maybeBinaryColumn || that_present_maybeBinaryColumn) {
+      if (!(this_present_maybeBinaryColumn && that_present_maybeBinaryColumn))
+        return false;
+      if (!this.maybeBinaryColumn.equals(that.maybeBinaryColumn))
+        return false;
+    }
+
+    boolean this_present_maybeStringColumn = true && this.isSetMaybeStringColumn();
+    boolean that_present_maybeStringColumn = true && that.isSetMaybeStringColumn();
+    if (this_present_maybeStringColumn || that_present_maybeStringColumn) {
+      if (!(this_present_maybeStringColumn && that_present_maybeStringColumn))
+        return false;
+      if (!this.maybeStringColumn.equals(that.maybeStringColumn))
+        return false;
+    }
+
+    boolean this_present_maybeEnumColumn = true && this.isSetMaybeEnumColumn();
+    boolean that_present_maybeEnumColumn = true && that.isSetMaybeEnumColumn();
+    if (this_present_maybeEnumColumn || that_present_maybeEnumColumn) {
+      if (!(this_present_maybeEnumColumn && that_present_maybeEnumColumn))
+        return false;
+      if (!this.maybeEnumColumn.equals(that.maybeEnumColumn))
+        return false;
+    }
+
+    boolean this_present_stringsColumn = true && this.isSetStringsColumn();
+    boolean that_present_stringsColumn = true && that.isSetStringsColumn();
+    if (this_present_stringsColumn || that_present_stringsColumn) {
+      if (!(this_present_stringsColumn && that_present_stringsColumn))
+        return false;
+      if (!this.stringsColumn.equals(that.stringsColumn))
+        return false;
+    }
+
+    boolean this_present_intSetColumn = true && this.isSetIntSetColumn();
+    boolean that_present_intSetColumn = true && that.isSetIntSetColumn();
+    if (this_present_intSetColumn || that_present_intSetColumn) {
+      if (!(this_present_intSetColumn && that_present_intSetColumn))
+        return false;
+      if (!this.intSetColumn.equals(that.intSetColumn))
+        return false;
+    }
+
+    boolean this_present_intToStringColumn = true && this.isSetIntToStringColumn();
+    boolean that_present_intToStringColumn = true && that.isSetIntToStringColumn();
+    if (this_present_intToStringColumn || that_present_intToStringColumn) {
+      if (!(this_present_intToStringColumn && that_present_intToStringColumn))
+        return false;
+      if (!this.intToStringColumn.equals(that.intToStringColumn))
+        return false;
+    }
+
+    boolean this_present_complexColumn = true && this.isSetComplexColumn();
+    boolean that_present_complexColumn = true && that.isSetComplexColumn();
+    if (this_present_complexColumn || that_present_complexColumn) {
+      if (!(this_present_complexColumn && that_present_complexColumn))
+        return false;
+      if (!this.complexColumn.equals(that.complexColumn))
+        return false;
+    }
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    List<Object> list = new ArrayList<Object>();
+
+    boolean present_boolColumn = true;
+    list.add(present_boolColumn);
+    if (present_boolColumn)
+      list.add(boolColumn);
+
+    boolean present_byteColumn = true;
+    list.add(present_byteColumn);
+    if (present_byteColumn)
+      list.add(byteColumn);
+
+    boolean present_shortColumn = true;
+    list.add(present_shortColumn);
+    if (present_shortColumn)
+      list.add(shortColumn);
+
+    boolean present_intColumn = true;
+    list.add(present_intColumn);
+    if (present_intColumn)
+      list.add(intColumn);
+
+    boolean present_longColumn = true;
+    list.add(present_longColumn);
+    if (present_longColumn)
+      list.add(longColumn);
+
+    boolean present_doubleColumn = true;
+    list.add(present_doubleColumn);
+    if (present_doubleColumn)
+      list.add(doubleColumn);
+
+    boolean present_binaryColumn = true && (isSetBinaryColumn());
+    list.add(present_binaryColumn);
+    if (present_binaryColumn)
+      list.add(binaryColumn);
+
+    boolean present_stringColumn = true && (isSetStringColumn());
+    list.add(present_stringColumn);
+    if (present_stringColumn)
+      list.add(stringColumn);
+
+    boolean present_enumColumn = true && (isSetEnumColumn());
+    list.add(present_enumColumn);
+    if (present_enumColumn)
+      list.add(enumColumn.getValue());
+
+    boolean present_maybeBoolColumn = true && (isSetMaybeBoolColumn());
+    list.add(present_maybeBoolColumn);
+    if (present_maybeBoolColumn)
+      list.add(maybeBoolColumn);
+
+    boolean present_maybeByteColumn = true && (isSetMaybeByteColumn());
+    list.add(present_maybeByteColumn);
+    if (present_maybeByteColumn)
+      list.add(maybeByteColumn);
+
+    boolean present_maybeShortColumn = true && (isSetMaybeShortColumn());
+    list.add(present_maybeShortColumn);
+    if (present_maybeShortColumn)
+      list.add(maybeShortColumn);
+
+    boolean present_maybeIntColumn = true && (isSetMaybeIntColumn());
+    list.add(present_maybeIntColumn);
+    if (present_maybeIntColumn)
+      list.add(maybeIntColumn);
+
+    boolean present_maybeLongColumn = true && (isSetMaybeLongColumn());
+    list.add(present_maybeLongColumn);
+    if (present_maybeLongColumn)
+      list.add(maybeLongColumn);
+
+    boolean present_maybeDoubleColumn = true && (isSetMaybeDoubleColumn());
+    list.add(present_maybeDoubleColumn);
+    if (present_maybeDoubleColumn)
+      list.add(maybeDoubleColumn);
+
+    boolean present_maybeBinaryColumn = true && (isSetMaybeBinaryColumn());
+    list.add(present_maybeBinaryColumn);
+    if (present_maybeBinaryColumn)
+      list.add(maybeBinaryColumn);
+
+    boolean present_maybeStringColumn = true && (isSetMaybeStringColumn());
+    list.add(present_maybeStringColumn);
+    if (present_maybeStringColumn)
+      list.add(maybeStringColumn);
+
+    boolean present_maybeEnumColumn = true && (isSetMaybeEnumColumn());
+    list.add(present_maybeEnumColumn);
+    if (present_maybeEnumColumn)
+      list.add(maybeEnumColumn.getValue());
+
+    boolean present_stringsColumn = true && (isSetStringsColumn());
+    list.add(present_stringsColumn);
+    if (present_stringsColumn)
+      list.add(stringsColumn);
+
+    boolean present_intSetColumn = true && (isSetIntSetColumn());
+    list.add(present_intSetColumn);
+    if (present_intSetColumn)
+      list.add(intSetColumn);
+
+    boolean present_intToStringColumn = true && (isSetIntToStringColumn());
+    list.add(present_intToStringColumn);
+    if (present_intToStringColumn)
+      list.add(intToStringColumn);
+
+    boolean present_complexColumn = true && (isSetComplexColumn());
+    list.add(present_complexColumn);
+    if (present_complexColumn)
+      list.add(complexColumn);
+
+    return list.hashCode();
+  }
+
+  @Override
+  public int compareTo(ParquetThriftCompat other) {
+    if (!getClass().equals(other.getClass())) {
+      return getClass().getName().compareTo(other.getClass().getName());
+    }
+
+    int lastComparison = 0;
+
+    lastComparison = Boolean.valueOf(isSetBoolColumn()).compareTo(other.isSetBoolColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetBoolColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.boolColumn, other.boolColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetByteColumn()).compareTo(other.isSetByteColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetByteColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.byteColumn, other.byteColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetShortColumn()).compareTo(other.isSetShortColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetShortColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.shortColumn, other.shortColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetIntColumn()).compareTo(other.isSetIntColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetIntColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.intColumn, other.intColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetLongColumn()).compareTo(other.isSetLongColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetLongColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.longColumn, other.longColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetDoubleColumn()).compareTo(other.isSetDoubleColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetDoubleColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.doubleColumn, other.doubleColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetBinaryColumn()).compareTo(other.isSetBinaryColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetBinaryColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.binaryColumn, other.binaryColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetStringColumn()).compareTo(other.isSetStringColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetStringColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.stringColumn, other.stringColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetEnumColumn()).compareTo(other.isSetEnumColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetEnumColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.enumColumn, other.enumColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeBoolColumn()).compareTo(other.isSetMaybeBoolColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeBoolColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeBoolColumn, other.maybeBoolColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeByteColumn()).compareTo(other.isSetMaybeByteColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeByteColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeByteColumn, other.maybeByteColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeShortColumn()).compareTo(other.isSetMaybeShortColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeShortColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeShortColumn, other.maybeShortColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeIntColumn()).compareTo(other.isSetMaybeIntColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeIntColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeIntColumn, other.maybeIntColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeLongColumn()).compareTo(other.isSetMaybeLongColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeLongColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeLongColumn, other.maybeLongColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeDoubleColumn()).compareTo(other.isSetMaybeDoubleColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeDoubleColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeDoubleColumn, other.maybeDoubleColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeBinaryColumn()).compareTo(other.isSetMaybeBinaryColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeBinaryColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeBinaryColumn, other.maybeBinaryColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeStringColumn()).compareTo(other.isSetMaybeStringColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeStringColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeStringColumn, other.maybeStringColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMaybeEnumColumn()).compareTo(other.isSetMaybeEnumColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMaybeEnumColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeEnumColumn, other.maybeEnumColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetStringsColumn()).compareTo(other.isSetStringsColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetStringsColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.stringsColumn, other.stringsColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetIntSetColumn()).compareTo(other.isSetIntSetColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetIntSetColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.intSetColumn, other.intSetColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetIntToStringColumn()).compareTo(other.isSetIntToStringColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetIntToStringColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.intToStringColumn, other.intToStringColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetComplexColumn()).compareTo(other.isSetComplexColumn());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetComplexColumn()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.complexColumn, other.complexColumn);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    return 0;
+  }
+
+  public _Fields fieldForId(int fieldId) {
+    return _Fields.findByThriftId(fieldId);
+  }
+
+  public void read(org.apache.thrift.protocol.TProtocol iprot) throws org.apache.thrift.TException {
+    schemes.get(iprot.getScheme()).getScheme().read(iprot, this);
+  }
+
+  public void write(org.apache.thrift.protocol.TProtocol oprot) throws org.apache.thrift.TException {
+    schemes.get(oprot.getScheme()).getScheme().write(oprot, this);
+  }
+
+  @Override
+  public String toString() {
+    StringBuilder sb = new StringBuilder("ParquetThriftCompat(");
+    boolean first = true;
+
+    sb.append("boolColumn:");
+    sb.append(this.boolColumn);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("byteColumn:");
+    sb.append(this.byteColumn);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("shortColumn:");
+    sb.append(this.shortColumn);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("intColumn:");
+    sb.append(this.intColumn);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("longColumn:");
+    sb.append(this.longColumn);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("doubleColumn:");
+    sb.append(this.doubleColumn);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("binaryColumn:");
+    if (this.binaryColumn == null) {
+      sb.append("null");
+    } else {
+      org.apache.thrift.TBaseHelper.toString(this.binaryColumn, sb);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("stringColumn:");
+    if (this.stringColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.stringColumn);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("enumColumn:");
+    if (this.enumColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.enumColumn);
+    }
+    first = false;
+    if (isSetMaybeBoolColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeBoolColumn:");
+      sb.append(this.maybeBoolColumn);
+      first = false;
+    }
+    if (isSetMaybeByteColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeByteColumn:");
+      sb.append(this.maybeByteColumn);
+      first = false;
+    }
+    if (isSetMaybeShortColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeShortColumn:");
+      sb.append(this.maybeShortColumn);
+      first = false;
+    }
+    if (isSetMaybeIntColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeIntColumn:");
+      sb.append(this.maybeIntColumn);
+      first = false;
+    }
+    if (isSetMaybeLongColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeLongColumn:");
+      sb.append(this.maybeLongColumn);
+      first = false;
+    }
+    if (isSetMaybeDoubleColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeDoubleColumn:");
+      sb.append(this.maybeDoubleColumn);
+      first = false;
+    }
+    if (isSetMaybeBinaryColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeBinaryColumn:");
+      if (this.maybeBinaryColumn == null) {
+        sb.append("null");
+      } else {
+        org.apache.thrift.TBaseHelper.toString(this.maybeBinaryColumn, sb);
+      }
+      first = false;
+    }
+    if (isSetMaybeStringColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeStringColumn:");
+      if (this.maybeStringColumn == null) {
+        sb.append("null");
+      } else {
+        sb.append(this.maybeStringColumn);
+      }
+      first = false;
+    }
+    if (isSetMaybeEnumColumn()) {
+      if (!first) sb.append(", ");
+      sb.append("maybeEnumColumn:");
+      if (this.maybeEnumColumn == null) {
+        sb.append("null");
+      } else {
+        sb.append(this.maybeEnumColumn);
+      }
+      first = false;
+    }
+    if (!first) sb.append(", ");
+    sb.append("stringsColumn:");
+    if (this.stringsColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.stringsColumn);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("intSetColumn:");
+    if (this.intSetColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.intSetColumn);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("intToStringColumn:");
+    if (this.intToStringColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.intToStringColumn);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("complexColumn:");
+    if (this.complexColumn == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.complexColumn);
+    }
+    first = false;
+    sb.append(")");
+    return sb.toString();
+  }
+
+  public void validate() throws org.apache.thrift.TException {
+    // check for required fields
+    // alas, we cannot check 'boolColumn' because it's a primitive and you chose the non-beans generator.
+    // alas, we cannot check 'byteColumn' because it's a primitive and you chose the non-beans generator.
+    // alas, we cannot check 'shortColumn' because it's a primitive and you chose the non-beans generator.
+    // alas, we cannot check 'intColumn' because it's a primitive and you chose the non-beans generator.
+    // alas, we cannot check 'longColumn' because it's a primitive and you chose the non-beans generator.
+    // alas, we cannot check 'doubleColumn' because it's a primitive and you chose the non-beans generator.
+    if (binaryColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'binaryColumn' was not present! Struct: " + toString());
+    }
+    if (stringColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'stringColumn' was not present! Struct: " + toString());
+    }
+    if (enumColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'enumColumn' was not present! Struct: " + toString());
+    }
+    if (stringsColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'stringsColumn' was not present! Struct: " + toString());
+    }
+    if (intSetColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'intSetColumn' was not present! Struct: " + toString());
+    }
+    if (intToStringColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'intToStringColumn' was not present! Struct: " + toString());
+    }
+    if (complexColumn == null) {
+      throw new org.apache.thrift.protocol.TProtocolException("Required field 'complexColumn' was not present! Struct: " + toString());
+    }
+    // check for sub-struct validity
+  }
+
+  private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException {
+    try {
+      write(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(out)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, ClassNotFoundException {
+    try {
+      // it doesn't seem like you should have to do this, but java serialization is wacky, and doesn't call the default constructor.
+      __isset_bitfield = 0;
+      read(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(in)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private static class ParquetThriftCompatStandardSchemeFactory implements SchemeFactory {
+    public ParquetThriftCompatStandardScheme getScheme() {
+      return new ParquetThriftCompatStandardScheme();
+    }
+  }
+
+  private static class ParquetThriftCompatStandardScheme extends StandardScheme<ParquetThriftCompat> {
+
+    public void read(org.apache.thrift.protocol.TProtocol iprot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
+      org.apache.thrift.protocol.TField schemeField;
+      iprot.readStructBegin();
+      while (true)
+      {
+        schemeField = iprot.readFieldBegin();
+        if (schemeField.type == org.apache.thrift.protocol.TType.STOP) { 
+          break;
+        }
+        switch (schemeField.id) {
+          case 1: // BOOL_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.BOOL) {
+              struct.boolColumn = iprot.readBool();
+              struct.setBoolColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 2: // BYTE_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.BYTE) {
+              struct.byteColumn = iprot.readByte();
+              struct.setByteColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 3: // SHORT_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I16) {
+              struct.shortColumn = iprot.readI16();
+              struct.setShortColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 4: // INT_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
+              struct.intColumn = iprot.readI32();
+              struct.setIntColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 5: // LONG_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I64) {
+              struct.longColumn = iprot.readI64();
+              struct.setLongColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 6: // DOUBLE_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.DOUBLE) {
+              struct.doubleColumn = iprot.readDouble();
+              struct.setDoubleColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 7: // BINARY_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
+              struct.binaryColumn = iprot.readBinary();
+              struct.setBinaryColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 8: // STRING_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
+              struct.stringColumn = iprot.readString();
+              struct.setStringColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 9: // ENUM_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
+              struct.enumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
+              struct.setEnumColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 10: // MAYBE_BOOL_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.BOOL) {
+              struct.maybeBoolColumn = iprot.readBool();
+              struct.setMaybeBoolColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 11: // MAYBE_BYTE_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.BYTE) {
+              struct.maybeByteColumn = iprot.readByte();
+              struct.setMaybeByteColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 12: // MAYBE_SHORT_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I16) {
+              struct.maybeShortColumn = iprot.readI16();
+              struct.setMaybeShortColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 13: // MAYBE_INT_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
+              struct.maybeIntColumn = iprot.readI32();
+              struct.setMaybeIntColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 14: // MAYBE_LONG_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I64) {
+              struct.maybeLongColumn = iprot.readI64();
+              struct.setMaybeLongColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 15: // MAYBE_DOUBLE_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.DOUBLE) {
+              struct.maybeDoubleColumn = iprot.readDouble();
+              struct.setMaybeDoubleColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 16: // MAYBE_BINARY_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
+              struct.maybeBinaryColumn = iprot.readBinary();
+              struct.setMaybeBinaryColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 17: // MAYBE_STRING_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
+              struct.maybeStringColumn = iprot.readString();
+              struct.setMaybeStringColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 18: // MAYBE_ENUM_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
+              struct.maybeEnumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
+              struct.setMaybeEnumColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 19: // STRINGS_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list8 = iprot.readListBegin();
+                struct.stringsColumn = new ArrayList<String>(_list8.size);
+                String _elem9;
+                for (int _i10 = 0; _i10 < _list8.size; ++_i10)
+                {
+                  _elem9 = iprot.readString();
+                  struct.stringsColumn.add(_elem9);
+                }
+                iprot.readListEnd();
+              }
+              struct.setStringsColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 20: // INT_SET_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.SET) {
+              {
+                org.apache.thrift.protocol.TSet _set11 = iprot.readSetBegin();
+                struct.intSetColumn = new HashSet<Integer>(2*_set11.size);
+                int _elem12;
+                for (int _i13 = 0; _i13 < _set11.size; ++_i13)
+                {
+                  _elem12 = iprot.readI32();
+                  struct.intSetColumn.add(_elem12);
+                }
+                iprot.readSetEnd();
+              }
+              struct.setIntSetColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 21: // INT_TO_STRING_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.MAP) {
+              {
+                org.apache.thrift.protocol.TMap _map14 = iprot.readMapBegin();
+                struct.intToStringColumn = new HashMap<Integer,String>(2*_map14.size);
+                int _key15;
+                String _val16;
+                for (int _i17 = 0; _i17 < _map14.size; ++_i17)
+                {
+                  _key15 = iprot.readI32();
+                  _val16 = iprot.readString();
+                  struct.intToStringColumn.put(_key15, _val16);
+                }
+                iprot.readMapEnd();
+              }
+              struct.setIntToStringColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 22: // COMPLEX_COLUMN
+            if (schemeField.type == org.apache.thrift.protocol.TType.MAP) {
+              {
+                org.apache.thrift.protocol.TMap _map18 = iprot.readMapBegin();
+                struct.complexColumn = new HashMap<Integer,List<Nested>>(2*_map18.size);
+                int _key19;
+                List<Nested> _val20;
+                for (int _i21 = 0; _i21 < _map18.size; ++_i21)
+                {
+                  _key19 = iprot.readI32();
+                  {
+                    org.apache.thrift.protocol.TList _list22 = iprot.readListBegin();
+                    _val20 = new ArrayList<Nested>(_list22.size);
+                    Nested _elem23;
+                    for (int _i24 = 0; _i24 < _list22.size; ++_i24)
+                    {
+                      _elem23 = new Nested();
+                      _elem23.read(iprot);
+                      _val20.add(_elem23);
+                    }
+                    iprot.readListEnd();
+                  }
+                  struct.complexColumn.put(_key19, _val20);
+                }
+                iprot.readMapEnd();
+              }
+              struct.setComplexColumnIsSet(true);
+            } else { 
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          default:
+            org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+        }
+        iprot.readFieldEnd();
+      }
+      iprot.readStructEnd();
+
+      // check for required fields of primitive type, which can't be checked in the validate method
+      if (!struct.isSetBoolColumn()) {
+        throw new org.apache.thrift.protocol.TProtocolException("Required field 'boolColumn' was not found in serialized data! Struct: " + toString());
+      }
+      if (!struct.isSetByteColumn()) {
+        throw new org.apache.thrift.protocol.TProtocolException("Required field 'byteColumn' was not found in serialized data! Struct: " + toString());
+      }
+      if (!struct.isSetShortColumn()) {
+        throw new org.apache.thrift.protocol.TProtocolException("Required field 'shortColumn' was not found in serialized data! Struct: " + toString());
+      }
+      if (!struct.isSetIntColumn()) {
+        throw new org.apache.thrift.protocol.TProtocolException("Required field 'intColumn' was not found in serialized data! Struct: " + toString());
+      }
+      if (!struct.isSetLongColumn()) {
+        throw new org.apache.thrift.protocol.TProtocolException("Required field 'longColumn' was not found in serialized data! Struct: " + toString());
+      }
+      if (!struct.isSetDoubleColumn()) {
+        throw new org.apache.thrift.protocol.TProtocolException("Required field 'doubleColumn' was not found in serialized data! Struct: " + toString());
+      }
+      struct.validate();
+    }
+
+    public void write(org.apache.thrift.protocol.TProtocol oprot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
+      struct.validate();
+
+      oprot.writeStructBegin(STRUCT_DESC);
+      oprot.writeFieldBegin(BOOL_COLUMN_FIELD_DESC);
+      oprot.writeBool(struct.boolColumn);
+      oprot.writeFieldEnd();
+      oprot.writeFieldBegin(BYTE_COLUMN_FIELD_DESC);
+      oprot.writeByte(struct.byteColumn);
+      oprot.writeFieldEnd();
+      oprot.writeFieldBegin(SHORT_COLUMN_FIELD_DESC);
+      oprot.writeI16(struct.shortColumn);
+      oprot.writeFieldEnd();
+      oprot.writeFieldBegin(INT_COLUMN_FIELD_DESC);
+      oprot.writeI32(struct.intColumn);
+      oprot.writeFieldEnd();
+      oprot.writeFieldBegin(LONG_COLUMN_FIELD_DESC);
+      oprot.writeI64(struct.longColumn);
+      oprot.writeFieldEnd();
+      oprot.writeFieldBegin(DOUBLE_COLUMN_FIELD_DESC);
+      oprot.writeDouble(struct.doubleColumn);
+      oprot.writeFieldEnd();
+      if (struct.binaryColumn != null) {
+        oprot.writeFieldBegin(BINARY_COLUMN_FIELD_DESC);
+        oprot.writeBinary(struct.binaryColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.stringColumn != null) {
+        oprot.writeFieldBegin(STRING_COLUMN_FIELD_DESC);
+        oprot.writeString(struct.stringColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.enumColumn != null) {
+        oprot.writeFieldBegin(ENUM_COLUMN_FIELD_DESC);
+        oprot.writeI32(struct.enumColumn.getValue());
+        oprot.writeFieldEnd();
+      }
+      if (struct.isSetMaybeBoolColumn()) {
+        oprot.writeFieldBegin(MAYBE_BOOL_COLUMN_FIELD_DESC);
+        oprot.writeBool(struct.maybeBoolColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.isSetMaybeByteColumn()) {
+        oprot.writeFieldBegin(MAYBE_BYTE_COLUMN_FIELD_DESC);
+        oprot.writeByte(struct.maybeByteColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.isSetMaybeShortColumn()) {
+        oprot.writeFieldBegin(MAYBE_SHORT_COLUMN_FIELD_DESC);
+        oprot.writeI16(struct.maybeShortColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.isSetMaybeIntColumn()) {
+        oprot.writeFieldBegin(MAYBE_INT_COLUMN_FIELD_DESC);
+        oprot.writeI32(struct.maybeIntColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.isSetMaybeLongColumn()) {
+        oprot.writeFieldBegin(MAYBE_LONG_COLUMN_FIELD_DESC);
+        oprot.writeI64(struct.maybeLongColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.isSetMaybeDoubleColumn()) {
+        oprot.writeFieldBegin(MAYBE_DOUBLE_COLUMN_FIELD_DESC);
+        oprot.writeDouble(struct.maybeDoubleColumn);
+        oprot.writeFieldEnd();
+      }
+      if (struct.maybeBinaryColumn != null) {
+        if (struct.isSetMaybeBinaryColumn()) {
+          oprot.writeFieldBegin(MAYBE_BINARY_COLUMN_FIELD_DESC);
+          oprot.writeBinary(struct.maybeBinaryColumn);
+          oprot.writeFieldEnd();
+        }
+      }
+      if (struct.maybeStringColumn != null) {
+        if (struct.isSetMaybeStringColumn()) {
+          oprot.writeFieldBegin(MAYBE_STRING_COLUMN_FIELD_DESC);
+          oprot.writeString(struct.maybeStringColumn);
+          oprot.writeFieldEnd();
+        }
+      }
+      if (struct.maybeEnumColumn != null) {
+        if (struct.isSetMaybeEnumColumn()) {
+          oprot.writeFieldBegin(MAYBE_ENUM_COLUMN_FIELD_DESC);
+          oprot.writeI32(struct.maybeEnumColumn.getValue());
+          oprot.writeFieldEnd();
+        }
+      }
+      if (struct.stringsColumn != null) {
+        oprot.writeFieldBegin(STRINGS_COLUMN_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, struct.stringsColumn.size()));
+          for (String _iter25 : struct.stringsColumn)
+          {
+            oprot.writeString(_iter25);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.intSetColumn != null) {
+        oprot.writeFieldBegin(INT_SET_COLUMN_FIELD_DESC);
+        {
+          oprot.writeSetBegin(new org.apache.thrift.protocol.TSet(org.apache.thrift.protocol.TType.I32, struct.intSetColumn.size()));
+          for (int _iter26 : struct.intSetColumn)
+          {
+            oprot.writeI32(_iter26);
+          }
+          oprot.writeSetEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.intToStringColumn != null) {
+        oprot.writeFieldBegin(INT_TO_STRING_COLUMN_FIELD_DESC);
+        {
+          oprot.writeMapBegin(new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.STRING, struct.intToStringColumn.size()));
+          for (Map.Entry<Integer, String> _iter27 : struct.intToStringColumn.entrySet())
+          {
+            oprot.writeI32(_iter27.getKey());
+            oprot.writeString(_iter27.getValue());
+          }
+          oprot.writeMapEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.complexColumn != null) {
+        oprot.writeFieldBegin(COMPLEX_COLUMN_FIELD_DESC);
+        {
+          oprot.writeMapBegin(new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.LIST, struct.complexColumn.size()));
+          for (Map.Entry<Integer, List<Nested>> _iter28 : struct.complexColumn.entrySet())
+          {
+            oprot.writeI32(_iter28.getKey());
+            {
+              oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, _iter28.getValue().size()));
+              for (Nested _iter29 : _iter28.getValue())
+              {
+                _iter29.write(oprot);
+              }
+              oprot.writeListEnd();
+            }
+          }
+          oprot.writeMapEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      oprot.writeFieldStop();
+      oprot.writeStructEnd();
+    }
+
+  }
+
+  private static class ParquetThriftCompatTupleSchemeFactory implements SchemeFactory {
+    public ParquetThriftCompatTupleScheme getScheme() {
+      return new ParquetThriftCompatTupleScheme();
+    }
+  }
+
+  private static class ParquetThriftCompatTupleScheme extends TupleScheme<ParquetThriftCompat> {
+
+    @Override
+    public void write(org.apache.thrift.protocol.TProtocol prot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
+      TTupleProtocol oprot = (TTupleProtocol) prot;
+      oprot.writeBool(struct.boolColumn);
+      oprot.writeByte(struct.byteColumn);
+      oprot.writeI16(struct.shortColumn);
+      oprot.writeI32(struct.intColumn);
+      oprot.writeI64(struct.longColumn);
+      oprot.writeDouble(struct.doubleColumn);
+      oprot.writeBinary(struct.binaryColumn);
+      oprot.writeString(struct.stringColumn);
+      oprot.writeI32(struct.enumColumn.getValue());
+      {
+        oprot.writeI32(struct.stringsColumn.size());
+        for (String _iter30 : struct.stringsColumn)
+        {
+          oprot.writeString(_iter30);
+        }
+      }
+      {
+        oprot.writeI32(struct.intSetColumn.size());
+        for (int _iter31 : struct.intSetColumn)
+        {
+          oprot.writeI32(_iter31);
+        }
+      }
+      {
+        oprot.writeI32(struct.intToStringColumn.size());
+        for (Map.Entry<Integer, String> _iter32 : struct.intToStringColumn.entrySet())
+        {
+          oprot.writeI32(_iter32.getKey());
+          oprot.writeString(_iter32.getValue());
+        }
+      }
+      {
+        oprot.writeI32(struct.complexColumn.size());
+        for (Map.Entry<Integer, List<Nested>> _iter33 : struct.complexColumn.entrySet())
+        {
+          oprot.writeI32(_iter33.getKey());
+          {
+            oprot.writeI32(_iter33.getValue().size());
+            for (Nested _iter34 : _iter33.getValue())
+            {
+              _iter34.write(oprot);
+            }
+          }
+        }
+      }
+      BitSet optionals = new BitSet();
+      if (struct.isSetMaybeBoolColumn()) {
+        optionals.set(0);
+      }
+      if (struct.isSetMaybeByteColumn()) {
+        optionals.set(1);
+      }
+      if (struct.isSetMaybeShortColumn()) {
+        optionals.set(2);
+      }
+      if (struct.isSetMaybeIntColumn()) {
+        optionals.set(3);
+      }
+      if (struct.isSetMaybeLongColumn()) {
+        optionals.set(4);
+      }
+      if (struct.isSetMaybeDoubleColumn()) {
+        optionals.set(5);
+      }
+      if (struct.isSetMaybeBinaryColumn()) {
+        optionals.set(6);
+      }
+      if (struct.isSetMaybeStringColumn()) {
+        optionals.set(7);
+      }
+      if (struct.isSetMaybeEnumColumn()) {
+        optionals.set(8);
+      }
+      oprot.writeBitSet(optionals, 9);
+      if (struct.isSetMaybeBoolColumn()) {
+        oprot.writeBool(struct.maybeBoolColumn);
+      }
+      if (struct.isSetMaybeByteColumn()) {
+        oprot.writeByte(struct.maybeByteColumn);
+      }
+      if (struct.isSetMaybeShortColumn()) {
+        oprot.writeI16(struct.maybeShortColumn);
+      }
+      if (struct.isSetMaybeIntColumn()) {
+        oprot.writeI32(struct.maybeIntColumn);
+      }
+      if (struct.isSetMaybeLongColumn()) {
+        oprot.writeI64(struct.maybeLongColumn);
+      }
+      if (struct.isSetMaybeDoubleColumn()) {
+        oprot.writeDouble(struct.maybeDoubleColumn);
+      }
+      if (struct.isSetMaybeBinaryColumn()) {
+        oprot.writeBinary(struct.maybeBinaryColumn);
+      }
+      if (struct.isSetMaybeStringColumn()) {
+        oprot.writeString(struct.maybeStringColumn);
+      }
+      if (struct.isSetMaybeEnumColumn()) {
+        oprot.writeI32(struct.maybeEnumColumn.getValue());
+      }
+    }
+
+    @Override
+    public void read(org.apache.thrift.protocol.TProtocol prot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
+      TTupleProtocol iprot = (TTupleProtocol) prot;
+      struct.boolColumn = iprot.readBool();
+      struct.setBoolColumnIsSet(true);
+      struct.byteColumn = iprot.readByte();
+      struct.setByteColumnIsSet(true);
+      struct.shortColumn = iprot.readI16();
+      struct.setShortColumnIsSet(true);
+      struct.intColumn = iprot.readI32();
+      struct.setIntColumnIsSet(true);
+      struct.longColumn = iprot.readI64();
+      struct.setLongColumnIsSet(true);
+      struct.doubleColumn = iprot.readDouble();
+      struct.setDoubleColumnIsSet(true);
+      struct.binaryColumn = iprot.readBinary();
+      struct.setBinaryColumnIsSet(true);
+      struct.stringColumn = iprot.readString();
+      struct.setStringColumnIsSet(true);
+      struct.enumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
+      struct.setEnumColumnIsSet(true);
+      {
+        org.apache.thrift.protocol.TList _list35 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, iprot.readI32());
+        struct.stringsColumn = new ArrayList<String>(_list35.size);
+        String _elem36;
+        for (int _i37 = 0; _i37 < _list35.size; ++_i37)
+        {
+          _elem36 = iprot.readString();
+          struct.stringsColumn.add(_elem36);
+        }
+      }
+      struct.setStringsColumnIsSet(true);
+      {
+        org.apache.thrift.protocol.TSet _set38 = new org.apache.thrift.protocol.TSet(org.apache.thrift.protocol.TType.I32, iprot.readI32());
+        struct.intSetColumn = new HashSet<Integer>(2*_set38.size);
+        int _elem39;
+        for (int _i40 = 0; _i40 < _set38.size; ++_i40)
+        {
+          _elem39 = iprot.readI32();
+          struct.intSetColumn.add(_elem39);
+        }
+      }
+      struct.setIntSetColumnIsSet(true);
+      {
+        org.apache.thrift.protocol.TMap _map41 = new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.STRING, iprot.readI32());
+        struct.intToStringColumn = new HashMap<Integer,String>(2*_map41.size);
+        int _key42;
+        String _val43;
+        for (int _i44 = 0; _i44 < _map41.size; ++_i44)
+        {
+          _key42 = iprot.readI32();
+          _val43 = iprot.readString();
+          struct.intToStringColumn.put(_key42, _val43);
+        }
+      }
+      struct.setIntToStringColumnIsSet(true);
+      {
+        org.apache.thrift.protocol.TMap _map45 = new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.LIST, iprot.readI32());
+        struct.complexColumn = new HashMap<Integer,List<Nested>>(2*_map45.size);
+        int _key46;
+        List<Nested> _val47;
+        for (int _i48 = 0; _i48 < _map45.size; ++_i48)
+        {
+          _key46 = iprot.readI32();
+          {
+            org.apache.thrift.protocol.TList _list49 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, iprot.readI32());
+            _val47 = new ArrayList<Nested>(_list49.size);
+            Nested _elem50;
+            for (int _i51 = 0; _i51 < _list49.size; ++_i51)
+            {
+              _elem50 = new Nested();
+              _elem50.read(iprot);
+              _val47.add(_elem50);
+            }
+          }
+          struct.complexColumn.put(_key46, _val47);
+        }
+      }
+      struct.setComplexColumnIsSet(true);
+      BitSet incoming = iprot.readBitSet(9);
+      if (incoming.get(0)) {
+        struct.maybeBoolColumn = iprot.readBool();
+        struct.setMaybeBoolColumnIsSet(true);
+      }
+      if (incoming.get(1)) {
+        struct.maybeByteColumn = iprot.readByte();
+        struct.setMaybeByteColumnIsSet(true);
+      }
+      if (incoming.get(2)) {
+        struct.maybeShortColumn = iprot.readI16();
+        struct.setMaybeShortColumnIsSet(true);
+      }
+      if (incoming.get(3)) {
+        struct.maybeIntColumn = iprot.readI32();
+        struct.setMaybeIntColumnIsSet(true);
+      }
+      if (incoming.get(4)) {
+        struct.maybeLongColumn = iprot.readI64();
+        struct.setMaybeLongColumnIsSet(true);
+      }
+      if (incoming.get(5)) {
+        struct.maybeDoubleColumn = iprot.readDouble();
+        struct.setMaybeDoubleColumnIsSet(true);
+      }
+      if (incoming.get(6)) {
+        struct.maybeBinaryColumn = iprot.readBinary();
+        struct.setMaybeBinaryColumnIsSet(true);
+      }
+      if (incoming.get(7)) {
+        struct.maybeStringColumn = iprot.readString();
+        struct.setMaybeStringColumnIsSet(true);
+      }
+      if (incoming.get(8)) {
+        struct.maybeEnumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
+        struct.setMaybeEnumColumnIsSet(true);
+      }
+    }
+  }
+
+}
+
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java
new file mode 100644
index 0000000000000..5315c6aae9372
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java
@@ -0,0 +1,51 @@
+/**
+ * Autogenerated by Thrift Compiler (0.9.2)
+ *
+ * DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
+ *  @generated
+ */
+package org.apache.spark.sql.parquet.test.thrift;
+
+
+import java.util.Map;
+import java.util.HashMap;
+import org.apache.thrift.TEnum;
+
+public enum Suit implements org.apache.thrift.TEnum {
+  SPADES(0),
+  HEARTS(1),
+  DIAMONDS(2),
+  CLUBS(3);
+
+  private final int value;
+
+  private Suit(int value) {
+    this.value = value;
+  }
+
+  /**
+   * Get the integer value of this enum value, as defined in the Thrift IDL.
+   */
+  public int getValue() {
+    return value;
+  }
+
+  /**
+   * Find a the enum type by its integer value, as defined in the Thrift IDL.
+   * @return null if the value is not found.
+   */
+  public static Suit findByValue(int value) { 
+    switch (value) {
+      case 0:
+        return SPADES;
+      case 1:
+        return HEARTS;
+      case 2:
+        return DIAMONDS;
+      case 3:
+        return CLUBS;
+      default:
+        return null;
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala
new file mode 100644
index 0000000000000..bfa427349ff6a
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import java.nio.ByteBuffer
+import java.util.{List => JList, Map => JMap}
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.fs.Path
+import org.apache.parquet.avro.AvroParquetWriter
+
+import org.apache.spark.sql.parquet.test.avro.{Nested, ParquetAvroCompat}
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.{Row, SQLContext}
+
+class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
+  import ParquetCompatibilityTest._
+
+  override val sqlContext: SQLContext = TestSQLContext
+
+  override protected def beforeAll(): Unit = {
+    super.beforeAll()
+
+    val writer =
+      new AvroParquetWriter[ParquetAvroCompat](
+        new Path(parquetStore.getCanonicalPath),
+        ParquetAvroCompat.getClassSchema)
+
+    (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
+    writer.close()
+  }
+
+  test("Read Parquet file generated by parquet-avro") {
+    logInfo(
+      s"""Schema of the Parquet file written by parquet-avro:
+         |${readParquetSchema(parquetStore.getCanonicalPath)}
+       """.stripMargin)
+
+    checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), (0 until 10).map { i =>
+      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
+
+      Row(
+        i % 2 == 0,
+        i,
+        i.toLong * 10,
+        i.toFloat + 0.1f,
+        i.toDouble + 0.2d,
+        s"val_$i".getBytes,
+        s"val_$i",
+
+        nullable(i % 2 == 0: java.lang.Boolean),
+        nullable(i: Integer),
+        nullable(i.toLong: java.lang.Long),
+        nullable(i.toFloat + 0.1f: java.lang.Float),
+        nullable(i.toDouble + 0.2d: java.lang.Double),
+        nullable(s"val_$i".getBytes),
+        nullable(s"val_$i"),
+
+        Seq.tabulate(3)(n => s"arr_${i + n}"),
+        Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap,
+        Seq.tabulate(3) { n =>
+          (i + n).toString -> Seq.tabulate(3) { m =>
+            Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
+          }
+        }.toMap)
+    })
+  }
+
+  def makeParquetAvroCompat(i: Int): ParquetAvroCompat = {
+    def nullable[T <: AnyRef] = makeNullable[T](i) _
+
+    def makeComplexColumn(i: Int): JMap[String, JList[Nested]] = {
+      mapAsJavaMap(Seq.tabulate(3) { n =>
+        (i + n).toString -> seqAsJavaList(Seq.tabulate(3) { m =>
+          Nested
+            .newBuilder()
+            .setNestedIntsColumn(seqAsJavaList(Seq.tabulate(3)(j => i + j + m)))
+            .setNestedStringColumn(s"val_${i + m}")
+            .build()
+        })
+      }.toMap)
+    }
+
+    ParquetAvroCompat
+      .newBuilder()
+      .setBoolColumn(i % 2 == 0)
+      .setIntColumn(i)
+      .setLongColumn(i.toLong * 10)
+      .setFloatColumn(i.toFloat + 0.1f)
+      .setDoubleColumn(i.toDouble + 0.2d)
+      .setBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes))
+      .setStringColumn(s"val_$i")
+
+      .setMaybeBoolColumn(nullable(i % 2 == 0: java.lang.Boolean))
+      .setMaybeIntColumn(nullable(i: Integer))
+      .setMaybeLongColumn(nullable(i.toLong: java.lang.Long))
+      .setMaybeFloatColumn(nullable(i.toFloat + 0.1f: java.lang.Float))
+      .setMaybeDoubleColumn(nullable(i.toDouble + 0.2d: java.lang.Double))
+      .setMaybeBinaryColumn(nullable(ByteBuffer.wrap(s"val_$i".getBytes)))
+      .setMaybeStringColumn(nullable(s"val_$i"))
+
+      .setStringsColumn(Seq.tabulate(3)(n => s"arr_${i + n}"))
+      .setStringToIntColumn(
+        mapAsJavaMap(Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap))
+      .setComplexColumn(makeComplexColumn(i))
+
+      .build()
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
new file mode 100644
index 0000000000000..b4cdfd9e98f6f
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+import java.io.File
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.fs.Path
+import org.apache.parquet.hadoop.ParquetFileReader
+import org.apache.parquet.schema.MessageType
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.sql.QueryTest
+import org.apache.spark.util.Utils
+
+abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with BeforeAndAfterAll {
+  protected var parquetStore: File = _
+
+  override protected def beforeAll(): Unit = {
+    parquetStore = Utils.createTempDir(namePrefix = "parquet-compat_")
+    parquetStore.delete()
+  }
+
+  override protected def afterAll(): Unit = {
+    Utils.deleteRecursively(parquetStore)
+  }
+
+  def readParquetSchema(path: String): MessageType = {
+    val fsPath = new Path(path)
+    val fs = fsPath.getFileSystem(configuration)
+    val parquetFiles = fs.listStatus(fsPath).toSeq.filterNot(_.getPath.getName.startsWith("_"))
+    val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
+    footers.head.getParquetMetadata.getFileMetaData.getSchema
+  }
+}
+
+object ParquetCompatibilityTest {
+  def makeNullable[T <: AnyRef](i: Int)(f: => T): T = {
+    if (i % 3 == 0) null.asInstanceOf[T] else f
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
new file mode 100644
index 0000000000000..d22066cabc567
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
@@ -0,0 +1,140 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import java.nio.ByteBuffer
+import java.util.{List => JList, Map => JMap}
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.fs.Path
+import org.apache.parquet.hadoop.metadata.CompressionCodecName
+import org.apache.parquet.thrift.ThriftParquetWriter
+
+import org.apache.spark.sql.parquet.test.thrift.{Nested, ParquetThriftCompat, Suit}
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.{Row, SQLContext}
+
+class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest {
+  import ParquetCompatibilityTest._
+
+  override val sqlContext: SQLContext = TestSQLContext
+
+  override protected def beforeAll(): Unit = {
+    super.beforeAll()
+
+    val writer =
+      new ThriftParquetWriter[ParquetThriftCompat](
+        new Path(parquetStore.getCanonicalPath),
+        classOf[ParquetThriftCompat],
+        CompressionCodecName.SNAPPY)
+
+    (0 until 10).foreach(i => writer.write(makeParquetThriftCompat(i)))
+    writer.close()
+  }
+
+  test("Read Parquet file generated by parquet-thrift") {
+    logInfo(
+      s"""Schema of the Parquet file written by parquet-thrift:
+         |${readParquetSchema(parquetStore.getCanonicalPath)}
+       """.stripMargin)
+
+    checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), (0 until 10).map { i =>
+      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
+
+      Row(
+        i % 2 == 0,
+        i.toByte,
+        (i + 1).toShort,
+        i + 2,
+        i.toLong * 10,
+        i.toDouble + 0.2d,
+        // Thrift `BINARY` values are actually unencoded `STRING` values, and thus are always
+        // treated as `BINARY (UTF8)` in parquet-thrift, since parquet-thrift always assume
+        // Thrift `STRING`s are encoded using UTF-8.
+        s"val_$i",
+        s"val_$i",
+        // Thrift ENUM values are converted to Parquet binaries containing UTF-8 strings
+        Suit.values()(i % 4).name(),
+
+        nullable(i % 2 == 0: java.lang.Boolean),
+        nullable(i.toByte: java.lang.Byte),
+        nullable((i + 1).toShort: java.lang.Short),
+        nullable(i + 2: Integer),
+        nullable((i * 10).toLong: java.lang.Long),
+        nullable(i.toDouble + 0.2d: java.lang.Double),
+        nullable(s"val_$i"),
+        nullable(s"val_$i"),
+        nullable(Suit.values()(i % 4).name()),
+
+        Seq.tabulate(3)(n => s"arr_${i + n}"),
+        // Thrift `SET`s are converted to Parquet `LIST`s
+        Seq(i),
+        Seq.tabulate(3)(n => (i + n: Integer) -> s"val_${i + n}").toMap,
+        Seq.tabulate(3) { n =>
+          (i + n) -> Seq.tabulate(3) { m =>
+            Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
+          }
+        }.toMap)
+    })
+  }
+
+  def makeParquetThriftCompat(i: Int): ParquetThriftCompat = {
+    def makeComplexColumn(i: Int): JMap[Integer, JList[Nested]] = {
+      mapAsJavaMap(Seq.tabulate(3) { n =>
+        (i + n: Integer) -> seqAsJavaList(Seq.tabulate(3) { m =>
+          new Nested(
+            seqAsJavaList(Seq.tabulate(3)(j => i + j + m)),
+            s"val_${i + m}")
+        })
+      }.toMap)
+    }
+
+    val value =
+      new ParquetThriftCompat(
+        i % 2 == 0,
+        i.toByte,
+        (i + 1).toShort,
+        i + 2,
+        i.toLong * 10,
+        i.toDouble + 0.2d,
+        ByteBuffer.wrap(s"val_$i".getBytes),
+        s"val_$i",
+        Suit.values()(i % 4),
+
+        seqAsJavaList(Seq.tabulate(3)(n => s"arr_${i + n}")),
+        setAsJavaSet(Set(i)),
+        mapAsJavaMap(Seq.tabulate(3)(n => (i + n: Integer) -> s"val_${i + n}").toMap),
+        makeComplexColumn(i))
+
+    if (i % 3 == 0) {
+      value
+    } else {
+      value
+        .setMaybeBoolColumn(i % 2 == 0)
+        .setMaybeByteColumn(i.toByte)
+        .setMaybeShortColumn((i + 1).toShort)
+        .setMaybeIntColumn(i + 2)
+        .setMaybeLongColumn(i.toLong * 10)
+        .setMaybeDoubleColumn(i.toDouble + 0.2d)
+        .setMaybeBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes))
+        .setMaybeStringColumn(s"val_$i")
+        .setMaybeEnumColumn(Suit.values()(i % 4))
+    }
+  }
+}
diff --git a/sql/core/src/test/scripts/gen-code.sh b/sql/core/src/test/scripts/gen-code.sh
new file mode 100755
index 0000000000000..5d8d8ad08555c
--- /dev/null
+++ b/sql/core/src/test/scripts/gen-code.sh
@@ -0,0 +1,31 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+cd $(dirname $0)/..
+BASEDIR=`pwd`
+cd -
+
+rm -rf $BASEDIR/gen-java
+mkdir -p $BASEDIR/gen-java
+
+thrift\
+    --gen java\
+    -out $BASEDIR/gen-java\
+    $BASEDIR/thrift/parquet-compat.thrift
+
+avro-tools idl $BASEDIR/avro/parquet-compat.avdl > $BASEDIR/avro/parquet-compat.avpr
+avro-tools compile -string protocol $BASEDIR/avro/parquet-compat.avpr $BASEDIR/gen-java
diff --git a/sql/core/src/test/thrift/parquet-compat.thrift b/sql/core/src/test/thrift/parquet-compat.thrift
new file mode 100644
index 0000000000000..fa5ed8c62306a
--- /dev/null
+++ b/sql/core/src/test/thrift/parquet-compat.thrift
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace java org.apache.spark.sql.parquet.test.thrift
+
+enum Suit {
+    SPADES,
+    HEARTS,
+    DIAMONDS,
+    CLUBS
+}
+
+struct Nested {
+    1: required list<i32> nestedIntsColumn;
+    2: required string nestedStringColumn;
+}
+
+/**
+ * This is a test struct for testing parquet-thrift compatibility.
+ */
+struct ParquetThriftCompat {
+    1: required bool boolColumn;
+    2: required byte byteColumn;
+    3: required i16 shortColumn;
+    4: required i32 intColumn;
+    5: required i64 longColumn;
+    6: required double doubleColumn;
+    7: required binary binaryColumn;
+    8: required string stringColumn;
+    9: required Suit enumColumn
+
+    10: optional bool maybeBoolColumn;
+    11: optional byte maybeByteColumn;
+    12: optional i16 maybeShortColumn;
+    13: optional i32 maybeIntColumn;
+    14: optional i64 maybeLongColumn;
+    15: optional double maybeDoubleColumn;
+    16: optional binary maybeBinaryColumn;
+    17: optional string maybeStringColumn;
+    18: optional Suit maybeEnumColumn;
+
+    19: required list<string> stringsColumn;
+    20: required set<i32> intSetColumn;
+    21: required map<i32, string> intToStringColumn;
+    22: required map<i32, list<Nested>> complexColumn;
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
new file mode 100644
index 0000000000000..bb5f1febe9ad4
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -0,0 +1,92 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.parquet.ParquetCompatibilityTest
+import org.apache.spark.sql.{Row, SQLConf, SQLContext}
+
+class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
+  import ParquetCompatibilityTest.makeNullable
+
+  override val sqlContext: SQLContext = TestHive
+
+  override protected def beforeAll(): Unit = {
+    super.beforeAll()
+
+    withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
+      withTempTable("data") {
+        sqlContext.sql(
+          s"""CREATE TABLE parquet_compat(
+             |  bool_column BOOLEAN,
+             |  byte_column TINYINT,
+             |  short_column SMALLINT,
+             |  int_column INT,
+             |  long_column BIGINT,
+             |  float_column FLOAT,
+             |  double_column DOUBLE,
+             |
+             |  strings_column ARRAY<STRING>,
+             |  int_to_string_column MAP<INT, STRING>
+             |)
+             |STORED AS PARQUET
+             |LOCATION '${parquetStore.getCanonicalPath}'
+           """.stripMargin)
+
+        val schema = sqlContext.table("parquet_compat").schema
+        val rowRDD = sqlContext.sparkContext.parallelize(makeRows).coalesce(1)
+        sqlContext.createDataFrame(rowRDD, schema).registerTempTable("data")
+        sqlContext.sql("INSERT INTO TABLE parquet_compat SELECT * FROM data")
+      }
+    }
+  }
+
+  override protected def afterAll(): Unit = {
+    sqlContext.sql("DROP TABLE parquet_compat")
+  }
+
+  test("Read Parquet file generated by parquet-hive") {
+    logInfo(
+      s"""Schema of the Parquet file written by parquet-hive:
+         |${readParquetSchema(parquetStore.getCanonicalPath)}
+       """.stripMargin)
+
+    // Unfortunately parquet-hive doesn't add `UTF8` annotation to BINARY when writing strings.
+    // Have to assume all BINARY values are strings here.
+    withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
+      checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), makeRows)
+    }
+  }
+
+  def makeRows: Seq[Row] = {
+    (0 until 10).map { i =>
+      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
+
+      Row(
+        nullable(i % 2 == 0: java.lang.Boolean),
+        nullable(i.toByte: java.lang.Byte),
+        nullable((i + 1).toShort: java.lang.Short),
+        nullable(i + 2: Integer),
+        nullable(i.toLong * 10: java.lang.Long),
+        nullable(i.toFloat + 0.1f: java.lang.Float),
+        nullable(i.toDouble + 0.2d: java.lang.Double),
+        nullable(Seq.tabulate(3)(n => s"arr_${i + n}")),
+        nullable(Seq.tabulate(3)(n => (i + n: Integer) -> s"val_${i + n}").toMap))
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index c2e09800933b5..9d79a4b007d66 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -21,14 +21,16 @@ import java.io.File
 
 import org.scalatest.BeforeAndAfterAll
 
+import org.apache.spark.sql._
 import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD}
 import org.apache.spark.sql.hive.execution.HiveTableScan
+import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.parquet.{ParquetRelation2, ParquetTableScan}
 import org.apache.spark.sql.sources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation}
+import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{DataFrame, QueryTest, Row, SQLConf, SaveMode}
 import org.apache.spark.util.Utils
 
 // The data where the partitioning key exists only in the directory structure.
@@ -685,6 +687,31 @@ class ParquetSourceSuiteBase extends ParquetPartitioningTest {
 
     sql("drop table spark_6016_fix")
   }
+
+  test("SPARK-8811: compatibility with array of struct in Hive") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withTable("array_of_struct") {
+        val conf = Seq(
+          HiveContext.CONVERT_METASTORE_PARQUET.key -> "false",
+          SQLConf.PARQUET_BINARY_AS_STRING.key -> "true",
+          SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key -> "true")
+
+        withSQLConf(conf: _*) {
+          sql(
+            s"""CREATE TABLE array_of_struct
+               |STORED AS PARQUET LOCATION '$path'
+               |AS SELECT '1st', '2nd', ARRAY(NAMED_STRUCT('a', 'val_a', 'b', 'val_b'))
+             """.stripMargin)
+
+          checkAnswer(
+            sqlContext.read.parquet(path),
+            Row("1st", "2nd", Seq(Row("val_a", "val_b"))))
+        }
+      }
+    }
+  }
 }
 
 class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase {
@@ -762,7 +789,9 @@ class ParquetDataSourceOffSourceSuite extends ParquetSourceSuiteBase {
 /**
  * A collection of tests for parquet data with various forms of partitioning.
  */
-abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll {
+abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
+  override def sqlContext: SQLContext = TestHive
+
   var partitionedTableDir: File = null
   var normalTableDir: File = null
   var partitionedTableDirWithKey: File = null

From 381cb161ba4e3a30f2da3c4ef4ee19869d51f101 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 8 Jul 2015 16:21:28 -0700
Subject: [PATCH 0289/1454] [SPARK-8068] [MLLIB] Add confusionMatrix method at
 class MulticlassMetrics in pyspark/mllib

Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7286 from yanboliang/spark-8068 and squashes the following commits:

6109fe1 [Yanbo Liang] Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib
---
 python/pyspark/mllib/evaluation.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py
index c5cf3a4e7ff22..f21403707e12a 100644
--- a/python/pyspark/mllib/evaluation.py
+++ b/python/pyspark/mllib/evaluation.py
@@ -152,6 +152,10 @@ class MulticlassMetrics(JavaModelWrapper):
     >>> predictionAndLabels = sc.parallelize([(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
     ...     (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)])
     >>> metrics = MulticlassMetrics(predictionAndLabels)
+    >>> metrics.confusionMatrix().toArray()
+    array([[ 2.,  1.,  1.],
+           [ 1.,  3.,  0.],
+           [ 0.,  0.,  1.]])
     >>> metrics.falsePositiveRate(0.0)
     0.2...
     >>> metrics.precision(1.0)
@@ -186,6 +190,13 @@ def __init__(self, predictionAndLabels):
         java_model = java_class(df._jdf)
         super(MulticlassMetrics, self).__init__(java_model)
 
+    def confusionMatrix(self):
+        """
+        Returns confusion matrix: predicted classes are in columns,
+        they are ordered by class label ascending, as in "labels".
+        """
+        return self.call("confusionMatrix")
+
     def truePositiveRate(self, label):
         """
         Returns true positive rate for a given label (category).

From 8c32b2e870c7c250a63e838718df833edf6dea07 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 8 Jul 2015 16:27:11 -0700
Subject: [PATCH 0290/1454] [SPARK-8877] [MLLIB] Public API for association
 rule generation

Adds FPGrowth.generateAssociationRules to public API for generating association rules after mining frequent itemsets.

Author: Feynman Liang <fliang@databricks.com>

Closes #7271 from feynmanliang/SPARK-8877 and squashes the following commits:

83b8baf [Feynman Liang] Add API Doc
867abff [Feynman Liang] Add FPGrowth.generateAssociationRules and change access modifiers for AssociationRules
---
 .../spark/mllib/fpm/AssociationRules.scala    |  5 ++-
 .../org/apache/spark/mllib/fpm/FPGrowth.scala | 11 ++++-
 .../spark/mllib/fpm/FPGrowthSuite.scala       | 42 +++++++++++++++++++
 3 files changed, 55 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 4a0f842f3338d..7e2bbfe31c1b7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -33,7 +33,7 @@ import org.apache.spark.rdd.RDD
  * association rules which have a single item as the consequent.
  */
 @Experimental
-class AssociationRules private (
+class AssociationRules private[fpm] (
     private var minConfidence: Double) extends Logging with Serializable {
 
   /**
@@ -45,6 +45,7 @@ class AssociationRules private (
    * Sets the minimal confidence (default: `0.8`).
    */
   def setMinConfidence(minConfidence: Double): this.type = {
+    require(minConfidence >= 0.0 && minConfidence <= 1.0)
     this.minConfidence = minConfidence
     this
   }
@@ -91,7 +92,7 @@ object AssociationRules {
    * @tparam Item item type
    */
   @Experimental
-  class Rule[Item] private[mllib] (
+  class Rule[Item] private[fpm] (
       val antecedent: Array[Item],
       val consequent: Array[Item],
       freqUnion: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index 0da59e812d5f9..9cb9a00dbd9c7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -40,7 +40,16 @@ import org.apache.spark.storage.StorageLevel
  * @tparam Item item type
  */
 @Experimental
-class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable
+class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
+  /**
+   * Generates association rules for the [[Item]]s in [[freqItemsets]].
+   * @param confidence minimal confidence of the rules produced
+   */
+  def generateAssociationRules(confidence: Double): RDD[AssociationRules.Rule[Item]] = {
+    val associationRules = new AssociationRules(confidence)
+    associationRules.run(freqItemsets)
+  }
+}
 
 /**
  * :: Experimental ::
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
index ddc296a428907..4a9bfdb348d9f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
@@ -132,6 +132,48 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(model1.freqItemsets.count() === 625)
   }
 
+  test("FP-Growth String type association rule generation") {
+    val transactions = Seq(
+      "r z h k p",
+      "z y x w v u t s",
+      "s x o n r",
+      "x z y m t s q e",
+      "z",
+      "x z y r q t p")
+      .map(_.split(" "))
+    val rdd = sc.parallelize(transactions, 2).cache()
+
+    /* Verify results using the `R` code:
+       transactions = as(sapply(
+         list("r z h k p",
+              "z y x w v u t s",
+              "s x o n r",
+              "x z y m t s q e",
+              "z",
+              "x z y r q t p"),
+         FUN=function(x) strsplit(x," ",fixed=TRUE)),
+         "transactions")
+       ars = apriori(transactions,
+                     parameter = list(support = 0.0, confidence = 0.5, target="rules", minlen=2))
+       arsDF = as(ars, "data.frame")
+       arsDF$support = arsDF$support * length(transactions)
+       names(arsDF)[names(arsDF) == "support"] = "freq"
+       > nrow(arsDF)
+       [1] 23
+       > sum(arsDF$confidence == 1)
+       [1] 23
+     */
+    val rules = (new FPGrowth())
+      .setMinSupport(0.5)
+      .setNumPartitions(2)
+      .run(rdd)
+      .generateAssociationRules(0.9)
+      .collect()
+
+    assert(rules.size === 23)
+    assert(rules.count(rule => math.abs(rule.confidence - 1.0D) < 1e-6) == 23)
+  }
+
   test("FP-Growth using Int type") {
     val transactions = Seq(
       "1 2 3",

From f472b8cdc00839780dc79be0bbe53a098cde230c Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 8 Jul 2015 16:32:00 -0700
Subject: [PATCH 0291/1454] [SPARK-5016] [MLLIB] Distribute GMM mixture
 components to executors

Distribute expensive portions of computation for Gaussian mixture components (in particular, pre-computation of `MultivariateGaussian.rootSigmaInv`, the inverse covariance matrix and covariance determinant) across executors. Repost of PR#4654.

Notes for reviewers:
 * What should be the policy for when to distribute computation. Always? When numClusters > threshold? User-specified param?

TODO:
 * Performance testing and comparison for large number of clusters

Author: Feynman Liang <fliang@databricks.com>

Closes #7166 from feynmanliang/GMM_parallel_mixtures and squashes the following commits:

4f351fa [Feynman Liang] Update heuristic and scaladoc
5ea947e [Feynman Liang] Fix parallelization logic
00eb7db [Feynman Liang] Add helper method for GMM's M step, remove distributeGaussians flag
e7c8127 [Feynman Liang] Add distributeGaussians flag and tests
1da3c7f [Feynman Liang] Distribute mixtures
---
 .../mllib/clustering/GaussianMixture.scala    | 44 +++++++++++++++----
 1 file changed, 36 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index fc509d2ba1470..e459367333d26 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -140,6 +140,10 @@ class GaussianMixture private (
     // Get length of the input vectors
     val d = breezeData.first().length
 
+    // Heuristic to distribute the computation of the [[MultivariateGaussian]]s, approximately when
+    // d > 25 except for when k is very small
+    val distributeGaussians = ((k - 1.0) / k) * d > 25
+
     // Determine initial weights and corresponding Gaussians.
     // If the user supplied an initial GMM, we use those values, otherwise
     // we start with uniform weights, a random mean from the data, and
@@ -171,14 +175,25 @@ class GaussianMixture private (
       // Create new distributions based on the partial assignments
       // (often referred to as the "M" step in literature)
       val sumWeights = sums.weights.sum
-      var i = 0
-      while (i < k) {
-        val mu = sums.means(i) / sums.weights(i)
-        BLAS.syr(-sums.weights(i), Vectors.fromBreeze(mu),
-          Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix])
-        weights(i) = sums.weights(i) / sumWeights
-        gaussians(i) = new MultivariateGaussian(mu, sums.sigmas(i) / sums.weights(i))
-        i = i + 1
+
+      if (distributeGaussians) {
+        val numPartitions = math.min(k, 1024)
+        val tuples =
+          Seq.tabulate(k)(i => (sums.means(i), sums.sigmas(i), sums.weights(i)))
+        val (ws, gs) = sc.parallelize(tuples, numPartitions).map { case (mean, sigma, weight) =>
+          updateWeightsAndGaussians(mean, sigma, weight, sumWeights)
+        }.collect.unzip
+        Array.copy(ws, 0, weights, 0, ws.length)
+        Array.copy(gs, 0, gaussians, 0, gs.length)
+      } else {
+        var i = 0
+        while (i < k) {
+          val (weight, gaussian) =
+            updateWeightsAndGaussians(sums.means(i), sums.sigmas(i), sums.weights(i), sumWeights)
+          weights(i) = weight
+          gaussians(i) = gaussian
+          i = i + 1
+        }
       }
 
       llhp = llh // current becomes previous
@@ -192,6 +207,19 @@ class GaussianMixture private (
   /** Java-friendly version of [[run()]] */
   def run(data: JavaRDD[Vector]): GaussianMixtureModel = run(data.rdd)
 
+  private def updateWeightsAndGaussians(
+      mean: BDV[Double],
+      sigma: BreezeMatrix[Double],
+      weight: Double,
+      sumWeights: Double): (Double, MultivariateGaussian) = {
+    val mu = (mean /= weight)
+    BLAS.syr(-weight, Vectors.fromBreeze(mu),
+      Matrices.fromBreeze(sigma).asInstanceOf[DenseMatrix])
+    val newWeight = weight / sumWeights
+    val newGaussian = new MultivariateGaussian(mu, sigma / weight)
+    (newWeight, newGaussian)
+  }
+
   /** Average of dense breeze vectors */
   private def vectorMean(x: IndexedSeq[BV[Double]]): BDV[Double] = {
     val v = BDV.zeros[Double](x(0).length)

From 2a4f88b6c16f2991e63b17c0e103bcd79f04dbbc Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 8 Jul 2015 18:09:39 -0700
Subject: [PATCH 0292/1454] [SPARK-8914][SQL] Remove RDDApi

As rxin suggested in #7298 , we should consider to remove `RDDApi`.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7302 from sarutak/remove-rddapi and squashes the following commits:

e495d35 [Kousuke Saruta] Fixed mima
cb7ebb9 [Kousuke Saruta] Removed overriding RDDApi
---
 project/MimaExcludes.scala                    |  5 ++
 .../org/apache/spark/sql/DataFrame.scala      | 39 ++++++-----
 .../scala/org/apache/spark/sql/RDDApi.scala   | 67 -------------------
 3 files changed, 24 insertions(+), 87 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 7346d804632bc..57a86bf8deb64 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -70,7 +70,12 @@ object MimaExcludes {
               "org.apache.spark.mllib.linalg.Matrix.numNonzeros"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.mllib.linalg.Matrix.numActives")
+          ) ++ Seq(
+            // SPARK-8914 Remove RDDApi
+            ProblemFilters.exclude[MissingClassProblem](
+            "org.apache.spark.sql.RDDApi")
           )
+
         case v if v.startsWith("1.4") =>
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index f33e19a0cb7dd..eeefc85255d14 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -115,8 +115,7 @@ private[sql] object DataFrame {
 @Experimental
 class DataFrame private[sql](
     @transient val sqlContext: SQLContext,
-    @DeveloperApi @transient val queryExecution: SQLContext#QueryExecution)
-  extends RDDApi[Row] with Serializable {
+    @DeveloperApi @transient val queryExecution: SQLContext#QueryExecution) extends Serializable {
 
   /**
    * A constructor that automatically analyzes the logical plan.
@@ -1320,14 +1319,14 @@ class DataFrame private[sql](
    * @group action
    * @since 1.3.0
    */
-  override def first(): Row = head()
+  def first(): Row = head()
 
   /**
    * Returns a new RDD by applying a function to all rows of this DataFrame.
    * @group rdd
    * @since 1.3.0
    */
-  override def map[R: ClassTag](f: Row => R): RDD[R] = rdd.map(f)
+  def map[R: ClassTag](f: Row => R): RDD[R] = rdd.map(f)
 
   /**
    * Returns a new RDD by first applying a function to all rows of this [[DataFrame]],
@@ -1335,14 +1334,14 @@ class DataFrame private[sql](
    * @group rdd
    * @since 1.3.0
    */
-  override def flatMap[R: ClassTag](f: Row => TraversableOnce[R]): RDD[R] = rdd.flatMap(f)
+  def flatMap[R: ClassTag](f: Row => TraversableOnce[R]): RDD[R] = rdd.flatMap(f)
 
   /**
    * Returns a new RDD by applying a function to each partition of this DataFrame.
    * @group rdd
    * @since 1.3.0
    */
-  override def mapPartitions[R: ClassTag](f: Iterator[Row] => Iterator[R]): RDD[R] = {
+  def mapPartitions[R: ClassTag](f: Iterator[Row] => Iterator[R]): RDD[R] = {
     rdd.mapPartitions(f)
   }
 
@@ -1351,49 +1350,49 @@ class DataFrame private[sql](
    * @group rdd
    * @since 1.3.0
    */
-  override def foreach(f: Row => Unit): Unit = rdd.foreach(f)
+  def foreach(f: Row => Unit): Unit = rdd.foreach(f)
 
   /**
    * Applies a function f to each partition of this [[DataFrame]].
    * @group rdd
    * @since 1.3.0
    */
-  override def foreachPartition(f: Iterator[Row] => Unit): Unit = rdd.foreachPartition(f)
+  def foreachPartition(f: Iterator[Row] => Unit): Unit = rdd.foreachPartition(f)
 
   /**
    * Returns the first `n` rows in the [[DataFrame]].
    * @group action
    * @since 1.3.0
    */
-  override def take(n: Int): Array[Row] = head(n)
+  def take(n: Int): Array[Row] = head(n)
 
   /**
    * Returns an array that contains all of [[Row]]s in this [[DataFrame]].
    * @group action
    * @since 1.3.0
    */
-  override def collect(): Array[Row] = queryExecution.executedPlan.executeCollect()
+  def collect(): Array[Row] = queryExecution.executedPlan.executeCollect()
 
   /**
    * Returns a Java list that contains all of [[Row]]s in this [[DataFrame]].
    * @group action
    * @since 1.3.0
    */
-  override def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() : _*)
+  def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() : _*)
 
   /**
    * Returns the number of rows in the [[DataFrame]].
    * @group action
    * @since 1.3.0
    */
-  override def count(): Long = groupBy().count().collect().head.getLong(0)
+  def count(): Long = groupBy().count().collect().head.getLong(0)
 
   /**
    * Returns a new [[DataFrame]] that has exactly `numPartitions` partitions.
    * @group rdd
    * @since 1.3.0
    */
-  override def repartition(numPartitions: Int): DataFrame = {
+  def repartition(numPartitions: Int): DataFrame = {
     Repartition(numPartitions, shuffle = true, logicalPlan)
   }
 
@@ -1405,7 +1404,7 @@ class DataFrame private[sql](
    * @group rdd
    * @since 1.4.0
    */
-  override def coalesce(numPartitions: Int): DataFrame = {
+  def coalesce(numPartitions: Int): DataFrame = {
     Repartition(numPartitions, shuffle = false, logicalPlan)
   }
 
@@ -1415,13 +1414,13 @@ class DataFrame private[sql](
    * @group dfops
    * @since 1.3.0
    */
-  override def distinct(): DataFrame = dropDuplicates()
+  def distinct(): DataFrame = dropDuplicates()
 
   /**
    * @group basic
    * @since 1.3.0
    */
-  override def persist(): this.type = {
+  def persist(): this.type = {
     sqlContext.cacheManager.cacheQuery(this)
     this
   }
@@ -1430,13 +1429,13 @@ class DataFrame private[sql](
    * @group basic
    * @since 1.3.0
    */
-  override def cache(): this.type = persist()
+  def cache(): this.type = persist()
 
   /**
    * @group basic
    * @since 1.3.0
    */
-  override def persist(newLevel: StorageLevel): this.type = {
+  def persist(newLevel: StorageLevel): this.type = {
     sqlContext.cacheManager.cacheQuery(this, None, newLevel)
     this
   }
@@ -1445,7 +1444,7 @@ class DataFrame private[sql](
    * @group basic
    * @since 1.3.0
    */
-  override def unpersist(blocking: Boolean): this.type = {
+  def unpersist(blocking: Boolean): this.type = {
     sqlContext.cacheManager.tryUncacheQuery(this, blocking)
     this
   }
@@ -1454,7 +1453,7 @@ class DataFrame private[sql](
    * @group basic
    * @since 1.3.0
    */
-  override def unpersist(): this.type = unpersist(blocking = false)
+  def unpersist(): this.type = unpersist(blocking = false)
 
   /////////////////////////////////////////////////////////////////////////////
   // I/O
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala b/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala
deleted file mode 100644
index 63dbab19947c0..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
-* Licensed to the Apache Software Foundation (ASF) under one or more
-* contributor license agreements.  See the NOTICE file distributed with
-* this work for additional information regarding copyright ownership.
-* The ASF licenses this file to You under the Apache License, Version 2.0
-* (the "License"); you may not use this file except in compliance with
-* the License.  You may obtain a copy of the License at
-*
-*    http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing, software
-* distributed under the License is distributed on an "AS IS" BASIS,
-* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-* See the License for the specific language governing permissions and
-* limitations under the License.
-*/
-
-package org.apache.spark.sql
-
-import scala.reflect.ClassTag
-
-import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.StorageLevel
-
-
-/**
- * An internal interface defining the RDD-like methods for [[DataFrame]].
- * Please use [[DataFrame]] directly, and do NOT use this.
- */
-private[sql] trait RDDApi[T] {
-
-  def cache(): this.type
-
-  def persist(): this.type
-
-  def persist(newLevel: StorageLevel): this.type
-
-  def unpersist(): this.type
-
-  def unpersist(blocking: Boolean): this.type
-
-  def map[R: ClassTag](f: T => R): RDD[R]
-
-  def flatMap[R: ClassTag](f: T => TraversableOnce[R]): RDD[R]
-
-  def mapPartitions[R: ClassTag](f: Iterator[T] => Iterator[R]): RDD[R]
-
-  def foreach(f: T => Unit): Unit
-
-  def foreachPartition(f: Iterator[T] => Unit): Unit
-
-  def take(n: Int): Array[T]
-
-  def collect(): Array[T]
-
-  def collectAsList(): java.util.List[T]
-
-  def count(): Long
-
-  def first(): T
-
-  def repartition(numPartitions: Int): DataFrame
-
-  def coalesce(numPartitions: Int): DataFrame
-
-  def distinct: DataFrame
-}

From 74d8d3d928cc9a7386b68588ac89ae042847d146 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 8 Jul 2015 18:22:53 -0700
Subject: [PATCH 0293/1454] [SPARK-8450] [SQL] [PYSARK] cleanup type converter
 for Python DataFrame

This PR fixes the converter for Python DataFrame, especially for DecimalType

Closes #7106

Author: Davies Liu <davies@databricks.com>

Closes #7131 from davies/decimal_python and squashes the following commits:

4d3c234 [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
20531d6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
7d73168 [Davies Liu] fix conflit
6cdd86a [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
7104e97 [Davies Liu] improve type infer
9cd5a21 [Davies Liu] run python tests with SPARK_PREPEND_CLASSES
829a05b [Davies Liu] fix UDT in python
c99e8c5 [Davies Liu] fix mima
c46814a [Davies Liu] convert decimal for Python DataFrames
---
 .../apache/spark/mllib/linalg/Matrices.scala  | 10 +-
 .../apache/spark/mllib/linalg/Vectors.scala   | 16 +---
 project/MimaExcludes.scala                    |  5 +-
 python/pyspark/sql/tests.py                   | 13 +++
 python/pyspark/sql/types.py                   |  4 +
 python/run-tests.py                           |  3 +-
 .../org/apache/spark/sql/DataFrame.scala      |  4 +-
 .../org/apache/spark/sql/SQLContext.scala     | 28 +-----
 .../spark/sql/execution/pythonUDFs.scala      | 95 ++++++++++---------
 9 files changed, 84 insertions(+), 94 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 75e7004464af9..0df07663405a3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -24,9 +24,9 @@ import scala.collection.mutable.{ArrayBuilder => MArrayBuilder, HashSet => MHash
 import breeze.linalg.{CSCMatrix => BSM, DenseMatrix => BDM, Matrix => BM}
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.types._
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types._
 
 /**
  * Trait for a local matrix.
@@ -147,7 +147,7 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
       ))
   }
 
-  override def serialize(obj: Any): Row = {
+  override def serialize(obj: Any): InternalRow = {
     val row = new GenericMutableRow(7)
     obj match {
       case sm: SparseMatrix =>
@@ -173,9 +173,7 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
 
   override def deserialize(datum: Any): Matrix = {
     datum match {
-      // TODO: something wrong with UDT serialization, should never happen.
-      case m: Matrix => m
-      case row: Row =>
+      case row: InternalRow =>
         require(row.length == 7,
           s"MatrixUDT.deserialize given row with length ${row.length} but requires length == 7")
         val tpe = row.getByte(0)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index c9c27425d2877..e048b01d92462 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -28,7 +28,7 @@ import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.util.NumericParser
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.types._
 
@@ -175,7 +175,7 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
       StructField("values", ArrayType(DoubleType, containsNull = false), nullable = true)))
   }
 
-  override def serialize(obj: Any): Row = {
+  override def serialize(obj: Any): InternalRow = {
     obj match {
       case SparseVector(size, indices, values) =>
         val row = new GenericMutableRow(4)
@@ -191,17 +191,12 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
         row.setNullAt(2)
         row.update(3, values.toSeq)
         row
-      // TODO: There are bugs in UDT serialization because we don't have a clear separation between
-      // TODO: internal SQL types and language specific types (including UDT). UDT serialize and
-      // TODO: deserialize may get called twice. See SPARK-7186.
-      case row: Row =>
-        row
     }
   }
 
   override def deserialize(datum: Any): Vector = {
     datum match {
-      case row: Row =>
+      case row: InternalRow =>
         require(row.length == 4,
           s"VectorUDT.deserialize given row with length ${row.length} but requires length == 4")
         val tpe = row.getByte(0)
@@ -215,11 +210,6 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
             val values = row.getAs[Iterable[Double]](3).toArray
             new DenseVector(values)
         }
-      // TODO: There are bugs in UDT serialization because we don't have a clear separation between
-      // TODO: internal SQL types and language specific types (including UDT). UDT serialize and
-      // TODO: deserialize may get called twice. See SPARK-7186.
-      case v: Vector =>
-        v
     }
   }
 
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 57a86bf8deb64..821aadd477ef3 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -63,7 +63,10 @@ object MimaExcludes {
             // SQL execution is considered private.
             excludePackage("org.apache.spark.sql.execution"),
             // Parquet support is considered private.
-            excludePackage("org.apache.spark.sql.parquet")
+            excludePackage("org.apache.spark.sql.parquet"),
+            // local function inside a method
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1")
           ) ++ Seq(
             // SPARK-8479 Add numNonzeros and numActives to Matrix.
             ProblemFilters.exclude[MissingMethodProblem](
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 333378c7f1854..66827d48850d9 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -700,6 +700,19 @@ def test_time_with_timezone(self):
         self.assertTrue(now - now1 < datetime.timedelta(0.001))
         self.assertTrue(now - utcnow1 < datetime.timedelta(0.001))
 
+    def test_decimal(self):
+        from decimal import Decimal
+        schema = StructType([StructField("decimal", DecimalType(10, 5))])
+        df = self.sqlCtx.createDataFrame([(Decimal("3.14159"),)], schema)
+        row = df.select(df.decimal + 1).first()
+        self.assertEqual(row[0], Decimal("4.14159"))
+        tmpPath = tempfile.mkdtemp()
+        shutil.rmtree(tmpPath)
+        df.write.parquet(tmpPath)
+        df2 = self.sqlCtx.read.parquet(tmpPath)
+        row = df2.first()
+        self.assertEqual(row[0], Decimal("3.14159"))
+
     def test_dropna(self):
         schema = StructType([
             StructField("name", StringType(), True),
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 160df40d65cc1..7e64cb0b54dba 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1069,6 +1069,10 @@ def _verify_type(obj, dataType):
     if obj is None:
         return
 
+    # StringType can work with any types
+    if isinstance(dataType, StringType):
+        return
+
     if isinstance(dataType, UserDefinedType):
         if not (hasattr(obj, '__UDT__') and obj.__UDT__ == dataType):
             raise ValueError("%r is not an instance of type %r" % (obj, dataType))
diff --git a/python/run-tests.py b/python/run-tests.py
index 7638854def2e8..cc560779373b3 100755
--- a/python/run-tests.py
+++ b/python/run-tests.py
@@ -72,7 +72,8 @@ def print_red(text):
 
 
 def run_individual_python_test(test_name, pyspark_python):
-    env = {'SPARK_TESTING': '1', 'PYSPARK_PYTHON': which(pyspark_python)}
+    env = dict(os.environ)
+    env.update({'SPARK_TESTING': '1', 'PYSPARK_PYTHON': which(pyspark_python)})
     LOGGER.debug("Starting test(%s): %s", pyspark_python, test_name)
     start_time = time.time()
     try:
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index eeefc85255d14..d9f987ae0252f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1549,8 +1549,8 @@ class DataFrame private[sql](
    * Converts a JavaRDD to a PythonRDD.
    */
   protected[sql] def javaToPython: JavaRDD[Array[Byte]] = {
-    val fieldTypes = schema.fields.map(_.dataType)
-    val jrdd = rdd.map(EvaluatePython.rowToArray(_, fieldTypes)).toJavaRDD()
+    val structType = schema  // capture it for closure
+    val jrdd = queryExecution.toRdd.map(EvaluatePython.toJava(_, structType)).toJavaRDD()
     SerDeUtil.javaToPython(jrdd)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 079f31ab8fe6d..477dea9164726 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -1044,33 +1044,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       rdd: RDD[Array[Any]],
       schema: StructType): DataFrame = {
 
-    def needsConversion(dataType: DataType): Boolean = dataType match {
-      case ByteType => true
-      case ShortType => true
-      case LongType => true
-      case FloatType => true
-      case DateType => true
-      case TimestampType => true
-      case StringType => true
-      case ArrayType(_, _) => true
-      case MapType(_, _, _) => true
-      case StructType(_) => true
-      case udt: UserDefinedType[_] => needsConversion(udt.sqlType)
-      case other => false
-    }
-
-    val convertedRdd = if (schema.fields.exists(f => needsConversion(f.dataType))) {
-      rdd.map(m => m.zip(schema.fields).map {
-        case (value, field) => EvaluatePython.fromJava(value, field.dataType)
-      })
-    } else {
-      rdd
-    }
-
-    val rowRdd = convertedRdd.mapPartitions { iter =>
-      iter.map { m => new GenericInternalRow(m): InternalRow}
-    }
-
+    val rowRdd = rdd.map(r => EvaluatePython.fromJava(r, schema).asInstanceOf[InternalRow])
     DataFrame(this, LogicalRDD(schema.toAttributes, rowRdd)(self))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 6946e798b71b0..1c8130b07c7fb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -24,20 +24,19 @@ import scala.collection.JavaConverters._
 
 import net.razorvine.pickle.{Pickler, Unpickler}
 
-import org.apache.spark.{Accumulator, Logging => SparkLogging}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.api.python.{PythonBroadcast, PythonRDD}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.{Accumulator, Logging => SparkLogging}
 
 /**
  * A serialized version of a Python lambda function.  Suitable for use in a [[PythonRDD]].
@@ -125,59 +124,86 @@ object EvaluatePython {
     new EvaluatePython(udf, child, AttributeReference("pythonUDF", udf.dataType)())
 
   /**
-   * Helper for converting a Scala object to a java suitable for pyspark serialization.
+   * Helper for converting from Catalyst type to java type suitable for Pyrolite.
    */
   def toJava(obj: Any, dataType: DataType): Any = (obj, dataType) match {
     case (null, _) => null
 
-    case (row: Row, struct: StructType) =>
+    case (row: InternalRow, struct: StructType) =>
       val fields = struct.fields.map(field => field.dataType)
-      row.toSeq.zip(fields).map {
-        case (obj, dataType) => toJava(obj, dataType)
-      }.toArray
+      rowToArray(row, fields)
 
     case (seq: Seq[Any], array: ArrayType) =>
       seq.map(x => toJava(x, array.elementType)).asJava
-    case (list: JList[_], array: ArrayType) =>
-      list.map(x => toJava(x, array.elementType)).asJava
-    case (arr, array: ArrayType) if arr.getClass.isArray =>
-      arr.asInstanceOf[Array[Any]].map(x => toJava(x, array.elementType))
 
     case (obj: Map[_, _], mt: MapType) => obj.map {
       case (k, v) => (toJava(k, mt.keyType), toJava(v, mt.valueType))
     }.asJava
 
-    case (ud, udt: UserDefinedType[_]) => toJava(udt.serialize(ud), udt.sqlType)
+    case (ud, udt: UserDefinedType[_]) => toJava(ud, udt.sqlType)
 
     case (date: Int, DateType) => DateTimeUtils.toJavaDate(date)
     case (t: Long, TimestampType) => DateTimeUtils.toJavaTimestamp(t)
+
+    case (d: Decimal, _) => d.toJavaBigDecimal
+
     case (s: UTF8String, StringType) => s.toString
 
-    // Pyrolite can handle Timestamp and Decimal
     case (other, _) => other
   }
 
   /**
    * Convert Row into Java Array (for pickled into Python)
    */
-  def rowToArray(row: Row, fields: Seq[DataType]): Array[Any] = {
+  def rowToArray(row: InternalRow, fields: Seq[DataType]): Array[Any] = {
     // TODO: this is slow!
     row.toSeq.zip(fields).map {case (obj, dt) => toJava(obj, dt)}.toArray
   }
 
-  // Converts value to the type specified by the data type.
-  // Because Python does not have data types for TimestampType, FloatType, ShortType, and
-  // ByteType, we need to explicitly convert values in columns of these data types to the desired
-  // JVM data types.
+  /**
+   * Converts `obj` to the type specified by the data type, or returns null if the type of obj is
+   * unexpected. Because Python doesn't enforce the type.
+   */
   def fromJava(obj: Any, dataType: DataType): Any = (obj, dataType) match {
-    // TODO: We should check nullable
     case (null, _) => null
 
+    case (c: Boolean, BooleanType) => c
+
+    case (c: Int, ByteType) => c.toByte
+    case (c: Long, ByteType) => c.toByte
+
+    case (c: Int, ShortType) => c.toShort
+    case (c: Long, ShortType) => c.toShort
+
+    case (c: Int, IntegerType) => c
+    case (c: Long, IntegerType) => c.toInt
+
+    case (c: Int, LongType) => c.toLong
+    case (c: Long, LongType) => c
+
+    case (c: Double, FloatType) => c.toFloat
+
+    case (c: Double, DoubleType) => c
+
+    case (c: java.math.BigDecimal, dt: DecimalType) => Decimal(c)
+
+    case (c: Int, DateType) => c
+
+    case (c: Long, TimestampType) => c
+
+    case (c: String, StringType) => UTF8String.fromString(c)
+    case (c, StringType) =>
+      // If we get here, c is not a string. Call toString on it.
+      UTF8String.fromString(c.toString)
+
+    case (c: String, BinaryType) => c.getBytes("utf-8")
+    case (c, BinaryType) if c.getClass.isArray && c.getClass.getComponentType.getName == "byte" => c
+
     case (c: java.util.List[_], ArrayType(elementType, _)) =>
-      c.map { e => fromJava(e, elementType)}: Seq[Any]
+      c.map { e => fromJava(e, elementType)}.toSeq
 
     case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
-      c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType)): Seq[Any]
+      c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType)).toSeq
 
     case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) => c.map {
       case (key, value) => (fromJava(key, keyType), fromJava(value, valueType))
@@ -188,30 +214,11 @@ object EvaluatePython {
         case (e, f) => fromJava(e, f.dataType)
       })
 
-    case (c: java.util.Calendar, DateType) =>
-      DateTimeUtils.fromJavaDate(new java.sql.Date(c.getTimeInMillis))
-
-    case (c: java.util.Calendar, TimestampType) =>
-      c.getTimeInMillis * 10000L
-    case (t: java.sql.Timestamp, TimestampType) =>
-      DateTimeUtils.fromJavaTimestamp(t)
-
-    case (_, udt: UserDefinedType[_]) =>
-      fromJava(obj, udt.sqlType)
-
-    case (c: Int, ByteType) => c.toByte
-    case (c: Long, ByteType) => c.toByte
-    case (c: Int, ShortType) => c.toShort
-    case (c: Long, ShortType) => c.toShort
-    case (c: Long, IntegerType) => c.toInt
-    case (c: Int, LongType) => c.toLong
-    case (c: Double, FloatType) => c.toFloat
-    case (c: String, StringType) => UTF8String.fromString(c)
-    case (c, StringType) =>
-      // If we get here, c is not a string. Call toString on it.
-      UTF8String.fromString(c.toString)
+    case (_, udt: UserDefinedType[_]) => fromJava(obj, udt.sqlType)
 
-    case (c, _) => c
+    // all other unexpected type should be null, or we will have runtime exception
+    // TODO(davies): we could improve this by try to cast the object to expected type
+    case (c, _) => null
   }
 }
 

From 28fa01e2ba146e823489f6d81c5eb3a76b20c71f Mon Sep 17 00:00:00 2001
From: Jonathan Alter <jonalter@users.noreply.github.com>
Date: Thu, 9 Jul 2015 03:28:51 +0100
Subject: [PATCH 0294/1454] [SPARK-8927] [DOCS] Format wrong for some config
 descriptions

A couple descriptions were not inside `<td></td>` and were being displayed immediately under the section title instead of in their row.

Author: Jonathan Alter <jonalter@users.noreply.github.com>

Closes #7292 from jonalter/docs-config and squashes the following commits:

5ce1570 [Jonathan Alter] [DOCS] Format wrong for some config descriptions
---
 docs/configuration.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index bebaf6f62e90a..892c02b27df32 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1007,9 +1007,9 @@ Apart from these, the following properties are also available, and may be useful
 <tr>
   <td><code>spark.rpc.numRetries</code></td>
   <td>3</td>
+  <td>
     Number of times to retry before an RPC task gives up.
     An RPC task will run at most times of this number.
-  <td>
   </td>
 </tr>
 <tr>
@@ -1029,8 +1029,8 @@ Apart from these, the following properties are also available, and may be useful
 <tr>
   <td><code>spark.rpc.lookupTimeout</code></td>
   <td>120s</td>
-    Duration for an RPC remote endpoint lookup operation to wait before timing out.
   <td>
+    Duration for an RPC remote endpoint lookup operation to wait before timing out.  
   </td>
 </tr>
 </table>

From a290814877308c6fa9b0f78b1a81145db7651ca4 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 8 Jul 2015 20:20:17 -0700
Subject: [PATCH 0295/1454] [SPARK-8866][SQL] use 1us precision for timestamp
 type

JIRA: https://issues.apache.org/jira/browse/SPARK-8866

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7283 from yijieshen/micro_timestamp and squashes the following commits:

dc735df [Yijie Shen] update CastSuite to avoid round error
714eaea [Yijie Shen] add timestamp_udf into blacklist due to precision lose
c3ca2f4 [Yijie Shen] fix unhandled case in CurrentTimestamp
8d4aa6b [Yijie Shen] use 1us precision for timestamp type
---
 python/pyspark/sql/types.py                   |  2 +-
 .../spark/sql/catalyst/expressions/Cast.scala | 18 ++++-----
 .../expressions/datetimeFunctions.scala       |  2 +-
 .../sql/catalyst/util/DateTimeUtils.scala     | 38 +++++++++----------
 .../sql/catalyst/expressions/CastSuite.scala  | 10 ++---
 .../catalyst/util/DateTimeUtilsSuite.scala    |  8 ++--
 .../apache/spark/sql/json/JacksonParser.scala |  4 +-
 .../org/apache/spark/sql/json/JsonRDD.scala   |  6 +--
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala |  2 +-
 .../execution/HiveCompatibilitySuite.scala    |  6 +--
 .../spark/sql/hive/HiveInspectors.scala       |  4 +-
 11 files changed, 50 insertions(+), 50 deletions(-)

diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 7e64cb0b54dba..fecfe6d71e9a7 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -775,7 +775,7 @@ def to_posix_timstamp(dt):
             if dt:
                 seconds = (calendar.timegm(dt.utctimetuple()) if dt.tzinfo
                            else time.mktime(dt.timetuple()))
-                return int(seconds * 1e7 + dt.microsecond * 10)
+                return int(seconds * 1e6 + dt.microsecond)
         return to_posix_timstamp
 
     else:
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 662ceeca7782d..567feca7136f9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -186,7 +186,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     case ByteType =>
       buildCast[Byte](_, b => longToTimestamp(b.toLong))
     case DateType =>
-      buildCast[Int](_, d => DateTimeUtils.daysToMillis(d) * 10000)
+      buildCast[Int](_, d => DateTimeUtils.daysToMillis(d) * 1000)
     // TimestampWritable.decimalToTimestamp
     case DecimalType() =>
       buildCast[Decimal](_, d => decimalToTimestamp(d))
@@ -207,16 +207,16 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   }
 
   private[this] def decimalToTimestamp(d: Decimal): Long = {
-    (d.toBigDecimal * 10000000L).longValue()
+    (d.toBigDecimal * 1000000L).longValue()
   }
 
-  // converting milliseconds to 100ns
-  private[this] def longToTimestamp(t: Long): Long = t * 10000L
-  // converting 100ns to seconds
-  private[this] def timestampToLong(ts: Long): Long = math.floor(ts.toDouble / 10000000L).toLong
-  // converting 100ns to seconds in double
+  // converting milliseconds to us
+  private[this] def longToTimestamp(t: Long): Long = t * 1000L
+  // converting us to seconds
+  private[this] def timestampToLong(ts: Long): Long = math.floor(ts.toDouble / 1000000L).toLong
+  // converting us to seconds in double
   private[this] def timestampToDouble(ts: Long): Double = {
-    ts / 10000000.0
+    ts / 1000000.0
   }
 
   // DateConverter
@@ -229,7 +229,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     case TimestampType =>
       // throw valid precision more than seconds, according to Hive.
       // Timestamp.nanos is in 0 to 999,999,999, no more than a second.
-      buildCast[Long](_, t => DateTimeUtils.millisToDays(t / 10000L))
+      buildCast[Long](_, t => DateTimeUtils.millisToDays(t / 1000L))
     // Hive throws this exception as a Semantic Exception
     // It is never possible to compare result when hive return with exception,
     // so we can return null
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index a492b966a5e31..dd5ec330a771b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -51,6 +51,6 @@ case class CurrentTimestamp() extends LeafExpression {
   override def dataType: DataType = TimestampType
 
   override def eval(input: InternalRow): Any = {
-    System.currentTimeMillis() * 10000L
+    System.currentTimeMillis() * 1000L
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 4269ad5d56737..c1ddee3ef0230 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -34,8 +34,8 @@ object DateTimeUtils {
   // see http://stackoverflow.com/questions/466321/convert-unix-timestamp-to-julian
   final val JULIAN_DAY_OF_EPOCH = 2440587  // and .5
   final val SECONDS_PER_DAY = 60 * 60 * 24L
-  final val HUNDRED_NANOS_PER_SECOND = 1000L * 1000L * 10L
-  final val NANOS_PER_SECOND = HUNDRED_NANOS_PER_SECOND * 100
+  final val MICROS_PER_SECOND = 1000L * 1000L
+  final val NANOS_PER_SECOND = MICROS_PER_SECOND * 1000L
 
 
   // Java TimeZone has no mention of thread safety. Use thread local instance to be safe.
@@ -77,8 +77,8 @@ object DateTimeUtils {
     threadLocalDateFormat.get.format(toJavaDate(days))
 
   // Converts Timestamp to string according to Hive TimestampWritable convention.
-  def timestampToString(num100ns: Long): String = {
-    val ts = toJavaTimestamp(num100ns)
+  def timestampToString(us: Long): String = {
+    val ts = toJavaTimestamp(us)
     val timestampString = ts.toString
     val formatted = threadLocalTimestampFormat.get.format(ts)
 
@@ -132,52 +132,52 @@ object DateTimeUtils {
   }
 
   /**
-   * Returns a java.sql.Timestamp from number of 100ns since epoch.
+   * Returns a java.sql.Timestamp from number of micros since epoch.
    */
-  def toJavaTimestamp(num100ns: Long): Timestamp = {
+  def toJavaTimestamp(us: Long): Timestamp = {
     // setNanos() will overwrite the millisecond part, so the milliseconds should be
     // cut off at seconds
-    var seconds = num100ns / HUNDRED_NANOS_PER_SECOND
-    var nanos = num100ns % HUNDRED_NANOS_PER_SECOND
+    var seconds = us / MICROS_PER_SECOND
+    var micros = us % MICROS_PER_SECOND
     // setNanos() can not accept negative value
-    if (nanos < 0) {
-      nanos += HUNDRED_NANOS_PER_SECOND
+    if (micros < 0) {
+      micros += MICROS_PER_SECOND
       seconds -= 1
     }
     val t = new Timestamp(seconds * 1000)
-    t.setNanos(nanos.toInt * 100)
+    t.setNanos(micros.toInt * 1000)
     t
   }
 
   /**
-   * Returns the number of 100ns since epoch from java.sql.Timestamp.
+   * Returns the number of micros since epoch from java.sql.Timestamp.
    */
   def fromJavaTimestamp(t: Timestamp): Long = {
     if (t != null) {
-      t.getTime() * 10000L + (t.getNanos().toLong / 100) % 10000L
+      t.getTime() * 1000L + (t.getNanos().toLong / 1000) % 1000L
     } else {
       0L
     }
   }
 
   /**
-   * Returns the number of 100ns (hundred of nanoseconds) since epoch from Julian day
+   * Returns the number of microseconds since epoch from Julian day
    * and nanoseconds in a day
    */
   def fromJulianDay(day: Int, nanoseconds: Long): Long = {
     // use Long to avoid rounding errors
     val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY - SECONDS_PER_DAY / 2
-    seconds * HUNDRED_NANOS_PER_SECOND + nanoseconds / 100L
+    seconds * MICROS_PER_SECOND + nanoseconds / 1000L
   }
 
   /**
-   * Returns Julian day and nanoseconds in a day from the number of 100ns (hundred of nanoseconds)
+   * Returns Julian day and nanoseconds in a day from the number of microseconds
    */
-  def toJulianDay(num100ns: Long): (Int, Long) = {
-    val seconds = num100ns / HUNDRED_NANOS_PER_SECOND + SECONDS_PER_DAY / 2
+  def toJulianDay(us: Long): (Int, Long) = {
+    val seconds = us / MICROS_PER_SECOND + SECONDS_PER_DAY / 2
     val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
     val secondsInDay = seconds % SECONDS_PER_DAY
-    val nanos = (num100ns % HUNDRED_NANOS_PER_SECOND) * 100L
+    val nanos = (us % MICROS_PER_SECOND) * 1000L
     (day.toInt, secondsInDay * NANOS_PER_SECOND + nanos)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 518961e38396f..919fdd470b79a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -293,15 +293,15 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("cast from timestamp") {
-    val millis = 15 * 1000 + 2
-    val seconds = millis * 1000 + 2
+    val millis = 15 * 1000 + 3
+    val seconds = millis * 1000 + 3
     val ts = new Timestamp(millis)
     val tss = new Timestamp(seconds)
     checkEvaluation(cast(ts, ShortType), 15.toShort)
     checkEvaluation(cast(ts, IntegerType), 15)
     checkEvaluation(cast(ts, LongType), 15.toLong)
-    checkEvaluation(cast(ts, FloatType), 15.002f)
-    checkEvaluation(cast(ts, DoubleType), 15.002)
+    checkEvaluation(cast(ts, FloatType), 15.003f)
+    checkEvaluation(cast(ts, DoubleType), 15.003)
     checkEvaluation(cast(cast(tss, ShortType), TimestampType), DateTimeUtils.fromJavaTimestamp(ts))
     checkEvaluation(cast(cast(tss, IntegerType), TimestampType),
       DateTimeUtils.fromJavaTimestamp(ts))
@@ -317,7 +317,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       Decimal(1))
 
     // A test for higher precision than millis
-    checkEvaluation(cast(cast(0.0000001, TimestampType), DoubleType), 0.0000001)
+    checkEvaluation(cast(cast(0.000001, TimestampType), DoubleType), 0.000001)
 
     checkEvaluation(cast(Double.NaN, TimestampType), null)
     checkEvaluation(cast(1.0 / 0.0, TimestampType), null)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 1d4a60c81efc5..f63ac191e7366 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -24,11 +24,11 @@ import org.apache.spark.SparkFunSuite
 
 class DateTimeUtilsSuite extends SparkFunSuite {
 
-  test("timestamp and 100ns") {
+  test("timestamp and us") {
     val now = new Timestamp(System.currentTimeMillis())
-    now.setNanos(100)
+    now.setNanos(1000)
     val ns = DateTimeUtils.fromJavaTimestamp(now)
-    assert(ns % 10000000L === 1)
+    assert(ns % 1000000L === 1)
     assert(DateTimeUtils.toJavaTimestamp(ns) === now)
 
     List(-111111111111L, -1L, 0, 1L, 111111111111L).foreach { t =>
@@ -38,7 +38,7 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     }
   }
 
-  test("100ns and julian day") {
+  test("us and julian day") {
     val (d, ns) = DateTimeUtils.toJulianDay(0)
     assert(d === DateTimeUtils.JULIAN_DAY_OF_EPOCH)
     assert(ns === DateTimeUtils.SECONDS_PER_DAY / 2 * DateTimeUtils.NANOS_PER_SECOND)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index 4b8ab63b5ab39..381e7ed54428f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -67,10 +67,10 @@ private[sql] object JacksonParser {
         DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(parser.getText).getTime)
 
       case (VALUE_STRING, TimestampType) =>
-        DateTimeUtils.stringToTime(parser.getText).getTime * 10000L
+        DateTimeUtils.stringToTime(parser.getText).getTime * 1000L
 
       case (VALUE_NUMBER_INT, TimestampType) =>
-        parser.getLongValue * 10000L
+        parser.getLongValue * 1000L
 
       case (_, StringType) =>
         val writer = new ByteArrayOutputStream()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index 01ba05cbd14f1..b392a51bf7dce 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -401,9 +401,9 @@ private[sql] object JsonRDD extends Logging {
 
   private def toTimestamp(value: Any): Long = {
     value match {
-      case value: java.lang.Integer => value.asInstanceOf[Int].toLong * 10000L
-      case value: java.lang.Long => value * 10000L
-      case value: java.lang.String => DateTimeUtils.stringToTime(value).getTime * 10000L
+      case value: java.lang.Integer => value.asInstanceOf[Int].toLong * 1000L
+      case value: java.lang.Long => value * 1000L
+      case value: java.lang.String => DateTimeUtils.stringToTime(value).getTime * 1000L
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 69ab1c292d221..566a52dc1b784 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -326,7 +326,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
     assert(cal.get(Calendar.HOUR) === 11)
     assert(cal.get(Calendar.MINUTE) === 22)
     assert(cal.get(Calendar.SECOND) === 33)
-    assert(rows(0).getAs[java.sql.Timestamp](2).getNanos === 543543500)
+    assert(rows(0).getAs[java.sql.Timestamp](2).getNanos === 543543000)
   }
 
   test("test DATE types") {
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 415a81644c58f..c884c399281a8 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -254,9 +254,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // the answer is sensitive for jdk version
     "udf_java_method",
 
-    // Spark SQL use Long for TimestampType, lose the precision under 100ns
+    // Spark SQL use Long for TimestampType, lose the precision under 1us
     "timestamp_1",
-    "timestamp_2"
+    "timestamp_2",
+    "timestamp_udf"
   )
 
   /**
@@ -803,7 +804,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "timestamp_comparison",
     "timestamp_lazy",
     "timestamp_null",
-    "timestamp_udf",
     "touch",
     "transform_ppr1",
     "transform_ppr2",
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 4cba17524af6c..a8f2ee37cb8ed 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -267,7 +267,7 @@ private[hive] trait HiveInspectors {
         poi.getWritableConstantValue.getHiveDecimal)
     case poi: WritableConstantTimestampObjectInspector =>
       val t = poi.getWritableConstantValue
-      t.getSeconds * 10000000L + t.getNanos / 100L
+      t.getSeconds * 1000000L + t.getNanos / 1000L
     case poi: WritableConstantIntObjectInspector =>
       poi.getWritableConstantValue.get()
     case poi: WritableConstantDoubleObjectInspector =>
@@ -332,7 +332,7 @@ private[hive] trait HiveInspectors {
       case x: DateObjectInspector => DateTimeUtils.fromJavaDate(x.getPrimitiveJavaObject(data))
       case x: TimestampObjectInspector if x.preferWritable() =>
         val t = x.getPrimitiveWritableObject(data)
-        t.getSeconds * 10000000L + t.getNanos / 100
+        t.getSeconds * 1000000L + t.getNanos / 1000L
       case ti: TimestampObjectInspector =>
         DateTimeUtils.fromJavaTimestamp(ti.getPrimitiveJavaObject(data))
       case _ => pi.getPrimitiveJavaObject(data)

From b55499a44ab74e33378211fb0d6940905d7c6318 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 8 Jul 2015 20:28:05 -0700
Subject: [PATCH 0296/1454] [SPARK-8932] Support copy() for UnsafeRows that do
 not use ObjectPools

We call Row.copy() in many places throughout SQL but UnsafeRow currently throws UnsupportedOperationException when copy() is called.

Supporting copying when ObjectPool is used may be difficult, since we may need to handle deep-copying of objects in the pool. In addition, this copy() method needs to produce a self-contained row object which may be passed around / buffered by downstream code which does not understand the UnsafeRow format.

In the long run, we'll need to figure out how to handle the ObjectPool corner cases, but this may be unnecessary if other changes are made. Therefore, in order to unblock my sort patch (#6444) I propose that we support copy() for the cases where UnsafeRow does not use an ObjectPool and continue to throw UnsupportedOperationException when an ObjectPool is used.

This patch accomplishes this by modifying UnsafeRow so that it knows the size of the row's backing data in order to be able to copy it into a byte array.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7306 from JoshRosen/SPARK-8932 and squashes the following commits:

338e6bf [Josh Rosen] Support copy for UnsafeRows that do not use ObjectPools.
---
 .../UnsafeFixedWidthAggregationMap.java       | 12 +++--
 .../sql/catalyst/expressions/UnsafeRow.java   | 32 +++++++++++-
 .../expressions/UnsafeRowConverter.scala      | 10 +++-
 .../expressions/UnsafeRowConverterSuite.scala | 52 ++++++++++++++-----
 4 files changed, 87 insertions(+), 19 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
index 1e79f4b2e88e5..79d55b36dab01 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
@@ -120,9 +120,11 @@ public UnsafeFixedWidthAggregationMap(
     this.bufferPool = new ObjectPool(initialCapacity);
 
     InternalRow initRow = initProjection.apply(emptyRow);
-    this.emptyBuffer = new byte[bufferConverter.getSizeRequirement(initRow)];
+    int emptyBufferSize = bufferConverter.getSizeRequirement(initRow);
+    this.emptyBuffer = new byte[emptyBufferSize];
     int writtenLength = bufferConverter.writeRow(
-      initRow, emptyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, bufferPool);
+      initRow, emptyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, emptyBufferSize,
+      bufferPool);
     assert (writtenLength == emptyBuffer.length): "Size requirement calculation was wrong!";
     // re-use the empty buffer only when there is no object saved in pool.
     reuseEmptyBuffer = bufferPool.size() == 0;
@@ -142,6 +144,7 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
       groupingKey,
       groupingKeyConversionScratchSpace,
       PlatformDependent.BYTE_ARRAY_OFFSET,
+      groupingKeySize,
       keyPool);
     assert (groupingKeySize == actualGroupingKeySize) : "Size requirement calculation was wrong!";
 
@@ -157,7 +160,7 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
         // There is some objects referenced by emptyBuffer, so generate a new one
         InternalRow initRow = initProjection.apply(emptyRow);
         bufferConverter.writeRow(initRow, emptyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET,
-          bufferPool);
+          groupingKeySize, bufferPool);
       }
       loc.putNewKey(
         groupingKeyConversionScratchSpace,
@@ -175,6 +178,7 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
       address.getBaseObject(),
       address.getBaseOffset(),
       bufferConverter.numFields(),
+      loc.getValueLength(),
       bufferPool
     );
     return currentBuffer;
@@ -214,12 +218,14 @@ public MapEntry next() {
           keyAddress.getBaseObject(),
           keyAddress.getBaseOffset(),
           keyConverter.numFields(),
+          loc.getKeyLength(),
           keyPool
         );
         entry.value.pointTo(
           valueAddress.getBaseObject(),
           valueAddress.getBaseOffset(),
           bufferConverter.numFields(),
+          loc.getValueLength(),
           bufferPool
         );
         return entry;
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index aeb64b045812f..edb7202245289 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -68,6 +68,9 @@ public final class UnsafeRow extends MutableRow {
   /** The number of fields in this row, used for calculating the bitset width (and in assertions) */
   private int numFields;
 
+  /** The size of this row's backing data, in bytes) */
+  private int sizeInBytes;
+
   public int length() { return numFields; }
 
   /** The width of the null tracking bit set, in bytes */
@@ -95,14 +98,17 @@ public UnsafeRow() { }
    * @param baseObject the base object
    * @param baseOffset the offset within the base object
    * @param numFields the number of fields in this row
+   * @param sizeInBytes the size of this row's backing data, in bytes
    * @param pool the object pool to hold arbitrary objects
    */
-  public void pointTo(Object baseObject, long baseOffset, int numFields, ObjectPool pool) {
+  public void pointTo(
+      Object baseObject, long baseOffset, int numFields, int sizeInBytes, ObjectPool pool) {
     assert numFields >= 0 : "numFields should >= 0";
     this.bitSetWidthInBytes = calculateBitSetWidthInBytes(numFields);
     this.baseObject = baseObject;
     this.baseOffset = baseOffset;
     this.numFields = numFields;
+    this.sizeInBytes = sizeInBytes;
     this.pool = pool;
   }
 
@@ -336,9 +342,31 @@ public double getDouble(int i) {
     }
   }
 
+  /**
+   * Copies this row, returning a self-contained UnsafeRow that stores its data in an internal
+   * byte array rather than referencing data stored in a data page.
+   * <p>
+   * This method is only supported on UnsafeRows that do not use ObjectPools.
+   */
   @Override
   public InternalRow copy() {
-    throw new UnsupportedOperationException();
+    if (pool != null) {
+      throw new UnsupportedOperationException(
+        "Copy is not supported for UnsafeRows that use object pools");
+    } else {
+      UnsafeRow rowCopy = new UnsafeRow();
+      final byte[] rowDataCopy = new byte[sizeInBytes];
+      PlatformDependent.copyMemory(
+        baseObject,
+        baseOffset,
+        rowDataCopy,
+        PlatformDependent.BYTE_ARRAY_OFFSET,
+        sizeInBytes
+      );
+      rowCopy.pointTo(
+        rowDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes, null);
+      return rowCopy;
+    }
   }
 
   @Override
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
index 1f395497a9839..6af5e6200e57b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
@@ -70,10 +70,16 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
    * @param row the row to convert
    * @param baseObject the base object of the destination address
    * @param baseOffset the base offset of the destination address
+   * @param rowLengthInBytes the length calculated by `getSizeRequirement(row)`
    * @return the number of bytes written. This should be equal to `getSizeRequirement(row)`.
    */
-  def writeRow(row: InternalRow, baseObject: Object, baseOffset: Long, pool: ObjectPool): Int = {
-    unsafeRow.pointTo(baseObject, baseOffset, writers.length, pool)
+  def writeRow(
+      row: InternalRow,
+      baseObject: Object,
+      baseOffset: Long,
+      rowLengthInBytes: Int,
+      pool: ObjectPool): Int = {
+    unsafeRow.pointTo(baseObject, baseOffset, writers.length, rowLengthInBytes, pool)
 
     if (writers.length > 0) {
       // zero-out the bitset
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 96d4e64ea344a..d00aeb4dfbf47 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -44,19 +44,32 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val sizeRequired: Int = converter.getSizeRequirement(row)
     assert(sizeRequired === 8 + (3 * 8))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+    val numBytesWritten =
+      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, null)
     assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
+    unsafeRow.pointTo(
+      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, null)
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getLong(1) === 1)
     assert(unsafeRow.getInt(2) === 2)
 
+    // We can copy UnsafeRows as long as they don't reference ObjectPools
+    val unsafeRowCopy = unsafeRow.copy()
+    assert(unsafeRowCopy.getLong(0) === 0)
+    assert(unsafeRowCopy.getLong(1) === 1)
+    assert(unsafeRowCopy.getInt(2) === 2)
+
     unsafeRow.setLong(1, 3)
     assert(unsafeRow.getLong(1) === 3)
     unsafeRow.setInt(2, 4)
     assert(unsafeRow.getInt(2) === 4)
+
+    // Mutating the original row should not have changed the copy
+    assert(unsafeRowCopy.getLong(0) === 0)
+    assert(unsafeRowCopy.getLong(1) === 1)
+    assert(unsafeRowCopy.getInt(2) === 2)
   }
 
   test("basic conversion with primitive, string and binary types") {
@@ -73,12 +86,14 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+    val numBytesWritten = converter.writeRow(
+      row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, null)
     assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
     val pool = new ObjectPool(10)
-    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, pool)
+    unsafeRow.pointTo(
+      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, pool)
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
     assert(unsafeRow.get(2) === "World".getBytes)
@@ -96,6 +111,11 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     unsafeRow.update(2, "Hello World".getBytes)
     assert(unsafeRow.get(2) === "Hello World".getBytes)
     assert(pool.size === 2)
+
+    // We do not support copy() for UnsafeRows that reference ObjectPools
+    intercept[UnsupportedOperationException] {
+      unsafeRow.copy()
+    }
   }
 
   test("basic conversion with primitive, decimal and array") {
@@ -111,12 +131,14 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val sizeRequired: Int = converter.getSizeRequirement(row)
     assert(sizeRequired === 8 + (8 * 3))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, pool)
+    val numBytesWritten =
+      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, pool)
     assert(numBytesWritten === sizeRequired)
     assert(pool.size === 2)
 
     val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, pool)
+    unsafeRow.pointTo(
+      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, pool)
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.get(1) === Decimal(1))
     assert(unsafeRow.get(2) === Array(2))
@@ -142,11 +164,13 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(sizeRequired === 8 + (8 * 4) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+    val numBytesWritten =
+      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, null)
     assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
+    unsafeRow.pointTo(
+      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, null)
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
     // Date is represented as Int in unsafeRow
@@ -190,12 +214,14 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val sizeRequired: Int = converter.getSizeRequirement(rowWithAllNullColumns)
     val createdFromNullBuffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten = converter.writeRow(
-      rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, null)
+      rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET,
+      sizeRequired, null)
     assert(numBytesWritten === sizeRequired)
 
     val createdFromNull = new UnsafeRow()
     createdFromNull.pointTo(
-      createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null)
+      createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length,
+      sizeRequired, null)
     for (i <- 0 to fieldTypes.length - 1) {
       assert(createdFromNull.isNullAt(i))
     }
@@ -233,10 +259,12 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val pool = new ObjectPool(1)
     val setToNullAfterCreationBuffer: Array[Long] = new Array[Long](sizeRequired / 8 + 2)
     converter.writeRow(
-      rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, pool)
+      rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET,
+      sizeRequired, pool)
     val setToNullAfterCreation = new UnsafeRow()
     setToNullAfterCreation.pointTo(
-      setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, pool)
+      setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length,
+      sizeRequired, pool)
 
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
     assert(setToNullAfterCreation.getBoolean(1) === rowWithNoNullColumns.getBoolean(1))

From 47ef423f860c3109d50c7e321616b267f4296e34 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 8 Jul 2015 20:29:08 -0700
Subject: [PATCH 0297/1454] [SPARK-8910] Fix MiMa flaky due to port contention
 issue

Due to the way MiMa works, we currently start a `SQLContext` pretty early on. This causes us to start a `SparkUI` that attempts to bind to port 4040. Because many tests run in parallel on the Jenkins machines, this  causes port contention sometimes and fails the MiMa tests.

Note that we already disabled the SparkUI for scalatests. However, the MiMa test is run before we even have a chance to load the default scalatest settings, so we need to explicitly disable the UI ourselves.

Author: Andrew Or <andrew@databricks.com>

Closes #7300 from andrewor14/mima-flaky and squashes the following commits:

b55a547 [Andrew Or] Do not enable SparkUI during tests
---
 .../scala/org/apache/spark/sql/test/TestSQLContext.scala  | 8 ++++----
 .../scala/org/apache/spark/sql/hive/test/TestHive.scala   | 7 ++++---
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
index 9fa394525d65c..b3a4231da91c2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -26,10 +26,10 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 /** A SQLContext that can be used for local testing. */
 class LocalSQLContext
   extends SQLContext(
-    new SparkContext(
-      "local[2]",
-      "TestSQLContext",
-      new SparkConf().set("spark.sql.testkey", "true"))) {
+    new SparkContext("local[2]", "TestSQLContext", new SparkConf()
+      .set("spark.sql.testkey", "true")
+      // SPARK-8910
+      .set("spark.ui.enabled", "false"))) {
 
   override protected[sql] def createSession(): SQLSession = {
     new this.SQLSession()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 7978fdacaedba..0f217bc66869f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -53,9 +53,10 @@ object TestHive
       "TestSQLContext",
       new SparkConf()
         .set("spark.sql.test", "")
-        .set(
-          "spark.sql.hive.metastore.barrierPrefixes",
-          "org.apache.spark.sql.hive.execution.PairSerDe")))
+        .set("spark.sql.hive.metastore.barrierPrefixes",
+          "org.apache.spark.sql.hive.execution.PairSerDe")
+        // SPARK-8910
+        .set("spark.ui.enabled", "false")))
 
 /**
  * A locally running test instance of Spark's Hive execution engine.

From aba5784dab24c03ddad89f7a1b5d3d0dc8d109be Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Thu, 9 Jul 2015 13:28:17 +0900
Subject: [PATCH 0298/1454] [SPARK-8937] [TEST] A setting
 `spark.unsafe.exceptionOnMemoryLeak ` is missing in ScalaTest config.

`spark.unsafe.exceptionOnMemoryLeak` is present in the config of surefire.

```
        <!-- Surefire runs all Java tests -->
        <plugin>
          <groupId>org.apache.maven.plugins</groupId>
          <artifactId>maven-surefire-plugin</artifactId>
          <version>2.18.1</version>
          <!-- Note config is repeated in scalatest config -->
...

<spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
            </systemProperties>
...
```

 but is absent in the config ScalaTest.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7308 from sarutak/add-setting-for-memory-leak and squashes the following commits:

95644e7 [Kousuke Saruta] Added a setting for memory leak
---
 pom.xml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pom.xml b/pom.xml
index 9cf2471b51304..529e47f8b5253 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1339,6 +1339,7 @@
               <spark.ui.enabled>false</spark.ui.enabled>
               <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
               <spark.driver.allowMultipleContexts>true</spark.driver.allowMultipleContexts>
+              <spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
             </systemProperties>
           </configuration>
           <executions>

From 768907eb7b0d3c11a420ef281454e36167011c89 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 8 Jul 2015 22:05:58 -0700
Subject: [PATCH 0299/1454] [SPARK-8926][SQL] Good errors for ExpectsInputType
 expressions

For example: `cannot resolve 'testfunction(null)' due to data type mismatch: argument 1 is expected to be of type int, however, null is of type datetype.`

Author: Michael Armbrust <michael@databricks.com>

Closes #7303 from marmbrus/expectsTypeErrors and squashes the following commits:

c654a0e [Michael Armbrust] fix udts and make errors pretty
137160d [Michael Armbrust] style
5428fda [Michael Armbrust] style
10fac82 [Michael Armbrust] [SPARK-8926][SQL] Good errors for ExpectsInputType expressions
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  12 +-
 .../expressions/ExpectsInputTypes.scala       |  13 +-
 .../spark/sql/types/AbstractDataType.scala    |  30 +++-
 .../apache/spark/sql/types/ArrayType.scala    |   8 +-
 .../org/apache/spark/sql/types/DataType.scala |   4 +-
 .../apache/spark/sql/types/DecimalType.scala  |   8 +-
 .../org/apache/spark/sql/types/MapType.scala  |   8 +-
 .../apache/spark/sql/types/StructType.scala   |   8 +-
 .../spark/sql/types/UserDefinedType.scala     |   5 +-
 .../analysis/AnalysisErrorSuite.scala         | 167 ++++++++++++++++++
 .../sql/catalyst/analysis/AnalysisSuite.scala | 126 ++-----------
 .../analysis/HiveTypeCoercionSuite.scala      |   8 +
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 13 files changed, 256 insertions(+), 143 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 5367b7f3308ee..8cb71995eb818 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -702,11 +702,19 @@ object HiveTypeCoercion {
       @Nullable val ret: Expression = (inType, expectedType) match {
 
         // If the expected type is already a parent of the input type, no need to cast.
-        case _ if expectedType.isParentOf(inType) => e
+        case _ if expectedType.isSameType(inType) => e
 
         // Cast null type (usually from null literals) into target types
         case (NullType, target) => Cast(e, target.defaultConcreteType)
 
+        // If the function accepts any numeric type (i.e. the ADT `NumericType`) and the input is
+        // already a number, leave it as is.
+        case (_: NumericType, NumericType) => e
+
+        // If the function accepts any numeric type and the input is a string, we follow the hive
+        // convention and cast that input into a double
+        case (StringType, NumericType) => Cast(e, NumericType.defaultConcreteType)
+
         // Implicit cast among numeric types
         // If input is a numeric type but not decimal, and we expect a decimal type,
         // cast the input to unlimited precision decimal.
@@ -732,7 +740,7 @@ object HiveTypeCoercion {
         // First see if we can find our input type in the type collection. If we can, then just
         // use the current expression; otherwise, find the first one we can implicitly cast.
         case (_, TypeCollection(types)) =>
-          if (types.exists(_.isParentOf(inType))) {
+          if (types.exists(_.isSameType(inType))) {
             e
           } else {
             types.flatMap(implicitCast(e, _)).headOption.orNull
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
index 916e30154d4f1..986cc09499d1f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -37,7 +37,16 @@ trait ExpectsInputTypes { self: Expression =>
   def inputTypes: Seq[AbstractDataType]
 
   override def checkInputDataTypes(): TypeCheckResult = {
-    // TODO: implement proper type checking.
-    TypeCheckResult.TypeCheckSuccess
+    val mismatches = children.zip(inputTypes).zipWithIndex.collect {
+      case ((child, expected), idx) if !expected.acceptsType(child.dataType) =>
+        s"Argument ${idx + 1} is expected to be of type ${expected.simpleString}, " +
+        s"however, ${child.prettyString} is of type ${child.dataType.simpleString}."
+    }
+
+    if (mismatches.isEmpty) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(mismatches.mkString(" "))
+    }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index fb1b47e946214..ad75fa2e31d90 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -34,9 +34,16 @@ private[sql] abstract class AbstractDataType {
   private[sql] def defaultConcreteType: DataType
 
   /**
-   * Returns true if this data type is a parent of the `childCandidate`.
+   * Returns true if this data type is the same type as `other`.  This is different that equality
+   * as equality will also consider data type parametrization, such as decimal precision.
    */
-  private[sql] def isParentOf(childCandidate: DataType): Boolean
+  private[sql] def isSameType(other: DataType): Boolean
+
+  /**
+   * Returns true if `other` is an acceptable input type for a function that expectes this,
+   * possibly abstract, DataType.
+   */
+  private[sql] def acceptsType(other: DataType): Boolean = isSameType(other)
 
   /** Readable string representation for the type. */
   private[sql] def simpleString: String
@@ -58,11 +65,14 @@ private[sql] class TypeCollection(private val types: Seq[AbstractDataType])
 
   require(types.nonEmpty, s"TypeCollection ($types) cannot be empty")
 
-  private[sql] override def defaultConcreteType: DataType = types.head.defaultConcreteType
+  override private[sql] def defaultConcreteType: DataType = types.head.defaultConcreteType
+
+  override private[sql] def isSameType(other: DataType): Boolean = false
 
-  private[sql] override def isParentOf(childCandidate: DataType): Boolean = false
+  override private[sql] def acceptsType(other: DataType): Boolean =
+    types.exists(_.isSameType(other))
 
-  private[sql] override def simpleString: String = {
+  override private[sql] def simpleString: String = {
     types.map(_.simpleString).mkString("(", " or ", ")")
   }
 }
@@ -108,7 +118,7 @@ abstract class NumericType extends AtomicType {
 }
 
 
-private[sql] object NumericType {
+private[sql] object NumericType extends AbstractDataType {
   /**
    * Enables matching against NumericType for expressions:
    * {{{
@@ -117,6 +127,14 @@ private[sql] object NumericType {
    * }}}
    */
   def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType]
+
+  override private[sql] def defaultConcreteType: DataType = DoubleType
+
+  override private[sql] def simpleString: String = "numeric"
+
+  override private[sql] def isSameType(other: DataType): Boolean = false
+
+  override private[sql] def acceptsType(other: DataType): Boolean = other.isInstanceOf[NumericType]
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index 43413ec761e6b..76ca7a84c1d1a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -26,13 +26,13 @@ object ArrayType extends AbstractDataType {
   /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is true. */
   def apply(elementType: DataType): ArrayType = ArrayType(elementType, containsNull = true)
 
-  private[sql] override def defaultConcreteType: DataType = ArrayType(NullType, containsNull = true)
+  override private[sql] def defaultConcreteType: DataType = ArrayType(NullType, containsNull = true)
 
-  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
-    childCandidate.isInstanceOf[ArrayType]
+  override private[sql] def isSameType(other: DataType): Boolean = {
+    other.isInstanceOf[ArrayType]
   }
 
-  private[sql] override def simpleString: String = "array"
+  override private[sql] def simpleString: String = "array"
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index a4c2da8e05f5d..57718228e490f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -76,9 +76,9 @@ abstract class DataType extends AbstractDataType {
    */
   private[spark] def asNullable: DataType
 
-  private[sql] override def defaultConcreteType: DataType = this
+  override private[sql] def defaultConcreteType: DataType = this
 
-  private[sql] override def isParentOf(childCandidate: DataType): Boolean = this == childCandidate
+  override private[sql] def isSameType(other: DataType): Boolean = this == other
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index 127b16ff85bed..a1cafeab1704d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -84,13 +84,13 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT
 /** Extra factory methods and pattern matchers for Decimals */
 object DecimalType extends AbstractDataType {
 
-  private[sql] override def defaultConcreteType: DataType = Unlimited
+  override private[sql] def defaultConcreteType: DataType = Unlimited
 
-  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
-    childCandidate.isInstanceOf[DecimalType]
+  override private[sql] def isSameType(other: DataType): Boolean = {
+    other.isInstanceOf[DecimalType]
   }
 
-  private[sql] override def simpleString: String = "decimal"
+  override private[sql] def simpleString: String = "decimal"
 
   val Unlimited: DecimalType = DecimalType(None)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
index 868dea13d971e..ddead10bc2171 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
@@ -69,13 +69,13 @@ case class MapType(
 
 object MapType extends AbstractDataType {
 
-  private[sql] override def defaultConcreteType: DataType = apply(NullType, NullType)
+  override private[sql] def defaultConcreteType: DataType = apply(NullType, NullType)
 
-  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
-    childCandidate.isInstanceOf[MapType]
+  override private[sql] def isSameType(other: DataType): Boolean = {
+    other.isInstanceOf[MapType]
   }
 
-  private[sql] override def simpleString: String = "map"
+  override private[sql] def simpleString: String = "map"
 
   /**
    * Construct a [[MapType]] object with the given key type and value type.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index e2d3f53f7d978..e0b8ff91786a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -303,13 +303,13 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
 
 object StructType extends AbstractDataType {
 
-  private[sql] override def defaultConcreteType: DataType = new StructType
+  override private[sql] def defaultConcreteType: DataType = new StructType
 
-  private[sql] override def isParentOf(childCandidate: DataType): Boolean = {
-    childCandidate.isInstanceOf[StructType]
+  override private[sql] def isSameType(other: DataType): Boolean = {
+    other.isInstanceOf[StructType]
   }
 
-  private[sql] override def simpleString: String = "struct"
+  override private[sql] def simpleString: String = "struct"
 
   private[sql] def fromString(raw: String): StructType = DataType.fromString(raw) match {
     case t: StructType => t
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala
index 6b20505c6009a..e47cfb4833bd8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala
@@ -77,5 +77,8 @@ abstract class UserDefinedType[UserType] extends DataType with Serializable {
    * For UDT, asNullable will not change the nullability of its internal sqlType and just returns
    * itself.
    */
-  private[spark] override def asNullable: UserDefinedType[UserType] = this
+  override private[spark] def asNullable: UserDefinedType[UserType] = this
+
+  override private[sql] def acceptsType(dataType: DataType) =
+    this.getClass == dataType.getClass
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
new file mode 100644
index 0000000000000..73236c3acbca2
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -0,0 +1,167 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.analysis
+
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.catalyst.{InternalRow, SimpleCatalystConf}
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
+
+case class TestFunction(
+    children: Seq[Expression],
+    inputTypes: Seq[AbstractDataType]) extends Expression with ExpectsInputTypes {
+  override def nullable: Boolean = true
+  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+  override def dataType: DataType = StringType
+}
+
+case class UnresolvedTestPlan() extends LeafNode {
+  override lazy val resolved = false
+  override def output: Seq[Attribute] = Nil
+}
+
+class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
+  import AnalysisSuite._
+
+  def errorTest(
+      name: String,
+      plan: LogicalPlan,
+      errorMessages: Seq[String],
+      caseSensitive: Boolean = true): Unit = {
+    test(name) {
+      val error = intercept[AnalysisException] {
+        if (caseSensitive) {
+          caseSensitiveAnalyze(plan)
+        } else {
+          caseInsensitiveAnalyze(plan)
+        }
+      }
+
+      errorMessages.foreach(m => assert(error.getMessage.toLowerCase contains m.toLowerCase))
+    }
+  }
+
+  val dateLit = Literal.create(null, DateType)
+
+  errorTest(
+    "single invalid type, single arg",
+    testRelation.select(TestFunction(dateLit :: Nil, IntegerType :: Nil).as('a)),
+    "cannot resolve" :: "testfunction" :: "argument 1" :: "expected to be of type int" ::
+    "null is of type date" ::Nil)
+
+  errorTest(
+    "single invalid type, second arg",
+    testRelation.select(
+      TestFunction(dateLit :: dateLit :: Nil, DateType :: IntegerType :: Nil).as('a)),
+    "cannot resolve" :: "testfunction" :: "argument 2" :: "expected to be of type int" ::
+    "null is of type date" ::Nil)
+
+  errorTest(
+    "multiple invalid type",
+    testRelation.select(
+      TestFunction(dateLit :: dateLit :: Nil, IntegerType :: IntegerType :: Nil).as('a)),
+    "cannot resolve" :: "testfunction" :: "argument 1" :: "argument 2" ::
+    "expected to be of type int" :: "null is of type date" ::Nil)
+
+  errorTest(
+    "unresolved window function",
+    testRelation2.select(
+      WindowExpression(
+        UnresolvedWindowFunction(
+          "lead",
+          UnresolvedAttribute("c") :: Nil),
+        WindowSpecDefinition(
+          UnresolvedAttribute("a") :: Nil,
+          SortOrder(UnresolvedAttribute("b"), Ascending) :: Nil,
+          UnspecifiedFrame)).as('window)),
+    "lead" :: "window functions currently requires a HiveContext" :: Nil)
+
+  errorTest(
+    "too many generators",
+    listRelation.select(Explode('list).as('a), Explode('list).as('b)),
+    "only one generator" :: "explode" :: Nil)
+
+  errorTest(
+    "unresolved attributes",
+    testRelation.select('abcd),
+    "cannot resolve" :: "abcd" :: Nil)
+
+  errorTest(
+    "bad casts",
+    testRelation.select(Literal(1).cast(BinaryType).as('badCast)),
+    "cannot cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil)
+
+  errorTest(
+    "non-boolean filters",
+    testRelation.where(Literal(1)),
+    "filter" :: "'1'" :: "not a boolean" :: Literal(1).dataType.simpleString :: Nil)
+
+  errorTest(
+    "missing group by",
+    testRelation2.groupBy('a)('b),
+    "'b'" :: "group by" :: Nil
+  )
+
+  errorTest(
+    "ambiguous field",
+    nestedRelation.select($"top.duplicateField"),
+    "Ambiguous reference to fields" :: "duplicateField" :: Nil,
+    caseSensitive = false)
+
+  errorTest(
+    "ambiguous field due to case insensitivity",
+    nestedRelation.select($"top.differentCase"),
+    "Ambiguous reference to fields" :: "differentCase" :: "differentcase" :: Nil,
+    caseSensitive = false)
+
+  errorTest(
+    "missing field",
+    nestedRelation2.select($"top.c"),
+    "No such struct field" :: "aField" :: "bField" :: "cField" :: Nil,
+    caseSensitive = false)
+
+  errorTest(
+    "catch all unresolved plan",
+    UnresolvedTestPlan(),
+    "unresolved" :: Nil)
+
+
+  test("SPARK-6452 regression test") {
+    // CheckAnalysis should throw AnalysisException when Aggregate contains missing attribute(s)
+    val plan =
+      Aggregate(
+        Nil,
+        Alias(Sum(AttributeReference("a", IntegerType)(exprId = ExprId(1))), "b")() :: Nil,
+        LocalRelation(
+          AttributeReference("a", IntegerType)(exprId = ExprId(2))))
+
+    assert(plan.resolved)
+
+    val message = intercept[AnalysisException] {
+      caseSensitiveAnalyze(plan)
+    }.getMessage
+
+    assert(message.contains("resolved attribute(s) a#1 missing from a#2"))
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 77ca080f366cd..58df1de983a09 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.catalyst.SimpleCatalystConf
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 
-class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
+object AnalysisSuite {
   val caseSensitiveConf = new SimpleCatalystConf(true)
   val caseInsensitiveConf = new SimpleCatalystConf(false)
 
@@ -61,25 +61,28 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
   val nestedRelation = LocalRelation(
     AttributeReference("top", StructType(
       StructField("duplicateField", StringType) ::
-      StructField("duplicateField", StringType) ::
-      StructField("differentCase", StringType) ::
-      StructField("differentcase", StringType) :: Nil
+        StructField("duplicateField", StringType) ::
+        StructField("differentCase", StringType) ::
+        StructField("differentcase", StringType) :: Nil
     ))())
 
   val nestedRelation2 = LocalRelation(
     AttributeReference("top", StructType(
       StructField("aField", StringType) ::
-      StructField("bField", StringType) ::
-      StructField("cField", StringType) :: Nil
+        StructField("bField", StringType) ::
+        StructField("cField", StringType) :: Nil
     ))())
 
   val listRelation = LocalRelation(
     AttributeReference("list", ArrayType(IntegerType))())
 
-  before {
-    caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-    caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-  }
+  caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
+  caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
+}
+
+
+class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
+  import AnalysisSuite._
 
   test("union project *") {
     val plan = (1 to 100)
@@ -149,91 +152,6 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
       caseInsensitiveAnalyzer.execute(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation)
   }
 
-  def errorTest(
-      name: String,
-      plan: LogicalPlan,
-      errorMessages: Seq[String],
-      caseSensitive: Boolean = true): Unit = {
-    test(name) {
-      val error = intercept[AnalysisException] {
-        if (caseSensitive) {
-          caseSensitiveAnalyze(plan)
-        } else {
-          caseInsensitiveAnalyze(plan)
-        }
-      }
-
-      errorMessages.foreach(m => assert(error.getMessage.toLowerCase contains m.toLowerCase))
-    }
-  }
-
-  errorTest(
-    "unresolved window function",
-    testRelation2.select(
-      WindowExpression(
-        UnresolvedWindowFunction(
-          "lead",
-          UnresolvedAttribute("c") :: Nil),
-        WindowSpecDefinition(
-          UnresolvedAttribute("a") :: Nil,
-          SortOrder(UnresolvedAttribute("b"), Ascending) :: Nil,
-          UnspecifiedFrame)).as('window)),
-      "lead" :: "window functions currently requires a HiveContext" :: Nil)
-
-  errorTest(
-    "too many generators",
-    listRelation.select(Explode('list).as('a), Explode('list).as('b)),
-    "only one generator" :: "explode" :: Nil)
-
-  errorTest(
-    "unresolved attributes",
-    testRelation.select('abcd),
-    "cannot resolve" :: "abcd" :: Nil)
-
-  errorTest(
-    "bad casts",
-    testRelation.select(Literal(1).cast(BinaryType).as('badCast)),
-    "cannot cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil)
-
-  errorTest(
-    "non-boolean filters",
-    testRelation.where(Literal(1)),
-    "filter" :: "'1'" :: "not a boolean" :: Literal(1).dataType.simpleString :: Nil)
-
-  errorTest(
-    "missing group by",
-    testRelation2.groupBy('a)('b),
-    "'b'" :: "group by" :: Nil
-  )
-
-  errorTest(
-    "ambiguous field",
-    nestedRelation.select($"top.duplicateField"),
-    "Ambiguous reference to fields" :: "duplicateField" :: Nil,
-    caseSensitive = false)
-
-  errorTest(
-    "ambiguous field due to case insensitivity",
-    nestedRelation.select($"top.differentCase"),
-    "Ambiguous reference to fields" :: "differentCase" :: "differentcase" :: Nil,
-    caseSensitive = false)
-
-  errorTest(
-    "missing field",
-    nestedRelation2.select($"top.c"),
-    "No such struct field" :: "aField" :: "bField" :: "cField" :: Nil,
-    caseSensitive = false)
-
-  case class UnresolvedTestPlan() extends LeafNode {
-    override lazy val resolved = false
-    override def output: Seq[Attribute] = Nil
-  }
-
-  errorTest(
-    "catch all unresolved plan",
-    UnresolvedTestPlan(),
-    "unresolved" :: Nil)
-
 
   test("divide should be casted into fractional types") {
     val testRelation2 = LocalRelation(
@@ -258,22 +176,4 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
     assert(pl(3).dataType == DecimalType.Unlimited)
     assert(pl(4).dataType == DoubleType)
   }
-
-  test("SPARK-6452 regression test") {
-    // CheckAnalysis should throw AnalysisException when Aggregate contains missing attribute(s)
-    val plan =
-      Aggregate(
-        Nil,
-        Alias(Sum(AttributeReference("a", IntegerType)(exprId = ExprId(1))), "b")() :: Nil,
-        LocalRelation(
-          AttributeReference("a", IntegerType)(exprId = ExprId(2))))
-
-    assert(plan.resolved)
-
-    val message = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan)
-    }.getMessage
-
-    assert(message.contains("resolved attribute(s) a#1 missing from a#2"))
-  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 93db33d44eb25..6e3aa0eebeb15 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -77,6 +77,14 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldCast(DecimalType(10, 2), TypeCollection(IntegerType, DecimalType), DecimalType(10, 2))
     shouldCast(DecimalType(10, 2), TypeCollection(DecimalType, IntegerType), DecimalType(10, 2))
     shouldCast(IntegerType, TypeCollection(DecimalType(10, 2), StringType), DecimalType(10, 2))
+
+    shouldCast(StringType, NumericType, DoubleType)
+
+    // NumericType should not be changed when function accepts any of them.
+    Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType,
+      DecimalType.Unlimited, DecimalType(10, 2)).foreach { tpe =>
+      shouldCast(tpe, NumericType, tpe)
+    }
   }
 
   test("ineligible implicit type cast") {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 439d8cab5f257..bbc39b892b79e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -359,7 +359,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     hiveconf.set(key, value)
   }
 
-  private[sql] override def setConf[T](entry: SQLConfEntry[T], value: T): Unit = {
+  override private[sql] def setConf[T](entry: SQLConfEntry[T], value: T): Unit = {
     setConf(entry.key, entry.stringConverter(value))
   }
 

From a240bf3b44b15d0da5182d6ebec281dbdc5439e8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 8 Jul 2015 22:08:50 -0700
Subject: [PATCH 0300/1454] Closes #7310.


From 3dab0da42940a46f0c4aa4853bdb5c64c4cb2613 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 8 Jul 2015 22:09:12 -0700
Subject: [PATCH 0301/1454] [SPARK-8928] [SQL] Makes CatalystSchemaConverter
 sticking to 1.4.x- when handling Parquet LISTs in compatible mode

This PR is based on #7209 authored by Sephiroth-Lin.

Author: Weizhong Lin <linweizhong@huawei.com>

Closes #7304 from liancheng/spark-8928 and squashes the following commits:

75267fe [Cheng Lian] Makes CatalystSchemaConverter sticking to 1.4.x- when handling LISTs in compatible mode
---
 .../spark/sql/parquet/CatalystSchemaConverter.scala    |  6 ++++--
 .../apache/spark/sql/parquet/ParquetSchemaSuite.scala  | 10 +++++-----
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index de3a72d8146c5..1ea6926af6d5b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -461,7 +461,8 @@ private[parquet] class CatalystSchemaConverter(
           field.name,
           Types
             .buildGroup(REPEATED)
-            .addField(convertField(StructField("element", elementType, nullable)))
+            // "array_element" is the name chosen by parquet-hive (1.7.0 and prior version)
+            .addField(convertField(StructField("array_element", elementType, nullable)))
             .named(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME))
 
       // Spark 1.4.x and prior versions convert ArrayType with non-nullable elements into a 2-level
@@ -474,7 +475,8 @@ private[parquet] class CatalystSchemaConverter(
         ConversionPatterns.listType(
           repetition,
           field.name,
-          convertField(StructField("element", elementType, nullable), REPEATED))
+          // "array" is the name chosen by parquet-avro (1.7.0 and prior version)
+          convertField(StructField("array", elementType, nullable), REPEATED))
 
       // Spark 1.4.x and prior versions convert MapType into a 3-level group annotated by
       // MAP_KEY_VALUE.  This is covered by `convertGroupField(field: GroupType): DataType`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
index 35d3c33f99a06..fa629392674bd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
@@ -174,7 +174,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
     """
       |message root {
       |  optional group _1 (LIST) {
-      |    repeated int32 element;
+      |    repeated int32 array;
       |  }
       |}
     """.stripMargin)
@@ -198,7 +198,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |message root {
       |  optional group _1 (LIST) {
       |    repeated group bag {
-      |      optional int32 element;
+      |      optional int32 array_element;
       |    }
       |  }
       |}
@@ -267,7 +267,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |        optional binary _1 (UTF8);
       |        optional group _2 (LIST) {
       |          repeated group bag {
-      |            optional group element {
+      |            optional group array_element {
       |              required int32 _1;
       |              required double _2;
       |            }
@@ -616,7 +616,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     """message root {
       |  optional group f1 (LIST) {
       |    repeated group bag {
-      |      optional int32 element;
+      |      optional int32 array_element;
       |    }
       |  }
       |}
@@ -648,7 +648,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
         nullable = true))),
     """message root {
       |  optional group f1 (LIST) {
-      |    repeated int32 element;
+      |    repeated int32 array;
       |  }
       |}
     """.stripMargin)

From c056484c0741e2a03d4a916538e1b9e3e65e71c3 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 8 Jul 2015 22:14:38 -0700
Subject: [PATCH 0302/1454] Revert "[SPARK-8928] [SQL] Makes
 CatalystSchemaConverter sticking to 1.4.x- when handling Parquet LISTs in
 compatible mode"

This reverts commit 3dab0da42940a46f0c4aa4853bdb5c64c4cb2613.
---
 .../spark/sql/parquet/CatalystSchemaConverter.scala    |  6 ++----
 .../apache/spark/sql/parquet/ParquetSchemaSuite.scala  | 10 +++++-----
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index 1ea6926af6d5b..de3a72d8146c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -461,8 +461,7 @@ private[parquet] class CatalystSchemaConverter(
           field.name,
           Types
             .buildGroup(REPEATED)
-            // "array_element" is the name chosen by parquet-hive (1.7.0 and prior version)
-            .addField(convertField(StructField("array_element", elementType, nullable)))
+            .addField(convertField(StructField("element", elementType, nullable)))
             .named(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME))
 
       // Spark 1.4.x and prior versions convert ArrayType with non-nullable elements into a 2-level
@@ -475,8 +474,7 @@ private[parquet] class CatalystSchemaConverter(
         ConversionPatterns.listType(
           repetition,
           field.name,
-          // "array" is the name chosen by parquet-avro (1.7.0 and prior version)
-          convertField(StructField("array", elementType, nullable), REPEATED))
+          convertField(StructField("element", elementType, nullable), REPEATED))
 
       // Spark 1.4.x and prior versions convert MapType into a 3-level group annotated by
       // MAP_KEY_VALUE.  This is covered by `convertGroupField(field: GroupType): DataType`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
index fa629392674bd..35d3c33f99a06 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
@@ -174,7 +174,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
     """
       |message root {
       |  optional group _1 (LIST) {
-      |    repeated int32 array;
+      |    repeated int32 element;
       |  }
       |}
     """.stripMargin)
@@ -198,7 +198,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |message root {
       |  optional group _1 (LIST) {
       |    repeated group bag {
-      |      optional int32 array_element;
+      |      optional int32 element;
       |    }
       |  }
       |}
@@ -267,7 +267,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |        optional binary _1 (UTF8);
       |        optional group _2 (LIST) {
       |          repeated group bag {
-      |            optional group array_element {
+      |            optional group element {
       |              required int32 _1;
       |              required double _2;
       |            }
@@ -616,7 +616,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     """message root {
       |  optional group f1 (LIST) {
       |    repeated group bag {
-      |      optional int32 array_element;
+      |      optional int32 element;
       |    }
       |  }
       |}
@@ -648,7 +648,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
         nullable = true))),
     """message root {
       |  optional group f1 (LIST) {
-      |    repeated int32 array;
+      |    repeated int32 element;
       |  }
       |}
     """.stripMargin)

From 851e247caad0977cfd4998254d9602624e06539f Mon Sep 17 00:00:00 2001
From: Weizhong Lin <linweizhong@huawei.com>
Date: Wed, 8 Jul 2015 22:18:39 -0700
Subject: [PATCH 0303/1454] [SPARK-8928] [SQL] Makes CatalystSchemaConverter
 sticking to 1.4.x- when handling Parquet LISTs in compatible mode

This PR is based on #7209 authored by Sephiroth-Lin.

Author: Weizhong Lin <linweizhong@huawei.com>

Closes #7314 from liancheng/spark-8928 and squashes the following commits:

75267fe [Cheng Lian] Makes CatalystSchemaConverter sticking to 1.4.x- when handling LISTs in compatible mode
---
 .../spark/sql/parquet/CatalystSchemaConverter.scala    |  6 ++++--
 .../apache/spark/sql/parquet/ParquetSchemaSuite.scala  | 10 +++++-----
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index de3a72d8146c5..1ea6926af6d5b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -461,7 +461,8 @@ private[parquet] class CatalystSchemaConverter(
           field.name,
           Types
             .buildGroup(REPEATED)
-            .addField(convertField(StructField("element", elementType, nullable)))
+            // "array_element" is the name chosen by parquet-hive (1.7.0 and prior version)
+            .addField(convertField(StructField("array_element", elementType, nullable)))
             .named(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME))
 
       // Spark 1.4.x and prior versions convert ArrayType with non-nullable elements into a 2-level
@@ -474,7 +475,8 @@ private[parquet] class CatalystSchemaConverter(
         ConversionPatterns.listType(
           repetition,
           field.name,
-          convertField(StructField("element", elementType, nullable), REPEATED))
+          // "array" is the name chosen by parquet-avro (1.7.0 and prior version)
+          convertField(StructField("array", elementType, nullable), REPEATED))
 
       // Spark 1.4.x and prior versions convert MapType into a 3-level group annotated by
       // MAP_KEY_VALUE.  This is covered by `convertGroupField(field: GroupType): DataType`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
index 35d3c33f99a06..fa629392674bd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
@@ -174,7 +174,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
     """
       |message root {
       |  optional group _1 (LIST) {
-      |    repeated int32 element;
+      |    repeated int32 array;
       |  }
       |}
     """.stripMargin)
@@ -198,7 +198,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |message root {
       |  optional group _1 (LIST) {
       |    repeated group bag {
-      |      optional int32 element;
+      |      optional int32 array_element;
       |    }
       |  }
       |}
@@ -267,7 +267,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |        optional binary _1 (UTF8);
       |        optional group _2 (LIST) {
       |          repeated group bag {
-      |            optional group element {
+      |            optional group array_element {
       |              required int32 _1;
       |              required double _2;
       |            }
@@ -616,7 +616,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     """message root {
       |  optional group f1 (LIST) {
       |    repeated group bag {
-      |      optional int32 element;
+      |      optional int32 array_element;
       |    }
       |  }
       |}
@@ -648,7 +648,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
         nullable = true))),
     """message root {
       |  optional group f1 (LIST) {
-      |    repeated int32 element;
+      |    repeated int32 array;
       |  }
       |}
     """.stripMargin)

From 09cb0d9c2dcb83818ced22ff9bd6a51688ea7ffe Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 9 Jul 2015 00:26:25 -0700
Subject: [PATCH 0304/1454] [SPARK-8942][SQL] use double not decimal when cast
 double and float to timestamp

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7312 from cloud-fan/minor and squashes the following commits:

a4589fa [Wenchen Fan] use double not decimal when cast double and float to timestamp
---
 .../spark/sql/catalyst/expressions/Cast.scala  | 18 ++++++------------
 1 file changed, 6 insertions(+), 12 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 567feca7136f9..7f2383dedc035 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -192,23 +192,18 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
       buildCast[Decimal](_, d => decimalToTimestamp(d))
     // TimestampWritable.doubleToTimestamp
     case DoubleType =>
-      buildCast[Double](_, d => try {
-        decimalToTimestamp(Decimal(d))
-      } catch {
-        case _: NumberFormatException => null
-      })
+      buildCast[Double](_, d => doubleToTimestamp(d))
     // TimestampWritable.floatToTimestamp
     case FloatType =>
-      buildCast[Float](_, f => try {
-        decimalToTimestamp(Decimal(f))
-      } catch {
-        case _: NumberFormatException => null
-      })
+      buildCast[Float](_, f => doubleToTimestamp(f.toDouble))
   }
 
   private[this] def decimalToTimestamp(d: Decimal): Long = {
     (d.toBigDecimal * 1000000L).longValue()
   }
+  private[this] def doubleToTimestamp(d: Double): Any = {
+    if (d.isNaN || d.isInfinite) null else (d * 1000000L).toLong
+  }
 
   // converting milliseconds to us
   private[this] def longToTimestamp(t: Long): Long = t * 1000L
@@ -396,8 +391,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     buildCast[InternalRow](_, row => {
       var i = 0
       while (i < row.length) {
-        val v = row(i)
-        newRow.update(i, if (v == null) null else casts(i)(v))
+        newRow.update(i, if (row.isNullAt(i)) null else casts(i)(row(i)))
         i += 1
       }
       newRow.copy()

From f88b12537ee81d914ef7c51a08f80cb28d93c8ed Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Thu, 9 Jul 2015 08:16:26 -0700
Subject: [PATCH 0305/1454] [SPARK-6266] [MLLIB] PySpark SparseVector missing
 doc for size, indices, values

Write missing pydocs in `SparseVector` attributes.

Author: lewuathe <lewuathe@me.com>

Closes #7290 from Lewuathe/SPARK-6266 and squashes the following commits:

51d9895 [lewuathe] Update docs
0480d35 [lewuathe] Merge branch 'master' into SPARK-6266
ba42cf3 [lewuathe] [SPARK-6266] PySpark SparseVector missing doc for size, indices, values
---
 python/pyspark/mllib/linalg.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 51ac198305711..040886f71775b 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -445,8 +445,10 @@ def __init__(self, size, *args):
         values (sorted by index).
 
         :param size: Size of the vector.
-        :param args: Non-zero entries, as a dictionary, list of tupes,
-               or two sorted lists containing indices and values.
+        :param args: Active entries, as a dictionary {index: value, ...},
+          a list of tuples [(index, value), ...], or a list of strictly i
+          ncreasing indices and a list of corresponding values [index, ...],
+          [value, ...]. Inactive entries are treated as zeros.
 
         >>> SparseVector(4, {1: 1.0, 3: 5.5})
         SparseVector(4, {1: 1.0, 3: 5.5})
@@ -456,6 +458,7 @@ def __init__(self, size, *args):
         SparseVector(4, {1: 1.0, 3: 5.5})
         """
         self.size = int(size)
+        """ Size of the vector. """
         assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments"
         if len(args) == 1:
             pairs = args[0]
@@ -463,7 +466,9 @@ def __init__(self, size, *args):
                 pairs = pairs.items()
             pairs = sorted(pairs)
             self.indices = np.array([p[0] for p in pairs], dtype=np.int32)
+            """ A list of indices corresponding to active entries. """
             self.values = np.array([p[1] for p in pairs], dtype=np.float64)
+            """ A list of values corresponding to active entries. """
         else:
             if isinstance(args[0], bytes):
                 assert isinstance(args[1], bytes), "values should be string too"

From 23448a9e988a1b92bd05ee8c6c1a096c83375a12 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 9 Jul 2015 09:20:16 -0700
Subject: [PATCH 0306/1454] [SPARK-8931] [SQL] Fallback to interpreted
 evaluation if failed to compile in codegen

Exception will not be catched during tests.

cc marmbrus rxin

Author: Davies Liu <davies@databricks.com>

Closes #7309 from davies/fallback and squashes the following commits:

969a612 [Davies Liu] throw exception during tests
f844f77 [Davies Liu] fallback
a3091bc [Davies Liu] Merge branch 'master' of github.com:apache/spark into fallback
364a0d6 [Davies Liu] fallback to interpret mode if failed to compile
---
 .../spark/sql/execution/SparkPlan.scala       | 51 +++++++++++++++++--
 .../apache/spark/sql/sources/commands.scala   | 13 ++++-
 2 files changed, 58 insertions(+), 6 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index ca53186383237..4d7d8626a0ecc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -153,12 +153,24 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     buf.toArray.map(converter(_).asInstanceOf[Row])
   }
 
+  private[this] def isTesting: Boolean = sys.props.contains("spark.testing")
+
   protected def newProjection(
       expressions: Seq[Expression], inputSchema: Seq[Attribute]): Projection = {
     log.debug(
       s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
     if (codegenEnabled) {
-      GenerateProjection.generate(expressions, inputSchema)
+      try {
+        GenerateProjection.generate(expressions, inputSchema)
+      } catch {
+        case e: Exception =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate projection, fallback to interpret", e)
+            new InterpretedProjection(expressions, inputSchema)
+          }
+      }
     } else {
       new InterpretedProjection(expressions, inputSchema)
     }
@@ -170,17 +182,36 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     log.debug(
       s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
     if(codegenEnabled) {
-      GenerateMutableProjection.generate(expressions, inputSchema)
+      try {
+        GenerateMutableProjection.generate(expressions, inputSchema)
+      } catch {
+        case e: Exception =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate mutable projection, fallback to interpreted", e)
+            () => new InterpretedMutableProjection(expressions, inputSchema)
+          }
+      }
     } else {
       () => new InterpretedMutableProjection(expressions, inputSchema)
     }
   }
 
-
   protected def newPredicate(
       expression: Expression, inputSchema: Seq[Attribute]): (InternalRow) => Boolean = {
     if (codegenEnabled) {
-      GeneratePredicate.generate(expression, inputSchema)
+      try {
+        GeneratePredicate.generate(expression, inputSchema)
+      } catch {
+        case e: Exception =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate predicate, fallback to interpreted", e)
+            InterpretedPredicate.create(expression, inputSchema)
+          }
+      }
     } else {
       InterpretedPredicate.create(expression, inputSchema)
     }
@@ -190,7 +221,17 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
       order: Seq[SortOrder],
       inputSchema: Seq[Attribute]): Ordering[InternalRow] = {
     if (codegenEnabled) {
-      GenerateOrdering.generate(order, inputSchema)
+      try {
+        GenerateOrdering.generate(order, inputSchema)
+      } catch {
+        case e: Exception =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate ordering, fallback to interpreted", e)
+            new RowOrdering(order, inputSchema)
+          }
+      }
     } else {
       new RowOrdering(order, inputSchema)
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index ecbc889770625..9189d176111d5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -276,7 +276,18 @@ private[sql] case class InsertIntoHadoopFsRelation(
     log.debug(
       s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
     if (codegenEnabled) {
-      GenerateProjection.generate(expressions, inputSchema)
+
+      try {
+        GenerateProjection.generate(expressions, inputSchema)
+      } catch {
+        case e: Exception =>
+          if (sys.props.contains("spark.testing")) {
+            throw e
+          } else {
+            log.error("failed to generate projection, fallback to interpreted", e)
+            new InterpretedProjection(expressions, inputSchema)
+          }
+      }
     } else {
       new InterpretedProjection(expressions, inputSchema)
     }

From a1964e9d902bb31f001893da8bc81f6dce08c908 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Thu, 9 Jul 2015 09:22:24 -0700
Subject: [PATCH 0307/1454] [SPARK-8830] [SQL] native levenshtein distance

Jira: https://issues.apache.org/jira/browse/SPARK-8830

rxin and HuJiayin can you have a look on it.

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7236 from tarekauel/native-levenshtein-distance and squashes the following commits:

ee4c4de [Tarek Auel] [SPARK-8830] implemented improvement proposals
c252e71 [Tarek Auel] [SPARK-8830] removed chartAt; use unsafe method for byte array comparison
ddf2222 [Tarek Auel] Merge branch 'master' into native-levenshtein-distance
179920a [Tarek Auel] [SPARK-8830] added description
5e9ed54 [Tarek Auel] [SPARK-8830] removed StringUtils import
dce4308 [Tarek Auel] [SPARK-8830] native levenshtein distance
---
 .../expressions/stringOperations.scala        |  9 ++-
 .../expressions/StringFunctionsSuite.scala    |  5 ++
 .../apache/spark/unsafe/types/UTF8String.java | 66 ++++++++++++++++++-
 .../spark/unsafe/types/UTF8StringSuite.java   | 24 +++++++
 4 files changed, 97 insertions(+), 7 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 47fc7cdaa826c..57f436485becf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -284,13 +284,12 @@ case class Levenshtein(left: Expression, right: Expression) extends BinaryExpres
 
   override def dataType: DataType = IntegerType
 
-  protected override def nullSafeEval(input1: Any, input2: Any): Any =
-    StringUtils.getLevenshteinDistance(input1.toString, input2.toString)
+  protected override def nullSafeEval(leftValue: Any, rightValue: Any): Any =
+    leftValue.asInstanceOf[UTF8String].levenshteinDistance(rightValue.asInstanceOf[UTF8String])
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val stringUtils = classOf[StringUtils].getName
-    defineCodeGen(ctx, ev, (left, right) =>
-      s"$stringUtils.getLevenshteinDistance($left.toString(), $right.toString())")
+    nullSafeCodeGen(ctx, ev, (left, right) =>
+      s"${ev.primitive} = $left.levenshteinDistance($right);")
   }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
index 1efbe1a245e83..69bef1c63e9dc 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
@@ -282,5 +282,10 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Levenshtein(Literal("abc"), Literal("abc")), 0)
     checkEvaluation(Levenshtein(Literal("kitten"), Literal("sitting")), 3)
     checkEvaluation(Levenshtein(Literal("frog"), Literal("fog")), 1)
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    checkEvaluation(Levenshtein(Literal("千世"), Literal("fog")), 3)
+    checkEvaluation(Levenshtein(Literal("世界千世"), Literal("大a界b")), 4)
+    // scalastyle:on
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index d2a25096a5e7a..847d80ad583f6 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -99,8 +99,6 @@ public int numBytes() {
 
   /**
    * Returns the number of code points in it.
-   *
-   * This is only used by Substring() when `start` is negative.
    */
   public int numChars() {
     int len = 0;
@@ -254,6 +252,70 @@ public boolean equals(final Object other) {
     }
   }
 
+  /**
+   * Levenshtein distance is a metric for measuring the distance of two strings. The distance is
+   * defined by the minimum number of single-character edits (i.e. insertions, deletions or
+   * substitutions) that are required to change one of the strings into the other.
+   */
+  public int levenshteinDistance(UTF8String other) {
+    // Implementation adopted from org.apache.common.lang3.StringUtils.getLevenshteinDistance
+
+    int n = numChars();
+    int m = other.numChars();
+
+    if (n == 0) {
+      return m;
+    } else if (m == 0) {
+      return n;
+    }
+
+    UTF8String s, t;
+
+    if (n <= m) {
+      s = this;
+      t = other;
+    } else {
+      s = other;
+      t = this;
+      int swap;
+      swap = n;
+      n = m;
+      m = swap;
+    }
+
+    int p[] = new int[n + 1];
+    int d[] = new int[n + 1];
+    int swap[];
+
+    int i, i_bytes, j, j_bytes, num_bytes_j, cost;
+
+    for (i = 0; i <= n; i++) {
+      p[i] = i;
+    }
+
+    for (j = 0, j_bytes = 0; j < m; j_bytes += num_bytes_j, j++) {
+      num_bytes_j = numBytesForFirstByte(t.getByte(j_bytes));
+      d[0] = j + 1;
+
+      for (i = 0, i_bytes = 0; i < n; i_bytes += numBytesForFirstByte(s.getByte(i_bytes)), i++) {
+        if (s.getByte(i_bytes) != t.getByte(j_bytes) ||
+              num_bytes_j != numBytesForFirstByte(s.getByte(i_bytes))) {
+          cost = 1;
+        } else {
+          cost = (ByteArrayMethods.arrayEquals(t.base, t.offset + j_bytes, s.base,
+              s.offset + i_bytes, num_bytes_j)) ? 0 : 1;
+        }
+        d[i + 1] = Math.min(Math.min(d[i] + 1, p[i + 1] + 1), p[i] + cost);
+      }
+
+      swap = p;
+      p = d;
+      d = swap;
+    }
+
+    return p[n];
+  }
+
   @Override
   public int hashCode() {
     int result = 1;
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 8ec69ebac8b37..fb463ba17f50b 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -128,4 +128,28 @@ public void substring() {
     assertEquals(fromString("数据砖头").substring(3, 5), fromString("头"));
     assertEquals(fromString("ߵ梷").substring(0, 2), fromString("ߵ梷"));
   }
+  
+  @Test
+  public void levenshteinDistance() {
+    assertEquals(
+        UTF8String.fromString("").levenshteinDistance(UTF8String.fromString("")), 0);
+    assertEquals(
+        UTF8String.fromString("").levenshteinDistance(UTF8String.fromString("a")), 1);
+    assertEquals(
+        UTF8String.fromString("aaapppp").levenshteinDistance(UTF8String.fromString("")), 7);
+    assertEquals(
+        UTF8String.fromString("frog").levenshteinDistance(UTF8String.fromString("fog")), 1);
+    assertEquals(
+        UTF8String.fromString("fly").levenshteinDistance(UTF8String.fromString("ant")),3);
+    assertEquals(
+        UTF8String.fromString("elephant").levenshteinDistance(UTF8String.fromString("hippo")), 7);
+    assertEquals(
+        UTF8String.fromString("hippo").levenshteinDistance(UTF8String.fromString("elephant")), 7);
+    assertEquals(
+        UTF8String.fromString("hippo").levenshteinDistance(UTF8String.fromString("zzzzzzzz")), 8);
+    assertEquals(
+        UTF8String.fromString("hello").levenshteinDistance(UTF8String.fromString("hallo")),1);
+    assertEquals(
+        UTF8String.fromString("世界千世").levenshteinDistance(UTF8String.fromString("千a世b")),4);
+  }
 }

From 59cc38944fe5c1dffc6551775bd939e2ac66c65e Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 9 Jul 2015 09:57:12 -0700
Subject: [PATCH 0308/1454] [SPARK-8940] [SPARKR] Don't overwrite given schema
 in createDataFrame

JIRA: https://issues.apache.org/jira/browse/SPARK-8940

The given `schema` parameter will be overwritten in `createDataFrame` now. If it is not null, we shouldn't overwrite it.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7311 from viirya/df_not_overwrite_schema and squashes the following commits:

2385139 [Liang-Chi Hsieh] Don't overwrite given schema if it is not null.
---
 R/pkg/R/SQLContext.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 9a743a3411533..30978bb50d339 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -86,7 +86,9 @@ infer_type <- function(x) {
 createDataFrame <- function(sqlContext, data, schema = NULL, samplingRatio = 1.0) {
   if (is.data.frame(data)) {
       # get the names of columns, they will be put into RDD
-      schema <- names(data)
+      if (is.null(schema)) {
+        schema <- names(data)
+      }
       n <- nrow(data)
       m <- ncol(data)
       # get rid of factor type

From e204d22bb70f28b1cc090ab60f12078479be4ae0 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 9 Jul 2015 10:01:01 -0700
Subject: [PATCH 0309/1454] [SPARK-8948][SQL] Remove ExtractValueWithOrdinal
 abstract class

Also added more documentation for the file.

Author: Reynold Xin <rxin@databricks.com>

Closes #7316 from rxin/extract-value and squashes the following commits:

069cb7e [Reynold Xin] Removed ExtractValueWithOrdinal.
621b705 [Reynold Xin] Reverted a line.
11ebd6c [Reynold Xin] [Minor][SQL] Improve documentation for complex type extractors.
---
 ...alue.scala => complexTypeExtractors.scala} | 54 ++++++++++++-------
 1 file changed, 34 insertions(+), 20 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{ExtractValue.scala => complexTypeExtractors.scala} (86%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
similarity index 86%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
index 2b25ba03579ec..73cc930c45832 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
@@ -25,6 +25,11 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types._
 
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// This file defines all the expressions to extract values out of complex types.
+// For example, getting a field out of an array, map, or struct.
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
 
 object ExtractValue {
   /**
@@ -73,11 +78,10 @@ object ExtractValue {
     }
   }
 
-  def unapply(g: ExtractValue): Option[(Expression, Expression)] = {
-    g match {
-      case o: ExtractValueWithOrdinal => Some((o.child, o.ordinal))
-      case s: ExtractValueWithStruct => Some((s.child, null))
-    }
+  def unapply(g: ExtractValue): Option[(Expression, Expression)] = g match {
+    case o: GetArrayItem => Some((o.child, o.ordinal))
+    case o: GetMapValue => Some((o.child, o.key))
+    case s: ExtractValueWithStruct => Some((s.child, null))
   }
 
   /**
@@ -117,6 +121,8 @@ abstract class ExtractValueWithStruct extends UnaryExpression with ExtractValue
 
 /**
  * Returns the value of fields in the Struct `child`.
+ *
+ * No need to do type checking since it is handled by [[ExtractValue]].
  */
 case class GetStructField(child: Expression, field: StructField, ordinal: Int)
   extends ExtractValueWithStruct {
@@ -142,6 +148,8 @@ case class GetStructField(child: Expression, field: StructField, ordinal: Int)
 
 /**
  * Returns the array of value of fields in the Array of Struct `child`.
+ *
+ * No need to do type checking since it is handled by [[ExtractValue]].
  */
 case class GetArrayStructFields(
     child: Expression,
@@ -178,25 +186,21 @@ case class GetArrayStructFields(
   }
 }
 
-abstract class ExtractValueWithOrdinal extends BinaryExpression with ExtractValue {
-  self: Product =>
+/**
+ * Returns the field at `ordinal` in the Array `child`.
+ *
+ * No need to do type checking since it is handled by [[ExtractValue]].
+ */
+case class GetArrayItem(child: Expression, ordinal: Expression)
+  extends BinaryExpression with ExtractValue {
 
-  def ordinal: Expression
-  def child: Expression
+  override def toString: String = s"$child[$ordinal]"
 
   override def left: Expression = child
   override def right: Expression = ordinal
 
   /** `Null` is returned for invalid ordinals. */
   override def nullable: Boolean = true
-  override def toString: String = s"$child[$ordinal]"
-}
-
-/**
- * Returns the field at `ordinal` in the Array `child`
- */
-case class GetArrayItem(child: Expression, ordinal: Expression)
-  extends ExtractValueWithOrdinal {
 
   override def dataType: DataType = child.dataType.asInstanceOf[ArrayType].elementType
 
@@ -227,10 +231,20 @@ case class GetArrayItem(child: Expression, ordinal: Expression)
 }
 
 /**
- * Returns the value of key `ordinal` in Map `child`
+ * Returns the value of key `ordinal` in Map `child`.
+ *
+ * No need to do type checking since it is handled by [[ExtractValue]].
  */
-case class GetMapValue(child: Expression, ordinal: Expression)
-  extends ExtractValueWithOrdinal {
+case class GetMapValue(child: Expression, key: Expression)
+  extends BinaryExpression with ExtractValue {
+
+  override def toString: String = s"$child[$key]"
+
+  override def left: Expression = child
+  override def right: Expression = key
+
+  /** `Null` is returned for invalid ordinals. */
+  override def nullable: Boolean = true
 
   override def dataType: DataType = child.dataType.asInstanceOf[MapType].valueType
 

From a870a82fb6f57bb63bd6f1e95da944a30f67519a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 9 Jul 2015 10:01:33 -0700
Subject: [PATCH 0310/1454] [SPARK-8926][SQL] Code review followup.

I merged https://github.com/apache/spark/pull/7303 so it unblocks another PR. This addresses my own code review comment for that PR.

Author: Reynold Xin <rxin@databricks.com>

Closes #7313 from rxin/adt and squashes the following commits:

7ade82b [Reynold Xin] Fixed unit tests.
f8d5533 [Reynold Xin] [SPARK-8926][SQL] Code review followup.
---
 .../catalyst/expressions/ExpectsInputTypes.scala |  4 ++--
 .../spark/sql/types/AbstractDataType.scala       | 16 ++++++++++++++++
 .../catalyst/analysis/AnalysisErrorSuite.scala   |  8 ++++----
 .../analysis/HiveTypeCoercionSuite.scala         |  1 +
 4 files changed, 23 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
index 986cc09499d1f..3eb0eb195c80d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -39,8 +39,8 @@ trait ExpectsInputTypes { self: Expression =>
   override def checkInputDataTypes(): TypeCheckResult = {
     val mismatches = children.zip(inputTypes).zipWithIndex.collect {
       case ((child, expected), idx) if !expected.acceptsType(child.dataType) =>
-        s"Argument ${idx + 1} is expected to be of type ${expected.simpleString}, " +
-        s"however, ${child.prettyString} is of type ${child.dataType.simpleString}."
+        s"argument ${idx + 1} is expected to be of type ${expected.simpleString}, " +
+        s"however, '${child.prettyString}' is of type ${child.dataType.simpleString}."
     }
 
     if (mismatches.isEmpty) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index ad75fa2e31d90..32f87440b4e37 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -36,12 +36,28 @@ private[sql] abstract class AbstractDataType {
   /**
    * Returns true if this data type is the same type as `other`.  This is different that equality
    * as equality will also consider data type parametrization, such as decimal precision.
+   *
+   * {{{
+   *   // this should return true
+   *   DecimalType.isSameType(DecimalType(10, 2))
+   *
+   *   // this should return false
+   *   NumericType.isSameType(DecimalType(10, 2))
+   * }}}
    */
   private[sql] def isSameType(other: DataType): Boolean
 
   /**
    * Returns true if `other` is an acceptable input type for a function that expectes this,
    * possibly abstract, DataType.
+   *
+   * {{{
+   *   // this should return true
+   *   DecimalType.isSameType(DecimalType(10, 2))
+   *
+   *   // this should return true as well
+   *   NumericType.acceptsType(DecimalType(10, 2))
+   * }}}
    */
   private[sql] def acceptsType(other: DataType): Boolean = isSameType(other)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 73236c3acbca2..9d0c69a2451d1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -58,7 +58,7 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
         }
       }
 
-      errorMessages.foreach(m => assert(error.getMessage.toLowerCase contains m.toLowerCase))
+      errorMessages.foreach(m => assert(error.getMessage.toLowerCase.contains(m.toLowerCase)))
     }
   }
 
@@ -68,21 +68,21 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
     "single invalid type, single arg",
     testRelation.select(TestFunction(dateLit :: Nil, IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 1" :: "expected to be of type int" ::
-    "null is of type date" ::Nil)
+    "'null' is of type date" ::Nil)
 
   errorTest(
     "single invalid type, second arg",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, DateType :: IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 2" :: "expected to be of type int" ::
-    "null is of type date" ::Nil)
+    "'null' is of type date" ::Nil)
 
   errorTest(
     "multiple invalid type",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, IntegerType :: IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 1" :: "argument 2" ::
-    "expected to be of type int" :: "null is of type date" ::Nil)
+    "expected to be of type int" :: "'null' is of type date" ::Nil)
 
   errorTest(
     "unresolved window function",
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 6e3aa0eebeb15..acb9a433de903 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -79,6 +79,7 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldCast(IntegerType, TypeCollection(DecimalType(10, 2), StringType), DecimalType(10, 2))
 
     shouldCast(StringType, NumericType, DoubleType)
+    shouldCast(StringType, TypeCollection(NumericType, BinaryType), DoubleType)
 
     // NumericType should not be changed when function accepts any of them.
     Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType,

From f6c0bd5c3755b2f9bab633a5d478240fdaf1c593 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 9 Jul 2015 10:04:42 -0700
Subject: [PATCH 0311/1454] [SPARK-8938][SQL] Implement toString for Interval
 data type

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7315 from cloud-fan/toString and squashes the following commits:

4fc8d80 [Wenchen Fan] Implement toString for Interval data type
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 24 ++++++--
 .../apache/spark/unsafe/types/Interval.java   | 42 +++++++++++++
 .../spark/unsafe/types/IntervalSuite.java     | 59 +++++++++++++++++++
 3 files changed, 119 insertions(+), 6 deletions(-)
 create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index dedd8c8fa3620..d4ef04c2294a2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -353,22 +353,34 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     integral <~ intervalUnit("microsecond") ^^ { case num => num.toLong }
 
   protected lazy val millisecond: Parser[Long] =
-    integral <~ intervalUnit("millisecond") ^^ { case num => num.toLong * 1000 }
+    integral <~ intervalUnit("millisecond") ^^ {
+      case num => num.toLong * Interval.MICROS_PER_MILLI
+    }
 
   protected lazy val second: Parser[Long] =
-    integral <~ intervalUnit("second") ^^ { case num => num.toLong * 1000 * 1000 }
+    integral <~ intervalUnit("second") ^^ {
+      case num => num.toLong * Interval.MICROS_PER_SECOND
+    }
 
   protected lazy val minute: Parser[Long] =
-    integral <~ intervalUnit("minute") ^^ { case num => num.toLong * 1000 * 1000 * 60 }
+    integral <~ intervalUnit("minute") ^^ {
+      case num => num.toLong * Interval.MICROS_PER_MINUTE
+    }
 
   protected lazy val hour: Parser[Long] =
-    integral <~ intervalUnit("hour") ^^ { case num => num.toLong * 1000 * 1000 * 3600 }
+    integral <~ intervalUnit("hour") ^^ {
+      case num => num.toLong * Interval.MICROS_PER_HOUR
+    }
 
   protected lazy val day: Parser[Long] =
-    integral <~ intervalUnit("day") ^^ { case num => num.toLong * 1000 * 1000 * 3600 * 24 }
+    integral <~ intervalUnit("day") ^^ {
+      case num => num.toLong * Interval.MICROS_PER_DAY
+    }
 
   protected lazy val week: Parser[Long] =
-    integral <~ intervalUnit("week") ^^ { case num => num.toLong * 1000 * 1000 * 3600 * 24 * 7 }
+    integral <~ intervalUnit("week") ^^ {
+      case num => num.toLong * Interval.MICROS_PER_WEEK
+    }
 
   protected lazy val intervalLiteral: Parser[Literal] =
     INTERVAL ~> year.? ~ month.? ~ week.? ~ day.? ~ hour.? ~ minute.? ~ second.? ~
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
index 3eb67ede062d9..0af982d4844c2 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
@@ -23,6 +23,13 @@
  * The internal representation of interval type.
  */
 public final class Interval implements Serializable {
+  public static final long MICROS_PER_MILLI = 1000L;
+  public static final long MICROS_PER_SECOND = MICROS_PER_MILLI * 1000;
+  public static final long MICROS_PER_MINUTE = MICROS_PER_SECOND * 60;
+  public static final long MICROS_PER_HOUR = MICROS_PER_MINUTE * 60;
+  public static final long MICROS_PER_DAY = MICROS_PER_HOUR * 24;
+  public static final long MICROS_PER_WEEK = MICROS_PER_DAY * 7;
+
   public final int months;
   public final long microseconds;
 
@@ -44,4 +51,39 @@ public boolean equals(Object other) {
   public int hashCode() {
     return 31 * months + (int) microseconds;
   }
+
+  @Override
+  public String toString() {
+    StringBuilder sb = new StringBuilder("interval");
+
+    if (months != 0) {
+      appendUnit(sb, months / 12, "year");
+      appendUnit(sb, months % 12, "month");
+    }
+
+    if (microseconds != 0) {
+      long rest = microseconds;
+      appendUnit(sb, rest / MICROS_PER_WEEK, "week");
+      rest %= MICROS_PER_WEEK;
+      appendUnit(sb, rest / MICROS_PER_DAY, "day");
+      rest %= MICROS_PER_DAY;
+      appendUnit(sb, rest / MICROS_PER_HOUR, "hour");
+      rest %= MICROS_PER_HOUR;
+      appendUnit(sb, rest / MICROS_PER_MINUTE, "minute");
+      rest %= MICROS_PER_MINUTE;
+      appendUnit(sb, rest / MICROS_PER_SECOND, "second");
+      rest %= MICROS_PER_SECOND;
+      appendUnit(sb, rest / MICROS_PER_MILLI, "millisecond");
+      rest %= MICROS_PER_MILLI;
+      appendUnit(sb, rest, "microsecond");
+    }
+
+    return sb.toString();
+  }
+
+  private void appendUnit(StringBuilder sb, long value, String unit) {
+    if (value != 0) {
+      sb.append(" " + value + " " + unit + "s");
+    }
+  }
 }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
new file mode 100644
index 0000000000000..0f4f38b2b03be
--- /dev/null
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
@@ -0,0 +1,59 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.unsafe.types;
+
+import org.junit.Test;
+
+import static junit.framework.Assert.*;
+import static org.apache.spark.unsafe.types.Interval.*;
+
+public class IntervalSuite {
+
+  @Test
+  public void equalsTest() {
+    Interval i1 = new Interval(3, 123);
+    Interval i2 = new Interval(3, 321);
+    Interval i3 = new Interval(1, 123);
+    Interval i4 = new Interval(3, 123);
+
+    assertNotSame(i1, i2);
+    assertNotSame(i1, i3);
+    assertNotSame(i2, i3);
+    assertEquals(i1, i4);
+  }
+
+  @Test
+  public void toStringTest() {
+    Interval i;
+
+    i = new Interval(34, 0);
+    assertEquals(i.toString(), "interval 2 years 10 months");
+
+    i = new Interval(-34, 0);
+    assertEquals(i.toString(), "interval -2 years -10 months");
+
+    i = new Interval(0, 3 * MICROS_PER_WEEK + 13 * MICROS_PER_HOUR + 123);
+    assertEquals(i.toString(), "interval 3 weeks 13 hours 123 microseconds");
+
+    i = new Interval(0, -3 * MICROS_PER_WEEK - 13 * MICROS_PER_HOUR - 123);
+    assertEquals(i.toString(), "interval -3 weeks -13 hours -123 microseconds");
+
+    i = new Interval(34, 3 * MICROS_PER_WEEK + 13 * MICROS_PER_HOUR + 123);
+    assertEquals(i.toString(), "interval 2 years 10 months 3 weeks 13 hours 123 microseconds");
+  }
+}

From c59e268d17cf10e46dbdbe760e2a7580a6364692 Mon Sep 17 00:00:00 2001
From: JPark <JPark@JPark.me>
Date: Thu, 9 Jul 2015 10:23:36 -0700
Subject: [PATCH 0312/1454] [SPARK-8863] [EC2] Check aws access key from aws
 credentials if there is no boto config

'spark_ec2.py' use boto to control ec2.
And boto can support '~/.aws/credentials' which is AWS CLI default configuration file.

We can check this information from ref of boto.

"A boto config file is a text file formatted like an .ini configuration file that specifies values for options that control the behavior of the boto library. In Unix/Linux systems, on startup, the boto library looks for configuration files in the following locations and in the following order:
/etc/boto.cfg - for site-wide settings that all users on this machine will use
(if profile is given) ~/.aws/credentials - for credentials shared between SDKs
(if profile is given) ~/.boto - for user-specific settings
~/.aws/credentials - for credentials shared between SDKs
~/.boto - for user-specific settings"

* ref of boto: http://boto.readthedocs.org/en/latest/boto_config_tut.html
* ref of aws cli : http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html

However 'spark_ec2.py' only check boto config & environment variable even if there is '~/.aws/credentials', and 'spark_ec2.py' is terminated.

So I changed to check '~/.aws/credentials'.

cc rxin

Jira : https://issues.apache.org/jira/browse/SPARK-8863

Author: JPark <JPark@JPark.me>

Closes #7252 from JuhongPark/master and squashes the following commits:

23c5792 [JPark] Check aws access key from aws credentials if there is no boto config
---
 ec2/spark_ec2.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index dd0c12d25980b..ae4f2ecc5bde7 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -325,14 +325,16 @@ def parse_args():
     home_dir = os.getenv('HOME')
     if home_dir is None or not os.path.isfile(home_dir + '/.boto'):
         if not os.path.isfile('/etc/boto.cfg'):
-            if os.getenv('AWS_ACCESS_KEY_ID') is None:
-                print("ERROR: The environment variable AWS_ACCESS_KEY_ID must be set",
-                      file=stderr)
-                sys.exit(1)
-            if os.getenv('AWS_SECRET_ACCESS_KEY') is None:
-                print("ERROR: The environment variable AWS_SECRET_ACCESS_KEY must be set",
-                      file=stderr)
-                sys.exit(1)
+            # If there is no boto config, check aws credentials
+            if not os.path.isfile(home_dir + '/.aws/credentials'):
+                if os.getenv('AWS_ACCESS_KEY_ID') is None:
+                    print("ERROR: The environment variable AWS_ACCESS_KEY_ID must be set",
+                          file=stderr)
+                    sys.exit(1)
+                if os.getenv('AWS_SECRET_ACCESS_KEY') is None:
+                    print("ERROR: The environment variable AWS_SECRET_ACCESS_KEY must be set",
+                          file=stderr)
+                    sys.exit(1)
     return (opts, action, cluster_name)
 
 

From 0cd84c86cac68600a74d84e50ad40c0c8b84822a Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Thu, 9 Jul 2015 10:26:38 -0700
Subject: [PATCH 0313/1454] [SPARK-8703] [ML] Add CountVectorizer as a ml
 transformer to convert document to words count vector

jira: https://issues.apache.org/jira/browse/SPARK-8703

Converts a text document to a sparse vector of token counts.

I can further add an estimator to extract vocabulary from corpus if that's appropriate.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7084 from hhbyyh/countVectorization and squashes the following commits:

5f3f655 [Yuhao Yang] text change
24728e4 [Yuhao Yang] style improvement
576728a [Yuhao Yang] rename to model and some fix
1deca28 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into countVectorization
99b0c14 [Yuhao Yang] undo extension from HashingTF
12c2dc8 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into countVectorization
7ee1c31 [Yuhao Yang] extends HashingTF
809fb59 [Yuhao Yang] minor fix for ut
7c61fb3 [Yuhao Yang] add countVectorizer
---
 .../ml/feature/CountVectorizerModel.scala     | 82 +++++++++++++++++++
 .../ml/feature/CountVectorizorSuite.scala     | 73 +++++++++++++++++
 2 files changed, 155 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
new file mode 100644
index 0000000000000..6b77de89a0330
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.feature
+
+import scala.collection.mutable
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.param.{ParamMap, ParamValidators, IntParam}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.linalg.{Vectors, VectorUDT, Vector}
+import org.apache.spark.sql.types.{StringType, ArrayType, DataType}
+
+/**
+ * :: Experimental ::
+ * Converts a text document to a sparse vector of token counts.
+ * @param vocabulary An Array over terms. Only the terms in the vocabulary will be counted.
+ */
+@Experimental
+class CountVectorizerModel (override val uid: String, val vocabulary: Array[String])
+  extends UnaryTransformer[Seq[String], Vector, CountVectorizerModel] {
+
+  def this(vocabulary: Array[String]) =
+    this(Identifiable.randomUID("cntVec"), vocabulary)
+
+  /**
+   * Corpus-specific filter to ignore scarce words in a document. For each document, terms with
+   * frequency (count) less than the given threshold are ignored.
+   * Default: 1
+   * @group param
+   */
+  val minTermFreq: IntParam = new IntParam(this, "minTermFreq",
+    "minimum frequency (count) filter used to neglect scarce words (>= 1). For each document, " +
+      "terms with frequency less than the given threshold are ignored.", ParamValidators.gtEq(1))
+
+  /** @group setParam */
+  def setMinTermFreq(value: Int): this.type = set(minTermFreq, value)
+
+  /** @group getParam */
+  def getMinTermFreq: Int = $(minTermFreq)
+
+  setDefault(minTermFreq -> 1)
+
+  override protected def createTransformFunc: Seq[String] => Vector = {
+    val dict = vocabulary.zipWithIndex.toMap
+    document =>
+      val termCounts = mutable.HashMap.empty[Int, Double]
+      document.foreach { term =>
+        dict.get(term) match {
+          case Some(index) => termCounts.put(index, termCounts.getOrElse(index, 0.0) + 1.0)
+          case None => // ignore terms not in the vocabulary
+        }
+      }
+      Vectors.sparse(dict.size, termCounts.filter(_._2 >= $(minTermFreq)).toSeq)
+  }
+
+  override protected def validateInputType(inputType: DataType): Unit = {
+    require(inputType.sameType(ArrayType(StringType)),
+      s"Input type must be ArrayType(StringType) but got $inputType.")
+  }
+
+  override protected def outputDataType: DataType = new VectorUDT()
+
+  override def copy(extra: ParamMap): CountVectorizerModel = {
+    val copied = new CountVectorizerModel(uid, vocabulary)
+    copyValues(copied, extra)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala
new file mode 100644
index 0000000000000..e90d9d4ef21ff
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+
+class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    ParamsSuite.checkParams(new CountVectorizerModel(Array("empty")))
+  }
+
+  test("CountVectorizerModel common cases") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, "a b c d".split(" ").toSeq,
+        Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0)))),
+      (1, "a b b c d  a".split(" ").toSeq,
+        Vectors.sparse(4, Seq((0, 2.0), (1, 2.0), (2, 1.0), (3, 1.0)))),
+      (2, "a".split(" ").toSeq, Vectors.sparse(4, Seq((0, 1.0)))),
+      (3, "".split(" ").toSeq, Vectors.sparse(4, Seq())), // empty string
+      (4, "a notInDict d".split(" ").toSeq,
+        Vectors.sparse(4, Seq((0, 1.0), (3, 1.0))))  // with words not in vocabulary
+    )).toDF("id", "words", "expected")
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+    val output = cv.transform(df).collect()
+    output.foreach { p =>
+      val features = p.getAs[Vector]("features")
+      val expected = p.getAs[Vector]("expected")
+      assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizerModel with minTermFreq") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, "a a a b b c c c d ".split(" ").toSeq, Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
+      (1, "c c c c c c".split(" ").toSeq, Vectors.sparse(4, Seq((2, 6.0)))),
+      (2, "a".split(" ").toSeq, Vectors.sparse(4, Seq())),
+      (3, "e e e e e".split(" ").toSeq, Vectors.sparse(4, Seq())))
+    ).toDF("id", "words", "expected")
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinTermFreq(3)
+    val output = cv.transform(df).collect()
+    output.foreach { p =>
+      val features = p.getAs[Vector]("features")
+      val expected = p.getAs[Vector]("expected")
+      assert(features ~== expected absTol 1e-14)
+    }
+  }
+}
+
+

From 0b0b9ceaf73de472198c9804fb7ae61fa2a2e097 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 9 Jul 2015 11:11:34 -0700
Subject: [PATCH 0314/1454] [SPARK-8247] [SPARK-8249] [SPARK-8252] [SPARK-8254]
 [SPARK-8257] [SPARK-8258] [SPARK-8259] [SPARK-8261] [SPARK-8262] [SPARK-8253]
 [SPARK-8260] [SPARK-8267] [SQL] Add String Expressions

Author: Cheng Hao <hao.cheng@intel.com>

Closes #6762 from chenghao-intel/str_funcs and squashes the following commits:

b09a909 [Cheng Hao] update the code as feedback
7ebbf4c [Cheng Hao] Add more string expressions
---
 .../catalyst/analysis/FunctionRegistry.scala  |  12 +
 .../expressions/stringOperations.scala        | 306 ++++++++++++++-
 .../expressions/StringFunctionsSuite.scala    | 138 +++++++
 .../org/apache/spark/sql/functions.scala      | 353 ++++++++++++++++++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 132 ++++++-
 .../apache/spark/unsafe/types/UTF8String.java | 191 ++++++++++
 .../spark/unsafe/types/UTF8StringSuite.java   |  94 ++++-
 7 files changed, 1202 insertions(+), 24 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 5c25181e1cf50..f62d79f8cea6d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -147,12 +147,24 @@ object FunctionRegistry {
     expression[Base64]("base64"),
     expression[Encode]("encode"),
     expression[Decode]("decode"),
+    expression[StringInstr]("instr"),
     expression[Lower]("lcase"),
     expression[Lower]("lower"),
     expression[StringLength]("length"),
     expression[Levenshtein]("levenshtein"),
+    expression[StringLocate]("locate"),
+    expression[StringLPad]("lpad"),
+    expression[StringTrimLeft]("ltrim"),
+    expression[StringFormat]("printf"),
+    expression[StringRPad]("rpad"),
+    expression[StringRepeat]("repeat"),
+    expression[StringReverse]("reverse"),
+    expression[StringTrimRight]("rtrim"),
+    expression[StringSpace]("space"),
+    expression[StringSplit]("split"),
     expression[Substring]("substr"),
     expression[Substring]("substring"),
+    expression[StringTrim]("trim"),
     expression[UnBase64]("unbase64"),
     expression[Upper]("ucase"),
     expression[Unhex]("unhex"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 57f436485becf..f64899c1ed84c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import java.util.Locale
 import java.util.regex.Pattern
 
 import org.apache.commons.lang3.StringUtils
@@ -104,7 +105,7 @@ case class RLike(left: Expression, right: Expression)
   override def toString: String = s"$left RLIKE $right"
 }
 
-trait CaseConversionExpression extends ExpectsInputTypes {
+trait String2StringExpression extends ExpectsInputTypes {
   self: UnaryExpression =>
 
   def convert(v: UTF8String): UTF8String
@@ -119,7 +120,7 @@ trait CaseConversionExpression extends ExpectsInputTypes {
 /**
  * A function that converts the characters of a string to uppercase.
  */
-case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
+case class Upper(child: Expression) extends UnaryExpression with String2StringExpression {
 
   override def convert(v: UTF8String): UTF8String = v.toUpperCase
 
@@ -131,7 +132,7 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE
 /**
  * A function that converts the characters of a string to lowercase.
  */
-case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
+case class Lower(child: Expression) extends UnaryExpression with String2StringExpression {
 
   override def convert(v: UTF8String): UTF8String = v.toLowerCase
 
@@ -187,6 +188,301 @@ case class EndsWith(left: Expression, right: Expression)
   }
 }
 
+/**
+ * A function that trim the spaces from both ends for the specified string.
+ */
+case class StringTrim(child: Expression)
+  extends UnaryExpression with String2StringExpression {
+
+  def convert(v: UTF8String): UTF8String = v.trim()
+
+  override def prettyName: String = "trim"
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, c => s"($c).trim()")
+  }
+}
+
+/**
+ * A function that trim the spaces from left end for given string.
+ */
+case class StringTrimLeft(child: Expression)
+  extends UnaryExpression with String2StringExpression {
+
+  def convert(v: UTF8String): UTF8String = v.trimLeft()
+
+  override def prettyName: String = "ltrim"
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, c => s"($c).trimLeft()")
+  }
+}
+
+/**
+ * A function that trim the spaces from right end for given string.
+ */
+case class StringTrimRight(child: Expression)
+  extends UnaryExpression with String2StringExpression {
+
+  def convert(v: UTF8String): UTF8String = v.trimRight()
+
+  override def prettyName: String = "rtrim"
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, c => s"($c).trimRight()")
+  }
+}
+
+/**
+ * A function that returns the position of the first occurrence of substr in the given string.
+ * Returns null if either of the arguments are null and
+ * returns 0 if substr could not be found in str.
+ *
+ * NOTE: that this is not zero based, but 1-based index. The first character in str has index 1.
+ */
+case class StringInstr(str: Expression, substr: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def left: Expression = str
+  override def right: Expression = substr
+  override def dataType: DataType = IntegerType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+
+  override def nullSafeEval(string: Any, sub: Any): Any = {
+    string.asInstanceOf[UTF8String].indexOf(sub.asInstanceOf[UTF8String], 0) + 1
+  }
+
+  override def prettyName: String = "instr"
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, (l, r) =>
+      s"($l).indexOf($r, 0) + 1")
+  }
+}
+
+/**
+ * A function that returns the position of the first occurrence of substr
+ * in given string after position pos.
+ */
+case class StringLocate(substr: Expression, str: Expression, start: Expression)
+  extends Expression with ExpectsInputTypes {
+
+  def this(substr: Expression, str: Expression) = {
+    this(substr, str, Literal(0))
+  }
+
+  override def children: Seq[Expression] = substr :: str :: start :: Nil
+  override def foldable: Boolean = children.forall(_.foldable)
+  override def nullable: Boolean = substr.nullable || str.nullable
+  override def dataType: DataType = IntegerType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType, IntegerType)
+
+  override def eval(input: InternalRow): Any = {
+    val s = start.eval(input)
+    if (s == null) {
+      // if the start position is null, we need to return 0, (conform to Hive)
+      0
+    } else {
+      val r = substr.eval(input)
+      if (r == null) {
+        null
+      } else {
+        val l = str.eval(input)
+        if (l == null) {
+          null
+        } else {
+          l.asInstanceOf[UTF8String].indexOf(
+            r.asInstanceOf[UTF8String],
+            s.asInstanceOf[Int]) + 1
+        }
+      }
+    }
+  }
+
+  override def prettyName: String = "locate"
+}
+
+/**
+ * Returns str, left-padded with pad to a length of len.
+ */
+case class StringLPad(str: Expression, len: Expression, pad: Expression)
+  extends Expression with ExpectsInputTypes {
+
+  override def children: Seq[Expression] = str :: len :: pad :: Nil
+  override def foldable: Boolean = children.forall(_.foldable)
+  override def nullable: Boolean = children.exists(_.nullable)
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, StringType)
+
+  override def eval(input: InternalRow): Any = {
+    val s = str.eval(input)
+    if (s == null) {
+      null
+    } else {
+      val l = len.eval(input)
+      if (l == null) {
+        null
+      } else {
+        val p = pad.eval(input)
+        if (p == null) {
+          null
+        } else {
+          val len = l.asInstanceOf[Int]
+          val str = s.asInstanceOf[UTF8String]
+          val pad = p.asInstanceOf[UTF8String]
+
+          str.lpad(len, pad)
+        }
+      }
+    }
+  }
+
+  override def prettyName: String = "lpad"
+}
+
+/**
+ * Returns str, right-padded with pad to a length of len.
+ */
+case class StringRPad(str: Expression, len: Expression, pad: Expression)
+  extends Expression with ExpectsInputTypes {
+
+  override def children: Seq[Expression] = str :: len :: pad :: Nil
+  override def foldable: Boolean = children.forall(_.foldable)
+  override def nullable: Boolean = children.exists(_.nullable)
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, StringType)
+
+  override def eval(input: InternalRow): Any = {
+    val s = str.eval(input)
+    if (s == null) {
+      null
+    } else {
+      val l = len.eval(input)
+      if (l == null) {
+        null
+      } else {
+        val p = pad.eval(input)
+        if (p == null) {
+          null
+        } else {
+          val len = l.asInstanceOf[Int]
+          val str = s.asInstanceOf[UTF8String]
+          val pad = p.asInstanceOf[UTF8String]
+
+          str.rpad(len, pad)
+        }
+      }
+    }
+  }
+
+  override def prettyName: String = "rpad"
+}
+
+/**
+ * Returns the input formatted according do printf-style format strings
+ */
+case class StringFormat(children: Expression*) extends Expression {
+
+  require(children.length >=1, "printf() should take at least 1 argument")
+
+  override def foldable: Boolean = children.forall(_.foldable)
+  override def nullable: Boolean = children(0).nullable
+  override def dataType: DataType = StringType
+  private def format: Expression = children(0)
+  private def args: Seq[Expression] = children.tail
+
+  override def eval(input: InternalRow): Any = {
+    val pattern = format.eval(input)
+    if (pattern == null) {
+      null
+    } else {
+      val sb = new StringBuffer()
+      val formatter = new java.util.Formatter(sb, Locale.US)
+
+      val arglist = args.map(_.eval(input).asInstanceOf[AnyRef])
+      formatter.format(pattern.asInstanceOf[UTF8String].toString(), arglist: _*)
+
+      UTF8String.fromString(sb.toString)
+    }
+  }
+
+  override def prettyName: String = "printf"
+}
+
+/**
+ * Returns the string which repeat the given string value n times.
+ */
+case class StringRepeat(str: Expression, times: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def left: Expression = str
+  override def right: Expression = times
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType)
+
+  override def nullSafeEval(string: Any, n: Any): Any = {
+    string.asInstanceOf[UTF8String].repeat(n.asInstanceOf[Integer])
+  }
+
+  override def prettyName: String = "repeat"
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, (l, r) => s"($l).repeat($r)")
+  }
+}
+
+/**
+ * Returns the reversed given string.
+ */
+case class StringReverse(child: Expression) extends UnaryExpression with String2StringExpression {
+  override def convert(v: UTF8String): UTF8String = v.reverse()
+
+  override def prettyName: String = "reverse"
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, c => s"($c).reverse()")
+  }
+}
+
+/**
+ * Returns a n spaces string.
+ */
+case class StringSpace(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(IntegerType)
+
+  override def nullSafeEval(s: Any): Any = {
+    val length = s.asInstanceOf[Integer]
+
+    val spaces = new Array[Byte](if (length < 0) 0 else length)
+    java.util.Arrays.fill(spaces, ' '.asInstanceOf[Byte])
+    UTF8String.fromBytes(spaces)
+  }
+
+  override def prettyName: String = "space"
+}
+
+/**
+ * Splits str around pat (pattern is a regular expression).
+ */
+case class StringSplit(str: Expression, pattern: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def left: Expression = str
+  override def right: Expression = pattern
+  override def dataType: DataType = ArrayType(StringType)
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+
+  override def nullSafeEval(string: Any, regex: Any): Any = {
+    val splits =
+      string.asInstanceOf[UTF8String].toString.split(regex.asInstanceOf[UTF8String].toString, -1)
+    splits.toSeq.map(UTF8String.fromString)
+  }
+
+  override def prettyName: String = "split"
+}
+
 /**
  * A function that takes a substring of its first argument starting at a given position.
  * Defined for String and Binary types.
@@ -199,8 +495,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
   }
 
   override def foldable: Boolean = str.foldable && pos.foldable && len.foldable
-
-  override  def nullable: Boolean = str.nullable || pos.nullable || len.nullable
+  override def nullable: Boolean = str.nullable || pos.nullable || len.nullable
 
   override def dataType: DataType = {
     if (!resolved) {
@@ -373,4 +668,3 @@ case class Encode(value: Expression, charset: Expression)
   }
 }
 
-
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
index 69bef1c63e9dc..b19f4ee37a109 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
@@ -288,4 +288,142 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Levenshtein(Literal("世界千世"), Literal("大a界b")), 4)
     // scalastyle:on
   }
+
+  test("TRIM/LTRIM/RTRIM") {
+    val s = 'a.string.at(0)
+    checkEvaluation(StringTrim(Literal(" aa  ")), "aa", create_row(" abdef "))
+    checkEvaluation(StringTrim(s), "abdef", create_row(" abdef "))
+
+    checkEvaluation(StringTrimLeft(Literal(" aa  ")), "aa  ", create_row(" abdef "))
+    checkEvaluation(StringTrimLeft(s), "abdef ", create_row(" abdef "))
+
+    checkEvaluation(StringTrimRight(Literal(" aa  ")), " aa", create_row(" abdef "))
+    checkEvaluation(StringTrimRight(s), " abdef", create_row(" abdef "))
+
+    // scalastyle:off
+    // non ascii characters are not allowed in the source code, so we disable the scalastyle.
+    checkEvaluation(StringTrimRight(s), "  花花世界", create_row("  花花世界 "))
+    checkEvaluation(StringTrimLeft(s), "花花世界 ", create_row("  花花世界 "))
+    checkEvaluation(StringTrim(s), "花花世界", create_row("  花花世界 "))
+    // scalastyle:on
+  }
+
+  test("FORMAT") {
+    val f = 'f.string.at(0)
+    val d1 = 'd.int.at(1)
+    val s1 = 's.int.at(2)
+
+    val row1 = create_row("aa%d%s", 12, "cc")
+    val row2 = create_row(null, 12, "cc")
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
+    checkEvaluation(StringFormat(Literal("aa")), "aa", create_row(null))
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
+
+    checkEvaluation(StringFormat(f, d1, s1), "aa12cc", row1)
+    checkEvaluation(StringFormat(f, d1, s1), null, row2)
+  }
+
+  test("INSTR") {
+    val s1 = 'a.string.at(0)
+    val s2 = 'b.string.at(1)
+    val s3 = 'c.string.at(2)
+    val row1 = create_row("aaads", "aa", "zz")
+
+    checkEvaluation(StringInstr(Literal("aaads"), Literal("aa")), 1, row1)
+    checkEvaluation(StringInstr(Literal("aaads"), Literal("de")), 0, row1)
+    checkEvaluation(StringInstr(Literal.create(null, StringType), Literal("de")), null, row1)
+    checkEvaluation(StringInstr(Literal("aaads"), Literal.create(null, StringType)), null, row1)
+
+    checkEvaluation(StringInstr(s1, s2), 1, row1)
+    checkEvaluation(StringInstr(s1, s3), 0, row1)
+
+    // scalastyle:off
+    // non ascii characters are not allowed in the source code, so we disable the scalastyle.
+    checkEvaluation(StringInstr(s1, s2), 3, create_row("花花世界", "世界"))
+    checkEvaluation(StringInstr(s1, s2), 1, create_row("花花世界", "花"))
+    checkEvaluation(StringInstr(s1, s2), 0, create_row("花花世界", "小"))
+    // scalastyle:on
+  }
+
+  test("LOCATE") {
+    val s1 = 'a.string.at(0)
+    val s2 = 'b.string.at(1)
+    val s3 = 'c.string.at(2)
+    val s4 = 'd.int.at(3)
+    val row1 = create_row("aaads", "aa", "zz", 1)
+
+    checkEvaluation(new StringLocate(Literal("aa"), Literal("aaads")), 1, row1)
+    checkEvaluation(StringLocate(Literal("aa"), Literal("aaads"), Literal(1)), 2, row1)
+    checkEvaluation(StringLocate(Literal("aa"), Literal("aaads"), Literal(2)), 0, row1)
+    checkEvaluation(new StringLocate(Literal("de"), Literal("aaads")), 0, row1)
+    checkEvaluation(StringLocate(Literal("de"), Literal("aaads"), 1), 0, row1)
+
+    checkEvaluation(new StringLocate(s2, s1), 1, row1)
+    checkEvaluation(StringLocate(s2, s1, s4), 2, row1)
+    checkEvaluation(new StringLocate(s3, s1), 0, row1)
+    checkEvaluation(StringLocate(s3, s1, Literal.create(null, IntegerType)), 0, row1)
+  }
+
+  test("LPAD/RPAD") {
+    val s1 = 'a.string.at(0)
+    val s2 = 'b.int.at(1)
+    val s3 = 'c.string.at(2)
+    val row1 = create_row("hi", 5, "??")
+    val row2 = create_row("hi", 1, "?")
+    val row3 = create_row(null, 1, "?")
+
+    checkEvaluation(StringLPad(Literal("hi"), Literal(5), Literal("??")), "???hi", row1)
+    checkEvaluation(StringLPad(Literal("hi"), Literal(1), Literal("??")), "h", row1)
+    checkEvaluation(StringLPad(s1, s2, s3), "???hi", row1)
+    checkEvaluation(StringLPad(s1, s2, s3), "h", row2)
+    checkEvaluation(StringLPad(s1, s2, s3), null, row3)
+
+    checkEvaluation(StringRPad(Literal("hi"), Literal(5), Literal("??")), "hi???", row1)
+    checkEvaluation(StringRPad(Literal("hi"), Literal(1), Literal("??")), "h", row1)
+    checkEvaluation(StringRPad(s1, s2, s3), "hi???", row1)
+    checkEvaluation(StringRPad(s1, s2, s3), "h", row2)
+    checkEvaluation(StringRPad(s1, s2, s3), null, row3)
+  }
+
+  test("REPEAT") {
+    val s1 = 'a.string.at(0)
+    val s2 = 'b.int.at(1)
+    val row1 = create_row("hi", 2)
+    val row2 = create_row(null, 1)
+
+    checkEvaluation(StringRepeat(Literal("hi"), Literal(2)), "hihi", row1)
+    checkEvaluation(StringRepeat(Literal("hi"), Literal(-1)), "", row1)
+    checkEvaluation(StringRepeat(s1, s2), "hihi", row1)
+    checkEvaluation(StringRepeat(s1, s2), null, row2)
+  }
+
+  test("REVERSE") {
+    val s = 'a.string.at(0)
+    val row1 = create_row("abccc")
+    checkEvaluation(StringReverse(Literal("abccc")), "cccba", row1)
+    checkEvaluation(StringReverse(s), "cccba", row1)
+  }
+
+  test("SPACE") {
+    val s1 = 'b.int.at(0)
+    val row1 = create_row(2)
+    val row2 = create_row(null)
+
+    checkEvaluation(StringSpace(Literal(2)), "  ", row1)
+    checkEvaluation(StringSpace(Literal(-1)), "", row1)
+    checkEvaluation(StringSpace(Literal(0)), "", row1)
+    checkEvaluation(StringSpace(s1), "  ", row1)
+    checkEvaluation(StringSpace(s1), null, row2)
+  }
+
+  test("SPLIT") {
+    val s1 = 'a.string.at(0)
+    val s2 = 'b.string.at(1)
+    val row1 = create_row("aa2bb3cc", "[1-9]+")
+
+    checkEvaluation(
+      StringSplit(Literal("aa2bb3cc"), Literal("[1-9]+")), Seq("aa", "bb", "cc"), row1)
+    checkEvaluation(
+      StringSplit(s1, s2), Seq("aa", "bb", "cc"), row1)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4da9ffc495e17..08bf37a5c223c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1626,6 +1626,179 @@ object functions {
    */
   def ascii(columnName: String): Column = ascii(Column(columnName))
 
+  /**
+   * Trim the spaces from both ends for the specified string value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def trim(e: Column): Column = StringTrim(e.expr)
+
+  /**
+   * Trim the spaces from both ends for the specified column.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def trim(columnName: String): Column = trim(Column(columnName))
+
+  /**
+   * Trim the spaces from left end for the specified string value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def ltrim(e: Column): Column = StringTrimLeft(e.expr)
+
+  /**
+   * Trim the spaces from left end for the specified column.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def ltrim(columnName: String): Column = ltrim(Column(columnName))
+
+  /**
+   * Trim the spaces from right end for the specified string value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def rtrim(e: Column): Column = StringTrimRight(e.expr)
+
+  /**
+   * Trim the spaces from right end for the specified column.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def rtrim(columnName: String): Column = rtrim(Column(columnName))
+
+  /**
+   * Format strings in printf-style.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def formatString(format: Column, arguments: Column*): Column = {
+    StringFormat((format +: arguments).map(_.expr): _*)
+  }
+
+  /**
+   * Format strings in printf-style.
+   * NOTE: `format` is the string value of the formatter, not column name.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def formatString(format: String, arguNames: String*): Column = {
+    StringFormat(lit(format).expr +: arguNames.map(Column(_).expr): _*)
+  }
+
+  /**
+   * Locate the position of the first occurrence of substr value in the given string.
+   * Returns null if either of the arguments are null.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def instr(substr: String, sub: String): Column = instr(Column(substr), Column(sub))
+
+  /**
+   * Locate the position of the first occurrence of substr column in the given string.
+   * Returns null if either of the arguments are null.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def instr(substr: Column, sub: Column): Column = StringInstr(substr.expr, sub.expr)
+
+  /**
+   * Locate the position of the first occurrence of substr.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def locate(substr: String, str: String): Column = {
+    locate(Column(substr), Column(str))
+  }
+
+  /**
+   * Locate the position of the first occurrence of substr.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def locate(substr: Column, str: Column): Column = {
+    new StringLocate(substr.expr, str.expr)
+  }
+
+  /**
+   * Locate the position of the first occurrence of substr in a given string after position pos.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def locate(substr: String, str: String, pos: String): Column = {
+    locate(Column(substr), Column(str), Column(pos))
+  }
+
+  /**
+   * Locate the position of the first occurrence of substr in a given string after position pos.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def locate(substr: Column, str: Column, pos: Column): Column = {
+    StringLocate(substr.expr, str.expr, pos.expr)
+  }
+
+  /**
+   * Locate the position of the first occurrence of substr in a given string after position pos.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def locate(substr: Column, str: Column, pos: Int): Column = {
+    StringLocate(substr.expr, str.expr, lit(pos).expr)
+  }
+
+  /**
+   * Locate the position of the first occurrence of substr in a given string after position pos.
+   *
+   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * could not be found in str.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def locate(substr: String, str: String, pos: Int): Column = {
+    locate(Column(substr), Column(str), lit(pos))
+  }
+
   /**
    * Computes the specified value from binary to a base64 string.
    *
@@ -1658,6 +1831,46 @@ object functions {
    */
   def unbase64(columnName: String): Column = unbase64(Column(columnName))
 
+  /**
+   * Left-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def lpad(str: String, len: String, pad: String): Column = {
+    lpad(Column(str), Column(len), Column(pad))
+  }
+
+  /**
+   * Left-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def lpad(str: Column, len: Column, pad: Column): Column = {
+    StringLPad(str.expr, len.expr, pad.expr)
+  }
+
+  /**
+   * Left-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def lpad(str: Column, len: Int, pad: Column): Column = {
+    StringLPad(str.expr, lit(len).expr, pad.expr)
+  }
+
+  /**
+   * Left-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def lpad(str: String, len: Int, pad: String): Column = {
+    lpad(Column(str), len, Column(pad))
+  }
+
   /**
    * Computes the first argument into a binary from a string using the provided character set
    * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
@@ -1702,6 +1915,146 @@ object functions {
   def decode(columnName: String, charset: String): Column =
     decode(Column(columnName), charset)
 
+  /**
+   * Right-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def rpad(str: String, len: String, pad: String): Column = {
+    rpad(Column(str), Column(len), Column(pad))
+  }
+
+  /**
+   * Right-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def rpad(str: Column, len: Column, pad: Column): Column = {
+    StringRPad(str.expr, len.expr, pad.expr)
+  }
+
+  /**
+   * Right-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def rpad(str: String, len: Int, pad: String): Column = {
+    rpad(Column(str), len, Column(pad))
+  }
+
+  /**
+   * Right-padded with pad to a length of len.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def rpad(str: Column, len: Int, pad: Column): Column = {
+    StringRPad(str.expr, lit(len).expr, pad.expr)
+  }
+
+  /**
+   * Repeat the string value of the specified column n times.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def repeat(strColumn: String, timesColumn: String): Column = {
+    repeat(Column(strColumn), Column(timesColumn))
+  }
+
+  /**
+   * Repeat the string expression value n times.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def repeat(str: Column, times: Column): Column = {
+    StringRepeat(str.expr, times.expr)
+  }
+
+  /**
+   * Repeat the string value of the specified column n times.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def repeat(strColumn: String, times: Int): Column = {
+    repeat(Column(strColumn), times)
+  }
+
+  /**
+   * Repeat the string expression value n times.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def repeat(str: Column, times: Int): Column = {
+    StringRepeat(str.expr, lit(times).expr)
+  }
+
+  /**
+   * Splits str around pattern (pattern is a regular expression).
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def split(strColumnName: String, pattern: String): Column = {
+    split(Column(strColumnName), pattern)
+  }
+
+  /**
+   * Splits str around pattern (pattern is a regular expression).
+   * NOTE: pattern is a string represent the regular expression.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def split(str: Column, pattern: String): Column = {
+    StringSplit(str.expr, lit(pattern).expr)
+  }
+
+  /**
+   * Reversed the string for the specified column.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def reverse(str: String): Column = {
+    reverse(Column(str))
+  }
+
+  /**
+   * Reversed the string for the specified value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def reverse(str: Column): Column = {
+    StringReverse(str.expr)
+  }
+
+  /**
+   * Make a n spaces of string.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def space(n: String): Column = {
+    space(Column(n))
+  }
+
+  /**
+   * Make a n spaces of string.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def space(n: Column): Column = {
+    StringSpace(n.expr)
+  }
 
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index afba28515e032..173280375c411 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -209,21 +209,14 @@ class DataFrameFunctionsSuite extends QueryTest {
   }
 
   test("string length function") {
+    val df = Seq(("abc", "")).toDF("a", "b")
     checkAnswer(
-      nullStrings.select(strlen($"s"), strlen("s")),
-      nullStrings.collect().toSeq.map { r =>
-        val v = r.getString(1)
-        val l = if (v == null) null else v.length
-        Row(l, l)
-      })
+      df.select(strlen($"a"), strlen("b")),
+      Row(3, 0))
 
     checkAnswer(
-      nullStrings.selectExpr("length(s)"),
-      nullStrings.collect().toSeq.map { r =>
-        val v = r.getString(1)
-        val l = if (v == null) null else v.length
-        Row(l)
-      })
+      df.selectExpr("length(a)", "length(b)"),
+      Row(3, 0))
   }
 
   test("Levenshtein distance") {
@@ -273,4 +266,119 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(bytes, "大千世界"))
     // scalastyle:on
   }
+
+  test("string trim functions") {
+    val df = Seq(("  example  ", "")).toDF("a", "b")
+
+    checkAnswer(
+      df.select(ltrim($"a"), rtrim($"a"), trim($"a")),
+      Row("example  ", "  example", "example"))
+
+    checkAnswer(
+      df.selectExpr("ltrim(a)", "rtrim(a)", "trim(a)"),
+      Row("example  ", "  example", "example"))
+  }
+
+  test("string formatString function") {
+    val df = Seq(("aa%d%s", 123, "cc")).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
+      Row("aa123cc", "aa123cc"))
+
+    checkAnswer(
+      df.selectExpr("printf(a, b, c)"),
+      Row("aa123cc"))
+  }
+
+  test("string instr function") {
+    val df = Seq(("aaads", "aa", "zz")).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(instr($"a", $"b"), instr("a", "b")),
+      Row(1, 1))
+
+    checkAnswer(
+      df.selectExpr("instr(a, b)"),
+      Row(1))
+  }
+
+  test("string locate function") {
+    val df = Seq(("aaads", "aa", "zz", 1)).toDF("a", "b", "c", "d")
+
+    checkAnswer(
+      df.select(
+        locate($"b", $"a"), locate("b", "a"), locate($"b", $"a", 1),
+        locate("b", "a", 1), locate($"b", $"a", $"d"), locate("b", "a", "d")),
+      Row(1, 1, 2, 2, 2, 2))
+
+    checkAnswer(
+      df.selectExpr("locate(b, a)", "locate(b, a, d)"),
+      Row(1, 2))
+  }
+
+  test("string padding functions") {
+    val df = Seq(("hi", 5, "??")).toDF("a", "b", "c")
+
+    checkAnswer(
+          df.select(
+            lpad($"a", $"b", $"c"), rpad("a", "b", "c"),
+            lpad($"a", 1, $"c"), rpad("a", 1, "c")),
+          Row("???hi", "hi???", "h", "h"))
+
+    checkAnswer(
+      df.selectExpr("lpad(a, b, c)", "rpad(a, b, c)", "lpad(a, 1, c)", "rpad(a, 1, c)"),
+      Row("???hi", "hi???", "h", "h"))
+  }
+
+  test("string repeat function") {
+    val df = Seq(("hi", 2)).toDF("a", "b")
+
+    checkAnswer(
+      df.select(
+        repeat($"a", 2), repeat("a", 2), repeat($"a", $"b"), repeat("a", "b")),
+      Row("hihi", "hihi", "hihi", "hihi"))
+
+    checkAnswer(
+      df.selectExpr("repeat(a, 2)", "repeat(a, b)"),
+      Row("hihi", "hihi"))
+  }
+
+  test("string reverse function") {
+    val df = Seq(("hi", "hhhi")).toDF("a", "b")
+
+    checkAnswer(
+      df.select(reverse($"a"), reverse("b")),
+      Row("ih", "ihhh"))
+
+    checkAnswer(
+      df.selectExpr("reverse(b)"),
+      Row("ihhh"))
+  }
+
+  test("string space function") {
+    val df = Seq((2, 3)).toDF("a", "b")
+
+    checkAnswer(
+      df.select(space($"a"), space("b")),
+      Row("  ", "   "))
+
+    checkAnswer(
+      df.selectExpr("space(b)"),
+      Row("   "))
+  }
+
+  test("string split function") {
+    val df = Seq(("aa2bb3cc", "[1-9]+")).toDF("a", "b")
+
+    checkAnswer(
+      df.select(
+        split($"a", "[1-9]+"),
+        split("a", "[1-9]+")),
+      Row(Seq("aa", "bb", "cc"), Seq("aa", "bb", "cc")))
+
+    checkAnswer(
+      df.selectExpr("split(a, '[1-9]+')"),
+      Row(Seq("aa", "bb", "cc")))
+  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 847d80ad583f6..60d050b0a0c97 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -25,6 +25,7 @@
 
 import static org.apache.spark.unsafe.PlatformDependent.*;
 
+
 /**
  * A UTF-8 String for internal Spark use.
  * <p>
@@ -204,6 +205,196 @@ public UTF8String toLowerCase() {
     return fromString(toString().toLowerCase());
   }
 
+  /**
+   * Copy the bytes from the current UTF8String, and make a new UTF8String.
+   * @param start the start position of the current UTF8String in bytes.
+   * @param end the end position of the current UTF8String in bytes.
+   * @return a new UTF8String in the position of [start, end] of current UTF8String bytes.
+   */
+  private UTF8String copyUTF8String(int start, int end) {
+    int len = end - start + 1;
+    byte[] newBytes = new byte[len];
+    copyMemory(base, offset + start, newBytes, BYTE_ARRAY_OFFSET, len);
+    return UTF8String.fromBytes(newBytes);
+  }
+
+  public UTF8String trim() {
+    int s = 0;
+    int e = this.numBytes - 1;
+    // skip all of the space (0x20) in the left side
+    while (s < this.numBytes && getByte(s) == 0x20) s++;
+    // skip all of the space (0x20) in the right side
+    while (e >= 0 && getByte(e) == 0x20) e--;
+
+    if (s > e) {
+      // empty string
+      return UTF8String.fromBytes(new byte[0]);
+    } else {
+      return copyUTF8String(s, e);
+    }
+  }
+
+  public UTF8String trimLeft() {
+    int s = 0;
+    // skip all of the space (0x20) in the left side
+    while (s < this.numBytes && getByte(s) == 0x20) s++;
+    if (s == this.numBytes) {
+      // empty string
+      return UTF8String.fromBytes(new byte[0]);
+    } else {
+      return copyUTF8String(s, this.numBytes - 1);
+    }
+  }
+
+  public UTF8String trimRight() {
+    int e = numBytes - 1;
+    // skip all of the space (0x20) in the right side
+    while (e >= 0 && getByte(e) == 0x20) e--;
+
+    if (e < 0) {
+      // empty string
+      return UTF8String.fromBytes(new byte[0]);
+    } else {
+      return copyUTF8String(0, e);
+    }
+  }
+
+  public UTF8String reverse() {
+    byte[] bytes = getBytes();
+    byte[] result = new byte[bytes.length];
+
+    int i = 0; // position in byte
+    while (i < numBytes) {
+      int len = numBytesForFirstByte(getByte(i));
+      System.arraycopy(bytes, i, result, result.length - i - len, len);
+
+      i += len;
+    }
+
+    return UTF8String.fromBytes(result);
+  }
+
+  public UTF8String repeat(int times) {
+    if (times <=0) {
+      return fromBytes(new byte[0]);
+    }
+
+    byte[] newBytes = new byte[numBytes * times];
+    System.arraycopy(getBytes(), 0, newBytes, 0, numBytes);
+
+    int copied = 1;
+    while (copied < times) {
+      int toCopy = Math.min(copied, times - copied);
+      System.arraycopy(newBytes, 0, newBytes, copied * numBytes, numBytes * toCopy);
+      copied += toCopy;
+    }
+
+    return UTF8String.fromBytes(newBytes);
+  }
+
+  /**
+   * Returns the position of the first occurrence of substr in
+   * current string from the specified position (0-based index).
+   *
+   * @param v the string to be searched
+   * @param start the start position of the current string for searching
+   * @return the position of the first occurrence of substr, if not found, -1 returned.
+   */
+  public int indexOf(UTF8String v, int start) {
+    if (v.numBytes() == 0) {
+      return 0;
+    }
+
+    // locate to the start position.
+    int i = 0; // position in byte
+    int c = 0; // position in character
+    while (i < numBytes && c < start) {
+      i += numBytesForFirstByte(getByte(i));
+      c += 1;
+    }
+
+    do {
+      if (i + v.numBytes > numBytes) {
+        return -1;
+      }
+      if (ByteArrayMethods.arrayEquals(base, offset + i, v.base, v.offset, v.numBytes)) {
+        return c;
+      }
+      i += numBytesForFirstByte(getByte(i));
+      c += 1;
+    } while(i < numBytes);
+
+    return -1;
+  }
+
+  /**
+   * Returns str, right-padded with pad to a length of len
+   * For example:
+   *   ('hi', 5, '??') => 'hi???'
+   *   ('hi', 1, '??') => 'h'
+   */
+  public UTF8String rpad(int len, UTF8String pad) {
+    int spaces = len - this.numChars(); // number of char need to pad
+    if (spaces <= 0) {
+      // no padding at all, return the substring of the current string
+      return substring(0, len);
+    } else {
+      int padChars = pad.numChars();
+      int count = spaces / padChars; // how many padding string needed
+      // the partial string of the padding
+      UTF8String remain = pad.substring(0, spaces - padChars * count);
+
+      byte[] data = new byte[this.numBytes + pad.numBytes * count + remain.numBytes];
+      System.arraycopy(getBytes(), 0, data, 0, this.numBytes);
+      int offset = this.numBytes;
+      int idx = 0;
+      byte[] padBytes = pad.getBytes();
+      while (idx < count) {
+        System.arraycopy(padBytes, 0, data, offset, pad.numBytes);
+        ++idx;
+        offset += pad.numBytes;
+      }
+      System.arraycopy(remain.getBytes(), 0, data, offset, remain.numBytes);
+
+      return UTF8String.fromBytes(data);
+    }
+  }
+
+  /**
+   * Returns str, left-padded with pad to a length of len.
+   * For example:
+   *   ('hi', 5, '??') => '???hi'
+   *   ('hi', 1, '??') => 'h'
+   */
+  public UTF8String lpad(int len, UTF8String pad) {
+    int spaces = len - this.numChars(); // number of char need to pad
+    if (spaces <= 0) {
+      // no padding at all, return the substring of the current string
+      return substring(0, len);
+    } else {
+      int padChars = pad.numChars();
+      int count = spaces / padChars; // how many padding string needed
+      // the partial string of the padding
+      UTF8String remain = pad.substring(0, spaces - padChars * count);
+
+      byte[] data = new byte[this.numBytes + pad.numBytes * count + remain.numBytes];
+
+      int offset = 0;
+      int idx = 0;
+      byte[] padBytes = pad.getBytes();
+      while (idx < count) {
+        System.arraycopy(padBytes, 0, data, offset, pad.numBytes);
+        ++idx;
+        offset += pad.numBytes;
+      }
+      System.arraycopy(remain.getBytes(), 0, data, offset, remain.numBytes);
+      offset += remain.numBytes;
+      System.arraycopy(getBytes(), 0, data, offset, numBytes());
+
+      return UTF8String.fromBytes(data);
+    }
+  }
+
   @Override
   public String toString() {
     try {
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index fb463ba17f50b..694bdc29f39d1 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -121,12 +121,94 @@ public void endsWith() {
 
   @Test
   public void substring() {
-    assertEquals(fromString("hello").substring(0, 0), fromString(""));
-    assertEquals(fromString("hello").substring(1, 3), fromString("el"));
-    assertEquals(fromString("数据砖头").substring(0, 1), fromString("数"));
-    assertEquals(fromString("数据砖头").substring(1, 3), fromString("据砖"));
-    assertEquals(fromString("数据砖头").substring(3, 5), fromString("头"));
-    assertEquals(fromString("ߵ梷").substring(0, 2), fromString("ߵ梷"));
+    assertEquals(fromString(""), fromString("hello").substring(0, 0));
+    assertEquals(fromString("el"), fromString("hello").substring(1, 3));
+    assertEquals(fromString("数"), fromString("数据砖头").substring(0, 1));
+    assertEquals(fromString("据砖"), fromString("数据砖头").substring(1, 3));
+    assertEquals(fromString("头"), fromString("数据砖头").substring(3, 5));
+    assertEquals(fromString("ߵ梷"), fromString("ߵ梷").substring(0, 2));
+  }
+
+  @Test
+  public void trims() {
+    assertEquals(fromString("hello"), fromString("  hello ").trim());
+    assertEquals(fromString("hello "), fromString("  hello ").trimLeft());
+    assertEquals(fromString("  hello"), fromString("  hello ").trimRight());
+
+    assertEquals(fromString(""), fromString("  ").trim());
+    assertEquals(fromString(""), fromString("  ").trimLeft());
+    assertEquals(fromString(""), fromString("  ").trimRight());
+
+    assertEquals(fromString("数据砖头"), fromString("  数据砖头 ").trim());
+    assertEquals(fromString("数据砖头 "), fromString("  数据砖头 ").trimLeft());
+    assertEquals(fromString("  数据砖头"), fromString("  数据砖头 ").trimRight());
+
+    assertEquals(fromString("数据砖头"), fromString("数据砖头").trim());
+    assertEquals(fromString("数据砖头"), fromString("数据砖头").trimLeft());
+    assertEquals(fromString("数据砖头"), fromString("数据砖头").trimRight());
+  }
+
+  @Test
+  public void indexOf() {
+    assertEquals(0, fromString("").indexOf(fromString(""), 0));
+    assertEquals(-1, fromString("").indexOf(fromString("l"), 0));
+    assertEquals(0, fromString("hello").indexOf(fromString(""), 0));
+    assertEquals(2, fromString("hello").indexOf(fromString("l"), 0));
+    assertEquals(3, fromString("hello").indexOf(fromString("l"), 3));
+    assertEquals(-1, fromString("hello").indexOf(fromString("a"), 0));
+    assertEquals(2, fromString("hello").indexOf(fromString("ll"), 0));
+    assertEquals(-1, fromString("hello").indexOf(fromString("ll"), 4));
+    assertEquals(1, fromString("数据砖头").indexOf(fromString("据砖"), 0));
+    assertEquals(-1, fromString("数据砖头").indexOf(fromString("数"), 3));
+    assertEquals(0, fromString("数据砖头").indexOf(fromString("数"), 0));
+    assertEquals(3, fromString("数据砖头").indexOf(fromString("头"), 0));
+  }
+
+  @Test
+  public void reverse() {
+    assertEquals(fromString("olleh"), fromString("hello").reverse());
+    assertEquals(fromString(""), fromString("").reverse());
+    assertEquals(fromString("者行孙"), fromString("孙行者").reverse());
+    assertEquals(fromString("者行孙 olleh"), fromString("hello 孙行者").reverse());
+  }
+
+  @Test
+  public void repeat() {
+    assertEquals(fromString("数d数d数d数d数d"), fromString("数d").repeat(5));
+    assertEquals(fromString("数d"), fromString("数d").repeat(1));
+    assertEquals(fromString(""), fromString("数d").repeat(-1));
+  }
+
+  @Test
+  public void pad() {
+    assertEquals(fromString("hel"), fromString("hello").lpad(3, fromString("????")));
+    assertEquals(fromString("hello"), fromString("hello").lpad(5, fromString("????")));
+    assertEquals(fromString("?hello"), fromString("hello").lpad(6, fromString("????")));
+    assertEquals(fromString("???????hello"), fromString("hello").lpad(12, fromString("????")));
+    assertEquals(fromString("?????hello"), fromString("hello").lpad(10, fromString("?????")));
+    assertEquals(fromString("???????"), fromString("").lpad(7, fromString("?????")));
+
+    assertEquals(fromString("hel"), fromString("hello").rpad(3, fromString("????")));
+    assertEquals(fromString("hello"), fromString("hello").rpad(5, fromString("????")));
+    assertEquals(fromString("hello?"), fromString("hello").rpad(6, fromString("????")));
+    assertEquals(fromString("hello???????"), fromString("hello").rpad(12, fromString("????")));
+    assertEquals(fromString("hello?????"), fromString("hello").rpad(10, fromString("?????")));
+    assertEquals(fromString("???????"), fromString("").rpad(7, fromString("?????")));
+
+
+    assertEquals(fromString("数据砖"), fromString("数据砖头").lpad(3, fromString("????")));
+    assertEquals(fromString("?数据砖头"), fromString("数据砖头").lpad(5, fromString("????")));
+    assertEquals(fromString("??数据砖头"), fromString("数据砖头").lpad(6, fromString("????")));
+    assertEquals(fromString("孙行数据砖头"), fromString("数据砖头").lpad(6, fromString("孙行者")));
+    assertEquals(fromString("孙行者数据砖头"), fromString("数据砖头").lpad(7, fromString("孙行者")));
+    assertEquals(fromString("孙行者孙行者孙行数据砖头"), fromString("数据砖头").lpad(12, fromString("孙行者")));
+
+    assertEquals(fromString("数据砖"), fromString("数据砖头").rpad(3, fromString("????")));
+    assertEquals(fromString("数据砖头?"), fromString("数据砖头").rpad(5, fromString("????")));
+    assertEquals(fromString("数据砖头??"), fromString("数据砖头").rpad(6, fromString("????")));
+    assertEquals(fromString("数据砖头孙行"), fromString("数据砖头").rpad(6, fromString("孙行者")));
+    assertEquals(fromString("数据砖头孙行者"), fromString("数据砖头").rpad(7, fromString("孙行者")));
+    assertEquals(fromString("数据砖头孙行者孙行者孙行"), fromString("数据砖头").rpad(12, fromString("孙行者")));
   }
   
   @Test

From 7ce3b818fb1ba3f291eda58988e4808e999cae3a Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 9 Jul 2015 13:19:36 -0700
Subject: [PATCH 0315/1454] [MINOR] [STREAMING] Fix log statements in
 ReceiverSupervisorImpl

Log statements incorrectly showed that the executor was being stopped when receiver was being stopped.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7328 from tdas/fix-log and squashes the following commits:

9cc6e99 [Tathagata Das] Fix log statements.
---
 .../spark/streaming/receiver/ReceiverSupervisor.scala       | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
index 33be067ebdaf2..eeb14ca3a49e9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
@@ -182,12 +182,12 @@ private[streaming] abstract class ReceiverSupervisor(
 
   /** Wait the thread until the supervisor is stopped */
   def awaitTermination() {
+    logInfo("Waiting for receiver to be stopped")
     stopLatch.await()
-    logInfo("Waiting for executor stop is over")
     if (stoppingError != null) {
-      logError("Stopped executor with error: " + stoppingError)
+      logError("Stopped receiver with error: " + stoppingError)
     } else {
-      logWarning("Stopped executor without error")
+      logInfo("Stopped receiver without error")
     }
     if (stoppingError != null) {
       throw stoppingError

From 930fe95350f8865e2af2d7afa5b717210933cd43 Mon Sep 17 00:00:00 2001
From: xutingjun <xutingjun@huawei.com>
Date: Thu, 9 Jul 2015 13:21:10 -0700
Subject: [PATCH 0316/1454] [SPARK-8953] SPARK_EXECUTOR_CORES is not read in
 SparkSubmit

The configuration ```SPARK_EXECUTOR_CORES``` won't put into ```SparkConf```, so it has no effect to the dynamic executor allocation.

Author: xutingjun <xutingjun@huawei.com>

Closes #7322 from XuTingjun/SPARK_EXECUTOR_CORES and squashes the following commits:

2cafa89 [xutingjun] make SPARK_EXECUTOR_CORES has effect to dynamicAllocation
---
 .../scala/org/apache/spark/deploy/SparkSubmitArguments.scala     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 73ab18332feb4..6e3c0b21b33c2 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -162,6 +162,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
       .orNull
     executorCores = Option(executorCores)
       .orElse(sparkProperties.get("spark.executor.cores"))
+      .orElse(env.get("SPARK_EXECUTOR_CORES"))
       .orNull
     totalExecutorCores = Option(totalExecutorCores)
       .orElse(sparkProperties.get("spark.cores.max"))

From 88bf430331eef3c02438ca441616034486e15789 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 9 Jul 2015 13:22:17 -0700
Subject: [PATCH 0317/1454] [SPARK-7419] [STREAMING] [TESTS] Fix
 CheckpointSuite.recovery with file input stream

Fix this failure: https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/2886/AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.3,label=centos/testReport/junit/org.apache.spark.streaming/CheckpointSuite/recovery_with_file_input_stream/

To reproduce this failure, you can add `Thread.sleep(2000)` before this line
https://github.com/apache/spark/blob/a9c4e29950a14e32acaac547e9a0e8879fd37fc9/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala#L477

Author: zsxwing <zsxwing@gmail.com>

Closes #7323 from zsxwing/SPARK-7419 and squashes the following commits:

b3caf58 [zsxwing] Fix CheckpointSuite.recovery with file input stream
---
 .../spark/streaming/CheckpointSuite.scala      | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index 6b0a3f91d4d06..6a94928076236 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -424,11 +424,11 @@ class CheckpointSuite extends TestSuiteBase {
             }
           }
         }
-        clock.advance(batchDuration.milliseconds)
         eventually(eventuallyTimeout) {
           // Wait until all files have been recorded and all batches have started
           assert(recordedFiles(ssc) === Seq(1, 2, 3) && batchCounter.getNumStartedBatches === 3)
         }
+        clock.advance(batchDuration.milliseconds)
         // Wait for a checkpoint to be written
         eventually(eventuallyTimeout) {
           assert(Checkpoint.getCheckpointFiles(checkpointDir).size === 6)
@@ -454,9 +454,12 @@ class CheckpointSuite extends TestSuiteBase {
       // recorded before failure were saved and successfully recovered
       logInfo("*********** RESTARTING ************")
       withStreamingContext(new StreamingContext(checkpointDir)) { ssc =>
-        // So that the restarted StreamingContext's clock has gone forward in time since failure
-        ssc.conf.set("spark.streaming.manualClock.jump", (batchDuration * 3).milliseconds.toString)
-        val oldClockTime = clock.getTimeMillis()
+        // "batchDuration.milliseconds * 3" has gone before restarting StreamingContext. And because
+        // the recovery time is read from the checkpoint time but the original clock doesn't align
+        // with the batch time, we need to add the offset "batchDuration.milliseconds / 2".
+        ssc.conf.set("spark.streaming.manualClock.jump",
+          (batchDuration.milliseconds / 2 + batchDuration.milliseconds * 3).toString)
+        val oldClockTime = clock.getTimeMillis() // 15000ms
         clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
         val batchCounter = new BatchCounter(ssc)
         val outputStream = ssc.graph.getOutputStreams().head.asInstanceOf[TestOutputStream[Int]]
@@ -467,10 +470,10 @@ class CheckpointSuite extends TestSuiteBase {
         ssc.start()
         // Verify that the clock has traveled forward to the expected time
         eventually(eventuallyTimeout) {
-          clock.getTimeMillis() === oldClockTime
+          assert(clock.getTimeMillis() === oldClockTime)
         }
-        // Wait for pre-failure batch to be recomputed (3 while SSC was down plus last batch)
-        val numBatchesAfterRestart = 4
+        // There are 5 batches between 6000ms and 15000ms (inclusive).
+        val numBatchesAfterRestart = 5
         eventually(eventuallyTimeout) {
           assert(batchCounter.getNumCompletedBatches === numBatchesAfterRestart)
         }
@@ -483,7 +486,6 @@ class CheckpointSuite extends TestSuiteBase {
             assert(batchCounter.getNumCompletedBatches === index + numBatchesAfterRestart + 1)
           }
         }
-        clock.advance(batchDuration.milliseconds)
         logInfo("Output after restart = " + outputStream.output.mkString("[", ", ", "]"))
         assert(outputStream.output.size > 0, "No files processed after restart")
         ssc.stop()

From ebdf58538058e57381c04b6725d4be0c37847ed3 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 9 Jul 2015 13:25:11 -0700
Subject: [PATCH 0318/1454] [SPARK-2017] [UI] Stage page hangs with many tasks

(This reopens a patch that was closed in the past: #6248)

When you view the stage page while running the following:
```
sc.parallelize(1 to X, 10000).count()
```
The page never loads, the job is stalled, and you end up running into an OOM:
```
HTTP ERROR 500

Problem accessing /stages/stage/. Reason:
    Server Error
Caused by:
java.lang.OutOfMemoryError: Java heap space
    at java.util.Arrays.copyOf(Arrays.java:2367)
    at java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:130)
```
This patch compresses Jetty responses in gzip. The correct long-term fix is to add pagination.

Author: Andrew Or <andrew@databricks.com>

Closes #7296 from andrewor14/gzip-jetty and squashes the following commits:

a051c64 [Andrew Or] Use GZIP to compress Jetty responses
---
 .../main/scala/org/apache/spark/ui/JettyUtils.scala    | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
index 06e616220c706..f413c1d37fbb6 100644
--- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
@@ -210,10 +210,16 @@ private[spark] object JettyUtils extends Logging {
       conf: SparkConf,
       serverName: String = ""): ServerInfo = {
 
-    val collection = new ContextHandlerCollection
-    collection.setHandlers(handlers.toArray)
     addFilters(handlers, conf)
 
+    val collection = new ContextHandlerCollection
+    val gzipHandlers = handlers.map { h =>
+      val gzipHandler = new GzipHandler
+      gzipHandler.setHandler(h)
+      gzipHandler
+    }
+    collection.setHandlers(gzipHandlers.toArray)
+
     // Bind to the given port, or throw a java.net.BindException if the port is occupied
     def connect(currentPort: Int): (Server, Int) = {
       val server = new Server(new InetSocketAddress(hostName, currentPort))

From c4830598b271cc6390d127bd4cf8ab02b28792e0 Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Thu, 9 Jul 2015 13:26:46 -0700
Subject: [PATCH 0319/1454] [SPARK-6287] [MESOS] Add dynamic allocation to the
 coarse-grained Mesos scheduler

This is largely based on extracting the dynamic allocation parts from tnachen's #3861.

Author: Iulian Dragos <jaguarul@gmail.com>

Closes #4984 from dragos/issue/mesos-coarse-dynamicAllocation and squashes the following commits:

39df8cd [Iulian Dragos] Update tests to latest changes in core.
9d2c9fa [Iulian Dragos] Remove adjustment of executorLimitOption in doKillExecutors.
8b00f52 [Iulian Dragos] Latest round of reviews.
0cd00e0 [Iulian Dragos] Add persistent shuffle directory
15c45c1 [Iulian Dragos] Add dynamic allocation to the Spark coarse-grained scheduler.
---
 .../scala/org/apache/spark/SparkContext.scala |  19 +-
 .../mesos/CoarseMesosSchedulerBackend.scala   | 136 +++++++++++---
 .../cluster/mesos/MesosSchedulerUtils.scala   |   4 +-
 .../spark/storage/DiskBlockManager.scala      |   8 +-
 .../scala/org/apache/spark/util/Utils.scala   |  45 +++--
 .../CoarseMesosSchedulerBackendSuite.scala    | 175 ++++++++++++++++++
 6 files changed, 331 insertions(+), 56 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index d2547eeff2b4e..82704b1ab2189 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -532,7 +532,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _executorAllocationManager =
       if (dynamicAllocationEnabled) {
         assert(supportDynamicAllocation,
-          "Dynamic allocation of executors is currently only supported in YARN mode")
+          "Dynamic allocation of executors is currently only supported in YARN and Mesos mode")
         Some(new ExecutorAllocationManager(this, listenerBus, _conf))
       } else {
         None
@@ -853,7 +853,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       minPartitions).setName(path)
   }
 
-
   /**
    * :: Experimental ::
    *
@@ -1364,10 +1363,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
 
   /**
    * Return whether dynamically adjusting the amount of resources allocated to
-   * this application is supported. This is currently only available for YARN.
+   * this application is supported. This is currently only available for YARN
+   * and Mesos coarse-grained mode.
    */
-  private[spark] def supportDynamicAllocation =
-    master.contains("yarn") || _conf.getBoolean("spark.dynamicAllocation.testing", false)
+  private[spark] def supportDynamicAllocation: Boolean = {
+    (master.contains("yarn")
+      || master.contains("mesos")
+      || _conf.getBoolean("spark.dynamicAllocation.testing", false))
+  }
 
   /**
    * :: DeveloperApi ::
@@ -1385,7 +1388,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    */
   private[spark] override def requestTotalExecutors(numExecutors: Int): Boolean = {
     assert(supportDynamicAllocation,
-      "Requesting executors is currently only supported in YARN mode")
+      "Requesting executors is currently only supported in YARN and Mesos modes")
     schedulerBackend match {
       case b: CoarseGrainedSchedulerBackend =>
         b.requestTotalExecutors(numExecutors)
@@ -1403,7 +1406,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   @DeveloperApi
   override def requestExecutors(numAdditionalExecutors: Int): Boolean = {
     assert(supportDynamicAllocation,
-      "Requesting executors is currently only supported in YARN mode")
+      "Requesting executors is currently only supported in YARN and Mesos modes")
     schedulerBackend match {
       case b: CoarseGrainedSchedulerBackend =>
         b.requestExecutors(numAdditionalExecutors)
@@ -1421,7 +1424,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   @DeveloperApi
   override def killExecutors(executorIds: Seq[String]): Boolean = {
     assert(supportDynamicAllocation,
-      "Killing executors is currently only supported in YARN mode")
+      "Killing executors is currently only supported in YARN and Mesos modes")
     schedulerBackend match {
       case b: CoarseGrainedSchedulerBackend =>
         b.killExecutors(executorIds)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index b68f8c7685eba..cbade131494bc 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -18,11 +18,14 @@
 package org.apache.spark.scheduler.cluster.mesos
 
 import java.io.File
-import java.util.{List => JList}
+import java.util.{List => JList, Collections}
+import java.util.concurrent.locks.ReentrantLock
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, HashSet}
 
+import com.google.common.collect.HashBiMap
+import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _}
 import org.apache.mesos.{Scheduler => MScheduler, _}
 import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _}
 import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState}
@@ -60,9 +63,27 @@ private[spark] class CoarseMesosSchedulerBackend(
 
   val slaveIdsWithExecutors = new HashSet[String]
 
-  val taskIdToSlaveId = new HashMap[Int, String]
-  val failuresBySlaveId = new HashMap[String, Int] // How many times tasks on each slave failed
+  val taskIdToSlaveId: HashBiMap[Int, String] = HashBiMap.create[Int, String]
+  // How many times tasks on each slave failed
+  val failuresBySlaveId: HashMap[String, Int] = new HashMap[String, Int]
+
+  /**
+   * The total number of executors we aim to have. Undefined when not using dynamic allocation
+   * and before the ExecutorAllocatorManager calls [[doRequesTotalExecutors]].
+   */
+  private var executorLimitOption: Option[Int] = None
+
+  /**
+   *  Return the current executor limit, which may be [[Int.MaxValue]]
+   *  before properly initialized.
+   */
+  private[mesos] def executorLimit: Int = executorLimitOption.getOrElse(Int.MaxValue)
+
+  private val pendingRemovedSlaveIds = new HashSet[String]
 
+  // private lock object protecting mutable state above. Using the intrinsic lock
+  // may lead to deadlocks since the superclass might also try to lock
+  private val stateLock = new ReentrantLock
 
   val extraCoresPerSlave = conf.getInt("spark.mesos.extra.cores", 0)
 
@@ -86,7 +107,7 @@ private[spark] class CoarseMesosSchedulerBackend(
     startScheduler(master, CoarseMesosSchedulerBackend.this, fwInfo)
   }
 
-  def createCommand(offer: Offer, numCores: Int): CommandInfo = {
+  def createCommand(offer: Offer, numCores: Int, taskId: Int): CommandInfo = {
     val executorSparkHome = conf.getOption("spark.mesos.executor.home")
       .orElse(sc.getSparkHome())
       .getOrElse {
@@ -120,10 +141,6 @@ private[spark] class CoarseMesosSchedulerBackend(
     }
     val command = CommandInfo.newBuilder()
       .setEnvironment(environment)
-    val driverUrl = sc.env.rpcEnv.uriOf(
-      SparkEnv.driverActorSystemName,
-      RpcAddress(conf.get("spark.driver.host"), conf.get("spark.driver.port").toInt),
-      CoarseGrainedSchedulerBackend.ENDPOINT_NAME)
 
     val uri = conf.getOption("spark.executor.uri")
       .orElse(Option(System.getenv("SPARK_EXECUTOR_URI")))
@@ -133,7 +150,7 @@ private[spark] class CoarseMesosSchedulerBackend(
       command.setValue(
         "%s \"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend"
           .format(prefixEnv, runScript) +
-        s" --driver-url $driverUrl" +
+        s" --driver-url $driverURL" +
         s" --executor-id ${offer.getSlaveId.getValue}" +
         s" --hostname ${offer.getHostname}" +
         s" --cores $numCores" +
@@ -142,11 +159,12 @@ private[spark] class CoarseMesosSchedulerBackend(
       // Grab everything to the first '.'. We'll use that and '*' to
       // glob the directory "correctly".
       val basename = uri.get.split('/').last.split('.').head
+      val executorId = sparkExecutorId(offer.getSlaveId.getValue, taskId.toString)
       command.setValue(
         s"cd $basename*; $prefixEnv " +
          "./bin/spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend" +
-        s" --driver-url $driverUrl" +
-        s" --executor-id ${offer.getSlaveId.getValue}" +
+        s" --driver-url $driverURL" +
+        s" --executor-id $executorId" +
         s" --hostname ${offer.getHostname}" +
         s" --cores $numCores" +
         s" --app-id $appId")
@@ -155,6 +173,17 @@ private[spark] class CoarseMesosSchedulerBackend(
     command.build()
   }
 
+  protected def driverURL: String = {
+    if (conf.contains("spark.testing")) {
+      "driverURL"
+    } else {
+      sc.env.rpcEnv.uriOf(
+        SparkEnv.driverActorSystemName,
+        RpcAddress(conf.get("spark.driver.host"), conf.get("spark.driver.port").toInt),
+        CoarseGrainedSchedulerBackend.ENDPOINT_NAME)
+    }
+  }
+
   override def offerRescinded(d: SchedulerDriver, o: OfferID) {}
 
   override def registered(d: SchedulerDriver, frameworkId: FrameworkID, masterInfo: MasterInfo) {
@@ -172,17 +201,18 @@ private[spark] class CoarseMesosSchedulerBackend(
    * unless we've already launched more than we wanted to.
    */
   override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) {
-    synchronized {
+    stateLock.synchronized {
       val filters = Filters.newBuilder().setRefuseSeconds(5).build()
       for (offer <- offers) {
         val offerAttributes = toAttributeMap(offer.getAttributesList)
         val meetsConstraints = matchesAttributeRequirements(slaveOfferConstraints, offerAttributes)
-        val slaveId = offer.getSlaveId.toString
+        val slaveId = offer.getSlaveId.getValue
         val mem = getResource(offer.getResourcesList, "mem")
         val cpus = getResource(offer.getResourcesList, "cpus").toInt
         val id = offer.getId.getValue
-        if (meetsConstraints &&
+        if (taskIdToSlaveId.size < executorLimit &&
             totalCoresAcquired < maxCores &&
+            meetsConstraints &&
             mem >= calculateTotalMemory(sc) &&
             cpus >= 1 &&
             failuresBySlaveId.getOrElse(slaveId, 0) < MAX_SLAVE_FAILURES &&
@@ -197,7 +227,7 @@ private[spark] class CoarseMesosSchedulerBackend(
           val task = MesosTaskInfo.newBuilder()
             .setTaskId(TaskID.newBuilder().setValue(taskId.toString).build())
             .setSlaveId(offer.getSlaveId)
-            .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave))
+            .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave, taskId))
             .setName("Task " + taskId)
             .addResources(createResource("cpus", cpusToUse))
             .addResources(createResource("mem", calculateTotalMemory(sc)))
@@ -209,7 +239,9 @@ private[spark] class CoarseMesosSchedulerBackend(
 
           // accept the offer and launch the task
           logDebug(s"Accepting offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
-          d.launchTasks(List(offer.getId), List(task.build()), filters)
+          d.launchTasks(
+            Collections.singleton(offer.getId),
+            Collections.singleton(task.build()), filters)
         } else {
           // Decline the offer
           logDebug(s"Declining offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
@@ -224,7 +256,7 @@ private[spark] class CoarseMesosSchedulerBackend(
     val taskId = status.getTaskId.getValue.toInt
     val state = status.getState
     logInfo("Mesos task " + taskId + " is now " + state)
-    synchronized {
+    stateLock.synchronized {
       if (TaskState.isFinished(TaskState.fromMesos(state))) {
         val slaveId = taskIdToSlaveId(taskId)
         slaveIdsWithExecutors -= slaveId
@@ -242,8 +274,9 @@ private[spark] class CoarseMesosSchedulerBackend(
                 "is Spark installed on it?")
           }
         }
+        executorTerminated(d, slaveId, s"Executor finished with state $state")
         // In case we'd rejected everything before but have now lost a node
-        mesosDriver.reviveOffers()
+        d.reviveOffers()
       }
     }
   }
@@ -262,18 +295,39 @@ private[spark] class CoarseMesosSchedulerBackend(
 
   override def frameworkMessage(d: SchedulerDriver, e: ExecutorID, s: SlaveID, b: Array[Byte]) {}
 
-  override def slaveLost(d: SchedulerDriver, slaveId: SlaveID) {
-    logInfo("Mesos slave lost: " + slaveId.getValue)
-    synchronized {
-      if (slaveIdsWithExecutors.contains(slaveId.getValue)) {
-        // Note that the slave ID corresponds to the executor ID on that slave
-        slaveIdsWithExecutors -= slaveId.getValue
-        removeExecutor(slaveId.getValue, "Mesos slave lost")
+  /**
+   * Called when a slave is lost or a Mesos task finished. Update local view on
+   * what tasks are running and remove the terminated slave from the list of pending
+   * slave IDs that we might have asked to be killed. It also notifies the driver
+   * that an executor was removed.
+   */
+  private def executorTerminated(d: SchedulerDriver, slaveId: String, reason: String): Unit = {
+    stateLock.synchronized {
+      if (slaveIdsWithExecutors.contains(slaveId)) {
+        val slaveIdToTaskId = taskIdToSlaveId.inverse()
+        if (slaveIdToTaskId.contains(slaveId)) {
+          val taskId: Int = slaveIdToTaskId.get(slaveId)
+          taskIdToSlaveId.remove(taskId)
+          removeExecutor(sparkExecutorId(slaveId, taskId.toString), reason)
+        }
+        // TODO: This assumes one Spark executor per Mesos slave,
+        // which may no longer be true after SPARK-5095
+        pendingRemovedSlaveIds -= slaveId
+        slaveIdsWithExecutors -= slaveId
       }
     }
   }
 
-  override def executorLost(d: SchedulerDriver, e: ExecutorID, s: SlaveID, status: Int) {
+  private def sparkExecutorId(slaveId: String, taskId: String): String = {
+    s"$slaveId/$taskId"
+  }
+
+  override def slaveLost(d: SchedulerDriver, slaveId: SlaveID): Unit = {
+    logInfo("Mesos slave lost: " + slaveId.getValue)
+    executorTerminated(d, slaveId.getValue, "Mesos slave lost: " + slaveId.getValue)
+  }
+
+  override def executorLost(d: SchedulerDriver, e: ExecutorID, s: SlaveID, status: Int): Unit = {
     logInfo("Executor lost: %s, marking slave %s as lost".format(e.getValue, s.getValue))
     slaveLost(d, s)
   }
@@ -284,4 +338,34 @@ private[spark] class CoarseMesosSchedulerBackend(
       super.applicationId
     }
 
+  override def doRequestTotalExecutors(requestedTotal: Int): Boolean = {
+    // We don't truly know if we can fulfill the full amount of executors
+    // since at coarse grain it depends on the amount of slaves available.
+    logInfo("Capping the total amount of executors to " + requestedTotal)
+    executorLimitOption = Some(requestedTotal)
+    true
+  }
+
+  override def doKillExecutors(executorIds: Seq[String]): Boolean = {
+    if (mesosDriver == null) {
+      logWarning("Asked to kill executors before the Mesos driver was started.")
+      return false
+    }
+
+    val slaveIdToTaskId = taskIdToSlaveId.inverse()
+    for (executorId <- executorIds) {
+      val slaveId = executorId.split("/")(0)
+      if (slaveIdToTaskId.contains(slaveId)) {
+        mesosDriver.killTask(
+          TaskID.newBuilder().setValue(slaveIdToTaskId.get(slaveId).toString).build())
+        pendingRemovedSlaveIds += slaveId
+      } else {
+        logWarning("Unable to find executor Id '" + executorId + "' in Mesos scheduler")
+      }
+    }
+    // no need to adjust `executorLimitOption` since the AllocationManager already communicated
+    // the desired limit through a call to `doRequestTotalExecutors`.
+    // See [[o.a.s.scheduler.cluster.CoarseGrainedSchedulerBackend.killExecutors]]
+    true
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
index d8a8c848bb4d1..925702e63afd3 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
@@ -24,7 +24,7 @@ import scala.collection.JavaConversions._
 import scala.util.control.NonFatal
 
 import com.google.common.base.Splitter
-import org.apache.mesos.{MesosSchedulerDriver, Protos, Scheduler}
+import org.apache.mesos.{MesosSchedulerDriver, SchedulerDriver, Scheduler, Protos}
 import org.apache.mesos.Protos._
 import org.apache.mesos.protobuf.GeneratedMessage
 import org.apache.spark.{Logging, SparkContext}
@@ -39,7 +39,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
   private final val registerLatch = new CountDownLatch(1)
 
   // Driver for talking to Mesos
-  protected var mesosDriver: MesosSchedulerDriver = null
+  protected var mesosDriver: SchedulerDriver = null
 
   /**
    * Starts the MesosSchedulerDriver with the provided information. This method returns
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 91ef86389a0c3..5f537692a16c5 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -124,10 +124,16 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
     (blockId, getFile(blockId))
   }
 
+  /**
+   * Create local directories for storing block data. These directories are
+   * located inside configured local directories and won't
+   * be deleted on JVM exit when using the external shuffle service.
+   */
   private def createLocalDirs(conf: SparkConf): Array[File] = {
-    Utils.getOrCreateLocalRootDirs(conf).flatMap { rootDir =>
+    Utils.getConfiguredLocalDirs(conf).flatMap { rootDir =>
       try {
         val localDir = Utils.createDirectory(rootDir, "blockmgr")
+        Utils.chmod700(localDir)
         logInfo(s"Created local directory at $localDir")
         Some(localDir)
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 944560a91354a..b6b932104a94d 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -733,7 +733,12 @@ private[spark] object Utils extends Logging {
     localRootDirs
   }
 
-  private def getOrCreateLocalRootDirsImpl(conf: SparkConf): Array[String] = {
+  /**
+   * Return the configured local directories where Spark can write files. This
+   * method does not create any directories on its own, it only encapsulates the
+   * logic of locating the local directories according to deployment mode.
+   */
+  def getConfiguredLocalDirs(conf: SparkConf): Array[String] = {
     if (isRunningInYarnContainer(conf)) {
       // If we are in yarn mode, systems can have different disk layouts so we must set it
       // to what Yarn on this system said was available. Note this assumes that Yarn has
@@ -749,27 +754,29 @@ private[spark] object Utils extends Logging {
       Option(conf.getenv("SPARK_LOCAL_DIRS"))
         .getOrElse(conf.get("spark.local.dir", System.getProperty("java.io.tmpdir")))
         .split(",")
-        .flatMap { root =>
-          try {
-            val rootDir = new File(root)
-            if (rootDir.exists || rootDir.mkdirs()) {
-              val dir = createTempDir(root)
-              chmod700(dir)
-              Some(dir.getAbsolutePath)
-            } else {
-              logError(s"Failed to create dir in $root. Ignoring this directory.")
-              None
-            }
-          } catch {
-            case e: IOException =>
-            logError(s"Failed to create local root dir in $root. Ignoring this directory.")
-            None
-          }
-        }
-        .toArray
     }
   }
 
+  private def getOrCreateLocalRootDirsImpl(conf: SparkConf): Array[String] = {
+    getConfiguredLocalDirs(conf).flatMap { root =>
+      try {
+        val rootDir = new File(root)
+        if (rootDir.exists || rootDir.mkdirs()) {
+          val dir = createTempDir(root)
+          chmod700(dir)
+          Some(dir.getAbsolutePath)
+        } else {
+          logError(s"Failed to create dir in $root. Ignoring this directory.")
+          None
+        }
+      } catch {
+        case e: IOException =>
+          logError(s"Failed to create local root dir in $root. Ignoring this directory.")
+          None
+      }
+    }.toArray
+  }
+
   /** Get the Yarn approved local directories. */
   private def getYarnLocalDirs(conf: SparkConf): String = {
     // Hadoop 0.23 and 2.x have different Environment variable names for the
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
new file mode 100644
index 0000000000000..3f1692917a357
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
@@ -0,0 +1,175 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler.cluster.mesos
+
+import java.util
+import java.util.Collections
+
+import org.apache.mesos.Protos.Value.Scalar
+import org.apache.mesos.Protos._
+import org.apache.mesos.SchedulerDriver
+import org.mockito.Matchers._
+import org.mockito.Mockito._
+import org.mockito.Matchers
+import org.scalatest.mock.MockitoSugar
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.scheduler.TaskSchedulerImpl
+import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkFunSuite}
+
+class CoarseMesosSchedulerBackendSuite extends SparkFunSuite
+    with LocalSparkContext
+    with MockitoSugar
+    with BeforeAndAfter {
+
+  private def createOffer(offerId: String, slaveId: String, mem: Int, cpu: Int): Offer = {
+    val builder = Offer.newBuilder()
+    builder.addResourcesBuilder()
+      .setName("mem")
+      .setType(Value.Type.SCALAR)
+      .setScalar(Scalar.newBuilder().setValue(mem))
+    builder.addResourcesBuilder()
+      .setName("cpus")
+      .setType(Value.Type.SCALAR)
+      .setScalar(Scalar.newBuilder().setValue(cpu))
+    builder.setId(OfferID.newBuilder()
+      .setValue(offerId).build())
+      .setFrameworkId(FrameworkID.newBuilder()
+        .setValue("f1"))
+      .setSlaveId(SlaveID.newBuilder().setValue(slaveId))
+      .setHostname(s"host${slaveId}")
+      .build()
+  }
+
+  private def createSchedulerBackend(
+      taskScheduler: TaskSchedulerImpl,
+      driver: SchedulerDriver): CoarseMesosSchedulerBackend = {
+    val backend = new CoarseMesosSchedulerBackend(taskScheduler, sc, "master") {
+      mesosDriver = driver
+      markRegistered()
+    }
+    backend.start()
+    backend
+  }
+
+  var sparkConf: SparkConf = _
+
+  before {
+    sparkConf = (new SparkConf)
+      .setMaster("local[*]")
+      .setAppName("test-mesos-dynamic-alloc")
+      .setSparkHome("/path")
+
+    sc = new SparkContext(sparkConf)
+  }
+
+  test("mesos supports killing and limiting executors") {
+    val driver = mock[SchedulerDriver]
+    val taskScheduler = mock[TaskSchedulerImpl]
+    when(taskScheduler.sc).thenReturn(sc)
+
+    sparkConf.set("spark.driver.host", "driverHost")
+    sparkConf.set("spark.driver.port", "1234")
+
+    val backend = createSchedulerBackend(taskScheduler, driver)
+    val minMem = backend.calculateTotalMemory(sc).toInt
+    val minCpu = 4
+
+    val mesosOffers = new java.util.ArrayList[Offer]
+    mesosOffers.add(createOffer("o1", "s1", minMem, minCpu))
+
+    val taskID0 = TaskID.newBuilder().setValue("0").build()
+
+    backend.resourceOffers(driver, mesosOffers)
+    verify(driver, times(1)).launchTasks(
+      Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)),
+      any[util.Collection[TaskInfo]],
+      any[Filters])
+
+    // simulate the allocation manager down-scaling executors
+    backend.doRequestTotalExecutors(0)
+    assert(backend.doKillExecutors(Seq("s1/0")))
+    verify(driver, times(1)).killTask(taskID0)
+
+    val mesosOffers2 = new java.util.ArrayList[Offer]
+    mesosOffers2.add(createOffer("o2", "s2", minMem, minCpu))
+    backend.resourceOffers(driver, mesosOffers2)
+
+    verify(driver, times(1))
+      .declineOffer(OfferID.newBuilder().setValue("o2").build())
+
+    // Verify we didn't launch any new executor
+    assert(backend.slaveIdsWithExecutors.size === 1)
+
+    backend.doRequestTotalExecutors(2)
+    backend.resourceOffers(driver, mesosOffers2)
+    verify(driver, times(1)).launchTasks(
+      Matchers.eq(Collections.singleton(mesosOffers2.get(0).getId)),
+      any[util.Collection[TaskInfo]],
+      any[Filters])
+
+    assert(backend.slaveIdsWithExecutors.size === 2)
+    backend.slaveLost(driver, SlaveID.newBuilder().setValue("s1").build())
+    assert(backend.slaveIdsWithExecutors.size === 1)
+  }
+
+  test("mesos supports killing and relaunching tasks with executors") {
+    val driver = mock[SchedulerDriver]
+    val taskScheduler = mock[TaskSchedulerImpl]
+    when(taskScheduler.sc).thenReturn(sc)
+
+    val backend = createSchedulerBackend(taskScheduler, driver)
+    val minMem = backend.calculateTotalMemory(sc).toInt + 1024
+    val minCpu = 4
+
+    val mesosOffers = new java.util.ArrayList[Offer]
+    val offer1 = createOffer("o1", "s1", minMem, minCpu)
+    mesosOffers.add(offer1)
+
+    val offer2 = createOffer("o2", "s1", minMem, 1);
+
+    backend.resourceOffers(driver, mesosOffers)
+
+    verify(driver, times(1)).launchTasks(
+      Matchers.eq(Collections.singleton(offer1.getId)),
+      anyObject(),
+      anyObject[Filters])
+
+    // Simulate task killed, executor no longer running
+    val status = TaskStatus.newBuilder()
+      .setTaskId(TaskID.newBuilder().setValue("0").build())
+      .setSlaveId(SlaveID.newBuilder().setValue("s1").build())
+      .setState(TaskState.TASK_KILLED)
+      .build
+
+    backend.statusUpdate(driver, status)
+    assert(!backend.slaveIdsWithExecutors.contains("s1"))
+
+    mesosOffers.clear()
+    mesosOffers.add(offer2)
+    backend.resourceOffers(driver, mesosOffers)
+    assert(backend.slaveIdsWithExecutors.contains("s1"))
+
+    verify(driver, times(1)).launchTasks(
+      Matchers.eq(Collections.singleton(offer2.getId)),
+      anyObject(),
+      anyObject[Filters])
+
+    verify(driver, times(1)).reviveOffers()
+  }
+}

From 1f6b0b1234cc03aa2e07aea7fec2de7563885238 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 9 Jul 2015 13:48:29 -0700
Subject: [PATCH 0320/1454] [SPARK-8701] [STREAMING] [WEBUI] Add input metadata
 in the batch page

This PR adds `metadata` to `InputInfo`. `InputDStream` can report its metadata for a batch and it will be shown in the batch page.

For example,

![screen shot](https://cloud.githubusercontent.com/assets/1000778/8403741/d6ffc7e2-1e79-11e5-9888-c78c1575123a.png)

FileInputDStream will display the new files for a batch, and DirectKafkaInputDStream will display its offset ranges.

Author: zsxwing <zsxwing@gmail.com>

Closes #7081 from zsxwing/input-metadata and squashes the following commits:

f7abd9b [zsxwing] Revert the space changes in project/MimaExcludes.scala
d906209 [zsxwing] Merge branch 'master' into input-metadata
74762da [zsxwing] Fix MiMa tests
7903e33 [zsxwing] Merge branch 'master' into input-metadata
450a46c [zsxwing] Address comments
1d94582 [zsxwing] Raname InputInfo to StreamInputInfo and change "metadata" to Map[String, Any]
d496ae9 [zsxwing] Add input metadata in the batch page
---
 .../kafka/DirectKafkaInputDStream.scala       | 23 ++++++++--
 .../spark/streaming/kafka/OffsetRange.scala   |  2 +-
 project/MimaExcludes.scala                    |  6 +++
 .../streaming/dstream/FileInputDStream.scala  | 10 ++++-
 .../dstream/ReceiverInputDStream.scala        |  4 +-
 .../spark/streaming/scheduler/BatchInfo.scala |  9 ++--
 .../scheduler/InputInfoTracker.scala          | 38 +++++++++++++---
 .../streaming/scheduler/JobGenerator.scala    |  3 +-
 .../spark/streaming/scheduler/JobSet.scala    |  4 +-
 .../apache/spark/streaming/ui/BatchPage.scala | 43 +++++++++++++++++--
 .../spark/streaming/ui/BatchUIData.scala      |  8 ++--
 .../ui/StreamingJobProgressListener.scala     |  5 ++-
 .../streaming/StreamingListenerSuite.scala    |  6 +--
 .../spark/streaming/TestSuiteBase.scala       |  2 +-
 .../scheduler/InputInfoTrackerSuite.scala     |  8 ++--
 .../StreamingJobProgressListenerSuite.scala   | 28 ++++++------
 16 files changed, 148 insertions(+), 51 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 876456c964770..48a1933d92f85 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.kafka
 
 import scala.annotation.tailrec
 import scala.collection.mutable
-import scala.reflect.{classTag, ClassTag}
+import scala.reflect.ClassTag
 
 import kafka.common.TopicAndPartition
 import kafka.message.MessageAndMetadata
@@ -29,7 +29,7 @@ import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
-import org.apache.spark.streaming.scheduler.InputInfo
+import org.apache.spark.streaming.scheduler.StreamInputInfo
 
 /**
  *  A stream of {@link org.apache.spark.streaming.kafka.KafkaRDD} where
@@ -119,8 +119,23 @@ class DirectKafkaInputDStream[
     val rdd = KafkaRDD[K, V, U, T, R](
       context.sparkContext, kafkaParams, currentOffsets, untilOffsets, messageHandler)
 
-    // Report the record number of this batch interval to InputInfoTracker.
-    val inputInfo = InputInfo(id, rdd.count)
+    // Report the record number and metadata of this batch interval to InputInfoTracker.
+    val offsetRanges = currentOffsets.map { case (tp, fo) =>
+      val uo = untilOffsets(tp)
+      OffsetRange(tp.topic, tp.partition, fo, uo.offset)
+    }
+    val description = offsetRanges.filter { offsetRange =>
+      // Don't display empty ranges.
+      offsetRange.fromOffset != offsetRange.untilOffset
+    }.map { offsetRange =>
+      s"topic: ${offsetRange.topic}\tpartition: ${offsetRange.partition}\t" +
+        s"offsets: ${offsetRange.fromOffset} to ${offsetRange.untilOffset}"
+    }.mkString("\n")
+    // Copy offsetRanges to immutable.List to prevent from being modified by the user
+    val metadata = Map(
+      "offsets" -> offsetRanges.toList,
+      StreamInputInfo.METADATA_KEY_DESCRIPTION -> description)
+    val inputInfo = StreamInputInfo(id, rdd.count, metadata)
     ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
 
     currentOffsets = untilOffsets.map(kv => kv._1 -> kv._2.offset)
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
index 2675042666304..f326e7f1f6f8d 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
@@ -75,7 +75,7 @@ final class OffsetRange private(
   }
 
   override def toString(): String = {
-    s"OffsetRange(topic: '$topic', partition: $partition, range: [$fromOffset -> $untilOffset]"
+    s"OffsetRange(topic: '$topic', partition: $partition, range: [$fromOffset -> $untilOffset])"
   }
 
   /** this is to avoid ClassNotFoundException during checkpoint restore */
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 821aadd477ef3..79089aae2a37c 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -77,6 +77,12 @@ object MimaExcludes {
             // SPARK-8914 Remove RDDApi
             ProblemFilters.exclude[MissingClassProblem](
             "org.apache.spark.sql.RDDApi")
+          ) ++ Seq(
+            // SPARK-8701 Add input metadata in the batch page.
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.streaming.scheduler.InputInfo$"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.streaming.scheduler.InputInfo")
           )
 
         case v if v.startsWith("1.4") =>
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
index 86a8e2beff57c..dd4da9d9ca6a2 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
@@ -28,6 +28,7 @@ import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 
 import org.apache.spark.rdd.{RDD, UnionRDD}
 import org.apache.spark.streaming._
+import org.apache.spark.streaming.scheduler.StreamInputInfo
 import org.apache.spark.util.{SerializableConfiguration, TimeStampedHashMap, Utils}
 
 /**
@@ -144,7 +145,14 @@ class FileInputDStream[K, V, F <: NewInputFormat[K, V]](
     logInfo("New files at time " + validTime + ":\n" + newFiles.mkString("\n"))
     batchTimeToSelectedFiles += ((validTime, newFiles))
     recentlySelectedFiles ++= newFiles
-    Some(filesToRDD(newFiles))
+    val rdds = Some(filesToRDD(newFiles))
+    // Copy newFiles to immutable.List to prevent from being modified by the user
+    val metadata = Map(
+      "files" -> newFiles.toList,
+      StreamInputInfo.METADATA_KEY_DESCRIPTION -> newFiles.mkString("\n"))
+    val inputInfo = StreamInputInfo(id, 0, metadata)
+    ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
+    rdds
   }
 
   /** Clear the old time-to-files mappings along with old RDDs */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index e76e7eb0dea19..a50f0efc030ce 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -24,7 +24,7 @@ import org.apache.spark.storage.BlockId
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
 import org.apache.spark.streaming.receiver.Receiver
-import org.apache.spark.streaming.scheduler.InputInfo
+import org.apache.spark.streaming.scheduler.StreamInputInfo
 import org.apache.spark.streaming.util.WriteAheadLogUtils
 
 /**
@@ -70,7 +70,7 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
         val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
 
         // Register the input blocks information into InputInfoTracker
-        val inputInfo = InputInfo(id, blockInfos.flatMap(_.numRecords).sum)
+        val inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum)
         ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
 
         if (blockInfos.nonEmpty) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala
index 5b9bfbf9b01e3..9922b6bc1201b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala
@@ -24,7 +24,7 @@ import org.apache.spark.streaming.Time
  * :: DeveloperApi ::
  * Class having information on completed batches.
  * @param batchTime   Time of the batch
- * @param streamIdToNumRecords A map of input stream id to record number
+ * @param streamIdToInputInfo A map of input stream id to its input info
  * @param submissionTime  Clock time of when jobs of this batch was submitted to
  *                        the streaming scheduler queue
  * @param processingStartTime Clock time of when the first job of this batch started processing
@@ -33,12 +33,15 @@ import org.apache.spark.streaming.Time
 @DeveloperApi
 case class BatchInfo(
     batchTime: Time,
-    streamIdToNumRecords: Map[Int, Long],
+    streamIdToInputInfo: Map[Int, StreamInputInfo],
     submissionTime: Long,
     processingStartTime: Option[Long],
     processingEndTime: Option[Long]
   ) {
 
+  @deprecated("Use streamIdToInputInfo instead", "1.5.0")
+  def streamIdToNumRecords: Map[Int, Long] = streamIdToInputInfo.mapValues(_.numRecords)
+
   /**
    * Time taken for the first job of this batch to start processing from the time this batch
    * was submitted to the streaming scheduler. Essentially, it is
@@ -63,5 +66,5 @@ case class BatchInfo(
   /**
    * The number of recorders received by the receivers in this batch.
    */
-  def numRecords: Long = streamIdToNumRecords.values.sum
+  def numRecords: Long = streamIdToInputInfo.values.map(_.numRecords).sum
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
index 7c0db8a863c67..363c03d431f04 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
@@ -20,11 +20,34 @@ package org.apache.spark.streaming.scheduler
 import scala.collection.mutable
 
 import org.apache.spark.Logging
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.streaming.{Time, StreamingContext}
 
-/** To track the information of input stream at specified batch time. */
-private[streaming] case class InputInfo(inputStreamId: Int, numRecords: Long) {
+/**
+ * :: DeveloperApi ::
+ * Track the information of input stream at specified batch time.
+ *
+ * @param inputStreamId the input stream id
+ * @param numRecords the number of records in a batch
+ * @param metadata metadata for this batch. It should contain at least one standard field named
+ *                 "Description" which maps to the content that will be shown in the UI.
+ */
+@DeveloperApi
+case class StreamInputInfo(
+    inputStreamId: Int, numRecords: Long, metadata: Map[String, Any] = Map.empty) {
   require(numRecords >= 0, "numRecords must not be negative")
+
+  def metadataDescription: Option[String] =
+    metadata.get(StreamInputInfo.METADATA_KEY_DESCRIPTION).map(_.toString)
+}
+
+@DeveloperApi
+object StreamInputInfo {
+
+  /**
+   * The key for description in `StreamInputInfo.metadata`.
+   */
+  val METADATA_KEY_DESCRIPTION: String = "Description"
 }
 
 /**
@@ -34,12 +57,13 @@ private[streaming] case class InputInfo(inputStreamId: Int, numRecords: Long) {
 private[streaming] class InputInfoTracker(ssc: StreamingContext) extends Logging {
 
   // Map to track all the InputInfo related to specific batch time and input stream.
-  private val batchTimeToInputInfos = new mutable.HashMap[Time, mutable.HashMap[Int, InputInfo]]
+  private val batchTimeToInputInfos =
+    new mutable.HashMap[Time, mutable.HashMap[Int, StreamInputInfo]]
 
   /** Report the input information with batch time to the tracker */
-  def reportInfo(batchTime: Time, inputInfo: InputInfo): Unit = synchronized {
+  def reportInfo(batchTime: Time, inputInfo: StreamInputInfo): Unit = synchronized {
     val inputInfos = batchTimeToInputInfos.getOrElseUpdate(batchTime,
-      new mutable.HashMap[Int, InputInfo]())
+      new mutable.HashMap[Int, StreamInputInfo]())
 
     if (inputInfos.contains(inputInfo.inputStreamId)) {
       throw new IllegalStateException(s"Input stream ${inputInfo.inputStreamId}} for batch" +
@@ -49,10 +73,10 @@ private[streaming] class InputInfoTracker(ssc: StreamingContext) extends Logging
   }
 
   /** Get the all the input stream's information of specified batch time */
-  def getInfo(batchTime: Time): Map[Int, InputInfo] = synchronized {
+  def getInfo(batchTime: Time): Map[Int, StreamInputInfo] = synchronized {
     val inputInfos = batchTimeToInputInfos.get(batchTime)
     // Convert mutable HashMap to immutable Map for the caller
-    inputInfos.map(_.toMap).getOrElse(Map[Int, InputInfo]())
+    inputInfos.map(_.toMap).getOrElse(Map[Int, StreamInputInfo]())
   }
 
   /** Cleanup the tracked input information older than threshold batch time */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
index 9f93d6cbc3c20..f5d41858646e4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
@@ -244,8 +244,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging {
     } match {
       case Success(jobs) =>
         val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
-        val streamIdToNumRecords = streamIdToInputInfos.mapValues(_.numRecords)
-        jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToNumRecords))
+        jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
       case Failure(e) =>
         jobScheduler.reportError("Error generating jobs for time " + time, e)
     }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala
index e6be63b2ddbdc..95833efc9417f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala
@@ -28,7 +28,7 @@ private[streaming]
 case class JobSet(
     time: Time,
     jobs: Seq[Job],
-    streamIdToNumRecords: Map[Int, Long] = Map.empty) {
+    streamIdToInputInfo: Map[Int, StreamInputInfo] = Map.empty) {
 
   private val incompleteJobs = new HashSet[Job]()
   private val submissionTime = System.currentTimeMillis() // when this jobset was submitted
@@ -64,7 +64,7 @@ case class JobSet(
   def toBatchInfo: BatchInfo = {
     new BatchInfo(
       time,
-      streamIdToNumRecords,
+      streamIdToInputInfo,
       submissionTime,
       if (processingStartTime >= 0 ) Some(processingStartTime) else None,
       if (processingEndTime >= 0 ) Some(processingEndTime) else None
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
index f75067669abe5..0c891662c264f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
@@ -17,11 +17,9 @@
 
 package org.apache.spark.streaming.ui
 
-import java.text.SimpleDateFormat
-import java.util.Date
 import javax.servlet.http.HttpServletRequest
 
-import scala.xml.{NodeSeq, Node, Text}
+import scala.xml.{NodeSeq, Node, Text, Unparsed}
 
 import org.apache.commons.lang3.StringEscapeUtils
 
@@ -303,6 +301,9 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
       batchUIData.processingDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
     val formattedTotalDelay = batchUIData.totalDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
 
+    val inputMetadatas = batchUIData.streamIdToInputInfo.values.flatMap { inputInfo =>
+      inputInfo.metadataDescription.map(desc => inputInfo.inputStreamId -> desc)
+    }.toSeq
     val summary: NodeSeq =
       <div>
         <ul class="unstyled">
@@ -326,6 +327,13 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
             <strong>Total delay: </strong>
             {formattedTotalDelay}
           </li>
+          {
+            if (inputMetadatas.nonEmpty) {
+              <li>
+                <strong>Input Metadata:</strong>{generateInputMetadataTable(inputMetadatas)}
+              </li>
+            }
+          }
         </ul>
       </div>
 
@@ -340,4 +348,33 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
 
     SparkUIUtils.headerSparkPage(s"Details of batch at $formattedBatchTime", content, parent)
   }
+
+  def generateInputMetadataTable(inputMetadatas: Seq[(Int, String)]): Seq[Node] = {
+    <table class={SparkUIUtils.TABLE_CLASS_STRIPED}>
+      <thead>
+        <tr>
+          <th>Input</th>
+          <th>Metadata</th>
+        </tr>
+      </thead>
+      <tbody>
+        {inputMetadatas.flatMap(generateInputMetadataRow)}
+      </tbody>
+    </table>
+  }
+
+  def generateInputMetadataRow(inputMetadata: (Int, String)): Seq[Node] = {
+    val streamId = inputMetadata._1
+
+    <tr>
+      <td>{streamingListener.streamName(streamId).getOrElse(s"Stream-$streamId")}</td>
+      <td>{metadataDescriptionToHTML(inputMetadata._2)}</td>
+    </tr>
+  }
+
+  private def metadataDescriptionToHTML(metadataDescription: String): Seq[Node] = {
+    // tab to 4 spaces and "\n" to "<br/>"
+    Unparsed(StringEscapeUtils.escapeHtml4(metadataDescription).
+      replaceAllLiterally("\t", "&nbsp;&nbsp;&nbsp;&nbsp;").replaceAllLiterally("\n", "<br/>"))
+  }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala
index a5514dfd71c9f..ae508c0e9577b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala
@@ -19,14 +19,14 @@
 package org.apache.spark.streaming.ui
 
 import org.apache.spark.streaming.Time
-import org.apache.spark.streaming.scheduler.BatchInfo
+import org.apache.spark.streaming.scheduler.{BatchInfo, StreamInputInfo}
 import org.apache.spark.streaming.ui.StreamingJobProgressListener._
 
 private[ui] case class OutputOpIdAndSparkJobId(outputOpId: OutputOpId, sparkJobId: SparkJobId)
 
 private[ui] case class BatchUIData(
     val batchTime: Time,
-    val streamIdToNumRecords: Map[Int, Long],
+    val streamIdToInputInfo: Map[Int, StreamInputInfo],
     val submissionTime: Long,
     val processingStartTime: Option[Long],
     val processingEndTime: Option[Long],
@@ -58,7 +58,7 @@ private[ui] case class BatchUIData(
   /**
    * The number of recorders received by the receivers in this batch.
    */
-  def numRecords: Long = streamIdToNumRecords.values.sum
+  def numRecords: Long = streamIdToInputInfo.values.map(_.numRecords).sum
 }
 
 private[ui] object BatchUIData {
@@ -66,7 +66,7 @@ private[ui] object BatchUIData {
   def apply(batchInfo: BatchInfo): BatchUIData = {
     new BatchUIData(
       batchInfo.batchTime,
-      batchInfo.streamIdToNumRecords,
+      batchInfo.streamIdToInputInfo,
       batchInfo.submissionTime,
       batchInfo.processingStartTime,
       batchInfo.processingEndTime
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
index 68e8ce98945e0..b77c555c68b8b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
@@ -192,7 +192,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
   def receivedEventRateWithBatchTime: Map[Int, Seq[(Long, Double)]] = synchronized {
     val _retainedBatches = retainedBatches
     val latestBatches = _retainedBatches.map { batchUIData =>
-      (batchUIData.batchTime.milliseconds, batchUIData.streamIdToNumRecords)
+      (batchUIData.batchTime.milliseconds, batchUIData.streamIdToInputInfo.mapValues(_.numRecords))
     }
     streamIds.map { streamId =>
       val eventRates = latestBatches.map {
@@ -205,7 +205,8 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
   }
 
   def lastReceivedBatchRecords: Map[Int, Long] = synchronized {
-    val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.streamIdToNumRecords)
+    val lastReceivedBlockInfoOption =
+      lastReceivedBatch.map(_.streamIdToInputInfo.mapValues(_.numRecords))
     lastReceivedBlockInfoOption.map { lastReceivedBlockInfo =>
       streamIds.map { streamId =>
         (streamId, lastReceivedBlockInfo.getOrElse(streamId, 0L))
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
index 7bc7727a9fbe4..4bc1dd4a30fc4 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
@@ -59,7 +59,7 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers {
 
     batchInfosSubmitted.foreach { info =>
       info.numRecords should be (1L)
-      info.streamIdToNumRecords should be (Map(0 -> 1L))
+      info.streamIdToInputInfo should be (Map(0 -> StreamInputInfo(0, 1L)))
     }
 
     isInIncreasingOrder(batchInfosSubmitted.map(_.submissionTime)) should be (true)
@@ -77,7 +77,7 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers {
 
     batchInfosStarted.foreach { info =>
       info.numRecords should be (1L)
-      info.streamIdToNumRecords should be (Map(0 -> 1L))
+      info.streamIdToInputInfo should be (Map(0 -> StreamInputInfo(0, 1L)))
     }
 
     isInIncreasingOrder(batchInfosStarted.map(_.submissionTime)) should be (true)
@@ -98,7 +98,7 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers {
 
     batchInfosCompleted.foreach { info =>
       info.numRecords should be (1L)
-      info.streamIdToNumRecords should be (Map(0 -> 1L))
+      info.streamIdToInputInfo should be (Map(0 -> StreamInputInfo(0, 1L)))
     }
 
     isInIncreasingOrder(batchInfosCompleted.map(_.submissionTime)) should be (true)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
index 31b1aebf6a8ec..0d58a7b54412f 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -76,7 +76,7 @@ class TestInputStream[T: ClassTag](ssc_ : StreamingContext, input: Seq[Seq[T]],
     }
 
     // Report the input data's information to InputInfoTracker for testing
-    val inputInfo = InputInfo(id, selectedInput.length.toLong)
+    val inputInfo = StreamInputInfo(id, selectedInput.length.toLong)
     ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
 
     val rdd = ssc.sc.makeRDD(selectedInput, numPartitions)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala
index 2e210397fe7c7..f5248acf712b9 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala
@@ -46,8 +46,8 @@ class InputInfoTrackerSuite extends SparkFunSuite with BeforeAndAfter {
     val streamId1 = 0
     val streamId2 = 1
     val time = Time(0L)
-    val inputInfo1 = InputInfo(streamId1, 100L)
-    val inputInfo2 = InputInfo(streamId2, 300L)
+    val inputInfo1 = StreamInputInfo(streamId1, 100L)
+    val inputInfo2 = StreamInputInfo(streamId2, 300L)
     inputInfoTracker.reportInfo(time, inputInfo1)
     inputInfoTracker.reportInfo(time, inputInfo2)
 
@@ -63,8 +63,8 @@ class InputInfoTrackerSuite extends SparkFunSuite with BeforeAndAfter {
     val inputInfoTracker = new InputInfoTracker(ssc)
 
     val streamId1 = 0
-    val inputInfo1 = InputInfo(streamId1, 100L)
-    val inputInfo2 = InputInfo(streamId1, 300L)
+    val inputInfo1 = StreamInputInfo(streamId1, 100L)
+    val inputInfo2 = StreamInputInfo(streamId1, 300L)
     inputInfoTracker.reportInfo(Time(0), inputInfo1)
     inputInfoTracker.reportInfo(Time(1), inputInfo2)
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
index c9175d61b1f49..40dc1fb601bd0 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
@@ -49,10 +49,12 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     val ssc = setupStreams(input, operation)
     val listener = new StreamingJobProgressListener(ssc)
 
-    val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L)
+    val streamIdToInputInfo = Map(
+      0 -> StreamInputInfo(0, 300L),
+      1 -> StreamInputInfo(1, 300L, Map(StreamInputInfo.METADATA_KEY_DESCRIPTION -> "test")))
 
     // onBatchSubmitted
-    val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, None, None)
+    val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToInputInfo, 1000, None, None)
     listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted))
     listener.waitingBatches should be (List(BatchUIData(batchInfoSubmitted)))
     listener.runningBatches should be (Nil)
@@ -64,7 +66,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     listener.numTotalReceivedRecords should be (0)
 
     // onBatchStarted
-    val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None)
+    val batchInfoStarted = BatchInfo(Time(1000), streamIdToInputInfo, 1000, Some(2000), None)
     listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted))
     listener.waitingBatches should be (Nil)
     listener.runningBatches should be (List(BatchUIData(batchInfoStarted)))
@@ -94,7 +96,9 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     batchUIData.get.schedulingDelay should be (batchInfoStarted.schedulingDelay)
     batchUIData.get.processingDelay should be (batchInfoStarted.processingDelay)
     batchUIData.get.totalDelay should be (batchInfoStarted.totalDelay)
-    batchUIData.get.streamIdToNumRecords should be (Map(0 -> 300L, 1 -> 300L))
+    batchUIData.get.streamIdToInputInfo should be (Map(
+      0 -> StreamInputInfo(0, 300L),
+      1 -> StreamInputInfo(1, 300L, Map(StreamInputInfo.METADATA_KEY_DESCRIPTION -> "test"))))
     batchUIData.get.numRecords should be(600)
     batchUIData.get.outputOpIdSparkJobIdPairs should be
       Seq(OutputOpIdAndSparkJobId(0, 0),
@@ -103,7 +107,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
         OutputOpIdAndSparkJobId(1, 1))
 
     // onBatchCompleted
-    val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None)
+    val batchInfoCompleted = BatchInfo(Time(1000), streamIdToInputInfo, 1000, Some(2000), None)
     listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted))
     listener.waitingBatches should be (Nil)
     listener.runningBatches should be (Nil)
@@ -141,9 +145,9 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
     val listener = new StreamingJobProgressListener(ssc)
 
-    val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L)
+    val streamIdToInputInfo = Map(0 -> StreamInputInfo(0, 300L), 1 -> StreamInputInfo(1, 300L))
 
-    val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None)
+    val batchInfoCompleted = BatchInfo(Time(1000), streamIdToInputInfo, 1000, Some(2000), None)
 
     for(_ <- 0 until (limit + 10)) {
       listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted))
@@ -182,7 +186,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     batchUIData.get.schedulingDelay should be (batchInfoSubmitted.schedulingDelay)
     batchUIData.get.processingDelay should be (batchInfoSubmitted.processingDelay)
     batchUIData.get.totalDelay should be (batchInfoSubmitted.totalDelay)
-    batchUIData.get.streamIdToNumRecords should be (Map.empty)
+    batchUIData.get.streamIdToInputInfo should be (Map.empty)
     batchUIData.get.numRecords should be (0)
     batchUIData.get.outputOpIdSparkJobIdPairs should be (Seq(OutputOpIdAndSparkJobId(0, 0)))
 
@@ -211,14 +215,14 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
 
     for (_ <- 0 until 2 * limit) {
-      val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L)
+      val streamIdToInputInfo = Map(0 -> StreamInputInfo(0, 300L), 1 -> StreamInputInfo(1, 300L))
 
       // onBatchSubmitted
-      val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, None, None)
+      val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToInputInfo, 1000, None, None)
       listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted))
 
       // onBatchStarted
-      val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None)
+      val batchInfoStarted = BatchInfo(Time(1000), streamIdToInputInfo, 1000, Some(2000), None)
       listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted))
 
       // onJobStart
@@ -235,7 +239,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
       listener.onJobStart(jobStart4)
 
       // onBatchCompleted
-      val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None)
+      val batchInfoCompleted = BatchInfo(Time(1000), streamIdToInputInfo, 1000, Some(2000), None)
       listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted))
     }
 

From 3ccebf36c5abe04702d4cf223552a94034d980fb Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Thu, 9 Jul 2015 13:54:44 -0700
Subject: [PATCH 0321/1454] [SPARK-8389] [STREAMING] [PYSPARK] Expose KafkaRDDs
 offsetRange in Python

This PR propose a simple way to expose OffsetRange in Python code, also the usage of offsetRanges is similar to Scala/Java way, here in Python we could get OffsetRange like:

```
dstream.foreachRDD(lambda r: KafkaUtils.offsetRanges(r))
```

Reason I didn't follow the way what SPARK-8389 suggested is that: Python Kafka API has one more step to decode the message compared to Scala/Java, Which makes Python API return a transformed RDD/DStream, not directly wrapped so-called JavaKafkaRDD, so it is hard to backtrack to the original RDD to get the offsetRange.

Author: jerryshao <saisai.shao@intel.com>

Closes #7185 from jerryshao/SPARK-8389 and squashes the following commits:

4c6d320 [jerryshao] Another way to fix subclass deserialization issue
e6a8011 [jerryshao] Address the comments
fd13937 [jerryshao] Fix serialization bug
7debf1c [jerryshao] bug fix
cff3893 [jerryshao] refactor the code according to the comments
2aabf9e [jerryshao] Style fix
848c708 [jerryshao] Add HasOffsetRanges for Python
---
 .../spark/streaming/kafka/KafkaUtils.scala    |  13 ++
 python/pyspark/streaming/kafka.py             | 123 ++++++++++++++++--
 python/pyspark/streaming/tests.py             |  64 +++++++++
 python/pyspark/streaming/util.py              |   7 +-
 4 files changed, 196 insertions(+), 11 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
index 0e33362d34acd..f3b01bd60b178 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
@@ -670,4 +670,17 @@ private class KafkaUtilsPythonHelper {
     TopicAndPartition(topic, partition)
 
   def createBroker(host: String, port: JInt): Broker = Broker(host, port)
+
+  def offsetRangesOfKafkaRDD(rdd: RDD[_]): JList[OffsetRange] = {
+    val parentRDDs = rdd.getNarrowAncestors
+    val kafkaRDDs = parentRDDs.filter(rdd => rdd.isInstanceOf[KafkaRDD[_, _, _, _, _]])
+
+    require(
+      kafkaRDDs.length == 1,
+      "Cannot get offset ranges, as there may be multiple Kafka RDDs or no Kafka RDD associated" +
+        "with this RDD, please call this method only on a Kafka RDD.")
+
+    val kafkaRDD = kafkaRDDs.head.asInstanceOf[KafkaRDD[_, _, _, _, _]]
+    kafkaRDD.offsetRanges.toSeq
+  }
 }
diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py
index 10a859a532e28..33dd596335b47 100644
--- a/python/pyspark/streaming/kafka.py
+++ b/python/pyspark/streaming/kafka.py
@@ -21,6 +21,8 @@
 from pyspark.storagelevel import StorageLevel
 from pyspark.serializers import PairDeserializer, NoOpSerializer
 from pyspark.streaming import DStream
+from pyspark.streaming.dstream import TransformedDStream
+from pyspark.streaming.util import TransformFunction
 
 __all__ = ['Broker', 'KafkaUtils', 'OffsetRange', 'TopicAndPartition', 'utf8_decoder']
 
@@ -122,8 +124,9 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
             raise e
 
         ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
-        stream = DStream(jstream, ssc, ser)
-        return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
+        stream = DStream(jstream, ssc, ser) \
+            .map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
+        return KafkaDStream(stream._jdstream, ssc, stream._jrdd_deserializer)
 
     @staticmethod
     def createRDD(sc, kafkaParams, offsetRanges, leaders={},
@@ -161,8 +164,8 @@ def createRDD(sc, kafkaParams, offsetRanges, leaders={},
             raise e
 
         ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
-        rdd = RDD(jrdd, sc, ser)
-        return rdd.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
+        rdd = RDD(jrdd, sc, ser).map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
+        return KafkaRDD(rdd._jrdd, rdd.ctx, rdd._jrdd_deserializer)
 
     @staticmethod
     def _printErrorMsg(sc):
@@ -200,14 +203,30 @@ def __init__(self, topic, partition, fromOffset, untilOffset):
         :param fromOffset: Inclusive starting offset.
         :param untilOffset: Exclusive ending offset.
         """
-        self._topic = topic
-        self._partition = partition
-        self._fromOffset = fromOffset
-        self._untilOffset = untilOffset
+        self.topic = topic
+        self.partition = partition
+        self.fromOffset = fromOffset
+        self.untilOffset = untilOffset
+
+    def __eq__(self, other):
+        if isinstance(other, self.__class__):
+            return (self.topic == other.topic
+                    and self.partition == other.partition
+                    and self.fromOffset == other.fromOffset
+                    and self.untilOffset == other.untilOffset)
+        else:
+            return False
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    def __str__(self):
+        return "OffsetRange(topic: %s, partition: %d, range: [%d -> %d]" \
+               % (self.topic, self.partition, self.fromOffset, self.untilOffset)
 
     def _jOffsetRange(self, helper):
-        return helper.createOffsetRange(self._topic, self._partition, self._fromOffset,
-                                        self._untilOffset)
+        return helper.createOffsetRange(self.topic, self.partition, self.fromOffset,
+                                        self.untilOffset)
 
 
 class TopicAndPartition(object):
@@ -244,3 +263,87 @@ def __init__(self, host, port):
 
     def _jBroker(self, helper):
         return helper.createBroker(self._host, self._port)
+
+
+class KafkaRDD(RDD):
+    """
+    A Python wrapper of KafkaRDD, to provide additional information on normal RDD.
+    """
+
+    def __init__(self, jrdd, ctx, jrdd_deserializer):
+        RDD.__init__(self, jrdd, ctx, jrdd_deserializer)
+
+    def offsetRanges(self):
+        """
+        Get the OffsetRange of specific KafkaRDD.
+        :return: A list of OffsetRange
+        """
+        try:
+            helperClass = self.ctx._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+                .loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper")
+            helper = helperClass.newInstance()
+            joffsetRanges = helper.offsetRangesOfKafkaRDD(self._jrdd.rdd())
+        except Py4JJavaError as e:
+            if 'ClassNotFoundException' in str(e.java_exception):
+                KafkaUtils._printErrorMsg(self.ctx)
+            raise e
+
+        ranges = [OffsetRange(o.topic(), o.partition(), o.fromOffset(), o.untilOffset())
+                  for o in joffsetRanges]
+        return ranges
+
+
+class KafkaDStream(DStream):
+    """
+    A Python wrapper of KafkaDStream
+    """
+
+    def __init__(self, jdstream, ssc, jrdd_deserializer):
+        DStream.__init__(self, jdstream, ssc, jrdd_deserializer)
+
+    def foreachRDD(self, func):
+        """
+        Apply a function to each RDD in this DStream.
+        """
+        if func.__code__.co_argcount == 1:
+            old_func = func
+            func = lambda r, rdd: old_func(rdd)
+        jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer) \
+            .rdd_wrapper(lambda jrdd, ctx, ser: KafkaRDD(jrdd, ctx, ser))
+        api = self._ssc._jvm.PythonDStream
+        api.callForeachRDD(self._jdstream, jfunc)
+
+    def transform(self, func):
+        """
+        Return a new DStream in which each RDD is generated by applying a function
+        on each RDD of this DStream.
+
+        `func` can have one argument of `rdd`, or have two arguments of
+        (`time`, `rdd`)
+        """
+        if func.__code__.co_argcount == 1:
+            oldfunc = func
+            func = lambda t, rdd: oldfunc(rdd)
+        assert func.__code__.co_argcount == 2, "func should take one or two arguments"
+
+        return KafkaTransformedDStream(self, func)
+
+
+class KafkaTransformedDStream(TransformedDStream):
+    """
+    Kafka specific wrapper of TransformedDStream to transform on Kafka RDD.
+    """
+
+    def __init__(self, prev, func):
+        TransformedDStream.__init__(self, prev, func)
+
+    @property
+    def _jdstream(self):
+        if self._jdstream_val is not None:
+            return self._jdstream_val
+
+        jfunc = TransformFunction(self._sc, self.func, self.prev._jrdd_deserializer) \
+            .rdd_wrapper(lambda jrdd, ctx, ser: KafkaRDD(jrdd, ctx, ser))
+        dstream = self._sc._jvm.PythonTransformedDStream(self.prev._jdstream.dstream(), jfunc)
+        self._jdstream_val = dstream.asJavaDStream()
+        return self._jdstream_val
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 188c8ff12067e..4ecae1e4bf282 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -678,6 +678,70 @@ def test_kafka_rdd_with_leaders(self):
         rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, leaders)
         self._validateRddResult(sendData, rdd)
 
+    @unittest.skipIf(sys.version >= "3", "long type not support")
+    def test_kafka_rdd_get_offsetRanges(self):
+        """Test Python direct Kafka RDD get OffsetRanges."""
+        topic = self._randomTopic()
+        sendData = {"a": 3, "b": 4, "c": 5}
+        offsetRanges = [OffsetRange(topic, 0, long(0), long(sum(sendData.values())))]
+        kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()}
+
+        self._kafkaTestUtils.createTopic(topic)
+        self._kafkaTestUtils.sendMessages(topic, sendData)
+        rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges)
+        self.assertEqual(offsetRanges, rdd.offsetRanges())
+
+    @unittest.skipIf(sys.version >= "3", "long type not support")
+    def test_kafka_direct_stream_foreach_get_offsetRanges(self):
+        """Test the Python direct Kafka stream foreachRDD get offsetRanges."""
+        topic = self._randomTopic()
+        sendData = {"a": 1, "b": 2, "c": 3}
+        kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(),
+                       "auto.offset.reset": "smallest"}
+
+        self._kafkaTestUtils.createTopic(topic)
+        self._kafkaTestUtils.sendMessages(topic, sendData)
+
+        stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams)
+
+        offsetRanges = []
+
+        def getOffsetRanges(_, rdd):
+            for o in rdd.offsetRanges():
+                offsetRanges.append(o)
+
+        stream.foreachRDD(getOffsetRanges)
+        self.ssc.start()
+        self.wait_for(offsetRanges, 1)
+
+        self.assertEqual(offsetRanges, [OffsetRange(topic, 0, long(0), long(6))])
+
+    @unittest.skipIf(sys.version >= "3", "long type not support")
+    def test_kafka_direct_stream_transform_get_offsetRanges(self):
+        """Test the Python direct Kafka stream transform get offsetRanges."""
+        topic = self._randomTopic()
+        sendData = {"a": 1, "b": 2, "c": 3}
+        kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(),
+                       "auto.offset.reset": "smallest"}
+
+        self._kafkaTestUtils.createTopic(topic)
+        self._kafkaTestUtils.sendMessages(topic, sendData)
+
+        stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams)
+
+        offsetRanges = []
+
+        def transformWithOffsetRanges(rdd):
+            for o in rdd.offsetRanges():
+                offsetRanges.append(o)
+            return rdd
+
+        stream.transform(transformWithOffsetRanges).foreachRDD(lambda rdd: rdd.count())
+        self.ssc.start()
+        self.wait_for(offsetRanges, 1)
+
+        self.assertEqual(offsetRanges, [OffsetRange(topic, 0, long(0), long(6))])
+
 
 class FlumeStreamTests(PySparkStreamingTestCase):
     timeout = 20  # seconds
diff --git a/python/pyspark/streaming/util.py b/python/pyspark/streaming/util.py
index a9bfec2aab8fc..b20613b1283bd 100644
--- a/python/pyspark/streaming/util.py
+++ b/python/pyspark/streaming/util.py
@@ -37,6 +37,11 @@ def __init__(self, ctx, func, *deserializers):
         self.ctx = ctx
         self.func = func
         self.deserializers = deserializers
+        self._rdd_wrapper = lambda jrdd, ctx, ser: RDD(jrdd, ctx, ser)
+
+    def rdd_wrapper(self, func):
+        self._rdd_wrapper = func
+        return self
 
     def call(self, milliseconds, jrdds):
         try:
@@ -51,7 +56,7 @@ def call(self, milliseconds, jrdds):
             if len(sers) < len(jrdds):
                 sers += (sers[0],) * (len(jrdds) - len(sers))
 
-            rdds = [RDD(jrdd, self.ctx, ser) if jrdd else None
+            rdds = [self._rdd_wrapper(jrdd, self.ctx, ser) if jrdd else None
                     for jrdd, ser in zip(jrdds, sers)]
             t = datetime.fromtimestamp(milliseconds / 1000.0)
             r = self.func(t, *rdds)

From c9e2ef52bb54f35a904427389dc492d61f29b018 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 9 Jul 2015 14:43:38 -0700
Subject: [PATCH 0322/1454] [SPARK-7902] [SPARK-6289] [SPARK-8685] [SQL]
 [PYSPARK] Refactor of serialization for Python DataFrame

This PR fix the long standing issue of serialization between Python RDD and DataFrame, it change to using a customized Pickler for InternalRow to enable customized unpickling (type conversion, especially for UDT), now we can support UDT for UDF, cc mengxr .

There is no generated `Row` anymore.

Author: Davies Liu <davies@databricks.com>

Closes #7301 from davies/sql_ser and squashes the following commits:

81bef71 [Davies Liu] address comments
e9217bd [Davies Liu] add regression tests
db34167 [Davies Liu] Refactor of serialization for Python DataFrame
---
 python/pyspark/sql/context.py                 |   5 +-
 python/pyspark/sql/dataframe.py               |  16 +-
 python/pyspark/sql/tests.py                   |  28 +-
 python/pyspark/sql/types.py                   | 419 ++++++------------
 .../spark/sql/catalyst/expressions/rows.scala |  12 +
 .../org/apache/spark/sql/DataFrame.scala      |   5 +-
 .../spark/sql/execution/pythonUDFs.scala      | 122 ++++-
 7 files changed, 292 insertions(+), 315 deletions(-)

diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 309c11faf9319..c93a15badae29 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -30,7 +30,7 @@
 from pyspark.serializers import AutoBatchedSerializer, PickleSerializer
 from pyspark.sql import since
 from pyspark.sql.types import Row, StringType, StructType, _verify_type, \
-    _infer_schema, _has_nulltype, _merge_type, _create_converter, _python_to_sql_converter
+    _infer_schema, _has_nulltype, _merge_type, _create_converter
 from pyspark.sql.dataframe import DataFrame
 from pyspark.sql.readwriter import DataFrameReader
 from pyspark.sql.utils import install_exception_handler
@@ -388,8 +388,7 @@ def createDataFrame(self, data, schema=None, samplingRatio=None):
             raise TypeError("schema should be StructType or list or None")
 
         # convert python objects to sql data
-        converter = _python_to_sql_converter(schema)
-        rdd = rdd.map(converter)
+        rdd = rdd.map(schema.toInternal)
 
         jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
         df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 1e9c657cf81b3..83e02b85f06f1 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -31,7 +31,7 @@
 from pyspark.storagelevel import StorageLevel
 from pyspark.traceback_utils import SCCallSiteSync
 from pyspark.sql import since
-from pyspark.sql.types import _create_cls, _parse_datatype_json_string
+from pyspark.sql.types import _parse_datatype_json_string
 from pyspark.sql.column import Column, _to_seq, _to_java_column
 from pyspark.sql.readwriter import DataFrameWriter
 from pyspark.sql.types import *
@@ -83,15 +83,7 @@ def rdd(self):
         """
         if self._lazy_rdd is None:
             jrdd = self._jdf.javaToPython()
-            rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
-            schema = self.schema
-
-            def applySchema(it):
-                cls = _create_cls(schema)
-                return map(cls, it)
-
-            self._lazy_rdd = rdd.mapPartitions(applySchema)
-
+            self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
         return self._lazy_rdd
 
     @property
@@ -287,9 +279,7 @@ def collect(self):
         """
         with SCCallSiteSync(self._sc) as css:
             port = self._sc._jvm.PythonRDD.collectAndServe(self._jdf.javaToPython().rdd())
-        rs = list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
-        cls = _create_cls(self.schema)
-        return [cls(r) for r in rs]
+        return list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
 
     @ignore_unicode_prefix
     @since(1.3)
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 66827d48850d9..4d7cad5a1ab88 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -151,6 +151,17 @@ def test_range(self):
         self.assertEqual(self.sqlCtx.range(-2).count(), 0)
         self.assertEqual(self.sqlCtx.range(3).count(), 3)
 
+    def test_duplicated_column_names(self):
+        df = self.sqlCtx.createDataFrame([(1, 2)], ["c", "c"])
+        row = df.select('*').first()
+        self.assertEqual(1, row[0])
+        self.assertEqual(2, row[1])
+        self.assertEqual("Row(c=1, c=2)", str(row))
+        # Cannot access columns
+        self.assertRaises(AnalysisException, lambda: df.select(df[0]).first())
+        self.assertRaises(AnalysisException, lambda: df.select(df.c).first())
+        self.assertRaises(AnalysisException, lambda: df.select(df["c"]).first())
+
     def test_explode(self):
         from pyspark.sql.functions import explode
         d = [Row(a=1, intlist=[1, 2, 3], mapfield={"a": "b"})]
@@ -401,6 +412,14 @@ def test_apply_schema_with_udt(self):
         point = df.head().point
         self.assertEquals(point, ExamplePoint(1.0, 2.0))
 
+    def test_udf_with_udt(self):
+        from pyspark.sql.tests import ExamplePoint
+        row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
+        df = self.sc.parallelize([row]).toDF()
+        self.assertEqual(1.0, df.map(lambda r: r.point.x).first())
+        udf = UserDefinedFunction(lambda p: p.y, DoubleType())
+        self.assertEqual(2.0, df.select(udf(df.point)).first()[0])
+
     def test_parquet_with_udt(self):
         from pyspark.sql.tests import ExamplePoint
         row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
@@ -693,12 +712,9 @@ def test_time_with_timezone(self):
         utcnow = datetime.datetime.fromtimestamp(ts, utc)
         df = self.sqlCtx.createDataFrame([(day, now, utcnow)])
         day1, now1, utcnow1 = df.first()
-        # Pyrolite serialize java.sql.Date as datetime, will be fixed in new version
-        self.assertEqual(day1.date(), day)
-        # Pyrolite does not support microsecond, the error should be
-        # less than 1 millisecond
-        self.assertTrue(now - now1 < datetime.timedelta(0.001))
-        self.assertTrue(now - utcnow1 < datetime.timedelta(0.001))
+        self.assertEqual(day1, day)
+        self.assertEqual(now, now1)
+        self.assertEqual(now, utcnow1)
 
     def test_decimal(self):
         from decimal import Decimal
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index fecfe6d71e9a7..d63857691675a 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -20,13 +20,9 @@
 import time
 import datetime
 import calendar
-import keyword
-import warnings
 import json
 import re
-import weakref
 from array import array
-from operator import itemgetter
 
 if sys.version >= "3":
     long = int
@@ -71,6 +67,26 @@ def json(self):
                           separators=(',', ':'),
                           sort_keys=True)
 
+    def needConversion(self):
+        """
+        Does this type need to conversion between Python object and internal SQL object.
+
+        This is used to avoid the unnecessary conversion for ArrayType/MapType/StructType.
+        """
+        return False
+
+    def toInternal(self, obj):
+        """
+        Converts a Python object into an internal SQL object.
+        """
+        return obj
+
+    def fromInternal(self, obj):
+        """
+        Converts an internal SQL object into a native Python object.
+        """
+        return obj
+
 
 # This singleton pattern does not work with pickle, you will get
 # another object after pickle and unpickle
@@ -143,6 +159,17 @@ class DateType(AtomicType):
 
     __metaclass__ = DataTypeSingleton
 
+    EPOCH_ORDINAL = datetime.datetime(1970, 1, 1).toordinal()
+
+    def needConversion(self):
+        return True
+
+    def toInternal(self, d):
+        return d and d.toordinal() - self.EPOCH_ORDINAL
+
+    def fromInternal(self, v):
+        return v and datetime.date.fromordinal(v + self.EPOCH_ORDINAL)
+
 
 class TimestampType(AtomicType):
     """Timestamp (datetime.datetime) data type.
@@ -150,6 +177,19 @@ class TimestampType(AtomicType):
 
     __metaclass__ = DataTypeSingleton
 
+    def needConversion(self):
+        return True
+
+    def toInternal(self, dt):
+        if dt is not None:
+            seconds = (calendar.timegm(dt.utctimetuple()) if dt.tzinfo
+                       else time.mktime(dt.timetuple()))
+            return int(seconds * 1e6 + dt.microsecond)
+
+    def fromInternal(self, ts):
+        if ts is not None:
+            return datetime.datetime.fromtimestamp(ts / 1e6)
+
 
 class DecimalType(FractionalType):
     """Decimal (decimal.Decimal) data type.
@@ -259,6 +299,19 @@ def fromJson(cls, json):
         return ArrayType(_parse_datatype_json_value(json["elementType"]),
                          json["containsNull"])
 
+    def needConversion(self):
+        return self.elementType.needConversion()
+
+    def toInternal(self, obj):
+        if not self.needConversion():
+            return obj
+        return obj and [self.elementType.toInternal(v) for v in obj]
+
+    def fromInternal(self, obj):
+        if not self.needConversion():
+            return obj
+        return obj and [self.elementType.fromInternal(v) for v in obj]
+
 
 class MapType(DataType):
     """Map data type.
@@ -304,6 +357,21 @@ def fromJson(cls, json):
                        _parse_datatype_json_value(json["valueType"]),
                        json["valueContainsNull"])
 
+    def needConversion(self):
+        return self.keyType.needConversion() or self.valueType.needConversion()
+
+    def toInternal(self, obj):
+        if not self.needConversion():
+            return obj
+        return obj and dict((self.keyType.toInternal(k), self.valueType.toInternal(v))
+                            for k, v in obj.items())
+
+    def fromInternal(self, obj):
+        if not self.needConversion():
+            return obj
+        return obj and dict((self.keyType.fromInternal(k), self.valueType.fromInternal(v))
+                            for k, v in obj.items())
+
 
 class StructField(DataType):
     """A field in :class:`StructType`.
@@ -311,7 +379,7 @@ class StructField(DataType):
     :param name: string, name of the field.
     :param dataType: :class:`DataType` of the field.
     :param nullable: boolean, whether the field can be null (None) or not.
-    :param metadata: a dict from string to simple type that can be serialized to JSON automatically
+    :param metadata: a dict from string to simple type that can be toInternald to JSON automatically
     """
 
     def __init__(self, name, dataType, nullable=True, metadata=None):
@@ -351,6 +419,15 @@ def fromJson(cls, json):
                            json["nullable"],
                            json["metadata"])
 
+    def needConversion(self):
+        return self.dataType.needConversion()
+
+    def toInternal(self, obj):
+        return self.dataType.toInternal(obj)
+
+    def fromInternal(self, obj):
+        return self.dataType.fromInternal(obj)
+
 
 class StructType(DataType):
     """Struct type, consisting of a list of :class:`StructField`.
@@ -371,10 +448,13 @@ def __init__(self, fields=None):
         """
         if not fields:
             self.fields = []
+            self.names = []
         else:
             self.fields = fields
+            self.names = [f.name for f in fields]
             assert all(isinstance(f, StructField) for f in fields),\
                 "fields should be a list of StructField"
+        self._needSerializeFields = None
 
     def add(self, field, data_type=None, nullable=True, metadata=None):
         """
@@ -406,6 +486,7 @@ def add(self, field, data_type=None, nullable=True, metadata=None):
         """
         if isinstance(field, StructField):
             self.fields.append(field)
+            self.names.append(field.name)
         else:
             if isinstance(field, str) and data_type is None:
                 raise ValueError("Must specify DataType if passing name of struct_field to create.")
@@ -415,6 +496,7 @@ def add(self, field, data_type=None, nullable=True, metadata=None):
             else:
                 data_type_f = data_type
             self.fields.append(StructField(field, data_type_f, nullable, metadata))
+            self.names.append(field)
         return self
 
     def simpleString(self):
@@ -432,6 +514,41 @@ def jsonValue(self):
     def fromJson(cls, json):
         return StructType([StructField.fromJson(f) for f in json["fields"]])
 
+    def needConversion(self):
+        # We need convert Row()/namedtuple into tuple()
+        return True
+
+    def toInternal(self, obj):
+        if obj is None:
+            return
+
+        if self._needSerializeFields is None:
+            self._needSerializeFields = any(f.needConversion() for f in self.fields)
+
+        if self._needSerializeFields:
+            if isinstance(obj, dict):
+                return tuple(f.toInternal(obj.get(n)) for n, f in zip(names, self.fields))
+            elif isinstance(obj, (tuple, list)):
+                return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
+            else:
+                raise ValueError("Unexpected tuple %r with StructType" % obj)
+        else:
+            if isinstance(obj, dict):
+                return tuple(obj.get(n) for n in self.names)
+            elif isinstance(obj, (list, tuple)):
+                return tuple(obj)
+            else:
+                raise ValueError("Unexpected tuple %r with StructType" % obj)
+
+    def fromInternal(self, obj):
+        if obj is None:
+            return
+        if isinstance(obj, Row):
+            # it's already converted by pickler
+            return obj
+        values = [f.dataType.fromInternal(v) for f, v in zip(self.fields, obj)]
+        return _create_row(self.names, values)
+
 
 class UserDefinedType(DataType):
     """User-defined type (UDT).
@@ -464,17 +581,35 @@ def scalaUDT(cls):
         """
         raise NotImplementedError("UDT must have a paired Scala UDT.")
 
+    def needConversion(self):
+        return True
+
+    @classmethod
+    def _cachedSqlType(cls):
+        """
+        Cache the sqlType() into class, because it's heavy used in `toInternal`.
+        """
+        if not hasattr(cls, "_cached_sql_type"):
+            cls._cached_sql_type = cls.sqlType()
+        return cls._cached_sql_type
+
+    def toInternal(self, obj):
+        return self._cachedSqlType().toInternal(self.serialize(obj))
+
+    def fromInternal(self, obj):
+        return self.deserialize(self._cachedSqlType().fromInternal(obj))
+
     def serialize(self, obj):
         """
         Converts the a user-type object into a SQL datum.
         """
-        raise NotImplementedError("UDT must implement serialize().")
+        raise NotImplementedError("UDT must implement toInternal().")
 
     def deserialize(self, datum):
         """
         Converts a SQL datum into a user-type object.
         """
-        raise NotImplementedError("UDT must implement deserialize().")
+        raise NotImplementedError("UDT must implement fromInternal().")
 
     def simpleString(self):
         return 'udt'
@@ -671,117 +806,6 @@ def _infer_schema(row):
     return StructType(fields)
 
 
-def _need_python_to_sql_conversion(dataType):
-    """
-    Checks whether we need python to sql conversion for the given type.
-    For now, only UDTs need this conversion.
-
-    >>> _need_python_to_sql_conversion(DoubleType())
-    False
-    >>> schema0 = StructType([StructField("indices", ArrayType(IntegerType(), False), False),
-    ...                       StructField("values", ArrayType(DoubleType(), False), False)])
-    >>> _need_python_to_sql_conversion(schema0)
-    True
-    >>> _need_python_to_sql_conversion(ExamplePointUDT())
-    True
-    >>> schema1 = ArrayType(ExamplePointUDT(), False)
-    >>> _need_python_to_sql_conversion(schema1)
-    True
-    >>> schema2 = StructType([StructField("label", DoubleType(), False),
-    ...                       StructField("point", ExamplePointUDT(), False)])
-    >>> _need_python_to_sql_conversion(schema2)
-    True
-    """
-    if isinstance(dataType, StructType):
-        # convert namedtuple or Row into tuple
-        return True
-    elif isinstance(dataType, ArrayType):
-        return _need_python_to_sql_conversion(dataType.elementType)
-    elif isinstance(dataType, MapType):
-        return _need_python_to_sql_conversion(dataType.keyType) or \
-            _need_python_to_sql_conversion(dataType.valueType)
-    elif isinstance(dataType, UserDefinedType):
-        return True
-    elif isinstance(dataType, (DateType, TimestampType)):
-        return True
-    else:
-        return False
-
-
-EPOCH_ORDINAL = datetime.datetime(1970, 1, 1).toordinal()
-
-
-def _python_to_sql_converter(dataType):
-    """
-    Returns a converter that converts a Python object into a SQL datum for the given type.
-
-    >>> conv = _python_to_sql_converter(DoubleType())
-    >>> conv(1.0)
-    1.0
-    >>> conv = _python_to_sql_converter(ArrayType(DoubleType(), False))
-    >>> conv([1.0, 2.0])
-    [1.0, 2.0]
-    >>> conv = _python_to_sql_converter(ExamplePointUDT())
-    >>> conv(ExamplePoint(1.0, 2.0))
-    [1.0, 2.0]
-    >>> schema = StructType([StructField("label", DoubleType(), False),
-    ...                      StructField("point", ExamplePointUDT(), False)])
-    >>> conv = _python_to_sql_converter(schema)
-    >>> conv((1.0, ExamplePoint(1.0, 2.0)))
-    (1.0, [1.0, 2.0])
-    """
-    if not _need_python_to_sql_conversion(dataType):
-        return lambda x: x
-
-    if isinstance(dataType, StructType):
-        names, types = zip(*[(f.name, f.dataType) for f in dataType.fields])
-        if any(_need_python_to_sql_conversion(t) for t in types):
-            converters = [_python_to_sql_converter(t) for t in types]
-
-            def converter(obj):
-                if isinstance(obj, dict):
-                    return tuple(c(obj.get(n)) for n, c in zip(names, converters))
-                elif isinstance(obj, tuple):
-                    if hasattr(obj, "__fields__") or hasattr(obj, "_fields"):
-                        return tuple(c(v) for c, v in zip(converters, obj))
-                    else:
-                        return tuple(c(v) for c, v in zip(converters, obj))
-                elif obj is not None:
-                    raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType))
-        else:
-            def converter(obj):
-                if isinstance(obj, dict):
-                    return tuple(obj.get(n) for n in names)
-                else:
-                    return tuple(obj)
-        return converter
-    elif isinstance(dataType, ArrayType):
-        element_converter = _python_to_sql_converter(dataType.elementType)
-        return lambda a: a and [element_converter(v) for v in a]
-    elif isinstance(dataType, MapType):
-        key_converter = _python_to_sql_converter(dataType.keyType)
-        value_converter = _python_to_sql_converter(dataType.valueType)
-        return lambda m: m and dict([(key_converter(k), value_converter(v)) for k, v in m.items()])
-
-    elif isinstance(dataType, UserDefinedType):
-        return lambda obj: obj and dataType.serialize(obj)
-
-    elif isinstance(dataType, DateType):
-        return lambda d: d and d.toordinal() - EPOCH_ORDINAL
-
-    elif isinstance(dataType, TimestampType):
-
-        def to_posix_timstamp(dt):
-            if dt:
-                seconds = (calendar.timegm(dt.utctimetuple()) if dt.tzinfo
-                           else time.mktime(dt.timetuple()))
-                return int(seconds * 1e6 + dt.microsecond)
-        return to_posix_timstamp
-
-    else:
-        raise ValueError("Unexpected type %r" % dataType)
-
-
 def _has_nulltype(dt):
     """ Return whether there is NullType in `dt` or not """
     if isinstance(dt, StructType):
@@ -1076,7 +1100,7 @@ def _verify_type(obj, dataType):
     if isinstance(dataType, UserDefinedType):
         if not (hasattr(obj, '__UDT__') and obj.__UDT__ == dataType):
             raise ValueError("%r is not an instance of type %r" % (obj, dataType))
-        _verify_type(dataType.serialize(obj), dataType.sqlType())
+        _verify_type(dataType.toInternal(obj), dataType.sqlType())
         return
 
     _type = type(dataType)
@@ -1086,7 +1110,7 @@ def _verify_type(obj, dataType):
         if not isinstance(obj, (tuple, list)):
             raise TypeError("StructType can not accept object in type %s" % type(obj))
     else:
-        # subclass of them can not be deserialized in JVM
+        # subclass of them can not be fromInternald in JVM
         if type(obj) not in _acceptable_types[_type]:
             raise TypeError("%s can not accept object in type %s" % (dataType, type(obj)))
 
@@ -1106,159 +1130,10 @@ def _verify_type(obj, dataType):
         for v, f in zip(obj, dataType.fields):
             _verify_type(v, f.dataType)
 
-_cached_cls = weakref.WeakValueDictionary()
-
-
-def _restore_object(dataType, obj):
-    """ Restore object during unpickling. """
-    # use id(dataType) as key to speed up lookup in dict
-    # Because of batched pickling, dataType will be the
-    # same object in most cases.
-    k = id(dataType)
-    cls = _cached_cls.get(k)
-    if cls is None or cls.__datatype is not dataType:
-        # use dataType as key to avoid create multiple class
-        cls = _cached_cls.get(dataType)
-        if cls is None:
-            cls = _create_cls(dataType)
-            _cached_cls[dataType] = cls
-        cls.__datatype = dataType
-        _cached_cls[k] = cls
-    return cls(obj)
-
-
-def _create_object(cls, v):
-    """ Create an customized object with class `cls`. """
-    # datetime.date would be deserialized as datetime.datetime
-    # from java type, so we need to set it back.
-    if cls is datetime.date and isinstance(v, datetime.datetime):
-        return v.date()
-    return cls(v) if v is not None else v
-
-
-def _create_getter(dt, i):
-    """ Create a getter for item `i` with schema """
-    cls = _create_cls(dt)
-
-    def getter(self):
-        return _create_object(cls, self[i])
-
-    return getter
-
-
-def _has_struct_or_date(dt):
-    """Return whether `dt` is or has StructType/DateType in it"""
-    if isinstance(dt, StructType):
-        return True
-    elif isinstance(dt, ArrayType):
-        return _has_struct_or_date(dt.elementType)
-    elif isinstance(dt, MapType):
-        return _has_struct_or_date(dt.keyType) or _has_struct_or_date(dt.valueType)
-    elif isinstance(dt, DateType):
-        return True
-    elif isinstance(dt, UserDefinedType):
-        return True
-    return False
-
-
-def _create_properties(fields):
-    """Create properties according to fields"""
-    ps = {}
-    for i, f in enumerate(fields):
-        name = f.name
-        if (name.startswith("__") and name.endswith("__")
-                or keyword.iskeyword(name)):
-            warnings.warn("field name %s can not be accessed in Python,"
-                          "use position to access it instead" % name)
-        if _has_struct_or_date(f.dataType):
-            # delay creating object until accessing it
-            getter = _create_getter(f.dataType, i)
-        else:
-            getter = itemgetter(i)
-        ps[name] = property(getter)
-    return ps
-
-
-def _create_cls(dataType):
-    """
-    Create an class by dataType
-
-    The created class is similar to namedtuple, but can have nested schema.
-
-    >>> schema = _parse_schema_abstract("a b c")
-    >>> row = (1, 1.0, "str")
-    >>> schema = _infer_schema_type(row, schema)
-    >>> obj = _create_cls(schema)(row)
-    >>> import pickle
-    >>> pickle.loads(pickle.dumps(obj))
-    Row(a=1, b=1.0, c='str')
-
-    >>> row = [[1], {"key": (1, 2.0)}]
-    >>> schema = _parse_schema_abstract("a[] b{c d}")
-    >>> schema = _infer_schema_type(row, schema)
-    >>> obj = _create_cls(schema)(row)
-    >>> pickle.loads(pickle.dumps(obj))
-    Row(a=[1], b={'key': Row(c=1, d=2.0)})
-    >>> pickle.loads(pickle.dumps(obj.a))
-    [1]
-    >>> pickle.loads(pickle.dumps(obj.b))
-    {'key': Row(c=1, d=2.0)}
-    """
-
-    if isinstance(dataType, ArrayType):
-        cls = _create_cls(dataType.elementType)
-
-        def List(l):
-            if l is None:
-                return
-            return [_create_object(cls, v) for v in l]
-
-        return List
-
-    elif isinstance(dataType, MapType):
-        kcls = _create_cls(dataType.keyType)
-        vcls = _create_cls(dataType.valueType)
-
-        def Dict(d):
-            if d is None:
-                return
-            return dict((_create_object(kcls, k), _create_object(vcls, v)) for k, v in d.items())
-
-        return Dict
-
-    elif isinstance(dataType, DateType):
-        return datetime.date
-
-    elif isinstance(dataType, UserDefinedType):
-        return lambda datum: dataType.deserialize(datum)
-
-    elif not isinstance(dataType, StructType):
-        # no wrapper for atomic types
-        return lambda x: x
-
-    class Row(tuple):
-
-        """ Row in DataFrame """
-        __datatype = dataType
-        __fields__ = tuple(f.name for f in dataType.fields)
-        __slots__ = ()
-
-        # create property for fast access
-        locals().update(_create_properties(dataType.fields))
-
-        def asDict(self):
-            """ Return as a dict """
-            return dict((n, getattr(self, n)) for n in self.__fields__)
-
-        def __repr__(self):
-            # call collect __repr__ for nested objects
-            return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n))
-                                          for n in self.__fields__))
-
-        def __reduce__(self):
-            return (_restore_object, (self.__datatype, tuple(self)))
 
-    return Row
+# This is used to unpickle a Row from JVM
+def _create_row_inbound_converter(dataType):
+    return lambda *a: dataType.fromInternal(a)
 
 
 def _create_row(fields, values):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 8b472a529e5c9..094904bbf9c15 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -132,6 +132,18 @@ class GenericInternalRow(protected[sql] val values: Array[Any])
   override def copy(): InternalRow = this
 }
 
+/**
+ * This is used for serialization of Python DataFrame
+ */
+class GenericInternalRowWithSchema(values: Array[Any], override val schema: StructType)
+  extends GenericInternalRow(values) {
+
+  /** No-arg constructor for serialization. */
+  protected def this() = this(null, null)
+
+  override def fieldIndex(name: String): Int = schema.fieldIndex(name)
+}
+
 class GenericMutableRow(val values: Array[Any]) extends MutableRow with ArrayBackedRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index d9f987ae0252f..d7966651b1948 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -30,7 +30,6 @@ import org.apache.commons.lang3.StringUtils
 
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaRDD
-import org.apache.spark.api.python.SerDeUtil
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis._
@@ -1550,8 +1549,8 @@ class DataFrame private[sql](
    */
   protected[sql] def javaToPython: JavaRDD[Array[Byte]] = {
     val structType = schema  // capture it for closure
-    val jrdd = queryExecution.toRdd.map(EvaluatePython.toJava(_, structType)).toJavaRDD()
-    SerDeUtil.javaToPython(jrdd)
+    val rdd = queryExecution.toRdd.map(EvaluatePython.toJava(_, structType))
+    EvaluatePython.javaToPython(rdd)
   }
 
   ////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 1c8130b07c7fb..6d6e67dace177 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -17,15 +17,16 @@
 
 package org.apache.spark.sql.execution
 
+import java.io.OutputStream
 import java.util.{List => JList, Map => JMap}
 
 import scala.collection.JavaConversions._
 import scala.collection.JavaConverters._
 
-import net.razorvine.pickle.{Pickler, Unpickler}
+import net.razorvine.pickle._
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.api.python.{PythonBroadcast, PythonRDD}
+import org.apache.spark.api.python.{PythonBroadcast, PythonRDD, SerDeUtil}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -33,7 +34,6 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.{Accumulator, Logging => SparkLogging}
@@ -130,8 +130,13 @@ object EvaluatePython {
     case (null, _) => null
 
     case (row: InternalRow, struct: StructType) =>
-      val fields = struct.fields.map(field => field.dataType)
-      rowToArray(row, fields)
+      val values = new Array[Any](row.size)
+      var i = 0
+      while (i < row.size) {
+        values(i) = toJava(row(i), struct.fields(i).dataType)
+        i += 1
+      }
+      new GenericInternalRowWithSchema(values, struct)
 
     case (seq: Seq[Any], array: ArrayType) =>
       seq.map(x => toJava(x, array.elementType)).asJava
@@ -142,9 +147,6 @@ object EvaluatePython {
 
     case (ud, udt: UserDefinedType[_]) => toJava(ud, udt.sqlType)
 
-    case (date: Int, DateType) => DateTimeUtils.toJavaDate(date)
-    case (t: Long, TimestampType) => DateTimeUtils.toJavaTimestamp(t)
-
     case (d: Decimal, _) => d.toJavaBigDecimal
 
     case (s: UTF8String, StringType) => s.toString
@@ -152,14 +154,6 @@ object EvaluatePython {
     case (other, _) => other
   }
 
-  /**
-   * Convert Row into Java Array (for pickled into Python)
-   */
-  def rowToArray(row: InternalRow, fields: Seq[DataType]): Array[Any] = {
-    // TODO: this is slow!
-    row.toSeq.zip(fields).map {case (obj, dt) => toJava(obj, dt)}.toArray
-  }
-
   /**
    * Converts `obj` to the type specified by the data type, or returns null if the type of obj is
    * unexpected. Because Python doesn't enforce the type.
@@ -220,6 +214,96 @@ object EvaluatePython {
     // TODO(davies): we could improve this by try to cast the object to expected type
     case (c, _) => null
   }
+
+
+  private val module = "pyspark.sql.types"
+
+  /**
+   * Pickler for StructType
+   */
+  private class StructTypePickler extends IObjectPickler {
+
+    private val cls = classOf[StructType]
+
+    def register(): Unit = {
+      Pickler.registerCustomPickler(cls, this)
+    }
+
+    def pickle(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      out.write(Opcodes.GLOBAL)
+      out.write((module + "\n" + "_parse_datatype_json_string" + "\n").getBytes("utf-8"))
+      val schema = obj.asInstanceOf[StructType]
+      pickler.save(schema.json)
+      out.write(Opcodes.TUPLE1)
+      out.write(Opcodes.REDUCE)
+    }
+  }
+
+  /**
+   * Pickler for InternalRow
+   */
+  private class RowPickler extends IObjectPickler {
+
+    private val cls = classOf[GenericInternalRowWithSchema]
+
+    // register this to Pickler and Unpickler
+    def register(): Unit = {
+      Pickler.registerCustomPickler(this.getClass, this)
+      Pickler.registerCustomPickler(cls, this)
+    }
+
+    def pickle(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      if (obj == this) {
+        out.write(Opcodes.GLOBAL)
+        out.write((module + "\n" + "_create_row_inbound_converter" + "\n").getBytes("utf-8"))
+      } else {
+        // it will be memorized by Pickler to save some bytes
+        pickler.save(this)
+        val row = obj.asInstanceOf[GenericInternalRowWithSchema]
+        // schema should always be same object for memoization
+        pickler.save(row.schema)
+        out.write(Opcodes.TUPLE1)
+        out.write(Opcodes.REDUCE)
+
+        out.write(Opcodes.MARK)
+        var i = 0
+        while (i < row.values.size) {
+          pickler.save(row.values(i))
+          i += 1
+        }
+        row.values.foreach(pickler.save)
+        out.write(Opcodes.TUPLE)
+        out.write(Opcodes.REDUCE)
+      }
+    }
+  }
+
+  private[this] var registered = false
+  /**
+   * This should be called before trying to serialize any above classes un cluster mode,
+   * this should be put in the closure
+   */
+  def registerPicklers(): Unit = {
+    synchronized {
+      if (!registered) {
+        SerDeUtil.initialize()
+        new StructTypePickler().register()
+        new RowPickler().register()
+        registered = true
+      }
+    }
+  }
+
+  /**
+   * Convert an RDD of Java objects to an RDD of serialized Python objects, that is usable by
+   * PySpark.
+   */
+  def javaToPython(rdd: RDD[Any]): RDD[Array[Byte]] = {
+    rdd.mapPartitions { iter =>
+      registerPicklers()  // let it called in executor
+      new SerDeUtil.AutoBatchedPickler(iter)
+    }
+  }
 }
 
 /**
@@ -254,12 +338,14 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
     val childResults = child.execute().map(_.copy())
 
     val parent = childResults.mapPartitions { iter =>
+      EvaluatePython.registerPicklers()  // register pickler for Row
       val pickle = new Pickler
       val currentRow = newMutableProjection(udf.children, child.output)()
       val fields = udf.children.map(_.dataType)
-      iter.grouped(1000).map { inputRows =>
+      val schema = new StructType(fields.map(t => new StructField("", t, true)).toArray)
+      iter.grouped(100).map { inputRows =>
         val toBePickled = inputRows.map { row =>
-          EvaluatePython.rowToArray(currentRow(row), fields)
+          EvaluatePython.toJava(currentRow(row), schema)
         }.toArray
         pickle.dumps(toBePickled)
       }

From 897700369f3aedf1a8fdb0984dd3d6d8e498e3af Mon Sep 17 00:00:00 2001
From: guowei2 <guowei@growingio.com>
Date: Thu, 9 Jul 2015 15:01:53 -0700
Subject: [PATCH 0323/1454] [SPARK-8865] [STREAMING] FIX BUG: check key in
 kafka params

Author: guowei2 <guowei@growingio.com>

Closes #7254 from guowei2/spark-8865 and squashes the following commits:

48ca17a [guowei2] fix contains key
---
 .../scala/org/apache/spark/streaming/kafka/KafkaCluster.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala
index 3e6b937af57b0..8465432c5850f 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala
@@ -410,7 +410,7 @@ object KafkaCluster {
       }
 
       Seq("zookeeper.connect", "group.id").foreach { s =>
-        if (!props.contains(s)) {
+        if (!props.containsKey(s)) {
           props.setProperty(s, "")
         }
       }

From 69165330303a71ea1da748eca7a780ec172b326f Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 9 Jul 2015 15:14:14 -0700
Subject: [PATCH 0324/1454] Closes #6837 Closes #7321 Closes #2634 Closes #4963
 Closes #2137


From e29ce319fa6ffb9c8e5110814d4923d433aa1b76 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 9 Jul 2015 15:49:30 -0700
Subject: [PATCH 0325/1454] [SPARK-8963][ML] cleanup tests in linear regression
 suite

Simplify model weight assertions to use vector comparision, switch to using absTol when comparing with 0.0 intercepts

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7327 from holdenk/SPARK-8913-cleanup-tests-from-SPARK-8700-logistic-regression and squashes the following commits:

5bac185 [Holden Karau] Simplify model weight assertions to use vector comparision, switch to using absTol when comparing with 0.0 intercepts
---
 .../ml/regression/LinearRegressionSuite.scala | 57 ++++++++-----------
 1 file changed, 24 insertions(+), 33 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 5f39d44f37352..4f6a57739558b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.DenseVector
+import org.apache.spark.mllib.linalg.{DenseVector, Vectors}
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
@@ -75,11 +75,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 7.198257
      */
     val interceptR = 6.298698
-    val weightsR = Array(4.700706, 7.199082)
+    val weightsR = Vectors.dense(4.700706, 7.199082)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -104,11 +103,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 6.995908
        as.numeric.data.V3. 5.275131
      */
-    val weightsR = Array(6.995908, 5.275131)
+    val weightsR = Vectors.dense(6.995908, 5.275131)
 
-    assert(model.intercept ~== 0 relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== 0 absTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
     /*
        Then again with the data with no intercept:
        > weightsWithoutIntercept
@@ -118,11 +116,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data3.V2. 4.70011
        as.numeric.data3.V3. 7.19943
      */
-    val weightsWithoutInterceptR = Array(4.70011, 7.19943)
+    val weightsWithoutInterceptR = Vectors.dense(4.70011, 7.19943)
 
-    assert(modelWithoutIntercept.intercept ~== 0 relTol 1E-3)
-    assert(modelWithoutIntercept.weights(0) ~== weightsWithoutInterceptR(0) relTol 1E-3)
-    assert(modelWithoutIntercept.weights(1) ~== weightsWithoutInterceptR(1) relTol 1E-3)
+    assert(modelWithoutIntercept.intercept ~== 0 absTol 1E-3)
+    assert(modelWithoutIntercept.weights ~= weightsWithoutInterceptR relTol 1E-3)
   }
 
   test("linear regression with intercept with L1 regularization") {
@@ -139,11 +136,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 6.679841
      */
     val interceptR = 6.24300
-    val weightsR = Array(4.024821, 6.679841)
+    val weightsR = Vectors.dense(4.024821, 6.679841)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -169,11 +165,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 4.772913
      */
     val interceptR = 0.0
-    val weightsR = Array(6.299752, 4.772913)
+    val weightsR = Vectors.dense(6.299752, 4.772913)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== interceptR absTol 1E-5)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -197,11 +192,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 4.926260
      */
     val interceptR = 5.269376
-    val weightsR = Array(3.736216, 5.712356)
+    val weightsR = Vectors.dense(3.736216, 5.712356)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -227,11 +221,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 4.214502
      */
     val interceptR = 0.0
-    val weightsR = Array(5.522875, 4.214502)
+    val weightsR = Vectors.dense(5.522875, 4.214502)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== interceptR absTol 1E-3)
+    assert(model.weights ~== weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -255,11 +248,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 5.200403
      */
     val interceptR = 5.696056
-    val weightsR = Array(3.670489, 6.001122)
+    val weightsR = Vectors.dense(3.670489, 6.001122)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~== weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -285,11 +277,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.dataM.V3. 4.322251
      */
     val interceptR = 0.0
-    val weightsR = Array(5.673348, 4.322251)
+    val weightsR = Vectors.dense(5.673348, 4.322251)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== interceptR absTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>

From a0cc3e5aa3fcfd0fce6813c520152657d327aaf2 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 9 Jul 2015 16:21:21 -0700
Subject: [PATCH 0326/1454] [SPARK-8538] [SPARK-8539] [ML] Linear Regression
 Training and Testing Results

Adds results (e.g. objective value at each iteration, residuals) on training and user-specified test sets for LinearRegressionModel.

Notes to Reviewers:
 * Are the `*TrainingResults` and `Results` classes too specialized for `LinearRegressionModel`? Where would be an appropriate level of abstraction?
 * Please check `transient` annotations are correct; the datasets should not be copied and kept during serialization.
 * Any thoughts on `RDD`s versus `DataFrame`s? If using `DataFrame`s, suggested schemas for each intermediate step? Also, how to create a "local DataFrame" without a `sqlContext`?

Author: Feynman Liang <fliang@databricks.com>

Closes #7099 from feynmanliang/SPARK-8538 and squashes the following commits:

d219fa4 [Feynman Liang] Update docs
4a42680 [Feynman Liang] Change Summary to hold values, move transient annotations down to metrics and predictions DF
6300031 [Feynman Liang] Code review changes
0a5e762 [Feynman Liang] Fix build error
e71102d [Feynman Liang] Merge branch 'master' into SPARK-8538
3367489 [Feynman Liang] Merge branch 'master' into SPARK-8538
70f267c [Feynman Liang] Make TrainingSummary transient and remove Serializable from *Summary and RegressionMetrics
1d9ea42 [Feynman Liang] Fix failing Java test
a65dfda [Feynman Liang] Make TrainingSummary and metrics serializable, prediction dataframe transient
0a605d8 [Feynman Liang] Replace Params from LinearRegression*Summary with private constructor vals
c2fe835 [Feynman Liang] Optimize imports
02d8a70 [Feynman Liang] Add Params to LinearModel*Summary, refactor tests and add test for evaluate()
8f999f4 [Feynman Liang] Refactor from jkbradley code review
072e948 [Feynman Liang] Style
509ae36 [Feynman Liang] Use DFs and localize serialization to LinearRegressionModel
9509c79 [Feynman Liang] Fix imports
b2bbaa3 [Feynman Liang] Refactored LinearRegressionResults API to be more private
ffceaec [Feynman Liang] Merge branch 'master' into SPARK-8538
1cedb2b [Feynman Liang] Add test for decreasing objective trace
dab0aff [Feynman Liang] Add LinearRegressionTrainingResults tests, make test suite code copy+pasteable
97b0a81 [Feynman Liang] Add LinearRegressionModel.evaluate() to get results on test sets
dc51bce [Feynman Liang] Style guide fixes
521f397 [Feynman Liang] Use RDD[(Double, Double)] instead of DF
2ff5710 [Feynman Liang] Add training results and model summary to ML LinearRegression
---
 .../ml/regression/LinearRegression.scala      | 139 +++++++++++++++++-
 .../ml/regression/LinearRegressionSuite.scala |  59 ++++++++
 2 files changed, 192 insertions(+), 6 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index f672c96576a33..8fc986056657d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -22,18 +22,20 @@ import scala.collection.mutable
 import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
 import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
-import org.apache.spark.{SparkException, Logging}
+import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.evaluation.RegressionMetrics
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.StatCounter
 
@@ -139,7 +141,16 @@ class LinearRegression(override val uid: String)
       logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " +
         s"and the intercept will be the mean of the label; as a result, training is not needed.")
       if (handlePersistence) instances.unpersist()
-      return new LinearRegressionModel(uid, Vectors.sparse(numFeatures, Seq()), yMean)
+      val weights = Vectors.sparse(numFeatures, Seq())
+      val intercept = yMean
+
+      val model = new LinearRegressionModel(uid, weights, intercept)
+      val trainingSummary = new LinearRegressionTrainingSummary(
+        model.transform(dataset).select($(predictionCol), $(labelCol)),
+        $(predictionCol),
+        $(labelCol),
+        Array(0D))
+      return copyValues(model.setSummary(trainingSummary))
     }
 
     val featuresMean = summarizer.mean.toArray
@@ -178,7 +189,6 @@ class LinearRegression(override val uid: String)
         state = states.next()
         arrayBuilder += state.adjustedValue
       }
-
       if (state == null) {
         val msg = s"${optimizer.getClass.getName} failed."
         logError(msg)
@@ -209,7 +219,13 @@ class LinearRegression(override val uid: String)
 
     if (handlePersistence) instances.unpersist()
 
-    copyValues(new LinearRegressionModel(uid, weights, intercept))
+    val model = copyValues(new LinearRegressionModel(uid, weights, intercept))
+    val trainingSummary = new LinearRegressionTrainingSummary(
+      model.transform(dataset).select($(predictionCol), $(labelCol)),
+      $(predictionCol),
+      $(labelCol),
+      objectiveHistory)
+    model.setSummary(trainingSummary)
   }
 
   override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra)
@@ -227,13 +243,124 @@ class LinearRegressionModel private[ml] (
   extends RegressionModel[Vector, LinearRegressionModel]
   with LinearRegressionParams {
 
+  private var trainingSummary: Option[LinearRegressionTrainingSummary] = None
+
+  /**
+   * Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is
+   * thrown if `trainingSummary == None`.
+   */
+  def summary: LinearRegressionTrainingSummary = trainingSummary match {
+    case Some(summ) => summ
+    case None =>
+      throw new SparkException(
+        "No training summary available for this LinearRegressionModel",
+        new NullPointerException())
+  }
+
+  private[regression] def setSummary(summary: LinearRegressionTrainingSummary): this.type = {
+    this.trainingSummary = Some(summary)
+    this
+  }
+
+  /** Indicates whether a training summary exists for this model instance. */
+  def hasSummary: Boolean = trainingSummary.isDefined
+
+  /**
+   * Evaluates the model on a testset.
+   * @param dataset Test dataset to evaluate model on.
+   */
+  // TODO: decide on a good name before exposing to public API
+  private[regression] def evaluate(dataset: DataFrame): LinearRegressionSummary = {
+    val t = udf { features: Vector => predict(features) }
+    val predictionAndObservations = dataset
+      .select(col($(labelCol)), t(col($(featuresCol))).as($(predictionCol)))
+
+    new LinearRegressionSummary(predictionAndObservations, $(predictionCol), $(labelCol))
+  }
+
   override protected def predict(features: Vector): Double = {
     dot(features, weights) + intercept
   }
 
   override def copy(extra: ParamMap): LinearRegressionModel = {
-    copyValues(new LinearRegressionModel(uid, weights, intercept), extra)
+    val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept))
+    if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
+    newModel
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Linear regression training results.
+ * @param predictions predictions outputted by the model's `transform` method.
+ * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
+ */
+@Experimental
+class LinearRegressionTrainingSummary private[regression] (
+    predictions: DataFrame,
+    predictionCol: String,
+    labelCol: String,
+    val objectiveHistory: Array[Double])
+  extends LinearRegressionSummary(predictions, predictionCol, labelCol) {
+
+  /** Number of training iterations until termination */
+  val totalIterations = objectiveHistory.length
+
+}
+
+/**
+ * :: Experimental ::
+ * Linear regression results evaluated on a dataset.
+ * @param predictions predictions outputted by the model's `transform` method.
+ */
+@Experimental
+class LinearRegressionSummary private[regression] (
+    @transient val predictions: DataFrame,
+    val predictionCol: String,
+    val labelCol: String) extends Serializable {
+
+  @transient private val metrics = new RegressionMetrics(
+    predictions
+      .select(predictionCol, labelCol)
+      .map { case Row(pred: Double, label: Double) => (pred, label) } )
+
+  /**
+   * Returns the explained variance regression score.
+   * explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
+   * Reference: [[http://en.wikipedia.org/wiki/Explained_variation]]
+   */
+  val explainedVariance: Double = metrics.explainedVariance
+
+  /**
+   * Returns the mean absolute error, which is a risk function corresponding to the
+   * expected value of the absolute error loss or l1-norm loss.
+   */
+  val meanAbsoluteError: Double = metrics.meanAbsoluteError
+
+  /**
+   * Returns the mean squared error, which is a risk function corresponding to the
+   * expected value of the squared error loss or quadratic loss.
+   */
+  val meanSquaredError: Double = metrics.meanSquaredError
+
+  /**
+   * Returns the root mean squared error, which is defined as the square root of
+   * the mean squared error.
+   */
+  val rootMeanSquaredError: Double = metrics.rootMeanSquaredError
+
+  /**
+   * Returns R^2^, the coefficient of determination.
+   * Reference: [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
+   */
+  val r2: Double = metrics.r2
+
+  /** Residuals (predicted value - label value) */
+  @transient lazy val residuals: DataFrame = {
+    val t = udf { (pred: Double, label: Double) => pred - label}
+    predictions.select(t(col(predictionCol), col(labelCol)).as("residuals"))
   }
+
 }
 
 /**
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 4f6a57739558b..cf120cf2a4b47 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -289,4 +289,63 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
+
+  test("linear regression model training summary") {
+    val trainer = new LinearRegression
+    val model = trainer.fit(dataset)
+
+    // Training results for the model should be available
+    assert(model.hasSummary)
+
+    // Residuals in [[LinearRegressionResults]] should equal those manually computed
+    val expectedResiduals = dataset.select("features", "label")
+      .map { case Row(features: DenseVector, label: Double) =>
+      val prediction =
+        features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+      prediction - label
+    }
+      .zip(model.summary.residuals.map(_.getDouble(0)))
+      .collect()
+      .foreach { case (manualResidual: Double, resultResidual: Double) =>
+      assert(manualResidual ~== resultResidual relTol 1E-5)
+    }
+
+    /*
+       Use the following R code to generate model training results.
+
+       predictions <- predict(fit, newx=features)
+       residuals <- predictions - label
+       > mean(residuals^2) # MSE
+       [1] 0.009720325
+       > mean(abs(residuals)) # MAD
+       [1] 0.07863206
+       > cor(predictions, label)^2# r^2
+               [,1]
+       s0 0.9998749
+     */
+    assert(model.summary.meanSquaredError ~== 0.00972035 relTol 1E-5)
+    assert(model.summary.meanAbsoluteError ~== 0.07863206  relTol 1E-5)
+    assert(model.summary.r2 ~== 0.9998749 relTol 1E-5)
+
+    // Objective function should be monotonically decreasing for linear regression
+    assert(
+      model.summary
+        .objectiveHistory
+        .sliding(2)
+        .forall(x => x(0) >= x(1)))
+  }
+
+  test("linear regression model testset evaluation summary") {
+    val trainer = new LinearRegression
+    val model = trainer.fit(dataset)
+
+    // Evaluating on training dataset should yield results summary equal to training summary
+    val testSummary = model.evaluate(dataset)
+    assert(model.summary.meanSquaredError ~== testSummary.meanSquaredError relTol 1E-5)
+    assert(model.summary.r2 ~== testSummary.r2 relTol 1E-5)
+    model.summary.residuals.select("residuals").collect()
+      .zip(testSummary.residuals.select("residuals").collect())
+      .forall { case (Row(r1: Double), Row(r2: Double)) => r1 ~== r2 relTol 1E-5 }
+  }
+
 }

From 2d45571fcb002cc9f03056c5a3f14493b83315a4 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 9 Jul 2015 17:09:16 -0700
Subject: [PATCH 0327/1454] [SPARK-8959] [SQL] [HOTFIX] Removes parquet-thrift
 and libthrift dependencies

These two dependencies were introduced in #7231 to help testing Parquet compatibility with `parquet-thrift`. However, they somehow crash the Scala compiler in Maven builds.

This PR fixes this issue by:

1. Removing these two dependencies, and
2. Instead of generating the testing Parquet file programmatically, checking in an actual testing Parquet file generated by `parquet-thrift` as a test resource.

This is just a quick fix to bring back Maven builds. Need to figure out the root case as binary Parquet files are harder to maintain.

Author: Cheng Lian <lian@databricks.com>

Closes #7330 from liancheng/spark-8959 and squashes the following commits:

cf69512 [Cheng Lian] Brings back Maven builds
---
 pom.xml                                       |   14 -
 sql/core/pom.xml                              |   10 -
 .../spark/sql/parquet/test/thrift/Nested.java |  541 ----
 .../test/thrift/ParquetThriftCompat.java      | 2808 -----------------
 .../spark/sql/parquet/test/thrift/Suit.java   |   51 -
 .../parquet-thrift-compat.snappy.parquet      |  Bin 0 -> 10550 bytes
 .../ParquetThriftCompatibilitySuite.scala     |   78 +-
 7 files changed, 8 insertions(+), 3494 deletions(-)
 delete mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java
 delete mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java
 delete mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java
 create mode 100755 sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet

diff --git a/pom.xml b/pom.xml
index 529e47f8b5253..1eda108dc065b 100644
--- a/pom.xml
+++ b/pom.xml
@@ -161,7 +161,6 @@
     <fasterxml.jackson.version>2.4.4</fasterxml.jackson.version>
     <snappy.version>1.1.1.7</snappy.version>
     <netlib.java.version>1.1.2</netlib.java.version>
-    <thrift.version>0.9.2</thrift.version>
     <!-- For maven shade plugin (see SPARK-8819) -->
     <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
 
@@ -181,7 +180,6 @@
     <hive.deps.scope>compile</hive.deps.scope>
     <parquet.deps.scope>compile</parquet.deps.scope>
     <parquet.test.deps.scope>test</parquet.test.deps.scope>
-    <thrift.test.deps.scope>test</thrift.test.deps.scope>
 
     <!--
       Overridable test home. So that you can call individual pom files directly without
@@ -1122,18 +1120,6 @@
         <version>${parquet.version}</version>
         <scope>${parquet.test.deps.scope}</scope>
       </dependency>
-      <dependency>
-        <groupId>org.apache.parquet</groupId>
-        <artifactId>parquet-thrift</artifactId>
-        <version>${parquet.version}</version>
-        <scope>${parquet.test.deps.scope}</scope>
-      </dependency>
-      <dependency>
-        <groupId>org.apache.thrift</groupId>
-        <artifactId>libthrift</artifactId>
-        <version>${thrift.version}</version>
-        <scope>${thrift.test.deps.scope}</scope>
-      </dependency>
       <dependency>
         <groupId>org.apache.flume</groupId>
         <artifactId>flume-ng-core</artifactId>
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index f90099f22d4bd..2c03fee9dbd71 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -106,16 +106,6 @@
       <artifactId>parquet-avro</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>org.apache.parquet</groupId>
-      <artifactId>parquet-thrift</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>org.apache.thrift</groupId>
-      <artifactId>libthrift</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java
deleted file mode 100644
index 281e60cc3ae34..0000000000000
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Nested.java
+++ /dev/null
@@ -1,541 +0,0 @@
-/**
- * Autogenerated by Thrift Compiler (0.9.2)
- *
- * DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
- *  @generated
- */
-package org.apache.spark.sql.parquet.test.thrift;
-
-import org.apache.thrift.scheme.IScheme;
-import org.apache.thrift.scheme.SchemeFactory;
-import org.apache.thrift.scheme.StandardScheme;
-
-import org.apache.thrift.scheme.TupleScheme;
-import org.apache.thrift.protocol.TTupleProtocol;
-import org.apache.thrift.protocol.TProtocolException;
-import org.apache.thrift.EncodingUtils;
-import org.apache.thrift.TException;
-import org.apache.thrift.async.AsyncMethodCallback;
-import org.apache.thrift.server.AbstractNonblockingServer.*;
-import java.util.List;
-import java.util.ArrayList;
-import java.util.Map;
-import java.util.HashMap;
-import java.util.EnumMap;
-import java.util.Set;
-import java.util.HashSet;
-import java.util.EnumSet;
-import java.util.Collections;
-import java.util.BitSet;
-import java.nio.ByteBuffer;
-import java.util.Arrays;
-import javax.annotation.Generated;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-@SuppressWarnings({"cast", "rawtypes", "serial", "unchecked"})
-@Generated(value = "Autogenerated by Thrift Compiler (0.9.2)", date = "2015-7-7")
-public class Nested implements org.apache.thrift.TBase<Nested, Nested._Fields>, java.io.Serializable, Cloneable, Comparable<Nested> {
-  private static final org.apache.thrift.protocol.TStruct STRUCT_DESC = new org.apache.thrift.protocol.TStruct("Nested");
-
-  private static final org.apache.thrift.protocol.TField NESTED_INTS_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("nestedIntsColumn", org.apache.thrift.protocol.TType.LIST, (short)1);
-  private static final org.apache.thrift.protocol.TField NESTED_STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("nestedStringColumn", org.apache.thrift.protocol.TType.STRING, (short)2);
-
-  private static final Map<Class<? extends IScheme>, SchemeFactory> schemes = new HashMap<Class<? extends IScheme>, SchemeFactory>();
-  static {
-    schemes.put(StandardScheme.class, new NestedStandardSchemeFactory());
-    schemes.put(TupleScheme.class, new NestedTupleSchemeFactory());
-  }
-
-  public List<Integer> nestedIntsColumn; // required
-  public String nestedStringColumn; // required
-
-  /** The set of fields this struct contains, along with convenience methods for finding and manipulating them. */
-  public enum _Fields implements org.apache.thrift.TFieldIdEnum {
-    NESTED_INTS_COLUMN((short)1, "nestedIntsColumn"),
-    NESTED_STRING_COLUMN((short)2, "nestedStringColumn");
-
-    private static final Map<String, _Fields> byName = new HashMap<String, _Fields>();
-
-    static {
-      for (_Fields field : EnumSet.allOf(_Fields.class)) {
-        byName.put(field.getFieldName(), field);
-      }
-    }
-
-    /**
-     * Find the _Fields constant that matches fieldId, or null if its not found.
-     */
-    public static _Fields findByThriftId(int fieldId) {
-      switch(fieldId) {
-        case 1: // NESTED_INTS_COLUMN
-          return NESTED_INTS_COLUMN;
-        case 2: // NESTED_STRING_COLUMN
-          return NESTED_STRING_COLUMN;
-        default:
-          return null;
-      }
-    }
-
-    /**
-     * Find the _Fields constant that matches fieldId, throwing an exception
-     * if it is not found.
-     */
-    public static _Fields findByThriftIdOrThrow(int fieldId) {
-      _Fields fields = findByThriftId(fieldId);
-      if (fields == null) throw new IllegalArgumentException("Field " + fieldId + " doesn't exist!");
-      return fields;
-    }
-
-    /**
-     * Find the _Fields constant that matches name, or null if its not found.
-     */
-    public static _Fields findByName(String name) {
-      return byName.get(name);
-    }
-
-    private final short _thriftId;
-    private final String _fieldName;
-
-    _Fields(short thriftId, String fieldName) {
-      _thriftId = thriftId;
-      _fieldName = fieldName;
-    }
-
-    public short getThriftFieldId() {
-      return _thriftId;
-    }
-
-    public String getFieldName() {
-      return _fieldName;
-    }
-  }
-
-  // isset id assignments
-  public static final Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> metaDataMap;
-  static {
-    Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> tmpMap = new EnumMap<_Fields, org.apache.thrift.meta_data.FieldMetaData>(_Fields.class);
-    tmpMap.put(_Fields.NESTED_INTS_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("nestedIntsColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST, 
-            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32))));
-    tmpMap.put(_Fields.NESTED_STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("nestedStringColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
-    metaDataMap = Collections.unmodifiableMap(tmpMap);
-    org.apache.thrift.meta_data.FieldMetaData.addStructMetaDataMap(Nested.class, metaDataMap);
-  }
-
-  public Nested() {
-  }
-
-  public Nested(
-    List<Integer> nestedIntsColumn,
-    String nestedStringColumn)
-  {
-    this();
-    this.nestedIntsColumn = nestedIntsColumn;
-    this.nestedStringColumn = nestedStringColumn;
-  }
-
-  /**
-   * Performs a deep copy on <i>other</i>.
-   */
-  public Nested(Nested other) {
-    if (other.isSetNestedIntsColumn()) {
-      List<Integer> __this__nestedIntsColumn = new ArrayList<Integer>(other.nestedIntsColumn);
-      this.nestedIntsColumn = __this__nestedIntsColumn;
-    }
-    if (other.isSetNestedStringColumn()) {
-      this.nestedStringColumn = other.nestedStringColumn;
-    }
-  }
-
-  public Nested deepCopy() {
-    return new Nested(this);
-  }
-
-  @Override
-  public void clear() {
-    this.nestedIntsColumn = null;
-    this.nestedStringColumn = null;
-  }
-
-  public int getNestedIntsColumnSize() {
-    return (this.nestedIntsColumn == null) ? 0 : this.nestedIntsColumn.size();
-  }
-
-  public java.util.Iterator<Integer> getNestedIntsColumnIterator() {
-    return (this.nestedIntsColumn == null) ? null : this.nestedIntsColumn.iterator();
-  }
-
-  public void addToNestedIntsColumn(int elem) {
-    if (this.nestedIntsColumn == null) {
-      this.nestedIntsColumn = new ArrayList<Integer>();
-    }
-    this.nestedIntsColumn.add(elem);
-  }
-
-  public List<Integer> getNestedIntsColumn() {
-    return this.nestedIntsColumn;
-  }
-
-  public Nested setNestedIntsColumn(List<Integer> nestedIntsColumn) {
-    this.nestedIntsColumn = nestedIntsColumn;
-    return this;
-  }
-
-  public void unsetNestedIntsColumn() {
-    this.nestedIntsColumn = null;
-  }
-
-  /** Returns true if field nestedIntsColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetNestedIntsColumn() {
-    return this.nestedIntsColumn != null;
-  }
-
-  public void setNestedIntsColumnIsSet(boolean value) {
-    if (!value) {
-      this.nestedIntsColumn = null;
-    }
-  }
-
-  public String getNestedStringColumn() {
-    return this.nestedStringColumn;
-  }
-
-  public Nested setNestedStringColumn(String nestedStringColumn) {
-    this.nestedStringColumn = nestedStringColumn;
-    return this;
-  }
-
-  public void unsetNestedStringColumn() {
-    this.nestedStringColumn = null;
-  }
-
-  /** Returns true if field nestedStringColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetNestedStringColumn() {
-    return this.nestedStringColumn != null;
-  }
-
-  public void setNestedStringColumnIsSet(boolean value) {
-    if (!value) {
-      this.nestedStringColumn = null;
-    }
-  }
-
-  public void setFieldValue(_Fields field, Object value) {
-    switch (field) {
-    case NESTED_INTS_COLUMN:
-      if (value == null) {
-        unsetNestedIntsColumn();
-      } else {
-        setNestedIntsColumn((List<Integer>)value);
-      }
-      break;
-
-    case NESTED_STRING_COLUMN:
-      if (value == null) {
-        unsetNestedStringColumn();
-      } else {
-        setNestedStringColumn((String)value);
-      }
-      break;
-
-    }
-  }
-
-  public Object getFieldValue(_Fields field) {
-    switch (field) {
-    case NESTED_INTS_COLUMN:
-      return getNestedIntsColumn();
-
-    case NESTED_STRING_COLUMN:
-      return getNestedStringColumn();
-
-    }
-    throw new IllegalStateException();
-  }
-
-  /** Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise */
-  public boolean isSet(_Fields field) {
-    if (field == null) {
-      throw new IllegalArgumentException();
-    }
-
-    switch (field) {
-    case NESTED_INTS_COLUMN:
-      return isSetNestedIntsColumn();
-    case NESTED_STRING_COLUMN:
-      return isSetNestedStringColumn();
-    }
-    throw new IllegalStateException();
-  }
-
-  @Override
-  public boolean equals(Object that) {
-    if (that == null)
-      return false;
-    if (that instanceof Nested)
-      return this.equals((Nested)that);
-    return false;
-  }
-
-  public boolean equals(Nested that) {
-    if (that == null)
-      return false;
-
-    boolean this_present_nestedIntsColumn = true && this.isSetNestedIntsColumn();
-    boolean that_present_nestedIntsColumn = true && that.isSetNestedIntsColumn();
-    if (this_present_nestedIntsColumn || that_present_nestedIntsColumn) {
-      if (!(this_present_nestedIntsColumn && that_present_nestedIntsColumn))
-        return false;
-      if (!this.nestedIntsColumn.equals(that.nestedIntsColumn))
-        return false;
-    }
-
-    boolean this_present_nestedStringColumn = true && this.isSetNestedStringColumn();
-    boolean that_present_nestedStringColumn = true && that.isSetNestedStringColumn();
-    if (this_present_nestedStringColumn || that_present_nestedStringColumn) {
-      if (!(this_present_nestedStringColumn && that_present_nestedStringColumn))
-        return false;
-      if (!this.nestedStringColumn.equals(that.nestedStringColumn))
-        return false;
-    }
-
-    return true;
-  }
-
-  @Override
-  public int hashCode() {
-    List<Object> list = new ArrayList<Object>();
-
-    boolean present_nestedIntsColumn = true && (isSetNestedIntsColumn());
-    list.add(present_nestedIntsColumn);
-    if (present_nestedIntsColumn)
-      list.add(nestedIntsColumn);
-
-    boolean present_nestedStringColumn = true && (isSetNestedStringColumn());
-    list.add(present_nestedStringColumn);
-    if (present_nestedStringColumn)
-      list.add(nestedStringColumn);
-
-    return list.hashCode();
-  }
-
-  @Override
-  public int compareTo(Nested other) {
-    if (!getClass().equals(other.getClass())) {
-      return getClass().getName().compareTo(other.getClass().getName());
-    }
-
-    int lastComparison = 0;
-
-    lastComparison = Boolean.valueOf(isSetNestedIntsColumn()).compareTo(other.isSetNestedIntsColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetNestedIntsColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.nestedIntsColumn, other.nestedIntsColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetNestedStringColumn()).compareTo(other.isSetNestedStringColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetNestedStringColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.nestedStringColumn, other.nestedStringColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    return 0;
-  }
-
-  public _Fields fieldForId(int fieldId) {
-    return _Fields.findByThriftId(fieldId);
-  }
-
-  public void read(org.apache.thrift.protocol.TProtocol iprot) throws org.apache.thrift.TException {
-    schemes.get(iprot.getScheme()).getScheme().read(iprot, this);
-  }
-
-  public void write(org.apache.thrift.protocol.TProtocol oprot) throws org.apache.thrift.TException {
-    schemes.get(oprot.getScheme()).getScheme().write(oprot, this);
-  }
-
-  @Override
-  public String toString() {
-    StringBuilder sb = new StringBuilder("Nested(");
-    boolean first = true;
-
-    sb.append("nestedIntsColumn:");
-    if (this.nestedIntsColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.nestedIntsColumn);
-    }
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("nestedStringColumn:");
-    if (this.nestedStringColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.nestedStringColumn);
-    }
-    first = false;
-    sb.append(")");
-    return sb.toString();
-  }
-
-  public void validate() throws org.apache.thrift.TException {
-    // check for required fields
-    if (nestedIntsColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'nestedIntsColumn' was not present! Struct: " + toString());
-    }
-    if (nestedStringColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'nestedStringColumn' was not present! Struct: " + toString());
-    }
-    // check for sub-struct validity
-  }
-
-  private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException {
-    try {
-      write(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(out)));
-    } catch (org.apache.thrift.TException te) {
-      throw new java.io.IOException(te);
-    }
-  }
-
-  private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, ClassNotFoundException {
-    try {
-      read(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(in)));
-    } catch (org.apache.thrift.TException te) {
-      throw new java.io.IOException(te);
-    }
-  }
-
-  private static class NestedStandardSchemeFactory implements SchemeFactory {
-    public NestedStandardScheme getScheme() {
-      return new NestedStandardScheme();
-    }
-  }
-
-  private static class NestedStandardScheme extends StandardScheme<Nested> {
-
-    public void read(org.apache.thrift.protocol.TProtocol iprot, Nested struct) throws org.apache.thrift.TException {
-      org.apache.thrift.protocol.TField schemeField;
-      iprot.readStructBegin();
-      while (true)
-      {
-        schemeField = iprot.readFieldBegin();
-        if (schemeField.type == org.apache.thrift.protocol.TType.STOP) { 
-          break;
-        }
-        switch (schemeField.id) {
-          case 1: // NESTED_INTS_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
-              {
-                org.apache.thrift.protocol.TList _list0 = iprot.readListBegin();
-                struct.nestedIntsColumn = new ArrayList<Integer>(_list0.size);
-                int _elem1;
-                for (int _i2 = 0; _i2 < _list0.size; ++_i2)
-                {
-                  _elem1 = iprot.readI32();
-                  struct.nestedIntsColumn.add(_elem1);
-                }
-                iprot.readListEnd();
-              }
-              struct.setNestedIntsColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 2: // NESTED_STRING_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
-              struct.nestedStringColumn = iprot.readString();
-              struct.setNestedStringColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          default:
-            org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-        }
-        iprot.readFieldEnd();
-      }
-      iprot.readStructEnd();
-
-      // check for required fields of primitive type, which can't be checked in the validate method
-      struct.validate();
-    }
-
-    public void write(org.apache.thrift.protocol.TProtocol oprot, Nested struct) throws org.apache.thrift.TException {
-      struct.validate();
-
-      oprot.writeStructBegin(STRUCT_DESC);
-      if (struct.nestedIntsColumn != null) {
-        oprot.writeFieldBegin(NESTED_INTS_COLUMN_FIELD_DESC);
-        {
-          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, struct.nestedIntsColumn.size()));
-          for (int _iter3 : struct.nestedIntsColumn)
-          {
-            oprot.writeI32(_iter3);
-          }
-          oprot.writeListEnd();
-        }
-        oprot.writeFieldEnd();
-      }
-      if (struct.nestedStringColumn != null) {
-        oprot.writeFieldBegin(NESTED_STRING_COLUMN_FIELD_DESC);
-        oprot.writeString(struct.nestedStringColumn);
-        oprot.writeFieldEnd();
-      }
-      oprot.writeFieldStop();
-      oprot.writeStructEnd();
-    }
-
-  }
-
-  private static class NestedTupleSchemeFactory implements SchemeFactory {
-    public NestedTupleScheme getScheme() {
-      return new NestedTupleScheme();
-    }
-  }
-
-  private static class NestedTupleScheme extends TupleScheme<Nested> {
-
-    @Override
-    public void write(org.apache.thrift.protocol.TProtocol prot, Nested struct) throws org.apache.thrift.TException {
-      TTupleProtocol oprot = (TTupleProtocol) prot;
-      {
-        oprot.writeI32(struct.nestedIntsColumn.size());
-        for (int _iter4 : struct.nestedIntsColumn)
-        {
-          oprot.writeI32(_iter4);
-        }
-      }
-      oprot.writeString(struct.nestedStringColumn);
-    }
-
-    @Override
-    public void read(org.apache.thrift.protocol.TProtocol prot, Nested struct) throws org.apache.thrift.TException {
-      TTupleProtocol iprot = (TTupleProtocol) prot;
-      {
-        org.apache.thrift.protocol.TList _list5 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, iprot.readI32());
-        struct.nestedIntsColumn = new ArrayList<Integer>(_list5.size);
-        int _elem6;
-        for (int _i7 = 0; _i7 < _list5.size; ++_i7)
-        {
-          _elem6 = iprot.readI32();
-          struct.nestedIntsColumn.add(_elem6);
-        }
-      }
-      struct.setNestedIntsColumnIsSet(true);
-      struct.nestedStringColumn = iprot.readString();
-      struct.setNestedStringColumnIsSet(true);
-    }
-  }
-
-}
-
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java
deleted file mode 100644
index 326ae9dbaa0d1..0000000000000
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/ParquetThriftCompat.java
+++ /dev/null
@@ -1,2808 +0,0 @@
-/**
- * Autogenerated by Thrift Compiler (0.9.2)
- *
- * DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
- *  @generated
- */
-package org.apache.spark.sql.parquet.test.thrift;
-
-import org.apache.thrift.scheme.IScheme;
-import org.apache.thrift.scheme.SchemeFactory;
-import org.apache.thrift.scheme.StandardScheme;
-
-import org.apache.thrift.scheme.TupleScheme;
-import org.apache.thrift.protocol.TTupleProtocol;
-import org.apache.thrift.protocol.TProtocolException;
-import org.apache.thrift.EncodingUtils;
-import org.apache.thrift.TException;
-import org.apache.thrift.async.AsyncMethodCallback;
-import org.apache.thrift.server.AbstractNonblockingServer.*;
-import java.util.List;
-import java.util.ArrayList;
-import java.util.Map;
-import java.util.HashMap;
-import java.util.EnumMap;
-import java.util.Set;
-import java.util.HashSet;
-import java.util.EnumSet;
-import java.util.Collections;
-import java.util.BitSet;
-import java.nio.ByteBuffer;
-import java.util.Arrays;
-import javax.annotation.Generated;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-@SuppressWarnings({"cast", "rawtypes", "serial", "unchecked"})
-/**
- * This is a test struct for testing parquet-thrift compatibility.
- */
-@Generated(value = "Autogenerated by Thrift Compiler (0.9.2)", date = "2015-7-7")
-public class ParquetThriftCompat implements org.apache.thrift.TBase<ParquetThriftCompat, ParquetThriftCompat._Fields>, java.io.Serializable, Cloneable, Comparable<ParquetThriftCompat> {
-  private static final org.apache.thrift.protocol.TStruct STRUCT_DESC = new org.apache.thrift.protocol.TStruct("ParquetThriftCompat");
-
-  private static final org.apache.thrift.protocol.TField BOOL_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("boolColumn", org.apache.thrift.protocol.TType.BOOL, (short)1);
-  private static final org.apache.thrift.protocol.TField BYTE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("byteColumn", org.apache.thrift.protocol.TType.BYTE, (short)2);
-  private static final org.apache.thrift.protocol.TField SHORT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("shortColumn", org.apache.thrift.protocol.TType.I16, (short)3);
-  private static final org.apache.thrift.protocol.TField INT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("intColumn", org.apache.thrift.protocol.TType.I32, (short)4);
-  private static final org.apache.thrift.protocol.TField LONG_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("longColumn", org.apache.thrift.protocol.TType.I64, (short)5);
-  private static final org.apache.thrift.protocol.TField DOUBLE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("doubleColumn", org.apache.thrift.protocol.TType.DOUBLE, (short)6);
-  private static final org.apache.thrift.protocol.TField BINARY_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("binaryColumn", org.apache.thrift.protocol.TType.STRING, (short)7);
-  private static final org.apache.thrift.protocol.TField STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("stringColumn", org.apache.thrift.protocol.TType.STRING, (short)8);
-  private static final org.apache.thrift.protocol.TField ENUM_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("enumColumn", org.apache.thrift.protocol.TType.I32, (short)9);
-  private static final org.apache.thrift.protocol.TField MAYBE_BOOL_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeBoolColumn", org.apache.thrift.protocol.TType.BOOL, (short)10);
-  private static final org.apache.thrift.protocol.TField MAYBE_BYTE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeByteColumn", org.apache.thrift.protocol.TType.BYTE, (short)11);
-  private static final org.apache.thrift.protocol.TField MAYBE_SHORT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeShortColumn", org.apache.thrift.protocol.TType.I16, (short)12);
-  private static final org.apache.thrift.protocol.TField MAYBE_INT_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeIntColumn", org.apache.thrift.protocol.TType.I32, (short)13);
-  private static final org.apache.thrift.protocol.TField MAYBE_LONG_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeLongColumn", org.apache.thrift.protocol.TType.I64, (short)14);
-  private static final org.apache.thrift.protocol.TField MAYBE_DOUBLE_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeDoubleColumn", org.apache.thrift.protocol.TType.DOUBLE, (short)15);
-  private static final org.apache.thrift.protocol.TField MAYBE_BINARY_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeBinaryColumn", org.apache.thrift.protocol.TType.STRING, (short)16);
-  private static final org.apache.thrift.protocol.TField MAYBE_STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeStringColumn", org.apache.thrift.protocol.TType.STRING, (short)17);
-  private static final org.apache.thrift.protocol.TField MAYBE_ENUM_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("maybeEnumColumn", org.apache.thrift.protocol.TType.I32, (short)18);
-  private static final org.apache.thrift.protocol.TField STRINGS_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("stringsColumn", org.apache.thrift.protocol.TType.LIST, (short)19);
-  private static final org.apache.thrift.protocol.TField INT_SET_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("intSetColumn", org.apache.thrift.protocol.TType.SET, (short)20);
-  private static final org.apache.thrift.protocol.TField INT_TO_STRING_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("intToStringColumn", org.apache.thrift.protocol.TType.MAP, (short)21);
-  private static final org.apache.thrift.protocol.TField COMPLEX_COLUMN_FIELD_DESC = new org.apache.thrift.protocol.TField("complexColumn", org.apache.thrift.protocol.TType.MAP, (short)22);
-
-  private static final Map<Class<? extends IScheme>, SchemeFactory> schemes = new HashMap<Class<? extends IScheme>, SchemeFactory>();
-  static {
-    schemes.put(StandardScheme.class, new ParquetThriftCompatStandardSchemeFactory());
-    schemes.put(TupleScheme.class, new ParquetThriftCompatTupleSchemeFactory());
-  }
-
-  public boolean boolColumn; // required
-  public byte byteColumn; // required
-  public short shortColumn; // required
-  public int intColumn; // required
-  public long longColumn; // required
-  public double doubleColumn; // required
-  public ByteBuffer binaryColumn; // required
-  public String stringColumn; // required
-  /**
-   * 
-   * @see Suit
-   */
-  public Suit enumColumn; // required
-  public boolean maybeBoolColumn; // optional
-  public byte maybeByteColumn; // optional
-  public short maybeShortColumn; // optional
-  public int maybeIntColumn; // optional
-  public long maybeLongColumn; // optional
-  public double maybeDoubleColumn; // optional
-  public ByteBuffer maybeBinaryColumn; // optional
-  public String maybeStringColumn; // optional
-  /**
-   * 
-   * @see Suit
-   */
-  public Suit maybeEnumColumn; // optional
-  public List<String> stringsColumn; // required
-  public Set<Integer> intSetColumn; // required
-  public Map<Integer,String> intToStringColumn; // required
-  public Map<Integer,List<Nested>> complexColumn; // required
-
-  /** The set of fields this struct contains, along with convenience methods for finding and manipulating them. */
-  public enum _Fields implements org.apache.thrift.TFieldIdEnum {
-    BOOL_COLUMN((short)1, "boolColumn"),
-    BYTE_COLUMN((short)2, "byteColumn"),
-    SHORT_COLUMN((short)3, "shortColumn"),
-    INT_COLUMN((short)4, "intColumn"),
-    LONG_COLUMN((short)5, "longColumn"),
-    DOUBLE_COLUMN((short)6, "doubleColumn"),
-    BINARY_COLUMN((short)7, "binaryColumn"),
-    STRING_COLUMN((short)8, "stringColumn"),
-    /**
-     * 
-     * @see Suit
-     */
-    ENUM_COLUMN((short)9, "enumColumn"),
-    MAYBE_BOOL_COLUMN((short)10, "maybeBoolColumn"),
-    MAYBE_BYTE_COLUMN((short)11, "maybeByteColumn"),
-    MAYBE_SHORT_COLUMN((short)12, "maybeShortColumn"),
-    MAYBE_INT_COLUMN((short)13, "maybeIntColumn"),
-    MAYBE_LONG_COLUMN((short)14, "maybeLongColumn"),
-    MAYBE_DOUBLE_COLUMN((short)15, "maybeDoubleColumn"),
-    MAYBE_BINARY_COLUMN((short)16, "maybeBinaryColumn"),
-    MAYBE_STRING_COLUMN((short)17, "maybeStringColumn"),
-    /**
-     * 
-     * @see Suit
-     */
-    MAYBE_ENUM_COLUMN((short)18, "maybeEnumColumn"),
-    STRINGS_COLUMN((short)19, "stringsColumn"),
-    INT_SET_COLUMN((short)20, "intSetColumn"),
-    INT_TO_STRING_COLUMN((short)21, "intToStringColumn"),
-    COMPLEX_COLUMN((short)22, "complexColumn");
-
-    private static final Map<String, _Fields> byName = new HashMap<String, _Fields>();
-
-    static {
-      for (_Fields field : EnumSet.allOf(_Fields.class)) {
-        byName.put(field.getFieldName(), field);
-      }
-    }
-
-    /**
-     * Find the _Fields constant that matches fieldId, or null if its not found.
-     */
-    public static _Fields findByThriftId(int fieldId) {
-      switch(fieldId) {
-        case 1: // BOOL_COLUMN
-          return BOOL_COLUMN;
-        case 2: // BYTE_COLUMN
-          return BYTE_COLUMN;
-        case 3: // SHORT_COLUMN
-          return SHORT_COLUMN;
-        case 4: // INT_COLUMN
-          return INT_COLUMN;
-        case 5: // LONG_COLUMN
-          return LONG_COLUMN;
-        case 6: // DOUBLE_COLUMN
-          return DOUBLE_COLUMN;
-        case 7: // BINARY_COLUMN
-          return BINARY_COLUMN;
-        case 8: // STRING_COLUMN
-          return STRING_COLUMN;
-        case 9: // ENUM_COLUMN
-          return ENUM_COLUMN;
-        case 10: // MAYBE_BOOL_COLUMN
-          return MAYBE_BOOL_COLUMN;
-        case 11: // MAYBE_BYTE_COLUMN
-          return MAYBE_BYTE_COLUMN;
-        case 12: // MAYBE_SHORT_COLUMN
-          return MAYBE_SHORT_COLUMN;
-        case 13: // MAYBE_INT_COLUMN
-          return MAYBE_INT_COLUMN;
-        case 14: // MAYBE_LONG_COLUMN
-          return MAYBE_LONG_COLUMN;
-        case 15: // MAYBE_DOUBLE_COLUMN
-          return MAYBE_DOUBLE_COLUMN;
-        case 16: // MAYBE_BINARY_COLUMN
-          return MAYBE_BINARY_COLUMN;
-        case 17: // MAYBE_STRING_COLUMN
-          return MAYBE_STRING_COLUMN;
-        case 18: // MAYBE_ENUM_COLUMN
-          return MAYBE_ENUM_COLUMN;
-        case 19: // STRINGS_COLUMN
-          return STRINGS_COLUMN;
-        case 20: // INT_SET_COLUMN
-          return INT_SET_COLUMN;
-        case 21: // INT_TO_STRING_COLUMN
-          return INT_TO_STRING_COLUMN;
-        case 22: // COMPLEX_COLUMN
-          return COMPLEX_COLUMN;
-        default:
-          return null;
-      }
-    }
-
-    /**
-     * Find the _Fields constant that matches fieldId, throwing an exception
-     * if it is not found.
-     */
-    public static _Fields findByThriftIdOrThrow(int fieldId) {
-      _Fields fields = findByThriftId(fieldId);
-      if (fields == null) throw new IllegalArgumentException("Field " + fieldId + " doesn't exist!");
-      return fields;
-    }
-
-    /**
-     * Find the _Fields constant that matches name, or null if its not found.
-     */
-    public static _Fields findByName(String name) {
-      return byName.get(name);
-    }
-
-    private final short _thriftId;
-    private final String _fieldName;
-
-    _Fields(short thriftId, String fieldName) {
-      _thriftId = thriftId;
-      _fieldName = fieldName;
-    }
-
-    public short getThriftFieldId() {
-      return _thriftId;
-    }
-
-    public String getFieldName() {
-      return _fieldName;
-    }
-  }
-
-  // isset id assignments
-  private static final int __BOOLCOLUMN_ISSET_ID = 0;
-  private static final int __BYTECOLUMN_ISSET_ID = 1;
-  private static final int __SHORTCOLUMN_ISSET_ID = 2;
-  private static final int __INTCOLUMN_ISSET_ID = 3;
-  private static final int __LONGCOLUMN_ISSET_ID = 4;
-  private static final int __DOUBLECOLUMN_ISSET_ID = 5;
-  private static final int __MAYBEBOOLCOLUMN_ISSET_ID = 6;
-  private static final int __MAYBEBYTECOLUMN_ISSET_ID = 7;
-  private static final int __MAYBESHORTCOLUMN_ISSET_ID = 8;
-  private static final int __MAYBEINTCOLUMN_ISSET_ID = 9;
-  private static final int __MAYBELONGCOLUMN_ISSET_ID = 10;
-  private static final int __MAYBEDOUBLECOLUMN_ISSET_ID = 11;
-  private short __isset_bitfield = 0;
-  private static final _Fields optionals[] = {_Fields.MAYBE_BOOL_COLUMN,_Fields.MAYBE_BYTE_COLUMN,_Fields.MAYBE_SHORT_COLUMN,_Fields.MAYBE_INT_COLUMN,_Fields.MAYBE_LONG_COLUMN,_Fields.MAYBE_DOUBLE_COLUMN,_Fields.MAYBE_BINARY_COLUMN,_Fields.MAYBE_STRING_COLUMN,_Fields.MAYBE_ENUM_COLUMN};
-  public static final Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> metaDataMap;
-  static {
-    Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> tmpMap = new EnumMap<_Fields, org.apache.thrift.meta_data.FieldMetaData>(_Fields.class);
-    tmpMap.put(_Fields.BOOL_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("boolColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BOOL)));
-    tmpMap.put(_Fields.BYTE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("byteColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BYTE)));
-    tmpMap.put(_Fields.SHORT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("shortColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I16)));
-    tmpMap.put(_Fields.INT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("intColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32)));
-    tmpMap.put(_Fields.LONG_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("longColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I64)));
-    tmpMap.put(_Fields.DOUBLE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("doubleColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.DOUBLE)));
-    tmpMap.put(_Fields.BINARY_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("binaryColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING        , true)));
-    tmpMap.put(_Fields.STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("stringColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
-    tmpMap.put(_Fields.ENUM_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("enumColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.EnumMetaData(org.apache.thrift.protocol.TType.ENUM, Suit.class)));
-    tmpMap.put(_Fields.MAYBE_BOOL_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeBoolColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BOOL)));
-    tmpMap.put(_Fields.MAYBE_BYTE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeByteColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.BYTE)));
-    tmpMap.put(_Fields.MAYBE_SHORT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeShortColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I16)));
-    tmpMap.put(_Fields.MAYBE_INT_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeIntColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32)));
-    tmpMap.put(_Fields.MAYBE_LONG_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeLongColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I64)));
-    tmpMap.put(_Fields.MAYBE_DOUBLE_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeDoubleColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.DOUBLE)));
-    tmpMap.put(_Fields.MAYBE_BINARY_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeBinaryColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING        , true)));
-    tmpMap.put(_Fields.MAYBE_STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeStringColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
-    tmpMap.put(_Fields.MAYBE_ENUM_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("maybeEnumColumn", org.apache.thrift.TFieldRequirementType.OPTIONAL, 
-        new org.apache.thrift.meta_data.EnumMetaData(org.apache.thrift.protocol.TType.ENUM, Suit.class)));
-    tmpMap.put(_Fields.STRINGS_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("stringsColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST, 
-            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
-    tmpMap.put(_Fields.INT_SET_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("intSetColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.SetMetaData(org.apache.thrift.protocol.TType.SET, 
-            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32))));
-    tmpMap.put(_Fields.INT_TO_STRING_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("intToStringColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.MapMetaData(org.apache.thrift.protocol.TType.MAP, 
-            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32), 
-            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
-    tmpMap.put(_Fields.COMPLEX_COLUMN, new org.apache.thrift.meta_data.FieldMetaData("complexColumn", org.apache.thrift.TFieldRequirementType.REQUIRED, 
-        new org.apache.thrift.meta_data.MapMetaData(org.apache.thrift.protocol.TType.MAP, 
-            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32), 
-            new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST, 
-                new org.apache.thrift.meta_data.StructMetaData(org.apache.thrift.protocol.TType.STRUCT, Nested.class)))));
-    metaDataMap = Collections.unmodifiableMap(tmpMap);
-    org.apache.thrift.meta_data.FieldMetaData.addStructMetaDataMap(ParquetThriftCompat.class, metaDataMap);
-  }
-
-  public ParquetThriftCompat() {
-  }
-
-  public ParquetThriftCompat(
-    boolean boolColumn,
-    byte byteColumn,
-    short shortColumn,
-    int intColumn,
-    long longColumn,
-    double doubleColumn,
-    ByteBuffer binaryColumn,
-    String stringColumn,
-    Suit enumColumn,
-    List<String> stringsColumn,
-    Set<Integer> intSetColumn,
-    Map<Integer,String> intToStringColumn,
-    Map<Integer,List<Nested>> complexColumn)
-  {
-    this();
-    this.boolColumn = boolColumn;
-    setBoolColumnIsSet(true);
-    this.byteColumn = byteColumn;
-    setByteColumnIsSet(true);
-    this.shortColumn = shortColumn;
-    setShortColumnIsSet(true);
-    this.intColumn = intColumn;
-    setIntColumnIsSet(true);
-    this.longColumn = longColumn;
-    setLongColumnIsSet(true);
-    this.doubleColumn = doubleColumn;
-    setDoubleColumnIsSet(true);
-    this.binaryColumn = org.apache.thrift.TBaseHelper.copyBinary(binaryColumn);
-    this.stringColumn = stringColumn;
-    this.enumColumn = enumColumn;
-    this.stringsColumn = stringsColumn;
-    this.intSetColumn = intSetColumn;
-    this.intToStringColumn = intToStringColumn;
-    this.complexColumn = complexColumn;
-  }
-
-  /**
-   * Performs a deep copy on <i>other</i>.
-   */
-  public ParquetThriftCompat(ParquetThriftCompat other) {
-    __isset_bitfield = other.__isset_bitfield;
-    this.boolColumn = other.boolColumn;
-    this.byteColumn = other.byteColumn;
-    this.shortColumn = other.shortColumn;
-    this.intColumn = other.intColumn;
-    this.longColumn = other.longColumn;
-    this.doubleColumn = other.doubleColumn;
-    if (other.isSetBinaryColumn()) {
-      this.binaryColumn = org.apache.thrift.TBaseHelper.copyBinary(other.binaryColumn);
-    }
-    if (other.isSetStringColumn()) {
-      this.stringColumn = other.stringColumn;
-    }
-    if (other.isSetEnumColumn()) {
-      this.enumColumn = other.enumColumn;
-    }
-    this.maybeBoolColumn = other.maybeBoolColumn;
-    this.maybeByteColumn = other.maybeByteColumn;
-    this.maybeShortColumn = other.maybeShortColumn;
-    this.maybeIntColumn = other.maybeIntColumn;
-    this.maybeLongColumn = other.maybeLongColumn;
-    this.maybeDoubleColumn = other.maybeDoubleColumn;
-    if (other.isSetMaybeBinaryColumn()) {
-      this.maybeBinaryColumn = org.apache.thrift.TBaseHelper.copyBinary(other.maybeBinaryColumn);
-    }
-    if (other.isSetMaybeStringColumn()) {
-      this.maybeStringColumn = other.maybeStringColumn;
-    }
-    if (other.isSetMaybeEnumColumn()) {
-      this.maybeEnumColumn = other.maybeEnumColumn;
-    }
-    if (other.isSetStringsColumn()) {
-      List<String> __this__stringsColumn = new ArrayList<String>(other.stringsColumn);
-      this.stringsColumn = __this__stringsColumn;
-    }
-    if (other.isSetIntSetColumn()) {
-      Set<Integer> __this__intSetColumn = new HashSet<Integer>(other.intSetColumn);
-      this.intSetColumn = __this__intSetColumn;
-    }
-    if (other.isSetIntToStringColumn()) {
-      Map<Integer,String> __this__intToStringColumn = new HashMap<Integer,String>(other.intToStringColumn);
-      this.intToStringColumn = __this__intToStringColumn;
-    }
-    if (other.isSetComplexColumn()) {
-      Map<Integer,List<Nested>> __this__complexColumn = new HashMap<Integer,List<Nested>>(other.complexColumn.size());
-      for (Map.Entry<Integer, List<Nested>> other_element : other.complexColumn.entrySet()) {
-
-        Integer other_element_key = other_element.getKey();
-        List<Nested> other_element_value = other_element.getValue();
-
-        Integer __this__complexColumn_copy_key = other_element_key;
-
-        List<Nested> __this__complexColumn_copy_value = new ArrayList<Nested>(other_element_value.size());
-        for (Nested other_element_value_element : other_element_value) {
-          __this__complexColumn_copy_value.add(new Nested(other_element_value_element));
-        }
-
-        __this__complexColumn.put(__this__complexColumn_copy_key, __this__complexColumn_copy_value);
-      }
-      this.complexColumn = __this__complexColumn;
-    }
-  }
-
-  public ParquetThriftCompat deepCopy() {
-    return new ParquetThriftCompat(this);
-  }
-
-  @Override
-  public void clear() {
-    setBoolColumnIsSet(false);
-    this.boolColumn = false;
-    setByteColumnIsSet(false);
-    this.byteColumn = 0;
-    setShortColumnIsSet(false);
-    this.shortColumn = 0;
-    setIntColumnIsSet(false);
-    this.intColumn = 0;
-    setLongColumnIsSet(false);
-    this.longColumn = 0;
-    setDoubleColumnIsSet(false);
-    this.doubleColumn = 0.0;
-    this.binaryColumn = null;
-    this.stringColumn = null;
-    this.enumColumn = null;
-    setMaybeBoolColumnIsSet(false);
-    this.maybeBoolColumn = false;
-    setMaybeByteColumnIsSet(false);
-    this.maybeByteColumn = 0;
-    setMaybeShortColumnIsSet(false);
-    this.maybeShortColumn = 0;
-    setMaybeIntColumnIsSet(false);
-    this.maybeIntColumn = 0;
-    setMaybeLongColumnIsSet(false);
-    this.maybeLongColumn = 0;
-    setMaybeDoubleColumnIsSet(false);
-    this.maybeDoubleColumn = 0.0;
-    this.maybeBinaryColumn = null;
-    this.maybeStringColumn = null;
-    this.maybeEnumColumn = null;
-    this.stringsColumn = null;
-    this.intSetColumn = null;
-    this.intToStringColumn = null;
-    this.complexColumn = null;
-  }
-
-  public boolean isBoolColumn() {
-    return this.boolColumn;
-  }
-
-  public ParquetThriftCompat setBoolColumn(boolean boolColumn) {
-    this.boolColumn = boolColumn;
-    setBoolColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetBoolColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __BOOLCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field boolColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetBoolColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __BOOLCOLUMN_ISSET_ID);
-  }
-
-  public void setBoolColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __BOOLCOLUMN_ISSET_ID, value);
-  }
-
-  public byte getByteColumn() {
-    return this.byteColumn;
-  }
-
-  public ParquetThriftCompat setByteColumn(byte byteColumn) {
-    this.byteColumn = byteColumn;
-    setByteColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetByteColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __BYTECOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field byteColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetByteColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __BYTECOLUMN_ISSET_ID);
-  }
-
-  public void setByteColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __BYTECOLUMN_ISSET_ID, value);
-  }
-
-  public short getShortColumn() {
-    return this.shortColumn;
-  }
-
-  public ParquetThriftCompat setShortColumn(short shortColumn) {
-    this.shortColumn = shortColumn;
-    setShortColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetShortColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __SHORTCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field shortColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetShortColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __SHORTCOLUMN_ISSET_ID);
-  }
-
-  public void setShortColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __SHORTCOLUMN_ISSET_ID, value);
-  }
-
-  public int getIntColumn() {
-    return this.intColumn;
-  }
-
-  public ParquetThriftCompat setIntColumn(int intColumn) {
-    this.intColumn = intColumn;
-    setIntColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetIntColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __INTCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field intColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetIntColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __INTCOLUMN_ISSET_ID);
-  }
-
-  public void setIntColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __INTCOLUMN_ISSET_ID, value);
-  }
-
-  public long getLongColumn() {
-    return this.longColumn;
-  }
-
-  public ParquetThriftCompat setLongColumn(long longColumn) {
-    this.longColumn = longColumn;
-    setLongColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetLongColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __LONGCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field longColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetLongColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __LONGCOLUMN_ISSET_ID);
-  }
-
-  public void setLongColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __LONGCOLUMN_ISSET_ID, value);
-  }
-
-  public double getDoubleColumn() {
-    return this.doubleColumn;
-  }
-
-  public ParquetThriftCompat setDoubleColumn(double doubleColumn) {
-    this.doubleColumn = doubleColumn;
-    setDoubleColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetDoubleColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __DOUBLECOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field doubleColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetDoubleColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __DOUBLECOLUMN_ISSET_ID);
-  }
-
-  public void setDoubleColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __DOUBLECOLUMN_ISSET_ID, value);
-  }
-
-  public byte[] getBinaryColumn() {
-    setBinaryColumn(org.apache.thrift.TBaseHelper.rightSize(binaryColumn));
-    return binaryColumn == null ? null : binaryColumn.array();
-  }
-
-  public ByteBuffer bufferForBinaryColumn() {
-    return org.apache.thrift.TBaseHelper.copyBinary(binaryColumn);
-  }
-
-  public ParquetThriftCompat setBinaryColumn(byte[] binaryColumn) {
-    this.binaryColumn = binaryColumn == null ? (ByteBuffer)null : ByteBuffer.wrap(Arrays.copyOf(binaryColumn, binaryColumn.length));
-    return this;
-  }
-
-  public ParquetThriftCompat setBinaryColumn(ByteBuffer binaryColumn) {
-    this.binaryColumn = org.apache.thrift.TBaseHelper.copyBinary(binaryColumn);
-    return this;
-  }
-
-  public void unsetBinaryColumn() {
-    this.binaryColumn = null;
-  }
-
-  /** Returns true if field binaryColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetBinaryColumn() {
-    return this.binaryColumn != null;
-  }
-
-  public void setBinaryColumnIsSet(boolean value) {
-    if (!value) {
-      this.binaryColumn = null;
-    }
-  }
-
-  public String getStringColumn() {
-    return this.stringColumn;
-  }
-
-  public ParquetThriftCompat setStringColumn(String stringColumn) {
-    this.stringColumn = stringColumn;
-    return this;
-  }
-
-  public void unsetStringColumn() {
-    this.stringColumn = null;
-  }
-
-  /** Returns true if field stringColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetStringColumn() {
-    return this.stringColumn != null;
-  }
-
-  public void setStringColumnIsSet(boolean value) {
-    if (!value) {
-      this.stringColumn = null;
-    }
-  }
-
-  /**
-   * 
-   * @see Suit
-   */
-  public Suit getEnumColumn() {
-    return this.enumColumn;
-  }
-
-  /**
-   * 
-   * @see Suit
-   */
-  public ParquetThriftCompat setEnumColumn(Suit enumColumn) {
-    this.enumColumn = enumColumn;
-    return this;
-  }
-
-  public void unsetEnumColumn() {
-    this.enumColumn = null;
-  }
-
-  /** Returns true if field enumColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetEnumColumn() {
-    return this.enumColumn != null;
-  }
-
-  public void setEnumColumnIsSet(boolean value) {
-    if (!value) {
-      this.enumColumn = null;
-    }
-  }
-
-  public boolean isMaybeBoolColumn() {
-    return this.maybeBoolColumn;
-  }
-
-  public ParquetThriftCompat setMaybeBoolColumn(boolean maybeBoolColumn) {
-    this.maybeBoolColumn = maybeBoolColumn;
-    setMaybeBoolColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetMaybeBoolColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEBOOLCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field maybeBoolColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeBoolColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __MAYBEBOOLCOLUMN_ISSET_ID);
-  }
-
-  public void setMaybeBoolColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEBOOLCOLUMN_ISSET_ID, value);
-  }
-
-  public byte getMaybeByteColumn() {
-    return this.maybeByteColumn;
-  }
-
-  public ParquetThriftCompat setMaybeByteColumn(byte maybeByteColumn) {
-    this.maybeByteColumn = maybeByteColumn;
-    setMaybeByteColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetMaybeByteColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEBYTECOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field maybeByteColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeByteColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __MAYBEBYTECOLUMN_ISSET_ID);
-  }
-
-  public void setMaybeByteColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEBYTECOLUMN_ISSET_ID, value);
-  }
-
-  public short getMaybeShortColumn() {
-    return this.maybeShortColumn;
-  }
-
-  public ParquetThriftCompat setMaybeShortColumn(short maybeShortColumn) {
-    this.maybeShortColumn = maybeShortColumn;
-    setMaybeShortColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetMaybeShortColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBESHORTCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field maybeShortColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeShortColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __MAYBESHORTCOLUMN_ISSET_ID);
-  }
-
-  public void setMaybeShortColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBESHORTCOLUMN_ISSET_ID, value);
-  }
-
-  public int getMaybeIntColumn() {
-    return this.maybeIntColumn;
-  }
-
-  public ParquetThriftCompat setMaybeIntColumn(int maybeIntColumn) {
-    this.maybeIntColumn = maybeIntColumn;
-    setMaybeIntColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetMaybeIntColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEINTCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field maybeIntColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeIntColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __MAYBEINTCOLUMN_ISSET_ID);
-  }
-
-  public void setMaybeIntColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEINTCOLUMN_ISSET_ID, value);
-  }
-
-  public long getMaybeLongColumn() {
-    return this.maybeLongColumn;
-  }
-
-  public ParquetThriftCompat setMaybeLongColumn(long maybeLongColumn) {
-    this.maybeLongColumn = maybeLongColumn;
-    setMaybeLongColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetMaybeLongColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBELONGCOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field maybeLongColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeLongColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __MAYBELONGCOLUMN_ISSET_ID);
-  }
-
-  public void setMaybeLongColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBELONGCOLUMN_ISSET_ID, value);
-  }
-
-  public double getMaybeDoubleColumn() {
-    return this.maybeDoubleColumn;
-  }
-
-  public ParquetThriftCompat setMaybeDoubleColumn(double maybeDoubleColumn) {
-    this.maybeDoubleColumn = maybeDoubleColumn;
-    setMaybeDoubleColumnIsSet(true);
-    return this;
-  }
-
-  public void unsetMaybeDoubleColumn() {
-    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __MAYBEDOUBLECOLUMN_ISSET_ID);
-  }
-
-  /** Returns true if field maybeDoubleColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeDoubleColumn() {
-    return EncodingUtils.testBit(__isset_bitfield, __MAYBEDOUBLECOLUMN_ISSET_ID);
-  }
-
-  public void setMaybeDoubleColumnIsSet(boolean value) {
-    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __MAYBEDOUBLECOLUMN_ISSET_ID, value);
-  }
-
-  public byte[] getMaybeBinaryColumn() {
-    setMaybeBinaryColumn(org.apache.thrift.TBaseHelper.rightSize(maybeBinaryColumn));
-    return maybeBinaryColumn == null ? null : maybeBinaryColumn.array();
-  }
-
-  public ByteBuffer bufferForMaybeBinaryColumn() {
-    return org.apache.thrift.TBaseHelper.copyBinary(maybeBinaryColumn);
-  }
-
-  public ParquetThriftCompat setMaybeBinaryColumn(byte[] maybeBinaryColumn) {
-    this.maybeBinaryColumn = maybeBinaryColumn == null ? (ByteBuffer)null : ByteBuffer.wrap(Arrays.copyOf(maybeBinaryColumn, maybeBinaryColumn.length));
-    return this;
-  }
-
-  public ParquetThriftCompat setMaybeBinaryColumn(ByteBuffer maybeBinaryColumn) {
-    this.maybeBinaryColumn = org.apache.thrift.TBaseHelper.copyBinary(maybeBinaryColumn);
-    return this;
-  }
-
-  public void unsetMaybeBinaryColumn() {
-    this.maybeBinaryColumn = null;
-  }
-
-  /** Returns true if field maybeBinaryColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeBinaryColumn() {
-    return this.maybeBinaryColumn != null;
-  }
-
-  public void setMaybeBinaryColumnIsSet(boolean value) {
-    if (!value) {
-      this.maybeBinaryColumn = null;
-    }
-  }
-
-  public String getMaybeStringColumn() {
-    return this.maybeStringColumn;
-  }
-
-  public ParquetThriftCompat setMaybeStringColumn(String maybeStringColumn) {
-    this.maybeStringColumn = maybeStringColumn;
-    return this;
-  }
-
-  public void unsetMaybeStringColumn() {
-    this.maybeStringColumn = null;
-  }
-
-  /** Returns true if field maybeStringColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeStringColumn() {
-    return this.maybeStringColumn != null;
-  }
-
-  public void setMaybeStringColumnIsSet(boolean value) {
-    if (!value) {
-      this.maybeStringColumn = null;
-    }
-  }
-
-  /**
-   * 
-   * @see Suit
-   */
-  public Suit getMaybeEnumColumn() {
-    return this.maybeEnumColumn;
-  }
-
-  /**
-   * 
-   * @see Suit
-   */
-  public ParquetThriftCompat setMaybeEnumColumn(Suit maybeEnumColumn) {
-    this.maybeEnumColumn = maybeEnumColumn;
-    return this;
-  }
-
-  public void unsetMaybeEnumColumn() {
-    this.maybeEnumColumn = null;
-  }
-
-  /** Returns true if field maybeEnumColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetMaybeEnumColumn() {
-    return this.maybeEnumColumn != null;
-  }
-
-  public void setMaybeEnumColumnIsSet(boolean value) {
-    if (!value) {
-      this.maybeEnumColumn = null;
-    }
-  }
-
-  public int getStringsColumnSize() {
-    return (this.stringsColumn == null) ? 0 : this.stringsColumn.size();
-  }
-
-  public java.util.Iterator<String> getStringsColumnIterator() {
-    return (this.stringsColumn == null) ? null : this.stringsColumn.iterator();
-  }
-
-  public void addToStringsColumn(String elem) {
-    if (this.stringsColumn == null) {
-      this.stringsColumn = new ArrayList<String>();
-    }
-    this.stringsColumn.add(elem);
-  }
-
-  public List<String> getStringsColumn() {
-    return this.stringsColumn;
-  }
-
-  public ParquetThriftCompat setStringsColumn(List<String> stringsColumn) {
-    this.stringsColumn = stringsColumn;
-    return this;
-  }
-
-  public void unsetStringsColumn() {
-    this.stringsColumn = null;
-  }
-
-  /** Returns true if field stringsColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetStringsColumn() {
-    return this.stringsColumn != null;
-  }
-
-  public void setStringsColumnIsSet(boolean value) {
-    if (!value) {
-      this.stringsColumn = null;
-    }
-  }
-
-  public int getIntSetColumnSize() {
-    return (this.intSetColumn == null) ? 0 : this.intSetColumn.size();
-  }
-
-  public java.util.Iterator<Integer> getIntSetColumnIterator() {
-    return (this.intSetColumn == null) ? null : this.intSetColumn.iterator();
-  }
-
-  public void addToIntSetColumn(int elem) {
-    if (this.intSetColumn == null) {
-      this.intSetColumn = new HashSet<Integer>();
-    }
-    this.intSetColumn.add(elem);
-  }
-
-  public Set<Integer> getIntSetColumn() {
-    return this.intSetColumn;
-  }
-
-  public ParquetThriftCompat setIntSetColumn(Set<Integer> intSetColumn) {
-    this.intSetColumn = intSetColumn;
-    return this;
-  }
-
-  public void unsetIntSetColumn() {
-    this.intSetColumn = null;
-  }
-
-  /** Returns true if field intSetColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetIntSetColumn() {
-    return this.intSetColumn != null;
-  }
-
-  public void setIntSetColumnIsSet(boolean value) {
-    if (!value) {
-      this.intSetColumn = null;
-    }
-  }
-
-  public int getIntToStringColumnSize() {
-    return (this.intToStringColumn == null) ? 0 : this.intToStringColumn.size();
-  }
-
-  public void putToIntToStringColumn(int key, String val) {
-    if (this.intToStringColumn == null) {
-      this.intToStringColumn = new HashMap<Integer,String>();
-    }
-    this.intToStringColumn.put(key, val);
-  }
-
-  public Map<Integer,String> getIntToStringColumn() {
-    return this.intToStringColumn;
-  }
-
-  public ParquetThriftCompat setIntToStringColumn(Map<Integer,String> intToStringColumn) {
-    this.intToStringColumn = intToStringColumn;
-    return this;
-  }
-
-  public void unsetIntToStringColumn() {
-    this.intToStringColumn = null;
-  }
-
-  /** Returns true if field intToStringColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetIntToStringColumn() {
-    return this.intToStringColumn != null;
-  }
-
-  public void setIntToStringColumnIsSet(boolean value) {
-    if (!value) {
-      this.intToStringColumn = null;
-    }
-  }
-
-  public int getComplexColumnSize() {
-    return (this.complexColumn == null) ? 0 : this.complexColumn.size();
-  }
-
-  public void putToComplexColumn(int key, List<Nested> val) {
-    if (this.complexColumn == null) {
-      this.complexColumn = new HashMap<Integer,List<Nested>>();
-    }
-    this.complexColumn.put(key, val);
-  }
-
-  public Map<Integer,List<Nested>> getComplexColumn() {
-    return this.complexColumn;
-  }
-
-  public ParquetThriftCompat setComplexColumn(Map<Integer,List<Nested>> complexColumn) {
-    this.complexColumn = complexColumn;
-    return this;
-  }
-
-  public void unsetComplexColumn() {
-    this.complexColumn = null;
-  }
-
-  /** Returns true if field complexColumn is set (has been assigned a value) and false otherwise */
-  public boolean isSetComplexColumn() {
-    return this.complexColumn != null;
-  }
-
-  public void setComplexColumnIsSet(boolean value) {
-    if (!value) {
-      this.complexColumn = null;
-    }
-  }
-
-  public void setFieldValue(_Fields field, Object value) {
-    switch (field) {
-    case BOOL_COLUMN:
-      if (value == null) {
-        unsetBoolColumn();
-      } else {
-        setBoolColumn((Boolean)value);
-      }
-      break;
-
-    case BYTE_COLUMN:
-      if (value == null) {
-        unsetByteColumn();
-      } else {
-        setByteColumn((Byte)value);
-      }
-      break;
-
-    case SHORT_COLUMN:
-      if (value == null) {
-        unsetShortColumn();
-      } else {
-        setShortColumn((Short)value);
-      }
-      break;
-
-    case INT_COLUMN:
-      if (value == null) {
-        unsetIntColumn();
-      } else {
-        setIntColumn((Integer)value);
-      }
-      break;
-
-    case LONG_COLUMN:
-      if (value == null) {
-        unsetLongColumn();
-      } else {
-        setLongColumn((Long)value);
-      }
-      break;
-
-    case DOUBLE_COLUMN:
-      if (value == null) {
-        unsetDoubleColumn();
-      } else {
-        setDoubleColumn((Double)value);
-      }
-      break;
-
-    case BINARY_COLUMN:
-      if (value == null) {
-        unsetBinaryColumn();
-      } else {
-        setBinaryColumn((ByteBuffer)value);
-      }
-      break;
-
-    case STRING_COLUMN:
-      if (value == null) {
-        unsetStringColumn();
-      } else {
-        setStringColumn((String)value);
-      }
-      break;
-
-    case ENUM_COLUMN:
-      if (value == null) {
-        unsetEnumColumn();
-      } else {
-        setEnumColumn((Suit)value);
-      }
-      break;
-
-    case MAYBE_BOOL_COLUMN:
-      if (value == null) {
-        unsetMaybeBoolColumn();
-      } else {
-        setMaybeBoolColumn((Boolean)value);
-      }
-      break;
-
-    case MAYBE_BYTE_COLUMN:
-      if (value == null) {
-        unsetMaybeByteColumn();
-      } else {
-        setMaybeByteColumn((Byte)value);
-      }
-      break;
-
-    case MAYBE_SHORT_COLUMN:
-      if (value == null) {
-        unsetMaybeShortColumn();
-      } else {
-        setMaybeShortColumn((Short)value);
-      }
-      break;
-
-    case MAYBE_INT_COLUMN:
-      if (value == null) {
-        unsetMaybeIntColumn();
-      } else {
-        setMaybeIntColumn((Integer)value);
-      }
-      break;
-
-    case MAYBE_LONG_COLUMN:
-      if (value == null) {
-        unsetMaybeLongColumn();
-      } else {
-        setMaybeLongColumn((Long)value);
-      }
-      break;
-
-    case MAYBE_DOUBLE_COLUMN:
-      if (value == null) {
-        unsetMaybeDoubleColumn();
-      } else {
-        setMaybeDoubleColumn((Double)value);
-      }
-      break;
-
-    case MAYBE_BINARY_COLUMN:
-      if (value == null) {
-        unsetMaybeBinaryColumn();
-      } else {
-        setMaybeBinaryColumn((ByteBuffer)value);
-      }
-      break;
-
-    case MAYBE_STRING_COLUMN:
-      if (value == null) {
-        unsetMaybeStringColumn();
-      } else {
-        setMaybeStringColumn((String)value);
-      }
-      break;
-
-    case MAYBE_ENUM_COLUMN:
-      if (value == null) {
-        unsetMaybeEnumColumn();
-      } else {
-        setMaybeEnumColumn((Suit)value);
-      }
-      break;
-
-    case STRINGS_COLUMN:
-      if (value == null) {
-        unsetStringsColumn();
-      } else {
-        setStringsColumn((List<String>)value);
-      }
-      break;
-
-    case INT_SET_COLUMN:
-      if (value == null) {
-        unsetIntSetColumn();
-      } else {
-        setIntSetColumn((Set<Integer>)value);
-      }
-      break;
-
-    case INT_TO_STRING_COLUMN:
-      if (value == null) {
-        unsetIntToStringColumn();
-      } else {
-        setIntToStringColumn((Map<Integer,String>)value);
-      }
-      break;
-
-    case COMPLEX_COLUMN:
-      if (value == null) {
-        unsetComplexColumn();
-      } else {
-        setComplexColumn((Map<Integer,List<Nested>>)value);
-      }
-      break;
-
-    }
-  }
-
-  public Object getFieldValue(_Fields field) {
-    switch (field) {
-    case BOOL_COLUMN:
-      return Boolean.valueOf(isBoolColumn());
-
-    case BYTE_COLUMN:
-      return Byte.valueOf(getByteColumn());
-
-    case SHORT_COLUMN:
-      return Short.valueOf(getShortColumn());
-
-    case INT_COLUMN:
-      return Integer.valueOf(getIntColumn());
-
-    case LONG_COLUMN:
-      return Long.valueOf(getLongColumn());
-
-    case DOUBLE_COLUMN:
-      return Double.valueOf(getDoubleColumn());
-
-    case BINARY_COLUMN:
-      return getBinaryColumn();
-
-    case STRING_COLUMN:
-      return getStringColumn();
-
-    case ENUM_COLUMN:
-      return getEnumColumn();
-
-    case MAYBE_BOOL_COLUMN:
-      return Boolean.valueOf(isMaybeBoolColumn());
-
-    case MAYBE_BYTE_COLUMN:
-      return Byte.valueOf(getMaybeByteColumn());
-
-    case MAYBE_SHORT_COLUMN:
-      return Short.valueOf(getMaybeShortColumn());
-
-    case MAYBE_INT_COLUMN:
-      return Integer.valueOf(getMaybeIntColumn());
-
-    case MAYBE_LONG_COLUMN:
-      return Long.valueOf(getMaybeLongColumn());
-
-    case MAYBE_DOUBLE_COLUMN:
-      return Double.valueOf(getMaybeDoubleColumn());
-
-    case MAYBE_BINARY_COLUMN:
-      return getMaybeBinaryColumn();
-
-    case MAYBE_STRING_COLUMN:
-      return getMaybeStringColumn();
-
-    case MAYBE_ENUM_COLUMN:
-      return getMaybeEnumColumn();
-
-    case STRINGS_COLUMN:
-      return getStringsColumn();
-
-    case INT_SET_COLUMN:
-      return getIntSetColumn();
-
-    case INT_TO_STRING_COLUMN:
-      return getIntToStringColumn();
-
-    case COMPLEX_COLUMN:
-      return getComplexColumn();
-
-    }
-    throw new IllegalStateException();
-  }
-
-  /** Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise */
-  public boolean isSet(_Fields field) {
-    if (field == null) {
-      throw new IllegalArgumentException();
-    }
-
-    switch (field) {
-    case BOOL_COLUMN:
-      return isSetBoolColumn();
-    case BYTE_COLUMN:
-      return isSetByteColumn();
-    case SHORT_COLUMN:
-      return isSetShortColumn();
-    case INT_COLUMN:
-      return isSetIntColumn();
-    case LONG_COLUMN:
-      return isSetLongColumn();
-    case DOUBLE_COLUMN:
-      return isSetDoubleColumn();
-    case BINARY_COLUMN:
-      return isSetBinaryColumn();
-    case STRING_COLUMN:
-      return isSetStringColumn();
-    case ENUM_COLUMN:
-      return isSetEnumColumn();
-    case MAYBE_BOOL_COLUMN:
-      return isSetMaybeBoolColumn();
-    case MAYBE_BYTE_COLUMN:
-      return isSetMaybeByteColumn();
-    case MAYBE_SHORT_COLUMN:
-      return isSetMaybeShortColumn();
-    case MAYBE_INT_COLUMN:
-      return isSetMaybeIntColumn();
-    case MAYBE_LONG_COLUMN:
-      return isSetMaybeLongColumn();
-    case MAYBE_DOUBLE_COLUMN:
-      return isSetMaybeDoubleColumn();
-    case MAYBE_BINARY_COLUMN:
-      return isSetMaybeBinaryColumn();
-    case MAYBE_STRING_COLUMN:
-      return isSetMaybeStringColumn();
-    case MAYBE_ENUM_COLUMN:
-      return isSetMaybeEnumColumn();
-    case STRINGS_COLUMN:
-      return isSetStringsColumn();
-    case INT_SET_COLUMN:
-      return isSetIntSetColumn();
-    case INT_TO_STRING_COLUMN:
-      return isSetIntToStringColumn();
-    case COMPLEX_COLUMN:
-      return isSetComplexColumn();
-    }
-    throw new IllegalStateException();
-  }
-
-  @Override
-  public boolean equals(Object that) {
-    if (that == null)
-      return false;
-    if (that instanceof ParquetThriftCompat)
-      return this.equals((ParquetThriftCompat)that);
-    return false;
-  }
-
-  public boolean equals(ParquetThriftCompat that) {
-    if (that == null)
-      return false;
-
-    boolean this_present_boolColumn = true;
-    boolean that_present_boolColumn = true;
-    if (this_present_boolColumn || that_present_boolColumn) {
-      if (!(this_present_boolColumn && that_present_boolColumn))
-        return false;
-      if (this.boolColumn != that.boolColumn)
-        return false;
-    }
-
-    boolean this_present_byteColumn = true;
-    boolean that_present_byteColumn = true;
-    if (this_present_byteColumn || that_present_byteColumn) {
-      if (!(this_present_byteColumn && that_present_byteColumn))
-        return false;
-      if (this.byteColumn != that.byteColumn)
-        return false;
-    }
-
-    boolean this_present_shortColumn = true;
-    boolean that_present_shortColumn = true;
-    if (this_present_shortColumn || that_present_shortColumn) {
-      if (!(this_present_shortColumn && that_present_shortColumn))
-        return false;
-      if (this.shortColumn != that.shortColumn)
-        return false;
-    }
-
-    boolean this_present_intColumn = true;
-    boolean that_present_intColumn = true;
-    if (this_present_intColumn || that_present_intColumn) {
-      if (!(this_present_intColumn && that_present_intColumn))
-        return false;
-      if (this.intColumn != that.intColumn)
-        return false;
-    }
-
-    boolean this_present_longColumn = true;
-    boolean that_present_longColumn = true;
-    if (this_present_longColumn || that_present_longColumn) {
-      if (!(this_present_longColumn && that_present_longColumn))
-        return false;
-      if (this.longColumn != that.longColumn)
-        return false;
-    }
-
-    boolean this_present_doubleColumn = true;
-    boolean that_present_doubleColumn = true;
-    if (this_present_doubleColumn || that_present_doubleColumn) {
-      if (!(this_present_doubleColumn && that_present_doubleColumn))
-        return false;
-      if (this.doubleColumn != that.doubleColumn)
-        return false;
-    }
-
-    boolean this_present_binaryColumn = true && this.isSetBinaryColumn();
-    boolean that_present_binaryColumn = true && that.isSetBinaryColumn();
-    if (this_present_binaryColumn || that_present_binaryColumn) {
-      if (!(this_present_binaryColumn && that_present_binaryColumn))
-        return false;
-      if (!this.binaryColumn.equals(that.binaryColumn))
-        return false;
-    }
-
-    boolean this_present_stringColumn = true && this.isSetStringColumn();
-    boolean that_present_stringColumn = true && that.isSetStringColumn();
-    if (this_present_stringColumn || that_present_stringColumn) {
-      if (!(this_present_stringColumn && that_present_stringColumn))
-        return false;
-      if (!this.stringColumn.equals(that.stringColumn))
-        return false;
-    }
-
-    boolean this_present_enumColumn = true && this.isSetEnumColumn();
-    boolean that_present_enumColumn = true && that.isSetEnumColumn();
-    if (this_present_enumColumn || that_present_enumColumn) {
-      if (!(this_present_enumColumn && that_present_enumColumn))
-        return false;
-      if (!this.enumColumn.equals(that.enumColumn))
-        return false;
-    }
-
-    boolean this_present_maybeBoolColumn = true && this.isSetMaybeBoolColumn();
-    boolean that_present_maybeBoolColumn = true && that.isSetMaybeBoolColumn();
-    if (this_present_maybeBoolColumn || that_present_maybeBoolColumn) {
-      if (!(this_present_maybeBoolColumn && that_present_maybeBoolColumn))
-        return false;
-      if (this.maybeBoolColumn != that.maybeBoolColumn)
-        return false;
-    }
-
-    boolean this_present_maybeByteColumn = true && this.isSetMaybeByteColumn();
-    boolean that_present_maybeByteColumn = true && that.isSetMaybeByteColumn();
-    if (this_present_maybeByteColumn || that_present_maybeByteColumn) {
-      if (!(this_present_maybeByteColumn && that_present_maybeByteColumn))
-        return false;
-      if (this.maybeByteColumn != that.maybeByteColumn)
-        return false;
-    }
-
-    boolean this_present_maybeShortColumn = true && this.isSetMaybeShortColumn();
-    boolean that_present_maybeShortColumn = true && that.isSetMaybeShortColumn();
-    if (this_present_maybeShortColumn || that_present_maybeShortColumn) {
-      if (!(this_present_maybeShortColumn && that_present_maybeShortColumn))
-        return false;
-      if (this.maybeShortColumn != that.maybeShortColumn)
-        return false;
-    }
-
-    boolean this_present_maybeIntColumn = true && this.isSetMaybeIntColumn();
-    boolean that_present_maybeIntColumn = true && that.isSetMaybeIntColumn();
-    if (this_present_maybeIntColumn || that_present_maybeIntColumn) {
-      if (!(this_present_maybeIntColumn && that_present_maybeIntColumn))
-        return false;
-      if (this.maybeIntColumn != that.maybeIntColumn)
-        return false;
-    }
-
-    boolean this_present_maybeLongColumn = true && this.isSetMaybeLongColumn();
-    boolean that_present_maybeLongColumn = true && that.isSetMaybeLongColumn();
-    if (this_present_maybeLongColumn || that_present_maybeLongColumn) {
-      if (!(this_present_maybeLongColumn && that_present_maybeLongColumn))
-        return false;
-      if (this.maybeLongColumn != that.maybeLongColumn)
-        return false;
-    }
-
-    boolean this_present_maybeDoubleColumn = true && this.isSetMaybeDoubleColumn();
-    boolean that_present_maybeDoubleColumn = true && that.isSetMaybeDoubleColumn();
-    if (this_present_maybeDoubleColumn || that_present_maybeDoubleColumn) {
-      if (!(this_present_maybeDoubleColumn && that_present_maybeDoubleColumn))
-        return false;
-      if (this.maybeDoubleColumn != that.maybeDoubleColumn)
-        return false;
-    }
-
-    boolean this_present_maybeBinaryColumn = true && this.isSetMaybeBinaryColumn();
-    boolean that_present_maybeBinaryColumn = true && that.isSetMaybeBinaryColumn();
-    if (this_present_maybeBinaryColumn || that_present_maybeBinaryColumn) {
-      if (!(this_present_maybeBinaryColumn && that_present_maybeBinaryColumn))
-        return false;
-      if (!this.maybeBinaryColumn.equals(that.maybeBinaryColumn))
-        return false;
-    }
-
-    boolean this_present_maybeStringColumn = true && this.isSetMaybeStringColumn();
-    boolean that_present_maybeStringColumn = true && that.isSetMaybeStringColumn();
-    if (this_present_maybeStringColumn || that_present_maybeStringColumn) {
-      if (!(this_present_maybeStringColumn && that_present_maybeStringColumn))
-        return false;
-      if (!this.maybeStringColumn.equals(that.maybeStringColumn))
-        return false;
-    }
-
-    boolean this_present_maybeEnumColumn = true && this.isSetMaybeEnumColumn();
-    boolean that_present_maybeEnumColumn = true && that.isSetMaybeEnumColumn();
-    if (this_present_maybeEnumColumn || that_present_maybeEnumColumn) {
-      if (!(this_present_maybeEnumColumn && that_present_maybeEnumColumn))
-        return false;
-      if (!this.maybeEnumColumn.equals(that.maybeEnumColumn))
-        return false;
-    }
-
-    boolean this_present_stringsColumn = true && this.isSetStringsColumn();
-    boolean that_present_stringsColumn = true && that.isSetStringsColumn();
-    if (this_present_stringsColumn || that_present_stringsColumn) {
-      if (!(this_present_stringsColumn && that_present_stringsColumn))
-        return false;
-      if (!this.stringsColumn.equals(that.stringsColumn))
-        return false;
-    }
-
-    boolean this_present_intSetColumn = true && this.isSetIntSetColumn();
-    boolean that_present_intSetColumn = true && that.isSetIntSetColumn();
-    if (this_present_intSetColumn || that_present_intSetColumn) {
-      if (!(this_present_intSetColumn && that_present_intSetColumn))
-        return false;
-      if (!this.intSetColumn.equals(that.intSetColumn))
-        return false;
-    }
-
-    boolean this_present_intToStringColumn = true && this.isSetIntToStringColumn();
-    boolean that_present_intToStringColumn = true && that.isSetIntToStringColumn();
-    if (this_present_intToStringColumn || that_present_intToStringColumn) {
-      if (!(this_present_intToStringColumn && that_present_intToStringColumn))
-        return false;
-      if (!this.intToStringColumn.equals(that.intToStringColumn))
-        return false;
-    }
-
-    boolean this_present_complexColumn = true && this.isSetComplexColumn();
-    boolean that_present_complexColumn = true && that.isSetComplexColumn();
-    if (this_present_complexColumn || that_present_complexColumn) {
-      if (!(this_present_complexColumn && that_present_complexColumn))
-        return false;
-      if (!this.complexColumn.equals(that.complexColumn))
-        return false;
-    }
-
-    return true;
-  }
-
-  @Override
-  public int hashCode() {
-    List<Object> list = new ArrayList<Object>();
-
-    boolean present_boolColumn = true;
-    list.add(present_boolColumn);
-    if (present_boolColumn)
-      list.add(boolColumn);
-
-    boolean present_byteColumn = true;
-    list.add(present_byteColumn);
-    if (present_byteColumn)
-      list.add(byteColumn);
-
-    boolean present_shortColumn = true;
-    list.add(present_shortColumn);
-    if (present_shortColumn)
-      list.add(shortColumn);
-
-    boolean present_intColumn = true;
-    list.add(present_intColumn);
-    if (present_intColumn)
-      list.add(intColumn);
-
-    boolean present_longColumn = true;
-    list.add(present_longColumn);
-    if (present_longColumn)
-      list.add(longColumn);
-
-    boolean present_doubleColumn = true;
-    list.add(present_doubleColumn);
-    if (present_doubleColumn)
-      list.add(doubleColumn);
-
-    boolean present_binaryColumn = true && (isSetBinaryColumn());
-    list.add(present_binaryColumn);
-    if (present_binaryColumn)
-      list.add(binaryColumn);
-
-    boolean present_stringColumn = true && (isSetStringColumn());
-    list.add(present_stringColumn);
-    if (present_stringColumn)
-      list.add(stringColumn);
-
-    boolean present_enumColumn = true && (isSetEnumColumn());
-    list.add(present_enumColumn);
-    if (present_enumColumn)
-      list.add(enumColumn.getValue());
-
-    boolean present_maybeBoolColumn = true && (isSetMaybeBoolColumn());
-    list.add(present_maybeBoolColumn);
-    if (present_maybeBoolColumn)
-      list.add(maybeBoolColumn);
-
-    boolean present_maybeByteColumn = true && (isSetMaybeByteColumn());
-    list.add(present_maybeByteColumn);
-    if (present_maybeByteColumn)
-      list.add(maybeByteColumn);
-
-    boolean present_maybeShortColumn = true && (isSetMaybeShortColumn());
-    list.add(present_maybeShortColumn);
-    if (present_maybeShortColumn)
-      list.add(maybeShortColumn);
-
-    boolean present_maybeIntColumn = true && (isSetMaybeIntColumn());
-    list.add(present_maybeIntColumn);
-    if (present_maybeIntColumn)
-      list.add(maybeIntColumn);
-
-    boolean present_maybeLongColumn = true && (isSetMaybeLongColumn());
-    list.add(present_maybeLongColumn);
-    if (present_maybeLongColumn)
-      list.add(maybeLongColumn);
-
-    boolean present_maybeDoubleColumn = true && (isSetMaybeDoubleColumn());
-    list.add(present_maybeDoubleColumn);
-    if (present_maybeDoubleColumn)
-      list.add(maybeDoubleColumn);
-
-    boolean present_maybeBinaryColumn = true && (isSetMaybeBinaryColumn());
-    list.add(present_maybeBinaryColumn);
-    if (present_maybeBinaryColumn)
-      list.add(maybeBinaryColumn);
-
-    boolean present_maybeStringColumn = true && (isSetMaybeStringColumn());
-    list.add(present_maybeStringColumn);
-    if (present_maybeStringColumn)
-      list.add(maybeStringColumn);
-
-    boolean present_maybeEnumColumn = true && (isSetMaybeEnumColumn());
-    list.add(present_maybeEnumColumn);
-    if (present_maybeEnumColumn)
-      list.add(maybeEnumColumn.getValue());
-
-    boolean present_stringsColumn = true && (isSetStringsColumn());
-    list.add(present_stringsColumn);
-    if (present_stringsColumn)
-      list.add(stringsColumn);
-
-    boolean present_intSetColumn = true && (isSetIntSetColumn());
-    list.add(present_intSetColumn);
-    if (present_intSetColumn)
-      list.add(intSetColumn);
-
-    boolean present_intToStringColumn = true && (isSetIntToStringColumn());
-    list.add(present_intToStringColumn);
-    if (present_intToStringColumn)
-      list.add(intToStringColumn);
-
-    boolean present_complexColumn = true && (isSetComplexColumn());
-    list.add(present_complexColumn);
-    if (present_complexColumn)
-      list.add(complexColumn);
-
-    return list.hashCode();
-  }
-
-  @Override
-  public int compareTo(ParquetThriftCompat other) {
-    if (!getClass().equals(other.getClass())) {
-      return getClass().getName().compareTo(other.getClass().getName());
-    }
-
-    int lastComparison = 0;
-
-    lastComparison = Boolean.valueOf(isSetBoolColumn()).compareTo(other.isSetBoolColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetBoolColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.boolColumn, other.boolColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetByteColumn()).compareTo(other.isSetByteColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetByteColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.byteColumn, other.byteColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetShortColumn()).compareTo(other.isSetShortColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetShortColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.shortColumn, other.shortColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetIntColumn()).compareTo(other.isSetIntColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetIntColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.intColumn, other.intColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetLongColumn()).compareTo(other.isSetLongColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetLongColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.longColumn, other.longColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetDoubleColumn()).compareTo(other.isSetDoubleColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetDoubleColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.doubleColumn, other.doubleColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetBinaryColumn()).compareTo(other.isSetBinaryColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetBinaryColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.binaryColumn, other.binaryColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetStringColumn()).compareTo(other.isSetStringColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetStringColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.stringColumn, other.stringColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetEnumColumn()).compareTo(other.isSetEnumColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetEnumColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.enumColumn, other.enumColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeBoolColumn()).compareTo(other.isSetMaybeBoolColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeBoolColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeBoolColumn, other.maybeBoolColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeByteColumn()).compareTo(other.isSetMaybeByteColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeByteColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeByteColumn, other.maybeByteColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeShortColumn()).compareTo(other.isSetMaybeShortColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeShortColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeShortColumn, other.maybeShortColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeIntColumn()).compareTo(other.isSetMaybeIntColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeIntColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeIntColumn, other.maybeIntColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeLongColumn()).compareTo(other.isSetMaybeLongColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeLongColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeLongColumn, other.maybeLongColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeDoubleColumn()).compareTo(other.isSetMaybeDoubleColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeDoubleColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeDoubleColumn, other.maybeDoubleColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeBinaryColumn()).compareTo(other.isSetMaybeBinaryColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeBinaryColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeBinaryColumn, other.maybeBinaryColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeStringColumn()).compareTo(other.isSetMaybeStringColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeStringColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeStringColumn, other.maybeStringColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetMaybeEnumColumn()).compareTo(other.isSetMaybeEnumColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetMaybeEnumColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.maybeEnumColumn, other.maybeEnumColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetStringsColumn()).compareTo(other.isSetStringsColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetStringsColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.stringsColumn, other.stringsColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetIntSetColumn()).compareTo(other.isSetIntSetColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetIntSetColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.intSetColumn, other.intSetColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetIntToStringColumn()).compareTo(other.isSetIntToStringColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetIntToStringColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.intToStringColumn, other.intToStringColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    lastComparison = Boolean.valueOf(isSetComplexColumn()).compareTo(other.isSetComplexColumn());
-    if (lastComparison != 0) {
-      return lastComparison;
-    }
-    if (isSetComplexColumn()) {
-      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.complexColumn, other.complexColumn);
-      if (lastComparison != 0) {
-        return lastComparison;
-      }
-    }
-    return 0;
-  }
-
-  public _Fields fieldForId(int fieldId) {
-    return _Fields.findByThriftId(fieldId);
-  }
-
-  public void read(org.apache.thrift.protocol.TProtocol iprot) throws org.apache.thrift.TException {
-    schemes.get(iprot.getScheme()).getScheme().read(iprot, this);
-  }
-
-  public void write(org.apache.thrift.protocol.TProtocol oprot) throws org.apache.thrift.TException {
-    schemes.get(oprot.getScheme()).getScheme().write(oprot, this);
-  }
-
-  @Override
-  public String toString() {
-    StringBuilder sb = new StringBuilder("ParquetThriftCompat(");
-    boolean first = true;
-
-    sb.append("boolColumn:");
-    sb.append(this.boolColumn);
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("byteColumn:");
-    sb.append(this.byteColumn);
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("shortColumn:");
-    sb.append(this.shortColumn);
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("intColumn:");
-    sb.append(this.intColumn);
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("longColumn:");
-    sb.append(this.longColumn);
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("doubleColumn:");
-    sb.append(this.doubleColumn);
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("binaryColumn:");
-    if (this.binaryColumn == null) {
-      sb.append("null");
-    } else {
-      org.apache.thrift.TBaseHelper.toString(this.binaryColumn, sb);
-    }
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("stringColumn:");
-    if (this.stringColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.stringColumn);
-    }
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("enumColumn:");
-    if (this.enumColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.enumColumn);
-    }
-    first = false;
-    if (isSetMaybeBoolColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeBoolColumn:");
-      sb.append(this.maybeBoolColumn);
-      first = false;
-    }
-    if (isSetMaybeByteColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeByteColumn:");
-      sb.append(this.maybeByteColumn);
-      first = false;
-    }
-    if (isSetMaybeShortColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeShortColumn:");
-      sb.append(this.maybeShortColumn);
-      first = false;
-    }
-    if (isSetMaybeIntColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeIntColumn:");
-      sb.append(this.maybeIntColumn);
-      first = false;
-    }
-    if (isSetMaybeLongColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeLongColumn:");
-      sb.append(this.maybeLongColumn);
-      first = false;
-    }
-    if (isSetMaybeDoubleColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeDoubleColumn:");
-      sb.append(this.maybeDoubleColumn);
-      first = false;
-    }
-    if (isSetMaybeBinaryColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeBinaryColumn:");
-      if (this.maybeBinaryColumn == null) {
-        sb.append("null");
-      } else {
-        org.apache.thrift.TBaseHelper.toString(this.maybeBinaryColumn, sb);
-      }
-      first = false;
-    }
-    if (isSetMaybeStringColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeStringColumn:");
-      if (this.maybeStringColumn == null) {
-        sb.append("null");
-      } else {
-        sb.append(this.maybeStringColumn);
-      }
-      first = false;
-    }
-    if (isSetMaybeEnumColumn()) {
-      if (!first) sb.append(", ");
-      sb.append("maybeEnumColumn:");
-      if (this.maybeEnumColumn == null) {
-        sb.append("null");
-      } else {
-        sb.append(this.maybeEnumColumn);
-      }
-      first = false;
-    }
-    if (!first) sb.append(", ");
-    sb.append("stringsColumn:");
-    if (this.stringsColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.stringsColumn);
-    }
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("intSetColumn:");
-    if (this.intSetColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.intSetColumn);
-    }
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("intToStringColumn:");
-    if (this.intToStringColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.intToStringColumn);
-    }
-    first = false;
-    if (!first) sb.append(", ");
-    sb.append("complexColumn:");
-    if (this.complexColumn == null) {
-      sb.append("null");
-    } else {
-      sb.append(this.complexColumn);
-    }
-    first = false;
-    sb.append(")");
-    return sb.toString();
-  }
-
-  public void validate() throws org.apache.thrift.TException {
-    // check for required fields
-    // alas, we cannot check 'boolColumn' because it's a primitive and you chose the non-beans generator.
-    // alas, we cannot check 'byteColumn' because it's a primitive and you chose the non-beans generator.
-    // alas, we cannot check 'shortColumn' because it's a primitive and you chose the non-beans generator.
-    // alas, we cannot check 'intColumn' because it's a primitive and you chose the non-beans generator.
-    // alas, we cannot check 'longColumn' because it's a primitive and you chose the non-beans generator.
-    // alas, we cannot check 'doubleColumn' because it's a primitive and you chose the non-beans generator.
-    if (binaryColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'binaryColumn' was not present! Struct: " + toString());
-    }
-    if (stringColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'stringColumn' was not present! Struct: " + toString());
-    }
-    if (enumColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'enumColumn' was not present! Struct: " + toString());
-    }
-    if (stringsColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'stringsColumn' was not present! Struct: " + toString());
-    }
-    if (intSetColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'intSetColumn' was not present! Struct: " + toString());
-    }
-    if (intToStringColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'intToStringColumn' was not present! Struct: " + toString());
-    }
-    if (complexColumn == null) {
-      throw new org.apache.thrift.protocol.TProtocolException("Required field 'complexColumn' was not present! Struct: " + toString());
-    }
-    // check for sub-struct validity
-  }
-
-  private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException {
-    try {
-      write(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(out)));
-    } catch (org.apache.thrift.TException te) {
-      throw new java.io.IOException(te);
-    }
-  }
-
-  private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, ClassNotFoundException {
-    try {
-      // it doesn't seem like you should have to do this, but java serialization is wacky, and doesn't call the default constructor.
-      __isset_bitfield = 0;
-      read(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(in)));
-    } catch (org.apache.thrift.TException te) {
-      throw new java.io.IOException(te);
-    }
-  }
-
-  private static class ParquetThriftCompatStandardSchemeFactory implements SchemeFactory {
-    public ParquetThriftCompatStandardScheme getScheme() {
-      return new ParquetThriftCompatStandardScheme();
-    }
-  }
-
-  private static class ParquetThriftCompatStandardScheme extends StandardScheme<ParquetThriftCompat> {
-
-    public void read(org.apache.thrift.protocol.TProtocol iprot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
-      org.apache.thrift.protocol.TField schemeField;
-      iprot.readStructBegin();
-      while (true)
-      {
-        schemeField = iprot.readFieldBegin();
-        if (schemeField.type == org.apache.thrift.protocol.TType.STOP) { 
-          break;
-        }
-        switch (schemeField.id) {
-          case 1: // BOOL_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.BOOL) {
-              struct.boolColumn = iprot.readBool();
-              struct.setBoolColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 2: // BYTE_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.BYTE) {
-              struct.byteColumn = iprot.readByte();
-              struct.setByteColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 3: // SHORT_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I16) {
-              struct.shortColumn = iprot.readI16();
-              struct.setShortColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 4: // INT_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
-              struct.intColumn = iprot.readI32();
-              struct.setIntColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 5: // LONG_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I64) {
-              struct.longColumn = iprot.readI64();
-              struct.setLongColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 6: // DOUBLE_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.DOUBLE) {
-              struct.doubleColumn = iprot.readDouble();
-              struct.setDoubleColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 7: // BINARY_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
-              struct.binaryColumn = iprot.readBinary();
-              struct.setBinaryColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 8: // STRING_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
-              struct.stringColumn = iprot.readString();
-              struct.setStringColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 9: // ENUM_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
-              struct.enumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
-              struct.setEnumColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 10: // MAYBE_BOOL_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.BOOL) {
-              struct.maybeBoolColumn = iprot.readBool();
-              struct.setMaybeBoolColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 11: // MAYBE_BYTE_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.BYTE) {
-              struct.maybeByteColumn = iprot.readByte();
-              struct.setMaybeByteColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 12: // MAYBE_SHORT_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I16) {
-              struct.maybeShortColumn = iprot.readI16();
-              struct.setMaybeShortColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 13: // MAYBE_INT_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
-              struct.maybeIntColumn = iprot.readI32();
-              struct.setMaybeIntColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 14: // MAYBE_LONG_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I64) {
-              struct.maybeLongColumn = iprot.readI64();
-              struct.setMaybeLongColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 15: // MAYBE_DOUBLE_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.DOUBLE) {
-              struct.maybeDoubleColumn = iprot.readDouble();
-              struct.setMaybeDoubleColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 16: // MAYBE_BINARY_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
-              struct.maybeBinaryColumn = iprot.readBinary();
-              struct.setMaybeBinaryColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 17: // MAYBE_STRING_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
-              struct.maybeStringColumn = iprot.readString();
-              struct.setMaybeStringColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 18: // MAYBE_ENUM_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
-              struct.maybeEnumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
-              struct.setMaybeEnumColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 19: // STRINGS_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
-              {
-                org.apache.thrift.protocol.TList _list8 = iprot.readListBegin();
-                struct.stringsColumn = new ArrayList<String>(_list8.size);
-                String _elem9;
-                for (int _i10 = 0; _i10 < _list8.size; ++_i10)
-                {
-                  _elem9 = iprot.readString();
-                  struct.stringsColumn.add(_elem9);
-                }
-                iprot.readListEnd();
-              }
-              struct.setStringsColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 20: // INT_SET_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.SET) {
-              {
-                org.apache.thrift.protocol.TSet _set11 = iprot.readSetBegin();
-                struct.intSetColumn = new HashSet<Integer>(2*_set11.size);
-                int _elem12;
-                for (int _i13 = 0; _i13 < _set11.size; ++_i13)
-                {
-                  _elem12 = iprot.readI32();
-                  struct.intSetColumn.add(_elem12);
-                }
-                iprot.readSetEnd();
-              }
-              struct.setIntSetColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 21: // INT_TO_STRING_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.MAP) {
-              {
-                org.apache.thrift.protocol.TMap _map14 = iprot.readMapBegin();
-                struct.intToStringColumn = new HashMap<Integer,String>(2*_map14.size);
-                int _key15;
-                String _val16;
-                for (int _i17 = 0; _i17 < _map14.size; ++_i17)
-                {
-                  _key15 = iprot.readI32();
-                  _val16 = iprot.readString();
-                  struct.intToStringColumn.put(_key15, _val16);
-                }
-                iprot.readMapEnd();
-              }
-              struct.setIntToStringColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          case 22: // COMPLEX_COLUMN
-            if (schemeField.type == org.apache.thrift.protocol.TType.MAP) {
-              {
-                org.apache.thrift.protocol.TMap _map18 = iprot.readMapBegin();
-                struct.complexColumn = new HashMap<Integer,List<Nested>>(2*_map18.size);
-                int _key19;
-                List<Nested> _val20;
-                for (int _i21 = 0; _i21 < _map18.size; ++_i21)
-                {
-                  _key19 = iprot.readI32();
-                  {
-                    org.apache.thrift.protocol.TList _list22 = iprot.readListBegin();
-                    _val20 = new ArrayList<Nested>(_list22.size);
-                    Nested _elem23;
-                    for (int _i24 = 0; _i24 < _list22.size; ++_i24)
-                    {
-                      _elem23 = new Nested();
-                      _elem23.read(iprot);
-                      _val20.add(_elem23);
-                    }
-                    iprot.readListEnd();
-                  }
-                  struct.complexColumn.put(_key19, _val20);
-                }
-                iprot.readMapEnd();
-              }
-              struct.setComplexColumnIsSet(true);
-            } else { 
-              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-            }
-            break;
-          default:
-            org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
-        }
-        iprot.readFieldEnd();
-      }
-      iprot.readStructEnd();
-
-      // check for required fields of primitive type, which can't be checked in the validate method
-      if (!struct.isSetBoolColumn()) {
-        throw new org.apache.thrift.protocol.TProtocolException("Required field 'boolColumn' was not found in serialized data! Struct: " + toString());
-      }
-      if (!struct.isSetByteColumn()) {
-        throw new org.apache.thrift.protocol.TProtocolException("Required field 'byteColumn' was not found in serialized data! Struct: " + toString());
-      }
-      if (!struct.isSetShortColumn()) {
-        throw new org.apache.thrift.protocol.TProtocolException("Required field 'shortColumn' was not found in serialized data! Struct: " + toString());
-      }
-      if (!struct.isSetIntColumn()) {
-        throw new org.apache.thrift.protocol.TProtocolException("Required field 'intColumn' was not found in serialized data! Struct: " + toString());
-      }
-      if (!struct.isSetLongColumn()) {
-        throw new org.apache.thrift.protocol.TProtocolException("Required field 'longColumn' was not found in serialized data! Struct: " + toString());
-      }
-      if (!struct.isSetDoubleColumn()) {
-        throw new org.apache.thrift.protocol.TProtocolException("Required field 'doubleColumn' was not found in serialized data! Struct: " + toString());
-      }
-      struct.validate();
-    }
-
-    public void write(org.apache.thrift.protocol.TProtocol oprot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
-      struct.validate();
-
-      oprot.writeStructBegin(STRUCT_DESC);
-      oprot.writeFieldBegin(BOOL_COLUMN_FIELD_DESC);
-      oprot.writeBool(struct.boolColumn);
-      oprot.writeFieldEnd();
-      oprot.writeFieldBegin(BYTE_COLUMN_FIELD_DESC);
-      oprot.writeByte(struct.byteColumn);
-      oprot.writeFieldEnd();
-      oprot.writeFieldBegin(SHORT_COLUMN_FIELD_DESC);
-      oprot.writeI16(struct.shortColumn);
-      oprot.writeFieldEnd();
-      oprot.writeFieldBegin(INT_COLUMN_FIELD_DESC);
-      oprot.writeI32(struct.intColumn);
-      oprot.writeFieldEnd();
-      oprot.writeFieldBegin(LONG_COLUMN_FIELD_DESC);
-      oprot.writeI64(struct.longColumn);
-      oprot.writeFieldEnd();
-      oprot.writeFieldBegin(DOUBLE_COLUMN_FIELD_DESC);
-      oprot.writeDouble(struct.doubleColumn);
-      oprot.writeFieldEnd();
-      if (struct.binaryColumn != null) {
-        oprot.writeFieldBegin(BINARY_COLUMN_FIELD_DESC);
-        oprot.writeBinary(struct.binaryColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.stringColumn != null) {
-        oprot.writeFieldBegin(STRING_COLUMN_FIELD_DESC);
-        oprot.writeString(struct.stringColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.enumColumn != null) {
-        oprot.writeFieldBegin(ENUM_COLUMN_FIELD_DESC);
-        oprot.writeI32(struct.enumColumn.getValue());
-        oprot.writeFieldEnd();
-      }
-      if (struct.isSetMaybeBoolColumn()) {
-        oprot.writeFieldBegin(MAYBE_BOOL_COLUMN_FIELD_DESC);
-        oprot.writeBool(struct.maybeBoolColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.isSetMaybeByteColumn()) {
-        oprot.writeFieldBegin(MAYBE_BYTE_COLUMN_FIELD_DESC);
-        oprot.writeByte(struct.maybeByteColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.isSetMaybeShortColumn()) {
-        oprot.writeFieldBegin(MAYBE_SHORT_COLUMN_FIELD_DESC);
-        oprot.writeI16(struct.maybeShortColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.isSetMaybeIntColumn()) {
-        oprot.writeFieldBegin(MAYBE_INT_COLUMN_FIELD_DESC);
-        oprot.writeI32(struct.maybeIntColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.isSetMaybeLongColumn()) {
-        oprot.writeFieldBegin(MAYBE_LONG_COLUMN_FIELD_DESC);
-        oprot.writeI64(struct.maybeLongColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.isSetMaybeDoubleColumn()) {
-        oprot.writeFieldBegin(MAYBE_DOUBLE_COLUMN_FIELD_DESC);
-        oprot.writeDouble(struct.maybeDoubleColumn);
-        oprot.writeFieldEnd();
-      }
-      if (struct.maybeBinaryColumn != null) {
-        if (struct.isSetMaybeBinaryColumn()) {
-          oprot.writeFieldBegin(MAYBE_BINARY_COLUMN_FIELD_DESC);
-          oprot.writeBinary(struct.maybeBinaryColumn);
-          oprot.writeFieldEnd();
-        }
-      }
-      if (struct.maybeStringColumn != null) {
-        if (struct.isSetMaybeStringColumn()) {
-          oprot.writeFieldBegin(MAYBE_STRING_COLUMN_FIELD_DESC);
-          oprot.writeString(struct.maybeStringColumn);
-          oprot.writeFieldEnd();
-        }
-      }
-      if (struct.maybeEnumColumn != null) {
-        if (struct.isSetMaybeEnumColumn()) {
-          oprot.writeFieldBegin(MAYBE_ENUM_COLUMN_FIELD_DESC);
-          oprot.writeI32(struct.maybeEnumColumn.getValue());
-          oprot.writeFieldEnd();
-        }
-      }
-      if (struct.stringsColumn != null) {
-        oprot.writeFieldBegin(STRINGS_COLUMN_FIELD_DESC);
-        {
-          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, struct.stringsColumn.size()));
-          for (String _iter25 : struct.stringsColumn)
-          {
-            oprot.writeString(_iter25);
-          }
-          oprot.writeListEnd();
-        }
-        oprot.writeFieldEnd();
-      }
-      if (struct.intSetColumn != null) {
-        oprot.writeFieldBegin(INT_SET_COLUMN_FIELD_DESC);
-        {
-          oprot.writeSetBegin(new org.apache.thrift.protocol.TSet(org.apache.thrift.protocol.TType.I32, struct.intSetColumn.size()));
-          for (int _iter26 : struct.intSetColumn)
-          {
-            oprot.writeI32(_iter26);
-          }
-          oprot.writeSetEnd();
-        }
-        oprot.writeFieldEnd();
-      }
-      if (struct.intToStringColumn != null) {
-        oprot.writeFieldBegin(INT_TO_STRING_COLUMN_FIELD_DESC);
-        {
-          oprot.writeMapBegin(new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.STRING, struct.intToStringColumn.size()));
-          for (Map.Entry<Integer, String> _iter27 : struct.intToStringColumn.entrySet())
-          {
-            oprot.writeI32(_iter27.getKey());
-            oprot.writeString(_iter27.getValue());
-          }
-          oprot.writeMapEnd();
-        }
-        oprot.writeFieldEnd();
-      }
-      if (struct.complexColumn != null) {
-        oprot.writeFieldBegin(COMPLEX_COLUMN_FIELD_DESC);
-        {
-          oprot.writeMapBegin(new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.LIST, struct.complexColumn.size()));
-          for (Map.Entry<Integer, List<Nested>> _iter28 : struct.complexColumn.entrySet())
-          {
-            oprot.writeI32(_iter28.getKey());
-            {
-              oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, _iter28.getValue().size()));
-              for (Nested _iter29 : _iter28.getValue())
-              {
-                _iter29.write(oprot);
-              }
-              oprot.writeListEnd();
-            }
-          }
-          oprot.writeMapEnd();
-        }
-        oprot.writeFieldEnd();
-      }
-      oprot.writeFieldStop();
-      oprot.writeStructEnd();
-    }
-
-  }
-
-  private static class ParquetThriftCompatTupleSchemeFactory implements SchemeFactory {
-    public ParquetThriftCompatTupleScheme getScheme() {
-      return new ParquetThriftCompatTupleScheme();
-    }
-  }
-
-  private static class ParquetThriftCompatTupleScheme extends TupleScheme<ParquetThriftCompat> {
-
-    @Override
-    public void write(org.apache.thrift.protocol.TProtocol prot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
-      TTupleProtocol oprot = (TTupleProtocol) prot;
-      oprot.writeBool(struct.boolColumn);
-      oprot.writeByte(struct.byteColumn);
-      oprot.writeI16(struct.shortColumn);
-      oprot.writeI32(struct.intColumn);
-      oprot.writeI64(struct.longColumn);
-      oprot.writeDouble(struct.doubleColumn);
-      oprot.writeBinary(struct.binaryColumn);
-      oprot.writeString(struct.stringColumn);
-      oprot.writeI32(struct.enumColumn.getValue());
-      {
-        oprot.writeI32(struct.stringsColumn.size());
-        for (String _iter30 : struct.stringsColumn)
-        {
-          oprot.writeString(_iter30);
-        }
-      }
-      {
-        oprot.writeI32(struct.intSetColumn.size());
-        for (int _iter31 : struct.intSetColumn)
-        {
-          oprot.writeI32(_iter31);
-        }
-      }
-      {
-        oprot.writeI32(struct.intToStringColumn.size());
-        for (Map.Entry<Integer, String> _iter32 : struct.intToStringColumn.entrySet())
-        {
-          oprot.writeI32(_iter32.getKey());
-          oprot.writeString(_iter32.getValue());
-        }
-      }
-      {
-        oprot.writeI32(struct.complexColumn.size());
-        for (Map.Entry<Integer, List<Nested>> _iter33 : struct.complexColumn.entrySet())
-        {
-          oprot.writeI32(_iter33.getKey());
-          {
-            oprot.writeI32(_iter33.getValue().size());
-            for (Nested _iter34 : _iter33.getValue())
-            {
-              _iter34.write(oprot);
-            }
-          }
-        }
-      }
-      BitSet optionals = new BitSet();
-      if (struct.isSetMaybeBoolColumn()) {
-        optionals.set(0);
-      }
-      if (struct.isSetMaybeByteColumn()) {
-        optionals.set(1);
-      }
-      if (struct.isSetMaybeShortColumn()) {
-        optionals.set(2);
-      }
-      if (struct.isSetMaybeIntColumn()) {
-        optionals.set(3);
-      }
-      if (struct.isSetMaybeLongColumn()) {
-        optionals.set(4);
-      }
-      if (struct.isSetMaybeDoubleColumn()) {
-        optionals.set(5);
-      }
-      if (struct.isSetMaybeBinaryColumn()) {
-        optionals.set(6);
-      }
-      if (struct.isSetMaybeStringColumn()) {
-        optionals.set(7);
-      }
-      if (struct.isSetMaybeEnumColumn()) {
-        optionals.set(8);
-      }
-      oprot.writeBitSet(optionals, 9);
-      if (struct.isSetMaybeBoolColumn()) {
-        oprot.writeBool(struct.maybeBoolColumn);
-      }
-      if (struct.isSetMaybeByteColumn()) {
-        oprot.writeByte(struct.maybeByteColumn);
-      }
-      if (struct.isSetMaybeShortColumn()) {
-        oprot.writeI16(struct.maybeShortColumn);
-      }
-      if (struct.isSetMaybeIntColumn()) {
-        oprot.writeI32(struct.maybeIntColumn);
-      }
-      if (struct.isSetMaybeLongColumn()) {
-        oprot.writeI64(struct.maybeLongColumn);
-      }
-      if (struct.isSetMaybeDoubleColumn()) {
-        oprot.writeDouble(struct.maybeDoubleColumn);
-      }
-      if (struct.isSetMaybeBinaryColumn()) {
-        oprot.writeBinary(struct.maybeBinaryColumn);
-      }
-      if (struct.isSetMaybeStringColumn()) {
-        oprot.writeString(struct.maybeStringColumn);
-      }
-      if (struct.isSetMaybeEnumColumn()) {
-        oprot.writeI32(struct.maybeEnumColumn.getValue());
-      }
-    }
-
-    @Override
-    public void read(org.apache.thrift.protocol.TProtocol prot, ParquetThriftCompat struct) throws org.apache.thrift.TException {
-      TTupleProtocol iprot = (TTupleProtocol) prot;
-      struct.boolColumn = iprot.readBool();
-      struct.setBoolColumnIsSet(true);
-      struct.byteColumn = iprot.readByte();
-      struct.setByteColumnIsSet(true);
-      struct.shortColumn = iprot.readI16();
-      struct.setShortColumnIsSet(true);
-      struct.intColumn = iprot.readI32();
-      struct.setIntColumnIsSet(true);
-      struct.longColumn = iprot.readI64();
-      struct.setLongColumnIsSet(true);
-      struct.doubleColumn = iprot.readDouble();
-      struct.setDoubleColumnIsSet(true);
-      struct.binaryColumn = iprot.readBinary();
-      struct.setBinaryColumnIsSet(true);
-      struct.stringColumn = iprot.readString();
-      struct.setStringColumnIsSet(true);
-      struct.enumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
-      struct.setEnumColumnIsSet(true);
-      {
-        org.apache.thrift.protocol.TList _list35 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, iprot.readI32());
-        struct.stringsColumn = new ArrayList<String>(_list35.size);
-        String _elem36;
-        for (int _i37 = 0; _i37 < _list35.size; ++_i37)
-        {
-          _elem36 = iprot.readString();
-          struct.stringsColumn.add(_elem36);
-        }
-      }
-      struct.setStringsColumnIsSet(true);
-      {
-        org.apache.thrift.protocol.TSet _set38 = new org.apache.thrift.protocol.TSet(org.apache.thrift.protocol.TType.I32, iprot.readI32());
-        struct.intSetColumn = new HashSet<Integer>(2*_set38.size);
-        int _elem39;
-        for (int _i40 = 0; _i40 < _set38.size; ++_i40)
-        {
-          _elem39 = iprot.readI32();
-          struct.intSetColumn.add(_elem39);
-        }
-      }
-      struct.setIntSetColumnIsSet(true);
-      {
-        org.apache.thrift.protocol.TMap _map41 = new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.STRING, iprot.readI32());
-        struct.intToStringColumn = new HashMap<Integer,String>(2*_map41.size);
-        int _key42;
-        String _val43;
-        for (int _i44 = 0; _i44 < _map41.size; ++_i44)
-        {
-          _key42 = iprot.readI32();
-          _val43 = iprot.readString();
-          struct.intToStringColumn.put(_key42, _val43);
-        }
-      }
-      struct.setIntToStringColumnIsSet(true);
-      {
-        org.apache.thrift.protocol.TMap _map45 = new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.I32, org.apache.thrift.protocol.TType.LIST, iprot.readI32());
-        struct.complexColumn = new HashMap<Integer,List<Nested>>(2*_map45.size);
-        int _key46;
-        List<Nested> _val47;
-        for (int _i48 = 0; _i48 < _map45.size; ++_i48)
-        {
-          _key46 = iprot.readI32();
-          {
-            org.apache.thrift.protocol.TList _list49 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, iprot.readI32());
-            _val47 = new ArrayList<Nested>(_list49.size);
-            Nested _elem50;
-            for (int _i51 = 0; _i51 < _list49.size; ++_i51)
-            {
-              _elem50 = new Nested();
-              _elem50.read(iprot);
-              _val47.add(_elem50);
-            }
-          }
-          struct.complexColumn.put(_key46, _val47);
-        }
-      }
-      struct.setComplexColumnIsSet(true);
-      BitSet incoming = iprot.readBitSet(9);
-      if (incoming.get(0)) {
-        struct.maybeBoolColumn = iprot.readBool();
-        struct.setMaybeBoolColumnIsSet(true);
-      }
-      if (incoming.get(1)) {
-        struct.maybeByteColumn = iprot.readByte();
-        struct.setMaybeByteColumnIsSet(true);
-      }
-      if (incoming.get(2)) {
-        struct.maybeShortColumn = iprot.readI16();
-        struct.setMaybeShortColumnIsSet(true);
-      }
-      if (incoming.get(3)) {
-        struct.maybeIntColumn = iprot.readI32();
-        struct.setMaybeIntColumnIsSet(true);
-      }
-      if (incoming.get(4)) {
-        struct.maybeLongColumn = iprot.readI64();
-        struct.setMaybeLongColumnIsSet(true);
-      }
-      if (incoming.get(5)) {
-        struct.maybeDoubleColumn = iprot.readDouble();
-        struct.setMaybeDoubleColumnIsSet(true);
-      }
-      if (incoming.get(6)) {
-        struct.maybeBinaryColumn = iprot.readBinary();
-        struct.setMaybeBinaryColumnIsSet(true);
-      }
-      if (incoming.get(7)) {
-        struct.maybeStringColumn = iprot.readString();
-        struct.setMaybeStringColumnIsSet(true);
-      }
-      if (incoming.get(8)) {
-        struct.maybeEnumColumn = org.apache.spark.sql.parquet.test.thrift.Suit.findByValue(iprot.readI32());
-        struct.setMaybeEnumColumnIsSet(true);
-      }
-    }
-  }
-
-}
-
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java b/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java
deleted file mode 100644
index 5315c6aae9372..0000000000000
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/thrift/Suit.java
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * Autogenerated by Thrift Compiler (0.9.2)
- *
- * DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
- *  @generated
- */
-package org.apache.spark.sql.parquet.test.thrift;
-
-
-import java.util.Map;
-import java.util.HashMap;
-import org.apache.thrift.TEnum;
-
-public enum Suit implements org.apache.thrift.TEnum {
-  SPADES(0),
-  HEARTS(1),
-  DIAMONDS(2),
-  CLUBS(3);
-
-  private final int value;
-
-  private Suit(int value) {
-    this.value = value;
-  }
-
-  /**
-   * Get the integer value of this enum value, as defined in the Thrift IDL.
-   */
-  public int getValue() {
-    return value;
-  }
-
-  /**
-   * Find a the enum type by its integer value, as defined in the Thrift IDL.
-   * @return null if the value is not found.
-   */
-  public static Suit findByValue(int value) { 
-    switch (value) {
-      case 0:
-        return SPADES;
-      case 1:
-        return HEARTS;
-      case 2:
-        return DIAMONDS;
-      case 3:
-        return CLUBS;
-      default:
-        return null;
-    }
-  }
-}
diff --git a/sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet b/sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet
new file mode 100755
index 0000000000000000000000000000000000000000..837e4876eea6385092610bb59016c3a7b97e1c72
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diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
index d22066cabc567..1c532d78790d2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
@@ -17,16 +17,6 @@
 
 package org.apache.spark.sql.parquet
 
-import java.nio.ByteBuffer
-import java.util.{List => JList, Map => JMap}
-
-import scala.collection.JavaConversions._
-
-import org.apache.hadoop.fs.Path
-import org.apache.parquet.hadoop.metadata.CompressionCodecName
-import org.apache.parquet.thrift.ThriftParquetWriter
-
-import org.apache.spark.sql.parquet.test.thrift.{Nested, ParquetThriftCompat, Suit}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.{Row, SQLContext}
 
@@ -35,28 +25,20 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest {
 
   override val sqlContext: SQLContext = TestSQLContext
 
-  override protected def beforeAll(): Unit = {
-    super.beforeAll()
-
-    val writer =
-      new ThriftParquetWriter[ParquetThriftCompat](
-        new Path(parquetStore.getCanonicalPath),
-        classOf[ParquetThriftCompat],
-        CompressionCodecName.SNAPPY)
-
-    (0 until 10).foreach(i => writer.write(makeParquetThriftCompat(i)))
-    writer.close()
-  }
+  private val parquetFilePath =
+    Thread.currentThread().getContextClassLoader.getResource("parquet-thrift-compat.snappy.parquet")
 
   test("Read Parquet file generated by parquet-thrift") {
     logInfo(
       s"""Schema of the Parquet file written by parquet-thrift:
-         |${readParquetSchema(parquetStore.getCanonicalPath)}
+         |${readParquetSchema(parquetFilePath.toString)}
        """.stripMargin)
 
-    checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), (0 until 10).map { i =>
+    checkAnswer(sqlContext.read.parquet(parquetFilePath.toString), (0 until 10).map { i =>
       def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
 
+      val suits = Array("SPADES", "HEARTS", "DIAMONDS", "CLUBS")
+
       Row(
         i % 2 == 0,
         i.toByte,
@@ -70,7 +52,7 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest {
         s"val_$i",
         s"val_$i",
         // Thrift ENUM values are converted to Parquet binaries containing UTF-8 strings
-        Suit.values()(i % 4).name(),
+        suits(i % 4),
 
         nullable(i % 2 == 0: java.lang.Boolean),
         nullable(i.toByte: java.lang.Byte),
@@ -80,7 +62,7 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest {
         nullable(i.toDouble + 0.2d: java.lang.Double),
         nullable(s"val_$i"),
         nullable(s"val_$i"),
-        nullable(Suit.values()(i % 4).name()),
+        nullable(suits(i % 4)),
 
         Seq.tabulate(3)(n => s"arr_${i + n}"),
         // Thrift `SET`s are converted to Parquet `LIST`s
@@ -93,48 +75,4 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest {
         }.toMap)
     })
   }
-
-  def makeParquetThriftCompat(i: Int): ParquetThriftCompat = {
-    def makeComplexColumn(i: Int): JMap[Integer, JList[Nested]] = {
-      mapAsJavaMap(Seq.tabulate(3) { n =>
-        (i + n: Integer) -> seqAsJavaList(Seq.tabulate(3) { m =>
-          new Nested(
-            seqAsJavaList(Seq.tabulate(3)(j => i + j + m)),
-            s"val_${i + m}")
-        })
-      }.toMap)
-    }
-
-    val value =
-      new ParquetThriftCompat(
-        i % 2 == 0,
-        i.toByte,
-        (i + 1).toShort,
-        i + 2,
-        i.toLong * 10,
-        i.toDouble + 0.2d,
-        ByteBuffer.wrap(s"val_$i".getBytes),
-        s"val_$i",
-        Suit.values()(i % 4),
-
-        seqAsJavaList(Seq.tabulate(3)(n => s"arr_${i + n}")),
-        setAsJavaSet(Set(i)),
-        mapAsJavaMap(Seq.tabulate(3)(n => (i + n: Integer) -> s"val_${i + n}").toMap),
-        makeComplexColumn(i))
-
-    if (i % 3 == 0) {
-      value
-    } else {
-      value
-        .setMaybeBoolColumn(i % 2 == 0)
-        .setMaybeByteColumn(i.toByte)
-        .setMaybeShortColumn((i + 1).toShort)
-        .setMaybeIntColumn(i + 2)
-        .setMaybeLongColumn(i.toLong * 10)
-        .setMaybeDoubleColumn(i.toDouble + 0.2d)
-        .setMaybeBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes))
-        .setMaybeStringColumn(s"val_$i")
-        .setMaybeEnumColumn(Suit.values()(i % 4))
-    }
-  }
 }

From 0e78e40c0b221e09d9ea22177a99f3bea4b2b4fb Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 9 Jul 2015 18:23:06 -0700
Subject: [PATCH 0328/1454] [SPARK-8852] [FLUME] Trim dependencies in flume
 assembly.

Also, add support for the *-provided profiles. This avoids repackaging
things that are already in the Spark assembly, or, in the case of the
*-provided profiles, are provided by the distribution.

The flume-ng-auth dependency was also excluded since it's not really
used by Spark.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7247 from vanzin/SPARK-8852 and squashes the following commits:

298a7d5 [Marcelo Vanzin] Feedback.
c962082 [Marcelo Vanzin] [SPARK-8852] [flume] Trim dependencies in flume assembly.
---
 external/flume-assembly/pom.xml | 169 ++++++++++++++++++--------------
 pom.xml                         |   4 +
 2 files changed, 100 insertions(+), 73 deletions(-)

diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
index 8565cd83edfa2..13189595d1d6c 100644
--- a/external/flume-assembly/pom.xml
+++ b/external/flume-assembly/pom.xml
@@ -32,6 +32,7 @@
   <url>http://spark.apache.org/</url>
 
   <properties>
+    <hadoop.deps.scope>provided</hadoop.deps.scope>
     <sbt.project.name>streaming-flume-assembly</sbt.project.name>
   </properties>
 
@@ -40,6 +41,16 @@
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-streaming-flume_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
+      <exclusions>
+        <exclusion>
+          <groupId>org.mortbay.jetty</groupId>
+          <artifactId>jetty</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.mortbay.jetty</groupId>
+          <artifactId>jetty-util</artifactId>
+        </exclusion>
+      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
@@ -47,89 +58,101 @@
       <version>${project.version}</version>
       <scope>provided</scope>
     </dependency>
+    <!--
+      Demote already included in the Spark assembly. These are transitive dependencies of flume
+      or spark-streaming-flume, and this need to be explicitly included even through the parent
+      pom may declare them with ${hadoop.deps.scope}.
+    -->
+    <dependency>
+      <groupId>commons-codec</groupId>
+      <artifactId>commons-codec</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>commons-net</groupId>
+      <artifactId>commons-net</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.avro</groupId>
       <artifactId>avro</artifactId>
-      <version>${avro.version}</version>
+      <scope>provided</scope>
     </dependency>
     <dependency>
       <groupId>org.apache.avro</groupId>
       <artifactId>avro-ipc</artifactId>
-      <version>${avro.version}</version>
-      <exclusions>
-        <exclusion>
-          <groupId>io.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.mortbay.jetty</groupId>
-          <artifactId>jetty</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.mortbay.jetty</groupId>
-          <artifactId>jetty-util</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.mortbay.jetty</groupId>
-          <artifactId>servlet-api</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.apache.velocity</groupId>
-          <artifactId>velocity</artifactId>
-        </exclusion>
-      </exclusions>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+      <scope>provided</scope>
     </dependency>
   </dependencies>
 
   <build>
-  <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
-  <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
-  <plugins>
-    <plugin>
-      <groupId>org.apache.maven.plugins</groupId>
-      <artifactId>maven-shade-plugin</artifactId>
-      <configuration>
-        <shadedArtifactAttached>false</shadedArtifactAttached>
-        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-flume-assembly-${project.version}.jar</outputFile>
-        <artifactSet>
-          <includes>
-            <include>*:*</include>
-          </includes>
-        </artifactSet>
-        <filters>
-          <filter>
-            <artifact>*:*</artifact>
-            <excludes>
-              <exclude>META-INF/*.SF</exclude>
-              <exclude>META-INF/*.DSA</exclude>
-              <exclude>META-INF/*.RSA</exclude>
-            </excludes>
-          </filter>
-        </filters>
-      </configuration>
-      <executions>
-        <execution>
-          <phase>package</phase>
-          <goals>
-            <goal>shade</goal>
-          </goals>
-          <configuration>
-            <transformers>
-              <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
-              <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
-                <resource>reference.conf</resource>
-              </transformer>
-              <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
-                <resource>log4j.properties</resource>
-              </transformer>
-              <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer"/>
-              <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/>
-            </transformers>
-          </configuration>
-        </execution>
-      </executions>
-    </plugin>
-  </plugins>
-</build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-shade-plugin</artifactId>
+        <configuration>
+          <shadedArtifactAttached>false</shadedArtifactAttached>
+          <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-flume-assembly-${project.version}.jar</outputFile>
+          <artifactSet>
+            <includes>
+              <include>*:*</include>
+            </includes>
+          </artifactSet>
+          <filters>
+            <filter>
+              <artifact>*:*</artifact>
+              <excludes>
+                <exclude>META-INF/*.SF</exclude>
+                <exclude>META-INF/*.DSA</exclude>
+                <exclude>META-INF/*.RSA</exclude>
+              </excludes>
+            </filter>
+          </filters>
+        </configuration>
+        <executions>
+          <execution>
+            <phase>package</phase>
+            <goals>
+              <goal>shade</goal>
+            </goals>
+            <configuration>
+              <transformers>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                  <resource>reference.conf</resource>
+                </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
+                  <resource>log4j.properties</resource>
+                </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/>
+              </transformers>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
+  </build>
+
+  <profiles>
+    <profile>
+      <id>flume-provided</id>
+      <properties>
+        <flume.deps.scope>provided</flume.deps.scope>
+      </properties>
+    </profile>
+  </profiles>
 </project>
 
diff --git a/pom.xml b/pom.xml
index 1eda108dc065b..172fdef4c73da 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1130,6 +1130,10 @@
             <groupId>io.netty</groupId>
             <artifactId>netty</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>org.apache.flume</groupId>
+            <artifactId>flume-ng-auth</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>org.apache.thrift</groupId>
             <artifactId>libthrift</artifactId>

From 2727304660663fcf1e41f7b666978c1443262e4e Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 9 Jul 2015 19:08:33 -0700
Subject: [PATCH 0329/1454] [SPARK-8913] [ML] Simplify LogisticRegression suite
 to use Vector Vector comparision

Cleanup tests from SPARK 8700.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7335 from holdenk/SPARK-8913-cleanup-tests-from-SPARK-8700-logistic-regression-r2-really-logistic-regression-this-time and squashes the following commits:

e5e2c5f [Holden Karau] Simplify LogisticRegression suite to use Vector <-> Vector comparisions instead of comparing element by element
---
 .../LogisticRegressionSuite.scala             | 135 +++++-------------
 1 file changed, 39 insertions(+), 96 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 27253c1db2fff..b7dd44753896a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -234,20 +234,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.7996864
      */
     val interceptR = 2.8366423
-    val weightsR = Array(-0.5895848, 0.8931147, -0.3925051, -0.7996864)
+    val weightsR = Vectors.dense(-0.5895848, 0.8931147, -0.3925051, -0.7996864)
 
     assert(model1.intercept ~== interceptR relTol 1E-3)
-    assert(model1.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model1.weights(1) ~== weightsR(1) relTol 1E-3)
-    assert(model1.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model1.weights(3) ~== weightsR(3) relTol 1E-3)
+    assert(model1.weights ~= weightsR relTol 1E-3)
 
     // Without regularization, with or without standardization will converge to the same solution.
     assert(model2.intercept ~== interceptR relTol 1E-3)
-    assert(model2.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model2.weights(1) ~== weightsR(1) relTol 1E-3)
-    assert(model2.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model2.weights(3) ~== weightsR(3) relTol 1E-3)
+    assert(model2.weights ~= weightsR relTol 1E-3)
   }
 
   test("binary logistic regression without intercept without regularization") {
@@ -277,20 +271,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.7407946
      */
     val interceptR = 0.0
-    val weightsR = Array(-0.3534996, 1.2964482, -0.3571741, -0.7407946)
+    val weightsR = Vectors.dense(-0.3534996, 1.2964482, -0.3571741, -0.7407946)
 
     assert(model1.intercept ~== interceptR relTol 1E-3)
-    assert(model1.weights(0) ~== weightsR(0) relTol 1E-2)
-    assert(model1.weights(1) ~== weightsR(1) relTol 1E-2)
-    assert(model1.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model1.weights(3) ~== weightsR(3) relTol 1E-3)
+    assert(model1.weights ~= weightsR relTol 1E-2)
 
     // Without regularization, with or without standardization should converge to the same solution.
     assert(model2.intercept ~== interceptR relTol 1E-3)
-    assert(model2.weights(0) ~== weightsR(0) relTol 1E-2)
-    assert(model2.weights(1) ~== weightsR(1) relTol 1E-2)
-    assert(model2.weights(2) ~== weightsR(2) relTol 1E-3)
-    assert(model2.weights(3) ~== weightsR(3) relTol 1E-3)
+    assert(model2.weights ~= weightsR relTol 1E-2)
   }
 
   test("binary logistic regression with intercept with L1 regularization") {
@@ -321,13 +309,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.02481551
      */
     val interceptR1 = -0.05627428
-    val weightsR1 = Array(0.0, 0.0, -0.04325749, -0.02481551)
+    val weightsR1 = Vectors.dense(0.0, 0.0, -0.04325749, -0.02481551)
 
     assert(model1.intercept ~== interceptR1 relTol 1E-2)
-    assert(model1.weights(0) ~== weightsR1(0) absTol 1E-3)
-    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-3)
-    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-2)
-    assert(model1.weights(3) ~== weightsR1(3) relTol 2E-2)
+    assert(model1.weights ~= weightsR1 absTol 2E-2)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -349,13 +334,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5       .
      */
     val interceptR2 = 0.3722152
-    val weightsR2 = Array(0.0, 0.0, -0.1665453, 0.0)
+    val weightsR2 = Vectors.dense(0.0, 0.0, -0.1665453, 0.0)
 
     assert(model2.intercept ~== interceptR2 relTol 1E-2)
-    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
-    assert(model2.weights(1) ~== weightsR2(1) absTol 1E-3)
-    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-2)
-    assert(model2.weights(3) ~== weightsR2(3) absTol 1E-3)
+    assert(model2.weights ~= weightsR2 absTol 1E-3)
   }
 
   test("binary logistic regression without intercept with L1 regularization") {
@@ -387,13 +369,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.03891782
      */
     val interceptR1 = 0.0
-    val weightsR1 = Array(0.0, 0.0, -0.05189203, -0.03891782)
+    val weightsR1 = Vectors.dense(0.0, 0.0, -0.05189203, -0.03891782)
 
     assert(model1.intercept ~== interceptR1 relTol 1E-3)
-    assert(model1.weights(0) ~== weightsR1(0) absTol 1E-3)
-    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-3)
-    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-2)
-    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-2)
+    assert(model1.weights ~= weightsR1 absTol 1E-3)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -415,13 +394,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5       .
      */
     val interceptR2 = 0.0
-    val weightsR2 = Array(0.0, 0.0, -0.08420782, 0.0)
+    val weightsR2 = Vectors.dense(0.0, 0.0, -0.08420782, 0.0)
 
-    assert(model2.intercept ~== interceptR2 relTol 1E-3)
-    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
-    assert(model2.weights(1) ~== weightsR2(1) absTol 1E-3)
-    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-2)
-    assert(model2.weights(3) ~== weightsR2(3) absTol 1E-3)
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 absTol 1E-3)
   }
 
   test("binary logistic regression with intercept with L2 regularization") {
@@ -452,13 +428,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.10062872
      */
     val interceptR1 = 0.15021751
-    val weightsR1 = Array(-0.07251837, 0.10724191, -0.04865309, -0.10062872)
+    val weightsR1 = Vectors.dense(-0.07251837, 0.10724191, -0.04865309, -0.10062872)
 
     assert(model1.intercept ~== interceptR1 relTol 1E-3)
-    assert(model1.weights(0) ~== weightsR1(0) relTol 1E-3)
-    assert(model1.weights(1) ~== weightsR1(1) relTol 1E-3)
-    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-3)
-    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -480,13 +453,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.06266838
      */
     val interceptR2 = 0.48657516
-    val weightsR2 = Array(-0.05155371, 0.02301057, -0.11482896, -0.06266838)
+    val weightsR2 = Vectors.dense(-0.05155371, 0.02301057, -0.11482896, -0.06266838)
 
     assert(model2.intercept ~== interceptR2 relTol 1E-3)
-    assert(model2.weights(0) ~== weightsR2(0) relTol 1E-3)
-    assert(model2.weights(1) ~== weightsR2(1) relTol 1E-3)
-    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-3)
-    assert(model2.weights(3) ~== weightsR2(3) relTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
   }
 
   test("binary logistic regression without intercept with L2 regularization") {
@@ -518,13 +488,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.09799775
      */
     val interceptR1 = 0.0
-    val weightsR1 = Array(-0.06099165, 0.12857058, -0.04708770, -0.09799775)
+    val weightsR1 = Vectors.dense(-0.06099165, 0.12857058, -0.04708770, -0.09799775)
 
-    assert(model1.intercept ~== interceptR1 relTol 1E-3)
-    assert(model1.weights(0) ~== weightsR1(0) relTol 1E-2)
-    assert(model1.weights(1) ~== weightsR1(1) relTol 1E-2)
-    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-3)
-    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-2)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -546,13 +513,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.053314311
      */
     val interceptR2 = 0.0
-    val weightsR2 = Array(-0.005679651, 0.048967094, -0.093714016, -0.053314311)
+    val weightsR2 = Vectors.dense(-0.005679651, 0.048967094, -0.093714016, -0.053314311)
 
-    assert(model2.intercept ~== interceptR2 relTol 1E-3)
-    assert(model2.weights(0) ~== weightsR2(0) relTol 1E-2)
-    assert(model2.weights(1) ~== weightsR2(1) relTol 1E-2)
-    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-3)
-    assert(model2.weights(3) ~== weightsR2(3) relTol 1E-3)
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-2)
   }
 
   test("binary logistic regression with intercept with ElasticNet regularization") {
@@ -583,13 +547,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.15458796
      */
     val interceptR1 = 0.57734851
-    val weightsR1 = Array(-0.05310287, 0.0, -0.08849250, -0.15458796)
+    val weightsR1 = Vectors.dense(-0.05310287, 0.0, -0.08849250, -0.15458796)
 
     assert(model1.intercept ~== interceptR1 relTol 6E-3)
-    assert(model1.weights(0) ~== weightsR1(0) relTol 5E-3)
-    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-3)
-    assert(model1.weights(2) ~== weightsR1(2) relTol 5E-3)
-    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-3)
+    assert(model1.weights ~== weightsR1 absTol 5E-3)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -611,13 +572,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.05350074
      */
     val interceptR2 = 0.51555993
-    val weightsR2 = Array(0.0, 0.0, -0.18807395, -0.05350074)
+    val weightsR2 = Vectors.dense(0.0, 0.0, -0.18807395, -0.05350074)
 
     assert(model2.intercept ~== interceptR2 relTol 6E-3)
-    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
-    assert(model2.weights(1) ~== weightsR2(1) absTol 1E-3)
-    assert(model2.weights(2) ~== weightsR2(2) relTol 5E-3)
-    assert(model2.weights(3) ~== weightsR2(3) relTol 1E-2)
+    assert(model2.weights ~= weightsR2 absTol 1E-3)
   }
 
   test("binary logistic regression without intercept with ElasticNet regularization") {
@@ -649,13 +607,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5     -0.142534158
      */
     val interceptR1 = 0.0
-    val weightsR1 = Array(-0.001005743, 0.072577857, -0.081203769, -0.142534158)
+    val weightsR1 = Vectors.dense(-0.001005743, 0.072577857, -0.081203769, -0.142534158)
 
     assert(model1.intercept ~== interceptR1 relTol 1E-3)
-    assert(model1.weights(0) ~== weightsR1(0) absTol 1E-2)
-    assert(model1.weights(1) ~== weightsR1(1) absTol 1E-2)
-    assert(model1.weights(2) ~== weightsR1(2) relTol 1E-3)
-    assert(model1.weights(3) ~== weightsR1(3) relTol 1E-2)
+    assert(model1.weights ~= weightsR1 absTol 1E-2)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -677,13 +632,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5       .
      */
     val interceptR2 = 0.0
-    val weightsR2 = Array(0.0, 0.03345223, -0.11304532, 0.0)
+    val weightsR2 = Vectors.dense(0.0, 0.03345223, -0.11304532, 0.0)
 
-    assert(model2.intercept ~== interceptR2 relTol 1E-3)
-    assert(model2.weights(0) ~== weightsR2(0) absTol 1E-3)
-    assert(model2.weights(1) ~== weightsR2(1) relTol 1E-2)
-    assert(model2.weights(2) ~== weightsR2(2) relTol 1E-2)
-    assert(model2.weights(3) ~== weightsR2(3) absTol 1E-3)
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 absTol 1E-3)
   }
 
   test("binary logistic regression with intercept with strong L1 regularization") {
@@ -717,19 +669,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        }}}
      */
     val interceptTheory = math.log(histogram(1).toDouble / histogram(0).toDouble)
-    val weightsTheory = Array(0.0, 0.0, 0.0, 0.0)
+    val weightsTheory = Vectors.dense(0.0, 0.0, 0.0, 0.0)
 
     assert(model1.intercept ~== interceptTheory relTol 1E-5)
-    assert(model1.weights(0) ~== weightsTheory(0) absTol 1E-6)
-    assert(model1.weights(1) ~== weightsTheory(1) absTol 1E-6)
-    assert(model1.weights(2) ~== weightsTheory(2) absTol 1E-6)
-    assert(model1.weights(3) ~== weightsTheory(3) absTol 1E-6)
+    assert(model1.weights ~= weightsTheory absTol 1E-6)
 
     assert(model2.intercept ~== interceptTheory relTol 1E-5)
-    assert(model2.weights(0) ~== weightsTheory(0) absTol 1E-6)
-    assert(model2.weights(1) ~== weightsTheory(1) absTol 1E-6)
-    assert(model2.weights(2) ~== weightsTheory(2) absTol 1E-6)
-    assert(model2.weights(3) ~== weightsTheory(3) absTol 1E-6)
+    assert(model2.weights ~= weightsTheory absTol 1E-6)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -750,12 +696,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        data.V5       .
      */
     val interceptR = -0.248065
-    val weightsR = Array(0.0, 0.0, 0.0, 0.0)
+    val weightsR = Vectors.dense(0.0, 0.0, 0.0, 0.0)
 
     assert(model1.intercept ~== interceptR relTol 1E-5)
-    assert(model1.weights(0) ~== weightsR(0) absTol 1E-6)
-    assert(model1.weights(1) ~== weightsR(1) absTol 1E-6)
-    assert(model1.weights(2) ~== weightsR(2) absTol 1E-6)
-    assert(model1.weights(3) ~== weightsR(3) absTol 1E-6)
+    assert(model1.weights ~= weightsR absTol 1E-6)
   }
 }

From 1903641e68ce7e7e657584bf45e91db6df357e41 Mon Sep 17 00:00:00 2001
From: huangzhaowei <carlmartinmax@gmail.com>
Date: Thu, 9 Jul 2015 19:31:31 -0700
Subject: [PATCH 0330/1454] [SPARK-8839] [SQL] ThriftServer2 will remove
 session and execution no matter it's finished or not.

In my test, `sessions` and `executions` in ThriftServer2 is not the same number as the connection number.
For example, if there are 200 clients connecting to the server,  but it will have more than 200 `sessions` and `executions`.
So if it reaches the `retainedStatements`, it has to remove some object which is not finished.
So it may cause the exception described in [Jira Address](https://issues.apache.org/jira/browse/SPARK-8839)

Author: huangzhaowei <carlmartinmax@gmail.com>

Closes #7239 from SaintBacchus/SPARK-8839 and squashes the following commits:

cf7ef40 [huangzhaowei] Remove the a meanless funciton call
3e9a5a6 [huangzhaowei] Add a filter before take
9d5ceb8 [huangzhaowei] [SPARK-8839][SQL]ThriftServer2 will remove session and execution no matter it's finished or not.
---
 .../spark/sql/hive/thriftserver/HiveThriftServer2.scala    | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index 700d994bb6a83..b7db80d93f852 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -179,6 +179,7 @@ object HiveThriftServer2 extends Logging {
     def onSessionClosed(sessionId: String): Unit = {
       sessionList(sessionId).finishTimestamp = System.currentTimeMillis
       onlineSessionNum -= 1
+      trimSessionIfNecessary()
     }
 
     def onStatementStart(
@@ -206,18 +207,20 @@ object HiveThriftServer2 extends Logging {
       executionList(id).detail = errorMessage
       executionList(id).state = ExecutionState.FAILED
       totalRunning -= 1
+      trimExecutionIfNecessary()
     }
 
     def onStatementFinish(id: String): Unit = {
       executionList(id).finishTimestamp = System.currentTimeMillis
       executionList(id).state = ExecutionState.FINISHED
       totalRunning -= 1
+      trimExecutionIfNecessary()
     }
 
     private def trimExecutionIfNecessary() = synchronized {
       if (executionList.size > retainedStatements) {
         val toRemove = math.max(retainedStatements / 10, 1)
-        executionList.take(toRemove).foreach { s =>
+        executionList.filter(_._2.finishTimestamp != 0).take(toRemove).foreach { s =>
           executionList.remove(s._1)
         }
       }
@@ -226,7 +229,7 @@ object HiveThriftServer2 extends Logging {
     private def trimSessionIfNecessary() = synchronized {
       if (sessionList.size > retainedSessions) {
         val toRemove = math.max(retainedSessions / 10, 1)
-        sessionList.take(toRemove).foreach { s =>
+        sessionList.filter(_._2.finishTimestamp != 0).take(toRemove).foreach { s =>
           sessionList.remove(s._1)
         }
       }

From d538919cc4fd3ab940d478c62dce1bae0270cfeb Mon Sep 17 00:00:00 2001
From: Michael Vogiatzis <michaelvogiatzis@gmail.com>
Date: Thu, 9 Jul 2015 19:53:23 -0700
Subject: [PATCH 0331/1454] [DOCS] Added important updateStateByKey details

Runs for *all* existing keys and returning "None" will remove the key-value pair.

Author: Michael Vogiatzis <michaelvogiatzis@gmail.com>

Closes #7229 from mvogiatzis/patch-1 and squashes the following commits:

e7a2946 [Michael Vogiatzis] Updated updateStateByKey text
00283ed [Michael Vogiatzis] Removed space
c2656f9 [Michael Vogiatzis] Moved description farther up
0a42551 [Michael Vogiatzis] Added important updateStateByKey details
---
 docs/streaming-programming-guide.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index e72d5580dae55..2f3013b533eb0 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -854,6 +854,8 @@ it with new information. To use this, you will have to do two steps.
 1. Define the state update function - Specify with a function how to update the state using the
 previous state and the new values from an input stream.
 
+In every batch, Spark will apply the state  update function for all existing keys, regardless of whether they have new data in a batch or not. If the update function returns `None` then the key-value pair will be eliminated.
+
 Let's illustrate this with an example. Say you want to maintain a running count of each word
 seen in a text data stream. Here, the running count is the state and it is an integer. We
 define the update function as:

From e14b545d2dcbc4587688b4c46718d3680b0a2f67 Mon Sep 17 00:00:00 2001
From: Jonathan Alter <jonalter@users.noreply.github.com>
Date: Fri, 10 Jul 2015 11:34:01 +0100
Subject: [PATCH 0332/1454] [SPARK-7977] [BUILD] Disallowing println

Author: Jonathan Alter <jonalter@users.noreply.github.com>

Closes #7093 from jonalter/SPARK-7977 and squashes the following commits:

ccd44cc [Jonathan Alter] Changed println to log in ThreadingSuite
7fcac3e [Jonathan Alter] Reverting to println in ThreadingSuite
10724b6 [Jonathan Alter] Changing some printlns to logs in tests
eeec1e7 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
0b1dcb4 [Jonathan Alter] More println cleanup
aedaf80 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
925fd98 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
0c16fa3 [Jonathan Alter] Replacing some printlns with logs
45c7e05 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
5c8e283 [Jonathan Alter] Allowing println in audit-release examples
5b50da1 [Jonathan Alter] Allowing printlns in example files
ca4b477 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
83ab635 [Jonathan Alter] Fixing new printlns
54b131f [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
1cd8a81 [Jonathan Alter] Removing some unnecessary comments and printlns
b837c3a [Jonathan Alter] Disallowing println
---
 .../main/scala/org/apache/spark/Logging.scala |  2 ++
 .../org/apache/spark/api/r/RBackend.scala     |  2 ++
 .../scala/org/apache/spark/api/r/RRDD.scala   |  2 ++
 .../org/apache/spark/deploy/Client.scala      | 30 ++++++++--------
 .../apache/spark/deploy/ClientArguments.scala |  4 +++
 .../org/apache/spark/deploy/RRunner.scala     |  2 ++
 .../org/apache/spark/deploy/SparkSubmit.scala | 18 ++++++++++
 .../spark/deploy/SparkSubmitArguments.scala   |  4 +++
 .../spark/deploy/client/TestExecutor.scala    |  2 ++
 .../history/HistoryServerArguments.scala      |  2 ++
 .../spark/deploy/master/MasterArguments.scala |  2 ++
 .../MesosClusterDispatcherArguments.scala     |  6 ++++
 .../spark/deploy/worker/DriverWrapper.scala   |  2 ++
 .../spark/deploy/worker/WorkerArguments.scala |  4 +++
 .../CoarseGrainedExecutorBackend.scala        |  4 +++
 .../input/FixedLengthBinaryInputFormat.scala  |  7 ++--
 .../spark/network/nio/BlockMessage.scala      | 22 ------------
 .../spark/network/nio/BlockMessageArray.scala | 34 ++++---------------
 .../spark/network/nio/ConnectionManager.scala |  4 +++
 .../scala/org/apache/spark/rdd/PipedRDD.scala |  4 +++
 .../scheduler/EventLoggingListener.scala      |  2 ++
 .../apache/spark/scheduler/JobLogger.scala    |  2 ++
 .../org/apache/spark/ui/JettyUtils.scala      |  2 ++
 .../apache/spark/ui/UIWorkloadGenerator.scala |  6 +++-
 .../org/apache/spark/util/Distribution.scala  |  6 ++++
 .../spark/util/random/XORShiftRandom.scala    |  2 ++
 .../org/apache/spark/DistributedSuite.scala   |  2 ++
 .../scala/org/apache/spark/FailureSuite.scala |  2 ++
 .../org/apache/spark/FileServerSuite.scala    |  2 ++
 .../org/apache/spark/ThreadingSuite.scala     |  6 ++--
 .../spark/deploy/SparkSubmitSuite.scala       |  4 +++
 .../spark/deploy/SparkSubmitUtilsSuite.scala  |  2 ++
 .../WholeTextFileRecordReaderSuite.scala      |  8 ++---
 .../metrics/InputOutputMetricsSuite.scala     |  2 ++
 .../spark/scheduler/ReplayListenerSuite.scala |  2 ++
 .../spark/util/ClosureCleanerSuite.scala      |  2 ++
 .../org/apache/spark/util/UtilsSuite.scala    |  2 ++
 .../util/collection/SizeTrackerSuite.scala    |  4 +++
 .../spark/util/collection/SorterSuite.scala   | 10 +++---
 .../src/main/scala/SparkApp.scala             |  2 ++
 .../src/main/scala/SparkApp.scala             |  2 ++
 .../src/main/scala/GraphxApp.scala            |  2 ++
 .../sbt_app_hive/src/main/scala/HiveApp.scala |  2 ++
 .../src/main/scala/SparkApp.scala             |  2 ++
 .../sbt_app_sql/src/main/scala/SqlApp.scala   |  2 ++
 .../src/main/scala/StreamingApp.scala         |  2 ++
 .../apache/spark/examples/BroadcastTest.scala |  2 ++
 .../spark/examples/CassandraCQLTest.scala     |  2 ++
 .../apache/spark/examples/CassandraTest.scala |  2 ++
 .../spark/examples/DFSReadWriteTest.scala     |  2 ++
 .../spark/examples/DriverSubmissionTest.scala |  2 ++
 .../apache/spark/examples/GroupByTest.scala   |  2 ++
 .../org/apache/spark/examples/HBaseTest.scala |  2 ++
 .../org/apache/spark/examples/HdfsTest.scala  |  2 ++
 .../org/apache/spark/examples/LocalALS.scala  |  2 ++
 .../apache/spark/examples/LocalFileLR.scala   |  2 ++
 .../apache/spark/examples/LocalKMeans.scala   |  2 ++
 .../org/apache/spark/examples/LocalLR.scala   |  2 ++
 .../org/apache/spark/examples/LocalPi.scala   |  2 ++
 .../org/apache/spark/examples/LogQuery.scala  |  2 ++
 .../spark/examples/MultiBroadcastTest.scala   |  2 ++
 .../examples/SimpleSkewedGroupByTest.scala    |  2 ++
 .../spark/examples/SkewedGroupByTest.scala    |  2 ++
 .../org/apache/spark/examples/SparkALS.scala  |  2 ++
 .../apache/spark/examples/SparkHdfsLR.scala   |  2 ++
 .../apache/spark/examples/SparkKMeans.scala   |  2 ++
 .../org/apache/spark/examples/SparkLR.scala   |  2 ++
 .../apache/spark/examples/SparkPageRank.scala |  2 ++
 .../org/apache/spark/examples/SparkPi.scala   |  2 ++
 .../org/apache/spark/examples/SparkTC.scala   |  2 ++
 .../spark/examples/SparkTachyonHdfsLR.scala   |  2 ++
 .../spark/examples/SparkTachyonPi.scala       |  2 ++
 .../spark/examples/graphx/Analytics.scala     |  2 ++
 .../examples/graphx/LiveJournalPageRank.scala |  2 ++
 .../examples/graphx/SynthBenchmark.scala      |  2 ++
 .../examples/ml/CrossValidatorExample.scala   |  2 ++
 .../examples/ml/DecisionTreeExample.scala     |  2 ++
 .../examples/ml/DeveloperApiExample.scala     |  2 ++
 .../apache/spark/examples/ml/GBTExample.scala |  2 ++
 .../examples/ml/LinearRegressionExample.scala |  2 ++
 .../ml/LogisticRegressionExample.scala        |  2 ++
 .../spark/examples/ml/MovieLensALS.scala      |  2 ++
 .../spark/examples/ml/OneVsRestExample.scala  |  2 ++
 .../examples/ml/RandomForestExample.scala     |  2 ++
 .../examples/ml/SimpleParamsExample.scala     |  2 ++
 .../ml/SimpleTextClassificationPipeline.scala |  2 ++
 .../examples/mllib/BinaryClassification.scala |  2 ++
 .../spark/examples/mllib/Correlations.scala   |  2 ++
 .../examples/mllib/CosineSimilarity.scala     |  2 ++
 .../spark/examples/mllib/DatasetExample.scala |  2 ++
 .../examples/mllib/DecisionTreeRunner.scala   |  2 ++
 .../examples/mllib/DenseGaussianMixture.scala |  2 ++
 .../spark/examples/mllib/DenseKMeans.scala    |  2 ++
 .../examples/mllib/FPGrowthExample.scala      |  2 ++
 .../mllib/GradientBoostedTreesRunner.scala    |  2 ++
 .../spark/examples/mllib/LDAExample.scala     |  2 ++
 .../examples/mllib/LinearRegression.scala     |  2 ++
 .../spark/examples/mllib/MovieLensALS.scala   |  2 ++
 .../mllib/MultivariateSummarizer.scala        |  2 ++
 .../PowerIterationClusteringExample.scala     |  3 +-
 .../examples/mllib/RandomRDDGeneration.scala  |  2 ++
 .../spark/examples/mllib/SampledRDDs.scala    |  2 ++
 .../examples/mllib/SparseNaiveBayes.scala     |  2 ++
 .../mllib/StreamingKMeansExample.scala        |  2 ++
 .../mllib/StreamingLinearRegression.scala     |  2 ++
 .../mllib/StreamingLogisticRegression.scala   |  2 ++
 .../spark/examples/mllib/TallSkinnyPCA.scala  |  2 ++
 .../spark/examples/mllib/TallSkinnySVD.scala  |  2 ++
 .../spark/examples/sql/RDDRelation.scala      |  2 ++
 .../examples/sql/hive/HiveFromSpark.scala     |  2 ++
 .../examples/streaming/ActorWordCount.scala   |  2 ++
 .../examples/streaming/CustomReceiver.scala   |  2 ++
 .../streaming/DirectKafkaWordCount.scala      |  2 ++
 .../examples/streaming/FlumeEventCount.scala  |  2 ++
 .../streaming/FlumePollingEventCount.scala    |  2 ++
 .../examples/streaming/HdfsWordCount.scala    |  2 ++
 .../examples/streaming/KafkaWordCount.scala   |  2 ++
 .../examples/streaming/MQTTWordCount.scala    |  4 +++
 .../examples/streaming/NetworkWordCount.scala |  2 ++
 .../examples/streaming/RawNetworkGrep.scala   |  2 ++
 .../RecoverableNetworkWordCount.scala         |  2 ++
 .../streaming/SqlNetworkWordCount.scala       |  2 ++
 .../streaming/StatefulNetworkWordCount.scala  |  2 ++
 .../streaming/TwitterAlgebirdCMS.scala        |  2 ++
 .../streaming/TwitterAlgebirdHLL.scala        |  2 ++
 .../streaming/TwitterPopularTags.scala        |  2 ++
 .../examples/streaming/ZeroMQWordCount.scala  |  2 ++
 .../clickstream/PageViewGenerator.scala       |  2 ++
 .../clickstream/PageViewStream.scala          |  2 ++
 .../kafka/DirectKafkaStreamSuite.scala        |  2 +-
 .../streaming/KinesisWordCountASL.scala       |  2 ++
 .../spark/graphx/util/BytecodeUtils.scala     |  1 -
 .../spark/graphx/util/GraphGenerators.scala   |  4 +--
 .../graphx/util/BytecodeUtilsSuite.scala      |  2 ++
 .../mllib/util/KMeansDataGenerator.scala      |  2 ++
 .../mllib/util/LinearDataGenerator.scala      |  2 ++
 .../LogisticRegressionDataGenerator.scala     |  2 ++
 .../spark/mllib/util/MFDataGenerator.scala    |  2 ++
 .../spark/mllib/util/SVMDataGenerator.scala   |  2 ++
 .../spark/ml/feature/VectorIndexerSuite.scala | 10 +++---
 .../spark/mllib/linalg/VectorsSuite.scala     |  6 ++--
 .../spark/mllib/stat/CorrelationSuite.scala   |  6 ++--
 .../tree/GradientBoostedTreesSuite.scala      | 10 +++---
 .../spark/mllib/util/NumericParserSuite.scala |  2 +-
 project/SparkBuild.scala                      |  4 +++
 .../apache/spark/repl/SparkCommandLine.scala  |  2 ++
 .../org/apache/spark/repl/SparkILoop.scala    |  2 ++
 .../apache/spark/repl/SparkILoopInit.scala    |  2 ++
 .../org/apache/spark/repl/SparkIMain.scala    |  2 ++
 .../org/apache/spark/repl/SparkILoop.scala    |  2 ++
 .../org/apache/spark/repl/SparkIMain.scala    |  4 +++
 .../apache/spark/repl/SparkReplReporter.scala |  2 ++
 scalastyle-config.xml                         | 12 +++----
 .../expressions/codegen/package.scala         |  2 ++
 .../spark/sql/catalyst/plans/QueryPlan.scala  |  2 ++
 .../spark/sql/catalyst/util/package.scala     |  2 ++
 .../apache/spark/sql/types/StructType.scala   |  2 ++
 .../scala/org/apache/spark/sql/Column.scala   |  2 ++
 .../org/apache/spark/sql/DataFrame.scala      |  6 ++++
 .../spark/sql/execution/debug/package.scala   | 16 ++++-----
 .../hive/thriftserver/SparkSQLCLIDriver.scala | 12 ++++---
 .../apache/spark/sql/hive/HiveContext.scala   |  5 +--
 .../org/apache/spark/sql/hive/HiveQl.scala    |  5 +--
 .../spark/sql/hive/client/ClientWrapper.scala |  2 ++
 .../regression-test-SPARK-8489/Main.scala     |  2 ++
 .../sql/hive/HiveMetastoreCatalogSuite.scala  |  6 ++--
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |  2 ++
 .../sql/hive/InsertIntoHiveTableSuite.scala   |  2 --
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  6 ++--
 .../sql/hive/execution/HiveUDFSuite.scala     |  1 -
 .../spark/streaming/dstream/DStream.scala     |  2 ++
 .../spark/streaming/util/RawTextSender.scala  |  2 ++
 .../spark/streaming/util/RecurringTimer.scala |  4 +--
 .../spark/streaming/MasterFailureTest.scala   |  4 +++
 .../scheduler/JobGeneratorSuite.scala         |  1 -
 .../spark/tools/GenerateMIMAIgnore.scala      |  8 +++++
 .../tools/JavaAPICompletenessChecker.scala    |  4 +++
 .../spark/tools/StoragePerfTester.scala       |  4 +++
 .../yarn/ApplicationMasterArguments.scala     |  4 +++
 .../org/apache/spark/deploy/yarn/Client.scala |  2 +-
 .../spark/deploy/yarn/ClientArguments.scala   |  4 +++
 .../spark/deploy/yarn/YarnClusterSuite.scala  |  4 +++
 182 files changed, 478 insertions(+), 135 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Logging.scala b/core/src/main/scala/org/apache/spark/Logging.scala
index 7fcb7830e7b0b..87ab099267b2f 100644
--- a/core/src/main/scala/org/apache/spark/Logging.scala
+++ b/core/src/main/scala/org/apache/spark/Logging.scala
@@ -121,6 +121,7 @@ trait Logging {
     if (usingLog4j12) {
       val log4j12Initialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements
       if (!log4j12Initialized) {
+        // scalastyle:off println
         if (Utils.isInInterpreter) {
           val replDefaultLogProps = "org/apache/spark/log4j-defaults-repl.properties"
           Option(Utils.getSparkClassLoader.getResource(replDefaultLogProps)) match {
@@ -141,6 +142,7 @@ trait Logging {
               System.err.println(s"Spark was unable to load $defaultLogProps")
           }
         }
+        // scalastyle:on println
       }
     }
     Logging.initialized = true
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala
index 1a5f2bca26c2b..b7e72d4d0ed0b 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala
@@ -95,7 +95,9 @@ private[spark] class RBackend {
 private[spark] object RBackend extends Logging {
   def main(args: Array[String]): Unit = {
     if (args.length < 1) {
+      // scalastyle:off println
       System.err.println("Usage: RBackend <tempFilePath>")
+      // scalastyle:on println
       System.exit(-1)
     }
     val sparkRBackend = new RBackend()
diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
index 524676544d6f5..ff1702f7dea48 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
@@ -161,7 +161,9 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag](
               dataOut.write(elem.asInstanceOf[Array[Byte]])
             } else if (deserializer == SerializationFormats.STRING) {
               // write string(for StringRRDD)
+              // scalastyle:off println
               printOut.println(elem)
+              // scalastyle:on println
             }
           }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala
index 71f7e2129116f..f03875a3e8c89 100644
--- a/core/src/main/scala/org/apache/spark/deploy/Client.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala
@@ -118,26 +118,26 @@ private class ClientEndpoint(
   def pollAndReportStatus(driverId: String) {
     // Since ClientEndpoint is the only RpcEndpoint in the process, blocking the event loop thread
     // is fine.
-    println("... waiting before polling master for driver state")
+    logInfo("... waiting before polling master for driver state")
     Thread.sleep(5000)
-    println("... polling master for driver state")
+    logInfo("... polling master for driver state")
     val statusResponse =
       activeMasterEndpoint.askWithRetry[DriverStatusResponse](RequestDriverStatus(driverId))
     statusResponse.found match {
       case false =>
-        println(s"ERROR: Cluster master did not recognize $driverId")
+        logError(s"ERROR: Cluster master did not recognize $driverId")
         System.exit(-1)
       case true =>
-        println(s"State of $driverId is ${statusResponse.state.get}")
+        logInfo(s"State of $driverId is ${statusResponse.state.get}")
         // Worker node, if present
         (statusResponse.workerId, statusResponse.workerHostPort, statusResponse.state) match {
           case (Some(id), Some(hostPort), Some(DriverState.RUNNING)) =>
-            println(s"Driver running on $hostPort ($id)")
+            logInfo(s"Driver running on $hostPort ($id)")
           case _ =>
         }
         // Exception, if present
         statusResponse.exception.map { e =>
-          println(s"Exception from cluster was: $e")
+          logError(s"Exception from cluster was: $e")
           e.printStackTrace()
           System.exit(-1)
         }
@@ -148,7 +148,7 @@ private class ClientEndpoint(
   override def receive: PartialFunction[Any, Unit] = {
 
     case SubmitDriverResponse(master, success, driverId, message) =>
-      println(message)
+      logInfo(message)
       if (success) {
         activeMasterEndpoint = master
         pollAndReportStatus(driverId.get)
@@ -158,7 +158,7 @@ private class ClientEndpoint(
 
 
     case KillDriverResponse(master, driverId, success, message) =>
-      println(message)
+      logInfo(message)
       if (success) {
         activeMasterEndpoint = master
         pollAndReportStatus(driverId)
@@ -169,13 +169,13 @@ private class ClientEndpoint(
 
   override def onDisconnected(remoteAddress: RpcAddress): Unit = {
     if (!lostMasters.contains(remoteAddress)) {
-      println(s"Error connecting to master $remoteAddress.")
+      logError(s"Error connecting to master $remoteAddress.")
       lostMasters += remoteAddress
       // Note that this heuristic does not account for the fact that a Master can recover within
       // the lifetime of this client. Thus, once a Master is lost it is lost to us forever. This
       // is not currently a concern, however, because this client does not retry submissions.
       if (lostMasters.size >= masterEndpoints.size) {
-        println("No master is available, exiting.")
+        logError("No master is available, exiting.")
         System.exit(-1)
       }
     }
@@ -183,18 +183,18 @@ private class ClientEndpoint(
 
   override def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = {
     if (!lostMasters.contains(remoteAddress)) {
-      println(s"Error connecting to master ($remoteAddress).")
-      println(s"Cause was: $cause")
+      logError(s"Error connecting to master ($remoteAddress).")
+      logError(s"Cause was: $cause")
       lostMasters += remoteAddress
       if (lostMasters.size >= masterEndpoints.size) {
-        println("No master is available, exiting.")
+        logError("No master is available, exiting.")
         System.exit(-1)
       }
     }
   }
 
   override def onError(cause: Throwable): Unit = {
-    println(s"Error processing messages, exiting.")
+    logError(s"Error processing messages, exiting.")
     cause.printStackTrace()
     System.exit(-1)
   }
@@ -209,10 +209,12 @@ private class ClientEndpoint(
  */
 object Client {
   def main(args: Array[String]) {
+    // scalastyle:off println
     if (!sys.props.contains("SPARK_SUBMIT")) {
       println("WARNING: This client is deprecated and will be removed in a future version of Spark")
       println("Use ./bin/spark-submit with \"--master spark://host:port\"")
     }
+    // scalastyle:on println
 
     val conf = new SparkConf()
     val driverArgs = new ClientArguments(args)
diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
index 42d3296062e6d..72cc330a398da 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala
@@ -72,9 +72,11 @@ private[deploy] class ClientArguments(args: Array[String]) {
       cmd = "launch"
 
       if (!ClientArguments.isValidJarUrl(_jarUrl)) {
+        // scalastyle:off println
         println(s"Jar url '${_jarUrl}' is not in valid format.")
         println(s"Must be a jar file path in URL format " +
           "(e.g. hdfs://host:port/XX.jar, file:///XX.jar)")
+        // scalastyle:on println
         printUsageAndExit(-1)
       }
 
@@ -110,7 +112,9 @@ private[deploy] class ClientArguments(args: Array[String]) {
       |                                  (default: $DEFAULT_SUPERVISE)
       |   -v, --verbose                  Print more debugging output
      """.stripMargin
+    // scalastyle:off println
     System.err.println(usage)
+    // scalastyle:on println
     System.exit(exitCode)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
index e99779f299785..4165740312e03 100644
--- a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
@@ -85,7 +85,9 @@ object RRunner {
       }
       System.exit(returnCode)
     } else {
+      // scalastyle:off println
       System.err.println("SparkR backend did not initialize in " + backendTimeout + " seconds")
+      // scalastyle:on println
       System.exit(-1)
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index b1d6ec209d62b..4cec9017b8adb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -82,6 +82,7 @@ object SparkSubmit {
 
   private val CLASS_NOT_FOUND_EXIT_STATUS = 101
 
+  // scalastyle:off println
   // Exposed for testing
   private[spark] var exitFn: Int => Unit = (exitCode: Int) => System.exit(exitCode)
   private[spark] var printStream: PrintStream = System.err
@@ -102,11 +103,14 @@ object SparkSubmit {
     printStream.println("Type --help for more information.")
     exitFn(0)
   }
+  // scalastyle:on println
 
   def main(args: Array[String]): Unit = {
     val appArgs = new SparkSubmitArguments(args)
     if (appArgs.verbose) {
+      // scalastyle:off println
       printStream.println(appArgs)
+      // scalastyle:on println
     }
     appArgs.action match {
       case SparkSubmitAction.SUBMIT => submit(appArgs)
@@ -160,7 +164,9 @@ object SparkSubmit {
             // makes the message printed to the output by the JVM not very helpful. Instead,
             // detect exceptions with empty stack traces here, and treat them differently.
             if (e.getStackTrace().length == 0) {
+              // scalastyle:off println
               printStream.println(s"ERROR: ${e.getClass().getName()}: ${e.getMessage()}")
+              // scalastyle:on println
               exitFn(1)
             } else {
               throw e
@@ -178,7 +184,9 @@ object SparkSubmit {
      // to use the legacy gateway if the master endpoint turns out to be not a REST server.
     if (args.isStandaloneCluster && args.useRest) {
       try {
+        // scalastyle:off println
         printStream.println("Running Spark using the REST application submission protocol.")
+        // scalastyle:on println
         doRunMain()
       } catch {
         // Fail over to use the legacy submission gateway
@@ -558,6 +566,7 @@ object SparkSubmit {
       sysProps: Map[String, String],
       childMainClass: String,
       verbose: Boolean): Unit = {
+    // scalastyle:off println
     if (verbose) {
       printStream.println(s"Main class:\n$childMainClass")
       printStream.println(s"Arguments:\n${childArgs.mkString("\n")}")
@@ -565,6 +574,7 @@ object SparkSubmit {
       printStream.println(s"Classpath elements:\n${childClasspath.mkString("\n")}")
       printStream.println("\n")
     }
+    // scalastyle:on println
 
     val loader =
       if (sysProps.getOrElse("spark.driver.userClassPathFirst", "false").toBoolean) {
@@ -592,8 +602,10 @@ object SparkSubmit {
       case e: ClassNotFoundException =>
         e.printStackTrace(printStream)
         if (childMainClass.contains("thriftserver")) {
+          // scalastyle:off println
           printStream.println(s"Failed to load main class $childMainClass.")
           printStream.println("You need to build Spark with -Phive and -Phive-thriftserver.")
+          // scalastyle:on println
         }
         System.exit(CLASS_NOT_FOUND_EXIT_STATUS)
     }
@@ -766,7 +778,9 @@ private[spark] object SparkSubmitUtils {
         brr.setRoot(repo)
         brr.setName(s"repo-${i + 1}")
         cr.add(brr)
+        // scalastyle:off println
         printStream.println(s"$repo added as a remote repository with the name: ${brr.getName}")
+        // scalastyle:on println
       }
     }
 
@@ -829,7 +843,9 @@ private[spark] object SparkSubmitUtils {
       val ri = ModuleRevisionId.newInstance(mvn.groupId, mvn.artifactId, mvn.version)
       val dd = new DefaultDependencyDescriptor(ri, false, false)
       dd.addDependencyConfiguration(ivyConfName, ivyConfName)
+      // scalastyle:off println
       printStream.println(s"${dd.getDependencyId} added as a dependency")
+      // scalastyle:on println
       md.addDependency(dd)
     }
   }
@@ -896,9 +912,11 @@ private[spark] object SparkSubmitUtils {
             ivySettings.setDefaultCache(new File(alternateIvyCache, "cache"))
             new File(alternateIvyCache, "jars")
           }
+        // scalastyle:off println
         printStream.println(
           s"Ivy Default Cache set to: ${ivySettings.getDefaultCache.getAbsolutePath}")
         printStream.println(s"The jars for the packages stored in: $packagesDirectory")
+        // scalastyle:on println
         // create a pattern matcher
         ivySettings.addMatcher(new GlobPatternMatcher)
         // create the dependency resolvers
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 6e3c0b21b33c2..ebb39c354dff1 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -79,6 +79,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
   /** Default properties present in the currently defined defaults file. */
   lazy val defaultSparkProperties: HashMap[String, String] = {
     val defaultProperties = new HashMap[String, String]()
+    // scalastyle:off println
     if (verbose) SparkSubmit.printStream.println(s"Using properties file: $propertiesFile")
     Option(propertiesFile).foreach { filename =>
       Utils.getPropertiesFromFile(filename).foreach { case (k, v) =>
@@ -86,6 +87,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
         if (verbose) SparkSubmit.printStream.println(s"Adding default property: $k=$v")
       }
     }
+    // scalastyle:on println
     defaultProperties
   }
 
@@ -452,6 +454,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
   }
 
   private def printUsageAndExit(exitCode: Int, unknownParam: Any = null): Unit = {
+    // scalastyle:off println
     val outStream = SparkSubmit.printStream
     if (unknownParam != null) {
       outStream.println("Unknown/unsupported param " + unknownParam)
@@ -541,6 +544,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
       outStream.println("CLI options:")
       outStream.println(getSqlShellOptions())
     }
+    // scalastyle:on println
 
     SparkSubmit.exitFn(exitCode)
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestExecutor.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestExecutor.scala
index c5ac45c6730d3..a98b1fa8f83a1 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/TestExecutor.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/TestExecutor.scala
@@ -19,7 +19,9 @@ package org.apache.spark.deploy.client
 
 private[spark] object TestExecutor {
   def main(args: Array[String]) {
+    // scalastyle:off println
     println("Hello world!")
+    // scalastyle:on println
     while (true) {
       Thread.sleep(1000)
     }
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala
index 4692d22651c93..18265df9faa2c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServerArguments.scala
@@ -56,6 +56,7 @@ private[history] class HistoryServerArguments(conf: SparkConf, args: Array[Strin
    Utils.loadDefaultSparkProperties(conf, propertiesFile)
 
   private def printUsageAndExit(exitCode: Int) {
+    // scalastyle:off println
     System.err.println(
       """
       |Usage: HistoryServer [options]
@@ -84,6 +85,7 @@ private[history] class HistoryServerArguments(conf: SparkConf, args: Array[Strin
       |  spark.history.fs.updateInterval    How often to reload log data from storage
       |                                     (in seconds, default: 10)
       |""".stripMargin)
+    // scalastyle:on println
     System.exit(exitCode)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala
index 435b9b12f83b8..44cefbc77f08e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala
@@ -85,6 +85,7 @@ private[master] class MasterArguments(args: Array[String], conf: SparkConf) {
    * Print usage and exit JVM with the given exit code.
    */
   private def printUsageAndExit(exitCode: Int) {
+    // scalastyle:off println
     System.err.println(
       "Usage: Master [options]\n" +
       "\n" +
@@ -95,6 +96,7 @@ private[master] class MasterArguments(args: Array[String], conf: SparkConf) {
       "  --webui-port PORT      Port for web UI (default: 8080)\n" +
       "  --properties-file FILE Path to a custom Spark properties file.\n" +
       "                         Default is conf/spark-defaults.conf.")
+    // scalastyle:on println
     System.exit(exitCode)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala
index 894cb78d8591a..5accaf78d0a51 100644
--- a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala
@@ -54,7 +54,9 @@ private[mesos] class MesosClusterDispatcherArguments(args: Array[String], conf:
 
     case ("--master" | "-m") :: value :: tail =>
       if (!value.startsWith("mesos://")) {
+        // scalastyle:off println
         System.err.println("Cluster dispatcher only supports mesos (uri begins with mesos://)")
+        // scalastyle:on println
         System.exit(1)
       }
       masterUrl = value.stripPrefix("mesos://")
@@ -73,7 +75,9 @@ private[mesos] class MesosClusterDispatcherArguments(args: Array[String], conf:
 
     case Nil => {
       if (masterUrl == null) {
+        // scalastyle:off println
         System.err.println("--master is required")
+        // scalastyle:on println
         printUsageAndExit(1)
       }
     }
@@ -83,6 +87,7 @@ private[mesos] class MesosClusterDispatcherArguments(args: Array[String], conf:
   }
 
   private def printUsageAndExit(exitCode: Int): Unit = {
+    // scalastyle:off println
     System.err.println(
       "Usage: MesosClusterDispatcher [options]\n" +
         "\n" +
@@ -96,6 +101,7 @@ private[mesos] class MesosClusterDispatcherArguments(args: Array[String], conf:
         "                          Zookeeper for persistence\n" +
         "  --properties-file FILE  Path to a custom Spark properties file.\n" +
         "                          Default is conf/spark-defaults.conf.")
+    // scalastyle:on println
     System.exit(exitCode)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
index d1a12b01e78f7..2d6be3042c905 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
@@ -60,7 +60,9 @@ object DriverWrapper {
         rpcEnv.shutdown()
 
       case _ =>
+        // scalastyle:off println
         System.err.println("Usage: DriverWrapper <workerUrl> <userJar> <driverMainClass> [options]")
+        // scalastyle:on println
         System.exit(-1)
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
index 1d2ecab517613..e89d076802215 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
@@ -121,6 +121,7 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) {
    * Print usage and exit JVM with the given exit code.
    */
   def printUsageAndExit(exitCode: Int) {
+    // scalastyle:off println
     System.err.println(
       "Usage: Worker [options] <master>\n" +
       "\n" +
@@ -136,6 +137,7 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) {
       "  --webui-port PORT        Port for web UI (default: 8081)\n" +
       "  --properties-file FILE   Path to a custom Spark properties file.\n" +
       "                           Default is conf/spark-defaults.conf.")
+    // scalastyle:on println
     System.exit(exitCode)
   }
 
@@ -160,7 +162,9 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) {
     } catch {
       case e: Exception => {
         totalMb = 2*1024
+        // scalastyle:off println
         System.out.println("Failed to get total physical memory. Using " + totalMb + " MB")
+        // scalastyle:on println
       }
     }
     // Leave out 1 GB for the operating system, but don't return a negative memory size
diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index 34d4cfdca7732..fcd76ec52742a 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -235,7 +235,9 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging {
           argv = tail
         case Nil =>
         case tail =>
+          // scalastyle:off println
           System.err.println(s"Unrecognized options: ${tail.mkString(" ")}")
+          // scalastyle:on println
           printUsageAndExit()
       }
     }
@@ -249,6 +251,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging {
   }
 
   private def printUsageAndExit() = {
+    // scalastyle:off println
     System.err.println(
       """
       |"Usage: CoarseGrainedExecutorBackend [options]
@@ -262,6 +265,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging {
       |   --worker-url <workerUrl>
       |   --user-class-path <url>
       |""".stripMargin)
+    // scalastyle:on println
     System.exit(1)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/input/FixedLengthBinaryInputFormat.scala b/core/src/main/scala/org/apache/spark/input/FixedLengthBinaryInputFormat.scala
index c219d21fbefa9..532850dd57716 100644
--- a/core/src/main/scala/org/apache/spark/input/FixedLengthBinaryInputFormat.scala
+++ b/core/src/main/scala/org/apache/spark/input/FixedLengthBinaryInputFormat.scala
@@ -21,6 +21,8 @@ import org.apache.hadoop.fs.Path
 import org.apache.hadoop.io.{BytesWritable, LongWritable}
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.mapreduce.{InputSplit, JobContext, RecordReader, TaskAttemptContext}
+
+import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkHadoopUtil
 
 /**
@@ -39,7 +41,8 @@ private[spark] object FixedLengthBinaryInputFormat {
 }
 
 private[spark] class FixedLengthBinaryInputFormat
-  extends FileInputFormat[LongWritable, BytesWritable] {
+  extends FileInputFormat[LongWritable, BytesWritable]
+  with Logging {
 
   private var recordLength = -1
 
@@ -51,7 +54,7 @@ private[spark] class FixedLengthBinaryInputFormat
       recordLength = FixedLengthBinaryInputFormat.getRecordLength(context)
     }
     if (recordLength <= 0) {
-      println("record length is less than 0, file cannot be split")
+      logDebug("record length is less than 0, file cannot be split")
       false
     } else {
       true
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
index 67a376102994c..79cb0640c8672 100644
--- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
@@ -57,16 +57,6 @@ private[nio] class BlockMessage() {
   }
 
   def set(buffer: ByteBuffer) {
-    /*
-    println()
-    println("BlockMessage: ")
-    while(buffer.remaining > 0) {
-      print(buffer.get())
-    }
-    buffer.rewind()
-    println()
-    println()
-    */
     typ = buffer.getInt()
     val idLength = buffer.getInt()
     val idBuilder = new StringBuilder(idLength)
@@ -138,18 +128,6 @@ private[nio] class BlockMessage() {
       buffers += data
     }
 
-    /*
-    println()
-    println("BlockMessage: ")
-    buffers.foreach(b => {
-      while(b.remaining > 0) {
-        print(b.get())
-      }
-      b.rewind()
-    })
-    println()
-    println()
-    */
     Message.createBufferMessage(buffers)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
index 7d0806f0c2580..f1c9ea8b64ca3 100644
--- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
@@ -43,16 +43,6 @@ class BlockMessageArray(var blockMessages: Seq[BlockMessage])
     val newBlockMessages = new ArrayBuffer[BlockMessage]()
     val buffer = bufferMessage.buffers(0)
     buffer.clear()
-    /*
-    println()
-    println("BlockMessageArray: ")
-    while(buffer.remaining > 0) {
-      print(buffer.get())
-    }
-    buffer.rewind()
-    println()
-    println()
-    */
     while (buffer.remaining() > 0) {
       val size = buffer.getInt()
       logDebug("Creating block message of size " + size + " bytes")
@@ -86,23 +76,11 @@ class BlockMessageArray(var blockMessages: Seq[BlockMessage])
 
     logDebug("Buffer list:")
     buffers.foreach((x: ByteBuffer) => logDebug("" + x))
-    /*
-    println()
-    println("BlockMessageArray: ")
-    buffers.foreach(b => {
-      while(b.remaining > 0) {
-        print(b.get())
-      }
-      b.rewind()
-    })
-    println()
-    println()
-    */
     Message.createBufferMessage(buffers)
   }
 }
 
-private[nio] object BlockMessageArray {
+private[nio] object BlockMessageArray extends Logging {
 
   def fromBufferMessage(bufferMessage: BufferMessage): BlockMessageArray = {
     val newBlockMessageArray = new BlockMessageArray()
@@ -123,10 +101,10 @@ private[nio] object BlockMessageArray {
         }
       }
     val blockMessageArray = new BlockMessageArray(blockMessages)
-    println("Block message array created")
+    logDebug("Block message array created")
 
     val bufferMessage = blockMessageArray.toBufferMessage
-    println("Converted to buffer message")
+    logDebug("Converted to buffer message")
 
     val totalSize = bufferMessage.size
     val newBuffer = ByteBuffer.allocate(totalSize)
@@ -138,10 +116,11 @@ private[nio] object BlockMessageArray {
     })
     newBuffer.flip
     val newBufferMessage = Message.createBufferMessage(newBuffer)
-    println("Copied to new buffer message, size = " + newBufferMessage.size)
+    logDebug("Copied to new buffer message, size = " + newBufferMessage.size)
 
     val newBlockMessageArray = BlockMessageArray.fromBufferMessage(newBufferMessage)
-    println("Converted back to block message array")
+    logDebug("Converted back to block message array")
+    // scalastyle:off println
     newBlockMessageArray.foreach(blockMessage => {
       blockMessage.getType match {
         case BlockMessage.TYPE_PUT_BLOCK => {
@@ -154,6 +133,7 @@ private[nio] object BlockMessageArray {
         }
       }
     })
+    // scalastyle:on println
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
index c0bca2c4bc994..9143918790381 100644
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
@@ -1016,7 +1016,9 @@ private[spark] object ConnectionManager {
     val conf = new SparkConf
     val manager = new ConnectionManager(9999, conf, new SecurityManager(conf))
     manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
+      // scalastyle:off println
       println("Received [" + msg + "] from [" + id + "]")
+      // scalastyle:on println
       None
     })
 
@@ -1033,6 +1035,7 @@ private[spark] object ConnectionManager {
     System.gc()
   }
 
+  // scalastyle:off println
   def testSequentialSending(manager: ConnectionManager) {
     println("--------------------------")
     println("Sequential Sending")
@@ -1150,4 +1153,5 @@ private[spark] object ConnectionManager {
       println()
     }
   }
+  // scalastyle:on println
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
index dc60d48927624..defdabf95ac4b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
@@ -123,7 +123,9 @@ private[spark] class PipedRDD[T: ClassTag](
     new Thread("stderr reader for " + command) {
       override def run() {
         for (line <- Source.fromInputStream(proc.getErrorStream).getLines) {
+          // scalastyle:off println
           System.err.println(line)
+          // scalastyle:on println
         }
       }
     }.start()
@@ -133,6 +135,7 @@ private[spark] class PipedRDD[T: ClassTag](
       override def run() {
         val out = new PrintWriter(proc.getOutputStream)
 
+        // scalastyle:off println
         // input the pipe context firstly
         if (printPipeContext != null) {
           printPipeContext(out.println(_))
@@ -144,6 +147,7 @@ private[spark] class PipedRDD[T: ClassTag](
             out.println(elem)
           }
         }
+        // scalastyle:on println
         out.close()
       }
     }.start()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index 529a5b2bf1a0d..62b05033a9281 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -140,7 +140,9 @@ private[spark] class EventLoggingListener(
   /** Log the event as JSON. */
   private def logEvent(event: SparkListenerEvent, flushLogger: Boolean = false) {
     val eventJson = JsonProtocol.sparkEventToJson(event)
+    // scalastyle:off println
     writer.foreach(_.println(compact(render(eventJson))))
+    // scalastyle:on println
     if (flushLogger) {
       writer.foreach(_.flush())
       hadoopDataStream.foreach(hadoopFlushMethod.invoke(_))
diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala
index e55b76c36cc5f..f96eb8ca0ae00 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala
@@ -125,7 +125,9 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener
       val date = new Date(System.currentTimeMillis())
       writeInfo = dateFormat.get.format(date) + ": " + info
     }
+    // scalastyle:off println
     jobIdToPrintWriter.get(jobId).foreach(_.println(writeInfo))
+    // scalastyle:on println
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
index f413c1d37fbb6..c8356467fab87 100644
--- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
@@ -68,7 +68,9 @@ private[spark] object JettyUtils extends Logging {
             response.setStatus(HttpServletResponse.SC_OK)
             val result = servletParams.responder(request)
             response.setHeader("Cache-Control", "no-cache, no-store, must-revalidate")
+            // scalastyle:off println
             response.getWriter.println(servletParams.extractFn(result))
+            // scalastyle:on println
           } else {
             response.setStatus(HttpServletResponse.SC_UNAUTHORIZED)
             response.setHeader("Cache-Control", "no-cache, no-store, must-revalidate")
diff --git a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala
index ba03acdb38cc5..5a8c2914314c2 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala
@@ -38,9 +38,11 @@ private[spark] object UIWorkloadGenerator {
 
   def main(args: Array[String]) {
     if (args.length < 3) {
+      // scalastyle:off println
       println(
-        "usage: ./bin/spark-class org.apache.spark.ui.UIWorkloadGenerator " +
+        "Usage: ./bin/spark-class org.apache.spark.ui.UIWorkloadGenerator " +
           "[master] [FIFO|FAIR] [#job set (4 jobs per set)]")
+      // scalastyle:on println
       System.exit(1)
     }
 
@@ -96,6 +98,7 @@ private[spark] object UIWorkloadGenerator {
       for ((desc, job) <- jobs) {
         new Thread {
           override def run() {
+            // scalastyle:off println
             try {
               setProperties(desc)
               job()
@@ -106,6 +109,7 @@ private[spark] object UIWorkloadGenerator {
             } finally {
               barrier.release()
             }
+            // scalastyle:on println
           }
         }.start
         Thread.sleep(INTER_JOB_WAIT_MS)
diff --git a/core/src/main/scala/org/apache/spark/util/Distribution.scala b/core/src/main/scala/org/apache/spark/util/Distribution.scala
index 1bab707235b89..950b69f7db641 100644
--- a/core/src/main/scala/org/apache/spark/util/Distribution.scala
+++ b/core/src/main/scala/org/apache/spark/util/Distribution.scala
@@ -52,9 +52,11 @@ private[spark] class Distribution(val data: Array[Double], val startIdx: Int, va
   }
 
   def showQuantiles(out: PrintStream = System.out): Unit = {
+    // scalastyle:off println
     out.println("min\t25%\t50%\t75%\tmax")
     getQuantiles(defaultProbabilities).foreach{q => out.print(q + "\t")}
     out.println
+    // scalastyle:on println
   }
 
   def statCounter: StatCounter = StatCounter(data.slice(startIdx, endIdx))
@@ -64,8 +66,10 @@ private[spark] class Distribution(val data: Array[Double], val startIdx: Int, va
    * @param out
    */
   def summary(out: PrintStream = System.out) {
+    // scalastyle:off println
     out.println(statCounter)
     showQuantiles(out)
+    // scalastyle:on println
   }
 }
 
@@ -80,8 +84,10 @@ private[spark] object Distribution {
   }
 
   def showQuantiles(out: PrintStream = System.out, quantiles: Traversable[Double]) {
+    // scalastyle:off println
     out.println("min\t25%\t50%\t75%\tmax")
     quantiles.foreach{q => out.print(q + "\t")}
     out.println
+    // scalastyle:on println
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala
index c4a7b4441c85c..85fb923cd9bc7 100644
--- a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala
@@ -70,12 +70,14 @@ private[spark] object XORShiftRandom {
    * @param args takes one argument - the number of random numbers to generate
    */
   def main(args: Array[String]): Unit = {
+    // scalastyle:off println
     if (args.length != 1) {
       println("Benchmark of XORShiftRandom vis-a-vis java.util.Random")
       println("Usage: XORShiftRandom number_of_random_numbers_to_generate")
       System.exit(1)
     }
     println(benchmark(args(0).toInt))
+    // scalastyle:on println
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/DistributedSuite.scala b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
index 9c191ed52206d..2300bcff4f118 100644
--- a/core/src/test/scala/org/apache/spark/DistributedSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
@@ -107,7 +107,9 @@ class DistributedSuite extends SparkFunSuite with Matchers with LocalSparkContex
     sc = new SparkContext(clusterUrl, "test")
     val accum = sc.accumulator(0)
     val thrown = intercept[SparkException] {
+      // scalastyle:off println
       sc.parallelize(1 to 10, 10).foreach(x => println(x / 0))
+      // scalastyle:on println
     }
     assert(thrown.getClass === classOf[SparkException])
     assert(thrown.getMessage.contains("failed 4 times"))
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index a8c8c6f73fb5a..b099cd3fb7965 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -130,7 +130,9 @@ class FailureSuite extends SparkFunSuite with LocalSparkContext {
 
     // Non-serializable closure in foreach function
     val thrown2 = intercept[SparkException] {
+      // scalastyle:off println
       sc.parallelize(1 to 10, 2).foreach(x => println(a))
+      // scalastyle:on println
     }
     assert(thrown2.getClass === classOf[SparkException])
     assert(thrown2.getMessage.contains("NotSerializableException") ||
diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala
index 6e65b0a8f6c76..876418aa13029 100644
--- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala
@@ -51,7 +51,9 @@ class FileServerSuite extends SparkFunSuite with LocalSparkContext {
 
     val textFile = new File(testTempDir, "FileServerSuite.txt")
     val pw = new PrintWriter(textFile)
+    // scalastyle:off println
     pw.println("100")
+    // scalastyle:on println
     pw.close()
 
     val jarFile = new File(testTempDir, "test.jar")
diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
index 6580139df6c60..48509f0759a3b 100644
--- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
@@ -36,7 +36,7 @@ object ThreadingSuiteState {
   }
 }
 
-class ThreadingSuite extends SparkFunSuite with LocalSparkContext {
+class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
 
   test("accessing SparkContext form a different thread") {
     sc = new SparkContext("local", "test")
@@ -130,8 +130,6 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext {
               Thread.sleep(100)
             }
             if (running.get() != 4) {
-              println("Waited 1 second without seeing runningThreads = 4 (it was " +
-                running.get() + "); failing test")
               ThreadingSuiteState.failed.set(true)
             }
             number
@@ -143,6 +141,8 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext {
     }
     sem.acquire(2)
     if (ThreadingSuiteState.failed.get()) {
+      logError("Waited 1 second without seeing runningThreads = 4 (it was " +
+                ThreadingSuiteState.runningThreads.get() + "); failing test")
       fail("One or more threads didn't see runningThreads = 4")
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 2e05dec99b6bf..1b64c329b5d4b 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -51,9 +51,11 @@ class SparkSubmitSuite
   /** Simple PrintStream that reads data into a buffer */
   private class BufferPrintStream extends PrintStream(noOpOutputStream) {
     var lineBuffer = ArrayBuffer[String]()
+    // scalastyle:off println
     override def println(line: String) {
       lineBuffer += line
     }
+    // scalastyle:on println
   }
 
   /** Returns true if the script exits and the given search string is printed. */
@@ -81,6 +83,7 @@ class SparkSubmitSuite
     }
   }
 
+  // scalastyle:off println
   test("prints usage on empty input") {
     testPrematureExit(Array[String](), "Usage: spark-submit")
   }
@@ -491,6 +494,7 @@ class SparkSubmitSuite
       appArgs.executorMemory should be ("2.3g")
     }
   }
+  // scalastyle:on println
 
   // NOTE: This is an expensive operation in terms of time (10 seconds+). Use sparingly.
   private def runSparkSubmit(args: Seq[String]): Unit = {
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
index c9b435a9228d3..01ece1a10f46d 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
@@ -41,9 +41,11 @@ class SparkSubmitUtilsSuite extends SparkFunSuite with BeforeAndAfterAll {
   /** Simple PrintStream that reads data into a buffer */
   private class BufferPrintStream extends PrintStream(noOpOutputStream) {
     var lineBuffer = ArrayBuffer[String]()
+    // scalastyle:off println
     override def println(line: String) {
       lineBuffer += line
     }
+    // scalastyle:on println
   }
 
   override def beforeAll() {
diff --git a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala
index 63947df3d43a2..8a199459c1ddf 100644
--- a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala
@@ -27,7 +27,7 @@ import org.scalatest.BeforeAndAfterAll
 
 import org.apache.hadoop.io.Text
 
-import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark.{Logging, SparkConf, SparkContext, SparkFunSuite}
 import org.apache.spark.util.Utils
 import org.apache.hadoop.io.compress.{DefaultCodec, CompressionCodecFactory, GzipCodec}
 
@@ -36,7 +36,7 @@ import org.apache.hadoop.io.compress.{DefaultCodec, CompressionCodecFactory, Gzi
  * [[org.apache.spark.input.WholeTextFileRecordReader WholeTextFileRecordReader]]. A temporary
  * directory is created as fake input. Temporal storage would be deleted in the end.
  */
-class WholeTextFileRecordReaderSuite extends SparkFunSuite with BeforeAndAfterAll {
+class WholeTextFileRecordReaderSuite extends SparkFunSuite with BeforeAndAfterAll with Logging {
   private var sc: SparkContext = _
   private var factory: CompressionCodecFactory = _
 
@@ -85,7 +85,7 @@ class WholeTextFileRecordReaderSuite extends SparkFunSuite with BeforeAndAfterAl
    */
   test("Correctness of WholeTextFileRecordReader.") {
     val dir = Utils.createTempDir()
-    println(s"Local disk address is ${dir.toString}.")
+    logInfo(s"Local disk address is ${dir.toString}.")
 
     WholeTextFileRecordReaderSuite.files.foreach { case (filename, contents) =>
       createNativeFile(dir, filename, contents, false)
@@ -109,7 +109,7 @@ class WholeTextFileRecordReaderSuite extends SparkFunSuite with BeforeAndAfterAl
 
   test("Correctness of WholeTextFileRecordReader with GzipCodec.") {
     val dir = Utils.createTempDir()
-    println(s"Local disk address is ${dir.toString}.")
+    logInfo(s"Local disk address is ${dir.toString}.")
 
     WholeTextFileRecordReaderSuite.files.foreach { case (filename, contents) =>
       createNativeFile(dir, filename, contents, true)
diff --git a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
index 9e4d34fb7d382..d3218a548efc7 100644
--- a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
@@ -60,7 +60,9 @@ class InputOutputMetricsSuite extends SparkFunSuite with SharedSparkContext
     tmpFile = new File(testTempDir, getClass.getSimpleName + ".txt")
     val pw = new PrintWriter(new FileWriter(tmpFile))
     for (x <- 1 to numRecords) {
+      // scalastyle:off println
       pw.println(RandomUtils.nextInt(0, numBuckets))
+      // scalastyle:on println
     }
     pw.close()
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
index ff3fa95ec32ae..4e3defb43a021 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
@@ -52,8 +52,10 @@ class ReplayListenerSuite extends SparkFunSuite with BeforeAndAfter {
     val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None,
       125L, "Mickey", None)
     val applicationEnd = SparkListenerApplicationEnd(1000L)
+    // scalastyle:off println
     writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationStart))))
     writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationEnd))))
+    // scalastyle:on println
     writer.close()
 
     val conf = EventLoggingListenerSuite.getLoggingConf(logFilePath)
diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
index 1053c6caf7718..480722a5ac182 100644
--- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
@@ -375,6 +375,7 @@ class TestCreateNullValue {
     // parameters of the closure constructor. This allows us to test whether
     // null values are created correctly for each type.
     val nestedClosure = () => {
+      // scalastyle:off println
       if (s.toString == "123") { // Don't really output them to avoid noisy
         println(bo)
         println(c)
@@ -389,6 +390,7 @@ class TestCreateNullValue {
       val closure = () => {
         println(getX)
       }
+      // scalastyle:on println
       ClosureCleaner.clean(closure)
     }
     nestedClosure()
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index 251a797dc28a2..c7638507c88c6 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -684,7 +684,9 @@ class UtilsSuite extends SparkFunSuite with ResetSystemProperties with Logging {
     val buffer = new CircularBuffer(25)
     val stream = new java.io.PrintStream(buffer, true, "UTF-8")
 
+    // scalastyle:off println
     stream.println("test circular test circular test circular test circular test circular")
+    // scalastyle:on println
     assert(buffer.toString === "t circular test circular\n")
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala
index 5a5919fca2469..4f382414a8dd7 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/SizeTrackerSuite.scala
@@ -103,7 +103,9 @@ private object SizeTrackerSuite {
    */
   def main(args: Array[String]): Unit = {
     if (args.size < 1) {
+      // scalastyle:off println
       println("Usage: SizeTrackerSuite [num elements]")
+      // scalastyle:on println
       System.exit(1)
     }
     val numElements = args(0).toInt
@@ -180,11 +182,13 @@ private object SizeTrackerSuite {
       baseTimes: Seq[Long],
       sampledTimes: Seq[Long],
       unsampledTimes: Seq[Long]): Unit = {
+    // scalastyle:off println
     println(s"Average times for $testName (ms):")
     println("  Base - " + averageTime(baseTimes))
     println("  SizeTracker (sampled) - " + averageTime(sampledTimes))
     println("  SizeEstimator (unsampled) - " + averageTime(unsampledTimes))
     println()
+    // scalastyle:on println
   }
 
   def time(f: => Unit): Long = {
diff --git a/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala
index b2f5d9009ee5d..fefa5165db197 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/SorterSuite.scala
@@ -20,10 +20,10 @@ package org.apache.spark.util.collection
 import java.lang.{Float => JFloat, Integer => JInteger}
 import java.util.{Arrays, Comparator}
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.util.random.XORShiftRandom
 
-class SorterSuite extends SparkFunSuite {
+class SorterSuite extends SparkFunSuite with Logging {
 
   test("equivalent to Arrays.sort") {
     val rand = new XORShiftRandom(123)
@@ -74,7 +74,7 @@ class SorterSuite extends SparkFunSuite {
   /** Runs an experiment several times. */
   def runExperiment(name: String, skip: Boolean = false)(f: => Unit, prepare: () => Unit): Unit = {
     if (skip) {
-      println(s"Skipped experiment $name.")
+      logInfo(s"Skipped experiment $name.")
       return
     }
 
@@ -86,11 +86,11 @@ class SorterSuite extends SparkFunSuite {
     while (i < 10) {
       val time = org.apache.spark.util.Utils.timeIt(1)(f, Some(prepare))
       next10 += time
-      println(s"$name: Took $time ms")
+      logInfo(s"$name: Took $time ms")
       i += 1
     }
 
-    println(s"$name: ($firstTry ms first try, ${next10 / 10} ms average)")
+    logInfo(s"$name: ($firstTry ms first try, ${next10 / 10} ms average)")
   }
 
   /**
diff --git a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala
index fc03fec9866a6..61d91c70e9709 100644
--- a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala
+++ b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package main.scala
 
 import scala.util.Try
@@ -59,3 +60,4 @@ object SimpleApp {
     }
   }
 }
+// scalastyle:on println
diff --git a/dev/audit-release/sbt_app_ganglia/src/main/scala/SparkApp.scala b/dev/audit-release/sbt_app_ganglia/src/main/scala/SparkApp.scala
index 0be8e64fbfabd..9f7ae75d0b477 100644
--- a/dev/audit-release/sbt_app_ganglia/src/main/scala/SparkApp.scala
+++ b/dev/audit-release/sbt_app_ganglia/src/main/scala/SparkApp.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package main.scala
 
 import scala.util.Try
@@ -37,3 +38,4 @@ object SimpleApp {
     }
   }
 }
+// scalastyle:on println
diff --git a/dev/audit-release/sbt_app_graphx/src/main/scala/GraphxApp.scala b/dev/audit-release/sbt_app_graphx/src/main/scala/GraphxApp.scala
index 24c7f8d667296..2f0b6ef9a5672 100644
--- a/dev/audit-release/sbt_app_graphx/src/main/scala/GraphxApp.scala
+++ b/dev/audit-release/sbt_app_graphx/src/main/scala/GraphxApp.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package main.scala
 
 import org.apache.spark.{SparkContext, SparkConf}
@@ -51,3 +52,4 @@ object GraphXApp {
     println("Test succeeded")
   }
 }
+// scalastyle:on println
diff --git a/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala
index 5111bc0adb772..4a980ec071ae4 100644
--- a/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala
+++ b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package main.scala
 
 import scala.collection.mutable.{ListBuffer, Queue}
@@ -55,3 +56,4 @@ object SparkSqlExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/dev/audit-release/sbt_app_kinesis/src/main/scala/SparkApp.scala b/dev/audit-release/sbt_app_kinesis/src/main/scala/SparkApp.scala
index 9f85066501472..adc25b57d6aa5 100644
--- a/dev/audit-release/sbt_app_kinesis/src/main/scala/SparkApp.scala
+++ b/dev/audit-release/sbt_app_kinesis/src/main/scala/SparkApp.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package main.scala
 
 import scala.util.Try
@@ -31,3 +32,4 @@ object SimpleApp {
     }
   }
 }
+// scalastyle:on println
diff --git a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala
index cc86ef45858c9..69c1154dc0955 100644
--- a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala
+++ b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package main.scala
 
 import scala.collection.mutable.{ListBuffer, Queue}
@@ -57,3 +58,4 @@ object SparkSqlExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala
index 58a662bd9b2e8..d6a074687f4a1 100644
--- a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala
+++ b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package main.scala
 
 import scala.collection.mutable.{ListBuffer, Queue}
@@ -61,3 +62,4 @@ object SparkStreamingExample {
     ssc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
index 4c129dbe2d12d..d812262fd87dc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -52,3 +53,4 @@ object BroadcastTest {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index 023bb3ee2d108..36832f51d2ad4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+ // scalastyle:off println
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
@@ -140,3 +141,4 @@ object CassandraCQLTest {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
index ec689474aecb0..96ef3e198e380 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
@@ -130,6 +131,7 @@ object CassandraTest {
     sc.stop()
   }
 }
+// scalastyle:on println
 
 /*
 create keyspace casDemo;
diff --git a/examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala b/examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala
index 1f12034ce0f57..d651fe4d6ee75 100644
--- a/examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.io.File
@@ -136,3 +137,4 @@ object DFSReadWriteTest {
 
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
index e757283823fc3..c42df2b8845d2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import scala.collection.JavaConversions._
@@ -46,3 +47,4 @@ object DriverSubmissionTest {
     }
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
index 15f6678648b29..fa4a3afeecd19 100644
--- a/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -53,3 +54,4 @@ object GroupByTest {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
index 95c96111c9b1f..244742327a907 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.hadoop.hbase.client.HBaseAdmin
@@ -62,3 +63,4 @@ object HBaseTest {
     admin.close()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
index ed2b38e2ca6f8..124dc9af6390f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.spark._
@@ -41,3 +42,4 @@ object HdfsTest {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
index 3d5259463003d..af5f216f28ba4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.commons.math3.linear._
@@ -142,3 +143,4 @@ object LocalALS {
     new Array2DRowRealMatrix(Array.fill(rows, cols)(math.random))
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
index ac2ea35bbd0e0..9c8aae53cf48d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -73,3 +74,4 @@ object LocalFileLR {
     println("Final w: " + w)
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
index 04fc0a033014a..e7b28d38bdfc6 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -119,3 +120,4 @@ object LocalKMeans {
     println("Final centers: " + kPoints)
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
index c3fc74a116c0a..4f6b092a59ca5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -77,3 +78,4 @@ object LocalLR {
     println("Final w: " + w)
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalPi.scala b/examples/src/main/scala/org/apache/spark/examples/LocalPi.scala
index ee6b3ee34aeb2..3d923625f11b6 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalPi.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalPi.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import scala.math.random
@@ -33,3 +34,4 @@ object LocalPi {
     println("Pi is roughly " + 4 * count / 100000.0)
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
index 75c82117cbad2..a80de10f4610a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -83,3 +84,4 @@ object LogQuery {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
index 2a5c0c0defe13..61ce9db914f9f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.spark.rdd.RDD
@@ -53,3 +54,4 @@ object MultiBroadcastTest {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
index 5291ab81f459e..3b0b00fe4dd0a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -67,3 +68,4 @@ object SimpleSkewedGroupByTest {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
index 017d4e1e5ce13..719e2176fed3f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -57,3 +58,4 @@ object SkewedGroupByTest {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
index 30c4261551837..69799b7c2bb30 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.commons.math3.linear._
@@ -144,3 +145,4 @@ object SparkALS {
     new Array2DRowRealMatrix(Array.fill(rows, cols)(math.random))
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
index 9099c2fcc90b3..505ea5a4c7a85 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -97,3 +98,4 @@ object SparkHdfsLR {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
index b514d9123f5e7..c56e1124ad415 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import breeze.linalg.{Vector, DenseVector, squaredDistance}
@@ -100,3 +101,4 @@ object SparkKMeans {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
index 1e6b4fb0c7514..d265c227f4ed2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -86,3 +87,4 @@ object SparkLR {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
index bd7894f184c4c..0fd79660dd196 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import org.apache.spark.SparkContext._
@@ -74,3 +75,4 @@ object SparkPageRank {
     ctx.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
index 35b8dd6c29b66..818d4f2b81f82 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import scala.math.random
@@ -37,3 +38,4 @@ object SparkPi {
     spark.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
index 772cd897f5140..95072071ccddb 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import scala.util.Random
@@ -70,3 +71,4 @@ object SparkTC {
     spark.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
index 4393b99e636b6..cfbdae02212a5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import java.util.Random
@@ -94,3 +95,4 @@ object SparkTachyonHdfsLR {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
index 7743f7968b100..e46ac655beb58 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples
 
 import scala.math.random
@@ -46,3 +47,4 @@ object SparkTachyonPi {
     spark.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala
index 409721b01c8fd..8dd6c9706e7df 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.graphx
 
 import scala.collection.mutable
@@ -151,3 +152,4 @@ object Analytics extends Logging {
     }
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
index f6f8d9f90c275..da3ffca1a6f2a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.graphx
 
 /**
@@ -42,3 +43,4 @@ object LiveJournalPageRank {
     Analytics.main(args.patch(0, List("pagerank"), 0))
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
index 3ec20d594b784..46e52aacd90bb 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.graphx
 
 import org.apache.spark.SparkContext._
@@ -128,3 +129,4 @@ object SynthBenchmark {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala
index 6c0af20461d3b..14b358d46f6ab 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -110,3 +111,4 @@ object CrossValidatorExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
index 54e4073941056..f28671f7869fc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import scala.collection.mutable
@@ -355,3 +356,4 @@ object DecisionTreeExample {
     println(s"  Root mean squared error (RMSE): $RMSE")
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
index 7b8cc21ed8982..78f31b4ffe56a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -181,3 +182,4 @@ private class MyLogisticRegressionModel(
     copyValues(new MyLogisticRegressionModel(uid, weights), extra)
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
index 33905277c7341..f4a15f806ea81 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import scala.collection.mutable
@@ -236,3 +237,4 @@ object GBTExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala
index b54466fd48bc5..b73299fb12d3f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import scala.collection.mutable
@@ -140,3 +141,4 @@ object LinearRegressionExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
index 3cf193f353fbc..7682557127b51 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import scala.collection.mutable
@@ -157,3 +158,4 @@ object LogisticRegressionExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
index 25f21113bf622..cd411397a4b9d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import scopt.OptionParser
@@ -178,3 +179,4 @@ object MovieLensALS {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
index 6927eb8f275cf..bab31f585b0ef 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import java.util.concurrent.TimeUnit.{NANOSECONDS => NANO}
@@ -183,3 +184,4 @@ object OneVsRestExample {
     (NANO.toSeconds(t1 - t0), result)
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
index 9f7cad68a4594..109178f4137b2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import scala.collection.mutable
@@ -244,3 +245,4 @@ object RandomForestExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
index a0561e2573fc9..58d7b67674ff7 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -100,3 +101,4 @@ object SimpleParamsExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala
index 1324b066c30c3..960280137cbf9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
 import scala.beans.BeanInfo
@@ -89,3 +90,4 @@ object SimpleTextClassificationPipeline {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
index a113653810b93..1a4016f76c2ad 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.log4j.{Level, Logger}
@@ -153,3 +154,4 @@ object BinaryClassification {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala
index e49129c4e7844..026d4ecc6d10a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import scopt.OptionParser
@@ -91,3 +92,4 @@ object Correlations {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/CosineSimilarity.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/CosineSimilarity.scala
index cb1abbd18fd4d..69988cc1b9334 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/CosineSimilarity.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/CosineSimilarity.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import scopt.OptionParser
@@ -106,3 +107,4 @@ object CosineSimilarity {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala
index 520893b26d595..dc13f82488af7 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import java.io.File
@@ -119,3 +120,4 @@ object DatasetExample {
   }
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index 3381941673db8..57ffe3dd2524f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import scala.language.reflectiveCalls
@@ -368,3 +369,4 @@ object DecisionTreeRunner {
   }
   // scalastyle:on structural.type
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala
index f8c71ccabc43b..1fce4ba7efd60 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGaussianMixture.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -65,3 +66,4 @@ object DenseGaussianMixture {
     println()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
index 14cc5cbb679c5..380d85d60e7b4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.log4j.{Level, Logger}
@@ -107,3 +108,4 @@ object DenseKMeans {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala
index 13f24a1e59610..14b930550d554 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import scopt.OptionParser
@@ -80,3 +81,4 @@ object FPGrowthExample {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
index 7416fb5a40848..e16a6bf033574 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import scopt.OptionParser
@@ -145,3 +146,4 @@ object GradientBoostedTreesRunner {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala
index 31d629f853161..75b0f69cf91aa 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import java.text.BreakIterator
@@ -302,3 +303,4 @@ private class SimpleTokenizer(sc: SparkContext, stopwordFile: String) extends Se
   }
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
index 6a456ba7ec07b..8878061a0970b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.log4j.{Level, Logger}
@@ -134,3 +135,4 @@ object LinearRegression {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
index 99588b0984ab2..e43a6f2864c73 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import scala.collection.mutable
@@ -189,3 +190,4 @@ object MovieLensALS {
     math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 - x._2)).mean())
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala
index 6e4e2d07f284b..5f839c75dd581 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import scopt.OptionParser
@@ -97,3 +98,4 @@ object MultivariateSummarizer {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala
index 6d8b806569dfd..0723223954610 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.log4j.{Level, Logger}
@@ -154,4 +155,4 @@ object PowerIterationClusteringExample {
     coeff * math.exp(expCoeff * ssquares)
   }
 }
-
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala
index 924b586e3af99..bee85ba0f9969 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.mllib.random.RandomRDDs
@@ -58,3 +59,4 @@ object RandomRDDGeneration {
   }
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala
index 663c12734af68..6963f43e082c4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.mllib.util.MLUtils
@@ -125,3 +126,4 @@ object SampledRDDs {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
index f1ff4e6911f5e..f81fc292a3bd1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.log4j.{Level, Logger}
@@ -100,3 +101,4 @@ object SparseNaiveBayes {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeansExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeansExample.scala
index 8bb12d2ee9ed2..af03724a8ac62 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeansExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeansExample.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.SparkConf
@@ -75,3 +76,4 @@ object StreamingKMeansExample {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala
index 1a95048bbfe2d..b4a5dca031abd 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.mllib.linalg.Vectors
@@ -69,3 +70,4 @@ object StreamingLinearRegression {
   }
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala
index e1998099c2d78..b42f4cb5f9338 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.mllib.linalg.Vectors
@@ -71,3 +72,4 @@ object StreamingLogisticRegression {
   }
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala
index 3cd9cb743e309..464fbd385ab5d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -58,3 +59,4 @@ object TallSkinnyPCA {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
index 4d6690318615a..65b4bc46f0266 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -58,3 +59,4 @@ object TallSkinnySVD {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
index b11e32047dc34..2cc56f04e5c1f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.sql
 
 import org.apache.spark.{SparkConf, SparkContext}
@@ -73,3 +74,4 @@ object RDDRelation {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
index b7ba60ec28155..bf40bd1ef13df 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.sql.hive
 
 import com.google.common.io.{ByteStreams, Files}
@@ -77,3 +78,4 @@ object HiveFromSpark {
     sc.stop()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
index 016de4c63d1d2..e9c9907198769 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import scala.collection.mutable.LinkedList
@@ -170,3 +171,4 @@ object ActorWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
index 30269a7ccae97..28e9bf520e568 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import java.io.{InputStreamReader, BufferedReader, InputStream}
@@ -100,3 +101,4 @@ class CustomReceiver(host: String, port: Int)
    }
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala
index fbe394de4a179..bd78526f8c299 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import kafka.serializer.StringDecoder
@@ -70,3 +71,4 @@ object DirectKafkaWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
index 20e7df7c45b1b..91e52e4eff5a7 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
@@ -66,3 +67,4 @@ object FlumeEventCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala
index 1cc8c8d5c23b6..2bdbc37e2a289 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
@@ -65,3 +66,4 @@ object FlumePollingEventCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
index 4b4667fec44e6..1f282d437dc38 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
@@ -53,3 +54,4 @@ object HdfsWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
index 60416ee343544..b40d17e9c2fa3 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import java.util.HashMap
@@ -101,3 +102,4 @@ object KafkaWordCountProducer {
   }
 
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
index 813c8554f5193..d772ae309f40d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.eclipse.paho.client.mqttv3._
@@ -96,8 +97,10 @@ object MQTTWordCount {
 
   def main(args: Array[String]) {
     if (args.length < 2) {
+      // scalastyle:off println
       System.err.println(
         "Usage: MQTTWordCount <MqttbrokerUrl> <topic>")
+      // scalastyle:on println
       System.exit(1)
     }
 
@@ -113,3 +116,4 @@ object MQTTWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
index 2cd8073dada14..9a57fe286d1ae 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
@@ -57,3 +58,4 @@ object NetworkWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
index a9aaa445bccb6..5322929d177b4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
@@ -58,3 +59,4 @@ object RawNetworkGrep {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
index 751b30ea15782..9916882e4f94a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import java.io.File
@@ -108,3 +109,4 @@ object RecoverableNetworkWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala
index 5a6b9216a3fbc..ed617754cbf1c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
@@ -99,3 +100,4 @@ object SQLContextSingleton {
     instance
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
index 345d0bc441351..02ba1c2eed0f7 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
@@ -78,3 +79,4 @@ object StatefulNetworkWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
index c10de84a80ffe..825c671a929b1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import com.twitter.algebird._
@@ -113,3 +114,4 @@ object TwitterAlgebirdCMS {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
index 62db5e663b8af..49826ede70418 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import com.twitter.algebird.HyperLogLogMonoid
@@ -90,3 +91,4 @@ object TwitterAlgebirdHLL {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
index f253d75b279f7..49cee1b43c2dc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.streaming.{Seconds, StreamingContext}
@@ -82,3 +83,4 @@ object TwitterPopularTags {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
index e99d1baa72b9f..6ac9a72c37941 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import akka.actor.ActorSystem
@@ -97,3 +98,4 @@ object ZeroMQWordCount {
     ssc.awaitTermination()
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
index 889f052c70263..bea7a47cb2855 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming.clickstream
 
 import java.net.ServerSocket
@@ -108,3 +109,4 @@ object PageViewGenerator {
     }
   }
 }
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
index fbacaee98690f..ec7d39da8b2e9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming.clickstream
 
 import org.apache.spark.SparkContext._
@@ -107,3 +108,4 @@ object PageViewStream {
     ssc.start()
   }
 }
+// scalastyle:on println
diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
index 8e1715f6dbb95..5b3c79444aa68 100644
--- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
+++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
@@ -111,7 +111,7 @@ class DirectKafkaStreamSuite
       rdd
     }.foreachRDD { rdd =>
       for (o <- offsetRanges) {
-        println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
+        logInfo(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
       }
       val collected = rdd.mapPartitionsWithIndex { (i, iter) =>
       // For each partition, get size of the range in the partition,
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
index be8b62d3cc6ba..de749626ec09c 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off println
 package org.apache.spark.examples.streaming
 
 import java.nio.ByteBuffer
@@ -272,3 +273,4 @@ private[streaming] object StreamingExamples extends Logging {
     }
   }
 }
+// scalastyle:on println
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala
index be6b9047d932d..5c07b415cd796 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala
@@ -66,7 +66,6 @@ private[graphx] object BytecodeUtils {
       val finder = new MethodInvocationFinder(c.getName, m)
       getClassReader(c).accept(finder, 0)
       for (classMethod <- finder.methodsInvoked) {
-        // println(classMethod)
         if (classMethod._1 == targetClass && classMethod._2 == targetMethod) {
           return true
         } else if (!seen.contains(classMethod)) {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
index 9591c4e9b8f4e..989e226305265 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
@@ -33,7 +33,7 @@ import org.apache.spark.graphx.Edge
 import org.apache.spark.graphx.impl.GraphImpl
 
 /** A collection of graph generating functions. */
-object GraphGenerators {
+object GraphGenerators extends Logging {
 
   val RMATa = 0.45
   val RMATb = 0.15
@@ -142,7 +142,7 @@ object GraphGenerators {
     var edges: Set[Edge[Int]] = Set()
     while (edges.size < numEdges) {
       if (edges.size % 100 == 0) {
-        println(edges.size + " edges")
+        logDebug(edges.size + " edges")
       }
       edges += addEdge(numVertices)
     }
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala
index 186d0cc2a977b..61e44dcab578c 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/util/BytecodeUtilsSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.graphx.util
 import org.apache.spark.SparkFunSuite
 
 
+// scalastyle:off println
 class BytecodeUtilsSuite extends SparkFunSuite {
 
   import BytecodeUtilsSuite.TestClass
@@ -102,6 +103,7 @@ class BytecodeUtilsSuite extends SparkFunSuite {
   private val c = {e: TestClass => println(e.baz)}
 }
 
+// scalastyle:on println
 
 object BytecodeUtilsSuite {
   class TestClass(val foo: Int, val bar: Long) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
index 6eaebaf7dba9f..e6bcff48b022c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
@@ -64,8 +64,10 @@ object KMeansDataGenerator {
 
   def main(args: Array[String]) {
     if (args.length < 6) {
+      // scalastyle:off println
       println("Usage: KMeansGenerator " +
         "<master> <output_dir> <num_points> <k> <d> <r> [<num_partitions>]")
+      // scalastyle:on println
       System.exit(1)
     }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index b4e33c98ba7e5..87eeb5db05d26 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -153,8 +153,10 @@ object LinearDataGenerator {
 
   def main(args: Array[String]) {
     if (args.length < 2) {
+      // scalastyle:off println
       println("Usage: LinearDataGenerator " +
         "<master> <output_dir> [num_examples] [num_features] [num_partitions]")
+      // scalastyle:on println
       System.exit(1)
     }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
index 9d802678c4a77..c09cbe69bb971 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
@@ -64,8 +64,10 @@ object LogisticRegressionDataGenerator {
 
   def main(args: Array[String]) {
     if (args.length != 5) {
+      // scalastyle:off println
       println("Usage: LogisticRegressionGenerator " +
         "<master> <output_dir> <num_examples> <num_features> <num_partitions>")
+      // scalastyle:on println
       System.exit(1)
     }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
index bd73a866c8a82..16f430599a515 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
@@ -55,8 +55,10 @@ import org.apache.spark.rdd.RDD
 object MFDataGenerator {
   def main(args: Array[String]) {
     if (args.length < 2) {
+      // scalastyle:off println
       println("Usage: MFDataGenerator " +
         "<master> <outputDir> [m] [n] [rank] [trainSampFact] [noise] [sigma] [test] [testSampFact]")
+      // scalastyle:on println
       System.exit(1)
     }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
index a8e30cc9d730c..ad20b7694a779 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
@@ -37,8 +37,10 @@ object SVMDataGenerator {
 
   def main(args: Array[String]) {
     if (args.length < 2) {
+      // scalastyle:off println
       println("Usage: SVMGenerator " +
         "<master> <output_dir> [num_examples] [num_features] [num_partitions]")
+      // scalastyle:on println
       System.exit(1)
     }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
index 8c85c96d5c6d8..03120c828ca96 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ml.feature
 
 import scala.beans.{BeanInfo, BeanProperty}
 
-import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.{Logging, SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors}
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
+class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext with Logging {
 
   import VectorIndexerSuite.FeatureData
 
@@ -113,11 +113,11 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
     model.transform(sparsePoints1) // should work
     intercept[SparkException] {
       model.transform(densePoints2).collect()
-      println("Did not throw error when fit, transform were called on vectors of different lengths")
+      logInfo("Did not throw error when fit, transform were called on vectors of different lengths")
     }
     intercept[SparkException] {
       vectorIndexer.fit(badPoints)
-      println("Did not throw error when fitting vectors of different lengths in same RDD.")
+      logInfo("Did not throw error when fitting vectors of different lengths in same RDD.")
     }
   }
 
@@ -196,7 +196,7 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
         }
       } catch {
         case e: org.scalatest.exceptions.TestFailedException =>
-          println(errMsg)
+          logError(errMsg)
           throw e
       }
     }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index c4ae0a16f7c04..178d95a7b94ec 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -21,10 +21,10 @@ import scala.util.Random
 
 import breeze.linalg.{DenseMatrix => BDM, squaredDistance => breezeSquaredDistance}
 
-import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.{Logging, SparkException, SparkFunSuite}
 import org.apache.spark.mllib.util.TestingUtils._
 
-class VectorsSuite extends SparkFunSuite {
+class VectorsSuite extends SparkFunSuite with Logging {
 
   val arr = Array(0.1, 0.0, 0.3, 0.4)
   val n = 4
@@ -142,7 +142,7 @@ class VectorsSuite extends SparkFunSuite {
     malformatted.foreach { s =>
       intercept[SparkException] {
         Vectors.parse(s)
-        println(s"Didn't detect malformatted string $s.")
+        logInfo(s"Didn't detect malformatted string $s.")
       }
     }
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
index c292ced75e870..c3eeda012571c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
@@ -19,13 +19,13 @@ package org.apache.spark.mllib.stat
 
 import breeze.linalg.{DenseMatrix => BDM, Matrix => BM}
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.stat.correlation.{Correlations, PearsonCorrelation,
   SpearmanCorrelation}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 
-class CorrelationSuite extends SparkFunSuite with MLlibTestSparkContext {
+class CorrelationSuite extends SparkFunSuite with MLlibTestSparkContext with Logging {
 
   // test input data
   val xData = Array(1.0, 0.0, -2.0)
@@ -146,7 +146,7 @@ class CorrelationSuite extends SparkFunSuite with MLlibTestSparkContext {
   def matrixApproxEqual(A: BM[Double], B: BM[Double], threshold: Double = 1e-6): Boolean = {
     for (i <- 0 until A.rows; j <- 0 until A.cols) {
       if (!approxEqual(A(i, j), B(i, j), threshold)) {
-        println("i, j = " + i + ", " + j + " actual: " + A(i, j) + " expected:" + B(i, j))
+        logInfo("i, j = " + i + ", " + j + " actual: " + A(i, j) + " expected:" + B(i, j))
         return false
       }
     }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
index 84dd3b342d4c0..2521b3342181a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Strategy}
@@ -31,7 +31,7 @@ import org.apache.spark.util.Utils
 /**
  * Test suite for [[GradientBoostedTrees]].
  */
-class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext {
+class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext with Logging {
 
   test("Regression with continuous features: SquaredError") {
     GradientBoostedTreesSuite.testCombinations.foreach {
@@ -50,7 +50,7 @@ class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext
           EnsembleTestHelper.validateRegressor(gbt, GradientBoostedTreesSuite.data, 0.06)
         } catch {
           case e: java.lang.AssertionError =>
-            println(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," +
+            logError(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," +
               s" subsamplingRate=$subsamplingRate")
             throw e
         }
@@ -80,7 +80,7 @@ class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext
           EnsembleTestHelper.validateRegressor(gbt, GradientBoostedTreesSuite.data, 0.85, "mae")
         } catch {
           case e: java.lang.AssertionError =>
-            println(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," +
+            logError(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," +
               s" subsamplingRate=$subsamplingRate")
             throw e
         }
@@ -111,7 +111,7 @@ class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext
           EnsembleTestHelper.validateClassifier(gbt, GradientBoostedTreesSuite.data, 0.9)
         } catch {
           case e: java.lang.AssertionError =>
-            println(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," +
+            logError(s"FAILED for numIterations=$numIterations, learningRate=$learningRate," +
               s" subsamplingRate=$subsamplingRate")
             throw e
         }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
index fa4f74d71b7e7..16d7c3ab39b03 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala
@@ -33,7 +33,7 @@ class NumericParserSuite extends SparkFunSuite {
     malformatted.foreach { s =>
       intercept[SparkException] {
         NumericParser.parse(s)
-        println(s"Didn't detect malformatted string $s.")
+        throw new RuntimeException(s"Didn't detect malformatted string $s.")
       }
     }
   }
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 3408c6d51ed4c..4291b0be2a616 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -69,6 +69,7 @@ object SparkBuild extends PomBuild {
     import scala.collection.mutable
     var isAlphaYarn = false
     var profiles: mutable.Seq[String] = mutable.Seq("sbt")
+    // scalastyle:off println
     if (Properties.envOrNone("SPARK_GANGLIA_LGPL").isDefined) {
       println("NOTE: SPARK_GANGLIA_LGPL is deprecated, please use -Pspark-ganglia-lgpl flag.")
       profiles ++= Seq("spark-ganglia-lgpl")
@@ -88,6 +89,7 @@ object SparkBuild extends PomBuild {
       println("NOTE: SPARK_YARN is deprecated, please use -Pyarn flag.")
       profiles ++= Seq("yarn")
     }
+    // scalastyle:on println
     profiles
   }
 
@@ -96,8 +98,10 @@ object SparkBuild extends PomBuild {
     case None => backwardCompatibility
     case Some(v) =>
       if (backwardCompatibility.nonEmpty)
+        // scalastyle:off println
         println("Note: We ignore environment variables, when use of profile is detected in " +
           "conjunction with environment variable.")
+        // scalastyle:on println
       v.split("(\\s+|,)").filterNot(_.isEmpty).map(_.trim.replaceAll("-P", "")).toSeq
     }
 
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala
index 6480e2d24e044..24fbbc12c08da 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala
@@ -39,6 +39,8 @@ class SparkCommandLine(args: List[String], override val settings: Settings)
   }
 
   def this(args: List[String]) {
+    // scalastyle:off println
     this(args, str => Console.println("Error: " + str))
+    // scalastyle:on println
   }
 }
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 2b235525250c2..8f7f9074d3f03 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -1101,7 +1101,9 @@ object SparkILoop extends Logging {
             val s = super.readLine()
             // helping out by printing the line being interpreted.
             if (s != null)
+              // scalastyle:off println
               output.println(s)
+              // scalastyle:on println
             s
           }
         }
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala
index 05faef8786d2c..bd3314d94eed6 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala
@@ -80,11 +80,13 @@ private[repl] trait SparkILoopInit {
     if (!initIsComplete)
       withLock { while (!initIsComplete) initLoopCondition.await() }
     if (initError != null) {
+      // scalastyle:off println
       println("""
         |Failed to initialize the REPL due to an unexpected error.
         |This is a bug, please, report it along with the error diagnostics printed below.
         |%s.""".stripMargin.format(initError)
       )
+      // scalastyle:on println
       false
     } else true
   }
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala
index 35fb625645022..8791618bd355e 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala
@@ -1761,7 +1761,9 @@ object SparkIMain {
         if (intp.totalSilence) ()
         else super.printMessage(msg)
       }
+      // scalastyle:off println
       else Console.println(msg)
+      // scalastyle:on println
     }
   }
 }
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 7a5e94da5cbf3..3c90287249497 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -943,7 +943,9 @@ object SparkILoop {
             val s = super.readLine()
             // helping out by printing the line being interpreted.
             if (s != null)
+              // scalastyle:off println
               output.println(s)
+              // scalastyle:on println
             s
           }
         }
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala
index 1cb910f376060..56c009a4e38e7 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala
@@ -129,7 +129,9 @@ class SparkIMain(@BeanProperty val factory: ScriptEngineFactory, initialSettings
   }
   private def tquoted(s: String) = "\"\"\"" + s + "\"\"\""
   private val logScope = scala.sys.props contains "scala.repl.scope"
+  // scalastyle:off println
   private def scopelog(msg: String) = if (logScope) Console.err.println(msg)
+  // scalastyle:on println
 
   // argument is a thunk to execute after init is done
   def initialize(postInitSignal: => Unit) {
@@ -1297,8 +1299,10 @@ class SparkISettings(intp: SparkIMain) {
   def deprecation_=(x: Boolean) = {
     val old = intp.settings.deprecation.value
     intp.settings.deprecation.value = x
+    // scalastyle:off println
     if (!old && x) println("Enabled -deprecation output.")
     else if (old && !x) println("Disabled -deprecation output.")
+    // scalastyle:on println
   }
   def deprecation: Boolean = intp.settings.deprecation.value
 
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala
index 0711ed4871bb6..272f81eca92c1 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala
@@ -42,7 +42,9 @@ class SparkReplReporter(intp: SparkIMain) extends ConsoleReporter(intp.settings,
       }
       else super.printMessage(msg)
     }
+    // scalastyle:off println
     else Console.println("[init] " + msg)
+    // scalastyle:on println
   }
 
   override def displayPrompt() {
diff --git a/scalastyle-config.xml b/scalastyle-config.xml
index d6f927b6fa803..49611703798e8 100644
--- a/scalastyle-config.xml
+++ b/scalastyle-config.xml
@@ -141,12 +141,8 @@ This file is divided into 3 sections:
     <customMessage>Tests must extend org.apache.spark.SparkFunSuite instead.</customMessage>
   </check>
 
-  <!-- ================================================================================ -->
-  <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
-  <!-- ================================================================================ -->
-
-  <!-- SPARK-7977 We should turn this on, but we'd need to add whitelist to files that are using it first. -->
-  <check customId="println" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="false">
+  <!-- As of SPARK-7977 all printlns need to be wrapped in '// scalastyle:off/on println' -->
+  <check customId="println" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
     <parameters><parameter name="regex">^println$</parameter></parameters>
     <customMessage><![CDATA[Are you sure you want to println? If yes, wrap the code block with
       // scalastyle:off println
@@ -154,6 +150,10 @@ This file is divided into 3 sections:
       // scalastyle:on println]]></customMessage>
   </check>
 
+  <!-- ================================================================================ -->
+  <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
+  <!-- ================================================================================ -->
+
   <!-- We cannot turn the following two on, because it'd fail a lot of string interpolation use cases. -->
   <!-- Ideally the following two rules should be configurable to rule out string interpolation. -->
   <check level="error" class="org.scalastyle.scalariform.NoWhitespaceBeforeLeftBracketChecker" enabled="false"></check>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala
index 7f1b12cdd5800..606fecbe06e47 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/package.scala
@@ -67,8 +67,10 @@ package object codegen {
       outfile.write(generatedBytes)
       outfile.close()
 
+      // scalastyle:off println
       println(
         s"javap -p -v -classpath ${dumpDirectory.getCanonicalPath} ${generatedClass.getName}".!!)
+      // scalastyle:on println
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index 2f545bb432165..b89e3382f06a9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -154,7 +154,9 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
   def schemaString: String = schema.treeString
 
   /** Prints out the schema in the tree format */
+  // scalastyle:off println
   def printSchema(): Unit = println(schemaString)
+  // scalastyle:on println
 
   /**
    * A prefix string used when printing the plan.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
index 07054166a5e88..71293475ca0f9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
@@ -124,7 +124,9 @@ package object util {
     val startTime = System.nanoTime()
     val ret = f
     val endTime = System.nanoTime()
+    // scalastyle:off println
     println(s"${(endTime - startTime).toDouble / 1000000}ms")
+    // scalastyle:on println
     ret
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index e0b8ff91786a7..b8097403ec3cc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -250,7 +250,9 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
     builder.toString()
   }
 
+  // scalastyle:off println
   def printTreeString(): Unit = println(treeString)
+  // scalastyle:on println
 
   private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = {
     fields.foreach(field => field.buildFormattedString(prefix, builder))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index f201c8ea8a110..10250264625b2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -860,11 +860,13 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    * @since 1.3.0
    */
   def explain(extended: Boolean): Unit = {
+    // scalastyle:off println
     if (extended) {
       println(expr)
     } else {
       println(expr.prettyString)
     }
+    // scalastyle:on println
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index d7966651b1948..830fba35bb7bc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -308,7 +308,9 @@ class DataFrame private[sql](
    * @group basic
    * @since 1.3.0
    */
+  // scalastyle:off println
   def printSchema(): Unit = println(schema.treeString)
+  // scalastyle:on println
 
   /**
    * Prints the plans (logical and physical) to the console for debugging purposes.
@@ -319,7 +321,9 @@ class DataFrame private[sql](
     ExplainCommand(
       queryExecution.logical,
       extended = extended).queryExecution.executedPlan.executeCollect().map {
+      // scalastyle:off println
       r => println(r.getString(0))
+      // scalastyle:on println
     }
   }
 
@@ -392,7 +396,9 @@ class DataFrame private[sql](
    * @group action
    * @since 1.5.0
    */
+  // scalastyle:off println
   def show(numRows: Int, truncate: Boolean): Unit = println(showString(numRows, truncate))
+  // scalastyle:on println
 
   /**
    * Returns a [[DataFrameNaFunctions]] for working with missing data.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index 2964edac1aba2..e6081cb05bc2d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -24,7 +24,7 @@ import org.apache.spark.unsafe.types.UTF8String
 
 import scala.collection.mutable.HashSet
 
-import org.apache.spark.{AccumulatorParam, Accumulator}
+import org.apache.spark.{AccumulatorParam, Accumulator, Logging}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.trees.TreeNodeRef
@@ -57,7 +57,7 @@ package object debug {
    * Augments [[DataFrame]]s with debug methods.
    */
   @DeveloperApi
-  implicit class DebugQuery(query: DataFrame) {
+  implicit class DebugQuery(query: DataFrame) extends Logging {
     def debug(): Unit = {
       val plan = query.queryExecution.executedPlan
       val visited = new collection.mutable.HashSet[TreeNodeRef]()
@@ -66,7 +66,7 @@ package object debug {
           visited += new TreeNodeRef(s)
           DebugNode(s)
       }
-      println(s"Results returned: ${debugPlan.execute().count()}")
+      logDebug(s"Results returned: ${debugPlan.execute().count()}")
       debugPlan.foreach {
         case d: DebugNode => d.dumpStats()
         case _ =>
@@ -82,11 +82,11 @@ package object debug {
           TypeCheck(s)
       }
       try {
-        println(s"Results returned: ${debugPlan.execute().count()}")
+        logDebug(s"Results returned: ${debugPlan.execute().count()}")
       } catch {
         case e: Exception =>
           def unwrap(e: Throwable): Throwable = if (e.getCause == null) e else unwrap(e.getCause)
-          println(s"Deepest Error: ${unwrap(e)}")
+          logDebug(s"Deepest Error: ${unwrap(e)}")
       }
     }
   }
@@ -119,11 +119,11 @@ package object debug {
     val columnStats: Array[ColumnMetrics] = Array.fill(child.output.size)(new ColumnMetrics())
 
     def dumpStats(): Unit = {
-      println(s"== ${child.simpleString} ==")
-      println(s"Tuples output: ${tupleCount.value}")
+      logDebug(s"== ${child.simpleString} ==")
+      logDebug(s"Tuples output: ${tupleCount.value}")
       child.output.zip(columnStats).foreach { case(attr, metric) =>
         val actualDataTypes = metric.elementTypes.value.mkString("{", ",", "}")
-        println(s" ${attr.name} ${attr.dataType}: $actualDataTypes")
+        logDebug(s" ${attr.name} ${attr.dataType}: $actualDataTypes")
       }
     }
 
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index 039cfa40d26b3..f66a17b20915f 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -40,7 +40,7 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.util.Utils
 
-private[hive] object SparkSQLCLIDriver {
+private[hive] object SparkSQLCLIDriver extends Logging {
   private var prompt = "spark-sql"
   private var continuedPrompt = "".padTo(prompt.length, ' ')
   private var transport: TSocket = _
@@ -164,7 +164,7 @@ private[hive] object SparkSQLCLIDriver {
       }
     } catch {
       case e: FileNotFoundException =>
-        System.err.println(s"Could not open input file for reading. (${e.getMessage})")
+        logError(s"Could not open input file for reading. (${e.getMessage})")
         System.exit(3)
     }
 
@@ -180,14 +180,14 @@ private[hive] object SparkSQLCLIDriver {
         val historyFile = historyDirectory + File.separator + ".hivehistory"
         reader.setHistory(new History(new File(historyFile)))
       } else {
-        System.err.println("WARNING: Directory for Hive history file: " + historyDirectory +
+        logWarning("WARNING: Directory for Hive history file: " + historyDirectory +
                            " does not exist.   History will not be available during this session.")
       }
     } catch {
       case e: Exception =>
-        System.err.println("WARNING: Encountered an error while trying to initialize Hive's " +
+        logWarning("WARNING: Encountered an error while trying to initialize Hive's " +
                            "history file.  History will not be available during this session.")
-        System.err.println(e.getMessage)
+        logWarning(e.getMessage)
     }
 
     val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
@@ -270,6 +270,7 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
       val proc: CommandProcessor = CommandProcessorFactory.get(Array(tokens(0)), hconf)
 
       if (proc != null) {
+        // scalastyle:off println
         if (proc.isInstanceOf[Driver] || proc.isInstanceOf[SetProcessor] ||
           proc.isInstanceOf[AddResourceProcessor]) {
           val driver = new SparkSQLDriver
@@ -336,6 +337,7 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
           }
           ret = proc.run(cmd_1).getResponseCode
         }
+        // scalastyle:on println
       }
       ret
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index bbc39b892b79e..4684d48aff889 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -34,6 +34,7 @@ import org.apache.hadoop.hive.ql.parse.VariableSubstitution
 import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.serde2.io.{DateWritable, TimestampWritable}
 
+import org.apache.spark.Logging
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
@@ -65,12 +66,12 @@ private[hive] class HiveQLDialect extends ParserDialect {
  *
  * @since 1.0.0
  */
-class HiveContext(sc: SparkContext) extends SQLContext(sc) {
+class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
   self =>
 
   import HiveContext._
 
-  println("create HiveContext")
+  logDebug("create HiveContext")
 
   /**
    * When true, enables an experimental feature where metastore tables that use the parquet SerDe
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 2de7a99c122fd..7fc517b646b20 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -28,6 +28,7 @@ import org.apache.hadoop.hive.ql.parse._
 import org.apache.hadoop.hive.ql.plan.PlanUtils
 import org.apache.hadoop.hive.ql.session.SessionState
 
+import org.apache.spark.Logging
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
@@ -73,7 +74,7 @@ private[hive] case class CreateTableAsSelect(
 }
 
 /** Provides a mapping from HiveQL statements to catalyst logical plans and expression trees. */
-private[hive] object HiveQl {
+private[hive] object HiveQl extends Logging {
   protected val nativeCommands = Seq(
     "TOK_ALTERDATABASE_OWNER",
     "TOK_ALTERDATABASE_PROPERTIES",
@@ -186,7 +187,7 @@ private[hive] object HiveQl {
             .map(ast => Option(ast).map(_.transform(rule)).orNull))
       } catch {
         case e: Exception =>
-          println(dumpTree(n))
+          logError(dumpTree(n).toString)
           throw e
       }
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index cbd2bf6b5eede..9d83ca6c113dc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -360,7 +360,9 @@ private[hive] class ClientWrapper(
 
         case _ =>
           if (state.out != null) {
+            // scalastyle:off println
             state.out.println(tokens(0) + " " + cmd_1)
+            // scalastyle:on println
           }
           Seq(proc.run(cmd_1).getResponseCode.toString)
       }
diff --git a/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala b/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala
index 0e428ba1d7456..2590040f2ec1c 100644
--- a/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala
+++ b/sql/hive/src/test/resources/regression-test-SPARK-8489/Main.scala
@@ -30,6 +30,7 @@ import org.apache.spark.sql.hive.HiveContext
  */
 object Main {
   def main(args: Array[String]) {
+    // scalastyle:off println
     println("Running regression test for SPARK-8489.")
     val sc = new SparkContext("local", "testing")
     val hc = new HiveContext(sc)
@@ -38,6 +39,7 @@ object Main {
     val df = hc.createDataFrame(Seq(MyCoolClass("1", "2", "3")))
     df.collect()
     println("Regression test for SPARK-8489 success!")
+    // scalastyle:on println
     sc.stop()
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index e9bb32667936c..983c013bcf86a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.hive.test.TestHive
 
 import org.apache.spark.sql.test.ExamplePointUDT
 import org.apache.spark.sql.types.StructType
 
-class HiveMetastoreCatalogSuite extends SparkFunSuite {
+class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
 
   test("struct field should accept underscore in sub-column name") {
     val metastr = "struct<a: int, b_1: string, c: string>"
@@ -41,7 +41,7 @@ class HiveMetastoreCatalogSuite extends SparkFunSuite {
   test("duplicated metastore relations") {
     import TestHive.implicits._
     val df = TestHive.sql("SELECT * FROM src")
-    println(df.queryExecution)
+    logInfo(df.queryExecution.toString)
     df.as('a).join(df.as('b), $"a.key" === $"b.key")
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index a38ed23b5cf9a..917900e5f46dc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -90,8 +90,10 @@ class HiveSparkSubmitSuite
       "SPARK_TESTING" -> "1",
       "SPARK_HOME" -> sparkHome
     ).run(ProcessLogger(
+      // scalastyle:off println
       (line: String) => { println(s"out> $line") },
       (line: String) => { println(s"err> $line") }
+      // scalastyle:on println
     ))
 
     try {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
index aa5dbe2db6903..508695919e9a7 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
@@ -86,8 +86,6 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
     val message = intercept[QueryExecutionException] {
       sql("CREATE TABLE doubleCreateAndInsertTest (key int, value string)")
     }.getMessage
-
-    println("message!!!!" + message)
   }
 
   test("Double create does not fail when allowExisting = true") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index cc294bc3e8bc3..d910af22c3dd1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -26,6 +26,7 @@ import org.scalatest.BeforeAndAfterAll
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapred.InvalidInputException
 
+import org.apache.spark.Logging
 import org.apache.spark.sql._
 import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive
@@ -40,7 +41,8 @@ import org.apache.spark.util.Utils
 /**
  * Tests for persisting tables created though the data sources API into the metastore.
  */
-class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
+class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll
+  with Logging {
   override val sqlContext = TestHive
 
   var jsonFilePath: String = _
@@ -415,7 +417,7 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeA
            |)
          """.stripMargin)
 
-      sql("DROP TABLE jsonTable").collect().foreach(println)
+      sql("DROP TABLE jsonTable").collect().foreach(i => logInfo(i.toString))
     }
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index eaaa88e17002b..1bde5922b5278 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -315,7 +315,6 @@ class PairUDF extends GenericUDF {
   )
 
   override def evaluate(args: Array[DeferredObject]): AnyRef = {
-    println("Type = %s".format(args(0).getClass.getName))
     Integer.valueOf(args(0).get.asInstanceOf[TestPair].entry._2)
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 192aa6a139bcb..1da0b0a54df07 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -720,12 +720,14 @@ abstract class DStream[T: ClassTag] (
     def foreachFunc: (RDD[T], Time) => Unit = {
       (rdd: RDD[T], time: Time) => {
         val firstNum = rdd.take(num + 1)
+        // scalastyle:off println
         println("-------------------------------------------")
         println("Time: " + time)
         println("-------------------------------------------")
         firstNum.take(num).foreach(println)
         if (firstNum.length > num) println("...")
         println()
+        // scalastyle:on println
       }
     }
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala
index ca2f319f174a2..6addb96752038 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala
@@ -35,7 +35,9 @@ private[streaming]
 object RawTextSender extends Logging {
   def main(args: Array[String]) {
     if (args.length != 4) {
+      // scalastyle:off println
       System.err.println("Usage: RawTextSender <port> <file> <blockSize> <bytesPerSec>")
+      // scalastyle:on println
       System.exit(1)
     }
     // Parse the arguments using a pattern match
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala
index c8eef833eb431..dd32ad5ad811d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala
@@ -106,7 +106,7 @@ class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name:
 }
 
 private[streaming]
-object RecurringTimer {
+object RecurringTimer extends Logging {
 
   def main(args: Array[String]) {
     var lastRecurTime = 0L
@@ -114,7 +114,7 @@ object RecurringTimer {
 
     def onRecur(time: Long) {
       val currentTime = System.currentTimeMillis()
-      println("" + currentTime + ": " + (currentTime - lastRecurTime))
+      logInfo("" + currentTime + ": " + (currentTime - lastRecurTime))
       lastRecurTime = currentTime
     }
     val timer = new  RecurringTimer(new SystemClock(), period, onRecur, "Test")
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala b/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala
index e0f14fd954280..6e9d4431090a2 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala
@@ -43,6 +43,7 @@ object MasterFailureTest extends Logging {
   @volatile var setupCalled = false
 
   def main(args: Array[String]) {
+    // scalastyle:off println
     if (args.size < 2) {
       println(
         "Usage: MasterFailureTest <local/HDFS directory> <# batches> " +
@@ -60,6 +61,7 @@ object MasterFailureTest extends Logging {
     testUpdateStateByKey(directory, numBatches, batchDuration)
 
     println("\n\nSUCCESS\n\n")
+    // scalastyle:on println
   }
 
   def testMap(directory: String, numBatches: Int, batchDuration: Duration) {
@@ -291,10 +293,12 @@ object MasterFailureTest extends Logging {
     }
 
     // Log the output
+    // scalastyle:off println
     println("Expected output, size = " + expectedOutput.size)
     println(expectedOutput.mkString("[", ",", "]"))
     println("Output, size = " + output.size)
     println(output.mkString("[", ",", "]"))
+    // scalastyle:on println
 
     // Match the output with the expected output
     output.foreach(o =>
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
index 7865b06c2e3c2..a2dbae149f311 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
@@ -76,7 +76,6 @@ class JobGeneratorSuite extends TestSuiteBase {
         if (time.milliseconds == longBatchTime) {
           while (waitLatch.getCount() > 0) {
             waitLatch.await()
-            println("Await over")
           }
         }
       })
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index 595ded6ae67fa..9483d2b692ab5 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -92,7 +92,9 @@ object GenerateMIMAIgnore {
         ignoredMembers ++= getAnnotatedOrPackagePrivateMembers(classSymbol)
 
       } catch {
+        // scalastyle:off println
         case _: Throwable => println("Error instrumenting class:" + className)
+        // scalastyle:on println
       }
     }
     (ignoredClasses.flatMap(c => Seq(c, c.replace("$", "#"))).toSet, ignoredMembers.toSet)
@@ -108,7 +110,9 @@ object GenerateMIMAIgnore {
         .filter(_.contains("$$")).map(classSymbol.fullName + "." + _)
     } catch {
       case t: Throwable =>
+        // scalastyle:off println
         println("[WARN] Unable to detect inner functions for class:" + classSymbol.fullName)
+        // scalastyle:on println
         Seq.empty[String]
     }
   }
@@ -128,12 +132,14 @@ object GenerateMIMAIgnore {
       getOrElse(Iterator.empty).mkString("\n")
     File(".generated-mima-class-excludes")
       .writeAll(previousContents + privateClasses.mkString("\n"))
+    // scalastyle:off println
     println("Created : .generated-mima-class-excludes in current directory.")
     val previousMembersContents = Try(File(".generated-mima-member-excludes").lines)
       .getOrElse(Iterator.empty).mkString("\n")
     File(".generated-mima-member-excludes").writeAll(previousMembersContents +
       privateMembers.mkString("\n"))
     println("Created : .generated-mima-member-excludes in current directory.")
+    // scalastyle:on println
   }
 
 
@@ -174,7 +180,9 @@ object GenerateMIMAIgnore {
       try {
         classes += Class.forName(entry.replace('/', '.').stripSuffix(".class"), false, classLoader)
       } catch {
+        // scalastyle:off println
         case _: Throwable => println("Unable to load:" + entry)
+        // scalastyle:on println
       }
     }
     classes
diff --git a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala
index 583823c90c5c6..856ea177a9a10 100644
--- a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala
@@ -323,11 +323,14 @@ object JavaAPICompletenessChecker {
     val missingMethods = javaEquivalents -- javaMethods
 
     for (method <- missingMethods) {
+      // scalastyle:off println
       println(method)
+      // scalastyle:on println
     }
   }
 
   def main(args: Array[String]) {
+    // scalastyle:off println
     println("Missing RDD methods")
     printMissingMethods(classOf[RDD[_]], classOf[JavaRDD[_]])
     println()
@@ -359,5 +362,6 @@ object JavaAPICompletenessChecker {
     println("Missing PairDStream methods")
     printMissingMethods(classOf[PairDStreamFunctions[_, _]], classOf[JavaPairDStream[_, _]])
     println()
+    // scalastyle:on println
   }
 }
diff --git a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala
index baa97616eaff3..0dc2861253f17 100644
--- a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala
@@ -85,7 +85,9 @@ object StoragePerfTester {
             latch.countDown()
           } catch {
             case e: Exception =>
+              // scalastyle:off println
               println("Exception in child thread: " + e + " " + e.getMessage)
+              // scalastyle:on println
               System.exit(1)
           }
         }
@@ -97,9 +99,11 @@ object StoragePerfTester {
     val bytesPerSecond = totalBytes.get() / time
     val bytesPerFile = (totalBytes.get() / (numOutputSplits * numMaps.toDouble)).toLong
 
+    // scalastyle:off println
     System.err.println("files_total\t\t%s".format(numMaps * numOutputSplits))
     System.err.println("bytes_per_file\t\t%s".format(Utils.bytesToString(bytesPerFile)))
     System.err.println("agg_throughput\t\t%s/s".format(Utils.bytesToString(bytesPerSecond.toLong)))
+    // scalastyle:on println
 
     executor.shutdown()
     sc.stop()
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index 68e9f6b4db7f4..37f793763367e 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -85,7 +85,9 @@ class ApplicationMasterArguments(val args: Array[String]) {
     }
 
     if (primaryPyFile != null && primaryRFile != null) {
+      // scalastyle:off println
       System.err.println("Cannot have primary-py-file and primary-r-file at the same time")
+      // scalastyle:on println
       System.exit(-1)
     }
 
@@ -93,6 +95,7 @@ class ApplicationMasterArguments(val args: Array[String]) {
   }
 
   def printUsageAndExit(exitCode: Int, unknownParam: Any = null) {
+    // scalastyle:off println
     if (unknownParam != null) {
       System.err.println("Unknown/unsupported param " + unknownParam)
     }
@@ -111,6 +114,7 @@ class ApplicationMasterArguments(val args: Array[String]) {
       |  --executor-cores NUM   Number of cores for the executors (Default: 1)
       |  --executor-memory MEM  Memory per executor (e.g. 1000M, 2G) (Default: 1G)
       """.stripMargin)
+    // scalastyle:on println
     System.exit(exitCode)
   }
 }
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 4d52ae774ea00..f0af6f875f523 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -938,7 +938,7 @@ private[spark] class Client(
 object Client extends Logging {
   def main(argStrings: Array[String]) {
     if (!sys.props.contains("SPARK_SUBMIT")) {
-      println("WARNING: This client is deprecated and will be removed in a " +
+      logWarning("WARNING: This client is deprecated and will be removed in a " +
         "future version of Spark. Use ./bin/spark-submit with \"--master yarn\"")
     }
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 19d1bbff9993f..20d63d40cf605 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -123,6 +123,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
       throw new SparkException("Executor cores must not be less than " +
         "spark.task.cpus.")
     }
+    // scalastyle:off println
     if (isClusterMode) {
       for (key <- Seq(amMemKey, amMemOverheadKey, amCoresKey)) {
         if (sparkConf.contains(key)) {
@@ -144,11 +145,13 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
         .map(_.toInt)
         .foreach { cores => amCores = cores }
     }
+    // scalastyle:on println
   }
 
   private def parseArgs(inputArgs: List[String]): Unit = {
     var args = inputArgs
 
+    // scalastyle:off println
     while (!args.isEmpty) {
       args match {
         case ("--jar") :: value :: tail =>
@@ -253,6 +256,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
           throw new IllegalArgumentException(getUsageMessage(args))
       }
     }
+    // scalastyle:on println
 
     if (primaryPyFile != null && primaryRFile != null) {
       throw new IllegalArgumentException("Cannot have primary-py-file and primary-r-file" +
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 335e966519c7c..547863d9a0739 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -328,12 +328,14 @@ private object YarnClusterDriver extends Logging with Matchers {
 
   def main(args: Array[String]): Unit = {
     if (args.length != 1) {
+      // scalastyle:off println
       System.err.println(
         s"""
         |Invalid command line: ${args.mkString(" ")}
         |
         |Usage: YarnClusterDriver [result file]
         """.stripMargin)
+      // scalastyle:on println
       System.exit(1)
     }
 
@@ -386,12 +388,14 @@ private object YarnClasspathTest {
 
   def main(args: Array[String]): Unit = {
     if (args.length != 2) {
+      // scalastyle:off println
       System.err.println(
         s"""
         |Invalid command line: ${args.mkString(" ")}
         |
         |Usage: YarnClasspathTest [driver result file] [executor result file]
         """.stripMargin)
+      // scalastyle:on println
       System.exit(1)
     }
 

From 11e22b74a080ea58fb9410b5cc6fa4c03f9198f2 Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Fri, 10 Jul 2015 16:22:49 +0100
Subject: [PATCH 0333/1454] [SPARK-7944] [SPARK-8013] Remove most of the Spark
 REPL fork for Scala 2.11
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR removes most of the code in the Spark REPL for Scala 2.11 and leaves just a couple of overridden methods in `SparkILoop` in order to:

- change welcome message
- restrict available commands (like `:power`)
- initialize Spark context

The two codebases have diverged and it's extremely hard to backport fixes from the upstream REPL. This somewhat radical step is absolutely necessary in order to fix other REPL tickets (like SPARK-8013 - Hive Thrift server for 2.11). BTW, the Scala REPL has fixed the serialization-unfriendly wrappers thanks to ScrapCodes's work in [#4522](https://github.com/scala/scala/pull/4522)

All tests pass and I tried the `spark-shell` on our Mesos cluster with some simple jobs (including with additional jars), everything looked good.

As soon as Scala 2.11.7 is out we need to upgrade and get a shaded `jline` dependency, clearing the way for SPARK-8013.

/cc pwendell

Author: Iulian Dragos <jaguarul@gmail.com>

Closes #6903 from dragos/issue/no-spark-repl-fork and squashes the following commits:

c596c6f [Iulian Dragos] Merge branch 'master' into issue/no-spark-repl-fork
2b1a305 [Iulian Dragos] Removed spaces around multiple imports.
0ce67a6 [Iulian Dragos] Remove -verbose flag for java compiler (added by mistake in an earlier commit).
10edaf9 [Iulian Dragos] Keep the jline dependency only in the 2.10 build.
529293b [Iulian Dragos] Add back Spark REPL files to rat-excludes, since they are part of the 2.10 real.
d85370d [Iulian Dragos] Remove jline dependency from the Spark REPL.
b541930 [Iulian Dragos] Merge branch 'master' into issue/no-spark-repl-fork
2b15962 [Iulian Dragos] Change jline dependency and bump Scala version.
b300183 [Iulian Dragos] Rename package and add license on top of the file, remove files from rat-excludes and removed `-Yrepl-sync` per reviewer’s request.
9d46d85 [Iulian Dragos] Fix SPARK-7944.
abcc7cb [Iulian Dragos] Remove the REPL forked code.
---
 pom.xml                                       |   18 +-
 repl/pom.xml                                  |   19 +-
 .../scala/org/apache/spark/repl/Main.scala    |   16 +-
 .../apache/spark/repl/SparkExprTyper.scala    |   86 --
 .../org/apache/spark/repl/SparkILoop.scala    |  971 +-----------
 .../org/apache/spark/repl/SparkIMain.scala    | 1323 -----------------
 .../org/apache/spark/repl/SparkImports.scala  |  201 ---
 .../spark/repl/SparkJLineCompletion.scala     |  350 -----
 .../spark/repl/SparkMemberHandlers.scala      |  221 ---
 .../apache/spark/repl/SparkReplReporter.scala |   55 -
 .../org/apache/spark/repl/ReplSuite.scala     |   11 +-
 11 files changed, 90 insertions(+), 3181 deletions(-)
 delete mode 100644 repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala
 delete mode 100644 repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala
 delete mode 100644 repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkImports.scala
 delete mode 100644 repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala
 delete mode 100644 repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala
 delete mode 100644 repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala

diff --git a/pom.xml b/pom.xml
index 172fdef4c73da..c2ebc1a11e770 100644
--- a/pom.xml
+++ b/pom.xml
@@ -341,11 +341,6 @@
   </dependencies>
   <dependencyManagement>
     <dependencies>
-      <dependency>
-        <groupId>${jline.groupid}</groupId>
-        <artifactId>jline</artifactId>
-        <version>${jline.version}</version>
-      </dependency>
       <dependency>
         <groupId>com.twitter</groupId>
         <artifactId>chill_${scala.binary.version}</artifactId>
@@ -1826,6 +1821,15 @@
         <jline.version>${scala.version}</jline.version>
         <jline.groupid>org.scala-lang</jline.groupid>
       </properties>
+      <dependencyManagement>
+        <dependencies>
+          <dependency>
+            <groupId>${jline.groupid}</groupId>
+            <artifactId>jline</artifactId>
+            <version>${jline.version}</version>
+          </dependency>
+        </dependencies>
+      </dependencyManagement>
     </profile>
 
     <profile>
@@ -1844,10 +1848,8 @@
         <property><name>scala-2.11</name></property>
       </activation>
       <properties>
-        <scala.version>2.11.6</scala.version>
+        <scala.version>2.11.7</scala.version>
         <scala.binary.version>2.11</scala.binary.version>
-        <jline.version>2.12.1</jline.version>
-        <jline.groupid>jline</jline.groupid>
       </properties>
     </profile>
 
diff --git a/repl/pom.xml b/repl/pom.xml
index 370b2bc2fa8ed..70c9bd7c01296 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -38,11 +38,6 @@
   </properties>
 
   <dependencies>
-    <dependency>
-      <groupId>${jline.groupid}</groupId>
-      <artifactId>jline</artifactId>
-      <version>${jline.version}</version>
-    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
@@ -161,6 +156,20 @@
     </plugins>
   </build>
   <profiles>
+    <profile>
+      <id>scala-2.10</id>
+      <activation>
+        <property><name>!scala-2.11</name></property>
+      </activation>
+      <dependencies>
+        <dependency>
+          <groupId>${jline.groupid}</groupId>
+          <artifactId>jline</artifactId>
+          <version>${jline.version}</version>
+        </dependency>
+      </dependencies>
+    </profile>
+
     <profile>
       <id>scala-2.11</id>
       <activation>
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
index f4f4b626988e9..eed4a379afa60 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
@@ -17,13 +17,14 @@
 
 package org.apache.spark.repl
 
+import java.io.File
+
+import scala.tools.nsc.Settings
+
 import org.apache.spark.util.Utils
 import org.apache.spark._
 import org.apache.spark.sql.SQLContext
 
-import scala.tools.nsc.Settings
-import scala.tools.nsc.interpreter.SparkILoop
-
 object Main extends Logging {
 
   val conf = new SparkConf()
@@ -32,7 +33,8 @@ object Main extends Logging {
   val outputDir = Utils.createTempDir(rootDir)
   val s = new Settings()
   s.processArguments(List("-Yrepl-class-based",
-    "-Yrepl-outdir", s"${outputDir.getAbsolutePath}", "-Yrepl-sync"), true)
+    "-Yrepl-outdir", s"${outputDir.getAbsolutePath}",
+    "-classpath", getAddedJars.mkString(File.pathSeparator)), true)
   val classServer = new HttpServer(conf, outputDir, new SecurityManager(conf))
   var sparkContext: SparkContext = _
   var sqlContext: SQLContext = _
@@ -48,7 +50,6 @@ object Main extends Logging {
     Option(sparkContext).map(_.stop)
   }
 
-
   def getAddedJars: Array[String] = {
     val envJars = sys.env.get("ADD_JARS")
     if (envJars.isDefined) {
@@ -84,10 +85,9 @@ object Main extends Logging {
     val loader = Utils.getContextOrSparkClassLoader
     try {
       sqlContext = loader.loadClass(name).getConstructor(classOf[SparkContext])
-        .newInstance(sparkContext).asInstanceOf[SQLContext] 
+        .newInstance(sparkContext).asInstanceOf[SQLContext]
       logInfo("Created sql context (with Hive support)..")
-    }
-    catch {
+    } catch {
       case _: java.lang.ClassNotFoundException | _: java.lang.NoClassDefFoundError =>
         sqlContext = new SQLContext(sparkContext)
         logInfo("Created sql context..")
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala
deleted file mode 100644
index 8e519fa67f649..0000000000000
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala
+++ /dev/null
@@ -1,86 +0,0 @@
-/* NSC -- new Scala compiler
- * Copyright 2005-2013 LAMP/EPFL
- * @author  Paul Phillips
- */
-
-package scala.tools.nsc
-package interpreter
-
-import scala.tools.nsc.ast.parser.Tokens.EOF
-
-trait SparkExprTyper {
-  val repl: SparkIMain
-
-  import repl._
-  import global.{ reporter => _, Import => _, _ }
-  import naming.freshInternalVarName
-
-  def symbolOfLine(code: String): Symbol = {
-    def asExpr(): Symbol = {
-      val name  = freshInternalVarName()
-      // Typing it with a lazy val would give us the right type, but runs
-      // into compiler bugs with things like existentials, so we compile it
-      // behind a def and strip the NullaryMethodType which wraps the expr.
-      val line = "def " + name + " = " + code
-
-      interpretSynthetic(line) match {
-        case IR.Success =>
-          val sym0 = symbolOfTerm(name)
-          // drop NullaryMethodType
-          sym0.cloneSymbol setInfo exitingTyper(sym0.tpe_*.finalResultType)
-        case _          => NoSymbol
-      }
-    }
-    def asDefn(): Symbol = {
-      val old = repl.definedSymbolList.toSet
-
-      interpretSynthetic(code) match {
-        case IR.Success =>
-          repl.definedSymbolList filterNot old match {
-            case Nil        => NoSymbol
-            case sym :: Nil => sym
-            case syms       => NoSymbol.newOverloaded(NoPrefix, syms)
-          }
-        case _ => NoSymbol
-      }
-    }
-    def asError(): Symbol = {
-      interpretSynthetic(code)
-      NoSymbol
-    }
-    beSilentDuring(asExpr()) orElse beSilentDuring(asDefn()) orElse asError()
-  }
-
-  private var typeOfExpressionDepth = 0
-  def typeOfExpression(expr: String, silent: Boolean = true): Type = {
-    if (typeOfExpressionDepth > 2) {
-      repldbg("Terminating typeOfExpression recursion for expression: " + expr)
-      return NoType
-    }
-    typeOfExpressionDepth += 1
-    // Don't presently have a good way to suppress undesirable success output
-    // while letting errors through, so it is first trying it silently: if there
-    // is an error, and errors are desired, then it re-evaluates non-silently
-    // to induce the error message.
-    try beSilentDuring(symbolOfLine(expr).tpe) match {
-      case NoType if !silent => symbolOfLine(expr).tpe // generate error
-      case tpe               => tpe
-    }
-    finally typeOfExpressionDepth -= 1
-  }
-
-  // This only works for proper types.
-  def typeOfTypeString(typeString: String): Type = {
-    def asProperType(): Option[Type] = {
-      val name = freshInternalVarName()
-      val line = "def %s: %s = ???" format (name, typeString)
-      interpretSynthetic(line) match {
-        case IR.Success =>
-          val sym0 = symbolOfTerm(name)
-          Some(sym0.asMethod.returnType)
-        case _          => None
-      }
-    }
-    beSilentDuring(asProperType()) getOrElse NoType
-  }
-}
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 3c90287249497..bf609ff0f65fc 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -1,88 +1,64 @@
-/* NSC -- new Scala compiler
- * Copyright 2005-2013 LAMP/EPFL
- * @author Alexander Spoon
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
  */
 
-package scala
-package tools.nsc
-package interpreter
+package org.apache.spark.repl
 
-import scala.language.{ implicitConversions, existentials }
-import scala.annotation.tailrec
-import Predef.{ println => _, _ }
-import interpreter.session._
-import StdReplTags._
-import scala.reflect.api.{Mirror, Universe, TypeCreator}
-import scala.util.Properties.{ jdkHome, javaVersion, versionString, javaVmName }
-import scala.tools.nsc.util.{ ClassPath, Exceptional, stringFromWriter, stringFromStream }
-import scala.reflect.{ClassTag, classTag}
-import scala.reflect.internal.util.{ BatchSourceFile, ScalaClassLoader }
-import ScalaClassLoader._
-import scala.reflect.io.{ File, Directory }
-import scala.tools.util._
-import scala.collection.generic.Clearable
-import scala.concurrent.{ ExecutionContext, Await, Future, future }
-import ExecutionContext.Implicits._
-import java.io.{ BufferedReader, FileReader }
+import java.io.{BufferedReader, FileReader}
 
-/** The Scala interactive shell.  It provides a read-eval-print loop
-  *  around the Interpreter class.
-  *  After instantiation, clients should call the main() method.
-  *
-  *  If no in0 is specified, then input will come from the console, and
-  *  the class will attempt to provide input editing feature such as
-  *  input history.
-  *
-  *  @author Moez A. Abdel-Gawad
-  *  @author  Lex Spoon
-  *  @version 1.2
-  */
-class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter)
-  extends AnyRef
-  with LoopCommands
-{
-  def this(in0: BufferedReader, out: JPrintWriter) = this(Some(in0), out)
-  def this() = this(None, new JPrintWriter(Console.out, true))
-//
-//  @deprecated("Use `intp` instead.", "2.9.0") def interpreter = intp
-//  @deprecated("Use `intp` instead.", "2.9.0") def interpreter_= (i: Interpreter): Unit = intp = i
-
-  var in: InteractiveReader = _   // the input stream from which commands come
-  var settings: Settings = _
-  var intp: SparkIMain = _
+import Predef.{println => _, _}
+import scala.util.Properties.{jdkHome, javaVersion, versionString, javaVmName}
 
-  var globalFuture: Future[Boolean] = _
+import scala.tools.nsc.interpreter.{JPrintWriter, ILoop}
+import scala.tools.nsc.Settings
+import scala.tools.nsc.util.stringFromStream
 
-  protected def asyncMessage(msg: String) {
-    if (isReplInfo || isReplPower)
-      echoAndRefresh(msg)
-  }
+/**
+ *  A Spark-specific interactive shell.
+ */
+class SparkILoop(in0: Option[BufferedReader], out: JPrintWriter)
+    extends ILoop(in0, out) {
+  def this(in0: BufferedReader, out: JPrintWriter) = this(Some(in0), out)
+  def this() = this(None, new JPrintWriter(Console.out, true))
 
   def initializeSpark() {
     intp.beQuietDuring {
-      command( """
+      processLine("""
          @transient val sc = {
            val _sc = org.apache.spark.repl.Main.createSparkContext()
            println("Spark context available as sc.")
            _sc
          }
         """)
-      command( """
+      processLine("""
          @transient val sqlContext = {
            val _sqlContext = org.apache.spark.repl.Main.createSQLContext()
            println("SQL context available as sqlContext.")
            _sqlContext
          }
         """)
-      command("import org.apache.spark.SparkContext._")
-      command("import sqlContext.implicits._")
-      command("import sqlContext.sql")
-      command("import org.apache.spark.sql.functions._")
+      processLine("import org.apache.spark.SparkContext._")
+      processLine("import sqlContext.implicits._")
+      processLine("import sqlContext.sql")
+      processLine("import org.apache.spark.sql.functions._")
     }
   }
 
   /** Print a welcome message */
-  def printWelcome() {
+  override def printWelcome() {
     import org.apache.spark.SPARK_VERSION
     echo("""Welcome to
       ____              __
@@ -98,877 +74,42 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter)
     echo("Type :help for more information.")
   }
 
-  override def echoCommandMessage(msg: String) {
-    intp.reporter printUntruncatedMessage msg
-  }
-
-  // lazy val power = new Power(intp, new StdReplVals(this))(tagOfStdReplVals, classTag[StdReplVals])
-  def history = in.history
-
-  // classpath entries added via :cp
-  var addedClasspath: String = ""
-
-  /** A reverse list of commands to replay if the user requests a :replay */
-  var replayCommandStack: List[String] = Nil
-
-  /** A list of commands to replay if the user requests a :replay */
-  def replayCommands = replayCommandStack.reverse
-
-  /** Record a command for replay should the user request a :replay */
-  def addReplay(cmd: String) = replayCommandStack ::= cmd
-
-  def savingReplayStack[T](body: => T): T = {
-    val saved = replayCommandStack
-    try body
-    finally replayCommandStack = saved
-  }
-  def savingReader[T](body: => T): T = {
-    val saved = in
-    try body
-    finally in = saved
-  }
-
-  /** Close the interpreter and set the var to null. */
-  def closeInterpreter() {
-    if (intp ne null) {
-      intp.close()
-      intp = null
-    }
-  }
-
-  class SparkILoopInterpreter extends SparkIMain(settings, out) {
-    outer =>
-
-    override lazy val formatting = new Formatting {
-      def prompt = SparkILoop.this.prompt
-    }
-    override protected def parentClassLoader =
-      settings.explicitParentLoader.getOrElse( classOf[SparkILoop].getClassLoader )
-  }
-
-  /** Create a new interpreter. */
-  def createInterpreter() {
-    if (addedClasspath != "")
-      settings.classpath append addedClasspath
-
-    intp = new SparkILoopInterpreter
-  }
-
-  /** print a friendly help message */
-  def helpCommand(line: String): Result = {
-    if (line == "") helpSummary()
-    else uniqueCommand(line) match {
-      case Some(lc) => echo("\n" + lc.help)
-      case _        => ambiguousError(line)
-    }
-  }
-  private def helpSummary() = {
-    val usageWidth  = commands map (_.usageMsg.length) max
-    val formatStr   = "%-" + usageWidth + "s %s"
-
-    echo("All commands can be abbreviated, e.g. :he instead of :help.")
-
-    commands foreach { cmd =>
-      echo(formatStr.format(cmd.usageMsg, cmd.help))
-    }
-  }
-  private def ambiguousError(cmd: String): Result = {
-    matchingCommands(cmd) match {
-      case Nil  => echo(cmd + ": no such command.  Type :help for help.")
-      case xs   => echo(cmd + " is ambiguous: did you mean " + xs.map(":" + _.name).mkString(" or ") + "?")
-    }
-    Result(keepRunning = true, None)
-  }
-  private def matchingCommands(cmd: String) = commands filter (_.name startsWith cmd)
-  private def uniqueCommand(cmd: String): Option[LoopCommand] = {
-    // this lets us add commands willy-nilly and only requires enough command to disambiguate
-    matchingCommands(cmd) match {
-      case List(x)  => Some(x)
-      // exact match OK even if otherwise appears ambiguous
-      case xs       => xs find (_.name == cmd)
-    }
-  }
-
-  /** Show the history */
-  lazy val historyCommand = new LoopCommand("history", "show the history (optional num is commands to show)") {
-    override def usage = "[num]"
-    def defaultLines = 20
-
-    def apply(line: String): Result = {
-      if (history eq NoHistory)
-        return "No history available."
-
-      val xs      = words(line)
-      val current = history.index
-      val count   = try xs.head.toInt catch { case _: Exception => defaultLines }
-      val lines   = history.asStrings takeRight count
-      val offset  = current - lines.size + 1
-
-      for ((line, index) <- lines.zipWithIndex)
-        echo("%3d  %s".format(index + offset, line))
-    }
-  }
-
-  // When you know you are most likely breaking into the middle
-  // of a line being typed.  This softens the blow.
-  protected def echoAndRefresh(msg: String) = {
-    echo("\n" + msg)
-    in.redrawLine()
-  }
-  protected def echo(msg: String) = {
-    out println msg
-    out.flush()
-  }
-
-  /** Search the history */
-  def searchHistory(_cmdline: String) {
-    val cmdline = _cmdline.toLowerCase
-    val offset  = history.index - history.size + 1
-
-    for ((line, index) <- history.asStrings.zipWithIndex ; if line.toLowerCase contains cmdline)
-      echo("%d %s".format(index + offset, line))
-  }
-
-  private val currentPrompt = Properties.shellPromptString
-
-  /** Prompt to print when awaiting input */
-  def prompt = currentPrompt
-
   import LoopCommand.{ cmd, nullary }
 
-  /** Standard commands **/
-  lazy val standardCommands = List(
-    cmd("cp", "<path>", "add a jar or directory to the classpath", addClasspath),
-    cmd("edit", "<id>|<line>", "edit history", editCommand),
-    cmd("help", "[command]", "print this summary or command-specific help", helpCommand),
-    historyCommand,
-    cmd("h?", "<string>", "search the history", searchHistory),
-    cmd("imports", "[name name ...]", "show import history, identifying sources of names", importsCommand),
-    //cmd("implicits", "[-v]", "show the implicits in scope", intp.implicitsCommand),
-    cmd("javap", "<path|class>", "disassemble a file or class name", javapCommand),
-    cmd("line", "<id>|<line>", "place line(s) at the end of history", lineCommand),
-    cmd("load", "<path>", "interpret lines in a file", loadCommand),
-    cmd("paste", "[-raw] [path]", "enter paste mode or paste a file", pasteCommand),
-    // nullary("power", "enable power user mode", powerCmd),
-    nullary("quit", "exit the interpreter", () => Result(keepRunning = false, None)),
-    nullary("replay", "reset execution and replay all previous commands", replay),
-    nullary("reset", "reset the repl to its initial state, forgetting all session entries", resetCommand),
-    cmd("save", "<path>", "save replayable session to a file", saveCommand),
-    shCommand,
-    cmd("settings", "[+|-]<options>", "+enable/-disable flags, set compiler options", changeSettings),
-    nullary("silent", "disable/enable automatic printing of results", verbosity),
-//    cmd("type", "[-v] <expr>", "display the type of an expression without evaluating it", typeCommand),
-//    cmd("kind", "[-v] <expr>", "display the kind of expression's type", kindCommand),
-    nullary("warnings", "show the suppressed warnings from the most recent line which had any", warningsCommand)
-  )
-
-  /** Power user commands */
-//  lazy val powerCommands: List[LoopCommand] = List(
-//    cmd("phase", "<phase>", "set the implicit phase for power commands", phaseCommand)
-//  )
-
-  private def importsCommand(line: String): Result = {
-    val tokens    = words(line)
-    val handlers  = intp.languageWildcardHandlers ++ intp.importHandlers
-
-    handlers.filterNot(_.importedSymbols.isEmpty).zipWithIndex foreach {
-      case (handler, idx) =>
-        val (types, terms) = handler.importedSymbols partition (_.name.isTypeName)
-        val imps           = handler.implicitSymbols
-        val found          = tokens filter (handler importsSymbolNamed _)
-        val typeMsg        = if (types.isEmpty) "" else types.size + " types"
-        val termMsg        = if (terms.isEmpty) "" else terms.size + " terms"
-        val implicitMsg    = if (imps.isEmpty) "" else imps.size + " are implicit"
-        val foundMsg       = if (found.isEmpty) "" else found.mkString(" // imports: ", ", ", "")
-        val statsMsg       = List(typeMsg, termMsg, implicitMsg) filterNot (_ == "") mkString ("(", ", ", ")")
-
-        intp.reporter.printMessage("%2d) %-30s %s%s".format(
-          idx + 1,
-          handler.importString,
-          statsMsg,
-          foundMsg
-        ))
-    }
-  }
-
-  private def findToolsJar() = PathResolver.SupplementalLocations.platformTools
+  private val blockedCommands = Set("implicits", "javap", "power", "type", "kind")
 
-  private def addToolsJarToLoader() = {
-    val cl = findToolsJar() match {
-      case Some(tools) => ScalaClassLoader.fromURLs(Seq(tools.toURL), intp.classLoader)
-      case _           => intp.classLoader
-    }
-    if (Javap.isAvailable(cl)) {
-      repldbg(":javap available.")
-      cl
-    }
-    else {
-      repldbg(":javap unavailable: no tools.jar at " + jdkHome)
-      intp.classLoader
-    }
-  }
-//
-//  protected def newJavap() =
-//    JavapClass(addToolsJarToLoader(), new IMain.ReplStrippingWriter(intp), Some(intp))
-//
-//  private lazy val javap = substituteAndLog[Javap]("javap", NoJavap)(newJavap())
-
-  // Still todo: modules.
-//  private def typeCommand(line0: String): Result = {
-//    line0.trim match {
-//      case "" => ":type [-v] <expression>"
-//      case s  => intp.typeCommandInternal(s stripPrefix "-v " trim, verbose = s startsWith "-v ")
-//    }
-//  }
-
-//  private def kindCommand(expr: String): Result = {
-//    expr.trim match {
-//      case "" => ":kind [-v] <expression>"
-//      case s  => intp.kindCommandInternal(s stripPrefix "-v " trim, verbose = s startsWith "-v ")
-//    }
-//  }
-
-  private def warningsCommand(): Result = {
-    if (intp.lastWarnings.isEmpty)
-      "Can't find any cached warnings."
-    else
-      intp.lastWarnings foreach { case (pos, msg) => intp.reporter.warning(pos, msg) }
-  }
-
-  private def changeSettings(args: String): Result = {
-    def showSettings() = {
-      for (s <- settings.userSetSettings.toSeq.sorted) echo(s.toString)
-    }
-    def updateSettings() = {
-      // put aside +flag options
-      val (pluses, rest) = (args split "\\s+").toList partition (_.startsWith("+"))
-      val tmps = new Settings
-      val (ok, leftover) = tmps.processArguments(rest, processAll = true)
-      if (!ok) echo("Bad settings request.")
-      else if (leftover.nonEmpty) echo("Unprocessed settings.")
-      else {
-        // boolean flags set-by-user on tmp copy should be off, not on
-        val offs = tmps.userSetSettings filter (_.isInstanceOf[Settings#BooleanSetting])
-        val (minuses, nonbools) = rest partition (arg => offs exists (_ respondsTo arg))
-        // update non-flags
-        settings.processArguments(nonbools, processAll = true)
-        // also snag multi-value options for clearing, e.g. -Ylog: and -language:
-        for {
-          s <- settings.userSetSettings
-          if s.isInstanceOf[Settings#MultiStringSetting] || s.isInstanceOf[Settings#PhasesSetting]
-          if nonbools exists (arg => arg.head == '-' && arg.last == ':' && (s respondsTo arg.init))
-        } s match {
-          case c: Clearable => c.clear()
-          case _ =>
-        }
-        def update(bs: Seq[String], name: String=>String, setter: Settings#Setting=>Unit) = {
-          for (b <- bs)
-            settings.lookupSetting(name(b)) match {
-              case Some(s) =>
-                if (s.isInstanceOf[Settings#BooleanSetting]) setter(s)
-                else echo(s"Not a boolean flag: $b")
-              case _ =>
-                echo(s"Not an option: $b")
-            }
-        }
-        update(minuses, identity, _.tryToSetFromPropertyValue("false"))  // turn off
-        update(pluses, "-" + _.drop(1), _.tryToSet(Nil))                 // turn on
-      }
-    }
-    if (args.isEmpty) showSettings() else updateSettings()
-  }
-
-  private def javapCommand(line: String): Result = {
-//    if (javap == null)
-//      ":javap unavailable, no tools.jar at %s.  Set JDK_HOME.".format(jdkHome)
-//    else if (line == "")
-//      ":javap [-lcsvp] [path1 path2 ...]"
-//    else
-//      javap(words(line)) foreach { res =>
-//        if (res.isError) return "Failed: " + res.value
-//        else res.show()
-//      }
-  }
-
-  private def pathToPhaseWrapper = intp.originalPath("$r") + ".phased.atCurrent"
-
-  private def phaseCommand(name: String): Result = {
-//    val phased: Phased = power.phased
-//    import phased.NoPhaseName
-//
-//    if (name == "clear") {
-//      phased.set(NoPhaseName)
-//      intp.clearExecutionWrapper()
-//      "Cleared active phase."
-//    }
-//    else if (name == "") phased.get match {
-//      case NoPhaseName => "Usage: :phase <expr> (e.g. typer, erasure.next, erasure+3)"
-//      case ph          => "Active phase is '%s'.  (To clear, :phase clear)".format(phased.get)
-//    }
-//    else {
-//      val what = phased.parse(name)
-//      if (what.isEmpty || !phased.set(what))
-//        "'" + name + "' does not appear to represent a valid phase."
-//      else {
-//        intp.setExecutionWrapper(pathToPhaseWrapper)
-//        val activeMessage =
-//          if (what.toString.length == name.length) "" + what
-//          else "%s (%s)".format(what, name)
-//
-//        "Active phase is now: " + activeMessage
-//      }
-//    }
-  }
+  /** Standard commands **/
+  lazy val sparkStandardCommands: List[SparkILoop.this.LoopCommand] =
+    standardCommands.filter(cmd => !blockedCommands(cmd.name))
 
   /** Available commands */
-  def commands: List[LoopCommand] = standardCommands ++ (
-    // if (isReplPower)
-    //  powerCommands
-    // else
-      Nil
-    )
-
-  val replayQuestionMessage =
-    """|That entry seems to have slain the compiler.  Shall I replay
-      |your session? I can re-run each line except the last one.
-      |[y/n]
-    """.trim.stripMargin
-
-  private val crashRecovery: PartialFunction[Throwable, Boolean] = {
-    case ex: Throwable =>
-      val (err, explain) = (
-        if (intp.isInitializeComplete)
-          (intp.global.throwableAsString(ex), "")
-        else
-          (ex.getMessage, "The compiler did not initialize.\n")
-        )
-      echo(err)
-
-      ex match {
-        case _: NoSuchMethodError | _: NoClassDefFoundError =>
-          echo("\nUnrecoverable error.")
-          throw ex
-        case _  =>
-          def fn(): Boolean =
-            try in.readYesOrNo(explain + replayQuestionMessage, { echo("\nYou must enter y or n.") ; fn() })
-            catch { case _: RuntimeException => false }
-
-          if (fn()) replay()
-          else echo("\nAbandoning crashed session.")
-      }
-      true
-  }
-
-  // return false if repl should exit
-  def processLine(line: String): Boolean = {
-    import scala.concurrent.duration._
-    Await.ready(globalFuture, 60.seconds)
-
-    (line ne null) && (command(line) match {
-      case Result(false, _)      => false
-      case Result(_, Some(line)) => addReplay(line) ; true
-      case _                     => true
-    })
-  }
-
-  private def readOneLine() = {
-    out.flush()
-    in readLine prompt
-  }
-
-  /** The main read-eval-print loop for the repl.  It calls
-    *  command() for each line of input, and stops when
-    *  command() returns false.
-    */
-  @tailrec final def loop() {
-    if ( try processLine(readOneLine()) catch crashRecovery )
-      loop()
-  }
-
-  /** interpret all lines from a specified file */
-  def interpretAllFrom(file: File) {
-    savingReader {
-      savingReplayStack {
-        file applyReader { reader =>
-          in = SimpleReader(reader, out, interactive = false)
-          echo("Loading " + file + "...")
-          loop()
-        }
-      }
-    }
-  }
-
-  /** create a new interpreter and replay the given commands */
-  def replay() {
-    reset()
-    if (replayCommandStack.isEmpty)
-      echo("Nothing to replay.")
-    else for (cmd <- replayCommands) {
-      echo("Replaying: " + cmd)  // flush because maybe cmd will have its own output
-      command(cmd)
-      echo("")
-    }
-  }
-  def resetCommand() {
-    echo("Resetting interpreter state.")
-    if (replayCommandStack.nonEmpty) {
-      echo("Forgetting this session history:\n")
-      replayCommands foreach echo
-      echo("")
-      replayCommandStack = Nil
-    }
-    if (intp.namedDefinedTerms.nonEmpty)
-      echo("Forgetting all expression results and named terms: " + intp.namedDefinedTerms.mkString(", "))
-    if (intp.definedTypes.nonEmpty)
-      echo("Forgetting defined types: " + intp.definedTypes.mkString(", "))
-
-    reset()
-  }
-  def reset() {
-    intp.reset()
-    unleashAndSetPhase()
-  }
-
-  def lineCommand(what: String): Result = editCommand(what, None)
-
-  // :edit id or :edit line
-  def editCommand(what: String): Result = editCommand(what, Properties.envOrNone("EDITOR"))
-
-  def editCommand(what: String, editor: Option[String]): Result = {
-    def diagnose(code: String) = {
-      echo("The edited code is incomplete!\n")
-      val errless = intp compileSources new BatchSourceFile("<pastie>", s"object pastel {\n$code\n}")
-      if (errless) echo("The compiler reports no errors.")
-    }
-    def historicize(text: String) = history match {
-      case jlh: JLineHistory => text.lines foreach jlh.add ; jlh.moveToEnd() ; true
-      case _ => false
-    }
-    def edit(text: String): Result = editor match {
-      case Some(ed) =>
-        val tmp = File.makeTemp()
-        tmp.writeAll(text)
-        try {
-          val pr = new ProcessResult(s"$ed ${tmp.path}")
-          pr.exitCode match {
-            case 0 =>
-              tmp.safeSlurp() match {
-                case Some(edited) if edited.trim.isEmpty => echo("Edited text is empty.")
-                case Some(edited) =>
-                  echo(edited.lines map ("+" + _) mkString "\n")
-                  val res = intp interpret edited
-                  if (res == IR.Incomplete) diagnose(edited)
-                  else {
-                    historicize(edited)
-                    Result(lineToRecord = Some(edited), keepRunning = true)
-                  }
-                case None => echo("Can't read edited text. Did you delete it?")
-              }
-            case x => echo(s"Error exit from $ed ($x), ignoring")
-          }
-        } finally {
-          tmp.delete()
-        }
-      case None =>
-        if (historicize(text)) echo("Placing text in recent history.")
-        else echo(f"No EDITOR defined and you can't change history, echoing your text:%n$text")
-    }
-
-    // if what is a number, use it as a line number or range in history
-    def isNum = what forall (c => c.isDigit || c == '-' || c == '+')
-    // except that "-" means last value
-    def isLast = (what == "-")
-    if (isLast || !isNum) {
-      val name = if (isLast) intp.mostRecentVar else what
-      val sym = intp.symbolOfIdent(name)
-      intp.prevRequestList collectFirst { case r if r.defines contains sym => r } match {
-        case Some(req) => edit(req.line)
-        case None      => echo(s"No symbol in scope: $what")
-      }
-    } else try {
-      val s = what
-      // line 123, 120+3, -3, 120-123, 120-, note -3 is not 0-3 but (cur-3,cur)
-      val (start, len) =
-        if ((s indexOf '+') > 0) {
-          val (a,b) = s splitAt (s indexOf '+')
-          (a.toInt, b.drop(1).toInt)
-        } else {
-          (s indexOf '-') match {
-            case -1 => (s.toInt, 1)
-            case 0  => val n = s.drop(1).toInt ; (history.index - n, n)
-            case _ if s.last == '-' => val n = s.init.toInt ; (n, history.index - n)
-            case i  => val n = s.take(i).toInt ; (n, s.drop(i+1).toInt - n)
-          }
-        }
-      import scala.collection.JavaConverters._
-      val index = (start - 1) max 0
-      val text = history match {
-        case jlh: JLineHistory => jlh.entries(index).asScala.take(len) map (_.value) mkString "\n"
-        case _ => history.asStrings.slice(index, index + len) mkString "\n"
-      }
-      edit(text)
-    } catch {
-      case _: NumberFormatException => echo(s"Bad range '$what'")
-        echo("Use line 123, 120+3, -3, 120-123, 120-, note -3 is not 0-3 but (cur-3,cur)")
-    }
-  }
-
-  /** fork a shell and run a command */
-  lazy val shCommand = new LoopCommand("sh", "run a shell command (result is implicitly => List[String])") {
-    override def usage = "<command line>"
-    def apply(line: String): Result = line match {
-      case ""   => showUsage()
-      case _    =>
-        val toRun = s"new ${classOf[ProcessResult].getName}(${string2codeQuoted(line)})"
-        intp interpret toRun
-        ()
-    }
-  }
-
-  def withFile[A](filename: String)(action: File => A): Option[A] = {
-    val res = Some(File(filename)) filter (_.exists) map action
-    if (res.isEmpty) echo("That file does not exist")  // courtesy side-effect
-    res
-  }
-
-  def loadCommand(arg: String) = {
-    var shouldReplay: Option[String] = None
-    withFile(arg)(f => {
-      interpretAllFrom(f)
-      shouldReplay = Some(":load " + arg)
-    })
-    Result(keepRunning = true, shouldReplay)
-  }
-
-  def saveCommand(filename: String): Result = (
-    if (filename.isEmpty) echo("File name is required.")
-    else if (replayCommandStack.isEmpty) echo("No replay commands in session")
-    else File(filename).printlnAll(replayCommands: _*)
-    )
-
-  def addClasspath(arg: String): Unit = {
-    val f = File(arg).normalize
-    if (f.exists) {
-      addedClasspath = ClassPath.join(addedClasspath, f.path)
-      val totalClasspath = ClassPath.join(settings.classpath.value, addedClasspath)
-      echo("Added '%s'.  Your new classpath is:\n\"%s\"".format(f.path, totalClasspath))
-      replay()
-    }
-    else echo("The path '" + f + "' doesn't seem to exist.")
-  }
-
-  def powerCmd(): Result = {
-    if (isReplPower) "Already in power mode."
-    else enablePowerMode(isDuringInit = false)
-  }
-  def enablePowerMode(isDuringInit: Boolean) = {
-    replProps.power setValue true
-    unleashAndSetPhase()
-    // asyncEcho(isDuringInit, power.banner)
-  }
-  private def unleashAndSetPhase() {
-    if (isReplPower) {
-    //  power.unleash()
-      // Set the phase to "typer"
-      // intp beSilentDuring phaseCommand("typer")
-    }
-  }
-
-  def asyncEcho(async: Boolean, msg: => String) {
-    if (async) asyncMessage(msg)
-    else echo(msg)
-  }
-
-  def verbosity() = {
-    val old = intp.printResults
-    intp.printResults = !old
-    echo("Switched " + (if (old) "off" else "on") + " result printing.")
-  }
-
-  /** Run one command submitted by the user.  Two values are returned:
-    * (1) whether to keep running, (2) the line to record for replay,
-    * if any. */
-  def command(line: String): Result = {
-    if (line startsWith ":") {
-      val cmd = line.tail takeWhile (x => !x.isWhitespace)
-      uniqueCommand(cmd) match {
-        case Some(lc) => lc(line.tail stripPrefix cmd dropWhile (_.isWhitespace))
-        case _        => ambiguousError(cmd)
-      }
-    }
-    else if (intp.global == null) Result(keepRunning = false, None)  // Notice failure to create compiler
-    else Result(keepRunning = true, interpretStartingWith(line))
-  }
-
-  private def readWhile(cond: String => Boolean) = {
-    Iterator continually in.readLine("") takeWhile (x => x != null && cond(x))
-  }
-
-  def pasteCommand(arg: String): Result = {
-    var shouldReplay: Option[String] = None
-    def result = Result(keepRunning = true, shouldReplay)
-    val (raw, file) =
-      if (arg.isEmpty) (false, None)
-      else {
-        val r = """(-raw)?(\s+)?([^\-]\S*)?""".r
-        arg match {
-          case r(flag, sep, name) =>
-            if (flag != null && name != null && sep == null)
-              echo(s"""I assume you mean "$flag $name"?""")
-            (flag != null, Option(name))
-          case _ =>
-            echo("usage: :paste -raw file")
-            return result
-        }
-      }
-    val code = file match {
-      case Some(name) =>
-        withFile(name)(f => {
-          shouldReplay = Some(s":paste $arg")
-          val s = f.slurp.trim
-          if (s.isEmpty) echo(s"File contains no code: $f")
-          else echo(s"Pasting file $f...")
-          s
-        }) getOrElse ""
-      case None =>
-        echo("// Entering paste mode (ctrl-D to finish)\n")
-        val text = (readWhile(_ => true) mkString "\n").trim
-        if (text.isEmpty) echo("\n// Nothing pasted, nothing gained.\n")
-        else echo("\n// Exiting paste mode, now interpreting.\n")
-        text
-    }
-    def interpretCode() = {
-      val res = intp interpret code
-      // if input is incomplete, let the compiler try to say why
-      if (res == IR.Incomplete) {
-        echo("The pasted code is incomplete!\n")
-        // Remembrance of Things Pasted in an object
-        val errless = intp compileSources new BatchSourceFile("<pastie>", s"object pastel {\n$code\n}")
-        if (errless) echo("...but compilation found no error? Good luck with that.")
-      }
-    }
-    def compileCode() = {
-      val errless = intp compileSources new BatchSourceFile("<pastie>", code)
-      if (!errless) echo("There were compilation errors!")
-    }
-    if (code.nonEmpty) {
-      if (raw) compileCode() else interpretCode()
-    }
-    result
-  }
-
-  private object paste extends Pasted {
-    val ContinueString = "     | "
-    val PromptString   = "scala> "
-
-    def interpret(line: String): Unit = {
-      echo(line.trim)
-      intp interpret line
-      echo("")
-    }
-
-    def transcript(start: String) = {
-      echo("\n// Detected repl transcript paste: ctrl-D to finish.\n")
-      apply(Iterator(start) ++ readWhile(_.trim != PromptString.trim))
-    }
-  }
-  import paste.{ ContinueString, PromptString }
-
-  /** Interpret expressions starting with the first line.
-    * Read lines until a complete compilation unit is available
-    * or until a syntax error has been seen.  If a full unit is
-    * read, go ahead and interpret it.  Return the full string
-    * to be recorded for replay, if any.
-    */
-  def interpretStartingWith(code: String): Option[String] = {
-    // signal completion non-completion input has been received
-    in.completion.resetVerbosity()
-
-    def reallyInterpret = {
-      val reallyResult = intp.interpret(code)
-      (reallyResult, reallyResult match {
-        case IR.Error       => None
-        case IR.Success     => Some(code)
-        case IR.Incomplete  =>
-          if (in.interactive && code.endsWith("\n\n")) {
-            echo("You typed two blank lines.  Starting a new command.")
-            None
-          }
-          else in.readLine(ContinueString) match {
-            case null =>
-              // we know compilation is going to fail since we're at EOF and the
-              // parser thinks the input is still incomplete, but since this is
-              // a file being read non-interactively we want to fail.  So we send
-              // it straight to the compiler for the nice error message.
-              intp.compileString(code)
-              None
-
-            case line => interpretStartingWith(code + "\n" + line)
-          }
-      })
-    }
-
-    /** Here we place ourselves between the user and the interpreter and examine
-      *  the input they are ostensibly submitting.  We intervene in several cases:
-      *
-      *  1) If the line starts with "scala> " it is assumed to be an interpreter paste.
-      *  2) If the line starts with "." (but not ".." or "./") it is treated as an invocation
-      *     on the previous result.
-      *  3) If the Completion object's execute returns Some(_), we inject that value
-      *     and avoid the interpreter, as it's likely not valid scala code.
-      */
-    if (code == "") None
-    else if (!paste.running && code.trim.startsWith(PromptString)) {
-      paste.transcript(code)
-      None
-    }
-    else if (Completion.looksLikeInvocation(code) && intp.mostRecentVar != "") {
-      interpretStartingWith(intp.mostRecentVar + code)
-    }
-    else if (code.trim startsWith "//") {
-      // line comment, do nothing
-      None
-    }
-    else
-      reallyInterpret._2
-  }
-
-  // runs :load `file` on any files passed via -i
-  def loadFiles(settings: Settings) = settings match {
-    case settings: GenericRunnerSettings =>
-      for (filename <- settings.loadfiles.value) {
-        val cmd = ":load " + filename
-        command(cmd)
-        addReplay(cmd)
-        echo("")
-      }
-    case _ =>
-  }
-
-  /** Tries to create a JLineReader, falling back to SimpleReader:
-    *  unless settings or properties are such that it should start
-    *  with SimpleReader.
-    */
-  def chooseReader(settings: Settings): InteractiveReader = {
-    if (settings.Xnojline || Properties.isEmacsShell)
-      SimpleReader()
-    else try new JLineReader(
-      if (settings.noCompletion) NoCompletion
-      else new SparkJLineCompletion(intp)
-    )
-    catch {
-      case ex @ (_: Exception | _: NoClassDefFoundError) =>
-        echo("Failed to created JLineReader: " + ex + "\nFalling back to SimpleReader.")
-        SimpleReader()
-    }
-  }
-  protected def tagOfStaticClass[T: ClassTag]: u.TypeTag[T] =
-    u.TypeTag[T](
-      m,
-      new TypeCreator {
-        def apply[U <: Universe with Singleton](m: Mirror[U]): U # Type =
-          m.staticClass(classTag[T].runtimeClass.getName).toTypeConstructor.asInstanceOf[U # Type]
-      })
-
-  private def loopPostInit() {
-    // Bind intp somewhere out of the regular namespace where
-    // we can get at it in generated code.
-    intp.quietBind(NamedParam[SparkIMain]("$intp", intp)(tagOfStaticClass[SparkIMain], classTag[SparkIMain]))
-    // Auto-run code via some setting.
-    ( replProps.replAutorunCode.option
-      flatMap (f => io.File(f).safeSlurp())
-      foreach (intp quietRun _)
-      )
-    // classloader and power mode setup
-    intp.setContextClassLoader()
-    if (isReplPower) {
-     // replProps.power setValue true
-     // unleashAndSetPhase()
-     // asyncMessage(power.banner)
-    }
-    // SI-7418 Now, and only now, can we enable TAB completion.
-    in match {
-      case x: JLineReader => x.consoleReader.postInit
-      case _              =>
-    }
-  }
-  def process(settings: Settings): Boolean = savingContextLoader {
-    this.settings = settings
-    createInterpreter()
-
-    // sets in to some kind of reader depending on environmental cues
-    in = in0.fold(chooseReader(settings))(r => SimpleReader(r, out, interactive = true))
-    globalFuture = future {
-      intp.initializeSynchronous()
-      loopPostInit()
-      !intp.reporter.hasErrors
-    }
-    import scala.concurrent.duration._
-    Await.ready(globalFuture, 10 seconds)
-    printWelcome()
+  override def commands: List[LoopCommand] = sparkStandardCommands
+
+  /** 
+   * We override `loadFiles` because we need to initialize Spark *before* the REPL
+   * sees any files, so that the Spark context is visible in those files. This is a bit of a
+   * hack, but there isn't another hook available to us at this point.
+   */
+  override def loadFiles(settings: Settings): Unit = {
     initializeSpark()
-    loadFiles(settings)
-
-    try loop()
-    catch AbstractOrMissingHandler()
-    finally closeInterpreter()
-
-    true
+    super.loadFiles(settings)
   }
-
-  @deprecated("Use `process` instead", "2.9.0")
-  def main(settings: Settings): Unit = process(settings) //used by sbt
 }
 
 object SparkILoop {
-  implicit def loopToInterpreter(repl: SparkILoop): SparkIMain = repl.intp
 
-  // Designed primarily for use by test code: take a String with a
-  // bunch of code, and prints out a transcript of what it would look
-  // like if you'd just typed it into the repl.
-  def runForTranscript(code: String, settings: Settings): String = {
-    import java.io.{ BufferedReader, StringReader, OutputStreamWriter }
-
-    stringFromStream { ostream =>
-      Console.withOut(ostream) {
-        val output = new JPrintWriter(new OutputStreamWriter(ostream), true) {
-          override def write(str: String) = {
-            // completely skip continuation lines
-            if (str forall (ch => ch.isWhitespace || ch == '|')) ()
-            else super.write(str)
-          }
-        }
-        val input = new BufferedReader(new StringReader(code.trim + "\n")) {
-          override def readLine(): String = {
-            val s = super.readLine()
-            // helping out by printing the line being interpreted.
-            if (s != null)
-              // scalastyle:off println
-              output.println(s)
-              // scalastyle:on println
-            s
-          }
-        }
-        val repl = new SparkILoop(input, output)
-        if (settings.classpath.isDefault)
-          settings.classpath.value = sys.props("java.class.path")
-
-        repl process settings
-      }
-    }
-  }
-
-  /** Creates an interpreter loop with default settings and feeds
-    *  the given code to it as input.
-    */
+  /** 
+   * Creates an interpreter loop with default settings and feeds
+   * the given code to it as input.
+   */
   def run(code: String, sets: Settings = new Settings): String = {
     import java.io.{ BufferedReader, StringReader, OutputStreamWriter }
 
     stringFromStream { ostream =>
       Console.withOut(ostream) {
-        val input    = new BufferedReader(new StringReader(code))
-        val output   = new JPrintWriter(new OutputStreamWriter(ostream), true)
-        val repl     = new SparkILoop(input, output)
+        val input = new BufferedReader(new StringReader(code))
+        val output = new JPrintWriter(new OutputStreamWriter(ostream), true)
+        val repl = new SparkILoop(input, output)
 
         if (sets.classpath.isDefault)
           sets.classpath.value = sys.props("java.class.path")
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala
deleted file mode 100644
index 56c009a4e38e7..0000000000000
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala
+++ /dev/null
@@ -1,1323 +0,0 @@
-/* NSC -- new Scala compiler
- * Copyright 2005-2013 LAMP/EPFL
- * @author  Martin Odersky
- */
-
-package scala
-package tools.nsc
-package interpreter
-
-import PartialFunction.cond
-import scala.language.implicitConversions
-import scala.beans.BeanProperty
-import scala.collection.mutable
-import scala.concurrent.{ Future, ExecutionContext }
-import scala.reflect.runtime.{ universe => ru }
-import scala.reflect.{ ClassTag, classTag }
-import scala.reflect.internal.util.{ BatchSourceFile, SourceFile }
-import scala.tools.util.PathResolver
-import scala.tools.nsc.io.AbstractFile
-import scala.tools.nsc.typechecker.{ TypeStrings, StructuredTypeStrings }
-import scala.tools.nsc.util.{ ScalaClassLoader, stringFromReader, stringFromWriter, StackTraceOps }
-import scala.tools.nsc.util.Exceptional.unwrap
-import javax.script.{AbstractScriptEngine, Bindings, ScriptContext, ScriptEngine, ScriptEngineFactory, ScriptException, CompiledScript, Compilable}
-
-/** An interpreter for Scala code.
-  *
-  *  The main public entry points are compile(), interpret(), and bind().
-  *  The compile() method loads a complete Scala file.  The interpret() method
-  *  executes one line of Scala code at the request of the user.  The bind()
-  *  method binds an object to a variable that can then be used by later
-  *  interpreted code.
-  *
-  *  The overall approach is based on compiling the requested code and then
-  *  using a Java classloader and Java reflection to run the code
-  *  and access its results.
-  *
-  *  In more detail, a single compiler instance is used
-  *  to accumulate all successfully compiled or interpreted Scala code.  To
-  *  "interpret" a line of code, the compiler generates a fresh object that
-  *  includes the line of code and which has public member(s) to export
-  *  all variables defined by that code.  To extract the result of an
-  *  interpreted line to show the user, a second "result object" is created
-  *  which imports the variables exported by the above object and then
-  *  exports members called "$eval" and "$print". To accomodate user expressions
-  *  that read from variables or methods defined in previous statements, "import"
-  *  statements are used.
-  *
-  *  This interpreter shares the strengths and weaknesses of using the
-  *  full compiler-to-Java.  The main strength is that interpreted code
-  *  behaves exactly as does compiled code, including running at full speed.
-  *  The main weakness is that redefining classes and methods is not handled
-  *  properly, because rebinding at the Java level is technically difficult.
-  *
-  *  @author Moez A. Abdel-Gawad
-  *  @author Lex Spoon
-  */
-class SparkIMain(@BeanProperty val factory: ScriptEngineFactory, initialSettings: Settings,
-  protected val out: JPrintWriter) extends AbstractScriptEngine with Compilable with SparkImports {
-  imain =>
-
-  setBindings(createBindings, ScriptContext.ENGINE_SCOPE)
-  object replOutput extends ReplOutput(settings.Yreploutdir) { }
-
-  @deprecated("Use replOutput.dir instead", "2.11.0")
-  def virtualDirectory = replOutput.dir
-  // Used in a test case.
-  def showDirectory() = replOutput.show(out)
-
-  private[nsc] var printResults               = true      // whether to print result lines
-  private[nsc] var totalSilence               = false     // whether to print anything
-  private var _initializeComplete             = false     // compiler is initialized
-  private var _isInitialized: Future[Boolean] = null      // set up initialization future
-  private var bindExceptions                  = true      // whether to bind the lastException variable
-  private var _executionWrapper               = ""        // code to be wrapped around all lines
-
-  /** We're going to go to some trouble to initialize the compiler asynchronously.
-    *  It's critical that nothing call into it until it's been initialized or we will
-    *  run into unrecoverable issues, but the perceived repl startup time goes
-    *  through the roof if we wait for it.  So we initialize it with a future and
-    *  use a lazy val to ensure that any attempt to use the compiler object waits
-    *  on the future.
-    */
-  private var _classLoader: util.AbstractFileClassLoader = null                              // active classloader
-  private val _compiler: ReplGlobal                 = newCompiler(settings, reporter)   // our private compiler
-
-  def compilerClasspath: Seq[java.net.URL] = (
-    if (isInitializeComplete) global.classPath.asURLs
-    else new PathResolver(settings).result.asURLs  // the compiler's classpath
-    )
-  def settings = initialSettings
-  // Run the code body with the given boolean settings flipped to true.
-  def withoutWarnings[T](body: => T): T = beQuietDuring {
-    val saved = settings.nowarn.value
-    if (!saved)
-      settings.nowarn.value = true
-
-    try body
-    finally if (!saved) settings.nowarn.value = false
-  }
-
-  /** construct an interpreter that reports to Console */
-  def this(settings: Settings, out: JPrintWriter) = this(null, settings, out)
-  def this(factory: ScriptEngineFactory, settings: Settings) = this(factory, settings, new NewLinePrintWriter(new ConsoleWriter, true))
-  def this(settings: Settings) = this(settings, new NewLinePrintWriter(new ConsoleWriter, true))
-  def this(factory: ScriptEngineFactory) = this(factory, new Settings())
-  def this() = this(new Settings())
-
-  lazy val formatting: Formatting = new Formatting {
-    val prompt = Properties.shellPromptString
-  }
-  lazy val reporter: SparkReplReporter = new SparkReplReporter(this)
-
-  import formatting._
-  import reporter.{ printMessage, printUntruncatedMessage }
-
-  // This exists mostly because using the reporter too early leads to deadlock.
-  private def echo(msg: String) { Console println msg }
-  private def _initSources = List(new BatchSourceFile("<init>", "class $repl_$init { }"))
-  private def _initialize() = {
-    try {
-      // if this crashes, REPL will hang its head in shame
-      val run = new _compiler.Run()
-      assert(run.typerPhase != NoPhase, "REPL requires a typer phase.")
-      run compileSources _initSources
-      _initializeComplete = true
-      true
-    }
-    catch AbstractOrMissingHandler()
-  }
-  private def tquoted(s: String) = "\"\"\"" + s + "\"\"\""
-  private val logScope = scala.sys.props contains "scala.repl.scope"
-  // scalastyle:off println
-  private def scopelog(msg: String) = if (logScope) Console.err.println(msg)
-  // scalastyle:on println
-
-  // argument is a thunk to execute after init is done
-  def initialize(postInitSignal: => Unit) {
-    synchronized {
-      if (_isInitialized == null) {
-        _isInitialized =
-          Future(try _initialize() finally postInitSignal)(ExecutionContext.global)
-      }
-    }
-  }
-  def initializeSynchronous(): Unit = {
-    if (!isInitializeComplete) {
-      _initialize()
-      assert(global != null, global)
-    }
-  }
-  def isInitializeComplete = _initializeComplete
-
-  lazy val global: Global = {
-    if (!isInitializeComplete) _initialize()
-    _compiler
-  }
-
-  import global._
-  import definitions.{ ObjectClass, termMember, dropNullaryMethod}
-
-  lazy val runtimeMirror = ru.runtimeMirror(classLoader)
-
-  private def noFatal(body: => Symbol): Symbol = try body catch { case _: FatalError => NoSymbol }
-
-  def getClassIfDefined(path: String)  = (
-    noFatal(runtimeMirror staticClass path)
-      orElse noFatal(rootMirror staticClass path)
-    )
-  def getModuleIfDefined(path: String) = (
-    noFatal(runtimeMirror staticModule path)
-      orElse noFatal(rootMirror staticModule path)
-    )
-
-  implicit class ReplTypeOps(tp: Type) {
-    def andAlso(fn: Type => Type): Type = if (tp eq NoType) tp else fn(tp)
-  }
-
-  // TODO: If we try to make naming a lazy val, we run into big time
-  // scalac unhappiness with what look like cycles.  It has not been easy to
-  // reduce, but name resolution clearly takes different paths.
-  object naming extends {
-    val global: imain.global.type = imain.global
-  } with Naming {
-    // make sure we don't overwrite their unwisely named res3 etc.
-    def freshUserTermName(): TermName = {
-      val name = newTermName(freshUserVarName())
-      if (replScope containsName name) freshUserTermName()
-      else name
-    }
-    def isInternalTermName(name: Name) = isInternalVarName("" + name)
-  }
-  import naming._
-
-  object deconstruct extends {
-    val global: imain.global.type = imain.global
-  } with StructuredTypeStrings
-
-  lazy val memberHandlers = new {
-    val intp: imain.type = imain
-  } with SparkMemberHandlers
-  import memberHandlers._
-
-  /** Temporarily be quiet */
-  def beQuietDuring[T](body: => T): T = {
-    val saved = printResults
-    printResults = false
-    try body
-    finally printResults = saved
-  }
-  def beSilentDuring[T](operation: => T): T = {
-    val saved = totalSilence
-    totalSilence = true
-    try operation
-    finally totalSilence = saved
-  }
-
-  def quietRun[T](code: String) = beQuietDuring(interpret(code))
-
-  /** takes AnyRef because it may be binding a Throwable or an Exceptional */
-  private def withLastExceptionLock[T](body: => T, alt: => T): T = {
-    assert(bindExceptions, "withLastExceptionLock called incorrectly.")
-    bindExceptions = false
-
-    try     beQuietDuring(body)
-    catch   logAndDiscard("withLastExceptionLock", alt)
-    finally bindExceptions = true
-  }
-
-  def executionWrapper = _executionWrapper
-  def setExecutionWrapper(code: String) = _executionWrapper = code
-  def clearExecutionWrapper() = _executionWrapper = ""
-
-  /** interpreter settings */
-  lazy val isettings = new SparkISettings(this)
-
-  /** Instantiate a compiler.  Overridable. */
-  protected def newCompiler(settings: Settings, reporter: reporters.Reporter): ReplGlobal = {
-    settings.outputDirs setSingleOutput replOutput.dir
-    settings.exposeEmptyPackage.value = true
-    new Global(settings, reporter) with ReplGlobal { override def toString: String = "<global>" }
-  }
-
-  /** Parent classloader.  Overridable. */
-  protected def parentClassLoader: ClassLoader =
-    settings.explicitParentLoader.getOrElse( this.getClass.getClassLoader() )
-
-  /* A single class loader is used for all commands interpreted by this Interpreter.
-     It would also be possible to create a new class loader for each command
-     to interpret.  The advantages of the current approach are:
-
-       - Expressions are only evaluated one time.  This is especially
-         significant for I/O, e.g. "val x = Console.readLine"
-
-     The main disadvantage is:
-
-       - Objects, classes, and methods cannot be rebound.  Instead, definitions
-         shadow the old ones, and old code objects refer to the old
-         definitions.
-  */
-  def resetClassLoader() = {
-    repldbg("Setting new classloader: was " + _classLoader)
-    _classLoader = null
-    ensureClassLoader()
-  }
-  final def ensureClassLoader() {
-    if (_classLoader == null)
-      _classLoader = makeClassLoader()
-  }
-  def classLoader: util.AbstractFileClassLoader = {
-    ensureClassLoader()
-    _classLoader
-  }
-
-  def backticked(s: String): String = (
-    (s split '.').toList map {
-      case "_"                               => "_"
-      case s if nme.keywords(newTermName(s)) => s"`$s`"
-      case s                                 => s
-    } mkString "."
-    )
-  def readRootPath(readPath: String) = getModuleIfDefined(readPath)
-
-  abstract class PhaseDependentOps {
-    def shift[T](op: => T): T
-
-    def path(name: => Name): String = shift(path(symbolOfName(name)))
-    def path(sym: Symbol): String = backticked(shift(sym.fullName))
-    def sig(sym: Symbol): String  = shift(sym.defString)
-  }
-  object typerOp extends PhaseDependentOps {
-    def shift[T](op: => T): T = exitingTyper(op)
-  }
-  object flatOp extends PhaseDependentOps {
-    def shift[T](op: => T): T = exitingFlatten(op)
-  }
-
-  def originalPath(name: String): String = originalPath(name: TermName)
-  def originalPath(name: Name): String   = typerOp path name
-  def originalPath(sym: Symbol): String  = typerOp path sym
-  def flatPath(sym: Symbol): String      = flatOp shift sym.javaClassName
-  def translatePath(path: String) = {
-    val sym = if (path endsWith "$") symbolOfTerm(path.init) else symbolOfIdent(path)
-    sym.toOption map flatPath
-  }
-  def translateEnclosingClass(n: String) = symbolOfTerm(n).enclClass.toOption map flatPath
-
-  private class TranslatingClassLoader(parent: ClassLoader) extends util.AbstractFileClassLoader(replOutput.dir, parent) {
-    /** Overridden here to try translating a simple name to the generated
-      *  class name if the original attempt fails.  This method is used by
-      *  getResourceAsStream as well as findClass.
-      */
-    override protected def findAbstractFile(name: String): AbstractFile =
-      super.findAbstractFile(name) match {
-        case null if _initializeComplete => translatePath(name) map (super.findAbstractFile(_)) orNull
-        case file => file
-      }
-  }
-  private def makeClassLoader(): util.AbstractFileClassLoader =
-    new TranslatingClassLoader(parentClassLoader match {
-      case null   => ScalaClassLoader fromURLs compilerClasspath
-      case p      => new ScalaClassLoader.URLClassLoader(compilerClasspath, p)
-    })
-
-  // Set the current Java "context" class loader to this interpreter's class loader
-  def setContextClassLoader() = classLoader.setAsContext()
-
-  def allDefinedNames: List[Name]  = exitingTyper(replScope.toList.map(_.name).sorted)
-  def unqualifiedIds: List[String] = allDefinedNames map (_.decode) sorted
-
-  /** Most recent tree handled which wasn't wholly synthetic. */
-  private def mostRecentlyHandledTree: Option[Tree] = {
-    prevRequests.reverse foreach { req =>
-      req.handlers.reverse foreach {
-        case x: MemberDefHandler if x.definesValue && !isInternalTermName(x.name) => return Some(x.member)
-        case _ => ()
-      }
-    }
-    None
-  }
-
-  private def updateReplScope(sym: Symbol, isDefined: Boolean) {
-    def log(what: String) {
-      val mark = if (sym.isType) "t " else "v "
-      val name = exitingTyper(sym.nameString)
-      val info = cleanTypeAfterTyper(sym)
-      val defn = sym defStringSeenAs info
-
-      scopelog(f"[$mark$what%6s] $name%-25s $defn%s")
-    }
-    if (ObjectClass isSubClass sym.owner) return
-    // unlink previous
-    replScope lookupAll sym.name foreach { sym =>
-      log("unlink")
-      replScope unlink sym
-    }
-    val what = if (isDefined) "define" else "import"
-    log(what)
-    replScope enter sym
-  }
-
-  def recordRequest(req: Request) {
-    if (req == null)
-      return
-
-    prevRequests += req
-
-    // warning about serially defining companions.  It'd be easy
-    // enough to just redefine them together but that may not always
-    // be what people want so I'm waiting until I can do it better.
-    exitingTyper {
-      req.defines filterNot (s => req.defines contains s.companionSymbol) foreach { newSym =>
-        val oldSym = replScope lookup newSym.name.companionName
-        if (Seq(oldSym, newSym).permutations exists { case Seq(s1, s2) => s1.isClass && s2.isModule }) {
-          replwarn(s"warning: previously defined $oldSym is not a companion to $newSym.")
-          replwarn("Companions must be defined together; you may wish to use :paste mode for this.")
-        }
-      }
-    }
-    exitingTyper {
-      req.imports foreach (sym => updateReplScope(sym, isDefined = false))
-      req.defines foreach (sym => updateReplScope(sym, isDefined = true))
-    }
-  }
-
-  private[nsc] def replwarn(msg: => String) {
-    if (!settings.nowarnings)
-      printMessage(msg)
-  }
-
-  def compileSourcesKeepingRun(sources: SourceFile*) = {
-    val run = new Run()
-    assert(run.typerPhase != NoPhase, "REPL requires a typer phase.")
-    reporter.reset()
-    run compileSources sources.toList
-    (!reporter.hasErrors, run)
-  }
-
-  /** Compile an nsc SourceFile.  Returns true if there are
-    *  no compilation errors, or false otherwise.
-    */
-  def compileSources(sources: SourceFile*): Boolean =
-    compileSourcesKeepingRun(sources: _*)._1
-
-  /** Compile a string.  Returns true if there are no
-    *  compilation errors, or false otherwise.
-    */
-  def compileString(code: String): Boolean =
-    compileSources(new BatchSourceFile("<script>", code))
-
-  /** Build a request from the user. `trees` is `line` after being parsed.
-    */
-  private def buildRequest(line: String, trees: List[Tree]): Request = {
-    executingRequest = new Request(line, trees)
-    executingRequest
-  }
-
-  private def safePos(t: Tree, alt: Int): Int =
-    try t.pos.start
-    catch { case _: UnsupportedOperationException => alt }
-
-  // Given an expression like 10 * 10 * 10 we receive the parent tree positioned
-  // at a '*'.  So look at each subtree and find the earliest of all positions.
-  private def earliestPosition(tree: Tree): Int = {
-    var pos = Int.MaxValue
-    tree foreach { t =>
-      pos = math.min(pos, safePos(t, Int.MaxValue))
-    }
-    pos
-  }
-
-  private def requestFromLine(line: String, synthetic: Boolean): Either[IR.Result, Request] = {
-    val content = indentCode(line)
-    val trees = parse(content) match {
-      case parse.Incomplete     => return Left(IR.Incomplete)
-      case parse.Error          => return Left(IR.Error)
-      case parse.Success(trees) => trees
-    }
-    repltrace(
-      trees map (t => {
-        // [Eugene to Paul] previously it just said `t map ...`
-        // because there was an implicit conversion from Tree to a list of Trees
-        // however Martin and I have removed the conversion
-        // (it was conflicting with the new reflection API),
-        // so I had to rewrite this a bit
-        val subs = t collect { case sub => sub }
-        subs map (t0 =>
-          "  " + safePos(t0, -1) + ": " + t0.shortClass + "\n"
-          ) mkString ""
-      }) mkString "\n"
-    )
-    // If the last tree is a bare expression, pinpoint where it begins using the
-    // AST node position and snap the line off there.  Rewrite the code embodied
-    // by the last tree as a ValDef instead, so we can access the value.
-    val last = trees.lastOption.getOrElse(EmptyTree)
-    last match {
-      case _:Assign                        => // we don't want to include assignments
-      case _:TermTree | _:Ident | _:Select => // ... but do want other unnamed terms.
-        val varName  = if (synthetic) freshInternalVarName() else freshUserVarName()
-        val rewrittenLine = (
-          // In theory this would come out the same without the 1-specific test, but
-          // it's a cushion against any more sneaky parse-tree position vs. code mismatches:
-          // this way such issues will only arise on multiple-statement repl input lines,
-          // which most people don't use.
-          if (trees.size == 1) "val " + varName + " =\n" + content
-          else {
-            // The position of the last tree
-            val lastpos0 = earliestPosition(last)
-            // Oh boy, the parser throws away parens so "(2+2)" is mispositioned,
-            // with increasingly hard to decipher positions as we move on to "() => 5",
-            // (x: Int) => x + 1, and more.  So I abandon attempts to finesse and just
-            // look for semicolons and newlines, which I'm sure is also buggy.
-            val (raw1, raw2) = content splitAt lastpos0
-            repldbg("[raw] " + raw1 + "   <--->   " + raw2)
-
-            val adjustment = (raw1.reverse takeWhile (ch => (ch != ';') && (ch != '\n'))).size
-            val lastpos = lastpos0 - adjustment
-
-            // the source code split at the laboriously determined position.
-            val (l1, l2) = content splitAt lastpos
-            repldbg("[adj] " + l1 + "   <--->   " + l2)
-
-            val prefix   = if (l1.trim == "") "" else l1 + ";\n"
-            // Note to self: val source needs to have this precise structure so that
-            // error messages print the user-submitted part without the "val res0 = " part.
-            val combined   = prefix + "val " + varName + " =\n" + l2
-
-            repldbg(List(
-              "    line" -> line,
-              " content" -> content,
-              "     was" -> l2,
-              "combined" -> combined) map {
-              case (label, s) => label + ": '" + s + "'"
-            } mkString "\n"
-            )
-            combined
-          }
-          )
-        // Rewriting    "foo ; bar ; 123"
-        // to           "foo ; bar ; val resXX = 123"
-        requestFromLine(rewrittenLine, synthetic) match {
-          case Right(req) => return Right(req withOriginalLine line)
-          case x          => return x
-        }
-      case _ =>
-    }
-    Right(buildRequest(line, trees))
-  }
-
-  // dealias non-public types so we don't see protected aliases like Self
-  def dealiasNonPublic(tp: Type) = tp match {
-    case TypeRef(_, sym, _) if sym.isAliasType && !sym.isPublic => tp.dealias
-    case _                                                      => tp
-  }
-
-  /**
-   *  Interpret one line of input. All feedback, including parse errors
-   *  and evaluation results, are printed via the supplied compiler's
-   *  reporter. Values defined are available for future interpreted strings.
-   *
-   *  The return value is whether the line was interpreter successfully,
-   *  e.g. that there were no parse errors.
-   */
-  def interpret(line: String): IR.Result = interpret(line, synthetic = false)
-  def interpretSynthetic(line: String): IR.Result = interpret(line, synthetic = true)
-  def interpret(line: String, synthetic: Boolean): IR.Result = compile(line, synthetic) match {
-    case Left(result) => result
-    case Right(req)   => new WrappedRequest(req).loadAndRunReq
-  }
-
-  private def compile(line: String, synthetic: Boolean): Either[IR.Result, Request] = {
-    if (global == null) Left(IR.Error)
-    else requestFromLine(line, synthetic) match {
-      case Left(result) => Left(result)
-      case Right(req)   =>
-        // null indicates a disallowed statement type; otherwise compile and
-        // fail if false (implying e.g. a type error)
-        if (req == null || !req.compile) Left(IR.Error) else Right(req)
-    }
-  }
-
-  var code = ""
-  var bound = false
-  def compiled(script: String): CompiledScript = {
-    if (!bound) {
-      quietBind("engine" -> this.asInstanceOf[ScriptEngine])
-      bound = true
-    }
-    val cat = code + script
-    compile(cat, false) match {
-      case Left(result) => result match {
-        case IR.Incomplete => {
-          code = cat + "\n"
-          new CompiledScript {
-            def eval(context: ScriptContext): Object = null
-            def getEngine: ScriptEngine = SparkIMain.this
-          }
-        }
-        case _ => {
-          code = ""
-          throw new ScriptException("compile-time error")
-        }
-      }
-      case Right(req)   => {
-        code = ""
-        new WrappedRequest(req)
-      }
-    }
-  }
-
-  private class WrappedRequest(val req: Request) extends CompiledScript {
-    var recorded = false
-
-    /** In Java we would have to wrap any checked exception in the declared
-      *  ScriptException. Runtime exceptions and errors would be ok and would
-      *  not need to be caught. So let us do the same in Scala : catch and
-      *  wrap any checked exception, and let runtime exceptions and errors
-      *  escape. We could have wrapped runtime exceptions just like other
-      *  exceptions in ScriptException, this is a choice.
-      */
-    @throws[ScriptException]
-    def eval(context: ScriptContext): Object = {
-      val result = req.lineRep.evalEither match {
-        case Left(e: RuntimeException) => throw e
-        case Left(e: Exception) => throw new ScriptException(e)
-        case Left(e) => throw e
-        case Right(result) => result.asInstanceOf[Object]
-      }
-      if (!recorded) {
-        recordRequest(req)
-        recorded = true
-      }
-      result
-    }
-
-    def loadAndRunReq = classLoader.asContext {
-      val (result, succeeded) = req.loadAndRun
-
-      /** To our displeasure, ConsoleReporter offers only printMessage,
-        *  which tacks a newline on the end.  Since that breaks all the
-        *  output checking, we have to take one off to balance.
-        */
-      if (succeeded) {
-        if (printResults && result != "")
-          printMessage(result stripSuffix "\n")
-        else if (isReplDebug) // show quiet-mode activity
-          printMessage(result.trim.lines map ("[quiet] " + _) mkString "\n")
-
-        // Book-keeping.  Have to record synthetic requests too,
-        // as they may have been issued for information, e.g. :type
-        recordRequest(req)
-        IR.Success
-      }
-      else {
-        // don't truncate stack traces
-        printUntruncatedMessage(result)
-        IR.Error
-      }
-    }
-
-    def getEngine: ScriptEngine = SparkIMain.this
-  }
-
-  /** Bind a specified name to a specified value.  The name may
-    *  later be used by expressions passed to interpret.
-    *
-    *  @param name      the variable name to bind
-    *  @param boundType the type of the variable, as a string
-    *  @param value     the object value to bind to it
-    *  @return          an indication of whether the binding succeeded
-    */
-  def bind(name: String, boundType: String, value: Any, modifiers: List[String] = Nil): IR.Result = {
-    val bindRep = new ReadEvalPrint()
-    bindRep.compile("""
-                      |object %s {
-                      |  var value: %s = _
-                      |  def set(x: Any) = value = x.asInstanceOf[%s]
-                      |}
-                    """.stripMargin.format(bindRep.evalName, boundType, boundType)
-    )
-    bindRep.callEither("set", value) match {
-      case Left(ex) =>
-        repldbg("Set failed in bind(%s, %s, %s)".format(name, boundType, value))
-        repldbg(util.stackTraceString(ex))
-        IR.Error
-
-      case Right(_) =>
-        val line = "%sval %s = %s.value".format(modifiers map (_ + " ") mkString, name, bindRep.evalPath)
-        repldbg("Interpreting: " + line)
-        interpret(line)
-    }
-  }
-  def directBind(name: String, boundType: String, value: Any): IR.Result = {
-    val result = bind(name, boundType, value)
-    if (result == IR.Success)
-      directlyBoundNames += newTermName(name)
-    result
-  }
-  def directBind(p: NamedParam): IR.Result                                    = directBind(p.name, p.tpe, p.value)
-  def directBind[T: ru.TypeTag : ClassTag](name: String, value: T): IR.Result = directBind((name, value))
-
-  def rebind(p: NamedParam): IR.Result = {
-    val name     = p.name
-    val newType  = p.tpe
-    val tempName = freshInternalVarName()
-
-    quietRun("val %s = %s".format(tempName, name))
-    quietRun("val %s = %s.asInstanceOf[%s]".format(name, tempName, newType))
-  }
-  def quietBind(p: NamedParam): IR.Result                               = beQuietDuring(bind(p))
-  def bind(p: NamedParam): IR.Result                                    = bind(p.name, p.tpe, p.value)
-  def bind[T: ru.TypeTag : ClassTag](name: String, value: T): IR.Result = bind((name, value))
-
-  /** Reset this interpreter, forgetting all user-specified requests. */
-  def reset() {
-    clearExecutionWrapper()
-    resetClassLoader()
-    resetAllCreators()
-    prevRequests.clear()
-    resetReplScope()
-    replOutput.dir.clear()
-  }
-
-  /** This instance is no longer needed, so release any resources
-    *  it is using.  The reporter's output gets flushed.
-    */
-  def close() {
-    reporter.flush()
-  }
-
-  /** Here is where we:
-    *
-    *  1) Read some source code, and put it in the "read" object.
-    *  2) Evaluate the read object, and put the result in the "eval" object.
-    *  3) Create a String for human consumption, and put it in the "print" object.
-    *
-    *  Read! Eval! Print! Some of that not yet centralized here.
-    */
-  class ReadEvalPrint(val lineId: Int) {
-    def this() = this(freshLineId())
-
-    val packageName = sessionNames.line + lineId
-    val readName    = sessionNames.read
-    val evalName    = sessionNames.eval
-    val printName   = sessionNames.print
-    val resultName  = sessionNames.result
-
-    def bindError(t: Throwable) = {
-      if (!bindExceptions) // avoid looping if already binding
-        throw t
-
-      val unwrapped = unwrap(t)
-
-      // Example input: $line3.$read$$iw$$iw$
-      val classNameRegex = (naming.lineRegex + ".*").r
-      def isWrapperInit(x: StackTraceElement) = cond(x.getClassName) {
-        case classNameRegex() if x.getMethodName == nme.CONSTRUCTOR.decoded => true
-      }
-      val stackTrace = unwrapped stackTracePrefixString (!isWrapperInit(_))
-
-      withLastExceptionLock[String]({
-        directBind[Throwable]("lastException", unwrapped)(StdReplTags.tagOfThrowable, classTag[Throwable])
-        stackTrace
-      }, stackTrace)
-    }
-
-    // TODO: split it out into a package object and a regular
-    // object and we can do that much less wrapping.
-    def packageDecl = "package " + packageName
-
-    def pathTo(name: String)   = packageName + "." + name
-    def packaged(code: String) = packageDecl + "\n\n" + code
-
-    def readPath  = pathTo(readName)
-    def evalPath  = pathTo(evalName)
-
-    def call(name: String, args: Any*): AnyRef = {
-      val m = evalMethod(name)
-      repldbg("Invoking: " + m)
-      if (args.nonEmpty)
-        repldbg("  with args: " + args.mkString(", "))
-
-      m.invoke(evalClass, args.map(_.asInstanceOf[AnyRef]): _*)
-    }
-
-    def callEither(name: String, args: Any*): Either[Throwable, AnyRef] =
-      try Right(call(name, args: _*))
-      catch { case ex: Throwable => Left(ex) }
-
-    class EvalException(msg: String, cause: Throwable) extends RuntimeException(msg, cause) { }
-
-    private def evalError(path: String, ex: Throwable) =
-      throw new EvalException("Failed to load '" + path + "': " + ex.getMessage, ex)
-
-    private def load(path: String): Class[_] = {
-      try Class.forName(path, true, classLoader)
-      catch { case ex: Throwable => evalError(path, unwrap(ex)) }
-    }
-
-    lazy val evalClass = load(evalPath)
-
-    def evalEither = callEither(resultName) match {
-      case Left(ex) => ex match {
-        case ex: NullPointerException => Right(null)
-        case ex => Left(unwrap(ex))
-      }
-      case Right(result) => Right(result)
-    }
-
-    def compile(source: String): Boolean = compileAndSaveRun("<console>", source)
-
-    /** The innermost object inside the wrapper, found by
-      * following accessPath into the outer one.
-      */
-    def resolvePathToSymbol(accessPath: String): Symbol = {
-      val readRoot: global.Symbol = readRootPath(readPath) // the outermost wrapper
-      ((".INSTANCE" + accessPath) split '.').foldLeft(readRoot: Symbol) {
-        case (sym, "")    => sym
-        case (sym, name)  => exitingTyper(termMember(sym, name))
-      }
-    }
-    /** We get a bunch of repeated warnings for reasons I haven't
-      *  entirely figured out yet.  For now, squash.
-      */
-    private def updateRecentWarnings(run: Run) {
-      def loop(xs: List[(Position, String)]): List[(Position, String)] = xs match {
-        case Nil                  => Nil
-        case ((pos, msg)) :: rest =>
-          val filtered = rest filter { case (pos0, msg0) =>
-            (msg != msg0) || (pos.lineContent.trim != pos0.lineContent.trim) || {
-              // same messages and same line content after whitespace removal
-              // but we want to let through multiple warnings on the same line
-              // from the same run.  The untrimmed line will be the same since
-              // there's no whitespace indenting blowing it.
-              (pos.lineContent == pos0.lineContent)
-            }
-          }
-          ((pos, msg)) :: loop(filtered)
-      }
-      val warnings = loop(run.reporting.allConditionalWarnings)
-      if (warnings.nonEmpty)
-        mostRecentWarnings = warnings
-    }
-    private def evalMethod(name: String) = evalClass.getMethods filter (_.getName == name) match {
-      case Array()       => null
-      case Array(method) => method
-      case xs            => sys.error("Internal error: eval object " + evalClass + ", " + xs.mkString("\n", "\n", ""))
-    }
-    private def compileAndSaveRun(label: String, code: String) = {
-      showCodeIfDebugging(code)
-      val (success, run) = compileSourcesKeepingRun(new BatchSourceFile(label, packaged(code)))
-      updateRecentWarnings(run)
-      success
-    }
-  }
-
-  /** One line of code submitted by the user for interpretation */
-  class Request(val line: String, val trees: List[Tree]) {
-    def defines    = defHandlers flatMap (_.definedSymbols)
-    def imports    = importedSymbols
-    def value      = Some(handlers.last) filter (h => h.definesValue) map (h => definedSymbols(h.definesTerm.get)) getOrElse NoSymbol
-
-    val lineRep = new ReadEvalPrint()
-
-    private var _originalLine: String = null
-    def withOriginalLine(s: String): this.type = { _originalLine = s ; this }
-    def originalLine = if (_originalLine == null) line else _originalLine
-
-    /** handlers for each tree in this request */
-    val handlers: List[MemberHandler] = trees map (memberHandlers chooseHandler _)
-    def defHandlers = handlers collect { case x: MemberDefHandler => x }
-
-    /** list of names used by this expression */
-    val referencedNames: List[Name] = handlers flatMap (_.referencedNames)
-
-    /** def and val names */
-    def termNames = handlers flatMap (_.definesTerm)
-    def typeNames = handlers flatMap (_.definesType)
-    def importedSymbols = handlers flatMap {
-      case x: ImportHandler => x.importedSymbols
-      case _                => Nil
-    }
-
-    val definedClasses = handlers.exists {
-      case _: ClassHandler => true
-      case _ => false
-    }
-    /** Code to import bound names from previous lines - accessPath is code to
-      * append to objectName to access anything bound by request.
-      */
-    lazy val ComputedImports(importsPreamble, importsTrailer, accessPath) =
-      exitingTyper(importsCode(referencedNames.toSet, ObjectSourceCode, definedClasses))
-
-    /** the line of code to compute */
-    def toCompute = line
-
-    /** The path of the value that contains the user code. */
-    def fullAccessPath = s"${lineRep.readPath}.INSTANCE$accessPath"
-
-    /** The path of the given member of the wrapping instance. */
-    def fullPath(vname: String) = s"$fullAccessPath.`$vname`"
-
-    /** generate the source code for the object that computes this request */
-    abstract class Wrapper extends SparkIMain.CodeAssembler[MemberHandler] {
-      def path = originalPath("$intp")
-      def envLines = {
-        if (!isReplPower) Nil // power mode only for now
-        else List("def %s = %s".format("$line", tquoted(originalLine)), "def %s = Nil".format("$trees"))
-      }
-      def preamble = s"""
-        |$preambleHeader
-        |%s%s%s
-      """.stripMargin.format(lineRep.readName, envLines.map("  " + _ + ";\n").mkString,
-          importsPreamble, indentCode(toCompute))
-
-      val generate = (m: MemberHandler) => m extraCodeToEvaluate Request.this
-
-      /** A format string with %s for $read, specifying the wrapper definition. */
-      def preambleHeader: String
-
-      /** Like preambleHeader for an import wrapper. */
-      def prewrap: String = preambleHeader + "\n"
-
-      /** Like postamble for an import wrapper. */
-      def postwrap: String
-    }
-
-    private class ObjectBasedWrapper extends Wrapper {
-      def preambleHeader = "object %s {"
-
-      def postamble = importsTrailer + "\n}"
-
-      def postwrap = "}\n"
-    }
-
-    private class ClassBasedWrapper extends Wrapper {
-      def preambleHeader = "class %s extends Serializable {"
-
-      /** Adds an object that instantiates the outer wrapping class. */
-      def postamble  = s"""
-                          |$importsTrailer
-                          |}
-                          |object ${lineRep.readName} {
-                          |   val INSTANCE = new ${lineRep.readName}();
-                          |}
-                          |""".stripMargin
-
-      import nme.{ INTERPRETER_IMPORT_WRAPPER => iw }
-
-      /** Adds a val that instantiates the wrapping class. */
-      def postwrap = s"}\nval $iw = new $iw\n"
-    }
-
-    private lazy val ObjectSourceCode: Wrapper = new ClassBasedWrapper
-    private object ResultObjectSourceCode extends SparkIMain.CodeAssembler[MemberHandler] {
-      /** We only want to generate this code when the result
-        *  is a value which can be referred to as-is.
-        */
-      val evalResult = Request.this.value match {
-        case NoSymbol => ""
-        case sym      =>
-          "lazy val %s = %s".format(lineRep.resultName,  fullPath(sym.decodedName))
-      }
-      // first line evaluates object to make sure constructor is run
-      // initial "" so later code can uniformly be: + etc
-      val preamble = """
-                       |object %s {
-                       |  %s
-                       |  lazy val %s: String = %s {
-                       |    %s
-                       |    (""
-                     """.stripMargin.format(
-          lineRep.evalName, evalResult, lineRep.printName,
-          executionWrapper, fullAccessPath
-        )
-
-      val postamble = """
-                        |    )
-                        |  }
-                        |}
-                      """.stripMargin
-      val generate = (m: MemberHandler) => m resultExtractionCode Request.this
-    }
-
-    /** Compile the object file.  Returns whether the compilation succeeded.
-      *  If all goes well, the "types" map is computed. */
-    lazy val compile: Boolean = {
-      // error counting is wrong, hence interpreter may overlook failure - so we reset
-      reporter.reset()
-
-      // compile the object containing the user's code
-      lineRep.compile(ObjectSourceCode(handlers)) && {
-        // extract and remember types
-        typeOf
-        typesOfDefinedTerms
-
-        // Assign symbols to the original trees
-        // TODO - just use the new trees.
-        defHandlers foreach { dh =>
-          val name = dh.member.name
-          definedSymbols get name foreach { sym =>
-            dh.member setSymbol sym
-            repldbg("Set symbol of " + name + " to " + symbolDefString(sym))
-          }
-        }
-
-        // compile the result-extraction object
-        val handls = if (printResults) handlers else Nil
-        withoutWarnings(lineRep compile ResultObjectSourceCode(handls))
-      }
-    }
-
-    lazy val resultSymbol =  lineRep.resolvePathToSymbol(accessPath)
-
-    def applyToResultMember[T](name: Name, f: Symbol => T) = exitingTyper(f(resultSymbol.info.nonPrivateDecl(name)))
-
-    /* typeOf lookup with encoding */
-    def lookupTypeOf(name: Name) = typeOf.getOrElse(name, typeOf(global.encode(name.toString)))
-
-    private def typeMap[T](f: Type => T) =
-      mapFrom[Name, Name, T](termNames ++ typeNames)(x => f(cleanMemberDecl(resultSymbol, x)))
-
-    /** Types of variables defined by this request. */
-    lazy val compilerTypeOf = typeMap[Type](x => x) withDefaultValue NoType
-    /** String representations of same. */
-    lazy val typeOf         = typeMap[String](tp => exitingTyper(tp.toString))
-
-    lazy val definedSymbols = (
-      termNames.map(x => x -> applyToResultMember(x, x => x)) ++
-        typeNames.map(x => x -> compilerTypeOf(x).typeSymbolDirect)
-      ).toMap[Name, Symbol] withDefaultValue NoSymbol
-
-    lazy val typesOfDefinedTerms = mapFrom[Name, Name, Type](termNames)(x => applyToResultMember(x, _.tpe))
-
-    /** load and run the code using reflection */
-    def loadAndRun: (String, Boolean) = {
-      try   { ("" + (lineRep call sessionNames.print), true) }
-      catch { case ex: Throwable => (lineRep.bindError(ex), false) }
-    }
-
-    override def toString = "Request(line=%s, %s trees)".format(line, trees.size)
-  }
-
-  def createBindings: Bindings = new IBindings {
-    override def put(name: String, value: Object): Object = {
-      val n = name.indexOf(":")
-      val p: NamedParam = if (n < 0) (name, value) else {
-        val nme = name.substring(0, n).trim
-        val tpe = name.substring(n + 1).trim
-        NamedParamClass(nme, tpe, value)
-      }
-      if (!p.name.startsWith("javax.script")) bind(p)
-      null
-    }
-  }
-
-  @throws[ScriptException]
-  def compile(script: String): CompiledScript = eval("new javax.script.CompiledScript { def eval(context: javax.script.ScriptContext): Object = { " + script + " }.asInstanceOf[Object]; def getEngine: javax.script.ScriptEngine = engine }").asInstanceOf[CompiledScript]
-
-  @throws[ScriptException]
-  def compile(reader: java.io.Reader): CompiledScript = compile(stringFromReader(reader))
-
-  @throws[ScriptException]
-  def eval(script: String, context: ScriptContext): Object = compiled(script).eval(context)
-
-  @throws[ScriptException]
-  def eval(reader: java.io.Reader, context: ScriptContext): Object = eval(stringFromReader(reader), context)
-
-  override def finalize = close
-
-  /** Returns the name of the most recent interpreter result.
-    *  Mostly this exists so you can conveniently invoke methods on
-    *  the previous result.
-    */
-  def mostRecentVar: String =
-    if (mostRecentlyHandledTree.isEmpty) ""
-    else "" + (mostRecentlyHandledTree.get match {
-      case x: ValOrDefDef           => x.name
-      case Assign(Ident(name), _)   => name
-      case ModuleDef(_, name, _)    => name
-      case _                        => naming.mostRecentVar
-    })
-
-  private var mostRecentWarnings: List[(global.Position, String)] = Nil
-  def lastWarnings = mostRecentWarnings
-
-  private lazy val importToGlobal  = global mkImporter ru
-  private lazy val importToRuntime = ru.internal createImporter global
-  private lazy val javaMirror = ru.rootMirror match {
-    case x: ru.JavaMirror => x
-    case _                => null
-  }
-  private implicit def importFromRu(sym: ru.Symbol): Symbol = importToGlobal importSymbol sym
-  private implicit def importToRu(sym: Symbol): ru.Symbol   = importToRuntime importSymbol sym
-
-  def classOfTerm(id: String): Option[JClass] = symbolOfTerm(id) match {
-    case NoSymbol => None
-    case sym      => Some(javaMirror runtimeClass importToRu(sym).asClass)
-  }
-
-  def typeOfTerm(id: String): Type = symbolOfTerm(id).tpe
-
-  def valueOfTerm(id: String): Option[Any] = exitingTyper {
-    def value() = {
-      val sym0    = symbolOfTerm(id)
-      val sym     = (importToRuntime importSymbol sym0).asTerm
-      val module  = runtimeMirror.reflectModule(sym.owner.companionSymbol.asModule).instance
-      val module1 = runtimeMirror.reflect(module)
-      val invoker = module1.reflectField(sym)
-
-      invoker.get
-    }
-
-    try Some(value()) catch { case _: Exception => None }
-  }
-
-  /** It's a bit of a shotgun approach, but for now we will gain in
-    *  robustness. Try a symbol-producing operation at phase typer, and
-    *  if that is NoSymbol, try again at phase flatten. I'll be able to
-    *  lose this and run only from exitingTyper as soon as I figure out
-    *  exactly where a flat name is sneaking in when calculating imports.
-    */
-  def tryTwice(op: => Symbol): Symbol = exitingTyper(op) orElse exitingFlatten(op)
-
-  def symbolOfIdent(id: String): Symbol  = symbolOfType(id) orElse symbolOfTerm(id)
-  def symbolOfType(id: String): Symbol   = tryTwice(replScope lookup (id: TypeName))
-  def symbolOfTerm(id: String): Symbol   = tryTwice(replScope lookup (id: TermName))
-  def symbolOfName(id: Name): Symbol     = replScope lookup id
-
-  def runtimeClassAndTypeOfTerm(id: String): Option[(JClass, Type)] = {
-    classOfTerm(id) flatMap { clazz =>
-      clazz.supers find (!_.isScalaAnonymous) map { nonAnon =>
-        (nonAnon, runtimeTypeOfTerm(id))
-      }
-    }
-  }
-
-  def runtimeTypeOfTerm(id: String): Type = {
-    typeOfTerm(id) andAlso { tpe =>
-      val clazz      = classOfTerm(id) getOrElse { return NoType }
-      val staticSym  = tpe.typeSymbol
-      val runtimeSym = getClassIfDefined(clazz.getName)
-
-      if ((runtimeSym != NoSymbol) && (runtimeSym != staticSym) && (runtimeSym isSubClass staticSym))
-        runtimeSym.info
-      else NoType
-    }
-  }
-
-  def cleanTypeAfterTyper(sym: => Symbol): Type = {
-    exitingTyper(
-      dealiasNonPublic(
-        dropNullaryMethod(
-          sym.tpe_*
-        )
-      )
-    )
-  }
-  def cleanMemberDecl(owner: Symbol, member: Name): Type =
-    cleanTypeAfterTyper(owner.info nonPrivateDecl member)
-
-  object exprTyper extends {
-    val repl: SparkIMain.this.type = imain
-  } with SparkExprTyper { }
-
-  /** Parse a line into and return parsing result (error, incomplete or success with list of trees) */
-  object parse {
-    abstract sealed class Result
-    case object Error extends Result
-    case object Incomplete extends Result
-    case class Success(trees: List[Tree]) extends Result
-
-    def apply(line: String): Result = debugging(s"""parse("$line")""")  {
-      var isIncomplete = false
-      currentRun.parsing.withIncompleteHandler((_, _) => isIncomplete = true) {
-        reporter.reset()
-        val trees = newUnitParser(line).parseStats()
-        if (reporter.hasErrors) Error
-        else if (isIncomplete) Incomplete
-        else Success(trees)
-      }
-    }
-  }
-
-  def symbolOfLine(code: String): Symbol =
-    exprTyper.symbolOfLine(code)
-
-  def typeOfExpression(expr: String, silent: Boolean = true): Type =
-    exprTyper.typeOfExpression(expr, silent)
-
-  protected def onlyTerms(xs: List[Name]): List[TermName] = xs collect { case x: TermName => x }
-  protected def onlyTypes(xs: List[Name]): List[TypeName] = xs collect { case x: TypeName => x }
-
-  def definedTerms      = onlyTerms(allDefinedNames) filterNot isInternalTermName
-  def definedTypes      = onlyTypes(allDefinedNames)
-  def definedSymbolList = prevRequestList flatMap (_.defines) filterNot (s => isInternalTermName(s.name))
-
-  // Terms with user-given names (i.e. not res0 and not synthetic)
-  def namedDefinedTerms = definedTerms filterNot (x => isUserVarName("" + x) || directlyBoundNames(x))
-
-  private var _replScope: Scope = _
-  private def resetReplScope() {
-    _replScope = newScope
-  }
-  def replScope = {
-    if (_replScope eq null)
-      _replScope = newScope
-
-    _replScope
-  }
-
-  private var executingRequest: Request = _
-  private val prevRequests       = mutable.ListBuffer[Request]()
-  private val directlyBoundNames = mutable.Set[Name]()
-
-  def allHandlers     = prevRequestList flatMap (_.handlers)
-  def lastRequest     = if (prevRequests.isEmpty) null else prevRequests.last
-  def prevRequestList = prevRequests.toList
-  def importHandlers  = allHandlers collect { case x: ImportHandler => x }
-
-  def withoutUnwrapping(op: => Unit): Unit = {
-    val saved = isettings.unwrapStrings
-    isettings.unwrapStrings = false
-    try op
-    finally isettings.unwrapStrings = saved
-  }
-
-  def symbolDefString(sym: Symbol) = {
-    TypeStrings.quieter(
-      exitingTyper(sym.defString),
-      sym.owner.name + ".this.",
-      sym.owner.fullName + "."
-    )
-  }
-
-  def showCodeIfDebugging(code: String) {
-    /** Secret bookcase entrance for repl debuggers: end the line
-      *  with "// show" and see what's going on.
-      */
-    def isShow = code.lines exists (_.trim endsWith "// show")
-    if (isReplDebug || isShow) {
-      beSilentDuring(parse(code)) match {
-        case parse.Success(ts) =>
-          ts foreach { t =>
-            withoutUnwrapping(echo(asCompactString(t)))
-          }
-        case _ =>
-      }
-    }
-  }
-
-  // debugging
-  def debugging[T](msg: String)(res: T) = {
-    repldbg(msg + " " + res)
-    res
-  }
-}
-
-/** Utility methods for the Interpreter. */
-object SparkIMain {
-  import java.util.Arrays.{ asList => asJavaList }
-
-  // The two name forms this is catching are the two sides of this assignment:
-  //
-  // $line3.$read.$iw.$iw.Bippy =
-  //   $line3.$read$$iw$$iw$Bippy@4a6a00ca
-  private def removeLineWrapper(s: String) = s.replaceAll("""\$line\d+[./]\$(read|eval|print)[$.]""", "")
-  private def removeIWPackages(s: String)  = s.replaceAll("""\$(iw|read|eval|print)[$.]""", "")
-  private def removeSparkVals(s: String) = s.replaceAll("""\$VAL[0-9]+[$.]""", "")
-  def stripString(s: String)               = removeSparkVals(removeIWPackages(removeLineWrapper(s)))
-  
-  trait CodeAssembler[T] {
-    def preamble: String
-    def generate: T => String
-    def postamble: String
-
-    def apply(contributors: List[T]): String = stringFromWriter { code =>
-      code println preamble
-      contributors map generate foreach (code println _)
-      code println postamble
-    }
-  }
-
-  trait StrippingWriter {
-    def isStripping: Boolean
-    def stripImpl(str: String): String
-    def strip(str: String): String = if (isStripping) stripImpl(str) else str
-  }
-  trait TruncatingWriter {
-    def maxStringLength: Int
-    def isTruncating: Boolean
-    def truncate(str: String): String = {
-      if (isTruncating && (maxStringLength != 0 && str.length > maxStringLength))
-        (str take maxStringLength - 3) + "..."
-      else str
-    }
-  }
-  abstract class StrippingTruncatingWriter(out: JPrintWriter)
-    extends JPrintWriter(out)
-    with StrippingWriter
-    with TruncatingWriter {
-    self =>
-
-    def clean(str: String): String = truncate(strip(str))
-    override def write(str: String) = super.write(clean(str))
-  }
-  class SparkReplStrippingWriter(intp: SparkIMain) extends StrippingTruncatingWriter(intp.out) {
-    import intp._
-    def maxStringLength    = isettings.maxPrintString
-    def isStripping        = isettings.unwrapStrings
-    def isTruncating       = reporter.truncationOK
-
-    def stripImpl(str: String): String = naming.unmangle(str)
-  }
-}
-
-/** Settings for the interpreter
-  *
-  * @version 1.0
-  * @author Lex Spoon, 2007/3/24
-  **/
-class SparkISettings(intp: SparkIMain) {
-  /** The maximum length of toString to use when printing the result
-    *  of an evaluation.  0 means no maximum.  If a printout requires
-    *  more than this number of characters, then the printout is
-    *  truncated.
-    */
-  var maxPrintString = replProps.maxPrintString.option.getOrElse(800)
-
-  /** The maximum number of completion candidates to print for tab
-    *  completion without requiring confirmation.
-    */
-  var maxAutoprintCompletion = 250
-
-  /** String unwrapping can be disabled if it is causing issues.
-    *  Setting this to false means you will see Strings like "$iw.$iw.".
-    */
-  var unwrapStrings = true
-
-  def deprecation_=(x: Boolean) = {
-    val old = intp.settings.deprecation.value
-    intp.settings.deprecation.value = x
-    // scalastyle:off println
-    if (!old && x) println("Enabled -deprecation output.")
-    else if (old && !x) println("Disabled -deprecation output.")
-    // scalastyle:on println
-  }
-  def deprecation: Boolean = intp.settings.deprecation.value
-
-  def allSettings = Map[String, Any](
-    "maxPrintString" -> maxPrintString,
-    "maxAutoprintCompletion" -> maxAutoprintCompletion,
-    "unwrapStrings" -> unwrapStrings,
-    "deprecation" -> deprecation
-  )
-
-  private def allSettingsString =
-    allSettings.toList sortBy (_._1) map { case (k, v) => "  " + k + " = " + v + "\n" } mkString
-
-  override def toString = """
-                            | SparkISettings {
-                            | %s
-                            | }""".stripMargin.format(allSettingsString)
-}
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkImports.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkImports.scala
deleted file mode 100644
index e60406d1e5ad8..0000000000000
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkImports.scala
+++ /dev/null
@@ -1,201 +0,0 @@
-/* NSC -- new Scala compiler
- * Copyright 2005-2013 LAMP/EPFL
- * @author  Paul Phillips
- */
-
-package scala.tools.nsc
-package interpreter
-
-import scala.collection.{ mutable, immutable }
-
-trait SparkImports {
-  self: SparkIMain =>
-
-  import global._
-  import definitions.{ ObjectClass, ScalaPackage, JavaLangPackage, PredefModule }
-  import memberHandlers._
-
-  /** Synthetic import handlers for the language defined imports. */
-  private def makeWildcardImportHandler(sym: Symbol): ImportHandler = {
-    val hd :: tl = sym.fullName.split('.').toList map newTermName
-    val tree = Import(
-      tl.foldLeft(Ident(hd): Tree)((x, y) => Select(x, y)),
-      ImportSelector.wildList
-    )
-    tree setSymbol sym
-    new ImportHandler(tree)
-  }
-
-  /** Symbols whose contents are language-defined to be imported. */
-  def languageWildcardSyms: List[Symbol] = List(JavaLangPackage, ScalaPackage, PredefModule)
-  def languageWildcardHandlers = languageWildcardSyms map makeWildcardImportHandler
-
-  def allImportedNames = importHandlers flatMap (_.importedNames)
-
-  /** Types which have been wildcard imported, such as:
-    *    val x = "abc" ; import x._  // type java.lang.String
-    *    import java.lang.String._   // object java.lang.String
-    *
-    *  Used by tab completion.
-    *
-    *  XXX right now this gets import x._ and import java.lang.String._,
-    *  but doesn't figure out import String._.  There's a lot of ad hoc
-    *  scope twiddling which should be swept away in favor of digging
-    *  into the compiler scopes.
-    */
-  def sessionWildcards: List[Type] = {
-    importHandlers filter (_.importsWildcard) map (_.targetType) distinct
-  }
-
-  def languageSymbols        = languageWildcardSyms flatMap membersAtPickler
-  def sessionImportedSymbols = importHandlers flatMap (_.importedSymbols)
-  def importedSymbols        = languageSymbols ++ sessionImportedSymbols
-  def importedTermSymbols    = importedSymbols collect { case x: TermSymbol => x }
-
-  /** Tuples of (source, imported symbols) in the order they were imported.
-    */
-  def importedSymbolsBySource: List[(Symbol, List[Symbol])] = {
-    val lang    = languageWildcardSyms map (sym => (sym, membersAtPickler(sym)))
-    val session = importHandlers filter (_.targetType != NoType) map { mh =>
-      (mh.targetType.typeSymbol, mh.importedSymbols)
-    }
-
-    lang ++ session
-  }
-  def implicitSymbolsBySource: List[(Symbol, List[Symbol])] = {
-    importedSymbolsBySource map {
-      case (k, vs) => (k, vs filter (_.isImplicit))
-    } filterNot (_._2.isEmpty)
-  }
-
-  /** Compute imports that allow definitions from previous
-    *  requests to be visible in a new request.  Returns
-    *  three pieces of related code:
-    *
-    *  1. An initial code fragment that should go before
-    *  the code of the new request.
-    *
-    *  2. A code fragment that should go after the code
-    *  of the new request.
-    *
-    *  3. An access path which can be traversed to access
-    *  any bindings inside code wrapped by #1 and #2 .
-    *
-    * The argument is a set of Names that need to be imported.
-    *
-    * Limitations: This method is not as precise as it could be.
-    * (1) It does not process wildcard imports to see what exactly
-    * they import.
-    * (2) If it imports any names from a request, it imports all
-    * of them, which is not really necessary.
-    * (3) It imports multiple same-named implicits, but only the
-    * last one imported is actually usable.
-    */
-  case class ComputedImports(prepend: String, append: String, access: String)
-  protected def importsCode(wanted: Set[Name], wrapper: Request#Wrapper, definedClass: Boolean): ComputedImports = {
-    /** Narrow down the list of requests from which imports
-      *  should be taken.  Removes requests which cannot contribute
-      *  useful imports for the specified set of wanted names.
-      */
-    case class ReqAndHandler(req: Request, handler: MemberHandler) { }
-
-    def reqsToUse: List[ReqAndHandler] = {
-      /** Loop through a list of MemberHandlers and select which ones to keep.
-        * 'wanted' is the set of names that need to be imported.
-        */
-      def select(reqs: List[ReqAndHandler], wanted: Set[Name]): List[ReqAndHandler] = {
-        // Single symbol imports might be implicits! See bug #1752.  Rather than
-        // try to finesse this, we will mimic all imports for now.
-        def keepHandler(handler: MemberHandler) = handler match {
-          case h: ImportHandler if definedClass => h.importedNames.exists(x => wanted.contains(x))
-          case _: ImportHandler => true
-          case x                => x.definesImplicit || (x.definedNames exists wanted)
-        }
-
-        reqs match {
-          case Nil                                    => Nil
-          case rh :: rest if !keepHandler(rh.handler) => select(rest, wanted)
-          case rh :: rest                             =>
-            import rh.handler._
-            val newWanted = wanted ++ referencedNames -- definedNames -- importedNames
-            rh :: select(rest, newWanted)
-        }
-      }
-
-      /** Flatten the handlers out and pair each with the original request */
-      select(allReqAndHandlers reverseMap { case (r, h) => ReqAndHandler(r, h) }, wanted).reverse
-    }
-
-    val code, trailingBraces, accessPath = new StringBuilder
-    val currentImps = mutable.HashSet[Name]()
-
-    // add code for a new object to hold some imports
-    def addWrapper() {
-      import nme.{ INTERPRETER_IMPORT_WRAPPER => iw }
-      code append (wrapper.prewrap format iw)
-      trailingBraces append wrapper.postwrap
-      accessPath append s".$iw"
-      currentImps.clear()
-    }
-
-    def maybeWrap(names: Name*) = if (names exists currentImps) addWrapper()
-
-    def wrapBeforeAndAfter[T](op: => T): T = {
-      addWrapper()
-      try op finally addWrapper()
-    }
-
-    // loop through previous requests, adding imports for each one
-    wrapBeforeAndAfter {
-      for (ReqAndHandler(req, handler) <- reqsToUse) {
-        handler match {
-          // If the user entered an import, then just use it; add an import wrapping
-          // level if the import might conflict with some other import
-          case x: ImportHandler if x.importsWildcard =>
-            wrapBeforeAndAfter(code append (x.member + "\n"))
-          case x: ImportHandler =>
-            maybeWrap(x.importedNames: _*)
-            code append (x.member + "\n")
-            currentImps ++= x.importedNames
-
-          case x: ClassHandler =>
-             for (imv <- x.definedNames) {
-              val objName = req.lineRep.readPath
-              code.append("import " + objName + ".INSTANCE" + req.accessPath + ".`" + imv + "`\n")
-            }
-
-          // For other requests, import each defined name.
-          // import them explicitly instead of with _, so that
-          // ambiguity errors will not be generated. Also, quote
-          // the name of the variable, so that we don't need to
-          // handle quoting keywords separately.
-          case x =>
-            for (imv <- x.definedNames) {
-              if (currentImps contains imv) addWrapper()
-              val objName = req.lineRep.readPath
-              val valName = "$VAL" + newValId()
-              if(!code.toString.endsWith(".`" + imv + "`;\n")) { // Which means already imported
-                code.append("val " + valName + " = " + objName + ".INSTANCE\n")
-                code.append("import " + valName + req.accessPath + ".`" + imv + "`;\n")
-              }
-              currentImps += imv
-            }
-        }
-      }
-    }
-    addWrapper()
-    ComputedImports(code.toString, trailingBraces.toString, accessPath.toString)
-  }
-  private var curValId = 0
-
-  private def newValId(): Int = {
-    curValId += 1
-    curValId
-  }
-
-  private def allReqAndHandlers =
-    prevRequestList flatMap (req => req.handlers map (req -> _))
-
-  private def membersAtPickler(sym: Symbol): List[Symbol] =
-    enteringPickler(sym.info.nonPrivateMembers.toList)
-}
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala
deleted file mode 100644
index 7fe6dcb32827f..0000000000000
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala
+++ /dev/null
@@ -1,350 +0,0 @@
-/* NSC -- new Scala compiler
- * Copyright 2005-2013 LAMP/EPFL
- * @author Paul Phillips
- */
-
-package scala.tools.nsc
-package interpreter
-
-import Completion._
-import scala.collection.mutable.ListBuffer
-import scala.reflect.internal.util.StringOps.longestCommonPrefix
-
-// REPL completor - queries supplied interpreter for valid
-// completions based on current contents of buffer.
-class SparkJLineCompletion(val intp: SparkIMain) extends Completion with CompletionOutput {
-  val global: intp.global.type = intp.global
-  import global._
-  import definitions._
-  import rootMirror.{ RootClass, getModuleIfDefined }
-  import intp.{ debugging }
-
-  // verbosity goes up with consecutive tabs
-  private var verbosity: Int = 0
-  def resetVerbosity() = verbosity = 0
-
-  def getSymbol(name: String, isModule: Boolean) = (
-    if (isModule) getModuleIfDefined(name)
-    else getModuleIfDefined(name)
-    )
-
-  trait CompilerCompletion {
-    def tp: Type
-    def effectiveTp = tp match {
-      case MethodType(Nil, resType)   => resType
-      case NullaryMethodType(resType) => resType
-      case _                          => tp
-    }
-
-    // for some reason any's members don't show up in subclasses, which
-    // we need so 5.<tab> offers asInstanceOf etc.
-    private def anyMembers = AnyTpe.nonPrivateMembers
-    def anyRefMethodsToShow = Set("isInstanceOf", "asInstanceOf", "toString")
-
-    def tos(sym: Symbol): String = sym.decodedName
-    def memberNamed(s: String) = exitingTyper(effectiveTp member newTermName(s))
-
-    // XXX we'd like to say "filterNot (_.isDeprecated)" but this causes the
-    // compiler to crash for reasons not yet known.
-    def members     = exitingTyper((effectiveTp.nonPrivateMembers.toList ++ anyMembers) filter (_.isPublic))
-    def methods     = members.toList filter (_.isMethod)
-    def packages    = members.toList filter (_.hasPackageFlag)
-    def aliases     = members.toList filter (_.isAliasType)
-
-    def memberNames   = members map tos
-    def methodNames   = methods map tos
-    def packageNames  = packages map tos
-    def aliasNames    = aliases map tos
-  }
-
-  object NoTypeCompletion extends TypeMemberCompletion(NoType) {
-    override def memberNamed(s: String) = NoSymbol
-    override def members = Nil
-    override def follow(s: String) = None
-    override def alternativesFor(id: String) = Nil
-  }
-
-  object TypeMemberCompletion {
-    def apply(tp: Type, runtimeType: Type, param: NamedParam): TypeMemberCompletion = {
-      new TypeMemberCompletion(tp) {
-        var upgraded = false
-        lazy val upgrade = {
-          intp rebind param
-          intp.reporter.printMessage("\nRebinding stable value %s from %s to %s".format(param.name, tp, param.tpe))
-          upgraded = true
-          new TypeMemberCompletion(runtimeType)
-        }
-        override def completions(verbosity: Int) = {
-          super.completions(verbosity) ++ (
-            if (verbosity == 0) Nil
-            else upgrade.completions(verbosity)
-            )
-        }
-        override def follow(s: String) = super.follow(s) orElse {
-          if (upgraded) upgrade.follow(s)
-          else None
-        }
-        override def alternativesFor(id: String) = super.alternativesFor(id) ++ (
-          if (upgraded) upgrade.alternativesFor(id)
-          else Nil
-          ) distinct
-      }
-    }
-    def apply(tp: Type): TypeMemberCompletion = {
-      if (tp eq NoType) NoTypeCompletion
-      else if (tp.typeSymbol.isPackageClass) new PackageCompletion(tp)
-      else new TypeMemberCompletion(tp)
-    }
-    def imported(tp: Type) = new ImportCompletion(tp)
-  }
-
-  class TypeMemberCompletion(val tp: Type) extends CompletionAware
-  with CompilerCompletion {
-    def excludeEndsWith: List[String] = Nil
-    def excludeStartsWith: List[String] = List("<") // <byname>, <repeated>, etc.
-    def excludeNames: List[String] = (anyref.methodNames filterNot anyRefMethodsToShow) :+ "_root_"
-
-    def methodSignatureString(sym: Symbol) = {
-      IMain stripString exitingTyper(new MethodSymbolOutput(sym).methodString())
-    }
-
-    def exclude(name: String): Boolean = (
-      (name contains "$") ||
-        (excludeNames contains name) ||
-        (excludeEndsWith exists (name endsWith _)) ||
-        (excludeStartsWith exists (name startsWith _))
-      )
-    def filtered(xs: List[String]) = xs filterNot exclude distinct
-
-    def completions(verbosity: Int) =
-      debugging(tp + " completions ==> ")(filtered(memberNames))
-
-    override def follow(s: String): Option[CompletionAware] =
-      debugging(tp + " -> '" + s + "' ==> ")(Some(TypeMemberCompletion(memberNamed(s).tpe)) filterNot (_ eq NoTypeCompletion))
-
-    override def alternativesFor(id: String): List[String] =
-      debugging(id + " alternatives ==> ") {
-        val alts = members filter (x => x.isMethod && tos(x) == id) map methodSignatureString
-
-        if (alts.nonEmpty) "" :: alts else Nil
-      }
-
-    override def toString = "%s (%d members)".format(tp, members.size)
-  }
-
-  class PackageCompletion(tp: Type) extends TypeMemberCompletion(tp) {
-    override def excludeNames = anyref.methodNames
-  }
-
-  class LiteralCompletion(lit: Literal) extends TypeMemberCompletion(lit.value.tpe) {
-    override def completions(verbosity: Int) = verbosity match {
-      case 0    => filtered(memberNames)
-      case _    => memberNames
-    }
-  }
-
-  class ImportCompletion(tp: Type) extends TypeMemberCompletion(tp) {
-    override def completions(verbosity: Int) = verbosity match {
-      case 0    => filtered(members filterNot (_.isSetter) map tos)
-      case _    => super.completions(verbosity)
-    }
-  }
-
-  // not for completion but for excluding
-  object anyref extends TypeMemberCompletion(AnyRefTpe) { }
-
-  // the unqualified vals/defs/etc visible in the repl
-  object ids extends CompletionAware {
-    override def completions(verbosity: Int) = intp.unqualifiedIds ++ List("classOf") //, "_root_")
-    // now we use the compiler for everything.
-    override def follow(id: String): Option[CompletionAware] = {
-      if (!completions(0).contains(id))
-        return None
-
-      val tpe = intp typeOfExpression id
-      if (tpe == NoType)
-        return None
-
-      def default = Some(TypeMemberCompletion(tpe))
-
-      // only rebinding vals in power mode for now.
-      if (!isReplPower) default
-      else intp runtimeClassAndTypeOfTerm id match {
-        case Some((clazz, runtimeType)) =>
-          val sym = intp.symbolOfTerm(id)
-          if (sym.isStable) {
-            val param = new NamedParam.Untyped(id, intp valueOfTerm id getOrElse null)
-            Some(TypeMemberCompletion(tpe, runtimeType, param))
-          }
-          else default
-        case _        =>
-          default
-      }
-    }
-    override def toString = "<repl ids> (%s)".format(completions(0).size)
-  }
-
-  // user-issued wildcard imports like "import global._" or "import String._"
-  private def imported = intp.sessionWildcards map TypeMemberCompletion.imported
-
-  // literal Ints, Strings, etc.
-  object literals extends CompletionAware {
-    def simpleParse(code: String): Option[Tree] = newUnitParser(code).parseStats().lastOption
-    def completions(verbosity: Int) = Nil
-
-    override def follow(id: String) = simpleParse(id).flatMap {
-      case x: Literal   => Some(new LiteralCompletion(x))
-      case _            => None
-    }
-  }
-
-  // top level packages
-  object rootClass extends TypeMemberCompletion(RootClass.tpe) {
-    override def completions(verbosity: Int) = super.completions(verbosity) :+ "_root_"
-    override def follow(id: String) = id match {
-      case "_root_" => Some(this)
-      case _        => super.follow(id)
-    }
-  }
-  // members of Predef
-  object predef extends TypeMemberCompletion(PredefModule.tpe) {
-    override def excludeEndsWith    = super.excludeEndsWith ++ List("Wrapper", "ArrayOps")
-    override def excludeStartsWith  = super.excludeStartsWith ++ List("wrap")
-    override def excludeNames       = anyref.methodNames
-
-    override def exclude(name: String) = super.exclude(name) || (
-      (name contains "2")
-      )
-
-    override def completions(verbosity: Int) = verbosity match {
-      case 0    => Nil
-      case _    => super.completions(verbosity)
-    }
-  }
-  // members of scala.*
-  object scalalang extends PackageCompletion(ScalaPackage.tpe) {
-    def arityClasses = List("Product", "Tuple", "Function")
-    def skipArity(name: String) = arityClasses exists (x => name != x && (name startsWith x))
-    override def exclude(name: String) = super.exclude(name) || (
-      skipArity(name)
-      )
-
-    override def completions(verbosity: Int) = verbosity match {
-      case 0    => filtered(packageNames ++ aliasNames)
-      case _    => super.completions(verbosity)
-    }
-  }
-  // members of java.lang.*
-  object javalang extends PackageCompletion(JavaLangPackage.tpe) {
-    override lazy val excludeEndsWith   = super.excludeEndsWith ++ List("Exception", "Error")
-    override lazy val excludeStartsWith = super.excludeStartsWith ++ List("CharacterData")
-
-    override def completions(verbosity: Int) = verbosity match {
-      case 0    => filtered(packageNames)
-      case _    => super.completions(verbosity)
-    }
-  }
-
-  // the list of completion aware objects which should be consulted
-  // for top level unqualified, it's too noisy to let much in.
-  lazy val topLevelBase: List[CompletionAware] = List(ids, rootClass, predef, scalalang, javalang, literals)
-  def topLevel = topLevelBase ++ imported
-  def topLevelThreshold = 50
-
-  // the first tier of top level objects (doesn't include file completion)
-  def topLevelFor(parsed: Parsed): List[String] = {
-    val buf = new ListBuffer[String]
-    topLevel foreach { ca =>
-      buf ++= (ca completionsFor parsed)
-
-      if (buf.size > topLevelThreshold)
-        return buf.toList.sorted
-    }
-    buf.toList
-  }
-
-  // the most recent result
-  def lastResult = Forwarder(() => ids follow intp.mostRecentVar)
-
-  def lastResultFor(parsed: Parsed) = {
-    /** The logic is a little tortured right now because normally '.' is
-      *  ignored as a delimiter, but on .<tab> it needs to be propagated.
-      */
-    val xs = lastResult completionsFor parsed
-    if (parsed.isEmpty) xs map ("." + _) else xs
-  }
-
-  def completer(): ScalaCompleter = new JLineTabCompletion
-
-  /** This gets a little bit hairy.  It's no small feat delegating everything
-    *  and also keeping track of exactly where the cursor is and where it's supposed
-    *  to end up.  The alternatives mechanism is a little hacky: if there is an empty
-    *  string in the list of completions, that means we are expanding a unique
-    *  completion, so don't update the "last" buffer because it'll be wrong.
-    */
-  class JLineTabCompletion extends ScalaCompleter {
-    // For recording the buffer on the last tab hit
-    private var lastBuf: String = ""
-    private var lastCursor: Int = -1
-
-    // Does this represent two consecutive tabs?
-    def isConsecutiveTabs(buf: String, cursor: Int) =
-      cursor == lastCursor && buf == lastBuf
-
-    // This is jline's entry point for completion.
-    override def complete(buf: String, cursor: Int): Candidates = {
-      verbosity = if (isConsecutiveTabs(buf, cursor)) verbosity + 1 else 0
-      repldbg(f"%ncomplete($buf, $cursor%d) last = ($lastBuf, $lastCursor%d), verbosity: $verbosity")
-
-      // we don't try lower priority completions unless higher ones return no results.
-      def tryCompletion(p: Parsed, completionFunction: Parsed => List[String]): Option[Candidates] = {
-        val winners = completionFunction(p)
-        if (winners.isEmpty)
-          return None
-        val newCursor =
-          if (winners contains "") p.cursor
-          else {
-            val advance = longestCommonPrefix(winners)
-            lastCursor = p.position + advance.length
-            lastBuf = (buf take p.position) + advance
-            repldbg(s"tryCompletion($p, _) lastBuf = $lastBuf, lastCursor = $lastCursor, p.position = ${p.position}")
-            p.position
-          }
-
-        Some(Candidates(newCursor, winners))
-      }
-
-      def mkDotted = Parsed.dotted(buf, cursor) withVerbosity verbosity
-
-      // a single dot is special cased to completion on the previous result
-      def lastResultCompletion =
-        if (!looksLikeInvocation(buf)) None
-        else tryCompletion(Parsed.dotted(buf drop 1, cursor), lastResultFor)
-
-      def tryAll = (
-        lastResultCompletion
-          orElse tryCompletion(mkDotted, topLevelFor)
-          getOrElse Candidates(cursor, Nil)
-        )
-
-      /**
-       *  This is the kickoff point for all manner of theoretically
-       *  possible compiler unhappiness. The fault may be here or
-       *  elsewhere, but we don't want to crash the repl regardless.
-       *  The compiler makes it impossible to avoid catching Throwable
-       *  with its unfortunate tendency to throw java.lang.Errors and
-       *  AssertionErrors as the hats drop. We take two swings at it
-       *  because there are some spots which like to throw an assertion
-       *  once, then work after that. Yeah, what can I say.
-       */
-      try tryAll
-      catch { case ex: Throwable =>
-        repldbg("Error: complete(%s, %s) provoked".format(buf, cursor) + ex)
-        Candidates(cursor,
-          if (isReplDebug) List("<error:" + ex + ">")
-          else Nil
-        )
-      }
-    }
-  }
-}
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala
deleted file mode 100644
index 0e22bc806d922..0000000000000
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala
+++ /dev/null
@@ -1,221 +0,0 @@
-/* NSC -- new Scala compiler
- * Copyright 2005-2013 LAMP/EPFL
- * @author  Martin Odersky
- */
-
-package scala.tools.nsc
-package interpreter
-
-import scala.collection.{ mutable, immutable }
-import scala.language.implicitConversions
-
-trait SparkMemberHandlers {
-  val intp: SparkIMain
-
-  import intp.{ Request, global, naming }
-  import global._
-  import naming._
-
-  private def codegenln(leadingPlus: Boolean, xs: String*): String = codegen(leadingPlus, (xs ++ Array("\n")): _*)
-  private def codegenln(xs: String*): String = codegenln(true, xs: _*)
-  private def codegen(leadingPlus: Boolean, xs: String*): String = {
-    val front = if (leadingPlus) "+ " else ""
-    front + (xs map string2codeQuoted mkString " + ")
-  }
-  private implicit def name2string(name: Name) = name.toString
-
-  /** A traverser that finds all mentioned identifiers, i.e. things
-    *  that need to be imported.  It might return extra names.
-    */
-  private class ImportVarsTraverser extends Traverser {
-    val importVars = new mutable.HashSet[Name]()
-
-    override def traverse(ast: Tree) = ast match {
-      case Ident(name) =>
-        // XXX this is obviously inadequate but it's going to require some effort
-        // to get right.
-        if (name.toString startsWith "x$") ()
-        else importVars += name
-      case _        => super.traverse(ast)
-    }
-  }
-  private object ImportVarsTraverser {
-    def apply(member: Tree) = {
-      val ivt = new ImportVarsTraverser()
-      ivt traverse member
-      ivt.importVars.toList
-    }
-  }
-
-  private def isTermMacro(ddef: DefDef): Boolean = ddef.mods.isMacro
-
-  def chooseHandler(member: Tree): MemberHandler = member match {
-    case member: DefDef if isTermMacro(member) => new TermMacroHandler(member)
-    case member: DefDef                        => new DefHandler(member)
-    case member: ValDef                        => new ValHandler(member)
-    case member: ModuleDef                     => new ModuleHandler(member)
-    case member: ClassDef                      => new ClassHandler(member)
-    case member: TypeDef                       => new TypeAliasHandler(member)
-    case member: Assign                        => new AssignHandler(member)
-    case member: Import                        => new ImportHandler(member)
-    case DocDef(_, documented)                 => chooseHandler(documented)
-    case member                                => new GenericHandler(member)
-  }
-
-  sealed abstract class MemberDefHandler(override val member: MemberDef) extends MemberHandler(member) {
-    override def name: Name = member.name
-    def mods: Modifiers     = member.mods
-    def keyword             = member.keyword
-    def prettyName          = name.decode
-
-    override def definesImplicit = member.mods.isImplicit
-    override def definesTerm: Option[TermName] = Some(name.toTermName) filter (_ => name.isTermName)
-    override def definesType: Option[TypeName] = Some(name.toTypeName) filter (_ => name.isTypeName)
-    override def definedSymbols = if (symbol.exists) symbol :: Nil else Nil
-  }
-
-  /** Class to handle one member among all the members included
-    *  in a single interpreter request.
-    */
-  sealed abstract class MemberHandler(val member: Tree) {
-    def name: Name      = nme.NO_NAME
-    def path            = intp.originalPath(symbol).replaceFirst("read", "read.INSTANCE")
-    def symbol          = if (member.symbol eq null) NoSymbol else member.symbol
-    def definesImplicit = false
-    def definesValue    = false
-
-    def definesTerm     = Option.empty[TermName]
-    def definesType     = Option.empty[TypeName]
-
-    private lazy val _referencedNames = ImportVarsTraverser(member)
-    def referencedNames = _referencedNames
-    def importedNames   = List[Name]()
-    def definedNames    = definesTerm.toList ++ definesType.toList
-    def definedSymbols  = List[Symbol]()
-
-    def extraCodeToEvaluate(req: Request): String = ""
-    def resultExtractionCode(req: Request): String = ""
-
-    private def shortName = this.getClass.toString split '.' last
-    override def toString = shortName + referencedNames.mkString(" (refs: ", ", ", ")")
-  }
-
-  class GenericHandler(member: Tree) extends MemberHandler(member)
-
-  class ValHandler(member: ValDef) extends MemberDefHandler(member) {
-    val maxStringElements = 1000  // no need to mkString billions of elements
-    override def definesValue = true
-
-    override def resultExtractionCode(req: Request): String = {
-
-      val isInternal = isUserVarName(name) && req.lookupTypeOf(name) == "Unit"
-      if (!mods.isPublic || isInternal) ""
-      else {
-        // if this is a lazy val we avoid evaluating it here
-        val resultString =
-          if (mods.isLazy) codegenln(false, "<lazy>")
-          else any2stringOf(path, maxStringElements)
-
-        val vidString =
-          if (replProps.vids) s"""" + " @ " + "%%8x".format(System.identityHashCode($path)) + " """.trim
-          else ""
-
-        """ + "%s%s: %s = " + %s""".format(string2code(prettyName), vidString, string2code(req typeOf name), resultString)
-      }
-    }
-  }
-
-  class DefHandler(member: DefDef) extends MemberDefHandler(member) {
-    override def definesValue = flattensToEmpty(member.vparamss) // true if 0-arity
-    override def resultExtractionCode(req: Request) =
-      if (mods.isPublic) codegenln(name, ": ", req.typeOf(name)) else ""
-  }
-
-  abstract class MacroHandler(member: DefDef) extends MemberDefHandler(member) {
-    override def referencedNames = super.referencedNames.flatMap(name => List(name.toTermName, name.toTypeName))
-    override def definesValue = false
-    override def definesTerm: Option[TermName] = Some(name.toTermName)
-    override def definesType: Option[TypeName] = None
-    override def resultExtractionCode(req: Request) = if (mods.isPublic) codegenln(notification(req)) else ""
-    def notification(req: Request): String
-  }
-
-  class TermMacroHandler(member: DefDef) extends MacroHandler(member) {
-    def notification(req: Request) = s"defined term macro $name: ${req.typeOf(name)}"
-  }
-
-  class AssignHandler(member: Assign) extends MemberHandler(member) {
-    val Assign(lhs, rhs) = member
-    override lazy val name = newTermName(freshInternalVarName())
-
-    override def definesTerm = Some(name)
-    override def definesValue = true
-    override def extraCodeToEvaluate(req: Request) =
-      """val %s = %s""".format(name, lhs)
-
-    /** Print out lhs instead of the generated varName */
-    override def resultExtractionCode(req: Request) = {
-      val lhsType = string2code(req lookupTypeOf name)
-      val res     = string2code(req fullPath name)
-      """ + "%s: %s = " + %s + "\n" """.format(string2code(lhs.toString), lhsType, res) + "\n"
-    }
-  }
-
-  class ModuleHandler(module: ModuleDef) extends MemberDefHandler(module) {
-    override def definesTerm = Some(name.toTermName)
-    override def definesValue = true
-
-    override def resultExtractionCode(req: Request) = codegenln("defined object ", name)
-  }
-
-  class ClassHandler(member: ClassDef) extends MemberDefHandler(member) {
-    override def definedSymbols = List(symbol, symbol.companionSymbol) filterNot (_ == NoSymbol)
-    override def definesType = Some(name.toTypeName)
-    override def definesTerm = Some(name.toTermName) filter (_ => mods.isCase)
-
-    override def resultExtractionCode(req: Request) =
-      codegenln("defined %s %s".format(keyword, name))
-  }
-
-  class TypeAliasHandler(member: TypeDef) extends MemberDefHandler(member) {
-    private def isAlias = mods.isPublic && treeInfo.isAliasTypeDef(member)
-    override def definesType = Some(name.toTypeName) filter (_ => isAlias)
-
-    override def resultExtractionCode(req: Request) =
-      codegenln("defined type alias ", name) + "\n"
-  }
-
-  class ImportHandler(imp: Import) extends MemberHandler(imp) {
-    val Import(expr, selectors) = imp
-    def targetType = intp.global.rootMirror.getModuleIfDefined("" + expr) match {
-      case NoSymbol => intp.typeOfExpression("" + expr)
-      case sym      => sym.thisType
-    }
-    private def importableTargetMembers = importableMembers(targetType).toList
-    // wildcard imports, e.g. import foo._
-    private def selectorWild    = selectors filter (_.name == nme.USCOREkw)
-    // renamed imports, e.g. import foo.{ bar => baz }
-    private def selectorRenames = selectors map (_.rename) filterNot (_ == null)
-
-    /** Whether this import includes a wildcard import */
-    val importsWildcard = selectorWild.nonEmpty
-
-    def implicitSymbols = importedSymbols filter (_.isImplicit)
-    def importedSymbols = individualSymbols ++ wildcardSymbols
-
-    private val selectorNames = selectorRenames filterNot (_ == nme.USCOREkw) flatMap (_.bothNames) toSet
-    lazy val individualSymbols: List[Symbol] = exitingTyper(importableTargetMembers filter (m => selectorNames(m.name)))
-    lazy val wildcardSymbols: List[Symbol]   = exitingTyper(if (importsWildcard) importableTargetMembers else Nil)
-
-    /** Complete list of names imported by a wildcard */
-    lazy val wildcardNames: List[Name]   = wildcardSymbols map (_.name)
-    lazy val individualNames: List[Name] = individualSymbols map (_.name)
-
-    /** The names imported by this statement */
-    override lazy val importedNames: List[Name] = wildcardNames ++ individualNames
-    lazy val importsSymbolNamed: Set[String] = importedNames map (_.toString) toSet
-
-    def importString = imp.toString
-    override def resultExtractionCode(req: Request) = codegenln(importString) + "\n"
-  }
-}
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala
deleted file mode 100644
index 272f81eca92c1..0000000000000
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkReplReporter.scala
+++ /dev/null
@@ -1,55 +0,0 @@
-/* NSC -- new Scala compiler
- * Copyright 2002-2013 LAMP/EPFL
- * @author Paul Phillips
- */
-
-package scala.tools.nsc
-package interpreter
-
-import reporters._
-import SparkIMain._
-
-import scala.reflect.internal.util.Position
-
-/** Like ReplGlobal, a layer for ensuring extra functionality.
-  */
-class SparkReplReporter(intp: SparkIMain) extends ConsoleReporter(intp.settings, Console.in, new SparkReplStrippingWriter(intp)) {
-  def printUntruncatedMessage(msg: String) = withoutTruncating(printMessage(msg))
-
-  /** Whether very long lines can be truncated.  This exists so important
-    *  debugging information (like printing the classpath) is not rendered
-    *  invisible due to the max message length.
-    */
-  private var _truncationOK: Boolean = !intp.settings.verbose
-  def truncationOK = _truncationOK
-  def withoutTruncating[T](body: => T): T = {
-    val saved = _truncationOK
-    _truncationOK = false
-    try body
-    finally _truncationOK = saved
-  }
-
-  override def warning(pos: Position, msg: String): Unit = withoutTruncating(super.warning(pos, msg))
-  override def error(pos: Position, msg: String): Unit   = withoutTruncating(super.error(pos, msg))
-
-  override def printMessage(msg: String) {
-    // Avoiding deadlock if the compiler starts logging before
-    // the lazy val is complete.
-    if (intp.isInitializeComplete) {
-      if (intp.totalSilence) {
-        if (isReplTrace)
-          super.printMessage("[silent] " + msg)
-      }
-      else super.printMessage(msg)
-    }
-    // scalastyle:off println
-    else Console.println("[init] " + msg)
-    // scalastyle:on println
-  }
-
-  override def displayPrompt() {
-    if (intp.totalSilence) ()
-    else super.displayPrompt()
-  }
-
-}
diff --git a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index 9ecc7c229e38a..e1cee97de32bc 100644
--- a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -22,14 +22,11 @@ import java.net.URLClassLoader
 
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
-import scala.tools.nsc.interpreter.SparkILoop
 
 import org.apache.commons.lang3.StringEscapeUtils
 import org.apache.spark.{SparkContext, SparkFunSuite}
 import org.apache.spark.util.Utils
 
-
-
 class ReplSuite extends SparkFunSuite {
 
   def runInterpreter(master: String, input: String): String = {
@@ -87,10 +84,6 @@ class ReplSuite extends SparkFunSuite {
       settings = new scala.tools.nsc.Settings
       settings.usejavacp.value = true
       org.apache.spark.repl.Main.interp = this
-      override def createInterpreter() {
-        intp = new SparkILoopInterpreter
-        intp.setContextClassLoader()
-      }
     }
 
     val out = new StringWriter()
@@ -274,7 +267,7 @@ class ReplSuite extends SparkFunSuite {
 
   test("SPARK-2632 importing a method from non serializable class and not using it.") {
     val output = runInterpreter("local",
-    """
+      """
       |class TestClass() { def testMethod = 3 }
       |val t = new TestClass
       |import t.testMethod
@@ -319,7 +312,7 @@ class ReplSuite extends SparkFunSuite {
     assertDoesNotContain("Exception", output)
     assertContains("ret: Array[Foo] = Array(Foo(1),", output)
   }
-  
+
   test("collecting objects of class defined in repl - shuffling") {
     val output = runInterpreter("local-cluster[1,1,512]",
       """

From 5dd45bde4ae6f5a10cfe005de3041abd075da4d6 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 10 Jul 2015 09:48:17 -0700
Subject: [PATCH 0334/1454] [SPARK-8958] Dynamic allocation: change cached
 timeout to infinity

pwendell and I discussed this a little more offline and concluded that it would be good to keep it more conservative. Losing cached blocks may be very expensive and we should only allow it if the user knows what he/she is doing.

FYI harishreedharan sryza.

Author: Andrew Or <andrew@databricks.com>

Closes #7329 from andrewor14/da-cached-timeout and squashes the following commits:

cef0b4e [Andrew Or] Change timeout to infinity
---
 .../scala/org/apache/spark/ExecutorAllocationManager.scala    | 2 +-
 docs/configuration.md                                         | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
index 49329423dca76..0c50b4002cf7b 100644
--- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
+++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
@@ -102,7 +102,7 @@ private[spark] class ExecutorAllocationManager(
     "spark.dynamicAllocation.executorIdleTimeout", "60s")
 
   private val cachedExecutorIdleTimeoutS = conf.getTimeAsSeconds(
-    "spark.dynamicAllocation.cachedExecutorIdleTimeout", s"${2 * executorIdleTimeoutS}s")
+    "spark.dynamicAllocation.cachedExecutorIdleTimeout", s"${Integer.MAX_VALUE}s")
 
   // During testing, the methods to actually kill and add executors are mocked out
   private val testing = conf.getBoolean("spark.dynamicAllocation.testing", false)
diff --git a/docs/configuration.md b/docs/configuration.md
index 892c02b27df32..443322e1eadf4 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1206,7 +1206,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.dynamicAllocation.cachedExecutorIdleTimeout</code></td>
-  <td>2 * executorIdleTimeout</td>
+  <td>infinity</td>
   <td>
     If dynamic allocation is enabled and an executor which has cached data blocks has been idle for more than this duration,
     the executor will be removed. For more details, see this
@@ -1222,7 +1222,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.dynamicAllocation.maxExecutors</code></td>
-  <td>Integer.MAX_VALUE</td>
+  <td>infinity</td>
   <td>
     Upper bound for the number of executors if dynamic allocation is enabled.
   </td>

From db6d57f87a9d657d764e01879014bf406971855a Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 10 Jul 2015 09:50:46 -0700
Subject: [PATCH 0335/1454] [CORE] [MINOR] change the log level to info

Too many logs even when set the log level to warning.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7340 from chenghao-intel/log and squashes the following commits:

59658cf [Cheng Hao] change the log level to info
---
 core/src/main/scala/org/apache/spark/executor/Executor.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 8f916e0502ecb..f7ef92bc80f91 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -443,7 +443,7 @@ private[spark] class Executor(
     try {
       val response = heartbeatReceiverRef.askWithRetry[HeartbeatResponse](message)
       if (response.reregisterBlockManager) {
-        logWarning("Told to re-register on heartbeat")
+        logInfo("Told to re-register on heartbeat")
         env.blockManager.reregister()
       }
     } catch {

From c185f3a45ddbc073192f7da41303941ee4cebd4f Mon Sep 17 00:00:00 2001
From: Min Zhou <coderplay@gmail.com>
Date: Fri, 10 Jul 2015 09:52:40 -0700
Subject: [PATCH 0336/1454] [SPARK-8675] Executors created by LocalBackend
 won't get the same classpath as other executor backends

AFAIK, some spark application always use LocalBackend to do some local initiatives, spark sql is an example. Starting a LocalPoint won't add user classpath into executor.
```java
  override def start() {
    localEndpoint = SparkEnv.get.rpcEnv.setupEndpoint(
      "LocalBackendEndpoint", new LocalEndpoint(SparkEnv.get.rpcEnv, scheduler, this, totalCores))
  }
```
Thus will cause local executor fail with these scenarios, loading hadoop built-in native libraries, loading other user defined native libraries, loading user jars, reading s3 config from a site.xml file, etc

Author: Min Zhou <coderplay@gmail.com>

Closes #7091 from coderplay/master and squashes the following commits:

365838f [Min Zhou] Fixed java.net.MalformedURLException, add default scheme, support relative path
d215b7f [Min Zhou] Follows spark standard scala style, make the auto testing happy
84ad2cd [Min Zhou] Use system specific path separator instead of ','
01f5d1a [Min Zhou] Merge branch 'master' of https://github.com/apache/spark
e528be7 [Min Zhou] Merge branch 'master' of https://github.com/apache/spark
45bf62c [Min Zhou] SPARK-8675 Executors created by LocalBackend won't get the same classpath as other executor backends
---
 .../spark/scheduler/local/LocalBackend.scala  | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
index 3078a1b10be8b..776e5d330e3c7 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.scheduler.local
 
+import java.io.File
+import java.net.URL
 import java.nio.ByteBuffer
 
 import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv, TaskState}
@@ -40,6 +42,7 @@ private case class StopExecutor()
  */
 private[spark] class LocalEndpoint(
     override val rpcEnv: RpcEnv,
+    userClassPath: Seq[URL],
     scheduler: TaskSchedulerImpl,
     executorBackend: LocalBackend,
     private val totalCores: Int)
@@ -51,7 +54,7 @@ private[spark] class LocalEndpoint(
   private val localExecutorHostname = "localhost"
 
   private val executor = new Executor(
-    localExecutorId, localExecutorHostname, SparkEnv.get, isLocal = true)
+    localExecutorId, localExecutorHostname, SparkEnv.get, userClassPath, isLocal = true)
 
   override def receive: PartialFunction[Any, Unit] = {
     case ReviveOffers =>
@@ -97,10 +100,22 @@ private[spark] class LocalBackend(
 
   private val appId = "local-" + System.currentTimeMillis
   var localEndpoint: RpcEndpointRef = null
+  private val userClassPath = getUserClasspath(conf)
+
+  /**
+   * Returns a list of URLs representing the user classpath.
+   *
+   * @param conf Spark configuration.
+   */
+  def getUserClasspath(conf: SparkConf): Seq[URL] = {
+    val userClassPathStr = conf.getOption("spark.executor.extraClassPath")
+    userClassPathStr.map(_.split(File.pathSeparator)).toSeq.flatten.map(new File(_).toURI.toURL)
+  }
 
   override def start() {
     localEndpoint = SparkEnv.get.rpcEnv.setupEndpoint(
-      "LocalBackendEndpoint", new LocalEndpoint(SparkEnv.get.rpcEnv, scheduler, this, totalCores))
+      "LocalBackendEndpoint",
+      new LocalEndpoint(SparkEnv.get.rpcEnv, userClassPath, scheduler, this, totalCores))
   }
 
   override def stop() {

From 05ac023dc8d9004a27c2f06ee875b0ff3743ccdd Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 10 Jul 2015 13:05:23 -0700
Subject: [PATCH 0337/1454] [HOTFIX] fix flaky test in PySpark SQL

It may loss precision in microseconds when using float for it.

Author: Davies Liu <davies@databricks.com>

Closes #7344 from davies/fix_date_test and squashes the following commits:

249ec61 [Davies Liu] fix flaky test
---
 python/pyspark/sql/tests.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 4d7cad5a1ab88..ed903334beafa 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -705,11 +705,12 @@ def test_filter_with_datetime(self):
     def test_time_with_timezone(self):
         day = datetime.date.today()
         now = datetime.datetime.now()
-        ts = time.mktime(now.timetuple()) + now.microsecond / 1e6
+        ts = time.mktime(now.timetuple())
         # class in __main__ is not serializable
         from pyspark.sql.tests import UTC
         utc = UTC()
-        utcnow = datetime.datetime.fromtimestamp(ts, utc)
+        utcnow = datetime.datetime.utcfromtimestamp(ts)  # without microseconds
+        utcnow = datetime.datetime(*(utcnow.timetuple()[:6] + (now.microsecond, utc)))
         df = self.sqlCtx.createDataFrame([(day, now, utcnow)])
         day1, now1, utcnow1 = df.first()
         self.assertEqual(day1, day)

From 0772026c2fc88aa85423034006b758f6ff0cc2ed Mon Sep 17 00:00:00 2001
From: rahulpalamuttam <rahulpalamut@gmail.com>
Date: Fri, 10 Jul 2015 16:07:31 -0700
Subject: [PATCH 0338/1454] [SPARK-8923] [DOCUMENTATION, MLLIB] Add @since tags
 to mllib.fpm

Author: rahulpalamuttam <rahulpalamut@gmail.com>

Closes #7341 from rahulpalamuttam/TaggingMLlibfpm and squashes the following commits:

bef2843 [rahulpalamuttam] fix @since tags in mmlib.fpm
cd86252 [rahulpalamuttam] Add @since tags to mllib.fpm
---
 .../spark/mllib/fpm/AssociationRules.scala    | 10 ++++++++++
 .../org/apache/spark/mllib/fpm/FPGrowth.scala | 19 +++++++++++++++++++
 2 files changed, 29 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 7e2bbfe31c1b7..72d0ea0c12e1e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -31,6 +31,8 @@ import org.apache.spark.rdd.RDD
  *
  * Generates association rules from a [[RDD[FreqItemset[Item]]]. This method only generates
  * association rules which have a single item as the consequent.
+ *
+ * @since 1.5.0
  */
 @Experimental
 class AssociationRules private[fpm] (
@@ -38,11 +40,15 @@ class AssociationRules private[fpm] (
 
   /**
    * Constructs a default instance with default parameters {minConfidence = 0.8}.
+   *
+   * @since 1.5.0
    */
   def this() = this(0.8)
 
   /**
    * Sets the minimal confidence (default: `0.8`).
+   *
+   * @since 1.5.0
    */
   def setMinConfidence(minConfidence: Double): this.type = {
     require(minConfidence >= 0.0 && minConfidence <= 1.0)
@@ -54,6 +60,8 @@ class AssociationRules private[fpm] (
    * Computes the association rules with confidence above [[minConfidence]].
    * @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
    * @return a [[Set[Rule[Item]]] containing the assocation rules.
+   *
+   * @since 1.5.0
    */
   def run[Item: ClassTag](freqItemsets: RDD[FreqItemset[Item]]): RDD[Rule[Item]] = {
     // For candidate rule X => Y, generate (X, (Y, freq(X union Y)))
@@ -90,6 +98,8 @@ object AssociationRules {
    * @param antecedent hypotheses of the rule
    * @param consequent conclusion of the rule
    * @tparam Item item type
+   *
+   * @since 1.5.0
    */
   @Experimental
   class Rule[Item] private[fpm] (
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index 9cb9a00dbd9c7..e2370a52f4930 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -38,12 +38,15 @@ import org.apache.spark.storage.StorageLevel
  * Model trained by [[FPGrowth]], which holds frequent itemsets.
  * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
+ *
+ * @since 1.3.0
  */
 @Experimental
 class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
   /**
    * Generates association rules for the [[Item]]s in [[freqItemsets]].
    * @param confidence minimal confidence of the rules produced
+   * @since 1.5.0
    */
   def generateAssociationRules(confidence: Double): RDD[AssociationRules.Rule[Item]] = {
     val associationRules = new AssociationRules(confidence)
@@ -67,6 +70,8 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
  *
  * @see [[http://en.wikipedia.org/wiki/Association_rule_learning Association rule learning
  *       (Wikipedia)]]
+ *
+ * @since 1.3.0
  */
 @Experimental
 class FPGrowth private (
@@ -76,11 +81,15 @@ class FPGrowth private (
   /**
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
    * as the input data}.
+   *
+   * @since 1.3.0
    */
   def this() = this(0.3, -1)
 
   /**
    * Sets the minimal support level (default: `0.3`).
+   *
+   * @since 1.3.0
    */
   def setMinSupport(minSupport: Double): this.type = {
     this.minSupport = minSupport
@@ -89,6 +98,8 @@ class FPGrowth private (
 
   /**
    * Sets the number of partitions used by parallel FP-growth (default: same as input data).
+   *
+   * @since 1.3.0
    */
   def setNumPartitions(numPartitions: Int): this.type = {
     this.numPartitions = numPartitions
@@ -99,6 +110,8 @@ class FPGrowth private (
    * Computes an FP-Growth model that contains frequent itemsets.
    * @param data input data set, each element contains a transaction
    * @return an [[FPGrowthModel]]
+   *
+   * @since 1.3.0
    */
   def run[Item: ClassTag](data: RDD[Array[Item]]): FPGrowthModel[Item] = {
     if (data.getStorageLevel == StorageLevel.NONE) {
@@ -199,6 +212,8 @@ class FPGrowth private (
 
 /**
  * :: Experimental ::
+ *
+ * @since 1.3.0
  */
 @Experimental
 object FPGrowth {
@@ -208,11 +223,15 @@ object FPGrowth {
    * @param items items in this itemset. Java users should call [[FreqItemset#javaItems]] instead.
    * @param freq frequency
    * @tparam Item item type
+   *
+   * @since 1.3.0
    */
   class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.
+     *
+     * @since 1.3.0
      */
     def javaItems: java.util.List[Item] = {
       items.toList.asJava

From fb8807c9b04f27467b36fc9d0177ef92dd012670 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 10 Jul 2015 16:44:51 -0700
Subject: [PATCH 0339/1454] [SPARK-7078] [SPARK-7079] Binary processing sort
 for Spark SQL

This patch adds a cache-friendly external sorter which operates on serialized bytes and uses this sorter to implement a new sort operator for Spark SQL and DataFrames.

### Overview of the new sorter

The new sorter design is inspired by [Alphasort](http://research.microsoft.com/pubs/68249/alphasort.doc) and implements a key-prefix optimization in order to improve the cache friendliness of the sort.  In naive sort implementations, the sorting algorithm operates on an array of record pointers.  To compare two records for ordering, the sorter must dereference these pointers, which likely involves random memory access, then compare the objects themselves.

![image](https://cloud.githubusercontent.com/assets/50748/8611390/3b1402ae-2675-11e5-8308-1a10bf347e6e.png)

In a key-prefix sort, the sort operates on an array which stores the record pointer alongside a prefix of the record's key. When comparing two records for ordering, the sorter first compares the the stored key prefixes. If the ordering can be determined from the key prefixes (i.e. the prefixes are unequal), then the sort can avoid directly comparing the records, avoiding random memory accesses and full record comparisons. For example, if we're sorting a list of strings then we can store the first 8 bytes of the UTF-8 encoded string as the key-prefix and can perform unsigned byte-at-a-time comparisons to determine the ordering of strings based on their prefixes, only resorting to full comparisons for strings that share a common prefix.  In cases where the sort key can fit entirely in the space allotted for the key prefix (e.g. the sorting key is an integer), we completely avoid direct record comparison.

In this patch's implementation of key-prefix sorting, our sorter's internal array stores a 64-bit long and 64-bit pointer for each record being sorted. The key prefixes are generated by the user when inserting records into the sorter, which uses a user-defined comparison function for comparing them.  The `PrefixComparators` object implements a set of comparators for many common types, including primitive numeric types and UTF-8 strings.

The actual sorting is implemented by `UnsafeInMemorySorter`.  Most consumers will not use this directly, but instead will use `UnsafeExternalSorter`, a class which implements a sort that can spill to disk in response to memory pressure.  Internally, `UnsafeExternalSorter` creates `UnsafeInMemorySorters` to perform sorting and uses `UnsafeSortSpillReader/Writer` to spill and read back runs of sorted records and `UnsafeSortSpillMerger` to merge multiple sorted spills into a single sorted iterator.  This external sorter integrates with Spark's existing ShuffleMemoryManager for controlling spilling.

Many parts of this sorter's design are based on / copied from the more specialized external sort implementation that I designed for the new UnsafeShuffleManager write path; see #5868 for more details on that patch.

### Sorting rows in Spark SQL

For now, `UnsafeExternalSorter` is only used by Spark SQL, which uses it to implement a new sort operator, `UnsafeExternalSort`.  This sort operator uses a SQL-specific class called `UnsafeExternalRowSorter` that configures an `UnsafeExternalSorter` to use prefix generators and comparators that operate on rows encoded in the UnsafeRow format that was designed for Project Tungsten.

I used some interesting unit-testing techniques to test this patch's SQL-specific components.  `UnsafeExternalSortSuite` uses the SQL random data generators introduced in #7176 to test the UnsafeSort operator with all atomic types both with and without nullability and in both ascending and descending sort orders.  `PrefixComparatorsSuite` contains a cool use of ScalaCheck + ScalaTest's `GeneratorDrivenPropertyChecks` in order to test UTF8String prefix comparison.

### Misc. additional improvements made in this patch

This patch made several miscellaneous improvements to related code in Spark SQL:

- The logic for selecting physical sort operator implementations, which was partially duplicated in both `Exchange` and `SparkStrategies, has now been consolidated into a `getSortOperator()` helper function in `SparkStrategies`.
- The `SparkPlanTest` unit testing helper trait has been extended with new methods for comparing the output produced by two different physical plans. This makes it easy to write tests which assert that two physical operator implementations should produce the same output.  I also added a method for disabling the implicit sorting of outputs prior to comparing them, a change which is necessary in order to be able to write proper SparkPlan tests for sort operators.

### Tasks deferred to followup patches

While most of this patch's features are reasonably well-tested and complete, there are a number of tasks that are intentionally being deferred to followup patches:

- Add tests which mock the ShuffleMemoryManager to check that memory pressure properly triggers spilling (there are examples of this type of test in #5868).
- Add tests to ensure that spill files are properly cleaned up after errors.  I'd like to do this in the context of a patch which introduces more general metrics for ensuring proper cleanup of tasks' temporary files; see https://issues.apache.org/jira/browse/SPARK-8966 for more details.
- Metrics integration: there are some open questions regarding how to track / report spill metrics for non-shuffle operations, so I've deferred most of the IO / shuffle metrics integration for now.
- Performance profiling.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/6444)
<!-- Reviewable:end -->

Author: Josh Rosen <joshrosen@databricks.com>

Closes #6444 from JoshRosen/sql-external-sort and squashes the following commits:

6beb467 [Josh Rosen] Remove a bunch of overloaded methods to avoid default args. issue
2bbac9c [Josh Rosen] Merge remote-tracking branch 'origin/master' into sql-external-sort
35dad9f [Josh Rosen] Make sortAnswers = false the default in SparkPlanTest
5135200 [Josh Rosen] Fix spill reading for large rows; add test
2f48777 [Josh Rosen] Add test and fix bug for sorting empty arrays
d1e28bc [Josh Rosen] Merge remote-tracking branch 'origin/master' into sql-external-sort
cd05866 [Josh Rosen] Fix scalastyle
3947fc1 [Josh Rosen] Merge remote-tracking branch 'origin/master' into sql-external-sort
d13ac55 [Josh Rosen] Hacky approach to copying of UnsafeRows for sort followed by limit.
845bea3 [Josh Rosen] Remove unnecessary zeroing of row conversion buffer
c56ec18 [Josh Rosen] Clean up final row copying code.
d31f180 [Josh Rosen] Re-enable NullType sorting test now that SPARK-8868 is fixed
844f4ca [Josh Rosen] Merge remote-tracking branch 'origin/master' into sql-external-sort
293f109 [Josh Rosen] Add missing license header.
f99a612 [Josh Rosen] Fix bugs in string prefix comparison.
9d00afc [Josh Rosen] Clean up prefix comparators for integral types
88aff18 [Josh Rosen] NULL_PREFIX has to be negative infinity for floating point types
613e16f [Josh Rosen] Test with larger data.
1d7ffaa [Josh Rosen] Somewhat hacky fix for descending sorts
08701e7 [Josh Rosen] Fix prefix comparison of null primitives.
b86e684 [Josh Rosen] Set global = true in UnsafeExternalSortSuite.
1c7bad8 [Josh Rosen] Make sorting of answers explicit in SparkPlanTest.checkAnswer().
b81a920 [Josh Rosen] Temporarily enable only the passing sort tests
5d6109d [Josh Rosen] Fix inconsistent handling / encoding of record lengths.
87b6ed9 [Josh Rosen] Fix critical issues in test which led to false negatives.
8d7fbe7 [Josh Rosen] Fixes to multiple spilling-related bugs.
82e21c1 [Josh Rosen] Force spilling in UnsafeExternalSortSuite.
88b72db [Josh Rosen] Test ascending and descending sort orders.
f27be09 [Josh Rosen] Fix tests by binding attributes.
0a79d39 [Josh Rosen] Revert "Undo part of a SparkPlanTest change in #7162 that broke my test."
7c3c864 [Josh Rosen] Undo part of a SparkPlanTest change in #7162 that broke my test.
9969c14 [Josh Rosen] Merge remote-tracking branch 'origin/master' into sql-external-sort
5822e6f [Josh Rosen] Fix test compilation issue
939f824 [Josh Rosen] Remove code gen experiment.
0dfe919 [Josh Rosen] Implement prefix sort for strings (albeit inefficiently).
66a813e [Josh Rosen] Prefix comparators for float and double
b310c88 [Josh Rosen] Integrate prefix comparators for Int and Long (others coming soon)
95058d9 [Josh Rosen] Add missing SortPrefixUtils file
4c37ba6 [Josh Rosen] Add tests for sorting on all primitive types.
6890863 [Josh Rosen] Fix memory leak on empty inputs.
d246e29 [Josh Rosen] Fix consideration of column types when choosing sort implementation.
6b156fb [Josh Rosen] Some WIP work on prefix comparison.
7f875f9 [Josh Rosen] Commit failing test demonstrating bug in handling objects in spills
41b8881 [Josh Rosen] Get UnsafeInMemorySorterSuite to pass (WIP)
90c2b6a [Josh Rosen] Update test name
6d6a1e6 [Josh Rosen] Centralize logic for picking sort operator implementations
9869ec2 [Josh Rosen] Clean up Exchange code a bit
82bb0ec [Josh Rosen] Fix IntelliJ complaint due to negated if condition
1db845a [Josh Rosen] Many more changes to harmonize with shuffle sorter
ebf9eea [Josh Rosen] Harmonization with shuffle's unsafe sorter
206bfa2 [Josh Rosen] Add some missing newlines at the ends of files
26c8931 [Josh Rosen] Back out some Hive changes that aren't needed anymore
62f0bb8 [Josh Rosen] Update to reflect SparkPlanTest changes
21d7d93 [Josh Rosen] Back out of BlockObjectWriter change
7eafecf [Josh Rosen] Port test to SparkPlanTest
d468a88 [Josh Rosen] Update for InternalRow refactoring
269cf86 [Josh Rosen] Back out SMJ operator change; isolate changes to selection of sort op.
1b841ca [Josh Rosen] WIP towards copying
b420a71 [Josh Rosen] Move most of the existing SMJ code into Java.
dfdb93f [Josh Rosen] SparkFunSuite change
73cc761 [Josh Rosen] Fix whitespace
9cc98f5 [Josh Rosen] Move more code to Java; fix bugs in UnsafeRowConverter length type.
c8792de [Josh Rosen] Remove some debug logging
dda6752 [Josh Rosen] Commit some missing code from an old git stash.
58f36d0 [Josh Rosen] Merge in a sketch of a unit test for the new sorter (now failing).
2bd8c9a [Josh Rosen] Import my original tests and get them to pass.
d5d3106 [Josh Rosen] WIP towards external sorter for Spark SQL.
---
 core/pom.xml                                  |  20 +-
 .../DummySerializerInstance.java              |   9 +-
 .../unsafe/UnsafeShuffleExternalSorter.java   |   1 +
 .../unsafe/sort/PrefixComparator.java         |  29 ++
 .../unsafe/sort/PrefixComparators.java        | 109 +++++++
 .../unsafe/sort/RecordComparator.java         |  37 +++
 .../sort/RecordPointerAndKeyPrefix.java       |  31 ++
 .../unsafe/sort/UnsafeExternalSorter.java     | 282 ++++++++++++++++++
 .../unsafe/sort/UnsafeInMemorySorter.java     | 189 ++++++++++++
 .../unsafe/sort/UnsafeSortDataFormat.java     |  80 +++++
 .../unsafe/sort/UnsafeSorterIterator.java     |  35 +++
 .../unsafe/sort/UnsafeSorterSpillMerger.java  |  91 ++++++
 .../unsafe/sort/UnsafeSorterSpillReader.java  |  98 ++++++
 .../unsafe/sort/UnsafeSorterSpillWriter.java  | 146 +++++++++
 .../sort/UnsafeExternalSorterSuite.java       | 202 +++++++++++++
 .../sort/UnsafeInMemorySorterSuite.java       | 139 +++++++++
 .../unsafe/sort/PrefixComparatorsSuite.scala  |  50 ++++
 .../sql/catalyst/expressions/UnsafeRow.java   |   7 +-
 .../execution/UnsafeExternalRowSorter.java    | 216 ++++++++++++++
 .../spark/sql/AbstractScalaRowIterator.scala  |  27 ++
 .../apache/spark/sql/execution/Exchange.scala |  13 +-
 .../spark/sql/execution/SortPrefixUtils.scala |  97 ++++++
 .../spark/sql/execution/SparkStrategies.scala |  22 +-
 .../spark/sql/execution/basicOperators.scala  |  73 +++++
 .../spark/sql/execution/SortSuite.scala       |  11 +-
 .../spark/sql/execution/SparkPlanTest.scala   | 253 ++++++++++------
 .../execution/UnsafeExternalSortSuite.scala   | 104 +++++++
 .../sql/execution/joins/OuterJoinSuite.scala  |  21 +-
 28 files changed, 2254 insertions(+), 138 deletions(-)
 rename core/src/main/java/org/apache/spark/{shuffle/unsafe => serializer}/DummySerializerInstance.java (91%)
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparator.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordPointerAndKeyPrefix.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSortDataFormat.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterIterator.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
 create mode 100644 core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
 create mode 100644 core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
 create mode 100644 core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
 create mode 100644 core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala

diff --git a/core/pom.xml b/core/pom.xml
index aee0d92620606..558cc3fb9f2f3 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -343,28 +343,28 @@
       <scope>test</scope>
     </dependency>
     <dependency>
-      <groupId>org.mockito</groupId>
-      <artifactId>mockito-core</artifactId>
+      <groupId>org.hamcrest</groupId>
+      <artifactId>hamcrest-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
-      <groupId>org.scalacheck</groupId>
-      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <groupId>org.hamcrest</groupId>
+      <artifactId>hamcrest-library</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
+      <groupId>org.mockito</groupId>
+      <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
-      <groupId>org.hamcrest</groupId>
-      <artifactId>hamcrest-core</artifactId>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
-      <groupId>org.hamcrest</groupId>
-      <artifactId>hamcrest-library</artifactId>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
       <scope>test</scope>
     </dependency>
     <dependency>
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/DummySerializerInstance.java b/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
similarity index 91%
rename from core/src/main/java/org/apache/spark/shuffle/unsafe/DummySerializerInstance.java
rename to core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
index 3f746b886bc9b..0399abc63c235 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/DummySerializerInstance.java
+++ b/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.shuffle.unsafe;
+package org.apache.spark.serializer;
 
 import java.io.IOException;
 import java.io.InputStream;
@@ -24,9 +24,7 @@
 
 import scala.reflect.ClassTag;
 
-import org.apache.spark.serializer.DeserializationStream;
-import org.apache.spark.serializer.SerializationStream;
-import org.apache.spark.serializer.SerializerInstance;
+import org.apache.spark.annotation.Private;
 import org.apache.spark.unsafe.PlatformDependent;
 
 /**
@@ -35,7 +33,8 @@
  * `write() OutputStream methods), but DiskBlockObjectWriter still calls some methods on it. To work
  * around this, we pass a dummy no-op serializer.
  */
-final class DummySerializerInstance extends SerializerInstance {
+@Private
+public final class DummySerializerInstance extends SerializerInstance {
 
   public static final DummySerializerInstance INSTANCE = new DummySerializerInstance();
 
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 9e9ed94b7890c..56289573209fb 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -30,6 +30,7 @@
 import org.apache.spark.SparkConf;
 import org.apache.spark.TaskContext;
 import org.apache.spark.executor.ShuffleWriteMetrics;
+import org.apache.spark.serializer.DummySerializerInstance;
 import org.apache.spark.serializer.SerializerInstance;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.storage.*;
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparator.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparator.java
new file mode 100644
index 0000000000000..45b78829e4cf7
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparator.java
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import org.apache.spark.annotation.Private;
+
+/**
+ * Compares 8-byte key prefixes in prefix sort. Subclasses may implement type-specific
+ * comparisons, such as lexicographic comparison for strings.
+ */
+@Private
+public abstract class PrefixComparator {
+  public abstract int compare(long prefix1, long prefix2);
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
new file mode 100644
index 0000000000000..438742565c51d
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -0,0 +1,109 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import com.google.common.base.Charsets;
+import com.google.common.primitives.Longs;
+import com.google.common.primitives.UnsignedBytes;
+
+import org.apache.spark.annotation.Private;
+import org.apache.spark.unsafe.types.UTF8String;
+
+@Private
+public class PrefixComparators {
+  private PrefixComparators() {}
+
+  public static final StringPrefixComparator STRING = new StringPrefixComparator();
+  public static final IntegralPrefixComparator INTEGRAL = new IntegralPrefixComparator();
+  public static final FloatPrefixComparator FLOAT = new FloatPrefixComparator();
+  public static final DoublePrefixComparator DOUBLE = new DoublePrefixComparator();
+
+  public static final class StringPrefixComparator extends PrefixComparator {
+    @Override
+    public int compare(long aPrefix, long bPrefix) {
+      // TODO: can done more efficiently
+      byte[] a = Longs.toByteArray(aPrefix);
+      byte[] b = Longs.toByteArray(bPrefix);
+      for (int i = 0; i < 8; i++) {
+        int c = UnsignedBytes.compare(a[i], b[i]);
+        if (c != 0) return c;
+      }
+      return 0;
+    }
+
+    public long computePrefix(byte[] bytes) {
+      if (bytes == null) {
+        return 0L;
+      } else {
+        byte[] padded = new byte[8];
+        System.arraycopy(bytes, 0, padded, 0, Math.min(bytes.length, 8));
+        return Longs.fromByteArray(padded);
+      }
+    }
+
+    public long computePrefix(String value) {
+      return value == null ? 0L : computePrefix(value.getBytes(Charsets.UTF_8));
+    }
+
+    public long computePrefix(UTF8String value) {
+      return value == null ? 0L : computePrefix(value.getBytes());
+    }
+  }
+
+  /**
+   * Prefix comparator for all integral types (boolean, byte, short, int, long).
+   */
+  public static final class IntegralPrefixComparator extends PrefixComparator {
+    @Override
+    public int compare(long a, long b) {
+      return (a < b) ? -1 : (a > b) ? 1 : 0;
+    }
+
+    public final long NULL_PREFIX = Long.MIN_VALUE;
+  }
+
+  public static final class FloatPrefixComparator extends PrefixComparator {
+    @Override
+    public int compare(long aPrefix, long bPrefix) {
+      float a = Float.intBitsToFloat((int) aPrefix);
+      float b = Float.intBitsToFloat((int) bPrefix);
+      return (a < b) ? -1 : (a > b) ? 1 : 0;
+    }
+
+    public long computePrefix(float value) {
+      return Float.floatToIntBits(value) & 0xffffffffL;
+    }
+
+    public final long NULL_PREFIX = computePrefix(Float.NEGATIVE_INFINITY);
+  }
+
+  public static final class DoublePrefixComparator extends PrefixComparator {
+    @Override
+    public int compare(long aPrefix, long bPrefix) {
+      double a = Double.longBitsToDouble(aPrefix);
+      double b = Double.longBitsToDouble(bPrefix);
+      return (a < b) ? -1 : (a > b) ? 1 : 0;
+    }
+
+    public long computePrefix(double value) {
+      return Double.doubleToLongBits(value);
+    }
+
+    public final long NULL_PREFIX = computePrefix(Double.NEGATIVE_INFINITY);
+  }
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java
new file mode 100644
index 0000000000000..09e4258792204
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordComparator.java
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+/**
+ * Compares records for ordering. In cases where the entire sorting key can fit in the 8-byte
+ * prefix, this may simply return 0.
+ */
+public abstract class RecordComparator {
+
+  /**
+   * Compare two records for order.
+   *
+   * @return a negative integer, zero, or a positive integer as the first record is less than,
+   *         equal to, or greater than the second.
+   */
+  public abstract int compare(
+    Object leftBaseObject,
+    long leftBaseOffset,
+    Object rightBaseObject,
+    long rightBaseOffset);
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordPointerAndKeyPrefix.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordPointerAndKeyPrefix.java
new file mode 100644
index 0000000000000..0c4ebde407cfc
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/RecordPointerAndKeyPrefix.java
@@ -0,0 +1,31 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+final class RecordPointerAndKeyPrefix {
+  /**
+   * A pointer to a record; see {@link org.apache.spark.unsafe.memory.TaskMemoryManager} for a
+   * description of how these addresses are encoded.
+   */
+  public long recordPointer;
+
+  /**
+   * A key prefix, for use in comparisons.
+   */
+  public long keyPrefix;
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
new file mode 100644
index 0000000000000..4d6731ee60af3
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -0,0 +1,282 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.io.IOException;
+import java.util.LinkedList;
+
+import com.google.common.annotations.VisibleForTesting;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.TaskContext;
+import org.apache.spark.executor.ShuffleWriteMetrics;
+import org.apache.spark.shuffle.ShuffleMemoryManager;
+import org.apache.spark.storage.BlockManager;
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.memory.MemoryBlock;
+import org.apache.spark.unsafe.memory.TaskMemoryManager;
+import org.apache.spark.util.Utils;
+
+/**
+ * External sorter based on {@link UnsafeInMemorySorter}.
+ */
+public final class UnsafeExternalSorter {
+
+  private final Logger logger = LoggerFactory.getLogger(UnsafeExternalSorter.class);
+
+  private static final int PAGE_SIZE = 1 << 27;  // 128 megabytes
+  @VisibleForTesting
+  static final int MAX_RECORD_SIZE = PAGE_SIZE - 4;
+
+  private final PrefixComparator prefixComparator;
+  private final RecordComparator recordComparator;
+  private final int initialSize;
+  private final TaskMemoryManager memoryManager;
+  private final ShuffleMemoryManager shuffleMemoryManager;
+  private final BlockManager blockManager;
+  private final TaskContext taskContext;
+  private ShuffleWriteMetrics writeMetrics;
+
+  /** The buffer size to use when writing spills using DiskBlockObjectWriter */
+  private final int fileBufferSizeBytes;
+
+  /**
+   * Memory pages that hold the records being sorted. The pages in this list are freed when
+   * spilling, although in principle we could recycle these pages across spills (on the other hand,
+   * this might not be necessary if we maintained a pool of re-usable pages in the TaskMemoryManager
+   * itself).
+   */
+  private final LinkedList<MemoryBlock> allocatedPages = new LinkedList<MemoryBlock>();
+
+  // These variables are reset after spilling:
+  private UnsafeInMemorySorter sorter;
+  private MemoryBlock currentPage = null;
+  private long currentPagePosition = -1;
+  private long freeSpaceInCurrentPage = 0;
+
+  private final LinkedList<UnsafeSorterSpillWriter> spillWriters = new LinkedList<>();
+
+  public UnsafeExternalSorter(
+      TaskMemoryManager memoryManager,
+      ShuffleMemoryManager shuffleMemoryManager,
+      BlockManager blockManager,
+      TaskContext taskContext,
+      RecordComparator recordComparator,
+      PrefixComparator prefixComparator,
+      int initialSize,
+      SparkConf conf) throws IOException {
+    this.memoryManager = memoryManager;
+    this.shuffleMemoryManager = shuffleMemoryManager;
+    this.blockManager = blockManager;
+    this.taskContext = taskContext;
+    this.recordComparator = recordComparator;
+    this.prefixComparator = prefixComparator;
+    this.initialSize = initialSize;
+    // Use getSizeAsKb (not bytes) to maintain backwards compatibility for units
+    this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
+    initializeForWriting();
+  }
+
+  // TODO: metrics tracking + integration with shuffle write metrics
+  // need to connect the write metrics to task metrics so we count the spill IO somewhere.
+
+  /**
+   * Allocates new sort data structures. Called when creating the sorter and after each spill.
+   */
+  private void initializeForWriting() throws IOException {
+    this.writeMetrics = new ShuffleWriteMetrics();
+    // TODO: move this sizing calculation logic into a static method of sorter:
+    final long memoryRequested = initialSize * 8L * 2;
+    final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryRequested);
+    if (memoryAcquired != memoryRequested) {
+      shuffleMemoryManager.release(memoryAcquired);
+      throw new IOException("Could not acquire " + memoryRequested + " bytes of memory");
+    }
+
+    this.sorter =
+      new UnsafeInMemorySorter(memoryManager, recordComparator, prefixComparator, initialSize);
+  }
+
+  /**
+   * Sort and spill the current records in response to memory pressure.
+   */
+  @VisibleForTesting
+  public void spill() throws IOException {
+    logger.info("Thread {} spilling sort data of {} to disk ({} {} so far)",
+      Thread.currentThread().getId(),
+      Utils.bytesToString(getMemoryUsage()),
+      spillWriters.size(),
+      spillWriters.size() > 1 ? " times" : " time");
+
+    final UnsafeSorterSpillWriter spillWriter =
+      new UnsafeSorterSpillWriter(blockManager, fileBufferSizeBytes, writeMetrics,
+        sorter.numRecords());
+    spillWriters.add(spillWriter);
+    final UnsafeSorterIterator sortedRecords = sorter.getSortedIterator();
+    while (sortedRecords.hasNext()) {
+      sortedRecords.loadNext();
+      final Object baseObject = sortedRecords.getBaseObject();
+      final long baseOffset = sortedRecords.getBaseOffset();
+      final int recordLength = sortedRecords.getRecordLength();
+      spillWriter.write(baseObject, baseOffset, recordLength, sortedRecords.getKeyPrefix());
+    }
+    spillWriter.close();
+    final long sorterMemoryUsage = sorter.getMemoryUsage();
+    sorter = null;
+    shuffleMemoryManager.release(sorterMemoryUsage);
+    final long spillSize = freeMemory();
+    taskContext.taskMetrics().incMemoryBytesSpilled(spillSize);
+    initializeForWriting();
+  }
+
+  private long getMemoryUsage() {
+    return sorter.getMemoryUsage() + (allocatedPages.size() * (long) PAGE_SIZE);
+  }
+
+  public long freeMemory() {
+    long memoryFreed = 0;
+    for (MemoryBlock block : allocatedPages) {
+      memoryManager.freePage(block);
+      shuffleMemoryManager.release(block.size());
+      memoryFreed += block.size();
+    }
+    allocatedPages.clear();
+    currentPage = null;
+    currentPagePosition = -1;
+    freeSpaceInCurrentPage = 0;
+    return memoryFreed;
+  }
+
+  /**
+   * Checks whether there is enough space to insert a new record into the sorter.
+   *
+   * @param requiredSpace the required space in the data page, in bytes, including space for storing
+   *                      the record size.
+
+   * @return true if the record can be inserted without requiring more allocations, false otherwise.
+   */
+  private boolean haveSpaceForRecord(int requiredSpace) {
+    assert (requiredSpace > 0);
+    return (sorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
+  }
+
+  /**
+   * Allocates more memory in order to insert an additional record. This will request additional
+   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
+   * obtained.
+   *
+   * @param requiredSpace the required space in the data page, in bytes, including space for storing
+   *                      the record size.
+   */
+  private void allocateSpaceForRecord(int requiredSpace) throws IOException {
+    // TODO: merge these steps to first calculate total memory requirements for this insert,
+    // then try to acquire; no point in acquiring sort buffer only to spill due to no space in the
+    // data page.
+    if (!sorter.hasSpaceForAnotherRecord()) {
+      logger.debug("Attempting to expand sort pointer array");
+      final long oldPointerArrayMemoryUsage = sorter.getMemoryUsage();
+      final long memoryToGrowPointerArray = oldPointerArrayMemoryUsage * 2;
+      final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryToGrowPointerArray);
+      if (memoryAcquired < memoryToGrowPointerArray) {
+        shuffleMemoryManager.release(memoryAcquired);
+        spill();
+      } else {
+        sorter.expandPointerArray();
+        shuffleMemoryManager.release(oldPointerArrayMemoryUsage);
+      }
+    }
+
+    if (requiredSpace > freeSpaceInCurrentPage) {
+      logger.trace("Required space {} is less than free space in current page ({})", requiredSpace,
+        freeSpaceInCurrentPage);
+      // TODO: we should track metrics on the amount of space wasted when we roll over to a new page
+      // without using the free space at the end of the current page. We should also do this for
+      // BytesToBytesMap.
+      if (requiredSpace > PAGE_SIZE) {
+        throw new IOException("Required space " + requiredSpace + " is greater than page size (" +
+          PAGE_SIZE + ")");
+      } else {
+        final long memoryAcquired = shuffleMemoryManager.tryToAcquire(PAGE_SIZE);
+        if (memoryAcquired < PAGE_SIZE) {
+          shuffleMemoryManager.release(memoryAcquired);
+          spill();
+          final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(PAGE_SIZE);
+          if (memoryAcquiredAfterSpilling != PAGE_SIZE) {
+            shuffleMemoryManager.release(memoryAcquiredAfterSpilling);
+            throw new IOException("Unable to acquire " + PAGE_SIZE + " bytes of memory");
+          }
+        }
+        currentPage = memoryManager.allocatePage(PAGE_SIZE);
+        currentPagePosition = currentPage.getBaseOffset();
+        freeSpaceInCurrentPage = PAGE_SIZE;
+        allocatedPages.add(currentPage);
+      }
+    }
+  }
+
+  /**
+   * Write a record to the sorter.
+   */
+  public void insertRecord(
+      Object recordBaseObject,
+      long recordBaseOffset,
+      int lengthInBytes,
+      long prefix) throws IOException {
+    // Need 4 bytes to store the record length.
+    final int totalSpaceRequired = lengthInBytes + 4;
+    if (!haveSpaceForRecord(totalSpaceRequired)) {
+      allocateSpaceForRecord(totalSpaceRequired);
+    }
+
+    final long recordAddress =
+      memoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
+    final Object dataPageBaseObject = currentPage.getBaseObject();
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, lengthInBytes);
+    currentPagePosition += 4;
+    PlatformDependent.copyMemory(
+      recordBaseObject,
+      recordBaseOffset,
+      dataPageBaseObject,
+      currentPagePosition,
+      lengthInBytes);
+    currentPagePosition += lengthInBytes;
+
+    sorter.insertRecord(recordAddress, prefix);
+  }
+
+  public UnsafeSorterIterator getSortedIterator() throws IOException {
+    final UnsafeSorterIterator inMemoryIterator = sorter.getSortedIterator();
+    int numIteratorsToMerge = spillWriters.size() + (inMemoryIterator.hasNext() ? 1 : 0);
+    if (spillWriters.isEmpty()) {
+      return inMemoryIterator;
+    } else {
+      final UnsafeSorterSpillMerger spillMerger =
+        new UnsafeSorterSpillMerger(recordComparator, prefixComparator, numIteratorsToMerge);
+      for (UnsafeSorterSpillWriter spillWriter : spillWriters) {
+        spillMerger.addSpill(spillWriter.getReader(blockManager));
+      }
+      spillWriters.clear();
+      if (inMemoryIterator.hasNext()) {
+        spillMerger.addSpill(inMemoryIterator);
+      }
+      return spillMerger.getSortedIterator();
+    }
+  }
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
new file mode 100644
index 0000000000000..fc34ad9cff369
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
@@ -0,0 +1,189 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.util.Comparator;
+
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.util.collection.Sorter;
+import org.apache.spark.unsafe.memory.TaskMemoryManager;
+
+/**
+ * Sorts records using an AlphaSort-style key-prefix sort. This sort stores pointers to records
+ * alongside a user-defined prefix of the record's sorting key. When the underlying sort algorithm
+ * compares records, it will first compare the stored key prefixes; if the prefixes are not equal,
+ * then we do not need to traverse the record pointers to compare the actual records. Avoiding these
+ * random memory accesses improves cache hit rates.
+ */
+public final class UnsafeInMemorySorter {
+
+  private static final class SortComparator implements Comparator<RecordPointerAndKeyPrefix> {
+
+    private final RecordComparator recordComparator;
+    private final PrefixComparator prefixComparator;
+    private final TaskMemoryManager memoryManager;
+
+    SortComparator(
+        RecordComparator recordComparator,
+        PrefixComparator prefixComparator,
+        TaskMemoryManager memoryManager) {
+      this.recordComparator = recordComparator;
+      this.prefixComparator = prefixComparator;
+      this.memoryManager = memoryManager;
+    }
+
+    @Override
+    public int compare(RecordPointerAndKeyPrefix r1, RecordPointerAndKeyPrefix r2) {
+      final int prefixComparisonResult = prefixComparator.compare(r1.keyPrefix, r2.keyPrefix);
+      if (prefixComparisonResult == 0) {
+        final Object baseObject1 = memoryManager.getPage(r1.recordPointer);
+        final long baseOffset1 = memoryManager.getOffsetInPage(r1.recordPointer) + 4; // skip length
+        final Object baseObject2 = memoryManager.getPage(r2.recordPointer);
+        final long baseOffset2 = memoryManager.getOffsetInPage(r2.recordPointer) + 4; // skip length
+        return recordComparator.compare(baseObject1, baseOffset1, baseObject2, baseOffset2);
+      } else {
+        return prefixComparisonResult;
+      }
+    }
+  }
+
+  private final TaskMemoryManager memoryManager;
+  private final Sorter<RecordPointerAndKeyPrefix, long[]> sorter;
+  private final Comparator<RecordPointerAndKeyPrefix> sortComparator;
+
+  /**
+   * Within this buffer, position {@code 2 * i} holds a pointer pointer to the record at
+   * index {@code i}, while position {@code 2 * i + 1} in the array holds an 8-byte key prefix.
+   */
+  private long[] pointerArray;
+
+  /**
+   * The position in the sort buffer where new records can be inserted.
+   */
+  private int pointerArrayInsertPosition = 0;
+
+  public UnsafeInMemorySorter(
+      final TaskMemoryManager memoryManager,
+      final RecordComparator recordComparator,
+      final PrefixComparator prefixComparator,
+      int initialSize) {
+    assert (initialSize > 0);
+    this.pointerArray = new long[initialSize * 2];
+    this.memoryManager = memoryManager;
+    this.sorter = new Sorter<>(UnsafeSortDataFormat.INSTANCE);
+    this.sortComparator = new SortComparator(recordComparator, prefixComparator, memoryManager);
+  }
+
+  /**
+   * @return the number of records that have been inserted into this sorter.
+   */
+  public int numRecords() {
+    return pointerArrayInsertPosition / 2;
+  }
+
+  public long getMemoryUsage() {
+    return pointerArray.length * 8L;
+  }
+
+  public boolean hasSpaceForAnotherRecord() {
+    return pointerArrayInsertPosition + 2 < pointerArray.length;
+  }
+
+  public void expandPointerArray() {
+    final long[] oldArray = pointerArray;
+    // Guard against overflow:
+    final int newLength = oldArray.length * 2 > 0 ? (oldArray.length * 2) : Integer.MAX_VALUE;
+    pointerArray = new long[newLength];
+    System.arraycopy(oldArray, 0, pointerArray, 0, oldArray.length);
+  }
+
+  /**
+   * Inserts a record to be sorted. Assumes that the record pointer points to a record length
+   * stored as a 4-byte integer, followed by the record's bytes.
+   *
+   * @param recordPointer pointer to a record in a data page, encoded by {@link TaskMemoryManager}.
+   * @param keyPrefix a user-defined key prefix
+   */
+  public void insertRecord(long recordPointer, long keyPrefix) {
+    if (!hasSpaceForAnotherRecord()) {
+      expandPointerArray();
+    }
+    pointerArray[pointerArrayInsertPosition] = recordPointer;
+    pointerArrayInsertPosition++;
+    pointerArray[pointerArrayInsertPosition] = keyPrefix;
+    pointerArrayInsertPosition++;
+  }
+
+  private static final class SortedIterator extends UnsafeSorterIterator {
+
+    private final TaskMemoryManager memoryManager;
+    private final int sortBufferInsertPosition;
+    private final long[] sortBuffer;
+    private int position = 0;
+    private Object baseObject;
+    private long baseOffset;
+    private long keyPrefix;
+    private int recordLength;
+
+    SortedIterator(
+        TaskMemoryManager memoryManager,
+        int sortBufferInsertPosition,
+        long[] sortBuffer) {
+      this.memoryManager = memoryManager;
+      this.sortBufferInsertPosition = sortBufferInsertPosition;
+      this.sortBuffer = sortBuffer;
+    }
+
+    @Override
+    public boolean hasNext() {
+      return position < sortBufferInsertPosition;
+    }
+
+    @Override
+    public void loadNext() {
+      // This pointer points to a 4-byte record length, followed by the record's bytes
+      final long recordPointer = sortBuffer[position];
+      baseObject = memoryManager.getPage(recordPointer);
+      baseOffset = memoryManager.getOffsetInPage(recordPointer) + 4;  // Skip over record length
+      recordLength = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset - 4);
+      keyPrefix = sortBuffer[position + 1];
+      position += 2;
+    }
+
+    @Override
+    public Object getBaseObject() { return baseObject; }
+
+    @Override
+    public long getBaseOffset() { return baseOffset; }
+
+    @Override
+    public int getRecordLength() { return recordLength; }
+
+    @Override
+    public long getKeyPrefix() { return keyPrefix; }
+  }
+
+  /**
+   * Return an iterator over record pointers in sorted order. For efficiency, all calls to
+   * {@code next()} will return the same mutable object.
+   */
+  public UnsafeSorterIterator getSortedIterator() {
+    sorter.sort(pointerArray, 0, pointerArrayInsertPosition / 2, sortComparator);
+    return new SortedIterator(memoryManager, pointerArrayInsertPosition, pointerArray);
+  }
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSortDataFormat.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSortDataFormat.java
new file mode 100644
index 0000000000000..d09c728a7a638
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSortDataFormat.java
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import org.apache.spark.util.collection.SortDataFormat;
+
+/**
+ * Supports sorting an array of (record pointer, key prefix) pairs.
+ * Used in {@link UnsafeInMemorySorter}.
+ * <p>
+ * Within each long[] buffer, position {@code 2 * i} holds a pointer pointer to the record at
+ * index {@code i}, while position {@code 2 * i + 1} in the array holds an 8-byte key prefix.
+ */
+final class UnsafeSortDataFormat extends SortDataFormat<RecordPointerAndKeyPrefix, long[]> {
+
+  public static final UnsafeSortDataFormat INSTANCE = new UnsafeSortDataFormat();
+
+  private UnsafeSortDataFormat() { }
+
+  @Override
+  public RecordPointerAndKeyPrefix getKey(long[] data, int pos) {
+    // Since we re-use keys, this method shouldn't be called.
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public RecordPointerAndKeyPrefix newKey() {
+    return new RecordPointerAndKeyPrefix();
+  }
+
+  @Override
+  public RecordPointerAndKeyPrefix getKey(long[] data, int pos, RecordPointerAndKeyPrefix reuse) {
+    reuse.recordPointer = data[pos * 2];
+    reuse.keyPrefix = data[pos * 2 + 1];
+    return reuse;
+  }
+
+  @Override
+  public void swap(long[] data, int pos0, int pos1) {
+    long tempPointer = data[pos0 * 2];
+    long tempKeyPrefix = data[pos0 * 2 + 1];
+    data[pos0 * 2] = data[pos1 * 2];
+    data[pos0 * 2 + 1] = data[pos1 * 2 + 1];
+    data[pos1 * 2] = tempPointer;
+    data[pos1 * 2 + 1] = tempKeyPrefix;
+  }
+
+  @Override
+  public void copyElement(long[] src, int srcPos, long[] dst, int dstPos) {
+    dst[dstPos * 2] = src[srcPos * 2];
+    dst[dstPos * 2 + 1] = src[srcPos * 2 + 1];
+  }
+
+  @Override
+  public void copyRange(long[] src, int srcPos, long[] dst, int dstPos, int length) {
+    System.arraycopy(src, srcPos * 2, dst, dstPos * 2, length * 2);
+  }
+
+  @Override
+  public long[] allocate(int length) {
+    assert (length < Integer.MAX_VALUE / 2) : "Length " + length + " is too large";
+    return new long[length * 2];
+  }
+
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterIterator.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterIterator.java
new file mode 100644
index 0000000000000..16ac2e8d821ba
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterIterator.java
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.io.IOException;
+
+public abstract class UnsafeSorterIterator {
+
+  public abstract boolean hasNext();
+
+  public abstract void loadNext() throws IOException;
+
+  public abstract Object getBaseObject();
+
+  public abstract long getBaseOffset();
+
+  public abstract int getRecordLength();
+
+  public abstract long getKeyPrefix();
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
new file mode 100644
index 0000000000000..8272c2a5be0d1
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.io.IOException;
+import java.util.Comparator;
+import java.util.PriorityQueue;
+
+final class UnsafeSorterSpillMerger {
+
+  private final PriorityQueue<UnsafeSorterIterator> priorityQueue;
+
+  public UnsafeSorterSpillMerger(
+      final RecordComparator recordComparator,
+      final PrefixComparator prefixComparator,
+      final int numSpills) {
+    final Comparator<UnsafeSorterIterator> comparator = new Comparator<UnsafeSorterIterator>() {
+
+      @Override
+      public int compare(UnsafeSorterIterator left, UnsafeSorterIterator right) {
+        final int prefixComparisonResult =
+          prefixComparator.compare(left.getKeyPrefix(), right.getKeyPrefix());
+        if (prefixComparisonResult == 0) {
+          return recordComparator.compare(
+            left.getBaseObject(), left.getBaseOffset(),
+            right.getBaseObject(), right.getBaseOffset());
+        } else {
+          return prefixComparisonResult;
+        }
+      }
+    };
+    priorityQueue = new PriorityQueue<UnsafeSorterIterator>(numSpills, comparator);
+  }
+
+  public void addSpill(UnsafeSorterIterator spillReader) throws IOException {
+    if (spillReader.hasNext()) {
+      spillReader.loadNext();
+    }
+    priorityQueue.add(spillReader);
+  }
+
+  public UnsafeSorterIterator getSortedIterator() throws IOException {
+    return new UnsafeSorterIterator() {
+
+      private UnsafeSorterIterator spillReader;
+
+      @Override
+      public boolean hasNext() {
+        return !priorityQueue.isEmpty() || (spillReader != null && spillReader.hasNext());
+      }
+
+      @Override
+      public void loadNext() throws IOException {
+        if (spillReader != null) {
+          if (spillReader.hasNext()) {
+            spillReader.loadNext();
+            priorityQueue.add(spillReader);
+          }
+        }
+        spillReader = priorityQueue.remove();
+      }
+
+      @Override
+      public Object getBaseObject() { return spillReader.getBaseObject(); }
+
+      @Override
+      public long getBaseOffset() { return spillReader.getBaseOffset(); }
+
+      @Override
+      public int getRecordLength() { return spillReader.getRecordLength(); }
+
+      @Override
+      public long getKeyPrefix() { return spillReader.getKeyPrefix(); }
+    };
+  }
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
new file mode 100644
index 0000000000000..29e9e0f30f934
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.io.*;
+
+import com.google.common.io.ByteStreams;
+
+import org.apache.spark.storage.BlockId;
+import org.apache.spark.storage.BlockManager;
+import org.apache.spark.unsafe.PlatformDependent;
+
+/**
+ * Reads spill files written by {@link UnsafeSorterSpillWriter} (see that class for a description
+ * of the file format).
+ */
+final class UnsafeSorterSpillReader extends UnsafeSorterIterator {
+
+  private InputStream in;
+  private DataInputStream din;
+
+  // Variables that change with every record read:
+  private int recordLength;
+  private long keyPrefix;
+  private int numRecordsRemaining;
+
+  private byte[] arr = new byte[1024 * 1024];
+  private Object baseObject = arr;
+  private final long baseOffset = PlatformDependent.BYTE_ARRAY_OFFSET;
+
+  public UnsafeSorterSpillReader(
+      BlockManager blockManager,
+      File file,
+      BlockId blockId) throws IOException {
+    assert (file.length() > 0);
+    final BufferedInputStream bs = new BufferedInputStream(new FileInputStream(file));
+    this.in = blockManager.wrapForCompression(blockId, bs);
+    this.din = new DataInputStream(this.in);
+    numRecordsRemaining = din.readInt();
+  }
+
+  @Override
+  public boolean hasNext() {
+    return (numRecordsRemaining > 0);
+  }
+
+  @Override
+  public void loadNext() throws IOException {
+    recordLength = din.readInt();
+    keyPrefix = din.readLong();
+    if (recordLength > arr.length) {
+      arr = new byte[recordLength];
+      baseObject = arr;
+    }
+    ByteStreams.readFully(in, arr, 0, recordLength);
+    numRecordsRemaining--;
+    if (numRecordsRemaining == 0) {
+      in.close();
+      in = null;
+      din = null;
+    }
+  }
+
+  @Override
+  public Object getBaseObject() {
+    return baseObject;
+  }
+
+  @Override
+  public long getBaseOffset() {
+    return baseOffset;
+  }
+
+  @Override
+  public int getRecordLength() {
+    return recordLength;
+  }
+
+  @Override
+  public long getKeyPrefix() {
+    return keyPrefix;
+  }
+}
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
new file mode 100644
index 0000000000000..b8d66659804ad
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
@@ -0,0 +1,146 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.io.File;
+import java.io.IOException;
+
+import scala.Tuple2;
+
+import org.apache.spark.executor.ShuffleWriteMetrics;
+import org.apache.spark.serializer.DummySerializerInstance;
+import org.apache.spark.storage.BlockId;
+import org.apache.spark.storage.BlockManager;
+import org.apache.spark.storage.BlockObjectWriter;
+import org.apache.spark.storage.TempLocalBlockId;
+import org.apache.spark.unsafe.PlatformDependent;
+
+/**
+ * Spills a list of sorted records to disk. Spill files have the following format:
+ *
+ *   [# of records (int)] [[len (int)][prefix (long)][data (bytes)]...]
+ */
+final class UnsafeSorterSpillWriter {
+
+  static final int DISK_WRITE_BUFFER_SIZE = 1024 * 1024;
+
+  // Small writes to DiskBlockObjectWriter will be fairly inefficient. Since there doesn't seem to
+  // be an API to directly transfer bytes from managed memory to the disk writer, we buffer
+  // data through a byte array.
+  private byte[] writeBuffer = new byte[DISK_WRITE_BUFFER_SIZE];
+
+  private final File file;
+  private final BlockId blockId;
+  private final int numRecordsToWrite;
+  private BlockObjectWriter writer;
+  private int numRecordsSpilled = 0;
+
+  public UnsafeSorterSpillWriter(
+      BlockManager blockManager,
+      int fileBufferSize,
+      ShuffleWriteMetrics writeMetrics,
+      int numRecordsToWrite) throws IOException {
+    final Tuple2<TempLocalBlockId, File> spilledFileInfo =
+      blockManager.diskBlockManager().createTempLocalBlock();
+    this.file = spilledFileInfo._2();
+    this.blockId = spilledFileInfo._1();
+    this.numRecordsToWrite = numRecordsToWrite;
+    // Unfortunately, we need a serializer instance in order to construct a DiskBlockObjectWriter.
+    // Our write path doesn't actually use this serializer (since we end up calling the `write()`
+    // OutputStream methods), but DiskBlockObjectWriter still calls some methods on it. To work
+    // around this, we pass a dummy no-op serializer.
+    writer = blockManager.getDiskWriter(
+      blockId, file, DummySerializerInstance.INSTANCE, fileBufferSize, writeMetrics);
+    // Write the number of records
+    writeIntToBuffer(numRecordsToWrite, 0);
+    writer.write(writeBuffer, 0, 4);
+  }
+
+  // Based on DataOutputStream.writeLong.
+  private void writeLongToBuffer(long v, int offset) throws IOException {
+    writeBuffer[offset + 0] = (byte)(v >>> 56);
+    writeBuffer[offset + 1] = (byte)(v >>> 48);
+    writeBuffer[offset + 2] = (byte)(v >>> 40);
+    writeBuffer[offset + 3] = (byte)(v >>> 32);
+    writeBuffer[offset + 4] = (byte)(v >>> 24);
+    writeBuffer[offset + 5] = (byte)(v >>> 16);
+    writeBuffer[offset + 6] = (byte)(v >>>  8);
+    writeBuffer[offset + 7] = (byte)(v >>>  0);
+  }
+
+  // Based on DataOutputStream.writeInt.
+  private void writeIntToBuffer(int v, int offset) throws IOException {
+    writeBuffer[offset + 0] = (byte)(v >>> 24);
+    writeBuffer[offset + 1] = (byte)(v >>> 16);
+    writeBuffer[offset + 2] = (byte)(v >>>  8);
+    writeBuffer[offset + 3] = (byte)(v >>>  0);
+  }
+
+  /**
+   * Write a record to a spill file.
+   *
+   * @param baseObject the base object / memory page containing the record
+   * @param baseOffset the base offset which points directly to the record data.
+   * @param recordLength the length of the record.
+   * @param keyPrefix a sort key prefix
+   */
+  public void write(
+      Object baseObject,
+      long baseOffset,
+      int recordLength,
+      long keyPrefix) throws IOException {
+    if (numRecordsSpilled == numRecordsToWrite) {
+      throw new IllegalStateException(
+        "Number of records written exceeded numRecordsToWrite = " + numRecordsToWrite);
+    } else {
+      numRecordsSpilled++;
+    }
+    writeIntToBuffer(recordLength, 0);
+    writeLongToBuffer(keyPrefix, 4);
+    int dataRemaining = recordLength;
+    int freeSpaceInWriteBuffer = DISK_WRITE_BUFFER_SIZE - 4 - 8; // space used by prefix + len
+    long recordReadPosition = baseOffset;
+    while (dataRemaining > 0) {
+      final int toTransfer = Math.min(freeSpaceInWriteBuffer, dataRemaining);
+      PlatformDependent.copyMemory(
+        baseObject,
+        recordReadPosition,
+        writeBuffer,
+        PlatformDependent.BYTE_ARRAY_OFFSET + (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer),
+        toTransfer);
+      writer.write(writeBuffer, 0, (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer) + toTransfer);
+      recordReadPosition += toTransfer;
+      dataRemaining -= toTransfer;
+      freeSpaceInWriteBuffer = DISK_WRITE_BUFFER_SIZE;
+    }
+    if (freeSpaceInWriteBuffer < DISK_WRITE_BUFFER_SIZE) {
+      writer.write(writeBuffer, 0, (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer));
+    }
+    writer.recordWritten();
+  }
+
+  public void close() throws IOException {
+    writer.commitAndClose();
+    writer = null;
+    writeBuffer = null;
+  }
+
+  public UnsafeSorterSpillReader getReader(BlockManager blockManager) throws IOException {
+    return new UnsafeSorterSpillReader(blockManager, file, blockId);
+  }
+}
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
new file mode 100644
index 0000000000000..ea8755e21eb68
--- /dev/null
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -0,0 +1,202 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.io.File;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.util.UUID;
+
+import scala.Tuple2;
+import scala.Tuple2$;
+import scala.runtime.AbstractFunction1;
+
+import org.junit.Before;
+import org.junit.Test;
+import org.mockito.Mock;
+import org.mockito.MockitoAnnotations;
+import org.mockito.invocation.InvocationOnMock;
+import org.mockito.stubbing.Answer;
+import static org.junit.Assert.*;
+import static org.mockito.AdditionalAnswers.returnsFirstArg;
+import static org.mockito.AdditionalAnswers.returnsSecondArg;
+import static org.mockito.Answers.RETURNS_SMART_NULLS;
+import static org.mockito.Mockito.*;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.TaskContext;
+import org.apache.spark.executor.ShuffleWriteMetrics;
+import org.apache.spark.executor.TaskMetrics;
+import org.apache.spark.serializer.SerializerInstance;
+import org.apache.spark.shuffle.ShuffleMemoryManager;
+import org.apache.spark.storage.*;
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
+import org.apache.spark.unsafe.memory.MemoryAllocator;
+import org.apache.spark.unsafe.memory.TaskMemoryManager;
+import org.apache.spark.util.Utils;
+
+public class UnsafeExternalSorterSuite {
+
+  final TaskMemoryManager memoryManager =
+    new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP));
+  // Use integer comparison for comparing prefixes (which are partition ids, in this case)
+  final PrefixComparator prefixComparator = new PrefixComparator() {
+    @Override
+    public int compare(long prefix1, long prefix2) {
+      return (int) prefix1 - (int) prefix2;
+    }
+  };
+  // Since the key fits within the 8-byte prefix, we don't need to do any record comparison, so
+  // use a dummy comparator
+  final RecordComparator recordComparator = new RecordComparator() {
+    @Override
+    public int compare(
+      Object leftBaseObject,
+      long leftBaseOffset,
+      Object rightBaseObject,
+      long rightBaseOffset) {
+      return 0;
+    }
+  };
+
+  @Mock(answer = RETURNS_SMART_NULLS) ShuffleMemoryManager shuffleMemoryManager;
+  @Mock(answer = RETURNS_SMART_NULLS) BlockManager blockManager;
+  @Mock(answer = RETURNS_SMART_NULLS) DiskBlockManager diskBlockManager;
+  @Mock(answer = RETURNS_SMART_NULLS) TaskContext taskContext;
+
+  File tempDir;
+
+  private static final class CompressStream extends AbstractFunction1<OutputStream, OutputStream> {
+    @Override
+    public OutputStream apply(OutputStream stream) {
+      return stream;
+    }
+  }
+
+  @Before
+  public void setUp() {
+    MockitoAnnotations.initMocks(this);
+    tempDir = new File(Utils.createTempDir$default$1());
+    taskContext = mock(TaskContext.class);
+    when(taskContext.taskMetrics()).thenReturn(new TaskMetrics());
+    when(shuffleMemoryManager.tryToAcquire(anyLong())).then(returnsFirstArg());
+    when(blockManager.diskBlockManager()).thenReturn(diskBlockManager);
+    when(diskBlockManager.createTempLocalBlock()).thenAnswer(new Answer<Tuple2<TempLocalBlockId, File>>() {
+      @Override
+      public Tuple2<TempLocalBlockId, File> answer(InvocationOnMock invocationOnMock) throws Throwable {
+        TempLocalBlockId blockId = new TempLocalBlockId(UUID.randomUUID());
+        File file = File.createTempFile("spillFile", ".spill", tempDir);
+        return Tuple2$.MODULE$.apply(blockId, file);
+      }
+    });
+    when(blockManager.getDiskWriter(
+      any(BlockId.class),
+      any(File.class),
+      any(SerializerInstance.class),
+      anyInt(),
+      any(ShuffleWriteMetrics.class))).thenAnswer(new Answer<DiskBlockObjectWriter>() {
+      @Override
+      public DiskBlockObjectWriter answer(InvocationOnMock invocationOnMock) throws Throwable {
+        Object[] args = invocationOnMock.getArguments();
+
+        return new DiskBlockObjectWriter(
+          (BlockId) args[0],
+          (File) args[1],
+          (SerializerInstance) args[2],
+          (Integer) args[3],
+          new CompressStream(),
+          false,
+          (ShuffleWriteMetrics) args[4]
+        );
+      }
+    });
+    when(blockManager.wrapForCompression(any(BlockId.class), any(InputStream.class)))
+      .then(returnsSecondArg());
+  }
+
+  private static void insertNumber(UnsafeExternalSorter sorter, int value) throws Exception {
+    final int[] arr = new int[] { value };
+    sorter.insertRecord(arr, PlatformDependent.INT_ARRAY_OFFSET, 4, value);
+  }
+
+  @Test
+  public void testSortingOnlyByPrefix() throws Exception {
+
+    final UnsafeExternalSorter sorter = new UnsafeExternalSorter(
+      memoryManager,
+      shuffleMemoryManager,
+      blockManager,
+      taskContext,
+      recordComparator,
+      prefixComparator,
+      1024,
+      new SparkConf());
+
+    insertNumber(sorter, 5);
+    insertNumber(sorter, 1);
+    insertNumber(sorter, 3);
+    sorter.spill();
+    insertNumber(sorter, 4);
+    sorter.spill();
+    insertNumber(sorter, 2);
+
+    UnsafeSorterIterator iter = sorter.getSortedIterator();
+
+    for (int i = 1; i <= 5; i++) {
+      iter.loadNext();
+      assertEquals(i, iter.getKeyPrefix());
+      assertEquals(4, iter.getRecordLength());
+      // TODO: read rest of value.
+    }
+
+    // TODO: test for cleanup:
+    // assert(tempDir.isEmpty)
+  }
+
+  @Test
+  public void testSortingEmptyArrays() throws Exception {
+
+    final UnsafeExternalSorter sorter = new UnsafeExternalSorter(
+      memoryManager,
+      shuffleMemoryManager,
+      blockManager,
+      taskContext,
+      recordComparator,
+      prefixComparator,
+      1024,
+      new SparkConf());
+
+    sorter.insertRecord(null, 0, 0, 0);
+    sorter.insertRecord(null, 0, 0, 0);
+    sorter.spill();
+    sorter.insertRecord(null, 0, 0, 0);
+    sorter.spill();
+    sorter.insertRecord(null, 0, 0, 0);
+    sorter.insertRecord(null, 0, 0, 0);
+
+    UnsafeSorterIterator iter = sorter.getSortedIterator();
+
+    for (int i = 1; i <= 5; i++) {
+      iter.loadNext();
+      assertEquals(0, iter.getKeyPrefix());
+      assertEquals(0, iter.getRecordLength());
+    }
+  }
+
+}
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
new file mode 100644
index 0000000000000..909500930539c
--- /dev/null
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
@@ -0,0 +1,139 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort;
+
+import java.util.Arrays;
+
+import org.junit.Test;
+import static org.hamcrest.MatcherAssert.assertThat;
+import static org.hamcrest.Matchers.*;
+import static org.junit.Assert.*;
+import static org.mockito.Mockito.mock;
+
+import org.apache.spark.HashPartitioner;
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
+import org.apache.spark.unsafe.memory.MemoryAllocator;
+import org.apache.spark.unsafe.memory.MemoryBlock;
+import org.apache.spark.unsafe.memory.TaskMemoryManager;
+
+public class UnsafeInMemorySorterSuite {
+
+  private static String getStringFromDataPage(Object baseObject, long baseOffset, int length) {
+    final byte[] strBytes = new byte[length];
+    PlatformDependent.copyMemory(
+      baseObject,
+      baseOffset,
+      strBytes,
+      PlatformDependent.BYTE_ARRAY_OFFSET, length);
+    return new String(strBytes);
+  }
+
+  @Test
+  public void testSortingEmptyInput() {
+    final UnsafeInMemorySorter sorter = new UnsafeInMemorySorter(
+      new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)),
+      mock(RecordComparator.class),
+      mock(PrefixComparator.class),
+      100);
+    final UnsafeSorterIterator iter = sorter.getSortedIterator();
+    assert(!iter.hasNext());
+  }
+
+  @Test
+  public void testSortingOnlyByIntegerPrefix() throws Exception {
+    final String[] dataToSort = new String[] {
+      "Boba",
+      "Pearls",
+      "Tapioca",
+      "Taho",
+      "Condensed Milk",
+      "Jasmine",
+      "Milk Tea",
+      "Lychee",
+      "Mango"
+    };
+    final TaskMemoryManager memoryManager =
+      new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP));
+    final MemoryBlock dataPage = memoryManager.allocatePage(2048);
+    final Object baseObject = dataPage.getBaseObject();
+    // Write the records into the data page:
+    long position = dataPage.getBaseOffset();
+    for (String str : dataToSort) {
+      final byte[] strBytes = str.getBytes("utf-8");
+      PlatformDependent.UNSAFE.putInt(baseObject, position, strBytes.length);
+      position += 4;
+      PlatformDependent.copyMemory(
+        strBytes,
+        PlatformDependent.BYTE_ARRAY_OFFSET,
+        baseObject,
+        position,
+        strBytes.length);
+      position += strBytes.length;
+    }
+    // Since the key fits within the 8-byte prefix, we don't need to do any record comparison, so
+    // use a dummy comparator
+    final RecordComparator recordComparator = new RecordComparator() {
+      @Override
+      public int compare(
+        Object leftBaseObject,
+        long leftBaseOffset,
+        Object rightBaseObject,
+        long rightBaseOffset) {
+        return 0;
+      }
+    };
+    // Compute key prefixes based on the records' partition ids
+    final HashPartitioner hashPartitioner = new HashPartitioner(4);
+    // Use integer comparison for comparing prefixes (which are partition ids, in this case)
+    final PrefixComparator prefixComparator = new PrefixComparator() {
+      @Override
+      public int compare(long prefix1, long prefix2) {
+        return (int) prefix1 - (int) prefix2;
+      }
+    };
+    UnsafeInMemorySorter sorter = new UnsafeInMemorySorter(memoryManager, recordComparator,
+      prefixComparator, dataToSort.length);
+    // Given a page of records, insert those records into the sorter one-by-one:
+    position = dataPage.getBaseOffset();
+    for (int i = 0; i < dataToSort.length; i++) {
+      // position now points to the start of a record (which holds its length).
+      final int recordLength = PlatformDependent.UNSAFE.getInt(baseObject, position);
+      final long address = memoryManager.encodePageNumberAndOffset(dataPage, position);
+      final String str = getStringFromDataPage(baseObject, position + 4, recordLength);
+      final int partitionId = hashPartitioner.getPartition(str);
+      sorter.insertRecord(address, partitionId);
+      position += 4 + recordLength;
+    }
+    final UnsafeSorterIterator iter = sorter.getSortedIterator();
+    int iterLength = 0;
+    long prevPrefix = -1;
+    Arrays.sort(dataToSort);
+    while (iter.hasNext()) {
+      iter.loadNext();
+      final String str =
+        getStringFromDataPage(iter.getBaseObject(), iter.getBaseOffset(), iter.getRecordLength());
+      final long keyPrefix = iter.getKeyPrefix();
+      assertThat(str, isIn(Arrays.asList(dataToSort)));
+      assertThat(keyPrefix, greaterThanOrEqualTo(prevPrefix));
+      prevPrefix = keyPrefix;
+      iterLength++;
+    }
+    assertEquals(dataToSort.length, iterLength);
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
new file mode 100644
index 0000000000000..dd505dfa7d758
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.collection.unsafe.sort
+
+import org.scalatest.prop.PropertyChecks
+
+import org.apache.spark.SparkFunSuite
+
+class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
+
+  test("String prefix comparator") {
+
+    def testPrefixComparison(s1: String, s2: String): Unit = {
+      val s1Prefix = PrefixComparators.STRING.computePrefix(s1)
+      val s2Prefix = PrefixComparators.STRING.computePrefix(s2)
+      val prefixComparisonResult = PrefixComparators.STRING.compare(s1Prefix, s2Prefix)
+      assert(
+        (prefixComparisonResult == 0) ||
+        (prefixComparisonResult < 0 && s1 < s2) ||
+        (prefixComparisonResult > 0 && s1 > s2))
+    }
+
+    // scalastyle:off
+    val regressionTests = Table(
+      ("s1", "s2"),
+      ("abc", "世界"),
+      ("你好", "世界"),
+      ("你好123", "你好122")
+    )
+    // scalastyle:on
+
+    forAll (regressionTests) { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
+    forAll { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
+  }
+}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index edb7202245289..4b99030d1046f 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -61,9 +61,10 @@ public final class UnsafeRow extends MutableRow {
   /** A pool to hold non-primitive objects */
   private ObjectPool pool;
 
-  Object getBaseObject() { return baseObject; }
-  long getBaseOffset() { return baseOffset; }
-  ObjectPool getPool() { return pool; }
+  public Object getBaseObject() { return baseObject; }
+  public long getBaseOffset() { return baseOffset; }
+  public int getSizeInBytes() { return sizeInBytes; }
+  public ObjectPool getPool() { return pool; }
 
   /** The number of fields in this row, used for calculating the bitset width (and in assertions) */
   private int numFields;
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
new file mode 100644
index 0000000000000..b94601cf6d818
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -0,0 +1,216 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution;
+
+import java.io.IOException;
+
+import scala.collection.Iterator;
+import scala.math.Ordering;
+
+import com.google.common.annotations.VisibleForTesting;
+
+import org.apache.spark.SparkEnv;
+import org.apache.spark.TaskContext;
+import org.apache.spark.sql.AbstractScalaRowIterator;
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.catalyst.expressions.ObjectUnsafeColumnWriter;
+import org.apache.spark.sql.catalyst.expressions.UnsafeColumnWriter;
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
+import org.apache.spark.sql.catalyst.expressions.UnsafeRowConverter;
+import org.apache.spark.sql.catalyst.util.ObjectPool;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
+import org.apache.spark.util.collection.unsafe.sort.RecordComparator;
+import org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter;
+import org.apache.spark.util.collection.unsafe.sort.UnsafeSorterIterator;
+
+final class UnsafeExternalRowSorter {
+
+  /**
+   * If positive, forces records to be spilled to disk at the given frequency (measured in numbers
+   * of records). This is only intended to be used in tests.
+   */
+  private int testSpillFrequency = 0;
+
+  private long numRowsInserted = 0;
+
+  private final StructType schema;
+  private final UnsafeRowConverter rowConverter;
+  private final PrefixComputer prefixComputer;
+  private final UnsafeExternalSorter sorter;
+  private byte[] rowConversionBuffer = new byte[1024 * 8];
+
+  public static abstract class PrefixComputer {
+    abstract long computePrefix(InternalRow row);
+  }
+
+  public UnsafeExternalRowSorter(
+      StructType schema,
+      Ordering<InternalRow> ordering,
+      PrefixComparator prefixComparator,
+      PrefixComputer prefixComputer) throws IOException {
+    this.schema = schema;
+    this.rowConverter = new UnsafeRowConverter(schema);
+    this.prefixComputer = prefixComputer;
+    final SparkEnv sparkEnv = SparkEnv.get();
+    final TaskContext taskContext = TaskContext.get();
+    sorter = new UnsafeExternalSorter(
+      taskContext.taskMemoryManager(),
+      sparkEnv.shuffleMemoryManager(),
+      sparkEnv.blockManager(),
+      taskContext,
+      new RowComparator(ordering, schema.length(), null),
+      prefixComparator,
+      4096,
+      sparkEnv.conf()
+    );
+  }
+
+  /**
+   * Forces spills to occur every `frequency` records. Only for use in tests.
+   */
+  @VisibleForTesting
+  void setTestSpillFrequency(int frequency) {
+    assert frequency > 0 : "Frequency must be positive";
+    testSpillFrequency = frequency;
+  }
+
+  @VisibleForTesting
+  void insertRow(InternalRow row) throws IOException {
+    final int sizeRequirement = rowConverter.getSizeRequirement(row);
+    if (sizeRequirement > rowConversionBuffer.length) {
+      rowConversionBuffer = new byte[sizeRequirement];
+    }
+    final int bytesWritten = rowConverter.writeRow(
+      row, rowConversionBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, sizeRequirement, null);
+    assert (bytesWritten == sizeRequirement);
+    final long prefix = prefixComputer.computePrefix(row);
+    sorter.insertRecord(
+      rowConversionBuffer,
+      PlatformDependent.BYTE_ARRAY_OFFSET,
+      sizeRequirement,
+      prefix
+    );
+    numRowsInserted++;
+    if (testSpillFrequency > 0 && (numRowsInserted % testSpillFrequency) == 0) {
+      spill();
+    }
+  }
+
+  @VisibleForTesting
+  void spill() throws IOException {
+    sorter.spill();
+  }
+
+  private void cleanupResources() {
+    sorter.freeMemory();
+  }
+
+  @VisibleForTesting
+  Iterator<InternalRow> sort() throws IOException {
+    try {
+      final UnsafeSorterIterator sortedIterator = sorter.getSortedIterator();
+      if (!sortedIterator.hasNext()) {
+        // Since we won't ever call next() on an empty iterator, we need to clean up resources
+        // here in order to prevent memory leaks.
+        cleanupResources();
+      }
+      return new AbstractScalaRowIterator() {
+
+        private final int numFields = schema.length();
+        private final UnsafeRow row = new UnsafeRow();
+
+        @Override
+        public boolean hasNext() {
+          return sortedIterator.hasNext();
+        }
+
+        @Override
+        public InternalRow next() {
+          try {
+            sortedIterator.loadNext();
+            row.pointTo(
+              sortedIterator.getBaseObject(),
+              sortedIterator.getBaseOffset(),
+              numFields,
+              sortedIterator.getRecordLength(),
+              null);
+            if (!hasNext()) {
+              row.copy(); // so that we don't have dangling pointers to freed page
+              cleanupResources();
+            }
+            return row;
+          } catch (IOException e) {
+            cleanupResources();
+            // Scala iterators don't declare any checked exceptions, so we need to use this hack
+            // to re-throw the exception:
+            PlatformDependent.throwException(e);
+          }
+          throw new RuntimeException("Exception should have been re-thrown in next()");
+        };
+      };
+    } catch (IOException e) {
+      cleanupResources();
+      throw e;
+    }
+  }
+
+
+  public Iterator<InternalRow> sort(Iterator<InternalRow> inputIterator) throws IOException {
+      while (inputIterator.hasNext()) {
+        insertRow(inputIterator.next());
+      }
+      return sort();
+  }
+
+  /**
+   * Return true if UnsafeExternalRowSorter can sort rows with the given schema, false otherwise.
+   */
+  public static boolean supportsSchema(StructType schema) {
+    // TODO: add spilling note to explain why we do this for now:
+    for (StructField field : schema.fields()) {
+      if (UnsafeColumnWriter.forType(field.dataType()) instanceof ObjectUnsafeColumnWriter) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  private static final class RowComparator extends RecordComparator {
+    private final Ordering<InternalRow> ordering;
+    private final int numFields;
+    private final ObjectPool objPool;
+    private final UnsafeRow row1 = new UnsafeRow();
+    private final UnsafeRow row2 = new UnsafeRow();
+
+    public RowComparator(Ordering<InternalRow> ordering, int numFields, ObjectPool objPool) {
+      this.numFields = numFields;
+      this.ordering = ordering;
+      this.objPool = objPool;
+    }
+
+    @Override
+    public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff2) {
+      row1.pointTo(baseObj1, baseOff1, numFields, -1, objPool);
+      row2.pointTo(baseObj2, baseOff2, numFields, -1, objPool);
+      return ordering.compare(row1, row2);
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala
new file mode 100644
index 0000000000000..cfefb13e7721e
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.catalyst.InternalRow
+
+/**
+ * Shim to allow us to implement [[scala.Iterator]] in Java. Scala 2.11+ has an AbstractIterator
+ * class for this, but that class is `private[scala]` in 2.10. We need to explicitly fix this to
+ * `Row` in order to work around a spurious IntelliJ compiler error.
+ */
+private[spark] abstract class AbstractScalaRowIterator extends Iterator[InternalRow]
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 74d933404551c..4b783e30d95e1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -289,11 +289,8 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
         }
 
         val withSort = if (needSort) {
-          if (sqlContext.conf.externalSortEnabled) {
-            ExternalSort(rowOrdering, global = false, withShuffle)
-          } else {
-            Sort(rowOrdering, global = false, withShuffle)
-          }
+          sqlContext.planner.BasicOperators.getSortOperator(
+            rowOrdering, global = false, withShuffle)
         } else {
           withShuffle
         }
@@ -321,11 +318,7 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
           case (UnspecifiedDistribution, Seq(), child) =>
             child
           case (UnspecifiedDistribution, rowOrdering, child) =>
-            if (sqlContext.conf.externalSortEnabled) {
-              ExternalSort(rowOrdering, global = false, child)
-            } else {
-              Sort(rowOrdering, global = false, child)
-            }
+            sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
 
           case (dist, ordering, _) =>
             sys.error(s"Don't know how to ensure $dist with ordering $ordering")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
new file mode 100644
index 0000000000000..2dee3542d6101
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.SortOrder
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.util.collection.unsafe.sort.{PrefixComparators, PrefixComparator}
+
+
+object SortPrefixUtils {
+
+  /**
+   * A dummy prefix comparator which always claims that prefixes are equal. This is used in cases
+   * where we don't know how to generate or compare prefixes for a SortOrder.
+   */
+  private object NoOpPrefixComparator extends PrefixComparator {
+    override def compare(prefix1: Long, prefix2: Long): Int = 0
+  }
+
+  def getPrefixComparator(sortOrder: SortOrder): PrefixComparator = {
+    sortOrder.dataType match {
+      case StringType => PrefixComparators.STRING
+      case BooleanType | ByteType | ShortType | IntegerType | LongType => PrefixComparators.INTEGRAL
+      case FloatType => PrefixComparators.FLOAT
+      case DoubleType => PrefixComparators.DOUBLE
+      case _ => NoOpPrefixComparator
+    }
+  }
+
+  def getPrefixComputer(sortOrder: SortOrder): InternalRow => Long = {
+    sortOrder.dataType match {
+      case StringType => (row: InternalRow) => {
+        PrefixComparators.STRING.computePrefix(sortOrder.child.eval(row).asInstanceOf[UTF8String])
+      }
+      case BooleanType =>
+        (row: InternalRow) => {
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else if (sortOrder.child.eval(row).asInstanceOf[Boolean]) 1
+          else 0
+        }
+      case ByteType =>
+        (row: InternalRow) => {
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Byte]
+        }
+      case ShortType =>
+        (row: InternalRow) => {
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Short]
+        }
+      case IntegerType =>
+        (row: InternalRow) => {
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Int]
+        }
+      case LongType =>
+        (row: InternalRow) => {
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Long]
+        }
+      case FloatType => (row: InternalRow) => {
+        val exprVal = sortOrder.child.eval(row)
+        if (exprVal == null) PrefixComparators.FLOAT.NULL_PREFIX
+        else PrefixComparators.FLOAT.computePrefix(sortOrder.child.eval(row).asInstanceOf[Float])
+      }
+      case DoubleType => (row: InternalRow) => {
+        val exprVal = sortOrder.child.eval(row)
+        if (exprVal == null) PrefixComparators.DOUBLE.NULL_PREFIX
+        else PrefixComparators.DOUBLE.computePrefix(sortOrder.child.eval(row).asInstanceOf[Double])
+      }
+      case _ => (row: InternalRow) => 0L
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 59b9b553a7ae5..ce25af58b6cab 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -302,6 +302,22 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   object BasicOperators extends Strategy {
     def numPartitions: Int = self.numPartitions
 
+    /**
+     * Picks an appropriate sort operator.
+     *
+     * @param global when true performs a global sort of all partitions by shuffling the data first
+     *               if necessary.
+     */
+    def getSortOperator(sortExprs: Seq[SortOrder], global: Boolean, child: SparkPlan): SparkPlan = {
+      if (sqlContext.conf.unsafeEnabled && UnsafeExternalSort.supportsSchema(child.schema)) {
+        execution.UnsafeExternalSort(sortExprs, global, child)
+      } else if (sqlContext.conf.externalSortEnabled) {
+        execution.ExternalSort(sortExprs, global, child)
+      } else {
+        execution.Sort(sortExprs, global, child)
+      }
+    }
+
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case r: RunnableCommand => ExecutedCommand(r) :: Nil
 
@@ -313,11 +329,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.SortPartitions(sortExprs, child) =>
         // This sort only sorts tuples within a partition. Its requiredDistribution will be
         // an UnspecifiedDistribution.
-        execution.Sort(sortExprs, global = false, planLater(child)) :: Nil
-      case logical.Sort(sortExprs, global, child) if sqlContext.conf.externalSortEnabled =>
-        execution.ExternalSort(sortExprs, global, planLater(child)):: Nil
+        getSortOperator(sortExprs, global = false, planLater(child)) :: Nil
       case logical.Sort(sortExprs, global, child) =>
-        execution.Sort(sortExprs, global, planLater(child)):: Nil
+        getSortOperator(sortExprs, global, planLater(child)):: Nil
       case logical.Project(projectList, child) =>
         execution.Project(projectList, planLater(child)) :: Nil
       case logical.Filter(condition, child) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index de14e6ad79ad6..4c063c299ba53 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.sql.types.StructType
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, ShuffledRDD}
 import org.apache.spark.shuffle.sort.SortShuffleManager
@@ -27,6 +28,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.util.collection.ExternalSorter
+import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
 import org.apache.spark.util.{CompletionIterator, MutablePair}
 import org.apache.spark.{HashPartitioner, SparkEnv}
 
@@ -246,6 +248,77 @@ case class ExternalSort(
   override def outputOrdering: Seq[SortOrder] = sortOrder
 }
 
+/**
+ * :: DeveloperApi ::
+ * Optimized version of [[ExternalSort]] that operates on binary data (implemented as part of
+ * Project Tungsten).
+ *
+ * @param global when true performs a global sort of all partitions by shuffling the data first
+ *               if necessary.
+ * @param testSpillFrequency Method for configuring periodic spilling in unit tests. If set, will
+ *                           spill every `frequency` records.
+ */
+@DeveloperApi
+case class UnsafeExternalSort(
+    sortOrder: Seq[SortOrder],
+    global: Boolean,
+    child: SparkPlan,
+    testSpillFrequency: Int = 0)
+  extends UnaryNode {
+
+  private[this] val schema: StructType = child.schema
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
+    assert(codegenEnabled, "UnsafeExternalSort requires code generation to be enabled")
+    def doSort(iterator: Iterator[InternalRow]): Iterator[InternalRow] = {
+      val ordering = newOrdering(sortOrder, child.output)
+      val boundSortExpression = BindReferences.bindReference(sortOrder.head, child.output)
+      // Hack until we generate separate comparator implementations for ascending vs. descending
+      // (or choose to codegen them):
+      val prefixComparator = {
+        val comp = SortPrefixUtils.getPrefixComparator(boundSortExpression)
+        if (sortOrder.head.direction == Descending) {
+          new PrefixComparator {
+            override def compare(p1: Long, p2: Long): Int = -1 * comp.compare(p1, p2)
+          }
+        } else {
+          comp
+        }
+      }
+      val prefixComputer = {
+        val prefixComputer = SortPrefixUtils.getPrefixComputer(boundSortExpression)
+        new UnsafeExternalRowSorter.PrefixComputer {
+          override def computePrefix(row: InternalRow): Long = prefixComputer(row)
+        }
+      }
+      val sorter = new UnsafeExternalRowSorter(schema, ordering, prefixComparator, prefixComputer)
+      if (testSpillFrequency > 0) {
+        sorter.setTestSpillFrequency(testSpillFrequency)
+      }
+      sorter.sort(iterator)
+    }
+    child.execute().mapPartitions(doSort, preservesPartitioning = true)
+  }
+
+  override def output: Seq[Attribute] = child.output
+
+  override def outputOrdering: Seq[SortOrder] = sortOrder
+}
+
+@DeveloperApi
+object UnsafeExternalSort {
+  /**
+   * Return true if UnsafeExternalSort can sort rows with the given schema, false otherwise.
+   */
+  def supportsSchema(schema: StructType): Boolean = {
+    UnsafeExternalRowSorter.supportsSchema(schema)
+  }
+}
+
+
 /**
  * :: DeveloperApi ::
  * Return a new RDD that has exactly `numPartitions` partitions.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
index a1e3ca11b1ad9..a2c10fdaf6cdb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.dsl.expressions._
 
 class SortSuite extends SparkPlanTest {
@@ -33,12 +34,14 @@ class SortSuite extends SparkPlanTest {
 
     checkAnswer(
       input.toDF("a", "b", "c"),
-      ExternalSort('a.asc :: 'b.asc :: Nil, global = false, _: SparkPlan),
-      input.sorted)
+      ExternalSort('a.asc :: 'b.asc :: Nil, global = true, _: SparkPlan),
+      input.sortBy(t => (t._1, t._2)).map(Row.fromTuple),
+      sortAnswers = false)
 
     checkAnswer(
       input.toDF("a", "b", "c"),
-      ExternalSort('b.asc :: 'a.asc :: Nil, global = false, _: SparkPlan),
-      input.sortBy(t => (t._2, t._1)))
+      ExternalSort('b.asc :: 'a.asc :: Nil, global = true, _: SparkPlan),
+      input.sortBy(t => (t._2, t._1)).map(Row.fromTuple),
+      sortAnswers = false)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index 108b1122f7bff..6a8f394545816 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -17,18 +17,15 @@
 
 package org.apache.spark.sql.execution
 
-import scala.language.implicitConversions
-import scala.reflect.runtime.universe.TypeTag
-import scala.util.control.NonFatal
-
 import org.apache.spark.SparkFunSuite
-
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.expressions.BoundReference
 import org.apache.spark.sql.catalyst.util._
-
 import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{DataFrameHolder, Row, DataFrame}
+import org.apache.spark.sql.{DataFrame, DataFrameHolder, Row}
+
+import scala.language.implicitConversions
+import scala.reflect.runtime.universe.TypeTag
+import scala.util.control.NonFatal
 
 /**
  * Base class for writing tests for individual physical operators. For an example of how this
@@ -49,12 +46,19 @@ class SparkPlanTest extends SparkFunSuite {
    * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
    *                     the physical operator that's being tested.
    * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
    */
   protected def checkAnswer(
       input: DataFrame,
       planFunction: SparkPlan => SparkPlan,
-      expectedAnswer: Seq[Row]): Unit = {
-    checkAnswer(input :: Nil, (plans: Seq[SparkPlan]) => planFunction(plans.head), expectedAnswer)
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean = true): Unit = {
+    doCheckAnswer(
+      input :: Nil,
+      (plans: Seq[SparkPlan]) => planFunction(plans.head),
+      expectedAnswer,
+      sortAnswers)
   }
 
   /**
@@ -64,86 +68,131 @@ class SparkPlanTest extends SparkFunSuite {
    * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
    *                     the physical operator that's being tested.
    * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
    */
-  protected def checkAnswer(
+  protected def checkAnswer2(
       left: DataFrame,
       right: DataFrame,
       planFunction: (SparkPlan, SparkPlan) => SparkPlan,
-      expectedAnswer: Seq[Row]): Unit = {
-    checkAnswer(left :: right :: Nil,
-      (plans: Seq[SparkPlan]) => planFunction(plans(0), plans(1)), expectedAnswer)
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean = true): Unit = {
+    doCheckAnswer(
+      left :: right :: Nil,
+      (plans: Seq[SparkPlan]) => planFunction(plans(0), plans(1)),
+      expectedAnswer,
+      sortAnswers)
   }
 
   /**
    * Runs the plan and makes sure the answer matches the expected result.
    * @param input the input data to be used.
-   * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
-   *                     the physical operator that's being tested.
+   * @param planFunction a function which accepts a sequence of input SparkPlans and uses them to
+   *                     instantiate the physical operator that's being tested.
    * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
    */
-  protected def checkAnswer(
+  protected def doCheckAnswer(
       input: Seq[DataFrame],
       planFunction: Seq[SparkPlan] => SparkPlan,
-      expectedAnswer: Seq[Row]): Unit = {
-    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer) match {
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean = true): Unit = {
+    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
   }
 
   /**
-   * Runs the plan and makes sure the answer matches the expected result.
+   * Runs the plan and makes sure the answer matches the result produced by a reference plan.
    * @param input the input data to be used.
    * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
    *                     the physical operator that's being tested.
-   * @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s.
+   * @param expectedPlanFunction a function which accepts the input SparkPlan and uses it to
+   *                             instantiate a reference implementation of the physical operator
+   *                             that's being tested. The result of executing this plan will be
+   *                             treated as the source-of-truth for the test.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
    */
-  protected def checkAnswer[A <: Product : TypeTag](
+  protected def checkThatPlansAgree(
       input: DataFrame,
       planFunction: SparkPlan => SparkPlan,
-      expectedAnswer: Seq[A]): Unit = {
-    val expectedRows = expectedAnswer.map(Row.fromTuple)
-    checkAnswer(input, planFunction, expectedRows)
+      expectedPlanFunction: SparkPlan => SparkPlan,
+      sortAnswers: Boolean = true): Unit = {
+    SparkPlanTest.checkAnswer(input, planFunction, expectedPlanFunction, sortAnswers) match {
+      case Some(errorMessage) => fail(errorMessage)
+      case None =>
+    }
   }
+}
 
-  /**
-   * Runs the plan and makes sure the answer matches the expected result.
-   * @param left the left input data to be used.
-   * @param right the right input data to be used.
-   * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
-   *                     the physical operator that's being tested.
-   * @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s.
-   */
-  protected def checkAnswer[A <: Product : TypeTag](
-      left: DataFrame,
-      right: DataFrame,
-      planFunction: (SparkPlan, SparkPlan) => SparkPlan,
-      expectedAnswer: Seq[A]): Unit = {
-    val expectedRows = expectedAnswer.map(Row.fromTuple)
-    checkAnswer(left, right, planFunction, expectedRows)
-  }
+/**
+ * Helper methods for writing tests of individual physical operators.
+ */
+object SparkPlanTest {
 
   /**
-   * Runs the plan and makes sure the answer matches the expected result.
+   * Runs the plan and makes sure the answer matches the result produced by a reference plan.
    * @param input the input data to be used.
    * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
    *                     the physical operator that's being tested.
-   * @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s.
+   * @param expectedPlanFunction a function which accepts the input SparkPlan and uses it to
+   *                             instantiate a reference implementation of the physical operator
+   *                             that's being tested. The result of executing this plan will be
+   *                             treated as the source-of-truth for the test.
    */
-  protected def checkAnswer[A <: Product : TypeTag](
-      input: Seq[DataFrame],
-      planFunction: Seq[SparkPlan] => SparkPlan,
-      expectedAnswer: Seq[A]): Unit = {
-    val expectedRows = expectedAnswer.map(Row.fromTuple)
-    checkAnswer(input, planFunction, expectedRows)
-  }
+  def checkAnswer(
+      input: DataFrame,
+      planFunction: SparkPlan => SparkPlan,
+      expectedPlanFunction: SparkPlan => SparkPlan,
+      sortAnswers: Boolean): Option[String] = {
 
-}
+    val outputPlan = planFunction(input.queryExecution.sparkPlan)
+    val expectedOutputPlan = expectedPlanFunction(input.queryExecution.sparkPlan)
 
-/**
- * Helper methods for writing tests of individual physical operators.
- */
-object SparkPlanTest {
+    val expectedAnswer: Seq[Row] = try {
+      executePlan(expectedOutputPlan)
+    } catch {
+      case NonFatal(e) =>
+        val errorMessage =
+          s"""
+             | Exception thrown while executing Spark plan to calculate expected answer:
+             | $expectedOutputPlan
+             | == Exception ==
+             | $e
+             | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
+          """.stripMargin
+        return Some(errorMessage)
+    }
+
+    val actualAnswer: Seq[Row] = try {
+      executePlan(outputPlan)
+    } catch {
+      case NonFatal(e) =>
+        val errorMessage =
+          s"""
+             | Exception thrown while executing Spark plan:
+             | $outputPlan
+             | == Exception ==
+             | $e
+             | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
+          """.stripMargin
+        return Some(errorMessage)
+    }
+
+    compareAnswers(actualAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
+      s"""
+         | Results do not match.
+         | Actual result Spark plan:
+         | $outputPlan
+         | Expected result Spark plan:
+         | $expectedOutputPlan
+         | $errorMessage
+       """.stripMargin
+    }
+  }
 
   /**
    * Runs the plan and makes sure the answer matches the expected result.
@@ -151,28 +200,45 @@ object SparkPlanTest {
    * @param planFunction a function which accepts the input SparkPlan and uses it to instantiate
    *                     the physical operator that's being tested.
    * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
    */
   def checkAnswer(
       input: Seq[DataFrame],
       planFunction: Seq[SparkPlan] => SparkPlan,
-      expectedAnswer: Seq[Row]): Option[String] = {
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean): Option[String] = {
 
     val outputPlan = planFunction(input.map(_.queryExecution.sparkPlan))
 
-    // A very simple resolver to make writing tests easier. In contrast to the real resolver
-    // this is always case sensitive and does not try to handle scoping or complex type resolution.
-    val resolvedPlan = TestSQLContext.prepareForExecution.execute(
-      outputPlan transform {
-        case plan: SparkPlan =>
-          val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
-          plan.transformExpressions {
-            case UnresolvedAttribute(Seq(u)) =>
-              inputMap.getOrElse(u,
-                sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap"))
-          }
-      }
-    )
+    val sparkAnswer: Seq[Row] = try {
+      executePlan(outputPlan)
+    } catch {
+      case NonFatal(e) =>
+        val errorMessage =
+          s"""
+             | Exception thrown while executing Spark plan:
+             | $outputPlan
+             | == Exception ==
+             | $e
+             | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
+          """.stripMargin
+        return Some(errorMessage)
+    }
 
+    compareAnswers(sparkAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
+      s"""
+         | Results do not match for Spark plan:
+         | $outputPlan
+         | $errorMessage
+       """.stripMargin
+    }
+  }
+
+  private def compareAnswers(
+      sparkAnswer: Seq[Row],
+      expectedAnswer: Seq[Row],
+      sort: Boolean): Option[String] = {
     def prepareAnswer(answer: Seq[Row]): Seq[Row] = {
       // Converts data to types that we can do equality comparison using Scala collections.
       // For BigDecimal type, the Scala type has a better definition of equality test (similar to
@@ -187,40 +253,43 @@ object SparkPlanTest {
           case o => o
         })
       }
-      converted.sortBy(_.toString())
-    }
-
-    val sparkAnswer: Seq[Row] = try {
-      resolvedPlan.executeCollect().toSeq
-    } catch {
-      case NonFatal(e) =>
-        val errorMessage =
-          s"""
-             | Exception thrown while executing Spark plan:
-             | $outputPlan
-             | == Exception ==
-             | $e
-             | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
-          """.stripMargin
-        return Some(errorMessage)
+      if (sort) {
+        converted.sortBy(_.toString())
+      } else {
+        converted
+      }
     }
-
     if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) {
       val errorMessage =
         s"""
-           | Results do not match for Spark plan:
-           | $outputPlan
            | == Results ==
            | ${sideBySide(
-              s"== Correct Answer - ${expectedAnswer.size} ==" +:
+              s"== Expected Answer - ${expectedAnswer.size} ==" +:
               prepareAnswer(expectedAnswer).map(_.toString()),
-              s"== Spark Answer - ${sparkAnswer.size} ==" +:
+              s"== Actual Answer - ${sparkAnswer.size} ==" +:
               prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")}
       """.stripMargin
-      return Some(errorMessage)
+      Some(errorMessage)
+    } else {
+      None
     }
+  }
 
-    None
+  private def executePlan(outputPlan: SparkPlan): Seq[Row] = {
+    // A very simple resolver to make writing tests easier. In contrast to the real resolver
+    // this is always case sensitive and does not try to handle scoping or complex type resolution.
+    val resolvedPlan = TestSQLContext.prepareForExecution.execute(
+      outputPlan transform {
+        case plan: SparkPlan =>
+          val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
+          plan.transformExpressions {
+            case UnresolvedAttribute(Seq(u)) =>
+              inputMap.getOrElse(u,
+                sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap"))
+          }
+      }
+    )
+    resolvedPlan.executeCollect().toSeq
   }
 }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
new file mode 100644
index 0000000000000..4f4c1f28564cb
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
@@ -0,0 +1,104 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.sql.{RandomDataGenerator, Row, SQLConf}
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.types._
+
+class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
+
+  override def beforeAll(): Unit = {
+    TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
+  }
+
+  override def afterAll(): Unit = {
+    TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
+  }
+
+  ignore("sort followed by limit should not leak memory") {
+    // TODO: this test is going to fail until we implement a proper iterator interface
+    // with a close() method.
+    TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "true")
+    checkThatPlansAgree(
+      (1 to 100).map(v => Tuple1(v)).toDF("a"),
+      (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
+      (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
+      sortAnswers = false
+    )
+  }
+
+  test("sort followed by limit") {
+    TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
+    try {
+      checkThatPlansAgree(
+        (1 to 100).map(v => Tuple1(v)).toDF("a"),
+        (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
+        (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
+        sortAnswers = false
+      )
+    } finally {
+      TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "true")
+
+    }
+  }
+
+  test("sorting does not crash for large inputs") {
+    val sortOrder = 'a.asc :: Nil
+    val stringLength = 1024 * 1024 * 2
+    checkThatPlansAgree(
+      Seq(Tuple1("a" * stringLength), Tuple1("b" * stringLength)).toDF("a").repartition(1),
+      UnsafeExternalSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 1),
+      Sort(sortOrder, global = true, _: SparkPlan),
+      sortAnswers = false
+    )
+  }
+
+  // Test sorting on different data types
+  for (
+    dataType <- DataTypeTestUtils.atomicTypes ++ Set(NullType)
+    if !dataType.isInstanceOf[DecimalType]; // We don't have an unsafe representation for decimals
+    nullable <- Seq(true, false);
+    sortOrder <- Seq('a.asc :: Nil, 'a.desc :: Nil);
+    randomDataGenerator <- RandomDataGenerator.forType(dataType, nullable)
+  ) {
+    test(s"sorting on $dataType with nullable=$nullable, sortOrder=$sortOrder") {
+      val inputData = Seq.fill(1000)(randomDataGenerator()).filter {
+        case d: Double => !d.isNaN
+        case f: Float => !java.lang.Float.isNaN(f)
+        case x => true
+      }
+      val inputDf = TestSQLContext.createDataFrame(
+        TestSQLContext.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
+        StructType(StructField("a", dataType, nullable = true) :: Nil)
+      )
+      assert(UnsafeExternalSort.supportsSchema(inputDf.schema))
+      checkThatPlansAgree(
+        inputDf,
+        UnsafeExternalSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 23),
+        Sort(sortOrder, global = true, _: SparkPlan),
+        sortAnswers = false
+      )
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index 5707d2fb300ae..2c27da596bc4f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.{Expression, LessThan}
 import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, RightOuter}
@@ -41,23 +42,23 @@ class OuterJoinSuite extends SparkPlanTest {
   val condition = Some(LessThan('b, 'd))
 
   test("shuffled hash outer join") {
-    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
       ShuffledHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
       Seq(
         (1, 2.0, null, null),
         (2, 1.0, 2, 3.0),
         (3, 3.0, null, null)
-      ))
+      ).map(Row.fromTuple))
 
-    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
       ShuffledHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
       Seq(
         (2, 1.0, 2, 3.0),
         (null, null, 3, 2.0),
         (null, null, 4, 1.0)
-      ))
+      ).map(Row.fromTuple))
 
-    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
       ShuffledHashOuterJoin(leftKeys, rightKeys, FullOuter, condition, left, right),
       Seq(
         (1, 2.0, null, null),
@@ -65,24 +66,24 @@ class OuterJoinSuite extends SparkPlanTest {
         (3, 3.0, null, null),
         (null, null, 3, 2.0),
         (null, null, 4, 1.0)
-      ))
+      ).map(Row.fromTuple))
   }
 
   test("broadcast hash outer join") {
-    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
       BroadcastHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
       Seq(
         (1, 2.0, null, null),
         (2, 1.0, 2, 3.0),
         (3, 3.0, null, null)
-      ))
+      ).map(Row.fromTuple))
 
-    checkAnswer(left, right, (left: SparkPlan, right: SparkPlan) =>
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
       BroadcastHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
       Seq(
         (2, 1.0, 2, 3.0),
         (null, null, 3, 2.0),
         (null, null, 4, 1.0)
-      ))
+      ).map(Row.fromTuple))
   }
 }

From 857e325f304a02b1cab2463c1e15af7627a600bd Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Fri, 10 Jul 2015 16:49:45 -0700
Subject: [PATCH 0340/1454] [SPARK-8990] [SQL] SPARK-8990
 DataFrameReader.parquet() should respect user specified options

Author: Cheng Lian <lian@databricks.com>

Closes #7347 from liancheng/spark-8990 and squashes the following commits:

045698c [Cheng Lian] SPARK-8990 DataFrameReader.parquet() should respect user specified options
---
 .../apache/spark/sql/DataFrameReader.scala    |  2 +-
 .../spark/sql/parquet/ParquetQuerySuite.scala | 21 +++++++++++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 1828ed1aab50b..9ad6e21da7bf7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -263,7 +263,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) {
       val globbedPaths = paths.map(new Path(_)).flatMap(SparkHadoopUtil.get.globPath).toArray
       sqlContext.baseRelationToDataFrame(
         new ParquetRelation2(
-          globbedPaths.map(_.toString), None, None, Map.empty[String, String])(sqlContext))
+          globbedPaths.map(_.toString), None, None, extraOptions.toMap)(sqlContext))
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index a0a81c4309c0f..21007d95ed752 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -142,6 +142,27 @@ class ParquetQuerySuiteBase extends QueryTest with ParquetTest {
       testSchemaMerging(2)
     }
   }
+
+  test("SPARK-8990 DataFrameReader.parquet() should respect user specified options") {
+    withTempPath { dir =>
+      val basePath = dir.getCanonicalPath
+      sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+      sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
+
+      // Disables the global SQL option for schema merging
+      withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "false") {
+        assertResult(2) {
+          // Disables schema merging via data source option
+          sqlContext.read.option("mergeSchema", "false").parquet(basePath).columns.length
+        }
+
+        assertResult(3) {
+          // Enables schema merging via data source option
+          sqlContext.read.option("mergeSchema", "true").parquet(basePath).columns.length
+        }
+      }
+    }
+  }
 }
 
 class ParquetDataSourceOnQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll {

From b6fc0adf6874fc26ab27cdaa8ebb28474c0681f0 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Fri, 10 Jul 2015 17:44:21 -0700
Subject: [PATCH 0341/1454] add inline comment for python tests

---
 python/pyspark/sql/tests.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index ed903334beafa..241eac45cfe36 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -710,6 +710,7 @@ def test_time_with_timezone(self):
         from pyspark.sql.tests import UTC
         utc = UTC()
         utcnow = datetime.datetime.utcfromtimestamp(ts)  # without microseconds
+        # add microseconds to utcnow (keeping year,month,day,hour,minute,second)
         utcnow = datetime.datetime(*(utcnow.timetuple()[:6] + (now.microsecond, utc)))
         df = self.sqlCtx.createDataFrame([(day, now, utcnow)])
         day1, now1, utcnow1 = df.first()

From 33630883685eafcc3ee4521ea8363be342f6e6b4 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Fri, 10 Jul 2015 18:15:36 -0700
Subject: [PATCH 0342/1454] [SPARK-8961] [SQL] Makes
 BaseWriterContainer.outputWriterForRow accepts InternalRow instead of Row

This is a follow-up of [SPARK-8888] [1], which also aims to optimize writing dynamic partitions.

Three more changes can be made here:

1. Using `InternalRow` instead of `Row` in `BaseWriterContainer.outputWriterForRow`
2. Using `Cast` expressions to convert partition columns to strings, so that we can leverage code generation.
3. Replacing the FP-style `zip` and `map` calls with a faster imperative `while` loop.

[1]: https://issues.apache.org/jira/browse/SPARK-8888

Author: Cheng Lian <lian@databricks.com>

Closes #7331 from liancheng/spark-8961 and squashes the following commits:

b5ab9ae [Cheng Lian] Casts Java iterator to Scala iterator explicitly
719e63b [Cheng Lian] Makes BaseWriterContainer.outputWriterForRow accepts InternalRow instead of Row
---
 .../apache/spark/sql/sources/commands.scala   | 73 +++++++++++--------
 1 file changed, 42 insertions(+), 31 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
index 9189d176111d5..5c6ef2dc90c73 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.sources
 
 import java.util.{Date, UUID}
 
+import scala.collection.JavaConversions.asScalaIterator
+
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
@@ -26,15 +28,14 @@ import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceF
 import org.apache.spark._
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.execution.RunnableCommand
-import org.apache.spark.sql.types.StructType
-import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext, SaveMode}
+import org.apache.spark.sql.types.StringType
 import org.apache.spark.util.SerializableConfiguration
 
 private[sql] case class InsertIntoDataSource(
@@ -170,14 +171,14 @@ private[sql] case class InsertIntoHadoopFsRelation(
       try {
         writerContainer.executorSideSetup(taskContext)
 
-        val converter = if (needsConversion) {
+        val converter: InternalRow => Row = if (needsConversion) {
           CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow => Row]
         } else {
           r: InternalRow => r.asInstanceOf[Row]
         }
         while (iterator.hasNext) {
-          val row = converter(iterator.next())
-          writerContainer.outputWriterForRow(row).write(row)
+          val internalRow = iterator.next()
+          writerContainer.outputWriterForRow(internalRow).write(converter(internalRow))
         }
 
         writerContainer.commitTask()
@@ -239,7 +240,9 @@ private[sql] case class InsertIntoHadoopFsRelation(
       try {
         writerContainer.executorSideSetup(taskContext)
 
-        val partitionProj = newProjection(codegenEnabled, partitionOutput, output)
+        // Projects all partition columns and casts them to strings to build partition directories.
+        val partitionCasts = partitionOutput.map(Cast(_, StringType))
+        val partitionProj = newProjection(codegenEnabled, partitionCasts, output)
         val dataProj = newProjection(codegenEnabled, dataOutput, output)
 
         val dataConverter: InternalRow => Row = if (needsConversion) {
@@ -247,15 +250,11 @@ private[sql] case class InsertIntoHadoopFsRelation(
         } else {
           r: InternalRow => r.asInstanceOf[Row]
         }
-        val partitionSchema = StructType.fromAttributes(partitionOutput)
-        val partConverter: InternalRow => Row =
-          CatalystTypeConverters.createToScalaConverter(partitionSchema)
-            .asInstanceOf[InternalRow => Row]
 
         while (iterator.hasNext) {
-          val row = iterator.next()
-          val partitionPart = partConverter(partitionProj(row))
-          val dataPart = dataConverter(dataProj(row))
+          val internalRow = iterator.next()
+          val partitionPart = partitionProj(internalRow)
+          val dataPart = dataConverter(dataProj(internalRow))
           writerContainer.outputWriterForRow(partitionPart).write(dataPart)
         }
 
@@ -435,7 +434,7 @@ private[sql] abstract class BaseWriterContainer(
   }
 
   // Called on executor side when writing rows
-  def outputWriterForRow(row: Row): OutputWriter
+  def outputWriterForRow(row: InternalRow): OutputWriter
 
   protected def initWriters(): Unit
 
@@ -477,7 +476,7 @@ private[sql] class DefaultWriterContainer(
     writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
   }
 
-  override def outputWriterForRow(row: Row): OutputWriter = writer
+  override def outputWriterForRow(row: InternalRow): OutputWriter = writer
 
   override def commitTask(): Unit = {
     try {
@@ -518,23 +517,36 @@ private[sql] class DynamicPartitionWriterContainer(
     outputWriters = new java.util.HashMap[String, OutputWriter]
   }
 
-  override def outputWriterForRow(row: Row): OutputWriter = {
-    // TODO (SPARK-8888): zip and all the stuff happening here is very inefficient.
-    val partitionPath = partitionColumns.zip(row.toSeq).map { case (col, rawValue) =>
-      val string = if (rawValue == null) null else String.valueOf(rawValue)
-      val valueString = if (string == null || string.isEmpty) {
-        defaultPartitionName
-      } else {
-        PartitioningUtils.escapePathName(string)
+  // The `row` argument is supposed to only contain partition column values which have been casted
+  // to strings.
+  override def outputWriterForRow(row: InternalRow): OutputWriter = {
+    val partitionPath = {
+      val partitionPathBuilder = new StringBuilder
+      var i = 0
+
+      while (i < partitionColumns.length) {
+        val col = partitionColumns(i)
+        val partitionValueString = {
+          val string = row.getString(i)
+          if (string.eq(null)) defaultPartitionName else PartitioningUtils.escapePathName(string)
+        }
+
+        if (i > 0) {
+          partitionPathBuilder.append(Path.SEPARATOR_CHAR)
+        }
+
+        partitionPathBuilder.append(s"$col=$partitionValueString")
+        i += 1
       }
-      s"/$col=$valueString"
-    }.mkString.stripPrefix(Path.SEPARATOR)
+
+      partitionPathBuilder.toString()
+    }
 
     val writer = outputWriters.get(partitionPath)
     if (writer.eq(null)) {
       val path = new Path(getWorkPath, partitionPath)
-      taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path",
-        new Path(outputPath, partitionPath).toString)
+      taskAttemptContext.getConfiguration.set(
+        "spark.sql.sources.output.path", new Path(outputPath, partitionPath).toString)
       val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
       outputWriters.put(partitionPath, newWriter)
       newWriter
@@ -545,8 +557,7 @@ private[sql] class DynamicPartitionWriterContainer(
 
   private def clearOutputWriters(): Unit = {
     if (!outputWriters.isEmpty) {
-      val iter = scala.collection.JavaConversions.asScalaIterator(outputWriters.values().iterator())
-      iter.foreach(_.close())
+      asScalaIterator(outputWriters.values().iterator()).foreach(_.close())
       outputWriters.clear()
     }
   }

From 6e1c7e2798bb0b7b120e28a07c5d70fa162dd724 Mon Sep 17 00:00:00 2001
From: Scott Taylor <github@megatron.me.uk>
Date: Fri, 10 Jul 2015 19:29:32 -0700
Subject: [PATCH 0343/1454] [SPARK-7735] [PYSPARK] Raise Exception on non-zero
 exit from pipe commands

This will allow problems with piped commands to be detected.
This will also allow tasks to be retried where errors are rare (such as network problems in piped commands).

Author: Scott Taylor <github@megatron.me.uk>

Closes #6262 from megatron-me-uk/patch-2 and squashes the following commits:

04ae1d5 [Scott Taylor] Remove spurious empty line
98fa101 [Scott Taylor] fix blank line style error
574b564 [Scott Taylor] Merge pull request #2 from megatron-me-uk/patch-4
0c1e762 [Scott Taylor] Update rdd pipe method for checkCode
ab9a2e1 [Scott Taylor] Update rdd pipe tests for checkCode
eb4801c [Scott Taylor] fix fail_condition
b0ac3a4 [Scott Taylor] Merge pull request #1 from megatron-me-uk/megatron-me-uk-patch-1
a307d13 [Scott Taylor] update rdd tests to test pipe modes
34fcdc3 [Scott Taylor] add optional argument 'mode' for rdd.pipe
a0c0161 [Scott Taylor] fix generator issue
8a9ef9c [Scott Taylor] make check_return_code an iterator
0486ae3 [Scott Taylor] style fixes
8ed89a6 [Scott Taylor] Chain generators to prevent potential deadlock
4153b02 [Scott Taylor] fix list.sort returns None
491d3fc [Scott Taylor] Pass a function handle to assertRaises
3344a21 [Scott Taylor] wrap assertRaises with QuietTest
3ab8c7a [Scott Taylor] remove whitespace for style
cc1a73d [Scott Taylor] fix style issues in pipe test
8db4073 [Scott Taylor] Add a test for rdd pipe functions
1b3dc4e [Scott Taylor] fix missing space around operator style
0974f98 [Scott Taylor] add space between words in multiline string
45f4977 [Scott Taylor] fix line too long style error
5745d85 [Scott Taylor] Remove space to fix style
f552d49 [Scott Taylor] Catch non-zero exit from pipe commands
---
 python/pyspark/rdd.py   | 16 ++++++++++++++--
 python/pyspark/tests.py | 12 ++++++++++++
 2 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 79dafb0a4ef27..3218bed5c74fc 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -700,12 +700,14 @@ def groupBy(self, f, numPartitions=None):
         return self.map(lambda x: (f(x), x)).groupByKey(numPartitions)
 
     @ignore_unicode_prefix
-    def pipe(self, command, env={}):
+    def pipe(self, command, env={}, checkCode=False):
         """
         Return an RDD created by piping elements to a forked external process.
 
         >>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
         [u'1', u'2', u'', u'3']
+
+        :param checkCode: whether or not to check the return value of the shell command.
         """
         def func(iterator):
             pipe = Popen(
@@ -717,7 +719,17 @@ def pipe_objs(out):
                     out.write(s.encode('utf-8'))
                 out.close()
             Thread(target=pipe_objs, args=[pipe.stdin]).start()
-            return (x.rstrip(b'\n').decode('utf-8') for x in iter(pipe.stdout.readline, b''))
+
+            def check_return_code():
+                pipe.wait()
+                if checkCode and pipe.returncode:
+                    raise Exception("Pipe function `%s' exited "
+                                    "with error code %d" % (command, pipe.returncode))
+                else:
+                    for i in range(0):
+                        yield i
+            return (x.rstrip(b'\n').decode('utf-8') for x in
+                    chain(iter(pipe.stdout.readline, b''), check_return_code()))
         return self.mapPartitions(func)
 
     def foreach(self, f):
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 17256dfc95744..c5c0add49d02c 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -885,6 +885,18 @@ def test_sortByKey_uses_all_partitions_not_only_first_and_last(self):
             for size in sizes:
                 self.assertGreater(size, 0)
 
+    def test_pipe_functions(self):
+        data = ['1', '2', '3']
+        rdd = self.sc.parallelize(data)
+        with QuietTest(self.sc):
+            self.assertEqual([], rdd.pipe('cc').collect())
+            self.assertRaises(Py4JJavaError, rdd.pipe('cc', checkCode=True).collect)
+        result = rdd.pipe('cat').collect()
+        result.sort()
+        [self.assertEqual(x, y) for x, y in zip(data, result)]
+        self.assertRaises(Py4JJavaError, rdd.pipe('grep 4', checkCode=True).collect)
+        self.assertEqual([], rdd.pipe('grep 4').collect())
+
 
 class ProfilerTests(PySparkTestCase):
 

From 9c5075775d741eacbeeb2df77ea30611356b6e1a Mon Sep 17 00:00:00 2001
From: "jose.cambronero" <jose.cambronero@cloudera.com>
Date: Fri, 10 Jul 2015 20:55:45 -0700
Subject: [PATCH 0344/1454] [SPARK-8598] [MLLIB] Implementation of 1-sample,
 two-sided, Kolmogorov Smirnov Test for RDDs

This contribution is my original work and I license it to the project under it's open source license.

Author: jose.cambronero <jose.cambronero@cloudera.com>

Closes #6994 from josepablocam/master and squashes the following commits:

bbb30b1 [jose.cambronero] renamed KSTestResult to KolmogorovSmirnovTestResult, to stay consistent with method name
0d0c201 [jose.cambronero] kstTest -> kolmogorovSmirnovTest in statistics.md
1f56371 [jose.cambronero] changed ksTest in public API to kolmogorovSmirnovTest for clarity
a48ae7b [jose.cambronero] refactor code to account for serializable RealDistribution. Reuse testOneSample( _, cdf)
1bb44bd [jose.cambronero]  style and doc changes. Factored out ks test into 2 separate tests
2ec2aa6 [jose.cambronero] initialize to stdnormal when no params passed (and log). Change unit tests to approximate equivalence rather than strict
a4bc0c7 [jose.cambronero] changed ksTest(data, distName) to ksTest(data, distName, params*) after api discussions. Changed tests and docs accordingly
7e66f57 [jose.cambronero] copied implementation note to public api docs, and added @see for links to wiki info
e760ebd [jose.cambronero] line length changes to fit style check
3288e42 [jose.cambronero] addressed style changes, correctness change to simpler approach, and fixed edge case for foldLeft in searchOneSampleCandidates when a partition is empty
9026895 [jose.cambronero] addressed style changes, correctness change to simpler approach, and fixed edge case for foldLeft in searchOneSampleCandidates when a partition is empty
1226b30 [jose.cambronero] reindent multi-line lambdas, prior intepretation of style guide was wrong on my part
9c0f1af [jose.cambronero] additional style changes incorporated and added documentation to mllib statistics docs
3f81ad2 [jose.cambronero] renamed ks1 sample test for clarity
992293b [jose.cambronero] Style changes as per comments and added implementation note explaining the distributed approach.
6a4784f [jose.cambronero] specified what distributions are available for the convenience method ksTest(data, name) (solely standard normal)
4b8ba61 [jose.cambronero] fixed off by 1/N in cases when post-constant adjustment ecdf is above cdf, but prior to adj it was below
0b5e8ec [jose.cambronero] changed KS one sample test to perform just 1 distributed pass (in addition to the sorting pass), operates on each partition separately. Implementation of Sandy Ryza's algorithm
16b5c4c [jose.cambronero] renamed dat to data and eliminated recalc of RDD size by sharing as argument between empirical and evalOneSampleP
c18dc66 [jose.cambronero] removed ksTestOpt from API and changed comments in HypothesisTestSuite accordingly
f6951b6 [jose.cambronero] changed style and some comments based on feedback from pull request
b9cff3a [jose.cambronero] made small changes to pass style check
ce8e9a1 [jose.cambronero] added kstest testing in HypothesisTestSuite
4da189b [jose.cambronero] added user facing ks test functions
c659ea1 [jose.cambronero] created KS test class
13dfe4d [jose.cambronero] created test result class for ks test
---
 docs/mllib-statistics.md                      |  37 +++-
 .../apache/spark/mllib/stat/Statistics.scala  |  40 +++-
 .../stat/test/KolmogorovSmirnovTest.scala     | 194 ++++++++++++++++++
 .../spark/mllib/stat/test/TestResult.scala    |  17 ++
 .../mllib/stat/HypothesisTestSuite.scala      | 101 +++++++++
 5 files changed, 387 insertions(+), 2 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala

diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index 887eae7f4f07b..de5d6485f9b5f 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -283,7 +283,7 @@ approxSample = data.sampleByKey(False, fractions);
 
 Hypothesis testing is a powerful tool in statistics to determine whether a result is statistically 
 significant, whether this result occurred by chance or not. MLlib currently supports Pearson's 
-chi-squared ( $\chi^2$) tests for goodness of fit and independence. The input data types determine 
+chi-squared ( $\chi^2$) tests for goodness of fit and independence. The input data types determine
 whether the goodness of fit or the independence test is conducted. The goodness of fit test requires 
 an input type of `Vector`, whereas the independence test requires a `Matrix` as input.
 
@@ -422,6 +422,41 @@ for i, result in enumerate(featureTestResults):
 
 </div>
 
+Additionally, MLlib provides a 1-sample, 2-sided implementation of the Kolmogorov-Smirnov (KS) test
+for equality of probability distributions. By providing the name of a theoretical distribution
+(currently solely supported for the normal distribution) and its parameters, or a function to 
+calculate the cumulative distribution according to a given theoretical distribution, the user can
+test the null hypothesis that their sample is drawn from that distribution. In the case that the
+user tests against the normal distribution (`distName="norm"`), but does not provide distribution
+parameters, the test initializes to the standard normal distribution and logs an appropriate 
+message.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[`Statistics`](api/scala/index.html#org.apache.spark.mllib.stat.Statistics$) provides methods to
+run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
+and interpret the hypothesis tests.
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.stat.Statistics._
+
+val data: RDD[Double] = ... // an RDD of sample data
+
+// run a KS test for the sample versus a standard normal distribution
+val testResult = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
+println(testResult) // summary of the test including the p-value, test statistic,
+                      // and null hypothesis
+                      // if our p-value indicates significance, we can reject the null hypothesis
+
+// perform a KS test using a cumulative distribution function of our making
+val myCDF: Double => Double = ...
+val testResult2 = Statistics.kolmogorovSmirnovTest(data, myCDF)
+{% endhighlight %}
+</div>
+</div>
+
+
 ## Random data generation
 
 Random data generation is useful for randomized algorithms, prototyping, and performance testing.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index 900007ec6bc74..90332028cfb3a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -17,13 +17,16 @@
 
 package org.apache.spark.mllib.stat
 
+import scala.annotation.varargs
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Matrix, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.stat.correlation.Correlations
-import org.apache.spark.mllib.stat.test.{ChiSqTest, ChiSqTestResult}
+import org.apache.spark.mllib.stat.test.{ChiSqTest, ChiSqTestResult, KolmogorovSmirnovTest,
+  KolmogorovSmirnovTestResult}
 import org.apache.spark.rdd.RDD
 
 /**
@@ -158,4 +161,39 @@ object Statistics {
   def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = {
     ChiSqTest.chiSquaredFeatures(data)
   }
+
+  /**
+   * Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a
+   * continuous distribution. By comparing the largest difference between the empirical cumulative
+   * distribution of the sample data and the theoretical distribution we can provide a test for the
+   * the null hypothesis that the sample data comes from that theoretical distribution.
+   * For more information on KS Test:
+   * @see [[https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test]]
+   *
+   * @param data an `RDD[Double]` containing the sample of data to test
+   * @param cdf a `Double => Double` function to calculate the theoretical CDF at a given value
+   * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] object containing test
+   *        statistic, p-value, and null hypothesis.
+   */
+  def kolmogorovSmirnovTest(data: RDD[Double], cdf: Double => Double)
+    : KolmogorovSmirnovTestResult = {
+    KolmogorovSmirnovTest.testOneSample(data, cdf)
+  }
+
+  /**
+   * Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability
+   * distribution equality. Currently supports the normal distribution, taking as parameters
+   * the mean and standard deviation.
+   * (distName = "norm")
+   * @param data an `RDD[Double]` containing the sample of data to test
+   * @param distName a `String` name for a theoretical distribution
+   * @param params `Double*` specifying the parameters to be used for the theoretical distribution
+   * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] object containing test
+   *        statistic, p-value, and null hypothesis.
+   */
+  @varargs
+  def kolmogorovSmirnovTest(data: RDD[Double], distName: String, params: Double*)
+    : KolmogorovSmirnovTestResult = {
+    KolmogorovSmirnovTest.testOneSample(data, distName, params: _*)
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala
new file mode 100644
index 0000000000000..d89b0059d83f3
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala
@@ -0,0 +1,194 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat.test
+
+import scala.annotation.varargs
+
+import org.apache.commons.math3.distribution.{NormalDistribution, RealDistribution}
+import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+
+/**
+ * Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a
+ * continuous distribution. By comparing the largest difference between the empirical cumulative
+ * distribution of the sample data and the theoretical distribution we can provide a test for the
+ * the null hypothesis that the sample data comes from that theoretical distribution.
+ * For more information on KS Test:
+ * @see [[https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test]]
+ *
+ * Implementation note: We seek to implement the KS test with a minimal number of distributed
+ * passes. We sort the RDD, and then perform the following operations on a per-partition basis:
+ * calculate an empirical cumulative distribution value for each observation, and a theoretical
+ * cumulative distribution value. We know the latter to be correct, while the former will be off by
+ * a constant (how large the constant is depends on how many values precede it in other partitions).
+ * However, given that this constant simply shifts the empirical CDF upwards, but doesn't
+ * change its shape, and furthermore, that constant is the same within a given partition, we can
+ * pick 2 values in each partition that can potentially resolve to the largest global distance.
+ * Namely, we pick the minimum distance and the maximum distance. Additionally, we keep track of how
+ * many elements are in each partition. Once these three values have been returned for every
+ * partition, we can collect and operate locally. Locally, we can now adjust each distance by the
+ * appropriate constant (the cumulative sum of number of elements in the prior partitions divided by
+ * thedata set size). Finally, we take the maximum absolute value, and this is the statistic.
+ */
+private[stat] object KolmogorovSmirnovTest extends Logging {
+
+  // Null hypothesis for the type of KS test to be included in the result.
+  object NullHypothesis extends Enumeration {
+    type NullHypothesis = Value
+    val OneSampleTwoSided = Value("Sample follows theoretical distribution")
+  }
+
+  /**
+   * Runs a KS test for 1 set of sample data, comparing it to a theoretical distribution
+   * @param data `RDD[Double]` data on which to run test
+   * @param cdf `Double => Double` function to calculate the theoretical CDF
+   * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] summarizing the test
+   *        results (p-value, statistic, and null hypothesis)
+   */
+  def testOneSample(data: RDD[Double], cdf: Double => Double): KolmogorovSmirnovTestResult = {
+    val n = data.count().toDouble
+    val localData = data.sortBy(x => x).mapPartitions { part =>
+      val partDiffs = oneSampleDifferences(part, n, cdf) // local distances
+      searchOneSampleCandidates(partDiffs) // candidates: local extrema
+    }.collect()
+    val ksStat = searchOneSampleStatistic(localData, n) // result: global extreme
+    evalOneSampleP(ksStat, n.toLong)
+  }
+
+  /**
+   * Runs a KS test for 1 set of sample data, comparing it to a theoretical distribution
+   * @param data `RDD[Double]` data on which to run test
+   * @param distObj `RealDistribution` a theoretical distribution
+   * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] summarizing the test
+   *        results (p-value, statistic, and null hypothesis)
+   */
+  def testOneSample(data: RDD[Double], distObj: RealDistribution): KolmogorovSmirnovTestResult = {
+    val cdf = (x: Double) => distObj.cumulativeProbability(x)
+    testOneSample(data, cdf)
+  }
+
+  /**
+   * Calculate unadjusted distances between the empirical CDF and the theoretical CDF in a
+   * partition
+   * @param partData `Iterator[Double]` 1 partition of a sorted RDD
+   * @param n `Double` the total size of the RDD
+   * @param cdf `Double => Double` a function the calculates the theoretical CDF of a value
+   * @return `Iterator[(Double, Double)] `Unadjusted (ie. off by a constant) potential extrema
+   *        in a partition. The first element corresponds to the (empirical CDF - 1/N) - CDF,
+   *        the second element corresponds to empirical CDF - CDF.  We can then search the resulting
+   *        iterator for the minimum of the first and the maximum of the second element, and provide
+   *        this as a partition's candidate extrema
+   */
+  private def oneSampleDifferences(partData: Iterator[Double], n: Double, cdf: Double => Double)
+    : Iterator[(Double, Double)] = {
+    // zip data with index (within that partition)
+    // calculate local (unadjusted) empirical CDF and subtract CDF
+    partData.zipWithIndex.map { case (v, ix) =>
+      // dp and dl are later adjusted by constant, when global info is available
+      val dp = (ix + 1) / n
+      val dl = ix / n
+      val cdfVal = cdf(v)
+      (dl - cdfVal, dp - cdfVal)
+    }
+  }
+
+  /**
+   * Search the unadjusted differences in a partition and return the
+   * two extrema (furthest below and furthest above CDF), along with a count of elements in that
+   * partition
+   * @param partDiffs `Iterator[(Double, Double)]` the unadjusted differences between empirical CDF
+   *                 and CDFin a partition, which come as a tuple of
+   *                 (empirical CDF - 1/N - CDF, empirical CDF - CDF)
+   * @return `Iterator[(Double, Double, Double)]` the local extrema and a count of elements
+   */
+  private def searchOneSampleCandidates(partDiffs: Iterator[(Double, Double)])
+    : Iterator[(Double, Double, Double)] = {
+    val initAcc = (Double.MaxValue, Double.MinValue, 0.0)
+    val pResults = partDiffs.foldLeft(initAcc) { case ((pMin, pMax, pCt), (dl, dp)) =>
+      (math.min(pMin, dl), math.max(pMax, dp), pCt + 1)
+    }
+    val results = if (pResults == initAcc) Array[(Double, Double, Double)]() else Array(pResults)
+    results.iterator
+  }
+
+  /**
+   * Find the global maximum distance between empirical CDF and CDF (i.e. the KS statistic) after
+   * adjusting local extrema estimates from individual partitions with the amount of elements in
+   * preceding partitions
+   * @param localData `Array[(Double, Double, Double)]` A local array containing the collected
+   *                 results of `searchOneSampleCandidates` across all partitions
+   * @param n `Double`The size of the RDD
+   * @return The one-sample Kolmogorov Smirnov Statistic
+   */
+  private def searchOneSampleStatistic(localData: Array[(Double, Double, Double)], n: Double)
+    : Double = {
+    val initAcc = (Double.MinValue, 0.0)
+    // adjust differences based on the number of elements preceding it, which should provide
+    // the correct distance between empirical CDF and CDF
+    val results = localData.foldLeft(initAcc) { case ((prevMax, prevCt), (minCand, maxCand, ct)) =>
+      val adjConst = prevCt / n
+      val dist1 = math.abs(minCand + adjConst)
+      val dist2 = math.abs(maxCand + adjConst)
+      val maxVal = Array(prevMax, dist1, dist2).max
+      (maxVal, prevCt + ct)
+    }
+    results._1
+  }
+
+  /**
+   * A convenience function that allows running the KS test for 1 set of sample data against
+   * a named distribution
+   * @param data the sample data that we wish to evaluate
+   * @param distName the name of the theoretical distribution
+   * @param params Variable length parameter for distribution's parameters
+   * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] summarizing the
+   *        test results (p-value, statistic, and null hypothesis)
+   */
+  @varargs
+  def testOneSample(data: RDD[Double], distName: String, params: Double*)
+    : KolmogorovSmirnovTestResult = {
+    val distObj =
+      distName match {
+        case "norm" => {
+          if (params.nonEmpty) {
+            // parameters are passed, then can only be 2
+            require(params.length == 2, "Normal distribution requires mean and standard " +
+              "deviation as parameters")
+            new NormalDistribution(params(0), params(1))
+          } else {
+            // if no parameters passed in initializes to standard normal
+            logInfo("No parameters specified for normal distribution," +
+              "initialized to standard normal (i.e. N(0, 1))")
+            new NormalDistribution(0, 1)
+          }
+        }
+        case  _ => throw new UnsupportedOperationException(s"$distName not yet supported through" +
+          s" convenience method. Current options are:['norm'].")
+      }
+
+    testOneSample(data, distObj)
+  }
+
+  private def evalOneSampleP(ksStat: Double, n: Long): KolmogorovSmirnovTestResult = {
+    val pval = 1 - new KolmogorovSmirnovTest().cdf(ksStat, n.toInt)
+    new KolmogorovSmirnovTestResult(pval, ksStat, NullHypothesis.OneSampleTwoSided.toString)
+  }
+}
+
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
index 4784f9e947908..f44be13706695 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
@@ -90,3 +90,20 @@ class ChiSqTestResult private[stat] (override val pValue: Double,
       super.toString
   }
 }
+
+/**
+ * :: Experimental ::
+ * Object containing the test results for the Kolmogorov-Smirnov test.
+ */
+@Experimental
+class KolmogorovSmirnovTestResult private[stat] (
+    override val pValue: Double,
+    override val statistic: Double,
+    override val nullHypothesis: String) extends TestResult[Int] {
+
+  override val degreesOfFreedom = 0
+
+  override def toString: String = {
+    "Kolmogorov-Smirnov test summary:\n" + super.toString
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala
index b084a5fb4313f..142b90e764a7c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala
@@ -19,6 +19,10 @@ package org.apache.spark.mllib.stat
 
 import java.util.Random
 
+import org.apache.commons.math3.distribution.{ExponentialDistribution,
+  NormalDistribution, UniformRealDistribution}
+import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
+
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.mllib.linalg.{DenseVector, Matrices, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -153,4 +157,101 @@ class HypothesisTestSuite extends SparkFunSuite with MLlibTestSparkContext {
       Statistics.chiSqTest(sc.parallelize(continuousFeature, 2))
     }
   }
+
+  test("1 sample Kolmogorov-Smirnov test: apache commons math3 implementation equivalence") {
+    // Create theoretical distributions
+    val stdNormalDist = new NormalDistribution(0, 1)
+    val expDist = new ExponentialDistribution(0.6)
+    val unifDist = new UniformRealDistribution()
+
+    // set seeds
+    val seed = 10L
+    stdNormalDist.reseedRandomGenerator(seed)
+    expDist.reseedRandomGenerator(seed)
+    unifDist.reseedRandomGenerator(seed)
+
+    // Sample data from the distributions and parallelize it
+    val n = 100000
+    val sampledNorm = sc.parallelize(stdNormalDist.sample(n), 10)
+    val sampledExp = sc.parallelize(expDist.sample(n), 10)
+    val sampledUnif = sc.parallelize(unifDist.sample(n), 10)
+
+    // Use a apache math commons local KS test to verify calculations
+    val ksTest = new KolmogorovSmirnovTest()
+    val pThreshold = 0.05
+
+    // Comparing a standard normal sample to a standard normal distribution
+    val result1 = Statistics.kolmogorovSmirnovTest(sampledNorm, "norm", 0, 1)
+    val referenceStat1 = ksTest.kolmogorovSmirnovStatistic(stdNormalDist, sampledNorm.collect())
+    val referencePVal1 = 1 - ksTest.cdf(referenceStat1, n)
+    // Verify vs apache math commons ks test
+    assert(result1.statistic ~== referenceStat1 relTol 1e-4)
+    assert(result1.pValue ~== referencePVal1 relTol 1e-4)
+    // Cannot reject null hypothesis
+    assert(result1.pValue > pThreshold)
+
+    // Comparing an exponential sample to a standard normal distribution
+    val result2 = Statistics.kolmogorovSmirnovTest(sampledExp, "norm", 0, 1)
+    val referenceStat2 = ksTest.kolmogorovSmirnovStatistic(stdNormalDist, sampledExp.collect())
+    val referencePVal2 = 1 - ksTest.cdf(referenceStat2, n)
+    // verify vs apache math commons ks test
+    assert(result2.statistic ~== referenceStat2 relTol 1e-4)
+    assert(result2.pValue ~== referencePVal2 relTol 1e-4)
+    // reject null hypothesis
+    assert(result2.pValue < pThreshold)
+
+    // Testing the use of a user provided CDF function
+    // Distribution is not serializable, so will have to create in the lambda
+    val expCDF = (x: Double) => new ExponentialDistribution(0.2).cumulativeProbability(x)
+
+    // Comparing an exponential sample with mean X to an exponential distribution with mean Y
+    // Where X != Y
+    val result3 = Statistics.kolmogorovSmirnovTest(sampledExp, expCDF)
+    val referenceStat3 = ksTest.kolmogorovSmirnovStatistic(new ExponentialDistribution(0.2),
+      sampledExp.collect())
+    val referencePVal3 = 1 - ksTest.cdf(referenceStat3, sampledNorm.count().toInt)
+    // verify vs apache math commons ks test
+    assert(result3.statistic ~== referenceStat3 relTol 1e-4)
+    assert(result3.pValue ~== referencePVal3 relTol 1e-4)
+    // reject null hypothesis
+    assert(result3.pValue < pThreshold)
+  }
+
+  test("1 sample Kolmogorov-Smirnov test: R implementation equivalence") {
+    /*
+      Comparing results with R's implementation of Kolmogorov-Smirnov for 1 sample
+      > sessionInfo()
+      R version 3.2.0 (2015-04-16)
+      Platform: x86_64-apple-darwin13.4.0 (64-bit)
+      > set.seed(20)
+      > v <- rnorm(20)
+      > v
+       [1]  1.16268529 -0.58592447  1.78546500 -1.33259371 -0.44656677  0.56960612
+       [7] -2.88971761 -0.86901834 -0.46170268 -0.55554091 -0.02013537 -0.15038222
+      [13] -0.62812676  1.32322085 -1.52135057 -0.43742787  0.97057758  0.02822264
+      [19] -0.08578219  0.38921440
+      > ks.test(v, pnorm, alternative = "two.sided")
+
+               One-sample Kolmogorov-Smirnov test
+
+      data:  v
+      D = 0.18874, p-value = 0.4223
+      alternative hypothesis: two-sided
+    */
+
+    val rKSStat = 0.18874
+    val rKSPVal = 0.4223
+    val rData = sc.parallelize(
+      Array(
+        1.1626852897838, -0.585924465893051, 1.78546500331661, -1.33259371048501,
+        -0.446566766553219, 0.569606122374976, -2.88971761441412, -0.869018343326555,
+        -0.461702683149641, -0.555540910137444, -0.0201353678515895, -0.150382224136063,
+        -0.628126755843964, 1.32322085193283, -1.52135057001199, -0.437427868856691,
+        0.970577579543399, 0.0282226444247749, -0.0857821886527593, 0.389214404984942
+      )
+    )
+    val rCompResult = Statistics.kolmogorovSmirnovTest(rData, "norm", 0, 1)
+    assert(rCompResult.statistic ~== rKSStat relTol 1e-4)
+    assert(rCompResult.pValue ~== rKSPVal relTol 1e-4)
+  }
 }

From 7f6be1f24d4f2fcb3d3ec181b5abf241709a8b6d Mon Sep 17 00:00:00 2001
From: zhangjiajin <zhangjiajin@huawei.com>
Date: Fri, 10 Jul 2015 21:11:46 -0700
Subject: [PATCH 0345/1454] [SPARK-6487] [MLLIB] Add sequential pattern mining
 algorithm PrefixSpan to Spark MLlib

Add parallel PrefixSpan algorithm and test file.
Support non-temporal sequences.

Author: zhangjiajin <zhangjiajin@huawei.com>
Author: zhang jiajin <zhangjiajin@huawei.com>

Closes #7258 from zhangjiajin/master and squashes the following commits:

ca9c4c8 [zhangjiajin] Modified the code according to the review comments.
574e56c [zhangjiajin] Add new object LocalPrefixSpan, and do some optimization.
ba5df34 [zhangjiajin] Fix a Scala style error.
4c60fb3 [zhangjiajin] Fix some Scala style errors.
1dd33ad [zhangjiajin] Modified the code according to the review comments.
89bc368 [zhangjiajin] Fixed a Scala style error.
a2eb14c [zhang jiajin] Delete PrefixspanSuite.scala
951fd42 [zhang jiajin] Delete Prefixspan.scala
575995f [zhangjiajin] Modified the code according to the review comments.
91fd7e6 [zhangjiajin] Add new algorithm PrefixSpan and test file.
---
 .../spark/mllib/fpm/LocalPrefixSpan.scala     | 113 +++++++++++++
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 157 ++++++++++++++++++
 .../spark/mllib/fpm/PrefixSpanSuite.scala     | 120 +++++++++++++
 3 files changed, 390 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
new file mode 100644
index 0000000000000..39c48b084e550
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
@@ -0,0 +1,113 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.fpm
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+
+/**
+ *
+ * :: Experimental ::
+ *
+ * Calculate all patterns of a projected database in local.
+ */
+@Experimental
+private[fpm] object LocalPrefixSpan extends Logging with Serializable {
+
+  /**
+   * Calculate all patterns of a projected database.
+   * @param minCount minimum count
+   * @param maxPatternLength maximum pattern length
+   * @param prefix prefix
+   * @param projectedDatabase the projected dabase
+   * @return a set of sequential pattern pairs,
+   *         the key of pair is sequential pattern (a list of items),
+   *         the value of pair is the pattern's count.
+   */
+  def run(
+      minCount: Long,
+      maxPatternLength: Int,
+      prefix: Array[Int],
+      projectedDatabase: Array[Array[Int]]): Array[(Array[Int], Long)] = {
+    val frequentPrefixAndCounts = getFreqItemAndCounts(minCount, projectedDatabase)
+    val frequentPatternAndCounts = frequentPrefixAndCounts
+      .map(x => (prefix ++ Array(x._1), x._2))
+    val prefixProjectedDatabases = getPatternAndProjectedDatabase(
+      prefix, frequentPrefixAndCounts.map(_._1), projectedDatabase)
+
+    val continueProcess = prefixProjectedDatabases.nonEmpty && prefix.length + 1 < maxPatternLength
+    if (continueProcess) {
+      val nextPatterns = prefixProjectedDatabases
+        .map(x => run(minCount, maxPatternLength, x._1, x._2))
+        .reduce(_ ++ _)
+      frequentPatternAndCounts ++ nextPatterns
+    } else {
+      frequentPatternAndCounts
+    }
+  }
+
+  /**
+   * calculate suffix sequence following a prefix in a sequence
+   * @param prefix prefix
+   * @param sequence sequence
+   * @return suffix sequence
+   */
+  def getSuffix(prefix: Int, sequence: Array[Int]): Array[Int] = {
+    val index = sequence.indexOf(prefix)
+    if (index == -1) {
+      Array()
+    } else {
+      sequence.drop(index + 1)
+    }
+  }
+
+  /**
+   * Generates frequent items by filtering the input data using minimal count level.
+   * @param minCount the absolute minimum count
+   * @param sequences sequences data
+   * @return array of item and count pair
+   */
+  private def getFreqItemAndCounts(
+      minCount: Long,
+      sequences: Array[Array[Int]]): Array[(Int, Long)] = {
+    sequences.flatMap(_.distinct)
+      .groupBy(x => x)
+      .mapValues(_.length.toLong)
+      .filter(_._2 >= minCount)
+      .toArray
+  }
+
+  /**
+   * Get the frequent prefixes' projected database.
+   * @param prePrefix the frequent prefixes' prefix
+   * @param frequentPrefixes frequent prefixes
+   * @param sequences sequences data
+   * @return prefixes and projected database
+   */
+  private def getPatternAndProjectedDatabase(
+      prePrefix: Array[Int],
+      frequentPrefixes: Array[Int],
+      sequences: Array[Array[Int]]): Array[(Array[Int], Array[Array[Int]])] = {
+    val filteredProjectedDatabase = sequences
+      .map(x => x.filter(frequentPrefixes.contains(_)))
+    frequentPrefixes.map { x =>
+      val sub = filteredProjectedDatabase.map(y => getSuffix(x, y)).filter(_.nonEmpty)
+      (prePrefix ++ Array(x), sub)
+    }.filter(x => x._2.nonEmpty)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
new file mode 100644
index 0000000000000..9d8c60ef0fc45
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -0,0 +1,157 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.fpm
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+/**
+ *
+ * :: Experimental ::
+ *
+ * A parallel PrefixSpan algorithm to mine sequential pattern.
+ * The PrefixSpan algorithm is described in
+ * [[http://doi.org/10.1109/ICDE.2001.914830]].
+ *
+ * @param minSupport the minimal support level of the sequential pattern, any pattern appears
+ *                   more than  (minSupport * size-of-the-dataset) times will be output
+ * @param maxPatternLength the maximal length of the sequential pattern, any pattern appears
+ *                   less than maxPatternLength will be output
+ *
+ * @see [[https://en.wikipedia.org/wiki/Sequential_Pattern_Mining Sequential Pattern Mining
+ *       (Wikipedia)]]
+ */
+@Experimental
+class PrefixSpan private (
+    private var minSupport: Double,
+    private var maxPatternLength: Int) extends Logging with Serializable {
+
+  /**
+   * Constructs a default instance with default parameters
+   * {minSupport: `0.1`, maxPatternLength: `10`}.
+   */
+  def this() = this(0.1, 10)
+
+  /**
+   * Sets the minimal support level (default: `0.1`).
+   */
+  def setMinSupport(minSupport: Double): this.type = {
+    require(minSupport >= 0 && minSupport <= 1,
+      "The minimum support value must be between 0 and 1, including 0 and 1.")
+    this.minSupport = minSupport
+    this
+  }
+
+  /**
+   * Sets maximal pattern length (default: `10`).
+   */
+  def setMaxPatternLength(maxPatternLength: Int): this.type = {
+    require(maxPatternLength >= 1,
+      "The maximum pattern length value must be greater than 0.")
+    this.maxPatternLength = maxPatternLength
+    this
+  }
+
+  /**
+   * Find the complete set of sequential patterns in the input sequences.
+   * @param sequences input data set, contains a set of sequences,
+   *                  a sequence is an ordered list of elements.
+   * @return a set of sequential pattern pairs,
+   *         the key of pair is pattern (a list of elements),
+   *         the value of pair is the pattern's count.
+   */
+  def run(sequences: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
+    if (sequences.getStorageLevel == StorageLevel.NONE) {
+      logWarning("Input data is not cached.")
+    }
+    val minCount = getMinCount(sequences)
+    val lengthOnePatternsAndCounts =
+      getFreqItemAndCounts(minCount, sequences).collect()
+    val prefixAndProjectedDatabase = getPrefixAndProjectedDatabase(
+      lengthOnePatternsAndCounts.map(_._1), sequences)
+    val groupedProjectedDatabase = prefixAndProjectedDatabase
+      .map(x => (x._1.toSeq, x._2))
+      .groupByKey()
+      .map(x => (x._1.toArray, x._2.toArray))
+    val nextPatterns = getPatternsInLocal(minCount, groupedProjectedDatabase)
+    val lengthOnePatternsAndCountsRdd =
+      sequences.sparkContext.parallelize(
+        lengthOnePatternsAndCounts.map(x => (Array(x._1), x._2)))
+    val allPatterns = lengthOnePatternsAndCountsRdd ++ nextPatterns
+    allPatterns
+  }
+
+  /**
+   * Get the minimum count (sequences count * minSupport).
+   * @param sequences input data set, contains a set of sequences,
+   * @return minimum count,
+   */
+  private def getMinCount(sequences: RDD[Array[Int]]): Long = {
+    if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
+  }
+
+  /**
+   * Generates frequent items by filtering the input data using minimal count level.
+   * @param minCount the absolute minimum count
+   * @param sequences original sequences data
+   * @return array of item and count pair
+   */
+  private def getFreqItemAndCounts(
+      minCount: Long,
+      sequences: RDD[Array[Int]]): RDD[(Int, Long)] = {
+    sequences.flatMap(_.distinct.map((_, 1L)))
+      .reduceByKey(_ + _)
+      .filter(_._2 >= minCount)
+  }
+
+  /**
+   * Get the frequent prefixes' projected database.
+   * @param frequentPrefixes frequent prefixes
+   * @param sequences sequences data
+   * @return prefixes and projected database
+   */
+  private def getPrefixAndProjectedDatabase(
+      frequentPrefixes: Array[Int],
+      sequences: RDD[Array[Int]]): RDD[(Array[Int], Array[Int])] = {
+    val filteredSequences = sequences.map { p =>
+      p.filter (frequentPrefixes.contains(_) )
+    }
+    filteredSequences.flatMap { x =>
+      frequentPrefixes.map { y =>
+        val sub = LocalPrefixSpan.getSuffix(y, x)
+        (Array(y), sub)
+      }.filter(_._2.nonEmpty)
+    }
+  }
+
+  /**
+   * calculate the patterns in local.
+   * @param minCount the absolute minimum count
+   * @param data patterns and projected sequences data data
+   * @return patterns
+   */
+  private def getPatternsInLocal(
+      minCount: Long,
+      data: RDD[(Array[Int], Array[Array[Int]])]): RDD[(Array[Int], Long)] = {
+    data.flatMap { x =>
+      LocalPrefixSpan.run(minCount, maxPatternLength, x._1, x._2)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
new file mode 100644
index 0000000000000..413436d3db85f
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -0,0 +1,120 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.mllib.fpm
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.rdd.RDD
+
+class PrefixspanSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("PrefixSpan using Integer type") {
+
+    /*
+      library("arulesSequences")
+      prefixSpanSeqs = read_baskets("prefixSpanSeqs", info = c("sequenceID","eventID","SIZE"))
+      freqItemSeq = cspade(
+        prefixSpanSeqs,
+        parameter = list(support =
+          2 / length(unique(transactionInfo(prefixSpanSeqs)$sequenceID)), maxlen = 2 ))
+      resSeq = as(freqItemSeq, "data.frame")
+      resSeq
+    */
+
+    val sequences = Array(
+      Array(1, 3, 4, 5),
+      Array(2, 3, 1),
+      Array(2, 4, 1),
+      Array(3, 1, 3, 4, 5),
+      Array(3, 4, 4, 3),
+      Array(6, 5, 3))
+
+    val rdd = sc.parallelize(sequences, 2).cache()
+
+    def compareResult(
+        expectedValue: Array[(Array[Int], Long)],
+        actualValue: Array[(Array[Int], Long)]): Boolean = {
+      val sortedExpectedValue = expectedValue.sortWith{ (x, y) =>
+        x._1.mkString(",") + ":" + x._2 < y._1.mkString(",") + ":" + y._2
+      }
+      val sortedActualValue = actualValue.sortWith{ (x, y) =>
+        x._1.mkString(",") + ":" + x._2 < y._1.mkString(",") + ":" + y._2
+      }
+      sortedExpectedValue.zip(sortedActualValue)
+        .map(x => x._1._1.mkString(",") == x._2._1.mkString(",") && x._1._2 == x._2._2)
+        .reduce(_&&_)
+    }
+
+    val prefixspan = new PrefixSpan()
+      .setMinSupport(0.33)
+      .setMaxPatternLength(50)
+    val result1 = prefixspan.run(rdd)
+    val expectedValue1 = Array(
+      (Array(1), 4L),
+      (Array(1, 3), 2L),
+      (Array(1, 3, 4), 2L),
+      (Array(1, 3, 4, 5), 2L),
+      (Array(1, 3, 5), 2L),
+      (Array(1, 4), 2L),
+      (Array(1, 4, 5), 2L),
+      (Array(1, 5), 2L),
+      (Array(2), 2L),
+      (Array(2, 1), 2L),
+      (Array(3), 5L),
+      (Array(3, 1), 2L),
+      (Array(3, 3), 2L),
+      (Array(3, 4), 3L),
+      (Array(3, 4, 5), 2L),
+      (Array(3, 5), 2L),
+      (Array(4), 4L),
+      (Array(4, 5), 2L),
+      (Array(5), 3L)
+    )
+    assert(compareResult(expectedValue1, result1.collect()))
+
+    prefixspan.setMinSupport(0.5).setMaxPatternLength(50)
+    val result2 = prefixspan.run(rdd)
+    val expectedValue2 = Array(
+      (Array(1), 4L),
+      (Array(3), 5L),
+      (Array(3, 4), 3L),
+      (Array(4), 4L),
+      (Array(5), 3L)
+    )
+    assert(compareResult(expectedValue2, result2.collect()))
+
+    prefixspan.setMinSupport(0.33).setMaxPatternLength(2)
+    val result3 = prefixspan.run(rdd)
+    val expectedValue3 = Array(
+      (Array(1), 4L),
+      (Array(1, 3), 2L),
+      (Array(1, 4), 2L),
+      (Array(1, 5), 2L),
+      (Array(2, 1), 2L),
+      (Array(2), 2L),
+      (Array(3), 5L),
+      (Array(3, 1), 2L),
+      (Array(3, 3), 2L),
+      (Array(3, 4), 3L),
+      (Array(3, 5), 2L),
+      (Array(4), 4L),
+      (Array(4, 5), 2L),
+      (Array(5), 3L)
+    )
+    assert(compareResult(expectedValue3, result3.collect()))
+  }
+}

From 0c5207c66db8cf76950d53404abd43e085f8d45b Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 10 Jul 2015 21:25:09 -0700
Subject: [PATCH 0346/1454] [SPARK-8994] [ML] tiny cleanups to Params, Pipeline

Made default impl of Params.validateParams empty
CC mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7349 from jkbradley/pipeline-small-cleanups and squashes the following commits:

4e0f013 [Joseph K. Bradley] small cleanups after SPARK-5956
---
 mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala     | 2 ++
 mllib/src/main/scala/org/apache/spark/ml/param/params.scala | 4 +---
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
index a1f3851d804ff..aef2c019d2871 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -95,6 +95,8 @@ class Pipeline(override val uid: String) extends Estimator[PipelineModel] {
   /** @group setParam */
   def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this }
 
+  // Below, we clone stages so that modifications to the list of stages will not change
+  // the Param value in the Pipeline.
   /** @group getParam */
   def getStages: Array[PipelineStage] = $(stages).clone()
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 50c0d855066f8..d034d7ec6b60e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -341,9 +341,7 @@ trait Params extends Identifiable with Serializable {
    * those are checked during schema validation.
    */
   def validateParams(): Unit = {
-    params.filter(isDefined).foreach { param =>
-      param.asInstanceOf[Param[Any]].validate($(param))
-    }
+    // Do nothing by default.  Override to handle Param interactions.
   }
 
   /**

From c472eb17ae7f0910f304e414ea5ccbb77a9e153a Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 10 Jul 2015 23:25:11 -0700
Subject: [PATCH 0347/1454] [SPARK-8970][SQL] remove unnecessary abstraction
 for ExtractValue

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7339 from cloud-fan/minor and squashes the following commits:

84a2128 [Wenchen Fan] remove unapply
6a37c12 [Wenchen Fan] remove unnecessary abstraction for ExtractValue
---
 .../sql/catalyst/analysis/Analyzer.scala      |  3 +-
 .../expressions/complexTypeExtractors.scala   | 36 ++++---------------
 .../sql/catalyst/optimizer/Optimizer.scala    |  8 +++--
 3 files changed, 15 insertions(+), 32 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 3fdc6d62bc786..891408e310049 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -141,7 +141,8 @@ class Analyzer(
           child match {
             case _: UnresolvedAttribute => u
             case ne: NamedExpression => ne
-            case ev: ExtractValueWithStruct => Alias(ev, ev.field.name)()
+            case g: GetStructField => Alias(g, g.field.name)()
+            case g: GetArrayStructFields => Alias(g, g.field.name)()
             case g: Generator if g.resolved && g.elementTypes.size > 1 => MultiAlias(g, Nil)
             case e if !e.resolved => u
             case other => Alias(other, s"_c$i")()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
index 73cc930c45832..5504781edca1b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
@@ -78,12 +78,6 @@ object ExtractValue {
     }
   }
 
-  def unapply(g: ExtractValue): Option[(Expression, Expression)] = g match {
-    case o: GetArrayItem => Some((o.child, o.ordinal))
-    case o: GetMapValue => Some((o.child, o.key))
-    case s: ExtractValueWithStruct => Some((s.child, null))
-  }
-
   /**
    * Find the ordinal of StructField, report error if no desired field or over one
    * desired fields are found.
@@ -103,32 +97,17 @@ object ExtractValue {
   }
 }
 
-/**
- * A common interface of all kinds of extract value expressions.
- * Note: concrete extract value expressions are created only by `ExtractValue.apply`,
- * we don't need to do type check for them.
- */
-trait ExtractValue {
-  self: Expression =>
-}
-
-abstract class ExtractValueWithStruct extends UnaryExpression with ExtractValue {
-  self: Product =>
-
-  def field: StructField
-  override def toString: String = s"$child.${field.name}"
-}
-
 /**
  * Returns the value of fields in the Struct `child`.
  *
  * No need to do type checking since it is handled by [[ExtractValue]].
  */
 case class GetStructField(child: Expression, field: StructField, ordinal: Int)
-  extends ExtractValueWithStruct {
+  extends UnaryExpression {
 
   override def dataType: DataType = field.dataType
   override def nullable: Boolean = child.nullable || field.nullable
+  override def toString: String = s"$child.${field.name}"
 
   protected override def nullSafeEval(input: Any): Any =
     input.asInstanceOf[InternalRow](ordinal)
@@ -155,10 +134,11 @@ case class GetArrayStructFields(
     child: Expression,
     field: StructField,
     ordinal: Int,
-    containsNull: Boolean) extends ExtractValueWithStruct {
+    containsNull: Boolean) extends UnaryExpression {
 
   override def dataType: DataType = ArrayType(field.dataType, containsNull)
   override def nullable: Boolean = child.nullable || containsNull || field.nullable
+  override def toString: String = s"$child.${field.name}"
 
   protected override def nullSafeEval(input: Any): Any = {
     input.asInstanceOf[Seq[InternalRow]].map { row =>
@@ -191,8 +171,7 @@ case class GetArrayStructFields(
  *
  * No need to do type checking since it is handled by [[ExtractValue]].
  */
-case class GetArrayItem(child: Expression, ordinal: Expression)
-  extends BinaryExpression with ExtractValue {
+case class GetArrayItem(child: Expression, ordinal: Expression) extends BinaryExpression {
 
   override def toString: String = s"$child[$ordinal]"
 
@@ -231,12 +210,11 @@ case class GetArrayItem(child: Expression, ordinal: Expression)
 }
 
 /**
- * Returns the value of key `ordinal` in Map `child`.
+ * Returns the value of key `key` in Map `child`.
  *
  * No need to do type checking since it is handled by [[ExtractValue]].
  */
-case class GetMapValue(child: Expression, key: Expression)
-  extends BinaryExpression with ExtractValue {
+case class GetMapValue(child: Expression, key: Expression) extends BinaryExpression {
 
   override def toString: String = s"$child[$key]"
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 7d41ef9aafa44..5d80214abf141 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -275,8 +275,12 @@ object NullPropagation extends Rule[LogicalPlan] {
       case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType)
       case e @ IsNull(c) if !c.nullable => Literal.create(false, BooleanType)
       case e @ IsNotNull(c) if !c.nullable => Literal.create(true, BooleanType)
-      case e @ ExtractValue(Literal(null, _), _) => Literal.create(null, e.dataType)
-      case e @ ExtractValue(_, Literal(null, _)) => Literal.create(null, e.dataType)
+      case e @ GetArrayItem(Literal(null, _), _) => Literal.create(null, e.dataType)
+      case e @ GetArrayItem(_, Literal(null, _)) => Literal.create(null, e.dataType)
+      case e @ GetMapValue(Literal(null, _), _) => Literal.create(null, e.dataType)
+      case e @ GetMapValue(_, Literal(null, _)) => Literal.create(null, e.dataType)
+      case e @ GetStructField(Literal(null, _), _, _) => Literal.create(null, e.dataType)
+      case e @ GetArrayStructFields(Literal(null, _), _, _, _) => Literal.create(null, e.dataType)
       case e @ EqualNullSafe(Literal(null, _), r) => IsNull(r)
       case e @ EqualNullSafe(l, Literal(null, _)) => IsNull(l)
       case e @ Count(expr) if !expr.nullable => Count(Literal(1))

From 30090884f96efde72d9653f2253070b68f87782c Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Sun, 12 Jul 2015 20:45:09 -0400
Subject: [PATCH 0348/1454] [SPARK-8880] Fix confusing Stage.attemptId member
 variable

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #7275 from kayousterhout/SPARK-8880 and squashes the following commits:

3e9ce7c [Kay Ousterhout] Added missing return type
e150278 [Kay Ousterhout] [SPARK-8880] Fix confusing Stage.attemptId member variable
---
 .../apache/spark/scheduler/DAGScheduler.scala |  6 +++---
 .../org/apache/spark/scheduler/Stage.scala    | 20 ++++++++++++-------
 .../apache/spark/scheduler/StageInfo.scala    |  4 ++--
 3 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 6841fa835747f..f3d87ee5c4fd1 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -872,7 +872,7 @@ class DAGScheduler(
     // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
     // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
     // event.
-    stage.latestInfo = StageInfo.fromStage(stage, Some(partitionsToCompute.size))
+    stage.makeNewStageAttempt(partitionsToCompute.size)
     outputCommitCoordinator.stageStart(stage.id)
     listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
 
@@ -937,8 +937,8 @@ class DAGScheduler(
       logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
       stage.pendingTasks ++= tasks
       logDebug("New pending tasks: " + stage.pendingTasks)
-      taskScheduler.submitTasks(
-        new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.firstJobId, properties))
+      taskScheduler.submitTasks(new TaskSet(
+        tasks.toArray, stage.id, stage.latestInfo.attemptId, stage.firstJobId, properties))
       stage.latestInfo.submissionTime = Some(clock.getTimeMillis())
     } else {
       // Because we posted SparkListenerStageSubmitted earlier, we should mark
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index c59d6e4f5bc04..b86724de2cb73 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -62,22 +62,28 @@ private[spark] abstract class Stage(
 
   var pendingTasks = new HashSet[Task[_]]
 
+  /** The ID to use for the next new attempt for this stage. */
   private var nextAttemptId: Int = 0
 
   val name = callSite.shortForm
   val details = callSite.longForm
 
-  /** Pointer to the latest [StageInfo] object, set by DAGScheduler. */
-  var latestInfo: StageInfo = StageInfo.fromStage(this)
+  /**
+   * Pointer to the [StageInfo] object for the most recent attempt. This needs to be initialized
+   * here, before any attempts have actually been created, because the DAGScheduler uses this
+   * StageInfo to tell SparkListeners when a job starts (which happens before any stage attempts
+   * have been created).
+   */
+  private var _latestInfo: StageInfo = StageInfo.fromStage(this, nextAttemptId)
 
-  /** Return a new attempt id, starting with 0. */
-  def newAttemptId(): Int = {
-    val id = nextAttemptId
+  /** Creates a new attempt for this stage by creating a new StageInfo with a new attempt ID. */
+  def makeNewStageAttempt(numPartitionsToCompute: Int): Unit = {
+    _latestInfo = StageInfo.fromStage(this, nextAttemptId, Some(numPartitionsToCompute))
     nextAttemptId += 1
-    id
   }
 
-  def attemptId: Int = nextAttemptId
+  /** Returns the StageInfo for the most recent attempt for this stage. */
+  def latestInfo: StageInfo = _latestInfo
 
   override final def hashCode(): Int = id
   override final def equals(other: Any): Boolean = other match {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
index e439d2a7e1229..5d2abbc67e9d9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
@@ -70,12 +70,12 @@ private[spark] object StageInfo {
    * shuffle dependencies. Therefore, all ancestor RDDs related to this Stage's RDD through a
    * sequence of narrow dependencies should also be associated with this Stage.
    */
-  def fromStage(stage: Stage, numTasks: Option[Int] = None): StageInfo = {
+  def fromStage(stage: Stage, attemptId: Int, numTasks: Option[Int] = None): StageInfo = {
     val ancestorRddInfos = stage.rdd.getNarrowAncestors.map(RDDInfo.fromRdd)
     val rddInfos = Seq(RDDInfo.fromRdd(stage.rdd)) ++ ancestorRddInfos
     new StageInfo(
       stage.id,
-      stage.attemptId,
+      attemptId,
       stage.name,
       numTasks.getOrElse(stage.numTasks),
       rddInfos,

From 20b474335c68c644150fdc8443a2d0d2dad5e27d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sun, 12 Jul 2015 20:25:06 -0700
Subject: [PATCH 0349/1454] [SPARK-9006] [PYSPARK] fix microsecond loss in
 Python 3

It may loss a microsecond if using timestamp as float, should be `int` instead.

Author: Davies Liu <davies@databricks.com>

Closes #7363 from davies/fix_microsecond and squashes the following commits:

36f6007 [Davies Liu] fix microsecond loss in Python 3
---
 python/pyspark/sql/types.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index d63857691675a..f75791fad1612 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -188,7 +188,8 @@ def toInternal(self, dt):
 
     def fromInternal(self, ts):
         if ts is not None:
-            return datetime.datetime.fromtimestamp(ts / 1e6)
+            # using int to avoid precision loss in float
+            return datetime.datetime.fromtimestamp(ts // 1000000).replace(microsecond=ts % 1000000)
 
 
 class DecimalType(FractionalType):

From 92540d22e45f9300f413f520a1770e9f36cfa833 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Mon, 13 Jul 2015 00:14:32 -0700
Subject: [PATCH 0350/1454] [SPARK-8203] [SPARK-8204] [SQL] conditional
 function: least/greatest

chenghao-intel zhichao-li qiansl127

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #6851 from adrian-wang/udflg and squashes the following commits:

0f1bff2 [Daoyuan Wang] address comments from davis
7a6bdbb [Daoyuan Wang] add '.' for hex()
c1f6824 [Daoyuan Wang] add codegen, test for all types
ec625b0 [Daoyuan Wang] conditional function: least/greatest
---
 .../catalyst/analysis/FunctionRegistry.scala  |   2 +
 .../catalyst/expressions/conditionals.scala   | 103 +++++++++++++++++-
 .../ConditionalExpressionSuite.scala          |  81 ++++++++++++++
 .../org/apache/spark/sql/functions.scala      |  60 +++++++++-
 .../spark/sql/DataFrameFunctionsSuite.scala   |  22 ++++
 5 files changed, 263 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index f62d79f8cea6d..ed69c42dcb825 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -76,9 +76,11 @@ object FunctionRegistry {
     expression[CreateArray]("array"),
     expression[Coalesce]("coalesce"),
     expression[Explode]("explode"),
+    expression[Greatest]("greatest"),
     expression[If]("if"),
     expression[IsNull]("isnull"),
     expression[IsNotNull]("isnotnull"),
+    expression[Least]("least"),
     expression[Coalesce]("nvl"),
     expression[Rand]("rand"),
     expression[Randn]("randn"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index 395e84f089e41..e6a705fb8055b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -20,7 +20,8 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.types.{BooleanType, DataType}
+import org.apache.spark.sql.catalyst.util.TypeUtils
+import org.apache.spark.sql.types.{NullType, BooleanType, DataType}
 
 
 case class If(predicate: Expression, trueValue: Expression, falseValue: Expression)
@@ -312,3 +313,103 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
     }.mkString
   }
 }
+
+case class Least(children: Expression*) extends Expression {
+  require(children.length > 1, "LEAST requires at least 2 arguments, got " + children.length)
+
+  override def nullable: Boolean = children.forall(_.nullable)
+  override def foldable: Boolean = children.forall(_.foldable)
+
+  private lazy val ordering = TypeUtils.getOrdering(dataType)
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (children.map(_.dataType).distinct.count(_ != NullType) > 1) {
+      TypeCheckResult.TypeCheckFailure(
+        s"The expressions should all have the same type," +
+          s" got LEAST (${children.map(_.dataType)}).")
+    } else {
+      TypeUtils.checkForOrderingExpr(dataType, "function " + prettyName)
+    }
+  }
+
+  override def dataType: DataType = children.head.dataType
+
+  override def eval(input: InternalRow): Any = {
+    children.foldLeft[Any](null)((r, c) => {
+      val evalc = c.eval(input)
+      if (evalc != null) {
+        if (r == null || ordering.lt(evalc, r)) evalc else r
+      } else {
+        r
+      }
+    })
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val evalChildren = children.map(_.gen(ctx))
+    def updateEval(i: Int): String =
+      s"""
+        if (!${evalChildren(i).isNull} && (${ev.isNull} ||
+          ${ctx.genComp(dataType, evalChildren(i).primitive, ev.primitive)} < 0)) {
+          ${ev.isNull} = false;
+          ${ev.primitive} = ${evalChildren(i).primitive};
+        }
+      """
+    s"""
+      ${evalChildren.map(_.code).mkString("\n")}
+      boolean ${ev.isNull} = true;
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      ${(0 until children.length).map(updateEval).mkString("\n")}
+    """
+  }
+}
+
+case class Greatest(children: Expression*) extends Expression {
+  require(children.length > 1, "GREATEST requires at least 2 arguments, got " + children.length)
+
+  override def nullable: Boolean = children.forall(_.nullable)
+  override def foldable: Boolean = children.forall(_.foldable)
+
+  private lazy val ordering = TypeUtils.getOrdering(dataType)
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (children.map(_.dataType).distinct.count(_ != NullType) > 1) {
+      TypeCheckResult.TypeCheckFailure(
+        s"The expressions should all have the same type," +
+          s" got GREATEST (${children.map(_.dataType)}).")
+    } else {
+      TypeUtils.checkForOrderingExpr(dataType, "function " + prettyName)
+    }
+  }
+
+  override def dataType: DataType = children.head.dataType
+
+  override def eval(input: InternalRow): Any = {
+    children.foldLeft[Any](null)((r, c) => {
+      val evalc = c.eval(input)
+      if (evalc != null) {
+        if (r == null || ordering.gt(evalc, r)) evalc else r
+      } else {
+        r
+      }
+    })
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val evalChildren = children.map(_.gen(ctx))
+    def updateEval(i: Int): String =
+      s"""
+        if (!${evalChildren(i).isNull} && (${ev.isNull} ||
+          ${ctx.genComp(dataType, evalChildren(i).primitive, ev.primitive)} > 0)) {
+          ${ev.isNull} = false;
+          ${ev.primitive} = ${evalChildren(i).primitive};
+        }
+      """
+    s"""
+      ${evalChildren.map(_.code).mkString("\n")}
+      boolean ${ev.isNull} = true;
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      ${(0 until children.length).map(updateEval).mkString("\n")}
+    """
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
index 372848ea9a596..aaf40cc83e762 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
@@ -17,7 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import java.sql.{Timestamp, Date}
+
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
 
@@ -134,4 +137,82 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(CaseKeyWhen(literalNull, Seq(c2, c5, c1, c6)), "c", row)
   }
 
+  test("function least") {
+    val row = create_row(1, 2, "a", "b", "c")
+    val c1 = 'a.int.at(0)
+    val c2 = 'a.int.at(1)
+    val c3 = 'a.string.at(2)
+    val c4 = 'a.string.at(3)
+    val c5 = 'a.string.at(4)
+    checkEvaluation(Least(c4, c3, c5), "a", row)
+    checkEvaluation(Least(c1, c2), 1, row)
+    checkEvaluation(Least(c1, c2, Literal(-1)), -1, row)
+    checkEvaluation(Least(c4, c5, c3, c3, Literal("a")), "a", row)
+
+    checkEvaluation(Least(Literal(null), Literal(null)), null, InternalRow.empty)
+    checkEvaluation(Least(Literal(-1.0), Literal(2.5)), -1.0, InternalRow.empty)
+    checkEvaluation(Least(Literal(-1), Literal(2)), -1, InternalRow.empty)
+    checkEvaluation(
+      Least(Literal((-1.0).toFloat), Literal(2.5.toFloat)), (-1.0).toFloat, InternalRow.empty)
+    checkEvaluation(
+      Least(Literal(Long.MaxValue), Literal(Long.MinValue)), Long.MinValue, InternalRow.empty)
+    checkEvaluation(Least(Literal(1.toByte), Literal(2.toByte)), 1.toByte, InternalRow.empty)
+    checkEvaluation(
+      Least(Literal(1.toShort), Literal(2.toByte.toShort)), 1.toShort, InternalRow.empty)
+    checkEvaluation(Least(Literal("abc"), Literal("aaaa")), "aaaa", InternalRow.empty)
+    checkEvaluation(Least(Literal(true), Literal(false)), false, InternalRow.empty)
+    checkEvaluation(
+      Least(
+        Literal(BigDecimal("1234567890987654321123456")),
+        Literal(BigDecimal("1234567890987654321123458"))),
+      BigDecimal("1234567890987654321123456"), InternalRow.empty)
+    checkEvaluation(
+      Least(Literal(Date.valueOf("2015-01-01")), Literal(Date.valueOf("2015-07-01"))),
+      Date.valueOf("2015-01-01"), InternalRow.empty)
+    checkEvaluation(
+      Least(
+        Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
+        Literal(Timestamp.valueOf("2015-07-01 10:00:00"))),
+      Timestamp.valueOf("2015-07-01 08:00:00"), InternalRow.empty)
+  }
+
+  test("function greatest") {
+    val row = create_row(1, 2, "a", "b", "c")
+    val c1 = 'a.int.at(0)
+    val c2 = 'a.int.at(1)
+    val c3 = 'a.string.at(2)
+    val c4 = 'a.string.at(3)
+    val c5 = 'a.string.at(4)
+    checkEvaluation(Greatest(c4, c5, c3), "c", row)
+    checkEvaluation(Greatest(c2, c1), 2, row)
+    checkEvaluation(Greatest(c1, c2, Literal(2)), 2, row)
+    checkEvaluation(Greatest(c4, c5, c3, Literal("ccc")), "ccc", row)
+
+    checkEvaluation(Greatest(Literal(null), Literal(null)), null, InternalRow.empty)
+    checkEvaluation(Greatest(Literal(-1.0), Literal(2.5)), 2.5, InternalRow.empty)
+    checkEvaluation(Greatest(Literal(-1), Literal(2)), 2, InternalRow.empty)
+    checkEvaluation(
+      Greatest(Literal((-1.0).toFloat), Literal(2.5.toFloat)), 2.5.toFloat, InternalRow.empty)
+    checkEvaluation(
+      Greatest(Literal(Long.MaxValue), Literal(Long.MinValue)), Long.MaxValue, InternalRow.empty)
+    checkEvaluation(Greatest(Literal(1.toByte), Literal(2.toByte)), 2.toByte, InternalRow.empty)
+    checkEvaluation(
+      Greatest(Literal(1.toShort), Literal(2.toByte.toShort)), 2.toShort, InternalRow.empty)
+    checkEvaluation(Greatest(Literal("abc"), Literal("aaaa")), "abc", InternalRow.empty)
+    checkEvaluation(Greatest(Literal(true), Literal(false)), true, InternalRow.empty)
+    checkEvaluation(
+      Greatest(
+        Literal(BigDecimal("1234567890987654321123456")),
+        Literal(BigDecimal("1234567890987654321123458"))),
+      BigDecimal("1234567890987654321123458"), InternalRow.empty)
+    checkEvaluation(
+      Greatest(Literal(Date.valueOf("2015-01-01")), Literal(Date.valueOf("2015-07-01"))),
+      Date.valueOf("2015-07-01"), InternalRow.empty)
+    checkEvaluation(
+      Greatest(
+        Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
+        Literal(Timestamp.valueOf("2015-07-01 10:00:00"))),
+      Timestamp.valueOf("2015-07-01 10:00:00"), InternalRow.empty)
+  }
+
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 08bf37a5c223c..ffa52f62588dc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -599,7 +599,7 @@ object functions {
   /**
    * Creates a new row for each element in the given array or map column.
    */
-   def explode(e: Column): Column = Explode(e.expr)
+  def explode(e: Column): Column = Explode(e.expr)
 
   /**
    * Converts a string exprsesion to lower case.
@@ -1073,15 +1073,41 @@ object functions {
   def floor(columnName: String): Column = floor(Column(columnName))
 
   /**
-   * Computes hex value of the given column
+   * Returns the greatest value of the list of values.
    *
-   * @group math_funcs
+   * @group normal_funcs
    * @since 1.5.0
    */
+  @scala.annotation.varargs
+  def greatest(exprs: Column*): Column = if (exprs.length < 2) {
+    sys.error("GREATEST takes at least 2 parameters")
+  } else {
+    Greatest(exprs.map(_.expr): _*)
+  }
+
+  /**
+   * Returns the greatest value of the list of column names.
+   *
+   * @group normal_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def greatest(columnName: String, columnNames: String*): Column = if (columnNames.isEmpty) {
+    sys.error("GREATEST takes at least 2 parameters")
+  } else {
+    greatest((columnName +: columnNames).map(Column.apply): _*)
+  }
+
+  /**
+    * Computes hex value of the given column.
+    *
+    * @group math_funcs
+    * @since 1.5.0
+    */
   def hex(column: Column): Column = Hex(column.expr)
 
   /**
-   * Computes hex value of the given input
+   * Computes hex value of the given input.
    *
    * @group math_funcs
    * @since 1.5.0
@@ -1171,6 +1197,32 @@ object functions {
    */
   def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName))
 
+  /**
+   * Returns the least value of the list of values.
+   *
+   * @group normal_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def least(exprs: Column*): Column = if (exprs.length < 2) {
+    sys.error("LEAST takes at least 2 parameters")
+  } else {
+    Least(exprs.map(_.expr): _*)
+  }
+
+  /**
+   * Returns the least value of the list of column names.
+   *
+   * @group normal_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def least(columnName: String, columnNames: String*): Column = if (columnNames.isEmpty) {
+    sys.error("LEAST takes at least 2 parameters")
+  } else {
+    least((columnName +: columnNames).map(Column.apply): _*)
+  }
+
   /**
    * Computes the natural logarithm of the given value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 173280375c411..6cebec95d2850 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -381,4 +381,26 @@ class DataFrameFunctionsSuite extends QueryTest {
       df.selectExpr("split(a, '[1-9]+')"),
       Row(Seq("aa", "bb", "cc")))
   }
+
+  test("conditional function: least") {
+    checkAnswer(
+      testData2.select(least(lit(-1), lit(0), col("a"), col("b"))).limit(1),
+      Row(-1)
+    )
+    checkAnswer(
+      ctx.sql("SELECT least(a, 2) as l from testData2 order by l"),
+      Seq(Row(1), Row(1), Row(2), Row(2), Row(2), Row(2))
+    )
+  }
+
+  test("conditional function: greatest") {
+    checkAnswer(
+      testData2.select(greatest(lit(2), lit(3), col("a"), col("b"))).limit(1),
+      Row(3)
+    )
+    checkAnswer(
+      ctx.sql("SELECT greatest(a, 2) as g from testData2 order by g"),
+      Seq(Row(2), Row(2), Row(2), Row(2), Row(3), Row(3))
+    )
+  }
 }

From 6b89943834a8d9d5d0ecfd97efcc10056d08532a Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 13 Jul 2015 00:49:39 -0700
Subject: [PATCH 0351/1454] [SPARK-8944][SQL] Support casting between
 IntervalType and StringType

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7355 from cloud-fan/fromString and squashes the following commits:

3bbb9d6 [Wenchen Fan] fix code gen
7dab957 [Wenchen Fan] naming fix
0fbbe19 [Wenchen Fan] address comments
ac1f3d1 [Wenchen Fan] Support casting between IntervalType and StringType
---
 .../spark/sql/catalyst/expressions/Cast.scala | 17 ++++++-
 .../sql/catalyst/expressions/CastSuite.scala  | 10 ++++
 .../apache/spark/unsafe/types/Interval.java   | 48 +++++++++++++++++++
 .../spark/unsafe/types/IntervalSuite.java     | 46 ++++++++++++++++++
 4 files changed, 120 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 7f2383dedc035..ab02addfb4d25 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.unsafe.types.{Interval, UTF8String}
 
 
 object Cast {
@@ -55,6 +55,9 @@ object Cast {
 
     case (_, DateType) => true
 
+    case (StringType, IntervalType) => true
+    case (IntervalType, StringType) => true
+
     case (StringType, _: NumericType) => true
     case (BooleanType, _: NumericType) => true
     case (DateType, _: NumericType) => true
@@ -232,6 +235,13 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     case _ => _ => null
   }
 
+  // IntervalConverter
+  private[this] def castToInterval(from: DataType): Any => Any = from match {
+    case StringType =>
+      buildCast[UTF8String](_, s => Interval.fromString(s.toString))
+    case _ => _ => null
+  }
+
   // LongConverter
   private[this] def castToLong(from: DataType): Any => Any = from match {
     case StringType =>
@@ -405,6 +415,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
     case DateType => castToDate(from)
     case decimal: DecimalType => castToDecimal(from, decimal)
     case TimestampType => castToTimestamp(from)
+    case IntervalType => castToInterval(from)
     case BooleanType => castToBoolean(from)
     case ByteType => castToByte(from)
     case ShortType => castToShort(from)
@@ -442,6 +453,10 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
       case (_, StringType) =>
         defineCodeGen(ctx, ev, c => s"${ctx.stringType}.fromString(String.valueOf($c))")
 
+      case (StringType, IntervalType) =>
+        defineCodeGen(ctx, ev, c =>
+          s"org.apache.spark.unsafe.types.Interval.fromString($c.toString())")
+
       // fallback for DecimalType, this must be before other numeric types
       case (_, dt: DecimalType) =>
         super.genCode(ctx, ev)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 919fdd470b79a..1de161c367a1d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -563,4 +563,14 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       InternalRow(0L)))
   }
 
+  test("case between string and interval") {
+    import org.apache.spark.unsafe.types.Interval
+
+    checkEvaluation(Cast(Literal("interval -3 month 7 hours"), IntervalType),
+      new Interval(-3, 7 * Interval.MICROS_PER_HOUR))
+    checkEvaluation(Cast(Literal.create(
+      new Interval(15, -3 * Interval.MICROS_PER_DAY), IntervalType), StringType),
+      "interval 1 years 3 months -3 days")
+  }
+
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
index 0af982d4844c2..eb7475e9df869 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
@@ -18,6 +18,8 @@
 package org.apache.spark.unsafe.types;
 
 import java.io.Serializable;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
 
 /**
  * The internal representation of interval type.
@@ -30,6 +32,52 @@ public final class Interval implements Serializable {
   public static final long MICROS_PER_DAY = MICROS_PER_HOUR * 24;
   public static final long MICROS_PER_WEEK = MICROS_PER_DAY * 7;
 
+  /**
+   * A function to generate regex which matches interval string's unit part like "3 years".
+   *
+   * First, we can leave out some units in interval string, and we only care about the value of
+   * unit, so here we use non-capturing group to wrap the actual regex.
+   * At the beginning of the actual regex, we should match spaces before the unit part.
+   * Next is the number part, starts with an optional "-" to represent negative value. We use
+   * capturing group to wrap this part as we need the value later.
+   * Finally is the unit name, ends with an optional "s".
+   */
+  private static String unitRegex(String unit) {
+    return "(?:\\s+(-?\\d+)\\s+" + unit + "s?)?";
+  }
+
+  private static Pattern p = Pattern.compile("interval" + unitRegex("year") + unitRegex("month") +
+    unitRegex("week") + unitRegex("day") + unitRegex("hour") + unitRegex("minute") +
+    unitRegex("second") + unitRegex("millisecond") + unitRegex("microsecond"));
+
+  private static long toLong(String s) {
+    if (s == null) {
+      return 0;
+    } else {
+      return Long.valueOf(s);
+    }
+  }
+
+  public static Interval fromString(String s) {
+    if (s == null) {
+      return null;
+    }
+    Matcher m = p.matcher(s);
+    if (!m.matches() || s.equals("interval")) {
+      return null;
+    } else {
+      long months = toLong(m.group(1)) * 12 + toLong(m.group(2));
+      long microseconds = toLong(m.group(3)) * MICROS_PER_WEEK;
+      microseconds += toLong(m.group(4)) * MICROS_PER_DAY;
+      microseconds += toLong(m.group(5)) * MICROS_PER_HOUR;
+      microseconds += toLong(m.group(6)) * MICROS_PER_MINUTE;
+      microseconds += toLong(m.group(7)) * MICROS_PER_SECOND;
+      microseconds += toLong(m.group(8)) * MICROS_PER_MILLI;
+      microseconds += toLong(m.group(9));
+      return new Interval((int) months, microseconds);
+    }
+  }
+
   public final int months;
   public final long microseconds;
 
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
index 0f4f38b2b03be..44a949a371f2b 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
@@ -56,4 +56,50 @@ public void toStringTest() {
     i = new Interval(34, 3 * MICROS_PER_WEEK + 13 * MICROS_PER_HOUR + 123);
     assertEquals(i.toString(), "interval 2 years 10 months 3 weeks 13 hours 123 microseconds");
   }
+
+  @Test
+  public void fromStringTest() {
+    testSingleUnit("year", 3, 36, 0);
+    testSingleUnit("month", 3, 3, 0);
+    testSingleUnit("week", 3, 0, 3 * MICROS_PER_WEEK);
+    testSingleUnit("day", 3, 0, 3 * MICROS_PER_DAY);
+    testSingleUnit("hour", 3, 0, 3 * MICROS_PER_HOUR);
+    testSingleUnit("minute", 3, 0, 3 * MICROS_PER_MINUTE);
+    testSingleUnit("second", 3, 0, 3 * MICROS_PER_SECOND);
+    testSingleUnit("millisecond", 3, 0, 3 * MICROS_PER_MILLI);
+    testSingleUnit("microsecond", 3, 0, 3);
+
+    String input;
+
+    input = "interval   -5  years  23   month";
+    Interval result = new Interval(-5 * 12 + 23, 0);
+    assertEquals(Interval.fromString(input), result);
+
+    // Error cases
+    input = "interval   3month 1 hour";
+    assertEquals(Interval.fromString(input), null);
+
+    input = "interval 3 moth 1 hour";
+    assertEquals(Interval.fromString(input), null);
+
+    input = "interval";
+    assertEquals(Interval.fromString(input), null);
+
+    input = "int";
+    assertEquals(Interval.fromString(input), null);
+
+    input = "";
+    assertEquals(Interval.fromString(input), null);
+
+    input = null;
+    assertEquals(Interval.fromString(input), null);
+  }
+
+  private void testSingleUnit(String unit, int number, int months, long microseconds) {
+    String input1 = "interval " + number + " " + unit;
+    String input2 = "interval " + number + " " + unit + "s";
+    Interval result = new Interval(months, microseconds);
+    assertEquals(Interval.fromString(input1), result);
+    assertEquals(Interval.fromString(input2), result);
+  }
 }

From a5bc803b7271028e34de1548d55b80ecfb812a7b Mon Sep 17 00:00:00 2001
From: "Vincent D. Warmerdam" <vincentwarmerdam@gmail.com>
Date: Mon, 13 Jul 2015 08:15:54 -0700
Subject: [PATCH 0352/1454] [SPARK-8596] Add module for rstudio link to spark

shivaram, added module for rstudio install

Author: Vincent D. Warmerdam <vincentwarmerdam@gmail.com>

Closes #7366 from koaning/rstudio-install and squashes the following commits:

e47c2da [Vincent D. Warmerdam] added rstudio module
---
 ec2/spark_ec2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index ae4f2ecc5bde7..7c83d68e7993e 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -793,7 +793,7 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key):
             ssh_write(slave_address, opts, ['tar', 'x'], dot_ssh_tar)
 
     modules = ['spark', 'ephemeral-hdfs', 'persistent-hdfs',
-               'mapreduce', 'spark-standalone', 'tachyon']
+               'mapreduce', 'spark-standalone', 'tachyon', 'rstudio']
 
     if opts.hadoop_major_version == "1":
         modules = list(filter(lambda x: x != "mapreduce", modules))

From 7f487c8bde14dbdd244a3493ad11a129ef2bb327 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Mon, 13 Jul 2015 08:21:47 -0700
Subject: [PATCH 0353/1454] [SPARK-6797] [SPARKR] Add support for YARN cluster
 mode.

This PR enables SparkR to dynamically ship the SparkR binary package to the AM node in YARN cluster mode, thus it is no longer required that the SparkR package be installed on each worker node.

This PR uses the JDK jar tool to package the SparkR package, because jar is thought to be available on both Linux/Windows platforms where JDK has been installed.

This PR does not address the R worker involved in RDD API. Will address it in a separate JIRA issue.

This PR does not address SBT build. SparkR installation and packaging by SBT will be addressed in a separate JIRA issue.

R/install-dev.bat is not tested. shivaram , Could you help to test it?

Author: Sun Rui <rui.sun@intel.com>

Closes #6743 from sun-rui/SPARK-6797 and squashes the following commits:

ca63c86 [Sun Rui] Adjust MimaExcludes after rebase.
7313374 [Sun Rui] Fix unit test errors.
72695fb [Sun Rui] Fix unit test failures.
193882f [Sun Rui] Fix Mima test error.
fe25a33 [Sun Rui] Fix Mima test error.
35ecfa3 [Sun Rui] Fix comments.
c38a005 [Sun Rui] Unzipped SparkR binary package is still required for standalone and Mesos modes.
b05340c [Sun Rui] Fix scala style.
2ca5048 [Sun Rui] Fix comments.
1acefd1 [Sun Rui] Fix scala style.
0aa1e97 [Sun Rui] Fix scala style.
41d4f17 [Sun Rui] Add support for locating SparkR package for R workers required by RDD APIs.
49ff948 [Sun Rui] Invoke jar.exe with full path in install-dev.bat.
7b916c5 [Sun Rui] Use 'rem' consistently.
3bed438 [Sun Rui] Add a comment.
681afb0 [Sun Rui] Fix a bug that RRunner does not handle client deployment modes.
cedfbe2 [Sun Rui] [SPARK-6797][SPARKR] Add support for YARN cluster mode.
---
 R/install-dev.bat                             |  5 ++
 R/install-dev.sh                              |  8 ++-
 R/pkg/DESCRIPTION                             |  1 -
 R/pkg/R/RDD.R                                 |  2 -
 R/pkg/R/pairRDD.R                             |  1 -
 R/pkg/R/sparkR.R                              | 10 ---
 R/pkg/R/zzz.R                                 | 20 ------
 R/pkg/inst/profile/general.R                  |  4 +-
 .../scala/org/apache/spark/api/r/RRDD.scala   | 21 +++---
 .../scala/org/apache/spark/api/r/RUtils.scala | 65 +++++++++++++++++++
 .../org/apache/spark/deploy/RRunner.scala     |  7 +-
 .../org/apache/spark/deploy/SparkSubmit.scala | 27 ++++++++
 .../spark/deploy/SparkSubmitSuite.scala       |  3 +-
 make-distribution.sh                          |  1 +
 project/MimaExcludes.scala                    | 12 ++++
 15 files changed, 133 insertions(+), 54 deletions(-)
 delete mode 100644 R/pkg/R/zzz.R
 create mode 100644 core/src/main/scala/org/apache/spark/api/r/RUtils.scala

diff --git a/R/install-dev.bat b/R/install-dev.bat
index 008a5c668bc45..f32670b67de96 100644
--- a/R/install-dev.bat
+++ b/R/install-dev.bat
@@ -25,3 +25,8 @@ set SPARK_HOME=%~dp0..
 MKDIR %SPARK_HOME%\R\lib
 
 R.exe CMD INSTALL --library="%SPARK_HOME%\R\lib"  %SPARK_HOME%\R\pkg\
+
+rem Zip the SparkR package so that it can be distributed to worker nodes on YARN
+pushd %SPARK_HOME%\R\lib
+%JAVA_HOME%\bin\jar.exe cfM "%SPARK_HOME%\R\lib\sparkr.zip" SparkR
+popd
diff --git a/R/install-dev.sh b/R/install-dev.sh
index 1edd551f8d243..4972bb9217072 100755
--- a/R/install-dev.sh
+++ b/R/install-dev.sh
@@ -34,7 +34,7 @@ LIB_DIR="$FWDIR/lib"
 
 mkdir -p $LIB_DIR
 
-pushd $FWDIR
+pushd $FWDIR > /dev/null
 
 # Generate Rd files if devtools is installed
 Rscript -e ' if("devtools" %in% rownames(installed.packages())) { library(devtools); devtools::document(pkg="./pkg", roclets=c("rd")) }'
@@ -42,4 +42,8 @@ Rscript -e ' if("devtools" %in% rownames(installed.packages())) { library(devtoo
 # Install SparkR to $LIB_DIR
 R CMD INSTALL --library=$LIB_DIR $FWDIR/pkg/
 
-popd
+# Zip the SparkR package so that it can be distributed to worker nodes on YARN
+cd $LIB_DIR
+jar cfM "$LIB_DIR/sparkr.zip" SparkR
+
+popd > /dev/null
diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index efc85bbc4b316..d028821534b1a 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -32,4 +32,3 @@ Collate:
     'serialize.R'
     'sparkR.R'
     'utils.R'
-    'zzz.R'
diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R
index 89511141d3ef7..d2d096709245d 100644
--- a/R/pkg/R/RDD.R
+++ b/R/pkg/R/RDD.R
@@ -165,7 +165,6 @@ setMethod("getJRDD", signature(rdd = "PipelinedRDD"),
                                    serializedFuncArr,
                                    rdd@env$prev_serializedMode,
                                    packageNamesArr,
-                                   as.character(.sparkREnv[["libname"]]),
                                    broadcastArr,
                                    callJMethod(prev_jrdd, "classTag"))
             } else {
@@ -175,7 +174,6 @@ setMethod("getJRDD", signature(rdd = "PipelinedRDD"),
                                    rdd@env$prev_serializedMode,
                                    serializedMode,
                                    packageNamesArr,
-                                   as.character(.sparkREnv[["libname"]]),
                                    broadcastArr,
                                    callJMethod(prev_jrdd, "classTag"))
             }
diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R
index 0f1179e0aa51a..ebc6ff65e9d0f 100644
--- a/R/pkg/R/pairRDD.R
+++ b/R/pkg/R/pairRDD.R
@@ -215,7 +215,6 @@ setMethod("partitionBy",
                                        serializedHashFuncBytes,
                                        getSerializedMode(x),
                                        packageNamesArr,
-                                       as.character(.sparkREnv$libname),
                                        broadcastArr,
                                        callJMethod(jrdd, "classTag"))
 
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 048eb8ed541e4..172335809dec2 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -17,10 +17,6 @@
 
 .sparkREnv <- new.env()
 
-sparkR.onLoad <- function(libname, pkgname) {
-  .sparkREnv$libname <- libname
-}
-
 # Utility function that returns TRUE if we have an active connection to the
 # backend and FALSE otherwise
 connExists <- function(env) {
@@ -80,7 +76,6 @@ sparkR.stop <- function() {
 #' @param sparkEnvir Named list of environment variables to set on worker nodes.
 #' @param sparkExecutorEnv Named list of environment variables to be used when launching executors.
 #' @param sparkJars Character string vector of jar files to pass to the worker nodes.
-#' @param sparkRLibDir The path where R is installed on the worker nodes.
 #' @param sparkPackages Character string vector of packages from spark-packages.org
 #' @export
 #' @examples
@@ -101,7 +96,6 @@ sparkR.init <- function(
   sparkEnvir = list(),
   sparkExecutorEnv = list(),
   sparkJars = "",
-  sparkRLibDir = "",
   sparkPackages = "") {
 
   if (exists(".sparkRjsc", envir = .sparkREnv)) {
@@ -170,10 +164,6 @@ sparkR.init <- function(
     sparkHome <- normalizePath(sparkHome)
   }
 
-  if (nchar(sparkRLibDir) != 0) {
-    .sparkREnv$libname <- sparkRLibDir
-  }
-
   sparkEnvirMap <- new.env()
   for (varname in names(sparkEnvir)) {
     sparkEnvirMap[[varname]] <- sparkEnvir[[varname]]
diff --git a/R/pkg/R/zzz.R b/R/pkg/R/zzz.R
deleted file mode 100644
index 301feade65fa3..0000000000000
--- a/R/pkg/R/zzz.R
+++ /dev/null
@@ -1,20 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-.onLoad <- function(libname, pkgname) {
-  sparkR.onLoad(libname, pkgname)
-}
diff --git a/R/pkg/inst/profile/general.R b/R/pkg/inst/profile/general.R
index 8fe711b622086..2a8a8213d0849 100644
--- a/R/pkg/inst/profile/general.R
+++ b/R/pkg/inst/profile/general.R
@@ -16,7 +16,7 @@
 #
 
 .First <- function() {
-  home <- Sys.getenv("SPARK_HOME")
-  .libPaths(c(file.path(home, "R", "lib"), .libPaths()))
+  packageDir <- Sys.getenv("SPARKR_PACKAGE_DIR")
+  .libPaths(c(packageDir, .libPaths()))
   Sys.setenv(NOAWT=1)
 }
diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
index ff1702f7dea48..23a470d6afcae 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
@@ -39,7 +39,6 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag](
     deserializer: String,
     serializer: String,
     packageNames: Array[Byte],
-    rLibDir: String,
     broadcastVars: Array[Broadcast[Object]])
   extends RDD[U](parent) with Logging {
   protected var dataStream: DataInputStream = _
@@ -60,7 +59,7 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag](
 
     // The stdout/stderr is shared by multiple tasks, because we use one daemon
     // to launch child process as worker.
-    val errThread = RRDD.createRWorker(rLibDir, listenPort)
+    val errThread = RRDD.createRWorker(listenPort)
 
     // We use two sockets to separate input and output, then it's easy to manage
     // the lifecycle of them to avoid deadlock.
@@ -235,11 +234,10 @@ private class PairwiseRRDD[T: ClassTag](
     hashFunc: Array[Byte],
     deserializer: String,
     packageNames: Array[Byte],
-    rLibDir: String,
     broadcastVars: Array[Object])
   extends BaseRRDD[T, (Int, Array[Byte])](
     parent, numPartitions, hashFunc, deserializer,
-    SerializationFormats.BYTE, packageNames, rLibDir,
+    SerializationFormats.BYTE, packageNames,
     broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) {
 
   override protected def readData(length: Int): (Int, Array[Byte]) = {
@@ -266,10 +264,9 @@ private class RRDD[T: ClassTag](
     deserializer: String,
     serializer: String,
     packageNames: Array[Byte],
-    rLibDir: String,
     broadcastVars: Array[Object])
   extends BaseRRDD[T, Array[Byte]](
-    parent, -1, func, deserializer, serializer, packageNames, rLibDir,
+    parent, -1, func, deserializer, serializer, packageNames,
     broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) {
 
   override protected def readData(length: Int): Array[Byte] = {
@@ -293,10 +290,9 @@ private class StringRRDD[T: ClassTag](
     func: Array[Byte],
     deserializer: String,
     packageNames: Array[Byte],
-    rLibDir: String,
     broadcastVars: Array[Object])
   extends BaseRRDD[T, String](
-    parent, -1, func, deserializer, SerializationFormats.STRING, packageNames, rLibDir,
+    parent, -1, func, deserializer, SerializationFormats.STRING, packageNames,
     broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) {
 
   override protected def readData(length: Int): String = {
@@ -392,9 +388,10 @@ private[r] object RRDD {
     thread
   }
 
-  private def createRProcess(rLibDir: String, port: Int, script: String): BufferedStreamThread = {
+  private def createRProcess(port: Int, script: String): BufferedStreamThread = {
     val rCommand = SparkEnv.get.conf.get("spark.sparkr.r.command", "Rscript")
     val rOptions = "--vanilla"
+    val rLibDir = RUtils.sparkRPackagePath(isDriver = false)
     val rExecScript = rLibDir + "/SparkR/worker/" + script
     val pb = new ProcessBuilder(List(rCommand, rOptions, rExecScript))
     // Unset the R_TESTS environment variable for workers.
@@ -413,7 +410,7 @@ private[r] object RRDD {
   /**
    * ProcessBuilder used to launch worker R processes.
    */
-  def createRWorker(rLibDir: String, port: Int): BufferedStreamThread = {
+  def createRWorker(port: Int): BufferedStreamThread = {
     val useDaemon = SparkEnv.get.conf.getBoolean("spark.sparkr.use.daemon", true)
     if (!Utils.isWindows && useDaemon) {
       synchronized {
@@ -421,7 +418,7 @@ private[r] object RRDD {
           // we expect one connections
           val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost"))
           val daemonPort = serverSocket.getLocalPort
-          errThread = createRProcess(rLibDir, daemonPort, "daemon.R")
+          errThread = createRProcess(daemonPort, "daemon.R")
           // the socket used to send out the input of task
           serverSocket.setSoTimeout(10000)
           val sock = serverSocket.accept()
@@ -443,7 +440,7 @@ private[r] object RRDD {
         errThread
       }
     } else {
-      createRProcess(rLibDir, port, "worker.R")
+      createRProcess(port, "worker.R")
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
new file mode 100644
index 0000000000000..d53abd3408c55
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.api.r
+
+import java.io.File
+
+import org.apache.spark.{SparkEnv, SparkException}
+
+private[spark] object RUtils {
+  /**
+   * Get the SparkR package path in the local spark distribution.
+   */
+  def localSparkRPackagePath: Option[String] = {
+    val sparkHome = sys.env.get("SPARK_HOME")
+    sparkHome.map(
+      Seq(_, "R", "lib").mkString(File.separator)
+    )
+  }
+
+  /**
+   * Get the SparkR package path in various deployment modes.
+   * This assumes that Spark properties `spark.master` and `spark.submit.deployMode`
+   * and environment variable `SPARK_HOME` are set.
+   */
+  def sparkRPackagePath(isDriver: Boolean): String = {
+    val (master, deployMode) =
+      if (isDriver) {
+        (sys.props("spark.master"), sys.props("spark.submit.deployMode"))
+      } else {
+        val sparkConf = SparkEnv.get.conf
+        (sparkConf.get("spark.master"), sparkConf.get("spark.submit.deployMode"))
+      }
+
+    val isYarnCluster = master.contains("yarn") && deployMode == "cluster"
+    val isYarnClient = master.contains("yarn") && deployMode == "client"
+
+    // In YARN mode, the SparkR package is distributed as an archive symbolically
+    // linked to the "sparkr" file in the current directory. Note that this does not apply
+    // to the driver in client mode because it is run outside of the cluster.
+    if (isYarnCluster || (isYarnClient && !isDriver)) {
+      new File("sparkr").getAbsolutePath
+    } else {
+      // Otherwise, assume the package is local
+      // TODO: support this for Mesos
+      localSparkRPackagePath.getOrElse {
+        throw new SparkException("SPARK_HOME not set. Can't locate SparkR package.")
+      }
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
index 4165740312e03..c0cab22fa8252 100644
--- a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
@@ -24,7 +24,7 @@ import scala.collection.JavaConversions._
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.api.r.RBackend
+import org.apache.spark.api.r.{RBackend, RUtils}
 import org.apache.spark.util.RedirectThread
 
 /**
@@ -71,9 +71,10 @@ object RRunner {
         val builder = new ProcessBuilder(Seq(rCommand, rFileNormalized) ++ otherArgs)
         val env = builder.environment()
         env.put("EXISTING_SPARKR_BACKEND_PORT", sparkRBackendPort.toString)
-        val sparkHome = System.getenv("SPARK_HOME")
+        val rPackageDir = RUtils.sparkRPackagePath(isDriver = true)
+        env.put("SPARKR_PACKAGE_DIR", rPackageDir)
         env.put("R_PROFILE_USER",
-          Seq(sparkHome, "R", "lib", "SparkR", "profile", "general.R").mkString(File.separator))
+          Seq(rPackageDir, "SparkR", "profile", "general.R").mkString(File.separator))
         builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize
         val process = builder.start()
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 4cec9017b8adb..7089a7e26707f 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -37,6 +37,7 @@ import org.apache.ivy.core.settings.IvySettings
 import org.apache.ivy.plugins.matcher.GlobPatternMatcher
 import org.apache.ivy.plugins.repository.file.FileRepository
 import org.apache.ivy.plugins.resolver.{FileSystemResolver, ChainResolver, IBiblioResolver}
+import org.apache.spark.api.r.RUtils
 import org.apache.spark.SPARK_VERSION
 import org.apache.spark.deploy.rest._
 import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils}
@@ -79,6 +80,7 @@ object SparkSubmit {
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
   private val SPARKR_SHELL = "sparkr-shell"
+  private val SPARKR_PACKAGE_ARCHIVE = "sparkr.zip"
 
   private val CLASS_NOT_FOUND_EXIT_STATUS = 101
 
@@ -262,6 +264,12 @@ object SparkSubmit {
       }
     }
 
+    // Update args.deployMode if it is null. It will be passed down as a Spark property later.
+    (args.deployMode, deployMode) match {
+      case (null, CLIENT) => args.deployMode = "client"
+      case (null, CLUSTER) => args.deployMode = "cluster"
+      case _ =>
+    }
     val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER
     val isMesosCluster = clusterManager == MESOS && deployMode == CLUSTER
 
@@ -347,6 +355,23 @@ object SparkSubmit {
       }
     }
 
+    // In YARN mode for an R app, add the SparkR package archive to archives
+    // that can be distributed with the job
+    if (args.isR && clusterManager == YARN) {
+      val rPackagePath = RUtils.localSparkRPackagePath
+      if (rPackagePath.isEmpty) {
+        printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
+      }
+      val rPackageFile = new File(rPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
+      if (!rPackageFile.exists()) {
+        printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
+      }
+      val localURI = Utils.resolveURI(rPackageFile.getAbsolutePath)
+
+      // Assigns a symbol link name "sparkr" to the shipped package.
+      args.archives = mergeFileLists(args.archives, localURI.toString + "#sparkr")
+    }
+
     // If we're running a R app, set the main class to our specific R runner
     if (args.isR && deployMode == CLIENT) {
       if (args.primaryResource == SPARKR_SHELL) {
@@ -375,6 +400,8 @@ object SparkSubmit {
 
       // All cluster managers
       OptionAssigner(args.master, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.master"),
+      OptionAssigner(args.deployMode, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
+        sysProp = "spark.submit.deployMode"),
       OptionAssigner(args.name, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.app.name"),
       OptionAssigner(args.jars, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.jars"),
       OptionAssigner(args.ivyRepoPath, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.jars.ivy"),
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 1b64c329b5d4b..e7878bde6fcb0 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -246,7 +246,7 @@ class SparkSubmitSuite
       mainClass should be ("org.apache.spark.deploy.Client")
     }
     classpath should have size 0
-    sysProps should have size 8
+    sysProps should have size 9
     sysProps.keys should contain ("SPARK_SUBMIT")
     sysProps.keys should contain ("spark.master")
     sysProps.keys should contain ("spark.app.name")
@@ -255,6 +255,7 @@ class SparkSubmitSuite
     sysProps.keys should contain ("spark.driver.cores")
     sysProps.keys should contain ("spark.driver.supervise")
     sysProps.keys should contain ("spark.shuffle.spill")
+    sysProps.keys should contain ("spark.submit.deployMode")
     sysProps("spark.shuffle.spill") should be ("false")
   }
 
diff --git a/make-distribution.sh b/make-distribution.sh
index 9f063da3a16c0..cac7032bb2e87 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -219,6 +219,7 @@ cp -r "$SPARK_HOME/ec2" "$DISTDIR"
 if [ -d "$SPARK_HOME"/R/lib/SparkR ]; then
   mkdir -p "$DISTDIR"/R/lib
   cp -r "$SPARK_HOME/R/lib/SparkR" "$DISTDIR"/R/lib
+  cp "$SPARK_HOME/R/lib/sparkr.zip" "$DISTDIR"/R/lib
 fi
 
 # Download and copy in tachyon, if requested
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 79089aae2a37c..4e4e810ec36e3 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -83,6 +83,18 @@ object MimaExcludes {
               "org.apache.spark.streaming.scheduler.InputInfo$"),
             ProblemFilters.exclude[MissingClassProblem](
               "org.apache.spark.streaming.scheduler.InputInfo")
+          ) ++ Seq(
+            // SPARK-6797 Support YARN modes for SparkR
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.r.PairwiseRRDD.this"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.r.RRDD.createRWorker"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.r.RRDD.this"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.r.StringRRDD.this"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.r.BaseRRDD.this")
           )
 
         case v if v.startsWith("1.4") =>

From 9b62e9375f032548d386aec7468e3d0f7c6da7b2 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Mon, 13 Jul 2015 09:47:53 -0700
Subject: [PATCH 0354/1454] [SPARK-8706] [PYSPARK] [PROJECT INFRA] Add pylint
 checks to PySpark

This adds Pylint checks to PySpark.

For now this lazy installs using easy_install to /dev/pylint (similar to the pep8 script).
We still need to figure out what rules to be allowed.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7241 from MechCoder/pylint and squashes the following commits:

8496834 [MechCoder] Silence warnings and make pylint tests fail to check if it works in jenkins
57393a3 [MechCoder] undefined-variable
a8e2547 [MechCoder] Minor changes
7753810 [MechCoder] remove trailing whitespace
75c5d2b [MechCoder] Remove blacklisted arguments and pointless statements check
6bde250 [MechCoder] Disable all checks for now
3464666 [MechCoder] Add pylint configuration file
d28109f [MechCoder] [SPARK-8706] [PySpark] [Project infra] Add pylint checks to PySpark
---
 dev/lint-python                   |  57 ++++-
 pylintrc                          | 404 ++++++++++++++++++++++++++++++
 python/pyspark/ml/param/shared.py |   4 +-
 3 files changed, 455 insertions(+), 10 deletions(-)
 create mode 100644 pylintrc

diff --git a/dev/lint-python b/dev/lint-python
index 0c3586462cb37..e02dff220eb87 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -21,12 +21,14 @@ SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
 SPARK_ROOT_DIR="$(dirname "$SCRIPT_DIR")"
 PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/ ./dev/sparktestsupport"
 PATHS_TO_CHECK="$PATHS_TO_CHECK ./dev/run-tests.py ./python/run-tests.py"
-PYTHON_LINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/python-lint-report.txt"
+PEP8_REPORT_PATH="$SPARK_ROOT_DIR/dev/pep8-report.txt"
+PYLINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/pylint-report.txt"
+PYLINT_INSTALL_INFO="$SPARK_ROOT_DIR/dev/pylint-info.txt"
 
 cd "$SPARK_ROOT_DIR"
 
 # compileall: https://docs.python.org/2/library/compileall.html
-python -B -m compileall -q -l $PATHS_TO_CHECK > "$PYTHON_LINT_REPORT_PATH"
+python -B -m compileall -q -l $PATHS_TO_CHECK > "$PEP8_REPORT_PATH"
 compile_status="${PIPESTATUS[0]}"
 
 # Get pep8 at runtime so that we don't rely on it being installed on the build server.
@@ -47,11 +49,36 @@ if [ ! -e "$PEP8_SCRIPT_PATH" ]; then
     fi
 fi
 
+# Easy install pylint in /dev/pylint. To easy_install into a directory, the PYTHONPATH should
+# be set to the directory.
+# dev/pylint should be appended to the PATH variable as well.
+# Jenkins by default installs the pylint3 version, so for now this just checks the code quality
+# of python3.
+export "PYTHONPATH=$SPARK_ROOT_DIR/dev/pylint"
+export "PYLINT_HOME=$PYTHONPATH"
+export "PATH=$PYTHONPATH:$PATH"
+
+if [ ! -d "$PYLINT_HOME" ]; then
+    mkdir "$PYLINT_HOME"
+    # Redirect the annoying pylint installation output.
+    easy_install -d "$PYLINT_HOME" pylint==1.4.4 &>> "$PYLINT_INSTALL_INFO"
+    easy_install_status="$?"
+
+    if [ "$easy_install_status" -ne 0 ]; then
+        echo "Unable to install pylint locally in \"$PYTHONPATH\"."
+        cat "$PYLINT_INSTALL_INFO"
+        exit "$easy_install_status"
+    fi
+
+    rm "$PYLINT_INSTALL_INFO"
+
+fi
+
 # There is no need to write this output to a file
 #+ first, but we do so so that the check status can
 #+ be output before the report, like with the
 #+ scalastyle and RAT checks.
-python "$PEP8_SCRIPT_PATH" --ignore=E402,E731,E241,W503,E226 $PATHS_TO_CHECK >> "$PYTHON_LINT_REPORT_PATH"
+python "$PEP8_SCRIPT_PATH" --ignore=E402,E731,E241,W503,E226 $PATHS_TO_CHECK >> "$PEP8_REPORT_PATH"
 pep8_status="${PIPESTATUS[0]}"
 
 if [ "$compile_status" -eq 0 -a "$pep8_status" -eq 0 ]; then
@@ -61,13 +88,27 @@ else
 fi
 
 if [ "$lint_status" -ne 0 ]; then
-    echo "Python lint checks failed."
-    cat "$PYTHON_LINT_REPORT_PATH"
+    echo "PEP8 checks failed."
+    cat "$PEP8_REPORT_PATH"
+else
+    echo "PEP8 checks passed."
+fi
+
+rm "$PEP8_REPORT_PATH"
+
+for to_be_checked in "$PATHS_TO_CHECK"
+do
+    pylint --rcfile="$SPARK_ROOT_DIR/pylintrc" $to_be_checked >> "$PYLINT_REPORT_PATH"
+done
+
+if [ "${PIPESTATUS[0]}" -ne 0 ]; then
+    lint_status=1
+    echo "Pylint checks failed."
+    cat "$PYLINT_REPORT_PATH"
 else
-    echo "Python lint checks passed."
+    echo "Pylint checks passed."
 fi
 
-# rm "$PEP8_SCRIPT_PATH"
-rm "$PYTHON_LINT_REPORT_PATH"
+rm "$PYLINT_REPORT_PATH"
 
 exit "$lint_status"
diff --git a/pylintrc b/pylintrc
new file mode 100644
index 0000000000000..061775960393b
--- /dev/null
+++ b/pylintrc
@@ -0,0 +1,404 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+[MASTER]
+
+# Specify a configuration file.
+#rcfile=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Profiled execution.
+profile=no
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=pyspark.heapq3
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Use multiple processes to speed up Pylint.
+jobs=1
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code
+extension-pkg-whitelist=
+
+# Allow optimization of some AST trees. This will activate a peephole AST
+# optimizer, which will apply various small optimizations. For instance, it can
+# be used to obtain the result of joining multiple strings with the addition
+# operator. Joining a lot of strings can lead to a maximum recursion error in
+# Pylint and this flag can prevent that. It has one side effect, the resulting
+# AST will be different than the one from reality.
+optimize-ast=no
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
+confidence=
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time. See also the "--disable" option for examples.
+enable=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once).You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use"--disable=all --enable=classes
+# --disable=W"
+
+# These errors are arranged in order of number of warning given in pylint.
+# If you would like to improve the code quality of pyspark, remove any of these disabled errors
+# run ./dev/lint-python and see if the errors raised by pylint can be fixed.
+
+disable=invalid-name,missing-docstring,protected-access,unused-argument,no-member,unused-wildcard-import,redefined-builtin,too-many-arguments,unused-variable,too-few-public-methods,bad-continuation,duplicate-code,redefined-outer-name,too-many-ancestors,import-error,superfluous-parens,unused-import,line-too-long,no-name-in-module,unnecessary-lambda,import-self,no-self-use,unidiomatic-typecheck,fixme,too-many-locals,cyclic-import,too-many-branches,bare-except,wildcard-import,dangerous-default-value,broad-except,too-many-public-methods,deprecated-lambda,anomalous-backslash-in-string,too-many-lines,reimported,too-many-statements,bad-whitespace,unpacking-non-sequence,too-many-instance-attributes,abstract-method,old-style-class,global-statement,attribute-defined-outside-init,arguments-differ,undefined-all-variable,no-init,useless-else-on-loop,super-init-not-called,notimplemented-raised,too-many-return-statements,pointless-string-statement,global-variable-undefined,bad-classmethod-argument,too-many-format-args,parse-error,no-self-argument,pointless-statement,undefined-variable
+
+
+[REPORTS]
+
+# Set the output format. Available formats are text, parseable, colorized, msvs
+# (visual studio) and html. You can also give a reporter class, eg
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Put messages in a separate file for each module / package specified on the
+# command line instead of printing them on stdout. Reports (if any) will be
+# written in a file name "pylint_global.[txt|html]".
+files-output=no
+
+# Tells whether to display a full report or only the messages
+reports=no
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Add a comment according to your evaluation note. This is used by the global
+# evaluation report (RP0004).
+comment=no
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details
+#msg-template=
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,XXX,TODO
+
+
+[BASIC]
+
+# Required attributes for module, separated by a comma
+required-attributes=
+
+# List of builtins function names that should not be used, separated by a comma
+bad-functions=
+
+# Good variable names which should always be accepted, separated by a comma
+good-names=i,j,k,ex,Run,_
+
+# Bad variable names which should always be refused, separated by a comma
+bad-names=baz,toto,tutu,tata
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Include a hint for the correct naming format with invalid-name
+include-naming-hint=no
+
+# Regular expression matching correct function names
+function-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for function names
+function-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct variable names
+variable-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for variable names
+variable-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct constant names
+const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Naming hint for constant names
+const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Regular expression matching correct attribute names
+attr-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for attribute names
+attr-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct argument names
+argument-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for argument names
+argument-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct class attribute names
+class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Naming hint for class attribute names
+class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Regular expression matching correct inline iteration names
+inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
+
+# Naming hint for inline iteration names
+inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
+
+# Regular expression matching correct class names
+class-rgx=[A-Z_][a-zA-Z0-9]+$
+
+# Naming hint for class names
+class-name-hint=[A-Z_][a-zA-Z0-9]+$
+
+# Regular expression matching correct module names
+module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Naming hint for module names
+module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Regular expression matching correct method names
+method-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for method names
+method-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=__.*__
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+
+[FORMAT]
+
+# Maximum number of characters on a single line.
+max-line-length=100
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+# List of optional constructs for which whitespace checking is disabled
+no-space-check=trailing-comma,dict-separator
+
+# Maximum number of lines in a module
+max-module-lines=1000
+
+# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren=4
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+
+[SIMILARITIES]
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+
+[VARIABLES]
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# A regular expression matching the name of dummy variables (i.e. expectedly
+# not used).
+dummy-variables-rgx=_$|dummy
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid to define new builtins when possible.
+additional-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,_cb
+
+
+[SPELLING]
+
+# Spelling dictionary name. Available dictionaries: none. To make it working
+# install python-enchant package.
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[LOGGING]
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format
+logging-modules=logging
+
+
+[TYPECHECK]
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis
+ignored-modules=
+
+# List of classes names for which member attributes should not be checked
+# (useful for classes with attributes dynamically set).
+ignored-classes=SQLObject
+
+# When zope mode is activated, add a predefined set of Zope acquired attributes
+# to generated-members.
+zope=no
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E0201 when accessed. Python regular
+# expressions are accepted.
+generated-members=REQUEST,acl_users,aq_parent
+
+
+[CLASSES]
+
+# List of interface methods to ignore, separated by a comma. This is used for
+# instance to not check methods defines in Zope's Interface base class.
+ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,__new__,setUp
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=mcs
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,_fields,_replace,_source,_make
+
+
+[IMPORTS]
+
+# Deprecated modules which should not be used, separated by a comma
+deprecated-modules=regsub,TERMIOS,Bastion,rexec
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled)
+import-graph=
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled)
+ext-import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled)
+int-import-graph=
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method
+max-args=5
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore
+ignored-argument-names=_.*
+
+# Maximum number of locals for function / method body
+max-locals=15
+
+# Maximum number of return / yield for function / method body
+max-returns=6
+
+# Maximum number of branch for function / method body
+max-branches=12
+
+# Maximum number of statements in function / method body
+max-statements=50
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception"
+overgeneral-exceptions=Exception
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index bc088e4c29e26..595124726366d 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -444,7 +444,7 @@ class DecisionTreeParams(Params):
     minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.")
     maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
     cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-    
+
 
     def __init__(self):
         super(DecisionTreeParams, self).__init__()
@@ -460,7 +460,7 @@ def __init__(self):
         self.maxMemoryInMB = Param(self, "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
         #: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.
         self.cacheNodeIds = Param(self, "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-        
+
     def setMaxDepth(self, value):
         """
         Sets the value of :py:attr:`maxDepth`.

From 5ca26fb64de99fa414dc59ce4cf29a0171894793 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Mon, 13 Jul 2015 11:20:04 -0700
Subject: [PATCH 0355/1454] [SPARK-8950] [WEBUI] Correct the calculation of
 SchedulerDelay in StagePage

In StagePage, the SchedulerDelay is calculated as totalExecutionTime - executorRunTime - executorOverhead - gettingResultTime.
But the totalExecutionTime is calculated in the way that doesn't include the gettingResultTime.

Author: Carson Wang <carson.wang@intel.com>

Closes #7319 from carsonwang/SchedulerDelayTime and squashes the following commits:

f66fb6e [Carson Wang] Update the code style
7d971ae [Carson Wang] Correct the calculation of SchedulerDelay
---
 .../org/apache/spark/ui/jobs/StagePage.scala  | 45 +++++++++----------
 1 file changed, 22 insertions(+), 23 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 60e3c6343122c..ff0a339a39c65 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -332,7 +332,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
             </td> +: getFormattedTimeQuantiles(serializationTimes)
 
           val gettingResultTimes = validTasks.map { case TaskUIData(info, _, _) =>
-            getGettingResultTime(info).toDouble
+            getGettingResultTime(info, currentTime).toDouble
           }
           val gettingResultQuantiles =
             <td>
@@ -346,7 +346,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
           // machine and to send back the result (but not the time to fetch the task result,
           // if it needed to be fetched from the block manager on the worker).
           val schedulerDelays = validTasks.map { case TaskUIData(info, metrics, _) =>
-            getSchedulerDelay(info, metrics.get).toDouble
+            getSchedulerDelay(info, metrics.get, currentTime).toDouble
           }
           val schedulerDelayTitle = <td><span data-toggle="tooltip"
             title={ToolTips.SCHEDULER_DELAY} data-placement="right">Scheduler Delay</span></td>
@@ -544,7 +544,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         val serializationTimeProportion = toProportion(serializationTime)
         val deserializationTime = metricsOpt.map(_.executorDeserializeTime).getOrElse(0L)
         val deserializationTimeProportion = toProportion(deserializationTime)
-        val gettingResultTime = getGettingResultTime(taskUIData.taskInfo)
+        val gettingResultTime = getGettingResultTime(taskUIData.taskInfo, currentTime)
         val gettingResultTimeProportion = toProportion(gettingResultTime)
         val schedulerDelay = totalExecutionTime -
           (executorComputingTime + shuffleReadTime + shuffleWriteTime +
@@ -685,11 +685,11 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         else metrics.map(_.executorRunTime).getOrElse(1L)
       val formatDuration = if (info.status == "RUNNING") UIUtils.formatDuration(duration)
         else metrics.map(m => UIUtils.formatDuration(m.executorRunTime)).getOrElse("")
-      val schedulerDelay = metrics.map(getSchedulerDelay(info, _)).getOrElse(0L)
+      val schedulerDelay = metrics.map(getSchedulerDelay(info, _, currentTime)).getOrElse(0L)
       val gcTime = metrics.map(_.jvmGCTime).getOrElse(0L)
       val taskDeserializationTime = metrics.map(_.executorDeserializeTime).getOrElse(0L)
       val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L)
-      val gettingResultTime = getGettingResultTime(info)
+      val gettingResultTime = getGettingResultTime(info, currentTime)
 
       val maybeAccumulators = info.accumulables
       val accumulatorsReadable = maybeAccumulators.map{acc => s"${acc.name}: ${acc.update.get}"}
@@ -852,32 +852,31 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
     <td>{errorSummary}{details}</td>
   }
 
-  private def getGettingResultTime(info: TaskInfo): Long = {
-    if (info.gettingResultTime > 0) {
-      if (info.finishTime > 0) {
+  private def getGettingResultTime(info: TaskInfo, currentTime: Long): Long = {
+    if (info.gettingResult) {
+      if (info.finished) {
         info.finishTime - info.gettingResultTime
       } else {
         // The task is still fetching the result.
-        System.currentTimeMillis - info.gettingResultTime
+        currentTime - info.gettingResultTime
       }
     } else {
       0L
     }
   }
 
-  private def getSchedulerDelay(info: TaskInfo, metrics: TaskMetrics): Long = {
-    val totalExecutionTime =
-      if (info.gettingResult) {
-        info.gettingResultTime - info.launchTime
-      } else if (info.finished) {
-        info.finishTime - info.launchTime
-      } else {
-        0
-      }
-    val executorOverhead = (metrics.executorDeserializeTime +
-      metrics.resultSerializationTime)
-    math.max(
-      0,
-      totalExecutionTime - metrics.executorRunTime - executorOverhead - getGettingResultTime(info))
+  private def getSchedulerDelay(info: TaskInfo, metrics: TaskMetrics, currentTime: Long): Long = {
+    if (info.finished) {
+      val totalExecutionTime = info.finishTime - info.launchTime
+      val executorOverhead = (metrics.executorDeserializeTime +
+        metrics.resultSerializationTime)
+      math.max(
+        0,
+        totalExecutionTime - metrics.executorRunTime - executorOverhead -
+          getGettingResultTime(info, currentTime))
+    } else {
+      // The task is still running and the metrics like executorRunTime are not available.
+      0L
+    }
   }
 }

From 79c35826e626805c5df776b3d22ed37829d239b6 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Mon, 13 Jul 2015 11:30:36 -0700
Subject: [PATCH 0356/1454] Revert "[SPARK-8706] [PYSPARK] [PROJECT INFRA] Add
 pylint checks to PySpark"

This reverts commit 9b62e9375f032548d386aec7468e3d0f7c6da7b2.
---
 dev/lint-python                   |  57 +----
 pylintrc                          | 404 ------------------------------
 python/pyspark/ml/param/shared.py |   4 +-
 3 files changed, 10 insertions(+), 455 deletions(-)
 delete mode 100644 pylintrc

diff --git a/dev/lint-python b/dev/lint-python
index e02dff220eb87..0c3586462cb37 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -21,14 +21,12 @@ SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
 SPARK_ROOT_DIR="$(dirname "$SCRIPT_DIR")"
 PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/ ./dev/sparktestsupport"
 PATHS_TO_CHECK="$PATHS_TO_CHECK ./dev/run-tests.py ./python/run-tests.py"
-PEP8_REPORT_PATH="$SPARK_ROOT_DIR/dev/pep8-report.txt"
-PYLINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/pylint-report.txt"
-PYLINT_INSTALL_INFO="$SPARK_ROOT_DIR/dev/pylint-info.txt"
+PYTHON_LINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/python-lint-report.txt"
 
 cd "$SPARK_ROOT_DIR"
 
 # compileall: https://docs.python.org/2/library/compileall.html
-python -B -m compileall -q -l $PATHS_TO_CHECK > "$PEP8_REPORT_PATH"
+python -B -m compileall -q -l $PATHS_TO_CHECK > "$PYTHON_LINT_REPORT_PATH"
 compile_status="${PIPESTATUS[0]}"
 
 # Get pep8 at runtime so that we don't rely on it being installed on the build server.
@@ -49,36 +47,11 @@ if [ ! -e "$PEP8_SCRIPT_PATH" ]; then
     fi
 fi
 
-# Easy install pylint in /dev/pylint. To easy_install into a directory, the PYTHONPATH should
-# be set to the directory.
-# dev/pylint should be appended to the PATH variable as well.
-# Jenkins by default installs the pylint3 version, so for now this just checks the code quality
-# of python3.
-export "PYTHONPATH=$SPARK_ROOT_DIR/dev/pylint"
-export "PYLINT_HOME=$PYTHONPATH"
-export "PATH=$PYTHONPATH:$PATH"
-
-if [ ! -d "$PYLINT_HOME" ]; then
-    mkdir "$PYLINT_HOME"
-    # Redirect the annoying pylint installation output.
-    easy_install -d "$PYLINT_HOME" pylint==1.4.4 &>> "$PYLINT_INSTALL_INFO"
-    easy_install_status="$?"
-
-    if [ "$easy_install_status" -ne 0 ]; then
-        echo "Unable to install pylint locally in \"$PYTHONPATH\"."
-        cat "$PYLINT_INSTALL_INFO"
-        exit "$easy_install_status"
-    fi
-
-    rm "$PYLINT_INSTALL_INFO"
-
-fi
-
 # There is no need to write this output to a file
 #+ first, but we do so so that the check status can
 #+ be output before the report, like with the
 #+ scalastyle and RAT checks.
-python "$PEP8_SCRIPT_PATH" --ignore=E402,E731,E241,W503,E226 $PATHS_TO_CHECK >> "$PEP8_REPORT_PATH"
+python "$PEP8_SCRIPT_PATH" --ignore=E402,E731,E241,W503,E226 $PATHS_TO_CHECK >> "$PYTHON_LINT_REPORT_PATH"
 pep8_status="${PIPESTATUS[0]}"
 
 if [ "$compile_status" -eq 0 -a "$pep8_status" -eq 0 ]; then
@@ -88,27 +61,13 @@ else
 fi
 
 if [ "$lint_status" -ne 0 ]; then
-    echo "PEP8 checks failed."
-    cat "$PEP8_REPORT_PATH"
-else
-    echo "PEP8 checks passed."
-fi
-
-rm "$PEP8_REPORT_PATH"
-
-for to_be_checked in "$PATHS_TO_CHECK"
-do
-    pylint --rcfile="$SPARK_ROOT_DIR/pylintrc" $to_be_checked >> "$PYLINT_REPORT_PATH"
-done
-
-if [ "${PIPESTATUS[0]}" -ne 0 ]; then
-    lint_status=1
-    echo "Pylint checks failed."
-    cat "$PYLINT_REPORT_PATH"
+    echo "Python lint checks failed."
+    cat "$PYTHON_LINT_REPORT_PATH"
 else
-    echo "Pylint checks passed."
+    echo "Python lint checks passed."
 fi
 
-rm "$PYLINT_REPORT_PATH"
+# rm "$PEP8_SCRIPT_PATH"
+rm "$PYTHON_LINT_REPORT_PATH"
 
 exit "$lint_status"
diff --git a/pylintrc b/pylintrc
deleted file mode 100644
index 061775960393b..0000000000000
--- a/pylintrc
+++ /dev/null
@@ -1,404 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-[MASTER]
-
-# Specify a configuration file.
-#rcfile=
-
-# Python code to execute, usually for sys.path manipulation such as
-# pygtk.require().
-#init-hook=
-
-# Profiled execution.
-profile=no
-
-# Add files or directories to the blacklist. They should be base names, not
-# paths.
-ignore=pyspark.heapq3
-
-# Pickle collected data for later comparisons.
-persistent=yes
-
-# List of plugins (as comma separated values of python modules names) to load,
-# usually to register additional checkers.
-load-plugins=
-
-# Use multiple processes to speed up Pylint.
-jobs=1
-
-# Allow loading of arbitrary C extensions. Extensions are imported into the
-# active Python interpreter and may run arbitrary code.
-unsafe-load-any-extension=no
-
-# A comma-separated list of package or module names from where C extensions may
-# be loaded. Extensions are loading into the active Python interpreter and may
-# run arbitrary code
-extension-pkg-whitelist=
-
-# Allow optimization of some AST trees. This will activate a peephole AST
-# optimizer, which will apply various small optimizations. For instance, it can
-# be used to obtain the result of joining multiple strings with the addition
-# operator. Joining a lot of strings can lead to a maximum recursion error in
-# Pylint and this flag can prevent that. It has one side effect, the resulting
-# AST will be different than the one from reality.
-optimize-ast=no
-
-
-[MESSAGES CONTROL]
-
-# Only show warnings with the listed confidence levels. Leave empty to show
-# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
-confidence=
-
-# Enable the message, report, category or checker with the given id(s). You can
-# either give multiple identifier separated by comma (,) or put this option
-# multiple time. See also the "--disable" option for examples.
-enable=
-
-# Disable the message, report, category or checker with the given id(s). You
-# can either give multiple identifiers separated by comma (,) or put this
-# option multiple times (only on the command line, not in the configuration
-# file where it should appear only once).You can also use "--disable=all" to
-# disable everything first and then reenable specific checks. For example, if
-# you want to run only the similarities checker, you can use "--disable=all
-# --enable=similarities". If you want to run only the classes checker, but have
-# no Warning level messages displayed, use"--disable=all --enable=classes
-# --disable=W"
-
-# These errors are arranged in order of number of warning given in pylint.
-# If you would like to improve the code quality of pyspark, remove any of these disabled errors
-# run ./dev/lint-python and see if the errors raised by pylint can be fixed.
-
-disable=invalid-name,missing-docstring,protected-access,unused-argument,no-member,unused-wildcard-import,redefined-builtin,too-many-arguments,unused-variable,too-few-public-methods,bad-continuation,duplicate-code,redefined-outer-name,too-many-ancestors,import-error,superfluous-parens,unused-import,line-too-long,no-name-in-module,unnecessary-lambda,import-self,no-self-use,unidiomatic-typecheck,fixme,too-many-locals,cyclic-import,too-many-branches,bare-except,wildcard-import,dangerous-default-value,broad-except,too-many-public-methods,deprecated-lambda,anomalous-backslash-in-string,too-many-lines,reimported,too-many-statements,bad-whitespace,unpacking-non-sequence,too-many-instance-attributes,abstract-method,old-style-class,global-statement,attribute-defined-outside-init,arguments-differ,undefined-all-variable,no-init,useless-else-on-loop,super-init-not-called,notimplemented-raised,too-many-return-statements,pointless-string-statement,global-variable-undefined,bad-classmethod-argument,too-many-format-args,parse-error,no-self-argument,pointless-statement,undefined-variable
-
-
-[REPORTS]
-
-# Set the output format. Available formats are text, parseable, colorized, msvs
-# (visual studio) and html. You can also give a reporter class, eg
-# mypackage.mymodule.MyReporterClass.
-output-format=text
-
-# Put messages in a separate file for each module / package specified on the
-# command line instead of printing them on stdout. Reports (if any) will be
-# written in a file name "pylint_global.[txt|html]".
-files-output=no
-
-# Tells whether to display a full report or only the messages
-reports=no
-
-# Python expression which should return a note less than 10 (10 is the highest
-# note). You have access to the variables errors warning, statement which
-# respectively contain the number of errors / warnings messages and the total
-# number of statements analyzed. This is used by the global evaluation report
-# (RP0004).
-evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
-
-# Add a comment according to your evaluation note. This is used by the global
-# evaluation report (RP0004).
-comment=no
-
-# Template used to display messages. This is a python new-style format string
-# used to format the message information. See doc for all details
-#msg-template=
-
-
-[MISCELLANEOUS]
-
-# List of note tags to take in consideration, separated by a comma.
-notes=FIXME,XXX,TODO
-
-
-[BASIC]
-
-# Required attributes for module, separated by a comma
-required-attributes=
-
-# List of builtins function names that should not be used, separated by a comma
-bad-functions=
-
-# Good variable names which should always be accepted, separated by a comma
-good-names=i,j,k,ex,Run,_
-
-# Bad variable names which should always be refused, separated by a comma
-bad-names=baz,toto,tutu,tata
-
-# Colon-delimited sets of names that determine each other's naming style when
-# the name regexes allow several styles.
-name-group=
-
-# Include a hint for the correct naming format with invalid-name
-include-naming-hint=no
-
-# Regular expression matching correct function names
-function-rgx=[a-z_][a-z0-9_]{2,30}$
-
-# Naming hint for function names
-function-name-hint=[a-z_][a-z0-9_]{2,30}$
-
-# Regular expression matching correct variable names
-variable-rgx=[a-z_][a-z0-9_]{2,30}$
-
-# Naming hint for variable names
-variable-name-hint=[a-z_][a-z0-9_]{2,30}$
-
-# Regular expression matching correct constant names
-const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
-
-# Naming hint for constant names
-const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
-
-# Regular expression matching correct attribute names
-attr-rgx=[a-z_][a-z0-9_]{2,30}$
-
-# Naming hint for attribute names
-attr-name-hint=[a-z_][a-z0-9_]{2,30}$
-
-# Regular expression matching correct argument names
-argument-rgx=[a-z_][a-z0-9_]{2,30}$
-
-# Naming hint for argument names
-argument-name-hint=[a-z_][a-z0-9_]{2,30}$
-
-# Regular expression matching correct class attribute names
-class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
-
-# Naming hint for class attribute names
-class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
-
-# Regular expression matching correct inline iteration names
-inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
-
-# Naming hint for inline iteration names
-inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
-
-# Regular expression matching correct class names
-class-rgx=[A-Z_][a-zA-Z0-9]+$
-
-# Naming hint for class names
-class-name-hint=[A-Z_][a-zA-Z0-9]+$
-
-# Regular expression matching correct module names
-module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
-
-# Naming hint for module names
-module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
-
-# Regular expression matching correct method names
-method-rgx=[a-z_][a-z0-9_]{2,30}$
-
-# Naming hint for method names
-method-name-hint=[a-z_][a-z0-9_]{2,30}$
-
-# Regular expression which should only match function or class names that do
-# not require a docstring.
-no-docstring-rgx=__.*__
-
-# Minimum line length for functions/classes that require docstrings, shorter
-# ones are exempt.
-docstring-min-length=-1
-
-
-[FORMAT]
-
-# Maximum number of characters on a single line.
-max-line-length=100
-
-# Regexp for a line that is allowed to be longer than the limit.
-ignore-long-lines=^\s*(# )?<?https?://\S+>?$
-
-# Allow the body of an if to be on the same line as the test if there is no
-# else.
-single-line-if-stmt=no
-
-# List of optional constructs for which whitespace checking is disabled
-no-space-check=trailing-comma,dict-separator
-
-# Maximum number of lines in a module
-max-module-lines=1000
-
-# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
-# tab).
-indent-string='    '
-
-# Number of spaces of indent required inside a hanging or continued line.
-indent-after-paren=4
-
-# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
-expected-line-ending-format=
-
-
-[SIMILARITIES]
-
-# Minimum lines number of a similarity.
-min-similarity-lines=4
-
-# Ignore comments when computing similarities.
-ignore-comments=yes
-
-# Ignore docstrings when computing similarities.
-ignore-docstrings=yes
-
-# Ignore imports when computing similarities.
-ignore-imports=no
-
-
-[VARIABLES]
-
-# Tells whether we should check for unused import in __init__ files.
-init-import=no
-
-# A regular expression matching the name of dummy variables (i.e. expectedly
-# not used).
-dummy-variables-rgx=_$|dummy
-
-# List of additional names supposed to be defined in builtins. Remember that
-# you should avoid to define new builtins when possible.
-additional-builtins=
-
-# List of strings which can identify a callback function by name. A callback
-# name must start or end with one of those strings.
-callbacks=cb_,_cb
-
-
-[SPELLING]
-
-# Spelling dictionary name. Available dictionaries: none. To make it working
-# install python-enchant package.
-spelling-dict=
-
-# List of comma separated words that should not be checked.
-spelling-ignore-words=
-
-# A path to a file that contains private dictionary; one word per line.
-spelling-private-dict-file=
-
-# Tells whether to store unknown words to indicated private dictionary in
-# --spelling-private-dict-file option instead of raising a message.
-spelling-store-unknown-words=no
-
-
-[LOGGING]
-
-# Logging modules to check that the string format arguments are in logging
-# function parameter format
-logging-modules=logging
-
-
-[TYPECHECK]
-
-# Tells whether missing members accessed in mixin class should be ignored. A
-# mixin class is detected if its name ends with "mixin" (case insensitive).
-ignore-mixin-members=yes
-
-# List of module names for which member attributes should not be checked
-# (useful for modules/projects where namespaces are manipulated during runtime
-# and thus existing member attributes cannot be deduced by static analysis
-ignored-modules=
-
-# List of classes names for which member attributes should not be checked
-# (useful for classes with attributes dynamically set).
-ignored-classes=SQLObject
-
-# When zope mode is activated, add a predefined set of Zope acquired attributes
-# to generated-members.
-zope=no
-
-# List of members which are set dynamically and missed by pylint inference
-# system, and so shouldn't trigger E0201 when accessed. Python regular
-# expressions are accepted.
-generated-members=REQUEST,acl_users,aq_parent
-
-
-[CLASSES]
-
-# List of interface methods to ignore, separated by a comma. This is used for
-# instance to not check methods defines in Zope's Interface base class.
-ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by
-
-# List of method names used to declare (i.e. assign) instance attributes.
-defining-attr-methods=__init__,__new__,setUp
-
-# List of valid names for the first argument in a class method.
-valid-classmethod-first-arg=cls
-
-# List of valid names for the first argument in a metaclass class method.
-valid-metaclass-classmethod-first-arg=mcs
-
-# List of member names, which should be excluded from the protected access
-# warning.
-exclude-protected=_asdict,_fields,_replace,_source,_make
-
-
-[IMPORTS]
-
-# Deprecated modules which should not be used, separated by a comma
-deprecated-modules=regsub,TERMIOS,Bastion,rexec
-
-# Create a graph of every (i.e. internal and external) dependencies in the
-# given file (report RP0402 must not be disabled)
-import-graph=
-
-# Create a graph of external dependencies in the given file (report RP0402 must
-# not be disabled)
-ext-import-graph=
-
-# Create a graph of internal dependencies in the given file (report RP0402 must
-# not be disabled)
-int-import-graph=
-
-
-[DESIGN]
-
-# Maximum number of arguments for function / method
-max-args=5
-
-# Argument names that match this expression will be ignored. Default to name
-# with leading underscore
-ignored-argument-names=_.*
-
-# Maximum number of locals for function / method body
-max-locals=15
-
-# Maximum number of return / yield for function / method body
-max-returns=6
-
-# Maximum number of branch for function / method body
-max-branches=12
-
-# Maximum number of statements in function / method body
-max-statements=50
-
-# Maximum number of parents for a class (see R0901).
-max-parents=7
-
-# Maximum number of attributes for a class (see R0902).
-max-attributes=7
-
-# Minimum number of public methods for a class (see R0903).
-min-public-methods=2
-
-# Maximum number of public methods for a class (see R0904).
-max-public-methods=20
-
-
-[EXCEPTIONS]
-
-# Exceptions that will emit a warning when being caught. Defaults to
-# "Exception"
-overgeneral-exceptions=Exception
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 595124726366d..bc088e4c29e26 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -444,7 +444,7 @@ class DecisionTreeParams(Params):
     minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.")
     maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
     cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-
+    
 
     def __init__(self):
         super(DecisionTreeParams, self).__init__()
@@ -460,7 +460,7 @@ def __init__(self):
         self.maxMemoryInMB = Param(self, "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
         #: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.
         self.cacheNodeIds = Param(self, "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-
+        
     def setMaxDepth(self, value):
         """
         Sets the value of :py:attr:`maxDepth`.

From 5c41691fe4da0017ccc7e372e3f655dc1c815349 Mon Sep 17 00:00:00 2001
From: yongtang <yongtang@users.noreply.github.com>
Date: Mon, 13 Jul 2015 12:01:23 -0700
Subject: [PATCH 0357/1454] [SPARK-8954] [BUILD] Remove unneeded deb repository
 from Dockerfile to fix build error in docker.

[SPARK-8954] [Build]
1. Remove unneeded deb repository from Dockerfile to fix build error in docker.
2. Remove unneeded /var/lib/apt/lists/* after install to reduce the docker image size (by ~30MB).

Author: yongtang <yongtang@users.noreply.github.com>

Closes #7346 from yongtang/SPARK-8954 and squashes the following commits:

36024a1 [yongtang] [SPARK-8954] [Build] Remove unneeded /var/lib/apt/lists/* after install to reduce the docker image size (by ~30MB)
7084941 [yongtang] [SPARK-8954] [Build] Remove unneeded deb repository from Dockerfile to fix build error in docker.
---
 docker/spark-test/base/Dockerfile | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docker/spark-test/base/Dockerfile b/docker/spark-test/base/Dockerfile
index 5956d59130fbf..5dbdb8b22a44f 100644
--- a/docker/spark-test/base/Dockerfile
+++ b/docker/spark-test/base/Dockerfile
@@ -17,13 +17,13 @@
 
 FROM ubuntu:precise
 
-RUN echo "deb http://archive.ubuntu.com/ubuntu precise main universe" > /etc/apt/sources.list
-
 # Upgrade package index
-RUN apt-get update
-
 # install a few other useful packages plus Open Jdk 7
-RUN apt-get install -y less openjdk-7-jre-headless net-tools vim-tiny sudo openssh-server
+# Remove unneeded /var/lib/apt/lists/* after install to reduce the
+# docker image size (by ~30MB)
+RUN apt-get update && \
+    apt-get install -y less openjdk-7-jre-headless net-tools vim-tiny sudo openssh-server && \
+    rm -rf /var/lib/apt/lists/*
 
 ENV SCALA_VERSION 2.10.4
 ENV CDH_VERSION cdh4

From 714fc55f4aadd5e7b7fb1e462910bfb6a82d9154 Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Mon, 13 Jul 2015 12:03:39 -0700
Subject: [PATCH 0358/1454] [SPARK-8991] [ML] Update SharedParamsCodeGen's
 Generated Documentation

Removed private[ml] from Generated documentation

Author: Vinod K C <vinod.kc@huawei.com>

Closes #7367 from vinodkc/fix_sharedparmascodegen and squashes the following commits:

4fa3c8f [Vinod K C] Adding auto generated code
7e19025 [Vinod K C] Removed private[ml]
---
 .../ml/param/shared/SharedParamsCodeGen.scala |  3 +-
 .../spark/ml/param/shared/sharedParams.scala  | 37 +++++++++----------
 2 files changed, 19 insertions(+), 21 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 66b751a1b02ee..f7ae1de522e01 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -134,7 +134,7 @@ private[shared] object SharedParamsCodeGen {
 
     s"""
       |/**
-      | * (private[ml]) Trait for shared param $name$defaultValueDoc.
+      | * Trait for shared param $name$defaultValueDoc.
       | */
       |private[ml] trait Has$Name extends Params {
       |
@@ -173,7 +173,6 @@ private[shared] object SharedParamsCodeGen {
         |package org.apache.spark.ml.param.shared
         |
         |import org.apache.spark.ml.param._
-        |import org.apache.spark.util.Utils
         |
         |// DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen.
         |
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index f81bd76c22376..65e48e4ee5083 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -18,14 +18,13 @@
 package org.apache.spark.ml.param.shared
 
 import org.apache.spark.ml.param._
-import org.apache.spark.util.Utils
 
 // DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen.
 
 // scalastyle:off
 
 /**
- * (private[ml]) Trait for shared param regParam.
+ * Trait for shared param regParam.
  */
 private[ml] trait HasRegParam extends Params {
 
@@ -40,7 +39,7 @@ private[ml] trait HasRegParam extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param maxIter.
+ * Trait for shared param maxIter.
  */
 private[ml] trait HasMaxIter extends Params {
 
@@ -55,7 +54,7 @@ private[ml] trait HasMaxIter extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param featuresCol (default: "features").
+ * Trait for shared param featuresCol (default: "features").
  */
 private[ml] trait HasFeaturesCol extends Params {
 
@@ -72,7 +71,7 @@ private[ml] trait HasFeaturesCol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param labelCol (default: "label").
+ * Trait for shared param labelCol (default: "label").
  */
 private[ml] trait HasLabelCol extends Params {
 
@@ -89,7 +88,7 @@ private[ml] trait HasLabelCol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param predictionCol (default: "prediction").
+ * Trait for shared param predictionCol (default: "prediction").
  */
 private[ml] trait HasPredictionCol extends Params {
 
@@ -106,7 +105,7 @@ private[ml] trait HasPredictionCol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param rawPredictionCol (default: "rawPrediction").
+ * Trait for shared param rawPredictionCol (default: "rawPrediction").
  */
 private[ml] trait HasRawPredictionCol extends Params {
 
@@ -123,7 +122,7 @@ private[ml] trait HasRawPredictionCol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param probabilityCol (default: "probability").
+ * Trait for shared param probabilityCol (default: "probability").
  */
 private[ml] trait HasProbabilityCol extends Params {
 
@@ -140,7 +139,7 @@ private[ml] trait HasProbabilityCol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param threshold.
+ * Trait for shared param threshold.
  */
 private[ml] trait HasThreshold extends Params {
 
@@ -155,7 +154,7 @@ private[ml] trait HasThreshold extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param inputCol.
+ * Trait for shared param inputCol.
  */
 private[ml] trait HasInputCol extends Params {
 
@@ -170,7 +169,7 @@ private[ml] trait HasInputCol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param inputCols.
+ * Trait for shared param inputCols.
  */
 private[ml] trait HasInputCols extends Params {
 
@@ -185,7 +184,7 @@ private[ml] trait HasInputCols extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param outputCol (default: uid + "__output").
+ * Trait for shared param outputCol (default: uid + "__output").
  */
 private[ml] trait HasOutputCol extends Params {
 
@@ -202,7 +201,7 @@ private[ml] trait HasOutputCol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param checkpointInterval.
+ * Trait for shared param checkpointInterval.
  */
 private[ml] trait HasCheckpointInterval extends Params {
 
@@ -217,7 +216,7 @@ private[ml] trait HasCheckpointInterval extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param fitIntercept (default: true).
+ * Trait for shared param fitIntercept (default: true).
  */
 private[ml] trait HasFitIntercept extends Params {
 
@@ -234,7 +233,7 @@ private[ml] trait HasFitIntercept extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param standardization (default: true).
+ * Trait for shared param standardization (default: true).
  */
 private[ml] trait HasStandardization extends Params {
 
@@ -251,7 +250,7 @@ private[ml] trait HasStandardization extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param seed (default: this.getClass.getName.hashCode.toLong).
+ * Trait for shared param seed (default: this.getClass.getName.hashCode.toLong).
  */
 private[ml] trait HasSeed extends Params {
 
@@ -268,7 +267,7 @@ private[ml] trait HasSeed extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param elasticNetParam.
+ * Trait for shared param elasticNetParam.
  */
 private[ml] trait HasElasticNetParam extends Params {
 
@@ -283,7 +282,7 @@ private[ml] trait HasElasticNetParam extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param tol.
+ * Trait for shared param tol.
  */
 private[ml] trait HasTol extends Params {
 
@@ -298,7 +297,7 @@ private[ml] trait HasTol extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param stepSize.
+ * Trait for shared param stepSize.
  */
 private[ml] trait HasStepSize extends Params {
 

From 4c797f2b0989317a2d004e5f72a0e593919737ea Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Mon, 13 Jul 2015 12:51:33 -0700
Subject: [PATCH 0359/1454] [SPARK-8636] [SQL] Fix equalNullSafe comparison

Author: Vinod K C <vinod.kc@huawei.com>

Closes #7040 from vinodkc/fix_CaseKeyWhen_equalNullSafe and squashes the following commits:

be5e641 [Vinod K C] Renamed equalNullSafe to threeValueEquals
aac9f67 [Vinod K C] Updated test suite and genCode method
f2d0b53 [Vinod K C]  Fix equalNullSafe comparison
---
 .../spark/sql/catalyst/expressions/conditionals.scala | 11 ++++-------
 .../expressions/ConditionalExpressionSuite.scala      |  4 ++--
 2 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index e6a705fb8055b..84c28c27f934b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -238,7 +238,7 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
     // If all branches fail and an elseVal is not provided, the whole statement
     // defaults to null, according to Hive's semantics.
     while (i < len - 1) {
-      if (equalNullSafe(evaluatedKey, branchesArr(i).eval(input))) {
+      if (threeValueEquals(evaluatedKey, branchesArr(i).eval(input))) {
         return branchesArr(i + 1).eval(input)
       }
       i += 2
@@ -261,8 +261,7 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
       s"""
         if (!$got) {
           ${cond.code}
-          if (${keyEval.isNull} && ${cond.isNull} ||
-            !${keyEval.isNull} && !${cond.isNull}
+          if (!${keyEval.isNull} && !${cond.isNull}
              && ${ctx.genEqual(key.dataType, keyEval.primitive, cond.primitive)}) {
             $got = true;
             ${res.code}
@@ -296,10 +295,8 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
     """
   }
 
-  private def equalNullSafe(l: Any, r: Any) = {
-    if (l == null && r == null) {
-      true
-    } else if (l == null || r == null) {
+  private def threeValueEquals(l: Any, r: Any) = {
+    if (l == null || r == null) {
       false
     } else {
       l == r
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
index aaf40cc83e762..adadc8c54fae3 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
@@ -125,7 +125,7 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     val literalString = Literal("a")
 
     checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, c5)), "b", row)
-    checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, literalNull, c5, c6)), "b", row)
+    checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, literalNull, c5, c6)), "c", row)
     checkEvaluation(CaseKeyWhen(c2, Seq(literalInt, c4, c5)), "a", row)
     checkEvaluation(CaseKeyWhen(c2, Seq(c1, c4, c5)), "b", row)
     checkEvaluation(CaseKeyWhen(c4, Seq(literalString, c2, c3)), 1, row)
@@ -134,7 +134,7 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(CaseKeyWhen(literalInt, Seq(c2, c4, c5)), "a", row)
     checkEvaluation(CaseKeyWhen(literalString, Seq(c5, c2, c4, c3)), 2, row)
     checkEvaluation(CaseKeyWhen(c6, Seq(c5, c2, c4, c3)), null, row)
-    checkEvaluation(CaseKeyWhen(literalNull, Seq(c2, c5, c1, c6)), "c", row)
+    checkEvaluation(CaseKeyWhen(literalNull, Seq(c2, c5, c1, c6)), null, row)
   }
 
   test("function least") {

From 0aed38e4498b24d372bfdc7001959e78536369a1 Mon Sep 17 00:00:00 2001
From: Hari Shreedharan <hshreedharan@apache.org>
Date: Mon, 13 Jul 2015 14:15:31 -0700
Subject: [PATCH 0360/1454] [SPARK-8533] [STREAMING] Upgrade Flume to 1.6.0

Author: Hari Shreedharan <hshreedharan@apache.org>

Closes #6939 from harishreedharan/upgrade-flume-1.6.0 and squashes the following commits:

94b80ae [Hari Shreedharan] [SPARK-8533][Streaming] Upgrade Flume to 1.6.0
---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index c2ebc1a11e770..370c95dd03632 100644
--- a/pom.xml
+++ b/pom.xml
@@ -128,7 +128,7 @@
     <yarn.version>${hadoop.version}</yarn.version>
     <hbase.version>0.98.7-hadoop2</hbase.version>
     <hbase.artifact>hbase</hbase.artifact>
-    <flume.version>1.4.0</flume.version>
+    <flume.version>1.6.0</flume.version>
     <zookeeper.version>3.4.5</zookeeper.version>
     <curator.version>2.4.0</curator.version>
     <hive.group>org.spark-project.hive</hive.group>

From b7bcbe25f90ba4e78b548465bc80d4de1d2c4a4a Mon Sep 17 00:00:00 2001
From: Neelesh Srinivas Salian <nsalian@cloudera.com>
Date: Mon, 13 Jul 2015 15:46:51 -0700
Subject: [PATCH 0361/1454] [SPARK-8743] [STREAMING] Deregister Codahale
 metrics for streaming when StreamingContext is closed

The issue link: https://issues.apache.org/jira/browse/SPARK-8743
Deregister Codahale metrics for streaming when StreamingContext is closed

Design:
Adding the method calls in the appropriate start() and stop () methods for the StreamingContext

Actions in the PullRequest:
1) Added the registerSource method call to the start method for the Streaming Context.
2) Added the removeSource method to the stop method.
3) Added comments for both 1 and 2 and comment to show initialization of the StreamingSource
4) Added a test case to check for both registration and de-registration of metrics

Previous closed PR for reference: https://github.com/apache/spark/pull/7250

Author: Neelesh Srinivas Salian <nsalian@cloudera.com>

Closes #7362 from nssalian/branch-SPARK-8743 and squashes the following commits:

7d998a3 [Neelesh Srinivas Salian] Removed the Thread.sleep() call
8b26397 [Neelesh Srinivas Salian] Moved the scalatest.{} import
0e8007a [Neelesh Srinivas Salian] moved import org.apache.spark{} to correct place
daedaa5 [Neelesh Srinivas Salian] Corrected Ordering of imports
8873180 [Neelesh Srinivas Salian] Removed redundancy in imports
59227a4 [Neelesh Srinivas Salian] Changed the ordering of the imports to classify  scala and spark imports
d8cb577 [Neelesh Srinivas Salian] Added registerSource to start() and removeSource to stop(). Wrote a test to check the registration and de-registration
---
 .../spark/streaming/StreamingContext.scala    | 10 +++--
 .../streaming/StreamingContextSuite.scala     | 41 ++++++++++++++++++-
 2 files changed, 45 insertions(+), 6 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index ec49d0f42d122..6b78a82e68c24 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -192,11 +192,8 @@ class StreamingContext private[streaming] (
       None
     }
 
-  /** Register streaming source to metrics system */
+  /* Initializing a streamingSource to register metrics */
   private val streamingSource = new StreamingSource(this)
-  assert(env != null)
-  assert(env.metricsSystem != null)
-  env.metricsSystem.registerSource(streamingSource)
 
   private var state: StreamingContextState = INITIALIZED
 
@@ -606,6 +603,9 @@ class StreamingContext private[streaming] (
         }
         shutdownHookRef = Utils.addShutdownHook(
           StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown)
+        // Registering Streaming Metrics at the start of the StreamingContext
+        assert(env.metricsSystem != null)
+        env.metricsSystem.registerSource(streamingSource)
         uiTab.foreach(_.attach())
         logInfo("StreamingContext started")
       case ACTIVE =>
@@ -682,6 +682,8 @@ class StreamingContext private[streaming] (
           logWarning("StreamingContext has already been stopped")
         case ACTIVE =>
           scheduler.stop(stopGracefully)
+          // Removing the streamingSource to de-register the metrics on stop()
+          env.metricsSystem.removeSource(streamingSource)
           uiTab.foreach(_.detach())
           StreamingContext.setActiveContext(null)
           waiter.notifyStop()
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 56b4ce5638a51..289a159d8990a 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -20,20 +20,23 @@ package org.apache.spark.streaming
 import java.io.{File, NotSerializableException}
 import java.util.concurrent.atomic.AtomicInteger
 
+import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.Queue
 
 import org.apache.commons.io.FileUtils
+import org.scalatest.{Assertions, BeforeAndAfter, PrivateMethodTester}
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.exceptions.TestFailedDueToTimeoutException
 import org.scalatest.time.SpanSugar._
-import org.scalatest.{Assertions, BeforeAndAfter}
 
+import org.apache.spark.{Logging, SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark.metrics.MetricsSystem
+import org.apache.spark.metrics.source.Source
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.streaming.receiver.Receiver
 import org.apache.spark.util.Utils
-import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException, SparkFunSuite}
 
 
 class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeouts with Logging {
@@ -299,6 +302,25 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
     Thread.sleep(100)
   }
 
+  test ("registering and de-registering of streamingSource") {
+    val conf = new SparkConf().setMaster(master).setAppName(appName)
+    ssc = new StreamingContext(conf, batchDuration)
+    assert(ssc.getState() === StreamingContextState.INITIALIZED)
+    addInputStream(ssc).register()
+    ssc.start()
+
+    val sources = StreamingContextSuite.getSources(ssc.env.metricsSystem)
+    val streamingSource = StreamingContextSuite.getStreamingSource(ssc)
+    assert(sources.contains(streamingSource))
+    assert(ssc.getState() === StreamingContextState.ACTIVE)
+
+    ssc.stop()
+    val sourcesAfterStop = StreamingContextSuite.getSources(ssc.env.metricsSystem)
+    val streamingSourceAfterStop = StreamingContextSuite.getStreamingSource(ssc)
+    assert(ssc.getState() === StreamingContextState.STOPPED)
+    assert(!sourcesAfterStop.contains(streamingSourceAfterStop))
+  }
+
   test("awaitTermination") {
     ssc = new StreamingContext(master, appName, batchDuration)
     val inputStream = addInputStream(ssc)
@@ -811,3 +833,18 @@ package object testPackage extends Assertions {
     }
   }
 }
+
+/**
+ * Helper methods for testing StreamingContextSuite
+ * This includes methods to access private methods and fields in StreamingContext and MetricsSystem
+ */
+private object StreamingContextSuite extends PrivateMethodTester {
+  private val _sources = PrivateMethod[ArrayBuffer[Source]]('sources)
+  private def getSources(metricsSystem: MetricsSystem): ArrayBuffer[Source] = {
+    metricsSystem.invokePrivate(_sources())
+  }
+  private val _streamingSource = PrivateMethod[StreamingSource]('streamingSource)
+  private def getStreamingSource(streamingContext: StreamingContext): StreamingSource = {
+    streamingContext.invokePrivate(_streamingSource())
+  }
+}

From 408b384de96b9dbe94945753f7947fbe84272ae1 Mon Sep 17 00:00:00 2001
From: Cheolsoo Park <cheolsoop@netflix.com>
Date: Mon, 13 Jul 2015 19:45:10 -0700
Subject: [PATCH 0362/1454] [SPARK-6910] [SQL] Support for pushing predicates
 down to metastore for partition pruning

This PR supersedes my old one #6921. Since my patch has changed quite a bit, I am opening a new PR to make it easier to review.

The changes include-
* Implement `toMetastoreFilter()` function in `HiveShim` that takes `Seq[Expression]` and converts them into a filter string for Hive metastore.
 * This functions matches all the `AttributeReference` + `BinaryComparisonOp` + `Integral/StringType` patterns in `Seq[Expression]` and fold them into a string.
* Change `hiveQlPartitions` field in `MetastoreRelation` to `getHiveQlPartitions()` function that takes a filter string parameter.
* Call `getHiveQlPartitions()` in `HiveTableScan` with a filter string.

But there are some cases in which predicate pushdown is disabled-

Case | Predicate pushdown
------- | -----------------------------
Hive integral and string types | Yes
Hive varchar type | No
Hive 0.13 and newer | Yes
Hive 0.12 and older | No
convertMetastoreParquet=false | Yes
convertMetastoreParquet=true | No

In case of `convertMetastoreParquet=true`, predicates are not pushed down because this conversion happens in an `Analyzer` rule (`HiveMetastoreCatalog.ParquetConversions`). At this point, `HiveTableScan` hasn't run, so predicates are not available. But reading the source code, I think it is intentional to convert the entire Hive table w/ all the partitions into `ParquetRelation` because then `ParquetRelation` can be cached and reused for any query against that table. Please correct me if I am wrong.

cc marmbrus

Author: Cheolsoo Park <cheolsoop@netflix.com>

Closes #7216 from piaozhexiu/SPARK-6910-2 and squashes the following commits:

aa1490f [Cheolsoo Park] Fix ordering of imports
c212c4d [Cheolsoo Park] Incorporate review comments
5e93f9d [Cheolsoo Park] Predicate pushdown into Hive metastore
---
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 58 ++++++++--------
 .../org/apache/spark/sql/hive/HiveShim.scala  |  1 +
 .../spark/sql/hive/HiveStrategies.scala       |  4 +-
 .../sql/hive/client/ClientInterface.scala     | 11 ++-
 .../spark/sql/hive/client/ClientWrapper.scala | 22 +++---
 .../spark/sql/hive/client/HiveShim.scala      | 68 ++++++++++++++++++-
 .../sql/hive/execution/HiveTableScan.scala    |  7 +-
 .../spark/sql/hive/client/VersionsSuite.scala |  8 +++
 .../sql/hive/execution/PruningSuite.scala     |  2 +-
 9 files changed, 137 insertions(+), 44 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 4b7a782c805a0..5bdf68c83fca7 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -301,7 +301,9 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     val result = if (metastoreRelation.hiveQlTable.isPartitioned) {
       val partitionSchema = StructType.fromAttributes(metastoreRelation.partitionKeys)
       val partitionColumnDataTypes = partitionSchema.map(_.dataType)
-      val partitions = metastoreRelation.hiveQlPartitions.map { p =>
+      // We're converting the entire table into ParquetRelation, so predicates to Hive metastore
+      // are empty.
+      val partitions = metastoreRelation.getHiveQlPartitions().map { p =>
         val location = p.getLocation
         val values = InternalRow.fromSeq(p.getValues.zip(partitionColumnDataTypes).map {
           case (rawValue, dataType) => Cast(Literal(rawValue), dataType).eval(null)
@@ -644,32 +646,6 @@ private[hive] case class MetastoreRelation
     new Table(tTable)
   }
 
-  @transient val hiveQlPartitions: Seq[Partition] = table.getAllPartitions.map { p =>
-    val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
-    tPartition.setDbName(databaseName)
-    tPartition.setTableName(tableName)
-    tPartition.setValues(p.values)
-
-    val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
-    tPartition.setSd(sd)
-    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
-
-    sd.setLocation(p.storage.location)
-    sd.setInputFormat(p.storage.inputFormat)
-    sd.setOutputFormat(p.storage.outputFormat)
-
-    val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
-    sd.setSerdeInfo(serdeInfo)
-    serdeInfo.setSerializationLib(p.storage.serde)
-
-    val serdeParameters = new java.util.HashMap[String, String]()
-    serdeInfo.setParameters(serdeParameters)
-    table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
-    p.storage.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
-
-    new Partition(hiveQlTable, tPartition)
-  }
-
   @transient override lazy val statistics: Statistics = Statistics(
     sizeInBytes = {
       val totalSize = hiveQlTable.getParameters.get(StatsSetupConst.TOTAL_SIZE)
@@ -690,6 +666,34 @@ private[hive] case class MetastoreRelation
     }
   )
 
+  def getHiveQlPartitions(predicates: Seq[Expression] = Nil): Seq[Partition] = {
+    table.getPartitions(predicates).map { p =>
+      val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
+      tPartition.setDbName(databaseName)
+      tPartition.setTableName(tableName)
+      tPartition.setValues(p.values)
+
+      val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
+      tPartition.setSd(sd)
+      sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+
+      sd.setLocation(p.storage.location)
+      sd.setInputFormat(p.storage.inputFormat)
+      sd.setOutputFormat(p.storage.outputFormat)
+
+      val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
+      sd.setSerdeInfo(serdeInfo)
+      serdeInfo.setSerializationLib(p.storage.serde)
+
+      val serdeParameters = new java.util.HashMap[String, String]()
+      serdeInfo.setParameters(serdeParameters)
+      table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+      p.storage.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+
+      new Partition(hiveQlTable, tPartition)
+    }
+  }
+
   /** Only compare database and tablename, not alias. */
   override def sameResult(plan: LogicalPlan): Boolean = {
     plan match {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
index d08c594151654..a357bb39ca7fd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -27,6 +27,7 @@ import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.Kryo
 import com.esotericsoftware.kryo.io.{Input, Output}
+
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.ql.exec.{UDF, Utilities}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index ed359620a5f7f..9638a8201e190 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -125,7 +125,7 @@ private[hive] trait HiveStrategies {
                 InterpretedPredicate.create(castedPredicate)
               }
 
-            val partitions = relation.hiveQlPartitions.filter { part =>
+            val partitions = relation.getHiveQlPartitions(pruningPredicates).filter { part =>
               val partitionValues = part.getValues
               var i = 0
               while (i < partitionValues.size()) {
@@ -213,7 +213,7 @@ private[hive] trait HiveStrategies {
           projectList,
           otherPredicates,
           identity[Seq[Expression]],
-          HiveTableScan(_, relation, pruningPredicates.reduceLeftOption(And))(hiveContext)) :: Nil
+          HiveTableScan(_, relation, pruningPredicates)(hiveContext)) :: Nil
       case _ =>
         Nil
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index 0a1d761a52f88..1656587d14835 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -21,6 +21,7 @@ import java.io.PrintStream
 import java.util.{Map => JMap}
 
 import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException}
+import org.apache.spark.sql.catalyst.expressions.Expression
 
 private[hive] case class HiveDatabase(
     name: String,
@@ -71,7 +72,12 @@ private[hive] case class HiveTable(
 
   def isPartitioned: Boolean = partitionColumns.nonEmpty
 
-  def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this)
+  def getPartitions(predicates: Seq[Expression]): Seq[HivePartition] = {
+    predicates match {
+      case Nil => client.getAllPartitions(this)
+      case _ => client.getPartitionsByFilter(this, predicates)
+    }
+  }
 
   // Hive does not support backticks when passing names to the client.
   def qualifiedName: String = s"$database.$name"
@@ -132,6 +138,9 @@ private[hive] trait ClientInterface {
   /** Returns all partitions for the given table. */
   def getAllPartitions(hTable: HiveTable): Seq[HivePartition]
 
+  /** Returns partitions filtered by predicates for the given table. */
+  def getPartitionsByFilter(hTable: HiveTable, predicates: Seq[Expression]): Seq[HivePartition]
+
   /** Loads a static partition into an existing table. */
   def loadPartition(
       loadPath: String,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 9d83ca6c113dc..1f280c642979a 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -17,27 +17,24 @@
 
 package org.apache.spark.sql.hive.client
 
-import java.io.{BufferedReader, InputStreamReader, File, PrintStream}
-import java.net.URI
-import java.util.{ArrayList => JArrayList, Map => JMap, List => JList, Set => JSet}
+import java.io.{File, PrintStream}
+import java.util.{Map => JMap}
 import javax.annotation.concurrent.GuardedBy
 
+import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.util.CircularBuffer
 
 import scala.collection.JavaConversions._
 import scala.language.reflectiveCalls
 
 import org.apache.hadoop.fs.Path
-import org.apache.hadoop.hive.metastore.api.Database
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.metastore.api.{Database, FieldSchema}
 import org.apache.hadoop.hive.metastore.{TableType => HTableType}
-import org.apache.hadoop.hive.metastore.api
-import org.apache.hadoop.hive.metastore.api.FieldSchema
-import org.apache.hadoop.hive.ql.metadata
 import org.apache.hadoop.hive.ql.metadata.Hive
-import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.ql.processors._
-import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.ql.{Driver, metadata}
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.execution.QueryExecutionException
@@ -316,6 +313,13 @@ private[hive] class ClientWrapper(
     shim.getAllPartitions(client, qlTable).map(toHivePartition)
   }
 
+  override def getPartitionsByFilter(
+      hTable: HiveTable,
+      predicates: Seq[Expression]): Seq[HivePartition] = withHiveState {
+    val qlTable = toQlTable(hTable)
+    shim.getPartitionsByFilter(client, qlTable, predicates).map(toHivePartition)
+  }
+
   override def listTables(dbName: String): Seq[String] = withHiveState {
     client.getAllTables(dbName)
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 1fa9d278e2a57..5542a521b173d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -31,6 +31,11 @@ import org.apache.hadoop.hive.ql.Driver
 import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table}
 import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
 import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.serde.serdeConstants
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions.{Expression, AttributeReference, BinaryComparison}
+import org.apache.spark.sql.types.{StringType, IntegralType}
 
 /**
  * A shim that defines the interface between ClientWrapper and the underlying Hive library used to
@@ -61,6 +66,8 @@ private[client] sealed abstract class Shim {
 
   def getAllPartitions(hive: Hive, table: Table): Seq[Partition]
 
+  def getPartitionsByFilter(hive: Hive, table: Table, predicates: Seq[Expression]): Seq[Partition]
+
   def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor
 
   def getDriverResults(driver: Driver): Seq[String]
@@ -109,7 +116,7 @@ private[client] sealed abstract class Shim {
 
 }
 
-private[client] class Shim_v0_12 extends Shim {
+private[client] class Shim_v0_12 extends Shim with Logging {
 
   private lazy val startMethod =
     findStaticMethod(
@@ -196,6 +203,17 @@ private[client] class Shim_v0_12 extends Shim {
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
     getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
 
+  override def getPartitionsByFilter(
+      hive: Hive,
+      table: Table,
+      predicates: Seq[Expression]): Seq[Partition] = {
+    // getPartitionsByFilter() doesn't support binary comparison ops in Hive 0.12.
+    // See HIVE-4888.
+    logDebug("Hive 0.12 doesn't support predicate pushdown to metastore. " +
+      "Please use Hive 0.13 or higher.")
+    getAllPartitions(hive, table)
+  }
+
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
     getCommandProcessorMethod.invoke(null, token, conf).asInstanceOf[CommandProcessor]
 
@@ -267,6 +285,12 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
       classOf[Hive],
       "getAllPartitionsOf",
       classOf[Table])
+  private lazy val getPartitionsByFilterMethod =
+    findMethod(
+      classOf[Hive],
+      "getPartitionsByFilter",
+      classOf[Table],
+      classOf[String])
   private lazy val getCommandProcessorMethod =
     findStaticMethod(
       classOf[CommandProcessorFactory],
@@ -288,6 +312,48 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
     getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
 
+  override def getPartitionsByFilter(
+      hive: Hive,
+      table: Table,
+      predicates: Seq[Expression]): Seq[Partition] = {
+    // hive varchar is treated as catalyst string, but hive varchar can't be pushed down.
+    val varcharKeys = table.getPartitionKeys
+      .filter(col => col.getType.startsWith(serdeConstants.VARCHAR_TYPE_NAME))
+      .map(col => col.getName).toSet
+
+    // Hive getPartitionsByFilter() takes a string that represents partition
+    // predicates like "str_key=\"value\" and int_key=1 ..."
+    val filter = predicates.flatMap { expr =>
+      expr match {
+        case op @ BinaryComparison(lhs, rhs) => {
+          lhs match {
+            case AttributeReference(_, _, _, _) => {
+              rhs.dataType match {
+                case _: IntegralType =>
+                  Some(lhs.prettyString + op.symbol + rhs.prettyString)
+                case _: StringType if (!varcharKeys.contains(lhs.prettyString)) =>
+                  Some(lhs.prettyString + op.symbol + "\"" + rhs.prettyString + "\"")
+                case _ => None
+              }
+            }
+            case _ => None
+          }
+        }
+        case _ => None
+      }
+    }.mkString(" and ")
+
+    val partitions =
+      if (filter.isEmpty) {
+        getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]]
+      } else {
+        logDebug(s"Hive metastore filter is '$filter'.")
+        getPartitionsByFilterMethod.invoke(hive, table, filter).asInstanceOf[JArrayList[Partition]]
+      }
+
+    partitions.toSeq
+  }
+
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
     getCommandProcessorMethod.invoke(null, Array(token), conf).asInstanceOf[CommandProcessor]
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index d33da8242cc1d..ba7eb15a1c0c6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -44,7 +44,7 @@ private[hive]
 case class HiveTableScan(
     requestedAttributes: Seq[Attribute],
     relation: MetastoreRelation,
-    partitionPruningPred: Option[Expression])(
+    partitionPruningPred: Seq[Expression])(
     @transient val context: HiveContext)
   extends LeafNode {
 
@@ -56,7 +56,7 @@ case class HiveTableScan(
 
   // Bind all partition key attribute references in the partition pruning predicate for later
   // evaluation.
-  private[this] val boundPruningPred = partitionPruningPred.map { pred =>
+  private[this] val boundPruningPred = partitionPruningPred.reduceLeftOption(And).map { pred =>
     require(
       pred.dataType == BooleanType,
       s"Data type of predicate $pred must be BooleanType rather than ${pred.dataType}.")
@@ -133,7 +133,8 @@ case class HiveTableScan(
   protected override def doExecute(): RDD[InternalRow] = if (!relation.hiveQlTable.isPartitioned) {
     hadoopReader.makeRDDForTable(relation.hiveQlTable)
   } else {
-    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
+    hadoopReader.makeRDDForPartitionedTable(
+      prunePartitions(relation.getHiveQlPartitions(partitionPruningPred)))
   }
 
   override def output: Seq[Attribute] = attributes
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index d52e162acbd04..3eb127e23d486 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -20,7 +20,9 @@ package org.apache.spark.sql.hive.client
 import java.io.File
 
 import org.apache.spark.{Logging, SparkFunSuite}
+import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
+import org.apache.spark.sql.types.IntegerType
 import org.apache.spark.util.Utils
 
 /**
@@ -151,6 +153,12 @@ class VersionsSuite extends SparkFunSuite with Logging {
       client.getAllPartitions(client.getTable("default", "src_part"))
     }
 
+    test(s"$version: getPartitionsByFilter") {
+      client.getPartitionsByFilter(client.getTable("default", "src_part"), Seq(EqualTo(
+        AttributeReference("key", IntegerType, false)(NamedExpression.newExprId),
+        Literal(1))))
+    }
+
     test(s"$version: loadPartition") {
       client.loadPartition(
         emptyDir,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index de6a41ce5bfcb..e83a7dc77e329 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -151,7 +151,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
         case p @ HiveTableScan(columns, relation, _) =>
           val columnNames = columns.map(_.name)
           val partValues = if (relation.table.isPartitioned) {
-            p.prunePartitions(relation.hiveQlPartitions).map(_.getValues)
+            p.prunePartitions(relation.getHiveQlPartitions()).map(_.getValues)
           } else {
             Seq.empty
           }

From 20c1434a8dbb25b98f6b434b158ae88e44ce3057 Mon Sep 17 00:00:00 2001
From: Joseph Gonzalez <joseph.e.gonzalez@gmail.com>
Date: Tue, 14 Jul 2015 00:32:29 -0700
Subject: [PATCH 0363/1454] [SPARK-9001] Fixing errors in javadocs that lead to
 failed build/sbt doc

These are minor corrections in the documentation of several classes that are preventing:

```bash
build/sbt publish-local
```

I believe this might be an issue associated with running JDK8 as ankurdave does not appear to have this issue in JDK7.

Author: Joseph Gonzalez <joseph.e.gonzalez@gmail.com>

Closes #7354 from jegonzal/FixingJavadocErrors and squashes the following commits:

6664b7e [Joseph Gonzalez] making requested changes
2e16d89 [Joseph Gonzalez] Fixing errors in javadocs that prevents build/sbt publish-local from completing.
---
 .../java/org/apache/spark/launcher/SparkLauncher.java  |  5 +++--
 .../java/org/apache/spark/launcher/package-info.java   | 10 +++++++---
 .../java/org/apache/spark/unsafe/bitset/BitSet.java    |  2 +-
 .../org/apache/spark/unsafe/bitset/BitSetMethods.java  |  2 +-
 .../org/apache/spark/unsafe/map/BytesToBytesMap.java   |  6 +++++-
 .../java/org/apache/spark/unsafe/types/UTF8String.java |  8 ++++----
 6 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
index d4cfeacb6ef18..c0f89c9230692 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
@@ -25,11 +25,12 @@
 
 import static org.apache.spark.launcher.CommandBuilderUtils.*;
 
-/**
+/** 
  * Launcher for Spark applications.
- * <p/>
+ * <p>
  * Use this class to start Spark applications programmatically. The class uses a builder pattern
  * to allow clients to configure the Spark application and launch it as a child process.
+ * </p>
  */
 public class SparkLauncher {
 
diff --git a/launcher/src/main/java/org/apache/spark/launcher/package-info.java b/launcher/src/main/java/org/apache/spark/launcher/package-info.java
index 7ed756f4b8591..7c97dba511b28 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/package-info.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/package-info.java
@@ -17,13 +17,17 @@
 
 /**
  * Library for launching Spark applications.
- * <p/>
+ * 
+ * <p>
  * This library allows applications to launch Spark programmatically. There's only one entry
  * point to the library - the {@link org.apache.spark.launcher.SparkLauncher} class.
- * <p/>
+ * </p>
+ *
+ * <p>
  * To launch a Spark application, just instantiate a {@link org.apache.spark.launcher.SparkLauncher}
  * and configure the application to run. For example:
- *
+ * </p>
+ * 
  * <pre>
  * {@code
  *   import org.apache.spark.launcher.SparkLauncher;
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java
index 28e23da108ebe..7c124173b0bbb 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java
@@ -90,7 +90,7 @@ public boolean isSet(int index) {
    * To iterate over the true bits in a BitSet, use the following loop:
    * <pre>
    * <code>
-   *  for (long i = bs.nextSetBit(0); i >= 0; i = bs.nextSetBit(i + 1)) {
+   *  for (long i = bs.nextSetBit(0); i &gt;= 0; i = bs.nextSetBit(i + 1)) {
    *    // operate on index i here
    *  }
    * </code>
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
index 0987191c1c636..27462c7fa5e62 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
@@ -87,7 +87,7 @@ public static boolean anySet(Object baseObject, long baseOffset, long bitSetWidt
    * To iterate over the true bits in a BitSet, use the following loop:
    * <pre>
    * <code>
-   *  for (long i = bs.nextSetBit(0, sizeInWords); i >= 0; i = bs.nextSetBit(i + 1, sizeInWords)) {
+   *  for (long i = bs.nextSetBit(0, sizeInWords); i &gt;= 0; i = bs.nextSetBit(i + 1, sizeInWords)) {
    *    // operate on index i here
    *  }
    * </code>
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 8d8c08de52b84..d0bde69cc1068 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -404,14 +404,17 @@ public int getValueLength() {
      * at the value address.
      * <p>
      * It is only valid to call this method immediately after calling `lookup()` using the same key.
+     * </p>
      * <p>
      * The key and value must be word-aligned (that is, their sizes must multiples of 8).
+     * </p>
      * <p>
      * After calling this method, calls to `get[Key|Value]Address()` and `get[Key|Value]Length`
      * will return information on the data stored by this `putNewKey` call.
+     * </p>
      * <p>
      * As an example usage, here's the proper way to store a new key:
-     * <p>
+     * </p>
      * <pre>
      *   Location loc = map.lookup(keyBaseObject, keyBaseOffset, keyLengthInBytes);
      *   if (!loc.isDefined()) {
@@ -420,6 +423,7 @@ public int getValueLength() {
      * </pre>
      * <p>
      * Unspecified behavior if the key is not defined.
+     * </p>
      */
     public void putNewKey(
         Object keyBaseObject,
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 60d050b0a0c97..e7f9fbb2bc682 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -330,8 +330,8 @@ public int indexOf(UTF8String v, int start) {
   /**
    * Returns str, right-padded with pad to a length of len
    * For example:
-   *   ('hi', 5, '??') => 'hi???'
-   *   ('hi', 1, '??') => 'h'
+   *   ('hi', 5, '??') =&gt; 'hi???'
+   *   ('hi', 1, '??') =&gt; 'h'
    */
   public UTF8String rpad(int len, UTF8String pad) {
     int spaces = len - this.numChars(); // number of char need to pad
@@ -363,8 +363,8 @@ public UTF8String rpad(int len, UTF8String pad) {
   /**
    * Returns str, left-padded with pad to a length of len.
    * For example:
-   *   ('hi', 5, '??') => '???hi'
-   *   ('hi', 1, '??') => 'h'
+   *   ('hi', 5, '??') =&gt; '???hi'
+   *   ('hi', 1, '??') =&gt; 'h'
    */
   public UTF8String lpad(int len, UTF8String pad) {
     int spaces = len - this.numChars(); // number of char need to pad

From c1feebd8fcba985667db8ccdafd2b5ec76dcfae7 Mon Sep 17 00:00:00 2001
From: zhaishidan <zhaishidan@haizhi.com>
Date: Tue, 14 Jul 2015 08:54:30 +0100
Subject: [PATCH 0364/1454] [SPARK-9010] [DOCUMENTATION] Improve the Spark
 Configuration document about `spark.kryoserializer.buffer`

The meaning of spark.kryoserializer.buffer should be "Initial size of Kryo's serialization buffer. Note that there will be one buffer per core on each worker. This buffer will grow up to spark.kryoserializer.buffer.max if needed.".

The spark.kryoserializer.buffer.max.mb is out-of-date in spark 1.4.

Author: zhaishidan <zhaishidan@haizhi.com>

Closes #7393 from stanzhai/master and squashes the following commits:

69729ef [zhaishidan] fix document error about spark.kryoserializer.buffer.max.mb
---
 docs/configuration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 443322e1eadf4..8a186ee51c1ca 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -665,7 +665,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>
     Initial size of Kryo's serialization buffer. Note that there will be one buffer
      <i>per core</i> on each worker. This buffer will grow up to
-     <code>spark.kryoserializer.buffer.max.mb</code> if needed.
+     <code>spark.kryoserializer.buffer.max</code> if needed.
   </td>
 </tr>
 <tr>

From 257236c3e17906098f801cbc2059e7a9054e8cab Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Tue, 14 Jul 2015 01:09:33 -0700
Subject: [PATCH 0365/1454] [SPARK-6851] [SQL] function least/greatest follow
 up

This is a follow up of remaining comments from #6851

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #7387 from adrian-wang/udflgfollow and squashes the following commits:

6163e62 [Daoyuan Wang] add skipping null values
e8c2e09 [Daoyuan Wang] use seq
8362966 [Daoyuan Wang] pr6851 follow up
---
 .../catalyst/expressions/conditionals.scala   | 16 +++-
 .../ConditionalExpressionSuite.scala          | 79 ++++++++++---------
 .../org/apache/spark/sql/functions.scala      | 16 ++--
 3 files changed, 62 insertions(+), 49 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index 84c28c27f934b..eea7706b9d391 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -311,7 +311,11 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
   }
 }
 
-case class Least(children: Expression*) extends Expression {
+/**
+ * A function that returns the least value of all parameters, skipping null values.
+ * It takes at least 2 parameters, and returns null iff all parameters are null.
+ */
+case class Least(children: Seq[Expression]) extends Expression {
   require(children.length > 1, "LEAST requires at least 2 arguments, got " + children.length)
 
   override def nullable: Boolean = children.forall(_.nullable)
@@ -356,12 +360,16 @@ case class Least(children: Expression*) extends Expression {
       ${evalChildren.map(_.code).mkString("\n")}
       boolean ${ev.isNull} = true;
       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      ${(0 until children.length).map(updateEval).mkString("\n")}
+      ${children.indices.map(updateEval).mkString("\n")}
     """
   }
 }
 
-case class Greatest(children: Expression*) extends Expression {
+/**
+ * A function that returns the greatest value of all parameters, skipping null values.
+ * It takes at least 2 parameters, and returns null iff all parameters are null.
+ */
+case class Greatest(children: Seq[Expression]) extends Expression {
   require(children.length > 1, "GREATEST requires at least 2 arguments, got " + children.length)
 
   override def nullable: Boolean = children.forall(_.nullable)
@@ -406,7 +414,7 @@ case class Greatest(children: Expression*) extends Expression {
       ${evalChildren.map(_.code).mkString("\n")}
       boolean ${ev.isNull} = true;
       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      ${(0 until children.length).map(updateEval).mkString("\n")}
+      ${children.indices.map(updateEval).mkString("\n")}
     """
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
index adadc8c54fae3..afa143bd5f331 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
@@ -144,35 +144,35 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     val c3 = 'a.string.at(2)
     val c4 = 'a.string.at(3)
     val c5 = 'a.string.at(4)
-    checkEvaluation(Least(c4, c3, c5), "a", row)
-    checkEvaluation(Least(c1, c2), 1, row)
-    checkEvaluation(Least(c1, c2, Literal(-1)), -1, row)
-    checkEvaluation(Least(c4, c5, c3, c3, Literal("a")), "a", row)
-
-    checkEvaluation(Least(Literal(null), Literal(null)), null, InternalRow.empty)
-    checkEvaluation(Least(Literal(-1.0), Literal(2.5)), -1.0, InternalRow.empty)
-    checkEvaluation(Least(Literal(-1), Literal(2)), -1, InternalRow.empty)
+    checkEvaluation(Least(Seq(c4, c3, c5)), "a", row)
+    checkEvaluation(Least(Seq(c1, c2)), 1, row)
+    checkEvaluation(Least(Seq(c1, c2, Literal(-1))), -1, row)
+    checkEvaluation(Least(Seq(c4, c5, c3, c3, Literal("a"))), "a", row)
+
+    checkEvaluation(Least(Seq(Literal(null), Literal(null))), null, InternalRow.empty)
+    checkEvaluation(Least(Seq(Literal(-1.0), Literal(2.5))), -1.0, InternalRow.empty)
+    checkEvaluation(Least(Seq(Literal(-1), Literal(2))), -1, InternalRow.empty)
     checkEvaluation(
-      Least(Literal((-1.0).toFloat), Literal(2.5.toFloat)), (-1.0).toFloat, InternalRow.empty)
+      Least(Seq(Literal((-1.0).toFloat), Literal(2.5.toFloat))), (-1.0).toFloat, InternalRow.empty)
     checkEvaluation(
-      Least(Literal(Long.MaxValue), Literal(Long.MinValue)), Long.MinValue, InternalRow.empty)
-    checkEvaluation(Least(Literal(1.toByte), Literal(2.toByte)), 1.toByte, InternalRow.empty)
+      Least(Seq(Literal(Long.MaxValue), Literal(Long.MinValue))), Long.MinValue, InternalRow.empty)
+    checkEvaluation(Least(Seq(Literal(1.toByte), Literal(2.toByte))), 1.toByte, InternalRow.empty)
     checkEvaluation(
-      Least(Literal(1.toShort), Literal(2.toByte.toShort)), 1.toShort, InternalRow.empty)
-    checkEvaluation(Least(Literal("abc"), Literal("aaaa")), "aaaa", InternalRow.empty)
-    checkEvaluation(Least(Literal(true), Literal(false)), false, InternalRow.empty)
+      Least(Seq(Literal(1.toShort), Literal(2.toByte.toShort))), 1.toShort, InternalRow.empty)
+    checkEvaluation(Least(Seq(Literal("abc"), Literal("aaaa"))), "aaaa", InternalRow.empty)
+    checkEvaluation(Least(Seq(Literal(true), Literal(false))), false, InternalRow.empty)
     checkEvaluation(
-      Least(
+      Least(Seq(
         Literal(BigDecimal("1234567890987654321123456")),
-        Literal(BigDecimal("1234567890987654321123458"))),
+        Literal(BigDecimal("1234567890987654321123458")))),
       BigDecimal("1234567890987654321123456"), InternalRow.empty)
     checkEvaluation(
-      Least(Literal(Date.valueOf("2015-01-01")), Literal(Date.valueOf("2015-07-01"))),
+      Least(Seq(Literal(Date.valueOf("2015-01-01")), Literal(Date.valueOf("2015-07-01")))),
       Date.valueOf("2015-01-01"), InternalRow.empty)
     checkEvaluation(
-      Least(
+      Least(Seq(
         Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
-        Literal(Timestamp.valueOf("2015-07-01 10:00:00"))),
+        Literal(Timestamp.valueOf("2015-07-01 10:00:00")))),
       Timestamp.valueOf("2015-07-01 08:00:00"), InternalRow.empty)
   }
 
@@ -183,35 +183,36 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     val c3 = 'a.string.at(2)
     val c4 = 'a.string.at(3)
     val c5 = 'a.string.at(4)
-    checkEvaluation(Greatest(c4, c5, c3), "c", row)
-    checkEvaluation(Greatest(c2, c1), 2, row)
-    checkEvaluation(Greatest(c1, c2, Literal(2)), 2, row)
-    checkEvaluation(Greatest(c4, c5, c3, Literal("ccc")), "ccc", row)
-
-    checkEvaluation(Greatest(Literal(null), Literal(null)), null, InternalRow.empty)
-    checkEvaluation(Greatest(Literal(-1.0), Literal(2.5)), 2.5, InternalRow.empty)
-    checkEvaluation(Greatest(Literal(-1), Literal(2)), 2, InternalRow.empty)
+    checkEvaluation(Greatest(Seq(c4, c5, c3)), "c", row)
+    checkEvaluation(Greatest(Seq(c2, c1)), 2, row)
+    checkEvaluation(Greatest(Seq(c1, c2, Literal(2))), 2, row)
+    checkEvaluation(Greatest(Seq(c4, c5, c3, Literal("ccc"))), "ccc", row)
+
+    checkEvaluation(Greatest(Seq(Literal(null), Literal(null))), null, InternalRow.empty)
+    checkEvaluation(Greatest(Seq(Literal(-1.0), Literal(2.5))), 2.5, InternalRow.empty)
+    checkEvaluation(Greatest(Seq(Literal(-1), Literal(2))), 2, InternalRow.empty)
     checkEvaluation(
-      Greatest(Literal((-1.0).toFloat), Literal(2.5.toFloat)), 2.5.toFloat, InternalRow.empty)
+      Greatest(Seq(Literal((-1.0).toFloat), Literal(2.5.toFloat))), 2.5.toFloat, InternalRow.empty)
+    checkEvaluation(Greatest(
+      Seq(Literal(Long.MaxValue), Literal(Long.MinValue))), Long.MaxValue, InternalRow.empty)
     checkEvaluation(
-      Greatest(Literal(Long.MaxValue), Literal(Long.MinValue)), Long.MaxValue, InternalRow.empty)
-    checkEvaluation(Greatest(Literal(1.toByte), Literal(2.toByte)), 2.toByte, InternalRow.empty)
+      Greatest(Seq(Literal(1.toByte), Literal(2.toByte))), 2.toByte, InternalRow.empty)
     checkEvaluation(
-      Greatest(Literal(1.toShort), Literal(2.toByte.toShort)), 2.toShort, InternalRow.empty)
-    checkEvaluation(Greatest(Literal("abc"), Literal("aaaa")), "abc", InternalRow.empty)
-    checkEvaluation(Greatest(Literal(true), Literal(false)), true, InternalRow.empty)
+      Greatest(Seq(Literal(1.toShort), Literal(2.toByte.toShort))), 2.toShort, InternalRow.empty)
+    checkEvaluation(Greatest(Seq(Literal("abc"), Literal("aaaa"))), "abc", InternalRow.empty)
+    checkEvaluation(Greatest(Seq(Literal(true), Literal(false))), true, InternalRow.empty)
     checkEvaluation(
-      Greatest(
+      Greatest(Seq(
         Literal(BigDecimal("1234567890987654321123456")),
-        Literal(BigDecimal("1234567890987654321123458"))),
+        Literal(BigDecimal("1234567890987654321123458")))),
       BigDecimal("1234567890987654321123458"), InternalRow.empty)
-    checkEvaluation(
-      Greatest(Literal(Date.valueOf("2015-01-01")), Literal(Date.valueOf("2015-07-01"))),
+    checkEvaluation(Greatest(
+      Seq(Literal(Date.valueOf("2015-01-01")), Literal(Date.valueOf("2015-07-01")))),
       Date.valueOf("2015-07-01"), InternalRow.empty)
     checkEvaluation(
-      Greatest(
+      Greatest(Seq(
         Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
-        Literal(Timestamp.valueOf("2015-07-01 10:00:00"))),
+        Literal(Timestamp.valueOf("2015-07-01 10:00:00")))),
       Timestamp.valueOf("2015-07-01 10:00:00"), InternalRow.empty)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index ffa52f62588dc..0d4e160ed8057 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1073,7 +1073,8 @@ object functions {
   def floor(columnName: String): Column = floor(Column(columnName))
 
   /**
-   * Returns the greatest value of the list of values.
+   * Returns the greatest value of the list of values, skipping null values.
+   * This function takes at least 2 parameters. It will return null iff all parameters are null.
    *
    * @group normal_funcs
    * @since 1.5.0
@@ -1082,11 +1083,12 @@ object functions {
   def greatest(exprs: Column*): Column = if (exprs.length < 2) {
     sys.error("GREATEST takes at least 2 parameters")
   } else {
-    Greatest(exprs.map(_.expr): _*)
+    Greatest(exprs.map(_.expr))
   }
 
   /**
-   * Returns the greatest value of the list of column names.
+   * Returns the greatest value of the list of column names, skipping null values.
+   * This function takes at least 2 parameters. It will return null iff all parameters are null.
    *
    * @group normal_funcs
    * @since 1.5.0
@@ -1198,7 +1200,8 @@ object functions {
   def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName))
 
   /**
-   * Returns the least value of the list of values.
+   * Returns the least value of the list of values, skipping null values.
+   * This function takes at least 2 parameters. It will return null iff all parameters are null.
    *
    * @group normal_funcs
    * @since 1.5.0
@@ -1207,11 +1210,12 @@ object functions {
   def least(exprs: Column*): Column = if (exprs.length < 2) {
     sys.error("LEAST takes at least 2 parameters")
   } else {
-    Least(exprs.map(_.expr): _*)
+    Least(exprs.map(_.expr))
   }
 
   /**
-   * Returns the least value of the list of column names.
+   * Returns the least value of the list of column names, skipping null values.
+   * This function takes at least 2 parameters. It will return null iff all parameters are null.
    *
    * @group normal_funcs
    * @since 1.5.0

From 59d820aa8dec08b744971237860b4c6bef577ddf Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 14 Jul 2015 10:20:15 -0700
Subject: [PATCH 0366/1454] [SPARK-9029] [SQL] shortcut CaseKeyWhen if key is
 null

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7389 from cloud-fan/case-when and squashes the following commits:

ea4b6ba [Wenchen Fan] shortcut for case key when
---
 .../catalyst/expressions/conditionals.scala   | 48 +++++++++----------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index eea7706b9d391..c7f039ede26b3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -230,24 +230,31 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
     }
   }
 
+  private def evalElse(input: InternalRow): Any = {
+    if (branchesArr.length % 2 == 0) {
+      null
+    } else {
+      branchesArr(branchesArr.length - 1).eval(input)
+    }
+  }
+
   /** Written in imperative fashion for performance considerations. */
   override def eval(input: InternalRow): Any = {
     val evaluatedKey = key.eval(input)
-    val len = branchesArr.length
-    var i = 0
-    // If all branches fail and an elseVal is not provided, the whole statement
-    // defaults to null, according to Hive's semantics.
-    while (i < len - 1) {
-      if (threeValueEquals(evaluatedKey, branchesArr(i).eval(input))) {
-        return branchesArr(i + 1).eval(input)
+    // If key is null, we can just return the else part or null if there is no else.
+    // If key is not null but doesn't match any when part, we need to return
+    // the else part or null if there is no else, according to Hive's semantics.
+    if (evaluatedKey != null) {
+      val len = branchesArr.length
+      var i = 0
+      while (i < len - 1) {
+        if (evaluatedKey ==  branchesArr(i).eval(input)) {
+          return branchesArr(i + 1).eval(input)
+        }
+        i += 2
       }
-      i += 2
     }
-    var res: Any = null
-    if (i == len - 1) {
-      res = branchesArr(i).eval(input)
-    }
-    return res
+    evalElse(input)
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -261,8 +268,7 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
       s"""
         if (!$got) {
           ${cond.code}
-          if (!${keyEval.isNull} && !${cond.isNull}
-             && ${ctx.genEqual(key.dataType, keyEval.primitive, cond.primitive)}) {
+          if (!${cond.isNull} && ${ctx.genEqual(key.dataType, keyEval.primitive, cond.primitive)}) {
             $got = true;
             ${res.code}
             ${ev.isNull} = ${res.isNull};
@@ -290,19 +296,13 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
       boolean ${ev.isNull} = true;
       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
       ${keyEval.code}
-      $cases
+      if (!${keyEval.isNull}) {
+        $cases
+      }
       $other
     """
   }
 
-  private def threeValueEquals(l: Any, r: Any) = {
-    if (l == null || r == null) {
-      false
-    } else {
-      l == r
-    }
-  }
-
   override def toString: String = {
     s"CASE $key" + branches.sliding(2, 2).map {
       case Seq(cond, value) => s" WHEN $cond THEN $value"

From 37f2d9635ff874fb8ad9d246e49faf6098d501c3 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 14 Jul 2015 11:22:09 -0700
Subject: [PATCH 0367/1454] [SPARK-9027] [SQL] Generalize metastore predicate
 pushdown

Add support for pushing down metastore filters that are in different orders and add some unit tests.

Author: Michael Armbrust <michael@databricks.com>

Closes #7386 from marmbrus/metastoreFilters and squashes the following commits:

05a4524 [Michael Armbrust] [SPARK-9027][SQL] Generalize metastore predicate pushdown
---
 .../spark/sql/hive/client/HiveShim.scala      | 54 +++++++------
 .../spark/sql/hive/client/FiltersSuite.scala  | 78 +++++++++++++++++++
 2 files changed, 107 insertions(+), 25 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 5542a521b173d..d12778c7583df 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -34,7 +34,7 @@ import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.serde.serdeConstants
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.expressions.{Expression, AttributeReference, BinaryComparison}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types.{StringType, IntegralType}
 
 /**
@@ -312,37 +312,41 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
     getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
 
-  override def getPartitionsByFilter(
-      hive: Hive,
-      table: Table,
-      predicates: Seq[Expression]): Seq[Partition] = {
+  /**
+   * Converts catalyst expression to the format that Hive's getPartitionsByFilter() expects, i.e.
+   * a string that represents partition predicates like "str_key=\"value\" and int_key=1 ...".
+   *
+   * Unsupported predicates are skipped.
+   */
+  def convertFilters(table: Table, filters: Seq[Expression]): String = {
     // hive varchar is treated as catalyst string, but hive varchar can't be pushed down.
     val varcharKeys = table.getPartitionKeys
       .filter(col => col.getType.startsWith(serdeConstants.VARCHAR_TYPE_NAME))
       .map(col => col.getName).toSet
 
-    // Hive getPartitionsByFilter() takes a string that represents partition
-    // predicates like "str_key=\"value\" and int_key=1 ..."
-    val filter = predicates.flatMap { expr =>
-      expr match {
-        case op @ BinaryComparison(lhs, rhs) => {
-          lhs match {
-            case AttributeReference(_, _, _, _) => {
-              rhs.dataType match {
-                case _: IntegralType =>
-                  Some(lhs.prettyString + op.symbol + rhs.prettyString)
-                case _: StringType if (!varcharKeys.contains(lhs.prettyString)) =>
-                  Some(lhs.prettyString + op.symbol + "\"" + rhs.prettyString + "\"")
-                case _ => None
-              }
-            }
-            case _ => None
-          }
-        }
-        case _ => None
-      }
+    filters.collect {
+      case op @ BinaryComparison(a: Attribute, Literal(v, _: IntegralType)) =>
+        s"${a.name} ${op.symbol} $v"
+      case op @ BinaryComparison(Literal(v, _: IntegralType), a: Attribute) =>
+        s"$v ${op.symbol} ${a.name}"
+
+      case op @ BinaryComparison(a: Attribute, Literal(v, _: StringType))
+          if !varcharKeys.contains(a.name) =>
+        s"""${a.name} ${op.symbol} "$v""""
+      case op @ BinaryComparison(Literal(v, _: StringType), a: Attribute)
+          if !varcharKeys.contains(a.name) =>
+        s""""$v" ${op.symbol} ${a.name}"""
     }.mkString(" and ")
+  }
+
+  override def getPartitionsByFilter(
+      hive: Hive,
+      table: Table,
+      predicates: Seq[Expression]): Seq[Partition] = {
 
+    // Hive getPartitionsByFilter() takes a string that represents partition
+    // predicates like "str_key=\"value\" and int_key=1 ..."
+    val filter = convertFilters(table, predicates)
     val partitions =
       if (filter.isEmpty) {
         getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]]
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
new file mode 100644
index 0000000000000..0efcf80bd4ea7
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.client
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+import org.apache.hadoop.hive.serde.serdeConstants
+
+import org.apache.spark.{Logging, SparkFunSuite}
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types._
+
+/**
+ * A set of tests for the filter conversion logic used when pushing partition pruning into the
+ * metastore
+ */
+class FiltersSuite extends SparkFunSuite with Logging {
+  private val shim = new Shim_v0_13
+
+  private val testTable = new org.apache.hadoop.hive.ql.metadata.Table("default", "test")
+  private val varCharCol = new FieldSchema()
+  varCharCol.setName("varchar")
+  varCharCol.setType(serdeConstants.VARCHAR_TYPE_NAME)
+  testTable.setPartCols(varCharCol :: Nil)
+
+  filterTest("string filter",
+    (a("stringcol", StringType) > Literal("test")) :: Nil,
+    "stringcol > \"test\"")
+
+  filterTest("string filter backwards",
+    (Literal("test") > a("stringcol", StringType)) :: Nil,
+    "\"test\" > stringcol")
+
+  filterTest("int filter",
+    (a("intcol", IntegerType) === Literal(1)) :: Nil,
+    "intcol = 1")
+
+  filterTest("int filter backwards",
+    (Literal(1) === a("intcol", IntegerType)) :: Nil,
+    "1 = intcol")
+
+  filterTest("int and string filter",
+    (Literal(1) === a("intcol", IntegerType)) :: (Literal("a") === a("strcol", IntegerType)) :: Nil,
+    "1 = intcol and \"a\" = strcol")
+
+  filterTest("skip varchar",
+    (Literal("") === a("varchar", StringType)) :: Nil,
+    "")
+
+  private def filterTest(name: String, filters: Seq[Expression], result: String) = {
+    test(name){
+      val converted = shim.convertFilters(testTable, filters)
+      if (converted != result) {
+        fail(
+          s"Expected filters ${filters.mkString(",")} to convert to '$result' but got '$converted'")
+      }
+    }
+  }
+
+  private def a(name: String, dataType: DataType) = AttributeReference(name, dataType)()
+}

From c4e98ff066cc6f0839d15140eb471d74a0d83e91 Mon Sep 17 00:00:00 2001
From: Brennon York <brennon.york@capitalone.com>
Date: Tue, 14 Jul 2015 11:43:26 -0700
Subject: [PATCH 0368/1454] [SPARK-8933] [BUILD] Provide a --force flag to
 build/mvn that always uses downloaded maven

added --force flag to manually download, if necessary, and use a built-in version of maven best for spark

Author: Brennon York <brennon.york@capitalone.com>

Closes #7374 from brennonyork/SPARK-8933 and squashes the following commits:

d673127 [Brennon York] added --force flag to manually download, if necessary, and use a built-in version of maven best for spark
---
 build/mvn | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/build/mvn b/build/mvn
index e8364181e8230..f62f61ee1c416 100755
--- a/build/mvn
+++ b/build/mvn
@@ -112,10 +112,17 @@ install_scala() {
 # the environment
 ZINC_PORT=${ZINC_PORT:-"3030"}
 
+# Check for the `--force` flag dictating that `mvn` should be downloaded
+# regardless of whether the system already has a `mvn` install
+if [ "$1" == "--force" ]; then
+  FORCE_MVN=1
+  shift
+fi
+
 # Install Maven if necessary
 MVN_BIN="$(command -v mvn)"
 
-if [ ! "$MVN_BIN" ]; then
+if [ ! "$MVN_BIN" -o -n "$FORCE_MVN" ]; then
   install_mvn
 fi
 
@@ -139,5 +146,7 @@ fi
 # Set any `mvn` options if not already present
 export MAVEN_OPTS=${MAVEN_OPTS:-"$_COMPILE_JVM_OPTS"}
 
+echo "Using \`mvn\` from path: $MVN_BIN"
+
 # Last, call the `mvn` command as usual
 ${MVN_BIN} "$@"

From 8fb3a65cbb714120d612e58ef9d12b0521a83260 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 14 Jul 2015 12:47:11 -0700
Subject: [PATCH 0369/1454] [SPARK-8911] Fix local mode endless heartbeats

As of #7173 we expect executors to properly register with the driver before responding to their heartbeats. This behavior is not matched in local mode. This patch adds the missing event that needs to be posted.

Author: Andrew Or <andrew@databricks.com>

Closes #7382 from andrewor14/fix-local-heartbeat and squashes the following commits:

1258bdf [Andrew Or] Post ExecutorAdded event to local executor
---
 .../spark/scheduler/local/LocalBackend.scala  | 20 ++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
index 776e5d330e3c7..4d48fcfea44e7 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
@@ -25,7 +25,8 @@ import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv, TaskState}
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.executor.{Executor, ExecutorBackend}
 import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}
-import org.apache.spark.scheduler.{SchedulerBackend, TaskSchedulerImpl, WorkerOffer}
+import org.apache.spark.scheduler._
+import org.apache.spark.scheduler.cluster.ExecutorInfo
 
 private case class ReviveOffers()
 
@@ -50,8 +51,8 @@ private[spark] class LocalEndpoint(
 
   private var freeCores = totalCores
 
-  private val localExecutorId = SparkContext.DRIVER_IDENTIFIER
-  private val localExecutorHostname = "localhost"
+  val localExecutorId = SparkContext.DRIVER_IDENTIFIER
+  val localExecutorHostname = "localhost"
 
   private val executor = new Executor(
     localExecutorId, localExecutorHostname, SparkEnv.get, userClassPath, isLocal = true)
@@ -99,8 +100,9 @@ private[spark] class LocalBackend(
   extends SchedulerBackend with ExecutorBackend with Logging {
 
   private val appId = "local-" + System.currentTimeMillis
-  var localEndpoint: RpcEndpointRef = null
+  private var localEndpoint: RpcEndpointRef = null
   private val userClassPath = getUserClasspath(conf)
+  private val listenerBus = scheduler.sc.listenerBus
 
   /**
    * Returns a list of URLs representing the user classpath.
@@ -113,9 +115,13 @@ private[spark] class LocalBackend(
   }
 
   override def start() {
-    localEndpoint = SparkEnv.get.rpcEnv.setupEndpoint(
-      "LocalBackendEndpoint",
-      new LocalEndpoint(SparkEnv.get.rpcEnv, userClassPath, scheduler, this, totalCores))
+    val rpcEnv = SparkEnv.get.rpcEnv
+    val executorEndpoint = new LocalEndpoint(rpcEnv, userClassPath, scheduler, this, totalCores)
+    localEndpoint = rpcEnv.setupEndpoint("LocalBackendEndpoint", executorEndpoint)
+    listenerBus.post(SparkListenerExecutorAdded(
+      System.currentTimeMillis,
+      executorEndpoint.localExecutorId,
+      new ExecutorInfo(executorEndpoint.localExecutorHostname, totalCores, Map.empty)))
   }
 
   override def stop() {

From d267c2834a639aaebd0559355c6a82613abb689b Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 14 Jul 2015 12:56:17 -0700
Subject: [PATCH 0370/1454] [SPARK-9031] Merge BlockObjectWriter and
 DiskBlockObject writer to remove abstract class

BlockObjectWriter has only one concrete non-test class, DiskBlockObjectWriter. In order to simplify the code in preparation for other refactorings, I think that we should remove this base class and have only DiskBlockObjectWriter.

While at one time we may have planned to have multiple BlockObjectWriter implementations, that doesn't seem to have happened, so the extra abstraction seems unnecessary.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7391 from JoshRosen/shuffle-write-interface-refactoring and squashes the following commits:

c418e33 [Josh Rosen] Fix compilation
5047995 [Josh Rosen] Fix comments
d5dc548 [Josh Rosen] Update references in comments
89dc797 [Josh Rosen] Rename test suite.
5755918 [Josh Rosen] Remove unnecessary val in case class
1607c91 [Josh Rosen] Merge BlockObjectWriter and DiskBlockObjectWriter
---
 .../sort/BypassMergeSortShuffleWriter.java    |  8 +-
 .../unsafe/UnsafeShuffleExternalSorter.java   |  2 +-
 .../unsafe/sort/UnsafeSorterSpillWriter.java  |  4 +-
 .../shuffle/FileShuffleBlockResolver.scala    |  8 +-
 .../shuffle/IndexShuffleBlockResolver.scala   |  2 +-
 .../shuffle/hash/HashShuffleWriter.scala      |  4 +-
 .../apache/spark/storage/BlockManager.scala   |  2 +-
 ...iter.scala => DiskBlockObjectWriter.scala} | 96 +++++++------------
 .../spark/util/collection/ChainedBuffer.scala |  2 +-
 .../util/collection/ExternalSorter.scala      |  4 +-
 .../collection/PartitionedPairBuffer.scala    |  1 -
 .../PartitionedSerializedPairBuffer.scala     |  5 +-
 .../WritablePartitionedPairCollection.scala   |  8 +-
 .../BypassMergeSortShuffleWriterSuite.scala   |  4 +-
 ...scala => DiskBlockObjectWriterSuite.scala} |  2 +-
 ...PartitionedSerializedPairBufferSuite.scala | 52 +++++-----
 16 files changed, 90 insertions(+), 114 deletions(-)
 rename core/src/main/scala/org/apache/spark/storage/{BlockObjectWriter.scala => DiskBlockObjectWriter.scala} (83%)
 rename core/src/test/scala/org/apache/spark/storage/{BlockObjectWriterSuite.scala => DiskBlockObjectWriterSuite.scala} (98%)

diff --git a/core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java
index d3d6280284beb..0b8b604e18494 100644
--- a/core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.java
@@ -75,7 +75,7 @@ final class BypassMergeSortShuffleWriter<K, V> implements SortShuffleFileWriter<
   private final Serializer serializer;
 
   /** Array of file writers, one for each partition */
-  private BlockObjectWriter[] partitionWriters;
+  private DiskBlockObjectWriter[] partitionWriters;
 
   public BypassMergeSortShuffleWriter(
       SparkConf conf,
@@ -101,7 +101,7 @@ public void insertAll(Iterator<Product2<K, V>> records) throws IOException {
     }
     final SerializerInstance serInstance = serializer.newInstance();
     final long openStartTime = System.nanoTime();
-    partitionWriters = new BlockObjectWriter[numPartitions];
+    partitionWriters = new DiskBlockObjectWriter[numPartitions];
     for (int i = 0; i < numPartitions; i++) {
       final Tuple2<TempShuffleBlockId, File> tempShuffleBlockIdPlusFile =
         blockManager.diskBlockManager().createTempShuffleBlock();
@@ -121,7 +121,7 @@ public void insertAll(Iterator<Product2<K, V>> records) throws IOException {
       partitionWriters[partitioner.getPartition(key)].write(key, record._2());
     }
 
-    for (BlockObjectWriter writer : partitionWriters) {
+    for (DiskBlockObjectWriter writer : partitionWriters) {
       writer.commitAndClose();
     }
   }
@@ -169,7 +169,7 @@ public void stop() throws IOException {
     if (partitionWriters != null) {
       try {
         final DiskBlockManager diskBlockManager = blockManager.diskBlockManager();
-        for (BlockObjectWriter writer : partitionWriters) {
+        for (DiskBlockObjectWriter writer : partitionWriters) {
           // This method explicitly does _not_ throw exceptions:
           writer.revertPartialWritesAndClose();
           if (!diskBlockManager.getFile(writer.blockId()).delete()) {
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 56289573209fb..1d460432be9ff 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -157,7 +157,7 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
 
     // Currently, we need to open a new DiskBlockObjectWriter for each partition; we can avoid this
     // after SPARK-5581 is fixed.
-    BlockObjectWriter writer;
+    DiskBlockObjectWriter writer;
 
     // Small writes to DiskBlockObjectWriter will be fairly inefficient. Since there doesn't seem to
     // be an API to directly transfer bytes from managed memory to the disk writer, we buffer
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
index b8d66659804ad..71eed29563d4a 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
@@ -26,7 +26,7 @@
 import org.apache.spark.serializer.DummySerializerInstance;
 import org.apache.spark.storage.BlockId;
 import org.apache.spark.storage.BlockManager;
-import org.apache.spark.storage.BlockObjectWriter;
+import org.apache.spark.storage.DiskBlockObjectWriter;
 import org.apache.spark.storage.TempLocalBlockId;
 import org.apache.spark.unsafe.PlatformDependent;
 
@@ -47,7 +47,7 @@ final class UnsafeSorterSpillWriter {
   private final File file;
   private final BlockId blockId;
   private final int numRecordsToWrite;
-  private BlockObjectWriter writer;
+  private DiskBlockObjectWriter writer;
   private int numRecordsSpilled = 0;
 
   public UnsafeSorterSpillWriter(
diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
index 6c3b3080d2605..f6a96d81e7aa9 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
@@ -35,7 +35,7 @@ import org.apache.spark.util.collection.{PrimitiveKeyOpenHashMap, PrimitiveVecto
 
 /** A group of writers for a ShuffleMapTask, one writer per reducer. */
 private[spark] trait ShuffleWriterGroup {
-  val writers: Array[BlockObjectWriter]
+  val writers: Array[DiskBlockObjectWriter]
 
   /** @param success Indicates all writes were successful. If false, no blocks will be recorded. */
   def releaseWriters(success: Boolean)
@@ -113,15 +113,15 @@ private[spark] class FileShuffleBlockResolver(conf: SparkConf)
 
       val openStartTime = System.nanoTime
       val serializerInstance = serializer.newInstance()
-      val writers: Array[BlockObjectWriter] = if (consolidateShuffleFiles) {
+      val writers: Array[DiskBlockObjectWriter] = if (consolidateShuffleFiles) {
         fileGroup = getUnusedFileGroup()
-        Array.tabulate[BlockObjectWriter](numBuckets) { bucketId =>
+        Array.tabulate[DiskBlockObjectWriter](numBuckets) { bucketId =>
           val blockId = ShuffleBlockId(shuffleId, mapId, bucketId)
           blockManager.getDiskWriter(blockId, fileGroup(bucketId), serializerInstance, bufferSize,
             writeMetrics)
         }
       } else {
-        Array.tabulate[BlockObjectWriter](numBuckets) { bucketId =>
+        Array.tabulate[DiskBlockObjectWriter](numBuckets) { bucketId =>
           val blockId = ShuffleBlockId(shuffleId, mapId, bucketId)
           val blockFile = blockManager.diskBlockManager.getFile(blockId)
           // Because of previous failures, the shuffle file may already exist on this machine.
diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
index d9c63b6e7bbb9..fae69551e7330 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
@@ -114,7 +114,7 @@ private[spark] class IndexShuffleBlockResolver(conf: SparkConf) extends ShuffleB
 }
 
 private[spark] object IndexShuffleBlockResolver {
-  // No-op reduce ID used in interactions with disk store and BlockObjectWriter.
+  // No-op reduce ID used in interactions with disk store and DiskBlockObjectWriter.
   // The disk store currently expects puts to relate to a (map, reduce) pair, but in the sort
   // shuffle outputs for several reduces are glommed into a single file.
   // TODO: Avoid this entirely by having the DiskBlockObjectWriter not require a BlockId.
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
index eb87cee15903c..41df70c602c30 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
@@ -22,7 +22,7 @@ import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle._
-import org.apache.spark.storage.BlockObjectWriter
+import org.apache.spark.storage.DiskBlockObjectWriter
 
 private[spark] class HashShuffleWriter[K, V](
     shuffleBlockResolver: FileShuffleBlockResolver,
@@ -102,7 +102,7 @@ private[spark] class HashShuffleWriter[K, V](
 
   private def commitWritesAndBuildStatus(): MapStatus = {
     // Commit the writes. Get the size of each bucket block (total block size).
-    val sizes: Array[Long] = shuffle.writers.map { writer: BlockObjectWriter =>
+    val sizes: Array[Long] = shuffle.writers.map { writer: DiskBlockObjectWriter =>
       writer.commitAndClose()
       writer.fileSegment().length
     }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 1beafa1771448..86493673d958d 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -648,7 +648,7 @@ private[spark] class BlockManager(
       file: File,
       serializerInstance: SerializerInstance,
       bufferSize: Int,
-      writeMetrics: ShuffleWriteMetrics): BlockObjectWriter = {
+      writeMetrics: ShuffleWriteMetrics): DiskBlockObjectWriter = {
     val compressStream: OutputStream => OutputStream = wrapForCompression(blockId, _)
     val syncWrites = conf.getBoolean("spark.shuffle.sync", false)
     new DiskBlockObjectWriter(blockId, file, serializerInstance, bufferSize, compressStream,
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala
similarity index 83%
rename from core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala
rename to core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala
index 7eeabd1e0489c..49d9154f95a5b 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala
@@ -26,66 +26,25 @@ import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.util.Utils
 
 /**
- * An interface for writing JVM objects to some underlying storage. This interface allows
- * appending data to an existing block, and can guarantee atomicity in the case of faults
- * as it allows the caller to revert partial writes.
+ * A class for writing JVM objects directly to a file on disk. This class allows data to be appended
+ * to an existing block and can guarantee atomicity in the case of faults as it allows the caller to
+ * revert partial writes.
  *
- * This interface does not support concurrent writes. Also, once the writer has
- * been opened, it cannot be reopened again.
- */
-private[spark] abstract class BlockObjectWriter(val blockId: BlockId) extends OutputStream {
-
-  def open(): BlockObjectWriter
-
-  def close()
-
-  def isOpen: Boolean
-
-  /**
-   * Flush the partial writes and commit them as a single atomic block.
-   */
-  def commitAndClose(): Unit
-
-  /**
-   * Reverts writes that haven't been flushed yet. Callers should invoke this function
-   * when there are runtime exceptions. This method will not throw, though it may be
-   * unsuccessful in truncating written data.
-   */
-  def revertPartialWritesAndClose()
-
-  /**
-   * Writes a key-value pair.
-   */
-  def write(key: Any, value: Any)
-
-  /**
-   * Notify the writer that a record worth of bytes has been written with OutputStream#write.
-   */
-  def recordWritten()
-
-  /**
-   * Returns the file segment of committed data that this Writer has written.
-   * This is only valid after commitAndClose() has been called.
-   */
-  def fileSegment(): FileSegment
-}
-
-/**
- * BlockObjectWriter which writes directly to a file on disk. Appends to the given file.
+ * This class does not support concurrent writes. Also, once the writer has been opened it cannot be
+ * reopened again.
  */
 private[spark] class DiskBlockObjectWriter(
-    blockId: BlockId,
+    val blockId: BlockId,
     file: File,
     serializerInstance: SerializerInstance,
     bufferSize: Int,
     compressStream: OutputStream => OutputStream,
     syncWrites: Boolean,
-    // These write metrics concurrently shared with other active BlockObjectWriter's who
+    // These write metrics concurrently shared with other active DiskBlockObjectWriters who
     // are themselves performing writes. All updates must be relative.
     writeMetrics: ShuffleWriteMetrics)
-  extends BlockObjectWriter(blockId)
-  with Logging
-{
+  extends OutputStream
+  with Logging {
 
   /** The file channel, used for repositioning / truncating the file. */
   private var channel: FileChannel = null
@@ -122,7 +81,7 @@ private[spark] class DiskBlockObjectWriter(
    */
   private var numRecordsWritten = 0
 
-  override def open(): BlockObjectWriter = {
+  def open(): DiskBlockObjectWriter = {
     if (hasBeenClosed) {
       throw new IllegalStateException("Writer already closed. Cannot be reopened.")
     }
@@ -159,9 +118,12 @@ private[spark] class DiskBlockObjectWriter(
     }
   }
 
-  override def isOpen: Boolean = objOut != null
+  def isOpen: Boolean = objOut != null
 
-  override def commitAndClose(): Unit = {
+  /**
+   * Flush the partial writes and commit them as a single atomic block.
+   */
+  def commitAndClose(): Unit = {
     if (initialized) {
       // NOTE: Because Kryo doesn't flush the underlying stream we explicitly flush both the
       //       serializer stream and the lower level stream.
@@ -177,9 +139,15 @@ private[spark] class DiskBlockObjectWriter(
     commitAndCloseHasBeenCalled = true
   }
 
-  // Discard current writes. We do this by flushing the outstanding writes and then
-  // truncating the file to its initial position.
-  override def revertPartialWritesAndClose() {
+
+  /**
+   * Reverts writes that haven't been flushed yet. Callers should invoke this function
+   * when there are runtime exceptions. This method will not throw, though it may be
+   * unsuccessful in truncating written data.
+   */
+  def revertPartialWritesAndClose() {
+    // Discard current writes. We do this by flushing the outstanding writes and then
+    // truncating the file to its initial position.
     try {
       if (initialized) {
         writeMetrics.decShuffleBytesWritten(reportedPosition - initialPosition)
@@ -201,7 +169,10 @@ private[spark] class DiskBlockObjectWriter(
     }
   }
 
-  override def write(key: Any, value: Any) {
+  /**
+   * Writes a key-value pair.
+   */
+  def write(key: Any, value: Any) {
     if (!initialized) {
       open()
     }
@@ -221,7 +192,10 @@ private[spark] class DiskBlockObjectWriter(
     bs.write(kvBytes, offs, len)
   }
 
-  override def recordWritten(): Unit = {
+  /**
+   * Notify the writer that a record worth of bytes has been written with OutputStream#write.
+   */
+  def recordWritten(): Unit = {
     numRecordsWritten += 1
     writeMetrics.incShuffleRecordsWritten(1)
 
@@ -230,7 +204,11 @@ private[spark] class DiskBlockObjectWriter(
     }
   }
 
-  override def fileSegment(): FileSegment = {
+  /**
+   * Returns the file segment of committed data that this Writer has written.
+   * This is only valid after commitAndClose() has been called.
+   */
+  def fileSegment(): FileSegment = {
     if (!commitAndCloseHasBeenCalled) {
       throw new IllegalStateException(
         "fileSegment() is only valid after commitAndClose() has been called")
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala
index 516aaa44d03fc..ae60f3b0cb555 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala
@@ -37,7 +37,7 @@ private[spark] class ChainedBuffer(chunkSize: Int) {
   private var _size: Long = 0
 
   /**
-   * Feed bytes from this buffer into a BlockObjectWriter.
+   * Feed bytes from this buffer into a DiskBlockObjectWriter.
    *
    * @param pos Offset in the buffer to read from.
    * @param os OutputStream to read into.
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index 757dec66c203b..ba7ec834d622d 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -30,7 +30,7 @@ import org.apache.spark._
 import org.apache.spark.serializer._
 import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.shuffle.sort.{SortShuffleFileWriter, SortShuffleWriter}
-import org.apache.spark.storage.{BlockId, BlockObjectWriter}
+import org.apache.spark.storage.{BlockId, DiskBlockObjectWriter}
 
 /**
  * Sorts and potentially merges a number of key-value pairs of type (K, V) to produce key-combiner
@@ -250,7 +250,7 @@ private[spark] class ExternalSorter[K, V, C](
     // These variables are reset after each flush
     var objectsWritten: Long = 0
     var spillMetrics: ShuffleWriteMetrics = null
-    var writer: BlockObjectWriter = null
+    var writer: DiskBlockObjectWriter = null
     def openWriter(): Unit = {
       assert (writer == null && spillMetrics == null)
       spillMetrics = new ShuffleWriteMetrics
diff --git a/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala
index 04bb7fc78c13b..f5844d5353be7 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala
@@ -19,7 +19,6 @@ package org.apache.spark.util.collection
 
 import java.util.Comparator
 
-import org.apache.spark.storage.BlockObjectWriter
 import org.apache.spark.util.collection.WritablePartitionedPairCollection._
 
 /**
diff --git a/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala
index ae9a48729e201..87a786b02d651 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala
@@ -21,9 +21,8 @@ import java.io.InputStream
 import java.nio.IntBuffer
 import java.util.Comparator
 
-import org.apache.spark.SparkEnv
 import org.apache.spark.serializer.{JavaSerializerInstance, SerializerInstance}
-import org.apache.spark.storage.BlockObjectWriter
+import org.apache.spark.storage.DiskBlockObjectWriter
 import org.apache.spark.util.collection.PartitionedSerializedPairBuffer._
 
 /**
@@ -136,7 +135,7 @@ private[spark] class PartitionedSerializedPairBuffer[K, V](
       // current position in the meta buffer in ints
       var pos = 0
 
-      def writeNext(writer: BlockObjectWriter): Unit = {
+      def writeNext(writer: DiskBlockObjectWriter): Unit = {
         val keyStart = getKeyStartPos(metaBuffer, pos)
         val keyValLen = metaBuffer.get(pos + KEY_VAL_LEN)
         pos += RECORD_SIZE
diff --git a/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala b/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala
index 7bc59898658e4..38848e9018c6c 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala
@@ -19,7 +19,7 @@ package org.apache.spark.util.collection
 
 import java.util.Comparator
 
-import org.apache.spark.storage.BlockObjectWriter
+import org.apache.spark.storage.DiskBlockObjectWriter
 
 /**
  * A common interface for size-tracking collections of key-value pairs that
@@ -51,7 +51,7 @@ private[spark] trait WritablePartitionedPairCollection[K, V] {
     new WritablePartitionedIterator {
       private[this] var cur = if (it.hasNext) it.next() else null
 
-      def writeNext(writer: BlockObjectWriter): Unit = {
+      def writeNext(writer: DiskBlockObjectWriter): Unit = {
         writer.write(cur._1._2, cur._2)
         cur = if (it.hasNext) it.next() else null
       }
@@ -91,11 +91,11 @@ private[spark] object WritablePartitionedPairCollection {
 }
 
 /**
- * Iterator that writes elements to a BlockObjectWriter instead of returning them. Each element
+ * Iterator that writes elements to a DiskBlockObjectWriter instead of returning them. Each element
  * has an associated partition.
  */
 private[spark] trait WritablePartitionedIterator {
-  def writeNext(writer: BlockObjectWriter): Unit
+  def writeNext(writer: DiskBlockObjectWriter): Unit
 
   def hasNext(): Boolean
 
diff --git a/core/src/test/scala/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriterSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriterSuite.scala
index 542f8f45125a4..cc7342f1ecd78 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriterSuite.scala
@@ -68,8 +68,8 @@ class BypassMergeSortShuffleWriterSuite extends SparkFunSuite with BeforeAndAfte
       any[SerializerInstance],
       anyInt(),
       any[ShuffleWriteMetrics]
-    )).thenAnswer(new Answer[BlockObjectWriter] {
-      override def answer(invocation: InvocationOnMock): BlockObjectWriter = {
+    )).thenAnswer(new Answer[DiskBlockObjectWriter] {
+      override def answer(invocation: InvocationOnMock): DiskBlockObjectWriter = {
         val args = invocation.getArguments
         new DiskBlockObjectWriter(
           args(0).asInstanceOf[BlockId],
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala
similarity index 98%
rename from core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala
rename to core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala
index 7bdea724fea58..66af6e1a79740 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.serializer.JavaSerializer
 import org.apache.spark.util.Utils
 
-class BlockObjectWriterSuite extends SparkFunSuite with BeforeAndAfterEach {
+class DiskBlockObjectWriterSuite extends SparkFunSuite with BeforeAndAfterEach {
 
   var tempDir: File = _
 
diff --git a/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala
index 6d2459d48d326..3b67f6206495a 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala
@@ -17,15 +17,20 @@
 
 package org.apache.spark.util.collection
 
-import java.io.{ByteArrayInputStream, ByteArrayOutputStream, InputStream}
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
 
 import com.google.common.io.ByteStreams
 
+import org.mockito.Matchers.any
+import org.mockito.Mockito._
+import org.mockito.Mockito.RETURNS_SMART_NULLS
+import org.mockito.invocation.InvocationOnMock
+import org.mockito.stubbing.Answer
 import org.scalatest.Matchers._
 
 import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.serializer.KryoSerializer
-import org.apache.spark.storage.{FileSegment, BlockObjectWriter}
+import org.apache.spark.storage.DiskBlockObjectWriter
 
 class PartitionedSerializedPairBufferSuite extends SparkFunSuite {
   test("OrderedInputStream single record") {
@@ -79,13 +84,13 @@ class PartitionedSerializedPairBufferSuite extends SparkFunSuite {
     val struct = SomeStruct("something", 5)
     buffer.insert(4, 10, struct)
     val it = buffer.destructiveSortedWritablePartitionedIterator(None)
-    val writer = new SimpleBlockObjectWriter
+    val (writer, baos) = createMockWriter()
     assert(it.hasNext)
     it.nextPartition should be (4)
     it.writeNext(writer)
     assert(!it.hasNext)
 
-    val stream = serializerInstance.deserializeStream(writer.getInputStream)
+    val stream = serializerInstance.deserializeStream(new ByteArrayInputStream(baos.toByteArray))
     stream.readObject[AnyRef]() should be (10)
     stream.readObject[AnyRef]() should be (struct)
   }
@@ -101,7 +106,7 @@ class PartitionedSerializedPairBufferSuite extends SparkFunSuite {
     buffer.insert(5, 3, struct3)
 
     val it = buffer.destructiveSortedWritablePartitionedIterator(None)
-    val writer = new SimpleBlockObjectWriter
+    val (writer, baos) = createMockWriter()
     assert(it.hasNext)
     it.nextPartition should be (4)
     it.writeNext(writer)
@@ -113,7 +118,7 @@ class PartitionedSerializedPairBufferSuite extends SparkFunSuite {
     it.writeNext(writer)
     assert(!it.hasNext)
 
-    val stream = serializerInstance.deserializeStream(writer.getInputStream)
+    val stream = serializerInstance.deserializeStream(new ByteArrayInputStream(baos.toByteArray))
     val iter = stream.asIterator
     iter.next() should be (2)
     iter.next() should be (struct2)
@@ -123,26 +128,21 @@ class PartitionedSerializedPairBufferSuite extends SparkFunSuite {
     iter.next() should be (struct1)
     assert(!iter.hasNext)
   }
-}
-
-case class SomeStruct(val str: String, val num: Int)
-
-class SimpleBlockObjectWriter extends BlockObjectWriter(null) {
-  val baos = new ByteArrayOutputStream()
 
-  override def write(bytes: Array[Byte], offs: Int, len: Int): Unit = {
-    baos.write(bytes, offs, len)
+  def createMockWriter(): (DiskBlockObjectWriter, ByteArrayOutputStream) = {
+    val writer = mock(classOf[DiskBlockObjectWriter], RETURNS_SMART_NULLS)
+    val baos = new ByteArrayOutputStream()
+    when(writer.write(any(), any(), any())).thenAnswer(new Answer[Unit] {
+      override def answer(invocationOnMock: InvocationOnMock): Unit = {
+        val args = invocationOnMock.getArguments
+        val bytes = args(0).asInstanceOf[Array[Byte]]
+        val offset = args(1).asInstanceOf[Int]
+        val length = args(2).asInstanceOf[Int]
+        baos.write(bytes, offset, length)
+      }
+    })
+    (writer, baos)
   }
-
-  def getInputStream(): InputStream = new ByteArrayInputStream(baos.toByteArray)
-
-  override def open(): BlockObjectWriter = this
-  override def close(): Unit = { }
-  override def isOpen: Boolean = true
-  override def commitAndClose(): Unit = { }
-  override def revertPartialWritesAndClose(): Unit = { }
-  override def fileSegment(): FileSegment = null
-  override def write(key: Any, value: Any): Unit = { }
-  override def recordWritten(): Unit = { }
-  override def write(b: Int): Unit = { }
 }
+
+case class SomeStruct(str: String, num: Int)

From 0a4071eab30db1db80f61ed2cb2e7243291183ce Mon Sep 17 00:00:00 2001
From: Andrew Ray <ray.andrew@gmail.com>
Date: Tue, 14 Jul 2015 13:14:47 -0700
Subject: [PATCH 0371/1454] [SPARK-8718] [GRAPHX] Improve EdgePartition2D for
 non perfect square number of partitions

See https://github.com/aray/e2d/blob/master/EdgePartition2D.ipynb

Author: Andrew Ray <ray.andrew@gmail.com>

Closes #7104 from aray/edge-partition-2d-improvement and squashes the following commits:

3729f84 [Andrew Ray] correct bounds and remove unneeded comments
97f8464 [Andrew Ray] change less
5141ab4 [Andrew Ray] Merge branch 'master' into edge-partition-2d-improvement
925fd2c [Andrew Ray] use new interface for partitioning
001bfd0 [Andrew Ray] Refactor PartitionStrategy so that we can return a prtition function for a given number of parts. To keep compatibility we define default methods that translate between the two implementation options. Made EdgePartition2D use old strategy when we have a perfect square and implement new interface.
5d42105 [Andrew Ray] % -> /
3560084 [Andrew Ray] Merge branch 'master' into edge-partition-2d-improvement
f006364 [Andrew Ray] remove unneeded comments
cfa2c5e [Andrew Ray] Modifications to EdgePartition2D so that it works for non perfect squares.
---
 .../spark/graphx/PartitionStrategy.scala      | 32 ++++++++++++-------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
index 7372dfbd9fe98..70a7592da8ae3 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
@@ -32,7 +32,7 @@ trait PartitionStrategy extends Serializable {
 object PartitionStrategy {
   /**
    * Assigns edges to partitions using a 2D partitioning of the sparse edge adjacency matrix,
-   * guaranteeing a `2 * sqrt(numParts) - 1` bound on vertex replication.
+   * guaranteeing a `2 * sqrt(numParts)` bound on vertex replication.
    *
    * Suppose we have a graph with 12 vertices that we want to partition
    * over 9 machines.  We can use the following sparse matrix representation:
@@ -61,26 +61,36 @@ object PartitionStrategy {
    * that edges adjacent to `v11` can only be in the first column of blocks `(P0, P3,
    * P6)` or the last
    * row of blocks `(P6, P7, P8)`.  As a consequence we can guarantee that `v11` will need to be
-   * replicated to at most `2 * sqrt(numParts) - 1` machines.
+   * replicated to at most `2 * sqrt(numParts)` machines.
    *
    * Notice that `P0` has many edges and as a consequence this partitioning would lead to poor work
    * balance.  To improve balance we first multiply each vertex id by a large prime to shuffle the
    * vertex locations.
    *
-   * One of the limitations of this approach is that the number of machines must either be a
-   * perfect square. We partially address this limitation by computing the machine assignment to
-   * the next
-   * largest perfect square and then mapping back down to the actual number of machines.
-   * Unfortunately, this can also lead to work imbalance and so it is suggested that a perfect
-   * square is used.
+   * When the number of partitions requested is not a perfect square we use a slightly different
+   * method where the last column can have a different number of rows than the others while still
+   * maintaining the same size per block.
    */
   case object EdgePartition2D extends PartitionStrategy {
     override def getPartition(src: VertexId, dst: VertexId, numParts: PartitionID): PartitionID = {
       val ceilSqrtNumParts: PartitionID = math.ceil(math.sqrt(numParts)).toInt
       val mixingPrime: VertexId = 1125899906842597L
-      val col: PartitionID = (math.abs(src * mixingPrime) % ceilSqrtNumParts).toInt
-      val row: PartitionID = (math.abs(dst * mixingPrime) % ceilSqrtNumParts).toInt
-      (col * ceilSqrtNumParts + row) % numParts
+      if (numParts == ceilSqrtNumParts * ceilSqrtNumParts) {
+        // Use old method for perfect squared to ensure we get same results
+        val col: PartitionID = (math.abs(src * mixingPrime) % ceilSqrtNumParts).toInt
+        val row: PartitionID = (math.abs(dst * mixingPrime) % ceilSqrtNumParts).toInt
+        (col * ceilSqrtNumParts + row) % numParts
+
+      } else {
+        // Otherwise use new method
+        val cols = ceilSqrtNumParts
+        val rows = (numParts + cols - 1) / cols
+        val lastColRows = numParts - rows * (cols - 1)
+        val col = (math.abs(src * mixingPrime) % numParts / rows).toInt
+        val row = (math.abs(dst * mixingPrime) % (if (col < cols - 1) rows else lastColRows)).toInt
+        col * rows + row
+
+      }
     }
   }
 

From fb1d06fc242ec00320f1a3049673fbb03c4a6eb9 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 14 Jul 2015 13:58:36 -0700
Subject: [PATCH 0372/1454] [SPARK-4072] [CORE] Display Streaming blocks in
 Streaming UI

Replace #6634

This PR adds `SparkListenerBlockUpdated` to SparkListener so that it can monitor all block update infos that are sent to `BlockManagerMasaterEndpoint`, and also add new tables in the Storage tab to display the stream block infos.

![screen shot 2015-07-01 at 5 19 46 pm](https://cloud.githubusercontent.com/assets/1000778/8451562/c291a6ec-2016-11e5-890d-0afc174e1f8c.png)

Author: zsxwing <zsxwing@gmail.com>

Closes #6672 from zsxwing/SPARK-4072-2 and squashes the following commits:

df2c1d8 [zsxwing] Use xml query to check the xml elements
54d54af [zsxwing] Add unit tests for StoragePage
e29fb53 [zsxwing] Update as per TD's comments
ccbee07 [zsxwing] Fix the code style
6dc42b4 [zsxwing] Fix the replication level of blocks
450fad1 [zsxwing] Merge branch 'master' into SPARK-4072-2
1e9ef52 [zsxwing] Don't categorize by Executor ID
ca0ab69 [zsxwing] Fix the code style
3de2762 [zsxwing] Make object BlockUpdatedInfo private
e95b594 [zsxwing] Add 'Aggregated Stream Block Metrics by Executor' table
ba5d0d1 [zsxwing] Refactor the unit test to improve the readability
4bbe341 [zsxwing] Revert JsonProtocol and don't log SparkListenerBlockUpdated
b464dd1 [zsxwing] Add onBlockUpdated to EventLoggingListener
5ba014c [zsxwing] Fix the code style
0b1e47b [zsxwing] Add a developer api BlockUpdatedInfo
04838a9 [zsxwing] Fix the code style
2baa161 [zsxwing] Add unit tests
80f6c6d [zsxwing] Address comments
797ee4b [zsxwing] Display Streaming blocks in Streaming UI
---
 .../org/apache/spark/JavaSparkListener.java   |  22 +-
 .../apache/spark/SparkFirehoseListener.java   |   6 +
 .../scheduler/EventLoggingListener.scala      |   3 +
 .../spark/scheduler/SparkListener.scala       |  10 +-
 .../spark/scheduler/SparkListenerBus.scala    |   2 +
 .../storage/BlockManagerMasterEndpoint.scala  |   3 +-
 .../spark/storage/BlockStatusListener.scala   | 105 ++++++++
 .../spark/storage/BlockUpdatedInfo.scala      |  47 ++++
 .../scala/org/apache/spark/ui/UIUtils.scala   |  14 +-
 .../apache/spark/ui/storage/StoragePage.scala | 148 ++++++++++-
 .../apache/spark/ui/storage/StorageTab.scala  |   3 +-
 .../storage/BlockStatusListenerSuite.scala    | 119 +++++++++
 .../spark/ui/storage/StoragePageSuite.scala   | 230 ++++++++++++++++++
 13 files changed, 684 insertions(+), 28 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/storage/BlockStatusListener.scala
 create mode 100644 core/src/main/scala/org/apache/spark/storage/BlockUpdatedInfo.scala
 create mode 100644 core/src/test/scala/org/apache/spark/storage/BlockStatusListenerSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/ui/storage/StoragePageSuite.scala

diff --git a/core/src/main/java/org/apache/spark/JavaSparkListener.java b/core/src/main/java/org/apache/spark/JavaSparkListener.java
index 646496f313507..fa9acf0a15b88 100644
--- a/core/src/main/java/org/apache/spark/JavaSparkListener.java
+++ b/core/src/main/java/org/apache/spark/JavaSparkListener.java
@@ -17,23 +17,7 @@
 
 package org.apache.spark;
 
-import org.apache.spark.scheduler.SparkListener;
-import org.apache.spark.scheduler.SparkListenerApplicationEnd;
-import org.apache.spark.scheduler.SparkListenerApplicationStart;
-import org.apache.spark.scheduler.SparkListenerBlockManagerAdded;
-import org.apache.spark.scheduler.SparkListenerBlockManagerRemoved;
-import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate;
-import org.apache.spark.scheduler.SparkListenerExecutorAdded;
-import org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate;
-import org.apache.spark.scheduler.SparkListenerExecutorRemoved;
-import org.apache.spark.scheduler.SparkListenerJobEnd;
-import org.apache.spark.scheduler.SparkListenerJobStart;
-import org.apache.spark.scheduler.SparkListenerStageCompleted;
-import org.apache.spark.scheduler.SparkListenerStageSubmitted;
-import org.apache.spark.scheduler.SparkListenerTaskEnd;
-import org.apache.spark.scheduler.SparkListenerTaskGettingResult;
-import org.apache.spark.scheduler.SparkListenerTaskStart;
-import org.apache.spark.scheduler.SparkListenerUnpersistRDD;
+import org.apache.spark.scheduler.*;
 
 /**
  * Java clients should extend this class instead of implementing
@@ -94,4 +78,8 @@ public void onExecutorAdded(SparkListenerExecutorAdded executorAdded) { }
 
   @Override
   public void onExecutorRemoved(SparkListenerExecutorRemoved executorRemoved) { }
+
+  @Override
+  public void onBlockUpdated(SparkListenerBlockUpdated blockUpdated) { }
+
 }
diff --git a/core/src/main/java/org/apache/spark/SparkFirehoseListener.java b/core/src/main/java/org/apache/spark/SparkFirehoseListener.java
index fbc5666959055..1214d05ba6063 100644
--- a/core/src/main/java/org/apache/spark/SparkFirehoseListener.java
+++ b/core/src/main/java/org/apache/spark/SparkFirehoseListener.java
@@ -112,4 +112,10 @@ public final void onExecutorAdded(SparkListenerExecutorAdded executorAdded) {
     public final void onExecutorRemoved(SparkListenerExecutorRemoved executorRemoved) {
         onEvent(executorRemoved);
     }
+
+    @Override
+    public void onBlockUpdated(SparkListenerBlockUpdated blockUpdated) {
+        onEvent(blockUpdated);
+    }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index 62b05033a9281..5a06ef02f5c57 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -199,6 +199,9 @@ private[spark] class EventLoggingListener(
     logEvent(event, flushLogger = true)
   }
 
+  // No-op because logging every update would be overkill
+  override def onBlockUpdated(event: SparkListenerBlockUpdated): Unit = {}
+
   // No-op because logging every update would be overkill
   override def onExecutorMetricsUpdate(event: SparkListenerExecutorMetricsUpdate): Unit = { }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
index 9620915f495ab..896f1743332f1 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
@@ -26,7 +26,7 @@ import org.apache.spark.{Logging, TaskEndReason}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler.cluster.ExecutorInfo
-import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.storage.{BlockManagerId, BlockUpdatedInfo}
 import org.apache.spark.util.{Distribution, Utils}
 
 @DeveloperApi
@@ -98,6 +98,9 @@ case class SparkListenerExecutorAdded(time: Long, executorId: String, executorIn
 case class SparkListenerExecutorRemoved(time: Long, executorId: String, reason: String)
   extends SparkListenerEvent
 
+@DeveloperApi
+case class SparkListenerBlockUpdated(blockUpdatedInfo: BlockUpdatedInfo) extends SparkListenerEvent
+
 /**
  * Periodic updates from executors.
  * @param execId executor id
@@ -215,6 +218,11 @@ trait SparkListener {
    * Called when the driver removes an executor.
    */
   def onExecutorRemoved(executorRemoved: SparkListenerExecutorRemoved) { }
+
+  /**
+   * Called when the driver receives a block update info.
+   */
+  def onBlockUpdated(blockUpdated: SparkListenerBlockUpdated) { }
 }
 
 /**
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
index 61e69ecc08387..04afde33f5aad 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala
@@ -58,6 +58,8 @@ private[spark] trait SparkListenerBus extends ListenerBus[SparkListener, SparkLi
         listener.onExecutorAdded(executorAdded)
       case executorRemoved: SparkListenerExecutorRemoved =>
         listener.onExecutorRemoved(executorRemoved)
+      case blockUpdated: SparkListenerBlockUpdated =>
+        listener.onBlockUpdated(blockUpdated)
       case logStart: SparkListenerLogStart => // ignore event log metadata
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
index 68ed9096731c5..5dc0c537cbb62 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
@@ -60,10 +60,11 @@ class BlockManagerMasterEndpoint(
       register(blockManagerId, maxMemSize, slaveEndpoint)
       context.reply(true)
 
-    case UpdateBlockInfo(
+    case _updateBlockInfo @ UpdateBlockInfo(
       blockManagerId, blockId, storageLevel, deserializedSize, size, externalBlockStoreSize) =>
       context.reply(updateBlockInfo(
         blockManagerId, blockId, storageLevel, deserializedSize, size, externalBlockStoreSize))
+      listenerBus.post(SparkListenerBlockUpdated(BlockUpdatedInfo(_updateBlockInfo)))
 
     case GetLocations(blockId) =>
       context.reply(getLocations(blockId))
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/BlockStatusListener.scala
new file mode 100644
index 0000000000000..2789e25b8d3ab
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/storage/BlockStatusListener.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import scala.collection.mutable
+
+import org.apache.spark.scheduler._
+
+private[spark] case class BlockUIData(
+    blockId: BlockId,
+    location: String,
+    storageLevel: StorageLevel,
+    memSize: Long,
+    diskSize: Long,
+    externalBlockStoreSize: Long)
+
+/**
+ * The aggregated status of stream blocks in an executor
+ */
+private[spark] case class ExecutorStreamBlockStatus(
+    executorId: String,
+    location: String,
+    blocks: Seq[BlockUIData]) {
+
+  def totalMemSize: Long = blocks.map(_.memSize).sum
+
+  def totalDiskSize: Long = blocks.map(_.diskSize).sum
+
+  def totalExternalBlockStoreSize: Long = blocks.map(_.externalBlockStoreSize).sum
+
+  def numStreamBlocks: Int = blocks.size
+
+}
+
+private[spark] class BlockStatusListener extends SparkListener {
+
+  private val blockManagers =
+    new mutable.HashMap[BlockManagerId, mutable.HashMap[BlockId, BlockUIData]]
+
+  override def onBlockUpdated(blockUpdated: SparkListenerBlockUpdated): Unit = {
+    val blockId = blockUpdated.blockUpdatedInfo.blockId
+    if (!blockId.isInstanceOf[StreamBlockId]) {
+      // Now we only monitor StreamBlocks
+      return
+    }
+    val blockManagerId = blockUpdated.blockUpdatedInfo.blockManagerId
+    val storageLevel = blockUpdated.blockUpdatedInfo.storageLevel
+    val memSize = blockUpdated.blockUpdatedInfo.memSize
+    val diskSize = blockUpdated.blockUpdatedInfo.diskSize
+    val externalBlockStoreSize = blockUpdated.blockUpdatedInfo.externalBlockStoreSize
+
+    synchronized {
+      // Drop the update info if the block manager is not registered
+      blockManagers.get(blockManagerId).foreach { blocksInBlockManager =>
+        if (storageLevel.isValid) {
+          blocksInBlockManager.put(blockId,
+            BlockUIData(
+              blockId,
+              blockManagerId.hostPort,
+              storageLevel,
+              memSize,
+              diskSize,
+              externalBlockStoreSize)
+          )
+        } else {
+          // If isValid is not true, it means we should drop the block.
+          blocksInBlockManager -= blockId
+        }
+      }
+    }
+  }
+
+  override def onBlockManagerAdded(blockManagerAdded: SparkListenerBlockManagerAdded): Unit = {
+    synchronized {
+      blockManagers.put(blockManagerAdded.blockManagerId, mutable.HashMap())
+    }
+  }
+
+  override def onBlockManagerRemoved(
+      blockManagerRemoved: SparkListenerBlockManagerRemoved): Unit = synchronized {
+    blockManagers -= blockManagerRemoved.blockManagerId
+  }
+
+  def allExecutorStreamBlockStatus: Seq[ExecutorStreamBlockStatus] = synchronized {
+    blockManagers.map { case (blockManagerId, blocks) =>
+      ExecutorStreamBlockStatus(
+        blockManagerId.executorId, blockManagerId.hostPort, blocks.values.toSeq)
+    }.toSeq
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockUpdatedInfo.scala b/core/src/main/scala/org/apache/spark/storage/BlockUpdatedInfo.scala
new file mode 100644
index 0000000000000..a5790e4454a89
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/storage/BlockUpdatedInfo.scala
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.storage.BlockManagerMessages.UpdateBlockInfo
+
+/**
+ * :: DeveloperApi ::
+ * Stores information about a block status in a block manager.
+ */
+@DeveloperApi
+case class BlockUpdatedInfo(
+    blockManagerId: BlockManagerId,
+    blockId: BlockId,
+    storageLevel: StorageLevel,
+    memSize: Long,
+    diskSize: Long,
+    externalBlockStoreSize: Long)
+
+private[spark] object BlockUpdatedInfo {
+
+  private[spark] def apply(updateBlockInfo: UpdateBlockInfo): BlockUpdatedInfo = {
+    BlockUpdatedInfo(
+      updateBlockInfo.blockManagerId,
+      updateBlockInfo.blockId,
+      updateBlockInfo.storageLevel,
+      updateBlockInfo.memSize,
+      updateBlockInfo.diskSize,
+      updateBlockInfo.externalBlockStoreSize)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index 7898039519201..718aea7e1dc22 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -27,7 +27,7 @@ import org.apache.spark.ui.scope.RDDOperationGraph
 
 /** Utility functions for generating XML pages with spark content. */
 private[spark] object UIUtils extends Logging {
-  val TABLE_CLASS_NOT_STRIPED = "table table-bordered table-condensed sortable"
+  val TABLE_CLASS_NOT_STRIPED = "table table-bordered table-condensed"
   val TABLE_CLASS_STRIPED = TABLE_CLASS_NOT_STRIPED + " table-striped"
 
   // SimpleDateFormat is not thread-safe. Don't expose it to avoid improper use.
@@ -267,9 +267,17 @@ private[spark] object UIUtils extends Logging {
       fixedWidth: Boolean = false,
       id: Option[String] = None,
       headerClasses: Seq[String] = Seq.empty,
-      stripeRowsWithCss: Boolean = true): Seq[Node] = {
+      stripeRowsWithCss: Boolean = true,
+      sortable: Boolean = true): Seq[Node] = {
 
-    val listingTableClass = if (stripeRowsWithCss) TABLE_CLASS_STRIPED else TABLE_CLASS_NOT_STRIPED
+    val listingTableClass = {
+      val _tableClass = if (stripeRowsWithCss) TABLE_CLASS_STRIPED else TABLE_CLASS_NOT_STRIPED
+      if (sortable) {
+        _tableClass + " sortable"
+      } else {
+        _tableClass
+      }
+    }
     val colWidth = 100.toDouble / headers.size
     val colWidthAttr = if (fixedWidth) colWidth + "%" else ""
 
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
index 07db783c572cf..04f584621e71e 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
@@ -21,7 +21,7 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.storage.RDDInfo
+import org.apache.spark.storage._
 import org.apache.spark.ui.{UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
@@ -30,13 +30,25 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") {
   private val listener = parent.listener
 
   def render(request: HttpServletRequest): Seq[Node] = {
-    val rdds = listener.rddInfoList
-    val content = UIUtils.listingTable(rddHeader, rddRow, rdds, id = Some("storage-by-rdd-table"))
+    val content = rddTable(listener.rddInfoList) ++
+      receiverBlockTables(listener.allExecutorStreamBlockStatus.sortBy(_.executorId))
     UIUtils.headerSparkPage("Storage", content, parent)
   }
 
+  private[storage] def rddTable(rdds: Seq[RDDInfo]): Seq[Node] = {
+    if (rdds.isEmpty) {
+      // Don't show the rdd table if there is no RDD persisted.
+      Nil
+    } else {
+      <div>
+        <h4>RDDs</h4>
+        {UIUtils.listingTable(rddHeader, rddRow, rdds, id = Some("storage-by-rdd-table"))}
+      </div>
+    }
+  }
+
   /** Header fields for the RDD table */
-  private def rddHeader = Seq(
+  private val rddHeader = Seq(
     "RDD Name",
     "Storage Level",
     "Cached Partitions",
@@ -56,7 +68,7 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") {
       </td>
       <td>{rdd.storageLevel.description}
       </td>
-      <td>{rdd.numCachedPartitions}</td>
+      <td>{rdd.numCachedPartitions.toString}</td>
       <td>{"%.0f%%".format(rdd.numCachedPartitions * 100.0 / rdd.numPartitions)}</td>
       <td sorttable_customkey={rdd.memSize.toString}>{Utils.bytesToString(rdd.memSize)}</td>
       <td sorttable_customkey={rdd.externalBlockStoreSize.toString}>{Utils.bytesToString(rdd.externalBlockStoreSize)}</td>
@@ -64,4 +76,130 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") {
     </tr>
     // scalastyle:on
   }
+
+  private[storage] def receiverBlockTables(statuses: Seq[ExecutorStreamBlockStatus]): Seq[Node] = {
+    if (statuses.map(_.numStreamBlocks).sum == 0) {
+      // Don't show the tables if there is no stream block
+      Nil
+    } else {
+      val blocks = statuses.flatMap(_.blocks).groupBy(_.blockId).toSeq.sortBy(_._1.toString)
+
+      <div>
+        <h4>Receiver Blocks</h4>
+        {executorMetricsTable(statuses)}
+        {streamBlockTable(blocks)}
+      </div>
+    }
+  }
+
+  private def executorMetricsTable(statuses: Seq[ExecutorStreamBlockStatus]): Seq[Node] = {
+    <div>
+      <h5>Aggregated Block Metrics by Executor</h5>
+      {UIUtils.listingTable(executorMetricsTableHeader, executorMetricsTableRow, statuses,
+        id = Some("storage-by-executor-stream-blocks"))}
+    </div>
+  }
+
+  private val executorMetricsTableHeader = Seq(
+    "Executor ID",
+    "Address",
+    "Total Size in Memory",
+    "Total Size in ExternalBlockStore",
+    "Total Size on Disk",
+    "Stream Blocks")
+
+  private def executorMetricsTableRow(status: ExecutorStreamBlockStatus): Seq[Node] = {
+    <tr>
+      <td>
+        {status.executorId}
+      </td>
+      <td>
+        {status.location}
+      </td>
+      <td sorttable_customkey={status.totalMemSize.toString}>
+        {Utils.bytesToString(status.totalMemSize)}
+      </td>
+      <td sorttable_customkey={status.totalExternalBlockStoreSize.toString}>
+        {Utils.bytesToString(status.totalExternalBlockStoreSize)}
+      </td>
+      <td sorttable_customkey={status.totalDiskSize.toString}>
+        {Utils.bytesToString(status.totalDiskSize)}
+      </td>
+      <td>
+        {status.numStreamBlocks.toString}
+      </td>
+    </tr>
+  }
+
+  private def streamBlockTable(blocks: Seq[(BlockId, Seq[BlockUIData])]): Seq[Node] = {
+    if (blocks.isEmpty) {
+      Nil
+    } else {
+      <div>
+        <h5>Blocks</h5>
+        {UIUtils.listingTable(
+          streamBlockTableHeader,
+          streamBlockTableRow,
+          blocks,
+          id = Some("storage-by-block-table"),
+          sortable = false)}
+      </div>
+    }
+  }
+
+  private val streamBlockTableHeader = Seq(
+    "Block ID",
+    "Replication Level",
+    "Location",
+    "Storage Level",
+    "Size")
+
+  /** Render a stream block */
+  private def streamBlockTableRow(block: (BlockId, Seq[BlockUIData])): Seq[Node] = {
+    val replications = block._2
+    assert(replications.size > 0) // This must be true because it's the result of "groupBy"
+    if (replications.size == 1) {
+      streamBlockTableSubrow(block._1, replications.head, replications.size, true)
+    } else {
+      streamBlockTableSubrow(block._1, replications.head, replications.size, true) ++
+        replications.tail.map(streamBlockTableSubrow(block._1, _, replications.size, false)).flatten
+    }
+  }
+
+  private def streamBlockTableSubrow(
+      blockId: BlockId, block: BlockUIData, replication: Int, firstSubrow: Boolean): Seq[Node] = {
+    val (storageLevel, size) = streamBlockStorageLevelDescriptionAndSize(block)
+
+    <tr>
+      {
+        if (firstSubrow) {
+          <td rowspan={replication.toString}>
+            {block.blockId.toString}
+          </td>
+          <td rowspan={replication.toString}>
+            {replication.toString}
+          </td>
+        }
+      }
+      <td>{block.location}</td>
+      <td>{storageLevel}</td>
+      <td>{Utils.bytesToString(size)}</td>
+    </tr>
+  }
+
+  private[storage] def streamBlockStorageLevelDescriptionAndSize(
+      block: BlockUIData): (String, Long) = {
+    if (block.storageLevel.useDisk) {
+      ("Disk", block.diskSize)
+    } else if (block.storageLevel.useMemory && block.storageLevel.deserialized) {
+      ("Memory", block.memSize)
+    } else if (block.storageLevel.useMemory && !block.storageLevel.deserialized) {
+      ("Memory Serialized", block.memSize)
+    } else if (block.storageLevel.useOffHeap) {
+      ("External", block.externalBlockStoreSize)
+    } else {
+      throw new IllegalStateException(s"Invalid Storage Level: ${block.storageLevel}")
+    }
+  }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
index 0351749700962..22e2993b3b5bd 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
@@ -39,7 +39,8 @@ private[ui] class StorageTab(parent: SparkUI) extends SparkUITab(parent, "storag
  * This class is thread-safe (unlike JobProgressListener)
  */
 @DeveloperApi
-class StorageListener(storageStatusListener: StorageStatusListener) extends SparkListener {
+class StorageListener(storageStatusListener: StorageStatusListener) extends BlockStatusListener {
+
   private[ui] val _rddInfoMap = mutable.Map[Int, RDDInfo]() // exposed for testing
 
   def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockStatusListenerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockStatusListenerSuite.scala
new file mode 100644
index 0000000000000..d7ffde1e7864e
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/storage/BlockStatusListenerSuite.scala
@@ -0,0 +1,119 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.scheduler._
+
+class BlockStatusListenerSuite extends SparkFunSuite {
+
+  test("basic functions") {
+    val blockManagerId = BlockManagerId("0", "localhost", 10000)
+    val listener = new BlockStatusListener()
+
+    // Add a block manager and a new block status
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(0, blockManagerId, 0))
+    listener.onBlockUpdated(SparkListenerBlockUpdated(
+      BlockUpdatedInfo(
+        blockManagerId,
+        StreamBlockId(0, 100),
+        StorageLevel.MEMORY_AND_DISK,
+        memSize = 100,
+        diskSize = 100,
+        externalBlockStoreSize = 0)))
+    // The new block status should be added to the listener
+    val expectedBlock = BlockUIData(
+      StreamBlockId(0, 100),
+      "localhost:10000",
+      StorageLevel.MEMORY_AND_DISK,
+      memSize = 100,
+      diskSize = 100,
+      externalBlockStoreSize = 0
+    )
+    val expectedExecutorStreamBlockStatus = Seq(
+      ExecutorStreamBlockStatus("0", "localhost:10000", Seq(expectedBlock))
+    )
+    assert(listener.allExecutorStreamBlockStatus === expectedExecutorStreamBlockStatus)
+
+    // Add the second block manager
+    val blockManagerId2 = BlockManagerId("1", "localhost", 10001)
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(0, blockManagerId2, 0))
+    // Add a new replication of the same block id from the second manager
+    listener.onBlockUpdated(SparkListenerBlockUpdated(
+      BlockUpdatedInfo(
+        blockManagerId2,
+        StreamBlockId(0, 100),
+        StorageLevel.MEMORY_AND_DISK,
+        memSize = 100,
+        diskSize = 100,
+        externalBlockStoreSize = 0)))
+    val expectedBlock2 = BlockUIData(
+      StreamBlockId(0, 100),
+      "localhost:10001",
+      StorageLevel.MEMORY_AND_DISK,
+      memSize = 100,
+      diskSize = 100,
+      externalBlockStoreSize = 0
+    )
+    // Each block manager should contain one block
+    val expectedExecutorStreamBlockStatus2 = Set(
+      ExecutorStreamBlockStatus("0", "localhost:10000", Seq(expectedBlock)),
+      ExecutorStreamBlockStatus("1", "localhost:10001", Seq(expectedBlock2))
+    )
+    assert(listener.allExecutorStreamBlockStatus.toSet === expectedExecutorStreamBlockStatus2)
+
+    // Remove a replication of the same block
+    listener.onBlockUpdated(SparkListenerBlockUpdated(
+      BlockUpdatedInfo(
+        blockManagerId2,
+        StreamBlockId(0, 100),
+        StorageLevel.NONE, // StorageLevel.NONE means removing it
+        memSize = 0,
+        diskSize = 0,
+        externalBlockStoreSize = 0)))
+    // Only the first block manager contains a block
+    val expectedExecutorStreamBlockStatus3 = Set(
+      ExecutorStreamBlockStatus("0", "localhost:10000", Seq(expectedBlock)),
+      ExecutorStreamBlockStatus("1", "localhost:10001", Seq.empty)
+    )
+    assert(listener.allExecutorStreamBlockStatus.toSet === expectedExecutorStreamBlockStatus3)
+
+    // Remove the second block manager at first but add a new block status
+    // from this removed block manager
+    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(0, blockManagerId2))
+    listener.onBlockUpdated(SparkListenerBlockUpdated(
+      BlockUpdatedInfo(
+        blockManagerId2,
+        StreamBlockId(0, 100),
+        StorageLevel.MEMORY_AND_DISK,
+        memSize = 100,
+        diskSize = 100,
+        externalBlockStoreSize = 0)))
+    // The second block manager is removed so we should not see the new block
+    val expectedExecutorStreamBlockStatus4 = Seq(
+      ExecutorStreamBlockStatus("0", "localhost:10000", Seq(expectedBlock))
+    )
+    assert(listener.allExecutorStreamBlockStatus === expectedExecutorStreamBlockStatus4)
+
+    // Remove the last block manager
+    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(0, blockManagerId))
+    // No block manager now so we should dop all block managers
+    assert(listener.allExecutorStreamBlockStatus.isEmpty)
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StoragePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StoragePageSuite.scala
new file mode 100644
index 0000000000000..3dab15a9d4691
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/ui/storage/StoragePageSuite.scala
@@ -0,0 +1,230 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui.storage
+
+import scala.xml.Utility
+
+import org.mockito.Mockito._
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.storage._
+
+class StoragePageSuite extends SparkFunSuite {
+
+  val storageTab = mock(classOf[StorageTab])
+  when(storageTab.basePath).thenReturn("http://localhost:4040")
+  val storagePage = new StoragePage(storageTab)
+
+  test("rddTable") {
+    val rdd1 = new RDDInfo(1,
+      "rdd1",
+      10,
+      StorageLevel.MEMORY_ONLY,
+      Seq.empty)
+    rdd1.memSize = 100
+    rdd1.numCachedPartitions = 10
+
+    val rdd2 = new RDDInfo(2,
+      "rdd2",
+      10,
+      StorageLevel.DISK_ONLY,
+      Seq.empty)
+    rdd2.diskSize = 200
+    rdd2.numCachedPartitions = 5
+
+    val rdd3 = new RDDInfo(3,
+      "rdd3",
+      10,
+      StorageLevel.MEMORY_AND_DISK_SER,
+      Seq.empty)
+    rdd3.memSize = 400
+    rdd3.diskSize = 500
+    rdd3.numCachedPartitions = 10
+
+    val xmlNodes = storagePage.rddTable(Seq(rdd1, rdd2, rdd3))
+
+    val headers = Seq(
+      "RDD Name",
+      "Storage Level",
+      "Cached Partitions",
+      "Fraction Cached",
+      "Size in Memory",
+      "Size in ExternalBlockStore",
+      "Size on Disk")
+    assert((xmlNodes \\ "th").map(_.text) === headers)
+
+    assert((xmlNodes \\ "tr").size === 3)
+    assert(((xmlNodes \\ "tr")(0) \\ "td").map(_.text.trim) ===
+      Seq("rdd1", "Memory Deserialized 1x Replicated", "10", "100%", "100.0 B", "0.0 B", "0.0 B"))
+    // Check the url
+    assert(((xmlNodes \\ "tr")(0) \\ "td" \ "a")(0).attribute("href").map(_.text) ===
+      Some("http://localhost:4040/storage/rdd?id=1"))
+
+    assert(((xmlNodes \\ "tr")(1) \\ "td").map(_.text.trim) ===
+      Seq("rdd2", "Disk Serialized 1x Replicated", "5", "50%", "0.0 B", "0.0 B", "200.0 B"))
+    // Check the url
+    assert(((xmlNodes \\ "tr")(1) \\ "td" \ "a")(0).attribute("href").map(_.text) ===
+      Some("http://localhost:4040/storage/rdd?id=2"))
+
+    assert(((xmlNodes \\ "tr")(2) \\ "td").map(_.text.trim) ===
+      Seq("rdd3", "Disk Memory Serialized 1x Replicated", "10", "100%", "400.0 B", "0.0 B",
+        "500.0 B"))
+    // Check the url
+    assert(((xmlNodes \\ "tr")(2) \\ "td" \ "a")(0).attribute("href").map(_.text) ===
+      Some("http://localhost:4040/storage/rdd?id=3"))
+  }
+
+  test("empty rddTable") {
+    assert(storagePage.rddTable(Seq.empty).isEmpty)
+  }
+
+  test("streamBlockStorageLevelDescriptionAndSize") {
+    val memoryBlock = BlockUIData(StreamBlockId(0, 0),
+      "localhost:1111",
+      StorageLevel.MEMORY_ONLY,
+      memSize = 100,
+      diskSize = 0,
+      externalBlockStoreSize = 0)
+    assert(("Memory", 100) === storagePage.streamBlockStorageLevelDescriptionAndSize(memoryBlock))
+
+    val memorySerializedBlock = BlockUIData(StreamBlockId(0, 0),
+      "localhost:1111",
+      StorageLevel.MEMORY_ONLY_SER,
+      memSize = 100,
+      diskSize = 0,
+      externalBlockStoreSize = 0)
+    assert(("Memory Serialized", 100) ===
+      storagePage.streamBlockStorageLevelDescriptionAndSize(memorySerializedBlock))
+
+    val diskBlock = BlockUIData(StreamBlockId(0, 0),
+      "localhost:1111",
+      StorageLevel.DISK_ONLY,
+      memSize = 0,
+      diskSize = 100,
+      externalBlockStoreSize = 0)
+    assert(("Disk", 100) === storagePage.streamBlockStorageLevelDescriptionAndSize(diskBlock))
+
+    val externalBlock = BlockUIData(StreamBlockId(0, 0),
+      "localhost:1111",
+      StorageLevel.OFF_HEAP,
+      memSize = 0,
+      diskSize = 0,
+      externalBlockStoreSize = 100)
+    assert(("External", 100) ===
+      storagePage.streamBlockStorageLevelDescriptionAndSize(externalBlock))
+  }
+
+  test("receiverBlockTables") {
+    val blocksForExecutor0 = Seq(
+      BlockUIData(StreamBlockId(0, 0),
+        "localhost:10000",
+        StorageLevel.MEMORY_ONLY,
+        memSize = 100,
+        diskSize = 0,
+        externalBlockStoreSize = 0),
+      BlockUIData(StreamBlockId(1, 1),
+        "localhost:10000",
+        StorageLevel.DISK_ONLY,
+        memSize = 0,
+        diskSize = 100,
+        externalBlockStoreSize = 0)
+    )
+    val executor0 = ExecutorStreamBlockStatus("0", "localhost:10000", blocksForExecutor0)
+
+    val blocksForExecutor1 = Seq(
+      BlockUIData(StreamBlockId(0, 0),
+        "localhost:10001",
+        StorageLevel.MEMORY_ONLY,
+        memSize = 100,
+        diskSize = 0,
+        externalBlockStoreSize = 0),
+      BlockUIData(StreamBlockId(2, 2),
+        "localhost:10001",
+        StorageLevel.OFF_HEAP,
+        memSize = 0,
+        diskSize = 0,
+        externalBlockStoreSize = 200),
+      BlockUIData(StreamBlockId(1, 1),
+        "localhost:10001",
+        StorageLevel.MEMORY_ONLY_SER,
+        memSize = 100,
+        diskSize = 0,
+        externalBlockStoreSize = 0)
+    )
+    val executor1 = ExecutorStreamBlockStatus("1", "localhost:10001", blocksForExecutor1)
+    val xmlNodes = storagePage.receiverBlockTables(Seq(executor0, executor1))
+
+    val executorTable = (xmlNodes \\ "table")(0)
+    val executorHeaders = Seq(
+      "Executor ID",
+      "Address",
+      "Total Size in Memory",
+      "Total Size in ExternalBlockStore",
+      "Total Size on Disk",
+      "Stream Blocks")
+    assert((executorTable \\ "th").map(_.text) === executorHeaders)
+
+    assert((executorTable \\ "tr").size === 2)
+    assert(((executorTable \\ "tr")(0) \\ "td").map(_.text.trim) ===
+      Seq("0", "localhost:10000", "100.0 B", "0.0 B", "100.0 B", "2"))
+    assert(((executorTable \\ "tr")(1) \\ "td").map(_.text.trim) ===
+      Seq("1", "localhost:10001", "200.0 B", "200.0 B", "0.0 B", "3"))
+
+    val blockTable = (xmlNodes \\ "table")(1)
+    val blockHeaders = Seq(
+      "Block ID",
+      "Replication Level",
+      "Location",
+      "Storage Level",
+      "Size")
+    assert((blockTable \\ "th").map(_.text) === blockHeaders)
+
+    assert((blockTable \\ "tr").size === 5)
+    assert(((blockTable \\ "tr")(0) \\ "td").map(_.text.trim) ===
+      Seq("input-0-0", "2", "localhost:10000", "Memory", "100.0 B"))
+    // Check "rowspan=2" for the first 2 columns
+    assert(((blockTable \\ "tr")(0) \\ "td")(0).attribute("rowspan").map(_.text) === Some("2"))
+    assert(((blockTable \\ "tr")(0) \\ "td")(1).attribute("rowspan").map(_.text) === Some("2"))
+
+    assert(((blockTable \\ "tr")(1) \\ "td").map(_.text.trim) ===
+      Seq("localhost:10001", "Memory", "100.0 B"))
+
+    assert(((blockTable \\ "tr")(2) \\ "td").map(_.text.trim) ===
+      Seq("input-1-1", "2", "localhost:10000", "Disk", "100.0 B"))
+    // Check "rowspan=2" for the first 2 columns
+    assert(((blockTable \\ "tr")(2) \\ "td")(0).attribute("rowspan").map(_.text) === Some("2"))
+    assert(((blockTable \\ "tr")(2) \\ "td")(1).attribute("rowspan").map(_.text) === Some("2"))
+
+    assert(((blockTable \\ "tr")(3) \\ "td").map(_.text.trim) ===
+      Seq("localhost:10001", "Memory Serialized", "100.0 B"))
+
+    assert(((blockTable \\ "tr")(4) \\ "td").map(_.text.trim) ===
+      Seq("input-2-2", "1", "localhost:10001", "External", "200.0 B"))
+    // Check "rowspan=1" for the first 2 columns
+    assert(((blockTable \\ "tr")(4) \\ "td")(0).attribute("rowspan").map(_.text) === Some("1"))
+    assert(((blockTable \\ "tr")(4) \\ "td")(1).attribute("rowspan").map(_.text) === Some("1"))
+  }
+
+  test("empty receiverBlockTables") {
+    assert(storagePage.receiverBlockTables(Seq.empty).isEmpty)
+
+    val executor0 = ExecutorStreamBlockStatus("0", "localhost:10000", Seq.empty)
+    val executor1 = ExecutorStreamBlockStatus("1", "localhost:10001", Seq.empty)
+    assert(storagePage.receiverBlockTables(Seq(executor0, executor1)).isEmpty)
+  }
+}

From 4b5cfc988f23988c2334882a255d494fc93d252e Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Tue, 14 Jul 2015 14:19:27 -0700
Subject: [PATCH 0373/1454] [SPARK-8800] [SQL] Fix inaccurate precision/scale
 of Decimal division operation

JIRA: https://issues.apache.org/jira/browse/SPARK-8800

Previously, we turn to Java BigDecimal's divide with specified ROUNDING_MODE to avoid non-terminating decimal expansion problem. However, as JihongMA reported, for the division operation on some specific values, we get inaccurate results.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #7212 from viirya/fix_decimal4 and squashes the following commits:

4205a0a [Liang-Chi Hsieh] Fix inaccuracy precision/scale of Decimal division operation.
---
 .../scala/org/apache/spark/sql/types/Decimal.scala | 14 +++++++++++---
 .../spark/sql/types/decimal/DecimalSuite.scala     | 10 +++++++++-
 2 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index 5a169488c97eb..f5bd068d60dc4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -145,6 +145,14 @@ final class Decimal extends Ordered[Decimal] with Serializable {
     }
   }
 
+  def toLimitedBigDecimal: BigDecimal = {
+    if (decimalVal.ne(null)) {
+      decimalVal
+    } else {
+      BigDecimal(longVal, _scale)
+    }
+  }
+
   def toJavaBigDecimal: java.math.BigDecimal = toBigDecimal.underlying()
 
   def toUnscaledLong: Long = {
@@ -269,9 +277,9 @@ final class Decimal extends Ordered[Decimal] with Serializable {
     if (that.isZero) {
       null
     } else {
-      // To avoid non-terminating decimal expansion problem, we turn to Java BigDecimal's divide
-      // with specified ROUNDING_MODE.
-      Decimal(toJavaBigDecimal.divide(that.toJavaBigDecimal, ROUNDING_MODE.id))
+      // To avoid non-terminating decimal expansion problem, we get scala's BigDecimal with limited
+      // precision and scala.
+      Decimal(toLimitedBigDecimal / that.toLimitedBigDecimal)
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
index 5f312964e5bf7..030bb6d21b18b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
@@ -170,6 +170,14 @@ class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
 
   test("fix non-terminating decimal expansion problem") {
     val decimal = Decimal(1.0, 10, 3) / Decimal(3.0, 10, 3)
-    assert(decimal.toString === "0.333")
+    // The difference between decimal should not be more than 0.001.
+    assert(decimal.toDouble - 0.333 < 0.001)
+  }
+
+  test("fix loss of precision/scale when doing division operation") {
+    val a = Decimal(2) / Decimal(3)
+    assert(a.toDouble < 1.0 && a.toDouble > 0.6)
+    val b = Decimal(1) / Decimal(8)
+    assert(b.toDouble === 0.125)
   }
 }

From 740b034f1ca885a386f5a9ef7e0c81c714b047ff Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 14 Jul 2015 22:44:54 +0100
Subject: [PATCH 0374/1454] [SPARK-4362] [MLLIB] Make prediction probability
 available in NaiveBayesModel

Add predictProbabilities to Naive Bayes, return class probabilities.

Continues https://github.com/apache/spark/pull/6761

Author: Sean Owen <sowen@cloudera.com>

Closes #7376 from srowen/SPARK-4362 and squashes the following commits:

23d5a76 [Sean Owen] Fix model.labels -> model.theta
95d91fb [Sean Owen] Check that predicted probabilities sum to 1
b32d1c8 [Sean Owen] Add predictProbabilities to Naive Bayes, return class probabilities
---
 .../mllib/classification/NaiveBayes.scala     | 76 +++++++++++++++----
 .../classification/NaiveBayesSuite.scala      | 55 +++++++++++++-
 2 files changed, 113 insertions(+), 18 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index f51ee36d0dfcb..9e379d7d74b2f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -93,26 +93,70 @@ class NaiveBayesModel private[mllib] (
   override def predict(testData: Vector): Double = {
     modelType match {
       case Multinomial =>
-        val prob = thetaMatrix.multiply(testData)
-        BLAS.axpy(1.0, piVector, prob)
-        labels(prob.argmax)
+        labels(multinomialCalculation(testData).argmax)
       case Bernoulli =>
-        testData.foreachActive { (index, value) =>
-          if (value != 0.0 && value != 1.0) {
-            throw new SparkException(
-              s"Bernoulli naive Bayes requires 0 or 1 feature values but found $testData.")
-          }
-        }
-        val prob = thetaMinusNegTheta.get.multiply(testData)
-        BLAS.axpy(1.0, piVector, prob)
-        BLAS.axpy(1.0, negThetaSum.get, prob)
-        labels(prob.argmax)
-      case _ =>
-        // This should never happen.
-        throw new UnknownError(s"Invalid modelType: $modelType.")
+        labels(bernoulliCalculation(testData).argmax)
+    }
+  }
+
+  /**
+   * Predict values for the given data set using the model trained.
+   *
+   * @param testData RDD representing data points to be predicted
+   * @return an RDD[Vector] where each entry contains the predicted posterior class probabilities,
+   *         in the same order as class labels
+   */
+  def predictProbabilities(testData: RDD[Vector]): RDD[Vector] = {
+    val bcModel = testData.context.broadcast(this)
+    testData.mapPartitions { iter =>
+      val model = bcModel.value
+      iter.map(model.predictProbabilities)
     }
   }
 
+  /**
+   * Predict posterior class probabilities for a single data point using the model trained.
+   *
+   * @param testData array representing a single data point
+   * @return predicted posterior class probabilities from the trained model,
+   *         in the same order as class labels
+   */
+  def predictProbabilities(testData: Vector): Vector = {
+    modelType match {
+      case Multinomial =>
+        posteriorProbabilities(multinomialCalculation(testData))
+      case Bernoulli =>
+        posteriorProbabilities(bernoulliCalculation(testData))
+    }
+  }
+
+  private def multinomialCalculation(testData: Vector) = {
+    val prob = thetaMatrix.multiply(testData)
+    BLAS.axpy(1.0, piVector, prob)
+    prob
+  }
+
+  private def bernoulliCalculation(testData: Vector) = {
+    testData.foreachActive((_, value) =>
+      if (value != 0.0 && value != 1.0) {
+        throw new SparkException(
+          s"Bernoulli naive Bayes requires 0 or 1 feature values but found $testData.")
+      }
+    )
+    val prob = thetaMinusNegTheta.get.multiply(testData)
+    BLAS.axpy(1.0, piVector, prob)
+    BLAS.axpy(1.0, negThetaSum.get, prob)
+    prob
+  }
+
+  private def posteriorProbabilities(logProb: DenseVector) = {
+    val logProbArray = logProb.toArray
+    val maxLog = logProbArray.max
+    val scaledProbs = logProbArray.map(lp => math.exp(lp - maxLog))
+    val probSum = scaledProbs.sum
+    new DenseVector(scaledProbs.map(_ / probSum))
+  }
+
   override def save(sc: SparkContext, path: String): Unit = {
     val data = NaiveBayesModel.SaveLoadV2_0.Data(labels, pi, theta, modelType)
     NaiveBayesModel.SaveLoadV2_0.save(sc, path, data)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
index f7fc8730606af..cffa1ab700f80 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
@@ -19,13 +19,14 @@ package org.apache.spark.mllib.classification
 
 import scala.util.Random
 
-import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, Vector => BV}
 import breeze.stats.distributions.{Multinomial => BrzMultinomial}
 
 import org.apache.spark.{SparkException, SparkFunSuite}
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.util.Utils
 
 object NaiveBayesSuite {
@@ -154,6 +155,29 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     // Test prediction on Array.
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+
+    // Test posteriors
+    validationData.map(_.features).foreach { features =>
+      val predicted = model.predictProbabilities(features).toArray
+      assert(predicted.sum ~== 1.0 relTol 1.0e-10)
+      val expected = expectedMultinomialProbabilities(model, features)
+      expected.zip(predicted).foreach { case (e, p) => assert(e ~== p relTol 1.0e-10) }
+    }
+  }
+
+  /**
+   * @param model Multinomial Naive Bayes model
+   * @param testData input to compute posterior probabilities for
+   * @return posterior class probabilities (in order of labels) for input
+   */
+  private def expectedMultinomialProbabilities(model: NaiveBayesModel, testData: Vector) = {
+    val piVector = new BDV(model.pi)
+    // model.theta is row-major; treat it as col-major representation of transpose, and transpose:
+    val thetaMatrix = new BDM(model.theta(0).length, model.theta.length, model.theta.flatten).t
+    val logClassProbs: BV[Double] = piVector + (thetaMatrix * testData.toBreeze)
+    val classProbs = logClassProbs.toArray.map(math.exp)
+    val classProbsSum = classProbs.sum
+    classProbs.map(_ / classProbsSum)
   }
 
   test("Naive Bayes Bernoulli") {
@@ -182,6 +206,33 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     // Test prediction on Array.
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+
+    // Test posteriors
+    validationData.map(_.features).foreach { features =>
+      val predicted = model.predictProbabilities(features).toArray
+      assert(predicted.sum ~== 1.0 relTol 1.0e-10)
+      val expected = expectedBernoulliProbabilities(model, features)
+      expected.zip(predicted).foreach { case (e, p) => assert(e ~== p relTol 1.0e-10) }
+    }
+  }
+
+  /**
+   * @param model Bernoulli Naive Bayes model
+   * @param testData input to compute posterior probabilities for
+   * @return posterior class probabilities (in order of labels) for input
+   */
+  private def expectedBernoulliProbabilities(model: NaiveBayesModel, testData: Vector) = {
+    val piVector = new BDV(model.pi)
+    val thetaMatrix = new BDM(model.theta(0).length, model.theta.length, model.theta.flatten).t
+    val negThetaMatrix = new BDM(model.theta(0).length, model.theta.length,
+      model.theta.flatten.map(v => math.log(1.0 - math.exp(v)))).t
+    val testBreeze = testData.toBreeze
+    val negTestBreeze = new BDV(Array.fill(testBreeze.size)(1.0)) - testBreeze
+    val piTheta: BV[Double] = piVector + (thetaMatrix * testBreeze)
+    val logClassProbs: BV[Double] = piTheta + (negThetaMatrix * negTestBreeze)
+    val classProbs = logClassProbs.toArray.map(math.exp)
+    val classProbsSum = classProbs.sum
+    classProbs.map(_ / classProbsSum)
   }
 
   test("detect negative values") {

From 11e5c372862ec00e57460b37ccfee51c6d93c5f7 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 14 Jul 2015 16:08:17 -0700
Subject: [PATCH 0375/1454] [SPARK-8962] Add Scalastyle rule to ban direct use
 of Class.forName; fix existing uses

This pull request adds a Scalastyle regex rule which fails the style check if `Class.forName` is used directly.  `Class.forName` always loads classes from the default / system classloader, but in a majority of cases, we should be using Spark's own `Utils.classForName` instead, which tries to load classes from the current thread's context classloader and falls back to the classloader which loaded Spark when the context classloader is not defined.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7350)
<!-- Reviewable:end -->

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7350 from JoshRosen/ban-Class.forName and squashes the following commits:

e3e96f7 [Josh Rosen] Merge remote-tracking branch 'origin/master' into ban-Class.forName
c0b7885 [Josh Rosen] Hopefully fix the last two cases
d707ba7 [Josh Rosen] Fix uses of Class.forName that I missed in my first cleanup pass
046470d [Josh Rosen] Merge remote-tracking branch 'origin/master' into ban-Class.forName
62882ee [Josh Rosen] Fix uses of Class.forName or add exclusion.
d9abade [Josh Rosen] Add stylechecker rule to ban uses of Class.forName
---
 .../main/scala/org/apache/spark/Logging.scala  |  2 +-
 .../scala/org/apache/spark/SparkContext.scala  | 11 +++++------
 .../main/scala/org/apache/spark/SparkEnv.scala |  2 +-
 .../apache/spark/api/r/RBackendHandler.scala   | 18 ++----------------
 .../spark/broadcast/BroadcastManager.scala     |  3 ++-
 .../apache/spark/deploy/SparkHadoopUtil.scala  |  4 ++--
 .../org/apache/spark/deploy/SparkSubmit.scala  |  2 +-
 .../spark/deploy/SparkSubmitArguments.scala    |  2 +-
 .../spark/deploy/history/HistoryServer.scala   |  2 +-
 .../apache/spark/deploy/master/Master.scala    |  2 +-
 .../rest/SubmitRestProtocolMessage.scala       |  2 +-
 .../spark/deploy/worker/DriverWrapper.scala    |  2 +-
 .../spark/deploy/worker/WorkerArguments.scala  |  2 ++
 .../org/apache/spark/executor/Executor.scala   |  2 +-
 .../org/apache/spark/io/CompressionCodec.scala |  3 +--
 .../spark/mapred/SparkHadoopMapRedUtil.scala   |  5 +++--
 .../mapreduce/SparkHadoopMapReduceUtil.scala   |  9 +++++----
 .../apache/spark/metrics/MetricsSystem.scala   |  6 ++++--
 .../scala/org/apache/spark/rdd/HadoopRDD.scala |  6 +++---
 .../scala/org/apache/spark/rpc/RpcEnv.scala    |  3 +--
 .../spark/serializer/JavaSerializer.scala      |  5 ++++-
 .../spark/serializer/KryoSerializer.scala      |  2 ++
 .../serializer/SerializationDebugger.scala     |  2 ++
 .../spark/storage/ExternalBlockStore.scala     |  2 +-
 .../org/apache/spark/util/ClosureCleaner.scala |  2 ++
 .../org/apache/spark/util/SizeEstimator.scala  |  2 ++
 .../scala/org/apache/spark/util/Utils.scala    | 11 +++++++++--
 .../scala/org/apache/spark/FileSuite.scala     |  2 ++
 .../SparkContextSchedulerCreationSuite.scala   |  3 ++-
 .../apache/spark/deploy/SparkSubmitSuite.scala |  4 ++--
 .../org/apache/spark/rdd/JdbcRDDSuite.scala    |  3 ++-
 .../KryoSerializerDistributedSuite.scala       |  2 ++
 .../util/MutableURLClassLoaderSuite.scala      |  2 ++
 .../spark/streaming/flume/sink/Logging.scala   |  2 ++
 .../spark/graphx/util/BytecodeUtils.scala      |  2 +-
 .../org/apache/spark/repl/SparkIMain.scala     |  2 ++
 scalastyle-config.xml                          | 11 +++++++++++
 .../org/apache/spark/sql/types/DataType.scala  |  3 ++-
 .../org/apache/spark/sql/SQLContext.scala      |  3 +--
 .../spark/sql/parquet/ParquetRelation.scala    |  7 ++++---
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala  |  3 ++-
 .../apache/spark/sql/jdbc/JDBCWriteSuite.scala |  3 ++-
 .../thriftserver/HiveThriftServer2Suites.scala |  2 +-
 .../apache/spark/sql/hive/TableReader.scala    |  4 +---
 .../spark/sql/hive/client/ClientWrapper.scala  |  9 ++++-----
 .../spark/sql/hive/HiveSparkSubmitSuite.scala  |  8 ++++----
 .../streaming/scheduler/JobGenerator.scala     |  6 +++---
 .../spark/tools/GenerateMIMAIgnore.scala       |  2 ++
 .../org/apache/spark/deploy/yarn/Client.scala  |  4 ++--
 49 files changed, 117 insertions(+), 84 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Logging.scala b/core/src/main/scala/org/apache/spark/Logging.scala
index 87ab099267b2f..f0598816d6c07 100644
--- a/core/src/main/scala/org/apache/spark/Logging.scala
+++ b/core/src/main/scala/org/apache/spark/Logging.scala
@@ -159,7 +159,7 @@ private object Logging {
   try {
     // We use reflection here to handle the case where users remove the
     // slf4j-to-jul bridge order to route their logs to JUL.
-    val bridgeClass = Class.forName("org.slf4j.bridge.SLF4JBridgeHandler")
+    val bridgeClass = Utils.classForName("org.slf4j.bridge.SLF4JBridgeHandler")
     bridgeClass.getMethod("removeHandlersForRootLogger").invoke(null)
     val installed = bridgeClass.getMethod("isInstalled").invoke(null).asInstanceOf[Boolean]
     if (!installed) {
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 82704b1ab2189..bd1cc332a63e7 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1968,7 +1968,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       for (className <- listenerClassNames) {
         // Use reflection to find the right constructor
         val constructors = {
-          val listenerClass = Class.forName(className)
+          val listenerClass = Utils.classForName(className)
           listenerClass.getConstructors.asInstanceOf[Array[Constructor[_ <: SparkListener]]]
         }
         val constructorTakingSparkConf = constructors.find { c =>
@@ -2503,7 +2503,7 @@ object SparkContext extends Logging {
             "\"yarn-standalone\" is deprecated as of Spark 1.0. Use \"yarn-cluster\" instead.")
         }
         val scheduler = try {
-          val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClusterScheduler")
+          val clazz = Utils.classForName("org.apache.spark.scheduler.cluster.YarnClusterScheduler")
           val cons = clazz.getConstructor(classOf[SparkContext])
           cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl]
         } catch {
@@ -2515,7 +2515,7 @@ object SparkContext extends Logging {
         }
         val backend = try {
           val clazz =
-            Class.forName("org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend")
+            Utils.classForName("org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend")
           val cons = clazz.getConstructor(classOf[TaskSchedulerImpl], classOf[SparkContext])
           cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend]
         } catch {
@@ -2528,8 +2528,7 @@ object SparkContext extends Logging {
 
       case "yarn-client" =>
         val scheduler = try {
-          val clazz =
-            Class.forName("org.apache.spark.scheduler.cluster.YarnScheduler")
+          val clazz = Utils.classForName("org.apache.spark.scheduler.cluster.YarnScheduler")
           val cons = clazz.getConstructor(classOf[SparkContext])
           cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl]
 
@@ -2541,7 +2540,7 @@ object SparkContext extends Logging {
 
         val backend = try {
           val clazz =
-            Class.forName("org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend")
+            Utils.classForName("org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend")
           val cons = clazz.getConstructor(classOf[TaskSchedulerImpl], classOf[SparkContext])
           cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend]
         } catch {
diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index d18fc599e9890..adfece4d6e7c0 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -261,7 +261,7 @@ object SparkEnv extends Logging {
 
     // Create an instance of the class with the given name, possibly initializing it with our conf
     def instantiateClass[T](className: String): T = {
-      val cls = Class.forName(className, true, Utils.getContextOrSparkClassLoader)
+      val cls = Utils.classForName(className)
       // Look for a constructor taking a SparkConf and a boolean isDriver, then one taking just
       // SparkConf, then one taking no arguments
       try {
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index 4b8f7fe9242e0..9658e9a696ffa 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -26,6 +26,7 @@ import io.netty.channel.{ChannelHandlerContext, SimpleChannelInboundHandler}
 
 import org.apache.spark.Logging
 import org.apache.spark.api.r.SerDe._
+import org.apache.spark.util.Utils
 
 /**
  * Handler for RBackend
@@ -88,21 +89,6 @@ private[r] class RBackendHandler(server: RBackend)
     ctx.close()
   }
 
-  // Looks up a class given a class name. This function first checks the
-  // current class loader and if a class is not found, it looks up the class
-  // in the context class loader. Address [SPARK-5185]
-  def getStaticClass(objId: String): Class[_] = {
-    try {
-      val clsCurrent = Class.forName(objId)
-      clsCurrent
-    } catch {
-      // Use contextLoader if we can't find the JAR in the system class loader
-      case e: ClassNotFoundException =>
-        val clsContext = Class.forName(objId, true, Thread.currentThread().getContextClassLoader)
-        clsContext
-      }
-    }
-
   def handleMethodCall(
       isStatic: Boolean,
       objId: String,
@@ -113,7 +99,7 @@ private[r] class RBackendHandler(server: RBackend)
     var obj: Object = null
     try {
       val cls = if (isStatic) {
-        getStaticClass(objId)
+        Utils.classForName(objId)
       } else {
         JVMObjectTracker.get(objId) match {
           case None => throw new IllegalArgumentException("Object not found " + objId)
diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
index 685313ac009ba..fac6666bb3410 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala
@@ -22,6 +22,7 @@ import java.util.concurrent.atomic.AtomicLong
 import scala.reflect.ClassTag
 
 import org.apache.spark._
+import org.apache.spark.util.Utils
 
 private[spark] class BroadcastManager(
     val isDriver: Boolean,
@@ -42,7 +43,7 @@ private[spark] class BroadcastManager(
           conf.get("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory")
 
         broadcastFactory =
-          Class.forName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory]
+          Utils.classForName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory]
 
         // Initialize appropriate BroadcastFactory and BroadcastObject
         broadcastFactory.initialize(isDriver, conf, securityManager)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 6d14590a1d192..9f94118829ff1 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -178,7 +178,7 @@ class SparkHadoopUtil extends Logging {
 
   private def getFileSystemThreadStatisticsMethod(methodName: String): Method = {
     val statisticsDataClass =
-      Class.forName("org.apache.hadoop.fs.FileSystem$Statistics$StatisticsData")
+      Utils.classForName("org.apache.hadoop.fs.FileSystem$Statistics$StatisticsData")
     statisticsDataClass.getDeclaredMethod(methodName)
   }
 
@@ -356,7 +356,7 @@ object SparkHadoopUtil {
         System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")))
     if (yarnMode) {
       try {
-        Class.forName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil")
+        Utils.classForName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil")
           .newInstance()
           .asInstanceOf[SparkHadoopUtil]
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 7089a7e26707f..036cb6e054791 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -624,7 +624,7 @@ object SparkSubmit {
     var mainClass: Class[_] = null
 
     try {
-      mainClass = Class.forName(childMainClass, true, loader)
+      mainClass = Utils.classForName(childMainClass)
     } catch {
       case e: ClassNotFoundException =>
         e.printStackTrace(printStream)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index ebb39c354dff1..b3710073e330c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -576,7 +576,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
       System.setSecurityManager(sm)
 
       try {
-        Class.forName(mainClass).getMethod("main", classOf[Array[String]])
+        Utils.classForName(mainClass).getMethod("main", classOf[Array[String]])
           .invoke(null, Array(HELP))
       } catch {
         case e: InvocationTargetException =>
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index 10638afb74900..a076a9c3f984d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -228,7 +228,7 @@ object HistoryServer extends Logging {
 
     val providerName = conf.getOption("spark.history.provider")
       .getOrElse(classOf[FsHistoryProvider].getName())
-    val provider = Class.forName(providerName)
+    val provider = Utils.classForName(providerName)
       .getConstructor(classOf[SparkConf])
       .newInstance(conf)
       .asInstanceOf[ApplicationHistoryProvider]
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 48070768f6edb..245b047e7dfbd 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -172,7 +172,7 @@ private[master] class Master(
           new FileSystemRecoveryModeFactory(conf, SerializationExtension(actorSystem))
         (fsFactory.createPersistenceEngine(), fsFactory.createLeaderElectionAgent(this))
       case "CUSTOM" =>
-        val clazz = Class.forName(conf.get("spark.deploy.recoveryMode.factory"))
+        val clazz = Utils.classForName(conf.get("spark.deploy.recoveryMode.factory"))
         val factory = clazz.getConstructor(classOf[SparkConf], classOf[Serialization])
           .newInstance(conf, SerializationExtension(actorSystem))
           .asInstanceOf[StandaloneRecoveryModeFactory]
diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala
index e6615a3174ce1..ef5a7e35ad562 100644
--- a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolMessage.scala
@@ -128,7 +128,7 @@ private[spark] object SubmitRestProtocolMessage {
    */
   def fromJson(json: String): SubmitRestProtocolMessage = {
     val className = parseAction(json)
-    val clazz = Class.forName(packagePrefix + "." + className)
+    val clazz = Utils.classForName(packagePrefix + "." + className)
       .asSubclass[SubmitRestProtocolMessage](classOf[SubmitRestProtocolMessage])
     fromJson(json, clazz)
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
index 2d6be3042c905..6799f78ec0c19 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala
@@ -53,7 +53,7 @@ object DriverWrapper {
         Thread.currentThread.setContextClassLoader(loader)
 
         // Delegate to supplied main class
-        val clazz = Class.forName(mainClass, true, loader)
+        val clazz = Utils.classForName(mainClass)
         val mainMethod = clazz.getMethod("main", classOf[Array[String]])
         mainMethod.invoke(null, extraArgs.toArray[String])
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
index e89d076802215..5181142c5f80e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala
@@ -149,6 +149,7 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) {
     val ibmVendor = System.getProperty("java.vendor").contains("IBM")
     var totalMb = 0
     try {
+      // scalastyle:off classforname
       val bean = ManagementFactory.getOperatingSystemMXBean()
       if (ibmVendor) {
         val beanClass = Class.forName("com.ibm.lang.management.OperatingSystemMXBean")
@@ -159,6 +160,7 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) {
         val method = beanClass.getDeclaredMethod("getTotalPhysicalMemorySize")
         totalMb = (method.invoke(bean).asInstanceOf[Long] / 1024 / 1024).toInt
       }
+      // scalastyle:on classforname
     } catch {
       case e: Exception => {
         totalMb = 2*1024
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index f7ef92bc80f91..1a02051c87f19 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -356,7 +356,7 @@ private[spark] class Executor(
       logInfo("Using REPL class URI: " + classUri)
       try {
         val _userClassPathFirst: java.lang.Boolean = userClassPathFirst
-        val klass = Class.forName("org.apache.spark.repl.ExecutorClassLoader")
+        val klass = Utils.classForName("org.apache.spark.repl.ExecutorClassLoader")
           .asInstanceOf[Class[_ <: ClassLoader]]
         val constructor = klass.getConstructor(classOf[SparkConf], classOf[String],
           classOf[ClassLoader], classOf[Boolean])
diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
index 0d8ac1f80a9f4..607d5a321efca 100644
--- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
+++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
@@ -63,8 +63,7 @@ private[spark] object CompressionCodec {
   def createCodec(conf: SparkConf, codecName: String): CompressionCodec = {
     val codecClass = shortCompressionCodecNames.getOrElse(codecName.toLowerCase, codecName)
     val codec = try {
-      val ctor = Class.forName(codecClass, true, Utils.getContextOrSparkClassLoader)
-        .getConstructor(classOf[SparkConf])
+      val ctor = Utils.classForName(codecClass).getConstructor(classOf[SparkConf])
       Some(ctor.newInstance(conf).asInstanceOf[CompressionCodec])
     } catch {
       case e: ClassNotFoundException => None
diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
index 818f7a4c8d422..87df42748be44 100644
--- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
+++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
@@ -26,6 +26,7 @@ import org.apache.hadoop.mapreduce.{OutputCommitter => MapReduceOutputCommitter}
 
 import org.apache.spark.executor.CommitDeniedException
 import org.apache.spark.{Logging, SparkEnv, TaskContext}
+import org.apache.spark.util.{Utils => SparkUtils}
 
 private[spark]
 trait SparkHadoopMapRedUtil {
@@ -64,10 +65,10 @@ trait SparkHadoopMapRedUtil {
 
   private def firstAvailableClass(first: String, second: String): Class[_] = {
     try {
-      Class.forName(first)
+      SparkUtils.classForName(first)
     } catch {
       case e: ClassNotFoundException =>
-        Class.forName(second)
+        SparkUtils.classForName(second)
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala
index 390d148bc97f9..943ebcb7bd0a1 100644
--- a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala
+++ b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala
@@ -21,6 +21,7 @@ import java.lang.{Boolean => JBoolean, Integer => JInteger}
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.mapreduce.{JobContext, JobID, TaskAttemptContext, TaskAttemptID}
+import org.apache.spark.util.Utils
 
 private[spark]
 trait SparkHadoopMapReduceUtil {
@@ -46,7 +47,7 @@ trait SparkHadoopMapReduceUtil {
       isMap: Boolean,
       taskId: Int,
       attemptId: Int): TaskAttemptID = {
-    val klass = Class.forName("org.apache.hadoop.mapreduce.TaskAttemptID")
+    val klass = Utils.classForName("org.apache.hadoop.mapreduce.TaskAttemptID")
     try {
       // First, attempt to use the old-style constructor that takes a boolean isMap
       // (not available in YARN)
@@ -57,7 +58,7 @@ trait SparkHadoopMapReduceUtil {
     } catch {
       case exc: NoSuchMethodException => {
         // If that failed, look for the new constructor that takes a TaskType (not available in 1.x)
-        val taskTypeClass = Class.forName("org.apache.hadoop.mapreduce.TaskType")
+        val taskTypeClass = Utils.classForName("org.apache.hadoop.mapreduce.TaskType")
           .asInstanceOf[Class[Enum[_]]]
         val taskType = taskTypeClass.getMethod("valueOf", classOf[String]).invoke(
           taskTypeClass, if (isMap) "MAP" else "REDUCE")
@@ -71,10 +72,10 @@ trait SparkHadoopMapReduceUtil {
 
   private def firstAvailableClass(first: String, second: String): Class[_] = {
     try {
-      Class.forName(first)
+      Utils.classForName(first)
     } catch {
       case e: ClassNotFoundException =>
-        Class.forName(second)
+        Utils.classForName(second)
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
index ed5131c79fdc5..67f64d5e278de 100644
--- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
+++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
@@ -20,6 +20,8 @@ package org.apache.spark.metrics
 import java.util.Properties
 import java.util.concurrent.TimeUnit
 
+import org.apache.spark.util.Utils
+
 import scala.collection.mutable
 
 import com.codahale.metrics.{Metric, MetricFilter, MetricRegistry}
@@ -166,7 +168,7 @@ private[spark] class MetricsSystem private (
     sourceConfigs.foreach { kv =>
       val classPath = kv._2.getProperty("class")
       try {
-        val source = Class.forName(classPath).newInstance()
+        val source = Utils.classForName(classPath).newInstance()
         registerSource(source.asInstanceOf[Source])
       } catch {
         case e: Exception => logError("Source class " + classPath + " cannot be instantiated", e)
@@ -182,7 +184,7 @@ private[spark] class MetricsSystem private (
       val classPath = kv._2.getProperty("class")
       if (null != classPath) {
         try {
-          val sink = Class.forName(classPath)
+          val sink = Utils.classForName(classPath)
             .getConstructor(classOf[Properties], classOf[MetricRegistry], classOf[SecurityManager])
             .newInstance(kv._2, registry, securityMgr)
           if (kv._1 == "servlet") {
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index bee59a437f120..f1c17369cb48c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -383,11 +383,11 @@ private[spark] object HadoopRDD extends Logging {
 
   private[spark] class SplitInfoReflections {
     val inputSplitWithLocationInfo =
-      Class.forName("org.apache.hadoop.mapred.InputSplitWithLocationInfo")
+      Utils.classForName("org.apache.hadoop.mapred.InputSplitWithLocationInfo")
     val getLocationInfo = inputSplitWithLocationInfo.getMethod("getLocationInfo")
-    val newInputSplit = Class.forName("org.apache.hadoop.mapreduce.InputSplit")
+    val newInputSplit = Utils.classForName("org.apache.hadoop.mapreduce.InputSplit")
     val newGetLocationInfo = newInputSplit.getMethod("getLocationInfo")
-    val splitLocationInfo = Class.forName("org.apache.hadoop.mapred.SplitLocationInfo")
+    val splitLocationInfo = Utils.classForName("org.apache.hadoop.mapred.SplitLocationInfo")
     val isInMemory = splitLocationInfo.getMethod("isInMemory")
     val getLocation = splitLocationInfo.getMethod("getLocation")
   }
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
index 1709bdf560b6f..c9fcc7a36cc04 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
@@ -39,8 +39,7 @@ private[spark] object RpcEnv {
     val rpcEnvNames = Map("akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory")
     val rpcEnvName = conf.get("spark.rpc", "akka")
     val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName)
-    Class.forName(rpcEnvFactoryClassName, true, Utils.getContextOrSparkClassLoader).
-      newInstance().asInstanceOf[RpcEnvFactory]
+    Utils.classForName(rpcEnvFactoryClassName).newInstance().asInstanceOf[RpcEnvFactory]
   }
 
   def create(
diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
index 698d1384d580d..4a5274b46b7a0 100644
--- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
@@ -62,8 +62,11 @@ private[spark] class JavaDeserializationStream(in: InputStream, loader: ClassLoa
   extends DeserializationStream {
 
   private val objIn = new ObjectInputStream(in) {
-    override def resolveClass(desc: ObjectStreamClass): Class[_] =
+    override def resolveClass(desc: ObjectStreamClass): Class[_] = {
+      // scalastyle:off classforname
       Class.forName(desc.getName, false, loader)
+      // scalastyle:on classforname
+    }
   }
 
   def readObject[T: ClassTag](): T = objIn.readObject().asInstanceOf[T]
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index ed35cffe968f8..7cb6e080533ad 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -102,6 +102,7 @@ class KryoSerializer(conf: SparkConf)
     kryo.register(classOf[PythonBroadcast], new KryoJavaSerializer())
 
     try {
+      // scalastyle:off classforname
       // Use the default classloader when calling the user registrator.
       Thread.currentThread.setContextClassLoader(classLoader)
       // Register classes given through spark.kryo.classesToRegister.
@@ -111,6 +112,7 @@ class KryoSerializer(conf: SparkConf)
       userRegistrator
         .map(Class.forName(_, true, classLoader).newInstance().asInstanceOf[KryoRegistrator])
         .foreach { reg => reg.registerClasses(kryo) }
+      // scalastyle:on classforname
     } catch {
       case e: Exception =>
         throw new SparkException(s"Failed to register classes with Kryo", e)
diff --git a/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala b/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala
index cc2f0506817d3..a1b1e1631eafb 100644
--- a/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala
@@ -407,7 +407,9 @@ private[spark] object SerializationDebugger extends Logging {
 
     /** ObjectStreamClass$ClassDataSlot.desc field */
     val DescField: Field = {
+      // scalastyle:off classforname
       val f = Class.forName("java.io.ObjectStreamClass$ClassDataSlot").getDeclaredField("desc")
+      // scalastyle:on classforname
       f.setAccessible(true)
       f
     }
diff --git a/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala
index 291394ed34816..db965d54bafd6 100644
--- a/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala
@@ -192,7 +192,7 @@ private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId:
       .getOrElse(ExternalBlockStore.DEFAULT_BLOCK_MANAGER_NAME)
 
     try {
-      val instance = Class.forName(clsName)
+      val instance = Utils.classForName(clsName)
         .newInstance()
         .asInstanceOf[ExternalBlockManager]
       instance.init(blockManager, executorId)
diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index 305de4c75539d..43626b4ef4880 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -448,10 +448,12 @@ private class InnerClosureFinder(output: Set[Class[_]]) extends ClassVisitor(ASM
         if (op == INVOKESPECIAL && name == "<init>" && argTypes.length > 0
             && argTypes(0).toString.startsWith("L") // is it an object?
             && argTypes(0).getInternalName == myName) {
+          // scalastyle:off classforname
           output += Class.forName(
               owner.replace('/', '.'),
               false,
               Thread.currentThread.getContextClassLoader)
+          // scalastyle:on classforname
         }
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
index 0180399c9dad5..7d84468f62ab1 100644
--- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
+++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
@@ -124,9 +124,11 @@ object SizeEstimator extends Logging {
       val server = ManagementFactory.getPlatformMBeanServer()
 
       // NOTE: This should throw an exception in non-Sun JVMs
+      // scalastyle:off classforname
       val hotSpotMBeanClass = Class.forName("com.sun.management.HotSpotDiagnosticMXBean")
       val getVMMethod = hotSpotMBeanClass.getDeclaredMethod("getVMOption",
           Class.forName("java.lang.String"))
+      // scalastyle:on classforname
 
       val bean = ManagementFactory.newPlatformMXBeanProxy(server,
         hotSpotMBeanName, hotSpotMBeanClass)
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index b6b932104a94d..e6374f17d858f 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -113,8 +113,11 @@ private[spark] object Utils extends Logging {
   def deserialize[T](bytes: Array[Byte], loader: ClassLoader): T = {
     val bis = new ByteArrayInputStream(bytes)
     val ois = new ObjectInputStream(bis) {
-      override def resolveClass(desc: ObjectStreamClass): Class[_] =
+      override def resolveClass(desc: ObjectStreamClass): Class[_] = {
+        // scalastyle:off classforname
         Class.forName(desc.getName, false, loader)
+        // scalastyle:on classforname
+      }
     }
     ois.readObject.asInstanceOf[T]
   }
@@ -177,12 +180,16 @@ private[spark] object Utils extends Logging {
 
   /** Determines whether the provided class is loadable in the current thread. */
   def classIsLoadable(clazz: String): Boolean = {
+    // scalastyle:off classforname
     Try { Class.forName(clazz, false, getContextOrSparkClassLoader) }.isSuccess
+    // scalastyle:on classforname
   }
 
+  // scalastyle:off classforname
   /** Preferred alternative to Class.forName(className) */
   def classForName(className: String): Class[_] = {
     Class.forName(className, true, getContextOrSparkClassLoader)
+    // scalastyle:on classforname
   }
 
   /**
@@ -2266,7 +2273,7 @@ private [util] class SparkShutdownHookManager {
     val hookTask = new Runnable() {
       override def run(): Unit = runAll()
     }
-    Try(Class.forName("org.apache.hadoop.util.ShutdownHookManager")) match {
+    Try(Utils.classForName("org.apache.hadoop.util.ShutdownHookManager")) match {
       case Success(shmClass) =>
         val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get()
           .asInstanceOf[Int]
diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala
index 1d8fade90f398..418763f4e5ffa 100644
--- a/core/src/test/scala/org/apache/spark/FileSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileSuite.scala
@@ -179,6 +179,7 @@ class FileSuite extends SparkFunSuite with LocalSparkContext {
   }
 
   test("object files of classes from a JAR") {
+    // scalastyle:off classforname
     val original = Thread.currentThread().getContextClassLoader
     val className = "FileSuiteObjectFileTest"
     val jar = TestUtils.createJarWithClasses(Seq(className))
@@ -201,6 +202,7 @@ class FileSuite extends SparkFunSuite with LocalSparkContext {
     finally {
       Thread.currentThread().setContextClassLoader(original)
     }
+    // scalastyle:on classforname
   }
 
   test("write SequenceFile using new Hadoop API") {
diff --git a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
index f89e3d0a49920..dba46f101c580 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark
 
 import org.scalatest.PrivateMethodTester
 
+import org.apache.spark.util.Utils
 import org.apache.spark.scheduler.{SchedulerBackend, TaskScheduler, TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.{SimrSchedulerBackend, SparkDeploySchedulerBackend}
 import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend}
@@ -131,7 +132,7 @@ class SparkContextSchedulerCreationSuite
   def testYarn(master: String, expectedClassName: String) {
     try {
       val sched = createTaskScheduler(master)
-      assert(sched.getClass === Class.forName(expectedClassName))
+      assert(sched.getClass === Utils.classForName(expectedClassName))
     } catch {
       case e: SparkException =>
         assert(e.getMessage.contains("YARN mode not available"))
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index e7878bde6fcb0..343d28eef8359 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -541,8 +541,8 @@ object JarCreationTest extends Logging {
     val result = sc.makeRDD(1 to 100, 10).mapPartitions { x =>
       var exception: String = null
       try {
-        Class.forName(args(0), true, Thread.currentThread().getContextClassLoader)
-        Class.forName(args(1), true, Thread.currentThread().getContextClassLoader)
+        Utils.classForName(args(0))
+        Utils.classForName(args(1))
       } catch {
         case t: Throwable =>
           exception = t + "\n" + t.getStackTraceString
diff --git a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala
index 08215a2bafc09..05013fbc49b8e 100644
--- a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala
@@ -22,11 +22,12 @@ import java.sql._
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.{LocalSparkContext, SparkContext, SparkFunSuite}
+import org.apache.spark.util.Utils
 
 class JdbcRDDSuite extends SparkFunSuite with BeforeAndAfter with LocalSparkContext {
 
   before {
-    Class.forName("org.apache.derby.jdbc.EmbeddedDriver")
+    Utils.classForName("org.apache.derby.jdbc.EmbeddedDriver")
     val conn = DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb;create=true")
     try {
 
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala
index 63a8480c9b57b..353b97469cd11 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala
@@ -59,7 +59,9 @@ object KryoDistributedTest {
   class AppJarRegistrator extends KryoRegistrator {
     override def registerClasses(k: Kryo) {
       val classLoader = Thread.currentThread.getContextClassLoader
+      // scalastyle:off classforname
       k.register(Class.forName(AppJarRegistrator.customClassName, true, classLoader))
+      // scalastyle:on classforname
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/util/MutableURLClassLoaderSuite.scala b/core/src/test/scala/org/apache/spark/util/MutableURLClassLoaderSuite.scala
index 42125547436cb..d3d464e84ffd7 100644
--- a/core/src/test/scala/org/apache/spark/util/MutableURLClassLoaderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/MutableURLClassLoaderSuite.scala
@@ -84,7 +84,9 @@ class MutableURLClassLoaderSuite extends SparkFunSuite {
     try {
       sc.makeRDD(1 to 5, 2).mapPartitions { x =>
         val loader = Thread.currentThread().getContextClassLoader
+        // scalastyle:off classforname
         Class.forName(className, true, loader).newInstance()
+        // scalastyle:on classforname
         Seq().iterator
       }.count()
     }
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala
index 17cbc6707b5ea..d87b86932dd41 100644
--- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/Logging.scala
@@ -113,7 +113,9 @@ private[sink] object Logging {
   try {
     // We use reflection here to handle the case where users remove the
     // slf4j-to-jul bridge order to route their logs to JUL.
+    // scalastyle:off classforname
     val bridgeClass = Class.forName("org.slf4j.bridge.SLF4JBridgeHandler")
+    // scalastyle:on classforname
     bridgeClass.getMethod("removeHandlersForRootLogger").invoke(null)
     val installed = bridgeClass.getMethod("isInstalled").invoke(null).asInstanceOf[Boolean]
     if (!installed) {
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala
index 5c07b415cd796..74a7de18d4161 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/BytecodeUtils.scala
@@ -121,7 +121,7 @@ private[graphx] object BytecodeUtils {
           override def visitMethodInsn(op: Int, owner: String, name: String, desc: String) {
             if (op == INVOKEVIRTUAL || op == INVOKESPECIAL || op == INVOKESTATIC) {
               if (!skipClass(owner)) {
-                methodsInvoked.add((Class.forName(owner.replace("/", ".")), name))
+                methodsInvoked.add((Utils.classForName(owner.replace("/", ".")), name))
               }
             }
           }
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala
index 8791618bd355e..4ee605fd7f11e 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkIMain.scala
@@ -1079,8 +1079,10 @@ import org.apache.spark.annotation.DeveloperApi
       throw new EvalException("Failed to load '" + path + "': " + ex.getMessage, ex)
 
     private def load(path: String): Class[_] = {
+      // scalastyle:off classforname
       try Class.forName(path, true, classLoader)
       catch { case ex: Throwable => evalError(path, unwrap(ex)) }
+      // scalastyle:on classforname
     }
 
     lazy val evalClass = load(evalPath)
diff --git a/scalastyle-config.xml b/scalastyle-config.xml
index 49611703798e8..b5e2e882d2254 100644
--- a/scalastyle-config.xml
+++ b/scalastyle-config.xml
@@ -150,6 +150,17 @@ This file is divided into 3 sections:
       // scalastyle:on println]]></customMessage>
   </check>
 
+  <check customId="classforname" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
+    <parameters><parameter name="regex">Class\.forName</parameter></parameters>
+    <customMessage><![CDATA[
+      Are you sure that you want to use Class.forName? In most cases, you should use Utils.classForName instead.
+      If you must use Class.forName, wrap the code block with
+      // scalastyle:off classforname
+      Class.forName(...)
+      // scalastyle:on classforname
+    ]]></customMessage>
+  </check>
+
   <!-- ================================================================================ -->
   <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
   <!-- ================================================================================ -->
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index 57718228e490f..da83a7f0ba379 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -27,6 +27,7 @@ import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.util.Utils
 
 
 /**
@@ -146,7 +147,7 @@ object DataType {
     ("pyClass", _),
     ("sqlType", _),
     ("type", JString("udt"))) =>
-      Class.forName(udtClass).newInstance().asInstanceOf[UserDefinedType[_]]
+      Utils.classForName(udtClass).newInstance().asInstanceOf[UserDefinedType[_]]
   }
 
   private def parseStructField(json: JValue): StructField = json match {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 477dea9164726..46bd60daa1f78 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -554,8 +554,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     val className = beanClass.getName
     val rowRdd = rdd.mapPartitions { iter =>
       // BeanInfo is not serializable so we must rediscover it remotely for each partition.
-      val localBeanInfo = Introspector.getBeanInfo(
-        Class.forName(className, true, Utils.getContextOrSparkClassLoader))
+      val localBeanInfo = Introspector.getBeanInfo(Utils.classForName(className))
       val extractors =
         localBeanInfo.getPropertyDescriptors.filterNot(_.getName == "class").map(_.getReadMethod)
       val methodsToConverts = extractors.zip(attributeSeq).map { case (e, attr) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 704cf56f38265..e0bea65a15f36 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -33,6 +33,7 @@ import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap}
 import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{DataFrame, SQLContext}
+import org.apache.spark.util.Utils
 
 /**
  * Relation that consists of data stored in a Parquet columnar format.
@@ -108,7 +109,7 @@ private[sql] object ParquetRelation {
     //
     // Therefore we need to force the class to be loaded.
     // This should really be resolved by Parquet.
-    Class.forName(classOf[ParquetLog].getName)
+    Utils.classForName(classOf[ParquetLog].getName)
 
     // Note: Logger.getLogger("parquet") has a default logger
     // that appends to Console which needs to be cleared.
@@ -119,12 +120,12 @@ private[sql] object ParquetRelation {
     // Disables a WARN log message in ParquetOutputCommitter.  We first ensure that
     // ParquetOutputCommitter is loaded and the static LOG field gets initialized.
     // See https://issues.apache.org/jira/browse/SPARK-5968 for details
-    Class.forName(classOf[ParquetOutputCommitter].getName)
+    Utils.classForName(classOf[ParquetOutputCommitter].getName)
     JLogger.getLogger(classOf[ParquetOutputCommitter].getName).setLevel(Level.OFF)
 
     // Similar as above, disables a unnecessary WARN log message in ParquetRecordReader.
     // See https://issues.apache.org/jira/browse/PARQUET-220 for details
-    Class.forName(classOf[ParquetRecordReader[_]].getName)
+    Utils.classForName(classOf[ParquetRecordReader[_]].getName)
     JLogger.getLogger(classOf[ParquetRecordReader[_]].getName).setLevel(Level.OFF)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 566a52dc1b784..0f82f13088d39 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -26,6 +26,7 @@ import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
 
 class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
   val url = "jdbc:h2:mem:testdb0"
@@ -46,7 +47,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
   import ctx.sql
 
   before {
-    Class.forName("org.h2.Driver")
+    Utils.classForName("org.h2.Driver")
     // Extra properties that will be specified for our database. We need these to test
     // usage of parameters from OPTIONS clause in queries.
     val properties = new Properties()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
index d949ef42267ec..84b52ca2c733c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
@@ -25,6 +25,7 @@ import org.scalatest.BeforeAndAfter
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.{SaveMode, Row}
 import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
 
 class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
   val url = "jdbc:h2:mem:testdb2"
@@ -41,7 +42,7 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
   import ctx.sql
 
   before {
-    Class.forName("org.h2.Driver")
+    Utils.classForName("org.h2.Driver")
     conn = DriverManager.getConnection(url)
     conn.prepareStatement("create schema test").executeUpdate()
 
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 301aa5a6411e2..39b31523e07cb 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -417,7 +417,7 @@ object ServerMode extends Enumeration {
 }
 
 abstract class HiveThriftJdbcTest extends HiveThriftServer2Test {
-  Class.forName(classOf[HiveDriver].getCanonicalName)
+  Utils.classForName(classOf[HiveDriver].getCanonicalName)
 
   private def jdbcUri = if (mode == ServerMode.http) {
     s"""jdbc:hive2://localhost:$serverPort/
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index d65d29daacf31..dc355690852bd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -78,9 +78,7 @@ class HadoopTableReader(
   override def makeRDDForTable(hiveTable: HiveTable): RDD[InternalRow] =
     makeRDDForTable(
       hiveTable,
-      Class.forName(
-        relation.tableDesc.getSerdeClassName, true, Utils.getContextOrSparkClassLoader)
-        .asInstanceOf[Class[Deserializer]],
+      Utils.classForName(relation.tableDesc.getSerdeClassName).asInstanceOf[Class[Deserializer]],
       filterOpt = None)
 
   /**
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 1f280c642979a..8adda54754230 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -21,9 +21,6 @@ import java.io.{File, PrintStream}
 import java.util.{Map => JMap}
 import javax.annotation.concurrent.GuardedBy
 
-import org.apache.spark.sql.catalyst.expressions.Expression
-import org.apache.spark.util.CircularBuffer
-
 import scala.collection.JavaConversions._
 import scala.language.reflectiveCalls
 
@@ -37,7 +34,9 @@ import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.ql.{Driver, metadata}
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.execution.QueryExecutionException
+import org.apache.spark.util.{CircularBuffer, Utils}
 
 
 /**
@@ -249,10 +248,10 @@ private[hive] class ClientWrapper(
   }
 
   private def toInputFormat(name: String) =
-    Class.forName(name).asInstanceOf[Class[_ <: org.apache.hadoop.mapred.InputFormat[_, _]]]
+    Utils.classForName(name).asInstanceOf[Class[_ <: org.apache.hadoop.mapred.InputFormat[_, _]]]
 
   private def toOutputFormat(name: String) =
-    Class.forName(name)
+    Utils.classForName(name)
       .asInstanceOf[Class[_ <: org.apache.hadoop.hive.ql.io.HiveOutputFormat[_, _]]]
 
   private def toQlTable(table: HiveTable): metadata.Table = {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 917900e5f46dc..bee2ecbedb244 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -120,8 +120,8 @@ object SparkSubmitClassLoaderTest extends Logging {
     logInfo("Testing load classes at the driver side.")
     // First, we load classes at driver side.
     try {
-      Class.forName(args(0), true, Thread.currentThread().getContextClassLoader)
-      Class.forName(args(1), true, Thread.currentThread().getContextClassLoader)
+      Utils.classForName(args(0))
+      Utils.classForName(args(1))
     } catch {
       case t: Throwable =>
         throw new Exception("Could not load user class from jar:\n", t)
@@ -131,8 +131,8 @@ object SparkSubmitClassLoaderTest extends Logging {
     val result = df.mapPartitions { x =>
       var exception: String = null
       try {
-        Class.forName(args(0), true, Thread.currentThread().getContextClassLoader)
-        Class.forName(args(1), true, Thread.currentThread().getContextClassLoader)
+        Utils.classForName(args(0))
+        Utils.classForName(args(1))
       } catch {
         case t: Throwable =>
           exception = t + "\n" + t.getStackTraceString
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
index f5d41858646e4..9f2117ada61c0 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
@@ -22,7 +22,7 @@ import scala.util.{Failure, Success, Try}
 import org.apache.spark.{SparkEnv, Logging}
 import org.apache.spark.streaming.{Checkpoint, CheckpointWriter, Time}
 import org.apache.spark.streaming.util.RecurringTimer
-import org.apache.spark.util.{Clock, EventLoop, ManualClock}
+import org.apache.spark.util.{Utils, Clock, EventLoop, ManualClock}
 
 /** Event classes for JobGenerator */
 private[scheduler] sealed trait JobGeneratorEvent
@@ -47,11 +47,11 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging {
     val clockClass = ssc.sc.conf.get(
       "spark.streaming.clock", "org.apache.spark.util.SystemClock")
     try {
-      Class.forName(clockClass).newInstance().asInstanceOf[Clock]
+      Utils.classForName(clockClass).newInstance().asInstanceOf[Clock]
     } catch {
       case e: ClassNotFoundException if clockClass.startsWith("org.apache.spark.streaming") =>
         val newClockClass = clockClass.replace("org.apache.spark.streaming", "org.apache.spark")
-        Class.forName(newClockClass).newInstance().asInstanceOf[Clock]
+        Utils.classForName(newClockClass).newInstance().asInstanceOf[Clock]
     }
   }
 
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index 9483d2b692ab5..9418beb6b3e3a 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 
+// scalastyle:off classforname
 package org.apache.spark.tools
 
 import java.io.File
@@ -188,3 +189,4 @@ object GenerateMIMAIgnore {
     classes
   }
 }
+// scalastyle:on classforname
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index f0af6f875f523..f86b6d1e5d7bc 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -732,9 +732,9 @@ private[spark] class Client(
       }
     val amClass =
       if (isClusterMode) {
-        Class.forName("org.apache.spark.deploy.yarn.ApplicationMaster").getName
+        Utils.classForName("org.apache.spark.deploy.yarn.ApplicationMaster").getName
       } else {
-        Class.forName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName
+        Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName
       }
     if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) {
       args.userArgs = ArrayBuffer(args.primaryRFile) ++ args.userArgs

From e965a798d09a9fba61b104c5cc0b65cdc28d27f6 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 14 Jul 2015 17:21:48 -0700
Subject: [PATCH 0376/1454] [SPARK-9045] Fix Scala 2.11 build break in
 UnsafeExternalRowSorter

This fixes a compilation break in under Scala 2.11:

```
[error] /home/jenkins/workspace/Spark-Master-Scala211-Compile/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java:135: error: <anonymous org.apache.spark.sql.execution.UnsafeExternalRowSorter$1> is not abstract and does not override abstract method <B>minBy(Function1<InternalRow,B>,Ordering<B>) in TraversableOnce
[error]       return new AbstractScalaRowIterator() {
[error]                                             ^
[error]   where B,A are type-variables:
[error]     B extends Object declared in method <B>minBy(Function1<A,B>,Ordering<B>)
[error]     A extends Object declared in interface TraversableOnce
[error] 1 error
```

The workaround for this is to make `AbstractScalaRowIterator` into a concrete class.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7405 from JoshRosen/SPARK-9045 and squashes the following commits:

cbcbb4c [Josh Rosen] Forgot that we can't use the ??? operator anymore
577ba60 [Josh Rosen] [SPARK-9045] Fix Scala 2.11 build break in UnsafeExternalRowSorter.
---
 .../apache/spark/sql/AbstractScalaRowIterator.scala   | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala
index cfefb13e7721e..1090bdb5a4bd3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/AbstractScalaRowIterator.scala
@@ -17,11 +17,14 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.catalyst.InternalRow
-
 /**
  * Shim to allow us to implement [[scala.Iterator]] in Java. Scala 2.11+ has an AbstractIterator
  * class for this, but that class is `private[scala]` in 2.10. We need to explicitly fix this to
- * `Row` in order to work around a spurious IntelliJ compiler error.
+ * `Row` in order to work around a spurious IntelliJ compiler error. This cannot be an abstract
+ * class because that leads to compilation errors under Scala 2.11.
  */
-private[spark] abstract class AbstractScalaRowIterator extends Iterator[InternalRow]
+private[spark] class AbstractScalaRowIterator[T] extends Iterator[T] {
+  override def hasNext: Boolean = throw new NotImplementedError
+
+  override def next(): T = throw new NotImplementedError
+}

From cc57d705e732aefc2f3d3f438e84d71705b2eb65 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 14 Jul 2015 18:55:34 -0700
Subject: [PATCH 0377/1454] [SPARK-9050] [SQL] Remove unused newOrdering
 argument from Exchange (cleanup after SPARK-8317)

SPARK-8317 changed the SQL Exchange operator so that it no longer pushed sorting into Spark's shuffle layer, a change which allowed more efficient SQL-specific sorters to be used.

This patch performs some leftover cleanup based on those changes:

- Exchange's constructor should no longer accept a `newOrdering` since it's no longer used and no longer works as expected.
- `addOperatorsIfNecessary` looked at shuffle input's output ordering to decide whether to sort, but this is the wrong node to be examining: it needs to look at whether the post-shuffle node has the right ordering, since shuffling will not preserve row orderings.  Thanks to davies for spotting this.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7407 from JoshRosen/SPARK-9050 and squashes the following commits:

e70be50 [Josh Rosen] No need to wrap line
e866494 [Josh Rosen] Refactor addOperatorsIfNecessary to make code clearer
2e467da [Josh Rosen] Remove `newOrdering` from Exchange.
---
 .../apache/spark/sql/execution/Exchange.scala | 37 ++++++++-----------
 .../spark/sql/execution/SparkStrategies.scala |  3 +-
 2 files changed, 16 insertions(+), 24 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 4b783e30d95e1..feea4f239c04d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -35,21 +35,13 @@ import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEn
 
 /**
  * :: DeveloperApi ::
- * Performs a shuffle that will result in the desired `newPartitioning`.  Optionally sorts each
- * resulting partition based on expressions from the partition key.  It is invalid to construct an
- * exchange operator with a `newOrdering` that cannot be calculated using the partitioning key.
+ * Performs a shuffle that will result in the desired `newPartitioning`.
  */
 @DeveloperApi
-case class Exchange(
-    newPartitioning: Partitioning,
-    newOrdering: Seq[SortOrder],
-    child: SparkPlan)
-  extends UnaryNode {
+case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode {
 
   override def outputPartitioning: Partitioning = newPartitioning
 
-  override def outputOrdering: Seq[SortOrder] = newOrdering
-
   override def output: Seq[Attribute] = child.output
 
   /**
@@ -279,23 +271,24 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
           partitioning: Partitioning,
           rowOrdering: Seq[SortOrder],
           child: SparkPlan): SparkPlan = {
-        val needSort = rowOrdering.nonEmpty && child.outputOrdering != rowOrdering
-        val needsShuffle = child.outputPartitioning != partitioning
 
-        val withShuffle = if (needsShuffle) {
-          Exchange(partitioning, Nil, child)
-        } else {
-          child
+        def addShuffleIfNecessary(child: SparkPlan): SparkPlan = {
+          if (child.outputPartitioning != partitioning) {
+            Exchange(partitioning, child)
+          } else {
+            child
+          }
         }
 
-        val withSort = if (needSort) {
-          sqlContext.planner.BasicOperators.getSortOperator(
-            rowOrdering, global = false, withShuffle)
-        } else {
-          withShuffle
+        def addSortIfNecessary(child: SparkPlan): SparkPlan = {
+          if (rowOrdering.nonEmpty && child.outputOrdering != rowOrdering) {
+            sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
+          } else {
+            child
+          }
         }
 
-        withSort
+        addSortIfNecessary(addShuffleIfNecessary(child))
       }
 
       if (meetsRequirements && compatible && !needsAnySort) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index ce25af58b6cab..73b463471ec5a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -360,8 +360,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.OneRowRelation =>
         execution.PhysicalRDD(Nil, singleRowRdd) :: Nil
       case logical.RepartitionByExpression(expressions, child) =>
-        execution.Exchange(
-          HashPartitioning(expressions, numPartitions), Nil, planLater(child)) :: Nil
+        execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil
       case e @ EvaluatePython(udf, child, _) =>
         BatchPythonEvaluation(udf, e.output, planLater(child)) :: Nil
       case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd) :: Nil

From f957796c4b3c3cd95edfc64500a045f7e810ee87 Mon Sep 17 00:00:00 2001
From: huangzhaowei <carlmartinmax@gmail.com>
Date: Tue, 14 Jul 2015 19:20:49 -0700
Subject: [PATCH 0378/1454] [SPARK-8820] [STREAMING] Add a configuration to set
 checkpoint dir.

Add a configuration to set checkpoint directory  for convenience to user.
[Jira Address](https://issues.apache.org/jira/browse/SPARK-8820)

Author: huangzhaowei <carlmartinmax@gmail.com>

Closes #7218 from SaintBacchus/SPARK-8820 and squashes the following commits:

d49fe4b [huangzhaowei] Rename the configuration name
66ea47c [huangzhaowei] Add the unit test.
dd0acc1 [huangzhaowei] [SPARK-8820][Streaming] Add a configuration to set checkpoint dir.
---
 .../org/apache/spark/streaming/StreamingContext.scala    | 2 ++
 .../apache/spark/streaming/StreamingContextSuite.scala   | 9 +++++++++
 2 files changed, 11 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 6b78a82e68c24..92438f1b1fbf7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -201,6 +201,8 @@ class StreamingContext private[streaming] (
 
   private var shutdownHookRef: AnyRef = _
 
+  conf.getOption("spark.streaming.checkpoint.directory").foreach(checkpoint)
+
   /**
    * Return the associated Spark context
    */
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 289a159d8990a..f588cf5bc1e7c 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -115,6 +115,15 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
     assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10)
   }
 
+  test("checkPoint from conf") {
+    val checkpointDirectory = Utils.createTempDir().getAbsolutePath()
+
+    val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName)
+    myConf.set("spark.streaming.checkpoint.directory", checkpointDirectory)
+    val ssc = new StreamingContext(myConf, batchDuration)
+    assert(ssc.checkpointDir != null)
+  }
+
   test("state matching") {
     import StreamingContextState._
     assert(INITIALIZED === INITIALIZED)

From bb870e72f42b6ce8d056df259f6fcf41808d7ed2 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Tue, 14 Jul 2015 19:54:02 -0700
Subject: [PATCH 0379/1454] [SPARK-5523] [CORE] [STREAMING] Add a cache for
 hostname in TaskMetrics to decrease the memory usage and GC overhead

Hostname in TaskMetrics will be created through deserialization, mostly the number of hostname is only the order of number of cluster node, so adding a cache layer to dedup the object could reduce the memory usage and alleviate GC overhead, especially for long-running and fast job generation applications like Spark Streaming.

Author: jerryshao <saisai.shao@intel.com>
Author: Saisai Shao <saisai.shao@intel.com>

Closes #5064 from jerryshao/SPARK-5523 and squashes the following commits:

3e2412a [jerryshao] Address the comments
b092a81 [Saisai Shao] Add a pool to cache the hostname
---
 .../apache/spark/executor/TaskMetrics.scala   | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
index a3b4561b07e7f..e80feeeab4142 100644
--- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
+++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
@@ -17,11 +17,15 @@
 
 package org.apache.spark.executor
 
+import java.io.{IOException, ObjectInputStream}
+import java.util.concurrent.ConcurrentHashMap
+
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.DataReadMethod.DataReadMethod
 import org.apache.spark.storage.{BlockId, BlockStatus}
+import org.apache.spark.util.Utils
 
 /**
  * :: DeveloperApi ::
@@ -210,10 +214,26 @@ class TaskMetrics extends Serializable {
   private[spark] def updateInputMetrics(): Unit = synchronized {
     inputMetrics.foreach(_.updateBytesRead())
   }
+
+  @throws(classOf[IOException])
+  private def readObject(in: ObjectInputStream): Unit = Utils.tryOrIOException {
+    in.defaultReadObject()
+    // Get the hostname from cached data, since hostname is the order of number of nodes in
+    // cluster, so using cached hostname will decrease the object number and alleviate the GC
+    // overhead.
+    _hostname = TaskMetrics.getCachedHostName(_hostname)
+  }
 }
 
 private[spark] object TaskMetrics {
+  private val hostNameCache = new ConcurrentHashMap[String, String]()
+
   def empty: TaskMetrics = new TaskMetrics
+
+  def getCachedHostName(host: String): String = {
+    val canonicalHost = hostNameCache.putIfAbsent(host, host)
+    if (canonicalHost != null) canonicalHost else host
+  }
 }
 
 /**

From 5572fd0c518acd2e4483ff41bea1eb1cffd543ce Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Tue, 14 Jul 2015 21:44:47 -0700
Subject: [PATCH 0380/1454] [HOTFIX] Adding new names to known contributors

---
 dev/create-release/known_translations | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/dev/create-release/known_translations b/dev/create-release/known_translations
index 5f2671a6e5053..e462302f28423 100644
--- a/dev/create-release/known_translations
+++ b/dev/create-release/known_translations
@@ -129,3 +129,12 @@ yongtang - Yong Tang
 ypcat - Pei-Lun Lee
 zhichao-li - Zhichao Li
 zzcclp - Zhichao Zhang
+979969786 - Yuming Wang
+Rosstin - Rosstin Murphy
+ameyc - Amey Chaugule
+animeshbaranawal - Animesh Baranawal
+cafreeman - Chris Freeman
+lee19 - Lee
+lockwobr - Brian Lockwood
+navis - Navis Ryu
+pparkkin - Paavo Parkkinen

From f650a005e03ecd800c9005a496cc6a0d8eb68c93 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Tue, 14 Jul 2015 22:21:01 -0700
Subject: [PATCH 0381/1454] [SPARK-8808] [SPARKR] Fix assignments in SparkR.

Author: Sun Rui <rui.sun@intel.com>

Closes #7395 from sun-rui/SPARK-8808 and squashes the following commits:

ce603bc [Sun Rui] Use '<-' instead of '='.
88590b1 [Sun Rui] Use '<-' instead of '='.
---
 R/pkg/R/DataFrame.R                     | 2 +-
 R/pkg/R/client.R                        | 4 ++--
 R/pkg/R/group.R                         | 4 ++--
 R/pkg/R/utils.R                         | 4 ++--
 R/pkg/inst/tests/test_binaryFile.R      | 2 +-
 R/pkg/inst/tests/test_binary_function.R | 2 +-
 R/pkg/inst/tests/test_rdd.R             | 4 ++--
 R/pkg/inst/tests/test_textFile.R        | 2 +-
 R/pkg/inst/tests/test_utils.R           | 2 +-
 9 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 60702824acb46..208813768e264 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1328,7 +1328,7 @@ setMethod("write.df",
             jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode)
             options <- varargsToEnv(...)
             if (!is.null(path)) {
-                options[['path']] = path
+                options[['path']] <- path
             }
             callJMethod(df@sdf, "save", source, jmode, options)
           })
diff --git a/R/pkg/R/client.R b/R/pkg/R/client.R
index 78c7a3037ffac..6f772158ddfe8 100644
--- a/R/pkg/R/client.R
+++ b/R/pkg/R/client.R
@@ -36,9 +36,9 @@ connectBackend <- function(hostname, port, timeout = 6000) {
 
 determineSparkSubmitBin <- function() {
   if (.Platform$OS.type == "unix") {
-    sparkSubmitBinName = "spark-submit"
+    sparkSubmitBinName <- "spark-submit"
   } else {
-    sparkSubmitBinName = "spark-submit.cmd"
+    sparkSubmitBinName <- "spark-submit.cmd"
   }
   sparkSubmitBinName
 }
diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R
index 8f1c68f7c4d28..576ac72f40fc0 100644
--- a/R/pkg/R/group.R
+++ b/R/pkg/R/group.R
@@ -87,7 +87,7 @@ setMethod("count",
 setMethod("agg",
           signature(x = "GroupedData"),
           function(x, ...) {
-            cols = list(...)
+            cols <- list(...)
             stopifnot(length(cols) > 0)
             if (is.character(cols[[1]])) {
               cols <- varargsToEnv(...)
@@ -97,7 +97,7 @@ setMethod("agg",
               if (!is.null(ns)) {
                 for (n in ns) {
                   if (n != "") {
-                    cols[[n]] = alias(cols[[n]], n)
+                    cols[[n]] <- alias(cols[[n]], n)
                   }
                 }
               }
diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index ea629a64f7158..950ba74dbe017 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -41,8 +41,8 @@ convertJListToRList <- function(jList, flatten, logicalUpperBound = NULL,
               if (isInstanceOf(obj, "scala.Tuple2")) {
                 # JavaPairRDD[Array[Byte], Array[Byte]].
 
-                keyBytes = callJMethod(obj, "_1")
-                valBytes = callJMethod(obj, "_2")
+                keyBytes <- callJMethod(obj, "_1")
+                valBytes <- callJMethod(obj, "_2")
                 res <- list(unserialize(keyBytes),
                   unserialize(valBytes))
               } else {
diff --git a/R/pkg/inst/tests/test_binaryFile.R b/R/pkg/inst/tests/test_binaryFile.R
index ccaea18ecab2a..f2452ed97d2ea 100644
--- a/R/pkg/inst/tests/test_binaryFile.R
+++ b/R/pkg/inst/tests/test_binaryFile.R
@@ -20,7 +20,7 @@ context("functions on binary files")
 # JavaSparkContext handle
 sc <- sparkR.init()
 
-mockFile = c("Spark is pretty.", "Spark is awesome.")
+mockFile <- c("Spark is pretty.", "Spark is awesome.")
 
 test_that("saveAsObjectFile()/objectFile() following textFile() works", {
   fileName1 <- tempfile(pattern="spark-test", fileext=".tmp")
diff --git a/R/pkg/inst/tests/test_binary_function.R b/R/pkg/inst/tests/test_binary_function.R
index 3be8c65a6c1a0..dca0657c57e0d 100644
--- a/R/pkg/inst/tests/test_binary_function.R
+++ b/R/pkg/inst/tests/test_binary_function.R
@@ -76,7 +76,7 @@ test_that("zipPartitions() on RDDs", {
   expect_equal(actual,
                list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6))))
 
-  mockFile = c("Spark is pretty.", "Spark is awesome.")
+  mockFile <- c("Spark is pretty.", "Spark is awesome.")
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
   writeLines(mockFile, fileName)
 
diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R
index b79692873cec3..6c3aaab8c711e 100644
--- a/R/pkg/inst/tests/test_rdd.R
+++ b/R/pkg/inst/tests/test_rdd.R
@@ -447,7 +447,7 @@ test_that("zipRDD() on RDDs", {
   expect_equal(actual,
                list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004)))
 
-  mockFile = c("Spark is pretty.", "Spark is awesome.")
+  mockFile <- c("Spark is pretty.", "Spark is awesome.")
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
   writeLines(mockFile, fileName)
 
@@ -483,7 +483,7 @@ test_that("cartesian() on RDDs", {
   actual <- collect(cartesian(rdd, emptyRdd))
   expect_equal(actual, list())
 
-  mockFile = c("Spark is pretty.", "Spark is awesome.")
+  mockFile <- c("Spark is pretty.", "Spark is awesome.")
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
   writeLines(mockFile, fileName)
 
diff --git a/R/pkg/inst/tests/test_textFile.R b/R/pkg/inst/tests/test_textFile.R
index 58318dfef71ab..a9cf83dbdbdb1 100644
--- a/R/pkg/inst/tests/test_textFile.R
+++ b/R/pkg/inst/tests/test_textFile.R
@@ -20,7 +20,7 @@ context("the textFile() function")
 # JavaSparkContext handle
 sc <- sparkR.init()
 
-mockFile = c("Spark is pretty.", "Spark is awesome.")
+mockFile <- c("Spark is pretty.", "Spark is awesome.")
 
 test_that("textFile() on a local file returns an RDD", {
   fileName <- tempfile(pattern="spark-test", fileext=".tmp")
diff --git a/R/pkg/inst/tests/test_utils.R b/R/pkg/inst/tests/test_utils.R
index aa0d2a66b9082..12df4cf4f65b7 100644
--- a/R/pkg/inst/tests/test_utils.R
+++ b/R/pkg/inst/tests/test_utils.R
@@ -119,7 +119,7 @@ test_that("cleanClosure on R functions", {
   # Test for overriding variables in base namespace (Issue: SparkR-196).
   nums <- as.list(1:10)
   rdd <- parallelize(sc, nums, 2L)
-  t = 4  # Override base::t in .GlobalEnv.
+  t <- 4  # Override base::t in .GlobalEnv.
   f <- function(x) { x > t }
   newF <- cleanClosure(f)
   env <- environment(newF)

From f23a721c10b64ec5c6768634fc5e9e7b60ee7ca8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 14 Jul 2015 22:52:53 -0700
Subject: [PATCH 0382/1454] [SPARK-8993][SQL] More comprehensive type checking
 in expressions.

This patch makes the following changes:

1. ExpectsInputTypes only defines expected input types, but does not perform any implicit type casting.
2. ImplicitCastInputTypes is a new trait that defines both expected input types, as well as performs implicit type casting.
3. BinaryOperator has a new abstract function "inputType", which defines the expected input type for both left/right. Concrete BinaryOperator expressions no longer perform any implicit type casting.
4. For BinaryOperators, convert NullType (i.e. null literals) into some accepted type so BinaryOperators don't need to handle NullTypes.

TODOs needed: fix unit tests for error reporting.

I'm intentionally not changing anything in aggregate expressions because yhuai is doing a big refactoring on that right now.

Author: Reynold Xin <rxin@databricks.com>

Closes #7348 from rxin/typecheck and squashes the following commits:

8fcf814 [Reynold Xin] Fixed ordering of cases.
3bb63e7 [Reynold Xin] Style fix.
f45408f [Reynold Xin] Comment update.
aa7790e [Reynold Xin] Moved RemoveNullTypes into ImplicitTypeCasts.
438ea07 [Reynold Xin] space
d55c9e5 [Reynold Xin] Removes NullTypes.
360d124 [Reynold Xin] Fixed the rule.
fb66657 [Reynold Xin] Convert NullType into some accepted type for BinaryOperators.
2e22330 [Reynold Xin] Fixed unit tests.
4932d57 [Reynold Xin] Style fix.
d061691 [Reynold Xin] Rename existing ExpectsInputTypes -> ImplicitCastInputTypes.
e4727cc [Reynold Xin] BinaryOperator should not be doing implicit cast.
d017861 [Reynold Xin] Improve expression type checking.
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../catalyst/analysis/HiveTypeCoercion.scala  | 43 ++++++----
 .../expressions/ExpectsInputTypes.scala       | 17 +++-
 .../sql/catalyst/expressions/Expression.scala | 44 +++++++++-
 .../sql/catalyst/expressions/ScalaUDF.scala   |  2 +-
 .../sql/catalyst/expressions/arithmetic.scala | 84 ++++++++-----------
 .../sql/catalyst/expressions/bitwise.scala    | 30 +++----
 .../spark/sql/catalyst/expressions/math.scala | 18 ++--
 .../spark/sql/catalyst/expressions/misc.scala |  8 +-
 .../sql/catalyst/expressions/predicates.scala | 83 ++++++++++--------
 .../expressions/stringOperations.scala        | 36 ++++----
 .../spark/sql/catalyst/util/TypeUtils.scala   |  8 --
 .../spark/sql/types/AbstractDataType.scala    | 35 ++++++++
 .../analysis/AnalysisErrorSuite.scala         |  2 +-
 .../ExpressionTypeCheckingSuite.scala         |  6 +-
 .../analysis/HiveTypeCoercionSuite.scala      | 56 +++++++++++++
 .../spark/sql/MathExpressionsSuite.scala      |  1 -
 17 files changed, 309 insertions(+), 165 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index ed69c42dcb825..6b1a94e4b2ad4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import scala.language.existentials
 import scala.reflect.ClassTag
 import scala.util.{Failure, Success, Try}
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 8cb71995eb818..15da5eecc8d3c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -214,19 +214,6 @@ object HiveTypeCoercion {
           }
 
         Union(newLeft, newRight)
-
-      // Also widen types for BinaryOperator.
-      case q: LogicalPlan => q transformExpressions {
-        // Skip nodes who's children have not been resolved yet.
-        case e if !e.childrenResolved => e
-
-        case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
-          findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { widestType =>
-            val newLeft = if (left.dataType == widestType) left else Cast(left, widestType)
-            val newRight = if (right.dataType == widestType) right else Cast(right, widestType)
-            b.makeCopy(Array(newLeft, newRight))
-          }.getOrElse(b)  // If there is no applicable conversion, leave expression unchanged.
-      }
     }
   }
 
@@ -672,20 +659,44 @@ object HiveTypeCoercion {
   }
 
   /**
-   * Casts types according to the expected input types for Expressions that have the trait
-   * [[ExpectsInputTypes]].
+   * Casts types according to the expected input types for [[Expression]]s.
    */
   object ImplicitTypeCasts extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      case e: ExpectsInputTypes if (e.inputTypes.nonEmpty) =>
+      case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
+        findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { commonType =>
+          if (b.inputType.acceptsType(commonType)) {
+            // If the expression accepts the tighest common type, cast to that.
+            val newLeft = if (left.dataType == commonType) left else Cast(left, commonType)
+            val newRight = if (right.dataType == commonType) right else Cast(right, commonType)
+            b.makeCopy(Array(newLeft, newRight))
+          } else {
+            // Otherwise, don't do anything with the expression.
+            b
+          }
+        }.getOrElse(b)  // If there is no applicable conversion, leave expression unchanged.
+
+      case e: ImplicitCastInputTypes if e.inputTypes.nonEmpty =>
         val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
           // If we cannot do the implicit cast, just use the original input.
           implicitCast(in, expected).getOrElse(in)
         }
         e.withNewChildren(children)
+
+      case e: ExpectsInputTypes if e.inputTypes.nonEmpty =>
+        // Convert NullType into some specific target type for ExpectsInputTypes that don't do
+        // general implicit casting.
+        val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
+          if (in.dataType == NullType && !expected.acceptsType(NullType)) {
+            Cast(in, expected.defaultConcreteType)
+          } else {
+            in
+          }
+        }
+        e.withNewChildren(children)
     }
 
     /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
index 3eb0eb195c80d..ded89e85dea79 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -19,10 +19,15 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.types.AbstractDataType
-
+import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion.ImplicitTypeCasts
 
 /**
  * An trait that gets mixin to define the expected input types of an expression.
+ *
+ * This trait is typically used by operator expressions (e.g. [[Add]], [[Subtract]]) to define
+ * expected input types without any implicit casting.
+ *
+ * Most function expressions (e.g. [[Substring]] should extends [[ImplicitCastInputTypes]]) instead.
  */
 trait ExpectsInputTypes { self: Expression =>
 
@@ -40,7 +45,7 @@ trait ExpectsInputTypes { self: Expression =>
     val mismatches = children.zip(inputTypes).zipWithIndex.collect {
       case ((child, expected), idx) if !expected.acceptsType(child.dataType) =>
         s"argument ${idx + 1} is expected to be of type ${expected.simpleString}, " +
-        s"however, '${child.prettyString}' is of type ${child.dataType.simpleString}."
+          s"however, '${child.prettyString}' is of type ${child.dataType.simpleString}."
     }
 
     if (mismatches.isEmpty) {
@@ -50,3 +55,11 @@ trait ExpectsInputTypes { self: Expression =>
     }
   }
 }
+
+
+/**
+ * A mixin for the analyzer to perform implicit type casting using [[ImplicitTypeCasts]].
+ */
+trait ImplicitCastInputTypes extends ExpectsInputTypes { self: Expression =>
+  // No other methods
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 54ec10444c4f3..3f19ac2b592b5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -24,8 +24,20 @@ import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types._
 
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// This file defines the basic expression abstract classes in Catalyst, including:
+// Expression: the base expression abstract class
+// LeafExpression
+// UnaryExpression
+// BinaryExpression
+// BinaryOperator
+//
+// For details, see their classdocs.
+////////////////////////////////////////////////////////////////////////////////////////////////////
 
 /**
+ * An expression in Catalyst.
+ *
  * If an expression wants to be exposed in the function registry (so users can call it with
  * "name(arguments...)", the concrete implementation must be a case class whose constructor
  * arguments are all Expressions types.
@@ -335,15 +347,41 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
 
 
 /**
- * An expression that has two inputs that are expected to the be same type. If the two inputs have
- * different types, the analyzer will find the tightest common type and do the proper type casting.
+ * A [[BinaryExpression]] that is an operator, with two properties:
+ *
+ * 1. The string representation is "x symbol y", rather than "funcName(x, y)".
+ * 2. Two inputs are expected to the be same type. If the two inputs have different types,
+ *    the analyzer will find the tightest common type and do the proper type casting.
  */
-abstract class BinaryOperator extends BinaryExpression {
+abstract class BinaryOperator extends BinaryExpression with ExpectsInputTypes {
   self: Product =>
 
+  /**
+   * Expected input type from both left/right child expressions, similar to the
+   * [[ImplicitCastInputTypes]] trait.
+   */
+  def inputType: AbstractDataType
+
   def symbol: String
 
   override def toString: String = s"($left $symbol $right)"
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(inputType, inputType)
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // First call the checker for ExpectsInputTypes, and then check whether left and right have
+    // the same type.
+    super.checkInputDataTypes() match {
+      case TypeCheckResult.TypeCheckSuccess =>
+        if (left.dataType != right.dataType) {
+          TypeCheckResult.TypeCheckFailure(s"differing types in '$prettyString' " +
+            s"(${left.dataType.simpleString} and ${right.dataType.simpleString}).")
+        } else {
+          TypeCheckResult.TypeCheckSuccess
+        }
+      case TypeCheckResult.TypeCheckFailure(msg) => TypeCheckResult.TypeCheckFailure(msg)
+    }
+  }
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index 6fb3343bb63f2..22687acd68a97 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -29,7 +29,7 @@ case class ScalaUDF(
     function: AnyRef,
     dataType: DataType,
     children: Seq[Expression],
-    inputTypes: Seq[DataType] = Nil) extends Expression with ExpectsInputTypes {
+    inputTypes: Seq[DataType] = Nil) extends Expression with ImplicitCastInputTypes {
 
   override def nullable: Boolean = true
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 8476af4a5d8d6..1a55a0876f303 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -18,23 +18,19 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
-abstract class UnaryArithmetic extends UnaryExpression {
-  self: Product =>
+
+case class UnaryMinus(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
 
   override def dataType: DataType = child.dataType
-}
 
-case class UnaryMinus(child: Expression) extends UnaryArithmetic {
   override def toString: String = s"-$child"
 
-  override def checkInputDataTypes(): TypeCheckResult =
-    TypeUtils.checkForNumericExpr(child.dataType, "operator -")
-
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
@@ -45,9 +41,13 @@ case class UnaryMinus(child: Expression) extends UnaryArithmetic {
   protected override def nullSafeEval(input: Any): Any = numeric.negate(input)
 }
 
-case class UnaryPositive(child: Expression) extends UnaryArithmetic {
+case class UnaryPositive(child: Expression) extends UnaryExpression with ExpectsInputTypes {
   override def prettyName: String = "positive"
 
+  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
+
+  override def dataType: DataType = child.dataType
+
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
     defineCodeGen(ctx, ev, c => c)
 
@@ -57,9 +57,11 @@ case class UnaryPositive(child: Expression) extends UnaryArithmetic {
 /**
  * A function that get the absolute value of the numeric value.
  */
-case class Abs(child: Expression) extends UnaryArithmetic {
-  override def checkInputDataTypes(): TypeCheckResult =
-    TypeUtils.checkForNumericExpr(child.dataType, "function abs")
+case class Abs(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
+
+  override def dataType: DataType = child.dataType
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
@@ -71,18 +73,6 @@ abstract class BinaryArithmetic extends BinaryOperator {
 
   override def dataType: DataType = left.dataType
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    if (left.dataType != right.dataType) {
-      TypeCheckResult.TypeCheckFailure(
-        s"differing types in ${this.getClass.getSimpleName} " +
-        s"(${left.dataType} and ${right.dataType}).")
-    } else {
-      checkTypesInternal(dataType)
-    }
-  }
-
-  protected def checkTypesInternal(t: DataType): TypeCheckResult
-
   /** Name of the function for this expression on a [[Decimal]] type. */
   def decimalMethod: String =
     sys.error("BinaryArithmetics must override either decimalMethod or genCode")
@@ -104,62 +94,61 @@ private[sql] object BinaryArithmetic {
 }
 
 case class Add(left: Expression, right: Expression) extends BinaryArithmetic {
+
+  override def inputType: AbstractDataType = NumericType
+
   override def symbol: String = "+"
   override def decimalMethod: String = "$plus"
 
   override lazy val resolved =
     childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForNumericExpr(t, "operator " + symbol)
-
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.plus(input1, input2)
 }
 
 case class Subtract(left: Expression, right: Expression) extends BinaryArithmetic {
+
+  override def inputType: AbstractDataType = NumericType
+
   override def symbol: String = "-"
   override def decimalMethod: String = "$minus"
 
   override lazy val resolved =
     childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForNumericExpr(t, "operator " + symbol)
-
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.minus(input1, input2)
 }
 
 case class Multiply(left: Expression, right: Expression) extends BinaryArithmetic {
+
+  override def inputType: AbstractDataType = NumericType
+
   override def symbol: String = "*"
   override def decimalMethod: String = "$times"
 
   override lazy val resolved =
     childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForNumericExpr(t, "operator " + symbol)
-
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.times(input1, input2)
 }
 
 case class Divide(left: Expression, right: Expression) extends BinaryArithmetic {
+
+  override def inputType: AbstractDataType = NumericType
+
   override def symbol: String = "/"
   override def decimalMethod: String = "$div"
-
   override def nullable: Boolean = true
 
   override lazy val resolved =
     childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForNumericExpr(t, "operator " + symbol)
-
   private lazy val div: (Any, Any) => Any = dataType match {
     case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div
     case it: IntegralType => it.integral.asInstanceOf[Integral[Any]].quot
@@ -215,17 +204,16 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic
 }
 
 case class Remainder(left: Expression, right: Expression) extends BinaryArithmetic {
+
+  override def inputType: AbstractDataType = NumericType
+
   override def symbol: String = "%"
   override def decimalMethod: String = "remainder"
-
   override def nullable: Boolean = true
 
   override lazy val resolved =
     childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForNumericExpr(t, "operator " + symbol)
-
   private lazy val integral = dataType match {
     case i: IntegralType => i.integral.asInstanceOf[Integral[Any]]
     case i: FractionalType => i.asIntegral.asInstanceOf[Integral[Any]]
@@ -281,10 +269,11 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet
 }
 
 case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
-  override def nullable: Boolean = left.nullable && right.nullable
+  // TODO: Remove MaxOf and MinOf, and replace its usage with Greatest and Least.
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForOrderingExpr(t, "function maxOf")
+  override def inputType: AbstractDataType = TypeCollection.Ordered
+
+  override def nullable: Boolean = left.nullable && right.nullable
 
   private lazy val ordering = TypeUtils.getOrdering(dataType)
 
@@ -335,10 +324,11 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
 }
 
 case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
-  override def nullable: Boolean = left.nullable && right.nullable
+  // TODO: Remove MaxOf and MinOf, and replace its usage with Greatest and Least.
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForOrderingExpr(t, "function minOf")
+  override def inputType: AbstractDataType = TypeCollection.Ordered
+
+  override def nullable: Boolean = left.nullable && right.nullable
 
   private lazy val ordering = TypeUtils.getOrdering(dataType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
index 2d47124d247e7..af1abbcd2239b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
@@ -17,9 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
 
@@ -29,10 +27,10 @@ import org.apache.spark.sql.types._
  * Code generation inherited from BinaryArithmetic.
  */
 case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithmetic {
-  override def symbol: String = "&"
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForBitwiseExpr(t, "operator " + symbol)
+  override def inputType: AbstractDataType = TypeCollection.Bitwise
+
+  override def symbol: String = "&"
 
   private lazy val and: (Any, Any) => Any = dataType match {
     case ByteType =>
@@ -54,10 +52,10 @@ case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithme
  * Code generation inherited from BinaryArithmetic.
  */
 case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmetic {
-  override def symbol: String = "|"
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForBitwiseExpr(t, "operator " + symbol)
+  override def inputType: AbstractDataType = TypeCollection.Bitwise
+
+  override def symbol: String = "|"
 
   private lazy val or: (Any, Any) => Any = dataType match {
     case ByteType =>
@@ -79,10 +77,10 @@ case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmet
  * Code generation inherited from BinaryArithmetic.
  */
 case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithmetic {
-  override def symbol: String = "^"
 
-  protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForBitwiseExpr(t, "operator " + symbol)
+  override def inputType: AbstractDataType = TypeCollection.Bitwise
+
+  override def symbol: String = "^"
 
   private lazy val xor: (Any, Any) => Any = dataType match {
     case ByteType =>
@@ -101,11 +99,13 @@ case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithme
 /**
  * A function that calculates bitwise not(~) of a number.
  */
-case class BitwiseNot(child: Expression) extends UnaryArithmetic {
-  override def toString: String = s"~$child"
+case class BitwiseNot(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
-  override def checkInputDataTypes(): TypeCheckResult =
-    TypeUtils.checkForBitwiseExpr(child.dataType, "operator ~")
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection.Bitwise)
+
+  override def dataType: DataType = child.dataType
+
+  override def toString: String = s"~$child"
 
   private lazy val not: (Any) => Any = dataType match {
     case ByteType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index c31890e27fb54..4b7fe05dd4980 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -55,7 +55,7 @@ abstract class LeafMathExpression(c: Double, name: String)
  * @param name The short name of the function
  */
 abstract class UnaryMathExpression(f: Double => Double, name: String)
-  extends UnaryExpression with Serializable with ExpectsInputTypes { self: Product =>
+  extends UnaryExpression with Serializable with ImplicitCastInputTypes { self: Product =>
 
   override def inputTypes: Seq[DataType] = Seq(DoubleType)
   override def dataType: DataType = DoubleType
@@ -89,7 +89,7 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
  * @param name The short name of the function
  */
 abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
-  extends BinaryExpression with Serializable with ExpectsInputTypes { self: Product =>
+  extends BinaryExpression with Serializable with ImplicitCastInputTypes { self: Product =>
 
   override def inputTypes: Seq[DataType] = Seq(DoubleType, DoubleType)
 
@@ -174,7 +174,7 @@ object Factorial {
   )
 }
 
-case class Factorial(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Factorial(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(IntegerType)
 
@@ -251,7 +251,7 @@ case class ToRadians(child: Expression) extends UnaryMathExpression(math.toRadia
 }
 
 case class Bin(child: Expression)
-  extends UnaryExpression with Serializable with ExpectsInputTypes {
+  extends UnaryExpression with Serializable with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(LongType)
   override def dataType: DataType = StringType
@@ -285,7 +285,7 @@ object Hex {
  * Otherwise if the number is a STRING, it converts each character into its hex representation
  * and returns the resulting STRING. Negative numbers would be treated as two's complement.
  */
-case class Hex(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Hex(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
   // TODO: Create code-gen version.
 
   override def inputTypes: Seq[AbstractDataType] =
@@ -329,7 +329,7 @@ case class Hex(child: Expression) extends UnaryExpression with ExpectsInputTypes
  * Performs the inverse operation of HEX.
  * Resulting characters are returned as a byte array.
  */
-case class Unhex(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Unhex(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
   // TODO: Create code-gen version.
 
   override def inputTypes: Seq[AbstractDataType] = Seq(StringType)
@@ -416,7 +416,7 @@ case class Pow(left: Expression, right: Expression)
  * @param right number of bits to left shift.
  */
 case class ShiftLeft(left: Expression, right: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(IntegerType, LongType), IntegerType)
@@ -442,7 +442,7 @@ case class ShiftLeft(left: Expression, right: Expression)
  * @param right number of bits to left shift.
  */
 case class ShiftRight(left: Expression, right: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(IntegerType, LongType), IntegerType)
@@ -468,7 +468,7 @@ case class ShiftRight(left: Expression, right: Expression)
  * @param right the number of bits to right shift.
  */
 case class ShiftRightUnsigned(left: Expression, right: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(IntegerType, LongType), IntegerType)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index 3b59cd431b871..a269ec4a1e6dc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -31,7 +31,7 @@ import org.apache.spark.unsafe.types.UTF8String
  * A function that calculates an MD5 128-bit checksum and returns it as a hex string
  * For input of type [[BinaryType]]
  */
-case class Md5(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Md5(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = StringType
 
@@ -55,7 +55,7 @@ case class Md5(child: Expression) extends UnaryExpression with ExpectsInputTypes
  * the hash length is not one of the permitted values, the return value is NULL.
  */
 case class Sha2(left: Expression, right: Expression)
-  extends BinaryExpression with Serializable with ExpectsInputTypes {
+  extends BinaryExpression with Serializable with ImplicitCastInputTypes {
 
   override def dataType: DataType = StringType
 
@@ -118,7 +118,7 @@ case class Sha2(left: Expression, right: Expression)
  * A function that calculates a sha1 hash value and returns it as a hex string
  * For input of type [[BinaryType]] or [[StringType]]
  */
-case class Sha1(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Sha1(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = StringType
 
@@ -138,7 +138,7 @@ case class Sha1(child: Expression) extends UnaryExpression with ExpectsInputType
  * A function that computes a cyclic redundancy check value and returns it as a bigint
  * For input of type [[BinaryType]]
  */
-case class Crc32(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Crc32(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = LongType
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index f74fd04619714..aa6c30e2f79f2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -33,12 +33,17 @@ object InterpretedPredicate {
   }
 }
 
+
+/**
+ * An [[Expression]] that returns a boolean value.
+ */
 trait Predicate extends Expression {
   self: Product =>
 
   override def dataType: DataType = BooleanType
 }
 
+
 trait PredicateHelper {
   protected def splitConjunctivePredicates(condition: Expression): Seq[Expression] = {
     condition match {
@@ -70,7 +75,10 @@ trait PredicateHelper {
     expr.references.subsetOf(plan.outputSet)
 }
 
-case class Not(child: Expression) extends UnaryExpression with Predicate with ExpectsInputTypes {
+
+case class Not(child: Expression)
+  extends UnaryExpression with Predicate with ImplicitCastInputTypes {
+
   override def toString: String = s"NOT $child"
 
   override def inputTypes: Seq[DataType] = Seq(BooleanType)
@@ -82,6 +90,7 @@ case class Not(child: Expression) extends UnaryExpression with Predicate with Ex
   }
 }
 
+
 /**
  * Evaluates to `true` if `list` contains `value`.
  */
@@ -97,6 +106,7 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate {
   }
 }
 
+
 /**
  * Optimized version of In clause, when all filter values of In clause are
  * static.
@@ -112,12 +122,12 @@ case class InSet(child: Expression, hset: Set[Any])
   }
 }
 
-case class And(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with ExpectsInputTypes {
 
-  override def toString: String = s"($left && $right)"
+case class And(left: Expression, right: Expression) extends BinaryOperator with Predicate {
+
+  override def inputType: AbstractDataType = BooleanType
 
-  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+  override def symbol: String = "&&"
 
   override def eval(input: InternalRow): Any = {
     val input1 = left.eval(input)
@@ -161,12 +171,12 @@ case class And(left: Expression, right: Expression)
   }
 }
 
-case class Or(left: Expression, right: Expression)
-  extends BinaryExpression with Predicate with ExpectsInputTypes {
 
-  override def toString: String = s"($left || $right)"
+case class Or(left: Expression, right: Expression) extends BinaryOperator with Predicate {
 
-  override def inputTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+  override def inputType: AbstractDataType = BooleanType
+
+  override def symbol: String = "||"
 
   override def eval(input: InternalRow): Any = {
     val input1 = left.eval(input)
@@ -210,21 +220,10 @@ case class Or(left: Expression, right: Expression)
   }
 }
 
+
 abstract class BinaryComparison extends BinaryOperator with Predicate {
   self: Product =>
 
-  override def checkInputDataTypes(): TypeCheckResult = {
-    if (left.dataType != right.dataType) {
-      TypeCheckResult.TypeCheckFailure(
-        s"differing types in ${this.getClass.getSimpleName} " +
-        s"(${left.dataType} and ${right.dataType}).")
-    } else {
-      checkTypesInternal(dataType)
-    }
-  }
-
-  protected def checkTypesInternal(t: DataType): TypeCheckResult
-
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     if (ctx.isPrimitiveType(left.dataType)) {
       // faster version
@@ -235,10 +234,12 @@ abstract class BinaryComparison extends BinaryOperator with Predicate {
   }
 }
 
+
 private[sql] object BinaryComparison {
   def unapply(e: BinaryComparison): Option[(Expression, Expression)] = Some((e.left, e.right))
 }
 
+
 /** An extractor that matches both standard 3VL equality and null-safe equality. */
 private[sql] object Equality {
   def unapply(e: BinaryComparison): Option[(Expression, Expression)] = e match {
@@ -248,10 +249,12 @@ private[sql] object Equality {
   }
 }
 
+
 case class EqualTo(left: Expression, right: Expression) extends BinaryComparison {
-  override def symbol: String = "="
 
-  override protected def checkTypesInternal(t: DataType) = TypeCheckResult.TypeCheckSuccess
+  override def inputType: AbstractDataType = AnyDataType
+
+  override def symbol: String = "="
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
     if (left.dataType != BinaryType) input1 == input2
@@ -263,13 +266,15 @@ case class EqualTo(left: Expression, right: Expression) extends BinaryComparison
   }
 }
 
+
 case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComparison {
+
+  override def inputType: AbstractDataType = AnyDataType
+
   override def symbol: String = "<=>"
 
   override def nullable: Boolean = false
 
-  override protected def checkTypesInternal(t: DataType) = TypeCheckResult.TypeCheckSuccess
-
   override def eval(input: InternalRow): Any = {
     val input1 = left.eval(input)
     val input2 = right.eval(input)
@@ -298,44 +303,48 @@ case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComp
   }
 }
 
+
 case class LessThan(left: Expression, right: Expression) extends BinaryComparison {
-  override def symbol: String = "<"
 
-  override protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol)
+  override def inputType: AbstractDataType = TypeCollection.Ordered
+
+  override def symbol: String = "<"
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lt(input1, input2)
 }
 
+
 case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison {
-  override def symbol: String = "<="
 
-  override protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol)
+  override def inputType: AbstractDataType = TypeCollection.Ordered
+
+  override def symbol: String = "<="
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lteq(input1, input2)
 }
 
+
 case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison {
-  override def symbol: String = ">"
 
-  override protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol)
+  override def inputType: AbstractDataType = TypeCollection.Ordered
+
+  override def symbol: String = ">"
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.gt(input1, input2)
 }
 
+
 case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison {
-  override def symbol: String = ">="
 
-  override protected def checkTypesInternal(t: DataType) =
-    TypeUtils.checkForOrderingExpr(left.dataType, "operator " + symbol)
+  override def inputType: AbstractDataType = TypeCollection.Ordered
+
+  override def symbol: String = ">="
 
   private lazy val ordering = TypeUtils.getOrdering(left.dataType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index f64899c1ed84c..03b55ce5fe7cc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -29,7 +29,7 @@ import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 
-trait StringRegexExpression extends ExpectsInputTypes {
+trait StringRegexExpression extends ImplicitCastInputTypes {
   self: BinaryExpression =>
 
   def escape(v: String): String
@@ -105,7 +105,7 @@ case class RLike(left: Expression, right: Expression)
   override def toString: String = s"$left RLIKE $right"
 }
 
-trait String2StringExpression extends ExpectsInputTypes {
+trait String2StringExpression extends ImplicitCastInputTypes {
   self: UnaryExpression =>
 
   def convert(v: UTF8String): UTF8String
@@ -142,7 +142,7 @@ case class Lower(child: Expression) extends UnaryExpression with String2StringEx
 }
 
 /** A base trait for functions that compare two strings, returning a boolean. */
-trait StringComparison extends ExpectsInputTypes {
+trait StringComparison extends ImplicitCastInputTypes {
   self: BinaryExpression =>
 
   def compare(l: UTF8String, r: UTF8String): Boolean
@@ -241,7 +241,7 @@ case class StringTrimRight(child: Expression)
  * NOTE: that this is not zero based, but 1-based index. The first character in str has index 1.
  */
 case class StringInstr(str: Expression, substr: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = str
   override def right: Expression = substr
@@ -265,7 +265,7 @@ case class StringInstr(str: Expression, substr: Expression)
  * in given string after position pos.
  */
 case class StringLocate(substr: Expression, str: Expression, start: Expression)
-  extends Expression with ExpectsInputTypes {
+  extends Expression with ImplicitCastInputTypes {
 
   def this(substr: Expression, str: Expression) = {
     this(substr, str, Literal(0))
@@ -306,7 +306,7 @@ case class StringLocate(substr: Expression, str: Expression, start: Expression)
  * Returns str, left-padded with pad to a length of len.
  */
 case class StringLPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ExpectsInputTypes {
+  extends Expression with ImplicitCastInputTypes {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
   override def foldable: Boolean = children.forall(_.foldable)
@@ -344,7 +344,7 @@ case class StringLPad(str: Expression, len: Expression, pad: Expression)
  * Returns str, right-padded with pad to a length of len.
  */
 case class StringRPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ExpectsInputTypes {
+  extends Expression with ImplicitCastInputTypes {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
   override def foldable: Boolean = children.forall(_.foldable)
@@ -413,7 +413,7 @@ case class StringFormat(children: Expression*) extends Expression {
  * Returns the string which repeat the given string value n times.
  */
 case class StringRepeat(str: Expression, times: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = str
   override def right: Expression = times
@@ -447,7 +447,7 @@ case class StringReverse(child: Expression) extends UnaryExpression with String2
 /**
  * Returns a n spaces string.
  */
-case class StringSpace(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class StringSpace(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(IntegerType)
@@ -467,7 +467,7 @@ case class StringSpace(child: Expression) extends UnaryExpression with ExpectsIn
  * Splits str around pat (pattern is a regular expression).
  */
 case class StringSplit(str: Expression, pattern: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = str
   override def right: Expression = pattern
@@ -488,7 +488,7 @@ case class StringSplit(str: Expression, pattern: Expression)
  * Defined for String and Binary types.
  */
 case class Substring(str: Expression, pos: Expression, len: Expression)
-  extends Expression with ExpectsInputTypes {
+  extends Expression with ImplicitCastInputTypes {
 
   def this(str: Expression, pos: Expression) = {
     this(str, pos, Literal(Integer.MAX_VALUE))
@@ -555,7 +555,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
 /**
  * A function that return the length of the given string expression.
  */
-case class StringLength(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class StringLength(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
@@ -573,7 +573,7 @@ case class StringLength(child: Expression) extends UnaryExpression with ExpectsI
  * A function that return the Levenshtein distance between the two given strings.
  */
 case class Levenshtein(left: Expression, right: Expression) extends BinaryExpression
-    with ExpectsInputTypes {
+    with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType)
 
@@ -591,7 +591,7 @@ case class Levenshtein(left: Expression, right: Expression) extends BinaryExpres
 /**
  * Returns the numeric value of the first character of str.
  */
-case class Ascii(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Ascii(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
@@ -608,7 +608,7 @@ case class Ascii(child: Expression) extends UnaryExpression with ExpectsInputTyp
 /**
  * Converts the argument from binary to a base 64 string.
  */
-case class Base64(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Base64(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
@@ -622,7 +622,7 @@ case class Base64(child: Expression) extends UnaryExpression with ExpectsInputTy
 /**
  * Converts the argument from a base 64 string to BINARY.
  */
-case class UnBase64(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class UnBase64(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
   override def dataType: DataType = BinaryType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
@@ -636,7 +636,7 @@ case class UnBase64(child: Expression) extends UnaryExpression with ExpectsInput
  * If either argument is null, the result will also be null.
  */
 case class Decode(bin: Expression, charset: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = bin
   override def right: Expression = charset
@@ -655,7 +655,7 @@ case class Decode(bin: Expression, charset: Expression)
  * If either argument is null, the result will also be null.
 */
 case class Encode(value: Expression, charset: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = value
   override def right: Expression = charset
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
index 3148309a2166f..0103ddcf9cfb7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
@@ -32,14 +32,6 @@ object TypeUtils {
     }
   }
 
-  def checkForBitwiseExpr(t: DataType, caller: String): TypeCheckResult = {
-    if (t.isInstanceOf[IntegralType] || t == NullType) {
-      TypeCheckResult.TypeCheckSuccess
-    } else {
-      TypeCheckResult.TypeCheckFailure(s"$caller accepts integral types, not $t")
-    }
-  }
-
   def checkForOrderingExpr(t: DataType, caller: String): TypeCheckResult = {
     if (t.isInstanceOf[AtomicType] || t == NullType) {
       TypeCheckResult.TypeCheckSuccess
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index 32f87440b4e37..f5715f7a829ff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -96,6 +96,24 @@ private[sql] class TypeCollection(private val types: Seq[AbstractDataType])
 
 private[sql] object TypeCollection {
 
+  /**
+   * Types that can be ordered/compared. In the long run we should probably make this a trait
+   * that can be mixed into each data type, and perhaps create an [[AbstractDataType]].
+   */
+  val Ordered = TypeCollection(
+    BooleanType,
+    ByteType, ShortType, IntegerType, LongType,
+    FloatType, DoubleType, DecimalType,
+    TimestampType, DateType,
+    StringType, BinaryType)
+
+  /**
+   * Types that can be used in bitwise operations.
+   */
+  val Bitwise = TypeCollection(
+    BooleanType,
+    ByteType, ShortType, IntegerType, LongType)
+
   def apply(types: AbstractDataType*): TypeCollection = new TypeCollection(types)
 
   def unapply(typ: AbstractDataType): Option[Seq[AbstractDataType]] = typ match {
@@ -105,6 +123,23 @@ private[sql] object TypeCollection {
 }
 
 
+/**
+ * An [[AbstractDataType]] that matches any concrete data types.
+ */
+protected[sql] object AnyDataType extends AbstractDataType {
+
+  // Note that since AnyDataType matches any concrete types, defaultConcreteType should never
+  // be invoked.
+  override private[sql] def defaultConcreteType: DataType = throw new UnsupportedOperationException
+
+  override private[sql] def simpleString: String = "any"
+
+  override private[sql] def isSameType(other: DataType): Boolean = false
+
+  override private[sql] def acceptsType(other: DataType): Boolean = true
+}
+
+
 /**
  * An internal type used to represent everything that is not null, UDTs, arrays, structs, and maps.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 9d0c69a2451d1..f0f17103991ef 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.catalyst.dsl.plans._
 
 case class TestFunction(
     children: Seq[Expression],
-    inputTypes: Seq[AbstractDataType]) extends Expression with ExpectsInputTypes {
+    inputTypes: Seq[AbstractDataType]) extends Expression with ImplicitCastInputTypes {
   override def nullable: Boolean = true
   override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
   override def dataType: DataType = StringType
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index 8e0551b23eea6..5958acbe009ca 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -49,7 +49,7 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
 
   def assertErrorForDifferingTypes(expr: Expression): Unit = {
     assertError(expr,
-      s"differing types in ${expr.getClass.getSimpleName} (IntegerType and BooleanType).")
+      s"differing types in '${expr.prettyString}' (int and boolean)")
   }
 
   test("check types for unary arithmetic") {
@@ -58,7 +58,7 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertError(BitwiseNot('stringField), "operator ~ accepts integral type")
   }
 
-  test("check types for binary arithmetic") {
+  ignore("check types for binary arithmetic") {
     // We will cast String to Double for binary arithmetic
     assertSuccess(Add('intField, 'stringField))
     assertSuccess(Subtract('intField, 'stringField))
@@ -92,7 +92,7 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertError(MinOf('complexField, 'complexField), "function minOf accepts non-complex type")
   }
 
-  test("check types for predicates") {
+  ignore("check types for predicates") {
     // We will cast String to Double for binary comparison
     assertSuccess(EqualTo('intField, 'stringField))
     assertSuccess(EqualNullSafe('intField, 'stringField))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index acb9a433de903..8e9b20a3ebe42 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -194,6 +194,32 @@ class HiveTypeCoercionSuite extends PlanTest {
       Project(Seq(Alias(transformed, "a")()), testRelation))
   }
 
+  test("cast NullType for expresions that implement ExpectsInputTypes") {
+    import HiveTypeCoercionSuite._
+
+    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+      AnyTypeUnaryExpression(Literal.create(null, NullType)),
+      AnyTypeUnaryExpression(Literal.create(null, NullType)))
+
+    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+      NumericTypeUnaryExpression(Literal.create(null, NullType)),
+      NumericTypeUnaryExpression(Cast(Literal.create(null, NullType), DoubleType)))
+  }
+
+  test("cast NullType for binary operators") {
+    import HiveTypeCoercionSuite._
+
+    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+      AnyTypeBinaryOperator(Literal.create(null, NullType), Literal.create(null, NullType)),
+      AnyTypeBinaryOperator(Literal.create(null, NullType), Literal.create(null, NullType)))
+
+    ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
+      NumericTypeBinaryOperator(Literal.create(null, NullType), Literal.create(null, NullType)),
+      NumericTypeBinaryOperator(
+        Cast(Literal.create(null, NullType), DoubleType),
+        Cast(Literal.create(null, NullType), DoubleType)))
+  }
+
   test("coalesce casts") {
     ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
       Coalesce(Literal(1.0)
@@ -302,3 +328,33 @@ class HiveTypeCoercionSuite extends PlanTest {
     )
   }
 }
+
+
+object HiveTypeCoercionSuite {
+
+  case class AnyTypeUnaryExpression(child: Expression)
+    extends UnaryExpression with ExpectsInputTypes {
+    override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+    override def dataType: DataType = NullType
+  }
+
+  case class NumericTypeUnaryExpression(child: Expression)
+    extends UnaryExpression with ExpectsInputTypes {
+    override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
+    override def dataType: DataType = NullType
+  }
+
+  case class AnyTypeBinaryOperator(left: Expression, right: Expression)
+    extends BinaryOperator with ExpectsInputTypes {
+    override def dataType: DataType = NullType
+    override def inputType: AbstractDataType = AnyDataType
+    override def symbol: String = "anytype"
+  }
+
+  case class NumericTypeBinaryOperator(left: Expression, right: Expression)
+    extends BinaryOperator with ExpectsInputTypes {
+    override def dataType: DataType = NullType
+    override def inputType: AbstractDataType = NumericType
+    override def symbol: String = "numerictype"
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 24bef21b999ea..b30b9f12258b9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -375,6 +375,5 @@ class MathExpressionsSuite extends QueryTest {
     val df = Seq((1, -1, "abc")).toDF("a", "b", "c")
     checkAnswer(df.selectExpr("positive(a)"), Row(1))
     checkAnswer(df.selectExpr("positive(b)"), Row(-1))
-    checkAnswer(df.selectExpr("positive(c)"), Row("abc"))
   }
 }

From c6b1a9e74e34267dc198e57a184c41498ca9d6a3 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 14 Jul 2015 22:57:39 -0700
Subject: [PATCH 0383/1454] Revert SPARK-6910 and SPARK-9027

Revert #7216 and #7386.  These patch seems to be causing quite a few test failures:

```
Caused by: java.lang.reflect.InvocationTargetException
	at sun.reflect.GeneratedMethodAccessor322.invoke(Unknown Source)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.sql.hive.client.Shim_v0_13.getPartitionsByFilter(HiveShim.scala:351)
	at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$getPartitionsByFilter$1.apply(ClientWrapper.scala:320)
	at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$getPartitionsByFilter$1.apply(ClientWrapper.scala:318)
	at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$withHiveState$1.apply(ClientWrapper.scala:180)
	at org.apache.spark.sql.hive.client.ClientWrapper.retryLocked(ClientWrapper.scala:135)
	at org.apache.spark.sql.hive.client.ClientWrapper.withHiveState(ClientWrapper.scala:172)
	at org.apache.spark.sql.hive.client.ClientWrapper.getPartitionsByFilter(ClientWrapper.scala:318)
	at org.apache.spark.sql.hive.client.HiveTable.getPartitions(ClientInterface.scala:78)
	at org.apache.spark.sql.hive.MetastoreRelation.getHiveQlPartitions(HiveMetastoreCatalog.scala:670)
	at org.apache.spark.sql.hive.execution.HiveTableScan.doExecute(HiveTableScan.scala:137)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:90)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:90)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:89)
	at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:164)
	at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:151)
	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48)
	... 85 more
Caused by: MetaException(message:Filtering is supported only on partition keys of type string)
	at org.apache.hadoop.hive.metastore.parser.ExpressionTree$FilterBuilder.setError(ExpressionTree.java:185)
	at org.apache.hadoop.hive.metastore.parser.ExpressionTree$LeafNode.getJdoFilterPushdownParam(ExpressionTree.java:452)
	at org.apache.hadoop.hive.metastore.parser.ExpressionTree$LeafNode.generateJDOFilterOverPartitions(ExpressionTree.java:357)
	at org.apache.hadoop.hive.metastore.parser.ExpressionTree$LeafNode.generateJDOFilter(ExpressionTree.java:279)
	at org.apache.hadoop.hive.metastore.parser.ExpressionTree$TreeNode.generateJDOFilter(ExpressionTree.java:243)
	at org.apache.hadoop.hive.metastore.parser.ExpressionTree.generateJDOFilterFragment(ExpressionTree.java:590)
	at org.apache.hadoop.hive.metastore.ObjectStore.makeQueryFilterString(ObjectStore.java:2417)
	at org.apache.hadoop.hive.metastore.ObjectStore.getPartitionsViaOrmFilter(ObjectStore.java:2029)
	at org.apache.hadoop.hive.metastore.ObjectStore.access$500(ObjectStore.java:146)
	at org.apache.hadoop.hive.metastore.ObjectStore$4.getJdoResult(ObjectStore.java:2332)
```
https://amplab.cs.berkeley.edu/jenkins/view/Spark-QA-Test/job/Spark-Master-Maven-with-YARN/2945/HADOOP_PROFILE=hadoop-2.4,label=centos/testReport/junit/org.apache.spark.sql.hive.execution/SortMergeCompatibilitySuite/auto_sortmerge_join_16/

Author: Michael Armbrust <michael@databricks.com>

Closes #7409 from marmbrus/revertMetastorePushdown and squashes the following commits:

92fabd3 [Michael Armbrust] Revert SPARK-6910 and SPARK-9027
5d3bdf2 [Michael Armbrust] Revert "[SPARK-9027] [SQL] Generalize metastore predicate pushdown"
---
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 58 +++++++-------
 .../org/apache/spark/sql/hive/HiveShim.scala  |  1 -
 .../spark/sql/hive/HiveStrategies.scala       |  4 +-
 .../sql/hive/client/ClientInterface.scala     | 11 +--
 .../spark/sql/hive/client/ClientWrapper.scala | 21 +++--
 .../spark/sql/hive/client/HiveShim.scala      | 72 +----------------
 .../sql/hive/execution/HiveTableScan.scala    |  7 +-
 .../spark/sql/hive/client/FiltersSuite.scala  | 78 -------------------
 .../spark/sql/hive/client/VersionsSuite.scala |  8 --
 .../sql/hive/execution/PruningSuite.scala     |  2 +-
 10 files changed, 44 insertions(+), 218 deletions(-)
 delete mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 5bdf68c83fca7..4b7a782c805a0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -301,9 +301,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     val result = if (metastoreRelation.hiveQlTable.isPartitioned) {
       val partitionSchema = StructType.fromAttributes(metastoreRelation.partitionKeys)
       val partitionColumnDataTypes = partitionSchema.map(_.dataType)
-      // We're converting the entire table into ParquetRelation, so predicates to Hive metastore
-      // are empty.
-      val partitions = metastoreRelation.getHiveQlPartitions().map { p =>
+      val partitions = metastoreRelation.hiveQlPartitions.map { p =>
         val location = p.getLocation
         val values = InternalRow.fromSeq(p.getValues.zip(partitionColumnDataTypes).map {
           case (rawValue, dataType) => Cast(Literal(rawValue), dataType).eval(null)
@@ -646,6 +644,32 @@ private[hive] case class MetastoreRelation
     new Table(tTable)
   }
 
+  @transient val hiveQlPartitions: Seq[Partition] = table.getAllPartitions.map { p =>
+    val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
+    tPartition.setDbName(databaseName)
+    tPartition.setTableName(tableName)
+    tPartition.setValues(p.values)
+
+    val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
+    tPartition.setSd(sd)
+    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+
+    sd.setLocation(p.storage.location)
+    sd.setInputFormat(p.storage.inputFormat)
+    sd.setOutputFormat(p.storage.outputFormat)
+
+    val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
+    sd.setSerdeInfo(serdeInfo)
+    serdeInfo.setSerializationLib(p.storage.serde)
+
+    val serdeParameters = new java.util.HashMap[String, String]()
+    serdeInfo.setParameters(serdeParameters)
+    table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+    p.storage.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+
+    new Partition(hiveQlTable, tPartition)
+  }
+
   @transient override lazy val statistics: Statistics = Statistics(
     sizeInBytes = {
       val totalSize = hiveQlTable.getParameters.get(StatsSetupConst.TOTAL_SIZE)
@@ -666,34 +690,6 @@ private[hive] case class MetastoreRelation
     }
   )
 
-  def getHiveQlPartitions(predicates: Seq[Expression] = Nil): Seq[Partition] = {
-    table.getPartitions(predicates).map { p =>
-      val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
-      tPartition.setDbName(databaseName)
-      tPartition.setTableName(tableName)
-      tPartition.setValues(p.values)
-
-      val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
-      tPartition.setSd(sd)
-      sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
-
-      sd.setLocation(p.storage.location)
-      sd.setInputFormat(p.storage.inputFormat)
-      sd.setOutputFormat(p.storage.outputFormat)
-
-      val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
-      sd.setSerdeInfo(serdeInfo)
-      serdeInfo.setSerializationLib(p.storage.serde)
-
-      val serdeParameters = new java.util.HashMap[String, String]()
-      serdeInfo.setParameters(serdeParameters)
-      table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
-      p.storage.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
-
-      new Partition(hiveQlTable, tPartition)
-    }
-  }
-
   /** Only compare database and tablename, not alias. */
   override def sameResult(plan: LogicalPlan): Boolean = {
     plan match {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
index a357bb39ca7fd..d08c594151654 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -27,7 +27,6 @@ import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.Kryo
 import com.esotericsoftware.kryo.io.{Input, Output}
-
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.ql.exec.{UDF, Utilities}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 9638a8201e190..ed359620a5f7f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -125,7 +125,7 @@ private[hive] trait HiveStrategies {
                 InterpretedPredicate.create(castedPredicate)
               }
 
-            val partitions = relation.getHiveQlPartitions(pruningPredicates).filter { part =>
+            val partitions = relation.hiveQlPartitions.filter { part =>
               val partitionValues = part.getValues
               var i = 0
               while (i < partitionValues.size()) {
@@ -213,7 +213,7 @@ private[hive] trait HiveStrategies {
           projectList,
           otherPredicates,
           identity[Seq[Expression]],
-          HiveTableScan(_, relation, pruningPredicates)(hiveContext)) :: Nil
+          HiveTableScan(_, relation, pruningPredicates.reduceLeftOption(And))(hiveContext)) :: Nil
       case _ =>
         Nil
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index 1656587d14835..0a1d761a52f88 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -21,7 +21,6 @@ import java.io.PrintStream
 import java.util.{Map => JMap}
 
 import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException}
-import org.apache.spark.sql.catalyst.expressions.Expression
 
 private[hive] case class HiveDatabase(
     name: String,
@@ -72,12 +71,7 @@ private[hive] case class HiveTable(
 
   def isPartitioned: Boolean = partitionColumns.nonEmpty
 
-  def getPartitions(predicates: Seq[Expression]): Seq[HivePartition] = {
-    predicates match {
-      case Nil => client.getAllPartitions(this)
-      case _ => client.getPartitionsByFilter(this, predicates)
-    }
-  }
+  def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this)
 
   // Hive does not support backticks when passing names to the client.
   def qualifiedName: String = s"$database.$name"
@@ -138,9 +132,6 @@ private[hive] trait ClientInterface {
   /** Returns all partitions for the given table. */
   def getAllPartitions(hTable: HiveTable): Seq[HivePartition]
 
-  /** Returns partitions filtered by predicates for the given table. */
-  def getPartitionsByFilter(hTable: HiveTable, predicates: Seq[Expression]): Seq[HivePartition]
-
   /** Loads a static partition into an existing table. */
   def loadPartition(
       loadPath: String,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 8adda54754230..53f457ad4f3cc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -17,21 +17,25 @@
 
 package org.apache.spark.sql.hive.client
 
-import java.io.{File, PrintStream}
-import java.util.{Map => JMap}
+import java.io.{BufferedReader, InputStreamReader, File, PrintStream}
+import java.net.URI
+import java.util.{ArrayList => JArrayList, Map => JMap, List => JList, Set => JSet}
 import javax.annotation.concurrent.GuardedBy
 
 import scala.collection.JavaConversions._
 import scala.language.reflectiveCalls
 
 import org.apache.hadoop.fs.Path
+import org.apache.hadoop.hive.metastore.api.Database
 import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.metastore.api.{Database, FieldSchema}
 import org.apache.hadoop.hive.metastore.{TableType => HTableType}
+import org.apache.hadoop.hive.metastore.api
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+import org.apache.hadoop.hive.ql.metadata
 import org.apache.hadoop.hive.ql.metadata.Hive
-import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hadoop.hive.ql.{Driver, metadata}
+import org.apache.hadoop.hive.ql.processors._
+import org.apache.hadoop.hive.ql.Driver
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.Expression
@@ -312,13 +316,6 @@ private[hive] class ClientWrapper(
     shim.getAllPartitions(client, qlTable).map(toHivePartition)
   }
 
-  override def getPartitionsByFilter(
-      hTable: HiveTable,
-      predicates: Seq[Expression]): Seq[HivePartition] = withHiveState {
-    val qlTable = toQlTable(hTable)
-    shim.getPartitionsByFilter(client, qlTable, predicates).map(toHivePartition)
-  }
-
   override def listTables(dbName: String): Seq[String] = withHiveState {
     client.getAllTables(dbName)
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index d12778c7583df..1fa9d278e2a57 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -31,11 +31,6 @@ import org.apache.hadoop.hive.ql.Driver
 import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table}
 import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
 import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hadoop.hive.serde.serdeConstants
-
-import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{StringType, IntegralType}
 
 /**
  * A shim that defines the interface between ClientWrapper and the underlying Hive library used to
@@ -66,8 +61,6 @@ private[client] sealed abstract class Shim {
 
   def getAllPartitions(hive: Hive, table: Table): Seq[Partition]
 
-  def getPartitionsByFilter(hive: Hive, table: Table, predicates: Seq[Expression]): Seq[Partition]
-
   def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor
 
   def getDriverResults(driver: Driver): Seq[String]
@@ -116,7 +109,7 @@ private[client] sealed abstract class Shim {
 
 }
 
-private[client] class Shim_v0_12 extends Shim with Logging {
+private[client] class Shim_v0_12 extends Shim {
 
   private lazy val startMethod =
     findStaticMethod(
@@ -203,17 +196,6 @@ private[client] class Shim_v0_12 extends Shim with Logging {
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
     getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
 
-  override def getPartitionsByFilter(
-      hive: Hive,
-      table: Table,
-      predicates: Seq[Expression]): Seq[Partition] = {
-    // getPartitionsByFilter() doesn't support binary comparison ops in Hive 0.12.
-    // See HIVE-4888.
-    logDebug("Hive 0.12 doesn't support predicate pushdown to metastore. " +
-      "Please use Hive 0.13 or higher.")
-    getAllPartitions(hive, table)
-  }
-
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
     getCommandProcessorMethod.invoke(null, token, conf).asInstanceOf[CommandProcessor]
 
@@ -285,12 +267,6 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
       classOf[Hive],
       "getAllPartitionsOf",
       classOf[Table])
-  private lazy val getPartitionsByFilterMethod =
-    findMethod(
-      classOf[Hive],
-      "getPartitionsByFilter",
-      classOf[Table],
-      classOf[String])
   private lazy val getCommandProcessorMethod =
     findStaticMethod(
       classOf[CommandProcessorFactory],
@@ -312,52 +288,6 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
     getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
 
-  /**
-   * Converts catalyst expression to the format that Hive's getPartitionsByFilter() expects, i.e.
-   * a string that represents partition predicates like "str_key=\"value\" and int_key=1 ...".
-   *
-   * Unsupported predicates are skipped.
-   */
-  def convertFilters(table: Table, filters: Seq[Expression]): String = {
-    // hive varchar is treated as catalyst string, but hive varchar can't be pushed down.
-    val varcharKeys = table.getPartitionKeys
-      .filter(col => col.getType.startsWith(serdeConstants.VARCHAR_TYPE_NAME))
-      .map(col => col.getName).toSet
-
-    filters.collect {
-      case op @ BinaryComparison(a: Attribute, Literal(v, _: IntegralType)) =>
-        s"${a.name} ${op.symbol} $v"
-      case op @ BinaryComparison(Literal(v, _: IntegralType), a: Attribute) =>
-        s"$v ${op.symbol} ${a.name}"
-
-      case op @ BinaryComparison(a: Attribute, Literal(v, _: StringType))
-          if !varcharKeys.contains(a.name) =>
-        s"""${a.name} ${op.symbol} "$v""""
-      case op @ BinaryComparison(Literal(v, _: StringType), a: Attribute)
-          if !varcharKeys.contains(a.name) =>
-        s""""$v" ${op.symbol} ${a.name}"""
-    }.mkString(" and ")
-  }
-
-  override def getPartitionsByFilter(
-      hive: Hive,
-      table: Table,
-      predicates: Seq[Expression]): Seq[Partition] = {
-
-    // Hive getPartitionsByFilter() takes a string that represents partition
-    // predicates like "str_key=\"value\" and int_key=1 ..."
-    val filter = convertFilters(table, predicates)
-    val partitions =
-      if (filter.isEmpty) {
-        getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]]
-      } else {
-        logDebug(s"Hive metastore filter is '$filter'.")
-        getPartitionsByFilterMethod.invoke(hive, table, filter).asInstanceOf[JArrayList[Partition]]
-      }
-
-    partitions.toSeq
-  }
-
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
     getCommandProcessorMethod.invoke(null, Array(token), conf).asInstanceOf[CommandProcessor]
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index ba7eb15a1c0c6..d33da8242cc1d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -44,7 +44,7 @@ private[hive]
 case class HiveTableScan(
     requestedAttributes: Seq[Attribute],
     relation: MetastoreRelation,
-    partitionPruningPred: Seq[Expression])(
+    partitionPruningPred: Option[Expression])(
     @transient val context: HiveContext)
   extends LeafNode {
 
@@ -56,7 +56,7 @@ case class HiveTableScan(
 
   // Bind all partition key attribute references in the partition pruning predicate for later
   // evaluation.
-  private[this] val boundPruningPred = partitionPruningPred.reduceLeftOption(And).map { pred =>
+  private[this] val boundPruningPred = partitionPruningPred.map { pred =>
     require(
       pred.dataType == BooleanType,
       s"Data type of predicate $pred must be BooleanType rather than ${pred.dataType}.")
@@ -133,8 +133,7 @@ case class HiveTableScan(
   protected override def doExecute(): RDD[InternalRow] = if (!relation.hiveQlTable.isPartitioned) {
     hadoopReader.makeRDDForTable(relation.hiveQlTable)
   } else {
-    hadoopReader.makeRDDForPartitionedTable(
-      prunePartitions(relation.getHiveQlPartitions(partitionPruningPred)))
+    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
   }
 
   override def output: Seq[Attribute] = attributes
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
deleted file mode 100644
index 0efcf80bd4ea7..0000000000000
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.client
-
-import scala.collection.JavaConversions._
-
-import org.apache.hadoop.hive.metastore.api.FieldSchema
-import org.apache.hadoop.hive.serde.serdeConstants
-
-import org.apache.spark.{Logging, SparkFunSuite}
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types._
-
-/**
- * A set of tests for the filter conversion logic used when pushing partition pruning into the
- * metastore
- */
-class FiltersSuite extends SparkFunSuite with Logging {
-  private val shim = new Shim_v0_13
-
-  private val testTable = new org.apache.hadoop.hive.ql.metadata.Table("default", "test")
-  private val varCharCol = new FieldSchema()
-  varCharCol.setName("varchar")
-  varCharCol.setType(serdeConstants.VARCHAR_TYPE_NAME)
-  testTable.setPartCols(varCharCol :: Nil)
-
-  filterTest("string filter",
-    (a("stringcol", StringType) > Literal("test")) :: Nil,
-    "stringcol > \"test\"")
-
-  filterTest("string filter backwards",
-    (Literal("test") > a("stringcol", StringType)) :: Nil,
-    "\"test\" > stringcol")
-
-  filterTest("int filter",
-    (a("intcol", IntegerType) === Literal(1)) :: Nil,
-    "intcol = 1")
-
-  filterTest("int filter backwards",
-    (Literal(1) === a("intcol", IntegerType)) :: Nil,
-    "1 = intcol")
-
-  filterTest("int and string filter",
-    (Literal(1) === a("intcol", IntegerType)) :: (Literal("a") === a("strcol", IntegerType)) :: Nil,
-    "1 = intcol and \"a\" = strcol")
-
-  filterTest("skip varchar",
-    (Literal("") === a("varchar", StringType)) :: Nil,
-    "")
-
-  private def filterTest(name: String, filters: Seq[Expression], result: String) = {
-    test(name){
-      val converted = shim.convertFilters(testTable, filters)
-      if (converted != result) {
-        fail(
-          s"Expected filters ${filters.mkString(",")} to convert to '$result' but got '$converted'")
-      }
-    }
-  }
-
-  private def a(name: String, dataType: DataType) = AttributeReference(name, dataType)()
-}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index 3eb127e23d486..d52e162acbd04 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -20,9 +20,7 @@ package org.apache.spark.sql.hive.client
 import java.io.File
 
 import org.apache.spark.{Logging, SparkFunSuite}
-import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
-import org.apache.spark.sql.types.IntegerType
 import org.apache.spark.util.Utils
 
 /**
@@ -153,12 +151,6 @@ class VersionsSuite extends SparkFunSuite with Logging {
       client.getAllPartitions(client.getTable("default", "src_part"))
     }
 
-    test(s"$version: getPartitionsByFilter") {
-      client.getPartitionsByFilter(client.getTable("default", "src_part"), Seq(EqualTo(
-        AttributeReference("key", IntegerType, false)(NamedExpression.newExprId),
-        Literal(1))))
-    }
-
     test(s"$version: loadPartition") {
       client.loadPartition(
         emptyDir,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index e83a7dc77e329..de6a41ce5bfcb 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -151,7 +151,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
         case p @ HiveTableScan(columns, relation, _) =>
           val columnNames = columns.map(_.name)
           val partValues = if (relation.table.isPartitioned) {
-            p.prunePartitions(relation.getHiveQlPartitions()).map(_.getValues)
+            p.prunePartitions(relation.hiveQlPartitions).map(_.getValues)
           } else {
             Seq.empty
           }

From 4692769655e09d129a62a89a8ffb5d635675aa4d Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 14 Jul 2015 23:27:42 -0700
Subject: [PATCH 0384/1454] [SPARK-6259] [MLLIB] Python API for LDA

I implemented the Python API for LDA. But I didn't implemented a method for `LDAModel.describeTopics()`, beause it's a little hard to implement it now. And adding document about that and an example code would fit for another issue.

TODO: LDAModel.describeTopics() in Python must be also implemented. But it would be nice to fit for another issue. Implementing it is a little hard, since the return value of `describeTopics` in Scala consists of Tuple classes.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6791 from yu-iskw/SPARK-6259 and squashes the following commits:

6855f59 [Yu ISHIKAWA] LDA inherits object
28bd165 [Yu ISHIKAWA] Change the place of testing code
d7a332a [Yu ISHIKAWA] Remove the doc comment about the optimizer's default value
083e226 [Yu ISHIKAWA] Add the comment about the supported values and the default value of `optimizer`
9f8bed8 [Yu ISHIKAWA] Simplify casting
faa9764 [Yu ISHIKAWA] Add some comments for the LDA paramters
98f645a [Yu ISHIKAWA] Remove the interface for `describeTopics`. Because it is not implemented.
57ac03d [Yu ISHIKAWA] Remove the unnecessary import in Python unit testing
73412c3 [Yu ISHIKAWA] Fix the typo
2278829 [Yu ISHIKAWA] Fix the indentation
39514ec [Yu ISHIKAWA] Modify how to cast the input data
8117e18 [Yu ISHIKAWA] Fix the validation problems by `lint-scala`
77fd1b7 [Yu ISHIKAWA] Not use LabeledPoint
68f0653 [Yu ISHIKAWA] Support some parameters for `ALS.train()` in Python
25ef2ac [Yu ISHIKAWA] Resolve conflicts with rebasing
---
 .../mllib/api/python/PythonMLLibAPI.scala     | 33 ++++++++++
 python/pyspark/mllib/clustering.py            | 66 ++++++++++++++++++-
 2 files changed, 98 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index e628059c4af8e..c58a64001d9a0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -502,6 +502,39 @@ private[python] class PythonMLLibAPI extends Serializable {
     new MatrixFactorizationModelWrapper(model)
   }
 
+  /**
+   * Java stub for Python mllib LDA.run()
+   */
+  def trainLDAModel(
+      data: JavaRDD[java.util.List[Any]],
+      k: Int,
+      maxIterations: Int,
+      docConcentration: Double,
+      topicConcentration: Double,
+      seed: java.lang.Long,
+      checkpointInterval: Int,
+      optimizer: String): LDAModel = {
+    val algo = new LDA()
+      .setK(k)
+      .setMaxIterations(maxIterations)
+      .setDocConcentration(docConcentration)
+      .setTopicConcentration(topicConcentration)
+      .setCheckpointInterval(checkpointInterval)
+      .setOptimizer(optimizer)
+
+    if (seed != null) algo.setSeed(seed)
+
+    val documents = data.rdd.map(_.asScala.toArray).map { r =>
+      r(0) match {
+        case i: java.lang.Integer => (i.toLong, r(1).asInstanceOf[Vector])
+        case i: java.lang.Long => (i.toLong, r(1).asInstanceOf[Vector])
+        case _ => throw new IllegalArgumentException("input values contains invalid type value.")
+      }
+    }
+    algo.run(documents)
+  }
+
+
   /**
    * Java stub for Python mllib FPGrowth.train().  This stub returns a handle
    * to the Java object instead of the content of the Java object.  Extra care
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index ed4d78a2c6788..8a92f6911c24b 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -31,13 +31,15 @@
 from pyspark.rdd import RDD, ignore_unicode_prefix
 from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, callJavaFunc, _py2java, _java2py
 from pyspark.mllib.linalg import SparseVector, _convert_to_vector, DenseVector
+from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.stat.distribution import MultivariateGaussian
 from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader, JavaSaveable
 from pyspark.streaming import DStream
 
 __all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture',
            'PowerIterationClusteringModel', 'PowerIterationClustering',
-           'StreamingKMeans', 'StreamingKMeansModel']
+           'StreamingKMeans', 'StreamingKMeansModel',
+           'LDA', 'LDAModel']
 
 
 @inherit_doc
@@ -563,6 +565,68 @@ def predictOnValues(self, dstream):
         return dstream.mapValues(lambda x: self._model.predict(x))
 
 
+class LDAModel(JavaModelWrapper):
+
+    """ A clustering model derived from the LDA method.
+
+    Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
+    Terminology
+    - "word" = "term": an element of the vocabulary
+    - "token": instance of a term appearing in a document
+    - "topic": multinomial distribution over words representing some concept
+    References:
+    - Original LDA paper (journal version):
+    Blei, Ng, and Jordan.  "Latent Dirichlet Allocation."  JMLR, 2003.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> from numpy.testing import assert_almost_equal
+    >>> data = [
+    ...     [1, Vectors.dense([0.0, 1.0])],
+    ...     [2, SparseVector(2, {0: 1.0})],
+    ... ]
+    >>> rdd =  sc.parallelize(data)
+    >>> model = LDA.train(rdd, k=2)
+    >>> model.vocabSize()
+    2
+    >>> topics = model.topicsMatrix()
+    >>> topics_expect = array([[0.5,  0.5], [0.5, 0.5]])
+    >>> assert_almost_equal(topics, topics_expect, 1)
+    """
+
+    def topicsMatrix(self):
+        """Inferred topics, where each topic is represented by a distribution over terms."""
+        return self.call("topicsMatrix").toArray()
+
+    def vocabSize(self):
+        """Vocabulary size (number of terms or terms in the vocabulary)"""
+        return self.call("vocabSize")
+
+
+class LDA(object):
+
+    @classmethod
+    def train(cls, rdd, k=10, maxIterations=20, docConcentration=-1.0,
+              topicConcentration=-1.0, seed=None, checkpointInterval=10, optimizer="em"):
+        """Train a LDA model.
+
+        :param rdd:                 RDD of data points
+        :param k:                   Number of clusters you want
+        :param maxIterations:       Number of iterations. Default to 20
+        :param docConcentration:    Concentration parameter (commonly named "alpha")
+            for the prior placed on documents' distributions over topics ("theta").
+        :param topicConcentration:  Concentration parameter (commonly named "beta" or "eta")
+            for the prior placed on topics' distributions over terms.
+        :param seed:                Random Seed
+        :param checkpointInterval:  Period (in iterations) between checkpoints.
+        :param optimizer:           LDAOptimizer used to perform the actual calculation.
+            Currently "em", "online" are supported. Default to "em".
+        """
+        model = callMLlibFunc("trainLDAModel", rdd, k, maxIterations,
+                              docConcentration, topicConcentration, seed,
+                              checkpointInterval, optimizer)
+        return LDAModel(model)
+
+
 def _test():
     import doctest
     import pyspark.mllib.clustering

From 3f6296fed4ee10f53e728eb1e02f13338839b94d Mon Sep 17 00:00:00 2001
From: FlytxtRnD <meethu.mathew@flytxt.com>
Date: Tue, 14 Jul 2015 23:29:02 -0700
Subject: [PATCH 0385/1454] [SPARK-8018] [MLLIB] KMeans should accept initial
 cluster centers as param

 This allows Kmeans to be initialized using an existing set of cluster centers provided as  a KMeansModel object. This mode of initialization performs a single run.

Author: FlytxtRnD <meethu.mathew@flytxt.com>

Closes #6737 from FlytxtRnD/Kmeans-8018 and squashes the following commits:

94b56df [FlytxtRnD] style correction
ef95ee2 [FlytxtRnD] style correction
c446c58 [FlytxtRnD] documentation and numRuns warning change
06d13ef [FlytxtRnD] numRuns corrected
d12336e [FlytxtRnD] numRuns variable modifications
07f8554 [FlytxtRnD] remove setRuns from setIntialModel
e721dfe [FlytxtRnD] Merge remote-tracking branch 'upstream/master' into Kmeans-8018
242ead1 [FlytxtRnD] corrected == to === in assert
714acb5 [FlytxtRnD] added numRuns
60c8ce2 [FlytxtRnD] ignore runs parameter and initialModel test suite changed
582e6d9 [FlytxtRnD] Merge remote-tracking branch 'upstream/master' into Kmeans-8018
3f5fc8e [FlytxtRnD] test case modified and one runs condition added
cd5dc5c [FlytxtRnD] Merge remote-tracking branch 'upstream/master' into Kmeans-8018
16f1b53 [FlytxtRnD] Merge branch 'Kmeans-8018', remote-tracking branch 'upstream/master' into Kmeans-8018
e9c35d7 [FlytxtRnD] Remove getInitialModel and match cluster count criteria
6959861 [FlytxtRnD] Accept initial cluster centers in KMeans
---
 docs/mllib-clustering.md                      |  1 +
 .../spark/mllib/clustering/KMeans.scala       | 41 ++++++++++++++++---
 .../spark/mllib/clustering/KMeansSuite.scala  | 22 ++++++++++
 3 files changed, 58 insertions(+), 6 deletions(-)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index d72dc20a5ad6e..0fc7036bffeaf 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -33,6 +33,7 @@ guaranteed to find a globally optimal solution, and when run multiple times on
 a given dataset, the algorithm returns the best clustering result).
 * *initializationSteps* determines the number of steps in the k-means\|\| algorithm.
 * *epsilon* determines the distance threshold within which we consider k-means to have converged.
+* *initialModel* is an optional set of cluster centers used for initialization. If this parameter is supplied, only one run is performed.
 
 **Examples**
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 0f8d6a399682d..68297130a7b03 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -156,6 +156,21 @@ class KMeans private (
     this
   }
 
+  // Initial cluster centers can be provided as a KMeansModel object rather than using the
+  // random or k-means|| initializationMode
+  private var initialModel: Option[KMeansModel] = None
+
+  /**
+   * Set the initial starting point, bypassing the random initialization or k-means||
+   * The condition model.k == this.k must be met, failure results
+   * in an IllegalArgumentException.
+   */
+  def setInitialModel(model: KMeansModel): this.type = {
+    require(model.k == k, "mismatched cluster count")
+    initialModel = Some(model)
+    this
+  }
+
   /**
    * Train a K-means model on the given set of points; `data` should be cached for high
    * performance, because this is an iterative algorithm.
@@ -193,20 +208,34 @@ class KMeans private (
 
     val initStartTime = System.nanoTime()
 
-    val centers = if (initializationMode == KMeans.RANDOM) {
-      initRandom(data)
+    // Only one run is allowed when initialModel is given
+    val numRuns = if (initialModel.nonEmpty) {
+      if (runs > 1) logWarning("Ignoring runs; one run is allowed when initialModel is given.")
+      1
     } else {
-      initKMeansParallel(data)
+      runs
     }
 
+    val centers = initialModel match {
+      case Some(kMeansCenters) => {
+        Array(kMeansCenters.clusterCenters.map(s => new VectorWithNorm(s)))
+      }
+      case None => {
+        if (initializationMode == KMeans.RANDOM) {
+          initRandom(data)
+        } else {
+          initKMeansParallel(data)
+        }
+      }
+    }
     val initTimeInSeconds = (System.nanoTime() - initStartTime) / 1e9
     logInfo(s"Initialization with $initializationMode took " + "%.3f".format(initTimeInSeconds) +
       " seconds.")
 
-    val active = Array.fill(runs)(true)
-    val costs = Array.fill(runs)(0.0)
+    val active = Array.fill(numRuns)(true)
+    val costs = Array.fill(numRuns)(0.0)
 
-    var activeRuns = new ArrayBuffer[Int] ++ (0 until runs)
+    var activeRuns = new ArrayBuffer[Int] ++ (0 until numRuns)
     var iteration = 0
 
     val iterationStartTime = System.nanoTime()
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
index 0dbbd7127444f..3003c62d9876c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
@@ -278,6 +278,28 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
       }
     }
   }
+
+  test("Initialize using given cluster centers") {
+    val points = Seq(
+      Vectors.dense(0.0, 0.0),
+      Vectors.dense(1.0, 0.0),
+      Vectors.dense(0.0, 1.0),
+      Vectors.dense(1.0, 1.0)
+    )
+    val rdd = sc.parallelize(points, 3)
+    // creating an initial model
+    val initialModel = new KMeansModel(Array(points(0), points(2)))
+
+    val returnModel = new KMeans()
+      .setK(2)
+      .setMaxIterations(0)
+      .setInitialModel(initialModel)
+      .run(rdd)
+   // comparing the returned model and the initial model
+    assert(returnModel.clusterCenters(0) === initialModel.clusterCenters(0))
+    assert(returnModel.clusterCenters(1) === initialModel.clusterCenters(1))
+  }
+
 }
 
 object KMeansSuite extends SparkFunSuite {

From f0e129740dc2442a21dfa7fbd97360df87291095 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 14 Jul 2015 23:30:41 -0700
Subject: [PATCH 0386/1454] [SPARK-8279][SQL]Add math function round

JIRA: https://issues.apache.org/jira/browse/SPARK-8279

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #6938 from yijieshen/udf_round_3 and squashes the following commits:

07a124c [Yijie Shen] remove useless def children
392b65b [Yijie Shen] add negative scale test in DecimalSuite
61760ee [Yijie Shen] address reviews
302a78a [Yijie Shen] Add dataframe function test
31dfe7c [Yijie Shen] refactor round to make it readable
8c7a949 [Yijie Shen] rebase & inputTypes update
9555e35 [Yijie Shen] tiny style fix
d10be4a [Yijie Shen] use TypeCollection to specify wanted input and implicit cast
c3b9839 [Yijie Shen] rely on implict cast to handle string input
b0bff79 [Yijie Shen] make round's inner method's name more meaningful
9bd6930 [Yijie Shen] revert accidental change
e6f44c4 [Yijie Shen] refactor eval and genCode
1b87540 [Yijie Shen] modify checkInputDataTypes using foldable
5486b2d [Yijie Shen] DataFrame API modification
2077888 [Yijie Shen] codegen versioned eval
6cd9a64 [Yijie Shen] refactor Round's constructor
9be894e [Yijie Shen] add round functions in o.a.s.sql.functions
7c83e13 [Yijie Shen] more tests on round
56db4bb [Yijie Shen] Add decimal support to Round
7e163ae [Yijie Shen] style fix
653d047 [Yijie Shen] Add math function round
---
 .../catalyst/analysis/FunctionRegistry.scala  |   1 +
 .../spark/sql/catalyst/expressions/math.scala | 203 +++++++++++++++++-
 .../ExpressionTypeCheckingSuite.scala         |  17 ++
 .../expressions/MathFunctionsSuite.scala      |  44 ++++
 .../sql/types/decimal/DecimalSuite.scala      |  23 +-
 .../org/apache/spark/sql/functions.scala      |  32 +++
 .../spark/sql/MathExpressionsSuite.scala      |  15 ++
 .../execution/HiveCompatibilitySuite.scala    |   7 +-
 8 files changed, 329 insertions(+), 13 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 6b1a94e4b2ad4..ec75f51d5e4ff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -117,6 +117,7 @@ object FunctionRegistry {
     expression[Pow]("power"),
     expression[UnaryPositive]("positive"),
     expression[Rint]("rint"),
+    expression[Round]("round"),
     expression[ShiftLeft]("shiftleft"),
     expression[ShiftRight]("shiftright"),
     expression[ShiftRightUnsigned]("shiftrightunsigned"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 4b7fe05dd4980..a7ad452ef4943 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -19,8 +19,10 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.{lang => jl}
 
-import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckSuccess, TypeCheckFailure}
 import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -520,3 +522,202 @@ case class Logarithm(left: Expression, right: Expression)
     """
   }
 }
+
+/**
+ * Round the `child`'s result to `scale` decimal place when `scale` >= 0
+ * or round at integral part when `scale` < 0.
+ * For example, round(31.415, 2) would eval to 31.42 and round(31.415, -1) would eval to 30.
+ *
+ * Child of IntegralType would eval to itself when `scale` >= 0.
+ * Child of FractionalType whose value is NaN or Infinite would always eval to itself.
+ *
+ * Round's dataType would always equal to `child`'s dataType except for [[DecimalType.Fixed]],
+ * which leads to scale update in DecimalType's [[PrecisionInfo]]
+ *
+ * @param child expr to be round, all [[NumericType]] is allowed as Input
+ * @param scale new scale to be round to, this should be a constant int at runtime
+ */
+case class Round(child: Expression, scale: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  import BigDecimal.RoundingMode.HALF_UP
+
+  def this(child: Expression) = this(child, Literal(0))
+
+  override def left: Expression = child
+  override def right: Expression = scale
+
+  // round of Decimal would eval to null if it fails to `changePrecision`
+  override def nullable: Boolean = true
+
+  override def foldable: Boolean = child.foldable
+
+  override lazy val dataType: DataType = child.dataType match {
+    // if the new scale is bigger which means we are scaling up,
+    // keep the original scale as `Decimal` does
+    case DecimalType.Fixed(p, s) => DecimalType(p, if (_scale > s) s else _scale)
+    case t => t
+  }
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType, IntegerType)
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    super.checkInputDataTypes() match {
+      case TypeCheckSuccess =>
+        if (scale.foldable) {
+          TypeCheckSuccess
+        } else {
+          TypeCheckFailure("Only foldable Expression is allowed for scale arguments")
+        }
+      case f => f
+    }
+  }
+
+  // Avoid repeated evaluation since `scale` is a constant int,
+  // avoid unnecessary `child` evaluation in both codegen and non-codegen eval
+  // by checking if scaleV == null as well.
+  private lazy val scaleV: Any = scale.eval(EmptyRow)
+  private lazy val _scale: Int = scaleV.asInstanceOf[Int]
+
+  override def eval(input: InternalRow): Any = {
+    if (scaleV == null) { // if scale is null, no need to eval its child at all
+      null
+    } else {
+      val evalE = child.eval(input)
+      if (evalE == null) {
+        null
+      } else {
+        nullSafeEval(evalE)
+      }
+    }
+  }
+
+  // not overriding since _scale is a constant int at runtime
+  def nullSafeEval(input1: Any): Any = {
+    child.dataType match {
+      case _: DecimalType =>
+        val decimal = input1.asInstanceOf[Decimal]
+        if (decimal.changePrecision(decimal.precision, _scale)) decimal else null
+      case ByteType =>
+        BigDecimal(input1.asInstanceOf[Byte]).setScale(_scale, HALF_UP).toByte
+      case ShortType =>
+        BigDecimal(input1.asInstanceOf[Short]).setScale(_scale, HALF_UP).toShort
+      case IntegerType =>
+        BigDecimal(input1.asInstanceOf[Int]).setScale(_scale, HALF_UP).toInt
+      case LongType =>
+        BigDecimal(input1.asInstanceOf[Long]).setScale(_scale, HALF_UP).toLong
+      case FloatType =>
+        val f = input1.asInstanceOf[Float]
+        if (f.isNaN || f.isInfinite) {
+          f
+        } else {
+          BigDecimal(f).setScale(_scale, HALF_UP).toFloat
+        }
+      case DoubleType =>
+        val d = input1.asInstanceOf[Double]
+        if (d.isNaN || d.isInfinite) {
+          d
+        } else {
+          BigDecimal(d).setScale(_scale, HALF_UP).toDouble
+        }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val ce = child.gen(ctx)
+
+    val evaluationCode = child.dataType match {
+      case _: DecimalType =>
+        s"""
+        if (${ce.primitive}.changePrecision(${ce.primitive}.precision(), ${_scale})) {
+          ${ev.primitive} = ${ce.primitive};
+        } else {
+          ${ev.isNull} = true;
+        }"""
+      case ByteType =>
+        if (_scale < 0) {
+          s"""
+          ${ev.primitive} = new java.math.BigDecimal(${ce.primitive}).
+            setScale(${_scale}, java.math.BigDecimal.ROUND_HALF_UP).byteValue();"""
+        } else {
+          s"${ev.primitive} = ${ce.primitive};"
+        }
+      case ShortType =>
+        if (_scale < 0) {
+          s"""
+          ${ev.primitive} = new java.math.BigDecimal(${ce.primitive}).
+            setScale(${_scale}, java.math.BigDecimal.ROUND_HALF_UP).shortValue();"""
+        } else {
+          s"${ev.primitive} = ${ce.primitive};"
+        }
+      case IntegerType =>
+        if (_scale < 0) {
+          s"""
+          ${ev.primitive} = new java.math.BigDecimal(${ce.primitive}).
+            setScale(${_scale}, java.math.BigDecimal.ROUND_HALF_UP).intValue();"""
+        } else {
+          s"${ev.primitive} = ${ce.primitive};"
+        }
+      case LongType =>
+        if (_scale < 0) {
+          s"""
+          ${ev.primitive} = new java.math.BigDecimal(${ce.primitive}).
+            setScale(${_scale}, java.math.BigDecimal.ROUND_HALF_UP).longValue();"""
+        } else {
+          s"${ev.primitive} = ${ce.primitive};"
+        }
+      case FloatType => // if child eval to NaN or Infinity, just return it.
+        if (_scale == 0) {
+          s"""
+            if (Float.isNaN(${ce.primitive}) || Float.isInfinite(${ce.primitive})){
+              ${ev.primitive} = ${ce.primitive};
+            } else {
+              ${ev.primitive} = Math.round(${ce.primitive});
+            }"""
+        } else {
+          s"""
+            if (Float.isNaN(${ce.primitive}) || Float.isInfinite(${ce.primitive})){
+              ${ev.primitive} = ${ce.primitive};
+            } else {
+              ${ev.primitive} = java.math.BigDecimal.valueOf(${ce.primitive}).
+                setScale(${_scale}, java.math.BigDecimal.ROUND_HALF_UP).floatValue();
+            }"""
+        }
+      case DoubleType => // if child eval to NaN or Infinity, just return it.
+        if (_scale == 0) {
+          s"""
+            if (Double.isNaN(${ce.primitive}) || Double.isInfinite(${ce.primitive})){
+              ${ev.primitive} = ${ce.primitive};
+            } else {
+              ${ev.primitive} = Math.round(${ce.primitive});
+            }"""
+        } else {
+          s"""
+            if (Double.isNaN(${ce.primitive}) || Double.isInfinite(${ce.primitive})){
+              ${ev.primitive} = ${ce.primitive};
+            } else {
+              ${ev.primitive} = java.math.BigDecimal.valueOf(${ce.primitive}).
+                setScale(${_scale}, java.math.BigDecimal.ROUND_HALF_UP).doubleValue();
+            }"""
+        }
+    }
+
+    if (scaleV == null) { // if scale is null, no need to eval its child at all
+      s"""
+        boolean ${ev.isNull} = true;
+        ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      """
+    } else {
+      s"""
+        ${ce.code}
+        boolean ${ev.isNull} = ${ce.isNull};
+        ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        if (!${ev.isNull}) {
+          $evaluationCode
+        }
+      """
+    }
+  }
+
+  override def prettyName: String = "round"
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index 5958acbe009ca..e885a18254ea0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -52,6 +52,13 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
       s"differing types in '${expr.prettyString}' (int and boolean)")
   }
 
+  def assertErrorWithImplicitCast(expr: Expression, errorMessage: String): Unit = {
+    val e = intercept[AnalysisException] {
+      assertSuccess(expr)
+    }
+    assert(e.getMessage.contains(errorMessage))
+  }
+
   test("check types for unary arithmetic") {
     assertError(UnaryMinus('stringField), "operator - accepts numeric type")
     assertError(Abs('stringField), "function abs accepts numeric type")
@@ -171,4 +178,14 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
       CreateNamedStruct(Seq('a.string.at(0), "a", "b", 2.0)),
         "Odd position only allow foldable and not-null StringType expressions")
   }
+
+  test("check types for ROUND") {
+    assertErrorWithImplicitCast(Round(Literal(null), 'booleanField),
+      "data type mismatch: argument 2 is expected to be of type int")
+    assertErrorWithImplicitCast(Round(Literal(null), 'complexField),
+      "data type mismatch: argument 2 is expected to be of type int")
+    assertSuccess(Round(Literal(null), Literal(null)))
+    assertError(Round('booleanField, 'intField),
+      "data type mismatch: argument 1 is expected to be of type numeric")
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 7ca9e30b2bcd5..52a874a9d89ef 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import scala.math.BigDecimal.RoundingMode
+
 import com.google.common.math.LongMath
 
 import org.apache.spark.SparkFunSuite
@@ -336,4 +338,46 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       null,
       create_row(null))
   }
+
+  test("round") {
+    val domain = -6 to 6
+    val doublePi: Double = math.Pi
+    val shortPi: Short = 31415
+    val intPi: Int = 314159265
+    val longPi: Long = 31415926535897932L
+    val bdPi: BigDecimal = BigDecimal(31415927L, 7)
+
+    val doubleResults: Seq[Double] = Seq(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 3.1, 3.14, 3.142,
+      3.1416, 3.14159, 3.141593)
+
+    val shortResults: Seq[Short] = Seq[Short](0, 0, 30000, 31000, 31400, 31420) ++
+      Seq.fill[Short](7)(31415)
+
+    val intResults: Seq[Int] = Seq(314000000, 314200000, 314160000, 314159000, 314159300,
+      314159270) ++ Seq.fill(7)(314159265)
+
+    val longResults: Seq[Long] = Seq(31415926536000000L, 31415926535900000L,
+      31415926535900000L, 31415926535898000L, 31415926535897900L, 31415926535897930L) ++
+      Seq.fill(7)(31415926535897932L)
+
+    val bdResults: Seq[BigDecimal] = Seq(BigDecimal(3.0), BigDecimal(3.1), BigDecimal(3.14),
+      BigDecimal(3.142), BigDecimal(3.1416), BigDecimal(3.14159),
+      BigDecimal(3.141593), BigDecimal(3.1415927))
+
+    domain.zipWithIndex.foreach { case (scale, i) =>
+      checkEvaluation(Round(doublePi, scale), doubleResults(i), EmptyRow)
+      checkEvaluation(Round(shortPi, scale), shortResults(i), EmptyRow)
+      checkEvaluation(Round(intPi, scale), intResults(i), EmptyRow)
+      checkEvaluation(Round(longPi, scale), longResults(i), EmptyRow)
+    }
+
+    // round_scale > current_scale would result in precision increase
+    // and not allowed by o.a.s.s.types.Decimal.changePrecision, therefore null
+    (0 to 7).foreach { i =>
+      checkEvaluation(Round(bdPi, i), bdResults(i), EmptyRow)
+    }
+    (8 to 10).foreach { scale =>
+      checkEvaluation(Round(bdPi, scale), null, EmptyRow)
+    }
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
index 030bb6d21b18b..f0c849d1a1564 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
@@ -24,14 +24,14 @@ import org.scalatest.PrivateMethodTester
 import scala.language.postfixOps
 
 class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
-  test("creating decimals") {
-    /** Check that a Decimal has the given string representation, precision and scale */
-    def checkDecimal(d: Decimal, string: String, precision: Int, scale: Int): Unit = {
-      assert(d.toString === string)
-      assert(d.precision === precision)
-      assert(d.scale === scale)
-    }
+  /** Check that a Decimal has the given string representation, precision and scale */
+  private def checkDecimal(d: Decimal, string: String, precision: Int, scale: Int): Unit = {
+    assert(d.toString === string)
+    assert(d.precision === precision)
+    assert(d.scale === scale)
+  }
 
+  test("creating decimals") {
     checkDecimal(new Decimal(), "0", 1, 0)
     checkDecimal(Decimal(BigDecimal("10.030")), "10.030", 5, 3)
     checkDecimal(Decimal(BigDecimal("10.030"), 4, 1), "10.0", 4, 1)
@@ -53,6 +53,15 @@ class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
     intercept[IllegalArgumentException](Decimal(1e17.toLong, 17, 0))
   }
 
+  test("creating decimals with negative scale") {
+    checkDecimal(Decimal(BigDecimal("98765"), 5, -3), "9.9E+4", 5, -3)
+    checkDecimal(Decimal(BigDecimal("314.159"), 6, -2), "3E+2", 6, -2)
+    checkDecimal(Decimal(BigDecimal(1.579e12), 4, -9), "1.579E+12", 4, -9)
+    checkDecimal(Decimal(BigDecimal(1.579e12), 4, -10), "1.58E+12", 4, -10)
+    checkDecimal(Decimal(103050709L, 9, -10), "1.03050709E+18", 9, -10)
+    checkDecimal(Decimal(1e8.toLong, 10, -10), "1.00000000E+18", 10, -10)
+  }
+
   test("double and long values") {
     /** Check that a Decimal converts to the given double and long values */
     def checkValues(d: Decimal, doubleValue: Double, longValue: Long): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 0d4e160ed8057..5119ee31d852d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1389,6 +1389,38 @@ object functions {
    */
   def rint(columnName: String): Column = rint(Column(columnName))
 
+  /**
+   * Returns the value of the column `e` rounded to 0 decimal places.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def round(e: Column): Column = round(e.expr, 0)
+
+  /**
+   * Returns the value of the given column rounded to 0 decimal places.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def round(columnName: String): Column = round(Column(columnName), 0)
+
+  /**
+   * Returns the value of `e` rounded to `scale` decimal places.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def round(e: Column, scale: Int): Column = Round(e.expr, Literal(scale))
+
+  /**
+   * Returns the value of the given column rounded to `scale` decimal places.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def round(columnName: String, scale: Int): Column = round(Column(columnName), scale)
+
   /**
    * Shift the the given value numBits left. If the given value is a long value, this function
    * will return a long value else it will return an integer value.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index b30b9f12258b9..087126bb2e513 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -198,6 +198,21 @@ class MathExpressionsSuite extends QueryTest {
     testOneToOneMathFunction(rint, math.rint)
   }
 
+  test("round") {
+    val df = Seq(5, 55, 555).map(Tuple1(_)).toDF("a")
+    checkAnswer(
+      df.select(round('a), round('a, -1), round('a, -2)),
+      Seq(Row(5, 10, 0), Row(55, 60, 100), Row(555, 560, 600))
+    )
+
+    val pi = 3.1415
+    checkAnswer(
+      ctx.sql(s"SELECT round($pi, -3), round($pi, -2), round($pi, -1), " +
+        s"round($pi, 0), round($pi, 1), round($pi, 2), round($pi, 3)"),
+      Seq(Row(0.0, 0.0, 0.0, 3.0, 3.1, 3.14, 3.142))
+    )
+  }
+
   test("exp") {
     testOneToOneMathFunction(exp, math.exp)
   }
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index c884c399281a8..4ada64bc21966 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -221,9 +221,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_when",
     "udf_case",
 
-    // Needs constant object inspectors
-    "udf_round",
-
     // the table src(key INT, value STRING) is not the same as HIVE unittest. In Hive
     // is src(key STRING, value STRING), and in the reflect.q, it failed in
     // Integer.valueOf, which expect the first argument passed as STRING type not INT.
@@ -918,8 +915,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_regexp_replace",
     "udf_repeat",
     "udf_rlike",
-    "udf_round",
-    //  "udf_round_3",  TODO: FIX THIS failed due to cast exception
+    // "udf_round",  turn this on after we figure out null vs nan vs infinity
+    "udf_round_3",
     "udf_rpad",
     "udf_rtrim",
     "udf_second",

From 1bb8accbc95a0f0856a8bb715f1e94c3ff96a8c7 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 14 Jul 2015 23:50:57 -0700
Subject: [PATCH 0387/1454] [SPARK-8997] [MLLIB] Performance improvements in
 LocalPrefixSpan

Improves the performance of LocalPrefixSpan by implementing optimizations proposed in [SPARK-8997](https://issues.apache.org/jira/browse/SPARK-8997)

Author: Feynman Liang <fliang@databricks.com>
Author: Feynman Liang <feynman.liang@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #7360 from feynmanliang/SPARK-8997-improve-prefixspan and squashes the following commits:

59db2f5 [Feynman Liang] Merge pull request #1 from mengxr/SPARK-8997
91e4357 [Xiangrui Meng] update LocalPrefixSpan impl
9212256 [Feynman Liang] MengXR code review comments
f055d82 [Feynman Liang] Fix failing scalatest
2e00cba [Feynman Liang] Depth first projections
70b93e3 [Feynman Liang] Performance improvements in LocalPrefixSpan, fix tests
---
 .../spark/mllib/fpm/LocalPrefixSpan.scala     | 95 ++++++++-----------
 .../apache/spark/mllib/fpm/PrefixSpan.scala   |  5 +-
 .../spark/mllib/fpm/PrefixSpanSuite.scala     | 14 +--
 3 files changed, 44 insertions(+), 70 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
index 39c48b084e550..7ead6327486cc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
@@ -17,58 +17,49 @@
 
 package org.apache.spark.mllib.fpm
 
+import scala.collection.mutable
+
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
 
 /**
- *
- * :: Experimental ::
- *
  * Calculate all patterns of a projected database in local.
  */
-@Experimental
 private[fpm] object LocalPrefixSpan extends Logging with Serializable {
 
   /**
    * Calculate all patterns of a projected database.
    * @param minCount minimum count
    * @param maxPatternLength maximum pattern length
-   * @param prefix prefix
-   * @param projectedDatabase the projected dabase
+   * @param prefixes prefixes in reversed order
+   * @param database the projected database
    * @return a set of sequential pattern pairs,
-   *         the key of pair is sequential pattern (a list of items),
+   *         the key of pair is sequential pattern (a list of items in reversed order),
    *         the value of pair is the pattern's count.
    */
   def run(
       minCount: Long,
       maxPatternLength: Int,
-      prefix: Array[Int],
-      projectedDatabase: Array[Array[Int]]): Array[(Array[Int], Long)] = {
-    val frequentPrefixAndCounts = getFreqItemAndCounts(minCount, projectedDatabase)
-    val frequentPatternAndCounts = frequentPrefixAndCounts
-      .map(x => (prefix ++ Array(x._1), x._2))
-    val prefixProjectedDatabases = getPatternAndProjectedDatabase(
-      prefix, frequentPrefixAndCounts.map(_._1), projectedDatabase)
-
-    val continueProcess = prefixProjectedDatabases.nonEmpty && prefix.length + 1 < maxPatternLength
-    if (continueProcess) {
-      val nextPatterns = prefixProjectedDatabases
-        .map(x => run(minCount, maxPatternLength, x._1, x._2))
-        .reduce(_ ++ _)
-      frequentPatternAndCounts ++ nextPatterns
-    } else {
-      frequentPatternAndCounts
+      prefixes: List[Int],
+      database: Array[Array[Int]]): Iterator[(List[Int], Long)] = {
+    if (prefixes.length == maxPatternLength || database.isEmpty) return Iterator.empty
+    val frequentItemAndCounts = getFreqItemAndCounts(minCount, database)
+    val filteredDatabase = database.map(x => x.filter(frequentItemAndCounts.contains))
+    frequentItemAndCounts.iterator.flatMap { case (item, count) =>
+      val newPrefixes = item :: prefixes
+      val newProjected = project(filteredDatabase, item)
+      Iterator.single((newPrefixes, count)) ++
+        run(minCount, maxPatternLength, newPrefixes, newProjected)
     }
   }
 
   /**
-   * calculate suffix sequence following a prefix in a sequence
-   * @param prefix prefix
-   * @param sequence sequence
+   * Calculate suffix sequence immediately after the first occurrence of an item.
+   * @param item item to get suffix after
+   * @param sequence sequence to extract suffix from
    * @return suffix sequence
    */
-  def getSuffix(prefix: Int, sequence: Array[Int]): Array[Int] = {
-    val index = sequence.indexOf(prefix)
+  def getSuffix(item: Int, sequence: Array[Int]): Array[Int] = {
+    val index = sequence.indexOf(item)
     if (index == -1) {
       Array()
     } else {
@@ -76,38 +67,28 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
     }
   }
 
+  def project(database: Array[Array[Int]], prefix: Int): Array[Array[Int]] = {
+    database
+      .map(getSuffix(prefix, _))
+      .filter(_.nonEmpty)
+  }
+
   /**
    * Generates frequent items by filtering the input data using minimal count level.
-   * @param minCount the absolute minimum count
-   * @param sequences sequences data
-   * @return array of item and count pair
+   * @param minCount the minimum count for an item to be frequent
+   * @param database database of sequences
+   * @return freq item to count map
    */
   private def getFreqItemAndCounts(
       minCount: Long,
-      sequences: Array[Array[Int]]): Array[(Int, Long)] = {
-    sequences.flatMap(_.distinct)
-      .groupBy(x => x)
-      .mapValues(_.length.toLong)
-      .filter(_._2 >= minCount)
-      .toArray
-  }
-
-  /**
-   * Get the frequent prefixes' projected database.
-   * @param prePrefix the frequent prefixes' prefix
-   * @param frequentPrefixes frequent prefixes
-   * @param sequences sequences data
-   * @return prefixes and projected database
-   */
-  private def getPatternAndProjectedDatabase(
-      prePrefix: Array[Int],
-      frequentPrefixes: Array[Int],
-      sequences: Array[Array[Int]]): Array[(Array[Int], Array[Array[Int]])] = {
-    val filteredProjectedDatabase = sequences
-      .map(x => x.filter(frequentPrefixes.contains(_)))
-    frequentPrefixes.map { x =>
-      val sub = filteredProjectedDatabase.map(y => getSuffix(x, y)).filter(_.nonEmpty)
-      (prePrefix ++ Array(x), sub)
-    }.filter(x => x._2.nonEmpty)
+      database: Array[Array[Int]]): mutable.Map[Int, Long] = {
+    // TODO: use PrimitiveKeyOpenHashMap
+    val counts = mutable.Map[Int, Long]().withDefaultValue(0L)
+    database.foreach { sequence =>
+      sequence.distinct.foreach { item =>
+        counts(item) += 1L
+      }
+    }
+    counts.filter(_._2 >= minCount)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index 9d8c60ef0fc45..6f52db7b073ae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -150,8 +150,9 @@ class PrefixSpan private (
   private def getPatternsInLocal(
       minCount: Long,
       data: RDD[(Array[Int], Array[Array[Int]])]): RDD[(Array[Int], Long)] = {
-    data.flatMap { x =>
-      LocalPrefixSpan.run(minCount, maxPatternLength, x._1, x._2)
+    data.flatMap { case (prefix, projDB) =>
+      LocalPrefixSpan.run(minCount, maxPatternLength, prefix.toList, projDB)
+        .map { case (pattern: List[Int], count: Long) => (pattern.toArray.reverse, count) }
     }
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
index 413436d3db85f..9f107c89f6d80 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -18,9 +18,8 @@ package org.apache.spark.mllib.fpm
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.rdd.RDD
 
-class PrefixspanSuite extends SparkFunSuite with MLlibTestSparkContext {
+class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("PrefixSpan using Integer type") {
 
@@ -48,15 +47,8 @@ class PrefixspanSuite extends SparkFunSuite with MLlibTestSparkContext {
     def compareResult(
         expectedValue: Array[(Array[Int], Long)],
         actualValue: Array[(Array[Int], Long)]): Boolean = {
-      val sortedExpectedValue = expectedValue.sortWith{ (x, y) =>
-        x._1.mkString(",") + ":" + x._2 < y._1.mkString(",") + ":" + y._2
-      }
-      val sortedActualValue = actualValue.sortWith{ (x, y) =>
-        x._1.mkString(",") + ":" + x._2 < y._1.mkString(",") + ":" + y._2
-      }
-      sortedExpectedValue.zip(sortedActualValue)
-        .map(x => x._1._1.mkString(",") == x._2._1.mkString(",") && x._1._2 == x._2._2)
-        .reduce(_&&_)
+      expectedValue.map(x => (x._1.toSeq, x._2)).toSet ==
+        actualValue.map(x => (x._1.toSeq, x._2)).toSet
     }
 
     val prefixspan = new PrefixSpan()

From 14935d846a4f6bcd4d2a448a8f112fa5dee769ba Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 15 Jul 2015 00:12:21 -0700
Subject: [PATCH 0388/1454] [HOTFIX][SQL] Unit test breaking.

---
 .../sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index e885a18254ea0..a4ce1825cab28 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -60,9 +60,9 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
   }
 
   test("check types for unary arithmetic") {
-    assertError(UnaryMinus('stringField), "operator - accepts numeric type")
-    assertError(Abs('stringField), "function abs accepts numeric type")
-    assertError(BitwiseNot('stringField), "operator ~ accepts integral type")
+    assertError(UnaryMinus('stringField), "expected to be of type numeric")
+    assertError(Abs('stringField), "expected to be of type numeric")
+    assertError(BitwiseNot('stringField), "type (boolean or tinyint or smallint or int or bigint)")
   }
 
   ignore("check types for binary arithmetic") {

From adb33d3665770daf2ccb8915d19e198be9dc3b47 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 15 Jul 2015 17:30:57 +0900
Subject: [PATCH 0389/1454] [SPARK-9012] [WEBUI] Escape Accumulators in the
 task table

If running the following codes, the task table will be broken because accumulators aren't escaped.
```
val a = sc.accumulator(1, "<table>")
sc.parallelize(1 to 10).foreach(i => a += i)
```

Before this fix,

<img width="1348" alt="screen shot 2015-07-13 at 8 02 44 pm" src="https://cloud.githubusercontent.com/assets/1000778/8649295/b17c491e-299b-11e5-97ee-4e6a64074c4f.png">

After this fix,

<img width="1355" alt="screen shot 2015-07-13 at 8 14 32 pm" src="https://cloud.githubusercontent.com/assets/1000778/8649337/f9e9c9ec-299b-11e5-927e-35c0a2f897f5.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #7369 from zsxwing/SPARK-9012 and squashes the following commits:

a83c9b6 [zsxwing] Escape Accumulators in the task table
---
 core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index ff0a339a39c65..27b82aaddd2e4 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -692,7 +692,9 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val gettingResultTime = getGettingResultTime(info, currentTime)
 
       val maybeAccumulators = info.accumulables
-      val accumulatorsReadable = maybeAccumulators.map{acc => s"${acc.name}: ${acc.update.get}"}
+      val accumulatorsReadable = maybeAccumulators.map { acc =>
+        StringEscapeUtils.escapeHtml4(s"${acc.name}: ${acc.update.get}")
+      }
 
       val maybeInput = metrics.flatMap(_.inputMetrics)
       val inputSortable = maybeInput.map(_.bytesRead.toString).getOrElse("")

From 20bb10f8644a92a57496b5df639008832b30e34d Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Wed, 15 Jul 2015 08:25:53 -0700
Subject: [PATCH 0390/1454] [SPARK-8706] [PYSPARK] [PROJECT INFRA] Add pylint
 checks to PySpark

This adds Pylint checks to PySpark.

For now this lazy installs using easy_install to /dev/pylint (similar to the pep8 script).
We still need to figure out what rules to be allowed.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7241 from MechCoder/pylint and squashes the following commits:

2fc7291 [MechCoder] Remove pylint test fail
6d883a2 [MechCoder] Silence warnings and make pylint tests fail to check if it works in jenkins
f3a5e17 [MechCoder] undefined-variable
ca8b749 [MechCoder] Minor changes
71629f8 [MechCoder] remove trailing whitespace
8498ff9 [MechCoder] Remove blacklisted arguments and pointless statements check
1dbd094 [MechCoder] Disable all checks for now
8b8aa8a [MechCoder] Add pylint configuration file
7871bb1 [MechCoder] [SPARK-8706] [PySpark] [Project infra] Add pylint checks to PySpark
---
 dev/lint-python                   |  57 ++++-
 pylintrc                          | 404 ++++++++++++++++++++++++++++++
 python/pyspark/ml/param/shared.py |   4 +-
 python/pyspark/tests.py           |   3 +-
 4 files changed, 457 insertions(+), 11 deletions(-)
 create mode 100644 pylintrc

diff --git a/dev/lint-python b/dev/lint-python
index 0c3586462cb37..e02dff220eb87 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -21,12 +21,14 @@ SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
 SPARK_ROOT_DIR="$(dirname "$SCRIPT_DIR")"
 PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/ ./dev/sparktestsupport"
 PATHS_TO_CHECK="$PATHS_TO_CHECK ./dev/run-tests.py ./python/run-tests.py"
-PYTHON_LINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/python-lint-report.txt"
+PEP8_REPORT_PATH="$SPARK_ROOT_DIR/dev/pep8-report.txt"
+PYLINT_REPORT_PATH="$SPARK_ROOT_DIR/dev/pylint-report.txt"
+PYLINT_INSTALL_INFO="$SPARK_ROOT_DIR/dev/pylint-info.txt"
 
 cd "$SPARK_ROOT_DIR"
 
 # compileall: https://docs.python.org/2/library/compileall.html
-python -B -m compileall -q -l $PATHS_TO_CHECK > "$PYTHON_LINT_REPORT_PATH"
+python -B -m compileall -q -l $PATHS_TO_CHECK > "$PEP8_REPORT_PATH"
 compile_status="${PIPESTATUS[0]}"
 
 # Get pep8 at runtime so that we don't rely on it being installed on the build server.
@@ -47,11 +49,36 @@ if [ ! -e "$PEP8_SCRIPT_PATH" ]; then
     fi
 fi
 
+# Easy install pylint in /dev/pylint. To easy_install into a directory, the PYTHONPATH should
+# be set to the directory.
+# dev/pylint should be appended to the PATH variable as well.
+# Jenkins by default installs the pylint3 version, so for now this just checks the code quality
+# of python3.
+export "PYTHONPATH=$SPARK_ROOT_DIR/dev/pylint"
+export "PYLINT_HOME=$PYTHONPATH"
+export "PATH=$PYTHONPATH:$PATH"
+
+if [ ! -d "$PYLINT_HOME" ]; then
+    mkdir "$PYLINT_HOME"
+    # Redirect the annoying pylint installation output.
+    easy_install -d "$PYLINT_HOME" pylint==1.4.4 &>> "$PYLINT_INSTALL_INFO"
+    easy_install_status="$?"
+
+    if [ "$easy_install_status" -ne 0 ]; then
+        echo "Unable to install pylint locally in \"$PYTHONPATH\"."
+        cat "$PYLINT_INSTALL_INFO"
+        exit "$easy_install_status"
+    fi
+
+    rm "$PYLINT_INSTALL_INFO"
+
+fi
+
 # There is no need to write this output to a file
 #+ first, but we do so so that the check status can
 #+ be output before the report, like with the
 #+ scalastyle and RAT checks.
-python "$PEP8_SCRIPT_PATH" --ignore=E402,E731,E241,W503,E226 $PATHS_TO_CHECK >> "$PYTHON_LINT_REPORT_PATH"
+python "$PEP8_SCRIPT_PATH" --ignore=E402,E731,E241,W503,E226 $PATHS_TO_CHECK >> "$PEP8_REPORT_PATH"
 pep8_status="${PIPESTATUS[0]}"
 
 if [ "$compile_status" -eq 0 -a "$pep8_status" -eq 0 ]; then
@@ -61,13 +88,27 @@ else
 fi
 
 if [ "$lint_status" -ne 0 ]; then
-    echo "Python lint checks failed."
-    cat "$PYTHON_LINT_REPORT_PATH"
+    echo "PEP8 checks failed."
+    cat "$PEP8_REPORT_PATH"
+else
+    echo "PEP8 checks passed."
+fi
+
+rm "$PEP8_REPORT_PATH"
+
+for to_be_checked in "$PATHS_TO_CHECK"
+do
+    pylint --rcfile="$SPARK_ROOT_DIR/pylintrc" $to_be_checked >> "$PYLINT_REPORT_PATH"
+done
+
+if [ "${PIPESTATUS[0]}" -ne 0 ]; then
+    lint_status=1
+    echo "Pylint checks failed."
+    cat "$PYLINT_REPORT_PATH"
 else
-    echo "Python lint checks passed."
+    echo "Pylint checks passed."
 fi
 
-# rm "$PEP8_SCRIPT_PATH"
-rm "$PYTHON_LINT_REPORT_PATH"
+rm "$PYLINT_REPORT_PATH"
 
 exit "$lint_status"
diff --git a/pylintrc b/pylintrc
new file mode 100644
index 0000000000000..061775960393b
--- /dev/null
+++ b/pylintrc
@@ -0,0 +1,404 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+[MASTER]
+
+# Specify a configuration file.
+#rcfile=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Profiled execution.
+profile=no
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=pyspark.heapq3
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Use multiple processes to speed up Pylint.
+jobs=1
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code
+extension-pkg-whitelist=
+
+# Allow optimization of some AST trees. This will activate a peephole AST
+# optimizer, which will apply various small optimizations. For instance, it can
+# be used to obtain the result of joining multiple strings with the addition
+# operator. Joining a lot of strings can lead to a maximum recursion error in
+# Pylint and this flag can prevent that. It has one side effect, the resulting
+# AST will be different than the one from reality.
+optimize-ast=no
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
+confidence=
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time. See also the "--disable" option for examples.
+enable=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once).You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use"--disable=all --enable=classes
+# --disable=W"
+
+# These errors are arranged in order of number of warning given in pylint.
+# If you would like to improve the code quality of pyspark, remove any of these disabled errors
+# run ./dev/lint-python and see if the errors raised by pylint can be fixed.
+
+disable=invalid-name,missing-docstring,protected-access,unused-argument,no-member,unused-wildcard-import,redefined-builtin,too-many-arguments,unused-variable,too-few-public-methods,bad-continuation,duplicate-code,redefined-outer-name,too-many-ancestors,import-error,superfluous-parens,unused-import,line-too-long,no-name-in-module,unnecessary-lambda,import-self,no-self-use,unidiomatic-typecheck,fixme,too-many-locals,cyclic-import,too-many-branches,bare-except,wildcard-import,dangerous-default-value,broad-except,too-many-public-methods,deprecated-lambda,anomalous-backslash-in-string,too-many-lines,reimported,too-many-statements,bad-whitespace,unpacking-non-sequence,too-many-instance-attributes,abstract-method,old-style-class,global-statement,attribute-defined-outside-init,arguments-differ,undefined-all-variable,no-init,useless-else-on-loop,super-init-not-called,notimplemented-raised,too-many-return-statements,pointless-string-statement,global-variable-undefined,bad-classmethod-argument,too-many-format-args,parse-error,no-self-argument,pointless-statement,undefined-variable
+
+
+[REPORTS]
+
+# Set the output format. Available formats are text, parseable, colorized, msvs
+# (visual studio) and html. You can also give a reporter class, eg
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Put messages in a separate file for each module / package specified on the
+# command line instead of printing them on stdout. Reports (if any) will be
+# written in a file name "pylint_global.[txt|html]".
+files-output=no
+
+# Tells whether to display a full report or only the messages
+reports=no
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Add a comment according to your evaluation note. This is used by the global
+# evaluation report (RP0004).
+comment=no
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details
+#msg-template=
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,XXX,TODO
+
+
+[BASIC]
+
+# Required attributes for module, separated by a comma
+required-attributes=
+
+# List of builtins function names that should not be used, separated by a comma
+bad-functions=
+
+# Good variable names which should always be accepted, separated by a comma
+good-names=i,j,k,ex,Run,_
+
+# Bad variable names which should always be refused, separated by a comma
+bad-names=baz,toto,tutu,tata
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Include a hint for the correct naming format with invalid-name
+include-naming-hint=no
+
+# Regular expression matching correct function names
+function-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for function names
+function-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct variable names
+variable-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for variable names
+variable-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct constant names
+const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Naming hint for constant names
+const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Regular expression matching correct attribute names
+attr-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for attribute names
+attr-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct argument names
+argument-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for argument names
+argument-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct class attribute names
+class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Naming hint for class attribute names
+class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Regular expression matching correct inline iteration names
+inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
+
+# Naming hint for inline iteration names
+inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
+
+# Regular expression matching correct class names
+class-rgx=[A-Z_][a-zA-Z0-9]+$
+
+# Naming hint for class names
+class-name-hint=[A-Z_][a-zA-Z0-9]+$
+
+# Regular expression matching correct module names
+module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Naming hint for module names
+module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Regular expression matching correct method names
+method-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for method names
+method-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=__.*__
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+
+[FORMAT]
+
+# Maximum number of characters on a single line.
+max-line-length=100
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+# List of optional constructs for which whitespace checking is disabled
+no-space-check=trailing-comma,dict-separator
+
+# Maximum number of lines in a module
+max-module-lines=1000
+
+# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren=4
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+
+[SIMILARITIES]
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+
+[VARIABLES]
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# A regular expression matching the name of dummy variables (i.e. expectedly
+# not used).
+dummy-variables-rgx=_$|dummy
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid to define new builtins when possible.
+additional-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,_cb
+
+
+[SPELLING]
+
+# Spelling dictionary name. Available dictionaries: none. To make it working
+# install python-enchant package.
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[LOGGING]
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format
+logging-modules=logging
+
+
+[TYPECHECK]
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis
+ignored-modules=
+
+# List of classes names for which member attributes should not be checked
+# (useful for classes with attributes dynamically set).
+ignored-classes=SQLObject
+
+# When zope mode is activated, add a predefined set of Zope acquired attributes
+# to generated-members.
+zope=no
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E0201 when accessed. Python regular
+# expressions are accepted.
+generated-members=REQUEST,acl_users,aq_parent
+
+
+[CLASSES]
+
+# List of interface methods to ignore, separated by a comma. This is used for
+# instance to not check methods defines in Zope's Interface base class.
+ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,__new__,setUp
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=mcs
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,_fields,_replace,_source,_make
+
+
+[IMPORTS]
+
+# Deprecated modules which should not be used, separated by a comma
+deprecated-modules=regsub,TERMIOS,Bastion,rexec
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled)
+import-graph=
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled)
+ext-import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled)
+int-import-graph=
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method
+max-args=5
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore
+ignored-argument-names=_.*
+
+# Maximum number of locals for function / method body
+max-locals=15
+
+# Maximum number of return / yield for function / method body
+max-returns=6
+
+# Maximum number of branch for function / method body
+max-branches=12
+
+# Maximum number of statements in function / method body
+max-statements=50
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception"
+overgeneral-exceptions=Exception
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index bc088e4c29e26..595124726366d 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -444,7 +444,7 @@ class DecisionTreeParams(Params):
     minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.")
     maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
     cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-    
+
 
     def __init__(self):
         super(DecisionTreeParams, self).__init__()
@@ -460,7 +460,7 @@ def __init__(self):
         self.maxMemoryInMB = Param(self, "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
         #: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.
         self.cacheNodeIds = Param(self, "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-        
+
     def setMaxDepth(self, value):
         """
         Sets the value of :py:attr:`maxDepth`.
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index c5c0add49d02c..21225016805bc 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -893,7 +893,8 @@ def test_pipe_functions(self):
             self.assertRaises(Py4JJavaError, rdd.pipe('cc', checkCode=True).collect)
         result = rdd.pipe('cat').collect()
         result.sort()
-        [self.assertEqual(x, y) for x, y in zip(data, result)]
+        for x, y in zip(data, result):
+            self.assertEqual(x, y)
         self.assertRaises(Py4JJavaError, rdd.pipe('grep 4', checkCode=True).collect)
         self.assertEqual([], rdd.pipe('grep 4').collect())
 

From 6f6902597d5d687049c103bc0cf6da30919b92d8 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 15 Jul 2015 09:48:33 -0700
Subject: [PATCH 0391/1454] [SPARK-8840] [SPARKR] Add float coercion on SparkR

JIRA: https://issues.apache.org/jira/browse/SPARK-8840

Currently the type coercion rules don't include float type. This PR simply adds it.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7280 from viirya/add_r_float_coercion and squashes the following commits:

c86dc0e [Liang-Chi Hsieh] For comments.
dbf0c1b [Liang-Chi Hsieh] Implicitly convert Double to Float based on provided schema.
733015a [Liang-Chi Hsieh] Add test case for DataFrame with float type.
30c2a40 [Liang-Chi Hsieh] Update test case.
52b5294 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into add_r_float_coercion
6f9159d [Liang-Chi Hsieh] Add another test case.
8db3244 [Liang-Chi Hsieh] schema also needs to support float. add test case.
0dcc992 [Liang-Chi Hsieh] Add float coercion on SparkR.
---
 R/pkg/R/deserialize.R                         |  1 +
 R/pkg/R/schema.R                              |  1 +
 R/pkg/inst/tests/test_sparkSQL.R              | 26 +++++++++++++++++++
 .../scala/org/apache/spark/api/r/SerDe.scala  |  4 +++
 .../org/apache/spark/sql/api/r/SQLUtils.scala | 15 ++++++++---
 5 files changed, 44 insertions(+), 3 deletions(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index d961bbc383688..7d1f6b0819ed0 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -23,6 +23,7 @@
 # Int -> integer
 # String -> character
 # Boolean -> logical
+# Float -> double
 # Double -> double
 # Long -> double
 # Array[Byte] -> raw
diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R
index 15e2bdbd55d79..06df430687682 100644
--- a/R/pkg/R/schema.R
+++ b/R/pkg/R/schema.R
@@ -123,6 +123,7 @@ structField.character <- function(x, type, nullable = TRUE) {
   }
   options <- c("byte",
                "integer",
+               "float",
                "double",
                "numeric",
                "character",
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index b0ea38854304e..76f74f80834a9 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -108,6 +108,32 @@ test_that("create DataFrame from RDD", {
   expect_equal(count(df), 10)
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
+
+  df <- jsonFile(sqlContext, jsonPathNa)
+  hiveCtx <- tryCatch({
+    newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc)
+  }, error = function(err) {
+    skip("Hive is not build with SparkSQL, skipped")
+  })
+  sql(hiveCtx, "CREATE TABLE people (name string, age double, height float)")
+  insertInto(df, "people")
+  expect_equal(sql(hiveCtx, "SELECT age from people WHERE name = 'Bob'"), c(16))
+  expect_equal(sql(hiveCtx, "SELECT height from people WHERE name ='Bob'"), c(176.5))
+
+  schema <- structType(structField("name", "string"), structField("age", "integer"),
+                       structField("height", "float"))
+  df2 <- createDataFrame(sqlContext, df.toRDD, schema)
+  expect_equal(columns(df2), c("name", "age", "height"))
+  expect_equal(dtypes(df2), list(c("name", "string"), c("age", "int"), c("height", "float")))
+  expect_equal(collect(where(df2, df2$name == "Bob")), c("Bob", 16, 176.5))
+
+  localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18), height=c(164.10, 181.4, 173.7))
+  df <- createDataFrame(sqlContext, localDF, schema)
+  expect_is(df, "DataFrame")
+  expect_equal(count(df), 3)
+  expect_equal(columns(df), c("name", "age", "height"))
+  expect_equal(dtypes(df), list(c("name", "string"), c("age", "int"), c("height", "float")))
+  expect_equal(collect(where(df, df$name == "John")), c("John", 19, 164.10))
 })
 
 test_that("convert NAs to null type in DataFrames", {
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 56adc857d4ce0..d5b4260bf4529 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -179,6 +179,7 @@ private[spark] object SerDe {
   // Int -> integer
   // String -> character
   // Boolean -> logical
+  // Float -> double
   // Double -> double
   // Long -> double
   // Array[Byte] -> raw
@@ -215,6 +216,9 @@ private[spark] object SerDe {
         case "long" | "java.lang.Long" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Long].toDouble)
+        case "float" | "java.lang.Float" =>
+          writeType(dos, "double")
+          writeDouble(dos, value.asInstanceOf[Float].toDouble)
         case "double" | "java.lang.Double" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Double])
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
index 43b62f0e822f8..92861ab038f19 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
@@ -47,6 +47,7 @@ private[r] object SQLUtils {
     dataType match {
       case "byte" => org.apache.spark.sql.types.ByteType
       case "integer" => org.apache.spark.sql.types.IntegerType
+      case "float" => org.apache.spark.sql.types.FloatType
       case "double" => org.apache.spark.sql.types.DoubleType
       case "numeric" => org.apache.spark.sql.types.DoubleType
       case "character" => org.apache.spark.sql.types.StringType
@@ -68,7 +69,7 @@ private[r] object SQLUtils {
 
   def createDF(rdd: RDD[Array[Byte]], schema: StructType, sqlContext: SQLContext): DataFrame = {
     val num = schema.fields.size
-    val rowRDD = rdd.map(bytesToRow)
+    val rowRDD = rdd.map(bytesToRow(_, schema))
     sqlContext.createDataFrame(rowRDD, schema)
   }
 
@@ -76,12 +77,20 @@ private[r] object SQLUtils {
     df.map(r => rowToRBytes(r))
   }
 
-  private[this] def bytesToRow(bytes: Array[Byte]): Row = {
+  private[this] def doConversion(data: Object, dataType: DataType): Object = {
+    data match {
+      case d: java.lang.Double if dataType == FloatType =>
+        new java.lang.Float(d)
+      case _ => data
+    }
+  }
+
+  private[this] def bytesToRow(bytes: Array[Byte], schema: StructType): Row = {
     val bis = new ByteArrayInputStream(bytes)
     val dis = new DataInputStream(bis)
     val num = SerDe.readInt(dis)
     Row.fromSeq((0 until num).map { i =>
-      SerDe.readObject(dis)
+      doConversion(SerDe.readObject(dis), schema.fields(i).dataType)
     }.toSeq)
   }
 

From fa4ec3606a965238423f977808163983c9d56e0a Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 15 Jul 2015 10:31:39 -0700
Subject: [PATCH 0392/1454] [SPARK-9020][SQL] Support mutable state in code gen
 expressions

We can keep expressions' mutable states in generated class(like `SpecificProjection`) as member variables, so that we can read and modify them inside codegened expressions.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7392 from cloud-fan/mutable-state and squashes the following commits:

eb3a221 [Wenchen Fan] fix order
73144d8 [Wenchen Fan] naming improvement
318f41d [Wenchen Fan] address more comments
d43b65d [Wenchen Fan] address comments
fd45c7a [Wenchen Fan] Support mutable state in code gen expressions
---
 .../scala/org/apache/spark/TaskContext.scala  | 15 ++-
 .../expressions/codegen/CodeGenerator.scala   | 17 +++-
 .../codegen/GenerateMutableProjection.scala   |  4 +
 .../codegen/GenerateOrdering.scala            | 38 +++++--
 .../codegen/GeneratePredicate.scala           |  4 +
 .../codegen/GenerateProjection.scala          | 99 ++++++++++---------
 .../sql/catalyst/expressions/random.scala     | 29 +++++-
 .../MonotonicallyIncreasingID.scala           | 19 +++-
 .../expressions/SparkPartitionID.scala        | 12 ++-
 9 files changed, 171 insertions(+), 66 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index d09e17dea0911..248339148d9b7 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -32,7 +32,20 @@ object TaskContext {
    */
   def get(): TaskContext = taskContext.get
 
-  private val taskContext: ThreadLocal[TaskContext] = new ThreadLocal[TaskContext]
+  /**
+   * Returns the partition id of currently active TaskContext. It will return 0
+   * if there is no active TaskContext for cases like local execution.
+   */
+  def getPartitionId(): Int = {
+    val tc = taskContext.get()
+    if (tc == null) {
+      0
+    } else {
+      tc.partitionId()
+    }
+  }
+
+  private[this] val taskContext: ThreadLocal[TaskContext] = new ThreadLocal[TaskContext]
 
   // Note: protected[spark] instead of private[spark] to prevent the following two from
   // showing up in JavaDoc.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 9f6329bbda4ec..328d635de8743 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -56,6 +56,18 @@ class CodeGenContext {
    */
   val references: mutable.ArrayBuffer[Expression] = new mutable.ArrayBuffer[Expression]()
 
+  /**
+   * Holding expressions' mutable states like `MonotonicallyIncreasingID.count` as a
+   * 3-tuple: java type, variable name, code to init it.
+   * They will be kept as member variables in generated classes like `SpecificProjection`.
+   */
+  val mutableStates: mutable.ArrayBuffer[(String, String, String)] =
+    mutable.ArrayBuffer.empty[(String, String, String)]
+
+  def addMutableState(javaType: String, variableName: String, initialValue: String): Unit = {
+    mutableStates += ((javaType, variableName, initialValue))
+  }
+
   val stringType: String = classOf[UTF8String].getName
   val decimalType: String = classOf[Decimal].getName
 
@@ -203,7 +215,10 @@ class CodeGenContext {
   def isPrimitiveType(dt: DataType): Boolean = isPrimitiveType(javaType(dt))
 }
 
-
+/**
+ * A wrapper for generated class, defines a `generate` method so that we can pass extra objects
+ * into generated class.
+ */
 abstract class GeneratedClass {
   def generate(expressions: Array[Expression]): Any
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index addb8023d9c0b..71e47d4f9b620 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -46,6 +46,9 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
             ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
         """
     }.mkString("\n")
+    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
+      s"private $javaType $variableName = $initialValue;"
+    }.mkString("\n      ")
     val code = s"""
       public Object generate($exprType[] expr) {
         return new SpecificProjection(expr);
@@ -55,6 +58,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
 
         private $exprType[] expressions = null;
         private $mutableRowType mutableRow = null;
+        $mutableStates
 
         public SpecificProjection($exprType[] expr) {
           expressions = expr;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index d05dfc108e63a..856ff9f1f96f8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -46,30 +46,47 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
   protected def create(ordering: Seq[SortOrder]): Ordering[InternalRow] = {
     val ctx = newCodeGenContext()
 
-    val comparisons = ordering.zipWithIndex.map { case (order, i) =>
-      val evalA = order.child.gen(ctx)
-      val evalB = order.child.gen(ctx)
+    val comparisons = ordering.map { order =>
+      val eval = order.child.gen(ctx)
       val asc = order.direction == Ascending
+      val isNullA = ctx.freshName("isNullA")
+      val primitiveA = ctx.freshName("primitiveA")
+      val isNullB = ctx.freshName("isNullB")
+      val primitiveB = ctx.freshName("primitiveB")
       s"""
           i = a;
-          ${evalA.code}
+          boolean $isNullA;
+          ${ctx.javaType(order.child.dataType)} $primitiveA;
+          {
+            ${eval.code}
+            $isNullA = ${eval.isNull};
+            $primitiveA = ${eval.primitive};
+          }
           i = b;
-          ${evalB.code}
-          if (${evalA.isNull} && ${evalB.isNull}) {
+          boolean $isNullB;
+          ${ctx.javaType(order.child.dataType)} $primitiveB;
+          {
+            ${eval.code}
+            $isNullB = ${eval.isNull};
+            $primitiveB = ${eval.primitive};
+          }
+          if ($isNullA && $isNullB) {
             // Nothing
-          } else if (${evalA.isNull}) {
+          } else if ($isNullA) {
             return ${if (order.direction == Ascending) "-1" else "1"};
-          } else if (${evalB.isNull}) {
+          } else if ($isNullB) {
             return ${if (order.direction == Ascending) "1" else "-1"};
           } else {
-            int comp = ${ctx.genComp(order.child.dataType, evalA.primitive, evalB.primitive)};
+            int comp = ${ctx.genComp(order.child.dataType, primitiveA, primitiveB)};
             if (comp != 0) {
               return ${if (asc) "comp" else "-comp"};
             }
           }
       """
     }.mkString("\n")
-
+    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
+      s"private $javaType $variableName = $initialValue;"
+    }.mkString("\n      ")
     val code = s"""
       public SpecificOrdering generate($exprType[] expr) {
         return new SpecificOrdering(expr);
@@ -78,6 +95,7 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
       class SpecificOrdering extends ${classOf[BaseOrdering].getName} {
 
         private $exprType[] expressions = null;
+        $mutableStates
 
         public SpecificOrdering($exprType[] expr) {
           expressions = expr;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
index 274a42cb69087..9e5a745d512e9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
@@ -40,6 +40,9 @@ object GeneratePredicate extends CodeGenerator[Expression, (InternalRow) => Bool
   protected def create(predicate: Expression): ((InternalRow) => Boolean) = {
     val ctx = newCodeGenContext()
     val eval = predicate.gen(ctx)
+    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
+      s"private $javaType $variableName = $initialValue;"
+    }.mkString("\n      ")
     val code = s"""
       public SpecificPredicate generate($exprType[] expr) {
         return new SpecificPredicate(expr);
@@ -47,6 +50,7 @@ object GeneratePredicate extends CodeGenerator[Expression, (InternalRow) => Bool
 
       class SpecificPredicate extends ${classOf[Predicate].getName} {
         private final $exprType[] expressions;
+        $mutableStates
         public SpecificPredicate($exprType[] expr) {
           expressions = expr;
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 3c7ee9cc16599..3e5ca308dc31d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -151,6 +151,10 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         s"""if (!nullBits[$i]) arr[$i] = c$i;"""
     }.mkString("\n      ")
 
+    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
+      s"private $javaType $variableName = $initialValue;"
+    }.mkString("\n      ")
+
     val code = s"""
     public SpecificProjection generate($exprType[] expr) {
       return new SpecificProjection(expr);
@@ -158,6 +162,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
 
     class SpecificProjection extends ${classOf[BaseProject].getName} {
       private $exprType[] expressions = null;
+      $mutableStates
 
       public SpecificProjection($exprType[] expr) {
         expressions = expr;
@@ -165,65 +170,65 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
 
       @Override
       public Object apply(Object r) {
-        return new SpecificRow(expressions, (InternalRow) r);
+        return new SpecificRow((InternalRow) r);
       }
-    }
 
-    final class SpecificRow extends ${classOf[MutableRow].getName} {
+      final class SpecificRow extends ${classOf[MutableRow].getName} {
 
-      $columns
+        $columns
 
-      public SpecificRow($exprType[] expressions, InternalRow i) {
-        $initColumns
-      }
+        public SpecificRow(InternalRow i) {
+          $initColumns
+        }
 
-      public int length() { return ${expressions.length};}
-      protected boolean[] nullBits = new boolean[${expressions.length}];
-      public void setNullAt(int i) { nullBits[i] = true; }
-      public boolean isNullAt(int i) { return nullBits[i]; }
+        public int length() { return ${expressions.length};}
+        protected boolean[] nullBits = new boolean[${expressions.length}];
+        public void setNullAt(int i) { nullBits[i] = true; }
+        public boolean isNullAt(int i) { return nullBits[i]; }
 
-      public Object get(int i) {
-        if (isNullAt(i)) return null;
-        switch (i) {
-        $getCases
+        public Object get(int i) {
+          if (isNullAt(i)) return null;
+          switch (i) {
+          $getCases
+          }
+          return null;
         }
-        return null;
-      }
-      public void update(int i, Object value) {
-        if (value == null) {
-          setNullAt(i);
-          return;
+        public void update(int i, Object value) {
+          if (value == null) {
+            setNullAt(i);
+            return;
+          }
+          nullBits[i] = false;
+          switch (i) {
+          $updateCases
+          }
         }
-        nullBits[i] = false;
-        switch (i) {
-        $updateCases
+        $specificAccessorFunctions
+        $specificMutatorFunctions
+
+        @Override
+        public int hashCode() {
+          int result = 37;
+          $hashUpdates
+          return result;
         }
-      }
-      $specificAccessorFunctions
-      $specificMutatorFunctions
-
-      @Override
-      public int hashCode() {
-        int result = 37;
-        $hashUpdates
-        return result;
-      }
 
-      @Override
-      public boolean equals(Object other) {
-        if (other instanceof SpecificRow) {
-          SpecificRow row = (SpecificRow) other;
-          $columnChecks
-          return true;
+        @Override
+        public boolean equals(Object other) {
+          if (other instanceof SpecificRow) {
+            SpecificRow row = (SpecificRow) other;
+            $columnChecks
+            return true;
+          }
+          return super.equals(other);
         }
-        return super.equals(other);
-      }
 
-      @Override
-      public InternalRow copy() {
-        Object[] arr = new Object[${expressions.length}];
-        ${copyColumns}
-        return new ${classOf[GenericInternalRow].getName}(arr);
+        @Override
+        public InternalRow copy() {
+          Object[] arr = new Object[${expressions.length}];
+          ${copyColumns}
+          return new ${classOf[GenericInternalRow].getName}(arr);
+        }
       }
     }
     """
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
index 6cdc3000382e2..e10ba55396664 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.types.{DataType, DoubleType}
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
@@ -38,11 +39,7 @@ abstract class RDG(seed: Long) extends LeafExpression with Serializable {
    * Record ID within each partition. By being transient, the Random Number Generator is
    * reset every time we serialize and deserialize it.
    */
-  @transient protected lazy val partitionId = TaskContext.get() match {
-    case null => 0
-    case _ => TaskContext.get().partitionId()
-  }
-  @transient protected lazy val rng = new XORShiftRandom(seed + partitionId)
+  @transient protected lazy val rng = new XORShiftRandom(seed + TaskContext.getPartitionId)
 
   override def deterministic: Boolean = false
 
@@ -61,6 +58,17 @@ case class Rand(seed: Long) extends RDG(seed) {
     case IntegerLiteral(s) => s
     case _ => throw new AnalysisException("Input argument to rand must be an integer literal.")
   })
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val rngTerm = ctx.freshName("rng")
+    val className = classOf[XORShiftRandom].getCanonicalName
+    ctx.addMutableState(className, rngTerm,
+      s"new $className($seed + org.apache.spark.TaskContext.getPartitionId())")
+    ev.isNull = "false"
+    s"""
+      final ${ctx.javaType(dataType)} ${ev.primitive} = $rngTerm.nextDouble();
+    """
+  }
 }
 
 /** Generate a random column with i.i.d. gaussian random distribution. */
@@ -73,4 +81,15 @@ case class Randn(seed: Long) extends RDG(seed) {
     case IntegerLiteral(s) => s
     case _ => throw new AnalysisException("Input argument to rand must be an integer literal.")
   })
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val rngTerm = ctx.freshName("rng")
+    val className = classOf[XORShiftRandom].getCanonicalName
+    ctx.addMutableState(className, rngTerm,
+      s"new $className($seed + org.apache.spark.TaskContext.getPartitionId())")
+    ev.isNull = "false"
+    s"""
+      final ${ctx.javaType(dataType)} ${ev.primitive} = $rngTerm.nextGaussian();
+    """
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
index 437d143e53f3f..69a37750d7525 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.expressions
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.LeafExpression
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types.{LongType, DataType}
 
 /**
@@ -40,6 +41,8 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression {
    */
   @transient private[this] var count: Long = 0L
 
+  @transient private lazy val partitionMask = TaskContext.getPartitionId.toLong << 33
+
   override def nullable: Boolean = false
 
   override def dataType: DataType = LongType
@@ -47,6 +50,20 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression {
   override def eval(input: InternalRow): Long = {
     val currentCount = count
     count += 1
-    (TaskContext.get().partitionId().toLong << 33) + currentCount
+    partitionMask + currentCount
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val countTerm = ctx.freshName("count")
+    val partitionMaskTerm = ctx.freshName("partitionMask")
+    ctx.addMutableState(ctx.JAVA_LONG, countTerm, "0L")
+    ctx.addMutableState(ctx.JAVA_LONG, partitionMaskTerm,
+      "((long) org.apache.spark.TaskContext.getPartitionId()) << 33")
+
+    ev.isNull = "false"
+    s"""
+      final ${ctx.javaType(dataType)} ${ev.primitive} = $partitionMaskTerm + $countTerm;
+      $countTerm++;
+    """
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
index 822d3d8c9108d..5f1b514f2cff2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.expressions
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.LeafExpression
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types.{IntegerType, DataType}
 
 
@@ -32,5 +33,14 @@ private[sql] case object SparkPartitionID extends LeafExpression {
 
   override def dataType: DataType = IntegerType
 
-  override def eval(input: InternalRow): Int = TaskContext.get().partitionId()
+  @transient private lazy val partitionId = TaskContext.getPartitionId
+
+  override def eval(input: InternalRow): Int = partitionId
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val idTerm = ctx.freshName("partitionId")
+    ctx.addMutableState(ctx.JAVA_INT, idTerm, "org.apache.spark.TaskContext.getPartitionId()")
+    ev.isNull = "false"
+    s"final ${ctx.javaType(dataType)} ${ev.primitive} = $idTerm;"
+  }
 }

From a9385271a9f6b97ec6aa619cf56ee556ba2fb0de Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Wed, 15 Jul 2015 10:43:38 -0700
Subject: [PATCH 0393/1454] [SPARK-8221][SQL]Add pmod function

https://issues.apache.org/jira/browse/SPARK-8221

One concern is the result would be negative if the divisor is not positive( i.e pmod(7, -3) ), but the behavior is the same as hive.

Author: zhichao.li <zhichao.li@intel.com>

Closes #6783 from zhichao-li/pmod2 and squashes the following commits:

7083eb9 [zhichao.li] update to the latest type checking
d26dba7 [zhichao.li] add pmod
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../catalyst/analysis/HiveTypeCoercion.scala  |  6 ++
 .../sql/catalyst/expressions/arithmetic.scala | 94 +++++++++++++++++++
 .../ArithmeticExpressionSuite.scala           | 16 +++-
 .../org/apache/spark/sql/functions.scala      | 17 ++++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 37 ++++++++
 6 files changed, 170 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index ec75f51d5e4ff..d2678ce860701 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -115,6 +115,7 @@ object FunctionRegistry {
     expression[Log2]("log2"),
     expression[Pow]("pow"),
     expression[Pow]("power"),
+    expression[Pmod]("pmod"),
     expression[UnaryPositive]("positive"),
     expression[Rint]("rint"),
     expression[Round]("round"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 15da5eecc8d3c..25087915b5c35 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -426,6 +426,12 @@ object HiveTypeCoercion {
             DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
           )
 
+        case Pmod(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
+          Cast(
+            Pmod(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)),
+            DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
+          )
+
         // When we compare 2 decimal types with different precisions, cast them to the smallest
         // common precision.
         case b @ BinaryComparison(e1 @ DecimalType.Expression(p1, s1),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 1a55a0876f303..394ef556e04a2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -377,3 +377,97 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
   override def symbol: String = "min"
   override def prettyName: String = symbol
 }
+
+case class Pmod(left: Expression, right: Expression) extends BinaryArithmetic {
+
+  override def toString: String = s"pmod($left, $right)"
+
+  override def symbol: String = "pmod"
+
+  protected def checkTypesInternal(t: DataType) =
+    TypeUtils.checkForNumericExpr(t, "pmod")
+
+  override def inputType: AbstractDataType = NumericType
+
+  protected override def nullSafeEval(left: Any, right: Any) =
+    dataType match {
+      case IntegerType => pmod(left.asInstanceOf[Int], right.asInstanceOf[Int])
+      case LongType => pmod(left.asInstanceOf[Long], right.asInstanceOf[Long])
+      case ShortType => pmod(left.asInstanceOf[Short], right.asInstanceOf[Short])
+      case ByteType => pmod(left.asInstanceOf[Byte], right.asInstanceOf[Byte])
+      case FloatType => pmod(left.asInstanceOf[Float], right.asInstanceOf[Float])
+      case DoubleType => pmod(left.asInstanceOf[Double], right.asInstanceOf[Double])
+      case _: DecimalType => pmod(left.asInstanceOf[Decimal], right.asInstanceOf[Decimal])
+    }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+      dataType match {
+        case dt: DecimalType =>
+          val decimalAdd = "$plus"
+          s"""
+            ${ctx.javaType(dataType)} r = $eval1.remainder($eval2);
+            if (r.compare(new org.apache.spark.sql.types.Decimal().set(0)) < 0) {
+              ${ev.primitive} = (r.$decimalAdd($eval2)).remainder($eval2);
+            } else {
+              ${ev.primitive} = r;
+            }
+          """
+        // byte and short are casted into int when add, minus, times or divide
+        case ByteType | ShortType =>
+          s"""
+            ${ctx.javaType(dataType)} r = (${ctx.javaType(dataType)})($eval1 % $eval2);
+            if (r < 0) {
+              ${ev.primitive} = (${ctx.javaType(dataType)})((r + $eval2) % $eval2);
+            } else {
+              ${ev.primitive} = r;
+            }
+          """
+        case _ =>
+          s"""
+            ${ctx.javaType(dataType)} r = $eval1 % $eval2;
+            if (r < 0) {
+              ${ev.primitive} = (r + $eval2) % $eval2;
+            } else {
+              ${ev.primitive} = r;
+            }
+          """
+      }
+    })
+  }
+
+  private def pmod(a: Int, n: Int): Int = {
+    val r = a % n
+    if (r < 0) {(r + n) % n} else r
+  }
+
+  private def pmod(a: Long, n: Long): Long = {
+    val r = a % n
+    if (r < 0) {(r + n) % n} else r
+  }
+
+  private def pmod(a: Byte, n: Byte): Byte = {
+    val r = a % n
+    if (r < 0) {((r + n) % n).toByte} else r.toByte
+  }
+
+  private def pmod(a: Double, n: Double): Double = {
+    val r = a % n
+    if (r < 0) {(r + n) % n} else r
+  }
+
+  private def pmod(a: Short, n: Short): Short = {
+    val r = a % n
+    if (r < 0) {((r + n) % n).toShort} else r.toShort
+  }
+
+  private def pmod(a: Float, n: Float): Float = {
+    val r = a % n
+    if (r < 0) {(r + n) % n} else r
+  }
+
+  private def pmod(a: Decimal, n: Decimal): Decimal = {
+    val r = a % n
+    if (r.compare(Decimal(0)) < 0) {(r + n) % n} else r
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index 6c93698f8017b..e7e5231d32c9e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -21,7 +21,6 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types.Decimal
 
-
 class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   /**
@@ -158,4 +157,19 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(MinOf(Array(1.toByte, 2.toByte), Array(1.toByte, 3.toByte)),
       Array(1.toByte, 2.toByte))
   }
+
+  test("pmod") {
+    testNumericDataTypes { convert =>
+      val left = Literal(convert(7))
+      val right = Literal(convert(3))
+      checkEvaluation(Pmod(left, right), convert(1))
+      checkEvaluation(Pmod(Literal.create(null, left.dataType), right), null)
+      checkEvaluation(Pmod(left, Literal.create(null, right.dataType)), null)
+      checkEvaluation(Remainder(left, Literal(convert(0))), null)  // mod by 0
+    }
+    checkEvaluation(Pmod(-7, 3), 2)
+    checkEvaluation(Pmod(7.2D, 4.1D), 3.1000000000000005)
+    checkEvaluation(Pmod(Decimal(0.7), Decimal(0.2)), Decimal(0.1))
+    checkEvaluation(Pmod(2L, Long.MaxValue), 2)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 5119ee31d852d..c7deaca8437a1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1371,6 +1371,23 @@ object functions {
    */
   def pow(l: Double, rightName: String): Column = pow(l, Column(rightName))
 
+  /**
+   * Returns the positive value of dividend mod divisor.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def pmod(dividend: Column, divisor: Column): Column = Pmod(dividend.expr, divisor.expr)
+
+  /**
+   * Returns the positive value of dividend mod divisor.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def pmod(dividendColName: String, divisorColName: String): Column =
+    pmod(Column(dividendColName), Column(divisorColName))
+
   /**
    * Returns the double value that is closest in value to the argument and
    * is equal to a mathematical integer.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 6cebec95d2850..70bd78737f69c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -403,4 +403,41 @@ class DataFrameFunctionsSuite extends QueryTest {
       Seq(Row(2), Row(2), Row(2), Row(2), Row(3), Row(3))
     )
   }
+
+  test("pmod") {
+    val intData = Seq((7, 3), (-7, 3)).toDF("a", "b")
+    checkAnswer(
+      intData.select(pmod('a, 'b)),
+      Seq(Row(1), Row(2))
+    )
+    checkAnswer(
+      intData.select(pmod('a, lit(3))),
+      Seq(Row(1), Row(2))
+    )
+    checkAnswer(
+      intData.select(pmod(lit(-7), 'b)),
+      Seq(Row(2), Row(2))
+    )
+    checkAnswer(
+      intData.selectExpr("pmod(a, b)"),
+      Seq(Row(1), Row(2))
+    )
+    checkAnswer(
+      intData.selectExpr("pmod(a, 3)"),
+      Seq(Row(1), Row(2))
+    )
+    checkAnswer(
+      intData.selectExpr("pmod(-7, b)"),
+      Seq(Row(2), Row(2))
+    )
+    val doubleData = Seq((7.2, 4.1)).toDF("a", "b")
+    checkAnswer(
+      doubleData.select(pmod('a, 'b)),
+      Seq(Row(3.1000000000000005))  // same as hive
+    )
+    checkAnswer(
+      doubleData.select(pmod(lit(2), lit(Int.MaxValue))),
+      Seq(Row(2))
+    )
+  }
 }

From 9716a727fb2d11380794549039e12e53c771e120 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 15 Jul 2015 10:46:22 -0700
Subject: [PATCH 0394/1454] [Minor][SQL] Allow spaces in the beginning and
 ending of string for Interval

This is a minor fixing for #7355 to allow spaces in the beginning and ending of string parsed to `Interval`.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7390 from viirya/fix_interval_string and squashes the following commits:

9eb6831 [Liang-Chi Hsieh] Use trim instead of modifying regex.
57861f7 [Liang-Chi Hsieh] Fix scala style.
815a9cb [Liang-Chi Hsieh] Slightly modify regex to allow spaces in the beginning and ending of string.
---
 .../main/java/org/apache/spark/unsafe/types/Interval.java   | 1 +
 .../java/org/apache/spark/unsafe/types/IntervalSuite.java   | 6 ++++++
 2 files changed, 7 insertions(+)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
index eb7475e9df869..905ea0b7b878c 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
@@ -62,6 +62,7 @@ public static Interval fromString(String s) {
     if (s == null) {
       return null;
     }
+    s = s.trim();
     Matcher m = p.matcher(s);
     if (!m.matches() || s.equals("interval")) {
       return null;
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
index 44a949a371f2b..1832d0bc65551 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
@@ -75,6 +75,12 @@ public void fromStringTest() {
     Interval result = new Interval(-5 * 12 + 23, 0);
     assertEquals(Interval.fromString(input), result);
 
+    input = "interval   -5  years  23   month   ";
+    assertEquals(Interval.fromString(input), result);
+
+    input = "  interval   -5  years  23   month   ";
+    assertEquals(Interval.fromString(input), result);
+
     // Error cases
     input = "interval   3month 1 hour";
     assertEquals(Interval.fromString(input), null);

From 303c1201c468d360a5f600ce37b8bee75a77a0e6 Mon Sep 17 00:00:00 2001
From: Shuo Xiang <shuoxiangpub@gmail.com>
Date: Wed, 15 Jul 2015 12:10:53 -0700
Subject: [PATCH 0395/1454] [SPARK-7555] [DOCS] Add doc for elastic net in
 ml-guide and mllib-guide

jkbradley I put the elastic net under the **Algorithm guide** section. Also add the formula of elastic net in mllib-linear `mllib-linear-methods#regularizers`.

dbtsai I left the code tab for you to add example code. Do you think it is the right place?

Author: Shuo Xiang <shuoxiangpub@gmail.com>

Closes #6504 from coderxiang/elasticnet and squashes the following commits:

f6061ee [Shuo Xiang] typo
90a7c88 [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into elasticnet
0610a36 [Shuo Xiang] move out the elastic net to ml-linear-methods
8747190 [Shuo Xiang] merge master
706d3f7 [Shuo Xiang] add python code
9bc2b4c [Shuo Xiang] typo
db32a60 [Shuo Xiang] java code sample
aab3b3a [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into elasticnet
a0dae07 [Shuo Xiang] simplify code
d8616fd [Shuo Xiang] Update the definition of elastic net. Add scala code; Mention Lasso and Ridge
df5bd14 [Shuo Xiang] use wikipeida page in ml-linear-methods.md
78d9366 [Shuo Xiang] address comments
8ce37c2 [Shuo Xiang] Merge branch 'elasticnet' of github.com:coderxiang/spark into elasticnet
8f24848 [Shuo Xiang] Merge branch 'elastic-net-doc' of github.com:coderxiang/spark into elastic-net-doc
998d766 [Shuo Xiang] Merge branch 'elastic-net-doc' of github.com:coderxiang/spark into elastic-net-doc
89f10e4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into elastic-net-doc
9262a72 [Shuo Xiang] update
7e07d12 [Shuo Xiang] update
b32f21a [Shuo Xiang] add doc for elastic net in sparkml
937eef1 [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into elastic-net-doc
180b496 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
aa0717d [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
5f109b4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
c5c5bfe [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
98804c9 [Shuo Xiang] fix bug in topBykey and update test
---
 docs/ml-guide.md             |  31 +++++++++
 docs/ml-linear-methods.md    | 129 +++++++++++++++++++++++++++++++++++
 docs/mllib-linear-methods.md |  53 +++++++-------
 3 files changed, 188 insertions(+), 25 deletions(-)
 create mode 100644 docs/ml-linear-methods.md

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index c74cb1f1ef8ea..8c46adf256a9a 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -3,6 +3,24 @@ layout: global
 title: Spark ML Programming Guide
 ---
 
+`\[
+\newcommand{\R}{\mathbb{R}}
+\newcommand{\E}{\mathbb{E}}
+\newcommand{\x}{\mathbf{x}}
+\newcommand{\y}{\mathbf{y}}
+\newcommand{\wv}{\mathbf{w}}
+\newcommand{\av}{\mathbf{\alpha}}
+\newcommand{\bv}{\mathbf{b}}
+\newcommand{\N}{\mathbb{N}}
+\newcommand{\id}{\mathbf{I}}
+\newcommand{\ind}{\mathbf{1}}
+\newcommand{\0}{\mathbf{0}}
+\newcommand{\unit}{\mathbf{e}}
+\newcommand{\one}{\mathbf{1}}
+\newcommand{\zero}{\mathbf{0}}
+\]`
+
+
 Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of
 high-level APIs that help users create and tune practical machine learning pipelines.
 
@@ -154,6 +172,19 @@ Parameters belong to specific instances of `Estimator`s and `Transformer`s.
 For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
 This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
 
+# Algorithm Guides
+
+There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
+
+**Pipelines API Algorithm Guides**
+
+* [Feature Extraction, Transformation, and Selection](ml-features.html)
+* [Ensembles](ml-ensembles.html)
+
+**Algorithms in `spark.ml`**
+
+* [Linear methods with elastic net regularization](ml-linear-methods.html)
+
 # Code Examples
 
 This section gives code examples illustrating the functionality discussed above.
diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
new file mode 100644
index 0000000000000..1ac83d94c9e81
--- /dev/null
+++ b/docs/ml-linear-methods.md
@@ -0,0 +1,129 @@
+---
+layout: global
+title: Linear Methods - ML
+displayTitle: <a href="ml-guide.html">ML</a> - Linear Methods
+---
+
+
+`\[
+\newcommand{\R}{\mathbb{R}}
+\newcommand{\E}{\mathbb{E}}
+\newcommand{\x}{\mathbf{x}}
+\newcommand{\y}{\mathbf{y}}
+\newcommand{\wv}{\mathbf{w}}
+\newcommand{\av}{\mathbf{\alpha}}
+\newcommand{\bv}{\mathbf{b}}
+\newcommand{\N}{\mathbb{N}}
+\newcommand{\id}{\mathbf{I}}
+\newcommand{\ind}{\mathbf{1}}
+\newcommand{\0}{\mathbf{0}}
+\newcommand{\unit}{\mathbf{e}}
+\newcommand{\one}{\mathbf{1}}
+\newcommand{\zero}{\mathbf{0}}
+\]`
+
+
+In MLlib, we implement popular linear methods such as logistic regression and linear least squares with L1 or L2 regularization. Refer to [the linear methods in mllib](mllib-linear-methods.html) for details. In `spark.ml`, we also include Pipelines API for [Elastic net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid of L1 and L2 regularization proposed in [this paper](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf). Mathematically it is defined as a linear combination of the L1-norm and the L2-norm:
+`\[
+\alpha \|\wv\|_1 + (1-\alpha) \frac{1}{2}\|\wv\|_2^2, \alpha \in [0, 1].
+\]`
+By setting $\alpha$ properly, it contains both L1 and L2 regularization as special cases. For example, if a [linear regression](https://en.wikipedia.org/wiki/Linear_regression) model is trained with the elastic net parameter $\alpha$ set to $1$, it is equivalent to a [Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model. On the other hand, if $\alpha$ is set to $0$, the trained model reduces to a [ridge regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model. We implement Pipelines API for both linear regression and logistic regression with elastic net regularization.
+
+**Examples**
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+
+import org.apache.spark.ml.classification.LogisticRegression
+import org.apache.spark.mllib.util.MLUtils
+
+// Load training data
+val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+val lr = new LogisticRegression()
+  .setMaxIter(10)
+  .setRegParam(0.3)
+  .setElasticNetParam(0.8)
+
+// Fit the model
+val lrModel = lr.fit(training)
+
+// Print the weights and intercept for logistic regression
+println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+
+import org.apache.spark.ml.classification.LogisticRegression;
+import org.apache.spark.ml.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class LogisticRegressionWithElasticNetExample {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf()
+      .setAppName("Logistic Regression with Elastic Net Example");
+
+    SparkContext sc = new SparkContext(conf);
+    SQLContext sql = new SQLContext(sc);
+    String path = "sample_libsvm_data.txt";
+
+    // Load training data
+    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+
+    LogisticRegression lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(0.3)
+      .setElasticNetParam(0.8)
+
+    // Fit the model
+    LogisticRegressionModel lrModel = lr.fit(training);
+
+    // Print the weights and intercept for logistic regression
+    System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
+  }
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+
+from pyspark.ml.classification import LogisticRegression
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.util import MLUtils
+
+# Load training data
+training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
+
+# Fit the model
+lrModel = lr.fit(training)
+
+# Print the weights and intercept for logistic regression
+print("Weights: " + str(lrModel.weights))
+print("Intercept: " + str(lrModel.intercept))
+{% endhighlight %}
+
+</div>
+
+</div>
+
+### Optimization
+
+The optimization algorithm underlies the implementation is called [Orthant-Wise Limited-memory QuasiNewton](http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf)
+(OWL-QN). It is an extension of L-BFGS that can effectively handle L1 regularization and elastic net.
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 3927d65fbf8fb..07655baa414b5 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -10,7 +10,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Linear Methods
 
 `\[
 \newcommand{\R}{\mathbb{R}}
-\newcommand{\E}{\mathbb{E}} 
+\newcommand{\E}{\mathbb{E}}
 \newcommand{\x}{\mathbf{x}}
 \newcommand{\y}{\mathbf{y}}
 \newcommand{\wv}{\mathbf{w}}
@@ -18,10 +18,10 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Linear Methods
 \newcommand{\bv}{\mathbf{b}}
 \newcommand{\N}{\mathbb{N}}
 \newcommand{\id}{\mathbf{I}}
-\newcommand{\ind}{\mathbf{1}} 
-\newcommand{\0}{\mathbf{0}} 
-\newcommand{\unit}{\mathbf{e}} 
-\newcommand{\one}{\mathbf{1}} 
+\newcommand{\ind}{\mathbf{1}}
+\newcommand{\0}{\mathbf{0}}
+\newcommand{\unit}{\mathbf{e}}
+\newcommand{\one}{\mathbf{1}}
 \newcommand{\zero}{\mathbf{0}}
 \]`
 
@@ -29,7 +29,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Linear Methods
 
 Many standard *machine learning* methods can be formulated as a convex optimization problem, i.e.
 the task of finding a minimizer of a convex function `$f$` that depends on a variable vector
-`$\wv$` (called `weights` in the code), which has `$d$` entries. 
+`$\wv$` (called `weights` in the code), which has `$d$` entries.
 Formally, we can write this as the optimization problem `$\min_{\wv \in\R^d} \; f(\wv)$`, where
 the objective function is of the form
 `\begin{equation}
@@ -39,7 +39,7 @@ the objective function is of the form
     \ .
 \end{equation}`
 Here the vectors `$\x_i\in\R^d$` are the training data examples, for `$1\le i\le n$`, and
-`$y_i\in\R$` are their corresponding labels, which we want to predict. 
+`$y_i\in\R$` are their corresponding labels, which we want to predict.
 We call the method *linear* if $L(\wv; \x, y)$ can be expressed as a function of $\wv^T x$ and $y$.
 Several of MLlib's classification and regression algorithms fall into this category,
 and are discussed here.
@@ -99,6 +99,9 @@ regularizers in MLlib:
     <tr>
       <td>L1</td><td>$\|\wv\|_1$</td><td>$\mathrm{sign}(\wv)$</td>
     </tr>
+    <tr>
+      <td>elastic net</td><td>$\alpha \|\wv\|_1 + (1-\alpha)\frac{1}{2}\|\wv\|_2^2$</td><td>$\alpha \mathrm{sign}(\wv) + (1-\alpha) \wv$</td>
+    </tr>
   </tbody>
 </table>
 
@@ -107,7 +110,7 @@ of `$\wv$`.
 
 L2-regularized problems are generally easier to solve than L1-regularized due to smoothness.
 However, L1 regularization can help promote sparsity in weights leading to smaller and more interpretable models, the latter of which can be useful for feature selection.
-It is not recommended to train models without any regularization,
+[Elastic net](http://en.wikipedia.org/wiki/Elastic_net_regularization) is a combination of L1 and L2 regularization. It is not recommended to train models without any regularization,
 especially when the number of training examples is small.
 
 ### Optimization
@@ -531,7 +534,7 @@ sameModel = LogisticRegressionModel.load(sc, "myModelPath")
 ### Linear least squares, Lasso, and ridge regression
 
 
-Linear least squares is the most common formulation for regression problems. 
+Linear least squares is the most common formulation for regression problems.
 It is a linear method as described above in equation `$\eqref{eq:regPrimal}$`, with the loss
 function in the formulation given by the squared loss:
 `\[
@@ -539,8 +542,8 @@ L(\wv;\x,y) :=  \frac{1}{2} (\wv^T \x - y)^2.
 \]`
 
 Various related regression methods are derived by using different types of regularization:
-[*ordinary least squares*](http://en.wikipedia.org/wiki/Ordinary_least_squares) or 
-[*linear least squares*](http://en.wikipedia.org/wiki/Linear_least_squares_(mathematics)) uses 
+[*ordinary least squares*](http://en.wikipedia.org/wiki/Ordinary_least_squares) or
+[*linear least squares*](http://en.wikipedia.org/wiki/Linear_least_squares_(mathematics)) uses
  no regularization; [*ridge regression*](http://en.wikipedia.org/wiki/Ridge_regression) uses L2
 regularization; and [*Lasso*](http://en.wikipedia.org/wiki/Lasso_(statistics)) uses L1
 regularization.  For all of these models, the average loss or training error, $\frac{1}{n} \sum_{i=1}^n (\wv^T x_i - y_i)^2$, is
@@ -552,7 +555,7 @@ known as the [mean squared error](http://en.wikipedia.org/wiki/Mean_squared_erro
 
 <div data-lang="scala" markdown="1">
 The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint.
-The example then uses LinearRegressionWithSGD to build a simple linear model to predict label 
+The example then uses LinearRegressionWithSGD to build a simple linear model to predict label
 values. We compute the mean squared error at the end to evaluate
 [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit).
 
@@ -614,7 +617,7 @@ public class LinearRegression {
   public static void main(String[] args) {
     SparkConf conf = new SparkConf().setAppName("Linear Regression Example");
     JavaSparkContext sc = new JavaSparkContext(conf);
-    
+
     // Load and parse the data
     String path = "data/mllib/ridge-data/lpsa.data";
     JavaRDD<String> data = sc.textFile(path);
@@ -634,7 +637,7 @@ public class LinearRegression {
 
     // Building the model
     int numIterations = 100;
-    final LinearRegressionModel model = 
+    final LinearRegressionModel model =
       LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations);
 
     // Evaluate model on training examples and compute training error
@@ -665,7 +668,7 @@ public class LinearRegression {
 
 <div data-lang="python" markdown="1">
 The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint.
-The example then uses LinearRegressionWithSGD to build a simple linear model to predict label 
+The example then uses LinearRegressionWithSGD to build a simple linear model to predict label
 values. We compute the mean squared error at the end to evaluate
 [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit).
 
@@ -706,8 +709,8 @@ a dependency.
 
 ###Streaming linear regression
 
-When data arrive in a streaming fashion, it is useful to fit regression models online, 
-updating the parameters of the model as new data arrives. MLlib currently supports 
+When data arrive in a streaming fashion, it is useful to fit regression models online,
+updating the parameters of the model as new data arrives. MLlib currently supports
 streaming linear regression using ordinary least squares. The fitting is similar
 to that performed offline, except fitting occurs on each batch of data, so that
 the model continually updates to reflect the data from the stream.
@@ -722,7 +725,7 @@ online to the first stream, and make predictions on the second stream.
 
 <div data-lang="scala" markdown="1">
 
-First, we import the necessary classes for parsing our input data and creating the model. 
+First, we import the necessary classes for parsing our input data and creating the model.
 
 {% highlight scala %}
 
@@ -734,7 +737,7 @@ import org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
 
 Then we make input streams for training and testing data. We assume a StreamingContext `ssc`
 has already been created, see [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing)
-for more info. For this example, we use labeled points in training and testing streams, 
+for more info. For this example, we use labeled points in training and testing streams,
 but in practice you will likely want to use unlabeled vectors for test data.
 
 {% highlight scala %}
@@ -754,7 +757,7 @@ val model = new StreamingLinearRegressionWithSGD()
 
 {% endhighlight %}
 
-Now we register the streams for training and testing and start the job. 
+Now we register the streams for training and testing and start the job.
 Printing predictions alongside true labels lets us easily see the result.
 
 {% highlight scala %}
@@ -764,14 +767,14 @@ model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
 
 ssc.start()
 ssc.awaitTermination()
- 
+
 {% endhighlight %}
 
 We can now save text files with data to the training or testing folders.
-Each line should be a data point formatted as `(y,[x1,x2,x3])` where `y` is the label 
-and `x1,x2,x3` are the features. Anytime a text file is placed in `/training/data/dir` 
-the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions. 
-As you feed more data to the training directory, the predictions 
+Each line should be a data point formatted as `(y,[x1,x2,x3])` where `y` is the label
+and `x1,x2,x3` are the features. Anytime a text file is placed in `/training/data/dir`
+the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions.
+As you feed more data to the training directory, the predictions
 will get better!
 
 </div>

From ec9b621647b893abae3afef219bceab382b99564 Mon Sep 17 00:00:00 2001
From: Steve Loughran <stevel@hortonworks.com>
Date: Wed, 15 Jul 2015 12:15:35 -0700
Subject: [PATCH 0396/1454] SPARK-9070 JavaDataFrameSuite teardown NPEs if
 setup failed

fix teardown to skip table delete if hive context is null

Author: Steve Loughran <stevel@hortonworks.com>

Closes #7425 from steveloughran/stevel/patches/SPARK-9070-JavaDataFrameSuite-NPE and squashes the following commits:

1982d38 [Steve Loughran] SPARK-9070 JavaDataFrameSuite teardown NPEs if setup failed
---
 .../test/org/apache/spark/sql/hive/JavaDataFrameSuite.java    | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index c4828c4717643..741a3cd31c603 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -61,7 +61,9 @@ public void setUp() throws IOException {
   @After
   public void tearDown() throws IOException {
     // Clean up tables.
-    hc.sql("DROP TABLE IF EXISTS window_table");
+    if (hc != null) {
+      hc.sql("DROP TABLE IF EXISTS window_table");
+    }
   }
 
   @Test

From 536533cad83a26f8fa7c60042904a31057ab56c2 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 15 Jul 2015 13:32:25 -0700
Subject: [PATCH 0397/1454] [SPARK-9005] [MLLIB] Fix RegressionMetrics
 computation of explainedVariance

Fixes implementation of `explainedVariance` and `r2` to be consistent with their definitions as described in [SPARK-9005](https://issues.apache.org/jira/browse/SPARK-9005).

Author: Feynman Liang <fliang@databricks.com>

Closes #7361 from feynmanliang/SPARK-9005-RegressionMetrics-bugs and squashes the following commits:

f1112fc [Feynman Liang] Add explainedVariance formula
1a3d098 [Feynman Liang] SROwen code review comments
08a0e1b [Feynman Liang] Fix pyspark tests
db8605a [Feynman Liang] Style fix
bde9761 [Feynman Liang] Fix RegressionMetrics tests, relax assumption predictor is unbiased
c235de0 [Feynman Liang] Fix RegressionMetrics tests
4c4e56f [Feynman Liang] Fix RegressionMetrics computation of explainedVariance and r2
---
 .../mllib/evaluation/RegressionMetrics.scala  | 27 +++++---
 .../evaluation/RegressionMetricsSuite.scala   | 69 +++++++++++++++++--
 python/pyspark/mllib/evaluation.py            |  2 +-
 3 files changed, 83 insertions(+), 15 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index e577bf87f885e..408847afa800d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -53,14 +53,22 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
       )
     summary
   }
+  private lazy val SSerr = math.pow(summary.normL2(1), 2)
+  private lazy val SStot = summary.variance(0) * (summary.count - 1)
+  private lazy val SSreg = {
+    val yMean = summary.mean(0)
+    predictionAndObservations.map {
+      case (prediction, _) => math.pow(prediction - yMean, 2)
+    }.sum()
+  }
 
   /**
-   * Returns the explained variance regression score.
-   * explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
-   * Reference: [[http://en.wikipedia.org/wiki/Explained_variation]]
+   * Returns the variance explained by regression.
+   * explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n
+   * @see [[https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained]]
    */
   def explainedVariance: Double = {
-    1 - summary.variance(1) / summary.variance(0)
+    SSreg / summary.count
   }
 
   /**
@@ -76,8 +84,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
    * expected value of the squared error loss or quadratic loss.
    */
   def meanSquaredError: Double = {
-    val rmse = summary.normL2(1) / math.sqrt(summary.count)
-    rmse * rmse
+    SSerr / summary.count
   }
 
   /**
@@ -85,14 +92,14 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
    * the mean squared error.
    */
   def rootMeanSquaredError: Double = {
-    summary.normL2(1) / math.sqrt(summary.count)
+    math.sqrt(this.meanSquaredError)
   }
 
   /**
-   * Returns R^2^, the coefficient of determination.
-   * Reference: [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
+   * Returns R^2^, the unadjusted coefficient of determination.
+   * @see [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
    */
   def r2: Double = {
-    1 - math.pow(summary.normL2(1), 2) / (summary.variance(0) * (summary.count - 1))
+    1 - SSerr / SStot
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
index 9de2bdb6d7246..4b7f1be58f99b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RegressionMetricsSuite.scala
@@ -23,24 +23,85 @@ import org.apache.spark.mllib.util.TestingUtils._
 
 class RegressionMetricsSuite extends SparkFunSuite with MLlibTestSparkContext {
 
-  test("regression metrics") {
+  test("regression metrics for unbiased (includes intercept term) predictor") {
+    /* Verify results in R:
+       preds = c(2.25, -0.25, 1.75, 7.75)
+       obs = c(3.0, -0.5, 2.0, 7.0)
+
+       SStot = sum((obs - mean(obs))^2)
+       SSreg = sum((preds - mean(obs))^2)
+       SSerr = sum((obs - preds)^2)
+
+       explainedVariance = SSreg / length(obs)
+       explainedVariance
+       > [1] 8.796875
+       meanAbsoluteError = mean(abs(preds - obs))
+       meanAbsoluteError
+       > [1] 0.5
+       meanSquaredError = mean((preds - obs)^2)
+       meanSquaredError
+       > [1] 0.3125
+       rmse = sqrt(meanSquaredError)
+       rmse
+       > [1] 0.559017
+       r2 = 1 - SSerr / SStot
+       r2
+       > [1] 0.9571734
+     */
+    val predictionAndObservations = sc.parallelize(
+      Seq((2.25, 3.0), (-0.25, -0.5), (1.75, 2.0), (7.75, 7.0)), 2)
+    val metrics = new RegressionMetrics(predictionAndObservations)
+    assert(metrics.explainedVariance ~== 8.79687 absTol 1E-5,
+      "explained variance regression score mismatch")
+    assert(metrics.meanAbsoluteError ~== 0.5 absTol 1E-5, "mean absolute error mismatch")
+    assert(metrics.meanSquaredError ~== 0.3125 absTol 1E-5, "mean squared error mismatch")
+    assert(metrics.rootMeanSquaredError ~== 0.55901 absTol 1E-5,
+      "root mean squared error mismatch")
+    assert(metrics.r2 ~== 0.95717 absTol 1E-5, "r2 score mismatch")
+  }
+
+  test("regression metrics for biased (no intercept term) predictor") {
+    /* Verify results in R:
+       preds = c(2.5, 0.0, 2.0, 8.0)
+       obs = c(3.0, -0.5, 2.0, 7.0)
+
+       SStot = sum((obs - mean(obs))^2)
+       SSreg = sum((preds - mean(obs))^2)
+       SSerr = sum((obs - preds)^2)
+
+       explainedVariance = SSreg / length(obs)
+       explainedVariance
+       > [1] 8.859375
+       meanAbsoluteError = mean(abs(preds - obs))
+       meanAbsoluteError
+       > [1] 0.5
+       meanSquaredError = mean((preds - obs)^2)
+       meanSquaredError
+       > [1] 0.375
+       rmse = sqrt(meanSquaredError)
+       rmse
+       > [1] 0.6123724
+       r2 = 1 - SSerr / SStot
+       r2
+       > [1] 0.9486081
+     */
     val predictionAndObservations = sc.parallelize(
       Seq((2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)), 2)
     val metrics = new RegressionMetrics(predictionAndObservations)
-    assert(metrics.explainedVariance ~== 0.95717 absTol 1E-5,
+    assert(metrics.explainedVariance ~== 8.85937 absTol 1E-5,
       "explained variance regression score mismatch")
     assert(metrics.meanAbsoluteError ~== 0.5 absTol 1E-5, "mean absolute error mismatch")
     assert(metrics.meanSquaredError ~== 0.375 absTol 1E-5, "mean squared error mismatch")
     assert(metrics.rootMeanSquaredError ~== 0.61237 absTol 1E-5,
       "root mean squared error mismatch")
-    assert(metrics.r2 ~== 0.94861 absTol 1E-5, "r2 score mismatch")
+    assert(metrics.r2 ~== 0.94860 absTol 1E-5, "r2 score mismatch")
   }
 
   test("regression metrics with complete fitting") {
     val predictionAndObservations = sc.parallelize(
       Seq((3.0, 3.0), (0.0, 0.0), (2.0, 2.0), (8.0, 8.0)), 2)
     val metrics = new RegressionMetrics(predictionAndObservations)
-    assert(metrics.explainedVariance ~== 1.0 absTol 1E-5,
+    assert(metrics.explainedVariance ~== 8.6875 absTol 1E-5,
       "explained variance regression score mismatch")
     assert(metrics.meanAbsoluteError ~== 0.0 absTol 1E-5, "mean absolute error mismatch")
     assert(metrics.meanSquaredError ~== 0.0 absTol 1E-5, "mean squared error mismatch")
diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py
index f21403707e12a..4398ca86f2ec2 100644
--- a/python/pyspark/mllib/evaluation.py
+++ b/python/pyspark/mllib/evaluation.py
@@ -82,7 +82,7 @@ class RegressionMetrics(JavaModelWrapper):
     ...     (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)])
     >>> metrics = RegressionMetrics(predictionAndObservations)
     >>> metrics.explainedVariance
-    0.95...
+    8.859...
     >>> metrics.meanAbsoluteError
     0.5...
     >>> metrics.meanSquaredError

From b9a922e260bec1b211437f020be37fab46a85db0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 15 Jul 2015 14:02:23 -0700
Subject: [PATCH 0398/1454] [SPARK-6602][Core]Replace Akka Serialization with
 Spark Serializer

Replace Akka Serialization with Spark Serializer and add unit tests.

Author: zsxwing <zsxwing@gmail.com>

Closes #7159 from zsxwing/remove-akka-serialization and squashes the following commits:

fc0fca3 [zsxwing] Merge branch 'master' into remove-akka-serialization
cf81a58 [zsxwing] Fix the code style
73251c6 [zsxwing] Add test scope
9ef4af9 [zsxwing] Add AkkaRpcEndpointRef.hashCode
433115c [zsxwing] Remove final
be3edb0 [zsxwing] Support deserializing RpcEndpointRef
ecec410 [zsxwing] Replace Akka Serialization with Spark Serializer
---
 core/pom.xml                                  |   5 +
 .../master/FileSystemPersistenceEngine.scala  |  35 ++---
 .../apache/spark/deploy/master/Master.scala   |  18 +--
 .../deploy/master/PersistenceEngine.scala     |   8 +-
 .../deploy/master/RecoveryModeFactory.scala   |   9 +-
 .../master/ZooKeeperPersistenceEngine.scala   |  16 +--
 .../scala/org/apache/spark/rpc/RpcEnv.scala   |   6 +
 .../apache/spark/rpc/akka/AkkaRpcEnv.scala    |  14 +-
 .../master/CustomRecoveryModeFactory.scala    |  31 ++---
 .../spark/deploy/master/MasterSuite.scala     |   2 +-
 .../master/PersistenceEngineSuite.scala       | 126 ++++++++++++++++++
 pom.xml                                       |   6 +
 12 files changed, 214 insertions(+), 62 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala

diff --git a/core/pom.xml b/core/pom.xml
index 558cc3fb9f2f3..73f7a75cab9d3 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -372,6 +372,11 @@
       <artifactId>junit-interface</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.curator</groupId>
+      <artifactId>curator-test</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>net.razorvine</groupId>
       <artifactId>pyrolite</artifactId>
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala
index f459ed5b3a1a1..aa379d4cd61e7 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala
@@ -21,9 +21,8 @@ import java.io._
 
 import scala.reflect.ClassTag
 
-import akka.serialization.Serialization
-
 import org.apache.spark.Logging
+import org.apache.spark.serializer.{DeserializationStream, SerializationStream, Serializer}
 import org.apache.spark.util.Utils
 
 
@@ -32,11 +31,11 @@ import org.apache.spark.util.Utils
  * Files are deleted when applications and workers are removed.
  *
  * @param dir Directory to store files. Created if non-existent (but not recursively).
- * @param serialization Used to serialize our objects.
+ * @param serializer Used to serialize our objects.
  */
 private[master] class FileSystemPersistenceEngine(
     val dir: String,
-    val serialization: Serialization)
+    val serializer: Serializer)
   extends PersistenceEngine with Logging {
 
   new File(dir).mkdir()
@@ -57,27 +56,31 @@ private[master] class FileSystemPersistenceEngine(
   private def serializeIntoFile(file: File, value: AnyRef) {
     val created = file.createNewFile()
     if (!created) { throw new IllegalStateException("Could not create file: " + file) }
-    val serializer = serialization.findSerializerFor(value)
-    val serialized = serializer.toBinary(value)
-    val out = new FileOutputStream(file)
+    val fileOut = new FileOutputStream(file)
+    var out: SerializationStream = null
     Utils.tryWithSafeFinally {
-      out.write(serialized)
+      out = serializer.newInstance().serializeStream(fileOut)
+      out.writeObject(value)
     } {
-      out.close()
+      fileOut.close()
+      if (out != null) {
+        out.close()
+      }
     }
   }
 
   private def deserializeFromFile[T](file: File)(implicit m: ClassTag[T]): T = {
-    val fileData = new Array[Byte](file.length().asInstanceOf[Int])
-    val dis = new DataInputStream(new FileInputStream(file))
+    val fileIn = new FileInputStream(file)
+    var in: DeserializationStream = null
     try {
-      dis.readFully(fileData)
+      in = serializer.newInstance().deserializeStream(fileIn)
+      in.readObject[T]()
     } finally {
-      dis.close()
+      fileIn.close()
+      if (in != null) {
+        in.close()
+      }
     }
-    val clazz = m.runtimeClass.asInstanceOf[Class[T]]
-    val serializer = serialization.serializerFor(clazz)
-    serializer.fromBinary(fileData).asInstanceOf[T]
   }
 
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 245b047e7dfbd..4615febf17d24 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -27,11 +27,8 @@ import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 import scala.language.postfixOps
 import scala.util.Random
 
-import akka.serialization.Serialization
-import akka.serialization.SerializationExtension
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.rpc.akka.AkkaRpcEnv
 import org.apache.spark.rpc._
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.deploy.{ApplicationDescription, DriverDescription,
@@ -44,6 +41,7 @@ import org.apache.spark.deploy.master.ui.MasterWebUI
 import org.apache.spark.deploy.rest.StandaloneRestServer
 import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.scheduler.{EventLoggingListener, ReplayListenerBus}
+import org.apache.spark.serializer.{JavaSerializer, Serializer}
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{ThreadUtils, SignalLogger, Utils}
 
@@ -58,9 +56,6 @@ private[master] class Master(
   private val forwardMessageThread =
     ThreadUtils.newDaemonSingleThreadScheduledExecutor("master-forward-message-thread")
 
-  // TODO Remove it once we don't use akka.serialization.Serialization
-  private val actorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem
-
   private val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
 
   private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs
@@ -161,20 +156,21 @@ private[master] class Master(
     masterMetricsSystem.getServletHandlers.foreach(webUi.attachHandler)
     applicationMetricsSystem.getServletHandlers.foreach(webUi.attachHandler)
 
+    val serializer = new JavaSerializer(conf)
     val (persistenceEngine_, leaderElectionAgent_) = RECOVERY_MODE match {
       case "ZOOKEEPER" =>
         logInfo("Persisting recovery state to ZooKeeper")
         val zkFactory =
-          new ZooKeeperRecoveryModeFactory(conf, SerializationExtension(actorSystem))
+          new ZooKeeperRecoveryModeFactory(conf, serializer)
         (zkFactory.createPersistenceEngine(), zkFactory.createLeaderElectionAgent(this))
       case "FILESYSTEM" =>
         val fsFactory =
-          new FileSystemRecoveryModeFactory(conf, SerializationExtension(actorSystem))
+          new FileSystemRecoveryModeFactory(conf, serializer)
         (fsFactory.createPersistenceEngine(), fsFactory.createLeaderElectionAgent(this))
       case "CUSTOM" =>
         val clazz = Utils.classForName(conf.get("spark.deploy.recoveryMode.factory"))
-        val factory = clazz.getConstructor(classOf[SparkConf], classOf[Serialization])
-          .newInstance(conf, SerializationExtension(actorSystem))
+        val factory = clazz.getConstructor(classOf[SparkConf], classOf[Serializer])
+          .newInstance(conf, serializer)
           .asInstanceOf[StandaloneRecoveryModeFactory]
         (factory.createPersistenceEngine(), factory.createLeaderElectionAgent(this))
       case _ =>
@@ -213,7 +209,7 @@ private[master] class Master(
 
   override def receive: PartialFunction[Any, Unit] = {
     case ElectedLeader => {
-      val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData()
+      val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData(rpcEnv)
       state = if (storedApps.isEmpty && storedDrivers.isEmpty && storedWorkers.isEmpty) {
         RecoveryState.ALIVE
       } else {
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala
index a03d460509e03..58a00bceee6af 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.deploy.master
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rpc.RpcEnv
 
 import scala.reflect.ClassTag
 
@@ -80,8 +81,11 @@ abstract class PersistenceEngine {
    * Returns the persisted data sorted by their respective ids (which implies that they're
    * sorted by time of creation).
    */
-  final def readPersistedData(): (Seq[ApplicationInfo], Seq[DriverInfo], Seq[WorkerInfo]) = {
-    (read[ApplicationInfo]("app_"), read[DriverInfo]("driver_"), read[WorkerInfo]("worker_"))
+  final def readPersistedData(
+      rpcEnv: RpcEnv): (Seq[ApplicationInfo], Seq[DriverInfo], Seq[WorkerInfo]) = {
+    rpcEnv.deserialize { () =>
+      (read[ApplicationInfo]("app_"), read[DriverInfo]("driver_"), read[WorkerInfo]("worker_"))
+    }
   }
 
   def close() {}
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala
index 351db8fab2041..c4c3283fb73f7 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala
@@ -17,10 +17,9 @@
 
 package org.apache.spark.deploy.master
 
-import akka.serialization.Serialization
-
 import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.serializer.Serializer
 
 /**
  * ::DeveloperApi::
@@ -30,7 +29,7 @@ import org.apache.spark.annotation.DeveloperApi
  *
  */
 @DeveloperApi
-abstract class StandaloneRecoveryModeFactory(conf: SparkConf, serializer: Serialization) {
+abstract class StandaloneRecoveryModeFactory(conf: SparkConf, serializer: Serializer) {
 
   /**
    * PersistenceEngine defines how the persistent data(Information about worker, driver etc..)
@@ -49,7 +48,7 @@ abstract class StandaloneRecoveryModeFactory(conf: SparkConf, serializer: Serial
  * LeaderAgent in this case is a no-op. Since leader is forever leader as the actual
  * recovery is made by restoring from filesystem.
  */
-private[master] class FileSystemRecoveryModeFactory(conf: SparkConf, serializer: Serialization)
+private[master] class FileSystemRecoveryModeFactory(conf: SparkConf, serializer: Serializer)
   extends StandaloneRecoveryModeFactory(conf, serializer) with Logging {
 
   val RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "")
@@ -64,7 +63,7 @@ private[master] class FileSystemRecoveryModeFactory(conf: SparkConf, serializer:
   }
 }
 
-private[master] class ZooKeeperRecoveryModeFactory(conf: SparkConf, serializer: Serialization)
+private[master] class ZooKeeperRecoveryModeFactory(conf: SparkConf, serializer: Serializer)
   extends StandaloneRecoveryModeFactory(conf, serializer) {
 
   def createPersistenceEngine(): PersistenceEngine = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
index 328d95a7a0c68..563831cc6b8dd 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.deploy.master
 
-import akka.serialization.Serialization
+import java.nio.ByteBuffer
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
@@ -27,9 +27,10 @@ import org.apache.zookeeper.CreateMode
 
 import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.deploy.SparkCuratorUtil
+import org.apache.spark.serializer.Serializer
 
 
-private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serialization: Serialization)
+private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serializer: Serializer)
   extends PersistenceEngine
   with Logging {
 
@@ -57,17 +58,16 @@ private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serializat
   }
 
   private def serializeIntoFile(path: String, value: AnyRef) {
-    val serializer = serialization.findSerializerFor(value)
-    val serialized = serializer.toBinary(value)
-    zk.create().withMode(CreateMode.PERSISTENT).forPath(path, serialized)
+    val serialized = serializer.newInstance().serialize(value)
+    val bytes = new Array[Byte](serialized.remaining())
+    serialized.get(bytes)
+    zk.create().withMode(CreateMode.PERSISTENT).forPath(path, bytes)
   }
 
   private def deserializeFromFile[T](filename: String)(implicit m: ClassTag[T]): Option[T] = {
     val fileData = zk.getData().forPath(WORKING_DIR + "/" + filename)
-    val clazz = m.runtimeClass.asInstanceOf[Class[T]]
-    val serializer = serialization.serializerFor(clazz)
     try {
-      Some(serializer.fromBinary(fileData).asInstanceOf[T])
+      Some(serializer.newInstance().deserialize[T](ByteBuffer.wrap(fileData)))
     } catch {
       case e: Exception => {
         logWarning("Exception while reading persisted file, deleting", e)
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
index c9fcc7a36cc04..29debe8081308 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala
@@ -139,6 +139,12 @@ private[spark] abstract class RpcEnv(conf: SparkConf) {
    * creating it manually because different [[RpcEnv]] may have different formats.
    */
   def uriOf(systemName: String, address: RpcAddress, endpointName: String): String
+
+  /**
+   * [[RpcEndpointRef]] cannot be deserialized without [[RpcEnv]]. So when deserializing any object
+   * that contains [[RpcEndpointRef]]s, the deserialization codes should be wrapped by this method.
+   */
+  def deserialize[T](deserializationAction: () => T): T
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
index f2d87f68341af..fc17542abf81d 100644
--- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
@@ -28,7 +28,7 @@ import akka.actor.{ActorSystem, ExtendedActorSystem, Actor, ActorRef, Props, Add
 import akka.event.Logging.Error
 import akka.pattern.{ask => akkaAsk}
 import akka.remote.{AssociationEvent, AssociatedEvent, DisassociatedEvent, AssociationErrorEvent}
-import com.google.common.util.concurrent.MoreExecutors
+import akka.serialization.JavaSerializer
 
 import org.apache.spark.{SparkException, Logging, SparkConf}
 import org.apache.spark.rpc._
@@ -239,6 +239,12 @@ private[spark] class AkkaRpcEnv private[akka] (
   }
 
   override def toString: String = s"${getClass.getSimpleName}($actorSystem)"
+
+  override def deserialize[T](deserializationAction: () => T): T = {
+    JavaSerializer.currentSystem.withValue(actorSystem.asInstanceOf[ExtendedActorSystem]) {
+      deserializationAction()
+    }
+  }
 }
 
 private[spark] class AkkaRpcEnvFactory extends RpcEnvFactory {
@@ -315,6 +321,12 @@ private[akka] class AkkaRpcEndpointRef(
 
   override def toString: String = s"${getClass.getSimpleName}($actorRef)"
 
+  final override def equals(that: Any): Boolean = that match {
+    case other: AkkaRpcEndpointRef => actorRef == other.actorRef
+    case _ => false
+  }
+
+  final override def hashCode(): Int = if (actorRef == null) 0 else actorRef.hashCode()
 }
 
 /**
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
index f4e56632e426a..8c96b0e71dfdd 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
@@ -19,18 +19,19 @@
 // when they are outside of org.apache.spark.
 package other.supplier
 
+import java.nio.ByteBuffer
+
 import scala.collection.mutable
 import scala.reflect.ClassTag
 
-import akka.serialization.Serialization
-
 import org.apache.spark.SparkConf
 import org.apache.spark.deploy.master._
+import org.apache.spark.serializer.Serializer
 
 class CustomRecoveryModeFactory(
   conf: SparkConf,
-  serialization: Serialization
-) extends StandaloneRecoveryModeFactory(conf, serialization) {
+  serializer: Serializer
+) extends StandaloneRecoveryModeFactory(conf, serializer) {
 
   CustomRecoveryModeFactory.instantiationAttempts += 1
 
@@ -40,7 +41,7 @@ class CustomRecoveryModeFactory(
    *
    */
   override def createPersistenceEngine(): PersistenceEngine =
-    new CustomPersistenceEngine(serialization)
+    new CustomPersistenceEngine(serializer)
 
   /**
    * Create an instance of LeaderAgent that decides who gets elected as master.
@@ -53,7 +54,7 @@ object CustomRecoveryModeFactory {
   @volatile var instantiationAttempts = 0
 }
 
-class CustomPersistenceEngine(serialization: Serialization) extends PersistenceEngine {
+class CustomPersistenceEngine(serializer: Serializer) extends PersistenceEngine {
   val data = mutable.HashMap[String, Array[Byte]]()
 
   CustomPersistenceEngine.lastInstance = Some(this)
@@ -64,10 +65,10 @@ class CustomPersistenceEngine(serialization: Serialization) extends PersistenceE
    */
   override def persist(name: String, obj: Object): Unit = {
     CustomPersistenceEngine.persistAttempts += 1
-    serialization.serialize(obj) match {
-      case util.Success(bytes) => data += name -> bytes
-      case util.Failure(cause) => throw new RuntimeException(cause)
-    }
+    val serialized = serializer.newInstance().serialize(obj)
+    val bytes = new Array[Byte](serialized.remaining())
+    serialized.get(bytes)
+    data += name -> bytes
   }
 
   /**
@@ -84,15 +85,9 @@ class CustomPersistenceEngine(serialization: Serialization) extends PersistenceE
    */
   override def read[T: ClassTag](prefix: String): Seq[T] = {
     CustomPersistenceEngine.readAttempts += 1
-    val clazz = implicitly[ClassTag[T]].runtimeClass.asInstanceOf[Class[T]]
     val results = for ((name, bytes) <- data; if name.startsWith(prefix))
-      yield serialization.deserialize(bytes, clazz)
-
-    results.find(_.isFailure).foreach {
-      case util.Failure(cause) => throw new RuntimeException(cause)
-    }
-
-    results.flatMap(_.toOption).toSeq
+      yield serializer.newInstance().deserialize[T](ByteBuffer.wrap(bytes))
+    results.toSeq
   }
 }
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 9cb6dd43bac47..a8fbaf1d9da0a 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -105,7 +105,7 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually {
       persistenceEngine.addDriver(driverToPersist)
       persistenceEngine.addWorker(workerToPersist)
 
-      val (apps, drivers, workers) = persistenceEngine.readPersistedData()
+      val (apps, drivers, workers) = persistenceEngine.readPersistedData(rpcEnv)
 
       apps.map(_.id) should contain(appToPersist.id)
       drivers.map(_.id) should contain(driverToPersist.id)
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
new file mode 100644
index 0000000000000..11e87bd1dd8eb
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
@@ -0,0 +1,126 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package org.apache.spark.deploy.master
+
+import java.net.ServerSocket
+
+import org.apache.commons.lang3.RandomUtils
+import org.apache.curator.test.TestingServer
+
+import org.apache.spark.{SecurityManager, SparkConf, SparkFunSuite}
+import org.apache.spark.rpc.{RpcEndpoint, RpcEnv}
+import org.apache.spark.serializer.{Serializer, JavaSerializer}
+import org.apache.spark.util.Utils
+
+class PersistenceEngineSuite extends SparkFunSuite {
+
+  test("FileSystemPersistenceEngine") {
+    val dir = Utils.createTempDir()
+    try {
+      val conf = new SparkConf()
+      testPersistenceEngine(conf, serializer =>
+        new FileSystemPersistenceEngine(dir.getAbsolutePath, serializer)
+      )
+    } finally {
+      Utils.deleteRecursively(dir)
+    }
+  }
+
+  test("ZooKeeperPersistenceEngine") {
+    val conf = new SparkConf()
+    // TestingServer logs the port conflict exception rather than throwing an exception.
+    // So we have to find a free port by ourselves. This approach cannot guarantee always starting
+    // zkTestServer successfully because there is a time gap between finding a free port and
+    // starting zkTestServer. But the failure possibility should be very low.
+    val zkTestServer = new TestingServer(findFreePort(conf))
+    try {
+      testPersistenceEngine(conf, serializer => {
+        conf.set("spark.deploy.zookeeper.url", zkTestServer.getConnectString)
+        new ZooKeeperPersistenceEngine(conf, serializer)
+      })
+    } finally {
+      zkTestServer.stop()
+    }
+  }
+
+  private def testPersistenceEngine(
+      conf: SparkConf, persistenceEngineCreator: Serializer => PersistenceEngine): Unit = {
+    val serializer = new JavaSerializer(conf)
+    val persistenceEngine = persistenceEngineCreator(serializer)
+    persistenceEngine.persist("test_1", "test_1_value")
+    assert(Seq("test_1_value") === persistenceEngine.read[String]("test_"))
+    persistenceEngine.persist("test_2", "test_2_value")
+    assert(Set("test_1_value", "test_2_value") === persistenceEngine.read[String]("test_").toSet)
+    persistenceEngine.unpersist("test_1")
+    assert(Seq("test_2_value") === persistenceEngine.read[String]("test_"))
+    persistenceEngine.unpersist("test_2")
+    assert(persistenceEngine.read[String]("test_").isEmpty)
+
+    // Test deserializing objects that contain RpcEndpointRef
+    val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
+    try {
+      // Create a real endpoint so that we can test RpcEndpointRef deserialization
+      val workerEndpoint = rpcEnv.setupEndpoint("worker", new RpcEndpoint {
+        override val rpcEnv: RpcEnv = rpcEnv
+      })
+
+      val workerToPersist = new WorkerInfo(
+        id = "test_worker",
+        host = "127.0.0.1",
+        port = 10000,
+        cores = 0,
+        memory = 0,
+        endpoint = workerEndpoint,
+        webUiPort = 0,
+        publicAddress = ""
+      )
+
+      persistenceEngine.addWorker(workerToPersist)
+
+      val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData(rpcEnv)
+
+      assert(storedApps.isEmpty)
+      assert(storedDrivers.isEmpty)
+
+      // Check deserializing WorkerInfo
+      assert(storedWorkers.size == 1)
+      val recoveryWorkerInfo = storedWorkers.head
+      assert(workerToPersist.id === recoveryWorkerInfo.id)
+      assert(workerToPersist.host === recoveryWorkerInfo.host)
+      assert(workerToPersist.port === recoveryWorkerInfo.port)
+      assert(workerToPersist.cores === recoveryWorkerInfo.cores)
+      assert(workerToPersist.memory === recoveryWorkerInfo.memory)
+      assert(workerToPersist.endpoint === recoveryWorkerInfo.endpoint)
+      assert(workerToPersist.webUiPort === recoveryWorkerInfo.webUiPort)
+      assert(workerToPersist.publicAddress === recoveryWorkerInfo.publicAddress)
+    } finally {
+      rpcEnv.shutdown()
+      rpcEnv.awaitTermination()
+    }
+  }
+
+  private def findFreePort(conf: SparkConf): Int = {
+    val candidatePort = RandomUtils.nextInt(1024, 65536)
+    Utils.startServiceOnPort(candidatePort, (trialPort: Int) => {
+      val socket = new ServerSocket(trialPort)
+      socket.close()
+      (null, trialPort)
+    }, conf)._2
+  }
+}
diff --git a/pom.xml b/pom.xml
index 370c95dd03632..aa49e2ab7294b 100644
--- a/pom.xml
+++ b/pom.xml
@@ -748,6 +748,12 @@
         <artifactId>curator-framework</artifactId>
         <version>${curator.version}</version>
       </dependency>
+      <dependency>
+        <groupId>org.apache.curator</groupId>
+        <artifactId>curator-test</artifactId>
+        <version>${curator.version}</version>
+        <scope>test</scope>
+      </dependency>
       <dependency>
         <groupId>org.apache.hadoop</groupId>
         <artifactId>hadoop-client</artifactId>

From 674eb2a4c3ff595760f990daf369ba75d2547593 Mon Sep 17 00:00:00 2001
From: KaiXinXiaoLei <huleilei1@huawei.com>
Date: Wed, 15 Jul 2015 22:31:10 +0100
Subject: [PATCH 0399/1454] [SPARK-8974] Catch exceptions in allocation
 schedule task.

I meet a problem. When I submit some tasks, the thread spark-dynamic-executor-allocation should seed the message about "requestTotalExecutors", and the new executor should start. But I meet a problem about this thread, like:

2015-07-14 19:02:17,461 | WARN  | [spark-dynamic-executor-allocation] | Error sending message [message = RequestExecutors(1)] in 1 attempts
java.util.concurrent.TimeoutException: Futures timed out after [120 seconds]
        at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
        at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
        at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
        at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
        at scala.concurrent.Await$.result(package.scala:107)
        at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:102)
        at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:78)
        at org.apache.spark.scheduler.cluster.YarnSchedulerBackend.doRequestTotalExecutors(YarnSchedulerBackend.scala:57)
        at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.requestTotalExecutors(CoarseGrainedSchedulerBackend.scala:351)
        at org.apache.spark.SparkContext.requestTotalExecutors(SparkContext.scala:1382)
        at org.apache.spark.ExecutorAllocationManager.addExecutors(ExecutorAllocationManager.scala:343)
        at org.apache.spark.ExecutorAllocationManager.updateAndSyncNumExecutorsTarget(ExecutorAllocationManager.scala:295)
        at org.apache.spark.ExecutorAllocationManager.org$apache$spark$ExecutorAllocationManager$$schedule(ExecutorAllocationManager.scala:248)

when after some minutes, I find a new ApplicationMaster start,  and tasks submitted start to run. The tasks Completed. And after long time (eg, ten minutes), the number of executor  does not reduce to zero.  I use the default value of "spark.dynamicAllocation.minExecutors".

Author: KaiXinXiaoLei <huleilei1@huawei.com>

Closes #7352 from KaiXinXiaoLei/dym and squashes the following commits:

3603631 [KaiXinXiaoLei] change logError to logWarning
efc4f24 [KaiXinXiaoLei] change file
---
 .../org/apache/spark/ExecutorAllocationManager.scala | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
index 0c50b4002cf7b..648bcfe28cad2 100644
--- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
+++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
@@ -20,6 +20,7 @@ package org.apache.spark
 import java.util.concurrent.TimeUnit
 
 import scala.collection.mutable
+import scala.util.control.ControlThrowable
 
 import com.codahale.metrics.{Gauge, MetricRegistry}
 
@@ -211,7 +212,16 @@ private[spark] class ExecutorAllocationManager(
     listenerBus.addListener(listener)
 
     val scheduleTask = new Runnable() {
-      override def run(): Unit = Utils.logUncaughtExceptions(schedule())
+      override def run(): Unit = {
+        try {
+          schedule()
+        } catch {
+          case ct: ControlThrowable =>
+            throw ct
+          case t: Throwable =>
+            logWarning(s"Uncaught exception in thread ${Thread.currentThread().getName}", t)
+        }
+      }
     }
     executor.scheduleAtFixedRate(scheduleTask, 0, intervalMillis, TimeUnit.MILLISECONDS)
   }

From affbe329ae0100bd50a3c3fb081b0f2b07efce33 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 15 Jul 2015 14:52:02 -0700
Subject: [PATCH 0400/1454] [SPARK-9071][SQL] MonotonicallyIncreasingID and
 SparkPartitionID should be marked as nondeterministic.

I also took the chance to more explicitly define the semantics of deterministic.

Author: Reynold Xin <rxin@databricks.com>

Closes #7428 from rxin/non-deterministic and squashes the following commits:

a760827 [Reynold Xin] [SPARK-9071][SQL] MonotonicallyIncreasingID and SparkPartitionID should be marked as nondeterministic.
---
 .../spark/sql/catalyst/expressions/Expression.scala    | 10 ++++++++--
 .../expressions/MonotonicallyIncreasingID.scala        |  4 +++-
 .../sql/execution/expressions/SparkPartitionID.scala   |  4 +++-
 3 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 3f19ac2b592b5..7b37ae7335253 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -61,9 +61,15 @@ abstract class Expression extends TreeNode[Expression] {
   def foldable: Boolean = false
 
   /**
-   * Returns true when the current expression always return the same result for fixed input values.
+   * Returns true when the current expression always return the same result for fixed inputs from
+   * children.
+   *
+   * Note that this means that an expression should be considered as non-deterministic if:
+   * - if it relies on some mutable internal state, or
+   * - if it relies on some implicit input that is not part of the children expression list.
+   *
+   * An example would be `SparkPartitionID` that relies on the partition id returned by TaskContext.
    */
-  // TODO: Need to define explicit input values vs implicit input values.
   def deterministic: Boolean = true
 
   def nullable: Boolean
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
index 69a37750d7525..fec403fe2d348 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
@@ -41,7 +41,9 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression {
    */
   @transient private[this] var count: Long = 0L
 
-  @transient private lazy val partitionMask = TaskContext.getPartitionId.toLong << 33
+  @transient private lazy val partitionMask = TaskContext.getPartitionId().toLong << 33
+
+  override def deterministic: Boolean = false
 
   override def nullable: Boolean = false
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
index 5f1b514f2cff2..7c790c549a5d8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
@@ -29,11 +29,13 @@ import org.apache.spark.sql.types.{IntegerType, DataType}
  */
 private[sql] case object SparkPartitionID extends LeafExpression {
 
+  override def deterministic: Boolean = false
+
   override def nullable: Boolean = false
 
   override def dataType: DataType = IntegerType
 
-  @transient private lazy val partitionId = TaskContext.getPartitionId
+  @transient private lazy val partitionId = TaskContext.getPartitionId()
 
   override def eval(input: InternalRow): Int = partitionId
 

From b0645195d0da57065885e078e08bd6c42f4f19b0 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 15 Jul 2015 17:50:11 -0700
Subject: [PATCH 0401/1454] [SPARK-9086][SQL] Remove BinaryNode from TreeNode.

These traits are not super useful, and yet cause problems with toString in expressions due to the orders they are mixed in.

Author: Reynold Xin <rxin@databricks.com>

Closes #7433 from rxin/remove-binary-node and squashes the following commits:

1881f78 [Reynold Xin] [SPARK-9086][SQL] Remove BinaryNode from TreeNode.
---
 .../sql/catalyst/expressions/Expression.scala   | 17 ++++++++++++++---
 .../catalyst/plans/logical/LogicalPlan.scala    |  7 ++++++-
 .../spark/sql/catalyst/trees/TreeNode.scala     |  9 ---------
 .../apache/spark/sql/execution/SparkPlan.scala  |  7 ++++++-
 4 files changed, 26 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 7b37ae7335253..87667316aca67 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -187,8 +187,10 @@ abstract class Expression extends TreeNode[Expression] {
 /**
  * A leaf expression, i.e. one without any child expressions.
  */
-abstract class LeafExpression extends Expression with trees.LeafNode[Expression] {
+abstract class LeafExpression extends Expression {
   self: Product =>
+
+  def children: Seq[Expression] = Nil
 }
 
 
@@ -196,9 +198,13 @@ abstract class LeafExpression extends Expression with trees.LeafNode[Expression]
  * An expression with one input and one output. The output is by default evaluated to null
  * if the input is evaluated to null.
  */
-abstract class UnaryExpression extends Expression with trees.UnaryNode[Expression] {
+abstract class UnaryExpression extends Expression {
   self: Product =>
 
+  def child: Expression
+
+  override def children: Seq[Expression] = child :: Nil
+
   override def foldable: Boolean = child.foldable
   override def nullable: Boolean = child.nullable
 
@@ -271,9 +277,14 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio
  * An expression with two inputs and one output. The output is by default evaluated to null
  * if any input is evaluated to null.
  */
-abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] {
+abstract class BinaryExpression extends Expression {
   self: Product =>
 
+  def left: Expression
+  def right: Expression
+
+  override def children: Seq[Expression] = Seq(left, right)
+
   override def foldable: Boolean = left.foldable && right.foldable
 
   override def nullable: Boolean = left.nullable || right.nullable
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index e911b907e8536..d7077a0ec907a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -291,6 +291,11 @@ abstract class UnaryNode extends LogicalPlan with trees.UnaryNode[LogicalPlan] {
 /**
  * A logical plan node with a left and right child.
  */
-abstract class BinaryNode extends LogicalPlan with trees.BinaryNode[LogicalPlan] {
+abstract class BinaryNode extends LogicalPlan {
   self: Product =>
+
+  def left: LogicalPlan
+  def right: LogicalPlan
+
+  override def children: Seq[LogicalPlan] = Seq(left, right)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 09f6c6b0ec423..16844b2f4b680 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -453,15 +453,6 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
   }
 }
 
-/**
- * A [[TreeNode]] that has two children, [[left]] and [[right]].
- */
-trait BinaryNode[BaseType <: TreeNode[BaseType]] {
-  def left: BaseType
-  def right: BaseType
-
-  def children: Seq[BaseType] = Seq(left, right)
-}
 
 /**
  * A [[TreeNode]] with no children.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 4d7d8626a0ecc..9dc7879fa4a1a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -247,6 +247,11 @@ private[sql] trait UnaryNode extends SparkPlan with trees.UnaryNode[SparkPlan] {
   override def outputPartitioning: Partitioning = child.outputPartitioning
 }
 
-private[sql] trait BinaryNode extends SparkPlan with trees.BinaryNode[SparkPlan] {
+private[sql] trait BinaryNode extends SparkPlan {
   self: Product =>
+
+  def left: SparkPlan
+  def right: SparkPlan
+
+  override def children: Seq[SparkPlan] = Seq(left, right)
 }

From 6960a7938c61cc07f181ca85e0d8152ceeb453d9 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Wed, 15 Jul 2015 20:33:06 -0700
Subject: [PATCH 0402/1454] [SPARK-8774] [ML] Add R model formula with basic
 support as a transformer

This implements minimal R formula support as a feature transformer. Both numeric and string labels are supported, but features must be numeric for now.

cc mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #7381 from ericl/spark-8774-1 and squashes the following commits:

d1959d2 [Eric Liang] clarify comment
2db68aa [Eric Liang] second round of comments
dc3c943 [Eric Liang] address comments
5765ec6 [Eric Liang] fix style checks
1f361b0 [Eric Liang] doc
fb0826b [Eric Liang] [SPARK-8774] Add R model formula with basic support as a transformer
---
 .../apache/spark/ml/feature/RFormula.scala    | 151 ++++++++++++++++++
 .../spark/ml/feature/VectorAssembler.scala    |   2 +-
 .../ml/feature/RFormulaParserSuite.scala      |  34 ++++
 .../spark/ml/feature/RFormulaSuite.scala      |  93 +++++++++++
 4 files changed, 279 insertions(+), 1 deletion(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
new file mode 100644
index 0000000000000..d9a36bda386b3
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import scala.util.parsing.combinator.RegexParsers
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.param.{Param, ParamMap}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+
+/**
+ * :: Experimental ::
+ * Implements the transforms required for fitting a dataset against an R model formula. Currently
+ * we support a limited subset of the R operators, including '~' and '+'. Also see the R formula
+ * docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+ */
+@Experimental
+class RFormula(override val uid: String)
+  extends Transformer with HasFeaturesCol with HasLabelCol {
+
+  def this() = this(Identifiable.randomUID("rFormula"))
+
+  /**
+   * R formula parameter. The formula is provided in string form.
+   * @group setParam
+   */
+  val formula: Param[String] = new Param(this, "formula", "R model formula")
+
+  private var parsedFormula: Option[ParsedRFormula] = None
+
+  /**
+   * Sets the formula to use for this transformer. Must be called before use.
+   * @group setParam
+   * @param value an R formula in string form (e.g. "y ~ x + z")
+   */
+  def setFormula(value: String): this.type = {
+    parsedFormula = Some(RFormulaParser.parse(value))
+    set(formula, value)
+    this
+  }
+
+  /** @group getParam */
+  def getFormula: String = $(formula)
+
+  /** @group getParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group getParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
+
+  override def transformSchema(schema: StructType): StructType = {
+    checkCanTransform(schema)
+    val withFeatures = transformFeatures.transformSchema(schema)
+    if (hasLabelCol(schema)) {
+      withFeatures
+    } else {
+      val nullable = schema(parsedFormula.get.label).dataType match {
+        case _: NumericType | BooleanType => false
+        case _ => true
+      }
+      StructType(withFeatures.fields :+ StructField($(labelCol), DoubleType, nullable))
+    }
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    checkCanTransform(dataset.schema)
+    transformLabel(transformFeatures.transform(dataset))
+  }
+
+  override def copy(extra: ParamMap): RFormula = defaultCopy(extra)
+
+  override def toString: String = s"RFormula(${get(formula)})"
+
+  private def transformLabel(dataset: DataFrame): DataFrame = {
+    if (hasLabelCol(dataset.schema)) {
+      dataset
+    } else {
+      val labelName = parsedFormula.get.label
+      dataset.schema(labelName).dataType match {
+        case _: NumericType | BooleanType =>
+          dataset.withColumn($(labelCol), dataset(labelName).cast(DoubleType))
+        // TODO(ekl) add support for string-type labels
+        case other =>
+          throw new IllegalArgumentException("Unsupported type for label: " + other)
+      }
+    }
+  }
+
+  private def transformFeatures: Transformer = {
+    // TODO(ekl) add support for non-numeric features and feature interactions
+    new VectorAssembler(uid)
+      .setInputCols(parsedFormula.get.terms.toArray)
+      .setOutputCol($(featuresCol))
+  }
+
+  private def checkCanTransform(schema: StructType) {
+    require(parsedFormula.isDefined, "Must call setFormula() first.")
+    val columnNames = schema.map(_.name)
+    require(!columnNames.contains($(featuresCol)), "Features column already exists.")
+    require(
+      !columnNames.contains($(labelCol)) || schema($(labelCol)).dataType == DoubleType,
+      "Label column already exists and is not of type DoubleType.")
+  }
+
+  private def hasLabelCol(schema: StructType): Boolean = {
+    schema.map(_.name).contains($(labelCol))
+  }
+}
+
+/**
+ * Represents a parsed R formula.
+ */
+private[ml] case class ParsedRFormula(label: String, terms: Seq[String])
+
+/**
+ * Limited implementation of R formula parsing. Currently supports: '~', '+'.
+ */
+private[ml] object RFormulaParser extends RegexParsers {
+  def term: Parser[String] = "([a-zA-Z]|\\.[a-zA-Z_])[a-zA-Z0-9._]*".r
+
+  def expr: Parser[List[String]] = term ~ rep("+" ~> term) ^^ { case a ~ list => a :: list }
+
+  def formula: Parser[ParsedRFormula] =
+    (term ~ "~" ~ expr) ^^ { case r ~ "~" ~ t => ParsedRFormula(r, t) }
+
+  def parse(value: String): ParsedRFormula = parseAll(formula, value) match {
+    case Success(result, _) => result
+    case failure: NoSuccess => throw new IllegalArgumentException(
+      "Could not parse formula: " + value)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 9f83c2ee16178..086917fa680f8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -116,7 +116,7 @@ class VectorAssembler(override val uid: String)
     if (schema.fieldNames.contains(outputColName)) {
       throw new IllegalArgumentException(s"Output column $outputColName already exists.")
     }
-    StructType(schema.fields :+ new StructField(outputColName, new VectorUDT, false))
+    StructType(schema.fields :+ new StructField(outputColName, new VectorUDT, true))
   }
 
   override def copy(extra: ParamMap): VectorAssembler = defaultCopy(extra)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
new file mode 100644
index 0000000000000..c8d065f37a605
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+
+class RFormulaParserSuite extends SparkFunSuite {
+  private def checkParse(formula: String, label: String, terms: Seq[String]) {
+    val parsed = RFormulaParser.parse(formula)
+    assert(parsed.label == label)
+    assert(parsed.terms == terms)
+  }
+
+  test("parse simple formulas") {
+    checkParse("y ~ x", "y", Seq("x"))
+    checkParse("y ~   ._foo  ", "y", Seq("._foo"))
+    checkParse("resp ~ A_VAR + B + c123", "resp", Seq("A_VAR", "B", "c123"))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
new file mode 100644
index 0000000000000..fa8611b243a9f
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext {
+  test("params") {
+    ParamsSuite.checkParams(new RFormula())
+  }
+
+  test("transform numeric data") {
+    val formula = new RFormula().setFormula("id ~ v1 + v2")
+    val original = sqlContext.createDataFrame(
+      Seq((0, 1.0, 3.0), (2, 2.0, 5.0))).toDF("id", "v1", "v2")
+    val result = formula.transform(original)
+    val resultSchema = formula.transformSchema(original.schema)
+    val expected = sqlContext.createDataFrame(
+      Seq(
+        (0, 1.0, 3.0, Vectors.dense(Array(1.0, 3.0)), 0.0),
+        (2, 2.0, 5.0, Vectors.dense(Array(2.0, 5.0)), 2.0))
+      ).toDF("id", "v1", "v2", "features", "label")
+    // TODO(ekl) make schema comparisons ignore metadata, to avoid .toString
+    assert(result.schema.toString == resultSchema.toString)
+    assert(resultSchema == expected.schema)
+    assert(result.collect().toSeq == expected.collect().toSeq)
+  }
+
+  test("features column already exists") {
+    val formula = new RFormula().setFormula("y ~ x").setFeaturesCol("x")
+    val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "y")
+    intercept[IllegalArgumentException] {
+      formula.transformSchema(original.schema)
+    }
+    intercept[IllegalArgumentException] {
+      formula.transform(original)
+    }
+  }
+
+  test("label column already exists") {
+    val formula = new RFormula().setFormula("y ~ x").setLabelCol("y")
+    val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "y")
+    val resultSchema = formula.transformSchema(original.schema)
+    assert(resultSchema.length == 3)
+    assert(resultSchema.toString == formula.transform(original).schema.toString)
+  }
+
+  test("label column already exists but is not double type") {
+    val formula = new RFormula().setFormula("y ~ x").setLabelCol("y")
+    val original = sqlContext.createDataFrame(Seq((0, 1), (2, 2))).toDF("x", "y")
+    intercept[IllegalArgumentException] {
+      formula.transformSchema(original.schema)
+    }
+    intercept[IllegalArgumentException] {
+      formula.transform(original)
+    }
+  }
+
+// TODO(ekl) enable after we implement string label support
+//  test("transform string label") {
+//    val formula = new RFormula().setFormula("name ~ id")
+//    val original = sqlContext.createDataFrame(
+//      Seq((1, "foo"), (2, "bar"), (3, "bar"))).toDF("id", "name")
+//    val result = formula.transform(original)
+//    val resultSchema = formula.transformSchema(original.schema)
+//    val expected = sqlContext.createDataFrame(
+//      Seq(
+//        (1, "foo", Vectors.dense(Array(1.0)), 1.0),
+//        (2, "bar", Vectors.dense(Array(2.0)), 0.0),
+//        (3, "bar", Vectors.dense(Array(3.0)), 0.0))
+//      ).toDF("id", "name", "features", "label")
+//    assert(result.schema.toString == resultSchema.toString)
+//    assert(result.collect().toSeq == expected.collect().toSeq)
+//  }
+}

From 73d92b00b9a6f5dfc2f8116447d17b381cd74f80 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 15 Jul 2015 21:02:42 -0700
Subject: [PATCH 0403/1454] [SPARK-9018] [MLLIB] add stopwatches

Add stopwatches for easy instrumentation of MLlib algorithms. This is based on the `TimeTracker` used in decision trees. The distributed version uses Spark accumulator. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #7415 from mengxr/SPARK-9018 and squashes the following commits:

40b4347 [Xiangrui Meng] == -> ===
c477745 [Xiangrui Meng] address Joseph's comments
f981a49 [Xiangrui Meng] add stopwatches
---
 .../apache/spark/ml/util/stopwatches.scala    | 151 ++++++++++++++++++
 .../apache/spark/ml/util/StopwatchSuite.scala | 109 +++++++++++++
 2 files changed, 260 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala b/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala
new file mode 100644
index 0000000000000..5fdf878a3df72
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.util
+
+import scala.collection.mutable
+
+import org.apache.spark.{Accumulator, SparkContext}
+
+/**
+ * Abstract class for stopwatches.
+ */
+private[spark] abstract class Stopwatch extends Serializable {
+
+  @transient private var running: Boolean = false
+  private var startTime: Long = _
+
+  /**
+   * Name of the stopwatch.
+   */
+  val name: String
+
+  /**
+   * Starts the stopwatch.
+   * Throws an exception if the stopwatch is already running.
+   */
+  def start(): Unit = {
+    assume(!running, "start() called but the stopwatch is already running.")
+    running = true
+    startTime = now
+  }
+
+  /**
+   * Stops the stopwatch and returns the duration of the last session in milliseconds.
+   * Throws an exception if the stopwatch is not running.
+   */
+  def stop(): Long = {
+    assume(running, "stop() called but the stopwatch is not running.")
+    val duration = now - startTime
+    add(duration)
+    running = false
+    duration
+  }
+
+  /**
+   * Checks whether the stopwatch is running.
+   */
+  def isRunning: Boolean = running
+
+  /**
+   * Returns total elapsed time in milliseconds, not counting the current session if the stopwatch
+   * is running.
+   */
+  def elapsed(): Long
+
+  /**
+   * Gets the current time in milliseconds.
+   */
+  protected def now: Long = System.currentTimeMillis()
+
+  /**
+   * Adds input duration to total elapsed time.
+   */
+  protected def add(duration: Long): Unit
+}
+
+/**
+ * A local [[Stopwatch]].
+ */
+private[spark] class LocalStopwatch(override val name: String) extends Stopwatch {
+
+  private var elapsedTime: Long = 0L
+
+  override def elapsed(): Long = elapsedTime
+
+  override protected def add(duration: Long): Unit = {
+    elapsedTime += duration
+  }
+}
+
+/**
+ * A distributed [[Stopwatch]] using Spark accumulator.
+ * @param sc SparkContext
+ */
+private[spark] class DistributedStopwatch(
+    sc: SparkContext,
+    override val name: String) extends Stopwatch {
+
+  private val elapsedTime: Accumulator[Long] = sc.accumulator(0L, s"DistributedStopwatch($name)")
+
+  override def elapsed(): Long = elapsedTime.value
+
+  override protected def add(duration: Long): Unit = {
+    elapsedTime += duration
+  }
+}
+
+/**
+ * A multiple stopwatch that contains local and distributed stopwatches.
+ * @param sc SparkContext
+ */
+private[spark] class MultiStopwatch(@transient private val sc: SparkContext) extends Serializable {
+
+  private val stopwatches: mutable.Map[String, Stopwatch] = mutable.Map.empty
+
+  /**
+   * Adds a local stopwatch.
+   * @param name stopwatch name
+   */
+  def addLocal(name: String): this.type = {
+    require(!stopwatches.contains(name), s"Stopwatch with name $name already exists.")
+    stopwatches(name) = new LocalStopwatch(name)
+    this
+  }
+
+  /**
+   * Adds a distributed stopwatch.
+   * @param name stopwatch name
+   */
+  def addDistributed(name: String): this.type = {
+    require(!stopwatches.contains(name), s"Stopwatch with name $name already exists.")
+    stopwatches(name) = new DistributedStopwatch(sc, name)
+    this
+  }
+
+  /**
+   * Gets a stopwatch.
+   * @param name stopwatch name
+   */
+  def apply(name: String): Stopwatch = stopwatches(name)
+
+  override def toString: String = {
+    stopwatches.values.toArray.sortBy(_.name)
+      .map(c => s"  ${c.name}: ${c.elapsed()}ms")
+      .mkString("{\n", ",\n", "\n}")
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala
new file mode 100644
index 0000000000000..8df6617fe0228
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala
@@ -0,0 +1,109 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.util
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class StopwatchSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  private def testStopwatchOnDriver(sw: Stopwatch): Unit = {
+    assert(sw.name === "sw")
+    assert(sw.elapsed() === 0L)
+    assert(!sw.isRunning)
+    intercept[AssertionError] {
+      sw.stop()
+    }
+    sw.start()
+    Thread.sleep(50)
+    val duration = sw.stop()
+    assert(duration >= 50 && duration < 100) // using a loose upper bound
+    val elapsed = sw.elapsed()
+    assert(elapsed === duration)
+    sw.start()
+    Thread.sleep(50)
+    val duration2 = sw.stop()
+    assert(duration2 >= 50 && duration2 < 100)
+    val elapsed2 = sw.elapsed()
+    assert(elapsed2 === duration + duration2)
+    sw.start()
+    assert(sw.isRunning)
+    intercept[AssertionError] {
+      sw.start()
+    }
+  }
+
+  test("LocalStopwatch") {
+    val sw = new LocalStopwatch("sw")
+    testStopwatchOnDriver(sw)
+  }
+
+  test("DistributedStopwatch on driver") {
+    val sw = new DistributedStopwatch(sc, "sw")
+    testStopwatchOnDriver(sw)
+  }
+
+  test("DistributedStopwatch on executors") {
+    val sw = new DistributedStopwatch(sc, "sw")
+    val rdd = sc.parallelize(0 until 4, 4)
+    rdd.foreach { i =>
+      sw.start()
+      Thread.sleep(50)
+      sw.stop()
+    }
+    assert(!sw.isRunning)
+    val elapsed = sw.elapsed()
+    assert(elapsed >= 200 && elapsed < 400) // using a loose upper bound
+  }
+
+  test("MultiStopwatch") {
+    val sw = new MultiStopwatch(sc)
+      .addLocal("local")
+      .addDistributed("spark")
+    assert(sw("local").name === "local")
+    assert(sw("spark").name === "spark")
+    intercept[NoSuchElementException] {
+      sw("some")
+    }
+    assert(sw.toString === "{\n  local: 0ms,\n  spark: 0ms\n}")
+    sw("local").start()
+    sw("spark").start()
+    Thread.sleep(50)
+    sw("local").stop()
+    Thread.sleep(50)
+    sw("spark").stop()
+    val localElapsed = sw("local").elapsed()
+    val sparkElapsed = sw("spark").elapsed()
+    assert(localElapsed >= 50 && localElapsed < 100)
+    assert(sparkElapsed >= 100 && sparkElapsed < 200)
+    assert(sw.toString ===
+      s"{\n  local: ${localElapsed}ms,\n  spark: ${sparkElapsed}ms\n}")
+    val rdd = sc.parallelize(0 until 4, 4)
+    rdd.foreach { i =>
+      sw("local").start()
+      sw("spark").start()
+      Thread.sleep(50)
+      sw("spark").stop()
+      sw("local").stop()
+    }
+    val localElapsed2 = sw("local").elapsed()
+    assert(localElapsed2 === localElapsed)
+    val sparkElapsed2 = sw("spark").elapsed()
+    assert(sparkElapsed2 >= 300 && sparkElapsed2 < 600)
+  }
+}

From 9c64a75bfc5e2566d1b4cd0d9b4585a818086ca6 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 15 Jul 2015 21:08:30 -0700
Subject: [PATCH 0404/1454] [SPARK-9060] [SQL] Revert SPARK-8359, SPARK-8800,
 and SPARK-8677

JIRA: https://issues.apache.org/jira/browse/SPARK-9060

This PR reverts:
* https://github.com/apache/spark/commit/31bd30687bc29c0e457c37308d489ae2b6e5b72a (SPARK-8359)
* https://github.com/apache/spark/commit/24fda7381171738cbbbacb5965393b660763e562 (SPARK-8677)
* https://github.com/apache/spark/commit/4b5cfc988f23988c2334882a255d494fc93d252e (SPARK-8800)

Author: Yin Huai <yhuai@databricks.com>

Closes #7426 from yhuai/SPARK-9060 and squashes the following commits:

651264d [Yin Huai] Revert "[SPARK-8359] [SQL] Fix incorrect decimal precision after multiplication"
cfda7e4 [Yin Huai] Revert "[SPARK-8677] [SQL] Fix non-terminating decimal expansion for decimal divide operation"
2de9afe [Yin Huai] Revert "[SPARK-8800] [SQL] Fix inaccurate precision/scale of Decimal division operation"
---
 .../org/apache/spark/sql/types/Decimal.scala  | 21 ++-----------------
 .../sql/types/decimal/DecimalSuite.scala      | 18 ----------------
 2 files changed, 2 insertions(+), 37 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index f5bd068d60dc4..a85af9e04aedb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.types
 
-import java.math.{MathContext, RoundingMode}
-
 import org.apache.spark.annotation.DeveloperApi
 
 /**
@@ -138,14 +136,6 @@ final class Decimal extends Ordered[Decimal] with Serializable {
   }
 
   def toBigDecimal: BigDecimal = {
-    if (decimalVal.ne(null)) {
-      decimalVal(MathContext.UNLIMITED)
-    } else {
-      BigDecimal(longVal, _scale)(MathContext.UNLIMITED)
-    }
-  }
-
-  def toLimitedBigDecimal: BigDecimal = {
     if (decimalVal.ne(null)) {
       decimalVal
     } else {
@@ -273,15 +263,8 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def * (that: Decimal): Decimal = Decimal(toBigDecimal * that.toBigDecimal)
 
-  def / (that: Decimal): Decimal = {
-    if (that.isZero) {
-      null
-    } else {
-      // To avoid non-terminating decimal expansion problem, we get scala's BigDecimal with limited
-      // precision and scala.
-      Decimal(toLimitedBigDecimal / that.toLimitedBigDecimal)
-    }
-  }
+  def / (that: Decimal): Decimal =
+    if (that.isZero) null else Decimal(toBigDecimal / that.toBigDecimal)
 
   def % (that: Decimal): Decimal =
     if (that.isZero) null else Decimal(toBigDecimal % that.toBigDecimal)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
index f0c849d1a1564..1d297beb3868d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
@@ -171,22 +171,4 @@ class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
     assert(new Decimal().set(100L, 10, 0).toUnscaledLong === 100L)
     assert(Decimal(Long.MaxValue, 100, 0).toUnscaledLong === Long.MaxValue)
   }
-
-  test("accurate precision after multiplication") {
-    val decimal = (Decimal(Long.MaxValue, 38, 0) * Decimal(Long.MaxValue, 38, 0)).toJavaBigDecimal
-    assert(decimal.unscaledValue.toString === "85070591730234615847396907784232501249")
-  }
-
-  test("fix non-terminating decimal expansion problem") {
-    val decimal = Decimal(1.0, 10, 3) / Decimal(3.0, 10, 3)
-    // The difference between decimal should not be more than 0.001.
-    assert(decimal.toDouble - 0.333 < 0.001)
-  }
-
-  test("fix loss of precision/scale when doing division operation") {
-    val a = Decimal(2) / Decimal(3)
-    assert(a.toDouble < 1.0 && a.toDouble > 0.6)
-    val b = Decimal(1) / Decimal(8)
-    assert(b.toDouble === 0.125)
-  }
 }

From 42dea3acf90ec506a0b79720b55ae1d753cc7544 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 15 Jul 2015 21:47:21 -0700
Subject: [PATCH 0405/1454] [SPARK-8245][SQL] FormatNumber/Length Support for
 Expression

- `BinaryType` for `Length`
- `FormatNumber`

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7034 from chenghao-intel/expression and squashes the following commits:

e534b87 [Cheng Hao] python api style issue
601bbf5 [Cheng Hao] add python API support
3ebe288 [Cheng Hao] update as feedback
52274f7 [Cheng Hao] add support for udf_format_number and length for binary
---
 python/pyspark/sql/functions.py               | 25 ++++-
 .../catalyst/analysis/FunctionRegistry.scala  |  5 +-
 .../expressions/stringOperations.scala        | 94 +++++++++++++++++--
 .../expressions/StringFunctionsSuite.scala    | 53 +++++++++--
 .../org/apache/spark/sql/functions.scala      | 32 ++++++-
 .../spark/sql/DataFrameFunctionsSuite.scala   | 93 +++++++++++++++---
 6 files changed, 261 insertions(+), 41 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index dca39fa833435..e0816b3e654bc 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -39,6 +39,8 @@
     'coalesce',
     'countDistinct',
     'explode',
+    'format_number',
+    'length',
     'log2',
     'md5',
     'monotonicallyIncreasingId',
@@ -47,7 +49,6 @@
     'sha1',
     'sha2',
     'sparkPartitionId',
-    'strlen',
     'struct',
     'udf',
     'when']
@@ -506,14 +507,28 @@ def sparkPartitionId():
 
 @ignore_unicode_prefix
 @since(1.5)
-def strlen(col):
-    """Calculates the length of a string expression.
+def length(col):
+    """Calculates the length of a string or binary expression.
 
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(strlen('a').alias('length')).collect()
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(length('a').alias('length')).collect()
     [Row(length=3)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.strlen(_to_java_column(col)))
+    return Column(sc._jvm.functions.length(_to_java_column(col)))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def format_number(col, d):
+    """Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
+       and returns the result as a string.
+    :param col: the column name of the numeric value to be formatted
+    :param d: the N decimal places
+    >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
+    [Row(v=u'5.0000')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.format_number(_to_java_column(col), d))
 
 
 @ignore_unicode_prefix
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index d2678ce860701..e0beafe710079 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -152,11 +152,12 @@ object FunctionRegistry {
     expression[Base64]("base64"),
     expression[Encode]("encode"),
     expression[Decode]("decode"),
-    expression[StringInstr]("instr"),
+    expression[FormatNumber]("format_number"),
     expression[Lower]("lcase"),
     expression[Lower]("lower"),
-    expression[StringLength]("length"),
+    expression[Length]("length"),
     expression[Levenshtein]("levenshtein"),
+    expression[StringInstr]("instr"),
     expression[StringLocate]("locate"),
     expression[StringLPad]("lpad"),
     expression[StringTrimLeft]("ltrim"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 03b55ce5fe7cc..c64afe7b3f19a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import java.text.DecimalFormat
 import java.util.Locale
 import java.util.regex.Pattern
 
-import org.apache.commons.lang3.StringUtils
-
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.catalyst.expressions.codegen._
@@ -553,17 +552,22 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
 }
 
 /**
- * A function that return the length of the given string expression.
+ * A function that return the length of the given string or binary expression.
  */
-case class StringLength(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+case class Length(child: Expression) extends UnaryExpression with ExpectsInputTypes {
   override def dataType: DataType = IntegerType
-  override def inputTypes: Seq[DataType] = Seq(StringType)
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(StringType, BinaryType))
 
-  protected override def nullSafeEval(string: Any): Any =
-    string.asInstanceOf[UTF8String].numChars
+  protected override def nullSafeEval(value: Any): Any = child.dataType match {
+    case StringType => value.asInstanceOf[UTF8String].numChars
+    case BinaryType => value.asInstanceOf[Array[Byte]].length
+  }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    defineCodeGen(ctx, ev, c => s"($c).numChars()")
+    child.dataType match {
+      case StringType => defineCodeGen(ctx, ev, c => s"($c).numChars()")
+      case BinaryType => defineCodeGen(ctx, ev, c => s"($c).length")
+    }
   }
 
   override def prettyName: String = "length"
@@ -668,3 +672,77 @@ case class Encode(value: Expression, charset: Expression)
   }
 }
 
+/**
+ * Formats the number X to a format like '#,###,###.##', rounded to D decimal places,
+ * and returns the result as a string. If D is 0, the result has no decimal point or
+ * fractional part.
+ */
+case class FormatNumber(x: Expression, d: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def left: Expression = x
+  override def right: Expression = d
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType, IntegerType)
+
+  // Associated with the pattern, for the last d value, and we will update the
+  // pattern (DecimalFormat) once the new coming d value differ with the last one.
+  @transient
+  private var lastDValue: Int = -100
+
+  // A cached DecimalFormat, for performance concern, we will change it
+  // only if the d value changed.
+  @transient
+  private val pattern: StringBuffer = new StringBuffer()
+
+  @transient
+  private val numberFormat: DecimalFormat = new DecimalFormat("")
+
+  override def eval(input: InternalRow): Any = {
+    val xObject = x.eval(input)
+    if (xObject == null) {
+      return null
+    }
+
+    val dObject = d.eval(input)
+
+    if (dObject == null || dObject.asInstanceOf[Int] < 0) {
+      return null
+    }
+    val dValue = dObject.asInstanceOf[Int]
+
+    if (dValue != lastDValue) {
+      // construct a new DecimalFormat only if a new dValue
+      pattern.delete(0, pattern.length())
+      pattern.append("#,###,###,###,###,###,##0")
+
+      // decimal place
+      if (dValue > 0) {
+        pattern.append(".")
+
+        var i = 0
+        while (i < dValue) {
+          i += 1
+          pattern.append("0")
+        }
+      }
+      val dFormat = new DecimalFormat(pattern.toString())
+      lastDValue = dValue;
+      numberFormat.applyPattern(dFormat.toPattern())
+    }
+
+    x.dataType match {
+      case ByteType => UTF8String.fromString(numberFormat.format(xObject.asInstanceOf[Byte]))
+      case ShortType => UTF8String.fromString(numberFormat.format(xObject.asInstanceOf[Short]))
+      case FloatType => UTF8String.fromString(numberFormat.format(xObject.asInstanceOf[Float]))
+      case IntegerType => UTF8String.fromString(numberFormat.format(xObject.asInstanceOf[Int]))
+      case LongType => UTF8String.fromString(numberFormat.format(xObject.asInstanceOf[Long]))
+      case DoubleType => UTF8String.fromString(numberFormat.format(xObject.asInstanceOf[Double]))
+      case _: DecimalType =>
+        UTF8String.fromString(numberFormat.format(xObject.asInstanceOf[Decimal].toJavaBigDecimal))
+    }
+  }
+
+  override def prettyName: String = "format_number"
+}
+
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
index b19f4ee37a109..5d7763bedf6bd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{BinaryType, IntegerType, StringType}
+import org.apache.spark.sql.types._
 
 
 class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -216,15 +216,6 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
-  test("length for string") {
-    val a = 'a.string.at(0)
-    checkEvaluation(StringLength(Literal("abc")), 3, create_row("abdef"))
-    checkEvaluation(StringLength(a), 5, create_row("abdef"))
-    checkEvaluation(StringLength(a), 0, create_row(""))
-    checkEvaluation(StringLength(a), null, create_row(null))
-    checkEvaluation(StringLength(Literal.create(null, StringType)), null, create_row("abdef"))
-  }
-
   test("ascii for string") {
     val a = 'a.string.at(0)
     checkEvaluation(Ascii(Literal("efg")), 101, create_row("abdef"))
@@ -426,4 +417,46 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       StringSplit(s1, s2), Seq("aa", "bb", "cc"), row1)
   }
+
+  test("length for string / binary") {
+    val a = 'a.string.at(0)
+    val b = 'b.binary.at(0)
+    val bytes = Array[Byte](1, 2, 3, 1, 2)
+    val string = "abdef"
+
+    // scalastyle:off
+    // non ascii characters are not allowed in the source code, so we disable the scalastyle.
+    checkEvaluation(Length(Literal("a花花c")), 4, create_row(string))
+    // scalastyle:on
+    checkEvaluation(Length(Literal(bytes)), 5, create_row(Array[Byte]()))
+
+    checkEvaluation(Length(a), 5, create_row(string))
+    checkEvaluation(Length(b), 5, create_row(bytes))
+
+    checkEvaluation(Length(a), 0, create_row(""))
+    checkEvaluation(Length(b), 0, create_row(Array[Byte]()))
+
+    checkEvaluation(Length(a), null, create_row(null))
+    checkEvaluation(Length(b), null, create_row(null))
+
+    checkEvaluation(Length(Literal.create(null, StringType)), null, create_row(string))
+    checkEvaluation(Length(Literal.create(null, BinaryType)), null, create_row(bytes))
+  }
+
+  test("number format") {
+    checkEvaluation(FormatNumber(Literal(4.asInstanceOf[Byte]), Literal(3)), "4.000")
+    checkEvaluation(FormatNumber(Literal(4.asInstanceOf[Short]), Literal(3)), "4.000")
+    checkEvaluation(FormatNumber(Literal(4.0f), Literal(3)), "4.000")
+    checkEvaluation(FormatNumber(Literal(4), Literal(3)), "4.000")
+    checkEvaluation(FormatNumber(Literal(12831273.23481d), Literal(3)), "12,831,273.235")
+    checkEvaluation(FormatNumber(Literal(12831273.83421d), Literal(0)), "12,831,274")
+    checkEvaluation(FormatNumber(Literal(123123324123L), Literal(3)), "123,123,324,123.000")
+    checkEvaluation(FormatNumber(Literal(123123324123L), Literal(-1)), null)
+    checkEvaluation(
+      FormatNumber(
+        Literal(Decimal(123123324123L) * Decimal(123123.21234d)), Literal(4)),
+      "15,159,339,180,002,773.2778")
+    checkEvaluation(FormatNumber(Literal.create(null, IntegerType), Literal(3)), null)
+    checkEvaluation(FormatNumber(Literal.create(null, NullType), Literal(3)), null)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index c7deaca8437a1..d6da284a4c788 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1685,20 +1685,44 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Computes the length of a given string value.
+   * Computes the length of a given string / binary value.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def strlen(e: Column): Column = StringLength(e.expr)
+  def length(e: Column): Column = Length(e.expr)
 
   /**
-   * Computes the length of a given string column.
+   * Computes the length of a given string / binary column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def strlen(columnName: String): Column = strlen(Column(columnName))
+  def length(columnName: String): Column = length(Column(columnName))
+
+  /**
+   * Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
+   * and returns the result as a string.
+   * If d is 0, the result has no decimal point or fractional part.
+   * If d < 0, the result will be null.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def format_number(x: Column, d: Int): Column = FormatNumber(x.expr, lit(d).expr)
+
+  /**
+   * Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
+   * and returns the result as a string.
+   * If d is 0, the result has no decimal point or fractional part.
+   * If d < 0, the result will be null.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def format_number(columnXName: String, d: Int): Column = {
+    format_number(Column(columnXName), d)
+  }
 
   /**
    * Computes the Levenshtein distance of the two given strings.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 70bd78737f69c..6dccdd857b453 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -208,17 +208,6 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(2743272264L, 2180413220L))
   }
 
-  test("string length function") {
-    val df = Seq(("abc", "")).toDF("a", "b")
-    checkAnswer(
-      df.select(strlen($"a"), strlen("b")),
-      Row(3, 0))
-
-    checkAnswer(
-      df.selectExpr("length(a)", "length(b)"),
-      Row(3, 0))
-  }
-
   test("Levenshtein distance") {
     val df = Seq(("kitten", "sitting"), ("frog", "fog")).toDF("l", "r")
     checkAnswer(df.select(levenshtein("l", "r")), Seq(Row(3), Row(1)))
@@ -433,11 +422,91 @@ class DataFrameFunctionsSuite extends QueryTest {
     val doubleData = Seq((7.2, 4.1)).toDF("a", "b")
     checkAnswer(
       doubleData.select(pmod('a, 'b)),
-      Seq(Row(3.1000000000000005))  // same as hive
+      Seq(Row(3.1000000000000005)) // same as hive
     )
     checkAnswer(
       doubleData.select(pmod(lit(2), lit(Int.MaxValue))),
       Seq(Row(2))
     )
   }
+
+  test("string / binary length function") {
+    val df = Seq(("123", Array[Byte](1, 2, 3, 4), 123)).toDF("a", "b", "c")
+    checkAnswer(
+      df.select(length($"a"), length("a"), length($"b"), length("b")),
+      Row(3, 3, 4, 4))
+
+    checkAnswer(
+      df.selectExpr("length(a)", "length(b)"),
+      Row(3, 4))
+
+    intercept[AnalysisException] {
+      checkAnswer(
+        df.selectExpr("length(c)"), // int type of the argument is unacceptable
+        Row("5.0000"))
+    }
+  }
+
+  test("number format function") {
+    val tuple =
+      ("aa", 1.asInstanceOf[Byte], 2.asInstanceOf[Short],
+        3.13223f, 4, 5L, 6.48173d, Decimal(7.128381))
+    val df =
+      Seq(tuple)
+        .toDF(
+          "a", // string "aa"
+          "b", // byte    1
+          "c", // short   2
+          "d", // float   3.13223f
+          "e", // integer 4
+          "f", // long    5L
+          "g", // double  6.48173d
+          "h") // decimal 7.128381
+
+    checkAnswer(
+      df.select(
+        format_number($"f", 4),
+        format_number("f", 4)),
+      Row("5.0000", "5.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(b, e)"), // convert the 1st argument to integer
+      Row("1.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(c, e)"), // convert the 1st argument to integer
+      Row("2.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(d, e)"), // convert the 1st argument to double
+      Row("3.1322"))
+
+    checkAnswer(
+      df.selectExpr("format_number(e, e)"), // not convert anything
+      Row("4.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(f, e)"), // not convert anything
+      Row("5.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(g, e)"), // not convert anything
+      Row("6.4817"))
+
+    checkAnswer(
+      df.selectExpr("format_number(h, e)"), // not convert anything
+      Row("7.1284"))
+
+    intercept[AnalysisException] {
+      checkAnswer(
+        df.selectExpr("format_number(a, e)"), // string type of the 1st argument is unacceptable
+        Row("5.0000"))
+    }
+
+    intercept[AnalysisException] {
+      checkAnswer(
+        df.selectExpr("format_number(e, g)"), // decimal type of the 2nd argument is unacceptable
+        Row("5.0000"))
+    }
+  }
 }

From ba33096846dc8061e97a7bf8f3b46f899d530159 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 15 Jul 2015 22:27:39 -0700
Subject: [PATCH 0406/1454] [SPARK-9068][SQL] refactor the implicit type cast
 code

based on https://github.com/apache/spark/pull/7348

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7420 from cloud-fan/type-check and squashes the following commits:

7633fa9 [Wenchen Fan] revert
fe169b0 [Wenchen Fan] improve test
03b70da [Wenchen Fan] enhance implicit type cast
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 33 +++-----
 .../sql/catalyst/expressions/Expression.scala | 20 +++--
 .../sql/catalyst/expressions/arithmetic.scala |  2 -
 .../sql/catalyst/expressions/bitwise.scala    |  8 +-
 .../catalyst/expressions/conditionals.scala   |  4 +-
 .../spark/sql/types/AbstractDataType.scala    | 45 +++--------
 .../apache/spark/sql/types/ArrayType.scala    |  2 +-
 .../org/apache/spark/sql/types/DataType.scala |  2 +-
 .../apache/spark/sql/types/DecimalType.scala  |  2 +-
 .../org/apache/spark/sql/types/MapType.scala  |  2 +-
 .../apache/spark/sql/types/StructType.scala   |  2 +-
 .../ExpressionTypeCheckingSuite.scala         | 75 +++++++++----------
 .../analysis/HiveTypeCoercionSuite.scala      | 10 +--
 13 files changed, 81 insertions(+), 126 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 25087915b5c35..50db7d21f01ca 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -675,10 +675,10 @@ object HiveTypeCoercion {
       case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
         findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { commonType =>
           if (b.inputType.acceptsType(commonType)) {
-            // If the expression accepts the tighest common type, cast to that.
+            // If the expression accepts the tightest common type, cast to that.
             val newLeft = if (left.dataType == commonType) left else Cast(left, commonType)
             val newRight = if (right.dataType == commonType) right else Cast(right, commonType)
-            b.makeCopy(Array(newLeft, newRight))
+            b.withNewChildren(Seq(newLeft, newRight))
           } else {
             // Otherwise, don't do anything with the expression.
             b
@@ -697,7 +697,7 @@ object HiveTypeCoercion {
         // general implicit casting.
         val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) =>
           if (in.dataType == NullType && !expected.acceptsType(NullType)) {
-            Cast(in, expected.defaultConcreteType)
+            Literal.create(null, expected.defaultConcreteType)
           } else {
             in
           }
@@ -719,27 +719,22 @@ object HiveTypeCoercion {
       @Nullable val ret: Expression = (inType, expectedType) match {
 
         // If the expected type is already a parent of the input type, no need to cast.
-        case _ if expectedType.isSameType(inType) => e
+        case _ if expectedType.acceptsType(inType) => e
 
         // Cast null type (usually from null literals) into target types
         case (NullType, target) => Cast(e, target.defaultConcreteType)
 
-        // If the function accepts any numeric type (i.e. the ADT `NumericType`) and the input is
-        // already a number, leave it as is.
-        case (_: NumericType, NumericType) => e
-
         // If the function accepts any numeric type and the input is a string, we follow the hive
         // convention and cast that input into a double
         case (StringType, NumericType) => Cast(e, NumericType.defaultConcreteType)
 
-        // Implicit cast among numeric types
+        // Implicit cast among numeric types. When we reach here, input type is not acceptable.
+
         // If input is a numeric type but not decimal, and we expect a decimal type,
         // cast the input to unlimited precision decimal.
-        case (_: NumericType, DecimalType) if !inType.isInstanceOf[DecimalType] =>
-          Cast(e, DecimalType.Unlimited)
+        case (_: NumericType, DecimalType) => Cast(e, DecimalType.Unlimited)
         // For any other numeric types, implicitly cast to each other, e.g. long -> int, int -> long
-        case (_: NumericType, target: NumericType) if e.dataType != target => Cast(e, target)
-        case (_: NumericType, target: NumericType) => e
+        case (_: NumericType, target: NumericType) => Cast(e, target)
 
         // Implicit cast between date time types
         case (DateType, TimestampType) => Cast(e, TimestampType)
@@ -753,15 +748,9 @@ object HiveTypeCoercion {
         case (StringType, BinaryType) => Cast(e, BinaryType)
         case (any, StringType) if any != StringType => Cast(e, StringType)
 
-        // Type collection.
-        // First see if we can find our input type in the type collection. If we can, then just
-        // use the current expression; otherwise, find the first one we can implicitly cast.
-        case (_, TypeCollection(types)) =>
-          if (types.exists(_.isSameType(inType))) {
-            e
-          } else {
-            types.flatMap(implicitCast(e, _)).headOption.orNull
-          }
+        // When we reach here, input type is not acceptable for any types in this type collection,
+        // try to find the first one we can implicitly cast.
+        case (_, TypeCollection(types)) => types.flatMap(implicitCast(e, _)).headOption.orNull
 
         // Else, just return the same input expression
         case _ => null
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 87667316aca67..a655cc8e48ae1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -386,17 +386,15 @@ abstract class BinaryOperator extends BinaryExpression with ExpectsInputTypes {
   override def inputTypes: Seq[AbstractDataType] = Seq(inputType, inputType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
-    // First call the checker for ExpectsInputTypes, and then check whether left and right have
-    // the same type.
-    super.checkInputDataTypes() match {
-      case TypeCheckResult.TypeCheckSuccess =>
-        if (left.dataType != right.dataType) {
-          TypeCheckResult.TypeCheckFailure(s"differing types in '$prettyString' " +
-            s"(${left.dataType.simpleString} and ${right.dataType.simpleString}).")
-        } else {
-          TypeCheckResult.TypeCheckSuccess
-        }
-      case TypeCheckResult.TypeCheckFailure(msg) => TypeCheckResult.TypeCheckFailure(msg)
+    // First check whether left and right have the same type, then check if the type is acceptable.
+    if (left.dataType != right.dataType) {
+      TypeCheckResult.TypeCheckFailure(s"differing types in '$prettyString' " +
+        s"(${left.dataType.simpleString} and ${right.dataType.simpleString}).")
+    } else if (!inputType.acceptsType(left.dataType)) {
+      TypeCheckResult.TypeCheckFailure(s"'$prettyString' accepts ${inputType.simpleString} type," +
+        s" not ${left.dataType.simpleString}")
+    } else {
+      TypeCheckResult.TypeCheckSuccess
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 394ef556e04a2..382cbe3b84a07 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -320,7 +320,6 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
   }
 
   override def symbol: String = "max"
-  override def prettyName: String = symbol
 }
 
 case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
@@ -375,7 +374,6 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
   }
 
   override def symbol: String = "min"
-  override def prettyName: String = symbol
 }
 
 case class Pmod(left: Expression, right: Expression) extends BinaryArithmetic {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
index af1abbcd2239b..a1e48c4210877 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.types._
  */
 case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithmetic {
 
-  override def inputType: AbstractDataType = TypeCollection.Bitwise
+  override def inputType: AbstractDataType = IntegralType
 
   override def symbol: String = "&"
 
@@ -53,7 +53,7 @@ case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithme
  */
 case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmetic {
 
-  override def inputType: AbstractDataType = TypeCollection.Bitwise
+  override def inputType: AbstractDataType = IntegralType
 
   override def symbol: String = "|"
 
@@ -78,7 +78,7 @@ case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmet
  */
 case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithmetic {
 
-  override def inputType: AbstractDataType = TypeCollection.Bitwise
+  override def inputType: AbstractDataType = IntegralType
 
   override def symbol: String = "^"
 
@@ -101,7 +101,7 @@ case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithme
  */
 case class BitwiseNot(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
-  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection.Bitwise)
+  override def inputTypes: Seq[AbstractDataType] = Seq(IntegralType)
 
   override def dataType: DataType = child.dataType
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index c7f039ede26b3..9162b73fe56eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -35,8 +35,8 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
       TypeCheckResult.TypeCheckFailure(
         s"type of predicate expression in If should be boolean, not ${predicate.dataType}")
     } else if (trueValue.dataType != falseValue.dataType) {
-      TypeCheckResult.TypeCheckFailure(
-        s"differing types in If (${trueValue.dataType} and ${falseValue.dataType}).")
+      TypeCheckResult.TypeCheckFailure(s"differing types in '$prettyString' " +
+        s"(${trueValue.dataType.simpleString} and ${falseValue.dataType.simpleString}).")
     } else {
       TypeCheckResult.TypeCheckSuccess
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index f5715f7a829ff..076d7b5a5118d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -34,32 +34,18 @@ private[sql] abstract class AbstractDataType {
   private[sql] def defaultConcreteType: DataType
 
   /**
-   * Returns true if this data type is the same type as `other`.  This is different that equality
-   * as equality will also consider data type parametrization, such as decimal precision.
+   * Returns true if `other` is an acceptable input type for a function that expects this,
+   * possibly abstract DataType.
    *
    * {{{
    *   // this should return true
-   *   DecimalType.isSameType(DecimalType(10, 2))
-   *
-   *   // this should return false
-   *   NumericType.isSameType(DecimalType(10, 2))
-   * }}}
-   */
-  private[sql] def isSameType(other: DataType): Boolean
-
-  /**
-   * Returns true if `other` is an acceptable input type for a function that expectes this,
-   * possibly abstract, DataType.
-   *
-   * {{{
-   *   // this should return true
-   *   DecimalType.isSameType(DecimalType(10, 2))
+   *   DecimalType.acceptsType(DecimalType(10, 2))
    *
    *   // this should return true as well
    *   NumericType.acceptsType(DecimalType(10, 2))
    * }}}
    */
-  private[sql] def acceptsType(other: DataType): Boolean = isSameType(other)
+  private[sql] def acceptsType(other: DataType): Boolean
 
   /** Readable string representation for the type. */
   private[sql] def simpleString: String
@@ -83,10 +69,8 @@ private[sql] class TypeCollection(private val types: Seq[AbstractDataType])
 
   override private[sql] def defaultConcreteType: DataType = types.head.defaultConcreteType
 
-  override private[sql] def isSameType(other: DataType): Boolean = false
-
   override private[sql] def acceptsType(other: DataType): Boolean =
-    types.exists(_.isSameType(other))
+    types.exists(_.acceptsType(other))
 
   override private[sql] def simpleString: String = {
     types.map(_.simpleString).mkString("(", " or ", ")")
@@ -107,13 +91,6 @@ private[sql] object TypeCollection {
     TimestampType, DateType,
     StringType, BinaryType)
 
-  /**
-   * Types that can be used in bitwise operations.
-   */
-  val Bitwise = TypeCollection(
-    BooleanType,
-    ByteType, ShortType, IntegerType, LongType)
-
   def apply(types: AbstractDataType*): TypeCollection = new TypeCollection(types)
 
   def unapply(typ: AbstractDataType): Option[Seq[AbstractDataType]] = typ match {
@@ -134,8 +111,6 @@ protected[sql] object AnyDataType extends AbstractDataType {
 
   override private[sql] def simpleString: String = "any"
 
-  override private[sql] def isSameType(other: DataType): Boolean = false
-
   override private[sql] def acceptsType(other: DataType): Boolean = true
 }
 
@@ -183,13 +158,11 @@ private[sql] object NumericType extends AbstractDataType {
 
   override private[sql] def simpleString: String = "numeric"
 
-  override private[sql] def isSameType(other: DataType): Boolean = false
-
   override private[sql] def acceptsType(other: DataType): Boolean = other.isInstanceOf[NumericType]
 }
 
 
-private[sql] object IntegralType {
+private[sql] object IntegralType extends AbstractDataType {
   /**
    * Enables matching against IntegralType for expressions:
    * {{{
@@ -198,6 +171,12 @@ private[sql] object IntegralType {
    * }}}
    */
   def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[IntegralType]
+
+  override private[sql] def defaultConcreteType: DataType = IntegerType
+
+  override private[sql] def simpleString: String = "integral"
+
+  override private[sql] def acceptsType(other: DataType): Boolean = other.isInstanceOf[IntegralType]
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index 76ca7a84c1d1a..5094058164b2f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -28,7 +28,7 @@ object ArrayType extends AbstractDataType {
 
   override private[sql] def defaultConcreteType: DataType = ArrayType(NullType, containsNull = true)
 
-  override private[sql] def isSameType(other: DataType): Boolean = {
+  override private[sql] def acceptsType(other: DataType): Boolean = {
     other.isInstanceOf[ArrayType]
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index da83a7f0ba379..2d133eea19fe0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -79,7 +79,7 @@ abstract class DataType extends AbstractDataType {
 
   override private[sql] def defaultConcreteType: DataType = this
 
-  override private[sql] def isSameType(other: DataType): Boolean = this == other
+  override private[sql] def acceptsType(other: DataType): Boolean = this == other
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index a1cafeab1704d..377c75f6e85a5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -86,7 +86,7 @@ object DecimalType extends AbstractDataType {
 
   override private[sql] def defaultConcreteType: DataType = Unlimited
 
-  override private[sql] def isSameType(other: DataType): Boolean = {
+  override private[sql] def acceptsType(other: DataType): Boolean = {
     other.isInstanceOf[DecimalType]
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
index ddead10bc2171..ac34b642827ca 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
@@ -71,7 +71,7 @@ object MapType extends AbstractDataType {
 
   override private[sql] def defaultConcreteType: DataType = apply(NullType, NullType)
 
-  override private[sql] def isSameType(other: DataType): Boolean = {
+  override private[sql] def acceptsType(other: DataType): Boolean = {
     other.isInstanceOf[MapType]
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index b8097403ec3cc..2ef97a427c37e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -307,7 +307,7 @@ object StructType extends AbstractDataType {
 
   override private[sql] def defaultConcreteType: DataType = new StructType
 
-  override private[sql] def isSameType(other: DataType): Boolean = {
+  override private[sql] def acceptsType(other: DataType): Boolean = {
     other.isInstanceOf[StructType]
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index a4ce1825cab28..ed0d20e7de80e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -23,7 +23,7 @@ import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
-import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.types.{TypeCollection, StringType}
 
 class ExpressionTypeCheckingSuite extends SparkFunSuite {
 
@@ -49,23 +49,16 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
 
   def assertErrorForDifferingTypes(expr: Expression): Unit = {
     assertError(expr,
-      s"differing types in '${expr.prettyString}' (int and boolean)")
-  }
-
-  def assertErrorWithImplicitCast(expr: Expression, errorMessage: String): Unit = {
-    val e = intercept[AnalysisException] {
-      assertSuccess(expr)
-    }
-    assert(e.getMessage.contains(errorMessage))
+      s"differing types in '${expr.prettyString}'")
   }
 
   test("check types for unary arithmetic") {
     assertError(UnaryMinus('stringField), "expected to be of type numeric")
     assertError(Abs('stringField), "expected to be of type numeric")
-    assertError(BitwiseNot('stringField), "type (boolean or tinyint or smallint or int or bigint)")
+    assertError(BitwiseNot('stringField), "expected to be of type integral")
   }
 
-  ignore("check types for binary arithmetic") {
+  test("check types for binary arithmetic") {
     // We will cast String to Double for binary arithmetic
     assertSuccess(Add('intField, 'stringField))
     assertSuccess(Subtract('intField, 'stringField))
@@ -85,21 +78,23 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertErrorForDifferingTypes(MaxOf('intField, 'booleanField))
     assertErrorForDifferingTypes(MinOf('intField, 'booleanField))
 
-    assertError(Add('booleanField, 'booleanField), "operator + accepts numeric type")
-    assertError(Subtract('booleanField, 'booleanField), "operator - accepts numeric type")
-    assertError(Multiply('booleanField, 'booleanField), "operator * accepts numeric type")
-    assertError(Divide('booleanField, 'booleanField), "operator / accepts numeric type")
-    assertError(Remainder('booleanField, 'booleanField), "operator % accepts numeric type")
+    assertError(Add('booleanField, 'booleanField), "accepts numeric type")
+    assertError(Subtract('booleanField, 'booleanField), "accepts numeric type")
+    assertError(Multiply('booleanField, 'booleanField), "accepts numeric type")
+    assertError(Divide('booleanField, 'booleanField), "accepts numeric type")
+    assertError(Remainder('booleanField, 'booleanField), "accepts numeric type")
 
-    assertError(BitwiseAnd('booleanField, 'booleanField), "operator & accepts integral type")
-    assertError(BitwiseOr('booleanField, 'booleanField), "operator | accepts integral type")
-    assertError(BitwiseXor('booleanField, 'booleanField), "operator ^ accepts integral type")
+    assertError(BitwiseAnd('booleanField, 'booleanField), "accepts integral type")
+    assertError(BitwiseOr('booleanField, 'booleanField), "accepts integral type")
+    assertError(BitwiseXor('booleanField, 'booleanField), "accepts integral type")
 
-    assertError(MaxOf('complexField, 'complexField), "function maxOf accepts non-complex type")
-    assertError(MinOf('complexField, 'complexField), "function minOf accepts non-complex type")
+    assertError(MaxOf('complexField, 'complexField),
+      s"accepts ${TypeCollection.Ordered.simpleString} type")
+    assertError(MinOf('complexField, 'complexField),
+      s"accepts ${TypeCollection.Ordered.simpleString} type")
   }
 
-  ignore("check types for predicates") {
+  test("check types for predicates") {
     // We will cast String to Double for binary comparison
     assertSuccess(EqualTo('intField, 'stringField))
     assertSuccess(EqualNullSafe('intField, 'stringField))
@@ -112,25 +107,23 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertSuccess(EqualTo('intField, 'booleanField))
     assertSuccess(EqualNullSafe('intField, 'booleanField))
 
-    assertError(EqualTo('intField, 'complexField), "differing types")
-    assertError(EqualNullSafe('intField, 'complexField), "differing types")
-
+    assertErrorForDifferingTypes(EqualTo('intField, 'complexField))
+    assertErrorForDifferingTypes(EqualNullSafe('intField, 'complexField))
     assertErrorForDifferingTypes(LessThan('intField, 'booleanField))
     assertErrorForDifferingTypes(LessThanOrEqual('intField, 'booleanField))
     assertErrorForDifferingTypes(GreaterThan('intField, 'booleanField))
     assertErrorForDifferingTypes(GreaterThanOrEqual('intField, 'booleanField))
 
-    assertError(
-      LessThan('complexField, 'complexField), "operator < accepts non-complex type")
-    assertError(
-      LessThanOrEqual('complexField, 'complexField), "operator <= accepts non-complex type")
-    assertError(
-      GreaterThan('complexField, 'complexField), "operator > accepts non-complex type")
-    assertError(
-      GreaterThanOrEqual('complexField, 'complexField), "operator >= accepts non-complex type")
+    assertError(LessThan('complexField, 'complexField),
+      s"accepts ${TypeCollection.Ordered.simpleString} type")
+    assertError(LessThanOrEqual('complexField, 'complexField),
+      s"accepts ${TypeCollection.Ordered.simpleString} type")
+    assertError(GreaterThan('complexField, 'complexField),
+      s"accepts ${TypeCollection.Ordered.simpleString} type")
+    assertError(GreaterThanOrEqual('complexField, 'complexField),
+      s"accepts ${TypeCollection.Ordered.simpleString} type")
 
-    assertError(
-      If('intField, 'stringField, 'stringField),
+    assertError(If('intField, 'stringField, 'stringField),
       "type of predicate expression in If should be boolean")
     assertErrorForDifferingTypes(If('booleanField, 'intField, 'booleanField))
 
@@ -180,12 +173,12 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
   }
 
   test("check types for ROUND") {
-    assertErrorWithImplicitCast(Round(Literal(null), 'booleanField),
-      "data type mismatch: argument 2 is expected to be of type int")
-    assertErrorWithImplicitCast(Round(Literal(null), 'complexField),
-      "data type mismatch: argument 2 is expected to be of type int")
     assertSuccess(Round(Literal(null), Literal(null)))
-    assertError(Round('booleanField, 'intField),
-      "data type mismatch: argument 1 is expected to be of type numeric")
+    assertSuccess(Round('intField, Literal(1)))
+
+    assertError(Round('intField, 'intField), "Only foldable Expression is allowed")
+    assertError(Round('intField, 'booleanField), "expected to be of type int")
+    assertError(Round('intField, 'complexField), "expected to be of type int")
+    assertError(Round('booleanField, 'intField), "expected to be of type numeric")
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 8e9b20a3ebe42..d0fd033b981c8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -203,7 +203,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 
     ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
       NumericTypeUnaryExpression(Literal.create(null, NullType)),
-      NumericTypeUnaryExpression(Cast(Literal.create(null, NullType), DoubleType)))
+      NumericTypeUnaryExpression(Literal.create(null, DoubleType)))
   }
 
   test("cast NullType for binary operators") {
@@ -215,9 +215,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 
     ruleTest(HiveTypeCoercion.ImplicitTypeCasts,
       NumericTypeBinaryOperator(Literal.create(null, NullType), Literal.create(null, NullType)),
-      NumericTypeBinaryOperator(
-        Cast(Literal.create(null, NullType), DoubleType),
-        Cast(Literal.create(null, NullType), DoubleType)))
+      NumericTypeBinaryOperator(Literal.create(null, DoubleType), Literal.create(null, DoubleType)))
   }
 
   test("coalesce casts") {
@@ -345,14 +343,14 @@ object HiveTypeCoercionSuite {
   }
 
   case class AnyTypeBinaryOperator(left: Expression, right: Expression)
-    extends BinaryOperator with ExpectsInputTypes {
+    extends BinaryOperator {
     override def dataType: DataType = NullType
     override def inputType: AbstractDataType = AnyDataType
     override def symbol: String = "anytype"
   }
 
   case class NumericTypeBinaryOperator(left: Expression, right: Expression)
-    extends BinaryOperator with ExpectsInputTypes {
+    extends BinaryOperator {
     override def dataType: DataType = NullType
     override def inputType: AbstractDataType = NumericType
     override def symbol: String = "numerictype"

From e27212317c7341852c52d9a85137b8f94cb0d935 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 15 Jul 2015 23:35:27 -0700
Subject: [PATCH 0407/1454] [SPARK-8972] [SQL] Incorrect result for rollup

We don't support the complex expression keys in the rollup/cube, and we even will not report it if we have the complex group by keys, that will cause very confusing/incorrect result.

e.g. `SELECT key%100 FROM src GROUP BY key %100 with ROLLUP`

This PR adds an additional project during the analyzing for the complex GROUP BY keys, and that projection will be the child of `Expand`, so to `Expand`, the GROUP BY KEY are always the simple key(attribute names).

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7343 from chenghao-intel/expand and squashes the following commits:

1ebbb59 [Cheng Hao] update the comment
827873f [Cheng Hao] update as feedback
34def69 [Cheng Hao] Add more unit test and comments
c695760 [Cheng Hao] fix bug of incorrect result for rollup
---
 .../sql/catalyst/analysis/Analyzer.scala      | 42 +++++++++++++--
 ...CUBE #1-0-63b61fb3f0e74226001ad279be440864 |  6 +++
 ...CUBE #2-0-7a511f02a16f0af4f810b1666cfcd896 | 10 ++++
 ...pingSet-0-8c14c24670a4b06c440346277ce9cf1c | 10 ++++
 ...llup #1-0-a78e3dbf242f240249e36b3d3fd0926a |  6 +++
 ...llup #2-0-bf180c9d1a18f61b9d9f31bb0115cf89 | 10 ++++
 ...llup #3-0-9257085d123728730be96b6d9fbb84ce | 10 ++++
 .../sql/hive/execution/HiveQuerySuite.scala   | 54 +++++++++++++++++++
 8 files changed, 145 insertions(+), 3 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #1-0-63b61fb3f0e74226001ad279be440864
 create mode 100644 sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #2-0-7a511f02a16f0af4f810b1666cfcd896
 create mode 100644 sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for GroupingSet-0-8c14c24670a4b06c440346277ce9cf1c
 create mode 100644 sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #1-0-a78e3dbf242f240249e36b3d3fd0926a
 create mode 100644 sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #2-0-bf180c9d1a18f61b9d9f31bb0115cf89
 create mode 100644 sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #3-0-9257085d123728730be96b6d9fbb84ce

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 891408e310049..df8e7f2381fbd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -194,16 +194,52 @@ class Analyzer(
     }
 
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      case a if !a.childrenResolved => a // be sure all of the children are resolved.
       case a: Cube =>
         GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations)
       case a: Rollup =>
         GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations)
       case x: GroupingSets =>
         val gid = AttributeReference(VirtualColumn.groupingIdName, IntegerType, false)()
+        // We will insert another Projection if the GROUP BY keys contains the
+        // non-attribute expressions. And the top operators can references those
+        // expressions by its alias.
+        // e.g. SELECT key%5 as c1 FROM src GROUP BY key%5 ==>
+        //      SELECT a as c1 FROM (SELECT key%5 AS a FROM src) GROUP BY a
+
+        // find all of the non-attribute expressions in the GROUP BY keys
+        val nonAttributeGroupByExpressions = new ArrayBuffer[Alias]()
+
+        // The pair of (the original GROUP BY key, associated attribute)
+        val groupByExprPairs = x.groupByExprs.map(_ match {
+          case e: NamedExpression => (e, e.toAttribute)
+          case other => {
+            val alias = Alias(other, other.toString)()
+            nonAttributeGroupByExpressions += alias // add the non-attributes expression alias
+            (other, alias.toAttribute)
+          }
+        })
+
+        // substitute the non-attribute expressions for aggregations.
+        val aggregation = x.aggregations.map(expr => expr.transformDown {
+          case e => groupByExprPairs.find(_._1.semanticEquals(e)).map(_._2).getOrElse(e)
+        }.asInstanceOf[NamedExpression])
+
+        // substitute the group by expressions.
+        val newGroupByExprs = groupByExprPairs.map(_._2)
+
+        val child = if (nonAttributeGroupByExpressions.length > 0) {
+          // insert additional projection if contains the
+          // non-attribute expressions in the GROUP BY keys
+          Project(x.child.output ++ nonAttributeGroupByExpressions, x.child)
+        } else {
+          x.child
+        }
+
         Aggregate(
-          x.groupByExprs :+ VirtualColumn.groupingIdAttribute,
-          x.aggregations,
-          Expand(x.bitmasks, x.groupByExprs, gid, x.child))
+          newGroupByExprs :+ VirtualColumn.groupingIdAttribute,
+          aggregation,
+          Expand(x.bitmasks, newGroupByExprs, gid, child))
     }
   }
 
diff --git a/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #1-0-63b61fb3f0e74226001ad279be440864 b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #1-0-63b61fb3f0e74226001ad279be440864
new file mode 100644
index 0000000000000..dac1b84b916d7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #1-0-63b61fb3f0e74226001ad279be440864	
@@ -0,0 +1,6 @@
+500	NULL	0
+91	0	1
+84	1	1
+105	2	1
+113	3	1
+107	4	1
diff --git a/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #2-0-7a511f02a16f0af4f810b1666cfcd896 b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #2-0-7a511f02a16f0af4f810b1666cfcd896
new file mode 100644
index 0000000000000..c7cb747c0a659
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for CUBE #2-0-7a511f02a16f0af4f810b1666cfcd896	
@@ -0,0 +1,10 @@
+1	NULL	-3	2
+1	NULL	-1	2
+1	NULL	3	2
+1	NULL	4	2
+1	NULL	5	2
+1	NULL	6	2
+1	NULL	12	2
+1	NULL	14	2
+1	NULL	15	2
+1	NULL	22	2
diff --git a/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for GroupingSet-0-8c14c24670a4b06c440346277ce9cf1c b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for GroupingSet-0-8c14c24670a4b06c440346277ce9cf1c
new file mode 100644
index 0000000000000..c7cb747c0a659
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for GroupingSet-0-8c14c24670a4b06c440346277ce9cf1c	
@@ -0,0 +1,10 @@
+1	NULL	-3	2
+1	NULL	-1	2
+1	NULL	3	2
+1	NULL	4	2
+1	NULL	5	2
+1	NULL	6	2
+1	NULL	12	2
+1	NULL	14	2
+1	NULL	15	2
+1	NULL	22	2
diff --git a/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #1-0-a78e3dbf242f240249e36b3d3fd0926a b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #1-0-a78e3dbf242f240249e36b3d3fd0926a
new file mode 100644
index 0000000000000..dac1b84b916d7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #1-0-a78e3dbf242f240249e36b3d3fd0926a	
@@ -0,0 +1,6 @@
+500	NULL	0
+91	0	1
+84	1	1
+105	2	1
+113	3	1
+107	4	1
diff --git a/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #2-0-bf180c9d1a18f61b9d9f31bb0115cf89 b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #2-0-bf180c9d1a18f61b9d9f31bb0115cf89
new file mode 100644
index 0000000000000..1eea4a9b23687
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #2-0-bf180c9d1a18f61b9d9f31bb0115cf89	
@@ -0,0 +1,10 @@
+1	0	5	3
+1	0	15	3
+1	0	25	3
+1	0	60	3
+1	0	75	3
+1	0	80	3
+1	0	100	3
+1	0	140	3
+1	0	145	3
+1	0	150	3
diff --git a/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #3-0-9257085d123728730be96b6d9fbb84ce b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #3-0-9257085d123728730be96b6d9fbb84ce
new file mode 100644
index 0000000000000..1eea4a9b23687
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/SPARK-8976 Wrong Result for Rollup #3-0-9257085d123728730be96b6d9fbb84ce	
@@ -0,0 +1,10 @@
+1	0	5	3
+1	0	15	3
+1	0	25	3
+1	0	60	3
+1	0	75	3
+1	0	80	3
+1	0	100	3
+1	0	140	3
+1	0	145	3
+1	0	150	3
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 991da2f829ae5..11a843becce69 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -85,6 +85,60 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     }
   }
 
+  createQueryTest("SPARK-8976 Wrong Result for Rollup #1",
+    """
+      SELECT count(*) AS cnt, key % 5,GROUPING__ID FROM src group by key%5 WITH ROLLUP
+    """.stripMargin)
+
+  createQueryTest("SPARK-8976 Wrong Result for Rollup #2",
+    """
+      SELECT
+        count(*) AS cnt,
+        key % 5 as k1,
+        key-5 as k2,
+        GROUPING__ID as k3
+      FROM src group by key%5, key-5
+      WITH ROLLUP ORDER BY cnt, k1, k2, k3 LIMIT 10
+    """.stripMargin)
+
+  createQueryTest("SPARK-8976 Wrong Result for Rollup #3",
+    """
+      SELECT
+        count(*) AS cnt,
+        key % 5 as k1,
+        key-5 as k2,
+        GROUPING__ID as k3
+      FROM (SELECT key, key%2, key - 5 FROM src) t group by key%5, key-5
+      WITH ROLLUP ORDER BY cnt, k1, k2, k3 LIMIT 10
+    """.stripMargin)
+
+  createQueryTest("SPARK-8976 Wrong Result for CUBE #1",
+    """
+      SELECT count(*) AS cnt, key % 5,GROUPING__ID FROM src group by key%5 WITH CUBE
+    """.stripMargin)
+
+  createQueryTest("SPARK-8976 Wrong Result for CUBE #2",
+    """
+      SELECT
+        count(*) AS cnt,
+        key % 5 as k1,
+        key-5 as k2,
+        GROUPING__ID as k3
+      FROM (SELECT key, key%2, key - 5 FROM src) t group by key%5, key-5
+      WITH CUBE ORDER BY cnt, k1, k2, k3 LIMIT 10
+    """.stripMargin)
+
+  createQueryTest("SPARK-8976 Wrong Result for GroupingSet",
+    """
+      SELECT
+        count(*) AS cnt,
+        key % 5 as k1,
+        key-5 as k2,
+        GROUPING__ID as k3
+      FROM (SELECT key, key%2, key - 5 FROM src) t group by key%5, key-5
+      GROUPING SETS (key%5, key-5) ORDER BY cnt, k1, k2, k3 LIMIT 10
+    """.stripMargin)
+
   createQueryTest("insert table with generator with column name",
     """
       |  CREATE TABLE gen_tmp (key Int);

From 0a795336df20c7ec969366e613286f0c060a4eeb Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 15 Jul 2015 23:36:57 -0700
Subject: [PATCH 0408/1454] [SPARK-8807] [SPARKR] Add between operator in
 SparkR

JIRA: https://issues.apache.org/jira/browse/SPARK-8807

Add between operator in SparkR.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7356 from viirya/add_r_between and squashes the following commits:

7f51b44 [Liang-Chi Hsieh] Add test for non-numeric column.
c6a25c5 [Liang-Chi Hsieh] Add between function.
---
 R/pkg/NAMESPACE                  |  1 +
 R/pkg/R/column.R                 | 17 +++++++++++++++++
 R/pkg/R/generics.R               |  4 ++++
 R/pkg/inst/tests/test_sparkSQL.R | 12 ++++++++++++
 4 files changed, 34 insertions(+)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 7f857222452d4..331307c2077a5 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -77,6 +77,7 @@ exportMethods("abs",
               "atan",
               "atan2",
               "avg",
+              "between",
               "cast",
               "cbrt",
               "ceiling",
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 8e4b0f5bf1c4d..2892e1416cc65 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -187,6 +187,23 @@ setMethod("substr", signature(x = "Column"),
             column(jc)
           })
 
+#' between
+#'
+#' Test if the column is between the lower bound and upper bound, inclusive.
+#'
+#' @rdname column
+#'
+#' @param bounds lower and upper bounds
+setMethod("between", signature(x = "Column"),
+          function(x, bounds) {
+            if (is.vector(bounds) && length(bounds) == 2) {
+              jc <- callJMethod(x@jc, "between", bounds[1], bounds[2])
+              column(jc)
+            } else {
+              stop("bounds should be a vector of lower and upper bounds")
+            }
+          })
+
 #' Casts the column to a different data type.
 #'
 #' @rdname column
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index fad9d71158c51..ebe6fbd97ce86 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -567,6 +567,10 @@ setGeneric("asc", function(x) { standardGeneric("asc") })
 #' @export
 setGeneric("avg", function(x, ...) { standardGeneric("avg") })
 
+#' @rdname column
+#' @export
+setGeneric("between", function(x, bounds) { standardGeneric("between") })
+
 #' @rdname column
 #' @export
 setGeneric("cast", function(x, dataType) { standardGeneric("cast") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 76f74f80834a9..cdfe6481f60ea 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -638,6 +638,18 @@ test_that("column functions", {
   c7 <- floor(c) + log(c) + log10(c) + log1p(c) + rint(c)
   c8 <- sign(c) + sin(c) + sinh(c) + tan(c) + tanh(c)
   c9 <- toDegrees(c) + toRadians(c)
+
+  df <- jsonFile(sqlContext, jsonPath)
+  df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))
+  expect_equal(collect(df2)[[2, 1]], TRUE)
+  expect_equal(collect(df2)[[2, 2]], FALSE)
+  expect_equal(collect(df2)[[3, 1]], FALSE)
+  expect_equal(collect(df2)[[3, 2]], TRUE)
+
+  df3 <- select(df, between(df$name, c("Apache", "Spark")))
+  expect_equal(collect(df3)[[1, 1]], TRUE)
+  expect_equal(collect(df3)[[2, 1]], FALSE)
+  expect_equal(collect(df3)[[3, 1]], TRUE)
 })
 
 test_that("column binary mathfunctions", {

From 011551620faa87107a787530f074af3d9be7e695 Mon Sep 17 00:00:00 2001
From: Daniel Darabos <darabos.daniel@gmail.com>
Date: Thu, 16 Jul 2015 08:16:54 +0100
Subject: [PATCH 0409/1454] [SPARK-8893] Add runtime checks against
 non-positive number of partitions

https://issues.apache.org/jira/browse/SPARK-8893

> What does `sc.parallelize(1 to 3).repartition(p).collect` return? I would expect `Array(1, 2, 3)` regardless of `p`. But if `p` < 1, it returns `Array()`. I think instead it should throw an `IllegalArgumentException`.

> I think the case is pretty clear for `p` < 0. But the behavior for `p` = 0 is also error prone. In fact that's how I found this strange behavior. I used `rdd.repartition(a/b)` with positive `a` and `b`, but `a/b` was rounded down to zero and the results surprised me. I'd prefer an exception instead of unexpected (corrupt) results.

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #7285 from darabos/patch-1 and squashes the following commits:

decba82 [Daniel Darabos] Allow repartitioning empty RDDs to zero partitions.
97de852 [Daniel Darabos] Allow zero partition count in HashPartitioner
f6ba5fb [Daniel Darabos] Use require() for simpler syntax.
d5e3df8 [Daniel Darabos] Require positive number of partitions in HashPartitioner
897c628 [Daniel Darabos] Require positive maxPartitions in CoalescedRDD
---
 core/src/main/scala/org/apache/spark/Partitioner.scala      | 2 ++
 core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala | 5 ++++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 82889bcd30988..ad68512dccb79 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -76,6 +76,8 @@ object Partitioner {
  * produce an unexpected or incorrect result.
  */
 class HashPartitioner(partitions: Int) extends Partitioner {
+  require(partitions >= 0, s"Number of partitions ($partitions) cannot be negative.")
+
   def numPartitions: Int = partitions
 
   def getPartition(key: Any): Int = key match {
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala
index 663eebb8e4191..90d9735cb3f69 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala
@@ -69,7 +69,7 @@ private[spark] case class CoalescedRDDPartition(
  * the preferred location of each new partition overlaps with as many preferred locations of its
  * parent partitions
  * @param prev RDD to be coalesced
- * @param maxPartitions number of desired partitions in the coalesced RDD
+ * @param maxPartitions number of desired partitions in the coalesced RDD (must be positive)
  * @param balanceSlack used to trade-off balance and locality. 1.0 is all locality, 0 is all balance
  */
 private[spark] class CoalescedRDD[T: ClassTag](
@@ -78,6 +78,9 @@ private[spark] class CoalescedRDD[T: ClassTag](
     balanceSlack: Double = 0.10)
   extends RDD[T](prev.context, Nil) {  // Nil since we implement getDependencies
 
+  require(maxPartitions > 0 || maxPartitions == prev.partitions.length,
+    s"Number of partitions ($maxPartitions) must be positive.")
+
   override def getPartitions: Array[Partition] = {
     val pc = new PartitionCoalescer(maxPartitions, prev, balanceSlack)
 

From 4ea6480a3ba4ca7e09089c9b99d4a855894b9015 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Thu, 16 Jul 2015 08:26:39 -0700
Subject: [PATCH 0410/1454] [SPARK-8995] [SQL] cast date strings like
 '2015-01-01 12:15:31' to date

Jira https://issues.apache.org/jira/browse/SPARK-8995

In PR #6981we noticed that we cannot cast date strings that contains a time, like '2015-03-18 12:39:40' to date. Besides it's not possible to cast a string like '18:03:20' to a timestamp.

If a time is passed without a date, today is inferred as date.

Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Tarek Auel <tarek.auel@gmail.com>

Closes #7353 from tarekauel/SPARK-8995 and squashes the following commits:

14f333b [Tarek Auel] [SPARK-8995] added tests for daylight saving time
ca1ae69 [Tarek Auel] [SPARK-8995] style fix
d20b8b4 [Tarek Auel] [SPARK-8995] bug fix: distinguish between 0 and null
ef05753 [Tarek Auel] [SPARK-8995] added check for year >= 1000
01c9ff3 [Tarek Auel] [SPARK-8995] support for time strings
34ec573 [Tarek Auel] fixed style
71622c0 [Tarek Auel] improved timestamp and date parsing
0e30c0a [Tarek Auel] Hive compatibility
cfbaed7 [Tarek Auel] fixed wrong checks
71f89c1 [Tarek Auel] [SPARK-8995] minor style fix
f7452fa [Tarek Auel] [SPARK-8995] removed old timestamp parsing
30e5aec [Tarek Auel] [SPARK-8995] date and timestamp cast
c1083fb [Tarek Auel] [SPARK-8995] cast date strings like '2015-01-01 12:15:31' to date or timestamp
---
 .../spark/sql/catalyst/expressions/Cast.scala |  17 +-
 .../sql/catalyst/util/DateTimeUtils.scala     | 198 ++++++++++++++++
 .../sql/catalyst/expressions/CastSuite.scala  | 144 ++++++++++++
 .../catalyst/util/DateTimeUtilsSuite.scala    | 218 ++++++++++++++++++
 4 files changed, 562 insertions(+), 15 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index ab02addfb4d25..83d5b3b76b0a3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -167,17 +167,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   // TimestampConverter
   private[this] def castToTimestamp(from: DataType): Any => Any = from match {
     case StringType =>
-      buildCast[UTF8String](_, utfs => {
-        // Throw away extra if more than 9 decimal places
-        val s = utfs.toString
-        val periodIdx = s.indexOf(".")
-        var n = s
-        if (periodIdx != -1 && n.length() - periodIdx > 9) {
-          n = n.substring(0, periodIdx + 10)
-        }
-        try DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf(n))
-        catch { case _: java.lang.IllegalArgumentException => null }
-      })
+      buildCast[UTF8String](_, utfs => DateTimeUtils.stringToTimestamp(utfs).orNull)
     case BooleanType =>
       buildCast[Boolean](_, b => if (b) 1L else 0)
     case LongType =>
@@ -220,10 +210,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
   // DateConverter
   private[this] def castToDate(from: DataType): Any => Any = from match {
     case StringType =>
-      buildCast[UTF8String](_, s =>
-        try DateTimeUtils.fromJavaDate(Date.valueOf(s.toString))
-        catch { case _: java.lang.IllegalArgumentException => null }
-      )
+      buildCast[UTF8String](_, s => DateTimeUtils.stringToDate(s).orNull)
     case TimestampType =>
       // throw valid precision more than seconds, according to Hive.
       // Timestamp.nanos is in 0 to 999,999,999, no more than a second.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index c1ddee3ef0230..53c32a0a9802b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -21,6 +21,8 @@ import java.sql.{Date, Timestamp}
 import java.text.{DateFormat, SimpleDateFormat}
 import java.util.{Calendar, TimeZone}
 
+import org.apache.spark.unsafe.types.UTF8String
+
 /**
  * Helper functions for converting between internal and external date and time representations.
  * Dates are exposed externally as java.sql.Date and are represented internally as the number of
@@ -180,4 +182,200 @@ object DateTimeUtils {
     val nanos = (us % MICROS_PER_SECOND) * 1000L
     (day.toInt, secondsInDay * NANOS_PER_SECOND + nanos)
   }
+
+  /**
+   * Parses a given UTF8 date string to the corresponding a corresponding [[Long]] value.
+   * The return type is [[Option]] in order to distinguish between 0L and null. The following
+   * formats are allowed:
+   *
+   * `yyyy`
+   * `yyyy-[m]m`
+   * `yyyy-[m]m-[d]d`
+   * `yyyy-[m]m-[d]d `
+   * `yyyy-[m]m-[d]d [h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]`
+   * `yyyy-[m]m-[d]d [h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]Z`
+   * `yyyy-[m]m-[d]d [h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]-[h]h:[m]m`
+   * `yyyy-[m]m-[d]d [h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]+[h]h:[m]m`
+   * `yyyy-[m]m-[d]dT[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]`
+   * `yyyy-[m]m-[d]dT[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]Z`
+   * `yyyy-[m]m-[d]dT[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]-[h]h:[m]m`
+   * `yyyy-[m]m-[d]dT[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]+[h]h:[m]m`
+   * `[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]`
+   * `[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]Z`
+   * `[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]-[h]h:[m]m`
+   * `[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]+[h]h:[m]m`
+   * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]`
+   * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]Z`
+   * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]-[h]h:[m]m`
+   * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]+[h]h:[m]m`
+   */
+  def stringToTimestamp(s: UTF8String): Option[Long] = {
+    if (s == null) {
+      return None
+    }
+    var timeZone: Option[Byte] = None
+    val segments: Array[Int] = Array[Int](1, 1, 1, 0, 0, 0, 0, 0, 0)
+    var i = 0
+    var currentSegmentValue = 0
+    val bytes = s.getBytes
+    var j = 0
+    var digitsMilli = 0
+    var justTime = false
+    while (j < bytes.length) {
+      val b = bytes(j)
+      val parsedValue = b - '0'.toByte
+      if (parsedValue < 0 || parsedValue > 9) {
+        if (j == 0 && b == 'T') {
+          justTime = true
+          i += 3
+        } else if (i < 2) {
+          if (b == '-') {
+            segments(i) = currentSegmentValue
+            currentSegmentValue = 0
+            i += 1
+          } else if (i == 0 && b == ':') {
+            justTime = true
+            segments(3) = currentSegmentValue
+            currentSegmentValue = 0
+            i = 4
+          } else {
+            return None
+          }
+        } else if (i == 2) {
+          if (b == ' ' || b == 'T') {
+            segments(i) = currentSegmentValue
+            currentSegmentValue = 0
+            i += 1
+          } else {
+            return None
+          }
+        } else if (i == 3 || i == 4) {
+          if (b == ':') {
+            segments(i) = currentSegmentValue
+            currentSegmentValue = 0
+            i += 1
+          } else {
+            return None
+          }
+        } else if (i == 5 || i == 6) {
+          if (b == 'Z') {
+            segments(i) = currentSegmentValue
+            currentSegmentValue = 0
+            i += 1
+            timeZone = Some(43)
+          } else if (b == '-' || b == '+') {
+            segments(i) = currentSegmentValue
+            currentSegmentValue = 0
+            i += 1
+            timeZone = Some(b)
+          } else if (b == '.' && i == 5) {
+            segments(i) = currentSegmentValue
+            currentSegmentValue = 0
+            i += 1
+          } else {
+            return None
+          }
+          if (i == 6  && b != '.') {
+            i += 1
+          }
+        } else {
+          if (b == ':' || b == ' ') {
+            segments(i) = currentSegmentValue
+            currentSegmentValue = 0
+            i += 1
+          } else {
+            return None
+          }
+        }
+      } else {
+        if (i == 6) {
+          digitsMilli += 1
+        }
+        currentSegmentValue = currentSegmentValue * 10 + parsedValue
+      }
+      j += 1
+    }
+
+    segments(i) = currentSegmentValue
+
+    while (digitsMilli < 6) {
+      segments(6) *= 10
+      digitsMilli += 1
+    }
+
+    if (!justTime && (segments(0) < 1000 || segments(0) > 9999 || segments(1) < 1 ||
+        segments(1) > 12 || segments(2) < 1 || segments(2) > 31)) {
+      return None
+    }
+
+    if (segments(3) < 0 || segments(3) > 23 || segments(4) < 0 || segments(4) > 59 ||
+        segments(5) < 0 || segments(5) > 59 || segments(6) < 0 || segments(6) > 999999 ||
+        segments(7) < 0 || segments(7) > 23 || segments(8) < 0 || segments(8) > 59) {
+      return None
+    }
+
+    val c = if (timeZone.isEmpty) {
+      Calendar.getInstance()
+    } else {
+      Calendar.getInstance(
+        TimeZone.getTimeZone(f"GMT${timeZone.get.toChar}${segments(7)}%02d:${segments(8)}%02d"))
+    }
+
+    if (justTime) {
+      c.set(Calendar.HOUR, segments(3))
+      c.set(Calendar.MINUTE, segments(4))
+      c.set(Calendar.SECOND, segments(5))
+    } else {
+      c.set(segments(0), segments(1) - 1, segments(2), segments(3), segments(4), segments(5))
+    }
+
+    Some(c.getTimeInMillis / 1000 * 1000000 + segments(6))
+  }
+
+  /**
+   * Parses a given UTF8 date string to the corresponding a corresponding [[Int]] value.
+   * The return type is [[Option]] in order to distinguish between 0 and null. The following
+   * formats are allowed:
+   *
+   * `yyyy`,
+   * `yyyy-[m]m`
+   * `yyyy-[m]m-[d]d`
+   * `yyyy-[m]m-[d]d `
+   * `yyyy-[m]m-[d]d *`
+   * `yyyy-[m]m-[d]dT*`
+   */
+  def stringToDate(s: UTF8String): Option[Int] = {
+    if (s == null) {
+      return None
+    }
+    val segments: Array[Int] = Array[Int](1, 1, 1)
+    var i = 0
+    var currentSegmentValue = 0
+    val bytes = s.getBytes
+    var j = 0
+    while (j < bytes.length && (i < 3 && !(bytes(j) == ' ' || bytes(j) == 'T'))) {
+      val b = bytes(j)
+      if (i < 2 && b == '-') {
+        segments(i) = currentSegmentValue
+        currentSegmentValue = 0
+        i += 1
+      } else {
+        val parsedValue = b - '0'.toByte
+        if (parsedValue < 0 || parsedValue > 9) {
+          return None
+        } else {
+          currentSegmentValue = currentSegmentValue * 10 + parsedValue
+        }
+      }
+      j += 1
+    }
+    segments(i) = currentSegmentValue
+    if (segments(0) < 1000 || segments(0) > 9999 || segments(1) < 1 || segments(1) > 12 ||
+        segments(2) < 1 || segments(2) > 31) {
+      return None
+    }
+    val c = Calendar.getInstance()
+    c.set(segments(0), segments(1) - 1, segments(2), 0, 0, 0)
+    Some((c.getTimeInMillis / 1000 / 3600 / 24).toInt)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 1de161c367a1d..ef8bcd41f7280 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.sql.{Timestamp, Date}
+import java.util.{TimeZone, Calendar}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
@@ -41,6 +42,137 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(v, Literal(expected).dataType), expected)
   }
 
+  test("cast string to date") {
+    var c = Calendar.getInstance()
+    c.set(2015, 0, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015"), DateType), new Date(c.getTimeInMillis))
+    c = Calendar.getInstance()
+    c.set(2015, 2, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03"), DateType), new Date(c.getTimeInMillis))
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03-18"), DateType), new Date(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18 "), DateType), new Date(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18 123142"), DateType), new Date(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18T123123"), DateType), new Date(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18T"), DateType), new Date(c.getTimeInMillis))
+
+    checkEvaluation(Cast(Literal("2015-03-18X"), DateType), null)
+    checkEvaluation(Cast(Literal("2015/03/18"), DateType), null)
+    checkEvaluation(Cast(Literal("2015.03.18"), DateType), null)
+    checkEvaluation(Cast(Literal("20150318"), DateType), null)
+    checkEvaluation(Cast(Literal("2015-031-8"), DateType), null)
+  }
+
+  test("cast string to timestamp") {
+    checkEvaluation(Cast(Literal("123"), TimestampType),
+      null)
+
+    var c = Calendar.getInstance()
+    c.set(2015, 0, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    c = Calendar.getInstance()
+    c.set(2015, 2, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03-18"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18 "), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18T"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03-18 12:03:17"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17Z"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18 12:03:17Z"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT-01:00"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17-1:0"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17-01:00"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17+07:30"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:03"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17+7:3"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    checkEvaluation(Cast(Literal("2015-03-18 12:03:17.123"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17.123"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 456)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17.456Z"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18 12:03:17.456Z"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT-01:00"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17.123-1:0"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17.123-01:00"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17.123+07:30"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:03"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17.123+7:3"), TimestampType),
+      new Timestamp(c.getTimeInMillis))
+
+    checkEvaluation(Cast(Literal("2015-03-18 123142"), TimestampType), null)
+    checkEvaluation(Cast(Literal("2015-03-18T123123"), TimestampType), null)
+    checkEvaluation(Cast(Literal("2015-03-18X"), TimestampType), null)
+    checkEvaluation(Cast(Literal("2015/03/18"), TimestampType), null)
+    checkEvaluation(Cast(Literal("2015.03.18"), TimestampType), null)
+    checkEvaluation(Cast(Literal("20150318"), TimestampType), null)
+    checkEvaluation(Cast(Literal("2015-031-8"), TimestampType), null)
+    checkEvaluation(Cast(Literal("2015-03-18T12:03:17-0:70"), TimestampType), null)
+  }
+
   test("cast from int") {
     checkCast(0, false)
     checkCast(1, true)
@@ -149,6 +281,18 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     val nts = sts + ".1"
     val ts = Timestamp.valueOf(nts)
 
+    val defaultTimeZone = TimeZone.getDefault
+    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+    var c = Calendar.getInstance()
+    c.set(2015, 2, 8, 2, 30, 0)
+    checkEvaluation(cast(cast(new Timestamp(c.getTimeInMillis), StringType), TimestampType),
+      c.getTimeInMillis * 1000)
+    c = Calendar.getInstance()
+    c.set(2015, 10, 1, 2, 30, 0)
+    checkEvaluation(cast(cast(new Timestamp(c.getTimeInMillis), StringType), TimestampType),
+      c.getTimeInMillis * 1000)
+    TimeZone.setDefault(defaultTimeZone)
+
     checkEvaluation(cast("abdef", StringType), "abdef")
     checkEvaluation(cast("abdef", DecimalType.Unlimited), null)
     checkEvaluation(cast("abdef", TimestampType), null)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index f63ac191e7366..c65fcbc4d1bc1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -19,8 +19,10 @@ package org.apache.spark.sql.catalyst.util
 
 import java.sql.{Date, Timestamp}
 import java.text.SimpleDateFormat
+import java.util.{TimeZone, Calendar}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.unsafe.types.UTF8String
 
 class DateTimeUtilsSuite extends SparkFunSuite {
 
@@ -86,4 +88,220 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     checkFromToJavaDate(new Date(df1.parse("1776-07-04 10:30:00").getTime))
     checkFromToJavaDate(new Date(df2.parse("1776-07-04 18:30:00 UTC").getTime))
   }
+
+  test("string to date") {
+    val millisPerDay = 1000L * 3600L * 24L
+    var c = Calendar.getInstance()
+    c.set(2015, 0, 28, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-01-28")).get ===
+      c.getTimeInMillis / millisPerDay)
+    c.set(2015, 0, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015")).get ===
+      c.getTimeInMillis / millisPerDay)
+    c = Calendar.getInstance()
+    c.set(2015, 2, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03")).get ===
+      c.getTimeInMillis / millisPerDay)
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18")).get ===
+      c.getTimeInMillis / millisPerDay)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18 ")).get ===
+      c.getTimeInMillis / millisPerDay)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18 123142")).get ===
+      c.getTimeInMillis / millisPerDay)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18T123123")).get ===
+      c.getTimeInMillis / millisPerDay)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18T")).get ===
+      c.getTimeInMillis / millisPerDay)
+
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18X")).isEmpty)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015/03/18")).isEmpty)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015.03.18")).isEmpty)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("20150318")).isEmpty)
+    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-031-8")).isEmpty)
+  }
+
+  test("string to timestamp") {
+    var c = Calendar.getInstance()
+    c.set(1969, 11, 31, 16, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("1969-12-31 16:00:00")).get ===
+      c.getTimeInMillis * 1000)
+    c.set(2015, 0, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015")).get ===
+      c.getTimeInMillis * 1000)
+    c = Calendar.getInstance()
+    c.set(2015, 2, 1, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03")).get ===
+      c.getTimeInMillis * 1000)
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18")).get ===
+      c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 ")).get ===
+      c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T")).get ===
+      c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 12:03:17")).get ===
+      c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17")).get ===
+      c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT-13:53"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17-13:53")).get === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17Z")).get ===
+      c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 12:03:17Z")).get ===
+      c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT-01:00"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17-1:0")).get ===
+      c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17-01:00")).get === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17+07:30")).get === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:03"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17+07:03")).get === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance()
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18 12:03:17.123")).get === c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.123")).get === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 456)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.456Z")).get  === c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18 12:03:17.456Z")).get  === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT-01:00"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.123-1:0")).get  === c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.123-01:00")).get ===  c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.123+07:30")).get ===  c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.123+07:30")).get === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.123121+7:30")).get ===
+        c.getTimeInMillis * 1000 + 121)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(2015, 2, 18, 12, 3, 17)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03:17.12312+7:30")).get ===
+        c.getTimeInMillis * 1000 + 120)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(Calendar.HOUR, 18)
+    c.set(Calendar.MINUTE, 12)
+    c.set(Calendar.SECOND, 15)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("T18:12:15.12312+7:30")).get ===
+      c.getTimeInMillis * 1000 + 120)
+
+    c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
+    c.set(Calendar.HOUR, 18)
+    c.set(Calendar.MINUTE, 12)
+    c.set(Calendar.SECOND, 15)
+    c.set(Calendar.MILLISECOND, 123)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("18:12:15.12312+7:30")).get ===
+      c.getTimeInMillis * 1000 + 120)
+
+    c = Calendar.getInstance()
+    c.set(2011, 4, 6, 7, 8, 9)
+    c.set(Calendar.MILLISECOND, 100)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2011-05-06 07:08:09.1000")).get === c.getTimeInMillis * 1000)
+
+    val defaultTimeZone = TimeZone.getDefault
+    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+
+    c = Calendar.getInstance()
+    c.set(2015, 2, 8, 2, 0, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-3-8 2:0:0")).get === c.getTimeInMillis * 1000)
+    c.add(Calendar.MINUTE, 30)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-3-8 3:30:0")).get === c.getTimeInMillis * 1000)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-3-8 2:30:0")).get === c.getTimeInMillis * 1000)
+
+    c = Calendar.getInstance()
+    c.set(2015, 10, 1, 1, 59, 0)
+    c.set(Calendar.MILLISECOND, 0)
+    c.add(Calendar.MINUTE, 31)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-11-1 2:30:0")).get === c.getTimeInMillis * 1000)
+    TimeZone.setDefault(defaultTimeZone)
+
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("238")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 123142")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T123123")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18X")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015/03/18")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015.03.18")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("20150318")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-031-8")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03.17-20:0")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03.17-0:70")).isEmpty)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("2015-03-18T12:03.17-1:0:0")).isEmpty)
+  }
 }

From b536d5dc6c2c712270b8130ddd9945dff19a27d9 Mon Sep 17 00:00:00 2001
From: Jan Prach <jendap@gmail.com>
Date: Thu, 16 Jul 2015 18:42:41 +0100
Subject: [PATCH 0411/1454] [SPARK-9015] [BUILD] Clean project import in scala
 ide

Cleanup maven for a clean import in scala-ide / eclipse.

* remove groovy plugin which is really not needed at all
* add-source from build-helper-maven-plugin is not needed as recent version of scala-maven-plugin do it automatically
* add lifecycle-mapping plugin to hide a few useless warnings from ide

Author: Jan Prach <jendap@gmail.com>

Closes #7375 from jendap/clean-project-import-in-scala-ide and squashes the following commits:

c4b4c0f [Jan Prach] fix whitespaces
5a83e07 [Jan Prach] Revert "remove java compiler warnings from java tests"
312007e [Jan Prach] scala-maven-plugin itself add scala sources by default
f47d856 [Jan Prach] remove spark-1.4-staging repository
c8a54db [Jan Prach] remove java compiler warnings from java tests
999a068 [Jan Prach] remove some maven warnings in scala ide
80fbdc5 [Jan Prach] remove groovy and gmavenplus plugin
---
 pom.xml          | 130 +++++++++++++++++++----------------------------
 repl/pom.xml     |   2 -
 sql/core/pom.xml |   1 -
 sql/hive/pom.xml |   1 -
 tools/pom.xml    |   4 --
 5 files changed, 53 insertions(+), 85 deletions(-)

diff --git a/pom.xml b/pom.xml
index aa49e2ab7294b..c5c655834bdeb 100644
--- a/pom.xml
+++ b/pom.xml
@@ -152,7 +152,6 @@
     <aws.kinesis.client.version>1.2.1</aws.kinesis.client.version>
     <commons.httpclient.version>4.3.2</commons.httpclient.version>
     <commons.math3.version>3.4.1</commons.math3.version>
-    <test_classpath_file>${project.build.directory}/spark-test-classpath.txt</test_classpath_file>
     <scala.version>2.10.4</scala.version>
     <scala.binary.version>2.10</scala.binary.version>
     <jline.version>${scala.version}</jline.version>
@@ -283,18 +282,6 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
-    <!-- TODO: This can be deleted after Spark 1.4 is posted -->
-    <repository>
-      <id>spark-1.4-staging</id>
-      <name>Spark 1.4 RC4 Staging Repository</name>
-      <url>https://repository.apache.org/content/repositories/orgapachespark-1112</url>
-      <releases>
-        <enabled>true</enabled>
-      </releases>
-      <snapshots>
-        <enabled>false</enabled>
-      </snapshots>
-    </repository>
   </repositories>
   <pluginRepositories>
     <pluginRepository>
@@ -318,17 +305,6 @@
       <artifactId>unused</artifactId>
       <version>1.0.0</version>
     </dependency>
-    <!--
-      This depndency has been added to provided scope as it is needed for executing build
-      specific groovy scripts using gmaven+ and not required for downstream project building
-      with spark.
-    -->
-    <dependency>
-      <groupId>org.codehaus.groovy</groupId>
-      <artifactId>groovy-all</artifactId>
-      <version>2.3.7</version>
-      <scope>provided</scope>
-    </dependency>
     <!--
          This is needed by the scalatest plugin, and so is declared here to be available in
          all child modules, just as scalatest is run in all children
@@ -1412,6 +1388,58 @@
           <artifactId>maven-deploy-plugin</artifactId>
           <version>2.8.2</version>
         </plugin>
+        <!-- This plugin's configuration is used to store Eclipse m2e settings only. -->
+        <!-- It has no influence on the Maven build itself. -->
+        <plugin>
+          <groupId>org.eclipse.m2e</groupId>
+          <artifactId>lifecycle-mapping</artifactId>
+          <version>1.0.0</version>
+          <configuration>
+            <lifecycleMappingMetadata>
+              <pluginExecutions>
+                <pluginExecution>
+                  <pluginExecutionFilter>
+                    <groupId>org.apache.maven.plugins</groupId>
+                    <artifactId>maven-dependency-plugin</artifactId>
+                    <versionRange>[2.8,)</versionRange>
+                    <goals>
+                      <goal>build-classpath</goal>
+                    </goals>
+                  </pluginExecutionFilter>
+                  <action>
+                    <ignore></ignore>
+                  </action>
+                </pluginExecution>
+                <pluginExecution>
+                  <pluginExecutionFilter>
+                    <groupId>org.apache.maven.plugins</groupId>
+                    <artifactId>maven-jar-plugin</artifactId>
+                    <versionRange>[2.6,)</versionRange>
+                    <goals>
+                      <goal>test-jar</goal>
+                    </goals>
+                  </pluginExecutionFilter>
+                  <action>
+                    <ignore></ignore>
+                  </action>
+                </pluginExecution>
+                <pluginExecution>
+                  <pluginExecutionFilter>
+                    <groupId>org.apache.maven.plugins</groupId>
+                    <artifactId>maven-antrun-plugin</artifactId>
+                    <versionRange>[1.8,)</versionRange>
+                    <goals>
+                      <goal>run</goal>
+                    </goals>
+                  </pluginExecutionFilter>
+                  <action>
+                    <ignore></ignore>
+                  </action>
+                </pluginExecution>
+              </pluginExecutions>
+            </lifecycleMappingMetadata>
+          </configuration>
+        </plugin>
       </plugins>
     </pluginManagement>
 
@@ -1429,34 +1457,12 @@
             </goals>
             <configuration>
               <includeScope>test</includeScope>
-              <outputFile>${test_classpath_file}</outputFile>
+              <outputProperty>test_classpath</outputProperty>
             </configuration>
           </execution>
         </executions>
       </plugin>
 
-      <!-- This plugin reads a file into maven property. And it lets us write groovy !! -->
-      <plugin>
-        <groupId>org.codehaus.gmavenplus</groupId>
-        <artifactId>gmavenplus-plugin</artifactId>
-        <version>1.5</version>
-        <executions>
-          <execution>
-            <phase>process-test-classes</phase>
-            <goals>
-              <goal>execute</goal>
-            </goals>
-            <configuration>
-              <scripts>
-                <script><![CDATA[
-                def file = new File(project.properties.test_classpath_file)
-                project.properties.test_classpath = file.getText().split().join(":")
-                ]]></script>
-              </scripts>
-            </configuration>
-          </execution>
-        </executions>
-      </plugin>
       <!--
         The shade plug-in is used here to create effective pom's (see SPARK-3812), and also
         remove references from the shaded libraries from artifacts published by Spark.
@@ -1523,36 +1529,6 @@
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-enforcer-plugin</artifactId>
       </plugin>
-      <plugin>
-        <groupId>org.codehaus.mojo</groupId>
-        <artifactId>build-helper-maven-plugin</artifactId>
-        <executions>
-          <execution>
-            <id>add-scala-sources</id>
-            <phase>generate-sources</phase>
-            <goals>
-              <goal>add-source</goal>
-            </goals>
-            <configuration>
-              <sources>
-                <source>src/main/scala</source>
-              </sources>
-            </configuration>
-          </execution>
-          <execution>
-            <id>add-scala-test-sources</id>
-            <phase>generate-test-sources</phase>
-            <goals>
-              <goal>add-test-source</goal>
-            </goals>
-            <configuration>
-              <sources>
-                <source>src/test/scala</source>
-              </sources>
-            </configuration>
-          </execution>
-        </executions>
-      </plugin>
       <plugin>
         <groupId>net.alchim31.maven</groupId>
         <artifactId>scala-maven-plugin</artifactId>
diff --git a/repl/pom.xml b/repl/pom.xml
index 70c9bd7c01296..a5a0f1fc2c857 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -133,7 +133,6 @@
             </goals>
             <configuration>
               <sources>
-                <source>src/main/scala</source>
                 <source>${extra.source.dir}</source>
               </sources>
             </configuration>
@@ -146,7 +145,6 @@
             </goals>
             <configuration>
               <sources>
-                <source>src/test/scala</source>
                 <source>${extra.testsource.dir}</source>
               </sources>
             </configuration>
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 2c03fee9dbd71..be0966641b5c4 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -123,7 +123,6 @@
             </goals>
             <configuration>
               <sources>
-                <source>src/test/scala</source>
                 <source>src/test/gen-java</source>
               </sources>
             </configuration>
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index a17546d706248..b00f320318be0 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -133,7 +133,6 @@
                 </goals>
                 <configuration>
                   <sources>
-                    <source>src/test/scala</source>
                     <source>compatibility/src/test/scala</source>
                   </sources>
                 </configuration>
diff --git a/tools/pom.xml b/tools/pom.xml
index feffde4c857eb..298ee2348b58e 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -76,10 +76,6 @@
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-source-plugin</artifactId>
       </plugin>
-      <plugin>
-        <groupId>org.codehaus.mojo</groupId>
-        <artifactId>build-helper-maven-plugin</artifactId>
-      </plugin>
     </plugins>
   </build>
 </project>

From 43dac2c880d6f310a958531aee0bb4ac1d9b7025 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Thu, 16 Jul 2015 10:52:09 -0700
Subject: [PATCH 0412/1454] [SPARK-6941] [SQL] Provide a better error message
 to when inserting into RDD based table

JIRA: https://issues.apache.org/jira/browse/SPARK-6941

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7342 from yijieshen/SPARK-6941 and squashes the following commits:

f82cbe7 [Yijie Shen] reorder import
dd67e40 [Yijie Shen] resolve comments
09518af [Yijie Shen] fix import order in DataframeSuite
0c635d4 [Yijie Shen] make match more specific
9df388d [Yijie Shen] move check into PreWriteCheck
847ab20 [Yijie Shen] Detect insertion error in DataSourceStrategy
---
 .../org/apache/spark/sql/sources/rules.scala  |  9 ++-
 .../org/apache/spark/sql/DataFrameSuite.scala | 55 ++++++++++++++++++-
 2 files changed, 60 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala
index a3fd7f13b3db7..40ee048e2653e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala
@@ -21,7 +21,7 @@ import org.apache.spark.sql.{SaveMode, AnalysisException}
 import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, Catalog}
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Cast, Alias}
 import org.apache.spark.sql.catalyst.plans.logical
-import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types.DataType
 
@@ -119,6 +119,13 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
         // The relation in l is not an InsertableRelation.
         failAnalysis(s"$l does not allow insertion.")
 
+      case logical.InsertIntoTable(t, _, _, _, _) =>
+        if (!t.isInstanceOf[LeafNode] || t == OneRowRelation || t.isInstanceOf[LocalRelation]) {
+          failAnalysis(s"Inserting into an RDD-based table is not allowed.")
+        } else {
+          // OK
+        }
+
       case CreateTableUsingAsSelect(tableName, _, _, _, SaveMode.Overwrite, _, query) =>
         // When the SaveMode is Overwrite, we need to check if the table is an input table of
         // the query. If so, we will throw an AnalysisException to let users know it is not allowed.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index f592a9934d0e6..23244fd310d0f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -17,19 +17,23 @@
 
 package org.apache.spark.sql
 
+import java.io.File
+
 import scala.language.postfixOps
 
+import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint}
-
+import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SQLTestUtils}
 
-class DataFrameSuite extends QueryTest {
+class DataFrameSuite extends QueryTest with SQLTestUtils {
   import org.apache.spark.sql.TestData._
 
   lazy val ctx = org.apache.spark.sql.test.TestSQLContext
   import ctx.implicits._
 
+  def sqlContext: SQLContext = ctx
+
   test("analysis error should be eagerly reported") {
     val oldSetting = ctx.conf.dataFrameEagerAnalysis
     // Eager analysis.
@@ -761,4 +765,49 @@ class DataFrameSuite extends QueryTest {
     assert(f.getMessage.contains("column3"))
     assert(!f.getMessage.contains("column2"))
   }
+
+  test("SPARK-6941: Better error message for inserting into RDD-based Table") {
+    withTempDir { dir =>
+
+      val tempParquetFile = new File(dir, "tmp_parquet")
+      val tempJsonFile = new File(dir, "tmp_json")
+
+      val df = Seq(Tuple1(1)).toDF()
+      val insertion = Seq(Tuple1(2)).toDF("col")
+
+      // pass case: parquet table (HadoopFsRelation)
+      df.write.mode(SaveMode.Overwrite).parquet(tempParquetFile.getCanonicalPath)
+      val pdf = ctx.read.parquet(tempParquetFile.getCanonicalPath)
+      pdf.registerTempTable("parquet_base")
+      insertion.write.insertInto("parquet_base")
+
+      // pass case: json table (InsertableRelation)
+      df.write.mode(SaveMode.Overwrite).json(tempJsonFile.getCanonicalPath)
+      val jdf = ctx.read.json(tempJsonFile.getCanonicalPath)
+      jdf.registerTempTable("json_base")
+      insertion.write.mode(SaveMode.Overwrite).insertInto("json_base")
+
+      // error cases: insert into an RDD
+      df.registerTempTable("rdd_base")
+      val e1 = intercept[AnalysisException] {
+        insertion.write.insertInto("rdd_base")
+      }
+      assert(e1.getMessage.contains("Inserting into an RDD-based table is not allowed."))
+
+      // error case: insert into a logical plan that is not a LeafNode
+      val indirectDS = pdf.select("_1").filter($"_1" > 5)
+      indirectDS.registerTempTable("indirect_ds")
+      val e2 = intercept[AnalysisException] {
+        insertion.write.insertInto("indirect_ds")
+      }
+      assert(e2.getMessage.contains("Inserting into an RDD-based table is not allowed."))
+
+      // error case: insert into an OneRowRelation
+      new DataFrame(ctx, OneRowRelation).registerTempTable("one_row")
+      val e3 = intercept[AnalysisException] {
+        insertion.write.insertInto("one_row")
+      }
+      assert(e3.getMessage.contains("Inserting into an RDD-based table is not allowed."))
+    }
+  }
 }

From fec10f0c63171bd5aff7a762a6c94df035f5fb52 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 16 Jul 2015 13:58:39 -0700
Subject: [PATCH 0413/1454] [SPARK-9085][SQL] Remove LeafNode, UnaryNode,
 BinaryNode from TreeNode.

This builds on #7433 but also removes LeafNode/UnaryNode. These are slightly more complicated to remove. I had to change some abstract classes to traits in order for it to work.

The problem with LeafNode/UnaryNode is that they are often mixed in at the end of an Expression, and then the toString function actually gets resolved to the ones defined in TreeNode, rather than in Expression.

Author: Reynold Xin <rxin@databricks.com>

Closes #7434 from rxin/remove-binary-unary-leaf-node and squashes the following commits:

9e8a4de [Reynold Xin] Generator should not be foldable.
3135a8b [Reynold Xin] SortOrder should not be foldable.
9c589cf [Reynold Xin] Fixed one more test case...
2225331 [Reynold Xin] Aggregate expressions should not be foldable.
16b5c90 [Reynold Xin] [SPARK-9085][SQL] Remove LeafNode, UnaryNode, BinaryNode from TreeNode.
---
 .../sql/catalyst/analysis/unresolved.scala    | 10 +++---
 .../catalyst/expressions/BoundAttribute.scala |  2 +-
 .../sql/catalyst/expressions/Expression.scala |  1 -
 .../sql/catalyst/expressions/SortOrder.scala  |  6 ++--
 .../sql/catalyst/expressions/aggregates.scala | 35 ++++++++++---------
 .../sql/catalyst/expressions/generators.scala | 12 +++----
 .../expressions/namedExpressions.scala        | 19 ++++++----
 .../sql/catalyst/optimizer/Optimizer.scala    |  4 +--
 .../catalyst/plans/logical/LogicalPlan.scala  | 11 ++++--
 .../spark/sql/catalyst/trees/TreeNode.scala   | 16 ---------
 .../spark/sql/execution/SparkPlan.scala       | 19 ++++++----
 11 files changed, 69 insertions(+), 66 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index f2e579afe833a..7089f079b6dde 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -50,8 +50,7 @@ case class UnresolvedRelation(
 /**
  * Holds the name of an attribute that has yet to be resolved.
  */
-case class UnresolvedAttribute(nameParts: Seq[String])
-  extends Attribute with trees.LeafNode[Expression] {
+case class UnresolvedAttribute(nameParts: Seq[String]) extends Attribute {
 
   def name: String =
     nameParts.map(n => if (n.contains(".")) s"`$n`" else n).mkString(".")
@@ -96,7 +95,7 @@ case class UnresolvedFunction(name: String, children: Seq[Expression]) extends E
  * Represents all of the input attributes to a given relational operator, for example in
  * "SELECT * FROM ...". A [[Star]] gets automatically expanded during analysis.
  */
-trait Star extends NamedExpression with trees.LeafNode[Expression] {
+abstract class Star extends LeafExpression with NamedExpression {
   self: Product =>
 
   override def name: String = throw new UnresolvedException(this, "name")
@@ -151,7 +150,7 @@ case class UnresolvedStar(table: Option[String]) extends Star {
  * @param names the names to be associated with each output of computing [[child]].
  */
 case class MultiAlias(child: Expression, names: Seq[String])
-  extends NamedExpression with trees.UnaryNode[Expression] {
+  extends UnaryExpression with NamedExpression {
 
   override def name: String = throw new UnresolvedException(this, "name")
 
@@ -210,8 +209,7 @@ case class UnresolvedExtractValue(child: Expression, extraction: Expression)
 /**
  * Holds the expression that has yet to be aliased.
  */
-case class UnresolvedAlias(child: Expression) extends NamedExpression
-  with trees.UnaryNode[Expression] {
+case class UnresolvedAlias(child: Expression) extends UnaryExpression with NamedExpression {
 
   override def toAttribute: Attribute = throw new UnresolvedException(this, "toAttribute")
   override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 3f0d7b803125f..b09aea03318da 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.types._
  * the layout of intermediate tuples, BindReferences should be run after all such transformations.
  */
 case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
-  extends NamedExpression with trees.LeafNode[Expression] {
+  extends LeafExpression with NamedExpression {
 
   override def toString: String = s"input[$ordinal]"
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index a655cc8e48ae1..f396bd08a8238 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types._
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index 8ab4ef060b68c..b8f7068c9e5e5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -30,8 +30,10 @@ case object Descending extends SortDirection
  * An expression that can be used to sort a tuple.  This class extends expression primarily so that
  * transformations over expression will descend into its child.
  */
-case class SortOrder(child: Expression, direction: SortDirection) extends Expression
-    with trees.UnaryNode[Expression] {
+case class SortOrder(child: Expression, direction: SortDirection) extends UnaryExpression {
+
+  /** Sort order is not foldable because we don't have an eval for it. */
+  override def foldable: Boolean = false
 
   override def dataType: DataType = child.dataType
   override def nullable: Boolean = child.nullable
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index c0e17f97e9b3c..71c943dc79e9e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -20,16 +20,20 @@ package org.apache.spark.sql.catalyst.expressions
 import com.clearspring.analytics.stream.cardinality.HyperLogLog
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
 
-abstract class AggregateExpression extends Expression {
+trait AggregateExpression extends Expression {
   self: Product =>
 
+  /**
+   * Aggregate expressions should not be foldable.
+   */
+  override def foldable: Boolean = false
+
   /**
    * Creates a new instance that can be used to compute this aggregate expression for a group
    * of input rows/
@@ -60,7 +64,7 @@ case class SplitEvaluation(
  * An [[AggregateExpression]] that can be partially computed without seeing all relevant tuples.
  * These partial evaluations can then be combined to compute the actual answer.
  */
-abstract class PartialAggregate extends AggregateExpression {
+trait PartialAggregate extends AggregateExpression {
   self: Product =>
 
   /**
@@ -74,7 +78,7 @@ abstract class PartialAggregate extends AggregateExpression {
  * [[AggregateExpression]] with an algorithm that will be used to compute one specific result.
  */
 abstract class AggregateFunction
-  extends AggregateExpression with Serializable with trees.LeafNode[Expression] {
+  extends LeafExpression with AggregateExpression with Serializable {
   self: Product =>
 
   /** Base should return the generic aggregate expression that this function is computing */
@@ -91,7 +95,7 @@ abstract class AggregateFunction
   }
 }
 
-case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+case class Min(child: Expression) extends UnaryExpression with PartialAggregate {
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
@@ -124,7 +128,7 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr
   override def eval(input: InternalRow): Any = currentMin.value
 }
 
-case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+case class Max(child: Expression) extends UnaryExpression with PartialAggregate {
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
@@ -157,7 +161,7 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr
   override def eval(input: InternalRow): Any = currentMax.value
 }
 
-case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+case class Count(child: Expression) extends UnaryExpression with PartialAggregate {
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
@@ -310,7 +314,7 @@ private[sql] case object HyperLogLogUDT extends UserDefinedType[HyperLogLog] {
 }
 
 case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
-  extends AggregateExpression with trees.UnaryNode[Expression] {
+  extends UnaryExpression with AggregateExpression {
 
   override def nullable: Boolean = false
   override def dataType: DataType = HyperLogLogUDT
@@ -340,7 +344,7 @@ case class ApproxCountDistinctPartitionFunction(
 }
 
 case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
-  extends AggregateExpression with trees.UnaryNode[Expression] {
+  extends UnaryExpression with AggregateExpression {
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
@@ -368,7 +372,7 @@ case class ApproxCountDistinctMergeFunction(
 }
 
 case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
-  extends PartialAggregate with trees.UnaryNode[Expression] {
+  extends UnaryExpression with PartialAggregate {
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
@@ -386,7 +390,7 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
   override def newInstance(): CountDistinctFunction = new CountDistinctFunction(child :: Nil, this)
 }
 
-case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+case class Average(child: Expression) extends UnaryExpression with PartialAggregate {
 
   override def prettyName: String = "avg"
 
@@ -479,7 +483,7 @@ case class AverageFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
-case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+case class Sum(child: Expression) extends UnaryExpression with PartialAggregate {
 
   override def nullable: Boolean = true
 
@@ -606,8 +610,7 @@ case class CombineSumFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
-case class SumDistinct(child: Expression)
-  extends PartialAggregate with trees.UnaryNode[Expression] {
+case class SumDistinct(child: Expression) extends UnaryExpression with PartialAggregate {
 
   def this() = this(null)
   override def nullable: Boolean = true
@@ -701,7 +704,7 @@ case class CombineSetsAndSumFunction(
   }
 }
 
-case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+case class First(child: Expression) extends UnaryExpression with PartialAggregate {
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
   override def toString: String = s"FIRST($child)"
@@ -729,7 +732,7 @@ case class FirstFunction(expr: Expression, base: AggregateExpression) extends Ag
   override def eval(input: InternalRow): Any = result
 }
 
-case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+case class Last(child: Expression) extends UnaryExpression with PartialAggregate {
   override def references: AttributeSet = child.references
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index b68d30a26abd8..51dc77ee3fc5f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -40,13 +40,14 @@ import org.apache.spark.sql.types._
  * requested.  The attributes produced by this function will be automatically copied anytime rules
  * result in changes to the Generator or its children.
  */
-abstract class Generator extends Expression {
-  self: Product =>
+trait Generator extends Expression { self: Product =>
 
   // TODO ideally we should return the type of ArrayType(StructType),
   // however, we don't keep the output field names in the Generator.
   override def dataType: DataType = throw new UnsupportedOperationException
 
+  override def foldable: Boolean = false
+
   override def nullable: Boolean = false
 
   /**
@@ -99,8 +100,9 @@ case class UserDefinedGenerator(
 /**
  * Given an input array produces a sequence of rows for each value in the array.
  */
-case class Explode(child: Expression)
-  extends Generator with trees.UnaryNode[Expression] {
+case class Explode(child: Expression) extends UnaryExpression with Generator {
+
+  override def children: Seq[Expression] = child :: Nil
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType]) {
@@ -127,6 +129,4 @@ case class Explode(child: Expression)
         else inputMap.map { case (k, v) => InternalRow(k, v) }
     }
   }
-
-  override def toString: String = s"explode($child)"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 6181c60c0e453..8bf7a7ce4e647 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -37,8 +37,13 @@ object NamedExpression {
  */
 case class ExprId(id: Long)
 
-abstract class NamedExpression extends Expression {
-  self: Product =>
+/**
+ * An [[Expression]] that is named.
+ */
+trait NamedExpression extends Expression { self: Product =>
+
+  /** We should never fold named expressions in order to not remove the alias. */
+  override def foldable: Boolean = false
 
   def name: String
   def exprId: ExprId
@@ -78,8 +83,7 @@ abstract class NamedExpression extends Expression {
     }
 }
 
-abstract class Attribute extends NamedExpression {
-  self: Product =>
+abstract class Attribute extends LeafExpression with NamedExpression { self: Product =>
 
   override def references: AttributeSet = AttributeSet(this)
 
@@ -110,7 +114,7 @@ case class Alias(child: Expression, name: String)(
     val exprId: ExprId = NamedExpression.newExprId,
     val qualifiers: Seq[String] = Nil,
     val explicitMetadata: Option[Metadata] = None)
-  extends NamedExpression with trees.UnaryNode[Expression] {
+  extends UnaryExpression with NamedExpression {
 
   // Alias(Generator, xx) need to be transformed into Generate(generator, ...)
   override lazy val resolved =
@@ -172,7 +176,8 @@ case class AttributeReference(
     nullable: Boolean = true,
     override val metadata: Metadata = Metadata.empty)(
     val exprId: ExprId = NamedExpression.newExprId,
-    val qualifiers: Seq[String] = Nil) extends Attribute with trees.LeafNode[Expression] {
+    val qualifiers: Seq[String] = Nil)
+  extends Attribute {
 
   /**
    * Returns true iff the expression id is the same for both attributes.
@@ -242,7 +247,7 @@ case class AttributeReference(
  * A place holder used when printing expressions without debugging information such as the
  * expression id or the unresolved indicator.
  */
-case class PrettyAttribute(name: String) extends Attribute with trees.LeafNode[Expression] {
+case class PrettyAttribute(name: String) extends Attribute {
 
   override def toString: String = name
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 5d80214abf141..2f94b457f4cdc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -342,7 +342,7 @@ object ConstantFolding extends Rule[LogicalPlan] {
       case l: Literal => l
 
       // Fold expressions that are foldable.
-      case e if e.foldable => Literal.create(e.eval(null), e.dataType)
+      case e if e.foldable => Literal.create(e.eval(EmptyRow), e.dataType)
 
       // Fold "literal in (item1, item2, ..., literal, ...)" into true directly.
       case In(Literal(v, _), list) if list.exists {
@@ -361,7 +361,7 @@ object OptimizeIn extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     case q: LogicalPlan => q transformExpressionsDown {
       case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) =>
-        val hSet = list.map(e => e.eval(null))
+        val hSet = list.map(e => e.eval(EmptyRow))
         InSet(v, HashSet() ++ hSet)
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index d7077a0ec907a..adac37231cc4a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -23,7 +23,6 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.trees.TreeNode
-import org.apache.spark.sql.catalyst.trees
 
 
 abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
@@ -277,15 +276,21 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
 /**
  * A logical plan node with no children.
  */
-abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
+abstract class LeafNode extends LogicalPlan {
   self: Product =>
+
+  override def children: Seq[LogicalPlan] = Nil
 }
 
 /**
  * A logical plan node with single child.
  */
-abstract class UnaryNode extends LogicalPlan with trees.UnaryNode[LogicalPlan] {
+abstract class UnaryNode extends LogicalPlan {
   self: Product =>
+
+  def child: LogicalPlan
+
+  override def children: Seq[LogicalPlan] = child :: Nil
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 16844b2f4b680..0f95ca688a7a8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -452,19 +452,3 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
     s"$nodeName(${args.mkString(",")})"
   }
 }
-
-
-/**
- * A [[TreeNode]] with no children.
- */
-trait LeafNode[BaseType <: TreeNode[BaseType]] {
-  def children: Seq[BaseType] = Nil
-}
-
-/**
- * A [[TreeNode]] with a single [[child]].
- */
-trait UnaryNode[BaseType <: TreeNode[BaseType]] {
-  def child: BaseType
-  def children: Seq[BaseType] = child :: Nil
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 9dc7879fa4a1a..632f633d82a2e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -17,20 +17,20 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.annotation.DeveloperApi
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.Logging
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees}
+import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 
-import scala.collection.mutable.ArrayBuffer
-
 object SparkPlan {
   protected[sql] val currentContext = new ThreadLocal[SQLContext]()
 }
@@ -238,12 +238,19 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   }
 }
 
-private[sql] trait LeafNode extends SparkPlan with trees.LeafNode[SparkPlan] {
+private[sql] trait LeafNode extends SparkPlan {
   self: Product =>
+
+  override def children: Seq[SparkPlan] = Nil
 }
 
-private[sql] trait UnaryNode extends SparkPlan with trees.UnaryNode[SparkPlan] {
+private[sql] trait UnaryNode extends SparkPlan {
   self: Product =>
+
+  def child: SparkPlan
+
+  override def children: Seq[SparkPlan] = child :: Nil
+
   override def outputPartitioning: Partitioning = child.outputPartitioning
 }
 

From 031d7d41430ec1f3c3353e33eab4821a9bcd58a5 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Thu, 16 Jul 2015 16:55:46 -0700
Subject: [PATCH 0414/1454] [SPARK-6304] [STREAMING] Fix checkpointing doesn't
 retain driver port issue.

Author: jerryshao <saisai.shao@intel.com>
Author: Saisai Shao <saisai.shao@intel.com>

Closes #5060 from jerryshao/SPARK-6304 and squashes the following commits:

89b01f5 [jerryshao] Update the unit test to add more cases
275d252 [jerryshao] Address the comments
7cc146d [jerryshao] Address the comments
2624723 [jerryshao] Fix rebase conflict
45befaa [Saisai Shao] Update the unit test
bbc1c9c [Saisai Shao] Fix checkpointing doesn't retain driver port issue
---
 .../apache/spark/streaming/Checkpoint.scala   |  2 +
 .../spark/streaming/CheckpointSuite.scala     | 45 ++++++++++++++++++-
 2 files changed, 46 insertions(+), 1 deletion(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 5279331c9e122..65d4e933bf8e9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -48,6 +48,8 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
     // Reload properties for the checkpoint application since user wants to set a reload property
     // or spark had changed its value and user wants to set it back.
     val propertiesToReload = List(
+      "spark.driver.host",
+      "spark.driver.port",
       "spark.master",
       "spark.yarn.keytab",
       "spark.yarn.principal")
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index 6a94928076236..d308ac05a54fe 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -191,8 +191,51 @@ class CheckpointSuite extends TestSuiteBase {
     }
   }
 
+  // This tests if "spark.driver.host" and "spark.driver.port" is set by user, can be recovered
+  // with correct value.
+  test("get correct spark.driver.[host|port] from checkpoint") {
+    val conf = Map("spark.driver.host" -> "localhost", "spark.driver.port" -> "9999")
+    conf.foreach(kv => System.setProperty(kv._1, kv._2))
+    ssc = new StreamingContext(master, framework, batchDuration)
+    val originalConf = ssc.conf
+    assert(originalConf.get("spark.driver.host") === "localhost")
+    assert(originalConf.get("spark.driver.port") === "9999")
+
+    val cp = new Checkpoint(ssc, Time(1000))
+    ssc.stop()
+
+    // Serialize/deserialize to simulate write to storage and reading it back
+    val newCp = Utils.deserialize[Checkpoint](Utils.serialize(cp))
+
+    val newCpConf = newCp.createSparkConf()
+    assert(newCpConf.contains("spark.driver.host"))
+    assert(newCpConf.contains("spark.driver.port"))
+    assert(newCpConf.get("spark.driver.host") === "localhost")
+    assert(newCpConf.get("spark.driver.port") === "9999")
+
+    // Check if all the parameters have been restored
+    ssc = new StreamingContext(null, newCp, null)
+    val restoredConf = ssc.conf
+    assert(restoredConf.get("spark.driver.host") === "localhost")
+    assert(restoredConf.get("spark.driver.port") === "9999")
+    ssc.stop()
+
+    // If spark.driver.host and spark.driver.host is not set in system property, these two
+    // parameters should not be presented in the newly recovered conf.
+    conf.foreach(kv => System.clearProperty(kv._1))
+    val newCpConf1 = newCp.createSparkConf()
+    assert(!newCpConf1.contains("spark.driver.host"))
+    assert(!newCpConf1.contains("spark.driver.port"))
+
+    // Spark itself will dispatch a random, not-used port for spark.driver.port if it is not set
+    // explicitly.
+    ssc = new StreamingContext(null, newCp, null)
+    val restoredConf1 = ssc.conf
+    assert(restoredConf1.get("spark.driver.host") === "localhost")
+    assert(restoredConf1.get("spark.driver.port") !== "9999")
+  }
 
-  // This tests whether the systm can recover from a master failure with simple
+  // This tests whether the system can recover from a master failure with simple
   // non-stateful operations. This assumes as reliable, replayable input
   // source - TestInputDStream.
   test("recovery with map and reduceByKey operations") {

From 57e9b13bf99ab82896114d47c62fbee648c9be7d Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Thu, 16 Jul 2015 18:14:45 -0700
Subject: [PATCH 0415/1454] [SPARK-8644] Include call site in SparkException
 stack traces thrown by job failures

Example exception (new part at bottom, clearly demarcated):

```
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.RuntimeException: uh-oh!
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37$$anonfun$38$$anonfun$apply$mcJ$sp$2.apply(DAGSchedulerSuite.scala:880)
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37$$anonfun$38$$anonfun$apply$mcJ$sp$2.apply(DAGSchedulerSuite.scala:880)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1640)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1099)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1099)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1777)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1777)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
	at org.apache.spark.scheduler.Task.run(Task.scala:70)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:744)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1298)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1289)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1288)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1288)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:755)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:755)
	at scala.Option.foreach(Option.scala:236)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:755)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1509)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1470)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1459)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:560)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1744)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1762)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1777)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1791)
	at org.apache.spark.rdd.RDD.count(RDD.scala:1099)
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37$$anonfun$38.apply$mcJ$sp(DAGSchedulerSuite.scala:880)
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37$$anonfun$38.apply(DAGSchedulerSuite.scala:880)
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37$$anonfun$38.apply(DAGSchedulerSuite.scala:880)
	at org.scalatest.Assertions$class.intercept(Assertions.scala:997)
	at org.scalatest.FunSuite.intercept(FunSuite.scala:1555)
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37.apply$mcV$sp(DAGSchedulerSuite.scala:879)
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37.apply(DAGSchedulerSuite.scala:878)
	at org.apache.spark.scheduler.DAGSchedulerSuite$$anonfun$37.apply(DAGSchedulerSuite.scala:878)
	at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
	at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
	at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
	at org.scalatest.Transformer.apply(Transformer.scala:22)
	at org.scalatest.Transformer.apply(Transformer.scala:20)
	at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
	at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:42)
	at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
	at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
	at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
	at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
	at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
	at org.apache.spark.scheduler.DAGSchedulerSuite.org$scalatest$BeforeAndAfter$$super$runTest(DAGSchedulerSuite.scala:70)
	at org.scalatest.BeforeAndAfter$class.runTest(BeforeAndAfter.scala:200)
	at org.apache.spark.scheduler.DAGSchedulerSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(DAGSchedulerSuite.scala:70)
	at org.scalatest.BeforeAndAfterEach$class.runTest(BeforeAndAfterEach.scala:255)
	at org.apache.spark.scheduler.DAGSchedulerSuite.runTest(DAGSchedulerSuite.scala:70)
	at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
	at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
	at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413)
	at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401)
	at scala.collection.immutable.List.foreach(List.scala:318)
	at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
	at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396)
	at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483)
	at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208)
	at org.scalatest.FunSuite.runTests(FunSuite.scala:1555)
	at org.scalatest.Suite$class.run(Suite.scala:1424)
	at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555)
	at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
	at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
	at org.scalatest.SuperEngine.runImpl(Engine.scala:545)
	at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212)
	at org.apache.spark.scheduler.DAGSchedulerSuite.org$scalatest$BeforeAndAfter$$super$run(DAGSchedulerSuite.scala:70)
	at org.scalatest.BeforeAndAfter$class.run(BeforeAndAfter.scala:241)
	at org.apache.spark.scheduler.DAGSchedulerSuite.org$scalatest$BeforeAndAfterAll$$super$run(DAGSchedulerSuite.scala:70)
	at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257)
	at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256)
	at org.apache.spark.scheduler.DAGSchedulerSuite.run(DAGSchedulerSuite.scala:70)
	at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:462)
	at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:671)
	at sbt.ForkMain$Run$2.call(ForkMain.java:294)
	at sbt.ForkMain$Run$2.call(ForkMain.java:284)
	at java.util.concurrent.FutureTask.run(FutureTask.java:262)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:744)
```

Author: Aaron Davidson <aaron@databricks.com>

Closes #7028 from aarondav/stack-trace and squashes the following commits:

4714664 [Aaron Davidson] [SPARK-8644] Include call site in SparkException stack traces thrown by job failures
---
 .../org/apache/spark/scheduler/DAGScheduler.scala |  3 +++
 .../spark/scheduler/DAGSchedulerSuite.scala       | 15 +++++++++++++++
 2 files changed, 18 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index f3d87ee5c4fd1..f8ba3d2d1a9b2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -556,6 +556,9 @@ class DAGScheduler(
       case JobFailed(exception: Exception) =>
         logInfo("Job %d failed: %s, took %f s".format
           (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
+        // SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
+        val callerStackTrace = Thread.currentThread().getStackTrace.tail
+        exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
         throw exception
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 6bc45f249f975..4f2b0fa162b72 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -875,6 +875,21 @@ class DAGSchedulerSuite
     assertDataStructuresEmpty
   }
 
+  test("Spark exceptions should include call site in stack trace") {
+    val e = intercept[SparkException] {
+      sc.parallelize(1 to 10, 2).map { _ => throw new RuntimeException("uh-oh!") }.count()
+    }
+
+    // Does not include message, ONLY stack trace.
+    val stackTraceString = e.getStackTraceString
+
+    // should actually include the RDD operation that invoked the method:
+    assert(stackTraceString.contains("org.apache.spark.rdd.RDD.count"))
+
+    // should include the FunSuite setup:
+    assert(stackTraceString.contains("org.scalatest.FunSuite"))
+  }
+
   /**
    * Assert that the supplied TaskSet has exactly the given hosts as its preferred locations.
    * Note that this checks only the host and not the executor ID.

From 49351c7f597c67950cc65e5014a89fad31b9a6f7 Mon Sep 17 00:00:00 2001
From: Lianhui Wang <lianhuiwang09@gmail.com>
Date: Thu, 16 Jul 2015 19:31:14 -0700
Subject: [PATCH 0416/1454] [SPARK-8646] PySpark does not run on YARN if master
 not provided in command line

andrewor14 davies vanzin can you take a look at this? thanks

Author: Lianhui Wang <lianhuiwang09@gmail.com>

Closes #7438 from lianhuiwang/SPARK-8646 and squashes the following commits:

cb3f12d [Lianhui Wang] add whitespace
6d874a6 [Lianhui Wang] support pyspark for yarn-client
---
 python/pyspark/context.py                                    | 5 +++++
 .../src/main/scala/org/apache/spark/deploy/yarn/Client.scala | 2 +-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index d7466729b8f36..43bde5ae41e23 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -152,6 +152,11 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize,
         self.master = self._conf.get("spark.master")
         self.appName = self._conf.get("spark.app.name")
         self.sparkHome = self._conf.get("spark.home", None)
+
+        # Let YARN know it's a pyspark app, so it distributes needed libraries.
+        if self.master == "yarn-client":
+            self._conf.set("spark.yarn.isPython", "true")
+
         for (k, v) in self._conf.getAll():
             if k.startswith("spark.executorEnv."):
                 varName = k[len("spark.executorEnv."):]
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index f86b6d1e5d7bc..b74ea9a10afb2 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -616,7 +616,7 @@ private[spark] class Client(
     val appId = newAppResponse.getApplicationId
     val appStagingDir = getAppStagingDir(appId)
     val pySparkArchives =
-      if (sys.props.getOrElse("spark.yarn.isPython", "false").toBoolean) {
+      if (sparkConf.getBoolean("spark.yarn.isPython", false)) {
         findPySparkArchives()
       } else {
         Nil

From d86bbb4e286f16f77ba125452b07827684eafeed Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Thu, 16 Jul 2015 19:36:45 -0700
Subject: [PATCH 0417/1454] [SPARK-6284] [MESOS] Add mesos role, principal and
 secret

Mesos supports framework authentication and role to be set per framework, which the role is used to identify the framework's role which impacts the sharing weight of resource allocation and optional authentication information to allow the framework to be connected to the master.

Author: Timothy Chen <tnachen@gmail.com>

Closes #4960 from tnachen/mesos_fw_auth and squashes the following commits:

0f9f03e [Timothy Chen] Fix review comments.
8f9488a [Timothy Chen] Fix rebase
f7fc2a9 [Timothy Chen] Add mesos role, auth and secret.
---
 .../mesos/CoarseMesosSchedulerBackend.scala   |  35 ++---
 .../cluster/mesos/MesosClusterScheduler.scala |  28 ++--
 .../cluster/mesos/MesosSchedulerBackend.scala | 118 ++++++++++------
 .../cluster/mesos/MesosSchedulerUtils.scala   | 126 +++++++++++++++---
 .../CoarseMesosSchedulerBackendSuite.scala    |  19 ++-
 .../mesos/MesosSchedulerBackendSuite.scala    | 106 ++++++++++++++-
 docs/running-on-mesos.md                      |  22 +++
 7 files changed, 358 insertions(+), 96 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index cbade131494bc..b7fde0d9b3265 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.scheduler.cluster.mesos
 
 import java.io.File
-import java.util.{List => JList, Collections}
 import java.util.concurrent.locks.ReentrantLock
+import java.util.{Collections, List => JList}
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, HashSet}
@@ -27,12 +27,11 @@ import scala.collection.mutable.{HashMap, HashSet}
 import com.google.common.collect.HashBiMap
 import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _}
 import org.apache.mesos.{Scheduler => MScheduler, _}
-import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _}
-import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState}
 import org.apache.spark.rpc.RpcAddress
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
+import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState}
 
 /**
  * A SchedulerBackend that runs tasks on Mesos, but uses "coarse-grained" tasks, where it holds
@@ -69,7 +68,7 @@ private[spark] class CoarseMesosSchedulerBackend(
 
   /**
    * The total number of executors we aim to have. Undefined when not using dynamic allocation
-   * and before the ExecutorAllocatorManager calls [[doRequesTotalExecutors]].
+   * and before the ExecutorAllocatorManager calls [[doRequestTotalExecutors]].
    */
   private var executorLimitOption: Option[Int] = None
 
@@ -103,8 +102,9 @@ private[spark] class CoarseMesosSchedulerBackend(
 
   override def start() {
     super.start()
-    val fwInfo = FrameworkInfo.newBuilder().setUser(sc.sparkUser).setName(sc.appName).build()
-    startScheduler(master, CoarseMesosSchedulerBackend.this, fwInfo)
+    val driver = createSchedulerDriver(
+      master, CoarseMesosSchedulerBackend.this, sc.sparkUser, sc.appName, sc.conf)
+    startScheduler(driver)
   }
 
   def createCommand(offer: Offer, numCores: Int, taskId: Int): CommandInfo = {
@@ -224,24 +224,29 @@ private[spark] class CoarseMesosSchedulerBackend(
           taskIdToSlaveId(taskId) = slaveId
           slaveIdsWithExecutors += slaveId
           coresByTaskId(taskId) = cpusToUse
-          val task = MesosTaskInfo.newBuilder()
+          // Gather cpu resources from the available resources and use them in the task.
+          val (remainingResources, cpuResourcesToUse) =
+            partitionResources(offer.getResourcesList, "cpus", cpusToUse)
+          val (_, memResourcesToUse) =
+            partitionResources(remainingResources, "mem", calculateTotalMemory(sc))
+          val taskBuilder = MesosTaskInfo.newBuilder()
             .setTaskId(TaskID.newBuilder().setValue(taskId.toString).build())
             .setSlaveId(offer.getSlaveId)
             .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave, taskId))
             .setName("Task " + taskId)
-            .addResources(createResource("cpus", cpusToUse))
-            .addResources(createResource("mem", calculateTotalMemory(sc)))
+            .addAllResources(cpuResourcesToUse)
+            .addAllResources(memResourcesToUse)
 
           sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image =>
             MesosSchedulerBackendUtil
-              .setupContainerBuilderDockerInfo(image, sc.conf, task.getContainerBuilder)
+              .setupContainerBuilderDockerInfo(image, sc.conf, taskBuilder.getContainerBuilder())
           }
 
           // accept the offer and launch the task
           logDebug(s"Accepting offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
           d.launchTasks(
             Collections.singleton(offer.getId),
-            Collections.singleton(task.build()), filters)
+            Collections.singleton(taskBuilder.build()), filters)
         } else {
           // Decline the offer
           logDebug(s"Declining offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
@@ -255,7 +260,7 @@ private[spark] class CoarseMesosSchedulerBackend(
   override def statusUpdate(d: SchedulerDriver, status: TaskStatus) {
     val taskId = status.getTaskId.getValue.toInt
     val state = status.getState
-    logInfo("Mesos task " + taskId + " is now " + state)
+    logInfo(s"Mesos task $taskId is now $state")
     stateLock.synchronized {
       if (TaskState.isFinished(TaskState.fromMesos(state))) {
         val slaveId = taskIdToSlaveId(taskId)
@@ -270,7 +275,7 @@ private[spark] class CoarseMesosSchedulerBackend(
         if (TaskState.isFailed(TaskState.fromMesos(state))) {
           failuresBySlaveId(slaveId) = failuresBySlaveId.getOrElse(slaveId, 0) + 1
           if (failuresBySlaveId(slaveId) >= MAX_SLAVE_FAILURES) {
-            logInfo("Blacklisting Mesos slave " + slaveId + " due to too many failures; " +
+            logInfo(s"Blacklisting Mesos slave $slaveId due to too many failures; " +
                 "is Spark installed on it?")
           }
         }
@@ -282,7 +287,7 @@ private[spark] class CoarseMesosSchedulerBackend(
   }
 
   override def error(d: SchedulerDriver, message: String) {
-    logError("Mesos error: " + message)
+    logError(s"Mesos error: $message")
     scheduler.error(message)
   }
 
@@ -323,7 +328,7 @@ private[spark] class CoarseMesosSchedulerBackend(
   }
 
   override def slaveLost(d: SchedulerDriver, slaveId: SlaveID): Unit = {
-    logInfo("Mesos slave lost: " + slaveId.getValue)
+    logInfo(s"Mesos slave lost: ${slaveId.getValue}")
     executorTerminated(d, slaveId.getValue, "Mesos slave lost: " + slaveId.getValue)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index d3a20f822176e..f078547e71352 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -295,20 +295,24 @@ private[spark] class MesosClusterScheduler(
   def start(): Unit = {
     // TODO: Implement leader election to make sure only one framework running in the cluster.
     val fwId = schedulerState.fetch[String]("frameworkId")
-    val builder = FrameworkInfo.newBuilder()
-      .setUser(Utils.getCurrentUserName())
-      .setName(appName)
-      .setWebuiUrl(frameworkUrl)
-      .setCheckpoint(true)
-      .setFailoverTimeout(Integer.MAX_VALUE) // Setting to max so tasks keep running on crash
     fwId.foreach { id =>
-      builder.setId(FrameworkID.newBuilder().setValue(id).build())
       frameworkId = id
     }
     recoverState()
     metricsSystem.registerSource(new MesosClusterSchedulerSource(this))
     metricsSystem.start()
-    startScheduler(master, MesosClusterScheduler.this, builder.build())
+    val driver = createSchedulerDriver(
+      master,
+      MesosClusterScheduler.this,
+      Utils.getCurrentUserName(),
+      appName,
+      conf,
+      Some(frameworkUrl),
+      Some(true),
+      Some(Integer.MAX_VALUE),
+      fwId)
+
+    startScheduler(driver)
     ready = true
   }
 
@@ -449,12 +453,8 @@ private[spark] class MesosClusterScheduler(
         offer.cpu -= driverCpu
         offer.mem -= driverMem
         val taskId = TaskID.newBuilder().setValue(submission.submissionId).build()
-        val cpuResource = Resource.newBuilder()
-          .setName("cpus").setType(Value.Type.SCALAR)
-          .setScalar(Value.Scalar.newBuilder().setValue(driverCpu)).build()
-        val memResource = Resource.newBuilder()
-          .setName("mem").setType(Value.Type.SCALAR)
-          .setScalar(Value.Scalar.newBuilder().setValue(driverMem)).build()
+        val cpuResource = createResource("cpus", driverCpu)
+        val memResource = createResource("mem", driverMem)
         val commandInfo = buildDriverCommand(submission)
         val appName = submission.schedulerProperties("spark.app.name")
         val taskInfo = TaskInfo.newBuilder()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index d72e2af456e15..3f63ec1c5832f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -32,6 +32,7 @@ import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.cluster.ExecutorInfo
 import org.apache.spark.util.Utils
 
+
 /**
  * A SchedulerBackend for running fine-grained tasks on Mesos. Each Spark task is mapped to a
  * separate Mesos task, allowing multiple applications to share cluster nodes both in space (tasks
@@ -45,8 +46,8 @@ private[spark] class MesosSchedulerBackend(
   with MScheduler
   with MesosSchedulerUtils {
 
-  // Which slave IDs we have executors on
-  val slaveIdsWithExecutors = new HashSet[String]
+  // Stores the slave ids that has launched a Mesos executor.
+  val slaveIdToExecutorInfo = new HashMap[String, MesosExecutorInfo]
   val taskIdToSlaveId = new HashMap[Long, String]
 
   // An ExecutorInfo for our tasks
@@ -66,12 +67,21 @@ private[spark] class MesosSchedulerBackend(
   @volatile var appId: String = _
 
   override def start() {
-    val fwInfo = FrameworkInfo.newBuilder().setUser(sc.sparkUser).setName(sc.appName).build()
     classLoader = Thread.currentThread.getContextClassLoader
-    startScheduler(master, MesosSchedulerBackend.this, fwInfo)
+    val driver = createSchedulerDriver(
+      master, MesosSchedulerBackend.this, sc.sparkUser, sc.appName, sc.conf)
+    startScheduler(driver)
   }
 
-  def createExecutorInfo(execId: String): MesosExecutorInfo = {
+  /**
+   * Creates a MesosExecutorInfo that is used to launch a Mesos executor.
+   * @param availableResources Available resources that is offered by Mesos
+   * @param execId The executor id to assign to this new executor.
+   * @return A tuple of the new mesos executor info and the remaining available resources.
+   */
+  def createExecutorInfo(
+      availableResources: JList[Resource],
+      execId: String): (MesosExecutorInfo, JList[Resource]) = {
     val executorSparkHome = sc.conf.getOption("spark.mesos.executor.home")
       .orElse(sc.getSparkHome()) // Fall back to driver Spark home for backward compatibility
       .getOrElse {
@@ -115,32 +125,25 @@ private[spark] class MesosSchedulerBackend(
       command.setValue(s"cd ${basename}*; $prefixEnv ./bin/spark-class $executorBackendName")
       command.addUris(CommandInfo.URI.newBuilder().setValue(uri.get))
     }
-    val cpus = Resource.newBuilder()
-      .setName("cpus")
-      .setType(Value.Type.SCALAR)
-      .setScalar(Value.Scalar.newBuilder()
-      .setValue(mesosExecutorCores).build())
-      .build()
-    val memory = Resource.newBuilder()
-      .setName("mem")
-      .setType(Value.Type.SCALAR)
-      .setScalar(
-        Value.Scalar.newBuilder()
-          .setValue(calculateTotalMemory(sc)).build())
-      .build()
-    val executorInfo = MesosExecutorInfo.newBuilder()
+    val builder = MesosExecutorInfo.newBuilder()
+    val (resourcesAfterCpu, usedCpuResources) =
+      partitionResources(availableResources, "cpus", scheduler.CPUS_PER_TASK)
+    val (resourcesAfterMem, usedMemResources) =
+      partitionResources(resourcesAfterCpu, "mem", calculateTotalMemory(sc))
+
+    builder.addAllResources(usedCpuResources)
+    builder.addAllResources(usedMemResources)
+    val executorInfo = builder
       .setExecutorId(ExecutorID.newBuilder().setValue(execId).build())
       .setCommand(command)
       .setData(ByteString.copyFrom(createExecArg()))
-      .addResources(cpus)
-      .addResources(memory)
 
     sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image =>
       MesosSchedulerBackendUtil
         .setupContainerBuilderDockerInfo(image, sc.conf, executorInfo.getContainerBuilder())
     }
 
-    executorInfo.build()
+    (executorInfo.build(), resourcesAfterMem)
   }
 
   /**
@@ -183,6 +186,18 @@ private[spark] class MesosSchedulerBackend(
 
   override def reregistered(d: SchedulerDriver, masterInfo: MasterInfo) {}
 
+  private def getTasksSummary(tasks: JArrayList[MesosTaskInfo]): String = {
+    val builder = new StringBuilder
+    tasks.foreach { t =>
+      builder.append("Task id: ").append(t.getTaskId.getValue).append("\n")
+        .append("Slave id: ").append(t.getSlaveId.getValue).append("\n")
+        .append("Task resources: ").append(t.getResourcesList).append("\n")
+        .append("Executor resources: ").append(t.getExecutor.getResourcesList)
+        .append("---------------------------------------------\n")
+    }
+    builder.toString()
+  }
+
   /**
    * Method called by Mesos to offer resources on slaves. We respond by asking our active task sets
    * for tasks in order of priority. We fill each node with tasks in a round-robin manner so that
@@ -207,7 +222,7 @@ private[spark] class MesosSchedulerBackend(
 
         val meetsRequirements =
           (meetsConstraints && meetsMemoryRequirements && meetsCPURequirements) ||
-          (slaveIdsWithExecutors.contains(slaveId) && cpus >= scheduler.CPUS_PER_TASK)
+          (slaveIdToExecutorInfo.contains(slaveId) && cpus >= scheduler.CPUS_PER_TASK)
 
         // add some debug messaging
         val debugstr = if (meetsRequirements) "Accepting" else "Declining"
@@ -221,7 +236,7 @@ private[spark] class MesosSchedulerBackend(
       unUsableOffers.foreach(o => d.declineOffer(o.getId))
 
       val workerOffers = usableOffers.map { o =>
-        val cpus = if (slaveIdsWithExecutors.contains(o.getSlaveId.getValue)) {
+        val cpus = if (slaveIdToExecutorInfo.contains(o.getSlaveId.getValue)) {
           getResource(o.getResourcesList, "cpus").toInt
         } else {
           // If the Mesos executor has not been started on this slave yet, set aside a few
@@ -236,6 +251,10 @@ private[spark] class MesosSchedulerBackend(
 
       val slaveIdToOffer = usableOffers.map(o => o.getSlaveId.getValue -> o).toMap
       val slaveIdToWorkerOffer = workerOffers.map(o => o.executorId -> o).toMap
+      val slaveIdToResources = new HashMap[String, JList[Resource]]()
+      usableOffers.foreach { o =>
+        slaveIdToResources(o.getSlaveId.getValue) = o.getResourcesList
+      }
 
       val mesosTasks = new HashMap[String, JArrayList[MesosTaskInfo]]
 
@@ -245,15 +264,19 @@ private[spark] class MesosSchedulerBackend(
       val acceptedOffers = scheduler.resourceOffers(workerOffers).filter(!_.isEmpty)
       acceptedOffers
         .foreach { offer =>
-        offer.foreach { taskDesc =>
-          val slaveId = taskDesc.executorId
-          slaveIdsWithExecutors += slaveId
-          slavesIdsOfAcceptedOffers += slaveId
-          taskIdToSlaveId(taskDesc.taskId) = slaveId
-          mesosTasks.getOrElseUpdate(slaveId, new JArrayList[MesosTaskInfo])
-            .add(createMesosTask(taskDesc, slaveId))
+          offer.foreach { taskDesc =>
+            val slaveId = taskDesc.executorId
+            slavesIdsOfAcceptedOffers += slaveId
+            taskIdToSlaveId(taskDesc.taskId) = slaveId
+            val (mesosTask, remainingResources) = createMesosTask(
+              taskDesc,
+              slaveIdToResources(slaveId),
+              slaveId)
+            mesosTasks.getOrElseUpdate(slaveId, new JArrayList[MesosTaskInfo])
+              .add(mesosTask)
+            slaveIdToResources(slaveId) = remainingResources
+          }
         }
-      }
 
       // Reply to the offers
       val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout?
@@ -264,6 +287,7 @@ private[spark] class MesosSchedulerBackend(
             // TODO: Add support for log urls for Mesos
             new ExecutorInfo(o.host, o.cores, Map.empty)))
         )
+        logTrace(s"Launching Mesos tasks on slave '$slaveId', tasks:\n${getTasksSummary(tasks)}")
         d.launchTasks(Collections.singleton(slaveIdToOffer(slaveId).getId), tasks, filters)
       }
 
@@ -272,26 +296,32 @@ private[spark] class MesosSchedulerBackend(
       for (o <- usableOffers if !slavesIdsOfAcceptedOffers.contains(o.getSlaveId.getValue)) {
         d.declineOffer(o.getId)
       }
-
     }
   }
 
-  /** Turn a Spark TaskDescription into a Mesos task */
-  def createMesosTask(task: TaskDescription, slaveId: String): MesosTaskInfo = {
+  /** Turn a Spark TaskDescription into a Mesos task and also resources unused by the task */
+  def createMesosTask(
+      task: TaskDescription,
+      resources: JList[Resource],
+      slaveId: String): (MesosTaskInfo, JList[Resource]) = {
     val taskId = TaskID.newBuilder().setValue(task.taskId.toString).build()
-    val cpuResource = Resource.newBuilder()
-      .setName("cpus")
-      .setType(Value.Type.SCALAR)
-      .setScalar(Value.Scalar.newBuilder().setValue(scheduler.CPUS_PER_TASK).build())
-      .build()
-    MesosTaskInfo.newBuilder()
+    val (executorInfo, remainingResources) = if (slaveIdToExecutorInfo.contains(slaveId)) {
+      (slaveIdToExecutorInfo(slaveId), resources)
+    } else {
+      createExecutorInfo(resources, slaveId)
+    }
+    slaveIdToExecutorInfo(slaveId) = executorInfo
+    val (finalResources, cpuResources) =
+      partitionResources(remainingResources, "cpus", scheduler.CPUS_PER_TASK)
+    val taskInfo = MesosTaskInfo.newBuilder()
       .setTaskId(taskId)
       .setSlaveId(SlaveID.newBuilder().setValue(slaveId).build())
-      .setExecutor(createExecutorInfo(slaveId))
+      .setExecutor(executorInfo)
       .setName(task.name)
-      .addResources(cpuResource)
+      .addAllResources(cpuResources)
       .setData(MesosTaskLaunchData(task.serializedTask, task.attemptNumber).toByteString)
       .build()
+    (taskInfo, finalResources)
   }
 
   override def statusUpdate(d: SchedulerDriver, status: TaskStatus) {
@@ -337,7 +367,7 @@ private[spark] class MesosSchedulerBackend(
   private def removeExecutor(slaveId: String, reason: String) = {
     synchronized {
       listenerBus.post(SparkListenerExecutorRemoved(System.currentTimeMillis(), slaveId, reason))
-      slaveIdsWithExecutors -= slaveId
+      slaveIdToExecutorInfo -= slaveId
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
index 925702e63afd3..c04920e4f5873 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
@@ -21,15 +21,17 @@ import java.util.{List => JList}
 import java.util.concurrent.CountDownLatch
 
 import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
 import scala.util.control.NonFatal
 
 import com.google.common.base.Splitter
 import org.apache.mesos.{MesosSchedulerDriver, SchedulerDriver, Scheduler, Protos}
 import org.apache.mesos.Protos._
-import org.apache.mesos.protobuf.GeneratedMessage
-import org.apache.spark.{Logging, SparkContext}
+import org.apache.mesos.protobuf.{ByteString, GeneratedMessage}
+import org.apache.spark.{SparkException, SparkConf, Logging, SparkContext}
 import org.apache.spark.util.Utils
 
+
 /**
  * Shared trait for implementing a Mesos Scheduler. This holds common state and helper
  * methods and Mesos scheduler will use.
@@ -42,13 +44,63 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
   protected var mesosDriver: SchedulerDriver = null
 
   /**
-   * Starts the MesosSchedulerDriver with the provided information. This method returns
-   * only after the scheduler has registered with Mesos.
-   * @param masterUrl Mesos master connection URL
-   * @param scheduler Scheduler object
-   * @param fwInfo FrameworkInfo to pass to the Mesos master
+   * Creates a new MesosSchedulerDriver that communicates to the Mesos master.
+   * @param masterUrl The url to connect to Mesos master
+   * @param scheduler the scheduler class to receive scheduler callbacks
+   * @param sparkUser User to impersonate with when running tasks
+   * @param appName The framework name to display on the Mesos UI
+   * @param conf Spark configuration
+   * @param webuiUrl The WebUI url to link from Mesos UI
+   * @param checkpoint Option to checkpoint tasks for failover
+   * @param failoverTimeout Duration Mesos master expect scheduler to reconnect on disconnect
+   * @param frameworkId The id of the new framework
    */
-  def startScheduler(masterUrl: String, scheduler: Scheduler, fwInfo: FrameworkInfo): Unit = {
+  protected def createSchedulerDriver(
+      masterUrl: String,
+      scheduler: Scheduler,
+      sparkUser: String,
+      appName: String,
+      conf: SparkConf,
+      webuiUrl: Option[String] = None,
+      checkpoint: Option[Boolean] = None,
+      failoverTimeout: Option[Double] = None,
+      frameworkId: Option[String] = None): SchedulerDriver = {
+    val fwInfoBuilder = FrameworkInfo.newBuilder().setUser(sparkUser).setName(appName)
+    val credBuilder = Credential.newBuilder()
+    webuiUrl.foreach { url => fwInfoBuilder.setWebuiUrl(url) }
+    checkpoint.foreach { checkpoint => fwInfoBuilder.setCheckpoint(checkpoint) }
+    failoverTimeout.foreach { timeout => fwInfoBuilder.setFailoverTimeout(timeout) }
+    frameworkId.foreach { id =>
+      fwInfoBuilder.setId(FrameworkID.newBuilder().setValue(id).build())
+    }
+    conf.getOption("spark.mesos.principal").foreach { principal =>
+      fwInfoBuilder.setPrincipal(principal)
+      credBuilder.setPrincipal(principal)
+    }
+    conf.getOption("spark.mesos.secret").foreach { secret =>
+      credBuilder.setSecret(ByteString.copyFromUtf8(secret))
+    }
+    if (credBuilder.hasSecret && !fwInfoBuilder.hasPrincipal) {
+      throw new SparkException(
+        "spark.mesos.principal must be configured when spark.mesos.secret is set")
+    }
+    conf.getOption("spark.mesos.role").foreach { role =>
+      fwInfoBuilder.setRole(role)
+    }
+    if (credBuilder.hasPrincipal) {
+      new MesosSchedulerDriver(
+        scheduler, fwInfoBuilder.build(), masterUrl, credBuilder.build())
+    } else {
+      new MesosSchedulerDriver(scheduler, fwInfoBuilder.build(), masterUrl)
+    }
+  }
+
+  /**
+   * Starts the MesosSchedulerDriver and stores the current running driver to this new instance.
+   * This driver is expected to not be running.
+   * This method returns only after the scheduler has registered with Mesos.
+   */
+  def startScheduler(newDriver: SchedulerDriver): Unit = {
     synchronized {
       if (mesosDriver != null) {
         registerLatch.await()
@@ -59,11 +111,11 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
         setDaemon(true)
 
         override def run() {
-          mesosDriver = new MesosSchedulerDriver(scheduler, fwInfo, masterUrl)
+          mesosDriver = newDriver
           try {
             val ret = mesosDriver.run()
             logInfo("driver.run() returned with code " + ret)
-            if (ret.equals(Status.DRIVER_ABORTED)) {
+            if (ret != null && ret.equals(Status.DRIVER_ABORTED)) {
               System.exit(1)
             }
           } catch {
@@ -82,18 +134,62 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
   /**
    * Signal that the scheduler has registered with Mesos.
    */
+  protected def getResource(res: JList[Resource], name: String): Double = {
+    // A resource can have multiple values in the offer since it can either be from
+    // a specific role or wildcard.
+    res.filter(_.getName == name).map(_.getScalar.getValue).sum
+  }
+
   protected def markRegistered(): Unit = {
     registerLatch.countDown()
   }
 
+  def createResource(name: String, amount: Double, role: Option[String] = None): Resource = {
+    val builder = Resource.newBuilder()
+      .setName(name)
+      .setType(Value.Type.SCALAR)
+      .setScalar(Value.Scalar.newBuilder().setValue(amount).build())
+
+    role.foreach { r => builder.setRole(r) }
+
+    builder.build()
+  }
+
   /**
-   * Get the amount of resources for the specified type from the resource list
+   * Partition the existing set of resources into two groups, those remaining to be
+   * scheduled and those requested to be used for a new task.
+   * @param resources The full list of available resources
+   * @param resourceName The name of the resource to take from the available resources
+   * @param amountToUse The amount of resources to take from the available resources
+   * @return The remaining resources list and the used resources list.
    */
-  protected def getResource(res: JList[Resource], name: String): Double = {
-    for (r <- res if r.getName == name) {
-      return r.getScalar.getValue
+  def partitionResources(
+      resources: JList[Resource],
+      resourceName: String,
+      amountToUse: Double): (List[Resource], List[Resource]) = {
+    var remain = amountToUse
+    var requestedResources = new ArrayBuffer[Resource]
+    val remainingResources = resources.map {
+      case r => {
+        if (remain > 0 &&
+          r.getType == Value.Type.SCALAR &&
+          r.getScalar.getValue > 0.0 &&
+          r.getName == resourceName) {
+          val usage = Math.min(remain, r.getScalar.getValue)
+          requestedResources += createResource(resourceName, usage, Some(r.getRole))
+          remain -= usage
+          createResource(resourceName, r.getScalar.getValue - usage, Some(r.getRole))
+        } else {
+          r
+        }
+      }
     }
-    0.0
+
+    // Filter any resource that has depleted.
+    val filteredResources =
+      remainingResources.filter(r => r.getType != Value.Type.SCALAR || r.getScalar.getValue > 0.0)
+
+    (filteredResources.toList, requestedResources.toList)
   }
 
   /** Helper method to get the key,value-set pair for a Mesos Attribute protobuf */
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
index 3f1692917a357..4b504df7b8851 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
@@ -22,7 +22,7 @@ import java.util.Collections
 
 import org.apache.mesos.Protos.Value.Scalar
 import org.apache.mesos.Protos._
-import org.apache.mesos.SchedulerDriver
+import org.apache.mesos.{Protos, Scheduler, SchedulerDriver}
 import org.mockito.Matchers._
 import org.mockito.Mockito._
 import org.mockito.Matchers
@@ -60,7 +60,16 @@ class CoarseMesosSchedulerBackendSuite extends SparkFunSuite
       taskScheduler: TaskSchedulerImpl,
       driver: SchedulerDriver): CoarseMesosSchedulerBackend = {
     val backend = new CoarseMesosSchedulerBackend(taskScheduler, sc, "master") {
-      mesosDriver = driver
+      override protected def createSchedulerDriver(
+        masterUrl: String,
+        scheduler: Scheduler,
+        sparkUser: String,
+        appName: String,
+        conf: SparkConf,
+        webuiUrl: Option[String] = None,
+        checkpoint: Option[Boolean] = None,
+        failoverTimeout: Option[Double] = None,
+        frameworkId: Option[String] = None): SchedulerDriver = driver
       markRegistered()
     }
     backend.start()
@@ -80,6 +89,7 @@ class CoarseMesosSchedulerBackendSuite extends SparkFunSuite
 
   test("mesos supports killing and limiting executors") {
     val driver = mock[SchedulerDriver]
+    when(driver.start()).thenReturn(Protos.Status.DRIVER_RUNNING)
     val taskScheduler = mock[TaskSchedulerImpl]
     when(taskScheduler.sc).thenReturn(sc)
 
@@ -87,7 +97,7 @@ class CoarseMesosSchedulerBackendSuite extends SparkFunSuite
     sparkConf.set("spark.driver.port", "1234")
 
     val backend = createSchedulerBackend(taskScheduler, driver)
-    val minMem = backend.calculateTotalMemory(sc).toInt
+    val minMem = backend.calculateTotalMemory(sc)
     val minCpu = 4
 
     val mesosOffers = new java.util.ArrayList[Offer]
@@ -130,11 +140,12 @@ class CoarseMesosSchedulerBackendSuite extends SparkFunSuite
 
   test("mesos supports killing and relaunching tasks with executors") {
     val driver = mock[SchedulerDriver]
+    when(driver.start()).thenReturn(Protos.Status.DRIVER_RUNNING)
     val taskScheduler = mock[TaskSchedulerImpl]
     when(taskScheduler.sc).thenReturn(sc)
 
     val backend = createSchedulerBackend(taskScheduler, driver)
-    val minMem = backend.calculateTotalMemory(sc).toInt + 1024
+    val minMem = backend.calculateTotalMemory(sc) + 1024
     val minCpu = 4
 
     val mesosOffers = new java.util.ArrayList[Offer]
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
index d01837fe78957..5ed30f64d705f 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
@@ -21,6 +21,7 @@ import java.nio.ByteBuffer
 import java.util
 import java.util.Collections
 
+import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -60,14 +61,17 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
 
     val mesosSchedulerBackend = new MesosSchedulerBackend(taskScheduler, sc, "master")
 
+    val resources = List(
+      mesosSchedulerBackend.createResource("cpus", 4),
+      mesosSchedulerBackend.createResource("mem", 1024))
     // uri is null.
-    val executorInfo = mesosSchedulerBackend.createExecutorInfo("test-id")
+    val (executorInfo, _) = mesosSchedulerBackend.createExecutorInfo(resources, "test-id")
     assert(executorInfo.getCommand.getValue ===
       s" /mesos-home/bin/spark-class ${classOf[MesosExecutorBackend].getName}")
 
     // uri exists.
     conf.set("spark.executor.uri", "hdfs:///test-app-1.0.0.tgz")
-    val executorInfo1 = mesosSchedulerBackend.createExecutorInfo("test-id")
+    val (executorInfo1, _) = mesosSchedulerBackend.createExecutorInfo(resources, "test-id")
     assert(executorInfo1.getCommand.getValue ===
       s"cd test-app-1*;  ./bin/spark-class ${classOf[MesosExecutorBackend].getName}")
   }
@@ -93,7 +97,8 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
 
     val backend = new MesosSchedulerBackend(taskScheduler, sc, "master")
 
-    val execInfo = backend.createExecutorInfo("mockExecutor")
+    val (execInfo, _) = backend.createExecutorInfo(
+      List(backend.createResource("cpus", 4)), "mockExecutor")
     assert(execInfo.getContainer.getDocker.getImage.equals("spark/mock"))
     val portmaps = execInfo.getContainer.getDocker.getPortMappingsList
     assert(portmaps.get(0).getHostPort.equals(80))
@@ -194,7 +199,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     )
     verify(driver, times(1)).declineOffer(mesosOffers.get(1).getId)
     verify(driver, times(1)).declineOffer(mesosOffers.get(2).getId)
-    assert(capture.getValue.size() == 1)
+    assert(capture.getValue.size() === 1)
     val taskInfo = capture.getValue.iterator().next()
     assert(taskInfo.getName.equals("n1"))
     val cpus = taskInfo.getResourcesList.get(0)
@@ -214,4 +219,97 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     backend.resourceOffers(driver, mesosOffers2)
     verify(driver, times(1)).declineOffer(mesosOffers2.get(0).getId)
   }
+
+  test("can handle multiple roles") {
+    val driver = mock[SchedulerDriver]
+    val taskScheduler = mock[TaskSchedulerImpl]
+
+    val listenerBus = mock[LiveListenerBus]
+    listenerBus.post(
+      SparkListenerExecutorAdded(anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty)))
+
+    val sc = mock[SparkContext]
+    when(sc.executorMemory).thenReturn(100)
+    when(sc.getSparkHome()).thenReturn(Option("/path"))
+    when(sc.executorEnvs).thenReturn(new mutable.HashMap[String, String])
+    when(sc.conf).thenReturn(new SparkConf)
+    when(sc.listenerBus).thenReturn(listenerBus)
+
+    val id = 1
+    val builder = Offer.newBuilder()
+    builder.addResourcesBuilder()
+      .setName("mem")
+      .setType(Value.Type.SCALAR)
+      .setRole("prod")
+      .setScalar(Scalar.newBuilder().setValue(500))
+    builder.addResourcesBuilder()
+      .setName("cpus")
+      .setRole("prod")
+      .setType(Value.Type.SCALAR)
+      .setScalar(Scalar.newBuilder().setValue(1))
+    builder.addResourcesBuilder()
+      .setName("mem")
+      .setRole("dev")
+      .setType(Value.Type.SCALAR)
+      .setScalar(Scalar.newBuilder().setValue(600))
+    builder.addResourcesBuilder()
+      .setName("cpus")
+      .setRole("dev")
+      .setType(Value.Type.SCALAR)
+      .setScalar(Scalar.newBuilder().setValue(2))
+    val offer = builder.setId(OfferID.newBuilder().setValue(s"o${id.toString}").build())
+      .setFrameworkId(FrameworkID.newBuilder().setValue("f1"))
+      .setSlaveId(SlaveID.newBuilder().setValue(s"s${id.toString}"))
+      .setHostname(s"host${id.toString}").build()
+
+
+    val mesosOffers = new java.util.ArrayList[Offer]
+    mesosOffers.add(offer)
+
+    val backend = new MesosSchedulerBackend(taskScheduler, sc, "master")
+
+    val expectedWorkerOffers = new ArrayBuffer[WorkerOffer](1)
+    expectedWorkerOffers.append(new WorkerOffer(
+      mesosOffers.get(0).getSlaveId.getValue,
+      mesosOffers.get(0).getHostname,
+      2 // Deducting 1 for executor
+    ))
+
+    val taskDesc = new TaskDescription(1L, 0, "s1", "n1", 0, ByteBuffer.wrap(new Array[Byte](0)))
+    when(taskScheduler.resourceOffers(expectedWorkerOffers)).thenReturn(Seq(Seq(taskDesc)))
+    when(taskScheduler.CPUS_PER_TASK).thenReturn(1)
+
+    val capture = ArgumentCaptor.forClass(classOf[util.Collection[TaskInfo]])
+    when(
+      driver.launchTasks(
+        Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)),
+        capture.capture(),
+        any(classOf[Filters])
+      )
+    ).thenReturn(Status.valueOf(1))
+
+    backend.resourceOffers(driver, mesosOffers)
+
+    verify(driver, times(1)).launchTasks(
+      Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)),
+      capture.capture(),
+      any(classOf[Filters])
+    )
+
+    assert(capture.getValue.size() === 1)
+    val taskInfo = capture.getValue.iterator().next()
+    assert(taskInfo.getName.equals("n1"))
+    assert(taskInfo.getResourcesCount === 1)
+    val cpusDev = taskInfo.getResourcesList.get(0)
+    assert(cpusDev.getName.equals("cpus"))
+    assert(cpusDev.getScalar.getValue.equals(1.0))
+    assert(cpusDev.getRole.equals("dev"))
+    val executorResources = taskInfo.getExecutor.getResourcesList
+    assert(executorResources.exists { r =>
+      r.getName.equals("mem") && r.getScalar.getValue.equals(484.0) && r.getRole.equals("prod")
+    })
+    assert(executorResources.exists { r =>
+      r.getName.equals("cpus") && r.getScalar.getValue.equals(1.0) && r.getRole.equals("prod")
+    })
+  }
 }
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 1f915d8ea1d73..debdd2adf22d6 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -306,6 +306,28 @@ See the [configuration page](configuration.html) for information on Spark config
     the final overhead will be this value.
   </td>
 </tr>
+<tr>
+  <td><code>spark.mesos.principal</code></td>
+  <td>Framework principal to authenticate to Mesos</td>
+  <td>
+    Set the principal with which Spark framework will use to authenticate with Mesos.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.mesos.secret</code></td>
+  <td>Framework secret to authenticate to Mesos</td>
+  <td>
+    Set the secret with which Spark framework will use to authenticate with Mesos.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.mesos.role</code></td>
+  <td>Role for the Spark framework</td>
+  <td>
+    Set the role of this Spark framework for Mesos. Roles are used in Mesos for reservations
+    and resource weight sharing.
+  </td>
+</tr>
 <tr>
   <td><code>spark.mesos.constraints</code></td>
   <td>Attribute based constraints to be matched against when accepting resource offers.</td>

From 96aa3340f41d8de4560caec97e8f3de23252c792 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 16 Jul 2015 19:39:54 -0700
Subject: [PATCH 0418/1454] [SPARK-8119] HeartbeatReceiver should replace
 executors, not kill

**Symptom.** If an executor in an application times out, `HeartbeatReceiver` attempts to kill it. After this happens, however, the application never gets an executor back even when there are cluster resources available.

**Cause.** The issue is that `sc.killExecutor` automatically assumes that the application wishes to adjust its resource requirements permanently downwards. This is not the intention in `HeartbeatReceiver`, however, which simply wants a replacement for the expired executor.

**Fix.** Differentiate between the intention to kill and the intention to replace an executor with a fresh one. More details can be found in the commit message.

Author: Andrew Or <andrew@databricks.com>

Closes #7107 from andrewor14/heartbeat-no-kill and squashes the following commits:

1cd2cd7 [Andrew Or] Add regression test for SPARK-8119
25a347d [Andrew Or] Reuse more code in scheduler backend
31ebd40 [Andrew Or] Differentiate between kill and replace
---
 .../org/apache/spark/HeartbeatReceiver.scala  |   4 +-
 .../scala/org/apache/spark/SparkContext.scala |  40 ++++-
 .../CoarseGrainedSchedulerBackend.scala       |  40 +++--
 .../apache/spark/HeartbeatReceiverSuite.scala | 147 +++++++++++++++---
 4 files changed, 194 insertions(+), 37 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
index 221b1dab43278..43dd4a170731d 100644
--- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
+++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
@@ -181,7 +181,9 @@ private[spark] class HeartbeatReceiver(sc: SparkContext, clock: Clock)
           // Asynchronously kill the executor to avoid blocking the current thread
           killExecutorThread.submit(new Runnable {
             override def run(): Unit = Utils.tryLogNonFatalError {
-              sc.killExecutor(executorId)
+              // Note: we want to get an executor back after expiring this one,
+              // so do not simply call `sc.killExecutor` here (SPARK-8119)
+              sc.killAndReplaceExecutor(executorId)
             }
           })
         }
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index bd1cc332a63e7..d00c012d80560 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1419,6 +1419,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   /**
    * :: DeveloperApi ::
    * Request that the cluster manager kill the specified executors.
+   *
+   * Note: This is an indication to the cluster manager that the application wishes to adjust
+   * its resource usage downwards. If the application wishes to replace the executors it kills
+   * through this method with new ones, it should follow up explicitly with a call to
+   * {{SparkContext#requestExecutors}}.
+   *
    * This is currently only supported in YARN mode. Return whether the request is received.
    */
   @DeveloperApi
@@ -1436,12 +1442,42 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
 
   /**
    * :: DeveloperApi ::
-   * Request that cluster manager the kill the specified executor.
-   * This is currently only supported in Yarn mode. Return whether the request is received.
+   * Request that the cluster manager kill the specified executor.
+   *
+   * Note: This is an indication to the cluster manager that the application wishes to adjust
+   * its resource usage downwards. If the application wishes to replace the executor it kills
+   * through this method with a new one, it should follow up explicitly with a call to
+   * {{SparkContext#requestExecutors}}.
+   *
+   * This is currently only supported in YARN mode. Return whether the request is received.
    */
   @DeveloperApi
   override def killExecutor(executorId: String): Boolean = super.killExecutor(executorId)
 
+  /**
+   * Request that the cluster manager kill the specified executor without adjusting the
+   * application resource requirements.
+   *
+   * The effect is that a new executor will be launched in place of the one killed by
+   * this request. This assumes the cluster manager will automatically and eventually
+   * fulfill all missing application resource requests.
+   *
+   * Note: The replace is by no means guaranteed; another application on the same cluster
+   * can steal the window of opportunity and acquire this application's resources in the
+   * mean time.
+   *
+   * This is currently only supported in YARN mode. Return whether the request is received.
+   */
+  private[spark] def killAndReplaceExecutor(executorId: String): Boolean = {
+    schedulerBackend match {
+      case b: CoarseGrainedSchedulerBackend =>
+        b.killExecutors(Seq(executorId), replace = true)
+      case _ =>
+        logWarning("Killing executors is only supported in coarse-grained mode")
+        false
+    }
+  }
+
   /** The version of Spark on which this application is running. */
   def version: String = SPARK_VERSION
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 7c7f70d8a193b..0e3215d6e9ec8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -371,26 +371,36 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
 
   /**
    * Request that the cluster manager kill the specified executors.
-   * Return whether the kill request is acknowledged.
+   * @return whether the kill request is acknowledged.
    */
   final override def killExecutors(executorIds: Seq[String]): Boolean = synchronized {
+    killExecutors(executorIds, replace = false)
+  }
+
+  /**
+   * Request that the cluster manager kill the specified executors.
+   *
+   * @param executorIds identifiers of executors to kill
+   * @param replace whether to replace the killed executors with new ones
+   * @return whether the kill request is acknowledged.
+   */
+  final def killExecutors(executorIds: Seq[String], replace: Boolean): Boolean = synchronized {
     logInfo(s"Requesting to kill executor(s) ${executorIds.mkString(", ")}")
-    val filteredExecutorIds = new ArrayBuffer[String]
-    executorIds.foreach { id =>
-      if (executorDataMap.contains(id)) {
-        filteredExecutorIds += id
-      } else {
-        logWarning(s"Executor to kill $id does not exist!")
-      }
+    val (knownExecutors, unknownExecutors) = executorIds.partition(executorDataMap.contains)
+    unknownExecutors.foreach { id =>
+      logWarning(s"Executor to kill $id does not exist!")
+    }
+
+    // If we do not wish to replace the executors we kill, sync the target number of executors
+    // with the cluster manager to avoid allocating new ones. When computing the new target,
+    // take into account executors that are pending to be added or removed.
+    if (!replace) {
+      doRequestTotalExecutors(numExistingExecutors + numPendingExecutors
+        - executorsPendingToRemove.size - knownExecutors.size)
     }
-    // Killing executors means effectively that we want less executors than before, so also update
-    // the target number of executors to avoid having the backend allocate new ones.
-    val newTotal = (numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size
-      - filteredExecutorIds.size)
-    doRequestTotalExecutors(newTotal)
 
-    executorsPendingToRemove ++= filteredExecutorIds
-    doKillExecutors(filteredExecutorIds)
+    executorsPendingToRemove ++= knownExecutors
+    doKillExecutors(knownExecutors)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
index b31b09196608f..5a2670e4d1cf0 100644
--- a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
+++ b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark
 
+import java.util.concurrent.{ExecutorService, TimeUnit}
+
+import scala.collection.mutable
 import scala.language.postfixOps
 
 import org.scalatest.{BeforeAndAfterEach, PrivateMethodTester}
@@ -25,11 +28,16 @@ import org.mockito.Matchers
 import org.mockito.Matchers._
 
 import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.rpc.{RpcCallContext, RpcEndpoint, RpcEnv, RpcEndpointRef}
 import org.apache.spark.scheduler._
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.storage.BlockManagerId
 import org.apache.spark.util.ManualClock
 
+/**
+ * A test suite for the heartbeating behavior between the driver and the executors.
+ */
 class HeartbeatReceiverSuite
   extends SparkFunSuite
   with BeforeAndAfterEach
@@ -40,23 +48,40 @@ class HeartbeatReceiverSuite
   private val executorId2 = "executor-2"
 
   // Shared state that must be reset before and after each test
-  private var scheduler: TaskScheduler = null
+  private var scheduler: TaskSchedulerImpl = null
   private var heartbeatReceiver: HeartbeatReceiver = null
   private var heartbeatReceiverRef: RpcEndpointRef = null
   private var heartbeatReceiverClock: ManualClock = null
 
+  // Helper private method accessors for HeartbeatReceiver
+  private val _executorLastSeen = PrivateMethod[collection.Map[String, Long]]('executorLastSeen)
+  private val _executorTimeoutMs = PrivateMethod[Long]('executorTimeoutMs)
+  private val _killExecutorThread = PrivateMethod[ExecutorService]('killExecutorThread)
+
+  /**
+   * Before each test, set up the SparkContext and a custom [[HeartbeatReceiver]]
+   * that uses a manual clock.
+   */
   override def beforeEach(): Unit = {
-    sc = spy(new SparkContext("local[2]", "test"))
-    scheduler = mock(classOf[TaskScheduler])
+    val conf = new SparkConf()
+      .setMaster("local[2]")
+      .setAppName("test")
+      .set("spark.dynamicAllocation.testing", "true")
+    sc = spy(new SparkContext(conf))
+    scheduler = mock(classOf[TaskSchedulerImpl])
     when(sc.taskScheduler).thenReturn(scheduler)
+    when(scheduler.sc).thenReturn(sc)
     heartbeatReceiverClock = new ManualClock
     heartbeatReceiver = new HeartbeatReceiver(sc, heartbeatReceiverClock)
     heartbeatReceiverRef = sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver)
     when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(true)
   }
 
+  /**
+   * After each test, clean up all state and stop the [[SparkContext]].
+   */
   override def afterEach(): Unit = {
-    resetSparkContext()
+    super.afterEach()
     scheduler = null
     heartbeatReceiver = null
     heartbeatReceiverRef = null
@@ -75,7 +100,7 @@ class HeartbeatReceiverSuite
     heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId2, null))
     triggerHeartbeat(executorId1, executorShouldReregister = false)
     triggerHeartbeat(executorId2, executorShouldReregister = false)
-    val trackedExecutors = executorLastSeen(heartbeatReceiver)
+    val trackedExecutors = heartbeatReceiver.invokePrivate(_executorLastSeen())
     assert(trackedExecutors.size === 2)
     assert(trackedExecutors.contains(executorId1))
     assert(trackedExecutors.contains(executorId2))
@@ -83,15 +108,15 @@ class HeartbeatReceiverSuite
 
   test("reregister if scheduler is not ready yet") {
     heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId1, null))
-    // Task scheduler not set in HeartbeatReceiver
+    // Task scheduler is not set yet in HeartbeatReceiver, so executors should reregister
     triggerHeartbeat(executorId1, executorShouldReregister = true)
   }
 
   test("reregister if heartbeat from unregistered executor") {
     heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
-    // Received heartbeat from unknown receiver, so we ask it to re-register
+    // Received heartbeat from unknown executor, so we ask it to re-register
     triggerHeartbeat(executorId1, executorShouldReregister = true)
-    assert(executorLastSeen(heartbeatReceiver).isEmpty)
+    assert(heartbeatReceiver.invokePrivate(_executorLastSeen()).isEmpty)
   }
 
   test("reregister if heartbeat from removed executor") {
@@ -104,14 +129,14 @@ class HeartbeatReceiverSuite
     // A heartbeat from the second executor should require reregistering
     triggerHeartbeat(executorId1, executorShouldReregister = false)
     triggerHeartbeat(executorId2, executorShouldReregister = true)
-    val trackedExecutors = executorLastSeen(heartbeatReceiver)
+    val trackedExecutors = heartbeatReceiver.invokePrivate(_executorLastSeen())
     assert(trackedExecutors.size === 1)
     assert(trackedExecutors.contains(executorId1))
     assert(!trackedExecutors.contains(executorId2))
   }
 
   test("expire dead hosts") {
-    val executorTimeout = executorTimeoutMs(heartbeatReceiver)
+    val executorTimeout = heartbeatReceiver.invokePrivate(_executorTimeoutMs())
     heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
     heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId1, null))
     heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId2, null))
@@ -124,12 +149,61 @@ class HeartbeatReceiverSuite
     heartbeatReceiverRef.askWithRetry[Boolean](ExpireDeadHosts)
     // Only the second executor should be expired as a dead host
     verify(scheduler).executorLost(Matchers.eq(executorId2), any())
-    val trackedExecutors = executorLastSeen(heartbeatReceiver)
+    val trackedExecutors = heartbeatReceiver.invokePrivate(_executorLastSeen())
     assert(trackedExecutors.size === 1)
     assert(trackedExecutors.contains(executorId1))
     assert(!trackedExecutors.contains(executorId2))
   }
 
+  test("expire dead hosts should kill executors with replacement (SPARK-8119)") {
+    // Set up a fake backend and cluster manager to simulate killing executors
+    val rpcEnv = sc.env.rpcEnv
+    val fakeClusterManager = new FakeClusterManager(rpcEnv)
+    val fakeClusterManagerRef = rpcEnv.setupEndpoint("fake-cm", fakeClusterManager)
+    val fakeSchedulerBackend = new FakeSchedulerBackend(scheduler, rpcEnv, fakeClusterManagerRef)
+    when(sc.schedulerBackend).thenReturn(fakeSchedulerBackend)
+
+    // Register fake executors with our fake scheduler backend
+    // This is necessary because the backend refuses to kill executors it does not know about
+    fakeSchedulerBackend.start()
+    val dummyExecutorEndpoint1 = new FakeExecutorEndpoint(rpcEnv)
+    val dummyExecutorEndpoint2 = new FakeExecutorEndpoint(rpcEnv)
+    val dummyExecutorEndpointRef1 = rpcEnv.setupEndpoint("fake-executor-1", dummyExecutorEndpoint1)
+    val dummyExecutorEndpointRef2 = rpcEnv.setupEndpoint("fake-executor-2", dummyExecutorEndpoint2)
+    fakeSchedulerBackend.driverEndpoint.askWithRetry[RegisteredExecutor.type](
+      RegisterExecutor(executorId1, dummyExecutorEndpointRef1, "dummy:4040", 0, Map.empty))
+    fakeSchedulerBackend.driverEndpoint.askWithRetry[RegisteredExecutor.type](
+      RegisterExecutor(executorId2, dummyExecutorEndpointRef2, "dummy:4040", 0, Map.empty))
+    heartbeatReceiverRef.askWithRetry[Boolean](TaskSchedulerIsSet)
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId1, null))
+    heartbeatReceiver.onExecutorAdded(SparkListenerExecutorAdded(0, executorId2, null))
+    triggerHeartbeat(executorId1, executorShouldReregister = false)
+    triggerHeartbeat(executorId2, executorShouldReregister = false)
+
+    // Adjust the target number of executors on the cluster manager side
+    assert(fakeClusterManager.getTargetNumExecutors === 0)
+    sc.requestTotalExecutors(2)
+    assert(fakeClusterManager.getTargetNumExecutors === 2)
+    assert(fakeClusterManager.getExecutorIdsToKill.isEmpty)
+
+    // Expire the executors. This should trigger our fake backend to kill the executors.
+    // Since the kill request is sent to the cluster manager asynchronously, we need to block
+    // on the kill thread to ensure that the cluster manager actually received our requests.
+    // Here we use a timeout of O(seconds), but in practice this whole test takes O(10ms).
+    val executorTimeout = heartbeatReceiver.invokePrivate(_executorTimeoutMs())
+    heartbeatReceiverClock.advance(executorTimeout * 2)
+    heartbeatReceiverRef.askWithRetry[Boolean](ExpireDeadHosts)
+    val killThread = heartbeatReceiver.invokePrivate(_killExecutorThread())
+    killThread.shutdown() // needed for awaitTermination
+    killThread.awaitTermination(10L, TimeUnit.SECONDS)
+
+    // The target number of executors should not change! Otherwise, having an expired
+    // executor means we permanently adjust the target number downwards until we
+    // explicitly request new executors. For more detail, see SPARK-8119.
+    assert(fakeClusterManager.getTargetNumExecutors === 2)
+    assert(fakeClusterManager.getExecutorIdsToKill === Set(executorId1, executorId2))
+  }
+
   /** Manually send a heartbeat and return the response. */
   private def triggerHeartbeat(
       executorId: String,
@@ -148,14 +222,49 @@ class HeartbeatReceiverSuite
     }
   }
 
-  // Helper methods to access private fields in HeartbeatReceiver
-  private val _executorLastSeen = PrivateMethod[collection.Map[String, Long]]('executorLastSeen)
-  private val _executorTimeoutMs = PrivateMethod[Long]('executorTimeoutMs)
-  private def executorLastSeen(receiver: HeartbeatReceiver): collection.Map[String, Long] = {
-    receiver invokePrivate _executorLastSeen()
+}
+
+// TODO: use these classes to add end-to-end tests for dynamic allocation!
+
+/**
+ * Dummy RPC endpoint to simulate executors.
+ */
+private class FakeExecutorEndpoint(override val rpcEnv: RpcEnv) extends RpcEndpoint
+
+/**
+ * Dummy scheduler backend to simulate executor allocation requests to the cluster manager.
+ */
+private class FakeSchedulerBackend(
+    scheduler: TaskSchedulerImpl,
+    rpcEnv: RpcEnv,
+    clusterManagerEndpoint: RpcEndpointRef)
+  extends CoarseGrainedSchedulerBackend(scheduler, rpcEnv) {
+
+  protected override def doRequestTotalExecutors(requestedTotal: Int): Boolean = {
+    clusterManagerEndpoint.askWithRetry[Boolean](RequestExecutors(requestedTotal))
   }
-  private def executorTimeoutMs(receiver: HeartbeatReceiver): Long = {
-    receiver invokePrivate _executorTimeoutMs()
+
+  protected override def doKillExecutors(executorIds: Seq[String]): Boolean = {
+    clusterManagerEndpoint.askWithRetry[Boolean](KillExecutors(executorIds))
   }
+}
 
+/**
+ * Dummy cluster manager to simulate responses to executor allocation requests.
+ */
+private class FakeClusterManager(override val rpcEnv: RpcEnv) extends RpcEndpoint {
+  private var targetNumExecutors = 0
+  private val executorIdsToKill = new mutable.HashSet[String]
+
+  def getTargetNumExecutors: Int = targetNumExecutors
+  def getExecutorIdsToKill: Set[String] = executorIdsToKill.toSet
+
+  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+    case RequestExecutors(requestedTotal) =>
+      targetNumExecutors = requestedTotal
+      context.reply(true)
+    case KillExecutors(executorIds) =>
+      executorIdsToKill ++= executorIds
+      context.reply(true)
+  }
 }

From 812b63bbee8d0b30884f7a96b207e8834b774957 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 16 Jul 2015 21:09:09 -0700
Subject: [PATCH 0419/1454] [SPARK-8857][SPARK-8859][Core]Add an internal flag
 to Accumulable and send internal accumulator updates to the driver via
 heartbeats

This PR includes the following changes:

1. Remove the thread local `Accumulators.localAccums`. Instead, all Accumulators in the executors will register with its TaskContext.
2. Add an internal flag to Accumulable. For internal Accumulators, their updates will be sent to the driver via heartbeats.

Author: zsxwing <zsxwing@gmail.com>

Closes #7448 from zsxwing/accumulators and squashes the following commits:

c24bc5b [zsxwing] Add comments
bd7dcf1 [zsxwing] Add an internal flag to Accumulable and send internal accumulator updates to the driver via heartbeats
---
 .../scala/org/apache/spark/Accumulators.scala | 68 ++++++++-----------
 .../scala/org/apache/spark/TaskContext.scala  | 18 +++++
 .../org/apache/spark/TaskContextImpl.scala    | 19 +++++-
 .../org/apache/spark/executor/Executor.scala  |  6 +-
 .../apache/spark/executor/TaskMetrics.scala   | 16 +++++
 .../apache/spark/scheduler/DAGScheduler.scala |  3 +-
 .../spark/scheduler/DAGSchedulerEvent.scala   |  2 +-
 .../org/apache/spark/scheduler/Task.scala     | 13 +++-
 .../apache/spark/scheduler/TaskResult.scala   |  8 ++-
 .../spark/scheduler/TaskSetManagerSuite.scala |  7 +-
 10 files changed, 104 insertions(+), 56 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 5a8d17bd99933..2f4fcac890eef 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -20,7 +20,8 @@ package org.apache.spark
 import java.io.{ObjectInputStream, Serializable}
 
 import scala.collection.generic.Growable
-import scala.collection.mutable.Map
+import scala.collection.Map
+import scala.collection.mutable
 import scala.ref.WeakReference
 import scala.reflect.ClassTag
 
@@ -39,25 +40,44 @@ import org.apache.spark.util.Utils
  * @param initialValue initial value of accumulator
  * @param param helper object defining how to add elements of type `R` and `T`
  * @param name human-readable name for use in Spark's web UI
+ * @param internal if this [[Accumulable]] is internal. Internal [[Accumulable]]s will be reported
+ *                 to the driver via heartbeats. For internal [[Accumulable]]s, `R` must be
+ *                 thread safe so that they can be reported correctly.
  * @tparam R the full accumulated data (result type)
  * @tparam T partial data that can be added in
  */
-class Accumulable[R, T] (
+class Accumulable[R, T] private[spark] (
     @transient initialValue: R,
     param: AccumulableParam[R, T],
-    val name: Option[String])
+    val name: Option[String],
+    internal: Boolean)
   extends Serializable {
 
+  private[spark] def this(
+      @transient initialValue: R, param: AccumulableParam[R, T], internal: Boolean) = {
+    this(initialValue, param, None, internal)
+  }
+
+  def this(@transient initialValue: R, param: AccumulableParam[R, T], name: Option[String]) =
+    this(initialValue, param, name, false)
+
   def this(@transient initialValue: R, param: AccumulableParam[R, T]) =
     this(initialValue, param, None)
 
   val id: Long = Accumulators.newId
 
-  @transient private var value_ = initialValue // Current value on master
+  @volatile @transient private var value_ : R = initialValue // Current value on master
   val zero = param.zero(initialValue)  // Zero value to be passed to workers
   private var deserialized = false
 
-  Accumulators.register(this, true)
+  Accumulators.register(this)
+
+  /**
+   * If this [[Accumulable]] is internal. Internal [[Accumulable]]s will be reported to the driver
+   * via heartbeats. For internal [[Accumulable]]s, `R` must be thread safe so that they can be
+   * reported correctly.
+   */
+  private[spark] def isInternal: Boolean = internal
 
   /**
    * Add more data to this accumulator / accumulable
@@ -132,7 +152,8 @@ class Accumulable[R, T] (
     in.defaultReadObject()
     value_ = zero
     deserialized = true
-    Accumulators.register(this, false)
+    val taskContext = TaskContext.get()
+    taskContext.registerAccumulator(this)
   }
 
   override def toString: String = if (value_ == null) "null" else value_.toString
@@ -284,16 +305,7 @@ private[spark] object Accumulators extends Logging {
    * It keeps weak references to these objects so that accumulators can be garbage-collected
    * once the RDDs and user-code that reference them are cleaned up.
    */
-  val originals = Map[Long, WeakReference[Accumulable[_, _]]]()
-
-  /**
-   * This thread-local map holds per-task copies of accumulators; it is used to collect the set
-   * of accumulator updates to send back to the driver when tasks complete. After tasks complete,
-   * this map is cleared by `Accumulators.clear()` (see Executor.scala).
-   */
-  private val localAccums = new ThreadLocal[Map[Long, Accumulable[_, _]]]() {
-    override protected def initialValue() = Map[Long, Accumulable[_, _]]()
-  }
+  val originals = mutable.Map[Long, WeakReference[Accumulable[_, _]]]()
 
   private var lastId: Long = 0
 
@@ -302,19 +314,8 @@ private[spark] object Accumulators extends Logging {
     lastId
   }
 
-  def register(a: Accumulable[_, _], original: Boolean): Unit = synchronized {
-    if (original) {
-      originals(a.id) = new WeakReference[Accumulable[_, _]](a)
-    } else {
-      localAccums.get()(a.id) = a
-    }
-  }
-
-  // Clear the local (non-original) accumulators for the current thread
-  def clear() {
-    synchronized {
-      localAccums.get.clear()
-    }
+  def register(a: Accumulable[_, _]): Unit = synchronized {
+    originals(a.id) = new WeakReference[Accumulable[_, _]](a)
   }
 
   def remove(accId: Long) {
@@ -323,15 +324,6 @@ private[spark] object Accumulators extends Logging {
     }
   }
 
-  // Get the values of the local accumulators for the current thread (by ID)
-  def values: Map[Long, Any] = synchronized {
-    val ret = Map[Long, Any]()
-    for ((id, accum) <- localAccums.get) {
-      ret(id) = accum.localValue
-    }
-    return ret
-  }
-
   // Add values to the original accumulators with some given IDs
   def add(values: Map[Long, Any]): Unit = synchronized {
     for ((id, value) <- values) {
diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index 248339148d9b7..345bb500a7dec 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -152,4 +152,22 @@ abstract class TaskContext extends Serializable {
    * Returns the manager for this task's managed memory.
    */
   private[spark] def taskMemoryManager(): TaskMemoryManager
+
+  /**
+   * Register an accumulator that belongs to this task. Accumulators must call this method when
+   * deserializing in executors.
+   */
+  private[spark] def registerAccumulator(a: Accumulable[_, _]): Unit
+
+  /**
+   * Return the local values of internal accumulators that belong to this task. The key of the Map
+   * is the accumulator id and the value of the Map is the latest accumulator local value.
+   */
+  private[spark] def collectInternalAccumulators(): Map[Long, Any]
+
+  /**
+   * Return the local values of accumulators that belong to this task. The key of the Map is the
+   * accumulator id and the value of the Map is the latest accumulator local value.
+   */
+  private[spark] def collectAccumulators(): Map[Long, Any]
 }
diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
index b4d572cb52313..6e394f1b12445 100644
--- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
@@ -17,12 +17,12 @@
 
 package org.apache.spark
 
+import scala.collection.mutable.{ArrayBuffer, HashMap}
+
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.unsafe.memory.TaskMemoryManager
 import org.apache.spark.util.{TaskCompletionListener, TaskCompletionListenerException}
 
-import scala.collection.mutable.ArrayBuffer
-
 private[spark] class TaskContextImpl(
     val stageId: Int,
     val partitionId: Int,
@@ -94,5 +94,18 @@ private[spark] class TaskContextImpl(
   override def isRunningLocally(): Boolean = runningLocally
 
   override def isInterrupted(): Boolean = interrupted
-}
 
+  @transient private val accumulators = new HashMap[Long, Accumulable[_, _]]
+
+  private[spark] override def registerAccumulator(a: Accumulable[_, _]): Unit = synchronized {
+    accumulators(a.id) = a
+  }
+
+  private[spark] override def collectInternalAccumulators(): Map[Long, Any] = synchronized {
+    accumulators.filter(_._2.isInternal).mapValues(_.localValue).toMap
+  }
+
+  private[spark] override def collectAccumulators(): Map[Long, Any] = synchronized {
+    accumulators.mapValues(_.localValue).toMap
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 1a02051c87f19..9087debde8c41 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -209,7 +209,7 @@ private[spark] class Executor(
 
         // Run the actual task and measure its runtime.
         taskStart = System.currentTimeMillis()
-        val value = try {
+        val (value, accumUpdates) = try {
           task.run(taskAttemptId = taskId, attemptNumber = attemptNumber)
         } finally {
           // Note: this memory freeing logic is duplicated in DAGScheduler.runLocallyWithinThread;
@@ -247,7 +247,6 @@ private[spark] class Executor(
           m.setResultSerializationTime(afterSerialization - beforeSerialization)
         }
 
-        val accumUpdates = Accumulators.values
         val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.orNull)
         val serializedDirectResult = ser.serialize(directResult)
         val resultSize = serializedDirectResult.limit
@@ -314,8 +313,6 @@ private[spark] class Executor(
         env.shuffleMemoryManager.releaseMemoryForThisThread()
         // Release memory used by this thread for unrolling blocks
         env.blockManager.memoryStore.releaseUnrollMemoryForThisThread()
-        // Release memory used by this thread for accumulators
-        Accumulators.clear()
         runningTasks.remove(taskId)
       }
     }
@@ -424,6 +421,7 @@ private[spark] class Executor(
           metrics.updateShuffleReadMetrics()
           metrics.updateInputMetrics()
           metrics.setJvmGCTime(curGCTime - taskRunner.startGCTime)
+          metrics.updateAccumulators()
 
           if (isLocal) {
             // JobProgressListener will hold an reference of it during
diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
index e80feeeab4142..42207a9553592 100644
--- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
+++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
@@ -223,6 +223,22 @@ class TaskMetrics extends Serializable {
     // overhead.
     _hostname = TaskMetrics.getCachedHostName(_hostname)
   }
+
+  private var _accumulatorUpdates: Map[Long, Any] = Map.empty
+  @transient private var _accumulatorsUpdater: () => Map[Long, Any] = null
+
+  private[spark] def updateAccumulators(): Unit = synchronized {
+    _accumulatorUpdates = _accumulatorsUpdater()
+  }
+
+  /**
+   * Return the latest updates of accumulators in this task.
+   */
+  def accumulatorUpdates(): Map[Long, Any] = _accumulatorUpdates
+
+  private[spark] def setAccumulatorsUpdater(accumulatorsUpdater: () => Map[Long, Any]): Unit = {
+    _accumulatorsUpdater = accumulatorsUpdater
+  }
 }
 
 private[spark] object TaskMetrics {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index f8ba3d2d1a9b2..dd55cd8054332 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -22,7 +22,8 @@ import java.util.Properties
 import java.util.concurrent.TimeUnit
 import java.util.concurrent.atomic.AtomicInteger
 
-import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map, Stack}
+import scala.collection.Map
+import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Stack}
 import scala.concurrent.duration._
 import scala.language.existentials
 import scala.language.postfixOps
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index 2b6f7e4205c32..a927eae2b04be 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -19,7 +19,7 @@ package org.apache.spark.scheduler
 
 import java.util.Properties
 
-import scala.collection.mutable.Map
+import scala.collection.Map
 import scala.language.existentials
 
 import org.apache.spark._
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
index 15101c64f0503..6a86f9d4b8530 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
@@ -45,14 +45,20 @@ import org.apache.spark.util.Utils
  */
 private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) extends Serializable {
 
+  /**
+   * The key of the Map is the accumulator id and the value of the Map is the latest accumulator
+   * local value.
+   */
+  type AccumulatorUpdates = Map[Long, Any]
+
   /**
    * Called by [[Executor]] to run this task.
    *
    * @param taskAttemptId an identifier for this task attempt that is unique within a SparkContext.
    * @param attemptNumber how many times this task has been attempted (0 for the first attempt)
-   * @return the result of the task
+   * @return the result of the task along with updates of Accumulators.
    */
-  final def run(taskAttemptId: Long, attemptNumber: Int): T = {
+  final def run(taskAttemptId: Long, attemptNumber: Int): (T, AccumulatorUpdates) = {
     context = new TaskContextImpl(
       stageId = stageId,
       partitionId = partitionId,
@@ -62,12 +68,13 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex
       runningLocally = false)
     TaskContext.setTaskContext(context)
     context.taskMetrics.setHostname(Utils.localHostName())
+    context.taskMetrics.setAccumulatorsUpdater(context.collectInternalAccumulators)
     taskThread = Thread.currentThread()
     if (_killed) {
       kill(interruptThread = false)
     }
     try {
-      runTask(context)
+      (runTask(context), context.collectAccumulators())
     } finally {
       context.markTaskCompleted()
       TaskContext.unset()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala
index 8b2a742b96988..b82c7f3fa54f8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala
@@ -20,7 +20,8 @@ package org.apache.spark.scheduler
 import java.io._
 import java.nio.ByteBuffer
 
-import scala.collection.mutable.Map
+import scala.collection.Map
+import scala.collection.mutable
 
 import org.apache.spark.SparkEnv
 import org.apache.spark.executor.TaskMetrics
@@ -69,10 +70,11 @@ class DirectTaskResult[T](var valueBytes: ByteBuffer, var accumUpdates: Map[Long
     if (numUpdates == 0) {
       accumUpdates = null
     } else {
-      accumUpdates = Map()
+      val _accumUpdates = mutable.Map[Long, Any]()
       for (i <- 0 until numUpdates) {
-        accumUpdates(in.readLong()) = in.readObject()
+        _accumUpdates(in.readLong()) = in.readObject()
       }
+      accumUpdates = _accumUpdates
     }
     metrics = in.readObject().asInstanceOf[TaskMetrics]
     valueObjectDeserialized = false
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index 0060f3396dcde..cdae0d83d01dc 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -19,12 +19,13 @@ package org.apache.spark.scheduler
 
 import java.util.Random
 
-import scala.collection.mutable.ArrayBuffer
+import scala.collection.Map
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark._
 import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.util.{ManualClock, Utils}
+import org.apache.spark.util.ManualClock
 
 class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler)
   extends DAGScheduler(sc) {
@@ -37,7 +38,7 @@ class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler)
       task: Task[_],
       reason: TaskEndReason,
       result: Any,
-      accumUpdates: mutable.Map[Long, Any],
+      accumUpdates: Map[Long, Any],
       taskInfo: TaskInfo,
       taskMetrics: TaskMetrics) {
     taskScheduler.endedTasks(taskInfo.index) = reason

From f893955b9cc6ea456fc5845890893c08d8878481 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 16 Jul 2015 21:41:36 -0700
Subject: [PATCH 0420/1454] [SPARK-8899] [SQL] remove duplicated equals method
 for Row

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7291 from cloud-fan/row and squashes the following commits:

a11addf [Wenchen Fan] move hashCode back to internal row
2de6180 [Wenchen Fan] making apply() call to get()
fbe1b24 [Wenchen Fan] add null check
ebdf148 [Wenchen Fan] address comments
25ef087 [Wenchen Fan] remove duplicated equals method for Row
---
 .../sql/catalyst/expressions/UnsafeRow.java   |  5 ---
 .../main/scala/org/apache/spark/sql/Row.scala | 44 +++++++++++++++++--
 .../spark/sql/catalyst/InternalRow.scala      | 37 +---------------
 .../spark/sql/catalyst/expressions/Cast.scala |  1 -
 .../sql/catalyst/expressions/Projection.scala | 12 ++---
 .../expressions/SpecificMutableRow.scala      |  2 +-
 .../spark/sql/catalyst/expressions/rows.scala | 23 +---------
 7 files changed, 50 insertions(+), 74 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 4b99030d1046f..87294a0e21441 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -246,11 +246,6 @@ public void setFloat(int ordinal, float value) {
     PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
-  @Override
-  public int size() {
-    return numFields;
-  }
-
   /**
    * Returns the object for column `i`, which should not be primitive type.
    */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 0f2fd6a86d177..5f0592dc1d77b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.sql.types.StructType
 
@@ -151,7 +152,7 @@ trait Row extends Serializable {
    *   StructType -> org.apache.spark.sql.Row
    * }}}
    */
-  def apply(i: Int): Any
+  def apply(i: Int): Any = get(i)
 
   /**
    * Returns the value at position i. If the value is null, null is returned. The following
@@ -176,10 +177,10 @@ trait Row extends Serializable {
    *   StructType -> org.apache.spark.sql.Row
    * }}}
    */
-  def get(i: Int): Any = apply(i)
+  def get(i: Int): Any
 
   /** Checks whether the value at position i is null. */
-  def isNullAt(i: Int): Boolean = apply(i) == null
+  def isNullAt(i: Int): Boolean = get(i) == null
 
   /**
    * Returns the value at position i as a primitive boolean.
@@ -311,7 +312,7 @@ trait Row extends Serializable {
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getAs[T](i: Int): T = apply(i).asInstanceOf[T]
+  def getAs[T](i: Int): T = get(i).asInstanceOf[T]
 
   /**
    * Returns the value of a given fieldName.
@@ -363,6 +364,41 @@ trait Row extends Serializable {
     false
   }
 
+  protected def canEqual(other: Any) =
+    other.isInstanceOf[Row] && !other.isInstanceOf[InternalRow]
+
+  override def equals(o: Any): Boolean = {
+    if (o == null || !canEqual(o)) return false
+
+    val other = o.asInstanceOf[Row]
+    if (length != other.length) {
+      return false
+    }
+
+    var i = 0
+    while (i < length) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = get(i)
+        val o2 = other.get(i)
+        if (o1.isInstanceOf[Array[Byte]]) {
+          // handle equality of Array[Byte]
+          val b1 = o1.asInstanceOf[Array[Byte]]
+          if (!o2.isInstanceOf[Array[Byte]] ||
+            !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+            return false
+          }
+        } else if (o1 != o2) {
+          return false
+        }
+      }
+      i += 1
+    }
+    return true
+  }
+
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 57de0f26a9720..e2fafb88ee43e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -53,41 +53,8 @@ abstract class InternalRow extends Row {
 
   // A default implementation to change the return type
   override def copy(): InternalRow = this
-  override def apply(i: Int): Any = get(i)
 
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[Row]) {
-      return false
-    }
-
-    val other = o.asInstanceOf[Row]
-    if (length != other.length) {
-      return false
-    }
-
-    var i = 0
-    while (i < length) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = apply(i)
-        val o2 = other.apply(i)
-        if (o1.isInstanceOf[Array[Byte]]) {
-          // handle equality of Array[Byte]
-          val b1 = o1.asInstanceOf[Array[Byte]]
-          if (!o2.isInstanceOf[Array[Byte]] ||
-            !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-            return false
-          }
-        } else if (o1 != o2) {
-          return false
-        }
-      }
-      i += 1
-    }
-    true
-  }
+  protected override def canEqual(other: Any) = other.isInstanceOf[InternalRow]
 
   // Custom hashCode function that matches the efficient code generated version.
   override def hashCode: Int = {
@@ -98,7 +65,7 @@ abstract class InternalRow extends Row {
         if (isNullAt(i)) {
           0
         } else {
-          apply(i) match {
+          get(i) match {
             case b: Boolean => if (b) 0 else 1
             case b: Byte => b.toInt
             case s: Short => s.toInt
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 83d5b3b76b0a3..65ae87fe6d166 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -56,7 +56,6 @@ object Cast {
     case (_, DateType) => true
 
     case (StringType, IntervalType) => true
-    case (IntervalType, StringType) => true
 
     case (StringType, _: NumericType) => true
     case (BooleanType, _: NumericType) => true
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 886a486bf5ee0..bf47a6c75b809 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -110,7 +110,7 @@ class JoinedRow extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
-  override def apply(i: Int): Any =
+  override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
   override def isNullAt(i: Int): Boolean =
@@ -204,7 +204,7 @@ class JoinedRow2 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
-  override def apply(i: Int): Any =
+  override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
   override def isNullAt(i: Int): Boolean =
@@ -292,7 +292,7 @@ class JoinedRow3 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
-  override def apply(i: Int): Any =
+  override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
   override def isNullAt(i: Int): Boolean =
@@ -380,7 +380,7 @@ class JoinedRow4 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
-  override def apply(i: Int): Any =
+  override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
   override def isNullAt(i: Int): Boolean =
@@ -468,7 +468,7 @@ class JoinedRow5 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
-  override def apply(i: Int): Any =
+  override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
   override def isNullAt(i: Int): Boolean =
@@ -556,7 +556,7 @@ class JoinedRow6 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
-  override def apply(i: Int): Any =
+  override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
   override def isNullAt(i: Int): Boolean =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index efa24710a5a67..6f291d2c86c1e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -219,7 +219,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
     values(i).isNull = true
   }
 
-  override def apply(i: Int): Any = values(i).boxed
+  override def get(i: Int): Any = values(i).boxed
 
   override def isNullAt(i: Int): Boolean = values(i).isNull
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 094904bbf9c15..d78be5a5958f9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -66,7 +66,7 @@ trait ArrayBackedRow {
 
   def length: Int = values.length
 
-  override def apply(i: Int): Any = values(i)
+  override def get(i: Int): Any = values(i)
 
   def setNullAt(i: Int): Unit = { values(i) = null}
 
@@ -84,27 +84,6 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row with ArrayBa
 
   def this(size: Int) = this(new Array[Any](size))
 
-  // This is used by test or outside
-  override def equals(o: Any): Boolean = o match {
-    case other: Row if other.length == length =>
-      var i = 0
-      while (i < length) {
-        if (isNullAt(i) != other.isNullAt(i)) {
-          return false
-        }
-        val equal = (apply(i), other.apply(i)) match {
-          case (a: Array[Byte], b: Array[Byte]) => java.util.Arrays.equals(a, b)
-          case (a, b) => a == b
-        }
-        if (!equal) {
-          return false
-        }
-        i += 1
-      }
-      true
-    case _ => false
-  }
-
   override def copy(): Row = this
 }
 

From 322d286bb7773389ed07df96290e427b21c775bd Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Thu, 16 Jul 2015 22:26:59 -0700
Subject: [PATCH 0421/1454] [SPARK-7131] [ML] Copy Decision Tree, Random Forest
 impl to spark.ml

This PR copies the RandomForest implementation from spark.mllib to spark.ml.  Note that this includes the DecisionTree implementation, but not the GradientBoostedTrees one (which will come later).

I essentially copied a minimal amount of code to spark.ml, removed the use of bins (and only used splits), and modified code only as much as necessary to get it to compile.  The spark.ml implementation still uses some spark.mllib classes (privately), which can be moved in future PRs.

This refactoring will be helpful in extending the node representation to include more information, such as class probabilities.

Specifically:
* Copied code from spark.mllib to spark.ml:
  * mllib.tree.DecisionTree, mllib.tree.RandomForest copied to ml.tree.impl.RandomForest (main implementation)
  * NodeIdCache (needed to use splits instead of bins)
  * TreePoint (use splits instead of bins)
* Added ml.tree.LearningNode used in RandomForest training (needed vars)
* Removed bins from implementation, and only used splits
* Small fix in JavaDecisionTreeRegressorSuite

CC: mengxr  manishamde  codedeft chouqin

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7294 from jkbradley/dt-move-impl and squashes the following commits:

48749be [Joseph K. Bradley] cleanups based on code review, mostly style
bea9703 [Joseph K. Bradley] scala style fixes.  added some scala doc
4e6d2a4 [Joseph K. Bradley] removed unnecessary use of copyValues, setParent for trees
9a4d721 [Joseph K. Bradley] cleanups. removed InfoGainStats from ml, using old one for now.
836e7d4 [Joseph K. Bradley] Fixed test suite failures
bd5e063 [Joseph K. Bradley] fixed bucketizing issue
0df3759 [Joseph K. Bradley] Need to remove use of Bucketizer
d5224a9 [Joseph K. Bradley] modified tree and forest to use moved impl
cc01823 [Joseph K. Bradley] still editing RF to get it to work
19143fb [Joseph K. Bradley] More progress, but not done yet.  Rebased with master after 1.4 release.
---
 .../DecisionTreeClassifier.scala              |   13 +-
 .../RandomForestClassifier.scala              |   16 +-
 .../ml/regression/DecisionTreeRegressor.scala |   13 +-
 .../ml/regression/RandomForestRegressor.scala |   15 +-
 .../scala/org/apache/spark/ml/tree/Node.scala |  129 ++
 .../org/apache/spark/ml/tree/Split.scala      |   30 +-
 .../spark/ml/tree/impl/NodeIdCache.scala      |  194 +++
 .../spark/ml/tree/impl/RandomForest.scala     | 1132 +++++++++++++++++
 .../apache/spark/ml/tree/impl/TreePoint.scala |  134 ++
 .../spark/mllib/tree/impl/BaggedPoint.scala   |   10 +-
 .../mllib/tree/impl/DTStatsAggregator.scala   |    2 +-
 .../tree/impl/DecisionTreeMetadata.scala      |    4 +-
 .../spark/mllib/tree/impl/NodeIdCache.scala   |    4 +-
 .../spark/mllib/tree/impl/TimeTracker.scala   |    2 +-
 .../spark/mllib/tree/impl/TreePoint.scala     |    4 +-
 .../spark/mllib/tree/impurity/Impurity.scala  |    4 +-
 .../tree/model/InformationGainStats.scala     |    2 +-
 .../JavaDecisionTreeRegressorSuite.java       |    2 +-
 18 files changed, 1678 insertions(+), 32 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 2dc1824964a42..36fe1bd40469c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -21,10 +21,10 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
+import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
 import org.apache.spark.rdd.RDD
@@ -75,8 +75,9 @@ final class DecisionTreeClassifier(override val uid: String)
     }
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy = getOldStrategy(categoricalFeatures, numClasses)
-    val oldModel = OldDecisionTree.train(oldDataset, strategy)
-    DecisionTreeClassificationModel.fromOld(oldModel, this, categoricalFeatures)
+    val trees = RandomForest.run(oldDataset, strategy, numTrees = 1, featureSubsetStrategy = "all",
+      seed = 0L, parentUID = Some(uid))
+    trees.head.asInstanceOf[DecisionTreeClassificationModel]
   }
 
   /** (private[ml]) Create a Strategy instance to use with the old API. */
@@ -112,6 +113,12 @@ final class DecisionTreeClassificationModel private[ml] (
   require(rootNode != null,
     "DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.")
 
+  /**
+   * Construct a decision tree classification model.
+   * @param rootNode  Root node of tree, with other nodes attached.
+   */
+  def this(rootNode: Node) = this(Identifiable.randomUID("dtc"), rootNode)
+
   override protected def predict(features: Vector): Double = {
     rootNode.predict(features)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index d3c67494a31e4..490f04c7c7172 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -20,13 +20,13 @@ package org.apache.spark.ml.classification
 import scala.collection.mutable
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
@@ -93,9 +93,10 @@ final class RandomForestClassifier(override val uid: String)
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)
-    val oldModel = OldRandomForest.trainClassifier(
-      oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt)
-    RandomForestClassificationModel.fromOld(oldModel, this, categoricalFeatures)
+    val trees =
+      RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
+        .map(_.asInstanceOf[DecisionTreeClassificationModel])
+    new RandomForestClassificationModel(trees)
   }
 
   override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra)
@@ -128,6 +129,13 @@ final class RandomForestClassificationModel private[ml] (
 
   require(numTrees > 0, "RandomForestClassificationModel requires at least 1 tree.")
 
+  /**
+   * Construct a random forest classification model, with all trees weighted equally.
+   * @param trees  Component trees
+   */
+  def this(trees: Array[DecisionTreeClassificationModel]) =
+    this(Identifiable.randomUID("rfc"), trees)
+
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
   // Note: We may add support for weights (based on tree performance) later on.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index be1f8063d41d8..6f3340c2f02be 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -21,10 +21,10 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeRegressorParams}
+import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
 import org.apache.spark.rdd.RDD
@@ -67,8 +67,9 @@ final class DecisionTreeRegressor(override val uid: String)
       MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy = getOldStrategy(categoricalFeatures)
-    val oldModel = OldDecisionTree.train(oldDataset, strategy)
-    DecisionTreeRegressionModel.fromOld(oldModel, this, categoricalFeatures)
+    val trees = RandomForest.run(oldDataset, strategy, numTrees = 1, featureSubsetStrategy = "all",
+      seed = 0L, parentUID = Some(uid))
+    trees.head.asInstanceOf[DecisionTreeRegressionModel]
   }
 
   /** (private[ml]) Create a Strategy instance to use with the old API. */
@@ -102,6 +103,12 @@ final class DecisionTreeRegressionModel private[ml] (
   require(rootNode != null,
     "DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.")
 
+  /**
+   * Construct a decision tree regression model.
+   * @param rootNode  Root node of tree, with other nodes attached.
+   */
+  def this(rootNode: Node) = this(Identifiable.randomUID("dtr"), rootNode)
+
   override protected def predict(features: Vector): Double = {
     rootNode.predict(features)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 21c59061a02fa..5fd5c7c7bd3fc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -21,10 +21,10 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams}
+import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
@@ -82,9 +82,10 @@ final class RandomForestRegressor(override val uid: String)
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity)
-    val oldModel = OldRandomForest.trainRegressor(
-      oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt)
-    RandomForestRegressionModel.fromOld(oldModel, this, categoricalFeatures)
+    val trees =
+      RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
+        .map(_.asInstanceOf[DecisionTreeRegressionModel])
+    new RandomForestRegressionModel(trees)
   }
 
   override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra)
@@ -115,6 +116,12 @@ final class RandomForestRegressionModel private[ml] (
 
   require(numTrees > 0, "RandomForestRegressionModel requires at least 1 tree.")
 
+  /**
+   * Construct a random forest regression model, with all trees weighted equally.
+   * @param trees  Component trees
+   */
+  def this(trees: Array[DecisionTreeRegressionModel]) = this(Identifiable.randomUID("rfr"), trees)
+
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
   // Note: We may add support for weights (based on tree performance) later on.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
index 4242154be14ce..bbc2427ca7d3d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
@@ -209,3 +209,132 @@ private object InternalNode {
     }
   }
 }
+
+/**
+ * Version of a node used in learning.  This uses vars so that we can modify nodes as we split the
+ * tree by adding children, etc.
+ *
+ * For now, we use node IDs.  These will be kept internal since we hope to remove node IDs
+ * in the future, or at least change the indexing (so that we can support much deeper trees).
+ *
+ * This node can either be:
+ *  - a leaf node, with leftChild, rightChild, split set to null, or
+ *  - an internal node, with all values set
+ *
+ * @param id  We currently use the same indexing as the old implementation in
+ *            [[org.apache.spark.mllib.tree.model.Node]], but this will change later.
+ * @param predictionStats  Predicted label + class probability (for classification).
+ *                         We will later modify this to store aggregate statistics for labels
+ *                         to provide all class probabilities (for classification) and maybe a
+ *                         distribution (for regression).
+ * @param isLeaf  Indicates whether this node will definitely be a leaf in the learned tree,
+ *                so that we do not need to consider splitting it further.
+ * @param stats  Old structure for storing stats about information gain, prediction, etc.
+ *               This is legacy and will be modified in the future.
+ */
+private[tree] class LearningNode(
+    var id: Int,
+    var predictionStats: OldPredict,
+    var impurity: Double,
+    var leftChild: Option[LearningNode],
+    var rightChild: Option[LearningNode],
+    var split: Option[Split],
+    var isLeaf: Boolean,
+    var stats: Option[OldInformationGainStats]) extends Serializable {
+
+  /**
+   * Convert this [[LearningNode]] to a regular [[Node]], and recurse on any children.
+   */
+  def toNode: Node = {
+    if (leftChild.nonEmpty) {
+      assert(rightChild.nonEmpty && split.nonEmpty && stats.nonEmpty,
+        "Unknown error during Decision Tree learning.  Could not convert LearningNode to Node.")
+      new InternalNode(predictionStats.predict, impurity, stats.get.gain,
+        leftChild.get.toNode, rightChild.get.toNode, split.get)
+    } else {
+      new LeafNode(predictionStats.predict, impurity)
+    }
+  }
+
+}
+
+private[tree] object LearningNode {
+
+  /** Create a node with some of its fields set. */
+  def apply(
+      id: Int,
+      predictionStats: OldPredict,
+      impurity: Double,
+      isLeaf: Boolean): LearningNode = {
+    new LearningNode(id, predictionStats, impurity, None, None, None, false, None)
+  }
+
+  /** Create an empty node with the given node index.  Values must be set later on. */
+  def emptyNode(nodeIndex: Int): LearningNode = {
+    new LearningNode(nodeIndex, new OldPredict(Double.NaN, Double.NaN), Double.NaN,
+      None, None, None, false, None)
+  }
+
+  // The below indexing methods were copied from spark.mllib.tree.model.Node
+
+  /**
+   * Return the index of the left child of this node.
+   */
+  def leftChildIndex(nodeIndex: Int): Int = nodeIndex << 1
+
+  /**
+   * Return the index of the right child of this node.
+   */
+  def rightChildIndex(nodeIndex: Int): Int = (nodeIndex << 1) + 1
+
+  /**
+   * Get the parent index of the given node, or 0 if it is the root.
+   */
+  def parentIndex(nodeIndex: Int): Int = nodeIndex >> 1
+
+  /**
+   * Return the level of a tree which the given node is in.
+   */
+  def indexToLevel(nodeIndex: Int): Int = if (nodeIndex == 0) {
+    throw new IllegalArgumentException(s"0 is not a valid node index.")
+  } else {
+    java.lang.Integer.numberOfTrailingZeros(java.lang.Integer.highestOneBit(nodeIndex))
+  }
+
+  /**
+   * Returns true if this is a left child.
+   * Note: Returns false for the root.
+   */
+  def isLeftChild(nodeIndex: Int): Boolean = nodeIndex > 1 && nodeIndex % 2 == 0
+
+  /**
+   * Return the maximum number of nodes which can be in the given level of the tree.
+   * @param level  Level of tree (0 = root).
+   */
+  def maxNodesInLevel(level: Int): Int = 1 << level
+
+  /**
+   * Return the index of the first node in the given level.
+   * @param level  Level of tree (0 = root).
+   */
+  def startIndexInLevel(level: Int): Int = 1 << level
+
+  /**
+   * Traces down from a root node to get the node with the given node index.
+   * This assumes the node exists.
+   */
+  def getNode(nodeIndex: Int, rootNode: LearningNode): LearningNode = {
+    var tmpNode: LearningNode = rootNode
+    var levelsToGo = indexToLevel(nodeIndex)
+    while (levelsToGo > 0) {
+      if ((nodeIndex & (1 << levelsToGo - 1)) == 0) {
+        tmpNode = tmpNode.leftChild.asInstanceOf[LearningNode]
+      } else {
+        tmpNode = tmpNode.rightChild.asInstanceOf[LearningNode]
+      }
+      levelsToGo -= 1
+    }
+    tmpNode
+  }
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
index 7acdeeee72d23..78199cc2df582 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
@@ -34,9 +34,19 @@ sealed trait Split extends Serializable {
   /** Index of feature which this split tests */
   def featureIndex: Int
 
-  /** Return true (split to left) or false (split to right) */
+  /**
+   * Return true (split to left) or false (split to right).
+   * @param features  Vector of features (original values, not binned).
+   */
   private[ml] def shouldGoLeft(features: Vector): Boolean
 
+  /**
+   * Return true (split to left) or false (split to right).
+   * @param binnedFeature Binned feature value.
+   * @param splits All splits for the given feature.
+   */
+  private[tree] def shouldGoLeft(binnedFeature: Int, splits: Array[Split]): Boolean
+
   /** Convert to old Split format */
   private[tree] def toOld: OldSplit
 }
@@ -94,6 +104,14 @@ final class CategoricalSplit private[ml] (
     }
   }
 
+  override private[tree] def shouldGoLeft(binnedFeature: Int, splits: Array[Split]): Boolean = {
+    if (isLeft) {
+      categories.contains(binnedFeature.toDouble)
+    } else {
+      !categories.contains(binnedFeature.toDouble)
+    }
+  }
+
   override def equals(o: Any): Boolean = {
     o match {
       case other: CategoricalSplit => featureIndex == other.featureIndex &&
@@ -144,6 +162,16 @@ final class ContinuousSplit private[ml] (override val featureIndex: Int, val thr
     features(featureIndex) <= threshold
   }
 
+  override private[tree] def shouldGoLeft(binnedFeature: Int, splits: Array[Split]): Boolean = {
+    if (binnedFeature == splits.length) {
+      // > last split, so split right
+      false
+    } else {
+      val featureValueUpperBound = splits(binnedFeature).asInstanceOf[ContinuousSplit].threshold
+      featureValueUpperBound <= threshold
+    }
+  }
+
   override def equals(o: Any): Boolean = {
     o match {
       case other: ContinuousSplit =>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala
new file mode 100644
index 0000000000000..488e8e4fb5dcd
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala
@@ -0,0 +1,194 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import java.io.IOException
+
+import scala.collection.mutable
+
+import org.apache.hadoop.fs.{Path, FileSystem}
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.tree.{LearningNode, Split}
+import org.apache.spark.mllib.tree.impl.BaggedPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+
+/**
+ * This is used by the node id cache to find the child id that a data point would belong to.
+ * @param split Split information.
+ * @param nodeIndex The current node index of a data point that this will update.
+ */
+private[tree] case class NodeIndexUpdater(split: Split, nodeIndex: Int) {
+
+  /**
+   * Determine a child node index based on the feature value and the split.
+   * @param binnedFeature Binned feature value.
+   * @param splits Split information to convert the bin indices to approximate feature values.
+   * @return Child node index to update to.
+   */
+  def updateNodeIndex(binnedFeature: Int, splits: Array[Split]): Int = {
+    if (split.shouldGoLeft(binnedFeature, splits)) {
+      LearningNode.leftChildIndex(nodeIndex)
+    } else {
+      LearningNode.rightChildIndex(nodeIndex)
+    }
+  }
+}
+
+/**
+ * Each TreePoint belongs to a particular node per tree.
+ * Each row in the nodeIdsForInstances RDD is an array over trees of the node index
+ * in each tree. Initially, values should all be 1 for root node.
+ * The nodeIdsForInstances RDD needs to be updated at each iteration.
+ * @param nodeIdsForInstances The initial values in the cache
+ *                           (should be an Array of all 1's (meaning the root nodes)).
+ * @param checkpointInterval The checkpointing interval
+ *                           (how often should the cache be checkpointed.).
+ */
+private[spark] class NodeIdCache(
+  var nodeIdsForInstances: RDD[Array[Int]],
+  val checkpointInterval: Int) extends Logging {
+
+  // Keep a reference to a previous node Ids for instances.
+  // Because we will keep on re-persisting updated node Ids,
+  // we want to unpersist the previous RDD.
+  private var prevNodeIdsForInstances: RDD[Array[Int]] = null
+
+  // To keep track of the past checkpointed RDDs.
+  private val checkpointQueue = mutable.Queue[RDD[Array[Int]]]()
+  private var rddUpdateCount = 0
+
+  // Indicates whether we can checkpoint
+  private val canCheckpoint = nodeIdsForInstances.sparkContext.getCheckpointDir.nonEmpty
+
+  // FileSystem instance for deleting checkpoints as needed
+  private val fs = FileSystem.get(nodeIdsForInstances.sparkContext.hadoopConfiguration)
+
+  /**
+   * Update the node index values in the cache.
+   * This updates the RDD and its lineage.
+   * TODO: Passing bin information to executors seems unnecessary and costly.
+   * @param data The RDD of training rows.
+   * @param nodeIdUpdaters A map of node index updaters.
+   *                       The key is the indices of nodes that we want to update.
+   * @param splits  Split information needed to find child node indices.
+   */
+  def updateNodeIndices(
+      data: RDD[BaggedPoint[TreePoint]],
+      nodeIdUpdaters: Array[mutable.Map[Int, NodeIndexUpdater]],
+      splits: Array[Array[Split]]): Unit = {
+    if (prevNodeIdsForInstances != null) {
+      // Unpersist the previous one if one exists.
+      prevNodeIdsForInstances.unpersist()
+    }
+
+    prevNodeIdsForInstances = nodeIdsForInstances
+    nodeIdsForInstances = data.zip(nodeIdsForInstances).map { case (point, ids) =>
+      var treeId = 0
+      while (treeId < nodeIdUpdaters.length) {
+        val nodeIdUpdater = nodeIdUpdaters(treeId).getOrElse(ids(treeId), null)
+        if (nodeIdUpdater != null) {
+          val featureIndex = nodeIdUpdater.split.featureIndex
+          val newNodeIndex = nodeIdUpdater.updateNodeIndex(
+            binnedFeature = point.datum.binnedFeatures(featureIndex),
+            splits = splits(featureIndex))
+          ids(treeId) = newNodeIndex
+        }
+        treeId += 1
+      }
+      ids
+    }
+
+    // Keep on persisting new ones.
+    nodeIdsForInstances.persist(StorageLevel.MEMORY_AND_DISK)
+    rddUpdateCount += 1
+
+    // Handle checkpointing if the directory is not None.
+    if (canCheckpoint && (rddUpdateCount % checkpointInterval) == 0) {
+      // Let's see if we can delete previous checkpoints.
+      var canDelete = true
+      while (checkpointQueue.size > 1 && canDelete) {
+        // We can delete the oldest checkpoint iff
+        // the next checkpoint actually exists in the file system.
+        if (checkpointQueue(1).getCheckpointFile.isDefined) {
+          val old = checkpointQueue.dequeue()
+          // Since the old checkpoint is not deleted by Spark, we'll manually delete it here.
+          try {
+            fs.delete(new Path(old.getCheckpointFile.get), true)
+          } catch {
+            case e: IOException =>
+              logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
+                s" file: ${old.getCheckpointFile.get}")
+          }
+        } else {
+          canDelete = false
+        }
+      }
+
+      nodeIdsForInstances.checkpoint()
+      checkpointQueue.enqueue(nodeIdsForInstances)
+    }
+  }
+
+  /**
+   * Call this after training is finished to delete any remaining checkpoints.
+   */
+  def deleteAllCheckpoints(): Unit = {
+    while (checkpointQueue.nonEmpty) {
+      val old = checkpointQueue.dequeue()
+      if (old.getCheckpointFile.isDefined) {
+        try {
+          fs.delete(new Path(old.getCheckpointFile.get), true)
+        } catch {
+          case e: IOException =>
+            logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
+              s" file: ${old.getCheckpointFile.get}")
+        }
+      }
+    }
+  }
+  if (prevNodeIdsForInstances != null) {
+    // Unpersist the previous one if one exists.
+    prevNodeIdsForInstances.unpersist()
+  }
+}
+
+@DeveloperApi
+private[spark] object NodeIdCache {
+  /**
+   * Initialize the node Id cache with initial node Id values.
+   * @param data The RDD of training rows.
+   * @param numTrees The number of trees that we want to create cache for.
+   * @param checkpointInterval The checkpointing interval
+   *                           (how often should the cache be checkpointed.).
+   * @param initVal The initial values in the cache.
+   * @return A node Id cache containing an RDD of initial root node Indices.
+   */
+  def init(
+      data: RDD[BaggedPoint[TreePoint]],
+      numTrees: Int,
+      checkpointInterval: Int,
+      initVal: Int = 1): NodeIdCache = {
+    new NodeIdCache(
+      data.map(_ => Array.fill[Int](numTrees)(initVal)),
+      checkpointInterval)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
new file mode 100644
index 0000000000000..15b56bd844bad
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
@@ -0,0 +1,1132 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import java.io.IOException
+
+import scala.collection.mutable
+import scala.util.Random
+
+import org.apache.spark.Logging
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.regression.DecisionTreeRegressionModel
+import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
+import org.apache.spark.mllib.tree.impl.{BaggedPoint, DTStatsAggregator, DecisionTreeMetadata,
+  TimeTracker}
+import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
+import org.apache.spark.mllib.tree.model.{InformationGainStats, Predict}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
+
+
+private[ml] object RandomForest extends Logging {
+
+  /**
+   * Train a random forest.
+   * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
+   * @return an unweighted set of trees
+   */
+  def run(
+      input: RDD[LabeledPoint],
+      strategy: OldStrategy,
+      numTrees: Int,
+      featureSubsetStrategy: String,
+      seed: Long,
+      parentUID: Option[String] = None): Array[DecisionTreeModel] = {
+
+    val timer = new TimeTracker()
+
+    timer.start("total")
+
+    timer.start("init")
+
+    val retaggedInput = input.retag(classOf[LabeledPoint])
+    val metadata =
+      DecisionTreeMetadata.buildMetadata(retaggedInput, strategy, numTrees, featureSubsetStrategy)
+    logDebug("algo = " + strategy.algo)
+    logDebug("numTrees = " + numTrees)
+    logDebug("seed = " + seed)
+    logDebug("maxBins = " + metadata.maxBins)
+    logDebug("featureSubsetStrategy = " + featureSubsetStrategy)
+    logDebug("numFeaturesPerNode = " + metadata.numFeaturesPerNode)
+    logDebug("subsamplingRate = " + strategy.subsamplingRate)
+
+    // Find the splits and the corresponding bins (interval between the splits) using a sample
+    // of the input data.
+    timer.start("findSplitsBins")
+    val splits = findSplits(retaggedInput, metadata)
+    timer.stop("findSplitsBins")
+    logDebug("numBins: feature: number of bins")
+    logDebug(Range(0, metadata.numFeatures).map { featureIndex =>
+      s"\t$featureIndex\t${metadata.numBins(featureIndex)}"
+    }.mkString("\n"))
+
+    // Bin feature values (TreePoint representation).
+    // Cache input RDD for speedup during multiple passes.
+    val treeInput = TreePoint.convertToTreeRDD(retaggedInput, splits, metadata)
+
+    val withReplacement = numTrees > 1
+
+    val baggedInput = BaggedPoint
+      .convertToBaggedRDD(treeInput, strategy.subsamplingRate, numTrees, withReplacement, seed)
+      .persist(StorageLevel.MEMORY_AND_DISK)
+
+    // depth of the decision tree
+    val maxDepth = strategy.maxDepth
+    require(maxDepth <= 30,
+      s"DecisionTree currently only supports maxDepth <= 30, but was given maxDepth = $maxDepth.")
+
+    // Max memory usage for aggregates
+    // TODO: Calculate memory usage more precisely.
+    val maxMemoryUsage: Long = strategy.maxMemoryInMB * 1024L * 1024L
+    logDebug("max memory usage for aggregates = " + maxMemoryUsage + " bytes.")
+    val maxMemoryPerNode = {
+      val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) {
+        // Find numFeaturesPerNode largest bins to get an upper bound on memory usage.
+        Some(metadata.numBins.zipWithIndex.sortBy(- _._1)
+          .take(metadata.numFeaturesPerNode).map(_._2))
+      } else {
+        None
+      }
+      RandomForest.aggregateSizeForNode(metadata, featureSubset) * 8L
+    }
+    require(maxMemoryPerNode <= maxMemoryUsage,
+      s"RandomForest/DecisionTree given maxMemoryInMB = ${strategy.maxMemoryInMB}," +
+        " which is too small for the given features." +
+        s"  Minimum value = ${maxMemoryPerNode / (1024L * 1024L)}")
+
+    timer.stop("init")
+
+    /*
+     * The main idea here is to perform group-wise training of the decision tree nodes thus
+     * reducing the passes over the data from (# nodes) to (# nodes / maxNumberOfNodesPerGroup).
+     * Each data sample is handled by a particular node (or it reaches a leaf and is not used
+     * in lower levels).
+     */
+
+    // Create an RDD of node Id cache.
+    // At first, all the rows belong to the root nodes (node Id == 1).
+    val nodeIdCache = if (strategy.useNodeIdCache) {
+      Some(NodeIdCache.init(
+        data = baggedInput,
+        numTrees = numTrees,
+        checkpointInterval = strategy.checkpointInterval,
+        initVal = 1))
+    } else {
+      None
+    }
+
+    // FIFO queue of nodes to train: (treeIndex, node)
+    val nodeQueue = new mutable.Queue[(Int, LearningNode)]()
+
+    val rng = new Random()
+    rng.setSeed(seed)
+
+    // Allocate and queue root nodes.
+    val topNodes = Array.fill[LearningNode](numTrees)(LearningNode.emptyNode(nodeIndex = 1))
+    Range(0, numTrees).foreach(treeIndex => nodeQueue.enqueue((treeIndex, topNodes(treeIndex))))
+
+    while (nodeQueue.nonEmpty) {
+      // Collect some nodes to split, and choose features for each node (if subsampling).
+      // Each group of nodes may come from one or multiple trees, and at multiple levels.
+      val (nodesForGroup, treeToNodeToIndexInfo) =
+        RandomForest.selectNodesToSplit(nodeQueue, maxMemoryUsage, metadata, rng)
+      // Sanity check (should never occur):
+      assert(nodesForGroup.nonEmpty,
+        s"RandomForest selected empty nodesForGroup.  Error for unknown reason.")
+
+      // Choose node splits, and enqueue new nodes as needed.
+      timer.start("findBestSplits")
+      RandomForest.findBestSplits(baggedInput, metadata, topNodes, nodesForGroup,
+        treeToNodeToIndexInfo, splits, nodeQueue, timer, nodeIdCache)
+      timer.stop("findBestSplits")
+    }
+
+    baggedInput.unpersist()
+
+    timer.stop("total")
+
+    logInfo("Internal timing for DecisionTree:")
+    logInfo(s"$timer")
+
+    // Delete any remaining checkpoints used for node Id cache.
+    if (nodeIdCache.nonEmpty) {
+      try {
+        nodeIdCache.get.deleteAllCheckpoints()
+      } catch {
+        case e: IOException =>
+          logWarning(s"delete all checkpoints failed. Error reason: ${e.getMessage}")
+      }
+    }
+
+    parentUID match {
+      case Some(uid) =>
+        if (strategy.algo == OldAlgo.Classification) {
+          topNodes.map(rootNode => new DecisionTreeClassificationModel(uid, rootNode.toNode))
+        } else {
+          topNodes.map(rootNode => new DecisionTreeRegressionModel(uid, rootNode.toNode))
+        }
+      case None =>
+        if (strategy.algo == OldAlgo.Classification) {
+          topNodes.map(rootNode => new DecisionTreeClassificationModel(rootNode.toNode))
+        } else {
+          topNodes.map(rootNode => new DecisionTreeRegressionModel(rootNode.toNode))
+        }
+    }
+  }
+
+  /**
+   * Get the node index corresponding to this data point.
+   * This function mimics prediction, passing an example from the root node down to a leaf
+   * or unsplit node; that node's index is returned.
+   *
+   * @param node  Node in tree from which to classify the given data point.
+   * @param binnedFeatures  Binned feature vector for data point.
+   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
+   * @return  Leaf index if the data point reaches a leaf.
+   *          Otherwise, last node reachable in tree matching this example.
+   *          Note: This is the global node index, i.e., the index used in the tree.
+   *                This index is different from the index used during training a particular
+   *                group of nodes on one call to [[findBestSplits()]].
+   */
+  private def predictNodeIndex(
+      node: LearningNode,
+      binnedFeatures: Array[Int],
+      splits: Array[Array[Split]]): Int = {
+    if (node.isLeaf || node.split.isEmpty) {
+      node.id
+    } else {
+      val split = node.split.get
+      val featureIndex = split.featureIndex
+      val splitLeft = split.shouldGoLeft(binnedFeatures(featureIndex), splits(featureIndex))
+      if (node.leftChild.isEmpty) {
+        // Not yet split. Return index from next layer of nodes to train
+        if (splitLeft) {
+          LearningNode.leftChildIndex(node.id)
+        } else {
+          LearningNode.rightChildIndex(node.id)
+        }
+      } else {
+        if (splitLeft) {
+          predictNodeIndex(node.leftChild.get, binnedFeatures, splits)
+        } else {
+          predictNodeIndex(node.rightChild.get, binnedFeatures, splits)
+        }
+      }
+    }
+  }
+
+  /**
+   * Helper for binSeqOp, for data which can contain a mix of ordered and unordered features.
+   *
+   * For ordered features, a single bin is updated.
+   * For unordered features, bins correspond to subsets of categories; either the left or right bin
+   * for each subset is updated.
+   *
+   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
+   *             each (feature, bin).
+   * @param treePoint  Data point being aggregated.
+   * @param splits possible splits indexed (numFeatures)(numSplits)
+   * @param unorderedFeatures  Set of indices of unordered features.
+   * @param instanceWeight  Weight (importance) of instance in dataset.
+   */
+  private def mixedBinSeqOp(
+      agg: DTStatsAggregator,
+      treePoint: TreePoint,
+      splits: Array[Array[Split]],
+      unorderedFeatures: Set[Int],
+      instanceWeight: Double,
+      featuresForNode: Option[Array[Int]]): Unit = {
+    val numFeaturesPerNode = if (featuresForNode.nonEmpty) {
+      // Use subsampled features
+      featuresForNode.get.length
+    } else {
+      // Use all features
+      agg.metadata.numFeatures
+    }
+    // Iterate over features.
+    var featureIndexIdx = 0
+    while (featureIndexIdx < numFeaturesPerNode) {
+      val featureIndex = if (featuresForNode.nonEmpty) {
+        featuresForNode.get.apply(featureIndexIdx)
+      } else {
+        featureIndexIdx
+      }
+      if (unorderedFeatures.contains(featureIndex)) {
+        // Unordered feature
+        val featureValue = treePoint.binnedFeatures(featureIndex)
+        val (leftNodeFeatureOffset, rightNodeFeatureOffset) =
+          agg.getLeftRightFeatureOffsets(featureIndexIdx)
+        // Update the left or right bin for each split.
+        val numSplits = agg.metadata.numSplits(featureIndex)
+        val featureSplits = splits(featureIndex)
+        var splitIndex = 0
+        while (splitIndex < numSplits) {
+          if (featureSplits(splitIndex).shouldGoLeft(featureValue, featureSplits)) {
+            agg.featureUpdate(leftNodeFeatureOffset, splitIndex, treePoint.label, instanceWeight)
+          } else {
+            agg.featureUpdate(rightNodeFeatureOffset, splitIndex, treePoint.label, instanceWeight)
+          }
+          splitIndex += 1
+        }
+      } else {
+        // Ordered feature
+        val binIndex = treePoint.binnedFeatures(featureIndex)
+        agg.update(featureIndexIdx, binIndex, treePoint.label, instanceWeight)
+      }
+      featureIndexIdx += 1
+    }
+  }
+
+  /**
+   * Helper for binSeqOp, for regression and for classification with only ordered features.
+   *
+   * For each feature, the sufficient statistics of one bin are updated.
+   *
+   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
+   *             each (feature, bin).
+   * @param treePoint  Data point being aggregated.
+   * @param instanceWeight  Weight (importance) of instance in dataset.
+   */
+  private def orderedBinSeqOp(
+      agg: DTStatsAggregator,
+      treePoint: TreePoint,
+      instanceWeight: Double,
+      featuresForNode: Option[Array[Int]]): Unit = {
+    val label = treePoint.label
+
+    // Iterate over features.
+    if (featuresForNode.nonEmpty) {
+      // Use subsampled features
+      var featureIndexIdx = 0
+      while (featureIndexIdx < featuresForNode.get.length) {
+        val binIndex = treePoint.binnedFeatures(featuresForNode.get.apply(featureIndexIdx))
+        agg.update(featureIndexIdx, binIndex, label, instanceWeight)
+        featureIndexIdx += 1
+      }
+    } else {
+      // Use all features
+      val numFeatures = agg.metadata.numFeatures
+      var featureIndex = 0
+      while (featureIndex < numFeatures) {
+        val binIndex = treePoint.binnedFeatures(featureIndex)
+        agg.update(featureIndex, binIndex, label, instanceWeight)
+        featureIndex += 1
+      }
+    }
+  }
+
+  /**
+   * Given a group of nodes, this finds the best split for each node.
+   *
+   * @param input Training data: RDD of [[org.apache.spark.mllib.tree.impl.TreePoint]]
+   * @param metadata Learning and dataset metadata
+   * @param topNodes Root node for each tree.  Used for matching instances with nodes.
+   * @param nodesForGroup Mapping: treeIndex --> nodes to be split in tree
+   * @param treeToNodeToIndexInfo Mapping: treeIndex --> nodeIndex --> nodeIndexInfo,
+   *                              where nodeIndexInfo stores the index in the group and the
+   *                              feature subsets (if using feature subsets).
+   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
+   * @param nodeQueue  Queue of nodes to split, with values (treeIndex, node).
+   *                   Updated with new non-leaf nodes which are created.
+   * @param nodeIdCache Node Id cache containing an RDD of Array[Int] where
+   *                    each value in the array is the data point's node Id
+   *                    for a corresponding tree. This is used to prevent the need
+   *                    to pass the entire tree to the executors during
+   *                    the node stat aggregation phase.
+   */
+  private[tree] def findBestSplits(
+      input: RDD[BaggedPoint[TreePoint]],
+      metadata: DecisionTreeMetadata,
+      topNodes: Array[LearningNode],
+      nodesForGroup: Map[Int, Array[LearningNode]],
+      treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]],
+      splits: Array[Array[Split]],
+      nodeQueue: mutable.Queue[(Int, LearningNode)],
+      timer: TimeTracker = new TimeTracker,
+      nodeIdCache: Option[NodeIdCache] = None): Unit = {
+
+    /*
+     * The high-level descriptions of the best split optimizations are noted here.
+     *
+     * *Group-wise training*
+     * We perform bin calculations for groups of nodes to reduce the number of
+     * passes over the data.  Each iteration requires more computation and storage,
+     * but saves several iterations over the data.
+     *
+     * *Bin-wise computation*
+     * We use a bin-wise best split computation strategy instead of a straightforward best split
+     * computation strategy. Instead of analyzing each sample for contribution to the left/right
+     * child node impurity of every split, we first categorize each feature of a sample into a
+     * bin. We exploit this structure to calculate aggregates for bins and then use these aggregates
+     * to calculate information gain for each split.
+     *
+     * *Aggregation over partitions*
+     * Instead of performing a flatMap/reduceByKey operation, we exploit the fact that we know
+     * the number of splits in advance. Thus, we store the aggregates (at the appropriate
+     * indices) in a single array for all bins and rely upon the RDD aggregate method to
+     * drastically reduce the communication overhead.
+     */
+
+    // numNodes:  Number of nodes in this group
+    val numNodes = nodesForGroup.values.map(_.length).sum
+    logDebug("numNodes = " + numNodes)
+    logDebug("numFeatures = " + metadata.numFeatures)
+    logDebug("numClasses = " + metadata.numClasses)
+    logDebug("isMulticlass = " + metadata.isMulticlass)
+    logDebug("isMulticlassWithCategoricalFeatures = " +
+      metadata.isMulticlassWithCategoricalFeatures)
+    logDebug("using nodeIdCache = " + nodeIdCache.nonEmpty.toString)
+
+    /**
+     * Performs a sequential aggregation over a partition for a particular tree and node.
+     *
+     * For each feature, the aggregate sufficient statistics are updated for the relevant
+     * bins.
+     *
+     * @param treeIndex Index of the tree that we want to perform aggregation for.
+     * @param nodeInfo The node info for the tree node.
+     * @param agg Array storing aggregate calculation, with a set of sufficient statistics
+     *            for each (node, feature, bin).
+     * @param baggedPoint Data point being aggregated.
+     */
+    def nodeBinSeqOp(
+        treeIndex: Int,
+        nodeInfo: NodeIndexInfo,
+        agg: Array[DTStatsAggregator],
+        baggedPoint: BaggedPoint[TreePoint]): Unit = {
+      if (nodeInfo != null) {
+        val aggNodeIndex = nodeInfo.nodeIndexInGroup
+        val featuresForNode = nodeInfo.featureSubset
+        val instanceWeight = baggedPoint.subsampleWeights(treeIndex)
+        if (metadata.unorderedFeatures.isEmpty) {
+          orderedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, instanceWeight, featuresForNode)
+        } else {
+          mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, splits,
+            metadata.unorderedFeatures, instanceWeight, featuresForNode)
+        }
+      }
+    }
+
+    /**
+     * Performs a sequential aggregation over a partition.
+     *
+     * Each data point contributes to one node. For each feature,
+     * the aggregate sufficient statistics are updated for the relevant bins.
+     *
+     * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
+     *             each (node, feature, bin).
+     * @param baggedPoint   Data point being aggregated.
+     * @return  agg
+     */
+    def binSeqOp(
+        agg: Array[DTStatsAggregator],
+        baggedPoint: BaggedPoint[TreePoint]): Array[DTStatsAggregator] = {
+      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
+        val nodeIndex =
+          predictNodeIndex(topNodes(treeIndex), baggedPoint.datum.binnedFeatures, splits)
+        nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null), agg, baggedPoint)
+      }
+      agg
+    }
+
+    /**
+     * Do the same thing as binSeqOp, but with nodeIdCache.
+     */
+    def binSeqOpWithNodeIdCache(
+        agg: Array[DTStatsAggregator],
+        dataPoint: (BaggedPoint[TreePoint], Array[Int])): Array[DTStatsAggregator] = {
+      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
+        val baggedPoint = dataPoint._1
+        val nodeIdCache = dataPoint._2
+        val nodeIndex = nodeIdCache(treeIndex)
+        nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null), agg, baggedPoint)
+      }
+
+      agg
+    }
+
+    /**
+     * Get node index in group --> features indices map,
+     * which is a short cut to find feature indices for a node given node index in group.
+     */
+    def getNodeToFeatures(
+        treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]]): Option[Map[Int, Array[Int]]] = {
+      if (!metadata.subsamplingFeatures) {
+        None
+      } else {
+        val mutableNodeToFeatures = new mutable.HashMap[Int, Array[Int]]()
+        treeToNodeToIndexInfo.values.foreach { nodeIdToNodeInfo =>
+          nodeIdToNodeInfo.values.foreach { nodeIndexInfo =>
+            assert(nodeIndexInfo.featureSubset.isDefined)
+            mutableNodeToFeatures(nodeIndexInfo.nodeIndexInGroup) = nodeIndexInfo.featureSubset.get
+          }
+        }
+        Some(mutableNodeToFeatures.toMap)
+      }
+    }
+
+    // array of nodes to train indexed by node index in group
+    val nodes = new Array[LearningNode](numNodes)
+    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
+      nodesForTree.foreach { node =>
+        nodes(treeToNodeToIndexInfo(treeIndex)(node.id).nodeIndexInGroup) = node
+      }
+    }
+
+    // Calculate best splits for all nodes in the group
+    timer.start("chooseSplits")
+
+    // In each partition, iterate all instances and compute aggregate stats for each node,
+    // yield an (nodeIndex, nodeAggregateStats) pair for each node.
+    // After a `reduceByKey` operation,
+    // stats of a node will be shuffled to a particular partition and be combined together,
+    // then best splits for nodes are found there.
+    // Finally, only best Splits for nodes are collected to driver to construct decision tree.
+    val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
+    val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
+
+    val partitionAggregates : RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
+      input.zip(nodeIdCache.get.nodeIdsForInstances).mapPartitions { points =>
+        // Construct a nodeStatsAggregators array to hold node aggregate stats,
+        // each node will have a nodeStatsAggregator
+        val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
+          val featuresForNode = nodeToFeaturesBc.value.flatMap { nodeToFeatures =>
+            Some(nodeToFeatures(nodeIndex))
+          }
+          new DTStatsAggregator(metadata, featuresForNode)
+        }
+
+        // iterator all instances in current partition and update aggregate stats
+        points.foreach(binSeqOpWithNodeIdCache(nodeStatsAggregators, _))
+
+        // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
+        // which can be combined with other partition using `reduceByKey`
+        nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
+      }
+    } else {
+      input.mapPartitions { points =>
+        // Construct a nodeStatsAggregators array to hold node aggregate stats,
+        // each node will have a nodeStatsAggregator
+        val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
+          val featuresForNode = nodeToFeaturesBc.value.flatMap { nodeToFeatures =>
+            Some(nodeToFeatures(nodeIndex))
+          }
+          new DTStatsAggregator(metadata, featuresForNode)
+        }
+
+        // iterator all instances in current partition and update aggregate stats
+        points.foreach(binSeqOp(nodeStatsAggregators, _))
+
+        // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
+        // which can be combined with other partition using `reduceByKey`
+        nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
+      }
+    }
+
+    val nodeToBestSplits = partitionAggregates.reduceByKey((a, b) => a.merge(b)).map {
+      case (nodeIndex, aggStats) =>
+        val featuresForNode = nodeToFeaturesBc.value.flatMap { nodeToFeatures =>
+          Some(nodeToFeatures(nodeIndex))
+        }
+
+        // find best split for each node
+        val (split: Split, stats: InformationGainStats, predict: Predict) =
+          binsToBestSplit(aggStats, splits, featuresForNode, nodes(nodeIndex))
+        (nodeIndex, (split, stats, predict))
+    }.collectAsMap()
+
+    timer.stop("chooseSplits")
+
+    val nodeIdUpdaters = if (nodeIdCache.nonEmpty) {
+      Array.fill[mutable.Map[Int, NodeIndexUpdater]](
+        metadata.numTrees)(mutable.Map[Int, NodeIndexUpdater]())
+    } else {
+      null
+    }
+    // Iterate over all nodes in this group.
+    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
+      nodesForTree.foreach { node =>
+        val nodeIndex = node.id
+        val nodeInfo = treeToNodeToIndexInfo(treeIndex)(nodeIndex)
+        val aggNodeIndex = nodeInfo.nodeIndexInGroup
+        val (split: Split, stats: InformationGainStats, predict: Predict) =
+          nodeToBestSplits(aggNodeIndex)
+        logDebug("best split = " + split)
+
+        // Extract info for this node.  Create children if not leaf.
+        val isLeaf =
+          (stats.gain <= 0) || (LearningNode.indexToLevel(nodeIndex) == metadata.maxDepth)
+        node.predictionStats = predict
+        node.isLeaf = isLeaf
+        node.stats = Some(stats)
+        node.impurity = stats.impurity
+        logDebug("Node = " + node)
+
+        if (!isLeaf) {
+          node.split = Some(split)
+          val childIsLeaf = (LearningNode.indexToLevel(nodeIndex) + 1) == metadata.maxDepth
+          val leftChildIsLeaf = childIsLeaf || (stats.leftImpurity == 0.0)
+          val rightChildIsLeaf = childIsLeaf || (stats.rightImpurity == 0.0)
+          node.leftChild = Some(LearningNode(LearningNode.leftChildIndex(nodeIndex),
+            stats.leftPredict, stats.leftImpurity, leftChildIsLeaf))
+          node.rightChild = Some(LearningNode(LearningNode.rightChildIndex(nodeIndex),
+            stats.rightPredict, stats.rightImpurity, rightChildIsLeaf))
+
+          if (nodeIdCache.nonEmpty) {
+            val nodeIndexUpdater = NodeIndexUpdater(
+              split = split,
+              nodeIndex = nodeIndex)
+            nodeIdUpdaters(treeIndex).put(nodeIndex, nodeIndexUpdater)
+          }
+
+          // enqueue left child and right child if they are not leaves
+          if (!leftChildIsLeaf) {
+            nodeQueue.enqueue((treeIndex, node.leftChild.get))
+          }
+          if (!rightChildIsLeaf) {
+            nodeQueue.enqueue((treeIndex, node.rightChild.get))
+          }
+
+          logDebug("leftChildIndex = " + node.leftChild.get.id +
+            ", impurity = " + stats.leftImpurity)
+          logDebug("rightChildIndex = " + node.rightChild.get.id +
+            ", impurity = " + stats.rightImpurity)
+        }
+      }
+    }
+
+    if (nodeIdCache.nonEmpty) {
+      // Update the cache if needed.
+      nodeIdCache.get.updateNodeIndices(input, nodeIdUpdaters, splits)
+    }
+  }
+
+  /**
+   * Calculate the information gain for a given (feature, split) based upon left/right aggregates.
+   * @param leftImpurityCalculator left node aggregates for this (feature, split)
+   * @param rightImpurityCalculator right node aggregate for this (feature, split)
+   * @return information gain and statistics for split
+   */
+  private def calculateGainForSplit(
+      leftImpurityCalculator: ImpurityCalculator,
+      rightImpurityCalculator: ImpurityCalculator,
+      metadata: DecisionTreeMetadata,
+      impurity: Double): InformationGainStats = {
+    val leftCount = leftImpurityCalculator.count
+    val rightCount = rightImpurityCalculator.count
+
+    // If left child or right child doesn't satisfy minimum instances per node,
+    // then this split is invalid, return invalid information gain stats.
+    if ((leftCount < metadata.minInstancesPerNode) ||
+      (rightCount < metadata.minInstancesPerNode)) {
+      return InformationGainStats.invalidInformationGainStats
+    }
+
+    val totalCount = leftCount + rightCount
+
+    val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
+    val rightImpurity = rightImpurityCalculator.calculate()
+
+    val leftWeight = leftCount / totalCount.toDouble
+    val rightWeight = rightCount / totalCount.toDouble
+
+    val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
+
+    // if information gain doesn't satisfy minimum information gain,
+    // then this split is invalid, return invalid information gain stats.
+    if (gain < metadata.minInfoGain) {
+      return InformationGainStats.invalidInformationGainStats
+    }
+
+    // calculate left and right predict
+    val leftPredict = calculatePredict(leftImpurityCalculator)
+    val rightPredict = calculatePredict(rightImpurityCalculator)
+
+    new InformationGainStats(gain, impurity, leftImpurity, rightImpurity,
+      leftPredict, rightPredict)
+  }
+
+  private def calculatePredict(impurityCalculator: ImpurityCalculator): Predict = {
+    val predict = impurityCalculator.predict
+    val prob = impurityCalculator.prob(predict)
+    new Predict(predict, prob)
+  }
+
+  /**
+   * Calculate predict value for current node, given stats of any split.
+   * Note that this function is called only once for each node.
+   * @param leftImpurityCalculator left node aggregates for a split
+   * @param rightImpurityCalculator right node aggregates for a split
+   * @return predict value and impurity for current node
+   */
+  private def calculatePredictImpurity(
+      leftImpurityCalculator: ImpurityCalculator,
+      rightImpurityCalculator: ImpurityCalculator): (Predict, Double) = {
+    val parentNodeAgg = leftImpurityCalculator.copy
+    parentNodeAgg.add(rightImpurityCalculator)
+    val predict = calculatePredict(parentNodeAgg)
+    val impurity = parentNodeAgg.calculate()
+
+    (predict, impurity)
+  }
+
+  /**
+   * Find the best split for a node.
+   * @param binAggregates Bin statistics.
+   * @return tuple for best split: (Split, information gain, prediction at node)
+   */
+  private def binsToBestSplit(
+      binAggregates: DTStatsAggregator,
+      splits: Array[Array[Split]],
+      featuresForNode: Option[Array[Int]],
+      node: LearningNode): (Split, InformationGainStats, Predict) = {
+
+    // Calculate prediction and impurity if current node is top node
+    val level = LearningNode.indexToLevel(node.id)
+    var predictionAndImpurity: Option[(Predict, Double)] = if (level == 0) {
+      None
+    } else {
+      Some((node.predictionStats, node.impurity))
+    }
+
+    // For each (feature, split), calculate the gain, and select the best (feature, split).
+    val (bestSplit, bestSplitStats) =
+      Range(0, binAggregates.metadata.numFeaturesPerNode).map { featureIndexIdx =>
+        val featureIndex = if (featuresForNode.nonEmpty) {
+          featuresForNode.get.apply(featureIndexIdx)
+        } else {
+          featureIndexIdx
+        }
+        val numSplits = binAggregates.metadata.numSplits(featureIndex)
+        if (binAggregates.metadata.isContinuous(featureIndex)) {
+          // Cumulative sum (scanLeft) of bin statistics.
+          // Afterwards, binAggregates for a bin is the sum of aggregates for
+          // that bin + all preceding bins.
+          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
+          var splitIndex = 0
+          while (splitIndex < numSplits) {
+            binAggregates.mergeForFeature(nodeFeatureOffset, splitIndex + 1, splitIndex)
+            splitIndex += 1
+          }
+          // Find best split.
+          val (bestFeatureSplitIndex, bestFeatureGainStats) =
+            Range(0, numSplits).map { case splitIdx =>
+              val leftChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, splitIdx)
+              val rightChildStats =
+                binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
+              rightChildStats.subtract(leftChildStats)
+              predictionAndImpurity = Some(predictionAndImpurity.getOrElse(
+                calculatePredictImpurity(leftChildStats, rightChildStats)))
+              val gainStats = calculateGainForSplit(leftChildStats,
+                rightChildStats, binAggregates.metadata, predictionAndImpurity.get._2)
+              (splitIdx, gainStats)
+            }.maxBy(_._2.gain)
+          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
+        } else if (binAggregates.metadata.isUnordered(featureIndex)) {
+          // Unordered categorical feature
+          val (leftChildOffset, rightChildOffset) =
+            binAggregates.getLeftRightFeatureOffsets(featureIndexIdx)
+          val (bestFeatureSplitIndex, bestFeatureGainStats) =
+            Range(0, numSplits).map { splitIndex =>
+              val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
+              val rightChildStats =
+                binAggregates.getImpurityCalculator(rightChildOffset, splitIndex)
+              predictionAndImpurity = Some(predictionAndImpurity.getOrElse(
+                calculatePredictImpurity(leftChildStats, rightChildStats)))
+              val gainStats = calculateGainForSplit(leftChildStats,
+                rightChildStats, binAggregates.metadata, predictionAndImpurity.get._2)
+              (splitIndex, gainStats)
+            }.maxBy(_._2.gain)
+          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
+        } else {
+          // Ordered categorical feature
+          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
+          val numCategories = binAggregates.metadata.numBins(featureIndex)
+
+          /* Each bin is one category (feature value).
+           * The bins are ordered based on centroidForCategories, and this ordering determines which
+           * splits are considered.  (With K categories, we consider K - 1 possible splits.)
+           *
+           * centroidForCategories is a list: (category, centroid)
+           */
+          val centroidForCategories = if (binAggregates.metadata.isMulticlass) {
+            // For categorical variables in multiclass classification,
+            // the bins are ordered by the impurity of their corresponding labels.
+            Range(0, numCategories).map { case featureValue =>
+              val categoryStats =
+                binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
+              val centroid = if (categoryStats.count != 0) {
+                categoryStats.calculate()
+              } else {
+                Double.MaxValue
+              }
+              (featureValue, centroid)
+            }
+          } else { // regression or binary classification
+            // For categorical variables in regression and binary classification,
+            // the bins are ordered by the centroid of their corresponding labels.
+            Range(0, numCategories).map { case featureValue =>
+              val categoryStats =
+                binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
+              val centroid = if (categoryStats.count != 0) {
+                categoryStats.predict
+              } else {
+                Double.MaxValue
+              }
+              (featureValue, centroid)
+            }
+          }
+
+          logDebug("Centroids for categorical variable: " + centroidForCategories.mkString(","))
+
+          // bins sorted by centroids
+          val categoriesSortedByCentroid = centroidForCategories.toList.sortBy(_._2)
+
+          logDebug("Sorted centroids for categorical variable = " +
+            categoriesSortedByCentroid.mkString(","))
+
+          // Cumulative sum (scanLeft) of bin statistics.
+          // Afterwards, binAggregates for a bin is the sum of aggregates for
+          // that bin + all preceding bins.
+          var splitIndex = 0
+          while (splitIndex < numSplits) {
+            val currentCategory = categoriesSortedByCentroid(splitIndex)._1
+            val nextCategory = categoriesSortedByCentroid(splitIndex + 1)._1
+            binAggregates.mergeForFeature(nodeFeatureOffset, nextCategory, currentCategory)
+            splitIndex += 1
+          }
+          // lastCategory = index of bin with total aggregates for this (node, feature)
+          val lastCategory = categoriesSortedByCentroid.last._1
+          // Find best split.
+          val (bestFeatureSplitIndex, bestFeatureGainStats) =
+            Range(0, numSplits).map { splitIndex =>
+              val featureValue = categoriesSortedByCentroid(splitIndex)._1
+              val leftChildStats =
+                binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
+              val rightChildStats =
+                binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
+              rightChildStats.subtract(leftChildStats)
+              predictionAndImpurity = Some(predictionAndImpurity.getOrElse(
+                calculatePredictImpurity(leftChildStats, rightChildStats)))
+              val gainStats = calculateGainForSplit(leftChildStats,
+                rightChildStats, binAggregates.metadata, predictionAndImpurity.get._2)
+              (splitIndex, gainStats)
+            }.maxBy(_._2.gain)
+          val categoriesForSplit =
+            categoriesSortedByCentroid.map(_._1.toDouble).slice(0, bestFeatureSplitIndex + 1)
+          val bestFeatureSplit =
+            new CategoricalSplit(featureIndex, categoriesForSplit.toArray, numCategories)
+          (bestFeatureSplit, bestFeatureGainStats)
+        }
+      }.maxBy(_._2.gain)
+
+    (bestSplit, bestSplitStats, predictionAndImpurity.get._1)
+  }
+
+  /**
+   * Returns splits and bins for decision tree calculation.
+   * Continuous and categorical features are handled differently.
+   *
+   * Continuous features:
+   *   For each feature, there are numBins - 1 possible splits representing the possible binary
+   *   decisions at each node in the tree.
+   *   This finds locations (feature values) for splits using a subsample of the data.
+   *
+   * Categorical features:
+   *   For each feature, there is 1 bin per split.
+   *   Splits and bins are handled in 2 ways:
+   *   (a) "unordered features"
+   *       For multiclass classification with a low-arity feature
+   *       (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits),
+   *       the feature is split based on subsets of categories.
+   *   (b) "ordered features"
+   *       For regression and binary classification,
+   *       and for multiclass classification with a high-arity feature,
+   *       there is one bin per category.
+   *
+   * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
+   * @param metadata Learning and dataset metadata
+   * @return A tuple of (splits, bins).
+   *         Splits is an Array of [[org.apache.spark.mllib.tree.model.Split]]
+   *          of size (numFeatures, numSplits).
+   *         Bins is an Array of [[org.apache.spark.mllib.tree.model.Bin]]
+   *          of size (numFeatures, numBins).
+   */
+  protected[tree] def findSplits(
+      input: RDD[LabeledPoint],
+      metadata: DecisionTreeMetadata): Array[Array[Split]] = {
+
+    logDebug("isMulticlass = " + metadata.isMulticlass)
+
+    val numFeatures = metadata.numFeatures
+
+    // Sample the input only if there are continuous features.
+    val hasContinuousFeatures = Range(0, numFeatures).exists(metadata.isContinuous)
+    val sampledInput = if (hasContinuousFeatures) {
+      // Calculate the number of samples for approximate quantile calculation.
+      val requiredSamples = math.max(metadata.maxBins * metadata.maxBins, 10000)
+      val fraction = if (requiredSamples < metadata.numExamples) {
+        requiredSamples.toDouble / metadata.numExamples
+      } else {
+        1.0
+      }
+      logDebug("fraction of data used for calculating quantiles = " + fraction)
+      input.sample(withReplacement = false, fraction, new XORShiftRandom(1).nextInt()).collect()
+    } else {
+      new Array[LabeledPoint](0)
+    }
+
+    val splits = new Array[Array[Split]](numFeatures)
+
+    // Find all splits.
+    // Iterate over all features.
+    var featureIndex = 0
+    while (featureIndex < numFeatures) {
+      if (metadata.isContinuous(featureIndex)) {
+        val featureSamples = sampledInput.map(_.features(featureIndex))
+        val featureSplits = findSplitsForContinuousFeature(featureSamples, metadata, featureIndex)
+
+        val numSplits = featureSplits.length
+        logDebug(s"featureIndex = $featureIndex, numSplits = $numSplits")
+        splits(featureIndex) = new Array[Split](numSplits)
+
+        var splitIndex = 0
+        while (splitIndex < numSplits) {
+          val threshold = featureSplits(splitIndex)
+          splits(featureIndex)(splitIndex) = new ContinuousSplit(featureIndex, threshold)
+          splitIndex += 1
+        }
+      } else {
+        // Categorical feature
+        if (metadata.isUnordered(featureIndex)) {
+          val numSplits = metadata.numSplits(featureIndex)
+          val featureArity = metadata.featureArity(featureIndex)
+          // TODO: Use an implicit representation mapping each category to a subset of indices.
+          //       I.e., track indices such that we can calculate the set of bins for which
+          //       feature value x splits to the left.
+          // Unordered features
+          // 2^(maxFeatureValue - 1) - 1 combinations
+          splits(featureIndex) = new Array[Split](numSplits)
+          var splitIndex = 0
+          while (splitIndex < numSplits) {
+            val categories: List[Double] =
+              extractMultiClassCategories(splitIndex + 1, featureArity)
+            splits(featureIndex)(splitIndex) =
+              new CategoricalSplit(featureIndex, categories.toArray, featureArity)
+            splitIndex += 1
+          }
+        } else {
+          // Ordered features
+          //   Bins correspond to feature values, so we do not need to compute splits or bins
+          //   beforehand.  Splits are constructed as needed during training.
+          splits(featureIndex) = new Array[Split](0)
+        }
+      }
+      featureIndex += 1
+    }
+    splits
+  }
+
+  /**
+   * Nested method to extract list of eligible categories given an index. It extracts the
+   * position of ones in a binary representation of the input. If binary
+   * representation of an number is 01101 (13), the output list should (3.0, 2.0,
+   * 0.0). The maxFeatureValue depict the number of rightmost digits that will be tested for ones.
+   */
+  private[tree] def extractMultiClassCategories(
+      input: Int,
+      maxFeatureValue: Int): List[Double] = {
+    var categories = List[Double]()
+    var j = 0
+    var bitShiftedInput = input
+    while (j < maxFeatureValue) {
+      if (bitShiftedInput % 2 != 0) {
+        // updating the list of categories.
+        categories = j.toDouble :: categories
+      }
+      // Right shift by one
+      bitShiftedInput = bitShiftedInput >> 1
+      j += 1
+    }
+    categories
+  }
+
+  /**
+   * Find splits for a continuous feature
+   * NOTE: Returned number of splits is set based on `featureSamples` and
+   *       could be different from the specified `numSplits`.
+   *       The `numSplits` attribute in the `DecisionTreeMetadata` class will be set accordingly.
+   * @param featureSamples feature values of each sample
+   * @param metadata decision tree metadata
+   *                 NOTE: `metadata.numbins` will be changed accordingly
+   *                       if there are not enough splits to be found
+   * @param featureIndex feature index to find splits
+   * @return array of splits
+   */
+  private[tree] def findSplitsForContinuousFeature(
+      featureSamples: Array[Double],
+      metadata: DecisionTreeMetadata,
+      featureIndex: Int): Array[Double] = {
+    require(metadata.isContinuous(featureIndex),
+      "findSplitsForContinuousFeature can only be used to find splits for a continuous feature.")
+
+    val splits = {
+      val numSplits = metadata.numSplits(featureIndex)
+
+      // get count for each distinct value
+      val valueCountMap = featureSamples.foldLeft(Map.empty[Double, Int]) { (m, x) =>
+        m + ((x, m.getOrElse(x, 0) + 1))
+      }
+      // sort distinct values
+      val valueCounts = valueCountMap.toSeq.sortBy(_._1).toArray
+
+      // if possible splits is not enough or just enough, just return all possible splits
+      val possibleSplits = valueCounts.length
+      if (possibleSplits <= numSplits) {
+        valueCounts.map(_._1)
+      } else {
+        // stride between splits
+        val stride: Double = featureSamples.length.toDouble / (numSplits + 1)
+        logDebug("stride = " + stride)
+
+        // iterate `valueCount` to find splits
+        val splitsBuilder = mutable.ArrayBuilder.make[Double]
+        var index = 1
+        // currentCount: sum of counts of values that have been visited
+        var currentCount = valueCounts(0)._2
+        // targetCount: target value for `currentCount`.
+        // If `currentCount` is closest value to `targetCount`,
+        // then current value is a split threshold.
+        // After finding a split threshold, `targetCount` is added by stride.
+        var targetCount = stride
+        while (index < valueCounts.length) {
+          val previousCount = currentCount
+          currentCount += valueCounts(index)._2
+          val previousGap = math.abs(previousCount - targetCount)
+          val currentGap = math.abs(currentCount - targetCount)
+          // If adding count of current value to currentCount
+          // makes the gap between currentCount and targetCount smaller,
+          // previous value is a split threshold.
+          if (previousGap < currentGap) {
+            splitsBuilder += valueCounts(index - 1)._1
+            targetCount += stride
+          }
+          index += 1
+        }
+
+        splitsBuilder.result()
+      }
+    }
+
+    // TODO: Do not fail; just ignore the useless feature.
+    assert(splits.length > 0,
+      s"DecisionTree could not handle feature $featureIndex since it had only 1 unique value." +
+        "  Please remove this feature and then try again.")
+    // set number of splits accordingly
+    metadata.setNumSplits(featureIndex, splits.length)
+
+    splits
+  }
+
+  private[tree] class NodeIndexInfo(
+      val nodeIndexInGroup: Int,
+      val featureSubset: Option[Array[Int]]) extends Serializable
+
+  /**
+   * Pull nodes off of the queue, and collect a group of nodes to be split on this iteration.
+   * This tracks the memory usage for aggregates and stops adding nodes when too much memory
+   * will be needed; this allows an adaptive number of nodes since different nodes may require
+   * different amounts of memory (if featureSubsetStrategy is not "all").
+   *
+   * @param nodeQueue  Queue of nodes to split.
+   * @param maxMemoryUsage  Bound on size of aggregate statistics.
+   * @return  (nodesForGroup, treeToNodeToIndexInfo).
+   *          nodesForGroup holds the nodes to split: treeIndex --> nodes in tree.
+   *
+   *          treeToNodeToIndexInfo holds indices selected features for each node:
+   *            treeIndex --> (global) node index --> (node index in group, feature indices).
+   *          The (global) node index is the index in the tree; the node index in group is the
+   *           index in [0, numNodesInGroup) of the node in this group.
+   *          The feature indices are None if not subsampling features.
+   */
+  private[tree] def selectNodesToSplit(
+      nodeQueue: mutable.Queue[(Int, LearningNode)],
+      maxMemoryUsage: Long,
+      metadata: DecisionTreeMetadata,
+      rng: Random): (Map[Int, Array[LearningNode]], Map[Int, Map[Int, NodeIndexInfo]]) = {
+    // Collect some nodes to split:
+    //  nodesForGroup(treeIndex) = nodes to split
+    val mutableNodesForGroup = new mutable.HashMap[Int, mutable.ArrayBuffer[LearningNode]]()
+    val mutableTreeToNodeToIndexInfo =
+      new mutable.HashMap[Int, mutable.HashMap[Int, NodeIndexInfo]]()
+    var memUsage: Long = 0L
+    var numNodesInGroup = 0
+    while (nodeQueue.nonEmpty && memUsage < maxMemoryUsage) {
+      val (treeIndex, node) = nodeQueue.head
+      // Choose subset of features for node (if subsampling).
+      val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) {
+        Some(SamplingUtils.reservoirSampleAndCount(Range(0,
+          metadata.numFeatures).iterator, metadata.numFeaturesPerNode, rng.nextLong())._1)
+      } else {
+        None
+      }
+      // Check if enough memory remains to add this node to the group.
+      val nodeMemUsage = RandomForest.aggregateSizeForNode(metadata, featureSubset) * 8L
+      if (memUsage + nodeMemUsage <= maxMemoryUsage) {
+        nodeQueue.dequeue()
+        mutableNodesForGroup.getOrElseUpdate(treeIndex, new mutable.ArrayBuffer[LearningNode]()) +=
+          node
+        mutableTreeToNodeToIndexInfo
+          .getOrElseUpdate(treeIndex, new mutable.HashMap[Int, NodeIndexInfo]())(node.id)
+          = new NodeIndexInfo(numNodesInGroup, featureSubset)
+      }
+      numNodesInGroup += 1
+      memUsage += nodeMemUsage
+    }
+    // Convert mutable maps to immutable ones.
+    val nodesForGroup: Map[Int, Array[LearningNode]] =
+      mutableNodesForGroup.mapValues(_.toArray).toMap
+    val treeToNodeToIndexInfo = mutableTreeToNodeToIndexInfo.mapValues(_.toMap).toMap
+    (nodesForGroup, treeToNodeToIndexInfo)
+  }
+
+  /**
+   * Get the number of values to be stored for this node in the bin aggregates.
+   * @param featureSubset  Indices of features which may be split at this node.
+   *                       If None, then use all features.
+   */
+  private def aggregateSizeForNode(
+      metadata: DecisionTreeMetadata,
+      featureSubset: Option[Array[Int]]): Long = {
+    val totalBins = if (featureSubset.nonEmpty) {
+      featureSubset.get.map(featureIndex => metadata.numBins(featureIndex).toLong).sum
+    } else {
+      metadata.numBins.map(_.toLong).sum
+    }
+    if (metadata.isClassification) {
+      metadata.numClasses * totalBins
+    } else {
+      3 * totalBins
+    }
+  }
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala
new file mode 100644
index 0000000000000..9fa27e5e1f721
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala
@@ -0,0 +1,134 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.apache.spark.ml.tree.{ContinuousSplit, Split}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.impl.DecisionTreeMetadata
+import org.apache.spark.rdd.RDD
+
+
+/**
+ * Internal representation of LabeledPoint for DecisionTree.
+ * This bins feature values based on a subsampled of data as follows:
+ *  (a) Continuous features are binned into ranges.
+ *  (b) Unordered categorical features are binned based on subsets of feature values.
+ *      "Unordered categorical features" are categorical features with low arity used in
+ *      multiclass classification.
+ *  (c) Ordered categorical features are binned based on feature values.
+ *      "Ordered categorical features" are categorical features with high arity,
+ *      or any categorical feature used in regression or binary classification.
+ *
+ * @param label  Label from LabeledPoint
+ * @param binnedFeatures  Binned feature values.
+ *                        Same length as LabeledPoint.features, but values are bin indices.
+ */
+private[spark] class TreePoint(val label: Double, val binnedFeatures: Array[Int])
+  extends Serializable {
+}
+
+private[spark] object TreePoint {
+
+  /**
+   * Convert an input dataset into its TreePoint representation,
+   * binning feature values in preparation for DecisionTree training.
+   * @param input     Input dataset.
+   * @param splits    Splits for features, of size (numFeatures, numSplits).
+   * @param metadata  Learning and dataset metadata
+   * @return  TreePoint dataset representation
+   */
+  def convertToTreeRDD(
+      input: RDD[LabeledPoint],
+      splits: Array[Array[Split]],
+      metadata: DecisionTreeMetadata): RDD[TreePoint] = {
+    // Construct arrays for featureArity for efficiency in the inner loop.
+    val featureArity: Array[Int] = new Array[Int](metadata.numFeatures)
+    var featureIndex = 0
+    while (featureIndex < metadata.numFeatures) {
+      featureArity(featureIndex) = metadata.featureArity.getOrElse(featureIndex, 0)
+      featureIndex += 1
+    }
+    val thresholds: Array[Array[Double]] = featureArity.zipWithIndex.map { case (arity, idx) =>
+      if (arity == 0) {
+        splits(idx).map(_.asInstanceOf[ContinuousSplit].threshold)
+      } else {
+        Array.empty[Double]
+      }
+    }
+    input.map { x =>
+      TreePoint.labeledPointToTreePoint(x, thresholds, featureArity)
+    }
+  }
+
+  /**
+   * Convert one LabeledPoint into its TreePoint representation.
+   * @param thresholds  For each feature, split thresholds for continuous features,
+   *                    empty for categorical features.
+   * @param featureArity  Array indexed by feature, with value 0 for continuous and numCategories
+   *                      for categorical features.
+   */
+  private def labeledPointToTreePoint(
+      labeledPoint: LabeledPoint,
+      thresholds: Array[Array[Double]],
+      featureArity: Array[Int]): TreePoint = {
+    val numFeatures = labeledPoint.features.size
+    val arr = new Array[Int](numFeatures)
+    var featureIndex = 0
+    while (featureIndex < numFeatures) {
+      arr(featureIndex) =
+        findBin(featureIndex, labeledPoint, featureArity(featureIndex), thresholds(featureIndex))
+      featureIndex += 1
+    }
+    new TreePoint(labeledPoint.label, arr)
+  }
+
+  /**
+   * Find discretized value for one (labeledPoint, feature).
+   *
+   * NOTE: We cannot use Bucketizer since it handles split thresholds differently than the old
+   *       (mllib) tree API.  We want to maintain the same behavior as the old tree API.
+   *
+   * @param featureArity  0 for continuous features; number of categories for categorical features.
+   */
+  private def findBin(
+      featureIndex: Int,
+      labeledPoint: LabeledPoint,
+      featureArity: Int,
+      thresholds: Array[Double]): Int = {
+    val featureValue = labeledPoint.features(featureIndex)
+
+    if (featureArity == 0) {
+      val idx = java.util.Arrays.binarySearch(thresholds, featureValue)
+      if (idx >= 0) {
+        idx
+      } else {
+        -idx - 1
+      }
+    } else {
+      // Categorical feature bins are indexed by feature values.
+      if (featureValue < 0 || featureValue >= featureArity) {
+        throw new IllegalArgumentException(
+          s"DecisionTree given invalid data:" +
+            s" Feature $featureIndex is categorical with values in {0,...,${featureArity - 1}," +
+            s" but a data point gives it value $featureValue.\n" +
+            "  Bad data point: " + labeledPoint.toString)
+      }
+      featureValue.toInt
+    }
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/BaggedPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/BaggedPoint.scala
index 089010c81ffb6..572815df0bc4a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/BaggedPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/BaggedPoint.scala
@@ -38,10 +38,10 @@ import org.apache.spark.util.random.XORShiftRandom
  * TODO: This does not currently support (Double) weighted instances.  Once MLlib has weighted
  *       dataset support, update.  (We store subsampleWeights as Double for this future extension.)
  */
-private[tree] class BaggedPoint[Datum](val datum: Datum, val subsampleWeights: Array[Double])
+private[spark] class BaggedPoint[Datum](val datum: Datum, val subsampleWeights: Array[Double])
   extends Serializable
 
-private[tree] object BaggedPoint {
+private[spark] object BaggedPoint {
 
   /**
    * Convert an input dataset into its BaggedPoint representation,
@@ -60,7 +60,7 @@ private[tree] object BaggedPoint {
       subsamplingRate: Double,
       numSubsamples: Int,
       withReplacement: Boolean,
-      seed: Int = Utils.random.nextInt()): RDD[BaggedPoint[Datum]] = {
+      seed: Long = Utils.random.nextLong()): RDD[BaggedPoint[Datum]] = {
     if (withReplacement) {
       convertToBaggedRDDSamplingWithReplacement(input, subsamplingRate, numSubsamples, seed)
     } else {
@@ -76,7 +76,7 @@ private[tree] object BaggedPoint {
       input: RDD[Datum],
       subsamplingRate: Double,
       numSubsamples: Int,
-      seed: Int): RDD[BaggedPoint[Datum]] = {
+      seed: Long): RDD[BaggedPoint[Datum]] = {
     input.mapPartitionsWithIndex { (partitionIndex, instances) =>
       // Use random seed = seed + partitionIndex + 1 to make generation reproducible.
       val rng = new XORShiftRandom
@@ -100,7 +100,7 @@ private[tree] object BaggedPoint {
       input: RDD[Datum],
       subsample: Double,
       numSubsamples: Int,
-      seed: Int): RDD[BaggedPoint[Datum]] = {
+      seed: Long): RDD[BaggedPoint[Datum]] = {
     input.mapPartitionsWithIndex { (partitionIndex, instances) =>
       // Use random seed = seed + partitionIndex + 1 to make generation reproducible.
       val poisson = new PoissonDistribution(subsample)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
index ce8825cc03229..7985ed4b4c0fa 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.tree.impurity._
  * and helps with indexing.
  * This class is abstract to support learning with and without feature subsampling.
  */
-private[tree] class DTStatsAggregator(
+private[spark] class DTStatsAggregator(
     val metadata: DecisionTreeMetadata,
     featureSubset: Option[Array[Int]]) extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index f73896e37c05e..380291ac22bd3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -37,7 +37,7 @@ import org.apache.spark.rdd.RDD
  *                      I.e., the feature takes values in {0, ..., arity - 1}.
  * @param numBins  Number of bins for each feature.
  */
-private[tree] class DecisionTreeMetadata(
+private[spark] class DecisionTreeMetadata(
     val numFeatures: Int,
     val numExamples: Long,
     val numClasses: Int,
@@ -94,7 +94,7 @@ private[tree] class DecisionTreeMetadata(
 
 }
 
-private[tree] object DecisionTreeMetadata extends Logging {
+private[spark] object DecisionTreeMetadata extends Logging {
 
   /**
    * Construct a [[DecisionTreeMetadata]] instance for this dataset and parameters.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/NodeIdCache.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/NodeIdCache.scala
index bdd0f576b048d..8f9eb24b57b55 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/NodeIdCache.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/NodeIdCache.scala
@@ -75,7 +75,7 @@ private[tree] case class NodeIndexUpdater(
  *                           (how often should the cache be checkpointed.).
  */
 @DeveloperApi
-private[tree] class NodeIdCache(
+private[spark] class NodeIdCache(
   var nodeIdsForInstances: RDD[Array[Int]],
   val checkpointInterval: Int) {
 
@@ -170,7 +170,7 @@ private[tree] class NodeIdCache(
 }
 
 @DeveloperApi
-private[tree] object NodeIdCache {
+private[spark] object NodeIdCache {
   /**
    * Initialize the node Id cache with initial node Id values.
    * @param data The RDD of training rows.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala
index d215d68c4279e..aac84243d5ce1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TimeTracker.scala
@@ -25,7 +25,7 @@ import org.apache.spark.annotation.Experimental
  * Time tracker implementation which holds labeled timers.
  */
 @Experimental
-private[tree] class TimeTracker extends Serializable {
+private[spark] class TimeTracker extends Serializable {
 
   private val starts: MutableHashMap[String, Long] = new MutableHashMap[String, Long]()
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala
index 50b292e71b067..21919d69a38a3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala
@@ -37,11 +37,11 @@ import org.apache.spark.rdd.RDD
  * @param binnedFeatures  Binned feature values.
  *                        Same length as LabeledPoint.features, but values are bin indices.
  */
-private[tree] class TreePoint(val label: Double, val binnedFeatures: Array[Int])
+private[spark] class TreePoint(val label: Double, val binnedFeatures: Array[Int])
   extends Serializable {
 }
 
-private[tree] object TreePoint {
+private[spark] object TreePoint {
 
   /**
    * Convert an input dataset into its TreePoint representation,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index 72eb24c49264a..578749d85a4e6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -57,7 +57,7 @@ trait Impurity extends Serializable {
  * Note: Instances of this class do not hold the data; they operate on views of the data.
  * @param statsSize  Length of the vector of sufficient statistics for one bin.
  */
-private[tree] abstract class ImpurityAggregator(val statsSize: Int) extends Serializable {
+private[spark] abstract class ImpurityAggregator(val statsSize: Int) extends Serializable {
 
   /**
    * Merge the stats from one bin into another.
@@ -95,7 +95,7 @@ private[tree] abstract class ImpurityAggregator(val statsSize: Int) extends Seri
  * (node, feature, bin).
  * @param stats  Array of sufficient statistics for a (node, feature, bin).
  */
-private[tree] abstract class ImpurityCalculator(val stats: Array[Double]) {
+private[spark] abstract class ImpurityCalculator(val stats: Array[Double]) {
 
   /**
    * Make a deep copy of this [[ImpurityCalculator]].
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index 2d087c967f679..dc9e0f9f51ffb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -67,7 +67,7 @@ class InformationGainStats(
 }
 
 
-private[tree] object InformationGainStats {
+private[spark] object InformationGainStats {
   /**
    * An [[org.apache.spark.mllib.tree.model.InformationGainStats]] object to
    * denote that current split doesn't satisfies minimum info gain or
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
index 71b041818d7ee..ebe800e749e05 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
@@ -57,7 +57,7 @@ public void runDT() {
     JavaRDD<LabeledPoint> data = sc.parallelize(
       LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
     Map<Integer, Integer> categoricalFeatures = new HashMap<Integer, Integer>();
-    DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2);
+    DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0);
 
     // This tests setters. Training with various options is tested in Scala.
     DecisionTreeRegressor dt = new DecisionTreeRegressor()

From 358e7bf652d6fedd9377593025cd661c142efeca Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 16 Jul 2015 23:02:06 -0700
Subject: [PATCH 0422/1454] [SPARK-9126] [MLLIB] do not assert on time taken by
 Thread.sleep()

Measure lower and upper bounds for task time and use them for validation. This PR also implements `Stopwatch.toString`. This suite should finish in less than 1 second.

jkbradley pwendell

Author: Xiangrui Meng <meng@databricks.com>

Closes #7457 from mengxr/SPARK-9126 and squashes the following commits:

4b40faa [Xiangrui Meng] simplify tests
739f5bd [Xiangrui Meng] do not assert on time taken by Thread.sleep()
---
 .../apache/spark/ml/util/stopwatches.scala    |  4 +-
 .../apache/spark/ml/util/StopwatchSuite.scala | 64 ++++++++++++-------
 2 files changed, 43 insertions(+), 25 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala b/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala
index 5fdf878a3df72..8d4174124b5c4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala
@@ -67,6 +67,8 @@ private[spark] abstract class Stopwatch extends Serializable {
    */
   def elapsed(): Long
 
+  override def toString: String = s"$name: ${elapsed()}ms"
+
   /**
    * Gets the current time in milliseconds.
    */
@@ -145,7 +147,7 @@ private[spark] class MultiStopwatch(@transient private val sc: SparkContext) ext
 
   override def toString: String = {
     stopwatches.values.toArray.sortBy(_.name)
-      .map(c => s"  ${c.name}: ${c.elapsed()}ms")
+      .map(c => s"  $c")
       .mkString("{\n", ",\n", "\n}")
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala
index 8df6617fe0228..9e6bc7193c13b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/util/StopwatchSuite.scala
@@ -17,11 +17,15 @@
 
 package org.apache.spark.ml.util
 
+import java.util.Random
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class StopwatchSuite extends SparkFunSuite with MLlibTestSparkContext {
 
+  import StopwatchSuite._
+
   private def testStopwatchOnDriver(sw: Stopwatch): Unit = {
     assert(sw.name === "sw")
     assert(sw.elapsed() === 0L)
@@ -29,18 +33,13 @@ class StopwatchSuite extends SparkFunSuite with MLlibTestSparkContext {
     intercept[AssertionError] {
       sw.stop()
     }
-    sw.start()
-    Thread.sleep(50)
-    val duration = sw.stop()
-    assert(duration >= 50 && duration < 100) // using a loose upper bound
+    val duration = checkStopwatch(sw)
     val elapsed = sw.elapsed()
     assert(elapsed === duration)
-    sw.start()
-    Thread.sleep(50)
-    val duration2 = sw.stop()
-    assert(duration2 >= 50 && duration2 < 100)
+    val duration2 = checkStopwatch(sw)
     val elapsed2 = sw.elapsed()
     assert(elapsed2 === duration + duration2)
+    assert(sw.toString === s"sw: ${elapsed2}ms")
     sw.start()
     assert(sw.isRunning)
     intercept[AssertionError] {
@@ -61,14 +60,13 @@ class StopwatchSuite extends SparkFunSuite with MLlibTestSparkContext {
   test("DistributedStopwatch on executors") {
     val sw = new DistributedStopwatch(sc, "sw")
     val rdd = sc.parallelize(0 until 4, 4)
+    val acc = sc.accumulator(0L)
     rdd.foreach { i =>
-      sw.start()
-      Thread.sleep(50)
-      sw.stop()
+      acc += checkStopwatch(sw)
     }
     assert(!sw.isRunning)
     val elapsed = sw.elapsed()
-    assert(elapsed >= 200 && elapsed < 400) // using a loose upper bound
+    assert(elapsed === acc.value)
   }
 
   test("MultiStopwatch") {
@@ -81,29 +79,47 @@ class StopwatchSuite extends SparkFunSuite with MLlibTestSparkContext {
       sw("some")
     }
     assert(sw.toString === "{\n  local: 0ms,\n  spark: 0ms\n}")
-    sw("local").start()
-    sw("spark").start()
-    Thread.sleep(50)
-    sw("local").stop()
-    Thread.sleep(50)
-    sw("spark").stop()
+    val localDuration = checkStopwatch(sw("local"))
+    val sparkDuration = checkStopwatch(sw("spark"))
     val localElapsed = sw("local").elapsed()
     val sparkElapsed = sw("spark").elapsed()
-    assert(localElapsed >= 50 && localElapsed < 100)
-    assert(sparkElapsed >= 100 && sparkElapsed < 200)
+    assert(localElapsed === localDuration)
+    assert(sparkElapsed === sparkDuration)
     assert(sw.toString ===
       s"{\n  local: ${localElapsed}ms,\n  spark: ${sparkElapsed}ms\n}")
     val rdd = sc.parallelize(0 until 4, 4)
+    val acc = sc.accumulator(0L)
     rdd.foreach { i =>
       sw("local").start()
-      sw("spark").start()
-      Thread.sleep(50)
-      sw("spark").stop()
+      val duration = checkStopwatch(sw("spark"))
       sw("local").stop()
+      acc += duration
     }
     val localElapsed2 = sw("local").elapsed()
     assert(localElapsed2 === localElapsed)
     val sparkElapsed2 = sw("spark").elapsed()
-    assert(sparkElapsed2 >= 300 && sparkElapsed2 < 600)
+    assert(sparkElapsed2 === sparkElapsed + acc.value)
   }
 }
+
+private object StopwatchSuite extends SparkFunSuite {
+
+  /**
+   * Checks the input stopwatch on a task that takes a random time (<10ms) to finish. Validates and
+   * returns the duration reported by the stopwatch.
+   */
+  def checkStopwatch(sw: Stopwatch): Long = {
+    val ubStart = now
+    sw.start()
+    val lbStart = now
+    Thread.sleep(new Random().nextInt(10))
+    val lb = now - lbStart
+    val duration = sw.stop()
+    val ub = now - ubStart
+    assert(duration >= lb && duration <= ub)
+    duration
+  }
+
+  /** The current time in milliseconds. */
+  private def now: Long = System.currentTimeMillis()
+}

From 111c05538d9dcee06e918dcd4481104ace712dc3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 16 Jul 2015 23:13:06 -0700
Subject: [PATCH 0423/1454] Added inline comment for the canEqual PR by
 @cloud-fan.

---
 sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 5f0592dc1d77b..3623fefbf2604 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -364,8 +364,13 @@ trait Row extends Serializable {
     false
   }
 
-  protected def canEqual(other: Any) =
+  protected def canEqual(other: Any) = {
+    // Note that InternalRow overrides canEqual. These two canEqual's together makes sure that
+    // comparing the external Row and InternalRow will always yield false.
+    // In the future, InternalRow should not extend Row. In that case, we can remove these
+    // canEqual methods.
     other.isInstanceOf[Row] && !other.isInstanceOf[InternalRow]
+  }
 
   override def equals(o: Any): Boolean = {
     if (o == null || !canEqual(o)) return false

From 3f6d28a5ca98cf7d20c2c029094350cc4f9545a0 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 17 Jul 2015 00:59:15 -0700
Subject: [PATCH 0424/1454] [SPARK-9102] [SQL] Improve project collapse with
 nondeterministic expressions

Currently we will stop project collapse when the lower projection has nondeterministic expressions. However it's overkill sometimes, we should be able to optimize `df.select(Rand(10)).select('a)` to `df.select('a)`

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7445 from cloud-fan/non-deterministic and squashes the following commits:

0deaef6 [Wenchen Fan] Improve project collapse with nondeterministic expressions
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 38 ++++++++++---------
 .../optimizer/ProjectCollapsingSuite.scala    | 26 +++++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 10 ++---
 3 files changed, 51 insertions(+), 23 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 2f94b457f4cdc..d5beeec0ffac1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -206,31 +206,33 @@ object ColumnPruning extends Rule[LogicalPlan] {
  */
 object ProjectCollapsing extends Rule[LogicalPlan] {
 
-  /** Returns true if any expression in projectList is non-deterministic. */
-  private def hasNondeterministic(projectList: Seq[NamedExpression]): Boolean = {
-    projectList.exists(expr => expr.find(!_.deterministic).isDefined)
-  }
-
   def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
-    // We only collapse these two Projects if the child Project's expressions are all
-    // deterministic.
-    case Project(projectList1, Project(projectList2, child))
-         if !hasNondeterministic(projectList2) =>
+    case p @ Project(projectList1, Project(projectList2, child)) =>
       // Create a map of Aliases to their values from the child projection.
       // e.g., 'SELECT ... FROM (SELECT a + b AS c, d ...)' produces Map(c -> Alias(a + b, c)).
       val aliasMap = AttributeMap(projectList2.collect {
-        case a @ Alias(e, _) => (a.toAttribute, a)
+        case a: Alias => (a.toAttribute, a)
       })
 
-      // Substitute any attributes that are produced by the child projection, so that we safely
-      // eliminate it.
-      // e.g., 'SELECT c + 1 FROM (SELECT a + b AS C ...' produces 'SELECT a + b + 1 ...'
-      // TODO: Fix TransformBase to avoid the cast below.
-      val substitutedProjection = projectList1.map(_.transform {
-        case a: Attribute if aliasMap.contains(a) => aliasMap(a)
-      }).asInstanceOf[Seq[NamedExpression]]
+      // We only collapse these two Projects if their overlapped expressions are all
+      // deterministic.
+      val hasNondeterministic = projectList1.flatMap(_.collect {
+        case a: Attribute if aliasMap.contains(a) => aliasMap(a).child
+      }).exists(_.find(!_.deterministic).isDefined)
 
-      Project(substitutedProjection, child)
+      if (hasNondeterministic) {
+        p
+      } else {
+        // Substitute any attributes that are produced by the child projection, so that we safely
+        // eliminate it.
+        // e.g., 'SELECT c + 1 FROM (SELECT a + b AS C ...' produces 'SELECT a + b + 1 ...'
+        // TODO: Fix TransformBase to avoid the cast below.
+        val substitutedProjection = projectList1.map(_.transform {
+          case a: Attribute => aliasMap.getOrElse(a, a)
+        }).asInstanceOf[Seq[NamedExpression]]
+
+        Project(substitutedProjection, child)
+      }
   }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ProjectCollapsingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ProjectCollapsingSuite.scala
index 151654bffbd66..1aa89991cc698 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ProjectCollapsingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ProjectCollapsingSuite.scala
@@ -70,4 +70,30 @@ class ProjectCollapsingSuite extends PlanTest {
 
     comparePlans(optimized, correctAnswer)
   }
+
+  test("collapse two nondeterministic, independent projects into one") {
+    val query = testRelation
+      .select(Rand(10).as('rand))
+      .select(Rand(20).as('rand2))
+
+    val optimized = Optimize.execute(query.analyze)
+
+    val correctAnswer = testRelation
+      .select(Rand(20).as('rand2)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("collapse one nondeterministic, one deterministic, independent projects into one") {
+    val query = testRelation
+      .select(Rand(10).as('rand), 'a)
+      .select(('a + 1).as('a_plus_1))
+
+    val optimized = Optimize.execute(query.analyze)
+
+    val correctAnswer = testRelation
+      .select(('a + 1).as('a_plus_1)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 23244fd310d0f..192cc0a6e5d7c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -745,8 +745,8 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   test("SPARK-8072: Better Exception for Duplicate Columns") {
     // only one duplicate column present
     val e = intercept[org.apache.spark.sql.AnalysisException] {
-      val df1 = Seq((1, 2, 3), (2, 3, 4), (3, 4, 5)).toDF("column1", "column2", "column1")
-                .write.format("parquet").save("temp")
+      Seq((1, 2, 3), (2, 3, 4), (3, 4, 5)).toDF("column1", "column2", "column1")
+        .write.format("parquet").save("temp")
     }
     assert(e.getMessage.contains("Duplicate column(s)"))
     assert(e.getMessage.contains("parquet"))
@@ -755,9 +755,9 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
 
     // multiple duplicate columns present
     val f = intercept[org.apache.spark.sql.AnalysisException] {
-      val df2 = Seq((1, 2, 3, 4, 5), (2, 3, 4, 5, 6), (3, 4, 5, 6, 7))
-                .toDF("column1", "column2", "column3", "column1", "column3")
-                .write.format("json").save("temp")
+      Seq((1, 2, 3, 4, 5), (2, 3, 4, 5, 6), (3, 4, 5, 6, 7))
+        .toDF("column1", "column2", "column3", "column1", "column3")
+        .write.format("json").save("temp")
     }
     assert(f.getMessage.contains("Duplicate column(s)"))
     assert(f.getMessage.contains("JSON"))

From 5a3c1ad087cb645a9496349ca021168e479ffae9 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Fri, 17 Jul 2015 17:00:50 +0900
Subject: [PATCH 0425/1454] [SPARK-9093] [SPARKR] Fix single-quotes strings in
 SparkR

[[SPARK-9093] Fix single-quotes strings in SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9093)

This is the result of lintr at the rivision:011551620faa87107a787530f074af3d9be7e695
[[SPARK-9093] The result of lintr at 011551620faa87107a787530f074af3d9be7e695](https://gist.github.com/yu-iskw/8c47acf3202796da4d01)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7439 from yu-iskw/SPARK-9093 and squashes the following commits:

61c391e [Yu ISHIKAWA] [SPARK-9093][SparkR] Fix single-quotes strings in SparkR
---
 R/pkg/R/DataFrame.R              | 10 +++++-----
 R/pkg/R/SQLContext.R             |  4 ++--
 R/pkg/R/serialize.R              |  4 ++--
 R/pkg/R/sparkR.R                 |  2 +-
 R/pkg/inst/tests/test_sparkSQL.R |  4 ++--
 5 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 208813768e264..a58433df3c8c1 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1314,7 +1314,7 @@ setMethod("except",
 #' write.df(df, "myfile", "parquet", "overwrite")
 #' }
 setMethod("write.df",
-          signature(df = "DataFrame", path = 'character'),
+          signature(df = "DataFrame", path = "character"),
           function(df, path, source = NULL, mode = "append", ...){
             if (is.null(source)) {
               sqlContext <- get(".sparkRSQLsc", envir = .sparkREnv)
@@ -1328,7 +1328,7 @@ setMethod("write.df",
             jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode)
             options <- varargsToEnv(...)
             if (!is.null(path)) {
-                options[['path']] <- path
+                options[["path"]] <- path
             }
             callJMethod(df@sdf, "save", source, jmode, options)
           })
@@ -1337,7 +1337,7 @@ setMethod("write.df",
 #' @aliases saveDF
 #' @export
 setMethod("saveDF",
-          signature(df = "DataFrame", path = 'character'),
+          signature(df = "DataFrame", path = "character"),
           function(df, path, source = NULL, mode = "append", ...){
             write.df(df, path, source, mode, ...)
           })
@@ -1375,8 +1375,8 @@ setMethod("saveDF",
 #' saveAsTable(df, "myfile")
 #' }
 setMethod("saveAsTable",
-          signature(df = "DataFrame", tableName = 'character', source = 'character',
-                    mode = 'character'),
+          signature(df = "DataFrame", tableName = "character", source = "character",
+                    mode = "character"),
           function(df, tableName, source = NULL, mode="append", ...){
             if (is.null(source)) {
               sqlContext <- get(".sparkRSQLsc", envir = .sparkREnv)
diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 30978bb50d339..110117a18ccbc 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -457,7 +457,7 @@ dropTempTable <- function(sqlContext, tableName) {
 read.df <- function(sqlContext, path = NULL, source = NULL, schema = NULL, ...) {
   options <- varargsToEnv(...)
   if (!is.null(path)) {
-    options[['path']] <- path
+    options[["path"]] <- path
   }
   if (is.null(source)) {
     sqlContext <- get(".sparkRSQLsc", envir = .sparkREnv)
@@ -506,7 +506,7 @@ loadDF <- function(sqlContext, path = NULL, source = NULL, schema = NULL, ...) {
 createExternalTable <- function(sqlContext, tableName, path = NULL, source = NULL, ...) {
   options <- varargsToEnv(...)
   if (!is.null(path)) {
-    options[['path']] <- path
+    options[["path"]] <- path
   }
   sdf <- callJMethod(sqlContext, "createExternalTable", tableName, source, options)
   dataFrame(sdf)
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R
index 78535eff0d2f6..311021e5d8473 100644
--- a/R/pkg/R/serialize.R
+++ b/R/pkg/R/serialize.R
@@ -140,8 +140,8 @@ writeType <- function(con, class) {
                  jobj = "j",
                  environment = "e",
                  Date = "D",
-                 POSIXlt = 't',
-                 POSIXct = 't',
+                 POSIXlt = "t",
+                 POSIXct = "t",
                  stop(paste("Unsupported type for serialization", class)))
   writeBin(charToRaw(type), con)
 }
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 172335809dec2..79b79d70943cb 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -140,7 +140,7 @@ sparkR.init <- function(
     if (!file.exists(path)) {
       stop("JVM is not ready after 10 seconds")
     }
-    f <- file(path, open='rb')
+    f <- file(path, open="rb")
     backendPort <- readInt(f)
     monitorPort <- readInt(f)
     close(f)
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index cdfe6481f60ea..a3039d36c9402 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -57,9 +57,9 @@ test_that("infer types", {
   expect_equal(infer_type(as.Date("2015-03-11")), "date")
   expect_equal(infer_type(as.POSIXlt("2015-03-11 12:13:04.043")), "timestamp")
   expect_equal(infer_type(c(1L, 2L)),
-               list(type = 'array', elementType = "integer", containsNull = TRUE))
+               list(type = "array", elementType = "integer", containsNull = TRUE))
   expect_equal(infer_type(list(1L, 2L)),
-               list(type = 'array', elementType = "integer", containsNull = TRUE))
+               list(type = "array", elementType = "integer", containsNull = TRUE))
   testStruct <- infer_type(list(a = 1L, b = "2"))
   expect_equal(class(testStruct), "structType")
   checkStructField(testStruct$fields()[[1]], "a", "IntegerType", TRUE)

From ec8973d1245d4a99edeb7365d7f4b0063ac31ddf Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 17 Jul 2015 01:27:14 -0700
Subject: [PATCH 0426/1454] [SPARK-9022] [SQL] Generated projections for
 UnsafeRow

Added two projections: GenerateUnsafeProjection and FromUnsafeProjection, which could be used to convert UnsafeRow from/to GenericInternalRow.

They will re-use the buffer during projection, similar to MutableProjection (without all the interface MutableProjection has).

cc rxin JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #7437 from davies/unsafe_proj2 and squashes the following commits:

dbf538e [Davies Liu] test with all the expression (only for supported types)
dc737b2 [Davies Liu] address comment
e424520 [Davies Liu] fix scala style
70e231c [Davies Liu] address comments
729138d [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_proj2
5a26373 [Davies Liu] unsafe projections
---
 .../execution/UnsafeExternalRowSorter.java    |  27 ++--
 .../spark/sql/catalyst/expressions/Cast.scala |   8 +-
 .../sql/catalyst/expressions/Projection.scala |  35 +++++
 .../expressions/UnsafeRowConverter.scala      |  69 +++++-----
 .../expressions/codegen/CodeGenerator.scala   |  15 ++-
 .../codegen/GenerateProjection.scala          |   4 +-
 .../codegen/GenerateUnsafeProjection.scala    | 125 ++++++++++++++++++
 .../expressions/decimalFunctions.scala        |   2 +-
 .../spark/sql/catalyst/expressions/math.scala |   2 +-
 .../spark/sql/catalyst/expressions/misc.scala |  17 ++-
 .../expressions/ExpressionEvalHelper.scala    |  34 ++++-
 11 files changed, 266 insertions(+), 72 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index b94601cf6d818..d1d81c87bb052 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -28,13 +28,11 @@
 import org.apache.spark.TaskContext;
 import org.apache.spark.sql.AbstractScalaRowIterator;
 import org.apache.spark.sql.catalyst.InternalRow;
-import org.apache.spark.sql.catalyst.expressions.ObjectUnsafeColumnWriter;
 import org.apache.spark.sql.catalyst.expressions.UnsafeColumnWriter;
+import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
-import org.apache.spark.sql.catalyst.expressions.UnsafeRowConverter;
 import org.apache.spark.sql.catalyst.util.ObjectPool;
-import org.apache.spark.sql.types.StructField;
-import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.*;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
 import org.apache.spark.util.collection.unsafe.sort.RecordComparator;
@@ -52,10 +50,9 @@ final class UnsafeExternalRowSorter {
   private long numRowsInserted = 0;
 
   private final StructType schema;
-  private final UnsafeRowConverter rowConverter;
+  private final UnsafeProjection unsafeProjection;
   private final PrefixComputer prefixComputer;
   private final UnsafeExternalSorter sorter;
-  private byte[] rowConversionBuffer = new byte[1024 * 8];
 
   public static abstract class PrefixComputer {
     abstract long computePrefix(InternalRow row);
@@ -67,7 +64,7 @@ public UnsafeExternalRowSorter(
       PrefixComparator prefixComparator,
       PrefixComputer prefixComputer) throws IOException {
     this.schema = schema;
-    this.rowConverter = new UnsafeRowConverter(schema);
+    this.unsafeProjection = UnsafeProjection.create(schema);
     this.prefixComputer = prefixComputer;
     final SparkEnv sparkEnv = SparkEnv.get();
     final TaskContext taskContext = TaskContext.get();
@@ -94,18 +91,12 @@ void setTestSpillFrequency(int frequency) {
 
   @VisibleForTesting
   void insertRow(InternalRow row) throws IOException {
-    final int sizeRequirement = rowConverter.getSizeRequirement(row);
-    if (sizeRequirement > rowConversionBuffer.length) {
-      rowConversionBuffer = new byte[sizeRequirement];
-    }
-    final int bytesWritten = rowConverter.writeRow(
-      row, rowConversionBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, sizeRequirement, null);
-    assert (bytesWritten == sizeRequirement);
+    UnsafeRow unsafeRow = unsafeProjection.apply(row);
     final long prefix = prefixComputer.computePrefix(row);
     sorter.insertRecord(
-      rowConversionBuffer,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      sizeRequirement,
+      unsafeRow.getBaseObject(),
+      unsafeRow.getBaseOffset(),
+      unsafeRow.getSizeInBytes(),
       prefix
     );
     numRowsInserted++;
@@ -186,7 +177,7 @@ public Iterator<InternalRow> sort(Iterator<InternalRow> inputIterator) throws IO
   public static boolean supportsSchema(StructType schema) {
     // TODO: add spilling note to explain why we do this for now:
     for (StructField field : schema.fields()) {
-      if (UnsafeColumnWriter.forType(field.dataType()) instanceof ObjectUnsafeColumnWriter) {
+      if (!UnsafeColumnWriter.canEmbed(field.dataType())) {
         return false;
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 65ae87fe6d166..692b9fddbb041 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -424,20 +424,20 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w
 
       case (BinaryType, StringType) =>
         defineCodeGen (ctx, ev, c =>
-          s"${ctx.stringType}.fromBytes($c)")
+          s"UTF8String.fromBytes($c)")
 
       case (DateType, StringType) =>
         defineCodeGen(ctx, ev, c =>
-          s"""${ctx.stringType}.fromString(
+          s"""UTF8String.fromString(
                 org.apache.spark.sql.catalyst.util.DateTimeUtils.dateToString($c))""")
 
       case (TimestampType, StringType) =>
         defineCodeGen(ctx, ev, c =>
-          s"""${ctx.stringType}.fromString(
+          s"""UTF8String.fromString(
                 org.apache.spark.sql.catalyst.util.DateTimeUtils.timestampToString($c))""")
 
       case (_, StringType) =>
-        defineCodeGen(ctx, ev, c => s"${ctx.stringType}.fromString(String.valueOf($c))")
+        defineCodeGen(ctx, ev, c => s"UTF8String.fromString(String.valueOf($c))")
 
       case (StringType, IntervalType) =>
         defineCodeGen(ctx, ev, c =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index bf47a6c75b809..24b01ea55110e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -18,6 +18,8 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GenerateMutableProjection}
+import org.apache.spark.sql.types.{StructType, DataType}
 
 /**
  * A [[Projection]] that is calculated by calling the `eval` of each of the specified expressions.
@@ -73,6 +75,39 @@ case class InterpretedMutableProjection(expressions: Seq[Expression]) extends Mu
   }
 }
 
+/**
+ * A projection that returns UnsafeRow.
+ */
+abstract class UnsafeProjection extends Projection {
+  override def apply(row: InternalRow): UnsafeRow
+}
+
+object UnsafeProjection {
+  def create(schema: StructType): UnsafeProjection = create(schema.fields.map(_.dataType))
+
+  def create(fields: Seq[DataType]): UnsafeProjection = {
+    val exprs = fields.zipWithIndex.map(x => new BoundReference(x._2, x._1, true))
+    GenerateUnsafeProjection.generate(exprs)
+  }
+}
+
+/**
+ * A projection that could turn UnsafeRow into GenericInternalRow
+ */
+case class FromUnsafeProjection(fields: Seq[DataType]) extends Projection {
+
+  private[this] val expressions = fields.zipWithIndex.map { case (dt, idx) =>
+    new BoundReference(idx, dt, true)
+  }
+
+  @transient private[this] lazy val generatedProj =
+    GenerateMutableProjection.generate(expressions)()
+
+  override def apply(input: InternalRow): InternalRow = {
+    generatedProj(input)
+  }
+}
+
 /**
  * A mutable wrapper that makes two rows appear as a single concatenated row.  Designed to
  * be instantiated once per thread and reused.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
index 6af5e6200e57b..885ab091fcdf5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
@@ -147,77 +147,73 @@ private object UnsafeColumnWriter {
       case t => ObjectUnsafeColumnWriter
     }
   }
+
+  /**
+   * Returns whether the dataType can be embedded into UnsafeRow (not using ObjectPool).
+   */
+  def canEmbed(dataType: DataType): Boolean = {
+    forType(dataType) != ObjectUnsafeColumnWriter
+  }
 }
 
 // ------------------------------------------------------------------------------------------------
 
-private object NullUnsafeColumnWriter extends NullUnsafeColumnWriter
-private object BooleanUnsafeColumnWriter extends BooleanUnsafeColumnWriter
-private object ByteUnsafeColumnWriter extends ByteUnsafeColumnWriter
-private object ShortUnsafeColumnWriter extends ShortUnsafeColumnWriter
-private object IntUnsafeColumnWriter extends IntUnsafeColumnWriter
-private object LongUnsafeColumnWriter extends LongUnsafeColumnWriter
-private object FloatUnsafeColumnWriter extends FloatUnsafeColumnWriter
-private object DoubleUnsafeColumnWriter extends DoubleUnsafeColumnWriter
-private object StringUnsafeColumnWriter extends StringUnsafeColumnWriter
-private object BinaryUnsafeColumnWriter extends BinaryUnsafeColumnWriter
-private object ObjectUnsafeColumnWriter extends ObjectUnsafeColumnWriter
 
 private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter {
   // Primitives don't write to the variable-length region:
   def getSize(sourceRow: InternalRow, column: Int): Int = 0
 }
 
-private class NullUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object NullUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setNullAt(column)
     0
   }
 }
 
-private class BooleanUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object BooleanUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setBoolean(column, source.getBoolean(column))
     0
   }
 }
 
-private class ByteUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object ByteUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setByte(column, source.getByte(column))
     0
   }
 }
 
-private class ShortUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object ShortUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setShort(column, source.getShort(column))
     0
   }
 }
 
-private class IntUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object IntUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setInt(column, source.getInt(column))
     0
   }
 }
 
-private class LongUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object LongUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setLong(column, source.getLong(column))
     0
   }
 }
 
-private class FloatUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object FloatUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setFloat(column, source.getFloat(column))
     0
   }
 }
 
-private class DoubleUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter {
+private object DoubleUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     target.setDouble(column, source.getDouble(column))
     0
@@ -226,18 +222,21 @@ private class DoubleUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWr
 
 private abstract class BytesUnsafeColumnWriter extends UnsafeColumnWriter {
 
-  def getBytes(source: InternalRow, column: Int): Array[Byte]
+  protected[this] def isString: Boolean
+  protected[this] def getBytes(source: InternalRow, column: Int): Array[Byte]
 
-  def getSize(source: InternalRow, column: Int): Int = {
+  override def getSize(source: InternalRow, column: Int): Int = {
     val numBytes = getBytes(source, column).length
     ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
   }
 
-  protected[this] def isString: Boolean
-
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    val offset = target.getBaseOffset + cursor
     val bytes = getBytes(source, column)
+    write(target, bytes, column, cursor)
+  }
+
+  def write(target: UnsafeRow, bytes: Array[Byte], column: Int, cursor: Int): Int = {
+    val offset = target.getBaseOffset + cursor
     val numBytes = bytes.length
     if ((numBytes & 0x07) > 0) {
       // zero-out the padding bytes
@@ -256,22 +255,32 @@ private abstract class BytesUnsafeColumnWriter extends UnsafeColumnWriter {
   }
 }
 
-private class StringUnsafeColumnWriter private() extends BytesUnsafeColumnWriter {
+private object StringUnsafeColumnWriter extends BytesUnsafeColumnWriter {
   protected[this] def isString: Boolean = true
   def getBytes(source: InternalRow, column: Int): Array[Byte] = {
     source.getAs[UTF8String](column).getBytes
   }
+  // TODO(davies): refactor this
+  // specialized for codegen
+  def getSize(value: UTF8String): Int =
+    ByteArrayMethods.roundNumberOfBytesToNearestWord(value.numBytes())
+  def write(target: UnsafeRow, value: UTF8String, column: Int, cursor: Int): Int = {
+    write(target, value.getBytes, column, cursor)
+  }
 }
 
-private class BinaryUnsafeColumnWriter private() extends BytesUnsafeColumnWriter {
-  protected[this] def isString: Boolean = false
-  def getBytes(source: InternalRow, column: Int): Array[Byte] = {
+private object BinaryUnsafeColumnWriter extends BytesUnsafeColumnWriter {
+  protected[this] override def isString: Boolean = false
+  override def getBytes(source: InternalRow, column: Int): Array[Byte] = {
     source.getAs[Array[Byte]](column)
   }
+  // specialized for codegen
+  def getSize(value: Array[Byte]): Int =
+    ByteArrayMethods.roundNumberOfBytesToNearestWord(value.length)
 }
 
-private class ObjectUnsafeColumnWriter private() extends UnsafeColumnWriter {
-  def getSize(sourceRow: InternalRow, column: Int): Int = 0
+private object ObjectUnsafeColumnWriter extends UnsafeColumnWriter {
+  override def getSize(sourceRow: InternalRow, column: Int): Int = 0
   override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
     val obj = source.get(column)
     val idx = target.getPool.put(obj)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 328d635de8743..45dc146488e12 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -24,6 +24,7 @@ import com.google.common.cache.{CacheBuilder, CacheLoader}
 import org.codehaus.janino.ClassBodyEvaluator
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -68,9 +69,6 @@ class CodeGenContext {
     mutableStates += ((javaType, variableName, initialValue))
   }
 
-  val stringType: String = classOf[UTF8String].getName
-  val decimalType: String = classOf[Decimal].getName
-
   final val JAVA_BOOLEAN = "boolean"
   final val JAVA_BYTE = "byte"
   final val JAVA_SHORT = "short"
@@ -136,9 +134,9 @@ class CodeGenContext {
     case LongType | TimestampType => JAVA_LONG
     case FloatType => JAVA_FLOAT
     case DoubleType => JAVA_DOUBLE
-    case dt: DecimalType => decimalType
+    case dt: DecimalType => "Decimal"
     case BinaryType => "byte[]"
-    case StringType => stringType
+    case StringType => "UTF8String"
     case _: StructType => "InternalRow"
     case _: ArrayType => s"scala.collection.Seq"
     case _: MapType => s"scala.collection.Map"
@@ -262,7 +260,12 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   private[this] def doCompile(code: String): GeneratedClass = {
     val evaluator = new ClassBodyEvaluator()
     evaluator.setParentClassLoader(getClass.getClassLoader)
-    evaluator.setDefaultImports(Array("org.apache.spark.sql.catalyst.InternalRow"))
+    evaluator.setDefaultImports(Array(
+      classOf[InternalRow].getName,
+      classOf[UnsafeRow].getName,
+      classOf[UTF8String].getName,
+      classOf[Decimal].getName
+    ))
     evaluator.setExtendedClass(classOf[GeneratedClass])
     try {
       evaluator.cook(code)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 3e5ca308dc31d..8f9fcbf810554 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -23,7 +23,7 @@ import org.apache.spark.sql.types._
 /**
  * Java can not access Projection (in package object)
  */
-abstract class BaseProject extends Projection {}
+abstract class BaseProjection extends Projection {}
 
 /**
  * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input
@@ -160,7 +160,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
       return new SpecificProjection(expr);
     }
 
-    class SpecificProjection extends ${classOf[BaseProject].getName} {
+    class SpecificProjection extends ${classOf[BaseProjection].getName} {
       private $exprType[] expressions = null;
       $mutableStates
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
new file mode 100644
index 0000000000000..a81d545a8ec63
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{NullType, BinaryType, StringType}
+
+
+/**
+ * Generates a [[Projection]] that returns an [[UnsafeRow]].
+ *
+ * It generates the code for all the expressions, compute the total length for all the columns
+ * (can be accessed via variables), and then copy the data into a scratch buffer space in the
+ * form of UnsafeRow (the scratch buffer will grow as needed).
+ *
+ * Note: The returned UnsafeRow will be pointed to a scratch buffer inside the projection.
+ */
+object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafeProjection] {
+
+  protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
+    in.map(ExpressionCanonicalizer.execute)
+
+  protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
+    in.map(BindReferences.bindReference(_, inputSchema))
+
+  protected def create(expressions: Seq[Expression]): UnsafeProjection = {
+    val ctx = newCodeGenContext()
+    val exprs = expressions.map(_.gen(ctx))
+    val allExprs = exprs.map(_.code).mkString("\n")
+    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
+    val stringWriter = "org.apache.spark.sql.catalyst.expressions.StringUnsafeColumnWriter"
+    val binaryWriter = "org.apache.spark.sql.catalyst.expressions.BinaryUnsafeColumnWriter"
+    val additionalSize = expressions.zipWithIndex.map { case (e, i) =>
+      e.dataType match {
+        case StringType =>
+          s" + (${exprs(i).isNull} ? 0 : $stringWriter.getSize(${exprs(i).primitive}))"
+        case BinaryType =>
+          s" + (${exprs(i).isNull} ? 0 : $binaryWriter.getSize(${exprs(i).primitive}))"
+        case _ => ""
+      }
+    }.mkString("")
+
+    val writers = expressions.zipWithIndex.map { case (e, i) =>
+      val update = e.dataType match {
+        case dt if ctx.isPrimitiveType(dt) =>
+          s"${ctx.setColumn("target", dt, i, exprs(i).primitive)}"
+        case StringType =>
+          s"cursor += $stringWriter.write(target, ${exprs(i).primitive}, $i, cursor)"
+        case BinaryType =>
+          s"cursor += $binaryWriter.write(target, ${exprs(i).primitive}, $i, cursor)"
+        case NullType => ""
+        case _ =>
+          throw new UnsupportedOperationException(s"Not supported DataType: ${e.dataType}")
+      }
+      s"""if (${exprs(i).isNull}) {
+            target.setNullAt($i);
+          } else {
+            $update;
+          }"""
+    }.mkString("\n          ")
+
+    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
+      s"private $javaType $variableName = $initialValue;"
+    }.mkString("\n      ")
+
+    val code = s"""
+    private $exprType[] expressions;
+
+    public Object generate($exprType[] expr) {
+      this.expressions = expr;
+      return new SpecificProjection();
+    }
+
+    class SpecificProjection extends ${classOf[UnsafeProjection].getName} {
+
+      private UnsafeRow target = new UnsafeRow();
+      private byte[] buffer = new byte[64];
+
+      $mutableStates
+
+      public SpecificProjection() {}
+
+      // Scala.Function1 need this
+      public Object apply(Object row) {
+        return apply((InternalRow) row);
+      }
+
+      public UnsafeRow apply(InternalRow i) {
+        ${allExprs}
+
+        // additionalSize had '+' in the beginning
+        int numBytes = $fixedSize $additionalSize;
+        if (numBytes > buffer.length) {
+          buffer = new byte[numBytes];
+        }
+        target.pointTo(buffer, org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
+          ${expressions.size}, numBytes, null);
+        int cursor = $fixedSize;
+        $writers
+        return target;
+      }
+    }
+    """
+
+    logDebug(s"code for ${expressions.mkString(",")}:\n$code")
+
+    val c = compile(code)
+    c.generate(ctx.references.toArray).asInstanceOf[UnsafeProjection]
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
index 2fa74b4ffc5da..b9d4736a65e26 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
@@ -54,7 +54,7 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, eval => {
       s"""
-        ${ev.primitive} = (new ${ctx.decimalType}()).setOrNull($eval, $precision, $scale);
+        ${ev.primitive} = (new Decimal()).setOrNull($eval, $precision, $scale);
         ${ev.isNull} = ${ev.primitive} == null;
       """
     })
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index a7ad452ef4943..84b289c4d1a68 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -263,7 +263,7 @@ case class Bin(child: Expression)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c) =>
-      s"${ctx.stringType}.fromString(java.lang.Long.toBinaryString($c))")
+      s"UTF8String.fromString(java.lang.Long.toBinaryString($c))")
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
index a269ec4a1e6dc..8d8d66ddeb341 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/misc.scala
@@ -17,12 +17,11 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import java.security.MessageDigest
-import java.security.NoSuchAlgorithmException
+import java.security.{MessageDigest, NoSuchAlgorithmException}
 import java.util.zip.CRC32
 
 import org.apache.commons.codec.digest.DigestUtils
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -42,7 +41,7 @@ case class Md5(child: Expression) extends UnaryExpression with ImplicitCastInput
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c =>
-      s"${ctx.stringType}.fromString(org.apache.commons.codec.digest.DigestUtils.md5Hex($c))")
+      s"UTF8String.fromString(org.apache.commons.codec.digest.DigestUtils.md5Hex($c))")
   }
 }
 
@@ -93,19 +92,19 @@ case class Sha2(left: Expression, right: Expression)
           try {
             java.security.MessageDigest md = java.security.MessageDigest.getInstance("SHA-224");
             md.update($eval1);
-            ${ev.primitive} = ${ctx.stringType}.fromBytes(md.digest());
+            ${ev.primitive} = UTF8String.fromBytes(md.digest());
           } catch (java.security.NoSuchAlgorithmException e) {
             ${ev.isNull} = true;
           }
         } else if ($eval2 == 256 || $eval2 == 0) {
           ${ev.primitive} =
-            ${ctx.stringType}.fromString($digestUtils.sha256Hex($eval1));
+            UTF8String.fromString($digestUtils.sha256Hex($eval1));
         } else if ($eval2 == 384) {
           ${ev.primitive} =
-            ${ctx.stringType}.fromString($digestUtils.sha384Hex($eval1));
+            UTF8String.fromString($digestUtils.sha384Hex($eval1));
         } else if ($eval2 == 512) {
           ${ev.primitive} =
-            ${ctx.stringType}.fromString($digestUtils.sha512Hex($eval1));
+            UTF8String.fromString($digestUtils.sha512Hex($eval1));
         } else {
           ${ev.isNull} = true;
         }
@@ -129,7 +128,7 @@ case class Sha1(child: Expression) extends UnaryExpression with ImplicitCastInpu
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, c =>
-      s"${ctx.stringType}.fromString(org.apache.commons.codec.digest.DigestUtils.shaHex($c))"
+      s"UTF8String.fromString(org.apache.commons.codec.digest.DigestUtils.shaHex($c))"
     )
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 43392df4bec2e..c43486b3ddcf5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -23,7 +23,7 @@ import org.scalatest.Matchers._
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
-import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateProjection, GenerateMutableProjection}
+import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GenerateProjection, GenerateMutableProjection}
 import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 
@@ -43,6 +43,9 @@ trait ExpressionEvalHelper {
     checkEvaluationWithoutCodegen(expression, catalystValue, inputRow)
     checkEvaluationWithGeneratedMutableProjection(expression, catalystValue, inputRow)
     checkEvaluationWithGeneratedProjection(expression, catalystValue, inputRow)
+    if (UnsafeColumnWriter.canEmbed(expression.dataType)) {
+      checkEvalutionWithUnsafeProjection(expression, catalystValue, inputRow)
+    }
     checkEvaluationWithOptimization(expression, catalystValue, inputRow)
   }
 
@@ -142,6 +145,35 @@ trait ExpressionEvalHelper {
     }
   }
 
+  protected def checkEvalutionWithUnsafeProjection(
+      expression: Expression,
+      expected: Any,
+      inputRow: InternalRow = EmptyRow): Unit = {
+    val ctx = GenerateUnsafeProjection.newCodeGenContext()
+    lazy val evaluated = expression.gen(ctx)
+
+    val plan = try {
+      GenerateUnsafeProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)
+    } catch {
+      case e: Throwable =>
+        fail(
+          s"""
+            |Code generation of $expression failed:
+            |${evaluated.code}
+            |$e
+          """.stripMargin)
+    }
+
+    val unsafeRow = plan(inputRow)
+    // UnsafeRow cannot be compared with GenericInternalRow directly
+    val actual = FromUnsafeProjection(expression.dataType :: Nil)(unsafeRow)
+    val expectedRow = InternalRow(expected)
+    if (actual != expectedRow) {
+      val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
+      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
+    }
+  }
+
   protected def checkEvaluationWithOptimization(
       expression: Expression,
       expected: Any,

From c043a3e9df55721f21332f7c44ff351832d20324 Mon Sep 17 00:00:00 2001
From: Hari Shreedharan <hshreedharan@apache.org>
Date: Fri, 17 Jul 2015 09:38:08 -0500
Subject: [PATCH 0427/1454] [SPARK-8851] [YARN] In Client mode, make sure the
 client logs in and updates tokens

In client side, the flow is SparkSubmit -> SparkContext -> yarn/Client. Since the yarn client only gets a cloned config and the staging dir is set here, it is not really possible to do re-logins in the SparkContext. So, do the initial logins in Spark Submit and do re-logins as we do now in the AM, but the Client behaves like an executor in this specific context and reads the credentials file to update the tokens. This way, even if the streaming context is started up from checkpoint - it is fine since we have logged in from SparkSubmit itself itself.

Author: Hari Shreedharan <hshreedharan@apache.org>

Closes #7394 from harishreedharan/yarn-client-login and squashes the following commits:

9a2166f [Hari Shreedharan] make it possible to use command line args and config parameters together.
de08f57 [Hari Shreedharan] Fix import order.
5c4fa63 [Hari Shreedharan] Add a comment explaining what is being done in YarnClientSchedulerBackend.
c872caa [Hari Shreedharan] Fix typo in log message.
2c80540 [Hari Shreedharan] Move token renewal to YarnClientSchedulerBackend.
0c48ac2 [Hari Shreedharan] Remove direct use of ExecutorDelegationTokenUpdater in Client.
26f8bfa [Hari Shreedharan] [SPARK-8851][YARN] In Client mode, make sure the client logs in and updates tokens.
58b1969 [Hari Shreedharan] Simple attempt 1.
---
 .../apache/spark/deploy/SparkHadoopUtil.scala | 29 ++++++++++-------
 .../org/apache/spark/deploy/SparkSubmit.scala | 10 ++++--
 .../org/apache/spark/deploy/yarn/Client.scala | 32 ++++++++++++-------
 .../cluster/YarnClientSchedulerBackend.scala  | 11 +++++--
 4 files changed, 56 insertions(+), 26 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 9f94118829ff1..6b14d407a6380 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -25,6 +25,7 @@ import java.util.{Arrays, Comparator}
 import scala.collection.JavaConversions._
 import scala.concurrent.duration._
 import scala.language.postfixOps
+import scala.util.control.NonFatal
 
 import com.google.common.primitives.Longs
 import org.apache.hadoop.conf.Configuration
@@ -248,19 +249,25 @@ class SparkHadoopUtil extends Logging {
       dir: Path,
       prefix: String,
       exclusionSuffix: String): Array[FileStatus] = {
-    val fileStatuses = remoteFs.listStatus(dir,
-      new PathFilter {
-        override def accept(path: Path): Boolean = {
-          val name = path.getName
-          name.startsWith(prefix) && !name.endsWith(exclusionSuffix)
+    try {
+      val fileStatuses = remoteFs.listStatus(dir,
+        new PathFilter {
+          override def accept(path: Path): Boolean = {
+            val name = path.getName
+            name.startsWith(prefix) && !name.endsWith(exclusionSuffix)
+          }
+        })
+      Arrays.sort(fileStatuses, new Comparator[FileStatus] {
+        override def compare(o1: FileStatus, o2: FileStatus): Int = {
+          Longs.compare(o1.getModificationTime, o2.getModificationTime)
         }
       })
-    Arrays.sort(fileStatuses, new Comparator[FileStatus] {
-      override def compare(o1: FileStatus, o2: FileStatus): Int = {
-        Longs.compare(o1.getModificationTime, o2.getModificationTime)
-      }
-    })
-    fileStatuses
+      fileStatuses
+    } catch {
+      case NonFatal(e) =>
+        logWarning("Error while attempting to list files from application staging dir", e)
+        Array.empty
+    }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 036cb6e054791..0b39ee8fe3ba0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -508,8 +508,14 @@ object SparkSubmit {
     }
 
     // Let YARN know it's a pyspark app, so it distributes needed libraries.
-    if (clusterManager == YARN && args.isPython) {
-      sysProps.put("spark.yarn.isPython", "true")
+    if (clusterManager == YARN) {
+      if (args.isPython) {
+        sysProps.put("spark.yarn.isPython", "true")
+      }
+      if (args.principal != null) {
+        require(args.keytab != null, "Keytab must be specified when the keytab is specified")
+        UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab)
+      }
     }
 
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index b74ea9a10afb2..bc28ce5eeae72 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -80,10 +80,12 @@ private[spark] class Client(
   private val isClusterMode = args.isClusterMode
 
   private var loginFromKeytab = false
+  private var principal: String = null
+  private var keytab: String = null
+
   private val fireAndForget = isClusterMode &&
     !sparkConf.getBoolean("spark.yarn.submit.waitAppCompletion", true)
 
-
   def stop(): Unit = yarnClient.stop()
 
   /**
@@ -339,7 +341,7 @@ private[spark] class Client(
     if (loginFromKeytab) {
       logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" +
         " via the YARN Secure Distributed Cache.")
-      val (_, localizedPath) = distribute(args.keytab,
+      val (_, localizedPath) = distribute(keytab,
         destName = Some(sparkConf.get("spark.yarn.keytab")),
         appMasterOnly = true)
       require(localizedPath != null, "Keytab file already distributed.")
@@ -785,19 +787,27 @@ private[spark] class Client(
   }
 
   def setupCredentials(): Unit = {
-    if (args.principal != null) {
-      require(args.keytab != null, "Keytab must be specified when principal is specified.")
+    loginFromKeytab = args.principal != null || sparkConf.contains("spark.yarn.principal")
+    if (loginFromKeytab) {
+      principal =
+        if (args.principal != null) args.principal else sparkConf.get("spark.yarn.principal")
+      keytab = {
+        if (args.keytab != null) {
+          args.keytab
+        } else {
+          sparkConf.getOption("spark.yarn.keytab").orNull
+        }
+      }
+
+      require(keytab != null, "Keytab must be specified when principal is specified.")
       logInfo("Attempting to login to the Kerberos" +
-        s" using principal: ${args.principal} and keytab: ${args.keytab}")
-      val f = new File(args.keytab)
+        s" using principal: $principal and keytab: $keytab")
+      val f = new File(keytab)
       // Generate a file name that can be used for the keytab file, that does not conflict
       // with any user file.
       val keytabFileName = f.getName + "-" + UUID.randomUUID().toString
-      UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab)
-      loginFromKeytab = true
       sparkConf.set("spark.yarn.keytab", keytabFileName)
-      sparkConf.set("spark.yarn.principal", args.principal)
-      logInfo("Successfully logged into the KDC.")
+      sparkConf.set("spark.yarn.principal", principal)
     }
     credentials = UserGroupInformation.getCurrentUser.getCredentials
   }
@@ -1162,7 +1172,7 @@ object Client extends Logging {
    *
    * If not a "local:" file and no alternate name, the environment is not modified.
    *
-   * @parma conf      Spark configuration.
+   * @param conf      Spark configuration.
    * @param uri       URI to add to classpath (optional).
    * @param fileName  Alternate name for the file (optional).
    * @param env       Map holding the environment variables.
diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 3a0b9443d2d7b..d97fa2e2151bc 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -20,10 +20,9 @@ package org.apache.spark.scheduler.cluster
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.yarn.api.records.{ApplicationId, YarnApplicationState}
-import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException
 
 import org.apache.spark.{SparkException, Logging, SparkContext}
-import org.apache.spark.deploy.yarn.{Client, ClientArguments}
+import org.apache.spark.deploy.yarn.{Client, ClientArguments, YarnSparkHadoopUtil}
 import org.apache.spark.scheduler.TaskSchedulerImpl
 
 private[spark] class YarnClientSchedulerBackend(
@@ -62,6 +61,13 @@ private[spark] class YarnClientSchedulerBackend(
     super.start()
 
     waitForApplication()
+
+    // SPARK-8851: In yarn-client mode, the AM still does the credentials refresh. The driver
+    // reads the credentials from HDFS, just like the executors and updates its own credentials
+    // cache.
+    if (conf.contains("spark.yarn.credentials.file")) {
+      YarnSparkHadoopUtil.get.startExecutorDelegationTokenRenewer(conf)
+    }
     monitorThread = asyncMonitorApplication()
     monitorThread.start()
   }
@@ -158,6 +164,7 @@ private[spark] class YarnClientSchedulerBackend(
     }
     super.stop()
     client.stop()
+    YarnSparkHadoopUtil.get.stopExecutorDelegationTokenRenewer()
     logInfo("Stopped")
   }
 

From 441e072a227378cae31afc45a608318b58ce2ac4 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 17 Jul 2015 09:00:41 -0700
Subject: [PATCH 0428/1454] [MINOR] [ML] fix wrong annotation of
 RFormula.formula

fix wrong annotation of RFormula.formula

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7470 from yanboliang/RFormula and squashes the following commits:

61f1919 [Yanbo Liang] fix wrong annotation
---
 mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index d9a36bda386b3..56169f2a01fc9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -42,7 +42,7 @@ class RFormula(override val uid: String)
 
   /**
    * R formula parameter. The formula is provided in string form.
-   * @group setParam
+   * @group param
    */
   val formula: Param[String] = new Param(this, "formula", "R model formula")
 

From 59d24c226a441db5f08c58ec407ba5873bd3b954 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 17 Jul 2015 09:31:13 -0700
Subject: [PATCH 0429/1454] [SPARK-9130][SQL] throw exception when check
 equality between external and internal row

instead of return false, throw exception when check equality between external and internal row is better.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7460 from cloud-fan/row-compare and squashes the following commits:

8a20911 [Wenchen Fan] improve equals
402daa8 [Wenchen Fan] throw exception when check equality between external and internal row
---
 .../main/scala/org/apache/spark/sql/Row.scala | 27 ++++++++++++++-----
 .../spark/sql/catalyst/InternalRow.scala      |  7 ++++-
 .../scala/org/apache/spark/sql/RowTest.scala  | 26 ++++++++++++++++++
 3 files changed, 53 insertions(+), 7 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 3623fefbf2604..2cb64d00935de 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -364,18 +364,33 @@ trait Row extends Serializable {
     false
   }
 
-  protected def canEqual(other: Any) = {
-    // Note that InternalRow overrides canEqual. These two canEqual's together makes sure that
-    // comparing the external Row and InternalRow will always yield false.
+  /**
+   * Returns true if we can check equality for these 2 rows.
+   * Equality check between external row and internal row is not allowed.
+   * Here we do this check to prevent call `equals` on external row with internal row.
+   */
+  protected def canEqual(other: Row) = {
+    // Note that `Row` is not only the interface of external row but also the parent
+    // of `InternalRow`, so we have to ensure `other` is not a internal row here to prevent
+    // call `equals` on external row with internal row.
+    // `InternalRow` overrides canEqual, and these two canEquals together makes sure that
+    // equality check between external Row and InternalRow will always fail.
     // In the future, InternalRow should not extend Row. In that case, we can remove these
     // canEqual methods.
-    other.isInstanceOf[Row] && !other.isInstanceOf[InternalRow]
+    !other.isInstanceOf[InternalRow]
   }
 
   override def equals(o: Any): Boolean = {
-    if (o == null || !canEqual(o)) return false
-
+    if (!o.isInstanceOf[Row]) return false
     val other = o.asInstanceOf[Row]
+
+    if (!canEqual(other)) {
+      throw new UnsupportedOperationException(
+        "cannot check equality between external and internal rows")
+    }
+
+    if (other eq null) return false
+
     if (length != other.length) {
       return false
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index e2fafb88ee43e..024973a6b9fcd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -54,7 +54,12 @@ abstract class InternalRow extends Row {
   // A default implementation to change the return type
   override def copy(): InternalRow = this
 
-  protected override def canEqual(other: Any) = other.isInstanceOf[InternalRow]
+  /**
+   * Returns true if we can check equality for these 2 rows.
+   * Equality check between external row and internal row is not allowed.
+   * Here we do this check to prevent call `equals` on internal row with external row.
+   */
+  protected override def canEqual(other: Row) = other.isInstanceOf[InternalRow]
 
   // Custom hashCode function that matches the efficient code generated version.
   override def hashCode: Int = {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala
index bbb9739e9cc76..878a1bb9b7e6d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{GenericRow, GenericRowWithSchema}
 import org.apache.spark.sql.types._
 import org.scalatest.{Matchers, FunSpec}
@@ -68,4 +69,29 @@ class RowTest extends FunSpec with Matchers {
       sampleRow.getValuesMap(List("col1", "col2")) shouldBe expected
     }
   }
+
+  describe("row equals") {
+    val externalRow = Row(1, 2)
+    val externalRow2 = Row(1, 2)
+    val internalRow = InternalRow(1, 2)
+    val internalRow2 = InternalRow(1, 2)
+
+    it("equality check for external rows") {
+      externalRow shouldEqual externalRow2
+    }
+
+    it("equality check for internal rows") {
+      internalRow shouldEqual internalRow2
+    }
+
+    it("throws an exception when check equality between external and internal rows") {
+      def assertError(f: => Unit): Unit = {
+        val e = intercept[UnsupportedOperationException](f)
+        e.getMessage.contains("cannot check equality between external and internal rows")
+      }
+
+      assertError(internalRow.equals(externalRow))
+      assertError(externalRow.equals(internalRow))
+    }
+  }
 }

From 305e77cd83f3dbe680a920d5329c2e8c58452d5b Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Fri, 17 Jul 2015 09:32:27 -0700
Subject: [PATCH 0430/1454] [SPARK-8209[SQL]Add function conv

cc chenghao-intel  adrian-wang

Author: zhichao.li <zhichao.li@intel.com>

Closes #6872 from zhichao-li/conv and squashes the following commits:

6ef3b37 [zhichao.li] add unittest and comments
78d9836 [zhichao.li] polish dataframe api and add unittest
e2bace3 [zhichao.li] update to use ImplicitCastInputTypes
cbcad3f [zhichao.li] add function conv
---
 .../catalyst/analysis/FunctionRegistry.scala  |   1 +
 .../spark/sql/catalyst/expressions/math.scala | 191 ++++++++++++++++++
 .../expressions/MathFunctionsSuite.scala      |  21 +-
 .../org/apache/spark/sql/functions.scala      |  18 ++
 .../spark/sql/MathExpressionsSuite.scala      |  13 ++
 5 files changed, 242 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index e0beafe710079..a45181712dbdf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -99,6 +99,7 @@ object FunctionRegistry {
     expression[Ceil]("ceil"),
     expression[Ceil]("ceiling"),
     expression[Cos]("cos"),
+    expression[Conv]("conv"),
     expression[EulerNumber]("e"),
     expression[Exp]("exp"),
     expression[Expm1]("expm1"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 84b289c4d1a68..7a543ff36afd1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.{lang => jl}
+import java.util.Arrays
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckSuccess, TypeCheckFailure}
@@ -139,6 +140,196 @@ case class Cos(child: Expression) extends UnaryMathExpression(math.cos, "COS")
 
 case class Cosh(child: Expression) extends UnaryMathExpression(math.cosh, "COSH")
 
+/**
+ * Convert a num from one base to another
+ * @param numExpr the number to be converted
+ * @param fromBaseExpr from which base
+ * @param toBaseExpr to which base
+ */
+case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expression)
+  extends Expression with ImplicitCastInputTypes{
+
+  override def foldable: Boolean = numExpr.foldable && fromBaseExpr.foldable && toBaseExpr.foldable
+
+  override def nullable: Boolean = numExpr.nullable || fromBaseExpr.nullable || toBaseExpr.nullable
+
+  override def children: Seq[Expression] = Seq(numExpr, fromBaseExpr, toBaseExpr)
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, IntegerType, IntegerType)
+
+  /** Returns the result of evaluating this expression on a given input Row */
+  override def eval(input: InternalRow): Any = {
+    val num = numExpr.eval(input)
+    val fromBase = fromBaseExpr.eval(input)
+    val toBase = toBaseExpr.eval(input)
+    if (num == null || fromBase == null || toBase == null) {
+      null
+    } else {
+      conv(num.asInstanceOf[UTF8String].getBytes,
+        fromBase.asInstanceOf[Int], toBase.asInstanceOf[Int])
+    }
+  }
+
+  /**
+   * Returns the [[DataType]] of the result of evaluating this expression.  It is
+   * invalid to query the dataType of an unresolved expression (i.e., when `resolved` == false).
+   */
+  override def dataType: DataType = StringType
+
+  private val value = new Array[Byte](64)
+
+  /**
+   * Divide x by m as if x is an unsigned 64-bit integer. Examples:
+   * unsignedLongDiv(-1, 2) == Long.MAX_VALUE unsignedLongDiv(6, 3) == 2
+   * unsignedLongDiv(0, 5) == 0
+   *
+   * @param x is treated as unsigned
+   * @param m is treated as signed
+   */
+  private def unsignedLongDiv(x: Long, m: Int): Long = {
+    if (x >= 0) {
+      x / m
+    } else {
+      // Let uval be the value of the unsigned long with the same bits as x
+      // Two's complement => x = uval - 2*MAX - 2
+      // => uval = x + 2*MAX + 2
+      // Now, use the fact: (a+b)/c = a/c + b/c + (a%c+b%c)/c
+      (x / m + 2 * (Long.MaxValue / m) + 2 / m + (x % m + 2 * (Long.MaxValue % m) + 2 % m) / m)
+    }
+  }
+
+  /**
+   * Decode v into value[].
+   *
+   * @param v is treated as an unsigned 64-bit integer
+   * @param radix must be between MIN_RADIX and MAX_RADIX
+   */
+  private def decode(v: Long, radix: Int): Unit = {
+    var tmpV = v
+    Arrays.fill(value, 0.asInstanceOf[Byte])
+    var i = value.length - 1
+    while (tmpV != 0) {
+      val q = unsignedLongDiv(tmpV, radix)
+      value(i) = (tmpV - q * radix).asInstanceOf[Byte]
+      tmpV = q
+      i -= 1
+    }
+  }
+
+  /**
+   * Convert value[] into a long. On overflow, return -1 (as mySQL does). If a
+   * negative digit is found, ignore the suffix starting there.
+   *
+   * @param radix  must be between MIN_RADIX and MAX_RADIX
+   * @param fromPos is the first element that should be conisdered
+   * @return the result should be treated as an unsigned 64-bit integer.
+   */
+  private def encode(radix: Int, fromPos: Int): Long = {
+    var v: Long = 0L
+    val bound = unsignedLongDiv(-1 - radix, radix) // Possible overflow once
+    // val
+    // exceeds this value
+    var i = fromPos
+    while (i < value.length && value(i) >= 0) {
+      if (v >= bound) {
+        // Check for overflow
+        if (unsignedLongDiv(-1 - value(i), radix) < v) {
+          return -1
+        }
+      }
+      v = v * radix + value(i)
+      i += 1
+    }
+    return v
+  }
+
+  /**
+   * Convert the bytes in value[] to the corresponding chars.
+   *
+   * @param radix must be between MIN_RADIX and MAX_RADIX
+   * @param fromPos is the first nonzero element
+   */
+  private def byte2char(radix: Int, fromPos: Int): Unit = {
+    var i = fromPos
+    while (i < value.length) {
+      value(i) = Character.toUpperCase(Character.forDigit(value(i), radix)).asInstanceOf[Byte]
+      i += 1
+    }
+  }
+
+  /**
+   * Convert the chars in value[] to the corresponding integers. Convert invalid
+   * characters to -1.
+   *
+   * @param radix must be between MIN_RADIX and MAX_RADIX
+   * @param fromPos is the first nonzero element
+   */
+  private def char2byte(radix: Int, fromPos: Int): Unit = {
+    var i = fromPos
+    while ( i < value.length) {
+      value(i) = Character.digit(value(i), radix).asInstanceOf[Byte]
+      i += 1
+    }
+  }
+
+  /**
+   * Convert numbers between different number bases. If toBase>0 the result is
+   * unsigned, otherwise it is signed.
+   * NB: This logic is borrowed from org.apache.hadoop.hive.ql.ud.UDFConv
+   */
+  private def conv(n: Array[Byte] , fromBase: Int, toBase: Int ): UTF8String = {
+    if (n == null || fromBase == null || toBase == null || n.isEmpty) {
+      return null
+    }
+
+    if (fromBase < Character.MIN_RADIX || fromBase > Character.MAX_RADIX
+      || Math.abs(toBase) < Character.MIN_RADIX
+      || Math.abs(toBase) > Character.MAX_RADIX) {
+      return null
+    }
+
+    var (negative, first) = if (n(0) == '-') (true, 1) else (false, 0)
+
+    // Copy the digits in the right side of the array
+    var i = 1
+    while (i <= n.length - first) {
+      value(value.length - i) = n(n.length - i)
+      i += 1
+    }
+    char2byte(fromBase, value.length - n.length + first)
+
+    // Do the conversion by going through a 64 bit integer
+    var v = encode(fromBase, value.length - n.length + first)
+    if (negative && toBase > 0) {
+      if (v < 0) {
+        v = -1
+      } else {
+        v = -v
+      }
+    }
+    if (toBase < 0 && v < 0) {
+      v = -v
+      negative = true
+    }
+    decode(v, Math.abs(toBase))
+
+    // Find the first non-zero digit or the last digits if all are zero.
+    val firstNonZeroPos = {
+      val firstNonZero = value.indexWhere( _ != 0)
+      if (firstNonZero != -1) firstNonZero else value.length - 1
+    }
+
+    byte2char(Math.abs(toBase), firstNonZeroPos)
+
+    var resultStartPos = firstNonZeroPos
+    if (negative && toBase < 0) {
+      resultStartPos = firstNonZeroPos - 1
+      value(resultStartPos) = '-'
+    }
+    UTF8String.fromBytes( Arrays.copyOfRange(value, resultStartPos, value.length))
+  }
+}
+
 case class Exp(child: Expression) extends UnaryMathExpression(math.exp, "EXP")
 
 case class Expm1(child: Expression) extends UnaryMathExpression(math.expm1, "EXPM1")
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 52a874a9d89ef..ca35c7ef8ae5d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -17,14 +17,13 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import scala.math.BigDecimal.RoundingMode
-
 import com.google.common.math.LongMath
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
 
+
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   /**
@@ -95,6 +94,24 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(c(Literal(1.0), Literal.create(null, DoubleType)), null, create_row(null))
   }
 
+  test("conv") {
+    checkEvaluation(Conv(Literal("3"), Literal(10), Literal(2)), "11")
+    checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(-16)), "-F")
+    checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(16)), "FFFFFFFFFFFFFFF1")
+    checkEvaluation(Conv(Literal("big"), Literal(36), Literal(16)), "3A48")
+    checkEvaluation(Conv(Literal(null), Literal(36), Literal(16)), null)
+    checkEvaluation(Conv(Literal("3"), Literal(null), Literal(16)), null)
+    checkEvaluation(
+      Conv(Literal("1234"), Literal(10), Literal(37)), null)
+    checkEvaluation(
+      Conv(Literal(""), Literal(10), Literal(16)), null)
+    checkEvaluation(
+      Conv(Literal("9223372036854775807"), Literal(36), Literal(16)), "FFFFFFFFFFFFFFFF")
+    // If there is an invalid digit in the number, the longest valid prefix should be converted.
+    checkEvaluation(
+      Conv(Literal("11abc"), Literal(10), Literal(16)), "B")
+  }
+
   test("e") {
     testLeaf(EulerNumber, math.E)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index d6da284a4c788..fe511c296cfd2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -68,6 +68,24 @@ object functions {
    */
   def column(colName: String): Column = Column(colName)
 
+  /**
+   * Convert a number from one base to another for the specified expressions
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def conv(num: Column, fromBase: Int, toBase: Int): Column =
+    Conv(num.expr, lit(fromBase).expr, lit(toBase).expr)
+
+  /**
+   * Convert a number from one base to another for the specified expressions
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def conv(numColName: String, fromBase: Int, toBase: Int): Column =
+    conv(Column(numColName), fromBase, toBase)
+
   /**
    * Creates a [[Column]] of literal value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 087126bb2e513..8eb3fec756b4c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -178,6 +178,19 @@ class MathExpressionsSuite extends QueryTest {
       Row(0.0, 1.0, 2.0))
   }
 
+  test("conv") {
+    val df = Seq(("333", 10, 2)).toDF("num", "fromBase", "toBase")
+    checkAnswer(df.select(conv('num, 10, 16)), Row("14D"))
+    checkAnswer(df.select(conv("num", 10, 16)), Row("14D"))
+    checkAnswer(df.select(conv(lit(100), 2, 16)), Row("4"))
+    checkAnswer(df.select(conv(lit(3122234455L), 10, 16)), Row("BA198457"))
+    checkAnswer(df.selectExpr("conv(num, fromBase, toBase)"), Row("101001101"))
+    checkAnswer(df.selectExpr("""conv("100", 2, 10)"""), Row("4"))
+    checkAnswer(df.selectExpr("""conv("-10", 16, -10)"""), Row("-16"))
+    checkAnswer(
+      df.selectExpr("""conv("9223372036854775807", 36, -16)"""), Row("-1")) // for overflow
+  }
+
   test("floor") {
     testOneToOneMathFunction(floor, math.floor)
   }

From eba6a1af4c8ffb21934a59a61a419d625f37cceb Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Fri, 17 Jul 2015 09:38:08 -0700
Subject: [PATCH 0431/1454] [SPARK-8945][SQL] Add add and subtract expressions
 for IntervalType

JIRA: https://issues.apache.org/jira/browse/SPARK-8945

Add add and subtract expressions for IntervalType.

Author: Liang-Chi Hsieh <viirya@appier.com>

This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>

Closes #7398 from viirya/interval_add_subtract and squashes the following commits:

acd1f1e [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into interval_add_subtract
5abae28 [Liang-Chi Hsieh] For comments.
6f5b72e [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into interval_add_subtract
dbe3906 [Liang-Chi Hsieh] For comments.
13a2fc5 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into interval_add_subtract
83ec129 [Liang-Chi Hsieh] Remove intervalMethod.
acfe1ab [Liang-Chi Hsieh] Fix scala style.
d3e9d0e [Liang-Chi Hsieh] Add add and subtract expressions for IntervalType.
---
 .../sql/catalyst/expressions/arithmetic.scala | 60 ++++++++++++++++---
 .../expressions/codegen/CodeGenerator.scala   |  4 +-
 .../sql/catalyst/expressions/literals.scala   |  3 +-
 .../spark/sql/types/AbstractDataType.scala    |  6 ++
 .../ExpressionTypeCheckingSuite.scala         |  6 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 17 ++++++
 .../apache/spark/unsafe/types/Interval.java   | 16 +++++
 .../spark/unsafe/types/IntervalSuite.java     | 38 ++++++++++++
 8 files changed, 136 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 382cbe3b84a07..1616d1bc0aed5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -21,11 +21,12 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.Interval
 
 
 case class UnaryMinus(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
-  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection.NumericAndInterval)
 
   override def dataType: DataType = child.dataType
 
@@ -36,15 +37,22 @@ case class UnaryMinus(child: Expression) extends UnaryExpression with ExpectsInp
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
     case dt: DecimalType => defineCodeGen(ctx, ev, c => s"$c.unary_$$minus()")
     case dt: NumericType => defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})(-($c))")
+    case dt: IntervalType => defineCodeGen(ctx, ev, c => s"$c.negate()")
   }
 
-  protected override def nullSafeEval(input: Any): Any = numeric.negate(input)
+  protected override def nullSafeEval(input: Any): Any = {
+    if (dataType.isInstanceOf[IntervalType]) {
+      input.asInstanceOf[Interval].negate()
+    } else {
+      numeric.negate(input)
+    }
+  }
 }
 
 case class UnaryPositive(child: Expression) extends UnaryExpression with ExpectsInputTypes {
   override def prettyName: String = "positive"
 
-  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection.NumericAndInterval)
 
   override def dataType: DataType = child.dataType
 
@@ -95,32 +103,66 @@ private[sql] object BinaryArithmetic {
 
 case class Add(left: Expression, right: Expression) extends BinaryArithmetic {
 
-  override def inputType: AbstractDataType = NumericType
+  override def inputType: AbstractDataType = TypeCollection.NumericAndInterval
 
   override def symbol: String = "+"
-  override def decimalMethod: String = "$plus"
 
   override lazy val resolved =
     childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
-  protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.plus(input1, input2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    if (dataType.isInstanceOf[IntervalType]) {
+      input1.asInstanceOf[Interval].add(input2.asInstanceOf[Interval])
+    } else {
+      numeric.plus(input1, input2)
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
+    case dt: DecimalType =>
+      defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.$$plus($eval2)")
+    case ByteType | ShortType =>
+      defineCodeGen(ctx, ev,
+        (eval1, eval2) => s"(${ctx.javaType(dataType)})($eval1 $symbol $eval2)")
+    case IntervalType =>
+      defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.add($eval2)")
+    case _ =>
+      defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1 $symbol $eval2")
+  }
 }
 
 case class Subtract(left: Expression, right: Expression) extends BinaryArithmetic {
 
-  override def inputType: AbstractDataType = NumericType
+  override def inputType: AbstractDataType = TypeCollection.NumericAndInterval
 
   override def symbol: String = "-"
-  override def decimalMethod: String = "$minus"
 
   override lazy val resolved =
     childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
-  protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.minus(input1, input2)
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    if (dataType.isInstanceOf[IntervalType]) {
+      input1.asInstanceOf[Interval].subtract(input2.asInstanceOf[Interval])
+    } else {
+      numeric.minus(input1, input2)
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
+    case dt: DecimalType =>
+      defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.$$minus($eval2)")
+    case ByteType | ShortType =>
+      defineCodeGen(ctx, ev,
+        (eval1, eval2) => s"(${ctx.javaType(dataType)})($eval1 $symbol $eval2)")
+    case IntervalType =>
+      defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.subtract($eval2)")
+    case _ =>
+      defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1 $symbol $eval2")
+  }
 }
 
 case class Multiply(left: Expression, right: Expression) extends BinaryArithmetic {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 45dc146488e12..7c388bc346306 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -27,7 +27,7 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.unsafe.types._
 
 
 // These classes are here to avoid issues with serialization and integration with quasiquotes.
@@ -69,6 +69,7 @@ class CodeGenContext {
     mutableStates += ((javaType, variableName, initialValue))
   }
 
+  final val intervalType: String = classOf[Interval].getName
   final val JAVA_BOOLEAN = "boolean"
   final val JAVA_BYTE = "byte"
   final val JAVA_SHORT = "short"
@@ -137,6 +138,7 @@ class CodeGenContext {
     case dt: DecimalType => "Decimal"
     case BinaryType => "byte[]"
     case StringType => "UTF8String"
+    case IntervalType => intervalType
     case _: StructType => "InternalRow"
     case _: ArrayType => s"scala.collection.Seq"
     case _: MapType => s"scala.collection.Map"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index 3a7a7ae440036..e1fdb29541fa8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.unsafe.types._
 
 object Literal {
   def apply(v: Any): Literal = v match {
@@ -42,6 +42,7 @@ object Literal {
     case t: Timestamp => Literal(DateTimeUtils.fromJavaTimestamp(t), TimestampType)
     case d: Date => Literal(DateTimeUtils.fromJavaDate(d), DateType)
     case a: Array[Byte] => Literal(a, BinaryType)
+    case i: Interval => Literal(i, IntervalType)
     case null => Literal(null, NullType)
     case _ =>
       throw new RuntimeException("Unsupported literal type " + v.getClass + " " + v)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index 076d7b5a5118d..40bf4b299c990 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -91,6 +91,12 @@ private[sql] object TypeCollection {
     TimestampType, DateType,
     StringType, BinaryType)
 
+  /**
+   * Types that include numeric types and interval type. They are only used in unary_minus,
+   * unary_positive, add and subtract operations.
+   */
+  val NumericAndInterval = TypeCollection(NumericType, IntervalType)
+
   def apply(types: AbstractDataType*): TypeCollection = new TypeCollection(types)
 
   def unapply(typ: AbstractDataType): Option[Seq[AbstractDataType]] = typ match {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index ed0d20e7de80e..ad15136ee9a2f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -53,7 +53,7 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
   }
 
   test("check types for unary arithmetic") {
-    assertError(UnaryMinus('stringField), "expected to be of type numeric")
+    assertError(UnaryMinus('stringField), "type (numeric or interval)")
     assertError(Abs('stringField), "expected to be of type numeric")
     assertError(BitwiseNot('stringField), "expected to be of type integral")
   }
@@ -78,8 +78,8 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertErrorForDifferingTypes(MaxOf('intField, 'booleanField))
     assertErrorForDifferingTypes(MinOf('intField, 'booleanField))
 
-    assertError(Add('booleanField, 'booleanField), "accepts numeric type")
-    assertError(Subtract('booleanField, 'booleanField), "accepts numeric type")
+    assertError(Add('booleanField, 'booleanField), "accepts (numeric or interval) type")
+    assertError(Subtract('booleanField, 'booleanField), "accepts (numeric or interval) type")
     assertError(Multiply('booleanField, 'booleanField), "accepts numeric type")
     assertError(Divide('booleanField, 'booleanField), "accepts numeric type")
     assertError(Remainder('booleanField, 'booleanField), "accepts numeric type")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 231440892bf0b..5b8b70ed5ae11 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1492,4 +1492,21 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     // Currently we don't yet support nanosecond
     checkIntervalParseError("select interval 23 nanosecond")
   }
+
+  test("SPARK-8945: add and subtract expressions for interval type") {
+    import org.apache.spark.unsafe.types.Interval
+
+    val df = sql("select interval 3 years -3 month 7 week 123 microseconds as i")
+    checkAnswer(df, Row(new Interval(12 * 3 - 3, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123)))
+
+    checkAnswer(df.select(df("i") + new Interval(2, 123)),
+      Row(new Interval(12 * 3 - 3 + 2, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123 + 123)))
+
+    checkAnswer(df.select(df("i") - new Interval(2, 123)),
+      Row(new Interval(12 * 3 - 3 - 2, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123 - 123)))
+
+    // unary minus
+    checkAnswer(df.select(-df("i")),
+      Row(new Interval(-(12 * 3 - 3), -(7L * 1000 * 1000 * 3600 * 24 * 7 + 123))))
+  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
index 905ea0b7b878c..71b1a85a818ea 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
@@ -87,6 +87,22 @@ public Interval(int months, long microseconds) {
     this.microseconds = microseconds;
   }
 
+  public Interval add(Interval that) {
+    int months = this.months + that.months;
+    long microseconds = this.microseconds + that.microseconds;
+    return new Interval(months, microseconds);
+  }
+
+  public Interval subtract(Interval that) {
+    int months = this.months - that.months;
+    long microseconds = this.microseconds - that.microseconds;
+    return new Interval(months, microseconds);
+  }
+
+  public Interval negate() {
+    return new Interval(-this.months, -this.microseconds);
+  }
+
   @Override
   public boolean equals(Object other) {
     if (this == other) return true;
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
index 1832d0bc65551..d29517cda66a3 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
@@ -101,6 +101,44 @@ public void fromStringTest() {
     assertEquals(Interval.fromString(input), null);
   }
 
+  @Test
+  public void addTest() {
+    String input = "interval 3 month 1 hour";
+    String input2 = "interval 2 month 100 hour";
+
+    Interval interval = Interval.fromString(input);
+    Interval interval2 = Interval.fromString(input2);
+
+    assertEquals(interval.add(interval2), new Interval(5, 101 * MICROS_PER_HOUR));
+
+    input = "interval -10 month -81 hour";
+    input2 = "interval 75 month 200 hour";
+
+    interval = Interval.fromString(input);
+    interval2 = Interval.fromString(input2);
+
+    assertEquals(interval.add(interval2), new Interval(65, 119 * MICROS_PER_HOUR));
+  }
+
+  @Test
+  public void subtractTest() {
+    String input = "interval 3 month 1 hour";
+    String input2 = "interval 2 month 100 hour";
+
+    Interval interval = Interval.fromString(input);
+    Interval interval2 = Interval.fromString(input2);
+
+    assertEquals(interval.subtract(interval2), new Interval(1, -99 * MICROS_PER_HOUR));
+
+    input = "interval -10 month -81 hour";
+    input2 = "interval 75 month 200 hour";
+
+    interval = Interval.fromString(input);
+    interval2 = Interval.fromString(input2);
+
+    assertEquals(interval.subtract(interval2), new Interval(-85, -281 * MICROS_PER_HOUR));
+  }
+
   private void testSingleUnit(String unit, int number, int months, long microseconds) {
     String input1 = "interval " + number + " " + unit;
     String input2 = "interval " + number + " " + unit + "s";

From 587c315b204f1439f696620543c38166d95f8a3d Mon Sep 17 00:00:00 2001
From: tien-dungle <tien-dung.le@realimpactanalytics.com>
Date: Fri, 17 Jul 2015 12:11:32 -0700
Subject: [PATCH 0432/1454] [SPARK-9109] [GRAPHX] Keep the cached edge in the
 graph

The change here is to keep the cached RDDs in the graph object so that when the graph.unpersist() is called these RDDs are correctly unpersisted.

```java
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD
import org.slf4j.LoggerFactory
import org.apache.spark.graphx.util.GraphGenerators

// Create an RDD for the vertices
val users: RDD[(VertexId, (String, String))] =
  sc.parallelize(Array((3L, ("rxin", "student")), (7L, ("jgonzal", "postdoc")),
                       (5L, ("franklin", "prof")), (2L, ("istoica", "prof"))))
// Create an RDD for edges
val relationships: RDD[Edge[String]] =
  sc.parallelize(Array(Edge(3L, 7L, "collab"),    Edge(5L, 3L, "advisor"),
                       Edge(2L, 5L, "colleague"), Edge(5L, 7L, "pi")))
// Define a default user in case there are relationship with missing user
val defaultUser = ("John Doe", "Missing")
// Build the initial Graph
val graph = Graph(users, relationships, defaultUser)
graph.cache().numEdges

graph.unpersist()

sc.getPersistentRDDs.foreach( r => println( r._2.toString))
```

Author: tien-dungle <tien-dung.le@realimpactanalytics.com>

Closes #7469 from tien-dungle/SPARK-9109_Graphx-unpersist and squashes the following commits:

8d87997 [tien-dungle] Keep the cached edge in the graph
---
 .../scala/org/apache/spark/graphx/impl/GraphImpl.scala   | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index 90a74d23a26cc..da95314440d86 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -332,9 +332,9 @@ object GraphImpl {
       edgeStorageLevel: StorageLevel,
       vertexStorageLevel: StorageLevel): GraphImpl[VD, ED] = {
     val edgeRDD = EdgeRDD.fromEdges(edges)(classTag[ED], classTag[VD])
-      .withTargetStorageLevel(edgeStorageLevel).cache()
+      .withTargetStorageLevel(edgeStorageLevel)
     val vertexRDD = VertexRDD(vertices, edgeRDD, defaultVertexAttr)
-      .withTargetStorageLevel(vertexStorageLevel).cache()
+      .withTargetStorageLevel(vertexStorageLevel)
     GraphImpl(vertexRDD, edgeRDD)
   }
 
@@ -346,9 +346,14 @@ object GraphImpl {
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: VertexRDD[VD],
       edges: EdgeRDD[ED]): GraphImpl[VD, ED] = {
+
+    vertices.cache()
+
     // Convert the vertex partitions in edges to the correct type
     val newEdges = edges.asInstanceOf[EdgeRDDImpl[ED, _]]
       .mapEdgePartitions((pid, part) => part.withoutVertexAttributes[VD])
+      .cache()
+
     GraphImpl.fromExistingRDDs(vertices, newEdges)
   }
 

From f9a82a884e7cb2a466a33ab64912924ce7ee30c1 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 17 Jul 2015 12:43:58 -0700
Subject: [PATCH 0433/1454] [SPARK-9138] [MLLIB] fix Vectors.dense

Vectors.dense() should accept numbers directly, like the one in Scala. We already use it in doctests, it worked by luck.

cc mengxr jkbradley

Author: Davies Liu <davies@databricks.com>

Closes #7476 from davies/fix_vectors_dense and squashes the following commits:

e0fd292 [Davies Liu] fix Vectors.dense
---
 python/pyspark/mllib/linalg.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 040886f71775b..529bd75894c96 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -30,6 +30,7 @@
     basestring = str
     xrange = range
     import copyreg as copy_reg
+    long = int
 else:
     from itertools import izip as zip
     import copy_reg
@@ -770,14 +771,18 @@ def sparse(size, *args):
         return SparseVector(size, *args)
 
     @staticmethod
-    def dense(elements):
+    def dense(*elements):
         """
-        Create a dense vector of 64-bit floats from a Python list. Always
-        returns a NumPy array.
+        Create a dense vector of 64-bit floats from a Python list or numbers.
 
         >>> Vectors.dense([1, 2, 3])
         DenseVector([1.0, 2.0, 3.0])
+        >>> Vectors.dense(1.0, 2.0)
+        DenseVector([1.0, 2.0])
         """
+        if len(elements) == 1 and not isinstance(elements[0], (float, int, long)):
+            # it's list, numpy.array or other iterable object.
+            elements = elements[0]
         return DenseVector(elements)
 
     @staticmethod

From 806c579f43ce66ac1398200cbc773fa3b69b5cb6 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Fri, 17 Jul 2015 13:43:19 -0700
Subject: [PATCH 0434/1454] [SPARK-9062] [ML] Change output type of Tokenizer
 to Array(String, true)

jira: https://issues.apache.org/jira/browse/SPARK-9062

Currently output type of Tokenizer is Array(String, false), which is not compatible with Word2Vec and Other transformers since their input type is Array(String, true). Seq[String] in udf will be treated as Array(String, true) by default.

I'm not sure what's the recommended way for Tokenizer to handle the null value in the input. Any suggestion will be welcome.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7414 from hhbyyh/tokenizer and squashes the following commits:

c01bd7a [Yuhao Yang] change output type of tokenizer
---
 .../main/scala/org/apache/spark/ml/feature/Tokenizer.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
index 5f9f57a2ebcfa..0b3af4747e693 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
@@ -42,7 +42,7 @@ class Tokenizer(override val uid: String) extends UnaryTransformer[String, Seq[S
     require(inputType == StringType, s"Input type must be string type but got $inputType.")
   }
 
-  override protected def outputDataType: DataType = new ArrayType(StringType, false)
+  override protected def outputDataType: DataType = new ArrayType(StringType, true)
 
   override def copy(extra: ParamMap): Tokenizer = defaultCopy(extra)
 }
@@ -113,7 +113,7 @@ class RegexTokenizer(override val uid: String)
     require(inputType == StringType, s"Input type must be string type but got $inputType.")
   }
 
-  override protected def outputDataType: DataType = new ArrayType(StringType, false)
+  override protected def outputDataType: DataType = new ArrayType(StringType, true)
 
   override def copy(extra: ParamMap): RegexTokenizer = defaultCopy(extra)
 }

From 9974642870404381fa425fadb966c6dd3ac4a94f Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 17 Jul 2015 13:55:17 -0700
Subject: [PATCH 0435/1454] [SPARK-8600] [ML] Naive Bayes API for spark.ml
 Pipelines

Naive Bayes API for spark.ml Pipelines

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7284 from yanboliang/spark-8600 and squashes the following commits:

bc890f7 [Yanbo Liang] remove labels valid check
c3de687 [Yanbo Liang] remove labels from ml.NaiveBayesModel
a2b3088 [Yanbo Liang] address comments
3220b82 [Yanbo Liang] trigger jenkins
3018a41 [Yanbo Liang] address comments
208e166 [Yanbo Liang] Naive Bayes API for spark.ml Pipelines
---
 .../spark/ml/classification/NaiveBayes.scala  | 178 ++++++++++++++++++
 .../mllib/classification/NaiveBayes.scala     |  10 +-
 .../apache/spark/mllib/linalg/Matrices.scala  |   6 +-
 .../classification/JavaNaiveBayesSuite.java   |  98 ++++++++++
 .../ml/classification/NaiveBayesSuite.scala   | 116 ++++++++++++
 5 files changed, 400 insertions(+), 8 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
new file mode 100644
index 0000000000000..1f547e4a98af7
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -0,0 +1,178 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification
+
+import org.apache.spark.SparkException
+import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
+import org.apache.spark.ml.param.{ParamMap, ParamValidators, Param, DoubleParam}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes}
+import org.apache.spark.mllib.classification.{NaiveBayesModel => OldNaiveBayesModel}
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.DataFrame
+
+/**
+ * Params for Naive Bayes Classifiers.
+ */
+private[ml] trait NaiveBayesParams extends PredictorParams {
+
+  /**
+   * The smoothing parameter.
+   * (default = 1.0).
+   * @group param
+   */
+  final val lambda: DoubleParam = new DoubleParam(this, "lambda", "The smoothing parameter.",
+    ParamValidators.gtEq(0))
+
+  /** @group getParam */
+  final def getLambda: Double = $(lambda)
+
+  /**
+   * The model type which is a string (case-sensitive).
+   * Supported options: "multinomial" and "bernoulli".
+   * (default = multinomial)
+   * @group param
+   */
+  final val modelType: Param[String] = new Param[String](this, "modelType", "The model type " +
+    "which is a string (case-sensitive). Supported options: multinomial (default) and bernoulli.",
+    ParamValidators.inArray[String](OldNaiveBayes.supportedModelTypes.toArray))
+
+  /** @group getParam */
+  final def getModelType: String = $(modelType)
+}
+
+/**
+ * Naive Bayes Classifiers.
+ * It supports both Multinomial NB
+ * ([[http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html]])
+ * which can handle finitely supported discrete data. For example, by converting documents into
+ * TF-IDF vectors, it can be used for document classification. By making every vector a
+ * binary (0/1) data, it can also be used as Bernoulli NB
+ * ([[http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html]]).
+ * The input feature values must be nonnegative.
+ */
+class NaiveBayes(override val uid: String)
+  extends Predictor[Vector, NaiveBayes, NaiveBayesModel]
+  with NaiveBayesParams {
+
+  def this() = this(Identifiable.randomUID("nb"))
+
+  /**
+   * Set the smoothing parameter.
+   * Default is 1.0.
+   * @group setParam
+   */
+  def setLambda(value: Double): this.type = set(lambda, value)
+  setDefault(lambda -> 1.0)
+
+  /**
+   * Set the model type using a string (case-sensitive).
+   * Supported options: "multinomial" and "bernoulli".
+   * Default is "multinomial"
+   */
+  def setModelType(value: String): this.type = set(modelType, value)
+  setDefault(modelType -> OldNaiveBayes.Multinomial)
+
+  override protected def train(dataset: DataFrame): NaiveBayesModel = {
+    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
+    val oldModel = OldNaiveBayes.train(oldDataset, $(lambda), $(modelType))
+    NaiveBayesModel.fromOld(oldModel, this)
+  }
+
+  override def copy(extra: ParamMap): NaiveBayes = defaultCopy(extra)
+}
+
+/**
+ * Model produced by [[NaiveBayes]]
+ */
+class NaiveBayesModel private[ml] (
+    override val uid: String,
+    val pi: Vector,
+    val theta: Matrix)
+  extends PredictionModel[Vector, NaiveBayesModel] with NaiveBayesParams {
+
+  import OldNaiveBayes.{Bernoulli, Multinomial}
+
+  /**
+   * Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0.
+   * This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra
+   * application of this condition (in predict function).
+   */
+  private lazy val (thetaMinusNegTheta, negThetaSum) = $(modelType) match {
+    case Multinomial => (None, None)
+    case Bernoulli =>
+      val negTheta = theta.map(value => math.log(1.0 - math.exp(value)))
+      val ones = new DenseVector(Array.fill(theta.numCols){1.0})
+      val thetaMinusNegTheta = theta.map { value =>
+        value - math.log(1.0 - math.exp(value))
+      }
+      (Option(thetaMinusNegTheta), Option(negTheta.multiply(ones)))
+    case _ =>
+      // This should never happen.
+      throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
+  }
+
+  override protected def predict(features: Vector): Double = {
+    $(modelType) match {
+      case Multinomial =>
+        val prob = theta.multiply(features)
+        BLAS.axpy(1.0, pi, prob)
+        prob.argmax
+      case Bernoulli =>
+        features.foreachActive{ (index, value) =>
+          if (value != 0.0 && value != 1.0) {
+            throw new SparkException(
+              s"Bernoulli naive Bayes requires 0 or 1 feature values but found $features")
+          }
+        }
+        val prob = thetaMinusNegTheta.get.multiply(features)
+        BLAS.axpy(1.0, pi, prob)
+        BLAS.axpy(1.0, negThetaSum.get, prob)
+        prob.argmax
+      case _ =>
+        // This should never happen.
+        throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
+    }
+  }
+
+  override def copy(extra: ParamMap): NaiveBayesModel = {
+    copyValues(new NaiveBayesModel(uid, pi, theta).setParent(this.parent), extra)
+  }
+
+  override def toString: String = {
+    s"NaiveBayesModel with ${pi.size} classes"
+  }
+
+}
+
+private[ml] object NaiveBayesModel {
+
+  /** Convert a model from the old API */
+  def fromOld(
+      oldModel: OldNaiveBayesModel,
+      parent: NaiveBayes): NaiveBayesModel = {
+    val uid = if (parent != null) parent.uid else Identifiable.randomUID("nb")
+    val labels = Vectors.dense(oldModel.labels)
+    val pi = Vectors.dense(oldModel.pi)
+    val theta = new DenseMatrix(oldModel.labels.length, oldModel.theta(0).length,
+      oldModel.theta.flatten, true)
+    new NaiveBayesModel(uid, pi, theta)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 9e379d7d74b2f..8cf4e15efe7a7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -40,7 +40,7 @@ import org.apache.spark.sql.{DataFrame, SQLContext}
  *              where D is number of features
  * @param modelType The type of NB model to fit  can be "multinomial" or "bernoulli"
  */
-class NaiveBayesModel private[mllib] (
+class NaiveBayesModel private[spark] (
     val labels: Array[Double],
     val pi: Array[Double],
     val theta: Array[Array[Double]],
@@ -382,7 +382,7 @@ class NaiveBayes private (
         BLAS.axpy(1.0, c2._2, c1._2)
         (c1._1 + c2._1, c1._2)
       }
-    ).collect()
+    ).collect().sortBy(_._1)
 
     val numLabels = aggregated.length
     var numDocuments = 0L
@@ -425,13 +425,13 @@ class NaiveBayes private (
 object NaiveBayes {
 
   /** String name for multinomial model type. */
-  private[classification] val Multinomial: String = "multinomial"
+  private[spark] val Multinomial: String = "multinomial"
 
   /** String name for Bernoulli model type. */
-  private[classification] val Bernoulli: String = "bernoulli"
+  private[spark] val Bernoulli: String = "bernoulli"
 
   /* Set of modelTypes that NaiveBayes supports */
-  private[classification] val supportedModelTypes = Set(Multinomial, Bernoulli)
+  private[spark] val supportedModelTypes = Set(Multinomial, Bernoulli)
 
   /**
    * Trains a Naive Bayes model given an RDD of `(label, features)` pairs.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 0df07663405a3..55da0e094d132 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -98,7 +98,7 @@ sealed trait Matrix extends Serializable {
   /** Map the values of this matrix using a function. Generates a new matrix. Performs the
     * function on only the backing array. For example, an operation such as addition or
     * subtraction will only be performed on the non-zero values in a `SparseMatrix`. */
-  private[mllib] def map(f: Double => Double): Matrix
+  private[spark] def map(f: Double => Double): Matrix
 
   /** Update all the values of this matrix using the function f. Performed in-place on the
     * backing array. For example, an operation such as addition or subtraction will only be
@@ -289,7 +289,7 @@ class DenseMatrix(
 
   override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone())
 
-  private[mllib] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
+  private[spark] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
     isTransposed)
 
   private[mllib] def update(f: Double => Double): DenseMatrix = {
@@ -555,7 +555,7 @@ class SparseMatrix(
     new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
   }
 
-  private[mllib] def map(f: Double => Double) =
+  private[spark] def map(f: Double => Double) =
     new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.map(f), isTransposed)
 
   private[mllib] def update(f: Double => Double): SparseMatrix = {
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
new file mode 100644
index 0000000000000..09a9fba0c19cf
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification;
+
+import java.io.Serializable;
+
+import com.google.common.collect.Lists;
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+public class JavaNaiveBayesSuite implements Serializable {
+
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
+    jsql = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  public void validatePrediction(DataFrame predictionAndLabels) {
+    for (Row r : predictionAndLabels.collect()) {
+      double prediction = r.getAs(0);
+      double label = r.getAs(1);
+      assert(prediction == label);
+    }
+  }
+
+  @Test
+  public void naiveBayesDefaultParams() {
+    NaiveBayes nb = new NaiveBayes();
+    assert(nb.getLabelCol() == "label");
+    assert(nb.getFeaturesCol() == "features");
+    assert(nb.getPredictionCol() == "prediction");
+    assert(nb.getLambda() == 1.0);
+    assert(nb.getModelType() == "multinomial");
+  }
+
+  @Test
+  public void testNaiveBayes() {
+    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+      RowFactory.create(0.0, Vectors.dense(1.0, 0.0, 0.0)),
+      RowFactory.create(0.0, Vectors.dense(2.0, 0.0, 0.0)),
+      RowFactory.create(1.0, Vectors.dense(0.0, 1.0, 0.0)),
+      RowFactory.create(1.0, Vectors.dense(0.0, 2.0, 0.0)),
+      RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 1.0)),
+      RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 2.0))
+    ));
+
+    StructType schema = new StructType(new StructField[]{
+      new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
+      new StructField("features", new VectorUDT(), false, Metadata.empty())
+    });
+
+    DataFrame dataset = jsql.createDataFrame(jrdd, schema);
+    NaiveBayes nb = new NaiveBayes().setLambda(0.5).setModelType("multinomial");
+    NaiveBayesModel model = nb.fit(dataset);
+
+    DataFrame predictionAndLabels = model.transform(dataset).select("prediction", "label");
+    validatePrediction(predictionAndLabels);
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
new file mode 100644
index 0000000000000..76381a2741296
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -0,0 +1,116 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.mllib.classification.NaiveBayesSuite._
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.Row
+
+class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  def validatePrediction(predictionAndLabels: DataFrame): Unit = {
+    val numOfErrorPredictions = predictionAndLabels.collect().count {
+      case Row(prediction: Double, label: Double) =>
+        prediction != label
+    }
+    // At least 80% of the predictions should be on.
+    assert(numOfErrorPredictions < predictionAndLabels.count() / 5)
+  }
+
+  def validateModelFit(
+      piData: Vector,
+      thetaData: Matrix,
+      model: NaiveBayesModel): Unit = {
+    assert(Vectors.dense(model.pi.toArray.map(math.exp)) ~==
+      Vectors.dense(piData.toArray.map(math.exp)) absTol 0.05, "pi mismatch")
+    assert(model.theta.map(math.exp) ~== thetaData.map(math.exp) absTol 0.05, "theta mismatch")
+  }
+
+  test("params") {
+    ParamsSuite.checkParams(new NaiveBayes)
+    val model = new NaiveBayesModel("nb", pi = Vectors.dense(Array(0.2, 0.8)),
+      theta = new DenseMatrix(2, 3, Array(0.1, 0.2, 0.3, 0.4, 0.6, 0.4)))
+    ParamsSuite.checkParams(model)
+  }
+
+  test("naive bayes: default params") {
+    val nb = new NaiveBayes
+    assert(nb.getLabelCol === "label")
+    assert(nb.getFeaturesCol === "features")
+    assert(nb.getPredictionCol === "prediction")
+    assert(nb.getLambda === 1.0)
+    assert(nb.getModelType === "multinomial")
+  }
+
+  test("Naive Bayes Multinomial") {
+    val nPoints = 1000
+    val piArray = Array(0.5, 0.1, 0.4).map(math.log)
+    val thetaArray = Array(
+      Array(0.70, 0.10, 0.10, 0.10), // label 0
+      Array(0.10, 0.70, 0.10, 0.10), // label 1
+      Array(0.10, 0.10, 0.70, 0.10)  // label 2
+    ).map(_.map(math.log))
+    val pi = Vectors.dense(piArray)
+    val theta = new DenseMatrix(3, 4, thetaArray.flatten, true)
+
+    val testDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
+      piArray, thetaArray, nPoints, 42, "multinomial"))
+    val nb = new NaiveBayes().setLambda(1.0).setModelType("multinomial")
+    val model = nb.fit(testDataset)
+
+    validateModelFit(pi, theta, model)
+    assert(model.hasParent)
+
+    val validationDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
+      piArray, thetaArray, nPoints, 17, "multinomial"))
+    val predictionAndLabels = model.transform(validationDataset).select("prediction", "label")
+
+    validatePrediction(predictionAndLabels)
+  }
+
+  test("Naive Bayes Bernoulli") {
+    val nPoints = 10000
+    val piArray = Array(0.5, 0.3, 0.2).map(math.log)
+    val thetaArray = Array(
+      Array(0.50, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.40), // label 0
+      Array(0.02, 0.70, 0.10, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02), // label 1
+      Array(0.02, 0.02, 0.60, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.30)  // label 2
+    ).map(_.map(math.log))
+    val pi = Vectors.dense(piArray)
+    val theta = new DenseMatrix(3, 12, thetaArray.flatten, true)
+
+    val testDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
+      piArray, thetaArray, nPoints, 45, "bernoulli"))
+    val nb = new NaiveBayes().setLambda(1.0).setModelType("bernoulli")
+    val model = nb.fit(testDataset)
+
+    validateModelFit(pi, theta, model)
+    assert(model.hasParent)
+
+    val validationDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
+      piArray, thetaArray, nPoints, 20, "bernoulli"))
+    val predictionAndLabels = model.transform(validationDataset).select("prediction", "label")
+
+    validatePrediction(predictionAndLabels)
+  }
+}

From 074085d6781a580017a45101b8b54ffd7bd31294 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 17 Jul 2015 13:57:31 -0700
Subject: [PATCH 0436/1454] [SPARK-9136] [SQL] fix several bugs in
 DateTimeUtils.stringToTimestamp

a follow up of https://github.com/apache/spark/pull/7353

1. we should use `Calendar.HOUR_OF_DAY` instead of `Calendar.HOUR`(this is for AM, PM).
2. we should call `c.set(Calendar.MILLISECOND, 0)` after `Calendar.getInstance`

I'm not sure why the tests didn't fail in jenkins, but I ran latest spark master branch locally and `DateTimeUtilsSuite` failed.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7473 from cloud-fan/datetime and squashes the following commits:

66cdaf2 [Wenchen Fan] fix several bugs in DateTimeUtils.stringToTimestamp
---
 .../spark/sql/catalyst/util/DateTimeUtils.scala     |  5 +++--
 .../sql/catalyst/util/DateTimeUtilsSuite.scala      | 13 +++++++++++--
 2 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 53c32a0a9802b..f33e34b380bcf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -320,16 +320,17 @@ object DateTimeUtils {
       Calendar.getInstance(
         TimeZone.getTimeZone(f"GMT${timeZone.get.toChar}${segments(7)}%02d:${segments(8)}%02d"))
     }
+    c.set(Calendar.MILLISECOND, 0)
 
     if (justTime) {
-      c.set(Calendar.HOUR, segments(3))
+      c.set(Calendar.HOUR_OF_DAY, segments(3))
       c.set(Calendar.MINUTE, segments(4))
       c.set(Calendar.SECOND, segments(5))
     } else {
       c.set(segments(0), segments(1) - 1, segments(2), segments(3), segments(4), segments(5))
     }
 
-    Some(c.getTimeInMillis / 1000 * 1000000 + segments(6))
+    Some(c.getTimeInMillis * 1000 + segments(6))
   }
 
   /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index c65fcbc4d1bc1..5c3a621c6d11f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -243,8 +243,17 @@ class DateTimeUtilsSuite extends SparkFunSuite {
       UTF8String.fromString("2015-03-18T12:03:17.12312+7:30")).get ===
         c.getTimeInMillis * 1000 + 120)
 
+    c = Calendar.getInstance()
+    c.set(Calendar.HOUR_OF_DAY, 18)
+    c.set(Calendar.MINUTE, 12)
+    c.set(Calendar.SECOND, 15)
+    c.set(Calendar.MILLISECOND, 0)
+    assert(DateTimeUtils.stringToTimestamp(
+      UTF8String.fromString("18:12:15")).get ===
+      c.getTimeInMillis * 1000)
+
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
-    c.set(Calendar.HOUR, 18)
+    c.set(Calendar.HOUR_OF_DAY, 18)
     c.set(Calendar.MINUTE, 12)
     c.set(Calendar.SECOND, 15)
     c.set(Calendar.MILLISECOND, 123)
@@ -253,7 +262,7 @@ class DateTimeUtilsSuite extends SparkFunSuite {
       c.getTimeInMillis * 1000 + 120)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
-    c.set(Calendar.HOUR, 18)
+    c.set(Calendar.HOUR_OF_DAY, 18)
     c.set(Calendar.MINUTE, 12)
     c.set(Calendar.SECOND, 15)
     c.set(Calendar.MILLISECOND, 123)

From ad0954f6de29761e0e7e543212c5bfe1fdcbed9f Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 17 Jul 2015 14:00:31 -0700
Subject: [PATCH 0437/1454] [SPARK-5681] [STREAMING] Move 'stopReceivers' to
 the event loop to resolve the race condition

This is an alternative way to fix `SPARK-5681`. It minimizes the changes.

Closes #4467

Author: zsxwing <zsxwing@gmail.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #6294 from zsxwing/pr4467 and squashes the following commits:

709ac1f [zsxwing] Fix the comment
e103e8a [zsxwing] Move ReceiverTracker.stop into ReceiverTracker.stop
f637142 [zsxwing] Address minor code style comments
a178d37 [zsxwing] Move 'stopReceivers' to the event looop to resolve the race condition
51fb07e [zsxwing] Fix the code style
3cb19a3 [zsxwing] Merge branch 'master' into pr4467
b4c29e7 [zsxwing] Stop receiver only if we start it
c41ee94 [zsxwing] Make stopReceivers private
7c73c1f [zsxwing] Use trackerStateLock to protect trackerState
a8120c0 [zsxwing] Merge branch 'master' into pr4467
7b1d9af [zsxwing] "case Throwable" => "case NonFatal"
15ed4a1 [zsxwing] Register before starting the receiver
fff63f9 [zsxwing] Use a lock to eliminate the race condition when stopping receivers and registering receivers happen at the same time.
e0ef72a [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into tracker_status_timeout
19b76d9 [Liang-Chi Hsieh] Remove timeout.
34c18dc [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into tracker_status_timeout
c419677 [Liang-Chi Hsieh] Fix style.
9e1a760 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into tracker_status_timeout
355f9ce [Liang-Chi Hsieh] Separate register and start events for receivers.
3d568e8 [Liang-Chi Hsieh] Let receivers get registered first before going started.
ae0d9fd [Liang-Chi Hsieh] Merge branch 'master' into tracker_status_timeout
77983f3 [Liang-Chi Hsieh] Add tracker status and stop to receive messages when stopping tracker.
---
 .../receiver/ReceiverSupervisor.scala         |  42 ++++--
 .../receiver/ReceiverSupervisorImpl.scala     |   2 +-
 .../streaming/scheduler/ReceiverTracker.scala | 139 ++++++++++++------
 .../spark/streaming/ReceiverSuite.scala       |   2 +
 .../streaming/StreamingContextSuite.scala     |  15 ++
 5 files changed, 138 insertions(+), 62 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
index eeb14ca3a49e9..6467029a277b2 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
@@ -22,6 +22,7 @@ import java.util.concurrent.CountDownLatch
 
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent._
+import scala.util.control.NonFatal
 
 import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.storage.StreamBlockId
@@ -36,7 +37,7 @@ private[streaming] abstract class ReceiverSupervisor(
     conf: SparkConf
   ) extends Logging {
 
-  /** Enumeration to identify current state of the StreamingContext */
+  /** Enumeration to identify current state of the Receiver */
   object ReceiverState extends Enumeration {
     type CheckpointState = Value
     val Initialized, Started, Stopped = Value
@@ -97,8 +98,8 @@ private[streaming] abstract class ReceiverSupervisor(
   /** Called when supervisor is stopped */
   protected def onStop(message: String, error: Option[Throwable]) { }
 
-  /** Called when receiver is started */
-  protected def onReceiverStart() { }
+  /** Called when receiver is started. Return true if the driver accepts us */
+  protected def onReceiverStart(): Boolean
 
   /** Called when receiver is stopped */
   protected def onReceiverStop(message: String, error: Option[Throwable]) { }
@@ -121,13 +122,17 @@ private[streaming] abstract class ReceiverSupervisor(
   /** Start receiver */
   def startReceiver(): Unit = synchronized {
     try {
-      logInfo("Starting receiver")
-      receiver.onStart()
-      logInfo("Called receiver onStart")
-      onReceiverStart()
-      receiverState = Started
+      if (onReceiverStart()) {
+        logInfo("Starting receiver")
+        receiverState = Started
+        receiver.onStart()
+        logInfo("Called receiver onStart")
+      } else {
+        // The driver refused us
+        stop("Registered unsuccessfully because Driver refused to start receiver " + streamId, None)
+      }
     } catch {
-      case t: Throwable =>
+      case NonFatal(t) =>
         stop("Error starting receiver " + streamId, Some(t))
     }
   }
@@ -136,12 +141,19 @@ private[streaming] abstract class ReceiverSupervisor(
   def stopReceiver(message: String, error: Option[Throwable]): Unit = synchronized {
     try {
       logInfo("Stopping receiver with message: " + message + ": " + error.getOrElse(""))
-      receiverState = Stopped
-      receiver.onStop()
-      logInfo("Called receiver onStop")
-      onReceiverStop(message, error)
+      receiverState match {
+        case Initialized =>
+          logWarning("Skip stopping receiver because it has not yet stared")
+        case Started =>
+          receiverState = Stopped
+          receiver.onStop()
+          logInfo("Called receiver onStop")
+          onReceiverStop(message, error)
+        case Stopped =>
+          logWarning("Receiver has been stopped")
+      }
     } catch {
-      case t: Throwable =>
+      case NonFatal(t) =>
         logError("Error stopping receiver " + streamId + t.getStackTraceString)
     }
   }
@@ -167,7 +179,7 @@ private[streaming] abstract class ReceiverSupervisor(
     }(futureExecutionContext)
   }
 
-  /** Check if receiver has been marked for stopping */
+  /** Check if receiver has been marked for starting */
   def isReceiverStarted(): Boolean = {
     logDebug("state = " + receiverState)
     receiverState == Started
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index 6078cdf8f8790..f6ba66b3ae036 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -162,7 +162,7 @@ private[streaming] class ReceiverSupervisorImpl(
     env.rpcEnv.stop(endpoint)
   }
 
-  override protected def onReceiverStart() {
+  override protected def onReceiverStart(): Boolean = {
     val msg = RegisterReceiver(
       streamId, receiver.getClass.getSimpleName, Utils.localHostName(), endpoint)
     trackerEndpoint.askWithRetry[Boolean](msg)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 644e581cd8279..6910d81d9866e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -20,7 +20,6 @@ package org.apache.spark.streaming.scheduler
 import scala.collection.mutable.{ArrayBuffer, HashMap, SynchronizedMap}
 import scala.language.existentials
 import scala.math.max
-import org.apache.spark.rdd._
 
 import org.apache.spark.streaming.util.WriteAheadLogUtils
 import org.apache.spark.{Logging, SparkEnv, SparkException}
@@ -47,6 +46,8 @@ private[streaming] case class ReportError(streamId: Int, message: String, error:
 private[streaming] case class DeregisterReceiver(streamId: Int, msg: String, error: String)
   extends ReceiverTrackerMessage
 
+private[streaming] case object StopAllReceivers extends ReceiverTrackerMessage
+
 /**
  * This class manages the execution of the receivers of ReceiverInputDStreams. Instance of
  * this class must be created after all input streams have been added and StreamingContext.start()
@@ -71,13 +72,23 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
   )
   private val listenerBus = ssc.scheduler.listenerBus
 
+  /** Enumeration to identify current state of the ReceiverTracker */
+  object TrackerState extends Enumeration {
+    type TrackerState = Value
+    val Initialized, Started, Stopping, Stopped = Value
+  }
+  import TrackerState._
+
+  /** State of the tracker. Protected by "trackerStateLock" */
+  @volatile private var trackerState = Initialized
+
   // endpoint is created when generator starts.
   // This not being null means the tracker has been started and not stopped
   private var endpoint: RpcEndpointRef = null
 
   /** Start the endpoint and receiver execution thread. */
   def start(): Unit = synchronized {
-    if (endpoint != null) {
+    if (isTrackerStarted) {
       throw new SparkException("ReceiverTracker already started")
     }
 
@@ -86,20 +97,46 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv))
       if (!skipReceiverLaunch) receiverExecutor.start()
       logInfo("ReceiverTracker started")
+      trackerState = Started
     }
   }
 
   /** Stop the receiver execution thread. */
   def stop(graceful: Boolean): Unit = synchronized {
-    if (!receiverInputStreams.isEmpty && endpoint != null) {
+    if (isTrackerStarted) {
       // First, stop the receivers
-      if (!skipReceiverLaunch) receiverExecutor.stop(graceful)
+      trackerState = Stopping
+      if (!skipReceiverLaunch) {
+        // Send the stop signal to all the receivers
+        endpoint.askWithRetry[Boolean](StopAllReceivers)
+
+        // Wait for the Spark job that runs the receivers to be over
+        // That is, for the receivers to quit gracefully.
+        receiverExecutor.awaitTermination(10000)
+
+        if (graceful) {
+          val pollTime = 100
+          logInfo("Waiting for receiver job to terminate gracefully")
+          while (receiverInfo.nonEmpty || receiverExecutor.running) {
+            Thread.sleep(pollTime)
+          }
+          logInfo("Waited for receiver job to terminate gracefully")
+        }
+
+        // Check if all the receivers have been deregistered or not
+        if (receiverInfo.nonEmpty) {
+          logWarning("Not all of the receivers have deregistered, " + receiverInfo)
+        } else {
+          logInfo("All of the receivers have deregistered successfully")
+        }
+      }
 
       // Finally, stop the endpoint
       ssc.env.rpcEnv.stop(endpoint)
       endpoint = null
       receivedBlockTracker.stop()
       logInfo("ReceiverTracker stopped")
+      trackerState = Stopped
     }
   }
 
@@ -145,14 +182,23 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
       host: String,
       receiverEndpoint: RpcEndpointRef,
       senderAddress: RpcAddress
-    ) {
+    ): Boolean = {
     if (!receiverInputStreamIds.contains(streamId)) {
       throw new SparkException("Register received for unexpected id " + streamId)
     }
-    receiverInfo(streamId) = ReceiverInfo(
-      streamId, s"${typ}-${streamId}", receiverEndpoint, true, host)
-    listenerBus.post(StreamingListenerReceiverStarted(receiverInfo(streamId)))
-    logInfo("Registered receiver for stream " + streamId + " from " + senderAddress)
+
+    if (isTrackerStopping || isTrackerStopped) {
+      false
+    } else {
+      // "stopReceivers" won't happen at the same time because both "registerReceiver" and are
+      // called in the event loop. So here we can assume "stopReceivers" has not yet been called. If
+      // "stopReceivers" is called later, it should be able to see this receiver.
+      receiverInfo(streamId) = ReceiverInfo(
+        streamId, s"${typ}-${streamId}", receiverEndpoint, true, host)
+      listenerBus.post(StreamingListenerReceiverStarted(receiverInfo(streamId)))
+      logInfo("Registered receiver for stream " + streamId + " from " + senderAddress)
+      true
+    }
   }
 
   /** Deregister a receiver */
@@ -220,20 +266,33 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
       case RegisterReceiver(streamId, typ, host, receiverEndpoint) =>
-        registerReceiver(streamId, typ, host, receiverEndpoint, context.sender.address)
-        context.reply(true)
+        val successful =
+          registerReceiver(streamId, typ, host, receiverEndpoint, context.sender.address)
+        context.reply(successful)
       case AddBlock(receivedBlockInfo) =>
         context.reply(addBlock(receivedBlockInfo))
       case DeregisterReceiver(streamId, message, error) =>
         deregisterReceiver(streamId, message, error)
         context.reply(true)
+      case StopAllReceivers =>
+        assert(isTrackerStopping || isTrackerStopped)
+        stopReceivers()
+        context.reply(true)
+    }
+
+    /** Send stop signal to the receivers. */
+    private def stopReceivers() {
+      // Signal the receivers to stop
+      receiverInfo.values.flatMap { info => Option(info.endpoint)}
+        .foreach { _.send(StopReceiver) }
+      logInfo("Sent stop signal to all " + receiverInfo.size + " receivers")
     }
   }
 
   /** This thread class runs all the receivers on the cluster.  */
   class ReceiverLauncher {
     @transient val env = ssc.env
-    @volatile @transient private var running = false
+    @volatile @transient var running = false
     @transient val thread = new Thread() {
       override def run() {
         try {
@@ -249,31 +308,6 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
       thread.start()
     }
 
-    def stop(graceful: Boolean) {
-      // Send the stop signal to all the receivers
-      stopReceivers()
-
-      // Wait for the Spark job that runs the receivers to be over
-      // That is, for the receivers to quit gracefully.
-      thread.join(10000)
-
-      if (graceful) {
-        val pollTime = 100
-        logInfo("Waiting for receiver job to terminate gracefully")
-        while (receiverInfo.nonEmpty || running) {
-          Thread.sleep(pollTime)
-        }
-        logInfo("Waited for receiver job to terminate gracefully")
-      }
-
-      // Check if all the receivers have been deregistered or not
-      if (receiverInfo.nonEmpty) {
-        logWarning("Not all of the receivers have deregistered, " + receiverInfo)
-      } else {
-        logInfo("All of the receivers have deregistered successfully")
-      }
-    }
-
     /**
      * Get the list of executors excluding driver
      */
@@ -358,17 +392,30 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
       // Distribute the receivers and start them
       logInfo("Starting " + receivers.length + " receivers")
       running = true
-      ssc.sparkContext.runJob(tempRDD, ssc.sparkContext.clean(startReceiver))
-      running = false
-      logInfo("All of the receivers have been terminated")
+      try {
+        ssc.sparkContext.runJob(tempRDD, ssc.sparkContext.clean(startReceiver))
+        logInfo("All of the receivers have been terminated")
+      } finally {
+        running = false
+      }
     }
 
-    /** Stops the receivers. */
-    private def stopReceivers() {
-      // Signal the receivers to stop
-      receiverInfo.values.flatMap { info => Option(info.endpoint)}
-                         .foreach { _.send(StopReceiver) }
-      logInfo("Sent stop signal to all " + receiverInfo.size + " receivers")
+    /**
+     * Wait until the Spark job that runs the receivers is terminated, or return when
+     * `milliseconds` elapses
+     */
+    def awaitTermination(milliseconds: Long): Unit = {
+      thread.join(milliseconds)
     }
   }
+
+  /** Check if tracker has been marked for starting */
+  private def isTrackerStarted(): Boolean = trackerState == Started
+
+  /** Check if tracker has been marked for stopping */
+  private def isTrackerStopping(): Boolean = trackerState == Stopping
+
+  /** Check if tracker has been marked for stopped */
+  private def isTrackerStopped(): Boolean = trackerState == Stopped
+
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
index 5d7127627eea5..13b4d17c86183 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
@@ -346,6 +346,8 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable {
     def reportError(message: String, throwable: Throwable) {
       errors += throwable
     }
+
+    override protected def onReceiverStart(): Boolean = true
   }
 
   /**
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index f588cf5bc1e7c..4bba9691f8aa5 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -285,6 +285,21 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
     }
   }
 
+  test("stop gracefully even if a receiver misses StopReceiver") {
+    // This is not a deterministic unit. But if this unit test is flaky, then there is definitely
+    // something wrong. See SPARK-5681
+    val conf = new SparkConf().setMaster(master).setAppName(appName)
+    sc = new SparkContext(conf)
+    ssc = new StreamingContext(sc, Milliseconds(100))
+    val input = ssc.receiverStream(new TestReceiver)
+    input.foreachRDD(_ => {})
+    ssc.start()
+    // Call `ssc.stop` at once so that it's possible that the receiver will miss "StopReceiver"
+    failAfter(30000 millis) {
+      ssc.stop(stopSparkContext = true, stopGracefully = true)
+    }
+  }
+
   test("stop slow receiver gracefully") {
     val conf = new SparkConf().setMaster(master).setAppName(appName)
     conf.set("spark.streaming.gracefulStopTimeout", "20000s")

From 6da1069696186572c66cbd83947c1a1dbd2bc827 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Fri, 17 Jul 2015 14:00:53 -0700
Subject: [PATCH 0438/1454] [SPARK-9090] [ML] Fix definition of residual in
 LinearRegressionSummary, EnsembleTestHelper, and SquaredError

Make the definition of residuals in Spark consistent with literature. We have been using `prediction - label` for residuals, but literature usually defines `residual = label - prediction`.

Author: Feynman Liang <fliang@databricks.com>

Closes #7435 from feynmanliang/SPARK-9090-Fix-LinearRegressionSummary-Residuals and squashes the following commits:

f4b39d8 [Feynman Liang] Fix doc
bc12a92 [Feynman Liang] Tweak EnsembleTestHelper and SquaredError residuals
63f0d60 [Feynman Liang] Fix definition of residual
---
 .../org/apache/spark/ml/regression/LinearRegression.scala     | 4 ++--
 .../scala/org/apache/spark/mllib/tree/loss/SquaredError.scala | 4 ++--
 .../apache/spark/ml/regression/LinearRegressionSuite.scala    | 4 ++--
 .../org/apache/spark/mllib/tree/EnsembleTestHelper.scala      | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 8fc986056657d..89718e0f3e15a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -355,9 +355,9 @@ class LinearRegressionSummary private[regression] (
    */
   val r2: Double = metrics.r2
 
-  /** Residuals (predicted value - label value) */
+  /** Residuals (label - predicted value) */
   @transient lazy val residuals: DataFrame = {
-    val t = udf { (pred: Double, label: Double) => pred - label}
+    val t = udf { (pred: Double, label: Double) => label - pred }
     predictions.select(t(col(predictionCol), col(labelCol)).as("residuals"))
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
index a5582d3ef3324..011a5d57422f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
@@ -42,11 +42,11 @@ object SquaredError extends Loss {
    * @return Loss gradient
    */
   override def gradient(prediction: Double, label: Double): Double = {
-    2.0 * (prediction - label)
+    - 2.0 * (label - prediction)
   }
 
   override private[mllib] def computeError(prediction: Double, label: Double): Double = {
-    val err = prediction - label
+    val err = label - prediction
     err * err
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index cf120cf2a4b47..374002c5b4fdd 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -302,7 +302,7 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .map { case Row(features: DenseVector, label: Double) =>
       val prediction =
         features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
-      prediction - label
+      label - prediction
     }
       .zip(model.summary.residuals.map(_.getDouble(0)))
       .collect()
@@ -314,7 +314,7 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        Use the following R code to generate model training results.
 
        predictions <- predict(fit, newx=features)
-       residuals <- predictions - label
+       residuals <- label - predictions
        > mean(residuals^2) # MSE
        [1] 0.009720325
        > mean(abs(residuals)) # MAD
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala
index 8972c229b7ecb..334bf3790fc7a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala
@@ -70,7 +70,7 @@ object EnsembleTestHelper {
       metricName: String = "mse") {
     val predictions = input.map(x => model.predict(x.features))
     val errors = predictions.zip(input.map(_.label)).map { case (prediction, label) =>
-      prediction - label
+      label - prediction
     }
     val metric = metricName match {
       case "mse" =>

From 830666f6fe1e77faa39eed2c1c3cd8e83bc93ef9 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 17 Jul 2015 14:08:06 -0700
Subject: [PATCH 0439/1454] [SPARK-8792] [ML] Add Python API for PCA
 transformer

Add Python API for PCA transformer

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7190 from yanboliang/spark-8792 and squashes the following commits:

8f4ac31 [Yanbo Liang] address comments
8a79cc0 [Yanbo Liang] Add Python API for PCA transformer
---
 python/pyspark/ml/feature.py | 64 +++++++++++++++++++++++++++++++++++-
 1 file changed, 63 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 9bca7cc000aa5..86e654dd0779f 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -24,7 +24,7 @@
 __all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
            'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
            'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
-           'Word2Vec', 'Word2VecModel']
+           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel']
 
 
 @inherit_doc
@@ -1048,6 +1048,68 @@ class Word2VecModel(JavaModel):
     """
 
 
+@inherit_doc
+class PCA(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    PCA trains a model to project vectors to a low-dimensional space using PCA.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
+    ...     (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
+    ...     (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
+    >>> df = sqlContext.createDataFrame(data,["features"])
+    >>> pca = PCA(k=2, inputCol="features", outputCol="pca_features")
+    >>> model = pca.fit(df)
+    >>> model.transform(df).collect()[0].pca_features
+    DenseVector([1.648..., -4.013...])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    k = Param(Params._dummy(), "k", "the number of principal components")
+
+    @keyword_only
+    def __init__(self, k=None, inputCol=None, outputCol=None):
+        """
+        __init__(self, k=None, inputCol=None, outputCol=None)
+        """
+        super(PCA, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.PCA", self.uid)
+        self.k = Param(self, "k", "the number of principal components")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, k=None, inputCol=None, outputCol=None):
+        """
+        setParams(self, k=None, inputCol=None, outputCol=None)
+        Set params for this PCA.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setK(self, value):
+        """
+        Sets the value of :py:attr:`k`.
+        """
+        self._paramMap[self.k] = value
+        return self
+
+    def getK(self):
+        """
+        Gets the value of k or its default value.
+        """
+        return self.getOrDefault(self.k)
+
+    def _create_model(self, java_model):
+        return PCAModel(java_model)
+
+
+class PCAModel(JavaModel):
+    """
+    Model fitted by PCA.
+    """
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

From 8b8be1f5d698e796b96a92f1ed2c13162a90944e Mon Sep 17 00:00:00 2001
From: Bryan Cutler <bjcutler@us.ibm.com>
Date: Fri, 17 Jul 2015 14:10:16 -0700
Subject: [PATCH 0440/1454] [SPARK-7127] [MLLIB] Adding broadcast of model
 before prediction for ensembles

Broadcast of ensemble models in transformImpl before call to predict

Author: Bryan Cutler <bjcutler@us.ibm.com>

Closes #6300 from BryanCutler/bcast-ensemble-models-7127 and squashes the following commits:

86e73de [Bryan Cutler] [SPARK-7127] Replaced deprecated callUDF with udf
40a139d [Bryan Cutler] Merge branch 'master' into bcast-ensemble-models-7127
9afad56 [Bryan Cutler] [SPARK-7127] Simplified calls by overriding transformImpl and using broadcasted model in callUDF to make prediction
1f34be4 [Bryan Cutler] [SPARK-7127] Removed accidental newline
171a6ce [Bryan Cutler] [SPARK-7127] Used modelAccessor parameter in predictImpl to access broadcasted model
6fd153c [Bryan Cutler] [SPARK-7127] Applied broadcasting to remaining ensemble models
aaad77b [Bryan Cutler] [SPARK-7127] Removed abstract class for broadcasting model, instead passing a prediction function as param to transform
83904bb [Bryan Cutler] [SPARK-7127] Adding broadcast of model before prediction in RandomForestClassifier
---
 .../main/scala/org/apache/spark/ml/Predictor.scala   | 12 ++++++++----
 .../spark/ml/classification/GBTClassifier.scala      | 11 ++++++++++-
 .../ml/classification/RandomForestClassifier.scala   | 11 ++++++++++-
 .../apache/spark/ml/regression/GBTRegressor.scala    | 11 ++++++++++-
 .../spark/ml/regression/RandomForestRegressor.scala  | 11 ++++++++++-
 5 files changed, 48 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
index 333b42711ec52..19fe039b8fd03 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -169,10 +169,7 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema, logging = true)
     if ($(predictionCol).nonEmpty) {
-      val predictUDF = udf { (features: Any) =>
-        predict(features.asInstanceOf[FeaturesType])
-      }
-      dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+      transformImpl(dataset)
     } else {
       this.logWarning(s"$uid: Predictor.transform() was called as NOOP" +
         " since no output columns were set.")
@@ -180,6 +177,13 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
     }
   }
 
+  protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val predictUDF = udf { (features: Any) =>
+      predict(features.asInstanceOf[FeaturesType])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   /**
    * Predict label for the given features.
    * This internal method is used to implement [[transform()]] and output [[predictionCol]].
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 554e3b8e052b2..eb0b1a0a405fc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -34,6 +34,8 @@ import org.apache.spark.mllib.tree.loss.{LogLoss => OldLogLoss, Loss => OldLoss}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -177,8 +179,15 @@ final class GBTClassificationModel(
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model: SPARK-7127
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
     val treePredictions = _trees.map(_.rootNode.predict(features))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 490f04c7c7172..fc0693f67cc2e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -31,6 +31,8 @@ import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -143,8 +145,15 @@ final class RandomForestClassificationModel private[ml] (
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model.  SPARK-7127
     // TODO: When we add a generic Bagging class, handle transform there: SPARK-7128
     // Classifies using majority votes.
     // Ignore the weights since all are 1.0 for now.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 47c110d027d67..e38dc73ee0ba7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -33,6 +33,8 @@ import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -167,8 +169,15 @@ final class GBTRegressionModel(
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model. SPARK-7127
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
     val treePredictions = _trees.map(_.rootNode.predict(features))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 5fd5c7c7bd3fc..506a878c2553b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -29,6 +29,8 @@ import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -129,8 +131,15 @@ final class RandomForestRegressionModel private[ml] (
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model.  SPARK-7127
     // TODO: When we add a generic Bagging class, handle transform there.  SPARK-7128
     // Predict average of tree predictions.
     // Ignore the weights since all are 1.0 for now.

From 42d8a012f6652df1fa3f560f87c53731ea070640 Mon Sep 17 00:00:00 2001
From: Joshi <rekhajoshm@gmail.com>
Date: Fri, 17 Jul 2015 22:47:28 +0100
Subject: [PATCH 0441/1454] [SPARK-8593] [CORE] Sort app attempts by start
 time.

This makes sure attempts are listed in the order they were executed, and that the
app's state matches the state of the most current attempt.

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #7253 from rekhajoshm/SPARK-8593 and squashes the following commits:

874dd80 [Joshi] History Server: updated order for multiple attempts(logcleaner)
716e0b1 [Joshi] History Server: updated order for multiple attempts(descending start time works everytime)
548c753 [Joshi] History Server: updated order for multiple attempts(descending start time works everytime)
83306a8 [Joshi] History Server: updated order for multiple attempts(descending start time)
b0fc922 [Joshi] History Server: updated order for multiple attempts(updated comment)
cc0fda7 [Joshi] History Server: updated order for multiple attempts(updated test)
304cb0b [Joshi] History Server: updated order for multiple attempts(reverted HistoryPage)
85024e8 [Joshi] History Server: updated order for multiple attempts
a41ac4b [Joshi] History Server: updated order for multiple attempts
ab65fa1 [Joshi] History Server: some attempt completed to work with showIncomplete
0be142d [Rekha Joshi] Merge pull request #3 from apache/master
106fd8e [Rekha Joshi] Merge pull request #2 from apache/master
e3677c9 [Rekha Joshi] Merge pull request #1 from apache/master
---
 .../deploy/history/FsHistoryProvider.scala    | 10 +++-----
 .../history/FsHistoryProviderSuite.scala      | 24 +++++++++----------
 2 files changed, 14 insertions(+), 20 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index 2cc465e55fceb..e3060ac3fa1a9 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -407,8 +407,8 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
 
   /**
    * Comparison function that defines the sort order for application attempts within the same
-   * application. Order is: running attempts before complete attempts, running attempts sorted
-   * by start time, completed attempts sorted by end time.
+   * application. Order is: attempts are sorted by descending start time.
+   * Most recent attempt state matches with current state of the app.
    *
    * Normally applications should have a single running attempt; but failure to call sc.stop()
    * may cause multiple running attempts to show up.
@@ -418,11 +418,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
   private def compareAttemptInfo(
       a1: FsApplicationAttemptInfo,
       a2: FsApplicationAttemptInfo): Boolean = {
-    if (a1.completed == a2.completed) {
-      if (a1.completed) a1.endTime >= a2.endTime else a1.startTime >= a2.startTime
-    } else {
-      !a1.completed
-    }
+    a1.startTime >= a2.startTime
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
index 2a62450bcdbad..73cff89544dc3 100644
--- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala
@@ -243,13 +243,12 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     appListAfterRename.size should be (1)
   }
 
-  test("apps with multiple attempts") {
+  test("apps with multiple attempts with order") {
     val provider = new FsHistoryProvider(createTestConf())
 
-    val attempt1 = newLogFile("app1", Some("attempt1"), inProgress = false)
+    val attempt1 = newLogFile("app1", Some("attempt1"), inProgress = true)
     writeFile(attempt1, true, None,
-      SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1")),
-      SparkListenerApplicationEnd(2L)
+      SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1"))
       )
 
     updateAndCheck(provider) { list =>
@@ -259,7 +258,7 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
 
     val attempt2 = newLogFile("app1", Some("attempt2"), inProgress = true)
     writeFile(attempt2, true, None,
-      SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2"))
+      SparkListenerApplicationStart("app1", Some("app1"), 2L, "test", Some("attempt2"))
       )
 
     updateAndCheck(provider) { list =>
@@ -268,22 +267,21 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
       list.head.attempts.head.attemptId should be (Some("attempt2"))
     }
 
-    val completedAttempt2 = newLogFile("app1", Some("attempt2"), inProgress = false)
-    attempt2.delete()
-    writeFile(attempt2, true, None,
-      SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")),
+    val attempt3 = newLogFile("app1", Some("attempt3"), inProgress = false)
+    writeFile(attempt3, true, None,
+      SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt3")),
       SparkListenerApplicationEnd(4L)
       )
 
     updateAndCheck(provider) { list =>
       list should not be (null)
       list.size should be (1)
-      list.head.attempts.size should be (2)
-      list.head.attempts.head.attemptId should be (Some("attempt2"))
+      list.head.attempts.size should be (3)
+      list.head.attempts.head.attemptId should be (Some("attempt3"))
     }
 
     val app2Attempt1 = newLogFile("app2", Some("attempt1"), inProgress = false)
-    writeFile(attempt2, true, None,
+    writeFile(attempt1, true, None,
       SparkListenerApplicationStart("app2", Some("app2"), 5L, "test", Some("attempt1")),
       SparkListenerApplicationEnd(6L)
       )
@@ -291,7 +289,7 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     updateAndCheck(provider) { list =>
       list.size should be (2)
       list.head.attempts.size should be (1)
-      list.last.attempts.size should be (2)
+      list.last.attempts.size should be (3)
       list.head.attempts.head.attemptId should be (Some("attempt1"))
 
       list.foreach { case app =>

From b2aa490bb60176631c94ecadf87c14564960f12c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 17 Jul 2015 15:02:13 -0700
Subject: [PATCH 0442/1454] [SPARK-9142] [SQL] Removing unnecessary self types
 in Catalyst.

Just a small change to add Product type to the base expression/plan abstract classes, based on suggestions on #7434 and offline discussions.

Author: Reynold Xin <rxin@databricks.com>

Closes #7479 from rxin/remove-self-types and squashes the following commits:

e407ffd [Reynold Xin] [SPARK-9142][SQL] Removing unnecessary self types in Catalyst.
---
 .../apache/spark/sql/catalyst/analysis/unresolved.scala  | 1 -
 .../spark/sql/catalyst/expressions/Expression.scala      | 7 +------
 .../spark/sql/catalyst/expressions/aggregates.scala      | 3 ---
 .../spark/sql/catalyst/expressions/arithmetic.scala      | 1 -
 .../spark/sql/catalyst/expressions/conditionals.scala    | 1 -
 .../spark/sql/catalyst/expressions/generators.scala      | 2 +-
 .../org/apache/spark/sql/catalyst/expressions/math.scala | 5 ++---
 .../sql/catalyst/expressions/namedExpressions.scala      | 4 ++--
 .../spark/sql/catalyst/expressions/predicates.scala      | 3 ---
 .../apache/spark/sql/catalyst/expressions/random.scala   | 1 -
 .../sql/catalyst/expressions/windowExpressions.scala     | 2 --
 .../spark/sql/catalyst/plans/logical/LogicalPlan.scala   | 9 +--------
 .../sql/catalyst/plans/logical/basicOperators.scala      | 2 +-
 .../spark/sql/catalyst/plans/logical/partitioning.scala  | 2 --
 .../scala/org/apache/spark/sql/execution/SparkPlan.scala | 9 +--------
 .../scala/org/apache/spark/sql/execution/commands.scala  | 2 --
 .../org/apache/spark/sql/parquet/ParquetRelation.scala   | 2 --
 .../org/apache/spark/sql/hive/HiveMetastoreCatalog.scala | 2 --
 18 files changed, 9 insertions(+), 49 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index 7089f079b6dde..4a1a1ed61ebe7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -96,7 +96,6 @@ case class UnresolvedFunction(name: String, children: Seq[Expression]) extends E
  * "SELECT * FROM ...". A [[Star]] gets automatically expanded during analysis.
  */
 abstract class Star extends LeafExpression with NamedExpression {
-  self: Product =>
 
   override def name: String = throw new UnresolvedException(this, "name")
   override def exprId: ExprId = throw new UnresolvedException(this, "exprId")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index f396bd08a8238..c70b5af4aa448 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -43,8 +43,7 @@ import org.apache.spark.sql.types._
  *
  * See [[Substring]] for an example.
  */
-abstract class Expression extends TreeNode[Expression] {
-  self: Product =>
+abstract class Expression extends TreeNode[Expression] with Product {
 
   /**
    * Returns true when an expression is a candidate for static evaluation before the query is
@@ -187,7 +186,6 @@ abstract class Expression extends TreeNode[Expression] {
  * A leaf expression, i.e. one without any child expressions.
  */
 abstract class LeafExpression extends Expression {
-  self: Product =>
 
   def children: Seq[Expression] = Nil
 }
@@ -198,7 +196,6 @@ abstract class LeafExpression extends Expression {
  * if the input is evaluated to null.
  */
 abstract class UnaryExpression extends Expression {
-  self: Product =>
 
   def child: Expression
 
@@ -277,7 +274,6 @@ abstract class UnaryExpression extends Expression {
  * if any input is evaluated to null.
  */
 abstract class BinaryExpression extends Expression {
-  self: Product =>
 
   def left: Expression
   def right: Expression
@@ -370,7 +366,6 @@ abstract class BinaryExpression extends Expression {
  *    the analyzer will find the tightest common type and do the proper type casting.
  */
 abstract class BinaryOperator extends BinaryExpression with ExpectsInputTypes {
-  self: Product =>
 
   /**
    * Expected input type from both left/right child expressions, similar to the
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 71c943dc79e9e..af9a674ab4958 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -27,7 +27,6 @@ import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
 
 trait AggregateExpression extends Expression {
-  self: Product =>
 
   /**
    * Aggregate expressions should not be foldable.
@@ -65,7 +64,6 @@ case class SplitEvaluation(
  * These partial evaluations can then be combined to compute the actual answer.
  */
 trait PartialAggregate extends AggregateExpression {
-  self: Product =>
 
   /**
    * Returns a [[SplitEvaluation]] that computes this aggregation using partial aggregation.
@@ -79,7 +77,6 @@ trait PartialAggregate extends AggregateExpression {
  */
 abstract class AggregateFunction
   extends LeafExpression with AggregateExpression with Serializable {
-  self: Product =>
 
   /** Base should return the generic aggregate expression that this function is computing */
   val base: AggregateExpression
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 1616d1bc0aed5..c5960eb390ea4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -77,7 +77,6 @@ case class Abs(child: Expression) extends UnaryExpression with ExpectsInputTypes
 }
 
 abstract class BinaryArithmetic extends BinaryOperator {
-  self: Product =>
 
   override def dataType: DataType = left.dataType
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index 9162b73fe56eb..15b33da884dcb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -77,7 +77,6 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
 }
 
 trait CaseWhenLike extends Expression {
-  self: Product =>
 
   // Note that `branches` are considered in consecutive pairs (cond, val), and the optional last
   // element is the value for the default catch-all case (if provided).
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index 51dc77ee3fc5f..c58a6d36141c1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -40,7 +40,7 @@ import org.apache.spark.sql.types._
  * requested.  The attributes produced by this function will be automatically copied anytime rules
  * result in changes to the Generator or its children.
  */
-trait Generator extends Expression { self: Product =>
+trait Generator extends Expression {
 
   // TODO ideally we should return the type of ArrayType(StructType),
   // however, we don't keep the output field names in the Generator.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 7a543ff36afd1..b05a7b3ed0ea4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -34,7 +34,6 @@ import org.apache.spark.unsafe.types.UTF8String
  */
 abstract class LeafMathExpression(c: Double, name: String)
   extends LeafExpression with Serializable {
-  self: Product =>
 
   override def dataType: DataType = DoubleType
   override def foldable: Boolean = true
@@ -58,7 +57,7 @@ abstract class LeafMathExpression(c: Double, name: String)
  * @param name The short name of the function
  */
 abstract class UnaryMathExpression(f: Double => Double, name: String)
-  extends UnaryExpression with Serializable with ImplicitCastInputTypes { self: Product =>
+  extends UnaryExpression with Serializable with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(DoubleType)
   override def dataType: DataType = DoubleType
@@ -92,7 +91,7 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
  * @param name The short name of the function
  */
 abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
-  extends BinaryExpression with Serializable with ImplicitCastInputTypes { self: Product =>
+  extends BinaryExpression with Serializable with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[DataType] = Seq(DoubleType, DoubleType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 8bf7a7ce4e647..c083ac08ded21 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -40,7 +40,7 @@ case class ExprId(id: Long)
 /**
  * An [[Expression]] that is named.
  */
-trait NamedExpression extends Expression { self: Product =>
+trait NamedExpression extends Expression {
 
   /** We should never fold named expressions in order to not remove the alias. */
   override def foldable: Boolean = false
@@ -83,7 +83,7 @@ trait NamedExpression extends Expression { self: Product =>
     }
 }
 
-abstract class Attribute extends LeafExpression with NamedExpression { self: Product =>
+abstract class Attribute extends LeafExpression with NamedExpression {
 
   override def references: AttributeSet = AttributeSet(this)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index aa6c30e2f79f2..7a6fb2b3788ca 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -38,8 +38,6 @@ object InterpretedPredicate {
  * An [[Expression]] that returns a boolean value.
  */
 trait Predicate extends Expression {
-  self: Product =>
-
   override def dataType: DataType = BooleanType
 }
 
@@ -222,7 +220,6 @@ case class Or(left: Expression, right: Expression) extends BinaryOperator with P
 
 
 abstract class BinaryComparison extends BinaryOperator with Predicate {
-  self: Product =>
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     if (ctx.isPrimitiveType(left.dataType)) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
index e10ba55396664..65093dc72264b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
@@ -33,7 +33,6 @@ import org.apache.spark.util.random.XORShiftRandom
  * Since this expression is stateful, it cannot be a case object.
  */
 abstract class RDG(seed: Long) extends LeafExpression with Serializable {
-  self: Product =>
 
   /**
    * Record ID within each partition. By being transient, the Random Number Generator is
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
index 344361685853f..c8aa571df64fc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
@@ -254,8 +254,6 @@ object SpecifiedWindowFrame {
  * to retrieve value corresponding with these n rows.
  */
 trait WindowFunction extends Expression {
-  self: Product =>
-
   def init(): Unit
 
   def reset(): Unit
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index adac37231cc4a..dd6c5d43f5714 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -25,8 +25,7 @@ import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.trees.TreeNode
 
 
-abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
-  self: Product =>
+abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging with Product{
 
   /**
    * Computes [[Statistics]] for this plan. The default implementation assumes the output
@@ -277,8 +276,6 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
  * A logical plan node with no children.
  */
 abstract class LeafNode extends LogicalPlan {
-  self: Product =>
-
   override def children: Seq[LogicalPlan] = Nil
 }
 
@@ -286,8 +283,6 @@ abstract class LeafNode extends LogicalPlan {
  * A logical plan node with single child.
  */
 abstract class UnaryNode extends LogicalPlan {
-  self: Product =>
-
   def child: LogicalPlan
 
   override def children: Seq[LogicalPlan] = child :: Nil
@@ -297,8 +292,6 @@ abstract class UnaryNode extends LogicalPlan {
  * A logical plan node with a left and right child.
  */
 abstract class BinaryNode extends LogicalPlan {
-  self: Product =>
-
   def left: LogicalPlan
   def right: LogicalPlan
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index fae339808c233..fbe104db016d6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -298,7 +298,7 @@ case class Expand(
 }
 
 trait GroupingAnalytics extends UnaryNode {
-  self: Product =>
+
   def groupByExprs: Seq[Expression]
   def aggregations: Seq[NamedExpression]
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala
index 63df2c1ee72ff..1f76b03bcb0f6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala
@@ -24,8 +24,6 @@ import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, SortOrd
  * result have expectations about the distribution and ordering of partitioned input data.
  */
 abstract class RedistributeData extends UnaryNode {
-  self: Product =>
-
   override def output: Seq[Attribute] = child.output
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 632f633d82a2e..ba12056ee7a1b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -39,8 +39,7 @@ object SparkPlan {
  * :: DeveloperApi ::
  */
 @DeveloperApi
-abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializable {
-  self: Product =>
+abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Product with Serializable {
 
   /**
    * A handle to the SQL Context that was used to create this plan.   Since many operators need
@@ -239,14 +238,10 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
 }
 
 private[sql] trait LeafNode extends SparkPlan {
-  self: Product =>
-
   override def children: Seq[SparkPlan] = Nil
 }
 
 private[sql] trait UnaryNode extends SparkPlan {
-  self: Product =>
-
   def child: SparkPlan
 
   override def children: Seq[SparkPlan] = child :: Nil
@@ -255,8 +250,6 @@ private[sql] trait UnaryNode extends SparkPlan {
 }
 
 private[sql] trait BinaryNode extends SparkPlan {
-  self: Product =>
-
   def left: SparkPlan
   def right: SparkPlan
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 5e9951f248ff2..bace3f8a9c8d4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -35,8 +35,6 @@ import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext}
  * wrapped in `ExecutedCommand` during execution.
  */
 private[sql] trait RunnableCommand extends LogicalPlan with logical.Command {
-  self: Product =>
-
   override def output: Seq[Attribute] = Seq.empty
   override def children: Seq[LogicalPlan] = Seq.empty
   def run(sqlContext: SQLContext): Seq[Row]
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index e0bea65a15f36..086559e9f7658 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -54,8 +54,6 @@ private[sql] case class ParquetRelation(
     partitioningAttributes: Seq[Attribute] = Nil)
   extends LeafNode with MultiInstanceRelation {
 
-  self: Product =>
-
   /** Schema derived from ParquetFile */
   def parquetSchema: MessageType =
     ParquetTypesConverter
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 4b7a782c805a0..6589bc6ea2921 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -596,8 +596,6 @@ private[hive] case class MetastoreRelation
     (@transient sqlContext: SQLContext)
   extends LeafNode with MultiInstanceRelation {
 
-  self: Product =>
-
   override def equals(other: Any): Boolean = other match {
     case relation: MetastoreRelation =>
       databaseName == relation.databaseName &&

From 15fc2ffe5530c43c64cfc37f2d1ce83f04ce3bd9 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Fri, 17 Jul 2015 15:49:31 -0700
Subject: [PATCH 0443/1454] [SPARK-9080][SQL] add isNaN predicate expression

JIRA: https://issues.apache.org/jira/browse/SPARK-9080

cc rxin

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7464 from yijieshen/isNaN and squashes the following commits:

11ae039 [Yijie Shen] add isNaN in functions
666718e [Yijie Shen] add isNaN predicate expression
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../sql/catalyst/expressions/predicates.scala | 50 +++++++++++++++++++
 .../catalyst/expressions/PredicateSuite.scala | 12 ++++-
 .../scala/org/apache/spark/sql/Column.scala   |  8 +++
 .../org/apache/spark/sql/functions.scala      | 10 +++-
 .../spark/sql/ColumnExpressionSuite.scala     | 21 ++++++++
 6 files changed, 100 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index a45181712dbdf..7bb2579506a8a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -79,6 +79,7 @@ object FunctionRegistry {
     expression[Explode]("explode"),
     expression[Greatest]("greatest"),
     expression[If]("if"),
+    expression[IsNaN]("isnan"),
     expression[IsNull]("isnull"),
     expression[IsNotNull]("isnotnull"),
     expression[Least]("least"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 7a6fb2b3788ca..2751c8e75f357 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -120,6 +120,56 @@ case class InSet(child: Expression, hset: Set[Any])
   }
 }
 
+/**
+ * Evaluates to `true` if it's NaN or null
+ */
+case class IsNaN(child: Expression) extends UnaryExpression
+    with Predicate with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(DoubleType, FloatType))
+
+  override def nullable: Boolean = false
+
+  override def eval(input: InternalRow): Any = {
+    val value = child.eval(input)
+    if (value == null) {
+      true
+    } else {
+      child.dataType match {
+        case DoubleType => value.asInstanceOf[Double].isNaN
+        case FloatType => value.asInstanceOf[Float].isNaN
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val eval = child.gen(ctx)
+    child.dataType match {
+      case FloatType =>
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = false;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (${eval.isNull}) {
+            ${ev.primitive} = true;
+          } else {
+            ${ev.primitive} = Float.isNaN(${eval.primitive});
+          }
+        """
+      case DoubleType =>
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = false;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (${eval.isNull}) {
+            ${ev.primitive} = true;
+          } else {
+            ${ev.primitive} = Double.isNaN(${eval.primitive});
+          }
+        """
+    }
+  }
+}
 
 case class And(left: Expression, right: Expression) extends BinaryOperator with Predicate {
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 188ecef9e7679..052abc51af5fd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -21,7 +21,7 @@ import scala.collection.immutable.HashSet
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{Decimal, IntegerType, BooleanType}
+import org.apache.spark.sql.types.{Decimal, DoubleType, IntegerType, BooleanType}
 
 
 class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -116,6 +116,16 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
       true)
   }
 
+  test("IsNaN") {
+    checkEvaluation(IsNaN(Literal(Double.NaN)), true)
+    checkEvaluation(IsNaN(Literal(Float.NaN)), true)
+    checkEvaluation(IsNaN(Literal(math.log(-3))), true)
+    checkEvaluation(IsNaN(Literal.create(null, DoubleType)), true)
+    checkEvaluation(IsNaN(Literal(Double.PositiveInfinity)), false)
+    checkEvaluation(IsNaN(Literal(Float.MaxValue)), false)
+    checkEvaluation(IsNaN(Literal(5.5f)), false)
+  }
+
   test("INSET") {
     val hS = HashSet[Any]() + 1 + 2
     val nS = HashSet[Any]() + 1 + 2 + null
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 10250264625b2..221cd04c6d288 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -400,6 +400,14 @@ class Column(protected[sql] val expr: Expression) extends Logging {
     (this >= lowerBound) && (this <= upperBound)
   }
 
+  /**
+   * True if the current expression is NaN or null
+   *
+   * @group expr_ops
+   * @since 1.5.0
+   */
+  def isNaN: Column = IsNaN(expr)
+
   /**
    * True if the current expression is null.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index fe511c296cfd2..b56fd9a71b321 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -620,7 +620,15 @@ object functions {
   def explode(e: Column): Column = Explode(e.expr)
 
   /**
-   * Converts a string exprsesion to lower case.
+   * Return true if the column is NaN or null
+   *
+   * @group normal_funcs
+   * @since 1.5.0
+   */
+  def isNaN(e: Column): Column = IsNaN(e.expr)
+
+  /**
+   * Converts a string expression to lower case.
    *
    * @group normal_funcs
    * @since 1.3.0
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 88bb743ab0bc9..8f15479308391 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -201,6 +201,27 @@ class ColumnExpressionSuite extends QueryTest {
       Row(false, true))
   }
 
+  test("isNaN") {
+    val testData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+      Row(Double.NaN, Float.NaN) ::
+      Row(math.log(-1), math.log(-3).toFloat) ::
+      Row(null, null) ::
+      Row(Double.MaxValue, Float.MinValue):: Nil),
+      StructType(Seq(StructField("a", DoubleType), StructField("b", FloatType))))
+
+    checkAnswer(
+      testData.select($"a".isNaN, $"b".isNaN),
+      Row(true, true) :: Row(true, true) :: Row(true, true) :: Row(false, false) :: Nil)
+
+    checkAnswer(
+      testData.select(isNaN($"a"), isNaN($"b")),
+      Row(true, true) :: Row(true, true) :: Row(true, true) :: Row(false, false) :: Nil)
+
+    checkAnswer(
+      ctx.sql("select isnan(15), isnan('invalid')"),
+      Row(false, true))
+  }
+
   test("===") {
     checkAnswer(
       testData2.filter($"a" === 1),

From fd6b3101fbb0a8c3ebcf89ce9b4e8664406d9869 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 17 Jul 2015 16:03:33 -0700
Subject: [PATCH 0444/1454] [SPARK-9113] [SQL] enable analysis check code for
 self join

The check was unreachable before, as `case operator: LogicalPlan` catches everything already.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7449 from cloud-fan/tmp and squashes the following commits:

2bb6637 [Wenchen Fan] add test
5493aea [Wenchen Fan] add the check back
27221a7 [Wenchen Fan] remove unnecessary analysis check code for self join
---
 .../sql/catalyst/analysis/Analyzer.scala      |  2 +-
 .../sql/catalyst/analysis/CheckAnalysis.scala | 28 +++++++++----------
 .../plans/logical/basicOperators.scala        |  6 ++--
 .../analysis/AnalysisErrorSuite.scala         | 14 ++++++++--
 4 files changed, 29 insertions(+), 21 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index df8e7f2381fbd..e58f3f64947f3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -316,7 +316,7 @@ class Analyzer(
         )
 
       // Special handling for cases when self-join introduce duplicate expression ids.
-      case j @ Join(left, right, _, _) if left.outputSet.intersect(right.outputSet).nonEmpty =>
+      case j @ Join(left, right, _, _) if !j.selfJoinResolved =>
         val conflictingAttributes = left.outputSet.intersect(right.outputSet)
         logDebug(s"Conflicting attributes ${conflictingAttributes.mkString(",")} in $j")
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 476ac2b7cb474..c7f9713344c50 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -109,29 +109,27 @@ trait CheckAnalysis {
               s"resolved attribute(s) $missingAttributes missing from $input " +
                 s"in operator ${operator.simpleString}")
 
-          case o if !o.resolved =>
-            failAnalysis(
-              s"unresolved operator ${operator.simpleString}")
-
           case p @ Project(exprs, _) if containsMultipleGenerators(exprs) =>
             failAnalysis(
               s"""Only a single table generating function is allowed in a SELECT clause, found:
                  | ${exprs.map(_.prettyString).mkString(",")}""".stripMargin)
 
+          // Special handling for cases when self-join introduce duplicate expression ids.
+          case j @ Join(left, right, _, _) if left.outputSet.intersect(right.outputSet).nonEmpty =>
+            val conflictingAttributes = left.outputSet.intersect(right.outputSet)
+            failAnalysis(
+              s"""
+                 |Failure when resolving conflicting references in Join:
+                 |$plan
+                  |Conflicting attributes: ${conflictingAttributes.mkString(",")}
+                  |""".stripMargin)
+
+          case o if !o.resolved =>
+            failAnalysis(
+              s"unresolved operator ${operator.simpleString}")
 
           case _ => // Analysis successful!
         }
-
-      // Special handling for cases when self-join introduce duplicate expression ids.
-      case j @ Join(left, right, _, _) if left.outputSet.intersect(right.outputSet).nonEmpty =>
-        val conflictingAttributes = left.outputSet.intersect(right.outputSet)
-        failAnalysis(
-          s"""
-             |Failure when resolving conflicting references in Join:
-             |$plan
-             |Conflicting attributes: ${conflictingAttributes.mkString(",")}
-             |""".stripMargin)
-
     }
     extendedCheckRules.foreach(_(plan))
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index fbe104db016d6..17a91247327f7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -123,11 +123,11 @@ case class Join(
     }
   }
 
-  private def selfJoinResolved: Boolean = left.outputSet.intersect(right.outputSet).isEmpty
+  def selfJoinResolved: Boolean = left.outputSet.intersect(right.outputSet).isEmpty
 
-  // Joins are only resolved if they don't introduce ambiguious expression ids.
+  // Joins are only resolved if they don't introduce ambiguous expression ids.
   override lazy val resolved: Boolean = {
-    childrenResolved && !expressions.exists(!_.resolved) && selfJoinResolved
+    childrenResolved && expressions.forall(_.resolved) && selfJoinResolved
   }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index f0f17103991ef..2147d07e09bd3 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -23,10 +23,11 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.types._
-import org.apache.spark.sql.catalyst.{InternalRow, SimpleCatalystConf}
+import org.apache.spark.sql.catalyst.plans.Inner
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.types._
 
 case class TestFunction(
     children: Seq[Expression],
@@ -164,4 +165,13 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
 
     assert(message.contains("resolved attribute(s) a#1 missing from a#2"))
   }
+
+  test("error test for self-join") {
+    val join = Join(testRelation, testRelation, Inner, None)
+    val error = intercept[AnalysisException] {
+      SimpleAnalyzer.checkAnalysis(join)
+    }
+    error.message.contains("Failure when resolving conflicting references in Join")
+    error.message.contains("Conflicting attributes")
+  }
 }

From bd903ee89f1d1bc4daf63f1f07958cb86d667e1e Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 17 Jul 2015 16:28:24 -0700
Subject: [PATCH 0445/1454] [SPARK-9117] [SQL] fix BooleanSimplification in
 case-insensitive

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7452 from cloud-fan/boolean-simplify and squashes the following commits:

2a6e692 [Wenchen Fan] fix style
d3cfd26 [Wenchen Fan] fix BooleanSimplification in case-insensitive
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 28 +++++-----
 .../BooleanSimplificationSuite.scala          | 55 +++++++++----------
 2 files changed, 40 insertions(+), 43 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index d5beeec0ffac1..0f28a0d2c8fff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -393,26 +393,26 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         // (a || b) && (a || c)  =>  a || (b && c)
         case _ =>
           // 1. Split left and right to get the disjunctive predicates,
-          //   i.e. lhsSet = (a, b), rhsSet = (a, c)
+          //   i.e. lhs = (a, b), rhs = (a, c)
           // 2. Find the common predict between lhsSet and rhsSet, i.e. common = (a)
           // 3. Remove common predict from lhsSet and rhsSet, i.e. ldiff = (b), rdiff = (c)
           // 4. Apply the formula, get the optimized predicate: common || (ldiff && rdiff)
-          val lhsSet = splitDisjunctivePredicates(left).toSet
-          val rhsSet = splitDisjunctivePredicates(right).toSet
-          val common = lhsSet.intersect(rhsSet)
+          val lhs = splitDisjunctivePredicates(left)
+          val rhs = splitDisjunctivePredicates(right)
+          val common = lhs.filter(e => rhs.exists(e.semanticEquals(_)))
           if (common.isEmpty) {
             // No common factors, return the original predicate
             and
           } else {
-            val ldiff = lhsSet.diff(common)
-            val rdiff = rhsSet.diff(common)
+            val ldiff = lhs.filterNot(e => common.exists(e.semanticEquals(_)))
+            val rdiff = rhs.filterNot(e => common.exists(e.semanticEquals(_)))
             if (ldiff.isEmpty || rdiff.isEmpty) {
               // (a || b || c || ...) && (a || b) => (a || b)
               common.reduce(Or)
             } else {
               // (a || b || c || ...) && (a || b || d || ...) =>
               // ((c || ...) && (d || ...)) || a || b
-              (common + And(ldiff.reduce(Or), rdiff.reduce(Or))).reduce(Or)
+              (common :+ And(ldiff.reduce(Or), rdiff.reduce(Or))).reduce(Or)
             }
           }
       }  // end of And(left, right)
@@ -431,26 +431,26 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         // (a && b) || (a && c)  =>  a && (b || c)
         case _ =>
            // 1. Split left and right to get the conjunctive predicates,
-           //   i.e.  lhsSet = (a, b), rhsSet = (a, c)
+           //   i.e.  lhs = (a, b), rhs = (a, c)
            // 2. Find the common predict between lhsSet and rhsSet, i.e. common = (a)
            // 3. Remove common predict from lhsSet and rhsSet, i.e. ldiff = (b), rdiff = (c)
            // 4. Apply the formula, get the optimized predicate: common && (ldiff || rdiff)
-          val lhsSet = splitConjunctivePredicates(left).toSet
-          val rhsSet = splitConjunctivePredicates(right).toSet
-          val common = lhsSet.intersect(rhsSet)
+          val lhs = splitConjunctivePredicates(left)
+          val rhs = splitConjunctivePredicates(right)
+          val common = lhs.filter(e => rhs.exists(e.semanticEquals(_)))
           if (common.isEmpty) {
             // No common factors, return the original predicate
             or
           } else {
-            val ldiff = lhsSet.diff(common)
-            val rdiff = rhsSet.diff(common)
+            val ldiff = lhs.filterNot(e => common.exists(e.semanticEquals(_)))
+            val rdiff = rhs.filterNot(e => common.exists(e.semanticEquals(_)))
             if (ldiff.isEmpty || rdiff.isEmpty) {
               // (a && b) || (a && b && c && ...) => a && b
               common.reduce(And)
             } else {
               // (a && b && c && ...) || (a && b && d && ...) =>
               // ((c && ...) || (d && ...)) && a && b
-              (common + Or(ldiff.reduce(And), rdiff.reduce(And))).reduce(And)
+              (common :+ Or(ldiff.reduce(And), rdiff.reduce(And))).reduce(And)
             }
           }
       }  // end of Or(left, right)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
index 465a5e6914204..d4916ea8d273a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.optimizer
 
-import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
+import org.apache.spark.sql.catalyst.analysis.{AnalysisSuite, EliminateSubQueries}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.PlanTest
@@ -40,29 +40,11 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper {
 
   val testRelation = LocalRelation('a.int, 'b.int, 'c.int, 'd.string)
 
-  // The `foldLeft` is required to handle cases like comparing `a && (b && c)` and `(a && b) && c`
-  def compareConditions(e1: Expression, e2: Expression): Boolean = (e1, e2) match {
-    case (lhs: And, rhs: And) =>
-      val lhsSet = splitConjunctivePredicates(lhs).toSet
-      val rhsSet = splitConjunctivePredicates(rhs).toSet
-      lhsSet.foldLeft(rhsSet) { (set, e) =>
-        set.find(compareConditions(_, e)).map(set - _).getOrElse(set)
-      }.isEmpty
-
-    case (lhs: Or, rhs: Or) =>
-      val lhsSet = splitDisjunctivePredicates(lhs).toSet
-      val rhsSet = splitDisjunctivePredicates(rhs).toSet
-      lhsSet.foldLeft(rhsSet) { (set, e) =>
-        set.find(compareConditions(_, e)).map(set - _).getOrElse(set)
-      }.isEmpty
-
-    case (l, r) => l == r
-  }
-
-  def checkCondition(input: Expression, expected: Expression): Unit = {
+  private def checkCondition(input: Expression, expected: Expression): Unit = {
     val plan = testRelation.where(input).analyze
-    val actual = Optimize.execute(plan).expressions.head
-    compareConditions(actual, expected)
+    val actual = Optimize.execute(plan)
+    val correctAnswer = testRelation.where(expected).analyze
+    comparePlans(actual, correctAnswer)
   }
 
   test("a && a => a") {
@@ -86,10 +68,8 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper {
       ('a === 'b && 'c < 1 && 'a === 5) ||
       ('a === 'b && 'b < 5 && 'a > 1)
 
-    val expected =
-      (((('b > 3) && ('c > 2)) ||
-        (('c < 1) && ('a === 5))) ||
-        (('b < 5) && ('a > 1))) && ('a === 'b)
+    val expected = 'a === 'b && (
+      ('b > 3 && 'c > 2) || ('c < 1 && 'a === 5) || ('b < 5 && 'a > 1))
 
     checkCondition(input, expected)
   }
@@ -101,10 +81,27 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper {
 
     checkCondition('a < 2 && ('a < 2 || 'a > 3 || 'b > 5) , 'a < 2)
 
-    checkCondition(('a < 2 || 'b > 3) && ('a < 2 || 'c > 5), ('b > 3 && 'c > 5) || 'a < 2)
+    checkCondition(('a < 2 || 'b > 3) && ('a < 2 || 'c > 5), 'a < 2 || ('b > 3 && 'c > 5))
 
     checkCondition(
       ('a === 'b || 'b > 3) && ('a === 'b || 'a > 3) && ('a === 'b || 'a < 5),
-      ('b > 3 && 'a > 3 && 'a < 5) || 'a === 'b)
+      ('a === 'b || 'b > 3 && 'a > 3 && 'a < 5))
+  }
+
+  private def caseInsensitiveAnalyse(plan: LogicalPlan) =
+    AnalysisSuite.caseInsensitiveAnalyzer.execute(plan)
+
+  test("(a && b) || (a && c) => a && (b || c) when case insensitive") {
+    val plan = caseInsensitiveAnalyse(testRelation.where(('a > 2 && 'b > 3) || ('A > 2 && 'b < 5)))
+    val actual = Optimize.execute(plan)
+    val expected = caseInsensitiveAnalyse(testRelation.where('a > 2 && ('b > 3 || 'b < 5)))
+    comparePlans(actual, expected)
+  }
+
+  test("(a || b) && (a || c) => a || (b && c) when case insensitive") {
+    val plan = caseInsensitiveAnalyse(testRelation.where(('a > 2 || 'b > 3) && ('A > 2 || 'b < 5)))
+    val actual = Optimize.execute(plan)
+    val expected = caseInsensitiveAnalyse(testRelation.where('a > 2 || ('b > 3 && 'b < 5)))
+    comparePlans(actual, expected)
   }
 }

From b13ef7723f254c10c685b93eb8dc08a52527ec73 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 17 Jul 2015 16:43:18 -0700
Subject: [PATCH 0446/1454] [SPARK-9030] [STREAMING] Add Kinesis.createStream
 unit tests that actual sends data

Current Kinesis unit tests do not test createStream by sending data. This PR is to add such unit test. Note that this unit will not run by default. It will only run when the relevant environment variables are set.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7413 from tdas/kinesis-tests and squashes the following commits:

0e16db5 [Tathagata Das] Added more comments regarding testOrIgnore
1ea5ce0 [Tathagata Das] Added more comments
c7caef7 [Tathagata Das] Address comments
a297b59 [Tathagata Das] Reverted unnecessary change in KafkaStreamSuite
90c9bde [Tathagata Das] Removed scalatest.FunSuite
deb7f4f [Tathagata Das] Removed scalatest.FunSuite
18c2208 [Tathagata Das] Changed how SparkFunSuite is inherited
dbb33a5 [Tathagata Das] Added license
88f6dab [Tathagata Das] Added scala docs
c6be0d7 [Tathagata Das] minor changes
24a992b [Tathagata Das] Moved KinesisTestUtils to src instead of test for future python usage
465b55d [Tathagata Das] Made unit tests optional in a nice way
4d70703 [Tathagata Das] Added license
129d436 [Tathagata Das] Minor updates
cc36510 [Tathagata Das] Added KinesisStreamSuite
---
 .../streaming/kinesis/KinesisTestUtils.scala  | 197 ++++++++++++++++++
 .../streaming/kinesis/KinesisFunSuite.scala   |  37 ++++
 .../kinesis/KinesisReceiverSuite.scala        |  17 --
 .../kinesis/KinesisStreamSuite.scala          | 120 +++++++++++
 4 files changed, 354 insertions(+), 17 deletions(-)
 create mode 100644 extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
 create mode 100644 extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
 create mode 100644 extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala

diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
new file mode 100644
index 0000000000000..f6bf552e6bb8e
--- /dev/null
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -0,0 +1,197 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kinesis
+
+import java.nio.ByteBuffer
+import java.util.concurrent.TimeUnit
+
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+import scala.util.{Failure, Random, Success, Try}
+
+import com.amazonaws.auth.{AWSCredentials, DefaultAWSCredentialsProviderChain}
+import com.amazonaws.regions.RegionUtils
+import com.amazonaws.services.dynamodbv2.AmazonDynamoDBClient
+import com.amazonaws.services.dynamodbv2.document.DynamoDB
+import com.amazonaws.services.kinesis.AmazonKinesisClient
+import com.amazonaws.services.kinesis.model._
+
+import org.apache.spark.Logging
+
+/**
+ * Shared utility methods for performing Kinesis tests that actually transfer data
+ */
+private class KinesisTestUtils(
+    val endpointUrl: String = "https://kinesis.us-west-2.amazonaws.com",
+    _regionName: String = "") extends Logging {
+
+  val regionName = if (_regionName.length == 0) {
+    RegionUtils.getRegionByEndpoint(endpointUrl).getName()
+  } else {
+    RegionUtils.getRegion(_regionName).getName()
+  }
+
+  val streamShardCount = 2
+
+  private val createStreamTimeoutSeconds = 300
+  private val describeStreamPollTimeSeconds = 1
+
+  @volatile
+  private var streamCreated = false
+  private var _streamName: String = _
+
+  private lazy val kinesisClient = {
+    val client = new AmazonKinesisClient(KinesisTestUtils.getAWSCredentials())
+    client.setEndpoint(endpointUrl)
+    client
+  }
+
+  private lazy val dynamoDB = {
+    val dynamoDBClient = new AmazonDynamoDBClient(new DefaultAWSCredentialsProviderChain())
+    dynamoDBClient.setRegion(RegionUtils.getRegion(regionName))
+    new DynamoDB(dynamoDBClient)
+  }
+
+  def streamName: String = {
+    require(streamCreated, "Stream not yet created, call createStream() to create one")
+    _streamName
+  }
+
+  def createStream(): Unit = {
+    logInfo("Creating stream")
+    require(!streamCreated, "Stream already created")
+    _streamName = findNonExistentStreamName()
+
+    // Create a stream. The number of shards determines the provisioned throughput.
+    val createStreamRequest = new CreateStreamRequest()
+    createStreamRequest.setStreamName(_streamName)
+    createStreamRequest.setShardCount(2)
+    kinesisClient.createStream(createStreamRequest)
+
+    // The stream is now being created. Wait for it to become active.
+    waitForStreamToBeActive(_streamName)
+    streamCreated = true
+    logInfo("Created stream")
+  }
+
+  /**
+   * Push data to Kinesis stream and return a map of
+   * shardId -> seq of (data, seq number) pushed to corresponding shard
+   */
+  def pushData(testData: Seq[Int]): Map[String, Seq[(Int, String)]] = {
+    require(streamCreated, "Stream not yet created, call createStream() to create one")
+    val shardIdToSeqNumbers = new mutable.HashMap[String, ArrayBuffer[(Int, String)]]()
+
+    testData.foreach { num =>
+      val str = num.toString
+      val putRecordRequest = new PutRecordRequest().withStreamName(streamName)
+        .withData(ByteBuffer.wrap(str.getBytes()))
+        .withPartitionKey(str)
+
+      val putRecordResult = kinesisClient.putRecord(putRecordRequest)
+      val shardId = putRecordResult.getShardId
+      val seqNumber = putRecordResult.getSequenceNumber()
+      val sentSeqNumbers = shardIdToSeqNumbers.getOrElseUpdate(shardId,
+        new ArrayBuffer[(Int, String)]())
+      sentSeqNumbers += ((num, seqNumber))
+    }
+
+    logInfo(s"Pushed $testData:\n\t ${shardIdToSeqNumbers.mkString("\n\t")}")
+    shardIdToSeqNumbers.toMap
+  }
+
+  def describeStream(streamNameToDescribe: String = streamName): Option[StreamDescription] = {
+    try {
+      val describeStreamRequest = new DescribeStreamRequest().withStreamName(streamNameToDescribe)
+      val desc = kinesisClient.describeStream(describeStreamRequest).getStreamDescription()
+      Some(desc)
+    } catch {
+      case rnfe: ResourceNotFoundException =>
+        None
+    }
+  }
+
+  def deleteStream(): Unit = {
+    try {
+      if (describeStream().nonEmpty) {
+        val deleteStreamRequest = new DeleteStreamRequest()
+        kinesisClient.deleteStream(streamName)
+      }
+    } catch {
+      case e: Exception =>
+        logWarning(s"Could not delete stream $streamName")
+    }
+  }
+
+  def deleteDynamoDBTable(tableName: String): Unit = {
+    try {
+      val table = dynamoDB.getTable(tableName)
+      table.delete()
+      table.waitForDelete()
+    } catch {
+      case e: Exception =>
+        logWarning(s"Could not delete DynamoDB table $tableName")
+    }
+  }
+
+  private def findNonExistentStreamName(): String = {
+    var testStreamName: String = null
+    do {
+      Thread.sleep(TimeUnit.SECONDS.toMillis(describeStreamPollTimeSeconds))
+      testStreamName = s"KinesisTestUtils-${math.abs(Random.nextLong())}"
+    } while (describeStream(testStreamName).nonEmpty)
+    testStreamName
+  }
+
+  private def waitForStreamToBeActive(streamNameToWaitFor: String): Unit = {
+    val startTime = System.currentTimeMillis()
+    val endTime = startTime + TimeUnit.SECONDS.toMillis(createStreamTimeoutSeconds)
+    while (System.currentTimeMillis() < endTime) {
+      Thread.sleep(TimeUnit.SECONDS.toMillis(describeStreamPollTimeSeconds))
+      describeStream(streamNameToWaitFor).foreach { description =>
+        val streamStatus = description.getStreamStatus()
+        logDebug(s"\t- current state: $streamStatus\n")
+        if ("ACTIVE".equals(streamStatus)) {
+          return
+        }
+      }
+    }
+    require(false, s"Stream $streamName never became active")
+  }
+}
+
+private[kinesis] object KinesisTestUtils {
+
+  val envVarName = "RUN_KINESIS_TESTS"
+
+  val shouldRunTests = sys.env.get(envVarName) == Some("1")
+
+  def isAWSCredentialsPresent: Boolean = {
+    Try { new DefaultAWSCredentialsProviderChain().getCredentials() }.isSuccess
+  }
+
+  def getAWSCredentials(): AWSCredentials = {
+    assert(shouldRunTests,
+      "Kinesis test not enabled, should not attempt to get AWS credentials")
+    Try { new DefaultAWSCredentialsProviderChain().getCredentials() } match {
+      case Success(cred) => cred
+      case Failure(e) =>
+        throw new Exception("Kinesis tests enabled, but could get not AWS credentials")
+    }
+  }
+}
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
new file mode 100644
index 0000000000000..6d011f295e7f7
--- /dev/null
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kinesis
+
+import org.apache.spark.SparkFunSuite
+
+/**
+ * Helper class that runs Kinesis real data transfer tests or
+ * ignores them based on env variable is set or not.
+ */
+trait KinesisSuiteHelper { self: SparkFunSuite =>
+  import KinesisTestUtils._
+
+  /** Run the test if environment variable is set or ignore the test */
+  def testOrIgnore(testName: String)(testBody: => Unit) {
+    if (shouldRunTests) {
+      test(testName)(testBody)
+    } else {
+      ignore(s"$testName [enable by setting env var $envVarName=1]")(testBody)
+    }
+  }
+}
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
index 2103dca6b766f..98f2c7c4f1bfb 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
@@ -73,23 +73,6 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
       checkpointStateMock, currentClockMock)
   }
 
-  test("KinesisUtils API") {
-    val ssc = new StreamingContext(master, framework, batchDuration)
-    // Tests the API, does not actually test data receiving
-    val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", Seconds(2),
-      InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2)
-    val kinesisStream2 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
-      InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2)
-    val kinesisStream3 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
-      InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2,
-      "awsAccessKey", "awsSecretKey")
-
-    ssc.stop()
-  }
-
   test("check serializability of SerializableAWSCredentials") {
     Utils.deserialize[SerializableAWSCredentials](
       Utils.serialize(new SerializableAWSCredentials("x", "y")))
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
new file mode 100644
index 0000000000000..d3dd541fe4371
--- /dev/null
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -0,0 +1,120 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kinesis
+
+import scala.collection.mutable
+import scala.concurrent.duration._
+import scala.language.postfixOps
+import scala.util.Random
+
+import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
+import org.scalatest.concurrent.Eventually
+import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
+
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming._
+import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
+
+class KinesisStreamSuite extends SparkFunSuite with KinesisSuiteHelper
+  with Eventually with BeforeAndAfter with BeforeAndAfterAll {
+
+  private val kinesisTestUtils = new KinesisTestUtils()
+
+  // This is the name that KCL uses to save metadata to DynamoDB
+  private val kinesisAppName = s"KinesisStreamSuite-${math.abs(Random.nextLong())}"
+
+  private var ssc: StreamingContext = _
+  private var sc: SparkContext = _
+
+  override def beforeAll(): Unit = {
+    kinesisTestUtils.createStream()
+    val conf = new SparkConf()
+      .setMaster("local[4]")
+      .setAppName("KinesisStreamSuite") // Setting Spark app name to Kinesis app name
+    sc = new SparkContext(conf)
+  }
+
+  override def afterAll(): Unit = {
+    sc.stop()
+    // Delete the Kinesis stream as well as the DynamoDB table generated by
+    // Kinesis Client Library when consuming the stream
+    kinesisTestUtils.deleteStream()
+    kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
+  }
+
+  before {
+    // Delete the DynamoDB table generated by Kinesis Client Library when
+    // consuming from the stream, so that each unit test can start from
+    // scratch without prior history of data consumption
+    kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
+  }
+
+  after {
+    if (ssc != null) {
+      ssc.stop(stopSparkContext = false)
+      ssc = null
+    }
+  }
+
+  test("KinesisUtils API") {
+    ssc = new StreamingContext(sc, Seconds(1))
+    // Tests the API, does not actually test data receiving
+    val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
+      "https://kinesis.us-west-2.amazonaws.com", Seconds(2),
+      InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2)
+    val kinesisStream2 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
+      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+      InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2)
+    val kinesisStream3 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
+      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+      InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2,
+      "awsAccessKey", "awsSecretKey")
+  }
+
+
+  /**
+   * Test the stream by sending data to a Kinesis stream and receiving from it.
+   * This test is not run by default as it requires AWS credentials that the test
+   * environment may not have. Even if there is AWS credentials available, the user
+   * may not want to run these tests to avoid the Kinesis costs. To enable this test,
+   * you must have AWS credentials available through the default AWS provider chain,
+   * and you have to set the system environment variable RUN_KINESIS_TESTS=1 .
+   */
+  testOrIgnore("basic operation") {
+    ssc = new StreamingContext(sc, Seconds(1))
+    val aWSCredentials = KinesisTestUtils.getAWSCredentials()
+    val stream = KinesisUtils.createStream(ssc, kinesisAppName, kinesisTestUtils.streamName,
+      kinesisTestUtils.endpointUrl, kinesisTestUtils.regionName, InitialPositionInStream.LATEST,
+      Seconds(10), StorageLevel.MEMORY_ONLY,
+      aWSCredentials.getAWSAccessKeyId, aWSCredentials.getAWSSecretKey)
+
+    val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
+    stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>
+      collected ++= rdd.collect()
+      logInfo("Collected = " + rdd.collect().toSeq.mkString(", "))
+    }
+    ssc.start()
+
+    val testData = 1 to 10
+    eventually(timeout(120 seconds), interval(10 second)) {
+      kinesisTestUtils.pushData(testData)
+      assert(collected === testData.toSet, "\nData received does not match data sent")
+    }
+    ssc.stop()
+  }
+}

From 1707238601690fd0e8e173e2c47f1b4286644a29 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Fri, 17 Jul 2015 16:45:46 -0700
Subject: [PATCH 0447/1454] [SPARK-7026] [SQL] fix left semi join with equi key
 and non-equi condition

When the `condition` extracted by `ExtractEquiJoinKeys` contain join Predicate for left semi join, we can not plan it as semiJoin. Such as

    SELECT * FROM testData2 x
    LEFT SEMI JOIN testData2 y
    ON x.b = y.b
    AND x.a >= y.a + 2
Condition `x.a >= y.a + 2` can not evaluate on table `x`, so it throw errors

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #5643 from adrian-wang/spark7026 and squashes the following commits:

cc09809 [Daoyuan Wang] refactor semijoin and add plan test
575a7c8 [Daoyuan Wang] fix notserializable
27841de [Daoyuan Wang] fix rebase
10bf124 [Daoyuan Wang] fix style
72baa02 [Daoyuan Wang] fix style
8e0afca [Daoyuan Wang] merge commits for rebase
---
 .../spark/sql/execution/SparkStrategies.scala | 10 +-
 .../joins/BroadcastLeftSemiJoinHash.scala     | 42 ++++-----
 .../sql/execution/joins/HashOuterJoin.scala   |  3 +-
 .../sql/execution/joins/HashSemiJoin.scala    | 91 +++++++++++++++++++
 .../execution/joins/LeftSemiJoinHash.scala    | 35 ++-----
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 12 +++
 .../sql/execution/joins/SemiJoinSuite.scala   | 74 +++++++++++++++
 7 files changed, 208 insertions(+), 59 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 73b463471ec5a..240332a80af0f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -38,14 +38,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right)
         if sqlContext.conf.autoBroadcastJoinThreshold > 0 &&
           right.statistics.sizeInBytes <= sqlContext.conf.autoBroadcastJoinThreshold =>
-        val semiJoin = joins.BroadcastLeftSemiJoinHash(
-          leftKeys, rightKeys, planLater(left), planLater(right))
-        condition.map(Filter(_, semiJoin)).getOrElse(semiJoin) :: Nil
+        joins.BroadcastLeftSemiJoinHash(
+          leftKeys, rightKeys, planLater(left), planLater(right), condition) :: Nil
       // Find left semi joins where at least some predicates can be evaluated by matching join keys
       case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right) =>
-        val semiJoin = joins.LeftSemiJoinHash(
-          leftKeys, rightKeys, planLater(left), planLater(right))
-        condition.map(Filter(_, semiJoin)).getOrElse(semiJoin) :: Nil
+        joins.LeftSemiJoinHash(
+          leftKeys, rightKeys, planLater(left), planLater(right), condition) :: Nil
       // no predicate can be evaluated by matching hash keys
       case logical.Join(left, right, LeftSemi, condition) =>
         joins.LeftSemiJoinBNL(planLater(left), planLater(right), condition) :: Nil
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index f7b46d6888d7d..2750f58b005ac 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -33,37 +33,27 @@ case class BroadcastLeftSemiJoinHash(
     leftKeys: Seq[Expression],
     rightKeys: Seq[Expression],
     left: SparkPlan,
-    right: SparkPlan) extends BinaryNode with HashJoin {
-
-  override val buildSide: BuildSide = BuildRight
-
-  override def output: Seq[Attribute] = left.output
+    right: SparkPlan,
+    condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
   protected override def doExecute(): RDD[InternalRow] = {
-    val buildIter = buildPlan.execute().map(_.copy()).collect().toIterator
-    val hashSet = new java.util.HashSet[InternalRow]()
-    var currentRow: InternalRow = null
+    val buildIter = right.execute().map(_.copy()).collect().toIterator
 
-    // Create a Hash set of buildKeys
-    while (buildIter.hasNext) {
-      currentRow = buildIter.next()
-      val rowKey = buildSideKeyGenerator(currentRow)
-      if (!rowKey.anyNull) {
-        val keyExists = hashSet.contains(rowKey)
-        if (!keyExists) {
-          // rowKey may be not serializable (from codegen)
-          hashSet.add(rowKey.copy())
-        }
-      }
-    }
+    if (condition.isEmpty) {
+      // rowKey may be not serializable (from codegen)
+      val hashSet = buildKeyHashSet(buildIter, copy = true)
+      val broadcastedRelation = sparkContext.broadcast(hashSet)
 
-    val broadcastedRelation = sparkContext.broadcast(hashSet)
+      left.execute().mapPartitions { streamIter =>
+        hashSemiJoin(streamIter, broadcastedRelation.value)
+      }
+    } else {
+      val hashRelation = HashedRelation(buildIter, rightKeyGenerator)
+      val broadcastedRelation = sparkContext.broadcast(hashRelation)
 
-    streamedPlan.execute().mapPartitions { streamIter =>
-      val joinKeys = streamSideKeyGenerator()
-      streamIter.filter(current => {
-        !joinKeys(current).anyNull && broadcastedRelation.value.contains(joinKeys.currentValue)
-      })
+      left.execute().mapPartitions { streamIter =>
+        hashSemiJoin(streamIter, broadcastedRelation.value)
+      }
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 0522ee85eeb8a..74a7db7761758 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -65,8 +65,7 @@ override def outputPartitioning: Partitioning = joinType match {
   @transient private[this] lazy val leftNullRow = new GenericInternalRow(left.output.length)
   @transient private[this] lazy val rightNullRow = new GenericInternalRow(right.output.length)
   @transient private[this] lazy val boundCondition =
-    condition.map(
-      newPredicate(_, left.output ++ right.output)).getOrElse((row: InternalRow) => true)
+    newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
   // TODO we need to rewrite all of the iterators with our own implementation instead of the Scala
   // iterator for performance purpose.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
new file mode 100644
index 0000000000000..1b983bc3a90f9
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.SparkPlan
+
+
+trait HashSemiJoin {
+  self: SparkPlan =>
+  val leftKeys: Seq[Expression]
+  val rightKeys: Seq[Expression]
+  val left: SparkPlan
+  val right: SparkPlan
+  val condition: Option[Expression]
+
+  override def output: Seq[Attribute] = left.output
+
+  @transient protected lazy val rightKeyGenerator: Projection =
+    newProjection(rightKeys, right.output)
+
+  @transient protected lazy val leftKeyGenerator: () => MutableProjection =
+    newMutableProjection(leftKeys, left.output)
+
+  @transient private lazy val boundCondition =
+    newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
+
+  protected def buildKeyHashSet(
+      buildIter: Iterator[InternalRow],
+      copy: Boolean): java.util.Set[InternalRow] = {
+    val hashSet = new java.util.HashSet[InternalRow]()
+    var currentRow: InternalRow = null
+
+    // Create a Hash set of buildKeys
+    while (buildIter.hasNext) {
+      currentRow = buildIter.next()
+      val rowKey = rightKeyGenerator(currentRow)
+      if (!rowKey.anyNull) {
+        val keyExists = hashSet.contains(rowKey)
+        if (!keyExists) {
+          if (copy) {
+            hashSet.add(rowKey.copy())
+          } else {
+            // rowKey may be not serializable (from codegen)
+            hashSet.add(rowKey)
+          }
+        }
+      }
+    }
+    hashSet
+  }
+
+  protected def hashSemiJoin(
+      streamIter: Iterator[InternalRow],
+      hashedRelation: HashedRelation): Iterator[InternalRow] = {
+    val joinKeys = leftKeyGenerator()
+    val joinedRow = new JoinedRow
+    streamIter.filter(current => {
+      lazy val rowBuffer = hashedRelation.get(joinKeys.currentValue)
+      !joinKeys(current).anyNull && rowBuffer != null && rowBuffer.exists {
+        (build: InternalRow) => boundCondition(joinedRow(current, build))
+      }
+    })
+  }
+
+  protected def hashSemiJoin(
+      streamIter: Iterator[InternalRow],
+      hashSet: java.util.Set[InternalRow]): Iterator[InternalRow] = {
+    val joinKeys = leftKeyGenerator()
+    val joinedRow = new JoinedRow
+    streamIter.filter(current => {
+      !joinKeys(current.copy()).anyNull && hashSet.contains(joinKeys.currentValue)
+    })
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
index 611ba928a16ec..9eaac817d9268 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.joins
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.ClusteredDistribution
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 
@@ -34,36 +34,21 @@ case class LeftSemiJoinHash(
     leftKeys: Seq[Expression],
     rightKeys: Seq[Expression],
     left: SparkPlan,
-    right: SparkPlan) extends BinaryNode with HashJoin {
-
-  override val buildSide: BuildSide = BuildRight
+    right: SparkPlan,
+    condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
   override def requiredChildDistribution: Seq[ClusteredDistribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
-  override def output: Seq[Attribute] = left.output
-
   protected override def doExecute(): RDD[InternalRow] = {
-    buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
-      val hashSet = new java.util.HashSet[InternalRow]()
-      var currentRow: InternalRow = null
-
-      // Create a Hash set of buildKeys
-      while (buildIter.hasNext) {
-        currentRow = buildIter.next()
-        val rowKey = buildSideKeyGenerator(currentRow)
-        if (!rowKey.anyNull) {
-          val keyExists = hashSet.contains(rowKey)
-          if (!keyExists) {
-            hashSet.add(rowKey)
-          }
-        }
+    right.execute().zipPartitions(left.execute()) { (buildIter, streamIter) =>
+      if (condition.isEmpty) {
+        val hashSet = buildKeyHashSet(buildIter, copy = false)
+        hashSemiJoin(streamIter, hashSet)
+      } else {
+        val hashRelation = HashedRelation(buildIter, rightKeyGenerator)
+        hashSemiJoin(streamIter, hashRelation)
       }
-
-      val joinKeys = streamSideKeyGenerator()
-      streamIter.filter(current => {
-        !joinKeys(current).anyNull && hashSet.contains(joinKeys.currentValue)
-      })
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 5b8b70ed5ae11..61d5f2061ae18 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -395,6 +395,18 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     )
   }
 
+  test("left semi greater than predicate and equal operator") {
+    checkAnswer(
+      sql("SELECT * FROM testData2 x LEFT SEMI JOIN testData2 y ON x.b = y.b and x.a >= y.a + 2"),
+      Seq(Row(3, 1), Row(3, 2))
+    )
+
+    checkAnswer(
+      sql("SELECT * FROM testData2 x LEFT SEMI JOIN testData2 y ON x.b = y.a and x.a >= y.b + 1"),
+      Seq(Row(2, 1), Row(2, 2), Row(3, 1), Row(3, 2))
+    )
+  }
+
   test("index into array of arrays") {
     checkAnswer(
       sql(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
new file mode 100644
index 0000000000000..927e85a7db3dc
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions.{LessThan, Expression}
+import org.apache.spark.sql.execution.{SparkPlan, SparkPlanTest}
+
+
+class SemiJoinSuite extends SparkPlanTest{
+  val left = Seq(
+    (1, 2.0),
+    (1, 2.0),
+    (2, 1.0),
+    (2, 1.0),
+    (3, 3.0)
+  ).toDF("a", "b")
+
+  val right = Seq(
+    (2, 3.0),
+    (2, 3.0),
+    (3, 2.0),
+    (4, 1.0)
+  ).toDF("c", "d")
+
+  val leftKeys: List[Expression] = 'a :: Nil
+  val rightKeys: List[Expression] = 'c :: Nil
+  val condition = Some(LessThan('b, 'd))
+
+  test("left semi join hash") {
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
+      LeftSemiJoinHash(leftKeys, rightKeys, left, right, condition),
+      Seq(
+        (2, 1.0),
+        (2, 1.0)
+      ).map(Row.fromTuple))
+  }
+
+  test("left semi join BNL") {
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
+      LeftSemiJoinBNL(left, right, condition),
+      Seq(
+        (1, 2.0),
+        (1, 2.0),
+        (2, 1.0),
+        (2, 1.0)
+      ).map(Row.fromTuple))
+  }
+
+  test("broadcast left semi join hash") {
+    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
+      BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, condition),
+      Seq(
+        (2, 1.0),
+        (2, 1.0)
+      ).map(Row.fromTuple))
+  }
+}

From 529a2c2d92fef062e0078a8608fa3a8ae848c139 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Fri, 17 Jul 2015 17:33:19 -0700
Subject: [PATCH 0448/1454] [SPARK-8280][SPARK-8281][SQL]Handle NaN, null and
 Infinity in math

JIRA:
https://issues.apache.org/jira/browse/SPARK-8280
https://issues.apache.org/jira/browse/SPARK-8281

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7451 from yijieshen/nan_null2 and squashes the following commits:

47a529d [Yijie Shen] style fix
63dee44 [Yijie Shen] handle log expressions similar to Hive
188be51 [Yijie Shen] null to nan in Math Expression
---
 .../catalyst/analysis/FunctionRegistry.scala  |   2 +-
 .../spark/sql/catalyst/expressions/math.scala |  97 ++++++++++-------
 .../expressions/MathFunctionsSuite.scala      | 102 +++++++++++++++---
 .../spark/sql/MathExpressionsSuite.scala      |   7 +-
 .../execution/HiveCompatibilitySuite.scala    |  12 ++-
 5 files changed, 157 insertions(+), 63 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 7bb2579506a8a..ce552a1d65eda 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -112,9 +112,9 @@ object FunctionRegistry {
     expression[Log]("ln"),
     expression[Log10]("log10"),
     expression[Log1p]("log1p"),
+    expression[Log2]("log2"),
     expression[UnaryMinus]("negative"),
     expression[Pi]("pi"),
-    expression[Log2]("log2"),
     expression[Pow]("pow"),
     expression[Pow]("power"),
     expression[Pmod]("pmod"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index b05a7b3ed0ea4..9101f11052218 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -65,22 +65,38 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
   override def toString: String = s"$name($child)"
 
   protected override def nullSafeEval(input: Any): Any = {
-    val result = f(input.asInstanceOf[Double])
-    if (result.isNaN) null else result
+    f(input.asInstanceOf[Double])
   }
 
   // name of function in java.lang.Math
   def funcName: String = name.toLowerCase
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, eval => {
+    defineCodeGen(ctx, ev, c => s"java.lang.Math.${funcName}($c)")
+  }
+}
+
+abstract class UnaryLogExpression(f: Double => Double, name: String)
+    extends UnaryMathExpression(f, name) { self: Product =>
+
+  // values less than or equal to yAsymptote eval to null in Hive, instead of NaN or -Infinity
+  protected val yAsymptote: Double = 0.0
+
+  protected override def nullSafeEval(input: Any): Any = {
+    val d = input.asInstanceOf[Double]
+    if (d <= yAsymptote) null else f(d)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, c =>
       s"""
-        ${ev.primitive} = java.lang.Math.${funcName}($eval);
-        if (Double.valueOf(${ev.primitive}).isNaN()) {
+        if ($c <= $yAsymptote) {
           ${ev.isNull} = true;
+        } else {
+          ${ev.primitive} = java.lang.Math.${funcName}($c);
         }
       """
-    })
+    )
   }
 }
 
@@ -100,8 +116,7 @@ abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
   override def dataType: DataType = DoubleType
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
-    val result = f(input1.asInstanceOf[Double], input2.asInstanceOf[Double])
-    if (result.isNaN) null else result
+    f(input1.asInstanceOf[Double], input2.asInstanceOf[Double])
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -398,25 +413,28 @@ case class Factorial(child: Expression) extends UnaryExpression with ImplicitCas
   }
 }
 
-case class Log(child: Expression) extends UnaryMathExpression(math.log, "LOG")
+case class Log(child: Expression) extends UnaryLogExpression(math.log, "LOG")
 
 case class Log2(child: Expression)
-  extends UnaryMathExpression((x: Double) => math.log(x) / math.log(2), "LOG2") {
+  extends UnaryLogExpression((x: Double) => math.log(x) / math.log(2), "LOG2") {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, eval => {
+    nullSafeCodeGen(ctx, ev, c =>
       s"""
-        ${ev.primitive} = java.lang.Math.log($eval) / java.lang.Math.log(2);
-        if (Double.valueOf(${ev.primitive}).isNaN()) {
+        if ($c <= $yAsymptote) {
           ${ev.isNull} = true;
+        } else {
+          ${ev.primitive} = java.lang.Math.log($c) / java.lang.Math.log(2);
         }
       """
-    })
+    )
   }
 }
 
-case class Log10(child: Expression) extends UnaryMathExpression(math.log10, "LOG10")
+case class Log10(child: Expression) extends UnaryLogExpression(math.log10, "LOG10")
 
-case class Log1p(child: Expression) extends UnaryMathExpression(math.log1p, "LOG1P")
+case class Log1p(child: Expression) extends UnaryLogExpression(math.log1p, "LOG1P") {
+  protected override val yAsymptote: Double = -1.0
+}
 
 case class Rint(child: Expression) extends UnaryMathExpression(math.rint, "ROUND") {
   override def funcName: String = "rint"
@@ -577,27 +595,18 @@ case class Atan2(left: Expression, right: Expression)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
     // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0
-    val result = math.atan2(input1.asInstanceOf[Double] + 0.0, input2.asInstanceOf[Double] + 0.0)
-    if (result.isNaN) null else result
+    math.atan2(input1.asInstanceOf[Double] + 0.0, input2.asInstanceOf[Double] + 0.0)
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.atan2($c1 + 0.0, $c2 + 0.0)") + s"""
-      if (Double.valueOf(${ev.primitive}).isNaN()) {
-        ${ev.isNull} = true;
-      }
-      """
+    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.atan2($c1 + 0.0, $c2 + 0.0)")
   }
 }
 
 case class Pow(left: Expression, right: Expression)
   extends BinaryMathExpression(math.pow, "POWER") {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.pow($c1, $c2)") + s"""
-      if (Double.valueOf(${ev.primitive}).isNaN()) {
-        ${ev.isNull} = true;
-      }
-      """
+    defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.pow($c1, $c2)")
   }
 }
 
@@ -699,17 +708,33 @@ case class Logarithm(left: Expression, right: Expression)
     this(EulerNumber(), child)
   }
 
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val dLeft = input1.asInstanceOf[Double]
+    val dRight = input2.asInstanceOf[Double]
+    // Unlike Hive, we support Log base in (0.0, 1.0]
+    if (dLeft <= 0.0 || dRight <= 0.0) null else math.log(dRight) / math.log(dLeft)
+  }
+
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val logCode = if (left.isInstanceOf[EulerNumber]) {
-      defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.log($c2)")
+    if (left.isInstanceOf[EulerNumber]) {
+      nullSafeCodeGen(ctx, ev, (c1, c2) =>
+        s"""
+          if ($c2 <= 0.0) {
+            ${ev.isNull} = true;
+          } else {
+            ${ev.primitive} = java.lang.Math.log($c2);
+          }
+        """)
     } else {
-      defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.log($c2) / java.lang.Math.log($c1)")
+      nullSafeCodeGen(ctx, ev, (c1, c2) =>
+        s"""
+          if ($c1 <= 0.0 || $c2 <= 0.0) {
+            ${ev.isNull} = true;
+          } else {
+            ${ev.primitive} = java.lang.Math.log($c2) / java.lang.Math.log($c1);
+          }
+        """)
     }
-    logCode + s"""
-      if (Double.isNaN(${ev.primitive})) {
-        ${ev.isNull} = true;
-      }
-    """
   }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index ca35c7ef8ae5d..df988f57fbfde 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -21,6 +21,10 @@ import com.google.common.math.LongMath
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateProjection, GenerateMutableProjection}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
+import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 import org.apache.spark.sql.types._
 
 
@@ -47,6 +51,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
    * @param f The functions in scala.math or elsewhere used to generate expected results
    * @param domain The set of values to run the function with
    * @param expectNull Whether the given values should return null or not
+   * @param expectNaN Whether the given values should eval to NaN or not
    * @tparam T Generic type for primitives
    * @tparam U Generic type for the output of the given function `f`
    */
@@ -55,11 +60,16 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       f: T => U,
       domain: Iterable[T] = (-20 to 20).map(_ * 0.1),
       expectNull: Boolean = false,
+      expectNaN: Boolean = false,
       evalType: DataType = DoubleType): Unit = {
     if (expectNull) {
       domain.foreach { value =>
         checkEvaluation(c(Literal(value)), null, EmptyRow)
       }
+    } else if (expectNaN) {
+      domain.foreach { value =>
+        checkNaN(c(Literal(value)), EmptyRow)
+      }
     } else {
       domain.foreach { value =>
         checkEvaluation(c(Literal(value)), f(value), EmptyRow)
@@ -74,16 +84,22 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
    * @param c The DataFrame function
    * @param f The functions in scala.math
    * @param domain The set of values to run the function with
+   * @param expectNull Whether the given values should return null or not
+   * @param expectNaN Whether the given values should eval to NaN or not
    */
   private def testBinary(
       c: (Expression, Expression) => Expression,
       f: (Double, Double) => Double,
       domain: Iterable[(Double, Double)] = (-20 to 20).map(v => (v * 0.1, v * -0.1)),
-      expectNull: Boolean = false): Unit = {
+      expectNull: Boolean = false, expectNaN: Boolean = false): Unit = {
     if (expectNull) {
       domain.foreach { case (v1, v2) =>
         checkEvaluation(c(Literal(v1), Literal(v2)), null, create_row(null))
       }
+    } else if (expectNaN) {
+      domain.foreach { case (v1, v2) =>
+        checkNaN(c(Literal(v1), Literal(v2)), EmptyRow)
+      }
     } else {
       domain.foreach { case (v1, v2) =>
         checkEvaluation(c(Literal(v1), Literal(v2)), f(v1 + 0.0, v2 + 0.0), EmptyRow)
@@ -112,6 +128,62 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       Conv(Literal("11abc"), Literal(10), Literal(16)), "B")
   }
 
+  private def checkNaN(
+      expression: Expression, inputRow: InternalRow = EmptyRow): Unit = {
+    checkNaNWithoutCodegen(expression, inputRow)
+    checkNaNWithGeneratedProjection(expression, inputRow)
+    checkNaNWithOptimization(expression, inputRow)
+  }
+
+  private def checkNaNWithoutCodegen(
+      expression: Expression,
+      expected: Any,
+      inputRow: InternalRow = EmptyRow): Unit = {
+    val actual = try evaluate(expression, inputRow) catch {
+      case e: Exception => fail(s"Exception evaluating $expression", e)
+    }
+    if (!actual.asInstanceOf[Double].isNaN) {
+      val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
+      fail(s"Incorrect evaluation (codegen off): $expression, " +
+        s"actual: $actual, " +
+        s"expected: NaN")
+    }
+  }
+
+
+  private def checkNaNWithGeneratedProjection(
+    expression: Expression,
+    inputRow: InternalRow = EmptyRow): Unit = {
+
+    val plan = try {
+      GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)()
+    } catch {
+      case e: Throwable =>
+        val ctx = GenerateProjection.newCodeGenContext()
+        val evaluated = expression.gen(ctx)
+        fail(
+          s"""
+             |Code generation of $expression failed:
+             |${evaluated.code}
+             |$e
+          """.stripMargin)
+    }
+
+    val actual = plan(inputRow).apply(0)
+    if (!actual.asInstanceOf[Double].isNaN) {
+      val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
+      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: NaN")
+    }
+  }
+
+  private def checkNaNWithOptimization(
+    expression: Expression,
+    inputRow: InternalRow = EmptyRow): Unit = {
+    val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation)
+    val optimizedPlan = DefaultOptimizer.execute(plan)
+    checkNaNWithoutCodegen(optimizedPlan.expressions.head, inputRow)
+  }
+
   test("e") {
     testLeaf(EulerNumber, math.E)
   }
@@ -126,7 +198,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("asin") {
     testUnary(Asin, math.asin, (-10 to 10).map(_ * 0.1))
-    testUnary(Asin, math.asin, (11 to 20).map(_ * 0.1), expectNull = true)
+    testUnary(Asin, math.asin, (11 to 20).map(_ * 0.1), expectNaN = true)
   }
 
   test("sinh") {
@@ -139,7 +211,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("acos") {
     testUnary(Acos, math.acos, (-10 to 10).map(_ * 0.1))
-    testUnary(Acos, math.acos, (11 to 20).map(_ * 0.1), expectNull = true)
+    testUnary(Acos, math.acos, (11 to 20).map(_ * 0.1), expectNaN = true)
   }
 
   test("cosh") {
@@ -204,18 +276,18 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("log") {
-    testUnary(Log, math.log, (0 to 20).map(_ * 0.1))
-    testUnary(Log, math.log, (-5 to -1).map(_ * 0.1), expectNull = true)
+    testUnary(Log, math.log, (1 to 20).map(_ * 0.1))
+    testUnary(Log, math.log, (-5 to 0).map(_ * 0.1), expectNull = true)
   }
 
   test("log10") {
-    testUnary(Log10, math.log10, (0 to 20).map(_ * 0.1))
-    testUnary(Log10, math.log10, (-5 to -1).map(_ * 0.1), expectNull = true)
+    testUnary(Log10, math.log10, (1 to 20).map(_ * 0.1))
+    testUnary(Log10, math.log10, (-5 to 0).map(_ * 0.1), expectNull = true)
   }
 
   test("log1p") {
-    testUnary(Log1p, math.log1p, (-1 to 20).map(_ * 0.1))
-    testUnary(Log1p, math.log1p, (-10 to -2).map(_ * 1.0), expectNull = true)
+    testUnary(Log1p, math.log1p, (0 to 20).map(_ * 0.1))
+    testUnary(Log1p, math.log1p, (-10 to -1).map(_ * 1.0), expectNull = true)
   }
 
   test("bin") {
@@ -237,22 +309,22 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("log2") {
     def f: (Double) => Double = (x: Double) => math.log(x) / math.log(2)
-    testUnary(Log2, f, (0 to 20).map(_ * 0.1))
-    testUnary(Log2, f, (-5 to -1).map(_ * 1.0), expectNull = true)
+    testUnary(Log2, f, (1 to 20).map(_ * 0.1))
+    testUnary(Log2, f, (-5 to 0).map(_ * 1.0), expectNull = true)
   }
 
   test("sqrt") {
     testUnary(Sqrt, math.sqrt, (0 to 20).map(_ * 0.1))
-    testUnary(Sqrt, math.sqrt, (-5 to -1).map(_ * 1.0), expectNull = true)
+    testUnary(Sqrt, math.sqrt, (-5 to -1).map(_ * 1.0), expectNaN = true)
 
     checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, create_row(null))
-    checkEvaluation(Sqrt(Literal(-1.0)), null, EmptyRow)
-    checkEvaluation(Sqrt(Literal(-1.5)), null, EmptyRow)
+    checkNaN(Sqrt(Literal(-1.0)), EmptyRow)
+    checkNaN(Sqrt(Literal(-1.5)), EmptyRow)
   }
 
   test("pow") {
     testBinary(Pow, math.pow, (-5 to 5).map(v => (v * 1.0, v * 1.0)))
-    testBinary(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), expectNull = true)
+    testBinary(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), expectNaN = true)
   }
 
   test("shift left") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 8eb3fec756b4c..a51523f1a7a0f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -68,12 +68,7 @@ class MathExpressionsSuite extends QueryTest {
     if (f(-1) === math.log1p(-1)) {
       checkAnswer(
         nnDoubleData.select(c('b)),
-        (1 to 9).map(n => Row(f(n * -0.1))) :+ Row(Double.NegativeInfinity)
-      )
-    } else {
-      checkAnswer(
-        nnDoubleData.select(c('b)),
-        (1 to 10).map(n => Row(null))
+        (1 to 9).map(n => Row(f(n * -0.1))) :+ Row(null)
       )
     }
 
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 4ada64bc21966..6b8f2f6217a54 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -254,7 +254,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // Spark SQL use Long for TimestampType, lose the precision under 1us
     "timestamp_1",
     "timestamp_2",
-    "timestamp_udf"
+    "timestamp_udf",
+
+    // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
+    "udf7"
   )
 
   /**
@@ -816,19 +819,18 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf2",
     "udf5",
     "udf6",
-    // "udf7",  turn this on after we figure out null vs nan vs infinity
     "udf8",
     "udf9",
     "udf_10_trims",
     "udf_E",
     "udf_PI",
     "udf_abs",
-    // "udf_acos",  turn this on after we figure out null vs nan vs infinity
+    "udf_acos",
     "udf_add",
     "udf_array",
     "udf_array_contains",
     "udf_ascii",
-    // "udf_asin",  turn this on after we figure out null vs nan vs infinity
+    "udf_asin",
     "udf_atan",
     "udf_avg",
     "udf_bigint",
@@ -915,7 +917,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_regexp_replace",
     "udf_repeat",
     "udf_rlike",
-    // "udf_round",  turn this on after we figure out null vs nan vs infinity
+    "udf_round",
     "udf_round_3",
     "udf_rpad",
     "udf_rtrim",

From 34a889db857f8752a0a78dcedec75ac6cd6cd48d Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Fri, 17 Jul 2015 18:30:04 -0700
Subject: [PATCH 0449/1454] [SPARK-7879] [MLLIB] KMeans API for spark.ml
 Pipelines

I Implemented the KMeans API for spark.ml Pipelines. But it doesn't include clustering abstractions for spark.ml (SPARK-7610). It would fit for another issues. And I'll try it later, since we are trying to add the hierarchical clustering algorithms in another issue. Thanks.

[SPARK-7879] KMeans API for spark.ml Pipelines - ASF JIRA https://issues.apache.org/jira/browse/SPARK-7879

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6756 from yu-iskw/SPARK-7879 and squashes the following commits:

be752de [Yu ISHIKAWA] Add assertions
a14939b [Yu ISHIKAWA] Fix the dashed line's length in pyspark.ml.rst
4c61693 [Yu ISHIKAWA] Remove the test about whether "features" and "prediction" columns exist or not in Python
fb2417c [Yu ISHIKAWA] Use getInt, instead of get
f397be4 [Yu ISHIKAWA] Switch the comparisons.
ca78b7d [Yu ISHIKAWA] Add the Scala docs about the constraints of each parameter.
effc650 [Yu ISHIKAWA] Using expertSetParam and expertGetParam
c8dc6e6 [Yu ISHIKAWA] Remove an unnecessary test
19a9d63 [Yu ISHIKAWA] Include spark.ml.clustering to python tests
1abb19c [Yu ISHIKAWA] Add the statements about spark.ml.clustering into pyspark.ml.rst
f8338bc [Yu ISHIKAWA] Add the placeholders in Python
4a03003 [Yu ISHIKAWA] Test for contains in Python
6566c8b [Yu ISHIKAWA] Use `get`, instead of `apply`
288e8d5 [Yu ISHIKAWA] Using `contains` to check the column names
5a7d574 [Yu ISHIKAWA] Renamce `validateInitializationMode` to `validateInitMode` and remove throwing exception
97cfae3 [Yu ISHIKAWA] Fix the type of return value of `KMeans.copy`
e933723 [Yu ISHIKAWA] Remove the default value of seed from the Model class
978ee2c [Yu ISHIKAWA] Modify the docs of KMeans, according to mllib's KMeans
2ec80bc [Yu ISHIKAWA] Fit on 1 line
e186be1 [Yu ISHIKAWA] Make a few variables, setters and getters be expert ones
b2c205c [Yu ISHIKAWA] Rename the method `getInitializationSteps` to `getInitSteps` and `setInitializationSteps` to `setInitSteps` in Scala and Python
f43f5b4 [Yu ISHIKAWA] Rename the method `getInitializationMode` to `getInitMode` and `setInitializationMode` to `setInitMode` in Scala and Python
3cb5ba4 [Yu ISHIKAWA] Modify the description about epsilon and the validation
4fa409b [Yu ISHIKAWA] Add a comment about the default value of epsilon
2f392e1 [Yu ISHIKAWA] Make some variables `final` and Use `IntParam` and `DoubleParam`
19326f8 [Yu ISHIKAWA] Use `udf`, instead of callUDF
4d2ad1e [Yu ISHIKAWA] Modify the indentations
0ae422f [Yu ISHIKAWA] Add a test for `setParams`
4ff7913 [Yu ISHIKAWA] Add "ml.clustering" to `javacOptions` in SparkBuild.scala
11ffdf1 [Yu ISHIKAWA] Use `===` and the variable
220a176 [Yu ISHIKAWA] Set a random seed in the unit testing
92c3efc [Yu ISHIKAWA] Make the points for a test be fewer
c758692 [Yu ISHIKAWA] Modify the parameters of KMeans in Python
6aca147 [Yu ISHIKAWA] Add some unit testings to validate the setter methods
687cacc [Yu ISHIKAWA] Alias mllib.KMeans as MLlibKMeans in KMeansSuite.scala
a4dfbef [Yu ISHIKAWA] Modify the last brace and indentations
5bedc51 [Yu ISHIKAWA] Remve an extra new line
444c289 [Yu ISHIKAWA] Add the validation for `runs`
e41989c [Yu ISHIKAWA] Modify how to validate `initStep`
7ea133a [Yu ISHIKAWA] Change how to validate `initMode`
7991e15 [Yu ISHIKAWA] Add a validation for `k`
c2df35d [Yu ISHIKAWA] Make `predict` private
93aa2ff [Yu ISHIKAWA] Use `withColumn` in `transform`
d3a79f7 [Yu ISHIKAWA] Remove the inhefited docs
e9532e1 [Yu ISHIKAWA] make `parentModel` of KMeansModel private
8559772 [Yu ISHIKAWA] Remove the `paramMap` parameter of KMeans
6684850 [Yu ISHIKAWA] Rename `initializationSteps` to `initSteps`
99b1b96 [Yu ISHIKAWA] Rename `initializationMode` to `initMode`
79ea82b [Yu ISHIKAWA] Modify the parameters of KMeans docs
6569bcd [Yu ISHIKAWA] Change how to set the default values with `setDefault`
20a795a [Yu ISHIKAWA] Change how to set the default values with `setDefault`
11c2a12 [Yu ISHIKAWA] Limit the imports
badb481 [Yu ISHIKAWA] Alias spark.mllib.{KMeans, KMeansModel}
f80319a [Yu ISHIKAWA] Rebase mater branch and add copy methods
85d92b1 [Yu ISHIKAWA] Add `KMeans.setPredictionCol`
aa9469d [Yu ISHIKAWA] Fix a python test suite error caused by python 3.x
c2d6bcb [Yu ISHIKAWA] ADD Java test suites of the KMeans API for spark.ml Pipeline
598ed2e [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Python
63ad785 [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Scala
---
 dev/sparktestsupport/modules.py               |   1 +
 .../apache/spark/ml/clustering/KMeans.scala   | 205 +++++++++++++++++
 .../spark/mllib/clustering/KMeans.scala       |  12 +-
 .../spark/ml/clustering/JavaKMeansSuite.java  |  72 ++++++
 .../spark/ml/clustering/KMeansSuite.scala     | 114 ++++++++++
 project/SparkBuild.scala                      |   4 +-
 python/docs/pyspark.ml.rst                    |   8 +
 python/pyspark/ml/clustering.py               | 206 ++++++++++++++++++
 8 files changed, 617 insertions(+), 5 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
 create mode 100644 python/pyspark/ml/clustering.py

diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 993583e2f4119..3073d489bad4a 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -338,6 +338,7 @@ def contains_file(self, filename):
     python_test_goals=[
         "pyspark.ml.feature",
         "pyspark.ml.classification",
+        "pyspark.ml.clustering",
         "pyspark.ml.recommendation",
         "pyspark.ml.regression",
         "pyspark.ml.tuning",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
new file mode 100644
index 0000000000000..dc192add6ca13
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.{Param, Params, IntParam, DoubleParam, ParamMap}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{IntegerType, StructType}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.util.Utils
+
+
+/**
+ * Common params for KMeans and KMeansModel
+ */
+private[clustering] trait KMeansParams
+    extends Params with HasMaxIter with HasFeaturesCol with HasSeed with HasPredictionCol {
+
+  /**
+   * Set the number of clusters to create (k). Must be > 1. Default: 2.
+   * @group param
+   */
+  final val k = new IntParam(this, "k", "number of clusters to create", (x: Int) => x > 1)
+
+  /** @group getParam */
+  def getK: Int = $(k)
+
+  /**
+   * Param the number of runs of the algorithm to execute in parallel. We initialize the algorithm
+   * this many times with random starting conditions (configured by the initialization mode), then
+   * return the best clustering found over any run. Must be >= 1. Default: 1.
+   * @group param
+   */
+  final val runs = new IntParam(this, "runs",
+    "number of runs of the algorithm to execute in parallel", (value: Int) => value >= 1)
+
+  /** @group getParam */
+  def getRuns: Int = $(runs)
+
+  /**
+   * Param the distance threshold within which we've consider centers to have converged.
+   * If all centers move less than this Euclidean distance, we stop iterating one run.
+   * Must be >= 0.0. Default: 1e-4
+   * @group param
+   */
+  final val epsilon = new DoubleParam(this, "epsilon",
+    "distance threshold within which we've consider centers to have converge",
+    (value: Double) => value >= 0.0)
+
+  /** @group getParam */
+  def getEpsilon: Double = $(epsilon)
+
+  /**
+   * Param for the initialization algorithm. This can be either "random" to choose random points as
+   * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
+   * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
+   * @group expertParam
+   */
+  final val initMode = new Param[String](this, "initMode", "initialization algorithm",
+    (value: String) => MLlibKMeans.validateInitMode(value))
+
+  /** @group expertGetParam */
+  def getInitMode: String = $(initMode)
+
+  /**
+   * Param for the number of steps for the k-means|| initialization mode. This is an advanced
+   * setting -- the default of 5 is almost always enough. Must be > 0. Default: 5.
+   * @group expertParam
+   */
+  final val initSteps = new IntParam(this, "initSteps", "number of steps for k-means||",
+    (value: Int) => value > 0)
+
+  /** @group expertGetParam */
+  def getInitSteps: Int = $(initSteps)
+
+  /**
+   * Validates and transforms the input schema.
+   * @param schema input schema
+   * @return output schema
+   */
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
+    SchemaUtils.appendColumn(schema, $(predictionCol), IntegerType)
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by KMeans.
+ *
+ * @param parentModel a model trained by spark.mllib.clustering.KMeans.
+ */
+@Experimental
+class KMeansModel private[ml] (
+    override val uid: String,
+    private val parentModel: MLlibKMeansModel) extends Model[KMeansModel] with KMeansParams {
+
+  override def copy(extra: ParamMap): KMeansModel = {
+    val copied = new KMeansModel(uid, parentModel)
+    copyValues(copied, extra)
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val predictUDF = udf((vector: Vector) => predict(vector))
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
+
+  def clusterCenters: Array[Vector] = parentModel.clusterCenters
+}
+
+/**
+ * :: Experimental ::
+ * K-means clustering with support for multiple parallel runs and a k-means++ like initialization
+ * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
+ * they are executed together with joint passes over the data for efficiency.
+ */
+@Experimental
+class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams {
+
+  setDefault(
+    k -> 2,
+    maxIter -> 20,
+    runs -> 1,
+    initMode -> MLlibKMeans.K_MEANS_PARALLEL,
+    initSteps -> 5,
+    epsilon -> 1e-4)
+
+  override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
+
+  def this() = this(Identifiable.randomUID("kmeans"))
+
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
+
+  /** @group setParam */
+  def setK(value: Int): this.type = set(k, value)
+
+  /** @group expertSetParam */
+  def setInitMode(value: String): this.type = set(initMode, value)
+
+  /** @group expertSetParam */
+  def setInitSteps(value: Int): this.type = set(initSteps, value)
+
+  /** @group setParam */
+  def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+  /** @group setParam */
+  def setRuns(value: Int): this.type = set(runs, value)
+
+  /** @group setParam */
+  def setEpsilon(value: Double): this.type = set(epsilon, value)
+
+  /** @group setParam */
+  def setSeed(value: Long): this.type = set(seed, value)
+
+  override def fit(dataset: DataFrame): KMeansModel = {
+    val rdd = dataset.select(col($(featuresCol))).map { case Row(point: Vector) => point }
+
+    val algo = new MLlibKMeans()
+      .setK($(k))
+      .setInitializationMode($(initMode))
+      .setInitializationSteps($(initSteps))
+      .setMaxIterations($(maxIter))
+      .setSeed($(seed))
+      .setEpsilon($(epsilon))
+      .setRuns($(runs))
+    val parentModel = algo.run(rdd)
+    val model = new KMeansModel(uid, parentModel)
+    copyValues(model)
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+}
+
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 68297130a7b03..0a65403f4ec95 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -85,9 +85,7 @@ class KMeans private (
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
    */
   def setInitializationMode(initializationMode: String): this.type = {
-    if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) {
-      throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode)
-    }
+    KMeans.validateInitMode(initializationMode)
     this.initializationMode = initializationMode
     this
   }
@@ -550,6 +548,14 @@ object KMeans {
       v2: VectorWithNorm): Double = {
     MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
   }
+
+  private[spark] def validateInitMode(initMode: String): Boolean = {
+    initMode match {
+      case KMeans.RANDOM => true
+      case KMeans.K_MEANS_PARALLEL => true
+      case _ => false
+    }
+  }
 }
 
 /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
new file mode 100644
index 0000000000000..d09fa7fd5637c
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering;
+
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class JavaKMeansSuite implements Serializable {
+
+  private transient int k = 5;
+  private transient JavaSparkContext sc;
+  private transient DataFrame dataset;
+  private transient SQLContext sql;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaKMeansSuite");
+    sql = new SQLContext(sc);
+
+    dataset = KMeansSuite.generateKMeansData(sql, 50, 3, k);
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void fitAndTransform() {
+    KMeans kmeans = new KMeans().setK(k).setSeed(1);
+    KMeansModel model = kmeans.fit(dataset);
+
+    Vector[] centers = model.clusterCenters();
+    assertEquals(k, centers.length);
+
+    DataFrame transformed = model.transform(dataset);
+    List<String> columns = Arrays.asList(transformed.columns());
+    List<String> expectedColumns = Arrays.asList("features", "prediction");
+    for (String column: expectedColumns) {
+      assertTrue(columns.contains(column));
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
new file mode 100644
index 0000000000000..1f15ac02f4008
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
@@ -0,0 +1,114 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, SQLContext}
+
+private[clustering] case class TestRow(features: Vector)
+
+object KMeansSuite {
+  def generateKMeansData(sql: SQLContext, rows: Int, dim: Int, k: Int): DataFrame = {
+    val sc = sql.sparkContext
+    val rdd = sc.parallelize(1 to rows).map(i => Vectors.dense(Array.fill(dim)((i % k).toDouble)))
+      .map(v => new TestRow(v))
+    sql.createDataFrame(rdd)
+  }
+}
+
+class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  final val k = 5
+  @transient var dataset: DataFrame = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+
+    dataset = KMeansSuite.generateKMeansData(sqlContext, 50, 3, k)
+  }
+
+  test("default parameters") {
+    val kmeans = new KMeans()
+
+    assert(kmeans.getK === 2)
+    assert(kmeans.getFeaturesCol === "features")
+    assert(kmeans.getPredictionCol === "prediction")
+    assert(kmeans.getMaxIter === 20)
+    assert(kmeans.getRuns === 1)
+    assert(kmeans.getInitMode === MLlibKMeans.K_MEANS_PARALLEL)
+    assert(kmeans.getInitSteps === 5)
+    assert(kmeans.getEpsilon === 1e-4)
+  }
+
+  test("set parameters") {
+    val kmeans = new KMeans()
+      .setK(9)
+      .setFeaturesCol("test_feature")
+      .setPredictionCol("test_prediction")
+      .setMaxIter(33)
+      .setRuns(7)
+      .setInitMode(MLlibKMeans.RANDOM)
+      .setInitSteps(3)
+      .setSeed(123)
+      .setEpsilon(1e-3)
+
+    assert(kmeans.getK === 9)
+    assert(kmeans.getFeaturesCol === "test_feature")
+    assert(kmeans.getPredictionCol === "test_prediction")
+    assert(kmeans.getMaxIter === 33)
+    assert(kmeans.getRuns === 7)
+    assert(kmeans.getInitMode === MLlibKMeans.RANDOM)
+    assert(kmeans.getInitSteps === 3)
+    assert(kmeans.getSeed === 123)
+    assert(kmeans.getEpsilon === 1e-3)
+  }
+
+  test("parameters validation") {
+    intercept[IllegalArgumentException] {
+      new KMeans().setK(1)
+    }
+    intercept[IllegalArgumentException] {
+      new KMeans().setInitMode("no_such_a_mode")
+    }
+    intercept[IllegalArgumentException] {
+      new KMeans().setInitSteps(0)
+    }
+    intercept[IllegalArgumentException] {
+      new KMeans().setRuns(0)
+    }
+  }
+
+  test("fit & transform") {
+    val predictionColName = "kmeans_prediction"
+    val kmeans = new KMeans().setK(k).setPredictionCol(predictionColName).setSeed(1)
+    val model = kmeans.fit(dataset)
+    assert(model.clusterCenters.length === k)
+
+    val transformed = model.transform(dataset)
+    val expectedColumns = Array("features", predictionColName)
+    expectedColumns.foreach { column =>
+      assert(transformed.columns.contains(column))
+    }
+    val clusters = transformed.select(predictionColName).map(_.getInt(0)).distinct().collect().toSet
+    assert(clusters.size === k)
+    assert(clusters === Set(0, 1, 2, 3, 4))
+  }
+}
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 4291b0be2a616..12828547d7077 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -481,8 +481,8 @@ object Unidoc {
         "mllib.tree.impurity", "mllib.tree.model", "mllib.util",
         "mllib.evaluation", "mllib.feature", "mllib.random", "mllib.stat.correlation",
         "mllib.stat.test", "mllib.tree.impl", "mllib.tree.loss",
-        "ml", "ml.attribute", "ml.classification", "ml.evaluation", "ml.feature", "ml.param",
-        "ml.recommendation", "ml.regression", "ml.tuning"
+        "ml", "ml.attribute", "ml.classification", "ml.clustering", "ml.evaluation", "ml.feature",
+        "ml.param", "ml.recommendation", "ml.regression", "ml.tuning"
       ),
       "-group", "Spark SQL", packageList("sql.api.java", "sql.api.java.types", "sql.hive.api.java"),
       "-noqualifier", "java.lang"
diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst
index 518b8e774dd5f..86d4186a2c798 100644
--- a/python/docs/pyspark.ml.rst
+++ b/python/docs/pyspark.ml.rst
@@ -33,6 +33,14 @@ pyspark.ml.classification module
     :undoc-members:
     :inherited-members:
 
+pyspark.ml.clustering module
+----------------------------
+
+.. automodule:: pyspark.ml.clustering
+    :members:
+    :undoc-members:
+    :inherited-members:
+
 pyspark.ml.recommendation module
 --------------------------------
 
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
new file mode 100644
index 0000000000000..b5e9b6549d9f1
--- /dev/null
+++ b/python/pyspark/ml/clustering.py
@@ -0,0 +1,206 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark.ml.util import keyword_only
+from pyspark.ml.wrapper import JavaEstimator, JavaModel
+from pyspark.ml.param.shared import *
+from pyspark.mllib.common import inherit_doc
+from pyspark.mllib.linalg import _convert_to_vector
+
+__all__ = ['KMeans', 'KMeansModel']
+
+
+class KMeansModel(JavaModel):
+    """
+    Model fitted by KMeans.
+    """
+
+    def clusterCenters(self):
+        """Get the cluster centers, represented as a list of NumPy arrays."""
+        return [c.toArray() for c in self._call_java("clusterCenters")]
+
+
+@inherit_doc
+class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
+    """
+    K-means Clustering
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
+    ...         (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
+    >>> df = sqlContext.createDataFrame(data, ["features"])
+    >>> kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol("features")
+    >>> model = kmeans.fit(df)
+    >>> centers = model.clusterCenters()
+    >>> len(centers)
+    2
+    >>> transformed = model.transform(df).select("features", "prediction")
+    >>> rows = transformed.collect()
+    >>> rows[0].prediction == rows[1].prediction
+    True
+    >>> rows[2].prediction == rows[3].prediction
+    True
+    """
+
+    # a placeholder to make it appear in the generated doc
+    k = Param(Params._dummy(), "k", "number of clusters to create")
+    epsilon = Param(Params._dummy(), "epsilon",
+                    "distance threshold within which " +
+                    "we've consider centers to have converged")
+    runs = Param(Params._dummy(), "runs", "number of runs of the algorithm to execute in parallel")
+    initMode = Param(Params._dummy(), "initMode",
+                     "the initialization algorithm. This can be either \"random\" to " +
+                     "choose random points as initial cluster centers, or \"k-means||\" " +
+                     "to use a parallel variant of k-means++")
+    initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode")
+
+    @keyword_only
+    def __init__(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initStep=5):
+        super(KMeans, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.KMeans", self.uid)
+        self.k = Param(self, "k", "number of clusters to create")
+        self.epsilon = Param(self, "epsilon",
+                             "distance threshold within which " +
+                             "we've consider centers to have converged")
+        self.runs = Param(self, "runs", "number of runs of the algorithm to execute in parallel")
+        self.seed = Param(self, "seed", "random seed")
+        self.initMode = Param(self, "initMode",
+                              "the initialization algorithm. This can be either \"random\" to " +
+                              "choose random points as initial cluster centers, or \"k-means||\" " +
+                              "to use a parallel variant of k-means++")
+        self.initSteps = Param(self, "initSteps", "steps for k-means initialization mode")
+        self._setDefault(k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    def _create_model(self, java_model):
+        return KMeansModel(java_model)
+
+    @keyword_only
+    def setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+        """
+        setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+
+        Sets params for KMeans.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setK(self, value):
+        """
+        Sets the value of :py:attr:`k`.
+
+        >>> algo = KMeans().setK(10)
+        >>> algo.getK()
+        10
+        """
+        self._paramMap[self.k] = value
+        return self
+
+    def getK(self):
+        """
+        Gets the value of `k`
+        """
+        return self.getOrDefault(self.k)
+
+    def setEpsilon(self, value):
+        """
+        Sets the value of :py:attr:`epsilon`.
+
+        >>> algo = KMeans().setEpsilon(1e-5)
+        >>> abs(algo.getEpsilon() - 1e-5) < 1e-5
+        True
+        """
+        self._paramMap[self.epsilon] = value
+        return self
+
+    def getEpsilon(self):
+        """
+        Gets the value of `epsilon`
+        """
+        return self.getOrDefault(self.epsilon)
+
+    def setRuns(self, value):
+        """
+        Sets the value of :py:attr:`runs`.
+
+        >>> algo = KMeans().setRuns(10)
+        >>> algo.getRuns()
+        10
+        """
+        self._paramMap[self.runs] = value
+        return self
+
+    def getRuns(self):
+        """
+        Gets the value of `runs`
+        """
+        return self.getOrDefault(self.runs)
+
+    def setInitMode(self, value):
+        """
+        Sets the value of :py:attr:`initMode`.
+
+        >>> algo = KMeans()
+        >>> algo.getInitMode()
+        'k-means||'
+        >>> algo = algo.setInitMode("random")
+        >>> algo.getInitMode()
+        'random'
+        """
+        self._paramMap[self.initMode] = value
+        return self
+
+    def getInitMode(self):
+        """
+        Gets the value of `initMode`
+        """
+        return self.getOrDefault(self.initMode)
+
+    def setInitSteps(self, value):
+        """
+        Sets the value of :py:attr:`initSteps`.
+
+        >>> algo = KMeans().setInitSteps(10)
+        >>> algo.getInitSteps()
+        10
+        """
+        self._paramMap[self.initSteps] = value
+        return self
+
+    def getInitSteps(self):
+        """
+        Gets the value of `initSteps`
+        """
+        return self.getOrDefault(self.initSteps)
+
+
+if __name__ == "__main__":
+    import doctest
+    from pyspark.context import SparkContext
+    from pyspark.sql import SQLContext
+    globs = globals().copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    sc = SparkContext("local[2]", "ml.clustering tests")
+    sqlContext = SQLContext(sc)
+    globs['sc'] = sc
+    globs['sqlContext'] = sqlContext
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
+    sc.stop()
+    if failure_count:
+        exit(-1)

From 1017908205b7690dc0b0ed4753b36fab5641f7ac Mon Sep 17 00:00:00 2001
From: Rekha Joshi <rekhajoshm@gmail.com>
Date: Fri, 17 Jul 2015 20:02:05 -0700
Subject: [PATCH 0450/1454] [SPARK-9118] [ML] Implement IntArrayParam in mllib

Implement IntArrayParam in mllib

Author: Rekha Joshi <rekhajoshm@gmail.com>
Author: Joshi <rekhajoshm@gmail.com>

Closes #7481 from rekhajoshm/SPARK-9118 and squashes the following commits:

d3b1766 [Joshi] Implement IntArrayParam
0be142d [Rekha Joshi] Merge pull request #3 from apache/master
106fd8e [Rekha Joshi] Merge pull request #2 from apache/master
e3677c9 [Rekha Joshi] Merge pull request #1 from apache/master
---
 .../scala/org/apache/spark/ml/param/params.scala | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index d034d7ec6b60e..824efa5ed4b28 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -295,6 +295,22 @@ class DoubleArrayParam(parent: Params, name: String, doc: String, isValid: Array
     w(value.asScala.map(_.asInstanceOf[Double]).toArray)
 }
 
+/**
+ * :: DeveloperApi ::
+ * Specialized version of [[Param[Array[Int]]]] for Java.
+ */
+@DeveloperApi
+class IntArrayParam(parent: Params, name: String, doc: String, isValid: Array[Int] => Boolean)
+  extends Param[Array[Int]](parent, name, doc, isValid) {
+
+  def this(parent: Params, name: String, doc: String) =
+    this(parent, name, doc, ParamValidators.alwaysTrue)
+
+  /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */
+  def w(value: java.util.List[java.lang.Integer]): ParamPair[Array[Int]] =
+    w(value.asScala.map(_.asInstanceOf[Int]).toArray)
+}
+
 /**
  * :: Experimental ::
  * A param and its value.

From b9ef7ac98c3dee3256c4a393e563b42b4612a4bf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Kozikowski?= <mupakoz@gmail.com>
Date: Sat, 18 Jul 2015 10:12:48 -0700
Subject: [PATCH 0451/1454] [MLLIB] [DOC] Seed fix in mllib naive bayes example
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Previous seed resulted in empty test data set.

Author: Paweł Kozikowski <mupakoz@gmail.com>

Closes #7477 from mupakoz/patch-1 and squashes the following commits:

f5d41ee [Paweł Kozikowski] Mllib Naive Bayes example data set enlarged
---
 data/mllib/sample_naive_bayes_data.txt | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/data/mllib/sample_naive_bayes_data.txt b/data/mllib/sample_naive_bayes_data.txt
index 981da382d6ac8..bd22bea3a59d6 100644
--- a/data/mllib/sample_naive_bayes_data.txt
+++ b/data/mllib/sample_naive_bayes_data.txt
@@ -1,6 +1,12 @@
 0,1 0 0
 0,2 0 0
+0,3 0 0
+0,4 0 0
 1,0 1 0
 1,0 2 0
+1,0 3 0
+1,0 4 0
 2,0 0 1
 2,0 0 2
+2,0 0 3
+2,0 0 4
\ No newline at end of file

From fba3f5ba85673336c0556ef8731dcbcd175c7418 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 11:06:46 -0700
Subject: [PATCH 0452/1454] [SPARK-9169][SQL] Improve unit test coverage for
 null expressions.

Author: Reynold Xin <rxin@databricks.com>

Closes #7490 from rxin/unit-test-null-funcs and squashes the following commits:

7b276f0 [Reynold Xin] Move isNaN.
8307287 [Reynold Xin] [SPARK-9169][SQL] Improve unit test coverage for null expressions.
---
 .../catalyst/expressions/nullFunctions.scala  | 81 +++++++++++++++++--
 .../sql/catalyst/expressions/predicates.scala | 51 ------------
 .../expressions/NullFunctionsSuite.scala      | 78 +++++++++---------
 .../catalyst/expressions/PredicateSuite.scala | 12 +--
 4 files changed, 119 insertions(+), 103 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 1522bcae08d17..98c67084642e3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -21,8 +21,19 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.TypeUtils
-import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.types._
 
+
+/**
+ * An expression that is evaluated to the first non-null input.
+ *
+ * {{{
+ *   coalesce(1, 2) => 1
+ *   coalesce(null, 1, 2) => 1
+ *   coalesce(null, null, 2) => 2
+ *   coalesce(null, null, null) => null
+ * }}}
+ */
 case class Coalesce(children: Seq[Expression]) extends Expression {
 
   /** Coalesce is nullable if all of its children are nullable, or if it has no children. */
@@ -70,6 +81,62 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
   }
 }
 
+
+/**
+ * Evaluates to `true` if it's NaN or null
+ */
+case class IsNaN(child: Expression) extends UnaryExpression
+  with Predicate with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(DoubleType, FloatType))
+
+  override def nullable: Boolean = false
+
+  override def eval(input: InternalRow): Any = {
+    val value = child.eval(input)
+    if (value == null) {
+      true
+    } else {
+      child.dataType match {
+        case DoubleType => value.asInstanceOf[Double].isNaN
+        case FloatType => value.asInstanceOf[Float].isNaN
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val eval = child.gen(ctx)
+    child.dataType match {
+      case FloatType =>
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = false;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (${eval.isNull}) {
+            ${ev.primitive} = true;
+          } else {
+            ${ev.primitive} = Float.isNaN(${eval.primitive});
+          }
+        """
+      case DoubleType =>
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = false;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (${eval.isNull}) {
+            ${ev.primitive} = true;
+          } else {
+            ${ev.primitive} = Double.isNaN(${eval.primitive});
+          }
+        """
+    }
+  }
+}
+
+
+/**
+ * An expression that is evaluated to true if the input is null.
+ */
 case class IsNull(child: Expression) extends UnaryExpression with Predicate {
   override def nullable: Boolean = false
 
@@ -83,13 +150,14 @@ case class IsNull(child: Expression) extends UnaryExpression with Predicate {
     ev.primitive = eval.isNull
     eval.code
   }
-
-  override def toString: String = s"IS NULL $child"
 }
 
+
+/**
+ * An expression that is evaluated to true if the input is not null.
+ */
 case class IsNotNull(child: Expression) extends UnaryExpression with Predicate {
   override def nullable: Boolean = false
-  override def toString: String = s"IS NOT NULL $child"
 
   override def eval(input: InternalRow): Any = {
     child.eval(input) != null
@@ -103,12 +171,13 @@ case class IsNotNull(child: Expression) extends UnaryExpression with Predicate {
   }
 }
 
+
 /**
- * A predicate that is evaluated to be true if there are at least `n` non-null values.
+ * A predicate that is evaluated to be true if there are at least `n` non-null and non-NaN values.
  */
 case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate {
   override def nullable: Boolean = false
-  override def foldable: Boolean = false
+  override def foldable: Boolean = children.forall(_.foldable)
   override def toString: String = s"AtLeastNNulls(n, ${children.mkString(",")})"
 
   private[this] val childrenArray = children.toArray
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 2751c8e75f357..bddd2a9eccfc0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
@@ -120,56 +119,6 @@ case class InSet(child: Expression, hset: Set[Any])
   }
 }
 
-/**
- * Evaluates to `true` if it's NaN or null
- */
-case class IsNaN(child: Expression) extends UnaryExpression
-    with Predicate with ImplicitCastInputTypes {
-
-  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(DoubleType, FloatType))
-
-  override def nullable: Boolean = false
-
-  override def eval(input: InternalRow): Any = {
-    val value = child.eval(input)
-    if (value == null) {
-      true
-    } else {
-      child.dataType match {
-        case DoubleType => value.asInstanceOf[Double].isNaN
-        case FloatType => value.asInstanceOf[Float].isNaN
-      }
-    }
-  }
-
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val eval = child.gen(ctx)
-    child.dataType match {
-      case FloatType =>
-        s"""
-          ${eval.code}
-          boolean ${ev.isNull} = false;
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (${eval.isNull}) {
-            ${ev.primitive} = true;
-          } else {
-            ${ev.primitive} = Float.isNaN(${eval.primitive});
-          }
-        """
-      case DoubleType =>
-        s"""
-          ${eval.code}
-          boolean ${ev.isNull} = false;
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (${eval.isNull}) {
-            ${ev.primitive} = true;
-          } else {
-            ${ev.primitive} = Double.isNaN(${eval.primitive});
-          }
-        """
-    }
-  }
-}
 
 case class And(left: Expression, right: Expression) extends BinaryOperator with Predicate {
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
index ccdada8b56f83..765cc7a969b5d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
@@ -18,48 +18,52 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{BooleanType, StringType, ShortType}
+import org.apache.spark.sql.types._
 
 class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
-  test("null checking") {
-    val row = create_row("^Ba*n", null, true, null)
-    val c1 = 'a.string.at(0)
-    val c2 = 'a.string.at(1)
-    val c3 = 'a.boolean.at(2)
-    val c4 = 'a.boolean.at(3)
-
-    checkEvaluation(c1.isNull, false, row)
-    checkEvaluation(c1.isNotNull, true, row)
-
-    checkEvaluation(c2.isNull, true, row)
-    checkEvaluation(c2.isNotNull, false, row)
-
-    checkEvaluation(Literal.create(1, ShortType).isNull, false)
-    checkEvaluation(Literal.create(1, ShortType).isNotNull, true)
-
-    checkEvaluation(Literal.create(null, ShortType).isNull, true)
-    checkEvaluation(Literal.create(null, ShortType).isNotNull, false)
+  def testAllTypes(testFunc: (Any, DataType) => Unit): Unit = {
+    testFunc(false, BooleanType)
+    testFunc(1.toByte, ByteType)
+    testFunc(1.toShort, ShortType)
+    testFunc(1, IntegerType)
+    testFunc(1L, LongType)
+    testFunc(1.0F, FloatType)
+    testFunc(1.0, DoubleType)
+    testFunc(Decimal(1.5), DecimalType.Unlimited)
+    testFunc(new java.sql.Date(10), DateType)
+    testFunc(new java.sql.Timestamp(10), TimestampType)
+    testFunc("abcd", StringType)
+  }
 
-    checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row)
-    checkEvaluation(Coalesce(Literal.create(null, StringType) :: Nil), null, row)
-    checkEvaluation(Coalesce(Literal.create(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row)
+  test("isnull and isnotnull") {
+    testAllTypes { (value: Any, tpe: DataType) =>
+      checkEvaluation(IsNull(Literal.create(value, tpe)), false)
+      checkEvaluation(IsNotNull(Literal.create(value, tpe)), true)
+      checkEvaluation(IsNull(Literal.create(null, tpe)), true)
+      checkEvaluation(IsNotNull(Literal.create(null, tpe)), false)
+    }
+  }
 
-    checkEvaluation(
-      If(c3, Literal.create("a", StringType), Literal.create("b", StringType)), "a", row)
-    checkEvaluation(If(c3, c1, c2), "^Ba*n", row)
-    checkEvaluation(If(c4, c2, c1), "^Ba*n", row)
-    checkEvaluation(If(Literal.create(null, BooleanType), c2, c1), "^Ba*n", row)
-    checkEvaluation(If(Literal.create(true, BooleanType), c1, c2), "^Ba*n", row)
-    checkEvaluation(If(Literal.create(false, BooleanType), c2, c1), "^Ba*n", row)
-    checkEvaluation(If(Literal.create(false, BooleanType),
-      Literal.create("a", StringType), Literal.create("b", StringType)), "b", row)
+  test("IsNaN") {
+    checkEvaluation(IsNaN(Literal(Double.NaN)), true)
+    checkEvaluation(IsNaN(Literal(Float.NaN)), true)
+    checkEvaluation(IsNaN(Literal(math.log(-3))), true)
+    checkEvaluation(IsNaN(Literal.create(null, DoubleType)), true)
+    checkEvaluation(IsNaN(Literal(Double.PositiveInfinity)), false)
+    checkEvaluation(IsNaN(Literal(Float.MaxValue)), false)
+    checkEvaluation(IsNaN(Literal(5.5f)), false)
+  }
 
-    checkEvaluation(c1 in (c1, c2), true, row)
-    checkEvaluation(
-      Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType)), true, row)
-    checkEvaluation(
-      Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType), c2), true, row)
+  test("coalesce") {
+    testAllTypes { (value: Any, tpe: DataType) =>
+      val lit = Literal.create(value, tpe)
+      val nullLit = Literal.create(null, tpe)
+      checkEvaluation(Coalesce(Seq(nullLit)), null)
+      checkEvaluation(Coalesce(Seq(lit)), value)
+      checkEvaluation(Coalesce(Seq(nullLit, lit)), value)
+      checkEvaluation(Coalesce(Seq(nullLit, lit, lit)), value)
+      checkEvaluation(Coalesce(Seq(nullLit, nullLit, lit)), value)
+    }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 052abc51af5fd..2173a0c25c645 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -114,16 +114,10 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       And(In(Literal(1), Seq(Literal(1), Literal(2))), In(Literal(2), Seq(Literal(1), Literal(2)))),
       true)
-  }
 
-  test("IsNaN") {
-    checkEvaluation(IsNaN(Literal(Double.NaN)), true)
-    checkEvaluation(IsNaN(Literal(Float.NaN)), true)
-    checkEvaluation(IsNaN(Literal(math.log(-3))), true)
-    checkEvaluation(IsNaN(Literal.create(null, DoubleType)), true)
-    checkEvaluation(IsNaN(Literal(Double.PositiveInfinity)), false)
-    checkEvaluation(IsNaN(Literal(Float.MaxValue)), false)
-    checkEvaluation(IsNaN(Literal(5.5f)), false)
+    checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("^Ba*n"))), true)
+    checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^Ba*n"))), true)
+    checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^n"))), false)
   }
 
   test("INSET") {

From b8aec6cd236f09881cad0fff9a6f1a5692934e21 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 18 Jul 2015 11:08:18 -0700
Subject: [PATCH 0453/1454] [SPARK-9143] [SQL] Add planner rule for
 automatically inserting Unsafe <-> Safe row format converters

Now that we have two different internal row formats, UnsafeRow and the old Java-object-based row format, we end up having to perform conversions between these two formats. These conversions should not be performed by the operators themselves; instead, the planner should be responsible for inserting appropriate format conversions when they are needed.

This patch makes the following changes:

- Add two new physical operators for performing row format conversions, `ConvertToUnsafe` and `ConvertFromUnsafe`.
- Add new methods to `SparkPlan` to allow operators to express whether they output UnsafeRows and whether they can handle safe or unsafe rows as inputs.
- Implement an `EnsureRowFormats` rule to automatically insert converter operators where necessary.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7482 from JoshRosen/unsafe-converter-planning and squashes the following commits:

7450fa5 [Josh Rosen] Resolve conflicts in favor of choosing UnsafeRow
5220cce [Josh Rosen] Add roundtrip converter test
2bb8da8 [Josh Rosen] Add Union unsafe support + tests to bump up test coverage
6f79449 [Josh Rosen] Add even more assertions to execute()
08ce199 [Josh Rosen] Rename ConvertFromUnsafe -> ConvertToSafe
0e2d548 [Josh Rosen] Add assertion if operators' input rows are in different formats
cabb703 [Josh Rosen] Add tests for Filter
3b11ce3 [Josh Rosen] Add missing test file.
ae2195a [Josh Rosen] Fixes
0fef0f8 [Josh Rosen] Rename file.
d5f9005 [Josh Rosen] Finish writing EnsureRowFormats planner rule
b5df19b [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-converter-planning
9ba3038 [Josh Rosen] WIP
---
 .../org/apache/spark/sql/SQLContext.scala     |   9 +-
 .../spark/sql/execution/SparkPlan.scala       |  24 ++++
 .../spark/sql/execution/basicOperators.scala  |  11 ++
 .../sql/execution/rowFormatConverters.scala   | 107 ++++++++++++++++++
 .../execution/RowFormatConvertersSuite.scala  |  91 +++++++++++++++
 5 files changed, 239 insertions(+), 3 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 46bd60daa1f78..2dda3ad1211fa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -921,12 +921,15 @@ class SQLContext(@transient val sparkContext: SparkContext)
   protected[sql] lazy val emptyResult = sparkContext.parallelize(Seq.empty[InternalRow], 1)
 
   /**
-   * Prepares a planned SparkPlan for execution by inserting shuffle operations as needed.
+   * Prepares a planned SparkPlan for execution by inserting shuffle operations and internal
+   * row format conversions as needed.
    */
   @transient
   protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] {
-    val batches =
-      Batch("Add exchange", Once, EnsureRequirements(self)) :: Nil
+    val batches = Seq(
+      Batch("Add exchange", Once, EnsureRequirements(self)),
+      Batch("Add row converters", Once, EnsureRowFormats)
+    )
   }
 
   protected[sql] def openSession(): SQLSession = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index ba12056ee7a1b..f363e9947d5f6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -79,12 +79,36 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Product
   /** Specifies sort order for each partition requirements on the input data for this operator. */
   def requiredChildOrdering: Seq[Seq[SortOrder]] = Seq.fill(children.size)(Nil)
 
+  /** Specifies whether this operator outputs UnsafeRows */
+  def outputsUnsafeRows: Boolean = false
+
+  /** Specifies whether this operator is capable of processing UnsafeRows */
+  def canProcessUnsafeRows: Boolean = false
+
+  /**
+   * Specifies whether this operator is capable of processing Java-object-based Rows (i.e. rows
+   * that are not UnsafeRows).
+   */
+  def canProcessSafeRows: Boolean = true
+
   /**
    * Returns the result of this query as an RDD[InternalRow] by delegating to doExecute
    * after adding query plan information to created RDDs for visualization.
    * Concrete implementations of SparkPlan should override doExecute instead.
    */
   final def execute(): RDD[InternalRow] = {
+    if (children.nonEmpty) {
+      val hasUnsafeInputs = children.exists(_.outputsUnsafeRows)
+      val hasSafeInputs = children.exists(!_.outputsUnsafeRows)
+      assert(!(hasSafeInputs && hasUnsafeInputs),
+        "Child operators should output rows in the same format")
+      assert(canProcessSafeRows || canProcessUnsafeRows,
+        "Operator must be able to process at least one row format")
+      assert(!hasSafeInputs || canProcessSafeRows,
+        "Operator will receive safe rows as input but cannot process safe rows")
+      assert(!hasUnsafeInputs || canProcessUnsafeRows,
+        "Operator will receive unsafe rows as input but cannot process unsafe rows")
+    }
     RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
       doExecute()
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 4c063c299ba53..82bef269b069f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -64,6 +64,12 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+  override def outputsUnsafeRows: Boolean = child.outputsUnsafeRows
+
+  override def canProcessUnsafeRows: Boolean = true
+
+  override def canProcessSafeRows: Boolean = true
 }
 
 /**
@@ -104,6 +110,9 @@ case class Sample(
 case class Union(children: Seq[SparkPlan]) extends SparkPlan {
   // TODO: attributes output by union should be distinct for nullability purposes
   override def output: Seq[Attribute] = children.head.output
+  override def outputsUnsafeRows: Boolean = children.forall(_.outputsUnsafeRows)
+  override def canProcessUnsafeRows: Boolean = true
+  override def canProcessSafeRows: Boolean = true
   protected override def doExecute(): RDD[InternalRow] =
     sparkContext.union(children.map(_.execute()))
 }
@@ -306,6 +315,8 @@ case class UnsafeExternalSort(
   override def output: Seq[Attribute] = child.output
 
   override def outputOrdering: Seq[SortOrder] = sortOrder
+
+  override def outputsUnsafeRows: Boolean = true
 }
 
 @DeveloperApi
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
new file mode 100644
index 0000000000000..421d510e6782d
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.rules.Rule
+
+/**
+ * :: DeveloperApi ::
+ * Converts Java-object-based rows into [[UnsafeRow]]s.
+ */
+@DeveloperApi
+case class ConvertToUnsafe(child: SparkPlan) extends UnaryNode {
+  override def output: Seq[Attribute] = child.output
+  override def outputsUnsafeRows: Boolean = true
+  override def canProcessUnsafeRows: Boolean = false
+  override def canProcessSafeRows: Boolean = true
+  override protected def doExecute(): RDD[InternalRow] = {
+    child.execute().mapPartitions { iter =>
+      val convertToUnsafe = UnsafeProjection.create(child.schema)
+      iter.map(convertToUnsafe)
+    }
+  }
+}
+
+/**
+ * :: DeveloperApi ::
+ * Converts [[UnsafeRow]]s back into Java-object-based rows.
+ */
+@DeveloperApi
+case class ConvertToSafe(child: SparkPlan) extends UnaryNode {
+  override def output: Seq[Attribute] = child.output
+  override def outputsUnsafeRows: Boolean = false
+  override def canProcessUnsafeRows: Boolean = true
+  override def canProcessSafeRows: Boolean = false
+  override protected def doExecute(): RDD[InternalRow] = {
+    child.execute().mapPartitions { iter =>
+      val convertToSafe = FromUnsafeProjection(child.output.map(_.dataType))
+      iter.map(convertToSafe)
+    }
+  }
+}
+
+private[sql] object EnsureRowFormats extends Rule[SparkPlan] {
+
+  private def onlyHandlesSafeRows(operator: SparkPlan): Boolean =
+    operator.canProcessSafeRows && !operator.canProcessUnsafeRows
+
+  private def onlyHandlesUnsafeRows(operator: SparkPlan): Boolean =
+    operator.canProcessUnsafeRows && !operator.canProcessSafeRows
+
+  private def handlesBothSafeAndUnsafeRows(operator: SparkPlan): Boolean =
+    operator.canProcessSafeRows && operator.canProcessUnsafeRows
+
+  override def apply(operator: SparkPlan): SparkPlan = operator.transformUp {
+    case operator: SparkPlan if onlyHandlesSafeRows(operator) =>
+      if (operator.children.exists(_.outputsUnsafeRows)) {
+        operator.withNewChildren {
+          operator.children.map {
+            c => if (c.outputsUnsafeRows) ConvertToSafe(c) else c
+          }
+        }
+      } else {
+        operator
+      }
+    case operator: SparkPlan if onlyHandlesUnsafeRows(operator) =>
+      if (operator.children.exists(!_.outputsUnsafeRows)) {
+        operator.withNewChildren {
+          operator.children.map {
+            c => if (!c.outputsUnsafeRows) ConvertToUnsafe(c) else c
+          }
+        }
+      } else {
+        operator
+      }
+    case operator: SparkPlan if handlesBothSafeAndUnsafeRows(operator) =>
+      if (operator.children.map(_.outputsUnsafeRows).toSet.size != 1) {
+        // If this operator's children produce both unsafe and safe rows, then convert everything
+        // to unsafe rows
+        operator.withNewChildren {
+          operator.children.map {
+            c => if (!c.outputsUnsafeRows) ConvertToUnsafe(c) else c
+          }
+        }
+      } else {
+        operator
+      }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
new file mode 100644
index 0000000000000..7b75f755918c1
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.expressions.IsNull
+import org.apache.spark.sql.test.TestSQLContext
+
+class RowFormatConvertersSuite extends SparkPlanTest {
+
+  private def getConverters(plan: SparkPlan): Seq[SparkPlan] = plan.collect {
+    case c: ConvertToUnsafe => c
+    case c: ConvertToSafe => c
+  }
+
+  private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  assert(!outputsSafe.outputsUnsafeRows)
+  private val outputsUnsafe = UnsafeExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  assert(outputsUnsafe.outputsUnsafeRows)
+
+  test("planner should insert unsafe->safe conversions when required") {
+    val plan = Limit(10, outputsUnsafe)
+    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    assert(preparedPlan.children.head.isInstanceOf[ConvertToSafe])
+  }
+
+  test("filter can process unsafe rows") {
+    val plan = Filter(IsNull(null), outputsUnsafe)
+    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    assert(getConverters(preparedPlan).isEmpty)
+    assert(preparedPlan.outputsUnsafeRows)
+  }
+
+  test("filter can process safe rows") {
+    val plan = Filter(IsNull(null), outputsSafe)
+    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    assert(getConverters(preparedPlan).isEmpty)
+    assert(!preparedPlan.outputsUnsafeRows)
+  }
+
+  test("execute() fails an assertion if inputs rows are of different formats") {
+    val e = intercept[AssertionError] {
+      Union(Seq(outputsSafe, outputsUnsafe)).execute()
+    }
+    assert(e.getMessage.contains("format"))
+  }
+
+  test("union requires all of its input rows' formats to agree") {
+    val plan = Union(Seq(outputsSafe, outputsUnsafe))
+    assert(plan.canProcessSafeRows && plan.canProcessUnsafeRows)
+    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    assert(preparedPlan.outputsUnsafeRows)
+  }
+
+  test("union can process safe rows") {
+    val plan = Union(Seq(outputsSafe, outputsSafe))
+    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    assert(!preparedPlan.outputsUnsafeRows)
+  }
+
+  test("union can process unsafe rows") {
+    val plan = Union(Seq(outputsUnsafe, outputsUnsafe))
+    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    assert(preparedPlan.outputsUnsafeRows)
+  }
+
+  test("round trip with ConvertToUnsafe and ConvertToSafe") {
+    val input = Seq(("hello", 1), ("world", 2))
+    checkAnswer(
+      TestSQLContext.createDataFrame(input),
+      plan => ConvertToSafe(ConvertToUnsafe(plan)),
+      input.map(Row.fromTuple)
+    )
+  }
+}

From 1b4ff05538fbcfe10ca4fa97606bd6e39a8450cb Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 18 Jul 2015 11:13:49 -0700
Subject: [PATCH 0454/1454] [SPARK-9142][SQL] remove more self type in catalyst

a follow up of https://github.com/apache/spark/pull/7479.
The `TreeNode` is the root case of the requirement of `self: Product =>` stuff, so why not make `TreeNode` extend `Product`?

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7495 from cloud-fan/self-type and squashes the following commits:

8676af7 [Wenchen Fan] remove more self type
---
 .../apache/spark/sql/catalyst/expressions/Expression.scala    | 2 +-
 .../org/apache/spark/sql/catalyst/expressions/math.scala      | 2 +-
 .../scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala | 2 +-
 .../apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala | 2 +-
 .../scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala  | 4 ++--
 .../main/scala/org/apache/spark/sql/execution/SparkPlan.scala | 2 +-
 6 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index c70b5af4aa448..0e128d8bdcd96 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -43,7 +43,7 @@ import org.apache.spark.sql.types._
  *
  * See [[Substring]] for an example.
  */
-abstract class Expression extends TreeNode[Expression] with Product {
+abstract class Expression extends TreeNode[Expression] {
 
   /**
    * Returns true when an expression is a candidate for static evaluation before the query is
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 9101f11052218..eb5c065a34123 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -77,7 +77,7 @@ abstract class UnaryMathExpression(f: Double => Double, name: String)
 }
 
 abstract class UnaryLogExpression(f: Double => Double, name: String)
-    extends UnaryMathExpression(f, name) { self: Product =>
+    extends UnaryMathExpression(f, name) {
 
   // values less than or equal to yAsymptote eval to null in Hive, instead of NaN or -Infinity
   protected val yAsymptote: Double = 0.0
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index b89e3382f06a9..d06a7a2add754 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -22,7 +22,7 @@ import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types.{ArrayType, DataType, StructField, StructType}
 
 abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanType] {
-  self: PlanType with Product =>
+  self: PlanType =>
 
   def output: Seq[Attribute]
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index dd6c5d43f5714..bedeaf06adf12 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -25,7 +25,7 @@ import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.trees.TreeNode
 
 
-abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging with Product{
+abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
 
   /**
    * Computes [[Statistics]] for this plan. The default implementation assumes the output
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 0f95ca688a7a8..122e9fc5ed77f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -54,8 +54,8 @@ object CurrentOrigin {
   }
 }
 
-abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
-  self: BaseType with Product =>
+abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
+  self: BaseType =>
 
   val origin: Origin = CurrentOrigin.get
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index f363e9947d5f6..b0d56b7bf0b86 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -39,7 +39,7 @@ object SparkPlan {
  * :: DeveloperApi ::
  */
 @DeveloperApi
-abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Product with Serializable {
+abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializable {
 
   /**
    * A handle to the SQL Context that was used to create this plan.   Since many operators need

From 692378c01d949dfe2b2a884add153cd5f8054b5a Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 18 Jul 2015 11:25:16 -0700
Subject: [PATCH 0455/1454] [SPARK-9167][SQL] use UTC Calendar in
 `stringToDate`

fix 2 bugs introduced in https://github.com/apache/spark/pull/7353

1. we should use UTC Calendar when cast string to date . Before #7353 , we use `DateTimeUtils.fromJavaDate(Date.valueOf(s.toString))` to cast string to date, and `fromJavaDate` will call `millisToDays` to avoid the time zone issue. Now we use `DateTimeUtils.stringToDate(s)`, we should create a Calendar with UTC in the begging.
2. we should not change the default time zone in test cases. The `threadLocalLocalTimeZone` and `threadLocalTimestampFormat` in `DateTimeUtils` will only be evaluated once for each thread, so we can't set the default time zone back anymore.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7488 from cloud-fan/datetime and squashes the following commits:

9cd6005 [Wenchen Fan] address comments
21ef293 [Wenchen Fan] fix 2 bugs in datetime
---
 .../sql/catalyst/util/DateTimeUtils.scala     |  9 +++++----
 .../sql/catalyst/expressions/CastSuite.scala  |  3 ---
 .../catalyst/util/DateTimeUtilsSuite.scala    | 19 ++++++++++---------
 3 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index f33e34b380bcf..45e45aef1a349 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -65,8 +65,8 @@ object DateTimeUtils {
   def millisToDays(millisUtc: Long): Int = {
     // SPARK-6785: use Math.floor so negative number of days (dates before 1970)
     // will correctly work as input for function toJavaDate(Int)
-    val millisLocal = millisUtc.toDouble + threadLocalLocalTimeZone.get().getOffset(millisUtc)
-    Math.floor(millisLocal / MILLIS_PER_DAY).toInt
+    val millisLocal = millisUtc + threadLocalLocalTimeZone.get().getOffset(millisUtc)
+    Math.floor(millisLocal.toDouble / MILLIS_PER_DAY).toInt
   }
 
   // reverse of millisToDays
@@ -375,8 +375,9 @@ object DateTimeUtils {
         segments(2) < 1 || segments(2) > 31) {
       return None
     }
-    val c = Calendar.getInstance()
+    val c = Calendar.getInstance(TimeZone.getTimeZone("GMT"))
     c.set(segments(0), segments(1) - 1, segments(2), 0, 0, 0)
-    Some((c.getTimeInMillis / 1000 / 3600 / 24).toInt)
+    c.set(Calendar.MILLISECOND, 0)
+    Some((c.getTimeInMillis / MILLIS_PER_DAY).toInt)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index ef8bcd41f7280..ccf448eee0688 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -281,8 +281,6 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     val nts = sts + ".1"
     val ts = Timestamp.valueOf(nts)
 
-    val defaultTimeZone = TimeZone.getDefault
-    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
     var c = Calendar.getInstance()
     c.set(2015, 2, 8, 2, 30, 0)
     checkEvaluation(cast(cast(new Timestamp(c.getTimeInMillis), StringType), TimestampType),
@@ -291,7 +289,6 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     c.set(2015, 10, 1, 2, 30, 0)
     checkEvaluation(cast(cast(new Timestamp(c.getTimeInMillis), StringType), TimestampType),
       c.getTimeInMillis * 1000)
-    TimeZone.setDefault(defaultTimeZone)
 
     checkEvaluation(cast("abdef", StringType), "abdef")
     checkEvaluation(cast("abdef", DecimalType.Unlimited), null)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 5c3a621c6d11f..04c5f09792ac3 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -90,34 +90,35 @@ class DateTimeUtilsSuite extends SparkFunSuite {
   }
 
   test("string to date") {
-    val millisPerDay = 1000L * 3600L * 24L
+    import DateTimeUtils.millisToDays
+
     var c = Calendar.getInstance()
     c.set(2015, 0, 28, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-01-28")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
     c.set(2015, 0, 1, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
     c = Calendar.getInstance()
     c.set(2015, 2, 1, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
     c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18 ")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18 123142")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18T123123")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18T")).get ===
-      c.getTimeInMillis / millisPerDay)
+      millisToDays(c.getTimeInMillis))
 
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18X")).isEmpty)
     assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015/03/18")).isEmpty)

From 86c50bf72c41d95107a55c16a6853dcda7f3e143 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 18 Jul 2015 11:58:53 -0700
Subject: [PATCH 0456/1454] [SPARK-9171][SQL] add and improve tests for
 nondeterministic expressions

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7496 from cloud-fan/tests and squashes the following commits:

0958f90 [Wenchen Fan] improve test for nondeterministic expressions
---
 .../scala/org/apache/spark/TaskContext.scala  |   2 +-
 .../expressions/ExpressionEvalHelper.scala    | 108 ++++++++++--------
 .../expressions/MathFunctionsSuite.scala      |  18 +--
 .../catalyst/expressions/RandomSuite.scala    |   6 +-
 .../spark/sql/ColumnExpressionSuite.scala     |   9 +-
 .../expression/NondeterministicSuite.scala    |  32 ++++++
 6 files changed, 103 insertions(+), 72 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index 345bb500a7dec..e93eb93124e51 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -38,7 +38,7 @@ object TaskContext {
    */
   def getPartitionId(): Int = {
     val tc = taskContext.get()
-    if (tc == null) {
+    if (tc eq null) {
       0
     } else {
       tc.partitionId()
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index c43486b3ddcf5..7a96044d35a09 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -23,7 +23,7 @@ import org.scalatest.Matchers._
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
-import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GenerateProjection, GenerateMutableProjection}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 
@@ -38,7 +38,7 @@ trait ExpressionEvalHelper {
   }
 
   protected def checkEvaluation(
-      expression: Expression, expected: Any, inputRow: InternalRow = EmptyRow): Unit = {
+      expression: => Expression, expected: Any, inputRow: InternalRow = EmptyRow): Unit = {
     val catalystValue = CatalystTypeConverters.convertToCatalyst(expected)
     checkEvaluationWithoutCodegen(expression, catalystValue, inputRow)
     checkEvaluationWithGeneratedMutableProjection(expression, catalystValue, inputRow)
@@ -51,12 +51,14 @@ trait ExpressionEvalHelper {
 
   /**
    * Check the equality between result of expression and expected value, it will handle
-   * Array[Byte].
+   * Array[Byte] and Spread[Double].
    */
   protected def checkResult(result: Any, expected: Any): Boolean = {
     (result, expected) match {
       case (result: Array[Byte], expected: Array[Byte]) =>
         java.util.Arrays.equals(result, expected)
+      case (result: Double, expected: Spread[Double]) =>
+        expected.isWithin(result)
       case _ => result == expected
     }
   }
@@ -65,10 +67,29 @@ trait ExpressionEvalHelper {
     expression.eval(inputRow)
   }
 
+  protected def generateProject(
+      generator: => Projection,
+      expression: Expression): Projection = {
+    try {
+      generator
+    } catch {
+      case e: Throwable =>
+        val ctx = new CodeGenContext
+        val evaluated = expression.gen(ctx)
+        fail(
+          s"""
+            |Code generation of $expression failed:
+            |${evaluated.code}
+            |$e
+          """.stripMargin)
+    }
+  }
+
   protected def checkEvaluationWithoutCodegen(
       expression: Expression,
       expected: Any,
       inputRow: InternalRow = EmptyRow): Unit = {
+
     val actual = try evaluate(expression, inputRow) catch {
       case e: Exception => fail(s"Exception evaluating $expression", e)
     }
@@ -85,21 +106,11 @@ trait ExpressionEvalHelper {
       expected: Any,
       inputRow: InternalRow = EmptyRow): Unit = {
 
-    val plan = try {
-      GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)()
-    } catch {
-      case e: Throwable =>
-        val ctx = GenerateProjection.newCodeGenContext()
-        val evaluated = expression.gen(ctx)
-        fail(
-          s"""
-            |Code generation of $expression failed:
-            |${evaluated.code}
-            |$e
-          """.stripMargin)
-    }
+    val plan = generateProject(
+      GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)(),
+      expression)
 
-    val actual = plan(inputRow).apply(0)
+    val actual = plan(inputRow).get(0)
     if (!checkResult(actual, expected)) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
       fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
@@ -110,24 +121,19 @@ trait ExpressionEvalHelper {
       expression: Expression,
       expected: Any,
       inputRow: InternalRow = EmptyRow): Unit = {
-    val ctx = GenerateProjection.newCodeGenContext()
-    lazy val evaluated = expression.gen(ctx)
 
-    val plan = try {
-      GenerateProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)
-    } catch {
-      case e: Throwable =>
-        fail(
-          s"""
-            |Code generation of $expression failed:
-            |${evaluated.code}
-            |$e
-          """.stripMargin)
-    }
+    val plan = generateProject(
+      GenerateProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil),
+      expression)
 
     val actual = plan(inputRow)
     val expectedRow = InternalRow(expected)
+
+    // We reimplement hashCode in generated `SpecificRow`, make sure it's consistent with our
+    // interpreted version.
     if (actual.hashCode() != expectedRow.hashCode()) {
+      val ctx = new CodeGenContext
+      val evaluated = expression.gen(ctx)
       fail(
         s"""
           |Mismatched hashCodes for values: $actual, $expectedRow
@@ -136,9 +142,10 @@ trait ExpressionEvalHelper {
           |Code: $evaluated
         """.stripMargin)
     }
+
     if (actual != expectedRow) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
-      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
+      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expectedRow$input")
     }
     if (actual.copy() != expectedRow) {
       fail(s"Copy of generated Row is wrong: actual: ${actual.copy()}, expected: $expectedRow")
@@ -149,20 +156,10 @@ trait ExpressionEvalHelper {
       expression: Expression,
       expected: Any,
       inputRow: InternalRow = EmptyRow): Unit = {
-    val ctx = GenerateUnsafeProjection.newCodeGenContext()
-    lazy val evaluated = expression.gen(ctx)
 
-    val plan = try {
-      GenerateUnsafeProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)
-    } catch {
-      case e: Throwable =>
-        fail(
-          s"""
-            |Code generation of $expression failed:
-            |${evaluated.code}
-            |$e
-          """.stripMargin)
-    }
+    val plan = generateProject(
+      GenerateUnsafeProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil),
+      expression)
 
     val unsafeRow = plan(inputRow)
     // UnsafeRow cannot be compared with GenericInternalRow directly
@@ -170,7 +167,7 @@ trait ExpressionEvalHelper {
     val expectedRow = InternalRow(expected)
     if (actual != expectedRow) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
-      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
+      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expectedRow$input")
     }
   }
 
@@ -184,12 +181,23 @@ trait ExpressionEvalHelper {
   }
 
   protected def checkDoubleEvaluation(
-      expression: Expression,
+      expression: => Expression,
       expected: Spread[Double],
       inputRow: InternalRow = EmptyRow): Unit = {
-    val actual = try evaluate(expression, inputRow) catch {
-      case e: Exception => fail(s"Exception evaluating $expression", e)
-    }
-    actual.asInstanceOf[Double] shouldBe expected
+    checkEvaluationWithoutCodegen(expression, expected)
+    checkEvaluationWithGeneratedMutableProjection(expression, expected)
+    checkEvaluationWithOptimization(expression, expected)
+
+    var plan = generateProject(
+      GenerateProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil),
+      expression)
+    var actual = plan(inputRow).get(0)
+    assert(checkResult(actual, expected))
+
+    plan = generateProject(
+      GenerateUnsafeProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil),
+      expression)
+    actual = FromUnsafeProjection(expression.dataType :: Nil)(plan(inputRow)).get(0)
+    assert(checkResult(actual, expected))
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index df988f57fbfde..04acd5b5ff4d1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -143,7 +143,6 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       case e: Exception => fail(s"Exception evaluating $expression", e)
     }
     if (!actual.asInstanceOf[Double].isNaN) {
-      val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
       fail(s"Incorrect evaluation (codegen off): $expression, " +
         s"actual: $actual, " +
         s"expected: NaN")
@@ -155,23 +154,12 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     expression: Expression,
     inputRow: InternalRow = EmptyRow): Unit = {
 
-    val plan = try {
-      GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)()
-    } catch {
-      case e: Throwable =>
-        val ctx = GenerateProjection.newCodeGenContext()
-        val evaluated = expression.gen(ctx)
-        fail(
-          s"""
-             |Code generation of $expression failed:
-             |${evaluated.code}
-             |$e
-          """.stripMargin)
-    }
+    val plan = generateProject(
+      GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)(),
+      expression)
 
     val actual = plan(inputRow).apply(0)
     if (!actual.asInstanceOf[Double].isNaN) {
-      val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
       fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: NaN")
     }
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
index 9be2b23a53f27..698c81ba24482 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
@@ -21,13 +21,13 @@ import org.scalatest.Matchers._
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{DoubleType, IntegerType}
+import org.apache.spark.sql.types.DoubleType
 
 
 class RandomSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("random") {
-    val row = create_row(1.1, 2.0, 3.1, null)
-    checkDoubleEvaluation(Rand(30), (0.7363714192755834 +- 0.001), row)
+    checkDoubleEvaluation(Rand(30), 0.7363714192755834 +- 0.001)
+    checkDoubleEvaluation(Randn(30), 0.5181478766595276 +- 0.001)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 8f15479308391..6bd5804196853 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -450,7 +450,7 @@ class ColumnExpressionSuite extends QueryTest {
 
   test("monotonicallyIncreasingId") {
     // Make sure we have 2 partitions, each with 2 records.
-    val df = ctx.sparkContext.parallelize(1 to 2, 2).mapPartitions { iter =>
+    val df = ctx.sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
       Iterator(Tuple1(1), Tuple1(2))
     }.toDF("a")
     checkAnswer(
@@ -460,10 +460,13 @@ class ColumnExpressionSuite extends QueryTest {
   }
 
   test("sparkPartitionId") {
-    val df = ctx.sparkContext.parallelize(1 to 1, 1).map(i => (i, i)).toDF("a", "b")
+    // Make sure we have 2 partitions, each with 2 records.
+    val df = ctx.sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
+      Iterator(Tuple1(1), Tuple1(2))
+    }.toDF("a")
     checkAnswer(
       df.select(sparkPartitionId()),
-      Row(0)
+      Row(0) :: Row(0) :: Row(1) :: Row(1) :: Nil
     )
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
new file mode 100644
index 0000000000000..99e11fd64b2b9
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.expression
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions. ExpressionEvalHelper
+import org.apache.spark.sql.execution.expressions.{SparkPartitionID, MonotonicallyIncreasingID}
+
+class NondeterministicSuite extends SparkFunSuite with ExpressionEvalHelper {
+  test("MonotonicallyIncreasingID") {
+    checkEvaluation(MonotonicallyIncreasingID(), 0)
+  }
+
+  test("SparkPartitionID") {
+    checkEvaluation(SparkPartitionID, 0)
+  }
+}

From 225de8da2b20ba03b358e222411610e8567aa88d Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Sat, 18 Jul 2015 12:11:37 -0700
Subject: [PATCH 0457/1454] [SPARK-9151][SQL] Implement code generation for Abs

JIRA: https://issues.apache.org/jira/browse/SPARK-9151

Add codegen support for `Abs`.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7498 from viirya/abs_codegen and squashes the following commits:

0c8410f [Liang-Chi Hsieh] Implement code generation for Abs.
---
 .../apache/spark/sql/catalyst/expressions/arithmetic.scala | 7 +++++++
 .../main/scala/org/apache/spark/sql/types/Decimal.scala    | 2 ++
 2 files changed, 9 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index c5960eb390ea4..e83650fc8cb0e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -73,6 +73,13 @@ case class Abs(child: Expression) extends UnaryExpression with ExpectsInputTypes
 
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
+    case dt: DecimalType =>
+      defineCodeGen(ctx, ev, c => s"$c.abs()")
+    case dt: NumericType =>
+      defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})(java.lang.Math.abs($c))")
+  }
+
   protected override def nullSafeEval(input: Any): Any = numeric.abs(input)
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index a85af9e04aedb..bc689810bc292 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -278,6 +278,8 @@ final class Decimal extends Ordered[Decimal] with Serializable {
       Decimal(-longVal, precision, scale)
     }
   }
+
+  def abs: Decimal = if (this.compare(Decimal(0)) < 0) this.unary_- else this
 }
 
 object Decimal {

From cdc36eef4160dbae32e19a1eadbb4cf062f2fb2b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 12:25:04 -0700
Subject: [PATCH 0458/1454] Closes #6122


From 3d2134fc0d90379b89da08de7614aef1ac674b1b Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sat, 18 Jul 2015 12:57:53 -0700
Subject: [PATCH 0459/1454] [SPARK-9055][SQL] WidenTypes should also support
 Intersect and Except

JIRA: https://issues.apache.org/jira/browse/SPARK-9055

cc rxin

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7491 from yijieshen/widen and squashes the following commits:

079fa52 [Yijie Shen] widenType support for intersect and expect
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 93 +++++++++++--------
 .../plans/logical/basicOperators.scala        |  8 ++
 .../analysis/HiveTypeCoercionSuite.scala      | 34 ++++++-
 3 files changed, 94 insertions(+), 41 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 50db7d21f01ca..ff20835e82ba7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.analysis
 import javax.annotation.Nullable
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project, Union}
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
 
@@ -168,52 +168,65 @@ object HiveTypeCoercion {
    * - LongType to DoubleType
    */
   object WidenTypes extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      // TODO: unions with fixed-precision decimals
-      case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
-        val castedInput = left.output.zip(right.output).map {
-          // When a string is found on one side, make the other side a string too.
-          case (lhs, rhs) if lhs.dataType == StringType && rhs.dataType != StringType =>
-            (lhs, Alias(Cast(rhs, StringType), rhs.name)())
-          case (lhs, rhs) if lhs.dataType != StringType && rhs.dataType == StringType =>
-            (Alias(Cast(lhs, StringType), lhs.name)(), rhs)
 
-          case (lhs, rhs) if lhs.dataType != rhs.dataType =>
-            logDebug(s"Resolving mismatched union input ${lhs.dataType}, ${rhs.dataType}")
-            findTightestCommonTypeOfTwo(lhs.dataType, rhs.dataType).map { widestType =>
-              val newLeft =
-                if (lhs.dataType == widestType) lhs else Alias(Cast(lhs, widestType), lhs.name)()
-              val newRight =
-                if (rhs.dataType == widestType) rhs else Alias(Cast(rhs, widestType), rhs.name)()
-
-              (newLeft, newRight)
-            }.getOrElse {
-              // If there is no applicable conversion, leave expression unchanged.
-              (lhs, rhs)
-            }
+    private[this] def widenOutputTypes(planName: String, left: LogicalPlan, right: LogicalPlan):
+        (LogicalPlan, LogicalPlan) = {
+
+      // TODO: with fixed-precision decimals
+      val castedInput = left.output.zip(right.output).map {
+        // When a string is found on one side, make the other side a string too.
+        case (lhs, rhs) if lhs.dataType == StringType && rhs.dataType != StringType =>
+          (lhs, Alias(Cast(rhs, StringType), rhs.name)())
+        case (lhs, rhs) if lhs.dataType != StringType && rhs.dataType == StringType =>
+          (Alias(Cast(lhs, StringType), lhs.name)(), rhs)
+
+        case (lhs, rhs) if lhs.dataType != rhs.dataType =>
+          logDebug(s"Resolving mismatched $planName input ${lhs.dataType}, ${rhs.dataType}")
+          findTightestCommonTypeOfTwo(lhs.dataType, rhs.dataType).map { widestType =>
+            val newLeft =
+              if (lhs.dataType == widestType) lhs else Alias(Cast(lhs, widestType), lhs.name)()
+            val newRight =
+              if (rhs.dataType == widestType) rhs else Alias(Cast(rhs, widestType), rhs.name)()
+
+            (newLeft, newRight)
+          }.getOrElse {
+            // If there is no applicable conversion, leave expression unchanged.
+            (lhs, rhs)
+          }
 
-          case other => other
-        }
+        case other => other
+      }
 
-        val (castedLeft, castedRight) = castedInput.unzip
+      val (castedLeft, castedRight) = castedInput.unzip
 
-        val newLeft =
-          if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) {
-            logDebug(s"Widening numeric types in union $castedLeft ${left.output}")
-            Project(castedLeft, left)
-          } else {
-            left
-          }
+      val newLeft =
+        if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) {
+          logDebug(s"Widening numeric types in $planName $castedLeft ${left.output}")
+          Project(castedLeft, left)
+        } else {
+          left
+        }
 
-        val newRight =
-          if (castedRight.map(_.dataType) != right.output.map(_.dataType)) {
-            logDebug(s"Widening numeric types in union $castedRight ${right.output}")
-            Project(castedRight, right)
-          } else {
-            right
-          }
+      val newRight =
+        if (castedRight.map(_.dataType) != right.output.map(_.dataType)) {
+          logDebug(s"Widening numeric types in $planName $castedRight ${right.output}")
+          Project(castedRight, right)
+        } else {
+          right
+        }
+      (newLeft, newRight)
+    }
 
+    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
+        val (newLeft, newRight) = widenOutputTypes(u.nodeName, left, right)
         Union(newLeft, newRight)
+      case e @ Except(left, right) if e.childrenResolved && !e.resolved =>
+        val (newLeft, newRight) = widenOutputTypes(e.nodeName, left, right)
+        Except(newLeft, newRight)
+      case i @ Intersect(left, right) if i.childrenResolved && !i.resolved =>
+        val (newLeft, newRight) = widenOutputTypes(i.nodeName, left, right)
+        Intersect(newLeft, newRight)
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 17a91247327f7..986c315b3173a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -141,6 +141,10 @@ case class BroadcastHint(child: LogicalPlan) extends UnaryNode {
 
 case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   override def output: Seq[Attribute] = left.output
+
+  override lazy val resolved: Boolean =
+    childrenResolved &&
+      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
 }
 
 case class InsertIntoTable(
@@ -437,4 +441,8 @@ case object OneRowRelation extends LeafNode {
 
 case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   override def output: Seq[Attribute] = left.output
+
+  override lazy val resolved: Boolean =
+    childrenResolved &&
+      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index d0fd033b981c8..c9b3c69c6de89 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.plans.PlanTest
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LocalRelation, Project}
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
 
@@ -305,6 +305,38 @@ class HiveTypeCoercionSuite extends PlanTest {
     )
   }
 
+  test("WidenTypes for union except and intersect") {
+    def checkOutput(logical: LogicalPlan, expectTypes: Seq[DataType]): Unit = {
+      logical.output.zip(expectTypes).foreach { case (attr, dt) =>
+        assert(attr.dataType === dt)
+      }
+    }
+
+    val left = LocalRelation(
+      AttributeReference("i", IntegerType)(),
+      AttributeReference("u", DecimalType.Unlimited)(),
+      AttributeReference("b", ByteType)(),
+      AttributeReference("d", DoubleType)())
+    val right = LocalRelation(
+      AttributeReference("s", StringType)(),
+      AttributeReference("d", DecimalType(2, 1))(),
+      AttributeReference("f", FloatType)(),
+      AttributeReference("l", LongType)())
+
+    val wt = HiveTypeCoercion.WidenTypes
+    val expectedTypes = Seq(StringType, DecimalType.Unlimited, FloatType, DoubleType)
+
+    val r1 = wt(Union(left, right)).asInstanceOf[Union]
+    val r2 = wt(Except(left, right)).asInstanceOf[Except]
+    val r3 = wt(Intersect(left, right)).asInstanceOf[Intersect]
+    checkOutput(r1.left, expectedTypes)
+    checkOutput(r1.right, expectedTypes)
+    checkOutput(r2.left, expectedTypes)
+    checkOutput(r2.right, expectedTypes)
+    checkOutput(r3.left, expectedTypes)
+    checkOutput(r3.right, expectedTypes)
+  }
+
   /**
    * There are rules that need to not fire before child expressions get resolved.
    * We use this test to make sure those rules do not fire early.

From 6e1e2eba696e89ba57bf5450b9c72c4386e43dc8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 14:07:56 -0700
Subject: [PATCH 0460/1454] [SPARK-8240][SQL] string function: concat

Author: Reynold Xin <rxin@databricks.com>

Closes #7486 from rxin/concat and squashes the following commits:

5217d6e [Reynold Xin] Removed Hive's concat test.
f5cb7a3 [Reynold Xin] Concat is never nullable.
ae4e61f [Reynold Xin] Removed extra import.
fddcbbd [Reynold Xin] Fixed NPE.
22e831c [Reynold Xin] Added missing file.
57a2352 [Reynold Xin] [SPARK-8240][SQL] string function: concat
---
 .../catalyst/analysis/FunctionRegistry.scala  |   1 +
 .../expressions/stringOperations.scala        |  37 +++
 ...ite.scala => StringExpressionsSuite.scala} |  24 +-
 .../org/apache/spark/sql/functions.scala      |  22 ++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 242 ---------------
 .../spark/sql/StringFunctionsSuite.scala      | 284 ++++++++++++++++++
 .../execution/HiveCompatibilitySuite.scala    |   4 +-
 .../apache/spark/unsafe/types/UTF8String.java |  40 ++-
 .../spark/unsafe/types/UTF8StringSuite.java   |  14 +
 9 files changed, 421 insertions(+), 247 deletions(-)
 rename sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/{StringFunctionsSuite.scala => StringExpressionsSuite.scala} (96%)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index ce552a1d65eda..d1cda6bc27095 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -152,6 +152,7 @@ object FunctionRegistry {
     // string functions
     expression[Ascii]("ascii"),
     expression[Base64]("base64"),
+    expression[Concat]("concat"),
     expression[Encode]("encode"),
     expression[Decode]("decode"),
     expression[FormatNumber]("format_number"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index c64afe7b3f19a..b36354eff092a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -27,6 +27,43 @@ import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// This file defines expressions for string operations.
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+/**
+ * An expression that concatenates multiple input strings into a single string.
+ * Input expressions that are evaluated to nulls are skipped.
+ *
+ * For example, `concat("a", null, "b")` is evaluated to `"ab"`.
+ *
+ * Note that this is different from Hive since Hive outputs null if any input is null.
+ * We never output null.
+ */
+case class Concat(children: Seq[Expression]) extends Expression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq.fill(children.size)(StringType)
+  override def dataType: DataType = StringType
+
+  override def nullable: Boolean = false
+  override def foldable: Boolean = children.forall(_.foldable)
+
+  override def eval(input: InternalRow): Any = {
+    val inputs = children.map(_.eval(input).asInstanceOf[UTF8String])
+    UTF8String.concat(inputs : _*)
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val evals = children.map(_.gen(ctx))
+    val inputs = evals.map { eval => s"${eval.isNull} ? null : ${eval.primitive}" }.mkString(", ")
+    evals.map(_.code).mkString("\n") + s"""
+      boolean ${ev.isNull} = false;
+      UTF8String ${ev.primitive} = UTF8String.concat($inputs);
+    """
+  }
+}
+
 
 trait StringRegexExpression extends ImplicitCastInputTypes {
   self: BinaryExpression =>
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
similarity index 96%
rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 5d7763bedf6bd..0ed567a90dd1f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -22,7 +22,29 @@ import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
 
 
-class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+
+  test("concat") {
+    def testConcat(inputs: String*): Unit = {
+      val expected = inputs.filter(_ != null).mkString
+      checkEvaluation(Concat(inputs.map(Literal.create(_, StringType))), expected, EmptyRow)
+    }
+
+    testConcat()
+    testConcat(null)
+    testConcat("")
+    testConcat("ab")
+    testConcat("a", "b")
+    testConcat("a", "b", "C")
+    testConcat("a", null, "C")
+    testConcat("a", null, null)
+    testConcat(null, null, null)
+
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    testConcat("数据", null, "砖头")
+    // scalastyle:on
+  }
 
   test("StringComparison") {
     val row = create_row("abc", null)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index b56fd9a71b321..c180407389136 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1710,6 +1710,28 @@ object functions {
   // String functions
   //////////////////////////////////////////////////////////////////////////////////////////////
 
+  /**
+   * Concatenates input strings together into a single string.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def concat(exprs: Column*): Column = Concat(exprs.map(_.expr))
+
+  /**
+   * Concatenates input strings together into a single string.
+   *
+   * This is the variant of concat that takes in the column names.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def concat(columnName: String, columnNames: String*): Column = {
+    concat((columnName +: columnNames).map(Column.apply): _*)
+  }
+
   /**
    * Computes the length of a given string / binary value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 6dccdd857b453..29f1197a8543c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -208,169 +208,6 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(2743272264L, 2180413220L))
   }
 
-  test("Levenshtein distance") {
-    val df = Seq(("kitten", "sitting"), ("frog", "fog")).toDF("l", "r")
-    checkAnswer(df.select(levenshtein("l", "r")), Seq(Row(3), Row(1)))
-    checkAnswer(df.selectExpr("levenshtein(l, r)"), Seq(Row(3), Row(1)))
-  }
-
-  test("string ascii function") {
-    val df = Seq(("abc", "")).toDF("a", "b")
-    checkAnswer(
-      df.select(ascii($"a"), ascii("b")),
-      Row(97, 0))
-
-    checkAnswer(
-      df.selectExpr("ascii(a)", "ascii(b)"),
-      Row(97, 0))
-  }
-
-  test("string base64/unbase64 function") {
-    val bytes = Array[Byte](1, 2, 3, 4)
-    val df = Seq((bytes, "AQIDBA==")).toDF("a", "b")
-    checkAnswer(
-      df.select(base64("a"), base64($"a"), unbase64("b"), unbase64($"b")),
-      Row("AQIDBA==", "AQIDBA==", bytes, bytes))
-
-    checkAnswer(
-      df.selectExpr("base64(a)", "unbase64(b)"),
-      Row("AQIDBA==", bytes))
-  }
-
-  test("string encode/decode function") {
-    val bytes = Array[Byte](-27, -92, -89, -27, -115, -125, -28, -72, -106, -25, -107, -116)
-    // scalastyle:off  
-    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
-    val df = Seq(("大千世界", "utf-8", bytes)).toDF("a", "b", "c")
-    checkAnswer(
-      df.select(
-        encode($"a", "utf-8"),
-        encode("a", "utf-8"),
-        decode($"c", "utf-8"),
-        decode("c", "utf-8")),
-      Row(bytes, bytes, "大千世界", "大千世界"))
-
-    checkAnswer(
-      df.selectExpr("encode(a, 'utf-8')", "decode(c, 'utf-8')"),
-      Row(bytes, "大千世界"))
-    // scalastyle:on
-  }
-
-  test("string trim functions") {
-    val df = Seq(("  example  ", "")).toDF("a", "b")
-
-    checkAnswer(
-      df.select(ltrim($"a"), rtrim($"a"), trim($"a")),
-      Row("example  ", "  example", "example"))
-
-    checkAnswer(
-      df.selectExpr("ltrim(a)", "rtrim(a)", "trim(a)"),
-      Row("example  ", "  example", "example"))
-  }
-
-  test("string formatString function") {
-    val df = Seq(("aa%d%s", 123, "cc")).toDF("a", "b", "c")
-
-    checkAnswer(
-      df.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
-      Row("aa123cc", "aa123cc"))
-
-    checkAnswer(
-      df.selectExpr("printf(a, b, c)"),
-      Row("aa123cc"))
-  }
-
-  test("string instr function") {
-    val df = Seq(("aaads", "aa", "zz")).toDF("a", "b", "c")
-
-    checkAnswer(
-      df.select(instr($"a", $"b"), instr("a", "b")),
-      Row(1, 1))
-
-    checkAnswer(
-      df.selectExpr("instr(a, b)"),
-      Row(1))
-  }
-
-  test("string locate function") {
-    val df = Seq(("aaads", "aa", "zz", 1)).toDF("a", "b", "c", "d")
-
-    checkAnswer(
-      df.select(
-        locate($"b", $"a"), locate("b", "a"), locate($"b", $"a", 1),
-        locate("b", "a", 1), locate($"b", $"a", $"d"), locate("b", "a", "d")),
-      Row(1, 1, 2, 2, 2, 2))
-
-    checkAnswer(
-      df.selectExpr("locate(b, a)", "locate(b, a, d)"),
-      Row(1, 2))
-  }
-
-  test("string padding functions") {
-    val df = Seq(("hi", 5, "??")).toDF("a", "b", "c")
-
-    checkAnswer(
-          df.select(
-            lpad($"a", $"b", $"c"), rpad("a", "b", "c"),
-            lpad($"a", 1, $"c"), rpad("a", 1, "c")),
-          Row("???hi", "hi???", "h", "h"))
-
-    checkAnswer(
-      df.selectExpr("lpad(a, b, c)", "rpad(a, b, c)", "lpad(a, 1, c)", "rpad(a, 1, c)"),
-      Row("???hi", "hi???", "h", "h"))
-  }
-
-  test("string repeat function") {
-    val df = Seq(("hi", 2)).toDF("a", "b")
-
-    checkAnswer(
-      df.select(
-        repeat($"a", 2), repeat("a", 2), repeat($"a", $"b"), repeat("a", "b")),
-      Row("hihi", "hihi", "hihi", "hihi"))
-
-    checkAnswer(
-      df.selectExpr("repeat(a, 2)", "repeat(a, b)"),
-      Row("hihi", "hihi"))
-  }
-
-  test("string reverse function") {
-    val df = Seq(("hi", "hhhi")).toDF("a", "b")
-
-    checkAnswer(
-      df.select(reverse($"a"), reverse("b")),
-      Row("ih", "ihhh"))
-
-    checkAnswer(
-      df.selectExpr("reverse(b)"),
-      Row("ihhh"))
-  }
-
-  test("string space function") {
-    val df = Seq((2, 3)).toDF("a", "b")
-
-    checkAnswer(
-      df.select(space($"a"), space("b")),
-      Row("  ", "   "))
-
-    checkAnswer(
-      df.selectExpr("space(b)"),
-      Row("   "))
-  }
-
-  test("string split function") {
-    val df = Seq(("aa2bb3cc", "[1-9]+")).toDF("a", "b")
-
-    checkAnswer(
-      df.select(
-        split($"a", "[1-9]+"),
-        split("a", "[1-9]+")),
-      Row(Seq("aa", "bb", "cc"), Seq("aa", "bb", "cc")))
-
-    checkAnswer(
-      df.selectExpr("split(a, '[1-9]+')"),
-      Row(Seq("aa", "bb", "cc")))
-  }
-
   test("conditional function: least") {
     checkAnswer(
       testData2.select(least(lit(-1), lit(0), col("a"), col("b"))).limit(1),
@@ -430,83 +267,4 @@ class DataFrameFunctionsSuite extends QueryTest {
     )
   }
 
-  test("string / binary length function") {
-    val df = Seq(("123", Array[Byte](1, 2, 3, 4), 123)).toDF("a", "b", "c")
-    checkAnswer(
-      df.select(length($"a"), length("a"), length($"b"), length("b")),
-      Row(3, 3, 4, 4))
-
-    checkAnswer(
-      df.selectExpr("length(a)", "length(b)"),
-      Row(3, 4))
-
-    intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("length(c)"), // int type of the argument is unacceptable
-        Row("5.0000"))
-    }
-  }
-
-  test("number format function") {
-    val tuple =
-      ("aa", 1.asInstanceOf[Byte], 2.asInstanceOf[Short],
-        3.13223f, 4, 5L, 6.48173d, Decimal(7.128381))
-    val df =
-      Seq(tuple)
-        .toDF(
-          "a", // string "aa"
-          "b", // byte    1
-          "c", // short   2
-          "d", // float   3.13223f
-          "e", // integer 4
-          "f", // long    5L
-          "g", // double  6.48173d
-          "h") // decimal 7.128381
-
-    checkAnswer(
-      df.select(
-        format_number($"f", 4),
-        format_number("f", 4)),
-      Row("5.0000", "5.0000"))
-
-    checkAnswer(
-      df.selectExpr("format_number(b, e)"), // convert the 1st argument to integer
-      Row("1.0000"))
-
-    checkAnswer(
-      df.selectExpr("format_number(c, e)"), // convert the 1st argument to integer
-      Row("2.0000"))
-
-    checkAnswer(
-      df.selectExpr("format_number(d, e)"), // convert the 1st argument to double
-      Row("3.1322"))
-
-    checkAnswer(
-      df.selectExpr("format_number(e, e)"), // not convert anything
-      Row("4.0000"))
-
-    checkAnswer(
-      df.selectExpr("format_number(f, e)"), // not convert anything
-      Row("5.0000"))
-
-    checkAnswer(
-      df.selectExpr("format_number(g, e)"), // not convert anything
-      Row("6.4817"))
-
-    checkAnswer(
-      df.selectExpr("format_number(h, e)"), // not convert anything
-      Row("7.1284"))
-
-    intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("format_number(a, e)"), // string type of the 1st argument is unacceptable
-        Row("5.0000"))
-    }
-
-    intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("format_number(e, g)"), // decimal type of the 2nd argument is unacceptable
-        Row("5.0000"))
-    }
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
new file mode 100644
index 0000000000000..4eff33ed45042
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -0,0 +1,284 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.Decimal
+
+
+class StringFunctionsSuite extends QueryTest {
+
+  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+  import ctx.implicits._
+
+  test("string concat") {
+    val df = Seq[(String, String, String)](("a", "b", null)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(concat($"a", $"b", $"c")),
+      Row("ab"))
+
+    checkAnswer(
+      df.selectExpr("concat(a, b, c)"),
+      Row("ab"))
+  }
+
+
+  test("string Levenshtein distance") {
+    val df = Seq(("kitten", "sitting"), ("frog", "fog")).toDF("l", "r")
+    checkAnswer(df.select(levenshtein("l", "r")), Seq(Row(3), Row(1)))
+    checkAnswer(df.selectExpr("levenshtein(l, r)"), Seq(Row(3), Row(1)))
+  }
+
+  test("string ascii function") {
+    val df = Seq(("abc", "")).toDF("a", "b")
+    checkAnswer(
+      df.select(ascii($"a"), ascii("b")),
+      Row(97, 0))
+
+    checkAnswer(
+      df.selectExpr("ascii(a)", "ascii(b)"),
+      Row(97, 0))
+  }
+
+  test("string base64/unbase64 function") {
+    val bytes = Array[Byte](1, 2, 3, 4)
+    val df = Seq((bytes, "AQIDBA==")).toDF("a", "b")
+    checkAnswer(
+      df.select(base64("a"), base64($"a"), unbase64("b"), unbase64($"b")),
+      Row("AQIDBA==", "AQIDBA==", bytes, bytes))
+
+    checkAnswer(
+      df.selectExpr("base64(a)", "unbase64(b)"),
+      Row("AQIDBA==", bytes))
+  }
+
+  test("string encode/decode function") {
+    val bytes = Array[Byte](-27, -92, -89, -27, -115, -125, -28, -72, -106, -25, -107, -116)
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    val df = Seq(("大千世界", "utf-8", bytes)).toDF("a", "b", "c")
+    checkAnswer(
+      df.select(
+        encode($"a", "utf-8"),
+        encode("a", "utf-8"),
+        decode($"c", "utf-8"),
+        decode("c", "utf-8")),
+      Row(bytes, bytes, "大千世界", "大千世界"))
+
+    checkAnswer(
+      df.selectExpr("encode(a, 'utf-8')", "decode(c, 'utf-8')"),
+      Row(bytes, "大千世界"))
+    // scalastyle:on
+  }
+
+  test("string trim functions") {
+    val df = Seq(("  example  ", "")).toDF("a", "b")
+
+    checkAnswer(
+      df.select(ltrim($"a"), rtrim($"a"), trim($"a")),
+      Row("example  ", "  example", "example"))
+
+    checkAnswer(
+      df.selectExpr("ltrim(a)", "rtrim(a)", "trim(a)"),
+      Row("example  ", "  example", "example"))
+  }
+
+  test("string formatString function") {
+    val df = Seq(("aa%d%s", 123, "cc")).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
+      Row("aa123cc", "aa123cc"))
+
+    checkAnswer(
+      df.selectExpr("printf(a, b, c)"),
+      Row("aa123cc"))
+  }
+
+  test("string instr function") {
+    val df = Seq(("aaads", "aa", "zz")).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(instr($"a", $"b"), instr("a", "b")),
+      Row(1, 1))
+
+    checkAnswer(
+      df.selectExpr("instr(a, b)"),
+      Row(1))
+  }
+
+  test("string locate function") {
+    val df = Seq(("aaads", "aa", "zz", 1)).toDF("a", "b", "c", "d")
+
+    checkAnswer(
+      df.select(
+        locate($"b", $"a"), locate("b", "a"), locate($"b", $"a", 1),
+        locate("b", "a", 1), locate($"b", $"a", $"d"), locate("b", "a", "d")),
+      Row(1, 1, 2, 2, 2, 2))
+
+    checkAnswer(
+      df.selectExpr("locate(b, a)", "locate(b, a, d)"),
+      Row(1, 2))
+  }
+
+  test("string padding functions") {
+    val df = Seq(("hi", 5, "??")).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(
+        lpad($"a", $"b", $"c"), rpad("a", "b", "c"),
+        lpad($"a", 1, $"c"), rpad("a", 1, "c")),
+      Row("???hi", "hi???", "h", "h"))
+
+    checkAnswer(
+      df.selectExpr("lpad(a, b, c)", "rpad(a, b, c)", "lpad(a, 1, c)", "rpad(a, 1, c)"),
+      Row("???hi", "hi???", "h", "h"))
+  }
+
+  test("string repeat function") {
+    val df = Seq(("hi", 2)).toDF("a", "b")
+
+    checkAnswer(
+      df.select(
+        repeat($"a", 2), repeat("a", 2), repeat($"a", $"b"), repeat("a", "b")),
+      Row("hihi", "hihi", "hihi", "hihi"))
+
+    checkAnswer(
+      df.selectExpr("repeat(a, 2)", "repeat(a, b)"),
+      Row("hihi", "hihi"))
+  }
+
+  test("string reverse function") {
+    val df = Seq(("hi", "hhhi")).toDF("a", "b")
+
+    checkAnswer(
+      df.select(reverse($"a"), reverse("b")),
+      Row("ih", "ihhh"))
+
+    checkAnswer(
+      df.selectExpr("reverse(b)"),
+      Row("ihhh"))
+  }
+
+  test("string space function") {
+    val df = Seq((2, 3)).toDF("a", "b")
+
+    checkAnswer(
+      df.select(space($"a"), space("b")),
+      Row("  ", "   "))
+
+    checkAnswer(
+      df.selectExpr("space(b)"),
+      Row("   "))
+  }
+
+  test("string split function") {
+    val df = Seq(("aa2bb3cc", "[1-9]+")).toDF("a", "b")
+
+    checkAnswer(
+      df.select(
+        split($"a", "[1-9]+"),
+        split("a", "[1-9]+")),
+      Row(Seq("aa", "bb", "cc"), Seq("aa", "bb", "cc")))
+
+    checkAnswer(
+      df.selectExpr("split(a, '[1-9]+')"),
+      Row(Seq("aa", "bb", "cc")))
+  }
+
+  test("string / binary length function") {
+    val df = Seq(("123", Array[Byte](1, 2, 3, 4), 123)).toDF("a", "b", "c")
+    checkAnswer(
+      df.select(length($"a"), length("a"), length($"b"), length("b")),
+      Row(3, 3, 4, 4))
+
+    checkAnswer(
+      df.selectExpr("length(a)", "length(b)"),
+      Row(3, 4))
+
+    intercept[AnalysisException] {
+      checkAnswer(
+        df.selectExpr("length(c)"), // int type of the argument is unacceptable
+        Row("5.0000"))
+    }
+  }
+
+  test("number format function") {
+    val tuple =
+      ("aa", 1.asInstanceOf[Byte], 2.asInstanceOf[Short],
+        3.13223f, 4, 5L, 6.48173d, Decimal(7.128381))
+    val df =
+      Seq(tuple)
+        .toDF(
+          "a", // string "aa"
+          "b", // byte    1
+          "c", // short   2
+          "d", // float   3.13223f
+          "e", // integer 4
+          "f", // long    5L
+          "g", // double  6.48173d
+          "h") // decimal 7.128381
+
+    checkAnswer(
+      df.select(
+        format_number($"f", 4),
+        format_number("f", 4)),
+      Row("5.0000", "5.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(b, e)"), // convert the 1st argument to integer
+      Row("1.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(c, e)"), // convert the 1st argument to integer
+      Row("2.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(d, e)"), // convert the 1st argument to double
+      Row("3.1322"))
+
+    checkAnswer(
+      df.selectExpr("format_number(e, e)"), // not convert anything
+      Row("4.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(f, e)"), // not convert anything
+      Row("5.0000"))
+
+    checkAnswer(
+      df.selectExpr("format_number(g, e)"), // not convert anything
+      Row("6.4817"))
+
+    checkAnswer(
+      df.selectExpr("format_number(h, e)"), // not convert anything
+      Row("7.1284"))
+
+    intercept[AnalysisException] {
+      checkAnswer(
+        df.selectExpr("format_number(a, e)"), // string type of the 1st argument is unacceptable
+        Row("5.0000"))
+    }
+
+    intercept[AnalysisException] {
+      checkAnswer(
+        df.selectExpr("format_number(e, g)"), // decimal type of the 2nd argument is unacceptable
+        Row("5.0000"))
+    }
+  }
+}
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 6b8f2f6217a54..299cc599ff8f7 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -256,6 +256,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "timestamp_2",
     "timestamp_udf",
 
+    // Hive outputs NULL if any concat input has null. We never output null for concat.
+    "udf_concat",
+
     // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
     "udf7"
   )
@@ -846,7 +849,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_case",
     "udf_ceil",
     "udf_ceiling",
-    "udf_concat",
     "udf_concat_insert1",
     "udf_concat_insert2",
     "udf_concat_ws",
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index e7f9fbb2bc682..9723b6e0834b2 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -21,6 +21,7 @@
 import java.io.Serializable;
 import java.io.UnsupportedEncodingException;
 
+import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 
 import static org.apache.spark.unsafe.PlatformDependent.*;
@@ -322,7 +323,7 @@ public int indexOf(UTF8String v, int start) {
       }
       i += numBytesForFirstByte(getByte(i));
       c += 1;
-    } while(i < numBytes);
+    } while (i < numBytes);
 
     return -1;
   }
@@ -395,6 +396,39 @@ public UTF8String lpad(int len, UTF8String pad) {
     }
   }
 
+  /**
+   * Concatenates input strings together into a single string. A null input is skipped.
+   * For example, concat("a", null, "c") would yield "ac".
+   */
+  public static UTF8String concat(UTF8String... inputs) {
+    if (inputs == null) {
+      return fromBytes(new byte[0]);
+    }
+
+    // Compute the total length of the result.
+    int totalLength = 0;
+    for (int i = 0; i < inputs.length; i++) {
+      if (inputs[i] != null) {
+        totalLength += inputs[i].numBytes;
+      }
+    }
+
+    // Allocate a new byte array, and copy the inputs one by one into it.
+    final byte[] result = new byte[totalLength];
+    int offset = 0;
+    for (int i = 0; i < inputs.length; i++) {
+      if (inputs[i] != null) {
+        int len = inputs[i].numBytes;
+        PlatformDependent.copyMemory(
+          inputs[i].base, inputs[i].offset,
+          result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+          len);
+        offset += len;
+      }
+    }
+    return fromBytes(result);
+  }
+
   @Override
   public String toString() {
     try {
@@ -413,7 +447,7 @@ public UTF8String clone() {
   }
 
   @Override
-  public int compareTo(final UTF8String other) {
+  public int compareTo(@Nonnull final UTF8String other) {
     int len = Math.min(numBytes, other.numBytes);
     // TODO: compare 8 bytes as unsigned long
     for (int i = 0; i < len; i ++) {
@@ -434,7 +468,7 @@ public int compare(final UTF8String other) {
   public boolean equals(final Object other) {
     if (other instanceof UTF8String) {
       UTF8String o = (UTF8String) other;
-      if (numBytes != o.numBytes){
+      if (numBytes != o.numBytes) {
         return false;
       }
       return ByteArrayMethods.arrayEquals(base, offset, o.base, o.offset, numBytes);
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 694bdc29f39d1..0db7522b50c1a 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -86,6 +86,20 @@ public void upperAndLower() {
     testUpperandLower("大千世界 数据砖头", "大千世界 数据砖头");
   }
 
+  @Test
+  public void concatTest() {
+    assertEquals(concat(), fromString(""));
+    assertEquals(concat(null), fromString(""));
+    assertEquals(concat(fromString("")), fromString(""));
+    assertEquals(concat(fromString("ab")), fromString("ab"));
+    assertEquals(concat(fromString("a"), fromString("b")), fromString("ab"));
+    assertEquals(concat(fromString("a"), fromString("b"), fromString("c")), fromString("abc"));
+    assertEquals(concat(fromString("a"), null, fromString("c")), fromString("ac"));
+    assertEquals(concat(fromString("a"), null, null), fromString("a"));
+    assertEquals(concat(null, null, null), fromString(""));
+    assertEquals(concat(fromString("数据"), fromString("砖头")), fromString("数据砖头"));
+  }
+
   @Test
   public void contains() {
     assertTrue(fromString("").contains(fromString("")));

From e16a19a39ed3369dffd375d712066d12add71c9e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 15:29:38 -0700
Subject: [PATCH 0461/1454] [SPARK-9174][SQL] Add documentation for all public
 SQLConfs.

Author: Reynold Xin <rxin@databricks.com>

Closes #7500 from rxin/sqlconf and squashes the following commits:

a5726c8 [Reynold Xin] [SPARK-9174][SQL] Add documentation for all public SQLConfs.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  | 144 +++++++-----------
 1 file changed, 53 insertions(+), 91 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 6005d35f015a9..2c2f7c35dfdce 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -26,6 +26,11 @@ import org.apache.parquet.hadoop.ParquetOutputCommitter
 
 import org.apache.spark.sql.catalyst.CatalystConf
 
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// This file defines the configuration options for Spark SQL.
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
 private[spark] object SQLConf {
 
   private val sqlConfEntries = java.util.Collections.synchronizedMap(
@@ -184,17 +189,20 @@ private[spark] object SQLConf {
   val COMPRESS_CACHED = booleanConf("spark.sql.inMemoryColumnarStorage.compressed",
     defaultValue = Some(true),
     doc = "When set to true Spark SQL will automatically select a compression codec for each " +
-      "column based on statistics of the data.")
+      "column based on statistics of the data.",
+    isPublic = false)
 
   val COLUMN_BATCH_SIZE = intConf("spark.sql.inMemoryColumnarStorage.batchSize",
     defaultValue = Some(10000),
     doc = "Controls the size of batches for columnar caching.  Larger batch sizes can improve " +
-      "memory utilization and compression, but risk OOMs when caching data.")
+      "memory utilization and compression, but risk OOMs when caching data.",
+    isPublic = false)
 
   val IN_MEMORY_PARTITION_PRUNING =
     booleanConf("spark.sql.inMemoryColumnarStorage.partitionPruning",
       defaultValue = Some(false),
-      doc = "<TODO>")
+      doc = "When true, enable partition pruning for in-memory columnar tables.",
+      isPublic = false)
 
   val AUTO_BROADCASTJOIN_THRESHOLD = intConf("spark.sql.autoBroadcastJoinThreshold",
     defaultValue = Some(10 * 1024 * 1024),
@@ -203,29 +211,35 @@ private[spark] object SQLConf {
       "Note that currently statistics are only supported for Hive Metastore tables where the " +
       "command<code>ANALYZE TABLE &lt;tableName&gt; COMPUTE STATISTICS noscan</code> has been run.")
 
-  val DEFAULT_SIZE_IN_BYTES = longConf("spark.sql.defaultSizeInBytes", isPublic = false)
+  val DEFAULT_SIZE_IN_BYTES = longConf(
+    "spark.sql.defaultSizeInBytes",
+    doc = "The default table size used in query planning. By default, it is set to a larger " +
+      "value than `spark.sql.autoBroadcastJoinThreshold` to be more conservative. That is to say " +
+      "by default the optimizer will not choose to broadcast a table unless it knows for sure its" +
+      "size is small enough.",
+    isPublic = false)
 
   val SHUFFLE_PARTITIONS = intConf("spark.sql.shuffle.partitions",
     defaultValue = Some(200),
-    doc = "Configures the number of partitions to use when shuffling data for joins or " +
-      "aggregations.")
+    doc = "The default number of partitions to use when shuffling data for joins or aggregations.")
 
   val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
     defaultValue = Some(true),
     doc = "When true, code will be dynamically generated at runtime for expression evaluation in" +
-      " a specific query. For some queries with complicated expression this option can lead to " +
-      "significant speed-ups. However, for simple queries this can actually slow down query " +
-      "execution.")
+      " a specific query.")
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
     defaultValue = Some(false),
-    doc = "<TDDO>")
+    doc = "When true, use the new optimized Tungsten physical execution backend.")
 
-  val DIALECT = stringConf("spark.sql.dialect", defaultValue = Some("sql"), doc = "<TODO>")
+  val DIALECT = stringConf(
+    "spark.sql.dialect",
+    defaultValue = Some("sql"),
+    doc = "The default SQL dialect to use.")
 
   val CASE_SENSITIVE = booleanConf("spark.sql.caseSensitive",
     defaultValue = Some(true),
-    doc = "<TODO>")
+    doc = "Whether the query analyzer should be case sensitive or not.")
 
   val PARQUET_SCHEMA_MERGING_ENABLED = booleanConf("spark.sql.parquet.mergeSchema",
     defaultValue = Some(true),
@@ -273,9 +287,8 @@ private[spark] object SQLConf {
   val PARQUET_FOLLOW_PARQUET_FORMAT_SPEC = booleanConf(
     key = "spark.sql.parquet.followParquetFormatSpec",
     defaultValue = Some(false),
-    doc = "Whether to stick to Parquet format specification when converting Parquet schema to " +
-      "Spark SQL schema and vice versa.  Sticks to the specification if set to true; falls back " +
-      "to compatible mode if set to false.",
+    doc = "Whether to follow Parquet's format specification when converting Parquet schema to " +
+      "Spark SQL schema and vice versa.",
     isPublic = false)
 
   val PARQUET_OUTPUT_COMMITTER_CLASS = stringConf(
@@ -290,7 +303,7 @@ private[spark] object SQLConf {
 
   val ORC_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.orc.filterPushdown",
     defaultValue = Some(false),
-    doc = "<TODO>")
+    doc = "When true, enable filter pushdown for ORC files.")
 
   val HIVE_VERIFY_PARTITION_PATH = booleanConf("spark.sql.hive.verifyPartitionPath",
     defaultValue = Some(true),
@@ -302,7 +315,7 @@ private[spark] object SQLConf {
 
   val BROADCAST_TIMEOUT = intConf("spark.sql.broadcastTimeout",
     defaultValue = Some(5 * 60),
-    doc = "<TODO>")
+    doc = "Timeout in seconds for the broadcast wait time in broadcast joins.")
 
   // Options that control which operators can be chosen by the query planner.  These should be
   // considered hints and may be ignored by future versions of Spark SQL.
@@ -313,7 +326,7 @@ private[spark] object SQLConf {
 
   val SORTMERGE_JOIN = booleanConf("spark.sql.planner.sortMergeJoin",
     defaultValue = Some(false),
-    doc = "<TODO>")
+    doc = "When true, use sort merge join (as opposed to hash join) by default for large joins.")
 
   // This is only used for the thriftserver
   val THRIFTSERVER_POOL = stringConf("spark.sql.thriftserver.scheduler.pool",
@@ -321,16 +334,16 @@ private[spark] object SQLConf {
 
   val THRIFTSERVER_UI_STATEMENT_LIMIT = intConf("spark.sql.thriftserver.ui.retainedStatements",
     defaultValue = Some(200),
-    doc = "<TODO>")
+    doc = "The number of SQL statements kept in the JDBC/ODBC web UI history.")
 
   val THRIFTSERVER_UI_SESSION_LIMIT = intConf("spark.sql.thriftserver.ui.retainedSessions",
     defaultValue = Some(200),
-    doc = "<TODO>")
+    doc = "The number of SQL client sessions kept in the JDBC/ODBC web UI history.")
 
   // This is used to set the default data source
   val DEFAULT_DATA_SOURCE_NAME = stringConf("spark.sql.sources.default",
     defaultValue = Some("org.apache.spark.sql.parquet"),
-    doc = "<TODO>")
+    doc = "The default data source to use in input/output.")
 
   // This is used to control the when we will split a schema's JSON string to multiple pieces
   // in order to fit the JSON string in metastore's table property (by default, the value has
@@ -338,18 +351,20 @@ private[spark] object SQLConf {
   // to its length exceeds the threshold.
   val SCHEMA_STRING_LENGTH_THRESHOLD = intConf("spark.sql.sources.schemaStringLengthThreshold",
     defaultValue = Some(4000),
-    doc = "<TODO>")
+    doc = "The maximum length allowed in a single cell when " +
+      "storing additional schema information in Hive's metastore.",
+    isPublic = false)
 
   // Whether to perform partition discovery when loading external data sources.  Default to true.
   val PARTITION_DISCOVERY_ENABLED = booleanConf("spark.sql.sources.partitionDiscovery.enabled",
     defaultValue = Some(true),
-    doc = "<TODO>")
+    doc = "When true, automtically discover data partitions.")
 
   // Whether to perform partition column type inference. Default to true.
   val PARTITION_COLUMN_TYPE_INFERENCE =
     booleanConf("spark.sql.sources.partitionColumnTypeInference.enabled",
       defaultValue = Some(true),
-      doc = "<TODO>")
+      doc = "When true, automatically infer the data types for partitioned columns.")
 
   // The output committer class used by HadoopFsRelation. The specified class needs to be a
   // subclass of org.apache.hadoop.mapreduce.OutputCommitter.
@@ -363,22 +378,28 @@ private[spark] object SQLConf {
 
   // Whether to perform eager analysis when constructing a dataframe.
   // Set to false when debugging requires the ability to look at invalid query plans.
-  val DATAFRAME_EAGER_ANALYSIS = booleanConf("spark.sql.eagerAnalysis",
+  val DATAFRAME_EAGER_ANALYSIS = booleanConf(
+    "spark.sql.eagerAnalysis",
     defaultValue = Some(true),
-    doc = "<TODO>")
+    doc = "When true, eagerly applies query analysis on DataFrame operations.",
+    isPublic = false)
 
   // Whether to automatically resolve ambiguity in join conditions for self-joins.
   // See SPARK-6231.
-  val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY =
-    booleanConf("spark.sql.selfJoinAutoResolveAmbiguity", defaultValue = Some(true), doc = "<TODO>")
+  val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY = booleanConf(
+    "spark.sql.selfJoinAutoResolveAmbiguity",
+    defaultValue = Some(true),
+    isPublic = false)
 
   // Whether to retain group by columns or not in GroupedData.agg.
-  val DATAFRAME_RETAIN_GROUP_COLUMNS = booleanConf("spark.sql.retainGroupColumns",
+  val DATAFRAME_RETAIN_GROUP_COLUMNS = booleanConf(
+    "spark.sql.retainGroupColumns",
     defaultValue = Some(true),
-    doc = "<TODO>")
+    isPublic = false)
 
-  val USE_SQL_SERIALIZER2 = booleanConf("spark.sql.useSerializer2",
-    defaultValue = Some(true), doc = "<TODO>")
+  val USE_SQL_SERIALIZER2 = booleanConf(
+    "spark.sql.useSerializer2",
+    defaultValue = Some(true), isPublic = false)
 
   val USE_JACKSON_STREAMING_API = booleanConf("spark.sql.json.useJacksonStreamingAPI",
     defaultValue = Some(true), doc = "<TODO>")
@@ -422,112 +443,53 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
    */
   private[spark] def dialect: String = getConf(DIALECT)
 
-  /** When true tables cached using the in-memory columnar caching will be compressed. */
   private[spark] def useCompression: Boolean = getConf(COMPRESS_CACHED)
 
-  /** The compression codec for writing to a Parquetfile */
   private[spark] def parquetCompressionCodec: String = getConf(PARQUET_COMPRESSION)
 
   private[spark] def parquetCacheMetadata: Boolean = getConf(PARQUET_CACHE_METADATA)
 
-  /** The number of rows that will be  */
   private[spark] def columnBatchSize: Int = getConf(COLUMN_BATCH_SIZE)
 
-  /** Number of partitions to use for shuffle operators. */
   private[spark] def numShufflePartitions: Int = getConf(SHUFFLE_PARTITIONS)
 
-  /** When true predicates will be passed to the parquet record reader when possible. */
   private[spark] def parquetFilterPushDown: Boolean = getConf(PARQUET_FILTER_PUSHDOWN_ENABLED)
 
-  /** When true uses Parquet implementation based on data source API */
   private[spark] def parquetUseDataSourceApi: Boolean = getConf(PARQUET_USE_DATA_SOURCE_API)
 
   private[spark] def orcFilterPushDown: Boolean = getConf(ORC_FILTER_PUSHDOWN_ENABLED)
 
-  /** When true uses verifyPartitionPath to prune the path which is not exists. */
   private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITION_PATH)
 
-  /** When true the planner will use the external sort, which may spill to disk. */
   private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT)
 
-  /**
-   * Sort merge join would sort the two side of join first, and then iterate both sides together
-   * only once to get all matches. Using sort merge join can save a lot of memory usage compared
-   * to HashJoin.
-   */
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
 
-  /**
-   * When set to true, Spark SQL will use the Janino at runtime to generate custom bytecode
-   * that evaluates expressions found in queries.  In general this custom code runs much faster
-   * than interpreted evaluation, but there are some start-up costs (5-10ms) due to compilation.
-   */
   private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED)
 
-  /**
-   * caseSensitive analysis true by default
-   */
   def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
 
-  /**
-   * When set to true, Spark SQL will use managed memory for certain operations.  This option only
-   * takes effect if codegen is enabled.
-   *
-   * Defaults to false as this feature is currently experimental.
-   */
   private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
 
   private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
 
-  /**
-   * Selects between the new (true) and old (false) JSON handlers, to be removed in Spark 1.5.0
-   */
   private[spark] def useJacksonStreamingAPI: Boolean = getConf(USE_JACKSON_STREAMING_API)
 
-  /**
-   * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
-   * a broadcast value during the physical executions of join operations.  Setting this to -1
-   * effectively disables auto conversion.
-   *
-   * Hive setting: hive.auto.convert.join.noconditionaltask.size, whose default value is 10000.
-   */
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
 
-  /**
-   * The default size in bytes to assign to a logical operator's estimation statistics.  By default,
-   * it is set to a larger value than `autoBroadcastJoinThreshold`, hence any logical operator
-   * without a properly implemented estimation of this statistic will not be incorrectly broadcasted
-   * in joins.
-   */
   private[spark] def defaultSizeInBytes: Long =
     getConf(DEFAULT_SIZE_IN_BYTES, autoBroadcastJoinThreshold + 1L)
 
-  /**
-   * When set to true, we always treat byte arrays in Parquet files as strings.
-   */
   private[spark] def isParquetBinaryAsString: Boolean = getConf(PARQUET_BINARY_AS_STRING)
 
-  /**
-   * When set to true, we always treat INT96Values in Parquet files as timestamp.
-   */
   private[spark] def isParquetINT96AsTimestamp: Boolean = getConf(PARQUET_INT96_AS_TIMESTAMP)
 
-  /**
-   * When set to true, sticks to Parquet format spec when converting Parquet schema to Spark SQL
-   * schema and vice versa.  Otherwise, falls back to compatible mode.
-   */
   private[spark] def followParquetFormatSpec: Boolean = getConf(PARQUET_FOLLOW_PARQUET_FORMAT_SPEC)
 
-  /**
-   * When set to true, partition pruning for in-memory columnar tables is enabled.
-   */
   private[spark] def inMemoryPartitionPruning: Boolean = getConf(IN_MEMORY_PARTITION_PRUNING)
 
   private[spark] def columnNameOfCorruptRecord: String = getConf(COLUMN_NAME_OF_CORRUPT_RECORD)
 
-  /**
-   * Timeout in seconds for the broadcast wait time in hash join
-   */
   private[spark] def broadcastTimeout: Int = getConf(BROADCAST_TIMEOUT)
 
   private[spark] def defaultDataSourceName: String = getConf(DEFAULT_DATA_SOURCE_NAME)

From 9914b1b2c5d5fe020f54d95f59f03023de2ea78a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 18:18:19 -0700
Subject: [PATCH 0462/1454] [SPARK-9150][SQL] Create CodegenFallback and
 Unevaluable trait

It is very hard to track which expressions have code gen implemented or not. This patch removes the default fallback gencode implementation from Expression, and moves that into a new trait called CodegenFallback. Each concrete expression needs to either implement code generation, or mix in CodegenFallback. This makes it very easy to track which expressions have code generation implemented already.

Additionally, this patch creates an Unevaluable trait that can be used to track expressions that don't support evaluation (e.g. Star).

Author: Reynold Xin <rxin@databricks.com>

Closes #7487 from rxin/codegenfallback and squashes the following commits:

14ebf38 [Reynold Xin] Fixed Conv
6c1c882 [Reynold Xin] Fixed Alias.
b42611b [Reynold Xin] [SPARK-9150][SQL] Create a trait to track code generation for expressions.
cb5c066 [Reynold Xin] Removed extra import.
39cbe40 [Reynold Xin] [SPARK-8240][SQL] string function: concat
---
 .../sql/catalyst/analysis/unresolved.scala    | 43 ++++---------
 .../spark/sql/catalyst/expressions/Cast.scala |  7 +--
 .../sql/catalyst/expressions/Expression.scala | 28 +++++----
 .../sql/catalyst/expressions/ScalaUDF.scala   |  4 +-
 .../sql/catalyst/expressions/SortOrder.scala  | 10 +--
 .../sql/catalyst/expressions/aggregates.scala | 10 +--
 .../sql/catalyst/expressions/arithmetic.scala |  5 +-
 .../expressions/codegen/CodegenFallback.scala | 40 ++++++++++++
 .../expressions/complexTypeCreator.scala      | 11 ++--
 .../expressions/datetimeFunctions.scala       |  5 +-
 .../sql/catalyst/expressions/generators.scala |  8 +--
 .../sql/catalyst/expressions/literals.scala   |  7 ++-
 .../spark/sql/catalyst/expressions/math.scala | 61 ++++++++++---------
 .../expressions/namedExpressions.scala        | 15 ++---
 .../sql/catalyst/expressions/predicates.scala |  7 ++-
 .../spark/sql/catalyst/expressions/sets.scala | 12 ++--
 .../expressions/stringOperations.scala        | 48 +++++++++------
 .../expressions/windowExpressions.scala       | 41 ++++---------
 .../plans/physical/partitioning.scala         | 16 +----
 .../analysis/AnalysisErrorSuite.scala         |  4 +-
 .../analysis/HiveTypeCoercionSuite.scala      |  8 +--
 .../sql/catalyst/trees/TreeNodeSuite.scala    |  3 +-
 .../spark/sql/execution/pythonUDFs.scala      |  6 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  | 25 ++++----
 24 files changed, 206 insertions(+), 218 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index 4a1a1ed61ebe7..0daee1990a6e0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -17,9 +17,8 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.{errors, trees}
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.errors
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.LeafNode
 import org.apache.spark.sql.catalyst.trees.TreeNode
@@ -50,7 +49,7 @@ case class UnresolvedRelation(
 /**
  * Holds the name of an attribute that has yet to be resolved.
  */
-case class UnresolvedAttribute(nameParts: Seq[String]) extends Attribute {
+case class UnresolvedAttribute(nameParts: Seq[String]) extends Attribute with Unevaluable {
 
   def name: String =
     nameParts.map(n => if (n.contains(".")) s"`$n`" else n).mkString(".")
@@ -66,10 +65,6 @@ case class UnresolvedAttribute(nameParts: Seq[String]) extends Attribute {
   override def withQualifiers(newQualifiers: Seq[String]): UnresolvedAttribute = this
   override def withName(newName: String): UnresolvedAttribute = UnresolvedAttribute.quoted(newName)
 
-  // Unresolved attributes are transient at compile time and don't get evaluated during execution.
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
-
   override def toString: String = s"'$name"
 }
 
@@ -78,16 +73,14 @@ object UnresolvedAttribute {
   def quoted(name: String): UnresolvedAttribute = new UnresolvedAttribute(Seq(name))
 }
 
-case class UnresolvedFunction(name: String, children: Seq[Expression]) extends Expression {
+case class UnresolvedFunction(name: String, children: Seq[Expression])
+  extends Expression with Unevaluable {
+
   override def dataType: DataType = throw new UnresolvedException(this, "dataType")
   override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
   override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
   override lazy val resolved = false
 
-  // Unresolved functions are transient at compile time and don't get evaluated during execution.
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
-
   override def toString: String = s"'$name(${children.mkString(",")})"
 }
 
@@ -105,10 +98,6 @@ abstract class Star extends LeafExpression with NamedExpression {
   override def toAttribute: Attribute = throw new UnresolvedException(this, "toAttribute")
   override lazy val resolved = false
 
-  // Star gets expanded at runtime so we never evaluate a Star.
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
-
   def expand(input: Seq[Attribute], resolver: Resolver): Seq[NamedExpression]
 }
 
@@ -120,7 +109,7 @@ abstract class Star extends LeafExpression with NamedExpression {
  * @param table an optional table that should be the target of the expansion.  If omitted all
  *              tables' columns are produced.
  */
-case class UnresolvedStar(table: Option[String]) extends Star {
+case class UnresolvedStar(table: Option[String]) extends Star with Unevaluable {
 
   override def expand(input: Seq[Attribute], resolver: Resolver): Seq[NamedExpression] = {
     val expandedAttributes: Seq[Attribute] = table match {
@@ -149,7 +138,7 @@ case class UnresolvedStar(table: Option[String]) extends Star {
  * @param names the names to be associated with each output of computing [[child]].
  */
 case class MultiAlias(child: Expression, names: Seq[String])
-  extends UnaryExpression with NamedExpression {
+  extends UnaryExpression with NamedExpression with CodegenFallback {
 
   override def name: String = throw new UnresolvedException(this, "name")
 
@@ -165,9 +154,6 @@ case class MultiAlias(child: Expression, names: Seq[String])
 
   override lazy val resolved = false
 
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
-
   override def toString: String = s"$child AS $names"
 
 }
@@ -178,7 +164,7 @@ case class MultiAlias(child: Expression, names: Seq[String])
  *
  * @param expressions Expressions to expand.
  */
-case class ResolvedStar(expressions: Seq[NamedExpression]) extends Star {
+case class ResolvedStar(expressions: Seq[NamedExpression]) extends Star with Unevaluable {
   override def expand(input: Seq[Attribute], resolver: Resolver): Seq[NamedExpression] = expressions
   override def toString: String = expressions.mkString("ResolvedStar(", ", ", ")")
 }
@@ -192,23 +178,21 @@ case class ResolvedStar(expressions: Seq[NamedExpression]) extends Star {
  *                   can be key of Map, index of Array, field name of Struct.
  */
 case class UnresolvedExtractValue(child: Expression, extraction: Expression)
-  extends UnaryExpression {
+  extends UnaryExpression with Unevaluable {
 
   override def dataType: DataType = throw new UnresolvedException(this, "dataType")
   override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
   override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
   override lazy val resolved = false
 
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
-
   override def toString: String = s"$child[$extraction]"
 }
 
 /**
  * Holds the expression that has yet to be aliased.
  */
-case class UnresolvedAlias(child: Expression) extends UnaryExpression with NamedExpression {
+case class UnresolvedAlias(child: Expression)
+  extends UnaryExpression with NamedExpression with Unevaluable {
 
   override def toAttribute: Attribute = throw new UnresolvedException(this, "toAttribute")
   override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers")
@@ -218,7 +202,4 @@ case class UnresolvedAlias(child: Expression) extends UnaryExpression with Named
   override def name: String = throw new UnresolvedException(this, "name")
 
   override lazy val resolved = false
-
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 692b9fddbb041..3346d3c9f9e61 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -18,12 +18,10 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.math.{BigDecimal => JavaBigDecimal}
-import java.sql.{Date, Timestamp}
 
-import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{Interval, UTF8String}
@@ -106,7 +104,8 @@ object Cast {
 }
 
 /** Cast the child expression to the target data type. */
-case class Cast(child: Expression, dataType: DataType) extends UnaryExpression with Logging {
+case class Cast(child: Expression, dataType: DataType)
+  extends UnaryExpression with CodegenFallback {
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (Cast.canCast(child.dataType, dataType)) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 0e128d8bdcd96..d0a1aa9a1e912 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -101,19 +101,7 @@ abstract class Expression extends TreeNode[Expression] {
    * @param ev an [[GeneratedExpressionCode]] with unique terms.
    * @return Java source code
    */
-  protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    ctx.references += this
-    val objectTerm = ctx.freshName("obj")
-    s"""
-      /* expression: ${this} */
-      Object $objectTerm = expressions[${ctx.references.size - 1}].eval(i);
-      boolean ${ev.isNull} = $objectTerm == null;
-      ${ctx.javaType(this.dataType)} ${ev.primitive} = ${ctx.defaultValue(this.dataType)};
-      if (!${ev.isNull}) {
-        ${ev.primitive} = (${ctx.boxedType(this.dataType)}) $objectTerm;
-      }
-    """
-  }
+  protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String
 
   /**
    * Returns `true` if this expression and all its children have been resolved to a specific schema
@@ -182,6 +170,20 @@ abstract class Expression extends TreeNode[Expression] {
 }
 
 
+/**
+ * An expression that cannot be evaluated. Some expressions don't live past analysis or optimization
+ * time (e.g. Star). This trait is used by those expressions.
+ */
+trait Unevaluable { self: Expression =>
+
+  override def eval(input: InternalRow = null): Any =
+    throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
+    throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
+}
+
+
 /**
  * A leaf expression, i.e. one without any child expressions.
  */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index 22687acd68a97..11c7950c0613b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -29,7 +30,8 @@ case class ScalaUDF(
     function: AnyRef,
     dataType: DataType,
     children: Seq[Expression],
-    inputTypes: Seq[DataType] = Nil) extends Expression with ImplicitCastInputTypes {
+    inputTypes: Seq[DataType] = Nil)
+  extends Expression with ImplicitCastInputTypes with CodegenFallback {
 
   override def nullable: Boolean = true
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index b8f7068c9e5e5..3f436c0eb893c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -17,9 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
-import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.types.DataType
 
 abstract sealed class SortDirection
@@ -30,7 +27,8 @@ case object Descending extends SortDirection
  * An expression that can be used to sort a tuple.  This class extends expression primarily so that
  * transformations over expression will descend into its child.
  */
-case class SortOrder(child: Expression, direction: SortDirection) extends UnaryExpression {
+case class SortOrder(child: Expression, direction: SortDirection)
+  extends UnaryExpression with Unevaluable {
 
   /** Sort order is not foldable because we don't have an eval for it. */
   override def foldable: Boolean = false
@@ -38,9 +36,5 @@ case class SortOrder(child: Expression, direction: SortDirection) extends UnaryE
   override def dataType: DataType = child.dataType
   override def nullable: Boolean = child.nullable
 
-  // SortOrder itself is never evaluated.
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
-
   override def toString: String = s"$child ${if (direction == Ascending) "ASC" else "DESC"}"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index af9a674ab4958..d705a1286065c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -26,7 +26,8 @@ import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
 
-trait AggregateExpression extends Expression {
+
+trait AggregateExpression extends Expression with Unevaluable {
 
   /**
    * Aggregate expressions should not be foldable.
@@ -38,13 +39,6 @@ trait AggregateExpression extends Expression {
    * of input rows/
    */
   def newInstance(): AggregateFunction
-
-  /**
-   * [[AggregateExpression.eval]] should never be invoked because [[AggregateExpression]]'s are
-   * replaced with a physical aggregate operator at runtime.
-   */
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index e83650fc8cb0e..05b5ad88fee8f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.Interval
@@ -65,7 +65,8 @@ case class UnaryPositive(child: Expression) extends UnaryExpression with Expects
 /**
  * A function that get the absolute value of the numeric value.
  */
-case class Abs(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+case class Abs(child: Expression)
+  extends UnaryExpression with ExpectsInputTypes with CodegenFallback {
 
   override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
new file mode 100644
index 0000000000000..bf4f600cb26e5
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.sql.catalyst.expressions.Expression
+
+/**
+ * A trait that can be used to provide a fallback mode for expression code generation.
+ */
+trait CodegenFallback { self: Expression =>
+
+  protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    ctx.references += this
+    val objectTerm = ctx.freshName("obj")
+    s"""
+      /* expression: ${this} */
+      Object $objectTerm = expressions[${ctx.references.size - 1}].eval(i);
+      boolean ${ev.isNull} = $objectTerm == null;
+      ${ctx.javaType(this.dataType)} ${ev.primitive} = ${ctx.defaultValue(this.dataType)};
+      if (!${ev.isNull}) {
+        ${ev.primitive} = (${ctx.boxedType(this.dataType)}) $objectTerm;
+      }
+    """
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index d1e4c458864f1..f9fd04c02aaef 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -19,13 +19,14 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
 /**
  * Returns an Array containing the evaluation of all children expressions.
  */
-case class CreateArray(children: Seq[Expression]) extends Expression {
+case class CreateArray(children: Seq[Expression]) extends Expression with CodegenFallback {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
@@ -51,7 +52,7 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
  * Returns a Row containing the evaluation of all children expressions.
  * TODO: [[CreateStruct]] does not support codegen.
  */
-case class CreateStruct(children: Seq[Expression]) extends Expression {
+case class CreateStruct(children: Seq[Expression]) extends Expression with CodegenFallback {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
@@ -83,7 +84,7 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
  *
  * @param children Seq(name1, val1, name2, val2, ...)
  */
-case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
+case class CreateNamedStruct(children: Seq[Expression]) extends Expression with CodegenFallback {
 
   private lazy val (nameExprs, valExprs) =
     children.grouped(2).map { case Seq(name, value) => (name, value) }.toList.unzip
@@ -103,11 +104,11 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (children.size % 2 != 0) {
-      TypeCheckResult.TypeCheckFailure("CreateNamedStruct expects an even number of arguments.")
+      TypeCheckResult.TypeCheckFailure(s"$prettyName expects an even number of arguments.")
     } else {
       val invalidNames =
         nameExprs.filterNot(e => e.foldable && e.dataType == StringType && !nullable)
-      if (invalidNames.size != 0) {
+      if (invalidNames.nonEmpty) {
         TypeCheckResult.TypeCheckFailure(
           s"Odd position only allow foldable and not-null StringType expressions, got :" +
             s" ${invalidNames.mkString(",")}")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index dd5ec330a771b..4bed140cffbfa 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 
@@ -27,7 +28,7 @@ import org.apache.spark.sql.types._
  *
  * There is no code generation since this expression should get constant folded by the optimizer.
  */
-case class CurrentDate() extends LeafExpression {
+case class CurrentDate() extends LeafExpression with CodegenFallback {
   override def foldable: Boolean = true
   override def nullable: Boolean = false
 
@@ -44,7 +45,7 @@ case class CurrentDate() extends LeafExpression {
  *
  * There is no code generation since this expression should get constant folded by the optimizer.
  */
-case class CurrentTimestamp() extends LeafExpression {
+case class CurrentTimestamp() extends LeafExpression with CodegenFallback {
   override def foldable: Boolean = true
   override def nullable: Boolean = false
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index c58a6d36141c1..2dbcf2830f876 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -20,9 +20,9 @@ package org.apache.spark.sql.catalyst.expressions
 import scala.collection.Map
 
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types._
 
 /**
@@ -73,7 +73,7 @@ case class UserDefinedGenerator(
     elementTypes: Seq[(DataType, Boolean)],
     function: Row => TraversableOnce[InternalRow],
     children: Seq[Expression])
-  extends Generator {
+  extends Generator with CodegenFallback {
 
   @transient private[this] var inputRow: InterpretedProjection = _
   @transient private[this] var convertToScala: (InternalRow) => Row = _
@@ -100,7 +100,7 @@ case class UserDefinedGenerator(
 /**
  * Given an input array produces a sequence of rows for each value in the array.
  */
-case class Explode(child: Expression) extends UnaryExpression with Generator {
+case class Explode(child: Expression) extends UnaryExpression with Generator with CodegenFallback {
 
   override def children: Seq[Expression] = child :: Nil
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index e1fdb29541fa8..f25ac32679587 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -21,7 +21,7 @@ import java.sql.{Date, Timestamp}
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types._
@@ -75,7 +75,8 @@ object IntegerLiteral {
 /**
  * In order to do type checking, use Literal.create() instead of constructor
  */
-case class Literal protected (value: Any, dataType: DataType) extends LeafExpression {
+case class Literal protected (value: Any, dataType: DataType)
+  extends LeafExpression with CodegenFallback {
 
   override def foldable: Boolean = true
   override def nullable: Boolean = value == null
@@ -142,7 +143,7 @@ case class Literal protected (value: Any, dataType: DataType) extends LeafExpres
 
 // TODO: Specialize
 case class MutableLiteral(var value: Any, dataType: DataType, nullable: Boolean = true)
-    extends LeafExpression {
+  extends LeafExpression with CodegenFallback {
 
   def update(expression: Expression, input: InternalRow): Unit = {
     value = expression.eval(input)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index eb5c065a34123..7ce64d29ba59a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.{lang => jl}
-import java.util.Arrays
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckSuccess, TypeCheckFailure}
@@ -29,11 +28,14 @@ import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * A leaf expression specifically for math constants. Math constants expect no input.
+ *
+ * There is no code generation because they should get constant folded by the optimizer.
+ *
  * @param c The math constant.
  * @param name The short name of the function
  */
 abstract class LeafMathExpression(c: Double, name: String)
-  extends LeafExpression with Serializable {
+  extends LeafExpression with CodegenFallback {
 
   override def dataType: DataType = DoubleType
   override def foldable: Boolean = true
@@ -41,13 +43,6 @@ abstract class LeafMathExpression(c: Double, name: String)
   override def toString: String = s"$name()"
 
   override def eval(input: InternalRow): Any = c
-
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    s"""
-      boolean ${ev.isNull} = false;
-      ${ctx.javaType(dataType)} ${ev.primitive} = java.lang.Math.$name;
-    """
-  }
 }
 
 /**
@@ -130,8 +125,16 @@ abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
+/**
+ * Euler's number. Note that there is no code generation because this is only
+ * evaluated by the optimizer during constant folding.
+ */
 case class EulerNumber() extends LeafMathExpression(math.E, "E")
 
+/**
+ * Pi. Note that there is no code generation because this is only
+ * evaluated by the optimizer during constant folding.
+ */
 case class Pi() extends LeafMathExpression(math.Pi, "PI")
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -161,7 +164,7 @@ case class Cosh(child: Expression) extends UnaryMathExpression(math.cosh, "COSH"
  * @param toBaseExpr to which base
  */
 case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expression)
-  extends Expression with ImplicitCastInputTypes{
+  extends Expression with ImplicitCastInputTypes with CodegenFallback {
 
   override def foldable: Boolean = numExpr.foldable && fromBaseExpr.foldable && toBaseExpr.foldable
 
@@ -171,6 +174,8 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
 
   override def inputTypes: Seq[AbstractDataType] = Seq(StringType, IntegerType, IntegerType)
 
+  override def dataType: DataType = StringType
+
   /** Returns the result of evaluating this expression on a given input Row */
   override def eval(input: InternalRow): Any = {
     val num = numExpr.eval(input)
@@ -179,17 +184,13 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
     if (num == null || fromBase == null || toBase == null) {
       null
     } else {
-      conv(num.asInstanceOf[UTF8String].getBytes,
-        fromBase.asInstanceOf[Int], toBase.asInstanceOf[Int])
+      conv(
+        num.asInstanceOf[UTF8String].getBytes,
+        fromBase.asInstanceOf[Int],
+        toBase.asInstanceOf[Int])
     }
   }
 
-  /**
-   * Returns the [[DataType]] of the result of evaluating this expression.  It is
-   * invalid to query the dataType of an unresolved expression (i.e., when `resolved` == false).
-   */
-  override def dataType: DataType = StringType
-
   private val value = new Array[Byte](64)
 
   /**
@@ -208,7 +209,7 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
       // Two's complement => x = uval - 2*MAX - 2
       // => uval = x + 2*MAX + 2
       // Now, use the fact: (a+b)/c = a/c + b/c + (a%c+b%c)/c
-      (x / m + 2 * (Long.MaxValue / m) + 2 / m + (x % m + 2 * (Long.MaxValue % m) + 2 % m) / m)
+      x / m + 2 * (Long.MaxValue / m) + 2 / m + (x % m + 2 * (Long.MaxValue % m) + 2 % m) / m
     }
   }
 
@@ -220,7 +221,7 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
    */
   private def decode(v: Long, radix: Int): Unit = {
     var tmpV = v
-    Arrays.fill(value, 0.asInstanceOf[Byte])
+    java.util.Arrays.fill(value, 0.asInstanceOf[Byte])
     var i = value.length - 1
     while (tmpV != 0) {
       val q = unsignedLongDiv(tmpV, radix)
@@ -254,7 +255,7 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
       v = v * radix + value(i)
       i += 1
     }
-    return v
+    v
   }
 
   /**
@@ -292,16 +293,16 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
    * NB: This logic is borrowed from org.apache.hadoop.hive.ql.ud.UDFConv
    */
   private def conv(n: Array[Byte] , fromBase: Int, toBase: Int ): UTF8String = {
-    if (n == null || fromBase == null || toBase == null || n.isEmpty) {
-      return null
-    }
-
     if (fromBase < Character.MIN_RADIX || fromBase > Character.MAX_RADIX
       || Math.abs(toBase) < Character.MIN_RADIX
       || Math.abs(toBase) > Character.MAX_RADIX) {
       return null
     }
 
+    if (n.length == 0) {
+      return null
+    }
+
     var (negative, first) = if (n(0) == '-') (true, 1) else (false, 0)
 
     // Copy the digits in the right side of the array
@@ -340,7 +341,7 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
       resultStartPos = firstNonZeroPos - 1
       value(resultStartPos) = '-'
     }
-    UTF8String.fromBytes( Arrays.copyOfRange(value, resultStartPos, value.length))
+    UTF8String.fromBytes(java.util.Arrays.copyOfRange(value, resultStartPos, value.length))
   }
 }
 
@@ -495,8 +496,8 @@ object Hex {
  * Otherwise if the number is a STRING, it converts each character into its hex representation
  * and returns the resulting STRING. Negative numbers would be treated as two's complement.
  */
-case class Hex(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
-  // TODO: Create code-gen version.
+case class Hex(child: Expression)
+  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
 
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(LongType, BinaryType, StringType))
@@ -539,8 +540,8 @@ case class Hex(child: Expression) extends UnaryExpression with ImplicitCastInput
  * Performs the inverse operation of HEX.
  * Resulting characters are returned as a byte array.
  */
-case class Unhex(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
-  // TODO: Create code-gen version.
+case class Unhex(child: Expression)
+  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
 
   override def inputTypes: Seq[AbstractDataType] = Seq(StringType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index c083ac08ded21..6f173b52ad9b9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -19,9 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.trees
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 
 object NamedExpression {
@@ -122,7 +120,9 @@ case class Alias(child: Expression, name: String)(
 
   override def eval(input: InternalRow): Any = child.eval(input)
 
+  /** Just a simple passthrough for code generation. */
   override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx)
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = ""
 
   override def dataType: DataType = child.dataType
   override def nullable: Boolean = child.nullable
@@ -177,7 +177,7 @@ case class AttributeReference(
     override val metadata: Metadata = Metadata.empty)(
     val exprId: ExprId = NamedExpression.newExprId,
     val qualifiers: Seq[String] = Nil)
-  extends Attribute {
+  extends Attribute with Unevaluable {
 
   /**
    * Returns true iff the expression id is the same for both attributes.
@@ -236,10 +236,6 @@ case class AttributeReference(
     }
   }
 
-  // Unresolved attributes are transient at compile time and don't get evaluated during execution.
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
-
   override def toString: String = s"$name#${exprId.id}$typeSuffix"
 }
 
@@ -247,7 +243,7 @@ case class AttributeReference(
  * A place holder used when printing expressions without debugging information such as the
  * expression id or the unresolved indicator.
  */
-case class PrettyAttribute(name: String) extends Attribute {
+case class PrettyAttribute(name: String) extends Attribute with Unevaluable {
 
   override def toString: String = name
 
@@ -259,7 +255,6 @@ case class PrettyAttribute(name: String) extends Attribute {
   override def withName(newName: String): Attribute = throw new UnsupportedOperationException
   override def qualifiers: Seq[String] = throw new UnsupportedOperationException
   override def exprId: ExprId = throw new UnsupportedOperationException
-  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
   override def nullable: Boolean = throw new UnsupportedOperationException
   override def dataType: DataType = NullType
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index bddd2a9eccfc0..40ec3df224ce1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -19,10 +19,11 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.TypeUtils
-import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.types._
 
+
 object InterpretedPredicate {
   def create(expression: Expression, inputSchema: Seq[Attribute]): (InternalRow => Boolean) =
     create(BindReferences.bindReference(expression, inputSchema))
@@ -91,7 +92,7 @@ case class Not(child: Expression)
 /**
  * Evaluates to `true` if `list` contains `value`.
  */
-case class In(value: Expression, list: Seq[Expression]) extends Predicate {
+case class In(value: Expression, list: Seq[Expression]) extends Predicate with CodegenFallback {
   override def children: Seq[Expression] = value +: list
 
   override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN.
@@ -109,7 +110,7 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate {
  * static.
  */
 case class InSet(child: Expression, hset: Set[Any])
-  extends UnaryExpression with Predicate {
+  extends UnaryExpression with Predicate with CodegenFallback {
 
   override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN.
   override def toString: String = s"$child INSET ${hset.mkString("(", ",", ")")}"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index 49b2026364cd6..5b0fe8dfe2fc8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
 
@@ -52,7 +52,7 @@ private[sql] class OpenHashSetUDT(
 /**
  * Creates a new set of the specified type
  */
-case class NewSet(elementType: DataType) extends LeafExpression {
+case class NewSet(elementType: DataType) extends LeafExpression with CodegenFallback {
 
   override def nullable: Boolean = false
 
@@ -82,7 +82,8 @@ case class NewSet(elementType: DataType) extends LeafExpression {
  * Note: this expression is internal and created only by the GeneratedAggregate,
  * we don't need to do type check for it.
  */
-case class AddItemToSet(item: Expression, set: Expression) extends Expression {
+case class AddItemToSet(item: Expression, set: Expression)
+  extends Expression with CodegenFallback {
 
   override def children: Seq[Expression] = item :: set :: Nil
 
@@ -134,7 +135,8 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression {
  * Note: this expression is internal and created only by the GeneratedAggregate,
  * we don't need to do type check for it.
  */
-case class CombineSets(left: Expression, right: Expression) extends BinaryExpression {
+case class CombineSets(left: Expression, right: Expression)
+  extends BinaryExpression with CodegenFallback {
 
   override def nullable: Boolean = left.nullable
   override def dataType: DataType = left.dataType
@@ -181,7 +183,7 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres
  * Note: this expression is internal and created only by the GeneratedAggregate,
  * we don't need to do type check for it.
  */
-case class CountSet(child: Expression) extends UnaryExpression {
+case class CountSet(child: Expression) extends UnaryExpression with CodegenFallback {
 
   override def dataType: DataType = LongType
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index b36354eff092a..560b1bc2d889f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -103,7 +103,7 @@ trait StringRegexExpression extends ImplicitCastInputTypes {
  * Simple RegEx pattern matching function
  */
 case class Like(left: Expression, right: Expression)
-  extends BinaryExpression with StringRegexExpression {
+  extends BinaryExpression with StringRegexExpression with CodegenFallback {
 
   // replace the _ with .{1} exactly match 1 time of any character
   // replace the % with .*, match 0 or more times with any character
@@ -133,14 +133,16 @@ case class Like(left: Expression, right: Expression)
   override def toString: String = s"$left LIKE $right"
 }
 
+
 case class RLike(left: Expression, right: Expression)
-  extends BinaryExpression with StringRegexExpression {
+  extends BinaryExpression with StringRegexExpression with CodegenFallback {
 
   override def escape(v: String): String = v
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
   override def toString: String = s"$left RLIKE $right"
 }
 
+
 trait String2StringExpression extends ImplicitCastInputTypes {
   self: UnaryExpression =>
 
@@ -156,7 +158,8 @@ trait String2StringExpression extends ImplicitCastInputTypes {
 /**
  * A function that converts the characters of a string to uppercase.
  */
-case class Upper(child: Expression) extends UnaryExpression with String2StringExpression {
+case class Upper(child: Expression)
+  extends UnaryExpression with String2StringExpression {
 
   override def convert(v: UTF8String): UTF8String = v.toUpperCase
 
@@ -301,7 +304,7 @@ case class StringInstr(str: Expression, substr: Expression)
  * in given string after position pos.
  */
 case class StringLocate(substr: Expression, str: Expression, start: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends Expression with ImplicitCastInputTypes with CodegenFallback {
 
   def this(substr: Expression, str: Expression) = {
     this(substr, str, Literal(0))
@@ -342,7 +345,7 @@ case class StringLocate(substr: Expression, str: Expression, start: Expression)
  * Returns str, left-padded with pad to a length of len.
  */
 case class StringLPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends Expression with ImplicitCastInputTypes with CodegenFallback {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
   override def foldable: Boolean = children.forall(_.foldable)
@@ -380,7 +383,7 @@ case class StringLPad(str: Expression, len: Expression, pad: Expression)
  * Returns str, right-padded with pad to a length of len.
  */
 case class StringRPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends Expression with ImplicitCastInputTypes with CodegenFallback {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
   override def foldable: Boolean = children.forall(_.foldable)
@@ -417,9 +420,9 @@ case class StringRPad(str: Expression, len: Expression, pad: Expression)
 /**
  * Returns the input formatted according do printf-style format strings
  */
-case class StringFormat(children: Expression*) extends Expression {
+case class StringFormat(children: Expression*) extends Expression with CodegenFallback {
 
-  require(children.length >=1, "printf() should take at least 1 argument")
+  require(children.nonEmpty, "printf() should take at least 1 argument")
 
   override def foldable: Boolean = children.forall(_.foldable)
   override def nullable: Boolean = children(0).nullable
@@ -436,7 +439,7 @@ case class StringFormat(children: Expression*) extends Expression {
       val formatter = new java.util.Formatter(sb, Locale.US)
 
       val arglist = args.map(_.eval(input).asInstanceOf[AnyRef])
-      formatter.format(pattern.asInstanceOf[UTF8String].toString(), arglist: _*)
+      formatter.format(pattern.asInstanceOf[UTF8String].toString, arglist: _*)
 
       UTF8String.fromString(sb.toString)
     }
@@ -483,7 +486,8 @@ case class StringReverse(child: Expression) extends UnaryExpression with String2
 /**
  * Returns a n spaces string.
  */
-case class StringSpace(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+case class StringSpace(child: Expression)
+  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
 
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(IntegerType)
@@ -503,7 +507,7 @@ case class StringSpace(child: Expression) extends UnaryExpression with ImplicitC
  * Splits str around pat (pattern is a regular expression).
  */
 case class StringSplit(str: Expression, pattern: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes with CodegenFallback {
 
   override def left: Expression = str
   override def right: Expression = pattern
@@ -524,7 +528,7 @@ case class StringSplit(str: Expression, pattern: Expression)
  * Defined for String and Binary types.
  */
 case class Substring(str: Expression, pos: Expression, len: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends Expression with ImplicitCastInputTypes with CodegenFallback {
 
   def this(str: Expression, pos: Expression) = {
     this(str, pos, Literal(Integer.MAX_VALUE))
@@ -606,8 +610,6 @@ case class Length(child: Expression) extends UnaryExpression with ExpectsInputTy
       case BinaryType => defineCodeGen(ctx, ev, c => s"($c).length")
     }
   }
-
-  override def prettyName: String = "length"
 }
 
 /**
@@ -632,7 +634,9 @@ case class Levenshtein(left: Expression, right: Expression) extends BinaryExpres
 /**
  * Returns the numeric value of the first character of str.
  */
-case class Ascii(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+case class Ascii(child: Expression)
+  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
+
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
@@ -649,7 +653,9 @@ case class Ascii(child: Expression) extends UnaryExpression with ImplicitCastInp
 /**
  * Converts the argument from binary to a base 64 string.
  */
-case class Base64(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+case class Base64(child: Expression)
+  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
+
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(BinaryType)
 
@@ -663,7 +669,9 @@ case class Base64(child: Expression) extends UnaryExpression with ImplicitCastIn
 /**
  * Converts the argument from a base 64 string to BINARY.
  */
-case class UnBase64(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+case class UnBase64(child: Expression)
+  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
+
   override def dataType: DataType = BinaryType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
@@ -677,7 +685,7 @@ case class UnBase64(child: Expression) extends UnaryExpression with ImplicitCast
  * If either argument is null, the result will also be null.
  */
 case class Decode(bin: Expression, charset: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes with CodegenFallback {
 
   override def left: Expression = bin
   override def right: Expression = charset
@@ -696,7 +704,7 @@ case class Decode(bin: Expression, charset: Expression)
  * If either argument is null, the result will also be null.
 */
 case class Encode(value: Expression, charset: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes with CodegenFallback {
 
   override def left: Expression = value
   override def right: Expression = charset
@@ -715,7 +723,7 @@ case class Encode(value: Expression, charset: Expression)
  * fractional part.
  */
 case class FormatNumber(x: Expression, d: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ExpectsInputTypes with CodegenFallback {
 
   override def left: Expression = x
   override def right: Expression = d
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
index c8aa571df64fc..50bbfd644d302 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.types.{DataType, NumericType}
 
 /**
@@ -37,7 +36,7 @@ sealed trait WindowSpec
 case class WindowSpecDefinition(
     partitionSpec: Seq[Expression],
     orderSpec: Seq[SortOrder],
-    frameSpecification: WindowFrame) extends Expression with WindowSpec {
+    frameSpecification: WindowFrame) extends Expression with WindowSpec with Unevaluable {
 
   def validate: Option[String] = frameSpecification match {
     case UnspecifiedFrame =>
@@ -75,7 +74,6 @@ case class WindowSpecDefinition(
 
   override def toString: String = simpleString
 
-  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
   override def nullable: Boolean = true
   override def foldable: Boolean = false
   override def dataType: DataType = throw new UnsupportedOperationException
@@ -274,60 +272,43 @@ trait WindowFunction extends Expression {
 case class UnresolvedWindowFunction(
     name: String,
     children: Seq[Expression])
-  extends Expression with WindowFunction {
+  extends Expression with WindowFunction with Unevaluable {
 
   override def dataType: DataType = throw new UnresolvedException(this, "dataType")
   override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
   override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
   override lazy val resolved = false
 
-  override def init(): Unit =
-    throw new UnresolvedException(this, "init")
-  override def reset(): Unit =
-    throw new UnresolvedException(this, "reset")
+  override def init(): Unit = throw new UnresolvedException(this, "init")
+  override def reset(): Unit = throw new UnresolvedException(this, "reset")
   override def prepareInputParameters(input: InternalRow): AnyRef =
     throw new UnresolvedException(this, "prepareInputParameters")
-  override def update(input: AnyRef): Unit =
-    throw new UnresolvedException(this, "update")
+  override def update(input: AnyRef): Unit = throw new UnresolvedException(this, "update")
   override def batchUpdate(inputs: Array[AnyRef]): Unit =
     throw new UnresolvedException(this, "batchUpdate")
-  override def evaluate(): Unit =
-    throw new UnresolvedException(this, "evaluate")
-  override def get(index: Int): Any =
-    throw new UnresolvedException(this, "get")
-  // Unresolved functions are transient at compile time and don't get evaluated during execution.
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+  override def evaluate(): Unit = throw new UnresolvedException(this, "evaluate")
+  override def get(index: Int): Any = throw new UnresolvedException(this, "get")
 
   override def toString: String = s"'$name(${children.mkString(",")})"
 
-  override def newInstance(): WindowFunction =
-    throw new UnresolvedException(this, "newInstance")
+  override def newInstance(): WindowFunction = throw new UnresolvedException(this, "newInstance")
 }
 
 case class UnresolvedWindowExpression(
     child: UnresolvedWindowFunction,
-    windowSpec: WindowSpecReference) extends UnaryExpression {
+    windowSpec: WindowSpecReference) extends UnaryExpression with Unevaluable {
 
   override def dataType: DataType = throw new UnresolvedException(this, "dataType")
   override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
   override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
   override lazy val resolved = false
-
-  // Unresolved functions are transient at compile time and don't get evaluated during execution.
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
 }
 
 case class WindowExpression(
     windowFunction: WindowFunction,
-    windowSpec: WindowSpecDefinition) extends Expression {
-
-  override def children: Seq[Expression] =
-    windowFunction :: windowSpec :: Nil
+    windowSpec: WindowSpecDefinition) extends Expression with Unevaluable {
 
-  override def eval(input: InternalRow): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+  override def children: Seq[Expression] = windowFunction :: windowSpec :: Nil
 
   override def dataType: DataType = windowFunction.dataType
   override def foldable: Boolean = windowFunction.foldable
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index 42dead7c28425..2dcfa19fec383 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -17,9 +17,7 @@
 
 package org.apache.spark.sql.catalyst.plans.physical
 
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
-import org.apache.spark.sql.catalyst.expressions.{Expression, SortOrder}
+import org.apache.spark.sql.catalyst.expressions.{Unevaluable, Expression, SortOrder}
 import org.apache.spark.sql.types.{DataType, IntegerType}
 
 /**
@@ -146,8 +144,7 @@ case object BroadcastPartitioning extends Partitioning {
  * in the same partition.
  */
 case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
-  extends Expression
-  with Partitioning {
+  extends Expression with Partitioning with Unevaluable {
 
   override def children: Seq[Expression] = expressions
   override def nullable: Boolean = false
@@ -169,9 +166,6 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
   }
 
   override def keyExpressions: Seq[Expression] = expressions
-
-  override def eval(input: InternalRow = null): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
 }
 
 /**
@@ -187,8 +181,7 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
  * into its child.
  */
 case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
-  extends Expression
-  with Partitioning {
+  extends Expression with Partitioning with Unevaluable {
 
   override def children: Seq[SortOrder] = ordering
   override def nullable: Boolean = false
@@ -213,7 +206,4 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
   }
 
   override def keyExpressions: Seq[Expression] = ordering.map(_.child)
-
-  override def eval(input: InternalRow): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 2147d07e09bd3..dca8c881f21ab 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -31,9 +31,9 @@ import org.apache.spark.sql.types._
 
 case class TestFunction(
     children: Seq[Expression],
-    inputTypes: Seq[AbstractDataType]) extends Expression with ImplicitCastInputTypes {
+    inputTypes: Seq[AbstractDataType])
+  extends Expression with ImplicitCastInputTypes with Unevaluable {
   override def nullable: Boolean = true
-  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
   override def dataType: DataType = StringType
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index c9b3c69c6de89..f9442bccc4a7a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -363,26 +363,26 @@ class HiveTypeCoercionSuite extends PlanTest {
 object HiveTypeCoercionSuite {
 
   case class AnyTypeUnaryExpression(child: Expression)
-    extends UnaryExpression with ExpectsInputTypes {
+    extends UnaryExpression with ExpectsInputTypes with Unevaluable {
     override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
     override def dataType: DataType = NullType
   }
 
   case class NumericTypeUnaryExpression(child: Expression)
-    extends UnaryExpression with ExpectsInputTypes {
+    extends UnaryExpression with ExpectsInputTypes with Unevaluable {
     override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
     override def dataType: DataType = NullType
   }
 
   case class AnyTypeBinaryOperator(left: Expression, right: Expression)
-    extends BinaryOperator {
+    extends BinaryOperator with Unevaluable {
     override def dataType: DataType = NullType
     override def inputType: AbstractDataType = AnyDataType
     override def symbol: String = "anytype"
   }
 
   case class NumericTypeBinaryOperator(left: Expression, right: Expression)
-    extends BinaryOperator {
+    extends BinaryOperator with Unevaluable {
     override def dataType: DataType = NullType
     override def inputType: AbstractDataType = NumericType
     override def symbol: String = "numerictype"
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index 1bd7d4e5cdf0f..8fff39906b342 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -22,9 +22,10 @@ import scala.collection.mutable.ArrayBuffer
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types.{IntegerType, StringType, NullType}
 
-case class Dummy(optKey: Option[Expression]) extends Expression {
+case class Dummy(optKey: Option[Expression]) extends Expression with CodegenFallback {
   override def children: Seq[Expression] = optKey.toSeq
   override def nullable: Boolean = true
   override def dataType: NullType = NullType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 6d6e67dace177..e6e27a87c7151 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -51,15 +51,11 @@ private[spark] case class PythonUDF(
     broadcastVars: JList[Broadcast[PythonBroadcast]],
     accumulator: Accumulator[JList[Array[Byte]]],
     dataType: DataType,
-    children: Seq[Expression]) extends Expression with SparkLogging {
+    children: Seq[Expression]) extends Expression with Unevaluable with SparkLogging {
 
   override def toString: String = s"PythonUDF#$name(${children.mkString(",")})"
 
   override def nullable: Boolean = true
-
-  override def eval(input: InternalRow): Any = {
-    throw new UnsupportedOperationException("PythonUDFs can not be directly evaluated.")
-  }
 }
 
 /**
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 0bc8adb16afc0..4d23c7035c03d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -36,8 +36,8 @@ import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.FunctionBuilder
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.hive.HiveShim._
@@ -81,7 +81,7 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
 }
 
 private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
-  extends Expression with HiveInspectors with Logging {
+  extends Expression with HiveInspectors with CodegenFallback with Logging {
 
   type UDFType = UDF
 
@@ -146,7 +146,7 @@ private[hive] class DeferredObjectAdapter(oi: ObjectInspector)
 }
 
 private[hive] case class HiveGenericUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
-  extends Expression with HiveInspectors with Logging {
+  extends Expression with HiveInspectors with CodegenFallback with Logging {
   type UDFType = GenericUDF
 
   override def deterministic: Boolean = isUDFDeterministic
@@ -166,8 +166,8 @@ private[hive] case class HiveGenericUDF(funcWrapper: HiveFunctionWrapper, childr
 
   @transient
   protected lazy val isUDFDeterministic = {
-    val udfType = function.getClass().getAnnotation(classOf[HiveUDFType])
-    (udfType != null && udfType.deterministic())
+    val udfType = function.getClass.getAnnotation(classOf[HiveUDFType])
+    udfType != null && udfType.deterministic()
   }
 
   override def foldable: Boolean =
@@ -301,7 +301,7 @@ private[hive] case class HiveWindowFunction(
     pivotResult: Boolean,
     isUDAFBridgeRequired: Boolean,
     children: Seq[Expression]) extends WindowFunction
-  with HiveInspectors {
+  with HiveInspectors with Unevaluable {
 
   // Hive window functions are based on GenericUDAFResolver2.
   type UDFType = GenericUDAFResolver2
@@ -330,7 +330,7 @@ private[hive] case class HiveWindowFunction(
     evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors)
   }
 
-  def dataType: DataType =
+  override def dataType: DataType =
     if (!pivotResult) {
       inspectorToDataType(returnInspector)
     } else {
@@ -344,10 +344,7 @@ private[hive] case class HiveWindowFunction(
       }
     }
 
-  def nullable: Boolean = true
-
-  override def eval(input: InternalRow): Any =
-    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+  override def nullable: Boolean = true
 
   @transient
   lazy val inputProjection = new InterpretedProjection(children)
@@ -406,7 +403,7 @@ private[hive] case class HiveWindowFunction(
     s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})"
   }
 
-  override def newInstance: WindowFunction =
+  override def newInstance(): WindowFunction =
     new HiveWindowFunction(funcWrapper, pivotResult, isUDAFBridgeRequired, children)
 }
 
@@ -476,7 +473,7 @@ private[hive] case class HiveUDAF(
 
 /**
  * Converts a Hive Generic User Defined Table Generating Function (UDTF) to a
- * [[catalyst.expressions.Generator Generator]].  Note that the semantics of Generators do not allow
+ * [[Generator]].  Note that the semantics of Generators do not allow
  * Generators to maintain state in between input rows.  Thus UDTFs that rely on partitioning
  * dependent operations like calls to `close()` before producing output will not operate the same as
  * in Hive.  However, in practice this should not affect compatibility for most sane UDTFs
@@ -488,7 +485,7 @@ private[hive] case class HiveUDAF(
 private[hive] case class HiveGenericUDTF(
     funcWrapper: HiveFunctionWrapper,
     children: Seq[Expression])
-  extends Generator with HiveInspectors {
+  extends Generator with HiveInspectors with CodegenFallback {
 
   @transient
   protected lazy val function: GenericUDTF = {

From 45d798c323ffe32bc2eba4dbd271c4572f5a30cf Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 20:27:55 -0700
Subject: [PATCH 0463/1454] [SPARK-8278] Remove non-streaming JSON reader.

Author: Reynold Xin <rxin@databricks.com>

Closes #7501 from rxin/jsonrdd and squashes the following commits:

767ec55 [Reynold Xin] More Mima
51f456e [Reynold Xin] Mima exclude.
789cb80 [Reynold Xin] Fixed compilation error.
b4cf50d [Reynold Xin] [SPARK-8278] Remove non-streaming JSON reader.
---
 project/MimaExcludes.scala                    |   3 +
 .../apache/spark/sql/DataFrameReader.scala    |  15 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |   5 -
 .../apache/spark/sql/json/JSONRelation.scala  |  48 +-
 .../org/apache/spark/sql/json/JsonRDD.scala   | 449 ------------------
 .../org/apache/spark/sql/json/JsonSuite.scala |  27 +-
 6 files changed, 29 insertions(+), 518 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 4e4e810ec36e3..36417f5df9f2d 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -64,6 +64,9 @@ object MimaExcludes {
             excludePackage("org.apache.spark.sql.execution"),
             // Parquet support is considered private.
             excludePackage("org.apache.spark.sql.parquet"),
+            // The old JSON RDD is removed in favor of streaming Jackson
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
             // local function inside a method
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 9ad6e21da7bf7..9b23df4843c06 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -27,7 +27,7 @@ import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation}
-import org.apache.spark.sql.json.{JsonRDD, JSONRelation}
+import org.apache.spark.sql.json.JSONRelation
 import org.apache.spark.sql.parquet.ParquetRelation2
 import org.apache.spark.sql.sources.{LogicalRelation, ResolvedDataSource}
 import org.apache.spark.sql.types.StructType
@@ -236,17 +236,8 @@ class DataFrameReader private[sql](sqlContext: SQLContext) {
    */
   def json(jsonRDD: RDD[String]): DataFrame = {
     val samplingRatio = extraOptions.getOrElse("samplingRatio", "1.0").toDouble
-    if (sqlContext.conf.useJacksonStreamingAPI) {
-      sqlContext.baseRelationToDataFrame(
-        new JSONRelation(() => jsonRDD, None, samplingRatio, userSpecifiedSchema)(sqlContext))
-    } else {
-      val columnNameOfCorruptJsonRecord = sqlContext.conf.columnNameOfCorruptRecord
-      val appliedSchema = userSpecifiedSchema.getOrElse(
-        JsonRDD.nullTypeToStringType(
-          JsonRDD.inferSchema(jsonRDD, 1.0, columnNameOfCorruptJsonRecord)))
-      val rowRDD = JsonRDD.jsonStringToRow(jsonRDD, appliedSchema, columnNameOfCorruptJsonRecord)
-      sqlContext.internalCreateDataFrame(rowRDD, appliedSchema)
-    }
+    sqlContext.baseRelationToDataFrame(
+      new JSONRelation(() => jsonRDD, None, samplingRatio, userSpecifiedSchema)(sqlContext))
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2c2f7c35dfdce..84d3271ceb738 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -401,9 +401,6 @@ private[spark] object SQLConf {
     "spark.sql.useSerializer2",
     defaultValue = Some(true), isPublic = false)
 
-  val USE_JACKSON_STREAMING_API = booleanConf("spark.sql.json.useJacksonStreamingAPI",
-    defaultValue = Some(true), doc = "<TODO>")
-
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
@@ -473,8 +470,6 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
 
-  private[spark] def useJacksonStreamingAPI: Boolean = getConf(USE_JACKSON_STREAMING_API)
-
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
 
   private[spark] def defaultSizeInBytes: Long =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 2361d3bf52d2b..25802d054ac00 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -157,51 +157,27 @@ private[sql] class JSONRelation(
     }
   }
 
-  private val useJacksonStreamingAPI: Boolean = sqlContext.conf.useJacksonStreamingAPI
-
   override val needConversion: Boolean = false
 
   override lazy val schema = userSpecifiedSchema.getOrElse {
-    if (useJacksonStreamingAPI) {
-      InferSchema(
-        baseRDD(),
-        samplingRatio,
-        sqlContext.conf.columnNameOfCorruptRecord)
-    } else {
-      JsonRDD.nullTypeToStringType(
-        JsonRDD.inferSchema(
-          baseRDD(),
-          samplingRatio,
-          sqlContext.conf.columnNameOfCorruptRecord))
-    }
+    InferSchema(
+      baseRDD(),
+      samplingRatio,
+      sqlContext.conf.columnNameOfCorruptRecord)
   }
 
   override def buildScan(): RDD[Row] = {
-    if (useJacksonStreamingAPI) {
-      JacksonParser(
-        baseRDD(),
-        schema,
-        sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
-    } else {
-      JsonRDD.jsonStringToRow(
-        baseRDD(),
-        schema,
-        sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
-    }
+    JacksonParser(
+      baseRDD(),
+      schema,
+      sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
   }
 
   override def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] = {
-    if (useJacksonStreamingAPI) {
-      JacksonParser(
-        baseRDD(),
-        StructType.fromAttributes(requiredColumns),
-        sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
-    } else {
-      JsonRDD.jsonStringToRow(
-        baseRDD(),
-        StructType.fromAttributes(requiredColumns),
-        sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
-    }
+    JacksonParser(
+      baseRDD(),
+      StructType.fromAttributes(requiredColumns),
+      sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
   }
 
   override def insert(data: DataFrame, overwrite: Boolean): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
deleted file mode 100644
index b392a51bf7dce..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ /dev/null
@@ -1,449 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.json
-
-import scala.collection.Map
-import scala.collection.convert.Wrappers.{JListWrapper, JMapWrapper}
-
-import com.fasterxml.jackson.core.JsonProcessingException
-import com.fasterxml.jackson.databind.ObjectMapper
-
-import org.apache.spark.Logging
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.ScalaReflection
-import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
-
-
-private[sql] object JsonRDD extends Logging {
-
-  private[sql] def jsonStringToRow(
-      json: RDD[String],
-      schema: StructType,
-      columnNameOfCorruptRecords: String): RDD[InternalRow] = {
-    parseJson(json, columnNameOfCorruptRecords).map(parsed => asRow(parsed, schema))
-  }
-
-  private[sql] def inferSchema(
-      json: RDD[String],
-      samplingRatio: Double = 1.0,
-      columnNameOfCorruptRecords: String): StructType = {
-    require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0")
-    val schemaData = if (samplingRatio > 0.99) json else json.sample(false, samplingRatio, 1)
-    val allKeys =
-      if (schemaData.isEmpty()) {
-        Set.empty[(String, DataType)]
-      } else {
-        parseJson(schemaData, columnNameOfCorruptRecords).map(allKeysWithValueTypes).reduce(_ ++ _)
-      }
-    createSchema(allKeys)
-  }
-
-  private def createSchema(allKeys: Set[(String, DataType)]): StructType = {
-    // Resolve type conflicts
-    val resolved = allKeys.groupBy {
-      case (key, dataType) => key
-    }.map {
-      // Now, keys and types are organized in the format of
-      // key -> Set(type1, type2, ...).
-      case (key, typeSet) => {
-        val fieldName = key.substring(1, key.length - 1).split("`.`").toSeq
-        val dataType = typeSet.map {
-          case (_, dataType) => dataType
-        }.reduce((type1: DataType, type2: DataType) => compatibleType(type1, type2))
-
-        (fieldName, dataType)
-      }
-    }
-
-    def makeStruct(values: Seq[Seq[String]], prefix: Seq[String]): StructType = {
-      val (topLevel, structLike) = values.partition(_.size == 1)
-
-      val topLevelFields = topLevel.filter {
-        name => resolved.get(prefix ++ name).get match {
-          case ArrayType(elementType, _) => {
-            def hasInnerStruct(t: DataType): Boolean = t match {
-              case s: StructType => true
-              case ArrayType(t1, _) => hasInnerStruct(t1)
-              case o => false
-            }
-
-            // Check if this array has inner struct.
-            !hasInnerStruct(elementType)
-          }
-          case struct: StructType => false
-          case _ => true
-        }
-      }.map {
-        a => StructField(a.head, resolved.get(prefix ++ a).get, nullable = true)
-      }
-      val topLevelFieldNameSet = topLevelFields.map(_.name)
-
-      val structFields: Seq[StructField] = structLike.groupBy(_(0)).filter {
-        case (name, _) => !topLevelFieldNameSet.contains(name)
-      }.map {
-        case (name, fields) => {
-          val nestedFields = fields.map(_.tail)
-          val structType = makeStruct(nestedFields, prefix :+ name)
-          val dataType = resolved.get(prefix :+ name).get
-          dataType match {
-            case array: ArrayType =>
-              // The pattern of this array is ArrayType(...(ArrayType(StructType))).
-              // Since the inner struct of array is a placeholder (StructType(Nil)),
-              // we need to replace this placeholder with the actual StructType (structType).
-              def getActualArrayType(
-                  innerStruct: StructType,
-                  currentArray: ArrayType): ArrayType = currentArray match {
-                case ArrayType(s: StructType, containsNull) =>
-                  ArrayType(innerStruct, containsNull)
-                case ArrayType(a: ArrayType, containsNull) =>
-                  ArrayType(getActualArrayType(innerStruct, a), containsNull)
-              }
-              Some(StructField(name, getActualArrayType(structType, array), nullable = true))
-            case struct: StructType => Some(StructField(name, structType, nullable = true))
-            // dataType is StringType means that we have resolved type conflicts involving
-            // primitive types and complex types. So, the type of name has been relaxed to
-            // StringType. Also, this field should have already been put in topLevelFields.
-            case StringType => None
-          }
-        }
-      }.flatMap(field => field).toSeq
-
-      StructType((topLevelFields ++ structFields).sortBy(_.name))
-    }
-
-    makeStruct(resolved.keySet.toSeq, Nil)
-  }
-
-  private[sql] def nullTypeToStringType(struct: StructType): StructType = {
-    val fields = struct.fields.map {
-      case StructField(fieldName, dataType, nullable, _) => {
-        val newType = dataType match {
-          case NullType => StringType
-          case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull)
-          case ArrayType(struct: StructType, containsNull) =>
-            ArrayType(nullTypeToStringType(struct), containsNull)
-          case struct: StructType => nullTypeToStringType(struct)
-          case other: DataType => other
-        }
-        StructField(fieldName, newType, nullable)
-      }
-    }
-
-    StructType(fields)
-  }
-
-  /**
-   * Returns the most general data type for two given data types.
-   */
-  private[json] def compatibleType(t1: DataType, t2: DataType): DataType = {
-    HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2) match {
-      case Some(commonType) => commonType
-      case None =>
-        // t1 or t2 is a StructType, ArrayType, or an unexpected type.
-        (t1, t2) match {
-          case (other: DataType, NullType) => other
-          case (NullType, other: DataType) => other
-          case (StructType(fields1), StructType(fields2)) => {
-            val newFields = (fields1 ++ fields2).groupBy(field => field.name).map {
-              case (name, fieldTypes) => {
-                val dataType = fieldTypes.map(field => field.dataType).reduce(
-                  (type1: DataType, type2: DataType) => compatibleType(type1, type2))
-                StructField(name, dataType, true)
-              }
-            }
-            StructType(newFields.toSeq.sortBy(_.name))
-          }
-          case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) =>
-            ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2)
-          // TODO: We should use JsonObjectStringType to mark that values of field will be
-          // strings and every string is a Json object.
-          case (_, _) => StringType
-        }
-    }
-  }
-
-  private def typeOfPrimitiveValue: PartialFunction[Any, DataType] = {
-    // For Integer values, use LongType by default.
-    val useLongType: PartialFunction[Any, DataType] = {
-      case value: IntegerType.InternalType => LongType
-    }
-
-    useLongType orElse ScalaReflection.typeOfObject orElse {
-      // Since we do not have a data type backed by BigInteger,
-      // when we see a Java BigInteger, we use DecimalType.
-      case value: java.math.BigInteger => DecimalType.Unlimited
-      // DecimalType's JVMType is scala BigDecimal.
-      case value: java.math.BigDecimal => DecimalType.Unlimited
-      // Unexpected data type.
-      case _ => StringType
-    }
-  }
-
-  /**
-   * Returns the element type of an JSON array. We go through all elements of this array
-   * to detect any possible type conflict. We use [[compatibleType]] to resolve
-   * type conflicts.
-   */
-  private def typeOfArray(l: Seq[Any]): ArrayType = {
-    val elements = l.flatMap(v => Option(v))
-    if (elements.isEmpty) {
-      // If this JSON array is empty, we use NullType as a placeholder.
-      // If this array is not empty in other JSON objects, we can resolve
-      // the type after we have passed through all JSON objects.
-      ArrayType(NullType, containsNull = true)
-    } else {
-      val elementType = elements.map {
-        e => e match {
-          case map: Map[_, _] => StructType(Nil)
-          // We have an array of arrays. If those element arrays do not have the same
-          // element types, we will return ArrayType[StringType].
-          case seq: Seq[_] => typeOfArray(seq)
-          case value => typeOfPrimitiveValue(value)
-        }
-      }.reduce((type1: DataType, type2: DataType) => compatibleType(type1, type2))
-
-      ArrayType(elementType, containsNull = true)
-    }
-  }
-
-  /**
-   * Figures out all key names and data types of values from a parsed JSON object
-   * (in the format of Map[Stirng, Any]). When the value of a key is an JSON object, we
-   * only use a placeholder (StructType(Nil)) to mark that it should be a struct
-   * instead of getting all fields of this struct because a field does not appear
-   * in this JSON object can appear in other JSON objects.
-   */
-  private def allKeysWithValueTypes(m: Map[String, Any]): Set[(String, DataType)] = {
-    val keyValuePairs = m.map {
-      // Quote the key with backticks to handle cases which have dots
-      // in the field name.
-      case (key, value) => (s"`$key`", value)
-    }.toSet
-    keyValuePairs.flatMap {
-      case (key: String, struct: Map[_, _]) => {
-        // The value associated with the key is an JSON object.
-        allKeysWithValueTypes(struct.asInstanceOf[Map[String, Any]]).map {
-          case (k, dataType) => (s"$key.$k", dataType)
-        } ++ Set((key, StructType(Nil)))
-      }
-      case (key: String, array: Seq[_]) => {
-        // The value associated with the key is an array.
-        // Handle inner structs of an array.
-        def buildKeyPathForInnerStructs(v: Any, t: DataType): Seq[(String, DataType)] = t match {
-          case ArrayType(e: StructType, _) => {
-            // The elements of this arrays are structs.
-            v.asInstanceOf[Seq[Map[String, Any]]].flatMap(Option(_)).flatMap {
-              element => allKeysWithValueTypes(element)
-            }.map {
-              case (k, t) => (s"$key.$k", t)
-            }
-          }
-          case ArrayType(t1, _) =>
-            v.asInstanceOf[Seq[Any]].flatMap(Option(_)).flatMap {
-              element => buildKeyPathForInnerStructs(element, t1)
-            }
-          case other => Nil
-        }
-        val elementType = typeOfArray(array)
-        buildKeyPathForInnerStructs(array, elementType) :+ (key, elementType)
-      }
-      // we couldn't tell what the type is if the value is null or empty string
-      case (key: String, value) if value == "" || value == null => (key, NullType) :: Nil
-      case (key: String, value) => (key, typeOfPrimitiveValue(value)) :: Nil
-    }
-  }
-
-  /**
-   * Converts a Java Map/List to a Scala Map/Seq.
-   * We do not use Jackson's scala module at here because
-   * DefaultScalaModule in jackson-module-scala will make
-   * the parsing very slow.
-   */
-  private def scalafy(obj: Any): Any = obj match {
-    case map: java.util.Map[_, _] =>
-      // .map(identity) is used as a workaround of non-serializable Map
-      // generated by .mapValues.
-      // This issue is documented at https://issues.scala-lang.org/browse/SI-7005
-      JMapWrapper(map).mapValues(scalafy).map(identity)
-    case list: java.util.List[_] =>
-      JListWrapper(list).map(scalafy)
-    case atom => atom
-  }
-
-  private def parseJson(
-      json: RDD[String],
-      columnNameOfCorruptRecords: String): RDD[Map[String, Any]] = {
-    // According to [Jackson-72: https://jira.codehaus.org/browse/JACKSON-72],
-    // ObjectMapper will not return BigDecimal when
-    // "DeserializationFeature.USE_BIG_DECIMAL_FOR_FLOATS" is disabled
-    // (see NumberDeserializer.deserialize for the logic).
-    // But, we do not want to enable this feature because it will use BigDecimal
-    // for every float number, which will be slow.
-    // So, right now, we will have Infinity for those BigDecimal number.
-    // TODO: Support BigDecimal.
-    json.mapPartitions(iter => {
-      // When there is a key appearing multiple times (a duplicate key),
-      // the ObjectMapper will take the last value associated with this duplicate key.
-      // For example: for {"key": 1, "key":2}, we will get "key"->2.
-      val mapper = new ObjectMapper()
-      iter.flatMap { record =>
-        try {
-          val parsed = mapper.readValue(record, classOf[Object]) match {
-            case map: java.util.Map[_, _] => scalafy(map).asInstanceOf[Map[String, Any]] :: Nil
-            case list: java.util.List[_] => scalafy(list).asInstanceOf[Seq[Map[String, Any]]]
-            case _ =>
-              sys.error(
-                s"Failed to parse record $record. Please make sure that each line of the file " +
-                "(or each string in the RDD) is a valid JSON object or an array of JSON objects.")
-          }
-
-          parsed
-        } catch {
-          case e: JsonProcessingException =>
-            Map(columnNameOfCorruptRecords -> UTF8String.fromString(record)) :: Nil
-        }
-      }
-    })
-  }
-
-  private def toLong(value: Any): Long = {
-    value match {
-      case value: java.lang.Integer => value.asInstanceOf[Int].toLong
-      case value: java.lang.Long => value.asInstanceOf[Long]
-    }
-  }
-
-  private def toDouble(value: Any): Double = {
-    value match {
-      case value: java.lang.Integer => value.asInstanceOf[Int].toDouble
-      case value: java.lang.Long => value.asInstanceOf[Long].toDouble
-      case value: java.lang.Double => value.asInstanceOf[Double]
-    }
-  }
-
-  private def toDecimal(value: Any): Decimal = {
-    value match {
-      case value: java.lang.Integer => Decimal(value)
-      case value: java.lang.Long => Decimal(value)
-      case value: java.math.BigInteger => Decimal(new java.math.BigDecimal(value))
-      case value: java.lang.Double => Decimal(value)
-      case value: java.math.BigDecimal => Decimal(value)
-    }
-  }
-
-  private def toJsonArrayString(seq: Seq[Any]): String = {
-    val builder = new StringBuilder
-    builder.append("[")
-    var count = 0
-    seq.foreach {
-      element =>
-        if (count > 0) builder.append(",")
-        count += 1
-        builder.append(toString(element))
-    }
-    builder.append("]")
-
-    builder.toString()
-  }
-
-  private def toJsonObjectString(map: Map[String, Any]): String = {
-    val builder = new StringBuilder
-    builder.append("{")
-    var count = 0
-    map.foreach {
-      case (key, value) =>
-        if (count > 0) builder.append(",")
-        count += 1
-        val stringValue = if (value.isInstanceOf[String]) s"""\"$value\"""" else toString(value)
-        builder.append(s"""\"${key}\":${stringValue}""")
-    }
-    builder.append("}")
-
-    builder.toString()
-  }
-
-  private def toString(value: Any): String = {
-    value match {
-      case value: Map[_, _] => toJsonObjectString(value.asInstanceOf[Map[String, Any]])
-      case value: Seq[_] => toJsonArrayString(value)
-      case value => Option(value).map(_.toString).orNull
-    }
-  }
-
-  private def toDate(value: Any): Int = {
-    value match {
-      // only support string as date
-      case value: java.lang.String =>
-        DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(value).getTime)
-      case value: java.sql.Date => DateTimeUtils.fromJavaDate(value)
-    }
-  }
-
-  private def toTimestamp(value: Any): Long = {
-    value match {
-      case value: java.lang.Integer => value.asInstanceOf[Int].toLong * 1000L
-      case value: java.lang.Long => value * 1000L
-      case value: java.lang.String => DateTimeUtils.stringToTime(value).getTime * 1000L
-    }
-  }
-
-  private[json] def enforceCorrectType(value: Any, desiredType: DataType): Any = {
-    if (value == null) {
-      null
-    } else {
-      desiredType match {
-        case StringType => UTF8String.fromString(toString(value))
-        case _ if value == null || value == "" => null // guard the non string type
-        case IntegerType => value.asInstanceOf[IntegerType.InternalType]
-        case LongType => toLong(value)
-        case DoubleType => toDouble(value)
-        case DecimalType() => toDecimal(value)
-        case BooleanType => value.asInstanceOf[BooleanType.InternalType]
-        case NullType => null
-        case ArrayType(elementType, _) =>
-          value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType))
-        case MapType(StringType, valueType, _) =>
-          val map = value.asInstanceOf[Map[String, Any]]
-          map.map {
-            case (k, v) =>
-              (UTF8String.fromString(k), enforceCorrectType(v, valueType))
-          }.map(identity)
-        case struct: StructType => asRow(value.asInstanceOf[Map[String, Any]], struct)
-        case DateType => toDate(value)
-        case TimestampType => toTimestamp(value)
-      }
-    }
-  }
-
-  private def asRow(json: Map[String, Any], schema: StructType): InternalRow = {
-    // TODO: Reuse the row instead of creating a new one for every record.
-    val row = new GenericMutableRow(schema.fields.length)
-    schema.fields.zipWithIndex.foreach {
-      case (StructField(name, dataType, _, _), i) =>
-        row.update(i, json.get(name).flatMap(v => Option(v)).map(
-          enforceCorrectType(_, dataType)).orNull)
-    }
-
-    row
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 8204a584179bb..3475f9dd6787e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -1079,28 +1079,23 @@ class JsonSuite extends QueryTest with TestJsonData {
   }
 
   test("SPARK-7565 MapType in JsonRDD") {
-    val useStreaming = ctx.conf.useJacksonStreamingAPI
     val oldColumnNameOfCorruptRecord = ctx.conf.columnNameOfCorruptRecord
     ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed")
 
     val schemaWithSimpleMap = StructType(
       StructField("map", MapType(StringType, IntegerType, true), false) :: Nil)
-    try{
-      for (useStreaming <- List(true, false)) {
-        ctx.setConf(SQLConf.USE_JACKSON_STREAMING_API, useStreaming)
-        val temp = Utils.createTempDir().getPath
-
-        val df = ctx.read.schema(schemaWithSimpleMap).json(mapType1)
-        df.write.mode("overwrite").parquet(temp)
-        // order of MapType is not defined
-        assert(ctx.read.parquet(temp).count() == 5)
-
-        val df2 = ctx.read.json(corruptRecords)
-        df2.write.mode("overwrite").parquet(temp)
-        checkAnswer(ctx.read.parquet(temp), df2.collect())
-      }
+    try {
+      val temp = Utils.createTempDir().getPath
+
+      val df = ctx.read.schema(schemaWithSimpleMap).json(mapType1)
+      df.write.mode("overwrite").parquet(temp)
+      // order of MapType is not defined
+      assert(ctx.read.parquet(temp).count() == 5)
+
+      val df2 = ctx.read.json(corruptRecords)
+      df2.write.mode("overwrite").parquet(temp)
+      checkAnswer(ctx.read.parquet(temp), df2.collect())
     } finally {
-      ctx.setConf(SQLConf.USE_JACKSON_STREAMING_API, useStreaming)
       ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord)
     }
   }

From 6cb6096c016178b9ce5c97592abe529ddb18cef2 Mon Sep 17 00:00:00 2001
From: Forest Fang <forest.fang@outlook.com>
Date: Sat, 18 Jul 2015 21:05:44 -0700
Subject: [PATCH 0464/1454] [SPARK-8443][SQL] Split GenerateMutableProjection
 Codegen due to JVM Code Size Limits

By grouping projection calls into multiple apply function, we are able to push the number of projections codegen can handle from ~1k to ~60k. I have set the unit test to test against 5k as 60k took 15s for the unit test to complete.

Author: Forest Fang <forest.fang@outlook.com>

Closes #7076 from saurfang/codegen_size_limit and squashes the following commits:

b7a7635 [Forest Fang] [SPARK-8443][SQL] Execute and verify split projections in test
adef95a [Forest Fang] [SPARK-8443][SQL] Use safer factor and rewrite splitting code
1b5aa7e [Forest Fang] [SPARK-8443][SQL] inline execution if one block only
9405680 [Forest Fang] [SPARK-8443][SQL] split projection code by size limit
---
 .../codegen/GenerateMutableProjection.scala   | 39 ++++++++++++++++++-
 .../expressions/CodeGenerationSuite.scala     | 14 ++++++-
 2 files changed, 50 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index 71e47d4f9b620..b82bd6814b487 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions._
 
+import scala.collection.mutable.ArrayBuffer
+
 // MutableProjection is not accessible in Java
 abstract class BaseMutableProjection extends MutableProjection
 
@@ -45,10 +47,41 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
           else
             ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
         """
-    }.mkString("\n")
+    }
+    // collect projections into blocks as function has 64kb codesize limit in JVM
+    val projectionBlocks = new ArrayBuffer[String]()
+    val blockBuilder = new StringBuilder()
+    for (projection <- projectionCode) {
+      if (blockBuilder.length > 16 * 1000) {
+        projectionBlocks.append(blockBuilder.toString())
+        blockBuilder.clear()
+      }
+      blockBuilder.append(projection)
+    }
+    projectionBlocks.append(blockBuilder.toString())
+
+    val (projectionFuns, projectionCalls) = {
+      // inline execution if codesize limit was not broken
+      if (projectionBlocks.length == 1) {
+        ("", projectionBlocks.head)
+      } else {
+        (
+          projectionBlocks.zipWithIndex.map { case (body, i) =>
+            s"""
+               |private void apply$i(InternalRow i) {
+               |  $body
+               |}
+             """.stripMargin
+          }.mkString,
+          projectionBlocks.indices.map(i => s"apply$i(i);").mkString("\n")
+        )
+      }
+    }
+
     val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
       s"private $javaType $variableName = $initialValue;"
     }.mkString("\n      ")
+
     val code = s"""
       public Object generate($exprType[] expr) {
         return new SpecificProjection(expr);
@@ -75,9 +108,11 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
           return (InternalRow) mutableRow;
         }
 
+        $projectionFuns
+
         public Object apply(Object _i) {
           InternalRow i = (InternalRow) _i;
-          $projectionCode
+          $projectionCalls
 
           return mutableRow;
         }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index 481b335d15dfd..e05218a23aa73 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen._
 /**
  * Additional tests for code generation.
  */
-class CodeGenerationSuite extends SparkFunSuite {
+class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("multithreaded eval") {
     import scala.concurrent._
@@ -42,4 +42,16 @@ class CodeGenerationSuite extends SparkFunSuite {
 
     futures.foreach(Await.result(_, 10.seconds))
   }
+
+  test("SPARK-8443: split wide projections into blocks due to JVM code size limit") {
+    val length = 5000
+    val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
+    val plan = GenerateMutableProjection.generate(expressions)()
+    val actual = plan(new GenericMutableRow(length)).toSeq
+    val expected = Seq.fill(length)(true)
+
+    if (!checkResult(actual, expected)) {
+      fail(s"Incorrect Evaluation: expressions: $expressions, actual: $actual, expected: $expected")
+    }
+  }
 }

From 83b682beec884da76708769414108f4316e620f2 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Sat, 18 Jul 2015 22:48:05 -0700
Subject: [PATCH 0465/1454] 
 [SPARK-8199][SPARK-8184][SPARK-8183][SPARK-8182][SPARK-8181][SPARK-8180][SPARK-8179][SPARK-8177][SPARK-8178][SPARK-9115][SQL]
 date functions

Jira:
https://issues.apache.org/jira/browse/SPARK-8199
https://issues.apache.org/jira/browse/SPARK-8184
https://issues.apache.org/jira/browse/SPARK-8183
https://issues.apache.org/jira/browse/SPARK-8182
https://issues.apache.org/jira/browse/SPARK-8181
https://issues.apache.org/jira/browse/SPARK-8180
https://issues.apache.org/jira/browse/SPARK-8179
https://issues.apache.org/jira/browse/SPARK-8177
https://issues.apache.org/jira/browse/SPARK-8179
https://issues.apache.org/jira/browse/SPARK-9115

Regarding `day`and `dayofmonth` are both necessary?

~~I am going to add `Quarter` to this PR as well.~~ Done.

~~As soon as the Scala coding is reviewed and discussed, I'll add the python api.~~ Done

Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Tarek Auel <tarek.auel@gmail.com>

Closes #6981 from tarekauel/SPARK-8199 and squashes the following commits:

f7b4c8c [Tarek Auel] [SPARK-8199] fixed bug in tests
bb567b6 [Tarek Auel] [SPARK-8199] fixed test
3e095ba [Tarek Auel] [SPARK-8199] style and timezone fix
256c357 [Tarek Auel] [SPARK-8199] code cleanup
5983dcc [Tarek Auel] [SPARK-8199] whitespace fix
6e0c78f [Tarek Auel] [SPARK-8199] removed setTimeZone in tests, according to cloud-fans comment in #7488
4afc09c [Tarek Auel] [SPARK-8199] concise leap year handling
ea6c110 [Tarek Auel] [SPARK-8199] fix after merging master
70238e0 [Tarek Auel] Merge branch 'master' into SPARK-8199
3c6ae2e [Tarek Auel] [SPARK-8199] removed binary search
fb98ba0 [Tarek Auel] [SPARK-8199] python docstring fix
cdfae27 [Tarek Auel] [SPARK-8199] cleanup & python docstring fix
746b80a [Tarek Auel] [SPARK-8199] build fix
0ad6db8 [Tarek Auel] [SPARK-8199] minor fix
523542d [Tarek Auel] [SPARK-8199] address comments
2259299 [Tarek Auel] [SPARK-8199] day_of_month alias
d01b977 [Tarek Auel] [SPARK-8199] python underscore
56c4a92 [Tarek Auel] [SPARK-8199] update python docu
e223bc0 [Tarek Auel] [SPARK-8199] refactoring
d6aa14e [Tarek Auel] [SPARK-8199] fixed Hive compatibility
b382267 [Tarek Auel] [SPARK-8199] fixed bug in day calculation; removed set TimeZone in HiveCompatibilitySuite for test purposes; removed Hive tests for second and minute, because we can cast '2015-03-18' to a timestamp and extract a minute/second from it
1b2e540 [Tarek Auel] [SPARK-8119] style fix
0852655 [Tarek Auel] [SPARK-8119] changed from ExpectsInputTypes to implicit casts
ec87c69 [Tarek Auel] [SPARK-8119] bug fixing and refactoring
1358cdc [Tarek Auel] Merge remote-tracking branch 'origin/master' into SPARK-8199
740af0e [Tarek Auel] implement date function using a calculation based on days
4fb66da [Tarek Auel] WIP: date functions on calculation only
1a436c9 [Tarek Auel] wip
f775f39 [Tarek Auel] fixed return type
ad17e96 [Tarek Auel] improved implementation
c42b444 [Tarek Auel] Removed merge conflict file
ccb723c [Tarek Auel] [SPARK-8199] style and fixed merge issues
10e4ad1 [Tarek Auel] Merge branch 'master' into date-functions-fast
7d9f0eb [Tarek Auel] [SPARK-8199] git renaming issue
f3e7a9f [Tarek Auel] [SPARK-8199] revert change in DataFrameFunctionsSuite
6f5d95c [Tarek Auel] [SPARK-8199] fixed year interval
d9f8ac3 [Tarek Auel] [SPARK-8199] implement fast track
7bc9d93 [Tarek Auel] Merge branch 'master' into SPARK-8199
5a105d9 [Tarek Auel] [SPARK-8199] rebase after #6985 got merged
eb6760d [Tarek Auel] Merge branch 'master' into SPARK-8199
f120415 [Tarek Auel] improved runtime
a8edebd [Tarek Auel] use Calendar instead of SimpleDateFormat
5fe74e1 [Tarek Auel] fixed python style
3bfac90 [Tarek Auel] fixed style
356df78 [Tarek Auel] rely on cast mechanism of Spark. Simplified implementation
02efc5d [Tarek Auel] removed doubled code
a5ea120 [Tarek Auel] added python api; changed test to be more meaningful
b680db6 [Tarek Auel] added codegeneration to all functions
c739788 [Tarek Auel] added support for quarter SPARK-8178
849fb41 [Tarek Auel] fixed stupid test
638596f [Tarek Auel] improved codegen
4d8049b [Tarek Auel] fixed tests and added type check
5ebb235 [Tarek Auel] resolved naming conflict
d0e2f99 [Tarek Auel] date functions
---
 python/pyspark/sql/functions.py               | 150 +++++++++++
 .../catalyst/analysis/FunctionRegistry.scala  |  14 +-
 .../expressions/datetimeFunctions.scala       | 206 +++++++++++++++
 .../sql/catalyst/util/DateTimeUtils.scala     | 195 +++++++++++++-
 .../expressions/DateFunctionsSuite.scala      | 249 ++++++++++++++++++
 .../catalyst/util/DateTimeUtilsSuite.scala    |  91 +++++--
 .../org/apache/spark/sql/functions.scala      | 176 +++++++++++++
 .../spark/sql/DateExpressionsSuite.scala      | 170 ++++++++++++
 .../execution/HiveCompatibilitySuite.scala    |   9 +-
 9 files changed, 1234 insertions(+), 26 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DateExpressionsSuite.scala

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index e0816b3e654bc..0aca3788922aa 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -652,6 +652,156 @@ def ntile(n):
     return Column(sc._jvm.functions.ntile(int(n)))
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def date_format(dateCol, format):
+    """
+    Converts a date/timestamp/string to a value of string in the format specified by the date
+    format given by the second argument.
+
+    A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
+    pattern letters of the Java class `java.text.SimpleDateFormat` can be used.
+
+    NOTE: Use when ever possible specialized functions like `year`. These benefit from a
+    specialized implementation.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
+    >>> df.select(date_format('a', 'MM/dd/yyy').alias('date')).collect()
+    [Row(date=u'04/08/2015')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.date_format(dateCol, format))
+
+
+@since(1.5)
+def year(col):
+    """
+    Extract the year of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
+    >>> df.select(year('a').alias('year')).collect()
+    [Row(year=2015)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.year(col))
+
+
+@since(1.5)
+def quarter(col):
+    """
+    Extract the quarter of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
+    >>> df.select(quarter('a').alias('quarter')).collect()
+    [Row(quarter=2)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.quarter(col))
+
+
+@since(1.5)
+def month(col):
+    """
+    Extract the month of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
+    >>> df.select(month('a').alias('month')).collect()
+    [Row(month=4)]
+   """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.month(col))
+
+
+@since(1.5)
+def day(col):
+    """
+    Extract the day of the month of a given date as integer.
+
+    >>> sqlContext.createDataFrame([('2015-04-08',)], ['a']).select(day('a').alias('day')).collect()
+    [Row(day=8)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.day(col))
+
+
+@since(1.5)
+def day_of_month(col):
+    """
+    Extract the day of the month of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
+    >>> df.select(day_of_month('a').alias('day')).collect()
+    [Row(day=8)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.day_of_month(col))
+
+
+@since(1.5)
+def day_in_year(col):
+    """
+    Extract the day of the year of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
+    >>> df.select(day_in_year('a').alias('day')).collect()
+    [Row(day=98)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.day_in_year(col))
+
+
+@since(1.5)
+def hour(col):
+    """
+    Extract the hours of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08 13:08:15',)], ['a'])
+    >>> df.select(hour('a').alias('hour')).collect()
+    [Row(hour=13)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.hour(col))
+
+
+@since(1.5)
+def minute(col):
+    """
+    Extract the minutes of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08 13:08:15',)], ['a'])
+    >>> df.select(minute('a').alias('minute')).collect()
+    [Row(minute=8)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.minute(col))
+
+
+@since(1.5)
+def second(col):
+    """
+    Extract the seconds of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08 13:08:15',)], ['a'])
+    >>> df.select(second('a').alias('second')).collect()
+    [Row(second=15)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.second(col))
+
+
+@since(1.5)
+def week_of_year(col):
+    """
+    Extract the week number of a given date as integer.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
+    >>> df.select(week_of_year('a').alias('week')).collect()
+    [Row(week=15)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.week_of_year(col))
+
+
 class UserDefinedFunction(object):
     """
     User defined function in Python
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index d1cda6bc27095..159f7eca7acfe 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -181,7 +181,19 @@ object FunctionRegistry {
 
     // datetime functions
     expression[CurrentDate]("current_date"),
-    expression[CurrentTimestamp]("current_timestamp")
+    expression[CurrentTimestamp]("current_timestamp"),
+    expression[DateFormatClass]("date_format"),
+    expression[Day]("day"),
+    expression[DayInYear]("day_in_year"),
+    expression[Day]("day_of_month"),
+    expression[Hour]("hour"),
+    expression[Month]("month"),
+    expression[Minute]("minute"),
+    expression[Quarter]("quarter"),
+    expression[Second]("second"),
+    expression[WeekOfYear]("week_of_year"),
+    expression[Year]("year")
+
   )
 
   val builtin: FunctionRegistry = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index 4bed140cffbfa..f9cbbb8c6bee0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -17,10 +17,16 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import java.sql.Date
+import java.text.SimpleDateFormat
+import java.util.{Calendar, TimeZone}
+
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * Returns the current date at the start of query evaluation.
@@ -55,3 +61,203 @@ case class CurrentTimestamp() extends LeafExpression with CodegenFallback {
     System.currentTimeMillis() * 1000L
   }
 }
+
+case class Hour(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType)
+
+  override def dataType: DataType = IntegerType
+
+  override protected def nullSafeEval(timestamp: Any): Any = {
+    DateTimeUtils.getHours(timestamp.asInstanceOf[Long])
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getHours($c)"""
+    )
+  }
+}
+
+case class Minute(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType)
+
+  override def dataType: DataType = IntegerType
+
+  override protected def nullSafeEval(timestamp: Any): Any = {
+    DateTimeUtils.getMinutes(timestamp.asInstanceOf[Long])
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getMinutes($c)"""
+    )
+  }
+}
+
+case class Second(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType)
+
+  override def dataType: DataType = IntegerType
+
+  override protected def nullSafeEval(timestamp: Any): Any = {
+    DateTimeUtils.getSeconds(timestamp.asInstanceOf[Long])
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getSeconds($c)"""
+    )
+  }
+}
+
+case class DayInYear(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = IntegerType
+
+  override def prettyName: String = "day_in_year"
+
+  override protected def nullSafeEval(date: Any): Any = {
+    DateTimeUtils.getDayInYear(date.asInstanceOf[Int])
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getDayInYear($c)"""
+    )
+  }
+}
+
+
+case class Year(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = IntegerType
+
+  override protected def nullSafeEval(date: Any): Any = {
+    DateTimeUtils.getYear(date.asInstanceOf[Int])
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getYear($c)"""
+    )
+  }
+}
+
+case class Quarter(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = IntegerType
+
+  override protected def nullSafeEval(date: Any): Any = {
+    DateTimeUtils.getQuarter(date.asInstanceOf[Int])
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getQuarter($c)"""
+    )
+  }
+}
+
+case class Month(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = IntegerType
+
+  override protected def nullSafeEval(date: Any): Any = {
+    DateTimeUtils.getMonth(date.asInstanceOf[Int])
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getMonth($c)"""
+    )
+  }
+}
+
+case class Day(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = IntegerType
+
+  override protected def nullSafeEval(date: Any): Any = {
+    DateTimeUtils.getDayOfMonth(date.asInstanceOf[Int])
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (c) =>
+      s"""$dtu.getDayOfMonth($c)"""
+    )
+  }
+}
+
+case class WeekOfYear(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = IntegerType
+
+  override def prettyName: String = "week_of_year"
+
+  override protected def nullSafeEval(date: Any): Any = {
+    val c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
+    c.setFirstDayOfWeek(Calendar.MONDAY)
+    c.setMinimalDaysInFirstWeek(4)
+    c.setTimeInMillis(date.asInstanceOf[Int] * 1000L * 3600L * 24L)
+    c.get(Calendar.WEEK_OF_YEAR)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
+    nullSafeCodeGen(ctx, ev, (time) => {
+      val cal = classOf[Calendar].getName
+      val c = ctx.freshName("cal")
+      s"""
+        $cal $c = $cal.getInstance(java.util.TimeZone.getTimeZone("UTC"));
+        $c.setFirstDayOfWeek($cal.MONDAY);
+        $c.setMinimalDaysInFirstWeek(4);
+        $c.setTimeInMillis($time * 1000L * 3600L * 24L);
+        ${ev.primitive} = $c.get($cal.WEEK_OF_YEAR);
+      """
+    })
+}
+
+case class DateFormatClass(left: Expression, right: Expression) extends BinaryExpression
+  with ImplicitCastInputTypes {
+
+  override def dataType: DataType = StringType
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType, StringType)
+
+  override def prettyName: String = "date_format"
+
+  override protected def nullSafeEval(timestamp: Any, format: Any): Any = {
+    val sdf = new SimpleDateFormat(format.toString)
+    UTF8String.fromString(sdf.format(new Date(timestamp.asInstanceOf[Long] / 1000)))
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val sdf = classOf[SimpleDateFormat].getName
+    defineCodeGen(ctx, ev, (timestamp, format) => {
+      s"""UTF8String.fromString((new $sdf($format.toString()))
+          .format(new java.sql.Date($timestamp / 1000)))"""
+    })
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 45e45aef1a349..a0da73a995a82 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.util
 
 import java.sql.{Date, Timestamp}
 import java.text.{DateFormat, SimpleDateFormat}
-import java.util.{Calendar, TimeZone}
+import java.util.{TimeZone, Calendar}
 
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -39,6 +39,15 @@ object DateTimeUtils {
   final val MICROS_PER_SECOND = 1000L * 1000L
   final val NANOS_PER_SECOND = MICROS_PER_SECOND * 1000L
 
+  // number of days in 400 years
+  final val daysIn400Years: Int = 146097
+  // number of days between 1.1.1970 and 1.1.2001
+  final val to2001 = -11323
+
+  // this is year -17999, calculation: 50 * daysIn400Year
+  final val toYearZero = to2001 + 7304850
+
+  @transient lazy val defaultTimeZone = TimeZone.getDefault
 
   // Java TimeZone has no mention of thread safety. Use thread local instance to be safe.
   private val threadLocalLocalTimeZone = new ThreadLocal[TimeZone] {
@@ -380,4 +389,188 @@ object DateTimeUtils {
     c.set(Calendar.MILLISECOND, 0)
     Some((c.getTimeInMillis / MILLIS_PER_DAY).toInt)
   }
+
+  /**
+   * Returns the hour value of a given timestamp value. The timestamp is expressed in microseconds.
+   */
+  def getHours(timestamp: Long): Int = {
+    val localTs = (timestamp / 1000) + defaultTimeZone.getOffset(timestamp / 1000)
+    ((localTs / 1000 / 3600) % 24).toInt
+  }
+
+  /**
+   * Returns the minute value of a given timestamp value. The timestamp is expressed in
+   * microseconds.
+   */
+  def getMinutes(timestamp: Long): Int = {
+    val localTs = (timestamp / 1000) + defaultTimeZone.getOffset(timestamp / 1000)
+    ((localTs / 1000 / 60) % 60).toInt
+  }
+
+  /**
+   * Returns the second value of a given timestamp value. The timestamp is expressed in
+   * microseconds.
+   */
+  def getSeconds(timestamp: Long): Int = {
+    ((timestamp / 1000 / 1000) % 60).toInt
+  }
+
+  private[this] def isLeapYear(year: Int): Boolean = {
+    (year % 4) == 0 && ((year % 100) != 0 || (year % 400) == 0)
+  }
+
+  /**
+   * Return the number of days since the start of 400 year period.
+   * The second year of a 400 year period (year 1) starts on day 365.
+   */
+  private[this] def yearBoundary(year: Int): Int = {
+    year * 365 + ((year / 4 ) - (year / 100) + (year / 400))
+  }
+
+  /**
+   * Calculates the number of years for the given number of days. This depends
+   * on a 400 year period.
+   * @param days days since the beginning of the 400 year period
+   * @return (number of year, days in year)
+   */
+  private[this] def numYears(days: Int): (Int, Int) = {
+    val year = days / 365
+    val boundary = yearBoundary(year)
+    if (days > boundary) (year, days - boundary) else (year - 1, days - yearBoundary(year - 1))
+  }
+
+  /**
+   * Calculates the year and and the number of the day in the year for the given
+   * number of days. The given days is the number of days since 1.1.1970.
+   *
+   * The calculation uses the fact that the period 1.1.2001 until 31.12.2400 is
+   * equals to the period 1.1.1601 until 31.12.2000.
+   */
+  private[this] def getYearAndDayInYear(daysSince1970: Int): (Int, Int) = {
+    // add the difference (in days) between 1.1.1970 and the artificial year 0 (-17999)
+    val daysNormalized = daysSince1970 + toYearZero
+    val numOfQuarterCenturies = daysNormalized / daysIn400Years
+    val daysInThis400 = daysNormalized % daysIn400Years + 1
+    val (years, dayInYear) = numYears(daysInThis400)
+    val year: Int = (2001 - 20000) + 400 * numOfQuarterCenturies + years
+    (year, dayInYear)
+  }
+
+  /**
+   * Returns the 'day in year' value for the given date. The date is expressed in days
+   * since 1.1.1970.
+   */
+  def getDayInYear(date: Int): Int = {
+    getYearAndDayInYear(date)._2
+  }
+
+  /**
+   * Returns the year value for the given date. The date is expressed in days
+   * since 1.1.1970.
+   */
+  def getYear(date: Int): Int = {
+    getYearAndDayInYear(date)._1
+  }
+
+  /**
+   * Returns the quarter for the given date. The date is expressed in days
+   * since 1.1.1970.
+   */
+  def getQuarter(date: Int): Int = {
+    var (year, dayInYear) = getYearAndDayInYear(date)
+    if (isLeapYear(year)) {
+      dayInYear = dayInYear - 1
+    }
+    if (dayInYear <= 90) {
+      1
+    } else if (dayInYear <= 181) {
+      2
+    } else if (dayInYear <= 273) {
+      3
+    } else {
+      4
+    }
+  }
+
+  /**
+   * Returns the month value for the given date. The date is expressed in days
+   * since 1.1.1970. January is month 1.
+   */
+  def getMonth(date: Int): Int = {
+    var (year, dayInYear) = getYearAndDayInYear(date)
+    if (isLeapYear(year)) {
+      if (dayInYear == 60) {
+        return 2
+      } else if (dayInYear > 60) {
+        dayInYear = dayInYear - 1
+      }
+    }
+
+    if (dayInYear <= 31) {
+      1
+    } else if (dayInYear <= 59) {
+      2
+    } else if (dayInYear <= 90) {
+      3
+    } else if (dayInYear <= 120) {
+      4
+    } else if (dayInYear <= 151) {
+      5
+    } else if (dayInYear <= 181) {
+      6
+    } else if (dayInYear <= 212) {
+      7
+    } else if (dayInYear <= 243) {
+      8
+    } else if (dayInYear <= 273) {
+      9
+    } else if (dayInYear <= 304) {
+      10
+    } else if (dayInYear <= 334) {
+      11
+    } else {
+      12
+    }
+  }
+
+  /**
+   * Returns the 'day of month' value for the given date. The date is expressed in days
+   * since 1.1.1970.
+   */
+  def getDayOfMonth(date: Int): Int = {
+    var (year, dayInYear) = getYearAndDayInYear(date)
+    if (isLeapYear(year)) {
+      if (dayInYear == 60) {
+        return 29
+      } else if (dayInYear > 60) {
+        dayInYear = dayInYear - 1
+      }
+    }
+
+    if (dayInYear <= 31) {
+      dayInYear
+    } else if (dayInYear <= 59) {
+      dayInYear - 31
+    } else if (dayInYear <= 90) {
+      dayInYear - 59
+    } else if (dayInYear <= 120) {
+      dayInYear - 90
+    } else if (dayInYear <= 151) {
+      dayInYear - 120
+    } else if (dayInYear <= 181) {
+      dayInYear - 151
+    } else if (dayInYear <= 212) {
+      dayInYear - 181
+    } else if (dayInYear <= 243) {
+      dayInYear - 212
+    } else if (dayInYear <= 273) {
+      dayInYear - 243
+    } else if (dayInYear <= 304) {
+      dayInYear - 273
+    } else if (dayInYear <= 334) {
+      dayInYear - 304
+    } else {
+      dayInYear - 334
+    }
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
new file mode 100644
index 0000000000000..49d0b0aceac0d
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
@@ -0,0 +1,249 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.sql.{Timestamp, Date}
+import java.text.SimpleDateFormat
+import java.util.{TimeZone, Calendar}
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.types.{StringType, TimestampType, DateType}
+
+class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+
+  val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+  val sdfDate = new SimpleDateFormat("yyyy-MM-dd")
+  val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
+  val ts = new Timestamp(sdf.parse("2013-11-08 13:10:15").getTime)
+
+  test("Day in Year") {
+    val sdfDay = new SimpleDateFormat("D")
+    (2002 to 2004).foreach { y =>
+      (0 to 11).foreach { m =>
+        (0 to 5).foreach { i =>
+          val c = Calendar.getInstance()
+          c.set(y, m, 28, 0, 0, 0)
+          c.add(Calendar.DATE, i)
+          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            sdfDay.format(c.getTime).toInt)
+        }
+      }
+    }
+
+    (1998 to 2002).foreach { y =>
+      (0 to 11).foreach { m =>
+        (0 to 5).foreach { i =>
+          val c = Calendar.getInstance()
+          c.set(y, m, 28, 0, 0, 0)
+          c.add(Calendar.DATE, 1)
+          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            sdfDay.format(c.getTime).toInt)
+        }
+      }
+    }
+
+    (1969 to 1970).foreach { y =>
+      (0 to 11).foreach { m =>
+        (0 to 5).foreach { i =>
+          val c = Calendar.getInstance()
+          c.set(y, m, 28, 0, 0, 0)
+          c.add(Calendar.DATE, 1)
+          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            sdfDay.format(c.getTime).toInt)
+        }
+      }
+    }
+
+    (2402 to 2404).foreach { y =>
+      (0 to 11).foreach { m =>
+        (0 to 5).foreach { i =>
+          val c = Calendar.getInstance()
+          c.set(y, m, 28, 0, 0, 0)
+          c.add(Calendar.DATE, 1)
+          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            sdfDay.format(c.getTime).toInt)
+        }
+      }
+    }
+
+    (2398 to 2402).foreach { y =>
+      (0 to 11).foreach { m =>
+        (0 to 5).foreach { i =>
+          val c = Calendar.getInstance()
+          c.set(y, m, 28, 0, 0, 0)
+          c.add(Calendar.DATE, 1)
+          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            sdfDay.format(c.getTime).toInt)
+        }
+      }
+    }
+  }
+
+  test("Year") {
+    checkEvaluation(Year(Literal.create(null, DateType)), null)
+    checkEvaluation(Year(Cast(Literal(d), DateType)), 2015)
+    checkEvaluation(Year(Cast(Literal(sdfDate.format(d)), DateType)), 2015)
+    checkEvaluation(Year(Cast(Literal(ts), DateType)), 2013)
+
+    val c = Calendar.getInstance()
+    (2000 to 2010).foreach { y =>
+      (0 to 11 by 11).foreach { m =>
+        c.set(y, m, 28)
+        (0 to 5 * 24).foreach { i =>
+          c.add(Calendar.HOUR_OF_DAY, 1)
+          checkEvaluation(Year(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            c.get(Calendar.YEAR))
+        }
+      }
+    }
+  }
+
+  test("Quarter") {
+    checkEvaluation(Quarter(Literal.create(null, DateType)), null)
+    checkEvaluation(Quarter(Cast(Literal(d), DateType)), 2)
+    checkEvaluation(Quarter(Cast(Literal(sdfDate.format(d)), DateType)), 2)
+    checkEvaluation(Quarter(Cast(Literal(ts), DateType)), 4)
+
+    val c = Calendar.getInstance()
+    (2003 to 2004).foreach { y =>
+      (0 to 11 by 3).foreach { m =>
+        c.set(y, m, 28, 0, 0, 0)
+        (0 to 5 * 24).foreach { i =>
+          c.add(Calendar.HOUR_OF_DAY, 1)
+          checkEvaluation(Quarter(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            c.get(Calendar.MONTH) / 3 + 1)
+        }
+      }
+    }
+  }
+
+  test("Month") {
+    checkEvaluation(Month(Literal.create(null, DateType)), null)
+    checkEvaluation(Month(Cast(Literal(d), DateType)), 4)
+    checkEvaluation(Month(Cast(Literal(sdfDate.format(d)), DateType)), 4)
+    checkEvaluation(Month(Cast(Literal(ts), DateType)), 11)
+
+    (2003 to 2004).foreach { y =>
+      (0 to 11).foreach { m =>
+        (0 to 5 * 24).foreach { i =>
+          val c = Calendar.getInstance()
+          c.set(y, m, 28, 0, 0, 0)
+          c.add(Calendar.HOUR_OF_DAY, i)
+          checkEvaluation(Month(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            c.get(Calendar.MONTH) + 1)
+        }
+      }
+    }
+
+    (1999 to 2000).foreach { y =>
+      (0 to 11).foreach { m =>
+        (0 to 5 * 24).foreach { i =>
+          val c = Calendar.getInstance()
+          c.set(y, m, 28, 0, 0, 0)
+          c.add(Calendar.HOUR_OF_DAY, i)
+          checkEvaluation(Month(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+            c.get(Calendar.MONTH) + 1)
+        }
+      }
+    }
+  }
+
+  test("Day") {
+    checkEvaluation(Day(Cast(Literal("2000-02-29"), DateType)), 29)
+    checkEvaluation(Day(Literal.create(null, DateType)), null)
+    checkEvaluation(Day(Cast(Literal(d), DateType)), 8)
+    checkEvaluation(Day(Cast(Literal(sdfDate.format(d)), DateType)), 8)
+    checkEvaluation(Day(Cast(Literal(ts), DateType)), 8)
+
+    (1999 to 2000).foreach { y =>
+      val c = Calendar.getInstance()
+      c.set(y, 0, 1, 0, 0, 0)
+      (0 to 365).foreach { d =>
+        c.add(Calendar.DATE, 1)
+        checkEvaluation(Day(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          c.get(Calendar.DAY_OF_MONTH))
+      }
+    }
+  }
+
+  test("Seconds") {
+    checkEvaluation(Second(Literal.create(null, DateType)), null)
+    checkEvaluation(Second(Cast(Literal(d), TimestampType)), 0)
+    checkEvaluation(Second(Cast(Literal(sdf.format(d)), TimestampType)), 15)
+    checkEvaluation(Second(Literal(ts)), 15)
+
+    val c = Calendar.getInstance()
+    (0 to 60 by 5).foreach { s =>
+      c.set(2015, 18, 3, 3, 5, s)
+      checkEvaluation(Second(Cast(Literal(new Timestamp(c.getTimeInMillis)), TimestampType)),
+        c.get(Calendar.SECOND))
+    }
+  }
+
+  test("WeekOfYear") {
+    checkEvaluation(WeekOfYear(Literal.create(null, DateType)), null)
+    checkEvaluation(WeekOfYear(Cast(Literal(d), DateType)), 15)
+    checkEvaluation(WeekOfYear(Cast(Literal(sdfDate.format(d)), DateType)), 15)
+    checkEvaluation(WeekOfYear(Cast(Literal(ts), DateType)), 45)
+    checkEvaluation(WeekOfYear(Cast(Literal("2011-05-06"), DateType)), 18)
+  }
+
+  test("DateFormat") {
+    checkEvaluation(DateFormatClass(Literal.create(null, TimestampType), Literal("y")), null)
+    checkEvaluation(DateFormatClass(Cast(Literal(d), TimestampType),
+      Literal.create(null, StringType)), null)
+    checkEvaluation(DateFormatClass(Cast(Literal(d), TimestampType),
+      Literal("y")), "2015")
+    checkEvaluation(DateFormatClass(Literal(ts), Literal("y")), "2013")
+  }
+
+  test("Hour") {
+    checkEvaluation(Hour(Literal.create(null, DateType)), null)
+    checkEvaluation(Hour(Cast(Literal(d), TimestampType)), 0)
+    checkEvaluation(Hour(Cast(Literal(sdf.format(d)), TimestampType)), 13)
+    checkEvaluation(Hour(Literal(ts)), 13)
+
+    val c = Calendar.getInstance()
+    (0 to 24).foreach { h =>
+      (0 to 60 by 15).foreach { m =>
+        (0 to 60 by 15).foreach { s =>
+          c.set(2015, 18, 3, h, m, s)
+          checkEvaluation(Hour(Cast(Literal(new Timestamp(c.getTimeInMillis)), TimestampType)),
+            c.get(Calendar.HOUR_OF_DAY))
+        }
+      }
+    }
+  }
+
+  test("Minute") {
+    checkEvaluation(Minute(Literal.create(null, DateType)), null)
+    checkEvaluation(Minute(Cast(Literal(d), TimestampType)), 0)
+    checkEvaluation(Minute(Cast(Literal(sdf.format(d)), TimestampType)), 10)
+    checkEvaluation(Minute(Literal(ts)), 10)
+
+    val c = Calendar.getInstance()
+    (0 to 60 by 5).foreach { m =>
+      (0 to 60 by 15).foreach { s =>
+        c.set(2015, 18, 3, 3, m, s)
+        checkEvaluation(Minute(Cast(Literal(new Timestamp(c.getTimeInMillis)), TimestampType)),
+          c.get(Calendar.MINUTE))
+      }
+    }
+  }
+
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 04c5f09792ac3..fab9eb9cd4c9f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -26,6 +26,11 @@ import org.apache.spark.unsafe.types.UTF8String
 
 class DateTimeUtilsSuite extends SparkFunSuite {
 
+  private[this] def getInUTCDays(timestamp: Long): Int = {
+    val tz = TimeZone.getDefault
+    ((timestamp + tz.getOffset(timestamp)) / DateTimeUtils.MILLIS_PER_DAY).toInt
+  }
+
   test("timestamp and us") {
     val now = new Timestamp(System.currentTimeMillis())
     now.setNanos(1000)
@@ -277,28 +282,6 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     assert(DateTimeUtils.stringToTimestamp(
       UTF8String.fromString("2011-05-06 07:08:09.1000")).get === c.getTimeInMillis * 1000)
 
-    val defaultTimeZone = TimeZone.getDefault
-    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
-
-    c = Calendar.getInstance()
-    c.set(2015, 2, 8, 2, 0, 0)
-    c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(
-      UTF8String.fromString("2015-3-8 2:0:0")).get === c.getTimeInMillis * 1000)
-    c.add(Calendar.MINUTE, 30)
-    assert(DateTimeUtils.stringToTimestamp(
-      UTF8String.fromString("2015-3-8 3:30:0")).get === c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(
-      UTF8String.fromString("2015-3-8 2:30:0")).get === c.getTimeInMillis * 1000)
-
-    c = Calendar.getInstance()
-    c.set(2015, 10, 1, 1, 59, 0)
-    c.set(Calendar.MILLISECOND, 0)
-    c.add(Calendar.MINUTE, 31)
-    assert(DateTimeUtils.stringToTimestamp(
-      UTF8String.fromString("2015-11-1 2:30:0")).get === c.getTimeInMillis * 1000)
-    TimeZone.setDefault(defaultTimeZone)
-
     assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("238")).isEmpty)
     assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 123142")).isEmpty)
     assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T123123")).isEmpty)
@@ -314,4 +297,68 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     assert(DateTimeUtils.stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03.17-1:0:0")).isEmpty)
   }
+
+  test("hours") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 13, 2, 11)
+    assert(DateTimeUtils.getHours(c.getTimeInMillis * 1000) === 13)
+    c.set(2015, 12, 8, 2, 7, 9)
+    assert(DateTimeUtils.getHours(c.getTimeInMillis * 1000) === 2)
+  }
+
+  test("minutes") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 13, 2, 11)
+    assert(DateTimeUtils.getMinutes(c.getTimeInMillis * 1000) === 2)
+    c.set(2015, 2, 8, 2, 7, 9)
+    assert(DateTimeUtils.getMinutes(c.getTimeInMillis * 1000) === 7)
+  }
+
+  test("seconds") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 13, 2, 11)
+    assert(DateTimeUtils.getSeconds(c.getTimeInMillis * 1000) === 11)
+    c.set(2015, 2, 8, 2, 7, 9)
+    assert(DateTimeUtils.getSeconds(c.getTimeInMillis * 1000) === 9)
+  }
+
+  test("get day in year") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    assert(DateTimeUtils.getDayInYear(getInUTCDays(c.getTimeInMillis)) === 77)
+    c.set(2012, 2, 18, 0, 0, 0)
+    assert(DateTimeUtils.getDayInYear(getInUTCDays(c.getTimeInMillis)) === 78)
+  }
+
+  test("get year") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    assert(DateTimeUtils.getYear(getInUTCDays(c.getTimeInMillis)) === 2015)
+    c.set(2012, 2, 18, 0, 0, 0)
+    assert(DateTimeUtils.getYear(getInUTCDays(c.getTimeInMillis)) === 2012)
+  }
+
+  test("get quarter") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    assert(DateTimeUtils.getQuarter(getInUTCDays(c.getTimeInMillis)) === 1)
+    c.set(2012, 11, 18, 0, 0, 0)
+    assert(DateTimeUtils.getQuarter(getInUTCDays(c.getTimeInMillis)) === 4)
+  }
+
+  test("get month") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    assert(DateTimeUtils.getMonth(getInUTCDays(c.getTimeInMillis)) === 3)
+    c.set(2012, 11, 18, 0, 0, 0)
+    assert(DateTimeUtils.getMonth(getInUTCDays(c.getTimeInMillis)) === 12)
+  }
+
+  test("get day of month") {
+    val c = Calendar.getInstance()
+    c.set(2015, 2, 18, 0, 0, 0)
+    assert(DateTimeUtils.getDayOfMonth(getInUTCDays(c.getTimeInMillis)) === 18)
+    c.set(2012, 11, 24, 0, 0, 0)
+    assert(DateTimeUtils.getDayOfMonth(getInUTCDays(c.getTimeInMillis)) === 24)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index c180407389136..cadb25d597d19 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1748,6 +1748,182 @@ object functions {
    */
   def length(columnName: String): Column = length(Column(columnName))
 
+  //////////////////////////////////////////////////////////////////////////////////////////////
+  // DateTime functions
+  //////////////////////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Converts a date/timestamp/string to a value of string in the format specified by the date
+   * format given by the second argument.
+   *
+   * A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
+   * pattern letters of [[java.text.SimpleDateFormat]] can be used.
+   *
+   * NOTE: Use when ever possible specialized functions like [[year]]. These benefit from a
+   * specialized implementation.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def date_format(dateExpr: Column, format: String): Column =
+    DateFormatClass(dateExpr.expr, Literal(format))
+
+  /**
+   * Converts a date/timestamp/string to a value of string in the format specified by the date
+   * format given by the second argument.
+   *
+   * A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
+   * pattern letters of [[java.text.SimpleDateFormat]] can be used.
+   *
+   * NOTE: Use when ever possible specialized functions like [[year]]. These benefit from a
+   * specialized implementation.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def date_format(dateColumnName: String, format: String): Column =
+    date_format(Column(dateColumnName), format)
+
+  /**
+   * Extracts the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def year(e: Column): Column = Year(e.expr)
+
+  /**
+   * Extracts the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def year(columnName: String): Column = year(Column(columnName))
+
+  /**
+   * Extracts the quarter as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def quarter(e: Column): Column = Quarter(e.expr)
+
+  /**
+   * Extracts the quarter as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def quarter(columnName: String): Column = quarter(Column(columnName))
+
+  /**
+   * Extracts the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def month(e: Column): Column = Month(e.expr)
+
+  /**
+   * Extracts the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def month(columnName: String): Column = month(Column(columnName))
+
+  /**
+   * Extracts the day of the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def day(e: Column): Column = Day(e.expr)
+
+  /**
+   * Extracts the day of the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def day(columnName: String): Column = day(Column(columnName))
+
+  /**
+   * Extracts the day of the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def day_of_month(e: Column): Column = Day(e.expr)
+
+  /**
+   * Extracts the day of the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def day_of_month(columnName: String): Column = day_of_month(Column(columnName))
+
+  /**
+   * Extracts the day of the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def day_in_year(e: Column): Column = DayInYear(e.expr)
+
+  /**
+   * Extracts the day of the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def day_in_year(columnName: String): Column = day_in_year(Column(columnName))
+
+  /**
+   * Extracts the hours as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def hour(e: Column): Column = Hour(e.expr)
+
+  /**
+   * Extracts the hours as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def hour(columnName: String): Column = hour(Column(columnName))
+
+  /**
+   * Extracts the minutes as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def minute(e: Column): Column = Minute(e.expr)
+
+  /**
+   * Extracts the minutes as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def minute(columnName: String): Column = minute(Column(columnName))
+
+  /**
+   * Extracts the seconds as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def second(e: Column): Column = Second(e.expr)
+
+  /**
+   * Extracts the seconds as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def second(columnName: String): Column = second(Column(columnName))
+
+  /**
+   * Extracts the week number as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def week_of_year(e: Column): Column = WeekOfYear(e.expr)
+
+  /**
+   * Extracts the week number as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def week_of_year(columnName: String): Column = week_of_year(Column(columnName))
+
   /**
    * Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
    * and returns the result as a string.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateExpressionsSuite.scala
new file mode 100644
index 0000000000000..d24e3ee1dd8f5
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateExpressionsSuite.scala
@@ -0,0 +1,170 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.sql.{Timestamp, Date}
+import java.text.SimpleDateFormat
+
+import org.apache.spark.sql.functions._
+
+class DateExpressionsSuite extends QueryTest {
+  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+
+  import ctx.implicits._
+
+  val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+  val sdfDate = new SimpleDateFormat("yyyy-MM-dd")
+  val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
+  val ts = new Timestamp(sdf.parse("2013-04-08 13:10:15").getTime)
+
+
+  test("date format") {
+    val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(date_format("a", "y"), date_format("b", "y"), date_format("c", "y")),
+      Row("2015", "2015", "2013"))
+
+    checkAnswer(
+      df.selectExpr("date_format(a, 'y')", "date_format(b, 'y')", "date_format(c, 'y')"),
+      Row("2015", "2015", "2013"))
+  }
+
+  test("year") {
+    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(year("a"), year("b"), year("c")),
+      Row(2015, 2015, 2013))
+
+    checkAnswer(
+      df.selectExpr("year(a)", "year(b)", "year(c)"),
+      Row(2015, 2015, 2013))
+  }
+
+  test("quarter") {
+    val ts = new Timestamp(sdf.parse("2013-11-08 13:10:15").getTime)
+
+    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(quarter("a"), quarter("b"), quarter("c")),
+      Row(2, 2, 4))
+
+    checkAnswer(
+      df.selectExpr("quarter(a)", "quarter(b)", "quarter(c)"),
+      Row(2, 2, 4))
+  }
+
+  test("month") {
+    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(month("a"), month("b"), month("c")),
+      Row(4, 4, 4))
+
+    checkAnswer(
+      df.selectExpr("month(a)", "month(b)", "month(c)"),
+      Row(4, 4, 4))
+  }
+
+  test("day") {
+    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(day("a"), day("b"), day("c")),
+      Row(8, 8, 8))
+
+    checkAnswer(
+      df.selectExpr("day(a)", "day(b)", "day(c)"),
+      Row(8, 8, 8))
+  }
+
+  test("day of month") {
+    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(day_of_month("a"), day_of_month("b"), day_of_month("c")),
+      Row(8, 8, 8))
+
+    checkAnswer(
+      df.selectExpr("day_of_month(a)", "day_of_month(b)", "day_of_month(c)"),
+      Row(8, 8, 8))
+  }
+
+  test("day in year") {
+    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(day_in_year("a"), day_in_year("b"), day_in_year("c")),
+      Row(98, 98, 98))
+
+    checkAnswer(
+      df.selectExpr("day_in_year(a)", "day_in_year(b)", "day_in_year(c)"),
+      Row(98, 98, 98))
+  }
+
+  test("hour") {
+    val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(hour("a"), hour("b"), hour("c")),
+      Row(0, 13, 13))
+
+    checkAnswer(
+      df.selectExpr("hour(a)", "hour(b)", "hour(c)"),
+      Row(0, 13, 13))
+  }
+
+  test("minute") {
+    val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(minute("a"), minute("b"), minute("c")),
+      Row(0, 10, 10))
+
+    checkAnswer(
+      df.selectExpr("minute(a)", "minute(b)", "minute(c)"),
+      Row(0, 10, 10))
+  }
+
+  test("second") {
+    val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(second("a"), second("b"), second("c")),
+      Row(0, 15, 15))
+
+    checkAnswer(
+      df.selectExpr("second(a)", "second(b)", "second(c)"),
+      Row(0, 15, 15))
+  }
+
+  test("week of year") {
+    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(week_of_year("a"), week_of_year("b"), week_of_year("c")),
+      Row(15, 15, 15))
+
+    checkAnswer(
+      df.selectExpr("week_of_year(a)", "week_of_year(b)", "week_of_year(c)"),
+      Row(15, 15, 15))
+  }
+
+}
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 299cc599ff8f7..2689d904d6541 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -115,6 +115,13 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // This test is totally fine except that it includes wrong queries and expects errors, but error
     // message format in Hive and Spark SQL differ. Should workaround this later.
     "udf_to_unix_timestamp",
+    // we can cast dates likes '2015-03-18' to a timestamp and extract the seconds.
+    // Hive returns null for second('2015-03-18')
+    "udf_second",
+    // we can cast dates likes '2015-03-18' to a timestamp and extract the minutes.
+    // Hive returns null for minute('2015-03-18')
+    "udf_minute",
+
 
     // Cant run without local map/reduce.
     "index_auto_update",
@@ -896,7 +903,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_lpad",
     "udf_ltrim",
     "udf_map",
-    "udf_minute",
     "udf_modulo",
     "udf_month",
     "udf_named_struct",
@@ -923,7 +929,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_round_3",
     "udf_rpad",
     "udf_rtrim",
-    "udf_second",
     "udf_sign",
     "udf_sin",
     "udf_smallint",

From 04c1b49f5eee915ad1159a32bf12836a3b9f2620 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 22:50:34 -0700
Subject: [PATCH 0466/1454] Fixed test cases.

---
 .../spark/sql/catalyst/expressions/DateFunctionsSuite.scala   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
index 49d0b0aceac0d..f469f42116d21 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
@@ -50,7 +50,7 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         (0 to 5).foreach { i =>
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, 1)
+          c.add(Calendar.DATE, i)
           checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }
@@ -62,7 +62,7 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         (0 to 5).foreach { i =>
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, 1)
+          c.add(Calendar.DATE, i)
           checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }

From a9a0d0cebf8ab3c539723488e5945794ebfd6104 Mon Sep 17 00:00:00 2001
From: Herman van Hovell <hvanhovell@questtec.nl>
Date: Sat, 18 Jul 2015 23:44:38 -0700
Subject: [PATCH 0467/1454] [SPARK-8638] [SQL] Window Function Performance
 Improvements

## Description
Performance improvements for Spark Window functions. This PR will also serve as the basis for moving away from Hive UDAFs to Spark UDAFs. See JIRA tickets SPARK-8638 and SPARK-7712 for more information.

## Improvements
* Much better performance (10x) in running cases (e.g. BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) and UNBOUDED FOLLOWING cases. The current implementation in spark uses a sliding window approach in these cases. This means that an aggregate is maintained for every row, so space usage is N (N being the number of rows). This also means that all these aggregates all need to be updated separately, this takes N*(N-1)/2 updates. The running case differs from the Sliding case because we are only adding data to an aggregate function (no reset is required), we only need to maintain one aggregate (like in the UNBOUNDED PRECEDING AND UNBOUNDED case), update the aggregate for each row, and get the aggregate value after each update. This is what the new implementation does. This approach only uses 1 buffer, and only requires N updates; I am currently working on data with window sizes of 500-1000 doing running sums and this saves a lot of time. The CURRENT ROW AND UNBOUNDED FOLLOWING case also uses this approach and the fact that aggregate operations are communitative, there is one twist though it will process the input buffer in reverse.
* Fewer comparisons in the sliding case. The current implementation determines frame boundaries for every input row. The new implementation makes more use of the fact that the window is sorted, maintains the boundaries, and only moves them when the current row order changes. This is a minor improvement.
* A single Window node is able to process all types of Frames for the same Partitioning/Ordering. This saves a little time/memory spent buffering and managing partitions. This will be enabled in a follow-up PR.
* A lot of the staging code is moved from the execution phase to the initialization phase. Minor performance improvement, and improves readability of the execution code.

## Benchmarking
I have done a small benchmark using [on time performance](http://www.transtats.bts.gov) data of the month april. I have used the origin as a partioning key, as a result there is quite some variation in window sizes. The code for the benchmark can be found in the JIRA ticket. These are the results per Frame type:

Frame | Master | SPARK-8638
----- | ------ | ----------
Entire Frame | 2 s | 1 s
Sliding | 18 s | 1 s
Growing | 14 s | 0.9 s
Shrinking | 13 s | 1 s

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #7057 from hvanhovell/SPARK-8638 and squashes the following commits:

3bfdc49 [Herman van Hovell] Fixed Perfomance Regression for Shrinking Window Frames (+Rebase)
2eb3b33 [Herman van Hovell] Corrected reverse range frame processing.
2cd2d5b [Herman van Hovell] Corrected reverse range frame processing.
b0654d7 [Herman van Hovell] Tests for exotic frame specifications.
e75b76e [Herman van Hovell] More docs, added support for reverse sliding range frames, and some reorganization of code.
1fdb558 [Herman van Hovell] Changed Data In HiveDataFrameWindowSuite.
ac2f682 [Herman van Hovell] Added a few more comments.
1938312 [Herman van Hovell] Added Documentation to the createBoundOrdering methods.
bb020e6 [Herman van Hovell] Major overhaul of Window operator.
---
 .../expressions/windowExpressions.scala       |   12 +
 .../apache/spark/sql/execution/Window.scala   | 1072 +++++++++++------
 .../sql/hive/HiveDataFrameWindowSuite.scala   |    6 +-
 .../sql/hive/execution/WindowSuite.scala      |   79 ++
 4 files changed, 765 insertions(+), 404 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
index 50bbfd644d302..09ec0e333aa44 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
@@ -316,3 +316,15 @@ case class WindowExpression(
 
   override def toString: String = s"$windowFunction $windowSpec"
 }
+
+/**
+ * Extractor for making working with frame boundaries easier.
+ */
+object FrameBoundaryExtractor {
+  def unapply(boundary: FrameBoundary): Option[Int] = boundary match {
+    case CurrentRow => Some(0)
+    case ValuePreceding(offset) => Some(-offset)
+    case ValueFollowing(offset) => Some(offset)
+    case _ => None
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
index 6e127e548a120..a054f52b8b489 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -19,18 +19,64 @@ package org.apache.spark.sql.execution
 
 import java.util
 
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, Partitioning}
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.types.IntegerType
+import org.apache.spark.rdd.RDD
 import org.apache.spark.util.collection.CompactBuffer
+import scala.collection.mutable
 
 /**
  * :: DeveloperApi ::
- * For every row, evaluates `windowExpression` containing Window Functions and attaches
- * the results with other regular expressions (presented by `projectList`).
- * Evert operator handles a single Window Specification, `windowSpec`.
+ * This class calculates and outputs (windowed) aggregates over the rows in a single (sorted)
+ * partition. The aggregates are calculated for each row in the group. Special processing
+ * instructions, frames, are used to calculate these aggregates. Frames are processed in the order
+ * specified in the window specification (the ORDER BY ... clause). There are four different frame
+ * types:
+ * - Entire partition: The frame is the entire partition, i.e.
+ *   UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. For this case, window function will take all
+ *   rows as inputs and be evaluated once.
+ * - Growing frame: We only add new rows into the frame, i.e. UNBOUNDED PRECEDING AND ....
+ *   Every time we move to a new row to process, we add some rows to the frame. We do not remove
+ *   rows from this frame.
+ * - Shrinking frame: We only remove rows from the frame, i.e. ... AND UNBOUNDED FOLLOWING.
+ *   Every time we move to a new row to process, we remove some rows from the frame. We do not add
+ *   rows to this frame.
+ * - Moving frame: Every time we move to a new row to process, we remove some rows from the frame
+ *   and we add some rows to the frame. Examples are:
+ *     1 PRECEDING AND CURRENT ROW and 1 FOLLOWING AND 2 FOLLOWING.
+ *
+ * Different frame boundaries can be used in Growing, Shrinking and Moving frames. A frame
+ * boundary can be either Row or Range based:
+ * - Row Based: A row based boundary is based on the position of the row within the partition.
+ *   An offset indicates the number of rows above or below the current row, the frame for the
+ *   current row starts or ends. For instance, given a row based sliding frame with a lower bound
+ *   offset of -1 and a upper bound offset of +2. The frame for row with index 5 would range from
+ *   index 4 to index 6.
+ * - Range based: A range based boundary is based on the actual value of the ORDER BY
+ *   expression(s). An offset is used to alter the value of the ORDER BY expression, for
+ *   instance if the current order by expression has a value of 10 and the lower bound offset
+ *   is -3, the resulting lower bound for the current row will be 10 - 3 = 7. This however puts a
+ *   number of constraints on the ORDER BY expressions: there can be only one expression and this
+ *   expression must have a numerical data type. An exception can be made when the offset is 0,
+ *   because no value modification is needed, in this case multiple and non-numeric ORDER BY
+ *   expression are allowed.
+ *
+ * This is quite an expensive operator because every row for a single group must be in the same
+ * partition and partitions must be sorted according to the grouping and sort order. The operator
+ * requires the planner to take care of the partitioning and sorting.
+ *
+ * The operator is semi-blocking. The window functions and aggregates are calculated one group at
+ * a time, the result will only be made available after the processing for the entire group has
+ * finished. The operator is able to process different frame configurations at the same time. This
+ * is done by delegating the actual frame processing (i.e. calculation of the window functions) to
+ * specialized classes, see [[WindowFunctionFrame]], which take care of their own frame type:
+ * Entire Partition, Sliding, Growing & Shrinking. Boundary evaluation is also delegated to a pair
+ * of specialized classes: [[RowBoundOrdering]] & [[RangeBoundOrdering]].
  */
+@DeveloperApi
 case class Window(
     projectList: Seq[Attribute],
     windowExpression: Seq[NamedExpression],
@@ -38,443 +84,667 @@ case class Window(
     child: SparkPlan)
   extends UnaryNode {
 
-  override def output: Seq[Attribute] =
-    (projectList ++ windowExpression).map(_.toAttribute)
+  override def output: Seq[Attribute] = projectList ++ windowExpression.map(_.toAttribute)
 
-  override def requiredChildDistribution: Seq[Distribution] =
+  override def requiredChildDistribution: Seq[Distribution] = {
     if (windowSpec.partitionSpec.isEmpty) {
-      // This operator will be very expensive.
+      // Only show warning when the number of bytes is larger than 100 MB?
+      logWarning("No Partition Defined for Window operation! Moving all data to a single "
+        + "partition, this can cause serious performance degradation.")
       AllTuples :: Nil
-    } else {
-      ClusteredDistribution(windowSpec.partitionSpec) :: Nil
-    }
-
-  // Since window functions are adding columns to the input rows, the child's outputPartitioning
-  // is preserved.
-  override def outputPartitioning: Partitioning = child.outputPartitioning
-
-  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
-    // The required child ordering has two parts.
-    // The first part is the expressions in the partition specification.
-    // We add these expressions to the required ordering to make sure input rows are grouped
-    // based on the partition specification. So, we only need to process a single partition
-    // at a time.
-    // The second part is the expressions specified in the ORDER BY cluase.
-    // Basically, we first use sort to group rows based on partition specifications and then sort
-    // Rows in a group based on the order specification.
-    (windowSpec.partitionSpec.map(SortOrder(_, Ascending)) ++ windowSpec.orderSpec) :: Nil
+    } else ClusteredDistribution(windowSpec.partitionSpec) :: Nil
   }
 
-  // Since window functions basically add columns to input rows, this operator
-  // will not change the ordering of input rows.
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+    Seq(windowSpec.partitionSpec.map(SortOrder(_, Ascending)) ++ windowSpec.orderSpec)
+
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
 
-  case class ComputedWindow(
-    unbound: WindowExpression,
-    windowFunction: WindowFunction,
-    resultAttribute: AttributeReference)
-
-  // A list of window functions that need to be computed for each group.
-  private[this] val computedWindowExpressions = windowExpression.flatMap { window =>
-    window.collect {
-      case w: WindowExpression =>
-        ComputedWindow(
-          w,
-          BindReferences.bindReference(w.windowFunction, child.output),
-          AttributeReference(s"windowResult:$w", w.dataType, w.nullable)())
+  /**
+   * Create a bound ordering object for a given frame type and offset. A bound ordering object is
+   * used to determine which input row lies within the frame boundaries of an output row.
+   *
+   * This method uses Code Generation. It can only be used on the executor side.
+   *
+   * @param frameType to evaluate. This can either be Row or Range based.
+   * @param offset with respect to the row.
+   * @return a bound ordering object.
+   */
+  private[this] def createBoundOrdering(frameType: FrameType, offset: Int): BoundOrdering = {
+    frameType match {
+      case RangeFrame =>
+        val (exprs, current, bound) = if (offset == 0) {
+          // Use the entire order expression when the offset is 0.
+          val exprs = windowSpec.orderSpec.map(_.child)
+          val projection = newMutableProjection(exprs, child.output)
+          (windowSpec.orderSpec, projection(), projection())
+        }
+        else if (windowSpec.orderSpec.size == 1) {
+          // Use only the first order expression when the offset is non-null.
+          val sortExpr = windowSpec.orderSpec.head
+          val expr = sortExpr.child
+          // Create the projection which returns the current 'value'.
+          val current = newMutableProjection(expr :: Nil, child.output)()
+          // Flip the sign of the offset when processing the order is descending
+          val boundOffset = if (sortExpr.direction == Descending) -offset
+          else offset
+          // Create the projection which returns the current 'value' modified by adding the offset.
+          val boundExpr = Add(expr, Cast(Literal.create(boundOffset, IntegerType), expr.dataType))
+          val bound = newMutableProjection(boundExpr :: Nil, child.output)()
+          (sortExpr :: Nil, current, bound)
+        }
+        else {
+          sys.error("Non-Zero range offsets are not supported for windows " +
+            "with multiple order expressions.")
+        }
+        // Construct the ordering. This is used to compare the result of current value projection
+        // to the result of bound value projection. This is done manually because we want to use
+        // Code Generation (if it is enabled).
+        val (sortExprs, schema) = exprs.map { case e =>
+          val ref = AttributeReference("ordExpr", e.dataType, e.nullable)()
+          (SortOrder(ref, e.direction), ref)
+        }.unzip
+        val ordering = newOrdering(sortExprs, schema)
+        RangeBoundOrdering(ordering, current, bound)
+      case RowFrame => RowBoundOrdering(offset)
     }
-  }.toArray
+  }
 
-  private[this] val windowFrame =
-    windowSpec.frameSpecification.asInstanceOf[SpecifiedWindowFrame]
+  /**
+   * Create a frame processor.
+   *
+   * This method uses Code Generation. It can only be used on the executor side.
+   *
+   * @param frame boundaries.
+   * @param functions to process in the frame.
+   * @param ordinal at which the processor starts writing to the output.
+   * @return a frame processor.
+   */
+  private[this] def createFrameProcessor(
+      frame: WindowFrame,
+      functions: Array[WindowFunction],
+      ordinal: Int): WindowFunctionFrame = frame match {
+    // Growing Frame.
+    case SpecifiedWindowFrame(frameType, UnboundedPreceding, FrameBoundaryExtractor(high)) =>
+      val uBoundOrdering = createBoundOrdering(frameType, high)
+      new UnboundedPrecedingWindowFunctionFrame(ordinal, functions, uBoundOrdering)
+
+    // Shrinking Frame.
+    case SpecifiedWindowFrame(frameType, FrameBoundaryExtractor(low), UnboundedFollowing) =>
+      val lBoundOrdering = createBoundOrdering(frameType, low)
+      new UnboundedFollowingWindowFunctionFrame(ordinal, functions, lBoundOrdering)
+
+    // Moving Frame.
+    case SpecifiedWindowFrame(frameType,
+        FrameBoundaryExtractor(low), FrameBoundaryExtractor(high)) =>
+      val lBoundOrdering = createBoundOrdering(frameType, low)
+      val uBoundOrdering = createBoundOrdering(frameType, high)
+      new SlidingWindowFunctionFrame(ordinal, functions, lBoundOrdering, uBoundOrdering)
+
+    // Entire Partition Frame.
+    case SpecifiedWindowFrame(_, UnboundedPreceding, UnboundedFollowing) =>
+      new UnboundedWindowFunctionFrame(ordinal, functions)
+
+    // Error
+    case fr =>
+      sys.error(s"Unsupported Frame $fr for functions: $functions")
+  }
 
-  // Create window functions.
-  private[this] def windowFunctions(): Array[WindowFunction] = {
-    val functions = new Array[WindowFunction](computedWindowExpressions.length)
-    var i = 0
-    while (i < computedWindowExpressions.length) {
-      functions(i) = computedWindowExpressions(i).windowFunction.newInstance()
-      functions(i).init()
-      i += 1
+  /**
+   * Create the resulting projection.
+   *
+   * This method uses Code Generation. It can only be used on the executor side.
+   *
+   * @param expressions unbound ordered function expressions.
+   * @return the final resulting projection.
+   */
+  private[this] def createResultProjection(
+      expressions: Seq[Expression]): MutableProjection = {
+    val unboundToAttr = expressions.map {
+      e => (e, AttributeReference("windowResult", e.dataType, e.nullable)())
     }
-    functions
+    val unboundToAttrMap = unboundToAttr.toMap
+    val patchedWindowExpression = windowExpression.map(_.transform(unboundToAttrMap))
+    newMutableProjection(
+      projectList ++ patchedWindowExpression,
+      child.output ++ unboundToAttr.map(_._2))()
   }
 
-  // The schema of the result of all window function evaluations
-  private[this] val computedSchema = computedWindowExpressions.map(_.resultAttribute)
-
-  private[this] val computedResultMap =
-    computedWindowExpressions.map { w => w.unbound -> w.resultAttribute }.toMap
+  protected override def doExecute(): RDD[InternalRow] = {
+    // Prepare processing.
+    // Group the window expression by their processing frame.
+    val windowExprs = windowExpression.flatMap {
+      _.collect {
+        case e: WindowExpression => e
+      }
+    }
 
-  private[this] val windowExpressionResult = windowExpression.map { window =>
-    window.transform {
-      case w: WindowExpression if computedResultMap.contains(w) => computedResultMap(w)
+    // Create Frame processor factories and order the unbound window expressions by the frame they
+    // are processed in; this is the order in which their results will be written to window
+    // function result buffer.
+    val framedWindowExprs = windowExprs.groupBy(_.windowSpec.frameSpecification)
+    val factories = Array.ofDim[() => WindowFunctionFrame](framedWindowExprs.size)
+    val unboundExpressions = mutable.Buffer.empty[Expression]
+    framedWindowExprs.zipWithIndex.foreach {
+      case ((frame, unboundFrameExpressions), index) =>
+        // Track the ordinal.
+        val ordinal = unboundExpressions.size
+
+        // Track the unbound expressions
+        unboundExpressions ++= unboundFrameExpressions
+
+        // Bind the expressions.
+        val functions = unboundFrameExpressions.map { e =>
+          BindReferences.bindReference(e.windowFunction, child.output)
+        }.toArray
+
+        // Create the frame processor factory.
+        factories(index) = () => createFrameProcessor(frame, functions, ordinal)
     }
-  }
 
-  protected override def doExecute(): RDD[InternalRow] = {
-    child.execute().mapPartitions { iter =>
+    // Start processing.
+    child.execute().mapPartitions { stream =>
       new Iterator[InternalRow] {
 
-        // Although input rows are grouped based on windowSpec.partitionSpec, we need to
-        // know when we have a new partition.
-        // This is to manually construct an ordering that can be used to compare rows.
-        // TODO: We may want to have a newOrdering that takes BoundReferences.
-        // So, we can take advantave of code gen.
-        private val partitionOrdering: Ordering[InternalRow] =
-          RowOrdering.forSchema(windowSpec.partitionSpec.map(_.dataType))
-
-        // This is used to project expressions for the partition specification.
-        protected val partitionGenerator =
-          newMutableProjection(windowSpec.partitionSpec, child.output)()
-
-        // This is ued to project expressions for the order specification.
-        protected val rowOrderGenerator =
-          newMutableProjection(windowSpec.orderSpec.map(_.child), child.output)()
-
-        // The position of next output row in the inputRowBuffer.
-        var rowPosition: Int = 0
-        // The number of buffered rows in the inputRowBuffer (the size of the current partition).
-        var partitionSize: Int = 0
-        // The buffer used to buffer rows in a partition.
-        var inputRowBuffer: CompactBuffer[InternalRow] = _
-        // The partition key of the current partition.
-        var currentPartitionKey: InternalRow = _
-        // The partition key of next partition.
-        var nextPartitionKey: InternalRow = _
-        // The first row of next partition.
-        var firstRowInNextPartition: InternalRow = _
-        // Indicates if this partition is the last one in the iter.
-        var lastPartition: Boolean = false
-
-        def createBoundaryEvaluator(): () => Unit = {
-          def findPhysicalBoundary(
-              boundary: FrameBoundary): () => Int = boundary match {
-            case UnboundedPreceding => () => 0
-            case UnboundedFollowing => () => partitionSize - 1
-            case CurrentRow => () => rowPosition
-            case ValuePreceding(value) =>
-              () =>
-                val newPosition = rowPosition - value
-                if (newPosition > 0) newPosition else 0
-            case ValueFollowing(value) =>
-              () =>
-                val newPosition = rowPosition + value
-                if (newPosition < partitionSize) newPosition else partitionSize - 1
+        // Get all relevant projections.
+        val result = createResultProjection(unboundExpressions)
+        val grouping = newProjection(windowSpec.partitionSpec, child.output)
+
+        // Manage the stream and the grouping.
+        var nextRow: InternalRow = EmptyRow
+        var nextGroup: InternalRow = EmptyRow
+        var nextRowAvailable: Boolean = false
+        private[this] def fetchNextRow() {
+          nextRowAvailable = stream.hasNext
+          if (nextRowAvailable) {
+            nextRow = stream.next()
+            nextGroup = grouping(nextRow)
+          } else {
+            nextRow = EmptyRow
+            nextGroup = EmptyRow
           }
-
-          def findLogicalBoundary(
-              boundary: FrameBoundary,
-              searchDirection: Int,
-              evaluator: Expression,
-              joinedRow: JoinedRow): () => Int = boundary match {
-            case UnboundedPreceding => () => 0
-            case UnboundedFollowing => () => partitionSize - 1
-            case other =>
-              () => {
-                // CurrentRow, ValuePreceding, or ValueFollowing.
-                var newPosition = rowPosition + searchDirection
-                var stopSearch = false
-                // rowOrderGenerator is a mutable projection.
-                // We need to make a copy of the returned by rowOrderGenerator since we will
-                // compare searched row with this currentOrderByValue.
-                val currentOrderByValue = rowOrderGenerator(inputRowBuffer(rowPosition)).copy()
-                while (newPosition >= 0 && newPosition < partitionSize && !stopSearch) {
-                  val r = rowOrderGenerator(inputRowBuffer(newPosition))
-                  stopSearch =
-                    !(evaluator.eval(joinedRow(currentOrderByValue, r)).asInstanceOf[Boolean])
-                  if (!stopSearch) {
-                    newPosition += searchDirection
-                  }
-                }
-                newPosition -= searchDirection
-
-                if (newPosition < 0) {
-                  0
-                } else if (newPosition >= partitionSize) {
-                  partitionSize - 1
-                } else {
-                  newPosition
-                }
-              }
+        }
+        fetchNextRow()
+
+        // Manage the current partition.
+        var rows: CompactBuffer[InternalRow] = _
+        val frames: Array[WindowFunctionFrame] = factories.map(_())
+        val numFrames = frames.length
+        private[this] def fetchNextPartition() {
+          // Collect all the rows in the current partition.
+          val currentGroup = nextGroup
+          rows = new CompactBuffer
+          while (nextRowAvailable && nextGroup == currentGroup) {
+            rows += nextRow.copy()
+            fetchNextRow()
           }
 
-          windowFrame.frameType match {
-            case RowFrame =>
-              val findStart = findPhysicalBoundary(windowFrame.frameStart)
-              val findEnd = findPhysicalBoundary(windowFrame.frameEnd)
-              () => {
-                frameStart = findStart()
-                frameEnd = findEnd()
-              }
-            case RangeFrame =>
-              val joinedRowForBoundaryEvaluation: JoinedRow = new JoinedRow()
-              val orderByExpr = windowSpec.orderSpec.head
-              val currentRowExpr =
-                BoundReference(0, orderByExpr.dataType, orderByExpr.nullable)
-              val examedRowExpr =
-                BoundReference(1, orderByExpr.dataType, orderByExpr.nullable)
-              val differenceExpr = Abs(Subtract(currentRowExpr, examedRowExpr))
-
-              val frameStartEvaluator = windowFrame.frameStart match {
-                case CurrentRow => EqualTo(currentRowExpr, examedRowExpr)
-                case ValuePreceding(value) =>
-                  LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
-                case ValueFollowing(value) =>
-                  GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
-                case o => Literal(true) // This is just a dummy expression, we will not use it.
-              }
-
-              val frameEndEvaluator = windowFrame.frameEnd match {
-                case CurrentRow => EqualTo(currentRowExpr, examedRowExpr)
-                case ValuePreceding(value) =>
-                  GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
-                case ValueFollowing(value) =>
-                  LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
-                case o => Literal(true) // This is just a dummy expression, we will not use it.
-              }
-
-              val findStart =
-                findLogicalBoundary(
-                  boundary = windowFrame.frameStart,
-                  searchDirection = -1,
-                  evaluator = frameStartEvaluator,
-                  joinedRow = joinedRowForBoundaryEvaluation)
-              val findEnd =
-                findLogicalBoundary(
-                  boundary = windowFrame.frameEnd,
-                  searchDirection = 1,
-                  evaluator = frameEndEvaluator,
-                  joinedRow = joinedRowForBoundaryEvaluation)
-              () => {
-                frameStart = findStart()
-                frameEnd = findEnd()
-              }
+          // Setup the frames.
+          var i = 0
+          while (i < numFrames) {
+            frames(i).prepare(rows)
+            i += 1
           }
+
+          // Setup iteration
+          rowIndex = 0
+          rowsSize = rows.size
         }
 
-        val boundaryEvaluator = createBoundaryEvaluator()
-        // Indicates if we the specified window frame requires us to maintain a sliding frame
-        // (e.g. RANGES BETWEEN 1 PRECEDING AND CURRENT ROW) or the window frame
-        // is the entire partition (e.g. ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING).
-        val requireUpdateFrame: Boolean = {
-          def requireUpdateBoundary(boundary: FrameBoundary): Boolean = boundary match {
-            case UnboundedPreceding => false
-            case UnboundedFollowing => false
-            case _ => true
-          }
+        // Iteration
+        var rowIndex = 0
+        var rowsSize = 0
+        override final def hasNext: Boolean = rowIndex < rowsSize || nextRowAvailable
 
-          requireUpdateBoundary(windowFrame.frameStart) ||
-            requireUpdateBoundary(windowFrame.frameEnd)
-        }
-        // The start position of the current frame in the partition.
-        var frameStart: Int = 0
-        // The end position of the current frame in the partition.
-        var frameEnd: Int = -1
-        // Window functions.
-        val functions: Array[WindowFunction] = windowFunctions()
-        // Buffers used to store input parameters for window functions. Because we may need to
-        // maintain a sliding frame, we use this buffer to avoid evaluate the parameters from
-        // the same row multiple times.
-        val windowFunctionParameterBuffers: Array[util.LinkedList[AnyRef]] =
-          functions.map(_ => new util.LinkedList[AnyRef]())
-
-        // The projection used to generate the final result rows of this operator.
-        private[this] val resultProjection =
-          newMutableProjection(
-            projectList ++ windowExpressionResult,
-            projectList ++ computedSchema)()
-
-        // The row used to hold results of window functions.
-        private[this] val windowExpressionResultRow =
-          new GenericMutableRow(computedSchema.length)
-
-        private[this] val joinedRow = new JoinedRow6
-
-        // Initialize this iterator.
-        initialize()
-
-        private def initialize(): Unit = {
-          if (iter.hasNext) {
-            val currentRow = iter.next().copy()
-            // partitionGenerator is a mutable projection. Since we need to track nextPartitionKey,
-            // we are making a copy of the returned partitionKey at here.
-            nextPartitionKey = partitionGenerator(currentRow).copy()
-            firstRowInNextPartition = currentRow
+        val join = new JoinedRow6
+        val windowFunctionResult = new GenericMutableRow(unboundExpressions.size)
+        override final def next(): InternalRow = {
+          // Load the next partition if we need to.
+          if (rowIndex >= rowsSize && nextRowAvailable) {
             fetchNextPartition()
-          } else {
-            // The iter is an empty one. So, we set all of the following variables
-            // to make sure hasNext will return false.
-            lastPartition = true
-            rowPosition = 0
-            partitionSize = 0
           }
-        }
-
-        // Indicates if we will have new output row.
-        override final def hasNext: Boolean = {
-          !lastPartition || (rowPosition < partitionSize)
-        }
 
-        override final def next(): InternalRow = {
-          if (hasNext) {
-            if (rowPosition == partitionSize) {
-              // All rows of this buffer have been consumed.
-              // We will move to next partition.
-              fetchNextPartition()
-            }
-            // Get the input row for the current output row.
-            val inputRow = inputRowBuffer(rowPosition)
-            // Get all results of the window functions for this output row.
+          if (rowIndex < rowsSize) {
+            // Get the results for the window frames.
             var i = 0
-            while (i < functions.length) {
-              windowExpressionResultRow.update(i, functions(i).get(rowPosition))
+            while (i < numFrames) {
+              frames(i).write(windowFunctionResult)
               i += 1
             }
 
-            // Construct the output row.
-            val outputRow = resultProjection(joinedRow(inputRow, windowExpressionResultRow))
-            // We will move to the next one.
-            rowPosition += 1
-            if (requireUpdateFrame && rowPosition < partitionSize) {
-              // If we need to maintain a sliding frame and
-              // we will still work on this partition when next is called next time, do the update.
-              updateFrame()
-            }
+            // 'Merge' the input row with the window function result
+            join(rows(rowIndex), windowFunctionResult)
+            rowIndex += 1
 
-            // Return the output row.
-            outputRow
-          } else {
-            // no more result
-            throw new NoSuchElementException
-          }
+            // Return the projection.
+            result(join)
+          } else throw new NoSuchElementException
         }
+      }
+    }
+  }
+}
 
-        // Fetch the next partition.
-        private def fetchNextPartition(): Unit = {
-          // Create a new buffer for input rows.
-          inputRowBuffer = new CompactBuffer[InternalRow]()
-          // We already have the first row for this partition
-          // (recorded in firstRowInNextPartition). Add it back.
-          inputRowBuffer += firstRowInNextPartition
-          // Set the current partition key.
-          currentPartitionKey = nextPartitionKey
-          // Now, we will start to find all rows belonging to this partition.
-          // Create a variable to track if we see the next partition.
-          var findNextPartition = false
-          // The search will stop when we see the next partition or there is no
-          // input row left in the iter.
-          while (iter.hasNext && !findNextPartition) {
-            // Make a copy of the input row since we will put it in the buffer.
-            val currentRow = iter.next().copy()
-            // Get the partition key based on the partition specification.
-            // For the below compare method, we do not need to make a copy of partitionKey.
-            val partitionKey = partitionGenerator(currentRow)
-            // Check if the current row belongs the current input row.
-            val comparing = partitionOrdering.compare(currentPartitionKey, partitionKey)
-            if (comparing == 0) {
-              // This row is still in the current partition.
-              inputRowBuffer += currentRow
-            } else {
-              // The current input row is in a different partition.
-              findNextPartition = true
-              // partitionGenerator is a mutable projection.
-              // Since we need to track nextPartitionKey and we determine that it should be set
-              // as partitionKey, we are making a copy of the partitionKey at here.
-              nextPartitionKey = partitionKey.copy()
-              firstRowInNextPartition = currentRow
-            }
-          }
+/**
+ * Function for comparing boundary values.
+ */
+private[execution] abstract class BoundOrdering {
+  def compare(input: Seq[InternalRow], inputIndex: Int, outputIndex: Int): Int
+}
 
-          // We have not seen a new partition. It means that there is no new row in the
-          // iter. The current partition is the last partition of the iter.
-          if (!findNextPartition) {
-            lastPartition = true
-          }
+/**
+ * Compare the input index to the bound of the output index.
+ */
+private[execution] final case class RowBoundOrdering(offset: Int) extends BoundOrdering {
+  override def compare(input: Seq[InternalRow], inputIndex: Int, outputIndex: Int): Int =
+    inputIndex - (outputIndex + offset)
+}
 
-          // We have got all rows for the current partition.
-          // Set rowPosition to 0 (the next output row will be based on the first
-          // input row of this partition).
-          rowPosition = 0
-          // The size of this partition.
-          partitionSize = inputRowBuffer.size
-          // Reset all parameter buffers of window functions.
-          var i = 0
-          while (i < windowFunctionParameterBuffers.length) {
-            windowFunctionParameterBuffers(i).clear()
-            i += 1
-          }
-          frameStart = 0
-          frameEnd = -1
-          // Create the first window frame for this partition.
-          // If we do not need to maintain a sliding frame, this frame will
-          // have the entire partition.
-          updateFrame()
-        }
+/**
+ * Compare the value of the input index to the value bound of the output index.
+ */
+private[execution] final case class RangeBoundOrdering(
+    ordering: Ordering[InternalRow],
+    current: Projection,
+    bound: Projection) extends BoundOrdering {
+  override def compare(input: Seq[InternalRow], inputIndex: Int, outputIndex: Int): Int =
+    ordering.compare(current(input(inputIndex)), bound(input(outputIndex)))
+}
 
-        /** The function used to maintain the sliding frame. */
-        private def updateFrame(): Unit = {
-          // Based on the difference between the new frame and old frame,
-          // updates the buffers holding input parameters of window functions.
-          // We will start to prepare input parameters starting from the row
-          // indicated by offset in the input row buffer.
-          def updateWindowFunctionParameterBuffers(
-              numToRemove: Int,
-              numToAdd: Int,
-              offset: Int): Unit = {
-            // First, remove unneeded entries from the head of every buffer.
-            var i = 0
-            while (i < numToRemove) {
-              var j = 0
-              while (j < windowFunctionParameterBuffers.length) {
-                windowFunctionParameterBuffers(j).remove()
-                j += 1
-              }
-              i += 1
-            }
-            // Then, add needed entries to the tail of every buffer.
-            i = 0
-            while (i < numToAdd) {
-              var j = 0
-              while (j < windowFunctionParameterBuffers.length) {
-                // Ask the function to prepare the input parameters.
-                val parameters = functions(j).prepareInputParameters(inputRowBuffer(i + offset))
-                windowFunctionParameterBuffers(j).add(parameters)
-                j += 1
-              }
-              i += 1
-            }
-          }
+/**
+ * A window function calculates the results of a number of window functions for a window frame.
+ * Before use a frame must be prepared by passing it all the rows in the current partition. After
+ * preparation the update method can be called to fill the output rows.
+ *
+ * TODO How to improve performance? A few thoughts:
+ * - Window functions are expensive due to its distribution and ordering requirements.
+ * Unfortunately it is up to the Spark engine to solve this. Improvements in the form of project
+ * Tungsten are on the way.
+ * - The window frame processing bit can be improved though. But before we start doing that we
+ * need to see how much of the time and resources are spent on partitioning and ordering, and
+ * how much time and resources are spent processing the partitions. There are a couple ways to
+ * improve on the current situation:
+ * - Reduce memory footprint by performing streaming calculations. This can only be done when
+ * there are no Unbound/Unbounded Following calculations present.
+ * - Use Tungsten style memory usage.
+ * - Use code generation in general, and use the approach to aggregation taken in the
+ *   GeneratedAggregate class in specific.
+ *
+ * @param ordinal of the first column written by this frame.
+ * @param functions to calculate the row values with.
+ */
+private[execution] abstract class WindowFunctionFrame(
+    ordinal: Int,
+    functions: Array[WindowFunction]) {
+
+  // Make sure functions are initialized.
+  functions.foreach(_.init())
+
+  /** Number of columns the window function frame is managing */
+  val numColumns = functions.length
+
+  /**
+   * Create a fresh thread safe copy of the frame.
+   *
+   * @return the copied frame.
+   */
+  def copy: WindowFunctionFrame
+
+  /**
+   * Create new instances of the functions.
+   *
+   * @return an array containing copies of the current window functions.
+   */
+  protected final def copyFunctions: Array[WindowFunction] = functions.map(_.newInstance())
+
+  /**
+   * Prepare the frame for calculating the results for a partition.
+   *
+   * @param rows to calculate the frame results for.
+   */
+  def prepare(rows: CompactBuffer[InternalRow]): Unit
+
+  /**
+   * Write the result for the current row to the given target row.
+   *
+   * @param target row to write the result for the current row to.
+   */
+  def write(target: GenericMutableRow): Unit
+
+  /** Reset the current window functions. */
+  protected final def reset(): Unit = {
+    var i = 0
+    while (i < numColumns) {
+      functions(i).reset()
+      i += 1
+    }
+  }
 
-          // Record the current frame start point and end point before
-          // we update them.
-          val previousFrameStart = frameStart
-          val previousFrameEnd = frameEnd
-          boundaryEvaluator()
-          updateWindowFunctionParameterBuffers(
-            frameStart - previousFrameStart,
-            frameEnd - previousFrameEnd,
-            previousFrameEnd + 1)
-          // Evaluate the current frame.
-          evaluateCurrentFrame()
-        }
+  /** Prepare an input row for processing. */
+  protected final def prepare(input: InternalRow): Array[AnyRef] = {
+    val prepared = new Array[AnyRef](numColumns)
+    var i = 0
+    while (i < numColumns) {
+      prepared(i) = functions(i).prepareInputParameters(input)
+      i += 1
+    }
+    prepared
+  }
 
-        /** Evaluate the current window frame. */
-        private def evaluateCurrentFrame(): Unit = {
-          var i = 0
-          while (i < functions.length) {
-            // Reset the state of the window function.
-            functions(i).reset()
-            // Get all buffered input parameters based on rows of this window frame.
-            val inputParameters = windowFunctionParameterBuffers(i).toArray()
-            // Send these input parameters to the window function.
-            functions(i).batchUpdate(inputParameters)
-            // Ask the function to evaluate based on this window frame.
-            functions(i).evaluate()
-            i += 1
-          }
-        }
+  /** Evaluate a prepared buffer (iterator). */
+  protected final def evaluatePrepared(iterator: java.util.Iterator[Array[AnyRef]]): Unit = {
+    reset()
+    while (iterator.hasNext) {
+      val prepared = iterator.next()
+      var i = 0
+      while (i < numColumns) {
+        functions(i).update(prepared(i))
+        i += 1
       }
     }
+    evaluate()
   }
+
+  /** Evaluate a prepared buffer (array). */
+  protected final def evaluatePrepared(prepared: Array[Array[AnyRef]],
+      fromIndex: Int, toIndex: Int): Unit = {
+    var i = 0
+    while (i < numColumns) {
+      val function = functions(i)
+      function.reset()
+      var j = fromIndex
+      while (j < toIndex) {
+        function.update(prepared(j)(i))
+        j += 1
+      }
+      function.evaluate()
+      i += 1
+    }
+  }
+
+  /** Update an array of window functions. */
+  protected final def update(input: InternalRow): Unit = {
+    var i = 0
+    while (i < numColumns) {
+      val aggregate = functions(i)
+      val preparedInput = aggregate.prepareInputParameters(input)
+      aggregate.update(preparedInput)
+      i += 1
+    }
+  }
+
+  /** Evaluate the window functions. */
+  protected final def evaluate(): Unit = {
+    var i = 0
+    while (i < numColumns) {
+      functions(i).evaluate()
+      i += 1
+    }
+  }
+
+  /** Fill a target row with the current window function results. */
+  protected final def fill(target: GenericMutableRow, rowIndex: Int): Unit = {
+    var i = 0
+    while (i < numColumns) {
+      target.update(ordinal + i, functions(i).get(rowIndex))
+      i += 1
+    }
+  }
+}
+
+/**
+ * The sliding window frame calculates frames with the following SQL form:
+ * ... BETWEEN 1 PRECEDING AND 1 FOLLOWING
+ *
+ * @param ordinal of the first column written by this frame.
+ * @param functions to calculate the row values with.
+ * @param lbound comparator used to identify the lower bound of an output row.
+ * @param ubound comparator used to identify the upper bound of an output row.
+ */
+private[execution] final class SlidingWindowFunctionFrame(
+    ordinal: Int,
+    functions: Array[WindowFunction],
+    lbound: BoundOrdering,
+    ubound: BoundOrdering) extends WindowFunctionFrame(ordinal, functions) {
+
+  /** Rows of the partition currently being processed. */
+  private[this] var input: CompactBuffer[InternalRow] = null
+
+  /** Index of the first input row with a value greater than the upper bound of the current
+    * output row. */
+  private[this] var inputHighIndex = 0
+
+  /** Index of the first input row with a value equal to or greater than the lower bound of the
+    * current output row. */
+  private[this] var inputLowIndex = 0
+
+  /** Buffer used for storing prepared input for the window functions. */
+  private[this] val buffer = new util.ArrayDeque[Array[AnyRef]]
+
+  /** Index of the row we are currently writing. */
+  private[this] var outputIndex = 0
+
+  /** Prepare the frame for calculating a new partition. Reset all variables. */
+  override def prepare(rows: CompactBuffer[InternalRow]): Unit = {
+    input = rows
+    inputHighIndex = 0
+    inputLowIndex = 0
+    outputIndex = 0
+    buffer.clear()
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(target: GenericMutableRow): Unit = {
+    var bufferUpdated = outputIndex == 0
+
+    // Add all rows to the buffer for which the input row value is equal to or less than
+    // the output row upper bound.
+    while (inputHighIndex < input.size &&
+        ubound.compare(input, inputHighIndex, outputIndex) <= 0) {
+      buffer.offer(prepare(input(inputHighIndex)))
+      inputHighIndex += 1
+      bufferUpdated = true
+    }
+
+    // Drop all rows from the buffer for which the input row value is smaller than
+    // the output row lower bound.
+    while (inputLowIndex < inputHighIndex &&
+        lbound.compare(input, inputLowIndex, outputIndex) < 0) {
+      buffer.pop()
+      inputLowIndex += 1
+      bufferUpdated = true
+    }
+
+    // Only recalculate and update when the buffer changes.
+    if (bufferUpdated) {
+      evaluatePrepared(buffer.iterator())
+      fill(target, outputIndex)
+    }
+
+    // Move to the next row.
+    outputIndex += 1
+  }
+
+  /** Copy the frame. */
+  override def copy: SlidingWindowFunctionFrame =
+    new SlidingWindowFunctionFrame(ordinal, copyFunctions, lbound, ubound)
+}
+
+/**
+ * The unbounded window frame calculates frames with the following SQL forms:
+ * ... (No Frame Definition)
+ * ... BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
+ *
+ * Its results are  the same for each and every row in the partition. This class can be seen as a
+ * special case of a sliding window, but is optimized for the unbound case.
+ *
+ * @param ordinal of the first column written by this frame.
+ * @param functions to calculate the row values with.
+ */
+private[execution] final class UnboundedWindowFunctionFrame(
+    ordinal: Int,
+    functions: Array[WindowFunction]) extends WindowFunctionFrame(ordinal, functions) {
+
+  /** Index of the row we are currently writing. */
+  private[this] var outputIndex = 0
+
+  /** Prepare the frame for calculating a new partition. Process all rows eagerly. */
+  override def prepare(rows: CompactBuffer[InternalRow]): Unit = {
+    reset()
+    outputIndex = 0
+    val iterator = rows.iterator
+    while (iterator.hasNext) {
+      update(iterator.next())
+    }
+    evaluate()
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(target: GenericMutableRow): Unit = {
+    fill(target, outputIndex)
+    outputIndex += 1
+  }
+
+  /** Copy the frame. */
+  override def copy: UnboundedWindowFunctionFrame =
+    new UnboundedWindowFunctionFrame(ordinal, copyFunctions)
+}
+
+/**
+ * The UnboundPreceding window frame calculates frames with the following SQL form:
+ * ... BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+ *
+ * There is only an upper bound. Very common use cases are for instance running sums or counts
+ * (row_number). Technically this is a special case of a sliding window. However a sliding window
+ * has to maintain a buffer, and it must do a full evaluation everytime the buffer changes. This
+ * is not the case when there is no lower bound, given the additive nature of most aggregates
+ * streaming updates and partial evaluation suffice and no buffering is needed.
+ *
+ * @param ordinal of the first column written by this frame.
+ * @param functions to calculate the row values with.
+ * @param ubound comparator used to identify the upper bound of an output row.
+ */
+private[execution] final class UnboundedPrecedingWindowFunctionFrame(
+    ordinal: Int,
+    functions: Array[WindowFunction],
+    ubound: BoundOrdering) extends WindowFunctionFrame(ordinal, functions) {
+
+  /** Rows of the partition currently being processed. */
+  private[this] var input: CompactBuffer[InternalRow] = null
+
+  /** Index of the first input row with a value greater than the upper bound of the current
+    * output row. */
+  private[this] var inputIndex = 0
+
+  /** Index of the row we are currently writing. */
+  private[this] var outputIndex = 0
+
+  /** Prepare the frame for calculating a new partition. */
+  override def prepare(rows: CompactBuffer[InternalRow]): Unit = {
+    reset()
+    input = rows
+    inputIndex = 0
+    outputIndex = 0
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(target: GenericMutableRow): Unit = {
+    var bufferUpdated = outputIndex == 0
+
+    // Add all rows to the aggregates for which the input row value is equal to or less than
+    // the output row upper bound.
+    while (inputIndex < input.size && ubound.compare(input, inputIndex, outputIndex) <= 0) {
+      update(input(inputIndex))
+      inputIndex += 1
+      bufferUpdated = true
+    }
+
+    // Only recalculate and update when the buffer changes.
+    if (bufferUpdated) {
+      evaluate()
+      fill(target, outputIndex)
+    }
+
+    // Move to the next row.
+    outputIndex += 1
+  }
+
+  /** Copy the frame. */
+  override def copy: UnboundedPrecedingWindowFunctionFrame =
+    new UnboundedPrecedingWindowFunctionFrame(ordinal, copyFunctions, ubound)
+}
+
+/**
+ * The UnboundFollowing window frame calculates frames with the following SQL form:
+ * ... BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
+ *
+ * There is only an upper bound. This is a slightly modified version of the sliding window. The
+ * sliding window operator has to check if both upper and the lower bound change when a new row
+ * gets processed, where as the unbounded following only has to check the lower bound.
+ *
+ * This is a very expensive operator to use, O(n * (n - 1) /2), because we need to maintain a
+ * buffer and must do full recalculation after each row. Reverse iteration would be possible, if
+ * the communitativity of the used window functions can be guaranteed.
+ *
+ * @param ordinal of the first column written by this frame.
+ * @param functions to calculate the row values with.
+ * @param lbound comparator used to identify the lower bound of an output row.
+ */
+private[execution] final class UnboundedFollowingWindowFunctionFrame(
+    ordinal: Int,
+    functions: Array[WindowFunction],
+    lbound: BoundOrdering) extends WindowFunctionFrame(ordinal, functions) {
+
+  /** Buffer used for storing prepared input for the window functions. */
+  private[this] var buffer: Array[Array[AnyRef]] = _
+
+  /** Rows of the partition currently being processed. */
+  private[this] var input: CompactBuffer[InternalRow] = null
+
+  /** Index of the first input row with a value equal to or greater than the lower bound of the
+    * current output row. */
+  private[this] var inputIndex = 0
+
+  /** Index of the row we are currently writing. */
+  private[this] var outputIndex = 0
+
+  /** Prepare the frame for calculating a new partition. */
+  override def prepare(rows: CompactBuffer[InternalRow]): Unit = {
+    input = rows
+    inputIndex = 0
+    outputIndex = 0
+    val size = input.size
+    buffer = Array.ofDim(size)
+    var i = 0
+    while (i < size) {
+      buffer(i) = prepare(input(i))
+      i += 1
+    }
+    evaluatePrepared(buffer, 0, buffer.length)
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(target: GenericMutableRow): Unit = {
+    var bufferUpdated = outputIndex == 0
+
+    // Drop all rows from the buffer for which the input row value is smaller than
+    // the output row lower bound.
+    while (inputIndex < input.size && lbound.compare(input, inputIndex, outputIndex) < 0) {
+      inputIndex += 1
+      bufferUpdated = true
+    }
+
+    // Only recalculate and update when the buffer changes.
+    if (bufferUpdated) {
+      evaluatePrepared(buffer, inputIndex, buffer.length)
+      fill(target, outputIndex)
+    }
+
+    // Move to the next row.
+    outputIndex += 1
+  }
+
+  /** Copy the frame. */
+  override def copy: UnboundedFollowingWindowFunctionFrame =
+    new UnboundedFollowingWindowFunctionFrame(ordinal, copyFunctions, lbound)
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
index efb3f2545db84..15b5f418f0a8c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
@@ -183,13 +183,13 @@ class HiveDataFrameWindowSuite extends QueryTest {
   }
 
   test("aggregation and range betweens with unbounded") {
-    val df = Seq((1, "1"), (2, "2"), (2, "2"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value")
+    val df = Seq((5, "1"), (5, "2"), (4, "2"), (6, "2"), (3, "1"), (2, "2")).toDF("key", "value")
     df.registerTempTable("window_table")
     checkAnswer(
       df.select(
         $"key",
         last("value").over(
-          Window.partitionBy($"value").orderBy($"key").rangeBetween(1, Long.MaxValue))
+          Window.partitionBy($"value").orderBy($"key").rangeBetween(-2, -1))
           .equalTo("2")
           .as("last_v"),
         avg("key").over(Window.partitionBy("value").orderBy("key").rangeBetween(Long.MinValue, 1))
@@ -203,7 +203,7 @@ class HiveDataFrameWindowSuite extends QueryTest {
         """SELECT
           | key,
           | last_value(value) OVER
-          |   (PARTITION BY value ORDER BY key RANGE 1 preceding) == "2",
+          |   (PARTITION BY value ORDER BY key RANGE BETWEEN 2 preceding and 1 preceding) == "2",
           | avg(key) OVER
           |   (PARTITION BY value ORDER BY key RANGE BETWEEN unbounded preceding and 1 following),
           | avg(key) OVER
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala
new file mode 100644
index 0000000000000..a089d0d165195
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala
@@ -0,0 +1,79 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.apache.spark.sql.{Row, QueryTest}
+import org.apache.spark.sql.expressions.Window
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.hive.test.TestHive.implicits._
+
+/**
+ * Window expressions are tested extensively by the following test suites:
+ * [[org.apache.spark.sql.hive.HiveDataFrameWindowSuite]]
+ * [[org.apache.spark.sql.hive.execution.HiveWindowFunctionQueryWithoutCodeGenSuite]]
+ * [[org.apache.spark.sql.hive.execution.HiveWindowFunctionQueryFileWithoutCodeGenSuite]]
+ * However these suites do not cover all possible (i.e. more exotic) settings. This suite fill
+ * this gap.
+ *
+ * TODO Move this class to the sql/core project when we move to Native Spark UDAFs.
+ */
+class WindowSuite extends QueryTest {
+
+  test("reverse sliding range frame") {
+    val df = Seq(
+      (1, "Thin", "Cell Phone", 6000),
+      (2, "Normal", "Tablet", 1500),
+      (3, "Mini", "Tablet", 5500),
+      (4, "Ultra thin", "Cell Phone", 5500),
+      (5, "Very thin", "Cell Phone", 6000),
+      (6, "Big", "Tablet", 2500),
+      (7, "Bendable", "Cell Phone", 3000),
+      (8, "Foldable", "Cell Phone", 3000),
+      (9, "Pro", "Tablet", 4500),
+      (10, "Pro2", "Tablet", 6500)).
+      toDF("id", "product", "category", "revenue")
+    val window = Window.
+      partitionBy($"category").
+      orderBy($"revenue".desc).
+      rangeBetween(-2000L, 1000L)
+    checkAnswer(
+      df.select(
+        $"id",
+        avg($"revenue").over(window).cast("int")),
+      Row(1, 5833) :: Row(2, 2000) :: Row(3, 5500) ::
+      Row(4, 5833) :: Row(5, 5833) :: Row(6, 2833) ::
+      Row(7, 3000) :: Row(8, 3000) :: Row(9, 5500) ::
+      Row(10, 6000) :: Nil)
+  }
+
+  // This is here to illustrate the fact that reverse order also reverses offsets.
+  test("reverse unbounded range frame") {
+    val df = Seq(1, 2, 4, 3, 2, 1).
+      map(Tuple1.apply).
+      toDF("value")
+    val window = Window.orderBy($"value".desc)
+    checkAnswer(
+      df.select(
+        $"value",
+         sum($"value").over(window.rangeBetween(Long.MinValue, 1)),
+         sum($"value").over(window.rangeBetween(1, Long.MaxValue))),
+      Row(1, 13, null) :: Row(2, 13, 2) :: Row(4, 7, 9) ::
+        Row(3, 11, 6) :: Row(2, 13, 2) :: Row(1, 13, null) :: Nil)
+
+  }
+}

From 89d135851d928f9d7dcebe785c1b3b6a4d8dfc87 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 18 Jul 2015 23:47:40 -0700
Subject: [PATCH 0468/1454] Closes #6775 since it is subsumbed by other
 patches.


From 9b644c41306cac53185ce0d2de4cb72127ada932 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Sun, 19 Jul 2015 00:32:56 -0700
Subject: [PATCH 0469/1454] [SPARK-9166][SQL][PYSPARK] Capture and hide
 IllegalArgumentException in Python API

JIRA: https://issues.apache.org/jira/browse/SPARK-9166

Simply capture and hide `IllegalArgumentException` in Python API.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7497 from viirya/hide_illegalargument and squashes the following commits:

8324dce [Liang-Chi Hsieh] Fix python style.
9ace67d [Liang-Chi Hsieh] Also check exception message.
8b2ce5c [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into hide_illegalargument
7be016a [Liang-Chi Hsieh] Capture and hide IllegalArgumentException in Python.
---
 python/pyspark/sql/tests.py | 11 +++++++++--
 python/pyspark/sql/utils.py |  8 ++++++++
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 241eac45cfe36..86706e2dc41a3 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -45,9 +45,9 @@
 from pyspark.sql.types import *
 from pyspark.sql.types import UserDefinedType, _infer_type
 from pyspark.tests import ReusedPySparkTestCase
-from pyspark.sql.functions import UserDefinedFunction
+from pyspark.sql.functions import UserDefinedFunction, sha2
 from pyspark.sql.window import Window
-from pyspark.sql.utils import AnalysisException
+from pyspark.sql.utils import AnalysisException, IllegalArgumentException
 
 
 class UTC(datetime.tzinfo):
@@ -894,6 +894,13 @@ def test_capture_analysis_exception(self):
         # RuntimeException should not be captured
         self.assertRaises(py4j.protocol.Py4JJavaError, lambda: self.sqlCtx.sql("abc"))
 
+    def test_capture_illegalargument_exception(self):
+        self.assertRaisesRegexp(IllegalArgumentException, "Setting negative mapred.reduce.tasks",
+                                lambda: self.sqlCtx.sql("SET mapred.reduce.tasks=-1"))
+        df = self.sqlCtx.createDataFrame([(1, 2)], ["a", "b"])
+        self.assertRaisesRegexp(IllegalArgumentException, "1024 is not in the permitted values",
+                                lambda: df.select(sha2(df.a, 1024)).collect())
+
 
 class HiveContextSQLTests(ReusedPySparkTestCase):
 
diff --git a/python/pyspark/sql/utils.py b/python/pyspark/sql/utils.py
index cc5b2c088b7cc..0f795ca35b38a 100644
--- a/python/pyspark/sql/utils.py
+++ b/python/pyspark/sql/utils.py
@@ -24,6 +24,12 @@ class AnalysisException(Exception):
     """
 
 
+class IllegalArgumentException(Exception):
+    """
+    Passed an illegal or inappropriate argument.
+    """
+
+
 def capture_sql_exception(f):
     def deco(*a, **kw):
         try:
@@ -32,6 +38,8 @@ def deco(*a, **kw):
             s = e.java_exception.toString()
             if s.startswith('org.apache.spark.sql.AnalysisException: '):
                 raise AnalysisException(s.split(': ', 1)[1])
+            if s.startswith('java.lang.IllegalArgumentException: '):
+                raise IllegalArgumentException(s.split(': ', 1)[1])
             raise
     return deco
 

From 344d1567e5ac28b3ab8f83f18d2fa9d98acef152 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carl=20Anders=20D=C3=BCvel?= <c.a.duevel@gmail.com>
Date: Sun, 19 Jul 2015 09:14:55 +0100
Subject: [PATCH 0470/1454] [SPARK-9094] [PARENT] Increased
 io.dropwizard.metrics from 3.1.0 to 3.1.2
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

We are running Spark 1.4.0 in production and ran into problems because after a network hiccup (which happens often in our current environment) no more metrics were reported to graphite leaving us blindfolded about the current state of our spark applications. [This problem](https://github.com/dropwizard/metrics/commit/70559816f1fc3a0a0122b5263d5478ff07396991) was fixed in the current version of the metrics library. We run spark with this change  in production now and have seen no problems. We also had a look at the commit history since 3.1.0 and did not detect any potentially  incompatible changes but many fixes which could potentially help other users as well.

Author: Carl Anders Düvel <c.a.duevel@gmail.com>

Closes #7493 from hackbert/bump-metrics-lib-version and squashes the following commits:

6677565 [Carl Anders Düvel] [SPARK-9094] [PARENT] Increased io.dropwizard.metrics from 3.1.0 to 3.1.2 in order to get this fix https://github.com/dropwizard/metrics/commit/70559816f1fc3a0a0122b5263d5478ff07396991
---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index c5c655834bdeb..2de0c35fbd51a 100644
--- a/pom.xml
+++ b/pom.xml
@@ -144,7 +144,7 @@
     <chill.version>0.5.0</chill.version>
     <ivy.version>2.4.0</ivy.version>
     <oro.version>2.0.8</oro.version>
-    <codahale.metrics.version>3.1.0</codahale.metrics.version>
+    <codahale.metrics.version>3.1.2</codahale.metrics.version>
     <avro.version>1.7.7</avro.version>
     <avro.mapred.classifier>hadoop2</avro.mapred.classifier>
     <jets3t.version>0.7.1</jets3t.version>

From a53d13f7aa5d44c706e5510f57399a32c7558b80 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Sun, 19 Jul 2015 01:16:01 -0700
Subject: [PATCH 0471/1454] [SPARK-8199][SQL] follow up; revert change in test

rxin / davies

Sorry for that unnecessary change. And thanks again for all your support!

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7505 from tarekauel/SPARK-8199-FollowUp and squashes the following commits:

d09321c [Tarek Auel] [SPARK-8199] follow up; revert change in test
c17397f [Tarek Auel] [SPARK-8199] follow up; revert change in test
67acfe6 [Tarek Auel] [SPARK-8199] follow up; revert change in test
---
 .../spark/sql/catalyst/expressions/DateFunctionsSuite.scala   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
index f469f42116d21..a0991ec998311 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
@@ -74,7 +74,7 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         (0 to 5).foreach { i =>
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, 1)
+          c.add(Calendar.DATE, i)
           checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }
@@ -86,7 +86,7 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         (0 to 5).foreach { i =>
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, 1)
+          c.add(Calendar.DATE, i)
           checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }

From 3427937ea2a4ed19142bd3d66707864879417d61 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 19 Jul 2015 01:17:22 -0700
Subject: [PATCH 0472/1454] [SQL] Make date/time functions more consistent with
 other database systems.

This pull request fixes some of the problems in #6981.

- Added date functions to `__all__` so they get exposed
- Rename day_of_month -> dayofmonth
- Rename day_in_year -> dayofyear
- Rename week_of_year -> weekofyear
- Removed "day" from Scala/Python API since it is ambiguous. Only leaving the alias in SQL.

Author: Reynold Xin <rxin@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>

Closes #7506 from rxin/datetime and squashes the following commits:

0cb24d9 [Reynold Xin] Export all functions in Python.
e44a4a0 [Reynold Xin] Removed day function from Scala and Python.
9c08fdc [Reynold Xin] [SQL] Make date/time functions more consistent with other database systems.
---
 python/pyspark/sql/functions.py               |  35 +-
 .../catalyst/analysis/FunctionRegistry.scala  |   8 +-
 .../expressions/datetimeFunctions.scala       |  13 +-
 .../sql/catalyst/util/DateTimeUtils.scala     |   4 +-
 ...Suite.scala => DateExpressionsSuite.scala} |  26 +-
 .../org/apache/spark/sql/functions.scala      | 338 +++++++++---------
 .../apache/spark/sql/DataFrameDateSuite.scala |  56 ---
 ...nsSuite.scala => DateFunctionsSuite.scala} |  61 ++--
 8 files changed, 239 insertions(+), 302 deletions(-)
 rename sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/{DateFunctionsSuite.scala => DateExpressionsSuite.scala} (91%)
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameDateSuite.scala
 rename sql/core/src/test/scala/org/apache/spark/sql/{DateExpressionsSuite.scala => DateFunctionsSuite.scala} (74%)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 0aca3788922aa..fd5a3ba8adab3 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -55,6 +55,11 @@
 
 __all__ += ['lag', 'lead', 'ntile']
 
+__all__ += [
+    'date_format',
+    'year', 'quarter', 'month', 'hour', 'minute', 'second',
+    'dayofmonth', 'dayofyear', 'weekofyear']
+
 
 def _create_function(name, doc=""):
     """ Create a function for aggregator by name"""
@@ -713,41 +718,29 @@ def month(col):
 
 
 @since(1.5)
-def day(col):
-    """
-    Extract the day of the month of a given date as integer.
-
-    >>> sqlContext.createDataFrame([('2015-04-08',)], ['a']).select(day('a').alias('day')).collect()
-    [Row(day=8)]
-    """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.day(col))
-
-
-@since(1.5)
-def day_of_month(col):
+def dayofmonth(col):
     """
     Extract the day of the month of a given date as integer.
 
     >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
-    >>> df.select(day_of_month('a').alias('day')).collect()
+    >>> df.select(dayofmonth('a').alias('day')).collect()
     [Row(day=8)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.day_of_month(col))
+    return Column(sc._jvm.functions.dayofmonth(col))
 
 
 @since(1.5)
-def day_in_year(col):
+def dayofyear(col):
     """
     Extract the day of the year of a given date as integer.
 
     >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
-    >>> df.select(day_in_year('a').alias('day')).collect()
+    >>> df.select(dayofyear('a').alias('day')).collect()
     [Row(day=98)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.day_in_year(col))
+    return Column(sc._jvm.functions.dayofyear(col))
 
 
 @since(1.5)
@@ -790,16 +783,16 @@ def second(col):
 
 
 @since(1.5)
-def week_of_year(col):
+def weekofyear(col):
     """
     Extract the week number of a given date as integer.
 
     >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
-    >>> df.select(week_of_year('a').alias('week')).collect()
+    >>> df.select(weekofyear('a').alias('week')).collect()
     [Row(week=15)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.week_of_year(col))
+    return Column(sc._jvm.functions.weekofyear(col))
 
 
 class UserDefinedFunction(object):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 159f7eca7acfe..4b256adcc60c6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -183,15 +183,15 @@ object FunctionRegistry {
     expression[CurrentDate]("current_date"),
     expression[CurrentTimestamp]("current_timestamp"),
     expression[DateFormatClass]("date_format"),
-    expression[Day]("day"),
-    expression[DayInYear]("day_in_year"),
-    expression[Day]("day_of_month"),
+    expression[DayOfMonth]("day"),
+    expression[DayOfYear]("dayofyear"),
+    expression[DayOfMonth]("dayofmonth"),
     expression[Hour]("hour"),
     expression[Month]("month"),
     expression[Minute]("minute"),
     expression[Quarter]("quarter"),
     expression[Second]("second"),
-    expression[WeekOfYear]("week_of_year"),
+    expression[WeekOfYear]("weekofyear"),
     expression[Year]("year")
 
   )
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index f9cbbb8c6bee0..802445509285d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -116,14 +116,12 @@ case class Second(child: Expression) extends UnaryExpression with ImplicitCastIn
   }
 }
 
-case class DayInYear(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+case class DayOfYear(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
 
   override def dataType: DataType = IntegerType
 
-  override def prettyName: String = "day_in_year"
-
   override protected def nullSafeEval(date: Any): Any = {
     DateTimeUtils.getDayInYear(date.asInstanceOf[Int])
   }
@@ -149,7 +147,7 @@ case class Year(child: Expression) extends UnaryExpression with ImplicitCastInpu
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
+    defineCodeGen(ctx, ev, c =>
       s"""$dtu.getYear($c)"""
     )
   }
@@ -191,7 +189,7 @@ case class Month(child: Expression) extends UnaryExpression with ImplicitCastInp
   }
 }
 
-case class Day(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+case class DayOfMonth(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
 
@@ -215,8 +213,6 @@ case class WeekOfYear(child: Expression) extends UnaryExpression with ImplicitCa
 
   override def dataType: DataType = IntegerType
 
-  override def prettyName: String = "week_of_year"
-
   override protected def nullSafeEval(date: Any): Any = {
     val c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
     c.setFirstDayOfWeek(Calendar.MONDAY)
@@ -225,7 +221,7 @@ case class WeekOfYear(child: Expression) extends UnaryExpression with ImplicitCa
     c.get(Calendar.WEEK_OF_YEAR)
   }
 
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, (time) => {
       val cal = classOf[Calendar].getName
       val c = ctx.freshName("cal")
@@ -237,6 +233,7 @@ case class WeekOfYear(child: Expression) extends UnaryExpression with ImplicitCa
         ${ev.primitive} = $c.get($cal.WEEK_OF_YEAR);
       """
     })
+  }
 }
 
 case class DateFormatClass(left: Expression, right: Expression) extends BinaryExpression
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index a0da73a995a82..07412e73b6a5b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -31,14 +31,14 @@ import org.apache.spark.unsafe.types.UTF8String
  * precision.
  */
 object DateTimeUtils {
-  final val MILLIS_PER_DAY = SECONDS_PER_DAY * 1000L
-
   // see http://stackoverflow.com/questions/466321/convert-unix-timestamp-to-julian
   final val JULIAN_DAY_OF_EPOCH = 2440587  // and .5
   final val SECONDS_PER_DAY = 60 * 60 * 24L
   final val MICROS_PER_SECOND = 1000L * 1000L
   final val NANOS_PER_SECOND = MICROS_PER_SECOND * 1000L
 
+  final val MILLIS_PER_DAY = SECONDS_PER_DAY * 1000L
+
   // number of days in 400 years
   final val daysIn400Years: Int = 146097
   // number of days between 1.1.1970 and 1.1.2001
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
similarity index 91%
rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index a0991ec998311..f01589c58ea86 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -19,19 +19,19 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.sql.{Timestamp, Date}
 import java.text.SimpleDateFormat
-import java.util.{TimeZone, Calendar}
+import java.util.Calendar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.types.{StringType, TimestampType, DateType}
 
-class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
   val sdfDate = new SimpleDateFormat("yyyy-MM-dd")
   val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
   val ts = new Timestamp(sdf.parse("2013-11-08 13:10:15").getTime)
 
-  test("Day in Year") {
+  test("DayOfYear") {
     val sdfDay = new SimpleDateFormat("D")
     (2002 to 2004).foreach { y =>
       (0 to 11).foreach { m =>
@@ -39,7 +39,7 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -51,7 +51,7 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -63,7 +63,7 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -163,19 +163,19 @@ class DateFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
-  test("Day") {
-    checkEvaluation(Day(Cast(Literal("2000-02-29"), DateType)), 29)
-    checkEvaluation(Day(Literal.create(null, DateType)), null)
-    checkEvaluation(Day(Cast(Literal(d), DateType)), 8)
-    checkEvaluation(Day(Cast(Literal(sdfDate.format(d)), DateType)), 8)
-    checkEvaluation(Day(Cast(Literal(ts), DateType)), 8)
+  test("Day / DayOfMonth") {
+    checkEvaluation(DayOfMonth(Cast(Literal("2000-02-29"), DateType)), 29)
+    checkEvaluation(DayOfMonth(Literal.create(null, DateType)), null)
+    checkEvaluation(DayOfMonth(Cast(Literal(d), DateType)), 8)
+    checkEvaluation(DayOfMonth(Cast(Literal(sdfDate.format(d)), DateType)), 8)
+    checkEvaluation(DayOfMonth(Cast(Literal(ts), DateType)), 8)
 
     (1999 to 2000).foreach { y =>
       val c = Calendar.getInstance()
       c.set(y, 0, 1, 0, 0, 0)
       (0 to 365).foreach { d =>
         c.add(Calendar.DATE, 1)
-        checkEvaluation(Day(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+        checkEvaluation(DayOfMonth(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
           c.get(Calendar.DAY_OF_MONTH))
       }
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index cadb25d597d19..f67c89437bb4a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1748,182 +1748,6 @@ object functions {
    */
   def length(columnName: String): Column = length(Column(columnName))
 
-  //////////////////////////////////////////////////////////////////////////////////////////////
-  // DateTime functions
-  //////////////////////////////////////////////////////////////////////////////////////////////
-
-  /**
-   * Converts a date/timestamp/string to a value of string in the format specified by the date
-   * format given by the second argument.
-   *
-   * A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
-   * pattern letters of [[java.text.SimpleDateFormat]] can be used.
-   *
-   * NOTE: Use when ever possible specialized functions like [[year]]. These benefit from a
-   * specialized implementation.
-   *
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def date_format(dateExpr: Column, format: String): Column =
-    DateFormatClass(dateExpr.expr, Literal(format))
-
-  /**
-   * Converts a date/timestamp/string to a value of string in the format specified by the date
-   * format given by the second argument.
-   *
-   * A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
-   * pattern letters of [[java.text.SimpleDateFormat]] can be used.
-   *
-   * NOTE: Use when ever possible specialized functions like [[year]]. These benefit from a
-   * specialized implementation.
-   *
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def date_format(dateColumnName: String, format: String): Column =
-    date_format(Column(dateColumnName), format)
-
-  /**
-   * Extracts the year as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def year(e: Column): Column = Year(e.expr)
-
-  /**
-   * Extracts the year as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def year(columnName: String): Column = year(Column(columnName))
-
-  /**
-   * Extracts the quarter as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def quarter(e: Column): Column = Quarter(e.expr)
-
-  /**
-   * Extracts the quarter as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def quarter(columnName: String): Column = quarter(Column(columnName))
-
-  /**
-   * Extracts the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def month(e: Column): Column = Month(e.expr)
-
-  /**
-   * Extracts the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def month(columnName: String): Column = month(Column(columnName))
-
-  /**
-   * Extracts the day of the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def day(e: Column): Column = Day(e.expr)
-
-  /**
-   * Extracts the day of the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def day(columnName: String): Column = day(Column(columnName))
-
-  /**
-   * Extracts the day of the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def day_of_month(e: Column): Column = Day(e.expr)
-
-  /**
-   * Extracts the day of the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def day_of_month(columnName: String): Column = day_of_month(Column(columnName))
-
-  /**
-   * Extracts the day of the year as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def day_in_year(e: Column): Column = DayInYear(e.expr)
-
-  /**
-   * Extracts the day of the year as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def day_in_year(columnName: String): Column = day_in_year(Column(columnName))
-
-  /**
-   * Extracts the hours as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def hour(e: Column): Column = Hour(e.expr)
-
-  /**
-   * Extracts the hours as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def hour(columnName: String): Column = hour(Column(columnName))
-
-  /**
-   * Extracts the minutes as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def minute(e: Column): Column = Minute(e.expr)
-
-  /**
-   * Extracts the minutes as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def minute(columnName: String): Column = minute(Column(columnName))
-
-  /**
-   * Extracts the seconds as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def second(e: Column): Column = Second(e.expr)
-
-  /**
-   * Extracts the seconds as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def second(columnName: String): Column = second(Column(columnName))
-
-  /**
-   * Extracts the week number as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def week_of_year(e: Column): Column = WeekOfYear(e.expr)
-
-  /**
-   * Extracts the week number as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def week_of_year(columnName: String): Column = week_of_year(Column(columnName))
-
   /**
    * Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
    * and returns the result as a string.
@@ -2409,6 +2233,168 @@ object functions {
     StringSpace(n.expr)
   }
 
+  //////////////////////////////////////////////////////////////////////////////////////////////
+  // DateTime functions
+  //////////////////////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Converts a date/timestamp/string to a value of string in the format specified by the date
+   * format given by the second argument.
+   *
+   * A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
+   * pattern letters of [[java.text.SimpleDateFormat]] can be used.
+   *
+   * NOTE: Use when ever possible specialized functions like [[year]]. These benefit from a
+   * specialized implementation.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def date_format(dateExpr: Column, format: String): Column =
+    DateFormatClass(dateExpr.expr, Literal(format))
+
+  /**
+   * Converts a date/timestamp/string to a value of string in the format specified by the date
+   * format given by the second argument.
+   *
+   * A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
+   * pattern letters of [[java.text.SimpleDateFormat]] can be used.
+   *
+   * NOTE: Use when ever possible specialized functions like [[year]]. These benefit from a
+   * specialized implementation.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def date_format(dateColumnName: String, format: String): Column =
+    date_format(Column(dateColumnName), format)
+
+  /**
+   * Extracts the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def year(e: Column): Column = Year(e.expr)
+
+  /**
+   * Extracts the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def year(columnName: String): Column = year(Column(columnName))
+
+  /**
+   * Extracts the quarter as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def quarter(e: Column): Column = Quarter(e.expr)
+
+  /**
+   * Extracts the quarter as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def quarter(columnName: String): Column = quarter(Column(columnName))
+
+  /**
+   * Extracts the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def month(e: Column): Column = Month(e.expr)
+
+  /**
+   * Extracts the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def month(columnName: String): Column = month(Column(columnName))
+
+  /**
+   * Extracts the day of the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def dayofmonth(e: Column): Column = DayOfMonth(e.expr)
+
+  /**
+   * Extracts the day of the month as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def dayofmonth(columnName: String): Column = dayofmonth(Column(columnName))
+
+  /**
+   * Extracts the day of the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def dayofyear(e: Column): Column = DayOfYear(e.expr)
+
+  /**
+   * Extracts the day of the year as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def dayofyear(columnName: String): Column = dayofyear(Column(columnName))
+
+  /**
+   * Extracts the hours as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def hour(e: Column): Column = Hour(e.expr)
+
+  /**
+   * Extracts the hours as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def hour(columnName: String): Column = hour(Column(columnName))
+
+  /**
+   * Extracts the minutes as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def minute(e: Column): Column = Minute(e.expr)
+
+  /**
+   * Extracts the minutes as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def minute(columnName: String): Column = minute(Column(columnName))
+
+  /**
+   * Extracts the seconds as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def second(e: Column): Column = Second(e.expr)
+
+  /**
+   * Extracts the seconds as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def second(columnName: String): Column = second(Column(columnName))
+
+  /**
+   * Extracts the week number as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def weekofyear(e: Column): Column = WeekOfYear(e.expr)
+
+  /**
+   * Extracts the week number as an integer from a given date/timestamp/string.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def weekofyear(columnName: String): Column = weekofyear(Column(columnName))
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameDateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameDateSuite.scala
deleted file mode 100644
index a4719a38de1d4..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameDateSuite.scala
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import java.sql.{Date, Timestamp}
-
-class DataFrameDateTimeSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-
-  test("timestamp comparison with date strings") {
-    val df = Seq(
-      (1, Timestamp.valueOf("2015-01-01 00:00:00")),
-      (2, Timestamp.valueOf("2014-01-01 00:00:00"))).toDF("i", "t")
-
-    checkAnswer(
-      df.select("t").filter($"t" <= "2014-06-01"),
-      Row(Timestamp.valueOf("2014-01-01 00:00:00")) :: Nil)
-
-
-    checkAnswer(
-      df.select("t").filter($"t" >= "2014-06-01"),
-      Row(Timestamp.valueOf("2015-01-01 00:00:00")) :: Nil)
-  }
-
-  test("date comparison with date strings") {
-    val df = Seq(
-      (1, Date.valueOf("2015-01-01")),
-      (2, Date.valueOf("2014-01-01"))).toDF("i", "t")
-
-    checkAnswer(
-      df.select("t").filter($"t" <= "2014-06-01"),
-      Row(Date.valueOf("2014-01-01")) :: Nil)
-
-
-    checkAnswer(
-      df.select("t").filter($"t" >= "2015"),
-      Row(Date.valueOf("2015-01-01")) :: Nil)
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
similarity index 74%
rename from sql/core/src/test/scala/org/apache/spark/sql/DateExpressionsSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index d24e3ee1dd8f5..9e80ae86920d9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -22,7 +22,7 @@ import java.text.SimpleDateFormat
 
 import org.apache.spark.sql.functions._
 
-class DateExpressionsSuite extends QueryTest {
+class DateFunctionsSuite extends QueryTest {
   private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
 
   import ctx.implicits._
@@ -32,6 +32,35 @@ class DateExpressionsSuite extends QueryTest {
   val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
   val ts = new Timestamp(sdf.parse("2013-04-08 13:10:15").getTime)
 
+  test("timestamp comparison with date strings") {
+    val df = Seq(
+      (1, Timestamp.valueOf("2015-01-01 00:00:00")),
+      (2, Timestamp.valueOf("2014-01-01 00:00:00"))).toDF("i", "t")
+
+    checkAnswer(
+      df.select("t").filter($"t" <= "2014-06-01"),
+      Row(Timestamp.valueOf("2014-01-01 00:00:00")) :: Nil)
+
+
+    checkAnswer(
+      df.select("t").filter($"t" >= "2014-06-01"),
+      Row(Timestamp.valueOf("2015-01-01 00:00:00")) :: Nil)
+  }
+
+  test("date comparison with date strings") {
+    val df = Seq(
+      (1, Date.valueOf("2015-01-01")),
+      (2, Date.valueOf("2014-01-01"))).toDF("i", "t")
+
+    checkAnswer(
+      df.select("t").filter($"t" <= "2014-06-01"),
+      Row(Date.valueOf("2014-01-01")) :: Nil)
+
+
+    checkAnswer(
+      df.select("t").filter($"t" >= "2015"),
+      Row(Date.valueOf("2015-01-01")) :: Nil)
+  }
 
   test("date format") {
     val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
@@ -83,39 +112,27 @@ class DateExpressionsSuite extends QueryTest {
       Row(4, 4, 4))
   }
 
-  test("day") {
-    val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
-
-    checkAnswer(
-      df.select(day("a"), day("b"), day("c")),
-      Row(8, 8, 8))
-
-    checkAnswer(
-      df.selectExpr("day(a)", "day(b)", "day(c)"),
-      Row(8, 8, 8))
-  }
-
-  test("day of month") {
+  test("dayofmonth") {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(day_of_month("a"), day_of_month("b"), day_of_month("c")),
+      df.select(dayofmonth("a"), dayofmonth("b"), dayofmonth("c")),
       Row(8, 8, 8))
 
     checkAnswer(
-      df.selectExpr("day_of_month(a)", "day_of_month(b)", "day_of_month(c)"),
+      df.selectExpr("day(a)", "day(b)", "dayofmonth(c)"),
       Row(8, 8, 8))
   }
 
-  test("day in year") {
+  test("dayofyear") {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(day_in_year("a"), day_in_year("b"), day_in_year("c")),
+      df.select(dayofyear("a"), dayofyear("b"), dayofyear("c")),
       Row(98, 98, 98))
 
     checkAnswer(
-      df.selectExpr("day_in_year(a)", "day_in_year(b)", "day_in_year(c)"),
+      df.selectExpr("dayofyear(a)", "dayofyear(b)", "dayofyear(c)"),
       Row(98, 98, 98))
   }
 
@@ -155,15 +172,15 @@ class DateExpressionsSuite extends QueryTest {
       Row(0, 15, 15))
   }
 
-  test("week of year") {
+  test("weekofyear") {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(week_of_year("a"), week_of_year("b"), week_of_year("c")),
+      df.select(weekofyear("a"), weekofyear("b"), weekofyear("c")),
       Row(15, 15, 15))
 
     checkAnswer(
-      df.selectExpr("week_of_year(a)", "week_of_year(b)", "week_of_year(c)"),
+      df.selectExpr("weekofyear(a)", "weekofyear(b)", "weekofyear(c)"),
       Row(15, 15, 15))
   }
 

From bc24289f5d54e4ff61cd75a5941338c9d946ff73 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sun, 19 Jul 2015 17:37:25 +0800
Subject: [PATCH 0473/1454] [SPARK-9179] [BUILD] Allows committers to specify
 primary author of the PR to be merged

It's a common case that some contributor contributes an initial version of a feature/bugfix, and later on some other people (mostly committers) fork and add more improvements. When merging these PRs, we probably want to specify the original author as the primary author. Currently we can only do this by running

```
$ git commit --amend --author="name <email>"
```

manually right before the merge script pushes to Apache Git repo. It would be nice if the script accepts user specified primary author information.

Author: Cheng Lian <lian@databricks.com>

Closes #7508 from liancheng/spark-9179 and squashes the following commits:

218d88e [Cheng Lian] Allows committers to specify primary author of the PR to be merged
---
 dev/merge_spark_pr.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index 4a17d48d8171d..d586a57481aa1 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -130,7 +130,10 @@ def merge_pr(pr_num, target_ref, title, body, pr_repo_desc):
                              '--pretty=format:%an <%ae>']).split("\n")
     distinct_authors = sorted(set(commit_authors),
                               key=lambda x: commit_authors.count(x), reverse=True)
-    primary_author = distinct_authors[0]
+    primary_author = raw_input(
+        "Enter primary author in the format of \"name <email>\" [%s]: " %
+        distinct_authors[0])
+
     commits = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name,
                       '--pretty=format:%h [%an] %s']).split("\n\n")
 
@@ -281,7 +284,7 @@ def get_version_json(version_str):
     resolve = filter(lambda a: a['name'] == "Resolve Issue", asf_jira.transitions(jira_id))[0]
     resolution = filter(lambda r: r.raw['name'] == "Fixed", asf_jira.resolutions())[0]
     asf_jira.transition_issue(
-        jira_id, resolve["id"], fixVersions = jira_fix_versions, 
+        jira_id, resolve["id"], fixVersions = jira_fix_versions,
         comment = comment, resolution = {'id': resolution.raw['id']})
 
     print "Successfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions)
@@ -300,7 +303,7 @@ def standardize_jira_ref(text):
     """
     Standardize the [SPARK-XXXXX] [MODULE] prefix
     Converts "[SPARK-XXX][mllib] Issue", "[MLLib] SPARK-XXX. Issue" or "SPARK XXX [MLLIB]: Issue" to "[SPARK-XXX] [MLLIB] Issue"
-    
+
     >>> standardize_jira_ref("[SPARK-5821] [SQL] ParquetRelation2 CTAS should check if delete is successful")
     '[SPARK-5821] [SQL] ParquetRelation2 CTAS should check if delete is successful'
     >>> standardize_jira_ref("[SPARK-4123][Project Infra][WIP]: Show new dependencies added in pull requests")
@@ -322,11 +325,11 @@ def standardize_jira_ref(text):
     """
     jira_refs = []
     components = []
-    
+
     # If the string is compliant, no need to process any further
     if (re.search(r'^\[SPARK-[0-9]{3,6}\] (\[[A-Z0-9_\s,]+\] )+\S+', text)):
         return text
-    
+
     # Extract JIRA ref(s):
     pattern = re.compile(r'(SPARK[-\s]*[0-9]{3,6})+', re.IGNORECASE)
     for ref in pattern.findall(text):
@@ -348,18 +351,18 @@ def standardize_jira_ref(text):
 
     # Assemble full text (JIRA ref(s), module(s), remaining text)
     clean_text = ' '.join(jira_refs).strip() + " " + ' '.join(components).strip() + " " + text.strip()
-    
+
     # Replace multiple spaces with a single space, e.g. if no jira refs and/or components were included
     clean_text = re.sub(r'\s+', ' ', clean_text.strip())
-    
+
     return clean_text
 
 def main():
     global original_head
-    
+
     os.chdir(SPARK_HOME)
     original_head = run_cmd("git rev-parse HEAD")[:8]
-    
+
     branches = get_json("%s/branches" % GITHUB_API_BASE)
     branch_names = filter(lambda x: x.startswith("branch-"), [x['name'] for x in branches])
     # Assumes branch names can be sorted lexicographically
@@ -448,5 +451,5 @@ def main():
     (failure_count, test_count) = doctest.testmod()
     if failure_count:
         exit(-1)
-    
+
     main()

From 34ed82bb44c4519819695ddc760e6c9a98bc2e40 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sun, 19 Jul 2015 18:58:19 +0800
Subject: [PATCH 0474/1454] [HOTFIX] [SQL] Fixes compilation error introduced
 by PR #7506

PR #7506 breaks master build because of compilation error. Note that #7506 itself looks good, but it seems that `git merge` did something stupid.

Author: Cheng Lian <lian@databricks.com>

Closes #7510 from liancheng/hotfix-for-pr-7506 and squashes the following commits:

7ea7e89 [Cheng Lian] Fixes compilation error
---
 .../spark/sql/catalyst/expressions/DateExpressionsSuite.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index f01589c58ea86..f724bab4d8839 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -75,7 +75,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -87,7 +87,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayInYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
             sdfDay.format(c.getTime).toInt)
         }
       }

From a803ac3e060d181c7b34d9501c9350e5f215ba85 Mon Sep 17 00:00:00 2001
From: Nicholas Hwang <moogling@gmail.com>
Date: Sun, 19 Jul 2015 10:30:28 -0700
Subject: [PATCH 0475/1454] [SPARK-9021] [PYSPARK] Change RDD.aggregate() to do
 reduce(mapPartitions()) instead of mapPartitions.fold()

I'm relatively new to Spark and functional programming, so forgive me if this pull request is just a result of my misunderstanding of how Spark should be used.

Currently, if one happens to use a mutable object as `zeroValue` for `RDD.aggregate()`, possibly unexpected behavior can occur.

This is because pyspark's current implementation of `RDD.aggregate()` does not serialize or make a copy of `zeroValue` before handing it off to `RDD.mapPartitions(...).fold(...)`. This results in a single reference to `zeroValue` being used for both `RDD.mapPartitions()` and `RDD.fold()` on each partition. This can result in strange accumulator values being fed into each partition's call to `RDD.fold()`, as the `zeroValue` may have been changed in-place during the `RDD.mapPartitions()` call.

As an illustrative example, submit the following to `spark-submit`:
```
from pyspark import SparkConf, SparkContext
import collections

def updateCounter(acc, val):
    print 'update acc:', acc
    print 'update val:', val
    acc[val] += 1
    return acc

def comboCounter(acc1, acc2):
    print 'combo acc1:', acc1
    print 'combo acc2:', acc2
    acc1.update(acc2)
    return acc1

def main():
    conf = SparkConf().setMaster("local").setAppName("Aggregate with Counter")
    sc = SparkContext(conf = conf)

    print '======= AGGREGATING with ONE PARTITION ======='
    print sc.parallelize(range(1,10), 1).aggregate(collections.Counter(), updateCounter, comboCounter)

    print '======= AGGREGATING with TWO PARTITIONS ======='
    print sc.parallelize(range(1,10), 2).aggregate(collections.Counter(), updateCounter, comboCounter)

if __name__ == "__main__":
    main()
```

One probably expects this to output the following:
```
Counter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1})
```

But it instead outputs this (regardless of the number of partitions):
```
Counter({1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2, 7: 2, 8: 2, 9: 2})
```

This is because (I believe) `zeroValue` gets passed correctly to each partition, but after `RDD.mapPartitions()` completes, the `zeroValue` object has been updated and is then passed to `RDD.fold()`, which results in all items being double-counted within each partition before being finally reduced at the calling node.

I realize that this type of calculation is typically done by `RDD.mapPartitions(...).reduceByKey(...)`, but hopefully this illustrates some potentially confusing behavior. I also noticed that other `RDD` methods use this `deepcopy` approach to creating unique copies of `zeroValue` (i.e., `RDD.aggregateByKey()` and `RDD.foldByKey()`), and that the Scala implementations do seem to serialize the `zeroValue` object appropriately to prevent this type of behavior.

Author: Nicholas Hwang <moogling@gmail.com>

Closes #7378 from njhwang/master and squashes the following commits:

659bb27 [Nicholas Hwang] Fixed RDD.aggregate() to perform a reduce operation on collected mapPartitions results, similar to how fold currently is implemented. This prevents an initial combOp being performed on each partition with zeroValue (which leads to unexpected behavior if zeroValue is a mutable object) before being combOp'ed with other partition results.
8d8d694 [Nicholas Hwang] Changed dict construction to be compatible with Python 2.6 (cannot use list comprehensions to make dicts)
56eb2ab [Nicholas Hwang] Fixed whitespace after colon to conform with PEP8
391de4a [Nicholas Hwang] Removed used of collections.Counter from RDD tests for Python 2.6 compatibility; used defaultdict(int) instead. Merged treeAggregate test with mutable zero value into aggregate test to reduce code duplication.
2fa4e4b [Nicholas Hwang] Merge branch 'master' of https://github.com/njhwang/spark
ba528bd [Nicholas Hwang] Updated comments regarding protection of zeroValue from mutation in RDD.aggregate(). Added regression tests for aggregate(), fold(), aggregateByKey(), foldByKey(), and treeAggregate(), all with both 1 and 2 partition RDDs. Confirmed that aggregate() is the only problematic implementation as of commit 257236c3e17906098f801cbc2059e7a9054e8cab. Also replaced some parallelizations of ranges with xranges, per the documentation's recommendations of preferring xrange over range.
7820391 [Nicholas Hwang] Updated comments regarding protection of zeroValue from mutation in RDD.aggregate(). Added regression tests for aggregate(), fold(), aggregateByKey(), foldByKey(), and treeAggregate(), all with both 1 and 2 partition RDDs. Confirmed that aggregate() is the only problematic implementation as of commit 257236c3e17906098f801cbc2059e7a9054e8cab.
90d1544 [Nicholas Hwang] Made sure RDD.aggregate() makes a deepcopy of zeroValue for all partitions; this ensures that the mapPartitions call works with unique copies of zeroValue in each partition, and prevents a single reference to zeroValue being used for both map and fold calls on each partition (resulting in possibly unexpected behavior).
---
 python/pyspark/rdd.py   |  10 ++-
 python/pyspark/tests.py | 141 ++++++++++++++++++++++++++++++++++++----
 2 files changed, 137 insertions(+), 14 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 3218bed5c74fc..7e788148d981c 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -862,6 +862,9 @@ def func(iterator):
             for obj in iterator:
                 acc = op(obj, acc)
             yield acc
+        # collecting result of mapPartitions here ensures that the copy of
+        # zeroValue provided to each partition is unique from the one provided
+        # to the final reduce call
         vals = self.mapPartitions(func).collect()
         return reduce(op, vals, zeroValue)
 
@@ -891,8 +894,11 @@ def func(iterator):
             for obj in iterator:
                 acc = seqOp(acc, obj)
             yield acc
-
-        return self.mapPartitions(func).fold(zeroValue, combOp)
+        # collecting result of mapPartitions here ensures that the copy of
+        # zeroValue provided to each partition is unique from the one provided
+        # to the final reduce call
+        vals = self.mapPartitions(func).collect()
+        return reduce(combOp, vals, zeroValue)
 
     def treeAggregate(self, zeroValue, seqOp, combOp, depth=2):
         """
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 21225016805bc..5be9937cb04b2 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -529,10 +529,127 @@ def test_deleting_input_files(self):
 
     def test_sampling_default_seed(self):
         # Test for SPARK-3995 (default seed setting)
-        data = self.sc.parallelize(range(1000), 1)
+        data = self.sc.parallelize(xrange(1000), 1)
         subset = data.takeSample(False, 10)
         self.assertEqual(len(subset), 10)
 
+    def test_aggregate_mutable_zero_value(self):
+        # Test for SPARK-9021; uses aggregate and treeAggregate to build dict
+        # representing a counter of ints
+        # NOTE: dict is used instead of collections.Counter for Python 2.6
+        # compatibility
+        from collections import defaultdict
+
+        # Show that single or multiple partitions work
+        data1 = self.sc.range(10, numSlices=1)
+        data2 = self.sc.range(10, numSlices=2)
+
+        def seqOp(x, y):
+            x[y] += 1
+            return x
+
+        def comboOp(x, y):
+            for key, val in y.items():
+                x[key] += val
+            return x
+
+        counts1 = data1.aggregate(defaultdict(int), seqOp, comboOp)
+        counts2 = data2.aggregate(defaultdict(int), seqOp, comboOp)
+        counts3 = data1.treeAggregate(defaultdict(int), seqOp, comboOp, 2)
+        counts4 = data2.treeAggregate(defaultdict(int), seqOp, comboOp, 2)
+
+        ground_truth = defaultdict(int, dict((i, 1) for i in range(10)))
+        self.assertEqual(counts1, ground_truth)
+        self.assertEqual(counts2, ground_truth)
+        self.assertEqual(counts3, ground_truth)
+        self.assertEqual(counts4, ground_truth)
+
+    def test_aggregate_by_key_mutable_zero_value(self):
+        # Test for SPARK-9021; uses aggregateByKey to make a pair RDD that
+        # contains lists of all values for each key in the original RDD
+
+        # list(range(...)) for Python 3.x compatibility (can't use * operator
+        # on a range object)
+        # list(zip(...)) for Python 3.x compatibility (want to parallelize a
+        # collection, not a zip object)
+        tuples = list(zip(list(range(10))*2, [1]*20))
+        # Show that single or multiple partitions work
+        data1 = self.sc.parallelize(tuples, 1)
+        data2 = self.sc.parallelize(tuples, 2)
+
+        def seqOp(x, y):
+            x.append(y)
+            return x
+
+        def comboOp(x, y):
+            x.extend(y)
+            return x
+
+        values1 = data1.aggregateByKey([], seqOp, comboOp).collect()
+        values2 = data2.aggregateByKey([], seqOp, comboOp).collect()
+        # Sort lists to ensure clean comparison with ground_truth
+        values1.sort()
+        values2.sort()
+
+        ground_truth = [(i, [1]*2) for i in range(10)]
+        self.assertEqual(values1, ground_truth)
+        self.assertEqual(values2, ground_truth)
+
+    def test_fold_mutable_zero_value(self):
+        # Test for SPARK-9021; uses fold to merge an RDD of dict counters into
+        # a single dict
+        # NOTE: dict is used instead of collections.Counter for Python 2.6
+        # compatibility
+        from collections import defaultdict
+
+        counts1 = defaultdict(int, dict((i, 1) for i in range(10)))
+        counts2 = defaultdict(int, dict((i, 1) for i in range(3, 8)))
+        counts3 = defaultdict(int, dict((i, 1) for i in range(4, 7)))
+        counts4 = defaultdict(int, dict((i, 1) for i in range(5, 6)))
+        all_counts = [counts1, counts2, counts3, counts4]
+        # Show that single or multiple partitions work
+        data1 = self.sc.parallelize(all_counts, 1)
+        data2 = self.sc.parallelize(all_counts, 2)
+
+        def comboOp(x, y):
+            for key, val in y.items():
+                x[key] += val
+            return x
+
+        fold1 = data1.fold(defaultdict(int), comboOp)
+        fold2 = data2.fold(defaultdict(int), comboOp)
+
+        ground_truth = defaultdict(int)
+        for counts in all_counts:
+            for key, val in counts.items():
+                ground_truth[key] += val
+        self.assertEqual(fold1, ground_truth)
+        self.assertEqual(fold2, ground_truth)
+
+    def test_fold_by_key_mutable_zero_value(self):
+        # Test for SPARK-9021; uses foldByKey to make a pair RDD that contains
+        # lists of all values for each key in the original RDD
+
+        tuples = [(i, range(i)) for i in range(10)]*2
+        # Show that single or multiple partitions work
+        data1 = self.sc.parallelize(tuples, 1)
+        data2 = self.sc.parallelize(tuples, 2)
+
+        def comboOp(x, y):
+            x.extend(y)
+            return x
+
+        values1 = data1.foldByKey([], comboOp).collect()
+        values2 = data2.foldByKey([], comboOp).collect()
+        # Sort lists to ensure clean comparison with ground_truth
+        values1.sort()
+        values2.sort()
+
+        # list(range(...)) for Python 3.x compatibility
+        ground_truth = [(i, list(range(i))*2) for i in range(10)]
+        self.assertEqual(values1, ground_truth)
+        self.assertEqual(values2, ground_truth)
+
     def test_aggregate_by_key(self):
         data = self.sc.parallelize([(1, 1), (1, 1), (3, 2), (5, 1), (5, 3)], 2)
 
@@ -624,8 +741,8 @@ def test_zip_with_different_serializers(self):
 
     def test_zip_with_different_object_sizes(self):
         # regress test for SPARK-5973
-        a = self.sc.parallelize(range(10000)).map(lambda i: '*' * i)
-        b = self.sc.parallelize(range(10000, 20000)).map(lambda i: '*' * i)
+        a = self.sc.parallelize(xrange(10000)).map(lambda i: '*' * i)
+        b = self.sc.parallelize(xrange(10000, 20000)).map(lambda i: '*' * i)
         self.assertEqual(10000, a.zip(b).count())
 
     def test_zip_with_different_number_of_items(self):
@@ -647,7 +764,7 @@ def test_zip_with_different_number_of_items(self):
             self.assertRaises(Exception, lambda: a.zip(b).count())
 
     def test_count_approx_distinct(self):
-        rdd = self.sc.parallelize(range(1000))
+        rdd = self.sc.parallelize(xrange(1000))
         self.assertTrue(950 < rdd.countApproxDistinct(0.03) < 1050)
         self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.03) < 1050)
         self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.03) < 1050)
@@ -777,7 +894,7 @@ def test_distinct(self):
     def test_external_group_by_key(self):
         self.sc._conf.set("spark.python.worker.memory", "1m")
         N = 200001
-        kv = self.sc.parallelize(range(N)).map(lambda x: (x % 3, x))
+        kv = self.sc.parallelize(xrange(N)).map(lambda x: (x % 3, x))
         gkv = kv.groupByKey().cache()
         self.assertEqual(3, gkv.count())
         filtered = gkv.filter(lambda kv: kv[0] == 1)
@@ -871,7 +988,7 @@ def test_narrow_dependency_in_join(self):
 
     # Regression test for SPARK-6294
     def test_take_on_jrdd(self):
-        rdd = self.sc.parallelize(range(1 << 20)).map(lambda x: str(x))
+        rdd = self.sc.parallelize(xrange(1 << 20)).map(lambda x: str(x))
         rdd._jrdd.first()
 
     def test_sortByKey_uses_all_partitions_not_only_first_and_last(self):
@@ -1517,13 +1634,13 @@ def run():
             self.fail("daemon had been killed")
 
         # run a normal job
-        rdd = self.sc.parallelize(range(100), 1)
+        rdd = self.sc.parallelize(xrange(100), 1)
         self.assertEqual(100, rdd.map(str).count())
 
     def test_after_exception(self):
         def raise_exception(_):
             raise Exception()
-        rdd = self.sc.parallelize(range(100), 1)
+        rdd = self.sc.parallelize(xrange(100), 1)
         with QuietTest(self.sc):
             self.assertRaises(Exception, lambda: rdd.foreach(raise_exception))
         self.assertEqual(100, rdd.map(str).count())
@@ -1539,22 +1656,22 @@ def test_after_jvm_exception(self):
         with QuietTest(self.sc):
             self.assertRaises(Exception, lambda: filtered_data.count())
 
-        rdd = self.sc.parallelize(range(100), 1)
+        rdd = self.sc.parallelize(xrange(100), 1)
         self.assertEqual(100, rdd.map(str).count())
 
     def test_accumulator_when_reuse_worker(self):
         from pyspark.accumulators import INT_ACCUMULATOR_PARAM
         acc1 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
-        self.sc.parallelize(range(100), 20).foreach(lambda x: acc1.add(x))
+        self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc1.add(x))
         self.assertEqual(sum(range(100)), acc1.value)
 
         acc2 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
-        self.sc.parallelize(range(100), 20).foreach(lambda x: acc2.add(x))
+        self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc2.add(x))
         self.assertEqual(sum(range(100)), acc2.value)
         self.assertEqual(sum(range(100)), acc1.value)
 
     def test_reuse_worker_after_take(self):
-        rdd = self.sc.parallelize(range(100000), 1)
+        rdd = self.sc.parallelize(xrange(100000), 1)
         self.assertEqual(0, rdd.first())
 
         def count():

From 7a81245345f2d6124423161786bb0d9f1c278ab8 Mon Sep 17 00:00:00 2001
From: Herman van Hovell <hvanhovell@questtec.nl>
Date: Sun, 19 Jul 2015 16:29:50 -0700
Subject: [PATCH 0476/1454] [SPARK-8638] [SQL] Window Function Performance
 Improvements - Cleanup

This PR contains a few clean-ups that are a part of SPARK-8638: a few style issues got fixed, and a few tests were moved.

Git commit message is wrong BTW :(...

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #7513 from hvanhovell/SPARK-8638-cleanup and squashes the following commits:

4e69d08 [Herman van Hovell] Fixed Perfomance Regression for Shrinking Window Frames (+Rebase)
---
 .../apache/spark/sql/execution/Window.scala   | 14 ++--
 .../sql/hive/HiveDataFrameWindowSuite.scala   | 43 ++++++++++
 .../sql/hive/execution/WindowSuite.scala      | 79 -------------------
 3 files changed, 51 insertions(+), 85 deletions(-)
 delete mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
index a054f52b8b489..de04132eb1104 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -118,22 +118,24 @@ case class Window(
           val exprs = windowSpec.orderSpec.map(_.child)
           val projection = newMutableProjection(exprs, child.output)
           (windowSpec.orderSpec, projection(), projection())
-        }
-        else if (windowSpec.orderSpec.size == 1) {
+        } else if (windowSpec.orderSpec.size == 1) {
           // Use only the first order expression when the offset is non-null.
           val sortExpr = windowSpec.orderSpec.head
           val expr = sortExpr.child
           // Create the projection which returns the current 'value'.
           val current = newMutableProjection(expr :: Nil, child.output)()
           // Flip the sign of the offset when processing the order is descending
-          val boundOffset = if (sortExpr.direction == Descending) -offset
-          else offset
+          val boundOffset =
+            if (sortExpr.direction == Descending) {
+              -offset
+            } else {
+              offset
+            }
           // Create the projection which returns the current 'value' modified by adding the offset.
           val boundExpr = Add(expr, Cast(Literal.create(boundOffset, IntegerType), expr.dataType))
           val bound = newMutableProjection(boundExpr :: Nil, child.output)()
           (sortExpr :: Nil, current, bound)
-        }
-        else {
+        } else {
           sys.error("Non-Zero range offsets are not supported for windows " +
             "with multiple order expressions.")
         }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
index 15b5f418f0a8c..c177cbdd991cf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
@@ -212,4 +212,47 @@ class HiveDataFrameWindowSuite extends QueryTest {
           |   (PARTITION BY value ORDER BY key RANGE BETWEEN 1 preceding and current row)
           | FROM window_table""".stripMargin).collect())
   }
+
+  test("reverse sliding range frame") {
+    val df = Seq(
+      (1, "Thin", "Cell Phone", 6000),
+      (2, "Normal", "Tablet", 1500),
+      (3, "Mini", "Tablet", 5500),
+      (4, "Ultra thin", "Cell Phone", 5500),
+      (5, "Very thin", "Cell Phone", 6000),
+      (6, "Big", "Tablet", 2500),
+      (7, "Bendable", "Cell Phone", 3000),
+      (8, "Foldable", "Cell Phone", 3000),
+      (9, "Pro", "Tablet", 4500),
+      (10, "Pro2", "Tablet", 6500)).
+      toDF("id", "product", "category", "revenue")
+    val window = Window.
+      partitionBy($"category").
+      orderBy($"revenue".desc).
+      rangeBetween(-2000L, 1000L)
+    checkAnswer(
+      df.select(
+        $"id",
+        avg($"revenue").over(window).cast("int")),
+      Row(1, 5833) :: Row(2, 2000) :: Row(3, 5500) ::
+        Row(4, 5833) :: Row(5, 5833) :: Row(6, 2833) ::
+        Row(7, 3000) :: Row(8, 3000) :: Row(9, 5500) ::
+        Row(10, 6000) :: Nil)
+  }
+
+  // This is here to illustrate the fact that reverse order also reverses offsets.
+  test("reverse unbounded range frame") {
+    val df = Seq(1, 2, 4, 3, 2, 1).
+      map(Tuple1.apply).
+      toDF("value")
+    val window = Window.orderBy($"value".desc)
+    checkAnswer(
+      df.select(
+        $"value",
+        sum($"value").over(window.rangeBetween(Long.MinValue, 1)),
+        sum($"value").over(window.rangeBetween(1, Long.MaxValue))),
+      Row(1, 13, null) :: Row(2, 13, 2) :: Row(4, 7, 9) ::
+        Row(3, 11, 6) :: Row(2, 13, 2) :: Row(1, 13, null) :: Nil)
+
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala
deleted file mode 100644
index a089d0d165195..0000000000000
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/WindowSuite.scala
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.execution
-
-import org.apache.spark.sql.{Row, QueryTest}
-import org.apache.spark.sql.expressions.Window
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
-
-/**
- * Window expressions are tested extensively by the following test suites:
- * [[org.apache.spark.sql.hive.HiveDataFrameWindowSuite]]
- * [[org.apache.spark.sql.hive.execution.HiveWindowFunctionQueryWithoutCodeGenSuite]]
- * [[org.apache.spark.sql.hive.execution.HiveWindowFunctionQueryFileWithoutCodeGenSuite]]
- * However these suites do not cover all possible (i.e. more exotic) settings. This suite fill
- * this gap.
- *
- * TODO Move this class to the sql/core project when we move to Native Spark UDAFs.
- */
-class WindowSuite extends QueryTest {
-
-  test("reverse sliding range frame") {
-    val df = Seq(
-      (1, "Thin", "Cell Phone", 6000),
-      (2, "Normal", "Tablet", 1500),
-      (3, "Mini", "Tablet", 5500),
-      (4, "Ultra thin", "Cell Phone", 5500),
-      (5, "Very thin", "Cell Phone", 6000),
-      (6, "Big", "Tablet", 2500),
-      (7, "Bendable", "Cell Phone", 3000),
-      (8, "Foldable", "Cell Phone", 3000),
-      (9, "Pro", "Tablet", 4500),
-      (10, "Pro2", "Tablet", 6500)).
-      toDF("id", "product", "category", "revenue")
-    val window = Window.
-      partitionBy($"category").
-      orderBy($"revenue".desc).
-      rangeBetween(-2000L, 1000L)
-    checkAnswer(
-      df.select(
-        $"id",
-        avg($"revenue").over(window).cast("int")),
-      Row(1, 5833) :: Row(2, 2000) :: Row(3, 5500) ::
-      Row(4, 5833) :: Row(5, 5833) :: Row(6, 2833) ::
-      Row(7, 3000) :: Row(8, 3000) :: Row(9, 5500) ::
-      Row(10, 6000) :: Nil)
-  }
-
-  // This is here to illustrate the fact that reverse order also reverses offsets.
-  test("reverse unbounded range frame") {
-    val df = Seq(1, 2, 4, 3, 2, 1).
-      map(Tuple1.apply).
-      toDF("value")
-    val window = Window.orderBy($"value".desc)
-    checkAnswer(
-      df.select(
-        $"value",
-         sum($"value").over(window.rangeBetween(Long.MinValue, 1)),
-         sum($"value").over(window.rangeBetween(1, Long.MaxValue))),
-      Row(1, 13, null) :: Row(2, 13, 2) :: Row(4, 7, 9) ::
-        Row(3, 11, 6) :: Row(2, 13, 2) :: Row(1, 13, null) :: Nil)
-
-  }
-}

From 163e3f1df94f6b7d3dadb46a87dbb3a2bade3f95 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 19 Jul 2015 16:48:47 -0700
Subject: [PATCH 0477/1454] [SPARK-8241][SQL] string function: concat_ws.

I also changed the semantics of concat w.r.t. null back to the same behavior as Hive.
That is to say, concat now returns null if any input is null.

Author: Reynold Xin <rxin@databricks.com>

Closes #7504 from rxin/concat_ws and squashes the following commits:

83fd950 [Reynold Xin] Fixed type casting.
3ae85f7 [Reynold Xin] Write null better.
cdc7be6 [Reynold Xin] Added code generation for pure string mode.
a61c4e4 [Reynold Xin] Updated comments.
2d51406 [Reynold Xin] [SPARK-8241][SQL] string function: concat_ws.
---
 .../catalyst/analysis/FunctionRegistry.scala  | 11 ++-
 .../expressions/stringOperations.scala        | 72 ++++++++++++++++---
 .../org/apache/spark/sql/types/DataType.scala |  2 +-
 .../analysis/HiveTypeCoercionSuite.scala      | 11 ++-
 .../expressions/StringExpressionsSuite.scala  | 31 +++++++-
 .../org/apache/spark/sql/functions.scala      | 24 +++++++
 .../spark/sql/StringFunctionsSuite.scala      | 19 +++--
 .../execution/HiveCompatibilitySuite.scala    |  4 +-
 .../apache/spark/unsafe/types/UTF8String.java | 58 +++++++++++++--
 .../spark/unsafe/types/UTF8StringSuite.java   | 62 ++++++++++++----
 10 files changed, 256 insertions(+), 38 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 4b256adcc60c6..71e87b98d86fc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -153,6 +153,7 @@ object FunctionRegistry {
     expression[Ascii]("ascii"),
     expression[Base64]("base64"),
     expression[Concat]("concat"),
+    expression[ConcatWs]("concat_ws"),
     expression[Encode]("encode"),
     expression[Decode]("decode"),
     expression[FormatNumber]("format_number"),
@@ -211,7 +212,10 @@ object FunctionRegistry {
     val builder = (expressions: Seq[Expression]) => {
       if (varargCtor.isDefined) {
         // If there is an apply method that accepts Seq[Expression], use that one.
-        varargCtor.get.newInstance(expressions).asInstanceOf[Expression]
+        Try(varargCtor.get.newInstance(expressions).asInstanceOf[Expression]) match {
+          case Success(e) => e
+          case Failure(e) => throw new AnalysisException(e.getMessage)
+        }
       } else {
         // Otherwise, find an ctor method that matches the number of arguments, and use that.
         val params = Seq.fill(expressions.size)(classOf[Expression])
@@ -221,7 +225,10 @@ object FunctionRegistry {
           case Failure(e) =>
             throw new AnalysisException(s"Invalid number of arguments for function $name")
         }
-        f.newInstance(expressions : _*).asInstanceOf[Expression]
+        Try(f.newInstance(expressions : _*).asInstanceOf[Expression]) match {
+          case Success(e) => e
+          case Failure(e) => throw new AnalysisException(e.getMessage)
+        }
       }
     }
     (name, builder)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 560b1bc2d889f..5f8ac716f79a1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -34,19 +34,14 @@ import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * An expression that concatenates multiple input strings into a single string.
- * Input expressions that are evaluated to nulls are skipped.
- *
- * For example, `concat("a", null, "b")` is evaluated to `"ab"`.
- *
- * Note that this is different from Hive since Hive outputs null if any input is null.
- * We never output null.
+ * If any input is null, concat returns null.
  */
 case class Concat(children: Seq[Expression]) extends Expression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] = Seq.fill(children.size)(StringType)
   override def dataType: DataType = StringType
 
-  override def nullable: Boolean = false
+  override def nullable: Boolean = children.exists(_.nullable)
   override def foldable: Boolean = children.forall(_.foldable)
 
   override def eval(input: InternalRow): Any = {
@@ -56,15 +51,76 @@ case class Concat(children: Seq[Expression]) extends Expression with ImplicitCas
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val evals = children.map(_.gen(ctx))
-    val inputs = evals.map { eval => s"${eval.isNull} ? null : ${eval.primitive}" }.mkString(", ")
+    val inputs = evals.map { eval =>
+      s"${eval.isNull} ? (UTF8String)null : ${eval.primitive}"
+    }.mkString(", ")
     evals.map(_.code).mkString("\n") + s"""
       boolean ${ev.isNull} = false;
       UTF8String ${ev.primitive} = UTF8String.concat($inputs);
+      if (${ev.primitive} == null) {
+        ${ev.isNull} = true;
+      }
     """
   }
 }
 
 
+/**
+ * An expression that concatenates multiple input strings or array of strings into a single string,
+ * using a given separator (the first child).
+ *
+ * Returns null if the separator is null. Otherwise, concat_ws skips all null values.
+ */
+case class ConcatWs(children: Seq[Expression])
+  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+
+  require(children.nonEmpty, s"$prettyName requires at least one argument.")
+
+  override def prettyName: String = "concat_ws"
+
+  /** The 1st child (separator) is str, and rest are either str or array of str. */
+  override def inputTypes: Seq[AbstractDataType] = {
+    val arrayOrStr = TypeCollection(ArrayType(StringType), StringType)
+    StringType +: Seq.fill(children.size - 1)(arrayOrStr)
+  }
+
+  override def dataType: DataType = StringType
+
+  override def nullable: Boolean = children.head.nullable
+  override def foldable: Boolean = children.forall(_.foldable)
+
+  override def eval(input: InternalRow): Any = {
+    val flatInputs = children.flatMap { child =>
+      child.eval(input) match {
+        case s: UTF8String => Iterator(s)
+        case arr: Seq[_] => arr.asInstanceOf[Seq[UTF8String]]
+        case null => Iterator(null.asInstanceOf[UTF8String])
+      }
+    }
+    UTF8String.concatWs(flatInputs.head, flatInputs.tail : _*)
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    if (children.forall(_.dataType == StringType)) {
+      // All children are strings. In that case we can construct a fixed size array.
+      val evals = children.map(_.gen(ctx))
+
+      val inputs = evals.map { eval =>
+        s"${eval.isNull} ? (UTF8String)null : ${eval.primitive}"
+      }.mkString(", ")
+
+      evals.map(_.code).mkString("\n") + s"""
+        UTF8String ${ev.primitive} = UTF8String.concatWs($inputs);
+        boolean ${ev.isNull} = ${ev.primitive} == null;
+      """
+    } else {
+      // Contains a mix of strings and array<string>s. Fall back to interpreted mode for now.
+      super.genCode(ctx, ev)
+    }
+  }
+}
+
+
 trait StringRegexExpression extends ImplicitCastInputTypes {
   self: BinaryExpression =>
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index 2d133eea19fe0..e98fd2583b931 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -79,7 +79,7 @@ abstract class DataType extends AbstractDataType {
 
   override private[sql] def defaultConcreteType: DataType = this
 
-  override private[sql] def acceptsType(other: DataType): Boolean = this == other
+  override private[sql] def acceptsType(other: DataType): Boolean = sameType(other)
 }
 
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index f9442bccc4a7a..7ee2333a81dfe 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -37,7 +37,6 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldCast(NullType, IntegerType, IntegerType)
     shouldCast(NullType, DecimalType, DecimalType.Unlimited)
 
-    // TODO: write the entire implicit cast table out for test cases.
     shouldCast(ByteType, IntegerType, IntegerType)
     shouldCast(IntegerType, IntegerType, IntegerType)
     shouldCast(IntegerType, LongType, LongType)
@@ -86,6 +85,16 @@ class HiveTypeCoercionSuite extends PlanTest {
       DecimalType.Unlimited, DecimalType(10, 2)).foreach { tpe =>
       shouldCast(tpe, NumericType, tpe)
     }
+
+    shouldCast(
+      ArrayType(StringType, false),
+      TypeCollection(ArrayType(StringType), StringType),
+      ArrayType(StringType, false))
+
+    shouldCast(
+      ArrayType(StringType, true),
+      TypeCollection(ArrayType(StringType), StringType),
+      ArrayType(StringType, true))
   }
 
   test("ineligible implicit type cast") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 0ed567a90dd1f..96f433be8b065 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -26,7 +26,7 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("concat") {
     def testConcat(inputs: String*): Unit = {
-      val expected = inputs.filter(_ != null).mkString
+      val expected = if (inputs.contains(null)) null else inputs.mkString
       checkEvaluation(Concat(inputs.map(Literal.create(_, StringType))), expected, EmptyRow)
     }
 
@@ -46,6 +46,35 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     // scalastyle:on
   }
 
+  test("concat_ws") {
+    def testConcatWs(expected: String, sep: String, inputs: Any*): Unit = {
+      val inputExprs = inputs.map {
+        case s: Seq[_] => Literal.create(s, ArrayType(StringType))
+        case null => Literal.create(null, StringType)
+        case s: String => Literal.create(s, StringType)
+      }
+      val sepExpr = Literal.create(sep, StringType)
+      checkEvaluation(ConcatWs(sepExpr +: inputExprs), expected, EmptyRow)
+    }
+
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    testConcatWs(null, null)
+    testConcatWs(null, null, "a", "b")
+    testConcatWs("", "")
+    testConcatWs("ab", "哈哈", "ab")
+    testConcatWs("a哈哈b", "哈哈", "a", "b")
+    testConcatWs("a哈哈b", "哈哈", "a", null, "b")
+    testConcatWs("a哈哈b哈哈c", "哈哈", null, "a", null, "b", "c")
+
+    testConcatWs("ab", "哈哈", Seq("ab"))
+    testConcatWs("a哈哈b", "哈哈", Seq("a", "b"))
+    testConcatWs("a哈哈b哈哈c哈哈d", "哈哈", Seq("a", null, "b"), null, "c", Seq(null, "d"))
+    testConcatWs("a哈哈b哈哈c", "哈哈", Seq("a", null, "b"), null, "c", Seq.empty[String])
+    testConcatWs("a哈哈b哈哈c", "哈哈", Seq("a", null, "b"), null, "c", Seq[String](null))
+    // scalastyle:on
+  }
+
   test("StringComparison") {
     val row = create_row("abc", null)
     val c1 = 'a.string.at(0)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index f67c89437bb4a..b5140dca0487f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1732,6 +1732,30 @@ object functions {
     concat((columnName +: columnNames).map(Column.apply): _*)
   }
 
+  /**
+   * Concatenates input strings together into a single string, using the given separator.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def concat_ws(sep: String, exprs: Column*): Column = {
+    ConcatWs(Literal.create(sep, StringType) +: exprs.map(_.expr))
+  }
+
+  /**
+   * Concatenates input strings together into a single string, using the given separator.
+   *
+   * This is the variant of concat_ws that takes in the column names.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def concat_ws(sep: String, columnName: String, columnNames: String*): Column = {
+    concat_ws(sep, (columnName +: columnNames).map(Column.apply) : _*)
+  }
+
   /**
    * Computes the length of a given string / binary value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 4eff33ed45042..fe4de8d8b855f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -30,14 +30,25 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq[(String, String, String)](("a", "b", null)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(concat($"a", $"b", $"c")),
-      Row("ab"))
+      df.select(concat($"a", $"b"), concat($"a", $"b", $"c")),
+      Row("ab", null))
 
     checkAnswer(
-      df.selectExpr("concat(a, b, c)"),
-      Row("ab"))
+      df.selectExpr("concat(a, b)", "concat(a, b, c)"),
+      Row("ab", null))
   }
 
+  test("string concat_ws") {
+    val df = Seq[(String, String, String)](("a", "b", null)).toDF("a", "b", "c")
+
+    checkAnswer(
+      df.select(concat_ws("||", $"a", $"b", $"c")),
+      Row("a||b"))
+
+    checkAnswer(
+      df.selectExpr("concat_ws('||', a, b, c)"),
+      Row("a||b"))
+  }
 
   test("string Levenshtein distance") {
     val df = Seq(("kitten", "sitting"), ("frog", "fog")).toDF("l", "r")
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 2689d904d6541..b12b3838e615c 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -263,9 +263,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "timestamp_2",
     "timestamp_udf",
 
-    // Hive outputs NULL if any concat input has null. We never output null for concat.
-    "udf_concat",
-
     // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
     "udf7"
   )
@@ -856,6 +853,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_case",
     "udf_ceil",
     "udf_ceiling",
+    "udf_concat",
     "udf_concat_insert1",
     "udf_concat_insert2",
     "udf_concat_ws",
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 9723b6e0834b2..3eecd657e6ef9 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -397,26 +397,62 @@ public UTF8String lpad(int len, UTF8String pad) {
   }
 
   /**
-   * Concatenates input strings together into a single string. A null input is skipped.
-   * For example, concat("a", null, "c") would yield "ac".
+   * Concatenates input strings together into a single string. Returns null if any input is null.
    */
   public static UTF8String concat(UTF8String... inputs) {
-    if (inputs == null) {
-      return fromBytes(new byte[0]);
-    }
-
     // Compute the total length of the result.
     int totalLength = 0;
     for (int i = 0; i < inputs.length; i++) {
       if (inputs[i] != null) {
         totalLength += inputs[i].numBytes;
+      } else {
+        return null;
       }
     }
 
     // Allocate a new byte array, and copy the inputs one by one into it.
     final byte[] result = new byte[totalLength];
     int offset = 0;
+    for (int i = 0; i < inputs.length; i++) {
+      int len = inputs[i].numBytes;
+      PlatformDependent.copyMemory(
+        inputs[i].base, inputs[i].offset,
+        result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+        len);
+      offset += len;
+    }
+    return fromBytes(result);
+  }
+
+  /**
+   * Concatenates input strings together into a single string using the separator.
+   * A null input is skipped. For example, concat(",", "a", null, "c") would yield "a,c".
+   */
+  public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
+    if (separator == null) {
+      return null;
+    }
+
+    int numInputBytes = 0;  // total number of bytes from the inputs
+    int numInputs = 0;      // number of non-null inputs
     for (int i = 0; i < inputs.length; i++) {
+      if (inputs[i] != null) {
+        numInputBytes += inputs[i].numBytes;
+        numInputs++;
+      }
+    }
+
+    if (numInputs == 0) {
+      // Return an empty string if there is no input, or all the inputs are null.
+      return fromBytes(new byte[0]);
+    }
+
+    // Allocate a new byte array, and copy the inputs one by one into it.
+    // The size of the new array is the size of all inputs, plus the separators.
+    final byte[] result = new byte[numInputBytes + (numInputs - 1) * separator.numBytes];
+    int offset = 0;
+
+    for (int i = 0, j = 0; i < inputs.length; i++) {
       if (inputs[i] != null) {
         int len = inputs[i].numBytes;
         PlatformDependent.copyMemory(
@@ -424,6 +460,16 @@ public static UTF8String concat(UTF8String... inputs) {
           result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
           len);
         offset += len;
+
+        j++;
+        // Add separator if this is not the last input.
+        if (j < numInputs) {
+          PlatformDependent.copyMemory(
+            separator.base, separator.offset,
+            result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+            separator.numBytes);
+          offset += separator.numBytes;
+        }
       }
     }
     return fromBytes(result);
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 0db7522b50c1a..7d0c49e2fb84c 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -88,16 +88,50 @@ public void upperAndLower() {
 
   @Test
   public void concatTest() {
-    assertEquals(concat(), fromString(""));
-    assertEquals(concat(null), fromString(""));
-    assertEquals(concat(fromString("")), fromString(""));
-    assertEquals(concat(fromString("ab")), fromString("ab"));
-    assertEquals(concat(fromString("a"), fromString("b")), fromString("ab"));
-    assertEquals(concat(fromString("a"), fromString("b"), fromString("c")), fromString("abc"));
-    assertEquals(concat(fromString("a"), null, fromString("c")), fromString("ac"));
-    assertEquals(concat(fromString("a"), null, null), fromString("a"));
-    assertEquals(concat(null, null, null), fromString(""));
-    assertEquals(concat(fromString("数据"), fromString("砖头")), fromString("数据砖头"));
+    assertEquals(fromString(""), concat());
+    assertEquals(null, concat((UTF8String) null));
+    assertEquals(fromString(""), concat(fromString("")));
+    assertEquals(fromString("ab"), concat(fromString("ab")));
+    assertEquals(fromString("ab"), concat(fromString("a"), fromString("b")));
+    assertEquals(fromString("abc"), concat(fromString("a"), fromString("b"), fromString("c")));
+    assertEquals(null, concat(fromString("a"), null, fromString("c")));
+    assertEquals(null, concat(fromString("a"), null, null));
+    assertEquals(null, concat(null, null, null));
+    assertEquals(fromString("数据砖头"), concat(fromString("数据"), fromString("砖头")));
+  }
+
+  @Test
+  public void concatWsTest() {
+    // Returns null if the separator is null
+    assertEquals(null, concatWs(null, (UTF8String)null));
+    assertEquals(null, concatWs(null, fromString("a")));
+
+    // If separator is null, concatWs should skip all null inputs and never return null.
+    UTF8String sep = fromString("哈哈");
+    assertEquals(
+      fromString(""),
+      concatWs(sep, fromString("")));
+    assertEquals(
+      fromString("ab"),
+      concatWs(sep, fromString("ab")));
+    assertEquals(
+      fromString("a哈哈b"),
+      concatWs(sep, fromString("a"), fromString("b")));
+    assertEquals(
+      fromString("a哈哈b哈哈c"),
+      concatWs(sep, fromString("a"), fromString("b"), fromString("c")));
+    assertEquals(
+      fromString("a哈哈c"),
+      concatWs(sep, fromString("a"), null, fromString("c")));
+    assertEquals(
+      fromString("a"),
+      concatWs(sep, fromString("a"), null, null));
+    assertEquals(
+      fromString(""),
+      concatWs(sep, null, null, null));
+    assertEquals(
+      fromString("数据哈哈砖头"),
+      concatWs(sep, fromString("数据"), fromString("砖头")));
   }
 
   @Test
@@ -215,14 +249,18 @@ public void pad() {
     assertEquals(fromString("??数据砖头"), fromString("数据砖头").lpad(6, fromString("????")));
     assertEquals(fromString("孙行数据砖头"), fromString("数据砖头").lpad(6, fromString("孙行者")));
     assertEquals(fromString("孙行者数据砖头"), fromString("数据砖头").lpad(7, fromString("孙行者")));
-    assertEquals(fromString("孙行者孙行者孙行数据砖头"), fromString("数据砖头").lpad(12, fromString("孙行者")));
+    assertEquals(
+      fromString("孙行者孙行者孙行数据砖头"),
+      fromString("数据砖头").lpad(12, fromString("孙行者")));
 
     assertEquals(fromString("数据砖"), fromString("数据砖头").rpad(3, fromString("????")));
     assertEquals(fromString("数据砖头?"), fromString("数据砖头").rpad(5, fromString("????")));
     assertEquals(fromString("数据砖头??"), fromString("数据砖头").rpad(6, fromString("????")));
     assertEquals(fromString("数据砖头孙行"), fromString("数据砖头").rpad(6, fromString("孙行者")));
     assertEquals(fromString("数据砖头孙行者"), fromString("数据砖头").rpad(7, fromString("孙行者")));
-    assertEquals(fromString("数据砖头孙行者孙行者孙行"), fromString("数据砖头").rpad(12, fromString("孙行者")));
+    assertEquals(
+      fromString("数据砖头孙行者孙行者孙行"),
+      fromString("数据砖头").rpad(12, fromString("孙行者")));
   }
   
   @Test

From 93eb2acfb287807355ba5d77989d239fdd6e2c30 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Sun, 19 Jul 2015 20:34:30 -0700
Subject: [PATCH 0478/1454] [SPARK-9030] [STREAMING] [HOTFIX] Make sure that no
 attempts to create Kinesis streams is made when no enabled

Problem: Even when the environment variable to enable tests are disabled, the `beforeAll` of the KinesisStreamSuite attempted to find AWS credentials to create Kinesis stream, and failed.

Solution: Made sure all accesses to KinesisTestUtils, that created streams, is under `testOrIgnore`

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7519 from tdas/kinesis-tests and squashes the following commits:

64d6d06 [Tathagata Das] Removed empty lines.
7c18473 [Tathagata Das] Putting all access to KinesisTestUtils inside testOrIgnore
---
 .../kinesis/KinesisStreamSuite.scala          | 57 +++++++++----------
 1 file changed, 26 insertions(+), 31 deletions(-)

diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
index d3dd541fe4371..50f71413abf37 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -33,8 +33,6 @@ import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
 class KinesisStreamSuite extends SparkFunSuite with KinesisSuiteHelper
   with Eventually with BeforeAndAfter with BeforeAndAfterAll {
 
-  private val kinesisTestUtils = new KinesisTestUtils()
-
   // This is the name that KCL uses to save metadata to DynamoDB
   private val kinesisAppName = s"KinesisStreamSuite-${math.abs(Random.nextLong())}"
 
@@ -42,7 +40,6 @@ class KinesisStreamSuite extends SparkFunSuite with KinesisSuiteHelper
   private var sc: SparkContext = _
 
   override def beforeAll(): Unit = {
-    kinesisTestUtils.createStream()
     val conf = new SparkConf()
       .setMaster("local[4]")
       .setAppName("KinesisStreamSuite") // Setting Spark app name to Kinesis app name
@@ -53,15 +50,6 @@ class KinesisStreamSuite extends SparkFunSuite with KinesisSuiteHelper
     sc.stop()
     // Delete the Kinesis stream as well as the DynamoDB table generated by
     // Kinesis Client Library when consuming the stream
-    kinesisTestUtils.deleteStream()
-    kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
-  }
-
-  before {
-    // Delete the DynamoDB table generated by Kinesis Client Library when
-    // consuming from the stream, so that each unit test can start from
-    // scratch without prior history of data consumption
-    kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
   }
 
   after {
@@ -96,25 +84,32 @@ class KinesisStreamSuite extends SparkFunSuite with KinesisSuiteHelper
    * and you have to set the system environment variable RUN_KINESIS_TESTS=1 .
    */
   testOrIgnore("basic operation") {
-    ssc = new StreamingContext(sc, Seconds(1))
-    val aWSCredentials = KinesisTestUtils.getAWSCredentials()
-    val stream = KinesisUtils.createStream(ssc, kinesisAppName, kinesisTestUtils.streamName,
-      kinesisTestUtils.endpointUrl, kinesisTestUtils.regionName, InitialPositionInStream.LATEST,
-      Seconds(10), StorageLevel.MEMORY_ONLY,
-      aWSCredentials.getAWSAccessKeyId, aWSCredentials.getAWSSecretKey)
-
-    val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
-    stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>
-      collected ++= rdd.collect()
-      logInfo("Collected = " + rdd.collect().toSeq.mkString(", "))
-    }
-    ssc.start()
-
-    val testData = 1 to 10
-    eventually(timeout(120 seconds), interval(10 second)) {
-      kinesisTestUtils.pushData(testData)
-      assert(collected === testData.toSet, "\nData received does not match data sent")
+    val kinesisTestUtils = new KinesisTestUtils()
+    try {
+      kinesisTestUtils.createStream()
+      ssc = new StreamingContext(sc, Seconds(1))
+      val aWSCredentials = KinesisTestUtils.getAWSCredentials()
+      val stream = KinesisUtils.createStream(ssc, kinesisAppName, kinesisTestUtils.streamName,
+        kinesisTestUtils.endpointUrl, kinesisTestUtils.regionName, InitialPositionInStream.LATEST,
+        Seconds(10), StorageLevel.MEMORY_ONLY,
+        aWSCredentials.getAWSAccessKeyId, aWSCredentials.getAWSSecretKey)
+
+      val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
+      stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>
+        collected ++= rdd.collect()
+        logInfo("Collected = " + rdd.collect().toSeq.mkString(", "))
+      }
+      ssc.start()
+
+      val testData = 1 to 10
+      eventually(timeout(120 seconds), interval(10 second)) {
+        kinesisTestUtils.pushData(testData)
+        assert(collected === testData.toSet, "\nData received does not match data sent")
+      }
+      ssc.stop()
+    } finally {
+      kinesisTestUtils.deleteStream()
+      kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
     }
-    ssc.stop()
   }
 }

From d743bec645fd2a65bd488d2d660b3aa2135b4da6 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Sun, 19 Jul 2015 20:53:18 -0700
Subject: [PATCH 0479/1454] [SPARK-9172][SQL] Make DecimalPrecision support for
 Intersect and Except

JIRA: https://issues.apache.org/jira/browse/SPARK-9172

Simply make `DecimalPrecision` support for `Intersect` and `Except` in addition to `Union`.

Besides, add unit test for `DecimalPrecision` as well.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7511 from viirya/more_decimalprecieion and squashes the following commits:

4d29d10 [Liang-Chi Hsieh] Fix code comment.
9fb0d49 [Liang-Chi Hsieh] Make DecimalPrecision support for Intersect and Except.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 86 +++++++++++--------
 .../analysis/HiveTypeCoercionSuite.scala      | 55 ++++++++++++
 2 files changed, 104 insertions(+), 37 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index ff20835e82ba7..e214545726249 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -335,7 +335,7 @@ object HiveTypeCoercion {
    * - INT gets turned into DECIMAL(10, 0)
    * - LONG gets turned into DECIMAL(20, 0)
    * - FLOAT and DOUBLE
-   *   1. Union operation:
+   *   1. Union, Intersect and Except operations:
    *      FLOAT gets turned into DECIMAL(7, 7), DOUBLE gets turned into DECIMAL(15, 15) (this is the
    *      same as Hive)
    *   2. Other operation:
@@ -362,47 +362,59 @@ object HiveTypeCoercion {
       DoubleType -> DecimalType(15, 15)
     )
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      // fix decimal precision for union
-      case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
-        val castedInput = left.output.zip(right.output).map {
-          case (lhs, rhs) if lhs.dataType != rhs.dataType =>
-            (lhs.dataType, rhs.dataType) match {
-              case (DecimalType.Fixed(p1, s1), DecimalType.Fixed(p2, s2)) =>
-                // Union decimals with precision/scale p1/s2 and p2/s2  will be promoted to
-                // DecimalType(max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2))
-                val fixedType = DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2), max(s1, s2))
-                (Alias(Cast(lhs, fixedType), lhs.name)(), Alias(Cast(rhs, fixedType), rhs.name)())
-              case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) =>
-                (Alias(Cast(lhs, intTypeToFixed(t)), lhs.name)(), rhs)
-              case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) =>
-                (lhs, Alias(Cast(rhs, intTypeToFixed(t)), rhs.name)())
-              case (t, DecimalType.Fixed(p, s)) if floatTypeToFixed.contains(t) =>
-                (Alias(Cast(lhs, floatTypeToFixed(t)), lhs.name)(), rhs)
-              case (DecimalType.Fixed(p, s), t) if floatTypeToFixed.contains(t) =>
-                (lhs, Alias(Cast(rhs, floatTypeToFixed(t)), rhs.name)())
-              case _ => (lhs, rhs)
-            }
-          case other => other
-        }
+    private def castDecimalPrecision(
+        left: LogicalPlan,
+        right: LogicalPlan): (LogicalPlan, LogicalPlan) = {
+      val castedInput = left.output.zip(right.output).map {
+        case (lhs, rhs) if lhs.dataType != rhs.dataType =>
+          (lhs.dataType, rhs.dataType) match {
+            case (DecimalType.Fixed(p1, s1), DecimalType.Fixed(p2, s2)) =>
+              // Decimals with precision/scale p1/s2 and p2/s2  will be promoted to
+              // DecimalType(max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2))
+              val fixedType = DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2), max(s1, s2))
+              (Alias(Cast(lhs, fixedType), lhs.name)(), Alias(Cast(rhs, fixedType), rhs.name)())
+            case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) =>
+              (Alias(Cast(lhs, intTypeToFixed(t)), lhs.name)(), rhs)
+            case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) =>
+              (lhs, Alias(Cast(rhs, intTypeToFixed(t)), rhs.name)())
+            case (t, DecimalType.Fixed(p, s)) if floatTypeToFixed.contains(t) =>
+              (Alias(Cast(lhs, floatTypeToFixed(t)), lhs.name)(), rhs)
+            case (DecimalType.Fixed(p, s), t) if floatTypeToFixed.contains(t) =>
+              (lhs, Alias(Cast(rhs, floatTypeToFixed(t)), rhs.name)())
+            case _ => (lhs, rhs)
+          }
+        case other => other
+      }
 
-        val (castedLeft, castedRight) = castedInput.unzip
+      val (castedLeft, castedRight) = castedInput.unzip
 
-        val newLeft =
-          if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) {
-            Project(castedLeft, left)
-          } else {
-            left
-          }
+      val newLeft =
+        if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) {
+          Project(castedLeft, left)
+        } else {
+          left
+        }
 
-        val newRight =
-          if (castedRight.map(_.dataType) != right.output.map(_.dataType)) {
-            Project(castedRight, right)
-          } else {
-            right
-          }
+      val newRight =
+        if (castedRight.map(_.dataType) != right.output.map(_.dataType)) {
+          Project(castedRight, right)
+        } else {
+          right
+        }
+      (newLeft, newRight)
+    }
 
+    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      // fix decimal precision for union, intersect and except
+      case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
+        val (newLeft, newRight) = castDecimalPrecision(left, right)
         Union(newLeft, newRight)
+      case i @ Intersect(left, right) if i.childrenResolved && !i.resolved =>
+        val (newLeft, newRight) = castDecimalPrecision(left, right)
+        Intersect(newLeft, newRight)
+      case e @ Except(left, right) if e.childrenResolved && !e.resolved =>
+        val (newLeft, newRight) = castDecimalPrecision(left, right)
+        Except(newLeft, newRight)
 
       // fix decimal precision for expressions
       case q => q.transformExpressions {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 7ee2333a81dfe..835220c563f41 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -346,6 +346,61 @@ class HiveTypeCoercionSuite extends PlanTest {
     checkOutput(r3.right, expectedTypes)
   }
 
+  test("Transform Decimal precision/scale for union except and intersect") {
+    def checkOutput(logical: LogicalPlan, expectTypes: Seq[DataType]): Unit = {
+      logical.output.zip(expectTypes).foreach { case (attr, dt) =>
+        assert(attr.dataType === dt)
+      }
+    }
+
+    val dp = HiveTypeCoercion.DecimalPrecision
+
+    val left1 = LocalRelation(
+      AttributeReference("l", DecimalType(10, 8))())
+    val right1 = LocalRelation(
+      AttributeReference("r", DecimalType(5, 5))())
+    val expectedType1 = Seq(DecimalType(math.max(8, 5) + math.max(10 - 8, 5 - 5), math.max(8, 5)))
+
+    val r1 = dp(Union(left1, right1)).asInstanceOf[Union]
+    val r2 = dp(Except(left1, right1)).asInstanceOf[Except]
+    val r3 = dp(Intersect(left1, right1)).asInstanceOf[Intersect]
+
+    checkOutput(r1.left, expectedType1)
+    checkOutput(r1.right, expectedType1)
+    checkOutput(r2.left, expectedType1)
+    checkOutput(r2.right, expectedType1)
+    checkOutput(r3.left, expectedType1)
+    checkOutput(r3.right, expectedType1)
+
+    val plan1 = LocalRelation(
+      AttributeReference("l", DecimalType(10, 10))())
+
+    val rightTypes = Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType)
+    val expectedTypes = Seq(DecimalType(3, 0), DecimalType(5, 0), DecimalType(10, 0),
+      DecimalType(20, 0), DecimalType(7, 7), DecimalType(15, 15))
+
+    rightTypes.zip(expectedTypes).map { case (rType, expectedType) =>
+      val plan2 = LocalRelation(
+        AttributeReference("r", rType)())
+
+      val r1 = dp(Union(plan1, plan2)).asInstanceOf[Union]
+      val r2 = dp(Except(plan1, plan2)).asInstanceOf[Except]
+      val r3 = dp(Intersect(plan1, plan2)).asInstanceOf[Intersect]
+
+      checkOutput(r1.right, Seq(expectedType))
+      checkOutput(r2.right, Seq(expectedType))
+      checkOutput(r3.right, Seq(expectedType))
+
+      val r4 = dp(Union(plan2, plan1)).asInstanceOf[Union]
+      val r5 = dp(Except(plan2, plan1)).asInstanceOf[Except]
+      val r6 = dp(Intersect(plan2, plan1)).asInstanceOf[Intersect]
+
+      checkOutput(r4.left, Seq(expectedType))
+      checkOutput(r5.left, Seq(expectedType))
+      checkOutput(r6.left, Seq(expectedType))
+    }
+  }
+
   /**
    * There are rules that need to not fire before child expressions get resolved.
    * We use this test to make sure those rules do not fire early.

From 930253e0766a7585347edfb73ed11b1bf78143fe Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sun, 19 Jul 2015 22:42:44 -0700
Subject: [PATCH 0480/1454] [SPARK-9185][SQL] improve code gen for mutable
 states to support complex initialization

Sometimes we need more than one step to initialize the mutable states in code gen like https://github.com/apache/spark/pull/7516

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7521 from cloud-fan/init and squashes the following commits:

2106445 [Wenchen Fan] improve code gen for mutable states
---
 .../expressions/codegen/CodeGenerator.scala   | 20 +++++++++++++++++--
 .../codegen/GenerateMutableProjection.scala   | 11 ++++------
 .../codegen/GenerateOrdering.scala            |  8 +++-----
 .../codegen/GeneratePredicate.scala           |  6 ++----
 .../codegen/GenerateProjection.scala          |  9 +++------
 .../codegen/GenerateUnsafeProjection.scala    | 11 ++++------
 .../sql/catalyst/expressions/random.scala     |  8 ++++----
 .../MonotonicallyIncreasingID.scala           |  4 ++--
 .../expressions/SparkPartitionID.scala        |  3 ++-
 9 files changed, 42 insertions(+), 38 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 7c388bc346306..b2468b6a181d7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -60,13 +60,19 @@ class CodeGenContext {
   /**
    * Holding expressions' mutable states like `MonotonicallyIncreasingID.count` as a
    * 3-tuple: java type, variable name, code to init it.
+   * As an example, ("int", "count", "count = 0;") will produce code:
+   * {{{
+   *   private int count;
+   *   count = 0;
+   * }}}
+   *
    * They will be kept as member variables in generated classes like `SpecificProjection`.
    */
   val mutableStates: mutable.ArrayBuffer[(String, String, String)] =
     mutable.ArrayBuffer.empty[(String, String, String)]
 
-  def addMutableState(javaType: String, variableName: String, initialValue: String): Unit = {
-    mutableStates += ((javaType, variableName, initialValue))
+  def addMutableState(javaType: String, variableName: String, initialCode: String): Unit = {
+    mutableStates += ((javaType, variableName, initialCode))
   }
 
   final val intervalType: String = classOf[Interval].getName
@@ -234,6 +240,16 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   protected val mutableRowType: String = classOf[MutableRow].getName
   protected val genericMutableRowType: String = classOf[GenericMutableRow].getName
 
+  protected def declareMutableStates(ctx: CodeGenContext) = {
+    ctx.mutableStates.map { case (javaType, variableName, _) =>
+      s"private $javaType $variableName;"
+    }.mkString("\n      ")
+  }
+
+  protected def initMutableStates(ctx: CodeGenContext) = {
+    ctx.mutableStates.map(_._3).mkString("\n        ")
+  }
+
   /**
    * Generates a class for a given input expression.  Called when there is not cached code
    * already available.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index b82bd6814b487..03b4b3c216f49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -78,10 +78,6 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
       }
     }
 
-    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
-      s"private $javaType $variableName = $initialValue;"
-    }.mkString("\n      ")
-
     val code = s"""
       public Object generate($exprType[] expr) {
         return new SpecificProjection(expr);
@@ -89,13 +85,14 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
 
       class SpecificProjection extends ${classOf[BaseMutableProjection].getName} {
 
-        private $exprType[] expressions = null;
-        private $mutableRowType mutableRow = null;
-        $mutableStates
+        private $exprType[] expressions;
+        private $mutableRowType mutableRow;
+        ${declareMutableStates(ctx)}
 
         public SpecificProjection($exprType[] expr) {
           expressions = expr;
           mutableRow = new $genericMutableRowType(${expressions.size});
+          ${initMutableStates(ctx)}
         }
 
         public ${classOf[BaseMutableProjection].getName} target($mutableRowType row) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index 856ff9f1f96f8..2e6f9e204d813 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -84,9 +84,6 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
           }
       """
     }.mkString("\n")
-    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
-      s"private $javaType $variableName = $initialValue;"
-    }.mkString("\n      ")
     val code = s"""
       public SpecificOrdering generate($exprType[] expr) {
         return new SpecificOrdering(expr);
@@ -94,11 +91,12 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
 
       class SpecificOrdering extends ${classOf[BaseOrdering].getName} {
 
-        private $exprType[] expressions = null;
-        $mutableStates
+        private $exprType[] expressions;
+        ${declareMutableStates(ctx)}
 
         public SpecificOrdering($exprType[] expr) {
           expressions = expr;
+          ${initMutableStates(ctx)}
         }
 
         @Override
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
index 9e5a745d512e9..1dda5992c3654 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
@@ -40,9 +40,6 @@ object GeneratePredicate extends CodeGenerator[Expression, (InternalRow) => Bool
   protected def create(predicate: Expression): ((InternalRow) => Boolean) = {
     val ctx = newCodeGenContext()
     val eval = predicate.gen(ctx)
-    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
-      s"private $javaType $variableName = $initialValue;"
-    }.mkString("\n      ")
     val code = s"""
       public SpecificPredicate generate($exprType[] expr) {
         return new SpecificPredicate(expr);
@@ -50,9 +47,10 @@ object GeneratePredicate extends CodeGenerator[Expression, (InternalRow) => Bool
 
       class SpecificPredicate extends ${classOf[Predicate].getName} {
         private final $exprType[] expressions;
-        $mutableStates
+        ${declareMutableStates(ctx)}
         public SpecificPredicate($exprType[] expr) {
           expressions = expr;
+          ${initMutableStates(ctx)}
         }
 
         @Override
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 8f9fcbf810554..405d6b0e3bc76 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -151,21 +151,18 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         s"""if (!nullBits[$i]) arr[$i] = c$i;"""
     }.mkString("\n      ")
 
-    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
-      s"private $javaType $variableName = $initialValue;"
-    }.mkString("\n      ")
-
     val code = s"""
     public SpecificProjection generate($exprType[] expr) {
       return new SpecificProjection(expr);
     }
 
     class SpecificProjection extends ${classOf[BaseProjection].getName} {
-      private $exprType[] expressions = null;
-      $mutableStates
+      private $exprType[] expressions;
+      ${declareMutableStates(ctx)}
 
       public SpecificProjection($exprType[] expr) {
         expressions = expr;
+        ${initMutableStates(ctx)}
       }
 
       @Override
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index a81d545a8ec63..3a8e8302b24fd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -74,10 +74,6 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           }"""
     }.mkString("\n          ")
 
-    val mutableStates = ctx.mutableStates.map { case (javaType, variableName, initialValue) =>
-      s"private $javaType $variableName = $initialValue;"
-    }.mkString("\n      ")
-
     val code = s"""
     private $exprType[] expressions;
 
@@ -90,10 +86,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
       private UnsafeRow target = new UnsafeRow();
       private byte[] buffer = new byte[64];
+      ${declareMutableStates(ctx)}
 
-      $mutableStates
-
-      public SpecificProjection() {}
+      public SpecificProjection() {
+        ${initMutableStates(ctx)}
+      }
 
       // Scala.Function1 need this
       public Object apply(Object row) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
index 65093dc72264b..822898e561cd6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
@@ -60,9 +60,9 @@ case class Rand(seed: Long) extends RDG(seed) {
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val rngTerm = ctx.freshName("rng")
-    val className = classOf[XORShiftRandom].getCanonicalName
+    val className = classOf[XORShiftRandom].getName
     ctx.addMutableState(className, rngTerm,
-      s"new $className($seed + org.apache.spark.TaskContext.getPartitionId())")
+      s"$rngTerm = new $className($seed + org.apache.spark.TaskContext.getPartitionId());")
     ev.isNull = "false"
     s"""
       final ${ctx.javaType(dataType)} ${ev.primitive} = $rngTerm.nextDouble();
@@ -83,9 +83,9 @@ case class Randn(seed: Long) extends RDG(seed) {
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val rngTerm = ctx.freshName("rng")
-    val className = classOf[XORShiftRandom].getCanonicalName
+    val className = classOf[XORShiftRandom].getName
     ctx.addMutableState(className, rngTerm,
-      s"new $className($seed + org.apache.spark.TaskContext.getPartitionId())")
+      s"$rngTerm = new $className($seed + org.apache.spark.TaskContext.getPartitionId());")
     ev.isNull = "false"
     s"""
       final ${ctx.javaType(dataType)} ${ev.primitive} = $rngTerm.nextGaussian();
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
index fec403fe2d348..4d8ed089731aa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
@@ -58,9 +58,9 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val countTerm = ctx.freshName("count")
     val partitionMaskTerm = ctx.freshName("partitionMask")
-    ctx.addMutableState(ctx.JAVA_LONG, countTerm, "0L")
+    ctx.addMutableState(ctx.JAVA_LONG, countTerm, s"$countTerm = 0L;")
     ctx.addMutableState(ctx.JAVA_LONG, partitionMaskTerm,
-      "((long) org.apache.spark.TaskContext.getPartitionId()) << 33")
+      s"$partitionMaskTerm = ((long) org.apache.spark.TaskContext.getPartitionId()) << 33;")
 
     ev.isNull = "false"
     s"""
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
index 7c790c549a5d8..43ffc9cc84738 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
@@ -41,7 +41,8 @@ private[sql] case object SparkPartitionID extends LeafExpression {
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val idTerm = ctx.freshName("partitionId")
-    ctx.addMutableState(ctx.JAVA_INT, idTerm, "org.apache.spark.TaskContext.getPartitionId()")
+    ctx.addMutableState(ctx.JAVA_INT, idTerm,
+      s"$idTerm = org.apache.spark.TaskContext.getPartitionId();")
     ev.isNull = "false"
     s"final ${ctx.javaType(dataType)} ${ev.primitive} = $idTerm;"
   }

From 5bdf16da90cf20f20e6fbb258ffd0888bf45e357 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 19 Jul 2015 22:45:56 -0700
Subject: [PATCH 0481/1454] Code review feedback for the previous patch.

---
 .../sql/catalyst/expressions/codegen/CodeGenerator.scala  | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index b2468b6a181d7..10f411ff7451a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -63,16 +63,20 @@ class CodeGenContext {
    * As an example, ("int", "count", "count = 0;") will produce code:
    * {{{
    *   private int count;
+   * }}}
+   * as a member variable, and add
+   * {{{
    *   count = 0;
    * }}}
+   * to the constructor.
    *
    * They will be kept as member variables in generated classes like `SpecificProjection`.
    */
   val mutableStates: mutable.ArrayBuffer[(String, String, String)] =
     mutable.ArrayBuffer.empty[(String, String, String)]
 
-  def addMutableState(javaType: String, variableName: String, initialCode: String): Unit = {
-    mutableStates += ((javaType, variableName, initialCode))
+  def addMutableState(javaType: String, variableName: String, initCode: String): Unit = {
+    mutableStates += ((javaType, variableName, initCode))
   }
 
   final val intervalType: String = classOf[Interval].getName

From 972d8900a1e2430d172968b11fdea14b289d7d4d Mon Sep 17 00:00:00 2001
From: Jacky Li <lee.unreal@gmail.com>
Date: Sun, 19 Jul 2015 23:19:17 -0700
Subject: [PATCH 0482/1454] [SQL][DOC] Minor document fix in
 HadoopFsRelationProvider

Catch this while reading the code

Author: Jacky Li <lee.unreal@gmail.com>
Author: Jacky Li <jackylk@users.noreply.github.com>

Closes #7524 from jackylk/patch-11 and squashes the following commits:

b679011 [Jacky Li] fix doc
e10e211 [Jacky Li] [SQL] Minor document fix in HadoopFsRelationProvider
---
 .../main/scala/org/apache/spark/sql/sources/interfaces.scala  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index b13c5313b25c9..5d7cc2ff55af1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -75,7 +75,7 @@ trait RelationProvider {
  * A new instance of this class with be instantiated each time a DDL call is made.
  *
  * The difference between a [[RelationProvider]] and a [[SchemaRelationProvider]] is that
- * users need to provide a schema when using a SchemaRelationProvider.
+ * users need to provide a schema when using a [[SchemaRelationProvider]].
  * A relation provider can inherits both [[RelationProvider]] and [[SchemaRelationProvider]]
  * if it can support both schema inference and user-specified schemas.
  *
@@ -111,7 +111,7 @@ trait SchemaRelationProvider {
  *
  * The difference between a [[RelationProvider]] and a [[HadoopFsRelationProvider]] is
  * that users need to provide a schema and a (possibly empty) list of partition columns when
- * using a SchemaRelationProvider. A relation provider can inherits both [[RelationProvider]],
+ * using a [[HadoopFsRelationProvider]]. A relation provider can inherits both [[RelationProvider]],
  * and [[HadoopFsRelationProvider]] if it can support schema inference, user-specified
  * schemas, and accessing partitioned relations.
  *

From 79ec07290d0b4d16f1643af83824d926304c8f46 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sun, 19 Jul 2015 23:41:28 -0700
Subject: [PATCH 0483/1454] [SPARK-9023] [SQL] Efficiency improvements for
 UnsafeRows in Exchange

This pull request aims to improve the performance of SQL's Exchange operator when shuffling UnsafeRows.  It also makes several general efficiency improvements to Exchange.

Key changes:

- When performing hash partitioning, the old Exchange projected the partitioning columns into a new row then passed a `(partitioningColumRow: InternalRow, row: InternalRow)` pair into the shuffle. This is very inefficient because it ends up redundantly serializing the partitioning columns only to immediately discard them after the shuffle.  After this patch's changes, Exchange now shuffles `(partitionId: Int, row: InternalRow)` pairs.  This still isn't optimal, since we're still shuffling extra data that we don't need, but it's significantly more efficient than the old implementation; in the future, we may be able to further optimize this once we implement a new shuffle write interface that accepts non-key-value-pair inputs.
- Exchange's `compute()` method has been significantly simplified; the new code has less duplication and thus is easier to understand.
- When the Exchange's input operator produces UnsafeRows, Exchange will use a specialized `UnsafeRowSerializer` to serialize these rows.  This serializer is significantly more efficient since it simply copies the UnsafeRow's underlying bytes.  Note that this approach does not work for UnsafeRows that use the ObjectPool mechanism; I did not add support for this because we are planning to remove ObjectPool in the next few weeks.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7456 from JoshRosen/unsafe-exchange and squashes the following commits:

7e75259 [Josh Rosen] Fix cast in SparkSqlSerializer2Suite
0082515 [Josh Rosen] Some additional comments + small cleanup to remove an unused parameter
a27cfc1 [Josh Rosen] Add missing newline
741973c [Josh Rosen] Add simple test of UnsafeRow shuffling in Exchange.
359c6a4 [Josh Rosen] Remove println() and add comments
93904e7 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-exchange
8dd3ff2 [Josh Rosen] Exchange outputs UnsafeRows when its child outputs them
dd9c66d [Josh Rosen] Fix for copying logic
035af21 [Josh Rosen] Add logic for choosing when to use UnsafeRowSerializer
7876f31 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-shuffle
cbea80b [Josh Rosen] Add UnsafeRowSerializer
0f2ac86 [Josh Rosen] Import ordering
3ca8515 [Josh Rosen] Big code simplification in Exchange
3526868 [Josh Rosen] Iniitial cut at removing shuffle on KV pairs
---
 .../apache/spark/sql/execution/Exchange.scala | 132 ++++++----------
 .../spark/sql/execution/ShuffledRowRDD.scala  |  80 ++++++++++
 .../sql/execution/SparkSqlSerializer2.scala   |  43 ++----
 .../sql/execution/UnsafeRowSerializer.scala   | 142 ++++++++++++++++++
 .../spark/sql/execution/basicOperators.scala  |   5 +-
 .../spark/sql/execution/ExchangeSuite.scala   |  32 ++++
 .../execution/SparkSqlSerializer2Suite.scala  |   4 +-
 .../execution/UnsafeRowSerializerSuite.scala  |  76 ++++++++++
 8 files changed, 398 insertions(+), 116 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index feea4f239c04d..2750053594f99 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.{RDD, ShuffledRDD}
+import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.hash.HashShuffleManager
 import org.apache.spark.shuffle.sort.SortShuffleManager
@@ -29,7 +29,6 @@ import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.types.DataType
 import org.apache.spark.util.MutablePair
 import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEnv}
 
@@ -44,6 +43,12 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
   override def output: Seq[Attribute] = child.output
 
+  override def outputsUnsafeRows: Boolean = child.outputsUnsafeRows
+
+  override def canProcessSafeRows: Boolean = true
+
+  override def canProcessUnsafeRows: Boolean = true
+
   /**
    * Determines whether records must be defensively copied before being sent to the shuffle.
    * Several of Spark's shuffle components will buffer deserialized Java objects in memory. The
@@ -112,109 +117,70 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
   @transient private lazy val sparkConf = child.sqlContext.sparkContext.getConf
 
-  private def getSerializer(
-      keySchema: Array[DataType],
-      valueSchema: Array[DataType],
-      numPartitions: Int): Serializer = {
+  private val serializer: Serializer = {
+    val rowDataTypes = child.output.map(_.dataType).toArray
     // It is true when there is no field that needs to be write out.
     // For now, we will not use SparkSqlSerializer2 when noField is true.
-    val noField =
-      (keySchema == null || keySchema.length == 0) &&
-      (valueSchema == null || valueSchema.length == 0)
+    val noField = rowDataTypes == null || rowDataTypes.length == 0
 
     val useSqlSerializer2 =
         child.sqlContext.conf.useSqlSerializer2 &&   // SparkSqlSerializer2 is enabled.
-        SparkSqlSerializer2.support(keySchema) &&    // The schema of key is supported.
-        SparkSqlSerializer2.support(valueSchema) &&  // The schema of value is supported.
+        SparkSqlSerializer2.support(rowDataTypes) &&  // The schema of row is supported.
         !noField
 
-    val serializer = if (useSqlSerializer2) {
+    if (child.outputsUnsafeRows) {
+      logInfo("Using UnsafeRowSerializer.")
+      new UnsafeRowSerializer(child.output.size)
+    } else if (useSqlSerializer2) {
       logInfo("Using SparkSqlSerializer2.")
-      new SparkSqlSerializer2(keySchema, valueSchema)
+      new SparkSqlSerializer2(rowDataTypes)
     } else {
       logInfo("Using SparkSqlSerializer.")
       new SparkSqlSerializer(sparkConf)
     }
-
-    serializer
   }
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this , "execute") {
-    newPartitioning match {
-      case HashPartitioning(expressions, numPartitions) =>
-        val keySchema = expressions.map(_.dataType).toArray
-        val valueSchema = child.output.map(_.dataType).toArray
-        val serializer = getSerializer(keySchema, valueSchema, numPartitions)
-        val part = new HashPartitioner(numPartitions)
-
-        val rdd = if (needToCopyObjectsBeforeShuffle(part, serializer)) {
-          child.execute().mapPartitions { iter =>
-            val hashExpressions = newMutableProjection(expressions, child.output)()
-            iter.map(r => (hashExpressions(r).copy(), r.copy()))
-          }
-        } else {
-          child.execute().mapPartitions { iter =>
-            val hashExpressions = newMutableProjection(expressions, child.output)()
-            val mutablePair = new MutablePair[InternalRow, InternalRow]()
-            iter.map(r => mutablePair.update(hashExpressions(r), r))
-          }
-        }
-        val shuffled = new ShuffledRDD[InternalRow, InternalRow, InternalRow](rdd, part)
-        shuffled.setSerializer(serializer)
-        shuffled.map(_._2)
-
+    val rdd = child.execute()
+    val part: Partitioner = newPartitioning match {
+      case HashPartitioning(expressions, numPartitions) => new HashPartitioner(numPartitions)
       case RangePartitioning(sortingExpressions, numPartitions) =>
-        val keySchema = child.output.map(_.dataType).toArray
-        val serializer = getSerializer(keySchema, null, numPartitions)
-
-        val childRdd = child.execute()
-        val part: Partitioner = {
-          // Internally, RangePartitioner runs a job on the RDD that samples keys to compute
-          // partition bounds. To get accurate samples, we need to copy the mutable keys.
-          val rddForSampling = childRdd.mapPartitions { iter =>
-            val mutablePair = new MutablePair[InternalRow, Null]()
-            iter.map(row => mutablePair.update(row.copy(), null))
-          }
-          // TODO: RangePartitioner should take an Ordering.
-          implicit val ordering = new RowOrdering(sortingExpressions, child.output)
-          new RangePartitioner(numPartitions, rddForSampling, ascending = true)
-        }
-
-        val rdd = if (needToCopyObjectsBeforeShuffle(part, serializer)) {
-          childRdd.mapPartitions { iter => iter.map(row => (row.copy(), null))}
-        } else {
-          childRdd.mapPartitions { iter =>
-            val mutablePair = new MutablePair[InternalRow, Null]()
-            iter.map(row => mutablePair.update(row, null))
-          }
+        // Internally, RangePartitioner runs a job on the RDD that samples keys to compute
+        // partition bounds. To get accurate samples, we need to copy the mutable keys.
+        val rddForSampling = rdd.mapPartitions { iter =>
+          val mutablePair = new MutablePair[InternalRow, Null]()
+          iter.map(row => mutablePair.update(row.copy(), null))
         }
-
-        val shuffled = new ShuffledRDD[InternalRow, Null, Null](rdd, part)
-        shuffled.setSerializer(serializer)
-        shuffled.map(_._1)
-
+        implicit val ordering = new RowOrdering(sortingExpressions, child.output)
+        new RangePartitioner(numPartitions, rddForSampling, ascending = true)
       case SinglePartition =>
-        val valueSchema = child.output.map(_.dataType).toArray
-        val serializer = getSerializer(null, valueSchema, numPartitions = 1)
-        val partitioner = new HashPartitioner(1)
-
-        val rdd = if (needToCopyObjectsBeforeShuffle(partitioner, serializer)) {
-          child.execute().mapPartitions {
-            iter => iter.map(r => (null, r.copy()))
-          }
-        } else {
-          child.execute().mapPartitions { iter =>
-            val mutablePair = new MutablePair[Null, InternalRow]()
-            iter.map(r => mutablePair.update(null, r))
-          }
+        new Partitioner {
+          override def numPartitions: Int = 1
+          override def getPartition(key: Any): Int = 0
         }
-        val shuffled = new ShuffledRDD[Null, InternalRow, InternalRow](rdd, partitioner)
-        shuffled.setSerializer(serializer)
-        shuffled.map(_._2)
-
       case _ => sys.error(s"Exchange not implemented for $newPartitioning")
       // TODO: Handle BroadcastPartitioning.
     }
+    def getPartitionKeyExtractor(): InternalRow => InternalRow = newPartitioning match {
+      case HashPartitioning(expressions, _) => newMutableProjection(expressions, child.output)()
+      case RangePartitioning(_, _) | SinglePartition => identity
+      case _ => sys.error(s"Exchange not implemented for $newPartitioning")
+    }
+    val rddWithPartitionIds: RDD[Product2[Int, InternalRow]] = {
+      if (needToCopyObjectsBeforeShuffle(part, serializer)) {
+        rdd.mapPartitions { iter =>
+          val getPartitionKey = getPartitionKeyExtractor()
+          iter.map { row => (part.getPartition(getPartitionKey(row)), row.copy()) }
+        }
+      } else {
+        rdd.mapPartitions { iter =>
+          val getPartitionKey = getPartitionKeyExtractor()
+          val mutablePair = new MutablePair[Int, InternalRow]()
+          iter.map { row => mutablePair.update(part.getPartition(getPartitionKey(row)), row) }
+        }
+      }
+    }
+    new ShuffledRowRDD(rddWithPartitionIds, serializer, part.numPartitions)
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala
new file mode 100644
index 0000000000000..88f5b13c8f248
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types.DataType
+
+private class ShuffledRowRDDPartition(val idx: Int) extends Partition {
+  override val index: Int = idx
+  override def hashCode(): Int = idx
+}
+
+/**
+ * A dummy partitioner for use with records whose partition ids have been pre-computed (i.e. for
+ * use on RDDs of (Int, Row) pairs where the Int is a partition id in the expected range).
+ */
+private class PartitionIdPassthrough(override val numPartitions: Int) extends Partitioner {
+  override def getPartition(key: Any): Int = key.asInstanceOf[Int]
+}
+
+/**
+ * This is a specialized version of [[org.apache.spark.rdd.ShuffledRDD]] that is optimized for
+ * shuffling rows instead of Java key-value pairs. Note that something like this should eventually
+ * be implemented in Spark core, but that is blocked by some more general refactorings to shuffle
+ * interfaces / internals.
+ *
+ * @param prev the RDD being shuffled. Elements of this RDD are (partitionId, Row) pairs.
+ *             Partition ids should be in the range [0, numPartitions - 1].
+ * @param serializer the serializer used during the shuffle.
+ * @param numPartitions the number of post-shuffle partitions.
+ */
+class ShuffledRowRDD(
+    @transient var prev: RDD[Product2[Int, InternalRow]],
+    serializer: Serializer,
+    numPartitions: Int)
+  extends RDD[InternalRow](prev.context, Nil) {
+
+  private val part: Partitioner = new PartitionIdPassthrough(numPartitions)
+
+  override def getDependencies: Seq[Dependency[_]] = {
+    List(new ShuffleDependency[Int, InternalRow, InternalRow](prev, part, Some(serializer)))
+  }
+
+  override val partitioner = Some(part)
+
+  override def getPartitions: Array[Partition] = {
+    Array.tabulate[Partition](part.numPartitions)(i => new ShuffledRowRDDPartition(i))
+  }
+
+  override def compute(split: Partition, context: TaskContext): Iterator[InternalRow] = {
+    val dep = dependencies.head.asInstanceOf[ShuffleDependency[Int, InternalRow, InternalRow]]
+    SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, split.index + 1, context)
+      .read()
+      .asInstanceOf[Iterator[Product2[Int, InternalRow]]]
+      .map(_._2)
+  }
+
+  override def clearDependencies() {
+    super.clearDependencies()
+    prev = null
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 6ed822dc70d68..c87e2064a8f33 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -45,14 +45,12 @@ import org.apache.spark.unsafe.types.UTF8String
  *     the comment of the `serializer` method in [[Exchange]] for more information on it.
  */
 private[sql] class Serializer2SerializationStream(
-    keySchema: Array[DataType],
-    valueSchema: Array[DataType],
+    rowSchema: Array[DataType],
     out: OutputStream)
   extends SerializationStream with Logging {
 
   private val rowOut = new DataOutputStream(new BufferedOutputStream(out))
-  private val writeKeyFunc = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut)
-  private val writeValueFunc = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut)
+  private val writeRowFunc = SparkSqlSerializer2.createSerializationFunction(rowSchema, rowOut)
 
   override def writeObject[T: ClassTag](t: T): SerializationStream = {
     val kv = t.asInstanceOf[Product2[Row, Row]]
@@ -63,12 +61,12 @@ private[sql] class Serializer2SerializationStream(
   }
 
   override def writeKey[T: ClassTag](t: T): SerializationStream = {
-    writeKeyFunc(t.asInstanceOf[Row])
+    // No-op.
     this
   }
 
   override def writeValue[T: ClassTag](t: T): SerializationStream = {
-    writeValueFunc(t.asInstanceOf[Row])
+    writeRowFunc(t.asInstanceOf[Row])
     this
   }
 
@@ -85,8 +83,7 @@ private[sql] class Serializer2SerializationStream(
  * The corresponding deserialization stream for [[Serializer2SerializationStream]].
  */
 private[sql] class Serializer2DeserializationStream(
-    keySchema: Array[DataType],
-    valueSchema: Array[DataType],
+    rowSchema: Array[DataType],
     in: InputStream)
   extends DeserializationStream with Logging  {
 
@@ -103,22 +100,20 @@ private[sql] class Serializer2DeserializationStream(
   }
 
   // Functions used to return rows for key and value.
-  private val getKey = rowGenerator(keySchema)
-  private val getValue = rowGenerator(valueSchema)
+  private val getRow = rowGenerator(rowSchema)
   // Functions used to read a serialized row from the InputStream and deserialize it.
-  private val readKeyFunc = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn)
-  private val readValueFunc = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn)
+  private val readRowFunc = SparkSqlSerializer2.createDeserializationFunction(rowSchema, rowIn)
 
   override def readObject[T: ClassTag](): T = {
-    (readKeyFunc(getKey()), readValueFunc(getValue())).asInstanceOf[T]
+    readValue()
   }
 
   override def readKey[T: ClassTag](): T = {
-    readKeyFunc(getKey()).asInstanceOf[T]
+    null.asInstanceOf[T] // intentionally left blank.
   }
 
   override def readValue[T: ClassTag](): T = {
-    readValueFunc(getValue()).asInstanceOf[T]
+    readRowFunc(getRow()).asInstanceOf[T]
   }
 
   override def close(): Unit = {
@@ -127,8 +122,7 @@ private[sql] class Serializer2DeserializationStream(
 }
 
 private[sql] class SparkSqlSerializer2Instance(
-    keySchema: Array[DataType],
-    valueSchema: Array[DataType])
+    rowSchema: Array[DataType])
   extends SerializerInstance {
 
   def serialize[T: ClassTag](t: T): ByteBuffer =
@@ -141,30 +135,25 @@ private[sql] class SparkSqlSerializer2Instance(
     throw new UnsupportedOperationException("Not supported.")
 
   def serializeStream(s: OutputStream): SerializationStream = {
-    new Serializer2SerializationStream(keySchema, valueSchema, s)
+    new Serializer2SerializationStream(rowSchema, s)
   }
 
   def deserializeStream(s: InputStream): DeserializationStream = {
-    new Serializer2DeserializationStream(keySchema, valueSchema, s)
+    new Serializer2DeserializationStream(rowSchema, s)
   }
 }
 
 /**
  * SparkSqlSerializer2 is a special serializer that creates serialization function and
  * deserialization function based on the schema of data. It assumes that values passed in
- * are key/value pairs and values returned from it are also key/value pairs.
- * The schema of keys is represented by `keySchema` and that of values is represented by
- * `valueSchema`.
+ * are Rows.
  */
-private[sql] class SparkSqlSerializer2(
-    keySchema: Array[DataType],
-    valueSchema: Array[DataType])
+private[sql] class SparkSqlSerializer2(rowSchema: Array[DataType])
   extends Serializer
   with Logging
   with Serializable{
 
-  def newInstance(): SerializerInstance =
-    new SparkSqlSerializer2Instance(keySchema, valueSchema)
+  def newInstance(): SerializerInstance = new SparkSqlSerializer2Instance(rowSchema)
 
   override def supportsRelocationOfSerializedObjects: Boolean = {
     // SparkSqlSerializer2 is stateless and writes no stream headers
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
new file mode 100644
index 0000000000000..19503ed00056c
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -0,0 +1,142 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import java.io.{DataInputStream, DataOutputStream, OutputStream, InputStream}
+import java.nio.ByteBuffer
+
+import scala.reflect.ClassTag
+
+import com.google.common.io.ByteStreams
+
+import org.apache.spark.serializer.{SerializationStream, DeserializationStream, SerializerInstance, Serializer}
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
+import org.apache.spark.unsafe.PlatformDependent
+
+/**
+ * Serializer for serializing [[UnsafeRow]]s during shuffle. Since UnsafeRows are already stored as
+ * bytes, this serializer simply copies those bytes to the underlying output stream. When
+ * deserializing a stream of rows, instances of this serializer mutate and return a single UnsafeRow
+ * instance that is backed by an on-heap byte array.
+ *
+ * Note that this serializer implements only the [[Serializer]] methods that are used during
+ * shuffle, so certain [[SerializerInstance]] methods will throw UnsupportedOperationException.
+ *
+ * This serializer does not support UnsafeRows that use
+ * [[org.apache.spark.sql.catalyst.util.ObjectPool]].
+ *
+ * @param numFields the number of fields in the row being serialized.
+ */
+private[sql] class UnsafeRowSerializer(numFields: Int) extends Serializer with Serializable {
+  override def newInstance(): SerializerInstance = new UnsafeRowSerializerInstance(numFields)
+  override private[spark] def supportsRelocationOfSerializedObjects: Boolean = true
+}
+
+private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInstance {
+
+  private[this] val EOF: Int = -1
+
+  override def serializeStream(out: OutputStream): SerializationStream = new SerializationStream {
+    private[this] var writeBuffer: Array[Byte] = new Array[Byte](4096)
+    private[this] val dOut: DataOutputStream = new DataOutputStream(out)
+
+    override def writeValue[T: ClassTag](value: T): SerializationStream = {
+      val row = value.asInstanceOf[UnsafeRow]
+      assert(row.getPool == null, "UnsafeRowSerializer does not support ObjectPool")
+      dOut.writeInt(row.getSizeInBytes)
+      var dataRemaining: Int = row.getSizeInBytes
+      val baseObject = row.getBaseObject
+      var rowReadPosition: Long = row.getBaseOffset
+      while (dataRemaining > 0) {
+        val toTransfer: Int = Math.min(writeBuffer.length, dataRemaining)
+        PlatformDependent.copyMemory(
+          baseObject,
+          rowReadPosition,
+          writeBuffer,
+          PlatformDependent.BYTE_ARRAY_OFFSET,
+          toTransfer)
+        out.write(writeBuffer, 0, toTransfer)
+        rowReadPosition += toTransfer
+        dataRemaining -= toTransfer
+      }
+      this
+    }
+    override def writeKey[T: ClassTag](key: T): SerializationStream = {
+      assert(key.isInstanceOf[Int])
+      this
+    }
+    override def writeAll[T: ClassTag](iter: Iterator[T]): SerializationStream =
+      throw new UnsupportedOperationException
+    override def writeObject[T: ClassTag](t: T): SerializationStream =
+      throw new UnsupportedOperationException
+    override def flush(): Unit = dOut.flush()
+    override def close(): Unit = {
+      writeBuffer = null
+      dOut.writeInt(EOF)
+      dOut.close()
+    }
+  }
+
+  override def deserializeStream(in: InputStream): DeserializationStream = {
+    new DeserializationStream {
+      private[this] val dIn: DataInputStream = new DataInputStream(in)
+      private[this] var rowBuffer: Array[Byte] = new Array[Byte](1024)
+      private[this] var row: UnsafeRow = new UnsafeRow()
+      private[this] var rowTuple: (Int, UnsafeRow) = (0, row)
+
+      override def asKeyValueIterator: Iterator[(Int, UnsafeRow)] = {
+        new Iterator[(Int, UnsafeRow)] {
+          private[this] var rowSize: Int = dIn.readInt()
+
+          override def hasNext: Boolean = rowSize != EOF
+
+          override def next(): (Int, UnsafeRow) = {
+            if (rowBuffer.length < rowSize) {
+              rowBuffer = new Array[Byte](rowSize)
+            }
+            ByteStreams.readFully(in, rowBuffer, 0, rowSize)
+            row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize, null)
+            rowSize = dIn.readInt() // read the next row's size
+            if (rowSize == EOF) { // We are returning the last row in this stream
+              val _rowTuple = rowTuple
+              // Null these out so that the byte array can be garbage collected once the entire
+              // iterator has been consumed
+              row = null
+              rowBuffer = null
+              rowTuple = null
+              _rowTuple
+            } else {
+              rowTuple
+            }
+          }
+        }
+      }
+      override def asIterator: Iterator[Any] = throw new UnsupportedOperationException
+      override def readKey[T: ClassTag](): T = throw new UnsupportedOperationException
+      override def readValue[T: ClassTag](): T = throw new UnsupportedOperationException
+      override def readObject[T: ClassTag](): T = throw new UnsupportedOperationException
+      override def close(): Unit = dIn.close()
+    }
+  }
+
+  override def serialize[T: ClassTag](t: T): ByteBuffer = throw new UnsupportedOperationException
+  override def deserialize[T: ClassTag](bytes: ByteBuffer): T =
+    throw new UnsupportedOperationException
+  override def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T =
+    throw new UnsupportedOperationException
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 82bef269b069f..fdd7ad59aba50 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -56,11 +56,8 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
-  @transient lazy val conditionEvaluator: (InternalRow) => Boolean =
-    newPredicate(condition, child.output)
-
   protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    iter.filter(conditionEvaluator)
+    iter.filter(newPredicate(condition, child.output))
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
new file mode 100644
index 0000000000000..79e903c2bbd40
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.plans.physical.SinglePartition
+
+class ExchangeSuite extends SparkPlanTest {
+  test("shuffling UnsafeRows in exchange") {
+    val input = (1 to 1000).map(Tuple1.apply)
+    checkAnswer(
+      input.toDF(),
+      plan => ConvertToSafe(Exchange(SinglePartition, ConvertToUnsafe(plan))),
+      input.map(Row.fromTuple)
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
index 71f6b26bcd01a..4a53fadd7e099 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
@@ -132,8 +132,8 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll
       expectedSerializerClass: Class[T]): Unit = {
     executedPlan.foreach {
       case exchange: Exchange =>
-        val shuffledRDD = exchange.execute().firstParent.asInstanceOf[ShuffledRDD[_, _, _]]
-        val dependency = shuffledRDD.getDependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
+        val shuffledRDD = exchange.execute()
+        val dependency = shuffledRDD.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
         val serializerNotSetMessage =
           s"Expected $expectedSerializerClass as the serializer of Exchange. " +
           s"However, the serializer was not set."
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
new file mode 100644
index 0000000000000..bd788ec8c14b1
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeRowConverter}
+import org.apache.spark.sql.catalyst.util.ObjectPool
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.PlatformDependent
+
+class UnsafeRowSerializerSuite extends SparkFunSuite {
+
+  private def toUnsafeRow(
+      row: Row,
+      schema: Array[DataType],
+      objPool: ObjectPool = null): UnsafeRow = {
+    val internalRow = CatalystTypeConverters.convertToCatalyst(row).asInstanceOf[InternalRow]
+    val rowConverter = new UnsafeRowConverter(schema)
+    val rowSizeInBytes = rowConverter.getSizeRequirement(internalRow)
+    val byteArray = new Array[Byte](rowSizeInBytes)
+    rowConverter.writeRow(
+      internalRow, byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, rowSizeInBytes, objPool)
+    val unsafeRow = new UnsafeRow()
+    unsafeRow.pointTo(
+      byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, row.length, rowSizeInBytes, objPool)
+    unsafeRow
+  }
+
+  test("toUnsafeRow() test helper method") {
+    val row = Row("Hello", 123)
+    val unsafeRow = toUnsafeRow(row, Array(StringType, IntegerType))
+    assert(row.getString(0) === unsafeRow.get(0).toString)
+    assert(row.getInt(1) === unsafeRow.getInt(1))
+  }
+
+  test("basic row serialization") {
+    val rows = Seq(Row("Hello", 1), Row("World", 2))
+    val unsafeRows = rows.map(row => toUnsafeRow(row, Array(StringType, IntegerType)))
+    val serializer = new UnsafeRowSerializer(numFields = 2).newInstance()
+    val baos = new ByteArrayOutputStream()
+    val serializerStream = serializer.serializeStream(baos)
+    for (unsafeRow <- unsafeRows) {
+      serializerStream.writeKey(0)
+      serializerStream.writeValue(unsafeRow)
+    }
+    serializerStream.close()
+    val deserializerIter = serializer.deserializeStream(
+      new ByteArrayInputStream(baos.toByteArray)).asKeyValueIterator
+    for (expectedRow <- unsafeRows) {
+      val actualRow = deserializerIter.next().asInstanceOf[(Integer, UnsafeRow)]._2
+      assert(expectedRow.getSizeInBytes === actualRow.getSizeInBytes)
+      assert(expectedRow.getString(0) === actualRow.getString(0))
+      assert(expectedRow.getInt(1) === actualRow.getInt(1))
+    }
+    assert(!deserializerIter.hasNext)
+  }
+}

From 3f7de7db4cf7c5e2824cb91087c5e9d4beb0f738 Mon Sep 17 00:00:00 2001
From: George Dittmar <georgedittmar@gmail.com>
Date: Mon, 20 Jul 2015 08:55:37 -0700
Subject: [PATCH 0484/1454] [SPARK-7422] [MLLIB] Add argmax to Vector,
 SparseVector

Modifying Vector, DenseVector, and SparseVector to implement argmax functionality. This work is to set the stage for changes to be done in Spark-7423.

Author: George Dittmar <georgedittmar@gmail.com>
Author: George <dittmar@Georges-MacBook-Pro.local>
Author: dittmarg <george.dittmar@webtrends.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #6112 from GeorgeDittmar/SPARK-7422 and squashes the following commits:

3e0a939 [George Dittmar] Merge pull request #1 from mengxr/SPARK-7422
127dec5 [Xiangrui Meng] update argmax impl
2ea6a55 [George Dittmar] Added MimaExcludes for Vectors.argmax
98058f4 [George Dittmar] Merge branch 'master' of github.com:apache/spark into SPARK-7422
5fd9380 [George Dittmar] fixing style check error
42341fb [George Dittmar] refactoring arg max check to better handle zero values
b22af46 [George Dittmar] Fixing spaces between commas in unit test
f2eba2f [George Dittmar] Cleaning up unit tests to be fewer lines
aa330e3 [George Dittmar] Fixing some last if else spacing issues
ac53c55 [George Dittmar] changing dense vector argmax unit test to be one line call vs 2
d5b5423 [George Dittmar] Fixing code style and updating if logic on when to check for zero values
ee1a85a [George Dittmar] Cleaning up unit tests a bit and modifying a few cases
3ee8711 [George Dittmar] Fixing corner case issue with zeros in the active values of the sparse vector. Updated unit tests
b1f059f [George Dittmar] Added comment before we start arg max calculation. Updated unit tests to cover corner cases
f21dcce [George Dittmar] commit
af17981 [dittmarg] Initial work fixing bug that was made clear in pr
eeda560 [George] Fixing SparseVector argmax function to ignore zero values while doing the calculation.
4526acc [George] Merge branch 'master' of github.com:apache/spark into SPARK-7422
df9538a [George] Added argmax to sparse vector and added unit test
3cffed4 [George] Adding unit tests for argmax functions for Dense and Sparse vectors
04677af [George] initial work on adding argmax to Vector and SparseVector
---
 .../apache/spark/mllib/linalg/Vectors.scala   | 57 +++++++++++++++++--
 .../spark/mllib/linalg/VectorsSuite.scala     | 39 +++++++++++++
 project/MimaExcludes.scala                    |  4 ++
 3 files changed, 95 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index e048b01d92462..9067b3ba9a7bb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -150,6 +150,12 @@ sealed trait Vector extends Serializable {
       toDense
     }
   }
+
+  /**
+   * Find the index of a maximal element.  Returns the first maximal element in case of a tie.
+   * Returns -1 if vector has length 0.
+   */
+  def argmax: Int
 }
 
 /**
@@ -588,11 +594,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     new SparseVector(size, ii, vv)
   }
 
-  /**
-   * Find the index of a maximal element.  Returns the first maximal element in case of a tie.
-   * Returns -1 if vector has length 0.
-   */
-  private[spark] def argmax: Int = {
+  override def argmax: Int = {
     if (size == 0) {
       -1
     } else {
@@ -717,6 +719,51 @@ class SparseVector(
       new SparseVector(size, ii, vv)
     }
   }
+
+  override def argmax: Int = {
+    if (size == 0) {
+      -1
+    } else {
+      // Find the max active entry.
+      var maxIdx = indices(0)
+      var maxValue = values(0)
+      var maxJ = 0
+      var j = 1
+      val na = numActives
+      while (j < na) {
+        val v = values(j)
+        if (v > maxValue) {
+          maxValue = v
+          maxIdx = indices(j)
+          maxJ = j
+        }
+        j += 1
+      }
+
+      // If the max active entry is nonpositive and there exists inactive ones, find the first zero.
+      if (maxValue <= 0.0 && na < size) {
+        if (maxValue == 0.0) {
+          // If there exists an inactive entry before maxIdx, find it and return its index.
+          if (maxJ < maxIdx) {
+            var k = 0
+            while (k < maxJ && indices(k) == k) {
+              k += 1
+            }
+            maxIdx = k
+          }
+        } else {
+          // If the max active value is negative, find and return the first inactive index.
+          var k = 0
+          while (k < na && indices(k) == k) {
+            k += 1
+          }
+          maxIdx = k
+        }
+      }
+
+      maxIdx
+    }
+  }
 }
 
 object SparseVector {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index 178d95a7b94ec..03be4119bdaca 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -62,11 +62,50 @@ class VectorsSuite extends SparkFunSuite with Logging {
     assert(vec.toArray.eq(arr))
   }
 
+  test("dense argmax") {
+    val vec = Vectors.dense(Array.empty[Double]).asInstanceOf[DenseVector]
+    assert(vec.argmax === -1)
+
+    val vec2 = Vectors.dense(arr).asInstanceOf[DenseVector]
+    assert(vec2.argmax === 3)
+
+    val vec3 = Vectors.dense(Array(-1.0, 0.0, -2.0, 1.0)).asInstanceOf[DenseVector]
+    assert(vec3.argmax === 3)
+  }
+
   test("sparse to array") {
     val vec = Vectors.sparse(n, indices, values).asInstanceOf[SparseVector]
     assert(vec.toArray === arr)
   }
 
+  test("sparse argmax") {
+    val vec = Vectors.sparse(0, Array.empty[Int], Array.empty[Double]).asInstanceOf[SparseVector]
+    assert(vec.argmax === -1)
+
+    val vec2 = Vectors.sparse(n, indices, values).asInstanceOf[SparseVector]
+    assert(vec2.argmax === 3)
+
+    val vec3 = Vectors.sparse(5, Array(2, 3, 4), Array(1.0, 0.0, -.7))
+    assert(vec3.argmax === 2)
+
+    // check for case that sparse vector is created with
+    // only negative values {0.0, 0.0,-1.0, -0.7, 0.0}
+    val vec4 = Vectors.sparse(5, Array(2, 3), Array(-1.0, -.7))
+    assert(vec4.argmax === 0)
+
+    val vec5 = Vectors.sparse(11, Array(0, 3, 10), Array(-1.0, -.7, 0.0))
+    assert(vec5.argmax === 1)
+
+    val vec6 = Vectors.sparse(11, Array(0, 1, 2), Array(-1.0, -.7, 0.0))
+    assert(vec6.argmax === 2)
+
+    val vec7 = Vectors.sparse(5, Array(0, 1, 3), Array(-1.0, 0.0, -.7))
+    assert(vec7.argmax === 1)
+
+    val vec8 = Vectors.sparse(5, Array(1, 2), Array(0.0, -1.0))
+    assert(vec8.argmax === 0)
+  }
+
   test("vector equals") {
     val dv1 = Vectors.dense(arr.clone())
     val dv2 = Vectors.dense(arr.clone())
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 36417f5df9f2d..dd852547492aa 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -98,6 +98,10 @@ object MimaExcludes {
               "org.apache.spark.api.r.StringRRDD.this"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.api.r.BaseRRDD.this")
+          ) ++ Seq(
+            // SPARK-7422 add argmax for sparse vectors
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.linalg.Vector.argmax")
           )
 
         case v if v.startsWith("1.4") =>

From d0b4e93f7e92ea59058cc457a5586a4d9a596d71 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Mon, 20 Jul 2015 09:00:01 -0700
Subject: [PATCH 0485/1454] [SPARK-8996] [MLLIB] [PYSPARK] Python API for
 Kolmogorov-Smirnov Test

Python API for the KS-test

Statistics.kolmogorovSmirnovTest(data, distName, *params)
I'm not quite sure how to support the callable function since it is not serializable.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7430 from MechCoder/spark-8996 and squashes the following commits:

2dd009d [MechCoder] minor
021d233 [MechCoder] Remove one wrapper and other minor stuff
49d07ab [MechCoder] [SPARK-8996] [MLlib] Python API for Kolmogorov-Smirnov Test
---
 .../mllib/api/python/PythonMLLibAPI.scala     | 14 +++-
 python/pyspark/mllib/stat/_statistics.py      | 67 ++++++++++++++++++-
 python/pyspark/mllib/stat/test.py             | 37 ++++++----
 python/pyspark/mllib/tests.py                 | 19 ++++++
 4 files changed, 123 insertions(+), 14 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index c58a64001d9a0..fda8d5a0b048f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -43,7 +43,7 @@ import org.apache.spark.mllib.recommendation._
 import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.stat.correlation.CorrelationNames
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
-import org.apache.spark.mllib.stat.test.ChiSqTestResult
+import org.apache.spark.mllib.stat.test.{ChiSqTestResult, KolmogorovSmirnovTestResult}
 import org.apache.spark.mllib.stat.{
   KernelDensity, MultivariateStatisticalSummary, Statistics}
 import org.apache.spark.mllib.tree.configuration.{Algo, BoostingStrategy, Strategy}
@@ -1093,6 +1093,18 @@ private[python] class PythonMLLibAPI extends Serializable {
     LinearDataGenerator.generateLinearRDD(
       sc, nexamples, nfeatures, eps, nparts, intercept)
   }
+
+  /**
+   * Java stub for Statistics.kolmogorovSmirnovTest()
+   */
+  def kolmogorovSmirnovTest(
+      data: JavaRDD[Double],
+      distName: String,
+      params: JList[Double]): KolmogorovSmirnovTestResult = {
+    val paramsSeq = params.asScala.toSeq
+    Statistics.kolmogorovSmirnovTest(data, distName, paramsSeq: _*)
+  }
+
 }
 
 /**
diff --git a/python/pyspark/mllib/stat/_statistics.py b/python/pyspark/mllib/stat/_statistics.py
index b475be4b4d953..36c8f48a4a882 100644
--- a/python/pyspark/mllib/stat/_statistics.py
+++ b/python/pyspark/mllib/stat/_statistics.py
@@ -15,11 +15,15 @@
 # limitations under the License.
 #
 
+import sys
+if sys.version >= '3':
+    basestring = str
+
 from pyspark.rdd import RDD, ignore_unicode_prefix
 from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
 from pyspark.mllib.linalg import Matrix, _convert_to_vector
 from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib.stat.test import ChiSqTestResult
+from pyspark.mllib.stat.test import ChiSqTestResult, KolmogorovSmirnovTestResult
 
 
 __all__ = ['MultivariateStatisticalSummary', 'Statistics']
@@ -238,6 +242,67 @@ def chiSqTest(observed, expected=None):
             jmodel = callMLlibFunc("chiSqTest", _convert_to_vector(observed), expected)
         return ChiSqTestResult(jmodel)
 
+    @staticmethod
+    @ignore_unicode_prefix
+    def kolmogorovSmirnovTest(data, distName="norm", *params):
+        """
+        .. note:: Experimental
+
+        Performs the Kolmogorov-Smirnov (KS) test for data sampled from
+        a continuous distribution. It tests the null hypothesis that
+        the data is generated from a particular distribution.
+
+        The given data is sorted and the Empirical Cumulative
+        Distribution Function (ECDF) is calculated
+        which for a given point is the number of points having a CDF
+        value lesser than it divided by the total number of points.
+
+        Since the data is sorted, this is a step function
+        that rises by (1 / length of data) for every ordered point.
+
+        The KS statistic gives us the maximum distance between the
+        ECDF and the CDF. Intuitively if this statistic is large, the
+        probabilty that the null hypothesis is true becomes small.
+        For specific details of the implementation, please have a look
+        at the Scala documentation.
+
+        :param data: RDD, samples from the data
+        :param distName: string, currently only "norm" is supported.
+                         (Normal distribution) to calculate the
+                         theoretical distribution of the data.
+        :param params: additional values which need to be provided for
+                       a certain distribution.
+                       If not provided, the default values are used.
+        :return: KolmogorovSmirnovTestResult object containing the test
+                 statistic, degrees of freedom, p-value,
+                 the method used, and the null hypothesis.
+
+        >>> kstest = Statistics.kolmogorovSmirnovTest
+        >>> data = sc.parallelize([-1.0, 0.0, 1.0])
+        >>> ksmodel = kstest(data, "norm")
+        >>> print(round(ksmodel.pValue, 3))
+        1.0
+        >>> print(round(ksmodel.statistic, 3))
+        0.175
+        >>> ksmodel.nullHypothesis
+        u'Sample follows theoretical distribution'
+
+        >>> data = sc.parallelize([2.0, 3.0, 4.0])
+        >>> ksmodel = kstest(data, "norm", 3.0, 1.0)
+        >>> print(round(ksmodel.pValue, 3))
+        1.0
+        >>> print(round(ksmodel.statistic, 3))
+        0.175
+        """
+        if not isinstance(data, RDD):
+            raise TypeError("data should be an RDD, got %s." % type(data))
+        if not isinstance(distName, basestring):
+            raise TypeError("distName should be a string, got %s." % type(distName))
+
+        params = [float(param) for param in params]
+        return KolmogorovSmirnovTestResult(
+            callMLlibFunc("kolmogorovSmirnovTest", data, distName, params))
+
 
 def _test():
     import doctest
diff --git a/python/pyspark/mllib/stat/test.py b/python/pyspark/mllib/stat/test.py
index 762506e952b43..0abe104049ff9 100644
--- a/python/pyspark/mllib/stat/test.py
+++ b/python/pyspark/mllib/stat/test.py
@@ -15,24 +15,16 @@
 # limitations under the License.
 #
 
-from pyspark.mllib.common import JavaModelWrapper
+from pyspark.mllib.common import inherit_doc, JavaModelWrapper
 
 
-__all__ = ["ChiSqTestResult"]
+__all__ = ["ChiSqTestResult", "KolmogorovSmirnovTestResult"]
 
 
-class ChiSqTestResult(JavaModelWrapper):
+class TestResult(JavaModelWrapper):
     """
-    .. note:: Experimental
-
-    Object containing the test results for the chi-squared hypothesis test.
+    Base class for all test results.
     """
-    @property
-    def method(self):
-        """
-        Name of the test method
-        """
-        return self._java_model.method()
 
     @property
     def pValue(self):
@@ -67,3 +59,24 @@ def nullHypothesis(self):
 
     def __str__(self):
         return self._java_model.toString()
+
+
+@inherit_doc
+class ChiSqTestResult(TestResult):
+    """
+    Contains test results for the chi-squared hypothesis test.
+    """
+
+    @property
+    def method(self):
+        """
+        Name of the test method
+        """
+        return self._java_model.method()
+
+
+@inherit_doc
+class KolmogorovSmirnovTestResult(TestResult):
+    """
+    Contains test results for the Kolmogorov-Smirnov test.
+    """
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index f2eab5b18f077..3f5a02af12e39 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -869,6 +869,25 @@ def test_right_number_of_results(self):
         self.assertIsNotNone(chi[1000])
 
 
+class KolmogorovSmirnovTest(MLlibTestCase):
+
+    def test_R_implementation_equivalence(self):
+        data = self.sc.parallelize([
+            1.1626852897838, -0.585924465893051, 1.78546500331661, -1.33259371048501,
+            -0.446566766553219, 0.569606122374976, -2.88971761441412, -0.869018343326555,
+            -0.461702683149641, -0.555540910137444, -0.0201353678515895, -0.150382224136063,
+            -0.628126755843964, 1.32322085193283, -1.52135057001199, -0.437427868856691,
+            0.970577579543399, 0.0282226444247749, -0.0857821886527593, 0.389214404984942
+        ])
+        model = Statistics.kolmogorovSmirnovTest(data, "norm")
+        self.assertAlmostEqual(model.statistic, 0.189, 3)
+        self.assertAlmostEqual(model.pValue, 0.422, 3)
+
+        model = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
+        self.assertAlmostEqual(model.statistic, 0.189, 3)
+        self.assertAlmostEqual(model.pValue, 0.422, 3)
+
+
 class SerDeTest(MLlibTestCase):
     def test_to_java_object_rdd(self):  # SPARK-6660
         data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0)

From 5112b7f58b9b8031ff79b9184dafe12b71ba1f79 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 09:35:45 -0700
Subject: [PATCH 0486/1454] [SPARK-9153][SQL] codegen StringLPad/StringRPad

Jira: https://issues.apache.org/jira/browse/SPARK-9153

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7527 from tarekauel/SPARK-9153 and squashes the following commits:

3840c6b [Tarek Auel] [SPARK-9153] removed codegen fallback
92b6a5d [Tarek Auel] [SPARK-9153] codegen lpad/rpad
---
 .../expressions/stringOperations.scala        | 54 ++++++++++++++++++-
 .../expressions/StringExpressionsSuite.scala  |  6 +++
 2 files changed, 58 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 5f8ac716f79a1..6608036f01318 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -401,7 +401,7 @@ case class StringLocate(substr: Expression, str: Expression, start: Expression)
  * Returns str, left-padded with pad to a length of len.
  */
 case class StringLPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+  extends Expression with ImplicitCastInputTypes {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
   override def foldable: Boolean = children.forall(_.foldable)
@@ -432,6 +432,31 @@ case class StringLPad(str: Expression, len: Expression, pad: Expression)
     }
   }
 
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val lenGen = len.gen(ctx)
+    val strGen = str.gen(ctx)
+    val padGen = pad.gen(ctx)
+
+    s"""
+      ${lenGen.code}
+      boolean ${ev.isNull} = ${lenGen.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${strGen.code}
+        if (!${strGen.isNull}) {
+          ${padGen.code}
+          if (!${padGen.isNull}) {
+            ${ev.primitive} = ${strGen.primitive}.lpad(${lenGen.primitive}, ${padGen.primitive});
+          } else {
+            ${ev.isNull} = true;
+          }
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+     """
+  }
+
   override def prettyName: String = "lpad"
 }
 
@@ -439,7 +464,7 @@ case class StringLPad(str: Expression, len: Expression, pad: Expression)
  * Returns str, right-padded with pad to a length of len.
  */
 case class StringRPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+  extends Expression with ImplicitCastInputTypes {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
   override def foldable: Boolean = children.forall(_.foldable)
@@ -470,6 +495,31 @@ case class StringRPad(str: Expression, len: Expression, pad: Expression)
     }
   }
 
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val lenGen = len.gen(ctx)
+    val strGen = str.gen(ctx)
+    val padGen = pad.gen(ctx)
+
+    s"""
+      ${lenGen.code}
+      boolean ${ev.isNull} = ${lenGen.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${strGen.code}
+        if (!${strGen.isNull}) {
+          ${padGen.code}
+          if (!${padGen.isNull}) {
+            ${ev.primitive} = ${strGen.primitive}.rpad(${lenGen.primitive}, ${padGen.primitive});
+          } else {
+            ${ev.isNull} = true;
+          }
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+     """
+  }
+
   override def prettyName: String = "rpad"
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 96f433be8b065..d5731229df3bb 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -413,18 +413,24 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val row1 = create_row("hi", 5, "??")
     val row2 = create_row("hi", 1, "?")
     val row3 = create_row(null, 1, "?")
+    val row4 = create_row("hi", null, "?")
+    val row5 = create_row("hi", 1, null)
 
     checkEvaluation(StringLPad(Literal("hi"), Literal(5), Literal("??")), "???hi", row1)
     checkEvaluation(StringLPad(Literal("hi"), Literal(1), Literal("??")), "h", row1)
     checkEvaluation(StringLPad(s1, s2, s3), "???hi", row1)
     checkEvaluation(StringLPad(s1, s2, s3), "h", row2)
     checkEvaluation(StringLPad(s1, s2, s3), null, row3)
+    checkEvaluation(StringLPad(s1, s2, s3), null, row4)
+    checkEvaluation(StringLPad(s1, s2, s3), null, row5)
 
     checkEvaluation(StringRPad(Literal("hi"), Literal(5), Literal("??")), "hi???", row1)
     checkEvaluation(StringRPad(Literal("hi"), Literal(1), Literal("??")), "h", row1)
     checkEvaluation(StringRPad(s1, s2, s3), "hi???", row1)
     checkEvaluation(StringRPad(s1, s2, s3), "h", row2)
     checkEvaluation(StringRPad(s1, s2, s3), null, row3)
+    checkEvaluation(StringRPad(s1, s2, s3), null, row4)
+    checkEvaluation(StringRPad(s1, s2, s3), null, row5)
   }
 
   test("REPEAT") {

From a15ecd057a6226e5cf83ca05c46748624a1cfc8c Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 09:41:25 -0700
Subject: [PATCH 0487/1454] [SPARK-9177][SQL] Reuse of calendar object in
 WeekOfYear

https://issues.apache.org/jira/browse/SPARK-9177

rxin Are we sure that this is thread safe? chenghao-intel explained in another PR that every partition (if I remember correctly) uses one expression instance. This instance isn't used by multiple threads, is it? If not, we are fine.

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7516 from tarekauel/SPARK-9177 and squashes the following commits:

0c1313a [Tarek Auel] [SPARK-9177] utilize more powerful addMutableState
6e2f03f [Tarek Auel] Merge branch 'master' into SPARK-9177
a69ec92 [Tarek Auel] [SPARK-9177] address comment
6cfb180 [Tarek Auel] [SPARK-9177] calendar as lazy transient val
ff97b09 [Tarek Auel] [SPARK-9177] Reuse calendar object in interpreted code and codegen
---
 .../catalyst/expressions/datetimeFunctions.scala  | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index 802445509285d..9e55f0546e123 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -213,10 +213,14 @@ case class WeekOfYear(child: Expression) extends UnaryExpression with ImplicitCa
 
   override def dataType: DataType = IntegerType
 
-  override protected def nullSafeEval(date: Any): Any = {
+  @transient private lazy val c = {
     val c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
     c.setFirstDayOfWeek(Calendar.MONDAY)
     c.setMinimalDaysInFirstWeek(4)
+    c
+  }
+
+  override protected def nullSafeEval(date: Any): Any = {
     c.setTimeInMillis(date.asInstanceOf[Int] * 1000L * 3600L * 24L)
     c.get(Calendar.WEEK_OF_YEAR)
   }
@@ -225,10 +229,13 @@ case class WeekOfYear(child: Expression) extends UnaryExpression with ImplicitCa
     nullSafeCodeGen(ctx, ev, (time) => {
       val cal = classOf[Calendar].getName
       val c = ctx.freshName("cal")
+      ctx.addMutableState(cal, c,
+        s"""
+          $c = $cal.getInstance(java.util.TimeZone.getTimeZone("UTC"));
+          $c.setFirstDayOfWeek($cal.MONDAY);
+          $c.setMinimalDaysInFirstWeek(4);
+         """)
       s"""
-        $cal $c = $cal.getInstance(java.util.TimeZone.getTimeZone("UTC"));
-        $c.setFirstDayOfWeek($cal.MONDAY);
-        $c.setMinimalDaysInFirstWeek(4);
         $c.setTimeInMillis($time * 1000L * 3600L * 24L);
         ${ev.primitive} = $c.get($cal.WEEK_OF_YEAR);
       """

From 04db58ae30d2f73af45b7e6813f97be62dc92095 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 20 Jul 2015 09:42:18 -0700
Subject: [PATCH 0488/1454] [SPARK-9186][SQL] make deterministic describing the
 tree rather than the expression

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7525 from cloud-fan/deterministic and squashes the following commits:

4189bfa [Wenchen Fan] make deterministic describing the tree rather than the expression
---
 .../spark/sql/catalyst/expressions/Expression.scala  | 12 +++++++++++-
 .../spark/sql/catalyst/expressions/random.scala      | 10 +++++-----
 .../spark/sql/catalyst/optimizer/Optimizer.scala     |  4 ++--
 .../expressions/MonotonicallyIncreasingID.scala      |  6 ++----
 .../sql/execution/expressions/SparkPartitionID.scala |  6 ++----
 5 files changed, 22 insertions(+), 16 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index d0a1aa9a1e912..da599b8963340 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -65,10 +65,12 @@ abstract class Expression extends TreeNode[Expression] {
    * Note that this means that an expression should be considered as non-deterministic if:
    * - if it relies on some mutable internal state, or
    * - if it relies on some implicit input that is not part of the children expression list.
+   * - if it has non-deterministic child or children.
    *
    * An example would be `SparkPartitionID` that relies on the partition id returned by TaskContext.
+   * By default leaf expressions are deterministic as Nil.forall(_.deterministic) returns true.
    */
-  def deterministic: Boolean = true
+  def deterministic: Boolean = children.forall(_.deterministic)
 
   def nullable: Boolean
 
@@ -183,6 +185,14 @@ trait Unevaluable { self: Expression =>
     throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
 }
 
+/**
+ * An expression that is nondeterministic.
+ */
+trait Nondeterministic { self: Expression =>
+
+  override def deterministic: Boolean = false
+}
+
 
 /**
  * A leaf expression, i.e. one without any child expressions.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
index 822898e561cd6..aef24a5486466 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
@@ -32,7 +32,9 @@ import org.apache.spark.util.random.XORShiftRandom
  *
  * Since this expression is stateful, it cannot be a case object.
  */
-abstract class RDG(seed: Long) extends LeafExpression with Serializable {
+abstract class RDG extends LeafExpression with Nondeterministic {
+
+  protected def seed: Long
 
   /**
    * Record ID within each partition. By being transient, the Random Number Generator is
@@ -40,15 +42,13 @@ abstract class RDG(seed: Long) extends LeafExpression with Serializable {
    */
   @transient protected lazy val rng = new XORShiftRandom(seed + TaskContext.getPartitionId)
 
-  override def deterministic: Boolean = false
-
   override def nullable: Boolean = false
 
   override def dataType: DataType = DoubleType
 }
 
 /** Generate a random column with i.i.d. uniformly distributed values in [0, 1). */
-case class Rand(seed: Long) extends RDG(seed) {
+case class Rand(seed: Long) extends RDG {
   override def eval(input: InternalRow): Double = rng.nextDouble()
 
   def this() = this(Utils.random.nextLong())
@@ -71,7 +71,7 @@ case class Rand(seed: Long) extends RDG(seed) {
 }
 
 /** Generate a random column with i.i.d. gaussian random distribution. */
-case class Randn(seed: Long) extends RDG(seed) {
+case class Randn(seed: Long) extends RDG {
   override def eval(input: InternalRow): Double = rng.nextGaussian()
 
   def this() = this(Utils.random.nextLong())
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 0f28a0d2c8fff..fafdae07c92f0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -216,9 +216,9 @@ object ProjectCollapsing extends Rule[LogicalPlan] {
 
       // We only collapse these two Projects if their overlapped expressions are all
       // deterministic.
-      val hasNondeterministic = projectList1.flatMap(_.collect {
+      val hasNondeterministic = projectList1.exists(_.collect {
         case a: Attribute if aliasMap.contains(a) => aliasMap(a).child
-      }).exists(_.find(!_.deterministic).isDefined)
+      }.exists(!_.deterministic))
 
       if (hasNondeterministic) {
         p
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
index 4d8ed089731aa..2645eb1854bce 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.expressions
 
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.LeafExpression
+import org.apache.spark.sql.catalyst.expressions.{Nondeterministic, LeafExpression}
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types.{LongType, DataType}
 
@@ -33,7 +33,7 @@ import org.apache.spark.sql.types.{LongType, DataType}
  *
  * Since this expression is stateful, it cannot be a case object.
  */
-private[sql] case class MonotonicallyIncreasingID() extends LeafExpression {
+private[sql] case class MonotonicallyIncreasingID() extends LeafExpression with Nondeterministic {
 
   /**
    * Record ID within each partition. By being transient, count's value is reset to 0 every time
@@ -43,8 +43,6 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression {
 
   @transient private lazy val partitionMask = TaskContext.getPartitionId().toLong << 33
 
-  override def deterministic: Boolean = false
-
   override def nullable: Boolean = false
 
   override def dataType: DataType = LongType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
index 43ffc9cc84738..53ddd47e3e0c1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.expressions
 
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.LeafExpression
+import org.apache.spark.sql.catalyst.expressions.{Nondeterministic, LeafExpression}
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types.{IntegerType, DataType}
 
@@ -27,9 +27,7 @@ import org.apache.spark.sql.types.{IntegerType, DataType}
 /**
  * Expression that returns the current partition id of the Spark task.
  */
-private[sql] case object SparkPartitionID extends LeafExpression {
-
-  override def deterministic: Boolean = false
+private[sql] case object SparkPartitionID extends LeafExpression with Nondeterministic {
 
   override def nullable: Boolean = false
 

From c6fe9b4a179eecce69a813501dd0f4a22ff5dd5b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 20 Jul 2015 09:43:25 -0700
Subject: [PATCH 0489/1454] [SQL] Remove space from DataFrame Scala/Java API.

I don't think this function is useful at all in Scala/Java, since users can easily compute n * space easily.

Author: Reynold Xin <rxin@databricks.com>

Closes #7530 from rxin/remove-space and squashes the following commits:

c147873 [Reynold Xin] [SQL] Remove space from DataFrame Scala/Java API.
---
 .../org/apache/spark/sql/functions.scala      | 20 -------------------
 .../spark/sql/StringFunctionsSuite.scala      |  4 ----
 2 files changed, 24 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index b5140dca0487f..41b25d1836481 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2237,26 +2237,6 @@ object functions {
     StringReverse(str.expr)
   }
 
-  /**
-   * Make a n spaces of string.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def space(n: String): Column = {
-    space(Column(n))
-  }
-
-  /**
-   * Make a n spaces of string.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def space(n: Column): Column = {
-    StringSpace(n.expr)
-  }
-
   //////////////////////////////////////////////////////////////////////////////////////////////
   // DateTime functions
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index fe4de8d8b855f..413f3858d6764 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -190,10 +190,6 @@ class StringFunctionsSuite extends QueryTest {
   test("string space function") {
     val df = Seq((2, 3)).toDF("a", "b")
 
-    checkAnswer(
-      df.select(space($"a"), space("b")),
-      Row("  ", "   "))
-
     checkAnswer(
       df.selectExpr("space(b)"),
       Row("   "))

From 80e2568b25780a7094199239da8ad6cfb6efc9f7 Mon Sep 17 00:00:00 2001
From: Imran Rashid <irashid@cloudera.com>
Date: Mon, 20 Jul 2015 10:28:32 -0700
Subject: [PATCH 0490/1454] [SPARK-8103][core] DAGScheduler should not submit
 multiple concurrent attempts for a stage

https://issues.apache.org/jira/browse/SPARK-8103

cc kayousterhout (thanks for the extra test case)

Author: Imran Rashid <irashid@cloudera.com>
Author: Kay Ousterhout <kayousterhout@gmail.com>
Author: Imran Rashid <squito@users.noreply.github.com>

Closes #6750 from squito/SPARK-8103 and squashes the following commits:

fb3acfc [Imran Rashid] fix log msg
e01b7aa [Imran Rashid] fix some comments, style
584acd4 [Imran Rashid] simplify going from taskId to taskSetMgr
e43ac25 [Imran Rashid] Merge branch 'master' into SPARK-8103
6bc23af [Imran Rashid] update log msg
4470fa1 [Imran Rashid] rename
c04707e [Imran Rashid] style
88b61cc [Imran Rashid] add tests to make sure that TaskSchedulerImpl schedules correctly with zombie attempts
d7f1ef2 [Imran Rashid] get rid of activeTaskSets
a21c8b5 [Imran Rashid] Merge branch 'master' into SPARK-8103
906d626 [Imran Rashid] fix merge
109900e [Imran Rashid] Merge branch 'master' into SPARK-8103
c0d4d90 [Imran Rashid] Revert "Index active task sets by stage Id rather than by task set id"
f025154 [Imran Rashid] Merge pull request #2 from kayousterhout/imran_SPARK-8103
baf46e1 [Kay Ousterhout] Index active task sets by stage Id rather than by task set id
19685bb [Imran Rashid] switch to using latestInfo.attemptId, and add comments
a5f7c8c [Imran Rashid] remove comment for reviewers
227b40d [Imran Rashid] style
517b6e5 [Imran Rashid] get rid of SparkIllegalStateException
b2faef5 [Imran Rashid] faster check for conflicting task sets
6542b42 [Imran Rashid] remove extra stageAttemptId
ada7726 [Imran Rashid] reviewer feedback
d8eb202 [Imran Rashid] Merge branch 'master' into SPARK-8103
46bc26a [Imran Rashid] more cleanup of debug garbage
cb245da [Imran Rashid] finally found the issue ... clean up debug stuff
8c29707 [Imran Rashid] Merge branch 'master' into SPARK-8103
89a59b6 [Imran Rashid] more printlns ...
9601b47 [Imran Rashid] more debug printlns
ecb4e7d [Imran Rashid] debugging printlns
b6bc248 [Imran Rashid] style
55f4a94 [Imran Rashid] get rid of more random test case since kays tests are clearer
7021d28 [Imran Rashid] update test since listenerBus.waitUntilEmpty now throws an exception instead of returning a boolean
883fe49 [Kay Ousterhout] Unit tests for concurrent stages issue
6e14683 [Imran Rashid] unit test just to make sure we fail fast on concurrent attempts
06a0af6 [Imran Rashid] ignore for jenkins
c443def [Imran Rashid] better fix and simpler test case
28d70aa [Imran Rashid] wip on getting a better test case ...
a9bf31f [Imran Rashid] wip
---
 .../apache/spark/scheduler/DAGScheduler.scala |  78 +++++-----
 .../apache/spark/scheduler/ResultTask.scala   |   3 +-
 .../spark/scheduler/ShuffleMapTask.scala      |   5 +-
 .../org/apache/spark/scheduler/Task.scala     |   5 +-
 .../spark/scheduler/TaskSchedulerImpl.scala   |  99 +++++++-----
 .../org/apache/spark/scheduler/TaskSet.scala  |   4 +-
 .../CoarseGrainedSchedulerBackend.scala       |   5 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   | 141 ++++++++++++++++++
 .../org/apache/spark/scheduler/FakeTask.scala |   8 +-
 .../scheduler/NotSerializableFakeTask.scala   |   2 +-
 .../spark/scheduler/TaskContextSuite.scala    |   4 +-
 .../scheduler/TaskSchedulerImplSuite.scala    | 113 +++++++++++++-
 .../spark/scheduler/TaskSetManagerSuite.scala |   2 +-
 13 files changed, 383 insertions(+), 86 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index dd55cd8054332..71a219a4f3414 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -857,7 +857,6 @@ class DAGScheduler(
     // Get our pending tasks and remember them in our pendingTasks entry
     stage.pendingTasks.clear()
 
-
     // First figure out the indexes of partition ids to compute.
     val partitionsToCompute: Seq[Int] = {
       stage match {
@@ -918,7 +917,7 @@ class DAGScheduler(
           partitionsToCompute.map { id =>
             val locs = getPreferredLocs(stage.rdd, id)
             val part = stage.rdd.partitions(id)
-            new ShuffleMapTask(stage.id, taskBinary, part, locs)
+            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs)
           }
 
         case stage: ResultStage =>
@@ -927,7 +926,7 @@ class DAGScheduler(
             val p: Int = job.partitions(id)
             val part = stage.rdd.partitions(p)
             val locs = getPreferredLocs(stage.rdd, p)
-            new ResultTask(stage.id, taskBinary, part, locs, id)
+            new ResultTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs, id)
           }
       }
     } catch {
@@ -1069,10 +1068,11 @@ class DAGScheduler(
             val execId = status.location.executorId
             logDebug("ShuffleMapTask finished on " + execId)
             if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) {
-              logInfo("Ignoring possibly bogus ShuffleMapTask completion from " + execId)
+              logInfo(s"Ignoring possibly bogus $smt completion from executor $execId")
             } else {
               shuffleStage.addOutputLoc(smt.partitionId, status)
             }
+
             if (runningStages.contains(shuffleStage) && shuffleStage.pendingTasks.isEmpty) {
               markStageAsFinished(shuffleStage)
               logInfo("looking for newly runnable stages")
@@ -1132,38 +1132,48 @@ class DAGScheduler(
         val failedStage = stageIdToStage(task.stageId)
         val mapStage = shuffleToMapStage(shuffleId)
 
-        // It is likely that we receive multiple FetchFailed for a single stage (because we have
-        // multiple tasks running concurrently on different executors). In that case, it is possible
-        // the fetch failure has already been handled by the scheduler.
-        if (runningStages.contains(failedStage)) {
-          logInfo(s"Marking $failedStage (${failedStage.name}) as failed " +
-            s"due to a fetch failure from $mapStage (${mapStage.name})")
-          markStageAsFinished(failedStage, Some(failureMessage))
-        }
+        if (failedStage.latestInfo.attemptId != task.stageAttemptId) {
+          logInfo(s"Ignoring fetch failure from $task as it's from $failedStage attempt" +
+            s" ${task.stageAttemptId} and there is a more recent attempt for that stage " +
+            s"(attempt ID ${failedStage.latestInfo.attemptId}) running")
+        } else {
 
-        if (disallowStageRetryForTest) {
-          abortStage(failedStage, "Fetch failure will not retry stage due to testing config")
-        } else if (failedStages.isEmpty) {
-          // Don't schedule an event to resubmit failed stages if failed isn't empty, because
-          // in that case the event will already have been scheduled.
-          // TODO: Cancel running tasks in the stage
-          logInfo(s"Resubmitting $mapStage (${mapStage.name}) and " +
-            s"$failedStage (${failedStage.name}) due to fetch failure")
-          messageScheduler.schedule(new Runnable {
-            override def run(): Unit = eventProcessLoop.post(ResubmitFailedStages)
-          }, DAGScheduler.RESUBMIT_TIMEOUT, TimeUnit.MILLISECONDS)
-        }
-        failedStages += failedStage
-        failedStages += mapStage
-        // Mark the map whose fetch failed as broken in the map stage
-        if (mapId != -1) {
-          mapStage.removeOutputLoc(mapId, bmAddress)
-          mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress)
-        }
+          // It is likely that we receive multiple FetchFailed for a single stage (because we have
+          // multiple tasks running concurrently on different executors). In that case, it is
+          // possible the fetch failure has already been handled by the scheduler.
+          if (runningStages.contains(failedStage)) {
+            logInfo(s"Marking $failedStage (${failedStage.name}) as failed " +
+              s"due to a fetch failure from $mapStage (${mapStage.name})")
+            markStageAsFinished(failedStage, Some(failureMessage))
+          } else {
+            logDebug(s"Received fetch failure from $task, but its from $failedStage which is no " +
+              s"longer running")
+          }
+
+          if (disallowStageRetryForTest) {
+            abortStage(failedStage, "Fetch failure will not retry stage due to testing config")
+          } else if (failedStages.isEmpty) {
+            // Don't schedule an event to resubmit failed stages if failed isn't empty, because
+            // in that case the event will already have been scheduled.
+            // TODO: Cancel running tasks in the stage
+            logInfo(s"Resubmitting $mapStage (${mapStage.name}) and " +
+              s"$failedStage (${failedStage.name}) due to fetch failure")
+            messageScheduler.schedule(new Runnable {
+              override def run(): Unit = eventProcessLoop.post(ResubmitFailedStages)
+            }, DAGScheduler.RESUBMIT_TIMEOUT, TimeUnit.MILLISECONDS)
+          }
+          failedStages += failedStage
+          failedStages += mapStage
+          // Mark the map whose fetch failed as broken in the map stage
+          if (mapId != -1) {
+            mapStage.removeOutputLoc(mapId, bmAddress)
+            mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress)
+          }
 
-        // TODO: mark the executor as failed only if there were lots of fetch failures on it
-        if (bmAddress != null) {
-          handleExecutorLost(bmAddress.executorId, fetchFailed = true, Some(task.epoch))
+          // TODO: mark the executor as failed only if there were lots of fetch failures on it
+          if (bmAddress != null) {
+            handleExecutorLost(bmAddress.executorId, fetchFailed = true, Some(task.epoch))
+          }
         }
 
       case commitDenied: TaskCommitDenied =>
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index c9a124113961f..9c2606e278c54 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -41,11 +41,12 @@ import org.apache.spark.rdd.RDD
  */
 private[spark] class ResultTask[T, U](
     stageId: Int,
+    stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
     @transient locs: Seq[TaskLocation],
     val outputId: Int)
-  extends Task[U](stageId, partition.index) with Serializable {
+  extends Task[U](stageId, stageAttemptId, partition.index) with Serializable {
 
   @transient private[this] val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index bd3dd23dfe1ac..14c8c00961487 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -40,14 +40,15 @@ import org.apache.spark.shuffle.ShuffleWriter
  */
 private[spark] class ShuffleMapTask(
     stageId: Int,
+    stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
     @transient private var locs: Seq[TaskLocation])
-  extends Task[MapStatus](stageId, partition.index) with Logging {
+  extends Task[MapStatus](stageId, stageAttemptId, partition.index) with Logging {
 
   /** A constructor used only in test suites. This does not require passing in an RDD. */
   def this(partitionId: Int) {
-    this(0, null, new Partition { override def index: Int = 0 }, null)
+    this(0, 0, null, new Partition { override def index: Int = 0 }, null)
   }
 
   @transient private val preferredLocs: Seq[TaskLocation] = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
index 6a86f9d4b8530..76a19aeac4679 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
@@ -43,7 +43,10 @@ import org.apache.spark.util.Utils
  * @param stageId id of the stage this task belongs to
  * @param partitionId index of the number in the RDD
  */
-private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) extends Serializable {
+private[spark] abstract class Task[T](
+    val stageId: Int,
+    val stageAttemptId: Int,
+    var partitionId: Int) extends Serializable {
 
   /**
    * The key of the Map is the accumulator id and the value of the Map is the latest accumulator
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index ed3dde0fc3055..1705e7f962de2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -75,9 +75,9 @@ private[spark] class TaskSchedulerImpl(
 
   // TaskSetManagers are not thread safe, so any access to one should be synchronized
   // on this class.
-  val activeTaskSets = new HashMap[String, TaskSetManager]
+  private val taskSetsByStageIdAndAttempt = new HashMap[Int, HashMap[Int, TaskSetManager]]
 
-  val taskIdToTaskSetId = new HashMap[Long, String]
+  private[scheduler] val taskIdToTaskSetManager = new HashMap[Long, TaskSetManager]
   val taskIdToExecutorId = new HashMap[Long, String]
 
   @volatile private var hasReceivedTask = false
@@ -162,7 +162,17 @@ private[spark] class TaskSchedulerImpl(
     logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
     this.synchronized {
       val manager = createTaskSetManager(taskSet, maxTaskFailures)
-      activeTaskSets(taskSet.id) = manager
+      val stage = taskSet.stageId
+      val stageTaskSets =
+        taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
+      stageTaskSets(taskSet.stageAttemptId) = manager
+      val conflictingTaskSet = stageTaskSets.exists { case (_, ts) =>
+        ts.taskSet != taskSet && !ts.isZombie
+      }
+      if (conflictingTaskSet) {
+        throw new IllegalStateException(s"more than one active taskSet for stage $stage:" +
+          s" ${stageTaskSets.toSeq.map{_._2.taskSet.id}.mkString(",")}")
+      }
       schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
 
       if (!isLocal && !hasReceivedTask) {
@@ -192,19 +202,21 @@ private[spark] class TaskSchedulerImpl(
 
   override def cancelTasks(stageId: Int, interruptThread: Boolean): Unit = synchronized {
     logInfo("Cancelling stage " + stageId)
-    activeTaskSets.find(_._2.stageId == stageId).foreach { case (_, tsm) =>
-      // There are two possible cases here:
-      // 1. The task set manager has been created and some tasks have been scheduled.
-      //    In this case, send a kill signal to the executors to kill the task and then abort
-      //    the stage.
-      // 2. The task set manager has been created but no tasks has been scheduled. In this case,
-      //    simply abort the stage.
-      tsm.runningTasksSet.foreach { tid =>
-        val execId = taskIdToExecutorId(tid)
-        backend.killTask(tid, execId, interruptThread)
+    taskSetsByStageIdAndAttempt.get(stageId).foreach { attempts =>
+      attempts.foreach { case (_, tsm) =>
+        // There are two possible cases here:
+        // 1. The task set manager has been created and some tasks have been scheduled.
+        //    In this case, send a kill signal to the executors to kill the task and then abort
+        //    the stage.
+        // 2. The task set manager has been created but no tasks has been scheduled. In this case,
+        //    simply abort the stage.
+        tsm.runningTasksSet.foreach { tid =>
+          val execId = taskIdToExecutorId(tid)
+          backend.killTask(tid, execId, interruptThread)
+        }
+        tsm.abort("Stage %s cancelled".format(stageId))
+        logInfo("Stage %d was cancelled".format(stageId))
       }
-      tsm.abort("Stage %s cancelled".format(stageId))
-      logInfo("Stage %d was cancelled".format(stageId))
     }
   }
 
@@ -214,7 +226,12 @@ private[spark] class TaskSchedulerImpl(
    * cleaned up.
    */
   def taskSetFinished(manager: TaskSetManager): Unit = synchronized {
-    activeTaskSets -= manager.taskSet.id
+    taskSetsByStageIdAndAttempt.get(manager.taskSet.stageId).foreach { taskSetsForStage =>
+      taskSetsForStage -= manager.taskSet.stageAttemptId
+      if (taskSetsForStage.isEmpty) {
+        taskSetsByStageIdAndAttempt -= manager.taskSet.stageId
+      }
+    }
     manager.parent.removeSchedulable(manager)
     logInfo("Removed TaskSet %s, whose tasks have all completed, from pool %s"
       .format(manager.taskSet.id, manager.parent.name))
@@ -235,7 +252,7 @@ private[spark] class TaskSchedulerImpl(
           for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
             tasks(i) += task
             val tid = task.taskId
-            taskIdToTaskSetId(tid) = taskSet.taskSet.id
+            taskIdToTaskSetManager(tid) = taskSet
             taskIdToExecutorId(tid) = execId
             executorsByHost(host) += execId
             availableCpus(i) -= CPUS_PER_TASK
@@ -319,26 +336,24 @@ private[spark] class TaskSchedulerImpl(
             failedExecutor = Some(execId)
           }
         }
-        taskIdToTaskSetId.get(tid) match {
-          case Some(taskSetId) =>
+        taskIdToTaskSetManager.get(tid) match {
+          case Some(taskSet) =>
             if (TaskState.isFinished(state)) {
-              taskIdToTaskSetId.remove(tid)
+              taskIdToTaskSetManager.remove(tid)
               taskIdToExecutorId.remove(tid)
             }
-            activeTaskSets.get(taskSetId).foreach { taskSet =>
-              if (state == TaskState.FINISHED) {
-                taskSet.removeRunningTask(tid)
-                taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData)
-              } else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) {
-                taskSet.removeRunningTask(tid)
-                taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData)
-              }
+            if (state == TaskState.FINISHED) {
+              taskSet.removeRunningTask(tid)
+              taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData)
+            } else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) {
+              taskSet.removeRunningTask(tid)
+              taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData)
             }
           case None =>
             logError(
               ("Ignoring update with state %s for TID %s because its task set is gone (this is " +
-               "likely the result of receiving duplicate task finished status updates)")
-              .format(state, tid))
+                "likely the result of receiving duplicate task finished status updates)")
+                .format(state, tid))
         }
       } catch {
         case e: Exception => logError("Exception in statusUpdate", e)
@@ -363,9 +378,9 @@ private[spark] class TaskSchedulerImpl(
 
     val metricsWithStageIds: Array[(Long, Int, Int, TaskMetrics)] = synchronized {
       taskMetrics.flatMap { case (id, metrics) =>
-        taskIdToTaskSetId.get(id)
-          .flatMap(activeTaskSets.get)
-          .map(taskSetMgr => (id, taskSetMgr.stageId, taskSetMgr.taskSet.attempt, metrics))
+        taskIdToTaskSetManager.get(id).map { taskSetMgr =>
+          (id, taskSetMgr.stageId, taskSetMgr.taskSet.stageAttemptId, metrics)
+        }
       }
     }
     dagScheduler.executorHeartbeatReceived(execId, metricsWithStageIds, blockManagerId)
@@ -397,9 +412,12 @@ private[spark] class TaskSchedulerImpl(
 
   def error(message: String) {
     synchronized {
-      if (activeTaskSets.nonEmpty) {
+      if (taskSetsByStageIdAndAttempt.nonEmpty) {
         // Have each task set throw a SparkException with the error
-        for ((taskSetId, manager) <- activeTaskSets) {
+        for {
+          attempts <- taskSetsByStageIdAndAttempt.values
+          manager <- attempts.values
+        } {
           try {
             manager.abort(message)
           } catch {
@@ -520,6 +538,17 @@ private[spark] class TaskSchedulerImpl(
 
   override def applicationAttemptId(): Option[String] = backend.applicationAttemptId()
 
+  private[scheduler] def taskSetManagerForAttempt(
+      stageId: Int,
+      stageAttemptId: Int): Option[TaskSetManager] = {
+    for {
+      attempts <- taskSetsByStageIdAndAttempt.get(stageId)
+      manager <- attempts.get(stageAttemptId)
+    } yield {
+      manager
+    }
+  }
+
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala
index c3ad325156f53..be8526ba9b94f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala
@@ -26,10 +26,10 @@ import java.util.Properties
 private[spark] class TaskSet(
     val tasks: Array[Task[_]],
     val stageId: Int,
-    val attempt: Int,
+    val stageAttemptId: Int,
     val priority: Int,
     val properties: Properties) {
-    val id: String = stageId + "." + attempt
+    val id: String = stageId + "." + stageAttemptId
 
   override def toString: String = "TaskSet " + id
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 0e3215d6e9ec8..f14c603ac6891 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -191,15 +191,14 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
       for (task <- tasks.flatten) {
         val serializedTask = ser.serialize(task)
         if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
-          val taskSetId = scheduler.taskIdToTaskSetId(task.taskId)
-          scheduler.activeTaskSets.get(taskSetId).foreach { taskSet =>
+          scheduler.taskIdToTaskSetManager.get(task.taskId).foreach { taskSetMgr =>
             try {
               var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
                 "spark.akka.frameSize (%d bytes) - reserved (%d bytes). Consider increasing " +
                 "spark.akka.frameSize or using broadcast variables for large values."
               msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize,
                 AkkaUtils.reservedSizeBytes)
-              taskSet.abort(msg)
+              taskSetMgr.abort(msg)
             } catch {
               case e: Exception => logError("Exception in error callback", e)
             }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 4f2b0fa162b72..86728cb2b62af 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -101,9 +101,15 @@ class DAGSchedulerSuite
   /** Length of time to wait while draining listener events. */
   val WAIT_TIMEOUT_MILLIS = 10000
   val sparkListener = new SparkListener() {
+    val submittedStageInfos = new HashSet[StageInfo]
     val successfulStages = new HashSet[Int]
     val failedStages = new ArrayBuffer[Int]
     val stageByOrderOfExecution = new ArrayBuffer[Int]
+
+    override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) {
+      submittedStageInfos += stageSubmitted.stageInfo
+    }
+
     override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) {
       val stageInfo = stageCompleted.stageInfo
       stageByOrderOfExecution += stageInfo.stageId
@@ -150,6 +156,7 @@ class DAGSchedulerSuite
     // Enable local execution for this test
     val conf = new SparkConf().set("spark.localExecution.enabled", "true")
     sc = new SparkContext("local", "DAGSchedulerSuite", conf)
+    sparkListener.submittedStageInfos.clear()
     sparkListener.successfulStages.clear()
     sparkListener.failedStages.clear()
     failure = null
@@ -547,6 +554,140 @@ class DAGSchedulerSuite
     assert(sparkListener.failedStages.size == 1)
   }
 
+  /**
+   * This tests the case where another FetchFailed comes in while the map stage is getting
+   * re-run.
+   */
+  test("late fetch failures don't cause multiple concurrent attempts for the same map stage") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+    submit(reduceRdd, Array(0, 1))
+
+    val mapStageId = 0
+    def countSubmittedMapStageAttempts(): Int = {
+      sparkListener.submittedStageInfos.count(_.stageId == mapStageId)
+    }
+
+    // The map stage should have been submitted.
+    assert(countSubmittedMapStageAttempts() === 1)
+
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", 2)),
+      (Success, makeMapStatus("hostB", 2))))
+    // The MapOutputTracker should know about both map output locations.
+    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) ===
+      Array("hostA", "hostB"))
+    assert(mapOutputTracker.getServerStatuses(shuffleId, 1).map(_._1.host) ===
+      Array("hostA", "hostB"))
+
+    // The first result task fails, with a fetch failure for the output from the first mapper.
+    runEvent(CompletionEvent(
+      taskSets(1).tasks(0),
+      FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0, "ignored"),
+      null,
+      Map[Long, Any](),
+      createFakeTaskInfo(),
+      null))
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+    assert(sparkListener.failedStages.contains(1))
+
+    // Trigger resubmission of the failed map stage.
+    runEvent(ResubmitFailedStages)
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+
+    // Another attempt for the map stage should have been submitted, resulting in 2 total attempts.
+    assert(countSubmittedMapStageAttempts() === 2)
+
+    // The second ResultTask fails, with a fetch failure for the output from the second mapper.
+    runEvent(CompletionEvent(
+      taskSets(1).tasks(1),
+      FetchFailed(makeBlockManagerId("hostB"), shuffleId, 1, 1, "ignored"),
+      null,
+      Map[Long, Any](),
+      createFakeTaskInfo(),
+      null))
+
+    // Another ResubmitFailedStages event should not result in another attempt for the map
+    // stage being run concurrently.
+    // NOTE: the actual ResubmitFailedStages may get called at any time during this, but it
+    // shouldn't effect anything -- our calling it just makes *SURE* it gets called between the
+    // desired event and our check.
+    runEvent(ResubmitFailedStages)
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+    assert(countSubmittedMapStageAttempts() === 2)
+
+  }
+
+  /**
+    * This tests the case where a late FetchFailed comes in after the map stage has finished getting
+    * retried and a new reduce stage starts running.
+    */
+  test("extremely late fetch failures don't cause multiple concurrent attempts for " +
+      "the same stage") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+    submit(reduceRdd, Array(0, 1))
+
+    def countSubmittedReduceStageAttempts(): Int = {
+      sparkListener.submittedStageInfos.count(_.stageId == 1)
+    }
+    def countSubmittedMapStageAttempts(): Int = {
+      sparkListener.submittedStageInfos.count(_.stageId == 0)
+    }
+
+    // The map stage should have been submitted.
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+    assert(countSubmittedMapStageAttempts() === 1)
+
+    // Complete the map stage.
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", 2)),
+      (Success, makeMapStatus("hostB", 2))))
+
+    // The reduce stage should have been submitted.
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+    assert(countSubmittedReduceStageAttempts() === 1)
+
+    // The first result task fails, with a fetch failure for the output from the first mapper.
+    runEvent(CompletionEvent(
+      taskSets(1).tasks(0),
+      FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0, "ignored"),
+      null,
+      Map[Long, Any](),
+      createFakeTaskInfo(),
+      null))
+
+    // Trigger resubmission of the failed map stage and finish the re-started map task.
+    runEvent(ResubmitFailedStages)
+    complete(taskSets(2), Seq((Success, makeMapStatus("hostA", 1))))
+
+    // Because the map stage finished, another attempt for the reduce stage should have been
+    // submitted, resulting in 2 total attempts for each the map and the reduce stage.
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+    assert(countSubmittedMapStageAttempts() === 2)
+    assert(countSubmittedReduceStageAttempts() === 2)
+
+    // A late FetchFailed arrives from the second task in the original reduce stage.
+    runEvent(CompletionEvent(
+      taskSets(1).tasks(1),
+      FetchFailed(makeBlockManagerId("hostB"), shuffleId, 1, 1, "ignored"),
+      null,
+      Map[Long, Any](),
+      createFakeTaskInfo(),
+      null))
+
+    // Running ResubmitFailedStages shouldn't result in any more attempts for the map stage, because
+    // the FetchFailed should have been ignored
+    runEvent(ResubmitFailedStages)
+
+    // The FetchFailed from the original reduce stage should be ignored.
+    assert(countSubmittedMapStageAttempts() === 2)
+  }
+
   test("ignore late map task completions") {
     val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
diff --git a/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
index 0a7cb69416a08..b3ca150195a5f 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
@@ -19,7 +19,7 @@ package org.apache.spark.scheduler
 
 import org.apache.spark.TaskContext
 
-class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId, 0) {
+class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId, 0, 0) {
   override def runTask(context: TaskContext): Int = 0
 
   override def preferredLocations: Seq[TaskLocation] = prefLocs
@@ -31,12 +31,16 @@ object FakeTask {
    * locations for each task (given as varargs) if this sequence is not empty.
    */
   def createTaskSet(numTasks: Int, prefLocs: Seq[TaskLocation]*): TaskSet = {
+    createTaskSet(numTasks, 0, prefLocs: _*)
+  }
+
+  def createTaskSet(numTasks: Int, stageAttemptId: Int, prefLocs: Seq[TaskLocation]*): TaskSet = {
     if (prefLocs.size != 0 && prefLocs.size != numTasks) {
       throw new IllegalArgumentException("Wrong number of task locations")
     }
     val tasks = Array.tabulate[Task[_]](numTasks) { i =>
       new FakeTask(i, if (prefLocs.size != 0) prefLocs(i) else Nil)
     }
-    new TaskSet(tasks, 0, 0, 0, null)
+    new TaskSet(tasks, 0, stageAttemptId, 0, null)
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
index 9b92f8de56759..383855caefa2f 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
@@ -25,7 +25,7 @@ import org.apache.spark.TaskContext
  * A Task implementation that fails to serialize.
  */
 private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int)
-  extends Task[Array[Byte]](stageId, 0) {
+  extends Task[Array[Byte]](stageId, 0, 0) {
 
   override def runTask(context: TaskContext): Array[Byte] = Array.empty[Byte]
   override def preferredLocations: Seq[TaskLocation] = Seq[TaskLocation]()
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
index 7c1adc1aef1b6..b9b0eccb0d834 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
@@ -41,8 +41,8 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark
     }
     val closureSerializer = SparkEnv.get.closureSerializer.newInstance()
     val func = (c: TaskContext, i: Iterator[String]) => i.next()
-    val task = new ResultTask[String, String](
-      0, sc.broadcast(closureSerializer.serialize((rdd, func)).array), rdd.partitions(0), Seq(), 0)
+    val task = new ResultTask[String, String](0, 0,
+      sc.broadcast(closureSerializer.serialize((rdd, func)).array), rdd.partitions(0), Seq(), 0)
     intercept[RuntimeException] {
       task.run(0, 0)
     }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
index a6d5232feb8de..c2edd4c317d6e 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala
@@ -33,7 +33,7 @@ class TaskSchedulerImplSuite extends SparkFunSuite with LocalSparkContext with L
     val taskScheduler = new TaskSchedulerImpl(sc)
     taskScheduler.initialize(new FakeSchedulerBackend)
     // Need to initialize a DAGScheduler for the taskScheduler to use for callbacks.
-    val dagScheduler = new DAGScheduler(sc, taskScheduler) {
+    new DAGScheduler(sc, taskScheduler) {
       override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
       override def executorAdded(execId: String, host: String) {}
     }
@@ -67,7 +67,7 @@ class TaskSchedulerImplSuite extends SparkFunSuite with LocalSparkContext with L
     val taskScheduler = new TaskSchedulerImpl(sc)
     taskScheduler.initialize(new FakeSchedulerBackend)
     // Need to initialize a DAGScheduler for the taskScheduler to use for callbacks.
-    val dagScheduler = new DAGScheduler(sc, taskScheduler) {
+    new DAGScheduler(sc, taskScheduler) {
       override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
       override def executorAdded(execId: String, host: String) {}
     }
@@ -128,4 +128,113 @@ class TaskSchedulerImplSuite extends SparkFunSuite with LocalSparkContext with L
     assert(taskDescriptions.map(_.executorId) === Seq("executor0"))
   }
 
+  test("refuse to schedule concurrent attempts for the same stage (SPARK-8103)") {
+    sc = new SparkContext("local", "TaskSchedulerImplSuite")
+    val taskScheduler = new TaskSchedulerImpl(sc)
+    taskScheduler.initialize(new FakeSchedulerBackend)
+    // Need to initialize a DAGScheduler for the taskScheduler to use for callbacks.
+    val dagScheduler = new DAGScheduler(sc, taskScheduler) {
+      override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
+      override def executorAdded(execId: String, host: String) {}
+    }
+    taskScheduler.setDAGScheduler(dagScheduler)
+    val attempt1 = FakeTask.createTaskSet(1, 0)
+    val attempt2 = FakeTask.createTaskSet(1, 1)
+    taskScheduler.submitTasks(attempt1)
+    intercept[IllegalStateException] { taskScheduler.submitTasks(attempt2) }
+
+    // OK to submit multiple if previous attempts are all zombie
+    taskScheduler.taskSetManagerForAttempt(attempt1.stageId, attempt1.stageAttemptId)
+      .get.isZombie = true
+    taskScheduler.submitTasks(attempt2)
+    val attempt3 = FakeTask.createTaskSet(1, 2)
+    intercept[IllegalStateException] { taskScheduler.submitTasks(attempt3) }
+    taskScheduler.taskSetManagerForAttempt(attempt2.stageId, attempt2.stageAttemptId)
+      .get.isZombie = true
+    taskScheduler.submitTasks(attempt3)
+  }
+
+  test("don't schedule more tasks after a taskset is zombie") {
+    sc = new SparkContext("local", "TaskSchedulerImplSuite")
+    val taskScheduler = new TaskSchedulerImpl(sc)
+    taskScheduler.initialize(new FakeSchedulerBackend)
+    // Need to initialize a DAGScheduler for the taskScheduler to use for callbacks.
+    new DAGScheduler(sc, taskScheduler) {
+      override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
+      override def executorAdded(execId: String, host: String) {}
+    }
+
+    val numFreeCores = 1
+    val workerOffers = Seq(new WorkerOffer("executor0", "host0", numFreeCores))
+    val attempt1 = FakeTask.createTaskSet(10)
+
+    // submit attempt 1, offer some resources, some tasks get scheduled
+    taskScheduler.submitTasks(attempt1)
+    val taskDescriptions = taskScheduler.resourceOffers(workerOffers).flatten
+    assert(1 === taskDescriptions.length)
+
+    // now mark attempt 1 as a zombie
+    taskScheduler.taskSetManagerForAttempt(attempt1.stageId, attempt1.stageAttemptId)
+      .get.isZombie = true
+
+    // don't schedule anything on another resource offer
+    val taskDescriptions2 = taskScheduler.resourceOffers(workerOffers).flatten
+    assert(0 === taskDescriptions2.length)
+
+    // if we schedule another attempt for the same stage, it should get scheduled
+    val attempt2 = FakeTask.createTaskSet(10, 1)
+
+    // submit attempt 2, offer some resources, some tasks get scheduled
+    taskScheduler.submitTasks(attempt2)
+    val taskDescriptions3 = taskScheduler.resourceOffers(workerOffers).flatten
+    assert(1 === taskDescriptions3.length)
+    val mgr = taskScheduler.taskIdToTaskSetManager.get(taskDescriptions3(0).taskId).get
+    assert(mgr.taskSet.stageAttemptId === 1)
+  }
+
+  test("if a zombie attempt finishes, continue scheduling tasks for non-zombie attempts") {
+    sc = new SparkContext("local", "TaskSchedulerImplSuite")
+    val taskScheduler = new TaskSchedulerImpl(sc)
+    taskScheduler.initialize(new FakeSchedulerBackend)
+    // Need to initialize a DAGScheduler for the taskScheduler to use for callbacks.
+    new DAGScheduler(sc, taskScheduler) {
+      override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
+      override def executorAdded(execId: String, host: String) {}
+    }
+
+    val numFreeCores = 10
+    val workerOffers = Seq(new WorkerOffer("executor0", "host0", numFreeCores))
+    val attempt1 = FakeTask.createTaskSet(10)
+
+    // submit attempt 1, offer some resources, some tasks get scheduled
+    taskScheduler.submitTasks(attempt1)
+    val taskDescriptions = taskScheduler.resourceOffers(workerOffers).flatten
+    assert(10 === taskDescriptions.length)
+
+    // now mark attempt 1 as a zombie
+    val mgr1 = taskScheduler.taskSetManagerForAttempt(attempt1.stageId, attempt1.stageAttemptId).get
+    mgr1.isZombie = true
+
+    // don't schedule anything on another resource offer
+    val taskDescriptions2 = taskScheduler.resourceOffers(workerOffers).flatten
+    assert(0 === taskDescriptions2.length)
+
+    // submit attempt 2
+    val attempt2 = FakeTask.createTaskSet(10, 1)
+    taskScheduler.submitTasks(attempt2)
+
+    // attempt 1 finished (this can happen even if it was marked zombie earlier -- all tasks were
+    // already submitted, and then they finish)
+    taskScheduler.taskSetFinished(mgr1)
+
+    // now with another resource offer, we should still schedule all the tasks in attempt2
+    val taskDescriptions3 = taskScheduler.resourceOffers(workerOffers).flatten
+    assert(10 === taskDescriptions3.length)
+
+    taskDescriptions3.foreach { task =>
+      val mgr = taskScheduler.taskIdToTaskSetManager.get(task.taskId).get
+      assert(mgr.taskSet.stageAttemptId === 1)
+    }
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index cdae0d83d01dc..3abb99c4b2b54 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -136,7 +136,7 @@ class FakeTaskScheduler(sc: SparkContext, liveExecutors: (String, String)* /* ex
 /**
  * A Task implementation that results in a large serialized task.
  */
-class LargeTask(stageId: Int) extends Task[Array[Byte]](stageId, 0) {
+class LargeTask(stageId: Int) extends Task[Array[Byte]](stageId, 0, 0) {
   val randomBuffer = new Array[Byte](TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024)
   val random = new Random(0)
   random.nextBytes(randomBuffer)

From 02181fb6d14833448fb5c501045655213d3cf340 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Mateusz=20Bu=C5=9Bkiewicz?= <mateusz.buskiewicz@getbase.com>
Date: Mon, 20 Jul 2015 12:00:48 -0700
Subject: [PATCH 0491/1454] [SPARK-9101] [PySpark] Add missing NullType
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

JIRA: https://issues.apache.org/jira/browse/SPARK-9101

Author: Mateusz Buśkiewicz <mateusz.buskiewicz@getbase.com>

Closes #7499 from sixers/spark-9101 and squashes the following commits:

dd75aa6 [Mateusz Buśkiewicz] [SPARK-9101] [PySpark] Test for selecting null literal
97e3f2f [Mateusz Buśkiewicz] [SPARK-9101] [PySpark] Add missing NullType to _atomic_types in pyspark.sql.types
---
 python/pyspark/sql/tests.py | 4 ++++
 python/pyspark/sql/types.py | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 86706e2dc41a3..7a55d801e48e6 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -333,6 +333,10 @@ def test_infer_nested_schema(self):
         df = self.sqlCtx.inferSchema(rdd)
         self.assertEquals(Row(field1=1, field2=u'row1'), df.first())
 
+    def test_select_null_literal(self):
+        df = self.sqlCtx.sql("select null as col")
+        self.assertEquals(Row(col=None), df.first())
+
     def test_apply_schema(self):
         from datetime import date, datetime
         rdd = self.sc.parallelize([(127, -128, -32768, 32767, 2147483647, 1.0,
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index f75791fad1612..10ad89ea14a8d 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -642,7 +642,7 @@ def __eq__(self, other):
 
 
 _atomic_types = [StringType, BinaryType, BooleanType, DecimalType, FloatType, DoubleType,
-                 ByteType, ShortType, IntegerType, LongType, DateType, TimestampType]
+                 ByteType, ShortType, IntegerType, LongType, DateType, TimestampType, NullType]
 _all_atomic_types = dict((t.typeName(), t) for t in _atomic_types)
 _all_complex_types = dict((v.typeName(), v)
                           for v in [ArrayType, MapType, StructType])

From 9f913c4fd6f0f223fd378e453d5b9a87beda1ac4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 20 Jul 2015 12:14:47 -0700
Subject: [PATCH 0492/1454] [SPARK-9114] [SQL] [PySpark] convert returned
 object from UDF into internal type

This PR also remove the duplicated code between registerFunction and UserDefinedFunction.

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #7450 from davies/fix_return_type and squashes the following commits:

e80bf9f [Davies Liu] remove debugging code
f94b1f6 [Davies Liu] fix mima
8f9c58b [Davies Liu] convert returned object from UDF into internal type
---
 project/MimaExcludes.scala                    |  4 +-
 python/pyspark/sql/context.py                 | 16 ++-----
 python/pyspark/sql/functions.py               | 15 ++++---
 python/pyspark/sql/tests.py                   |  4 +-
 .../apache/spark/sql/UDFRegistration.scala    | 44 ++++---------------
 .../spark/sql/UserDefinedFunction.scala       | 10 +++--
 6 files changed, 32 insertions(+), 61 deletions(-)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index dd852547492aa..a2595ff6c22f4 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -69,7 +69,9 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
             // local function inside a method
             ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1")
+              "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.sql.UDFRegistration.org$apache$spark$sql$UDFRegistration$$builder$24")
           ) ++ Seq(
             // SPARK-8479 Add numNonzeros and numActives to Matrix.
             ProblemFilters.exclude[MissingMethodProblem](
diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index c93a15badae29..abb6522dde7b0 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -34,6 +34,7 @@
 from pyspark.sql.dataframe import DataFrame
 from pyspark.sql.readwriter import DataFrameReader
 from pyspark.sql.utils import install_exception_handler
+from pyspark.sql.functions import UserDefinedFunction
 
 try:
     import pandas
@@ -191,19 +192,8 @@ def registerFunction(self, name, f, returnType=StringType()):
         >>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
         [Row(_c0=4)]
         """
-        func = lambda _, it: map(lambda x: f(*x), it)
-        ser = AutoBatchedSerializer(PickleSerializer())
-        command = (func, None, ser, ser)
-        pickled_cmd, bvars, env, includes = _prepare_for_python_RDD(self._sc, command, self)
-        self._ssql_ctx.udf().registerPython(name,
-                                            bytearray(pickled_cmd),
-                                            env,
-                                            includes,
-                                            self._sc.pythonExec,
-                                            self._sc.pythonVer,
-                                            bvars,
-                                            self._sc._javaAccumulator,
-                                            returnType.json())
+        udf = UserDefinedFunction(f, returnType, name)
+        self._ssql_ctx.udf().registerPython(name, udf._judf)
 
     def _inferSchemaFromList(self, data):
         """
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index fd5a3ba8adab3..031745a1c4d3b 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -801,23 +801,24 @@ class UserDefinedFunction(object):
 
     .. versionadded:: 1.3
     """
-    def __init__(self, func, returnType):
+    def __init__(self, func, returnType, name=None):
         self.func = func
         self.returnType = returnType
         self._broadcast = None
-        self._judf = self._create_judf()
+        self._judf = self._create_judf(name)
 
-    def _create_judf(self):
-        f = self.func  # put it in closure `func`
-        func = lambda _, it: map(lambda x: f(*x), it)
+    def _create_judf(self, name):
+        f, returnType = self.func, self.returnType  # put them in closure `func`
+        func = lambda _, it: map(lambda x: returnType.toInternal(f(*x)), it)
         ser = AutoBatchedSerializer(PickleSerializer())
         command = (func, None, ser, ser)
         sc = SparkContext._active_spark_context
         pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self)
         ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
         jdt = ssql_ctx.parseDataType(self.returnType.json())
-        fname = f.__name__ if hasattr(f, '__name__') else f.__class__.__name__
-        judf = sc._jvm.UserDefinedPythonFunction(fname, bytearray(pickled_command), env, includes,
+        if name is None:
+            name = f.__name__ if hasattr(f, '__name__') else f.__class__.__name__
+        judf = sc._jvm.UserDefinedPythonFunction(name, bytearray(pickled_command), env, includes,
                                                  sc.pythonExec, sc.pythonVer, broadcast_vars,
                                                  sc._javaAccumulator, jdt)
         return judf
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 7a55d801e48e6..ea821f486f13a 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -417,12 +417,14 @@ def test_apply_schema_with_udt(self):
         self.assertEquals(point, ExamplePoint(1.0, 2.0))
 
     def test_udf_with_udt(self):
-        from pyspark.sql.tests import ExamplePoint
+        from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
         row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
         df = self.sc.parallelize([row]).toDF()
         self.assertEqual(1.0, df.map(lambda r: r.point.x).first())
         udf = UserDefinedFunction(lambda p: p.y, DoubleType())
         self.assertEqual(2.0, df.select(udf(df.point)).first()[0])
+        udf2 = UserDefinedFunction(lambda p: ExamplePoint(p.x + 1, p.y + 1), ExamplePointUDT())
+        self.assertEqual(ExamplePoint(2.0, 3.0), df.select(udf2(df.point)).first()[0])
 
     def test_parquet_with_udt(self):
         from pyspark.sql.tests import ExamplePoint
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
index d35d37d017198..7cd7421a518c9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
@@ -22,13 +22,10 @@ import java.util.{List => JList, Map => JMap}
 import scala.reflect.runtime.universe.TypeTag
 import scala.util.Try
 
-import org.apache.spark.{Accumulator, Logging}
-import org.apache.spark.api.python.PythonBroadcast
-import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.Logging
 import org.apache.spark.sql.api.java._
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
-import org.apache.spark.sql.execution.PythonUDF
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -40,44 +37,19 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
 
   private val functionRegistry = sqlContext.functionRegistry
 
-  protected[sql] def registerPython(
-      name: String,
-      command: Array[Byte],
-      envVars: JMap[String, String],
-      pythonIncludes: JList[String],
-      pythonExec: String,
-      pythonVer: String,
-      broadcastVars: JList[Broadcast[PythonBroadcast]],
-      accumulator: Accumulator[JList[Array[Byte]]],
-      stringDataType: String): Unit = {
+  protected[sql] def registerPython(name: String, udf: UserDefinedPythonFunction): Unit = {
     log.debug(
       s"""
         | Registering new PythonUDF:
         | name: $name
-        | command: ${command.toSeq}
-        | envVars: $envVars
-        | pythonIncludes: $pythonIncludes
-        | pythonExec: $pythonExec
-        | dataType: $stringDataType
+        | command: ${udf.command.toSeq}
+        | envVars: ${udf.envVars}
+        | pythonIncludes: ${udf.pythonIncludes}
+        | pythonExec: ${udf.pythonExec}
+        | dataType: ${udf.dataType}
       """.stripMargin)
 
-
-    val dataType = sqlContext.parseDataType(stringDataType)
-
-    def builder(e: Seq[Expression]): PythonUDF =
-      PythonUDF(
-        name,
-        command,
-        envVars,
-        pythonIncludes,
-        pythonExec,
-        pythonVer,
-        broadcastVars,
-        accumulator,
-        dataType,
-        e)
-
-    functionRegistry.registerFunction(name, builder)
+    functionRegistry.registerFunction(name, udf.builder)
   }
 
   // scalastyle:off
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
index b14e00ab9b163..0f8cd280b5acb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UserDefinedFunction.scala
@@ -23,7 +23,7 @@ import org.apache.spark.Accumulator
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.python.PythonBroadcast
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.sql.catalyst.expressions.ScalaUDF
+import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
 import org.apache.spark.sql.execution.PythonUDF
 import org.apache.spark.sql.types.DataType
 
@@ -66,10 +66,14 @@ private[sql] case class UserDefinedPythonFunction(
     accumulator: Accumulator[JList[Array[Byte]]],
     dataType: DataType) {
 
+  def builder(e: Seq[Expression]): PythonUDF = {
+    PythonUDF(name, command, envVars, pythonIncludes, pythonExec, pythonVer, broadcastVars,
+      accumulator, dataType, e)
+  }
+
   /** Returns a [[Column]] that will evaluate to calling this UDF with the given input. */
   def apply(exprs: Column*): Column = {
-    val udf = PythonUDF(name, command, envVars, pythonIncludes, pythonExec, pythonVer,
-      broadcastVars, accumulator, dataType, exprs.map(_.expr))
+    val udf = builder(exprs.map(_.expr))
     Column(udf)
   }
 }

From dde0e12f32e3a0448d8308ec78ad59cbb2c55d23 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 20 Jul 2015 15:12:06 -0700
Subject: [PATCH 0493/1454] [SPARK-6910] [SQL] Support for pushing predicates
 down to metastore for partition pruning

This PR forks PR #7421 authored by piaozhexiu and adds [a workaround] [1] for fixing the occasional test failures occurred in PR #7421. Please refer to these [two] [2] [comments] [3] for details.

[1]: https://github.com/liancheng/spark/commit/536ac41a7e6b2abeb1f6ec1a6491bbf09ed3e591
[2]: https://github.com/apache/spark/pull/7421#issuecomment-122527391
[3]: https://github.com/apache/spark/pull/7421#issuecomment-122528059

Author: Cheolsoo Park <cheolsoop@netflix.com>
Author: Cheng Lian <lian@databricks.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #7492 from liancheng/pr-7421-workaround and squashes the following commits:

5599cc4 [Cheolsoo Park] Predicate pushdown to hive metastore
536ac41 [Cheng Lian] Sets hive.metastore.integral.jdo.pushdown to true to workaround test failures caused by in #7421
---
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 58 +++++++-------
 .../org/apache/spark/sql/hive/HiveShim.scala  |  1 +
 .../spark/sql/hive/HiveStrategies.scala       |  4 +-
 .../sql/hive/client/ClientInterface.scala     | 11 ++-
 .../spark/sql/hive/client/ClientWrapper.scala | 21 ++---
 .../spark/sql/hive/client/HiveShim.scala      | 71 ++++++++++++++++-
 .../sql/hive/execution/HiveTableScan.scala    |  7 +-
 .../apache/spark/sql/hive/test/TestHive.scala |  5 +-
 .../spark/sql/hive/client/FiltersSuite.scala  | 78 +++++++++++++++++++
 .../spark/sql/hive/client/VersionsSuite.scala |  8 ++
 .../sql/hive/execution/PruningSuite.scala     |  2 +-
 11 files changed, 221 insertions(+), 45 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 6589bc6ea2921..b15261b7914dd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -301,7 +301,9 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     val result = if (metastoreRelation.hiveQlTable.isPartitioned) {
       val partitionSchema = StructType.fromAttributes(metastoreRelation.partitionKeys)
       val partitionColumnDataTypes = partitionSchema.map(_.dataType)
-      val partitions = metastoreRelation.hiveQlPartitions.map { p =>
+      // We're converting the entire table into ParquetRelation, so predicates to Hive metastore
+      // are empty.
+      val partitions = metastoreRelation.getHiveQlPartitions().map { p =>
         val location = p.getLocation
         val values = InternalRow.fromSeq(p.getValues.zip(partitionColumnDataTypes).map {
           case (rawValue, dataType) => Cast(Literal(rawValue), dataType).eval(null)
@@ -642,32 +644,6 @@ private[hive] case class MetastoreRelation
     new Table(tTable)
   }
 
-  @transient val hiveQlPartitions: Seq[Partition] = table.getAllPartitions.map { p =>
-    val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
-    tPartition.setDbName(databaseName)
-    tPartition.setTableName(tableName)
-    tPartition.setValues(p.values)
-
-    val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
-    tPartition.setSd(sd)
-    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
-
-    sd.setLocation(p.storage.location)
-    sd.setInputFormat(p.storage.inputFormat)
-    sd.setOutputFormat(p.storage.outputFormat)
-
-    val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
-    sd.setSerdeInfo(serdeInfo)
-    serdeInfo.setSerializationLib(p.storage.serde)
-
-    val serdeParameters = new java.util.HashMap[String, String]()
-    serdeInfo.setParameters(serdeParameters)
-    table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
-    p.storage.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
-
-    new Partition(hiveQlTable, tPartition)
-  }
-
   @transient override lazy val statistics: Statistics = Statistics(
     sizeInBytes = {
       val totalSize = hiveQlTable.getParameters.get(StatsSetupConst.TOTAL_SIZE)
@@ -688,6 +664,34 @@ private[hive] case class MetastoreRelation
     }
   )
 
+  def getHiveQlPartitions(predicates: Seq[Expression] = Nil): Seq[Partition] = {
+    table.getPartitions(predicates).map { p =>
+      val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
+      tPartition.setDbName(databaseName)
+      tPartition.setTableName(tableName)
+      tPartition.setValues(p.values)
+
+      val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
+      tPartition.setSd(sd)
+      sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+
+      sd.setLocation(p.storage.location)
+      sd.setInputFormat(p.storage.inputFormat)
+      sd.setOutputFormat(p.storage.outputFormat)
+
+      val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
+      sd.setSerdeInfo(serdeInfo)
+      serdeInfo.setSerializationLib(p.storage.serde)
+
+      val serdeParameters = new java.util.HashMap[String, String]()
+      serdeInfo.setParameters(serdeParameters)
+      table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+      p.storage.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+
+      new Partition(hiveQlTable, tPartition)
+    }
+  }
+
   /** Only compare database and tablename, not alias. */
   override def sameResult(plan: LogicalPlan): Boolean = {
     plan match {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
index d08c594151654..a357bb39ca7fd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -27,6 +27,7 @@ import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.Kryo
 import com.esotericsoftware.kryo.io.{Input, Output}
+
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.ql.exec.{UDF, Utilities}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index ed359620a5f7f..9638a8201e190 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -125,7 +125,7 @@ private[hive] trait HiveStrategies {
                 InterpretedPredicate.create(castedPredicate)
               }
 
-            val partitions = relation.hiveQlPartitions.filter { part =>
+            val partitions = relation.getHiveQlPartitions(pruningPredicates).filter { part =>
               val partitionValues = part.getValues
               var i = 0
               while (i < partitionValues.size()) {
@@ -213,7 +213,7 @@ private[hive] trait HiveStrategies {
           projectList,
           otherPredicates,
           identity[Seq[Expression]],
-          HiveTableScan(_, relation, pruningPredicates.reduceLeftOption(And))(hiveContext)) :: Nil
+          HiveTableScan(_, relation, pruningPredicates)(hiveContext)) :: Nil
       case _ =>
         Nil
     }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index 0a1d761a52f88..1656587d14835 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -21,6 +21,7 @@ import java.io.PrintStream
 import java.util.{Map => JMap}
 
 import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException}
+import org.apache.spark.sql.catalyst.expressions.Expression
 
 private[hive] case class HiveDatabase(
     name: String,
@@ -71,7 +72,12 @@ private[hive] case class HiveTable(
 
   def isPartitioned: Boolean = partitionColumns.nonEmpty
 
-  def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this)
+  def getPartitions(predicates: Seq[Expression]): Seq[HivePartition] = {
+    predicates match {
+      case Nil => client.getAllPartitions(this)
+      case _ => client.getPartitionsByFilter(this, predicates)
+    }
+  }
 
   // Hive does not support backticks when passing names to the client.
   def qualifiedName: String = s"$database.$name"
@@ -132,6 +138,9 @@ private[hive] trait ClientInterface {
   /** Returns all partitions for the given table. */
   def getAllPartitions(hTable: HiveTable): Seq[HivePartition]
 
+  /** Returns partitions filtered by predicates for the given table. */
+  def getPartitionsByFilter(hTable: HiveTable, predicates: Seq[Expression]): Seq[HivePartition]
+
   /** Loads a static partition into an existing table. */
   def loadPartition(
       loadPath: String,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 53f457ad4f3cc..8adda54754230 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -17,25 +17,21 @@
 
 package org.apache.spark.sql.hive.client
 
-import java.io.{BufferedReader, InputStreamReader, File, PrintStream}
-import java.net.URI
-import java.util.{ArrayList => JArrayList, Map => JMap, List => JList, Set => JSet}
+import java.io.{File, PrintStream}
+import java.util.{Map => JMap}
 import javax.annotation.concurrent.GuardedBy
 
 import scala.collection.JavaConversions._
 import scala.language.reflectiveCalls
 
 import org.apache.hadoop.fs.Path
-import org.apache.hadoop.hive.metastore.api.Database
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.metastore.api.{Database, FieldSchema}
 import org.apache.hadoop.hive.metastore.{TableType => HTableType}
-import org.apache.hadoop.hive.metastore.api
-import org.apache.hadoop.hive.metastore.api.FieldSchema
-import org.apache.hadoop.hive.ql.metadata
 import org.apache.hadoop.hive.ql.metadata.Hive
-import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.ql.processors._
-import org.apache.hadoop.hive.ql.Driver
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.ql.{Driver, metadata}
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.Expression
@@ -316,6 +312,13 @@ private[hive] class ClientWrapper(
     shim.getAllPartitions(client, qlTable).map(toHivePartition)
   }
 
+  override def getPartitionsByFilter(
+      hTable: HiveTable,
+      predicates: Seq[Expression]): Seq[HivePartition] = withHiveState {
+    val qlTable = toQlTable(hTable)
+    shim.getPartitionsByFilter(client, qlTable, predicates).map(toHivePartition)
+  }
+
   override def listTables(dbName: String): Seq[String] = withHiveState {
     client.getAllTables(dbName)
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 1fa9d278e2a57..956997e5f9dce 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -31,6 +31,11 @@ import org.apache.hadoop.hive.ql.Driver
 import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table}
 import org.apache.hadoop.hive.ql.processors.{CommandProcessor, CommandProcessorFactory}
 import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.serde.serdeConstants
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{StringType, IntegralType}
 
 /**
  * A shim that defines the interface between ClientWrapper and the underlying Hive library used to
@@ -61,6 +66,8 @@ private[client] sealed abstract class Shim {
 
   def getAllPartitions(hive: Hive, table: Table): Seq[Partition]
 
+  def getPartitionsByFilter(hive: Hive, table: Table, predicates: Seq[Expression]): Seq[Partition]
+
   def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor
 
   def getDriverResults(driver: Driver): Seq[String]
@@ -109,7 +116,7 @@ private[client] sealed abstract class Shim {
 
 }
 
-private[client] class Shim_v0_12 extends Shim {
+private[client] class Shim_v0_12 extends Shim with Logging {
 
   private lazy val startMethod =
     findStaticMethod(
@@ -196,6 +203,17 @@ private[client] class Shim_v0_12 extends Shim {
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
     getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
 
+  override def getPartitionsByFilter(
+      hive: Hive,
+      table: Table,
+      predicates: Seq[Expression]): Seq[Partition] = {
+    // getPartitionsByFilter() doesn't support binary comparison ops in Hive 0.12.
+    // See HIVE-4888.
+    logDebug("Hive 0.12 doesn't support predicate pushdown to metastore. " +
+      "Please use Hive 0.13 or higher.")
+    getAllPartitions(hive, table)
+  }
+
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
     getCommandProcessorMethod.invoke(null, token, conf).asInstanceOf[CommandProcessor]
 
@@ -267,6 +285,12 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
       classOf[Hive],
       "getAllPartitionsOf",
       classOf[Table])
+  private lazy val getPartitionsByFilterMethod =
+    findMethod(
+      classOf[Hive],
+      "getPartitionsByFilter",
+      classOf[Table],
+      classOf[String])
   private lazy val getCommandProcessorMethod =
     findStaticMethod(
       classOf[CommandProcessorFactory],
@@ -288,6 +312,51 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
     getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
 
+  /**
+   * Converts catalyst expression to the format that Hive's getPartitionsByFilter() expects, i.e.
+   * a string that represents partition predicates like "str_key=\"value\" and int_key=1 ...".
+   *
+   * Unsupported predicates are skipped.
+   */
+  def convertFilters(table: Table, filters: Seq[Expression]): String = {
+    // hive varchar is treated as catalyst string, but hive varchar can't be pushed down.
+    val varcharKeys = table.getPartitionKeys
+      .filter(col => col.getType.startsWith(serdeConstants.VARCHAR_TYPE_NAME))
+      .map(col => col.getName).toSet
+
+    filters.collect {
+      case op @ BinaryComparison(a: Attribute, Literal(v, _: IntegralType)) =>
+        s"${a.name} ${op.symbol} $v"
+      case op @ BinaryComparison(Literal(v, _: IntegralType), a: Attribute) =>
+        s"$v ${op.symbol} ${a.name}"
+      case op @ BinaryComparison(a: Attribute, Literal(v, _: StringType))
+          if !varcharKeys.contains(a.name) =>
+        s"""${a.name} ${op.symbol} "$v""""
+      case op @ BinaryComparison(Literal(v, _: StringType), a: Attribute)
+          if !varcharKeys.contains(a.name) =>
+        s""""$v" ${op.symbol} ${a.name}"""
+    }.mkString(" and ")
+  }
+
+  override def getPartitionsByFilter(
+      hive: Hive,
+      table: Table,
+      predicates: Seq[Expression]): Seq[Partition] = {
+
+    // Hive getPartitionsByFilter() takes a string that represents partition
+    // predicates like "str_key=\"value\" and int_key=1 ..."
+    val filter = convertFilters(table, predicates)
+    val partitions =
+      if (filter.isEmpty) {
+        getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]]
+      } else {
+        logDebug(s"Hive metastore filter is '$filter'.")
+        getPartitionsByFilterMethod.invoke(hive, table, filter).asInstanceOf[JArrayList[Partition]]
+      }
+
+    partitions.toSeq
+  }
+
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
     getCommandProcessorMethod.invoke(null, Array(token), conf).asInstanceOf[CommandProcessor]
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index d33da8242cc1d..ba7eb15a1c0c6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -44,7 +44,7 @@ private[hive]
 case class HiveTableScan(
     requestedAttributes: Seq[Attribute],
     relation: MetastoreRelation,
-    partitionPruningPred: Option[Expression])(
+    partitionPruningPred: Seq[Expression])(
     @transient val context: HiveContext)
   extends LeafNode {
 
@@ -56,7 +56,7 @@ case class HiveTableScan(
 
   // Bind all partition key attribute references in the partition pruning predicate for later
   // evaluation.
-  private[this] val boundPruningPred = partitionPruningPred.map { pred =>
+  private[this] val boundPruningPred = partitionPruningPred.reduceLeftOption(And).map { pred =>
     require(
       pred.dataType == BooleanType,
       s"Data type of predicate $pred must be BooleanType rather than ${pred.dataType}.")
@@ -133,7 +133,8 @@ case class HiveTableScan(
   protected override def doExecute(): RDD[InternalRow] = if (!relation.hiveQlTable.isPartitioned) {
     hadoopReader.makeRDDForTable(relation.hiveQlTable)
   } else {
-    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
+    hadoopReader.makeRDDForPartitionedTable(
+      prunePartitions(relation.getHiveQlPartitions(partitionPruningPred)))
   }
 
   override def output: Seq[Attribute] = attributes
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 0f217bc66869f..3662a4352f55d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -21,6 +21,7 @@ import java.io.File
 import java.util.{Set => JavaSet}
 
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.exec.FunctionRegistry
 import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat}
 import org.apache.hadoop.hive.ql.metadata.Table
@@ -87,7 +88,9 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
 
   /** Sets up the system initially or after a RESET command */
   protected override def configure(): Map[String, String] =
-    temporaryConfig ++ Map("hive.metastore.warehouse.dir" -> warehousePath.toString)
+    temporaryConfig ++ Map(
+      ConfVars.METASTOREWAREHOUSE.varname -> warehousePath.toString,
+      ConfVars.METASTORE_INTEGER_JDO_PUSHDOWN.varname -> "true")
 
   val testTempDir = Utils.createTempDir()
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
new file mode 100644
index 0000000000000..0efcf80bd4ea7
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.client
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+import org.apache.hadoop.hive.serde.serdeConstants
+
+import org.apache.spark.{Logging, SparkFunSuite}
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types._
+
+/**
+ * A set of tests for the filter conversion logic used when pushing partition pruning into the
+ * metastore
+ */
+class FiltersSuite extends SparkFunSuite with Logging {
+  private val shim = new Shim_v0_13
+
+  private val testTable = new org.apache.hadoop.hive.ql.metadata.Table("default", "test")
+  private val varCharCol = new FieldSchema()
+  varCharCol.setName("varchar")
+  varCharCol.setType(serdeConstants.VARCHAR_TYPE_NAME)
+  testTable.setPartCols(varCharCol :: Nil)
+
+  filterTest("string filter",
+    (a("stringcol", StringType) > Literal("test")) :: Nil,
+    "stringcol > \"test\"")
+
+  filterTest("string filter backwards",
+    (Literal("test") > a("stringcol", StringType)) :: Nil,
+    "\"test\" > stringcol")
+
+  filterTest("int filter",
+    (a("intcol", IntegerType) === Literal(1)) :: Nil,
+    "intcol = 1")
+
+  filterTest("int filter backwards",
+    (Literal(1) === a("intcol", IntegerType)) :: Nil,
+    "1 = intcol")
+
+  filterTest("int and string filter",
+    (Literal(1) === a("intcol", IntegerType)) :: (Literal("a") === a("strcol", IntegerType)) :: Nil,
+    "1 = intcol and \"a\" = strcol")
+
+  filterTest("skip varchar",
+    (Literal("") === a("varchar", StringType)) :: Nil,
+    "")
+
+  private def filterTest(name: String, filters: Seq[Expression], result: String) = {
+    test(name){
+      val converted = shim.convertFilters(testTable, filters)
+      if (converted != result) {
+        fail(
+          s"Expected filters ${filters.mkString(",")} to convert to '$result' but got '$converted'")
+      }
+    }
+  }
+
+  private def a(name: String, dataType: DataType) = AttributeReference(name, dataType)()
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index d52e162acbd04..3eb127e23d486 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -20,7 +20,9 @@ package org.apache.spark.sql.hive.client
 import java.io.File
 
 import org.apache.spark.{Logging, SparkFunSuite}
+import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
+import org.apache.spark.sql.types.IntegerType
 import org.apache.spark.util.Utils
 
 /**
@@ -151,6 +153,12 @@ class VersionsSuite extends SparkFunSuite with Logging {
       client.getAllPartitions(client.getTable("default", "src_part"))
     }
 
+    test(s"$version: getPartitionsByFilter") {
+      client.getPartitionsByFilter(client.getTable("default", "src_part"), Seq(EqualTo(
+        AttributeReference("key", IntegerType, false)(NamedExpression.newExprId),
+        Literal(1))))
+    }
+
     test(s"$version: loadPartition") {
       client.loadPartition(
         emptyDir,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index de6a41ce5bfcb..e83a7dc77e329 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -151,7 +151,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
         case p @ HiveTableScan(columns, relation, _) =>
           val columnNames = columns.map(_.name)
           val partValues = if (relation.table.isPartitioned) {
-            p.prunePartitions(relation.hiveQlPartitions).map(_.getValues)
+            p.prunePartitions(relation.getHiveQlPartitions()).map(_.getValues)
           } else {
             Seq.empty
           }

From 4863c11ea9d9f94799fbe6ae5a60860f0740a44d Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 15:23:28 -0700
Subject: [PATCH 0494/1454] [SPARK-9155][SQL] codegen StringSpace

Jira https://issues.apache.org/jira/browse/SPARK-9155

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7531 from tarekauel/SPARK-9155 and squashes the following commits:

423c426 [Tarek Auel] [SPARK-9155] language typo fix
e34bd1b [Tarek Auel] [SPARK-9155] moved creation of blank string to UTF8String
4bc33e6 [Tarek Auel] [SPARK-9155] codegen StringSpace
---
 .../sql/catalyst/expressions/stringOperations.scala  | 12 +++++++-----
 .../org/apache/spark/unsafe/types/UTF8String.java    | 10 ++++++++++
 .../apache/spark/unsafe/types/UTF8StringSuite.java   |  8 ++++++++
 3 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 6608036f01318..e42be85367aeb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -593,17 +593,19 @@ case class StringReverse(child: Expression) extends UnaryExpression with String2
  * Returns a n spaces string.
  */
 case class StringSpace(child: Expression)
-  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
+  extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(IntegerType)
 
   override def nullSafeEval(s: Any): Any = {
-    val length = s.asInstanceOf[Integer]
+    val length = s.asInstanceOf[Int]
+    UTF8String.blankString(if (length < 0) 0 else length)
+  }
 
-    val spaces = new Array[Byte](if (length < 0) 0 else length)
-    java.util.Arrays.fill(spaces, ' '.asInstanceOf[Byte])
-    UTF8String.fromBytes(spaces)
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (length) =>
+      s"""${ev.primitive} = UTF8String.blankString(($length < 0) ? 0 : $length);""")
   }
 
   override def prettyName: String = "space"
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 3eecd657e6ef9..819639f300177 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -20,6 +20,7 @@
 import javax.annotation.Nonnull;
 import java.io.Serializable;
 import java.io.UnsupportedEncodingException;
+import java.util.Arrays;
 
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
@@ -77,6 +78,15 @@ public static UTF8String fromString(String str) {
     }
   }
 
+  /**
+   * Creates an UTF8String that contains `length` spaces.
+   */
+  public static UTF8String blankString(int length) {
+    byte[] spaces = new byte[length];
+    Arrays.fill(spaces, (byte) ' ');
+    return fromBytes(spaces);
+  }
+
   protected UTF8String(Object base, long offset, int size) {
     this.base = base;
     this.offset = offset;
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 7d0c49e2fb84c..6a21c27461163 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -286,4 +286,12 @@ public void levenshteinDistance() {
     assertEquals(
         UTF8String.fromString("世界千世").levenshteinDistance(UTF8String.fromString("千a世b")),4);
   }
+
+  @Test
+  public void createBlankString() {
+    assertEquals(fromString(" "), blankString(1));
+    assertEquals(fromString("  "), blankString(2));
+    assertEquals(fromString("   "), blankString(3));
+    assertEquals(fromString(""), blankString(0));
+  }
 }

From c9db8eaa42387c03cde12c1d145a6f72872def71 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 15:32:46 -0700
Subject: [PATCH 0495/1454] [SPARK-9159][SQL] codegen ascii, base64, unbase64

Jira: https://issues.apache.org/jira/browse/SPARK-9159

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7542 from tarekauel/SPARK-9159 and squashes the following commits:

772e6bc [Tarek Auel] [SPARK-9159][SQL] codegen ascii, base64, unbase64
---
 .../expressions/stringOperations.scala        | 37 ++++++++++++++++---
 .../expressions/StringExpressionsSuite.scala  |  2 +-
 2 files changed, 32 insertions(+), 7 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index e42be85367aeb..e660d499fabd3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -742,8 +742,7 @@ case class Levenshtein(left: Expression, right: Expression) extends BinaryExpres
 /**
  * Returns the numeric value of the first character of str.
  */
-case class Ascii(child: Expression)
-  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
+case class Ascii(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType)
@@ -756,13 +755,25 @@ case class Ascii(child: Expression)
       0
     }
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (child) => {
+      val bytes = ctx.freshName("bytes")
+      s"""
+        byte[] $bytes = $child.getBytes();
+        if ($bytes.length > 0) {
+          ${ev.primitive} = (int) $bytes[0];
+        } else {
+          ${ev.primitive} = 0;
+        }
+       """})
+  }
 }
 
 /**
  * Converts the argument from binary to a base 64 string.
  */
-case class Base64(child: Expression)
-  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
+case class Base64(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(BinaryType)
@@ -772,19 +783,33 @@ case class Base64(child: Expression)
       org.apache.commons.codec.binary.Base64.encodeBase64(
         bytes.asInstanceOf[Array[Byte]]))
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (child) => {
+      s"""${ev.primitive} = UTF8String.fromBytes(
+            org.apache.commons.codec.binary.Base64.encodeBase64($child));
+       """})
+  }
+
 }
 
 /**
  * Converts the argument from a base 64 string to BINARY.
  */
-case class UnBase64(child: Expression)
-  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
+case class UnBase64(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def dataType: DataType = BinaryType
   override def inputTypes: Seq[DataType] = Seq(StringType)
 
   protected override def nullSafeEval(string: Any): Any =
     org.apache.commons.codec.binary.Base64.decodeBase64(string.asInstanceOf[UTF8String].toString)
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (child) => {
+      s"""
+         ${ev.primitive} = org.apache.commons.codec.binary.Base64.decodeBase64($child.toString());
+       """})
+  }
 }
 
 /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index d5731229df3bb..67d97cd30b039 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -290,7 +290,7 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Base64(b), "AQIDBA==", create_row(bytes))
     checkEvaluation(Base64(b), "", create_row(Array[Byte]()))
     checkEvaluation(Base64(b), null, create_row(null))
-    checkEvaluation(Base64(Literal.create(null, StringType)), null, create_row("abdef"))
+    checkEvaluation(Base64(Literal.create(null, BinaryType)), null, create_row("abdef"))
 
     checkEvaluation(UnBase64(a), null, create_row(null))
     checkEvaluation(UnBase64(Literal.create(null, StringType)), null, create_row("abdef"))

From dac7dbf5a6bd663ef3acaa5b2249b31140fa6857 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 16:11:56 -0700
Subject: [PATCH 0496/1454] [SPARK-9160][SQL] codegen encode, decode

Jira: https://issues.apache.org/jira/browse/SPARK-9160

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7543 from tarekauel/SPARK-9160 and squashes the following commits:

7528f0e [Tarek Auel] [SPARK-9160][SQL] codegen encode, decode
---
 .../expressions/stringOperations.scala        | 25 +++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index e660d499fabd3..a5682428b3d40 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -818,7 +818,7 @@ case class UnBase64(child: Expression) extends UnaryExpression with ImplicitCast
  * If either argument is null, the result will also be null.
  */
 case class Decode(bin: Expression, charset: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes with CodegenFallback {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = bin
   override def right: Expression = charset
@@ -829,6 +829,17 @@ case class Decode(bin: Expression, charset: Expression)
     val fromCharset = input2.asInstanceOf[UTF8String].toString
     UTF8String.fromString(new String(input1.asInstanceOf[Array[Byte]], fromCharset))
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (bytes, charset) =>
+      s"""
+        try {
+          ${ev.primitive} = UTF8String.fromString(new String($bytes, $charset.toString()));
+        } catch (java.io.UnsupportedEncodingException e) {
+          org.apache.spark.unsafe.PlatformDependent.throwException(e);
+        }
+      """)
+  }
 }
 
 /**
@@ -837,7 +848,7 @@ case class Decode(bin: Expression, charset: Expression)
  * If either argument is null, the result will also be null.
 */
 case class Encode(value: Expression, charset: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes with CodegenFallback {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = value
   override def right: Expression = charset
@@ -848,6 +859,16 @@ case class Encode(value: Expression, charset: Expression)
     val toCharset = input2.asInstanceOf[UTF8String].toString
     input1.asInstanceOf[UTF8String].toString.getBytes(toCharset)
   }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (string, charset) =>
+      s"""
+        try {
+          ${ev.primitive} = $string.toString().getBytes($charset.toString());
+        } catch (java.io.UnsupportedEncodingException e) {
+          org.apache.spark.unsafe.PlatformDependent.throwException(e);
+        }""")
+  }
 }
 
 /**

From a1064df0ee3daf496800be84293345a10e1497d9 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 20 Jul 2015 16:42:43 -0700
Subject: [PATCH 0497/1454] [SPARK-8125] [SQL] Accelerates Parquet schema
 merging and partition discovery

This PR tries to accelerate Parquet schema discovery and `HadoopFsRelation` partition discovery.  The acceleration is done by the following means:

- Turning off schema merging by default

  Schema merging is not the most common case, but requires reading footers of all Parquet part-files and can be very slow.

- Avoiding `FileSystem.globStatus()` call when possible

  `FileSystem.globStatus()` may issue multiple synchronous RPC calls, and can be very slow (esp. on S3).  This PR adds `SparkHadoopUtil.globPathIfNecessary()`, which only issues RPC calls when the path contain glob-pattern specific character(s) (`{}[]*?\`).

  This is especially useful when converting a metastore Parquet table with lots of partitions, since Spark SQL adds all partition directories as the input paths, and currently we do a `globStatus` call on each input path sequentially.

- Listing leaf files in parallel when the number of input paths exceeds a threshold

  Listing leaf files is required by partition discovery.  Currently it is done on driver side, and can be slow when there are lots of (nested) directories, since each `FileSystem.listStatus()` call issues an RPC.  In this PR, we list leaf files in a BFS style, and resort to a Spark job once we found that the number of directories need to be listed exceed a threshold.

  The threshold is controlled by `SQLConf` option `spark.sql.sources.parallelPartitionDiscovery.threshold`, which defaults to 32.

- Discovering Parquet schema in parallel

  Currently, schema merging is also done on driver side, and needs to read footers of all part-files.  This PR uses a Spark job to do schema merging.  Together with task side metadata reading in Parquet 1.7.0, we never read any footers on driver side now.

Author: Cheng Lian <lian@databricks.com>

Closes #7396 from liancheng/accel-parquet and squashes the following commits:

5598efc [Cheng Lian] Uses ParquetInputFormat[InternalRow] instead of ParquetInputFormat[Row]
ff32cd0 [Cheng Lian] Excludes directories while listing leaf files
3c580f1 [Cheng Lian] Fixes test failure caused by making "mergeSchema" default to "false"
b1646aa [Cheng Lian] Should allow empty input paths
32e5f0d [Cheng Lian] Moves schema merging to executor side
---
 .../apache/spark/deploy/SparkHadoopUtil.scala |   8 +
 .../apache/spark/sql/DataFrameReader.scala    |  12 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  10 +-
 .../sql/parquet/ParquetTableOperations.scala  |  14 +-
 .../apache/spark/sql/parquet/newParquet.scala | 158 +++++++++++++-----
 .../org/apache/spark/sql/sources/ddl.scala    |   8 +-
 .../apache/spark/sql/sources/interfaces.scala | 120 ++++++++++---
 .../ParquetPartitionDiscoverySuite.scala      |  18 +-
 8 files changed, 258 insertions(+), 90 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 6b14d407a6380..e06b06e06fb4a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -239,6 +239,14 @@ class SparkHadoopUtil extends Logging {
     }.getOrElse(Seq.empty[Path])
   }
 
+  def globPathIfNecessary(pattern: Path): Seq[Path] = {
+    if (pattern.toString.exists("{}[]*?\\".toSet.contains)) {
+      globPath(pattern)
+    } else {
+      Seq(pattern)
+    }
+  }
+
   /**
    * Lists all the files in a directory with the specified prefix, and does not end with the
    * given suffix. The returned {{FileStatus}} instances are sorted by the modification times of
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 9b23df4843c06..0e37ad3e12e08 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql
 import java.util.Properties
 
 import org.apache.hadoop.fs.Path
-import org.apache.spark.Partition
+import org.apache.spark.{Logging, Partition}
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
@@ -40,7 +40,7 @@ import org.apache.spark.sql.types.StructType
  * @since 1.4.0
  */
 @Experimental
-class DataFrameReader private[sql](sqlContext: SQLContext) {
+class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
 
   /**
    * Specifies the input data source format.
@@ -251,7 +251,13 @@ class DataFrameReader private[sql](sqlContext: SQLContext) {
     if (paths.isEmpty) {
       sqlContext.emptyDataFrame
     } else {
-      val globbedPaths = paths.map(new Path(_)).flatMap(SparkHadoopUtil.get.globPath).toArray
+      val globbedPaths = paths.flatMap { path =>
+        val hdfsPath = new Path(path)
+        val fs = hdfsPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+        val qualified = hdfsPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+        SparkHadoopUtil.get.globPathIfNecessary(qualified)
+      }.toArray
+
       sqlContext.baseRelationToDataFrame(
         new ParquetRelation2(
           globbedPaths.map(_.toString), None, None, extraOptions.toMap)(sqlContext))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 84d3271ceb738..78c780bdc5797 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -242,7 +242,7 @@ private[spark] object SQLConf {
     doc = "Whether the query analyzer should be case sensitive or not.")
 
   val PARQUET_SCHEMA_MERGING_ENABLED = booleanConf("spark.sql.parquet.mergeSchema",
-    defaultValue = Some(true),
+    defaultValue = Some(false),
     doc = "When true, the Parquet data source merges schemas collected from all data files, " +
           "otherwise the schema is picked from the summary file or a random data file " +
           "if no summary file is available.")
@@ -376,6 +376,11 @@ private[spark] object SQLConf {
   val OUTPUT_COMMITTER_CLASS =
     stringConf("spark.sql.sources.outputCommitterClass", isPublic = false)
 
+  val PARALLEL_PARTITION_DISCOVERY_THRESHOLD = intConf(
+    key = "spark.sql.sources.parallelPartitionDiscovery.threshold",
+    defaultValue = Some(32),
+    doc = "<TODO>")
+
   // Whether to perform eager analysis when constructing a dataframe.
   // Set to false when debugging requires the ability to look at invalid query plans.
   val DATAFRAME_EAGER_ANALYSIS = booleanConf(
@@ -495,6 +500,9 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
   private[spark] def partitionColumnTypeInferenceEnabled(): Boolean =
     getConf(SQLConf.PARTITION_COLUMN_TYPE_INFERENCE)
 
+  private[spark] def parallelPartitionDiscoveryThreshold: Int =
+    getConf(SQLConf.PARALLEL_PARTITION_DISCOVERY_THRESHOLD)
+
   // Do not use a value larger than 4000 as the default value of this property.
   // See the comments of SCHEMA_STRING_LENGTH_THRESHOLD above for more information.
   private[spark] def schemaStringLengthThreshold: Int = getConf(SCHEMA_STRING_LENGTH_THRESHOLD)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index 9058b09375291..28cba5e54d69e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -426,6 +426,7 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int)
   }
 }
 
+// TODO Removes this class after removing old Parquet support code
 /**
  * We extend ParquetInputFormat in order to have more control over which
  * RecordFilter we want to use.
@@ -433,8 +434,6 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int)
 private[parquet] class FilteringParquetRowInputFormat
   extends org.apache.parquet.hadoop.ParquetInputFormat[InternalRow] with Logging {
 
-  private var fileStatuses = Map.empty[Path, FileStatus]
-
   override def createRecordReader(
       inputSplit: InputSplit,
       taskAttemptContext: TaskAttemptContext): RecordReader[Void, InternalRow] = {
@@ -455,17 +454,6 @@ private[parquet] class FilteringParquetRowInputFormat
 
 }
 
-private[parquet] object FilteringParquetRowInputFormat {
-  private val footerCache = CacheBuilder.newBuilder()
-    .maximumSize(20000)
-    .build[FileStatus, Footer]()
-
-  private val blockLocationCache = CacheBuilder.newBuilder()
-    .maximumSize(20000)
-    .expireAfterWrite(15, TimeUnit.MINUTES)  // Expire locations since HDFS files might move
-    .build[FileStatus, Array[BlockLocation]]()
-}
-
 private[parquet] object FileSystemHelper {
   def listFiles(pathStr: String, conf: Configuration): Seq[Path] = {
     val origPath = new Path(pathStr)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index 01dd6f471bd7c..e683eb0126004 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -22,7 +22,7 @@ import java.util.{List => JList}
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable
-import scala.util.Try
+import scala.util.{Failure, Try}
 
 import com.google.common.base.Objects
 import org.apache.hadoop.fs.{FileStatus, Path}
@@ -31,12 +31,11 @@ import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.parquet.filter2.predicate.FilterApi
 import org.apache.parquet.hadoop._
-import org.apache.parquet.hadoop.metadata.{FileMetaData, CompressionCodecName}
+import org.apache.parquet.hadoop.metadata.CompressionCodecName
 import org.apache.parquet.hadoop.util.ContextUtil
 import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.RDD._
 import org.apache.spark.sql._
@@ -278,19 +277,13 @@ private[sql] class ParquetRelation2(
     // Create the function to set input paths at the driver side.
     val setInputPaths = ParquetRelation2.initializeDriverSideJobFunc(inputFiles) _
 
-    val footers = inputFiles.map(f => metadataCache.footers(f.getPath))
-
     Utils.withDummyCallSite(sqlContext.sparkContext) {
-      // TODO Stop using `FilteringParquetRowInputFormat` and overriding `getPartition`.
-      // After upgrading to Parquet 1.6.0, we should be able to stop caching `FileStatus` objects
-      // and footers. Especially when a global arbitrative schema (either from metastore or data
-      // source DDL) is available.
       new SqlNewHadoopRDD(
         sc = sqlContext.sparkContext,
         broadcastedConf = broadcastedConf,
         initDriverSideJobFuncOpt = Some(setInputPaths),
         initLocalJobFuncOpt = Some(initLocalJobFuncOpt),
-        inputFormatClass = classOf[FilteringParquetRowInputFormat],
+        inputFormatClass = classOf[ParquetInputFormat[InternalRow]],
         keyClass = classOf[Void],
         valueClass = classOf[InternalRow]) {
 
@@ -306,12 +299,6 @@ private[sql] class ParquetRelation2(
             f.getAccessTime, f.getPermission, f.getOwner, f.getGroup, pathWithEscapedAuthority)
         }.toSeq
 
-        @transient val cachedFooters = footers.map { f =>
-          // In order to encode the authority of a Path containing special characters such as /,
-          // we need to use the string returned by the URI of the path to create a new Path.
-          new Footer(escapePathUserInfo(f.getFile), f.getParquetMetadata)
-        }.toSeq
-
         private def escapePathUserInfo(path: Path): Path = {
           val uri = path.toUri
           new Path(new URI(
@@ -321,13 +308,10 @@ private[sql] class ParquetRelation2(
 
         // Overridden so we can inject our own cached files statuses.
         override def getPartitions: Array[SparkPartition] = {
-          val inputFormat = if (cacheMetadata) {
-            new FilteringParquetRowInputFormat {
-              override def listStatus(jobContext: JobContext): JList[FileStatus] = cachedStatuses
-              override def getFooters(jobContext: JobContext): JList[Footer] = cachedFooters
+          val inputFormat = new ParquetInputFormat[InternalRow] {
+            override def listStatus(jobContext: JobContext): JList[FileStatus] = {
+              if (cacheMetadata) cachedStatuses else super.listStatus(jobContext)
             }
-          } else {
-            new FilteringParquetRowInputFormat
           }
 
           val jobContext = newJobContext(getConf(isDriverSide = true), jobId)
@@ -348,9 +332,6 @@ private[sql] class ParquetRelation2(
     // `FileStatus` objects of all "_common_metadata" files.
     private var commonMetadataStatuses: Array[FileStatus] = _
 
-    // Parquet footer cache.
-    var footers: Map[Path, Footer] = _
-
     // `FileStatus` objects of all data files (Parquet part-files).
     var dataStatuses: Array[FileStatus] = _
 
@@ -376,20 +357,6 @@ private[sql] class ParquetRelation2(
       commonMetadataStatuses =
         leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE)
 
-      footers = {
-        val conf = SparkHadoopUtil.get.conf
-        val taskSideMetaData = conf.getBoolean(ParquetInputFormat.TASK_SIDE_METADATA, true)
-        val rawFooters = if (shouldMergeSchemas) {
-          ParquetFileReader.readAllFootersInParallel(
-            conf, seqAsJavaList(leaves), taskSideMetaData)
-        } else {
-          ParquetFileReader.readAllFootersInParallelUsingSummaryFiles(
-            conf, seqAsJavaList(leaves), taskSideMetaData)
-        }
-
-        rawFooters.map(footer => footer.getFile -> footer).toMap
-      }
-
       // If we already get the schema, don't need to re-compute it since the schema merging is
       // time-consuming.
       if (dataSchema == null) {
@@ -422,7 +389,7 @@ private[sql] class ParquetRelation2(
       // Always tries the summary files first if users don't require a merged schema.  In this case,
       // "_common_metadata" is more preferable than "_metadata" because it doesn't contain row
       // groups information, and could be much smaller for large Parquet files with lots of row
-      // groups.
+      // groups.  If no summary file is available, falls back to some random part-file.
       //
       // NOTE: Metadata stored in the summary files are merged from all part-files.  However, for
       // user defined key-value metadata (in which we store Spark SQL schema), Parquet doesn't know
@@ -457,10 +424,10 @@ private[sql] class ParquetRelation2(
 
       assert(
         filesToTouch.nonEmpty || maybeDataSchema.isDefined || maybeMetastoreSchema.isDefined,
-        "No schema defined, " +
-          s"and no Parquet data file or summary file found under ${paths.mkString(", ")}.")
+        "No predefined schema found, " +
+          s"and no Parquet data files or summary files found under ${paths.mkString(", ")}.")
 
-      ParquetRelation2.readSchema(filesToTouch.map(f => footers.apply(f.getPath)), sqlContext)
+      ParquetRelation2.mergeSchemasInParallel(filesToTouch, sqlContext)
     }
   }
 }
@@ -519,6 +486,7 @@ private[sql] object ParquetRelation2 extends Logging {
   private[parquet] def initializeDriverSideJobFunc(
       inputFiles: Array[FileStatus])(job: Job): Unit = {
     // We side the input paths at the driver side.
+    logInfo(s"Reading Parquet file(s) from ${inputFiles.map(_.getPath).mkString(", ")}")
     if (inputFiles.nonEmpty) {
       FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*)
     }
@@ -543,7 +511,7 @@ private[sql] object ParquetRelation2 extends Logging {
         .getKeyValueMetaData
         .toMap
         .get(RowReadSupport.SPARK_METADATA_KEY)
-      if (serializedSchema == None) {
+      if (serializedSchema.isEmpty) {
         // Falls back to Parquet schema if no Spark SQL schema found.
         Some(parseParquetSchema(metadata.getSchema))
       } else if (!seen.contains(serializedSchema.get)) {
@@ -646,4 +614,106 @@ private[sql] object ParquetRelation2 extends Logging {
       .filter(_.nullable)
     StructType(parquetSchema ++ missingFields)
   }
+
+  /**
+   * Figures out a merged Parquet schema with a distributed Spark job.
+   *
+   * Note that locality is not taken into consideration here because:
+   *
+   *  1. For a single Parquet part-file, in most cases the footer only resides in the last block of
+   *     that file.  Thus we only need to retrieve the location of the last block.  However, Hadoop
+   *     `FileSystem` only provides API to retrieve locations of all blocks, which can be
+   *     potentially expensive.
+   *
+   *  2. This optimization is mainly useful for S3, where file metadata operations can be pretty
+   *     slow.  And basically locality is not available when using S3 (you can't run computation on
+   *     S3 nodes).
+   */
+  def mergeSchemasInParallel(
+      filesToTouch: Seq[FileStatus], sqlContext: SQLContext): Option[StructType] = {
+    val assumeBinaryIsString = sqlContext.conf.isParquetBinaryAsString
+    val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
+    val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
+    val serializedConf = new SerializableConfiguration(sqlContext.sparkContext.hadoopConfiguration)
+
+    // HACK ALERT:
+    //
+    // Parquet requires `FileStatus`es to read footers.  Here we try to send cached `FileStatus`es
+    // to executor side to avoid fetching them again.  However, `FileStatus` is not `Serializable`
+    // but only `Writable`.  What makes it worth, for some reason, `FileStatus` doesn't play well
+    // with `SerializableWritable[T]` and always causes a weird `IllegalStateException`.  These
+    // facts virtually prevents us to serialize `FileStatus`es.
+    //
+    // Since Parquet only relies on path and length information of those `FileStatus`es to read
+    // footers, here we just extract them (which can be easily serialized), send them to executor
+    // side, and resemble fake `FileStatus`es there.
+    val partialFileStatusInfo = filesToTouch.map(f => (f.getPath.toString, f.getLen))
+
+    // Issues a Spark job to read Parquet schema in parallel.
+    val partiallyMergedSchemas =
+      sqlContext
+        .sparkContext
+        .parallelize(partialFileStatusInfo)
+        .mapPartitions { iterator =>
+          // Resembles fake `FileStatus`es with serialized path and length information.
+          val fakeFileStatuses = iterator.map { case (path, length) =>
+            new FileStatus(length, false, 0, 0, 0, 0, null, null, null, new Path(path))
+          }.toSeq
+
+          // Skips row group information since we only need the schema
+          val skipRowGroups = true
+
+          // Reads footers in multi-threaded manner within each task
+          val footers =
+            ParquetFileReader.readAllFootersInParallel(
+              serializedConf.value, fakeFileStatuses, skipRowGroups)
+
+          // Converter used to convert Parquet `MessageType` to Spark SQL `StructType`
+          val converter =
+            new CatalystSchemaConverter(
+              assumeBinaryIsString = assumeBinaryIsString,
+              assumeInt96IsTimestamp = assumeInt96IsTimestamp,
+              followParquetFormatSpec = followParquetFormatSpec)
+
+          footers.map { footer =>
+            ParquetRelation2.readSchemaFromFooter(footer, converter)
+          }.reduceOption(_ merge _).iterator
+        }.collect()
+
+    partiallyMergedSchemas.reduceOption(_ merge _)
+  }
+
+  /**
+   * Reads Spark SQL schema from a Parquet footer.  If a valid serialized Spark SQL schema string
+   * can be found in the file metadata, returns the deserialized [[StructType]], otherwise, returns
+   * a [[StructType]] converted from the [[MessageType]] stored in this footer.
+   */
+  def readSchemaFromFooter(
+      footer: Footer, converter: CatalystSchemaConverter): StructType = {
+    val fileMetaData = footer.getParquetMetadata.getFileMetaData
+    fileMetaData
+      .getKeyValueMetaData
+      .toMap
+      .get(RowReadSupport.SPARK_METADATA_KEY)
+      .flatMap(deserializeSchemaString)
+      .getOrElse(converter.convert(fileMetaData.getSchema))
+  }
+
+  private def deserializeSchemaString(schemaString: String): Option[StructType] = {
+    // Tries to deserialize the schema string as JSON first, then falls back to the case class
+    // string parser (data generated by older versions of Spark SQL uses this format).
+    Try(DataType.fromJson(schemaString).asInstanceOf[StructType]).recover {
+      case _: Throwable =>
+        logInfo(
+          s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " +
+            "falling back to the deprecated DataType.fromCaseClassString parser.")
+        DataType.fromCaseClassString(schemaString).asInstanceOf[StructType]
+    }.recoverWith {
+      case cause: Throwable =>
+        logWarning(
+          "Failed to parse and ignored serialized Spark schema in " +
+            s"Parquet key-value metadata:\n\t$schemaString", cause)
+        Failure(cause)
+    }.toOption
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
index d7440c55bd4a6..5a8c97c773ee6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
@@ -247,7 +247,9 @@ private[sql] object ResolvedDataSource {
           val caseInsensitiveOptions = new CaseInsensitiveMap(options)
           val paths = {
             val patternPath = new Path(caseInsensitiveOptions("path"))
-            SparkHadoopUtil.get.globPath(patternPath).map(_.toString).toArray
+            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
           }
 
           val dataSchema =
@@ -272,7 +274,9 @@ private[sql] object ResolvedDataSource {
           val caseInsensitiveOptions = new CaseInsensitiveMap(options)
           val paths = {
             val patternPath = new Path(caseInsensitiveOptions("path"))
-            SparkHadoopUtil.get.globPath(patternPath).map(_.toString).toArray
+            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
           }
           dataSource.createRelation(sqlContext, paths, None, None, caseInsensitiveOptions)
         case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 5d7cc2ff55af1..2cd8b358d81c6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -18,21 +18,23 @@
 package org.apache.spark.sql.sources
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 import scala.util.Try
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
+import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Row, SaveMode, SQLContext}
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.execution.RDDConversions
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
+import org.apache.spark.sql.execution.RDDConversions
 import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql._
 import org.apache.spark.util.SerializableConfiguration
 
 /**
@@ -367,7 +369,9 @@ abstract class OutputWriter {
  */
 @Experimental
 abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[PartitionSpec])
-  extends BaseRelation {
+  extends BaseRelation with Logging {
+
+  logInfo("Constructing HadoopFsRelation")
 
   def this() = this(None)
 
@@ -382,36 +386,40 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
 
     var leafDirToChildrenFiles = mutable.Map.empty[Path, Array[FileStatus]]
 
-    def refresh(): Unit = {
-      // We don't filter files/directories whose name start with "_" except "_temporary" here, as
-      // specific data sources may take advantages over them (e.g. Parquet _metadata and
-      // _common_metadata files). "_temporary" directories are explicitly ignored since failed
-      // tasks/jobs may leave partial/corrupted data files there.
-      def listLeafFilesAndDirs(fs: FileSystem, status: FileStatus): Set[FileStatus] = {
-        if (status.getPath.getName.toLowerCase == "_temporary") {
-          Set.empty
+    private def listLeafFiles(paths: Array[String]): Set[FileStatus] = {
+      if (paths.length >= sqlContext.conf.parallelPartitionDiscoveryThreshold) {
+        HadoopFsRelation.listLeafFilesInParallel(paths, hadoopConf, sqlContext.sparkContext)
+      } else {
+        val statuses = paths.flatMap { path =>
+          val hdfsPath = new Path(path)
+          val fs = hdfsPath.getFileSystem(hadoopConf)
+          val qualified = hdfsPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+
+          logInfo(s"Listing $qualified on driver")
+          Try(fs.listStatus(qualified)).getOrElse(Array.empty)
+        }.filterNot { status =>
+          val name = status.getPath.getName
+          name.toLowerCase == "_temporary" || name.startsWith(".")
+        }
+
+        val (dirs, files) = statuses.partition(_.isDir)
+
+        if (dirs.isEmpty) {
+          files.toSet
         } else {
-          val (dirs, files) = fs.listStatus(status.getPath).partition(_.isDir)
-          val leafDirs = if (dirs.isEmpty) Set(status) else Set.empty[FileStatus]
-          files.toSet ++ leafDirs ++ dirs.flatMap(dir => listLeafFilesAndDirs(fs, dir))
+          files.toSet ++ listLeafFiles(dirs.map(_.getPath.toString))
         }
       }
+    }
 
-      leafFiles.clear()
+    def refresh(): Unit = {
+      val files = listLeafFiles(paths)
 
-      val statuses = paths.flatMap { path =>
-        val hdfsPath = new Path(path)
-        val fs = hdfsPath.getFileSystem(hadoopConf)
-        val qualified = hdfsPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
-        Try(fs.getFileStatus(qualified)).toOption.toArray.flatMap(listLeafFilesAndDirs(fs, _))
-      }.filterNot { status =>
-        // SPARK-8037: Ignores files like ".DS_Store" and other hidden files/directories
-        status.getPath.getName.startsWith(".")
-      }
+      leafFiles.clear()
+      leafDirToChildrenFiles.clear()
 
-      val files = statuses.filterNot(_.isDir)
       leafFiles ++= files.map(f => f.getPath -> f).toMap
-      leafDirToChildrenFiles ++= files.groupBy(_.getPath.getParent)
+      leafDirToChildrenFiles ++= files.toArray.groupBy(_.getPath.getParent)
     }
   }
 
@@ -666,3 +674,63 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
    */
   def prepareJobForWrite(job: Job): OutputWriterFactory
 }
+
+private[sql] object HadoopFsRelation extends Logging {
+  // We don't filter files/directories whose name start with "_" except "_temporary" here, as
+  // specific data sources may take advantages over them (e.g. Parquet _metadata and
+  // _common_metadata files). "_temporary" directories are explicitly ignored since failed
+  // tasks/jobs may leave partial/corrupted data files there.  Files and directories whose name
+  // start with "." are also ignored.
+  def listLeafFiles(fs: FileSystem, status: FileStatus): Array[FileStatus] = {
+    logInfo(s"Listing ${status.getPath}")
+    val name = status.getPath.getName.toLowerCase
+    if (name == "_temporary" || name.startsWith(".")) {
+      Array.empty
+    } else {
+      val (dirs, files) = fs.listStatus(status.getPath).partition(_.isDir)
+      files ++ dirs.flatMap(dir => listLeafFiles(fs, dir))
+    }
+  }
+
+  // `FileStatus` is Writable but not serializable.  What make it worse, somehow it doesn't play
+  // well with `SerializableWritable`.  So there seems to be no way to serialize a `FileStatus`.
+  // Here we use `FakeFileStatus` to extract key components of a `FileStatus` to serialize it from
+  // executor side and reconstruct it on driver side.
+  case class FakeFileStatus(
+      path: String,
+      length: Long,
+      isDir: Boolean,
+      blockReplication: Short,
+      blockSize: Long,
+      modificationTime: Long,
+      accessTime: Long)
+
+  def listLeafFilesInParallel(
+      paths: Array[String],
+      hadoopConf: Configuration,
+      sparkContext: SparkContext): Set[FileStatus] = {
+    logInfo(s"Listing leaf files and directories in parallel under: ${paths.mkString(", ")}")
+
+    val serializableConfiguration = new SerializableConfiguration(hadoopConf)
+    val fakeStatuses = sparkContext.parallelize(paths).flatMap { path =>
+      val hdfsPath = new Path(path)
+      val fs = hdfsPath.getFileSystem(serializableConfiguration.value)
+      val qualified = hdfsPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+      Try(listLeafFiles(fs, fs.getFileStatus(qualified))).getOrElse(Array.empty)
+    }.map { status =>
+      FakeFileStatus(
+        status.getPath.toString,
+        status.getLen,
+        status.isDir,
+        status.getReplication,
+        status.getBlockSize,
+        status.getModificationTime,
+        status.getAccessTime)
+    }.collect()
+
+    fakeStatuses.map { f =>
+      new FileStatus(
+        f.length, f.isDir, f.blockReplication, f.blockSize, f.modificationTime, new Path(f.path))
+    }.toSet
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
index d0ebb11b063f0..37b0a9fbf7a4e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
@@ -447,7 +447,12 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest {
         (1 to 10).map(i => (i, i.toString)).toDF("intField", "stringField"),
         makePartitionDir(base, defaultPartitionName, "pi" -> 2))
 
-      sqlContext.read.format("parquet").load(base.getCanonicalPath).registerTempTable("t")
+      sqlContext
+        .read
+        .option("mergeSchema", "true")
+        .format("parquet")
+        .load(base.getCanonicalPath)
+        .registerTempTable("t")
 
       withTempTable("t") {
         checkAnswer(
@@ -583,4 +588,15 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest {
           Seq("a", "a, b"),
           Seq("file:/tmp/foo/a=1", "file:/tmp/foo/a=1/b=foo")))
   }
+
+  test("Parallel partition discovery") {
+    withTempPath { dir =>
+      withSQLConf(SQLConf.PARALLEL_PARTITION_DISCOVERY_THRESHOLD.key -> "1") {
+        val path = dir.getCanonicalPath
+        val df = sqlContext.range(5).select('id as 'a, 'id as 'b, 'id as 'c).coalesce(1)
+        df.write.partitionBy("b", "c").parquet(path)
+        checkAnswer(sqlContext.read.parquet(path), df)
+      }
+    }
+  }
 }

From a5d05819afcc9b19aeae4817d842205f32b34335 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Mon, 20 Jul 2015 16:49:55 -0700
Subject: [PATCH 0498/1454] [SPARK-9198] [MLLIB] [PYTHON] Fixed typo in pyspark
 sparsevector doc tests

Several places in the PySpark SparseVector docs have one defined as:
```
SparseVector(4, [2, 4], [1.0, 2.0])
```
The index 4 goes out of bounds (but this is not checked).

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7541 from jkbradley/sparsevec-doc-typo-fix and squashes the following commits:

c806a65 [Joseph K. Bradley] fixed doc test
e2dcb23 [Joseph K. Bradley] Fixed typo in pyspark sparsevector doc tests
---
 python/pyspark/mllib/linalg.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 529bd75894c96..334dc8e38bb8f 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -566,7 +566,7 @@ def dot(self, other):
         25.0
         >>> a.dot(array.array('d', [1., 2., 3., 4.]))
         22.0
-        >>> b = SparseVector(4, [2, 4], [1.0, 2.0])
+        >>> b = SparseVector(4, [2], [1.0])
         >>> a.dot(b)
         0.0
         >>> a.dot(np.array([[1, 1], [2, 2], [3, 3], [4, 4]]))
@@ -624,11 +624,11 @@ def squared_distance(self, other):
         11.0
         >>> a.squared_distance(np.array([1., 2., 3., 4.]))
         11.0
-        >>> b = SparseVector(4, [2, 4], [1.0, 2.0])
+        >>> b = SparseVector(4, [2], [1.0])
         >>> a.squared_distance(b)
-        30.0
+        26.0
         >>> b.squared_distance(a)
-        30.0
+        26.0
         >>> b.squared_distance([1., 2.])
         Traceback (most recent call last):
             ...

From ff3c72dbafa16c6158fc36619f3c38344c452ba0 Mon Sep 17 00:00:00 2001
From: Meihua Wu <meihuawu@umich.edu>
Date: Mon, 20 Jul 2015 17:03:46 -0700
Subject: [PATCH 0499/1454] [SPARK-9175] [MLLIB] BLAS.gemm fails to update
 matrix C when alpha==0 and beta!=1

Fix BLAS.gemm to update matrix C when alpha==0 and beta!=1
Also include unit tests to verify the fix.

mengxr brkyvz

Author: Meihua Wu <meihuawu@umich.edu>

Closes #7503 from rotationsymmetry/fix_BLAS_gemm and squashes the following commits:

fce199c [Meihua Wu] Fix BLAS.gemm to update C when alpha==0 and beta!=1
---
 .../org/apache/spark/mllib/linalg/BLAS.scala     |  4 ++--
 .../apache/spark/mllib/linalg/BLASSuite.scala    | 16 ++++++++++++++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index 3523f1804325d..9029093e0fa08 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -303,8 +303,8 @@ private[spark] object BLAS extends Serializable with Logging {
       C: DenseMatrix): Unit = {
     require(!C.isTransposed,
       "The matrix C cannot be the product of a transpose() call. C.isTransposed must be false.")
-    if (alpha == 0.0) {
-      logDebug("gemm: alpha is equal to 0. Returning C.")
+    if (alpha == 0.0 && beta == 1.0) {
+      logDebug("gemm: alpha is equal to 0 and beta is equal to 1. Returning C.")
     } else {
       A match {
         case sparse: SparseMatrix => gemm(alpha, sparse, B, beta, C)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
index b0f3f71113c57..d119e0b50a393 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
@@ -200,8 +200,14 @@ class BLASSuite extends SparkFunSuite {
     val C10 = C1.copy
     val C11 = C1.copy
     val C12 = C1.copy
+    val C13 = C1.copy
+    val C14 = C1.copy
+    val C15 = C1.copy
+    val C16 = C1.copy
     val expected2 = new DenseMatrix(4, 2, Array(2.0, 1.0, 4.0, 2.0, 4.0, 0.0, 4.0, 3.0))
     val expected3 = new DenseMatrix(4, 2, Array(2.0, 2.0, 4.0, 2.0, 8.0, 0.0, 6.0, 6.0))
+    val expected4 = new DenseMatrix(4, 2, Array(5.0, 0.0, 10.0, 5.0, 0.0, 0.0, 5.0, 0.0))
+    val expected5 = C1.copy
 
     gemm(1.0, dA, B, 2.0, C1)
     gemm(1.0, sA, B, 2.0, C2)
@@ -248,6 +254,16 @@ class BLASSuite extends SparkFunSuite {
     assert(C10 ~== expected2 absTol 1e-15)
     assert(C11 ~== expected3 absTol 1e-15)
     assert(C12 ~== expected3 absTol 1e-15)
+
+    gemm(0, dA, B, 5, C13)
+    gemm(0, sA, B, 5, C14)
+    gemm(0, dA, B, 1, C15)
+    gemm(0, sA, B, 1, C16)
+    assert(C13 ~== expected4 absTol 1e-15)
+    assert(C14 ~== expected4 absTol 1e-15)
+    assert(C15 ~== expected5 absTol 1e-15)
+    assert(C16 ~== expected5 absTol 1e-15)
+
   }
 
   test("gemv") {

From 66bb8003b949860b8652542e1232bc48665448c2 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Mon, 20 Jul 2015 18:08:59 -0700
Subject: [PATCH 0500/1454] [SPARK-9187] [WEBUI] Timeline view may show
 negative value for running tasks

For running tasks, the executorRunTime metrics is 0 which causes negative executorComputingTime in the timeline. It also causes an incorrect SchedulerDelay time.
![timelinenegativevalue](https://cloud.githubusercontent.com/assets/9278199/8770953/f4362378-2eec-11e5-81e6-a06a07c04794.png)

Author: Carson Wang <carson.wang@intel.com>

Closes #7526 from carsonwang/timeline-negValue and squashes the following commits:

7b17db2 [Carson Wang] Fix negative value in timeline view
---
 .../org/apache/spark/ui/jobs/StagePage.scala  | 23 ++++++++++++-------
 1 file changed, 15 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 27b82aaddd2e4..6e077bf3e70d5 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -537,20 +537,27 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
           (metricsOpt.flatMap(_.shuffleWriteMetrics
             .map(_.shuffleWriteTime)).getOrElse(0L) / 1e6).toLong
         val shuffleWriteTimeProportion = toProportion(shuffleWriteTime)
-        val executorComputingTime = metricsOpt.map(_.executorRunTime).getOrElse(0L) -
-          shuffleReadTime - shuffleWriteTime
-        val executorComputingTimeProportion = toProportion(executorComputingTime)
+
         val serializationTime = metricsOpt.map(_.resultSerializationTime).getOrElse(0L)
         val serializationTimeProportion = toProportion(serializationTime)
         val deserializationTime = metricsOpt.map(_.executorDeserializeTime).getOrElse(0L)
         val deserializationTimeProportion = toProportion(deserializationTime)
         val gettingResultTime = getGettingResultTime(taskUIData.taskInfo, currentTime)
         val gettingResultTimeProportion = toProportion(gettingResultTime)
-        val schedulerDelay = totalExecutionTime -
-          (executorComputingTime + shuffleReadTime + shuffleWriteTime +
-            serializationTime + deserializationTime + gettingResultTime)
-        val schedulerDelayProportion =
-          (100 - executorComputingTimeProportion - shuffleReadTimeProportion -
+        val schedulerDelay =
+          metricsOpt.map(getSchedulerDelay(taskInfo, _, currentTime)).getOrElse(0L)
+        val schedulerDelayProportion = toProportion(schedulerDelay)
+
+        val executorOverhead = serializationTime + deserializationTime
+        val executorRunTime = if (taskInfo.running) {
+          totalExecutionTime - executorOverhead - gettingResultTime
+        } else {
+          metricsOpt.map(_.executorRunTime).getOrElse(
+            totalExecutionTime - executorOverhead - gettingResultTime)
+        }
+        val executorComputingTime = executorRunTime - shuffleReadTime - shuffleWriteTime
+        val executorComputingTimeProportion =
+          (100 - schedulerDelayProportion - shuffleReadTimeProportion -
             shuffleWriteTimeProportion - serializationTimeProportion -
             deserializationTimeProportion - gettingResultTimeProportion)
 

From 047ccc8c9a88e74f7bc87709ee5d531f1d7a4228 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 18:16:49 -0700
Subject: [PATCH 0501/1454] [SPARK-9178][SQL] Add an empty string constant to
 UTF8String

Jira: https://issues.apache.org/jira/browse/SPARK-9178

In order to avoid calls of `UTF8String.fromString("")` this pr adds an `EMPTY_STRING` constant to `UTF8String`. An `UTF8String` is immutable, so we can use a constant, isn't it?

I searched for current usage of `UTF8String.fromString("")` with
`grep -R  "UTF8String.fromString(\"\")" .`

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7509 from tarekauel/SPARK-9178 and squashes the following commits:

8d6c405 [Tarek Auel] [SPARK-9178] revert intellij indents
3627b80 [Tarek Auel] [SPARK-9178] revert concat tests changes
3f5fbf5 [Tarek Auel] [SPARK-9178] rebase and add final to UTF8String.EMPTY_UTF8
47cda68 [Tarek Auel] Merge branch 'master' into SPARK-9178
4a37344 [Tarek Auel] [SPARK-9178] changed name to EMPTY_UTF8, added tests
748b87a [Tarek Auel] [SPARK-9178] Add empty string constant to UTF8String
---
 .../apache/spark/unsafe/types/UTF8String.java |  2 +
 .../spark/unsafe/types/UTF8StringSuite.java   | 76 +++++++++----------
 2 files changed, 39 insertions(+), 39 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 819639f300177..fc63fe537d226 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -50,6 +50,8 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
     5, 5, 5, 5,
     6, 6, 6, 6};
 
+  public static final UTF8String EMPTY_UTF8 = UTF8String.fromString("");
+
   /**
    * Creates an UTF8String from byte array, which should be encoded in UTF-8.
    *
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 6a21c27461163..d730b1d1384f5 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -54,6 +54,14 @@ public void basicTest() throws UnsupportedEncodingException {
     checkBasic("大 千 世 界", 7);
   }
 
+  @Test
+  public void emptyStringTest() {
+    assertEquals(fromString(""), EMPTY_UTF8);
+    assertEquals(fromBytes(new byte[0]), EMPTY_UTF8);
+    assertEquals(0, EMPTY_UTF8.numChars());
+    assertEquals(0, EMPTY_UTF8.numBytes());
+  }
+
   @Test
   public void compareTo() {
     assertTrue(fromString("abc").compareTo(fromString("ABC")) > 0);
@@ -88,9 +96,9 @@ public void upperAndLower() {
 
   @Test
   public void concatTest() {
-    assertEquals(fromString(""), concat());
+    assertEquals(EMPTY_UTF8, concat());
     assertEquals(null, concat((UTF8String) null));
-    assertEquals(fromString(""), concat(fromString("")));
+    assertEquals(EMPTY_UTF8, concat(EMPTY_UTF8));
     assertEquals(fromString("ab"), concat(fromString("ab")));
     assertEquals(fromString("ab"), concat(fromString("a"), fromString("b")));
     assertEquals(fromString("abc"), concat(fromString("a"), fromString("b"), fromString("c")));
@@ -109,8 +117,8 @@ public void concatWsTest() {
     // If separator is null, concatWs should skip all null inputs and never return null.
     UTF8String sep = fromString("哈哈");
     assertEquals(
-      fromString(""),
-      concatWs(sep, fromString("")));
+      EMPTY_UTF8,
+      concatWs(sep, EMPTY_UTF8));
     assertEquals(
       fromString("ab"),
       concatWs(sep, fromString("ab")));
@@ -127,7 +135,7 @@ public void concatWsTest() {
       fromString("a"),
       concatWs(sep, fromString("a"), null, null));
     assertEquals(
-      fromString(""),
+      EMPTY_UTF8,
       concatWs(sep, null, null, null));
     assertEquals(
       fromString("数据哈哈砖头"),
@@ -136,7 +144,7 @@ public void concatWsTest() {
 
   @Test
   public void contains() {
-    assertTrue(fromString("").contains(fromString("")));
+    assertTrue(EMPTY_UTF8.contains(EMPTY_UTF8));
     assertTrue(fromString("hello").contains(fromString("ello")));
     assertFalse(fromString("hello").contains(fromString("vello")));
     assertFalse(fromString("hello").contains(fromString("hellooo")));
@@ -147,7 +155,7 @@ public void contains() {
 
   @Test
   public void startsWith() {
-    assertTrue(fromString("").startsWith(fromString("")));
+    assertTrue(EMPTY_UTF8.startsWith(EMPTY_UTF8));
     assertTrue(fromString("hello").startsWith(fromString("hell")));
     assertFalse(fromString("hello").startsWith(fromString("ell")));
     assertFalse(fromString("hello").startsWith(fromString("hellooo")));
@@ -158,7 +166,7 @@ public void startsWith() {
 
   @Test
   public void endsWith() {
-    assertTrue(fromString("").endsWith(fromString("")));
+    assertTrue(EMPTY_UTF8.endsWith(EMPTY_UTF8));
     assertTrue(fromString("hello").endsWith(fromString("ello")));
     assertFalse(fromString("hello").endsWith(fromString("ellov")));
     assertFalse(fromString("hello").endsWith(fromString("hhhello")));
@@ -169,7 +177,7 @@ public void endsWith() {
 
   @Test
   public void substring() {
-    assertEquals(fromString(""), fromString("hello").substring(0, 0));
+    assertEquals(EMPTY_UTF8, fromString("hello").substring(0, 0));
     assertEquals(fromString("el"), fromString("hello").substring(1, 3));
     assertEquals(fromString("数"), fromString("数据砖头").substring(0, 1));
     assertEquals(fromString("据砖"), fromString("数据砖头").substring(1, 3));
@@ -183,9 +191,9 @@ public void trims() {
     assertEquals(fromString("hello "), fromString("  hello ").trimLeft());
     assertEquals(fromString("  hello"), fromString("  hello ").trimRight());
 
-    assertEquals(fromString(""), fromString("  ").trim());
-    assertEquals(fromString(""), fromString("  ").trimLeft());
-    assertEquals(fromString(""), fromString("  ").trimRight());
+    assertEquals(EMPTY_UTF8, fromString("  ").trim());
+    assertEquals(EMPTY_UTF8, fromString("  ").trimLeft());
+    assertEquals(EMPTY_UTF8, fromString("  ").trimRight());
 
     assertEquals(fromString("数据砖头"), fromString("  数据砖头 ").trim());
     assertEquals(fromString("数据砖头 "), fromString("  数据砖头 ").trimLeft());
@@ -198,9 +206,9 @@ public void trims() {
 
   @Test
   public void indexOf() {
-    assertEquals(0, fromString("").indexOf(fromString(""), 0));
-    assertEquals(-1, fromString("").indexOf(fromString("l"), 0));
-    assertEquals(0, fromString("hello").indexOf(fromString(""), 0));
+    assertEquals(0, EMPTY_UTF8.indexOf(EMPTY_UTF8, 0));
+    assertEquals(-1, EMPTY_UTF8.indexOf(fromString("l"), 0));
+    assertEquals(0, fromString("hello").indexOf(EMPTY_UTF8, 0));
     assertEquals(2, fromString("hello").indexOf(fromString("l"), 0));
     assertEquals(3, fromString("hello").indexOf(fromString("l"), 3));
     assertEquals(-1, fromString("hello").indexOf(fromString("a"), 0));
@@ -215,7 +223,7 @@ public void indexOf() {
   @Test
   public void reverse() {
     assertEquals(fromString("olleh"), fromString("hello").reverse());
-    assertEquals(fromString(""), fromString("").reverse());
+    assertEquals(EMPTY_UTF8, EMPTY_UTF8.reverse());
     assertEquals(fromString("者行孙"), fromString("孙行者").reverse());
     assertEquals(fromString("者行孙 olleh"), fromString("hello 孙行者").reverse());
   }
@@ -224,7 +232,7 @@ public void reverse() {
   public void repeat() {
     assertEquals(fromString("数d数d数d数d数d"), fromString("数d").repeat(5));
     assertEquals(fromString("数d"), fromString("数d").repeat(1));
-    assertEquals(fromString(""), fromString("数d").repeat(-1));
+    assertEquals(EMPTY_UTF8, fromString("数d").repeat(-1));
   }
 
   @Test
@@ -234,14 +242,14 @@ public void pad() {
     assertEquals(fromString("?hello"), fromString("hello").lpad(6, fromString("????")));
     assertEquals(fromString("???????hello"), fromString("hello").lpad(12, fromString("????")));
     assertEquals(fromString("?????hello"), fromString("hello").lpad(10, fromString("?????")));
-    assertEquals(fromString("???????"), fromString("").lpad(7, fromString("?????")));
+    assertEquals(fromString("???????"), EMPTY_UTF8.lpad(7, fromString("?????")));
 
     assertEquals(fromString("hel"), fromString("hello").rpad(3, fromString("????")));
     assertEquals(fromString("hello"), fromString("hello").rpad(5, fromString("????")));
     assertEquals(fromString("hello?"), fromString("hello").rpad(6, fromString("????")));
     assertEquals(fromString("hello???????"), fromString("hello").rpad(12, fromString("????")));
     assertEquals(fromString("hello?????"), fromString("hello").rpad(10, fromString("?????")));
-    assertEquals(fromString("???????"), fromString("").rpad(7, fromString("?????")));
+    assertEquals(fromString("???????"), EMPTY_UTF8.rpad(7, fromString("?????")));
 
 
     assertEquals(fromString("数据砖"), fromString("数据砖头").lpad(3, fromString("????")));
@@ -265,26 +273,16 @@ public void pad() {
   
   @Test
   public void levenshteinDistance() {
-    assertEquals(
-        UTF8String.fromString("").levenshteinDistance(UTF8String.fromString("")), 0);
-    assertEquals(
-        UTF8String.fromString("").levenshteinDistance(UTF8String.fromString("a")), 1);
-    assertEquals(
-        UTF8String.fromString("aaapppp").levenshteinDistance(UTF8String.fromString("")), 7);
-    assertEquals(
-        UTF8String.fromString("frog").levenshteinDistance(UTF8String.fromString("fog")), 1);
-    assertEquals(
-        UTF8String.fromString("fly").levenshteinDistance(UTF8String.fromString("ant")),3);
-    assertEquals(
-        UTF8String.fromString("elephant").levenshteinDistance(UTF8String.fromString("hippo")), 7);
-    assertEquals(
-        UTF8String.fromString("hippo").levenshteinDistance(UTF8String.fromString("elephant")), 7);
-    assertEquals(
-        UTF8String.fromString("hippo").levenshteinDistance(UTF8String.fromString("zzzzzzzz")), 8);
-    assertEquals(
-        UTF8String.fromString("hello").levenshteinDistance(UTF8String.fromString("hallo")),1);
-    assertEquals(
-        UTF8String.fromString("世界千世").levenshteinDistance(UTF8String.fromString("千a世b")),4);
+    assertEquals(EMPTY_UTF8.levenshteinDistance(EMPTY_UTF8), 0);
+    assertEquals(EMPTY_UTF8.levenshteinDistance(fromString("a")), 1);
+    assertEquals(fromString("aaapppp").levenshteinDistance(EMPTY_UTF8), 7);
+    assertEquals(fromString("frog").levenshteinDistance(fromString("fog")), 1);
+    assertEquals(fromString("fly").levenshteinDistance(fromString("ant")),3);
+    assertEquals(fromString("elephant").levenshteinDistance(fromString("hippo")), 7);
+    assertEquals(fromString("hippo").levenshteinDistance(fromString("elephant")), 7);
+    assertEquals(fromString("hippo").levenshteinDistance(fromString("zzzzzzzz")), 8);
+    assertEquals(fromString("hello").levenshteinDistance(fromString("hallo")),1);
+    assertEquals(fromString("世界千世").levenshteinDistance(fromString("千a世b")),4);
   }
 
   @Test

From 6853ac7c8c76003160fc861ddcc8e8e39e4a5924 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 18:21:05 -0700
Subject: [PATCH 0502/1454] [SPARK-9156][SQL] codegen StringSplit

Jira: https://issues.apache.org/jira/browse/SPARK-9156

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7547 from tarekauel/SPARK-9156 and squashes the following commits:

0be2700 [Tarek Auel] [SPARK-9156][SQL] indention fix
b860eaf [Tarek Auel] [SPARK-9156][SQL] codegen StringSplit
5ad6a1f [Tarek Auel] [SPARK-9156] codegen StringSplit
---
 .../sql/catalyst/expressions/stringOperations.scala  | 12 ++++++++----
 .../org/apache/spark/unsafe/types/UTF8String.java    |  9 +++++++++
 .../apache/spark/unsafe/types/UTF8StringSuite.java   | 11 +++++++++++
 3 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index a5682428b3d40..5c1908d55576a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -615,7 +615,7 @@ case class StringSpace(child: Expression)
  * Splits str around pat (pattern is a regular expression).
  */
 case class StringSplit(str: Expression, pattern: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes with CodegenFallback {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   override def left: Expression = str
   override def right: Expression = pattern
@@ -623,9 +623,13 @@ case class StringSplit(str: Expression, pattern: Expression)
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
   override def nullSafeEval(string: Any, regex: Any): Any = {
-    val splits =
-      string.asInstanceOf[UTF8String].toString.split(regex.asInstanceOf[UTF8String].toString, -1)
-    splits.toSeq.map(UTF8String.fromString)
+    string.asInstanceOf[UTF8String].split(regex.asInstanceOf[UTF8String], -1).toSeq
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (str, pattern) =>
+      s"""${ev.primitive} = scala.collection.JavaConversions.asScalaBuffer(
+            java.util.Arrays.asList($str.split($pattern, -1)));""")
   }
 
   override def prettyName: String = "split"
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index fc63fe537d226..ed354f7f877f1 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -487,6 +487,15 @@ public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
     return fromBytes(result);
   }
 
+  public UTF8String[] split(UTF8String pattern, int limit) {
+    String[] splits = toString().split(pattern.toString(), limit);
+    UTF8String[] res = new UTF8String[splits.length];
+    for (int i = 0; i < res.length; i++) {
+      res[i] = fromString(splits[i]);
+    }
+    return res;
+  }
+
   @Override
   public String toString() {
     try {
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index d730b1d1384f5..1f5572c509bdb 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -18,6 +18,7 @@
 package org.apache.spark.unsafe.types;
 
 import java.io.UnsupportedEncodingException;
+import java.util.Arrays;
 
 import org.junit.Test;
 
@@ -270,6 +271,16 @@ public void pad() {
       fromString("数据砖头孙行者孙行者孙行"),
       fromString("数据砖头").rpad(12, fromString("孙行者")));
   }
+
+  @Test
+  public void split() {
+    assertTrue(Arrays.equals(fromString("ab,def,ghi").split(fromString(","), -1),
+      new UTF8String[]{fromString("ab"), fromString("def"), fromString("ghi")}));
+    assertTrue(Arrays.equals(fromString("ab,def,ghi").split(fromString(","), 2),
+      new UTF8String[]{fromString("ab"), fromString("def,ghi")}));
+    assertTrue(Arrays.equals(fromString("ab,def,ghi").split(fromString(","), 2),
+      new UTF8String[]{fromString("ab"), fromString("def,ghi")}));
+  }
   
   @Test
   public void levenshteinDistance() {

From e90543e5366808332bbde18d78cccd4d064a3338 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 20 Jul 2015 18:23:51 -0700
Subject: [PATCH 0503/1454] [SPARK-9142][SQL] Removing unnecessary self types
 in expressions.

Also added documentation to expressions to explain the important traits and abstract classes.

Author: Reynold Xin <rxin@databricks.com>

Closes #7550 from rxin/remove-self-types and squashes the following commits:

b2a3ec1 [Reynold Xin] [SPARK-9142][SQL] Removing unnecessary self types in expressions.
---
 .../expressions/ExpectsInputTypes.scala       |  4 +--
 .../sql/catalyst/expressions/Expression.scala | 33 +++++++++++--------
 .../expressions/codegen/CodegenFallback.scala |  2 +-
 3 files changed, 22 insertions(+), 17 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
index ded89e85dea79..abe6457747550 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -29,7 +29,7 @@ import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion.ImplicitTypeCasts
  *
  * Most function expressions (e.g. [[Substring]] should extends [[ImplicitCastInputTypes]]) instead.
  */
-trait ExpectsInputTypes { self: Expression =>
+trait ExpectsInputTypes extends Expression {
 
   /**
    * Expected input types from child expressions. The i-th position in the returned seq indicates
@@ -60,6 +60,6 @@ trait ExpectsInputTypes { self: Expression =>
 /**
  * A mixin for the analyzer to perform implicit type casting using [[ImplicitTypeCasts]].
  */
-trait ImplicitCastInputTypes extends ExpectsInputTypes { self: Expression =>
+trait ImplicitCastInputTypes extends ExpectsInputTypes {
   // No other methods
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index da599b8963340..aada25276adb7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -19,19 +19,12 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, UnresolvedAttribute}
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types._
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
-// This file defines the basic expression abstract classes in Catalyst, including:
-// Expression: the base expression abstract class
-// LeafExpression
-// UnaryExpression
-// BinaryExpression
-// BinaryOperator
-//
-// For details, see their classdocs.
+// This file defines the basic expression abstract classes in Catalyst.
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
 /**
@@ -39,9 +32,21 @@ import org.apache.spark.sql.types._
  *
  * If an expression wants to be exposed in the function registry (so users can call it with
  * "name(arguments...)", the concrete implementation must be a case class whose constructor
- * arguments are all Expressions types.
+ * arguments are all Expressions types. See [[Substring]] for an example.
+ *
+ * There are a few important traits:
+ *
+ * - [[Nondeterministic]]: an expression that is not deterministic.
+ * - [[Unevaluable]]: an expression that is not supposed to be evaluated.
+ * - [[CodegenFallback]]: an expression that does not have code gen implemented and falls back to
+ *                        interpreted mode.
+ *
+ * - [[LeafExpression]]: an expression that has no child.
+ * - [[UnaryExpression]]: an expression that has one child.
+ * - [[BinaryExpression]]: an expression that has two children.
+ * - [[BinaryOperator]]: a special case of [[BinaryExpression]] that requires two children to have
+ *                       the same output data type.
  *
- * See [[Substring]] for an example.
  */
 abstract class Expression extends TreeNode[Expression] {
 
@@ -176,7 +181,7 @@ abstract class Expression extends TreeNode[Expression] {
  * An expression that cannot be evaluated. Some expressions don't live past analysis or optimization
  * time (e.g. Star). This trait is used by those expressions.
  */
-trait Unevaluable { self: Expression =>
+trait Unevaluable extends Expression {
 
   override def eval(input: InternalRow = null): Any =
     throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
@@ -185,11 +190,11 @@ trait Unevaluable { self: Expression =>
     throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
 }
 
+
 /**
  * An expression that is nondeterministic.
  */
-trait Nondeterministic { self: Expression =>
-
+trait Nondeterministic extends Expression {
   override def deterministic: Boolean = false
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
index bf4f600cb26e5..6b187f05604fd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
@@ -22,7 +22,7 @@ import org.apache.spark.sql.catalyst.expressions.Expression
 /**
  * A trait that can be used to provide a fallback mode for expression code generation.
  */
-trait CodegenFallback { self: Expression =>
+trait CodegenFallback extends Expression {
 
   protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     ctx.references += this

From 936a96cb31a6dd7d8685bce05103e779ca02e763 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 19:17:59 -0700
Subject: [PATCH 0504/1454] [SPARK-9164] [SQL] codegen hex/unhex

Jira: https://issues.apache.org/jira/browse/SPARK-9164

The diff looks heavy, but I just moved the `hex` and `unhex` methods to `object Hex`.  This allows me to call them from `eval` and `codeGen`

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7548 from tarekauel/SPARK-9164 and squashes the following commits:

dd91c57 [Tarek Auel] [SPARK-9164][SQL] codegen hex/unhex
---
 .../spark/sql/catalyst/expressions/math.scala | 96 +++++++++++--------
 1 file changed, 57 insertions(+), 39 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 7ce64d29ba59a..7a9be02ba45b3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -489,28 +489,8 @@ object Hex {
     (0 to 5).foreach(i => array('a' + i) = (i + 10).toByte)
     array
   }
-}
 
-/**
- * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
- * Otherwise if the number is a STRING, it converts each character into its hex representation
- * and returns the resulting STRING. Negative numbers would be treated as two's complement.
- */
-case class Hex(child: Expression)
-  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
-
-  override def inputTypes: Seq[AbstractDataType] =
-    Seq(TypeCollection(LongType, BinaryType, StringType))
-
-  override def dataType: DataType = StringType
-
-  protected override def nullSafeEval(num: Any): Any = child.dataType match {
-    case LongType => hex(num.asInstanceOf[Long])
-    case BinaryType => hex(num.asInstanceOf[Array[Byte]])
-    case StringType => hex(num.asInstanceOf[UTF8String].getBytes)
-  }
-
-  private[this] def hex(bytes: Array[Byte]): UTF8String = {
+  def hex(bytes: Array[Byte]): UTF8String = {
     val length = bytes.length
     val value = new Array[Byte](length * 2)
     var i = 0
@@ -522,7 +502,7 @@ case class Hex(child: Expression)
     UTF8String.fromBytes(value)
   }
 
-  private def hex(num: Long): UTF8String = {
+  def hex(num: Long): UTF8String = {
     // Extract the hex digits of num into value[] from right to left
     val value = new Array[Byte](16)
     var numBuf = num
@@ -534,24 +514,8 @@ case class Hex(child: Expression)
     } while (numBuf != 0)
     UTF8String.fromBytes(java.util.Arrays.copyOfRange(value, value.length - len, value.length))
   }
-}
 
-/**
- * Performs the inverse operation of HEX.
- * Resulting characters are returned as a byte array.
- */
-case class Unhex(child: Expression)
-  extends UnaryExpression with ImplicitCastInputTypes with CodegenFallback {
-
-  override def inputTypes: Seq[AbstractDataType] = Seq(StringType)
-
-  override def nullable: Boolean = true
-  override def dataType: DataType = BinaryType
-
-  protected override def nullSafeEval(num: Any): Any =
-    unhex(num.asInstanceOf[UTF8String].getBytes)
-
-  private[this] def unhex(bytes: Array[Byte]): Array[Byte] = {
+  def unhex(bytes: Array[Byte]): Array[Byte] = {
     val out = new Array[Byte]((bytes.length + 1) >> 1)
     var i = 0
     if ((bytes.length & 0x01) != 0) {
@@ -583,6 +547,60 @@ case class Unhex(child: Expression)
   }
 }
 
+/**
+ * If the argument is an INT or binary, hex returns the number as a STRING in hexadecimal format.
+ * Otherwise if the number is a STRING, it converts each character into its hex representation
+ * and returns the resulting STRING. Negative numbers would be treated as two's complement.
+ */
+case class Hex(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(LongType, BinaryType, StringType))
+
+  override def dataType: DataType = StringType
+
+  protected override def nullSafeEval(num: Any): Any = child.dataType match {
+    case LongType => Hex.hex(num.asInstanceOf[Long])
+    case BinaryType => Hex.hex(num.asInstanceOf[Array[Byte]])
+    case StringType => Hex.hex(num.asInstanceOf[UTF8String].getBytes)
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (c) => {
+      val hex = Hex.getClass.getName.stripSuffix("$")
+      s"${ev.primitive} = " + (child.dataType match {
+        case StringType => s"""$hex.hex($c.getBytes());"""
+        case _ => s"""$hex.hex($c);"""
+      })
+    })
+  }
+}
+
+/**
+ * Performs the inverse operation of HEX.
+ * Resulting characters are returned as a byte array.
+ */
+case class Unhex(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType)
+
+  override def nullable: Boolean = true
+  override def dataType: DataType = BinaryType
+
+  protected override def nullSafeEval(num: Any): Any =
+    Hex.unhex(num.asInstanceOf[UTF8String].getBytes)
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (c) => {
+      val hex = Hex.getClass.getName.stripSuffix("$")
+      s"""
+        ${ev.primitive} = $hex.unhex($c.getBytes());
+        ${ev.isNull} = ${ev.primitive} == null;
+       """
+    })
+  }
+}
+
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 ////////////////////////////////////////////////////////////////////////////////////////////////////

From 2bdf9914ab709bf9c1cdd17fc5dd7a69f6d46f29 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 21 Jul 2015 11:38:22 +0900
Subject: [PATCH 0505/1454] [SPARK-9052] [SPARKR] Fix comments after curly
 braces

[[SPARK-9052] Fix comments after curly braces - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9052)

This is the full result of lintr at the rivision:011551620faa87107a787530f074af3d9be7e695.
[[SPARK-9052] the result of lint-r at the revision:011551620faa87107a787530f074af3d9be7e695](https://gist.github.com/yu-iskw/e7246041b173a3f29482)

This is the difference of the result between before and after.
https://gist.github.com/yu-iskw/e7246041b173a3f29482/revisions

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7440 from yu-iskw/SPARK-9052 and squashes the following commits:

015d738 [Yu ISHIKAWA] Fix the indentations and move the placement of commna
5cc30fe [Yu ISHIKAWA] Fix the indentation in a condition
4ead0e5 [Yu ISHIKAWA] [SPARK-9052][SparkR] Fix comments after curly braces
---
 R/pkg/R/schema.R | 13 ++++++++-----
 R/pkg/R/utils.R  | 33 ++++++++++++++++++++++-----------
 2 files changed, 30 insertions(+), 16 deletions(-)

diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R
index 06df430687682..79c744ef29c23 100644
--- a/R/pkg/R/schema.R
+++ b/R/pkg/R/schema.R
@@ -69,11 +69,14 @@ structType.structField <- function(x, ...) {
 #' @param ... further arguments passed to or from other methods
 print.structType <- function(x, ...) {
   cat("StructType\n",
-      sapply(x$fields(), function(field) { paste("|-", "name = \"", field$name(),
-                                           "\", type = \"", field$dataType.toString(),
-                                           "\", nullable = ", field$nullable(), "\n",
-                                           sep = "") })
-      , sep = "")
+      sapply(x$fields(),
+             function(field) {
+               paste("|-", "name = \"", field$name(),
+                     "\", type = \"", field$dataType.toString(),
+                     "\", nullable = ", field$nullable(), "\n",
+                     sep = "")
+             }),
+      sep = "")
 }
 
 #' structField
diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 950ba74dbe017..3f45589a50443 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -390,14 +390,17 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
       for (i in 1:nodeLen) {
         processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv)
       }
-    } else {  # if node[[1]] is length of 1, check for some R special functions.
+    } else {
+      # if node[[1]] is length of 1, check for some R special functions.
       nodeChar <- as.character(node[[1]])
-      if (nodeChar == "{" || nodeChar == "(") {  # Skip start symbol.
+      if (nodeChar == "{" || nodeChar == "(") {
+        # Skip start symbol.
         for (i in 2:nodeLen) {
           processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv)
         }
       } else if (nodeChar == "<-" || nodeChar == "=" ||
-                   nodeChar == "<<-") { # Assignment Ops.
+                   nodeChar == "<<-") {
+        # Assignment Ops.
         defVar <- node[[2]]
         if (length(defVar) == 1 && typeof(defVar) == "symbol") {
           # Add the defined variable name into defVars.
@@ -408,14 +411,16 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
         for (i in 3:nodeLen) {
           processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv)
         }
-      } else if (nodeChar == "function") {  # Function definition.
+      } else if (nodeChar == "function") {
+        # Function definition.
         # Add parameter names.
         newArgs <- names(node[[2]])
         lapply(newArgs, function(arg) { addItemToAccumulator(defVars, arg) })
         for (i in 3:nodeLen) {
           processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv)
         }
-      } else if (nodeChar == "$") {  # Skip the field.
+      } else if (nodeChar == "$") {
+        # Skip the field.
         processClosure(node[[2]], oldEnv, defVars, checkedFuncs, newEnv)
       } else if (nodeChar == "::" || nodeChar == ":::") {
         processClosure(node[[3]], oldEnv, defVars, checkedFuncs, newEnv)
@@ -429,7 +434,8 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
                (typeof(node) == "symbol" || typeof(node) == "language")) {
     # Base case: current AST node is a leaf node and a symbol or a function call.
     nodeChar <- as.character(node)
-    if (!nodeChar %in% defVars$data) {  # Not a function parameter or local variable.
+    if (!nodeChar %in% defVars$data) {
+      # Not a function parameter or local variable.
       func.env <- oldEnv
       topEnv <- parent.env(.GlobalEnv)
       # Search in function environment, and function's enclosing environments
@@ -439,20 +445,24 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
       while (!identical(func.env, topEnv)) {
         # Namespaces other than "SparkR" will not be searched.
         if (!isNamespace(func.env) ||
-              (getNamespaceName(func.env) == "SparkR" &&
-              !(nodeChar %in% getNamespaceExports("SparkR")))) {  # Only include SparkR internals.
+            (getNamespaceName(func.env) == "SparkR" &&
+               !(nodeChar %in% getNamespaceExports("SparkR")))) {
+          # Only include SparkR internals.
+
           # Set parameter 'inherits' to FALSE since we do not need to search in
           # attached package environments.
           if (tryCatch(exists(nodeChar, envir = func.env, inherits = FALSE),
                        error = function(e) { FALSE })) {
             obj <- get(nodeChar, envir = func.env, inherits = FALSE)
-            if (is.function(obj)) {  # If the node is a function call.
+            if (is.function(obj)) {
+              # If the node is a function call.
               funcList <- mget(nodeChar, envir = checkedFuncs, inherits = F,
                                ifnotfound = list(list(NULL)))[[1]]
               found <- sapply(funcList, function(func) {
                 ifelse(identical(func, obj), TRUE, FALSE)
               })
-              if (sum(found) > 0) {  # If function has been examined, ignore.
+              if (sum(found) > 0) {
+                # If function has been examined, ignore.
                 break
               }
               # Function has not been examined, record it and recursively clean its closure.
@@ -495,7 +505,8 @@ cleanClosure <- function(func, checkedFuncs = new.env()) {
     # environment. First, function's arguments are added to defVars.
     defVars <- initAccumulator()
     argNames <- names(as.list(args(func)))
-    for (i in 1:(length(argNames) - 1)) {  # Remove the ending NULL in pairlist.
+    for (i in 1:(length(argNames) - 1)) {
+      # Remove the ending NULL in pairlist.
       addItemToAccumulator(defVars, argNames[i])
     }
     # Recursively examine variables in the function body.

From 1cbdd8991898912a8471a7070c472a0edb92487c Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Mon, 20 Jul 2015 20:49:38 -0700
Subject: [PATCH 0506/1454] [SPARK-9201] [ML] Initial integration of MLlib +
 SparkR using RFormula

This exposes the SparkR:::glm() and SparkR:::predict() APIs. It was necessary to change RFormula to silently drop the label column if it was missing from the input dataset, which is kind of a hack but necessary to integrate with the Pipeline API.

The umbrella design doc for MLlib + SparkR integration can be viewed here: https://docs.google.com/document/d/10NZNSEurN2EdWM31uFYsgayIPfCFHiuIu3pCWrUmP_c/edit

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #7483 from ericl/spark-8774 and squashes the following commits:

3dfac0c [Eric Liang] update
17ef516 [Eric Liang] more comments
1753a0f [Eric Liang] make glm generic
b0f50f8 [Eric Liang] equivalence test
550d56d [Eric Liang] export methods
c015697 [Eric Liang] second pass
117949a [Eric Liang] comments
5afbc67 [Eric Liang] test label columns
6b7f15f [Eric Liang] Fri Jul 17 14:20:22 PDT 2015
3a63ae5 [Eric Liang] Fri Jul 17 13:41:52 PDT 2015
ce61367 [Eric Liang] Fri Jul 17 13:41:17 PDT 2015
0299c59 [Eric Liang] Fri Jul 17 13:40:32 PDT 2015
e37603f [Eric Liang] Fri Jul 17 12:15:03 PDT 2015
d417d0c [Eric Liang] Merge remote-tracking branch 'upstream/master' into spark-8774
29a2ce7 [Eric Liang] Merge branch 'spark-8774-1' into spark-8774
d1959d2 [Eric Liang] clarify comment
2db68aa [Eric Liang] second round of comments
dc3c943 [Eric Liang] address comments
5765ec6 [Eric Liang] fix style checks
1f361b0 [Eric Liang] doc
d33211b [Eric Liang] r support
fb0826b [Eric Liang] [SPARK-8774] Add R model formula with basic support as a transformer
---
 R/pkg/DESCRIPTION                             |  1 +
 R/pkg/NAMESPACE                               |  4 +
 R/pkg/R/generics.R                            |  4 +
 R/pkg/R/mllib.R                               | 73 +++++++++++++++++++
 R/pkg/inst/tests/test_mllib.R                 | 42 +++++++++++
 .../apache/spark/ml/feature/RFormula.scala    | 14 +++-
 .../apache/spark/ml/r/SparkRWrappers.scala    | 41 +++++++++++
 .../spark/ml/feature/RFormulaSuite.scala      |  9 +++
 8 files changed, 185 insertions(+), 3 deletions(-)
 create mode 100644 R/pkg/R/mllib.R
 create mode 100644 R/pkg/inst/tests/test_mllib.R
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index d028821534b1a..4949d86d20c91 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -29,6 +29,7 @@ Collate:
     'client.R'
     'context.R'
     'deserialize.R'
+    'mllib.R'
     'serialize.R'
     'sparkR.R'
     'utils.R'
diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 331307c2077a5..5834813319bfd 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -10,6 +10,10 @@ export("sparkR.init")
 export("sparkR.stop")
 export("print.jobj")
 
+# MLlib integration
+exportMethods("glm",
+              "predict")
+
 # Job group lifecycle management methods
 export("setJobGroup",
        "clearJobGroup",
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index ebe6fbd97ce86..39b5586f7c90e 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -661,3 +661,7 @@ setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 #' @rdname column
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
+
+#' @rdname glm
+#' @export
+setGeneric("glm")
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
new file mode 100644
index 0000000000000..258e354081fc1
--- /dev/null
+++ b/R/pkg/R/mllib.R
@@ -0,0 +1,73 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib.R: Provides methods for MLlib integration
+
+#' @title S4 class that represents a PipelineModel
+#' @param model A Java object reference to the backing Scala PipelineModel
+#' @export
+setClass("PipelineModel", representation(model = "jobj"))
+
+#' Fits a generalized linear model
+#'
+#' Fits a generalized linear model, similarly to R's glm(). Also see the glmnet package.
+#'
+#' @param formula A symbolic description of the model to be fitted. Currently only a few formula
+#'                operators are supported, including '~' and '+'.
+#' @param data DataFrame for training
+#' @param family Error distribution. "gaussian" -> linear regression, "binomial" -> logistic reg.
+#' @param lambda Regularization parameter
+#' @param alpha Elastic-net mixing parameter (see glmnet's documentation for details)
+#' @return a fitted MLlib model
+#' @rdname glm
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' data(iris)
+#' df <- createDataFrame(sqlContext, iris)
+#' model <- glm(Sepal_Length ~ Sepal_Width, df)
+#'}
+setMethod("glm", signature(formula = "formula", family = "ANY", data = "DataFrame"),
+          function(formula, family = c("gaussian", "binomial"), data, lambda = 0, alpha = 0) {
+            family <- match.arg(family)
+            model <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
+                                 "fitRModelFormula", deparse(formula), data@sdf, family, lambda,
+                                 alpha)
+            return(new("PipelineModel", model = model))
+          })
+
+#' Make predictions from a model
+#'
+#' Makes predictions from a model produced by glm(), similarly to R's predict().
+#'
+#' @param model A fitted MLlib model
+#' @param newData DataFrame for testing
+#' @return DataFrame containing predicted values
+#' @rdname glm
+#' @export
+#' @examples
+#'\dontrun{
+#' model <- glm(y ~ x, trainingData)
+#' predicted <- predict(model, testData)
+#' showDF(predicted)
+#'}
+setMethod("predict", signature(object = "PipelineModel"),
+          function(object, newData) {
+            return(dataFrame(callJMethod(object@model, "transform", newData@sdf)))
+          })
diff --git a/R/pkg/inst/tests/test_mllib.R b/R/pkg/inst/tests/test_mllib.R
new file mode 100644
index 0000000000000..a492763344ae6
--- /dev/null
+++ b/R/pkg/inst/tests/test_mllib.R
@@ -0,0 +1,42 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+library(testthat)
+
+context("MLlib functions")
+
+# Tests for MLlib functions in SparkR
+
+sc <- sparkR.init()
+
+sqlContext <- sparkRSQL.init(sc)
+
+test_that("glm and predict", {
+  training <- createDataFrame(sqlContext, iris)
+  test <- select(training, "Sepal_Length")
+  model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian")
+  prediction <- predict(model, test)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
+})
+
+test_that("predictions match with native glm", {
+  training <- createDataFrame(sqlContext, iris)
+  model <- glm(Sepal_Width ~ Sepal_Length, data = training)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Sepal.Length, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-9), rVals - vals)
+})
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index 56169f2a01fc9..f7b46efa10e90 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -73,12 +73,16 @@ class RFormula(override val uid: String)
     val withFeatures = transformFeatures.transformSchema(schema)
     if (hasLabelCol(schema)) {
       withFeatures
-    } else {
+    } else if (schema.exists(_.name == parsedFormula.get.label)) {
       val nullable = schema(parsedFormula.get.label).dataType match {
         case _: NumericType | BooleanType => false
         case _ => true
       }
       StructType(withFeatures.fields :+ StructField($(labelCol), DoubleType, nullable))
+    } else {
+      // Ignore the label field. This is a hack so that this transformer can also work on test
+      // datasets in a Pipeline.
+      withFeatures
     }
   }
 
@@ -92,10 +96,10 @@ class RFormula(override val uid: String)
   override def toString: String = s"RFormula(${get(formula)})"
 
   private def transformLabel(dataset: DataFrame): DataFrame = {
+    val labelName = parsedFormula.get.label
     if (hasLabelCol(dataset.schema)) {
       dataset
-    } else {
-      val labelName = parsedFormula.get.label
+    } else if (dataset.schema.exists(_.name == labelName)) {
       dataset.schema(labelName).dataType match {
         case _: NumericType | BooleanType =>
           dataset.withColumn($(labelCol), dataset(labelName).cast(DoubleType))
@@ -103,6 +107,10 @@ class RFormula(override val uid: String)
         case other =>
           throw new IllegalArgumentException("Unsupported type for label: " + other)
       }
+    } else {
+      // Ignore the label field. This is a hack so that this transformer can also work on test
+      // datasets in a Pipeline.
+      dataset
     }
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
new file mode 100644
index 0000000000000..1ee080641e3e3
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.api.r
+
+import org.apache.spark.ml.feature.RFormula
+import org.apache.spark.ml.classification.LogisticRegression
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.{Pipeline, PipelineModel}
+import org.apache.spark.sql.DataFrame
+
+private[r] object SparkRWrappers {
+  def fitRModelFormula(
+      value: String,
+      df: DataFrame,
+      family: String,
+      lambda: Double,
+      alpha: Double): PipelineModel = {
+    val formula = new RFormula().setFormula(value)
+    val estimator = family match {
+      case "gaussian" => new LinearRegression().setRegParam(lambda).setElasticNetParam(alpha)
+      case "binomial" => new LogisticRegression().setRegParam(lambda).setElasticNetParam(alpha)
+    }
+    val pipeline = new Pipeline().setStages(Array(formula, estimator))
+    pipeline.fit(df)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
index fa8611b243a9f..79c4ccf02d4e0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
@@ -74,6 +74,15 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("allow missing label column for test datasets") {
+    val formula = new RFormula().setFormula("y ~ x").setLabelCol("label")
+    val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "_not_y")
+    val resultSchema = formula.transformSchema(original.schema)
+    assert(resultSchema.length == 3)
+    assert(!resultSchema.exists(_.name == "label"))
+    assert(resultSchema.toString == formula.transform(original).schema.toString)
+  }
+
 // TODO(ekl) enable after we implement string label support
 //  test("transform string label") {
 //    val formula = new RFormula().setFormula("name ~ id")

From a3c7a3ce32697ad293b8bcaf29f9384c8255b37f Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 22:08:12 -0700
Subject: [PATCH 0507/1454] [SPARK-9132][SPARK-9163][SQL] codegen conv

Jira: https://issues.apache.org/jira/browse/SPARK-9132
https://issues.apache.org/jira/browse/SPARK-9163

rxin as you proposed in the Jira ticket, I just moved the logic to a separate object. I haven't changed anything of the logic of `NumberConverter`.

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7552 from tarekauel/SPARK-9163 and squashes the following commits:

40dcde9 [Tarek Auel] [SPARK-9132][SPARK-9163][SQL] style fix
fa985bd [Tarek Auel] [SPARK-9132][SPARK-9163][SQL] codegen conv
---
 .../spark/sql/catalyst/expressions/math.scala | 204 ++++--------------
 .../sql/catalyst/util/NumberConverter.scala   | 176 +++++++++++++++
 .../expressions/MathFunctionsSuite.scala      |   4 +-
 .../catalyst/util/NumberConverterSuite.scala  |  40 ++++
 4 files changed, 263 insertions(+), 161 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/NumberConverter.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/NumberConverterSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 7a9be02ba45b3..68cca0ad3d067 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckSuccess, TypeCheckFailure}
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.util.NumberConverter
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -164,7 +165,7 @@ case class Cosh(child: Expression) extends UnaryMathExpression(math.cosh, "COSH"
  * @param toBaseExpr to which base
  */
 case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expression)
-  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+  extends Expression with ImplicitCastInputTypes {
 
   override def foldable: Boolean = numExpr.foldable && fromBaseExpr.foldable && toBaseExpr.foldable
 
@@ -179,169 +180,54 @@ case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expre
   /** Returns the result of evaluating this expression on a given input Row */
   override def eval(input: InternalRow): Any = {
     val num = numExpr.eval(input)
-    val fromBase = fromBaseExpr.eval(input)
-    val toBase = toBaseExpr.eval(input)
-    if (num == null || fromBase == null || toBase == null) {
-      null
-    } else {
-      conv(
-        num.asInstanceOf[UTF8String].getBytes,
-        fromBase.asInstanceOf[Int],
-        toBase.asInstanceOf[Int])
-    }
-  }
-
-  private val value = new Array[Byte](64)
-
-  /**
-   * Divide x by m as if x is an unsigned 64-bit integer. Examples:
-   * unsignedLongDiv(-1, 2) == Long.MAX_VALUE unsignedLongDiv(6, 3) == 2
-   * unsignedLongDiv(0, 5) == 0
-   *
-   * @param x is treated as unsigned
-   * @param m is treated as signed
-   */
-  private def unsignedLongDiv(x: Long, m: Int): Long = {
-    if (x >= 0) {
-      x / m
-    } else {
-      // Let uval be the value of the unsigned long with the same bits as x
-      // Two's complement => x = uval - 2*MAX - 2
-      // => uval = x + 2*MAX + 2
-      // Now, use the fact: (a+b)/c = a/c + b/c + (a%c+b%c)/c
-      x / m + 2 * (Long.MaxValue / m) + 2 / m + (x % m + 2 * (Long.MaxValue % m) + 2 % m) / m
-    }
-  }
-
-  /**
-   * Decode v into value[].
-   *
-   * @param v is treated as an unsigned 64-bit integer
-   * @param radix must be between MIN_RADIX and MAX_RADIX
-   */
-  private def decode(v: Long, radix: Int): Unit = {
-    var tmpV = v
-    java.util.Arrays.fill(value, 0.asInstanceOf[Byte])
-    var i = value.length - 1
-    while (tmpV != 0) {
-      val q = unsignedLongDiv(tmpV, radix)
-      value(i) = (tmpV - q * radix).asInstanceOf[Byte]
-      tmpV = q
-      i -= 1
-    }
-  }
-
-  /**
-   * Convert value[] into a long. On overflow, return -1 (as mySQL does). If a
-   * negative digit is found, ignore the suffix starting there.
-   *
-   * @param radix  must be between MIN_RADIX and MAX_RADIX
-   * @param fromPos is the first element that should be conisdered
-   * @return the result should be treated as an unsigned 64-bit integer.
-   */
-  private def encode(radix: Int, fromPos: Int): Long = {
-    var v: Long = 0L
-    val bound = unsignedLongDiv(-1 - radix, radix) // Possible overflow once
-    // val
-    // exceeds this value
-    var i = fromPos
-    while (i < value.length && value(i) >= 0) {
-      if (v >= bound) {
-        // Check for overflow
-        if (unsignedLongDiv(-1 - value(i), radix) < v) {
-          return -1
+    if (num != null) {
+      val fromBase = fromBaseExpr.eval(input)
+      if (fromBase != null) {
+        val toBase = toBaseExpr.eval(input)
+        if (toBase != null) {
+          NumberConverter.convert(
+            num.asInstanceOf[UTF8String].getBytes,
+            fromBase.asInstanceOf[Int],
+            toBase.asInstanceOf[Int])
+        } else {
+          null
         }
-      }
-      v = v * radix + value(i)
-      i += 1
-    }
-    v
-  }
-
-  /**
-   * Convert the bytes in value[] to the corresponding chars.
-   *
-   * @param radix must be between MIN_RADIX and MAX_RADIX
-   * @param fromPos is the first nonzero element
-   */
-  private def byte2char(radix: Int, fromPos: Int): Unit = {
-    var i = fromPos
-    while (i < value.length) {
-      value(i) = Character.toUpperCase(Character.forDigit(value(i), radix)).asInstanceOf[Byte]
-      i += 1
-    }
-  }
-
-  /**
-   * Convert the chars in value[] to the corresponding integers. Convert invalid
-   * characters to -1.
-   *
-   * @param radix must be between MIN_RADIX and MAX_RADIX
-   * @param fromPos is the first nonzero element
-   */
-  private def char2byte(radix: Int, fromPos: Int): Unit = {
-    var i = fromPos
-    while ( i < value.length) {
-      value(i) = Character.digit(value(i), radix).asInstanceOf[Byte]
-      i += 1
-    }
-  }
-
-  /**
-   * Convert numbers between different number bases. If toBase>0 the result is
-   * unsigned, otherwise it is signed.
-   * NB: This logic is borrowed from org.apache.hadoop.hive.ql.ud.UDFConv
-   */
-  private def conv(n: Array[Byte] , fromBase: Int, toBase: Int ): UTF8String = {
-    if (fromBase < Character.MIN_RADIX || fromBase > Character.MAX_RADIX
-      || Math.abs(toBase) < Character.MIN_RADIX
-      || Math.abs(toBase) > Character.MAX_RADIX) {
-      return null
-    }
-
-    if (n.length == 0) {
-      return null
-    }
-
-    var (negative, first) = if (n(0) == '-') (true, 1) else (false, 0)
-
-    // Copy the digits in the right side of the array
-    var i = 1
-    while (i <= n.length - first) {
-      value(value.length - i) = n(n.length - i)
-      i += 1
-    }
-    char2byte(fromBase, value.length - n.length + first)
-
-    // Do the conversion by going through a 64 bit integer
-    var v = encode(fromBase, value.length - n.length + first)
-    if (negative && toBase > 0) {
-      if (v < 0) {
-        v = -1
       } else {
-        v = -v
+        null
       }
+    } else {
+      null
     }
-    if (toBase < 0 && v < 0) {
-      v = -v
-      negative = true
-    }
-    decode(v, Math.abs(toBase))
-
-    // Find the first non-zero digit or the last digits if all are zero.
-    val firstNonZeroPos = {
-      val firstNonZero = value.indexWhere( _ != 0)
-      if (firstNonZero != -1) firstNonZero else value.length - 1
-    }
-
-    byte2char(Math.abs(toBase), firstNonZeroPos)
+  }
 
-    var resultStartPos = firstNonZeroPos
-    if (negative && toBase < 0) {
-      resultStartPos = firstNonZeroPos - 1
-      value(resultStartPos) = '-'
-    }
-    UTF8String.fromBytes(java.util.Arrays.copyOfRange(value, resultStartPos, value.length))
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val numGen = numExpr.gen(ctx)
+    val from = fromBaseExpr.gen(ctx)
+    val to = toBaseExpr.gen(ctx)
+
+    val numconv = NumberConverter.getClass.getName.stripSuffix("$")
+    s"""
+       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+       ${numGen.code}
+       boolean ${ev.isNull} = ${numGen.isNull};
+       if (!${ev.isNull}) {
+         ${from.code}
+         if (!${from.isNull}) {
+           ${to.code}
+           if (!${to.isNull}) {
+             ${ev.primitive} = $numconv.convert(${numGen.primitive}.getBytes(),
+               ${from.primitive}, ${to.primitive});
+             if (${ev.primitive} == null) {
+               ${ev.isNull} = true;
+             }
+           } else {
+             ${ev.isNull} = true;
+           }
+         } else {
+           ${ev.isNull} = true;
+         }
+       }
+     """
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/NumberConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/NumberConverter.scala
new file mode 100644
index 0000000000000..9fefc5656aac0
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/NumberConverter.scala
@@ -0,0 +1,176 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util
+
+import org.apache.spark.unsafe.types.UTF8String
+
+object NumberConverter {
+
+  private val value = new Array[Byte](64)
+
+  /**
+   * Divide x by m as if x is an unsigned 64-bit integer. Examples:
+   * unsignedLongDiv(-1, 2) == Long.MAX_VALUE unsignedLongDiv(6, 3) == 2
+   * unsignedLongDiv(0, 5) == 0
+   *
+   * @param x is treated as unsigned
+   * @param m is treated as signed
+   */
+  private def unsignedLongDiv(x: Long, m: Int): Long = {
+    if (x >= 0) {
+      x / m
+    } else {
+      // Let uval be the value of the unsigned long with the same bits as x
+      // Two's complement => x = uval - 2*MAX - 2
+      // => uval = x + 2*MAX + 2
+      // Now, use the fact: (a+b)/c = a/c + b/c + (a%c+b%c)/c
+      x / m + 2 * (Long.MaxValue / m) + 2 / m + (x % m + 2 * (Long.MaxValue % m) + 2 % m) / m
+    }
+  }
+
+  /**
+   * Decode v into value[].
+   *
+   * @param v is treated as an unsigned 64-bit integer
+   * @param radix must be between MIN_RADIX and MAX_RADIX
+   */
+  private def decode(v: Long, radix: Int): Unit = {
+    var tmpV = v
+    java.util.Arrays.fill(value, 0.asInstanceOf[Byte])
+    var i = value.length - 1
+    while (tmpV != 0) {
+      val q = unsignedLongDiv(tmpV, radix)
+      value(i) = (tmpV - q * radix).asInstanceOf[Byte]
+      tmpV = q
+      i -= 1
+    }
+  }
+
+  /**
+   * Convert value[] into a long. On overflow, return -1 (as mySQL does). If a
+   * negative digit is found, ignore the suffix starting there.
+   *
+   * @param radix  must be between MIN_RADIX and MAX_RADIX
+   * @param fromPos is the first element that should be conisdered
+   * @return the result should be treated as an unsigned 64-bit integer.
+   */
+  private def encode(radix: Int, fromPos: Int): Long = {
+    var v: Long = 0L
+    val bound = unsignedLongDiv(-1 - radix, radix) // Possible overflow once
+    // val
+    // exceeds this value
+    var i = fromPos
+    while (i < value.length && value(i) >= 0) {
+      if (v >= bound) {
+        // Check for overflow
+        if (unsignedLongDiv(-1 - value(i), radix) < v) {
+          return -1
+        }
+      }
+      v = v * radix + value(i)
+      i += 1
+    }
+    v
+  }
+
+  /**
+   * Convert the bytes in value[] to the corresponding chars.
+   *
+   * @param radix must be between MIN_RADIX and MAX_RADIX
+   * @param fromPos is the first nonzero element
+   */
+  private def byte2char(radix: Int, fromPos: Int): Unit = {
+    var i = fromPos
+    while (i < value.length) {
+      value(i) = Character.toUpperCase(Character.forDigit(value(i), radix)).asInstanceOf[Byte]
+      i += 1
+    }
+  }
+
+  /**
+   * Convert the chars in value[] to the corresponding integers. Convert invalid
+   * characters to -1.
+   *
+   * @param radix must be between MIN_RADIX and MAX_RADIX
+   * @param fromPos is the first nonzero element
+   */
+  private def char2byte(radix: Int, fromPos: Int): Unit = {
+    var i = fromPos
+    while ( i < value.length) {
+      value(i) = Character.digit(value(i), radix).asInstanceOf[Byte]
+      i += 1
+    }
+  }
+
+  /**
+   * Convert numbers between different number bases. If toBase>0 the result is
+   * unsigned, otherwise it is signed.
+   * NB: This logic is borrowed from org.apache.hadoop.hive.ql.ud.UDFConv
+   */
+  def convert(n: Array[Byte] , fromBase: Int, toBase: Int ): UTF8String = {
+    if (fromBase < Character.MIN_RADIX || fromBase > Character.MAX_RADIX
+      || Math.abs(toBase) < Character.MIN_RADIX
+      || Math.abs(toBase) > Character.MAX_RADIX) {
+      return null
+    }
+
+    if (n.length == 0) {
+      return null
+    }
+
+    var (negative, first) = if (n(0) == '-') (true, 1) else (false, 0)
+
+    // Copy the digits in the right side of the array
+    var i = 1
+    while (i <= n.length - first) {
+      value(value.length - i) = n(n.length - i)
+      i += 1
+    }
+    char2byte(fromBase, value.length - n.length + first)
+
+    // Do the conversion by going through a 64 bit integer
+    var v = encode(fromBase, value.length - n.length + first)
+    if (negative && toBase > 0) {
+      if (v < 0) {
+        v = -1
+      } else {
+        v = -v
+      }
+    }
+    if (toBase < 0 && v < 0) {
+      v = -v
+      negative = true
+    }
+    decode(v, Math.abs(toBase))
+
+    // Find the first non-zero digit or the last digits if all are zero.
+    val firstNonZeroPos = {
+      val firstNonZero = value.indexWhere( _ != 0)
+      if (firstNonZero != -1) firstNonZero else value.length - 1
+    }
+
+    byte2char(Math.abs(toBase), firstNonZeroPos)
+
+    var resultStartPos = firstNonZeroPos
+    if (negative && toBase < 0) {
+      resultStartPos = firstNonZeroPos - 1
+      value(resultStartPos) = '-'
+    }
+    UTF8String.fromBytes(java.util.Arrays.copyOfRange(value, resultStartPos, value.length))
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 04acd5b5ff4d1..a2b0fad7b7a04 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -115,8 +115,8 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(-16)), "-F")
     checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(16)), "FFFFFFFFFFFFFFF1")
     checkEvaluation(Conv(Literal("big"), Literal(36), Literal(16)), "3A48")
-    checkEvaluation(Conv(Literal(null), Literal(36), Literal(16)), null)
-    checkEvaluation(Conv(Literal("3"), Literal(null), Literal(16)), null)
+    checkEvaluation(Conv(Literal.create(null, StringType), Literal(36), Literal(16)), null)
+    checkEvaluation(Conv(Literal("3"), Literal.create(null, IntegerType), Literal(16)), null)
     checkEvaluation(
       Conv(Literal("1234"), Literal(10), Literal(37)), null)
     checkEvaluation(
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/NumberConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/NumberConverterSuite.scala
new file mode 100644
index 0000000000000..13265a1ff1c7f
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/NumberConverterSuite.scala
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.util.NumberConverter.convert
+import org.apache.spark.unsafe.types.UTF8String
+
+class NumberConverterSuite extends SparkFunSuite {
+
+  private[this] def checkConv(n: String, fromBase: Int, toBase: Int, expected: String): Unit = {
+    assert(convert(UTF8String.fromString(n).getBytes, fromBase, toBase) ===
+      UTF8String.fromString(expected))
+  }
+
+  test("convert") {
+    checkConv("3", 10, 2, "11")
+    checkConv("-15", 10, -16, "-F")
+    checkConv("-15", 10, 16, "FFFFFFFFFFFFFFF1")
+    checkConv("big", 36, 16, "3A48")
+    checkConv("9223372036854775807", 36, 16, "FFFFFFFFFFFFFFFF")
+    checkConv("11abc", 10, 16, "B")
+  }
+
+}

From 4d97be95300f729391c17b4c162e3c7fba09b8bf Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Mon, 20 Jul 2015 22:15:10 -0700
Subject: [PATCH 0508/1454] [SPARK-9204][ML] Add default params test for
 linearyregression suite

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7553 from holdenk/SPARK-9204-add-default-params-test-to-linear-regression and squashes the following commits:

630ba19 [Holden Karau] style fix
faa08a3 [Holden Karau] Add default params test for linearyregression suite
---
 .../ml/regression/LinearRegressionSuite.scala | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 374002c5b4fdd..7cdda3db88ad1 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.mllib.linalg.{DenseVector, Vectors}
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
 import org.apache.spark.mllib.util.TestingUtils._
@@ -55,6 +56,30 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
 
   }
 
+  test("params") {
+    ParamsSuite.checkParams(new LinearRegression)
+    val model = new LinearRegressionModel("linearReg", Vectors.dense(0.0), 0.0)
+    ParamsSuite.checkParams(model)
+  }
+
+  test("linear regression: default params") {
+    val lir = new LinearRegression
+    assert(lir.getLabelCol === "label")
+    assert(lir.getFeaturesCol === "features")
+    assert(lir.getPredictionCol === "prediction")
+    assert(lir.getRegParam === 0.0)
+    assert(lir.getElasticNetParam === 0.0)
+    assert(lir.getFitIntercept)
+    val model = lir.fit(dataset)
+    model.transform(dataset)
+      .select("label", "prediction")
+      .collect()
+    assert(model.getFeaturesCol === "features")
+    assert(model.getPredictionCol === "prediction")
+    assert(model.intercept !== 0.0)
+    assert(model.hasParent)
+  }
+
   test("linear regression with intercept without regularization") {
     val trainer = new LinearRegression
     val model = trainer.fit(dataset)

From c032b0bf92130dc4facb003f0deaeb1228aefded Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 20 Jul 2015 22:38:05 -0700
Subject: [PATCH 0509/1454] [SPARK-8797] [SPARK-9146] [SPARK-9145] [SPARK-9147]
 Support NaN ordering and equality comparisons in Spark SQL

This patch addresses an issue where queries that sorted float or double columns containing NaN values could fail with "Comparison method violates its general contract!" errors from TimSort.  The root of this problem is that `NaN > anything`, `NaN == anything`, and `NaN < anything` all return `false`.

Per the design specified in SPARK-9079, we have decided that `NaN = NaN` should return true and that NaN should appear last when sorting in ascending order (i.e. it is larger than any other numeric value).

In addition to implementing these semantics, this patch also adds canonicalization of NaN values in UnsafeRow, which is necessary in order to be able to do binary equality comparisons on equal NaNs that might have different bit representations (see SPARK-9147).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7194 from JoshRosen/nan and squashes the following commits:

983d4fc [Josh Rosen] Merge remote-tracking branch 'origin/master' into nan
88bd73c [Josh Rosen] Fix Row.equals()
a702e2e [Josh Rosen] normalization -> canonicalization
a7267cf [Josh Rosen] Normalize NaNs in UnsafeRow
fe629ae [Josh Rosen] Merge remote-tracking branch 'origin/master' into nan
fbb2a29 [Josh Rosen] Fix NaN comparisons in BinaryComparison expressions
c1fd4fe [Josh Rosen] Fold NaN test into existing test framework
b31eb19 [Josh Rosen] Uncomment failing tests
7fe67af [Josh Rosen] Support NaN == NaN (SPARK-9145)
58bad2c [Josh Rosen] Revert "Compare rows' string representations to work around NaN incomparability."
fc6b4d2 [Josh Rosen] Update CodeGenerator
3998ef2 [Josh Rosen] Remove unused code
a2ba2e7 [Josh Rosen] Fix prefix comparision for NaNs
a30d371 [Josh Rosen] Compare rows' string representations to work around NaN incomparability.
6f03f85 [Josh Rosen] Fix bug in Double / Float ordering
42a1ad5 [Josh Rosen] Stop filtering NaNs in UnsafeExternalSortSuite
bfca524 [Josh Rosen] Change ordering so that NaN is maximum value.
8d7be61 [Josh Rosen] Update randomized test to use ScalaTest's assume()
b20837b [Josh Rosen] Add failing test for new NaN comparision ordering
5b88b2b [Josh Rosen] Fix compilation of CodeGenerationSuite
d907b5b [Josh Rosen] Merge remote-tracking branch 'origin/master' into nan
630ebc5 [Josh Rosen] Specify an ordering for NaN values.
9bf195a [Josh Rosen] Re-enable NaNs in CodeGenerationSuite to produce more regression tests
13fc06a [Josh Rosen] Add regression test for NaN sorting issue
f9efbb5 [Josh Rosen] Fix ORDER BY NULL
e7dc4fb [Josh Rosen] Add very generic test for ordering
7d5c13e [Josh Rosen] Add regression test for SPARK-8782 (ORDER BY NULL)
b55875a [Josh Rosen] Generate doubles and floats over entire possible range.
5acdd5c [Josh Rosen] Infinity and NaN are interesting.
ab76cbd [Josh Rosen] Move code to Catalyst package.
d2b4a4a [Josh Rosen] Add random data generator test utilities to Spark SQL.
---
 .../unsafe/sort/PrefixComparators.java        |  5 ++-
 .../scala/org/apache/spark/util/Utils.scala   | 28 +++++++++++++
 .../org/apache/spark/util/UtilsSuite.scala    | 31 +++++++++++++++
 .../unsafe/sort/PrefixComparatorsSuite.scala  | 25 ++++++++++++
 .../sql/catalyst/expressions/UnsafeRow.java   |  6 +++
 .../main/scala/org/apache/spark/sql/Row.scala | 24 ++++++++----
 .../expressions/codegen/CodeGenerator.scala   |  4 ++
 .../sql/catalyst/expressions/predicates.scala | 22 +++++++++--
 .../apache/spark/sql/types/DoubleType.scala   |  5 ++-
 .../apache/spark/sql/types/FloatType.scala    |  5 ++-
 .../expressions/CodeGenerationSuite.scala     | 39 +++++++++++++++++++
 .../catalyst/expressions/PredicateSuite.scala | 13 ++++---
 .../expressions/UnsafeRowConverterSuite.scala | 22 +++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 22 +++++++++++
 .../scala/org/apache/spark/sql/RowSuite.scala | 12 ++++++
 .../execution/UnsafeExternalSortSuite.scala   |  6 +--
 16 files changed, 243 insertions(+), 26 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index 438742565c51d..bf1bc5dffba78 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -23,6 +23,7 @@
 
 import org.apache.spark.annotation.Private;
 import org.apache.spark.unsafe.types.UTF8String;
+import org.apache.spark.util.Utils;
 
 @Private
 public class PrefixComparators {
@@ -82,7 +83,7 @@ public static final class FloatPrefixComparator extends PrefixComparator {
     public int compare(long aPrefix, long bPrefix) {
       float a = Float.intBitsToFloat((int) aPrefix);
       float b = Float.intBitsToFloat((int) bPrefix);
-      return (a < b) ? -1 : (a > b) ? 1 : 0;
+      return Utils.nanSafeCompareFloats(a, b);
     }
 
     public long computePrefix(float value) {
@@ -97,7 +98,7 @@ public static final class DoublePrefixComparator extends PrefixComparator {
     public int compare(long aPrefix, long bPrefix) {
       double a = Double.longBitsToDouble(aPrefix);
       double b = Double.longBitsToDouble(bPrefix);
-      return (a < b) ? -1 : (a > b) ? 1 : 0;
+      return Utils.nanSafeCompareDoubles(a, b);
     }
 
     public long computePrefix(double value) {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index e6374f17d858f..c5816949cd360 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1586,6 +1586,34 @@ private[spark] object Utils extends Logging {
     hashAbs
   }
 
+  /**
+   * NaN-safe version of [[java.lang.Double.compare()]] which allows NaN values to be compared
+   * according to semantics where NaN == NaN and NaN > any non-NaN double.
+   */
+  def nanSafeCompareDoubles(x: Double, y: Double): Int = {
+    val xIsNan: Boolean = java.lang.Double.isNaN(x)
+    val yIsNan: Boolean = java.lang.Double.isNaN(y)
+    if ((xIsNan && yIsNan) || (x == y)) 0
+    else if (xIsNan) 1
+    else if (yIsNan) -1
+    else if (x > y) 1
+    else -1
+  }
+
+  /**
+   * NaN-safe version of [[java.lang.Float.compare()]] which allows NaN values to be compared
+   * according to semantics where NaN == NaN and NaN > any non-NaN float.
+   */
+  def nanSafeCompareFloats(x: Float, y: Float): Int = {
+    val xIsNan: Boolean = java.lang.Float.isNaN(x)
+    val yIsNan: Boolean = java.lang.Float.isNaN(y)
+    if ((xIsNan && yIsNan) || (x == y)) 0
+    else if (xIsNan) 1
+    else if (yIsNan) -1
+    else if (x > y) 1
+    else -1
+  }
+
   /** Returns the system properties map that is thread-safe to iterator over. It gets the
     * properties which have been set explicitly, as well as those for which only a default value
     * has been defined. */
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index c7638507c88c6..8f7e402d5f2a6 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.util
 
 import java.io.{File, ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream}
+import java.lang.{Double => JDouble, Float => JFloat}
 import java.net.{BindException, ServerSocket, URI}
 import java.nio.{ByteBuffer, ByteOrder}
 import java.text.DecimalFormatSymbols
@@ -689,4 +690,34 @@ class UtilsSuite extends SparkFunSuite with ResetSystemProperties with Logging {
     // scalastyle:on println
     assert(buffer.toString === "t circular test circular\n")
   }
+
+  test("nanSafeCompareDoubles") {
+    def shouldMatchDefaultOrder(a: Double, b: Double): Unit = {
+      assert(Utils.nanSafeCompareDoubles(a, b) === JDouble.compare(a, b))
+      assert(Utils.nanSafeCompareDoubles(b, a) === JDouble.compare(b, a))
+    }
+    shouldMatchDefaultOrder(0d, 0d)
+    shouldMatchDefaultOrder(0d, 1d)
+    shouldMatchDefaultOrder(Double.MinValue, Double.MaxValue)
+    assert(Utils.nanSafeCompareDoubles(Double.NaN, Double.NaN) === 0)
+    assert(Utils.nanSafeCompareDoubles(Double.NaN, Double.PositiveInfinity) === 1)
+    assert(Utils.nanSafeCompareDoubles(Double.NaN, Double.NegativeInfinity) === 1)
+    assert(Utils.nanSafeCompareDoubles(Double.PositiveInfinity, Double.NaN) === -1)
+    assert(Utils.nanSafeCompareDoubles(Double.NegativeInfinity, Double.NaN) === -1)
+  }
+
+  test("nanSafeCompareFloats") {
+    def shouldMatchDefaultOrder(a: Float, b: Float): Unit = {
+      assert(Utils.nanSafeCompareFloats(a, b) === JFloat.compare(a, b))
+      assert(Utils.nanSafeCompareFloats(b, a) === JFloat.compare(b, a))
+    }
+    shouldMatchDefaultOrder(0f, 0f)
+    shouldMatchDefaultOrder(1f, 1f)
+    shouldMatchDefaultOrder(Float.MinValue, Float.MaxValue)
+    assert(Utils.nanSafeCompareFloats(Float.NaN, Float.NaN) === 0)
+    assert(Utils.nanSafeCompareFloats(Float.NaN, Float.PositiveInfinity) === 1)
+    assert(Utils.nanSafeCompareFloats(Float.NaN, Float.NegativeInfinity) === 1)
+    assert(Utils.nanSafeCompareFloats(Float.PositiveInfinity, Float.NaN) === -1)
+    assert(Utils.nanSafeCompareFloats(Float.NegativeInfinity, Float.NaN) === -1)
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
index dd505dfa7d758..dc03e374b51db 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -47,4 +47,29 @@ class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
     forAll (regressionTests) { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
     forAll { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
   }
+
+  test("float prefix comparator handles NaN properly") {
+    val nan1: Float = java.lang.Float.intBitsToFloat(0x7f800001)
+    val nan2: Float = java.lang.Float.intBitsToFloat(0x7fffffff)
+    assert(nan1.isNaN)
+    assert(nan2.isNaN)
+    val nan1Prefix = PrefixComparators.FLOAT.computePrefix(nan1)
+    val nan2Prefix = PrefixComparators.FLOAT.computePrefix(nan2)
+    assert(nan1Prefix === nan2Prefix)
+    val floatMaxPrefix = PrefixComparators.FLOAT.computePrefix(Float.MaxValue)
+    assert(PrefixComparators.FLOAT.compare(nan1Prefix, floatMaxPrefix) === 1)
+  }
+
+  test("double prefix comparator handles NaNs properly") {
+    val nan1: Double = java.lang.Double.longBitsToDouble(0x7ff0000000000001L)
+    val nan2: Double = java.lang.Double.longBitsToDouble(0x7fffffffffffffffL)
+    assert(nan1.isNaN)
+    assert(nan2.isNaN)
+    val nan1Prefix = PrefixComparators.DOUBLE.computePrefix(nan1)
+    val nan2Prefix = PrefixComparators.DOUBLE.computePrefix(nan2)
+    assert(nan1Prefix === nan2Prefix)
+    val doubleMaxPrefix = PrefixComparators.DOUBLE.computePrefix(Double.MaxValue)
+    assert(PrefixComparators.DOUBLE.compare(nan1Prefix, doubleMaxPrefix) === 1)
+  }
+
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 87294a0e21441..8cd9e7bc60a03 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -215,6 +215,9 @@ public void setLong(int ordinal, long value) {
   public void setDouble(int ordinal, double value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
+    if (Double.isNaN(value)) {
+      value = Double.NaN;
+    }
     PlatformDependent.UNSAFE.putDouble(baseObject, getFieldOffset(ordinal), value);
   }
 
@@ -243,6 +246,9 @@ public void setByte(int ordinal, byte value) {
   public void setFloat(int ordinal, float value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
+    if (Float.isNaN(value)) {
+      value = Float.NaN;
+    }
     PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 2cb64d00935de..91449479fa539 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -403,20 +403,28 @@ trait Row extends Serializable {
       if (!isNullAt(i)) {
         val o1 = get(i)
         val o2 = other.get(i)
-        if (o1.isInstanceOf[Array[Byte]]) {
-          // handle equality of Array[Byte]
-          val b1 = o1.asInstanceOf[Array[Byte]]
-          if (!o2.isInstanceOf[Array[Byte]] ||
-            !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+                !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
             return false
           }
-        } else if (o1 != o2) {
-          return false
         }
       }
       i += 1
     }
-    return true
+    true
   }
 
   /* ---------------------- utility methods for Scala ---------------------- */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 10f411ff7451a..606f770cb4f7b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -194,6 +194,8 @@ class CodeGenContext {
    */
   def genEqual(dataType: DataType, c1: String, c2: String): String = dataType match {
     case BinaryType => s"java.util.Arrays.equals($c1, $c2)"
+    case FloatType => s"(java.lang.Float.isNaN($c1) && java.lang.Float.isNaN($c2)) || $c1 == $c2"
+    case DoubleType => s"(java.lang.Double.isNaN($c1) && java.lang.Double.isNaN($c2)) || $c1 == $c2"
     case dt: DataType if isPrimitiveType(dt) => s"$c1 == $c2"
     case other => s"$c1.equals($c2)"
   }
@@ -204,6 +206,8 @@ class CodeGenContext {
   def genComp(dataType: DataType, c1: String, c2: String): String = dataType match {
     // java boolean doesn't support > or < operator
     case BooleanType => s"($c1 == $c2 ? 0 : ($c1 ? 1 : -1))"
+    case DoubleType => s"org.apache.spark.util.Utils.nanSafeCompareDoubles($c1, $c2)"
+    case FloatType => s"org.apache.spark.util.Utils.nanSafeCompareFloats($c1, $c2)"
     // use c1 - c2 may overflow
     case dt: DataType if isPrimitiveType(dt) => s"($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)"
     case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 40ec3df224ce1..a53ec31ee6a4b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -22,6 +22,7 @@ import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
 
 
 object InterpretedPredicate {
@@ -222,7 +223,9 @@ case class Or(left: Expression, right: Expression) extends BinaryOperator with P
 abstract class BinaryComparison extends BinaryOperator with Predicate {
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    if (ctx.isPrimitiveType(left.dataType)) {
+    if (ctx.isPrimitiveType(left.dataType)
+        && left.dataType != FloatType
+        && left.dataType != DoubleType) {
       // faster version
       defineCodeGen(ctx, ev, (c1, c2) => s"$c1 $symbol $c2")
     } else {
@@ -254,8 +257,15 @@ case class EqualTo(left: Expression, right: Expression) extends BinaryComparison
   override def symbol: String = "="
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
-    if (left.dataType != BinaryType) input1 == input2
-    else java.util.Arrays.equals(input1.asInstanceOf[Array[Byte]], input2.asInstanceOf[Array[Byte]])
+    if (left.dataType == FloatType) {
+      Utils.nanSafeCompareFloats(input1.asInstanceOf[Float], input2.asInstanceOf[Float]) == 0
+    } else if (left.dataType == DoubleType) {
+      Utils.nanSafeCompareDoubles(input1.asInstanceOf[Double], input2.asInstanceOf[Double]) == 0
+    } else if (left.dataType != BinaryType) {
+      input1 == input2
+    } else {
+      java.util.Arrays.equals(input1.asInstanceOf[Array[Byte]], input2.asInstanceOf[Array[Byte]])
+    }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -280,7 +290,11 @@ case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComp
     } else if (input1 == null || input2 == null) {
       false
     } else {
-      if (left.dataType != BinaryType) {
+      if (left.dataType == FloatType) {
+        Utils.nanSafeCompareFloats(input1.asInstanceOf[Float], input2.asInstanceOf[Float]) == 0
+      } else if (left.dataType == DoubleType) {
+        Utils.nanSafeCompareDoubles(input1.asInstanceOf[Double], input2.asInstanceOf[Double]) == 0
+      } else if (left.dataType != BinaryType) {
         input1 == input2
       } else {
         java.util.Arrays.equals(input1.asInstanceOf[Array[Byte]], input2.asInstanceOf[Array[Byte]])
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala
index 986c2ab055386..2a1bf0938e5a8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala
@@ -23,6 +23,7 @@ import scala.reflect.runtime.universe.typeTag
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.ScalaReflectionLock
+import org.apache.spark.util.Utils
 
 /**
  * :: DeveloperApi ::
@@ -37,7 +38,9 @@ class DoubleType private() extends FractionalType {
   @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] }
   private[sql] val numeric = implicitly[Numeric[Double]]
   private[sql] val fractional = implicitly[Fractional[Double]]
-  private[sql] val ordering = implicitly[Ordering[InternalType]]
+  private[sql] val ordering = new Ordering[Double] {
+    override def compare(x: Double, y: Double): Int = Utils.nanSafeCompareDoubles(x, y)
+  }
   private[sql] val asIntegral = DoubleAsIfIntegral
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala
index 9bd48ece83a1c..08e22252aef82 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala
@@ -23,6 +23,7 @@ import scala.reflect.runtime.universe.typeTag
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.ScalaReflectionLock
+import org.apache.spark.util.Utils
 
 /**
  * :: DeveloperApi ::
@@ -37,7 +38,9 @@ class FloatType private() extends FractionalType {
   @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] }
   private[sql] val numeric = implicitly[Numeric[Float]]
   private[sql] val fractional = implicitly[Fractional[Float]]
-  private[sql] val ordering = implicitly[Ordering[InternalType]]
+  private[sql] val ordering = new Ordering[Float] {
+    override def compare(x: Float, y: Float): Int = Utils.nanSafeCompareFloats(x, y)
+  }
   private[sql] val asIntegral = FloatAsIfIntegral
 
   /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index e05218a23aa73..f4fbc49677ca3 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -17,9 +17,14 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import scala.math._
+
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.types.{DataTypeTestUtils, NullType, StructField, StructType}
 
 /**
  * Additional tests for code generation.
@@ -43,6 +48,40 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     futures.foreach(Await.result(_, 10.seconds))
   }
 
+  // Test GenerateOrdering for all common types. For each type, we construct random input rows that
+  // contain two columns of that type, then for pairs of randomly-generated rows we check that
+  // GenerateOrdering agrees with RowOrdering.
+  (DataTypeTestUtils.atomicTypes ++ Set(NullType)).foreach { dataType =>
+    test(s"GenerateOrdering with $dataType") {
+      val rowOrdering = RowOrdering.forSchema(Seq(dataType, dataType))
+      val genOrdering = GenerateOrdering.generate(
+        BoundReference(0, dataType, nullable = true).asc ::
+          BoundReference(1, dataType, nullable = true).asc :: Nil)
+      val rowType = StructType(
+        StructField("a", dataType, nullable = true) ::
+          StructField("b", dataType, nullable = true) :: Nil)
+      val maybeDataGenerator = RandomDataGenerator.forType(rowType, nullable = false)
+      assume(maybeDataGenerator.isDefined)
+      val randGenerator = maybeDataGenerator.get
+      val toCatalyst = CatalystTypeConverters.createToCatalystConverter(rowType)
+      for (_ <- 1 to 50) {
+        val a = toCatalyst(randGenerator()).asInstanceOf[InternalRow]
+        val b = toCatalyst(randGenerator()).asInstanceOf[InternalRow]
+        withClue(s"a = $a, b = $b") {
+          assert(genOrdering.compare(a, a) === 0)
+          assert(genOrdering.compare(b, b) === 0)
+          assert(rowOrdering.compare(a, a) === 0)
+          assert(rowOrdering.compare(b, b) === 0)
+          assert(signum(genOrdering.compare(a, b)) === -1 * signum(genOrdering.compare(b, a)))
+          assert(signum(rowOrdering.compare(a, b)) === -1 * signum(rowOrdering.compare(b, a)))
+          assert(
+            signum(rowOrdering.compare(a, b)) === signum(genOrdering.compare(a, b)),
+            "Generated and non-generated orderings should agree")
+        }
+      }
+    }
+  }
+
   test("SPARK-8443: split wide projections into blocks due to JVM code size limit") {
     val length = 5000
     val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 2173a0c25c645..0bc2812a5dc83 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -136,11 +136,14 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true)
   }
 
-  private val smallValues = Seq(1, Decimal(1), Array(1.toByte), "a").map(Literal(_))
-  private val largeValues = Seq(2, Decimal(2), Array(2.toByte), "b").map(Literal(_))
-
-  private val equalValues1 = smallValues
-  private val equalValues2 = Seq(1, Decimal(1), Array(1.toByte), "a").map(Literal(_))
+  private val smallValues = Seq(1, Decimal(1), Array(1.toByte), "a", 0f, 0d).map(Literal(_))
+  private val largeValues =
+    Seq(2, Decimal(2), Array(2.toByte), "b", Float.NaN, Double.NaN).map(Literal(_))
+
+  private val equalValues1 =
+    Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN).map(Literal(_))
+  private val equalValues2 =
+    Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN).map(Literal(_))
 
   test("BinaryComparison: <") {
     for (i <- 0 until smallValues.length) {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index d00aeb4dfbf47..dff5faf9f6ec8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -316,4 +316,26 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
   }
 
+  test("NaN canonicalization") {
+    val fieldTypes: Array[DataType] = Array(FloatType, DoubleType)
+
+    val row1 = new SpecificMutableRow(fieldTypes)
+    row1.setFloat(0, java.lang.Float.intBitsToFloat(0x7f800001))
+    row1.setDouble(1, java.lang.Double.longBitsToDouble(0x7ff0000000000001L))
+
+    val row2 = new SpecificMutableRow(fieldTypes)
+    row2.setFloat(0, java.lang.Float.intBitsToFloat(0x7fffffff))
+    row2.setDouble(1, java.lang.Double.longBitsToDouble(0x7fffffffffffffffL))
+
+    val converter = new UnsafeRowConverter(fieldTypes)
+    val row1Buffer = new Array[Byte](converter.getSizeRequirement(row1))
+    val row2Buffer = new Array[Byte](converter.getSizeRequirement(row2))
+    converter.writeRow(
+      row1, row1Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row1Buffer.length, null)
+    converter.writeRow(
+      row2, row2Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row2Buffer.length, null)
+
+    assert(row1Buffer.toSeq === row2Buffer.toSeq)
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 192cc0a6e5d7c..f67f2c60c0e16 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.io.File
 
 import scala.language.postfixOps
+import scala.util.Random
 
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
 import org.apache.spark.sql.functions._
@@ -742,6 +743,27 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     df.col("t.``")
   }
 
+  test("SPARK-8797: sort by float column containing NaN should not crash") {
+    val inputData = Seq.fill(10)(Tuple1(Float.NaN)) ++ (1 to 1000).map(x => Tuple1(x.toFloat))
+    val df = Random.shuffle(inputData).toDF("a")
+    df.orderBy("a").collect()
+  }
+
+  test("SPARK-8797: sort by double column containing NaN should not crash") {
+    val inputData = Seq.fill(10)(Tuple1(Double.NaN)) ++ (1 to 1000).map(x => Tuple1(x.toDouble))
+    val df = Random.shuffle(inputData).toDF("a")
+    df.orderBy("a").collect()
+  }
+
+  test("NaN is greater than all other non-NaN numeric values") {
+    val maxDouble = Seq(Double.NaN, Double.PositiveInfinity, Double.MaxValue)
+      .map(Tuple1.apply).toDF("a").selectExpr("max(a)").first()
+    assert(java.lang.Double.isNaN(maxDouble.getDouble(0)))
+    val maxFloat = Seq(Float.NaN, Float.PositiveInfinity, Float.MaxValue)
+      .map(Tuple1.apply).toDF("a").selectExpr("max(a)").first()
+    assert(java.lang.Float.isNaN(maxFloat.getFloat(0)))
+  }
+
   test("SPARK-8072: Better Exception for Duplicate Columns") {
     // only one duplicate column present
     val e = intercept[org.apache.spark.sql.AnalysisException] {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index d84b57af9c882..7cc6ffd7548d0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -73,4 +73,16 @@ class RowSuite extends SparkFunSuite {
       row.getAs[Int]("c")
     }
   }
+
+  test("float NaN == NaN") {
+    val r1 = Row(Float.NaN)
+    val r2 = Row(Float.NaN)
+    assert(r1 === r2)
+  }
+
+  test("double NaN == NaN") {
+    val r1 = Row(Double.NaN)
+    val r2 = Row(Double.NaN)
+    assert(r1 === r2)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
index 4f4c1f28564cb..5fe73f7e0b072 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
@@ -83,11 +83,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     randomDataGenerator <- RandomDataGenerator.forType(dataType, nullable)
   ) {
     test(s"sorting on $dataType with nullable=$nullable, sortOrder=$sortOrder") {
-      val inputData = Seq.fill(1000)(randomDataGenerator()).filter {
-        case d: Double => !d.isNaN
-        case f: Float => !java.lang.Float.isNaN(f)
-        case x => true
-      }
+      val inputData = Seq.fill(1000)(randomDataGenerator())
       val inputDf = TestSQLContext.createDataFrame(
         TestSQLContext.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)

From 560b355ccd038ca044726c9c9fcffd14d02e6696 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 22:43:30 -0700
Subject: [PATCH 0510/1454] [SPARK-9157] [SQL] codegen substring

https://issues.apache.org/jira/browse/SPARK-9157

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7534 from tarekauel/SPARK-9157 and squashes the following commits:

e65e3e9 [Tarek Auel] [SPARK-9157] indent fix
44e89f8 [Tarek Auel] [SPARK-9157] use EMPTY_UTF8
37d54c4 [Tarek Auel] Merge branch 'master' into SPARK-9157
60732ea [Tarek Auel] [SPARK-9157] created substringSQL in UTF8String
18c3576 [Tarek Auel] [SPARK-9157][SQL] remove slice pos
1a2e611 [Tarek Auel] [SPARK-9157][SQL] codegen substring
---
 .../expressions/stringOperations.scala        | 87 ++++++++++---------
 .../apache/spark/unsafe/types/UTF8String.java | 12 +++
 .../spark/unsafe/types/UTF8StringSuite.java   | 19 ++++
 3 files changed, 75 insertions(+), 43 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 5c1908d55576a..438215e8e6e37 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -640,7 +640,7 @@ case class StringSplit(str: Expression, pattern: Expression)
  * Defined for String and Binary types.
  */
 case class Substring(str: Expression, pos: Expression, len: Expression)
-  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+  extends Expression with ImplicitCastInputTypes {
 
   def this(str: Expression, pos: Expression) = {
     this(str, pos, Literal(Integer.MAX_VALUE))
@@ -649,58 +649,59 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
   override def foldable: Boolean = str.foldable && pos.foldable && len.foldable
   override def nullable: Boolean = str.nullable || pos.nullable || len.nullable
 
-  override def dataType: DataType = {
-    if (!resolved) {
-      throw new UnresolvedException(this, s"Cannot resolve since $children are not resolved")
-    }
-    if (str.dataType == BinaryType) str.dataType else StringType
-  }
+  override def dataType: DataType = StringType
 
   override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, IntegerType)
 
   override def children: Seq[Expression] = str :: pos :: len :: Nil
 
-  @inline
-  def slicePos(startPos: Int, sliceLen: Int, length: () => Int): (Int, Int) = {
-    // Hive and SQL use one-based indexing for SUBSTR arguments but also accept zero and
-    // negative indices for start positions. If a start index i is greater than 0, it
-    // refers to element i-1 in the sequence. If a start index i is less than 0, it refers
-    // to the -ith element before the end of the sequence. If a start index i is 0, it
-    // refers to the first element.
-
-    val start = startPos match {
-      case pos if pos > 0 => pos - 1
-      case neg if neg < 0 => length() + neg
-      case _ => 0
-    }
-
-    val end = sliceLen match {
-      case max if max == Integer.MAX_VALUE => max
-      case x => start + x
+  override def eval(input: InternalRow): Any = {
+    val stringEval = str.eval(input)
+    if (stringEval != null) {
+      val posEval = pos.eval(input)
+      if (posEval != null) {
+        val lenEval = len.eval(input)
+        if (lenEval != null) {
+          stringEval.asInstanceOf[UTF8String]
+            .substringSQL(posEval.asInstanceOf[Int], lenEval.asInstanceOf[Int])
+        } else {
+          null
+        }
+      } else {
+        null
+      }
+    } else {
+      null
     }
-
-    (start, end)
   }
 
-  override def eval(input: InternalRow): Any = {
-    val string = str.eval(input)
-    val po = pos.eval(input)
-    val ln = len.eval(input)
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val strGen = str.gen(ctx)
+    val posGen = pos.gen(ctx)
+    val lenGen = len.gen(ctx)
 
-    if ((string == null) || (po == null) || (ln == null)) {
-      null
-    } else {
-      val start = po.asInstanceOf[Int]
-      val length = ln.asInstanceOf[Int]
-      string match {
-        case ba: Array[Byte] =>
-          val (st, end) = slicePos(start, length, () => ba.length)
-          ba.slice(st, end)
-        case s: UTF8String =>
-          val (st, end) = slicePos(start, length, () => s.numChars())
-          s.substring(st, end)
+    val start = ctx.freshName("start")
+    val end = ctx.freshName("end")
+
+    s"""
+      ${strGen.code}
+      boolean ${ev.isNull} = ${strGen.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${posGen.code}
+        if (!${posGen.isNull}) {
+          ${lenGen.code}
+          if (!${lenGen.isNull}) {
+            ${ev.primitive} = ${strGen.primitive}
+              .substringSQL(${posGen.primitive}, ${lenGen.primitive});
+          } else {
+            ${ev.isNull} = true;
+          }
+        } else {
+          ${ev.isNull} = true;
+        }
       }
-    }
+     """
   }
 }
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index ed354f7f877f1..946d355f1fc28 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -165,6 +165,18 @@ public UTF8String substring(final int start, final int until) {
     return fromBytes(bytes);
   }
 
+  public UTF8String substringSQL(int pos, int length) {
+    // Information regarding the pos calculation:
+    // Hive and SQL use one-based indexing for SUBSTR arguments but also accept zero and
+    // negative indices for start positions. If a start index i is greater than 0, it
+    // refers to element i-1 in the sequence. If a start index i is less than 0, it refers
+    // to the -ith element before the end of the sequence. If a start index i is 0, it
+    // refers to the first element.
+    int start = (pos > 0) ? pos -1 : ((pos < 0) ? numChars() + pos : 0);
+    int end = (length == Integer.MAX_VALUE) ? Integer.MAX_VALUE : start + length;
+    return substring(start, end);
+  }
+
   /**
    * Returns whether this contains `substring` or not.
    */
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 1f5572c509bdb..e2a5628ff4d93 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -272,6 +272,25 @@ public void pad() {
       fromString("数据砖头").rpad(12, fromString("孙行者")));
   }
 
+  @Test
+  public void substringSQL() {
+    UTF8String e = fromString("example");
+    assertEquals(e.substringSQL(0, 2), fromString("ex"));
+    assertEquals(e.substringSQL(1, 2), fromString("ex"));
+    assertEquals(e.substringSQL(0, 7), fromString("example"));
+    assertEquals(e.substringSQL(1, 2), fromString("ex"));
+    assertEquals(e.substringSQL(0, 100), fromString("example"));
+    assertEquals(e.substringSQL(1, 100), fromString("example"));
+    assertEquals(e.substringSQL(2, 2), fromString("xa"));
+    assertEquals(e.substringSQL(1, 6), fromString("exampl"));
+    assertEquals(e.substringSQL(2, 100), fromString("xample"));
+    assertEquals(e.substringSQL(0, 0), fromString(""));
+    assertEquals(e.substringSQL(100, 4), EMPTY_UTF8);
+    assertEquals(e.substringSQL(0, Integer.MAX_VALUE), fromString("example"));
+    assertEquals(e.substringSQL(1, Integer.MAX_VALUE), fromString("example"));
+    assertEquals(e.substringSQL(2, Integer.MAX_VALUE), fromString("xample"));
+  }
+
   @Test
   public void split() {
     assertTrue(Arrays.equals(fromString("ab,def,ghi").split(fromString(","), -1),

From 67570beed5950974126a91eacd48fd0fedfeb141 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 20 Jul 2015 22:48:13 -0700
Subject: [PATCH 0511/1454] [SPARK-9208][SQL] Remove variant of DataFrame
 string functions that accept column names.

It can be ambiguous whether that is a string literal or a column name.

cc marmbrus

Author: Reynold Xin <rxin@databricks.com>

Closes #7556 from rxin/str-exprs and squashes the following commits:

92afa83 [Reynold Xin] [SPARK-9208][SQL] Remove variant of DataFrame string functions that accept column names.
---
 .../sql/catalyst/optimizer/Optimizer.scala    |   2 +-
 .../org/apache/spark/sql/functions.scala      | 459 ++----------------
 .../spark/sql/DataFrameFunctionsSuite.scala   |   8 +-
 .../spark/sql/MathExpressionsSuite.scala      |   1 -
 .../spark/sql/StringFunctionsSuite.scala      |  59 +--
 5 files changed, 74 insertions(+), 455 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index fafdae07c92f0..9c45b196245da 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -684,7 +684,7 @@ object CombineLimits extends Rule[LogicalPlan] {
 }
 
 /**
- * Removes the inner [[CaseConversionExpression]] that are unnecessary because
+ * Removes the inner case conversion expressions that are unnecessary because
  * the inner conversion is overwritten by the outer one.
  */
 object SimplifyCaseConversionExpressions extends Rule[LogicalPlan] {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 41b25d1836481..8fa017610b63c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -69,7 +69,7 @@ object functions {
   def column(colName: String): Column = Column(colName)
 
   /**
-   * Convert a number from one base to another for the specified expressions
+   * Convert a number in string format from one base to another.
    *
    * @group math_funcs
    * @since 1.5.0
@@ -77,15 +77,6 @@ object functions {
   def conv(num: Column, fromBase: Int, toBase: Int): Column =
     Conv(num.expr, lit(fromBase).expr, lit(toBase).expr)
 
-  /**
-   * Convert a number from one base to another for the specified expressions
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def conv(numColName: String, fromBase: Int, toBase: Int): Column =
-    conv(Column(numColName), fromBase, toBase)
-
   /**
    * Creates a [[Column]] of literal value.
    *
@@ -627,14 +618,6 @@ object functions {
    */
   def isNaN(e: Column): Column = IsNaN(e.expr)
 
-  /**
-   * Converts a string expression to lower case.
-   *
-   * @group normal_funcs
-   * @since 1.3.0
-   */
-  def lower(e: Column): Column = Lower(e.expr)
-
   /**
    * A column expression that generates monotonically increasing 64-bit integers.
    *
@@ -791,14 +774,6 @@ object functions {
     struct((colName +: colNames).map(col) : _*)
   }
 
-  /**
-   * Converts a string expression to upper case.
-   *
-   * @group normal_funcs
-   * @since 1.3.0
-   */
-  def upper(e: Column): Column = Upper(e.expr)
-
   /**
    * Computes bitwise NOT.
    *
@@ -1106,9 +1081,8 @@ object functions {
    * @since 1.5.0
    */
   @scala.annotation.varargs
-  def greatest(exprs: Column*): Column = if (exprs.length < 2) {
-    sys.error("GREATEST takes at least 2 parameters")
-  } else {
+  def greatest(exprs: Column*): Column = {
+    require(exprs.length > 1, "greatest requires at least 2 arguments.")
     Greatest(exprs.map(_.expr))
   }
 
@@ -1120,9 +1094,7 @@ object functions {
    * @since 1.5.0
    */
   @scala.annotation.varargs
-  def greatest(columnName: String, columnNames: String*): Column = if (columnNames.isEmpty) {
-    sys.error("GREATEST takes at least 2 parameters")
-  } else {
+  def greatest(columnName: String, columnNames: String*): Column = {
     greatest((columnName +: columnNames).map(Column.apply): _*)
   }
 
@@ -1134,14 +1106,6 @@ object functions {
     */
   def hex(column: Column): Column = Hex(column.expr)
 
-  /**
-   * Computes hex value of the given input.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def hex(colName: String): Column = hex(Column(colName))
-
   /**
    * Inverse of hex. Interprets each pair of characters as a hexadecimal number
    * and converts to the byte representation of number.
@@ -1151,15 +1115,6 @@ object functions {
    */
   def unhex(column: Column): Column = Unhex(column.expr)
 
-  /**
-   * Inverse of hex. Interprets each pair of characters as a hexadecimal number
-   * and converts to the byte representation of number.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def unhex(colName: String): Column = unhex(Column(colName))
-
   /**
    * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
    *
@@ -1233,9 +1188,8 @@ object functions {
    * @since 1.5.0
    */
   @scala.annotation.varargs
-  def least(exprs: Column*): Column = if (exprs.length < 2) {
-    sys.error("LEAST takes at least 2 parameters")
-  } else {
+  def least(exprs: Column*): Column = {
+    require(exprs.length > 1, "least requires at least 2 arguments.")
     Least(exprs.map(_.expr))
   }
 
@@ -1247,9 +1201,7 @@ object functions {
    * @since 1.5.0
    */
   @scala.annotation.varargs
-  def least(columnName: String, columnNames: String*): Column = if (columnNames.isEmpty) {
-    sys.error("LEAST takes at least 2 parameters")
-  } else {
+  def least(columnName: String, columnNames: String*): Column = {
     least((columnName +: columnNames).map(Column.apply): _*)
   }
 
@@ -1639,7 +1591,8 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Calculates the MD5 digest and returns the value as a 32 character hex string.
+   * Calculates the MD5 digest of a binary column and returns the value
+   * as a 32 character hex string.
    *
    * @group misc_funcs
    * @since 1.5.0
@@ -1647,15 +1600,8 @@ object functions {
   def md5(e: Column): Column = Md5(e.expr)
 
   /**
-   * Calculates the MD5 digest and returns the value as a 32 character hex string.
-   *
-   * @group misc_funcs
-   * @since 1.5.0
-   */
-  def md5(columnName: String): Column = md5(Column(columnName))
-
-  /**
-   * Calculates the SHA-1 digest and returns the value as a 40 character hex string.
+   * Calculates the SHA-1 digest of a binary column and returns the value
+   * as a 40 character hex string.
    *
    * @group misc_funcs
    * @since 1.5.0
@@ -1663,15 +1609,11 @@ object functions {
   def sha1(e: Column): Column = Sha1(e.expr)
 
   /**
-   * Calculates the SHA-1 digest and returns the value as a 40 character hex string.
+   * Calculates the SHA-2 family of hash functions of a binary column and
+   * returns the value as a hex string.
    *
-   * @group misc_funcs
-   * @since 1.5.0
-   */
-  def sha1(columnName: String): Column = sha1(Column(columnName))
-
-  /**
-   * Calculates the SHA-2 family of hash functions and returns the value as a hex string.
+   * @param e column to compute SHA-2 on.
+   * @param numBits one of 224, 256, 384, or 512.
    *
    * @group misc_funcs
    * @since 1.5.0
@@ -1683,29 +1625,14 @@ object functions {
   }
 
   /**
-   * Calculates the SHA-2 family of hash functions and returns the value as a hex string.
-   *
-   * @group misc_funcs
-   * @since 1.5.0
-   */
-  def sha2(columnName: String, numBits: Int): Column = sha2(Column(columnName), numBits)
-
-  /**
-   * Calculates the cyclic redundancy check value and returns the value as a bigint.
+   * Calculates the cyclic redundancy check value  (CRC32) of a binary column and
+   * returns the value as a bigint.
    *
    * @group misc_funcs
    * @since 1.5.0
    */
   def crc32(e: Column): Column = Crc32(e.expr)
 
-  /**
-   * Calculates the cyclic redundancy check value and returns the value as a bigint.
-   *
-   * @group misc_funcs
-   * @since 1.5.0
-   */
-  def crc32(columnName: String): Column = crc32(Column(columnName))
-
   //////////////////////////////////////////////////////////////////////////////////////////////
   // String functions
   //////////////////////////////////////////////////////////////////////////////////////////////
@@ -1719,19 +1646,6 @@ object functions {
   @scala.annotation.varargs
   def concat(exprs: Column*): Column = Concat(exprs.map(_.expr))
 
-  /**
-   * Concatenates input strings together into a single string.
-   *
-   * This is the variant of concat that takes in the column names.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  @scala.annotation.varargs
-  def concat(columnName: String, columnNames: String*): Column = {
-    concat((columnName +: columnNames).map(Column.apply): _*)
-  }
-
   /**
    * Concatenates input strings together into a single string, using the given separator.
    *
@@ -1743,19 +1657,6 @@ object functions {
     ConcatWs(Literal.create(sep, StringType) +: exprs.map(_.expr))
   }
 
-  /**
-   * Concatenates input strings together into a single string, using the given separator.
-   *
-   * This is the variant of concat_ws that takes in the column names.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  @scala.annotation.varargs
-  def concat_ws(sep: String, columnName: String, columnNames: String*): Column = {
-    concat_ws(sep, (columnName +: columnNames).map(Column.apply) : _*)
-  }
-
   /**
    * Computes the length of a given string / binary value.
    *
@@ -1765,23 +1666,20 @@ object functions {
   def length(e: Column): Column = Length(e.expr)
 
   /**
-   * Computes the length of a given string / binary column.
+   * Converts a string expression to lower case.
    *
    * @group string_funcs
-   * @since 1.5.0
+   * @since 1.3.0
    */
-  def length(columnName: String): Column = length(Column(columnName))
+  def lower(e: Column): Column = Lower(e.expr)
 
   /**
-   * Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
-   * and returns the result as a string.
-   * If d is 0, the result has no decimal point or fractional part.
-   * If d < 0, the result will be null.
+   * Converts a string expression to upper case.
    *
    * @group string_funcs
-   * @since 1.5.0
+   * @since 1.3.0
    */
-  def format_number(x: Column, d: Int): Column = FormatNumber(x.expr, lit(d).expr)
+  def upper(e: Column): Column = Upper(e.expr)
 
   /**
    * Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
@@ -1792,57 +1690,31 @@ object functions {
    * @group string_funcs
    * @since 1.5.0
    */
-  def format_number(columnXName: String, d: Int): Column = {
-    format_number(Column(columnXName), d)
-  }
+  def format_number(x: Column, d: Int): Column = FormatNumber(x.expr, lit(d).expr)
 
   /**
-   * Computes the Levenshtein distance of the two given strings.
+   * Computes the Levenshtein distance of the two given string columns.
    * @group string_funcs
    * @since 1.5.0
    */
   def levenshtein(l: Column, r: Column): Column = Levenshtein(l.expr, r.expr)
 
-  /**
-   * Computes the Levenshtein distance of the two given strings.
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def levenshtein(leftColumnName: String, rightColumnName: String): Column =
-    levenshtein(Column(leftColumnName), Column(rightColumnName))
-
-  /**
-   * Computes the numeric value of the first character of the specified string value.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def ascii(e: Column): Column = Ascii(e.expr)
-
   /**
    * Computes the numeric value of the first character of the specified string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def ascii(columnName: String): Column = ascii(Column(columnName))
+  def ascii(e: Column): Column = Ascii(e.expr)
 
   /**
-   * Trim the spaces from both ends for the specified string value.
+   * Trim the spaces from both ends for the specified string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
   def trim(e: Column): Column = StringTrim(e.expr)
 
-  /**
-   * Trim the spaces from both ends for the specified column.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def trim(columnName: String): Column = trim(Column(columnName))
-
   /**
    * Trim the spaces from left end for the specified string value.
    *
@@ -1851,14 +1723,6 @@ object functions {
    */
   def ltrim(e: Column): Column = StringTrimLeft(e.expr)
 
-  /**
-   * Trim the spaces from left end for the specified column.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def ltrim(columnName: String): Column = ltrim(Column(columnName))
-
   /**
    * Trim the spaces from right end for the specified string value.
    *
@@ -1867,25 +1731,6 @@ object functions {
    */
   def rtrim(e: Column): Column = StringTrimRight(e.expr)
 
-  /**
-   * Trim the spaces from right end for the specified column.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def rtrim(columnName: String): Column = rtrim(Column(columnName))
-
-  /**
-   * Format strings in printf-style.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  @scala.annotation.varargs
-  def formatString(format: Column, arguments: Column*): Column = {
-    StringFormat((format +: arguments).map(_.expr): _*)
-  }
-
   /**
    * Format strings in printf-style.
    * NOTE: `format` is the string value of the formatter, not column name.
@@ -1898,18 +1743,6 @@ object functions {
     StringFormat(lit(format).expr +: arguNames.map(Column(_).expr): _*)
   }
 
-  /**
-   * Locate the position of the first occurrence of substr value in the given string.
-   * Returns null if either of the arguments are null.
-   *
-   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
-   * could not be found in str.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def instr(substr: String, sub: String): Column = instr(Column(substr), Column(sub))
-
   /**
    * Locate the position of the first occurrence of substr column in the given string.
    * Returns null if either of the arguments are null.
@@ -1920,10 +1753,10 @@ object functions {
    * @group string_funcs
    * @since 1.5.0
    */
-  def instr(substr: Column, sub: Column): Column = StringInstr(substr.expr, sub.expr)
+  def instr(str: Column, substring: String): Column = StringInstr(str.expr, lit(substring).expr)
 
   /**
-   * Locate the position of the first occurrence of substr.
+   * Locate the position of the first occurrence of substr in a string column.
    *
    * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
    * could not be found in str.
@@ -1931,77 +1764,26 @@ object functions {
    * @group string_funcs
    * @since 1.5.0
    */
-  def locate(substr: String, str: String): Column = {
-    locate(Column(substr), Column(str))
+  def locate(substr: String, str: Column): Column = {
+    new StringLocate(lit(substr).expr, str.expr)
   }
 
   /**
-   * Locate the position of the first occurrence of substr.
+   * Locate the position of the first occurrence of substr in a string column, after position pos.
    *
-   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+   * NOTE: The position is not zero based, but 1 based index. returns 0 if substr
    * could not be found in str.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def locate(substr: Column, str: Column): Column = {
-    new StringLocate(substr.expr, str.expr)
+  def locate(substr: String, str: Column, pos: Int): Column = {
+    StringLocate(lit(substr).expr, str.expr, lit(pos).expr)
   }
 
   /**
-   * Locate the position of the first occurrence of substr in a given string after position pos.
-   *
-   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
-   * could not be found in str.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def locate(substr: String, str: String, pos: String): Column = {
-    locate(Column(substr), Column(str), Column(pos))
-  }
-
-  /**
-   * Locate the position of the first occurrence of substr in a given string after position pos.
-   *
-   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
-   * could not be found in str.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def locate(substr: Column, str: Column, pos: Column): Column = {
-    StringLocate(substr.expr, str.expr, pos.expr)
-  }
-
-  /**
-   * Locate the position of the first occurrence of substr in a given string after position pos.
-   *
-   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
-   * could not be found in str.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def locate(substr: Column, str: Column, pos: Int): Column = {
-    StringLocate(substr.expr, str.expr, lit(pos).expr)
-  }
-
-  /**
-   * Locate the position of the first occurrence of substr in a given string after position pos.
-   *
-   * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
-   * could not be found in str.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def locate(substr: String, str: String, pos: Int): Column = {
-    locate(Column(substr), Column(str), lit(pos))
-  }
-
-  /**
-   * Computes the specified value from binary to a base64 string.
+   * Computes the BASE64 encoding of a binary column and returns it as a string column.
+   * This is the reverse of unbase64.
    *
    * @group string_funcs
    * @since 1.5.0
@@ -2009,67 +1791,22 @@ object functions {
   def base64(e: Column): Column = Base64(e.expr)
 
   /**
-   * Computes the specified column from binary to a base64 string.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def base64(columnName: String): Column = base64(Column(columnName))
-
-  /**
-   * Computes the specified value from a base64 string to binary.
+   * Decodes a BASE64 encoded string column and returns it as a binary column.
+   * This is the reverse of base64.
    *
    * @group string_funcs
    * @since 1.5.0
    */
   def unbase64(e: Column): Column = UnBase64(e.expr)
 
-  /**
-   * Computes the specified column from a base64 string to binary.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def unbase64(columnName: String): Column = unbase64(Column(columnName))
-
   /**
    * Left-padded with pad to a length of len.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def lpad(str: String, len: String, pad: String): Column = {
-    lpad(Column(str), Column(len), Column(pad))
-  }
-
-  /**
-   * Left-padded with pad to a length of len.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def lpad(str: Column, len: Column, pad: Column): Column = {
-    StringLPad(str.expr, len.expr, pad.expr)
-  }
-
-  /**
-   * Left-padded with pad to a length of len.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def lpad(str: Column, len: Int, pad: Column): Column = {
-    StringLPad(str.expr, lit(len).expr, pad.expr)
-  }
-
-  /**
-   * Left-padded with pad to a length of len.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def lpad(str: String, len: Int, pad: String): Column = {
-    lpad(Column(str), len, Column(pad))
+  def lpad(str: Column, len: Int, pad: String): Column = {
+    StringLPad(str.expr, lit(len).expr, lit(pad).expr)
   }
 
   /**
@@ -2082,18 +1819,6 @@ object functions {
    */
   def encode(value: Column, charset: String): Column = Encode(value.expr, lit(charset).expr)
 
-  /**
-   * Computes the first argument into a binary from a string using the provided character set
-   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
-   * If either argument is null, the result will also be null.
-   * NOTE: charset represents the string value of the character set, not the column name.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def encode(columnName: String, charset: String): Column =
-    encode(Column(columnName), charset)
-
   /**
    * Computes the first argument into a string from a binary using the provided character set
    * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
@@ -2104,106 +1829,24 @@ object functions {
    */
   def decode(value: Column, charset: String): Column = Decode(value.expr, lit(charset).expr)
 
-  /**
-   * Computes the first argument into a string from a binary using the provided character set
-   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
-   * If either argument is null, the result will also be null.
-   * NOTE: charset represents the string value of the character set, not the column name.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def decode(columnName: String, charset: String): Column =
-    decode(Column(columnName), charset)
-
-  /**
-   * Right-padded with pad to a length of len.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def rpad(str: String, len: String, pad: String): Column = {
-    rpad(Column(str), Column(len), Column(pad))
-  }
-
-  /**
-   * Right-padded with pad to a length of len.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def rpad(str: Column, len: Column, pad: Column): Column = {
-    StringRPad(str.expr, len.expr, pad.expr)
-  }
-
-  /**
-   * Right-padded with pad to a length of len.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def rpad(str: String, len: Int, pad: String): Column = {
-    rpad(Column(str), len, Column(pad))
-  }
-
   /**
    * Right-padded with pad to a length of len.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def rpad(str: Column, len: Int, pad: Column): Column = {
-    StringRPad(str.expr, lit(len).expr, pad.expr)
-  }
-
-  /**
-   * Repeat the string value of the specified column n times.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def repeat(strColumn: String, timesColumn: String): Column = {
-    repeat(Column(strColumn), Column(timesColumn))
+  def rpad(str: Column, len: Int, pad: String): Column = {
+    StringRPad(str.expr, lit(len).expr, lit(pad).expr)
   }
 
   /**
-   * Repeat the string expression value n times.
+   * Repeats a string column n times, and returns it as a new string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def repeat(str: Column, times: Column): Column = {
-    StringRepeat(str.expr, times.expr)
-  }
-
-  /**
-   * Repeat the string value of the specified column n times.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def repeat(strColumn: String, times: Int): Column = {
-    repeat(Column(strColumn), times)
-  }
-
-  /**
-   * Repeat the string expression value n times.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def repeat(str: Column, times: Int): Column = {
-    StringRepeat(str.expr, lit(times).expr)
-  }
-
-  /**
-   * Splits str around pattern (pattern is a regular expression).
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def split(strColumnName: String, pattern: String): Column = {
-    split(Column(strColumnName), pattern)
+  def repeat(str: Column, n: Int): Column = {
+    StringRepeat(str.expr, lit(n).expr)
   }
 
   /**
@@ -2217,16 +1860,6 @@ object functions {
     StringSplit(str.expr, lit(pattern).expr)
   }
 
-  /**
-   * Reversed the string for the specified column.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def reverse(str: String): Column = {
-    reverse(Column(str))
-  }
-
   /**
    * Reversed the string for the specified value.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 29f1197a8543c..8d2ff2f9690d6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -160,7 +160,7 @@ class DataFrameFunctionsSuite extends QueryTest {
   test("misc md5 function") {
     val df = Seq(("ABC", Array[Byte](1, 2, 3, 4, 5, 6))).toDF("a", "b")
     checkAnswer(
-      df.select(md5($"a"), md5("b")),
+      df.select(md5($"a"), md5($"b")),
       Row("902fbdd2b1df0c4f70b4a5d23525e932", "6ac1e56bc78f031059be7be854522c4c"))
 
     checkAnswer(
@@ -171,7 +171,7 @@ class DataFrameFunctionsSuite extends QueryTest {
   test("misc sha1 function") {
     val df = Seq(("ABC", "ABC".getBytes)).toDF("a", "b")
     checkAnswer(
-      df.select(sha1($"a"), sha1("b")),
+      df.select(sha1($"a"), sha1($"b")),
       Row("3c01bdbb26f358bab27f267924aa2c9a03fcfdb8", "3c01bdbb26f358bab27f267924aa2c9a03fcfdb8"))
 
     val dfEmpty = Seq(("", "".getBytes)).toDF("a", "b")
@@ -183,7 +183,7 @@ class DataFrameFunctionsSuite extends QueryTest {
   test("misc sha2 function") {
     val df = Seq(("ABC", Array[Byte](1, 2, 3, 4, 5, 6))).toDF("a", "b")
     checkAnswer(
-      df.select(sha2($"a", 256), sha2("b", 256)),
+      df.select(sha2($"a", 256), sha2($"b", 256)),
       Row("b5d4045c3f466fa91fe2cc6abe79232a1a57cdf104f7a26e716e0a1e2789df78",
         "7192385c3c0605de55bb9476ce1d90748190ecb32a8eed7f5207b30cf6a1fe89"))
 
@@ -200,7 +200,7 @@ class DataFrameFunctionsSuite extends QueryTest {
   test("misc crc32 function") {
     val df = Seq(("ABC", Array[Byte](1, 2, 3, 4, 5, 6))).toDF("a", "b")
     checkAnswer(
-      df.select(crc32($"a"), crc32("b")),
+      df.select(crc32($"a"), crc32($"b")),
       Row(2743272264L, 2180413220L))
 
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index a51523f1a7a0f..21256704a5b16 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -176,7 +176,6 @@ class MathExpressionsSuite extends QueryTest {
   test("conv") {
     val df = Seq(("333", 10, 2)).toDF("num", "fromBase", "toBase")
     checkAnswer(df.select(conv('num, 10, 16)), Row("14D"))
-    checkAnswer(df.select(conv("num", 10, 16)), Row("14D"))
     checkAnswer(df.select(conv(lit(100), 2, 16)), Row("4"))
     checkAnswer(df.select(conv(lit(3122234455L), 10, 16)), Row("BA198457"))
     checkAnswer(df.selectExpr("conv(num, fromBase, toBase)"), Row("101001101"))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 413f3858d6764..4551192b157ff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -52,14 +52,14 @@ class StringFunctionsSuite extends QueryTest {
 
   test("string Levenshtein distance") {
     val df = Seq(("kitten", "sitting"), ("frog", "fog")).toDF("l", "r")
-    checkAnswer(df.select(levenshtein("l", "r")), Seq(Row(3), Row(1)))
+    checkAnswer(df.select(levenshtein($"l", $"r")), Seq(Row(3), Row(1)))
     checkAnswer(df.selectExpr("levenshtein(l, r)"), Seq(Row(3), Row(1)))
   }
 
   test("string ascii function") {
     val df = Seq(("abc", "")).toDF("a", "b")
     checkAnswer(
-      df.select(ascii($"a"), ascii("b")),
+      df.select(ascii($"a"), ascii($"b")),
       Row(97, 0))
 
     checkAnswer(
@@ -71,8 +71,8 @@ class StringFunctionsSuite extends QueryTest {
     val bytes = Array[Byte](1, 2, 3, 4)
     val df = Seq((bytes, "AQIDBA==")).toDF("a", "b")
     checkAnswer(
-      df.select(base64("a"), base64($"a"), unbase64("b"), unbase64($"b")),
-      Row("AQIDBA==", "AQIDBA==", bytes, bytes))
+      df.select(base64($"a"), unbase64($"b")),
+      Row("AQIDBA==", bytes))
 
     checkAnswer(
       df.selectExpr("base64(a)", "unbase64(b)"),
@@ -85,12 +85,8 @@ class StringFunctionsSuite extends QueryTest {
     // non ascii characters are not allowed in the code, so we disable the scalastyle here.
     val df = Seq(("大千世界", "utf-8", bytes)).toDF("a", "b", "c")
     checkAnswer(
-      df.select(
-        encode($"a", "utf-8"),
-        encode("a", "utf-8"),
-        decode($"c", "utf-8"),
-        decode("c", "utf-8")),
-      Row(bytes, bytes, "大千世界", "大千世界"))
+      df.select(encode($"a", "utf-8"), decode($"c", "utf-8")),
+      Row(bytes, "大千世界"))
 
     checkAnswer(
       df.selectExpr("encode(a, 'utf-8')", "decode(c, 'utf-8')"),
@@ -114,8 +110,8 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("aa%d%s", 123, "cc")).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
-      Row("aa123cc", "aa123cc"))
+      df.select(formatString("aa%d%s", "b", "c")),
+      Row("aa123cc"))
 
     checkAnswer(
       df.selectExpr("printf(a, b, c)"),
@@ -126,8 +122,8 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("aaads", "aa", "zz")).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(instr($"a", $"b"), instr("a", "b")),
-      Row(1, 1))
+      df.select(instr($"a", "aa")),
+      Row(1))
 
     checkAnswer(
       df.selectExpr("instr(a, b)"),
@@ -138,10 +134,8 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("aaads", "aa", "zz", 1)).toDF("a", "b", "c", "d")
 
     checkAnswer(
-      df.select(
-        locate($"b", $"a"), locate("b", "a"), locate($"b", $"a", 1),
-        locate("b", "a", 1), locate($"b", $"a", $"d"), locate("b", "a", "d")),
-      Row(1, 1, 2, 2, 2, 2))
+      df.select(locate("aa", $"a"), locate("aa", $"a", 1)),
+      Row(1, 2))
 
     checkAnswer(
       df.selectExpr("locate(b, a)", "locate(b, a, d)"),
@@ -152,10 +146,8 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("hi", 5, "??")).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(
-        lpad($"a", $"b", $"c"), rpad("a", "b", "c"),
-        lpad($"a", 1, $"c"), rpad("a", 1, "c")),
-      Row("???hi", "hi???", "h", "h"))
+      df.select(lpad($"a", 1, "c"), lpad($"a", 5, "??"), rpad($"a", 1, "c"), rpad($"a", 5, "??")),
+      Row("h", "???hi", "h", "hi???"))
 
     checkAnswer(
       df.selectExpr("lpad(a, b, c)", "rpad(a, b, c)", "lpad(a, 1, c)", "rpad(a, 1, c)"),
@@ -166,9 +158,8 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("hi", 2)).toDF("a", "b")
 
     checkAnswer(
-      df.select(
-        repeat($"a", 2), repeat("a", 2), repeat($"a", $"b"), repeat("a", "b")),
-      Row("hihi", "hihi", "hihi", "hihi"))
+      df.select(repeat($"a", 2)),
+      Row("hihi"))
 
     checkAnswer(
       df.selectExpr("repeat(a, 2)", "repeat(a, b)"),
@@ -179,7 +170,7 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("hi", "hhhi")).toDF("a", "b")
 
     checkAnswer(
-      df.select(reverse($"a"), reverse("b")),
+      df.select(reverse($"a"), reverse($"b")),
       Row("ih", "ihhh"))
 
     checkAnswer(
@@ -199,10 +190,8 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("aa2bb3cc", "[1-9]+")).toDF("a", "b")
 
     checkAnswer(
-      df.select(
-        split($"a", "[1-9]+"),
-        split("a", "[1-9]+")),
-      Row(Seq("aa", "bb", "cc"), Seq("aa", "bb", "cc")))
+      df.select(split($"a", "[1-9]+")),
+      Row(Seq("aa", "bb", "cc")))
 
     checkAnswer(
       df.selectExpr("split(a, '[1-9]+')"),
@@ -212,8 +201,8 @@ class StringFunctionsSuite extends QueryTest {
   test("string / binary length function") {
     val df = Seq(("123", Array[Byte](1, 2, 3, 4), 123)).toDF("a", "b", "c")
     checkAnswer(
-      df.select(length($"a"), length("a"), length($"b"), length("b")),
-      Row(3, 3, 4, 4))
+      df.select(length($"a"), length($"b")),
+      Row(3, 4))
 
     checkAnswer(
       df.selectExpr("length(a)", "length(b)"),
@@ -243,10 +232,8 @@ class StringFunctionsSuite extends QueryTest {
           "h") // decimal 7.128381
 
     checkAnswer(
-      df.select(
-        format_number($"f", 4),
-        format_number("f", 4)),
-      Row("5.0000", "5.0000"))
+      df.select(format_number($"f", 4)),
+      Row("5.0000"))
 
     checkAnswer(
       df.selectExpr("format_number(b, e)"), // convert the 1st argument to integer

From 48f8fd46b32973f1f3b865da80345698cb1a71c7 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 20 Jul 2015 23:28:35 -0700
Subject: [PATCH 0512/1454] [SPARK-9023] [SQL] Followup for #7456 (Efficiency
 improvements for UnsafeRows in Exchange)

This patch addresses code review feedback from #7456.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7551 from JoshRosen/unsafe-exchange-followup and squashes the following commits:

76dbdf8 [Josh Rosen] Add comments + more methods to UnsafeRowSerializer
3d7a1f2 [Josh Rosen] Add writeToStream() method to UnsafeRow
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 33 ++++++++
 .../sql/execution/UnsafeRowSerializer.scala   | 80 +++++++++++++------
 .../org/apache/spark/sql/UnsafeRowSuite.scala | 71 ++++++++++++++++
 3 files changed, 161 insertions(+), 23 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 8cd9e7bc60a03..6ce03a48e9538 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
+import java.io.IOException;
+import java.io.OutputStream;
+
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.catalyst.util.ObjectPool;
 import org.apache.spark.unsafe.PlatformDependent;
@@ -371,6 +374,36 @@ public InternalRow copy() {
     }
   }
 
+  /**
+   * Write this UnsafeRow's underlying bytes to the given OutputStream.
+   *
+   * @param out the stream to write to.
+   * @param writeBuffer a byte array for buffering chunks of off-heap data while writing to the
+   *                    output stream. If this row is backed by an on-heap byte array, then this
+   *                    buffer will not be used and may be null.
+   */
+  public void writeToStream(OutputStream out, byte[] writeBuffer) throws IOException {
+    if (baseObject instanceof byte[]) {
+      int offsetInByteArray = (int) (PlatformDependent.BYTE_ARRAY_OFFSET - baseOffset);
+      out.write((byte[]) baseObject, offsetInByteArray, sizeInBytes);
+    } else {
+      int dataRemaining = sizeInBytes;
+      long rowReadPosition = baseOffset;
+      while (dataRemaining > 0) {
+        int toTransfer = Math.min(writeBuffer.length, dataRemaining);
+        PlatformDependent.copyMemory(
+          baseObject,
+          rowReadPosition,
+          writeBuffer,
+          PlatformDependent.BYTE_ARRAY_OFFSET,
+          toTransfer);
+        out.write(writeBuffer, 0, toTransfer);
+        rowReadPosition += toTransfer;
+        dataRemaining -= toTransfer;
+      }
+    }
+  }
+
   @Override
   public boolean anyNull() {
     return BitSetMethods.anySet(baseObject, baseOffset, bitSetWidthInBytes);
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 19503ed00056c..318550e5ed899 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -49,8 +49,16 @@ private[sql] class UnsafeRowSerializer(numFields: Int) extends Serializer with S
 
 private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInstance {
 
+  /**
+   * Marks the end of a stream written with [[serializeStream()]].
+   */
   private[this] val EOF: Int = -1
 
+  /**
+   * Serializes a stream of UnsafeRows. Within the stream, each record consists of a record
+   * length (stored as a 4-byte integer, written high byte first), followed by the record's bytes.
+   * The end of the stream is denoted by a record with the special length `EOF` (-1).
+   */
   override def serializeStream(out: OutputStream): SerializationStream = new SerializationStream {
     private[this] var writeBuffer: Array[Byte] = new Array[Byte](4096)
     private[this] val dOut: DataOutputStream = new DataOutputStream(out)
@@ -59,32 +67,31 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
       val row = value.asInstanceOf[UnsafeRow]
       assert(row.getPool == null, "UnsafeRowSerializer does not support ObjectPool")
       dOut.writeInt(row.getSizeInBytes)
-      var dataRemaining: Int = row.getSizeInBytes
-      val baseObject = row.getBaseObject
-      var rowReadPosition: Long = row.getBaseOffset
-      while (dataRemaining > 0) {
-        val toTransfer: Int = Math.min(writeBuffer.length, dataRemaining)
-        PlatformDependent.copyMemory(
-          baseObject,
-          rowReadPosition,
-          writeBuffer,
-          PlatformDependent.BYTE_ARRAY_OFFSET,
-          toTransfer)
-        out.write(writeBuffer, 0, toTransfer)
-        rowReadPosition += toTransfer
-        dataRemaining -= toTransfer
-      }
+      row.writeToStream(out, writeBuffer)
       this
     }
+
     override def writeKey[T: ClassTag](key: T): SerializationStream = {
+      // The key is only needed on the map side when computing partition ids. It does not need to
+      // be shuffled.
       assert(key.isInstanceOf[Int])
       this
     }
-    override def writeAll[T: ClassTag](iter: Iterator[T]): SerializationStream =
+
+    override def writeAll[T: ClassTag](iter: Iterator[T]): SerializationStream = {
+      // This method is never called by shuffle code.
       throw new UnsupportedOperationException
-    override def writeObject[T: ClassTag](t: T): SerializationStream =
+    }
+
+    override def writeObject[T: ClassTag](t: T): SerializationStream = {
+      // This method is never called by shuffle code.
       throw new UnsupportedOperationException
-    override def flush(): Unit = dOut.flush()
+    }
+
+    override def flush(): Unit = {
+      dOut.flush()
+    }
+
     override def close(): Unit = {
       writeBuffer = null
       dOut.writeInt(EOF)
@@ -95,6 +102,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
   override def deserializeStream(in: InputStream): DeserializationStream = {
     new DeserializationStream {
       private[this] val dIn: DataInputStream = new DataInputStream(in)
+      // 1024 is a default buffer size; this buffer will grow to accommodate larger rows
       private[this] var rowBuffer: Array[Byte] = new Array[Byte](1024)
       private[this] var row: UnsafeRow = new UnsafeRow()
       private[this] var rowTuple: (Int, UnsafeRow) = (0, row)
@@ -126,14 +134,40 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
           }
         }
       }
-      override def asIterator: Iterator[Any] = throw new UnsupportedOperationException
-      override def readKey[T: ClassTag](): T = throw new UnsupportedOperationException
-      override def readValue[T: ClassTag](): T = throw new UnsupportedOperationException
-      override def readObject[T: ClassTag](): T = throw new UnsupportedOperationException
-      override def close(): Unit = dIn.close()
+
+      override def asIterator: Iterator[Any] = {
+        // This method is never called by shuffle code.
+        throw new UnsupportedOperationException
+      }
+
+      override def readKey[T: ClassTag](): T = {
+        // We skipped serialization of the key in writeKey(), so just return a dummy value since
+        // this is going to be discarded anyways.
+        null.asInstanceOf[T]
+      }
+
+      override def readValue[T: ClassTag](): T = {
+        val rowSize = dIn.readInt()
+        if (rowBuffer.length < rowSize) {
+          rowBuffer = new Array[Byte](rowSize)
+        }
+        ByteStreams.readFully(in, rowBuffer, 0, rowSize)
+        row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize, null)
+        row.asInstanceOf[T]
+      }
+
+      override def readObject[T: ClassTag](): T = {
+        // This method is never called by shuffle code.
+        throw new UnsupportedOperationException
+      }
+
+      override def close(): Unit = {
+        dIn.close()
+      }
     }
   }
 
+  // These methods are never called by shuffle code.
   override def serialize[T: ClassTag](t: T): ByteBuffer = throw new UnsupportedOperationException
   override def deserialize[T: ClassTag](bytes: ByteBuffer): T =
     throw new UnsupportedOperationException
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
new file mode 100644
index 0000000000000..3854dc1b7a3d1
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.io.ByteArrayOutputStream
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
+import org.apache.spark.sql.types.{IntegerType, StringType}
+import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.memory.MemoryAllocator
+import org.apache.spark.unsafe.types.UTF8String
+
+class UnsafeRowSuite extends SparkFunSuite {
+  test("writeToStream") {
+    val row = InternalRow.apply(UTF8String.fromString("hello"), UTF8String.fromString("world"), 123)
+    val arrayBackedUnsafeRow: UnsafeRow =
+      UnsafeProjection.create(Seq(StringType, StringType, IntegerType)).apply(row)
+    assert(arrayBackedUnsafeRow.getBaseObject.isInstanceOf[Array[Byte]])
+    val bytesFromArrayBackedRow: Array[Byte] = {
+      val baos = new ByteArrayOutputStream()
+      arrayBackedUnsafeRow.writeToStream(baos, null)
+      baos.toByteArray
+    }
+    val bytesFromOffheapRow: Array[Byte] = {
+      val offheapRowPage = MemoryAllocator.UNSAFE.allocate(arrayBackedUnsafeRow.getSizeInBytes)
+      try {
+        PlatformDependent.copyMemory(
+          arrayBackedUnsafeRow.getBaseObject,
+          arrayBackedUnsafeRow.getBaseOffset,
+          offheapRowPage.getBaseObject,
+          offheapRowPage.getBaseOffset,
+          arrayBackedUnsafeRow.getSizeInBytes
+        )
+        val offheapUnsafeRow: UnsafeRow = new UnsafeRow()
+        offheapUnsafeRow.pointTo(
+          offheapRowPage.getBaseObject,
+          offheapRowPage.getBaseOffset,
+          3, // num fields
+          arrayBackedUnsafeRow.getSizeInBytes,
+          null // object pool
+        )
+        assert(offheapUnsafeRow.getBaseObject === null)
+        val baos = new ByteArrayOutputStream()
+        val writeBuffer = new Array[Byte](1024)
+        offheapUnsafeRow.writeToStream(baos, writeBuffer)
+        baos.toByteArray
+      } finally {
+        MemoryAllocator.UNSAFE.free(offheapRowPage)
+      }
+    }
+
+    assert(bytesFromArrayBackedRow === bytesFromOffheapRow)
+  }
+}

From 228ab65a4eeef8a42eb4713edf72b50590f63176 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Mon, 20 Jul 2015 23:31:08 -0700
Subject: [PATCH 0513/1454] [SPARK-9179] [BUILD] Use default primary author if
 unspecified

Fixes feature introduced in #7508 to use the default value if nothing is specified in command line

cc liancheng rxin pwendell

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7558 from shivaram/merge-script-fix and squashes the following commits:

7092141 [Shivaram Venkataraman] Use default primary author if unspecified
---
 dev/merge_spark_pr.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index d586a57481aa1..ad4b76695c9ff 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -133,6 +133,8 @@ def merge_pr(pr_num, target_ref, title, body, pr_repo_desc):
     primary_author = raw_input(
         "Enter primary author in the format of \"name <email>\" [%s]: " %
         distinct_authors[0])
+    if primary_author == "":
+        primary_author = distinct_authors[0]
 
     commits = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name,
                       '--pretty=format:%h [%an] %s']).split("\n\n")

From 1ddd0f2f1688560f88470e312b72af04364e2d49 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Mon, 20 Jul 2015 23:33:07 -0700
Subject: [PATCH 0514/1454] [SPARK-9161][SQL] codegen FormatNumber

Jira https://issues.apache.org/jira/browse/SPARK-9161

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7545 from tarekauel/SPARK-9161 and squashes the following commits:

21425c8 [Tarek Auel] [SPARK-9161][SQL] codegen FormatNumber
---
 .../expressions/stringOperations.scala        | 68 +++++++++++++++----
 1 file changed, 54 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 438215e8e6e37..92fefe1585b23 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -902,22 +902,15 @@ case class FormatNumber(x: Expression, d: Expression)
   @transient
   private val numberFormat: DecimalFormat = new DecimalFormat("")
 
-  override def eval(input: InternalRow): Any = {
-    val xObject = x.eval(input)
-    if (xObject == null) {
+  override protected def nullSafeEval(xObject: Any, dObject: Any): Any = {
+    val dValue = dObject.asInstanceOf[Int]
+    if (dValue < 0) {
       return null
     }
 
-    val dObject = d.eval(input)
-
-    if (dObject == null || dObject.asInstanceOf[Int] < 0) {
-      return null
-    }
-    val dValue = dObject.asInstanceOf[Int]
-
     if (dValue != lastDValue) {
       // construct a new DecimalFormat only if a new dValue
-      pattern.delete(0, pattern.length())
+      pattern.delete(0, pattern.length)
       pattern.append("#,###,###,###,###,###,##0")
 
       // decimal place
@@ -930,9 +923,10 @@ case class FormatNumber(x: Expression, d: Expression)
           pattern.append("0")
         }
       }
-      val dFormat = new DecimalFormat(pattern.toString())
-      lastDValue = dValue;
-      numberFormat.applyPattern(dFormat.toPattern())
+      val dFormat = new DecimalFormat(pattern.toString)
+      lastDValue = dValue
+
+      numberFormat.applyPattern(dFormat.toPattern)
     }
 
     x.dataType match {
@@ -947,6 +941,52 @@ case class FormatNumber(x: Expression, d: Expression)
     }
   }
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (num, d) => {
+
+      def typeHelper(p: String): String = {
+        x.dataType match {
+          case _ : DecimalType => s"""$p.toJavaBigDecimal()"""
+          case _ => s"$p"
+        }
+      }
+
+      val sb = classOf[StringBuffer].getName
+      val df = classOf[DecimalFormat].getName
+      val lastDValue = ctx.freshName("lastDValue")
+      val pattern = ctx.freshName("pattern")
+      val numberFormat = ctx.freshName("numberFormat")
+      val i = ctx.freshName("i")
+      val dFormat = ctx.freshName("dFormat")
+      ctx.addMutableState("int", lastDValue, s"$lastDValue = -100;")
+      ctx.addMutableState(sb, pattern, s"$pattern = new $sb();")
+      ctx.addMutableState(df, numberFormat, s"""$numberFormat = new $df("");""")
+
+      s"""
+        if ($d >= 0) {
+          $pattern.delete(0, $pattern.length());
+          if ($d != $lastDValue) {
+            $pattern.append("#,###,###,###,###,###,##0");
+
+            if ($d > 0) {
+              $pattern.append(".");
+              for (int $i = 0; $i < $d; $i++) {
+                $pattern.append("0");
+              }
+            }
+            $df $dFormat = new $df($pattern.toString());
+            $lastDValue = $d;
+            $numberFormat.applyPattern($dFormat.toPattern());
+            ${ev.primitive} = UTF8String.fromString($numberFormat.format(${typeHelper(num)}));
+          }
+        } else {
+          ${ev.primitive} = null;
+          ${ev.isNull} = true;
+        }
+       """
+    })
+  }
+
   override def prettyName: String = "format_number"
 }
 

From d38c5029a2ca845e2782096044a6412b653c9f95 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 21 Jul 2015 15:08:44 +0800
Subject: [PATCH 0515/1454] [SPARK-9100] [SQL] Adds DataFrame reader/writer
 shortcut methods for ORC

This PR adds DataFrame reader/writer shortcut methods for ORC in both Scala and Python.

Author: Cheng Lian <lian@databricks.com>

Closes #7444 from liancheng/spark-9100 and squashes the following commits:

284d043 [Cheng Lian] Fixes PySpark test cases and addresses PR comments
e0b09fb [Cheng Lian] Adds DataFrame reader/writer shortcut methods for ORC
---
 python/pyspark/sql/readwriter.py              |  44 ++++++++++++++++--
 .../sql/orc_partitioned/._SUCCESS.crc         | Bin 0 -> 8 bytes
 .../test_support/sql/orc_partitioned/_SUCCESS |   0
 ...9af031-b970-49d6-ad39-30460a0be2c8.orc.crc | Bin 0 -> 12 bytes
 ...0-829af031-b970-49d6-ad39-30460a0be2c8.orc | Bin 0 -> 168 bytes
 ...9af031-b970-49d6-ad39-30460a0be2c8.orc.crc | Bin 0 -> 12 bytes
 ...0-829af031-b970-49d6-ad39-30460a0be2c8.orc | Bin 0 -> 168 bytes
 .../apache/spark/sql/DataFrameReader.scala    |   9 ++++
 .../apache/spark/sql/DataFrameWriter.scala    |  12 +++++
 .../hive/orc/OrcHadoopFsRelationSuite.scala   |   3 +-
 .../hive/orc/OrcPartitionDiscoverySuite.scala |  14 +++---
 .../spark/sql/hive/orc/OrcQuerySuite.scala    |  12 ++---
 .../apache/spark/sql/hive/orc/OrcTest.scala   |   8 ++--
 13 files changed, 79 insertions(+), 23 deletions(-)
 create mode 100644 python/test_support/sql/orc_partitioned/._SUCCESS.crc
 create mode 100755 python/test_support/sql/orc_partitioned/_SUCCESS
 create mode 100644 python/test_support/sql/orc_partitioned/b=0/c=0/.part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc.crc
 create mode 100755 python/test_support/sql/orc_partitioned/b=0/c=0/part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc
 create mode 100644 python/test_support/sql/orc_partitioned/b=1/c=1/.part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc.crc
 create mode 100755 python/test_support/sql/orc_partitioned/b=1/c=1/part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 882a03090ec13..dea8bad79e187 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -146,14 +146,28 @@ def table(self, tableName):
         return self._df(self._jreader.table(tableName))
 
     @since(1.4)
-    def parquet(self, *path):
+    def parquet(self, *paths):
         """Loads a Parquet file, returning the result as a :class:`DataFrame`.
 
         >>> df = sqlContext.read.parquet('python/test_support/sql/parquet_partitioned')
         >>> df.dtypes
         [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
         """
-        return self._df(self._jreader.parquet(_to_seq(self._sqlContext._sc, path)))
+        return self._df(self._jreader.parquet(_to_seq(self._sqlContext._sc, paths)))
+
+    @since(1.5)
+    def orc(self, path):
+        """
+        Loads an ORC file, returning the result as a :class:`DataFrame`.
+
+        ::Note: Currently ORC support is only available together with
+        :class:`HiveContext`.
+
+        >>> df = hiveContext.read.orc('python/test_support/sql/orc_partitioned')
+        >>> df.dtypes
+        [('a', 'bigint'), ('b', 'int'), ('c', 'int')]
+        """
+        return self._df(self._jreader.orc(path))
 
     @since(1.4)
     def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
@@ -378,6 +392,29 @@ def parquet(self, path, mode=None, partitionBy=None):
             self.partitionBy(partitionBy)
         self._jwrite.parquet(path)
 
+    def orc(self, path, mode=None, partitionBy=None):
+        """Saves the content of the :class:`DataFrame` in ORC format at the specified path.
+
+        ::Note: Currently ORC support is only available together with
+        :class:`HiveContext`.
+
+        :param path: the path in any Hadoop supported file system
+        :param mode: specifies the behavior of the save operation when data already exists.
+
+            * ``append``: Append contents of this :class:`DataFrame` to existing data.
+            * ``overwrite``: Overwrite existing data.
+            * ``ignore``: Silently ignore this operation if data already exists.
+            * ``error`` (default case): Throw an exception if data already exists.
+        :param partitionBy: names of partitioning columns
+
+        >>> orc_df = hiveContext.read.orc('python/test_support/sql/orc_partitioned')
+        >>> orc_df.write.orc(os.path.join(tempfile.mkdtemp(), 'data'))
+        """
+        self.mode(mode)
+        if partitionBy is not None:
+            self.partitionBy(partitionBy)
+        self._jwrite.orc(path)
+
     @since(1.4)
     def jdbc(self, url, table, mode=None, properties={}):
         """Saves the content of the :class:`DataFrame` to a external database table via JDBC.
@@ -408,7 +445,7 @@ def _test():
     import os
     import tempfile
     from pyspark.context import SparkContext
-    from pyspark.sql import Row, SQLContext
+    from pyspark.sql import Row, SQLContext, HiveContext
     import pyspark.sql.readwriter
 
     os.chdir(os.environ["SPARK_HOME"])
@@ -420,6 +457,7 @@ def _test():
     globs['os'] = os
     globs['sc'] = sc
     globs['sqlContext'] = SQLContext(sc)
+    globs['hiveContext'] = HiveContext(sc)
     globs['df'] = globs['sqlContext'].read.parquet('python/test_support/sql/parquet_partitioned')
 
     (failure_count, test_count) = doctest.testmod(
diff --git a/python/test_support/sql/orc_partitioned/._SUCCESS.crc b/python/test_support/sql/orc_partitioned/._SUCCESS.crc
new file mode 100644
index 0000000000000000000000000000000000000000..3b7b044936a890cd8d651d349a752d819d71d22c
GIT binary patch
literal 8
PcmYc;N@ieSU}69O2$TUk

literal 0
HcmV?d00001

diff --git a/python/test_support/sql/orc_partitioned/_SUCCESS b/python/test_support/sql/orc_partitioned/_SUCCESS
new file mode 100755
index 0000000000000..e69de29bb2d1d
diff --git a/python/test_support/sql/orc_partitioned/b=0/c=0/.part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc.crc b/python/test_support/sql/orc_partitioned/b=0/c=0/.part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc.crc
new file mode 100644
index 0000000000000000000000000000000000000000..834cf0b7f227244a3ccda18809a0bb49d27b59d2
GIT binary patch
literal 12
TcmYc;N@ieSU}CV!x?uzW5r_im

literal 0
HcmV?d00001

diff --git a/python/test_support/sql/orc_partitioned/b=0/c=0/part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc b/python/test_support/sql/orc_partitioned/b=0/c=0/part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc
new file mode 100755
index 0000000000000000000000000000000000000000..49438018733565be297429b4f9349450441230f9
GIT binary patch
literal 168
zcmeYda^_`V;9?PC;$Tz<G6cDp85kIZxHuSv*f<yj7$g`t85j-#h0GWj9w$9c5Kfrz
z;0e>k9-$<MS%My$xeW^q7z~9ORDoIrxPWGGA?ZwKVBlaDaF^l&lJXMl3UV5ZN*o+K
wLX3=3jEPE294rQmRzNLQNLm~w_;HvCFiJEu2q-Zz@MwrOePL$y4{{a-0D%M+=Kufz

literal 0
HcmV?d00001

diff --git a/python/test_support/sql/orc_partitioned/b=1/c=1/.part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc.crc b/python/test_support/sql/orc_partitioned/b=1/c=1/.part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc.crc
new file mode 100644
index 0000000000000000000000000000000000000000..693dceeee3ef2ff6fb24e8ac7fd006ffd7463074
GIT binary patch
literal 12
TcmYc;N@ieSU}8vHaCb8R6J-O@

literal 0
HcmV?d00001

diff --git a/python/test_support/sql/orc_partitioned/b=1/c=1/part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc b/python/test_support/sql/orc_partitioned/b=1/c=1/part-r-00000-829af031-b970-49d6-ad39-30460a0be2c8.orc
new file mode 100755
index 0000000000000000000000000000000000000000..4cbb95ae0242cd99ed08f5d28ae7531dc55ccee3
GIT binary patch
literal 168
zcmeYda^_`V;9?PC;$Tz<G6cDp85kIZxHuSv*f^L3m?W4u85j;QFfo`hFg#9roFJSq
z;lUH8i#<X~4zmP3Hgg*m8Za0NGpGW!3UC3<;6l=w&cMLIEZ{E11tjGq*cIe77?n6U
xc!U@kr5F>Hm^fGr7_ER>tdO)gOz`6{6JV5RXb@0hV&KsbZTiB@>>uPT3IK<`7W)7I

literal 0
HcmV?d00001

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 0e37ad3e12e08..f1c1ddf898986 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -264,6 +264,15 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
     }
   }
 
+  /**
+   * Loads an ORC file and returns the result as a [[DataFrame]].
+   *
+   * @param path input path
+   * @since 1.5.0
+   * @note Currently, this method can only be used together with `HiveContext`.
+   */
+  def orc(path: String): DataFrame = format("orc").load(path)
+
   /**
    * Returns the specified table as a [[DataFrame]].
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 5548b26cb8f80..3e7b9cd7976c3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -280,6 +280,18 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    */
   def parquet(path: String): Unit = format("parquet").save(path)
 
+  /**
+   * Saves the content of the [[DataFrame]] in ORC format at the specified path.
+   * This is equivalent to:
+   * {{{
+   *   format("orc").save(path)
+   * }}}
+   *
+   * @since 1.5.0
+   * @note Currently, this method can only be used together with `HiveContext`.
+   */
+  def orc(path: String): Unit = format("orc").save(path)
+
   ///////////////////////////////////////////////////////////////////////////////////////
   // Builder pattern config options
   ///////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
index 080af5bb23c16..af3f468aaa5e9 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
@@ -41,8 +41,7 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
           .parallelize(for (i <- 1 to 3) yield (i, s"val_$i", p1))
           .toDF("a", "b", "p1")
           .write
-          .format("orc")
-          .save(partitionDir.toString)
+          .orc(partitionDir.toString)
       }
 
       val dataSchemaWithPartition =
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
index 3c2efe329bfd5..d463e8fd626f9 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
@@ -49,13 +49,13 @@ class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
 
   def makeOrcFile[T <: Product: ClassTag: TypeTag](
       data: Seq[T], path: File): Unit = {
-    data.toDF().write.format("orc").mode("overwrite").save(path.getCanonicalPath)
+    data.toDF().write.mode("overwrite").orc(path.getCanonicalPath)
   }
 
 
   def makeOrcFile[T <: Product: ClassTag: TypeTag](
       df: DataFrame, path: File): Unit = {
-    df.write.format("orc").mode("overwrite").save(path.getCanonicalPath)
+    df.write.mode("overwrite").orc(path.getCanonicalPath)
   }
 
   protected def withTempTable(tableName: String)(f: => Unit): Unit = {
@@ -90,7 +90,7 @@ class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
           makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps))
       }
 
-      read.format("orc").load(base.getCanonicalPath).registerTempTable("t")
+      read.orc(base.getCanonicalPath).registerTempTable("t")
 
       withTempTable("t") {
         checkAnswer(
@@ -137,7 +137,7 @@ class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
           makePartitionDir(base, defaultPartitionName, "pi" -> pi, "ps" -> ps))
       }
 
-      read.format("orc").load(base.getCanonicalPath).registerTempTable("t")
+      read.orc(base.getCanonicalPath).registerTempTable("t")
 
       withTempTable("t") {
         checkAnswer(
@@ -187,9 +187,8 @@ class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
       }
 
       read
-        .format("orc")
         .option(ConfVars.DEFAULTPARTITIONNAME.varname, defaultPartitionName)
-        .load(base.getCanonicalPath)
+        .orc(base.getCanonicalPath)
         .registerTempTable("t")
 
       withTempTable("t") {
@@ -230,9 +229,8 @@ class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
       }
 
       read
-        .format("orc")
         .option(ConfVars.DEFAULTPARTITIONNAME.varname, defaultPartitionName)
-        .load(base.getCanonicalPath)
+        .orc(base.getCanonicalPath)
         .registerTempTable("t")
 
       withTempTable("t") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
index ca131faaeef05..744d462938141 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
@@ -63,14 +63,14 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
 
     withOrcFile(data) { file =>
       checkAnswer(
-        sqlContext.read.format("orc").load(file),
+        sqlContext.read.orc(file),
         data.toDF().collect())
     }
   }
 
   test("Read/write binary data") {
     withOrcFile(BinaryData("test".getBytes("utf8")) :: Nil) { file =>
-      val bytes = read.format("orc").load(file).head().getAs[Array[Byte]](0)
+      val bytes = read.orc(file).head().getAs[Array[Byte]](0)
       assert(new String(bytes, "utf8") === "test")
     }
   }
@@ -88,7 +88,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
 
     withOrcFile(data) { file =>
       checkAnswer(
-        read.format("orc").load(file),
+        read.orc(file),
         data.toDF().collect())
     }
   }
@@ -158,7 +158,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
 
     withOrcFile(data) { file =>
       checkAnswer(
-        read.format("orc").load(file),
+        read.orc(file),
         Row(Seq.fill(5)(null): _*))
     }
   }
@@ -310,7 +310,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
              """.stripMargin)
 
           val errorMessage = intercept[AnalysisException] {
-            sqlContext.read.format("orc").load(path)
+            sqlContext.read.orc(path)
           }.getMessage
 
           assert(errorMessage.contains("Failed to discover schema from ORC files"))
@@ -323,7 +323,7 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
                |SELECT key, value FROM single
              """.stripMargin)
 
-          val df = sqlContext.read.format("orc").load(path)
+          val df = sqlContext.read.orc(path)
           assert(df.schema === singleRowDF.schema.asNullable)
           checkAnswer(df, singleRowDF)
         }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
index 5daf691aa8c53..9d76d6503a3e6 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
@@ -39,7 +39,7 @@ private[sql] trait OrcTest extends SQLTestUtils {
       (data: Seq[T])
       (f: String => Unit): Unit = {
     withTempPath { file =>
-      sparkContext.parallelize(data).toDF().write.format("orc").save(file.getCanonicalPath)
+      sparkContext.parallelize(data).toDF().write.orc(file.getCanonicalPath)
       f(file.getCanonicalPath)
     }
   }
@@ -51,7 +51,7 @@ private[sql] trait OrcTest extends SQLTestUtils {
   protected def withOrcDataFrame[T <: Product: ClassTag: TypeTag]
       (data: Seq[T])
       (f: DataFrame => Unit): Unit = {
-    withOrcFile(data)(path => f(sqlContext.read.format("orc").load(path)))
+    withOrcFile(data)(path => f(sqlContext.read.orc(path)))
   }
 
   /**
@@ -70,11 +70,11 @@ private[sql] trait OrcTest extends SQLTestUtils {
 
   protected def makeOrcFile[T <: Product: ClassTag: TypeTag](
       data: Seq[T], path: File): Unit = {
-    data.toDF().write.format("orc").mode(SaveMode.Overwrite).save(path.getCanonicalPath)
+    data.toDF().write.mode(SaveMode.Overwrite).orc(path.getCanonicalPath)
   }
 
   protected def makeOrcFile[T <: Product: ClassTag: TypeTag](
       df: DataFrame, path: File): Unit = {
-    df.write.format("orc").mode(SaveMode.Overwrite).save(path.getCanonicalPath)
+    df.write.mode(SaveMode.Overwrite).orc(path.getCanonicalPath)
   }
 }

From 8c8f0ef59e12b6f13d5a0bf2d7bf1248b5c1e369 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 21 Jul 2015 00:48:07 -0700
Subject: [PATCH 0516/1454] [SPARK-8255] [SPARK-8256] [SQL] Add
 regex_extract/regex_replace

Add expressions `regex_extract` & `regex_replace`

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7468 from chenghao-intel/regexp and squashes the following commits:

e5ea476 [Cheng Hao] minor update for documentation
ef96fd6 [Cheng Hao] update the code gen
72cf28f [Cheng Hao] Add more log for compilation error
4e11381 [Cheng Hao] Add regexp_replace / regexp_extract support
---
 python/pyspark/sql/functions.py               |  30 +++
 .../catalyst/analysis/FunctionRegistry.scala  |   2 +
 .../expressions/codegen/CodeGenerator.scala   |   5 +-
 .../expressions/stringOperations.scala        | 217 +++++++++++++++++-
 .../expressions/ExpressionEvalHelper.scala    |   1 -
 .../expressions/StringExpressionsSuite.scala  |  35 +++
 .../org/apache/spark/sql/functions.scala      |  21 ++
 .../spark/sql/StringFunctionsSuite.scala      |  16 ++
 8 files changed, 323 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 031745a1c4d3b..3c134faa0a765 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -46,6 +46,8 @@
     'monotonicallyIncreasingId',
     'rand',
     'randn',
+    'regexp_extract',
+    'regexp_replace',
     'sha1',
     'sha2',
     'sparkPartitionId',
@@ -343,6 +345,34 @@ def levenshtein(left, right):
     return Column(jc)
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def regexp_extract(str, pattern, idx):
+    """Extract a specific(idx) group identified by a java regex, from the specified string column.
+
+    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
+    >>> df.select(regexp_extract('str', '(\d+)-(\d+)', 1).alias('d')).collect()
+    [Row(d=u'100')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.regexp_extract(_to_java_column(str), pattern, idx)
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def regexp_replace(str, pattern, replacement):
+    """Replace all substrings of the specified string value that match regexp with rep.
+
+    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
+    >>> df.select(regexp_replace('str', '(\\d+)', '##').alias('d')).collect()
+    [Row(d=u'##-##')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.regexp_replace(_to_java_column(str), pattern, replacement)
+    return Column(jc)
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def md5(col):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 71e87b98d86fc..aec392379c186 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -161,6 +161,8 @@ object FunctionRegistry {
     expression[Lower]("lower"),
     expression[Length]("length"),
     expression[Levenshtein]("levenshtein"),
+    expression[RegExpExtract]("regexp_extract"),
+    expression[RegExpReplace]("regexp_replace"),
     expression[StringInstr]("instr"),
     expression[StringLocate]("locate"),
     expression[StringLPad]("lpad"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 606f770cb4f7b..319dcd1c04316 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -297,8 +297,9 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       evaluator.cook(code)
     } catch {
       case e: Exception =>
-        logError(s"failed to compile:\n $code", e)
-        throw e
+        val msg = s"failed to compile:\n $code"
+        logError(msg, e)
+        throw new Exception(msg, e)
     }
     evaluator.getClazz().newInstance().asInstanceOf[GeneratedClass]
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 92fefe1585b23..fe57d17f1ec14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.text.DecimalFormat
 import java.util.Locale
-import java.util.regex.Pattern
+import java.util.regex.{MatchResult, Pattern}
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
@@ -876,6 +876,221 @@ case class Encode(value: Expression, charset: Expression)
   }
 }
 
+/**
+ * Replace all substrings of str that match regexp with rep.
+ *
+ * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
+ */
+case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expression)
+  extends Expression with ImplicitCastInputTypes {
+
+  // last regex in string, we will update the pattern iff regexp value changed.
+  @transient private var lastRegex: UTF8String = _
+  // last regex pattern, we cache it for performance concern
+  @transient private var pattern: Pattern = _
+  // last replacement string, we don't want to convert a UTF8String => java.langString every time.
+  @transient private var lastReplacement: String = _
+  @transient private var lastReplacementInUTF8: UTF8String = _
+  // result buffer write by Matcher
+  @transient private val result: StringBuffer = new StringBuffer
+
+  override def nullable: Boolean = subject.nullable || regexp.nullable || rep.nullable
+  override def foldable: Boolean = subject.foldable && regexp.foldable && rep.foldable
+
+  override def eval(input: InternalRow): Any = {
+    val s = subject.eval(input)
+    if (null != s) {
+      val p = regexp.eval(input)
+      if (null != p) {
+        val r = rep.eval(input)
+        if (null != r) {
+          if (!p.equals(lastRegex)) {
+            // regex value changed
+            lastRegex = p.asInstanceOf[UTF8String]
+            pattern = Pattern.compile(lastRegex.toString)
+          }
+          if (!r.equals(lastReplacementInUTF8)) {
+            // replacement string changed
+            lastReplacementInUTF8 = r.asInstanceOf[UTF8String]
+            lastReplacement = lastReplacementInUTF8.toString
+          }
+          val m = pattern.matcher(s.toString())
+          result.delete(0, result.length())
+
+          while (m.find) {
+            m.appendReplacement(result, lastReplacement)
+          }
+          m.appendTail(result)
+
+          return UTF8String.fromString(result.toString)
+        }
+      }
+    }
+
+    null
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, StringType)
+  override def children: Seq[Expression] = subject :: regexp :: rep :: Nil
+  override def prettyName: String = "regexp_replace"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastRegex = ctx.freshName("lastRegex")
+    val termPattern = ctx.freshName("pattern")
+
+    val termLastReplacement = ctx.freshName("lastReplacement")
+    val termLastReplacementInUTF8 = ctx.freshName("lastReplacementInUTF8")
+
+    val termResult = ctx.freshName("result")
+
+    val classNameUTF8String = classOf[UTF8String].getCanonicalName
+    val classNamePattern = classOf[Pattern].getCanonicalName
+    val classNameString = classOf[java.lang.String].getCanonicalName
+    val classNameStringBuffer = classOf[java.lang.StringBuffer].getCanonicalName
+
+    ctx.addMutableState(classNameUTF8String,
+      termLastRegex, s"${termLastRegex} = null;")
+    ctx.addMutableState(classNamePattern,
+      termPattern, s"${termPattern} = null;")
+    ctx.addMutableState(classNameString,
+      termLastReplacement, s"${termLastReplacement} = null;")
+    ctx.addMutableState(classNameUTF8String,
+      termLastReplacementInUTF8, s"${termLastReplacementInUTF8} = null;")
+    ctx.addMutableState(classNameStringBuffer,
+      termResult, s"${termResult} = new $classNameStringBuffer();")
+
+    val evalSubject = subject.gen(ctx)
+    val evalRegexp = regexp.gen(ctx)
+    val evalRep = rep.gen(ctx)
+
+    s"""
+      ${evalSubject.code}
+      boolean ${ev.isNull} = ${evalSubject.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${evalSubject.isNull}) {
+        ${evalRegexp.code}
+        if (!${evalRegexp.isNull}) {
+          ${evalRep.code}
+          if (!${evalRep.isNull}) {
+            if (!${evalRegexp.primitive}.equals(${termLastRegex})) {
+              // regex value changed
+              ${termLastRegex} = ${evalRegexp.primitive};
+              ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
+            }
+            if (!${evalRep.primitive}.equals(${termLastReplacementInUTF8})) {
+              // replacement string changed
+              ${termLastReplacementInUTF8} = ${evalRep.primitive};
+              ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
+            }
+            ${termResult}.delete(0, ${termResult}.length());
+            ${classOf[java.util.regex.Matcher].getCanonicalName} m =
+                                   ${termPattern}.matcher(${evalSubject.primitive}.toString());
+
+            while (m.find()) {
+              m.appendReplacement(${termResult}, ${termLastReplacement});
+            }
+            m.appendTail(${termResult});
+            ${ev.primitive} = ${classNameUTF8String}.fromString(${termResult}.toString());
+            ${ev.isNull} = false;
+          }
+        }
+      }
+    """
+  }
+}
+
+/**
+ * Extract a specific(idx) group identified by a Java regex.
+ *
+ * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
+ */
+case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expression)
+  extends Expression with ImplicitCastInputTypes {
+  def this(s: Expression, r: Expression) = this(s, r, Literal(1))
+
+  // last regex in string, we will update the pattern iff regexp value changed.
+  @transient private var lastRegex: UTF8String = _
+  // last regex pattern, we cache it for performance concern
+  @transient private var pattern: Pattern = _
+
+  override def nullable: Boolean = subject.nullable || regexp.nullable || idx.nullable
+  override def foldable: Boolean = subject.foldable && regexp.foldable && idx.foldable
+
+  override def eval(input: InternalRow): Any = {
+    val s = subject.eval(input)
+    if (null != s) {
+      val p = regexp.eval(input)
+      if (null != p) {
+        val r = idx.eval(input)
+        if (null != r) {
+          if (!p.equals(lastRegex)) {
+            // regex value changed
+            lastRegex = p.asInstanceOf[UTF8String]
+            pattern = Pattern.compile(lastRegex.toString)
+          }
+          val m = pattern.matcher(s.toString())
+          if (m.find) {
+            val mr: MatchResult = m.toMatchResult
+            return UTF8String.fromString(mr.group(r.asInstanceOf[Int]))
+          }
+          return UTF8String.EMPTY_UTF8
+        }
+      }
+    }
+
+    null
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, IntegerType)
+  override def children: Seq[Expression] = subject :: regexp :: idx :: Nil
+  override def prettyName: String = "regexp_extract"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastRegex = ctx.freshName("lastRegex")
+    val termPattern = ctx.freshName("pattern")
+    val classNameUTF8String = classOf[UTF8String].getCanonicalName
+    val classNamePattern = classOf[Pattern].getCanonicalName
+
+    ctx.addMutableState(classNameUTF8String, termLastRegex, s"${termLastRegex} = null;")
+    ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
+
+    val evalSubject = subject.gen(ctx)
+    val evalRegexp = regexp.gen(ctx)
+    val evalIdx = idx.gen(ctx)
+
+    s"""
+      ${ctx.javaType(dataType)} ${ev.primitive} = null;
+      boolean ${ev.isNull} = true;
+      ${evalSubject.code}
+      if (!${evalSubject.isNull}) {
+        ${evalRegexp.code}
+        if (!${evalRegexp.isNull}) {
+          ${evalIdx.code}
+          if (!${evalIdx.isNull}) {
+            if (!${evalRegexp.primitive}.equals(${termLastRegex})) {
+              // regex value changed
+              ${termLastRegex} = ${evalRegexp.primitive};
+              ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
+            }
+            ${classOf[java.util.regex.Matcher].getCanonicalName} m =
+                                   ${termPattern}.matcher(${evalSubject.primitive}.toString());
+            if (m.find()) {
+              ${classOf[java.util.regex.MatchResult].getCanonicalName} mr = m.toMatchResult();
+              ${ev.primitive} = ${classNameUTF8String}.fromString(mr.group(${evalIdx.primitive}));
+              ${ev.isNull} = false;
+            } else {
+              ${ev.primitive} = ${classNameUTF8String}.EMPTY_UTF8;
+              ${ev.isNull} = false;
+            }
+          }
+        }
+      }
+    """
+  }
+}
+
 /**
  * Formats the number X to a format like '#,###,###.##', rounded to D decimal places,
  * and returns the result as a string. If D is 0, the result has no decimal point or
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 7a96044d35a09..6e17ffcda9dc4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -79,7 +79,6 @@ trait ExpressionEvalHelper {
         fail(
           s"""
             |Code generation of $expression failed:
-            |${evaluated.code}
             |$e
           """.stripMargin)
     }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 67d97cd30b039..96c540ab36f08 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -464,6 +464,41 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(StringSpace(s1), null, row2)
   }
 
+  test("RegexReplace") {
+    val row1 = create_row("100-200", "(\\d+)", "num")
+    val row2 = create_row("100-200", "(\\d+)", "###")
+    val row3 = create_row("100-200", "(-)", "###")
+
+    val s = 's.string.at(0)
+    val p = 'p.string.at(1)
+    val r = 'r.string.at(2)
+
+    val expr = RegExpReplace(s, p, r)
+    checkEvaluation(expr, "num-num", row1)
+    checkEvaluation(expr, "###-###", row2)
+    checkEvaluation(expr, "100###200", row3)
+  }
+
+  test("RegexExtract") {
+    val row1 = create_row("100-200", "(\\d+)-(\\d+)", 1)
+    val row2 = create_row("100-200", "(\\d+)-(\\d+)", 2)
+    val row3 = create_row("100-200", "(\\d+).*", 1)
+    val row4 = create_row("100-200", "([a-z])", 1)
+
+    val s = 's.string.at(0)
+    val p = 'p.string.at(1)
+    val r = 'r.int.at(2)
+
+    val expr = RegExpExtract(s, p, r)
+    checkEvaluation(expr, "100", row1)
+    checkEvaluation(expr, "200", row2)
+    checkEvaluation(expr, "100", row3)
+    checkEvaluation(expr, "", row4) // will not match anything, empty string get
+
+    val expr1 = new RegExpExtract(s, p)
+    checkEvaluation(expr1, "100", row1)
+  }
+
   test("SPLIT") {
     val s1 = 'a.string.at(0)
     val s2 = 'b.string.at(1)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 8fa017610b63c..6d60dae624b0c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1781,6 +1781,27 @@ object functions {
     StringLocate(lit(substr).expr, str.expr, lit(pos).expr)
   }
 
+
+  /**
+   * Extract a specific(idx) group identified by a java regex, from the specified string column.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def regexp_extract(e: Column, exp: String, groupIdx: Int): Column = {
+    RegExpExtract(e.expr, lit(exp).expr, lit(groupIdx).expr)
+  }
+
+  /**
+   * Replace all substrings of the specified string value that match regexp with rep.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def regexp_replace(e: Column, pattern: String, replacement: String): Column = {
+    RegExpReplace(e.expr, lit(pattern).expr, lit(replacement).expr)
+  }
+
   /**
    * Computes the BASE64 encoding of a binary column and returns it as a string column.
    * This is the reverse of unbase64.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 4551192b157ff..d1f855903ca4b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -56,6 +56,22 @@ class StringFunctionsSuite extends QueryTest {
     checkAnswer(df.selectExpr("levenshtein(l, r)"), Seq(Row(3), Row(1)))
   }
 
+  test("string regex_replace / regex_extract") {
+    val df = Seq(("100-200", "")).toDF("a", "b")
+
+    checkAnswer(
+      df.select(
+        regexp_replace($"a", "(\\d+)", "num"),
+        regexp_extract($"a", "(\\d+)-(\\d+)", 1)),
+      Row("num-num", "100"))
+
+    checkAnswer(
+      df.selectExpr(
+        "regexp_replace(a, '(\\d+)', 'num')",
+        "regexp_extract(a, '(\\d+)-(\\d+)', 2)"),
+      Row("num-num", "200"))
+  }
+
   test("string ascii function") {
     val df = Seq(("abc", "")).toDF("a", "b")
     checkAnswer(

From 560c658a7462844c698b5bda09a4cfb4094fd65b Mon Sep 17 00:00:00 2001
From: Pedro Rodriguez <ski.rodriguez@gmail.com>
Date: Tue, 21 Jul 2015 00:53:20 -0700
Subject: [PATCH 0517/1454] [SPARK-8230][SQL] Add array/map size method

Pull Request for: https://issues.apache.org/jira/browse/SPARK-8230

Primary issue resolved is to implement array/map size for Spark SQL. Code is ready for review by a committer. Chen Hao is on the JIRA ticket, but I don't know his username on github, rxin is also on JIRA ticket.

Things to review:
1. Where to put added functions namespace wise, they seem to be part of a few operations on collections which includes `sort_array` and `array_contains`. Hence the name given `collectionOperations.scala` and `_collection_functions` in python.
2. In Python code, should it be in a `1.5.0` function array or in a collections array?
3. Are there any missing methods on the `Size` case class? Looks like many of these functions have generated Java code, is that also needed in this case?
4. Something else?

Author: Pedro Rodriguez <ski.rodriguez@gmail.com>
Author: Pedro Rodriguez <prodriguez@trulia.com>

Closes #7462 from EntilZha/SPARK-8230 and squashes the following commits:

9a442ae [Pedro Rodriguez] fixed functions and sorted __all__
9aea3bb [Pedro Rodriguez] removed imports from python docs
15d4bf1 [Pedro Rodriguez] Added null test case and changed to nullSafeCodeGen
d88247c [Pedro Rodriguez] removed python code
bd5f0e4 [Pedro Rodriguez] removed duplicate function from rebase/merge
59931b4 [Pedro Rodriguez] fixed compile bug instroduced when merging
c187175 [Pedro Rodriguez] updated code to add size to __all__ directly and removed redundent pretty print
130839f [Pedro Rodriguez] fixed failing test
aa9bade [Pedro Rodriguez] fix style
e093473 [Pedro Rodriguez] updated python code with docs, switched classes/traits implemented, added (failing) expression tests
0449377 [Pedro Rodriguez] refactored code to use better abstract classes/traits and implementations
9a1a2ff [Pedro Rodriguez] added unit tests for map size
2bfbcb6 [Pedro Rodriguez] added unit test for size
20df2b4 [Pedro Rodriguez] Finished working version of size function and added it to python
b503e75 [Pedro Rodriguez] First attempt at implementing size for maps and arrays
99a6a5c [Pedro Rodriguez] fixed failing test
cac75ac [Pedro Rodriguez] fix style
933d843 [Pedro Rodriguez] updated python code with docs, switched classes/traits implemented, added (failing) expression tests
42bb7d4 [Pedro Rodriguez] refactored code to use better abstract classes/traits and implementations
f9c3b8a [Pedro Rodriguez] added unit tests for map size
2515d9f [Pedro Rodriguez] added documentation
0e60541 [Pedro Rodriguez] added unit test for size
acf9853 [Pedro Rodriguez] Finished working version of size function and added it to python
84a5d38 [Pedro Rodriguez] First attempt at implementing size for maps and arrays
---
 python/pyspark/sql/functions.py               | 15 ++++++
 .../catalyst/analysis/FunctionRegistry.scala  |  4 +-
 .../expressions/collectionOperations.scala    | 37 +++++++++++++++
 .../CollectionFunctionsSuite.scala            | 46 +++++++++++++++++++
 .../org/apache/spark/sql/functions.scala      | 20 ++++++++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 31 +++++++++++++
 6 files changed, 152 insertions(+), 1 deletion(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 3c134faa0a765..719e623a1a11f 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -50,6 +50,7 @@
     'regexp_replace',
     'sha1',
     'sha2',
+    'size',
     'sparkPartitionId',
     'struct',
     'udf',
@@ -825,6 +826,20 @@ def weekofyear(col):
     return Column(sc._jvm.functions.weekofyear(col))
 
 
+@since(1.5)
+def size(col):
+    """
+    Collection function: returns the length of the array or map stored in the column.
+    :param col: name of column or expression
+
+    >>> df = sqlContext.createDataFrame([([1, 2, 3],),([1],),([],)], ['data'])
+    >>> df.select(size(df.data)).collect()
+    [Row(size(data)=3), Row(size(data)=1), Row(size(data)=0)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.size(_to_java_column(col)))
+
+
 class UserDefinedFunction(object):
     """
     User defined function in Python
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index aec392379c186..13523720daff0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -195,8 +195,10 @@ object FunctionRegistry {
     expression[Quarter]("quarter"),
     expression[Second]("second"),
     expression[WeekOfYear]("weekofyear"),
-    expression[Year]("year")
+    expression[Year]("year"),
 
+    // collection functions
+    expression[Size]("size")
   )
 
   val builtin: FunctionRegistry = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
new file mode 100644
index 0000000000000..2d92dcf23a86e
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.types._
+
+/**
+ * Given an array or map, returns its size.
+ */
+case class Size(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+  override def dataType: DataType = IntegerType
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(ArrayType, MapType))
+
+  override def nullSafeEval(value: Any): Int = child.dataType match {
+    case ArrayType(_, _) => value.asInstanceOf[Seq[Any]].size
+    case MapType(_, _, _) => value.asInstanceOf[Map[Any, Any]].size
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).size();")
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
new file mode 100644
index 0000000000000..28c41b57169f9
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.types._
+
+
+class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+
+  test("Array and Map Size") {
+    val a0 = Literal.create(Seq(1, 2, 3), ArrayType(IntegerType))
+    val a1 = Literal.create(Seq[Integer](), ArrayType(IntegerType))
+    val a2 = Literal.create(Seq(1, 2), ArrayType(IntegerType))
+
+    checkEvaluation(Size(a0), 3)
+    checkEvaluation(Size(a1), 0)
+    checkEvaluation(Size(a2), 2)
+
+    val m0 = Literal.create(Map("a" -> "a", "b" -> "b"), MapType(StringType, StringType))
+    val m1 = Literal.create(Map[String, String](), MapType(StringType, StringType))
+    val m2 = Literal.create(Map("a" -> "a"), MapType(StringType, StringType))
+
+    checkEvaluation(Size(m0), 2)
+    checkEvaluation(Size(m1), 0)
+    checkEvaluation(Size(m2), 1)
+
+    checkEvaluation(Literal.create(null, MapType(StringType, StringType)), null)
+    checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 6d60dae624b0c..60b089180c876 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -42,6 +42,7 @@ import org.apache.spark.util.Utils
  * @groupname misc_funcs Misc functions
  * @groupname window_funcs Window functions
  * @groupname string_funcs String functions
+ * @groupname collection_funcs Collection functions
  * @groupname Ungrouped Support functions for DataFrames.
  * @since 1.3.0
  */
@@ -2053,6 +2054,25 @@ object functions {
    */
   def weekofyear(columnName: String): Column = weekofyear(Column(columnName))
 
+  //////////////////////////////////////////////////////////////////////////////////////////////
+  // Collection functions
+  //////////////////////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Returns length of array or map
+   * @group collection_funcs
+   * @since 1.5.0
+   */
+  def size(columnName: String): Column = size(Column(columnName))
+
+  /**
+   * Returns length of array or map
+   * @group collection_funcs
+   * @since 1.5.0
+   */
+  def size(column: Column): Column = Size(column.expr)
+
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 8d2ff2f9690d6..1baec5d37699d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -267,4 +267,35 @@ class DataFrameFunctionsSuite extends QueryTest {
     )
   }
 
+  test("array size function") {
+    val df = Seq(
+      (Array[Int](1, 2), "x"),
+      (Array[Int](), "y"),
+      (Array[Int](1, 2, 3), "z")
+    ).toDF("a", "b")
+    checkAnswer(
+      df.select(size("a")),
+      Seq(Row(2), Row(0), Row(3))
+    )
+    checkAnswer(
+      df.selectExpr("size(a)"),
+      Seq(Row(2), Row(0), Row(3))
+    )
+  }
+
+  test("map size function") {
+    val df = Seq(
+      (Map[Int, Int](1 -> 1, 2 -> 2), "x"),
+      (Map[Int, Int](), "y"),
+      (Map[Int, Int](1 -> 1, 2 -> 2, 3 -> 3), "z")
+    ).toDF("a", "b")
+    checkAnswer(
+      df.select(size("a")),
+      Seq(Row(2), Row(0), Row(3))
+    )
+    checkAnswer(
+      df.selectExpr("size(a)"),
+      Seq(Row(2), Row(0), Row(3))
+    )
+  }
 }

From ae230596b866d8e369bd061256c4cc569dba430a Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 21 Jul 2015 00:56:57 -0700
Subject: [PATCH 0518/1454] [SPARK-9173][SQL]UnionPushDown should also support
 Intersect and Except

JIRA: https://issues.apache.org/jira/browse/SPARK-9173

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7540 from yjshen/union_pushdown and squashes the following commits:

278510a [Yijie Shen] rename UnionPushDown to SetOperationPushDown
91741c1 [Yijie Shen] Add UnionPushDown support for intersect and except
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 47 +++++++++--
 .../optimizer/SetOperationPushDownSuite.scala | 82 +++++++++++++++++++
 .../optimizer/UnionPushdownSuite.scala        | 61 --------------
 3 files changed, 120 insertions(+), 70 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SetOperationPushDownSuite.scala
 delete mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 9c45b196245da..e42f0b9a247e3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -40,7 +40,7 @@ object DefaultOptimizer extends Optimizer {
       ReplaceDistinctWithAggregate) ::
     Batch("Operator Optimizations", FixedPoint(100),
       // Operator push down
-      UnionPushDown,
+      SetOperationPushDown,
       SamplePushDown,
       PushPredicateThroughJoin,
       PushPredicateThroughProject,
@@ -84,23 +84,24 @@ object SamplePushDown extends Rule[LogicalPlan] {
 }
 
 /**
- * Pushes operations to either side of a Union.
+ * Pushes operations to either side of a Union, Intersect or Except.
  */
-object UnionPushDown extends Rule[LogicalPlan] {
+object SetOperationPushDown extends Rule[LogicalPlan] {
 
   /**
    * Maps Attributes from the left side to the corresponding Attribute on the right side.
    */
-  private def buildRewrites(union: Union): AttributeMap[Attribute] = {
-    assert(union.left.output.size == union.right.output.size)
+  private def buildRewrites(bn: BinaryNode): AttributeMap[Attribute] = {
+    assert(bn.isInstanceOf[Union] || bn.isInstanceOf[Intersect] || bn.isInstanceOf[Except])
+    assert(bn.left.output.size == bn.right.output.size)
 
-    AttributeMap(union.left.output.zip(union.right.output))
+    AttributeMap(bn.left.output.zip(bn.right.output))
   }
 
   /**
-   * Rewrites an expression so that it can be pushed to the right side of a Union operator.
-   * This method relies on the fact that the output attributes of a union are always equal
-   * to the left child's output.
+   * Rewrites an expression so that it can be pushed to the right side of a
+   * Union, Intersect or Except operator. This method relies on the fact that the output attributes
+   * of a union/intersect/except are always equal to the left child's output.
    */
   private def pushToRight[A <: Expression](e: A, rewrites: AttributeMap[Attribute]) = {
     val result = e transform {
@@ -126,6 +127,34 @@ object UnionPushDown extends Rule[LogicalPlan] {
       Union(
         Project(projectList, left),
         Project(projectList.map(pushToRight(_, rewrites)), right))
+
+    // Push down filter into intersect
+    case Filter(condition, i @ Intersect(left, right)) =>
+      val rewrites = buildRewrites(i)
+      Intersect(
+        Filter(condition, left),
+        Filter(pushToRight(condition, rewrites), right))
+
+    // Push down projection into intersect
+    case Project(projectList, i @ Intersect(left, right)) =>
+      val rewrites = buildRewrites(i)
+      Intersect(
+        Project(projectList, left),
+        Project(projectList.map(pushToRight(_, rewrites)), right))
+
+    // Push down filter into except
+    case Filter(condition, e @ Except(left, right)) =>
+      val rewrites = buildRewrites(e)
+      Except(
+        Filter(condition, left),
+        Filter(pushToRight(condition, rewrites), right))
+
+    // Push down projection into except
+    case Project(projectList, e @ Except(left, right)) =>
+      val rewrites = buildRewrites(e)
+      Except(
+        Project(projectList, left),
+        Project(projectList.map(pushToRight(_, rewrites)), right))
   }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SetOperationPushDownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SetOperationPushDownSuite.scala
new file mode 100644
index 0000000000000..49c979bc7d72c
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SetOperationPushDownSuite.scala
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
+import org.apache.spark.sql.catalyst.plans.PlanTest
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.catalyst.dsl.expressions._
+
+class SetOperationPushDownSuite extends PlanTest {
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches =
+      Batch("Subqueries", Once,
+        EliminateSubQueries) ::
+      Batch("Union Pushdown", Once,
+        SetOperationPushDown) :: Nil
+  }
+
+  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)
+  val testRelation2 = LocalRelation('d.int, 'e.int, 'f.int)
+  val testUnion = Union(testRelation, testRelation2)
+  val testIntersect = Intersect(testRelation, testRelation2)
+  val testExcept = Except(testRelation, testRelation2)
+
+  test("union/intersect/except: filter to each side") {
+    val unionQuery = testUnion.where('a === 1)
+    val intersectQuery = testIntersect.where('b < 10)
+    val exceptQuery = testExcept.where('c >= 5)
+
+    val unionOptimized = Optimize.execute(unionQuery.analyze)
+    val intersectOptimized = Optimize.execute(intersectQuery.analyze)
+    val exceptOptimized = Optimize.execute(exceptQuery.analyze)
+
+    val unionCorrectAnswer =
+      Union(testRelation.where('a === 1), testRelation2.where('d === 1)).analyze
+    val intersectCorrectAnswer =
+      Intersect(testRelation.where('b < 10), testRelation2.where('e < 10)).analyze
+    val exceptCorrectAnswer =
+      Except(testRelation.where('c >= 5), testRelation2.where('f >= 5)).analyze
+
+    comparePlans(unionOptimized, unionCorrectAnswer)
+    comparePlans(intersectOptimized, intersectCorrectAnswer)
+    comparePlans(exceptOptimized, exceptCorrectAnswer)
+  }
+
+  test("union/intersect/except: project to each side") {
+    val unionQuery = testUnion.select('a)
+    val intersectQuery = testIntersect.select('b, 'c)
+    val exceptQuery = testExcept.select('a, 'b, 'c)
+
+    val unionOptimized = Optimize.execute(unionQuery.analyze)
+    val intersectOptimized = Optimize.execute(intersectQuery.analyze)
+    val exceptOptimized = Optimize.execute(exceptQuery.analyze)
+
+    val unionCorrectAnswer =
+      Union(testRelation.select('a), testRelation2.select('d)).analyze
+    val intersectCorrectAnswer =
+      Intersect(testRelation.select('b, 'c), testRelation2.select('e, 'f)).analyze
+    val exceptCorrectAnswer =
+      Except(testRelation.select('a, 'b, 'c), testRelation2.select('d, 'e, 'f)).analyze
+
+    comparePlans(unionOptimized, unionCorrectAnswer)
+    comparePlans(intersectOptimized, intersectCorrectAnswer)
+    comparePlans(exceptOptimized, exceptCorrectAnswer)  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala
deleted file mode 100644
index ec379489a6d1e..0000000000000
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.optimizer
-
-import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
-import org.apache.spark.sql.catalyst.plans.PlanTest
-import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.rules._
-import org.apache.spark.sql.catalyst.dsl.plans._
-import org.apache.spark.sql.catalyst.dsl.expressions._
-
-class UnionPushDownSuite extends PlanTest {
-  object Optimize extends RuleExecutor[LogicalPlan] {
-    val batches =
-      Batch("Subqueries", Once,
-        EliminateSubQueries) ::
-      Batch("Union Pushdown", Once,
-        UnionPushDown) :: Nil
-  }
-
-  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)
-  val testRelation2 = LocalRelation('d.int, 'e.int, 'f.int)
-  val testUnion = Union(testRelation, testRelation2)
-
-  test("union: filter to each side") {
-    val query = testUnion.where('a === 1)
-
-    val optimized = Optimize.execute(query.analyze)
-
-    val correctAnswer =
-      Union(testRelation.where('a === 1), testRelation2.where('d === 1)).analyze
-
-    comparePlans(optimized, correctAnswer)
-  }
-
-  test("union: project to each side") {
-    val query = testUnion.select('b)
-
-    val optimized = Optimize.execute(query.analyze)
-
-    val correctAnswer =
-      Union(testRelation.select('b), testRelation2.select('e)).analyze
-
-    comparePlans(optimized, correctAnswer)
-  }
-}

From 6364735bcc67ecb0e9c7e5076d214ed88e927430 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Tue, 21 Jul 2015 01:12:51 -0700
Subject: [PATCH 0519/1454] [SPARK-8875] Remove BlockStoreShuffleFetcher class

The shuffle code has gotten increasingly difficult to read as it has evolved, and many classes
have evolved significantly since they were originally created. The BlockStoreShuffleFetcher class
now serves little purpose other than to make the code more difficult to read; this commit moves its
functionality into the ShuffleBlockFetcherIterator class.

cc massie JoshRosen (Josh, this PR also removes the Try you pointed out as being confusing / not necessarily useful in a previous comment).  Matt, would be helpful to know whether this will interfere in any negative ways with your new shuffle PR (I took a look and it seems like this should still cleanly integrate with your parquet work, but want to double check).

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #7268 from kayousterhout/SPARK-8875 and squashes the following commits:

2b24a97 [Kay Ousterhout] Fixed DAGSchedulerSuite compile error
98a1831 [Kay Ousterhout] Merge remote-tracking branch 'upstream/master' into SPARK-8875
90f0e89 [Kay Ousterhout] Fixed broken test
14bfcbb [Kay Ousterhout] Last style fix
bc69d2b [Kay Ousterhout] Style improvements based on Josh's code review
ad3c8d1 [Kay Ousterhout] Better documentation for MapOutputTracker methods
0bc0e59 [Kay Ousterhout] [SPARK-8875] Remove BlockStoreShuffleFetcher class
---
 .../org/apache/spark/MapOutputTracker.scala   | 62 ++++++++++----
 .../hash/BlockStoreShuffleFetcher.scala       | 85 -------------------
 .../shuffle/hash/HashShuffleReader.scala      | 19 +++--
 .../storage/ShuffleBlockFetcherIterator.scala | 72 ++++++++++------
 .../apache/spark/MapOutputTrackerSuite.scala  | 28 +++---
 .../scala/org/apache/spark/ShuffleSuite.scala | 12 +--
 .../spark/scheduler/DAGSchedulerSuite.scala   | 32 +++----
 .../shuffle/hash/HashShuffleReaderSuite.scala | 14 +--
 .../ShuffleBlockFetcherIteratorSuite.scala    | 18 ++--
 .../apache/spark/util/AkkaUtilsSuite.scala    | 22 +++--
 10 files changed, 172 insertions(+), 192 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala

diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index 862ffe868f58f..92218832d256f 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -21,14 +21,14 @@ import java.io._
 import java.util.concurrent.ConcurrentHashMap
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
-import scala.collection.mutable.{HashMap, HashSet, Map}
+import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map}
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
 
 import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, RpcEndpoint}
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.shuffle.MetadataFetchFailedException
-import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockId}
 import org.apache.spark.util._
 
 private[spark] sealed trait MapOutputTrackerMessage
@@ -124,10 +124,18 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
   }
 
   /**
-   * Called from executors to get the server URIs and output sizes of the map outputs of
-   * a given shuffle.
+   * Called from executors to get the server URIs and output sizes for each shuffle block that
+   * needs to be read from a given reduce task.
+   *
+   * @return A sequence of 2-item tuples, where the first item in the tuple is a BlockManagerId,
+   *         and the second item is a sequence of (shuffle block id, shuffle block size) tuples
+   *         describing the shuffle blocks that are stored at that block manager.
    */
-  def getServerStatuses(shuffleId: Int, reduceId: Int): Array[(BlockManagerId, Long)] = {
+  def getMapSizesByExecutorId(shuffleId: Int, reduceId: Int)
+  : Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
+    logDebug(s"Fetching outputs for shuffle $shuffleId, reduce $reduceId")
+    val startTime = System.currentTimeMillis
+
     val statuses = mapStatuses.get(shuffleId).orNull
     if (statuses == null) {
       logInfo("Don't have map outputs for shuffle " + shuffleId + ", fetching them")
@@ -167,6 +175,9 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
           }
         }
       }
+      logDebug(s"Fetching map output location for shuffle $shuffleId, reduce $reduceId took " +
+        s"${System.currentTimeMillis - startTime} ms")
+
       if (fetchedStatuses != null) {
         fetchedStatuses.synchronized {
           return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, fetchedStatuses)
@@ -421,23 +432,38 @@ private[spark] object MapOutputTracker extends Logging {
     }
   }
 
-  // Convert an array of MapStatuses to locations and sizes for a given reduce ID. If
-  // any of the statuses is null (indicating a missing location due to a failed mapper),
-  // throw a FetchFailedException.
+  /**
+   * Converts an array of MapStatuses for a given reduce ID to a sequence that, for each block
+   * manager ID, lists the shuffle block ids and corresponding shuffle block sizes stored at that
+   * block manager.
+   *
+   * If any of the statuses is null (indicating a missing location due to a failed mapper),
+   * throws a FetchFailedException.
+   *
+   * @param shuffleId Identifier for the shuffle
+   * @param reduceId Identifier for the reduce task
+   * @param statuses List of map statuses, indexed by map ID.
+   * @return A sequence of 2-item tuples, where the first item in the tuple is a BlockManagerId,
+   *         and the second item is a sequence of (shuffle block id, shuffle block size) tuples
+   *         describing the shuffle blocks that are stored at that block manager.
+   */
   private def convertMapStatuses(
       shuffleId: Int,
       reduceId: Int,
-      statuses: Array[MapStatus]): Array[(BlockManagerId, Long)] = {
+      statuses: Array[MapStatus]): Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
     assert (statuses != null)
-    statuses.map {
-      status =>
-        if (status == null) {
-          logError("Missing an output location for shuffle " + shuffleId)
-          throw new MetadataFetchFailedException(
-            shuffleId, reduceId, "Missing an output location for shuffle " + shuffleId)
-        } else {
-          (status.location, status.getSizeForBlock(reduceId))
-        }
+    val splitsByAddress = new HashMap[BlockManagerId, ArrayBuffer[(BlockId, Long)]]
+    for ((status, mapId) <- statuses.zipWithIndex) {
+      if (status == null) {
+        val errorMessage = s"Missing an output location for shuffle $shuffleId"
+        logError(errorMessage)
+        throw new MetadataFetchFailedException(shuffleId, reduceId, errorMessage)
+      } else {
+        splitsByAddress.getOrElseUpdate(status.location, ArrayBuffer()) +=
+          ((ShuffleBlockId(shuffleId, mapId, reduceId), status.getSizeForBlock(reduceId)))
+      }
     }
+
+    splitsByAddress.toSeq
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
deleted file mode 100644
index 9d8e7e9f03aea..0000000000000
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.shuffle.hash
-
-import java.io.InputStream
-
-import scala.collection.mutable.{ArrayBuffer, HashMap}
-import scala.util.{Failure, Success}
-
-import org.apache.spark._
-import org.apache.spark.shuffle.FetchFailedException
-import org.apache.spark.storage.{BlockId, BlockManager, BlockManagerId, ShuffleBlockFetcherIterator,
-  ShuffleBlockId}
-
-private[hash] object BlockStoreShuffleFetcher extends Logging {
-  def fetchBlockStreams(
-      shuffleId: Int,
-      reduceId: Int,
-      context: TaskContext,
-      blockManager: BlockManager,
-      mapOutputTracker: MapOutputTracker)
-    : Iterator[(BlockId, InputStream)] =
-  {
-    logDebug("Fetching outputs for shuffle %d, reduce %d".format(shuffleId, reduceId))
-
-    val startTime = System.currentTimeMillis
-    val statuses = mapOutputTracker.getServerStatuses(shuffleId, reduceId)
-    logDebug("Fetching map output location for shuffle %d, reduce %d took %d ms".format(
-      shuffleId, reduceId, System.currentTimeMillis - startTime))
-
-    val splitsByAddress = new HashMap[BlockManagerId, ArrayBuffer[(Int, Long)]]
-    for (((address, size), index) <- statuses.zipWithIndex) {
-      splitsByAddress.getOrElseUpdate(address, ArrayBuffer()) += ((index, size))
-    }
-
-    val blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])] = splitsByAddress.toSeq.map {
-      case (address, splits) =>
-        (address, splits.map(s => (ShuffleBlockId(shuffleId, s._1, reduceId), s._2)))
-    }
-
-    val blockFetcherItr = new ShuffleBlockFetcherIterator(
-      context,
-      blockManager.shuffleClient,
-      blockManager,
-      blocksByAddress,
-      // Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
-      SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024)
-
-    // Make sure that fetch failures are wrapped inside a FetchFailedException for the scheduler
-    blockFetcherItr.map { blockPair =>
-      val blockId = blockPair._1
-      val blockOption = blockPair._2
-      blockOption match {
-        case Success(inputStream) => {
-          (blockId, inputStream)
-        }
-        case Failure(e) => {
-          blockId match {
-            case ShuffleBlockId(shufId, mapId, _) =>
-              val address = statuses(mapId.toInt)._1
-              throw new FetchFailedException(address, shufId.toInt, mapId.toInt, reduceId, e)
-            case _ =>
-              throw new SparkException(
-                "Failed to get block " + blockId + ", which is not a shuffle block", e)
-          }
-        }
-      }
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index d5c9880659dd3..de79fa56f017b 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.shuffle.hash
 
-import org.apache.spark.{InterruptibleIterator, MapOutputTracker, SparkEnv, TaskContext}
+import org.apache.spark.{InterruptibleIterator, Logging, MapOutputTracker, SparkEnv, TaskContext}
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
-import org.apache.spark.storage.BlockManager
+import org.apache.spark.storage.{BlockManager, ShuffleBlockFetcherIterator}
 import org.apache.spark.util.CompletionIterator
 import org.apache.spark.util.collection.ExternalSorter
 
@@ -31,8 +31,8 @@ private[spark] class HashShuffleReader[K, C](
     context: TaskContext,
     blockManager: BlockManager = SparkEnv.get.blockManager,
     mapOutputTracker: MapOutputTracker = SparkEnv.get.mapOutputTracker)
-  extends ShuffleReader[K, C]
-{
+  extends ShuffleReader[K, C] with Logging {
+
   require(endPartition == startPartition + 1,
     "Hash shuffle currently only supports fetching one partition")
 
@@ -40,11 +40,16 @@ private[spark] class HashShuffleReader[K, C](
 
   /** Read the combined key-values for this reduce task */
   override def read(): Iterator[Product2[K, C]] = {
-    val blockStreams = BlockStoreShuffleFetcher.fetchBlockStreams(
-      handle.shuffleId, startPartition, context, blockManager, mapOutputTracker)
+    val blockFetcherItr = new ShuffleBlockFetcherIterator(
+      context,
+      blockManager.shuffleClient,
+      blockManager,
+      mapOutputTracker.getMapSizesByExecutorId(handle.shuffleId, startPartition),
+      // Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
+      SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024)
 
     // Wrap the streams for compression based on configuration
-    val wrappedStreams = blockStreams.map { case (blockId, inputStream) =>
+    val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
       blockManager.wrapForCompression(blockId, inputStream)
     }
 
diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
index e49e39679e940..a759ceb96ec1e 100644
--- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
@@ -21,18 +21,19 @@ import java.io.InputStream
 import java.util.concurrent.LinkedBlockingQueue
 
 import scala.collection.mutable.{ArrayBuffer, HashSet, Queue}
-import scala.util.{Failure, Try}
+import scala.util.control.NonFatal
 
-import org.apache.spark.{Logging, TaskContext}
+import org.apache.spark.{Logging, SparkException, TaskContext}
 import org.apache.spark.network.buffer.ManagedBuffer
 import org.apache.spark.network.shuffle.{BlockFetchingListener, ShuffleClient}
+import org.apache.spark.shuffle.FetchFailedException
 import org.apache.spark.util.Utils
 
 /**
  * An iterator that fetches multiple blocks. For local blocks, it fetches from the local block
  * manager. For remote blocks, it fetches them using the provided BlockTransferService.
  *
- * This creates an iterator of (BlockID, Try[InputStream]) tuples so the caller can handle blocks
+ * This creates an iterator of (BlockID, InputStream) tuples so the caller can handle blocks
  * in a pipelined fashion as they are received.
  *
  * The implementation throttles the remote fetches to they don't exceed maxBytesInFlight to avoid
@@ -53,7 +54,7 @@ final class ShuffleBlockFetcherIterator(
     blockManager: BlockManager,
     blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
     maxBytesInFlight: Long)
-  extends Iterator[(BlockId, Try[InputStream])] with Logging {
+  extends Iterator[(BlockId, InputStream)] with Logging {
 
   import ShuffleBlockFetcherIterator._
 
@@ -115,7 +116,7 @@ final class ShuffleBlockFetcherIterator(
   private[storage] def releaseCurrentResultBuffer(): Unit = {
     // Release the current buffer if necessary
     currentResult match {
-      case SuccessFetchResult(_, _, buf) => buf.release()
+      case SuccessFetchResult(_, _, _, buf) => buf.release()
       case _ =>
     }
     currentResult = null
@@ -132,7 +133,7 @@ final class ShuffleBlockFetcherIterator(
     while (iter.hasNext) {
       val result = iter.next()
       result match {
-        case SuccessFetchResult(_, _, buf) => buf.release()
+        case SuccessFetchResult(_, _, _, buf) => buf.release()
         case _ =>
       }
     }
@@ -157,7 +158,7 @@ final class ShuffleBlockFetcherIterator(
             // Increment the ref count because we need to pass this to a different thread.
             // This needs to be released after use.
             buf.retain()
-            results.put(new SuccessFetchResult(BlockId(blockId), sizeMap(blockId), buf))
+            results.put(new SuccessFetchResult(BlockId(blockId), address, sizeMap(blockId), buf))
             shuffleMetrics.incRemoteBytesRead(buf.size)
             shuffleMetrics.incRemoteBlocksFetched(1)
           }
@@ -166,7 +167,7 @@ final class ShuffleBlockFetcherIterator(
 
         override def onBlockFetchFailure(blockId: String, e: Throwable): Unit = {
           logError(s"Failed to get block(s) from ${req.address.host}:${req.address.port}", e)
-          results.put(new FailureFetchResult(BlockId(blockId), e))
+          results.put(new FailureFetchResult(BlockId(blockId), address, e))
         }
       }
     )
@@ -238,12 +239,12 @@ final class ShuffleBlockFetcherIterator(
         shuffleMetrics.incLocalBlocksFetched(1)
         shuffleMetrics.incLocalBytesRead(buf.size)
         buf.retain()
-        results.put(new SuccessFetchResult(blockId, 0, buf))
+        results.put(new SuccessFetchResult(blockId, blockManager.blockManagerId, 0, buf))
       } catch {
         case e: Exception =>
           // If we see an exception, stop immediately.
           logError(s"Error occurred while fetching local blocks", e)
-          results.put(new FailureFetchResult(blockId, e))
+          results.put(new FailureFetchResult(blockId, blockManager.blockManagerId, e))
           return
       }
     }
@@ -275,12 +276,14 @@ final class ShuffleBlockFetcherIterator(
   override def hasNext: Boolean = numBlocksProcessed < numBlocksToFetch
 
   /**
-   * Fetches the next (BlockId, Try[InputStream]). If a task fails, the ManagedBuffers
+   * Fetches the next (BlockId, InputStream). If a task fails, the ManagedBuffers
    * underlying each InputStream will be freed by the cleanup() method registered with the
    * TaskCompletionListener. However, callers should close() these InputStreams
    * as soon as they are no longer needed, in order to release memory as early as possible.
+   *
+   * Throws a FetchFailedException if the next block could not be fetched.
    */
-  override def next(): (BlockId, Try[InputStream]) = {
+  override def next(): (BlockId, InputStream) = {
     numBlocksProcessed += 1
     val startFetchWait = System.currentTimeMillis()
     currentResult = results.take()
@@ -289,7 +292,7 @@ final class ShuffleBlockFetcherIterator(
     shuffleMetrics.incFetchWaitTime(stopFetchWait - startFetchWait)
 
     result match {
-      case SuccessFetchResult(_, size, _) => bytesInFlight -= size
+      case SuccessFetchResult(_, _, size, _) => bytesInFlight -= size
       case _ =>
     }
     // Send fetch requests up to maxBytesInFlight
@@ -298,19 +301,28 @@ final class ShuffleBlockFetcherIterator(
       sendRequest(fetchRequests.dequeue())
     }
 
-    val iteratorTry: Try[InputStream] = result match {
-      case FailureFetchResult(_, e) =>
-        Failure(e)
-      case SuccessFetchResult(blockId, _, buf) =>
-        // There is a chance that createInputStream can fail (e.g. fetching a local file that does
-        // not exist, SPARK-4085). In that case, we should propagate the right exception so
-        // the scheduler gets a FetchFailedException.
-        Try(buf.createInputStream()).map { inputStream =>
-          new BufferReleasingInputStream(inputStream, this)
+    result match {
+      case FailureFetchResult(blockId, address, e) =>
+        throwFetchFailedException(blockId, address, e)
+
+      case SuccessFetchResult(blockId, address, _, buf) =>
+        try {
+          (result.blockId, new BufferReleasingInputStream(buf.createInputStream(), this))
+        } catch {
+          case NonFatal(t) =>
+            throwFetchFailedException(blockId, address, t)
         }
     }
+  }
 
-    (result.blockId, iteratorTry)
+  private def throwFetchFailedException(blockId: BlockId, address: BlockManagerId, e: Throwable) = {
+    blockId match {
+      case ShuffleBlockId(shufId, mapId, reduceId) =>
+        throw new FetchFailedException(address, shufId.toInt, mapId.toInt, reduceId, e)
+      case _ =>
+        throw new SparkException(
+          "Failed to get block " + blockId + ", which is not a shuffle block", e)
+    }
   }
 }
 
@@ -366,16 +378,22 @@ object ShuffleBlockFetcherIterator {
    */
   private[storage] sealed trait FetchResult {
     val blockId: BlockId
+    val address: BlockManagerId
   }
 
   /**
    * Result of a fetch from a remote block successfully.
    * @param blockId block id
+   * @param address BlockManager that the block was fetched from.
    * @param size estimated size of the block, used to calculate bytesInFlight.
    *             Note that this is NOT the exact bytes.
    * @param buf [[ManagedBuffer]] for the content.
    */
-  private[storage] case class SuccessFetchResult(blockId: BlockId, size: Long, buf: ManagedBuffer)
+  private[storage] case class SuccessFetchResult(
+      blockId: BlockId,
+      address: BlockManagerId,
+      size: Long,
+      buf: ManagedBuffer)
     extends FetchResult {
     require(buf != null)
     require(size >= 0)
@@ -384,8 +402,12 @@ object ShuffleBlockFetcherIterator {
   /**
    * Result of a fetch from a remote block unsuccessfully.
    * @param blockId block id
+   * @param address BlockManager that the block was attempted to be fetched from
    * @param e the failure exception
    */
-  private[storage] case class FailureFetchResult(blockId: BlockId, e: Throwable)
+  private[storage] case class FailureFetchResult(
+      blockId: BlockId,
+      address: BlockManagerId,
+      e: Throwable)
     extends FetchResult
 }
diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
index 7a1961137cce5..af4e68950f75a 100644
--- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
@@ -17,13 +17,15 @@
 
 package org.apache.spark
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.mockito.Mockito._
 import org.mockito.Matchers.{any, isA}
 
 import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcCallContext, RpcEnv}
 import org.apache.spark.scheduler.{CompressedMapStatus, MapStatus}
 import org.apache.spark.shuffle.FetchFailedException
-import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.storage.{BlockManagerId, ShuffleBlockId}
 
 class MapOutputTrackerSuite extends SparkFunSuite {
   private val conf = new SparkConf
@@ -55,9 +57,11 @@ class MapOutputTrackerSuite extends SparkFunSuite {
         Array(1000L, 10000L)))
     tracker.registerMapOutput(10, 1, MapStatus(BlockManagerId("b", "hostB", 1000),
         Array(10000L, 1000L)))
-    val statuses = tracker.getServerStatuses(10, 0)
-    assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000),
-                                  (BlockManagerId("b", "hostB", 1000), size10000)))
+    val statuses = tracker.getMapSizesByExecutorId(10, 0)
+    assert(statuses.toSet ===
+      Seq((BlockManagerId("a", "hostA", 1000), ArrayBuffer((ShuffleBlockId(10, 0, 0), size1000))),
+          (BlockManagerId("b", "hostB", 1000), ArrayBuffer((ShuffleBlockId(10, 1, 0), size10000))))
+        .toSet)
     tracker.stop()
     rpcEnv.shutdown()
   }
@@ -75,10 +79,10 @@ class MapOutputTrackerSuite extends SparkFunSuite {
     tracker.registerMapOutput(10, 1, MapStatus(BlockManagerId("b", "hostB", 1000),
       Array(compressedSize10000, compressedSize1000)))
     assert(tracker.containsShuffle(10))
-    assert(tracker.getServerStatuses(10, 0).nonEmpty)
+    assert(tracker.getMapSizesByExecutorId(10, 0).nonEmpty)
     tracker.unregisterShuffle(10)
     assert(!tracker.containsShuffle(10))
-    assert(tracker.getServerStatuses(10, 0).isEmpty)
+    assert(tracker.getMapSizesByExecutorId(10, 0).isEmpty)
 
     tracker.stop()
     rpcEnv.shutdown()
@@ -104,7 +108,7 @@ class MapOutputTrackerSuite extends SparkFunSuite {
     // The remaining reduce task might try to grab the output despite the shuffle failure;
     // this should cause it to fail, and the scheduler will ignore the failure due to the
     // stage already being aborted.
-    intercept[FetchFailedException] { tracker.getServerStatuses(10, 1) }
+    intercept[FetchFailedException] { tracker.getMapSizesByExecutorId(10, 1) }
 
     tracker.stop()
     rpcEnv.shutdown()
@@ -126,23 +130,23 @@ class MapOutputTrackerSuite extends SparkFunSuite {
     masterTracker.registerShuffle(10, 1)
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
-    intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) }
+    intercept[FetchFailedException] { slaveTracker.getMapSizesByExecutorId(10, 0) }
 
     val size1000 = MapStatus.decompressSize(MapStatus.compressSize(1000L))
     masterTracker.registerMapOutput(10, 0, MapStatus(
       BlockManagerId("a", "hostA", 1000), Array(1000L)))
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
-    assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-      Seq((BlockManagerId("a", "hostA", 1000), size1000)))
+    assert(slaveTracker.getMapSizesByExecutorId(10, 0) ===
+      Seq((BlockManagerId("a", "hostA", 1000), ArrayBuffer((ShuffleBlockId(10, 0, 0), size1000)))))
 
     masterTracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000))
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
-    intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) }
+    intercept[FetchFailedException] { slaveTracker.getMapSizesByExecutorId(10, 0) }
 
     // failure should be cached
-    intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) }
+    intercept[FetchFailedException] { slaveTracker.getMapSizesByExecutorId(10, 0) }
 
     masterTracker.stop()
     slaveTracker.stop()
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index c3c2b1ffc1efa..b68102bfb949f 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -66,8 +66,8 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
     // All blocks must have non-zero size
     (0 until NUM_BLOCKS).foreach { id =>
-      val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id)
-      assert(statuses.forall(s => s._2 > 0))
+      val statuses = SparkEnv.get.mapOutputTracker.getMapSizesByExecutorId(shuffleId, id)
+      assert(statuses.forall(_._2.forall(blockIdSizePair => blockIdSizePair._2 > 0)))
     }
   }
 
@@ -105,8 +105,8 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
     assert(c.count === 4)
 
     val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>
-      val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id)
-      statuses.map(x => x._2)
+      val statuses = SparkEnv.get.mapOutputTracker.getMapSizesByExecutorId(shuffleId, id)
+      statuses.flatMap(_._2.map(_._2))
     }
     val nonEmptyBlocks = blockSizes.filter(x => x > 0)
 
@@ -130,8 +130,8 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
     assert(c.count === 4)
 
     val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>
-      val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id)
-      statuses.map(x => x._2)
+      val statuses = SparkEnv.get.mapOutputTracker.getMapSizesByExecutorId(shuffleId, id)
+      statuses.flatMap(_._2.map(_._2))
     }
     val nonEmptyBlocks = blockSizes.filter(x => x > 0)
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 86728cb2b62af..3462a82c9cdd3 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -483,8 +483,8 @@ class DAGSchedulerSuite
     complete(taskSets(0), Seq(
         (Success, makeMapStatus("hostA", 1)),
         (Success, makeMapStatus("hostB", 1))))
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) ===
-           Array(makeBlockManagerId("hostA"), makeBlockManagerId("hostB")))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostA"), makeBlockManagerId("hostB")))
     complete(taskSets(1), Seq((Success, 42)))
     assert(results === Map(0 -> 42))
     assertDataStructuresEmpty()
@@ -510,8 +510,8 @@ class DAGSchedulerSuite
     // have the 2nd attempt pass
     complete(taskSets(2), Seq((Success, makeMapStatus("hostA", reduceRdd.partitions.size))))
     // we can see both result blocks now
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) ===
-      Array("hostA", "hostB"))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1.host).toSet ===
+      HashSet("hostA", "hostB"))
     complete(taskSets(3), Seq((Success, 43)))
     assert(results === Map(0 -> 42, 1 -> 43))
     assertDataStructuresEmpty()
@@ -527,8 +527,8 @@ class DAGSchedulerSuite
       (Success, makeMapStatus("hostA", reduceRdd.partitions.size)),
       (Success, makeMapStatus("hostB", reduceRdd.partitions.size))))
     // The MapOutputTracker should know about both map output locations.
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) ===
-      Array("hostA", "hostB"))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1.host).toSet ===
+      HashSet("hostA", "hostB"))
 
     // The first result task fails, with a fetch failure for the output from the first mapper.
     runEvent(CompletionEvent(
@@ -577,10 +577,10 @@ class DAGSchedulerSuite
       (Success, makeMapStatus("hostA", 2)),
       (Success, makeMapStatus("hostB", 2))))
     // The MapOutputTracker should know about both map output locations.
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) ===
-      Array("hostA", "hostB"))
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 1).map(_._1.host) ===
-      Array("hostA", "hostB"))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1.host).toSet ===
+      HashSet("hostA", "hostB"))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 1).map(_._1.host).toSet ===
+      HashSet("hostA", "hostB"))
 
     // The first result task fails, with a fetch failure for the output from the first mapper.
     runEvent(CompletionEvent(
@@ -713,8 +713,8 @@ class DAGSchedulerSuite
     taskSet.tasks(1).epoch = newEpoch
     runEvent(CompletionEvent(taskSet.tasks(1), Success, makeMapStatus("hostA",
       reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) ===
-           Array(makeBlockManagerId("hostB"), makeBlockManagerId("hostA")))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
+           HashSet(makeBlockManagerId("hostB"), makeBlockManagerId("hostA")))
     complete(taskSets(1), Seq((Success, 42), (Success, 43)))
     assert(results === Map(0 -> 42, 1 -> 43))
     assertDataStructuresEmpty()
@@ -809,8 +809,8 @@ class DAGSchedulerSuite
        (Success, makeMapStatus("hostB", 1))))
     // have hostC complete the resubmitted task
     complete(taskSets(1), Seq((Success, makeMapStatus("hostC", 1))))
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) ===
-           Array(makeBlockManagerId("hostC"), makeBlockManagerId("hostB")))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
+           HashSet(makeBlockManagerId("hostC"), makeBlockManagerId("hostB")))
     complete(taskSets(2), Seq((Success, 42)))
     assert(results === Map(0 -> 42))
     assertDataStructuresEmpty()
@@ -981,8 +981,8 @@ class DAGSchedulerSuite
     submit(reduceRdd, Array(0))
     complete(taskSets(0), Seq(
         (Success, makeMapStatus("hostA", 1))))
-    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) ===
-           Array(makeBlockManagerId("hostA")))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
+           HashSet(makeBlockManagerId("hostA")))
 
     // Reducer should run on the same host that map task ran
     val reduceTaskSet = taskSets(1)
diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
index 28ca68698e3dc..6c9cb448e7833 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
@@ -115,11 +115,15 @@ class HashShuffleReaderSuite extends SparkFunSuite with LocalSparkContext {
     // Make a mocked MapOutputTracker for the shuffle reader to use to determine what
     // shuffle data to read.
     val mapOutputTracker = mock(classOf[MapOutputTracker])
-    // Test a scenario where all data is local, just to avoid creating a bunch of additional mocks
-    // for the code to read data over the network.
-    val statuses: Array[(BlockManagerId, Long)] =
-      Array.fill(numMaps)((localBlockManagerId, byteOutputStream.size().toLong))
-    when(mapOutputTracker.getServerStatuses(shuffleId, reduceId)).thenReturn(statuses)
+    when(mapOutputTracker.getMapSizesByExecutorId(shuffleId, reduceId)).thenReturn {
+      // Test a scenario where all data is local, to avoid creating a bunch of additional mocks
+      // for the code to read data over the network.
+      val shuffleBlockIdsAndSizes = (0 until numMaps).map { mapId =>
+        val shuffleBlockId = ShuffleBlockId(shuffleId, mapId, reduceId)
+        (shuffleBlockId, byteOutputStream.size().toLong)
+      }
+      Seq((localBlockManagerId, shuffleBlockIdsAndSizes))
+    }
 
     // Create a mocked shuffle handle to pass into HashShuffleReader.
     val shuffleHandle = {
diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
index 9ced4148d7206..64f3fbdcebed9 100644
--- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
@@ -33,6 +33,7 @@ import org.apache.spark.{SparkFunSuite, TaskContextImpl}
 import org.apache.spark.network._
 import org.apache.spark.network.buffer.ManagedBuffer
 import org.apache.spark.network.shuffle.BlockFetchingListener
+import org.apache.spark.shuffle.FetchFailedException
 
 
 class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodTester {
@@ -106,13 +107,11 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     for (i <- 0 until 5) {
       assert(iterator.hasNext, s"iterator should have 5 elements but actually has $i elements")
       val (blockId, inputStream) = iterator.next()
-      assert(inputStream.isSuccess,
-        s"iterator should have 5 elements defined but actually has $i elements")
 
       // Make sure we release buffers when a wrapped input stream is closed.
       val mockBuf = localBlocks.getOrElse(blockId, remoteBlocks(blockId))
       // Note: ShuffleBlockFetcherIterator wraps input streams in a BufferReleasingInputStream
-      val wrappedInputStream = inputStream.get.asInstanceOf[BufferReleasingInputStream]
+      val wrappedInputStream = inputStream.asInstanceOf[BufferReleasingInputStream]
       verify(mockBuf, times(0)).release()
       val delegateAccess = PrivateMethod[InputStream]('delegate)
 
@@ -175,11 +174,11 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
       48 * 1024 * 1024)
 
     verify(blocks(ShuffleBlockId(0, 0, 0)), times(0)).release()
-    iterator.next()._2.get.close() // close() first block's input stream
+    iterator.next()._2.close() // close() first block's input stream
     verify(blocks(ShuffleBlockId(0, 0, 0)), times(1)).release()
 
     // Get the 2nd block but do not exhaust the iterator
-    val subIter = iterator.next()._2.get
+    val subIter = iterator.next()._2
 
     // Complete the task; then the 2nd block buffer should be exhausted
     verify(blocks(ShuffleBlockId(0, 1, 0)), times(0)).release()
@@ -239,9 +238,10 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     // Continue only after the mock calls onBlockFetchFailure
     sem.acquire()
 
-    // The first block should be defined, and the last two are not defined (due to failure)
-    assert(iterator.next()._2.isSuccess)
-    assert(iterator.next()._2.isFailure)
-    assert(iterator.next()._2.isFailure)
+    // The first block should be returned without an exception, and the last two should throw
+    // FetchFailedExceptions (due to failure)
+    iterator.next()
+    intercept[FetchFailedException] { iterator.next() }
+    intercept[FetchFailedException] { iterator.next() }
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
index 6c40685484ed4..61601016e005e 100644
--- a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.util
 
+import scala.collection.mutable.ArrayBuffer
+
 import java.util.concurrent.TimeoutException
 
 import akka.actor.ActorNotFound
@@ -24,7 +26,7 @@ import akka.actor.ActorNotFound
 import org.apache.spark._
 import org.apache.spark.rpc.RpcEnv
 import org.apache.spark.scheduler.MapStatus
-import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.storage.{BlockManagerId, ShuffleBlockId}
 import org.apache.spark.SSLSampleConfigs._
 
 
@@ -107,8 +109,9 @@ class AkkaUtilsSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
     slaveTracker.updateEpoch(masterTracker.getEpoch)
 
     // this should succeed since security off
-    assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-           Seq((BlockManagerId("a", "hostA", 1000), size1000)))
+    assert(slaveTracker.getMapSizesByExecutorId(10, 0).toSeq ===
+           Seq((BlockManagerId("a", "hostA", 1000),
+             ArrayBuffer((ShuffleBlockId(10, 0, 0), size1000)))))
 
     rpcEnv.shutdown()
     slaveRpcEnv.shutdown()
@@ -153,8 +156,9 @@ class AkkaUtilsSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
     slaveTracker.updateEpoch(masterTracker.getEpoch)
 
     // this should succeed since security on and passwords match
-    assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-           Seq((BlockManagerId("a", "hostA", 1000), size1000)))
+    assert(slaveTracker.getMapSizesByExecutorId(10, 0) ===
+           Seq((BlockManagerId("a", "hostA", 1000),
+             ArrayBuffer((ShuffleBlockId(10, 0, 0), size1000)))))
 
     rpcEnv.shutdown()
     slaveRpcEnv.shutdown()
@@ -232,8 +236,8 @@ class AkkaUtilsSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
     slaveTracker.updateEpoch(masterTracker.getEpoch)
 
     // this should succeed since security off
-    assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-      Seq((BlockManagerId("a", "hostA", 1000), size1000)))
+    assert(slaveTracker.getMapSizesByExecutorId(10, 0) ===
+      Seq((BlockManagerId("a", "hostA", 1000), ArrayBuffer((ShuffleBlockId(10, 0, 0), size1000)))))
 
     rpcEnv.shutdown()
     slaveRpcEnv.shutdown()
@@ -278,8 +282,8 @@ class AkkaUtilsSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
 
-    assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-      Seq((BlockManagerId("a", "hostA", 1000), size1000)))
+    assert(slaveTracker.getMapSizesByExecutorId(10, 0) ===
+      Seq((BlockManagerId("a", "hostA", 1000), ArrayBuffer((ShuffleBlockId(10, 0, 0), size1000)))))
 
     rpcEnv.shutdown()
     slaveRpcEnv.shutdown()

From f5b6dc5e3e7e3b586096b71164f052318b840e8a Mon Sep 17 00:00:00 2001
From: Michael Allman <michael@videoamp.com>
Date: Tue, 21 Jul 2015 11:14:31 +0100
Subject: [PATCH 0520/1454] [SPARK-8401] [BUILD] Scala version switching build
 enhancements

These commits address a few minor issues in the Scala cross-version support in the build:

  1. Correct two missing `${scala.binary.version}` pom file substitutions.
  2. Don't update `scala.binary.version` in parent POM. This property is set through profiles.
  3. Update the source of the generated scaladocs in `docs/_plugins/copy_api_dirs.rb`.
  4. Factor common code out of `dev/change-version-to-*.sh` and add some validation. We also test `sed` to see if it's GNU sed and try `gsed` as an alternative if not. This prevents the script from running with a non-GNU sed.

This is my original work and I license this work to the Spark project under the Apache License.

Author: Michael Allman <michael@videoamp.com>

Closes #6832 from mallman/scala-versions and squashes the following commits:

cde2f17 [Michael Allman] Delete dev/change-version-to-*.sh, replacing them with single dev/change-scala-version.sh script that takes a version as argument
02296f2 [Michael Allman] Make the scala version change scripts cross-platform by restricting ourselves to POSIX sed syntax instead of looking for GNU sed
ad9b40a [Michael Allman] Factor change-scala-version.sh out of change-version-to-*.sh, adding command line argument validation and testing for GNU sed
bdd20bf [Michael Allman] Update source of scaladocs when changing Scala version
475088e [Michael Allman] Replace jackson-module-scala_2.10 with jackson-module-scala_${scala.binary.version}
---
 core/pom.xml                         |  2 +-
 dev/change-scala-version.sh          | 66 ++++++++++++++++++++++++++++
 dev/change-version-to-2.10.sh        | 26 -----------
 dev/change-version-to-2.11.sh        | 26 -----------
 dev/create-release/create-release.sh |  6 +--
 docs/building-spark.md               |  2 +-
 pom.xml                              |  2 +-
 7 files changed, 72 insertions(+), 58 deletions(-)
 create mode 100755 dev/change-scala-version.sh
 delete mode 100755 dev/change-version-to-2.10.sh
 delete mode 100755 dev/change-version-to-2.11.sh

diff --git a/core/pom.xml b/core/pom.xml
index 73f7a75cab9d3..95f36eb348698 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -261,7 +261,7 @@
     </dependency>
     <dependency>
       <groupId>com.fasterxml.jackson.module</groupId>
-      <artifactId>jackson-module-scala_2.10</artifactId>
+      <artifactId>jackson-module-scala_${scala.binary.version}</artifactId>
     </dependency>
     <dependency>
       <groupId>org.apache.derby</groupId>
diff --git a/dev/change-scala-version.sh b/dev/change-scala-version.sh
new file mode 100755
index 0000000000000..b81c00c9d6d9d
--- /dev/null
+++ b/dev/change-scala-version.sh
@@ -0,0 +1,66 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+set -e
+
+usage() {
+  echo "Usage: $(basename $0) <version>" 1>&2
+  exit 1
+}
+
+if [ $# -ne 1 ]; then
+  usage
+fi
+
+TO_VERSION=$1
+
+VALID_VERSIONS=( 2.10 2.11 )
+
+check_scala_version() {
+  for i in ${VALID_VERSIONS[*]}; do [ $i = "$1" ] && return 0; done
+  echo "Invalid Scala version: $1. Valid versions: ${VALID_VERSIONS[*]}" 1>&2
+  exit 1
+}
+
+check_scala_version "$TO_VERSION"
+
+if [ $TO_VERSION = "2.11" ]; then
+  FROM_VERSION="2.10"
+else
+  FROM_VERSION="2.11"
+fi
+
+sed_i() {
+  sed -e "$1" "$2" > "$2.tmp" && mv "$2.tmp" "$2"
+}
+
+export -f sed_i
+
+BASEDIR=$(dirname $0)/..
+find "$BASEDIR" -name 'pom.xml' -not -path '*target*' -print \
+  -exec bash -c "sed_i 's/\(artifactId.*\)_'$FROM_VERSION'/\1_'$TO_VERSION'/g' {}" \;
+
+# Also update <scala.binary.version> in parent POM
+# Match any scala binary version to ensure idempotency
+sed_i '1,/<scala\.binary\.version>[0-9]*\.[0-9]*</s/<scala\.binary\.version>[0-9]*\.[0-9]*</<scala.binary.version>'$TO_VERSION'</' \
+  "$BASEDIR/pom.xml"
+
+# Update source of scaladocs
+echo "$BASEDIR/docs/_plugins/copy_api_dirs.rb"
+sed_i 's/scala\-'$FROM_VERSION'/scala\-'$TO_VERSION'/' "$BASEDIR/docs/_plugins/copy_api_dirs.rb"
diff --git a/dev/change-version-to-2.10.sh b/dev/change-version-to-2.10.sh
deleted file mode 100755
index c4adb1f96b7d3..0000000000000
--- a/dev/change-version-to-2.10.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Note that this will not necessarily work as intended with non-GNU sed (e.g. OS X)
-BASEDIR=$(dirname $0)/..
-find $BASEDIR -name 'pom.xml' | grep -v target \
-  | xargs -I {} sed -i -e 's/\(artifactId.*\)_2.11/\1_2.10/g' {}
-
-# Also update <scala.binary.version> in parent POM
-sed -i -e '0,/<scala\.binary\.version>2.11</s//<scala.binary.version>2.10</' $BASEDIR/pom.xml
diff --git a/dev/change-version-to-2.11.sh b/dev/change-version-to-2.11.sh
deleted file mode 100755
index d370019dec34d..0000000000000
--- a/dev/change-version-to-2.11.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Note that this will not necessarily work as intended with non-GNU sed (e.g. OS X)
-BASEDIR=$(dirname $0)/..
-find $BASEDIR -name 'pom.xml' | grep -v target \
-  | xargs -I {} sed -i -e 's/\(artifactId.*\)_2.10/\1_2.11/g' {}
-
-# Also update <scala.binary.version> in parent POM
-sed -i -e '0,/<scala\.binary\.version>2.10</s//<scala.binary.version>2.11</' $BASEDIR/pom.xml
diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 30190dcd41ec5..86a7a4068c40e 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -122,13 +122,13 @@ if [[ ! "$@" =~ --skip-publish ]]; then
     -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
-  ./dev/change-version-to-2.11.sh
+  ./dev/change-scala-version.sh 2.11
   
   build/mvn -DskipTests -Pyarn -Phive -Prelease\
     -Dscala-2.11 -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
-  ./dev/change-version-to-2.10.sh
+  ./dev/change-scala-version.sh 2.10
 
   pushd $SPARK_REPO
 
@@ -205,7 +205,7 @@ if [[ ! "$@" =~ --skip-package ]]; then
 
     # TODO There should probably be a flag to make-distribution to allow 2.11 support
     if [[ $FLAGS == *scala-2.11* ]]; then
-      ./dev/change-version-to-2.11.sh
+      ./dev/change-scala-version.sh 2.11
     fi
 
     export ZINC_PORT=$ZINC_PORT
diff --git a/docs/building-spark.md b/docs/building-spark.md
index 2128fdffecc05..a5da3b39502e2 100644
--- a/docs/building-spark.md
+++ b/docs/building-spark.md
@@ -124,7 +124,7 @@ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -Dskip
 # Building for Scala 2.11
 To produce a Spark package compiled with Scala 2.11, use the `-Dscala-2.11` property:
 
-    dev/change-version-to-2.11.sh
+    dev/change-scala-version.sh 2.11
     mvn -Pyarn -Phadoop-2.4 -Dscala-2.11 -DskipTests clean package
 
 Spark does not yet support its JDBC component for Scala 2.11.
diff --git a/pom.xml b/pom.xml
index 2de0c35fbd51a..1f44dc8abe1d4 100644
--- a/pom.xml
+++ b/pom.xml
@@ -614,7 +614,7 @@
            15.0, which causes runtime incompatibility issues. -->
       <dependency>
         <groupId>com.fasterxml.jackson.module</groupId>
-        <artifactId>jackson-module-scala_2.10</artifactId>
+        <artifactId>jackson-module-scala_${scala.binary.version}</artifactId>
         <version>${fasterxml.jackson.version}</version>
         <exclusions>
           <exclusion>

From be5c5d3741256697cc76938a8ed6f609eb2d4b11 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 21 Jul 2015 08:25:50 -0700
Subject: [PATCH 0521/1454] [SPARK-9081] [SPARK-9168] [SQL] nanvl &
 dropna/fillna supporting nan as well

JIRA:
https://issues.apache.org/jira/browse/SPARK-9081
https://issues.apache.org/jira/browse/SPARK-9168

This PR target at two modifications:
1.  Change `isNaN` to return `false` on `null` input
2.  Make `dropna` and `fillna` to fill/drop NaN values as well
3.  Implement `nanvl`

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7523 from yjshen/fillna_dropna and squashes the following commits:

f0a51db [Yijie Shen] make coalesce untouched and implement nanvl
1d3e35f [Yijie Shen] make Coalesce aware of NaN in order to support fillna
2760cbc [Yijie Shen] change isNaN(null) to false as well as implement dropna
---
 .../catalyst/analysis/FunctionRegistry.scala  |   1 +
 .../catalyst/expressions/nullFunctions.scala  | 104 ++++++++++++++----
 .../sql/catalyst/expressions/predicates.scala |   5 +-
 .../expressions/NullFunctionsSuite.scala      |  39 ++++++-
 .../scala/org/apache/spark/sql/Column.scala   |   2 +-
 .../spark/sql/DataFrameNaFunctions.scala      |  52 +++++----
 .../org/apache/spark/sql/functions.scala      |  13 ++-
 .../spark/sql/ColumnExpressionSuite.scala     |  25 ++++-
 .../spark/sql/DataFrameNaFunctionsSuite.scala |  69 ++++++------
 9 files changed, 222 insertions(+), 88 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 13523720daff0..e3d8d2adf2135 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -89,6 +89,7 @@ object FunctionRegistry {
     expression[CreateStruct]("struct"),
     expression[CreateNamedStruct]("named_struct"),
     expression[Sqrt]("sqrt"),
+    expression[NaNvl]("nanvl"),
 
     // math functions
     expression[Acos]("acos"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 98c67084642e3..287718fab7f0d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -83,7 +83,7 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
 
 
 /**
- * Evaluates to `true` if it's NaN or null
+ * Evaluates to `true` iff it's NaN.
  */
 case class IsNaN(child: Expression) extends UnaryExpression
   with Predicate with ImplicitCastInputTypes {
@@ -95,7 +95,7 @@ case class IsNaN(child: Expression) extends UnaryExpression
   override def eval(input: InternalRow): Any = {
     val value = child.eval(input)
     if (value == null) {
-      true
+      false
     } else {
       child.dataType match {
         case DoubleType => value.asInstanceOf[Double].isNaN
@@ -107,26 +107,65 @@ case class IsNaN(child: Expression) extends UnaryExpression
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val eval = child.gen(ctx)
     child.dataType match {
-      case FloatType =>
+      case DoubleType | FloatType =>
         s"""
           ${eval.code}
           boolean ${ev.isNull} = false;
           ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (${eval.isNull}) {
-            ${ev.primitive} = true;
-          } else {
-            ${ev.primitive} = Float.isNaN(${eval.primitive});
-          }
+          ${ev.primitive} = !${eval.isNull} && Double.isNaN(${eval.primitive});
         """
-      case DoubleType =>
+    }
+  }
+}
+
+/**
+ * An Expression evaluates to `left` iff it's not NaN, or evaluates to `right` otherwise.
+ * This Expression is useful for mapping NaN values to null.
+ */
+case class NaNvl(left: Expression, right: Expression)
+    extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def dataType: DataType = left.dataType
+
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(DoubleType, FloatType), TypeCollection(DoubleType, FloatType))
+
+  override def eval(input: InternalRow): Any = {
+    val value = left.eval(input)
+    if (value == null) {
+      null
+    } else {
+      left.dataType match {
+        case DoubleType =>
+          if (!value.asInstanceOf[Double].isNaN) value else right.eval(input)
+        case FloatType =>
+          if (!value.asInstanceOf[Float].isNaN) value else right.eval(input)
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val leftGen = left.gen(ctx)
+    val rightGen = right.gen(ctx)
+    left.dataType match {
+      case DoubleType | FloatType =>
         s"""
-          ${eval.code}
+          ${leftGen.code}
           boolean ${ev.isNull} = false;
           ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (${eval.isNull}) {
-            ${ev.primitive} = true;
+          if (${leftGen.isNull}) {
+            ${ev.isNull} = true;
           } else {
-            ${ev.primitive} = Double.isNaN(${eval.primitive});
+            if (!Double.isNaN(${leftGen.primitive})) {
+              ${ev.primitive} = ${leftGen.primitive};
+            } else {
+              ${rightGen.code}
+              if (${rightGen.isNull}) {
+                ${ev.isNull} = true;
+              } else {
+                ${ev.primitive} = ${rightGen.primitive};
+              }
+            }
           }
         """
     }
@@ -186,8 +225,15 @@ case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate
     var numNonNulls = 0
     var i = 0
     while (i < childrenArray.length && numNonNulls < n) {
-      if (childrenArray(i).eval(input) != null) {
-        numNonNulls += 1
+      val evalC = childrenArray(i).eval(input)
+      if (evalC != null) {
+        childrenArray(i).dataType match {
+          case DoubleType =>
+            if (!evalC.asInstanceOf[Double].isNaN) numNonNulls += 1
+          case FloatType =>
+            if (!evalC.asInstanceOf[Float].isNaN) numNonNulls += 1
+          case _ => numNonNulls += 1
+        }
       }
       i += 1
     }
@@ -198,14 +244,26 @@ case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate
     val nonnull = ctx.freshName("nonnull")
     val code = children.map { e =>
       val eval = e.gen(ctx)
-      s"""
-        if ($nonnull < $n) {
-          ${eval.code}
-          if (!${eval.isNull}) {
-            $nonnull += 1;
-          }
-        }
-      """
+      e.dataType match {
+        case DoubleType | FloatType =>
+          s"""
+            if ($nonnull < $n) {
+              ${eval.code}
+              if (!${eval.isNull} && !Double.isNaN(${eval.primitive})) {
+                $nonnull += 1;
+              }
+            }
+          """
+        case _ =>
+          s"""
+            if ($nonnull < $n) {
+              ${eval.code}
+              if (!${eval.isNull}) {
+                $nonnull += 1;
+              }
+            }
+          """
+      }
     }.mkString("\n")
     s"""
       int $nonnull = 0;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index a53ec31ee6a4b..3f1bd2a925fe7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.util.TypeUtils
-import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -121,7 +121,6 @@ case class InSet(child: Expression, hset: Set[Any])
   }
 }
 
-
 case class And(left: Expression, right: Expression) extends BinaryOperator with Predicate {
 
   override def inputType: AbstractDataType = BooleanType
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
index 765cc7a969b5d..0728f6695c39d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
@@ -49,12 +49,22 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(IsNaN(Literal(Double.NaN)), true)
     checkEvaluation(IsNaN(Literal(Float.NaN)), true)
     checkEvaluation(IsNaN(Literal(math.log(-3))), true)
-    checkEvaluation(IsNaN(Literal.create(null, DoubleType)), true)
+    checkEvaluation(IsNaN(Literal.create(null, DoubleType)), false)
     checkEvaluation(IsNaN(Literal(Double.PositiveInfinity)), false)
     checkEvaluation(IsNaN(Literal(Float.MaxValue)), false)
     checkEvaluation(IsNaN(Literal(5.5f)), false)
   }
 
+  test("nanvl") {
+    checkEvaluation(NaNvl(Literal(5.0), Literal.create(null, DoubleType)), 5.0)
+    checkEvaluation(NaNvl(Literal.create(null, DoubleType), Literal(5.0)), null)
+    checkEvaluation(NaNvl(Literal.create(null, DoubleType), Literal(Double.NaN)), null)
+    checkEvaluation(NaNvl(Literal(Double.NaN), Literal(5.0)), 5.0)
+    checkEvaluation(NaNvl(Literal(Double.NaN), Literal.create(null, DoubleType)), null)
+    assert(NaNvl(Literal(Double.NaN), Literal(Double.NaN)).
+      eval(EmptyRow).asInstanceOf[Double].isNaN)
+  }
+
   test("coalesce") {
     testAllTypes { (value: Any, tpe: DataType) =>
       val lit = Literal.create(value, tpe)
@@ -66,4 +76,31 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(Coalesce(Seq(nullLit, nullLit, lit)), value)
     }
   }
+
+  test("AtLeastNNonNulls") {
+    val mix = Seq(Literal("x"),
+      Literal.create(null, StringType),
+      Literal.create(null, DoubleType),
+      Literal(Double.NaN),
+      Literal(5f))
+
+    val nanOnly = Seq(Literal("x"),
+      Literal(10.0),
+      Literal(Float.NaN),
+      Literal(math.log(-2)),
+      Literal(Double.MaxValue))
+
+    val nullOnly = Seq(Literal("x"),
+      Literal.create(null, DoubleType),
+      Literal.create(null, DecimalType.Unlimited),
+      Literal(Float.MaxValue),
+      Literal(false))
+
+    checkEvaluation(AtLeastNNonNulls(2, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, mix), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, nanOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(4, nanOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(4, nullOnly), false, EmptyRow)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 221cd04c6d288..6e2a6525bf17e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -401,7 +401,7 @@ class Column(protected[sql] val expr: Expression) extends Logging {
   }
 
   /**
-   * True if the current expression is NaN or null
+   * True if the current expression is NaN.
    *
    * @group expr_ops
    * @since 1.5.0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index 8681a56c82f1e..a4fd4cf3b330b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -37,24 +37,24 @@ import org.apache.spark.sql.types._
 final class DataFrameNaFunctions private[sql](df: DataFrame) {
 
   /**
-   * Returns a new [[DataFrame]] that drops rows containing any null values.
+   * Returns a new [[DataFrame]] that drops rows containing any null or NaN values.
    *
    * @since 1.3.1
    */
   def drop(): DataFrame = drop("any", df.columns)
 
   /**
-   * Returns a new [[DataFrame]] that drops rows containing null values.
+   * Returns a new [[DataFrame]] that drops rows containing null or NaN values.
    *
-   * If `how` is "any", then drop rows containing any null values.
-   * If `how` is "all", then drop rows only if every column is null for that row.
+   * If `how` is "any", then drop rows containing any null or NaN values.
+   * If `how` is "all", then drop rows only if every column is null or NaN for that row.
    *
    * @since 1.3.1
    */
   def drop(how: String): DataFrame = drop(how, df.columns)
 
   /**
-   * Returns a new [[DataFrame]] that drops rows containing any null values
+   * Returns a new [[DataFrame]] that drops rows containing any null or NaN values
    * in the specified columns.
    *
    * @since 1.3.1
@@ -62,7 +62,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   def drop(cols: Array[String]): DataFrame = drop(cols.toSeq)
 
   /**
-   * (Scala-specific) Returns a new [[DataFrame ]] that drops rows containing any null values
+   * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing any null or NaN values
    * in the specified columns.
    *
    * @since 1.3.1
@@ -70,22 +70,22 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   def drop(cols: Seq[String]): DataFrame = drop(cols.size, cols)
 
   /**
-   * Returns a new [[DataFrame]] that drops rows containing null values
+   * Returns a new [[DataFrame]] that drops rows containing null or NaN values
    * in the specified columns.
    *
-   * If `how` is "any", then drop rows containing any null values in the specified columns.
-   * If `how` is "all", then drop rows only if every specified column is null for that row.
+   * If `how` is "any", then drop rows containing any null or NaN values in the specified columns.
+   * If `how` is "all", then drop rows only if every specified column is null or NaN for that row.
    *
    * @since 1.3.1
    */
   def drop(how: String, cols: Array[String]): DataFrame = drop(how, cols.toSeq)
 
   /**
-   * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing null values
+   * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing null or NaN values
    * in the specified columns.
    *
-   * If `how` is "any", then drop rows containing any null values in the specified columns.
-   * If `how` is "all", then drop rows only if every specified column is null for that row.
+   * If `how` is "any", then drop rows containing any null or NaN values in the specified columns.
+   * If `how` is "all", then drop rows only if every specified column is null or NaN for that row.
    *
    * @since 1.3.1
    */
@@ -98,15 +98,16 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   }
 
   /**
-   * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null values.
+   * Returns a new [[DataFrame]] that drops rows containing
+   * less than `minNonNulls` non-null and non-NaN values.
    *
    * @since 1.3.1
    */
   def drop(minNonNulls: Int): DataFrame = drop(minNonNulls, df.columns)
 
   /**
-   * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null
-   * values in the specified columns.
+   * Returns a new [[DataFrame]] that drops rows containing
+   * less than `minNonNulls` non-null and non-NaN values in the specified columns.
    *
    * @since 1.3.1
    */
@@ -114,32 +115,33 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
 
   /**
    * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing less than
-   * `minNonNulls` non-null values in the specified columns.
+   * `minNonNulls` non-null and non-NaN values in the specified columns.
    *
    * @since 1.3.1
    */
   def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = {
-    // Filtering condition -- only keep the row if it has at least `minNonNulls` non-null values.
+    // Filtering condition:
+    // only keep the row if it has at least `minNonNulls` non-null and non-NaN values.
     val predicate = AtLeastNNonNulls(minNonNulls, cols.map(name => df.resolve(name)))
     df.filter(Column(predicate))
   }
 
   /**
-   * Returns a new [[DataFrame]] that replaces null values in numeric columns with `value`.
+   * Returns a new [[DataFrame]] that replaces null or NaN values in numeric columns with `value`.
    *
    * @since 1.3.1
    */
   def fill(value: Double): DataFrame = fill(value, df.columns)
 
   /**
-   * Returns a new [[DataFrame ]] that replaces null values in string columns with `value`.
+   * Returns a new [[DataFrame]] that replaces null values in string columns with `value`.
    *
    * @since 1.3.1
    */
   def fill(value: String): DataFrame = fill(value, df.columns)
 
   /**
-   * Returns a new [[DataFrame]] that replaces null values in specified numeric columns.
+   * Returns a new [[DataFrame]] that replaces null or NaN values in specified numeric columns.
    * If a specified column is not a numeric column, it is ignored.
    *
    * @since 1.3.1
@@ -147,7 +149,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   def fill(value: Double, cols: Array[String]): DataFrame = fill(value, cols.toSeq)
 
   /**
-   * (Scala-specific) Returns a new [[DataFrame]] that replaces null values in specified
+   * (Scala-specific) Returns a new [[DataFrame]] that replaces null or NaN values in specified
    * numeric columns. If a specified column is not a numeric column, it is ignored.
    *
    * @since 1.3.1
@@ -391,7 +393,13 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * Returns a [[Column]] expression that replaces null value in `col` with `replacement`.
    */
   private def fillCol[T](col: StructField, replacement: T): Column = {
-    coalesce(df.col("`" + col.name + "`"), lit(replacement).cast(col.dataType)).as(col.name)
+    col.dataType match {
+      case DoubleType | FloatType =>
+        coalesce(nanvl(df.col("`" + col.name + "`"), lit(null)),
+          lit(replacement).cast(col.dataType)).as(col.name)
+      case _ =>
+        coalesce(df.col("`" + col.name + "`"), lit(replacement).cast(col.dataType)).as(col.name)
+    }
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 60b089180c876..d94d7335828c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -595,7 +595,7 @@ object functions {
   }
 
   /**
-   * Returns the first column that is not null.
+   * Returns the first column that is not null and not NaN.
    * {{{
    *   df.select(coalesce(df("a"), df("b")))
    * }}}
@@ -612,7 +612,7 @@ object functions {
   def explode(e: Column): Column = Explode(e.expr)
 
   /**
-   * Return true if the column is NaN or null
+   * Return true iff the column is NaN.
    *
    * @group normal_funcs
    * @since 1.5.0
@@ -636,6 +636,15 @@ object functions {
    */
   def monotonicallyIncreasingId(): Column = execution.expressions.MonotonicallyIncreasingID()
 
+  /**
+   * Return an alternative value `r` if `l` is NaN.
+   * This function is useful for mapping NaN values to null.
+   *
+   * @group normal_funcs
+   * @since 1.5.0
+   */
+  def nanvl(l: Column, r: Column): Column = NaNvl(l.expr, r.expr)
+
   /**
    * Unary minus, i.e. negate the expression.
    * {{{
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 6bd5804196853..1f9f7118c3f04 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -211,15 +211,34 @@ class ColumnExpressionSuite extends QueryTest {
 
     checkAnswer(
       testData.select($"a".isNaN, $"b".isNaN),
-      Row(true, true) :: Row(true, true) :: Row(true, true) :: Row(false, false) :: Nil)
+      Row(true, true) :: Row(true, true) :: Row(false, false) :: Row(false, false) :: Nil)
 
     checkAnswer(
       testData.select(isNaN($"a"), isNaN($"b")),
-      Row(true, true) :: Row(true, true) :: Row(true, true) :: Row(false, false) :: Nil)
+      Row(true, true) :: Row(true, true) :: Row(false, false) :: Row(false, false) :: Nil)
 
     checkAnswer(
       ctx.sql("select isnan(15), isnan('invalid')"),
-      Row(false, true))
+      Row(false, false))
+  }
+
+  test("nanvl") {
+    val testData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+      Row(null, 3.0, Double.NaN, Double.PositiveInfinity) :: Nil),
+      StructType(Seq(StructField("a", DoubleType), StructField("b", DoubleType),
+        StructField("c", DoubleType), StructField("d", DoubleType))))
+
+    checkAnswer(
+      testData.select(
+        nanvl($"a", lit(5)), nanvl($"b", lit(10)),
+        nanvl($"c", lit(null).cast(DoubleType)), nanvl($"d", lit(10))),
+      Row(null, 3.0, null, Double.PositiveInfinity)
+    )
+    testData.registerTempTable("t")
+    checkAnswer(
+      ctx.sql("select nanvl(a, 5), nanvl(b, 10), nanvl(c, null), nanvl(d, 10) from t"),
+      Row(null, 3.0, null, Double.PositiveInfinity)
+    )
   }
 
   test("===") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
index 495701d4f616c..dbe3b44ee2c79 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
@@ -30,8 +30,10 @@ class DataFrameNaFunctionsSuite extends QueryTest {
       ("Bob", 16, 176.5),
       ("Alice", null, 164.3),
       ("David", 60, null),
+      ("Nina", 25, Double.NaN),
       ("Amy", null, null),
-      (null, null, null)).toDF("name", "age", "height")
+      (null, null, null)
+      ).toDF("name", "age", "height")
   }
 
   test("drop") {
@@ -39,12 +41,12 @@ class DataFrameNaFunctionsSuite extends QueryTest {
     val rows = input.collect()
 
     checkAnswer(
-      input.na.drop("name" :: Nil),
-      rows(0) :: rows(1) :: rows(2) :: rows(3) :: Nil)
+      input.na.drop("name" :: Nil).select("name"),
+      Row("Bob") :: Row("Alice") :: Row("David") :: Row("Nina") :: Row("Amy") :: Nil)
 
     checkAnswer(
-      input.na.drop("age" :: Nil),
-      rows(0) :: rows(2) :: Nil)
+      input.na.drop("age" :: Nil).select("name"),
+      Row("Bob") :: Row("David") :: Row("Nina") :: Nil)
 
     checkAnswer(
       input.na.drop("age" :: "height" :: Nil),
@@ -67,8 +69,8 @@ class DataFrameNaFunctionsSuite extends QueryTest {
     val rows = input.collect()
 
     checkAnswer(
-      input.na.drop("all"),
-      rows(0) :: rows(1) :: rows(2) :: rows(3) :: Nil)
+      input.na.drop("all").select("name"),
+      Row("Bob") :: Row("Alice") :: Row("David") :: Row("Nina") :: Row("Amy") :: Nil)
 
     checkAnswer(
       input.na.drop("any"),
@@ -79,8 +81,8 @@ class DataFrameNaFunctionsSuite extends QueryTest {
       rows(0) :: Nil)
 
     checkAnswer(
-      input.na.drop("all", Seq("age", "height")),
-      rows(0) :: rows(1) :: rows(2) :: Nil)
+      input.na.drop("all", Seq("age", "height")).select("name"),
+      Row("Bob") :: Row("Alice") :: Row("David") :: Row("Nina") :: Nil)
   }
 
   test("drop with threshold") {
@@ -108,6 +110,7 @@ class DataFrameNaFunctionsSuite extends QueryTest {
       Row("Bob", 16, 176.5) ::
         Row("Alice", 50, 164.3) ::
         Row("David", 60, 50.6) ::
+        Row("Nina", 25, 50.6) ::
         Row("Amy", 50, 50.6) ::
         Row(null, 50, 50.6) :: Nil)
 
@@ -117,17 +120,19 @@ class DataFrameNaFunctionsSuite extends QueryTest {
     // string
     checkAnswer(
       input.na.fill("unknown").select("name"),
-      Row("Bob") :: Row("Alice") :: Row("David") :: Row("Amy") :: Row("unknown") :: Nil)
+      Row("Bob") :: Row("Alice") :: Row("David") ::
+        Row("Nina") :: Row("Amy") :: Row("unknown") :: Nil)
     assert(input.na.fill("unknown").columns.toSeq === input.columns.toSeq)
 
     // fill double with subset columns
     checkAnswer(
-      input.na.fill(50.6, "age" :: Nil),
-      Row("Bob", 16, 176.5) ::
-        Row("Alice", 50, 164.3) ::
-        Row("David", 60, null) ::
-        Row("Amy", 50, null) ::
-        Row(null, 50, null) :: Nil)
+      input.na.fill(50.6, "age" :: Nil).select("name", "age"),
+      Row("Bob", 16) ::
+        Row("Alice", 50) ::
+        Row("David", 60) ::
+        Row("Nina", 25) ::
+        Row("Amy", 50) ::
+        Row(null, 50) :: Nil)
 
     // fill string with subset columns
     checkAnswer(
@@ -164,29 +169,27 @@ class DataFrameNaFunctionsSuite extends QueryTest {
       16 -> 61,
       60 -> 6,
       164.3 -> 461.3  // Alice is really tall
-    ))
+    )).collect()
 
-    checkAnswer(
-      out,
-      Row("Bob", 61, 176.5) ::
-        Row("Alice", null, 461.3) ::
-        Row("David", 6, null) ::
-        Row("Amy", null, null) ::
-        Row(null, null, null) :: Nil)
+    assert(out(0) === Row("Bob", 61, 176.5))
+    assert(out(1) === Row("Alice", null, 461.3))
+    assert(out(2) === Row("David", 6, null))
+    assert(out(3).get(2).asInstanceOf[Double].isNaN)
+    assert(out(4) === Row("Amy", null, null))
+    assert(out(5) === Row(null, null, null))
 
     // Replace only the age column
     val out1 = input.na.replace("age", Map(
       16 -> 61,
       60 -> 6,
       164.3 -> 461.3  // Alice is really tall
-    ))
-
-    checkAnswer(
-      out1,
-      Row("Bob", 61, 176.5) ::
-        Row("Alice", null, 164.3) ::
-        Row("David", 6, null) ::
-        Row("Amy", null, null) ::
-        Row(null, null, null) :: Nil)
+    )).collect()
+
+    assert(out1(0) === Row("Bob", 61, 176.5))
+    assert(out1(1) === Row("Alice", null, 164.3))
+    assert(out1(2) === Row("David", 6, null))
+    assert(out1(3).get(2).asInstanceOf[Double].isNaN)
+    assert(out1(4) === Row("Amy", null, null))
+    assert(out1(5) === Row(null, null, null))
   }
 }

From df4ddb3120be28df381c11a36312620e58034b93 Mon Sep 17 00:00:00 2001
From: petz2000 <petz2000@gmail.com>
Date: Tue, 21 Jul 2015 08:50:43 -0700
Subject: [PATCH 0522/1454] [SPARK-8915] [DOCUMENTATION, MLLIB] Added @since
 tags to mllib.classification

Created since tags for methods in mllib.classification

Author: petz2000 <petz2000@gmail.com>

Closes #7371 from petz2000/add_since_mllib.classification and squashes the following commits:

39fe291 [petz2000] Removed whitespace in block comment
c9b1e03 [petz2000] Removed @since tags again from protected and private methods
cd759b6 [petz2000] Added @since tags to methods
---
 .../classification/ClassificationModel.scala    |  3 +++
 .../classification/LogisticRegression.scala     | 17 +++++++++++++++++
 .../spark/mllib/classification/NaiveBayes.scala |  3 +++
 .../apache/spark/mllib/classification/SVM.scala | 16 ++++++++++++++++
 4 files changed, 39 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index 35a0db76f3a8c..ba73024e3c04d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -36,6 +36,7 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData RDD representing data points to be predicted
    * @return an RDD[Double] where each entry contains the corresponding prediction
+   * @since 0.8.0
    */
   def predict(testData: RDD[Vector]): RDD[Double]
 
@@ -44,6 +45,7 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData array representing a single data point
    * @return predicted category from the trained model
+   * @since 0.8.0
    */
   def predict(testData: Vector): Double
 
@@ -51,6 +53,7 @@ trait ClassificationModel extends Serializable {
    * Predict values for examples stored in a JavaRDD.
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
+   * @since 0.8.0
    */
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 2df4d21e8cd55..268642ac6a2f6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -85,6 +85,7 @@ class LogisticRegressionModel (
    * in Binary Logistic Regression. An example with prediction score greater than or equal to
    * this threshold is identified as an positive, and negative otherwise. The default value is 0.5.
    * It is only used for binary classification.
+   * @since 1.0.0
    */
   @Experimental
   def setThreshold(threshold: Double): this.type = {
@@ -96,6 +97,7 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
    * It is only used for binary classification.
+   * @since 1.3.0
    */
   @Experimental
   def getThreshold: Option[Double] = threshold
@@ -104,6 +106,7 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    * It is only used for binary classification.
+   * @since 1.0.0
    */
   @Experimental
   def clearThreshold(): this.type = {
@@ -155,6 +158,9 @@ class LogisticRegressionModel (
     }
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures, numClasses, weights, intercept, threshold)
@@ -162,6 +168,9 @@ class LogisticRegressionModel (
 
   override protected def formatVersion: String = "1.0"
 
+  /**
+   * @since 1.4.0
+   */
   override def toString: String = {
     s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.getOrElse("None")}"
   }
@@ -169,6 +178,9 @@ class LogisticRegressionModel (
 
 object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): LogisticRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -249,6 +261,7 @@ object LogisticRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -271,6 +284,7 @@ object LogisticRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
 
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -292,6 +306,7 @@ object LogisticRegressionWithSGD {
 
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -309,6 +324,7 @@ object LogisticRegressionWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -345,6 +361,7 @@ class LogisticRegressionWithLBFGS
    * Set the number of possible outcomes for k classes classification problem in
    * Multinomial Logistic Regression.
    * By default, it is binary logistic regression so k will be set to 2.
+   * @since 1.3.0
    */
   @Experimental
   def setNumClasses(numClasses: Int): this.type = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 8cf4e15efe7a7..2df91c09421e9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -444,6 +444,7 @@ object NaiveBayes {
    *
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
+   * @since 0.9.0
    */
   def train(input: RDD[LabeledPoint]): NaiveBayesModel = {
     new NaiveBayes().run(input)
@@ -459,6 +460,7 @@ object NaiveBayes {
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
    * @param lambda The smoothing parameter
+   * @since 0.9.0
    */
   def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
     new NaiveBayes(lambda, Multinomial).run(input)
@@ -481,6 +483,7 @@ object NaiveBayes {
    *
    * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be
    *              multinomial or bernoulli
+   * @since 0.9.0
    */
   def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
     require(supportedModelTypes.contains(modelType),
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 348485560713e..5b54feeb10467 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -46,6 +46,7 @@ class SVMModel (
    * Sets the threshold that separates positive predictions from negative predictions. An example
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
+   * @since 1.3.0
    */
   @Experimental
   def setThreshold(threshold: Double): this.type = {
@@ -56,6 +57,7 @@ class SVMModel (
   /**
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
+   * @since 1.3.0
    */
   @Experimental
   def getThreshold: Option[Double] = threshold
@@ -63,6 +65,7 @@ class SVMModel (
   /**
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
+   * @since 1.0.0
    */
   @Experimental
   def clearThreshold(): this.type = {
@@ -81,6 +84,9 @@ class SVMModel (
     }
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures = weights.size, numClasses = 2, weights, intercept, threshold)
@@ -88,6 +94,9 @@ class SVMModel (
 
   override protected def formatVersion: String = "1.0"
 
+  /**
+   * @since 1.4.0
+   */
   override def toString: String = {
     s"${super.toString}, numClasses = 2, threshold = ${threshold.getOrElse("None")}"
   }
@@ -95,6 +104,9 @@ class SVMModel (
 
 object SVMModel extends Loader[SVMModel] {
 
+   /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): SVMModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -173,6 +185,7 @@ object SVMWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -196,6 +209,7 @@ object SVMWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -217,6 +231,7 @@ object SVMWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -235,6 +250,7 @@ object SVMWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
+   * @since 0.8.0
    */
   def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel = {
     train(input, numIterations, 1.0, 0.01, 1.0)

From 6592a6058eee6a27a5c91281ca19076284d62483 Mon Sep 17 00:00:00 2001
From: Grace <jie.huang@intel.com>
Date: Tue, 21 Jul 2015 11:35:49 -0500
Subject: [PATCH 0523/1454] [SPARK-9193] Avoid assigning tasks to "lost"
 executor(s)

Now, when some executors are killed by dynamic-allocation, it leads to some mis-assignment onto lost executors sometimes. Such kind of mis-assignment causes task failure(s) or even job failure if it repeats that errors for 4 times.

The root cause is that ***killExecutors*** doesn't remove those executors under killing ASAP. It depends on the ***OnDisassociated*** event to refresh the active working list later. The delay time really depends on your cluster status (from several milliseconds to sub-minute). When new tasks to be scheduled during that period of time, it will be assigned to those "active" but "under killing" executors. Then the tasks will be failed due to "executor lost". The better way is to exclude those executors under killing in the makeOffers(). Then all those tasks won't be allocated onto those executors "to be lost" any more.

Author: Grace <jie.huang@intel.com>

Closes #7528 from GraceH/AssignToLostExecutor and squashes the following commits:

ecc1da6 [Grace] scala style fix
6e2ed96 [Grace] Re-word makeOffers by more readable lines
b5546ce [Grace] Add comments about the fix
30a9ad0 [Grace] Avoid assigning tasks to lost executors
---
 .../cluster/CoarseGrainedSchedulerBackend.scala | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index f14c603ac6891..c65b3e517773e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -169,9 +169,12 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
 
     // Make fake resource offers on all executors
     private def makeOffers() {
-      launchTasks(scheduler.resourceOffers(executorDataMap.map { case (id, executorData) =>
+      // Filter out executors under killing
+      val activeExecutors = executorDataMap.filterKeys(!executorsPendingToRemove.contains(_))
+      val workOffers = activeExecutors.map { case (id, executorData) =>
         new WorkerOffer(id, executorData.executorHost, executorData.freeCores)
-      }.toSeq))
+      }.toSeq
+      launchTasks(scheduler.resourceOffers(workOffers))
     }
 
     override def onDisconnected(remoteAddress: RpcAddress): Unit = {
@@ -181,9 +184,13 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
 
     // Make fake resource offers on just one executor
     private def makeOffers(executorId: String) {
-      val executorData = executorDataMap(executorId)
-      launchTasks(scheduler.resourceOffers(
-        Seq(new WorkerOffer(executorId, executorData.executorHost, executorData.freeCores))))
+      // Filter out executors under killing
+      if (!executorsPendingToRemove.contains(executorId)) {
+        val executorData = executorDataMap(executorId)
+        val workOffers = Seq(
+          new WorkerOffer(executorId, executorData.executorHost, executorData.freeCores))
+        launchTasks(scheduler.resourceOffers(workOffers))
+      }
     }
 
     // Launch tasks returned by a set of resource offers

From f67da43c394c27ceb4e6bfd49e81be05e406aa29 Mon Sep 17 00:00:00 2001
From: Ben <benjaminpiering@gmail.com>
Date: Tue, 21 Jul 2015 09:51:13 -0700
Subject: [PATCH 0524/1454] [SPARK-9036] [CORE]
 SparkListenerExecutorMetricsUpdate messages not included in JsonProtocol

This PR implements a JSON serializer and deserializer in the JSONProtocol to handle the (de)serialization of SparkListenerExecutorMetricsUpdate events. It also includes a unit test in the JSONProtocolSuite file. This was implemented to satisfy the improvement request in the JIRA  issue SPARK-9036.

Author: Ben <benjaminpiering@gmail.com>

Closes #7555 from NamelessAnalyst/master and squashes the following commits:

fb4e3cc [Ben] Update JSON Protocol and tests
aa69517 [Ben] Update JSON Protocol and tests --Corrected Stage Attempt to Stage Attempt ID
33e5774 [Ben] Update JSON Protocol Tests
3f237e7 [Ben] Update JSON Protocol Tests
84ca798 [Ben] Update JSON Protocol Tests
cde57a0 [Ben] Update JSON Protocol Tests
8049600 [Ben] Update JSON Protocol Tests
c5bc061 [Ben] Update JSON Protocol Tests
6f25785 [Ben] Merge remote-tracking branch 'origin/master'
df2a609 [Ben] Update JSON Protocol
dcda80b [Ben] Update JSON Protocol
---
 .../org/apache/spark/util/JsonProtocol.scala  | 31 ++++++++-
 .../apache/spark/util/JsonProtocolSuite.scala | 69 ++++++++++++++++++-
 2 files changed, 96 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index adf69a4e78e71..a078f14af52a1 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -92,8 +92,8 @@ private[spark] object JsonProtocol {
         executorRemovedToJson(executorRemoved)
       case logStart: SparkListenerLogStart =>
         logStartToJson(logStart)
-      // These aren't used, but keeps compiler happy
-      case SparkListenerExecutorMetricsUpdate(_, _) => JNothing
+      case metricsUpdate: SparkListenerExecutorMetricsUpdate =>
+        executorMetricsUpdateToJson(metricsUpdate)
     }
   }
 
@@ -224,6 +224,19 @@ private[spark] object JsonProtocol {
     ("Spark Version" -> SPARK_VERSION)
   }
 
+  def executorMetricsUpdateToJson(metricsUpdate: SparkListenerExecutorMetricsUpdate): JValue = {
+    val execId = metricsUpdate.execId
+    val taskMetrics = metricsUpdate.taskMetrics
+    ("Event" -> Utils.getFormattedClassName(metricsUpdate)) ~
+    ("Executor ID" -> execId) ~
+    ("Metrics Updated" -> taskMetrics.map { case (taskId, stageId, stageAttemptId, metrics) =>
+      ("Task ID" -> taskId) ~
+      ("Stage ID" -> stageId) ~
+      ("Stage Attempt ID" -> stageAttemptId) ~
+      ("Task Metrics" -> taskMetricsToJson(metrics))
+    })
+  }
+
   /** ------------------------------------------------------------------- *
    * JSON serialization methods for classes SparkListenerEvents depend on |
    * -------------------------------------------------------------------- */
@@ -463,6 +476,7 @@ private[spark] object JsonProtocol {
     val executorAdded = Utils.getFormattedClassName(SparkListenerExecutorAdded)
     val executorRemoved = Utils.getFormattedClassName(SparkListenerExecutorRemoved)
     val logStart = Utils.getFormattedClassName(SparkListenerLogStart)
+    val metricsUpdate = Utils.getFormattedClassName(SparkListenerExecutorMetricsUpdate)
 
     (json \ "Event").extract[String] match {
       case `stageSubmitted` => stageSubmittedFromJson(json)
@@ -481,6 +495,7 @@ private[spark] object JsonProtocol {
       case `executorAdded` => executorAddedFromJson(json)
       case `executorRemoved` => executorRemovedFromJson(json)
       case `logStart` => logStartFromJson(json)
+      case `metricsUpdate` => executorMetricsUpdateFromJson(json)
     }
   }
 
@@ -598,6 +613,18 @@ private[spark] object JsonProtocol {
     SparkListenerLogStart(sparkVersion)
   }
 
+  def executorMetricsUpdateFromJson(json: JValue): SparkListenerExecutorMetricsUpdate = {
+    val execInfo = (json \ "Executor ID").extract[String]
+    val taskMetrics = (json \ "Metrics Updated").extract[List[JValue]].map { json =>
+      val taskId = (json \ "Task ID").extract[Long]
+      val stageId = (json \ "Stage ID").extract[Int]
+      val stageAttemptId = (json \ "Stage Attempt ID").extract[Int]
+      val metrics = taskMetricsFromJson(json \ "Task Metrics")
+      (taskId, stageId, stageAttemptId, metrics)
+    }
+    SparkListenerExecutorMetricsUpdate(execInfo, taskMetrics)
+  }
+
   /** --------------------------------------------------------------------- *
    * JSON deserialization methods for classes SparkListenerEvents depend on |
    * ---------------------------------------------------------------------- */
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index e0ef9c70a5fc3..dde95f3778434 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -83,6 +83,9 @@ class JsonProtocolSuite extends SparkFunSuite {
     val executorAdded = SparkListenerExecutorAdded(executorAddedTime, "exec1",
       new ExecutorInfo("Hostee.awesome.com", 11, logUrlMap))
     val executorRemoved = SparkListenerExecutorRemoved(executorRemovedTime, "exec2", "test reason")
+    val executorMetricsUpdate = SparkListenerExecutorMetricsUpdate("exec3", Seq(
+      (1L, 2, 3, makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800,
+        hasHadoopInput = true, hasOutput = true))))
 
     testEvent(stageSubmitted, stageSubmittedJsonString)
     testEvent(stageCompleted, stageCompletedJsonString)
@@ -102,6 +105,7 @@ class JsonProtocolSuite extends SparkFunSuite {
     testEvent(applicationEnd, applicationEndJsonString)
     testEvent(executorAdded, executorAddedJsonString)
     testEvent(executorRemoved, executorRemovedJsonString)
+    testEvent(executorMetricsUpdate, executorMetricsUpdateJsonString)
   }
 
   test("Dependent Classes") {
@@ -440,10 +444,20 @@ class JsonProtocolSuite extends SparkFunSuite {
       case (e1: SparkListenerEnvironmentUpdate, e2: SparkListenerEnvironmentUpdate) =>
         assertEquals(e1.environmentDetails, e2.environmentDetails)
       case (e1: SparkListenerExecutorAdded, e2: SparkListenerExecutorAdded) =>
-        assert(e1.executorId == e1.executorId)
+        assert(e1.executorId === e1.executorId)
         assertEquals(e1.executorInfo, e2.executorInfo)
       case (e1: SparkListenerExecutorRemoved, e2: SparkListenerExecutorRemoved) =>
-        assert(e1.executorId == e1.executorId)
+        assert(e1.executorId === e1.executorId)
+      case (e1: SparkListenerExecutorMetricsUpdate, e2: SparkListenerExecutorMetricsUpdate) =>
+        assert(e1.execId === e2.execId)
+        assertSeqEquals[(Long, Int, Int, TaskMetrics)](e1.taskMetrics, e2.taskMetrics, (a, b) => {
+          val (taskId1, stageId1, stageAttemptId1, metrics1) = a
+          val (taskId2, stageId2, stageAttemptId2, metrics2) = b
+          assert(taskId1 === taskId2)
+          assert(stageId1 === stageId2)
+          assert(stageAttemptId1 === stageAttemptId2)
+          assertEquals(metrics1, metrics2)
+        })
       case (e1, e2) =>
         assert(e1 === e2)
       case _ => fail("Events don't match in types!")
@@ -1598,4 +1612,55 @@ class JsonProtocolSuite extends SparkFunSuite {
       |  "Removed Reason": "test reason"
       |}
     """
+
+  private val executorMetricsUpdateJsonString =
+  s"""
+     |{
+     |  "Event": "SparkListenerExecutorMetricsUpdate",
+     |  "Executor ID": "exec3",
+     |  "Metrics Updated": [
+     |  {
+     |    "Task ID": 1,
+     |    "Stage ID": 2,
+     |    "Stage Attempt ID": 3,
+     |    "Task Metrics": {
+     |    "Host Name": "localhost",
+     |    "Executor Deserialize Time": 300,
+     |    "Executor Run Time": 400,
+     |    "Result Size": 500,
+     |    "JVM GC Time": 600,
+     |    "Result Serialization Time": 700,
+     |    "Memory Bytes Spilled": 800,
+     |    "Disk Bytes Spilled": 0,
+     |    "Input Metrics": {
+     |      "Data Read Method": "Hadoop",
+     |      "Bytes Read": 2100,
+     |      "Records Read": 21
+     |    },
+     |    "Output Metrics": {
+     |      "Data Write Method": "Hadoop",
+     |      "Bytes Written": 1200,
+     |      "Records Written": 12
+     |    },
+     |    "Updated Blocks": [
+     |      {
+     |        "Block ID": "rdd_0_0",
+     |        "Status": {
+     |          "Storage Level": {
+     |            "Use Disk": true,
+     |            "Use Memory": true,
+     |            "Use ExternalBlockStore": false,
+     |            "Deserialized": false,
+     |            "Replication": 2
+     |          },
+     |          "Memory Size": 0,
+     |          "ExternalBlockStore Size": 0,
+     |          "Disk Size": 0
+     |        }
+     |      }
+     |    ]
+     |  }
+     |  }]
+     |}
+   """.stripMargin
 }

From 9a4fd875b39b6a1ef7038823d1c49b0826110fbc Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Tue, 21 Jul 2015 09:52:27 -0700
Subject: [PATCH 0525/1454] [SPARK-9128] [CORE] Get outerclasses and objects
 with only one method calling in ClosureCleaner

JIRA: https://issues.apache.org/jira/browse/SPARK-9128

Currently, in `ClosureCleaner`, the outerclasses and objects are retrieved using two different methods. However, the logic of the two methods is the same, and we can get both the outerclasses and objects with only one method calling.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7459 from viirya/remove_extra_closurecleaner and squashes the following commits:

7c9858d [Liang-Chi Hsieh] For comments.
a096941 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into remove_extra_closurecleaner
2ec5ce1 [Liang-Chi Hsieh] Remove unnecessary methods.
4df5a51 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into remove_extra_closurecleaner
dc110d1 [Liang-Chi Hsieh] Add method to get outerclasses and objects at the same time.
---
 .../apache/spark/util/ClosureCleaner.scala    | 32 +++--------
 .../spark/util/ClosureCleanerSuite2.scala     | 54 ++++++++-----------
 2 files changed, 28 insertions(+), 58 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index 43626b4ef4880..ebead830c6466 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -49,45 +49,28 @@ private[spark] object ClosureCleaner extends Logging {
     cls.getName.contains("$anonfun$")
   }
 
-  // Get a list of the classes of the outer objects of a given closure object, obj;
+  // Get a list of the outer objects and their classes of a given closure object, obj;
   // the outer objects are defined as any closures that obj is nested within, plus
   // possibly the class that the outermost closure is in, if any. We stop searching
   // for outer objects beyond that because cloning the user's object is probably
   // not a good idea (whereas we can clone closure objects just fine since we
   // understand how all their fields are used).
-  private def getOuterClasses(obj: AnyRef): List[Class[_]] = {
+  private def getOuterClassesAndObjects(obj: AnyRef): (List[Class[_]], List[AnyRef]) = {
     for (f <- obj.getClass.getDeclaredFields if f.getName == "$outer") {
       f.setAccessible(true)
       val outer = f.get(obj)
       // The outer pointer may be null if we have cleaned this closure before
       if (outer != null) {
         if (isClosure(f.getType)) {
-          return f.getType :: getOuterClasses(outer)
+          val recurRet = getOuterClassesAndObjects(outer)
+          return (f.getType :: recurRet._1, outer :: recurRet._2)
         } else {
-          return f.getType :: Nil // Stop at the first $outer that is not a closure
+          return (f.getType :: Nil, outer :: Nil) // Stop at the first $outer that is not a closure
         }
       }
     }
-    Nil
+    (Nil, Nil)
   }
-
-  // Get a list of the outer objects for a given closure object.
-  private def getOuterObjects(obj: AnyRef): List[AnyRef] = {
-    for (f <- obj.getClass.getDeclaredFields if f.getName == "$outer") {
-      f.setAccessible(true)
-      val outer = f.get(obj)
-      // The outer pointer may be null if we have cleaned this closure before
-      if (outer != null) {
-        if (isClosure(f.getType)) {
-          return outer :: getOuterObjects(outer)
-        } else {
-          return outer :: Nil // Stop at the first $outer that is not a closure
-        }
-      }
-    }
-    Nil
-  }
-
   /**
    * Return a list of classes that represent closures enclosed in the given closure object.
    */
@@ -205,8 +188,7 @@ private[spark] object ClosureCleaner extends Logging {
 
     // A list of enclosing objects and their respective classes, from innermost to outermost
     // An outer object at a given index is of type outer class at the same index
-    val outerClasses = getOuterClasses(func)
-    val outerObjects = getOuterObjects(func)
+    val (outerClasses, outerObjects) = getOuterClassesAndObjects(func)
 
     // For logging purposes only
     val declaredFields = func.getClass.getDeclaredFields
diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala
index 3147c937769d2..a829b099025e9 100644
--- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala
+++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala
@@ -120,8 +120,8 @@ class ClosureCleanerSuite2 extends SparkFunSuite with BeforeAndAfterAll with Pri
   // Accessors for private methods
   private val _isClosure = PrivateMethod[Boolean]('isClosure)
   private val _getInnerClosureClasses = PrivateMethod[List[Class[_]]]('getInnerClosureClasses)
-  private val _getOuterClasses = PrivateMethod[List[Class[_]]]('getOuterClasses)
-  private val _getOuterObjects = PrivateMethod[List[AnyRef]]('getOuterObjects)
+  private val _getOuterClassesAndObjects =
+    PrivateMethod[(List[Class[_]], List[AnyRef])]('getOuterClassesAndObjects)
 
   private def isClosure(obj: AnyRef): Boolean = {
     ClosureCleaner invokePrivate _isClosure(obj)
@@ -131,12 +131,8 @@ class ClosureCleanerSuite2 extends SparkFunSuite with BeforeAndAfterAll with Pri
     ClosureCleaner invokePrivate _getInnerClosureClasses(closure)
   }
 
-  private def getOuterClasses(closure: AnyRef): List[Class[_]] = {
-    ClosureCleaner invokePrivate _getOuterClasses(closure)
-  }
-
-  private def getOuterObjects(closure: AnyRef): List[AnyRef] = {
-    ClosureCleaner invokePrivate _getOuterObjects(closure)
+  private def getOuterClassesAndObjects(closure: AnyRef): (List[Class[_]], List[AnyRef]) = {
+    ClosureCleaner invokePrivate _getOuterClassesAndObjects(closure)
   }
 
   test("get inner closure classes") {
@@ -171,14 +167,11 @@ class ClosureCleanerSuite2 extends SparkFunSuite with BeforeAndAfterAll with Pri
     val closure2 = () => localValue
     val closure3 = () => someSerializableValue
     val closure4 = () => someSerializableMethod()
-    val outerClasses1 = getOuterClasses(closure1)
-    val outerClasses2 = getOuterClasses(closure2)
-    val outerClasses3 = getOuterClasses(closure3)
-    val outerClasses4 = getOuterClasses(closure4)
-    val outerObjects1 = getOuterObjects(closure1)
-    val outerObjects2 = getOuterObjects(closure2)
-    val outerObjects3 = getOuterObjects(closure3)
-    val outerObjects4 = getOuterObjects(closure4)
+
+    val (outerClasses1, outerObjects1) = getOuterClassesAndObjects(closure1)
+    val (outerClasses2, outerObjects2) = getOuterClassesAndObjects(closure2)
+    val (outerClasses3, outerObjects3) = getOuterClassesAndObjects(closure3)
+    val (outerClasses4, outerObjects4) = getOuterClassesAndObjects(closure4)
 
     // The classes and objects should have the same size
     assert(outerClasses1.size === outerObjects1.size)
@@ -211,10 +204,8 @@ class ClosureCleanerSuite2 extends SparkFunSuite with BeforeAndAfterAll with Pri
       val x = 1
       val closure1 = () => 1
       val closure2 = () => x
-      val outerClasses1 = getOuterClasses(closure1)
-      val outerClasses2 = getOuterClasses(closure2)
-      val outerObjects1 = getOuterObjects(closure1)
-      val outerObjects2 = getOuterObjects(closure2)
+      val (outerClasses1, outerObjects1) = getOuterClassesAndObjects(closure1)
+      val (outerClasses2, outerObjects2) = getOuterClassesAndObjects(closure2)
       assert(outerClasses1.size === outerObjects1.size)
       assert(outerClasses2.size === outerObjects2.size)
       // These inner closures only reference local variables, and so do not have $outer pointers
@@ -227,12 +218,9 @@ class ClosureCleanerSuite2 extends SparkFunSuite with BeforeAndAfterAll with Pri
       val closure1 = () => 1
       val closure2 = () => y
       val closure3 = () => localValue
-      val outerClasses1 = getOuterClasses(closure1)
-      val outerClasses2 = getOuterClasses(closure2)
-      val outerClasses3 = getOuterClasses(closure3)
-      val outerObjects1 = getOuterObjects(closure1)
-      val outerObjects2 = getOuterObjects(closure2)
-      val outerObjects3 = getOuterObjects(closure3)
+      val (outerClasses1, outerObjects1) = getOuterClassesAndObjects(closure1)
+      val (outerClasses2, outerObjects2) = getOuterClassesAndObjects(closure2)
+      val (outerClasses3, outerObjects3) = getOuterClassesAndObjects(closure3)
       assert(outerClasses1.size === outerObjects1.size)
       assert(outerClasses2.size === outerObjects2.size)
       assert(outerClasses3.size === outerObjects3.size)
@@ -265,9 +253,9 @@ class ClosureCleanerSuite2 extends SparkFunSuite with BeforeAndAfterAll with Pri
     val closure1 = () => 1
     val closure2 = () => localValue
     val closure3 = () => someSerializableValue
-    val outerClasses1 = getOuterClasses(closure1)
-    val outerClasses2 = getOuterClasses(closure2)
-    val outerClasses3 = getOuterClasses(closure3)
+    val (outerClasses1, _) = getOuterClassesAndObjects(closure1)
+    val (outerClasses2, _) = getOuterClassesAndObjects(closure2)
+    val (outerClasses3, _) = getOuterClassesAndObjects(closure3)
 
     val fields1 = findAccessedFields(closure1, outerClasses1, findTransitively = false)
     val fields2 = findAccessedFields(closure2, outerClasses2, findTransitively = false)
@@ -307,10 +295,10 @@ class ClosureCleanerSuite2 extends SparkFunSuite with BeforeAndAfterAll with Pri
       val closure2 = () => a
       val closure3 = () => localValue
       val closure4 = () => someSerializableValue
-      val outerClasses1 = getOuterClasses(closure1)
-      val outerClasses2 = getOuterClasses(closure2)
-      val outerClasses3 = getOuterClasses(closure3)
-      val outerClasses4 = getOuterClasses(closure4)
+      val (outerClasses1, _) = getOuterClassesAndObjects(closure1)
+      val (outerClasses2, _) = getOuterClassesAndObjects(closure2)
+      val (outerClasses3, _) = getOuterClassesAndObjects(closure3)
+      val (outerClasses4, _) = getOuterClassesAndObjects(closure4)
 
       // First, find only fields accessed directly, not transitively, by these closures
       val fields1 = findAccessedFields(closure1, outerClasses1, findTransitively = false)

From 31954910d67c29874d2af22ee30590a7346a464c Mon Sep 17 00:00:00 2001
From: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Date: Tue, 21 Jul 2015 09:53:33 -0700
Subject: [PATCH 0526/1454] [SPARK-7171] Added a method to retrieve metrics
 sources in TaskContext

Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>

Closes #5805 from jacek-lewandowski/SPARK-7171 and squashes the following commits:

ed20bda [Jacek Lewandowski] SPARK-7171: Added a method to retrieve metrics sources in TaskContext
---
 .../scala/org/apache/spark/TaskContext.scala  |  9 ++++++++
 .../org/apache/spark/TaskContextImpl.scala    |  6 +++++
 .../org/apache/spark/executor/Executor.scala  |  5 ++++-
 .../apache/spark/metrics/MetricsSystem.scala  |  3 +++
 .../apache/spark/scheduler/DAGScheduler.scala |  1 +
 .../org/apache/spark/scheduler/Task.scala     |  8 ++++++-
 .../java/org/apache/spark/JavaAPISuite.java   |  2 +-
 .../org/apache/spark/CacheManagerSuite.scala  |  8 +++----
 .../org/apache/spark/rdd/PipedRDDSuite.scala  |  2 +-
 .../spark/scheduler/TaskContextSuite.scala    | 22 ++++++++++++++++---
 .../shuffle/hash/HashShuffleReaderSuite.scala |  2 +-
 .../ShuffleBlockFetcherIteratorSuite.scala    |  6 ++---
 12 files changed, 59 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index e93eb93124e51..b48836d5c8897 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -21,6 +21,7 @@ import java.io.Serializable
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.metrics.source.Source
 import org.apache.spark.unsafe.memory.TaskMemoryManager
 import org.apache.spark.util.TaskCompletionListener
 
@@ -148,6 +149,14 @@ abstract class TaskContext extends Serializable {
   @DeveloperApi
   def taskMetrics(): TaskMetrics
 
+  /**
+   * ::DeveloperApi::
+   * Returns all metrics sources with the given name which are associated with the instance
+   * which runs the task. For more information see [[org.apache.spark.metrics.MetricsSystem!]].
+   */
+  @DeveloperApi
+  def getMetricsSources(sourceName: String): Seq[Source]
+
   /**
    * Returns the manager for this task's managed memory.
    */
diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
index 6e394f1b12445..9ee168ae016f8 100644
--- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
@@ -20,6 +20,8 @@ package org.apache.spark
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 
 import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.metrics.MetricsSystem
+import org.apache.spark.metrics.source.Source
 import org.apache.spark.unsafe.memory.TaskMemoryManager
 import org.apache.spark.util.{TaskCompletionListener, TaskCompletionListenerException}
 
@@ -29,6 +31,7 @@ private[spark] class TaskContextImpl(
     override val taskAttemptId: Long,
     override val attemptNumber: Int,
     override val taskMemoryManager: TaskMemoryManager,
+    @transient private val metricsSystem: MetricsSystem,
     val runningLocally: Boolean = false,
     val taskMetrics: TaskMetrics = TaskMetrics.empty)
   extends TaskContext
@@ -95,6 +98,9 @@ private[spark] class TaskContextImpl(
 
   override def isInterrupted(): Boolean = interrupted
 
+  override def getMetricsSources(sourceName: String): Seq[Source] =
+    metricsSystem.getSourcesByName(sourceName)
+
   @transient private val accumulators = new HashMap[Long, Accumulable[_, _]]
 
   private[spark] override def registerAccumulator(a: Accumulable[_, _]): Unit = synchronized {
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 9087debde8c41..66624ffbe4790 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -210,7 +210,10 @@ private[spark] class Executor(
         // Run the actual task and measure its runtime.
         taskStart = System.currentTimeMillis()
         val (value, accumUpdates) = try {
-          task.run(taskAttemptId = taskId, attemptNumber = attemptNumber)
+          task.run(
+            taskAttemptId = taskId,
+            attemptNumber = attemptNumber,
+            metricsSystem = env.metricsSystem)
         } finally {
           // Note: this memory freeing logic is duplicated in DAGScheduler.runLocallyWithinThread;
           // when changing this, make sure to update both copies.
diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
index 67f64d5e278de..4517f465ebd3b 100644
--- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
+++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
@@ -142,6 +142,9 @@ private[spark] class MetricsSystem private (
     } else { defaultName }
   }
 
+  def getSourcesByName(sourceName: String): Seq[Source] =
+    sources.filter(_.sourceName == sourceName)
+
   def registerSource(source: Source) {
     sources += source
     try {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 71a219a4f3414..b829d06923404 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -682,6 +682,7 @@ class DAGScheduler(
           taskAttemptId = 0,
           attemptNumber = 0,
           taskMemoryManager = taskMemoryManager,
+          metricsSystem = env.metricsSystem,
           runningLocally = true)
       TaskContext.setTaskContext(taskContext)
       try {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
index 76a19aeac4679..d11a00956a9a9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
@@ -22,6 +22,7 @@ import java.nio.ByteBuffer
 
 import scala.collection.mutable.HashMap
 
+import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.{TaskContextImpl, TaskContext}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.serializer.SerializerInstance
@@ -61,13 +62,18 @@ private[spark] abstract class Task[T](
    * @param attemptNumber how many times this task has been attempted (0 for the first attempt)
    * @return the result of the task along with updates of Accumulators.
    */
-  final def run(taskAttemptId: Long, attemptNumber: Int): (T, AccumulatorUpdates) = {
+  final def run(
+    taskAttemptId: Long,
+    attemptNumber: Int,
+    metricsSystem: MetricsSystem)
+  : (T, AccumulatorUpdates) = {
     context = new TaskContextImpl(
       stageId = stageId,
       partitionId = partitionId,
       taskAttemptId = taskAttemptId,
       attemptNumber = attemptNumber,
       taskMemoryManager = taskMemoryManager,
+      metricsSystem = metricsSystem,
       runningLocally = false)
     TaskContext.setTaskContext(context)
     context.taskMetrics.setHostname(Utils.localHostName())
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index dfd86d3e51e7d..1b04a3b1cff0e 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -1011,7 +1011,7 @@ public void persist() {
   @Test
   public void iterator() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2);
-    TaskContext context = new TaskContextImpl(0, 0, 0L, 0, null, false, new TaskMetrics());
+    TaskContext context = new TaskContextImpl(0, 0, 0L, 0, null, null, false, new TaskMetrics());
     Assert.assertEquals(1, rdd.iterator(rdd.partitions().get(0), context).next().intValue());
   }
 
diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
index af81e46a657d3..618a5fb24710f 100644
--- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
@@ -65,7 +65,7 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     // in blockManager.put is a losing battle. You have been warned.
     blockManager = sc.env.blockManager
     cacheManager = sc.env.cacheManager
-    val context = new TaskContextImpl(0, 0, 0, 0, null)
+    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
     val computeValue = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     val getValue = blockManager.get(RDDBlockId(rdd.id, split.index))
     assert(computeValue.toList === List(1, 2, 3, 4))
@@ -77,7 +77,7 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12)
     when(blockManager.get(RDDBlockId(0, 0))).thenReturn(Some(result))
 
-    val context = new TaskContextImpl(0, 0, 0, 0, null)
+    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
     val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     assert(value.toList === List(5, 6, 7))
   }
@@ -86,14 +86,14 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     // Local computation should not persist the resulting value, so don't expect a put().
     when(blockManager.get(RDDBlockId(0, 0))).thenReturn(None)
 
-    val context = new TaskContextImpl(0, 0, 0, 0, null, true)
+    val context = new TaskContextImpl(0, 0, 0, 0, null, null, true)
     val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     assert(value.toList === List(1, 2, 3, 4))
   }
 
   test("verify task metrics updated correctly") {
     cacheManager = sc.env.cacheManager
-    val context = new TaskContextImpl(0, 0, 0, 0, null)
+    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
     cacheManager.getOrCompute(rdd3, split, context, StorageLevel.MEMORY_ONLY)
     assert(context.taskMetrics.updatedBlocks.getOrElse(Seq()).size === 2)
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
index 32f04d54eff94..3e8816a4c65be 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
@@ -175,7 +175,7 @@ class PipedRDDSuite extends SparkFunSuite with SharedSparkContext {
       }
       val hadoopPart1 = generateFakeHadoopPartition()
       val pipedRdd = new PipedRDD(nums, "printenv " + varName)
-      val tContext = new TaskContextImpl(0, 0, 0, 0, null)
+      val tContext = new TaskContextImpl(0, 0, 0, 0, null, null)
       val rddIter = pipedRdd.compute(hadoopPart1, tContext)
       val arr = rddIter.toArray
       assert(arr(0) == "/some/path")
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
index b9b0eccb0d834..9201d1e1f328b 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
@@ -24,11 +24,27 @@ import org.scalatest.BeforeAndAfter
 
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
-import org.apache.spark.util.{TaskCompletionListenerException, TaskCompletionListener}
+import org.apache.spark.util.{TaskCompletionListener, TaskCompletionListenerException}
+import org.apache.spark.metrics.source.JvmSource
 
 
 class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSparkContext {
 
+  test("provide metrics sources") {
+    val filePath = getClass.getClassLoader.getResource("test_metrics_config.properties").getFile
+    val conf = new SparkConf(loadDefaults = false)
+      .set("spark.metrics.conf", filePath)
+    sc = new SparkContext("local", "test", conf)
+    val rdd = sc.makeRDD(1 to 1)
+    val result = sc.runJob(rdd, (tc: TaskContext, it: Iterator[Int]) => {
+      tc.getMetricsSources("jvm").count {
+        case source: JvmSource => true
+        case _ => false
+      }
+    }).sum
+    assert(result > 0)
+  }
+
   test("calls TaskCompletionListener after failure") {
     TaskContextSuite.completed = false
     sc = new SparkContext("local", "test")
@@ -44,13 +60,13 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark
     val task = new ResultTask[String, String](0, 0,
       sc.broadcast(closureSerializer.serialize((rdd, func)).array), rdd.partitions(0), Seq(), 0)
     intercept[RuntimeException] {
-      task.run(0, 0)
+      task.run(0, 0, null)
     }
     assert(TaskContextSuite.completed === true)
   }
 
   test("all TaskCompletionListeners should be called even if some fail") {
-    val context = new TaskContextImpl(0, 0, 0, 0, null)
+    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
     val listener = mock(classOf[TaskCompletionListener])
     context.addTaskCompletionListener(_ => throw new Exception("blah"))
     context.addTaskCompletionListener(listener)
diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
index 6c9cb448e7833..db718ecabbdb9 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
@@ -138,7 +138,7 @@ class HashShuffleReaderSuite extends SparkFunSuite with LocalSparkContext {
       shuffleHandle,
       reduceId,
       reduceId + 1,
-      new TaskContextImpl(0, 0, 0, 0, null),
+      new TaskContextImpl(0, 0, 0, 0, null, null),
       blockManager,
       mapOutputTracker)
 
diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
index 64f3fbdcebed9..cf8bd8ae69625 100644
--- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
@@ -95,7 +95,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     )
 
     val iterator = new ShuffleBlockFetcherIterator(
-      new TaskContextImpl(0, 0, 0, 0, null),
+      new TaskContextImpl(0, 0, 0, 0, null, null),
       transfer,
       blockManager,
       blocksByAddress,
@@ -165,7 +165,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq))
 
-    val taskContext = new TaskContextImpl(0, 0, 0, 0, null)
+    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null)
     val iterator = new ShuffleBlockFetcherIterator(
       taskContext,
       transfer,
@@ -227,7 +227,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq))
 
-    val taskContext = new TaskContextImpl(0, 0, 0, 0, null)
+    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null)
     val iterator = new ShuffleBlockFetcherIterator(
       taskContext,
       transfer,

From 4f7f1ee378e80b33686508d56e133fc25dec5316 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 21 Jul 2015 09:54:39 -0700
Subject: [PATCH 0527/1454] [SPARK-4598] [WEBUI] Task table pagination for the
 Stage page

This PR adds pagination for the task table to solve the scalability issue of the stage page. Here is the initial screenshot:
<img width="1347" alt="pagination" src="https://cloud.githubusercontent.com/assets/1000778/8679669/9e63863c-2a8e-11e5-94e4-994febcd6717.png">
The task table only shows 100 tasks. There is a page navigation above the table. Users can click the page navigation or type the page number to jump to another page. The table can be sorted by clicking the headers. However, unlike previous implementation, the sorting work is done in the server now. So clicking a table column to sort needs to refresh the web page.

Author: zsxwing <zsxwing@gmail.com>

Closes #7399 from zsxwing/task-table-pagination and squashes the following commits:

144f513 [zsxwing] Display the page navigation when the page number is out of range
a3eee22 [zsxwing] Add extra space for the error message
54c5b84 [zsxwing] Reset page to 1 if the user changes the page size
c2f7f39 [zsxwing] Add a text field to let users fill the page size
bad52eb [zsxwing] Display user-friendly error messages
410586b [zsxwing] Scroll down to the tasks table if the url contains any sort column
a0746d1 [zsxwing] Use expand-dag-viz-arrow-job and expand-dag-viz-arrow-stage instead of expand-dag-viz-arrow-true and expand-dag-viz-arrow-false
b123f67 [zsxwing] Use localStorage to remember the user's actions and replay them when loading the page
894a342 [zsxwing] Show the link cursor when hovering for headers and page links and other minor fix
4d4fecf [zsxwing] Address Carson's comments
d9285f0 [zsxwing] Add comments and fix the style
74285fa [zsxwing] Merge branch 'master' into task-table-pagination
db6c859 [zsxwing] Task table pagination for the Stage page
---
 .../spark/ui/static/additional-metrics.js     |  34 +-
 .../apache/spark/ui/static/spark-dag-viz.js   |  27 +
 .../apache/spark/ui/static/timeline-view.js   |  39 +
 .../org/apache/spark/ui/PagedTable.scala      | 246 +++++
 .../org/apache/spark/ui/jobs/StagePage.scala  | 879 +++++++++++++-----
 .../org/apache/spark/ui/PagedTableSuite.scala |  99 ++
 6 files changed, 1102 insertions(+), 222 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/ui/PagedTable.scala
 create mode 100644 core/src/test/scala/org/apache/spark/ui/PagedTableSuite.scala

diff --git a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js
index 0b450dc76bc38..3c8ddddf07b1e 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js
@@ -19,6 +19,9 @@
  * to be registered after the page loads. */
 $(function() {
     $("span.expand-additional-metrics").click(function(){
+        var status = window.localStorage.getItem("expand-additional-metrics") == "true";
+        status = !status;
+
         // Expand the list of additional metrics.
         var additionalMetricsDiv = $(this).parent().find('.additional-metrics');
         $(additionalMetricsDiv).toggleClass('collapsed');
@@ -26,17 +29,31 @@ $(function() {
         // Switch the class of the arrow from open to closed.
         $(this).find('.expand-additional-metrics-arrow').toggleClass('arrow-open');
         $(this).find('.expand-additional-metrics-arrow').toggleClass('arrow-closed');
+
+        window.localStorage.setItem("expand-additional-metrics", "" + status);
     });
 
+    if (window.localStorage.getItem("expand-additional-metrics") == "true") {
+        // Set it to false so that the click function can revert it
+        window.localStorage.setItem("expand-additional-metrics", "false");
+        $("span.expand-additional-metrics").trigger("click");
+    }
+
     stripeSummaryTable();
 
     $('input[type="checkbox"]').click(function() {
-        var column = "table ." + $(this).attr("name");
+        var name = $(this).attr("name")
+        var column = "table ." + name;
+        var status = window.localStorage.getItem(name) == "true";
+        status = !status;
         $(column).toggle();
         stripeSummaryTable();
+        window.localStorage.setItem(name, "" + status);
     });
 
     $("#select-all-metrics").click(function() {
+       var status = window.localStorage.getItem("select-all-metrics") == "true";
+       status = !status;
        if (this.checked) {
           // Toggle all un-checked options.
           $('input[type="checkbox"]:not(:checked)').trigger('click');
@@ -44,6 +61,21 @@ $(function() {
           // Toggle all checked options.
           $('input[type="checkbox"]:checked').trigger('click');
        }
+       window.localStorage.setItem("select-all-metrics", "" + status);
+    });
+
+    if (window.localStorage.getItem("select-all-metrics") == "true") {
+        $("#select-all-metrics").attr('checked', status);
+    }
+
+    $("span.additional-metric-title").parent().find('input[type="checkbox"]').each(function() {
+        var name = $(this).attr("name")
+        // If name is undefined, then skip it because it's the "select-all-metrics" checkbox
+        if (name && window.localStorage.getItem(name) == "true") {
+            // Set it to false so that the click function can revert it
+            window.localStorage.setItem(name, "false");
+            $(this).trigger("click")
+        }
     });
 
     // Trigger a click on the checkbox if a user clicks the label next to it.
diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index 9fa53baaf4212..4a893bc0189aa 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -72,6 +72,14 @@ var StagePageVizConstants = {
   rankSep: 40
 };
 
+/*
+ * Return "expand-dag-viz-arrow-job" if forJob is true.
+ * Otherwise, return "expand-dag-viz-arrow-stage".
+ */
+function expandDagVizArrowKey(forJob) {
+  return forJob ? "expand-dag-viz-arrow-job" : "expand-dag-viz-arrow-stage";
+}
+
 /*
  * Show or hide the RDD DAG visualization.
  *
@@ -79,6 +87,9 @@ var StagePageVizConstants = {
  * This is the narrow interface called from the Scala UI code.
  */
 function toggleDagViz(forJob) {
+  var status = window.localStorage.getItem(expandDagVizArrowKey(forJob)) == "true";
+  status = !status;
+
   var arrowSelector = ".expand-dag-viz-arrow";
   $(arrowSelector).toggleClass('arrow-closed');
   $(arrowSelector).toggleClass('arrow-open');
@@ -93,8 +104,24 @@ function toggleDagViz(forJob) {
     // Save the graph for later so we don't have to render it again
     graphContainer().style("display", "none");
   }
+
+  window.localStorage.setItem(expandDagVizArrowKey(forJob), "" + status);
 }
 
+$(function (){
+  if (window.localStorage.getItem(expandDagVizArrowKey(false)) == "true") {
+    // Set it to false so that the click function can revert it
+    window.localStorage.setItem(expandDagVizArrowKey(false), "false");
+    toggleDagViz(false);
+  }
+
+  if (window.localStorage.getItem(expandDagVizArrowKey(true)) == "true") {
+    // Set it to false so that the click function can revert it
+    window.localStorage.setItem(expandDagVizArrowKey(true), "false");
+    toggleDagViz(true);
+  }
+});
+
 /*
  * Render the RDD DAG visualization.
  *
diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js
index ca74ef9d7e94e..f4453c71df1ea 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js
@@ -66,14 +66,27 @@ function drawApplicationTimeline(groupArray, eventObjArray, startTime) {
   setupJobEventAction();
 
   $("span.expand-application-timeline").click(function() {
+    var status = window.localStorage.getItem("expand-application-timeline") == "true";
+    status = !status;
+
     $("#application-timeline").toggleClass('collapsed');
 
     // Switch the class of the arrow from open to closed.
     $(this).find('.expand-application-timeline-arrow').toggleClass('arrow-open');
     $(this).find('.expand-application-timeline-arrow').toggleClass('arrow-closed');
+
+    window.localStorage.setItem("expand-application-timeline", "" + status);
   });
 }
 
+$(function (){
+  if (window.localStorage.getItem("expand-application-timeline") == "true") {
+    // Set it to false so that the click function can revert it
+    window.localStorage.setItem("expand-application-timeline", "false");
+    $("span.expand-application-timeline").trigger('click');
+  }
+});
+
 function drawJobTimeline(groupArray, eventObjArray, startTime) {
   var groups = new vis.DataSet(groupArray);
   var items = new vis.DataSet(eventObjArray);
@@ -125,14 +138,27 @@ function drawJobTimeline(groupArray, eventObjArray, startTime) {
   setupStageEventAction();
 
   $("span.expand-job-timeline").click(function() {
+    var status = window.localStorage.getItem("expand-job-timeline") == "true";
+    status = !status;
+
     $("#job-timeline").toggleClass('collapsed');
 
     // Switch the class of the arrow from open to closed.
     $(this).find('.expand-job-timeline-arrow').toggleClass('arrow-open');
     $(this).find('.expand-job-timeline-arrow').toggleClass('arrow-closed');
+
+    window.localStorage.setItem("expand-job-timeline", "" + status);
   });
 }
 
+$(function (){
+  if (window.localStorage.getItem("expand-job-timeline") == "true") {
+    // Set it to false so that the click function can revert it
+    window.localStorage.setItem("expand-job-timeline", "false");
+    $("span.expand-job-timeline").trigger('click');
+  }
+});
+
 function drawTaskAssignmentTimeline(groupArray, eventObjArray, minLaunchTime, maxFinishTime) {
   var groups = new vis.DataSet(groupArray);
   var items = new vis.DataSet(eventObjArray);
@@ -176,14 +202,27 @@ function drawTaskAssignmentTimeline(groupArray, eventObjArray, minLaunchTime, ma
   setupZoomable("#task-assignment-timeline-zoom-lock", taskTimeline);
 
   $("span.expand-task-assignment-timeline").click(function() {
+    var status = window.localStorage.getItem("expand-task-assignment-timeline") == "true";
+    status = !status;
+
     $("#task-assignment-timeline").toggleClass("collapsed");
 
      // Switch the class of the arrow from open to closed.
     $(this).find(".expand-task-assignment-timeline-arrow").toggleClass("arrow-open");
     $(this).find(".expand-task-assignment-timeline-arrow").toggleClass("arrow-closed");
+
+    window.localStorage.setItem("expand-task-assignment-timeline", "" + status);
   });
 }
 
+$(function (){
+  if (window.localStorage.getItem("expand-task-assignment-timeline") == "true") {
+    // Set it to false so that the click function can revert it
+    window.localStorage.setItem("expand-task-assignment-timeline", "false");
+    $("span.expand-task-assignment-timeline").trigger('click');
+  }
+});
+
 function setupExecutorEventAction() {
   $(".item.box.executor").each(function () {
     $(this).hover(
diff --git a/core/src/main/scala/org/apache/spark/ui/PagedTable.scala b/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
new file mode 100644
index 0000000000000..17d7b39c2d951
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
@@ -0,0 +1,246 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui
+
+import scala.xml.{Node, Unparsed}
+
+/**
+ * A data source that provides data for a page.
+ *
+ * @param pageSize the number of rows in a page
+ */
+private[ui] abstract class PagedDataSource[T](val pageSize: Int) {
+
+  if (pageSize <= 0) {
+    throw new IllegalArgumentException("Page size must be positive")
+  }
+
+  /**
+   * Return the size of all data.
+   */
+  protected def dataSize: Int
+
+  /**
+   * Slice a range of data.
+   */
+  protected def sliceData(from: Int, to: Int): Seq[T]
+
+  /**
+   * Slice the data for this page
+   */
+  def pageData(page: Int): PageData[T] = {
+    val totalPages = (dataSize + pageSize - 1) / pageSize
+    if (page <= 0 || page > totalPages) {
+      throw new IndexOutOfBoundsException(
+        s"Page $page is out of range. Please select a page number between 1 and $totalPages.")
+    }
+    val from = (page - 1) * pageSize
+    val to = dataSize.min(page * pageSize)
+    PageData(totalPages, sliceData(from, to))
+  }
+
+}
+
+/**
+ * The data returned by `PagedDataSource.pageData`, including the page number, the number of total
+ * pages and the data in this page.
+ */
+private[ui] case class PageData[T](totalPage: Int, data: Seq[T])
+
+/**
+ * A paged table that will generate a HTML table for a specified page and also the page navigation.
+ */
+private[ui] trait PagedTable[T] {
+
+  def tableId: String
+
+  def tableCssClass: String
+
+  def dataSource: PagedDataSource[T]
+
+  def headers: Seq[Node]
+
+  def row(t: T): Seq[Node]
+
+  def table(page: Int): Seq[Node] = {
+    val _dataSource = dataSource
+    try {
+      val PageData(totalPages, data) = _dataSource.pageData(page)
+      <div>
+        {pageNavigation(page, _dataSource.pageSize, totalPages)}
+        <table class={tableCssClass} id={tableId}>
+          {headers}
+          <tbody>
+            {data.map(row)}
+          </tbody>
+        </table>
+      </div>
+    } catch {
+      case e: IndexOutOfBoundsException =>
+        val PageData(totalPages, _) = _dataSource.pageData(1)
+        <div>
+          {pageNavigation(1, _dataSource.pageSize, totalPages)}
+          <div class="alert alert-error">{e.getMessage}</div>
+        </div>
+    }
+  }
+
+  /**
+   * Return a page navigation.
+   * <ul>
+   *   <li>If the totalPages is 1, the page navigation will be empty</li>
+   *   <li>
+   *     If the totalPages is more than 1, it will create a page navigation including a group of
+   *     page numbers and a form to submit the page number.
+   *   </li>
+   * </ul>
+   *
+   * Here are some examples of the page navigation:
+   * {{{
+   * << < 11 12 13* 14 15 16 17 18 19 20 > >>
+   *
+   * This is the first group, so "<<" is hidden.
+   * < 1 2* 3 4 5 6 7 8 9 10 > >>
+   *
+   * This is the first group and the first page, so "<<" and "<" are hidden.
+   * 1* 2 3 4 5 6 7 8 9 10 > >>
+   *
+   * Assume totalPages is 19. This is the last group, so ">>" is hidden.
+   * << < 11 12 13* 14 15 16 17 18 19 >
+   *
+   * Assume totalPages is 19. This is the last group and the last page, so ">>" and ">" are hidden.
+   * << < 11 12 13 14 15 16 17 18 19*
+   *
+   * * means the current page number
+   * << means jumping to the first page of the previous group.
+   * < means jumping to the previous page.
+   * >> means jumping to the first page of the next group.
+   * > means jumping to the next page.
+   * }}}
+   */
+  private[ui] def pageNavigation(page: Int, pageSize: Int, totalPages: Int): Seq[Node] = {
+    if (totalPages == 1) {
+      Nil
+    } else {
+      // A group includes all page numbers will be shown in the page navigation.
+      // The size of group is 10 means there are 10 page numbers will be shown.
+      // The first group is 1 to 10, the second is 2 to 20, and so on
+      val groupSize = 10
+      val firstGroup = 0
+      val lastGroup = (totalPages - 1) / groupSize
+      val currentGroup = (page - 1) / groupSize
+      val startPage = currentGroup * groupSize + 1
+      val endPage = totalPages.min(startPage + groupSize - 1)
+      val pageTags = (startPage to endPage).map { p =>
+        if (p == page) {
+          // The current page should be disabled so that it cannot be clicked.
+          <li class="disabled"><a href="#">{p}</a></li>
+        } else {
+          <li><a href={pageLink(p)}>{p}</a></li>
+        }
+      }
+      val (goButtonJsFuncName, goButtonJsFunc) = goButtonJavascriptFunction
+      // When clicking the "Go" button, it will call this javascript method and then call
+      // "goButtonJsFuncName"
+      val formJs =
+        s"""$$(function(){
+          |  $$( "#form-task-page" ).submit(function(event) {
+          |    var page = $$("#form-task-page-no").val()
+          |    var pageSize = $$("#form-task-page-size").val()
+          |    pageSize = pageSize ? pageSize: 100;
+          |    if (page != "") {
+          |      ${goButtonJsFuncName}(page, pageSize);
+          |    }
+          |    event.preventDefault();
+          |  });
+          |});
+        """.stripMargin
+
+      <div>
+        <div>
+          <form id="form-task-page" class="form-inline pull-right" style="margin-bottom: 0px;">
+            <label>{totalPages} Pages. Jump to</label>
+            <input type="text" id="form-task-page-no" value={page.toString} class="span1" />
+            <label>. Show </label>
+            <input type="text" id="form-task-page-size" value={pageSize.toString} class="span1" />
+            <label>tasks in a page.</label>
+            <button type="submit" class="btn">Go</button>
+          </form>
+        </div>
+        <div class="pagination" style="margin-bottom: 0px;">
+          <span style="float: left; padding-top: 4px; padding-right: 4px;">Page: </span>
+          <ul>
+            {if (currentGroup > firstGroup) {
+            <li>
+              <a href={pageLink(startPage - groupSize)} aria-label="Previous Group">
+                <span aria-hidden="true">
+                  &lt;&lt;
+                </span>
+              </a>
+            </li>
+            }}
+            {if (page > 1) {
+            <li>
+            <a href={pageLink(page - 1)} aria-label="Previous">
+              <span aria-hidden="true">
+                &lt;
+              </span>
+            </a>
+            </li>
+            }}
+            {pageTags}
+            {if (page < totalPages) {
+            <li>
+              <a href={pageLink(page + 1)} aria-label="Next">
+                <span aria-hidden="true">&gt;</span>
+              </a>
+            </li>
+            }}
+            {if (currentGroup < lastGroup) {
+            <li>
+              <a href={pageLink(startPage + groupSize)} aria-label="Next Group">
+                <span aria-hidden="true">
+                  &gt;&gt;
+                </span>
+              </a>
+            </li>
+          }}
+          </ul>
+        </div>
+        <script>
+          {Unparsed(goButtonJsFunc)}
+
+          {Unparsed(formJs)}
+        </script>
+      </div>
+    }
+  }
+
+  /**
+   * Return a link to jump to a page.
+   */
+  def pageLink(page: Int): String
+
+  /**
+   * Only the implementation knows how to create the url with a page number and the page size, so we
+   * leave this one to the implementation. The implementation should create a JavaScript method that
+   * accepts a page number along with the page size and jumps to the page. The return value is this
+   * method name and its JavaScript codes.
+   */
+  def goButtonJavascriptFunction: (String, String)
+}
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 6e077bf3e70d5..cf04b5e59239b 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ui.jobs
 
+import java.net.URLEncoder
 import java.util.Date
 import javax.servlet.http.HttpServletRequest
 
@@ -27,13 +28,14 @@ import org.apache.commons.lang3.StringEscapeUtils
 
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
-import org.apache.spark.ui.{ToolTips, WebUIPage, UIUtils}
+import org.apache.spark.ui._
 import org.apache.spark.ui.jobs.UIData._
-import org.apache.spark.ui.scope.RDDOperationGraph
 import org.apache.spark.util.{Utils, Distribution}
 
 /** Page showing statistics and task list for a given stage */
 private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
+  import StagePage._
+
   private val progressListener = parent.progressListener
   private val operationGraphListener = parent.operationGraphListener
 
@@ -74,6 +76,16 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val parameterAttempt = request.getParameter("attempt")
       require(parameterAttempt != null && parameterAttempt.nonEmpty, "Missing attempt parameter")
 
+      val parameterTaskPage = request.getParameter("task.page")
+      val parameterTaskSortColumn = request.getParameter("task.sort")
+      val parameterTaskSortDesc = request.getParameter("task.desc")
+      val parameterTaskPageSize = request.getParameter("task.pageSize")
+
+      val taskPage = Option(parameterTaskPage).map(_.toInt).getOrElse(1)
+      val taskSortColumn = Option(parameterTaskSortColumn).getOrElse("Index")
+      val taskSortDesc = Option(parameterTaskSortDesc).map(_.toBoolean).getOrElse(false)
+      val taskPageSize = Option(parameterTaskPageSize).map(_.toInt).getOrElse(100)
+
       // If this is set, expand the dag visualization by default
       val expandDagVizParam = request.getParameter("expandDagViz")
       val expandDagViz = expandDagVizParam != null && expandDagVizParam.toBoolean
@@ -231,52 +243,47 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         accumulableRow,
         accumulables.values.toSeq)
 
-      val taskHeadersAndCssClasses: Seq[(String, String)] =
-        Seq(
-          ("Index", ""), ("ID", ""), ("Attempt", ""), ("Status", ""), ("Locality Level", ""),
-          ("Executor ID / Host", ""), ("Launch Time", ""), ("Duration", ""),
-          ("Scheduler Delay", TaskDetailsClassNames.SCHEDULER_DELAY),
-          ("Task Deserialization Time", TaskDetailsClassNames.TASK_DESERIALIZATION_TIME),
-          ("GC Time", ""),
-          ("Result Serialization Time", TaskDetailsClassNames.RESULT_SERIALIZATION_TIME),
-          ("Getting Result Time", TaskDetailsClassNames.GETTING_RESULT_TIME)) ++
-        {if (hasAccumulators) Seq(("Accumulators", "")) else Nil} ++
-        {if (stageData.hasInput) Seq(("Input Size / Records", "")) else Nil} ++
-        {if (stageData.hasOutput) Seq(("Output Size / Records", "")) else Nil} ++
-        {if (stageData.hasShuffleRead) {
-          Seq(("Shuffle Read Blocked Time", TaskDetailsClassNames.SHUFFLE_READ_BLOCKED_TIME),
-            ("Shuffle Read Size / Records", ""),
-            ("Shuffle Remote Reads", TaskDetailsClassNames.SHUFFLE_READ_REMOTE_SIZE))
-        } else {
-          Nil
-        }} ++
-        {if (stageData.hasShuffleWrite) {
-          Seq(("Write Time", ""), ("Shuffle Write Size / Records", ""))
-        } else {
-          Nil
-        }} ++
-        {if (stageData.hasBytesSpilled) {
-          Seq(("Shuffle Spill (Memory)", ""), ("Shuffle Spill (Disk)", ""))
-        } else {
-          Nil
-        }} ++
-        Seq(("Errors", ""))
-
-      val unzipped = taskHeadersAndCssClasses.unzip
-
       val currentTime = System.currentTimeMillis()
-      val taskTable = UIUtils.listingTable(
-        unzipped._1,
-        taskRow(
+      val (taskTable, taskTableHTML) = try {
+        val _taskTable = new TaskPagedTable(
+          UIUtils.prependBaseUri(parent.basePath) +
+            s"/stages/stage?id=${stageId}&attempt=${stageAttemptId}",
+          tasks,
           hasAccumulators,
           stageData.hasInput,
           stageData.hasOutput,
           stageData.hasShuffleRead,
           stageData.hasShuffleWrite,
           stageData.hasBytesSpilled,
-          currentTime),
-        tasks,
-        headerClasses = unzipped._2)
+          currentTime,
+          pageSize = taskPageSize,
+          sortColumn = taskSortColumn,
+          desc = taskSortDesc
+        )
+        (_taskTable, _taskTable.table(taskPage))
+      } catch {
+        case e @ (_ : IllegalArgumentException | _ : IndexOutOfBoundsException) =>
+          (null, <div class="alert alert-error">{e.getMessage}</div>)
+      }
+
+      val jsForScrollingDownToTaskTable =
+        <script>
+          {Unparsed {
+            """
+              |$(function() {
+              |  if (/.*&task.sort=.*$/.test(location.search)) {
+              |    var topOffset = $("#tasks-section").offset().top;
+              |    $("html,body").animate({scrollTop: topOffset}, 200);
+              |  }
+              |});
+            """.stripMargin
+           }
+          }
+        </script>
+
+      val taskIdsInPage = if (taskTable == null) Set.empty[Long]
+        else taskTable.dataSource.slicedTaskIds
+
       // Excludes tasks which failed and have incomplete metrics
       val validTasks = tasks.filter(t => t.taskInfo.status == "SUCCESS" && t.taskMetrics.isDefined)
 
@@ -499,12 +506,15 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         dagViz ++
         maybeExpandDagViz ++
         showAdditionalMetrics ++
-        makeTimeline(stageData.taskData.values.toSeq, currentTime) ++
+        makeTimeline(
+          // Only show the tasks in the table
+          stageData.taskData.values.toSeq.filter(t => taskIdsInPage.contains(t.taskInfo.taskId)),
+          currentTime) ++
         <h4>Summary Metrics for {numCompleted} Completed Tasks</h4> ++
         <div>{summaryTable.getOrElse("No tasks have reported metrics yet.")}</div> ++
         <h4>Aggregated Metrics by Executor</h4> ++ executorTable.toNodeSeq ++
         maybeAccumulableTable ++
-        <h4>Tasks</h4> ++ taskTable
+        <h4 id="tasks-section">Tasks</h4> ++ taskTableHTML ++ jsForScrollingDownToTaskTable
       UIUtils.headerSparkPage(stageHeader, content, parent, showVisualization = true)
     }
   }
@@ -679,164 +689,619 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
     </script>
   }
 
-  def taskRow(
-      hasAccumulators: Boolean,
-      hasInput: Boolean,
-      hasOutput: Boolean,
-      hasShuffleRead: Boolean,
-      hasShuffleWrite: Boolean,
-      hasBytesSpilled: Boolean,
-      currentTime: Long)(taskData: TaskUIData): Seq[Node] = {
-    taskData match { case TaskUIData(info, metrics, errorMessage) =>
-      val duration = if (info.status == "RUNNING") info.timeRunning(currentTime)
-        else metrics.map(_.executorRunTime).getOrElse(1L)
-      val formatDuration = if (info.status == "RUNNING") UIUtils.formatDuration(duration)
-        else metrics.map(m => UIUtils.formatDuration(m.executorRunTime)).getOrElse("")
-      val schedulerDelay = metrics.map(getSchedulerDelay(info, _, currentTime)).getOrElse(0L)
-      val gcTime = metrics.map(_.jvmGCTime).getOrElse(0L)
-      val taskDeserializationTime = metrics.map(_.executorDeserializeTime).getOrElse(0L)
-      val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L)
-      val gettingResultTime = getGettingResultTime(info, currentTime)
-
-      val maybeAccumulators = info.accumulables
-      val accumulatorsReadable = maybeAccumulators.map { acc =>
-        StringEscapeUtils.escapeHtml4(s"${acc.name}: ${acc.update.get}")
+}
+
+private[ui] object StagePage {
+  private[ui] def getGettingResultTime(info: TaskInfo, currentTime: Long): Long = {
+    if (info.gettingResult) {
+      if (info.finished) {
+        info.finishTime - info.gettingResultTime
+      } else {
+        // The task is still fetching the result.
+        currentTime - info.gettingResultTime
       }
+    } else {
+      0L
+    }
+  }
 
-      val maybeInput = metrics.flatMap(_.inputMetrics)
-      val inputSortable = maybeInput.map(_.bytesRead.toString).getOrElse("")
-      val inputReadable = maybeInput
-        .map(m => s"${Utils.bytesToString(m.bytesRead)} (${m.readMethod.toString.toLowerCase()})")
-        .getOrElse("")
-      val inputRecords = maybeInput.map(_.recordsRead.toString).getOrElse("")
-
-      val maybeOutput = metrics.flatMap(_.outputMetrics)
-      val outputSortable = maybeOutput.map(_.bytesWritten.toString).getOrElse("")
-      val outputReadable = maybeOutput
-        .map(m => s"${Utils.bytesToString(m.bytesWritten)}")
-        .getOrElse("")
-      val outputRecords = maybeOutput.map(_.recordsWritten.toString).getOrElse("")
-
-      val maybeShuffleRead = metrics.flatMap(_.shuffleReadMetrics)
-      val shuffleReadBlockedTimeSortable = maybeShuffleRead
-        .map(_.fetchWaitTime.toString).getOrElse("")
-      val shuffleReadBlockedTimeReadable =
-        maybeShuffleRead.map(ms => UIUtils.formatDuration(ms.fetchWaitTime)).getOrElse("")
-
-      val totalShuffleBytes = maybeShuffleRead.map(_.totalBytesRead)
-      val shuffleReadSortable = totalShuffleBytes.map(_.toString).getOrElse("")
-      val shuffleReadReadable = totalShuffleBytes.map(Utils.bytesToString).getOrElse("")
-      val shuffleReadRecords = maybeShuffleRead.map(_.recordsRead.toString).getOrElse("")
-
-      val remoteShuffleBytes = maybeShuffleRead.map(_.remoteBytesRead)
-      val shuffleReadRemoteSortable = remoteShuffleBytes.map(_.toString).getOrElse("")
-      val shuffleReadRemoteReadable = remoteShuffleBytes.map(Utils.bytesToString).getOrElse("")
-
-      val maybeShuffleWrite = metrics.flatMap(_.shuffleWriteMetrics)
-      val shuffleWriteSortable = maybeShuffleWrite.map(_.shuffleBytesWritten.toString).getOrElse("")
-      val shuffleWriteReadable = maybeShuffleWrite
-        .map(m => s"${Utils.bytesToString(m.shuffleBytesWritten)}").getOrElse("")
-      val shuffleWriteRecords = maybeShuffleWrite
-        .map(_.shuffleRecordsWritten.toString).getOrElse("")
-
-      val maybeWriteTime = metrics.flatMap(_.shuffleWriteMetrics).map(_.shuffleWriteTime)
-      val writeTimeSortable = maybeWriteTime.map(_.toString).getOrElse("")
-      val writeTimeReadable = maybeWriteTime.map(t => t / (1000 * 1000)).map { ms =>
-        if (ms == 0) "" else UIUtils.formatDuration(ms)
-      }.getOrElse("")
-
-      val maybeMemoryBytesSpilled = metrics.map(_.memoryBytesSpilled)
-      val memoryBytesSpilledSortable = maybeMemoryBytesSpilled.map(_.toString).getOrElse("")
-      val memoryBytesSpilledReadable =
-        maybeMemoryBytesSpilled.map(Utils.bytesToString).getOrElse("")
-
-      val maybeDiskBytesSpilled = metrics.map(_.diskBytesSpilled)
-      val diskBytesSpilledSortable = maybeDiskBytesSpilled.map(_.toString).getOrElse("")
-      val diskBytesSpilledReadable = maybeDiskBytesSpilled.map(Utils.bytesToString).getOrElse("")
-
-      <tr>
-        <td>{info.index}</td>
-        <td>{info.taskId}</td>
-        <td sorttable_customkey={info.attempt.toString}>{
-          if (info.speculative) s"${info.attempt} (speculative)" else info.attempt.toString
-        }</td>
-        <td>{info.status}</td>
-        <td>{info.taskLocality}</td>
-        <td>{info.executorId} / {info.host}</td>
-        <td>{UIUtils.formatDate(new Date(info.launchTime))}</td>
-        <td sorttable_customkey={duration.toString}>
-          {formatDuration}
-        </td>
-        <td sorttable_customkey={schedulerDelay.toString}
-            class={TaskDetailsClassNames.SCHEDULER_DELAY}>
-          {UIUtils.formatDuration(schedulerDelay.toLong)}
-        </td>
-        <td sorttable_customkey={taskDeserializationTime.toString}
-            class={TaskDetailsClassNames.TASK_DESERIALIZATION_TIME}>
-          {UIUtils.formatDuration(taskDeserializationTime.toLong)}
-        </td>
-        <td sorttable_customkey={gcTime.toString}>
-          {if (gcTime > 0) UIUtils.formatDuration(gcTime) else ""}
-        </td>
-        <td sorttable_customkey={serializationTime.toString}
-            class={TaskDetailsClassNames.RESULT_SERIALIZATION_TIME}>
-          {UIUtils.formatDuration(serializationTime)}
-        </td>
-        <td sorttable_customkey={gettingResultTime.toString}
-            class={TaskDetailsClassNames.GETTING_RESULT_TIME}>
-          {UIUtils.formatDuration(gettingResultTime)}
-        </td>
-        {if (hasAccumulators) {
-          <td>
-            {Unparsed(accumulatorsReadable.mkString("<br/>"))}
-          </td>
-        }}
-        {if (hasInput) {
-          <td sorttable_customkey={inputSortable}>
-            {s"$inputReadable / $inputRecords"}
-          </td>
-        }}
-        {if (hasOutput) {
-          <td sorttable_customkey={outputSortable}>
-            {s"$outputReadable / $outputRecords"}
-          </td>
-        }}
+  private[ui] def getSchedulerDelay(
+      info: TaskInfo, metrics: TaskMetrics, currentTime: Long): Long = {
+    if (info.finished) {
+      val totalExecutionTime = info.finishTime - info.launchTime
+      val executorOverhead = (metrics.executorDeserializeTime +
+        metrics.resultSerializationTime)
+      math.max(
+        0,
+        totalExecutionTime - metrics.executorRunTime - executorOverhead -
+          getGettingResultTime(info, currentTime))
+    } else {
+      // The task is still running and the metrics like executorRunTime are not available.
+      0L
+    }
+  }
+}
+
+private[ui] case class TaskTableRowInputData(inputSortable: Long, inputReadable: String)
+
+private[ui] case class TaskTableRowOutputData(outputSortable: Long, outputReadable: String)
+
+private[ui] case class TaskTableRowShuffleReadData(
+    shuffleReadBlockedTimeSortable: Long,
+    shuffleReadBlockedTimeReadable: String,
+    shuffleReadSortable: Long,
+    shuffleReadReadable: String,
+    shuffleReadRemoteSortable: Long,
+    shuffleReadRemoteReadable: String)
+
+private[ui] case class TaskTableRowShuffleWriteData(
+    writeTimeSortable: Long,
+    writeTimeReadable: String,
+    shuffleWriteSortable: Long,
+    shuffleWriteReadable: String)
+
+private[ui] case class TaskTableRowBytesSpilledData(
+    memoryBytesSpilledSortable: Long,
+    memoryBytesSpilledReadable: String,
+    diskBytesSpilledSortable: Long,
+    diskBytesSpilledReadable: String)
+
+/**
+ * Contains all data that needs for sorting and generating HTML. Using this one rather than
+ * TaskUIData to avoid creating duplicate contents during sorting the data.
+ */
+private[ui] case class TaskTableRowData(
+    index: Int,
+    taskId: Long,
+    attempt: Int,
+    speculative: Boolean,
+    status: String,
+    taskLocality: String,
+    executorIdAndHost: String,
+    launchTime: Long,
+    duration: Long,
+    formatDuration: String,
+    schedulerDelay: Long,
+    taskDeserializationTime: Long,
+    gcTime: Long,
+    serializationTime: Long,
+    gettingResultTime: Long,
+    accumulators: Option[String], // HTML
+    input: Option[TaskTableRowInputData],
+    output: Option[TaskTableRowOutputData],
+    shuffleRead: Option[TaskTableRowShuffleReadData],
+    shuffleWrite: Option[TaskTableRowShuffleWriteData],
+    bytesSpilled: Option[TaskTableRowBytesSpilledData],
+    error: String)
+
+private[ui] class TaskDataSource(
+    tasks: Seq[TaskUIData],
+    hasAccumulators: Boolean,
+    hasInput: Boolean,
+    hasOutput: Boolean,
+    hasShuffleRead: Boolean,
+    hasShuffleWrite: Boolean,
+    hasBytesSpilled: Boolean,
+    currentTime: Long,
+    pageSize: Int,
+    sortColumn: String,
+    desc: Boolean) extends PagedDataSource[TaskTableRowData](pageSize) {
+  import StagePage._
+
+  // Convert TaskUIData to TaskTableRowData which contains the final contents to show in the table
+  // so that we can avoid creating duplicate contents during sorting the data
+  private val data = tasks.map(taskRow).sorted(ordering(sortColumn, desc))
+
+  private var _slicedTaskIds: Set[Long] = null
+
+  override def dataSize: Int = data.size
+
+  override def sliceData(from: Int, to: Int): Seq[TaskTableRowData] = {
+    val r = data.slice(from, to)
+    _slicedTaskIds = r.map(_.taskId).toSet
+    r
+  }
+
+  def slicedTaskIds: Set[Long] = _slicedTaskIds
+
+  private def taskRow(taskData: TaskUIData): TaskTableRowData = {
+    val TaskUIData(info, metrics, errorMessage) = taskData
+    val duration = if (info.status == "RUNNING") info.timeRunning(currentTime)
+      else metrics.map(_.executorRunTime).getOrElse(1L)
+    val formatDuration = if (info.status == "RUNNING") UIUtils.formatDuration(duration)
+      else metrics.map(m => UIUtils.formatDuration(m.executorRunTime)).getOrElse("")
+    val schedulerDelay = metrics.map(getSchedulerDelay(info, _, currentTime)).getOrElse(0L)
+    val gcTime = metrics.map(_.jvmGCTime).getOrElse(0L)
+    val taskDeserializationTime = metrics.map(_.executorDeserializeTime).getOrElse(0L)
+    val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L)
+    val gettingResultTime = getGettingResultTime(info, currentTime)
+
+    val maybeAccumulators = info.accumulables
+    val accumulatorsReadable = maybeAccumulators.map { acc =>
+      StringEscapeUtils.escapeHtml4(s"${acc.name}: ${acc.update.get}")
+    }
+
+    val maybeInput = metrics.flatMap(_.inputMetrics)
+    val inputSortable = maybeInput.map(_.bytesRead).getOrElse(0L)
+    val inputReadable = maybeInput
+      .map(m => s"${Utils.bytesToString(m.bytesRead)} (${m.readMethod.toString.toLowerCase()})")
+      .getOrElse("")
+    val inputRecords = maybeInput.map(_.recordsRead.toString).getOrElse("")
+
+    val maybeOutput = metrics.flatMap(_.outputMetrics)
+    val outputSortable = maybeOutput.map(_.bytesWritten).getOrElse(0L)
+    val outputReadable = maybeOutput
+      .map(m => s"${Utils.bytesToString(m.bytesWritten)}")
+      .getOrElse("")
+    val outputRecords = maybeOutput.map(_.recordsWritten.toString).getOrElse("")
+
+    val maybeShuffleRead = metrics.flatMap(_.shuffleReadMetrics)
+    val shuffleReadBlockedTimeSortable = maybeShuffleRead.map(_.fetchWaitTime).getOrElse(0L)
+    val shuffleReadBlockedTimeReadable =
+      maybeShuffleRead.map(ms => UIUtils.formatDuration(ms.fetchWaitTime)).getOrElse("")
+
+    val totalShuffleBytes = maybeShuffleRead.map(_.totalBytesRead)
+    val shuffleReadSortable = totalShuffleBytes.getOrElse(0L)
+    val shuffleReadReadable = totalShuffleBytes.map(Utils.bytesToString).getOrElse("")
+    val shuffleReadRecords = maybeShuffleRead.map(_.recordsRead.toString).getOrElse("")
+
+    val remoteShuffleBytes = maybeShuffleRead.map(_.remoteBytesRead)
+    val shuffleReadRemoteSortable = remoteShuffleBytes.getOrElse(0L)
+    val shuffleReadRemoteReadable = remoteShuffleBytes.map(Utils.bytesToString).getOrElse("")
+
+    val maybeShuffleWrite = metrics.flatMap(_.shuffleWriteMetrics)
+    val shuffleWriteSortable = maybeShuffleWrite.map(_.shuffleBytesWritten).getOrElse(0L)
+    val shuffleWriteReadable = maybeShuffleWrite
+      .map(m => s"${Utils.bytesToString(m.shuffleBytesWritten)}").getOrElse("")
+    val shuffleWriteRecords = maybeShuffleWrite
+      .map(_.shuffleRecordsWritten.toString).getOrElse("")
+
+    val maybeWriteTime = metrics.flatMap(_.shuffleWriteMetrics).map(_.shuffleWriteTime)
+    val writeTimeSortable = maybeWriteTime.getOrElse(0L)
+    val writeTimeReadable = maybeWriteTime.map(t => t / (1000 * 1000)).map { ms =>
+      if (ms == 0) "" else UIUtils.formatDuration(ms)
+    }.getOrElse("")
+
+    val maybeMemoryBytesSpilled = metrics.map(_.memoryBytesSpilled)
+    val memoryBytesSpilledSortable = maybeMemoryBytesSpilled.getOrElse(0L)
+    val memoryBytesSpilledReadable =
+      maybeMemoryBytesSpilled.map(Utils.bytesToString).getOrElse("")
+
+    val maybeDiskBytesSpilled = metrics.map(_.diskBytesSpilled)
+    val diskBytesSpilledSortable = maybeDiskBytesSpilled.getOrElse(0L)
+    val diskBytesSpilledReadable = maybeDiskBytesSpilled.map(Utils.bytesToString).getOrElse("")
+
+    val input =
+      if (hasInput) {
+        Some(TaskTableRowInputData(inputSortable, s"$inputReadable / $inputRecords"))
+      } else {
+        None
+      }
+
+    val output =
+      if (hasOutput) {
+        Some(TaskTableRowOutputData(outputSortable, s"$outputReadable / $outputRecords"))
+      } else {
+        None
+      }
+
+    val shuffleRead =
+      if (hasShuffleRead) {
+        Some(TaskTableRowShuffleReadData(
+          shuffleReadBlockedTimeSortable,
+          shuffleReadBlockedTimeReadable,
+          shuffleReadSortable,
+          s"$shuffleReadReadable / $shuffleReadRecords",
+          shuffleReadRemoteSortable,
+          shuffleReadRemoteReadable
+        ))
+      } else {
+        None
+      }
+
+    val shuffleWrite =
+      if (hasShuffleWrite) {
+        Some(TaskTableRowShuffleWriteData(
+          writeTimeSortable,
+          writeTimeReadable,
+          shuffleWriteSortable,
+          s"$shuffleWriteReadable / $shuffleWriteRecords"
+        ))
+      } else {
+        None
+      }
+
+    val bytesSpilled =
+      if (hasBytesSpilled) {
+        Some(TaskTableRowBytesSpilledData(
+          memoryBytesSpilledSortable,
+          memoryBytesSpilledReadable,
+          diskBytesSpilledSortable,
+          diskBytesSpilledReadable
+        ))
+      } else {
+        None
+      }
+
+    TaskTableRowData(
+      info.index,
+      info.taskId,
+      info.attempt,
+      info.speculative,
+      info.status,
+      info.taskLocality.toString,
+      s"${info.executorId} / ${info.host}",
+      info.launchTime,
+      duration,
+      formatDuration,
+      schedulerDelay,
+      taskDeserializationTime,
+      gcTime,
+      serializationTime,
+      gettingResultTime,
+      if (hasAccumulators) Some(accumulatorsReadable.mkString("<br/>")) else None,
+      input,
+      output,
+      shuffleRead,
+      shuffleWrite,
+      bytesSpilled,
+      errorMessage.getOrElse("")
+    )
+  }
+
+  /**
+   * Return Ordering according to sortColumn and desc
+   */
+  private def ordering(sortColumn: String, desc: Boolean): Ordering[TaskTableRowData] = {
+    val ordering = sortColumn match {
+      case "Index" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Int.compare(x.index, y.index)
+      }
+      case "ID" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.taskId, y.taskId)
+      }
+      case "Attempt" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Int.compare(x.attempt, y.attempt)
+      }
+      case "Status" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.String.compare(x.status, y.status)
+      }
+      case "Locality Level" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.String.compare(x.taskLocality, y.taskLocality)
+      }
+      case "Executor ID / Host" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.String.compare(x.executorIdAndHost, y.executorIdAndHost)
+      }
+      case "Launch Time" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.launchTime, y.launchTime)
+      }
+      case "Duration" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.duration, y.duration)
+      }
+      case "Scheduler Delay" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.schedulerDelay, y.schedulerDelay)
+      }
+      case "Task Deserialization Time" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.taskDeserializationTime, y.taskDeserializationTime)
+      }
+      case "GC Time" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.gcTime, y.gcTime)
+      }
+      case "Result Serialization Time" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.serializationTime, y.serializationTime)
+      }
+      case "Getting Result Time" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.gettingResultTime, y.gettingResultTime)
+      }
+      case "Accumulators" =>
+        if (hasAccumulators) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.String.compare(x.accumulators.get, y.accumulators.get)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Accumulators because of no accumulators")
+        }
+      case "Input Size / Records" =>
+        if (hasInput) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.input.get.inputSortable, y.input.get.inputSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Input Size / Records because of no inputs")
+        }
+      case "Output Size / Records" =>
+        if (hasOutput) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.output.get.outputSortable, y.output.get.outputSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Output Size / Records because of no outputs")
+        }
+      // ShuffleRead
+      case "Shuffle Read Blocked Time" =>
+        if (hasShuffleRead) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.shuffleRead.get.shuffleReadBlockedTimeSortable,
+                y.shuffleRead.get.shuffleReadBlockedTimeSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Shuffle Read Blocked Time because of no shuffle reads")
+        }
+      case "Shuffle Read Size / Records" =>
+        if (hasShuffleRead) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.shuffleRead.get.shuffleReadSortable,
+                y.shuffleRead.get.shuffleReadSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Shuffle Read Size / Records because of no shuffle reads")
+        }
+      case "Shuffle Remote Reads" =>
+        if (hasShuffleRead) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.shuffleRead.get.shuffleReadRemoteSortable,
+                y.shuffleRead.get.shuffleReadRemoteSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Shuffle Remote Reads because of no shuffle reads")
+        }
+      // ShuffleWrite
+      case "Write Time" =>
+        if (hasShuffleWrite) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.shuffleWrite.get.writeTimeSortable,
+                y.shuffleWrite.get.writeTimeSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Write Time because of no shuffle writes")
+        }
+      case "Shuffle Write Size / Records" =>
+        if (hasShuffleWrite) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.shuffleWrite.get.shuffleWriteSortable,
+                y.shuffleWrite.get.shuffleWriteSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Shuffle Write Size / Records because of no shuffle writes")
+        }
+      // BytesSpilled
+      case "Shuffle Spill (Memory)" =>
+        if (hasBytesSpilled) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.bytesSpilled.get.memoryBytesSpilledSortable,
+                y.bytesSpilled.get.memoryBytesSpilledSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Shuffle Spill (Memory) because of no spills")
+        }
+      case "Shuffle Spill (Disk)" =>
+        if (hasBytesSpilled) {
+          new Ordering[TaskTableRowData] {
+            override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+              Ordering.Long.compare(x.bytesSpilled.get.diskBytesSpilledSortable,
+                y.bytesSpilled.get.diskBytesSpilledSortable)
+          }
+        } else {
+          throw new IllegalArgumentException(
+            "Cannot sort by Shuffle Spill (Disk) because of no spills")
+        }
+      case "Errors" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.String.compare(x.error, y.error)
+      }
+      case unknownColumn => throw new IllegalArgumentException(s"Unknown column: $unknownColumn")
+    }
+    if (desc) {
+      ordering.reverse
+    } else {
+      ordering
+    }
+  }
+
+}
+
+private[ui] class TaskPagedTable(
+    basePath: String,
+    data: Seq[TaskUIData],
+    hasAccumulators: Boolean,
+    hasInput: Boolean,
+    hasOutput: Boolean,
+    hasShuffleRead: Boolean,
+    hasShuffleWrite: Boolean,
+    hasBytesSpilled: Boolean,
+    currentTime: Long,
+    pageSize: Int,
+    sortColumn: String,
+    desc: Boolean) extends PagedTable[TaskTableRowData]{
+
+  override def tableId: String = ""
+
+  override def tableCssClass: String = "table table-bordered table-condensed table-striped"
+
+  override val dataSource: TaskDataSource = new TaskDataSource(
+    data,
+    hasAccumulators,
+    hasInput,
+    hasOutput,
+    hasShuffleRead,
+    hasShuffleWrite,
+    hasBytesSpilled,
+    currentTime,
+    pageSize,
+    sortColumn,
+    desc
+  )
+
+  override def pageLink(page: Int): String = {
+    val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
+    s"${basePath}&task.page=$page&task.sort=${encodedSortColumn}&task.desc=${desc}" +
+      s"&task.pageSize=${pageSize}"
+  }
+
+  override def goButtonJavascriptFunction: (String, String) = {
+    val jsFuncName = "goToTaskPage"
+    val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
+    val jsFunc = s"""
+      |currentTaskPageSize = ${pageSize}
+      |function goToTaskPage(page, pageSize) {
+      |  // Set page to 1 if the page size changes
+      |  page = pageSize == currentTaskPageSize ? page : 1;
+      |  var url = "${basePath}&task.sort=${encodedSortColumn}&task.desc=${desc}" +
+      |    "&task.page=" + page + "&task.pageSize=" + pageSize;
+      |  window.location.href = url;
+      |}
+     """.stripMargin
+    (jsFuncName, jsFunc)
+  }
+
+  def headers: Seq[Node] = {
+    val taskHeadersAndCssClasses: Seq[(String, String)] =
+      Seq(
+        ("Index", ""), ("ID", ""), ("Attempt", ""), ("Status", ""), ("Locality Level", ""),
+        ("Executor ID / Host", ""), ("Launch Time", ""), ("Duration", ""),
+        ("Scheduler Delay", TaskDetailsClassNames.SCHEDULER_DELAY),
+        ("Task Deserialization Time", TaskDetailsClassNames.TASK_DESERIALIZATION_TIME),
+        ("GC Time", ""),
+        ("Result Serialization Time", TaskDetailsClassNames.RESULT_SERIALIZATION_TIME),
+        ("Getting Result Time", TaskDetailsClassNames.GETTING_RESULT_TIME)) ++
+        {if (hasAccumulators) Seq(("Accumulators", "")) else Nil} ++
+        {if (hasInput) Seq(("Input Size / Records", "")) else Nil} ++
+        {if (hasOutput) Seq(("Output Size / Records", "")) else Nil} ++
         {if (hasShuffleRead) {
-           <td sorttable_customkey={shuffleReadBlockedTimeSortable}
-             class={TaskDetailsClassNames.SHUFFLE_READ_BLOCKED_TIME}>
-             {shuffleReadBlockedTimeReadable}
-           </td>
-           <td sorttable_customkey={shuffleReadSortable}>
-             {s"$shuffleReadReadable / $shuffleReadRecords"}
-           </td>
-           <td sorttable_customkey={shuffleReadRemoteSortable}
-               class={TaskDetailsClassNames.SHUFFLE_READ_REMOTE_SIZE}>
-             {shuffleReadRemoteReadable}
-           </td>
-        }}
+          Seq(("Shuffle Read Blocked Time", TaskDetailsClassNames.SHUFFLE_READ_BLOCKED_TIME),
+            ("Shuffle Read Size / Records", ""),
+            ("Shuffle Remote Reads", TaskDetailsClassNames.SHUFFLE_READ_REMOTE_SIZE))
+        } else {
+          Nil
+        }} ++
         {if (hasShuffleWrite) {
-           <td sorttable_customkey={writeTimeSortable}>
-             {writeTimeReadable}
-           </td>
-           <td sorttable_customkey={shuffleWriteSortable}>
-             {s"$shuffleWriteReadable / $shuffleWriteRecords"}
-           </td>
-        }}
+          Seq(("Write Time", ""), ("Shuffle Write Size / Records", ""))
+        } else {
+          Nil
+        }} ++
         {if (hasBytesSpilled) {
-          <td sorttable_customkey={memoryBytesSpilledSortable}>
-            {memoryBytesSpilledReadable}
-          </td>
-          <td sorttable_customkey={diskBytesSpilledSortable}>
-            {diskBytesSpilledReadable}
-          </td>
-        }}
-        {errorMessageCell(errorMessage)}
-      </tr>
+          Seq(("Shuffle Spill (Memory)", ""), ("Shuffle Spill (Disk)", ""))
+        } else {
+          Nil
+        }} ++
+        Seq(("Errors", ""))
+
+    if (!taskHeadersAndCssClasses.map(_._1).contains(sortColumn)) {
+      new IllegalArgumentException(s"Unknown column: $sortColumn")
     }
+
+    val headerRow: Seq[Node] = {
+      taskHeadersAndCssClasses.map { case (header, cssClass) =>
+        if (header == sortColumn) {
+          val headerLink =
+            s"$basePath&task.sort=${URLEncoder.encode(header, "UTF-8")}&task.desc=${!desc}" +
+              s"&task.pageSize=${pageSize}"
+          val js = Unparsed(s"window.location.href='${headerLink}'")
+          val arrow = if (desc) "&#x25BE;" else "&#x25B4;" // UP or DOWN
+          <th class={cssClass} onclick={js} style="cursor: pointer;">
+            {header}
+            <span>&nbsp;{Unparsed(arrow)}</span>
+          </th>
+        } else {
+          val headerLink =
+            s"$basePath&task.sort=${URLEncoder.encode(header, "UTF-8")}&task.pageSize=${pageSize}"
+          val js = Unparsed(s"window.location.href='${headerLink}'")
+          <th class={cssClass} onclick={js} style="cursor: pointer;">
+            {header}
+          </th>
+        }
+      }
+    }
+    <thead>{headerRow}</thead>
+  }
+
+  def row(task: TaskTableRowData): Seq[Node] = {
+    <tr>
+      <td>{task.index}</td>
+      <td>{task.taskId}</td>
+      <td>{if (task.speculative) s"${task.attempt} (speculative)" else task.attempt.toString}</td>
+      <td>{task.status}</td>
+      <td>{task.taskLocality}</td>
+      <td>{task.executorIdAndHost}</td>
+      <td>{UIUtils.formatDate(new Date(task.launchTime))}</td>
+      <td>{task.formatDuration}</td>
+      <td class={TaskDetailsClassNames.SCHEDULER_DELAY}>
+        {UIUtils.formatDuration(task.schedulerDelay)}
+      </td>
+      <td class={TaskDetailsClassNames.TASK_DESERIALIZATION_TIME}>
+        {UIUtils.formatDuration(task.taskDeserializationTime)}
+      </td>
+      <td>
+        {if (task.gcTime > 0) UIUtils.formatDuration(task.gcTime) else ""}
+      </td>
+      <td class={TaskDetailsClassNames.RESULT_SERIALIZATION_TIME}>
+        {UIUtils.formatDuration(task.serializationTime)}
+      </td>
+      <td class={TaskDetailsClassNames.GETTING_RESULT_TIME}>
+        {UIUtils.formatDuration(task.gettingResultTime)}
+      </td>
+      {if (task.accumulators.nonEmpty) {
+        <td>{Unparsed(task.accumulators.get)}</td>
+      }}
+      {if (task.input.nonEmpty) {
+        <td>{task.input.get.inputReadable}</td>
+      }}
+      {if (task.output.nonEmpty) {
+        <td>{task.output.get.outputReadable}</td>
+      }}
+      {if (task.shuffleRead.nonEmpty) {
+        <td class={TaskDetailsClassNames.SHUFFLE_READ_BLOCKED_TIME}>
+          {task.shuffleRead.get.shuffleReadBlockedTimeReadable}
+        </td>
+        <td>{task.shuffleRead.get.shuffleReadReadable}</td>
+        <td class={TaskDetailsClassNames.SHUFFLE_READ_REMOTE_SIZE}>
+          {task.shuffleRead.get.shuffleReadRemoteReadable}
+        </td>
+      }}
+      {if (task.shuffleWrite.nonEmpty) {
+        <td>{task.shuffleWrite.get.writeTimeReadable}</td>
+        <td>{task.shuffleWrite.get.shuffleWriteReadable}</td>
+      }}
+      {if (task.bytesSpilled.nonEmpty) {
+        <td>{task.bytesSpilled.get.memoryBytesSpilledReadable}</td>
+        <td>{task.bytesSpilled.get.diskBytesSpilledReadable}</td>
+      }}
+      {errorMessageCell(task.error)}
+    </tr>
   }
 
-  private def errorMessageCell(errorMessage: Option[String]): Seq[Node] = {
-    val error = errorMessage.getOrElse("")
+  private def errorMessageCell(error: String): Seq[Node] = {
     val isMultiline = error.indexOf('\n') >= 0
     // Display the first line by default
     val errorSummary = StringEscapeUtils.escapeHtml4(
@@ -860,32 +1325,4 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
     }
     <td>{errorSummary}{details}</td>
   }
-
-  private def getGettingResultTime(info: TaskInfo, currentTime: Long): Long = {
-    if (info.gettingResult) {
-      if (info.finished) {
-        info.finishTime - info.gettingResultTime
-      } else {
-        // The task is still fetching the result.
-        currentTime - info.gettingResultTime
-      }
-    } else {
-      0L
-    }
-  }
-
-  private def getSchedulerDelay(info: TaskInfo, metrics: TaskMetrics, currentTime: Long): Long = {
-    if (info.finished) {
-      val totalExecutionTime = info.finishTime - info.launchTime
-      val executorOverhead = (metrics.executorDeserializeTime +
-        metrics.resultSerializationTime)
-      math.max(
-        0,
-        totalExecutionTime - metrics.executorRunTime - executorOverhead -
-          getGettingResultTime(info, currentTime))
-    } else {
-      // The task is still running and the metrics like executorRunTime are not available.
-      0L
-    }
-  }
 }
diff --git a/core/src/test/scala/org/apache/spark/ui/PagedTableSuite.scala b/core/src/test/scala/org/apache/spark/ui/PagedTableSuite.scala
new file mode 100644
index 0000000000000..cc76c141c53cc
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/ui/PagedTableSuite.scala
@@ -0,0 +1,99 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui
+
+import scala.xml.Node
+
+import org.apache.spark.SparkFunSuite
+
+class PagedDataSourceSuite extends SparkFunSuite {
+
+  test("basic") {
+    val dataSource1 = new SeqPagedDataSource[Int](1 to 5, pageSize = 2)
+    assert(dataSource1.pageData(1) === PageData(3, (1 to 2)))
+
+    val dataSource2 = new SeqPagedDataSource[Int](1 to 5, pageSize = 2)
+    assert(dataSource2.pageData(2) === PageData(3, (3 to 4)))
+
+    val dataSource3 = new SeqPagedDataSource[Int](1 to 5, pageSize = 2)
+    assert(dataSource3.pageData(3) === PageData(3, Seq(5)))
+
+    val dataSource4 = new SeqPagedDataSource[Int](1 to 5, pageSize = 2)
+    val e1 = intercept[IndexOutOfBoundsException] {
+      dataSource4.pageData(4)
+    }
+    assert(e1.getMessage === "Page 4 is out of range. Please select a page number between 1 and 3.")
+
+    val dataSource5 = new SeqPagedDataSource[Int](1 to 5, pageSize = 2)
+    val e2 = intercept[IndexOutOfBoundsException] {
+      dataSource5.pageData(0)
+    }
+    assert(e2.getMessage === "Page 0 is out of range. Please select a page number between 1 and 3.")
+
+  }
+}
+
+class PagedTableSuite extends SparkFunSuite {
+  test("pageNavigation") {
+    // Create a fake PagedTable to test pageNavigation
+    val pagedTable = new PagedTable[Int] {
+      override def tableId: String = ""
+
+      override def tableCssClass: String = ""
+
+      override def dataSource: PagedDataSource[Int] = null
+
+      override def pageLink(page: Int): String = page.toString
+
+      override def headers: Seq[Node] = Nil
+
+      override def row(t: Int): Seq[Node] = Nil
+
+      override def goButtonJavascriptFunction: (String, String) = ("", "")
+    }
+
+    assert(pagedTable.pageNavigation(1, 10, 1) === Nil)
+    assert(
+      (pagedTable.pageNavigation(1, 10, 2).head \\ "li").map(_.text.trim) === Seq("1", "2", ">"))
+    assert(
+      (pagedTable.pageNavigation(2, 10, 2).head \\ "li").map(_.text.trim) === Seq("<", "1", "2"))
+
+    assert((pagedTable.pageNavigation(1, 10, 100).head \\ "li").map(_.text.trim) ===
+      (1 to 10).map(_.toString) ++ Seq(">", ">>"))
+    assert((pagedTable.pageNavigation(2, 10, 100).head \\ "li").map(_.text.trim) ===
+      Seq("<") ++ (1 to 10).map(_.toString) ++ Seq(">", ">>"))
+
+    assert((pagedTable.pageNavigation(100, 10, 100).head \\ "li").map(_.text.trim) ===
+      Seq("<<", "<") ++ (91 to 100).map(_.toString))
+    assert((pagedTable.pageNavigation(99, 10, 100).head \\ "li").map(_.text.trim) ===
+      Seq("<<", "<") ++ (91 to 100).map(_.toString) ++ Seq(">"))
+
+    assert((pagedTable.pageNavigation(11, 10, 100).head \\ "li").map(_.text.trim) ===
+      Seq("<<", "<") ++ (11 to 20).map(_.toString) ++ Seq(">", ">>"))
+    assert((pagedTable.pageNavigation(93, 10, 97).head \\ "li").map(_.text.trim) ===
+      Seq("<<", "<") ++ (91 to 97).map(_.toString) ++ Seq(">"))
+  }
+}
+
+private[spark] class SeqPagedDataSource[T](seq: Seq[T], pageSize: Int)
+  extends PagedDataSource[T](pageSize) {
+
+  override protected def dataSize: Int = seq.size
+
+  override protected def sliceData(from: Int, to: Int): Seq[T] = seq.slice(from, to)
+}

From d45355ee224b734727255ff278a47801f5da7e93 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 21 Jul 2015 09:55:42 -0700
Subject: [PATCH 0528/1454] [SPARK-5423] [CORE] Register a
 TaskCompletionListener to make sure release all resources

Make `DiskMapIterator.cleanup` idempotent and register a TaskCompletionListener to make sure call `cleanup`.

Author: zsxwing <zsxwing@gmail.com>

Closes #7529 from zsxwing/SPARK-5423 and squashes the following commits:

3e3c413 [zsxwing] Remove TODO
9556c78 [zsxwing] Fix NullPointerException for tests
3d574d9 [zsxwing] Register a TaskCompletionListener to make sure release all resources
---
 .../collection/ExternalAppendOnlyMap.scala    | 25 ++++++++++++++-----
 1 file changed, 19 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index 1e4531ef395ae..d166037351c31 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -26,7 +26,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.io.ByteStreams
 
-import org.apache.spark.{Logging, SparkEnv}
+import org.apache.spark.{Logging, SparkEnv, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.serializer.{DeserializationStream, Serializer}
 import org.apache.spark.storage.{BlockId, BlockManager}
@@ -470,14 +470,27 @@ class ExternalAppendOnlyMap[K, V, C](
       item
     }
 
-    // TODO: Ensure this gets called even if the iterator isn't drained.
     private def cleanup() {
       batchIndex = batchOffsets.length  // Prevent reading any other batch
       val ds = deserializeStream
-      deserializeStream = null
-      fileStream = null
-      ds.close()
-      file.delete()
+      if (ds != null) {
+        ds.close()
+        deserializeStream = null
+      }
+      if (fileStream != null) {
+        fileStream.close()
+        fileStream = null
+      }
+      if (file.exists()) {
+        file.delete()
+      }
+    }
+
+    val context = TaskContext.get()
+    // context is null in some tests of ExternalAppendOnlyMapSuite because these tests don't run in
+    // a TaskContext.
+    if (context != null) {
+      context.addTaskCompletionListener(context => cleanup())
     }
   }
 

From 7f072c3d5ec50c65d76bd9f28fac124fce96a89e Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Tue, 21 Jul 2015 09:58:16 -0700
Subject: [PATCH 0529/1454] [SPARK-9154] [SQL] codegen StringFormat

Jira: https://issues.apache.org/jira/browse/SPARK-9154

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7546 from tarekauel/SPARK-9154 and squashes the following commits:

a943d3e [Tarek Auel] [SPARK-9154] implicit input cast, added tests for null, support for null primitives
10b4de8 [Tarek Auel] [SPARK-9154][SQL] codegen removed fallback trait
cd8322b [Tarek Auel] [SPARK-9154][SQL] codegen string format
086caba [Tarek Auel] [SPARK-9154][SQL] codegen string format
---
 .../expressions/stringOperations.scala        | 42 ++++++++++++++++++-
 .../expressions/StringExpressionsSuite.scala  | 18 ++++----
 .../spark/sql/StringFunctionsSuite.scala      | 10 +++++
 3 files changed, 59 insertions(+), 11 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index fe57d17f1ec14..280ae0e546358 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -526,7 +526,7 @@ case class StringRPad(str: Expression, len: Expression, pad: Expression)
 /**
  * Returns the input formatted according do printf-style format strings
  */
-case class StringFormat(children: Expression*) extends Expression with CodegenFallback {
+case class StringFormat(children: Expression*) extends Expression with ImplicitCastInputTypes {
 
   require(children.nonEmpty, "printf() should take at least 1 argument")
 
@@ -536,6 +536,10 @@ case class StringFormat(children: Expression*) extends Expression with CodegenFa
   private def format: Expression = children(0)
   private def args: Seq[Expression] = children.tail
 
+  override def inputTypes: Seq[AbstractDataType] =
+    children.zipWithIndex.map(x => if (x._2 == 0) StringType else AnyDataType)
+
+
   override def eval(input: InternalRow): Any = {
     val pattern = format.eval(input)
     if (pattern == null) {
@@ -551,6 +555,42 @@ case class StringFormat(children: Expression*) extends Expression with CodegenFa
     }
   }
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val pattern = children.head.gen(ctx)
+
+    val argListGen = children.tail.map(x => (x.dataType, x.gen(ctx)))
+    val argListCode = argListGen.map(_._2.code + "\n")
+
+    val argListString = argListGen.foldLeft("")((s, v) => {
+      val nullSafeString =
+        if (ctx.boxedType(v._1) != ctx.javaType(v._1)) {
+          // Java primitives get boxed in order to allow null values.
+          s"(${v._2.isNull}) ? (${ctx.boxedType(v._1)}) null : " +
+            s"new ${ctx.boxedType(v._1)}(${v._2.primitive})"
+        } else {
+          s"(${v._2.isNull}) ? null : ${v._2.primitive}"
+        }
+      s + "," + nullSafeString
+    })
+
+    val form = ctx.freshName("formatter")
+    val formatter = classOf[java.util.Formatter].getName
+    val sb = ctx.freshName("sb")
+    val stringBuffer = classOf[StringBuffer].getName
+    s"""
+      ${pattern.code}
+      boolean ${ev.isNull} = ${pattern.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${argListCode.mkString}
+        $stringBuffer $sb = new $stringBuffer();
+        $formatter $form = new $formatter($sb, ${classOf[Locale].getName}.US);
+        $form.format(${pattern.primitive}.toString() $argListString);
+        ${ev.primitive} = UTF8String.fromString($sb.toString());
+      }
+     """
+  }
+
   override def prettyName: String = "printf"
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 96c540ab36f08..3c2d88731beb4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -351,18 +351,16 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("FORMAT") {
-    val f = 'f.string.at(0)
-    val d1 = 'd.int.at(1)
-    val s1 = 's.int.at(2)
-
-    val row1 = create_row("aa%d%s", 12, "cc")
-    val row2 = create_row(null, 12, "cc")
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
     checkEvaluation(StringFormat(Literal("aa")), "aa", create_row(null))
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
+    checkEvaluation(StringFormat(Literal("aa%d%s"), 12, "cc"), "aa12cc")
 
-    checkEvaluation(StringFormat(f, d1, s1), "aa12cc", row1)
-    checkEvaluation(StringFormat(f, d1, s1), null, row2)
+    checkEvaluation(StringFormat(Literal.create(null, StringType), 12, "cc"), null)
+    checkEvaluation(
+      StringFormat(Literal("aa%d%s"), Literal.create(null, IntegerType), "cc"), "aanullcc")
+    checkEvaluation(
+      StringFormat(Literal("aa%d%s"), 12, Literal.create(null, StringType)), "aa12null")
   }
 
   test("INSTR") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index d1f855903ca4b..3702e73b4e74f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -132,6 +132,16 @@ class StringFunctionsSuite extends QueryTest {
     checkAnswer(
       df.selectExpr("printf(a, b, c)"),
       Row("aa123cc"))
+
+    val df2 = Seq(("aa%d%s".getBytes, 123, "cc")).toDF("a", "b", "c")
+
+    checkAnswer(
+      df2.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
+      Row("aa123cc", "aa123cc"))
+
+    checkAnswer(
+      df2.selectExpr("printf(a, b, c)"),
+      Row("aa123cc"))
   }
 
   test("string instr function") {

From 89db3c0b6edcffed7e1e12c202e6827271ddba26 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 21 Jul 2015 10:31:31 -0700
Subject: [PATCH 0530/1454] [SPARK-5989] [MLLIB] Model save/load for LDA

Add support for saving and loading LDA both the local and distributed versions.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #6948 from MechCoder/lda_save_load and squashes the following commits:

49bcdce [MechCoder] minor style fixes
cc14054 [MechCoder] minor
4587d1d [MechCoder] Minor changes
c753122 [MechCoder] Load and save the model in private methods
2782326 [MechCoder] [SPARK-5989] Model save/load for LDA
---
 docs/mllib-clustering.md                      |  10 +-
 .../spark/mllib/clustering/LDAModel.scala     | 228 +++++++++++++++++-
 .../spark/mllib/clustering/LDASuite.scala     |  41 ++++
 3 files changed, 274 insertions(+), 5 deletions(-)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 0fc7036bffeaf..bb875ae2ae6cb 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -472,7 +472,7 @@ to the algorithm. We then output the topics, represented as probability distribu
 <div data-lang="scala" markdown="1">
 
 {% highlight scala %}
-import org.apache.spark.mllib.clustering.LDA
+import org.apache.spark.mllib.clustering.{LDA, DistributedLDAModel}
 import org.apache.spark.mllib.linalg.Vectors
 
 // Load and parse the data
@@ -492,6 +492,11 @@ for (topic <- Range(0, 3)) {
   for (word <- Range(0, ldaModel.vocabSize)) { print(" " + topics(word, topic)); }
   println()
 }
+
+// Save and load model.
+ldaModel.save(sc, "myLDAModel")
+val sameModel = DistributedLDAModel.load(sc, "myLDAModel")
+
 {% endhighlight %}
 </div>
 
@@ -551,6 +556,9 @@ public class JavaLDAExample {
       }
       System.out.println();
     }
+
+    ldaModel.save(sc.sc(), "myLDAModel");
+    DistributedLDAModel sameModel = DistributedLDAModel.load(sc.sc(), "myLDAModel");
   }
 }
 {% endhighlight %}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 974b26924dfb8..920b57756b625 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -17,15 +17,25 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM, normalize, sum => brzSum}
+import breeze.linalg.{DenseMatrix => BDM, normalize, sum => brzSum, DenseVector => BDV}
 
+import org.apache.hadoop.fs.Path
+
+import org.json4s.DefaultFormats
+import org.json4s.JsonDSL._
+import org.json4s.jackson.JsonMethods._
+
+import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaPairRDD
-import org.apache.spark.graphx.{VertexId, EdgeContext, Graph}
-import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix}
+import org.apache.spark.graphx.{VertexId, Edge, EdgeContext, Graph}
+import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix, DenseVector}
+import org.apache.spark.mllib.util.{Saveable, Loader}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{SQLContext, Row}
 import org.apache.spark.util.BoundedPriorityQueue
 
+
 /**
  * :: Experimental ::
  *
@@ -35,7 +45,7 @@ import org.apache.spark.util.BoundedPriorityQueue
  * including local and distributed data structures.
  */
 @Experimental
-abstract class LDAModel private[clustering] {
+abstract class LDAModel private[clustering] extends Saveable {
 
   /** Number of topics */
   def k: Int
@@ -176,6 +186,11 @@ class LocalLDAModel private[clustering] (
     }.toArray
   }
 
+  override protected def formatVersion = "1.0"
+
+  override def save(sc: SparkContext, path: String): Unit = {
+    LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix)
+  }
   // TODO
   // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ???
 
@@ -184,6 +199,80 @@ class LocalLDAModel private[clustering] (
 
 }
 
+@Experimental
+object LocalLDAModel extends Loader[LocalLDAModel] {
+
+  private object SaveLoadV1_0 {
+
+    val thisFormatVersion = "1.0"
+
+    val thisClassName = "org.apache.spark.mllib.clustering.LocalLDAModel"
+
+    // Store the distribution of terms of each topic and the column index in topicsMatrix
+    // as a Row in data.
+    case class Data(topic: Vector, index: Int)
+
+    def save(sc: SparkContext, path: String, topicsMatrix: Matrix): Unit = {
+      val sqlContext = SQLContext.getOrCreate(sc)
+      import sqlContext.implicits._
+
+      val k = topicsMatrix.numCols
+      val metadata = compact(render
+        (("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~
+         ("k" -> k) ~ ("vocabSize" -> topicsMatrix.numRows)))
+      sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
+
+      val topicsDenseMatrix = topicsMatrix.toBreeze.toDenseMatrix
+      val topics = Range(0, k).map { topicInd =>
+        Data(Vectors.dense((topicsDenseMatrix(::, topicInd).toArray)), topicInd)
+      }.toSeq
+      sc.parallelize(topics, 1).toDF().write.parquet(Loader.dataPath(path))
+    }
+
+    def load(sc: SparkContext, path: String): LocalLDAModel = {
+      val dataPath = Loader.dataPath(path)
+      val sqlContext = SQLContext.getOrCreate(sc)
+      val dataFrame = sqlContext.read.parquet(dataPath)
+
+      Loader.checkSchema[Data](dataFrame.schema)
+      val topics = dataFrame.collect()
+      val vocabSize = topics(0).getAs[Vector](0).size
+      val k = topics.size
+
+      val brzTopics = BDM.zeros[Double](vocabSize, k)
+      topics.foreach { case Row(vec: Vector, ind: Int) =>
+        brzTopics(::, ind) := vec.toBreeze
+      }
+      new LocalLDAModel(Matrices.fromBreeze(brzTopics))
+    }
+  }
+
+  override def load(sc: SparkContext, path: String): LocalLDAModel = {
+    val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
+    implicit val formats = DefaultFormats
+    val expectedK = (metadata \ "k").extract[Int]
+    val expectedVocabSize = (metadata \ "vocabSize").extract[Int]
+    val classNameV1_0 = SaveLoadV1_0.thisClassName
+
+    val model = (loadedClassName, loadedVersion) match {
+      case (className, "1.0") if className == classNameV1_0 =>
+        SaveLoadV1_0.load(sc, path)
+      case _ => throw new Exception(
+        s"LocalLDAModel.load did not recognize model with (className, format version):" +
+        s"($loadedClassName, $loadedVersion).  Supported:\n" +
+        s"  ($classNameV1_0, 1.0)")
+    }
+
+    val topicsMatrix = model.topicsMatrix
+    require(expectedK == topicsMatrix.numCols,
+      s"LocalLDAModel requires $expectedK topics, got ${topicsMatrix.numCols} topics")
+    require(expectedVocabSize == topicsMatrix.numRows,
+      s"LocalLDAModel requires $expectedVocabSize terms for each topic, " +
+      s"but got ${topicsMatrix.numRows}")
+    model
+  }
+}
+
 /**
  * :: Experimental ::
  *
@@ -354,4 +443,135 @@ class DistributedLDAModel private (
   // TODO:
   // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ???
 
+  override protected def formatVersion = "1.0"
+
+  override def save(sc: SparkContext, path: String): Unit = {
+    DistributedLDAModel.SaveLoadV1_0.save(
+      sc, path, graph, globalTopicTotals, k, vocabSize, docConcentration, topicConcentration,
+      iterationTimes)
+  }
+}
+
+
+@Experimental
+object DistributedLDAModel extends Loader[DistributedLDAModel] {
+
+  private object SaveLoadV1_0 {
+
+    val thisFormatVersion = "1.0"
+
+    val classNameV1_0 = "org.apache.spark.mllib.clustering.DistributedLDAModel"
+
+    // Store globalTopicTotals as a Vector.
+    case class Data(globalTopicTotals: Vector)
+
+    // Store each term and document vertex with an id and the topicWeights.
+    case class VertexData(id: Long, topicWeights: Vector)
+
+    // Store each edge with the source id, destination id and tokenCounts.
+    case class EdgeData(srcId: Long, dstId: Long, tokenCounts: Double)
+
+    def save(
+        sc: SparkContext,
+        path: String,
+        graph: Graph[LDA.TopicCounts, LDA.TokenCount],
+        globalTopicTotals: LDA.TopicCounts,
+        k: Int,
+        vocabSize: Int,
+        docConcentration: Double,
+        topicConcentration: Double,
+        iterationTimes: Array[Double]): Unit = {
+      val sqlContext = SQLContext.getOrCreate(sc)
+      import sqlContext.implicits._
+
+      val metadata = compact(render
+        (("class" -> classNameV1_0) ~ ("version" -> thisFormatVersion) ~
+         ("k" -> k) ~ ("vocabSize" -> vocabSize) ~ ("docConcentration" -> docConcentration) ~
+         ("topicConcentration" -> topicConcentration) ~
+         ("iterationTimes" -> iterationTimes.toSeq)))
+      sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
+
+      val newPath = new Path(Loader.dataPath(path), "globalTopicTotals").toUri.toString
+      sc.parallelize(Seq(Data(Vectors.fromBreeze(globalTopicTotals)))).toDF()
+        .write.parquet(newPath)
+
+      val verticesPath = new Path(Loader.dataPath(path), "topicCounts").toUri.toString
+      graph.vertices.map { case (ind, vertex) =>
+        VertexData(ind, Vectors.fromBreeze(vertex))
+      }.toDF().write.parquet(verticesPath)
+
+      val edgesPath = new Path(Loader.dataPath(path), "tokenCounts").toUri.toString
+      graph.edges.map { case Edge(srcId, dstId, prop) =>
+        EdgeData(srcId, dstId, prop)
+      }.toDF().write.parquet(edgesPath)
+    }
+
+    def load(
+        sc: SparkContext,
+        path: String,
+        vocabSize: Int,
+        docConcentration: Double,
+        topicConcentration: Double,
+        iterationTimes: Array[Double]): DistributedLDAModel = {
+      val dataPath = new Path(Loader.dataPath(path), "globalTopicTotals").toUri.toString
+      val vertexDataPath = new Path(Loader.dataPath(path), "topicCounts").toUri.toString
+      val edgeDataPath = new Path(Loader.dataPath(path), "tokenCounts").toUri.toString
+      val sqlContext = SQLContext.getOrCreate(sc)
+      val dataFrame = sqlContext.read.parquet(dataPath)
+      val vertexDataFrame = sqlContext.read.parquet(vertexDataPath)
+      val edgeDataFrame = sqlContext.read.parquet(edgeDataPath)
+
+      Loader.checkSchema[Data](dataFrame.schema)
+      Loader.checkSchema[VertexData](vertexDataFrame.schema)
+      Loader.checkSchema[EdgeData](edgeDataFrame.schema)
+      val globalTopicTotals: LDA.TopicCounts =
+        dataFrame.first().getAs[Vector](0).toBreeze.toDenseVector
+      val vertices: RDD[(VertexId, LDA.TopicCounts)] = vertexDataFrame.map {
+        case Row(ind: Long, vec: Vector) => (ind, vec.toBreeze.toDenseVector)
+      }
+
+      val edges: RDD[Edge[LDA.TokenCount]] = edgeDataFrame.map {
+        case Row(srcId: Long, dstId: Long, prop: Double) => Edge(srcId, dstId, prop)
+      }
+      val graph: Graph[LDA.TopicCounts, LDA.TokenCount] = Graph(vertices, edges)
+
+      new DistributedLDAModel(graph, globalTopicTotals, globalTopicTotals.length, vocabSize,
+        docConcentration, topicConcentration, iterationTimes)
+    }
+
+  }
+
+  override def load(sc: SparkContext, path: String): DistributedLDAModel = {
+    val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
+    implicit val formats = DefaultFormats
+    val expectedK = (metadata \ "k").extract[Int]
+    val vocabSize = (metadata \ "vocabSize").extract[Int]
+    val docConcentration = (metadata \ "docConcentration").extract[Double]
+    val topicConcentration = (metadata \ "topicConcentration").extract[Double]
+    val iterationTimes = (metadata \ "iterationTimes").extract[Seq[Double]]
+    val classNameV1_0 = SaveLoadV1_0.classNameV1_0
+
+    val model = (loadedClassName, loadedVersion) match {
+      case (className, "1.0") if className == classNameV1_0 => {
+        DistributedLDAModel.SaveLoadV1_0.load(
+          sc, path, vocabSize, docConcentration, topicConcentration, iterationTimes.toArray)
+      }
+      case _ => throw new Exception(
+        s"DistributedLDAModel.load did not recognize model with (className, format version):" +
+        s"($loadedClassName, $loadedVersion).  Supported: ($classNameV1_0, 1.0)")
+    }
+
+    require(model.vocabSize == vocabSize,
+      s"DistributedLDAModel requires $vocabSize vocabSize, got ${model.vocabSize} vocabSize")
+    require(model.docConcentration == docConcentration,
+      s"DistributedLDAModel requires $docConcentration docConcentration, " +
+      s"got ${model.docConcentration} docConcentration")
+    require(model.topicConcentration == topicConcentration,
+      s"DistributedLDAModel requires $topicConcentration docConcentration, " +
+      s"got ${model.topicConcentration} docConcentration")
+    require(expectedK == model.k,
+      s"DistributedLDAModel requires $expectedK topics, got ${model.k} topics")
+    model
+  }
+
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 03a8a2538b464..721a065658951 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -23,6 +23,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.util.Utils
 
 class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
@@ -217,6 +218,46 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("model save/load") {
+    // Test for LocalLDAModel.
+    val localModel = new LocalLDAModel(tinyTopics)
+    val tempDir1 = Utils.createTempDir()
+    val path1 = tempDir1.toURI.toString
+
+    // Test for DistributedLDAModel.
+    val k = 3
+    val docConcentration = 1.2
+    val topicConcentration = 1.5
+    val lda = new LDA()
+    lda.setK(k)
+      .setDocConcentration(docConcentration)
+      .setTopicConcentration(topicConcentration)
+      .setMaxIterations(5)
+      .setSeed(12345)
+    val corpus = sc.parallelize(tinyCorpus, 2)
+    val distributedModel: DistributedLDAModel = lda.run(corpus).asInstanceOf[DistributedLDAModel]
+    val tempDir2 = Utils.createTempDir()
+    val path2 = tempDir2.toURI.toString
+
+    try {
+      localModel.save(sc, path1)
+      distributedModel.save(sc, path2)
+      val samelocalModel = LocalLDAModel.load(sc, path1)
+      assert(samelocalModel.topicsMatrix === localModel.topicsMatrix)
+      assert(samelocalModel.k === localModel.k)
+      assert(samelocalModel.vocabSize === localModel.vocabSize)
+
+      val sameDistributedModel = DistributedLDAModel.load(sc, path2)
+      assert(distributedModel.topicsMatrix === sameDistributedModel.topicsMatrix)
+      assert(distributedModel.k === sameDistributedModel.k)
+      assert(distributedModel.vocabSize === sameDistributedModel.vocabSize)
+      assert(distributedModel.iterationTimes === sameDistributedModel.iterationTimes)
+    } finally {
+      Utils.deleteRecursively(tempDir1)
+      Utils.deleteRecursively(tempDir2)
+    }
+  }
+
 }
 
 private[clustering] object LDASuite {

From 87d890cc105a7f41478433b28f53c9aa431db211 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 21 Jul 2015 11:18:39 -0700
Subject: [PATCH 0531/1454] Revert "[SPARK-9154] [SQL] codegen StringFormat"

This reverts commit 7f072c3d5ec50c65d76bd9f28fac124fce96a89e.

Revert #7546

Author: Michael Armbrust <michael@databricks.com>

Closes #7570 from marmbrus/revert9154 and squashes the following commits:

ed2c32a [Michael Armbrust] Revert "[SPARK-9154] [SQL] codegen StringFormat"
---
 .../expressions/stringOperations.scala        | 42 +------------------
 .../expressions/StringExpressionsSuite.scala  | 18 ++++----
 .../spark/sql/StringFunctionsSuite.scala      | 10 -----
 3 files changed, 11 insertions(+), 59 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 280ae0e546358..fe57d17f1ec14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -526,7 +526,7 @@ case class StringRPad(str: Expression, len: Expression, pad: Expression)
 /**
  * Returns the input formatted according do printf-style format strings
  */
-case class StringFormat(children: Expression*) extends Expression with ImplicitCastInputTypes {
+case class StringFormat(children: Expression*) extends Expression with CodegenFallback {
 
   require(children.nonEmpty, "printf() should take at least 1 argument")
 
@@ -536,10 +536,6 @@ case class StringFormat(children: Expression*) extends Expression with ImplicitC
   private def format: Expression = children(0)
   private def args: Seq[Expression] = children.tail
 
-  override def inputTypes: Seq[AbstractDataType] =
-    children.zipWithIndex.map(x => if (x._2 == 0) StringType else AnyDataType)
-
-
   override def eval(input: InternalRow): Any = {
     val pattern = format.eval(input)
     if (pattern == null) {
@@ -555,42 +551,6 @@ case class StringFormat(children: Expression*) extends Expression with ImplicitC
     }
   }
 
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val pattern = children.head.gen(ctx)
-
-    val argListGen = children.tail.map(x => (x.dataType, x.gen(ctx)))
-    val argListCode = argListGen.map(_._2.code + "\n")
-
-    val argListString = argListGen.foldLeft("")((s, v) => {
-      val nullSafeString =
-        if (ctx.boxedType(v._1) != ctx.javaType(v._1)) {
-          // Java primitives get boxed in order to allow null values.
-          s"(${v._2.isNull}) ? (${ctx.boxedType(v._1)}) null : " +
-            s"new ${ctx.boxedType(v._1)}(${v._2.primitive})"
-        } else {
-          s"(${v._2.isNull}) ? null : ${v._2.primitive}"
-        }
-      s + "," + nullSafeString
-    })
-
-    val form = ctx.freshName("formatter")
-    val formatter = classOf[java.util.Formatter].getName
-    val sb = ctx.freshName("sb")
-    val stringBuffer = classOf[StringBuffer].getName
-    s"""
-      ${pattern.code}
-      boolean ${ev.isNull} = ${pattern.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${argListCode.mkString}
-        $stringBuffer $sb = new $stringBuffer();
-        $formatter $form = new $formatter($sb, ${classOf[Locale].getName}.US);
-        $form.format(${pattern.primitive}.toString() $argListString);
-        ${ev.primitive} = UTF8String.fromString($sb.toString());
-      }
-     """
-  }
-
   override def prettyName: String = "printf"
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 3c2d88731beb4..96c540ab36f08 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -351,16 +351,18 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("FORMAT") {
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
+    val f = 'f.string.at(0)
+    val d1 = 'd.int.at(1)
+    val s1 = 's.int.at(2)
+
+    val row1 = create_row("aa%d%s", 12, "cc")
+    val row2 = create_row(null, 12, "cc")
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
     checkEvaluation(StringFormat(Literal("aa")), "aa", create_row(null))
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
-    checkEvaluation(StringFormat(Literal("aa%d%s"), 12, "cc"), "aa12cc")
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
 
-    checkEvaluation(StringFormat(Literal.create(null, StringType), 12, "cc"), null)
-    checkEvaluation(
-      StringFormat(Literal("aa%d%s"), Literal.create(null, IntegerType), "cc"), "aanullcc")
-    checkEvaluation(
-      StringFormat(Literal("aa%d%s"), 12, Literal.create(null, StringType)), "aa12null")
+    checkEvaluation(StringFormat(f, d1, s1), "aa12cc", row1)
+    checkEvaluation(StringFormat(f, d1, s1), null, row2)
   }
 
   test("INSTR") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 3702e73b4e74f..d1f855903ca4b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -132,16 +132,6 @@ class StringFunctionsSuite extends QueryTest {
     checkAnswer(
       df.selectExpr("printf(a, b, c)"),
       Row("aa123cc"))
-
-    val df2 = Seq(("aa%d%s".getBytes, 123, "cc")).toDF("a", "b", "c")
-
-    checkAnswer(
-      df2.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
-      Row("aa123cc", "aa123cc"))
-
-    checkAnswer(
-      df2.selectExpr("printf(a, b, c)"),
-      Row("aa123cc"))
   }
 
   test("string instr function") {

From 9ba7c64decfc92853bd281e9e7bfb95211080dd4 Mon Sep 17 00:00:00 2001
From: "navis.ryu" <navis@apache.org>
Date: Tue, 21 Jul 2015 11:52:52 -0700
Subject: [PATCH 0532/1454] [SPARK-8357] Fix unsafe memory leak on empty inputs
 in GeneratedAggregate

This patch fixes a managed memory leak in GeneratedAggregate.  The leak occurs when the unsafe aggregation path is used to perform grouped aggregation on an empty input; in this case, GeneratedAggregate allocates an UnsafeFixedWidthAggregationMap that is never cleaned up because `next()` is never called on the aggregate result iterator.

This patch fixes this by short-circuiting on empty inputs.

This patch is an updated version of #6810.

Closes #6810.

Author: navis.ryu <navis@apache.org>
Author: Josh Rosen <joshrosen@databricks.com>

Closes #7560 from JoshRosen/SPARK-8357 and squashes the following commits:

3486ce4 [Josh Rosen] Some minor cleanup
c649310 [Josh Rosen] Revert SparkPlan change:
3c7db0f [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-8357
adc8239 [Josh Rosen] Back out Projection changes.
c5419b3 [navis.ryu] addressed comments
143e1ef [navis.ryu] fixed format & added test for CCE case
735972f [navis.ryu] used new conf apis
1a02a55 [navis.ryu] Rolled-back test-conf cleanup & fixed possible CCE & added more tests
51178e8 [navis.ryu] addressed comments
4d326b9 [navis.ryu] fixed test fails
15c5afc [navis.ryu] added a test as suggested by JoshRosen
d396589 [navis.ryu] added comments
1b07556 [navis.ryu] [SPARK-8357] [SQL] Memory leakage on unsafe aggregation path with empty input
---
 .../sql/execution/GeneratedAggregate.scala    | 14 +++++-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  9 ++++
 .../spark/sql/execution/AggregateSuite.scala  | 48 +++++++++++++++++++
 3 files changed, 70 insertions(+), 1 deletion(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index c069da016f9f0..ecde9c57139a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -266,7 +266,18 @@ case class GeneratedAggregate(
 
       val joinedRow = new JoinedRow3
 
-      if (groupingExpressions.isEmpty) {
+      if (!iter.hasNext) {
+        // This is an empty input, so return early so that we do not allocate data structures
+        // that won't be cleaned up (see SPARK-8357).
+        if (groupingExpressions.isEmpty) {
+          // This is a global aggregate, so return an empty aggregation buffer.
+          val resultProjection = resultProjectionBuilder()
+          Iterator(resultProjection(newAggregationBuffer(EmptyRow)))
+        } else {
+          // This is a grouped aggregate, so return an empty iterator.
+          Iterator[InternalRow]()
+        }
+      } else if (groupingExpressions.isEmpty) {
         // TODO: Codegening anything other than the updateProjection is probably over kill.
         val buffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
         var currentRow: InternalRow = null
@@ -280,6 +291,7 @@ case class GeneratedAggregate(
         val resultProjection = resultProjectionBuilder()
         Iterator(resultProjection(buffer))
       } else if (unsafeEnabled) {
+        assert(iter.hasNext, "There should be at least one row for this path")
         log.info("Using Unsafe-based aggregator")
         val aggregationMap = new UnsafeFixedWidthAggregationMap(
           newAggregationBuffer,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 61d5f2061ae18..beee10173fbc4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -648,6 +648,15 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(2, 1, 2, 2, 1))
   }
 
+  test("count of empty table") {
+    withTempTable("t") {
+      Seq.empty[(Int, Int)].toDF("a", "b").registerTempTable("t")
+      checkAnswer(
+        sql("select count(a) from t"),
+        Row(0))
+    }
+  }
+
   test("inner join where, one match per row") {
     checkAnswer(
       sql("SELECT * FROM upperCaseData JOIN lowerCaseData WHERE n = N"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala
new file mode 100644
index 0000000000000..20def6bef0c17
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.test.TestSQLContext
+
+class AggregateSuite extends SparkPlanTest {
+
+  test("SPARK-8357 unsafe aggregation path should not leak memory with empty input") {
+    val codegenDefault = TestSQLContext.getConf(SQLConf.CODEGEN_ENABLED)
+    val unsafeDefault = TestSQLContext.getConf(SQLConf.UNSAFE_ENABLED)
+    try {
+      TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, true)
+      TestSQLContext.setConf(SQLConf.UNSAFE_ENABLED, true)
+      val df = Seq.empty[(Int, Int)].toDF("a", "b")
+      checkAnswer(
+        df,
+        GeneratedAggregate(
+          partial = true,
+          Seq(df.col("b").expr),
+          Seq(Alias(Count(df.col("a").expr), "cnt")()),
+          unsafeEnabled = true,
+          _: SparkPlan),
+        Seq.empty
+      )
+    } finally {
+      TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
+      TestSQLContext.setConf(SQLConf.UNSAFE_ENABLED, unsafeDefault)
+    }
+  }
+}

From 60c0ce134d90ef18852ed2c637d2f240b7f99ab9 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 21 Jul 2015 11:56:38 -0700
Subject: [PATCH 0533/1454] [SPARK-8906][SQL] Move all internal data source
 classes into execution.datasources.

This way, the sources package contains only public facing interfaces.

Author: Reynold Xin <rxin@databricks.com>

Closes #7565 from rxin/move-ds and squashes the following commits:

7661aff [Reynold Xin] Mima
9d5196a [Reynold Xin] Rearranged imports.
3dd7174 [Reynold Xin] [SPARK-8906][SQL] Move all internal data source classes into execution.datasources.
---
 project/MimaExcludes.scala                    | 47 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrame.scala      |  2 +-
 .../apache/spark/sql/DataFrameReader.scala    |  4 +-
 .../apache/spark/sql/DataFrameWriter.scala    |  2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  9 ++--
 .../spark/sql/execution/SparkStrategies.scala |  4 +-
 .../SqlNewHadoopRDD.scala                     |  9 ++--
 .../datasources}/DataSourceStrategy.scala     | 11 ++---
 .../datasources}/LogicalRelation.scala        |  7 +--
 .../datasources}/PartitioningUtils.scala      |  5 +-
 .../datasources}/commands.scala               |  5 +-
 .../datasources}/ddl.scala                    |  9 ++--
 .../datasources}/rules.scala                  | 10 ++--
 .../apache/spark/sql/parquet/newParquet.scala |  5 +-
 .../apache/spark/sql/sources/filters.scala    |  4 ++
 .../apache/spark/sql/sources/interfaces.scala |  4 +-
 .../org/apache/spark/sql/json/JsonSuite.scala |  2 +-
 .../sql/parquet/ParquetFilterSuite.scala      |  2 +-
 .../ParquetPartitionDiscoverySuite.scala      |  4 +-
 .../sources/CreateTableAsSelectSuite.scala    |  1 +
 .../sql/sources/ResolvedDataSourceSuite.scala |  1 +
 .../apache/spark/sql/hive/HiveContext.scala   |  6 +--
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 11 +++--
 .../org/apache/spark/sql/hive/HiveQl.scala    |  2 +-
 .../spark/sql/hive/HiveStrategies.scala       |  2 +-
 .../spark/sql/hive/execution/commands.scala   |  1 +
 .../spark/sql/hive/orc/OrcRelation.scala      |  1 +
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  2 +-
 .../hive/execution/HiveComparisonTest.scala   |  4 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |  2 +-
 .../apache/spark/sql/hive/parquetSuites.scala |  2 +-
 .../sql/sources/hadoopFsRelationSuites.scala  |  6 ++-
 32 files changed, 124 insertions(+), 62 deletions(-)
 rename sql/core/src/main/scala/org/apache/spark/sql/{sources => execution}/SqlNewHadoopRDD.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{sources => execution/datasources}/DataSourceStrategy.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{sources => execution/datasources}/LogicalRelation.scala (88%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{sources => execution/datasources}/PartitioningUtils.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{sources => execution/datasources}/commands.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{sources => execution/datasources}/ddl.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{sources => execution/datasources}/rules.scala (94%)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index a2595ff6c22f4..fa36629c37a35 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -104,6 +104,53 @@ object MimaExcludes {
             // SPARK-7422 add argmax for sparse vectors
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.mllib.linalg.Vector.argmax")
+          ) ++ Seq(
+            // SPARK-8906 Move all internal data source classes into execution.datasources
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopPartition"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DefaultWriterContainer"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DynamicPartitionWriterContainer"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.BaseWriterContainer"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLParser"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CaseInsensitiveMap"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException")
           )
 
         case v if v.startsWith("1.4") =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 830fba35bb7bc..323ff17357fda 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -38,8 +38,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
 import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD}
+import org.apache.spark.sql.execution.datasources.CreateTableUsingAsSelect
 import org.apache.spark.sql.json.JacksonGenerator
-import org.apache.spark.sql.sources.CreateTableUsingAsSelect
 import org.apache.spark.sql.types._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.Utils
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index f1c1ddf898986..e9d782cdcd667 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -20,16 +20,16 @@ package org.apache.spark.sql
 import java.util.Properties
 
 import org.apache.hadoop.fs.Path
-import org.apache.spark.{Logging, Partition}
 
+import org.apache.spark.{Logging, Partition}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
 import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation}
 import org.apache.spark.sql.json.JSONRelation
 import org.apache.spark.sql.parquet.ParquetRelation2
-import org.apache.spark.sql.sources.{LogicalRelation, ResolvedDataSource}
 import org.apache.spark.sql.types.StructType
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 3e7b9cd7976c3..ee0201a9d4cb2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -22,8 +22,8 @@ import java.util.Properties
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.plans.logical.InsertIntoTable
+import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, ResolvedDataSource}
 import org.apache.spark.sql.jdbc.{JDBCWriteDetails, JdbcUtils}
-import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect}
 
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 2dda3ad1211fa..8b4528b5d52fe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -39,8 +39,9 @@ import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer}
 import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
-import org.apache.spark.sql.execution.{Filter, _}
-import org.apache.spark.sql.sources._
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.datasources._
+import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
@@ -146,11 +147,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
     new Analyzer(catalog, functionRegistry, conf) {
       override val extendedResolutionRules =
         ExtractPythonUDFs ::
-        sources.PreInsertCastAndRename ::
+        PreInsertCastAndRename ::
         Nil
 
       override val extendedCheckRules = Seq(
-        sources.PreWriteCheck(catalog)
+        datasources.PreWriteCheck(catalog)
       )
     }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 240332a80af0f..8cef7f200d2dc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.sql.{SQLContext, Strategy, execution}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
@@ -25,10 +26,9 @@ import org.apache.spark.sql.catalyst.plans.logical.{BroadcastHint, LogicalPlan}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation}
 import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand}
+import org.apache.spark.sql.execution.datasources.{CreateTableUsing, CreateTempTableUsing, DescribeCommand => LogicalDescribeCommand, _}
 import org.apache.spark.sql.parquet._
-import org.apache.spark.sql.sources.{CreateTableUsing, CreateTempTableUsing, DescribeCommand => LogicalDescribeCommand, _}
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{SQLContext, Strategy, execution}
 
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   self: SQLContext#SparkPlanner =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/SqlNewHadoopRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/sources/SqlNewHadoopRDD.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
index 2bdc341021256..e1c1a6c06268f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/SqlNewHadoopRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
@@ -15,24 +15,23 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.sources
+package org.apache.spark.sql.execution
 
 import java.text.SimpleDateFormat
 import java.util.Date
 
+import org.apache.spark.{Partition => SparkPartition, _}
 import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{CombineFileSplit, FileSplit}
-import org.apache.spark.broadcast.Broadcast
-
-import org.apache.spark.{Partition => SparkPartition, _}
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
-import org.apache.spark.rdd.{RDD, HadoopRDD}
 import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD
+import org.apache.spark.rdd.{HadoopRDD, RDD}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 70c9e06927582..2b400926177fe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -15,22 +15,21 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.sources
+package org.apache.spark.sql.execution.datasources
 
 import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
-import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{StringType, StructType}
-import org.apache.spark.sql.{SaveMode, Strategy, execution, sources}
-import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
 import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 /**
  * A Strategy for planning scans over data sources defined using the sources API.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/LogicalRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/LogicalRelation.scala
similarity index 88%
rename from sql/core/src/main/scala/org/apache/spark/sql/sources/LogicalRelation.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/LogicalRelation.scala
index f374abffdd505..a7123dc845fa2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/LogicalRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/LogicalRelation.scala
@@ -14,11 +14,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.spark.sql.sources
+package org.apache.spark.sql.execution.datasources
 
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, AttributeMap}
-import org.apache.spark.sql.catalyst.plans.logical.{Statistics, LeafNode, LogicalPlan}
+import org.apache.spark.sql.catalyst.expressions.{AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics}
+import org.apache.spark.sql.sources.BaseRelation
 
 /**
  * Used to link a [[BaseRelation]] in to a logical query plan.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
index 8b2a45d8e970a..6b4a359db22d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
@@ -15,9 +15,9 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.sources
+package org.apache.spark.sql.execution.datasources
 
-import java.lang.{Double => JDouble, Float => JFloat, Integer => JInteger, Long => JLong}
+import java.lang.{Double => JDouble, Long => JLong}
 import java.math.{BigDecimal => JBigDecimal}
 
 import scala.collection.mutable.ArrayBuffer
@@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
 import org.apache.spark.sql.types._
 
+
 private[sql] case class Partition(values: InternalRow, path: String)
 
 private[sql] case class PartitionSpec(partitionColumns: StructType, partitions: Seq[Partition])
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
index 5c6ef2dc90c73..84a0441e145c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.sources
+package org.apache.spark.sql.execution.datasources
 
 import java.util.{Date, UUID}
 
@@ -24,7 +24,6 @@ import scala.collection.JavaConversions.asScalaIterator
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
-
 import org.apache.spark._
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
@@ -35,9 +34,11 @@ import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.execution.RunnableCommand
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StringType
 import org.apache.spark.util.SerializableConfiguration
 
+
 private[sql] case class InsertIntoDataSource(
     logicalRelation: LogicalRelation,
     query: LogicalPlan,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index 5a8c97c773ee6..c8033d3c0470a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -15,23 +15,22 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.sources
+package org.apache.spark.sql.execution.datasources
 
 import scala.language.{existentials, implicitConversions}
 import scala.util.matching.Regex
 
 import org.apache.hadoop.fs.Path
-
 import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.catalyst.AbstractSparkSQLParser
+import org.apache.spark.sql.{AnalysisException, DataFrame, SQLContext, SaveMode}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.{AbstractSparkSQLParser, InternalRow}
 import org.apache.spark.sql.execution.RunnableCommand
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{AnalysisException, DataFrame, SQLContext, SaveMode}
 import org.apache.spark.util.Utils
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
similarity index 94%
rename from sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
index 40ee048e2653e..11bb49b8d83de 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
@@ -15,15 +15,15 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.sources
+package org.apache.spark.sql.execution.datasources
 
-import org.apache.spark.sql.{SaveMode, AnalysisException}
-import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, Catalog}
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Cast, Alias}
+import org.apache.spark.sql.{AnalysisException, SaveMode}
+import org.apache.spark.sql.catalyst.analysis.{Catalog, EliminateSubQueries}
+import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, Cast}
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.sources.{BaseRelation, HadoopFsRelation, InsertableRelation}
 
 /**
  * A rule to do pre-insert data type casting and field renaming. Before we insert into
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index e683eb0126004..2f9f880c70690 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -35,15 +35,18 @@ import org.apache.parquet.hadoop.metadata.CompressionCodecName
 import org.apache.parquet.hadoop.util.ContextUtil
 import org.apache.parquet.schema.MessageType
 
+import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.RDD._
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.{SqlNewHadoopPartition, SqlNewHadoopRDD}
+import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{DataType, StructType}
 import org.apache.spark.util.{SerializableConfiguration, Utils}
-import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
+
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider {
   override def createRelation(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
index 24e86ca415c51..4d942e4f9287a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
@@ -17,6 +17,10 @@
 
 package org.apache.spark.sql.sources
 
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// This file defines all the filters that we can push down to the data sources.
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
 /**
  * A filter predicate for data sources.
  *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 2cd8b358d81c6..7cd005b959488 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.sources
 
 import scala.collection.mutable
-import scala.collection.mutable.ArrayBuffer
 import scala.util.Try
 
 import org.apache.hadoop.conf.Configuration
@@ -33,6 +32,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
 import org.apache.spark.sql.execution.RDDConversions
+import org.apache.spark.sql.execution.datasources.{PartitioningUtils, PartitionSpec, Partition}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql._
 import org.apache.spark.util.SerializableConfiguration
@@ -523,7 +523,7 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
     })
   }
 
-  private[sources] final def buildScan(
+  private[sql] final def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
       inputPaths: Array[String],
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 3475f9dd6787e..1d04513a44672 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -26,8 +26,8 @@ import org.scalactic.Tolerance._
 import org.apache.spark.sql.{QueryTest, Row, SQLConf}
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.json.InferSchema.compatibleType
-import org.apache.spark.sql.sources.LogicalRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
index a2763c78b6450..23df102cd951d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
@@ -24,7 +24,7 @@ import org.apache.parquet.filter2.predicate.{FilterPredicate, Operators}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
-import org.apache.spark.sql.sources.LogicalRelation
+import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
index 37b0a9fbf7a4e..4f98776b91160 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
@@ -28,11 +28,11 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Literal
-import org.apache.spark.sql.sources.PartitioningUtils._
-import org.apache.spark.sql.sources.{LogicalRelation, Partition, PartitionSpec}
+import org.apache.spark.sql.execution.datasources.{LogicalRelation, PartitionSpec, Partition, PartitioningUtils}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql._
 import org.apache.spark.unsafe.types.UTF8String
+import PartitioningUtils._
 
 // The data where the partitioning key exists only in the directory structure.
 case class ParquetData(intField: Int, stringField: String)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
index a71088430bfd5..1907e643c85dd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
@@ -22,6 +22,7 @@ import java.io.{File, IOException}
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.execution.datasources.DDLException
 import org.apache.spark.util.Utils
 
 class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
index 296b0d6f74a0c..3cbf5467b253a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.sources
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.execution.datasources.ResolvedDataSource
 
 class ResolvedDataSourceSuite extends SparkFunSuite {
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 4684d48aff889..cec7685bb6859 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -44,9 +44,9 @@ import org.apache.spark.sql.catalyst.ParserDialect
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUDFs, SetCommand}
+import org.apache.spark.sql.execution.datasources.{PreWriteCheck, PreInsertCastAndRename, DataSourceStrategy}
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNativeCommand}
-import org.apache.spark.sql.sources.DataSourceStrategy
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -384,11 +384,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
         catalog.PreInsertionCasts ::
         ExtractPythonUDFs ::
         ResolveHiveWindowFunction ::
-        sources.PreInsertCastAndRename ::
+        PreInsertCastAndRename ::
         Nil
 
       override val extendedCheckRules = Seq(
-        sources.PreWriteCheck(catalog)
+        PreWriteCheck(catalog)
       )
     }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index b15261b7914dd..0a2121c955871 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import scala.collection.JavaConversions._
+
 import com.google.common.base.Objects
 import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache}
 
@@ -28,6 +30,7 @@ import org.apache.hadoop.hive.ql.metadata._
 import org.apache.hadoop.hive.ql.plan.TableDesc
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{Catalog, MultiInstanceRelation, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions._
@@ -35,14 +38,12 @@ import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.execution.datasources
+import org.apache.spark.sql.execution.datasources.{Partition => ParquetPartition, PartitionSpec, CreateTableUsingAsSelect, ResolvedDataSource, LogicalRelation}
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.parquet.ParquetRelation2
-import org.apache.spark.sql.sources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode, sources}
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
 
 private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: HiveContext)
   extends Catalog with Logging {
@@ -278,7 +279,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
             parquetRelation.paths.toSet == pathsInMetastore.toSet &&
             logical.schema.sameType(metastoreSchema) &&
             parquetRelation.partitionSpec == partitionSpecInMetastore.getOrElse {
-              PartitionSpec(StructType(Nil), Array.empty[sources.Partition])
+              PartitionSpec(StructType(Nil), Array.empty[datasources.Partition])
             }
 
           if (useCached) {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 7fc517b646b20..f5574509b0b38 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -37,7 +37,7 @@ import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.trees.CurrentOrigin
 import org.apache.spark.sql.execution.ExplainCommand
-import org.apache.spark.sql.sources.DescribeCommand
+import org.apache.spark.sql.execution.datasources.DescribeCommand
 import org.apache.spark.sql.hive.HiveShim._
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.hive.execution.{HiveNativeCommand, DropTable, AnalyzeTable, HiveScriptIOSchema}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 9638a8201e190..a22c3292eff94 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -30,9 +30,9 @@ import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand, _}
+import org.apache.spark.sql.execution.datasources.{CreateTableUsing, CreateTableUsingAsSelect, DescribeCommand}
 import org.apache.spark.sql.hive.execution._
 import org.apache.spark.sql.parquet.ParquetRelation
-import org.apache.spark.sql.sources.{CreateTableUsing, CreateTableUsingAsSelect, DescribeCommand}
 import org.apache.spark.sql.types.StringType
 
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index 71fa3e9c33ad9..a47f9a4feb21b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.execution.RunnableCommand
+import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 48d35a60a759b..de63ee56dd8e6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -37,6 +37,7 @@ import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.{HadoopRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.hive.{HiveContext, HiveInspectors, HiveMetastoreTypes, HiveShim}
 import org.apache.spark.sql.sources.{Filter, _}
 import org.apache.spark.sql.types.StructType
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index d910af22c3dd1..e403f32efaf91 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -28,12 +28,12 @@ import org.apache.hadoop.mapred.InvalidInputException
 
 import org.apache.spark.Logging
 import org.apache.spark.sql._
+import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.parquet.ParquetRelation2
-import org.apache.spark.sql.sources.LogicalRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index c9dd4c0935a72..efb04bf3d5097 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -22,11 +22,11 @@ import java.io._
 import org.scalatest.{BeforeAndAfterAll, GivenWhenThen}
 
 import org.apache.spark.{Logging, SparkFunSuite}
-import org.apache.spark.sql.sources.DescribeCommand
-import org.apache.spark.sql.execution.{SetCommand, ExplainCommand}
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.execution.{SetCommand, ExplainCommand}
+import org.apache.spark.sql.execution.datasources.DescribeCommand
 import org.apache.spark.sql.hive.test.TestHive
 
 /**
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 05a1f0094e5e1..03428265422e6 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -23,12 +23,12 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
 import org.apache.spark.sql.catalyst.errors.DialectException
+import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
 import org.apache.spark.sql.parquet.ParquetRelation2
-import org.apache.spark.sql.sources.LogicalRelation
 import org.apache.spark.sql.types._
 
 case class Nested1(f1: Nested2)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index 9d79a4b007d66..82a8daf8b4b09 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -23,12 +23,12 @@ import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD}
+import org.apache.spark.sql.execution.datasources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation}
 import org.apache.spark.sql.hive.execution.HiveTableScan
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.parquet.{ParquetRelation2, ParquetTableScan}
-import org.apache.spark.sql.sources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation}
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index afecf9675e11f..1cef83fd5e990 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.sql.sources
 
-import scala.collection.JavaConversions._
-
 import java.io.File
 
+import scala.collection.JavaConversions._
+
 import com.google.common.io.Files
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
@@ -31,10 +31,12 @@ import org.apache.parquet.hadoop.ParquetOutputCommitter
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql._
+import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 
+
 abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
   override lazy val sqlContext: SQLContext = TestHive
 

From c07838b5a9cdf96c0f49055ea1c397e0f0e915d2 Mon Sep 17 00:00:00 2001
From: Dennis Huo <dhuo@google.com>
Date: Tue, 21 Jul 2015 13:12:11 -0700
Subject: [PATCH 0534/1454] [SPARK-9206] [SQL] Fix HiveContext classloading for
 GCS connector.

IsolatedClientLoader.isSharedClass includes all of com.google.\*, presumably
for Guava, protobuf, and/or other shared Google libraries, but needs to
count com.google.cloud.\* as "hive classes" when determining which ClassLoader
to use. Otherwise, things like HiveContext.parquetFile will throw a
ClassCastException when fs.defaultFS is set to a Google Cloud Storage (gs://)
path. On StackOverflow: http://stackoverflow.com/questions/31478955

EDIT: Adding yhuai who worked on the relevant classloading isolation pieces.

Author: Dennis Huo <dhuo@google.com>

Closes #7549 from dennishuo/dhuo-fix-hivecontext-gcs and squashes the following commits:

1f8db07 [Dennis Huo] Fix HiveContext classloading for GCS connector.
---
 .../org/apache/spark/sql/hive/client/IsolatedClientLoader.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index 3d609a66f3664..97fb98199991b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -125,7 +125,7 @@ private[hive] class IsolatedClientLoader(
     name.contains("log4j") ||
     name.startsWith("org.apache.spark.") ||
     name.startsWith("scala.") ||
-    name.startsWith("com.google") ||
+    (name.startsWith("com.google") && !name.startsWith("com.google.cloud")) ||
     name.startsWith("java.lang.") ||
     name.startsWith("java.net") ||
     sharedPrefixes.exists(name.startsWith)

From d4c7a7a3642a74ad40093c96c4bf45a62a470605 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Tue, 21 Jul 2015 15:47:40 -0700
Subject: [PATCH 0535/1454] [SPARK-9154] [SQL] codegen StringFormat

Jira: https://issues.apache.org/jira/browse/SPARK-9154

fixes bug of #7546

marmbrus I can't reopen the other PR, because I didn't closed it. Can you trigger Jenkins?

Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7571 from tarekauel/SPARK-9154 and squashes the following commits:

dcae272 [Tarek Auel] [SPARK-9154][SQL] build fix
1487602 [Tarek Auel] Merge remote-tracking branch 'upstream/master' into SPARK-9154
f512c5f [Tarek Auel] [SPARK-9154][SQL] build fix
a943d3e [Tarek Auel] [SPARK-9154] implicit input cast, added tests for null, support for null primitives
10b4de8 [Tarek Auel] [SPARK-9154][SQL] codegen removed fallback trait
cd8322b [Tarek Auel] [SPARK-9154][SQL] codegen string format
086caba [Tarek Auel] [SPARK-9154][SQL] codegen string format
---
 .../expressions/stringOperations.scala        | 42 ++++++++++++++++++-
 .../expressions/StringExpressionsSuite.scala  | 18 ++++----
 .../org/apache/spark/sql/functions.scala      | 11 +++++
 .../spark/sql/StringFunctionsSuite.scala      | 10 +++++
 4 files changed, 70 insertions(+), 11 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index fe57d17f1ec14..1f18a6e9ff8a5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -526,7 +526,7 @@ case class StringRPad(str: Expression, len: Expression, pad: Expression)
 /**
  * Returns the input formatted according do printf-style format strings
  */
-case class StringFormat(children: Expression*) extends Expression with CodegenFallback {
+case class StringFormat(children: Expression*) extends Expression with ImplicitCastInputTypes {
 
   require(children.nonEmpty, "printf() should take at least 1 argument")
 
@@ -536,6 +536,10 @@ case class StringFormat(children: Expression*) extends Expression with CodegenFa
   private def format: Expression = children(0)
   private def args: Seq[Expression] = children.tail
 
+  override def inputTypes: Seq[AbstractDataType] =
+    StringType :: List.fill(children.size - 1)(AnyDataType)
+
+
   override def eval(input: InternalRow): Any = {
     val pattern = format.eval(input)
     if (pattern == null) {
@@ -551,6 +555,42 @@ case class StringFormat(children: Expression*) extends Expression with CodegenFa
     }
   }
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val pattern = children.head.gen(ctx)
+
+    val argListGen = children.tail.map(x => (x.dataType, x.gen(ctx)))
+    val argListCode = argListGen.map(_._2.code + "\n")
+
+    val argListString = argListGen.foldLeft("")((s, v) => {
+      val nullSafeString =
+        if (ctx.boxedType(v._1) != ctx.javaType(v._1)) {
+          // Java primitives get boxed in order to allow null values.
+          s"(${v._2.isNull}) ? (${ctx.boxedType(v._1)}) null : " +
+            s"new ${ctx.boxedType(v._1)}(${v._2.primitive})"
+        } else {
+          s"(${v._2.isNull}) ? null : ${v._2.primitive}"
+        }
+      s + "," + nullSafeString
+    })
+
+    val form = ctx.freshName("formatter")
+    val formatter = classOf[java.util.Formatter].getName
+    val sb = ctx.freshName("sb")
+    val stringBuffer = classOf[StringBuffer].getName
+    s"""
+      ${pattern.code}
+      boolean ${ev.isNull} = ${pattern.isNull};
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${ev.isNull}) {
+        ${argListCode.mkString}
+        $stringBuffer $sb = new $stringBuffer();
+        $formatter $form = new $formatter($sb, ${classOf[Locale].getName}.US);
+        $form.format(${pattern.primitive}.toString() $argListString);
+        ${ev.primitive} = UTF8String.fromString($sb.toString());
+      }
+     """
+  }
+
   override def prettyName: String = "printf"
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 96c540ab36f08..3c2d88731beb4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -351,18 +351,16 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("FORMAT") {
-    val f = 'f.string.at(0)
-    val d1 = 'd.int.at(1)
-    val s1 = 's.int.at(2)
-
-    val row1 = create_row("aa%d%s", 12, "cc")
-    val row2 = create_row(null, 12, "cc")
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
     checkEvaluation(StringFormat(Literal("aa")), "aa", create_row(null))
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a", row1)
+    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
+    checkEvaluation(StringFormat(Literal("aa%d%s"), 12, "cc"), "aa12cc")
 
-    checkEvaluation(StringFormat(f, d1, s1), "aa12cc", row1)
-    checkEvaluation(StringFormat(f, d1, s1), null, row2)
+    checkEvaluation(StringFormat(Literal.create(null, StringType), 12, "cc"), null)
+    checkEvaluation(
+      StringFormat(Literal("aa%d%s"), Literal.create(null, IntegerType), "cc"), "aanullcc")
+    checkEvaluation(
+      StringFormat(Literal("aa%d%s"), 12, Literal.create(null, StringType)), "aa12null")
   }
 
   test("INSTR") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index d94d7335828c5..e5ff8ae7e3179 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1741,6 +1741,17 @@ object functions {
    */
   def rtrim(e: Column): Column = StringTrimRight(e.expr)
 
+  /**
+   * Format strings in printf-style.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def formatString(format: Column, arguments: Column*): Column = {
+    StringFormat((format +: arguments).map(_.expr): _*)
+  }
+
   /**
    * Format strings in printf-style.
    * NOTE: `format` is the string value of the formatter, not column name.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index d1f855903ca4b..3702e73b4e74f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -132,6 +132,16 @@ class StringFunctionsSuite extends QueryTest {
     checkAnswer(
       df.selectExpr("printf(a, b, c)"),
       Row("aa123cc"))
+
+    val df2 = Seq(("aa%d%s".getBytes, 123, "cc")).toDF("a", "b", "c")
+
+    checkAnswer(
+      df2.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
+      Row("aa123cc", "aa123cc"))
+
+    checkAnswer(
+      df2.selectExpr("printf(a, b, c)"),
+      Row("aa123cc"))
   }
 
   test("string instr function") {

From a4c83cb1e4b066cd60264b6572fd3e51d160d26a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 21 Jul 2015 19:14:07 -0700
Subject: [PATCH 0536/1454] [SPARK-9154][SQL] Rename formatString to
 format_string.

Also make format_string the canonical form, rather than printf.

Author: Reynold Xin <rxin@databricks.com>

Closes #7579 from rxin/format_strings and squashes the following commits:

53ee54f [Reynold Xin] Fixed unit tests.
52357e1 [Reynold Xin] Add format_string alias.
b40a42a [Reynold Xin] [SPARK-9154][SQL] Rename formatString to format_string.
---
 .../catalyst/analysis/FunctionRegistry.scala   |  3 ++-
 .../expressions/stringOperations.scala         | 13 +++++--------
 .../expressions/StringExpressionsSuite.scala   | 14 +++++++-------
 .../scala/org/apache/spark/sql/functions.scala | 18 +++---------------
 .../spark/sql/StringFunctionsSuite.scala       | 12 +-----------
 5 files changed, 18 insertions(+), 42 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index e3d8d2adf2135..9c349838c28a1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -168,7 +168,8 @@ object FunctionRegistry {
     expression[StringLocate]("locate"),
     expression[StringLPad]("lpad"),
     expression[StringTrimLeft]("ltrim"),
-    expression[StringFormat]("printf"),
+    expression[FormatString]("format_string"),
+    expression[FormatString]("printf"),
     expression[StringRPad]("rpad"),
     expression[StringRepeat]("repeat"),
     expression[StringReverse]("reverse"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 1f18a6e9ff8a5..cf187ad5a0a9f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -526,29 +526,26 @@ case class StringRPad(str: Expression, len: Expression, pad: Expression)
 /**
  * Returns the input formatted according do printf-style format strings
  */
-case class StringFormat(children: Expression*) extends Expression with ImplicitCastInputTypes {
+case class FormatString(children: Expression*) extends Expression with ImplicitCastInputTypes {
 
-  require(children.nonEmpty, "printf() should take at least 1 argument")
+  require(children.nonEmpty, "format_string() should take at least 1 argument")
 
   override def foldable: Boolean = children.forall(_.foldable)
   override def nullable: Boolean = children(0).nullable
   override def dataType: DataType = StringType
-  private def format: Expression = children(0)
-  private def args: Seq[Expression] = children.tail
 
   override def inputTypes: Seq[AbstractDataType] =
     StringType :: List.fill(children.size - 1)(AnyDataType)
 
-
   override def eval(input: InternalRow): Any = {
-    val pattern = format.eval(input)
+    val pattern = children(0).eval(input)
     if (pattern == null) {
       null
     } else {
       val sb = new StringBuffer()
       val formatter = new java.util.Formatter(sb, Locale.US)
 
-      val arglist = args.map(_.eval(input).asInstanceOf[AnyRef])
+      val arglist = children.tail.map(_.eval(input).asInstanceOf[AnyRef])
       formatter.format(pattern.asInstanceOf[UTF8String].toString, arglist: _*)
 
       UTF8String.fromString(sb.toString)
@@ -591,7 +588,7 @@ case class StringFormat(children: Expression*) extends Expression with ImplicitC
      """
   }
 
-  override def prettyName: String = "printf"
+  override def prettyName: String = "format_string"
 }
 
 /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 3c2d88731beb4..3d294fda5d103 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -351,16 +351,16 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("FORMAT") {
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
-    checkEvaluation(StringFormat(Literal("aa")), "aa", create_row(null))
-    checkEvaluation(StringFormat(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
-    checkEvaluation(StringFormat(Literal("aa%d%s"), 12, "cc"), "aa12cc")
+    checkEvaluation(FormatString(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
+    checkEvaluation(FormatString(Literal("aa")), "aa", create_row(null))
+    checkEvaluation(FormatString(Literal("aa%d%s"), Literal(123), Literal("a")), "aa123a")
+    checkEvaluation(FormatString(Literal("aa%d%s"), 12, "cc"), "aa12cc")
 
-    checkEvaluation(StringFormat(Literal.create(null, StringType), 12, "cc"), null)
+    checkEvaluation(FormatString(Literal.create(null, StringType), 12, "cc"), null)
     checkEvaluation(
-      StringFormat(Literal("aa%d%s"), Literal.create(null, IntegerType), "cc"), "aanullcc")
+      FormatString(Literal("aa%d%s"), Literal.create(null, IntegerType), "cc"), "aanullcc")
     checkEvaluation(
-      StringFormat(Literal("aa%d%s"), 12, Literal.create(null, StringType)), "aa12null")
+      FormatString(Literal("aa%d%s"), 12, Literal.create(null, StringType)), "aa12null")
   }
 
   test("INSTR") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index e5ff8ae7e3179..28159cbd5ab96 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1742,26 +1742,14 @@ object functions {
   def rtrim(e: Column): Column = StringTrimRight(e.expr)
 
   /**
-   * Format strings in printf-style.
+   * Formats the arguments in printf-style and returns the result as a string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
   @scala.annotation.varargs
-  def formatString(format: Column, arguments: Column*): Column = {
-    StringFormat((format +: arguments).map(_.expr): _*)
-  }
-
-  /**
-   * Format strings in printf-style.
-   * NOTE: `format` is the string value of the formatter, not column name.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  @scala.annotation.varargs
-  def formatString(format: String, arguNames: String*): Column = {
-    StringFormat(lit(format).expr +: arguNames.map(Column(_).expr): _*)
+  def format_string(format: String, arguments: Column*): Column = {
+    FormatString((lit(format) +: arguments).map(_.expr): _*)
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 3702e73b4e74f..0f9c986f649a1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -126,22 +126,12 @@ class StringFunctionsSuite extends QueryTest {
     val df = Seq(("aa%d%s", 123, "cc")).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(formatString("aa%d%s", "b", "c")),
+      df.select(format_string("aa%d%s", $"b", $"c")),
       Row("aa123cc"))
 
     checkAnswer(
       df.selectExpr("printf(a, b, c)"),
       Row("aa123cc"))
-
-    val df2 = Seq(("aa%d%s".getBytes, 123, "cc")).toDF("a", "b", "c")
-
-    checkAnswer(
-      df2.select(formatString($"a", $"b", $"c"), formatString("aa%d%s", "b", "c")),
-      Row("aa123cc", "aa123cc"))
-
-    checkAnswer(
-      df2.selectExpr("printf(a, b, c)"),
-      Row("aa123cc"))
   }
 
   test("string instr function") {

From 63f4bcc73f5a09c1790cc3c333f08b18609de6a4 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 21 Jul 2015 22:50:27 -0700
Subject: [PATCH 0537/1454] [SPARK-9121] [SPARKR] Get rid of the warnings about
 `no visible global function definition` in SparkR

[[SPARK-9121] Get rid of the warnings about `no visible global function definition` in SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9121)

## The Result of `dev/lint-r`
[The result of lint-r for SPARK-9121 at the revision:1ddd0f2f1688560f88470e312b72af04364e2d49 when I have sent a PR](https://gist.github.com/yu-iskw/6f55953425901725edf6)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7567 from yu-iskw/SPARK-9121 and squashes the following commits:

c8cfd63 [Yu ISHIKAWA] Fix the typo
b1f19ed [Yu ISHIKAWA] Add a validate statement for local SparkR
1a03987 [Yu ISHIKAWA] Load the `testthat` package in `dev/lint-r.R`, instead of using the full path of function.
3a5e0ab [Yu ISHIKAWA] [SPARK-9121][SparkR] Get rid of the warnings about `no visible global function definition` in SparkR
---
 dev/lint-r.R | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/dev/lint-r.R b/dev/lint-r.R
index dcb1a184291e1..48bd6246096ae 100644
--- a/dev/lint-r.R
+++ b/dev/lint-r.R
@@ -15,15 +15,21 @@
 # limitations under the License.
 #
 
+argv <- commandArgs(TRUE)
+SPARK_ROOT_DIR <- as.character(argv[1])
+
 # Installs lintr from Github.
 # NOTE: The CRAN's version is too old to adapt to our rules.
 if ("lintr" %in% row.names(installed.packages())  == FALSE) {
   devtools::install_github("jimhester/lintr")
 }
-library(lintr)
 
-argv <- commandArgs(TRUE)
-SPARK_ROOT_DIR <- as.character(argv[1])
+library(lintr)
+library(methods)
+library(testthat)
+if (! library(SparkR, lib.loc = file.path(SPARK_ROOT_DIR, "R", "lib"), logical.return = TRUE)) {
+  stop("You should install SparkR in a local directory with `R/install-dev.sh`.")
+}
 
 path.to.package <- file.path(SPARK_ROOT_DIR, "R", "pkg")
 lint_package(path.to.package, cache = FALSE)

From f4785f5b82c57bce41d3dc26ed9e3c9e794c7558 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 21 Jul 2015 23:00:13 -0700
Subject: [PATCH 0538/1454] [SPARK-9232] [SQL] Duplicate code in JSONRelation

Author: Andrew Or <andrew@databricks.com>

Closes #7576 from andrewor14/clean-up-json-relation and squashes the following commits:

ea80803 [Andrew Or] Clean up duplicate code
---
 .../apache/spark/sql/json/JSONRelation.scala  | 50 ++++++++-----------
 1 file changed, 21 insertions(+), 29 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 25802d054ac00..922794ac9aac5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.json
 
 import java.io.IOException
 
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{FileSystem, Path}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.AnalysisException
@@ -87,20 +87,7 @@ private[sql] class DefaultSource
         case SaveMode.Append =>
           sys.error(s"Append mode is not supported by ${this.getClass.getCanonicalName}")
         case SaveMode.Overwrite => {
-          var success: Boolean = false
-          try {
-            success = fs.delete(filesystemPath, true)
-          } catch {
-            case e: IOException =>
-              throw new IOException(
-                s"Unable to clear output directory ${filesystemPath.toString} prior"
-                  + s" to writing to JSON table:\n${e.toString}")
-          }
-          if (!success) {
-            throw new IOException(
-              s"Unable to clear output directory ${filesystemPath.toString} prior"
-                + s" to writing to JSON table.")
-          }
+          JSONRelation.delete(filesystemPath, fs)
           true
         }
         case SaveMode.ErrorIfExists =>
@@ -195,20 +182,7 @@ private[sql] class JSONRelation(
 
     if (overwrite) {
       if (fs.exists(filesystemPath)) {
-        var success: Boolean = false
-        try {
-          success = fs.delete(filesystemPath, true)
-        } catch {
-          case e: IOException =>
-            throw new IOException(
-              s"Unable to clear output directory ${filesystemPath.toString} prior"
-                + s" to writing to JSON table:\n${e.toString}")
-        }
-        if (!success) {
-          throw new IOException(
-            s"Unable to clear output directory ${filesystemPath.toString} prior"
-              + s" to writing to JSON table.")
-        }
+        JSONRelation.delete(filesystemPath, fs)
       }
       // Write the data.
       data.toJSON.saveAsTextFile(filesystemPath.toString)
@@ -228,3 +202,21 @@ private[sql] class JSONRelation(
     case _ => false
   }
 }
+
+private object JSONRelation {
+
+  /** Delete the specified directory to overwrite it with new JSON data. */
+  def delete(dir: Path, fs: FileSystem): Unit = {
+    var success: Boolean = false
+    val failMessage = s"Unable to clear output directory $dir prior to writing to JSON table"
+    try {
+      success = fs.delete(dir, true /* recursive */)
+    } catch {
+      case e: IOException =>
+        throw new IOException(s"$failMessage\n${e.toString}")
+    }
+    if (!success) {
+      throw new IOException(failMessage)
+    }
+  }
+}

From c03299a18b4e076cabb4b7833a1e7632c5c0dabe Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 21 Jul 2015 23:26:11 -0700
Subject: [PATCH 0539/1454] [SPARK-4233] [SPARK-4367] [SPARK-3947] [SPARK-3056]
 [SQL] Aggregation Improvement

This is the first PR for the aggregation improvement, which is tracked by https://issues.apache.org/jira/browse/SPARK-4366 (umbrella JIRA). This PR contains work for its subtasks, SPARK-3056, SPARK-3947, SPARK-4233, and SPARK-4367.

This PR introduces a new code path for evaluating aggregate functions. This code path is guarded by `spark.sql.useAggregate2` and by default the value of this flag is true.

This new code path contains:
* A new aggregate function interface (`AggregateFunction2`) and 7 built-int aggregate functions based on this new interface (`AVG`, `COUNT`, `FIRST`, `LAST`, `MAX`, `MIN`, `SUM`)
* A UDAF interface (`UserDefinedAggregateFunction`) based on the new code path and two example UDAFs (`MyDoubleAvg` and `MyDoubleSum`).
* A sort-based aggregate operator (`Aggregate2Sort`) for the new aggregate function interface .
* A sort-based aggregate operator (`FinalAndCompleteAggregate2Sort`) for distinct aggregations (for distinct aggregations the query plan will use `Aggregate2Sort` and `FinalAndCompleteAggregate2Sort` together).

With this change, `spark.sql.useAggregate2` is `true`, the flow of compiling an aggregation query is:
1. Our analyzer looks up functions and returns aggregate functions built based on the old aggregate function interface.
2. When our planner is compiling the physical plan, it tries try to convert all aggregate functions to the ones built based on the new interface. The planner will fallback to the old code path if any of the following two conditions is true:
* code-gen is disabled.
* there is any function that cannot be converted (right now, Hive UDAFs).
* the schema of grouping expressions contain any complex data type.
* There are multiple distinct columns.

Right now, the new code path handles a single distinct column in the query (you can have multiple aggregate functions using that distinct column). For a query having a aggregate function with DISTINCT and regular aggregate functions, the generated plan will do partial aggregations for those regular aggregate function.

Thanks chenghao-intel for his initial work on it.

Author: Yin Huai <yhuai@databricks.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #7458 from yhuai/UDAF and squashes the following commits:

7865f5e [Yin Huai] Put the catalyst expression in the comment of the generated code for it.
b04d6c8 [Yin Huai] Remove unnecessary change.
f1d5901 [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
35b0520 [Yin Huai] Use semanticEquals to replace grouping expressions in the output of the aggregate operator.
3b43b24 [Yin Huai] bug fix.
00eb298 [Yin Huai] Make it compile.
a3ca551 [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
e0afca3 [Yin Huai] Gracefully fallback to old aggregation code path.
8a8ac4a [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
88c7d4d [Yin Huai] Enable spark.sql.useAggregate2 by default for testing purpose.
dc96fd1 [Yin Huai] Many updates:
85c9c4b [Yin Huai] newline.
43de3de [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
c3614d7 [Yin Huai] Handle single distinct column.
68b8ee9 [Yin Huai] Support single distinct column set. WIP
3013579 [Yin Huai] Format.
d678aee [Yin Huai] Remove AggregateExpressionSuite.scala since our built-in aggregate functions will be based on AlgebraicAggregate and we need to have another way to test it.
e243ca6 [Yin Huai] Add aggregation iterators.
a101960 [Yin Huai] Change MyJavaUDAF to MyDoubleSum.
594cdf5 [Yin Huai] Change existing AggregateExpression to AggregateExpression1 and add an AggregateExpression as the common interface for both AggregateExpression1 and AggregateExpression2.
380880f [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
0a827b3 [Yin Huai] Add comments and doc. Move some classes to the right places.
a19fea6 [Yin Huai] Add UDAF interface.
262d4c4 [Yin Huai] Make it compile.
b2e358e [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
6edb5ac [Yin Huai] Format update.
70b169c [Yin Huai] Remove groupOrdering.
4721936 [Yin Huai] Add CheckAggregateFunction to extendedCheckRules.
d821a34 [Yin Huai] Cleanup.
32aea9c [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
5b46d41 [Yin Huai] Bug fix.
aff9534 [Yin Huai] Make Aggregate2Sort work with both algebraic AggregateFunctions and non-algebraic AggregateFunctions.
2857b55 [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
4435f20 [Yin Huai] Add ConvertAggregateFunction to HiveContext's analyzer.
1b490ed [Michael Armbrust] make hive test
8cfa6a9 [Michael Armbrust] add test
1b0bb3f [Yin Huai] Do not bind references in AlgebraicAggregate and use code gen for all places.
072209f [Yin Huai] Bug fix: Handle expressions in grouping columns that are not attribute references.
f7d9e54 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into UDAF
39ee975 [Yin Huai] Code cleanup: Remove unnecesary AttributeReferences.
b7720ba [Yin Huai] Add an analysis rule to convert aggregate function to the new version.
5c00f3f [Michael Armbrust] First draft of codegen
6bbc6ba [Michael Armbrust] now with correct answers\!
f7996d0 [Michael Armbrust] Add AlgebraicAggregate
dded1c5 [Yin Huai] wip
---
 .../apache/spark/sql/catalyst/SqlParser.scala |   3 +-
 .../sql/catalyst/analysis/Analyzer.scala      |  24 +-
 .../sql/catalyst/analysis/CheckAnalysis.scala |   1 +
 .../sql/catalyst/analysis/unresolved.scala    |   5 +-
 .../catalyst/expressions/BoundAttribute.scala |   2 +-
 .../sql/catalyst/expressions/Expression.scala |   3 +-
 .../expressions/aggregate/functions.scala     | 292 +++++++
 .../expressions/aggregate/interfaces.scala    | 206 +++++
 .../sql/catalyst/expressions/aggregates.scala | 100 +--
 .../codegen/GenerateMutableProjection.scala   |  21 +-
 .../sql/catalyst/planning/patterns.scala      |   4 +-
 .../plans/logical/basicOperators.scala        |   1 +
 .../scala/org/apache/spark/sql/SQLConf.scala  |   5 +
 .../org/apache/spark/sql/SQLContext.scala     |   4 +
 .../apache/spark/sql/UDAFRegistration.scala   |  35 +
 .../spark/sql/execution/Aggregate.scala       |  12 +-
 .../apache/spark/sql/execution/Exchange.scala |  11 +-
 .../sql/execution/GeneratedAggregate.scala    |   2 +-
 .../spark/sql/execution/SparkStrategies.scala | 100 ++-
 .../aggregate/aggregateOperators.scala        | 173 ++++
 .../aggregate/sortBasedIterators.scala        | 749 ++++++++++++++++++
 .../spark/sql/execution/aggregate/utils.scala | 364 +++++++++
 .../sql/expressions/aggregate/udaf.scala      | 280 +++++++
 .../org/apache/spark/sql/functions.scala      |   4 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |   4 +-
 .../spark/sql/execution/PlannerSuite.scala    |  26 +-
 .../HiveWindowFunctionQuerySuite.scala        |   1 +
 .../SortMergeCompatibilitySuite.scala         |   7 +
 .../apache/spark/sql/hive/HiveContext.scala   |   1 +
 .../org/apache/spark/sql/hive/HiveQl.scala    |   7 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |   8 +-
 .../spark/sql/hive/aggregate/MyDoubleAvg.java | 107 +++
 .../spark/sql/hive/aggregate/MyDoubleSum.java | 100 +++
 ...f_unhex-0-50131c0ba7b7a6b65c789a5a8497bada |   1 +
 ...f_unhex-1-11eb3cc5216d5446f4165007203acc47 |   1 +
 ...f_unhex-2-a660886085b8651852b9b77934848ae4 |  14 +
 ...f_unhex-3-4b2cf4050af229fde91ab53fd9f3af3e |   1 +
 ...f_unhex-4-7d3e094f139892ecef17de3fd63ca3c3 |   1 +
 .../execution/AggregationQuerySuite.scala     | 507 ++++++++++++
 39 files changed, 3087 insertions(+), 100 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
 create mode 100644 sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
 create mode 100644 sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
 create mode 100644 sql/hive/src/test/resources/golden/udf_unhex-0-50131c0ba7b7a6b65c789a5a8497bada
 create mode 100644 sql/hive/src/test/resources/golden/udf_unhex-1-11eb3cc5216d5446f4165007203acc47
 create mode 100644 sql/hive/src/test/resources/golden/udf_unhex-2-a660886085b8651852b9b77934848ae4
 create mode 100644 sql/hive/src/test/resources/golden/udf_unhex-3-4b2cf4050af229fde91ab53fd9f3af3e
 create mode 100644 sql/hive/src/test/resources/golden/udf_unhex-4-7d3e094f139892ecef17de3fd63ca3c3
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index d4ef04c2294a2..c04bd6cd85187 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -266,11 +266,12 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
       }
     }
     | ident ~ ("(" ~> repsep(expression, ",")) <~ ")" ^^
-      { case udfName ~ exprs => UnresolvedFunction(udfName, exprs) }
+      { case udfName ~ exprs => UnresolvedFunction(udfName, exprs, isDistinct = false) }
     | ident ~ ("(" ~ DISTINCT ~> repsep(expression, ",")) <~ ")" ^^ { case udfName ~ exprs =>
       lexical.normalizeKeyword(udfName) match {
         case "sum" => SumDistinct(exprs.head)
         case "count" => CountDistinct(exprs)
+        case name => UnresolvedFunction(name, exprs, isDistinct = true)
         case _ => throw new AnalysisException(s"function $udfName does not support DISTINCT")
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index e58f3f64947f3..8cadbc57e87e1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.analysis
 
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.expressions.aggregate.{Complete, AggregateExpression2, AggregateFunction2}
 import org.apache.spark.sql.catalyst.{SimpleCatalystConf, CatalystConf}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
@@ -277,7 +278,7 @@ class Analyzer(
         Project(
           projectList.flatMap {
             case s: Star => s.expand(child.output, resolver)
-            case UnresolvedAlias(f @ UnresolvedFunction(_, args)) if containsStar(args) =>
+            case UnresolvedAlias(f @ UnresolvedFunction(_, args, _)) if containsStar(args) =>
               val expandedArgs = args.flatMap {
                 case s: Star => s.expand(child.output, resolver)
                 case o => o :: Nil
@@ -517,9 +518,26 @@ class Analyzer(
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
       case q: LogicalPlan =>
         q transformExpressions {
-          case u @ UnresolvedFunction(name, children) =>
+          case u @ UnresolvedFunction(name, children, isDistinct) =>
             withPosition(u) {
-              registry.lookupFunction(name, children)
+              registry.lookupFunction(name, children) match {
+                // We get an aggregate function built based on AggregateFunction2 interface.
+                // So, we wrap it in AggregateExpression2.
+                case agg2: AggregateFunction2 => AggregateExpression2(agg2, Complete, isDistinct)
+                // Currently, our old aggregate function interface supports SUM(DISTINCT ...)
+                // and COUTN(DISTINCT ...).
+                case sumDistinct: SumDistinct => sumDistinct
+                case countDistinct: CountDistinct => countDistinct
+                // DISTINCT is not meaningful with Max and Min.
+                case max: Max if isDistinct => max
+                case min: Min if isDistinct => min
+                // For other aggregate functions, DISTINCT keyword is not supported for now.
+                // Once we converted to the new code path, we will allow using DISTINCT keyword.
+                case other if isDistinct =>
+                  failAnalysis(s"$name does not support DISTINCT keyword.")
+                // If it does not have DISTINCT keyword, we will return it as is.
+                case other => other
+              }
             }
         }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index c7f9713344c50..c203fcecf20fb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.analysis
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.types._
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index 0daee1990a6e0..03da45b09f928 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -73,7 +73,10 @@ object UnresolvedAttribute {
   def quoted(name: String): UnresolvedAttribute = new UnresolvedAttribute(Seq(name))
 }
 
-case class UnresolvedFunction(name: String, children: Seq[Expression])
+case class UnresolvedFunction(
+    name: String,
+    children: Seq[Expression],
+    isDistinct: Boolean)
   extends Expression with Unevaluable {
 
   override def dataType: DataType = throw new UnresolvedException(this, "dataType")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index b09aea03318da..b10a3c877434b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.types._
 case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
   extends LeafExpression with NamedExpression {
 
-  override def toString: String = s"input[$ordinal]"
+  override def toString: String = s"input[$ordinal, $dataType]"
 
   override def eval(input: InternalRow): Any = input(ordinal)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index aada25276adb7..29ae47e842ddb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -96,7 +96,8 @@ abstract class Expression extends TreeNode[Expression] {
     val primitive = ctx.freshName("primitive")
     val ve = GeneratedExpressionCode("", isNull, primitive)
     ve.code = genCode(ctx, ve)
-    ve
+    // Add `this` in the comment.
+    ve.copy(s"/* $this */\n" + ve.code)
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
new file mode 100644
index 0000000000000..b924af4cc84d8
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
@@ -0,0 +1,292 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.aggregate
+
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types._
+
+case class Average(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  // Return data type.
+  override def dataType: DataType = resultType
+
+  // Expected input data type.
+  // TODO: Once we remove the old code path, we can use our analyzer to cast NullType
+  // to the default data type of the NumericType.
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(NumericType, NullType))
+
+  private val resultType = child.dataType match {
+    case DecimalType.Fixed(precision, scale) =>
+      DecimalType(precision + 4, scale + 4)
+    case DecimalType.Unlimited => DecimalType.Unlimited
+    case _ => DoubleType
+  }
+
+  private val sumDataType = child.dataType match {
+    case _ @ DecimalType() => DecimalType.Unlimited
+    case _ => DoubleType
+  }
+
+  private val currentSum = AttributeReference("currentSum", sumDataType)()
+  private val currentCount = AttributeReference("currentCount", LongType)()
+
+  override val bufferAttributes = currentSum :: currentCount :: Nil
+
+  override val initialValues = Seq(
+    /* currentSum = */ Cast(Literal(0), sumDataType),
+    /* currentCount = */ Literal(0L)
+  )
+
+  override val updateExpressions = Seq(
+    /* currentSum = */
+    Add(
+      currentSum,
+      Coalesce(Cast(child, sumDataType) :: Cast(Literal(0), sumDataType) :: Nil)),
+    /* currentCount = */ If(IsNull(child), currentCount, currentCount + 1L)
+  )
+
+  override val mergeExpressions = Seq(
+    /* currentSum = */ currentSum.left + currentSum.right,
+    /* currentCount = */ currentCount.left + currentCount.right
+  )
+
+  // If all input are nulls, currentCount will be 0 and we will get null after the division.
+  override val evaluateExpression = Cast(currentSum, resultType) / Cast(currentCount, resultType)
+}
+
+case class Count(child: Expression) extends AlgebraicAggregate {
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = false
+
+  // Return data type.
+  override def dataType: DataType = LongType
+
+  // Expected input data type.
+  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+
+  private val currentCount = AttributeReference("currentCount", LongType)()
+
+  override val bufferAttributes = currentCount :: Nil
+
+  override val initialValues = Seq(
+    /* currentCount = */ Literal(0L)
+  )
+
+  override val updateExpressions = Seq(
+    /* currentCount = */ If(IsNull(child), currentCount, currentCount + 1L)
+  )
+
+  override val mergeExpressions = Seq(
+    /* currentCount = */ currentCount.left + currentCount.right
+  )
+
+  override val evaluateExpression = Cast(currentCount, LongType)
+}
+
+case class First(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  // First is not a deterministic function.
+  override def deterministic: Boolean = false
+
+  // Return data type.
+  override def dataType: DataType = child.dataType
+
+  // Expected input data type.
+  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+
+  private val first = AttributeReference("first", child.dataType)()
+
+  override val bufferAttributes = first :: Nil
+
+  override val initialValues = Seq(
+    /* first = */ Literal.create(null, child.dataType)
+  )
+
+  override val updateExpressions = Seq(
+    /* first = */ If(IsNull(first), child, first)
+  )
+
+  override val mergeExpressions = Seq(
+    /* first = */ If(IsNull(first.left), first.right, first.left)
+  )
+
+  override val evaluateExpression = first
+}
+
+case class Last(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  // Last is not a deterministic function.
+  override def deterministic: Boolean = false
+
+  // Return data type.
+  override def dataType: DataType = child.dataType
+
+  // Expected input data type.
+  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+
+  private val last = AttributeReference("last", child.dataType)()
+
+  override val bufferAttributes = last :: Nil
+
+  override val initialValues = Seq(
+    /* last = */ Literal.create(null, child.dataType)
+  )
+
+  override val updateExpressions = Seq(
+    /* last = */ If(IsNull(child), last, child)
+  )
+
+  override val mergeExpressions = Seq(
+    /* last = */ If(IsNull(last.right), last.left, last.right)
+  )
+
+  override val evaluateExpression = last
+}
+
+case class Max(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  // Return data type.
+  override def dataType: DataType = child.dataType
+
+  // Expected input data type.
+  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+
+  private val max = AttributeReference("max", child.dataType)()
+
+  override val bufferAttributes = max :: Nil
+
+  override val initialValues = Seq(
+    /* max = */ Literal.create(null, child.dataType)
+  )
+
+  override val updateExpressions = Seq(
+    /* max = */ If(IsNull(child), max, If(IsNull(max), child, Greatest(Seq(max, child))))
+  )
+
+  override val mergeExpressions = {
+    val greatest = Greatest(Seq(max.left, max.right))
+    Seq(
+      /* max = */ If(IsNull(max.right), max.left, If(IsNull(max.left), max.right, greatest))
+    )
+  }
+
+  override val evaluateExpression = max
+}
+
+case class Min(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  // Return data type.
+  override def dataType: DataType = child.dataType
+
+  // Expected input data type.
+  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+
+  private val min = AttributeReference("min", child.dataType)()
+
+  override val bufferAttributes = min :: Nil
+
+  override val initialValues = Seq(
+    /* min = */ Literal.create(null, child.dataType)
+  )
+
+  override val updateExpressions = Seq(
+    /* min = */ If(IsNull(child), min, If(IsNull(min), child, Least(Seq(min, child))))
+  )
+
+  override val mergeExpressions = {
+    val least = Least(Seq(min.left, min.right))
+    Seq(
+      /* min = */ If(IsNull(min.right), min.left, If(IsNull(min.left), min.right, least))
+    )
+  }
+
+  override val evaluateExpression = min
+}
+
+case class Sum(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  // Return data type.
+  override def dataType: DataType = resultType
+
+  // Expected input data type.
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(LongType, DoubleType, DecimalType, NullType))
+
+  private val resultType = child.dataType match {
+    case DecimalType.Fixed(precision, scale) =>
+      DecimalType(precision + 4, scale + 4)
+    case DecimalType.Unlimited => DecimalType.Unlimited
+    case _ => child.dataType
+  }
+
+  private val sumDataType = child.dataType match {
+    case _ @ DecimalType() => DecimalType.Unlimited
+    case _ => child.dataType
+  }
+
+  private val currentSum = AttributeReference("currentSum", sumDataType)()
+
+  private val zero = Cast(Literal(0), sumDataType)
+
+  override val bufferAttributes = currentSum :: Nil
+
+  override val initialValues = Seq(
+    /* currentSum = */ Literal.create(null, sumDataType)
+  )
+
+  override val updateExpressions = Seq(
+    /* currentSum = */
+    Coalesce(Seq(Add(Coalesce(Seq(currentSum, zero)), Cast(child, sumDataType)), currentSum))
+  )
+
+  override val mergeExpressions = {
+    val add = Add(Coalesce(Seq(currentSum.left, zero)), Cast(currentSum.right, sumDataType))
+    Seq(
+      /* currentSum = */
+      Coalesce(Seq(add, currentSum.left))
+    )
+  }
+
+  override val evaluateExpression = Cast(currentSum, resultType)
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
new file mode 100644
index 0000000000000..577ede73cb01f
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -0,0 +1,206 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.aggregate
+
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types._
+
+/** The mode of an [[AggregateFunction1]]. */
+private[sql] sealed trait AggregateMode
+
+/**
+ * An [[AggregateFunction1]] with [[Partial]] mode is used for partial aggregation.
+ * This function updates the given aggregation buffer with the original input of this
+ * function. When it has processed all input rows, the aggregation buffer is returned.
+ */
+private[sql] case object Partial extends AggregateMode
+
+/**
+ * An [[AggregateFunction1]] with [[PartialMerge]] mode is used to merge aggregation buffers
+ * containing intermediate results for this function.
+ * This function updates the given aggregation buffer by merging multiple aggregation buffers.
+ * When it has processed all input rows, the aggregation buffer is returned.
+ */
+private[sql] case object PartialMerge extends AggregateMode
+
+/**
+ * An [[AggregateFunction1]] with [[PartialMerge]] mode is used to merge aggregation buffers
+ * containing intermediate results for this function and the generate final result.
+ * This function updates the given aggregation buffer by merging multiple aggregation buffers.
+ * When it has processed all input rows, the final result of this function is returned.
+ */
+private[sql] case object Final extends AggregateMode
+
+/**
+ * An [[AggregateFunction2]] with [[Partial]] mode is used to evaluate this function directly
+ * from original input rows without any partial aggregation.
+ * This function updates the given aggregation buffer with the original input of this
+ * function. When it has processed all input rows, the final result of this function is returned.
+ */
+private[sql] case object Complete extends AggregateMode
+
+/**
+ * A place holder expressions used in code-gen, it does not change the corresponding value
+ * in the row.
+ */
+private[sql] case object NoOp extends Expression with Unevaluable {
+  override def nullable: Boolean = true
+  override def eval(input: InternalRow): Any = {
+    throw new TreeNodeException(
+      this, s"No function to evaluate expression. type: ${this.nodeName}")
+  }
+  override def dataType: DataType = NullType
+  override def children: Seq[Expression] = Nil
+}
+
+/**
+ * A container for an [[AggregateFunction2]] with its [[AggregateMode]] and a field
+ * (`isDistinct`) indicating if DISTINCT keyword is specified for this function.
+ * @param aggregateFunction
+ * @param mode
+ * @param isDistinct
+ */
+private[sql] case class AggregateExpression2(
+    aggregateFunction: AggregateFunction2,
+    mode: AggregateMode,
+    isDistinct: Boolean) extends AggregateExpression {
+
+  override def children: Seq[Expression] = aggregateFunction :: Nil
+  override def dataType: DataType = aggregateFunction.dataType
+  override def foldable: Boolean = false
+  override def nullable: Boolean = aggregateFunction.nullable
+
+  override def references: AttributeSet = {
+    val childReferemces = mode match {
+      case Partial | Complete => aggregateFunction.references.toSeq
+      case PartialMerge | Final => aggregateFunction.bufferAttributes
+    }
+
+    AttributeSet(childReferemces)
+  }
+
+  override def toString: String = s"(${aggregateFunction}2,mode=$mode,isDistinct=$isDistinct)"
+}
+
+abstract class AggregateFunction2
+  extends Expression with ImplicitCastInputTypes {
+
+  self: Product =>
+
+  /** An aggregate function is not foldable. */
+  override def foldable: Boolean = false
+
+  /**
+   * The offset of this function's buffer in the underlying buffer shared with other functions.
+   */
+  var bufferOffset: Int = 0
+
+  /** The schema of the aggregation buffer. */
+  def bufferSchema: StructType
+
+  /** Attributes of fields in bufferSchema. */
+  def bufferAttributes: Seq[AttributeReference]
+
+  /** Clones bufferAttributes. */
+  def cloneBufferAttributes: Seq[Attribute]
+
+  /**
+   * Initializes its aggregation buffer located in `buffer`.
+   * It will use bufferOffset to find the starting point of
+   * its buffer in the given `buffer` shared with other functions.
+   */
+  def initialize(buffer: MutableRow): Unit
+
+  /**
+   * Updates its aggregation buffer located in `buffer` based on the given `input`.
+   * It will use bufferOffset to find the starting point of its buffer in the given `buffer`
+   * shared with other functions.
+   */
+  def update(buffer: MutableRow, input: InternalRow): Unit
+
+  /**
+   * Updates its aggregation buffer located in `buffer1` by combining intermediate results
+   * in the current buffer and intermediate results from another buffer `buffer2`.
+   * It will use bufferOffset to find the starting point of its buffer in the given `buffer1`
+   * and `buffer2`.
+   */
+  def merge(buffer1: MutableRow, buffer2: InternalRow): Unit
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
+    throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
+}
+
+/**
+ * A helper class for aggregate functions that can be implemented in terms of catalyst expressions.
+ */
+abstract class AlgebraicAggregate extends AggregateFunction2 with Serializable {
+  self: Product =>
+
+  val initialValues: Seq[Expression]
+  val updateExpressions: Seq[Expression]
+  val mergeExpressions: Seq[Expression]
+  val evaluateExpression: Expression
+
+  override lazy val cloneBufferAttributes = bufferAttributes.map(_.newInstance())
+
+  /**
+   * A helper class for representing an attribute used in merging two
+   * aggregation buffers. When merging two buffers, `bufferLeft` and `bufferRight`,
+   * we merge buffer values and then update bufferLeft. A [[RichAttribute]]
+   * of an [[AttributeReference]] `a` has two functions `left` and `right`,
+   * which represent `a` in `bufferLeft` and `bufferRight`, respectively.
+   * @param a
+   */
+  implicit class RichAttribute(a: AttributeReference) {
+    /** Represents this attribute at the mutable buffer side. */
+    def left: AttributeReference = a
+
+    /** Represents this attribute at the input buffer side (the data value is read-only). */
+    def right: AttributeReference = cloneBufferAttributes(bufferAttributes.indexOf(a))
+  }
+
+  /** An AlgebraicAggregate's bufferSchema is derived from bufferAttributes. */
+  override def bufferSchema: StructType = StructType.fromAttributes(bufferAttributes)
+
+  override def initialize(buffer: MutableRow): Unit = {
+    var i = 0
+    while (i < bufferAttributes.size) {
+      buffer(i + bufferOffset) = initialValues(i).eval()
+      i += 1
+    }
+  }
+
+  override def update(buffer: MutableRow, input: InternalRow): Unit = {
+    throw new UnsupportedOperationException(
+      "AlgebraicAggregate's update should not be called directly")
+  }
+
+  override def merge(buffer1: MutableRow, buffer2: InternalRow): Unit = {
+    throw new UnsupportedOperationException(
+      "AlgebraicAggregate's merge should not be called directly")
+  }
+
+  override def eval(buffer: InternalRow): Any = {
+    throw new UnsupportedOperationException(
+      "AlgebraicAggregate's eval should not be called directly")
+  }
+}
+
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index d705a1286065c..e07c920a41d0a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -27,7 +27,9 @@ import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
 
 
-trait AggregateExpression extends Expression with Unevaluable {
+trait AggregateExpression extends Expression with Unevaluable
+
+trait AggregateExpression1 extends AggregateExpression {
 
   /**
    * Aggregate expressions should not be foldable.
@@ -38,7 +40,7 @@ trait AggregateExpression extends Expression with Unevaluable {
    * Creates a new instance that can be used to compute this aggregate expression for a group
    * of input rows/
    */
-  def newInstance(): AggregateFunction
+  def newInstance(): AggregateFunction1
 }
 
 /**
@@ -54,10 +56,10 @@ case class SplitEvaluation(
     partialEvaluations: Seq[NamedExpression])
 
 /**
- * An [[AggregateExpression]] that can be partially computed without seeing all relevant tuples.
+ * An [[AggregateExpression1]] that can be partially computed without seeing all relevant tuples.
  * These partial evaluations can then be combined to compute the actual answer.
  */
-trait PartialAggregate extends AggregateExpression {
+trait PartialAggregate1 extends AggregateExpression1 {
 
   /**
    * Returns a [[SplitEvaluation]] that computes this aggregation using partial aggregation.
@@ -67,13 +69,13 @@ trait PartialAggregate extends AggregateExpression {
 
 /**
  * A specific implementation of an aggregate function. Used to wrap a generic
- * [[AggregateExpression]] with an algorithm that will be used to compute one specific result.
+ * [[AggregateExpression1]] with an algorithm that will be used to compute one specific result.
  */
-abstract class AggregateFunction
-  extends LeafExpression with AggregateExpression with Serializable {
+abstract class AggregateFunction1
+  extends LeafExpression with AggregateExpression1 with Serializable {
 
   /** Base should return the generic aggregate expression that this function is computing */
-  val base: AggregateExpression
+  val base: AggregateExpression1
 
   override def nullable: Boolean = base.nullable
   override def dataType: DataType = base.dataType
@@ -81,12 +83,12 @@ abstract class AggregateFunction
   def update(input: InternalRow): Unit
 
   // Do we really need this?
-  override def newInstance(): AggregateFunction = {
+  override def newInstance(): AggregateFunction1 = {
     makeCopy(productIterator.map { case a: AnyRef => a }.toArray)
   }
 }
 
-case class Min(child: Expression) extends UnaryExpression with PartialAggregate {
+case class Min(child: Expression) extends UnaryExpression with PartialAggregate1 {
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
@@ -102,7 +104,7 @@ case class Min(child: Expression) extends UnaryExpression with PartialAggregate
     TypeUtils.checkForOrderingExpr(child.dataType, "function min")
 }
 
-case class MinFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+case class MinFunction(expr: Expression, base: AggregateExpression1) extends AggregateFunction1 {
   def this() = this(null, null) // Required for serialization.
 
   val currentMin: MutableLiteral = MutableLiteral(null, expr.dataType)
@@ -119,7 +121,7 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr
   override def eval(input: InternalRow): Any = currentMin.value
 }
 
-case class Max(child: Expression) extends UnaryExpression with PartialAggregate {
+case class Max(child: Expression) extends UnaryExpression with PartialAggregate1 {
 
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
@@ -135,7 +137,7 @@ case class Max(child: Expression) extends UnaryExpression with PartialAggregate
     TypeUtils.checkForOrderingExpr(child.dataType, "function max")
 }
 
-case class MaxFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+case class MaxFunction(expr: Expression, base: AggregateExpression1) extends AggregateFunction1 {
   def this() = this(null, null) // Required for serialization.
 
   val currentMax: MutableLiteral = MutableLiteral(null, expr.dataType)
@@ -152,7 +154,7 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr
   override def eval(input: InternalRow): Any = currentMax.value
 }
 
-case class Count(child: Expression) extends UnaryExpression with PartialAggregate {
+case class Count(child: Expression) extends UnaryExpression with PartialAggregate1 {
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
@@ -165,7 +167,7 @@ case class Count(child: Expression) extends UnaryExpression with PartialAggregat
   override def newInstance(): CountFunction = new CountFunction(child, this)
 }
 
-case class CountFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+case class CountFunction(expr: Expression, base: AggregateExpression1) extends AggregateFunction1 {
   def this() = this(null, null) // Required for serialization.
 
   var count: Long = _
@@ -180,7 +182,7 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag
   override def eval(input: InternalRow): Any = count
 }
 
-case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate {
+case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate1 {
   def this() = this(null)
 
   override def children: Seq[Expression] = expressions
@@ -200,8 +202,8 @@ case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate
 
 case class CountDistinctFunction(
     @transient expr: Seq[Expression],
-    @transient base: AggregateExpression)
-  extends AggregateFunction {
+    @transient base: AggregateExpression1)
+  extends AggregateFunction1 {
 
   def this() = this(null, null) // Required for serialization.
 
@@ -220,7 +222,7 @@ case class CountDistinctFunction(
   override def eval(input: InternalRow): Any = seen.size.toLong
 }
 
-case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpression {
+case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpression1 {
   def this() = this(null)
 
   override def children: Seq[Expression] = expressions
@@ -233,8 +235,8 @@ case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpress
 
 case class CollectHashSetFunction(
     @transient expr: Seq[Expression],
-    @transient base: AggregateExpression)
-  extends AggregateFunction {
+    @transient base: AggregateExpression1)
+  extends AggregateFunction1 {
 
   def this() = this(null, null) // Required for serialization.
 
@@ -255,7 +257,7 @@ case class CollectHashSetFunction(
   }
 }
 
-case class CombineSetsAndCount(inputSet: Expression) extends AggregateExpression {
+case class CombineSetsAndCount(inputSet: Expression) extends AggregateExpression1 {
   def this() = this(null)
 
   override def children: Seq[Expression] = inputSet :: Nil
@@ -269,8 +271,8 @@ case class CombineSetsAndCount(inputSet: Expression) extends AggregateExpression
 
 case class CombineSetsAndCountFunction(
     @transient inputSet: Expression,
-    @transient base: AggregateExpression)
-  extends AggregateFunction {
+    @transient base: AggregateExpression1)
+  extends AggregateFunction1 {
 
   def this() = this(null, null) // Required for serialization.
 
@@ -305,7 +307,7 @@ private[sql] case object HyperLogLogUDT extends UserDefinedType[HyperLogLog] {
 }
 
 case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
-  extends UnaryExpression with AggregateExpression {
+  extends UnaryExpression with AggregateExpression1 {
 
   override def nullable: Boolean = false
   override def dataType: DataType = HyperLogLogUDT
@@ -317,9 +319,9 @@ case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
 
 case class ApproxCountDistinctPartitionFunction(
     expr: Expression,
-    base: AggregateExpression,
+    base: AggregateExpression1,
     relativeSD: Double)
-  extends AggregateFunction {
+  extends AggregateFunction1 {
   def this() = this(null, null, 0) // Required for serialization.
 
   private val hyperLogLog = new HyperLogLog(relativeSD)
@@ -335,7 +337,7 @@ case class ApproxCountDistinctPartitionFunction(
 }
 
 case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
-  extends UnaryExpression with AggregateExpression {
+  extends UnaryExpression with AggregateExpression1 {
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
@@ -347,9 +349,9 @@ case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
 
 case class ApproxCountDistinctMergeFunction(
     expr: Expression,
-    base: AggregateExpression,
+    base: AggregateExpression1,
     relativeSD: Double)
-  extends AggregateFunction {
+  extends AggregateFunction1 {
   def this() = this(null, null, 0) // Required for serialization.
 
   private val hyperLogLog = new HyperLogLog(relativeSD)
@@ -363,7 +365,7 @@ case class ApproxCountDistinctMergeFunction(
 }
 
 case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
-  extends UnaryExpression with PartialAggregate {
+  extends UnaryExpression with PartialAggregate1 {
 
   override def nullable: Boolean = false
   override def dataType: LongType.type = LongType
@@ -381,7 +383,7 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
   override def newInstance(): CountDistinctFunction = new CountDistinctFunction(child :: Nil, this)
 }
 
-case class Average(child: Expression) extends UnaryExpression with PartialAggregate {
+case class Average(child: Expression) extends UnaryExpression with PartialAggregate1 {
 
   override def prettyName: String = "avg"
 
@@ -427,8 +429,8 @@ case class Average(child: Expression) extends UnaryExpression with PartialAggreg
     TypeUtils.checkForNumericExpr(child.dataType, "function average")
 }
 
-case class AverageFunction(expr: Expression, base: AggregateExpression)
-  extends AggregateFunction {
+case class AverageFunction(expr: Expression, base: AggregateExpression1)
+  extends AggregateFunction1 {
 
   def this() = this(null, null) // Required for serialization.
 
@@ -474,7 +476,7 @@ case class AverageFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
-case class Sum(child: Expression) extends UnaryExpression with PartialAggregate {
+case class Sum(child: Expression) extends UnaryExpression with PartialAggregate1 {
 
   override def nullable: Boolean = true
 
@@ -509,7 +511,7 @@ case class Sum(child: Expression) extends UnaryExpression with PartialAggregate
     TypeUtils.checkForNumericExpr(child.dataType, "function sum")
 }
 
-case class SumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+case class SumFunction(expr: Expression, base: AggregateExpression1) extends AggregateFunction1 {
   def this() = this(null, null) // Required for serialization.
 
   private val calcType =
@@ -554,7 +556,7 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr
  *          <-- null         <-- no data
  * null     <-- null         <-- no data
  */
-case class CombineSum(child: Expression) extends AggregateExpression {
+case class CombineSum(child: Expression) extends AggregateExpression1 {
   def this() = this(null)
 
   override def children: Seq[Expression] = child :: Nil
@@ -564,8 +566,8 @@ case class CombineSum(child: Expression) extends AggregateExpression {
   override def newInstance(): CombineSumFunction = new CombineSumFunction(child, this)
 }
 
-case class CombineSumFunction(expr: Expression, base: AggregateExpression)
-  extends AggregateFunction {
+case class CombineSumFunction(expr: Expression, base: AggregateExpression1)
+  extends AggregateFunction1 {
 
   def this() = this(null, null) // Required for serialization.
 
@@ -601,7 +603,7 @@ case class CombineSumFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
-case class SumDistinct(child: Expression) extends UnaryExpression with PartialAggregate {
+case class SumDistinct(child: Expression) extends UnaryExpression with PartialAggregate1 {
 
   def this() = this(null)
   override def nullable: Boolean = true
@@ -627,8 +629,8 @@ case class SumDistinct(child: Expression) extends UnaryExpression with PartialAg
     TypeUtils.checkForNumericExpr(child.dataType, "function sumDistinct")
 }
 
-case class SumDistinctFunction(expr: Expression, base: AggregateExpression)
-  extends AggregateFunction {
+case class SumDistinctFunction(expr: Expression, base: AggregateExpression1)
+  extends AggregateFunction1 {
 
   def this() = this(null, null) // Required for serialization.
 
@@ -653,7 +655,7 @@ case class SumDistinctFunction(expr: Expression, base: AggregateExpression)
   }
 }
 
-case class CombineSetsAndSum(inputSet: Expression, base: Expression) extends AggregateExpression {
+case class CombineSetsAndSum(inputSet: Expression, base: Expression) extends AggregateExpression1 {
   def this() = this(null, null)
 
   override def children: Seq[Expression] = inputSet :: Nil
@@ -667,8 +669,8 @@ case class CombineSetsAndSum(inputSet: Expression, base: Expression) extends Agg
 
 case class CombineSetsAndSumFunction(
     @transient inputSet: Expression,
-    @transient base: AggregateExpression)
-  extends AggregateFunction {
+    @transient base: AggregateExpression1)
+  extends AggregateFunction1 {
 
   def this() = this(null, null) // Required for serialization.
 
@@ -695,7 +697,7 @@ case class CombineSetsAndSumFunction(
   }
 }
 
-case class First(child: Expression) extends UnaryExpression with PartialAggregate {
+case class First(child: Expression) extends UnaryExpression with PartialAggregate1 {
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
   override def toString: String = s"FIRST($child)"
@@ -709,7 +711,7 @@ case class First(child: Expression) extends UnaryExpression with PartialAggregat
   override def newInstance(): FirstFunction = new FirstFunction(child, this)
 }
 
-case class FirstFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+case class FirstFunction(expr: Expression, base: AggregateExpression1) extends AggregateFunction1 {
   def this() = this(null, null) // Required for serialization.
 
   var result: Any = null
@@ -723,7 +725,7 @@ case class FirstFunction(expr: Expression, base: AggregateExpression) extends Ag
   override def eval(input: InternalRow): Any = result
 }
 
-case class Last(child: Expression) extends UnaryExpression with PartialAggregate {
+case class Last(child: Expression) extends UnaryExpression with PartialAggregate1 {
   override def references: AttributeSet = child.references
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
@@ -738,7 +740,7 @@ case class Last(child: Expression) extends UnaryExpression with PartialAggregate
   override def newInstance(): LastFunction = new LastFunction(child, this)
 }
 
-case class LastFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+case class LastFunction(expr: Expression, base: AggregateExpression1) extends AggregateFunction1 {
   def this() = this(null, null) // Required for serialization.
 
   var result: Any = null
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index 03b4b3c216f49..d838268f46956 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
 
 import scala.collection.mutable.ArrayBuffer
 
@@ -38,15 +39,17 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
 
   protected def create(expressions: Seq[Expression]): (() => MutableProjection) = {
     val ctx = newCodeGenContext()
-    val projectionCode = expressions.zipWithIndex.map { case (e, i) =>
-      val evaluationCode = e.gen(ctx)
-      evaluationCode.code +
-        s"""
-          if(${evaluationCode.isNull})
-            mutableRow.setNullAt($i);
-          else
-            ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
-        """
+    val projectionCode = expressions.zipWithIndex.map {
+      case (NoOp, _) => ""
+      case (e, i) =>
+        val evaluationCode = e.gen(ctx)
+        evaluationCode.code +
+          s"""
+            if(${evaluationCode.isNull})
+              mutableRow.setNullAt($i);
+            else
+              ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
+          """
     }
     // collect projections into blocks as function has 64kb codesize limit in JVM
     val projectionBlocks = new ArrayBuffer[String]()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 179a348d5baac..b8e3b0d53a505 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -129,10 +129,10 @@ object PartialAggregation {
     case logical.Aggregate(groupingExpressions, aggregateExpressions, child) =>
       // Collect all aggregate expressions.
       val allAggregates =
-        aggregateExpressions.flatMap(_ collect { case a: AggregateExpression => a})
+        aggregateExpressions.flatMap(_ collect { case a: AggregateExpression1 => a})
       // Collect all aggregate expressions that can be computed partially.
       val partialAggregates =
-        aggregateExpressions.flatMap(_ collect { case p: PartialAggregate => p})
+        aggregateExpressions.flatMap(_ collect { case p: PartialAggregate1 => p})
 
       // Only do partial aggregation if supported by all aggregate expressions.
       if (allAggregates.size == partialAggregates.size) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 986c315b3173a..6aefa9f67556a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 78c780bdc5797..1474b170ba896 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -402,6 +402,9 @@ private[spark] object SQLConf {
     defaultValue = Some(true),
     isPublic = false)
 
+  val USE_SQL_AGGREGATE2 = booleanConf("spark.sql.useAggregate2",
+    defaultValue = Some(true), doc = "<TODO>")
+
   val USE_SQL_SERIALIZER2 = booleanConf(
     "spark.sql.useSerializer2",
     defaultValue = Some(true), isPublic = false)
@@ -473,6 +476,8 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
 
+  private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
+
   private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
 
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 8b4528b5d52fe..49bfe74b680af 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -285,6 +285,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
   @transient
   val udf: UDFRegistration = new UDFRegistration(this)
 
+  @transient
+  val udaf: UDAFRegistration = new UDAFRegistration(this)
+
   /**
    * Returns true if the table is currently cached in-memory.
    * @group cachemgmt
@@ -863,6 +866,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       DDLStrategy ::
       TakeOrderedAndProject ::
       HashAggregation ::
+      Aggregation ::
       LeftSemiJoin ::
       HashJoin ::
       InMemoryScans ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
new file mode 100644
index 0000000000000..5b872f5e3eecd
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions.{Expression}
+import org.apache.spark.sql.expressions.aggregate.{ScalaUDAF, UserDefinedAggregateFunction}
+
+class UDAFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
+
+  private val functionRegistry = sqlContext.functionRegistry
+
+  def register(
+      name: String,
+      func: UserDefinedAggregateFunction): UserDefinedAggregateFunction = {
+    def builder(children: Seq[Expression]) = ScalaUDAF(children, func)
+    functionRegistry.registerFunction(name, builder)
+    func
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index 3cd60a2aa55ed..c2c945321db95 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -68,14 +68,14 @@ case class Aggregate(
    *                        output.
    */
   case class ComputedAggregate(
-      unbound: AggregateExpression,
-      aggregate: AggregateExpression,
+      unbound: AggregateExpression1,
+      aggregate: AggregateExpression1,
       resultAttribute: AttributeReference)
 
   /** A list of aggregates that need to be computed for each group. */
   private[this] val computedAggregates = aggregateExpressions.flatMap { agg =>
     agg.collect {
-      case a: AggregateExpression =>
+      case a: AggregateExpression1 =>
         ComputedAggregate(
           a,
           BindReferences.bindReference(a, child.output),
@@ -87,8 +87,8 @@ case class Aggregate(
   private[this] val computedSchema = computedAggregates.map(_.resultAttribute)
 
   /** Creates a new aggregate buffer for a group. */
-  private[this] def newAggregateBuffer(): Array[AggregateFunction] = {
-    val buffer = new Array[AggregateFunction](computedAggregates.length)
+  private[this] def newAggregateBuffer(): Array[AggregateFunction1] = {
+    val buffer = new Array[AggregateFunction1](computedAggregates.length)
     var i = 0
     while (i < computedAggregates.length) {
       buffer(i) = computedAggregates(i).aggregate.newInstance()
@@ -146,7 +146,7 @@ case class Aggregate(
       }
     } else {
       child.execute().mapPartitions { iter =>
-        val hashTable = new HashMap[InternalRow, Array[AggregateFunction]]
+        val hashTable = new HashMap[InternalRow, Array[AggregateFunction1]]
         val groupingProjection = new InterpretedMutableProjection(groupingExpressions, child.output)
 
         var currentRow: InternalRow = null
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 2750053594f99..d31e265a293e9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -247,8 +247,15 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
         }
 
         def addSortIfNecessary(child: SparkPlan): SparkPlan = {
-          if (rowOrdering.nonEmpty && child.outputOrdering != rowOrdering) {
-            sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
+
+          if (rowOrdering.nonEmpty) {
+            // If child.outputOrdering is [a, b] and rowOrdering is [a], we do not need to sort.
+            val minSize = Seq(rowOrdering.size, child.outputOrdering.size).min
+            if (minSize == 0 || rowOrdering.take(minSize) != child.outputOrdering.take(minSize)) {
+              sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
+            } else {
+              child
+            }
           } else {
             child
           }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index ecde9c57139a6..0e63f2fe29cb3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -69,7 +69,7 @@ case class GeneratedAggregate(
 
   protected override def doExecute(): RDD[InternalRow] = {
     val aggregatesToCompute = aggregateExpressions.flatMap { a =>
-      a.collect { case agg: AggregateExpression => agg}
+      a.collect { case agg: AggregateExpression1 => agg}
     }
 
     // If you add any new function support, please add tests in org.apache.spark.sql.SQLQuerySuite
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 8cef7f200d2dc..f54aa2027f6a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution
 import org.apache.spark.sql.{SQLContext, Strategy, execution}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2}
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.{BroadcastHint, LogicalPlan}
@@ -148,7 +149,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
              if canBeCodeGened(
                   allAggregates(partialComputation) ++
                   allAggregates(rewrittenAggregateExpressions)) &&
-               codegenEnabled =>
+               codegenEnabled &&
+               !canBeConvertedToNewAggregation(plan) =>
           execution.GeneratedAggregate(
             partial = false,
             namedGroupingAttributes,
@@ -167,7 +169,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
              rewrittenAggregateExpressions,
              groupingExpressions,
              partialComputation,
-             child) =>
+             child) if !canBeConvertedToNewAggregation(plan) =>
         execution.Aggregate(
           partial = false,
           namedGroupingAttributes,
@@ -181,7 +183,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case _ => Nil
     }
 
-    def canBeCodeGened(aggs: Seq[AggregateExpression]): Boolean = !aggs.exists {
+    def canBeConvertedToNewAggregation(plan: LogicalPlan): Boolean = {
+      aggregate.Utils.tryConvert(
+        plan,
+        sqlContext.conf.useSqlAggregate2,
+        sqlContext.conf.codegenEnabled).isDefined
+    }
+
+    def canBeCodeGened(aggs: Seq[AggregateExpression1]): Boolean = !aggs.exists {
       case _: CombineSum | _: Sum | _: Count | _: Max | _: Min |  _: CombineSetsAndCount => false
       // The generated set implementation is pretty limited ATM.
       case CollectHashSet(exprs) if exprs.size == 1  &&
@@ -189,10 +198,74 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case _ => true
     }
 
-    def allAggregates(exprs: Seq[Expression]): Seq[AggregateExpression] =
-      exprs.flatMap(_.collect { case a: AggregateExpression => a })
+    def allAggregates(exprs: Seq[Expression]): Seq[AggregateExpression1] =
+      exprs.flatMap(_.collect { case a: AggregateExpression1 => a })
   }
 
+  /**
+   * Used to plan the aggregate operator for expressions based on the AggregateFunction2 interface.
+   */
+  object Aggregation extends Strategy {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case p: logical.Aggregate =>
+        val converted =
+          aggregate.Utils.tryConvert(
+            p,
+            sqlContext.conf.useSqlAggregate2,
+            sqlContext.conf.codegenEnabled)
+        converted match {
+          case None => Nil // Cannot convert to new aggregation code path.
+          case Some(logical.Aggregate(groupingExpressions, resultExpressions, child)) =>
+            // Extracts all distinct aggregate expressions from the resultExpressions.
+            val aggregateExpressions = resultExpressions.flatMap { expr =>
+              expr.collect {
+                case agg: AggregateExpression2 => agg
+              }
+            }.toSet.toSeq
+            // For those distinct aggregate expressions, we create a map from the
+            // aggregate function to the corresponding attribute of the function.
+            val aggregateFunctionMap = aggregateExpressions.map { agg =>
+              val aggregateFunction = agg.aggregateFunction
+              (aggregateFunction, agg.isDistinct) ->
+                Alias(aggregateFunction, aggregateFunction.toString)().toAttribute
+            }.toMap
+
+            val (functionsWithDistinct, functionsWithoutDistinct) =
+              aggregateExpressions.partition(_.isDistinct)
+            if (functionsWithDistinct.map(_.aggregateFunction.children).distinct.length > 1) {
+              // This is a sanity check. We should not reach here when we have multiple distinct
+              // column sets (aggregate.NewAggregation will not match).
+              sys.error(
+                "Multiple distinct column sets are not supported by the new aggregation" +
+                  "code path.")
+            }
+
+            val aggregateOperator =
+              if (functionsWithDistinct.isEmpty) {
+                aggregate.Utils.planAggregateWithoutDistinct(
+                  groupingExpressions,
+                  aggregateExpressions,
+                  aggregateFunctionMap,
+                  resultExpressions,
+                  planLater(child))
+              } else {
+                aggregate.Utils.planAggregateWithOneDistinct(
+                  groupingExpressions,
+                  functionsWithDistinct,
+                  functionsWithoutDistinct,
+                  aggregateFunctionMap,
+                  resultExpressions,
+                  planLater(child))
+              }
+
+            aggregateOperator
+        }
+
+      case _ => Nil
+    }
+  }
+
+
   object BroadcastNestedLoopJoin extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.Join(left, right, joinType, condition) =>
@@ -336,8 +409,21 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Filter(condition, planLater(child)) :: Nil
       case e @ logical.Expand(_, _, _, child) =>
         execution.Expand(e.projections, e.output, planLater(child)) :: Nil
-      case logical.Aggregate(group, agg, child) =>
-        execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
+      case a @ logical.Aggregate(group, agg, child) => {
+        val useNewAggregation =
+          aggregate.Utils.tryConvert(
+            a,
+            sqlContext.conf.useSqlAggregate2,
+            sqlContext.conf.codegenEnabled).isDefined
+        if (useNewAggregation) {
+          // If this logical.Aggregate can be planned to use new aggregation code path
+          // (i.e. it can be planned by the Strategy Aggregation), we will not use the old
+          // aggregation code path.
+          Nil
+        } else {
+          execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
+        }
+      }
       case logical.Window(projectList, windowExpressions, spec, child) =>
         execution.Window(projectList, windowExpressions, spec, planLater(child)) :: Nil
       case logical.Sample(lb, ub, withReplacement, seed, child) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
new file mode 100644
index 0000000000000..0c9082897f390
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
@@ -0,0 +1,173 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.errors._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, UnspecifiedDistribution}
+import org.apache.spark.sql.execution.{SparkPlan, UnaryNode}
+
+case class Aggregate2Sort(
+    requiredChildDistributionExpressions: Option[Seq[Expression]],
+    groupingExpressions: Seq[NamedExpression],
+    aggregateExpressions: Seq[AggregateExpression2],
+    aggregateAttributes: Seq[Attribute],
+    resultExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def canProcessUnsafeRows: Boolean = true
+
+  override def references: AttributeSet = {
+    val referencesInResults =
+      AttributeSet(resultExpressions.flatMap(_.references)) -- AttributeSet(aggregateAttributes)
+
+    AttributeSet(
+      groupingExpressions.flatMap(_.references) ++
+      aggregateExpressions.flatMap(_.references) ++
+      referencesInResults)
+  }
+
+  override def requiredChildDistribution: List[Distribution] = {
+    requiredChildDistributionExpressions match {
+      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
+      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
+      case None => UnspecifiedDistribution :: Nil
+    }
+  }
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
+    // TODO: We should not sort the input rows if they are just in reversed order.
+    groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
+  }
+
+  override def outputOrdering: Seq[SortOrder] = {
+    // It is possible that the child.outputOrdering starts with the required
+    // ordering expressions (e.g. we require [a] as the sort expression and the
+    // child's outputOrdering is [a, b]). We can only guarantee the output rows
+    // are sorted by values of groupingExpressions.
+    groupingExpressions.map(SortOrder(_, Ascending))
+  }
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    child.execute().mapPartitions { iter =>
+      if (aggregateExpressions.length == 0) {
+        new GroupingIterator(
+          groupingExpressions,
+          resultExpressions,
+          newMutableProjection,
+          child.output,
+          iter)
+      } else {
+        val aggregationIterator: SortAggregationIterator = {
+          aggregateExpressions.map(_.mode).distinct.toList match {
+            case Partial :: Nil =>
+              new PartialSortAggregationIterator(
+                groupingExpressions,
+                aggregateExpressions,
+                newMutableProjection,
+                child.output,
+                iter)
+            case PartialMerge :: Nil =>
+              new PartialMergeSortAggregationIterator(
+                groupingExpressions,
+                aggregateExpressions,
+                newMutableProjection,
+                child.output,
+                iter)
+            case Final :: Nil =>
+              new FinalSortAggregationIterator(
+                groupingExpressions,
+                aggregateExpressions,
+                aggregateAttributes,
+                resultExpressions,
+                newMutableProjection,
+                child.output,
+                iter)
+            case other =>
+              sys.error(
+                s"Could not evaluate ${aggregateExpressions} because we do not support evaluate " +
+                  s"modes $other in this operator.")
+          }
+        }
+
+        aggregationIterator
+      }
+    }
+  }
+}
+
+case class FinalAndCompleteAggregate2Sort(
+    previousGroupingExpressions: Seq[NamedExpression],
+    groupingExpressions: Seq[NamedExpression],
+    finalAggregateExpressions: Seq[AggregateExpression2],
+    finalAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    resultExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+  override def references: AttributeSet = {
+    val referencesInResults =
+      AttributeSet(resultExpressions.flatMap(_.references)) --
+        AttributeSet(finalAggregateExpressions) --
+        AttributeSet(completeAggregateExpressions)
+
+    AttributeSet(
+      groupingExpressions.flatMap(_.references) ++
+        finalAggregateExpressions.flatMap(_.references) ++
+        completeAggregateExpressions.flatMap(_.references) ++
+        referencesInResults)
+  }
+
+  override def requiredChildDistribution: List[Distribution] = {
+    if (groupingExpressions.isEmpty) {
+      AllTuples :: Nil
+    } else {
+      ClusteredDistribution(groupingExpressions) :: Nil
+    }
+  }
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+    groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    child.execute().mapPartitions { iter =>
+
+      new FinalAndCompleteSortAggregationIterator(
+        previousGroupingExpressions.length,
+        groupingExpressions,
+        finalAggregateExpressions,
+        finalAggregateAttributes,
+        completeAggregateExpressions,
+        completeAggregateAttributes,
+        resultExpressions,
+        newMutableProjection,
+        child.output,
+        iter)
+    }
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
new file mode 100644
index 0000000000000..ce1cbdc9cb090
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
@@ -0,0 +1,749 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.types.NullType
+
+import scala.collection.mutable.ArrayBuffer
+
+/**
+ * An iterator used to evaluate aggregate functions. It assumes that input rows
+ * are already grouped by values of `groupingExpressions`.
+ */
+private[sql] abstract class SortAggregationIterator(
+    groupingExpressions: Seq[NamedExpression],
+    aggregateExpressions: Seq[AggregateExpression2],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    inputAttributes: Seq[Attribute],
+    inputIter: Iterator[InternalRow])
+  extends Iterator[InternalRow] {
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Static fields for this iterator
+  ///////////////////////////////////////////////////////////////////////////
+
+  protected val aggregateFunctions: Array[AggregateFunction2] = {
+    var bufferOffset = initialBufferOffset
+    val functions = new Array[AggregateFunction2](aggregateExpressions.length)
+    var i = 0
+    while (i < aggregateExpressions.length) {
+      val func = aggregateExpressions(i).aggregateFunction
+      val funcWithBoundReferences = aggregateExpressions(i).mode match {
+        case Partial | Complete if !func.isInstanceOf[AlgebraicAggregate] =>
+          // We need to create BoundReferences if the function is not an
+          // AlgebraicAggregate (it does not support code-gen) and the mode of
+          // this function is Partial or Complete because we will call eval of this
+          // function's children in the update method of this aggregate function.
+          // Those eval calls require BoundReferences to work.
+          BindReferences.bindReference(func, inputAttributes)
+        case _ => func
+      }
+      // Set bufferOffset for this function. It is important that setting bufferOffset
+      // happens after all potential bindReference operations because bindReference
+      // will create a new instance of the function.
+      funcWithBoundReferences.bufferOffset = bufferOffset
+      bufferOffset += funcWithBoundReferences.bufferSchema.length
+      functions(i) = funcWithBoundReferences
+      i += 1
+    }
+    functions
+  }
+
+  // All non-algebraic aggregate functions.
+  protected val nonAlgebraicAggregateFunctions: Array[AggregateFunction2] = {
+    aggregateFunctions.collect {
+      case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
+    }.toArray
+  }
+
+  // Positions of those non-algebraic aggregate functions in aggregateFunctions.
+  // For example, we have func1, func2, func3, func4 in aggregateFunctions, and
+  // func2 and func3 are non-algebraic aggregate functions.
+  // nonAlgebraicAggregateFunctionPositions will be [1, 2].
+  protected val nonAlgebraicAggregateFunctionPositions: Array[Int] = {
+    val positions = new ArrayBuffer[Int]()
+    var i = 0
+    while (i < aggregateFunctions.length) {
+      aggregateFunctions(i) match {
+        case agg: AlgebraicAggregate =>
+        case _ => positions += i
+      }
+      i += 1
+    }
+    positions.toArray
+  }
+
+  // This is used to project expressions for the grouping expressions.
+  protected val groupGenerator =
+    newMutableProjection(groupingExpressions, inputAttributes)()
+
+  // The underlying buffer shared by all aggregate functions.
+  protected val buffer: MutableRow = {
+    // The number of elements of the underlying buffer of this operator.
+    // All aggregate functions are sharing this underlying buffer and they find their
+    // buffer values through bufferOffset.
+    var size = initialBufferOffset
+    var i = 0
+    while (i < aggregateFunctions.length) {
+      size += aggregateFunctions(i).bufferSchema.length
+      i += 1
+    }
+    new GenericMutableRow(size)
+  }
+
+  protected val joinedRow = new JoinedRow4
+
+  protected val placeholderExpressions = Seq.fill(initialBufferOffset)(NoOp)
+
+  // This projection is used to initialize buffer values for all AlgebraicAggregates.
+  protected val algebraicInitialProjection = {
+    val initExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
+      case ae: AlgebraicAggregate => ae.initialValues
+      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+    }
+    newMutableProjection(initExpressions, Nil)().target(buffer)
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Mutable states
+  ///////////////////////////////////////////////////////////////////////////
+
+  // The partition key of the current partition.
+  protected var currentGroupingKey: InternalRow = _
+  // The partition key of next partition.
+  protected var nextGroupingKey: InternalRow = _
+  // The first row of next partition.
+  protected var firstRowInNextGroup: InternalRow = _
+  // Indicates if we has new group of rows to process.
+  protected var hasNewGroup: Boolean = true
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Private methods
+  ///////////////////////////////////////////////////////////////////////////
+
+  /** Initializes buffer values for all aggregate functions. */
+  protected def initializeBuffer(): Unit = {
+    algebraicInitialProjection(EmptyRow)
+    var i = 0
+    while (i < nonAlgebraicAggregateFunctions.length) {
+      nonAlgebraicAggregateFunctions(i).initialize(buffer)
+      i += 1
+    }
+  }
+
+  protected def initialize(): Unit = {
+    if (inputIter.hasNext) {
+      initializeBuffer()
+      val currentRow = inputIter.next().copy()
+      // partitionGenerator is a mutable projection. Since we need to track nextGroupingKey,
+      // we are making a copy at here.
+      nextGroupingKey = groupGenerator(currentRow).copy()
+      firstRowInNextGroup = currentRow
+    } else {
+      // This iter is an empty one.
+      hasNewGroup = false
+    }
+  }
+
+  /** Processes rows in the current group. It will stop when it find a new group. */
+  private def processCurrentGroup(): Unit = {
+    currentGroupingKey = nextGroupingKey
+    // Now, we will start to find all rows belonging to this group.
+    // We create a variable to track if we see the next group.
+    var findNextPartition = false
+    // firstRowInNextGroup is the first row of this group. We first process it.
+    processRow(firstRowInNextGroup)
+    // The search will stop when we see the next group or there is no
+    // input row left in the iter.
+    while (inputIter.hasNext && !findNextPartition) {
+      val currentRow = inputIter.next()
+      // Get the grouping key based on the grouping expressions.
+      // For the below compare method, we do not need to make a copy of groupingKey.
+      val groupingKey = groupGenerator(currentRow)
+      // Check if the current row belongs the current input row.
+      currentGroupingKey.equals(groupingKey)
+
+      if (currentGroupingKey == groupingKey) {
+        processRow(currentRow)
+      } else {
+        // We find a new group.
+        findNextPartition = true
+        nextGroupingKey = groupingKey.copy()
+        firstRowInNextGroup = currentRow.copy()
+      }
+    }
+    // We have not seen a new group. It means that there is no new row in the input
+    // iter. The current group is the last group of the iter.
+    if (!findNextPartition) {
+      hasNewGroup = false
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Public methods
+  ///////////////////////////////////////////////////////////////////////////
+
+  override final def hasNext: Boolean = hasNewGroup
+
+  override final def next(): InternalRow = {
+    if (hasNext) {
+      // Process the current group.
+      processCurrentGroup()
+      // Generate output row for the current group.
+      val outputRow = generateOutput()
+      // Initilize buffer values for the next group.
+      initializeBuffer()
+
+      outputRow
+    } else {
+      // no more result
+      throw new NoSuchElementException
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Methods that need to be implemented
+  ///////////////////////////////////////////////////////////////////////////
+
+  protected def initialBufferOffset: Int
+
+  protected def processRow(row: InternalRow): Unit
+
+  protected def generateOutput(): InternalRow
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Initialize this iterator
+  ///////////////////////////////////////////////////////////////////////////
+
+  initialize()
+}
+
+/**
+ * An iterator only used to group input rows according to values of `groupingExpressions`.
+ * It assumes that input rows are already grouped by values of `groupingExpressions`.
+ */
+class GroupingIterator(
+    groupingExpressions: Seq[NamedExpression],
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    inputAttributes: Seq[Attribute],
+    inputIter: Iterator[InternalRow])
+  extends SortAggregationIterator(
+    groupingExpressions,
+    Nil,
+    newMutableProjection,
+    inputAttributes,
+    inputIter) {
+
+  private val resultProjection =
+    newMutableProjection(resultExpressions, groupingExpressions.map(_.toAttribute))()
+
+  override protected def initialBufferOffset: Int = 0
+
+  override protected def processRow(row: InternalRow): Unit = {
+    // Since we only do grouping, there is nothing to do at here.
+  }
+
+  override protected def generateOutput(): InternalRow = {
+    resultProjection(currentGroupingKey)
+  }
+}
+
+/**
+ * An iterator used to do partial aggregations (for those aggregate functions with mode Partial).
+ * It assumes that input rows are already grouped by values of `groupingExpressions`.
+ * The format of its output rows is:
+ * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
+ */
+class PartialSortAggregationIterator(
+    groupingExpressions: Seq[NamedExpression],
+    aggregateExpressions: Seq[AggregateExpression2],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    inputAttributes: Seq[Attribute],
+    inputIter: Iterator[InternalRow])
+  extends SortAggregationIterator(
+    groupingExpressions,
+    aggregateExpressions,
+    newMutableProjection,
+    inputAttributes,
+    inputIter) {
+
+  // This projection is used to update buffer values for all AlgebraicAggregates.
+  private val algebraicUpdateProjection = {
+    val bufferSchema = aggregateFunctions.flatMap {
+      case ae: AlgebraicAggregate => ae.bufferAttributes
+      case agg: AggregateFunction2 => agg.bufferAttributes
+    }
+    val updateExpressions = aggregateFunctions.flatMap {
+      case ae: AlgebraicAggregate => ae.updateExpressions
+      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+    }
+    newMutableProjection(updateExpressions, bufferSchema ++ inputAttributes)().target(buffer)
+  }
+
+  override protected def initialBufferOffset: Int = 0
+
+  override protected def processRow(row: InternalRow): Unit = {
+    // Process all algebraic aggregate functions.
+    algebraicUpdateProjection(joinedRow(buffer, row))
+    // Process all non-algebraic aggregate functions.
+    var i = 0
+    while (i < nonAlgebraicAggregateFunctions.length) {
+      nonAlgebraicAggregateFunctions(i).update(buffer, row)
+      i += 1
+    }
+  }
+
+  override protected def generateOutput(): InternalRow = {
+    // We just output the grouping expressions and the underlying buffer.
+    joinedRow(currentGroupingKey, buffer).copy()
+  }
+}
+
+/**
+ * An iterator used to do partial merge aggregations (for those aggregate functions with mode
+ * PartialMerge). It assumes that input rows are already grouped by values of
+ * `groupingExpressions`.
+ * The format of its input rows is:
+ * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
+ *
+ * The format of its internal buffer is:
+ * |placeholder1|...|placeholderN|aggregationBuffer1|...|aggregationBufferN|
+ * Every placeholder is for a grouping expression.
+ * The actual buffers are stored after placeholderN.
+ * The reason that we have placeholders at here is to make our underlying buffer have the same
+ * length with a input row.
+ *
+ * The format of its output rows is:
+ * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
+ */
+class PartialMergeSortAggregationIterator(
+    groupingExpressions: Seq[NamedExpression],
+    aggregateExpressions: Seq[AggregateExpression2],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    inputAttributes: Seq[Attribute],
+    inputIter: Iterator[InternalRow])
+  extends SortAggregationIterator(
+    groupingExpressions,
+    aggregateExpressions,
+    newMutableProjection,
+    inputAttributes,
+    inputIter) {
+
+  private val placeholderAttribtues =
+    Seq.fill(initialBufferOffset)(AttributeReference("placeholder", NullType)())
+
+  // This projection is used to merge buffer values for all AlgebraicAggregates.
+  private val algebraicMergeProjection = {
+    val bufferSchemata =
+      placeholderAttribtues ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.bufferAttributes
+        case agg: AggregateFunction2 => agg.bufferAttributes
+      } ++ placeholderAttribtues ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
+        case agg: AggregateFunction2 => agg.cloneBufferAttributes
+      }
+    val mergeExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
+      case ae: AlgebraicAggregate => ae.mergeExpressions
+      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+    }
+
+    newMutableProjection(mergeExpressions, bufferSchemata)()
+  }
+
+  // This projection is used to extract aggregation buffers from the underlying buffer.
+  // We need it because the underlying buffer has placeholders at its beginning.
+  private val extractsBufferValues = {
+    val expressions = aggregateFunctions.flatMap {
+      case agg => agg.bufferAttributes
+    }
+
+    newMutableProjection(expressions, inputAttributes)()
+  }
+
+  override protected def initialBufferOffset: Int = groupingExpressions.length
+
+  override protected def processRow(row: InternalRow): Unit = {
+    // Process all algebraic aggregate functions.
+    algebraicMergeProjection.target(buffer)(joinedRow(buffer, row))
+    // Process all non-algebraic aggregate functions.
+    var i = 0
+    while (i < nonAlgebraicAggregateFunctions.length) {
+      nonAlgebraicAggregateFunctions(i).merge(buffer, row)
+      i += 1
+    }
+  }
+
+  override protected def generateOutput(): InternalRow = {
+    // We output grouping expressions and aggregation buffers.
+    joinedRow(currentGroupingKey, extractsBufferValues(buffer))
+  }
+}
+
+/**
+ * An iterator used to do final aggregations (for those aggregate functions with mode
+ * Final). It assumes that input rows are already grouped by values of
+ * `groupingExpressions`.
+ * The format of its input rows is:
+ * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
+ *
+ * The format of its internal buffer is:
+ * |placeholder1|...|placeholder N|aggregationBuffer1|...|aggregationBufferN|
+ * Every placeholder is for a grouping expression.
+ * The actual buffers are stored after placeholderN.
+ * The reason that we have placeholders at here is to make our underlying buffer have the same
+ * length with a input row.
+ *
+ * The format of its output rows is represented by the schema of `resultExpressions`.
+ */
+class FinalSortAggregationIterator(
+    groupingExpressions: Seq[NamedExpression],
+    aggregateExpressions: Seq[AggregateExpression2],
+    aggregateAttributes: Seq[Attribute],
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    inputAttributes: Seq[Attribute],
+    inputIter: Iterator[InternalRow])
+  extends SortAggregationIterator(
+    groupingExpressions,
+    aggregateExpressions,
+    newMutableProjection,
+    inputAttributes,
+    inputIter) {
+
+  // The result of aggregate functions.
+  private val aggregateResult: MutableRow = new GenericMutableRow(aggregateAttributes.length)
+
+  // The projection used to generate the output rows of this operator.
+  // This is only used when we are generating final results of aggregate functions.
+  private val resultProjection =
+    newMutableProjection(
+      resultExpressions, groupingExpressions.map(_.toAttribute) ++ aggregateAttributes)()
+
+  private val offsetAttributes =
+    Seq.fill(initialBufferOffset)(AttributeReference("placeholder", NullType)())
+
+  // This projection is used to merge buffer values for all AlgebraicAggregates.
+  private val algebraicMergeProjection = {
+    val bufferSchemata =
+      offsetAttributes ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.bufferAttributes
+        case agg: AggregateFunction2 => agg.bufferAttributes
+      } ++ offsetAttributes ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
+        case agg: AggregateFunction2 => agg.cloneBufferAttributes
+      }
+    val mergeExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
+      case ae: AlgebraicAggregate => ae.mergeExpressions
+      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+    }
+
+    newMutableProjection(mergeExpressions, bufferSchemata)()
+  }
+
+  // This projection is used to evaluate all AlgebraicAggregates.
+  private val algebraicEvalProjection = {
+    val bufferSchemata =
+      offsetAttributes ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.bufferAttributes
+        case agg: AggregateFunction2 => agg.bufferAttributes
+      } ++ offsetAttributes ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
+        case agg: AggregateFunction2 => agg.cloneBufferAttributes
+      }
+    val evalExpressions = aggregateFunctions.map {
+      case ae: AlgebraicAggregate => ae.evaluateExpression
+      case agg: AggregateFunction2 => NoOp
+    }
+
+    newMutableProjection(evalExpressions, bufferSchemata)()
+  }
+
+  override protected def initialBufferOffset: Int = groupingExpressions.length
+
+  override def initialize(): Unit = {
+    if (inputIter.hasNext) {
+      initializeBuffer()
+      val currentRow = inputIter.next().copy()
+      // partitionGenerator is a mutable projection. Since we need to track nextGroupingKey,
+      // we are making a copy at here.
+      nextGroupingKey = groupGenerator(currentRow).copy()
+      firstRowInNextGroup = currentRow
+    } else {
+      if (groupingExpressions.isEmpty) {
+        // If there is no grouping expression, we need to generate a single row as the output.
+        initializeBuffer()
+        // Right now, the buffer only contains initial buffer values. Because
+        // merging two buffers with initial values will generate a row that
+        // still store initial values. We set the currentRow as the copy of the current buffer.
+        val currentRow = buffer.copy()
+        nextGroupingKey = groupGenerator(currentRow).copy()
+        firstRowInNextGroup = currentRow
+      } else {
+        // This iter is an empty one.
+        hasNewGroup = false
+      }
+    }
+  }
+
+  override protected def processRow(row: InternalRow): Unit = {
+    // Process all algebraic aggregate functions.
+    algebraicMergeProjection.target(buffer)(joinedRow(buffer, row))
+    // Process all non-algebraic aggregate functions.
+    var i = 0
+    while (i < nonAlgebraicAggregateFunctions.length) {
+      nonAlgebraicAggregateFunctions(i).merge(buffer, row)
+      i += 1
+    }
+  }
+
+  override protected def generateOutput(): InternalRow = {
+    // Generate results for all algebraic aggregate functions.
+    algebraicEvalProjection.target(aggregateResult)(buffer)
+    // Generate results for all non-algebraic aggregate functions.
+    var i = 0
+    while (i < nonAlgebraicAggregateFunctions.length) {
+      aggregateResult.update(
+        nonAlgebraicAggregateFunctionPositions(i),
+        nonAlgebraicAggregateFunctions(i).eval(buffer))
+      i += 1
+    }
+    resultProjection(joinedRow(currentGroupingKey, aggregateResult))
+  }
+}
+
+/**
+ * An iterator used to do both final aggregations (for those aggregate functions with mode
+ * Final) and complete aggregations (for those aggregate functions with mode Complete).
+ * It assumes that input rows are already grouped by values of `groupingExpressions`.
+ * The format of its input rows is:
+ * |groupingExpr1|...|groupingExprN|col1|...|colM|aggregationBuffer1|...|aggregationBufferN|
+ * col1 to colM are columns used by aggregate functions with Complete mode.
+ * aggregationBuffer1 to aggregationBufferN are buffers used by aggregate functions with
+ * Final mode.
+ *
+ * The format of its internal buffer is:
+ * |placeholder1|...|placeholder(N+M)|aggregationBuffer1|...|aggregationBuffer(N+M)|
+ * The first N placeholders represent slots of grouping expressions.
+ * Then, next M placeholders represent slots of col1 to colM.
+ * For aggregation buffers, first N aggregation buffers are used by N aggregate functions with
+ * mode Final. Then, the last M aggregation buffers are used by M aggregate functions with mode
+ * Complete. The reason that we have placeholders at here is to make our underlying buffer
+ * have the same length with a input row.
+ *
+ * The format of its output rows is represented by the schema of `resultExpressions`.
+ */
+class FinalAndCompleteSortAggregationIterator(
+    override protected val initialBufferOffset: Int,
+    groupingExpressions: Seq[NamedExpression],
+    finalAggregateExpressions: Seq[AggregateExpression2],
+    finalAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    inputAttributes: Seq[Attribute],
+    inputIter: Iterator[InternalRow])
+  extends SortAggregationIterator(
+    groupingExpressions,
+    // TODO: document the ordering
+    finalAggregateExpressions ++ completeAggregateExpressions,
+    newMutableProjection,
+    inputAttributes,
+    inputIter) {
+
+  // The result of aggregate functions.
+  private val aggregateResult: MutableRow =
+    new GenericMutableRow(completeAggregateAttributes.length + finalAggregateAttributes.length)
+
+  // The projection used to generate the output rows of this operator.
+  // This is only used when we are generating final results of aggregate functions.
+  private val resultProjection = {
+    val inputSchema =
+      groupingExpressions.map(_.toAttribute) ++
+        finalAggregateAttributes ++
+        completeAggregateAttributes
+    newMutableProjection(resultExpressions, inputSchema)()
+  }
+
+  private val offsetAttributes =
+    Seq.fill(initialBufferOffset)(AttributeReference("placeholder", NullType)())
+
+  // All aggregate functions with mode Final.
+  private val finalAggregateFunctions: Array[AggregateFunction2] = {
+    val functions = new Array[AggregateFunction2](finalAggregateExpressions.length)
+    var i = 0
+    while (i < finalAggregateExpressions.length) {
+      functions(i) = aggregateFunctions(i)
+      i += 1
+    }
+    functions
+  }
+
+  // All non-algebraic aggregate functions with mode Final.
+  private val finalNonAlgebraicAggregateFunctions: Array[AggregateFunction2] = {
+    finalAggregateFunctions.collect {
+      case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
+    }.toArray
+  }
+
+  // All aggregate functions with mode Complete.
+  private val completeAggregateFunctions: Array[AggregateFunction2] = {
+    val functions = new Array[AggregateFunction2](completeAggregateExpressions.length)
+    var i = 0
+    while (i < completeAggregateExpressions.length) {
+      functions(i) = aggregateFunctions(finalAggregateFunctions.length + i)
+      i += 1
+    }
+    functions
+  }
+
+  // All non-algebraic aggregate functions with mode Complete.
+  private val completeNonAlgebraicAggregateFunctions: Array[AggregateFunction2] = {
+    completeAggregateFunctions.collect {
+      case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
+    }.toArray
+  }
+
+  // This projection is used to merge buffer values for all AlgebraicAggregates with mode
+  // Final.
+  private val finalAlgebraicMergeProjection = {
+    val numCompleteOffsetAttributes =
+      completeAggregateFunctions.map(_.bufferAttributes.length).sum
+    val completeOffsetAttributes =
+      Seq.fill(numCompleteOffsetAttributes)(AttributeReference("placeholder", NullType)())
+    val completeOffsetExpressions = Seq.fill(numCompleteOffsetAttributes)(NoOp)
+
+    val bufferSchemata =
+      offsetAttributes ++ finalAggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.bufferAttributes
+        case agg: AggregateFunction2 => agg.bufferAttributes
+      } ++ completeOffsetAttributes ++ offsetAttributes ++ finalAggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
+        case agg: AggregateFunction2 => agg.cloneBufferAttributes
+      } ++ completeOffsetAttributes
+    val mergeExpressions =
+      placeholderExpressions ++ finalAggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.mergeExpressions
+        case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+      } ++ completeOffsetExpressions
+
+    newMutableProjection(mergeExpressions, bufferSchemata)()
+  }
+
+  // This projection is used to update buffer values for all AlgebraicAggregates with mode
+  // Complete.
+  private val completeAlgebraicUpdateProjection = {
+    val numFinalOffsetAttributes = finalAggregateFunctions.map(_.bufferAttributes.length).sum
+    val finalOffsetAttributes =
+      Seq.fill(numFinalOffsetAttributes)(AttributeReference("placeholder", NullType)())
+    val finalOffsetExpressions = Seq.fill(numFinalOffsetAttributes)(NoOp)
+
+    val bufferSchema =
+      offsetAttributes ++ finalOffsetAttributes ++ completeAggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.bufferAttributes
+        case agg: AggregateFunction2 => agg.bufferAttributes
+      }
+    val updateExpressions =
+      placeholderExpressions ++ finalOffsetExpressions ++ completeAggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.updateExpressions
+        case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+      }
+    newMutableProjection(updateExpressions, bufferSchema ++ inputAttributes)().target(buffer)
+  }
+
+  // This projection is used to evaluate all AlgebraicAggregates.
+  private val algebraicEvalProjection = {
+    val bufferSchemata =
+      offsetAttributes ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.bufferAttributes
+        case agg: AggregateFunction2 => agg.bufferAttributes
+      } ++ offsetAttributes ++ aggregateFunctions.flatMap {
+        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
+        case agg: AggregateFunction2 => agg.cloneBufferAttributes
+      }
+    val evalExpressions = aggregateFunctions.map {
+      case ae: AlgebraicAggregate => ae.evaluateExpression
+      case agg: AggregateFunction2 => NoOp
+    }
+
+    newMutableProjection(evalExpressions, bufferSchemata)()
+  }
+
+  override def initialize(): Unit = {
+    if (inputIter.hasNext) {
+      initializeBuffer()
+      val currentRow = inputIter.next().copy()
+      // partitionGenerator is a mutable projection. Since we need to track nextGroupingKey,
+      // we are making a copy at here.
+      nextGroupingKey = groupGenerator(currentRow).copy()
+      firstRowInNextGroup = currentRow
+    } else {
+      if (groupingExpressions.isEmpty) {
+        // If there is no grouping expression, we need to generate a single row as the output.
+        initializeBuffer()
+        // Right now, the buffer only contains initial buffer values. Because
+        // merging two buffers with initial values will generate a row that
+        // still store initial values. We set the currentRow as the copy of the current buffer.
+        val currentRow = buffer.copy()
+        nextGroupingKey = groupGenerator(currentRow).copy()
+        firstRowInNextGroup = currentRow
+      } else {
+        // This iter is an empty one.
+        hasNewGroup = false
+      }
+    }
+  }
+
+  override protected def processRow(row: InternalRow): Unit = {
+    val input = joinedRow(buffer, row)
+    // For all aggregate functions with mode Complete, update buffers.
+    completeAlgebraicUpdateProjection(input)
+    var i = 0
+    while (i < completeNonAlgebraicAggregateFunctions.length) {
+      completeNonAlgebraicAggregateFunctions(i).update(buffer, row)
+      i += 1
+    }
+
+    // For all aggregate functions with mode Final, merge buffers.
+    finalAlgebraicMergeProjection.target(buffer)(input)
+    i = 0
+    while (i < finalNonAlgebraicAggregateFunctions.length) {
+      finalNonAlgebraicAggregateFunctions(i).merge(buffer, row)
+      i += 1
+    }
+  }
+
+  override protected def generateOutput(): InternalRow = {
+    // Generate results for all algebraic aggregate functions.
+    algebraicEvalProjection.target(aggregateResult)(buffer)
+    // Generate results for all non-algebraic aggregate functions.
+    var i = 0
+    while (i < nonAlgebraicAggregateFunctions.length) {
+      aggregateResult.update(
+        nonAlgebraicAggregateFunctionPositions(i),
+        nonAlgebraicAggregateFunctions(i).eval(buffer))
+      i += 1
+    }
+
+    resultProjection(joinedRow(currentGroupingKey, aggregateResult))
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
new file mode 100644
index 0000000000000..1cb27710e0480
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
@@ -0,0 +1,364 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan}
+import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.types.{StructType, MapType, ArrayType}
+
+/**
+ * Utility functions used by the query planner to convert our plan to new aggregation code path.
+ */
+object Utils {
+  // Right now, we do not support complex types in the grouping key schema.
+  private def supportsGroupingKeySchema(aggregate: Aggregate): Boolean = {
+    val hasComplexTypes = aggregate.groupingExpressions.map(_.dataType).exists {
+      case array: ArrayType => true
+      case map: MapType => true
+      case struct: StructType => true
+      case _ => false
+    }
+
+    !hasComplexTypes
+  }
+
+  private def tryConvert(plan: LogicalPlan): Option[Aggregate] = plan match {
+    case p: Aggregate if supportsGroupingKeySchema(p) =>
+      val converted = p.transformExpressionsDown {
+        case expressions.Average(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Average(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Count(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Count(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        // We do not support multiple COUNT DISTINCT columns for now.
+        case expressions.CountDistinct(children) if children.length == 1 =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Count(children.head),
+            mode = aggregate.Complete,
+            isDistinct = true)
+
+        case expressions.First(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.First(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Last(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Last(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Max(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Max(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Min(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Min(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Sum(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Sum(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.SumDistinct(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Sum(child),
+            mode = aggregate.Complete,
+            isDistinct = true)
+      }
+      // Check if there is any expressions.AggregateExpression1 left.
+      // If so, we cannot convert this plan.
+      val hasAggregateExpression1 = converted.aggregateExpressions.exists { expr =>
+        // For every expressions, check if it contains AggregateExpression1.
+        expr.find {
+          case agg: expressions.AggregateExpression1 => true
+          case other => false
+        }.isDefined
+      }
+
+      // Check if there are multiple distinct columns.
+      val aggregateExpressions = converted.aggregateExpressions.flatMap { expr =>
+        expr.collect {
+          case agg: AggregateExpression2 => agg
+        }
+      }.toSet.toSeq
+      val functionsWithDistinct = aggregateExpressions.filter(_.isDistinct)
+      val hasMultipleDistinctColumnSets =
+        if (functionsWithDistinct.map(_.aggregateFunction.children).distinct.length > 1) {
+          true
+        } else {
+          false
+        }
+
+      if (!hasAggregateExpression1 && !hasMultipleDistinctColumnSets) Some(converted) else None
+
+    case other => None
+  }
+
+  private def checkInvalidAggregateFunction2(aggregate: Aggregate): Unit = {
+    // If the plan cannot be converted, we will do a final round check to if the original
+    // logical.Aggregate contains both AggregateExpression1 and AggregateExpression2. If so,
+    // we need to throw an exception.
+    val aggregateFunction2s = aggregate.aggregateExpressions.flatMap { expr =>
+      expr.collect {
+        case agg: AggregateExpression2 => agg.aggregateFunction
+      }
+    }.distinct
+    if (aggregateFunction2s.nonEmpty) {
+      // For functions implemented based on the new interface, prepare a list of function names.
+      val invalidFunctions = {
+        if (aggregateFunction2s.length > 1) {
+          s"${aggregateFunction2s.tail.map(_.nodeName).mkString(",")} " +
+            s"and ${aggregateFunction2s.head.nodeName} are"
+        } else {
+          s"${aggregateFunction2s.head.nodeName} is"
+        }
+      }
+      val errorMessage =
+        s"${invalidFunctions} implemented based on the new Aggregate Function " +
+          s"interface and it cannot be used with functions implemented based on " +
+          s"the old Aggregate Function interface."
+      throw new AnalysisException(errorMessage)
+    }
+  }
+
+  def tryConvert(
+      plan: LogicalPlan,
+      useNewAggregation: Boolean,
+      codeGenEnabled: Boolean): Option[Aggregate] = plan match {
+    case p: Aggregate if useNewAggregation && codeGenEnabled =>
+      val converted = tryConvert(p)
+      if (converted.isDefined) {
+        converted
+      } else {
+        checkInvalidAggregateFunction2(p)
+        None
+      }
+    case p: Aggregate =>
+      checkInvalidAggregateFunction2(p)
+      None
+    case other => None
+  }
+
+  def planAggregateWithoutDistinct(
+      groupingExpressions: Seq[Expression],
+      aggregateExpressions: Seq[AggregateExpression2],
+      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), Attribute],
+      resultExpressions: Seq[NamedExpression],
+      child: SparkPlan): Seq[SparkPlan] = {
+    // 1. Create an Aggregate Operator for partial aggregations.
+    val namedGroupingExpressions = groupingExpressions.map {
+      case ne: NamedExpression => ne -> ne
+      // If the expression is not a NamedExpressions, we add an alias.
+      // So, when we generate the result of the operator, the Aggregate Operator
+      // can directly get the Seq of attributes representing the grouping expressions.
+      case other =>
+        val withAlias = Alias(other, other.toString)()
+        other -> withAlias
+    }
+    val groupExpressionMap = namedGroupingExpressions.toMap
+    val namedGroupingAttributes = namedGroupingExpressions.map(_._2.toAttribute)
+    val partialAggregateExpressions = aggregateExpressions.map {
+      case AggregateExpression2(aggregateFunction, mode, isDistinct) =>
+        AggregateExpression2(aggregateFunction, Partial, isDistinct)
+    }
+    val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
+      agg.aggregateFunction.bufferAttributes
+    }
+    val partialAggregate =
+      Aggregate2Sort(
+        None: Option[Seq[Expression]],
+        namedGroupingExpressions.map(_._2),
+        partialAggregateExpressions,
+        partialAggregateAttributes,
+        namedGroupingAttributes ++ partialAggregateAttributes,
+        child)
+
+    // 2. Create an Aggregate Operator for final aggregations.
+    val finalAggregateExpressions = aggregateExpressions.map {
+      case AggregateExpression2(aggregateFunction, mode, isDistinct) =>
+        AggregateExpression2(aggregateFunction, Final, isDistinct)
+    }
+    val finalAggregateAttributes =
+      finalAggregateExpressions.map {
+        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+      }
+    val rewrittenResultExpressions = resultExpressions.map { expr =>
+      expr.transformDown {
+        case agg: AggregateExpression2 =>
+          aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct).toAttribute
+        case expression =>
+          // We do not rely on the equality check at here since attributes may
+          // different cosmetically. Instead, we use semanticEquals.
+          groupExpressionMap.collectFirst {
+            case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+          }.getOrElse(expression)
+      }.asInstanceOf[NamedExpression]
+    }
+    val finalAggregate = Aggregate2Sort(
+      Some(namedGroupingAttributes),
+      namedGroupingAttributes,
+      finalAggregateExpressions,
+      finalAggregateAttributes,
+      rewrittenResultExpressions,
+      partialAggregate)
+
+    finalAggregate :: Nil
+  }
+
+  def planAggregateWithOneDistinct(
+      groupingExpressions: Seq[Expression],
+      functionsWithDistinct: Seq[AggregateExpression2],
+      functionsWithoutDistinct: Seq[AggregateExpression2],
+      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), Attribute],
+      resultExpressions: Seq[NamedExpression],
+      child: SparkPlan): Seq[SparkPlan] = {
+
+    // 1. Create an Aggregate Operator for partial aggregations.
+    // The grouping expressions are original groupingExpressions and
+    // distinct columns. For example, for avg(distinct value) ... group by key
+    // the grouping expressions of this Aggregate Operator will be [key, value].
+    val namedGroupingExpressions = groupingExpressions.map {
+      case ne: NamedExpression => ne -> ne
+      // If the expression is not a NamedExpressions, we add an alias.
+      // So, when we generate the result of the operator, the Aggregate Operator
+      // can directly get the Seq of attributes representing the grouping expressions.
+      case other =>
+        val withAlias = Alias(other, other.toString)()
+        other -> withAlias
+    }
+    val groupExpressionMap = namedGroupingExpressions.toMap
+    val namedGroupingAttributes = namedGroupingExpressions.map(_._2.toAttribute)
+
+    // It is safe to call head at here since functionsWithDistinct has at least one
+    // AggregateExpression2.
+    val distinctColumnExpressions =
+      functionsWithDistinct.head.aggregateFunction.children
+    val namedDistinctColumnExpressions = distinctColumnExpressions.map {
+      case ne: NamedExpression => ne -> ne
+      case other =>
+        val withAlias = Alias(other, other.toString)()
+        other -> withAlias
+    }
+    val distinctColumnExpressionMap = namedDistinctColumnExpressions.toMap
+    val distinctColumnAttributes = namedDistinctColumnExpressions.map(_._2.toAttribute)
+
+    val partialAggregateExpressions = functionsWithoutDistinct.map {
+      case AggregateExpression2(aggregateFunction, mode, _) =>
+        AggregateExpression2(aggregateFunction, Partial, false)
+    }
+    val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
+      agg.aggregateFunction.bufferAttributes
+    }
+    val partialAggregate =
+      Aggregate2Sort(
+        None: Option[Seq[Expression]],
+        (namedGroupingExpressions ++ namedDistinctColumnExpressions).map(_._2),
+        partialAggregateExpressions,
+        partialAggregateAttributes,
+        namedGroupingAttributes ++ distinctColumnAttributes ++ partialAggregateAttributes,
+        child)
+
+    // 2. Create an Aggregate Operator for partial merge aggregations.
+    val partialMergeAggregateExpressions = functionsWithoutDistinct.map {
+      case AggregateExpression2(aggregateFunction, mode, _) =>
+        AggregateExpression2(aggregateFunction, PartialMerge, false)
+    }
+    val partialMergeAggregateAttributes =
+      partialMergeAggregateExpressions.map {
+        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+      }
+    val partialMergeAggregate =
+      Aggregate2Sort(
+        Some(namedGroupingAttributes),
+        namedGroupingAttributes ++ distinctColumnAttributes,
+        partialMergeAggregateExpressions,
+        partialMergeAggregateAttributes,
+        namedGroupingAttributes ++ distinctColumnAttributes ++ partialMergeAggregateAttributes,
+        partialAggregate)
+
+    // 3. Create an Aggregate Operator for partial merge aggregations.
+    val finalAggregateExpressions = functionsWithoutDistinct.map {
+      case AggregateExpression2(aggregateFunction, mode, _) =>
+        AggregateExpression2(aggregateFunction, Final, false)
+    }
+    val finalAggregateAttributes =
+      finalAggregateExpressions.map {
+        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+      }
+    val (completeAggregateExpressions, completeAggregateAttributes) = functionsWithDistinct.map {
+      // Children of an AggregateFunction with DISTINCT keyword has already
+      // been evaluated. At here, we need to replace original children
+      // to AttributeReferences.
+      case agg @ AggregateExpression2(aggregateFunction, mode, isDistinct) =>
+        val rewrittenAggregateFunction = aggregateFunction.transformDown {
+          case expr if distinctColumnExpressionMap.contains(expr) =>
+            distinctColumnExpressionMap(expr).toAttribute
+        }.asInstanceOf[AggregateFunction2]
+        // We rewrite the aggregate function to a non-distinct aggregation because
+        // its input will have distinct arguments.
+        val rewrittenAggregateExpression =
+          AggregateExpression2(rewrittenAggregateFunction, Complete, false)
+
+        val aggregateFunctionAttribute = aggregateFunctionMap(agg.aggregateFunction, isDistinct)
+        (rewrittenAggregateExpression -> aggregateFunctionAttribute)
+    }.unzip
+
+    val rewrittenResultExpressions = resultExpressions.map { expr =>
+      expr.transform {
+        case agg: AggregateExpression2 =>
+          aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct).toAttribute
+        case expression =>
+          // We do not rely on the equality check at here since attributes may
+          // different cosmetically. Instead, we use semanticEquals.
+          groupExpressionMap.collectFirst {
+            case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+          }.getOrElse(expression)
+      }.asInstanceOf[NamedExpression]
+    }
+    val finalAndCompleteAggregate = FinalAndCompleteAggregate2Sort(
+      namedGroupingAttributes ++ distinctColumnAttributes,
+      namedGroupingAttributes,
+      finalAggregateExpressions,
+      finalAggregateAttributes,
+      completeAggregateExpressions,
+      completeAggregateAttributes,
+      rewrittenResultExpressions,
+      partialMergeAggregate)
+
+    finalAndCompleteAggregate :: Nil
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
new file mode 100644
index 0000000000000..6c49a906c848a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
@@ -0,0 +1,280 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.expressions.aggregate
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
+import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateFunction2
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.Row
+
+/**
+ * The abstract class for implementing user-defined aggregate function.
+ */
+abstract class UserDefinedAggregateFunction extends Serializable {
+
+  /**
+   * A [[StructType]] represents data types of input arguments of this aggregate function.
+   * For example, if a [[UserDefinedAggregateFunction]] expects two input arguments
+   * with type of [[DoubleType]] and [[LongType]], the returned [[StructType]] will look like
+   *
+   * ```
+   *   StructType(Seq(StructField("doubleInput", DoubleType), StructField("longInput", LongType)))
+   * ```
+   *
+   * The name of a field of this [[StructType]] is only used to identify the corresponding
+   * input argument. Users can choose names to identify the input arguments.
+   */
+  def inputSchema: StructType
+
+  /**
+   * A [[StructType]] represents data types of values in the aggregation buffer.
+   * For example, if a [[UserDefinedAggregateFunction]]'s buffer has two values
+   * (i.e. two intermediate values) with type of [[DoubleType]] and [[LongType]],
+   * the returned [[StructType]] will look like
+   *
+   * ```
+   *   StructType(Seq(StructField("doubleInput", DoubleType), StructField("longInput", LongType)))
+   * ```
+   *
+   * The name of a field of this [[StructType]] is only used to identify the corresponding
+   * buffer value. Users can choose names to identify the input arguments.
+   */
+  def bufferSchema: StructType
+
+  /**
+   * The [[DataType]] of the returned value of this [[UserDefinedAggregateFunction]].
+   */
+  def returnDataType: DataType
+
+  /** Indicates if this function is deterministic. */
+  def deterministic: Boolean
+
+  /**
+   *  Initializes the given aggregation buffer. Initial values set by this method should satisfy
+   *  the condition that when merging two buffers with initial values, the new buffer should
+   *  still store initial values.
+   */
+  def initialize(buffer: MutableAggregationBuffer): Unit
+
+  /** Updates the given aggregation buffer `buffer` with new input data from `input`. */
+  def update(buffer: MutableAggregationBuffer, input: Row): Unit
+
+  /** Merges two aggregation buffers and stores the updated buffer values back in `buffer1`. */
+  def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit
+
+  /**
+   * Calculates the final result of this [[UserDefinedAggregateFunction]] based on the given
+   * aggregation buffer.
+   */
+  def evaluate(buffer: Row): Any
+}
+
+private[sql] abstract class AggregationBuffer(
+    toCatalystConverters: Array[Any => Any],
+    toScalaConverters: Array[Any => Any],
+    bufferOffset: Int)
+  extends Row {
+
+  override def length: Int = toCatalystConverters.length
+
+  protected val offsets: Array[Int] = {
+    val newOffsets = new Array[Int](length)
+    var i = 0
+    while (i < newOffsets.length) {
+      newOffsets(i) = bufferOffset + i
+      i += 1
+    }
+    newOffsets
+  }
+}
+
+/**
+ * A Mutable [[Row]] representing an mutable aggregation buffer.
+ */
+class MutableAggregationBuffer private[sql] (
+    toCatalystConverters: Array[Any => Any],
+    toScalaConverters: Array[Any => Any],
+    bufferOffset: Int,
+    var underlyingBuffer: MutableRow)
+  extends AggregationBuffer(toCatalystConverters, toScalaConverters, bufferOffset) {
+
+  override def get(i: Int): Any = {
+    if (i >= length || i < 0) {
+      throw new IllegalArgumentException(
+        s"Could not access ${i}th value in this buffer because it only has $length values.")
+    }
+    toScalaConverters(i)(underlyingBuffer(offsets(i)))
+  }
+
+  def update(i: Int, value: Any): Unit = {
+    if (i >= length || i < 0) {
+      throw new IllegalArgumentException(
+        s"Could not update ${i}th value in this buffer because it only has $length values.")
+    }
+    underlyingBuffer.update(offsets(i), toCatalystConverters(i)(value))
+  }
+
+  override def copy(): MutableAggregationBuffer = {
+    new MutableAggregationBuffer(
+      toCatalystConverters,
+      toScalaConverters,
+      bufferOffset,
+      underlyingBuffer)
+  }
+}
+
+/**
+ * A [[Row]] representing an immutable aggregation buffer.
+ */
+class InputAggregationBuffer private[sql] (
+    toCatalystConverters: Array[Any => Any],
+    toScalaConverters: Array[Any => Any],
+    bufferOffset: Int,
+    var underlyingInputBuffer: Row)
+  extends AggregationBuffer(toCatalystConverters, toScalaConverters, bufferOffset) {
+
+  override def get(i: Int): Any = {
+    if (i >= length || i < 0) {
+      throw new IllegalArgumentException(
+        s"Could not access ${i}th value in this buffer because it only has $length values.")
+    }
+    toScalaConverters(i)(underlyingInputBuffer(offsets(i)))
+  }
+
+  override def copy(): InputAggregationBuffer = {
+    new InputAggregationBuffer(
+      toCatalystConverters,
+      toScalaConverters,
+      bufferOffset,
+      underlyingInputBuffer)
+  }
+}
+
+/**
+ * The internal wrapper used to hook a [[UserDefinedAggregateFunction]] `udaf` in the
+ * internal aggregation code path.
+ * @param children
+ * @param udaf
+ */
+case class ScalaUDAF(
+    children: Seq[Expression],
+    udaf: UserDefinedAggregateFunction)
+  extends AggregateFunction2 with Logging {
+
+  require(
+    children.length == udaf.inputSchema.length,
+    s"$udaf only accepts ${udaf.inputSchema.length} arguments, " +
+      s"but ${children.length} are provided.")
+
+  override def nullable: Boolean = true
+
+  override def dataType: DataType = udaf.returnDataType
+
+  override def deterministic: Boolean = udaf.deterministic
+
+  override val inputTypes: Seq[DataType] = udaf.inputSchema.map(_.dataType)
+
+  override val bufferSchema: StructType = udaf.bufferSchema
+
+  override val bufferAttributes: Seq[AttributeReference] = bufferSchema.toAttributes
+
+  override lazy val cloneBufferAttributes = bufferAttributes.map(_.newInstance())
+
+  val childrenSchema: StructType = {
+    val inputFields = children.zipWithIndex.map {
+      case (child, index) =>
+        StructField(s"input$index", child.dataType, child.nullable, Metadata.empty)
+    }
+    StructType(inputFields)
+  }
+
+  lazy val inputProjection = {
+    val inputAttributes = childrenSchema.toAttributes
+    log.debug(
+      s"Creating MutableProj: $children, inputSchema: $inputAttributes.")
+    try {
+      GenerateMutableProjection.generate(children, inputAttributes)()
+    } catch {
+      case e: Exception =>
+        log.error("Failed to generate mutable projection, fallback to interpreted", e)
+        new InterpretedMutableProjection(children, inputAttributes)
+    }
+  }
+
+  val inputToScalaConverters: Any => Any =
+    CatalystTypeConverters.createToScalaConverter(childrenSchema)
+
+  val bufferValuesToCatalystConverters: Array[Any => Any] = bufferSchema.fields.map { field =>
+    CatalystTypeConverters.createToCatalystConverter(field.dataType)
+  }
+
+  val bufferValuesToScalaConverters: Array[Any => Any] = bufferSchema.fields.map { field =>
+    CatalystTypeConverters.createToScalaConverter(field.dataType)
+  }
+
+  lazy val inputAggregateBuffer: InputAggregationBuffer =
+    new InputAggregationBuffer(
+      bufferValuesToCatalystConverters,
+      bufferValuesToScalaConverters,
+      bufferOffset,
+      null)
+
+  lazy val mutableAggregateBuffer: MutableAggregationBuffer =
+    new MutableAggregationBuffer(
+      bufferValuesToCatalystConverters,
+      bufferValuesToScalaConverters,
+      bufferOffset,
+      null)
+
+
+  override def initialize(buffer: MutableRow): Unit = {
+    mutableAggregateBuffer.underlyingBuffer = buffer
+
+    udaf.initialize(mutableAggregateBuffer)
+  }
+
+  override def update(buffer: MutableRow, input: InternalRow): Unit = {
+    mutableAggregateBuffer.underlyingBuffer = buffer
+
+    udaf.update(
+      mutableAggregateBuffer,
+      inputToScalaConverters(inputProjection(input)).asInstanceOf[Row])
+  }
+
+  override def merge(buffer1: MutableRow, buffer2: InternalRow): Unit = {
+    mutableAggregateBuffer.underlyingBuffer = buffer1
+    inputAggregateBuffer.underlyingInputBuffer = buffer2
+
+    udaf.merge(mutableAggregateBuffer, inputAggregateBuffer)
+  }
+
+  override def eval(buffer: InternalRow = null): Any = {
+    inputAggregateBuffer.underlyingInputBuffer = buffer
+
+    udaf.evaluate(inputAggregateBuffer)
+  }
+
+  override def toString: String = {
+    s"""${udaf.getClass.getSimpleName}(${children.mkString(",")})"""
+  }
+
+  override def nodeName: String = udaf.getClass.getSimpleName
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 28159cbd5ab96..bfeecbe8b2ab5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2420,7 +2420,7 @@ object functions {
    * @since 1.5.0
    */
   def callUDF(udfName: String, cols: Column*): Column = {
-    UnresolvedFunction(udfName, cols.map(_.expr))
+    UnresolvedFunction(udfName, cols.map(_.expr), isDistinct = false)
   }
 
   /**
@@ -2449,7 +2449,7 @@ object functions {
       exprs(i) = cols(i).expr
       i += 1
     }
-    UnresolvedFunction(udfName, exprs)
+    UnresolvedFunction(udfName, exprs, isDistinct = false)
   }
 
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index beee10173fbc4..ab8dce603c117 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -23,6 +23,7 @@ import java.sql.Timestamp
 
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
+import org.apache.spark.sql.execution.aggregate.Aggregate2Sort
 import org.apache.spark.sql.execution.GeneratedAggregate
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.TestData._
@@ -204,6 +205,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       var hasGeneratedAgg = false
       df.queryExecution.executedPlan.foreach {
         case generatedAgg: GeneratedAggregate => hasGeneratedAgg = true
+        case newAggregate: Aggregate2Sort => hasGeneratedAgg = true
         case _ =>
       }
       if (!hasGeneratedAgg) {
@@ -285,7 +287,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       // Aggregate with Code generation handling all null values
       testCodeGen(
         "SELECT  sum('a'), avg('a'), count(null) FROM testData",
-        Row(0, null, 0) :: Nil)
+        Row(null, null, 0) :: Nil)
     } finally {
       sqlContext.dropTempTable("testData3x")
       sqlContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 3dd24130af81a..3d71deb13e884 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.test.TestSQLContext._
@@ -30,6 +31,20 @@ import org.apache.spark.sql.{Row, SQLConf, execution}
 
 
 class PlannerSuite extends SparkFunSuite {
+  private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
+    val plannedOption = HashAggregation(query).headOption.orElse(Aggregation(query).headOption)
+    val planned =
+      plannedOption.getOrElse(
+        fail(s"Could query play aggregation query $query. Is it an aggregation query?"))
+    val aggregations = planned.collect { case n if n.nodeName contains "Aggregate" => n }
+
+    // For the new aggregation code path, there will be three aggregate operator for
+    // distinct aggregations.
+    assert(
+      aggregations.size == 2 || aggregations.size == 3,
+      s"The plan of query $query does not have partial aggregations.")
+  }
+
   test("unions are collapsed") {
     val query = testData.unionAll(testData).unionAll(testData).logicalPlan
     val planned = BasicOperators(query).head
@@ -42,23 +57,18 @@ class PlannerSuite extends SparkFunSuite {
 
   test("count is partially aggregated") {
     val query = testData.groupBy('value).agg(count('key)).queryExecution.analyzed
-    val planned = HashAggregation(query).head
-    val aggregations = planned.collect { case n if n.nodeName contains "Aggregate" => n }
-
-    assert(aggregations.size === 2)
+    testPartialAggregationPlan(query)
   }
 
   test("count distinct is partially aggregated") {
     val query = testData.groupBy('value).agg(countDistinct('key)).queryExecution.analyzed
-    val planned = HashAggregation(query)
-    assert(planned.nonEmpty)
+    testPartialAggregationPlan(query)
   }
 
   test("mixed aggregates are partially aggregated") {
     val query =
       testData.groupBy('value).agg(count('value), countDistinct('key)).queryExecution.analyzed
-    val planned = HashAggregation(query)
-    assert(planned.nonEmpty)
+    testPartialAggregationPlan(query)
   }
 
   test("sizeInBytes estimation of limit operator for broadcast hash join optimization") {
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
index 31a49a3683338..24a758f53170a 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
@@ -833,6 +833,7 @@ abstract class HiveWindowFunctionQueryFileBaseSuite
     "windowing_adjust_rowcontainer_sz"
   )
 
+  // Only run those query tests in the realWhileList (do not try other ignored query files).
   override def testCases: Seq[(String, File)] = super.testCases.filter {
     case (name, _) => realWhiteList.contains(name)
   }
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala
index f458567e5d7ea..1fe4fe9629c02 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive.execution
 
+import java.io.File
+
 import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.hive.test.TestHive
 
@@ -159,4 +161,9 @@ class SortMergeCompatibilitySuite extends HiveCompatibilitySuite {
     "join_reorder4",
     "join_star"
   )
+
+  // Only run those query tests in the realWhileList (do not try other ignored query files).
+  override def testCases: Seq[(String, File)] = super.testCases.filter {
+    case (name, _) => realWhiteList.contains(name)
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index cec7685bb6859..4cdb83c5116f9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -451,6 +451,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
       DataSinks,
       Scripts,
       HashAggregation,
+      Aggregation,
       LeftSemiJoin,
       HashJoin,
       BasicOperators,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index f5574509b0b38..8518e333e8058 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -1464,9 +1464,12 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
     /* UDFs - Must be last otherwise will preempt built in functions */
     case Token("TOK_FUNCTION", Token(name, Nil) :: args) =>
-      UnresolvedFunction(name, args.map(nodeToExpr))
+      UnresolvedFunction(name, args.map(nodeToExpr), isDistinct = false)
+    // Aggregate function with DISTINCT keyword.
+    case Token("TOK_FUNCTIONDI", Token(name, Nil) :: args) =>
+      UnresolvedFunction(name, args.map(nodeToExpr), isDistinct = true)
     case Token("TOK_FUNCTIONSTAR", Token(name, Nil) :: args) =>
-      UnresolvedFunction(name, UnresolvedStar(None) :: Nil)
+      UnresolvedFunction(name, UnresolvedStar(None) :: Nil, isDistinct = false)
 
     /* Literals */
     case Token("TOK_NULL", Nil) => Literal.create(null, NullType)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 4d23c7035c03d..3259b50acc765 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -409,7 +409,7 @@ private[hive] case class HiveWindowFunction(
 
 private[hive] case class HiveGenericUDAF(
     funcWrapper: HiveFunctionWrapper,
-    children: Seq[Expression]) extends AggregateExpression
+    children: Seq[Expression]) extends AggregateExpression1
   with HiveInspectors {
 
   type UDFType = AbstractGenericUDAFResolver
@@ -441,7 +441,7 @@ private[hive] case class HiveGenericUDAF(
 /** It is used as a wrapper for the hive functions which uses UDAF interface */
 private[hive] case class HiveUDAF(
     funcWrapper: HiveFunctionWrapper,
-    children: Seq[Expression]) extends AggregateExpression
+    children: Seq[Expression]) extends AggregateExpression1
   with HiveInspectors {
 
   type UDFType = UDAF
@@ -550,9 +550,9 @@ private[hive] case class HiveGenericUDTF(
 private[hive] case class HiveUDAFFunction(
     funcWrapper: HiveFunctionWrapper,
     exprs: Seq[Expression],
-    base: AggregateExpression,
+    base: AggregateExpression1,
     isUDAFBridgeRequired: Boolean = false)
-  extends AggregateFunction
+  extends AggregateFunction1
   with HiveInspectors {
 
   def this() = this(null, null, null)
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
new file mode 100644
index 0000000000000..5c9d0e97a99c6
--- /dev/null
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package test.org.apache.spark.sql.hive.aggregate;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.expressions.aggregate.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.aggregate.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+public class MyDoubleAvg extends UserDefinedAggregateFunction {
+
+  private StructType _inputDataType;
+
+  private StructType _bufferSchema;
+
+  private DataType _returnDataType;
+
+  public MyDoubleAvg() {
+    List<StructField> inputfields = new ArrayList<StructField>();
+    inputfields.add(DataTypes.createStructField("inputDouble", DataTypes.DoubleType, true));
+    _inputDataType = DataTypes.createStructType(inputfields);
+
+    List<StructField> bufferFields = new ArrayList<StructField>();
+    bufferFields.add(DataTypes.createStructField("bufferSum", DataTypes.DoubleType, true));
+    bufferFields.add(DataTypes.createStructField("bufferCount", DataTypes.LongType, true));
+    _bufferSchema = DataTypes.createStructType(bufferFields);
+
+    _returnDataType = DataTypes.DoubleType;
+  }
+
+  @Override public StructType inputSchema() {
+    return _inputDataType;
+  }
+
+  @Override public StructType bufferSchema() {
+    return _bufferSchema;
+  }
+
+  @Override public DataType returnDataType() {
+    return _returnDataType;
+  }
+
+  @Override public boolean deterministic() {
+    return true;
+  }
+
+  @Override public void initialize(MutableAggregationBuffer buffer) {
+    buffer.update(0, null);
+    buffer.update(1, 0L);
+  }
+
+  @Override public void update(MutableAggregationBuffer buffer, Row input) {
+    if (!input.isNullAt(0)) {
+      if (buffer.isNullAt(0)) {
+        buffer.update(0, input.getDouble(0));
+        buffer.update(1, 1L);
+      } else {
+        Double newValue = input.getDouble(0) + buffer.getDouble(0);
+        buffer.update(0, newValue);
+        buffer.update(1, buffer.getLong(1) + 1L);
+      }
+    }
+  }
+
+  @Override public void merge(MutableAggregationBuffer buffer1, Row buffer2) {
+    if (!buffer2.isNullAt(0)) {
+      if (buffer1.isNullAt(0)) {
+        buffer1.update(0, buffer2.getDouble(0));
+        buffer1.update(1, buffer2.getLong(1));
+      } else {
+        Double newValue = buffer2.getDouble(0) + buffer1.getDouble(0);
+        buffer1.update(0, newValue);
+        buffer1.update(1, buffer1.getLong(1) + buffer2.getLong(1));
+      }
+    }
+  }
+
+  @Override public Object evaluate(Row buffer) {
+    if (buffer.isNullAt(0)) {
+      return null;
+    } else {
+      return buffer.getDouble(0) / buffer.getLong(1) + 100.0;
+    }
+  }
+}
+
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
new file mode 100644
index 0000000000000..1d4587a27c787
--- /dev/null
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
@@ -0,0 +1,100 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package test.org.apache.spark.sql.hive.aggregate;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.apache.spark.sql.expressions.aggregate.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.aggregate.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.Row;
+
+public class MyDoubleSum extends UserDefinedAggregateFunction {
+
+  private StructType _inputDataType;
+
+  private StructType _bufferSchema;
+
+  private DataType _returnDataType;
+
+  public MyDoubleSum() {
+    List<StructField> inputfields = new ArrayList<StructField>();
+    inputfields.add(DataTypes.createStructField("inputDouble", DataTypes.DoubleType, true));
+    _inputDataType = DataTypes.createStructType(inputfields);
+
+    List<StructField> bufferFields = new ArrayList<StructField>();
+    bufferFields.add(DataTypes.createStructField("bufferDouble", DataTypes.DoubleType, true));
+    _bufferSchema = DataTypes.createStructType(bufferFields);
+
+    _returnDataType = DataTypes.DoubleType;
+  }
+
+  @Override public StructType inputSchema() {
+    return _inputDataType;
+  }
+
+  @Override public StructType bufferSchema() {
+    return _bufferSchema;
+  }
+
+  @Override public DataType returnDataType() {
+    return _returnDataType;
+  }
+
+  @Override public boolean deterministic() {
+    return true;
+  }
+
+  @Override public void initialize(MutableAggregationBuffer buffer) {
+    buffer.update(0, null);
+  }
+
+  @Override public void update(MutableAggregationBuffer buffer, Row input) {
+    if (!input.isNullAt(0)) {
+      if (buffer.isNullAt(0)) {
+        buffer.update(0, input.getDouble(0));
+      } else {
+        Double newValue = input.getDouble(0) + buffer.getDouble(0);
+        buffer.update(0, newValue);
+      }
+    }
+  }
+
+  @Override public void merge(MutableAggregationBuffer buffer1, Row buffer2) {
+    if (!buffer2.isNullAt(0)) {
+      if (buffer1.isNullAt(0)) {
+        buffer1.update(0, buffer2.getDouble(0));
+      } else {
+        Double newValue = buffer2.getDouble(0) + buffer1.getDouble(0);
+        buffer1.update(0, newValue);
+      }
+    }
+  }
+
+  @Override public Object evaluate(Row buffer) {
+    if (buffer.isNullAt(0)) {
+      return null;
+    } else {
+      return buffer.getDouble(0);
+    }
+  }
+}
diff --git a/sql/hive/src/test/resources/golden/udf_unhex-0-50131c0ba7b7a6b65c789a5a8497bada b/sql/hive/src/test/resources/golden/udf_unhex-0-50131c0ba7b7a6b65c789a5a8497bada
new file mode 100644
index 0000000000000..573541ac9702d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unhex-0-50131c0ba7b7a6b65c789a5a8497bada
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/udf_unhex-1-11eb3cc5216d5446f4165007203acc47 b/sql/hive/src/test/resources/golden/udf_unhex-1-11eb3cc5216d5446f4165007203acc47
new file mode 100644
index 0000000000000..44b2a42cc26c5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unhex-1-11eb3cc5216d5446f4165007203acc47
@@ -0,0 +1 @@
+unhex(str) - Converts hexadecimal argument to binary
diff --git a/sql/hive/src/test/resources/golden/udf_unhex-2-a660886085b8651852b9b77934848ae4 b/sql/hive/src/test/resources/golden/udf_unhex-2-a660886085b8651852b9b77934848ae4
new file mode 100644
index 0000000000000..97af3b812a429
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unhex-2-a660886085b8651852b9b77934848ae4
@@ -0,0 +1,14 @@
+unhex(str) - Converts hexadecimal argument to binary
+Performs the inverse operation of HEX(str). That is, it interprets
+each pair of hexadecimal digits in the argument as a number and
+converts it to the byte representation of the number. The
+resulting characters are returned as a binary string.
+
+Example:
+> SELECT DECODE(UNHEX('4D7953514C'), 'UTF-8') from src limit 1;
+'MySQL'
+
+The characters in the argument string must be legal hexadecimal
+digits: '0' .. '9', 'A' .. 'F', 'a' .. 'f'. If UNHEX() encounters
+any nonhexadecimal digits in the argument, it returns NULL. Also,
+if there are an odd number of characters a leading 0 is appended.
diff --git a/sql/hive/src/test/resources/golden/udf_unhex-3-4b2cf4050af229fde91ab53fd9f3af3e b/sql/hive/src/test/resources/golden/udf_unhex-3-4b2cf4050af229fde91ab53fd9f3af3e
new file mode 100644
index 0000000000000..b4a6f2b692227
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unhex-3-4b2cf4050af229fde91ab53fd9f3af3e
@@ -0,0 +1 @@
+MySQL	1267	a	-4	
diff --git a/sql/hive/src/test/resources/golden/udf_unhex-4-7d3e094f139892ecef17de3fd63ca3c3 b/sql/hive/src/test/resources/golden/udf_unhex-4-7d3e094f139892ecef17de3fd63ca3c3
new file mode 100644
index 0000000000000..3a67adaf0a9a8
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/udf_unhex-4-7d3e094f139892ecef17de3fd63ca3c3
@@ -0,0 +1 @@
+NULL	NULL	NULL
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
new file mode 100644
index 0000000000000..0375eb79add95
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -0,0 +1,507 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.apache.spark.sql.execution.aggregate.Aggregate2Sort
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
+import org.apache.spark.sql.{AnalysisException, QueryTest, Row}
+import org.scalatest.BeforeAndAfterAll
+import test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
+
+class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
+
+  override val sqlContext = TestHive
+  import sqlContext.implicits._
+
+  var originalUseAggregate2: Boolean = _
+
+  override def beforeAll(): Unit = {
+    originalUseAggregate2 = sqlContext.conf.useSqlAggregate2
+    sqlContext.sql("set spark.sql.useAggregate2=true")
+    val data1 = Seq[(Integer, Integer)](
+      (1, 10),
+      (null, -60),
+      (1, 20),
+      (1, 30),
+      (2, 0),
+      (null, -10),
+      (2, -1),
+      (2, null),
+      (2, null),
+      (null, 100),
+      (3, null),
+      (null, null),
+      (3, null)).toDF("key", "value")
+    data1.write.saveAsTable("agg1")
+
+    val data2 = Seq[(Integer, Integer, Integer)](
+      (1, 10, -10),
+      (null, -60, 60),
+      (1, 30, -30),
+      (1, 30, 30),
+      (2, 1, 1),
+      (null, -10, 10),
+      (2, -1, null),
+      (2, 1, 1),
+      (2, null, 1),
+      (null, 100, -10),
+      (3, null, 3),
+      (null, null, null),
+      (3, null, null)).toDF("key", "value1", "value2")
+    data2.write.saveAsTable("agg2")
+
+    val emptyDF = sqlContext.createDataFrame(
+      sqlContext.sparkContext.emptyRDD[Row],
+      StructType(StructField("key", StringType) :: StructField("value", IntegerType) :: Nil))
+    emptyDF.registerTempTable("emptyTable")
+
+    // Register UDAFs
+    sqlContext.udaf.register("mydoublesum", new MyDoubleSum)
+    sqlContext.udaf.register("mydoubleavg", new MyDoubleAvg)
+  }
+
+  override def afterAll(): Unit = {
+    sqlContext.sql("DROP TABLE IF EXISTS agg1")
+    sqlContext.sql("DROP TABLE IF EXISTS agg2")
+    sqlContext.dropTempTable("emptyTable")
+    sqlContext.sql(s"set spark.sql.useAggregate2=$originalUseAggregate2")
+  }
+
+  test("empty table") {
+    // If there is no GROUP BY clause and the table is empty, we will generate a single row.
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  AVG(value),
+          |  COUNT(*),
+          |  COUNT(key),
+          |  COUNT(value),
+          |  FIRST(key),
+          |  LAST(value),
+          |  MAX(key),
+          |  MIN(value),
+          |  SUM(key)
+          |FROM emptyTable
+        """.stripMargin),
+      Row(null, 0, 0, 0, null, null, null, null, null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  AVG(value),
+          |  COUNT(*),
+          |  COUNT(key),
+          |  COUNT(value),
+          |  FIRST(key),
+          |  LAST(value),
+          |  MAX(key),
+          |  MIN(value),
+          |  SUM(key),
+          |  COUNT(DISTINCT value)
+          |FROM emptyTable
+        """.stripMargin),
+      Row(null, 0, 0, 0, null, null, null, null, null, 0) :: Nil)
+
+    // If there is a GROUP BY clause and the table is empty, there is no output.
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  AVG(value),
+          |  COUNT(*),
+          |  COUNT(value),
+          |  FIRST(value),
+          |  LAST(value),
+          |  MAX(value),
+          |  MIN(value),
+          |  SUM(value),
+          |  COUNT(DISTINCT value)
+          |FROM emptyTable
+          |GROUP BY key
+        """.stripMargin),
+      Nil)
+  }
+
+  test("only do grouping") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT key
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(1) :: Row(2) :: Row(3) :: Row(null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT DISTINCT value1, key
+          |FROM agg2
+        """.stripMargin),
+      Row(10, 1) ::
+        Row(-60, null) ::
+        Row(30, 1) ::
+        Row(1, 2) ::
+        Row(-10, null) ::
+        Row(-1, 2) ::
+        Row(null, 2) ::
+        Row(100, null) ::
+        Row(null, 3) ::
+        Row(null, null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT value1, key
+          |FROM agg2
+          |GROUP BY key, value1
+        """.stripMargin),
+      Row(10, 1) ::
+        Row(-60, null) ::
+        Row(30, 1) ::
+        Row(1, 2) ::
+        Row(-10, null) ::
+        Row(-1, 2) ::
+        Row(null, 2) ::
+        Row(100, null) ::
+        Row(null, 3) ::
+        Row(null, null) :: Nil)
+  }
+
+  test("case in-sensitive resolution") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT avg(value), kEY - 100
+          |FROM agg1
+          |GROUP BY Key - 100
+        """.stripMargin),
+      Row(20.0, -99) :: Row(-0.5, -98) :: Row(null, -97) :: Row(10.0, null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT sum(distinct value1), kEY - 100, count(distinct value1)
+          |FROM agg2
+          |GROUP BY Key - 100
+        """.stripMargin),
+      Row(40, -99, 2) :: Row(0, -98, 2) :: Row(null, -97, 0) :: Row(30, null, 3) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT valUe * key - 100
+          |FROM agg1
+          |GROUP BY vAlue * keY - 100
+        """.stripMargin),
+      Row(-90) ::
+        Row(-80) ::
+        Row(-70) ::
+        Row(-100) ::
+        Row(-102) ::
+        Row(null) :: Nil)
+  }
+
+  test("test average no key in output") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT avg(value)
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(-0.5) :: Row(20.0) :: Row(null) :: Row(10.0) :: Nil)
+  }
+
+  test("test average") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT key, avg(value)
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(1, 20.0) :: Row(2, -0.5) :: Row(3, null) :: Row(null, 10.0) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT avg(value), key
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(20.0, 1) :: Row(-0.5, 2) :: Row(null, 3) :: Row(10.0, null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT avg(value) + 1.5, key + 10
+          |FROM agg1
+          |GROUP BY key + 10
+        """.stripMargin),
+      Row(21.5, 11) :: Row(1.0, 12) :: Row(null, 13) :: Row(11.5, null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT avg(value) FROM agg1
+        """.stripMargin),
+      Row(11.125) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT avg(null)
+        """.stripMargin),
+      Row(null) :: Nil)
+  }
+
+  test("udaf") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  key,
+          |  mydoublesum(value + 1.5 * key),
+          |  mydoubleavg(value),
+          |  avg(value - key),
+          |  mydoublesum(value - 1.5 * key),
+          |  avg(value)
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(1, 64.5, 120.0, 19.0, 55.5, 20.0) ::
+        Row(2, 5.0, 99.5, -2.5, -7.0, -0.5) ::
+        Row(3, null, null, null, null, null) ::
+        Row(null, null, 110.0, null, null, 10.0) :: Nil)
+  }
+
+  test("non-AlgebraicAggregate aggreguate function") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT mydoublesum(value), key
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(60.0, 1) :: Row(-1.0, 2) :: Row(null, 3) :: Row(30.0, null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT mydoublesum(value) FROM agg1
+        """.stripMargin),
+      Row(89.0) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT mydoublesum(null)
+        """.stripMargin),
+      Row(null) :: Nil)
+  }
+
+  test("non-AlgebraicAggregate and AlgebraicAggregate aggreguate function") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT mydoublesum(value), key, avg(value)
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(60.0, 1, 20.0) ::
+        Row(-1.0, 2, -0.5) ::
+        Row(null, 3, null) ::
+        Row(30.0, null, 10.0) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  mydoublesum(value + 1.5 * key),
+          |  avg(value - key),
+          |  key,
+          |  mydoublesum(value - 1.5 * key),
+          |  avg(value)
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin),
+      Row(64.5, 19.0, 1, 55.5, 20.0) ::
+        Row(5.0, -2.5, 2, -7.0, -0.5) ::
+        Row(null, null, 3, null, null) ::
+        Row(null, null, null, null, 10.0) :: Nil)
+  }
+
+  test("single distinct column set") {
+    // DISTINCT is not meaningful with Max and Min, so we just ignore the DISTINCT keyword.
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  min(distinct value1),
+          |  sum(distinct value1),
+          |  avg(value1),
+          |  avg(value2),
+          |  max(distinct value1)
+          |FROM agg2
+        """.stripMargin),
+      Row(-60, 70.0, 101.0/9.0, 5.6, 100.0))
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  mydoubleavg(distinct value1),
+          |  avg(value1),
+          |  avg(value2),
+          |  key,
+          |  mydoubleavg(value1 - 1),
+          |  mydoubleavg(distinct value1) * 0.1,
+          |  avg(value1 + value2)
+          |FROM agg2
+          |GROUP BY key
+        """.stripMargin),
+      Row(120.0, 70.0/3.0, -10.0/3.0, 1, 67.0/3.0 + 100.0, 12.0, 20.0) ::
+        Row(100.0, 1.0/3.0, 1.0, 2, -2.0/3.0 + 100.0, 10.0, 2.0) ::
+        Row(null, null, 3.0, 3, null, null, null) ::
+        Row(110.0, 10.0, 20.0, null, 109.0, 11.0, 30.0) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  key,
+          |  mydoubleavg(distinct value1),
+          |  mydoublesum(value2),
+          |  mydoublesum(distinct value1),
+          |  mydoubleavg(distinct value1),
+          |  mydoubleavg(value1)
+          |FROM agg2
+          |GROUP BY key
+        """.stripMargin),
+      Row(1, 120.0, -10.0, 40.0, 120.0, 70.0/3.0 + 100.0) ::
+        Row(2, 100.0, 3.0, 0.0, 100.0, 1.0/3.0 + 100.0) ::
+        Row(3, null, 3.0, null, null, null) ::
+        Row(null, 110.0, 60.0, 30.0, 110.0, 110.0) :: Nil)
+  }
+
+  test("test count") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  count(value2),
+          |  value1,
+          |  count(*),
+          |  count(1),
+          |  key
+          |FROM agg2
+          |GROUP BY key, value1
+        """.stripMargin),
+      Row(1, 10, 1, 1, 1) ::
+        Row(1, -60, 1, 1, null) ::
+        Row(2, 30, 2, 2, 1) ::
+        Row(2, 1, 2, 2, 2) ::
+        Row(1, -10, 1, 1, null) ::
+        Row(0, -1, 1, 1, 2) ::
+        Row(1, null, 1, 1, 2) ::
+        Row(1, 100, 1, 1, null) ::
+        Row(1, null, 2, 2, 3) ::
+        Row(0, null, 1, 1, null) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  count(value2),
+          |  value1,
+          |  count(*),
+          |  count(1),
+          |  key,
+          |  count(DISTINCT abs(value2))
+          |FROM agg2
+          |GROUP BY key, value1
+        """.stripMargin),
+      Row(1, 10, 1, 1, 1, 1) ::
+        Row(1, -60, 1, 1, null, 1) ::
+        Row(2, 30, 2, 2, 1, 1) ::
+        Row(2, 1, 2, 2, 2, 1) ::
+        Row(1, -10, 1, 1, null, 1) ::
+        Row(0, -1, 1, 1, 2, 0) ::
+        Row(1, null, 1, 1, 2, 1) ::
+        Row(1, 100, 1, 1, null, 1) ::
+        Row(1, null, 2, 2, 3, 1) ::
+        Row(0, null, 1, 1, null, 0) :: Nil)
+  }
+
+  test("error handling") {
+    sqlContext.sql(s"set spark.sql.useAggregate2=false")
+    var errorMessage = intercept[AnalysisException] {
+      sqlContext.sql(
+        """
+          |SELECT
+          |  key,
+          |  sum(value + 1.5 * key),
+          |  mydoublesum(value),
+          |  mydoubleavg(value)
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin).collect()
+    }.getMessage
+    assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
+
+    // TODO: once we support Hive UDAF in the new interface,
+    // we can remove the following two tests.
+    sqlContext.sql(s"set spark.sql.useAggregate2=true")
+    errorMessage = intercept[AnalysisException] {
+      sqlContext.sql(
+        """
+          |SELECT
+          |  key,
+          |  mydoublesum(value + 1.5 * key),
+          |  stddev_samp(value)
+          |FROM agg1
+          |GROUP BY key
+        """.stripMargin).collect()
+    }.getMessage
+    assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
+
+    // This will fall back to the old aggregate
+    val newAggregateOperators = sqlContext.sql(
+      """
+        |SELECT
+        |  key,
+        |  sum(value + 1.5 * key),
+        |  stddev_samp(value)
+        |FROM agg1
+        |GROUP BY key
+      """.stripMargin).queryExecution.executedPlan.collect {
+      case agg: Aggregate2Sort => agg
+    }
+    val message =
+      "We should fallback to the old aggregation code path if there is any aggregate function " +
+        "that cannot be converted to the new interface."
+    assert(newAggregateOperators.isEmpty, message)
+
+    sqlContext.sql(s"set spark.sql.useAggregate2=true")
+  }
+}

From b55a36bc30a628d76baa721d38789fc219eccc27 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 22 Jul 2015 09:32:42 -0700
Subject: [PATCH 0540/1454] [SPARK-9254] [BUILD] [HOTFIX] sbt-launch-lib.bash
 should support HTTP/HTTPS redirection

Target file(s) can be hosted on CDN nodes. HTTP/HTTPS redirection must be supported to download these files.

Author: Cheng Lian <lian@databricks.com>

Closes #7597 from liancheng/spark-9254 and squashes the following commits:

fd266ca [Cheng Lian] Uses `--fail' to make curl return non-zero value and remove garbage output when the download fails
a7cbfb3 [Cheng Lian] Supports HTTP/HTTPS redirection
---
 build/sbt-launch-lib.bash | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/build/sbt-launch-lib.bash b/build/sbt-launch-lib.bash
index 504be48b358fa..7930a38b9674a 100755
--- a/build/sbt-launch-lib.bash
+++ b/build/sbt-launch-lib.bash
@@ -51,9 +51,13 @@ acquire_sbt_jar () {
     printf "Attempting to fetch sbt\n"
     JAR_DL="${JAR}.part"
     if [ $(command -v curl) ]; then
-      (curl --silent ${URL1} > "${JAR_DL}" || curl --silent ${URL2} > "${JAR_DL}") && mv "${JAR_DL}" "${JAR}"
+      (curl --fail --location --silent ${URL1} > "${JAR_DL}" ||\
+        (rm -f "${JAR_DL}" && curl --fail --location --silent ${URL2} > "${JAR_DL}")) &&\
+        mv "${JAR_DL}" "${JAR}"
     elif [ $(command -v wget) ]; then
-      (wget --quiet ${URL1} -O "${JAR_DL}" || wget --quiet ${URL2} -O "${JAR_DL}") && mv "${JAR_DL}" "${JAR}"
+      (wget --quiet ${URL1} -O "${JAR_DL}" ||\
+        (rm -f "${JAR_DL}" && wget --quiet ${URL2} -O "${JAR_DL}")) &&\
+        mv "${JAR_DL}" "${JAR}"
     else
       printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n"
       exit -1

From 76520955fddbda87a5c53d0a394dedc91dce67e8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 22 Jul 2015 11:45:51 -0700
Subject: [PATCH 0541/1454] [SPARK-9082] [SQL] Filter using non-deterministic
 expressions should not be pushed down

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7446 from cloud-fan/filter and squashes the following commits:

330021e [Wenchen Fan] add exists to tree node
2cab68c [Wenchen Fan] more enhance
949be07 [Wenchen Fan] push down part of predicate if possible
3912f84 [Wenchen Fan] address comments
8ce15ca [Wenchen Fan] fix bug
557158e [Wenchen Fan] Filter using non-deterministic expressions should not be pushed down
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 50 +++++++++++++++----
 .../optimizer/FilterPushdownSuite.scala       | 45 ++++++++++++++++-
 2 files changed, 84 insertions(+), 11 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index e42f0b9a247e3..d2db3dd3d078e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -541,20 +541,50 @@ object SimplifyFilters extends Rule[LogicalPlan] {
  *
  * This heuristic is valid assuming the expression evaluation cost is minimal.
  */
-object PushPredicateThroughProject extends Rule[LogicalPlan] {
+object PushPredicateThroughProject extends Rule[LogicalPlan] with PredicateHelper {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     case filter @ Filter(condition, project @ Project(fields, grandChild)) =>
-      val sourceAliases = fields.collect { case a @ Alias(c, _) =>
-        (a.toAttribute: Attribute) -> c
-      }.toMap
-      project.copy(child = filter.copy(
-        replaceAlias(condition, sourceAliases),
-        grandChild))
+      // Create a map of Aliases to their values from the child projection.
+      // e.g., 'SELECT a + b AS c, d ...' produces Map(c -> a + b).
+      val aliasMap = AttributeMap(fields.collect {
+        case a: Alias => (a.toAttribute, a.child)
+      })
+
+      // Split the condition into small conditions by `And`, so that we can push down part of this
+      // condition without nondeterministic expressions.
+      val andConditions = splitConjunctivePredicates(condition)
+      val nondeterministicConditions = andConditions.filter(hasNondeterministic(_, aliasMap))
+
+      // If there is no nondeterministic conditions, push down the whole condition.
+      if (nondeterministicConditions.isEmpty) {
+        project.copy(child = Filter(replaceAlias(condition, aliasMap), grandChild))
+      } else {
+        // If they are all nondeterministic conditions, leave it un-changed.
+        if (nondeterministicConditions.length == andConditions.length) {
+          filter
+        } else {
+          val deterministicConditions = andConditions.filterNot(hasNondeterministic(_, aliasMap))
+          // Push down the small conditions without nondeterministic expressions.
+          val pushedCondition = deterministicConditions.map(replaceAlias(_, aliasMap)).reduce(And)
+          Filter(nondeterministicConditions.reduce(And),
+            project.copy(child = Filter(pushedCondition, grandChild)))
+        }
+      }
+  }
+
+  private def hasNondeterministic(
+      condition: Expression,
+      sourceAliases: AttributeMap[Expression]) = {
+    condition.collect {
+      case a: Attribute if sourceAliases.contains(a) => sourceAliases(a)
+    }.exists(!_.deterministic)
   }
 
-  private def replaceAlias(condition: Expression, sourceAliases: Map[Attribute, Expression]) = {
-    condition transform {
-      case a: AttributeReference => sourceAliases.getOrElse(a, a)
+  // Substitute any attributes that are produced by the child projection, so that we safely
+  // eliminate it.
+  private def replaceAlias(condition: Expression, sourceAliases: AttributeMap[Expression]) = {
+    condition.transform {
+      case a: Attribute => sourceAliases.getOrElse(a, a)
     }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
index dc28b3ffb59ee..0f1fde2fb0f67 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.optimizer
 
 import org.apache.spark.sql.catalyst.analysis
 import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
-import org.apache.spark.sql.catalyst.expressions.{SortOrder, Ascending, Count, Explode}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{LeftSemi, PlanTest, LeftOuter, RightOuter}
 import org.apache.spark.sql.catalyst.rules._
@@ -146,6 +146,49 @@ class FilterPushdownSuite extends PlanTest {
     comparePlans(optimized, correctAnswer)
   }
 
+  test("nondeterministic: can't push down filter through project") {
+    val originalQuery = testRelation
+      .select(Rand(10).as('rand), 'a)
+      .where('rand > 5 || 'a > 5)
+      .analyze
+
+    val optimized = Optimize.execute(originalQuery)
+
+    comparePlans(optimized, originalQuery)
+  }
+
+  test("nondeterministic: push down part of filter through project") {
+    val originalQuery = testRelation
+      .select(Rand(10).as('rand), 'a)
+      .where('rand > 5 && 'a > 5)
+      .analyze
+
+    val optimized = Optimize.execute(originalQuery)
+
+    val correctAnswer = testRelation
+      .where('a > 5)
+      .select(Rand(10).as('rand), 'a)
+      .where('rand > 5)
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("nondeterministic: push down filter through project") {
+    val originalQuery = testRelation
+      .select(Rand(10).as('rand), 'a)
+      .where('a > 5 && 'a < 10)
+      .analyze
+
+    val optimized = Optimize.execute(originalQuery)
+    val correctAnswer = testRelation
+      .where('a > 5 && 'a < 10)
+      .select(Rand(10).as('rand), 'a)
+      .analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
   test("filters: combines filters") {
     val originalQuery = testRelation
       .select('a)

From 86f80e2b4759e574fe3eb91695f81b644db87242 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 22 Jul 2015 12:19:59 -0700
Subject: [PATCH 0542/1454] [SPARK-9165] [SQL] codegen for CreateArray,
 CreateStruct and CreateNamedStruct

JIRA: https://issues.apache.org/jira/browse/SPARK-9165

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7537 from yjshen/array_struct_codegen and squashes the following commits:

3a6dce6 [Yijie Shen] use infix notion in createArray test
5e90f0a [Yijie Shen] resolve comments: classOf
39cefb8 [Yijie Shen] codegen for createArray createStruct & createNamedStruct
---
 .../expressions/complexTypeCreator.scala      | 65 +++++++++++++++++--
 .../expressions/ComplexTypeSuite.scala        | 16 +++++
 2 files changed, 76 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index f9fd04c02aaef..20b1eaab8e303 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -17,16 +17,18 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import scala.collection.mutable
+
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
 /**
  * Returns an Array containing the evaluation of all children expressions.
  */
-case class CreateArray(children: Seq[Expression]) extends Expression with CodegenFallback {
+case class CreateArray(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
@@ -45,14 +47,31 @@ case class CreateArray(children: Seq[Expression]) extends Expression with Codege
     children.map(_.eval(input))
   }
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val arraySeqClass = classOf[mutable.ArraySeq[Any]].getName
+    s"""
+      boolean ${ev.isNull} = false;
+      $arraySeqClass<Object> ${ev.primitive} = new $arraySeqClass<Object>(${children.size});
+    """ +
+      children.zipWithIndex.map { case (e, i) =>
+        val eval = e.gen(ctx)
+        eval.code + s"""
+          if (${eval.isNull}) {
+            ${ev.primitive}.update($i, null);
+          } else {
+            ${ev.primitive}.update($i, ${eval.primitive});
+          }
+         """
+      }.mkString("\n")
+  }
+
   override def prettyName: String = "array"
 }
 
 /**
  * Returns a Row containing the evaluation of all children expressions.
- * TODO: [[CreateStruct]] does not support codegen.
  */
-case class CreateStruct(children: Seq[Expression]) extends Expression with CodegenFallback {
+case class CreateStruct(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
@@ -76,6 +95,24 @@ case class CreateStruct(children: Seq[Expression]) extends Expression with Codeg
     InternalRow(children.map(_.eval(input)): _*)
   }
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val rowClass = classOf[GenericMutableRow].getName
+    s"""
+      boolean ${ev.isNull} = false;
+      final $rowClass ${ev.primitive} = new $rowClass(${children.size});
+    """ +
+      children.zipWithIndex.map { case (e, i) =>
+        val eval = e.gen(ctx)
+        eval.code + s"""
+        if (${eval.isNull}) {
+          ${ev.primitive}.update($i, null);
+        } else {
+          ${ev.primitive}.update($i, ${eval.primitive});
+        }
+       """
+      }.mkString("\n")
+  }
+
   override def prettyName: String = "struct"
 }
 
@@ -84,7 +121,7 @@ case class CreateStruct(children: Seq[Expression]) extends Expression with Codeg
  *
  * @param children Seq(name1, val1, name2, val2, ...)
  */
-case class CreateNamedStruct(children: Seq[Expression]) extends Expression with CodegenFallback {
+case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
 
   private lazy val (nameExprs, valExprs) =
     children.grouped(2).map { case Seq(name, value) => (name, value) }.toList.unzip
@@ -122,5 +159,23 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression with
     InternalRow(valExprs.map(_.eval(input)): _*)
   }
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val rowClass = classOf[GenericMutableRow].getName
+    s"""
+      boolean ${ev.isNull} = false;
+      final $rowClass ${ev.primitive} = new $rowClass(${valExprs.size});
+    """ +
+      valExprs.zipWithIndex.map { case (e, i) =>
+        val eval = e.gen(ctx)
+        eval.code + s"""
+        if (${eval.isNull}) {
+          ${ev.primitive}.update($i, null);
+        } else {
+          ${ev.primitive}.update($i, ${eval.primitive});
+        }
+       """
+      }.mkString("\n")
+  }
+
   override def prettyName: String = "named_struct"
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index e3042143632aa..a8aee8f634e03 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -117,6 +117,22 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(getArrayStructFields(nullArrayStruct, "a"), null)
   }
 
+  test("CreateArray") {
+    val intSeq = Seq(5, 10, 15, 20, 25)
+    val longSeq = intSeq.map(_.toLong)
+    val strSeq = intSeq.map(_.toString)
+    checkEvaluation(CreateArray(intSeq.map(Literal(_))), intSeq, EmptyRow)
+    checkEvaluation(CreateArray(longSeq.map(Literal(_))), longSeq, EmptyRow)
+    checkEvaluation(CreateArray(strSeq.map(Literal(_))), strSeq, EmptyRow)
+
+    val intWithNull = intSeq.map(Literal(_)) :+ Literal.create(null, IntegerType)
+    val longWithNull = longSeq.map(Literal(_)) :+ Literal.create(null, LongType)
+    val strWithNull = strSeq.map(Literal(_)) :+ Literal.create(null, StringType)
+    checkEvaluation(CreateArray(intWithNull), intSeq :+ null, EmptyRow)
+    checkEvaluation(CreateArray(longWithNull), longSeq :+ null, EmptyRow)
+    checkEvaluation(CreateArray(strWithNull), strSeq :+ null, EmptyRow)
+  }
+
   test("CreateStruct") {
     val row = create_row(1, 2, 3)
     val c1 = 'a.int.at(0)

From e0b7ba59a1ace9b78a1ad6f3f07fe153db20b52c Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 22 Jul 2015 13:02:43 -0700
Subject: [PATCH 0543/1454] [SPARK-9024] Unsafe
 HashJoin/HashOuterJoin/HashSemiJoin

This PR introduce unsafe version (using UnsafeRow) of HashJoin, HashOuterJoin and HashSemiJoin, including the broadcast one and shuffle one (except FullOuterJoin, which is better to be implemented using SortMergeJoin).

It use HashMap to store UnsafeRow right now, will change to use BytesToBytesMap for better performance (in another PR).

Author: Davies Liu <davies@databricks.com>

Closes #7480 from davies/unsafe_join and squashes the following commits:

6294b1e [Davies Liu] fix projection
10583f1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
dede020 [Davies Liu] fix test
84c9807 [Davies Liu] address comments
a05b4f6 [Davies Liu] support UnsafeRow in LeftSemiJoinBNL and BroadcastNestedLoopJoin
611d2ed [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
9481ae8 [Davies Liu] return UnsafeRow after join()
ca2b40f [Davies Liu] revert unrelated change
68f5cd9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
0f4380d [Davies Liu] ada a comment
69e38f5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
1a40f02 [Davies Liu] refactor
ab1690f [Davies Liu] address comments
60371f2 [Davies Liu] use UnsafeRow in SemiJoin
a6c0b7d [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
184b852 [Davies Liu] fix style
6acbb11 [Davies Liu] fix tests
95d0762 [Davies Liu] remove println
bea4a50 [Davies Liu] Unsafe HashJoin
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 50 ++++++++++-
 .../execution/UnsafeExternalRowSorter.java    | 10 +--
 .../catalyst/expressions/BoundAttribute.scala | 19 ++++-
 .../sql/catalyst/expressions/Projection.scala | 34 +++++++-
 .../execution/joins/BroadcastHashJoin.scala   |  2 +-
 .../joins/BroadcastHashOuterJoin.scala        | 32 ++-----
 .../joins/BroadcastLeftSemiJoinHash.scala     |  5 +-
 .../joins/BroadcastNestedLoopJoin.scala       | 37 +++++---
 .../spark/sql/execution/joins/HashJoin.scala  | 43 ++++++++--
 .../sql/execution/joins/HashOuterJoin.scala   | 82 +++++++++++++++---
 .../sql/execution/joins/HashSemiJoin.scala    | 74 ++++++++++------
 .../sql/execution/joins/HashedRelation.scala  | 85 ++++++++++++++++++-
 .../sql/execution/joins/LeftSemiJoinBNL.scala |  3 +
 .../execution/joins/LeftSemiJoinHash.scala    |  4 +-
 .../execution/joins/ShuffledHashJoin.scala    |  2 +-
 .../joins/ShuffledHashOuterJoin.scala         | 13 +--
 .../sql/execution/rowFormatConverters.scala   | 21 +++--
 .../org/apache/spark/sql/UnsafeRowSuite.scala |  4 +-
 .../execution/joins/HashedRelationSuite.scala | 49 ++++++++---
 .../spark/unsafe/hash/Murmur3_x86_32.java     | 10 ++-
 20 files changed, 444 insertions(+), 135 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 6ce03a48e9538..7f08bf7b742dc 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -20,10 +20,11 @@
 import java.io.IOException;
 import java.io.OutputStream;
 
-import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.catalyst.util.ObjectPool;
 import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
+import org.apache.spark.unsafe.hash.Murmur3_x86_32;
 import org.apache.spark.unsafe.types.UTF8String;
 
 
@@ -354,7 +355,7 @@ public double getDouble(int i) {
    * This method is only supported on UnsafeRows that do not use ObjectPools.
    */
   @Override
-  public InternalRow copy() {
+  public UnsafeRow copy() {
     if (pool != null) {
       throw new UnsupportedOperationException(
         "Copy is not supported for UnsafeRows that use object pools");
@@ -404,8 +405,51 @@ public void writeToStream(OutputStream out, byte[] writeBuffer) throws IOExcepti
     }
   }
 
+  @Override
+  public int hashCode() {
+    return Murmur3_x86_32.hashUnsafeWords(baseObject, baseOffset, sizeInBytes, 42);
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other instanceof UnsafeRow) {
+      UnsafeRow o = (UnsafeRow) other;
+      return (sizeInBytes == o.sizeInBytes) &&
+        ByteArrayMethods.arrayEquals(baseObject, baseOffset, o.baseObject, o.baseOffset,
+          sizeInBytes);
+    }
+    return false;
+  }
+
+  /**
+   * Returns the underlying bytes for this UnsafeRow.
+   */
+  public byte[] getBytes() {
+    if (baseObject instanceof byte[] && baseOffset == PlatformDependent.BYTE_ARRAY_OFFSET
+        && (((byte[]) baseObject).length == sizeInBytes)) {
+      return (byte[]) baseObject;
+    } else {
+      byte[] bytes = new byte[sizeInBytes];
+      PlatformDependent.copyMemory(baseObject, baseOffset, bytes,
+        PlatformDependent.BYTE_ARRAY_OFFSET, sizeInBytes);
+      return bytes;
+    }
+  }
+
+  // This is for debugging
+  @Override
+  public String toString() {
+    StringBuilder build = new StringBuilder("[");
+    for (int i = 0; i < sizeInBytes; i += 8) {
+      build.append(PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + i));
+      build.append(',');
+    }
+    build.append(']');
+    return build.toString();
+  }
+
   @Override
   public boolean anyNull() {
-    return BitSetMethods.anySet(baseObject, baseOffset, bitSetWidthInBytes);
+    return BitSetMethods.anySet(baseObject, baseOffset, bitSetWidthInBytes / 8);
   }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index d1d81c87bb052..39fd6e1bc6d13 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -28,11 +28,10 @@
 import org.apache.spark.TaskContext;
 import org.apache.spark.sql.AbstractScalaRowIterator;
 import org.apache.spark.sql.catalyst.InternalRow;
-import org.apache.spark.sql.catalyst.expressions.UnsafeColumnWriter;
 import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
 import org.apache.spark.sql.catalyst.util.ObjectPool;
-import org.apache.spark.sql.types.*;
+import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
 import org.apache.spark.util.collection.unsafe.sort.RecordComparator;
@@ -176,12 +175,7 @@ public Iterator<InternalRow> sort(Iterator<InternalRow> inputIterator) throws IO
    */
   public static boolean supportsSchema(StructType schema) {
     // TODO: add spilling note to explain why we do this for now:
-    for (StructField field : schema.fields()) {
-      if (!UnsafeColumnWriter.canEmbed(field.dataType())) {
-        return false;
-      }
-    }
-    return true;
+    return UnsafeProjection.canSupport(schema);
   }
 
   private static final class RowComparator extends RecordComparator {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index b10a3c877434b..4a13b687bf4ce 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -21,7 +21,6 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.types._
 
 /**
@@ -34,7 +33,23 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
 
   override def toString: String = s"input[$ordinal, $dataType]"
 
-  override def eval(input: InternalRow): Any = input(ordinal)
+  // Use special getter for primitive types (for UnsafeRow)
+  override def eval(input: InternalRow): Any = {
+    if (input.isNullAt(ordinal)) {
+      null
+    } else {
+      dataType match {
+        case BooleanType => input.getBoolean(ordinal)
+        case ByteType => input.getByte(ordinal)
+        case ShortType => input.getShort(ordinal)
+        case IntegerType | DateType => input.getInt(ordinal)
+        case LongType | TimestampType => input.getLong(ordinal)
+        case FloatType => input.getFloat(ordinal)
+        case DoubleType => input.getDouble(ordinal)
+        case _ => input.get(ordinal)
+      }
+    }
+  }
 
   override def name: String = s"i[$ordinal]"
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 24b01ea55110e..69758e653eba0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -83,12 +83,42 @@ abstract class UnsafeProjection extends Projection {
 }
 
 object UnsafeProjection {
+
+  /*
+   * Returns whether UnsafeProjection can support given StructType, Array[DataType] or
+   * Seq[Expression].
+   */
+  def canSupport(schema: StructType): Boolean = canSupport(schema.fields.map(_.dataType))
+  def canSupport(types: Array[DataType]): Boolean = types.forall(UnsafeColumnWriter.canEmbed(_))
+  def canSupport(exprs: Seq[Expression]): Boolean = canSupport(exprs.map(_.dataType).toArray)
+
+  /**
+   * Returns an UnsafeProjection for given StructType.
+   */
   def create(schema: StructType): UnsafeProjection = create(schema.fields.map(_.dataType))
 
-  def create(fields: Seq[DataType]): UnsafeProjection = {
+  /**
+   * Returns an UnsafeProjection for given Array of DataTypes.
+   */
+  def create(fields: Array[DataType]): UnsafeProjection = {
     val exprs = fields.zipWithIndex.map(x => new BoundReference(x._2, x._1, true))
+    create(exprs)
+  }
+
+  /**
+   * Returns an UnsafeProjection for given sequence of Expressions (bounded).
+   */
+  def create(exprs: Seq[Expression]): UnsafeProjection = {
     GenerateUnsafeProjection.generate(exprs)
   }
+
+  /**
+   * Returns an UnsafeProjection for given sequence of Expressions, which will be bound to
+   * `inputSchema`.
+   */
+  def create(exprs: Seq[Expression], inputSchema: Seq[Attribute]): UnsafeProjection = {
+    create(exprs.map(BindReferences.bindReference(_, inputSchema)))
+  }
 }
 
 /**
@@ -96,6 +126,8 @@ object UnsafeProjection {
  */
 case class FromUnsafeProjection(fields: Seq[DataType]) extends Projection {
 
+  def this(schema: StructType) = this(schema.fields.map(_.dataType))
+
   private[this] val expressions = fields.zipWithIndex.map { case (dt, idx) =>
     new BoundReference(idx, dt, true)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index 7ffdce60d2955..abaa4a6ce86a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -62,7 +62,7 @@ case class BroadcastHashJoin(
   private val broadcastFuture = future {
     // Note that we use .execute().collect() because we don't want to convert data to Scala types
     val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.length)
+    val hashed = buildHashRelation(input.iterator)
     sparkContext.broadcast(hashed)
   }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index ab757fc7de6cd..c9d1a880f4ef4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.execution.joins
 
+import scala.concurrent._
+import scala.concurrent.duration._
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -26,10 +29,6 @@ import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 import org.apache.spark.util.ThreadUtils
 
-import scala.collection.JavaConversions._
-import scala.concurrent._
-import scala.concurrent.duration._
-
 /**
  * :: DeveloperApi ::
  * Performs a outer hash join for two child relations.  When the output RDD of this operator is
@@ -58,28 +57,11 @@ case class BroadcastHashOuterJoin(
   override def requiredChildDistribution: Seq[Distribution] =
     UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
 
-  private[this] lazy val (buildPlan, streamedPlan) = joinType match {
-    case RightOuter => (left, right)
-    case LeftOuter => (right, left)
-    case x =>
-      throw new IllegalArgumentException(
-        s"BroadcastHashOuterJoin should not take $x as the JoinType")
-  }
-
-  private[this] lazy val (buildKeys, streamedKeys) = joinType match {
-    case RightOuter => (leftKeys, rightKeys)
-    case LeftOuter => (rightKeys, leftKeys)
-    case x =>
-      throw new IllegalArgumentException(
-        s"BroadcastHashOuterJoin should not take $x as the JoinType")
-  }
-
   @transient
   private val broadcastFuture = future {
     // Note that we use .execute().collect() because we don't want to convert data to Scala types
     val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    // buildHashTable uses code-generated rows as keys, which are not serializable
-    val hashed = buildHashTable(input.iterator, newProjection(buildKeys, buildPlan.output))
+    val hashed = buildHashRelation(input.iterator)
     sparkContext.broadcast(hashed)
   }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
 
@@ -89,21 +71,21 @@ case class BroadcastHashOuterJoin(
     streamedPlan.execute().mapPartitions { streamedIter =>
       val joinedRow = new JoinedRow()
       val hashTable = broadcastRelation.value
-      val keyGenerator = newProjection(streamedKeys, streamedPlan.output)
+      val keyGenerator = streamedKeyGenerator
 
       joinType match {
         case LeftOuter =>
           streamedIter.flatMap(currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashTable.getOrElse(rowKey, EMPTY_LIST))
+            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey))
           })
 
         case RightOuter =>
           streamedIter.flatMap(currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashTable.getOrElse(rowKey, EMPTY_LIST), joinedRow)
+            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow)
           })
 
         case x =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index 2750f58b005ac..f71c0ce352904 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -40,15 +40,14 @@ case class BroadcastLeftSemiJoinHash(
     val buildIter = right.execute().map(_.copy()).collect().toIterator
 
     if (condition.isEmpty) {
-      // rowKey may be not serializable (from codegen)
-      val hashSet = buildKeyHashSet(buildIter, copy = true)
+      val hashSet = buildKeyHashSet(buildIter)
       val broadcastedRelation = sparkContext.broadcast(hashSet)
 
       left.execute().mapPartitions { streamIter =>
         hashSemiJoin(streamIter, broadcastedRelation.value)
       }
     } else {
-      val hashRelation = HashedRelation(buildIter, rightKeyGenerator)
+      val hashRelation = buildHashRelation(buildIter)
       val broadcastedRelation = sparkContext.broadcast(hashRelation)
 
       left.execute().mapPartitions { streamIter =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 60b4266fad8b1..700636966f8be 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -44,6 +44,19 @@ case class BroadcastNestedLoopJoin(
     case BuildLeft => (right, left)
   }
 
+  override def outputsUnsafeRows: Boolean = left.outputsUnsafeRows || right.outputsUnsafeRows
+  override def canProcessUnsafeRows: Boolean = true
+
+  @transient private[this] lazy val resultProjection: Projection = {
+    if (outputsUnsafeRows) {
+      UnsafeProjection.create(schema)
+    } else {
+      new Projection {
+        override def apply(r: InternalRow): InternalRow = r
+      }
+    }
+  }
+
   override def outputPartitioning: Partitioning = streamed.outputPartitioning
 
   override def output: Seq[Attribute] = {
@@ -74,6 +87,7 @@ case class BroadcastNestedLoopJoin(
       val includedBroadcastTuples =
         new scala.collection.mutable.BitSet(broadcastedRelation.value.size)
       val joinedRow = new JoinedRow
+
       val leftNulls = new GenericMutableRow(left.output.size)
       val rightNulls = new GenericMutableRow(right.output.size)
 
@@ -86,11 +100,11 @@ case class BroadcastNestedLoopJoin(
           val broadcastedRow = broadcastedRelation.value(i)
           buildSide match {
             case BuildRight if boundCondition(joinedRow(streamedRow, broadcastedRow)) =>
-              matchedRows += joinedRow(streamedRow, broadcastedRow).copy()
+              matchedRows += resultProjection(joinedRow(streamedRow, broadcastedRow)).copy()
               streamRowMatched = true
               includedBroadcastTuples += i
             case BuildLeft if boundCondition(joinedRow(broadcastedRow, streamedRow)) =>
-              matchedRows += joinedRow(broadcastedRow, streamedRow).copy()
+              matchedRows += resultProjection(joinedRow(broadcastedRow, streamedRow)).copy()
               streamRowMatched = true
               includedBroadcastTuples += i
             case _ =>
@@ -100,9 +114,9 @@ case class BroadcastNestedLoopJoin(
 
         (streamRowMatched, joinType, buildSide) match {
           case (false, LeftOuter | FullOuter, BuildRight) =>
-            matchedRows += joinedRow(streamedRow, rightNulls).copy()
+            matchedRows += resultProjection(joinedRow(streamedRow, rightNulls)).copy()
           case (false, RightOuter | FullOuter, BuildLeft) =>
-            matchedRows += joinedRow(leftNulls, streamedRow).copy()
+            matchedRows += resultProjection(joinedRow(leftNulls, streamedRow)).copy()
           case _ =>
         }
       }
@@ -110,12 +124,9 @@ case class BroadcastNestedLoopJoin(
     }
 
     val includedBroadcastTuples = matchesOrStreamedRowsWithNulls.map(_._2)
-    val allIncludedBroadcastTuples =
-      if (includedBroadcastTuples.count == 0) {
-        new scala.collection.mutable.BitSet(broadcastedRelation.value.size)
-      } else {
-        includedBroadcastTuples.reduce(_ ++ _)
-      }
+    val allIncludedBroadcastTuples = includedBroadcastTuples.fold(
+      new scala.collection.mutable.BitSet(broadcastedRelation.value.size)
+    )(_ ++ _)
 
     val leftNulls = new GenericMutableRow(left.output.size)
     val rightNulls = new GenericMutableRow(right.output.size)
@@ -127,8 +138,10 @@ case class BroadcastNestedLoopJoin(
       while (i < rel.length) {
         if (!allIncludedBroadcastTuples.contains(i)) {
           (joinType, buildSide) match {
-            case (RightOuter | FullOuter, BuildRight) => buf += new JoinedRow(leftNulls, rel(i))
-            case (LeftOuter | FullOuter, BuildLeft) => buf += new JoinedRow(rel(i), rightNulls)
+            case (RightOuter | FullOuter, BuildRight) =>
+              buf += resultProjection(new JoinedRow(leftNulls, rel(i)))
+            case (LeftOuter | FullOuter, BuildLeft) =>
+              buf += resultProjection(new JoinedRow(rel(i), rightNulls))
             case _ =>
           }
         }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index ff85ea3f6a410..ae34409bcfcca 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -44,11 +44,20 @@ trait HashJoin {
 
   override def output: Seq[Attribute] = left.output ++ right.output
 
-  @transient protected lazy val buildSideKeyGenerator: Projection =
-    newProjection(buildKeys, buildPlan.output)
+  protected[this] def supportUnsafe: Boolean = {
+    (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
+      && UnsafeProjection.canSupport(self.schema))
+  }
+
+  override def outputsUnsafeRows: Boolean = supportUnsafe
+  override def canProcessUnsafeRows: Boolean = supportUnsafe
 
-  @transient protected lazy val streamSideKeyGenerator: () => MutableProjection =
-    newMutableProjection(streamedKeys, streamedPlan.output)
+  @transient protected lazy val streamSideKeyGenerator: Projection =
+    if (supportUnsafe) {
+      UnsafeProjection.create(streamedKeys, streamedPlan.output)
+    } else {
+      newMutableProjection(streamedKeys, streamedPlan.output)()
+    }
 
   protected def hashJoin(
       streamIter: Iterator[InternalRow],
@@ -61,8 +70,17 @@ trait HashJoin {
 
       // Mutable per row objects.
       private[this] val joinRow = new JoinedRow2
+      private[this] val resultProjection: Projection = {
+        if (supportUnsafe) {
+          UnsafeProjection.create(self.schema)
+        } else {
+          new Projection {
+            override def apply(r: InternalRow): InternalRow = r
+          }
+        }
+      }
 
-      private[this] val joinKeys = streamSideKeyGenerator()
+      private[this] val joinKeys = streamSideKeyGenerator
 
       override final def hasNext: Boolean =
         (currentMatchPosition != -1 && currentMatchPosition < currentHashMatches.size) ||
@@ -74,7 +92,7 @@ trait HashJoin {
           case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow)
         }
         currentMatchPosition += 1
-        ret
+        resultProjection(ret)
       }
 
       /**
@@ -89,8 +107,9 @@ trait HashJoin {
 
         while (currentHashMatches == null && streamIter.hasNext) {
           currentStreamedRow = streamIter.next()
-          if (!joinKeys(currentStreamedRow).anyNull) {
-            currentHashMatches = hashedRelation.get(joinKeys.currentValue)
+          val key = joinKeys(currentStreamedRow)
+          if (!key.anyNull) {
+            currentHashMatches = hashedRelation.get(key)
           }
         }
 
@@ -103,4 +122,12 @@ trait HashJoin {
       }
     }
   }
+
+  protected[this] def buildHashRelation(buildIter: Iterator[InternalRow]): HashedRelation = {
+    if (supportUnsafe) {
+      UnsafeHashedRelation(buildIter, buildKeys, buildPlan)
+    } else {
+      HashedRelation(buildIter, newProjection(buildKeys, buildPlan.output))
+    }
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 74a7db7761758..6bf2f82954046 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -23,7 +23,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning}
-import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.util.collection.CompactBuffer
 
@@ -38,7 +38,7 @@ trait HashOuterJoin {
   val left: SparkPlan
   val right: SparkPlan
 
-override def outputPartitioning: Partitioning = joinType match {
+  override def outputPartitioning: Partitioning = joinType match {
     case LeftOuter => left.outputPartitioning
     case RightOuter => right.outputPartitioning
     case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
@@ -59,6 +59,49 @@ override def outputPartitioning: Partitioning = joinType match {
     }
   }
 
+  protected[this] lazy val (buildPlan, streamedPlan) = joinType match {
+    case RightOuter => (left, right)
+    case LeftOuter => (right, left)
+    case x =>
+      throw new IllegalArgumentException(
+        s"HashOuterJoin should not take $x as the JoinType")
+  }
+
+  protected[this] lazy val (buildKeys, streamedKeys) = joinType match {
+    case RightOuter => (leftKeys, rightKeys)
+    case LeftOuter => (rightKeys, leftKeys)
+    case x =>
+      throw new IllegalArgumentException(
+        s"HashOuterJoin should not take $x as the JoinType")
+  }
+
+  protected[this] def supportUnsafe: Boolean = {
+    (self.codegenEnabled && joinType != FullOuter
+      && UnsafeProjection.canSupport(buildKeys)
+      && UnsafeProjection.canSupport(self.schema))
+  }
+
+  override def outputsUnsafeRows: Boolean = supportUnsafe
+  override def canProcessUnsafeRows: Boolean = supportUnsafe
+
+  protected[this] def streamedKeyGenerator(): Projection = {
+    if (supportUnsafe) {
+      UnsafeProjection.create(streamedKeys, streamedPlan.output)
+    } else {
+      newProjection(streamedKeys, streamedPlan.output)
+    }
+  }
+
+  @transient private[this] lazy val resultProjection: Projection = {
+    if (supportUnsafe) {
+      UnsafeProjection.create(self.schema)
+    } else {
+      new Projection {
+        override def apply(r: InternalRow): InternalRow = r
+      }
+    }
+  }
+
   @transient private[this] lazy val DUMMY_LIST = CompactBuffer[InternalRow](null)
   @transient protected[this] lazy val EMPTY_LIST = CompactBuffer[InternalRow]()
 
@@ -76,16 +119,20 @@ override def outputPartitioning: Partitioning = joinType match {
       rightIter: Iterable[InternalRow]): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
-        val temp = rightIter.collect {
-          case r if boundCondition(joinedRow.withRight(r)) => joinedRow.copy()
+        val temp = if (rightIter != null) {
+          rightIter.collect {
+            case r if boundCondition(joinedRow.withRight(r)) => resultProjection(joinedRow).copy()
+          }
+        } else {
+          List.empty
         }
         if (temp.isEmpty) {
-          joinedRow.withRight(rightNullRow).copy :: Nil
+          resultProjection(joinedRow.withRight(rightNullRow)).copy :: Nil
         } else {
           temp
         }
       } else {
-        joinedRow.withRight(rightNullRow).copy :: Nil
+        resultProjection(joinedRow.withRight(rightNullRow)).copy :: Nil
       }
     }
     ret.iterator
@@ -97,17 +144,21 @@ override def outputPartitioning: Partitioning = joinType match {
       joinedRow: JoinedRow): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
-        val temp = leftIter.collect {
-          case l if boundCondition(joinedRow.withLeft(l)) =>
-            joinedRow.copy()
+        val temp = if (leftIter != null) {
+          leftIter.collect {
+            case l if boundCondition(joinedRow.withLeft(l)) =>
+              resultProjection(joinedRow).copy()
+          }
+        } else {
+          List.empty
         }
         if (temp.isEmpty) {
-          joinedRow.withLeft(leftNullRow).copy :: Nil
+          resultProjection(joinedRow.withLeft(leftNullRow)).copy :: Nil
         } else {
           temp
         }
       } else {
-        joinedRow.withLeft(leftNullRow).copy :: Nil
+        resultProjection(joinedRow.withLeft(leftNullRow)).copy :: Nil
       }
     }
     ret.iterator
@@ -159,6 +210,7 @@ override def outputPartitioning: Partitioning = joinType match {
     }
   }
 
+  // This is only used by FullOuter
   protected[this] def buildHashTable(
       iter: Iterator[InternalRow],
       keyGenerator: Projection): JavaHashMap[InternalRow, CompactBuffer[InternalRow]] = {
@@ -178,4 +230,12 @@ override def outputPartitioning: Partitioning = joinType match {
 
     hashTable
   }
+
+  protected[this] def buildHashRelation(buildIter: Iterator[InternalRow]): HashedRelation = {
+    if (supportUnsafe) {
+      UnsafeHashedRelation(buildIter, buildKeys, buildPlan)
+    } else {
+      HashedRelation(buildIter, newProjection(buildKeys, buildPlan.output))
+    }
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 1b983bc3a90f9..7f49264d40354 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -32,34 +32,45 @@ trait HashSemiJoin {
 
   override def output: Seq[Attribute] = left.output
 
-  @transient protected lazy val rightKeyGenerator: Projection =
-    newProjection(rightKeys, right.output)
+  protected[this] def supportUnsafe: Boolean = {
+    (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
+      && UnsafeProjection.canSupport(rightKeys)
+      && UnsafeProjection.canSupport(left.schema))
+  }
+
+  override def outputsUnsafeRows: Boolean = right.outputsUnsafeRows
+  override def canProcessUnsafeRows: Boolean = supportUnsafe
+
+  @transient protected lazy val leftKeyGenerator: Projection =
+    if (supportUnsafe) {
+      UnsafeProjection.create(leftKeys, left.output)
+    } else {
+      newMutableProjection(leftKeys, left.output)()
+    }
 
-  @transient protected lazy val leftKeyGenerator: () => MutableProjection =
-    newMutableProjection(leftKeys, left.output)
+  @transient protected lazy val rightKeyGenerator: Projection =
+    if (supportUnsafe) {
+      UnsafeProjection.create(rightKeys, right.output)
+    } else {
+      newMutableProjection(rightKeys, right.output)()
+    }
 
   @transient private lazy val boundCondition =
     newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
-  protected def buildKeyHashSet(
-      buildIter: Iterator[InternalRow],
-      copy: Boolean): java.util.Set[InternalRow] = {
+  protected def buildKeyHashSet(buildIter: Iterator[InternalRow]): java.util.Set[InternalRow] = {
     val hashSet = new java.util.HashSet[InternalRow]()
     var currentRow: InternalRow = null
 
     // Create a Hash set of buildKeys
+    val rightKey = rightKeyGenerator
     while (buildIter.hasNext) {
       currentRow = buildIter.next()
-      val rowKey = rightKeyGenerator(currentRow)
+      val rowKey = rightKey(currentRow)
       if (!rowKey.anyNull) {
         val keyExists = hashSet.contains(rowKey)
         if (!keyExists) {
-          if (copy) {
-            hashSet.add(rowKey.copy())
-          } else {
-            // rowKey may be not serializable (from codegen)
-            hashSet.add(rowKey)
-          }
+          hashSet.add(rowKey.copy())
         }
       }
     }
@@ -67,25 +78,34 @@ trait HashSemiJoin {
   }
 
   protected def hashSemiJoin(
-      streamIter: Iterator[InternalRow],
-      hashedRelation: HashedRelation): Iterator[InternalRow] = {
-    val joinKeys = leftKeyGenerator()
-    val joinedRow = new JoinedRow
+    streamIter: Iterator[InternalRow],
+    hashSet: java.util.Set[InternalRow]): Iterator[InternalRow] = {
+    val joinKeys = leftKeyGenerator
     streamIter.filter(current => {
-      lazy val rowBuffer = hashedRelation.get(joinKeys.currentValue)
-      !joinKeys(current).anyNull && rowBuffer != null && rowBuffer.exists {
-        (build: InternalRow) => boundCondition(joinedRow(current, build))
-      }
+      val key = joinKeys(current)
+      !key.anyNull && hashSet.contains(key)
     })
   }
 
+  protected def buildHashRelation(buildIter: Iterator[InternalRow]): HashedRelation = {
+    if (supportUnsafe) {
+      UnsafeHashedRelation(buildIter, rightKeys, right)
+    } else {
+      HashedRelation(buildIter, newProjection(rightKeys, right.output))
+    }
+  }
+
   protected def hashSemiJoin(
       streamIter: Iterator[InternalRow],
-      hashSet: java.util.Set[InternalRow]): Iterator[InternalRow] = {
-    val joinKeys = leftKeyGenerator()
+      hashedRelation: HashedRelation): Iterator[InternalRow] = {
+    val joinKeys = leftKeyGenerator
     val joinedRow = new JoinedRow
-    streamIter.filter(current => {
-      !joinKeys(current.copy()).anyNull && hashSet.contains(joinKeys.currentValue)
-    })
+    streamIter.filter { current =>
+      val key = joinKeys(current)
+      lazy val rowBuffer = hashedRelation.get(key)
+      !key.anyNull && rowBuffer != null && rowBuffer.exists {
+        (row: InternalRow) => boundCondition(joinedRow(current, row))
+      }
+    }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 6b51f5d4151d3..8d5731afd59b8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.sql.execution.joins
 
-import java.io.{ObjectInput, ObjectOutput, Externalizable}
+import java.io.{Externalizable, ObjectInput, ObjectOutput}
 import java.util.{HashMap => JavaHashMap}
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.Projection
-import org.apache.spark.sql.execution.SparkSqlSerializer
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.{SparkPlan, SparkSqlSerializer}
+import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.CompactBuffer
 
 
@@ -98,7 +99,6 @@ final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalR
   }
 }
 
-
 // TODO(rxin): a version of [[HashedRelation]] backed by arrays for consecutive integer keys.
 
 
@@ -148,3 +148,80 @@ private[joins] object HashedRelation {
     }
   }
 }
+
+
+/**
+ * A HashedRelation for UnsafeRow, which is backed by BytesToBytesMap that maps the key into a
+ * sequence of values.
+ *
+ * TODO(davies): use BytesToBytesMap
+ */
+private[joins] final class UnsafeHashedRelation(
+    private var hashTable: JavaHashMap[UnsafeRow, CompactBuffer[UnsafeRow]])
+  extends HashedRelation with Externalizable {
+
+  def this() = this(null)  // Needed for serialization
+
+  override def get(key: InternalRow): CompactBuffer[InternalRow] = {
+    val unsafeKey = key.asInstanceOf[UnsafeRow]
+    // Thanks to type eraser
+    hashTable.get(unsafeKey).asInstanceOf[CompactBuffer[InternalRow]]
+  }
+
+  override def writeExternal(out: ObjectOutput): Unit = {
+    writeBytes(out, SparkSqlSerializer.serialize(hashTable))
+  }
+
+  override def readExternal(in: ObjectInput): Unit = {
+    hashTable = SparkSqlSerializer.deserialize(readBytes(in))
+  }
+}
+
+private[joins] object UnsafeHashedRelation {
+
+  def apply(
+      input: Iterator[InternalRow],
+      buildKeys: Seq[Expression],
+      buildPlan: SparkPlan,
+      sizeEstimate: Int = 64): HashedRelation = {
+    val boundedKeys = buildKeys.map(BindReferences.bindReference(_, buildPlan.output))
+    apply(input, boundedKeys, buildPlan.schema, sizeEstimate)
+  }
+
+  // Used for tests
+  def apply(
+      input: Iterator[InternalRow],
+      buildKeys: Seq[Expression],
+      rowSchema: StructType,
+      sizeEstimate: Int): HashedRelation = {
+
+    // TODO: Use BytesToBytesMap.
+    val hashTable = new JavaHashMap[UnsafeRow, CompactBuffer[UnsafeRow]](sizeEstimate)
+    val toUnsafe = UnsafeProjection.create(rowSchema)
+    val keyGenerator = UnsafeProjection.create(buildKeys)
+
+    // Create a mapping of buildKeys -> rows
+    while (input.hasNext) {
+      val currentRow = input.next()
+      val unsafeRow = if (currentRow.isInstanceOf[UnsafeRow]) {
+        currentRow.asInstanceOf[UnsafeRow]
+      } else {
+        toUnsafe(currentRow)
+      }
+      val rowKey = keyGenerator(unsafeRow)
+      if (!rowKey.anyNull) {
+        val existingMatchList = hashTable.get(rowKey)
+        val matchList = if (existingMatchList == null) {
+          val newMatchList = new CompactBuffer[UnsafeRow]()
+          hashTable.put(rowKey.copy(), newMatchList)
+          newMatchList
+        } else {
+          existingMatchList
+        }
+        matchList += unsafeRow.copy()
+      }
+    }
+
+    new UnsafeHashedRelation(hashTable)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
index db5be9f453674..4443455ef11fe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
@@ -39,6 +39,9 @@ case class LeftSemiJoinBNL(
 
   override def output: Seq[Attribute] = left.output
 
+  override def outputsUnsafeRows: Boolean = streamed.outputsUnsafeRows
+  override def canProcessUnsafeRows: Boolean = true
+
   /** The Streamed Relation */
   override def left: SparkPlan = streamed
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
index 9eaac817d9268..874712a4e739f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
@@ -43,10 +43,10 @@ case class LeftSemiJoinHash(
   protected override def doExecute(): RDD[InternalRow] = {
     right.execute().zipPartitions(left.execute()) { (buildIter, streamIter) =>
       if (condition.isEmpty) {
-        val hashSet = buildKeyHashSet(buildIter, copy = false)
+        val hashSet = buildKeyHashSet(buildIter)
         hashSemiJoin(streamIter, hashSet)
       } else {
-        val hashRelation = HashedRelation(buildIter, rightKeyGenerator)
+        val hashRelation = buildHashRelation(buildIter)
         hashSemiJoin(streamIter, hashRelation)
       }
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
index 5439e10a60b2a..948d0ccebceb0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
@@ -45,7 +45,7 @@ case class ShuffledHashJoin(
 
   protected override def doExecute(): RDD[InternalRow] = {
     buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
-      val hashed = HashedRelation(buildIter, buildSideKeyGenerator)
+      val hashed = buildHashRelation(buildIter)
       hashJoin(streamIter, hashed)
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index ab0a6ad56acde..f54f1edd38ec8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -50,24 +50,25 @@ case class ShuffledHashOuterJoin(
       // TODO this probably can be replaced by external sort (sort merged join?)
       joinType match {
         case LeftOuter =>
-          val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
-          val keyGenerator = newProjection(leftKeys, left.output)
+          val hashed = buildHashRelation(rightIter)
+          val keyGenerator = streamedKeyGenerator()
           leftIter.flatMap( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, rightHashTable.getOrElse(rowKey, EMPTY_LIST))
+            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey))
           })
 
         case RightOuter =>
-          val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
-          val keyGenerator = newProjection(rightKeys, right.output)
+          val hashed = buildHashRelation(leftIter)
+          val keyGenerator = streamedKeyGenerator()
           rightIter.flatMap ( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, leftHashTable.getOrElse(rowKey, EMPTY_LIST), joinedRow)
+            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow)
           })
 
         case FullOuter =>
+          // TODO(davies): use UnsafeRow
           val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
           val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
           (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
index 421d510e6782d..29f3beb3cb3c8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
@@ -29,6 +29,9 @@ import org.apache.spark.sql.catalyst.rules.Rule
  */
 @DeveloperApi
 case class ConvertToUnsafe(child: SparkPlan) extends UnaryNode {
+
+  require(UnsafeProjection.canSupport(child.schema), s"Cannot convert ${child.schema} to Unsafe")
+
   override def output: Seq[Attribute] = child.output
   override def outputsUnsafeRows: Boolean = true
   override def canProcessUnsafeRows: Boolean = false
@@ -93,11 +96,19 @@ private[sql] object EnsureRowFormats extends Rule[SparkPlan] {
       }
     case operator: SparkPlan if handlesBothSafeAndUnsafeRows(operator) =>
       if (operator.children.map(_.outputsUnsafeRows).toSet.size != 1) {
-        // If this operator's children produce both unsafe and safe rows, then convert everything
-        // to unsafe rows
-        operator.withNewChildren {
-          operator.children.map {
-            c => if (!c.outputsUnsafeRows) ConvertToUnsafe(c) else c
+        // If this operator's children produce both unsafe and safe rows,
+        // convert everything unsafe rows if all the schema of them are support by UnsafeRow
+        if (operator.children.forall(c => UnsafeProjection.canSupport(c.schema))) {
+          operator.withNewChildren {
+            operator.children.map {
+              c => if (!c.outputsUnsafeRows) ConvertToUnsafe(c) else c
+            }
+          }
+        } else {
+          operator.withNewChildren {
+            operator.children.map {
+              c => if (c.outputsUnsafeRows) ConvertToSafe(c) else c
+            }
           }
         }
       } else {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index 3854dc1b7a3d1..d36e2639376e7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -22,7 +22,7 @@ import java.io.ByteArrayOutputStream
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
-import org.apache.spark.sql.types.{IntegerType, StringType}
+import org.apache.spark.sql.types.{DataType, IntegerType, StringType}
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.memory.MemoryAllocator
 import org.apache.spark.unsafe.types.UTF8String
@@ -31,7 +31,7 @@ class UnsafeRowSuite extends SparkFunSuite {
   test("writeToStream") {
     val row = InternalRow.apply(UTF8String.fromString("hello"), UTF8String.fromString("world"), 123)
     val arrayBackedUnsafeRow: UnsafeRow =
-      UnsafeProjection.create(Seq(StringType, StringType, IntegerType)).apply(row)
+      UnsafeProjection.create(Array[DataType](StringType, StringType, IntegerType)).apply(row)
     assert(arrayBackedUnsafeRow.getBaseObject.isInstanceOf[Array[Byte]])
     val bytesFromArrayBackedRow: Array[Byte] = {
       val baos = new ByteArrayOutputStream()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 9d9858b1c6151..9dd2220f0967e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -19,7 +19,9 @@ package org.apache.spark.sql.execution.joins
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.Projection
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.SparkSqlSerializer
+import org.apache.spark.sql.types.{StructField, StructType, IntegerType}
 import org.apache.spark.util.collection.CompactBuffer
 
 
@@ -35,13 +37,13 @@ class HashedRelationSuite extends SparkFunSuite {
     val hashed = HashedRelation(data.iterator, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
 
-    assert(hashed.get(data(0)) == CompactBuffer[InternalRow](data(0)))
-    assert(hashed.get(data(1)) == CompactBuffer[InternalRow](data(1)))
+    assert(hashed.get(data(0)) === CompactBuffer[InternalRow](data(0)))
+    assert(hashed.get(data(1)) === CompactBuffer[InternalRow](data(1)))
     assert(hashed.get(InternalRow(10)) === null)
 
     val data2 = CompactBuffer[InternalRow](data(2))
     data2 += data(2)
-    assert(hashed.get(data(2)) == data2)
+    assert(hashed.get(data(2)) === data2)
   }
 
   test("UniqueKeyHashedRelation") {
@@ -49,15 +51,40 @@ class HashedRelationSuite extends SparkFunSuite {
     val hashed = HashedRelation(data.iterator, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
 
-    assert(hashed.get(data(0)) == CompactBuffer[InternalRow](data(0)))
-    assert(hashed.get(data(1)) == CompactBuffer[InternalRow](data(1)))
-    assert(hashed.get(data(2)) == CompactBuffer[InternalRow](data(2)))
+    assert(hashed.get(data(0)) === CompactBuffer[InternalRow](data(0)))
+    assert(hashed.get(data(1)) === CompactBuffer[InternalRow](data(1)))
+    assert(hashed.get(data(2)) === CompactBuffer[InternalRow](data(2)))
     assert(hashed.get(InternalRow(10)) === null)
 
     val uniqHashed = hashed.asInstanceOf[UniqueKeyHashedRelation]
-    assert(uniqHashed.getValue(data(0)) == data(0))
-    assert(uniqHashed.getValue(data(1)) == data(1))
-    assert(uniqHashed.getValue(data(2)) == data(2))
-    assert(uniqHashed.getValue(InternalRow(10)) == null)
+    assert(uniqHashed.getValue(data(0)) === data(0))
+    assert(uniqHashed.getValue(data(1)) === data(1))
+    assert(uniqHashed.getValue(data(2)) === data(2))
+    assert(uniqHashed.getValue(InternalRow(10)) === null)
+  }
+
+  test("UnsafeHashedRelation") {
+    val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
+    val buildKey = Seq(BoundReference(0, IntegerType, false))
+    val schema = StructType(StructField("a", IntegerType, true) :: Nil)
+    val hashed = UnsafeHashedRelation(data.iterator, buildKey, schema, 1)
+    assert(hashed.isInstanceOf[UnsafeHashedRelation])
+
+    val toUnsafeKey = UnsafeProjection.create(schema)
+    val unsafeData = data.map(toUnsafeKey(_).copy()).toArray
+    assert(hashed.get(unsafeData(0)) === CompactBuffer[InternalRow](unsafeData(0)))
+    assert(hashed.get(unsafeData(1)) === CompactBuffer[InternalRow](unsafeData(1)))
+    assert(hashed.get(toUnsafeKey(InternalRow(10))) === null)
+
+    val data2 = CompactBuffer[InternalRow](unsafeData(2).copy())
+    data2 += unsafeData(2).copy()
+    assert(hashed.get(unsafeData(2)) === data2)
+
+    val hashed2 = SparkSqlSerializer.deserialize(SparkSqlSerializer.serialize(hashed))
+      .asInstanceOf[UnsafeHashedRelation]
+    assert(hashed2.get(unsafeData(0)) === CompactBuffer[InternalRow](unsafeData(0)))
+    assert(hashed2.get(unsafeData(1)) === CompactBuffer[InternalRow](unsafeData(1)))
+    assert(hashed2.get(toUnsafeKey(InternalRow(10))) === null)
+    assert(hashed2.get(unsafeData(2)) === data2)
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
index 85cd02469adb7..61f483ced3217 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
@@ -44,12 +44,16 @@ public int hashInt(int input) {
     return fmix(h1, 4);
   }
 
-  public int hashUnsafeWords(Object baseObject, long baseOffset, int lengthInBytes) {
+  public int hashUnsafeWords(Object base, long offset, int lengthInBytes) {
+    return hashUnsafeWords(base, offset, lengthInBytes, seed);
+  }
+
+  public static int hashUnsafeWords(Object base, long offset, int lengthInBytes, int seed) {
     // This is based on Guava's `Murmur32_Hasher.processRemaining(ByteBuffer)` method.
     assert (lengthInBytes % 8 == 0): "lengthInBytes must be a multiple of 8 (word-aligned)";
     int h1 = seed;
-    for (int offset = 0; offset < lengthInBytes; offset += 4) {
-      int halfWord = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + offset);
+    for (int i = 0; i < lengthInBytes; i += 4) {
+      int halfWord = PlatformDependent.UNSAFE.getInt(base, offset + i);
       int k1 = mixK1(halfWord);
       h1 = mixH1(h1, k1);
     }

From 8486cd853104255b4eb013860bba793eef4e74e7 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 22 Jul 2015 13:06:01 -0700
Subject: [PATCH 0544/1454] [SPARK-9224] [MLLIB] OnlineLDA Performance
 Improvements

In-place updates, reduce number of transposes, and vectorize operations in OnlineLDA implementation.

Author: Feynman Liang <fliang@databricks.com>

Closes #7454 from feynmanliang/OnlineLDA-perf-improvements and squashes the following commits:

78b0f5a [Feynman Liang] Make in-place variables vals, fix BLAS error
7f62a55 [Feynman Liang] --amend
c62cb1e [Feynman Liang] Outer product for stats, revert Range slicing
aead650 [Feynman Liang] Range slice, in-place update, reduce transposes
---
 .../spark/mllib/clustering/LDAOptimizer.scala | 59 +++++++++----------
 1 file changed, 27 insertions(+), 32 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 8e5154b902d1d..b960ae6c0708d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -19,15 +19,15 @@ package org.apache.spark.mllib.clustering
 
 import java.util.Random
 
-import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, sum, normalize, kron}
-import breeze.numerics.{digamma, exp, abs}
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, normalize, sum}
+import breeze.numerics.{abs, digamma, exp}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
 import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
-import org.apache.spark.mllib.linalg.{Matrices, SparseVector, DenseVector, Vector}
+import org.apache.spark.mllib.linalg.{DenseVector, Matrices, SparseVector, Vector}
 import org.apache.spark.rdd.RDD
 
 /**
@@ -370,7 +370,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     iteration += 1
     val k = this.k
     val vocabSize = this.vocabSize
-    val Elogbeta = dirichletExpectation(lambda)
+    val Elogbeta = dirichletExpectation(lambda).t
     val expElogbeta = exp(Elogbeta)
     val alpha = this.alpha
     val gammaShape = this.gammaShape
@@ -385,41 +385,36 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
           case v => throw new IllegalArgumentException("Online LDA does not support vector type "
             + v.getClass)
         }
+        if (!ids.isEmpty) {
+
+          // Initialize the variational distribution q(theta|gamma) for the mini-batch
+          val gammad: BDV[Double] =
+            new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k) // K
+          val expElogthetad: BDV[Double] = exp(digamma(gammad) - digamma(sum(gammad))) // K
+          val expElogbetad: BDM[Double] = expElogbeta(ids, ::).toDenseMatrix // ids * K
+
+          val phinorm: BDV[Double] = expElogbetad * expElogthetad :+ 1e-100 // ids
+          var meanchange = 1D
+          val ctsVector = new BDV[Double](cts) // ids
+
+          // Iterate between gamma and phi until convergence
+          while (meanchange > 1e-3) {
+            val lastgamma = gammad.copy
+            //        K                  K * ids               ids
+            gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/ phinorm))) :+ alpha
+            expElogthetad := exp(digamma(gammad) - digamma(sum(gammad)))
+            phinorm := expElogbetad * expElogthetad :+ 1e-100
+            meanchange = sum(abs(gammad - lastgamma)) / k
+          }
 
-        // Initialize the variational distribution q(theta|gamma) for the mini-batch
-        var gammad = new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k).t // 1 * K
-        var Elogthetad = digamma(gammad) - digamma(sum(gammad))     // 1 * K
-        var expElogthetad = exp(Elogthetad)                         // 1 * K
-        val expElogbetad = expElogbeta(::, ids).toDenseMatrix       // K * ids
-
-        var phinorm = expElogthetad * expElogbetad + 1e-100         // 1 * ids
-        var meanchange = 1D
-        val ctsVector = new BDV[Double](cts).t                      // 1 * ids
-
-        // Iterate between gamma and phi until convergence
-        while (meanchange > 1e-3) {
-          val lastgamma = gammad
-          //        1*K                  1 * ids               ids * k
-          gammad = (expElogthetad :* ((ctsVector / phinorm) * expElogbetad.t)) + alpha
-          Elogthetad = digamma(gammad) - digamma(sum(gammad))
-          expElogthetad = exp(Elogthetad)
-          phinorm = expElogthetad * expElogbetad + 1e-100
-          meanchange = sum(abs(gammad - lastgamma)) / k
-        }
-
-        val m1 = expElogthetad.t
-        val m2 = (ctsVector / phinorm).t.toDenseVector
-        var i = 0
-        while (i < ids.size) {
-          stat(::, ids(i)) := stat(::, ids(i)) + m1 * m2(i)
-          i += 1
+          stat(::, ids) := expElogthetad.asDenseMatrix.t * (ctsVector :/ phinorm).asDenseMatrix
         }
       }
       Iterator(stat)
     }
 
     val statsSum: BDM[Double] = stats.reduce(_ += _)
-    val batchResult = statsSum :* expElogbeta
+    val batchResult = statsSum :* expElogbeta.t
 
     // Note that this is an optimization to avoid batch.count
     update(batchResult, iteration, (miniBatchFraction * corpusSize).ceil.toInt)

From cf21d05f8b5fae52b118fb8846f43d6fda1aea41 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 22 Jul 2015 13:28:09 -0700
Subject: [PATCH 0545/1454] [SPARK-4366] [SQL] [Follow-up] Fix SqlParser
 compiling warning.

Author: Yin Huai <yhuai@databricks.com>

Closes #7588 from yhuai/SPARK-4366-update1 and squashes the following commits:

25f5f36 [Yin Huai] Fix SqlParser Warning.
---
 .../main/scala/org/apache/spark/sql/catalyst/SqlParser.scala   | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index c04bd6cd85187..29cfc064da89a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -271,8 +271,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
       lexical.normalizeKeyword(udfName) match {
         case "sum" => SumDistinct(exprs.head)
         case "count" => CountDistinct(exprs)
-        case name => UnresolvedFunction(name, exprs, isDistinct = true)
-        case _ => throw new AnalysisException(s"function $udfName does not support DISTINCT")
+        case _ => UnresolvedFunction(udfName, exprs, isDistinct = true)
       }
     }
     | APPROXIMATE ~> ident ~ ("(" ~ DISTINCT ~> expression <~ ")") ^^ { case udfName ~ exp =>

From 1aca9c13c144fa336af6afcfa666128bf77c49d4 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 22 Jul 2015 15:07:05 -0700
Subject: [PATCH 0546/1454] [SPARK-8536] [MLLIB] Generalize OnlineLDAOptimizer
 to asymmetric document-topic Dirichlet priors

Modify `LDA` to take asymmetric document-topic prior distributions and `OnlineLDAOptimizer` to use the asymmetric prior during variational inference.

This PR only generalizes `OnlineLDAOptimizer` and the associated `LocalLDAModel`; `EMLDAOptimizer` and `DistributedLDAModel` still only support symmetric `alpha` (checked during `EMLDAOptimizer.initialize`).

Author: Feynman Liang <fliang@databricks.com>

Closes #7575 from feynmanliang/SPARK-8536-LDA-asymmetric-priors and squashes the following commits:

af8fbb7 [Feynman Liang] Fix merge errors
ef5821d [Feynman Liang] Merge remote-tracking branch 'apache/master' into SPARK-8536-LDA-asymmetric-priors
58f1d7b [Feynman Liang] Fix from review feedback
a6dcf70 [Feynman Liang] Change docConcentration interface and move LDAOptimizer validation to initialize, add sad path tests
72038ff [Feynman Liang] Add tests referenced against gensim
d4284fa [Feynman Liang] Generalize OnlineLDA to asymmetric priors, no tests
---
 .../apache/spark/mllib/clustering/LDA.scala   | 49 +++++++----
 .../spark/mllib/clustering/LDAOptimizer.scala | 27 ++++--
 .../spark/mllib/clustering/LDASuite.scala     | 82 +++++++++++++++++--
 3 files changed, 126 insertions(+), 32 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index a410547a72fda..ab124e6d77c5e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -23,11 +23,10 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx._
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
-
 /**
  * :: Experimental ::
  *
@@ -49,14 +48,15 @@ import org.apache.spark.util.Utils
 class LDA private (
     private var k: Int,
     private var maxIterations: Int,
-    private var docConcentration: Double,
+    private var docConcentration: Vector,
     private var topicConcentration: Double,
     private var seed: Long,
     private var checkpointInterval: Int,
     private var ldaOptimizer: LDAOptimizer) extends Logging {
 
-  def this() = this(k = 10, maxIterations = 20, docConcentration = -1, topicConcentration = -1,
-    seed = Utils.random.nextLong(), checkpointInterval = 10, ldaOptimizer = new EMLDAOptimizer)
+  def this() = this(k = 10, maxIterations = 20, docConcentration = Vectors.dense(-1),
+    topicConcentration = -1, seed = Utils.random.nextLong(), checkpointInterval = 10,
+    ldaOptimizer = new EMLDAOptimizer)
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
@@ -77,37 +77,50 @@ class LDA private (
    * Concentration parameter (commonly named "alpha") for the prior placed on documents'
    * distributions over topics ("theta").
    *
-   * This is the parameter to a symmetric Dirichlet distribution.
+   * This is the parameter to a Dirichlet distribution.
    */
-  def getDocConcentration: Double = this.docConcentration
+  def getDocConcentration: Vector = this.docConcentration
 
   /**
    * Concentration parameter (commonly named "alpha") for the prior placed on documents'
    * distributions over topics ("theta").
    *
-   * This is the parameter to a symmetric Dirichlet distribution, where larger values
-   * mean more smoothing (more regularization).
+   * This is the parameter to a Dirichlet distribution, where larger values mean more smoothing
+   * (more regularization).
    *
-   * If set to -1, then docConcentration is set automatically.
-   *  (default = -1 = automatic)
+   * If set to a singleton vector Vector(-1), then docConcentration is set automatically. If set to
+   * singleton vector Vector(t) where t != -1, then t is replicated to a vector of length k during
+   * [[LDAOptimizer.initialize()]]. Otherwise, the [[docConcentration]] vector must be length k.
+   * (default = Vector(-1) = automatic)
    *
    * Optimizer-specific parameter settings:
    *  - EM
-   *     - Value should be > 1.0
-   *     - default = (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows
-   *       Asuncion et al. (2009), who recommend a +1 adjustment for EM.
+   *     - Currently only supports symmetric distributions, so all values in the vector should be
+   *       the same.
+   *     - Values should be > 1.0
+   *     - default = uniformly (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows
+   *       from Asuncion et al. (2009), who recommend a +1 adjustment for EM.
    *  - Online
-   *     - Value should be >= 0
-   *     - default = (1.0 / k), following the implementation from
+   *     - Values should be >= 0
+   *     - default = uniformly (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
    */
-  def setDocConcentration(docConcentration: Double): this.type = {
+  def setDocConcentration(docConcentration: Vector): this.type = {
     this.docConcentration = docConcentration
     this
   }
 
+  /** Replicates Double to create a symmetric prior */
+  def setDocConcentration(docConcentration: Double): this.type = {
+    this.docConcentration = Vectors.dense(docConcentration)
+    this
+  }
+
   /** Alias for [[getDocConcentration]] */
-  def getAlpha: Double = getDocConcentration
+  def getAlpha: Vector = getDocConcentration
+
+  /** Alias for [[setDocConcentration()]] */
+  def setAlpha(alpha: Vector): this.type = setDocConcentration(alpha)
 
   /** Alias for [[setDocConcentration()]] */
   def setAlpha(alpha: Double): this.type = setDocConcentration(alpha)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index b960ae6c0708d..f4170a3d98dd8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -27,7 +27,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
 import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
-import org.apache.spark.mllib.linalg.{DenseVector, Matrices, SparseVector, Vector}
+import org.apache.spark.mllib.linalg.{DenseVector, Matrices, SparseVector, Vector, Vectors}
 import org.apache.spark.rdd.RDD
 
 /**
@@ -95,8 +95,11 @@ final class EMLDAOptimizer extends LDAOptimizer {
    * Compute bipartite term/doc graph.
    */
   override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer = {
+    val docConcentration = lda.getDocConcentration(0)
+    require({
+      lda.getDocConcentration.toArray.forall(_ == docConcentration)
+    }, "EMLDAOptimizer currently only supports symmetric document-topic priors")
 
-    val docConcentration = lda.getDocConcentration
     val topicConcentration = lda.getTopicConcentration
     val k = lda.getK
 
@@ -229,10 +232,10 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   private var vocabSize: Int = 0
 
   /** alias for docConcentration */
-  private var alpha: Double = 0
+  private var alpha: Vector = Vectors.dense(0)
 
   /** (private[clustering] for debugging)  Get docConcentration */
-  private[clustering] def getAlpha: Double = alpha
+  private[clustering] def getAlpha: Vector = alpha
 
   /** alias for topicConcentration */
   private var eta: Double = 0
@@ -343,7 +346,19 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     this.k = lda.getK
     this.corpusSize = docs.count()
     this.vocabSize = docs.first()._2.size
-    this.alpha = if (lda.getDocConcentration == -1) 1.0 / k else lda.getDocConcentration
+    this.alpha = if (lda.getDocConcentration.size == 1) {
+      if (lda.getDocConcentration(0) == -1) Vectors.dense(Array.fill(k)(1.0 / k))
+      else {
+        require(lda.getDocConcentration(0) >= 0, s"all entries in alpha must be >=0, got: $alpha")
+        Vectors.dense(Array.fill(k)(lda.getDocConcentration(0)))
+      }
+    } else {
+      require(lda.getDocConcentration.size == k, s"alpha must have length k, got: $alpha")
+      lda.getDocConcentration.foreachActive { case (_, x) =>
+        require(x >= 0, s"all entries in alpha must be >= 0, got: $alpha")
+      }
+      lda.getDocConcentration
+    }
     this.eta = if (lda.getTopicConcentration == -1) 1.0 / k else lda.getTopicConcentration
     this.randomGenerator = new Random(lda.getSeed)
 
@@ -372,7 +387,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     val vocabSize = this.vocabSize
     val Elogbeta = dirichletExpectation(lambda).t
     val expElogbeta = exp(Elogbeta)
-    val alpha = this.alpha
+    val alpha = this.alpha.toBreeze
     val gammaShape = this.gammaShape
 
     val stats: RDD[BDM[Double]] = batch.mapPartitions { docs =>
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 721a065658951..da70d9bd7c790 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import breeze.linalg.{DenseMatrix => BDM}
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors}
+import org.apache.spark.mllib.linalg.{DenseMatrix, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.util.Utils
@@ -132,22 +132,38 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("setter alias") {
     val lda = new LDA().setAlpha(2.0).setBeta(3.0)
-    assert(lda.getAlpha === 2.0)
-    assert(lda.getDocConcentration === 2.0)
+    assert(lda.getAlpha.toArray.forall(_ === 2.0))
+    assert(lda.getDocConcentration.toArray.forall(_ === 2.0))
     assert(lda.getBeta === 3.0)
     assert(lda.getTopicConcentration === 3.0)
   }
 
+  test("initializing with alpha length != k or 1 fails") {
+    intercept[IllegalArgumentException] {
+      val lda = new LDA().setK(2).setAlpha(Vectors.dense(1, 2, 3, 4))
+      val corpus = sc.parallelize(tinyCorpus, 2)
+      lda.run(corpus)
+    }
+  }
+
+  test("initializing with elements in alpha < 0 fails") {
+    intercept[IllegalArgumentException] {
+      val lda = new LDA().setK(4).setAlpha(Vectors.dense(-1, 2, 3, 4))
+      val corpus = sc.parallelize(tinyCorpus, 2)
+      lda.run(corpus)
+    }
+  }
+
   test("OnlineLDAOptimizer initialization") {
     val lda = new LDA().setK(2)
     val corpus = sc.parallelize(tinyCorpus, 2)
     val op = new OnlineLDAOptimizer().initialize(corpus, lda)
     op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau0(567)
-    assert(op.getAlpha == 0.5) // default 1.0 / k
-    assert(op.getEta == 0.5)   // default 1.0 / k
-    assert(op.getKappa == 0.9876)
-    assert(op.getMiniBatchFraction == 0.123)
-    assert(op.getTau0 == 567)
+    assert(op.getAlpha.toArray.forall(_ === 0.5)) // default 1.0 / k
+    assert(op.getEta === 0.5)   // default 1.0 / k
+    assert(op.getKappa === 0.9876)
+    assert(op.getMiniBatchFraction === 0.123)
+    assert(op.getTau0 === 567)
   }
 
   test("OnlineLDAOptimizer one iteration") {
@@ -218,6 +234,56 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("OnlineLDAOptimizer with asymmetric prior") {
+    def toydata: Array[(Long, Vector)] = Array(
+      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
+      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
+      Vectors.sparse(6, Array(4, 5), Array(1, 1))
+    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
+
+    val docs = sc.parallelize(toydata)
+    val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
+      .setGammaShape(1e10)
+    val lda = new LDA().setK(2)
+      .setDocConcentration(Vectors.dense(0.00001, 0.1))
+      .setTopicConcentration(0.01)
+      .setMaxIterations(100)
+      .setOptimizer(op)
+      .setSeed(12345)
+
+    val ldaModel = lda.run(docs)
+    val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 10)
+    val topics = topicIndices.map { case (terms, termWeights) =>
+      terms.zip(termWeights)
+    }
+
+    /* Verify results with Python:
+
+       import numpy as np
+       from gensim import models
+       corpus = [
+           [(0, 1.0), (1, 1.0)],
+           [(1, 1.0), (2, 1.0)],
+           [(0, 1.0), (2, 1.0)],
+           [(3, 1.0), (4, 1.0)],
+           [(3, 1.0), (5, 1.0)],
+           [(4, 1.0), (5, 1.0)]]
+       np.random.seed(10)
+       lda = models.ldamodel.LdaModel(
+           corpus=corpus, alpha=np.array([0.00001, 0.1]), num_topics=2, update_every=0, passes=100)
+       lda.print_topics()
+
+       > ['0.167*0 + 0.167*1 + 0.167*2 + 0.167*3 + 0.167*4 + 0.167*5',
+          '0.167*0 + 0.167*1 + 0.167*2 + 0.167*4 + 0.167*3 + 0.167*5']
+     */
+    topics.foreach { topic =>
+      assert(topic.forall { case (_, p) => p ~= 0.167 absTol 0.05 })
+    }
+  }
+
   test("model save/load") {
     // Test for LocalLDAModel.
     val localModel = new LocalLDAModel(tinyTopics)

From fe26584a1f5b472fb2e87aa7259aec822a619a3b Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Wed, 22 Jul 2015 15:28:09 -0700
Subject: [PATCH 0547/1454] [SPARK-9244] Increase some memory defaults

There are a few memory limits that people hit often and that we could
make higher, especially now that memory sizes have grown.

- spark.akka.frameSize: This defaults at 10 but is often hit for map
  output statuses in large shuffles. This memory is not fully allocated
  up-front, so we can just make this larger and still not affect jobs
  that never sent a status that large. We increase it to 128.

- spark.executor.memory: Defaults at 512m, which is really small. We
  increase it to 1g.

Author: Matei Zaharia <matei@databricks.com>

Closes #7586 from mateiz/configs and squashes the following commits:

ce0038a [Matei Zaharia] [SPARK-9244] Increase some memory defaults
---
 .../scala/org/apache/spark/SparkContext.scala |  2 +-
 .../org/apache/spark/util/AkkaUtils.scala     |  2 +-
 .../java/org/apache/spark/JavaAPISuite.java   |  2 +-
 .../apache/spark/ContextCleanerSuite.scala    |  4 ++--
 .../org/apache/spark/DistributedSuite.scala   | 16 +++++++--------
 .../scala/org/apache/spark/DriverSuite.scala  |  2 +-
 .../spark/ExternalShuffleServiceSuite.scala   |  2 +-
 .../org/apache/spark/FileServerSuite.scala    |  6 +++---
 .../apache/spark/JobCancellationSuite.scala   |  4 ++--
 .../scala/org/apache/spark/ShuffleSuite.scala | 20 +++++++++----------
 .../SparkContextSchedulerCreationSuite.scala  |  2 +-
 .../spark/broadcast/BroadcastSuite.scala      |  8 ++++----
 .../spark/deploy/LogUrlsStandaloneSuite.scala |  4 ++--
 .../spark/deploy/SparkSubmitSuite.scala       |  4 ++--
 .../CoarseGrainedSchedulerBackendSuite.scala  |  2 +-
 .../scheduler/EventLoggingListenerSuite.scala |  2 +-
 .../spark/scheduler/ReplayListenerSuite.scala |  2 +-
 .../SparkListenerWithClusterSuite.scala       |  2 +-
 .../KryoSerializerDistributedSuite.scala      |  2 +-
 .../ExternalAppendOnlyMapSuite.scala          | 10 +++++-----
 .../util/collection/ExternalSorterSuite.scala | 14 ++++++-------
 docs/configuration.md                         | 16 +++++++--------
 .../mllib/util/LocalClusterSparkContext.scala |  2 +-
 python/pyspark/tests.py                       |  6 +++---
 .../org/apache/spark/repl/ReplSuite.scala     | 10 +++++-----
 .../org/apache/spark/repl/ReplSuite.scala     |  8 ++++----
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |  4 ++--
 27 files changed, 78 insertions(+), 80 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index d00c012d80560..4976e5eb49468 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -471,7 +471,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       .orElse(Option(System.getenv("SPARK_MEM"))
       .map(warnSparkMem))
       .map(Utils.memoryStringToMb)
-      .getOrElse(512)
+      .getOrElse(1024)
 
     // Convert java options to env vars as a work around
     // since we can't set env vars directly in sbt.
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index c179833e5b06a..78e7ddc27d1c7 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -128,7 +128,7 @@ private[spark] object AkkaUtils extends Logging {
 
   /** Returns the configured max frame size for Akka messages in bytes. */
   def maxFrameSizeBytes(conf: SparkConf): Int = {
-    val frameSizeInMB = conf.getInt("spark.akka.frameSize", 10)
+    val frameSizeInMB = conf.getInt("spark.akka.frameSize", 128)
     if (frameSizeInMB > AKKA_MAX_FRAME_SIZE_IN_MB) {
       throw new IllegalArgumentException(
         s"spark.akka.frameSize should not be greater than $AKKA_MAX_FRAME_SIZE_IN_MB MB")
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index 1b04a3b1cff0e..e948ca33471a4 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -1783,7 +1783,7 @@ public void testGuavaOptional() {
     // Stop the context created in setUp() and start a local-cluster one, to force usage of the
     // assembly.
     sc.stop();
-    JavaSparkContext localCluster = new JavaSparkContext("local-cluster[1,1,512]", "JavaAPISuite");
+    JavaSparkContext localCluster = new JavaSparkContext("local-cluster[1,1,1024]", "JavaAPISuite");
     try {
       JavaRDD<Integer> rdd1 = localCluster.parallelize(Arrays.asList(1, 2, null), 3);
       JavaRDD<Optional<Integer>> rdd2 = rdd1.map(
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 501fe186bfd7c..26858ef2774fc 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -292,7 +292,7 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase {
     sc.stop()
 
     val conf2 = new SparkConf()
-      .setMaster("local-cluster[2, 1, 512]")
+      .setMaster("local-cluster[2, 1, 1024]")
       .setAppName("ContextCleanerSuite")
       .set("spark.cleaner.referenceTracking.blocking", "true")
       .set("spark.cleaner.referenceTracking.blocking.shuffle", "true")
@@ -370,7 +370,7 @@ class SortShuffleContextCleanerSuite extends ContextCleanerSuiteBase(classOf[Sor
     sc.stop()
 
     val conf2 = new SparkConf()
-      .setMaster("local-cluster[2, 1, 512]")
+      .setMaster("local-cluster[2, 1, 1024]")
       .setAppName("ContextCleanerSuite")
       .set("spark.cleaner.referenceTracking.blocking", "true")
       .set("spark.cleaner.referenceTracking.blocking.shuffle", "true")
diff --git a/core/src/test/scala/org/apache/spark/DistributedSuite.scala b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
index 2300bcff4f118..600c1403b0344 100644
--- a/core/src/test/scala/org/apache/spark/DistributedSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
@@ -29,7 +29,7 @@ class NotSerializableExn(val notSer: NotSerializableClass) extends Throwable() {
 
 class DistributedSuite extends SparkFunSuite with Matchers with LocalSparkContext {
 
-  val clusterUrl = "local-cluster[2,1,512]"
+  val clusterUrl = "local-cluster[2,1,1024]"
 
   test("task throws not serializable exception") {
     // Ensures that executors do not crash when an exn is not serializable. If executors crash,
@@ -40,7 +40,7 @@ class DistributedSuite extends SparkFunSuite with Matchers with LocalSparkContex
     val numSlaves = 3
     val numPartitions = 10
 
-    sc = new SparkContext("local-cluster[%s,1,512]".format(numSlaves), "test")
+    sc = new SparkContext("local-cluster[%s,1,1024]".format(numSlaves), "test")
     val data = sc.parallelize(1 to 100, numPartitions).
       map(x => throw new NotSerializableExn(new NotSerializableClass))
     intercept[SparkException] {
@@ -50,16 +50,16 @@ class DistributedSuite extends SparkFunSuite with Matchers with LocalSparkContex
   }
 
   test("local-cluster format") {
-    sc = new SparkContext("local-cluster[2,1,512]", "test")
+    sc = new SparkContext("local-cluster[2,1,1024]", "test")
     assert(sc.parallelize(1 to 2, 2).count() == 2)
     resetSparkContext()
-    sc = new SparkContext("local-cluster[2 , 1 , 512]", "test")
+    sc = new SparkContext("local-cluster[2 , 1 , 1024]", "test")
     assert(sc.parallelize(1 to 2, 2).count() == 2)
     resetSparkContext()
-    sc = new SparkContext("local-cluster[2, 1, 512]", "test")
+    sc = new SparkContext("local-cluster[2, 1, 1024]", "test")
     assert(sc.parallelize(1 to 2, 2).count() == 2)
     resetSparkContext()
-    sc = new SparkContext("local-cluster[ 2, 1, 512 ]", "test")
+    sc = new SparkContext("local-cluster[ 2, 1, 1024 ]", "test")
     assert(sc.parallelize(1 to 2, 2).count() == 2)
     resetSparkContext()
   }
@@ -276,7 +276,7 @@ class DistributedSuite extends SparkFunSuite with Matchers with LocalSparkContex
     DistributedSuite.amMaster = true
     // Using more than two nodes so we don't have a symmetric communication pattern and might
     // cache a partially correct list of peers.
-    sc = new SparkContext("local-cluster[3,1,512]", "test")
+    sc = new SparkContext("local-cluster[3,1,1024]", "test")
     for (i <- 1 to 3) {
       val data = sc.parallelize(Seq(true, false, false, false), 4)
       data.persist(StorageLevel.MEMORY_ONLY_2)
@@ -294,7 +294,7 @@ class DistributedSuite extends SparkFunSuite with Matchers with LocalSparkContex
 
   test("unpersist RDDs") {
     DistributedSuite.amMaster = true
-    sc = new SparkContext("local-cluster[3,1,512]", "test")
+    sc = new SparkContext("local-cluster[3,1,1024]", "test")
     val data = sc.parallelize(Seq(true, false, false, false), 4)
     data.persist(StorageLevel.MEMORY_ONLY_2)
     data.count
diff --git a/core/src/test/scala/org/apache/spark/DriverSuite.scala b/core/src/test/scala/org/apache/spark/DriverSuite.scala
index b2262033ca238..454b7e607a51b 100644
--- a/core/src/test/scala/org/apache/spark/DriverSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DriverSuite.scala
@@ -29,7 +29,7 @@ class DriverSuite extends SparkFunSuite with Timeouts {
 
   ignore("driver should exit after finishing without cleanup (SPARK-530)") {
     val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
-    val masters = Table("master", "local", "local-cluster[2,1,512]")
+    val masters = Table("master", "local", "local-cluster[2,1,1024]")
     forAll(masters) { (master: String) =>
       val process = Utils.executeCommand(
         Seq(s"$sparkHome/bin/spark-class", "org.apache.spark.DriverWithoutCleanup", master),
diff --git a/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala b/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
index 140012226fdbb..c38d70252add1 100644
--- a/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
@@ -51,7 +51,7 @@ class ExternalShuffleServiceSuite extends ShuffleSuite with BeforeAndAfterAll {
 
   // This test ensures that the external shuffle service is actually in use for the other tests.
   test("using external shuffle service") {
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     sc.env.blockManager.externalShuffleServiceEnabled should equal(true)
     sc.env.blockManager.shuffleClient.getClass should equal(classOf[ExternalShuffleClient])
 
diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala
index 876418aa13029..1255e71af6c0b 100644
--- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala
@@ -139,7 +139,7 @@ class FileServerSuite extends SparkFunSuite with LocalSparkContext {
   }
 
   test("Distributing files on a standalone cluster") {
-    sc = new SparkContext("local-cluster[1,1,512]", "test", newConf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", newConf)
     sc.addFile(tmpFile.toString)
     val testData = Array((1, 1), (1, 1), (2, 1), (3, 5), (2, 2), (3, 0))
     val result = sc.parallelize(testData).reduceByKey {
@@ -153,7 +153,7 @@ class FileServerSuite extends SparkFunSuite with LocalSparkContext {
   }
 
   test ("Dynamically adding JARS on a standalone cluster") {
-    sc = new SparkContext("local-cluster[1,1,512]", "test", newConf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", newConf)
     sc.addJar(tmpJarUrl)
     val testData = Array((1, 1))
     sc.parallelize(testData).foreach { x =>
@@ -164,7 +164,7 @@ class FileServerSuite extends SparkFunSuite with LocalSparkContext {
   }
 
   test ("Dynamically adding JARS on a standalone cluster using local: URL") {
-    sc = new SparkContext("local-cluster[1,1,512]", "test", newConf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", newConf)
     sc.addJar(tmpJarUrl.replace("file", "local"))
     val testData = Array((1, 1))
     sc.parallelize(testData).foreach { x =>
diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala
index 340a9e327107e..1168eb0b802f2 100644
--- a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala
@@ -64,7 +64,7 @@ class JobCancellationSuite extends SparkFunSuite with Matchers with BeforeAndAft
 
   test("cluster mode, FIFO scheduler") {
     val conf = new SparkConf().set("spark.scheduler.mode", "FIFO")
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     testCount()
     testTake()
     // Make sure we can still launch tasks.
@@ -75,7 +75,7 @@ class JobCancellationSuite extends SparkFunSuite with Matchers with BeforeAndAft
     val conf = new SparkConf().set("spark.scheduler.mode", "FAIR")
     val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile()
     conf.set("spark.scheduler.allocation.file", xmlPath)
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     testCount()
     testTake()
     // Make sure we can still launch tasks.
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index b68102bfb949f..d91b799ecfc08 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -47,7 +47,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
   }
 
   test("shuffle non-zero block size") {
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     val NUM_BLOCKS = 3
 
     val a = sc.parallelize(1 to 10, 2)
@@ -73,7 +73,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
   test("shuffle serializer") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     val a = sc.parallelize(1 to 10, 2)
     val b = a.map { x =>
       (x, new NonJavaSerializableClass(x * 2))
@@ -89,7 +89,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
   test("zero sized blocks") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
 
     // 201 partitions (greater than "spark.shuffle.sort.bypassMergeThreshold") from 4 keys
     val NUM_BLOCKS = 201
@@ -116,7 +116,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
   test("zero sized blocks without kryo") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
 
     // 201 partitions (greater than "spark.shuffle.sort.bypassMergeThreshold") from 4 keys
     val NUM_BLOCKS = 201
@@ -141,7 +141,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
   test("shuffle on mutable pairs") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
     val data = Array(p(1, 1), p(1, 2), p(1, 3), p(2, 1))
     val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2)
@@ -154,7 +154,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
   test("sorting on mutable pairs") {
     // This is not in SortingSuite because of the local cluster setup.
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
     val data = Array(p(1, 11), p(3, 33), p(100, 100), p(2, 22))
     val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2)
@@ -168,7 +168,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
   test("cogroup using mutable pairs") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
     val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1))
     val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"), p(3, "3"))
@@ -195,7 +195,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
   test("subtract mutable pairs") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
     val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1), p(3, 33))
     val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"))
@@ -210,7 +210,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
   test("sort with Java non serializable class - Kryo") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
     val myConf = conf.clone().set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-    sc = new SparkContext("local-cluster[2,1,512]", "test", myConf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", myConf)
     val a = sc.parallelize(1 to 10, 2)
     val b = a.map { x =>
       (new NonJavaSerializableClass(x), x)
@@ -223,7 +223,7 @@ abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkC
 
   test("sort with Java non serializable class - Java") {
     // Use a local cluster with 2 processes to make sure there are both local and remote blocks
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     val a = sc.parallelize(1 to 10, 2)
     val b = a.map { x =>
       (new NonJavaSerializableClass(x), x)
diff --git a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
index dba46f101c580..e5a14a69ef05f 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
@@ -123,7 +123,7 @@ class SparkContextSchedulerCreationSuite
   }
 
   test("local-cluster") {
-    createTaskScheduler("local-cluster[3, 14, 512]").backend match {
+    createTaskScheduler("local-cluster[3, 14, 1024]").backend match {
       case s: SparkDeploySchedulerBackend => // OK
       case _ => fail()
     }
diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
index c054c718075f8..48e74f06f79b1 100644
--- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
+++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
@@ -69,7 +69,7 @@ class BroadcastSuite extends SparkFunSuite with LocalSparkContext {
     val conf = httpConf.clone
     conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
     conf.set("spark.broadcast.compress", "true")
-    sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", conf)
+    sc = new SparkContext("local-cluster[%d, 1, 1024]".format(numSlaves), "test", conf)
     val list = List[Int](1, 2, 3, 4)
     val broadcast = sc.broadcast(list)
     val results = sc.parallelize(1 to numSlaves).map(x => (x, broadcast.value.sum))
@@ -97,7 +97,7 @@ class BroadcastSuite extends SparkFunSuite with LocalSparkContext {
     val conf = torrentConf.clone
     conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
     conf.set("spark.broadcast.compress", "true")
-    sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", conf)
+    sc = new SparkContext("local-cluster[%d, 1, 1024]".format(numSlaves), "test", conf)
     val list = List[Int](1, 2, 3, 4)
     val broadcast = sc.broadcast(list)
     val results = sc.parallelize(1 to numSlaves).map(x => (x, broadcast.value.sum))
@@ -125,7 +125,7 @@ class BroadcastSuite extends SparkFunSuite with LocalSparkContext {
   test("Test Lazy Broadcast variables with TorrentBroadcast") {
     val numSlaves = 2
     val conf = torrentConf.clone
-    sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", conf)
+    sc = new SparkContext("local-cluster[%d, 1, 1024]".format(numSlaves), "test", conf)
     val rdd = sc.parallelize(1 to numSlaves)
 
     val results = new DummyBroadcastClass(rdd).doSomething()
@@ -308,7 +308,7 @@ class BroadcastSuite extends SparkFunSuite with LocalSparkContext {
 
     sc = if (distributed) {
       val _sc =
-        new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", broadcastConf)
+        new SparkContext("local-cluster[%d, 1, 1024]".format(numSlaves), "test", broadcastConf)
       // Wait until all salves are up
       _sc.jobProgressListener.waitUntilExecutorsUp(numSlaves, 10000)
       _sc
diff --git a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
index ddc92814c0acf..cbd2aee10c0e2 100644
--- a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
@@ -33,7 +33,7 @@ class LogUrlsStandaloneSuite extends SparkFunSuite with LocalSparkContext {
   private val WAIT_TIMEOUT_MILLIS = 10000
 
   test("verify that correct log urls get propagated from workers") {
-    sc = new SparkContext("local-cluster[2,1,512]", "test")
+    sc = new SparkContext("local-cluster[2,1,1024]", "test")
 
     val listener = new SaveExecutorInfo
     sc.addSparkListener(listener)
@@ -66,7 +66,7 @@ class LogUrlsStandaloneSuite extends SparkFunSuite with LocalSparkContext {
     }
     val conf = new MySparkConf().set(
       "spark.extraListeners", classOf[SaveExecutorInfo].getName)
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
 
     // Trigger a job so that executors get added
     sc.parallelize(1 to 100, 4).map(_.toString).count()
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 343d28eef8359..aa78bfe30974c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -337,7 +337,7 @@ class SparkSubmitSuite
     val args = Seq(
       "--class", JarCreationTest.getClass.getName.stripSuffix("$"),
       "--name", "testApp",
-      "--master", "local-cluster[2,1,512]",
+      "--master", "local-cluster[2,1,1024]",
       "--jars", jarsString,
       unusedJar.toString, "SparkSubmitClassA", "SparkSubmitClassB")
     runSparkSubmit(args)
@@ -352,7 +352,7 @@ class SparkSubmitSuite
       val args = Seq(
         "--class", JarCreationTest.getClass.getName.stripSuffix("$"),
         "--name", "testApp",
-        "--master", "local-cluster[2,1,512]",
+        "--master", "local-cluster[2,1,1024]",
         "--packages", Seq(main, dep).mkString(","),
         "--repositories", repo,
         "--conf", "spark.ui.enabled=false",
diff --git a/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
index 34145691153ce..eef6aafa624ee 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/CoarseGrainedSchedulerBackendSuite.scala
@@ -26,7 +26,7 @@ class CoarseGrainedSchedulerBackendSuite extends SparkFunSuite with LocalSparkCo
     val conf = new SparkConf
     conf.set("spark.akka.frameSize", "1")
     conf.set("spark.default.parallelism", "1")
-    sc = new SparkContext("local-cluster[2 , 1 , 512]", "test", conf)
+    sc = new SparkContext("local-cluster[2, 1, 1024]", "test", conf)
     val frameSize = AkkaUtils.maxFrameSizeBytes(sc.conf)
     val buffer = new SerializableBuffer(java.nio.ByteBuffer.allocate(2 * frameSize))
     val larger = sc.parallelize(Seq(buffer))
diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
index f681f21b6205e..5cb2d4225d281 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
@@ -180,7 +180,7 @@ class EventLoggingListenerSuite extends SparkFunSuite with LocalSparkContext wit
     // into SPARK-6688.
     val conf = getLoggingConf(testDirPath, compressionCodec)
       .set("spark.hadoop.fs.defaultFS", "unsupported://example.com")
-    val sc = new SparkContext("local-cluster[2,2,512]", "test", conf)
+    val sc = new SparkContext("local-cluster[2,2,1024]", "test", conf)
     assert(sc.eventLogger.isDefined)
     val eventLogger = sc.eventLogger.get
     val eventLogPath = eventLogger.logPath
diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
index 4e3defb43a021..103fc19369c97 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
@@ -102,7 +102,7 @@ class ReplayListenerSuite extends SparkFunSuite with BeforeAndAfter {
     fileSystem.mkdirs(logDirPath)
 
     val conf = EventLoggingListenerSuite.getLoggingConf(logDirPath, codecName)
-    val sc = new SparkContext("local-cluster[2,1,512]", "Test replay", conf)
+    val sc = new SparkContext("local-cluster[2,1,1024]", "Test replay", conf)
 
     // Run a few jobs
     sc.parallelize(1 to 100, 1).count()
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerWithClusterSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerWithClusterSuite.scala
index d97fba00976d2..d1e23ed527ff1 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerWithClusterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerWithClusterSuite.scala
@@ -34,7 +34,7 @@ class SparkListenerWithClusterSuite extends SparkFunSuite with LocalSparkContext
   val WAIT_TIMEOUT_MILLIS = 10000
 
   before {
-    sc = new SparkContext("local-cluster[2,1,512]", "SparkListenerSuite")
+    sc = new SparkContext("local-cluster[2,1,1024]", "SparkListenerSuite")
   }
 
   test("SparkListener sends executor added message") {
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala
index 353b97469cd11..935a091f14f9b 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerDistributedSuite.scala
@@ -35,7 +35,7 @@ class KryoSerializerDistributedSuite extends SparkFunSuite {
     val jar = TestUtils.createJarWithClasses(List(AppJarRegistrator.customClassName))
     conf.setJars(List(jar.getPath))
 
-    val sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    val sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
     val original = Thread.currentThread.getContextClassLoader
     val loader = new java.net.URLClassLoader(Array(jar), Utils.getContextOrSparkClassLoader)
     SparkEnv.get.serializer.setDefaultClassLoader(loader)
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index 79eba61a87251..9c362f0de7076 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -244,7 +244,7 @@ class ExternalAppendOnlyMapSuite extends SparkFunSuite with LocalSparkContext {
   private def testSimpleSpilling(codec: Option[String] = None): Unit = {
     val conf = createSparkConf(loadDefaults = true, codec)  // Load defaults for Spark home
     conf.set("spark.shuffle.memoryFraction", "0.001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
 
     // reduceByKey - should spill ~8 times
     val rddA = sc.parallelize(0 until 100000).map(i => (i/2, i))
@@ -292,7 +292,7 @@ class ExternalAppendOnlyMapSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with hash collisions") {
     val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
     val map = createExternalMap[String]
 
     val collisionPairs = Seq(
@@ -341,7 +341,7 @@ class ExternalAppendOnlyMapSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with many hash collisions") {
     val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.0001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
     val map = new ExternalAppendOnlyMap[FixedHashObject, Int, Int](_ => 1, _ + _, _ + _)
 
     // Insert 10 copies each of lots of objects whose hash codes are either 0 or 1. This causes
@@ -366,7 +366,7 @@ class ExternalAppendOnlyMapSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with hash collisions using the Int.MaxValue key") {
     val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
     val map = createExternalMap[Int]
 
     (1 to 100000).foreach { i => map.insert(i, i) }
@@ -383,7 +383,7 @@ class ExternalAppendOnlyMapSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with null keys and values") {
     val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
     val map = createExternalMap[Int]
 
     map.insertAll((1 to 100000).iterator.map(i => (i, i)))
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
index 9cefa612f5491..986cd8623d145 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
@@ -176,7 +176,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
   def testSpillingInLocalCluster(conf: SparkConf) {
     conf.set("spark.shuffle.memoryFraction", "0.001")
     conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
 
     // reduceByKey - should spill ~8 times
     val rddA = sc.parallelize(0 until 100000).map(i => (i/2, i))
@@ -254,7 +254,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
   def spillingInLocalClusterWithManyReduceTasks(conf: SparkConf) {
     conf.set("spark.shuffle.memoryFraction", "0.001")
     conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager")
-    sc = new SparkContext("local-cluster[2,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
 
     // reduceByKey - should spill ~4 times per executor
     val rddA = sc.parallelize(0 until 100000).map(i => (i/2, i))
@@ -554,7 +554,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with hash collisions") {
     val conf = createSparkConf(true, false)
     conf.set("spark.shuffle.memoryFraction", "0.001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
 
     def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer[String](i)
     def mergeValue(buffer: ArrayBuffer[String], i: String): ArrayBuffer[String] = buffer += i
@@ -611,7 +611,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with many hash collisions") {
     val conf = createSparkConf(true, false)
     conf.set("spark.shuffle.memoryFraction", "0.0001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
 
     val agg = new Aggregator[FixedHashObject, Int, Int](_ => 1, _ + _, _ + _)
     val sorter = new ExternalSorter[FixedHashObject, Int, Int](Some(agg), None, None, None)
@@ -634,7 +634,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with hash collisions using the Int.MaxValue key") {
     val conf = createSparkConf(true, false)
     conf.set("spark.shuffle.memoryFraction", "0.001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
 
     def createCombiner(i: Int): ArrayBuffer[Int] = ArrayBuffer[Int](i)
     def mergeValue(buffer: ArrayBuffer[Int], i: Int): ArrayBuffer[Int] = buffer += i
@@ -658,7 +658,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
   test("spilling with null keys and values") {
     val conf = createSparkConf(true, false)
     conf.set("spark.shuffle.memoryFraction", "0.001")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
 
     def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer[String](i)
     def mergeValue(buffer: ArrayBuffer[String], i: String): ArrayBuffer[String] = buffer += i
@@ -695,7 +695,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
   def sortWithoutBreakingSortingContracts(conf: SparkConf) {
     conf.set("spark.shuffle.memoryFraction", "0.01")
     conf.set("spark.shuffle.manager", "sort")
-    sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+    sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
 
     // Using wrongOrdering to show integer overflow introduced exception.
     val rand = new Random(100L)
diff --git a/docs/configuration.md b/docs/configuration.md
index 8a186ee51c1ca..fea259204ae68 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -31,7 +31,6 @@ which can help detect bugs that only exist when we run in a distributed context.
 val conf = new SparkConf()
              .setMaster("local[2]")
              .setAppName("CountingSheep")
-             .set("spark.executor.memory", "1g")
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
@@ -84,7 +83,7 @@ Running `./bin/spark-submit --help` will show the entire list of these options.
 each line consists of a key and a value separated by whitespace. For example:
 
     spark.master            spark://5.6.7.8:7077
-    spark.executor.memory   512m
+    spark.executor.memory   4g
     spark.eventLog.enabled  true
     spark.serializer        org.apache.spark.serializer.KryoSerializer
 
@@ -150,10 +149,9 @@ of the most common options to set are:
 </tr>
 <tr>
   <td><code>spark.executor.memory</code></td>
-  <td>512m</td>
+  <td>1g</td>
   <td>
-    Amount of memory to use per executor process, in the same format as JVM memory strings
-    (e.g. <code>512m</code>, <code>2g</code>).
+    Amount of memory to use per executor process (e.g. <code>2g</code>, <code>8g</code>).
   </td>
 </tr>
 <tr>
@@ -886,11 +884,11 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.akka.frameSize</code></td>
-  <td>10</td>
+  <td>128</td>
   <td>
-    Maximum message size to allow in "control plane" communication (for serialized tasks and task
-    results), in MB. Increase this if your tasks need to send back large results to the driver
-    (e.g. using <code>collect()</code> on a large dataset).
+    Maximum message size to allow in "control plane" communication; generally only applies to map
+    output size information sent between executors and the driver. Increase this if you are running
+    jobs with many thousands of map and reduce tasks and see messages about the frame size.
   </td>
 </tr>
 <tr>
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala
index 5e9101cdd3804..525ab68c7921a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/LocalClusterSparkContext.scala
@@ -26,7 +26,7 @@ trait LocalClusterSparkContext extends BeforeAndAfterAll { self: Suite =>
 
   override def beforeAll() {
     val conf = new SparkConf()
-      .setMaster("local-cluster[2, 1, 512]")
+      .setMaster("local-cluster[2, 1, 1024]")
       .setAppName("test-cluster")
       .set("spark.akka.frameSize", "1") // set to 1MB to detect direct serialization of data
     sc = new SparkContext(conf)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 5be9937cb04b2..8bfed074c9052 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -1823,7 +1823,7 @@ def test_module_dependency_on_cluster(self):
             |    return x + 1
             """)
         proc = subprocess.Popen([self.sparkSubmit, "--py-files", zip, "--master",
-                                "local-cluster[1,1,512]", script],
+                                "local-cluster[1,1,1024]", script],
                                 stdout=subprocess.PIPE)
         out, err = proc.communicate()
         self.assertEqual(0, proc.returncode)
@@ -1857,7 +1857,7 @@ def test_package_dependency_on_cluster(self):
         self.create_spark_package("a:mylib:0.1")
         proc = subprocess.Popen([self.sparkSubmit, "--packages", "a:mylib:0.1", "--repositories",
                                  "file:" + self.programDir, "--master",
-                                 "local-cluster[1,1,512]", script], stdout=subprocess.PIPE)
+                                 "local-cluster[1,1,1024]", script], stdout=subprocess.PIPE)
         out, err = proc.communicate()
         self.assertEqual(0, proc.returncode)
         self.assertIn("[2, 3, 4]", out.decode('utf-8'))
@@ -1876,7 +1876,7 @@ def test_single_script_on_cluster(self):
         # this will fail if you have different spark.executor.memory
         # in conf/spark-defaults.conf
         proc = subprocess.Popen(
-            [self.sparkSubmit, "--master", "local-cluster[1,1,512]", script],
+            [self.sparkSubmit, "--master", "local-cluster[1,1,1024]", script],
             stdout=subprocess.PIPE)
         out, err = proc.communicate()
         self.assertEqual(0, proc.returncode)
diff --git a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index f150fec7db945..5674dcd669bee 100644
--- a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -211,7 +211,7 @@ class ReplSuite extends SparkFunSuite {
   }
 
   test("local-cluster mode") {
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |var v = 7
         |def getV() = v
@@ -233,7 +233,7 @@ class ReplSuite extends SparkFunSuite {
   }
 
   test("SPARK-1199 two instances of same class don't type check.") {
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |case class Sum(exp: String, exp2: String)
         |val a = Sum("A", "B")
@@ -256,7 +256,7 @@ class ReplSuite extends SparkFunSuite {
 
   test("SPARK-2576 importing SQLContext.implicits._") {
     // We need to use local-cluster to test this case.
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |val sqlContext = new org.apache.spark.sql.SQLContext(sc)
         |import sqlContext.implicits._
@@ -325,9 +325,9 @@ class ReplSuite extends SparkFunSuite {
     assertDoesNotContain("Exception", output)
     assertContains("ret: Array[Foo] = Array(Foo(1),", output)
   }
-  
+
   test("collecting objects of class defined in repl - shuffling") {
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |case class Foo(i: Int)
         |val list = List((1, Foo(1)), (1, Foo(2)))
diff --git a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
index e1cee97de32bc..bf8997998e00d 100644
--- a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
+++ b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
@@ -209,7 +209,7 @@ class ReplSuite extends SparkFunSuite {
   }
 
   test("local-cluster mode") {
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |var v = 7
         |def getV() = v
@@ -231,7 +231,7 @@ class ReplSuite extends SparkFunSuite {
   }
 
   test("SPARK-1199 two instances of same class don't type check.") {
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |case class Sum(exp: String, exp2: String)
         |val a = Sum("A", "B")
@@ -254,7 +254,7 @@ class ReplSuite extends SparkFunSuite {
 
   test("SPARK-2576 importing SQLContext.createDataFrame.") {
     // We need to use local-cluster to test this case.
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |val sqlContext = new org.apache.spark.sql.SQLContext(sc)
         |import sqlContext.implicits._
@@ -314,7 +314,7 @@ class ReplSuite extends SparkFunSuite {
   }
 
   test("collecting objects of class defined in repl - shuffling") {
-    val output = runInterpreter("local-cluster[1,1,512]",
+    val output = runInterpreter("local-cluster[1,1,1024]",
       """
         |case class Foo(i: Int)
         |val list = List((1, Foo(1)), (1, Foo(2)))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index bee2ecbedb244..72b35959a491b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -53,7 +53,7 @@ class HiveSparkSubmitSuite
     val args = Seq(
       "--class", SparkSubmitClassLoaderTest.getClass.getName.stripSuffix("$"),
       "--name", "SparkSubmitClassLoaderTest",
-      "--master", "local-cluster[2,1,512]",
+      "--master", "local-cluster[2,1,1024]",
       "--jars", jarsString,
       unusedJar.toString, "SparkSubmitClassA", "SparkSubmitClassB")
     runSparkSubmit(args)
@@ -64,7 +64,7 @@ class HiveSparkSubmitSuite
     val args = Seq(
       "--class", SparkSQLConfTest.getClass.getName.stripSuffix("$"),
       "--name", "SparkSQLConfTest",
-      "--master", "local-cluster[2,1,512]",
+      "--master", "local-cluster[2,1,1024]",
       unusedJar.toString)
     runSparkSubmit(args)
   }

From 798dff7b4baa952c609725b852bcb6a9c9e5a317 Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Wed, 22 Jul 2015 15:54:08 -0700
Subject: [PATCH 0548/1454] [SPARK-8975] [STREAMING] Adds a mechanism to send a
 new rate from the driver to the block generator
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

First step for [SPARK-7398](https://issues.apache.org/jira/browse/SPARK-7398).

tdas huitseeker

Author: Iulian Dragos <jaguarul@gmail.com>
Author: François Garillot <francois@garillot.net>

Closes #7471 from dragos/topic/streaming-bp/dynamic-rate and squashes the following commits:

8941cf9 [Iulian Dragos] Renames and other nitpicks.
162d9e5 [Iulian Dragos] Use Reflection for accessing truly private `executor` method and use the listener bus to know when receivers have registered (`onStart` is called before receivers have registered, leading to flaky behavior).
210f495 [Iulian Dragos] Revert "Added a few tests that measure the receiver’s rate."
0c51959 [Iulian Dragos] Added a few tests that measure the receiver’s rate.
261a051 [Iulian Dragos] - removed field to hold the current rate limit in rate limiter - made rate limit a Long and default to Long.MaxValue (consequence of the above) - removed custom `waitUntil` and replaced it by `eventually`
cd1397d [Iulian Dragos] Add a test for the propagation of a new rate limit from driver to receivers.
6369b30 [Iulian Dragos] Merge pull request #15 from huitseeker/SPARK-8975
d15de42 [François Garillot] [SPARK-8975][Streaming] Adds Ratelimiter unit tests w.r.t. spark.streaming.receiver.maxRate
4721c7d [François Garillot] [SPARK-8975][Streaming] Add a mechanism to send a new rate from the driver to the block generator
---
 .../streaming/receiver/RateLimiter.scala      | 30 +++++++--
 .../spark/streaming/receiver/Receiver.scala   |  2 +-
 .../streaming/receiver/ReceiverMessage.scala  |  3 +-
 .../receiver/ReceiverSupervisor.scala         |  3 +
 .../receiver/ReceiverSupervisorImpl.scala     |  6 ++
 .../streaming/scheduler/ReceiverTracker.scala |  9 ++-
 .../streaming/receiver/RateLimiterSuite.scala | 46 ++++++++++++++
 .../scheduler/ReceiverTrackerSuite.scala      | 62 +++++++++++++++++++
 8 files changed, 153 insertions(+), 8 deletions(-)
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/receiver/RateLimiterSuite.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
index 8df542b367d27..f663def4c0511 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
@@ -34,12 +34,32 @@ import org.apache.spark.{Logging, SparkConf}
   */
 private[receiver] abstract class RateLimiter(conf: SparkConf) extends Logging {
 
-  private val desiredRate = conf.getInt("spark.streaming.receiver.maxRate", 0)
-  private lazy val rateLimiter = GuavaRateLimiter.create(desiredRate)
+  // treated as an upper limit
+  private val maxRateLimit = conf.getLong("spark.streaming.receiver.maxRate", Long.MaxValue)
+  private lazy val rateLimiter = GuavaRateLimiter.create(maxRateLimit.toDouble)
 
   def waitToPush() {
-    if (desiredRate > 0) {
-      rateLimiter.acquire()
-    }
+    rateLimiter.acquire()
   }
+
+  /**
+   * Return the current rate limit. If no limit has been set so far, it returns {{{Long.MaxValue}}}.
+   */
+  def getCurrentLimit: Long =
+    rateLimiter.getRate.toLong
+
+  /**
+   * Set the rate limit to `newRate`. The new rate will not exceed the maximum rate configured by
+   * {{{spark.streaming.receiver.maxRate}}}, even if `newRate` is higher than that.
+   *
+   * @param newRate A new rate in events per second. It has no effect if it's 0 or negative.
+   */
+  private[receiver] def updateRate(newRate: Long): Unit =
+    if (newRate > 0) {
+      if (maxRateLimit > 0) {
+        rateLimiter.setRate(newRate.min(maxRateLimit))
+      } else {
+        rateLimiter.setRate(newRate)
+      }
+    }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index 5b5a3fe648602..7504fa44d9fae 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -271,7 +271,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
   }
 
   /** Get the attached executor. */
-  private def executor = {
+  private def executor: ReceiverSupervisor = {
     assert(executor_ != null, "Executor has not been attached to this receiver")
     executor_
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala
index 7bf3c33319491..1eb55affaa9d0 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverMessage.scala
@@ -23,4 +23,5 @@ import org.apache.spark.streaming.Time
 private[streaming] sealed trait ReceiverMessage extends Serializable
 private[streaming] object StopReceiver extends ReceiverMessage
 private[streaming] case class CleanupOldBlocks(threshTime: Time) extends ReceiverMessage
-
+private[streaming] case class UpdateRateLimit(elementsPerSecond: Long)
+                   extends ReceiverMessage
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
index 6467029a277b2..a7c220f426ecf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
@@ -59,6 +59,9 @@ private[streaming] abstract class ReceiverSupervisor(
   /** Time between a receiver is stopped and started again */
   private val defaultRestartDelay = conf.getInt("spark.streaming.receiverRestartDelay", 2000)
 
+  /** The current maximum rate limit for this receiver. */
+  private[streaming] def getCurrentRateLimit: Option[Long] = None
+
   /** Exception associated with the stopping of the receiver */
   @volatile protected var stoppingError: Throwable = null
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index f6ba66b3ae036..2f6841ee8879c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -77,6 +77,9 @@ private[streaming] class ReceiverSupervisorImpl(
         case CleanupOldBlocks(threshTime) =>
           logDebug("Received delete old batch signal")
           cleanupOldBlocks(threshTime)
+        case UpdateRateLimit(eps) =>
+          logInfo(s"Received a new rate limit: $eps.")
+          blockGenerator.updateRate(eps)
       }
     })
 
@@ -98,6 +101,9 @@ private[streaming] class ReceiverSupervisorImpl(
     }
   }, streamId, env.conf)
 
+  override private[streaming] def getCurrentRateLimit: Option[Long] =
+    Some(blockGenerator.getCurrentLimit)
+
   /** Push a single record of received data into block generator. */
   def pushSingle(data: Any) {
     blockGenerator.addData(data)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 6910d81d9866e..9cc6ffcd12f61 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -26,7 +26,7 @@ import org.apache.spark.{Logging, SparkEnv, SparkException}
 import org.apache.spark.rpc._
 import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.receiver.{CleanupOldBlocks, Receiver, ReceiverSupervisorImpl,
-  StopReceiver}
+  StopReceiver, UpdateRateLimit}
 import org.apache.spark.util.SerializableConfiguration
 
 /**
@@ -226,6 +226,13 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     logError(s"Deregistered receiver for stream $streamId: $messageWithError")
   }
 
+  /** Update a receiver's maximum ingestion rate */
+  def sendRateUpdate(streamUID: Int, newRate: Long): Unit = {
+    for (info <- receiverInfo.get(streamUID); eP <- Option(info.endpoint)) {
+      eP.send(UpdateRateLimit(newRate))
+    }
+  }
+
   /** Add new blocks for the given stream */
   private def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
     receivedBlockTracker.addBlock(receivedBlockInfo)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/receiver/RateLimiterSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/receiver/RateLimiterSuite.scala
new file mode 100644
index 0000000000000..c6330eb3673fb
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/receiver/RateLimiterSuite.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.receiver
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkFunSuite
+
+/** Testsuite for testing the network receiver behavior */
+class RateLimiterSuite extends SparkFunSuite {
+
+  test("rate limiter initializes even without a maxRate set") {
+    val conf = new SparkConf()
+    val rateLimiter = new RateLimiter(conf){}
+    rateLimiter.updateRate(105)
+    assert(rateLimiter.getCurrentLimit == 105)
+  }
+
+  test("rate limiter updates when below maxRate") {
+    val conf = new SparkConf().set("spark.streaming.receiver.maxRate", "110")
+    val rateLimiter = new RateLimiter(conf){}
+    rateLimiter.updateRate(105)
+    assert(rateLimiter.getCurrentLimit == 105)
+  }
+
+  test("rate limiter stays below maxRate despite large updates") {
+    val conf = new SparkConf().set("spark.streaming.receiver.maxRate", "100")
+    val rateLimiter = new RateLimiter(conf){}
+    rateLimiter.updateRate(105)
+    assert(rateLimiter.getCurrentLimit === 100)
+  }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index a6e783861dbe6..aadb7231757b8 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -17,11 +17,17 @@
 
 package org.apache.spark.streaming.scheduler
 
+import org.scalatest.concurrent.Eventually._
+import org.scalatest.concurrent.Timeouts
+import org.scalatest.time.SpanSugar._
 import org.apache.spark.streaming._
 import org.apache.spark.SparkConf
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.receiver._
 import org.apache.spark.util.Utils
+import org.apache.spark.streaming.dstream.InputDStream
+import scala.reflect.ClassTag
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
 
 /** Testsuite for receiver scheduling */
 class ReceiverTrackerSuite extends TestSuiteBase {
@@ -72,8 +78,64 @@ class ReceiverTrackerSuite extends TestSuiteBase {
     assert(locations(0).length === 1)
     assert(locations(3).length === 1)
   }
+
+  test("Receiver tracker - propagates rate limit") {
+    object ReceiverStartedWaiter extends StreamingListener {
+      @volatile
+      var started = false
+
+      override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = {
+        started = true
+      }
+    }
+
+    ssc.addStreamingListener(ReceiverStartedWaiter)
+    ssc.scheduler.listenerBus.start(ssc.sc)
+
+    val newRateLimit = 100L
+    val inputDStream = new RateLimitInputDStream(ssc)
+    val tracker = new ReceiverTracker(ssc)
+    tracker.start()
+
+    // we wait until the Receiver has registered with the tracker,
+    // otherwise our rate update is lost
+    eventually(timeout(5 seconds)) {
+      assert(ReceiverStartedWaiter.started)
+    }
+    tracker.sendRateUpdate(inputDStream.id, newRateLimit)
+    // this is an async message, we need to wait a bit for it to be processed
+    eventually(timeout(3 seconds)) {
+      assert(inputDStream.getCurrentRateLimit.get === newRateLimit)
+    }
+  }
 }
 
+/** An input DStream with a hard-coded receiver that gives access to internals for testing. */
+private class RateLimitInputDStream(@transient ssc_ : StreamingContext)
+  extends ReceiverInputDStream[Int](ssc_) {
+
+  override def getReceiver(): DummyReceiver = SingletonDummyReceiver
+
+  def getCurrentRateLimit: Option[Long] = {
+    invokeExecutorMethod.getCurrentRateLimit
+  }
+
+  private def invokeExecutorMethod: ReceiverSupervisor = {
+    val c = classOf[Receiver[_]]
+    val ex = c.getDeclaredMethod("executor")
+    ex.setAccessible(true)
+    ex.invoke(SingletonDummyReceiver).asInstanceOf[ReceiverSupervisor]
+  }
+}
+
+/**
+ * A Receiver as an object so we can read its rate limit.
+ *
+ * @note It's necessary to be a top-level object, or else serialization would create another
+ *       one on the executor side and we won't be able to read its rate limit.
+ */
+private object SingletonDummyReceiver extends DummyReceiver
+
 /**
  * Dummy receiver implementation
  */

From 430cd7815dc7875edd126af4b90752ba8a380cf2 Mon Sep 17 00:00:00 2001
From: Kenichi Maehashi <webmaster@kenichimaehashi.com>
Date: Wed, 22 Jul 2015 16:15:44 -0700
Subject: [PATCH 0549/1454] [SPARK-9180] fix spark-shell to accept --name
 option

This patch fixes [[SPARK-9180]](https://issues.apache.org/jira/browse/SPARK-9180).
Users can now set the app name of spark-shell using `spark-shell --name "whatever"`.

Author: Kenichi Maehashi <webmaster@kenichimaehashi.com>

Closes #7512 from kmaehashi/fix-spark-shell-app-name and squashes the following commits:

e24991a [Kenichi Maehashi] use setIfMissing instead of setAppName
18aa4ad [Kenichi Maehashi] fix spark-shell to accept --name option
---
 bin/spark-shell                                               | 4 ++--
 bin/spark-shell2.cmd                                          | 2 +-
 .../src/main/scala/org/apache/spark/repl/SparkILoop.scala     | 2 +-
 .../src/main/scala/org/apache/spark/repl/Main.scala           | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/bin/spark-shell b/bin/spark-shell
index a6dc863d83fc6..00ab7afd118b5 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -47,11 +47,11 @@ function main() {
     # (see https://github.com/sbt/sbt/issues/562).
     stty -icanon min 1 -echo > /dev/null 2>&1
     export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-    "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "$@"
+    "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main --name "Spark shell" "$@"
     stty icanon echo > /dev/null 2>&1
   else
     export SPARK_SUBMIT_OPTS
-    "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "$@"
+    "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main --name "Spark shell" "$@"
   fi
 }
 
diff --git a/bin/spark-shell2.cmd b/bin/spark-shell2.cmd
index 251309d67f860..b9b0f510d7f5d 100644
--- a/bin/spark-shell2.cmd
+++ b/bin/spark-shell2.cmd
@@ -32,4 +32,4 @@ if "x%SPARK_SUBMIT_OPTS%"=="x" (
 set SPARK_SUBMIT_OPTS="%SPARK_SUBMIT_OPTS% -Dscala.usejavacp=true"
 
 :run_shell
-%SPARK_HOME%\bin\spark-submit2.cmd --class org.apache.spark.repl.Main %*
+%SPARK_HOME%\bin\spark-submit2.cmd --class org.apache.spark.repl.Main --name "Spark shell" %*
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 8f7f9074d3f03..8130868fe1487 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -1008,9 +1008,9 @@ class SparkILoop(
     val jars = SparkILoop.getAddedJars
     val conf = new SparkConf()
       .setMaster(getMaster())
-      .setAppName("Spark shell")
       .setJars(jars)
       .set("spark.repl.class.uri", intp.classServerUri)
+      .setIfMissing("spark.app.name", "Spark shell")
     if (execUri != null) {
       conf.set("spark.executor.uri", execUri)
     }
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
index eed4a379afa60..be31eb2eda546 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
@@ -65,9 +65,9 @@ object Main extends Logging {
     val jars = getAddedJars
     val conf = new SparkConf()
       .setMaster(getMaster)
-      .setAppName("Spark shell")
       .setJars(jars)
       .set("spark.repl.class.uri", classServer.uri)
+      .setIfMissing("spark.app.name", "Spark shell")
     logInfo("Spark class server started at " + classServer.uri)
     if (execUri != null) {
       conf.set("spark.executor.uri", execUri)

From 5307c9d3f7a35c0276b72e743e3a62a44d2bd0f5 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Wed, 22 Jul 2015 17:22:12 -0700
Subject: [PATCH 0550/1454] [SPARK-9223] [PYSPARK] [MLLIB] Support model
 save/load in LDA

Since save / load has been merged in LDA, it takes no time to write the wrappers in Python as well.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7587 from MechCoder/python_lda_save_load and squashes the following commits:

c8e4ea7 [MechCoder] [SPARK-9223] [PySpark] Support model save/load in LDA
---
 python/pyspark/mllib/clustering.py | 43 +++++++++++++++++++++++++++++-
 1 file changed, 42 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index 8a92f6911c24b..58ad99d46e23b 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -20,6 +20,7 @@
 
 if sys.version > '3':
     xrange = range
+    basestring = str
 
 from math import exp, log
 
@@ -579,7 +580,7 @@ class LDAModel(JavaModelWrapper):
     Blei, Ng, and Jordan.  "Latent Dirichlet Allocation."  JMLR, 2003.
 
     >>> from pyspark.mllib.linalg import Vectors
-    >>> from numpy.testing import assert_almost_equal
+    >>> from numpy.testing import assert_almost_equal, assert_equal
     >>> data = [
     ...     [1, Vectors.dense([0.0, 1.0])],
     ...     [2, SparseVector(2, {0: 1.0})],
@@ -591,6 +592,19 @@ class LDAModel(JavaModelWrapper):
     >>> topics = model.topicsMatrix()
     >>> topics_expect = array([[0.5,  0.5], [0.5, 0.5]])
     >>> assert_almost_equal(topics, topics_expect, 1)
+
+    >>> import os, tempfile
+    >>> from shutil import rmtree
+    >>> path = tempfile.mkdtemp()
+    >>> model.save(sc, path)
+    >>> sameModel = LDAModel.load(sc, path)
+    >>> assert_equal(sameModel.topicsMatrix(), model.topicsMatrix())
+    >>> sameModel.vocabSize() == model.vocabSize()
+    True
+    >>> try:
+    ...     rmtree(path)
+    ... except OSError:
+    ...     pass
     """
 
     def topicsMatrix(self):
@@ -601,6 +615,33 @@ def vocabSize(self):
         """Vocabulary size (number of terms or terms in the vocabulary)"""
         return self.call("vocabSize")
 
+    def save(self, sc, path):
+        """Save the LDAModel on to disk.
+
+        :param sc: SparkContext
+        :param path: str, path to where the model needs to be stored.
+        """
+        if not isinstance(sc, SparkContext):
+            raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
+        if not isinstance(path, basestring):
+            raise TypeError("path should be a basestring, got type %s" % type(path))
+        self._java_model.save(sc._jsc.sc(), path)
+
+    @classmethod
+    def load(cls, sc, path):
+        """Load the LDAModel from disk.
+
+        :param sc: SparkContext
+        :param path: str, path to where the model is stored.
+        """
+        if not isinstance(sc, SparkContext):
+            raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
+        if not isinstance(path, basestring):
+            raise TypeError("path should be a basestring, got type %s" % type(path))
+        java_model = sc._jvm.org.apache.spark.mllib.clustering.DistributedLDAModel.load(
+            sc._jsc.sc(), path)
+        return cls(java_model)
+
 
 class LDA(object):
 

From a721ee52705100dbd7852f80f92cde4375517e48 Mon Sep 17 00:00:00 2001
From: martinzapletal <zapletal-martin@email.cz>
Date: Wed, 22 Jul 2015 17:35:05 -0700
Subject: [PATCH 0551/1454] [SPARK-8484] [ML] Added TrainValidationSplit for
 hyper-parameter tuning.

- [X] Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model. It should be similar to CrossValidator, but simpler and less expensive.
- [X] Simplified replacement of https://github.com/apache/spark/pull/6996

Author: martinzapletal <zapletal-martin@email.cz>

Closes #7337 from zapletal-martin/SPARK-8484-TrainValidationSplit and squashes the following commits:

cafc949 [martinzapletal] Review comments https://github.com/apache/spark/pull/7337.
511b398 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8484-TrainValidationSplit
f4fc9c4 [martinzapletal] SPARK-8484 Resolved feedback to https://github.com/apache/spark/pull/7337
00c4f5a [martinzapletal] SPARK-8484. Styling.
d699506 [martinzapletal] SPARK-8484. Styling.
93ed2ee [martinzapletal] Styling.
3bc1853 [martinzapletal] SPARK-8484. Styling.
2aa6f43 [martinzapletal] SPARK-8484. Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model.
21662eb [martinzapletal] SPARK-8484. Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model.
---
 .../spark/ml/tuning/CrossValidator.scala      |  33 +---
 .../ml/tuning/TrainValidationSplit.scala      | 168 ++++++++++++++++++
 .../spark/ml/tuning/ValidatorParams.scala     |  60 +++++++
 .../ml/tuning/TrainValidationSplitSuite.scala | 139 +++++++++++++++
 4 files changed, 368 insertions(+), 32 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tuning/ValidatorParams.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index e2444ab65b43b..f979319cc4b58 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -32,38 +32,7 @@ import org.apache.spark.sql.types.StructType
 /**
  * Params for [[CrossValidator]] and [[CrossValidatorModel]].
  */
-private[ml] trait CrossValidatorParams extends Params {
-
-  /**
-   * param for the estimator to be cross-validated
-   * @group param
-   */
-  val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection")
-
-  /** @group getParam */
-  def getEstimator: Estimator[_] = $(estimator)
-
-  /**
-   * param for estimator param maps
-   * @group param
-   */
-  val estimatorParamMaps: Param[Array[ParamMap]] =
-    new Param(this, "estimatorParamMaps", "param maps for the estimator")
-
-  /** @group getParam */
-  def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
-
-  /**
-   * param for the evaluator used to select hyper-parameters that maximize the cross-validated
-   * metric
-   * @group param
-   */
-  val evaluator: Param[Evaluator] = new Param(this, "evaluator",
-    "evaluator used to select hyper-parameters that maximize the cross-validated metric")
-
-  /** @group getParam */
-  def getEvaluator: Evaluator = $(evaluator)
-
+private[ml] trait CrossValidatorParams extends ValidatorParams {
   /**
    * Param for number of folds for cross validation.  Must be >= 2.
    * Default: 3
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
new file mode 100644
index 0000000000000..c0edc730b6fd6
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
@@ -0,0 +1,168 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tuning
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.evaluation.Evaluator
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.param.{DoubleParam, ParamMap, ParamValidators}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Params for [[TrainValidationSplit]] and [[TrainValidationSplitModel]].
+ */
+private[ml] trait TrainValidationSplitParams extends ValidatorParams {
+  /**
+   * Param for ratio between train and validation data. Must be between 0 and 1.
+   * Default: 0.75
+   * @group param
+   */
+  val trainRatio: DoubleParam = new DoubleParam(this, "trainRatio",
+    "ratio between training set and validation set (>= 0 && <= 1)", ParamValidators.inRange(0, 1))
+
+  /** @group getParam */
+  def getTrainRatio: Double = $(trainRatio)
+
+  setDefault(trainRatio -> 0.75)
+}
+
+/**
+ * :: Experimental ::
+ * Validation for hyper-parameter tuning.
+ * Randomly splits the input dataset into train and validation sets,
+ * and uses evaluation metric on the validation set to select the best model.
+ * Similar to [[CrossValidator]], but only splits the set once.
+ */
+@Experimental
+class TrainValidationSplit(override val uid: String) extends Estimator[TrainValidationSplitModel]
+  with TrainValidationSplitParams with Logging {
+
+  def this() = this(Identifiable.randomUID("tvs"))
+
+  /** @group setParam */
+  def setEstimator(value: Estimator[_]): this.type = set(estimator, value)
+
+  /** @group setParam */
+  def setEstimatorParamMaps(value: Array[ParamMap]): this.type = set(estimatorParamMaps, value)
+
+  /** @group setParam */
+  def setEvaluator(value: Evaluator): this.type = set(evaluator, value)
+
+  /** @group setParam */
+  def setTrainRatio(value: Double): this.type = set(trainRatio, value)
+
+  override def fit(dataset: DataFrame): TrainValidationSplitModel = {
+    val schema = dataset.schema
+    transformSchema(schema, logging = true)
+    val sqlCtx = dataset.sqlContext
+    val est = $(estimator)
+    val eval = $(evaluator)
+    val epm = $(estimatorParamMaps)
+    val numModels = epm.length
+    val metrics = new Array[Double](epm.length)
+
+    val Array(training, validation) =
+      dataset.rdd.randomSplit(Array($(trainRatio), 1 - $(trainRatio)))
+    val trainingDataset = sqlCtx.createDataFrame(training, schema).cache()
+    val validationDataset = sqlCtx.createDataFrame(validation, schema).cache()
+
+    // multi-model training
+    logDebug(s"Train split with multiple sets of parameters.")
+    val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
+    trainingDataset.unpersist()
+    var i = 0
+    while (i < numModels) {
+      // TODO: duplicate evaluator to take extra params from input
+      val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
+      logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
+      metrics(i) += metric
+      i += 1
+    }
+    validationDataset.unpersist()
+
+    logInfo(s"Train validation split metrics: ${metrics.toSeq}")
+    val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
+    logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
+    logInfo(s"Best train validation split metric: $bestMetric.")
+    val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
+    copyValues(new TrainValidationSplitModel(uid, bestModel, metrics).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    $(estimator).transformSchema(schema)
+  }
+
+  override def validateParams(): Unit = {
+    super.validateParams()
+    val est = $(estimator)
+    for (paramMap <- $(estimatorParamMaps)) {
+      est.copy(paramMap).validateParams()
+    }
+  }
+
+  override def copy(extra: ParamMap): TrainValidationSplit = {
+    val copied = defaultCopy(extra).asInstanceOf[TrainValidationSplit]
+    if (copied.isDefined(estimator)) {
+      copied.setEstimator(copied.getEstimator.copy(extra))
+    }
+    if (copied.isDefined(evaluator)) {
+      copied.setEvaluator(copied.getEvaluator.copy(extra))
+    }
+    copied
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Model from train validation split.
+ *
+ * @param uid Id.
+ * @param bestModel Estimator determined best model.
+ * @param validationMetrics Evaluated validation metrics.
+ */
+@Experimental
+class TrainValidationSplitModel private[ml] (
+    override val uid: String,
+    val bestModel: Model[_],
+    val validationMetrics: Array[Double])
+  extends Model[TrainValidationSplitModel] with TrainValidationSplitParams {
+
+  override def validateParams(): Unit = {
+    bestModel.validateParams()
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    bestModel.transform(dataset)
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    bestModel.transformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): TrainValidationSplitModel = {
+    val copied = new TrainValidationSplitModel (
+      uid,
+      bestModel.copy(extra).asInstanceOf[Model[_]],
+      validationMetrics.clone())
+    copyValues(copied, extra)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/ValidatorParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/ValidatorParams.scala
new file mode 100644
index 0000000000000..8897ab0825acd
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/ValidatorParams.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tuning
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.Estimator
+import org.apache.spark.ml.evaluation.Evaluator
+import org.apache.spark.ml.param.{ParamMap, Param, Params}
+
+/**
+ * :: DeveloperApi ::
+ * Common params for [[TrainValidationSplitParams]] and [[CrossValidatorParams]].
+ */
+@DeveloperApi
+private[ml] trait ValidatorParams extends Params {
+
+  /**
+   * param for the estimator to be validated
+   * @group param
+   */
+  val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection")
+
+  /** @group getParam */
+  def getEstimator: Estimator[_] = $(estimator)
+
+  /**
+   * param for estimator param maps
+   * @group param
+   */
+  val estimatorParamMaps: Param[Array[ParamMap]] =
+    new Param(this, "estimatorParamMaps", "param maps for the estimator")
+
+  /** @group getParam */
+  def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
+
+  /**
+   * param for the evaluator used to select hyper-parameters that maximize the validated metric
+   * @group param
+   */
+  val evaluator: Param[Evaluator] = new Param(this, "evaluator",
+    "evaluator used to select hyper-parameters that maximize the validated metric")
+
+  /** @group getParam */
+  def getEvaluator: Evaluator = $(evaluator)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
new file mode 100644
index 0000000000000..c8e58f216cceb
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
@@ -0,0 +1,139 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tuning
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.classification.LogisticRegression
+import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
+import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.shared.HasInputCol
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
+import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.types.StructType
+
+class TrainValidationSplitSuite extends SparkFunSuite with MLlibTestSparkContext {
+  test("train validation with logistic regression") {
+    val dataset = sqlContext.createDataFrame(
+      sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2))
+
+    val lr = new LogisticRegression
+    val lrParamMaps = new ParamGridBuilder()
+      .addGrid(lr.regParam, Array(0.001, 1000.0))
+      .addGrid(lr.maxIter, Array(0, 10))
+      .build()
+    val eval = new BinaryClassificationEvaluator
+    val cv = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEstimatorParamMaps(lrParamMaps)
+      .setEvaluator(eval)
+      .setTrainRatio(0.5)
+    val cvModel = cv.fit(dataset)
+    val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression]
+    assert(cv.getTrainRatio === 0.5)
+    assert(parent.getRegParam === 0.001)
+    assert(parent.getMaxIter === 10)
+    assert(cvModel.validationMetrics.length === lrParamMaps.length)
+  }
+
+  test("train validation with linear regression") {
+    val dataset = sqlContext.createDataFrame(
+        sc.parallelize(LinearDataGenerator.generateLinearInput(
+            6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2))
+
+    val trainer = new LinearRegression
+    val lrParamMaps = new ParamGridBuilder()
+      .addGrid(trainer.regParam, Array(1000.0, 0.001))
+      .addGrid(trainer.maxIter, Array(0, 10))
+      .build()
+    val eval = new RegressionEvaluator()
+    val cv = new TrainValidationSplit()
+      .setEstimator(trainer)
+      .setEstimatorParamMaps(lrParamMaps)
+      .setEvaluator(eval)
+      .setTrainRatio(0.5)
+    val cvModel = cv.fit(dataset)
+    val parent = cvModel.bestModel.parent.asInstanceOf[LinearRegression]
+    assert(parent.getRegParam === 0.001)
+    assert(parent.getMaxIter === 10)
+    assert(cvModel.validationMetrics.length === lrParamMaps.length)
+
+      eval.setMetricName("r2")
+    val cvModel2 = cv.fit(dataset)
+    val parent2 = cvModel2.bestModel.parent.asInstanceOf[LinearRegression]
+    assert(parent2.getRegParam === 0.001)
+    assert(parent2.getMaxIter === 10)
+    assert(cvModel2.validationMetrics.length === lrParamMaps.length)
+  }
+
+  test("validateParams should check estimatorParamMaps") {
+    import TrainValidationSplitSuite._
+
+    val est = new MyEstimator("est")
+    val eval = new MyEvaluator
+    val paramMaps = new ParamGridBuilder()
+      .addGrid(est.inputCol, Array("input1", "input2"))
+      .build()
+
+    val cv = new TrainValidationSplit()
+      .setEstimator(est)
+      .setEstimatorParamMaps(paramMaps)
+      .setEvaluator(eval)
+      .setTrainRatio(0.5)
+    cv.validateParams() // This should pass.
+
+    val invalidParamMaps = paramMaps :+ ParamMap(est.inputCol -> "")
+    cv.setEstimatorParamMaps(invalidParamMaps)
+    intercept[IllegalArgumentException] {
+      cv.validateParams()
+    }
+  }
+}
+
+object TrainValidationSplitSuite {
+
+  abstract class MyModel extends Model[MyModel]
+
+  class MyEstimator(override val uid: String) extends Estimator[MyModel] with HasInputCol {
+
+    override def validateParams(): Unit = require($(inputCol).nonEmpty)
+
+    override def fit(dataset: DataFrame): MyModel = {
+      throw new UnsupportedOperationException
+    }
+
+    override def transformSchema(schema: StructType): StructType = {
+      throw new UnsupportedOperationException
+    }
+
+    override def copy(extra: ParamMap): MyEstimator = defaultCopy(extra)
+  }
+
+  class MyEvaluator extends Evaluator {
+
+    override def evaluate(dataset: DataFrame): Double = {
+      throw new UnsupportedOperationException
+    }
+
+    override val uid: String = "eval"
+
+    override def copy(extra: ParamMap): MyEvaluator = defaultCopy(extra)
+  }
+}

From d71a13f475df2d05a7db9e25738d1353cbc8cfc7 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 22 Jul 2015 21:02:19 -0700
Subject: [PATCH 0552/1454] [SPARK-9262][build] Treat Scala compiler warnings
 as errors

I've seen a few cases in the past few weeks that the compiler is throwing warnings that are caused by legitimate bugs. This patch upgrades warnings to errors, except deprecation warnings.

Note that ideally we should be able to mark deprecation warnings as errors as well. However, due to the lack of ability to suppress individual warning messages in the Scala compiler, we cannot do that (since we do need to access deprecated APIs in Hadoop).

Most of the work are done by ericl.

Author: Reynold Xin <rxin@databricks.com>
Author: Eric Liang <ekl@databricks.com>

Closes #7598 from rxin/warnings and squashes the following commits:

beb311b [Reynold Xin] Fixed tests.
542c031 [Reynold Xin] Fixed one more warning.
87c354a [Reynold Xin] Fixed all non-deprecation warnings.
78660ac [Eric Liang] first effort to fix warnings
---
 .../apache/spark/api/r/RBackendHandler.scala  |  1 +
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |  7 ++--
 .../org/apache/spark/util/JsonProtocol.scala  |  2 ++
 .../util/SerializableConfiguration.scala      |  2 --
 .../spark/util/SerializableJobConf.scala      |  2 --
 .../stat/test/KolmogorovSmirnovTest.scala     |  4 +--
 project/SparkBuild.scala                      | 33 ++++++++++++++++++-
 .../sql/catalyst/CatalystTypeConverters.scala |  3 +-
 .../apache/spark/sql/DataFrameWriter.scala    |  3 ++
 .../sql/execution/datasources/commands.scala  |  4 ++-
 .../spark/sql/hive/orc/OrcFilters.scala       |  6 ++--
 .../sql/sources/hadoopFsRelationSuites.scala  |  6 ++--
 12 files changed, 55 insertions(+), 18 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index 9658e9a696ffa..a5de10fe89c42 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -20,6 +20,7 @@ package org.apache.spark.api.r
 import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream}
 
 import scala.collection.mutable.HashMap
+import scala.language.existentials
 
 import io.netty.channel.ChannelHandler.Sharable
 import io.netty.channel.{ChannelHandlerContext, SimpleChannelInboundHandler}
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 658e8c8b89318..130b58882d8ee 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -94,13 +94,14 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
   }
 
   override def getDependencies: Seq[Dependency[_]] = {
-    rdds.map { rdd: RDD[_ <: Product2[K, _]] =>
+    rdds.map { rdd: RDD[_] =>
       if (rdd.partitioner == Some(part)) {
         logDebug("Adding one-to-one dependency with " + rdd)
         new OneToOneDependency(rdd)
       } else {
         logDebug("Adding shuffle dependency with " + rdd)
-        new ShuffleDependency[K, Any, CoGroupCombiner](rdd, part, serializer)
+        new ShuffleDependency[K, Any, CoGroupCombiner](
+          rdd.asInstanceOf[RDD[_ <: Product2[K, _]]], part, serializer)
       }
     }
   }
@@ -133,7 +134,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
     // A list of (rdd iterator, dependency number) pairs
     val rddIterators = new ArrayBuffer[(Iterator[Product2[K, Any]], Int)]
     for ((dep, depNum) <- dependencies.zipWithIndex) dep match {
-      case oneToOneDependency: OneToOneDependency[Product2[K, Any]] =>
+      case oneToOneDependency: OneToOneDependency[Product2[K, Any]] @unchecked =>
         val dependencyPartition = split.narrowDeps(depNum).get.split
         // Read them from the parent
         val it = oneToOneDependency.rdd.iterator(dependencyPartition, context)
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index a078f14af52a1..c600319d9ddb4 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -94,6 +94,8 @@ private[spark] object JsonProtocol {
         logStartToJson(logStart)
       case metricsUpdate: SparkListenerExecutorMetricsUpdate =>
         executorMetricsUpdateToJson(metricsUpdate)
+      case blockUpdated: SparkListenerBlockUpdated =>
+        throw new MatchError(blockUpdated)  // TODO(ekl) implement this
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/util/SerializableConfiguration.scala b/core/src/main/scala/org/apache/spark/util/SerializableConfiguration.scala
index 30bcf1d2f24d5..3354a923273ff 100644
--- a/core/src/main/scala/org/apache/spark/util/SerializableConfiguration.scala
+++ b/core/src/main/scala/org/apache/spark/util/SerializableConfiguration.scala
@@ -20,8 +20,6 @@ import java.io.{ObjectInputStream, ObjectOutputStream}
 
 import org.apache.hadoop.conf.Configuration
 
-import org.apache.spark.util.Utils
-
 private[spark]
 class SerializableConfiguration(@transient var value: Configuration) extends Serializable {
   private def writeObject(out: ObjectOutputStream): Unit = Utils.tryOrIOException {
diff --git a/core/src/main/scala/org/apache/spark/util/SerializableJobConf.scala b/core/src/main/scala/org/apache/spark/util/SerializableJobConf.scala
index afbcc6efc850c..cadae472b3f85 100644
--- a/core/src/main/scala/org/apache/spark/util/SerializableJobConf.scala
+++ b/core/src/main/scala/org/apache/spark/util/SerializableJobConf.scala
@@ -21,8 +21,6 @@ import java.io.{ObjectInputStream, ObjectOutputStream}
 
 import org.apache.hadoop.mapred.JobConf
 
-import org.apache.spark.util.Utils
-
 private[spark]
 class SerializableJobConf(@transient var value: JobConf) extends Serializable {
   private def writeObject(out: ObjectOutputStream): Unit = Utils.tryOrIOException {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala
index d89b0059d83f3..2b3ed6df486c9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.stat.test
 import scala.annotation.varargs
 
 import org.apache.commons.math3.distribution.{NormalDistribution, RealDistribution}
-import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
+import org.apache.commons.math3.stat.inference.{KolmogorovSmirnovTest => CommonMathKolmogorovSmirnovTest}
 
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
@@ -187,7 +187,7 @@ private[stat] object KolmogorovSmirnovTest extends Logging {
   }
 
   private def evalOneSampleP(ksStat: Double, n: Long): KolmogorovSmirnovTestResult = {
-    val pval = 1 - new KolmogorovSmirnovTest().cdf(ksStat, n.toInt)
+    val pval = 1 - new CommonMathKolmogorovSmirnovTest().cdf(ksStat, n.toInt)
     new KolmogorovSmirnovTestResult(pval, ksStat, NullHypothesis.OneSampleTwoSided.toString)
   }
 }
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 12828547d7077..61a05d375d99e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -154,7 +154,38 @@ object SparkBuild extends PomBuild {
       if (major.toInt >= 1 && minor.toInt >= 8) Seq("-Xdoclint:all", "-Xdoclint:-missing") else Seq.empty
     },
 
-    javacOptions in Compile ++= Seq("-encoding", "UTF-8")
+    javacOptions in Compile ++= Seq("-encoding", "UTF-8"),
+
+    // Implements -Xfatal-warnings, ignoring deprecation warnings.
+    // Code snippet taken from https://issues.scala-lang.org/browse/SI-8410.
+    compile in Compile := {
+      val analysis = (compile in Compile).value
+      val s = streams.value
+
+      def logProblem(l: (=> String) => Unit, f: File, p: xsbti.Problem) = {
+        l(f.toString + ":" + p.position.line.fold("")(_ + ":") + " " + p.message)
+        l(p.position.lineContent)
+        l("")
+      }
+
+      var failed = 0
+      analysis.infos.allInfos.foreach { case (k, i) =>
+        i.reportedProblems foreach { p =>
+          val deprecation = p.message.contains("is deprecated")
+
+          if (!deprecation) {
+            failed = failed + 1
+          }
+
+          logProblem(if (deprecation) s.log.warn else s.log.error, k, p)
+        }
+      }
+
+      if (failed > 0) {
+        sys.error(s"$failed fatal warnings")
+      }
+      analysis
+    }
   )
 
   def enable(settings: Seq[Setting[_]])(projectRef: ProjectRef) = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 8f63d2120ad0e..ae0ab2f4c63f5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -24,6 +24,7 @@ import java.util.{Map => JavaMap}
 import javax.annotation.Nullable
 
 import scala.collection.mutable.HashMap
+import scala.language.existentials
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
@@ -401,7 +402,7 @@ object CatalystTypeConverters {
     case seq: Seq[Any] => seq.map(convertToCatalyst)
     case r: Row => InternalRow(r.toSeq.map(convertToCatalyst): _*)
     case arr: Array[Any] => arr.toSeq.map(convertToCatalyst).toArray
-    case m: Map[Any, Any] =>
+    case m: Map[_, _] =>
       m.map { case (k, v) => (convertToCatalyst(k), convertToCatalyst(v)) }.toMap
     case other => other
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index ee0201a9d4cb2..05da05d7b8050 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -197,6 +197,9 @@ final class DataFrameWriter private[sql](df: DataFrame) {
           // the table. But, insertInto with Overwrite requires the schema of data be the same
           // the schema of the table.
           insertInto(tableName)
+
+        case SaveMode.Overwrite =>
+          throw new UnsupportedOperationException("overwrite mode unsupported.")
       }
     } else {
       val cmd =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
index 84a0441e145c5..cd2aa7f7433c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
@@ -100,7 +100,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
     val pathExists = fs.exists(qualifiedOutputPath)
     val doInsertion = (mode, pathExists) match {
       case (SaveMode.ErrorIfExists, true) =>
-        sys.error(s"path $qualifiedOutputPath already exists.")
+        throw new AnalysisException(s"path $qualifiedOutputPath already exists.")
       case (SaveMode.Overwrite, true) =>
         fs.delete(qualifiedOutputPath, true)
         true
@@ -108,6 +108,8 @@ private[sql] case class InsertIntoHadoopFsRelation(
         true
       case (SaveMode.Ignore, exists) =>
         !exists
+      case (s, exists) =>
+        throw new IllegalStateException(s"unsupported save mode $s ($exists)")
     }
     // If we are appending data to an existing dir.
     val isAppend = pathExists && (mode == SaveMode.Append)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
index 250e73a4dba92..ddd5d24717add 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
@@ -41,10 +41,10 @@ private[orc] object OrcFilters extends Logging {
   private def buildSearchArgument(expression: Filter, builder: Builder): Option[Builder] = {
     def newBuilder = SearchArgument.FACTORY.newBuilder()
 
-    def isSearchableLiteral(value: Any) = value match {
+    def isSearchableLiteral(value: Any): Boolean = value match {
       // These are types recognized by the `SearchArgumentImpl.BuilderImpl.boxLiteral()` method.
-      case _: String | _: Long | _: Double | _: DateWritable | _: HiveDecimal | _: HiveChar |
-           _: HiveVarchar | _: Byte | _: Short | _: Integer | _: Float => true
+      case _: String | _: Long | _: Double | _: Byte | _: Short | _: Integer | _: Float => true
+      case _: DateWritable | _: HiveDecimal | _: HiveChar | _: HiveVarchar => true
       case _ => false
     }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 1cef83fd5e990..2a8748d913569 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -134,7 +134,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
 
   test("save()/load() - non-partitioned table - ErrorIfExists") {
     withTempDir { file =>
-      intercept[RuntimeException] {
+      intercept[AnalysisException] {
         testDF.write.format(dataSourceName).mode(SaveMode.ErrorIfExists).save(file.getCanonicalPath)
       }
     }
@@ -233,7 +233,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
 
   test("save()/load() - partitioned table - ErrorIfExists") {
     withTempDir { file =>
-      intercept[RuntimeException] {
+      intercept[AnalysisException] {
         partitionedTestDF.write
           .format(dataSourceName)
           .mode(SaveMode.ErrorIfExists)
@@ -696,7 +696,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
       // This should only complain that the destination directory already exists, rather than file
       // "empty" is not a Parquet file.
       assert {
-        intercept[RuntimeException] {
+        intercept[AnalysisException] {
           df.write.format("parquet").mode(SaveMode.ErrorIfExists).save(path)
         }.getMessage.contains("already exists")
       }

From b217230f2a96c6d5a0554c593bdf1d1374878688 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 22 Jul 2015 21:04:04 -0700
Subject: [PATCH 0553/1454] [SPARK-9144] Remove
 DAGScheduler.runLocallyWithinThread and spark.localExecution.enabled

Spark has an option called spark.localExecution.enabled; according to the docs:

> Enables Spark to run certain jobs, such as first() or take() on the driver, without sending tasks to the cluster. This can make certain jobs execute very quickly, but may require shipping a whole partition of data to the driver.

This feature ends up adding quite a bit of complexity to DAGScheduler, especially in the runLocallyWithinThread method, but as far as I know nobody uses this feature (I searched the mailing list and haven't seen any recent mentions of the configuration nor stacktraces including the runLocally method). As a step towards scheduler complexity reduction, I propose that we remove this feature and all code related to it for Spark 1.5.

This pull request simply brings #7484 up to date.

Author: Josh Rosen <joshrosen@databricks.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #7585 from rxin/remove-local-exec and squashes the following commits:

84bd10e [Reynold Xin] Python fix.
1d9739a [Reynold Xin] Merge pull request #7484 from JoshRosen/remove-localexecution
eec39fa [Josh Rosen] Remove allowLocal(); deprecate user-facing uses of it.
b0835dc [Josh Rosen] Remove local execution code in DAGScheduler
8975d96 [Josh Rosen] Remove local execution tests.
ffa8c9b [Josh Rosen] Remove documentation for configuration
---
 .../scala/org/apache/spark/SparkContext.scala |  86 ++++++++++---
 .../apache/spark/api/java/JavaRDDLike.scala   |   2 +-
 .../apache/spark/api/python/PythonRDD.scala   |   5 +-
 .../org/apache/spark/executor/Executor.scala  |   2 -
 .../apache/spark/rdd/PairRDDFunctions.scala   |   2 +-
 .../main/scala/org/apache/spark/rdd/RDD.scala |   4 +-
 .../apache/spark/rdd/ZippedWithIndexRDD.scala |   3 +-
 .../apache/spark/scheduler/DAGScheduler.scala | 117 +++---------------
 .../spark/scheduler/DAGSchedulerEvent.scala   |   1 -
 .../scala/org/apache/spark/rdd/RDDSuite.scala |   2 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  83 ++-----------
 .../OutputCommitCoordinatorSuite.scala        |   4 +-
 .../spark/scheduler/SparkListenerSuite.scala  |   2 +-
 docs/configuration.md                         |   9 --
 .../spark/streaming/kafka/KafkaRDD.scala      |   3 +-
 .../apache/spark/mllib/rdd/SlidingRDD.scala   |   2 +-
 python/pyspark/context.py                     |   3 +-
 python/pyspark/rdd.py                         |   4 +-
 .../spark/sql/execution/SparkPlan.scala       |   3 +-
 19 files changed, 108 insertions(+), 229 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 4976e5eb49468..6a6b94a271cfc 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1758,16 +1758,13 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
 
   /**
    * Run a function on a given set of partitions in an RDD and pass the results to the given
-   * handler function. This is the main entry point for all actions in Spark. The allowLocal
-   * flag specifies whether the scheduler can run the computation on the driver rather than
-   * shipping it out to the cluster, for short actions like first().
+   * handler function. This is the main entry point for all actions in Spark.
    */
   def runJob[T, U: ClassTag](
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
       partitions: Seq[Int],
-      allowLocal: Boolean,
-      resultHandler: (Int, U) => Unit) {
+      resultHandler: (Int, U) => Unit): Unit = {
     if (stopped.get()) {
       throw new IllegalStateException("SparkContext has been shutdown")
     }
@@ -1777,54 +1774,104 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     if (conf.getBoolean("spark.logLineage", false)) {
       logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
     }
-    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal,
-      resultHandler, localProperties.get)
+    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
     progressBar.foreach(_.finishAll())
     rdd.doCheckpoint()
   }
 
   /**
-   * Run a function on a given set of partitions in an RDD and return the results as an array. The
-   * allowLocal flag specifies whether the scheduler can run the computation on the driver rather
-   * than shipping it out to the cluster, for short actions like first().
+   * Run a function on a given set of partitions in an RDD and return the results as an array.
+   */
+  def runJob[T, U: ClassTag](
+      rdd: RDD[T],
+      func: (TaskContext, Iterator[T]) => U,
+      partitions: Seq[Int]): Array[U] = {
+    val results = new Array[U](partitions.size)
+    runJob[T, U](rdd, func, partitions, (index, res) => results(index) = res)
+    results
+  }
+
+  /**
+   * Run a job on a given set of partitions of an RDD, but take a function of type
+   * `Iterator[T] => U` instead of `(TaskContext, Iterator[T]) => U`.
+   */
+  def runJob[T, U: ClassTag](
+      rdd: RDD[T],
+      func: Iterator[T] => U,
+      partitions: Seq[Int]): Array[U] = {
+    val cleanedFunc = clean(func)
+    runJob(rdd, (ctx: TaskContext, it: Iterator[T]) => cleanedFunc(it), partitions)
+  }
+
+
+  /**
+   * Run a function on a given set of partitions in an RDD and pass the results to the given
+   * handler function. This is the main entry point for all actions in Spark.
+   *
+   * The allowLocal flag is deprecated as of Spark 1.5.0+.
+   */
+  @deprecated("use the version of runJob without the allowLocal parameter", "1.5.0")
+  def runJob[T, U: ClassTag](
+      rdd: RDD[T],
+      func: (TaskContext, Iterator[T]) => U,
+      partitions: Seq[Int],
+      allowLocal: Boolean,
+      resultHandler: (Int, U) => Unit): Unit = {
+    if (allowLocal) {
+      logWarning("sc.runJob with allowLocal=true is deprecated in Spark 1.5.0+")
+    }
+    runJob(rdd, func, partitions, resultHandler)
+  }
+
+  /**
+   * Run a function on a given set of partitions in an RDD and return the results as an array.
+   *
+   * The allowLocal flag is deprecated as of Spark 1.5.0+.
    */
+  @deprecated("use the version of runJob without the allowLocal parameter", "1.5.0")
   def runJob[T, U: ClassTag](
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
       partitions: Seq[Int],
       allowLocal: Boolean
       ): Array[U] = {
-    val results = new Array[U](partitions.size)
-    runJob[T, U](rdd, func, partitions, allowLocal, (index, res) => results(index) = res)
-    results
+    if (allowLocal) {
+      logWarning("sc.runJob with allowLocal=true is deprecated in Spark 1.5.0+")
+    }
+    runJob(rdd, func, partitions)
   }
 
   /**
    * Run a job on a given set of partitions of an RDD, but take a function of type
    * `Iterator[T] => U` instead of `(TaskContext, Iterator[T]) => U`.
+   *
+   * The allowLocal argument is deprecated as of Spark 1.5.0+.
    */
+  @deprecated("use the version of runJob without the allowLocal parameter", "1.5.0")
   def runJob[T, U: ClassTag](
       rdd: RDD[T],
       func: Iterator[T] => U,
       partitions: Seq[Int],
       allowLocal: Boolean
       ): Array[U] = {
-    val cleanedFunc = clean(func)
-    runJob(rdd, (ctx: TaskContext, it: Iterator[T]) => cleanedFunc(it), partitions, allowLocal)
+    if (allowLocal) {
+      logWarning("sc.runJob with allowLocal=true is deprecated in Spark 1.5.0+")
+    }
+    runJob(rdd, func, partitions)
   }
 
   /**
    * Run a job on all partitions in an RDD and return the results in an array.
    */
   def runJob[T, U: ClassTag](rdd: RDD[T], func: (TaskContext, Iterator[T]) => U): Array[U] = {
-    runJob(rdd, func, 0 until rdd.partitions.size, false)
+    runJob(rdd, func, 0 until rdd.partitions.length)
   }
 
   /**
    * Run a job on all partitions in an RDD and return the results in an array.
    */
   def runJob[T, U: ClassTag](rdd: RDD[T], func: Iterator[T] => U): Array[U] = {
-    runJob(rdd, func, 0 until rdd.partitions.size, false)
+    runJob(rdd, func, 0 until rdd.partitions.length)
   }
 
   /**
@@ -1835,7 +1882,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     processPartition: (TaskContext, Iterator[T]) => U,
     resultHandler: (Int, U) => Unit)
   {
-    runJob[T, U](rdd, processPartition, 0 until rdd.partitions.size, false, resultHandler)
+    runJob[T, U](rdd, processPartition, 0 until rdd.partitions.length, resultHandler)
   }
 
   /**
@@ -1847,7 +1894,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       resultHandler: (Int, U) => Unit)
   {
     val processFunc = (context: TaskContext, iter: Iterator[T]) => processPartition(iter)
-    runJob[T, U](rdd, processFunc, 0 until rdd.partitions.size, false, resultHandler)
+    runJob[T, U](rdd, processFunc, 0 until rdd.partitions.length, resultHandler)
   }
 
   /**
@@ -1892,7 +1939,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       (context: TaskContext, iter: Iterator[T]) => cleanF(iter),
       partitions,
       callSite,
-      allowLocal = false,
       resultHandler,
       localProperties.get)
     new SimpleFutureAction(waiter, resultFunc)
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index c95615a5a9307..829fae1d1d9bf 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -364,7 +364,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
     // This is useful for implementing `take` from other language frontends
     // like Python where the data is serialized.
     import scala.collection.JavaConversions._
-    val res = context.runJob(rdd, (it: Iterator[T]) => it.toArray, partitionIds, true)
+    val res = context.runJob(rdd, (it: Iterator[T]) => it.toArray, partitionIds)
     res.map(x => new java.util.ArrayList(x.toSeq)).toArray
   }
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index dc9f62f39e6d5..598953ac3bcc8 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -358,12 +358,11 @@ private[spark] object PythonRDD extends Logging {
   def runJob(
       sc: SparkContext,
       rdd: JavaRDD[Array[Byte]],
-      partitions: JArrayList[Int],
-      allowLocal: Boolean): Int = {
+      partitions: JArrayList[Int]): Int = {
     type ByteArray = Array[Byte]
     type UnrolledPartition = Array[ByteArray]
     val allPartitions: Array[UnrolledPartition] =
-      sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions, allowLocal)
+      sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions)
     val flattenedPartition: UnrolledPartition = Array.concat(allPartitions: _*)
     serveIterator(flattenedPartition.iterator,
       s"serve RDD ${rdd.id} with partitions ${partitions.mkString(",")}")
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 66624ffbe4790..581b40003c6c4 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -215,8 +215,6 @@ private[spark] class Executor(
             attemptNumber = attemptNumber,
             metricsSystem = env.metricsSystem)
         } finally {
-          // Note: this memory freeing logic is duplicated in DAGScheduler.runLocallyWithinThread;
-          // when changing this, make sure to update both copies.
           val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory()
           if (freedMemory > 0) {
             val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId"
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 91a6a2d039852..326fafb230a40 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -881,7 +881,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
           }
           buf
         } : Seq[V]
-        val res = self.context.runJob(self, process, Array(index), false)
+        val res = self.context.runJob(self, process, Array(index))
         res(0)
       case None =>
         self.filter(_._1 == key).map(_._2).collect()
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 9f7ebae3e9af3..394c6686cbabd 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -897,7 +897,7 @@ abstract class RDD[T: ClassTag](
    */
   def toLocalIterator: Iterator[T] = withScope {
     def collectPartition(p: Int): Array[T] = {
-      sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal = false).head
+      sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p)).head
     }
     (0 until partitions.length).iterator.flatMap(i => collectPartition(i))
   }
@@ -1273,7 +1273,7 @@ abstract class RDD[T: ClassTag](
 
         val left = num - buf.size
         val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts)
-        val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true)
+        val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p)
 
         res.foreach(buf ++= _.take(num - buf.size))
         partsScanned += numPartsToTry
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
index 523aaf2b860b5..e277ae28d588f 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
@@ -50,8 +50,7 @@ class ZippedWithIndexRDD[T: ClassTag](@transient prev: RDD[T]) extends RDD[(T, L
       prev.context.runJob(
         prev,
         Utils.getIteratorSize _,
-        0 until n - 1, // do not need to count the last partition
-        allowLocal = false
+        0 until n - 1 // do not need to count the last partition
       ).scanLeft(0L)(_ + _)
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index b829d06923404..552dabcfa5139 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -38,7 +38,6 @@ import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rpc.RpcTimeout
 import org.apache.spark.storage._
-import org.apache.spark.unsafe.memory.TaskMemoryManager
 import org.apache.spark.util._
 import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
 
@@ -128,10 +127,6 @@ class DAGScheduler(
   // This is only safe because DAGScheduler runs in a single thread.
   private val closureSerializer = SparkEnv.get.closureSerializer.newInstance()
 
-
-  /** If enabled, we may run certain actions like take() and first() locally. */
-  private val localExecutionEnabled = sc.getConf.getBoolean("spark.localExecution.enabled", false)
-
   /** If enabled, FetchFailed will not cause stage retry, in order to surface the problem. */
   private val disallowStageRetryForTest = sc.getConf.getBoolean("spark.test.noStageRetry", false)
 
@@ -515,7 +510,6 @@ class DAGScheduler(
       func: (TaskContext, Iterator[T]) => U,
       partitions: Seq[Int],
       callSite: CallSite,
-      allowLocal: Boolean,
       resultHandler: (Int, U) => Unit,
       properties: Properties): JobWaiter[U] = {
     // Check to make sure we are not launching a task on a partition that does not exist.
@@ -535,7 +529,7 @@ class DAGScheduler(
     val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
     val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
     eventProcessLoop.post(JobSubmitted(
-      jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter,
+      jobId, rdd, func2, partitions.toArray, callSite, waiter,
       SerializationUtils.clone(properties)))
     waiter
   }
@@ -545,11 +539,10 @@ class DAGScheduler(
       func: (TaskContext, Iterator[T]) => U,
       partitions: Seq[Int],
       callSite: CallSite,
-      allowLocal: Boolean,
       resultHandler: (Int, U) => Unit,
       properties: Properties): Unit = {
     val start = System.nanoTime
-    val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties)
+    val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
     waiter.awaitResult() match {
       case JobSucceeded =>
         logInfo("Job %d finished: %s, took %f s".format
@@ -576,8 +569,7 @@ class DAGScheduler(
     val partitions = (0 until rdd.partitions.size).toArray
     val jobId = nextJobId.getAndIncrement()
     eventProcessLoop.post(JobSubmitted(
-      jobId, rdd, func2, partitions, allowLocal = false, callSite, listener,
-      SerializationUtils.clone(properties)))
+      jobId, rdd, func2, partitions, callSite, listener, SerializationUtils.clone(properties)))
     listener.awaitResult()    // Will throw an exception if the job fails
   }
 
@@ -654,74 +646,6 @@ class DAGScheduler(
     }
   }
 
-  /**
-   * Run a job on an RDD locally, assuming it has only a single partition and no dependencies.
-   * We run the operation in a separate thread just in case it takes a bunch of time, so that we
-   * don't block the DAGScheduler event loop or other concurrent jobs.
-   */
-  protected def runLocally(job: ActiveJob) {
-    logInfo("Computing the requested partition locally")
-    new Thread("Local computation of job " + job.jobId) {
-      override def run() {
-        runLocallyWithinThread(job)
-      }
-    }.start()
-  }
-
-  // Broken out for easier testing in DAGSchedulerSuite.
-  protected def runLocallyWithinThread(job: ActiveJob) {
-    var jobResult: JobResult = JobSucceeded
-    try {
-      val rdd = job.finalStage.rdd
-      val split = rdd.partitions(job.partitions(0))
-      val taskMemoryManager = new TaskMemoryManager(env.executorMemoryManager)
-      val taskContext =
-        new TaskContextImpl(
-          job.finalStage.id,
-          job.partitions(0),
-          taskAttemptId = 0,
-          attemptNumber = 0,
-          taskMemoryManager = taskMemoryManager,
-          metricsSystem = env.metricsSystem,
-          runningLocally = true)
-      TaskContext.setTaskContext(taskContext)
-      try {
-        val result = job.func(taskContext, rdd.iterator(split, taskContext))
-        job.listener.taskSucceeded(0, result)
-      } finally {
-        taskContext.markTaskCompleted()
-        TaskContext.unset()
-        // Note: this memory freeing logic is duplicated in Executor.run(); when changing this,
-        // make sure to update both copies.
-        val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory()
-        if (freedMemory > 0) {
-          if (sc.getConf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) {
-            throw new SparkException(s"Managed memory leak detected; size = $freedMemory bytes")
-          } else {
-            logError(s"Managed memory leak detected; size = $freedMemory bytes")
-          }
-        }
-      }
-    } catch {
-      case e: Exception =>
-        val exception = new SparkDriverExecutionException(e)
-        jobResult = JobFailed(exception)
-        job.listener.jobFailed(exception)
-      case oom: OutOfMemoryError =>
-        val exception = new SparkException("Local job aborted due to out of memory error", oom)
-        jobResult = JobFailed(exception)
-        job.listener.jobFailed(exception)
-    } finally {
-      val s = job.finalStage
-      // clean up data structures that were populated for a local job,
-      // but that won't get cleaned up via the normal paths through
-      // completion events or stage abort
-      stageIdToStage -= s.id
-      jobIdToStageIds -= job.jobId
-      listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), jobResult))
-    }
-  }
-
   /** Finds the earliest-created active job that needs the stage */
   // TODO: Probably should actually find among the active jobs that need this
   // stage the one with the highest priority (highest-priority pool, earliest created).
@@ -784,7 +708,6 @@ class DAGScheduler(
       finalRDD: RDD[_],
       func: (TaskContext, Iterator[_]) => _,
       partitions: Array[Int],
-      allowLocal: Boolean,
       callSite: CallSite,
       listener: JobListener,
       properties: Properties) {
@@ -802,29 +725,20 @@ class DAGScheduler(
     if (finalStage != null) {
       val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
       clearCacheLocs()
-      logInfo("Got job %s (%s) with %d output partitions (allowLocal=%s)".format(
-        job.jobId, callSite.shortForm, partitions.length, allowLocal))
+      logInfo("Got job %s (%s) with %d output partitions".format(
+        job.jobId, callSite.shortForm, partitions.length))
       logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
       logInfo("Parents of final stage: " + finalStage.parents)
       logInfo("Missing parents: " + getMissingParentStages(finalStage))
-      val shouldRunLocally =
-        localExecutionEnabled && allowLocal && finalStage.parents.isEmpty && partitions.length == 1
       val jobSubmissionTime = clock.getTimeMillis()
-      if (shouldRunLocally) {
-        // Compute very short actions like first() or take() with no parent stages locally.
-        listenerBus.post(
-          SparkListenerJobStart(job.jobId, jobSubmissionTime, Seq.empty, properties))
-        runLocally(job)
-      } else {
-        jobIdToActiveJob(jobId) = job
-        activeJobs += job
-        finalStage.resultOfJob = Some(job)
-        val stageIds = jobIdToStageIds(jobId).toArray
-        val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
-        listenerBus.post(
-          SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
-        submitStage(finalStage)
-      }
+      jobIdToActiveJob(jobId) = job
+      activeJobs += job
+      finalStage.resultOfJob = Some(job)
+      val stageIds = jobIdToStageIds(jobId).toArray
+      val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
+      listenerBus.post(
+        SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
+      submitStage(finalStage)
     }
     submitWaitingStages()
   }
@@ -1486,9 +1400,8 @@ private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler
   }
 
   private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
-    case JobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite, listener, properties) =>
-      dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite,
-        listener, properties)
+    case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
+      dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
 
     case StageCancelled(stageId) =>
       dagScheduler.handleStageCancellation(stageId)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index a927eae2b04be..a213d419cf033 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -40,7 +40,6 @@ private[scheduler] case class JobSubmitted(
     finalRDD: RDD[_],
     func: (TaskContext, Iterator[_]) => _,
     partitions: Array[Int],
-    allowLocal: Boolean,
     callSite: CallSite,
     listener: JobListener,
     properties: Properties = null)
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index f6da9f98ad253..5f718ea9f7be1 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -679,7 +679,7 @@ class RDDSuite extends SparkFunSuite with SharedSparkContext {
 
   test("runJob on an invalid partition") {
     intercept[IllegalArgumentException] {
-      sc.runJob(sc.parallelize(1 to 10, 2), {iter: Iterator[Int] => iter.size}, Seq(0, 1, 2), false)
+      sc.runJob(sc.parallelize(1 to 10, 2), {iter: Iterator[Int] => iter.size}, Seq(0, 1, 2))
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 3462a82c9cdd3..86dff8fb577d5 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -153,9 +153,7 @@ class DAGSchedulerSuite
   }
 
   before {
-    // Enable local execution for this test
-    val conf = new SparkConf().set("spark.localExecution.enabled", "true")
-    sc = new SparkContext("local", "DAGSchedulerSuite", conf)
+    sc = new SparkContext("local", "DAGSchedulerSuite")
     sparkListener.submittedStageInfos.clear()
     sparkListener.successfulStages.clear()
     sparkListener.failedStages.clear()
@@ -172,12 +170,7 @@ class DAGSchedulerSuite
         sc.listenerBus,
         mapOutputTracker,
         blockManagerMaster,
-        sc.env) {
-      override def runLocally(job: ActiveJob) {
-        // don't bother with the thread while unit testing
-        runLocallyWithinThread(job)
-      }
-    }
+        sc.env)
     dagEventProcessLoopTester = new DAGSchedulerEventProcessLoopTester(scheduler)
   }
 
@@ -241,10 +234,9 @@ class DAGSchedulerSuite
       rdd: RDD[_],
       partitions: Array[Int],
       func: (TaskContext, Iterator[_]) => _ = jobComputeFunc,
-      allowLocal: Boolean = false,
       listener: JobListener = jobListener): Int = {
     val jobId = scheduler.nextJobId.getAndIncrement()
-    runEvent(JobSubmitted(jobId, rdd, func, partitions, allowLocal, CallSite("", ""), listener))
+    runEvent(JobSubmitted(jobId, rdd, func, partitions, CallSite("", ""), listener))
     jobId
   }
 
@@ -284,37 +276,6 @@ class DAGSchedulerSuite
     assertDataStructuresEmpty()
   }
 
-  test("local job") {
-    val rdd = new PairOfIntsRDD(sc, Nil) {
-      override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
-        Array(42 -> 0).iterator
-      override def getPartitions: Array[Partition] =
-        Array( new Partition { override def index: Int = 0 } )
-      override def getPreferredLocations(split: Partition): List[String] = Nil
-      override def toString: String = "DAGSchedulerSuite Local RDD"
-    }
-    val jobId = scheduler.nextJobId.getAndIncrement()
-    runEvent(
-      JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener))
-    assert(results === Map(0 -> 42))
-    assertDataStructuresEmpty()
-  }
-
-  test("local job oom") {
-    val rdd = new PairOfIntsRDD(sc, Nil) {
-      override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] =
-        throw new java.lang.OutOfMemoryError("test local job oom")
-      override def getPartitions = Array( new Partition { override def index = 0 } )
-      override def getPreferredLocations(split: Partition) = Nil
-      override def toString = "DAGSchedulerSuite Local RDD"
-    }
-    val jobId = scheduler.nextJobId.getAndIncrement()
-    runEvent(
-      JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener))
-    assert(results.size == 0)
-    assertDataStructuresEmpty()
-  }
-
   test("run trivial job w/ dependency") {
     val baseRdd = new MyRDD(sc, 1, Nil)
     val finalRdd = new MyRDD(sc, 1, List(new OneToOneDependency(baseRdd)))
@@ -452,12 +413,7 @@ class DAGSchedulerSuite
       sc.listenerBus,
       mapOutputTracker,
       blockManagerMaster,
-      sc.env) {
-      override def runLocally(job: ActiveJob) {
-        // don't bother with the thread while unit testing
-        runLocallyWithinThread(job)
-      }
-    }
+      sc.env)
     dagEventProcessLoopTester = new DAGSchedulerEventProcessLoopTester(noKillScheduler)
     val jobId = submit(new MyRDD(sc, 1, Nil), Array(0))
     cancel(jobId)
@@ -889,40 +845,23 @@ class DAGSchedulerSuite
     // Run this on executors
     sc.parallelize(1 to 10, 2).foreach { item => acc.add(1) }
 
-    // Run this within a local thread
-    sc.parallelize(1 to 10, 2).map { item => acc.add(1) }.take(1)
-
-    // Make sure we can still run local commands as well as cluster commands.
+    // Make sure we can still run commands
     assert(sc.parallelize(1 to 10, 2).count() === 10)
-    assert(sc.parallelize(1 to 10, 2).first() === 1)
   }
 
   test("misbehaved resultHandler should not crash DAGScheduler and SparkContext") {
-    val e1 = intercept[SparkDriverExecutionException] {
-      val rdd = sc.parallelize(1 to 10, 2)
-      sc.runJob[Int, Int](
-        rdd,
-        (context: TaskContext, iter: Iterator[Int]) => iter.size,
-        Seq(0),
-        allowLocal = true,
-        (part: Int, result: Int) => throw new DAGSchedulerSuiteDummyException)
-    }
-    assert(e1.getCause.isInstanceOf[DAGSchedulerSuiteDummyException])
-
-    val e2 = intercept[SparkDriverExecutionException] {
+    val e = intercept[SparkDriverExecutionException] {
       val rdd = sc.parallelize(1 to 10, 2)
       sc.runJob[Int, Int](
         rdd,
         (context: TaskContext, iter: Iterator[Int]) => iter.size,
         Seq(0, 1),
-        allowLocal = false,
         (part: Int, result: Int) => throw new DAGSchedulerSuiteDummyException)
     }
-    assert(e2.getCause.isInstanceOf[DAGSchedulerSuiteDummyException])
+    assert(e.getCause.isInstanceOf[DAGSchedulerSuiteDummyException])
 
-    // Make sure we can still run local commands as well as cluster commands.
+    // Make sure we can still run commands
     assert(sc.parallelize(1 to 10, 2).count() === 10)
-    assert(sc.parallelize(1 to 10, 2).first() === 1)
   }
 
   test("getPartitions exceptions should not crash DAGScheduler and SparkContext (SPARK-8606)") {
@@ -935,9 +874,8 @@ class DAGSchedulerSuite
       rdd.reduceByKey(_ + _, 1).count()
     }
 
-    // Make sure we can still run local commands as well as cluster commands.
+    // Make sure we can still run commands
     assert(sc.parallelize(1 to 10, 2).count() === 10)
-    assert(sc.parallelize(1 to 10, 2).first() === 1)
   }
 
   test("getPreferredLocations errors should not crash DAGScheduler and SparkContext (SPARK-8606)") {
@@ -951,9 +889,8 @@ class DAGSchedulerSuite
     }
     assert(e1.getMessage.contains(classOf[DAGSchedulerSuiteDummyException].getName))
 
-    // Make sure we can still run local commands as well as cluster commands.
+    // Make sure we can still run commands
     assert(sc.parallelize(1 to 10, 2).count() === 10)
-    assert(sc.parallelize(1 to 10, 2).first() === 1)
   }
 
   test("accumulator not calculated for resubmitted result stage") {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala
index a9036da9cc93d..e5ecd4b7c2610 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala
@@ -134,14 +134,14 @@ class OutputCommitCoordinatorSuite extends SparkFunSuite with BeforeAndAfter {
   test("Only one of two duplicate commit tasks should commit") {
     val rdd = sc.parallelize(Seq(1), 1)
     sc.runJob(rdd, OutputCommitFunctions(tempDir.getAbsolutePath).commitSuccessfully _,
-      0 until rdd.partitions.size, allowLocal = false)
+      0 until rdd.partitions.size)
     assert(tempDir.list().size === 1)
   }
 
   test("If commit fails, if task is retried it should not be locked, and will succeed.") {
     val rdd = sc.parallelize(Seq(1), 1)
     sc.runJob(rdd, OutputCommitFunctions(tempDir.getAbsolutePath).failFirstCommitAttempt _,
-      0 until rdd.partitions.size, allowLocal = false)
+      0 until rdd.partitions.size)
     assert(tempDir.list().size === 1)
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 651295b7344c5..730535ece7878 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -188,7 +188,7 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match
     sc.addSparkListener(listener)
     val rdd1 = sc.parallelize(1 to 100, 4)
     val rdd2 = rdd1.map(_.toString)
-    sc.runJob(rdd2, (items: Iterator[String]) => items.size, Seq(0, 1), true)
+    sc.runJob(rdd2, (items: Iterator[String]) => items.size, Seq(0, 1))
 
     sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
 
diff --git a/docs/configuration.md b/docs/configuration.md
index fea259204ae68..200f3cd212e46 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1048,15 +1048,6 @@ Apart from these, the following properties are also available, and may be useful
     infinite (all available cores) on Mesos.
   </td>
 </tr>
-<tr>
-  <td><code>spark.localExecution.enabled</code></td>
-  <td>false</td>
-  <td>
-    Enables Spark to run certain jobs, such as first() or take() on the driver, without sending
-    tasks to the cluster. This can make certain jobs execute very quickly, but may require
-    shipping a whole partition of data to the driver.
-  </td>
-</tr>
 <tr>
   <td><code>spark.locality.wait</code></td>
   <td>3s</td>
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
index c5cd2154772ac..1a9d78c0d4f59 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
@@ -98,8 +98,7 @@ class KafkaRDD[
     val res = context.runJob(
       this,
       (tc: TaskContext, it: Iterator[R]) => it.take(parts(tc.partitionId)).toArray,
-      parts.keys.toArray,
-      allowLocal = true)
+      parts.keys.toArray)
     res.foreach(buf ++= _)
     buf.toArray
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/SlidingRDD.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/SlidingRDD.scala
index 35e81fcb3de0d..1facf83d806d0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/SlidingRDD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/SlidingRDD.scala
@@ -72,7 +72,7 @@ class SlidingRDD[T: ClassTag](@transient val parent: RDD[T], val windowSize: Int
       val w1 = windowSize - 1
       // Get the first w1 items of each partition, starting from the second partition.
       val nextHeads =
-        parent.context.runJob(parent, (iter: Iterator[T]) => iter.take(w1).toArray, 1 until n, true)
+        parent.context.runJob(parent, (iter: Iterator[T]) => iter.take(w1).toArray, 1 until n)
       val partitions = mutable.ArrayBuffer[SlidingRDDPartition[T]]()
       var i = 0
       var partitionIndex = 0
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 43bde5ae41e23..eb5b0bbbdac4b 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -913,8 +913,7 @@ def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False):
         # by runJob() in order to avoid having to pass a Python lambda into
         # SparkContext#runJob.
         mappedRDD = rdd.mapPartitions(partitionFunc)
-        port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions,
-                                          allowLocal)
+        port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
         return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
 
     def show_profiles(self):
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 7e788148d981c..fa8e0a0574a62 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1293,7 +1293,7 @@ def takeUpToNumLeft(iterator):
                     taken += 1
 
             p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-            res = self.context.runJob(self, takeUpToNumLeft, p, True)
+            res = self.context.runJob(self, takeUpToNumLeft, p)
 
             items += res
             partsScanned += numPartsToTry
@@ -2193,7 +2193,7 @@ def lookup(self, key):
         values = self.filter(lambda kv: kv[0] == key).values()
 
         if self.partitioner is not None:
-            return self.ctx.runJob(values, lambda x: x, [self.partitioner(key)], False)
+            return self.ctx.runJob(values, lambda x: x, [self.partitioner(key)])
 
         return values.collect()
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index b0d56b7bf0b86..50c27def8ea54 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -165,8 +165,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
       val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts)
       val sc = sqlContext.sparkContext
       val res =
-        sc.runJob(childRDD, (it: Iterator[InternalRow]) => it.take(left).toArray, p,
-          allowLocal = false)
+        sc.runJob(childRDD, (it: Iterator[InternalRow]) => it.take(left).toArray, p)
 
       res.foreach(buf ++= _.take(n - buf.size))
       partsScanned += numPartsToTry

From 2f5cbd860e487e7339e627dd7e2c9baa5116b819 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 22 Jul 2015 21:40:23 -0700
Subject: [PATCH 0554/1454] [SPARK-8364] [SPARKR] Add crosstab to SparkR
 DataFrames

Add `crosstab` to SparkR DataFrames, which takes two column names and returns a local R data.frame. This is similar to `table` in R. However, `table` in SparkR is used for loading SQL tables as DataFrames. The return type is data.frame instead table for `crosstab` to be compatible with Scala/Python.

I couldn't run R tests successfully on my local. Many unit tests failed. So let's try Jenkins.

Author: Xiangrui Meng <meng@databricks.com>

Closes #7318 from mengxr/SPARK-8364 and squashes the following commits:

d75e894 [Xiangrui Meng] fix tests
53f6ddd [Xiangrui Meng] fix tests
f1348d6 [Xiangrui Meng] update test
47cb088 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-8364
5621262 [Xiangrui Meng] first version without test
---
 R/pkg/NAMESPACE                  |  1 +
 R/pkg/R/DataFrame.R              | 28 ++++++++++++++++++++++++++++
 R/pkg/R/generics.R               |  4 ++++
 R/pkg/inst/tests/test_sparkSQL.R | 13 +++++++++++++
 4 files changed, 46 insertions(+)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 5834813319bfd..7f7a8a2e4de24 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -26,6 +26,7 @@ exportMethods("arrange",
               "collect",
               "columns",
               "count",
+              "crosstab",
               "describe",
               "distinct",
               "dropna",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index a58433df3c8c1..06dd6b75dff3d 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1554,3 +1554,31 @@ setMethod("fillna",
             }
             dataFrame(sdf)
           })
+
+#' crosstab
+#'
+#' Computes a pair-wise frequency table of the given columns. Also known as a contingency
+#' table. The number of distinct values for each column should be less than 1e4. At most 1e6
+#' non-zero pair frequencies will be returned.
+#'
+#' @param col1 name of the first column. Distinct items will make the first item of each row.
+#' @param col2 name of the second column. Distinct items will make the column names of the output.
+#' @return a local R data.frame representing the contingency table. The first column of each row
+#'         will be the distinct values of `col1` and the column names will be the distinct values
+#'         of `col2`. The name of the first column will be `$col1_$col2`. Pairs that have no
+#'         occurrences will have `null` as their counts.
+#'
+#' @rdname statfunctions
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- jsonFile(sqlCtx, "/path/to/file.json")
+#' ct = crosstab(df, "title", "gender")
+#' }
+setMethod("crosstab",
+          signature(x = "DataFrame", col1 = "character", col2 = "character"),
+          function(x, col1, col2) {
+            statFunctions <- callJMethod(x@sdf, "stat")
+            sct <- callJMethod(statFunctions, "crosstab", col1, col2)
+            collect(dataFrame(sct))
+          })
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 39b5586f7c90e..836e0175c391f 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -59,6 +59,10 @@ setGeneric("count", function(x) { standardGeneric("count") })
 # @export
 setGeneric("countByValue", function(x) { standardGeneric("countByValue") })
 
+# @rdname statfunctions
+# @export
+setGeneric("crosstab", function(x, col1, col2) { standardGeneric("crosstab") })
+
 # @rdname distinct
 # @export
 setGeneric("distinct", function(x, numPartitions = 1) { standardGeneric("distinct") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index a3039d36c9402..62fe48a5d6c7b 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -987,6 +987,19 @@ test_that("fillna() on a DataFrame", {
   expect_identical(expected, actual)
 })
 
+test_that("crosstab() on a DataFrame", {
+  rdd <- lapply(parallelize(sc, 0:3), function(x) {
+    list(paste0("a", x %% 3), paste0("b", x %% 2))
+  })
+  df <- toDF(rdd, list("a", "b"))
+  ct <- crosstab(df, "a", "b")
+  ordered <- ct[order(ct$a_b),]
+  row.names(ordered) <- NULL
+  expected <- data.frame("a_b" = c("a0", "a1", "a2"), "b0" = c(1, 0, 1), "b1" = c(1, 1, 0),
+                         stringsAsFactors = FALSE, row.names = NULL)
+  expect_identical(expected, ordered)
+})
+
 unlink(parquetPath)
 unlink(jsonPath)
 unlink(jsonPathNa)

From 410dd41cf6618b93b6daa6147d17339deeaa49ae Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 22 Jul 2015 23:27:25 -0700
Subject: [PATCH 0555/1454] [SPARK-9268] [ML] Removed varargs annotation from
 Params.setDefault taking multiple params

Removed varargs annotation from Params.setDefault taking multiple params.

Though varargs is technically correct, it often requires that developers do clean assembly, rather than (not clean) assembly, which is a nuisance during development.

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7604 from jkbradley/params-setdefault-varargs and squashes the following commits:

6016dc6 [Joseph K. Bradley] removed varargs annotation from Params.setDefault taking multiple params
---
 mllib/src/main/scala/org/apache/spark/ml/param/params.scala  | 5 ++++-
 .../test/java/org/apache/spark/ml/param/JavaTestParams.java  | 3 ---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 824efa5ed4b28..954aa17e26a02 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -476,11 +476,14 @@ trait Params extends Identifiable with Serializable {
   /**
    * Sets default values for a list of params.
    *
+   * Note: Java developers should use the single-parameter [[setDefault()]].
+   *       Annotating this with varargs can cause compilation failures due to a Scala compiler bug.
+   *       See SPARK-9268.
+   *
    * @param paramPairs  a list of param pairs that specify params and their default values to set
    *                    respectively. Make sure that the params are initialized before this method
    *                    gets called.
    */
-  @varargs
   protected final def setDefault(paramPairs: ParamPair[_]*): this.type = {
     paramPairs.foreach { p =>
       setDefault(p.param.asInstanceOf[Param[Any]], p.value)
diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
index 3ae09d39ef500..dc6ce8061f62b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
+++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
@@ -96,11 +96,8 @@ private void init() {
       new DoubleArrayParam(this, "myDoubleArrayParam", "this is a double param");
 
     setDefault(myIntParam(), 1);
-    setDefault(myIntParam().w(1));
     setDefault(myDoubleParam(), 0.5);
-    setDefault(myIntParam().w(1), myDoubleParam().w(0.5));
     setDefault(myDoubleArrayParam(), new double[] {1.0, 2.0});
-    setDefault(myDoubleArrayParam().w(new double[] {1.0, 2.0}));
   }
 
   @Override

From 825ab1e4526059a77e3278769797c4d065f48bd3 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 22 Jul 2015 23:29:26 -0700
Subject: [PATCH 0556/1454] [SPARK-7254] [MLLIB] Run PowerIterationClustering
 directly on graph

JIRA: https://issues.apache.org/jira/browse/SPARK-7254

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #6054 from viirya/pic_on_graph and squashes the following commits:

8b87b81 [Liang-Chi Hsieh] Fix scala style.
a22fb8b [Liang-Chi Hsieh] For comment.
ef565a0 [Liang-Chi Hsieh] Fix indentation.
d249aa1 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into pic_on_graph
82d7351 [Liang-Chi Hsieh] Run PowerIterationClustering directly on graph.
---
 .../clustering/PowerIterationClustering.scala | 46 ++++++++++++++++++
 .../PowerIterationClusteringSuite.scala       | 48 +++++++++++++++++++
 2 files changed, 94 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index e7a243f854e33..407e43a024a2e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -153,6 +153,27 @@ class PowerIterationClustering private[clustering] (
     this
   }
 
+  /**
+   * Run the PIC algorithm on Graph.
+   *
+   * @param graph an affinity matrix represented as graph, which is the matrix A in the PIC paper.
+   *              The similarity s,,ij,, represented as the edge between vertices (i, j) must
+   *              be nonnegative. This is a symmetric matrix and hence s,,ij,, = s,,ji,,. For
+   *              any (i, j) with nonzero similarity, there should be either (i, j, s,,ij,,)
+   *              or (j, i, s,,ji,,) in the input. Tuples with i = j are ignored, because we
+   *              assume s,,ij,, = 0.0.
+   *
+   * @return a [[PowerIterationClusteringModel]] that contains the clustering result
+   */
+  def run(graph: Graph[Double, Double]): PowerIterationClusteringModel = {
+    val w = normalize(graph)
+    val w0 = initMode match {
+      case "random" => randomInit(w)
+      case "degree" => initDegreeVector(w)
+    }
+    pic(w0)
+  }
+
   /**
    * Run the PIC algorithm.
    *
@@ -212,6 +233,31 @@ object PowerIterationClustering extends Logging {
   @Experimental
   case class Assignment(id: Long, cluster: Int)
 
+  /**
+   * Normalizes the affinity graph (A) and returns the normalized affinity matrix (W).
+   */
+  private[clustering]
+  def normalize(graph: Graph[Double, Double]): Graph[Double, Double] = {
+    val vD = graph.aggregateMessages[Double](
+      sendMsg = ctx => {
+        val i = ctx.srcId
+        val j = ctx.dstId
+        val s = ctx.attr
+        if (s < 0.0) {
+          throw new SparkException("Similarity must be nonnegative but found s($i, $j) = $s.")
+        }
+        if (s > 0.0) {
+          ctx.sendToSrc(s)
+        }
+      },
+      mergeMsg = _ + _,
+      TripletFields.EdgeOnly)
+    GraphImpl.fromExistingRDDs(vD, graph.edges)
+      .mapTriplets(
+        e => e.attr / math.max(e.srcAttr, MLUtils.EPSILON),
+        TripletFields.Src)
+  }
+
   /**
    * Normalizes the affinity matrix (A) by row sums and returns the normalized affinity matrix (W).
    */
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala
index 19e65f1b53ab5..189000512155f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala
@@ -68,6 +68,54 @@ class PowerIterationClusteringSuite extends SparkFunSuite with MLlibTestSparkCon
     assert(predictions2.toSet == Set((0 to 3).toSet, (4 to 15).toSet))
   }
 
+  test("power iteration clustering on graph") {
+    /*
+     We use the following graph to test PIC. All edges are assigned similarity 1.0 except 0.1 for
+     edge (3, 4).
+
+     15-14 -13 -12
+     |           |
+     4 . 3 - 2  11
+     |   | x |   |
+     5   0 - 1  10
+     |           |
+     6 - 7 - 8 - 9
+     */
+
+    val similarities = Seq[(Long, Long, Double)]((0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0),
+      (1, 3, 1.0), (2, 3, 1.0), (3, 4, 0.1), // (3, 4) is a weak edge
+      (4, 5, 1.0), (4, 15, 1.0), (5, 6, 1.0), (6, 7, 1.0), (7, 8, 1.0), (8, 9, 1.0), (9, 10, 1.0),
+      (10, 11, 1.0), (11, 12, 1.0), (12, 13, 1.0), (13, 14, 1.0), (14, 15, 1.0))
+
+    val edges = similarities.flatMap { case (i, j, s) =>
+      if (i != j) {
+        Seq(Edge(i, j, s), Edge(j, i, s))
+      } else {
+        None
+      }
+    }
+    val graph = Graph.fromEdges(sc.parallelize(edges, 2), 0.0)
+
+    val model = new PowerIterationClustering()
+      .setK(2)
+      .run(graph)
+    val predictions = Array.fill(2)(mutable.Set.empty[Long])
+    model.assignments.collect().foreach { a =>
+      predictions(a.cluster) += a.id
+    }
+    assert(predictions.toSet == Set((0 to 3).toSet, (4 to 15).toSet))
+
+    val model2 = new PowerIterationClustering()
+      .setK(2)
+      .setInitializationMode("degree")
+      .run(sc.parallelize(similarities, 2))
+    val predictions2 = Array.fill(2)(mutable.Set.empty[Long])
+    model2.assignments.collect().foreach { a =>
+      predictions2(a.cluster) += a.id
+    }
+    assert(predictions2.toSet == Set((0 to 3).toSet, (4 to 15).toSet))
+  }
+
   test("normalize and powerIter") {
     /*
      Test normalize() with the following graph:

From 6d0d8b406942edcf9fc97e76fb227ff1eb35ca3a Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 22 Jul 2015 23:44:08 -0700
Subject: [PATCH 0557/1454] [SPARK-8935] [SQL] Implement code generation for
 all casts

JIRA: https://issues.apache.org/jira/browse/SPARK-8935

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7365 from yjshen/cast_codegen and squashes the following commits:

ef6e8b5 [Yijie Shen] getColumn and setColumn in struct cast, autounboxing in array and map
eaece18 [Yijie Shen] remove null case in cast code gen
fd7eba4 [Yijie Shen] resolve comments
80378a5 [Yijie Shen] the missing self cast
611d66e [Yijie Shen] Bug fix: NullType & primitive object unboxing
6d5c0fe [Yijie Shen] rebase and add Interval codegen
9424b65 [Yijie Shen] tiny style fix
4a1c801 [Yijie Shen] remove CodeHolder class, use function instead.
3f5df88 [Yijie Shen] CodeHolder for complex dataTypes
c286f13 [Yijie Shen] moved all the cast code into class body
4edfd76 [Yijie Shen] [WIP] finished primitive part
---
 .../spark/sql/catalyst/expressions/Cast.scala | 523 ++++++++++++++++--
 .../expressions/DateExpressionsSuite.scala    |  36 +-
 2 files changed, 508 insertions(+), 51 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 3346d3c9f9e61..e66cd828481bf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -26,6 +26,8 @@ import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{Interval, UTF8String}
 
+import scala.collection.mutable
+
 
 object Cast {
 
@@ -418,51 +420,506 @@ case class Cast(child: Expression, dataType: DataType)
   protected override def nullSafeEval(input: Any): Any = cast(input)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    // TODO: Add support for more data types.
-    (child.dataType, dataType) match {
+    val eval = child.gen(ctx)
+    val nullSafeCast = nullSafeCastFunction(child.dataType, dataType, ctx)
+    eval.code +
+      castCode(ctx, eval.primitive, eval.isNull, ev.primitive, ev.isNull, dataType, nullSafeCast)
+  }
+
+  // three function arguments are: child.primitive, result.primitive and result.isNull
+  // it returns the code snippets to be put in null safe evaluation region
+  private[this] type CastFunction = (String, String, String) => String
+
+  private[this] def nullSafeCastFunction(
+      from: DataType,
+      to: DataType,
+      ctx: CodeGenContext): CastFunction = to match {
+
+    case _ if from == NullType => (c, evPrim, evNull) => s"$evNull = true;"
+    case _ if to == from => (c, evPrim, evNull) => s"$evPrim = $c;"
+    case StringType => castToStringCode(from, ctx)
+    case BinaryType => castToBinaryCode(from)
+    case DateType => castToDateCode(from, ctx)
+    case decimal: DecimalType => castToDecimalCode(from, decimal)
+    case TimestampType => castToTimestampCode(from, ctx)
+    case IntervalType => castToIntervalCode(from)
+    case BooleanType => castToBooleanCode(from)
+    case ByteType => castToByteCode(from)
+    case ShortType => castToShortCode(from)
+    case IntegerType => castToIntCode(from)
+    case FloatType => castToFloatCode(from)
+    case LongType => castToLongCode(from)
+    case DoubleType => castToDoubleCode(from)
+
+    case array: ArrayType => castArrayCode(from.asInstanceOf[ArrayType], array, ctx)
+    case map: MapType => castMapCode(from.asInstanceOf[MapType], map, ctx)
+    case struct: StructType => castStructCode(from.asInstanceOf[StructType], struct, ctx)
+  }
+
+  // Since we need to cast child expressions recursively inside ComplexTypes, such as Map's
+  // Key and Value, Struct's field, we need to name out all the variable names involved in a cast.
+  private[this] def castCode(ctx: CodeGenContext, childPrim: String, childNull: String,
+    resultPrim: String, resultNull: String, resultType: DataType, cast: CastFunction): String = {
+    s"""
+      boolean $resultNull = $childNull;
+      ${ctx.javaType(resultType)} $resultPrim = ${ctx.defaultValue(resultType)};
+      if (!${childNull}) {
+        ${cast(childPrim, resultPrim, resultNull)}
+      }
+    """
+  }
+
+  private[this] def castToStringCode(from: DataType, ctx: CodeGenContext): CastFunction = {
+    from match {
+      case BinaryType =>
+        (c, evPrim, evNull) => s"$evPrim = UTF8String.fromBytes($c);"
+      case DateType =>
+        (c, evPrim, evNull) => s"""$evPrim = UTF8String.fromString(
+          org.apache.spark.sql.catalyst.util.DateTimeUtils.dateToString($c));"""
+      case TimestampType =>
+        (c, evPrim, evNull) => s"""$evPrim = UTF8String.fromString(
+          org.apache.spark.sql.catalyst.util.DateTimeUtils.timestampToString($c));"""
+      case _ =>
+        (c, evPrim, evNull) => s"$evPrim = UTF8String.fromString(String.valueOf($c));"
+    }
+  }
+
+  private[this] def castToBinaryCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) => s"$evPrim = $c.getBytes();"
+  }
+
+  private[this] def castToDateCode(
+      from: DataType,
+      ctx: CodeGenContext): CastFunction = from match {
+    case StringType =>
+      val intOpt = ctx.freshName("intOpt")
+      (c, evPrim, evNull) => s"""
+        scala.Option<Integer> $intOpt =
+          org.apache.spark.sql.catalyst.util.DateTimeUtils.stringToDate($c);
+        if ($intOpt.isDefined()) {
+          $evPrim = ((Integer) $intOpt.get()).intValue();
+        } else {
+          $evNull = true;
+        }
+       """
+    case TimestampType =>
+      (c, evPrim, evNull) =>
+        s"$evPrim = org.apache.spark.sql.catalyst.util.DateTimeUtils.millisToDays($c / 1000L);";
+    case _ =>
+      (c, evPrim, evNull) => s"$evNull = true;"
+  }
+
+  private[this] def changePrecision(d: String, decimalType: DecimalType,
+      evPrim: String, evNull: String): String = {
+    decimalType match {
+      case DecimalType.Unlimited =>
+        s"$evPrim = $d;"
+      case DecimalType.Fixed(precision, scale) =>
+        s"""
+          if ($d.changePrecision($precision, $scale)) {
+            $evPrim = $d;
+          } else {
+            $evNull = true;
+          }
+        """
+    }
+  }
 
-      case (BinaryType, StringType) =>
-        defineCodeGen (ctx, ev, c =>
-          s"UTF8String.fromBytes($c)")
+  private[this] def castToDecimalCode(from: DataType, target: DecimalType): CastFunction = {
+    from match {
+      case StringType =>
+        (c, evPrim, evNull) =>
+          s"""
+            try {
+              org.apache.spark.sql.types.Decimal tmpDecimal =
+                new org.apache.spark.sql.types.Decimal().set(
+                  new scala.math.BigDecimal(
+                    new java.math.BigDecimal($c.toString())));
+              ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            } catch (java.lang.NumberFormatException e) {
+              $evNull = true;
+            }
+          """
+      case BooleanType =>
+        (c, evPrim, evNull) =>
+          s"""
+            org.apache.spark.sql.types.Decimal tmpDecimal = null;
+            if ($c) {
+              tmpDecimal = new org.apache.spark.sql.types.Decimal().set(1);
+            } else {
+              tmpDecimal = new org.apache.spark.sql.types.Decimal().set(0);
+            }
+            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+          """
+      case DateType =>
+        // date can't cast to decimal in Hive
+        (c, evPrim, evNull) => s"$evNull = true;"
+      case TimestampType =>
+        // Note that we lose precision here.
+        (c, evPrim, evNull) =>
+          s"""
+            org.apache.spark.sql.types.Decimal tmpDecimal =
+              new org.apache.spark.sql.types.Decimal().set(
+                scala.math.BigDecimal.valueOf(${timestampToDoubleCode(c)}));
+            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+          """
+      case DecimalType() =>
+        (c, evPrim, evNull) =>
+          s"""
+            org.apache.spark.sql.types.Decimal tmpDecimal = $c.clone();
+            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+          """
+      case LongType =>
+        (c, evPrim, evNull) =>
+          s"""
+            org.apache.spark.sql.types.Decimal tmpDecimal =
+              new org.apache.spark.sql.types.Decimal().set($c);
+            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+          """
+      case x: NumericType =>
+        // All other numeric types can be represented precisely as Doubles
+        (c, evPrim, evNull) =>
+          s"""
+            try {
+              org.apache.spark.sql.types.Decimal tmpDecimal =
+                new org.apache.spark.sql.types.Decimal().set(
+                  scala.math.BigDecimal.valueOf((double) $c));
+              ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            } catch (java.lang.NumberFormatException e) {
+              $evNull = true;
+            }
+          """
+    }
+  }
 
-      case (DateType, StringType) =>
-        defineCodeGen(ctx, ev, c =>
-          s"""UTF8String.fromString(
-                org.apache.spark.sql.catalyst.util.DateTimeUtils.dateToString($c))""")
+  private[this] def castToTimestampCode(
+      from: DataType,
+      ctx: CodeGenContext): CastFunction = from match {
+    case StringType =>
+      val longOpt = ctx.freshName("longOpt")
+      (c, evPrim, evNull) =>
+        s"""
+          scala.Option<Long> $longOpt =
+            org.apache.spark.sql.catalyst.util.DateTimeUtils.stringToTimestamp($c);
+          if ($longOpt.isDefined()) {
+            $evPrim = ((Long) $longOpt.get()).longValue();
+          } else {
+            $evNull = true;
+          }
+         """
+    case BooleanType =>
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1L : 0;"
+    case _: IntegralType =>
+      (c, evPrim, evNull) => s"$evPrim = ${longToTimeStampCode(c)};"
+    case DateType =>
+      (c, evPrim, evNull) =>
+        s"$evPrim = org.apache.spark.sql.catalyst.util.DateTimeUtils.daysToMillis($c) * 1000;"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = ${decimalToTimestampCode(c)};"
+    case DoubleType =>
+      (c, evPrim, evNull) =>
+        s"""
+          if (Double.isNaN($c) || Double.isInfinite($c)) {
+            $evNull = true;
+          } else {
+            $evPrim = (long)($c * 1000000L);
+          }
+        """
+    case FloatType =>
+      (c, evPrim, evNull) =>
+        s"""
+          if (Float.isNaN($c) || Float.isInfinite($c)) {
+            $evNull = true;
+          } else {
+            $evPrim = (long)($c * 1000000L);
+          }
+        """
+  }
 
-      case (TimestampType, StringType) =>
-        defineCodeGen(ctx, ev, c =>
-          s"""UTF8String.fromString(
-                org.apache.spark.sql.catalyst.util.DateTimeUtils.timestampToString($c))""")
+  private[this] def castToIntervalCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) =>
+        s"$evPrim = org.apache.spark.unsafe.types.Interval.fromString($c.toString());"
+  }
+
+  private[this] def decimalToTimestampCode(d: String): String =
+    s"($d.toBigDecimal().bigDecimal().multiply(new java.math.BigDecimal(1000000L))).longValue()"
+  private[this] def longToTimeStampCode(l: String): String = s"$l * 1000L"
+  private[this] def timestampToIntegerCode(ts: String): String =
+    s"java.lang.Math.floor((double) $ts / 1000000L)"
+  private[this] def timestampToDoubleCode(ts: String): String = s"$ts / 1000000.0"
+
+  private[this] def castToBooleanCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) => s"$evPrim = $c.numBytes() != 0;"
+    case TimestampType =>
+      (c, evPrim, evNull) => s"$evPrim = $c != 0;"
+    case DateType =>
+      // Hive would return null when cast from date to boolean
+      (c, evPrim, evNull) => s"$evNull = true;"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = !$c.isZero();"
+    case n: NumericType =>
+      (c, evPrim, evNull) => s"$evPrim = $c != 0;"
+  }
+
+  private[this] def castToByteCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) =>
+        s"""
+          try {
+            $evPrim = Byte.valueOf($c.toString());
+          } catch (java.lang.NumberFormatException e) {
+            $evNull = true;
+          }
+        """
+    case BooleanType =>
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+    case DateType =>
+      (c, evPrim, evNull) => s"$evNull = true;"
+    case TimestampType =>
+      (c, evPrim, evNull) => s"$evPrim = (byte) ${timestampToIntegerCode(c)};"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = $c.toByte();"
+    case x: NumericType =>
+      (c, evPrim, evNull) => s"$evPrim = (byte) $c;"
+  }
 
-      case (_, StringType) =>
-        defineCodeGen(ctx, ev, c => s"UTF8String.fromString(String.valueOf($c))")
+  private[this] def castToShortCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) =>
+        s"""
+          try {
+            $evPrim = Short.valueOf($c.toString());
+          } catch (java.lang.NumberFormatException e) {
+            $evNull = true;
+          }
+        """
+    case BooleanType =>
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+    case DateType =>
+      (c, evPrim, evNull) => s"$evNull = true;"
+    case TimestampType =>
+      (c, evPrim, evNull) => s"$evPrim = (short) ${timestampToIntegerCode(c)};"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = $c.toShort();"
+    case x: NumericType =>
+      (c, evPrim, evNull) => s"$evPrim = (short) $c;"
+  }
 
-      case (StringType, IntervalType) =>
-        defineCodeGen(ctx, ev, c =>
-          s"org.apache.spark.unsafe.types.Interval.fromString($c.toString())")
+  private[this] def castToIntCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) =>
+        s"""
+          try {
+            $evPrim = Integer.valueOf($c.toString());
+          } catch (java.lang.NumberFormatException e) {
+            $evNull = true;
+          }
+        """
+    case BooleanType =>
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+    case DateType =>
+      (c, evPrim, evNull) => s"$evNull = true;"
+    case TimestampType =>
+      (c, evPrim, evNull) => s"$evPrim = (int) ${timestampToIntegerCode(c)};"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = $c.toInt();"
+    case x: NumericType =>
+      (c, evPrim, evNull) => s"$evPrim = (int) $c;"
+  }
 
-      // fallback for DecimalType, this must be before other numeric types
-      case (_, dt: DecimalType) =>
-        super.genCode(ctx, ev)
+  private[this] def castToLongCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) =>
+        s"""
+          try {
+            $evPrim = Long.valueOf($c.toString());
+          } catch (java.lang.NumberFormatException e) {
+            $evNull = true;
+          }
+        """
+    case BooleanType =>
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+    case DateType =>
+      (c, evPrim, evNull) => s"$evNull = true;"
+    case TimestampType =>
+      (c, evPrim, evNull) => s"$evPrim = (long) ${timestampToIntegerCode(c)};"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = $c.toLong();"
+    case x: NumericType =>
+      (c, evPrim, evNull) => s"$evPrim = (long) $c;"
+  }
 
-      case (BooleanType, dt: NumericType) =>
-        defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})($c ? 1 : 0)")
+  private[this] def castToFloatCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) =>
+        s"""
+          try {
+            $evPrim = Float.valueOf($c.toString());
+          } catch (java.lang.NumberFormatException e) {
+            $evNull = true;
+          }
+        """
+    case BooleanType =>
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+    case DateType =>
+      (c, evPrim, evNull) => s"$evNull = true;"
+    case TimestampType =>
+      (c, evPrim, evNull) => s"$evPrim = (float) (${timestampToDoubleCode(c)});"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = $c.toFloat();"
+    case x: NumericType =>
+      (c, evPrim, evNull) => s"$evPrim = (float) $c;"
+  }
 
-      case (dt: DecimalType, BooleanType) =>
-        defineCodeGen(ctx, ev, c => s"!$c.isZero()")
+  private[this] def castToDoubleCode(from: DataType): CastFunction = from match {
+    case StringType =>
+      (c, evPrim, evNull) =>
+        s"""
+          try {
+            $evPrim = Double.valueOf($c.toString());
+          } catch (java.lang.NumberFormatException e) {
+            $evNull = true;
+          }
+        """
+    case BooleanType =>
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+    case DateType =>
+      (c, evPrim, evNull) => s"$evNull = true;"
+    case TimestampType =>
+      (c, evPrim, evNull) => s"$evPrim = ${timestampToDoubleCode(c)};"
+    case DecimalType() =>
+      (c, evPrim, evNull) => s"$evPrim = $c.toDouble();"
+    case x: NumericType =>
+      (c, evPrim, evNull) => s"$evPrim = (double) $c;"
+  }
 
-      case (dt: NumericType, BooleanType) =>
-        defineCodeGen(ctx, ev, c => s"$c != 0")
+  private[this] def castArrayCode(
+      from: ArrayType, to: ArrayType, ctx: CodeGenContext): CastFunction = {
+    val elementCast = nullSafeCastFunction(from.elementType, to.elementType, ctx)
+
+    val arraySeqClass = classOf[mutable.ArraySeq[Any]].getName
+    val fromElementNull = ctx.freshName("feNull")
+    val fromElementPrim = ctx.freshName("fePrim")
+    val toElementNull = ctx.freshName("teNull")
+    val toElementPrim = ctx.freshName("tePrim")
+    val size = ctx.freshName("n")
+    val j = ctx.freshName("j")
+    val result = ctx.freshName("result")
+
+    (c, evPrim, evNull) =>
+      s"""
+        final int $size = $c.size();
+        final $arraySeqClass<Object> $result = new $arraySeqClass<Object>($size);
+        for (int $j = 0; $j < $size; $j ++) {
+          if ($c.apply($j) == null) {
+            $result.update($j, null);
+          } else {
+            boolean $fromElementNull = false;
+            ${ctx.javaType(from.elementType)} $fromElementPrim =
+              (${ctx.boxedType(from.elementType)}) $c.apply($j);
+            ${castCode(ctx, fromElementPrim,
+              fromElementNull, toElementPrim, toElementNull, to.elementType, elementCast)}
+            if ($toElementNull) {
+              $result.update($j, null);
+            } else {
+              $result.update($j, $toElementPrim);
+            }
+          }
+        }
+        $evPrim = $result;
+      """
+  }
 
-      case (_: DecimalType, dt: NumericType) =>
-        defineCodeGen(ctx, ev, c => s"($c).to${ctx.primitiveTypeName(dt)}()")
+  private[this] def castMapCode(from: MapType, to: MapType, ctx: CodeGenContext): CastFunction = {
+    val keyCast = nullSafeCastFunction(from.keyType, to.keyType, ctx)
+    val valueCast = nullSafeCastFunction(from.valueType, to.valueType, ctx)
+
+    val hashMapClass = classOf[mutable.HashMap[Any, Any]].getName
+    val fromKeyPrim = ctx.freshName("fkp")
+    val fromKeyNull = ctx.freshName("fkn")
+    val fromValuePrim = ctx.freshName("fvp")
+    val fromValueNull = ctx.freshName("fvn")
+    val toKeyPrim = ctx.freshName("tkp")
+    val toKeyNull = ctx.freshName("tkn")
+    val toValuePrim = ctx.freshName("tvp")
+    val toValueNull = ctx.freshName("tvn")
+    val result = ctx.freshName("result")
+
+    (c, evPrim, evNull) =>
+      s"""
+        final $hashMapClass $result = new $hashMapClass();
+        scala.collection.Iterator iter = $c.iterator();
+        while (iter.hasNext()) {
+          scala.Tuple2 kv = (scala.Tuple2) iter.next();
+          boolean $fromKeyNull = false;
+          ${ctx.javaType(from.keyType)} $fromKeyPrim =
+            (${ctx.boxedType(from.keyType)}) kv._1();
+          ${castCode(ctx, fromKeyPrim,
+            fromKeyNull, toKeyPrim, toKeyNull, to.keyType, keyCast)}
+
+          boolean $fromValueNull = kv._2() == null;
+          if ($fromValueNull) {
+            $result.put($toKeyPrim, null);
+          } else {
+            ${ctx.javaType(from.valueType)} $fromValuePrim =
+              (${ctx.boxedType(from.valueType)}) kv._2();
+            ${castCode(ctx, fromValuePrim,
+              fromValueNull, toValuePrim, toValueNull, to.valueType, valueCast)}
+            if ($toValueNull) {
+              $result.put($toKeyPrim, null);
+            } else {
+              $result.put($toKeyPrim, $toValuePrim);
+            }
+          }
+        }
+        $evPrim = $result;
+      """
+  }
 
-      case (_: NumericType, dt: NumericType) =>
-        defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})($c)")
+  private[this] def castStructCode(
+      from: StructType, to: StructType, ctx: CodeGenContext): CastFunction = {
 
-      case other =>
-        super.genCode(ctx, ev)
+    val fieldsCasts = from.fields.zip(to.fields).map {
+      case (fromField, toField) => nullSafeCastFunction(fromField.dataType, toField.dataType, ctx)
     }
+    val rowClass = classOf[GenericMutableRow].getName
+    val result = ctx.freshName("result")
+    val tmpRow = ctx.freshName("tmpRow")
+
+    val fieldsEvalCode = fieldsCasts.zipWithIndex.map { case (cast, i) => {
+      val fromFieldPrim = ctx.freshName("ffp")
+      val fromFieldNull = ctx.freshName("ffn")
+      val toFieldPrim = ctx.freshName("tfp")
+      val toFieldNull = ctx.freshName("tfn")
+      val fromType = ctx.javaType(from.fields(i).dataType)
+      s"""
+        boolean $fromFieldNull = $tmpRow.isNullAt($i);
+        if ($fromFieldNull) {
+          $result.setNullAt($i);
+        } else {
+          $fromType $fromFieldPrim =
+            ${ctx.getColumn(tmpRow, from.fields(i).dataType, i)};
+          ${castCode(ctx, fromFieldPrim,
+            fromFieldNull, toFieldPrim, toFieldNull, to.fields(i).dataType, cast)}
+          if ($toFieldNull) {
+            $result.setNullAt($i);
+          } else {
+            ${ctx.setColumn(result, to.fields(i).dataType, i, toFieldPrim)};
+          }
+        }
+       """
+      }
+    }.mkString("\n")
+
+    (c, evPrim, evNull) =>
+      s"""
+        final $rowClass $result = new $rowClass(${fieldsCasts.size});
+        final InternalRow $tmpRow = $c;
+        $fieldsEvalCode
+        $evPrim = $result.copy();
+      """
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index f724bab4d8839..bdba6ce891386 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -39,7 +39,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -51,7 +51,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -63,7 +63,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -75,7 +75,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -87,7 +87,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
             sdfDay.format(c.getTime).toInt)
         }
       }
@@ -96,7 +96,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("Year") {
     checkEvaluation(Year(Literal.create(null, DateType)), null)
-    checkEvaluation(Year(Cast(Literal(d), DateType)), 2015)
+    checkEvaluation(Year(Literal(d)), 2015)
     checkEvaluation(Year(Cast(Literal(sdfDate.format(d)), DateType)), 2015)
     checkEvaluation(Year(Cast(Literal(ts), DateType)), 2013)
 
@@ -106,7 +106,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         c.set(y, m, 28)
         (0 to 5 * 24).foreach { i =>
           c.add(Calendar.HOUR_OF_DAY, 1)
-          checkEvaluation(Year(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(Year(Literal(new Date(c.getTimeInMillis))),
             c.get(Calendar.YEAR))
         }
       }
@@ -115,7 +115,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("Quarter") {
     checkEvaluation(Quarter(Literal.create(null, DateType)), null)
-    checkEvaluation(Quarter(Cast(Literal(d), DateType)), 2)
+    checkEvaluation(Quarter(Literal(d)), 2)
     checkEvaluation(Quarter(Cast(Literal(sdfDate.format(d)), DateType)), 2)
     checkEvaluation(Quarter(Cast(Literal(ts), DateType)), 4)
 
@@ -125,7 +125,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         c.set(y, m, 28, 0, 0, 0)
         (0 to 5 * 24).foreach { i =>
           c.add(Calendar.HOUR_OF_DAY, 1)
-          checkEvaluation(Quarter(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(Quarter(Literal(new Date(c.getTimeInMillis))),
             c.get(Calendar.MONTH) / 3 + 1)
         }
       }
@@ -134,7 +134,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("Month") {
     checkEvaluation(Month(Literal.create(null, DateType)), null)
-    checkEvaluation(Month(Cast(Literal(d), DateType)), 4)
+    checkEvaluation(Month(Literal(d)), 4)
     checkEvaluation(Month(Cast(Literal(sdfDate.format(d)), DateType)), 4)
     checkEvaluation(Month(Cast(Literal(ts), DateType)), 11)
 
@@ -144,7 +144,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.HOUR_OF_DAY, i)
-          checkEvaluation(Month(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(Month(Literal(new Date(c.getTimeInMillis))),
             c.get(Calendar.MONTH) + 1)
         }
       }
@@ -156,7 +156,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.HOUR_OF_DAY, i)
-          checkEvaluation(Month(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+          checkEvaluation(Month(Literal(new Date(c.getTimeInMillis))),
             c.get(Calendar.MONTH) + 1)
         }
       }
@@ -166,7 +166,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("Day / DayOfMonth") {
     checkEvaluation(DayOfMonth(Cast(Literal("2000-02-29"), DateType)), 29)
     checkEvaluation(DayOfMonth(Literal.create(null, DateType)), null)
-    checkEvaluation(DayOfMonth(Cast(Literal(d), DateType)), 8)
+    checkEvaluation(DayOfMonth(Literal(d)), 8)
     checkEvaluation(DayOfMonth(Cast(Literal(sdfDate.format(d)), DateType)), 8)
     checkEvaluation(DayOfMonth(Cast(Literal(ts), DateType)), 8)
 
@@ -175,7 +175,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       c.set(y, 0, 1, 0, 0, 0)
       (0 to 365).foreach { d =>
         c.add(Calendar.DATE, 1)
-        checkEvaluation(DayOfMonth(Cast(Literal(new Date(c.getTimeInMillis)), DateType)),
+        checkEvaluation(DayOfMonth(Literal(new Date(c.getTimeInMillis))),
           c.get(Calendar.DAY_OF_MONTH))
       }
     }
@@ -190,14 +190,14 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val c = Calendar.getInstance()
     (0 to 60 by 5).foreach { s =>
       c.set(2015, 18, 3, 3, 5, s)
-      checkEvaluation(Second(Cast(Literal(new Timestamp(c.getTimeInMillis)), TimestampType)),
+      checkEvaluation(Second(Literal(new Timestamp(c.getTimeInMillis))),
         c.get(Calendar.SECOND))
     }
   }
 
   test("WeekOfYear") {
     checkEvaluation(WeekOfYear(Literal.create(null, DateType)), null)
-    checkEvaluation(WeekOfYear(Cast(Literal(d), DateType)), 15)
+    checkEvaluation(WeekOfYear(Literal(d)), 15)
     checkEvaluation(WeekOfYear(Cast(Literal(sdfDate.format(d)), DateType)), 15)
     checkEvaluation(WeekOfYear(Cast(Literal(ts), DateType)), 45)
     checkEvaluation(WeekOfYear(Cast(Literal("2011-05-06"), DateType)), 18)
@@ -223,7 +223,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       (0 to 60 by 15).foreach { m =>
         (0 to 60 by 15).foreach { s =>
           c.set(2015, 18, 3, h, m, s)
-          checkEvaluation(Hour(Cast(Literal(new Timestamp(c.getTimeInMillis)), TimestampType)),
+          checkEvaluation(Hour(Literal(new Timestamp(c.getTimeInMillis))),
             c.get(Calendar.HOUR_OF_DAY))
         }
       }
@@ -240,7 +240,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     (0 to 60 by 5).foreach { m =>
       (0 to 60 by 15).foreach { s =>
         c.set(2015, 18, 3, 3, m, s)
-        checkEvaluation(Minute(Cast(Literal(new Timestamp(c.getTimeInMillis)), TimestampType)),
+        checkEvaluation(Minute(Literal(new Timestamp(c.getTimeInMillis))),
           c.get(Calendar.MINUTE))
       }
     }

From b983d493b490ca8bafe7eb988b62a250987ae353 Mon Sep 17 00:00:00 2001
From: "Perinkulam I. Ganesh" <gip@us.ibm.com>
Date: Thu, 23 Jul 2015 07:46:20 +0100
Subject: [PATCH 0558/1454] [SPARK-8695] [CORE] [MLLIB] TreeAggregation
 shouldn't be triggered when it doesn't save wall-clock time.

Author: Perinkulam I. Ganesh <gip@us.ibm.com>

Closes #7397 from piganesh/SPARK-8695 and squashes the following commits:

041620c [Perinkulam I. Ganesh] [SPARK-8695][CORE][MLlib] TreeAggregation shouldn't be triggered when it doesn't save wall-clock time.
9ad067c [Perinkulam I. Ganesh] [SPARK-8695] [core] [WIP] TreeAggregation shouldn't be triggered for 5 partitions
a6fed07 [Perinkulam I. Ganesh] [SPARK-8695] [core] [WIP] TreeAggregation shouldn't be triggered for 5 partitions
---
 core/src/main/scala/org/apache/spark/rdd/RDD.scala | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 394c6686cbabd..6d61d227382d7 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1082,7 +1082,9 @@ abstract class RDD[T: ClassTag](
       val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
       // If creating an extra level doesn't help reduce
       // the wall-clock time, we stop tree aggregation.
-      while (numPartitions > scale + numPartitions / scale) {
+
+      // Don't trigger TreeAggregation when it doesn't save wall-clock time
+      while (numPartitions > scale + math.ceil(numPartitions.toDouble / scale)) {
         numPartitions /= scale
         val curNumPartitions = numPartitions
         partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex {

From ac3ae0f2be88e0b53f65342efe5fcbe67b5c2106 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Thu, 23 Jul 2015 00:43:26 -0700
Subject: [PATCH 0559/1454] [SPARK-9266] Prevent "managed memory leak detected"
 exception from masking original exception

When a task fails with an exception and also fails to properly clean up its managed memory, the `spark.unsafe.exceptionOnMemoryLeak` memory leak detection mechanism's exceptions will mask the original exception that caused the task to fail. We should throw the memory leak exception only if no other exception occurred.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7603 from JoshRosen/SPARK-9266 and squashes the following commits:

c268cb5 [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-9266
c1f0167 [Josh Rosen] Fix the error masking problem
448eae8 [Josh Rosen] Add regression test
---
 .../org/apache/spark/executor/Executor.scala  |  7 ++++--
 .../scala/org/apache/spark/FailureSuite.scala | 25 +++++++++++++++++++
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 581b40003c6c4..e76664f1bd7b0 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -209,16 +209,19 @@ private[spark] class Executor(
 
         // Run the actual task and measure its runtime.
         taskStart = System.currentTimeMillis()
+        var threwException = true
         val (value, accumUpdates) = try {
-          task.run(
+          val res = task.run(
             taskAttemptId = taskId,
             attemptNumber = attemptNumber,
             metricsSystem = env.metricsSystem)
+          threwException = false
+          res
         } finally {
           val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory()
           if (freedMemory > 0) {
             val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId"
-            if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) {
+            if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false) && !threwException) {
               throw new SparkException(errMsg)
             } else {
               logError(errMsg)
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index b099cd3fb7965..69cb4b44cf7ef 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -141,5 +141,30 @@ class FailureSuite extends SparkFunSuite with LocalSparkContext {
     FailureSuiteState.clear()
   }
 
+  test("managed memory leak error should not mask other failures (SPARK-9266") {
+    val conf = new SparkConf().set("spark.unsafe.exceptionOnMemoryLeak", "true")
+    sc = new SparkContext("local[1,1]", "test", conf)
+
+    // If a task leaks memory but fails due to some other cause, then make sure that the original
+    // cause is preserved
+    val thrownDueToTaskFailure = intercept[SparkException] {
+      sc.parallelize(Seq(0)).mapPartitions { iter =>
+        TaskContext.get().taskMemoryManager().allocate(128)
+        throw new Exception("intentional task failure")
+        iter
+      }.count()
+    }
+    assert(thrownDueToTaskFailure.getMessage.contains("intentional task failure"))
+
+    // If the task succeeded but memory was leaked, then the task should fail due to that leak
+    val thrownDueToMemoryLeak = intercept[SparkException] {
+      sc.parallelize(Seq(0)).mapPartitions { iter =>
+        TaskContext.get().taskMemoryManager().allocate(128)
+        iter
+      }.count()
+    }
+    assert(thrownDueToMemoryLeak.getMessage.contains("memory leak"))
+  }
+
   // TODO: Need to add tests with shuffle fetch failures.
 }

From fb36397b3ce569d77db26df07ac339731cc07b1c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 23 Jul 2015 01:51:34 -0700
Subject: [PATCH 0560/1454] Revert "[SPARK-8579] [SQL] support arbitrary object
 in UnsafeRow"

Reverts ObjectPool. As it stands, it has a few problems:

1. ObjectPool doesn't work with spilling and memory accounting.
2. I don't think in the long run the idea of an object pool is what we want to support, since it essentially goes back to unmanaged memory, and creates pressure on GC, and is hard to account for the total in memory size.
3. The ObjectPool patch removed the specialized getters for strings and binary, and as a result, actually introduced branches when reading non primitive data types.

If we do want to support arbitrary user defined types in the future, I think we can just add an object array in UnsafeRow, rather than relying on indirect memory addressing through a pool. We also need to pick execution strategies that are optimized for those, rather than keeping a lot of unserialized JVM objects in memory during aggregation.

This is probably the hardest thing I had to revert in Spark, due to recent patches that also change the same part of the code. Would be great to get a careful look.

Author: Reynold Xin <rxin@databricks.com>

Closes #7591 from rxin/revert-object-pool and squashes the following commits:

01db0bc [Reynold Xin] Scala style.
eda89fc [Reynold Xin] Fixed describe.
2967118 [Reynold Xin] Fixed accessor for JoinedRow.
e3294eb [Reynold Xin] Merge branch 'master' into revert-object-pool
657855f [Reynold Xin] Temp commit.
c20f2c8 [Reynold Xin] Style fix.
fe37079 [Reynold Xin] Revert "[SPARK-8579] [SQL] support arbitrary object in UnsafeRow"
---
 project/SparkBuild.scala                      |   2 +-
 .../UnsafeFixedWidthAggregationMap.java       | 150 ++++++------
 .../sql/catalyst/expressions/UnsafeRow.java   | 229 ++++++++----------
 .../spark/sql/catalyst/util/ObjectPool.java   |  78 ------
 .../sql/catalyst/util/UniqueObjectPool.java   |  59 -----
 .../execution/UnsafeExternalRowSorter.java    |  16 +-
 .../sql/catalyst/CatalystTypeConverters.scala |   3 +-
 .../spark/sql/catalyst/InternalRow.scala      |   9 +-
 .../catalyst/expressions/BoundAttribute.scala |   2 +
 .../sql/catalyst/expressions/Projection.scala |  53 ++++
 .../expressions/UnsafeRowConverter.scala      |  42 ++--
 .../expressions/codegen/CodeGenerator.scala   |   9 +-
 .../codegen/GenerateUnsafeProjection.scala    |   4 +-
 .../plans/logical/LocalRelation.scala         |   7 +
 .../UnsafeFixedWidthAggregationMapSuite.scala |  65 ++---
 .../expressions/UnsafeRowConverterSuite.scala | 137 +++--------
 .../sql/catalyst/util/ObjectPoolSuite.scala   |  57 -----
 .../org/apache/spark/sql/DataFrame.scala      |  13 +-
 .../sql/execution/GeneratedAggregate.scala    |  17 +-
 .../spark/sql/execution/LocalTableScan.scala  |   2 -
 .../sql/execution/UnsafeRowSerializer.scala   |   8 +-
 .../org/apache/spark/sql/UnsafeRowSuite.scala |   3 +-
 .../execution/UnsafeExternalSortSuite.scala   |   7 +-
 .../execution/UnsafeRowSerializerSuite.scala  |  14 +-
 24 files changed, 355 insertions(+), 631 deletions(-)
 delete mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java
 delete mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java
 delete mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 61a05d375d99e..b5b0adf630b9e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -543,7 +543,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.ui.enabled=false",
     javaOptions in Test += "-Dspark.ui.showConsoleProgress=false",
     javaOptions in Test += "-Dspark.driver.allowMultipleContexts=true",
-    javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true",
+    //javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true",
     javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test += "-Dderby.system.durability=test",
     javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark")
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
index 79d55b36dab01..2f7e84a7f59e2 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
@@ -19,11 +19,9 @@
 
 import java.util.Iterator;
 
-import scala.Function1;
-
 import org.apache.spark.sql.catalyst.InternalRow;
-import org.apache.spark.sql.catalyst.util.ObjectPool;
-import org.apache.spark.sql.catalyst.util.UniqueObjectPool;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
 import org.apache.spark.unsafe.memory.MemoryLocation;
@@ -40,48 +38,26 @@ public final class UnsafeFixedWidthAggregationMap {
    * An empty aggregation buffer, encoded in UnsafeRow format. When inserting a new key into the
    * map, we copy this buffer and use it as the value.
    */
-  private final byte[] emptyBuffer;
+  private final byte[] emptyAggregationBuffer;
 
-  /**
-   * An empty row used by `initProjection`
-   */
-  private static final InternalRow emptyRow = new GenericInternalRow();
+  private final StructType aggregationBufferSchema;
 
-  /**
-   * Whether can the empty aggregation buffer be reuse without calling `initProjection` or not.
-   */
-  private final boolean reuseEmptyBuffer;
+  private final StructType groupingKeySchema;
 
   /**
-   * The projection used to initialize the emptyBuffer
+   * Encodes grouping keys as UnsafeRows.
    */
-  private final Function1<InternalRow, InternalRow> initProjection;
-
-  /**
-   * Encodes grouping keys or buffers as UnsafeRows.
-   */
-  private final UnsafeRowConverter keyConverter;
-  private final UnsafeRowConverter bufferConverter;
+  private final UnsafeRowConverter groupingKeyToUnsafeRowConverter;
 
   /**
    * A hashmap which maps from opaque bytearray keys to bytearray values.
    */
   private final BytesToBytesMap map;
 
-  /**
-   * An object pool for objects that are used in grouping keys.
-   */
-  private final UniqueObjectPool keyPool;
-
-  /**
-   * An object pool for objects that are used in aggregation buffers.
-   */
-  private final ObjectPool bufferPool;
-
   /**
    * Re-used pointer to the current aggregation buffer
    */
-  private final UnsafeRow currentBuffer = new UnsafeRow();
+  private final UnsafeRow currentAggregationBuffer = new UnsafeRow();
 
   /**
    * Scratch space that is used when encoding grouping keys into UnsafeRow format.
@@ -93,41 +69,69 @@ public final class UnsafeFixedWidthAggregationMap {
 
   private final boolean enablePerfMetrics;
 
+  /**
+   * @return true if UnsafeFixedWidthAggregationMap supports grouping keys with the given schema,
+   *         false otherwise.
+   */
+  public static boolean supportsGroupKeySchema(StructType schema) {
+    for (StructField field: schema.fields()) {
+      if (!UnsafeRow.readableFieldTypes.contains(field.dataType())) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  /**
+   * @return true if UnsafeFixedWidthAggregationMap supports aggregation buffers with the given
+   *         schema, false otherwise.
+   */
+  public static boolean supportsAggregationBufferSchema(StructType schema) {
+    for (StructField field: schema.fields()) {
+      if (!UnsafeRow.settableFieldTypes.contains(field.dataType())) {
+        return false;
+      }
+    }
+    return true;
+  }
+
   /**
    * Create a new UnsafeFixedWidthAggregationMap.
    *
-   * @param initProjection the default value for new keys (a "zero" of the agg. function)
-   * @param keyConverter the converter of the grouping key, used for row conversion.
-   * @param bufferConverter the converter of the aggregation buffer, used for row conversion.
+   * @param emptyAggregationBuffer the default value for new keys (a "zero" of the agg. function)
+   * @param aggregationBufferSchema the schema of the aggregation buffer, used for row conversion.
+   * @param groupingKeySchema the schema of the grouping key, used for row conversion.
    * @param memoryManager the memory manager used to allocate our Unsafe memory structures.
    * @param initialCapacity the initial capacity of the map (a sizing hint to avoid re-hashing).
    * @param enablePerfMetrics if true, performance metrics will be recorded (has minor perf impact)
    */
   public UnsafeFixedWidthAggregationMap(
-      Function1<InternalRow, InternalRow> initProjection,
-      UnsafeRowConverter keyConverter,
-      UnsafeRowConverter bufferConverter,
+      InternalRow emptyAggregationBuffer,
+      StructType aggregationBufferSchema,
+      StructType groupingKeySchema,
       TaskMemoryManager memoryManager,
       int initialCapacity,
       boolean enablePerfMetrics) {
-    this.initProjection = initProjection;
-    this.keyConverter = keyConverter;
-    this.bufferConverter = bufferConverter;
-    this.enablePerfMetrics = enablePerfMetrics;
-
+    this.emptyAggregationBuffer =
+      convertToUnsafeRow(emptyAggregationBuffer, aggregationBufferSchema);
+    this.aggregationBufferSchema = aggregationBufferSchema;
+    this.groupingKeyToUnsafeRowConverter = new UnsafeRowConverter(groupingKeySchema);
+    this.groupingKeySchema = groupingKeySchema;
     this.map = new BytesToBytesMap(memoryManager, initialCapacity, enablePerfMetrics);
-    this.keyPool = new UniqueObjectPool(100);
-    this.bufferPool = new ObjectPool(initialCapacity);
+    this.enablePerfMetrics = enablePerfMetrics;
+  }
 
-    InternalRow initRow = initProjection.apply(emptyRow);
-    int emptyBufferSize = bufferConverter.getSizeRequirement(initRow);
-    this.emptyBuffer = new byte[emptyBufferSize];
-    int writtenLength = bufferConverter.writeRow(
-      initRow, emptyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, emptyBufferSize,
-      bufferPool);
-    assert (writtenLength == emptyBuffer.length): "Size requirement calculation was wrong!";
-    // re-use the empty buffer only when there is no object saved in pool.
-    reuseEmptyBuffer = bufferPool.size() == 0;
+  /**
+   * Convert a Java object row into an UnsafeRow, allocating it into a new byte array.
+   */
+  private static byte[] convertToUnsafeRow(InternalRow javaRow, StructType schema) {
+    final UnsafeRowConverter converter = new UnsafeRowConverter(schema);
+    final int size = converter.getSizeRequirement(javaRow);
+    final byte[] unsafeRow = new byte[size];
+    final int writtenLength =
+      converter.writeRow(javaRow, unsafeRow, PlatformDependent.BYTE_ARRAY_OFFSET, size);
+    assert (writtenLength == unsafeRow.length): "Size requirement calculation was wrong!";
+    return unsafeRow;
   }
 
   /**
@@ -135,17 +139,16 @@ public UnsafeFixedWidthAggregationMap(
    * return the same object.
    */
   public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
-    final int groupingKeySize = keyConverter.getSizeRequirement(groupingKey);
+    final int groupingKeySize = groupingKeyToUnsafeRowConverter.getSizeRequirement(groupingKey);
     // Make sure that the buffer is large enough to hold the key. If it's not, grow it:
     if (groupingKeySize > groupingKeyConversionScratchSpace.length) {
       groupingKeyConversionScratchSpace = new byte[groupingKeySize];
     }
-    final int actualGroupingKeySize = keyConverter.writeRow(
+    final int actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow(
       groupingKey,
       groupingKeyConversionScratchSpace,
       PlatformDependent.BYTE_ARRAY_OFFSET,
-      groupingKeySize,
-      keyPool);
+      groupingKeySize);
     assert (groupingKeySize == actualGroupingKeySize) : "Size requirement calculation was wrong!";
 
     // Probe our map using the serialized key
@@ -156,32 +159,25 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     if (!loc.isDefined()) {
       // This is the first time that we've seen this grouping key, so we'll insert a copy of the
       // empty aggregation buffer into the map:
-      if (!reuseEmptyBuffer) {
-        // There is some objects referenced by emptyBuffer, so generate a new one
-        InternalRow initRow = initProjection.apply(emptyRow);
-        bufferConverter.writeRow(initRow, emptyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET,
-          groupingKeySize, bufferPool);
-      }
       loc.putNewKey(
         groupingKeyConversionScratchSpace,
         PlatformDependent.BYTE_ARRAY_OFFSET,
         groupingKeySize,
-        emptyBuffer,
+        emptyAggregationBuffer,
         PlatformDependent.BYTE_ARRAY_OFFSET,
-        emptyBuffer.length
+        emptyAggregationBuffer.length
       );
     }
 
     // Reset the pointer to point to the value that we just stored or looked up:
     final MemoryLocation address = loc.getValueAddress();
-    currentBuffer.pointTo(
+    currentAggregationBuffer.pointTo(
       address.getBaseObject(),
       address.getBaseOffset(),
-      bufferConverter.numFields(),
-      loc.getValueLength(),
-      bufferPool
+      aggregationBufferSchema.length(),
+      loc.getValueLength()
     );
-    return currentBuffer;
+    return currentAggregationBuffer;
   }
 
   /**
@@ -217,16 +213,14 @@ public MapEntry next() {
         entry.key.pointTo(
           keyAddress.getBaseObject(),
           keyAddress.getBaseOffset(),
-          keyConverter.numFields(),
-          loc.getKeyLength(),
-          keyPool
+          groupingKeySchema.length(),
+          loc.getKeyLength()
         );
         entry.value.pointTo(
           valueAddress.getBaseObject(),
           valueAddress.getBaseOffset(),
-          bufferConverter.numFields(),
-          loc.getValueLength(),
-          bufferPool
+          aggregationBufferSchema.length(),
+          loc.getValueLength()
         );
         return entry;
       }
@@ -254,8 +248,6 @@ public void printPerfMetrics() {
     System.out.println("Number of hash collisions: " + map.getNumHashCollisions());
     System.out.println("Time spent resizing (ns): " + map.getTimeSpentResizingNs());
     System.out.println("Total memory consumption (bytes): " + map.getTotalMemoryConsumption());
-    System.out.println("Number of unique objects in keys: " + keyPool.size());
-    System.out.println("Number of objects in buffers: " + bufferPool.size());
   }
 
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 7f08bf7b742dc..fa1216b455a9e 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -19,14 +19,19 @@
 
 import java.io.IOException;
 import java.io.OutputStream;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashSet;
+import java.util.Set;
 
-import org.apache.spark.sql.catalyst.util.ObjectPool;
+import org.apache.spark.sql.types.DataType;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
 import org.apache.spark.unsafe.types.UTF8String;
 
+import static org.apache.spark.sql.types.DataTypes.*;
 
 /**
  * An Unsafe implementation of Row which is backed by raw memory instead of Java objects.
@@ -40,20 +45,7 @@
  * primitive types, such as long, double, or int, we store the value directly in the word. For
  * fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the
  * base address of the row) that points to the beginning of the variable-length field, and length
- * (they are combined into a long). For other objects, they are stored in a pool, the indexes of
- * them are hold in the the word.
- *
- * In order to support fast hashing and equality checks for UnsafeRows that contain objects
- * when used as grouping key in BytesToBytesMap, we put the objects in an UniqueObjectPool to make
- * sure all the key have the same index for same object, then we can hash/compare the objects by
- * hash/compare the index.
- *
- * For non-primitive types, the word of a field could be:
- *   UNION {
- *     [1] [offset: 31bits] [length: 31bits]  // StringType
- *     [0] [offset: 31bits] [length: 31bits]  // BinaryType
- *     - [index: 63bits]                      // StringType, Binary, index to object in pool
- *   }
+ * (they are combined into a long).
  *
  * Instances of `UnsafeRow` act as pointers to row data stored in this format.
  */
@@ -62,13 +54,9 @@ public final class UnsafeRow extends MutableRow {
   private Object baseObject;
   private long baseOffset;
 
-  /** A pool to hold non-primitive objects */
-  private ObjectPool pool;
-
   public Object getBaseObject() { return baseObject; }
   public long getBaseOffset() { return baseOffset; }
   public int getSizeInBytes() { return sizeInBytes; }
-  public ObjectPool getPool() { return pool; }
 
   /** The number of fields in this row, used for calculating the bitset width (and in assertions) */
   private int numFields;
@@ -89,7 +77,42 @@ public static int calculateBitSetWidthInBytes(int numFields) {
     return ((numFields / 64) + (numFields % 64 == 0 ? 0 : 1)) * 8;
   }
 
-  public static final long OFFSET_BITS = 31L;
+  /**
+   * Field types that can be updated in place in UnsafeRows (e.g. we support set() for these types)
+   */
+  public static final Set<DataType> settableFieldTypes;
+
+  /**
+   * Fields types can be read(but not set (e.g. set() will throw UnsupportedOperationException).
+   */
+  public static final Set<DataType> readableFieldTypes;
+
+  // TODO: support DecimalType
+  static {
+    settableFieldTypes = Collections.unmodifiableSet(
+      new HashSet<>(
+        Arrays.asList(new DataType[] {
+          NullType,
+          BooleanType,
+          ByteType,
+          ShortType,
+          IntegerType,
+          LongType,
+          FloatType,
+          DoubleType,
+          DateType,
+          TimestampType
+    })));
+
+    // We support get() on a superset of the types for which we support set():
+    final Set<DataType> _readableFieldTypes = new HashSet<>(
+      Arrays.asList(new DataType[]{
+        StringType,
+        BinaryType
+      }));
+    _readableFieldTypes.addAll(settableFieldTypes);
+    readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes);
+  }
 
   /**
    * Construct a new UnsafeRow. The resulting row won't be usable until `pointTo()` has been called,
@@ -104,17 +127,14 @@ public UnsafeRow() { }
    * @param baseOffset the offset within the base object
    * @param numFields the number of fields in this row
    * @param sizeInBytes the size of this row's backing data, in bytes
-   * @param pool the object pool to hold arbitrary objects
    */
-  public void pointTo(
-      Object baseObject, long baseOffset, int numFields, int sizeInBytes, ObjectPool pool) {
+  public void pointTo(Object baseObject, long baseOffset, int numFields, int sizeInBytes) {
     assert numFields >= 0 : "numFields should >= 0";
     this.bitSetWidthInBytes = calculateBitSetWidthInBytes(numFields);
     this.baseObject = baseObject;
     this.baseOffset = baseOffset;
     this.numFields = numFields;
     this.sizeInBytes = sizeInBytes;
-    this.pool = pool;
   }
 
   private void assertIndexIsValid(int index) {
@@ -137,68 +157,9 @@ private void setNotNullAt(int i) {
     BitSetMethods.unset(baseObject, baseOffset, i);
   }
 
-  /**
-   * Updates the column `i` as Object `value`, which cannot be primitive types.
-   */
   @Override
-  public void update(int i, Object value) {
-    if (value == null) {
-      if (!isNullAt(i)) {
-        // remove the old value from pool
-        long idx = getLong(i);
-        if (idx <= 0) {
-          // this is the index of old value in pool, remove it
-          pool.replace((int)-idx, null);
-        } else {
-          // there will be some garbage left (UTF8String or byte[])
-        }
-        setNullAt(i);
-      }
-      return;
-    }
-
-    if (isNullAt(i)) {
-      // there is not an old value, put the new value into pool
-      int idx = pool.put(value);
-      setLong(i, (long)-idx);
-    } else {
-      // there is an old value, check the type, then replace it or update it
-      long v = getLong(i);
-      if (v <= 0) {
-        // it's the index in the pool, replace old value with new one
-        int idx = (int)-v;
-        pool.replace(idx, value);
-      } else {
-        // old value is UTF8String or byte[], try to reuse the space
-        boolean isString;
-        byte[] newBytes;
-        if (value instanceof UTF8String) {
-          newBytes = ((UTF8String) value).getBytes();
-          isString = true;
-        } else {
-          newBytes = (byte[]) value;
-          isString = false;
-        }
-        int offset = (int) ((v >> OFFSET_BITS) & Integer.MAX_VALUE);
-        int oldLength = (int) (v & Integer.MAX_VALUE);
-        if (newBytes.length <= oldLength) {
-          // the new value can fit in the old buffer, re-use it
-          PlatformDependent.copyMemory(
-            newBytes,
-            PlatformDependent.BYTE_ARRAY_OFFSET,
-            baseObject,
-            baseOffset + offset,
-            newBytes.length);
-          long flag = isString ? 1L << (OFFSET_BITS * 2) : 0L;
-          setLong(i, flag | (((long) offset) << OFFSET_BITS) | (long) newBytes.length);
-        } else {
-          // Cannot fit in the buffer
-          int idx = pool.put(value);
-          setLong(i, (long) -idx);
-        }
-      }
-    }
-    setNotNullAt(i);
+  public void update(int ordinal, Object value) {
+    throw new UnsupportedOperationException();
   }
 
   @Override
@@ -256,40 +217,14 @@ public void setFloat(int ordinal, float value) {
     PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
-  /**
-   * Returns the object for column `i`, which should not be primitive type.
-   */
+  @Override
+  public int size() {
+    return numFields;
+  }
+
   @Override
   public Object get(int i) {
-    assertIndexIsValid(i);
-    if (isNullAt(i)) {
-      return null;
-    }
-    long v = PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(i));
-    if (v <= 0) {
-      // It's an index to object in the pool.
-      int idx = (int)-v;
-      return pool.get(idx);
-    } else {
-      // The column could be StingType or BinaryType
-      boolean isString = (v >> (OFFSET_BITS * 2)) > 0;
-      int offset = (int) ((v >> OFFSET_BITS) & Integer.MAX_VALUE);
-      int size = (int) (v & Integer.MAX_VALUE);
-      final byte[] bytes = new byte[size];
-      // TODO(davies): Avoid the copy once we can manage the life cycle of Row well.
-      PlatformDependent.copyMemory(
-        baseObject,
-        baseOffset + offset,
-        bytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        size
-      );
-      if (isString) {
-        return UTF8String.fromBytes(bytes);
-      } else {
-        return bytes;
-      }
-    }
+    throw new UnsupportedOperationException();
   }
 
   @Override
@@ -348,6 +283,38 @@ public double getDouble(int i) {
     }
   }
 
+  @Override
+  public UTF8String getUTF8String(int i) {
+    assertIndexIsValid(i);
+    return isNullAt(i) ? null : UTF8String.fromBytes(getBinary(i));
+  }
+
+  @Override
+  public byte[] getBinary(int i) {
+    if (isNullAt(i)) {
+      return null;
+    } else {
+      assertIndexIsValid(i);
+      final long offsetAndSize = getLong(i);
+      final int offset = (int) (offsetAndSize >> 32);
+      final int size = (int) (offsetAndSize & ((1L << 32) - 1));
+      final byte[] bytes = new byte[size];
+      PlatformDependent.copyMemory(
+        baseObject,
+        baseOffset + offset,
+        bytes,
+        PlatformDependent.BYTE_ARRAY_OFFSET,
+        size
+      );
+      return bytes;
+    }
+  }
+
+  @Override
+  public String getString(int i) {
+    return getUTF8String(i).toString();
+  }
+
   /**
    * Copies this row, returning a self-contained UnsafeRow that stores its data in an internal
    * byte array rather than referencing data stored in a data page.
@@ -356,23 +323,17 @@ public double getDouble(int i) {
    */
   @Override
   public UnsafeRow copy() {
-    if (pool != null) {
-      throw new UnsupportedOperationException(
-        "Copy is not supported for UnsafeRows that use object pools");
-    } else {
-      UnsafeRow rowCopy = new UnsafeRow();
-      final byte[] rowDataCopy = new byte[sizeInBytes];
-      PlatformDependent.copyMemory(
-        baseObject,
-        baseOffset,
-        rowDataCopy,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        sizeInBytes
-      );
-      rowCopy.pointTo(
-        rowDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes, null);
-      return rowCopy;
-    }
+    UnsafeRow rowCopy = new UnsafeRow();
+    final byte[] rowDataCopy = new byte[sizeInBytes];
+    PlatformDependent.copyMemory(
+      baseObject,
+      baseOffset,
+      rowDataCopy,
+      PlatformDependent.BYTE_ARRAY_OFFSET,
+      sizeInBytes
+    );
+    rowCopy.pointTo(rowDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
+    return rowCopy;
   }
 
   /**
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java
deleted file mode 100644
index 97f89a7d0b758..0000000000000
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/ObjectPool.java
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.util;
-
-/**
- * A object pool stores a collection of objects in array, then they can be referenced by the
- * pool plus an index.
- */
-public class ObjectPool {
-
-  /**
-   * An array to hold objects, which will grow as needed.
-   */
-  private Object[] objects;
-
-  /**
-   * How many objects in the pool.
-   */
-  private int numObj;
-
-  public ObjectPool(int capacity) {
-    objects = new Object[capacity];
-    numObj = 0;
-  }
-
-  /**
-   * Returns how many objects in the pool.
-   */
-  public int size() {
-    return numObj;
-  }
-
-  /**
-   * Returns the object at position `idx` in the array.
-   */
-  public Object get(int idx) {
-    assert (idx < numObj);
-    return objects[idx];
-  }
-
-  /**
-   * Puts an object `obj` at the end of array, returns the index of it.
-   * <p/>
-   * The array will grow as needed.
-   */
-  public int put(Object obj) {
-    if (numObj >= objects.length) {
-      Object[] tmp = new Object[objects.length * 2];
-      System.arraycopy(objects, 0, tmp, 0, objects.length);
-      objects = tmp;
-    }
-    objects[numObj++] = obj;
-    return numObj - 1;
-  }
-
-  /**
-   * Replaces the object at `idx` with new one `obj`.
-   */
-  public void replace(int idx, Object obj) {
-    assert (idx < numObj);
-    objects[idx] = obj;
-  }
-}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java
deleted file mode 100644
index d512392dcaacc..0000000000000
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/util/UniqueObjectPool.java
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.util;
-
-import java.util.HashMap;
-
-/**
- * An unique object pool stores a collection of unique objects in it.
- */
-public class UniqueObjectPool extends ObjectPool {
-
-  /**
-   * A hash map from objects to their indexes in the array.
-   */
-  private HashMap<Object, Integer> objIndex;
-
-  public UniqueObjectPool(int capacity) {
-    super(capacity);
-    objIndex = new HashMap<Object, Integer>();
-  }
-
-  /**
-   * Put an object `obj` into the pool. If there is an existing object equals to `obj`, it will
-   * return the index of the existing one.
-   */
-  @Override
-  public int put(Object obj) {
-    if (objIndex.containsKey(obj)) {
-      return objIndex.get(obj);
-    } else {
-      int idx = super.put(obj);
-      objIndex.put(obj, idx);
-      return idx;
-    }
-  }
-
-  /**
-   * The objects can not be replaced.
-   */
-  @Override
-  public void replace(int idx, Object obj) {
-    throw new UnsupportedOperationException();
-  }
-}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 39fd6e1bc6d13..be4ff400c4754 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -30,7 +30,6 @@
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
-import org.apache.spark.sql.catalyst.util.ObjectPool;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
@@ -72,7 +71,7 @@ public UnsafeExternalRowSorter(
       sparkEnv.shuffleMemoryManager(),
       sparkEnv.blockManager(),
       taskContext,
-      new RowComparator(ordering, schema.length(), null),
+      new RowComparator(ordering, schema.length()),
       prefixComparator,
       4096,
       sparkEnv.conf()
@@ -140,8 +139,7 @@ public InternalRow next() {
               sortedIterator.getBaseObject(),
               sortedIterator.getBaseOffset(),
               numFields,
-              sortedIterator.getRecordLength(),
-              null);
+              sortedIterator.getRecordLength());
             if (!hasNext()) {
               row.copy(); // so that we don't have dangling pointers to freed page
               cleanupResources();
@@ -174,27 +172,25 @@ public Iterator<InternalRow> sort(Iterator<InternalRow> inputIterator) throws IO
    * Return true if UnsafeExternalRowSorter can sort rows with the given schema, false otherwise.
    */
   public static boolean supportsSchema(StructType schema) {
-    // TODO: add spilling note to explain why we do this for now:
     return UnsafeProjection.canSupport(schema);
   }
 
   private static final class RowComparator extends RecordComparator {
     private final Ordering<InternalRow> ordering;
     private final int numFields;
-    private final ObjectPool objPool;
     private final UnsafeRow row1 = new UnsafeRow();
     private final UnsafeRow row2 = new UnsafeRow();
 
-    public RowComparator(Ordering<InternalRow> ordering, int numFields, ObjectPool objPool) {
+    public RowComparator(Ordering<InternalRow> ordering, int numFields) {
       this.numFields = numFields;
       this.ordering = ordering;
-      this.objPool = objPool;
     }
 
     @Override
     public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff2) {
-      row1.pointTo(baseObj1, baseOff1, numFields, -1, objPool);
-      row2.pointTo(baseObj2, baseOff2, numFields, -1, objPool);
+      // TODO: Why are the sizes -1?
+      row1.pointTo(baseObj1, baseOff1, numFields, -1);
+      row2.pointTo(baseObj2, baseOff2, numFields, -1);
       return ordering.compare(row1, row2);
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index ae0ab2f4c63f5..4067833d5e648 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -281,7 +281,8 @@ object CatalystTypeConverters {
     }
     override def toScala(catalystValue: UTF8String): String =
       if (catalystValue == null) null else catalystValue.toString
-    override def toScalaImpl(row: InternalRow, column: Int): String = row(column).toString
+    override def toScalaImpl(row: InternalRow, column: Int): String =
+      row.getUTF8String(column).toString
   }
 
   private object DateConverter extends CatalystTypeConverter[Date, Date, Any] {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 024973a6b9fcd..c7ec49b3d6c3d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -27,11 +27,12 @@ import org.apache.spark.unsafe.types.UTF8String
  */
 abstract class InternalRow extends Row {
 
+  def getUTF8String(i: Int): UTF8String = getAs[UTF8String](i)
+
+  def getBinary(i: Int): Array[Byte] = getAs[Array[Byte]](i)
+
   // This is only use for test
-  override def getString(i: Int): String = {
-    val str = getAs[UTF8String](i)
-    if (str != null) str.toString else null
-  }
+  override def getString(i: Int): String = getAs[UTF8String](i).toString
 
   // These expensive API should not be used internally.
   final override def getDecimal(i: Int): java.math.BigDecimal =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 4a13b687bf4ce..6aa4930cb8587 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -46,6 +46,8 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
         case LongType | TimestampType => input.getLong(ordinal)
         case FloatType => input.getFloat(ordinal)
         case DoubleType => input.getDouble(ordinal)
+        case StringType => input.getUTF8String(ordinal)
+        case BinaryType => input.getBinary(ordinal)
         case _ => input.get(ordinal)
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 69758e653eba0..04872fbc8b091 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GenerateMutableProjection}
 import org.apache.spark.sql.types.{StructType, DataType}
+import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * A [[Projection]] that is calculated by calling the `eval` of each of the specified expressions.
@@ -177,6 +178,14 @@ class JoinedRow extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
+  override def getUTF8String(i: Int): UTF8String = {
+    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+  }
+
+  override def getBinary(i: Int): Array[Byte] = {
+    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+  }
+
   override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
@@ -271,6 +280,14 @@ class JoinedRow2 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
+  override def getUTF8String(i: Int): UTF8String = {
+    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+  }
+
+  override def getBinary(i: Int): Array[Byte] = {
+    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+  }
+
   override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
@@ -359,6 +376,15 @@ class JoinedRow3 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
+  override def getUTF8String(i: Int): UTF8String = {
+    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+  }
+
+  override def getBinary(i: Int): Array[Byte] = {
+    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+  }
+
+
   override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
@@ -447,6 +473,15 @@ class JoinedRow4 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
+  override def getUTF8String(i: Int): UTF8String = {
+    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+  }
+
+  override def getBinary(i: Int): Array[Byte] = {
+    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+  }
+
+
   override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
@@ -535,6 +570,15 @@ class JoinedRow5 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
+  override def getUTF8String(i: Int): UTF8String = {
+    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+  }
+
+  override def getBinary(i: Int): Array[Byte] = {
+    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+  }
+
+
   override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
@@ -623,6 +667,15 @@ class JoinedRow6 extends InternalRow {
 
   override def length: Int = row1.length + row2.length
 
+  override def getUTF8String(i: Int): UTF8String = {
+    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+  }
+
+  override def getBinary(i: Int): Array[Byte] = {
+    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+  }
+
+
   override def get(i: Int): Any =
     if (i < row1.length) row1(i) else row2(i - row1.length)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
index 885ab091fcdf5..c47b16c0f8585 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
@@ -17,13 +17,15 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import scala.util.Try
+
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.util.ObjectPool
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
 import org.apache.spark.unsafe.types.UTF8String
 
+
 /**
  * Converts Rows into UnsafeRow format. This class is NOT thread-safe.
  *
@@ -35,8 +37,6 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
     this(schema.fields.map(_.dataType))
   }
 
-  def numFields: Int = fieldTypes.length
-
   /** Re-used pointer to the unsafe row being written */
   private[this] val unsafeRow = new UnsafeRow()
 
@@ -77,9 +77,8 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
       row: InternalRow,
       baseObject: Object,
       baseOffset: Long,
-      rowLengthInBytes: Int,
-      pool: ObjectPool): Int = {
-    unsafeRow.pointTo(baseObject, baseOffset, writers.length, rowLengthInBytes, pool)
+      rowLengthInBytes: Int): Int = {
+    unsafeRow.pointTo(baseObject, baseOffset, writers.length, rowLengthInBytes)
 
     if (writers.length > 0) {
       // zero-out the bitset
@@ -94,16 +93,16 @@ class UnsafeRowConverter(fieldTypes: Array[DataType]) {
     }
 
     var fieldNumber = 0
-    var cursor: Int = fixedLengthSize
+    var appendCursor: Int = fixedLengthSize
     while (fieldNumber < writers.length) {
       if (row.isNullAt(fieldNumber)) {
         unsafeRow.setNullAt(fieldNumber)
       } else {
-        cursor += writers(fieldNumber).write(row, unsafeRow, fieldNumber, cursor)
+        appendCursor += writers(fieldNumber).write(row, unsafeRow, fieldNumber, appendCursor)
       }
       fieldNumber += 1
     }
-    cursor
+    appendCursor
   }
 
 }
@@ -118,11 +117,11 @@ private abstract class UnsafeColumnWriter {
    * @param source the row being converted
    * @param target a pointer to the converted unsafe row
    * @param column the column to write
-   * @param cursor the offset from the start of the unsafe row to the end of the row;
+   * @param appendCursor the offset from the start of the unsafe row to the end of the row;
    *                     used for calculating where variable-length data should be written
    * @return the number of variable-length bytes written
    */
-  def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int
+  def write(source: InternalRow, target: UnsafeRow, column: Int, appendCursor: Int): Int
 
   /**
    * Return the number of bytes that are needed to write this variable-length value.
@@ -144,21 +143,19 @@ private object UnsafeColumnWriter {
       case DoubleType => DoubleUnsafeColumnWriter
       case StringType => StringUnsafeColumnWriter
       case BinaryType => BinaryUnsafeColumnWriter
-      case t => ObjectUnsafeColumnWriter
+      case t =>
+        throw new UnsupportedOperationException(s"Do not know how to write columns of type $t")
     }
   }
 
   /**
    * Returns whether the dataType can be embedded into UnsafeRow (not using ObjectPool).
    */
-  def canEmbed(dataType: DataType): Boolean = {
-    forType(dataType) != ObjectUnsafeColumnWriter
-  }
+  def canEmbed(dataType: DataType): Boolean = Try(forType(dataType)).isSuccess
 }
 
 // ------------------------------------------------------------------------------------------------
 
-
 private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter {
   // Primitives don't write to the variable-length region:
   def getSize(sourceRow: InternalRow, column: Int): Int = 0
@@ -249,8 +246,7 @@ private abstract class BytesUnsafeColumnWriter extends UnsafeColumnWriter {
       offset,
       numBytes
     )
-    val flag = if (isString) 1L << (UnsafeRow.OFFSET_BITS * 2) else 0
-    target.setLong(column, flag | (cursor.toLong << UnsafeRow.OFFSET_BITS) | numBytes.toLong)
+    target.setLong(column, (cursor.toLong << 32) | numBytes.toLong)
     ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
   }
 }
@@ -278,13 +274,3 @@ private object BinaryUnsafeColumnWriter extends BytesUnsafeColumnWriter {
   def getSize(value: Array[Byte]): Int =
     ByteArrayMethods.roundNumberOfBytesToNearestWord(value.length)
 }
-
-private object ObjectUnsafeColumnWriter extends UnsafeColumnWriter {
-  override def getSize(sourceRow: InternalRow, column: Int): Int = 0
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    val obj = source.get(column)
-    val idx = target.getPool.put(obj)
-    target.setLong(column, - idx)
-    0
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 319dcd1c04316..48225e1574600 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -105,10 +105,11 @@ class CodeGenContext {
    */
   def getColumn(row: String, dataType: DataType, ordinal: Int): String = {
     val jt = javaType(dataType)
-    if (isPrimitiveType(jt)) {
-      s"$row.get${primitiveTypeName(jt)}($ordinal)"
-    } else {
-      s"($jt)$row.apply($ordinal)"
+    dataType match {
+      case _ if isPrimitiveType(jt) => s"$row.get${primitiveTypeName(jt)}($ordinal)"
+      case StringType => s"$row.getUTF8String($ordinal)"
+      case BinaryType => s"$row.getBinary($ordinal)"
+      case _ => s"($jt)$row.apply($ordinal)"
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 3a8e8302b24fd..d65e5c38ebf5c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -98,7 +98,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       }
 
       public UnsafeRow apply(InternalRow i) {
-        ${allExprs}
+        $allExprs
 
         // additionalSize had '+' in the beginning
         int numBytes = $fixedSize $additionalSize;
@@ -106,7 +106,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           buffer = new byte[numBytes];
         }
         target.pointTo(buffer, org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
-          ${expressions.size}, numBytes, null);
+          ${expressions.size}, numBytes);
         int cursor = $fixedSize;
         $writers
         return target;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala
index 1868f119f0e97..e3e7a11dba973 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.plans.logical
 
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, analysis}
 import org.apache.spark.sql.types.{StructField, StructType}
@@ -28,6 +29,12 @@ object LocalRelation {
     new LocalRelation(StructType(output1 +: output).toAttributes)
   }
 
+  def fromExternalRows(output: Seq[Attribute], data: Seq[Row]): LocalRelation = {
+    val schema = StructType.fromAttributes(output)
+    val converter = CatalystTypeConverters.createToCatalystConverter(schema)
+    LocalRelation(output, data.map(converter(_).asInstanceOf[InternalRow]))
+  }
+
   def fromProduct(output: Seq[Attribute], data: Seq[Product]): LocalRelation = {
     val schema = StructType.fromAttributes(output)
     val converter = CatalystTypeConverters.createToCatalystConverter(schema)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
index c9667e90a0aaa..7566cb59e34ee 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
@@ -24,9 +24,8 @@ import org.scalatest.{BeforeAndAfterEach, Matchers}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
+import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, TaskMemoryManager, MemoryAllocator}
 import org.apache.spark.unsafe.types.UTF8String
 
 
@@ -35,10 +34,10 @@ class UnsafeFixedWidthAggregationMapSuite
   with Matchers
   with BeforeAndAfterEach {
 
+  import UnsafeFixedWidthAggregationMap._
+
   private val groupKeySchema = StructType(StructField("product", StringType) :: Nil)
   private val aggBufferSchema = StructType(StructField("salePrice", IntegerType) :: Nil)
-  private def emptyProjection: Projection =
-    GenerateProjection.generate(Seq(Literal(0)), Seq(AttributeReference("price", IntegerType)()))
   private def emptyAggregationBuffer: InternalRow = InternalRow(0)
 
   private var memoryManager: TaskMemoryManager = null
@@ -54,11 +53,21 @@ class UnsafeFixedWidthAggregationMapSuite
     }
   }
 
+  test("supported schemas") {
+    assert(!supportsAggregationBufferSchema(StructType(StructField("x", StringType) :: Nil)))
+    assert(supportsGroupKeySchema(StructType(StructField("x", StringType) :: Nil)))
+
+    assert(
+      !supportsAggregationBufferSchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil)))
+    assert(
+      !supportsGroupKeySchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil)))
+  }
+
   test("empty map") {
     val map = new UnsafeFixedWidthAggregationMap(
-      emptyProjection,
-      new UnsafeRowConverter(groupKeySchema),
-      new UnsafeRowConverter(aggBufferSchema),
+      emptyAggregationBuffer,
+      aggBufferSchema,
+      groupKeySchema,
       memoryManager,
       1024, // initial capacity
       false // disable perf metrics
@@ -69,9 +78,9 @@ class UnsafeFixedWidthAggregationMapSuite
 
   test("updating values for a single key") {
     val map = new UnsafeFixedWidthAggregationMap(
-      emptyProjection,
-      new UnsafeRowConverter(groupKeySchema),
-      new UnsafeRowConverter(aggBufferSchema),
+      emptyAggregationBuffer,
+      aggBufferSchema,
+      groupKeySchema,
       memoryManager,
       1024, // initial capacity
       false // disable perf metrics
@@ -95,9 +104,9 @@ class UnsafeFixedWidthAggregationMapSuite
 
   test("inserting large random keys") {
     val map = new UnsafeFixedWidthAggregationMap(
-      emptyProjection,
-      new UnsafeRowConverter(groupKeySchema),
-      new UnsafeRowConverter(aggBufferSchema),
+      emptyAggregationBuffer,
+      aggBufferSchema,
+      groupKeySchema,
       memoryManager,
       128, // initial capacity
       false // disable perf metrics
@@ -112,36 +121,6 @@ class UnsafeFixedWidthAggregationMapSuite
     }.toSet
     seenKeys.size should be (groupKeys.size)
     seenKeys should be (groupKeys)
-
-    map.free()
-  }
-
-  test("with decimal in the key and values") {
-    val groupKeySchema = StructType(StructField("price", DecimalType(10, 0)) :: Nil)
-    val aggBufferSchema = StructType(StructField("amount", DecimalType.Unlimited) :: Nil)
-    val emptyProjection = GenerateProjection.generate(Seq(Literal(Decimal(0))),
-      Seq(AttributeReference("price", DecimalType.Unlimited)()))
-    val map = new UnsafeFixedWidthAggregationMap(
-      emptyProjection,
-      new UnsafeRowConverter(groupKeySchema),
-      new UnsafeRowConverter(aggBufferSchema),
-      memoryManager,
-      1, // initial capacity
-      false // disable perf metrics
-    )
-
-    (0 until 100).foreach { i =>
-      val groupKey = InternalRow(Decimal(i % 10))
-      val row = map.getAggregationBuffer(groupKey)
-      row.update(0, Decimal(i))
-    }
-    val seenKeys: Set[Int] = map.iterator().asScala.map { entry =>
-      entry.key.getAs[Decimal](0).toInt
-    }.toSet
-    seenKeys.size should be (10)
-    seenKeys should be ((0 until 10).toSet)
-
-    map.free()
   }
 
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index dff5faf9f6ec8..8819234e78e60 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -24,7 +24,7 @@ import org.scalatest.Matchers
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.util.{ObjectPool, DateTimeUtils}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
@@ -45,12 +45,11 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(sizeRequired === 8 + (3 * 8))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten =
-      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, null)
+      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired)
     assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(
-      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, null)
+    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getLong(1) === 1)
     assert(unsafeRow.getInt(2) === 2)
@@ -87,67 +86,15 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten = converter.writeRow(
-      row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, null)
+      row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired)
     assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
-    val pool = new ObjectPool(10)
     unsafeRow.pointTo(
-      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, pool)
+      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
-    assert(unsafeRow.get(2) === "World".getBytes)
-
-    unsafeRow.update(1, UTF8String.fromString("World"))
-    assert(unsafeRow.getString(1) === "World")
-    assert(pool.size === 0)
-    unsafeRow.update(1, UTF8String.fromString("Hello World"))
-    assert(unsafeRow.getString(1) === "Hello World")
-    assert(pool.size === 1)
-
-    unsafeRow.update(2, "World".getBytes)
-    assert(unsafeRow.get(2) === "World".getBytes)
-    assert(pool.size === 1)
-    unsafeRow.update(2, "Hello World".getBytes)
-    assert(unsafeRow.get(2) === "Hello World".getBytes)
-    assert(pool.size === 2)
-
-    // We do not support copy() for UnsafeRows that reference ObjectPools
-    intercept[UnsupportedOperationException] {
-      unsafeRow.copy()
-    }
-  }
-
-  test("basic conversion with primitive, decimal and array") {
-    val fieldTypes: Array[DataType] = Array(LongType, DecimalType(10, 0), ArrayType(StringType))
-    val converter = new UnsafeRowConverter(fieldTypes)
-
-    val row = new SpecificMutableRow(fieldTypes)
-    row.setLong(0, 0)
-    row.update(1, Decimal(1))
-    row.update(2, Array(2))
-
-    val pool = new ObjectPool(10)
-    val sizeRequired: Int = converter.getSizeRequirement(row)
-    assert(sizeRequired === 8 + (8 * 3))
-    val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten =
-      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, pool)
-    assert(numBytesWritten === sizeRequired)
-    assert(pool.size === 2)
-
-    val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(
-      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, pool)
-    assert(unsafeRow.getLong(0) === 0)
-    assert(unsafeRow.get(1) === Decimal(1))
-    assert(unsafeRow.get(2) === Array(2))
-
-    unsafeRow.update(1, Decimal(2))
-    assert(unsafeRow.get(1) === Decimal(2))
-    unsafeRow.update(2, Array(3, 4))
-    assert(unsafeRow.get(2) === Array(3, 4))
-    assert(pool.size === 2)
+    assert(unsafeRow.getBinary(2) === "World".getBytes)
   }
 
   test("basic conversion with primitive, string, date and timestamp types") {
@@ -165,25 +112,25 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length))
     val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten =
-      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired, null)
+      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired)
     assert(numBytesWritten === sizeRequired)
 
     val unsafeRow = new UnsafeRow()
     unsafeRow.pointTo(
-      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired, null)
+      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
     // Date is represented as Int in unsafeRow
     assert(DateTimeUtils.toJavaDate(unsafeRow.getInt(2)) === Date.valueOf("1970-01-01"))
     // Timestamp is represented as Long in unsafeRow
     DateTimeUtils.toJavaTimestamp(unsafeRow.getLong(3)) should be
-      (Timestamp.valueOf("2015-05-08 08:10:25"))
+    (Timestamp.valueOf("2015-05-08 08:10:25"))
 
     unsafeRow.setInt(2, DateTimeUtils.fromJavaDate(Date.valueOf("2015-06-22")))
     assert(DateTimeUtils.toJavaDate(unsafeRow.getInt(2)) === Date.valueOf("2015-06-22"))
     unsafeRow.setLong(3, DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2015-06-22 08:10:25")))
     DateTimeUtils.toJavaTimestamp(unsafeRow.getLong(3)) should be
-      (Timestamp.valueOf("2015-06-22 08:10:25"))
+    (Timestamp.valueOf("2015-06-22 08:10:25"))
   }
 
   test("null handling") {
@@ -197,9 +144,9 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       FloatType,
       DoubleType,
       StringType,
-      BinaryType,
-      DecimalType.Unlimited,
-      ArrayType(IntegerType)
+      BinaryType
+      // DecimalType.Unlimited,
+      // ArrayType(IntegerType)
     )
     val converter = new UnsafeRowConverter(fieldTypes)
 
@@ -215,14 +162,13 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val createdFromNullBuffer: Array[Long] = new Array[Long](sizeRequired / 8)
     val numBytesWritten = converter.writeRow(
       rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET,
-      sizeRequired, null)
+      sizeRequired)
     assert(numBytesWritten === sizeRequired)
 
     val createdFromNull = new UnsafeRow()
     createdFromNull.pointTo(
-      createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length,
-      sizeRequired, null)
-    for (i <- 0 to fieldTypes.length - 1) {
+      createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
+    for (i <- fieldTypes.indices) {
       assert(createdFromNull.isNullAt(i))
     }
     assert(createdFromNull.getBoolean(1) === false)
@@ -232,10 +178,10 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(createdFromNull.getLong(5) === 0)
     assert(java.lang.Float.isNaN(createdFromNull.getFloat(6)))
     assert(java.lang.Double.isNaN(createdFromNull.getDouble(7)))
-    assert(createdFromNull.getString(8) === null)
-    assert(createdFromNull.get(9) === null)
-    assert(createdFromNull.get(10) === null)
-    assert(createdFromNull.get(11) === null)
+    assert(createdFromNull.getUTF8String(8) === null)
+    assert(createdFromNull.getBinary(9) === null)
+    // assert(createdFromNull.get(10) === null)
+    // assert(createdFromNull.get(11) === null)
 
     // If we have an UnsafeRow with columns that are initially non-null and we null out those
     // columns, then the serialized row representation should be identical to what we would get by
@@ -252,19 +198,18 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       r.setDouble(7, 700)
       r.update(8, UTF8String.fromString("hello"))
       r.update(9, "world".getBytes)
-      r.update(10, Decimal(10))
-      r.update(11, Array(11))
+      // r.update(10, Decimal(10))
+      // r.update(11, Array(11))
       r
     }
-    val pool = new ObjectPool(1)
     val setToNullAfterCreationBuffer: Array[Long] = new Array[Long](sizeRequired / 8 + 2)
     converter.writeRow(
       rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET,
-      sizeRequired, pool)
+      sizeRequired)
     val setToNullAfterCreation = new UnsafeRow()
     setToNullAfterCreation.pointTo(
       setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length,
-      sizeRequired, pool)
+      sizeRequired)
 
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
     assert(setToNullAfterCreation.getBoolean(1) === rowWithNoNullColumns.getBoolean(1))
@@ -275,14 +220,11 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.getFloat(6) === rowWithNoNullColumns.getFloat(6))
     assert(setToNullAfterCreation.getDouble(7) === rowWithNoNullColumns.getDouble(7))
     assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
-    assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
-    assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
-    assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
+    assert(setToNullAfterCreation.getBinary(9) === rowWithNoNullColumns.get(9))
+    // assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
+    // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
 
-    for (i <- 0 to fieldTypes.length - 1) {
-      if (i >= 8) {
-        setToNullAfterCreation.update(i, null)
-      }
+    for (i <- fieldTypes.indices) {
       setToNullAfterCreation.setNullAt(i)
     }
     // There are some garbage left in the var-length area
@@ -297,10 +239,10 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     setToNullAfterCreation.setLong(5, 500)
     setToNullAfterCreation.setFloat(6, 600)
     setToNullAfterCreation.setDouble(7, 700)
-    setToNullAfterCreation.update(8, UTF8String.fromString("hello"))
-    setToNullAfterCreation.update(9, "world".getBytes)
-    setToNullAfterCreation.update(10, Decimal(10))
-    setToNullAfterCreation.update(11, Array(11))
+    // setToNullAfterCreation.update(8, UTF8String.fromString("hello"))
+    // setToNullAfterCreation.update(9, "world".getBytes)
+    // setToNullAfterCreation.update(10, Decimal(10))
+    // setToNullAfterCreation.update(11, Array(11))
 
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
     assert(setToNullAfterCreation.getBoolean(1) === rowWithNoNullColumns.getBoolean(1))
@@ -310,10 +252,10 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.getLong(5) === rowWithNoNullColumns.getLong(5))
     assert(setToNullAfterCreation.getFloat(6) === rowWithNoNullColumns.getFloat(6))
     assert(setToNullAfterCreation.getDouble(7) === rowWithNoNullColumns.getDouble(7))
-    assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
-    assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
-    assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
-    assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
+    // assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
+    // assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
+    // assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
+    // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
   }
 
   test("NaN canonicalization") {
@@ -330,12 +272,9 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val converter = new UnsafeRowConverter(fieldTypes)
     val row1Buffer = new Array[Byte](converter.getSizeRequirement(row1))
     val row2Buffer = new Array[Byte](converter.getSizeRequirement(row2))
-    converter.writeRow(
-      row1, row1Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row1Buffer.length, null)
-    converter.writeRow(
-      row2, row2Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row2Buffer.length, null)
+    converter.writeRow(row1, row1Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row1Buffer.length)
+    converter.writeRow(row2, row2Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row2Buffer.length)
 
     assert(row1Buffer.toSeq === row2Buffer.toSeq)
   }
-
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala
deleted file mode 100644
index 94764df4b9cdb..0000000000000
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ObjectPoolSuite.scala
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.util
-
-import org.scalatest.Matchers
-
-import org.apache.spark.SparkFunSuite
-
-class ObjectPoolSuite extends SparkFunSuite with Matchers {
-
-  test("pool") {
-    val pool = new ObjectPool(1)
-    assert(pool.put(1) === 0)
-    assert(pool.put("hello") === 1)
-    assert(pool.put(false) === 2)
-
-    assert(pool.get(0) === 1)
-    assert(pool.get(1) === "hello")
-    assert(pool.get(2) === false)
-    assert(pool.size() === 3)
-
-    pool.replace(1, "world")
-    assert(pool.get(1) === "world")
-    assert(pool.size() === 3)
-  }
-
-  test("unique pool") {
-    val pool = new UniqueObjectPool(1)
-    assert(pool.put(1) === 0)
-    assert(pool.put("hello") === 1)
-    assert(pool.put(1) === 0)
-    assert(pool.put("hello") === 1)
-
-    assert(pool.get(0) === 1)
-    assert(pool.get(1) === "hello")
-    assert(pool.size() === 2)
-
-    intercept[UnsupportedOperationException] {
-      pool.replace(1, "world")
-    }
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 323ff17357fda..fa942a1f8fd93 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -20,6 +20,8 @@ package org.apache.spark.sql
 import java.io.CharArrayWriter
 import java.util.Properties
 
+import org.apache.spark.unsafe.types.UTF8String
+
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
@@ -1282,7 +1284,7 @@ class DataFrame private[sql](
 
     val outputCols = (if (cols.isEmpty) numericColumns.map(_.prettyString) else cols).toList
 
-    val ret: Seq[InternalRow] = if (outputCols.nonEmpty) {
+    val ret: Seq[Row] = if (outputCols.nonEmpty) {
       val aggExprs = statistics.flatMap { case (_, colToAgg) =>
         outputCols.map(c => Column(Cast(colToAgg(Column(c).expr), StringType)).as(c))
       }
@@ -1290,19 +1292,18 @@ class DataFrame private[sql](
       val row = agg(aggExprs.head, aggExprs.tail: _*).head().toSeq
 
       // Pivot the data so each summary is one row
-      row.grouped(outputCols.size).toSeq.zip(statistics).map {
-        case (aggregation, (statistic, _)) =>
-          InternalRow(statistic :: aggregation.toList: _*)
+      row.grouped(outputCols.size).toSeq.zip(statistics).map { case (aggregation, (statistic, _)) =>
+        Row(statistic :: aggregation.toList: _*)
       }
     } else {
       // If there are no output columns, just output a single column that contains the stats.
-      statistics.map { case (name, _) => InternalRow(name) }
+      statistics.map { case (name, _) => Row(name) }
     }
 
     // All columns are string type
     val schema = StructType(
       StructField("summary", StringType) :: outputCols.map(StructField(_, StringType))).toAttributes
-    LocalRelation(schema, ret)
+    LocalRelation.fromExternalRows(schema, ret)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 0e63f2fe29cb3..16176abe3a51d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -239,6 +239,11 @@ case class GeneratedAggregate(
       StructType(fields)
     }
 
+    val schemaSupportsUnsafe: Boolean = {
+      UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
+        UnsafeFixedWidthAggregationMap.supportsGroupKeySchema(groupKeySchema)
+    }
+
     child.execute().mapPartitions { iter =>
       // Builds a new custom class for holding the results of aggregation for a group.
       val initialValues = computeFunctions.flatMap(_.initialValues)
@@ -290,13 +295,14 @@ case class GeneratedAggregate(
 
         val resultProjection = resultProjectionBuilder()
         Iterator(resultProjection(buffer))
-      } else if (unsafeEnabled) {
+
+      } else if (unsafeEnabled && schemaSupportsUnsafe) {
         assert(iter.hasNext, "There should be at least one row for this path")
         log.info("Using Unsafe-based aggregator")
         val aggregationMap = new UnsafeFixedWidthAggregationMap(
-          newAggregationBuffer,
-          new UnsafeRowConverter(groupKeySchema),
-          new UnsafeRowConverter(aggregationBufferSchema),
+          newAggregationBuffer(EmptyRow),
+          aggregationBufferSchema,
+          groupKeySchema,
           TaskContext.get.taskMemoryManager(),
           1024 * 16, // initial capacity
           false // disable tracking of performance metrics
@@ -331,6 +337,9 @@ case class GeneratedAggregate(
           }
         }
       } else {
+        if (unsafeEnabled) {
+          log.info("Not using Unsafe-based aggregator because it is not supported for this schema")
+        }
         val buffers = new java.util.HashMap[InternalRow, MutableRow]()
 
         var currentRow: InternalRow = null
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
index cd341180b6100..34e926e4582be 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
@@ -34,13 +34,11 @@ private[sql] case class LocalTableScan(
 
   protected override def doExecute(): RDD[InternalRow] = rdd
 
-
   override def executeCollect(): Array[Row] = {
     val converter = CatalystTypeConverters.createToScalaConverter(schema)
     rows.map(converter(_).asInstanceOf[Row]).toArray
   }
 
-
   override def executeTake(limit: Int): Array[Row] = {
     val converter = CatalystTypeConverters.createToScalaConverter(schema)
     rows.map(converter(_).asInstanceOf[Row]).take(limit).toArray
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 318550e5ed899..16498da080c88 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -37,9 +37,6 @@ import org.apache.spark.unsafe.PlatformDependent
  * Note that this serializer implements only the [[Serializer]] methods that are used during
  * shuffle, so certain [[SerializerInstance]] methods will throw UnsupportedOperationException.
  *
- * This serializer does not support UnsafeRows that use
- * [[org.apache.spark.sql.catalyst.util.ObjectPool]].
- *
  * @param numFields the number of fields in the row being serialized.
  */
 private[sql] class UnsafeRowSerializer(numFields: Int) extends Serializer with Serializable {
@@ -65,7 +62,6 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
 
     override def writeValue[T: ClassTag](value: T): SerializationStream = {
       val row = value.asInstanceOf[UnsafeRow]
-      assert(row.getPool == null, "UnsafeRowSerializer does not support ObjectPool")
       dOut.writeInt(row.getSizeInBytes)
       row.writeToStream(out, writeBuffer)
       this
@@ -118,7 +114,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
               rowBuffer = new Array[Byte](rowSize)
             }
             ByteStreams.readFully(in, rowBuffer, 0, rowSize)
-            row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize, null)
+            row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
             rowSize = dIn.readInt() // read the next row's size
             if (rowSize == EOF) { // We are returning the last row in this stream
               val _rowTuple = rowTuple
@@ -152,7 +148,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
           rowBuffer = new Array[Byte](rowSize)
         }
         ByteStreams.readFully(in, rowBuffer, 0, rowSize)
-        row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize, null)
+        row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
         row.asInstanceOf[T]
       }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index d36e2639376e7..ad3bb1744cb3c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -53,8 +53,7 @@ class UnsafeRowSuite extends SparkFunSuite {
           offheapRowPage.getBaseObject,
           offheapRowPage.getBaseOffset,
           3, // num fields
-          arrayBackedUnsafeRow.getSizeInBytes,
-          null // object pool
+          arrayBackedUnsafeRow.getSizeInBytes
         )
         assert(offheapUnsafeRow.getBaseObject === null)
         val baos = new ByteArrayOutputStream()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
index 5fe73f7e0b072..7a4baa9e4a49d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
@@ -39,7 +39,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
   ignore("sort followed by limit should not leak memory") {
     // TODO: this test is going to fail until we implement a proper iterator interface
     // with a close() method.
-    TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "true")
+    TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
     checkThatPlansAgree(
       (1 to 100).map(v => Tuple1(v)).toDF("a"),
       (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
@@ -58,7 +58,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
         sortAnswers = false
       )
     } finally {
-      TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "true")
+      TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
 
     }
   }
@@ -91,7 +91,8 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
       assert(UnsafeExternalSort.supportsSchema(inputDf.schema))
       checkThatPlansAgree(
         inputDf,
-        UnsafeExternalSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 23),
+        plan => ConvertToSafe(
+          UnsafeExternalSort(sortOrder, global = true, plan: SparkPlan, testSpillFrequency = 23)),
         Sort(sortOrder, global = true, _: SparkPlan),
         sortAnswers = false
       )
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
index bd788ec8c14b1..a1e1695717e23 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
@@ -23,29 +23,25 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeRowConverter}
-import org.apache.spark.sql.catalyst.util.ObjectPool
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 
 class UnsafeRowSerializerSuite extends SparkFunSuite {
 
-  private def toUnsafeRow(
-      row: Row,
-      schema: Array[DataType],
-      objPool: ObjectPool = null): UnsafeRow = {
+  private def toUnsafeRow(row: Row, schema: Array[DataType]): UnsafeRow = {
     val internalRow = CatalystTypeConverters.convertToCatalyst(row).asInstanceOf[InternalRow]
     val rowConverter = new UnsafeRowConverter(schema)
     val rowSizeInBytes = rowConverter.getSizeRequirement(internalRow)
     val byteArray = new Array[Byte](rowSizeInBytes)
     rowConverter.writeRow(
-      internalRow, byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, rowSizeInBytes, objPool)
+      internalRow, byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, rowSizeInBytes)
     val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(
-      byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, row.length, rowSizeInBytes, objPool)
+    unsafeRow.pointTo(byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, row.length, rowSizeInBytes)
     unsafeRow
   }
 
-  test("toUnsafeRow() test helper method") {
+  ignore("toUnsafeRow() test helper method") {
+    // This currently doesnt work because the generic getter throws an exception.
     val row = Row("Hello", 123)
     val unsafeRow = toUnsafeRow(row, Array(StringType, IntegerType))
     assert(row.getString(0) === unsafeRow.get(0).toString)

From 26ed22aec8af42c6dc161e0a2827a4235a49a9a4 Mon Sep 17 00:00:00 2001
From: "Zhang, Liye" <liye.zhang@intel.com>
Date: Thu, 23 Jul 2015 12:43:54 +0100
Subject: [PATCH 0561/1454] [SPARK-9212] [CORE] upgrade Netty version to
 4.0.29.Final

related JIRA: [SPARK-9212](https://issues.apache.org/jira/browse/SPARK-9212) and [SPARK-8101](https://issues.apache.org/jira/browse/SPARK-8101)

Author: Zhang, Liye <liye.zhang@intel.com>

Closes #7562 from liyezhang556520/SPARK-9212 and squashes the following commits:

1917729 [Zhang, Liye] SPARK-9212 upgrade Netty version to 4.0.29.Final
---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index 1f44dc8abe1d4..35fc8c44bc1b0 100644
--- a/pom.xml
+++ b/pom.xml
@@ -573,7 +573,7 @@
       <dependency>
         <groupId>io.netty</groupId>
         <artifactId>netty-all</artifactId>
-        <version>4.0.28.Final</version>
+        <version>4.0.29.Final</version>
       </dependency>
       <dependency>
         <groupId>org.apache.derby</groupId>

From 52ef76de219c4bf19c54c99414b89a67d0bf457b Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 23 Jul 2015 09:37:53 -0700
Subject: [PATCH 0562/1454] [SPARK-9082] [SQL] [FOLLOW-UP] use `partition` in
 `PushPredicateThroughProject`

a follow up of https://github.com/apache/spark/pull/7446

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7607 from cloud-fan/tmp and squashes the following commits:

7106989 [Wenchen Fan] use `partition` in `PushPredicateThroughProject`
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 22 +++++++------------
 1 file changed, 8 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index d2db3dd3d078e..b59f800e7cc0f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -553,33 +553,27 @@ object PushPredicateThroughProject extends Rule[LogicalPlan] with PredicateHelpe
       // Split the condition into small conditions by `And`, so that we can push down part of this
       // condition without nondeterministic expressions.
       val andConditions = splitConjunctivePredicates(condition)
-      val nondeterministicConditions = andConditions.filter(hasNondeterministic(_, aliasMap))
+
+      val (deterministic, nondeterministic) = andConditions.partition(_.collect {
+        case a: Attribute if aliasMap.contains(a) => aliasMap(a)
+      }.forall(_.deterministic))
 
       // If there is no nondeterministic conditions, push down the whole condition.
-      if (nondeterministicConditions.isEmpty) {
+      if (nondeterministic.isEmpty) {
         project.copy(child = Filter(replaceAlias(condition, aliasMap), grandChild))
       } else {
         // If they are all nondeterministic conditions, leave it un-changed.
-        if (nondeterministicConditions.length == andConditions.length) {
+        if (deterministic.isEmpty) {
           filter
         } else {
-          val deterministicConditions = andConditions.filterNot(hasNondeterministic(_, aliasMap))
           // Push down the small conditions without nondeterministic expressions.
-          val pushedCondition = deterministicConditions.map(replaceAlias(_, aliasMap)).reduce(And)
-          Filter(nondeterministicConditions.reduce(And),
+          val pushedCondition = deterministic.map(replaceAlias(_, aliasMap)).reduce(And)
+          Filter(nondeterministic.reduce(And),
             project.copy(child = Filter(pushedCondition, grandChild)))
         }
       }
   }
 
-  private def hasNondeterministic(
-      condition: Expression,
-      sourceAliases: AttributeMap[Expression]) = {
-    condition.collect {
-      case a: Attribute if sourceAliases.contains(a) => sourceAliases(a)
-    }.exists(!_.deterministic)
-  }
-
   // Substitute any attributes that are produced by the child projection, so that we safely
   // eliminate it.
   private def replaceAlias(condition: Expression, sourceAliases: AttributeMap[Expression]) = {

From 19aeab57c1b0c739edb5ba351f98e930e1a0f984 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 23 Jul 2015 10:28:20 -0700
Subject: [PATCH 0563/1454] [Build][Minor] Fix building error & performance

1. When build the latest code with sbt, it throws exception like:
[error] /home/hcheng/git/catalyst/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala:78: match may not be exhaustive.
[error] It would fail on the following input: UNKNOWN
[error]       val classNameByStatus = status match {
[error]

2. Potential performance issue when implicitly convert an Array[Any] to Seq[Any]

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7611 from chenghao-intel/toseq and squashes the following commits:

cab75c5 [Cheng Hao] remove the toArray
24df682 [Cheng Hao] fix building error & performance
---
 core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala  | 1 +
 .../org/apache/spark/sql/catalyst/CatalystTypeConverters.scala  | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
index 2ce670ad02e97..e72547df7254b 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
@@ -79,6 +79,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
         case JobExecutionStatus.SUCCEEDED => "succeeded"
         case JobExecutionStatus.FAILED => "failed"
         case JobExecutionStatus.RUNNING => "running"
+        case JobExecutionStatus.UNKNOWN => "unknown"
       }
 
       // The timeline library treats contents as HTML, so we have to escape them; for the
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 4067833d5e648..bfaee04f33b7f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -402,7 +402,7 @@ object CatalystTypeConverters {
     case d: JavaBigDecimal => BigDecimalConverter.toCatalyst(d)
     case seq: Seq[Any] => seq.map(convertToCatalyst)
     case r: Row => InternalRow(r.toSeq.map(convertToCatalyst): _*)
-    case arr: Array[Any] => arr.toSeq.map(convertToCatalyst).toArray
+    case arr: Array[Any] => arr.map(convertToCatalyst)
     case m: Map[_, _] =>
       m.map { case (k, v) => (convertToCatalyst(k), convertToCatalyst(v)) }.toMap
     case other => other

From d2666a3c70dad037776dc4015fa561356381357b Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Thu, 23 Jul 2015 10:31:12 -0700
Subject: [PATCH 0564/1454] [SPARK-9183] confusing error message when looking
 up missing function in Spark SQL

JIRA: https://issues.apache.org/jira/browse/SPARK-9183

cc rxin

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7613 from yjshen/npe_udf and squashes the following commits:

44f58f2 [Yijie Shen] add jira ticket number
903c963 [Yijie Shen] add explanation comments
f44dd3c [Yijie Shen] Change two hive class LogLevel to avoid annoying messages
---
 conf/log4j.properties.template                                | 4 ++++
 .../resources/org/apache/spark/log4j-defaults-repl.properties | 4 ++++
 .../main/resources/org/apache/spark/log4j-defaults.properties | 4 ++++
 3 files changed, 12 insertions(+)

diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template
index 3a2a88219818f..27006e45e932b 100644
--- a/conf/log4j.properties.template
+++ b/conf/log4j.properties.template
@@ -10,3 +10,7 @@ log4j.logger.org.spark-project.jetty=WARN
 log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
+
+# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
+log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
+log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR
diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults-repl.properties b/core/src/main/resources/org/apache/spark/log4j-defaults-repl.properties
index b146f8a784127..689afea64f8db 100644
--- a/core/src/main/resources/org/apache/spark/log4j-defaults-repl.properties
+++ b/core/src/main/resources/org/apache/spark/log4j-defaults-repl.properties
@@ -10,3 +10,7 @@ log4j.logger.org.spark-project.jetty=WARN
 log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
+
+# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
+log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
+log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR
diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults.properties b/core/src/main/resources/org/apache/spark/log4j-defaults.properties
index 3a2a88219818f..27006e45e932b 100644
--- a/core/src/main/resources/org/apache/spark/log4j-defaults.properties
+++ b/core/src/main/resources/org/apache/spark/log4j-defaults.properties
@@ -10,3 +10,7 @@ log4j.logger.org.spark-project.jetty=WARN
 log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
+
+# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
+log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
+log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

From ecfb3127670c7f15e3a15e7f51fa578532480cda Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 23 Jul 2015 10:32:11 -0700
Subject: [PATCH 0565/1454] [SPARK-9243] [Documentation] null -> zero in
 crosstab doc

We forgot to update doc. brkyvz

Author: Xiangrui Meng <meng@databricks.com>

Closes #7608 from mengxr/SPARK-9243 and squashes the following commits:

0ea3236 [Xiangrui Meng] null -> zero in crosstab doc
---
 R/pkg/R/DataFrame.R                                             | 2 +-
 python/pyspark/sql/dataframe.py                                 | 2 +-
 .../scala/org/apache/spark/sql/DataFrameStatFunctions.scala     | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 06dd6b75dff3d..f4c93d3c7dd67 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1566,7 +1566,7 @@ setMethod("fillna",
 #' @return a local R data.frame representing the contingency table. The first column of each row
 #'         will be the distinct values of `col1` and the column names will be the distinct values
 #'         of `col2`. The name of the first column will be `$col1_$col2`. Pairs that have no
-#'         occurrences will have `null` as their counts.
+#'         occurrences will have zero as their counts.
 #'
 #' @rdname statfunctions
 #' @export
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 83e02b85f06f1..d76e051bd73a1 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -1130,7 +1130,7 @@ def crosstab(self, col1, col2):
         non-zero pair frequencies will be returned.
         The first column of each row will be the distinct values of `col1` and the column names
         will be the distinct values of `col2`. The name of the first column will be `$col1_$col2`.
-        Pairs that have no occurrences will have `null` as their counts.
+        Pairs that have no occurrences will have zero as their counts.
         :func:`DataFrame.crosstab` and :func:`DataFrameStatFunctions.crosstab` are aliases.
 
         :param col1: The name of the first column. Distinct items will make the first item of
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index 587869e57f96e..4ec58082e7aef 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -77,7 +77,7 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * pair frequencies will be returned.
    * The first column of each row will be the distinct values of `col1` and the column names will
    * be the distinct values of `col2`. The name of the first column will be `$col1_$col2`. Counts
-   * will be returned as `Long`s. Pairs that have no occurrences will have `null` as their counts.
+   * will be returned as `Long`s. Pairs that have no occurrences will have zero as their counts.
    * Null elements will be replaced by "null", and back ticks will be dropped from elements if they
    * exist.
    *

From 662d60db3f4a758b6869de5bd971d23bd5962c3b Mon Sep 17 00:00:00 2001
From: David Arroyo Cazorla <darroyo@stratio.com>
Date: Thu, 23 Jul 2015 10:34:32 -0700
Subject: [PATCH 0566/1454] [SPARK-5447][SQL] Replace reference 'schema rdd'
 with DataFrame @rxin.

Author: David Arroyo Cazorla <darroyo@stratio.com>

Closes #7618 from darroyocazorla/master and squashes the following commits:

5f91379 [David Arroyo Cazorla] [SPARK-5447][SQL] Replace reference 'schema rdd' with DataFrame
---
 .../scala/org/apache/spark/sql/execution/CacheManager.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
index a4b38d364d54a..d3e5c378d037d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
@@ -84,7 +84,7 @@ private[sql] class CacheManager(sqlContext: SQLContext) extends Logging {
   }
 
   /**
-   * Caches the data produced by the logical representation of the given schema rdd.  Unlike
+   * Caches the data produced by the logical representation of the given [[DataFrame]]. Unlike
    * `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because recomputing
    * the in-memory columnar representation of the underlying table is expensive.
    */

From b2f3aca1e8c182b93e250f9d9c4aa69f97eaa11a Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Thu, 23 Jul 2015 16:08:07 -0700
Subject: [PATCH 0567/1454] [SPARK-9286] [SQL] Methods in Unevaluable should be
 final and AlgebraicAggregate should extend Unevaluable.

This patch marks the Unevaluable.eval() and UnevaluablegenCode() methods as final and fixes two cases where they were overridden.  It also updates AggregateFunction2 to extend Unevaluable.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7627 from JoshRosen/unevaluable-fix and squashes the following commits:

8d9ed22 [Josh Rosen] AlgebraicAggregate should extend Unevaluable
65329c2 [Josh Rosen] Do not have AggregateFunction1 inherit from AggregateExpression1
fa68a22 [Josh Rosen] Make eval() and genCode() final
---
 .../sql/catalyst/expressions/Expression.scala     |  4 ++--
 .../expressions/aggregate/interfaces.scala        | 15 +++------------
 .../sql/catalyst/expressions/aggregates.scala     | 11 +++++------
 3 files changed, 10 insertions(+), 20 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 29ae47e842ddb..3f72e6e184db1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -184,10 +184,10 @@ abstract class Expression extends TreeNode[Expression] {
  */
 trait Unevaluable extends Expression {
 
-  override def eval(input: InternalRow = null): Any =
+  final override def eval(input: InternalRow = null): Any =
     throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
 
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
+  final override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String =
     throw new UnsupportedOperationException(s"Cannot evaluate expression: $this")
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
index 577ede73cb01f..d3fee1ade05e6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -63,10 +63,6 @@ private[sql] case object Complete extends AggregateMode
  */
 private[sql] case object NoOp extends Expression with Unevaluable {
   override def nullable: Boolean = true
-  override def eval(input: InternalRow): Any = {
-    throw new TreeNodeException(
-      this, s"No function to evaluate expression. type: ${this.nodeName}")
-  }
   override def dataType: DataType = NullType
   override def children: Seq[Expression] = Nil
 }
@@ -151,8 +147,7 @@ abstract class AggregateFunction2
 /**
  * A helper class for aggregate functions that can be implemented in terms of catalyst expressions.
  */
-abstract class AlgebraicAggregate extends AggregateFunction2 with Serializable {
-  self: Product =>
+abstract class AlgebraicAggregate extends AggregateFunction2 with Serializable with Unevaluable {
 
   val initialValues: Seq[Expression]
   val updateExpressions: Seq[Expression]
@@ -188,19 +183,15 @@ abstract class AlgebraicAggregate extends AggregateFunction2 with Serializable {
     }
   }
 
-  override def update(buffer: MutableRow, input: InternalRow): Unit = {
+  override final def update(buffer: MutableRow, input: InternalRow): Unit = {
     throw new UnsupportedOperationException(
       "AlgebraicAggregate's update should not be called directly")
   }
 
-  override def merge(buffer1: MutableRow, buffer2: InternalRow): Unit = {
+  override final def merge(buffer1: MutableRow, buffer2: InternalRow): Unit = {
     throw new UnsupportedOperationException(
       "AlgebraicAggregate's merge should not be called directly")
   }
 
-  override def eval(buffer: InternalRow): Any = {
-    throw new UnsupportedOperationException(
-      "AlgebraicAggregate's eval should not be called directly")
-  }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index e07c920a41d0a..d3295b8bafa80 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -20,8 +20,8 @@ package org.apache.spark.sql.catalyst.expressions
 import com.clearspring.analytics.stream.cardinality.HyperLogLog
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
@@ -71,8 +71,7 @@ trait PartialAggregate1 extends AggregateExpression1 {
  * A specific implementation of an aggregate function. Used to wrap a generic
  * [[AggregateExpression1]] with an algorithm that will be used to compute one specific result.
  */
-abstract class AggregateFunction1
-  extends LeafExpression with AggregateExpression1 with Serializable {
+abstract class AggregateFunction1 extends LeafExpression with Serializable {
 
   /** Base should return the generic aggregate expression that this function is computing */
   val base: AggregateExpression1
@@ -82,9 +81,9 @@ abstract class AggregateFunction1
 
   def update(input: InternalRow): Unit
 
-  // Do we really need this?
-  override def newInstance(): AggregateFunction1 = {
-    makeCopy(productIterator.map { case a: AnyRef => a }.toArray)
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    throw new UnsupportedOperationException(
+      "AggregateFunction1 should not be used for generated aggregates")
   }
 }
 

From bebe3f7b45f7b0a96f20d5af9b80633fd40cff06 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 23 Jul 2015 17:49:33 -0700
Subject: [PATCH 0568/1454] [SPARK-9207] [SQL] Enables Parquet filter push-down
 by default

PARQUET-136 and PARQUET-173 have been fixed in parquet-mr 1.7.0. It's time to enable filter push-down by default now.

Author: Cheng Lian <lian@databricks.com>

Closes #7612 from liancheng/spark-9207 and squashes the following commits:

77e6b5e [Cheng Lian] Enables Parquet filter push-down by default
---
 docs/sql-programming-guide.md                            | 9 ++-------
 .../src/main/scala/org/apache/spark/sql/SQLConf.scala    | 8 ++------
 2 files changed, 4 insertions(+), 13 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 5838bc172fe86..95945eb7fc8a0 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1332,13 +1332,8 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
 </tr>
 <tr>
   <td><code>spark.sql.parquet.filterPushdown</code></td>
-  <td>false</td>
-  <td>
-    Turn on Parquet filter pushdown optimization. This feature is turned off by default because of a known
-    bug in Parquet 1.6.0rc3 (<a href="https://issues.apache.org/jira/browse/PARQUET-136">PARQUET-136</a>).
-    However, if your table doesn't contain any nullable string or binary columns, it's still safe to turn
-    this feature on.
-  </td>
+  <td>true</td>
+  <td>Enables Parquet filter push-down optimization when set to true.</td>
 </tr>
 <tr>
   <td><code>spark.sql.hive.convertMetastoreParquet</code></td>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 1474b170ba896..2a641b9d64a95 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -273,12 +273,8 @@ private[spark] object SQLConf {
       "uncompressed, snappy, gzip, lzo.")
 
   val PARQUET_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.parquet.filterPushdown",
-    defaultValue = Some(false),
-    doc = "Turn on Parquet filter pushdown optimization. This feature is turned off by default " +
-      "because of a known bug in Parquet 1.6.0rc3 " +
-      "(PARQUET-136, https://issues.apache.org/jira/browse/PARQUET-136). However, " +
-      "if your table doesn't contain any nullable string or binary columns, it's still safe to " +
-      "turn this feature on.")
+    defaultValue = Some(true),
+    doc = "Enables Parquet filter push-down optimization when set to true.")
 
   val PARQUET_USE_DATA_SOURCE_API = booleanConf("spark.sql.parquet.useDataSourceApi",
     defaultValue = Some(true),

From 8a94eb23d53e291441e3144a1b800fe054457040 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 23 Jul 2015 18:31:13 -0700
Subject: [PATCH 0569/1454] [SPARK-9069] [SPARK-9264] [SQL] remove unlimited
 precision support for DecimalType

Romove Decimal.Unlimited (change to support precision up to 38, to match with Hive and other databases).

In order to keep backward source compatibility, Decimal.Unlimited is still there, but change to Decimal(38, 18).

If no precision and scale is provide, it's Decimal(10, 0) as before.

Author: Davies Liu <davies@databricks.com>

Closes #7605 from davies/decimal_unlimited and squashes the following commits:

aa3f115 [Davies Liu] fix tests and style
fb0d20d [Davies Liu] address comments
bfaae35 [Davies Liu] fix style
df93657 [Davies Liu] address comments and clean up
06727fd [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_unlimited
4c28969 [Davies Liu] fix tests
8d783cc [Davies Liu] fix tests
788631c [Davies Liu] fix double with decimal in Union/except
1779bde [Davies Liu] fix scala style
c9c7c78 [Davies Liu] remove Decimal.Unlimited
---
 .../spark/ml/attribute/AttributeSuite.scala   |   2 +-
 python/pyspark/sql/types.py                   |  36 +--
 .../org/apache/spark/sql/types/DataTypes.java |   8 +-
 .../sql/catalyst/JavaTypeInference.scala      |   2 +-
 .../spark/sql/catalyst/ScalaReflection.scala  |  10 +-
 .../apache/spark/sql/catalyst/SqlParser.scala |   5 +-
 .../catalyst/analysis/HiveTypeCoercion.scala  | 255 +++++++-----------
 .../spark/sql/catalyst/dsl/package.scala      |   2 +-
 .../spark/sql/catalyst/expressions/Cast.scala |   7 +-
 .../expressions/aggregate/functions.scala     |  24 +-
 .../sql/catalyst/expressions/aggregates.scala |  46 ++--
 .../sql/catalyst/expressions/arithmetic.scala |  17 +-
 .../sql/catalyst/expressions/literals.scala   |   6 +-
 .../spark/sql/catalyst/plans/QueryPlan.scala  |   4 +-
 .../org/apache/spark/sql/types/DataType.scala |   4 +-
 .../spark/sql/types/DataTypeParser.scala      |   2 +-
 .../apache/spark/sql/types/DecimalType.scala  | 110 +++++---
 .../spark/sql/RandomDataGenerator.scala       |   4 +-
 .../spark/sql/RandomDataGeneratorSuite.scala  |   4 +-
 .../sql/catalyst/ScalaReflectionSuite.scala   |  14 +-
 .../sql/catalyst/analysis/AnalysisSuite.scala |   6 +-
 .../analysis/DecimalPrecisionSuite.scala      |  54 ++--
 .../analysis/HiveTypeCoercionSuite.scala      |  45 ++--
 .../sql/catalyst/expressions/CastSuite.scala  |  46 ++--
 .../ConditionalExpressionSuite.scala          |   2 +-
 .../expressions/LiteralExpressionSuite.scala  |   2 +-
 .../expressions/NullFunctionsSuite.scala      |   2 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala |   2 +
 .../expressions/UnsafeRowConverterSuite.scala |   2 +-
 .../spark/sql/types/DataTypeParserSuite.scala |   4 +-
 .../spark/sql/types/DataTypeSuite.scala       |   4 +-
 .../spark/sql/types/DataTypeTestUtils.scala   |   2 +-
 .../scala/org/apache/spark/sql/Column.scala   |   2 +-
 .../spark/sql/columnar/ColumnType.scala       |   2 +-
 .../sql/execution/GeneratedAggregate.scala    |  10 +-
 .../datasources/PartitioningUtils.scala       |   7 +-
 .../org/apache/spark/sql/jdbc/JDBCRDD.scala   |  22 +-
 .../org/apache/spark/sql/jdbc/jdbc.scala      |   7 +-
 .../apache/spark/sql/json/InferSchema.scala   |  11 +-
 .../sql/parquet/CatalystSchemaConverter.scala |   4 -
 .../sql/parquet/ParquetTableSupport.scala     |   8 +-
 .../spark/sql/JavaApplySchemaSuite.java       |  14 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |   2 +-
 .../spark/sql/execution/PlannerSuite.scala    |   2 +-
 .../execution/SparkSqlSerializer2Suite.scala  |   4 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala |  20 +-
 .../org/apache/spark/sql/json/JsonSuite.scala |  57 ++--
 .../spark/sql/parquet/ParquetIOSuite.scala    |   8 -
 .../ParquetPartitionDiscoverySuite.scala      |   2 +-
 .../spark/sql/sources/DDLTestSuite.scala      |   2 +-
 .../spark/sql/sources/TableScanSuite.scala    |   2 +-
 .../spark/sql/hive/HiveInspectors.scala       |   9 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |   4 +-
 53 files changed, 459 insertions(+), 473 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala
index c5fd2f9d5a22a..6355e0f179496 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala
@@ -218,7 +218,7 @@ class AttributeSuite extends SparkFunSuite {
     // Attribute.fromStructField should accept any NumericType, not just DoubleType
     val longFldWithMeta = new StructField("x", LongType, false, metadata)
     assert(Attribute.fromStructField(longFldWithMeta).isNumeric)
-    val decimalFldWithMeta = new StructField("x", DecimalType(None), false, metadata)
+    val decimalFldWithMeta = new StructField("x", DecimalType(38, 18), false, metadata)
     assert(Attribute.fromStructField(decimalFldWithMeta).isNumeric)
   }
 }
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 10ad89ea14a8d..b97d50c945f24 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -194,30 +194,33 @@ def fromInternal(self, ts):
 
 class DecimalType(FractionalType):
     """Decimal (decimal.Decimal) data type.
+
+    The DecimalType must have fixed precision (the maximum total number of digits)
+    and scale (the number of digits on the right of dot). For example, (5, 2) can
+    support the value from [-999.99 to 999.99].
+
+    The precision can be up to 38, the scale must less or equal to precision.
+
+    When create a DecimalType, the default precision and scale is (10, 0). When infer
+    schema from decimal.Decimal objects, it will be DecimalType(38, 18).
+
+    :param precision: the maximum total number of digits (default: 10)
+    :param scale: the number of digits on right side of dot. (default: 0)
     """
 
-    def __init__(self, precision=None, scale=None):
+    def __init__(self, precision=10, scale=0):
         self.precision = precision
         self.scale = scale
-        self.hasPrecisionInfo = precision is not None
+        self.hasPrecisionInfo = True  # this is public API
 
     def simpleString(self):
-        if self.hasPrecisionInfo:
-            return "decimal(%d,%d)" % (self.precision, self.scale)
-        else:
-            return "decimal(10,0)"
+        return "decimal(%d,%d)" % (self.precision, self.scale)
 
     def jsonValue(self):
-        if self.hasPrecisionInfo:
-            return "decimal(%d,%d)" % (self.precision, self.scale)
-        else:
-            return "decimal"
+        return "decimal(%d,%d)" % (self.precision, self.scale)
 
     def __repr__(self):
-        if self.hasPrecisionInfo:
-            return "DecimalType(%d,%d)" % (self.precision, self.scale)
-        else:
-            return "DecimalType()"
+        return "DecimalType(%d,%d)" % (self.precision, self.scale)
 
 
 class DoubleType(FractionalType):
@@ -761,7 +764,10 @@ def _infer_type(obj):
         return obj.__UDT__
 
     dataType = _type_mappings.get(type(obj))
-    if dataType is not None:
+    if dataType is DecimalType:
+        # the precision and scale of `obj` may be different from row to row.
+        return DecimalType(38, 18)
+    elif dataType is not None:
         return dataType()
 
     if isinstance(obj, dict):
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java b/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
index d22ad6794d608..5703de42393de 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
@@ -111,12 +111,18 @@ public static ArrayType createArrayType(DataType elementType, boolean containsNu
     return new ArrayType(elementType, containsNull);
   }
 
+  /**
+   * Creates a DecimalType by specifying the precision and scale.
+   */
   public static DecimalType createDecimalType(int precision, int scale) {
     return DecimalType$.MODULE$.apply(precision, scale);
   }
 
+  /**
+   * Creates a DecimalType with default precision and scale, which are 10 and 0.
+   */
   public static DecimalType createDecimalType() {
-    return DecimalType$.MODULE$.Unlimited();
+    return DecimalType$.MODULE$.USER_DEFAULT();
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala
index 9a3f9694e4c48..88a457f87ce4e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala
@@ -75,7 +75,7 @@ private [sql] object JavaTypeInference {
       case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true)
       case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true)
 
-      case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType(), true)
+      case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType.SYSTEM_DEFAULT, true)
       case c: Class[_] if c == classOf[java.sql.Date] => (DateType, true)
       case c: Class[_] if c == classOf[java.sql.Timestamp] => (TimestampType, true)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 21b1de1ab9cb1..2442341da106d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -131,10 +131,10 @@ trait ScalaReflection {
       case t if t <:< localTypeOf[String] => Schema(StringType, nullable = true)
       case t if t <:< localTypeOf[java.sql.Timestamp] => Schema(TimestampType, nullable = true)
       case t if t <:< localTypeOf[java.sql.Date] => Schema(DateType, nullable = true)
-      case t if t <:< localTypeOf[BigDecimal] => Schema(DecimalType.Unlimited, nullable = true)
+      case t if t <:< localTypeOf[BigDecimal] => Schema(DecimalType.SYSTEM_DEFAULT, nullable = true)
       case t if t <:< localTypeOf[java.math.BigDecimal] =>
-        Schema(DecimalType.Unlimited, nullable = true)
-      case t if t <:< localTypeOf[Decimal] => Schema(DecimalType.Unlimited, nullable = true)
+        Schema(DecimalType.SYSTEM_DEFAULT, nullable = true)
+      case t if t <:< localTypeOf[Decimal] => Schema(DecimalType.SYSTEM_DEFAULT, nullable = true)
       case t if t <:< localTypeOf[java.lang.Integer] => Schema(IntegerType, nullable = true)
       case t if t <:< localTypeOf[java.lang.Long] => Schema(LongType, nullable = true)
       case t if t <:< localTypeOf[java.lang.Double] => Schema(DoubleType, nullable = true)
@@ -167,8 +167,8 @@ trait ScalaReflection {
     case obj: Float => FloatType
     case obj: Double => DoubleType
     case obj: java.sql.Date => DateType
-    case obj: java.math.BigDecimal => DecimalType.Unlimited
-    case obj: Decimal => DecimalType.Unlimited
+    case obj: java.math.BigDecimal => DecimalType.SYSTEM_DEFAULT
+    case obj: Decimal => DecimalType.SYSTEM_DEFAULT
     case obj: java.sql.Timestamp => TimestampType
     case null => NullType
     // For other cases, there is no obvious mapping from the type of the given object to a
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 29cfc064da89a..c494e5d704213 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -322,7 +322,10 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
 
   protected lazy val numericLiteral: Parser[Literal] =
     ( integral  ^^ { case i => Literal(toNarrowestIntegerType(i)) }
-    | sign.? ~ unsignedFloat ^^ { case s ~ f => Literal((s.getOrElse("") + f).toDouble) }
+    | sign.? ~ unsignedFloat ^^ {
+      // TODO(davies): some precisions may loss, we should create decimal literal
+      case s ~ f => Literal(BigDecimal(s.getOrElse("") + f).doubleValue())
+    }
     )
 
   protected lazy val unsignedFloat: Parser[String] =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index e214545726249..d56ceeadc9e85 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -19,7 +19,9 @@ package org.apache.spark.sql.catalyst.analysis
 
 import javax.annotation.Nullable
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
@@ -58,8 +60,7 @@ object HiveTypeCoercion {
       IntegerType,
       LongType,
       FloatType,
-      DoubleType,
-      DecimalType.Unlimited)
+      DoubleType)
 
   /**
    * Find the tightest common type of two types that might be used in a binary expression.
@@ -72,15 +73,16 @@ object HiveTypeCoercion {
     case (NullType, t1) => Some(t1)
     case (t1, NullType) => Some(t1)
 
-    // Promote numeric types to the highest of the two and all numeric types to unlimited decimal
+    case (t1: IntegralType, t2: DecimalType) if t2.isWiderThan(t1) =>
+      Some(t2)
+    case (t1: DecimalType, t2: IntegralType) if t1.isWiderThan(t2) =>
+      Some(t1)
+
+    // Promote numeric types to the highest of the two
     case (t1, t2) if Seq(t1, t2).forall(numericPrecedence.contains) =>
       val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2)
       Some(numericPrecedence(index))
 
-    // Fixed-precision decimals can up-cast into unlimited
-    case (DecimalType.Unlimited, _: DecimalType) => Some(DecimalType.Unlimited)
-    case (_: DecimalType, DecimalType.Unlimited) => Some(DecimalType.Unlimited)
-
     case _ => None
   }
 
@@ -101,7 +103,7 @@ object HiveTypeCoercion {
     types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match {
       case None => None
       case Some(d) =>
-        findTightestCommonTypeOfTwo(d, c).orElse(findTightestCommonTypeToString(d, c))
+        findTightestCommonTypeToString(d, c)
     })
   }
 
@@ -158,6 +160,9 @@ object HiveTypeCoercion {
    *     converted to DOUBLE.
    *   - TINYINT, SMALLINT, and INT can all be converted to FLOAT.
    *   - BOOLEAN types cannot be converted to any other type.
+   *   - Any integral numeric type can be implicitly converted to decimal type.
+   *   - two different decimal types will be converted into a wider decimal type for both of them.
+   *   - decimal type will be converted into double if there float or double together with it.
    *
    * Additionally, all types when UNION-ed with strings will be promoted to strings.
    * Other string conversions are handled by PromoteStrings.
@@ -166,55 +171,50 @@ object HiveTypeCoercion {
    * - IntegerType to FloatType
    * - LongType to FloatType
    * - LongType to DoubleType
+   * - DecimalType to Double
+   *
+   * This rule is only applied to Union/Except/Intersect
    */
   object WidenTypes extends Rule[LogicalPlan] {
 
-    private[this] def widenOutputTypes(planName: String, left: LogicalPlan, right: LogicalPlan):
-        (LogicalPlan, LogicalPlan) = {
-
-      // TODO: with fixed-precision decimals
-      val castedInput = left.output.zip(right.output).map {
-        // When a string is found on one side, make the other side a string too.
-        case (lhs, rhs) if lhs.dataType == StringType && rhs.dataType != StringType =>
-          (lhs, Alias(Cast(rhs, StringType), rhs.name)())
-        case (lhs, rhs) if lhs.dataType != StringType && rhs.dataType == StringType =>
-          (Alias(Cast(lhs, StringType), lhs.name)(), rhs)
+    private[this] def widenOutputTypes(
+        planName: String,
+        left: LogicalPlan,
+        right: LogicalPlan): (LogicalPlan, LogicalPlan) = {
 
+      val castedTypes = left.output.zip(right.output).map {
         case (lhs, rhs) if lhs.dataType != rhs.dataType =>
-          logDebug(s"Resolving mismatched $planName input ${lhs.dataType}, ${rhs.dataType}")
-          findTightestCommonTypeOfTwo(lhs.dataType, rhs.dataType).map { widestType =>
-            val newLeft =
-              if (lhs.dataType == widestType) lhs else Alias(Cast(lhs, widestType), lhs.name)()
-            val newRight =
-              if (rhs.dataType == widestType) rhs else Alias(Cast(rhs, widestType), rhs.name)()
-
-            (newLeft, newRight)
-          }.getOrElse {
-            // If there is no applicable conversion, leave expression unchanged.
-            (lhs, rhs)
+          (lhs.dataType, rhs.dataType) match {
+            case (t1: DecimalType, t2: DecimalType) =>
+              Some(DecimalPrecision.widerDecimalType(t1, t2))
+            case (t: IntegralType, d: DecimalType) =>
+              Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
+            case (d: DecimalType, t: IntegralType) =>
+              Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
+            case (t: FractionalType, d: DecimalType) =>
+              Some(DoubleType)
+            case (d: DecimalType, t: FractionalType) =>
+              Some(DoubleType)
+            case _ =>
+              findTightestCommonTypeToString(lhs.dataType, rhs.dataType)
           }
-
-        case other => other
+        case other => None
       }
 
-      val (castedLeft, castedRight) = castedInput.unzip
-
-      val newLeft =
-        if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) {
-          logDebug(s"Widening numeric types in $planName $castedLeft ${left.output}")
-          Project(castedLeft, left)
-        } else {
-          left
+      def castOutput(plan: LogicalPlan): LogicalPlan = {
+        val casted = plan.output.zip(castedTypes).map {
+          case (hs, Some(dt)) if dt != hs.dataType =>
+            Alias(Cast(hs, dt), hs.name)()
+          case (hs, _) => hs
         }
+        Project(casted, plan)
+      }
 
-      val newRight =
-        if (castedRight.map(_.dataType) != right.output.map(_.dataType)) {
-          logDebug(s"Widening numeric types in $planName $castedRight ${right.output}")
-          Project(castedRight, right)
-        } else {
-          right
-        }
-      (newLeft, newRight)
+      if (castedTypes.exists(_.isDefined)) {
+        (castOutput(left), castOutput(right))
+      } else {
+        (left, right)
+      }
     }
 
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
@@ -334,144 +334,94 @@ object HiveTypeCoercion {
    * - SHORT gets turned into DECIMAL(5, 0)
    * - INT gets turned into DECIMAL(10, 0)
    * - LONG gets turned into DECIMAL(20, 0)
-   * - FLOAT and DOUBLE
-   *   1. Union, Intersect and Except operations:
-   *      FLOAT gets turned into DECIMAL(7, 7), DOUBLE gets turned into DECIMAL(15, 15) (this is the
-   *      same as Hive)
-   *   2. Other operation:
-   *      FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive,
-   *   but note that unlimited decimals are considered bigger than doubles in WidenTypes)
+   * - FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE
+   *
+   * Note: Union/Except/Interact is handled by WidenTypes
    */
   // scalastyle:on
   object DecimalPrecision extends Rule[LogicalPlan] {
     import scala.math.{max, min}
 
-    // Conversion rules for integer types into fixed-precision decimals
-    private val intTypeToFixed: Map[DataType, DecimalType] = Map(
-      ByteType -> DecimalType(3, 0),
-      ShortType -> DecimalType(5, 0),
-      IntegerType -> DecimalType(10, 0),
-      LongType -> DecimalType(20, 0)
-    )
-
     private def isFloat(t: DataType): Boolean = t == FloatType || t == DoubleType
 
-    // Conversion rules for float and double into fixed-precision decimals
-    private val floatTypeToFixed: Map[DataType, DecimalType] = Map(
-      FloatType -> DecimalType(7, 7),
-      DoubleType -> DecimalType(15, 15)
-    )
-
-    private def castDecimalPrecision(
-        left: LogicalPlan,
-        right: LogicalPlan): (LogicalPlan, LogicalPlan) = {
-      val castedInput = left.output.zip(right.output).map {
-        case (lhs, rhs) if lhs.dataType != rhs.dataType =>
-          (lhs.dataType, rhs.dataType) match {
-            case (DecimalType.Fixed(p1, s1), DecimalType.Fixed(p2, s2)) =>
-              // Decimals with precision/scale p1/s2 and p2/s2  will be promoted to
-              // DecimalType(max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2))
-              val fixedType = DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2), max(s1, s2))
-              (Alias(Cast(lhs, fixedType), lhs.name)(), Alias(Cast(rhs, fixedType), rhs.name)())
-            case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) =>
-              (Alias(Cast(lhs, intTypeToFixed(t)), lhs.name)(), rhs)
-            case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) =>
-              (lhs, Alias(Cast(rhs, intTypeToFixed(t)), rhs.name)())
-            case (t, DecimalType.Fixed(p, s)) if floatTypeToFixed.contains(t) =>
-              (Alias(Cast(lhs, floatTypeToFixed(t)), lhs.name)(), rhs)
-            case (DecimalType.Fixed(p, s), t) if floatTypeToFixed.contains(t) =>
-              (lhs, Alias(Cast(rhs, floatTypeToFixed(t)), rhs.name)())
-            case _ => (lhs, rhs)
-          }
-        case other => other
-      }
-
-      val (castedLeft, castedRight) = castedInput.unzip
+    // Returns the wider decimal type that's wider than both of them
+    def widerDecimalType(d1: DecimalType, d2: DecimalType): DecimalType = {
+      widerDecimalType(d1.precision, d1.scale, d2.precision, d2.scale)
+    }
+    // max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2)
+    def widerDecimalType(p1: Int, s1: Int, p2: Int, s2: Int): DecimalType = {
+      val scale = max(s1, s2)
+      val range = max(p1 - s1, p2 - s2)
+      DecimalType.bounded(range + scale, scale)
+    }
 
-      val newLeft =
-        if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) {
-          Project(castedLeft, left)
-        } else {
-          left
-        }
+    /**
+     * An expression used to wrap the children when promote the precision of DecimalType to avoid
+     * promote multiple times.
+     */
+    case class ChangePrecision(child: Expression) extends UnaryExpression {
+      override def dataType: DataType = child.dataType
+      override def eval(input: InternalRow): Any = child.eval(input)
+      override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx)
+      override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = ""
+      override def prettyName: String = "change_precision"
+    }
 
-      val newRight =
-        if (castedRight.map(_.dataType) != right.output.map(_.dataType)) {
-          Project(castedRight, right)
-        } else {
-          right
-        }
-      (newLeft, newRight)
+    def changePrecision(e: Expression, dataType: DataType): Expression = {
+      ChangePrecision(Cast(e, dataType))
     }
 
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      // fix decimal precision for union, intersect and except
-      case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
-        val (newLeft, newRight) = castDecimalPrecision(left, right)
-        Union(newLeft, newRight)
-      case i @ Intersect(left, right) if i.childrenResolved && !i.resolved =>
-        val (newLeft, newRight) = castDecimalPrecision(left, right)
-        Intersect(newLeft, newRight)
-      case e @ Except(left, right) if e.childrenResolved && !e.resolved =>
-        val (newLeft, newRight) = castDecimalPrecision(left, right)
-        Except(newLeft, newRight)
-
       // fix decimal precision for expressions
       case q => q.transformExpressions {
         // Skip nodes whose children have not been resolved yet
         case e if !e.childrenResolved => e
 
+        // Skip nodes who is already promoted
+        case e: BinaryArithmetic if e.left.isInstanceOf[ChangePrecision] => e
+
         case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          Cast(
-            Add(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)),
-            DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
-          )
+          val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
+          Add(changePrecision(e1, dt), changePrecision(e2, dt))
 
         case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          Cast(
-            Subtract(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)),
-            DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
-          )
+          val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
+          Subtract(changePrecision(e1, dt), changePrecision(e2, dt))
 
         case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          Cast(
-            Multiply(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)),
-            DecimalType(p1 + p2 + 1, s1 + s2)
-          )
+          val dt = DecimalType.bounded(p1 + p2 + 1, s1 + s2)
+          Multiply(changePrecision(e1, dt), changePrecision(e2, dt))
 
         case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          Cast(
-            Divide(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)),
-            DecimalType(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1))
-          )
+          val dt = DecimalType.bounded(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1))
+          Divide(changePrecision(e1, dt), changePrecision(e2, dt))
 
         case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          Cast(
-            Remainder(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)),
-            DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
-          )
+          val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
+          // resultType may have lower precision, so we cast them into wider type first.
+          val widerType = widerDecimalType(p1, s1, p2, s2)
+          Cast(Remainder(changePrecision(e1, widerType), changePrecision(e2, widerType)),
+            resultType)
 
         case Pmod(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          Cast(
-            Pmod(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)),
-            DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
-          )
+          val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
+          // resultType may have lower precision, so we cast them into wider type first.
+          val widerType = widerDecimalType(p1, s1, p2, s2)
+          Cast(Pmod(changePrecision(e1, widerType), changePrecision(e2, widerType)), resultType)
 
-        // When we compare 2 decimal types with different precisions, cast them to the smallest
-        // common precision.
         case b @ BinaryComparison(e1 @ DecimalType.Expression(p1, s1),
                                   e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
-          val resultType = DecimalType(max(p1, p2), max(s1, s2))
+          val resultType = widerDecimalType(p1, s1, p2, s2)
           b.makeCopy(Array(Cast(e1, resultType), Cast(e2, resultType)))
 
         // Promote integers inside a binary expression with fixed-precision decimals to decimals,
         // and fixed-precision decimals in an expression with floats / doubles to doubles
         case b @ BinaryOperator(left, right) if left.dataType != right.dataType =>
           (left.dataType, right.dataType) match {
-            case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) =>
-              b.makeCopy(Array(Cast(left, intTypeToFixed(t)), right))
-            case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) =>
-              b.makeCopy(Array(left, Cast(right, intTypeToFixed(t))))
+            case (t: IntegralType, DecimalType.Fixed(p, s)) =>
+              b.makeCopy(Array(Cast(left, DecimalType.forType(t)), right))
+            case (DecimalType.Fixed(p, s), t: IntegralType) =>
+              b.makeCopy(Array(left, Cast(right, DecimalType.forType(t))))
             case (t, DecimalType.Fixed(p, s)) if isFloat(t) =>
               b.makeCopy(Array(left, Cast(right, DoubleType)))
             case (DecimalType.Fixed(p, s), t) if isFloat(t) =>
@@ -485,7 +435,6 @@ object HiveTypeCoercion {
         // SUM and AVERAGE are handled by the implementations of those expressions
       }
     }
-
   }
 
   /**
@@ -563,7 +512,7 @@ object HiveTypeCoercion {
       case e if !e.childrenResolved => e
 
       case Cast(e @ StringType(), t: IntegralType) =>
-        Cast(Cast(e, DecimalType.Unlimited), t)
+        Cast(Cast(e, DecimalType.forType(LongType)), t)
     }
   }
 
@@ -756,8 +705,8 @@ object HiveTypeCoercion {
         // Implicit cast among numeric types. When we reach here, input type is not acceptable.
 
         // If input is a numeric type but not decimal, and we expect a decimal type,
-        // cast the input to unlimited precision decimal.
-        case (_: NumericType, DecimalType) => Cast(e, DecimalType.Unlimited)
+        // cast the input to decimal.
+        case (d: NumericType, DecimalType) => Cast(e, DecimalType.forType(d))
         // For any other numeric types, implicitly cast to each other, e.g. long -> int, int -> long
         case (_: NumericType, target: NumericType) => Cast(e, target)
 
@@ -766,7 +715,7 @@ object HiveTypeCoercion {
         case (TimestampType, DateType) => Cast(e, DateType)
 
         // Implicit cast from/to string
-        case (StringType, DecimalType) => Cast(e, DecimalType.Unlimited)
+        case (StringType, DecimalType) => Cast(e, DecimalType.SYSTEM_DEFAULT)
         case (StringType, target: NumericType) => Cast(e, target)
         case (StringType, DateType) => Cast(e, DateType)
         case (StringType, TimestampType) => Cast(e, TimestampType)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index 51821757967d2..a7e3a49327655 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -201,7 +201,7 @@ package object dsl {
 
       /** Creates a new AttributeReference of type decimal */
       def decimal: AttributeReference =
-        AttributeReference(s, DecimalType.Unlimited, nullable = true)()
+        AttributeReference(s, DecimalType.SYSTEM_DEFAULT, nullable = true)()
 
       /** Creates a new AttributeReference of type decimal */
       def decimal(precision: Int, scale: Int): AttributeReference =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index e66cd828481bf..c66854d52c50b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -300,12 +300,7 @@ case class Cast(child: Expression, dataType: DataType)
    * NOTE: this modifies `value` in-place, so don't call it on external data.
    */
   private[this] def changePrecision(value: Decimal, decimalType: DecimalType): Decimal = {
-    decimalType match {
-      case DecimalType.Unlimited =>
-        value
-      case DecimalType.Fixed(precision, scale) =>
-        if (value.changePrecision(precision, scale)) value else null
-    }
+    if (value.changePrecision(decimalType.precision, decimalType.scale)) value else null
   }
 
   private[this] def castToDecimal(from: DataType, target: DecimalType): Any => Any = from match {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
index b924af4cc84d8..88fb516e64aaf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
@@ -36,14 +36,13 @@ case class Average(child: Expression) extends AlgebraicAggregate {
   override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(NumericType, NullType))
 
   private val resultType = child.dataType match {
-    case DecimalType.Fixed(precision, scale) =>
-      DecimalType(precision + 4, scale + 4)
-    case DecimalType.Unlimited => DecimalType.Unlimited
+    case DecimalType.Fixed(p, s) =>
+      DecimalType.bounded(p + 4, s + 4)
     case _ => DoubleType
   }
 
   private val sumDataType = child.dataType match {
-    case _ @ DecimalType() => DecimalType.Unlimited
+    case _ @ DecimalType.Fixed(p, s) => DecimalType.bounded(p + 10, s)
     case _ => DoubleType
   }
 
@@ -71,7 +70,14 @@ case class Average(child: Expression) extends AlgebraicAggregate {
   )
 
   // If all input are nulls, currentCount will be 0 and we will get null after the division.
-  override val evaluateExpression = Cast(currentSum, resultType) / Cast(currentCount, resultType)
+  override val evaluateExpression = child.dataType match {
+    case DecimalType.Fixed(p, s) =>
+      // increase the precision and scale to prevent precision loss
+      val dt = DecimalType.bounded(p + 14, s + 4)
+      Cast(Cast(currentSum, dt) / Cast(currentCount, dt), resultType)
+    case _ =>
+      Cast(currentSum, resultType) / Cast(currentCount, resultType)
+  }
 }
 
 case class Count(child: Expression) extends AlgebraicAggregate {
@@ -255,15 +261,11 @@ case class Sum(child: Expression) extends AlgebraicAggregate {
 
   private val resultType = child.dataType match {
     case DecimalType.Fixed(precision, scale) =>
-      DecimalType(precision + 4, scale + 4)
-    case DecimalType.Unlimited => DecimalType.Unlimited
+      DecimalType.bounded(precision + 10, scale)
     case _ => child.dataType
   }
 
-  private val sumDataType = child.dataType match {
-    case _ @ DecimalType() => DecimalType.Unlimited
-    case _ => child.dataType
-  }
+  private val sumDataType = resultType
 
   private val currentSum = AttributeReference("currentSum", sumDataType)()
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index d3295b8bafa80..73fde4e9164d7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -390,22 +390,21 @@ case class Average(child: Expression) extends UnaryExpression with PartialAggreg
 
   override def dataType: DataType = child.dataType match {
     case DecimalType.Fixed(precision, scale) =>
-      DecimalType(precision + 4, scale + 4)  // Add 4 digits after decimal point, like Hive
-    case DecimalType.Unlimited =>
-      DecimalType.Unlimited
+      // Add 4 digits after decimal point, like Hive
+      DecimalType.bounded(precision + 4, scale + 4)
     case _ =>
       DoubleType
   }
 
   override def asPartial: SplitEvaluation = {
     child.dataType match {
-      case DecimalType.Fixed(_, _) | DecimalType.Unlimited =>
-        // Turn the child to unlimited decimals for calculation, before going back to fixed
-        val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")()
+      case DecimalType.Fixed(precision, scale) =>
+        val partialSum = Alias(Sum(child), "PartialSum")()
         val partialCount = Alias(Count(child), "PartialCount")()
 
-        val castedSum = Cast(Sum(partialSum.toAttribute), DecimalType.Unlimited)
-        val castedCount = Cast(Sum(partialCount.toAttribute), DecimalType.Unlimited)
+        // partialSum already increase the precision by 10
+        val castedSum = Cast(Sum(partialSum.toAttribute), partialSum.dataType)
+        val castedCount = Sum(partialCount.toAttribute)
         SplitEvaluation(
           Cast(Divide(castedSum, castedCount), dataType),
           partialCount :: partialSum :: Nil)
@@ -435,8 +434,8 @@ case class AverageFunction(expr: Expression, base: AggregateExpression1)
 
   private val calcType =
     expr.dataType match {
-      case DecimalType.Fixed(_, _) =>
-        DecimalType.Unlimited
+      case DecimalType.Fixed(precision, scale) =>
+        DecimalType.bounded(precision + 10, scale)
       case _ =>
         expr.dataType
     }
@@ -454,10 +453,9 @@ case class AverageFunction(expr: Expression, base: AggregateExpression1)
       null
     } else {
       expr.dataType match {
-        case DecimalType.Fixed(_, _) =>
-          Cast(Divide(
-            Cast(sum, DecimalType.Unlimited),
-            Cast(Literal(count), DecimalType.Unlimited)), dataType).eval(null)
+        case DecimalType.Fixed(precision, scale) =>
+          val dt = DecimalType.bounded(precision + 14, scale + 4)
+          Cast(Divide(Cast(sum, dt), Cast(Literal(count), dt)), dataType).eval(null)
         case _ =>
           Divide(
             Cast(sum, dataType),
@@ -481,9 +479,8 @@ case class Sum(child: Expression) extends UnaryExpression with PartialAggregate1
 
   override def dataType: DataType = child.dataType match {
     case DecimalType.Fixed(precision, scale) =>
-      DecimalType(precision + 10, scale)  // Add 10 digits left of decimal point, like Hive
-    case DecimalType.Unlimited =>
-      DecimalType.Unlimited
+      // Add 10 digits left of decimal point, like Hive
+      DecimalType.bounded(precision + 10, scale)
     case _ =>
       child.dataType
   }
@@ -491,7 +488,7 @@ case class Sum(child: Expression) extends UnaryExpression with PartialAggregate1
   override def asPartial: SplitEvaluation = {
     child.dataType match {
       case DecimalType.Fixed(_, _) =>
-        val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")()
+        val partialSum = Alias(Sum(child), "PartialSum")()
         SplitEvaluation(
           Cast(CombineSum(partialSum.toAttribute), dataType),
           partialSum :: Nil)
@@ -515,8 +512,8 @@ case class SumFunction(expr: Expression, base: AggregateExpression1) extends Agg
 
   private val calcType =
     expr.dataType match {
-      case DecimalType.Fixed(_, _) =>
-        DecimalType.Unlimited
+      case DecimalType.Fixed(precision, scale) =>
+        DecimalType.bounded(precision + 10, scale)
       case _ =>
         expr.dataType
     }
@@ -572,8 +569,8 @@ case class CombineSumFunction(expr: Expression, base: AggregateExpression1)
 
   private val calcType =
     expr.dataType match {
-      case DecimalType.Fixed(_, _) =>
-        DecimalType.Unlimited
+      case DecimalType.Fixed(precision, scale) =>
+        DecimalType.bounded(precision + 10, scale)
       case _ =>
         expr.dataType
     }
@@ -608,9 +605,8 @@ case class SumDistinct(child: Expression) extends UnaryExpression with PartialAg
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType match {
     case DecimalType.Fixed(precision, scale) =>
-      DecimalType(precision + 10, scale)  // Add 10 digits left of decimal point, like Hive
-    case DecimalType.Unlimited =>
-      DecimalType.Unlimited
+      // Add 10 digits left of decimal point, like Hive
+      DecimalType.bounded(precision + 10, scale)
     case _ =>
       child.dataType
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 05b5ad88fee8f..7c254a8750a9f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -88,6 +88,8 @@ abstract class BinaryArithmetic extends BinaryOperator {
 
   override def dataType: DataType = left.dataType
 
+  override lazy val resolved = childrenResolved && checkInputDataTypes().isSuccess
+
   /** Name of the function for this expression on a [[Decimal]] type. */
   def decimalMethod: String =
     sys.error("BinaryArithmetics must override either decimalMethod or genCode")
@@ -114,9 +116,6 @@ case class Add(left: Expression, right: Expression) extends BinaryArithmetic {
 
   override def symbol: String = "+"
 
-  override lazy val resolved =
-    childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
-
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
@@ -146,9 +145,6 @@ case class Subtract(left: Expression, right: Expression) extends BinaryArithmeti
 
   override def symbol: String = "-"
 
-  override lazy val resolved =
-    childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
-
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
@@ -179,9 +175,6 @@ case class Multiply(left: Expression, right: Expression) extends BinaryArithmeti
   override def symbol: String = "*"
   override def decimalMethod: String = "$times"
 
-  override lazy val resolved =
-    childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
-
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = numeric.times(input1, input2)
@@ -195,9 +188,6 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic
   override def decimalMethod: String = "$div"
   override def nullable: Boolean = true
 
-  override lazy val resolved =
-    childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
-
   private lazy val div: (Any, Any) => Any = dataType match {
     case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div
     case it: IntegralType => it.integral.asInstanceOf[Integral[Any]].quot
@@ -260,9 +250,6 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet
   override def decimalMethod: String = "remainder"
   override def nullable: Boolean = true
 
-  override lazy val resolved =
-    childrenResolved && checkInputDataTypes().isSuccess && !DecimalType.isFixed(dataType)
-
   private lazy val integral = dataType match {
     case i: IntegralType => i.integral.asInstanceOf[Integral[Any]]
     case i: FractionalType => i.asIntegral.asInstanceOf[Integral[Any]]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index f25ac32679587..85060b7893556 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -36,9 +36,9 @@ object Literal {
     case s: Short => Literal(s, ShortType)
     case s: String => Literal(UTF8String.fromString(s), StringType)
     case b: Boolean => Literal(b, BooleanType)
-    case d: BigDecimal => Literal(Decimal(d), DecimalType.Unlimited)
-    case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType.Unlimited)
-    case d: Decimal => Literal(d, DecimalType.Unlimited)
+    case d: BigDecimal => Literal(Decimal(d), DecimalType(d.precision, d.scale))
+    case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType(d.precision(), d.scale()))
+    case d: Decimal => Literal(d, DecimalType(d.precision, d.scale))
     case t: Timestamp => Literal(DateTimeUtils.fromJavaTimestamp(t), TimestampType)
     case d: Date => Literal(DateTimeUtils.fromJavaDate(d), DateType)
     case a: Array[Byte] => Literal(a, BinaryType)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index d06a7a2add754..c610f70d38437 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -17,9 +17,9 @@
 
 package org.apache.spark.sql.catalyst.plans
 
-import org.apache.spark.sql.catalyst.expressions.{VirtualColumn, Attribute, AttributeSet, Expression}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression, VirtualColumn}
 import org.apache.spark.sql.catalyst.trees.TreeNode
-import org.apache.spark.sql.types.{ArrayType, DataType, StructField, StructType}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanType] {
   self: PlanType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index e98fd2583b931..591fb26e67c4a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -106,7 +106,7 @@ object DataType {
   private def nameToType(name: String): DataType = {
     val FIXED_DECIMAL = """decimal\(\s*(\d+)\s*,\s*(\d+)\s*\)""".r
     name match {
-      case "decimal" => DecimalType.Unlimited
+      case "decimal" => DecimalType.USER_DEFAULT
       case FIXED_DECIMAL(precision, scale) => DecimalType(precision.toInt, scale.toInt)
       case other => nonDecimalNameToType(other)
     }
@@ -177,7 +177,7 @@ object DataType {
         | "BinaryType" ^^^ BinaryType
         | "BooleanType" ^^^ BooleanType
         | "DateType" ^^^ DateType
-        | "DecimalType()" ^^^ DecimalType.Unlimited
+        | "DecimalType()" ^^^ DecimalType.USER_DEFAULT
         | fixedDecimalType
         | "TimestampType" ^^^ TimestampType
         )
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala
index 6b43224feb1f2..6e081ea9237bd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala
@@ -48,7 +48,7 @@ private[sql] trait DataTypeParser extends StandardTokenParsers {
     "(?i)binary".r ^^^ BinaryType |
     "(?i)boolean".r ^^^ BooleanType |
     fixedDecimalType |
-    "(?i)decimal".r ^^^ DecimalType.Unlimited |
+    "(?i)decimal".r ^^^ DecimalType.USER_DEFAULT |
     "(?i)date".r ^^^ DateType |
     "(?i)timestamp".r ^^^ TimestampType |
     varchar
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index 377c75f6e85a5..26b24616d98ec 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -26,25 +26,46 @@ import org.apache.spark.sql.catalyst.expressions.Expression
 
 
 /** Precision parameters for a Decimal */
+@deprecated("Use DecimalType(precision, scale) directly", "1.5")
 case class PrecisionInfo(precision: Int, scale: Int) {
   if (scale > precision) {
     throw new AnalysisException(
       s"Decimal scale ($scale) cannot be greater than precision ($precision).")
   }
+  if (precision > DecimalType.MAX_PRECISION) {
+    throw new AnalysisException(
+      s"DecimalType can only support precision up to 38"
+    )
+  }
 }
 
 /**
  * :: DeveloperApi ::
  * The data type representing `java.math.BigDecimal` values.
- * A Decimal that might have fixed precision and scale, or unlimited values for these.
+ * A Decimal that must have fixed precision (the maximum number of digits) and scale (the number
+ * of digits on right side of dot).
+ *
+ * The precision can be up to 38, scale can also be up to 38 (less or equal to precision).
+ *
+ * The default precision and scale is (10, 0).
  *
  * Please use [[DataTypes.createDecimalType()]] to create a specific instance.
  */
 @DeveloperApi
-case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalType {
+case class DecimalType(precision: Int, scale: Int) extends FractionalType {
+
+  // default constructor for Java
+  def this(precision: Int) = this(precision, 0)
+  def this() = this(10)
+
+  @deprecated("Use DecimalType(precision, scale) instead", "1.5")
+  def this(precisionInfo: Option[PrecisionInfo]) {
+    this(precisionInfo.getOrElse(PrecisionInfo(10, 0)).precision,
+      precisionInfo.getOrElse(PrecisionInfo(10, 0)).scale)
+  }
 
-  /** No-arg constructor for kryo. */
-  protected def this() = this(null)
+  @deprecated("Use DecimalType.precision and DecimalType.scale instead", "1.5")
+  val precisionInfo = Some(PrecisionInfo(precision, scale))
 
   private[sql] type InternalType = Decimal
   @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] }
@@ -53,18 +74,16 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT
   private[sql] val ordering = Decimal.DecimalIsFractional
   private[sql] val asIntegral = Decimal.DecimalAsIfIntegral
 
-  def precision: Int = precisionInfo.map(_.precision).getOrElse(-1)
-
-  def scale: Int = precisionInfo.map(_.scale).getOrElse(-1)
+  override def typeName: String = s"decimal($precision,$scale)"
 
-  override def typeName: String = precisionInfo match {
-    case Some(PrecisionInfo(precision, scale)) => s"decimal($precision,$scale)"
-    case None => "decimal"
-  }
+  override def toString: String = s"DecimalType($precision,$scale)"
 
-  override def toString: String = precisionInfo match {
-    case Some(PrecisionInfo(precision, scale)) => s"DecimalType($precision,$scale)"
-    case None => "DecimalType()"
+  private[sql] def isWiderThan(other: DataType): Boolean = other match {
+    case dt: DecimalType =>
+      (precision - scale) >= (dt.precision - dt.scale) && scale >= dt.scale
+    case dt: IntegralType =>
+      isWiderThan(DecimalType.forType(dt))
+    case _ => false
   }
 
   /**
@@ -72,10 +91,7 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT
    */
   override def defaultSize: Int = 4096
 
-  override def simpleString: String = precisionInfo match {
-    case Some(PrecisionInfo(precision, scale)) => s"decimal($precision,$scale)"
-    case None => "decimal(10,0)"
-  }
+  override def simpleString: String = s"decimal($precision,$scale)"
 
   private[spark] override def asNullable: DecimalType = this
 }
@@ -83,8 +99,47 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT
 
 /** Extra factory methods and pattern matchers for Decimals */
 object DecimalType extends AbstractDataType {
+  import scala.math.min
+
+  val MAX_PRECISION = 38
+  val MAX_SCALE = 38
+  val SYSTEM_DEFAULT: DecimalType = DecimalType(MAX_PRECISION, 18)
+  val USER_DEFAULT: DecimalType = DecimalType(10, 0)
+
+  @deprecated("Does not support unlimited precision, please specify the precision and scale", "1.5")
+  val Unlimited: DecimalType = SYSTEM_DEFAULT
+
+  // The decimal types compatible with other numberic types
+  private[sql] val ByteDecimal = DecimalType(3, 0)
+  private[sql] val ShortDecimal = DecimalType(5, 0)
+  private[sql] val IntDecimal = DecimalType(10, 0)
+  private[sql] val LongDecimal = DecimalType(20, 0)
+  private[sql] val FloatDecimal = DecimalType(14, 7)
+  private[sql] val DoubleDecimal = DecimalType(30, 15)
+
+  private[sql] def forType(dataType: DataType): DecimalType = dataType match {
+    case ByteType => ByteDecimal
+    case ShortType => ShortDecimal
+    case IntegerType => IntDecimal
+    case LongType => LongDecimal
+    case FloatType => FloatDecimal
+    case DoubleType => DoubleDecimal
+  }
 
-  override private[sql] def defaultConcreteType: DataType = Unlimited
+  @deprecated("please specify precision and scale", "1.5")
+  def apply(): DecimalType = USER_DEFAULT
+
+  @deprecated("Use DecimalType(precision, scale) instead", "1.5")
+  def apply(precisionInfo: Option[PrecisionInfo]) {
+    this(precisionInfo.getOrElse(PrecisionInfo(10, 0)).precision,
+      precisionInfo.getOrElse(PrecisionInfo(10, 0)).scale)
+  }
+
+  private[sql] def bounded(precision: Int, scale: Int): DecimalType = {
+    DecimalType(min(precision, MAX_PRECISION), min(scale, MAX_SCALE))
+  }
+
+  override private[sql] def defaultConcreteType: DataType = SYSTEM_DEFAULT
 
   override private[sql] def acceptsType(other: DataType): Boolean = {
     other.isInstanceOf[DecimalType]
@@ -92,31 +147,18 @@ object DecimalType extends AbstractDataType {
 
   override private[sql] def simpleString: String = "decimal"
 
-  val Unlimited: DecimalType = DecimalType(None)
-
   private[sql] object Fixed {
-    def unapply(t: DecimalType): Option[(Int, Int)] =
-      t.precisionInfo.map(p => (p.precision, p.scale))
+    def unapply(t: DecimalType): Option[(Int, Int)] = Some((t.precision, t.scale))
   }
 
   private[sql] object Expression {
     def unapply(e: Expression): Option[(Int, Int)] = e.dataType match {
-      case t: DecimalType => t.precisionInfo.map(p => (p.precision, p.scale))
+      case t: DecimalType => Some((t.precision, t.scale))
       case _ => None
     }
   }
 
-  def apply(): DecimalType = Unlimited
-
-  def apply(precision: Int, scale: Int): DecimalType =
-    DecimalType(Some(PrecisionInfo(precision, scale)))
-
   def unapply(t: DataType): Boolean = t.isInstanceOf[DecimalType]
 
   def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[DecimalType]
-
-  def isFixed(dataType: DataType): Boolean = dataType match {
-    case DecimalType.Fixed(_, _) => true
-    case _ => false
-  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
index 13aad467fa578..b9f2ad7ec0481 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -94,8 +94,8 @@ object RandomDataGenerator {
       case BooleanType => Some(() => rand.nextBoolean())
       case DateType => Some(() => new java.sql.Date(rand.nextInt()))
       case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
-      case DecimalType.Unlimited => Some(
-        () => BigDecimal.apply(rand.nextLong, rand.nextInt, MathContext.UNLIMITED))
+      case DecimalType.Fixed(precision, scale) => Some(
+        () => BigDecimal.apply(rand.nextLong, rand.nextInt, new MathContext(precision)))
       case DoubleType => randomNumeric[Double](
         rand, r => longBitsToDouble(r.nextLong()), Seq(Double.MinValue, Double.MinPositiveValue,
           Double.MaxValue, Double.PositiveInfinity, Double.NegativeInfinity, Double.NaN, 0.0))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
index dbba93dba668e..677ba0a18040c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
@@ -50,9 +50,7 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
   for (
     dataType <- DataTypeTestUtils.atomicTypes;
     nullable <- Seq(true, false)
-    if !dataType.isInstanceOf[DecimalType] ||
-      dataType.asInstanceOf[DecimalType].precisionInfo.isEmpty
-  ) {
+    if !dataType.isInstanceOf[DecimalType]) {
     test(s"$dataType (nullable=$nullable)") {
       testRandomDataGeneration(dataType)
     }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
index b4b00f558463f..3b848cfdf737f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
@@ -102,7 +102,7 @@ class ScalaReflectionSuite extends SparkFunSuite {
         StructField("byteField", ByteType, nullable = true),
         StructField("booleanField", BooleanType, nullable = true),
         StructField("stringField", StringType, nullable = true),
-        StructField("decimalField", DecimalType.Unlimited, nullable = true),
+        StructField("decimalField", DecimalType.SYSTEM_DEFAULT, nullable = true),
         StructField("dateField", DateType, nullable = true),
         StructField("timestampField", TimestampType, nullable = true),
         StructField("binaryField", BinaryType, nullable = true))),
@@ -216,7 +216,7 @@ class ScalaReflectionSuite extends SparkFunSuite {
     assert(DoubleType === typeOfObject(1.7976931348623157E308))
 
     // DecimalType
-    assert(DecimalType.Unlimited ===
+    assert(DecimalType.SYSTEM_DEFAULT ===
       typeOfObject(new java.math.BigDecimal("1.7976931348623157E318")))
 
     // DateType
@@ -229,19 +229,19 @@ class ScalaReflectionSuite extends SparkFunSuite {
     assert(NullType === typeOfObject(null))
 
     def typeOfObject1: PartialFunction[Any, DataType] = typeOfObject orElse {
-      case value: java.math.BigInteger => DecimalType.Unlimited
-      case value: java.math.BigDecimal => DecimalType.Unlimited
+      case value: java.math.BigInteger => DecimalType.SYSTEM_DEFAULT
+      case value: java.math.BigDecimal => DecimalType.SYSTEM_DEFAULT
       case _ => StringType
     }
 
-    assert(DecimalType.Unlimited === typeOfObject1(
+    assert(DecimalType.SYSTEM_DEFAULT === typeOfObject1(
       new BigInteger("92233720368547758070")))
-    assert(DecimalType.Unlimited === typeOfObject1(
+    assert(DecimalType.SYSTEM_DEFAULT === typeOfObject1(
       new java.math.BigDecimal("1.7976931348623157E318")))
     assert(StringType === typeOfObject1(BigInt("92233720368547758070")))
 
     def typeOfObject2: PartialFunction[Any, DataType] = typeOfObject orElse {
-      case value: java.math.BigInteger => DecimalType.Unlimited
+      case value: java.math.BigInteger => DecimalType.SYSTEM_DEFAULT
     }
 
     intercept[MatchError](typeOfObject2(BigInt("92233720368547758070")))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 58df1de983a09..7e67427237a65 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -55,7 +55,7 @@ object AnalysisSuite {
     AttributeReference("a", StringType)(),
     AttributeReference("b", StringType)(),
     AttributeReference("c", DoubleType)(),
-    AttributeReference("d", DecimalType.Unlimited)(),
+    AttributeReference("d", DecimalType.SYSTEM_DEFAULT)(),
     AttributeReference("e", ShortType)())
 
   val nestedRelation = LocalRelation(
@@ -158,7 +158,7 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
       AttributeReference("a", StringType)(),
       AttributeReference("b", StringType)(),
       AttributeReference("c", DoubleType)(),
-      AttributeReference("d", DecimalType.Unlimited)(),
+      AttributeReference("d", DecimalType(10, 2))(),
       AttributeReference("e", ShortType)())
 
     val plan = caseInsensitiveAnalyzer.execute(
@@ -173,7 +173,7 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
     assert(pl(0).dataType == DoubleType)
     assert(pl(1).dataType == DoubleType)
     assert(pl(2).dataType == DoubleType)
-    assert(pl(3).dataType == DecimalType.Unlimited)
+    assert(pl(3).dataType == DoubleType)  // StringType will be promoted into Double
     assert(pl(4).dataType == DoubleType)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
index 7bac97b7894f5..f9f15e7a6608d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
@@ -34,7 +34,7 @@ class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter {
     AttributeReference("i", IntegerType)(),
     AttributeReference("d1", DecimalType(2, 1))(),
     AttributeReference("d2", DecimalType(5, 2))(),
-    AttributeReference("u", DecimalType.Unlimited)(),
+    AttributeReference("u", DecimalType.SYSTEM_DEFAULT)(),
     AttributeReference("f", FloatType)(),
     AttributeReference("b", DoubleType)()
   )
@@ -92,11 +92,11 @@ class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter {
   }
 
   test("Comparison operations") {
-    checkComparison(EqualTo(i, d1), DecimalType(10, 1))
+    checkComparison(EqualTo(i, d1), DecimalType(11, 1))
     checkComparison(EqualNullSafe(d2, d1), DecimalType(5, 2))
-    checkComparison(LessThan(i, d1), DecimalType(10, 1))
+    checkComparison(LessThan(i, d1), DecimalType(11, 1))
     checkComparison(LessThanOrEqual(d1, d2), DecimalType(5, 2))
-    checkComparison(GreaterThan(d2, u), DecimalType.Unlimited)
+    checkComparison(GreaterThan(d2, u), DecimalType.SYSTEM_DEFAULT)
     checkComparison(GreaterThanOrEqual(d1, f), DoubleType)
     checkComparison(GreaterThan(d2, d2), DecimalType(5, 2))
   }
@@ -106,12 +106,12 @@ class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter {
     checkUnion(i, d2, DecimalType(12, 2))
     checkUnion(d1, d2, DecimalType(5, 2))
     checkUnion(d2, d1, DecimalType(5, 2))
-    checkUnion(d1, f, DecimalType(8, 7))
-    checkUnion(f, d2, DecimalType(10, 7))
-    checkUnion(d1, b, DecimalType(16, 15))
-    checkUnion(b, d2, DecimalType(18, 15))
-    checkUnion(d1, u, DecimalType.Unlimited)
-    checkUnion(u, d2, DecimalType.Unlimited)
+    checkUnion(d1, f, DoubleType)
+    checkUnion(f, d2, DoubleType)
+    checkUnion(d1, b, DoubleType)
+    checkUnion(b, d2, DoubleType)
+    checkUnion(d1, u, DecimalType.SYSTEM_DEFAULT)
+    checkUnion(u, d2, DecimalType.SYSTEM_DEFAULT)
   }
 
   test("bringing in primitive types") {
@@ -125,13 +125,33 @@ class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter {
     checkType(Add(d1, Cast(i, DoubleType)), DoubleType)
   }
 
-  test("unlimited decimals make everything else cast up") {
-    for (expr <- Seq(d1, d2, i, f, u)) {
-      checkType(Add(expr, u), DecimalType.Unlimited)
-      checkType(Subtract(expr, u), DecimalType.Unlimited)
-      checkType(Multiply(expr, u), DecimalType.Unlimited)
-      checkType(Divide(expr, u), DecimalType.Unlimited)
-      checkType(Remainder(expr, u), DecimalType.Unlimited)
+  test("maximum decimals") {
+    for (expr <- Seq(d1, d2, i, u)) {
+      checkType(Add(expr, u), DecimalType.SYSTEM_DEFAULT)
+      checkType(Subtract(expr, u), DecimalType.SYSTEM_DEFAULT)
+    }
+
+    checkType(Multiply(d1, u), DecimalType(38, 19))
+    checkType(Multiply(d2, u), DecimalType(38, 20))
+    checkType(Multiply(i, u), DecimalType(38, 18))
+    checkType(Multiply(u, u), DecimalType(38, 36))
+
+    checkType(Divide(u, d1), DecimalType(38, 21))
+    checkType(Divide(u, d2), DecimalType(38, 24))
+    checkType(Divide(u, i), DecimalType(38, 29))
+    checkType(Divide(u, u), DecimalType(38, 38))
+
+    checkType(Remainder(d1, u), DecimalType(19, 18))
+    checkType(Remainder(d2, u), DecimalType(21, 18))
+    checkType(Remainder(i, u), DecimalType(28, 18))
+    checkType(Remainder(u, u), DecimalType.SYSTEM_DEFAULT)
+
+    for (expr <- Seq(f, b)) {
+      checkType(Add(expr, u), DoubleType)
+      checkType(Subtract(expr, u), DoubleType)
+      checkType(Multiply(expr, u), DoubleType)
+      checkType(Divide(expr, u), DoubleType)
+      checkType(Remainder(expr, u), DoubleType)
     }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 835220c563f41..d0fb95b580ad2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -35,14 +35,14 @@ class HiveTypeCoercionSuite extends PlanTest {
 
     shouldCast(NullType, NullType, NullType)
     shouldCast(NullType, IntegerType, IntegerType)
-    shouldCast(NullType, DecimalType, DecimalType.Unlimited)
+    shouldCast(NullType, DecimalType, DecimalType.SYSTEM_DEFAULT)
 
     shouldCast(ByteType, IntegerType, IntegerType)
     shouldCast(IntegerType, IntegerType, IntegerType)
     shouldCast(IntegerType, LongType, LongType)
-    shouldCast(IntegerType, DecimalType, DecimalType.Unlimited)
+    shouldCast(IntegerType, DecimalType, DecimalType(10, 0))
     shouldCast(LongType, IntegerType, IntegerType)
-    shouldCast(LongType, DecimalType, DecimalType.Unlimited)
+    shouldCast(LongType, DecimalType, DecimalType(20, 0))
 
     shouldCast(DateType, TimestampType, TimestampType)
     shouldCast(TimestampType, DateType, DateType)
@@ -71,8 +71,8 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldCast(IntegerType, TypeCollection(StringType, BinaryType), StringType)
     shouldCast(IntegerType, TypeCollection(BinaryType, StringType), StringType)
 
-    shouldCast(
-      DecimalType.Unlimited, TypeCollection(IntegerType, DecimalType), DecimalType.Unlimited)
+    shouldCast(DecimalType.SYSTEM_DEFAULT,
+      TypeCollection(IntegerType, DecimalType), DecimalType.SYSTEM_DEFAULT)
     shouldCast(DecimalType(10, 2), TypeCollection(IntegerType, DecimalType), DecimalType(10, 2))
     shouldCast(DecimalType(10, 2), TypeCollection(DecimalType, IntegerType), DecimalType(10, 2))
     shouldCast(IntegerType, TypeCollection(DecimalType(10, 2), StringType), DecimalType(10, 2))
@@ -82,7 +82,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 
     // NumericType should not be changed when function accepts any of them.
     Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType,
-      DecimalType.Unlimited, DecimalType(10, 2)).foreach { tpe =>
+      DecimalType.SYSTEM_DEFAULT, DecimalType(10, 2)).foreach { tpe =>
       shouldCast(tpe, NumericType, tpe)
     }
 
@@ -107,8 +107,8 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldNotCast(IntegerType, TimestampType)
     shouldNotCast(LongType, DateType)
     shouldNotCast(LongType, TimestampType)
-    shouldNotCast(DecimalType.Unlimited, DateType)
-    shouldNotCast(DecimalType.Unlimited, TimestampType)
+    shouldNotCast(DecimalType.SYSTEM_DEFAULT, DateType)
+    shouldNotCast(DecimalType.SYSTEM_DEFAULT, TimestampType)
 
     shouldNotCast(IntegerType, TypeCollection(DateType, TimestampType))
 
@@ -160,14 +160,6 @@ class HiveTypeCoercionSuite extends PlanTest {
     widenTest(LongType, FloatType, Some(FloatType))
     widenTest(LongType, DoubleType, Some(DoubleType))
 
-    // Casting up to unlimited-precision decimal
-    widenTest(IntegerType, DecimalType.Unlimited, Some(DecimalType.Unlimited))
-    widenTest(DoubleType, DecimalType.Unlimited, Some(DecimalType.Unlimited))
-    widenTest(DecimalType(3, 2), DecimalType.Unlimited, Some(DecimalType.Unlimited))
-    widenTest(DecimalType.Unlimited, IntegerType, Some(DecimalType.Unlimited))
-    widenTest(DecimalType.Unlimited, DoubleType, Some(DecimalType.Unlimited))
-    widenTest(DecimalType.Unlimited, DecimalType(3, 2), Some(DecimalType.Unlimited))
-
     // No up-casting for fixed-precision decimal (this is handled by arithmetic rules)
     widenTest(DecimalType(2, 1), DecimalType(3, 2), None)
     widenTest(DecimalType(2, 1), DoubleType, None)
@@ -242,9 +234,9 @@ class HiveTypeCoercionSuite extends PlanTest {
         :: Literal(1)
         :: Literal(new java.math.BigDecimal("1000000000000000000000"))
         :: Nil),
-      Coalesce(Cast(Literal(1L), DecimalType())
-        :: Cast(Literal(1), DecimalType())
-        :: Cast(Literal(new java.math.BigDecimal("1000000000000000000000")), DecimalType())
+      Coalesce(Cast(Literal(1L), DecimalType(22, 0))
+        :: Cast(Literal(1), DecimalType(22, 0))
+        :: Cast(Literal(new java.math.BigDecimal("1000000000000000000000")), DecimalType(22, 0))
         :: Nil))
   }
 
@@ -323,7 +315,7 @@ class HiveTypeCoercionSuite extends PlanTest {
 
     val left = LocalRelation(
       AttributeReference("i", IntegerType)(),
-      AttributeReference("u", DecimalType.Unlimited)(),
+      AttributeReference("u", DecimalType.SYSTEM_DEFAULT)(),
       AttributeReference("b", ByteType)(),
       AttributeReference("d", DoubleType)())
     val right = LocalRelation(
@@ -333,7 +325,7 @@ class HiveTypeCoercionSuite extends PlanTest {
       AttributeReference("l", LongType)())
 
     val wt = HiveTypeCoercion.WidenTypes
-    val expectedTypes = Seq(StringType, DecimalType.Unlimited, FloatType, DoubleType)
+    val expectedTypes = Seq(StringType, DecimalType.SYSTEM_DEFAULT, FloatType, DoubleType)
 
     val r1 = wt(Union(left, right)).asInstanceOf[Union]
     val r2 = wt(Except(left, right)).asInstanceOf[Except]
@@ -353,13 +345,13 @@ class HiveTypeCoercionSuite extends PlanTest {
       }
     }
 
-    val dp = HiveTypeCoercion.DecimalPrecision
+    val dp = HiveTypeCoercion.WidenTypes
 
     val left1 = LocalRelation(
       AttributeReference("l", DecimalType(10, 8))())
     val right1 = LocalRelation(
       AttributeReference("r", DecimalType(5, 5))())
-    val expectedType1 = Seq(DecimalType(math.max(8, 5) + math.max(10 - 8, 5 - 5), math.max(8, 5)))
+    val expectedType1 = Seq(DecimalType(10, 8))
 
     val r1 = dp(Union(left1, right1)).asInstanceOf[Union]
     val r2 = dp(Except(left1, right1)).asInstanceOf[Except]
@@ -372,12 +364,11 @@ class HiveTypeCoercionSuite extends PlanTest {
     checkOutput(r3.left, expectedType1)
     checkOutput(r3.right, expectedType1)
 
-    val plan1 = LocalRelation(
-      AttributeReference("l", DecimalType(10, 10))())
+    val plan1 = LocalRelation(AttributeReference("l", DecimalType(10, 5))())
 
     val rightTypes = Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType)
-    val expectedTypes = Seq(DecimalType(3, 0), DecimalType(5, 0), DecimalType(10, 0),
-      DecimalType(20, 0), DecimalType(7, 7), DecimalType(15, 15))
+    val expectedTypes = Seq(DecimalType(10, 5), DecimalType(10, 5), DecimalType(15, 5),
+      DecimalType(25, 5), DoubleType, DoubleType)
 
     rightTypes.zip(expectedTypes).map { case (rType, expectedType) =>
       val plan2 = LocalRelation(
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index ccf448eee0688..facf65c155148 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -185,7 +185,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkCast(1, 1.0)
     checkCast(123, "123")
 
-    checkEvaluation(cast(123, DecimalType.Unlimited), Decimal(123))
+    checkEvaluation(cast(123, DecimalType.USER_DEFAULT), Decimal(123))
     checkEvaluation(cast(123, DecimalType(3, 0)), Decimal(123))
     checkEvaluation(cast(123, DecimalType(3, 1)), null)
     checkEvaluation(cast(123, DecimalType(2, 0)), null)
@@ -203,7 +203,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkCast(1L, 1.0)
     checkCast(123L, "123")
 
-    checkEvaluation(cast(123L, DecimalType.Unlimited), Decimal(123))
+    checkEvaluation(cast(123L, DecimalType.USER_DEFAULT), Decimal(123))
     checkEvaluation(cast(123L, DecimalType(3, 0)), Decimal(123))
     checkEvaluation(cast(123L, DecimalType(3, 1)), Decimal(123.0))
 
@@ -225,7 +225,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(cast(1000, TimestampType), LongType), 1.toLong)
     checkEvaluation(cast(cast(-1200, TimestampType), LongType), -2.toLong)
 
-    checkEvaluation(cast(123, DecimalType.Unlimited), Decimal(123))
+    checkEvaluation(cast(123, DecimalType.USER_DEFAULT), Decimal(123))
     checkEvaluation(cast(123, DecimalType(3, 0)), Decimal(123))
     checkEvaluation(cast(123, DecimalType(3, 1)), null)
     checkEvaluation(cast(123, DecimalType(2, 0)), null)
@@ -267,7 +267,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     assert(cast("abcdef", IntegerType).nullable === true)
     assert(cast("abcdef", ShortType).nullable === true)
     assert(cast("abcdef", ByteType).nullable === true)
-    assert(cast("abcdef", DecimalType.Unlimited).nullable === true)
+    assert(cast("abcdef", DecimalType.USER_DEFAULT).nullable === true)
     assert(cast("abcdef", DecimalType(4, 2)).nullable === true)
     assert(cast("abcdef", DoubleType).nullable === true)
     assert(cast("abcdef", FloatType).nullable === true)
@@ -291,9 +291,9 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       c.getTimeInMillis * 1000)
 
     checkEvaluation(cast("abdef", StringType), "abdef")
-    checkEvaluation(cast("abdef", DecimalType.Unlimited), null)
+    checkEvaluation(cast("abdef", DecimalType.USER_DEFAULT), null)
     checkEvaluation(cast("abdef", TimestampType), null)
-    checkEvaluation(cast("12.65", DecimalType.Unlimited), Decimal(12.65))
+    checkEvaluation(cast("12.65", DecimalType.SYSTEM_DEFAULT), Decimal(12.65))
 
     checkEvaluation(cast(cast(sd, DateType), StringType), sd)
     checkEvaluation(cast(cast(d, StringType), DateType), 0)
@@ -311,20 +311,20 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       5.toLong)
     checkEvaluation(
       cast(cast(cast(cast(cast(cast("5", ByteType), TimestampType),
-        DecimalType.Unlimited), LongType), StringType), ShortType),
+        DecimalType.SYSTEM_DEFAULT), LongType), StringType), ShortType),
       0.toShort)
     checkEvaluation(
       cast(cast(cast(cast(cast(cast("5", TimestampType), ByteType),
-        DecimalType.Unlimited), LongType), StringType), ShortType),
+        DecimalType.SYSTEM_DEFAULT), LongType), StringType), ShortType),
       null)
-    checkEvaluation(cast(cast(cast(cast(cast(cast("5", DecimalType.Unlimited),
+    checkEvaluation(cast(cast(cast(cast(cast(cast("5", DecimalType.SYSTEM_DEFAULT),
       ByteType), TimestampType), LongType), StringType), ShortType),
       0.toShort)
 
     checkEvaluation(cast("23", DoubleType), 23d)
     checkEvaluation(cast("23", IntegerType), 23)
     checkEvaluation(cast("23", FloatType), 23f)
-    checkEvaluation(cast("23", DecimalType.Unlimited), Decimal(23))
+    checkEvaluation(cast("23", DecimalType.USER_DEFAULT), Decimal(23))
     checkEvaluation(cast("23", ByteType), 23.toByte)
     checkEvaluation(cast("23", ShortType), 23.toShort)
     checkEvaluation(cast("2012-12-11", DoubleType), null)
@@ -338,7 +338,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Add(Literal(23d), cast(true, DoubleType)), 24d)
     checkEvaluation(Add(Literal(23), cast(true, IntegerType)), 24)
     checkEvaluation(Add(Literal(23f), cast(true, FloatType)), 24f)
-    checkEvaluation(Add(Literal(Decimal(23)), cast(true, DecimalType.Unlimited)), Decimal(24))
+    checkEvaluation(Add(Literal(Decimal(23)), cast(true, DecimalType.USER_DEFAULT)), Decimal(24))
     checkEvaluation(Add(Literal(23.toByte), cast(true, ByteType)), 24.toByte)
     checkEvaluation(Add(Literal(23.toShort), cast(true, ShortType)), 24.toShort)
   }
@@ -362,10 +362,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     // - Values that would overflow the target precision should turn into null
     // - Because of this, casts to fixed-precision decimals should be nullable
 
-    assert(cast(123, DecimalType.Unlimited).nullable === false)
-    assert(cast(10.03f, DecimalType.Unlimited).nullable === true)
-    assert(cast(10.03, DecimalType.Unlimited).nullable === true)
-    assert(cast(Decimal(10.03), DecimalType.Unlimited).nullable === false)
+    assert(cast(123, DecimalType.USER_DEFAULT).nullable === true)
+    assert(cast(10.03f, DecimalType.SYSTEM_DEFAULT).nullable === true)
+    assert(cast(10.03, DecimalType.SYSTEM_DEFAULT).nullable === true)
+    assert(cast(Decimal(10.03), DecimalType.SYSTEM_DEFAULT).nullable === true)
 
     assert(cast(123, DecimalType(2, 1)).nullable === true)
     assert(cast(10.03f, DecimalType(2, 1)).nullable === true)
@@ -373,7 +373,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     assert(cast(Decimal(10.03), DecimalType(2, 1)).nullable === true)
 
 
-    checkEvaluation(cast(10.03, DecimalType.Unlimited), Decimal(10.03))
+    checkEvaluation(cast(10.03, DecimalType.SYSTEM_DEFAULT), Decimal(10.03))
     checkEvaluation(cast(10.03, DecimalType(4, 2)), Decimal(10.03))
     checkEvaluation(cast(10.03, DecimalType(3, 1)), Decimal(10.0))
     checkEvaluation(cast(10.03, DecimalType(2, 0)), Decimal(10))
@@ -383,7 +383,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(Decimal(10.03), DecimalType(3, 1)), Decimal(10.0))
     checkEvaluation(cast(Decimal(10.03), DecimalType(3, 2)), null)
 
-    checkEvaluation(cast(10.05, DecimalType.Unlimited), Decimal(10.05))
+    checkEvaluation(cast(10.05, DecimalType.SYSTEM_DEFAULT), Decimal(10.05))
     checkEvaluation(cast(10.05, DecimalType(4, 2)), Decimal(10.05))
     checkEvaluation(cast(10.05, DecimalType(3, 1)), Decimal(10.1))
     checkEvaluation(cast(10.05, DecimalType(2, 0)), Decimal(10))
@@ -409,10 +409,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(Decimal(-9.95), DecimalType(3, 1)), Decimal(-10.0))
     checkEvaluation(cast(Decimal(-9.95), DecimalType(1, 0)), null)
 
-    checkEvaluation(cast(Double.NaN, DecimalType.Unlimited), null)
-    checkEvaluation(cast(1.0 / 0.0, DecimalType.Unlimited), null)
-    checkEvaluation(cast(Float.NaN, DecimalType.Unlimited), null)
-    checkEvaluation(cast(1.0f / 0.0f, DecimalType.Unlimited), null)
+    checkEvaluation(cast(Double.NaN, DecimalType.SYSTEM_DEFAULT), null)
+    checkEvaluation(cast(1.0 / 0.0, DecimalType.SYSTEM_DEFAULT), null)
+    checkEvaluation(cast(Float.NaN, DecimalType.SYSTEM_DEFAULT), null)
+    checkEvaluation(cast(1.0f / 0.0f, DecimalType.SYSTEM_DEFAULT), null)
 
     checkEvaluation(cast(Double.NaN, DecimalType(2, 1)), null)
     checkEvaluation(cast(1.0 / 0.0, DecimalType(2, 1)), null)
@@ -427,7 +427,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(d, LongType), null)
     checkEvaluation(cast(d, FloatType), null)
     checkEvaluation(cast(d, DoubleType), null)
-    checkEvaluation(cast(d, DecimalType.Unlimited), null)
+    checkEvaluation(cast(d, DecimalType.SYSTEM_DEFAULT), null)
     checkEvaluation(cast(d, DecimalType(10, 2)), null)
     checkEvaluation(cast(d, StringType), "1970-01-01")
     checkEvaluation(cast(cast(d, TimestampType), StringType), "1970-01-01 00:00:00")
@@ -454,7 +454,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       cast(cast(millis.toDouble / 1000, TimestampType), DoubleType),
       millis.toDouble / 1000)
     checkEvaluation(
-      cast(cast(Decimal(1), TimestampType), DecimalType.Unlimited),
+      cast(cast(Decimal(1), TimestampType), DecimalType.SYSTEM_DEFAULT),
       Decimal(1))
 
     // A test for higher precision than millis
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
index afa143bd5f331..b31d6661c8c1c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
@@ -60,7 +60,7 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
 
     testIf(_.toFloat, FloatType)
     testIf(_.toDouble, DoubleType)
-    testIf(Decimal(_), DecimalType.Unlimited)
+    testIf(Decimal(_), DecimalType.USER_DEFAULT)
 
     testIf(identity, DateType)
     testIf(_.toLong, TimestampType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
index d924ff7a102f6..f6404d21611e5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
@@ -33,7 +33,7 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, LongType), null)
     checkEvaluation(Literal.create(null, StringType), null)
     checkEvaluation(Literal.create(null, BinaryType), null)
-    checkEvaluation(Literal.create(null, DecimalType()), null)
+    checkEvaluation(Literal.create(null, DecimalType.USER_DEFAULT), null)
     checkEvaluation(Literal.create(null, ArrayType(ByteType, true)), null)
     checkEvaluation(Literal.create(null, MapType(StringType, IntegerType)), null)
     checkEvaluation(Literal.create(null, StructType(Seq.empty)), null)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
index 0728f6695c39d..9efe44c83293d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
@@ -30,7 +30,7 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     testFunc(1L, LongType)
     testFunc(1.0F, FloatType)
     testFunc(1.0, DoubleType)
-    testFunc(Decimal(1.5), DecimalType.Unlimited)
+    testFunc(Decimal(1.5), DecimalType(2, 1))
     testFunc(new java.sql.Date(10), DateType)
     testFunc(new java.sql.Timestamp(10), TimestampType)
     testFunc("abcd", StringType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
index 7566cb59e34ee..48b7dc57451a3 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
@@ -121,6 +121,8 @@ class UnsafeFixedWidthAggregationMapSuite
     }.toSet
     seenKeys.size should be (groupKeys.size)
     seenKeys should be (groupKeys)
+
+    map.free()
   }
 
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 8819234e78e60..a5d9806c20463 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -145,7 +145,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       DoubleType,
       StringType,
       BinaryType
-      // DecimalType.Unlimited,
+      // DecimalType.Default,
       // ArrayType(IntegerType)
     )
     val converter = new UnsafeRowConverter(fieldTypes)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala
index c6171b7b6916d..1ba290753ce48 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala
@@ -44,7 +44,7 @@ class DataTypeParserSuite extends SparkFunSuite {
   checkDataType("float", FloatType)
   checkDataType("dOUBle", DoubleType)
   checkDataType("decimal(10, 5)", DecimalType(10, 5))
-  checkDataType("decimal", DecimalType.Unlimited)
+  checkDataType("decimal", DecimalType.USER_DEFAULT)
   checkDataType("DATE", DateType)
   checkDataType("timestamp", TimestampType)
   checkDataType("string", StringType)
@@ -87,7 +87,7 @@ class DataTypeParserSuite extends SparkFunSuite {
     StructType(
       StructField("struct",
         StructType(
-          StructField("deciMal", DecimalType.Unlimited, true) ::
+          StructField("deciMal", DecimalType.USER_DEFAULT, true) ::
           StructField("anotherDecimal", DecimalType(5, 2), true) :: Nil), true) ::
       StructField("MAP", MapType(TimestampType, StringType), true) ::
       StructField("arrAy", ArrayType(DoubleType, true), true) :: Nil)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
index 14e7b4a9561b6..88b221cd81d74 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
@@ -185,7 +185,7 @@ class DataTypeSuite extends SparkFunSuite {
   checkDataTypeJsonRepr(FloatType)
   checkDataTypeJsonRepr(DoubleType)
   checkDataTypeJsonRepr(DecimalType(10, 5))
-  checkDataTypeJsonRepr(DecimalType.Unlimited)
+  checkDataTypeJsonRepr(DecimalType.SYSTEM_DEFAULT)
   checkDataTypeJsonRepr(DateType)
   checkDataTypeJsonRepr(TimestampType)
   checkDataTypeJsonRepr(StringType)
@@ -219,7 +219,7 @@ class DataTypeSuite extends SparkFunSuite {
   checkDefaultSize(FloatType, 4)
   checkDefaultSize(DoubleType, 8)
   checkDefaultSize(DecimalType(10, 5), 4096)
-  checkDefaultSize(DecimalType.Unlimited, 4096)
+  checkDefaultSize(DecimalType.SYSTEM_DEFAULT, 4096)
   checkDefaultSize(DateType, 4)
   checkDefaultSize(TimestampType, 8)
   checkDefaultSize(StringType, 4096)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
index 32632b5d6e342..0ee9ddac815b8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
@@ -34,7 +34,7 @@ object DataTypeTestUtils {
    * decimal types.
    */
   val fractionalTypes: Set[FractionalType] = Set(
-    DecimalType(precisionInfo = None),
+    DecimalType.SYSTEM_DEFAULT,
     DecimalType(2, 1),
     DoubleType,
     FloatType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 6e2a6525bf17e..b25dcbca82b9f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -996,7 +996,7 @@ class ColumnName(name: String) extends Column(name) {
    * Creates a new [[StructField]] of type decimal.
    * @since 1.3.0
    */
-  def decimal: StructField = StructField(name, DecimalType.Unlimited)
+  def decimal: StructField = StructField(name, DecimalType.USER_DEFAULT)
 
   /**
    * Creates a new [[StructField]] of type decimal.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index fc72360c88fe1..9d8415f06399c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -375,7 +375,7 @@ private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 8) {
 
 private[sql] case class FIXED_DECIMAL(precision: Int, scale: Int)
   extends NativeColumnType(
-    DecimalType(Some(PrecisionInfo(precision, scale))),
+    DecimalType(precision, scale),
     10,
     FIXED_DECIMAL.defaultSize) {
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 16176abe3a51d..5ed158b3d2912 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -21,9 +21,9 @@ import org.apache.spark.TaskContext
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.trees._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.catalyst.trees._
 import org.apache.spark.sql.types._
 
 case class AggregateEvaluation(
@@ -92,8 +92,8 @@ case class GeneratedAggregate(
       case s @ Sum(expr) =>
         val calcType =
           expr.dataType match {
-            case DecimalType.Fixed(_, _) =>
-              DecimalType.Unlimited
+            case DecimalType.Fixed(p, s) =>
+              DecimalType.bounded(p + 10, s)
             case _ =>
               expr.dataType
           }
@@ -121,8 +121,8 @@ case class GeneratedAggregate(
       case cs @ CombineSum(expr) =>
         val calcType =
           expr.dataType match {
-            case DecimalType.Fixed(_, _) =>
-              DecimalType.Unlimited
+            case DecimalType.Fixed(p, s) =>
+              DecimalType.bounded(p + 10, s)
             case _ =>
               expr.dataType
           }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
index 6b4a359db22d1..9d0fa894b9942 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
@@ -25,6 +25,7 @@ import scala.util.Try
 
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.util.Shell
+
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
 import org.apache.spark.sql.types._
@@ -236,7 +237,7 @@ private[sql] object PartitioningUtils {
 
   /**
    * Converts a string to a [[Literal]] with automatic type inference.  Currently only supports
-   * [[IntegerType]], [[LongType]], [[DoubleType]], [[DecimalType.Unlimited]], and
+   * [[IntegerType]], [[LongType]], [[DoubleType]], [[DecimalType.SYSTEM_DEFAULT]], and
    * [[StringType]].
    */
   private[sql] def inferPartitionColumnValue(
@@ -249,7 +250,7 @@ private[sql] object PartitioningUtils {
         .orElse(Try(Literal.create(JLong.parseLong(raw), LongType)))
         // Then falls back to fractional types
         .orElse(Try(Literal.create(JDouble.parseDouble(raw), DoubleType)))
-        .orElse(Try(Literal.create(new JBigDecimal(raw), DecimalType.Unlimited)))
+        .orElse(Try(Literal(new JBigDecimal(raw))))
         // Then falls back to string
         .getOrElse {
           if (raw == defaultPartitionName) {
@@ -268,7 +269,7 @@ private[sql] object PartitioningUtils {
   }
 
   private val upCastingOrder: Seq[DataType] =
-    Seq(NullType, IntegerType, LongType, FloatType, DoubleType, DecimalType.Unlimited, StringType)
+    Seq(NullType, IntegerType, LongType, FloatType, DoubleType, StringType)
 
   /**
    * Given a collection of [[Literal]]s, resolves possible type conflicts by up-casting "lower"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
index 7a27fba1780b9..3cf70db6b7b09 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
@@ -66,8 +66,8 @@ private[sql] object JDBCRDD extends Logging {
       case java.sql.Types.DATALINK      => null
       case java.sql.Types.DATE          => DateType
       case java.sql.Types.DECIMAL
-        if precision != 0 || scale != 0 => DecimalType(precision, scale)
-      case java.sql.Types.DECIMAL       => DecimalType.Unlimited
+        if precision != 0 || scale != 0 => DecimalType.bounded(precision, scale)
+      case java.sql.Types.DECIMAL       => DecimalType.SYSTEM_DEFAULT
       case java.sql.Types.DISTINCT      => null
       case java.sql.Types.DOUBLE        => DoubleType
       case java.sql.Types.FLOAT         => FloatType
@@ -80,8 +80,8 @@ private[sql] object JDBCRDD extends Logging {
       case java.sql.Types.NCLOB         => StringType
       case java.sql.Types.NULL          => null
       case java.sql.Types.NUMERIC
-        if precision != 0 || scale != 0 => DecimalType(precision, scale)
-      case java.sql.Types.NUMERIC       => DecimalType.Unlimited
+        if precision != 0 || scale != 0 => DecimalType.bounded(precision, scale)
+      case java.sql.Types.NUMERIC       => DecimalType.SYSTEM_DEFAULT
       case java.sql.Types.NVARCHAR      => StringType
       case java.sql.Types.OTHER         => null
       case java.sql.Types.REAL          => DoubleType
@@ -314,7 +314,7 @@ private[sql] class JDBCRDD(
   abstract class JDBCConversion
   case object BooleanConversion extends JDBCConversion
   case object DateConversion extends JDBCConversion
-  case class  DecimalConversion(precisionInfo: Option[(Int, Int)]) extends JDBCConversion
+  case class  DecimalConversion(precision: Int, scale: Int) extends JDBCConversion
   case object DoubleConversion extends JDBCConversion
   case object FloatConversion extends JDBCConversion
   case object IntegerConversion extends JDBCConversion
@@ -331,8 +331,7 @@ private[sql] class JDBCRDD(
     schema.fields.map(sf => sf.dataType match {
       case BooleanType => BooleanConversion
       case DateType => DateConversion
-      case DecimalType.Unlimited => DecimalConversion(None)
-      case DecimalType.Fixed(d) => DecimalConversion(Some(d))
+      case DecimalType.Fixed(p, s) => DecimalConversion(p, s)
       case DoubleType => DoubleConversion
       case FloatType => FloatConversion
       case IntegerType => IntegerConversion
@@ -399,20 +398,13 @@ private[sql] class JDBCRDD(
             // DecimalType(12, 2). Thus, after saving the dataframe into parquet file and then
             // retrieve it, you will get wrong result 199.99.
             // So it is needed to set precision and scale for Decimal based on JDBC metadata.
-            case DecimalConversion(Some((p, s))) =>
+            case DecimalConversion(p, s) =>
               val decimalVal = rs.getBigDecimal(pos)
               if (decimalVal == null) {
                 mutableRow.update(i, null)
               } else {
                 mutableRow.update(i, Decimal(decimalVal, p, s))
               }
-            case DecimalConversion(None) =>
-              val decimalVal = rs.getBigDecimal(pos)
-              if (decimalVal == null) {
-                mutableRow.update(i, null)
-              } else {
-                mutableRow.update(i, Decimal(decimalVal))
-              }
             case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos))
             case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos))
             case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
index f7ea852fe7f58..035e0510080ff 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
@@ -89,8 +89,7 @@ package object jdbc {
                   case BinaryType => stmt.setBytes(i + 1, row.getAs[Array[Byte]](i))
                   case TimestampType => stmt.setTimestamp(i + 1, row.getAs[java.sql.Timestamp](i))
                   case DateType => stmt.setDate(i + 1, row.getAs[java.sql.Date](i))
-                  case DecimalType.Unlimited => stmt.setBigDecimal(i + 1,
-                      row.getAs[java.math.BigDecimal](i))
+                  case t: DecimalType => stmt.setBigDecimal(i + 1, row.getDecimal(i))
                   case _ => throw new IllegalArgumentException(
                       s"Can't translate non-null value for field $i")
                 }
@@ -145,7 +144,7 @@ package object jdbc {
             case BinaryType => "BLOB"
             case TimestampType => "TIMESTAMP"
             case DateType => "DATE"
-            case DecimalType.Unlimited => "DECIMAL(40,20)"
+            case t: DecimalType => s"DECIMAL(${t.precision}},${t.scale}})"
             case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
           })
         val nullable = if (field.nullable) "" else "NOT NULL"
@@ -177,7 +176,7 @@ package object jdbc {
             case BinaryType => java.sql.Types.BLOB
             case TimestampType => java.sql.Types.TIMESTAMP
             case DateType => java.sql.Types.DATE
-            case DecimalType.Unlimited => java.sql.Types.DECIMAL
+            case t: DecimalType => java.sql.Types.DECIMAL
             case _ => throw new IllegalArgumentException(
               s"Can't translate null value for field $field")
           })
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
index afe2c6c11ac69..0eb3b04007f8d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
@@ -113,7 +113,7 @@ private[sql] object InferSchema {
           case INT | LONG => LongType
           // Since we do not have a data type backed by BigInteger,
           // when we see a Java BigInteger, we use DecimalType.
-          case BIG_INTEGER | BIG_DECIMAL => DecimalType.Unlimited
+          case BIG_INTEGER | BIG_DECIMAL => DecimalType.SYSTEM_DEFAULT
           case FLOAT | DOUBLE => DoubleType
         }
 
@@ -168,8 +168,13 @@ private[sql] object InferSchema {
     HiveTypeCoercion.findTightestCommonTypeOfTwo(t1, t2).getOrElse {
       // t1 or t2 is a StructType, ArrayType, or an unexpected type.
       (t1, t2) match {
-        case (other: DataType, NullType) => other
-        case (NullType, other: DataType) => other
+        // Double support larger range than fixed decimal, DecimalType.Maximum should be enough
+        // in most case, also have better precision.
+        case (DoubleType, t: DecimalType) =>
+          if (t == DecimalType.SYSTEM_DEFAULT) t else DoubleType
+        case (t: DecimalType, DoubleType) =>
+          if (t == DecimalType.SYSTEM_DEFAULT) t else DoubleType
+
         case (StructType(fields1), StructType(fields2)) =>
           val newFields = (fields1 ++ fields2).groupBy(field => field.name).map {
             case (name, fieldTypes) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index 1ea6926af6d5b..1d3a0d15d336e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -439,10 +439,6 @@ private[parquet] class CatalystSchemaConverter(
           .length(minBytesForPrecision(precision))
           .named(field.name)
 
-      case dec @ DecimalType.Unlimited if followParquetFormatSpec =>
-        throw new AnalysisException(
-          s"Data type $dec is not supported. Decimal precision and scale must be specified.")
-
       // ===================================================
       // ArrayType and MapType (for Spark versions <= 1.4.x)
       // ===================================================
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index e8851ddb68026..d1040bf5562a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -261,10 +261,10 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
         case BinaryType => writer.addBinary(
           Binary.fromByteArray(value.asInstanceOf[Array[Byte]]))
         case d: DecimalType =>
-          if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) {
+          if (d.precision > 18) {
             sys.error(s"Unsupported datatype $d, cannot write to consumer")
           }
-          writeDecimal(value.asInstanceOf[Decimal], d.precisionInfo.get.precision)
+          writeDecimal(value.asInstanceOf[Decimal], d.precision)
         case _ => sys.error(s"Do not know how to writer $schema to consumer")
       }
     }
@@ -415,10 +415,10 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
       case BinaryType => writer.addBinary(
         Binary.fromByteArray(record(index).asInstanceOf[Array[Byte]]))
       case d: DecimalType =>
-        if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) {
+        if (d.precision > 18) {
           sys.error(s"Unsupported datatype $d, cannot write to consumer")
         }
-        writeDecimal(record(index).asInstanceOf[Decimal], d.precisionInfo.get.precision)
+        writeDecimal(record(index).asInstanceOf[Decimal], d.precision)
       case _ => sys.error(s"Unsupported datatype $ctype, cannot write to consumer")
     }
   }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index fcb8f5499cf84..cb84e78d628ca 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -22,7 +22,6 @@
 import java.util.Arrays;
 import java.util.List;
 
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -31,8 +30,14 @@
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
-import org.apache.spark.sql.*;
-import org.apache.spark.sql.types.*;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.test.TestSQLContext$;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
 
 // The test suite itself is Serializable so that anonymous Function implementations can be
 // serialized, as an alternative to converting these anonymous classes to static inner classes;
@@ -159,7 +164,8 @@ public void applySchemaToJSON() {
         "\"bigInteger\":92233720368547758069, \"double\":1.7976931348623157E305, " +
         "\"boolean\":false, \"null\":null}"));
     List<StructField> fields = new ArrayList<StructField>(7);
-    fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(), true));
+    fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(38, 18),
+      true));
     fields.add(DataTypes.createStructField("boolean", DataTypes.BooleanType, true));
     fields.add(DataTypes.createStructField("double", DataTypes.DoubleType, true));
     fields.add(DataTypes.createStructField("integer", DataTypes.LongType, true));
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 01bc23277fa88..037e2048a8631 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -148,7 +148,7 @@ class InMemoryColumnarQuerySuite extends QueryTest {
     val dataTypes =
       Seq(StringType, BinaryType, NullType, BooleanType,
         ByteType, ShortType, IntegerType, LongType,
-        FloatType, DoubleType, DecimalType.Unlimited, DecimalType(6, 5),
+        FloatType, DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType(6, 5),
         DateType, TimestampType,
         ArrayType(IntegerType), MapType(StringType, LongType), struct)
     val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 3d71deb13e884..845ce669f0b33 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -109,7 +109,7 @@ class PlannerSuite extends SparkFunSuite {
       FloatType ::
       DoubleType ::
       DecimalType(10, 5) ::
-      DecimalType.Unlimited ::
+      DecimalType.SYSTEM_DEFAULT ::
       DateType ::
       TimestampType ::
       StringType ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
index 4a53fadd7e099..54f82f89ed18a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
@@ -54,7 +54,7 @@ class SparkSqlSerializer2DataTypeSuite extends SparkFunSuite {
   checkSupported(StringType, isSupported = true)
   checkSupported(BinaryType, isSupported = true)
   checkSupported(DecimalType(10, 5), isSupported = true)
-  checkSupported(DecimalType.Unlimited, isSupported = true)
+  checkSupported(DecimalType.SYSTEM_DEFAULT, isSupported = true)
 
   // If NullType is the only data type in the schema, we do not support it.
   checkSupported(NullType, isSupported = false)
@@ -86,7 +86,7 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll
     val supportedTypes =
       Seq(StringType, BinaryType, NullType, BooleanType,
         ByteType, ShortType, IntegerType, LongType,
-        FloatType, DoubleType, DecimalType.Unlimited, DecimalType(6, 5),
+        FloatType, DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType(6, 5),
         DateType, TimestampType)
 
     val fields = supportedTypes.zipWithIndex.map { case (dataType, index) =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 0f82f13088d39..42f2449afb0f9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -134,7 +134,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
       """.stripMargin.replaceAll("\n", " "))
 
 
-    conn.prepareStatement("create table test.flttypes (a DOUBLE, b REAL, c DECIMAL(40, 20))"
+    conn.prepareStatement("create table test.flttypes (a DOUBLE, b REAL, c DECIMAL(38, 18))"
         ).executeUpdate()
     conn.prepareStatement("insert into test.flttypes values ("
       + "1.0000000000000002220446049250313080847263336181640625, "
@@ -152,7 +152,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
       s"""
         |create table test.nulltypes (a INT, b BOOLEAN, c TINYINT, d BINARY(20), e VARCHAR(20),
         |f VARCHAR_IGNORECASE(20), g CHAR(20), h BLOB, i CLOB, j TIME, k DATE, l TIMESTAMP,
-        |m DOUBLE, n REAL, o DECIMAL(40, 20))
+        |m DOUBLE, n REAL, o DECIMAL(38, 18))
       """.stripMargin.replaceAll("\n", " ")).executeUpdate()
     conn.prepareStatement("insert into test.nulltypes values ("
       + "null, null, null, null, null, null, null, null, null, "
@@ -357,14 +357,14 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
 
   test("H2 floating-point types") {
     val rows = sql("SELECT * FROM flttypes").collect()
-    assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==.
-    assert(rows(0).getDouble(1) === 1.00000011920928955) // Yes, I meant ==.
-    assert(rows(0).getAs[BigDecimal](2)
-      .equals(new BigDecimal("123456789012345.54321543215432100000")))
-    assert(rows(0).schema.fields(2).dataType === DecimalType(40, 20))
-    val compareDecimal = sql("SELECT C FROM flttypes where C > C - 1").collect()
-    assert(compareDecimal(0).getAs[BigDecimal](0)
-      .equals(new BigDecimal("123456789012345.54321543215432100000")))
+    assert(rows(0).getDouble(0) === 1.00000000000000022)
+    assert(rows(0).getDouble(1) === 1.00000011920928955)
+    assert(rows(0).getAs[BigDecimal](2) ===
+      new BigDecimal("123456789012345.543215432154321000"))
+    assert(rows(0).schema.fields(2).dataType === DecimalType(38, 18))
+    val result = sql("SELECT C FROM flttypes where C > C - 1").collect()
+    assert(result(0).getAs[BigDecimal](0) ===
+      new BigDecimal("123456789012345.543215432154321000"))
   }
 
   test("SQL query as table name") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 1d04513a44672..3ac312d6f4c50 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -63,18 +63,18 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkTypePromotion(intNumber.toLong, enforceCorrectType(intNumber, LongType))
     checkTypePromotion(intNumber.toDouble, enforceCorrectType(intNumber, DoubleType))
     checkTypePromotion(
-      Decimal(intNumber), enforceCorrectType(intNumber, DecimalType.Unlimited))
+      Decimal(intNumber), enforceCorrectType(intNumber, DecimalType.SYSTEM_DEFAULT))
 
     val longNumber: Long = 9223372036854775807L
     checkTypePromotion(longNumber, enforceCorrectType(longNumber, LongType))
     checkTypePromotion(longNumber.toDouble, enforceCorrectType(longNumber, DoubleType))
     checkTypePromotion(
-      Decimal(longNumber), enforceCorrectType(longNumber, DecimalType.Unlimited))
+      Decimal(longNumber), enforceCorrectType(longNumber, DecimalType.SYSTEM_DEFAULT))
 
     val doubleNumber: Double = 1.7976931348623157E308d
     checkTypePromotion(doubleNumber.toDouble, enforceCorrectType(doubleNumber, DoubleType))
     checkTypePromotion(
-      Decimal(doubleNumber), enforceCorrectType(doubleNumber, DecimalType.Unlimited))
+      Decimal(doubleNumber), enforceCorrectType(doubleNumber, DecimalType.SYSTEM_DEFAULT))
 
     checkTypePromotion(DateTimeUtils.fromJavaTimestamp(new Timestamp(intNumber)),
         enforceCorrectType(intNumber, TimestampType))
@@ -115,7 +115,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkDataType(NullType, IntegerType, IntegerType)
     checkDataType(NullType, LongType, LongType)
     checkDataType(NullType, DoubleType, DoubleType)
-    checkDataType(NullType, DecimalType.Unlimited, DecimalType.Unlimited)
+    checkDataType(NullType, DecimalType.SYSTEM_DEFAULT, DecimalType.SYSTEM_DEFAULT)
     checkDataType(NullType, StringType, StringType)
     checkDataType(NullType, ArrayType(IntegerType), ArrayType(IntegerType))
     checkDataType(NullType, StructType(Nil), StructType(Nil))
@@ -126,7 +126,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkDataType(BooleanType, IntegerType, StringType)
     checkDataType(BooleanType, LongType, StringType)
     checkDataType(BooleanType, DoubleType, StringType)
-    checkDataType(BooleanType, DecimalType.Unlimited, StringType)
+    checkDataType(BooleanType, DecimalType.SYSTEM_DEFAULT, StringType)
     checkDataType(BooleanType, StringType, StringType)
     checkDataType(BooleanType, ArrayType(IntegerType), StringType)
     checkDataType(BooleanType, StructType(Nil), StringType)
@@ -135,7 +135,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkDataType(IntegerType, IntegerType, IntegerType)
     checkDataType(IntegerType, LongType, LongType)
     checkDataType(IntegerType, DoubleType, DoubleType)
-    checkDataType(IntegerType, DecimalType.Unlimited, DecimalType.Unlimited)
+    checkDataType(IntegerType, DecimalType.SYSTEM_DEFAULT, DecimalType.SYSTEM_DEFAULT)
     checkDataType(IntegerType, StringType, StringType)
     checkDataType(IntegerType, ArrayType(IntegerType), StringType)
     checkDataType(IntegerType, StructType(Nil), StringType)
@@ -143,23 +143,24 @@ class JsonSuite extends QueryTest with TestJsonData {
     // LongType
     checkDataType(LongType, LongType, LongType)
     checkDataType(LongType, DoubleType, DoubleType)
-    checkDataType(LongType, DecimalType.Unlimited, DecimalType.Unlimited)
+    checkDataType(LongType, DecimalType.SYSTEM_DEFAULT, DecimalType.SYSTEM_DEFAULT)
     checkDataType(LongType, StringType, StringType)
     checkDataType(LongType, ArrayType(IntegerType), StringType)
     checkDataType(LongType, StructType(Nil), StringType)
 
     // DoubleType
     checkDataType(DoubleType, DoubleType, DoubleType)
-    checkDataType(DoubleType, DecimalType.Unlimited, DecimalType.Unlimited)
+    checkDataType(DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType.SYSTEM_DEFAULT)
     checkDataType(DoubleType, StringType, StringType)
     checkDataType(DoubleType, ArrayType(IntegerType), StringType)
     checkDataType(DoubleType, StructType(Nil), StringType)
 
-    // DoubleType
-    checkDataType(DecimalType.Unlimited, DecimalType.Unlimited, DecimalType.Unlimited)
-    checkDataType(DecimalType.Unlimited, StringType, StringType)
-    checkDataType(DecimalType.Unlimited, ArrayType(IntegerType), StringType)
-    checkDataType(DecimalType.Unlimited, StructType(Nil), StringType)
+    // DecimalType
+    checkDataType(DecimalType.SYSTEM_DEFAULT, DecimalType.SYSTEM_DEFAULT,
+      DecimalType.SYSTEM_DEFAULT)
+    checkDataType(DecimalType.SYSTEM_DEFAULT, StringType, StringType)
+    checkDataType(DecimalType.SYSTEM_DEFAULT, ArrayType(IntegerType), StringType)
+    checkDataType(DecimalType.SYSTEM_DEFAULT, StructType(Nil), StringType)
 
     // StringType
     checkDataType(StringType, StringType, StringType)
@@ -213,7 +214,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkDataType(
       StructType(
         StructField("f1", IntegerType, true) :: Nil),
-      DecimalType.Unlimited,
+      DecimalType.SYSTEM_DEFAULT,
       StringType)
   }
 
@@ -240,7 +241,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val jsonDF = ctx.read.json(primitiveFieldAndType)
 
     val expectedSchema = StructType(
-      StructField("bigInteger", DecimalType.Unlimited, true) ::
+      StructField("bigInteger", DecimalType.SYSTEM_DEFAULT, true) ::
       StructField("boolean", BooleanType, true) ::
       StructField("double", DoubleType, true) ::
       StructField("integer", LongType, true) ::
@@ -270,7 +271,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val expectedSchema = StructType(
       StructField("arrayOfArray1", ArrayType(ArrayType(StringType, true), true), true) ::
       StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType, true), true), true) ::
-      StructField("arrayOfBigInteger", ArrayType(DecimalType.Unlimited, true), true) ::
+      StructField("arrayOfBigInteger", ArrayType(DecimalType.SYSTEM_DEFAULT, true), true) ::
       StructField("arrayOfBoolean", ArrayType(BooleanType, true), true) ::
       StructField("arrayOfDouble", ArrayType(DoubleType, true), true) ::
       StructField("arrayOfInteger", ArrayType(LongType, true), true) ::
@@ -284,7 +285,7 @@ class JsonSuite extends QueryTest with TestJsonData {
           StructField("field3", StringType, true) :: Nil), true), true) ::
       StructField("struct", StructType(
         StructField("field1", BooleanType, true) ::
-        StructField("field2", DecimalType.Unlimited, true) :: Nil), true) ::
+        StructField("field2", DecimalType.SYSTEM_DEFAULT, true) :: Nil), true) ::
       StructField("structWithArrayFields", StructType(
         StructField("field1", ArrayType(LongType, true), true) ::
         StructField("field2", ArrayType(StringType, true), true) :: Nil), true) :: Nil)
@@ -385,7 +386,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val expectedSchema = StructType(
       StructField("num_bool", StringType, true) ::
       StructField("num_num_1", LongType, true) ::
-      StructField("num_num_2", DecimalType.Unlimited, true) ::
+      StructField("num_num_2", DecimalType.SYSTEM_DEFAULT, true) ::
       StructField("num_num_3", DoubleType, true) ::
       StructField("num_str", StringType, true) ::
       StructField("str_bool", StringType, true) :: Nil)
@@ -421,11 +422,11 @@ class JsonSuite extends QueryTest with TestJsonData {
       Row(-89) :: Row(21474836370L) :: Row(21474836470L) :: Nil
     )
 
-    // Widening to DecimalType
+    // Widening to DoubleType
     checkAnswer(
-      sql("select num_num_2 + 1.2 from jsonTable where num_num_2 > 1.1"),
-      Row(new java.math.BigDecimal("21474836472.1")) ::
-        Row(new java.math.BigDecimal("92233720368547758071.2")) :: Nil
+      sql("select num_num_2 + 1.3 from jsonTable where num_num_2 > 1.1"),
+      Row(21474836472.2) ::
+        Row(92233720368547758071.3) :: Nil
     )
 
     // Widening to DoubleType
@@ -442,8 +443,8 @@ class JsonSuite extends QueryTest with TestJsonData {
 
     // Number and String conflict: resolve the type as number in this query.
     checkAnswer(
-      sql("select num_str + 1.2 from jsonTable where num_str > 92233720368547758060"),
-      Row(new java.math.BigDecimal("92233720368547758061.2").doubleValue)
+      sql("select num_str + 1.2 from jsonTable where num_str >= 92233720368547758060"),
+      Row(new java.math.BigDecimal("92233720368547758071.2").doubleValue)
     )
 
     // String and Boolean conflict: resolve the type as string.
@@ -489,9 +490,9 @@ class JsonSuite extends QueryTest with TestJsonData {
     // in the Project.
     checkAnswer(
       jsonDF.
-        where('num_str > BigDecimal("92233720368547758060")).
+        where('num_str >= BigDecimal("92233720368547758060")).
         select(('num_str + 1.2).as("num")),
-      Row(new java.math.BigDecimal("92233720368547758061.2"))
+      Row(new java.math.BigDecimal("92233720368547758071.2").doubleValue())
     )
 
     // The following test will fail. The type of num_str is StringType.
@@ -610,7 +611,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val jsonDF = ctx.read.json(path)
 
     val expectedSchema = StructType(
-      StructField("bigInteger", DecimalType.Unlimited, true) ::
+      StructField("bigInteger", DecimalType.SYSTEM_DEFAULT, true) ::
       StructField("boolean", BooleanType, true) ::
       StructField("double", DoubleType, true) ::
       StructField("integer", LongType, true) ::
@@ -668,7 +669,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
 
     val schema = StructType(
-      StructField("bigInteger", DecimalType.Unlimited, true) ::
+      StructField("bigInteger", DecimalType.SYSTEM_DEFAULT, true) ::
       StructField("boolean", BooleanType, true) ::
       StructField("double", DoubleType, true) ::
       StructField("integer", IntegerType, true) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
index 7b16eba00d6fb..3a5b860484e86 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
@@ -122,14 +122,6 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest {
         sqlContext.read.parquet(dir.getCanonicalPath).collect()
       }
     }
-
-    // Unlimited-length decimals are not yet supported
-    intercept[Throwable] {
-      withTempPath { dir =>
-        makeDecimalRDD(DecimalType.Unlimited).write.parquet(dir.getCanonicalPath)
-        sqlContext.read.parquet(dir.getCanonicalPath).collect()
-      }
-    }
   }
 
   test("date type") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
index 4f98776b91160..7f16b1125c7a5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
@@ -509,7 +509,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest {
         FloatType,
         DoubleType,
         DecimalType(10, 5),
-        DecimalType.Unlimited,
+        DecimalType.SYSTEM_DEFAULT,
         DateType,
         TimestampType,
         StringType)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
index 54e1efb6e36e7..da53ec16b5c41 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
@@ -44,7 +44,7 @@ case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlCo
       StructField("doubleType", DoubleType, nullable = false),
       StructField("bigintType", LongType, nullable = false),
       StructField("tinyintType", ByteType, nullable = false),
-      StructField("decimalType", DecimalType.Unlimited, nullable = false),
+      StructField("decimalType", DecimalType.USER_DEFAULT, nullable = false),
       StructField("fixedDecimalType", DecimalType(5, 1), nullable = false),
       StructField("binaryType", BinaryType, nullable = false),
       StructField("booleanType", BooleanType, nullable = false),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index 2c916f3322b6d..143aadc08b1c4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -202,7 +202,7 @@ class TableScanSuite extends DataSourceTest {
       StructField("longField_:,<>=+/~^", LongType, true) ::
       StructField("floatField", FloatType, true) ::
       StructField("doubleField", DoubleType, true) ::
-      StructField("decimalField1", DecimalType.Unlimited, true) ::
+      StructField("decimalField1", DecimalType.USER_DEFAULT, true) ::
       StructField("decimalField2", DecimalType(9, 2), true) ::
       StructField("dateField", DateType, true) ::
       StructField("timestampField", TimestampType, true) ::
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index a8f2ee37cb8ed..592cfa0ee8380 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -179,7 +179,7 @@ private[hive] trait HiveInspectors {
     // writable
     case c: Class[_] if c == classOf[hadoopIo.DoubleWritable] => DoubleType
     case c: Class[_] if c == classOf[hiveIo.DoubleWritable] => DoubleType
-    case c: Class[_] if c == classOf[hiveIo.HiveDecimalWritable] => DecimalType.Unlimited
+    case c: Class[_] if c == classOf[hiveIo.HiveDecimalWritable] => DecimalType.SYSTEM_DEFAULT
     case c: Class[_] if c == classOf[hiveIo.ByteWritable] => ByteType
     case c: Class[_] if c == classOf[hiveIo.ShortWritable] => ShortType
     case c: Class[_] if c == classOf[hiveIo.DateWritable] => DateType
@@ -195,8 +195,8 @@ private[hive] trait HiveInspectors {
     case c: Class[_] if c == classOf[java.lang.String] => StringType
     case c: Class[_] if c == classOf[java.sql.Date] => DateType
     case c: Class[_] if c == classOf[java.sql.Timestamp] => TimestampType
-    case c: Class[_] if c == classOf[HiveDecimal] => DecimalType.Unlimited
-    case c: Class[_] if c == classOf[java.math.BigDecimal] => DecimalType.Unlimited
+    case c: Class[_] if c == classOf[HiveDecimal] => DecimalType.SYSTEM_DEFAULT
+    case c: Class[_] if c == classOf[java.math.BigDecimal] => DecimalType.SYSTEM_DEFAULT
     case c: Class[_] if c == classOf[Array[Byte]] => BinaryType
     case c: Class[_] if c == classOf[java.lang.Short] => ShortType
     case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType
@@ -813,9 +813,6 @@ private[hive] trait HiveInspectors {
 
     private def decimalTypeInfo(decimalType: DecimalType): TypeInfo = decimalType match {
       case DecimalType.Fixed(precision, scale) => new DecimalTypeInfo(precision, scale)
-      case _ => new DecimalTypeInfo(
-        HiveShim.UNLIMITED_DECIMAL_PRECISION,
-        HiveShim.UNLIMITED_DECIMAL_SCALE)
     }
 
     def toTypeInfo: TypeInfo = dt match {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 8518e333e8058..620b8a44d8a9b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -377,7 +377,7 @@ private[hive] object HiveQl extends Logging {
       DecimalType(precision.getText.toInt, scale.getText.toInt)
     case Token("TOK_DECIMAL", precision :: Nil) =>
       DecimalType(precision.getText.toInt, 0)
-    case Token("TOK_DECIMAL", Nil) => DecimalType.Unlimited
+    case Token("TOK_DECIMAL", Nil) => DecimalType.USER_DEFAULT
     case Token("TOK_BIGINT", Nil) => LongType
     case Token("TOK_INT", Nil) => IntegerType
     case Token("TOK_TINYINT", Nil) => ByteType
@@ -1369,7 +1369,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     case Token("TOK_FUNCTION", Token("TOK_DECIMAL", precision :: Nil) :: arg :: Nil) =>
       Cast(nodeToExpr(arg), DecimalType(precision.getText.toInt, 0))
     case Token("TOK_FUNCTION", Token("TOK_DECIMAL", Nil) :: arg :: Nil) =>
-      Cast(nodeToExpr(arg), DecimalType.Unlimited)
+      Cast(nodeToExpr(arg), DecimalType.USER_DEFAULT)
     case Token("TOK_FUNCTION", Token("TOK_TIMESTAMP", Nil) :: arg :: Nil) =>
       Cast(nodeToExpr(arg), TimestampType)
     case Token("TOK_FUNCTION", Token("TOK_DATE", Nil) :: arg :: Nil) =>

From 52de3acca4ce8c36fd4c9ce162473a091701bbc7 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 23 Jul 2015 18:53:07 -0700
Subject: [PATCH 0570/1454] [SPARK-9122] [MLLIB] [PySpark] spark.mllib
 regression support batch predict

spark.mllib support batch predict for LinearRegressionModel, RidgeRegressionModel and LassoModel.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7614 from yanboliang/spark-9122 and squashes the following commits:

4e610c0 [Yanbo Liang] spark.mllib regression support batch predict
---
 python/pyspark/mllib/regression.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 8e90adee5f4c2..5b7afc15ddfba 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -97,9 +97,11 @@ class LinearRegressionModelBase(LinearModel):
 
     def predict(self, x):
         """
-        Predict the value of the dependent variable given a vector x
-        containing values for the independent variables.
+        Predict the value of the dependent variable given a vector or
+        an RDD of vectors containing values for the independent variables.
         """
+        if isinstance(x, RDD):
+            return x.map(self.predict)
         x = _convert_to_vector(x)
         return self.weights.dot(x) + self.intercept
 
@@ -124,6 +126,8 @@ class LinearRegressionModel(LinearRegressionModelBase):
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> abs(lrm.predict(sc.parallelize([[1.0]])).collect()[0] - 1) < 0.5
+    True
     >>> import os, tempfile
     >>> path = tempfile.mkdtemp()
     >>> lrm.save(sc, path)
@@ -267,6 +271,8 @@ class LassoModel(LinearRegressionModelBase):
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> abs(lrm.predict(sc.parallelize([[1.0]])).collect()[0] - 1) < 0.5
+    True
     >>> import os, tempfile
     >>> path = tempfile.mkdtemp()
     >>> lrm.save(sc, path)
@@ -382,6 +388,8 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> abs(lrm.predict(sc.parallelize([[1.0]])).collect()[0] - 1) < 0.5
+    True
     >>> import os, tempfile
     >>> path = tempfile.mkdtemp()
     >>> lrm.save(sc, path)

From d249636e59fabd8ca57a47dc2cbad9c4a4e7a750 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 23 Jul 2015 20:06:54 -0700
Subject: [PATCH 0571/1454] [SPARK-9216] [STREAMING] Define
 KinesisBackedBlockRDDs

For more information see master JIRA: https://issues.apache.org/jira/browse/SPARK-9215
Design Doc: https://docs.google.com/document/d/1k0dl270EnK7uExrsCE7jYw7PYx0YC935uBcxn3p0f58/edit

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7578 from tdas/kinesis-rdd and squashes the following commits:

543d208 [Tathagata Das] Fixed scala style
5082a30 [Tathagata Das] Fixed scala style
3f40c2d [Tathagata Das] Addressed comments
c4f25d2 [Tathagata Das] Addressed comment
d3d64d1 [Tathagata Das] Minor update
f6e35c8 [Tathagata Das] Added retry logic to make it more robust
8874b70 [Tathagata Das] Updated Kinesis RDD
575bdbc [Tathagata Das] Fix scala style issues
4a36096 [Tathagata Das] Add license
5da3995 [Tathagata Das] Changed KinesisSuiteHelper to KinesisFunSuite
528e206 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into kinesis-rdd
3ae0814 [Tathagata Das] Added KinesisBackedBlockRDD
---
 .../kinesis/KinesisBackedBlockRDD.scala       | 285 ++++++++++++++++++
 .../streaming/kinesis/KinesisTestUtils.scala  |   2 +-
 .../kinesis/KinesisBackedBlockRDDSuite.scala  | 246 +++++++++++++++
 .../streaming/kinesis/KinesisFunSuite.scala   |  13 +-
 .../kinesis/KinesisStreamSuite.scala          |   4 +-
 5 files changed, 545 insertions(+), 5 deletions(-)
 create mode 100644 extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
 create mode 100644 extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala

diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
new file mode 100644
index 0000000000000..8f144a4d974a8
--- /dev/null
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
@@ -0,0 +1,285 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kinesis
+
+import scala.collection.JavaConversions._
+import scala.util.control.NonFatal
+
+import com.amazonaws.auth.{AWSCredentials, DefaultAWSCredentialsProviderChain}
+import com.amazonaws.services.kinesis.AmazonKinesisClient
+import com.amazonaws.services.kinesis.model._
+
+import org.apache.spark._
+import org.apache.spark.rdd.{BlockRDD, BlockRDDPartition}
+import org.apache.spark.storage.BlockId
+import org.apache.spark.util.NextIterator
+
+
+/** Class representing a range of Kinesis sequence numbers. Both sequence numbers are inclusive. */
+private[kinesis]
+case class SequenceNumberRange(
+    streamName: String, shardId: String, fromSeqNumber: String, toSeqNumber: String)
+
+/** Class representing an array of Kinesis sequence number ranges */
+private[kinesis]
+case class SequenceNumberRanges(ranges: Array[SequenceNumberRange]) {
+  def isEmpty(): Boolean = ranges.isEmpty
+  def nonEmpty(): Boolean = ranges.nonEmpty
+  override def toString(): String = ranges.mkString("SequenceNumberRanges(", ", ", ")")
+}
+
+private[kinesis]
+object SequenceNumberRanges {
+  def apply(range: SequenceNumberRange): SequenceNumberRanges = {
+    new SequenceNumberRanges(Array(range))
+  }
+}
+
+
+/** Partition storing the information of the ranges of Kinesis sequence numbers to read */
+private[kinesis]
+class KinesisBackedBlockRDDPartition(
+    idx: Int,
+    blockId: BlockId,
+    val isBlockIdValid: Boolean,
+    val seqNumberRanges: SequenceNumberRanges
+  ) extends BlockRDDPartition(blockId, idx)
+
+/**
+ * A BlockRDD where the block data is backed by Kinesis, which can accessed using the
+ * sequence numbers of the corresponding blocks.
+ */
+private[kinesis]
+class KinesisBackedBlockRDD(
+    sc: SparkContext,
+    regionId: String,
+    endpointUrl: String,
+    @transient blockIds: Array[BlockId],
+    @transient arrayOfseqNumberRanges: Array[SequenceNumberRanges],
+    @transient isBlockIdValid: Array[Boolean] = Array.empty,
+    retryTimeoutMs: Int = 10000,
+    awsCredentialsOption: Option[SerializableAWSCredentials] = None
+  ) extends BlockRDD[Array[Byte]](sc, blockIds) {
+
+  require(blockIds.length == arrayOfseqNumberRanges.length,
+    "Number of blockIds is not equal to the number of sequence number ranges")
+
+  override def isValid(): Boolean = true
+
+  override def getPartitions: Array[Partition] = {
+    Array.tabulate(blockIds.length) { i =>
+      val isValid = if (isBlockIdValid.length == 0) true else isBlockIdValid(i)
+      new KinesisBackedBlockRDDPartition(i, blockIds(i), isValid, arrayOfseqNumberRanges(i))
+    }
+  }
+
+  override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = {
+    val blockManager = SparkEnv.get.blockManager
+    val partition = split.asInstanceOf[KinesisBackedBlockRDDPartition]
+    val blockId = partition.blockId
+
+    def getBlockFromBlockManager(): Option[Iterator[Array[Byte]]] = {
+      logDebug(s"Read partition data of $this from block manager, block $blockId")
+      blockManager.get(blockId).map(_.data.asInstanceOf[Iterator[Array[Byte]]])
+    }
+
+    def getBlockFromKinesis(): Iterator[Array[Byte]] = {
+      val credenentials = awsCredentialsOption.getOrElse {
+        new DefaultAWSCredentialsProviderChain().getCredentials()
+      }
+      partition.seqNumberRanges.ranges.iterator.flatMap { range =>
+        new KinesisSequenceRangeIterator(
+          credenentials, endpointUrl, regionId, range, retryTimeoutMs)
+      }
+    }
+    if (partition.isBlockIdValid) {
+      getBlockFromBlockManager().getOrElse { getBlockFromKinesis() }
+    } else {
+      getBlockFromKinesis()
+    }
+  }
+}
+
+
+/**
+ * An iterator that return the Kinesis data based on the given range of sequence numbers.
+ * Internally, it repeatedly fetches sets of records starting from the fromSequenceNumber,
+ * until the endSequenceNumber is reached.
+ */
+private[kinesis]
+class KinesisSequenceRangeIterator(
+    credentials: AWSCredentials,
+    endpointUrl: String,
+    regionId: String,
+    range: SequenceNumberRange,
+    retryTimeoutMs: Int
+  ) extends NextIterator[Array[Byte]] with Logging {
+
+  private val client = new AmazonKinesisClient(credentials)
+  private val streamName = range.streamName
+  private val shardId = range.shardId
+
+  private var toSeqNumberReceived = false
+  private var lastSeqNumber: String = null
+  private var internalIterator: Iterator[Record] = null
+
+  client.setEndpoint(endpointUrl, "kinesis", regionId)
+
+  override protected def getNext(): Array[Byte] = {
+    var nextBytes: Array[Byte] = null
+    if (toSeqNumberReceived) {
+      finished = true
+    } else {
+
+      if (internalIterator == null) {
+
+        // If the internal iterator has not been initialized,
+        // then fetch records from starting sequence number
+        internalIterator = getRecords(ShardIteratorType.AT_SEQUENCE_NUMBER, range.fromSeqNumber)
+      } else if (!internalIterator.hasNext) {
+
+        // If the internal iterator does not have any more records,
+        // then fetch more records after the last consumed sequence number
+        internalIterator = getRecords(ShardIteratorType.AFTER_SEQUENCE_NUMBER, lastSeqNumber)
+      }
+
+      if (!internalIterator.hasNext) {
+
+        // If the internal iterator still does not have any data, then throw exception
+        // and terminate this iterator
+        finished = true
+        throw new SparkException(
+          s"Could not read until the end sequence number of the range: $range")
+      } else {
+
+        // Get the record, copy the data into a byte array and remember its sequence number
+        val nextRecord: Record = internalIterator.next()
+        val byteBuffer = nextRecord.getData()
+        nextBytes = new Array[Byte](byteBuffer.remaining())
+        byteBuffer.get(nextBytes)
+        lastSeqNumber = nextRecord.getSequenceNumber()
+
+        // If the this record's sequence number matches the stopping sequence number, then make sure
+        // the iterator is marked finished next time getNext() is called
+        if (nextRecord.getSequenceNumber == range.toSeqNumber) {
+          toSeqNumberReceived = true
+        }
+      }
+
+    }
+    nextBytes
+  }
+
+  override protected def close(): Unit = {
+    client.shutdown()
+  }
+
+  /**
+   * Get records starting from or after the given sequence number.
+   */
+  private def getRecords(iteratorType: ShardIteratorType, seqNum: String): Iterator[Record] = {
+    val shardIterator = getKinesisIterator(iteratorType, seqNum)
+    val result = getRecordsAndNextKinesisIterator(shardIterator)
+    result._1
+  }
+
+  /**
+   * Get the records starting from using a Kinesis shard iterator (which is a progress handle
+   * to get records from Kinesis), and get the next shard iterator for next consumption.
+   */
+  private def getRecordsAndNextKinesisIterator(
+      shardIterator: String): (Iterator[Record], String) = {
+    val getRecordsRequest = new GetRecordsRequest
+    getRecordsRequest.setRequestCredentials(credentials)
+    getRecordsRequest.setShardIterator(shardIterator)
+    val getRecordsResult = retryOrTimeout[GetRecordsResult](
+      s"getting records using shard iterator") {
+        client.getRecords(getRecordsRequest)
+      }
+    (getRecordsResult.getRecords.iterator(), getRecordsResult.getNextShardIterator)
+  }
+
+  /**
+   * Get the Kinesis shard iterator for getting records starting from or after the given
+   * sequence number.
+   */
+  private def getKinesisIterator(
+      iteratorType: ShardIteratorType,
+      sequenceNumber: String): String = {
+    val getShardIteratorRequest = new GetShardIteratorRequest
+    getShardIteratorRequest.setRequestCredentials(credentials)
+    getShardIteratorRequest.setStreamName(streamName)
+    getShardIteratorRequest.setShardId(shardId)
+    getShardIteratorRequest.setShardIteratorType(iteratorType.toString)
+    getShardIteratorRequest.setStartingSequenceNumber(sequenceNumber)
+    val getShardIteratorResult = retryOrTimeout[GetShardIteratorResult](
+        s"getting shard iterator from sequence number $sequenceNumber") {
+          client.getShardIterator(getShardIteratorRequest)
+        }
+    getShardIteratorResult.getShardIterator
+  }
+
+  /** Helper method to retry Kinesis API request with exponential backoff and timeouts */
+  private def retryOrTimeout[T](message: String)(body: => T): T = {
+    import KinesisSequenceRangeIterator._
+
+    var startTimeMs = System.currentTimeMillis()
+    var retryCount = 0
+    var waitTimeMs = MIN_RETRY_WAIT_TIME_MS
+    var result: Option[T] = None
+    var lastError: Throwable = null
+
+    def isTimedOut = (System.currentTimeMillis() - startTimeMs) >= retryTimeoutMs
+    def isMaxRetryDone = retryCount >= MAX_RETRIES
+
+    while (result.isEmpty && !isTimedOut && !isMaxRetryDone) {
+      if (retryCount > 0) {  // wait only if this is a retry
+        Thread.sleep(waitTimeMs)
+        waitTimeMs *= 2  // if you have waited, then double wait time for next round
+      }
+      try {
+        result = Some(body)
+      } catch {
+        case NonFatal(t) =>
+          lastError = t
+           t match {
+             case ptee: ProvisionedThroughputExceededException =>
+               logWarning(s"Error while $message [attempt = ${retryCount + 1}]", ptee)
+             case e: Throwable =>
+               throw new SparkException(s"Error while $message", e)
+           }
+      }
+      retryCount += 1
+    }
+    result.getOrElse {
+      if (isTimedOut) {
+        throw new SparkException(
+          s"Timed out after $retryTimeoutMs ms while $message, last exception: ", lastError)
+      } else {
+        throw new SparkException(
+          s"Gave up after $retryCount retries while $message, last exception: ", lastError)
+      }
+    }
+  }
+}
+
+private[streaming]
+object KinesisSequenceRangeIterator {
+  val MAX_RETRIES = 3
+  val MIN_RETRY_WAIT_TIME_MS = 100
+}
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index f6bf552e6bb8e..0ff1b7ed0fd90 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -177,7 +177,7 @@ private class KinesisTestUtils(
 
 private[kinesis] object KinesisTestUtils {
 
-  val envVarName = "RUN_KINESIS_TESTS"
+  val envVarName = "ENABLE_KINESIS_TESTS"
 
   val shouldRunTests = sys.env.get(envVarName) == Some("1")
 
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
new file mode 100644
index 0000000000000..b2e2a4246dbd5
--- /dev/null
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
@@ -0,0 +1,246 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kinesis
+
+import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
+
+import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBlockId}
+import org.apache.spark.{SparkConf, SparkContext, SparkException, SparkFunSuite}
+
+class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll {
+
+  private val regionId = "us-east-1"
+  private val endpointUrl = "https://kinesis.us-east-1.amazonaws.com"
+  private val testData = 1 to 8
+
+  private var testUtils: KinesisTestUtils = null
+  private var shardIds: Seq[String] = null
+  private var shardIdToData: Map[String, Seq[Int]] = null
+  private var shardIdToSeqNumbers: Map[String, Seq[String]] = null
+  private var shardIdToDataAndSeqNumbers: Map[String, Seq[(Int, String)]] = null
+  private var shardIdToRange: Map[String, SequenceNumberRange] = null
+  private var allRanges: Seq[SequenceNumberRange] = null
+
+  private var sc: SparkContext = null
+  private var blockManager: BlockManager = null
+
+
+  override def beforeAll(): Unit = {
+    runIfTestsEnabled("Prepare KinesisTestUtils") {
+      testUtils = new KinesisTestUtils(endpointUrl)
+      testUtils.createStream()
+
+      shardIdToDataAndSeqNumbers = testUtils.pushData(testData)
+      require(shardIdToDataAndSeqNumbers.size > 1, "Need data to be sent to multiple shards")
+
+      shardIds = shardIdToDataAndSeqNumbers.keySet.toSeq
+      shardIdToData = shardIdToDataAndSeqNumbers.mapValues { _.map { _._1 }}
+      shardIdToSeqNumbers = shardIdToDataAndSeqNumbers.mapValues { _.map { _._2 }}
+      shardIdToRange = shardIdToSeqNumbers.map { case (shardId, seqNumbers) =>
+        val seqNumRange = SequenceNumberRange(
+          testUtils.streamName, shardId, seqNumbers.head, seqNumbers.last)
+        (shardId, seqNumRange)
+      }
+      allRanges = shardIdToRange.values.toSeq
+
+      val conf = new SparkConf().setMaster("local[4]").setAppName("KinesisBackedBlockRDDSuite")
+      sc = new SparkContext(conf)
+      blockManager = sc.env.blockManager
+    }
+  }
+
+  override def afterAll(): Unit = {
+    if (sc != null) {
+      sc.stop()
+    }
+  }
+
+  testIfEnabled("Basic reading from Kinesis") {
+    // Verify all data using multiple ranges in a single RDD partition
+    val receivedData1 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+      fakeBlockIds(1),
+      Array(SequenceNumberRanges(allRanges.toArray))
+    ).map { bytes => new String(bytes).toInt }.collect()
+    assert(receivedData1.toSet === testData.toSet)
+
+    // Verify all data using one range in each of the multiple RDD partitions
+    val receivedData2 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+      fakeBlockIds(allRanges.size),
+      allRanges.map { range => SequenceNumberRanges(Array(range)) }.toArray
+    ).map { bytes => new String(bytes).toInt }.collect()
+    assert(receivedData2.toSet === testData.toSet)
+
+    // Verify ordering within each partition
+    val receivedData3 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+      fakeBlockIds(allRanges.size),
+      allRanges.map { range => SequenceNumberRanges(Array(range)) }.toArray
+    ).map { bytes => new String(bytes).toInt }.collectPartitions()
+    assert(receivedData3.length === allRanges.size)
+    for (i <- 0 until allRanges.size) {
+      assert(receivedData3(i).toSeq === shardIdToData(allRanges(i).shardId))
+    }
+  }
+
+  testIfEnabled("Read data available in both block manager and Kinesis") {
+    testRDD(numPartitions = 2, numPartitionsInBM = 2, numPartitionsInKinesis = 2)
+  }
+
+  testIfEnabled("Read data available only in block manager, not in Kinesis") {
+    testRDD(numPartitions = 2, numPartitionsInBM = 2, numPartitionsInKinesis = 0)
+  }
+
+  testIfEnabled("Read data available only in Kinesis, not in block manager") {
+    testRDD(numPartitions = 2, numPartitionsInBM = 0, numPartitionsInKinesis = 2)
+  }
+
+  testIfEnabled("Read data available partially in block manager, rest in Kinesis") {
+    testRDD(numPartitions = 2, numPartitionsInBM = 1, numPartitionsInKinesis = 1)
+  }
+
+  testIfEnabled("Test isBlockValid skips block fetching from block manager") {
+    testRDD(numPartitions = 2, numPartitionsInBM = 2, numPartitionsInKinesis = 0,
+      testIsBlockValid = true)
+  }
+
+  testIfEnabled("Test whether RDD is valid after removing blocks from block anager") {
+    testRDD(numPartitions = 2, numPartitionsInBM = 2, numPartitionsInKinesis = 2,
+      testBlockRemove = true)
+  }
+
+  /**
+   * Test the WriteAheadLogBackedRDD, by writing some partitions of the data to block manager
+   * and the rest to a write ahead log, and then reading reading it all back using the RDD.
+   * It can also test if the partitions that were read from the log were again stored in
+   * block manager.
+   *
+   *
+   *
+   * @param numPartitions Number of partitions in RDD
+   * @param numPartitionsInBM Number of partitions to write to the BlockManager.
+   *                          Partitions 0 to (numPartitionsInBM-1) will be written to BlockManager
+   * @param numPartitionsInKinesis Number of partitions to write to the Kinesis.
+   *                           Partitions (numPartitions - 1 - numPartitionsInKinesis) to
+   *                           (numPartitions - 1) will be written to Kinesis
+   * @param testIsBlockValid Test whether setting isBlockValid to false skips block fetching
+   * @param testBlockRemove Test whether calling rdd.removeBlock() makes the RDD still usable with
+   *                        reads falling back to the WAL
+   * Example with numPartitions = 5, numPartitionsInBM = 3, and numPartitionsInWAL = 4
+   *
+   *   numPartitionsInBM = 3
+   *   |------------------|
+   *   |                  |
+   *    0       1       2       3       4
+   *           |                         |
+   *           |-------------------------|
+   *              numPartitionsInKinesis = 4
+   */
+  private def testRDD(
+      numPartitions: Int,
+      numPartitionsInBM: Int,
+      numPartitionsInKinesis: Int,
+      testIsBlockValid: Boolean = false,
+      testBlockRemove: Boolean = false
+    ): Unit = {
+    require(shardIds.size > 1, "Need at least 2 shards to test")
+    require(numPartitionsInBM <= shardIds.size ,
+      "Number of partitions in BlockManager cannot be more than the Kinesis test shards available")
+    require(numPartitionsInKinesis <= shardIds.size ,
+      "Number of partitions in Kinesis cannot be more than the Kinesis test shards available")
+    require(numPartitionsInBM <= numPartitions,
+      "Number of partitions in BlockManager cannot be more than that in RDD")
+    require(numPartitionsInKinesis <= numPartitions,
+      "Number of partitions in Kinesis cannot be more than that in RDD")
+
+    // Put necessary blocks in the block manager
+    val blockIds = fakeBlockIds(numPartitions)
+    blockIds.foreach(blockManager.removeBlock(_))
+    (0 until numPartitionsInBM).foreach { i =>
+      val blockData = shardIdToData(shardIds(i)).iterator.map { _.toString.getBytes() }
+      blockManager.putIterator(blockIds(i), blockData, StorageLevel.MEMORY_ONLY)
+    }
+
+    // Create the necessary ranges to use in the RDD
+    val fakeRanges = Array.fill(numPartitions - numPartitionsInKinesis)(
+      SequenceNumberRanges(SequenceNumberRange("fakeStream", "fakeShardId", "xxx", "yyy")))
+    val realRanges = Array.tabulate(numPartitionsInKinesis) { i =>
+      val range = shardIdToRange(shardIds(i + (numPartitions - numPartitionsInKinesis)))
+      SequenceNumberRanges(Array(range))
+    }
+    val ranges = (fakeRanges ++ realRanges)
+
+
+    // Make sure that the left `numPartitionsInBM` blocks are in block manager, and others are not
+    require(
+      blockIds.take(numPartitionsInBM).forall(blockManager.get(_).nonEmpty),
+      "Expected blocks not in BlockManager"
+    )
+
+    require(
+      blockIds.drop(numPartitionsInBM).forall(blockManager.get(_).isEmpty),
+      "Unexpected blocks in BlockManager"
+    )
+
+    // Make sure that the right sequence `numPartitionsInKinesis` are configured, and others are not
+    require(
+      ranges.takeRight(numPartitionsInKinesis).forall {
+        _.ranges.forall { _.streamName == testUtils.streamName }
+      }, "Incorrect configuration of RDD, expected ranges not set: "
+    )
+
+    require(
+      ranges.dropRight(numPartitionsInKinesis).forall {
+        _.ranges.forall { _.streamName != testUtils.streamName }
+      }, "Incorrect configuration of RDD, unexpected ranges set"
+    )
+
+    val rdd = new KinesisBackedBlockRDD(sc, regionId, endpointUrl, blockIds, ranges)
+    val collectedData = rdd.map { bytes =>
+      new String(bytes).toInt
+    }.collect()
+    assert(collectedData.toSet === testData.toSet)
+
+    // Verify that the block fetching is skipped when isBlockValid is set to false.
+    // This is done by using a RDD whose data is only in memory but is set to skip block fetching
+    // Using that RDD will throw exception, as it skips block fetching even if the blocks are in
+    // in BlockManager.
+    if (testIsBlockValid) {
+      require(numPartitionsInBM === numPartitions, "All partitions must be in BlockManager")
+      require(numPartitionsInKinesis === 0, "No partitions must be in Kinesis")
+      val rdd2 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl, blockIds.toArray,
+        ranges, isBlockIdValid = Array.fill(blockIds.length)(false))
+      intercept[SparkException] {
+        rdd2.collect()
+      }
+    }
+
+    // Verify that the RDD is not invalid after the blocks are removed and can still read data
+    // from write ahead log
+    if (testBlockRemove) {
+      require(numPartitions === numPartitionsInKinesis,
+        "All partitions must be in WAL for this test")
+      require(numPartitionsInBM > 0, "Some partitions must be in BlockManager for this test")
+      rdd.removeBlocks()
+      assert(rdd.map { bytes => new String(bytes).toInt }.collect().toSet === testData.toSet)
+    }
+  }
+
+  /** Generate fake block ids */
+  private def fakeBlockIds(num: Int): Array[BlockId] = {
+    Array.tabulate(num) { i => new StreamBlockId(0, i) }
+  }
+}
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
index 6d011f295e7f7..8373138785a89 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
@@ -23,15 +23,24 @@ import org.apache.spark.SparkFunSuite
  * Helper class that runs Kinesis real data transfer tests or
  * ignores them based on env variable is set or not.
  */
-trait KinesisSuiteHelper { self: SparkFunSuite =>
+trait KinesisFunSuite extends SparkFunSuite  {
   import KinesisTestUtils._
 
   /** Run the test if environment variable is set or ignore the test */
-  def testOrIgnore(testName: String)(testBody: => Unit) {
+  def testIfEnabled(testName: String)(testBody: => Unit) {
     if (shouldRunTests) {
       test(testName)(testBody)
     } else {
       ignore(s"$testName [enable by setting env var $envVarName=1]")(testBody)
     }
   }
+
+  /** Run the give body of code only if Kinesis tests are enabled */
+  def runIfTestsEnabled(message: String)(body: => Unit): Unit = {
+    if (shouldRunTests) {
+      body
+    } else {
+      ignore(s"$message [enable by setting env var $envVarName=1]")()
+    }
+  }
 }
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
index 50f71413abf37..f9c952b9468bb 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -30,7 +30,7 @@ import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
 import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
 
-class KinesisStreamSuite extends SparkFunSuite with KinesisSuiteHelper
+class KinesisStreamSuite extends KinesisFunSuite
   with Eventually with BeforeAndAfter with BeforeAndAfterAll {
 
   // This is the name that KCL uses to save metadata to DynamoDB
@@ -83,7 +83,7 @@ class KinesisStreamSuite extends SparkFunSuite with KinesisSuiteHelper
    * you must have AWS credentials available through the default AWS provider chain,
    * and you have to set the system environment variable RUN_KINESIS_TESTS=1 .
    */
-  testOrIgnore("basic operation") {
+  testIfEnabled("basic operation") {
     val kinesisTestUtils = new KinesisTestUtils()
     try {
       kinesisTestUtils.createStream()

From d4d762f275749a923356cd84de549b14c22cc3eb Mon Sep 17 00:00:00 2001
From: Ram Sriharsha <rsriharsha@hw11853.local>
Date: Thu, 23 Jul 2015 22:35:41 -0700
Subject: [PATCH 0572/1454] [SPARK-8092] [ML] Allow OneVsRest Classifier
 feature and label column names to be configurable.

The base classifier input and output columns are ignored in favor of  the ones specified in OneVsRest.

Author: Ram Sriharsha <rsriharsha@hw11853.local>

Closes #6631 from harsha2010/SPARK-8092 and squashes the following commits:

6591dc6 [Ram Sriharsha] add documentation for params
b7024b1 [Ram Sriharsha] cleanup
f0e2bfb [Ram Sriharsha] merge with master
108d3d7 [Ram Sriharsha] merge with master
4f74126 [Ram Sriharsha] Allow label/ features columns to be configurable
---
 .../spark/ml/classification/OneVsRest.scala   | 17 ++++++++++++-
 .../ml/classification/OneVsRestSuite.scala    | 24 +++++++++++++++++++
 2 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index ea757c5e40c76..1741f19dc911c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -47,6 +47,8 @@ private[ml] trait OneVsRestParams extends PredictorParams {
 
   /**
    * param for the base binary classifier that we reduce multiclass classification into.
+   * The base classifier input and output columns are ignored in favor of
+   * the ones specified in [[OneVsRest]].
    * @group param
    */
   val classifier: Param[ClassifierType] = new Param(this, "classifier", "base binary classifier")
@@ -160,6 +162,15 @@ final class OneVsRest(override val uid: String)
     set(classifier, value.asInstanceOf[ClassifierType])
   }
 
+  /** @group setParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
+
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
+
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema, fitting = true, getClassifier.featuresDataType)
   }
@@ -195,7 +206,11 @@ final class OneVsRest(override val uid: String)
       val labelUDFWithNewMeta = labelUDF(col($(labelCol))).as(labelColName, newLabelMeta)
       val trainingDataset = multiclassLabeled.withColumn(labelColName, labelUDFWithNewMeta)
       val classifier = getClassifier
-      classifier.fit(trainingDataset, classifier.labelCol -> labelColName)
+      val paramMap = new ParamMap()
+      paramMap.put(classifier.labelCol -> labelColName)
+      paramMap.put(classifier.featuresCol -> getFeaturesCol)
+      paramMap.put(classifier.predictionCol -> getPredictionCol)
+      classifier.fit(trainingDataset, paramMap)
     }.toArray[ClassificationModel[_, _]]
 
     if (handlePersistence) {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index 75cf5bd4ead4f..3775292f6dca7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.NominalAttribute
+import org.apache.spark.ml.feature.StringIndexer
 import org.apache.spark.ml.param.{ParamMap, ParamsSuite}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
@@ -104,6 +105,29 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext {
     ova.fit(datasetWithLabelMetadata)
   }
 
+  test("SPARK-8092: ensure label features and prediction cols are configurable") {
+    val labelIndexer = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("indexed")
+
+    val indexedDataset = labelIndexer
+      .fit(dataset)
+      .transform(dataset)
+      .drop("label")
+      .withColumnRenamed("features", "f")
+
+    val ova = new OneVsRest()
+    ova.setClassifier(new LogisticRegression())
+      .setLabelCol(labelIndexer.getOutputCol)
+      .setFeaturesCol("f")
+      .setPredictionCol("p")
+
+    val ovaModel = ova.fit(indexedDataset)
+    val transformedDataset = ovaModel.transform(indexedDataset)
+    val outputFields = transformedDataset.schema.fieldNames.toSet
+    assert(outputFields.contains("p"))
+  }
+
   test("SPARK-8049: OneVsRest shouldn't output temp columns") {
     val logReg = new LogisticRegression()
       .setMaxIter(1)

From 408e64b284ef8bd6796d815b5eb603312d090b74 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 23 Jul 2015 23:40:01 -0700
Subject: [PATCH 0573/1454] [SPARK-9294][SQL] cleanup comments, code style,
 naming typo for the new aggregation

fix some comments and code style for https://github.com/apache/spark/pull/7458

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7619 from cloud-fan/agg-clean and squashes the following commits:

3925457 [Wenchen Fan] one more...
cc78357 [Wenchen Fan] one more cleanup
26f6a93 [Wenchen Fan] some minor cleanup for the new aggregation
---
 .../sql/catalyst/analysis/Analyzer.scala      |  2 +-
 .../expressions/aggregate/interfaces.scala    | 18 ++--
 .../apache/spark/sql/execution/Exchange.scala |  6 +-
 .../spark/sql/execution/SparkStrategies.scala |  8 +-
 .../aggregate/sortBasedIterators.scala        | 82 ++++++-------------
 .../spark/sql/execution/aggregate/utils.scala | 10 +--
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  9 +-
 7 files changed, 46 insertions(+), 89 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 8cadbc57e87e1..e916887187dc8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -533,7 +533,7 @@ class Analyzer(
                 case min: Min if isDistinct => min
                 // For other aggregate functions, DISTINCT keyword is not supported for now.
                 // Once we converted to the new code path, we will allow using DISTINCT keyword.
-                case other if isDistinct =>
+                case other: AggregateExpression1 if isDistinct =>
                   failAnalysis(s"$name does not support DISTINCT keyword.")
                 // If it does not have DISTINCT keyword, we will return it as is.
                 case other => other
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
index d3fee1ade05e6..10bd19c8a840f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -23,18 +23,18 @@ import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCod
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
-/** The mode of an [[AggregateFunction1]]. */
+/** The mode of an [[AggregateFunction2]]. */
 private[sql] sealed trait AggregateMode
 
 /**
- * An [[AggregateFunction1]] with [[Partial]] mode is used for partial aggregation.
+ * An [[AggregateFunction2]] with [[Partial]] mode is used for partial aggregation.
  * This function updates the given aggregation buffer with the original input of this
  * function. When it has processed all input rows, the aggregation buffer is returned.
  */
 private[sql] case object Partial extends AggregateMode
 
 /**
- * An [[AggregateFunction1]] with [[PartialMerge]] mode is used to merge aggregation buffers
+ * An [[AggregateFunction2]] with [[PartialMerge]] mode is used to merge aggregation buffers
  * containing intermediate results for this function.
  * This function updates the given aggregation buffer by merging multiple aggregation buffers.
  * When it has processed all input rows, the aggregation buffer is returned.
@@ -42,8 +42,8 @@ private[sql] case object Partial extends AggregateMode
 private[sql] case object PartialMerge extends AggregateMode
 
 /**
- * An [[AggregateFunction1]] with [[PartialMerge]] mode is used to merge aggregation buffers
- * containing intermediate results for this function and the generate final result.
+ * An [[AggregateFunction2]] with [[PartialMerge]] mode is used to merge aggregation buffers
+ * containing intermediate results for this function and then generate final result.
  * This function updates the given aggregation buffer by merging multiple aggregation buffers.
  * When it has processed all input rows, the final result of this function is returned.
  */
@@ -85,12 +85,12 @@ private[sql] case class AggregateExpression2(
   override def nullable: Boolean = aggregateFunction.nullable
 
   override def references: AttributeSet = {
-    val childReferemces = mode match {
+    val childReferences = mode match {
       case Partial | Complete => aggregateFunction.references.toSeq
       case PartialMerge | Final => aggregateFunction.bufferAttributes
     }
 
-    AttributeSet(childReferemces)
+    AttributeSet(childReferences)
   }
 
   override def toString: String = s"(${aggregateFunction}2,mode=$mode,isDistinct=$isDistinct)"
@@ -99,10 +99,8 @@ private[sql] case class AggregateExpression2(
 abstract class AggregateFunction2
   extends Expression with ImplicitCastInputTypes {
 
-  self: Product =>
-
   /** An aggregate function is not foldable. */
-  override def foldable: Boolean = false
+  final override def foldable: Boolean = false
 
   /**
    * The offset of this function's buffer in the underlying buffer shared with other functions.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index d31e265a293e9..41a0c519ba527 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -224,13 +224,13 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
       // compatible.
       // TODO: ASSUMES TRANSITIVITY?
       def compatible: Boolean =
-        !operator.children
+        operator.children
           .map(_.outputPartitioning)
           .sliding(2)
-          .map {
+          .forall {
             case Seq(a) => true
             case Seq(a, b) => a.compatibleWith(b)
-          }.exists(!_)
+          }
 
       // Adds Exchange or Sort operators as required
       def addOperatorsIfNecessary(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index f54aa2027f6a6..eb4be1900b153 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -190,12 +190,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         sqlContext.conf.codegenEnabled).isDefined
     }
 
-    def canBeCodeGened(aggs: Seq[AggregateExpression1]): Boolean = !aggs.exists {
-      case _: CombineSum | _: Sum | _: Count | _: Max | _: Min |  _: CombineSetsAndCount => false
+    def canBeCodeGened(aggs: Seq[AggregateExpression1]): Boolean = aggs.forall {
+      case _: CombineSum | _: Sum | _: Count | _: Max | _: Min |  _: CombineSetsAndCount => true
       // The generated set implementation is pretty limited ATM.
       case CollectHashSet(exprs) if exprs.size == 1  &&
-           Seq(IntegerType, LongType).contains(exprs.head.dataType) => false
-      case _ => true
+           Seq(IntegerType, LongType).contains(exprs.head.dataType) => true
+      case _ => false
     }
 
     def allAggregates(exprs: Seq[Expression]): Seq[AggregateExpression1] =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
index ce1cbdc9cb090..b8e95a5a2a4da 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
@@ -67,13 +67,6 @@ private[sql] abstract class SortAggregationIterator(
     functions
   }
 
-  // All non-algebraic aggregate functions.
-  protected val nonAlgebraicAggregateFunctions: Array[AggregateFunction2] = {
-    aggregateFunctions.collect {
-      case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
-    }.toArray
-  }
-
   // Positions of those non-algebraic aggregate functions in aggregateFunctions.
   // For example, we have func1, func2, func3, func4 in aggregateFunctions, and
   // func2 and func3 are non-algebraic aggregate functions.
@@ -91,6 +84,10 @@ private[sql] abstract class SortAggregationIterator(
     positions.toArray
   }
 
+  // All non-algebraic aggregate functions.
+  protected val nonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
+    nonAlgebraicAggregateFunctionPositions.map(aggregateFunctions)
+
   // This is used to project expressions for the grouping expressions.
   protected val groupGenerator =
     newMutableProjection(groupingExpressions, inputAttributes)()
@@ -179,8 +176,6 @@ private[sql] abstract class SortAggregationIterator(
       // For the below compare method, we do not need to make a copy of groupingKey.
       val groupingKey = groupGenerator(currentRow)
       // Check if the current row belongs the current input row.
-      currentGroupingKey.equals(groupingKey)
-
       if (currentGroupingKey == groupingKey) {
         processRow(currentRow)
       } else {
@@ -288,10 +283,7 @@ class PartialSortAggregationIterator(
 
   // This projection is used to update buffer values for all AlgebraicAggregates.
   private val algebraicUpdateProjection = {
-    val bufferSchema = aggregateFunctions.flatMap {
-      case ae: AlgebraicAggregate => ae.bufferAttributes
-      case agg: AggregateFunction2 => agg.bufferAttributes
-    }
+    val bufferSchema = aggregateFunctions.flatMap(_.bufferAttributes)
     val updateExpressions = aggregateFunctions.flatMap {
       case ae: AlgebraicAggregate => ae.updateExpressions
       case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
@@ -348,19 +340,14 @@ class PartialMergeSortAggregationIterator(
     inputAttributes,
     inputIter) {
 
-  private val placeholderAttribtues =
+  private val placeholderAttributes =
     Seq.fill(initialBufferOffset)(AttributeReference("placeholder", NullType)())
 
   // This projection is used to merge buffer values for all AlgebraicAggregates.
   private val algebraicMergeProjection = {
     val bufferSchemata =
-      placeholderAttribtues ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.bufferAttributes
-        case agg: AggregateFunction2 => agg.bufferAttributes
-      } ++ placeholderAttribtues ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
-        case agg: AggregateFunction2 => agg.cloneBufferAttributes
-      }
+      placeholderAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
+        placeholderAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
     val mergeExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
       case ae: AlgebraicAggregate => ae.mergeExpressions
       case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
@@ -444,13 +431,8 @@ class FinalSortAggregationIterator(
   // This projection is used to merge buffer values for all AlgebraicAggregates.
   private val algebraicMergeProjection = {
     val bufferSchemata =
-      offsetAttributes ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.bufferAttributes
-        case agg: AggregateFunction2 => agg.bufferAttributes
-      } ++ offsetAttributes ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
-        case agg: AggregateFunction2 => agg.cloneBufferAttributes
-      }
+      offsetAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
+        offsetAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
     val mergeExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
       case ae: AlgebraicAggregate => ae.mergeExpressions
       case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
@@ -462,13 +444,8 @@ class FinalSortAggregationIterator(
   // This projection is used to evaluate all AlgebraicAggregates.
   private val algebraicEvalProjection = {
     val bufferSchemata =
-      offsetAttributes ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.bufferAttributes
-        case agg: AggregateFunction2 => agg.bufferAttributes
-      } ++ offsetAttributes ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
-        case agg: AggregateFunction2 => agg.cloneBufferAttributes
-      }
+      offsetAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
+        offsetAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
     val evalExpressions = aggregateFunctions.map {
       case ae: AlgebraicAggregate => ae.evaluateExpression
       case agg: AggregateFunction2 => NoOp
@@ -599,11 +576,10 @@ class FinalAndCompleteSortAggregationIterator(
   }
 
   // All non-algebraic aggregate functions with mode Final.
-  private val finalNonAlgebraicAggregateFunctions: Array[AggregateFunction2] = {
+  private val finalNonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
     finalAggregateFunctions.collect {
       case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
-    }.toArray
-  }
+    }
 
   // All aggregate functions with mode Complete.
   private val completeAggregateFunctions: Array[AggregateFunction2] = {
@@ -617,11 +593,10 @@ class FinalAndCompleteSortAggregationIterator(
   }
 
   // All non-algebraic aggregate functions with mode Complete.
-  private val completeNonAlgebraicAggregateFunctions: Array[AggregateFunction2] = {
+  private val completeNonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
     completeAggregateFunctions.collect {
       case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
-    }.toArray
-  }
+    }
 
   // This projection is used to merge buffer values for all AlgebraicAggregates with mode
   // Final.
@@ -633,13 +608,9 @@ class FinalAndCompleteSortAggregationIterator(
     val completeOffsetExpressions = Seq.fill(numCompleteOffsetAttributes)(NoOp)
 
     val bufferSchemata =
-      offsetAttributes ++ finalAggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.bufferAttributes
-        case agg: AggregateFunction2 => agg.bufferAttributes
-      } ++ completeOffsetAttributes ++ offsetAttributes ++ finalAggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
-        case agg: AggregateFunction2 => agg.cloneBufferAttributes
-      } ++ completeOffsetAttributes
+      offsetAttributes ++ finalAggregateFunctions.flatMap(_.bufferAttributes) ++
+        completeOffsetAttributes ++ offsetAttributes ++
+        finalAggregateFunctions.flatMap(_.cloneBufferAttributes) ++ completeOffsetAttributes
     val mergeExpressions =
       placeholderExpressions ++ finalAggregateFunctions.flatMap {
         case ae: AlgebraicAggregate => ae.mergeExpressions
@@ -658,10 +629,8 @@ class FinalAndCompleteSortAggregationIterator(
     val finalOffsetExpressions = Seq.fill(numFinalOffsetAttributes)(NoOp)
 
     val bufferSchema =
-      offsetAttributes ++ finalOffsetAttributes ++ completeAggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.bufferAttributes
-        case agg: AggregateFunction2 => agg.bufferAttributes
-      }
+      offsetAttributes ++ finalOffsetAttributes ++
+        completeAggregateFunctions.flatMap(_.bufferAttributes)
     val updateExpressions =
       placeholderExpressions ++ finalOffsetExpressions ++ completeAggregateFunctions.flatMap {
         case ae: AlgebraicAggregate => ae.updateExpressions
@@ -673,13 +642,8 @@ class FinalAndCompleteSortAggregationIterator(
   // This projection is used to evaluate all AlgebraicAggregates.
   private val algebraicEvalProjection = {
     val bufferSchemata =
-      offsetAttributes ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.bufferAttributes
-        case agg: AggregateFunction2 => agg.bufferAttributes
-      } ++ offsetAttributes ++ aggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.cloneBufferAttributes
-        case agg: AggregateFunction2 => agg.cloneBufferAttributes
-      }
+      offsetAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
+        offsetAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
     val evalExpressions = aggregateFunctions.map {
       case ae: AlgebraicAggregate => ae.evaluateExpression
       case agg: AggregateFunction2 => NoOp
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
index 1cb27710e0480..5bbe6c162ff4b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
@@ -191,10 +191,7 @@ object Utils {
     }
     val groupExpressionMap = namedGroupingExpressions.toMap
     val namedGroupingAttributes = namedGroupingExpressions.map(_._2.toAttribute)
-    val partialAggregateExpressions = aggregateExpressions.map {
-      case AggregateExpression2(aggregateFunction, mode, isDistinct) =>
-        AggregateExpression2(aggregateFunction, Partial, isDistinct)
-    }
+    val partialAggregateExpressions = aggregateExpressions.map(_.copy(mode = Partial))
     val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
       agg.aggregateFunction.bufferAttributes
     }
@@ -208,10 +205,7 @@ object Utils {
         child)
 
     // 2. Create an Aggregate Operator for final aggregations.
-    val finalAggregateExpressions = aggregateExpressions.map {
-      case AggregateExpression2(aggregateFunction, mode, isDistinct) =>
-        AggregateExpression2(aggregateFunction, Final, isDistinct)
-    }
+    val finalAggregateExpressions = aggregateExpressions.map(_.copy(mode = Final))
     val finalAggregateAttributes =
       finalAggregateExpressions.map {
         expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index ab8dce603c117..95a1106cf072d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1518,18 +1518,19 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
   test("SPARK-8945: add and subtract expressions for interval type") {
     import org.apache.spark.unsafe.types.Interval
+    import org.apache.spark.unsafe.types.Interval.MICROS_PER_WEEK
 
     val df = sql("select interval 3 years -3 month 7 week 123 microseconds as i")
-    checkAnswer(df, Row(new Interval(12 * 3 - 3, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123)))
+    checkAnswer(df, Row(new Interval(12 * 3 - 3, 7L * MICROS_PER_WEEK + 123)))
 
     checkAnswer(df.select(df("i") + new Interval(2, 123)),
-      Row(new Interval(12 * 3 - 3 + 2, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123 + 123)))
+      Row(new Interval(12 * 3 - 3 + 2, 7L * MICROS_PER_WEEK + 123 + 123)))
 
     checkAnswer(df.select(df("i") - new Interval(2, 123)),
-      Row(new Interval(12 * 3 - 3 - 2, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123 - 123)))
+      Row(new Interval(12 * 3 - 3 - 2, 7L * MICROS_PER_WEEK + 123 - 123)))
 
     // unary minus
     checkAnswer(df.select(-df("i")),
-      Row(new Interval(-(12 * 3 - 3), -(7L * 1000 * 1000 * 3600 * 24 * 7 + 123))))
+      Row(new Interval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 }

From cb8c241f05b9ab4ad0cd07df14d454cc5a4554cc Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 24 Jul 2015 01:18:43 -0700
Subject: [PATCH 0574/1454] [SPARK-9200][SQL] Don't implicitly cast non-atomic
 types to string type.

Author: Reynold Xin <rxin@databricks.com>

Closes #7636 from rxin/complex-string-implicit-cast and squashes the following commits:

3e67327 [Reynold Xin] [SPARK-9200][SQL] Don't implicitly cast non-atomic types to string type.
---
 .../spark/sql/catalyst/analysis/HiveTypeCoercion.scala    | 3 ++-
 .../sql/catalyst/analysis/HiveTypeCoercionSuite.scala     | 8 ++++++++
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index d56ceeadc9e85..87ffbfe791b93 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -720,7 +720,8 @@ object HiveTypeCoercion {
         case (StringType, DateType) => Cast(e, DateType)
         case (StringType, TimestampType) => Cast(e, TimestampType)
         case (StringType, BinaryType) => Cast(e, BinaryType)
-        case (any, StringType) if any != StringType => Cast(e, StringType)
+        // Cast any atomic type to string.
+        case (any: AtomicType, StringType) if any != StringType => Cast(e, StringType)
 
         // When we reach here, input type is not acceptable for any types in this type collection,
         // try to find the first one we can implicitly cast.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index d0fb95b580ad2..55865bdb534b4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -115,6 +115,14 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldNotCast(IntegerType, ArrayType)
     shouldNotCast(IntegerType, MapType)
     shouldNotCast(IntegerType, StructType)
+
+    shouldNotCast(IntervalType, StringType)
+
+    // Don't implicitly cast complex types to string.
+    shouldNotCast(ArrayType(StringType), StringType)
+    shouldNotCast(MapType(StringType, StringType), StringType)
+    shouldNotCast(new StructType().add("a1", StringType), StringType)
+    shouldNotCast(MapType(StringType, StringType), StringType)
   }
 
   test("tightest common bound for types") {

From 8fe32b4f7d49607ad5f2479d454b33ab3f079f7c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 24 Jul 2015 01:47:13 -0700
Subject: [PATCH 0575/1454] [build] Enable memory leak detection for Tungsten.

This was turned off accidentally in #7591.

Author: Reynold Xin <rxin@databricks.com>

Closes #7637 from rxin/enable-mem-leak-detect and squashes the following commits:

34bc3ef [Reynold Xin] Enable memory leak detection for Tungsten.
---
 project/SparkBuild.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index b5b0adf630b9e..61a05d375d99e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -543,7 +543,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.ui.enabled=false",
     javaOptions in Test += "-Dspark.ui.showConsoleProgress=false",
     javaOptions in Test += "-Dspark.driver.allowMultipleContexts=true",
-    //javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true",
+    javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true",
     javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test += "-Dderby.system.durability=test",
     javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark")

From 6a7e537f3a4fd5e99a905f9842dc0ad4c348e4fd Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Fri, 24 Jul 2015 17:39:57 +0800
Subject: [PATCH 0576/1454] [SPARK-8756] [SQL] Keep cached information and
 avoid re-calculating footers in ParquetRelation2

JIRA: https://issues.apache.org/jira/browse/SPARK-8756

Currently, in ParquetRelation2, footers are re-read every time refresh() is called. But we can check if it is possibly changed before we do the reading because reading all footers will be expensive when there are too many partitions. This pr fixes this by keeping some cached information to check it.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7154 from viirya/cached_footer_parquet_relation and squashes the following commits:

92e9347 [Liang-Chi Hsieh] Fix indentation.
ae0ec64 [Liang-Chi Hsieh] Fix wrong assignment.
c8fdfb7 [Liang-Chi Hsieh] Fix it.
a52b6d1 [Liang-Chi Hsieh] For comments.
c2a2420 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into cached_footer_parquet_relation
fa5458f [Liang-Chi Hsieh] Use Map to cache FileStatus and do merging previously loaded schema and newly loaded one.
6ae0911 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into cached_footer_parquet_relation
21bbdec [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into cached_footer_parquet_relation
12a0ed9 [Liang-Chi Hsieh] Add check of FileStatus's modification time.
186429d [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into cached_footer_parquet_relation
0ef8caf [Liang-Chi Hsieh] Keep cached information and avoid re-calculating footers.
---
 .../apache/spark/sql/parquet/newParquet.scala | 38 ++++++++++++-------
 1 file changed, 24 insertions(+), 14 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index 2f9f880c70690..c384697c0ee62 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -345,24 +345,34 @@ private[sql] class ParquetRelation2(
     // Schema of the whole table, including partition columns.
     var schema: StructType = _
 
+    // Cached leaves
+    var cachedLeaves: Set[FileStatus] = null
+
     /**
      * Refreshes `FileStatus`es, footers, partition spec, and table schema.
      */
     def refresh(): Unit = {
-      // Lists `FileStatus`es of all leaf nodes (files) under all base directories.
-      val leaves = cachedLeafStatuses().filter { f =>
-        isSummaryFile(f.getPath) ||
-          !(f.getPath.getName.startsWith("_") || f.getPath.getName.startsWith("."))
-      }.toArray
-
-      dataStatuses = leaves.filterNot(f => isSummaryFile(f.getPath))
-      metadataStatuses = leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_METADATA_FILE)
-      commonMetadataStatuses =
-        leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE)
-
-      // If we already get the schema, don't need to re-compute it since the schema merging is
-      // time-consuming.
-      if (dataSchema == null) {
+      val currentLeafStatuses = cachedLeafStatuses()
+
+      // Check if cachedLeafStatuses is changed or not
+      val leafStatusesChanged = (cachedLeaves == null) ||
+        !cachedLeaves.equals(currentLeafStatuses)
+
+      if (leafStatusesChanged) {
+        cachedLeaves = currentLeafStatuses.toIterator.toSet
+
+        // Lists `FileStatus`es of all leaf nodes (files) under all base directories.
+        val leaves = currentLeafStatuses.filter { f =>
+          isSummaryFile(f.getPath) ||
+            !(f.getPath.getName.startsWith("_") || f.getPath.getName.startsWith("."))
+        }.toArray
+
+        dataStatuses = leaves.filterNot(f => isSummaryFile(f.getPath))
+        metadataStatuses =
+          leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_METADATA_FILE)
+        commonMetadataStatuses =
+          leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE)
+
         dataSchema = {
           val dataSchema0 = maybeDataSchema
             .orElse(readSchema())

From 6cd28cc21ed585ab8d1e0e7147a1a48b044c9c8e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Garillot?= <francois@garillot.net>
Date: Fri, 24 Jul 2015 15:41:13 +0100
Subject: [PATCH 0577/1454] [SPARK-9236] [CORE] Make defaultPartitioner not
 reuse a parent RDD's partitioner if it has 0 partitions
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

See also comments on https://issues.apache.org/jira/browse/SPARK-9236

Author: François Garillot <francois@garillot.net>

Closes #7616 from huitseeker/issue/SPARK-9236 and squashes the following commits:

217f902 [François Garillot] [SPARK-9236] Make defaultPartitioner not reuse a parent RDD's partitioner if it has 0 partitions
---
 .../scala/org/apache/spark/Partitioner.scala  |  2 +-
 .../spark/rdd/PairRDDFunctionsSuite.scala     | 23 +++++++++++++++++++
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index ad68512dccb79..4b9d59975bdc2 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -56,7 +56,7 @@ object Partitioner {
    */
   def defaultPartitioner(rdd: RDD[_], others: RDD[_]*): Partitioner = {
     val bySize = (Seq(rdd) ++ others).sortBy(_.partitions.size).reverse
-    for (r <- bySize if r.partitioner.isDefined) {
+    for (r <- bySize if r.partitioner.isDefined && r.partitioner.get.numPartitions > 0) {
       return r.partitioner.get
     }
     if (rdd.context.conf.contains("spark.default.parallelism")) {
diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
index dfa102f432a02..1321ec84735b5 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
@@ -282,6 +282,29 @@ class PairRDDFunctionsSuite extends SparkFunSuite with SharedSparkContext {
     ))
   }
 
+  // See SPARK-9326
+  test("cogroup with empty RDD") {
+    import scala.reflect.classTag
+    val intPairCT = classTag[(Int, Int)]
+
+    val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
+    val rdd2 = sc.emptyRDD[(Int, Int)](intPairCT)
+
+    val joined = rdd1.cogroup(rdd2).collect()
+    assert(joined.size > 0)
+  }
+
+  // See SPARK-9326
+  test("cogroup with groupByed RDD having 0 partitions") {
+    import scala.reflect.classTag
+    val intCT = classTag[Int]
+
+    val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
+    val rdd2 = sc.emptyRDD[Int](intCT).groupBy((x) => 5)
+    val joined = rdd1.cogroup(rdd2).collect()
+    assert(joined.size > 0)
+  }
+
   test("rightOuterJoin") {
     val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
     val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))

From dfb18be0366376be3b928dbf4570448c60fe652b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 24 Jul 2015 08:24:13 -0700
Subject: [PATCH 0578/1454] [SPARK-9069] [SQL] follow up

Address comments for #7605

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7634 from davies/decimal_unlimited2 and squashes the following commits:

b2d8b0d [Davies Liu] add doc and test for DecimalType.isWiderThan
65b251c [Davies Liu] fix test
6a91f32 [Davies Liu] fix style
ca9c973 [Davies Liu] address comments
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 30 +++++--------------
 .../expressions/decimalFunctions.scala        | 13 ++++++++
 .../apache/spark/sql/types/DecimalType.scala  |  6 +++-
 .../analysis/DecimalPrecisionSuite.scala      | 26 ++++++++++++++++
 .../analysis/HiveTypeCoercionSuite.scala      |  6 ++--
 5 files changed, 55 insertions(+), 26 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 87ffbfe791b93..e0527503442f0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -19,9 +19,7 @@ package org.apache.spark.sql.catalyst.analysis
 
 import javax.annotation.Nullable
 
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
@@ -38,7 +36,7 @@ object HiveTypeCoercion {
   val typeCoercionRules =
     PropagateTypes ::
       InConversion ::
-      WidenTypes ::
+      WidenSetOperationTypes ::
       PromoteStrings ::
       DecimalPrecision ::
       BooleanEquality ::
@@ -175,7 +173,7 @@ object HiveTypeCoercion {
    *
    * This rule is only applied to Union/Except/Intersect
    */
-  object WidenTypes extends Rule[LogicalPlan] {
+  object WidenSetOperationTypes extends Rule[LogicalPlan] {
 
     private[this] def widenOutputTypes(
         planName: String,
@@ -203,9 +201,9 @@ object HiveTypeCoercion {
 
       def castOutput(plan: LogicalPlan): LogicalPlan = {
         val casted = plan.output.zip(castedTypes).map {
-          case (hs, Some(dt)) if dt != hs.dataType =>
-            Alias(Cast(hs, dt), hs.name)()
-          case (hs, _) => hs
+          case (e, Some(dt)) if e.dataType != dt =>
+            Alias(Cast(e, dt), e.name)()
+          case (e, _) => e
         }
         Project(casted, plan)
       }
@@ -355,20 +353,8 @@ object HiveTypeCoercion {
       DecimalType.bounded(range + scale, scale)
     }
 
-    /**
-     * An expression used to wrap the children when promote the precision of DecimalType to avoid
-     * promote multiple times.
-     */
-    case class ChangePrecision(child: Expression) extends UnaryExpression {
-      override def dataType: DataType = child.dataType
-      override def eval(input: InternalRow): Any = child.eval(input)
-      override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx)
-      override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = ""
-      override def prettyName: String = "change_precision"
-    }
-
-    def changePrecision(e: Expression, dataType: DataType): Expression = {
-      ChangePrecision(Cast(e, dataType))
+    private def changePrecision(e: Expression, dataType: DataType): Expression = {
+      ChangeDecimalPrecision(Cast(e, dataType))
     }
 
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
@@ -378,7 +364,7 @@ object HiveTypeCoercion {
         case e if !e.childrenResolved => e
 
         // Skip nodes who is already promoted
-        case e: BinaryArithmetic if e.left.isInstanceOf[ChangePrecision] => e
+        case e: BinaryArithmetic if e.left.isInstanceOf[ChangeDecimalPrecision] => e
 
         case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
index b9d4736a65e26..adb33e4c8d4a1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 
@@ -60,3 +61,15 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un
     })
   }
 }
+
+/**
+ * An expression used to wrap the children when promote the precision of DecimalType to avoid
+ * promote multiple times.
+ */
+case class ChangeDecimalPrecision(child: Expression) extends UnaryExpression {
+  override def dataType: DataType = child.dataType
+  override def eval(input: InternalRow): Any = child.eval(input)
+  override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx)
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = ""
+  override def prettyName: String = "change_decimal_precision"
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
index 26b24616d98ec..0cd352d0fa928 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala
@@ -78,6 +78,10 @@ case class DecimalType(precision: Int, scale: Int) extends FractionalType {
 
   override def toString: String = s"DecimalType($precision,$scale)"
 
+  /**
+   * Returns whether this DecimalType is wider than `other`. If yes, it means `other`
+   * can be casted into `this` safely without losing any precision or range.
+   */
   private[sql] def isWiderThan(other: DataType): Boolean = other match {
     case dt: DecimalType =>
       (precision - scale) >= (dt.precision - dt.scale) && scale >= dt.scale
@@ -109,7 +113,7 @@ object DecimalType extends AbstractDataType {
   @deprecated("Does not support unlimited precision, please specify the precision and scale", "1.5")
   val Unlimited: DecimalType = SYSTEM_DEFAULT
 
-  // The decimal types compatible with other numberic types
+  // The decimal types compatible with other numeric types
   private[sql] val ByteDecimal = DecimalType(3, 0)
   private[sql] val ShortDecimal = DecimalType(5, 0)
   private[sql] val IntDecimal = DecimalType(10, 0)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
index f9f15e7a6608d..fc11627da6fd1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
@@ -154,4 +154,30 @@ class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter {
       checkType(Remainder(expr, u), DoubleType)
     }
   }
+
+  test("DecimalType.isWiderThan") {
+    val d0 = DecimalType(2, 0)
+    val d1 = DecimalType(2, 1)
+    val d2 = DecimalType(5, 2)
+    val d3 = DecimalType(15, 3)
+    val d4 = DecimalType(25, 4)
+
+    assert(d0.isWiderThan(d1) === false)
+    assert(d1.isWiderThan(d0) === false)
+    assert(d1.isWiderThan(d2) === false)
+    assert(d2.isWiderThan(d1) === true)
+    assert(d2.isWiderThan(d3) === false)
+    assert(d3.isWiderThan(d2) === true)
+    assert(d4.isWiderThan(d3) === true)
+
+    assert(d1.isWiderThan(ByteType) === false)
+    assert(d2.isWiderThan(ByteType) === true)
+    assert(d2.isWiderThan(ShortType) === false)
+    assert(d3.isWiderThan(ShortType) === true)
+    assert(d3.isWiderThan(IntegerType) === true)
+    assert(d3.isWiderThan(LongType) === false)
+    assert(d4.isWiderThan(LongType) === true)
+    assert(d4.isWiderThan(FloatType) === false)
+    assert(d4.isWiderThan(DoubleType) === false)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 55865bdb534b4..4454d51b75877 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -314,7 +314,7 @@ class HiveTypeCoercionSuite extends PlanTest {
     )
   }
 
-  test("WidenTypes for union except and intersect") {
+  test("WidenSetOperationTypes for union except and intersect") {
     def checkOutput(logical: LogicalPlan, expectTypes: Seq[DataType]): Unit = {
       logical.output.zip(expectTypes).foreach { case (attr, dt) =>
         assert(attr.dataType === dt)
@@ -332,7 +332,7 @@ class HiveTypeCoercionSuite extends PlanTest {
       AttributeReference("f", FloatType)(),
       AttributeReference("l", LongType)())
 
-    val wt = HiveTypeCoercion.WidenTypes
+    val wt = HiveTypeCoercion.WidenSetOperationTypes
     val expectedTypes = Seq(StringType, DecimalType.SYSTEM_DEFAULT, FloatType, DoubleType)
 
     val r1 = wt(Union(left, right)).asInstanceOf[Union]
@@ -353,7 +353,7 @@ class HiveTypeCoercionSuite extends PlanTest {
       }
     }
 
-    val dp = HiveTypeCoercion.WidenTypes
+    val dp = HiveTypeCoercion.WidenSetOperationTypes
 
     val left1 = LocalRelation(
       AttributeReference("l", DecimalType(10, 8))())

From 846cf46282da8f4b87aeee64e407a38cdc80e13b Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Fri, 24 Jul 2015 08:34:50 -0700
Subject: [PATCH 0579/1454] [SPARK-9238] [SQL] Remove two extra useless entries
 for bytesOfCodePointInUTF8

Only a trial thing, not sure if I understand correctly or not but I guess only 2 entries in `bytesOfCodePointInUTF8` for the case of 6 bytes codepoint(1111110x) is enough.
Details can be found from https://en.wikipedia.org/wiki/UTF-8 in "Description" section.

Author: zhichao.li <zhichao.li@intel.com>

Closes #7582 from zhichao-li/utf8 and squashes the following commits:

8bddd01 [zhichao.li] two extra entries
---
 .../src/main/java/org/apache/spark/unsafe/types/UTF8String.java | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 946d355f1fc28..6d8dcb1cbf876 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -48,7 +48,7 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
     3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
     4, 4, 4, 4, 4, 4, 4, 4,
     5, 5, 5, 5,
-    6, 6, 6, 6};
+    6, 6};
 
   public static final UTF8String EMPTY_UTF8 = UTF8String.fromString("");
 

From 428cde5d1c46adad344255447283dfb9716d65cb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Garillot?= <francois@garillot.net>
Date: Fri, 24 Jul 2015 17:09:33 +0100
Subject: [PATCH 0580/1454] [SPARK-9250] Make change-scala-version more helpful
 w.r.t. valid Scala versions
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Author: François Garillot <francois@garillot.net>

Closes #7595 from huitseeker/issue/SPARK-9250 and squashes the following commits:

80a0218 [François Garillot] [SPARK-9250] Make change-scala-version's usage more explicit, introduce a -h|--help option.
---
 dev/change-scala-version.sh | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/dev/change-scala-version.sh b/dev/change-scala-version.sh
index b81c00c9d6d9d..d7975dfb6475c 100755
--- a/dev/change-scala-version.sh
+++ b/dev/change-scala-version.sh
@@ -19,19 +19,23 @@
 
 set -e
 
+VALID_VERSIONS=( 2.10 2.11 )
+
 usage() {
-  echo "Usage: $(basename $0) <version>" 1>&2
+  echo "Usage: $(basename $0) [-h|--help] <version>
+where :
+  -h| --help Display this help text
+  valid version values : ${VALID_VERSIONS[*]}
+" 1>&2
   exit 1
 }
 
-if [ $# -ne 1 ]; then
+if [[ ($# -ne 1) || ( $1 == "--help") ||  $1 == "-h" ]]; then
   usage
 fi
 
 TO_VERSION=$1
 
-VALID_VERSIONS=( 2.10 2.11 )
-
 check_scala_version() {
   for i in ${VALID_VERSIONS[*]}; do [ $i = "$1" ] && return 0; done
   echo "Invalid Scala version: $1. Valid versions: ${VALID_VERSIONS[*]}" 1>&2

From 3aec9f4e2d8fcce9ddf84ab4d0e10147c18afa16 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Fri, 24 Jul 2015 09:10:11 -0700
Subject: [PATCH 0581/1454] [SPARK-9249] [SPARKR] local variable assigned but
 may not be used

[[SPARK-9249] local variable assigned but may not be used - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9249)

https://gist.github.com/yu-iskw/0e5b0253c11769457ea5

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7640 from yu-iskw/SPARK-9249 and squashes the following commits:

7a51cab [Yu ISHIKAWA] [SPARK-9249][SparkR] local variable assigned but may not be used
---
 R/pkg/R/deserialize.R | 4 ++--
 R/pkg/R/sparkR.R      | 3 ---
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 7d1f6b0819ed0..6d364f77be7ee 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -102,11 +102,11 @@ readList <- function(con) {
 
 readRaw <- function(con) {
   dataLen <- readInt(con)
-  data <- readBin(con, raw(), as.integer(dataLen), endian = "big")
+  readBin(con, raw(), as.integer(dataLen), endian = "big")
 }
 
 readRawLen <- function(con, dataLen) {
-  data <- readBin(con, raw(), as.integer(dataLen), endian = "big")
+  readBin(con, raw(), as.integer(dataLen), endian = "big")
 }
 
 readDeserialize <- function(con) {
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 79b79d70943cb..76c15875b50d5 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -104,16 +104,13 @@ sparkR.init <- function(
     return(get(".sparkRjsc", envir = .sparkREnv))
   }
 
-  sparkMem <- Sys.getenv("SPARK_MEM", "1024m")
   jars <- suppressWarnings(normalizePath(as.character(sparkJars)))
 
   # Classpath separator is ";" on Windows
   # URI needs four /// as from http://stackoverflow.com/a/18522792
   if (.Platform$OS.type == "unix") {
-    collapseChar <- ":"
     uriSep <- "//"
   } else {
-    collapseChar <- ";"
     uriSep <- "////"
   }
 

From 431ca39be51352dfcdacc87de7e64c2af313558d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 24 Jul 2015 09:37:36 -0700
Subject: [PATCH 0582/1454] [SPARK-9285][SQL] Remove InternalRow's inheritance
 from Row.

I also changed InternalRow's size/length function to numFields, to make it more obvious that it is not about bytes, but the number of fields.

Author: Reynold Xin <rxin@databricks.com>

Closes #7626 from rxin/internalRow and squashes the following commits:

e124daf [Reynold Xin] Fixed test case.
805ceb7 [Reynold Xin] Commented out the failed test suite.
f8a9ca5 [Reynold Xin] Fixed more bugs. Still at least one more remaining.
76d9081 [Reynold Xin] Fixed data sources.
7807f70 [Reynold Xin] Fixed DataFrameSuite.
cb60cd2 [Reynold Xin] Code review & small bug fixes.
0a2948b [Reynold Xin] Fixed style.
3280d03 [Reynold Xin] [SPARK-9285][SQL] Remove InternalRow's inheritance from Row.
---
 .../apache/spark/mllib/linalg/Matrices.scala  |   4 +-
 .../apache/spark/mllib/linalg/Vectors.scala   |   4 +-
 .../sql/catalyst/expressions/UnsafeRow.java   |   9 +-
 .../sql/catalyst/CatalystTypeConverters.scala |  14 +-
 .../spark/sql/catalyst/InternalRow.scala      | 153 ++++++++++++----
 .../spark/sql/catalyst/expressions/Cast.scala |   4 +-
 .../sql/catalyst/expressions/Projection.scala | 168 +++++++++---------
 .../expressions/SpecificMutableRow.scala      |   4 +-
 .../sql/catalyst/expressions/aggregates.scala |   4 +-
 .../codegen/GenerateProjection.scala          |   2 +-
 .../expressions/complexTypeExtractors.scala   |   4 +-
 .../spark/sql/catalyst/expressions/rows.scala |  57 +++---
 .../scala/org/apache/spark/sql/RowTest.scala  |  10 --
 .../sql/catalyst/expressions/CastSuite.scala  |  24 ++-
 .../expressions/ComplexTypeSuite.scala        |   7 +-
 .../spark/sql/columnar/ColumnType.scala       |   2 +-
 .../columnar/InMemoryColumnarTableScan.scala  |  12 +-
 .../sql/execution/SparkSqlSerializer2.scala   |  10 +-
 .../datasources/DataSourceStrategy.scala      |   4 +-
 .../sql/execution/datasources/commands.scala  |  53 ++++--
 .../spark/sql/execution/datasources/ddl.scala |  16 +-
 .../spark/sql/execution/pythonUDFs.scala      |   4 +-
 .../sql/expressions/aggregate/udaf.scala      |   3 +-
 .../apache/spark/sql/jdbc/JDBCRelation.scala  |   3 +-
 .../apache/spark/sql/json/JSONRelation.scala  |   6 +-
 .../sql/parquet/CatalystRowConverter.scala    |  10 +-
 .../sql/parquet/ParquetTableOperations.scala  |   2 +-
 .../sql/parquet/ParquetTableSupport.scala     |  12 +-
 .../apache/spark/sql/parquet/newParquet.scala |   6 +-
 .../apache/spark/sql/sources/interfaces.scala |  22 ++-
 .../scala/org/apache/spark/sql/RowSuite.scala |   4 +-
 .../spark/sql/sources/DDLTestSuite.scala      |   5 +-
 .../spark/sql/sources/PrunedScanSuite.scala   |   2 +-
 .../spark/sql/sources/TableScanSuite.scala    |   2 +-
 .../hive/execution/InsertIntoHiveTable.scala  |   9 +-
 .../spark/sql/hive/hiveWriterContainers.scala |   8 +-
 .../spark/sql/hive/orc/OrcRelation.scala      |   8 +-
 .../CommitFailureTestRelationSuite.scala      |  47 +++++
 .../ParquetHadoopFsRelationSuite.scala        | 139 +++++++++++++++
 .../SimpleTextHadoopFsRelationSuite.scala     |  57 ++++++
 .../sql/sources/hadoopFsRelationSuites.scala  | 166 -----------------
 41 files changed, 647 insertions(+), 433 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 55da0e094d132..b6e2c30fbf104 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -174,8 +174,8 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
   override def deserialize(datum: Any): Matrix = {
     datum match {
       case row: InternalRow =>
-        require(row.length == 7,
-          s"MatrixUDT.deserialize given row with length ${row.length} but requires length == 7")
+        require(row.numFields == 7,
+          s"MatrixUDT.deserialize given row with length ${row.numFields} but requires length == 7")
         val tpe = row.getByte(0)
         val numRows = row.getInt(1)
         val numCols = row.getInt(2)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 9067b3ba9a7bb..c884aad08889f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -203,8 +203,8 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
   override def deserialize(datum: Any): Vector = {
     datum match {
       case row: InternalRow =>
-        require(row.length == 4,
-          s"VectorUDT.deserialize given row with length ${row.length} but requires length == 4")
+        require(row.numFields == 4,
+          s"VectorUDT.deserialize given row with length ${row.numFields} but requires length == 4")
         val tpe = row.getByte(0)
         tpe match {
           case 0 =>
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index fa1216b455a9e..a8986608855e2 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -64,7 +64,8 @@ public final class UnsafeRow extends MutableRow {
   /** The size of this row's backing data, in bytes) */
   private int sizeInBytes;
 
-  public int length() { return numFields; }
+  @Override
+  public int numFields() { return numFields; }
 
   /** The width of the null tracking bit set, in bytes */
   private int bitSetWidthInBytes;
@@ -218,12 +219,12 @@ public void setFloat(int ordinal, float value) {
   }
 
   @Override
-  public int size() {
-    return numFields;
+  public Object get(int i) {
+    throw new UnsupportedOperationException();
   }
 
   @Override
-  public Object get(int i) {
+  public <T> T getAs(int i) {
     throw new UnsupportedOperationException();
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index bfaee04f33b7f..5c3072a77aeba 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -140,14 +140,14 @@ object CatalystTypeConverters {
   private object IdentityConverter extends CatalystTypeConverter[Any, Any, Any] {
     override def toCatalystImpl(scalaValue: Any): Any = scalaValue
     override def toScala(catalystValue: Any): Any = catalystValue
-    override def toScalaImpl(row: InternalRow, column: Int): Any = row(column)
+    override def toScalaImpl(row: InternalRow, column: Int): Any = row.get(column)
   }
 
   private case class UDTConverter(
       udt: UserDefinedType[_]) extends CatalystTypeConverter[Any, Any, Any] {
     override def toCatalystImpl(scalaValue: Any): Any = udt.serialize(scalaValue)
     override def toScala(catalystValue: Any): Any = udt.deserialize(catalystValue)
-    override def toScalaImpl(row: InternalRow, column: Int): Any = toScala(row(column))
+    override def toScalaImpl(row: InternalRow, column: Int): Any = toScala(row.get(column))
   }
 
   /** Converter for arrays, sequences, and Java iterables. */
@@ -184,7 +184,7 @@ object CatalystTypeConverters {
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Seq[Any] =
-      toScala(row(column).asInstanceOf[Seq[Any]])
+      toScala(row.get(column).asInstanceOf[Seq[Any]])
   }
 
   private case class MapConverter(
@@ -227,7 +227,7 @@ object CatalystTypeConverters {
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Map[Any, Any] =
-      toScala(row(column).asInstanceOf[Map[Any, Any]])
+      toScala(row.get(column).asInstanceOf[Map[Any, Any]])
   }
 
   private case class StructConverter(
@@ -260,9 +260,9 @@ object CatalystTypeConverters {
       if (row == null) {
         null
       } else {
-        val ar = new Array[Any](row.size)
+        val ar = new Array[Any](row.numFields)
         var idx = 0
-        while (idx < row.size) {
+        while (idx < row.numFields) {
           ar(idx) = converters(idx).toScala(row, idx)
           idx += 1
         }
@@ -271,7 +271,7 @@ object CatalystTypeConverters {
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Row =
-      toScala(row(column).asInstanceOf[InternalRow])
+      toScala(row.get(column).asInstanceOf[InternalRow])
   }
 
   private object StringConverter extends CatalystTypeConverter[Any, String, UTF8String] {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index c7ec49b3d6c3d..efc4faea569b2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -25,48 +25,139 @@ import org.apache.spark.unsafe.types.UTF8String
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
  * internal types.
  */
-abstract class InternalRow extends Row {
+abstract class InternalRow extends Serializable {
 
-  def getUTF8String(i: Int): UTF8String = getAs[UTF8String](i)
+  def numFields: Int
 
-  def getBinary(i: Int): Array[Byte] = getAs[Array[Byte]](i)
+  def get(i: Int): Any
 
-  // This is only use for test
-  override def getString(i: Int): String = getAs[UTF8String](i).toString
-
-  // These expensive API should not be used internally.
-  final override def getDecimal(i: Int): java.math.BigDecimal =
-    throw new UnsupportedOperationException
-  final override def getDate(i: Int): java.sql.Date =
-    throw new UnsupportedOperationException
-  final override def getTimestamp(i: Int): java.sql.Timestamp =
-    throw new UnsupportedOperationException
-  final override def getSeq[T](i: Int): Seq[T] = throw new UnsupportedOperationException
-  final override def getList[T](i: Int): java.util.List[T] = throw new UnsupportedOperationException
-  final override def getMap[K, V](i: Int): scala.collection.Map[K, V] =
-    throw new UnsupportedOperationException
-  final override def getJavaMap[K, V](i: Int): java.util.Map[K, V] =
-    throw new UnsupportedOperationException
-  final override def getStruct(i: Int): Row = throw new UnsupportedOperationException
-  final override def getAs[T](fieldName: String): T = throw new UnsupportedOperationException
-  final override def getValuesMap[T](fieldNames: Seq[String]): Map[String, T] =
-    throw new UnsupportedOperationException
-
-  // A default implementation to change the return type
-  override def copy(): InternalRow = this
+  // TODO: Remove this.
+  def apply(i: Int): Any = get(i)
+
+  def getAs[T](i: Int): T = get(i).asInstanceOf[T]
+
+  def isNullAt(i: Int): Boolean = get(i) == null
+
+  def getBoolean(i: Int): Boolean = getAs[Boolean](i)
+
+  def getByte(i: Int): Byte = getAs[Byte](i)
+
+  def getShort(i: Int): Short = getAs[Short](i)
+
+  def getInt(i: Int): Int = getAs[Int](i)
+
+  def getLong(i: Int): Long = getAs[Long](i)
+
+  def getFloat(i: Int): Float = getAs[Float](i)
+
+  def getDouble(i: Int): Double = getAs[Double](i)
+
+  override def toString: String = s"[${this.mkString(",")}]"
+
+  /**
+   * Make a copy of the current [[InternalRow]] object.
+   */
+  def copy(): InternalRow = this
+
+  /** Returns true if there are any NULL values in this row. */
+  def anyNull: Boolean = {
+    val len = numFields
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i)) { return true }
+      i += 1
+    }
+    false
+  }
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[InternalRow]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[InternalRow]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numFields
+    if (len != other.numFields) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = get(i)
+        val o2 = other.get(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
-   * Returns true if we can check equality for these 2 rows.
-   * Equality check between external row and internal row is not allowed.
-   * Here we do this check to prevent call `equals` on internal row with external row.
+   * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  protected override def canEqual(other: Row) = other.isInstanceOf[InternalRow]
+  def toSeq: Seq[Any] = {
+    val n = numFields
+    val values = new Array[Any](n)
+    var i = 0
+    while (i < n) {
+      values.update(i, get(i))
+      i += 1
+    }
+    values.toSeq
+  }
+
+  /** Displays all elements of this sequence in a string (without a separator). */
+  def mkString: String = toSeq.mkString
+
+  /** Displays all elements of this sequence in a string using a separator string. */
+  def mkString(sep: String): String = toSeq.mkString(sep)
+
+  /**
+   * Displays all elements of this traversable or iterator in a string using
+   * start, end, and separator strings.
+   */
+  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
+
+  def getUTF8String(i: Int): UTF8String = getAs[UTF8String](i)
+
+  def getBinary(i: Int): Array[Byte] = getAs[Array[Byte]](i)
+
+  // This is only use for test
+  def getString(i: Int): String = getAs[UTF8String](i).toString
 
   // Custom hashCode function that matches the efficient code generated version.
   override def hashCode: Int = {
     var result: Int = 37
     var i = 0
-    while (i < length) {
+    val len = numFields
+    while (i < len) {
       val update: Int =
         if (isNullAt(i)) {
           0
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index c66854d52c50b..47ad3e089e4c7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -382,8 +382,8 @@ case class Cast(child: Expression, dataType: DataType)
     val newRow = new GenericMutableRow(from.fields.length)
     buildCast[InternalRow](_, row => {
       var i = 0
-      while (i < row.length) {
-        newRow.update(i, if (row.isNullAt(i)) null else casts(i)(row(i)))
+      while (i < row.numFields) {
+        newRow.update(i, if (row.isNullAt(i)) null else casts(i)(row.get(i)))
         i += 1
       }
       newRow.copy()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 04872fbc8b091..dbda05a792cbf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -176,49 +176,49 @@ class JoinedRow extends InternalRow {
 
   override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
-  override def length: Int = row1.length + row2.length
+  override def numFields: Int = row1.numFields + row2.numFields
 
   override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
   }
 
   override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
   override def get(i: Int): Any =
-    if (i < row1.length) row1(i) else row2(i - row1.length)
+    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
-    if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
 
   override def getInt(i: Int): Int =
-    if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
 
   override def getLong(i: Int): Long =
-    if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
 
   override def getDouble(i: Int): Double =
-    if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
 
   override def getBoolean(i: Int): Boolean =
-    if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
 
   override def getShort(i: Int): Short =
-    if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
 
   override def getByte(i: Int): Byte =
-    if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
 
   override def getFloat(i: Int): Float =
-    if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
   override def copy(): InternalRow = {
-    val totalSize = row1.length + row2.length
+    val totalSize = row1.numFields + row2.numFields
     val copiedValues = new Array[Any](totalSize)
     var i = 0
     while(i < totalSize) {
-      copiedValues(i) = apply(i)
+      copiedValues(i) = get(i)
       i += 1
     }
     new GenericInternalRow(copiedValues)
@@ -278,49 +278,49 @@ class JoinedRow2 extends InternalRow {
 
   override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
-  override def length: Int = row1.length + row2.length
+  override def numFields: Int = row1.numFields + row2.numFields
 
   override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
   }
 
   override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
   override def get(i: Int): Any =
-    if (i < row1.length) row1(i) else row2(i - row1.length)
+    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
-    if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
 
   override def getInt(i: Int): Int =
-    if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
 
   override def getLong(i: Int): Long =
-    if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
 
   override def getDouble(i: Int): Double =
-    if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
 
   override def getBoolean(i: Int): Boolean =
-    if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
 
   override def getShort(i: Int): Short =
-    if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
 
   override def getByte(i: Int): Byte =
-    if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
 
   override def getFloat(i: Int): Float =
-    if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
   override def copy(): InternalRow = {
-    val totalSize = row1.length + row2.length
+    val totalSize = row1.numFields + row2.numFields
     val copiedValues = new Array[Any](totalSize)
     var i = 0
     while(i < totalSize) {
-      copiedValues(i) = apply(i)
+      copiedValues(i) = get(i)
       i += 1
     }
     new GenericInternalRow(copiedValues)
@@ -374,50 +374,50 @@ class JoinedRow3 extends InternalRow {
 
   override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
-  override def length: Int = row1.length + row2.length
+  override def numFields: Int = row1.numFields + row2.numFields
 
   override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
   }
 
   override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
 
   override def get(i: Int): Any =
-    if (i < row1.length) row1(i) else row2(i - row1.length)
+    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
-    if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
 
   override def getInt(i: Int): Int =
-    if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
 
   override def getLong(i: Int): Long =
-    if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
 
   override def getDouble(i: Int): Double =
-    if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
 
   override def getBoolean(i: Int): Boolean =
-    if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
 
   override def getShort(i: Int): Short =
-    if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
 
   override def getByte(i: Int): Byte =
-    if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
 
   override def getFloat(i: Int): Float =
-    if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
   override def copy(): InternalRow = {
-    val totalSize = row1.length + row2.length
+    val totalSize = row1.numFields + row2.numFields
     val copiedValues = new Array[Any](totalSize)
     var i = 0
     while(i < totalSize) {
-      copiedValues(i) = apply(i)
+      copiedValues(i) = get(i)
       i += 1
     }
     new GenericInternalRow(copiedValues)
@@ -471,50 +471,50 @@ class JoinedRow4 extends InternalRow {
 
   override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
-  override def length: Int = row1.length + row2.length
+  override def numFields: Int = row1.numFields + row2.numFields
 
   override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
   }
 
   override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
 
   override def get(i: Int): Any =
-    if (i < row1.length) row1(i) else row2(i - row1.length)
+    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
-    if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
 
   override def getInt(i: Int): Int =
-    if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
 
   override def getLong(i: Int): Long =
-    if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
 
   override def getDouble(i: Int): Double =
-    if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
 
   override def getBoolean(i: Int): Boolean =
-    if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
 
   override def getShort(i: Int): Short =
-    if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
 
   override def getByte(i: Int): Byte =
-    if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
 
   override def getFloat(i: Int): Float =
-    if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
   override def copy(): InternalRow = {
-    val totalSize = row1.length + row2.length
+    val totalSize = row1.numFields + row2.numFields
     val copiedValues = new Array[Any](totalSize)
     var i = 0
     while(i < totalSize) {
-      copiedValues(i) = apply(i)
+      copiedValues(i) = get(i)
       i += 1
     }
     new GenericInternalRow(copiedValues)
@@ -568,50 +568,50 @@ class JoinedRow5 extends InternalRow {
 
   override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
-  override def length: Int = row1.length + row2.length
+  override def numFields: Int = row1.numFields + row2.numFields
 
   override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
   }
 
   override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
 
   override def get(i: Int): Any =
-    if (i < row1.length) row1(i) else row2(i - row1.length)
+    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
-    if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
 
   override def getInt(i: Int): Int =
-    if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
 
   override def getLong(i: Int): Long =
-    if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
 
   override def getDouble(i: Int): Double =
-    if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
 
   override def getBoolean(i: Int): Boolean =
-    if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
 
   override def getShort(i: Int): Short =
-    if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
 
   override def getByte(i: Int): Byte =
-    if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
 
   override def getFloat(i: Int): Float =
-    if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
   override def copy(): InternalRow = {
-    val totalSize = row1.length + row2.length
+    val totalSize = row1.numFields + row2.numFields
     val copiedValues = new Array[Any](totalSize)
     var i = 0
     while(i < totalSize) {
-      copiedValues(i) = apply(i)
+      copiedValues(i) = get(i)
       i += 1
     }
     new GenericInternalRow(copiedValues)
@@ -665,50 +665,50 @@ class JoinedRow6 extends InternalRow {
 
   override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
-  override def length: Int = row1.length + row2.length
+  override def numFields: Int = row1.numFields + row2.numFields
 
   override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.length) row1.getUTF8String(i) else row2.getUTF8String(i - row1.length)
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
   }
 
   override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.length) row1.getBinary(i) else row2.getBinary(i - row1.length)
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
 
   override def get(i: Int): Any =
-    if (i < row1.length) row1(i) else row2(i - row1.length)
+    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
-    if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
 
   override def getInt(i: Int): Int =
-    if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
 
   override def getLong(i: Int): Long =
-    if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
 
   override def getDouble(i: Int): Double =
-    if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
 
   override def getBoolean(i: Int): Boolean =
-    if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
 
   override def getShort(i: Int): Short =
-    if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
 
   override def getByte(i: Int): Byte =
-    if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
 
   override def getFloat(i: Int): Float =
-    if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
   override def copy(): InternalRow = {
-    val totalSize = row1.length + row2.length
+    val totalSize = row1.numFields + row2.numFields
     val copiedValues = new Array[Any](totalSize)
     var i = 0
     while(i < totalSize) {
-      copiedValues(i) = apply(i)
+      copiedValues(i) = get(i)
       i += 1
     }
     new GenericInternalRow(copiedValues)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 6f291d2c86c1e..4b4833bd06a3b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -211,7 +211,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
 
   def this() = this(Seq.empty)
 
-  override def length: Int = values.length
+  override def numFields: Int = values.length
 
   override def toSeq: Seq[Any] = values.map(_.boxed).toSeq
 
@@ -245,7 +245,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
   override def setString(ordinal: Int, value: String): Unit =
     update(ordinal, UTF8String.fromString(value))
 
-  override def getString(ordinal: Int): String = apply(ordinal).toString
+  override def getString(ordinal: Int): String = get(ordinal).toString
 
   override def setInt(ordinal: Int, value: Int): Unit = {
     val currentValue = values(ordinal).asInstanceOf[MutableInt]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 73fde4e9164d7..62b6cc834c9c9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -675,7 +675,7 @@ case class CombineSetsAndSumFunction(
     val inputSetEval = inputSet.eval(input).asInstanceOf[OpenHashSet[Any]]
     val inputIterator = inputSetEval.iterator
     while (inputIterator.hasNext) {
-      seen.add(inputIterator.next)
+      seen.add(inputIterator.next())
     }
   }
 
@@ -685,7 +685,7 @@ case class CombineSetsAndSumFunction(
       null
     } else {
       Cast(Literal(
-        casted.iterator.map(f => f.apply(0)).reduceLeft(
+        casted.iterator.map(f => f.get(0)).reduceLeft(
           base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus)),
         base.dataType).eval(null)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 405d6b0e3bc76..f0efc4bff12ba 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -178,7 +178,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
           $initColumns
         }
 
-        public int length() { return ${expressions.length};}
+        public int numFields() { return ${expressions.length};}
         protected boolean[] nullBits = new boolean[${expressions.length}];
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
index 5504781edca1b..c91122cda2a41 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
@@ -110,7 +110,7 @@ case class GetStructField(child: Expression, field: StructField, ordinal: Int)
   override def toString: String = s"$child.${field.name}"
 
   protected override def nullSafeEval(input: Any): Any =
-    input.asInstanceOf[InternalRow](ordinal)
+    input.asInstanceOf[InternalRow].get(ordinal)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, eval => {
@@ -142,7 +142,7 @@ case class GetArrayStructFields(
 
   protected override def nullSafeEval(input: Any): Any = {
     input.asInstanceOf[Seq[InternalRow]].map { row =>
-      if (row == null) null else row(ordinal)
+      if (row == null) null else row.get(ordinal)
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index d78be5a5958f9..53779dd4049d1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -44,9 +44,10 @@ abstract class MutableRow extends InternalRow {
   }
 
   override def copy(): InternalRow = {
-    val arr = new Array[Any](length)
+    val n = numFields
+    val arr = new Array[Any](n)
     var i = 0
-    while (i < length) {
+    while (i < n) {
       arr(i) = get(i)
       i += 1
     }
@@ -54,36 +55,23 @@ abstract class MutableRow extends InternalRow {
   }
 }
 
-/**
- * A row implementation that uses an array of objects as the underlying storage.
- */
-trait ArrayBackedRow {
-  self: Row =>
-
-  protected val values: Array[Any]
-
-  override def toSeq: Seq[Any] = values.toSeq
-
-  def length: Int = values.length
-
-  override def get(i: Int): Any = values(i)
-
-  def setNullAt(i: Int): Unit = { values(i) = null}
-
-  def update(i: Int, value: Any): Unit = { values(i) = value }
-}
-
 /**
  * A row implementation that uses an array of objects as the underlying storage.  Note that, while
  * the array is not copied, and thus could technically be mutated after creation, this is not
  * allowed.
  */
-class GenericRow(protected[sql] val values: Array[Any]) extends Row with ArrayBackedRow {
+class GenericRow(protected[sql] val values: Array[Any]) extends Row {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
+  override def length: Int = values.length
+
+  override def get(i: Int): Any = values(i)
+
+  override def toSeq: Seq[Any] = values.toSeq
+
   override def copy(): Row = this
 }
 
@@ -101,34 +89,49 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(protected[sql] val values: Array[Any])
-    extends InternalRow with ArrayBackedRow {
+class GenericInternalRow(protected[sql] val values: Array[Any]) extends InternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
+  override def toSeq: Seq[Any] = values.toSeq
+
+  override def numFields: Int = values.length
+
+  override def get(i: Int): Any = values(i)
+
   override def copy(): InternalRow = this
 }
 
 /**
  * This is used for serialization of Python DataFrame
  */
-class GenericInternalRowWithSchema(values: Array[Any], override val schema: StructType)
+class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   extends GenericInternalRow(values) {
 
   /** No-arg constructor for serialization. */
   protected def this() = this(null, null)
 
-  override def fieldIndex(name: String): Int = schema.fieldIndex(name)
+  def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(val values: Array[Any]) extends MutableRow with ArrayBackedRow {
+class GenericMutableRow(val values: Array[Any]) extends MutableRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
+  override def toSeq: Seq[Any] = values.toSeq
+
+  override def numFields: Int = values.length
+
+  override def get(i: Int): Any = values(i)
+
+  override def setNullAt(i: Int): Unit = { values(i) = null}
+
+  override def update(i: Int, value: Any): Unit = { values(i) = value }
+
   override def copy(): InternalRow = new GenericInternalRow(values.clone())
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala
index 878a1bb9b7e6d..01ff84cb56054 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala
@@ -83,15 +83,5 @@ class RowTest extends FunSpec with Matchers {
     it("equality check for internal rows") {
       internalRow shouldEqual internalRow2
     }
-
-    it("throws an exception when check equality between external and internal rows") {
-      def assertError(f: => Unit): Unit = {
-        val e = intercept[UnsupportedOperationException](f)
-        e.getMessage.contains("cannot check equality between external and internal rows")
-      }
-
-      assertError(internalRow.equals(externalRow))
-      assertError(externalRow.equals(internalRow))
-    }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index facf65c155148..408353cf70a49 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -24,6 +24,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * Test suite for data type casting expression [[Cast]].
@@ -580,14 +581,21 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("cast from struct") {
     val struct = Literal.create(
-      InternalRow("123", "abc", "", null),
+      InternalRow(
+        UTF8String.fromString("123"),
+        UTF8String.fromString("abc"),
+        UTF8String.fromString(""),
+        null),
       StructType(Seq(
         StructField("a", StringType, nullable = true),
         StructField("b", StringType, nullable = true),
         StructField("c", StringType, nullable = true),
         StructField("d", StringType, nullable = true))))
     val struct_notNull = Literal.create(
-      InternalRow("123", "abc", ""),
+      InternalRow(
+        UTF8String.fromString("123"),
+        UTF8String.fromString("abc"),
+        UTF8String.fromString("")),
       StructType(Seq(
         StructField("a", StringType, nullable = false),
         StructField("b", StringType, nullable = false),
@@ -676,8 +684,11 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("complex casting") {
     val complex = Literal.create(
       InternalRow(
-        Seq("123", "abc", ""),
-        Map("a" -> "123", "b" -> "abc", "c" -> ""),
+        Seq(UTF8String.fromString("123"), UTF8String.fromString("abc"), UTF8String.fromString("")),
+        Map(
+          UTF8String.fromString("a") -> UTF8String.fromString("123"),
+          UTF8String.fromString("b") -> UTF8String.fromString("abc"),
+          UTF8String.fromString("c") -> UTF8String.fromString("")),
         InternalRow(0)),
       StructType(Seq(
         StructField("a",
@@ -700,7 +711,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     assert(ret.resolved === true)
     checkEvaluation(ret, InternalRow(
       Seq(123, null, null),
-      Map("a" -> true, "b" -> true, "c" -> false),
+      Map(
+        UTF8String.fromString("a") -> true,
+        UTF8String.fromString("b") -> true,
+        UTF8String.fromString("c") -> false),
       InternalRow(0L)))
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index a8aee8f634e03..fc842772f3480 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -22,6 +22,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedExtractValue
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 
 class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -150,12 +151,14 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("CreateNamedStruct with literal field") {
     val row = InternalRow(1, 2, 3)
     val c1 = 'a.int.at(0)
-    checkEvaluation(CreateNamedStruct(Seq("a", c1, "b", "y")), InternalRow(1, "y"), row)
+    checkEvaluation(CreateNamedStruct(Seq("a", c1, "b", "y")),
+      InternalRow(1, UTF8String.fromString("y")), row)
   }
 
   test("CreateNamedStruct from all literal fields") {
     checkEvaluation(
-      CreateNamedStruct(Seq("a", "x", "b", 2.0)), InternalRow("x", 2.0), InternalRow.empty)
+      CreateNamedStruct(Seq("a", "x", "b", 2.0)),
+      InternalRow(UTF8String.fromString("x"), 2.0), InternalRow.empty)
   }
 
   test("test dsl for complex type") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 9d8415f06399c..ac42bde07c37d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -309,7 +309,7 @@ private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) {
 
 private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
   override def actualSize(row: InternalRow, ordinal: Int): Int = {
-    row.getString(ordinal).getBytes("utf-8").length + 4
+    row.getUTF8String(ordinal).numBytes() + 4
   }
 
   override def append(v: UTF8String, buffer: ByteBuffer): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 38720968c1313..5d5b0697d7016 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -134,13 +134,13 @@ private[sql] case class InMemoryRelation(
             // may result malformed rows, causing ArrayIndexOutOfBoundsException, which is somewhat
             // hard to decipher.
             assert(
-              row.size == columnBuilders.size,
-              s"""Row column number mismatch, expected ${output.size} columns, but got ${row.size}.
-                 |Row content: $row
-               """.stripMargin)
+              row.numFields == columnBuilders.size,
+              s"Row column number mismatch, expected ${output.size} columns, " +
+                s"but got ${row.numFields}." +
+                s"\nRow content: $row")
 
             var i = 0
-            while (i < row.length) {
+            while (i < row.numFields) {
               columnBuilders(i).appendFrom(row, i)
               i += 1
             }
@@ -304,7 +304,7 @@ private[sql] case class InMemoryColumnarTableScan(
 
           // Extract rows via column accessors
           new Iterator[InternalRow] {
-            private[this] val rowLen = nextRow.length
+            private[this] val rowLen = nextRow.numFields
             override def next(): InternalRow = {
               var i = 0
               while (i < rowLen) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index c87e2064a8f33..83c4e8733f15f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -25,7 +25,6 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
 import org.apache.spark.serializer._
-import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{MutableRow, SpecificMutableRow}
 import org.apache.spark.sql.types._
@@ -53,7 +52,7 @@ private[sql] class Serializer2SerializationStream(
   private val writeRowFunc = SparkSqlSerializer2.createSerializationFunction(rowSchema, rowOut)
 
   override def writeObject[T: ClassTag](t: T): SerializationStream = {
-    val kv = t.asInstanceOf[Product2[Row, Row]]
+    val kv = t.asInstanceOf[Product2[InternalRow, InternalRow]]
     writeKey(kv._1)
     writeValue(kv._2)
 
@@ -66,7 +65,7 @@ private[sql] class Serializer2SerializationStream(
   }
 
   override def writeValue[T: ClassTag](t: T): SerializationStream = {
-    writeRowFunc(t.asInstanceOf[Row])
+    writeRowFunc(t.asInstanceOf[InternalRow])
     this
   }
 
@@ -205,8 +204,9 @@ private[sql] object SparkSqlSerializer2 {
   /**
    * The util function to create the serialization function based on the given schema.
    */
-  def createSerializationFunction(schema: Array[DataType], out: DataOutputStream): Row => Unit = {
-    (row: Row) =>
+  def createSerializationFunction(schema: Array[DataType], out: DataOutputStream)
+    : InternalRow => Unit = {
+    (row: InternalRow) =>
       // If the schema is null, the returned function does nothing when it get called.
       if (schema != null) {
         var i = 0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 2b400926177fe..7f452daef33c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -206,7 +206,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         val mutableRow = new SpecificMutableRow(dataTypes)
         iterator.map { dataRow =>
           var i = 0
-          while (i < mutableRow.length) {
+          while (i < mutableRow.numFields) {
             mergers(i)(mutableRow, dataRow, i)
             i += 1
           }
@@ -315,7 +315,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     if (relation.relation.needConversion) {
       execution.RDDConversions.rowToRowRdd(rdd, output.map(_.dataType))
     } else {
-      rdd.map(_.asInstanceOf[InternalRow])
+      rdd.asInstanceOf[RDD[InternalRow]]
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
index cd2aa7f7433c5..d551f386eee6e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
@@ -174,14 +174,19 @@ private[sql] case class InsertIntoHadoopFsRelation(
       try {
         writerContainer.executorSideSetup(taskContext)
 
-        val converter: InternalRow => Row = if (needsConversion) {
-          CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow => Row]
+        if (needsConversion) {
+          val converter = CatalystTypeConverters.createToScalaConverter(dataSchema)
+            .asInstanceOf[InternalRow => Row]
+          while (iterator.hasNext) {
+            val internalRow = iterator.next()
+            writerContainer.outputWriterForRow(internalRow).write(converter(internalRow))
+          }
         } else {
-          r: InternalRow => r.asInstanceOf[Row]
-        }
-        while (iterator.hasNext) {
-          val internalRow = iterator.next()
-          writerContainer.outputWriterForRow(internalRow).write(converter(internalRow))
+          while (iterator.hasNext) {
+            val internalRow = iterator.next()
+            writerContainer.outputWriterForRow(internalRow)
+              .asInstanceOf[OutputWriterInternal].writeInternal(internalRow)
+          }
         }
 
         writerContainer.commitTask()
@@ -248,17 +253,23 @@ private[sql] case class InsertIntoHadoopFsRelation(
         val partitionProj = newProjection(codegenEnabled, partitionCasts, output)
         val dataProj = newProjection(codegenEnabled, dataOutput, output)
 
-        val dataConverter: InternalRow => Row = if (needsConversion) {
-          CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow => Row]
+        if (needsConversion) {
+          val converter = CatalystTypeConverters.createToScalaConverter(dataSchema)
+            .asInstanceOf[InternalRow => Row]
+          while (iterator.hasNext) {
+            val internalRow = iterator.next()
+            val partitionPart = partitionProj(internalRow)
+            val dataPart = converter(dataProj(internalRow))
+            writerContainer.outputWriterForRow(partitionPart).write(dataPart)
+          }
         } else {
-          r: InternalRow => r.asInstanceOf[Row]
-        }
-
-        while (iterator.hasNext) {
-          val internalRow = iterator.next()
-          val partitionPart = partitionProj(internalRow)
-          val dataPart = dataConverter(dataProj(internalRow))
-          writerContainer.outputWriterForRow(partitionPart).write(dataPart)
+          while (iterator.hasNext) {
+            val internalRow = iterator.next()
+            val partitionPart = partitionProj(internalRow)
+            val dataPart = dataProj(internalRow)
+            writerContainer.outputWriterForRow(partitionPart)
+              .asInstanceOf[OutputWriterInternal].writeInternal(dataPart)
+          }
         }
 
         writerContainer.commitTask()
@@ -530,8 +541,12 @@ private[sql] class DynamicPartitionWriterContainer(
       while (i < partitionColumns.length) {
         val col = partitionColumns(i)
         val partitionValueString = {
-          val string = row.getString(i)
-          if (string.eq(null)) defaultPartitionName else PartitioningUtils.escapePathName(string)
+          val string = row.getUTF8String(i)
+          if (string.eq(null)) {
+            defaultPartitionName
+          } else {
+            PartitioningUtils.escapePathName(string.toString)
+          }
         }
 
         if (i > 0) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index c8033d3c0470a..1f2797ec5527a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -23,11 +23,11 @@ import scala.util.matching.Regex
 import org.apache.hadoop.fs.Path
 import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.{AnalysisException, DataFrame, SQLContext, SaveMode}
+import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SQLContext, SaveMode}
+import org.apache.spark.sql.catalyst.AbstractSparkSQLParser
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.{AbstractSparkSQLParser, InternalRow}
 import org.apache.spark.sql.execution.RunnableCommand
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
@@ -415,12 +415,12 @@ private[sql] case class CreateTempTableUsing(
     provider: String,
     options: Map[String, String]) extends RunnableCommand {
 
-  def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  def run(sqlContext: SQLContext): Seq[Row] = {
     val resolved = ResolvedDataSource(
       sqlContext, userSpecifiedSchema, Array.empty[String], provider, options)
     sqlContext.registerDataFrameAsTable(
       DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName)
-    Seq.empty
+    Seq.empty[Row]
   }
 }
 
@@ -432,20 +432,20 @@ private[sql] case class CreateTempTableUsingAsSelect(
     options: Map[String, String],
     query: LogicalPlan) extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val df = DataFrame(sqlContext, query)
     val resolved = ResolvedDataSource(sqlContext, provider, partitionColumns, mode, options, df)
     sqlContext.registerDataFrameAsTable(
       DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName)
 
-    Seq.empty
+    Seq.empty[Row]
   }
 }
 
 private[sql] case class RefreshTable(databaseName: String, tableName: String)
   extends RunnableCommand {
 
-  override def run(sqlContext: SQLContext): Seq[InternalRow] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     // Refresh the given table's metadata first.
     sqlContext.catalog.refreshTable(databaseName, tableName)
 
@@ -464,7 +464,7 @@ private[sql] case class RefreshTable(databaseName: String, tableName: String)
       sqlContext.cacheManager.cacheQuery(df, Some(tableName))
     }
 
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index e6e27a87c7151..40bf03a3f1a62 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -126,9 +126,9 @@ object EvaluatePython {
     case (null, _) => null
 
     case (row: InternalRow, struct: StructType) =>
-      val values = new Array[Any](row.size)
+      val values = new Array[Any](row.numFields)
       var i = 0
-      while (i < row.size) {
+      while (i < row.numFields) {
         values(i) = toJava(row(i), struct.fields(i).dataType)
         i += 1
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
index 6c49a906c848a..46f0fac861282 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
@@ -148,7 +148,7 @@ class InputAggregationBuffer private[sql] (
     toCatalystConverters: Array[Any => Any],
     toScalaConverters: Array[Any => Any],
     bufferOffset: Int,
-    var underlyingInputBuffer: Row)
+    var underlyingInputBuffer: InternalRow)
   extends AggregationBuffer(toCatalystConverters, toScalaConverters, bufferOffset) {
 
   override def get(i: Int): Any = {
@@ -156,6 +156,7 @@ class InputAggregationBuffer private[sql] (
       throw new IllegalArgumentException(
         s"Could not access ${i}th value in this buffer because it only has $length values.")
     }
+    // TODO: Use buffer schema to avoid using generic getter.
     toScalaConverters(i)(underlyingInputBuffer(offsets(i)))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
index 4d3aac464c538..41d0ecb4bbfbf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
@@ -128,6 +128,7 @@ private[sql] case class JDBCRelation(
 
   override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = {
     val driver: String = DriverRegistry.getDriverClassName(url)
+    // Rely on a type erasure hack to pass RDD[InternalRow] back as RDD[Row]
     JDBCRDD.scanTable(
       sqlContext.sparkContext,
       schema,
@@ -137,7 +138,7 @@ private[sql] case class JDBCRelation(
       table,
       requiredColumns,
       filters,
-      parts).map(_.asInstanceOf[Row])
+      parts).asInstanceOf[RDD[Row]]
   }
 
   override def insert(data: DataFrame, overwrite: Boolean): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 922794ac9aac5..562b058414d07 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -154,17 +154,19 @@ private[sql] class JSONRelation(
   }
 
   override def buildScan(): RDD[Row] = {
+    // Rely on type erasure hack to pass RDD[InternalRow] back as RDD[Row]
     JacksonParser(
       baseRDD(),
       schema,
-      sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
+      sqlContext.conf.columnNameOfCorruptRecord).asInstanceOf[RDD[Row]]
   }
 
   override def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] = {
+    // Rely on a type erasure hack to pass RDD[InternalRow] back as RDD[Row]
     JacksonParser(
       baseRDD(),
       StructType.fromAttributes(requiredColumns),
-      sqlContext.conf.columnNameOfCorruptRecord).map(_.asInstanceOf[Row])
+      sqlContext.conf.columnNameOfCorruptRecord).asInstanceOf[RDD[Row]]
   }
 
   override def insert(data: DataFrame, overwrite: Boolean): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
index 0c3d8fdab6bd2..b5e4263008f56 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
@@ -28,7 +28,7 @@ import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveCo
 import org.apache.parquet.schema.Type.Repetition
 import org.apache.parquet.schema.{GroupType, PrimitiveType, Type}
 
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
@@ -55,8 +55,8 @@ private[parquet] trait ParentContainerUpdater {
 private[parquet] object NoopUpdater extends ParentContainerUpdater
 
 /**
- * A [[CatalystRowConverter]] is used to convert Parquet "structs" into Spark SQL [[Row]]s.  Since
- * any Parquet record is also a struct, this converter can also be used as root converter.
+ * A [[CatalystRowConverter]] is used to convert Parquet "structs" into Spark SQL [[InternalRow]]s.
+ * Since any Parquet record is also a struct, this converter can also be used as root converter.
  *
  * When used as a root converter, [[NoopUpdater]] should be used since root converters don't have
  * any "parent" container.
@@ -108,7 +108,7 @@ private[parquet] class CatalystRowConverter(
 
   override def start(): Unit = {
     var i = 0
-    while (i < currentRow.length) {
+    while (i < currentRow.numFields) {
       currentRow.setNullAt(i)
       i += 1
     }
@@ -178,7 +178,7 @@ private[parquet] class CatalystRowConverter(
 
       case t: StructType =>
         new CatalystRowConverter(parquetType.asGroupType(), t, new ParentContainerUpdater {
-          override def set(value: Any): Unit = updater.set(value.asInstanceOf[Row].copy())
+          override def set(value: Any): Unit = updater.set(value.asInstanceOf[InternalRow].copy())
         })
 
       case t: UserDefinedType[_] =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index 28cba5e54d69e..8cab27d6e1c46 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -178,7 +178,7 @@ private[sql] case class ParquetTableScan(
               val row = iter.next()._2.asInstanceOf[InternalRow]
 
               var i = 0
-              while (i < row.size) {
+              while (i < row.numFields) {
                 mutableRow(i) = row(i)
                 i += 1
               }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index d1040bf5562a2..c7c58e69d42ef 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -208,9 +208,9 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
 
   override def write(record: InternalRow): Unit = {
     val attributesSize = attributes.size
-    if (attributesSize > record.size) {
-      throw new IndexOutOfBoundsException(
-        s"Trying to write more fields than contained in row ($attributesSize > ${record.size})")
+    if (attributesSize > record.numFields) {
+      throw new IndexOutOfBoundsException("Trying to write more fields than contained in row " +
+        s"($attributesSize > ${record.numFields})")
     }
 
     var index = 0
@@ -378,9 +378,9 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
 private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
   override def write(record: InternalRow): Unit = {
     val attributesSize = attributes.size
-    if (attributesSize > record.size) {
-      throw new IndexOutOfBoundsException(
-        s"Trying to write more fields than contained in row ($attributesSize > ${record.size})")
+    if (attributesSize > record.numFields) {
+      throw new IndexOutOfBoundsException("Trying to write more fields than contained in row " +
+        s"($attributesSize > ${record.numFields})")
     }
 
     var index = 0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
index c384697c0ee62..8ec228c2b25bc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
@@ -61,7 +61,7 @@ private[sql] class DefaultSource extends HadoopFsRelationProvider {
 
 // NOTE: This class is instantiated and used on executor side only, no need to be serializable.
 private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext)
-  extends OutputWriter {
+  extends OutputWriterInternal {
 
   private val recordWriter: RecordWriter[Void, InternalRow] = {
     val outputFormat = {
@@ -86,7 +86,7 @@ private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext
     outputFormat.getRecordWriter(context)
   }
 
-  override def write(row: Row): Unit = recordWriter.write(null, row.asInstanceOf[InternalRow])
+  override def writeInternal(row: InternalRow): Unit = recordWriter.write(null, row)
 
   override def close(): Unit = recordWriter.close(context)
 }
@@ -324,7 +324,7 @@ private[sql] class ParquetRelation2(
             new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
           }
         }
-      }.values.map(_.asInstanceOf[Row])
+      }.values.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 7cd005b959488..119bac786d478 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -344,6 +344,18 @@ abstract class OutputWriter {
   def close(): Unit
 }
 
+/**
+ * This is an internal, private version of [[OutputWriter]] with an writeInternal method that
+ * accepts an [[InternalRow]] rather than an [[Row]]. Data sources that return this must have
+ * the conversion flag set to false.
+ */
+private[sql] abstract class OutputWriterInternal extends OutputWriter {
+
+  override def write(row: Row): Unit = throw new UnsupportedOperationException
+
+  def writeInternal(row: InternalRow): Unit
+}
+
 /**
  * ::Experimental::
  * A [[BaseRelation]] that provides much of the common code required for formats that store their
@@ -592,12 +604,12 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
       BoundReference(dataSchema.fieldIndex(col), field.dataType, field.nullable)
     }.toSeq
 
-    val rdd = buildScan(inputFiles)
-    val converted =
+    val rdd: RDD[Row] = buildScan(inputFiles)
+    val converted: RDD[InternalRow] =
       if (needConversion) {
         RDDConversions.rowToRowRdd(rdd, dataSchema.fields.map(_.dataType))
       } else {
-        rdd.map(_.asInstanceOf[InternalRow])
+        rdd.asInstanceOf[RDD[InternalRow]]
       }
     converted.mapPartitions { rows =>
       val buildProjection = if (codegenEnabled) {
@@ -606,8 +618,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
         () => new InterpretedMutableProjection(requiredOutput, dataSchema.toAttributes)
       }
       val mutableProjection = buildProjection()
-      rows.map(r => mutableProjection(r).asInstanceOf[Row])
-    }
+      rows.map(r => mutableProjection(r))
+    }.asInstanceOf[RDD[Row]]
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 7cc6ffd7548d0..0e5c5abff85f6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -35,14 +35,14 @@ class RowSuite extends SparkFunSuite {
     expected.update(2, false)
     expected.update(3, null)
     val actual1 = Row(2147483647, "this is a string", false, null)
-    assert(expected.size === actual1.size)
+    assert(expected.numFields === actual1.size)
     assert(expected.getInt(0) === actual1.getInt(0))
     assert(expected.getString(1) === actual1.getString(1))
     assert(expected.getBoolean(2) === actual1.getBoolean(2))
     assert(expected(3) === actual1(3))
 
     val actual2 = Row.fromSeq(Seq(2147483647, "this is a string", false, null))
-    assert(expected.size === actual2.size)
+    assert(expected.numFields === actual2.size)
     assert(expected.getInt(0) === actual2.getInt(0))
     assert(expected.getString(1) === actual2.getString(1))
     assert(expected.getBoolean(2) === actual2.getBoolean(2))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
index da53ec16b5c41..84855ce45e918 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
@@ -61,9 +61,10 @@ case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlCo
   override def needConversion: Boolean = false
 
   override def buildScan(): RDD[Row] = {
+    // Rely on a type erasure hack to pass RDD[InternalRow] back as RDD[Row]
     sqlContext.sparkContext.parallelize(from to to).map { e =>
-      InternalRow(UTF8String.fromString(s"people$e"), e * 2): Row
-    }
+      InternalRow(UTF8String.fromString(s"people$e"), e * 2)
+    }.asInstanceOf[RDD[Row]]
   }
 }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
index 257526feab945..0d5183444af78 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
@@ -131,7 +131,7 @@ class PrunedScanSuite extends DataSourceTest {
             queryExecution)
       }
 
-      if (rawOutput.size != expectedColumns.size) {
+      if (rawOutput.numFields != expectedColumns.size) {
         fail(s"Wrong output row. Got $rawOutput\n$queryExecution")
       }
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index 143aadc08b1c4..5e189c3563ca8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -93,7 +93,7 @@ case class AllDataTypesScan(
         InternalRow(i, UTF8String.fromString(i.toString)),
         InternalRow(Seq(UTF8String.fromString(s"str_$i"), UTF8String.fromString(s"str_${i + 1}")),
           InternalRow(Seq(DateTimeUtils.fromJavaDate(new Date(1970, 1, i + 1))))))
-    }
+    }.asInstanceOf[RDD[Row]]
   }
 }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 8202e553afbfe..34b629403e128 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -122,7 +122,7 @@ case class InsertIntoHiveTable(
    *
    * Note: this is run once and then kept to avoid double insertions.
    */
-  protected[sql] lazy val sideEffectResult: Seq[InternalRow] = {
+  protected[sql] lazy val sideEffectResult: Seq[Row] = {
     // Have to pass the TableDesc object to RDD.mapPartitions and then instantiate new serializer
     // instances within the closure, since Serializer is not serializable while TableDesc is.
     val tableDesc = table.tableDesc
@@ -252,13 +252,12 @@ case class InsertIntoHiveTable(
     // however for now we return an empty list to simplify compatibility checks with hive, which
     // does not return anything for insert operations.
     // TODO: implement hive compatibility as rules.
-    Seq.empty[InternalRow]
+    Seq.empty[Row]
   }
 
-  override def executeCollect(): Array[Row] =
-    sideEffectResult.toArray
+  override def executeCollect(): Array[Row] = sideEffectResult.toArray
 
   protected override def doExecute(): RDD[InternalRow] = {
-    sqlContext.sparkContext.parallelize(sideEffectResult, 1)
+    sqlContext.sparkContext.parallelize(sideEffectResult.asInstanceOf[Seq[InternalRow]], 1)
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index ecc78a5f8d321..8850e060d2a73 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -34,6 +34,7 @@ import org.apache.hadoop.hive.common.FileUtils
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.sql.Row
 import org.apache.spark.{Logging, SerializableWritable, SparkHadoopWriter}
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc}
 import org.apache.spark.sql.types._
@@ -94,7 +95,9 @@ private[hive] class SparkHiveWriterContainer(
     "part-" + numberFormat.format(splitID) + extension
   }
 
-  def getLocalFileWriter(row: Row, schema: StructType): FileSinkOperator.RecordWriter = writer
+  def getLocalFileWriter(row: InternalRow, schema: StructType): FileSinkOperator.RecordWriter = {
+    writer
+  }
 
   def close() {
     // Seems the boolean value passed into close does not matter.
@@ -197,7 +200,8 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
     jobConf.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, oldMarker)
   }
 
-  override def getLocalFileWriter(row: Row, schema: StructType): FileSinkOperator.RecordWriter = {
+  override def getLocalFileWriter(row: InternalRow, schema: StructType)
+    : FileSinkOperator.RecordWriter = {
     def convertToHiveRawString(col: String, value: Any): String = {
       val raw = String.valueOf(value)
       schema(col).dataType match {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index de63ee56dd8e6..10623dc820316 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -66,7 +66,7 @@ private[orc] class OrcOutputWriter(
     path: String,
     dataSchema: StructType,
     context: TaskAttemptContext)
-  extends OutputWriter with SparkHadoopMapRedUtil with HiveInspectors {
+  extends OutputWriterInternal with SparkHadoopMapRedUtil with HiveInspectors {
 
   private val serializer = {
     val table = new Properties()
@@ -119,9 +119,9 @@ private[orc] class OrcOutputWriter(
     ).asInstanceOf[RecordWriter[NullWritable, Writable]]
   }
 
-  override def write(row: Row): Unit = {
+  override def writeInternal(row: InternalRow): Unit = {
     var i = 0
-    while (i < row.length) {
+    while (i < row.numFields) {
       reusableOutputBuffer(i) = wrappers(i)(row(i))
       i += 1
     }
@@ -192,7 +192,7 @@ private[sql] class OrcRelation(
       filters: Array[Filter],
       inputPaths: Array[FileStatus]): RDD[Row] = {
     val output = StructType(requiredColumns.map(dataSchema(_))).toAttributes
-    OrcTableScan(output, this, filters, inputPaths).execute().map(_.asInstanceOf[Row])
+    OrcTableScan(output, this, filters, inputPaths).execute().asInstanceOf[RDD[Row]]
   }
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
new file mode 100644
index 0000000000000..e976125b3706d
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.sources
+
+import org.apache.hadoop.fs.Path
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.test.SQLTestUtils
+
+
+class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils {
+  override val sqlContext = TestHive
+
+  // When committing a task, `CommitFailureTestSource` throws an exception for testing purpose.
+  val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName
+
+  test("SPARK-7684: commitTask() failure should fallback to abortTask()") {
+    withTempPath { file =>
+      // Here we coalesce partition number to 1 to ensure that only a single task is issued.  This
+      // prevents race condition happened when FileOutputCommitter tries to remove the `_temporary`
+      // directory while committing/aborting the job.  See SPARK-8513 for more details.
+      val df = sqlContext.range(0, 10).coalesce(1)
+      intercept[SparkException] {
+        df.write.format(dataSourceName).save(file.getCanonicalPath)
+      }
+
+      val fs = new Path(file.getCanonicalPath).getFileSystem(SparkHadoopUtil.get.conf)
+      assert(!fs.exists(new Path(file.getCanonicalPath, "_temporary")))
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
new file mode 100644
index 0000000000000..d280543a071d9
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -0,0 +1,139 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.sources
+
+import java.io.File
+
+import com.google.common.io.Files
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.{AnalysisException, SaveMode, parquet}
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+
+
+class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
+  override val dataSourceName: String = classOf[parquet.DefaultSource].getCanonicalName
+
+  import sqlContext._
+  import sqlContext.implicits._
+
+  test("save()/load() - partitioned table - simple queries - partition columns in data") {
+    withTempDir { file =>
+      val basePath = new Path(file.getCanonicalPath)
+      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
+      val qualifiedBasePath = fs.makeQualified(basePath)
+
+      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
+        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
+        sparkContext
+          .parallelize(for (i <- 1 to 3) yield (i, s"val_$i", p1))
+          .toDF("a", "b", "p1")
+          .write.parquet(partitionDir.toString)
+      }
+
+      val dataSchemaWithPartition =
+        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
+
+      checkQueries(
+        read.format(dataSourceName)
+          .option("dataSchema", dataSchemaWithPartition.json)
+          .load(file.getCanonicalPath))
+    }
+  }
+
+  test("SPARK-7868: _temporary directories should be ignored") {
+    withTempPath { dir =>
+      val df = Seq("a", "b", "c").zipWithIndex.toDF()
+
+      df.write
+        .format("parquet")
+        .save(dir.getCanonicalPath)
+
+      df.write
+        .format("parquet")
+        .save(s"${dir.getCanonicalPath}/_temporary")
+
+      checkAnswer(read.format("parquet").load(dir.getCanonicalPath), df.collect())
+    }
+  }
+
+  test("SPARK-8014: Avoid scanning output directory when SaveMode isn't SaveMode.Append") {
+    withTempDir { dir =>
+      val path = dir.getCanonicalPath
+      val df = Seq(1 -> "a").toDF()
+
+      // Creates an arbitrary file.  If this directory gets scanned, ParquetRelation2 will throw
+      // since it's not a valid Parquet file.
+      val emptyFile = new File(path, "empty")
+      Files.createParentDirs(emptyFile)
+      Files.touch(emptyFile)
+
+      // This shouldn't throw anything.
+      df.write.format("parquet").mode(SaveMode.Ignore).save(path)
+
+      // This should only complain that the destination directory already exists, rather than file
+      // "empty" is not a Parquet file.
+      assert {
+        intercept[AnalysisException] {
+          df.write.format("parquet").mode(SaveMode.ErrorIfExists).save(path)
+        }.getMessage.contains("already exists")
+      }
+
+      // This shouldn't throw anything.
+      df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
+      checkAnswer(read.format("parquet").load(path), df)
+    }
+  }
+
+  test("SPARK-8079: Avoid NPE thrown from BaseWriterContainer.abortJob") {
+    withTempPath { dir =>
+      intercept[AnalysisException] {
+        // Parquet doesn't allow field names with spaces.  Here we are intentionally making an
+        // exception thrown from the `ParquetRelation2.prepareForWriteJob()` method to trigger
+        // the bug.  Please refer to spark-8079 for more details.
+        range(1, 10)
+          .withColumnRenamed("id", "a b")
+          .write
+          .format("parquet")
+          .save(dir.getCanonicalPath)
+      }
+    }
+  }
+
+  test("SPARK-8604: Parquet data source should write summary file while doing appending") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(0, 5)
+      df.write.mode(SaveMode.Overwrite).parquet(path)
+
+      val summaryPath = new Path(path, "_metadata")
+      val commonSummaryPath = new Path(path, "_common_metadata")
+
+      val fs = summaryPath.getFileSystem(configuration)
+      fs.delete(summaryPath, true)
+      fs.delete(commonSummaryPath, true)
+
+      df.write.mode(SaveMode.Append).parquet(path)
+      checkAnswer(sqlContext.read.parquet(path), df.unionAll(df))
+
+      assert(fs.exists(summaryPath))
+      assert(fs.exists(commonSummaryPath))
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
new file mode 100644
index 0000000000000..d761909d60e21
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.sources
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+
+/*
+This is commented out due a bug in the data source API (SPARK-9291).
+
+
+class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
+  override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
+
+  import sqlContext._
+
+  test("save()/load() - partitioned table - simple queries - partition columns in data") {
+    withTempDir { file =>
+      val basePath = new Path(file.getCanonicalPath)
+      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
+      val qualifiedBasePath = fs.makeQualified(basePath)
+
+      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
+        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
+        sparkContext
+          .parallelize(for (i <- 1 to 3) yield s"$i,val_$i,$p1")
+          .saveAsTextFile(partitionDir.toString)
+      }
+
+      val dataSchemaWithPartition =
+        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
+
+      checkQueries(
+        read.format(dataSourceName)
+          .option("dataSchema", dataSchemaWithPartition.json)
+          .load(file.getCanonicalPath))
+    }
+  }
+}
+*/
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 2a8748d913569..dd274023a1cf5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -17,18 +17,14 @@
 
 package org.apache.spark.sql.sources
 
-import java.io.File
-
 import scala.collection.JavaConversions._
 
-import com.google.common.io.Files
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext}
 import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
 import org.apache.parquet.hadoop.ParquetOutputCommitter
 
-import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.LogicalRelation
@@ -581,165 +577,3 @@ class AlwaysFailParquetOutputCommitter(
     sys.error("Intentional job commitment failure for testing purpose.")
   }
 }
-
-class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
-
-  import sqlContext._
-
-  test("save()/load() - partitioned table - simple queries - partition columns in data") {
-    withTempDir { file =>
-      val basePath = new Path(file.getCanonicalPath)
-      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
-      val qualifiedBasePath = fs.makeQualified(basePath)
-
-      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
-        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
-        sparkContext
-          .parallelize(for (i <- 1 to 3) yield s"$i,val_$i,$p1")
-          .saveAsTextFile(partitionDir.toString)
-      }
-
-      val dataSchemaWithPartition =
-        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
-
-      checkQueries(
-        read.format(dataSourceName)
-          .option("dataSchema", dataSchemaWithPartition.json)
-          .load(file.getCanonicalPath))
-    }
-  }
-}
-
-class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils {
-  override val sqlContext = TestHive
-
-  // When committing a task, `CommitFailureTestSource` throws an exception for testing purpose.
-  val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName
-
-  test("SPARK-7684: commitTask() failure should fallback to abortTask()") {
-    withTempPath { file =>
-      // Here we coalesce partition number to 1 to ensure that only a single task is issued.  This
-      // prevents race condition happened when FileOutputCommitter tries to remove the `_temporary`
-      // directory while committing/aborting the job.  See SPARK-8513 for more details.
-      val df = sqlContext.range(0, 10).coalesce(1)
-      intercept[SparkException] {
-        df.write.format(dataSourceName).save(file.getCanonicalPath)
-      }
-
-      val fs = new Path(file.getCanonicalPath).getFileSystem(SparkHadoopUtil.get.conf)
-      assert(!fs.exists(new Path(file.getCanonicalPath, "_temporary")))
-    }
-  }
-}
-
-class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = classOf[parquet.DefaultSource].getCanonicalName
-
-  import sqlContext._
-  import sqlContext.implicits._
-
-  test("save()/load() - partitioned table - simple queries - partition columns in data") {
-    withTempDir { file =>
-      val basePath = new Path(file.getCanonicalPath)
-      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
-      val qualifiedBasePath = fs.makeQualified(basePath)
-
-      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
-        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
-        sparkContext
-          .parallelize(for (i <- 1 to 3) yield (i, s"val_$i", p1))
-          .toDF("a", "b", "p1")
-          .write.parquet(partitionDir.toString)
-      }
-
-      val dataSchemaWithPartition =
-        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
-
-      checkQueries(
-        read.format(dataSourceName)
-          .option("dataSchema", dataSchemaWithPartition.json)
-          .load(file.getCanonicalPath))
-    }
-  }
-
-  test("SPARK-7868: _temporary directories should be ignored") {
-    withTempPath { dir =>
-      val df = Seq("a", "b", "c").zipWithIndex.toDF()
-
-      df.write
-        .format("parquet")
-        .save(dir.getCanonicalPath)
-
-      df.write
-        .format("parquet")
-        .save(s"${dir.getCanonicalPath}/_temporary")
-
-      checkAnswer(read.format("parquet").load(dir.getCanonicalPath), df.collect())
-    }
-  }
-
-  test("SPARK-8014: Avoid scanning output directory when SaveMode isn't SaveMode.Append") {
-    withTempDir { dir =>
-      val path = dir.getCanonicalPath
-      val df = Seq(1 -> "a").toDF()
-
-      // Creates an arbitrary file.  If this directory gets scanned, ParquetRelation2 will throw
-      // since it's not a valid Parquet file.
-      val emptyFile = new File(path, "empty")
-      Files.createParentDirs(emptyFile)
-      Files.touch(emptyFile)
-
-      // This shouldn't throw anything.
-      df.write.format("parquet").mode(SaveMode.Ignore).save(path)
-
-      // This should only complain that the destination directory already exists, rather than file
-      // "empty" is not a Parquet file.
-      assert {
-        intercept[AnalysisException] {
-          df.write.format("parquet").mode(SaveMode.ErrorIfExists).save(path)
-        }.getMessage.contains("already exists")
-      }
-
-      // This shouldn't throw anything.
-      df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
-      checkAnswer(read.format("parquet").load(path), df)
-    }
-  }
-
-  test("SPARK-8079: Avoid NPE thrown from BaseWriterContainer.abortJob") {
-    withTempPath { dir =>
-      intercept[AnalysisException] {
-        // Parquet doesn't allow field names with spaces.  Here we are intentionally making an
-        // exception thrown from the `ParquetRelation2.prepareForWriteJob()` method to trigger
-        // the bug.  Please refer to spark-8079 for more details.
-        range(1, 10)
-          .withColumnRenamed("id", "a b")
-          .write
-          .format("parquet")
-          .save(dir.getCanonicalPath)
-      }
-    }
-  }
-
-  test("SPARK-8604: Parquet data source should write summary file while doing appending") {
-    withTempPath { dir =>
-      val path = dir.getCanonicalPath
-      val df = sqlContext.range(0, 5)
-      df.write.mode(SaveMode.Overwrite).parquet(path)
-
-      val summaryPath = new Path(path, "_metadata")
-      val commonSummaryPath = new Path(path, "_common_metadata")
-
-      val fs = summaryPath.getFileSystem(configuration)
-      fs.delete(summaryPath, true)
-      fs.delete(commonSummaryPath, true)
-
-      df.write.mode(SaveMode.Append).parquet(path)
-      checkAnswer(sqlContext.read.parquet(path), df.unionAll(df))
-
-      assert(fs.exists(summaryPath))
-      assert(fs.exists(commonSummaryPath))
-    }
-  }
-}

From c8d71a4183dfc83ff257047857af0b6d66c6b90d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 24 Jul 2015 09:38:13 -0700
Subject: [PATCH 0583/1454] [SPARK-9305] Rename org.apache.spark.Row to Item.

It's a thing used in test cases, but named Row. Pretty annoying because everytime I search for Row, it shows up before the Spark SQL Row, which is what a developer wants most of the time.

Author: Reynold Xin <rxin@databricks.com>

Closes #7638 from rxin/remove-row and squashes the following commits:

aeda52d [Reynold Xin] [SPARK-9305] Rename org.apache.spark.Row to Item.
---
 .../scala/org/apache/spark/PartitioningSuite.scala     | 10 +++++-----
 .../org/apache/spark/sql/RandomDataGenerator.scala     |  3 +--
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
index 3316f561a4949..aa8028792cb41 100644
--- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
+++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala
@@ -91,13 +91,13 @@ class PartitioningSuite extends SparkFunSuite with SharedSparkContext with Priva
 
   test("RangePartitioner for keys that are not Comparable (but with Ordering)") {
     // Row does not extend Comparable, but has an implicit Ordering defined.
-    implicit object RowOrdering extends Ordering[Row] {
-      override def compare(x: Row, y: Row): Int = x.value - y.value
+    implicit object RowOrdering extends Ordering[Item] {
+      override def compare(x: Item, y: Item): Int = x.value - y.value
     }
 
-    val rdd = sc.parallelize(1 to 4500).map(x => (Row(x), Row(x)))
+    val rdd = sc.parallelize(1 to 4500).map(x => (Item(x), Item(x)))
     val partitioner = new RangePartitioner(1500, rdd)
-    partitioner.getPartition(Row(100))
+    partitioner.getPartition(Item(100))
   }
 
   test("RangPartitioner.sketch") {
@@ -252,4 +252,4 @@ class PartitioningSuite extends SparkFunSuite with SharedSparkContext with Priva
 }
 
 
-private sealed case class Row(value: Int)
+private sealed case class Item(value: Int)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
index b9f2ad7ec0481..75ae29d690770 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -69,8 +69,7 @@ object RandomDataGenerator {
    * Returns a function which generates random values for the given [[DataType]], or `None` if no
    * random data generator is defined for that data type. The generated values will use an external
    * representation of the data type; for example, the random generator for [[DateType]] will return
-   * instances of [[java.sql.Date]] and the generator for [[StructType]] will return a
-   * [[org.apache.spark.Row]].
+   * instances of [[java.sql.Date]] and the generator for [[StructType]] will return a [[Row]].
    *
    * @param dataType the type to generate values for
    * @param nullable whether null values should be generated

From c2b50d693e469558e3b3c9cbb9d76089d259b587 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 24 Jul 2015 09:49:50 -0700
Subject: [PATCH 0584/1454] [SPARK-9292] Analysis should check that join
 conditions' data types are BooleanType

This patch adds an analysis check to ensure that join conditions' data types are BooleanType. This check is necessary in order to report proper errors for non-boolean DataFrame join conditions.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7630 from JoshRosen/SPARK-9292 and squashes the following commits:

aec6c7b [Josh Rosen] Check condition type in resolved()
75a3ea6 [Josh Rosen] Fix SPARK-9292.
---
 .../apache/spark/sql/catalyst/analysis/CheckAnalysis.scala   | 5 +++++
 .../spark/sql/catalyst/plans/logical/basicOperators.scala    | 5 ++++-
 .../spark/sql/catalyst/analysis/AnalysisErrorSuite.scala     | 5 +++++
 3 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index c203fcecf20fb..c23ab3c74338d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -83,6 +83,11 @@ trait CheckAnalysis {
               s"filter expression '${f.condition.prettyString}' " +
                 s"of type ${f.condition.dataType.simpleString} is not a boolean.")
 
+          case j @ Join(_, _, _, Some(condition)) if condition.dataType != BooleanType =>
+            failAnalysis(
+              s"join condition '${condition.prettyString}' " +
+                s"of type ${condition.dataType.simpleString} is not a boolean.")
+
           case Aggregate(groupingExprs, aggregateExprs, child) =>
             def checkValidAggregateExpression(expr: Expression): Unit = expr match {
               case _: AggregateExpression => // OK
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 6aefa9f67556a..57a12820fa4c6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -128,7 +128,10 @@ case class Join(
 
   // Joins are only resolved if they don't introduce ambiguous expression ids.
   override lazy val resolved: Boolean = {
-    childrenResolved && expressions.forall(_.resolved) && selfJoinResolved
+    childrenResolved &&
+      expressions.forall(_.resolved) &&
+      selfJoinResolved &&
+      condition.forall(_.dataType == BooleanType)
   }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index dca8c881f21ab..7bf678ebf71ce 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -118,6 +118,11 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
     testRelation.where(Literal(1)),
     "filter" :: "'1'" :: "not a boolean" :: Literal(1).dataType.simpleString :: Nil)
 
+  errorTest(
+    "non-boolean join conditions",
+    testRelation.join(testRelation, condition = Some(Literal(1))),
+    "condition" :: "'1'" :: "not a boolean" :: Literal(1).dataType.simpleString :: Nil)
+
   errorTest(
     "missing group by",
     testRelation2.groupBy('a)('b),

From e25312451322969ad716dddf8248b8c17f68323b Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Fri, 24 Jul 2015 10:56:48 -0700
Subject: [PATCH 0585/1454] [SPARK-9222] [MLlib] Make class instantiation
 variables in DistributedLDAModel private[clustering]

This makes it easier to test all the class variables of the DistributedLDAmodel.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7573 from MechCoder/lda_test and squashes the following commits:

2f1a293 [MechCoder] [SPARK-9222] [MLlib] Make class instantiation variables in DistributedLDAModel private[clustering]
---
 .../apache/spark/mllib/clustering/LDAModel.scala  |  8 ++++----
 .../apache/spark/mllib/clustering/LDASuite.scala  | 15 +++++++++++++++
 2 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 920b57756b625..31c1d520fd659 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -283,12 +283,12 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
  */
 @Experimental
 class DistributedLDAModel private (
-    private val graph: Graph[LDA.TopicCounts, LDA.TokenCount],
-    private val globalTopicTotals: LDA.TopicCounts,
+    private[clustering] val graph: Graph[LDA.TopicCounts, LDA.TokenCount],
+    private[clustering] val globalTopicTotals: LDA.TopicCounts,
     val k: Int,
     val vocabSize: Int,
-    private val docConcentration: Double,
-    private val topicConcentration: Double,
+    private[clustering] val docConcentration: Double,
+    private[clustering] val topicConcentration: Double,
     private[spark] val iterationTimes: Array[Double]) extends LDAModel {
 
   import LDA._
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index da70d9bd7c790..376a87f0511b4 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.mllib.clustering
 import breeze.linalg.{DenseMatrix => BDM}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.graphx.Edge
 import org.apache.spark.mllib.linalg.{DenseMatrix, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -318,6 +319,20 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
       assert(distributedModel.k === sameDistributedModel.k)
       assert(distributedModel.vocabSize === sameDistributedModel.vocabSize)
       assert(distributedModel.iterationTimes === sameDistributedModel.iterationTimes)
+      assert(distributedModel.docConcentration === sameDistributedModel.docConcentration)
+      assert(distributedModel.topicConcentration === sameDistributedModel.topicConcentration)
+      assert(distributedModel.globalTopicTotals === sameDistributedModel.globalTopicTotals)
+
+      val graph = distributedModel.graph
+      val sameGraph = sameDistributedModel.graph
+      assert(graph.vertices.sortByKey().collect() === sameGraph.vertices.sortByKey().collect())
+      val edge = graph.edges.map {
+        case Edge(sid: Long, did: Long, nos: Double) => (sid, did, nos)
+      }.sortBy(x => (x._1, x._2)).collect()
+      val sameEdge = sameGraph.edges.map {
+        case Edge(sid: Long, did: Long, nos: Double) => (sid, did, nos)
+      }.sortBy(x => (x._1, x._2)).collect()
+      assert(edge === sameEdge)
     } finally {
       Utils.deleteRecursively(tempDir1)
       Utils.deleteRecursively(tempDir2)

From 6aceaf3d62ee335570ddc07ccaf07e8c3776f517 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 24 Jul 2015 11:34:23 -0700
Subject: [PATCH 0586/1454] [SPARK-9295] Analysis should detect sorting on
 unsupported column types

This patch extends CheckAnalysis to throw errors for queries that try to sort on unsupported column types, such as ArrayType.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7633 from JoshRosen/SPARK-9295 and squashes the following commits:

23b2fbf [Josh Rosen] Embed function in foreach
bfe1451 [Josh Rosen] Update to allow sorting by null literals
2f1b802 [Josh Rosen] Add analysis rule to detect sorting on unsupported column types (SPARK-9295)
---
 .../spark/sql/catalyst/analysis/CheckAnalysis.scala    | 10 ++++++++++
 .../sql/catalyst/analysis/AnalysisErrorSuite.scala     |  5 +++++
 2 files changed, 15 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index c23ab3c74338d..81d473c1130f7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -103,6 +103,16 @@ trait CheckAnalysis {
 
             aggregateExprs.foreach(checkValidAggregateExpression)
 
+          case Sort(orders, _, _) =>
+            orders.foreach { order =>
+              order.dataType match {
+                case t: AtomicType => // OK
+                case NullType => // OK
+                case t =>
+                  failAnalysis(s"Sorting is not supported for columns of type ${t.simpleString}")
+              }
+            }
+
           case _ => // Fallbacks to the following checks
         }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 7bf678ebf71ce..2588df98246dd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -113,6 +113,11 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
     testRelation.select(Literal(1).cast(BinaryType).as('badCast)),
     "cannot cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil)
 
+  errorTest(
+    "sorting by unsupported column types",
+    listRelation.orderBy('list.asc),
+    "sorting" :: "type" :: "array<int>" :: Nil)
+
   errorTest(
     "non-boolean filters",
     testRelation.where(Literal(1)),

From 8399ba14873854ab2f80a0ccaf6adba499060365 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 24 Jul 2015 11:53:16 -0700
Subject: [PATCH 0587/1454] [SPARK-9261] [STREAMING] Avoid calling APIs that
 expose shaded classes.

Doing this may cause weird errors when tests are run on maven, depending
on the flags used. Instead, expose the needed functionality through methods
that do not expose shaded classes.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7601 from vanzin/SPARK-9261 and squashes the following commits:

4f64a16 [Marcelo Vanzin] [SPARK-9261] [streaming] Avoid calling APIs that expose shaded classes.
---
 .../scala/org/apache/spark/ui/WebUI.scala     | 19 +++++++++++++++++++
 .../spark/streaming/ui/StreamingTab.scala     | 12 +++---------
 2 files changed, 22 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/WebUI.scala b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
index 2c84e4485996e..61449847add3d 100644
--- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
@@ -107,6 +107,25 @@ private[spark] abstract class WebUI(
     }
   }
 
+  /**
+   * Add a handler for static content.
+   *
+   * @param resourceBase Root of where to find resources to serve.
+   * @param path Path in UI where to mount the resources.
+   */
+  def addStaticHandler(resourceBase: String, path: String): Unit = {
+    attachHandler(JettyUtils.createStaticHandler(resourceBase, path))
+  }
+
+  /**
+   * Remove a static content handler.
+   *
+   * @param path Path in UI to unmount.
+   */
+  def removeStaticHandler(path: String): Unit = {
+    handlers.find(_.getContextPath() == path).foreach(detachHandler)
+  }
+
   /** Initialize all components of the server. */
   def initialize()
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
index e0c0f57212f55..bc53f2a31f6d1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
@@ -17,11 +17,9 @@
 
 package org.apache.spark.streaming.ui
 
-import org.eclipse.jetty.servlet.ServletContextHandler
-
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.ui.{JettyUtils, SparkUI, SparkUITab}
+import org.apache.spark.ui.{SparkUI, SparkUITab}
 
 import StreamingTab._
 
@@ -42,18 +40,14 @@ private[spark] class StreamingTab(val ssc: StreamingContext)
   attachPage(new StreamingPage(this))
   attachPage(new BatchPage(this))
 
-  var staticHandler: ServletContextHandler = null
-
   def attach() {
     getSparkUI(ssc).attachTab(this)
-    staticHandler = JettyUtils.createStaticHandler(STATIC_RESOURCE_DIR, "/static/streaming")
-    getSparkUI(ssc).attachHandler(staticHandler)
+    getSparkUI(ssc).addStaticHandler(STATIC_RESOURCE_DIR, "/static/streaming")
   }
 
   def detach() {
     getSparkUI(ssc).detachTab(this)
-    getSparkUI(ssc).detachHandler(staticHandler)
-    staticHandler = null
+    getSparkUI(ssc).removeStaticHandler("/static/streaming")
   }
 }
 

From 9a11396113d4bb0e76e0520df4fc58e7a8ec9f69 Mon Sep 17 00:00:00 2001
From: Cheolsoo Park <cheolsoop@netflix.com>
Date: Fri, 24 Jul 2015 11:56:55 -0700
Subject: [PATCH 0588/1454] [SPARK-9270] [PYSPARK] allow --name option in
 pyspark

This is continuation of #7512 which added `--name` option to spark-shell. This PR adds the same option to pyspark.

Note that `--conf spark.app.name` in command-line has no effect in spark-shell and pyspark. Instead, `--name` must be used. This is in fact inconsistency with spark-sql which doesn't accept `--name` option while it accepts `--conf spark.app.name`. I am not fixing this inconsistency in this PR. IMO, one of `--name` and `--conf spark.app.name` is needed not both. But since I cannot decide which to choose, I am not making any change here.

Author: Cheolsoo Park <cheolsoop@netflix.com>

Closes #7610 from piaozhexiu/SPARK-9270 and squashes the following commits:

763e86d [Cheolsoo Park] Update windows script
400b7f9 [Cheolsoo Park] Allow --name option to pyspark
---
 bin/pyspark             | 2 +-
 bin/pyspark2.cmd        | 2 +-
 python/pyspark/shell.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/bin/pyspark b/bin/pyspark
index f9dbddfa53560..8f2a3b5a7717b 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -82,4 +82,4 @@ fi
 
 export PYSPARK_DRIVER_PYTHON
 export PYSPARK_DRIVER_PYTHON_OPTS
-exec "$SPARK_HOME"/bin/spark-submit pyspark-shell-main "$@"
+exec "$SPARK_HOME"/bin/spark-submit pyspark-shell-main --name "PySparkShell" "$@"
diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd
index 45e9e3def5121..3c6169983e76b 100644
--- a/bin/pyspark2.cmd
+++ b/bin/pyspark2.cmd
@@ -35,4 +35,4 @@ set PYTHONPATH=%SPARK_HOME%\python\lib\py4j-0.8.2.1-src.zip;%PYTHONPATH%
 set OLD_PYTHONSTARTUP=%PYTHONSTARTUP%
 set PYTHONSTARTUP=%SPARK_HOME%\python\pyspark\shell.py
 
-call %SPARK_HOME%\bin\spark-submit2.cmd pyspark-shell-main %*
+call %SPARK_HOME%\bin\spark-submit2.cmd pyspark-shell-main --name "PySparkShell" %*
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index 144cdf0b0cdd5..99331297c19f0 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -40,7 +40,7 @@
 if os.environ.get("SPARK_EXECUTOR_URI"):
     SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"])
 
-sc = SparkContext(appName="PySparkShell", pyFiles=add_files)
+sc = SparkContext(pyFiles=add_files)
 atexit.register(lambda: sc.stop())
 
 try:

From 64135cbb3363e3b74dad3c0498cb9959c047d381 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Fri, 24 Jul 2015 12:36:44 -0700
Subject: [PATCH 0589/1454] [SPARK-9067] [SQL] Close reader in NewHadoopRDD
 early if there is no more data

JIRA: https://issues.apache.org/jira/browse/SPARK-9067

According to the description of the JIRA ticket, calling `reader.close()` only after the task is finished will cause memory and file open limit problem since these resources are occupied even we don't need that anymore.

This PR simply closes the reader early when we know there is no more data to read.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7424 from viirya/close_reader and squashes the following commits:

3ff64e5 [Liang-Chi Hsieh] For comments.
3d20267 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into close_reader
e152182 [Liang-Chi Hsieh] For comments.
5116cbe [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into close_reader
3ceb755 [Liang-Chi Hsieh] For comments.
e34d98e [Liang-Chi Hsieh] For comments.
50ed729 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into close_reader
216912f [Liang-Chi Hsieh] Fix it.
f429016 [Liang-Chi Hsieh] Release reader if we don't need it.
a305621 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into close_reader
67569da [Liang-Chi Hsieh] Close reader early if there is no more data.
---
 .../org/apache/spark/rdd/NewHadoopRDD.scala   | 37 ++++++++++++-------
 .../spark/sql/execution/SqlNewHadoopRDD.scala | 36 +++++++++++-------
 2 files changed, 47 insertions(+), 26 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index f827270ee6a44..f83a051f5da11 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -128,7 +128,7 @@ class NewHadoopRDD[K, V](
           configurable.setConf(conf)
         case _ =>
       }
-      val reader = format.createRecordReader(
+      private var reader = format.createRecordReader(
         split.serializableHadoopSplit.value, hadoopAttemptContext)
       reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
 
@@ -141,6 +141,12 @@ class NewHadoopRDD[K, V](
       override def hasNext: Boolean = {
         if (!finished && !havePair) {
           finished = !reader.nextKeyValue
+          if (finished) {
+            // Close and release the reader here; close() will also be called when the task
+            // completes, but for tasks that read from many files, it helps to release the
+            // resources early.
+            close()
+          }
           havePair = !finished
         }
         !finished
@@ -159,18 +165,23 @@ class NewHadoopRDD[K, V](
 
       private def close() {
         try {
-          reader.close()
-          if (bytesReadCallback.isDefined) {
-            inputMetrics.updateBytesRead()
-          } else if (split.serializableHadoopSplit.value.isInstanceOf[FileSplit] ||
-                     split.serializableHadoopSplit.value.isInstanceOf[CombineFileSplit]) {
-            // If we can't get the bytes read from the FS stats, fall back to the split size,
-            // which may be inaccurate.
-            try {
-              inputMetrics.incBytesRead(split.serializableHadoopSplit.value.getLength)
-            } catch {
-              case e: java.io.IOException =>
-                logWarning("Unable to get input size to set InputMetrics for task", e)
+          if (reader != null) {
+            // Close reader and release it
+            reader.close()
+            reader = null
+
+            if (bytesReadCallback.isDefined) {
+              inputMetrics.updateBytesRead()
+            } else if (split.serializableHadoopSplit.value.isInstanceOf[FileSplit] ||
+                       split.serializableHadoopSplit.value.isInstanceOf[CombineFileSplit]) {
+              // If we can't get the bytes read from the FS stats, fall back to the split size,
+              // which may be inaccurate.
+              try {
+                inputMetrics.incBytesRead(split.serializableHadoopSplit.value.getLength)
+              } catch {
+                case e: java.io.IOException =>
+                  logWarning("Unable to get input size to set InputMetrics for task", e)
+              }
             }
           }
         } catch {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
index e1c1a6c06268f..3d75b6a91def6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
@@ -147,7 +147,7 @@ private[sql] class SqlNewHadoopRDD[K, V](
           configurable.setConf(conf)
         case _ =>
       }
-      val reader = format.createRecordReader(
+      private var reader = format.createRecordReader(
         split.serializableHadoopSplit.value, hadoopAttemptContext)
       reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
 
@@ -160,6 +160,12 @@ private[sql] class SqlNewHadoopRDD[K, V](
       override def hasNext: Boolean = {
         if (!finished && !havePair) {
           finished = !reader.nextKeyValue
+          if (finished) {
+            // Close and release the reader here; close() will also be called when the task
+            // completes, but for tasks that read from many files, it helps to release the
+            // resources early.
+            close()
+          }
           havePair = !finished
         }
         !finished
@@ -178,18 +184,22 @@ private[sql] class SqlNewHadoopRDD[K, V](
 
       private def close() {
         try {
-          reader.close()
-          if (bytesReadCallback.isDefined) {
-            inputMetrics.updateBytesRead()
-          } else if (split.serializableHadoopSplit.value.isInstanceOf[FileSplit] ||
-                     split.serializableHadoopSplit.value.isInstanceOf[CombineFileSplit]) {
-            // If we can't get the bytes read from the FS stats, fall back to the split size,
-            // which may be inaccurate.
-            try {
-              inputMetrics.incBytesRead(split.serializableHadoopSplit.value.getLength)
-            } catch {
-              case e: java.io.IOException =>
-                logWarning("Unable to get input size to set InputMetrics for task", e)
+          if (reader != null) {
+            reader.close()
+            reader = null
+
+            if (bytesReadCallback.isDefined) {
+              inputMetrics.updateBytesRead()
+            } else if (split.serializableHadoopSplit.value.isInstanceOf[FileSplit] ||
+                       split.serializableHadoopSplit.value.isInstanceOf[CombineFileSplit]) {
+              // If we can't get the bytes read from the FS stats, fall back to the split size,
+              // which may be inaccurate.
+              try {
+                inputMetrics.incBytesRead(split.serializableHadoopSplit.value.getLength)
+              } catch {
+                case e: java.io.IOException =>
+                  logWarning("Unable to get input size to set InputMetrics for task", e)
+              }
             }
           }
         } catch {

From a400ab516fa93185aa683a596f9d7c6c1a02f330 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Fri, 24 Jul 2015 14:58:07 -0700
Subject: [PATCH 0590/1454] [SPARK-7045] [MLLIB] Avoid intermediate
 representation when creating model

Word2Vec used to convert from an Array[Float] representation to a Map[String, Array[Float]] and then back to an Array[Float] through Word2VecModel.

This prevents this conversion while still supporting the older method of supplying a Map.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #5748 from MechCoder/spark-7045 and squashes the following commits:

e308913 [MechCoder] move docs
5703116 [MechCoder] minor
fa04313 [MechCoder] style fixes
b1d61c4 [MechCoder] better errors and tests
3b32c8c [MechCoder] [SPARK-7045] Avoid intermediate representation when creating model
---
 .../apache/spark/mllib/feature/Word2Vec.scala | 85 +++++++++++--------
 .../spark/mllib/feature/Word2VecSuite.scala   |  6 ++
 2 files changed, 55 insertions(+), 36 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index f087d06d2a46a..cbbd2b0c8d060 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -403,17 +403,8 @@ class Word2Vec extends Serializable with Logging {
     }
     newSentences.unpersist()
 
-    val word2VecMap = mutable.HashMap.empty[String, Array[Float]]
-    var i = 0
-    while (i < vocabSize) {
-      val word = bcVocab.value(i).word
-      val vector = new Array[Float](vectorSize)
-      Array.copy(syn0Global, i * vectorSize, vector, 0, vectorSize)
-      word2VecMap += word -> vector
-      i += 1
-    }
-
-    new Word2VecModel(word2VecMap.toMap)
+    val wordArray = vocab.map(_.word)
+    new Word2VecModel(wordArray.zipWithIndex.toMap, syn0Global)
   }
 
   /**
@@ -429,38 +420,42 @@ class Word2Vec extends Serializable with Logging {
 /**
  * :: Experimental ::
  * Word2Vec model
+ * @param wordIndex maps each word to an index, which can retrieve the corresponding
+ *                  vector from wordVectors
+ * @param wordVectors array of length numWords * vectorSize, vector corresponding
+ *                    to the word mapped with index i can be retrieved by the slice
+ *                    (i * vectorSize, i * vectorSize + vectorSize)
  */
 @Experimental
-class Word2VecModel private[spark] (
-    model: Map[String, Array[Float]]) extends Serializable with Saveable {
-
-  // wordList: Ordered list of words obtained from model.
-  private val wordList: Array[String] = model.keys.toArray
-
-  // wordIndex: Maps each word to an index, which can retrieve the corresponding
-  //            vector from wordVectors (see below).
-  private val wordIndex: Map[String, Int] = wordList.zip(0 until model.size).toMap
+class Word2VecModel private[mllib] (
+    private val wordIndex: Map[String, Int],
+    private val wordVectors: Array[Float]) extends Serializable with Saveable {
 
-  // vectorSize: Dimension of each word's vector.
-  private val vectorSize = model.head._2.size
   private val numWords = wordIndex.size
+  // vectorSize: Dimension of each word's vector.
+  private val vectorSize = wordVectors.length / numWords
+
+  // wordList: Ordered list of words obtained from wordIndex.
+  private val wordList: Array[String] = {
+    val (wl, _) = wordIndex.toSeq.sortBy(_._2).unzip
+    wl.toArray
+  }
 
-  // wordVectors: Array of length numWords * vectorSize, vector corresponding to the word
-  //              mapped with index i can be retrieved by the slice
-  //              (ind * vectorSize, ind * vectorSize + vectorSize)
   // wordVecNorms: Array of length numWords, each value being the Euclidean norm
   //               of the wordVector.
-  private val (wordVectors: Array[Float], wordVecNorms: Array[Double]) = {
-    val wordVectors = new Array[Float](vectorSize * numWords)
+  private val wordVecNorms: Array[Double] = {
     val wordVecNorms = new Array[Double](numWords)
     var i = 0
     while (i < numWords) {
-      val vec = model.get(wordList(i)).get
-      Array.copy(vec, 0, wordVectors, i * vectorSize, vectorSize)
+      val vec = wordVectors.slice(i * vectorSize, i * vectorSize + vectorSize)
       wordVecNorms(i) = blas.snrm2(vectorSize, vec, 1)
       i += 1
     }
-    (wordVectors, wordVecNorms)
+    wordVecNorms
+  }
+
+  def this(model: Map[String, Array[Float]]) = {
+    this(Word2VecModel.buildWordIndex(model), Word2VecModel.buildWordVectors(model))
   }
 
   private def cosineSimilarity(v1: Array[Float], v2: Array[Float]): Double = {
@@ -484,8 +479,9 @@ class Word2VecModel private[spark] (
    * @return vector representation of word
    */
   def transform(word: String): Vector = {
-    model.get(word) match {
-      case Some(vec) =>
+    wordIndex.get(word) match {
+      case Some(ind) =>
+        val vec = wordVectors.slice(ind * vectorSize, ind * vectorSize + vectorSize)
         Vectors.dense(vec.map(_.toDouble))
       case None =>
         throw new IllegalStateException(s"$word not in vocabulary")
@@ -511,7 +507,7 @@ class Word2VecModel private[spark] (
    */
   def findSynonyms(vector: Vector, num: Int): Array[(String, Double)] = {
     require(num > 0, "Number of similar words should > 0")
-
+    // TODO: optimize top-k
     val fVector = vector.toArray.map(_.toFloat)
     val cosineVec = Array.fill[Float](numWords)(0)
     val alpha: Float = 1
@@ -521,13 +517,13 @@ class Word2VecModel private[spark] (
       "T", vectorSize, numWords, alpha, wordVectors, vectorSize, fVector, 1, beta, cosineVec, 1)
 
     // Need not divide with the norm of the given vector since it is constant.
-    val updatedCosines = new Array[Double](numWords)
+    val cosVec = cosineVec.map(_.toDouble)
     var ind = 0
     while (ind < numWords) {
-      updatedCosines(ind) = cosineVec(ind) / wordVecNorms(ind)
+      cosVec(ind) /= wordVecNorms(ind)
       ind += 1
     }
-    wordList.zip(updatedCosines)
+    wordList.zip(cosVec)
       .toSeq
       .sortBy(- _._2)
       .take(num + 1)
@@ -548,6 +544,23 @@ class Word2VecModel private[spark] (
 @Experimental
 object Word2VecModel extends Loader[Word2VecModel] {
 
+  private def buildWordIndex(model: Map[String, Array[Float]]): Map[String, Int] = {
+    model.keys.zipWithIndex.toMap
+  }
+
+  private def buildWordVectors(model: Map[String, Array[Float]]): Array[Float] = {
+    require(model.nonEmpty, "Word2VecMap should be non-empty")
+    val (vectorSize, numWords) = (model.head._2.size, model.size)
+    val wordList = model.keys.toArray
+    val wordVectors = new Array[Float](vectorSize * numWords)
+    var i = 0
+    while (i < numWords) {
+      Array.copy(model(wordList(i)), 0, wordVectors, i * vectorSize, vectorSize)
+      i += 1
+    }
+    wordVectors
+  }
+
   private object SaveLoadV1_0 {
 
     val formatVersionV1_0 = "1.0"
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
index b6818369208d7..4cc8d1129b858 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
@@ -37,6 +37,12 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(syms.length == 2)
     assert(syms(0)._1 == "b")
     assert(syms(1)._1 == "c")
+
+    // Test that model built using Word2Vec, i.e wordVectors and wordIndec
+    // and a Word2VecMap give the same values.
+    val word2VecMap = model.getVectors
+    val newModel = new Word2VecModel(word2VecMap)
+    assert(newModel.getVectors.mapValues(_.toSeq) === word2VecMap.mapValues(_.toSeq))
   }
 
   test("Word2VecModel") {

From f99cb5615cbc0b469d52af6bd08f8bf888af58f3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 24 Jul 2015 19:29:01 -0700
Subject: [PATCH 0591/1454] [SPARK-9330][SQL] Create specialized getStruct
 getter in InternalRow.

Also took the chance to rearrange some of the methods in UnsafeRow to group static/private/public things together.

Author: Reynold Xin <rxin@databricks.com>

Closes #7654 from rxin/getStruct and squashes the following commits:

b491a09 [Reynold Xin] Fixed typo.
48d77e5 [Reynold Xin] [SPARK-9330][SQL] Create specialized getStruct getter in InternalRow.
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 87 ++++++++++++-------
 .../sql/catalyst/CatalystTypeConverters.scala |  2 +-
 .../spark/sql/catalyst/InternalRow.scala      | 22 +++--
 .../catalyst/expressions/BoundAttribute.scala |  1 +
 .../expressions/codegen/CodeGenerator.scala   |  5 +-
 5 files changed, 77 insertions(+), 40 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index a8986608855e2..225f6e6553d19 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -51,28 +51,9 @@
  */
 public final class UnsafeRow extends MutableRow {
 
-  private Object baseObject;
-  private long baseOffset;
-
-  public Object getBaseObject() { return baseObject; }
-  public long getBaseOffset() { return baseOffset; }
-  public int getSizeInBytes() { return sizeInBytes; }
-
-  /** The number of fields in this row, used for calculating the bitset width (and in assertions) */
-  private int numFields;
-
-  /** The size of this row's backing data, in bytes) */
-  private int sizeInBytes;
-
-  @Override
-  public int numFields() { return numFields; }
-
-  /** The width of the null tracking bit set, in bytes */
-  private int bitSetWidthInBytes;
-
-  private long getFieldOffset(int ordinal) {
-   return baseOffset + bitSetWidthInBytes + ordinal * 8L;
-  }
+  //////////////////////////////////////////////////////////////////////////////
+  // Static methods
+  //////////////////////////////////////////////////////////////////////////////
 
   public static int calculateBitSetWidthInBytes(int numFields) {
     return ((numFields / 64) + (numFields % 64 == 0 ? 0 : 1)) * 8;
@@ -103,7 +84,7 @@ public static int calculateBitSetWidthInBytes(int numFields) {
           DoubleType,
           DateType,
           TimestampType
-    })));
+        })));
 
     // We support get() on a superset of the types for which we support set():
     final Set<DataType> _readableFieldTypes = new HashSet<>(
@@ -115,12 +96,48 @@ public static int calculateBitSetWidthInBytes(int numFields) {
     readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes);
   }
 
+  //////////////////////////////////////////////////////////////////////////////
+  // Private fields and methods
+  //////////////////////////////////////////////////////////////////////////////
+
+  private Object baseObject;
+  private long baseOffset;
+
+  /** The number of fields in this row, used for calculating the bitset width (and in assertions) */
+  private int numFields;
+
+  /** The size of this row's backing data, in bytes) */
+  private int sizeInBytes;
+
+  private void setNotNullAt(int i) {
+    assertIndexIsValid(i);
+    BitSetMethods.unset(baseObject, baseOffset, i);
+  }
+
+  /** The width of the null tracking bit set, in bytes */
+  private int bitSetWidthInBytes;
+
+  private long getFieldOffset(int ordinal) {
+    return baseOffset + bitSetWidthInBytes + ordinal * 8L;
+  }
+
+  //////////////////////////////////////////////////////////////////////////////
+  // Public methods
+  //////////////////////////////////////////////////////////////////////////////
+
   /**
    * Construct a new UnsafeRow. The resulting row won't be usable until `pointTo()` has been called,
    * since the value returned by this constructor is equivalent to a null pointer.
    */
   public UnsafeRow() { }
 
+  public Object getBaseObject() { return baseObject; }
+  public long getBaseOffset() { return baseOffset; }
+  public int getSizeInBytes() { return sizeInBytes; }
+
+  @Override
+  public int numFields() { return numFields; }
+
   /**
    * Update this UnsafeRow to point to different backing data.
    *
@@ -130,7 +147,7 @@ public UnsafeRow() { }
    * @param sizeInBytes the size of this row's backing data, in bytes
    */
   public void pointTo(Object baseObject, long baseOffset, int numFields, int sizeInBytes) {
-    assert numFields >= 0 : "numFields should >= 0";
+    assert numFields >= 0 : "numFields (" + numFields + ") should >= 0";
     this.bitSetWidthInBytes = calculateBitSetWidthInBytes(numFields);
     this.baseObject = baseObject;
     this.baseOffset = baseOffset;
@@ -153,11 +170,6 @@ public void setNullAt(int i) {
     PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(i), 0);
   }
 
-  private void setNotNullAt(int i) {
-    assertIndexIsValid(i);
-    BitSetMethods.unset(baseObject, baseOffset, i);
-  }
-
   @Override
   public void update(int ordinal, Object value) {
     throw new UnsupportedOperationException();
@@ -316,6 +328,21 @@ public String getString(int i) {
     return getUTF8String(i).toString();
   }
 
+  @Override
+  public UnsafeRow getStruct(int i, int numFields) {
+    if (isNullAt(i)) {
+      return null;
+    } else {
+      assertIndexIsValid(i);
+      final long offsetAndSize = getLong(i);
+      final int offset = (int) (offsetAndSize >> 32);
+      final int size = (int) (offsetAndSize & ((1L << 32) - 1));
+      final UnsafeRow row = new UnsafeRow();
+      row.pointTo(baseObject, baseOffset + offset, numFields, size);
+      return row;
+    }
+  }
+
   /**
    * Copies this row, returning a self-contained UnsafeRow that stores its data in an internal
    * byte array rather than referencing data stored in a data page.
@@ -388,7 +415,7 @@ public boolean equals(Object other) {
    */
   public byte[] getBytes() {
     if (baseObject instanceof byte[] && baseOffset == PlatformDependent.BYTE_ARRAY_OFFSET
-        && (((byte[]) baseObject).length == sizeInBytes)) {
+      && (((byte[]) baseObject).length == sizeInBytes)) {
       return (byte[]) baseObject;
     } else {
       byte[] bytes = new byte[sizeInBytes];
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 5c3072a77aeba..7416ddbaef3fc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -271,7 +271,7 @@ object CatalystTypeConverters {
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Row =
-      toScala(row.get(column).asInstanceOf[InternalRow])
+      toScala(row.getStruct(column, structType.size))
   }
 
   private object StringConverter extends CatalystTypeConverter[Any, String, UTF8String] {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index efc4faea569b2..f248b1f338acc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -52,6 +52,21 @@ abstract class InternalRow extends Serializable {
 
   def getDouble(i: Int): Double = getAs[Double](i)
 
+  def getUTF8String(i: Int): UTF8String = getAs[UTF8String](i)
+
+  def getBinary(i: Int): Array[Byte] = getAs[Array[Byte]](i)
+
+  // This is only use for test
+  def getString(i: Int): String = getAs[UTF8String](i).toString
+
+  /**
+   * Returns a struct from ordinal position.
+   *
+   * @param ordinal position to get the struct from.
+   * @param numFields number of fields the struct type has
+   */
+  def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs[InternalRow](ordinal)
+
   override def toString: String = s"[${this.mkString(",")}]"
 
   /**
@@ -145,13 +160,6 @@ abstract class InternalRow extends Serializable {
    */
   def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
 
-  def getUTF8String(i: Int): UTF8String = getAs[UTF8String](i)
-
-  def getBinary(i: Int): Array[Byte] = getAs[Array[Byte]](i)
-
-  // This is only use for test
-  def getString(i: Int): String = getAs[UTF8String](i).toString
-
   // Custom hashCode function that matches the efficient code generated version.
   override def hashCode: Int = {
     var result: Int = 37
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 6aa4930cb8587..1f7adcd36ec14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -48,6 +48,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
         case DoubleType => input.getDouble(ordinal)
         case StringType => input.getUTF8String(ordinal)
         case BinaryType => input.getBinary(ordinal)
+        case t: StructType => input.getStruct(ordinal, t.size)
         case _ => input.get(ordinal)
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 48225e1574600..4a90f1b559896 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -109,6 +109,7 @@ class CodeGenContext {
       case _ if isPrimitiveType(jt) => s"$row.get${primitiveTypeName(jt)}($ordinal)"
       case StringType => s"$row.getUTF8String($ordinal)"
       case BinaryType => s"$row.getBinary($ordinal)"
+      case t: StructType => s"$row.getStruct($ordinal, ${t.size})"
       case _ => s"($jt)$row.apply($ordinal)"
     }
   }
@@ -249,13 +250,13 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   protected val mutableRowType: String = classOf[MutableRow].getName
   protected val genericMutableRowType: String = classOf[GenericMutableRow].getName
 
-  protected def declareMutableStates(ctx: CodeGenContext) = {
+  protected def declareMutableStates(ctx: CodeGenContext): String = {
     ctx.mutableStates.map { case (javaType, variableName, _) =>
       s"private $javaType $variableName;"
     }.mkString("\n      ")
   }
 
-  protected def initMutableStates(ctx: CodeGenContext) = {
+  protected def initMutableStates(ctx: CodeGenContext): String = {
     ctx.mutableStates.map(_._3).mkString("\n        ")
   }
 

From c84acd4aa4f8bee98baa550cd6801c797a8a7a25 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 24 Jul 2015 19:35:24 -0700
Subject: [PATCH 0592/1454] [SPARK-9331][SQL] Add a code formatter to
 auto-format generated code.

The generated expression code can be hard to read since they are not indented well. This patch adds a code formatter that formats code automatically when we output them to the screen.

Author: Reynold Xin <rxin@databricks.com>

Closes #7656 from rxin/codeformatter and squashes the following commits:

5ba0e90 [Reynold Xin] [SPARK-9331][SQL] Add a code formatter to auto-format generated code.
---
 .../expressions/codegen/CodeFormatter.scala   | 60 +++++++++++++++
 .../expressions/codegen/CodeGenerator.scala   |  2 +-
 .../codegen/GenerateMutableProjection.scala   | 11 +--
 .../codegen/GenerateOrdering.scala            |  2 +-
 .../codegen/GeneratePredicate.scala           |  2 +-
 .../codegen/GenerateProjection.scala          |  3 +-
 .../codegen/GenerateUnsafeProjection.scala    |  2 +-
 .../codegen/CodeFormatterSuite.scala          | 76 +++++++++++++++++++
 8 files changed, 148 insertions(+), 10 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala
new file mode 100644
index 0000000000000..2087cc7f109bc
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+/**
+ * An utility class that indents a block of code based on the curly braces.
+ *
+ * This is used to prettify generated code when in debug mode (or exceptions).
+ *
+ * Written by Matei Zaharia.
+ */
+object CodeFormatter {
+  def format(code: String): String = new CodeFormatter().addLines(code).result()
+}
+
+private class CodeFormatter {
+  private val code = new StringBuilder
+  private var indentLevel = 0
+  private val indentSize = 2
+  private var indentString = ""
+
+  private def addLine(line: String): Unit = {
+    val indentChange = line.count(_ == '{') - line.count(_ == '}')
+    val newIndentLevel = math.max(0, indentLevel + indentChange)
+    // Lines starting with '}' should be de-indented even if they contain '{' after;
+    // in addition, lines ending with ':' are typically labels
+    val thisLineIndent = if (line.startsWith("}") || line.endsWith(":")) {
+      " " * (indentSize * (indentLevel - 1))
+    } else {
+      indentString
+    }
+    code.append(thisLineIndent)
+    code.append(line)
+    code.append("\n")
+    indentLevel = newIndentLevel
+    indentString = " " * (indentSize * newIndentLevel)
+  }
+
+  private def addLines(code: String): CodeFormatter = {
+    code.split('\n').foreach(s => addLine(s.trim()))
+    this
+  }
+
+  private def result(): String = code.result()
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 4a90f1b559896..508882acbee5a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -299,7 +299,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       evaluator.cook(code)
     } catch {
       case e: Exception =>
-        val msg = s"failed to compile:\n $code"
+        val msg = "failed to compile:\n " + CodeFormatter.format(code)
         logError(msg, e)
         throw new Exception(msg, e)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index d838268f46956..825031a4faf5e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
 
-import scala.collection.mutable.ArrayBuffer
-
 // MutableProjection is not accessible in Java
 abstract class BaseMutableProjection extends MutableProjection
 
@@ -45,10 +45,11 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
         val evaluationCode = e.gen(ctx)
         evaluationCode.code +
           s"""
-            if(${evaluationCode.isNull})
+            if (${evaluationCode.isNull}) {
               mutableRow.setNullAt($i);
-            else
+            } else {
               ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
+            }
           """
     }
     // collect projections into blocks as function has 64kb codesize limit in JVM
@@ -119,7 +120,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
       }
     """
 
-    logDebug(s"code for ${expressions.mkString(",")}:\n$code")
+    logDebug(s"code for ${expressions.mkString(",")}:\n${CodeFormatter.format(code)}")
 
     val c = compile(code)
     () => {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index 2e6f9e204d813..dbd4616d281c8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -107,7 +107,7 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
         }
       }"""
 
-    logDebug(s"Generated Ordering: $code")
+    logDebug(s"Generated Ordering: ${CodeFormatter.format(code)}")
 
     compile(code).generate(ctx.references.toArray).asInstanceOf[BaseOrdering]
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
index 1dda5992c3654..dfd593fb7c064 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
@@ -60,7 +60,7 @@ object GeneratePredicate extends CodeGenerator[Expression, (InternalRow) => Bool
         }
       }"""
 
-    logDebug(s"Generated predicate '$predicate':\n$code")
+    logDebug(s"Generated predicate '$predicate':\n${CodeFormatter.format(code)}")
 
     val p = compile(code).generate(ctx.references.toArray).asInstanceOf[Predicate]
     (r: InternalRow) => p.eval(r)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index f0efc4bff12ba..a361b216eb472 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -230,7 +230,8 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
     }
     """
 
-    logDebug(s"MutableRow, initExprs: ${expressions.mkString(",")} code:\n${code}")
+    logDebug(s"MutableRow, initExprs: ${expressions.mkString(",")} code:\n" +
+      CodeFormatter.format(code))
 
     compile(code).generate(ctx.references.toArray).asInstanceOf[Projection]
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index d65e5c38ebf5c..0320bcb827bf7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -114,7 +114,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     }
     """
 
-    logDebug(s"code for ${expressions.mkString(",")}:\n$code")
+    logDebug(s"code for ${expressions.mkString(",")}:\n${CodeFormatter.format(code)}")
 
     val c = compile(code)
     c.generate(ctx.references.toArray).asInstanceOf[UnsafeProjection]
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala
new file mode 100644
index 0000000000000..478702fea6146
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.SparkFunSuite
+
+
+class CodeFormatterSuite extends SparkFunSuite {
+
+  def testCase(name: String)(input: String)(expected: String): Unit = {
+    test(name) {
+      assert(CodeFormatter.format(input).trim === expected.trim)
+    }
+  }
+
+  testCase("basic example") {
+    """
+      |class A {
+      |blahblah;
+      |}
+    """.stripMargin
+  }{
+    """
+      |class A {
+      |  blahblah;
+      |}
+    """.stripMargin
+  }
+
+  testCase("nested example") {
+    """
+      |class A {
+      | if (c) {
+      |duh;
+      |}
+      |}
+    """.stripMargin
+  } {
+    """
+      |class A {
+      |  if (c) {
+      |    duh;
+      |  }
+      |}
+    """.stripMargin
+  }
+
+  testCase("single line") {
+    """
+      |class A {
+      | if (c) {duh;}
+      |}
+    """.stripMargin
+  }{
+    """
+      |class A {
+      |  if (c) {duh;}
+      |}
+    """.stripMargin
+  }
+}

From 19bcd6ab12bf355bc5d774905ec7fe3b5fc8e0e2 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Fri, 24 Jul 2015 22:57:01 -0700
Subject: [PATCH 0593/1454] [HOTFIX] - Disable Kinesis tests due to rate limits

---
 .../apache/spark/streaming/kinesis/KinesisStreamSuite.scala   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
index f9c952b9468bb..4992b041765e9 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -59,7 +59,7 @@ class KinesisStreamSuite extends KinesisFunSuite
     }
   }
 
-  test("KinesisUtils API") {
+  ignore("KinesisUtils API") {
     ssc = new StreamingContext(sc, Seconds(1))
     // Tests the API, does not actually test data receiving
     val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
@@ -83,7 +83,7 @@ class KinesisStreamSuite extends KinesisFunSuite
    * you must have AWS credentials available through the default AWS provider chain,
    * and you have to set the system environment variable RUN_KINESIS_TESTS=1 .
    */
-  testIfEnabled("basic operation") {
+  ignore("basic operation") {
     val kinesisTestUtils = new KinesisTestUtils()
     try {
       kinesisTestUtils.createStream()

From 723db13e0688bf20e2a5f02ad170397c3a287712 Mon Sep 17 00:00:00 2001
From: JD <jd@csh.rit.edu>
Date: Sat, 25 Jul 2015 00:34:59 -0700
Subject: [PATCH 0594/1454] [Spark-8668][SQL] Adding expr to functions

Author: JD <jd@csh.rit.edu>
Author: Joseph Batchik <josephbatchik@gmail.com>

Closes #7606 from JDrit/expr and squashes the following commits:

ad7f607 [Joseph Batchik] fixing python linter error
9d6daea [Joseph Batchik] removed order by per @rxin's comment
707d5c6 [Joseph Batchik] Added expr to fuctions.py
79df83c [JD] added example to the docs
b89eec8 [JD] moved function up as per @rxin's comment
4960909 [JD] updated per @JoshRosen's comment
2cb329c [JD] updated per @rxin's comment
9a9ad0c [JD] removing unused import
6dc26d0 [JD] removed split
7f2222c [JD] Adding expr function as per SPARK-8668
---
 python/pyspark/sql/functions.py                   | 10 ++++++++++
 python/pyspark/sql/tests.py                       |  7 +++++++
 .../scala/org/apache/spark/sql/functions.scala    | 15 +++++++++++++--
 .../org/apache/spark/sql/SQLQuerySuite.scala      | 11 +++++++++++
 4 files changed, 41 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 719e623a1a11f..d930f7db25d25 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -541,6 +541,16 @@ def sparkPartitionId():
     return Column(sc._jvm.functions.sparkPartitionId())
 
 
+def expr(str):
+    """Parses the expression string into the column that it represents
+
+    >>> df.select(expr("length(name)")).collect()
+    [Row('length(name)=5), Row('length(name)=3)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.expr(str))
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def length(col):
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index ea821f486f13a..5aa6135dc1ee7 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -846,6 +846,13 @@ def test_bitwise_operations(self):
         result = df.select(functions.bitwiseNOT(df.b)).collect()[0].asDict()
         self.assertEqual(~75, result['~b'])
 
+    def test_expr(self):
+        from pyspark.sql import functions
+        row = Row(a="length string", b=75)
+        df = self.sqlCtx.createDataFrame([row])
+        result = df.select(functions.expr("length(a)")).collect()[0].asDict()
+        self.assertEqual(13, result["'length(a)"])
+
     def test_replace(self):
         schema = StructType([
             StructField("name", StringType(), True),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index bfeecbe8b2ab5..cab3db609dd4b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -22,7 +22,7 @@ import scala.reflect.runtime.universe.{TypeTag, typeTag}
 import scala.util.Try
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.sql.catalyst.ScalaReflection
+import org.apache.spark.sql.catalyst.{SqlParser, ScalaReflection}
 import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.BroadcastHint
@@ -792,6 +792,18 @@ object functions {
    */
   def bitwiseNOT(e: Column): Column = BitwiseNot(e.expr)
 
+  /**
+   * Parses the expression string into the column that it represents, similar to
+   * DataFrame.selectExpr
+   * {{{
+   *   // get the number of words of each length
+   *   df.groupBy(expr("length(word)")).count()
+   * }}}
+   *
+   * @group normal_funcs
+   */
+  def expr(expr: String): Column = Column(new SqlParser().parseExpression(expr))
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   // Math Functions
   //////////////////////////////////////////////////////////////////////////////////////////////
@@ -2451,5 +2463,4 @@ object functions {
     }
     UnresolvedFunction(udfName, exprs, isDistinct = false)
   }
-
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 95a1106cf072d..cd386b7a3ecf9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -112,6 +112,17 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil)
   }
 
+  test("SPARK-8668 expr function") {
+    checkAnswer(Seq((1, "Bobby G."))
+      .toDF("id", "name")
+      .select(expr("length(name)"), expr("abs(id)")), Row(8, 1))
+
+    checkAnswer(Seq((1, "building burrito tunnels"), (1, "major projects"))
+      .toDF("id", "saying")
+      .groupBy(expr("length(saying)"))
+      .count(), Row(24, 1) :: Row(14, 1) :: Nil)
+  }
+
   test("SQL Dialect Switching to a new SQL parser") {
     val newContext = new SQLContext(sqlContext.sparkContext)
     newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName())

From f0ebab3f6d3a9231474acf20110db72c0fb51882 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 25 Jul 2015 01:28:46 -0700
Subject: [PATCH 0595/1454] [SPARK-9336][SQL] Remove extra JoinedRows

They were added to improve performance (so JIT can inline the JoinedRow calls). However, we can also just improve it by projecting output out to UnsafeRow in Tungsten variant of the operators.

Author: Reynold Xin <rxin@databricks.com>

Closes #7659 from rxin/remove-joinedrows and squashes the following commits:

7510447 [Reynold Xin] [SPARK-9336][SQL] Remove extra JoinedRows
---
 .../sql/catalyst/expressions/Projection.scala | 494 +-----------------
 .../spark/sql/execution/Aggregate.scala       |   2 +-
 .../sql/execution/GeneratedAggregate.scala    |   2 +-
 .../apache/spark/sql/execution/Window.scala   |   2 +-
 .../aggregate/sortBasedIterators.scala        |   2 +-
 .../spark/sql/execution/joins/HashJoin.scala  |   2 +-
 .../sql/execution/joins/SortMergeJoin.scala   |   2 +-
 7 files changed, 8 insertions(+), 498 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index dbda05a792cbf..6023a2c564389 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -44,7 +44,7 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection {
     new GenericInternalRow(outputArray)
   }
 
-  override def toString: String = s"Row => [${exprArray.mkString(",")}]"
+  override def toString(): String = s"Row => [${exprArray.mkString(",")}]"
 }
 
 /**
@@ -58,7 +58,7 @@ case class InterpretedMutableProjection(expressions: Seq[Expression]) extends Mu
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
   private[this] val exprArray = expressions.toArray
-  private[this] var mutableRow: MutableRow = new GenericMutableRow(exprArray.size)
+  private[this] var mutableRow: MutableRow = new GenericMutableRow(exprArray.length)
   def currentValue: InternalRow = mutableRow
 
   override def target(row: MutableRow): MutableProjection = {
@@ -186,496 +186,6 @@ class JoinedRow extends InternalRow {
     if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
-  override def get(i: Int): Any =
-    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
-
-  override def isNullAt(i: Int): Boolean =
-    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
-
-  override def getInt(i: Int): Int =
-    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
-
-  override def getLong(i: Int): Long =
-    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
-
-  override def getDouble(i: Int): Double =
-    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
-
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
-
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
-
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
-
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
-
-  override def copy(): InternalRow = {
-    val totalSize = row1.numFields + row2.numFields
-    val copiedValues = new Array[Any](totalSize)
-    var i = 0
-    while(i < totalSize) {
-      copiedValues(i) = get(i)
-      i += 1
-    }
-    new GenericInternalRow(copiedValues)
-  }
-
-  override def toString: String = {
-    // Make sure toString never throws NullPointerException.
-    if ((row1 eq null) && (row2 eq null)) {
-      "[ empty row ]"
-    } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
-    } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
-    } else {
-      mkString("[", ",", "]")
-    }
-  }
-}
-
-/**
- * JIT HACK: Replace with macros
- * The `JoinedRow` class is used in many performance critical situation.  Unfortunately, since there
- * are multiple different types of `Rows` that could be stored as `row1` and `row2` most of the
- * calls in the critical path are polymorphic.  By creating special versions of this class that are
- * used in only a single location of the code, we increase the chance that only a single type of
- * Row will be referenced, increasing the opportunity for the JIT to play tricks.  This sounds
- * crazy but in benchmarks it had noticeable effects.
- */
-class JoinedRow2 extends InternalRow {
-  private[this] var row1: InternalRow = _
-  private[this] var row2: InternalRow = _
-
-  def this(left: InternalRow, right: InternalRow) = {
-    this()
-    row1 = left
-    row2 = right
-  }
-
-  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
-    row1 = r1
-    row2 = r2
-    this
-  }
-
-  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
-    row1 = newLeft
-    this
-  }
-
-  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
-    row2 = newRight
-    this
-  }
-
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
-
-  override def numFields: Int = row1.numFields + row2.numFields
-
-  override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-  }
-
-  override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-  }
-
-  override def get(i: Int): Any =
-    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
-
-  override def isNullAt(i: Int): Boolean =
-    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
-
-  override def getInt(i: Int): Int =
-    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
-
-  override def getLong(i: Int): Long =
-    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
-
-  override def getDouble(i: Int): Double =
-    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
-
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
-
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
-
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
-
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
-
-  override def copy(): InternalRow = {
-    val totalSize = row1.numFields + row2.numFields
-    val copiedValues = new Array[Any](totalSize)
-    var i = 0
-    while(i < totalSize) {
-      copiedValues(i) = get(i)
-      i += 1
-    }
-    new GenericInternalRow(copiedValues)
-  }
-
-  override def toString: String = {
-    // Make sure toString never throws NullPointerException.
-    if ((row1 eq null) && (row2 eq null)) {
-      "[ empty row ]"
-    } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
-    } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
-    } else {
-      mkString("[", ",", "]")
-    }
-  }
-}
-
-/**
- * JIT HACK: Replace with macros
- */
-class JoinedRow3 extends InternalRow {
-  private[this] var row1: InternalRow = _
-  private[this] var row2: InternalRow = _
-
-  def this(left: InternalRow, right: InternalRow) = {
-    this()
-    row1 = left
-    row2 = right
-  }
-
-  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
-    row1 = r1
-    row2 = r2
-    this
-  }
-
-  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
-    row1 = newLeft
-    this
-  }
-
-  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
-    row2 = newRight
-    this
-  }
-
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
-
-  override def numFields: Int = row1.numFields + row2.numFields
-
-  override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-  }
-
-  override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-  }
-
-
-  override def get(i: Int): Any =
-    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
-
-  override def isNullAt(i: Int): Boolean =
-    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
-
-  override def getInt(i: Int): Int =
-    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
-
-  override def getLong(i: Int): Long =
-    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
-
-  override def getDouble(i: Int): Double =
-    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
-
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
-
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
-
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
-
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
-
-  override def copy(): InternalRow = {
-    val totalSize = row1.numFields + row2.numFields
-    val copiedValues = new Array[Any](totalSize)
-    var i = 0
-    while(i < totalSize) {
-      copiedValues(i) = get(i)
-      i += 1
-    }
-    new GenericInternalRow(copiedValues)
-  }
-
-  override def toString: String = {
-    // Make sure toString never throws NullPointerException.
-    if ((row1 eq null) && (row2 eq null)) {
-      "[ empty row ]"
-    } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
-    } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
-    } else {
-      mkString("[", ",", "]")
-    }
-  }
-}
-
-/**
- * JIT HACK: Replace with macros
- */
-class JoinedRow4 extends InternalRow {
-  private[this] var row1: InternalRow = _
-  private[this] var row2: InternalRow = _
-
-  def this(left: InternalRow, right: InternalRow) = {
-    this()
-    row1 = left
-    row2 = right
-  }
-
-  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
-    row1 = r1
-    row2 = r2
-    this
-  }
-
-  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
-    row1 = newLeft
-    this
-  }
-
-  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
-    row2 = newRight
-    this
-  }
-
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
-
-  override def numFields: Int = row1.numFields + row2.numFields
-
-  override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-  }
-
-  override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-  }
-
-
-  override def get(i: Int): Any =
-    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
-
-  override def isNullAt(i: Int): Boolean =
-    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
-
-  override def getInt(i: Int): Int =
-    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
-
-  override def getLong(i: Int): Long =
-    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
-
-  override def getDouble(i: Int): Double =
-    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
-
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
-
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
-
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
-
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
-
-  override def copy(): InternalRow = {
-    val totalSize = row1.numFields + row2.numFields
-    val copiedValues = new Array[Any](totalSize)
-    var i = 0
-    while(i < totalSize) {
-      copiedValues(i) = get(i)
-      i += 1
-    }
-    new GenericInternalRow(copiedValues)
-  }
-
-  override def toString: String = {
-    // Make sure toString never throws NullPointerException.
-    if ((row1 eq null) && (row2 eq null)) {
-      "[ empty row ]"
-    } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
-    } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
-    } else {
-      mkString("[", ",", "]")
-    }
-  }
-}
-
-/**
- * JIT HACK: Replace with macros
- */
-class JoinedRow5 extends InternalRow {
-  private[this] var row1: InternalRow = _
-  private[this] var row2: InternalRow = _
-
-  def this(left: InternalRow, right: InternalRow) = {
-    this()
-    row1 = left
-    row2 = right
-  }
-
-  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
-    row1 = r1
-    row2 = r2
-    this
-  }
-
-  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
-    row1 = newLeft
-    this
-  }
-
-  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
-    row2 = newRight
-    this
-  }
-
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
-
-  override def numFields: Int = row1.numFields + row2.numFields
-
-  override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-  }
-
-  override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-  }
-
-
-  override def get(i: Int): Any =
-    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
-
-  override def isNullAt(i: Int): Boolean =
-    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
-
-  override def getInt(i: Int): Int =
-    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
-
-  override def getLong(i: Int): Long =
-    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
-
-  override def getDouble(i: Int): Double =
-    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
-
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
-
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
-
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
-
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
-
-  override def copy(): InternalRow = {
-    val totalSize = row1.numFields + row2.numFields
-    val copiedValues = new Array[Any](totalSize)
-    var i = 0
-    while(i < totalSize) {
-      copiedValues(i) = get(i)
-      i += 1
-    }
-    new GenericInternalRow(copiedValues)
-  }
-
-  override def toString: String = {
-    // Make sure toString never throws NullPointerException.
-    if ((row1 eq null) && (row2 eq null)) {
-      "[ empty row ]"
-    } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
-    } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
-    } else {
-      mkString("[", ",", "]")
-    }
-  }
-}
-
-/**
- * JIT HACK: Replace with macros
- */
-class JoinedRow6 extends InternalRow {
-  private[this] var row1: InternalRow = _
-  private[this] var row2: InternalRow = _
-
-  def this(left: InternalRow, right: InternalRow) = {
-    this()
-    row1 = left
-    row2 = right
-  }
-
-  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
-    row1 = r1
-    row2 = r2
-    this
-  }
-
-  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
-    row1 = newLeft
-    this
-  }
-
-  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
-    row2 = newRight
-    this
-  }
-
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
-
-  override def numFields: Int = row1.numFields + row2.numFields
-
-  override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-  }
-
-  override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-  }
-
-
   override def get(i: Int): Any =
     if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index c2c945321db95..e8c6a0f8f801d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -172,7 +172,7 @@ case class Aggregate(
           private[this] val resultProjection =
             new InterpretedMutableProjection(
               resultExpressions, computedSchema ++ namedGroups.map(_._2))
-          private[this] val joinedRow = new JoinedRow4
+          private[this] val joinedRow = new JoinedRow
 
           override final def hasNext: Boolean = hashTableIter.hasNext
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 5ed158b3d2912..5ad4691a5ca07 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -269,7 +269,7 @@ case class GeneratedAggregate(
           namedGroups.map(_._2) ++ computationSchema)
       log.info(s"Result Projection: ${resultExpressions.mkString(",")}")
 
-      val joinedRow = new JoinedRow3
+      val joinedRow = new JoinedRow
 
       if (!iter.hasNext) {
         // This is an empty input, so return early so that we do not allocate data structures
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
index de04132eb1104..91c8a02e2b5bc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -298,7 +298,7 @@ case class Window(
         var rowsSize = 0
         override final def hasNext: Boolean = rowIndex < rowsSize || nextRowAvailable
 
-        val join = new JoinedRow6
+        val join = new JoinedRow
         val windowFunctionResult = new GenericMutableRow(unboundExpressions.size)
         override final def next(): InternalRow = {
           // Load the next partition if we need to.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
index b8e95a5a2a4da..1b89edafa8dad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
@@ -106,7 +106,7 @@ private[sql] abstract class SortAggregationIterator(
     new GenericMutableRow(size)
   }
 
-  protected val joinedRow = new JoinedRow4
+  protected val joinedRow = new JoinedRow
 
   protected val placeholderExpressions = Seq.fill(initialBufferOffset)(NoOp)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index ae34409bcfcca..46ab5b0d1cc6d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -69,7 +69,7 @@ trait HashJoin {
       private[this] var currentMatchPosition: Int = -1
 
       // Mutable per row objects.
-      private[this] val joinRow = new JoinedRow2
+      private[this] val joinRow = new JoinedRow
       private[this] val resultProjection: Projection = {
         if (supportUnsafe) {
           UnsafeProjection.create(self.schema)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 981447eacad74..bb18b5403f8e8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -66,7 +66,7 @@ case class SortMergeJoin(
     leftResults.zipPartitions(rightResults) { (leftIter, rightIter) =>
       new Iterator[InternalRow] {
         // Mutable per row objects.
-        private[this] val joinRow = new JoinedRow5
+        private[this] val joinRow = new JoinedRow
         private[this] var leftElement: InternalRow = _
         private[this] var rightElement: InternalRow = _
         private[this] var leftKey: InternalRow = _

From 215713e19924dff69d226a97f1860a5470464d15 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 25 Jul 2015 01:37:41 -0700
Subject: [PATCH 0596/1454] [SPARK-9334][SQL] Remove UnsafeRowConverter in
 favor of UnsafeProjection.

The two are redundant.

Once this patch is merged, I plan to remove the inbound conversions from unsafe aggregates.

Author: Reynold Xin <rxin@databricks.com>

Closes #7658 from rxin/unsafeconverters and squashes the following commits:

ed19e6c [Reynold Xin] Updated support types.
2a56d7e [Reynold Xin] [SPARK-9334][SQL] Remove UnsafeRowConverter in favor of UnsafeProjection.
---
 .../UnsafeFixedWidthAggregationMap.java       |  55 +---
 .../expressions/UnsafeRowWriters.java         |  83 ++++++
 .../sql/catalyst/expressions/Projection.scala |   4 +-
 .../expressions/UnsafeRowConverter.scala      | 276 ------------------
 .../codegen/GenerateUnsafeProjection.scala    | 131 ++++++---
 .../expressions/ExpressionEvalHelper.scala    |   2 +-
 .../expressions/UnsafeRowConverterSuite.scala |  79 ++---
 .../execution/UnsafeRowSerializerSuite.scala  |  17 +-
 .../apache/spark/unsafe/types/ByteArray.java  |  38 +++
 .../apache/spark/unsafe/types/UTF8String.java |  15 +
 10 files changed, 262 insertions(+), 438 deletions(-)
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
 delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
 create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
index 2f7e84a7f59e2..684de6e81d67c 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
@@ -47,7 +47,7 @@ public final class UnsafeFixedWidthAggregationMap {
   /**
    * Encodes grouping keys as UnsafeRows.
    */
-  private final UnsafeRowConverter groupingKeyToUnsafeRowConverter;
+  private final UnsafeProjection groupingKeyProjection;
 
   /**
    * A hashmap which maps from opaque bytearray keys to bytearray values.
@@ -59,14 +59,6 @@ public final class UnsafeFixedWidthAggregationMap {
    */
   private final UnsafeRow currentAggregationBuffer = new UnsafeRow();
 
-  /**
-   * Scratch space that is used when encoding grouping keys into UnsafeRow format.
-   *
-   * By default, this is a 8 kb array, but it will grow as necessary in case larger keys are
-   * encountered.
-   */
-  private byte[] groupingKeyConversionScratchSpace = new byte[1024 * 8];
-
   private final boolean enablePerfMetrics;
 
   /**
@@ -112,26 +104,17 @@ public UnsafeFixedWidthAggregationMap(
       TaskMemoryManager memoryManager,
       int initialCapacity,
       boolean enablePerfMetrics) {
-    this.emptyAggregationBuffer =
-      convertToUnsafeRow(emptyAggregationBuffer, aggregationBufferSchema);
     this.aggregationBufferSchema = aggregationBufferSchema;
-    this.groupingKeyToUnsafeRowConverter = new UnsafeRowConverter(groupingKeySchema);
+    this.groupingKeyProjection = UnsafeProjection.create(groupingKeySchema);
     this.groupingKeySchema = groupingKeySchema;
     this.map = new BytesToBytesMap(memoryManager, initialCapacity, enablePerfMetrics);
     this.enablePerfMetrics = enablePerfMetrics;
-  }
 
-  /**
-   * Convert a Java object row into an UnsafeRow, allocating it into a new byte array.
-   */
-  private static byte[] convertToUnsafeRow(InternalRow javaRow, StructType schema) {
-    final UnsafeRowConverter converter = new UnsafeRowConverter(schema);
-    final int size = converter.getSizeRequirement(javaRow);
-    final byte[] unsafeRow = new byte[size];
-    final int writtenLength =
-      converter.writeRow(javaRow, unsafeRow, PlatformDependent.BYTE_ARRAY_OFFSET, size);
-    assert (writtenLength == unsafeRow.length): "Size requirement calculation was wrong!";
-    return unsafeRow;
+    // Initialize the buffer for aggregation value
+    final UnsafeProjection valueProjection = UnsafeProjection.create(aggregationBufferSchema);
+    this.emptyAggregationBuffer = valueProjection.apply(emptyAggregationBuffer).getBytes();
+    assert(this.emptyAggregationBuffer.length == aggregationBufferSchema.length() * 8 +
+      UnsafeRow.calculateBitSetWidthInBytes(aggregationBufferSchema.length()));
   }
 
   /**
@@ -139,30 +122,20 @@ private static byte[] convertToUnsafeRow(InternalRow javaRow, StructType schema)
    * return the same object.
    */
   public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
-    final int groupingKeySize = groupingKeyToUnsafeRowConverter.getSizeRequirement(groupingKey);
-    // Make sure that the buffer is large enough to hold the key. If it's not, grow it:
-    if (groupingKeySize > groupingKeyConversionScratchSpace.length) {
-      groupingKeyConversionScratchSpace = new byte[groupingKeySize];
-    }
-    final int actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow(
-      groupingKey,
-      groupingKeyConversionScratchSpace,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      groupingKeySize);
-    assert (groupingKeySize == actualGroupingKeySize) : "Size requirement calculation was wrong!";
+    final UnsafeRow unsafeGroupingKeyRow = this.groupingKeyProjection.apply(groupingKey);
 
     // Probe our map using the serialized key
     final BytesToBytesMap.Location loc = map.lookup(
-      groupingKeyConversionScratchSpace,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      groupingKeySize);
+      unsafeGroupingKeyRow.getBaseObject(),
+      unsafeGroupingKeyRow.getBaseOffset(),
+      unsafeGroupingKeyRow.getSizeInBytes());
     if (!loc.isDefined()) {
       // This is the first time that we've seen this grouping key, so we'll insert a copy of the
       // empty aggregation buffer into the map:
       loc.putNewKey(
-        groupingKeyConversionScratchSpace,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        groupingKeySize,
+        unsafeGroupingKeyRow.getBaseObject(),
+        unsafeGroupingKeyRow.getBaseOffset(),
+        unsafeGroupingKeyRow.getSizeInBytes(),
         emptyAggregationBuffer,
         PlatformDependent.BYTE_ARRAY_OFFSET,
         emptyAggregationBuffer.length
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
new file mode 100644
index 0000000000000..87521d1f23c99
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -0,0 +1,83 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
+import org.apache.spark.unsafe.types.ByteArray;
+import org.apache.spark.unsafe.types.UTF8String;
+
+/**
+ * A set of helper methods to write data into {@link UnsafeRow}s,
+ * used by {@link org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection}.
+ */
+public class UnsafeRowWriters {
+
+  /** Writer for UTF8String. */
+  public static class UTF8StringWriter {
+
+    public static int getSize(UTF8String input) {
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(input.numBytes());
+    }
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, UTF8String input) {
+      final long offset = target.getBaseOffset() + cursor;
+      final int numBytes = input.numBytes();
+
+      // zero-out the padding bytes
+      if ((numBytes & 0x07) > 0) {
+        PlatformDependent.UNSAFE.putLong(
+          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+      }
+
+      // Write the string to the variable length portion.
+      input.writeToMemory(target.getBaseObject(), offset);
+
+      // Set the fixed length portion.
+      target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes);
+    }
+  }
+
+  /** Writer for bianry (byte array) type. */
+  public static class BinaryWriter {
+
+    public static int getSize(byte[] input) {
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(input.length);
+    }
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, byte[] input) {
+      final long offset = target.getBaseOffset() + cursor;
+      final int numBytes = input.length;
+
+      // zero-out the padding bytes
+      if ((numBytes & 0x07) > 0) {
+        PlatformDependent.UNSAFE.putLong(
+          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+      }
+
+      // Write the string to the variable length portion.
+      ByteArray.writeToMemory(input, target.getBaseObject(), offset);
+
+      // Set the fixed length portion.
+      target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes);
+    }
+  }
+
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 6023a2c564389..fb873e7e99547 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -90,8 +90,10 @@ object UnsafeProjection {
    * Seq[Expression].
    */
   def canSupport(schema: StructType): Boolean = canSupport(schema.fields.map(_.dataType))
-  def canSupport(types: Array[DataType]): Boolean = types.forall(UnsafeColumnWriter.canEmbed(_))
   def canSupport(exprs: Seq[Expression]): Boolean = canSupport(exprs.map(_.dataType).toArray)
+  private def canSupport(types: Array[DataType]): Boolean = {
+    types.forall(GenerateUnsafeProjection.canSupport)
+  }
 
   /**
    * Returns an UnsafeProjection for given StructType.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
deleted file mode 100644
index c47b16c0f8585..0000000000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala
+++ /dev/null
@@ -1,276 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.expressions
-
-import scala.util.Try
-
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
-import org.apache.spark.unsafe.array.ByteArrayMethods
-import org.apache.spark.unsafe.types.UTF8String
-
-
-/**
- * Converts Rows into UnsafeRow format. This class is NOT thread-safe.
- *
- * @param fieldTypes the data types of the row's columns.
- */
-class UnsafeRowConverter(fieldTypes: Array[DataType]) {
-
-  def this(schema: StructType) {
-    this(schema.fields.map(_.dataType))
-  }
-
-  /** Re-used pointer to the unsafe row being written */
-  private[this] val unsafeRow = new UnsafeRow()
-
-  /** Functions for encoding each column */
-  private[this] val writers: Array[UnsafeColumnWriter] = {
-    fieldTypes.map(t => UnsafeColumnWriter.forType(t))
-  }
-
-  /** The size, in bytes, of the fixed-length portion of the row, including the null bitmap */
-  private[this] val fixedLengthSize: Int =
-    (8 * fieldTypes.length) + UnsafeRow.calculateBitSetWidthInBytes(fieldTypes.length)
-
-  /**
-   * Compute the amount of space, in bytes, required to encode the given row.
-   */
-  def getSizeRequirement(row: InternalRow): Int = {
-    var fieldNumber = 0
-    var variableLengthFieldSize: Int = 0
-    while (fieldNumber < writers.length) {
-      if (!row.isNullAt(fieldNumber)) {
-        variableLengthFieldSize += writers(fieldNumber).getSize(row, fieldNumber)
-      }
-      fieldNumber += 1
-    }
-    fixedLengthSize + variableLengthFieldSize
-  }
-
-  /**
-   * Convert the given row into UnsafeRow format.
-   *
-   * @param row the row to convert
-   * @param baseObject the base object of the destination address
-   * @param baseOffset the base offset of the destination address
-   * @param rowLengthInBytes the length calculated by `getSizeRequirement(row)`
-   * @return the number of bytes written. This should be equal to `getSizeRequirement(row)`.
-   */
-  def writeRow(
-      row: InternalRow,
-      baseObject: Object,
-      baseOffset: Long,
-      rowLengthInBytes: Int): Int = {
-    unsafeRow.pointTo(baseObject, baseOffset, writers.length, rowLengthInBytes)
-
-    if (writers.length > 0) {
-      // zero-out the bitset
-      var n = writers.length / 64
-      while (n >= 0) {
-        PlatformDependent.UNSAFE.putLong(
-          unsafeRow.getBaseObject,
-          unsafeRow.getBaseOffset + n * 8,
-          0L)
-        n -= 1
-      }
-    }
-
-    var fieldNumber = 0
-    var appendCursor: Int = fixedLengthSize
-    while (fieldNumber < writers.length) {
-      if (row.isNullAt(fieldNumber)) {
-        unsafeRow.setNullAt(fieldNumber)
-      } else {
-        appendCursor += writers(fieldNumber).write(row, unsafeRow, fieldNumber, appendCursor)
-      }
-      fieldNumber += 1
-    }
-    appendCursor
-  }
-
-}
-
-/**
- * Function for writing a column into an UnsafeRow.
- */
-private abstract class UnsafeColumnWriter {
-  /**
-   * Write a value into an UnsafeRow.
-   *
-   * @param source the row being converted
-   * @param target a pointer to the converted unsafe row
-   * @param column the column to write
-   * @param appendCursor the offset from the start of the unsafe row to the end of the row;
-   *                     used for calculating where variable-length data should be written
-   * @return the number of variable-length bytes written
-   */
-  def write(source: InternalRow, target: UnsafeRow, column: Int, appendCursor: Int): Int
-
-  /**
-   * Return the number of bytes that are needed to write this variable-length value.
-   */
-  def getSize(source: InternalRow, column: Int): Int
-}
-
-private object UnsafeColumnWriter {
-
-  def forType(dataType: DataType): UnsafeColumnWriter = {
-    dataType match {
-      case NullType => NullUnsafeColumnWriter
-      case BooleanType => BooleanUnsafeColumnWriter
-      case ByteType => ByteUnsafeColumnWriter
-      case ShortType => ShortUnsafeColumnWriter
-      case IntegerType | DateType => IntUnsafeColumnWriter
-      case LongType | TimestampType => LongUnsafeColumnWriter
-      case FloatType => FloatUnsafeColumnWriter
-      case DoubleType => DoubleUnsafeColumnWriter
-      case StringType => StringUnsafeColumnWriter
-      case BinaryType => BinaryUnsafeColumnWriter
-      case t =>
-        throw new UnsupportedOperationException(s"Do not know how to write columns of type $t")
-    }
-  }
-
-  /**
-   * Returns whether the dataType can be embedded into UnsafeRow (not using ObjectPool).
-   */
-  def canEmbed(dataType: DataType): Boolean = Try(forType(dataType)).isSuccess
-}
-
-// ------------------------------------------------------------------------------------------------
-
-private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter {
-  // Primitives don't write to the variable-length region:
-  def getSize(sourceRow: InternalRow, column: Int): Int = 0
-}
-
-private object NullUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setNullAt(column)
-    0
-  }
-}
-
-private object BooleanUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setBoolean(column, source.getBoolean(column))
-    0
-  }
-}
-
-private object ByteUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setByte(column, source.getByte(column))
-    0
-  }
-}
-
-private object ShortUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setShort(column, source.getShort(column))
-    0
-  }
-}
-
-private object IntUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setInt(column, source.getInt(column))
-    0
-  }
-}
-
-private object LongUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setLong(column, source.getLong(column))
-    0
-  }
-}
-
-private object FloatUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setFloat(column, source.getFloat(column))
-    0
-  }
-}
-
-private object DoubleUnsafeColumnWriter extends PrimitiveUnsafeColumnWriter {
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    target.setDouble(column, source.getDouble(column))
-    0
-  }
-}
-
-private abstract class BytesUnsafeColumnWriter extends UnsafeColumnWriter {
-
-  protected[this] def isString: Boolean
-  protected[this] def getBytes(source: InternalRow, column: Int): Array[Byte]
-
-  override def getSize(source: InternalRow, column: Int): Int = {
-    val numBytes = getBytes(source, column).length
-    ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
-  }
-
-  override def write(source: InternalRow, target: UnsafeRow, column: Int, cursor: Int): Int = {
-    val bytes = getBytes(source, column)
-    write(target, bytes, column, cursor)
-  }
-
-  def write(target: UnsafeRow, bytes: Array[Byte], column: Int, cursor: Int): Int = {
-    val offset = target.getBaseOffset + cursor
-    val numBytes = bytes.length
-    if ((numBytes & 0x07) > 0) {
-      // zero-out the padding bytes
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject, offset + ((numBytes >> 3) << 3), 0L)
-    }
-    PlatformDependent.copyMemory(
-      bytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      target.getBaseObject,
-      offset,
-      numBytes
-    )
-    target.setLong(column, (cursor.toLong << 32) | numBytes.toLong)
-    ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes)
-  }
-}
-
-private object StringUnsafeColumnWriter extends BytesUnsafeColumnWriter {
-  protected[this] def isString: Boolean = true
-  def getBytes(source: InternalRow, column: Int): Array[Byte] = {
-    source.getAs[UTF8String](column).getBytes
-  }
-  // TODO(davies): refactor this
-  // specialized for codegen
-  def getSize(value: UTF8String): Int =
-    ByteArrayMethods.roundNumberOfBytesToNearestWord(value.numBytes())
-  def write(target: UnsafeRow, value: UTF8String, column: Int, cursor: Int): Int = {
-    write(target, value.getBytes, column, cursor)
-  }
-}
-
-private object BinaryUnsafeColumnWriter extends BytesUnsafeColumnWriter {
-  protected[this] override def isString: Boolean = false
-  override def getBytes(source: InternalRow, column: Int): Array[Byte] = {
-    source.getAs[Array[Byte]](column)
-  }
-  // specialized for codegen
-  def getSize(value: Array[Byte]): Int =
-    ByteArrayMethods.roundNumberOfBytesToNearestWord(value.length)
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 0320bcb827bf7..afd0d9cfa1ddd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{NullType, BinaryType, StringType}
-
+import org.apache.spark.sql.types._
 
 /**
  * Generates a [[Projection]] that returns an [[UnsafeRow]].
@@ -32,25 +31,43 @@ import org.apache.spark.sql.types.{NullType, BinaryType, StringType}
  */
 object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafeProjection] {
 
-  protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
-    in.map(ExpressionCanonicalizer.execute)
+  private val StringWriter = classOf[UnsafeRowWriters.UTF8StringWriter].getName
+  private val BinaryWriter = classOf[UnsafeRowWriters.BinaryWriter].getName
 
-  protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
-    in.map(BindReferences.bindReference(_, inputSchema))
+  /** Returns true iff we support this data type. */
+  def canSupport(dataType: DataType): Boolean = dataType match {
+    case t: AtomicType if !t.isInstanceOf[DecimalType] => true
+    case NullType => true
+    case _ => false
+  }
+
+  /**
+   * Generates the code to create an [[UnsafeRow]] object based on the input expressions.
+   * @param ctx context for code generation
+   * @param ev specifies the name of the variable for the output [[UnsafeRow]] object
+   * @param expressions input expressions
+   * @return generated code to put the expression output into an [[UnsafeRow]]
+   */
+  def createCode(ctx: CodeGenContext, ev: GeneratedExpressionCode, expressions: Seq[Expression])
+    : String = {
+
+    val ret = ev.primitive
+    ctx.addMutableState("UnsafeRow", ret, s"$ret = new UnsafeRow();")
+    val bufferTerm = ctx.freshName("buffer")
+    ctx.addMutableState("byte[]", bufferTerm, s"$bufferTerm = new byte[64];")
+    val cursorTerm = ctx.freshName("cursor")
+    val numBytesTerm = ctx.freshName("numBytes")
 
-  protected def create(expressions: Seq[Expression]): UnsafeProjection = {
-    val ctx = newCodeGenContext()
     val exprs = expressions.map(_.gen(ctx))
     val allExprs = exprs.map(_.code).mkString("\n")
     val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
-    val stringWriter = "org.apache.spark.sql.catalyst.expressions.StringUnsafeColumnWriter"
-    val binaryWriter = "org.apache.spark.sql.catalyst.expressions.BinaryUnsafeColumnWriter"
+
     val additionalSize = expressions.zipWithIndex.map { case (e, i) =>
       e.dataType match {
         case StringType =>
-          s" + (${exprs(i).isNull} ? 0 : $stringWriter.getSize(${exprs(i).primitive}))"
+          s" + (${exprs(i).isNull} ? 0 : $StringWriter.getSize(${exprs(i).primitive}))"
         case BinaryType =>
-          s" + (${exprs(i).isNull} ? 0 : $binaryWriter.getSize(${exprs(i).primitive}))"
+          s" + (${exprs(i).isNull} ? 0 : $BinaryWriter.getSize(${exprs(i).primitive}))"
         case _ => ""
       }
     }.mkString("")
@@ -58,63 +75,85 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     val writers = expressions.zipWithIndex.map { case (e, i) =>
       val update = e.dataType match {
         case dt if ctx.isPrimitiveType(dt) =>
-          s"${ctx.setColumn("target", dt, i, exprs(i).primitive)}"
+          s"${ctx.setColumn(ret, dt, i, exprs(i).primitive)}"
         case StringType =>
-          s"cursor += $stringWriter.write(target, ${exprs(i).primitive}, $i, cursor)"
+          s"$cursorTerm += $StringWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
         case BinaryType =>
-          s"cursor += $binaryWriter.write(target, ${exprs(i).primitive}, $i, cursor)"
+          s"$cursorTerm += $BinaryWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
         case NullType => ""
         case _ =>
           throw new UnsupportedOperationException(s"Not supported DataType: ${e.dataType}")
       }
       s"""if (${exprs(i).isNull}) {
-            target.setNullAt($i);
+            $ret.setNullAt($i);
           } else {
             $update;
           }"""
     }.mkString("\n          ")
 
-    val code = s"""
-    private $exprType[] expressions;
+    s"""
+      $allExprs
+      int $numBytesTerm = $fixedSize $additionalSize;
+      if ($numBytesTerm > $bufferTerm.length) {
+        $bufferTerm = new byte[$numBytesTerm];
+      }
 
-    public Object generate($exprType[] expr) {
-      this.expressions = expr;
-      return new SpecificProjection();
-    }
+      $ret.pointTo(
+        $bufferTerm,
+        org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
+        ${expressions.size},
+        $numBytesTerm);
+      int $cursorTerm = $fixedSize;
 
-    class SpecificProjection extends ${classOf[UnsafeProjection].getName} {
 
-      private UnsafeRow target = new UnsafeRow();
-      private byte[] buffer = new byte[64];
-      ${declareMutableStates(ctx)}
+      $writers
+      boolean ${ev.isNull} = false;
+     """
+  }
 
-      public SpecificProjection() {
-        ${initMutableStates(ctx)}
-      }
+  protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
+    in.map(ExpressionCanonicalizer.execute)
+
+  protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
+    in.map(BindReferences.bindReference(_, inputSchema))
+
+  protected def create(expressions: Seq[Expression]): UnsafeProjection = {
+    val ctx = newCodeGenContext()
+
+    val isNull = ctx.freshName("retIsNull")
+    val primitive = ctx.freshName("retValue")
+    val eval = GeneratedExpressionCode("", isNull, primitive)
+    eval.code = createCode(ctx, eval, expressions)
 
-      // Scala.Function1 need this
-      public Object apply(Object row) {
-        return apply((InternalRow) row);
+    val code = s"""
+      private $exprType[] expressions;
+
+      public Object generate($exprType[] expr) {
+        this.expressions = expr;
+        return new SpecificProjection();
       }
 
-      public UnsafeRow apply(InternalRow i) {
-        $allExprs
+      class SpecificProjection extends ${classOf[UnsafeProjection].getName} {
+
+        ${declareMutableStates(ctx)}
+
+        public SpecificProjection() {
+          ${initMutableStates(ctx)}
+        }
+
+        // Scala.Function1 need this
+        public Object apply(Object row) {
+          return apply((InternalRow) row);
+        }
 
-        // additionalSize had '+' in the beginning
-        int numBytes = $fixedSize $additionalSize;
-        if (numBytes > buffer.length) {
-          buffer = new byte[numBytes];
+        public UnsafeRow apply(InternalRow i) {
+          ${eval.code}
+          return ${eval.primitive};
         }
-        target.pointTo(buffer, org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
-          ${expressions.size}, numBytes);
-        int cursor = $fixedSize;
-        $writers
-        return target;
       }
-    }
-    """
+      """
 
-    logDebug(s"code for ${expressions.mkString(",")}:\n${CodeFormatter.format(code)}")
+    logDebug(s"code for ${expressions.mkString(",")}:\n$code")
 
     val c = compile(code)
     c.generate(ctx.references.toArray).asInstanceOf[UnsafeProjection]
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 6e17ffcda9dc4..4930219aa63cb 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -43,7 +43,7 @@ trait ExpressionEvalHelper {
     checkEvaluationWithoutCodegen(expression, catalystValue, inputRow)
     checkEvaluationWithGeneratedMutableProjection(expression, catalystValue, inputRow)
     checkEvaluationWithGeneratedProjection(expression, catalystValue, inputRow)
-    if (UnsafeColumnWriter.canEmbed(expression.dataType)) {
+    if (GenerateUnsafeProjection.canSupport(expression.dataType)) {
       checkEvalutionWithUnsafeProjection(expression, catalystValue, inputRow)
     }
     checkEvaluationWithOptimization(expression, catalystValue, inputRow)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index a5d9806c20463..4606bcb57311d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -26,7 +26,6 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.array.ByteArrayMethods
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -34,22 +33,15 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
   test("basic conversion with only primitive types") {
     val fieldTypes: Array[DataType] = Array(LongType, LongType, IntegerType)
-    val converter = new UnsafeRowConverter(fieldTypes)
+    val converter = UnsafeProjection.create(fieldTypes)
 
     val row = new SpecificMutableRow(fieldTypes)
     row.setLong(0, 0)
     row.setLong(1, 1)
     row.setInt(2, 2)
 
-    val sizeRequired: Int = converter.getSizeRequirement(row)
-    assert(sizeRequired === 8 + (3 * 8))
-    val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten =
-      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired)
-    assert(numBytesWritten === sizeRequired)
-
-    val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
+    val unsafeRow: UnsafeRow = converter.apply(row)
+    assert(converter.apply(row).getSizeInBytes === 8 + (3 * 8))
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getLong(1) === 1)
     assert(unsafeRow.getInt(2) === 2)
@@ -73,25 +65,18 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
   test("basic conversion with primitive, string and binary types") {
     val fieldTypes: Array[DataType] = Array(LongType, StringType, BinaryType)
-    val converter = new UnsafeRowConverter(fieldTypes)
+    val converter = UnsafeProjection.create(fieldTypes)
 
     val row = new SpecificMutableRow(fieldTypes)
     row.setLong(0, 0)
     row.update(1, UTF8String.fromString("Hello"))
     row.update(2, "World".getBytes)
 
-    val sizeRequired: Int = converter.getSizeRequirement(row)
-    assert(sizeRequired === 8 + (8 * 3) +
+    val unsafeRow: UnsafeRow = converter.apply(row)
+    assert(unsafeRow.getSizeInBytes === 8 + (8 * 3) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length))
-    val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(
-      row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired)
-    assert(numBytesWritten === sizeRequired)
-
-    val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(
-      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
+
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
     assert(unsafeRow.getBinary(2) === "World".getBytes)
@@ -99,7 +84,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
   test("basic conversion with primitive, string, date and timestamp types") {
     val fieldTypes: Array[DataType] = Array(LongType, StringType, DateType, TimestampType)
-    val converter = new UnsafeRowConverter(fieldTypes)
+    val converter = UnsafeProjection.create(fieldTypes)
 
     val row = new SpecificMutableRow(fieldTypes)
     row.setLong(0, 0)
@@ -107,17 +92,10 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     row.update(2, DateTimeUtils.fromJavaDate(Date.valueOf("1970-01-01")))
     row.update(3, DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2015-05-08 08:10:25")))
 
-    val sizeRequired: Int = converter.getSizeRequirement(row)
-    assert(sizeRequired === 8 + (8 * 4) +
+    val unsafeRow: UnsafeRow = converter.apply(row)
+    assert(unsafeRow.getSizeInBytes === 8 + (8 * 4) +
       ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length))
-    val buffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten =
-      converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET, sizeRequired)
-    assert(numBytesWritten === sizeRequired)
-
-    val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(
-      buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
+
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
     // Date is represented as Int in unsafeRow
@@ -148,26 +126,18 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       // DecimalType.Default,
       // ArrayType(IntegerType)
     )
-    val converter = new UnsafeRowConverter(fieldTypes)
+    val converter = UnsafeProjection.create(fieldTypes)
 
     val rowWithAllNullColumns: InternalRow = {
       val r = new SpecificMutableRow(fieldTypes)
-      for (i <- 0 to fieldTypes.length - 1) {
+      for (i <- fieldTypes.indices) {
         r.setNullAt(i)
       }
       r
     }
 
-    val sizeRequired: Int = converter.getSizeRequirement(rowWithAllNullColumns)
-    val createdFromNullBuffer: Array[Long] = new Array[Long](sizeRequired / 8)
-    val numBytesWritten = converter.writeRow(
-      rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET,
-      sizeRequired)
-    assert(numBytesWritten === sizeRequired)
+    val createdFromNull: UnsafeRow = converter.apply(rowWithAllNullColumns)
 
-    val createdFromNull = new UnsafeRow()
-    createdFromNull.pointTo(
-      createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, sizeRequired)
     for (i <- fieldTypes.indices) {
       assert(createdFromNull.isNullAt(i))
     }
@@ -202,15 +172,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       // r.update(11, Array(11))
       r
     }
-    val setToNullAfterCreationBuffer: Array[Long] = new Array[Long](sizeRequired / 8 + 2)
-    converter.writeRow(
-      rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET,
-      sizeRequired)
-    val setToNullAfterCreation = new UnsafeRow()
-    setToNullAfterCreation.pointTo(
-      setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length,
-      sizeRequired)
 
+    val setToNullAfterCreation = converter.apply(rowWithNoNullColumns)
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
     assert(setToNullAfterCreation.getBoolean(1) === rowWithNoNullColumns.getBoolean(1))
     assert(setToNullAfterCreation.getByte(2) === rowWithNoNullColumns.getByte(2))
@@ -228,8 +191,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       setToNullAfterCreation.setNullAt(i)
     }
     // There are some garbage left in the var-length area
-    assert(Arrays.equals(createdFromNullBuffer,
-      java.util.Arrays.copyOf(setToNullAfterCreationBuffer, sizeRequired / 8)))
+    assert(Arrays.equals(createdFromNull.getBytes, setToNullAfterCreation.getBytes()))
 
     setToNullAfterCreation.setNullAt(0)
     setToNullAfterCreation.setBoolean(1, false)
@@ -269,12 +231,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     row2.setFloat(0, java.lang.Float.intBitsToFloat(0x7fffffff))
     row2.setDouble(1, java.lang.Double.longBitsToDouble(0x7fffffffffffffffL))
 
-    val converter = new UnsafeRowConverter(fieldTypes)
-    val row1Buffer = new Array[Byte](converter.getSizeRequirement(row1))
-    val row2Buffer = new Array[Byte](converter.getSizeRequirement(row2))
-    converter.writeRow(row1, row1Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row1Buffer.length)
-    converter.writeRow(row2, row2Buffer, PlatformDependent.BYTE_ARRAY_OFFSET, row2Buffer.length)
-
-    assert(row1Buffer.toSeq === row2Buffer.toSeq)
+    val converter = UnsafeProjection.create(fieldTypes)
+    assert(converter.apply(row1).getBytes === converter.apply(row2).getBytes)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
index a1e1695717e23..40b47ae18d648 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
@@ -22,29 +22,22 @@ import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeRowConverter}
+import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, UnsafeRow}
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
 
 class UnsafeRowSerializerSuite extends SparkFunSuite {
 
   private def toUnsafeRow(row: Row, schema: Array[DataType]): UnsafeRow = {
     val internalRow = CatalystTypeConverters.convertToCatalyst(row).asInstanceOf[InternalRow]
-    val rowConverter = new UnsafeRowConverter(schema)
-    val rowSizeInBytes = rowConverter.getSizeRequirement(internalRow)
-    val byteArray = new Array[Byte](rowSizeInBytes)
-    rowConverter.writeRow(
-      internalRow, byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, rowSizeInBytes)
-    val unsafeRow = new UnsafeRow()
-    unsafeRow.pointTo(byteArray, PlatformDependent.BYTE_ARRAY_OFFSET, row.length, rowSizeInBytes)
-    unsafeRow
+    val converter = UnsafeProjection.create(schema)
+    converter.apply(internalRow)
   }
 
-  ignore("toUnsafeRow() test helper method") {
+  test("toUnsafeRow() test helper method") {
     // This currently doesnt work because the generic getter throws an exception.
     val row = Row("Hello", 123)
     val unsafeRow = toUnsafeRow(row, Array(StringType, IntegerType))
-    assert(row.getString(0) === unsafeRow.get(0).toString)
+    assert(row.getString(0) === unsafeRow.getUTF8String(0).toString)
     assert(row.getInt(1) === unsafeRow.getInt(1))
   }
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
new file mode 100644
index 0000000000000..69b0e206cef18
--- /dev/null
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.unsafe.types;
+
+import org.apache.spark.unsafe.PlatformDependent;
+
+public class ByteArray {
+
+  /**
+   * Writes the content of a byte array into a memory address, identified by an object and an
+   * offset. The target memory address must already been allocated, and have enough space to
+   * hold all the bytes in this string.
+   */
+  public static void writeToMemory(byte[] src, Object target, long targetOffset) {
+    PlatformDependent.copyMemory(
+      src,
+      PlatformDependent.BYTE_ARRAY_OFFSET,
+      target,
+      targetOffset,
+      src.length
+    );
+  }
+}
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 6d8dcb1cbf876..85381cf0ef425 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -95,6 +95,21 @@ protected UTF8String(Object base, long offset, int size) {
     this.numBytes = size;
   }
 
+  /**
+   * Writes the content of this string into a memory address, identified by an object and an offset.
+   * The target memory address must already been allocated, and have enough space to hold all the
+   * bytes in this string.
+   */
+  public void writeToMemory(Object target, long targetOffset) {
+    PlatformDependent.copyMemory(
+      base,
+      offset,
+      target,
+      targetOffset,
+      numBytes
+    );
+  }
+
   /**
    * Returns the number of bytes for a code point with the first byte as `b`
    * @param b The first byte of a code point

From c980e20cf17f2980c564beab9b241022872e29ea Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sat, 25 Jul 2015 11:05:08 +0100
Subject: [PATCH 0597/1454] [SPARK-9304] [BUILD] Improve backwards
 compatibility of SPARK-8401

Add back change-version-to-X.sh scripts, as wrappers for new script, for backwards compatibility

Author: Sean Owen <sowen@cloudera.com>

Closes #7639 from srowen/SPARK-9304 and squashes the following commits:

9ab2681 [Sean Owen] Add deprecation message to wrappers
3c8c202 [Sean Owen] Add back change-version-to-X.sh scripts, as wrappers for new script, for backwards compatibility
---
 dev/change-version-to-2.10.sh | 23 +++++++++++++++++++++++
 dev/change-version-to-2.11.sh | 23 +++++++++++++++++++++++
 2 files changed, 46 insertions(+)
 create mode 100755 dev/change-version-to-2.10.sh
 create mode 100755 dev/change-version-to-2.11.sh

diff --git a/dev/change-version-to-2.10.sh b/dev/change-version-to-2.10.sh
new file mode 100755
index 0000000000000..0962d34c52f28
--- /dev/null
+++ b/dev/change-version-to-2.10.sh
@@ -0,0 +1,23 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This script exists for backwards compability. Use change-scala-version.sh instead.
+echo "This script is deprecated. Please instead run: change-scala-version.sh 2.10"
+
+$(dirname $0)/change-scala-version.sh 2.10
diff --git a/dev/change-version-to-2.11.sh b/dev/change-version-to-2.11.sh
new file mode 100755
index 0000000000000..4ccfeef09fd04
--- /dev/null
+++ b/dev/change-version-to-2.11.sh
@@ -0,0 +1,23 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This script exists for backwards compability. Use change-scala-version.sh instead.
+echo "This script is deprecated. Please instead run: change-scala-version.sh 2.11"
+
+$(dirname $0)/change-scala-version.sh 2.11

From e2ec018e37cb699077b5fa2bd662f2055cb42296 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sat, 25 Jul 2015 11:42:49 -0700
Subject: [PATCH 0598/1454] [SPARK-9285] [SQL] Fixes Row/InternalRow conversion
 for HadoopFsRelation

This is a follow-up of #7626. It fixes `Row`/`InternalRow` conversion for data sources extending `HadoopFsRelation` with `needConversion` being `true`.

Author: Cheng Lian <lian@databricks.com>

Closes #7649 from liancheng/spark-9285-conversion-fix and squashes the following commits:

036a50c [Cheng Lian] Addresses PR comment
f6d7c6a [Cheng Lian] Fixes Row/InternalRow conversion for HadoopFsRelation
---
 .../apache/spark/sql/sources/interfaces.scala | 23 ++++++++++++++++---
 .../SimpleTextHadoopFsRelationSuite.scala     |  5 ----
 2 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 119bac786d478..7126145ddc010 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -28,7 +28,7 @@ import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
 import org.apache.spark.sql.execution.RDDConversions
@@ -593,6 +593,11 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
    *
    * @since 1.4.0
    */
+  // TODO Tries to eliminate the extra Catalyst-to-Scala conversion when `needConversion` is true
+  //
+  // PR #7626 separated `Row` and `InternalRow` completely.  One of the consequences is that we can
+  // no longer treat an `InternalRow` containing Catalyst values as a `Row`.  Thus we have to
+  // introduce another row value conversion for data sources whose `needConversion` is true.
   def buildScan(requiredColumns: Array[String], inputFiles: Array[FileStatus]): RDD[Row] = {
     // Yeah, to workaround serialization...
     val dataSchema = this.dataSchema
@@ -611,14 +616,26 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
       } else {
         rdd.asInstanceOf[RDD[InternalRow]]
       }
+
     converted.mapPartitions { rows =>
       val buildProjection = if (codegenEnabled) {
         GenerateMutableProjection.generate(requiredOutput, dataSchema.toAttributes)
       } else {
         () => new InterpretedMutableProjection(requiredOutput, dataSchema.toAttributes)
       }
-      val mutableProjection = buildProjection()
-      rows.map(r => mutableProjection(r))
+
+      val projectedRows = {
+        val mutableProjection = buildProjection()
+        rows.map(r => mutableProjection(r))
+      }
+
+      if (needConversion) {
+        val requiredSchema = StructType(requiredColumns.map(dataSchema(_)))
+        val toScala = CatalystTypeConverters.createToScalaConverter(requiredSchema)
+        projectedRows.map(toScala(_).asInstanceOf[Row])
+      } else {
+        projectedRows
+      }
     }.asInstanceOf[RDD[Row]]
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index d761909d60e21..e8975e5f5cd08 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -22,10 +22,6 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
-/*
-This is commented out due a bug in the data source API (SPARK-9291).
-
-
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
 
@@ -54,4 +50,3 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
     }
   }
 }
-*/

From 2c94d0f24a37fa079b56d534b0b0a4574209215b Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 25 Jul 2015 12:10:02 -0700
Subject: [PATCH 0599/1454] [SPARK-9192][SQL] add initialization phase for
 nondeterministic expression

Currently nondeterministic expression is broken without a explicit initialization phase.

Let me take `MonotonicallyIncreasingID` as an example. This expression need a mutable state to remember how many times it has been evaluated, so we use `transient var count: Long` there. By being transient, the `count` will be reset to 0 and **only** to 0 when serialize and deserialize it, as deserialize transient variable will result to default value. There is *no way* to use another initial value for `count`, until we add the explicit initialization phase.

Another use case is local execution for `LocalRelation`, there is no serialize and deserialize phase and thus we can't reset mutable states for it.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7535 from cloud-fan/init and squashes the following commits:

6c6f332 [Wenchen Fan] add test
ef68ff4 [Wenchen Fan] fix comments
9eac85e [Wenchen Fan] move init code to interpreted class
bb7d838 [Wenchen Fan] pulls out nondeterministic expressions into a project
b4a4fc7 [Wenchen Fan] revert a refactor
86fee36 [Wenchen Fan] add initialization phase for nondeterministic expression
---
 .../sql/catalyst/analysis/Analyzer.scala      |  35 +++++-
 .../sql/catalyst/analysis/CheckAnalysis.scala |  19 +++-
 .../sql/catalyst/expressions/Expression.scala |  21 +++-
 .../sql/catalyst/expressions/Projection.scala |  10 ++
 .../sql/catalyst/expressions/predicates.scala |   4 +
 .../sql/catalyst/expressions/random.scala     |  12 +-
 .../plans/logical/basicOperators.scala        |   3 +-
 .../sql/catalyst/analysis/AnalysisSuite.scala |  96 +++++++---------
 .../sql/catalyst/analysis/AnalysisTest.scala  | 105 ++++++++++++++++++
 .../expressions/ExpressionEvalHelper.scala    |   4 +
 .../MonotonicallyIncreasingID.scala           |  13 ++-
 .../expressions/SparkPartitionID.scala        |   8 +-
 12 files changed, 254 insertions(+), 76 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index e916887187dc8..a723e92114b32 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -19,10 +19,11 @@ package org.apache.spark.sql.catalyst.analysis
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions.aggregate.{Complete, AggregateExpression2, AggregateFunction2}
-import org.apache.spark.sql.catalyst.{SimpleCatalystConf, CatalystConf}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.catalyst.trees.TreeNodeRef
+import org.apache.spark.sql.catalyst.{SimpleCatalystConf, CatalystConf}
 import org.apache.spark.sql.types._
 import scala.collection.mutable.ArrayBuffer
 
@@ -78,7 +79,9 @@ class Analyzer(
       GlobalAggregates ::
       UnresolvedHavingClauseAttributes ::
       HiveTypeCoercion.typeCoercionRules ++
-      extendedResolutionRules : _*)
+      extendedResolutionRules : _*),
+    Batch("Nondeterministic", Once,
+      PullOutNondeterministic)
   )
 
   /**
@@ -910,6 +913,34 @@ class Analyzer(
         Project(finalProjectList, withWindow)
     }
   }
+
+  /**
+   * Pulls out nondeterministic expressions from LogicalPlan which is not Project or Filter,
+   * put them into an inner Project and finally project them away at the outer Project.
+   */
+  object PullOutNondeterministic extends Rule[LogicalPlan] {
+    override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      case p: Project => p
+      case f: Filter => f
+
+      // todo: It's hard to write a general rule to pull out nondeterministic expressions
+      // from LogicalPlan, currently we only do it for UnaryNode which has same output
+      // schema with its child.
+      case p: UnaryNode if p.output == p.child.output && p.expressions.exists(!_.deterministic) =>
+        val nondeterministicExprs = p.expressions.filterNot(_.deterministic).map { e =>
+          val ne = e match {
+            case n: NamedExpression => n
+            case _ => Alias(e, "_nondeterministic")()
+          }
+          new TreeNodeRef(e) -> ne
+        }.toMap
+        val newPlan = p.transformExpressions { case e =>
+          nondeterministicExprs.get(new TreeNodeRef(e)).map(_.toAttribute).getOrElse(e)
+        }
+        val newChild = Project(p.child.output ++ nondeterministicExprs.values, p.child)
+        Project(p.output, newPlan.withNewChildren(newChild :: Nil))
+    }
+  }
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 81d473c1130f7..a373714832962 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.analysis
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.types._
 
@@ -38,10 +37,10 @@ trait CheckAnalysis {
     throw new AnalysisException(msg)
   }
 
-  def containsMultipleGenerators(exprs: Seq[Expression]): Boolean = {
+  protected def containsMultipleGenerators(exprs: Seq[Expression]): Boolean = {
     exprs.flatMap(_.collect {
-      case e: Generator => true
-    }).nonEmpty
+      case e: Generator => e
+    }).length > 1
   }
 
   def checkAnalysis(plan: LogicalPlan): Unit = {
@@ -137,13 +136,21 @@ trait CheckAnalysis {
               s"""
                  |Failure when resolving conflicting references in Join:
                  |$plan
-                  |Conflicting attributes: ${conflictingAttributes.mkString(",")}
-                  |""".stripMargin)
+                 |Conflicting attributes: ${conflictingAttributes.mkString(",")}
+                 |""".stripMargin)
 
           case o if !o.resolved =>
             failAnalysis(
               s"unresolved operator ${operator.simpleString}")
 
+          case o if o.expressions.exists(!_.deterministic) &&
+            !o.isInstanceOf[Project] && !o.isInstanceOf[Filter] =>
+            failAnalysis(
+              s"""nondeterministic expressions are only allowed in Project or Filter, found:
+                 | ${o.expressions.map(_.prettyString).mkString(",")}
+                 |in operator ${operator.simpleString}
+             """.stripMargin)
+
           case _ => // Analysis successful!
         }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 3f72e6e184db1..cb4c3f24b2721 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -196,7 +196,26 @@ trait Unevaluable extends Expression {
  * An expression that is nondeterministic.
  */
 trait Nondeterministic extends Expression {
-  override def deterministic: Boolean = false
+  final override def deterministic: Boolean = false
+  final override def foldable: Boolean = false
+
+  private[this] var initialized = false
+
+  final def initialize(): Unit = {
+    if (!initialized) {
+      initInternal()
+      initialized = true
+    }
+  }
+
+  protected def initInternal(): Unit
+
+  final override def eval(input: InternalRow = null): Any = {
+    require(initialized, "nondeterministic expression should be initialized before evaluate")
+    evalInternal(input)
+  }
+
+  protected def evalInternal(input: InternalRow): Any
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index fb873e7e99547..c1ed9cf7ed6a0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -31,6 +31,11 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection {
   def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
+  expressions.foreach(_.foreach {
+    case n: Nondeterministic => n.initialize()
+    case _ =>
+  })
+
   // null check is required for when Kryo invokes the no-arg constructor.
   protected val exprArray = if (expressions != null) expressions.toArray else null
 
@@ -57,6 +62,11 @@ case class InterpretedMutableProjection(expressions: Seq[Expression]) extends Mu
   def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
+  expressions.foreach(_.foreach {
+    case n: Nondeterministic => n.initialize()
+    case _ =>
+  })
+
   private[this] val exprArray = expressions.toArray
   private[this] var mutableRow: MutableRow = new GenericMutableRow(exprArray.length)
   def currentValue: InternalRow = mutableRow
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 3f1bd2a925fe7..5bfe1cad24a3e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -30,6 +30,10 @@ object InterpretedPredicate {
     create(BindReferences.bindReference(expression, inputSchema))
 
   def create(expression: Expression): (InternalRow => Boolean) = {
+    expression.foreach {
+      case n: Nondeterministic => n.initialize()
+      case _ =>
+    }
     (r: InternalRow) => expression.eval(r).asInstanceOf[Boolean]
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
index aef24a5486466..8f30519697a37 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
@@ -38,9 +38,13 @@ abstract class RDG extends LeafExpression with Nondeterministic {
 
   /**
    * Record ID within each partition. By being transient, the Random Number Generator is
-   * reset every time we serialize and deserialize it.
+   * reset every time we serialize and deserialize and initialize it.
    */
-  @transient protected lazy val rng = new XORShiftRandom(seed + TaskContext.getPartitionId)
+  @transient protected var rng: XORShiftRandom = _
+
+  override protected def initInternal(): Unit = {
+    rng = new XORShiftRandom(seed + TaskContext.getPartitionId)
+  }
 
   override def nullable: Boolean = false
 
@@ -49,7 +53,7 @@ abstract class RDG extends LeafExpression with Nondeterministic {
 
 /** Generate a random column with i.i.d. uniformly distributed values in [0, 1). */
 case class Rand(seed: Long) extends RDG {
-  override def eval(input: InternalRow): Double = rng.nextDouble()
+  override protected def evalInternal(input: InternalRow): Double = rng.nextDouble()
 
   def this() = this(Utils.random.nextLong())
 
@@ -72,7 +76,7 @@ case class Rand(seed: Long) extends RDG {
 
 /** Generate a random column with i.i.d. gaussian random distribution. */
 case class Randn(seed: Long) extends RDG {
-  override def eval(input: InternalRow): Double = rng.nextGaussian()
+  override protected def evalInternal(input: InternalRow): Double = rng.nextGaussian()
 
   def this() = this(Utils.random.nextLong())
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 57a12820fa4c6..8e1a236e2988c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
@@ -379,7 +378,7 @@ case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
   override lazy val statistics: Statistics = {
-    val limit = limitExpr.eval(null).asInstanceOf[Int]
+    val limit = limitExpr.eval().asInstanceOf[Int]
     val sizeInBytes = (limit: Long) * output.map(a => a.dataType.defaultSize).sum
     Statistics(sizeInBytes = sizeInBytes)
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 7e67427237a65..ed645b618dc9b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -17,10 +17,6 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.scalatest.BeforeAndAfter
-
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.types._
@@ -28,6 +24,7 @@ import org.apache.spark.sql.catalyst.SimpleCatalystConf
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 
+// todo: remove this and use AnalysisTest instead.
 object AnalysisSuite {
   val caseSensitiveConf = new SimpleCatalystConf(true)
   val caseInsensitiveConf = new SimpleCatalystConf(false)
@@ -55,7 +52,7 @@ object AnalysisSuite {
     AttributeReference("a", StringType)(),
     AttributeReference("b", StringType)(),
     AttributeReference("c", DoubleType)(),
-    AttributeReference("d", DecimalType.SYSTEM_DEFAULT)(),
+    AttributeReference("d", DecimalType(10, 2))(),
     AttributeReference("e", ShortType)())
 
   val nestedRelation = LocalRelation(
@@ -81,8 +78,7 @@ object AnalysisSuite {
 }
 
 
-class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
-  import AnalysisSuite._
+class AnalysisSuite extends AnalysisTest {
 
   test("union project *") {
     val plan = (1 to 100)
@@ -91,7 +87,7 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
         a.select(UnresolvedStar(None)).select('a).unionAll(b.select(UnresolvedStar(None)))
       }
 
-    assert(caseInsensitiveAnalyzer.execute(plan).resolved)
+    assertAnalysisSuccess(plan)
   }
 
   test("check project's resolved") {
@@ -106,61 +102,40 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
   }
 
   test("analyze project") {
-    assert(
-      caseSensitiveAnalyzer.execute(Project(Seq(UnresolvedAttribute("a")), testRelation)) ===
-        Project(testRelation.output, testRelation))
-
-    assert(
-      caseSensitiveAnalyzer.execute(
-        Project(Seq(UnresolvedAttribute("TbL.a")),
-          UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) ===
-        Project(testRelation.output, testRelation))
-
-    val e = intercept[AnalysisException] {
-      caseSensitiveAnalyze(
-        Project(Seq(UnresolvedAttribute("tBl.a")),
-          UnresolvedRelation(Seq("TaBlE"), Some("TbL"))))
-    }
-    assert(e.getMessage().toLowerCase.contains("cannot resolve"))
-
-    assert(
-      caseInsensitiveAnalyzer.execute(
-        Project(Seq(UnresolvedAttribute("TbL.a")),
-          UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) ===
-        Project(testRelation.output, testRelation))
-
-    assert(
-      caseInsensitiveAnalyzer.execute(
-        Project(Seq(UnresolvedAttribute("tBl.a")),
-          UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) ===
-        Project(testRelation.output, testRelation))
+    checkAnalysis(
+      Project(Seq(UnresolvedAttribute("a")), testRelation),
+      Project(testRelation.output, testRelation))
+
+    checkAnalysis(
+      Project(Seq(UnresolvedAttribute("TbL.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL"))),
+      Project(testRelation.output, testRelation))
+
+    assertAnalysisError(
+      Project(Seq(UnresolvedAttribute("tBl.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL"))),
+      Seq("cannot resolve"))
+
+    checkAnalysis(
+      Project(Seq(UnresolvedAttribute("TbL.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL"))),
+      Project(testRelation.output, testRelation),
+      caseSensitive = false)
+
+    checkAnalysis(
+      Project(Seq(UnresolvedAttribute("tBl.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL"))),
+      Project(testRelation.output, testRelation),
+      caseSensitive = false)
   }
 
   test("resolve relations") {
-    val e = intercept[RuntimeException] {
-      caseSensitiveAnalyze(UnresolvedRelation(Seq("tAbLe"), None))
-    }
-    assert(e.getMessage == "Table Not Found: tAbLe")
+    assertAnalysisError(UnresolvedRelation(Seq("tAbLe"), None), Seq("Table Not Found: tAbLe"))
 
-    assert(
-      caseSensitiveAnalyzer.execute(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation)
+    checkAnalysis(UnresolvedRelation(Seq("TaBlE"), None), testRelation)
 
-    assert(
-      caseInsensitiveAnalyzer.execute(UnresolvedRelation(Seq("tAbLe"), None)) === testRelation)
+    checkAnalysis(UnresolvedRelation(Seq("tAbLe"), None), testRelation, caseSensitive = false)
 
-    assert(
-      caseInsensitiveAnalyzer.execute(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation)
+    checkAnalysis(UnresolvedRelation(Seq("TaBlE"), None), testRelation, caseSensitive = false)
   }
 
-
   test("divide should be casted into fractional types") {
-    val testRelation2 = LocalRelation(
-      AttributeReference("a", StringType)(),
-      AttributeReference("b", StringType)(),
-      AttributeReference("c", DoubleType)(),
-      AttributeReference("d", DecimalType(10, 2))(),
-      AttributeReference("e", ShortType)())
-
     val plan = caseInsensitiveAnalyzer.execute(
       testRelation2.select(
         'a / Literal(2) as 'div1,
@@ -170,10 +145,21 @@ class AnalysisSuite extends SparkFunSuite with BeforeAndAfter {
         'e / 'e as 'div5))
     val pl = plan.asInstanceOf[Project].projectList
 
+    // StringType will be promoted into Double
     assert(pl(0).dataType == DoubleType)
     assert(pl(1).dataType == DoubleType)
     assert(pl(2).dataType == DoubleType)
-    assert(pl(3).dataType == DoubleType)  // StringType will be promoted into Double
+    assert(pl(3).dataType == DoubleType)
     assert(pl(4).dataType == DoubleType)
   }
+
+  test("pull out nondeterministic expressions from unary LogicalPlan") {
+    val plan = RepartitionByExpression(Seq(Rand(33)), testRelation)
+    val projected = Alias(Rand(33), "_nondeterministic")()
+    val expected =
+      Project(testRelation.output,
+        RepartitionByExpression(Seq(projected.toAttribute),
+          Project(testRelation.output :+ projected, testRelation)))
+    checkAnalysis(plan, expected)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
new file mode 100644
index 0000000000000..fdb4f28950daf
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.analysis
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.PlanTest
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.SimpleCatalystConf
+import org.apache.spark.sql.types._
+
+trait AnalysisTest extends PlanTest {
+  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
+
+  val testRelation2 = LocalRelation(
+    AttributeReference("a", StringType)(),
+    AttributeReference("b", StringType)(),
+    AttributeReference("c", DoubleType)(),
+    AttributeReference("d", DecimalType(10, 2))(),
+    AttributeReference("e", ShortType)())
+
+  val nestedRelation = LocalRelation(
+    AttributeReference("top", StructType(
+      StructField("duplicateField", StringType) ::
+        StructField("duplicateField", StringType) ::
+        StructField("differentCase", StringType) ::
+        StructField("differentcase", StringType) :: Nil
+    ))())
+
+  val nestedRelation2 = LocalRelation(
+    AttributeReference("top", StructType(
+      StructField("aField", StringType) ::
+        StructField("bField", StringType) ::
+        StructField("cField", StringType) :: Nil
+    ))())
+
+  val listRelation = LocalRelation(
+    AttributeReference("list", ArrayType(IntegerType))())
+
+  val (caseSensitiveAnalyzer, caseInsensitiveAnalyzer) = {
+    val caseSensitiveConf = new SimpleCatalystConf(true)
+    val caseInsensitiveConf = new SimpleCatalystConf(false)
+
+    val caseSensitiveCatalog = new SimpleCatalog(caseSensitiveConf)
+    val caseInsensitiveCatalog = new SimpleCatalog(caseInsensitiveConf)
+
+    caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
+    caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
+
+    new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitiveConf) {
+      override val extendedResolutionRules = EliminateSubQueries :: Nil
+    } ->
+    new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseInsensitiveConf) {
+      override val extendedResolutionRules = EliminateSubQueries :: Nil
+    }
+  }
+
+  protected def getAnalyzer(caseSensitive: Boolean) = {
+    if (caseSensitive) caseSensitiveAnalyzer else caseInsensitiveAnalyzer
+  }
+
+  protected def checkAnalysis(
+      inputPlan: LogicalPlan,
+      expectedPlan: LogicalPlan,
+      caseSensitive: Boolean = true): Unit = {
+    val analyzer = getAnalyzer(caseSensitive)
+    val actualPlan = analyzer.execute(inputPlan)
+    analyzer.checkAnalysis(actualPlan)
+    comparePlans(actualPlan, expectedPlan)
+  }
+
+  protected def assertAnalysisSuccess(
+      inputPlan: LogicalPlan,
+      caseSensitive: Boolean = true): Unit = {
+    val analyzer = getAnalyzer(caseSensitive)
+    analyzer.checkAnalysis(analyzer.execute(inputPlan))
+  }
+
+  protected def assertAnalysisError(
+      inputPlan: LogicalPlan,
+      expectedErrors: Seq[String],
+      caseSensitive: Boolean = true): Unit = {
+    val analyzer = getAnalyzer(caseSensitive)
+    // todo: make sure we throw AnalysisException during analysis
+    val e = intercept[Exception] {
+      analyzer.checkAnalysis(analyzer.execute(inputPlan))
+    }
+    expectedErrors.forall(e.getMessage.contains)
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 4930219aa63cb..852a8b235f127 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -64,6 +64,10 @@ trait ExpressionEvalHelper {
   }
 
   protected def evaluate(expression: Expression, inputRow: InternalRow = EmptyRow): Any = {
+    expression.foreach {
+      case n: Nondeterministic => n.initialize()
+      case _ =>
+    }
     expression.eval(inputRow)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
index 2645eb1854bce..eca36b3274420 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
@@ -37,17 +37,22 @@ private[sql] case class MonotonicallyIncreasingID() extends LeafExpression with
 
   /**
    * Record ID within each partition. By being transient, count's value is reset to 0 every time
-   * we serialize and deserialize it.
+   * we serialize and deserialize and initialize it.
    */
-  @transient private[this] var count: Long = 0L
+  @transient private[this] var count: Long = _
 
-  @transient private lazy val partitionMask = TaskContext.getPartitionId().toLong << 33
+  @transient private[this] var partitionMask: Long = _
+
+  override protected def initInternal(): Unit = {
+    count = 0L
+    partitionMask = TaskContext.getPartitionId().toLong << 33
+  }
 
   override def nullable: Boolean = false
 
   override def dataType: DataType = LongType
 
-  override def eval(input: InternalRow): Long = {
+  override protected def evalInternal(input: InternalRow): Long = {
     val currentCount = count
     count += 1
     partitionMask + currentCount
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
index 53ddd47e3e0c1..61ef079d89af5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
@@ -33,9 +33,13 @@ private[sql] case object SparkPartitionID extends LeafExpression with Nondetermi
 
   override def dataType: DataType = IntegerType
 
-  @transient private lazy val partitionId = TaskContext.getPartitionId()
+  @transient private[this] var partitionId: Int = _
 
-  override def eval(input: InternalRow): Int = partitionId
+  override protected def initInternal(): Unit = {
+    partitionId = TaskContext.getPartitionId()
+  }
+
+  override protected def evalInternal(input: InternalRow): Int = partitionId
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val idTerm = ctx.freshName("partitionId")

From b1f4b4abfd8d038c3684685b245b5fd31b927da0 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 25 Jul 2015 18:41:51 -0700
Subject: [PATCH 0600/1454] [SPARK-9348][SQL] Remove apply method on
 InternalRow.

Author: Reynold Xin <rxin@databricks.com>

Closes #7665 from rxin/remove-row-apply and squashes the following commits:

0b43001 [Reynold Xin] support getString in UnsafeRow.
176d633 [Reynold Xin] apply -> get.
2941324 [Reynold Xin] [SPARK-9348][SQL] Remove apply method on InternalRow.
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 88 +++++++++----------
 .../spark/sql/catalyst/InternalRow.scala      | 32 +++----
 .../expressions/codegen/CodeGenerator.scala   |  2 +-
 .../expressions/MathFunctionsSuite.scala      |  2 +-
 .../spark/sql/columnar/ColumnStats.scala      |  4 +-
 .../spark/sql/columnar/ColumnType.scala       | 16 ++--
 .../compression/compressionSchemes.scala      |  2 +-
 .../sql/execution/SparkSqlSerializer2.scala   |  4 +-
 .../datasources/DataSourceStrategy.scala      |  6 +-
 .../spark/sql/execution/debug/package.scala   |  2 +-
 .../spark/sql/execution/pythonUDFs.scala      |  2 +-
 .../sql/expressions/aggregate/udaf.scala      |  4 +-
 .../sql/parquet/ParquetTableOperations.scala  |  6 +-
 .../sql/parquet/ParquetTableSupport.scala     | 22 ++---
 .../scala/org/apache/spark/sql/RowSuite.scala |  4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala | 12 +--
 .../NullableColumnAccessorSuite.scala         |  2 +-
 .../columnar/NullableColumnBuilderSuite.scala |  2 +-
 .../compression/BooleanBitSetSuite.scala      |  2 +-
 .../spark/sql/hive/HiveInspectors.scala       |  6 +-
 .../hive/execution/InsertIntoHiveTable.scala  |  2 +-
 .../spark/sql/hive/orc/OrcRelation.scala      |  2 +-
 22 files changed, 113 insertions(+), 111 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 225f6e6553d19..9be9089493335 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -231,84 +231,89 @@ public void setFloat(int ordinal, float value) {
   }
 
   @Override
-  public Object get(int i) {
+  public Object get(int ordinal) {
     throw new UnsupportedOperationException();
   }
 
   @Override
-  public <T> T getAs(int i) {
+  public <T> T getAs(int ordinal) {
     throw new UnsupportedOperationException();
   }
 
   @Override
-  public boolean isNullAt(int i) {
-    assertIndexIsValid(i);
-    return BitSetMethods.isSet(baseObject, baseOffset, i);
+  public boolean isNullAt(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return BitSetMethods.isSet(baseObject, baseOffset, ordinal);
   }
 
   @Override
-  public boolean getBoolean(int i) {
-    assertIndexIsValid(i);
-    return PlatformDependent.UNSAFE.getBoolean(baseObject, getFieldOffset(i));
+  public boolean getBoolean(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return PlatformDependent.UNSAFE.getBoolean(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
-  public byte getByte(int i) {
-    assertIndexIsValid(i);
-    return PlatformDependent.UNSAFE.getByte(baseObject, getFieldOffset(i));
+  public byte getByte(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return PlatformDependent.UNSAFE.getByte(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
-  public short getShort(int i) {
-    assertIndexIsValid(i);
-    return PlatformDependent.UNSAFE.getShort(baseObject, getFieldOffset(i));
+  public short getShort(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return PlatformDependent.UNSAFE.getShort(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
-  public int getInt(int i) {
-    assertIndexIsValid(i);
-    return PlatformDependent.UNSAFE.getInt(baseObject, getFieldOffset(i));
+  public int getInt(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return PlatformDependent.UNSAFE.getInt(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
-  public long getLong(int i) {
-    assertIndexIsValid(i);
-    return PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(i));
+  public long getLong(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
-  public float getFloat(int i) {
-    assertIndexIsValid(i);
-    if (isNullAt(i)) {
+  public float getFloat(int ordinal) {
+    assertIndexIsValid(ordinal);
+    if (isNullAt(ordinal)) {
       return Float.NaN;
     } else {
-      return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(i));
+      return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(ordinal));
     }
   }
 
   @Override
-  public double getDouble(int i) {
-    assertIndexIsValid(i);
-    if (isNullAt(i)) {
+  public double getDouble(int ordinal) {
+    assertIndexIsValid(ordinal);
+    if (isNullAt(ordinal)) {
       return Float.NaN;
     } else {
-      return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(i));
+      return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
     }
   }
 
   @Override
-  public UTF8String getUTF8String(int i) {
-    assertIndexIsValid(i);
-    return isNullAt(i) ? null : UTF8String.fromBytes(getBinary(i));
+  public UTF8String getUTF8String(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return isNullAt(ordinal) ? null : UTF8String.fromBytes(getBinary(ordinal));
   }
 
   @Override
-  public byte[] getBinary(int i) {
-    if (isNullAt(i)) {
+  public String getString(int ordinal) {
+    return getUTF8String(ordinal).toString();
+  }
+
+  @Override
+  public byte[] getBinary(int ordinal) {
+    if (isNullAt(ordinal)) {
       return null;
     } else {
-      assertIndexIsValid(i);
-      final long offsetAndSize = getLong(i);
+      assertIndexIsValid(ordinal);
+      final long offsetAndSize = getLong(ordinal);
       final int offset = (int) (offsetAndSize >> 32);
       final int size = (int) (offsetAndSize & ((1L << 32) - 1));
       final byte[] bytes = new byte[size];
@@ -324,17 +329,12 @@ public byte[] getBinary(int i) {
   }
 
   @Override
-  public String getString(int i) {
-    return getUTF8String(i).toString();
-  }
-
-  @Override
-  public UnsafeRow getStruct(int i, int numFields) {
-    if (isNullAt(i)) {
+  public UnsafeRow getStruct(int ordinal, int numFields) {
+    if (isNullAt(ordinal)) {
       return null;
     } else {
-      assertIndexIsValid(i);
-      final long offsetAndSize = getLong(i);
+      assertIndexIsValid(ordinal);
+      final long offsetAndSize = getLong(ordinal);
       final int offset = (int) (offsetAndSize >> 32);
       final int size = (int) (offsetAndSize & ((1L << 32) - 1));
       final UnsafeRow row = new UnsafeRow();
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index f248b1f338acc..37f0f57e9e6d3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.Decimal
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -29,35 +30,34 @@ abstract class InternalRow extends Serializable {
 
   def numFields: Int
 
-  def get(i: Int): Any
+  def get(ordinal: Int): Any
 
-  // TODO: Remove this.
-  def apply(i: Int): Any = get(i)
+  def getAs[T](ordinal: Int): T = get(ordinal).asInstanceOf[T]
 
-  def getAs[T](i: Int): T = get(i).asInstanceOf[T]
+  def isNullAt(ordinal: Int): Boolean = get(ordinal) == null
 
-  def isNullAt(i: Int): Boolean = get(i) == null
+  def getBoolean(ordinal: Int): Boolean = getAs[Boolean](ordinal)
 
-  def getBoolean(i: Int): Boolean = getAs[Boolean](i)
+  def getByte(ordinal: Int): Byte = getAs[Byte](ordinal)
 
-  def getByte(i: Int): Byte = getAs[Byte](i)
+  def getShort(ordinal: Int): Short = getAs[Short](ordinal)
 
-  def getShort(i: Int): Short = getAs[Short](i)
+  def getInt(ordinal: Int): Int = getAs[Int](ordinal)
 
-  def getInt(i: Int): Int = getAs[Int](i)
+  def getLong(ordinal: Int): Long = getAs[Long](ordinal)
 
-  def getLong(i: Int): Long = getAs[Long](i)
+  def getFloat(ordinal: Int): Float = getAs[Float](ordinal)
 
-  def getFloat(i: Int): Float = getAs[Float](i)
+  def getDouble(ordinal: Int): Double = getAs[Double](ordinal)
 
-  def getDouble(i: Int): Double = getAs[Double](i)
+  def getUTF8String(ordinal: Int): UTF8String = getAs[UTF8String](ordinal)
 
-  def getUTF8String(i: Int): UTF8String = getAs[UTF8String](i)
+  def getBinary(ordinal: Int): Array[Byte] = getAs[Array[Byte]](ordinal)
 
-  def getBinary(i: Int): Array[Byte] = getAs[Array[Byte]](i)
+  def getDecimal(ordinal: Int): Decimal = getAs[Decimal](ordinal)
 
-  // This is only use for test
-  def getString(i: Int): String = getAs[UTF8String](i).toString
+  // This is only use for test and will throw a null pointer exception if the position is null.
+  def getString(ordinal: Int): String = getAs[UTF8String](ordinal).toString
 
   /**
    * Returns a struct from ordinal position.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 508882acbee5a..2a1e288cb8377 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -110,7 +110,7 @@ class CodeGenContext {
       case StringType => s"$row.getUTF8String($ordinal)"
       case BinaryType => s"$row.getBinary($ordinal)"
       case t: StructType => s"$row.getStruct($ordinal, ${t.size})"
-      case _ => s"($jt)$row.apply($ordinal)"
+      case _ => s"($jt)$row.get($ordinal)"
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index a2b0fad7b7a04..6caf8baf24a81 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -158,7 +158,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)(),
       expression)
 
-    val actual = plan(inputRow).apply(0)
+    val actual = plan(inputRow).get(0)
     if (!actual.asInstanceOf[Double].isNaN) {
       fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: NaN")
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 00374d1fa3ef1..7c63179af6470 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -211,7 +211,7 @@ private[sql] class StringColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
     if (!row.isNullAt(ordinal)) {
-      val value = row(ordinal).asInstanceOf[UTF8String]
+      val value = row.getUTF8String(ordinal)
       if (upper == null || value.compareTo(upper) > 0) upper = value
       if (lower == null || value.compareTo(lower) < 0) lower = value
       sizeInBytes += STRING.actualSize(row, ordinal)
@@ -241,7 +241,7 @@ private[sql] class FixedDecimalColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
     if (!row.isNullAt(ordinal)) {
-      val value = row(ordinal).asInstanceOf[Decimal]
+      val value = row.getDecimal(ordinal)
       if (upper == null || value.compareTo(upper) > 0) upper = value
       if (lower == null || value.compareTo(lower) < 0) lower = value
       sizeInBytes += FIXED_DECIMAL.defaultSize
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index ac42bde07c37d..c0ca52751b66c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -90,7 +90,7 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
    * boxing/unboxing costs whenever possible.
    */
   def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
-    to(toOrdinal) = from(fromOrdinal)
+    to(toOrdinal) = from.get(fromOrdinal)
   }
 
   /**
@@ -329,11 +329,11 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
   }
 
   override def getField(row: InternalRow, ordinal: Int): UTF8String = {
-    row(ordinal).asInstanceOf[UTF8String]
+    row.getUTF8String(ordinal)
   }
 
   override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
-    to.update(toOrdinal, from(fromOrdinal))
+    to.update(toOrdinal, from.getUTF8String(fromOrdinal))
   }
 }
 
@@ -347,7 +347,7 @@ private[sql] object DATE extends NativeColumnType(DateType, 8, 4) {
   }
 
   override def getField(row: InternalRow, ordinal: Int): Int = {
-    row(ordinal).asInstanceOf[Int]
+    row.getInt(ordinal)
   }
 
   def setField(row: MutableRow, ordinal: Int, value: Int): Unit = {
@@ -365,7 +365,7 @@ private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 9, 8) {
   }
 
   override def getField(row: InternalRow, ordinal: Int): Long = {
-    row(ordinal).asInstanceOf[Long]
+    row.getLong(ordinal)
   }
 
   override def setField(row: MutableRow, ordinal: Int, value: Long): Unit = {
@@ -388,7 +388,7 @@ private[sql] case class FIXED_DECIMAL(precision: Int, scale: Int)
   }
 
   override def getField(row: InternalRow, ordinal: Int): Decimal = {
-    row(ordinal).asInstanceOf[Decimal]
+    row.getDecimal(ordinal)
   }
 
   override def setField(row: MutableRow, ordinal: Int, value: Decimal): Unit = {
@@ -427,7 +427,7 @@ private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](11, 16)
   }
 
   override def getField(row: InternalRow, ordinal: Int): Array[Byte] = {
-    row(ordinal).asInstanceOf[Array[Byte]]
+    row.getBinary(ordinal)
   }
 }
 
@@ -440,7 +440,7 @@ private[sql] object GENERIC extends ByteArrayColumnType[DataType](12, 16) {
   }
 
   override def getField(row: InternalRow, ordinal: Int): Array[Byte] = {
-    SparkSqlSerializer.serialize(row(ordinal))
+    SparkSqlSerializer.serialize(row.get(ordinal))
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
index 5abc1259a19ab..6150df6930b32 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
@@ -128,7 +128,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
         while (from.hasRemaining) {
           columnType.extract(from, value, 0)
 
-          if (value(0) == currentValue(0)) {
+          if (value.get(0) == currentValue.get(0)) {
             currentRun += 1
           } else {
             // Writes current run
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 83c4e8733f15f..6ee833c7b2c94 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -278,7 +278,7 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val bytes = row.getAs[UTF8String](i).getBytes
+                val bytes = row.getUTF8String(i).getBytes
                 out.writeInt(bytes.length)
                 out.write(bytes)
               }
@@ -298,7 +298,7 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val value = row.apply(i).asInstanceOf[Decimal]
+                val value = row.getAs[Decimal](i)
                 val javaBigDecimal = value.toJavaBigDecimal
                 // First, write out the unscaled value.
                 val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 7f452daef33c5..cdbe42381a7e4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -170,6 +170,8 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     execution.PhysicalRDD(projections.map(_.toAttribute), unionedRows)
   }
 
+  // TODO: refactor this thing. It is very complicated because it does projection internally.
+  // We should just put a project on top of this.
   private def mergeWithPartitionValues(
       schema: StructType,
       requiredColumns: Array[String],
@@ -187,13 +189,13 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         if (i != -1) {
           // If yes, gets column value from partition values.
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = partitionValues(i)
+            mutableRow(ordinal) = partitionValues.get(i)
           }
         } else {
           // Otherwise, inherits the value from scanned data.
           val i = nonPartitionColumns.indexOf(name)
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = dataRow(i)
+            mutableRow(ordinal) = dataRow.get(i)
           }
         }
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index e6081cb05bc2d..1fdcc6a850602 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -136,7 +136,7 @@ package object debug {
             tupleCount += 1
             var i = 0
             while (i < numColumns) {
-              val value = currentRow(i)
+              val value = currentRow.get(i)
               if (value != null) {
                 columnStats(i).elementTypes += HashSet(value.getClass.getName)
               }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 40bf03a3f1a62..970c40dc61a3c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -129,7 +129,7 @@ object EvaluatePython {
       val values = new Array[Any](row.numFields)
       var i = 0
       while (i < row.numFields) {
-        values(i) = toJava(row(i), struct.fields(i).dataType)
+        values(i) = toJava(row.get(i), struct.fields(i).dataType)
         i += 1
       }
       new GenericInternalRowWithSchema(values, struct)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
index 46f0fac861282..7a6e86779b185 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
@@ -121,7 +121,7 @@ class MutableAggregationBuffer private[sql] (
       throw new IllegalArgumentException(
         s"Could not access ${i}th value in this buffer because it only has $length values.")
     }
-    toScalaConverters(i)(underlyingBuffer(offsets(i)))
+    toScalaConverters(i)(underlyingBuffer.get(offsets(i)))
   }
 
   def update(i: Int, value: Any): Unit = {
@@ -157,7 +157,7 @@ class InputAggregationBuffer private[sql] (
         s"Could not access ${i}th value in this buffer because it only has $length values.")
     }
     // TODO: Use buffer schema to avoid using generic getter.
-    toScalaConverters(i)(underlyingInputBuffer(offsets(i)))
+    toScalaConverters(i)(underlyingInputBuffer.get(offsets(i)))
   }
 
   override def copy(): InputAggregationBuffer = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index 8cab27d6e1c46..38bb1e3967642 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -159,7 +159,7 @@ private[sql] case class ParquetTableScan(
 
               // Parquet will leave partitioning columns empty, so we fill them in here.
               var i = 0
-              while (i < requestedPartitionOrdinals.size) {
+              while (i < requestedPartitionOrdinals.length) {
                 row(requestedPartitionOrdinals(i)._2) =
                   partitionRowValues(requestedPartitionOrdinals(i)._1)
                 i += 1
@@ -179,12 +179,12 @@ private[sql] case class ParquetTableScan(
 
               var i = 0
               while (i < row.numFields) {
-                mutableRow(i) = row(i)
+                mutableRow(i) = row.get(i)
                 i += 1
               }
               // Parquet will leave partitioning columns empty, so we fill them in here.
               i = 0
-              while (i < requestedPartitionOrdinals.size) {
+              while (i < requestedPartitionOrdinals.length) {
                 mutableRow(requestedPartitionOrdinals(i)._2) =
                   partitionRowValues(requestedPartitionOrdinals(i)._1)
                 i += 1
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index c7c58e69d42ef..2c23d4e8a8146 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -217,9 +217,9 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
     writer.startMessage()
     while(index < attributesSize) {
       // null values indicate optional fields but we do not check currently
-      if (record(index) != null) {
+      if (!record.isNullAt(index)) {
         writer.startField(attributes(index).name, index)
-        writeValue(attributes(index).dataType, record(index))
+        writeValue(attributes(index).dataType, record.get(index))
         writer.endField(attributes(index).name, index)
       }
       index = index + 1
@@ -277,10 +277,10 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
       val fields = schema.fields.toArray
       writer.startGroup()
       var i = 0
-      while(i < fields.size) {
-        if (struct(i) != null) {
+      while(i < fields.length) {
+        if (!struct.isNullAt(i)) {
           writer.startField(fields(i).name, i)
-          writeValue(fields(i).dataType, struct(i))
+          writeValue(fields(i).dataType, struct.get(i))
           writer.endField(fields(i).name, i)
         }
         i = i + 1
@@ -387,7 +387,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
     writer.startMessage()
     while(index < attributesSize) {
       // null values indicate optional fields but we do not check currently
-      if (record(index) != null && record(index) != Nil) {
+      if (!record.isNullAt(index) && !record.isNullAt(index)) {
         writer.startField(attributes(index).name, index)
         consumeType(attributes(index).dataType, record, index)
         writer.endField(attributes(index).name, index)
@@ -410,15 +410,15 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
       case TimestampType => writeTimestamp(record.getLong(index))
       case FloatType => writer.addFloat(record.getFloat(index))
       case DoubleType => writer.addDouble(record.getDouble(index))
-      case StringType => writer.addBinary(
-        Binary.fromByteArray(record(index).asInstanceOf[UTF8String].getBytes))
-      case BinaryType => writer.addBinary(
-        Binary.fromByteArray(record(index).asInstanceOf[Array[Byte]]))
+      case StringType =>
+        writer.addBinary(Binary.fromByteArray(record.getUTF8String(index).getBytes))
+      case BinaryType =>
+        writer.addBinary(Binary.fromByteArray(record.getBinary(index)))
       case d: DecimalType =>
         if (d.precision > 18) {
           sys.error(s"Unsupported datatype $d, cannot write to consumer")
         }
-        writeDecimal(record(index).asInstanceOf[Decimal], d.precision)
+        writeDecimal(record.getDecimal(index), d.precision)
       case _ => sys.error(s"Unsupported datatype $ctype, cannot write to consumer")
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 0e5c5abff85f6..c6804e84827c0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -39,14 +39,14 @@ class RowSuite extends SparkFunSuite {
     assert(expected.getInt(0) === actual1.getInt(0))
     assert(expected.getString(1) === actual1.getString(1))
     assert(expected.getBoolean(2) === actual1.getBoolean(2))
-    assert(expected(3) === actual1(3))
+    assert(expected.get(3) === actual1.get(3))
 
     val actual2 = Row.fromSeq(Seq(2147483647, "this is a string", false, null))
     assert(expected.numFields === actual2.size)
     assert(expected.getInt(0) === actual2.getInt(0))
     assert(expected.getString(1) === actual2.getString(1))
     assert(expected.getBoolean(2) === actual2.getBoolean(2))
-    assert(expected(3) === actual2(3))
+    assert(expected.get(3) === actual2.get(3))
   }
 
   test("SpecificMutableRow.update with null") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 3333fee6711c0..31e7b0e72e510 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -58,15 +58,15 @@ class ColumnStatsSuite extends SparkFunSuite {
       val rows = Seq.fill(10)(makeRandomRow(columnType)) ++ Seq.fill(10)(makeNullRow(1))
       rows.foreach(columnStats.gatherStats(_, 0))
 
-      val values = rows.take(10).map(_(0).asInstanceOf[T#InternalType])
+      val values = rows.take(10).map(_.get(0).asInstanceOf[T#InternalType])
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats(1))
-      assertResult(10, "Wrong null count")(stats(2))
-      assertResult(20, "Wrong row count")(stats(3))
-      assertResult(stats(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1))
+      assertResult(10, "Wrong null count")(stats.get(2))
+      assertResult(20, "Wrong row count")(stats.get(3))
+      assertResult(stats.get(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
index 9eaa769846088..d421f4d8d091e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
@@ -75,7 +75,7 @@ class NullableColumnAccessorSuite extends SparkFunSuite {
       (0 until 4).foreach { _ =>
         assert(accessor.hasNext)
         accessor.extractTo(row, 0)
-        assert(row(0) === randomRow(0))
+        assert(row.get(0) === randomRow.get(0))
 
         assert(accessor.hasNext)
         accessor.extractTo(row, 0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
index 17e9ae464bcc0..cd8bf75ff1752 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
@@ -98,7 +98,7 @@ class NullableColumnBuilderSuite extends SparkFunSuite {
           columnType.extract(buffer)
         }
 
-        assert(actual === randomRow(0), "Extracted value didn't equal to the original one")
+        assert(actual === randomRow.get(0), "Extracted value didn't equal to the original one")
       }
 
       assert(!buffer.hasRemaining)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
index f606e2133bedc..33092c83a1a1c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
@@ -33,7 +33,7 @@ class BooleanBitSetSuite extends SparkFunSuite {
 
     val builder = TestCompressibleColumnBuilder(new NoopColumnStats, BOOLEAN, BooleanBitSet)
     val rows = Seq.fill[InternalRow](count)(makeRandomRow(BOOLEAN))
-    val values = rows.map(_(0))
+    val values = rows.map(_.get(0))
 
     rows.foreach(builder.appendFrom(_, 0))
     val buffer = builder.build()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 592cfa0ee8380..16977ce30cfff 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -497,7 +497,7 @@ private[hive] trait HiveInspectors {
         x.setStructFieldData(
           result,
           fieldRefs.get(i),
-          wrap(row(i), fieldRefs.get(i).getFieldObjectInspector))
+          wrap(row.get(i), fieldRefs.get(i).getFieldObjectInspector))
         i += 1
       }
 
@@ -508,7 +508,7 @@ private[hive] trait HiveInspectors {
       val result = new java.util.ArrayList[AnyRef](fieldRefs.length)
       var i = 0
       while (i < fieldRefs.length) {
-        result.add(wrap(row(i), fieldRefs.get(i).getFieldObjectInspector))
+        result.add(wrap(row.get(i), fieldRefs.get(i).getFieldObjectInspector))
         i += 1
       }
 
@@ -536,7 +536,7 @@ private[hive] trait HiveInspectors {
       cache: Array[AnyRef]): Array[AnyRef] = {
     var i = 0
     while (i < inspectors.length) {
-      cache(i) = wrap(row(i), inspectors(i))
+      cache(i) = wrap(row.get(i), inspectors(i))
       i += 1
     }
     cache
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 34b629403e128..f0e0ca05a8aad 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -102,7 +102,7 @@ case class InsertIntoHiveTable(
       iterator.foreach { row =>
         var i = 0
         while (i < fieldOIs.length) {
-          outputData(i) = if (row.isNullAt(i)) null else wrappers(i)(row(i))
+          outputData(i) = if (row.isNullAt(i)) null else wrappers(i)(row.get(i))
           i += 1
         }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 10623dc820316..58445095ad74f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -122,7 +122,7 @@ private[orc] class OrcOutputWriter(
   override def writeInternal(row: InternalRow): Unit = {
     var i = 0
     while (i < row.numFields) {
-      reusableOutputBuffer(i) = wrappers(i)(row(i))
+      reusableOutputBuffer(i) = wrappers(i)(row.get(i))
       i += 1
     }
 

From 41a7cdf85de2d583d8b8759941a9d6c6e98cae4d Mon Sep 17 00:00:00 2001
From: Nishkam Ravi <nravi@cloudera.com>
Date: Sat, 25 Jul 2015 22:56:25 -0700
Subject: [PATCH 0601/1454] [SPARK-8881] [SPARK-9260] Fix algorithm for
 scheduling executors on workers

Current scheduling algorithm allocates one core at a time and in doing so ends up ignoring spark.executor.cores. As a result, when spark.cores.max/spark.executor.cores (i.e, num_executors) < num_workers, executors are not launched and the app hangs. This PR fixes and refactors the scheduling algorithm.

andrewor14

Author: Nishkam Ravi <nravi@cloudera.com>
Author: nishkamravi2 <nishkamravi@gmail.com>

Closes #7274 from nishkamravi2/master_scheduler and squashes the following commits:

b998097 [nishkamravi2] Update Master.scala
da0f491 [Nishkam Ravi] Update Master.scala
79084e8 [Nishkam Ravi] Update Master.scala
1daf25f [Nishkam Ravi] Update Master.scala
f279cdf [Nishkam Ravi] Update Master.scala
adec84b [Nishkam Ravi] Update Master.scala
a06da76 [nishkamravi2] Update Master.scala
40c8f9f [nishkamravi2] Update Master.scala (to trigger retest)
c11c689 [nishkamravi2] Update EventLoggingListenerSuite.scala
5d6a19c [nishkamravi2] Update Master.scala (for the purpose of issuing a retest)
2d6371c [Nishkam Ravi] Update Master.scala
66362d5 [nishkamravi2] Update Master.scala
ee7cf0e [Nishkam Ravi] Improved scheduling algorithm for executors
---
 .../apache/spark/deploy/master/Master.scala   | 112 ++++++++++++------
 1 file changed, 75 insertions(+), 37 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 4615febf17d24..029f94d1020be 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -541,6 +541,7 @@ private[master] class Master(
 
   /**
    * Schedule executors to be launched on the workers.
+   * Returns an array containing number of cores assigned to each worker.
    *
    * There are two modes of launching executors. The first attempts to spread out an application's
    * executors on as many workers as possible, while the second does the opposite (i.e. launch them
@@ -551,39 +552,73 @@ private[master] class Master(
    * multiple executors from the same application may be launched on the same worker if the worker
    * has enough cores and memory. Otherwise, each executor grabs all the cores available on the
    * worker by default, in which case only one executor may be launched on each worker.
+   *
+   * It is important to allocate coresPerExecutor on each worker at a time (instead of 1 core
+   * at a time). Consider the following example: cluster has 4 workers with 16 cores each.
+   * User requests 3 executors (spark.cores.max = 48, spark.executor.cores = 16). If 1 core is
+   * allocated at a time, 12 cores from each worker would be assigned to each executor.
+   * Since 12 < 16, no executors would launch [SPARK-8881].
    */
-  private def startExecutorsOnWorkers(): Unit = {
-    // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app
-    // in the queue, then the second app, etc.
-    if (spreadOutApps) {
-      // Try to spread out each app among all the workers, until it has all its cores
-      for (app <- waitingApps if app.coresLeft > 0) {
-        val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
-          .filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB &&
-            worker.coresFree >= app.desc.coresPerExecutor.getOrElse(1))
-          .sortBy(_.coresFree).reverse
-        val numUsable = usableWorkers.length
-        val assigned = new Array[Int](numUsable) // Number of cores to give on each node
-        var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
-        var pos = 0
-        while (toAssign > 0) {
-          if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
-            toAssign -= 1
-            assigned(pos) += 1
+  private[master] def scheduleExecutorsOnWorkers(
+      app: ApplicationInfo,
+      usableWorkers: Array[WorkerInfo],
+      spreadOutApps: Boolean): Array[Int] = {
+    // If the number of cores per executor is not specified, then we can just schedule
+    // 1 core at a time since we expect a single executor to be launched on each worker
+    val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(1)
+    val memoryPerExecutor = app.desc.memoryPerExecutorMB
+    val numUsable = usableWorkers.length
+    val assignedCores = new Array[Int](numUsable) // Number of cores to give to each worker
+    val assignedMemory = new Array[Int](numUsable) // Amount of memory to give to each worker
+    var coresToAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
+    var freeWorkers = (0 until numUsable).toIndexedSeq
+
+    def canLaunchExecutor(pos: Int): Boolean = {
+      usableWorkers(pos).coresFree - assignedCores(pos) >= coresPerExecutor &&
+      usableWorkers(pos).memoryFree - assignedMemory(pos) >= memoryPerExecutor
+    }
+
+    while (coresToAssign >= coresPerExecutor && freeWorkers.nonEmpty) {
+      freeWorkers = freeWorkers.filter(canLaunchExecutor)
+      freeWorkers.foreach { pos =>
+        var keepScheduling = true
+        while (keepScheduling && canLaunchExecutor(pos) && coresToAssign >= coresPerExecutor) {
+          coresToAssign -= coresPerExecutor
+          assignedCores(pos) += coresPerExecutor
+          assignedMemory(pos) += memoryPerExecutor
+
+          // Spreading out an application means spreading out its executors across as
+          // many workers as possible. If we are not spreading out, then we should keep
+          // scheduling executors on this worker until we use all of its resources.
+          // Otherwise, just move on to the next worker.
+          if (spreadOutApps) {
+            keepScheduling = false
           }
-          pos = (pos + 1) % numUsable
-        }
-        // Now that we've decided how many cores to give on each node, let's actually give them
-        for (pos <- 0 until numUsable if assigned(pos) > 0) {
-          allocateWorkerResourceToExecutors(app, assigned(pos), usableWorkers(pos))
         }
       }
-    } else {
-      // Pack each app into as few workers as possible until we've assigned all its cores
-      for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
-        for (app <- waitingApps if app.coresLeft > 0) {
-          allocateWorkerResourceToExecutors(app, app.coresLeft, worker)
-        }
+    }
+    assignedCores
+  }
+
+  /**
+   * Schedule and launch executors on workers
+   */
+  private def startExecutorsOnWorkers(): Unit = {
+    // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app
+    // in the queue, then the second app, etc.
+    for (app <- waitingApps if app.coresLeft > 0) {
+      val coresPerExecutor: Option[Int] = app.desc.coresPerExecutor
+      // Filter out workers that don't have enough resources to launch an executor
+      val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
+        .filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB &&
+          worker.coresFree >= coresPerExecutor.getOrElse(1))
+        .sortBy(_.coresFree).reverse
+      val assignedCores = scheduleExecutorsOnWorkers(app, usableWorkers, spreadOutApps)
+
+      // Now that we've decided how many cores to allocate on each worker, let's allocate them
+      for (pos <- 0 until usableWorkers.length if assignedCores(pos) > 0) {
+        allocateWorkerResourceToExecutors(
+          app, assignedCores(pos), coresPerExecutor, usableWorkers(pos))
       }
     }
   }
@@ -591,19 +626,22 @@ private[master] class Master(
   /**
    * Allocate a worker's resources to one or more executors.
    * @param app the info of the application which the executors belong to
-   * @param coresToAllocate cores on this worker to be allocated to this application
+   * @param assignedCores number of cores on this worker for this application
+   * @param coresPerExecutor number of cores per executor
    * @param worker the worker info
    */
   private def allocateWorkerResourceToExecutors(
       app: ApplicationInfo,
-      coresToAllocate: Int,
+      assignedCores: Int,
+      coresPerExecutor: Option[Int],
       worker: WorkerInfo): Unit = {
-    val memoryPerExecutor = app.desc.memoryPerExecutorMB
-    val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(coresToAllocate)
-    var coresLeft = coresToAllocate
-    while (coresLeft >= coresPerExecutor && worker.memoryFree >= memoryPerExecutor) {
-      val exec = app.addExecutor(worker, coresPerExecutor)
-      coresLeft -= coresPerExecutor
+    // If the number of cores per executor is specified, we divide the cores assigned
+    // to this worker evenly among the executors with no remainder.
+    // Otherwise, we launch a single executor that grabs all the assignedCores on this worker.
+    val numExecutors = coresPerExecutor.map { assignedCores / _ }.getOrElse(1)
+    val coresToAssign = coresPerExecutor.getOrElse(assignedCores)
+    for (i <- 1 to numExecutors) {
+      val exec = app.addExecutor(worker, coresToAssign)
       launchExecutor(worker, exec)
       app.state = ApplicationState.RUNNING
     }

From 4a01bfc2a2e664186028ea32095d32d29c9f9e38 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 25 Jul 2015 23:52:37 -0700
Subject: [PATCH 0602/1454] [SPARK-9350][SQL] Introduce an InternalRow generic
 getter that requires a DataType

Currently UnsafeRow cannot support a generic getter. However, if the data type is known, we can support a generic getter.

Author: Reynold Xin <rxin@databricks.com>

Closes #7666 from rxin/generic-getter-with-datatype and squashes the following commits:

ee2874c [Reynold Xin] Add a default implementation for getStruct.
1e109a0 [Reynold Xin] [SPARK-9350][SQL] Introduce an InternalRow generic getter that requires a DataType.
033ee88 [Reynold Xin] Removed getAs in non test code.
---
 .../apache/spark/mllib/linalg/Matrices.scala  |  8 +++--
 .../apache/spark/mllib/linalg/Vectors.scala   |  9 ++++--
 .../sql/catalyst/expressions/UnsafeRow.java   |  5 ---
 .../sql/catalyst/CatalystTypeConverters.scala | 16 ++++++----
 .../spark/sql/catalyst/InternalRow.scala      | 32 +++++++++++--------
 .../catalyst/expressions/BoundAttribute.scala |  2 +-
 .../spark/sql/catalyst/expressions/Cast.scala |  5 +--
 .../sql/catalyst/expressions/Projection.scala |  8 +++++
 .../expressions/SpecificMutableRow.scala      | 10 +++---
 .../sql/catalyst/expressions/aggregates.scala |  2 +-
 .../expressions/complexTypeCreator.scala      |  2 +-
 .../expressions/complexTypeExtractors.scala   |  4 +--
 .../spark/sql/catalyst/expressions/rows.scala |  8 +++++
 .../expressions/ExpressionEvalHelper.scala    |  7 ++--
 .../expressions/MathFunctionsSuite.scala      |  2 +-
 .../expressions/UnsafeRowConverterSuite.scala |  2 +-
 .../sql/execution/SparkSqlSerializer2.scala   |  4 +--
 .../spark/sql/execution/basicOperators.scala  |  4 +--
 .../datasources/DataSourceStrategy.scala      |  4 +--
 .../spark/sql/execution/debug/package.scala   |  2 +-
 .../spark/sql/execution/pythonUDFs.scala      |  2 +-
 .../sql/execution/stat/FrequentItems.scala    |  8 ++---
 .../sql/expressions/aggregate/udaf.scala      | 10 ++++--
 .../sql/parquet/ParquetTableOperations.scala  |  2 +-
 .../sql/parquet/ParquetTableSupport.scala     |  4 +--
 .../scala/org/apache/spark/sql/RowSuite.scala | 11 ++++---
 .../hive/execution/InsertIntoHiveTable.scala  |  4 ++-
 .../spark/sql/hive/orc/OrcRelation.scala      |  2 +-
 28 files changed, 105 insertions(+), 74 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index b6e2c30fbf104..d82ba2456df1a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -179,12 +179,14 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
         val tpe = row.getByte(0)
         val numRows = row.getInt(1)
         val numCols = row.getInt(2)
-        val values = row.getAs[Iterable[Double]](5).toArray
+        val values = row.getAs[Seq[Double]](5, ArrayType(DoubleType, containsNull = false)).toArray
         val isTransposed = row.getBoolean(6)
         tpe match {
           case 0 =>
-            val colPtrs = row.getAs[Iterable[Int]](3).toArray
-            val rowIndices = row.getAs[Iterable[Int]](4).toArray
+            val colPtrs =
+              row.getAs[Seq[Int]](3, ArrayType(IntegerType, containsNull = false)).toArray
+            val rowIndices =
+              row.getAs[Seq[Int]](4, ArrayType(IntegerType, containsNull = false)).toArray
             new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values, isTransposed)
           case 1 =>
             new DenseMatrix(numRows, numCols, values, isTransposed)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index c884aad08889f..0cb28d78bec05 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -209,11 +209,14 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
         tpe match {
           case 0 =>
             val size = row.getInt(1)
-            val indices = row.getAs[Iterable[Int]](2).toArray
-            val values = row.getAs[Iterable[Double]](3).toArray
+            val indices =
+              row.getAs[Seq[Int]](2, ArrayType(IntegerType, containsNull = false)).toArray
+            val values =
+              row.getAs[Seq[Double]](3, ArrayType(DoubleType, containsNull = false)).toArray
             new SparseVector(size, indices, values)
           case 1 =>
-            val values = row.getAs[Iterable[Double]](3).toArray
+            val values =
+              row.getAs[Seq[Double]](3, ArrayType(DoubleType, containsNull = false)).toArray
             new DenseVector(values)
         }
     }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 9be9089493335..87e5a89c19658 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -235,11 +235,6 @@ public Object get(int ordinal) {
     throw new UnsupportedOperationException();
   }
 
-  @Override
-  public <T> T getAs(int ordinal) {
-    throw new UnsupportedOperationException();
-  }
-
   @Override
   public boolean isNullAt(int ordinal) {
     assertIndexIsValid(ordinal);
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 7416ddbaef3fc..d1d89a1f48329 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -77,7 +77,7 @@ object CatalystTypeConverters {
       case LongType => LongConverter
       case FloatType => FloatConverter
       case DoubleType => DoubleConverter
-      case _ => IdentityConverter
+      case dataType: DataType => IdentityConverter(dataType)
     }
     converter.asInstanceOf[CatalystTypeConverter[Any, Any, Any]]
   }
@@ -137,17 +137,19 @@ object CatalystTypeConverters {
     protected def toScalaImpl(row: InternalRow, column: Int): ScalaOutputType
   }
 
-  private object IdentityConverter extends CatalystTypeConverter[Any, Any, Any] {
+  private case class IdentityConverter(dataType: DataType)
+    extends CatalystTypeConverter[Any, Any, Any] {
     override def toCatalystImpl(scalaValue: Any): Any = scalaValue
     override def toScala(catalystValue: Any): Any = catalystValue
-    override def toScalaImpl(row: InternalRow, column: Int): Any = row.get(column)
+    override def toScalaImpl(row: InternalRow, column: Int): Any = row.get(column, dataType)
   }
 
   private case class UDTConverter(
       udt: UserDefinedType[_]) extends CatalystTypeConverter[Any, Any, Any] {
     override def toCatalystImpl(scalaValue: Any): Any = udt.serialize(scalaValue)
     override def toScala(catalystValue: Any): Any = udt.deserialize(catalystValue)
-    override def toScalaImpl(row: InternalRow, column: Int): Any = toScala(row.get(column))
+    override def toScalaImpl(row: InternalRow, column: Int): Any =
+      toScala(row.get(column, udt.sqlType))
   }
 
   /** Converter for arrays, sequences, and Java iterables. */
@@ -184,7 +186,7 @@ object CatalystTypeConverters {
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Seq[Any] =
-      toScala(row.get(column).asInstanceOf[Seq[Any]])
+      toScala(row.get(column, ArrayType(elementType)).asInstanceOf[Seq[Any]])
   }
 
   private case class MapConverter(
@@ -227,7 +229,7 @@ object CatalystTypeConverters {
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Map[Any, Any] =
-      toScala(row.get(column).asInstanceOf[Map[Any, Any]])
+      toScala(row.get(column, MapType(keyType, valueType)).asInstanceOf[Map[Any, Any]])
   }
 
   private case class StructConverter(
@@ -311,7 +313,7 @@ object CatalystTypeConverters {
     }
     override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal
     override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal =
-      row.get(column).asInstanceOf[Decimal].toJavaBigDecimal
+      row.getDecimal(column).toJavaBigDecimal
   }
 
   private abstract class PrimitiveConverter[T] extends CatalystTypeConverter[T, Any, Any] {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 37f0f57e9e6d3..385d9671386dc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.Decimal
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -32,32 +32,36 @@ abstract class InternalRow extends Serializable {
 
   def get(ordinal: Int): Any
 
-  def getAs[T](ordinal: Int): T = get(ordinal).asInstanceOf[T]
+  def genericGet(ordinal: Int): Any = get(ordinal, null)
+
+  def get(ordinal: Int, dataType: DataType): Any = get(ordinal)
+
+  def getAs[T](ordinal: Int, dataType: DataType): T = get(ordinal, dataType).asInstanceOf[T]
 
   def isNullAt(ordinal: Int): Boolean = get(ordinal) == null
 
-  def getBoolean(ordinal: Int): Boolean = getAs[Boolean](ordinal)
+  def getBoolean(ordinal: Int): Boolean = getAs[Boolean](ordinal, BooleanType)
 
-  def getByte(ordinal: Int): Byte = getAs[Byte](ordinal)
+  def getByte(ordinal: Int): Byte = getAs[Byte](ordinal, ByteType)
 
-  def getShort(ordinal: Int): Short = getAs[Short](ordinal)
+  def getShort(ordinal: Int): Short = getAs[Short](ordinal, ShortType)
 
-  def getInt(ordinal: Int): Int = getAs[Int](ordinal)
+  def getInt(ordinal: Int): Int = getAs[Int](ordinal, IntegerType)
 
-  def getLong(ordinal: Int): Long = getAs[Long](ordinal)
+  def getLong(ordinal: Int): Long = getAs[Long](ordinal, LongType)
 
-  def getFloat(ordinal: Int): Float = getAs[Float](ordinal)
+  def getFloat(ordinal: Int): Float = getAs[Float](ordinal, FloatType)
 
-  def getDouble(ordinal: Int): Double = getAs[Double](ordinal)
+  def getDouble(ordinal: Int): Double = getAs[Double](ordinal, DoubleType)
 
-  def getUTF8String(ordinal: Int): UTF8String = getAs[UTF8String](ordinal)
+  def getUTF8String(ordinal: Int): UTF8String = getAs[UTF8String](ordinal, StringType)
 
-  def getBinary(ordinal: Int): Array[Byte] = getAs[Array[Byte]](ordinal)
+  def getBinary(ordinal: Int): Array[Byte] = getAs[Array[Byte]](ordinal, BinaryType)
 
-  def getDecimal(ordinal: Int): Decimal = getAs[Decimal](ordinal)
+  def getDecimal(ordinal: Int): Decimal = getAs[Decimal](ordinal, DecimalType.SYSTEM_DEFAULT)
 
   // This is only use for test and will throw a null pointer exception if the position is null.
-  def getString(ordinal: Int): String = getAs[UTF8String](ordinal).toString
+  def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
   /**
    * Returns a struct from ordinal position.
@@ -65,7 +69,7 @@ abstract class InternalRow extends Serializable {
    * @param ordinal position to get the struct from.
    * @param numFields number of fields the struct type has
    */
-  def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs[InternalRow](ordinal)
+  def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs[InternalRow](ordinal, null)
 
   override def toString: String = s"[${this.mkString(",")}]"
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 1f7adcd36ec14..6b5c450e3fb0a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -49,7 +49,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
         case StringType => input.getUTF8String(ordinal)
         case BinaryType => input.getBinary(ordinal)
         case t: StructType => input.getStruct(ordinal, t.size)
-        case _ => input.get(ordinal)
+        case dataType => input.get(ordinal, dataType)
       }
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 47ad3e089e4c7..e5b83cd31bf0f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -375,7 +375,7 @@ case class Cast(child: Expression, dataType: DataType)
   }
 
   private[this] def castStruct(from: StructType, to: StructType): Any => Any = {
-    val casts = from.fields.zip(to.fields).map {
+    val castFuncs: Array[(Any) => Any] = from.fields.zip(to.fields).map {
       case (fromField, toField) => cast(fromField.dataType, toField.dataType)
     }
     // TODO: Could be faster?
@@ -383,7 +383,8 @@ case class Cast(child: Expression, dataType: DataType)
     buildCast[InternalRow](_, row => {
       var i = 0
       while (i < row.numFields) {
-        newRow.update(i, if (row.isNullAt(i)) null else casts(i)(row.get(i)))
+        newRow.update(i,
+          if (row.isNullAt(i)) null else castFuncs(i)(row.get(i, from.apply(i).dataType)))
         i += 1
       }
       newRow.copy()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index c1ed9cf7ed6a0..cc89d74146b34 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -225,6 +225,14 @@ class JoinedRow extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
+  override def getStruct(i: Int, numFields: Int): InternalRow = {
+    if (i < row1.numFields) {
+      row1.getStruct(i, numFields)
+    } else {
+      row2.getStruct(i - row1.numFields, numFields)
+    }
+  }
+
   override def copy(): InternalRow = {
     val totalSize = row1.numFields + row2.numFields
     val copiedValues = new Array[Any](totalSize)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 4b4833bd06a3b..5953a093dc684 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -221,6 +221,10 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
 
   override def get(i: Int): Any = values(i).boxed
 
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
+    values(ordinal).boxed.asInstanceOf[InternalRow]
+  }
+
   override def isNullAt(i: Int): Boolean = values(i).isNull
 
   override def copy(): InternalRow = {
@@ -245,8 +249,6 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
   override def setString(ordinal: Int, value: String): Unit =
     update(ordinal, UTF8String.fromString(value))
 
-  override def getString(ordinal: Int): String = get(ordinal).toString
-
   override def setInt(ordinal: Int, value: Int): Unit = {
     val currentValue = values(ordinal).asInstanceOf[MutableInt]
     currentValue.isNull = false
@@ -316,8 +318,4 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
   override def getByte(i: Int): Byte = {
     values(i).asInstanceOf[MutableByte].value
   }
-
-  override def getAs[T](i: Int): T = {
-    values(i).boxed.asInstanceOf[T]
-  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 62b6cc834c9c9..42343d4d8d79c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -685,7 +685,7 @@ case class CombineSetsAndSumFunction(
       null
     } else {
       Cast(Literal(
-        casted.iterator.map(f => f.get(0)).reduceLeft(
+        casted.iterator.map(f => f.genericGet(0)).reduceLeft(
           base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus)),
         base.dataType).eval(null)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 20b1eaab8e303..119168fa59f15 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
+import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
index c91122cda2a41..6331a9eb603ca 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
@@ -110,7 +110,7 @@ case class GetStructField(child: Expression, field: StructField, ordinal: Int)
   override def toString: String = s"$child.${field.name}"
 
   protected override def nullSafeEval(input: Any): Any =
-    input.asInstanceOf[InternalRow].get(ordinal)
+    input.asInstanceOf[InternalRow].get(ordinal, field.dataType)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, eval => {
@@ -142,7 +142,7 @@ case class GetArrayStructFields(
 
   protected override def nullSafeEval(input: Any): Any = {
     input.asInstanceOf[Seq[InternalRow]].map { row =>
-      if (row == null) null else row.get(ordinal)
+      if (row == null) null else row.get(ordinal, field.dataType)
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 53779dd4049d1..daeabe8e90f1d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -101,6 +101,10 @@ class GenericInternalRow(protected[sql] val values: Array[Any]) extends Internal
 
   override def get(i: Int): Any = values(i)
 
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
+    values(ordinal).asInstanceOf[InternalRow]
+  }
+
   override def copy(): InternalRow = this
 }
 
@@ -128,6 +132,10 @@ class GenericMutableRow(val values: Array[Any]) extends MutableRow {
 
   override def get(i: Int): Any = values(i)
 
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
+    values(ordinal).asInstanceOf[InternalRow]
+  }
+
   override def setNullAt(i: Int): Unit = { values(i) = null}
 
   override def update(i: Int, value: Any): Unit = { values(i) = value }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 852a8b235f127..8b0f90cf3a623 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -113,7 +113,7 @@ trait ExpressionEvalHelper {
       GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)(),
       expression)
 
-    val actual = plan(inputRow).get(0)
+    val actual = plan(inputRow).get(0, expression.dataType)
     if (!checkResult(actual, expected)) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
       fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
@@ -194,13 +194,14 @@ trait ExpressionEvalHelper {
     var plan = generateProject(
       GenerateProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil),
       expression)
-    var actual = plan(inputRow).get(0)
+    var actual = plan(inputRow).get(0, expression.dataType)
     assert(checkResult(actual, expected))
 
     plan = generateProject(
       GenerateUnsafeProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil),
       expression)
-    actual = FromUnsafeProjection(expression.dataType :: Nil)(plan(inputRow)).get(0)
+    actual = FromUnsafeProjection(expression.dataType :: Nil)(
+      plan(inputRow)).get(0, expression.dataType)
     assert(checkResult(actual, expected))
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 6caf8baf24a81..21459a7c69838 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -158,7 +158,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)(),
       expression)
 
-    val actual = plan(inputRow).get(0)
+    val actual = plan(inputRow).get(0, expression.dataType)
     if (!actual.asInstanceOf[Double].isNaN) {
       fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: NaN")
     }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 4606bcb57311d..2834b54e8fb2e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -183,7 +183,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.getFloat(6) === rowWithNoNullColumns.getFloat(6))
     assert(setToNullAfterCreation.getDouble(7) === rowWithNoNullColumns.getDouble(7))
     assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
-    assert(setToNullAfterCreation.getBinary(9) === rowWithNoNullColumns.get(9))
+    assert(setToNullAfterCreation.getBinary(9) === rowWithNoNullColumns.getBinary(9))
     // assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
     // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 6ee833c7b2c94..c808442a4849b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -288,7 +288,7 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val bytes = row.getAs[Array[Byte]](i)
+                val bytes = row.getBinary(i)
                 out.writeInt(bytes.length)
                 out.write(bytes)
               }
@@ -298,7 +298,7 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val value = row.getAs[Decimal](i)
+                val value = row.getDecimal(i)
                 val javaBigDecimal = value.toJavaBigDecimal
                 // First, write out the unscaled value.
                 val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index fdd7ad59aba50..fe429d862a0a3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -17,16 +17,16 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.sql.types.StructType
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, ShuffledRDD}
 import org.apache.spark.shuffle.sort.SortShuffleManager
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.ExternalSorter
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
 import org.apache.spark.util.{CompletionIterator, MutablePair}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index cdbe42381a7e4..6b91e51ca52fb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -189,13 +189,13 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         if (i != -1) {
           // If yes, gets column value from partition values.
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = partitionValues.get(i)
+            mutableRow(ordinal) = partitionValues.genericGet(i)
           }
         } else {
           // Otherwise, inherits the value from scanned data.
           val i = nonPartitionColumns.indexOf(name)
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = dataRow.get(i)
+            mutableRow(ordinal) = dataRow.genericGet(i)
           }
         }
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index 1fdcc6a850602..aeeb0e45270dd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -136,7 +136,7 @@ package object debug {
             tupleCount += 1
             var i = 0
             while (i < numColumns) {
-              val value = currentRow.get(i)
+              val value = currentRow.get(i, output(i).dataType)
               if (value != null) {
                 columnStats(i).elementTypes += HashSet(value.getClass.getName)
               }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 970c40dc61a3c..ec084a299649e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -129,7 +129,7 @@ object EvaluatePython {
       val values = new Array[Any](row.numFields)
       var i = 0
       while (i < row.numFields) {
-        values(i) = toJava(row.get(i), struct.fields(i).dataType)
+        values(i) = toJava(row.get(i, struct.fields(i).dataType), struct.fields(i).dataType)
         i += 1
       }
       new GenericInternalRowWithSchema(values, struct)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
index ec5c6950f37ad..78da2840dad69 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.{Map => MutableMap}
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
-import org.apache.spark.sql.types.{ArrayType, StructField, StructType}
+import org.apache.spark.sql.types.{DataType, ArrayType, StructField, StructType}
 import org.apache.spark.sql.{Column, DataFrame}
 
 private[sql] object FrequentItems extends Logging {
@@ -85,17 +85,17 @@ private[sql] object FrequentItems extends Logging {
     val sizeOfMap = (1 / support).toInt
     val countMaps = Seq.tabulate(numCols)(i => new FreqItemCounter(sizeOfMap))
     val originalSchema = df.schema
-    val colInfo = cols.map { name =>
+    val colInfo: Array[(String, DataType)] = cols.map { name =>
       val index = originalSchema.fieldIndex(name)
       (name, originalSchema.fields(index).dataType)
-    }
+    }.toArray
 
     val freqItems = df.select(cols.map(Column(_)) : _*).queryExecution.toRdd.aggregate(countMaps)(
       seqOp = (counts, row) => {
         var i = 0
         while (i < numCols) {
           val thisMap = counts(i)
-          val key = row.get(i)
+          val key = row.get(i, colInfo(i)._2)
           thisMap.add(key, 1L)
           i += 1
         }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
index 7a6e86779b185..4ada9eca7a035 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
@@ -110,6 +110,7 @@ private[sql] abstract class AggregationBuffer(
  * A Mutable [[Row]] representing an mutable aggregation buffer.
  */
 class MutableAggregationBuffer private[sql] (
+    schema: StructType,
     toCatalystConverters: Array[Any => Any],
     toScalaConverters: Array[Any => Any],
     bufferOffset: Int,
@@ -121,7 +122,7 @@ class MutableAggregationBuffer private[sql] (
       throw new IllegalArgumentException(
         s"Could not access ${i}th value in this buffer because it only has $length values.")
     }
-    toScalaConverters(i)(underlyingBuffer.get(offsets(i)))
+    toScalaConverters(i)(underlyingBuffer.get(offsets(i), schema(i).dataType))
   }
 
   def update(i: Int, value: Any): Unit = {
@@ -134,6 +135,7 @@ class MutableAggregationBuffer private[sql] (
 
   override def copy(): MutableAggregationBuffer = {
     new MutableAggregationBuffer(
+      schema,
       toCatalystConverters,
       toScalaConverters,
       bufferOffset,
@@ -145,6 +147,7 @@ class MutableAggregationBuffer private[sql] (
  * A [[Row]] representing an immutable aggregation buffer.
  */
 class InputAggregationBuffer private[sql] (
+    schema: StructType,
     toCatalystConverters: Array[Any => Any],
     toScalaConverters: Array[Any => Any],
     bufferOffset: Int,
@@ -157,11 +160,12 @@ class InputAggregationBuffer private[sql] (
         s"Could not access ${i}th value in this buffer because it only has $length values.")
     }
     // TODO: Use buffer schema to avoid using generic getter.
-    toScalaConverters(i)(underlyingInputBuffer.get(offsets(i)))
+    toScalaConverters(i)(underlyingInputBuffer.get(offsets(i), schema(i).dataType))
   }
 
   override def copy(): InputAggregationBuffer = {
     new InputAggregationBuffer(
+      schema,
       toCatalystConverters,
       toScalaConverters,
       bufferOffset,
@@ -233,6 +237,7 @@ case class ScalaUDAF(
 
   lazy val inputAggregateBuffer: InputAggregationBuffer =
     new InputAggregationBuffer(
+      bufferSchema,
       bufferValuesToCatalystConverters,
       bufferValuesToScalaConverters,
       bufferOffset,
@@ -240,6 +245,7 @@ case class ScalaUDAF(
 
   lazy val mutableAggregateBuffer: MutableAggregationBuffer =
     new MutableAggregationBuffer(
+      bufferSchema,
       bufferValuesToCatalystConverters,
       bufferValuesToScalaConverters,
       bufferOffset,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index 38bb1e3967642..75cbbde4f1512 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -179,7 +179,7 @@ private[sql] case class ParquetTableScan(
 
               var i = 0
               while (i < row.numFields) {
-                mutableRow(i) = row.get(i)
+                mutableRow(i) = row.genericGet(i)
                 i += 1
               }
               // Parquet will leave partitioning columns empty, so we fill them in here.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 2c23d4e8a8146..7b6a7f65d69db 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -219,7 +219,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
       // null values indicate optional fields but we do not check currently
       if (!record.isNullAt(index)) {
         writer.startField(attributes(index).name, index)
-        writeValue(attributes(index).dataType, record.get(index))
+        writeValue(attributes(index).dataType, record.get(index, attributes(index).dataType))
         writer.endField(attributes(index).name, index)
       }
       index = index + 1
@@ -280,7 +280,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
       while(i < fields.length) {
         if (!struct.isNullAt(i)) {
           writer.startField(fields(i).name, i)
-          writeValue(fields(i).dataType, struct.get(i))
+          writeValue(fields(i).dataType, struct.get(i, fields(i).dataType))
           writer.endField(fields(i).name, i)
         }
         i = i + 1
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index c6804e84827c0..01b7c21e84159 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -30,23 +30,24 @@ class RowSuite extends SparkFunSuite {
 
   test("create row") {
     val expected = new GenericMutableRow(4)
-    expected.update(0, 2147483647)
+    expected.setInt(0, 2147483647)
     expected.setString(1, "this is a string")
-    expected.update(2, false)
-    expected.update(3, null)
+    expected.setBoolean(2, false)
+    expected.setNullAt(3)
+
     val actual1 = Row(2147483647, "this is a string", false, null)
     assert(expected.numFields === actual1.size)
     assert(expected.getInt(0) === actual1.getInt(0))
     assert(expected.getString(1) === actual1.getString(1))
     assert(expected.getBoolean(2) === actual1.getBoolean(2))
-    assert(expected.get(3) === actual1.get(3))
+    assert(expected.isNullAt(3) === actual1.isNullAt(3))
 
     val actual2 = Row.fromSeq(Seq(2147483647, "this is a string", false, null))
     assert(expected.numFields === actual2.size)
     assert(expected.getInt(0) === actual2.getInt(0))
     assert(expected.getString(1) === actual2.getString(1))
     assert(expected.getBoolean(2) === actual2.getBoolean(2))
-    assert(expected.get(3) === actual2.get(3))
+    assert(expected.isNullAt(3) === actual2.isNullAt(3))
   }
 
   test("SpecificMutableRow.update with null") {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index f0e0ca05a8aad..e4944caeff924 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -36,6 +36,7 @@ import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
 import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc}
 import org.apache.spark.sql.hive._
+import org.apache.spark.sql.types.DataType
 import org.apache.spark.{SparkException, TaskContext}
 
 import scala.collection.JavaConversions._
@@ -96,13 +97,14 @@ case class InsertIntoHiveTable(
       val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector).toArray
       val wrappers = fieldOIs.map(wrapperFor)
       val outputData = new Array[Any](fieldOIs.length)
+      val dataTypes: Array[DataType] = child.output.map(_.dataType).toArray
 
       writerContainer.executorSideSetup(context.stageId, context.partitionId, context.attemptNumber)
 
       iterator.foreach { row =>
         var i = 0
         while (i < fieldOIs.length) {
-          outputData(i) = if (row.isNullAt(i)) null else wrappers(i)(row.get(i))
+          outputData(i) = if (row.isNullAt(i)) null else wrappers(i)(row.get(i, dataTypes(i)))
           i += 1
         }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 58445095ad74f..924f4d37ce21f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -122,7 +122,7 @@ private[orc] class OrcOutputWriter(
   override def writeInternal(row: InternalRow): Unit = {
     var i = 0
     while (i < row.numFields) {
-      reusableOutputBuffer(i) = wrappers(i)(row.get(i))
+      reusableOutputBuffer(i) = wrappers(i)(row.get(i, dataSchema(i).dataType))
       i += 1
     }
 

From b79bf1df6238c087c3ec524344f1fc179719c5de Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Sun, 26 Jul 2015 14:02:20 +0100
Subject: [PATCH 0603/1454] [SPARK-9337] [MLLIB] Add an ut for Word2Vec to
 verify the empty vocabulary check

jira: https://issues.apache.org/jira/browse/SPARK-9337

Word2Vec should throw exception when vocabulary is empty

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7660 from hhbyyh/ut4Word2vec and squashes the following commits:

17a18cb [Yuhao Yang] add ut for word2vec
---
 .../org/apache/spark/mllib/feature/Word2VecSuite.scala | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
index 4cc8d1129b858..a864eec460f2b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala
@@ -45,6 +45,16 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(newModel.getVectors.mapValues(_.toSeq) === word2VecMap.mapValues(_.toSeq))
   }
 
+  test("Word2Vec throws exception when vocabulary is empty") {
+    intercept[IllegalArgumentException] {
+      val sentence = "a b c"
+      val localDoc = Seq(sentence, sentence)
+      val doc = sc.parallelize(localDoc)
+        .map(line => line.split(" ").toSeq)
+      new Word2Vec().setMinCount(10).fit(doc)
+    }
+  }
+
   test("Word2VecModel") {
     val num = 2
     val word2VecMap = Map(

From 6c400b4f39be3fb5f473b8d2db11d239ea8ddf42 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 26 Jul 2015 10:27:39 -0700
Subject: [PATCH 0604/1454] [SPARK-9354][SQL] Remove InternalRow.get generic
 getter call in Hive integration code.

Replaced them with get(ordinal, datatype) so we can use UnsafeRow here.

I passed the data types throughout.

Author: Reynold Xin <rxin@databricks.com>

Closes #7669 from rxin/row-generic-getter-hive and squashes the following commits:

3467d8e [Reynold Xin] [SPARK-9354][SQL] Remove Internal.get generic getter call in Hive integration code.
---
 .../spark/sql/hive/HiveInspectors.scala       | 43 ++++++-----
 .../org/apache/spark/sql/hive/hiveUDFs.scala  | 74 ++++++++++++-------
 .../spark/sql/hive/HiveInspectorSuite.scala   | 53 +++++++------
 3 files changed, 102 insertions(+), 68 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 16977ce30cfff..f467500259c91 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -46,7 +46,7 @@ import scala.collection.JavaConversions._
  *     long / scala.Long
  *     short / scala.Short
  *     byte / scala.Byte
- *     org.apache.spark.sql.types.Decimal
+ *     [[org.apache.spark.sql.types.Decimal]]
  *     Array[Byte]
  *     java.sql.Date
  *     java.sql.Timestamp
@@ -54,7 +54,7 @@ import scala.collection.JavaConversions._
  *    Map: scala.collection.immutable.Map
  *    List: scala.collection.immutable.Seq
  *    Struct:
- *           org.apache.spark.sql.catalyst.expression.Row
+ *           [[org.apache.spark.sql.catalyst.InternalRow]]
  *    Union: NOT SUPPORTED YET
  *  The Complex types plays as a container, which can hold arbitrary data types.
  *
@@ -454,7 +454,7 @@ private[hive] trait HiveInspectors {
    *
    *  NOTICE: the complex data type requires recursive wrapping.
    */
-  def wrap(a: Any, oi: ObjectInspector): AnyRef = oi match {
+  def wrap(a: Any, oi: ObjectInspector, dataType: DataType): AnyRef = oi match {
     case x: ConstantObjectInspector => x.getWritableConstantValue
     case _ if a == null => null
     case x: PrimitiveObjectInspector => x match {
@@ -488,43 +488,50 @@ private[hive] trait HiveInspectors {
     }
     case x: SettableStructObjectInspector =>
       val fieldRefs = x.getAllStructFieldRefs
+      val structType = dataType.asInstanceOf[StructType]
       val row = a.asInstanceOf[InternalRow]
       // 1. create the pojo (most likely) object
       val result = x.create()
       var i = 0
       while (i < fieldRefs.length) {
         // 2. set the property for the pojo
+        val tpe = structType(i).dataType
         x.setStructFieldData(
           result,
           fieldRefs.get(i),
-          wrap(row.get(i), fieldRefs.get(i).getFieldObjectInspector))
+          wrap(row.get(i, tpe), fieldRefs.get(i).getFieldObjectInspector, tpe))
         i += 1
       }
 
       result
     case x: StructObjectInspector =>
       val fieldRefs = x.getAllStructFieldRefs
+      val structType = dataType.asInstanceOf[StructType]
       val row = a.asInstanceOf[InternalRow]
       val result = new java.util.ArrayList[AnyRef](fieldRefs.length)
       var i = 0
       while (i < fieldRefs.length) {
-        result.add(wrap(row.get(i), fieldRefs.get(i).getFieldObjectInspector))
+        val tpe = structType(i).dataType
+        result.add(wrap(row.get(i, tpe), fieldRefs.get(i).getFieldObjectInspector, tpe))
         i += 1
       }
 
       result
     case x: ListObjectInspector =>
       val list = new java.util.ArrayList[Object]
+      val tpe = dataType.asInstanceOf[ArrayType].elementType
       a.asInstanceOf[Seq[_]].foreach {
-        v => list.add(wrap(v, x.getListElementObjectInspector))
+        v => list.add(wrap(v, x.getListElementObjectInspector, tpe))
       }
       list
     case x: MapObjectInspector =>
+      val keyType = dataType.asInstanceOf[MapType].keyType
+      val valueType = dataType.asInstanceOf[MapType].valueType
       // Some UDFs seem to assume we pass in a HashMap.
       val hashMap = new java.util.HashMap[AnyRef, AnyRef]()
-      hashMap.putAll(a.asInstanceOf[Map[_, _]].map {
-        case (k, v) =>
-          wrap(k, x.getMapKeyObjectInspector) -> wrap(v, x.getMapValueObjectInspector)
+      hashMap.putAll(a.asInstanceOf[Map[_, _]].map { case (k, v) =>
+        wrap(k, x.getMapKeyObjectInspector, keyType) ->
+          wrap(v, x.getMapValueObjectInspector, valueType)
       })
 
       hashMap
@@ -533,22 +540,24 @@ private[hive] trait HiveInspectors {
   def wrap(
       row: InternalRow,
       inspectors: Seq[ObjectInspector],
-      cache: Array[AnyRef]): Array[AnyRef] = {
+      cache: Array[AnyRef],
+      dataTypes: Array[DataType]): Array[AnyRef] = {
     var i = 0
     while (i < inspectors.length) {
-      cache(i) = wrap(row.get(i), inspectors(i))
+      cache(i) = wrap(row.get(i, dataTypes(i)), inspectors(i), dataTypes(i))
       i += 1
     }
     cache
   }
 
   def wrap(
-    row: Seq[Any],
-    inspectors: Seq[ObjectInspector],
-    cache: Array[AnyRef]): Array[AnyRef] = {
+      row: Seq[Any],
+      inspectors: Seq[ObjectInspector],
+      cache: Array[AnyRef],
+      dataTypes: Array[DataType]): Array[AnyRef] = {
     var i = 0
     while (i < inspectors.length) {
-      cache(i) = wrap(row(i), inspectors(i))
+      cache(i) = wrap(row(i), inspectors(i), dataTypes(i))
       i += 1
     }
     cache
@@ -625,7 +634,7 @@ private[hive] trait HiveInspectors {
         ObjectInspectorFactory.getStandardConstantListObjectInspector(listObjectInspector, null)
       } else {
         val list = new java.util.ArrayList[Object]()
-        value.asInstanceOf[Seq[_]].foreach(v => list.add(wrap(v, listObjectInspector)))
+        value.asInstanceOf[Seq[_]].foreach(v => list.add(wrap(v, listObjectInspector, dt)))
         ObjectInspectorFactory.getStandardConstantListObjectInspector(listObjectInspector, list)
       }
     case Literal(value, MapType(keyType, valueType, _)) =>
@@ -636,7 +645,7 @@ private[hive] trait HiveInspectors {
       } else {
         val map = new java.util.HashMap[Object, Object]()
         value.asInstanceOf[Map[_, _]].foreach (entry => {
-          map.put(wrap(entry._1, keyOI), wrap(entry._2, valueOI))
+          map.put(wrap(entry._1, keyOI, keyType), wrap(entry._2, valueOI, valueType))
         })
         ObjectInspectorFactory.getStandardConstantMapObjectInspector(keyOI, valueOI, map)
       }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 3259b50acc765..54bf6bd67ff84 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -83,24 +83,22 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
 private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
   extends Expression with HiveInspectors with CodegenFallback with Logging {
 
-  type UDFType = UDF
-
   override def deterministic: Boolean = isUDFDeterministic
 
   override def nullable: Boolean = true
 
   @transient
-  lazy val function = funcWrapper.createFunction[UDFType]()
+  lazy val function = funcWrapper.createFunction[UDF]()
 
   @transient
-  protected lazy val method =
+  private lazy val method =
     function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo))
 
   @transient
-  protected lazy val arguments = children.map(toInspector).toArray
+  private lazy val arguments = children.map(toInspector).toArray
 
   @transient
-  protected lazy val isUDFDeterministic = {
+  private lazy val isUDFDeterministic = {
     val udfType = function.getClass().getAnnotation(classOf[HiveUDFType])
     udfType != null && udfType.deterministic()
   }
@@ -109,7 +107,7 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
 
   // Create parameter converters
   @transient
-  protected lazy val conversionHelper = new ConversionHelper(method, arguments)
+  private lazy val conversionHelper = new ConversionHelper(method, arguments)
 
   @transient
   lazy val dataType = javaClassToDataType(method.getReturnType)
@@ -119,14 +117,19 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
     method.getGenericReturnType(), ObjectInspectorOptions.JAVA)
 
   @transient
-  protected lazy val cached: Array[AnyRef] = new Array[AnyRef](children.length)
+  private lazy val cached: Array[AnyRef] = new Array[AnyRef](children.length)
+
+  @transient
+  private lazy val inputDataTypes: Array[DataType] = children.map(_.dataType).toArray
 
   // TODO: Finish input output types.
   override def eval(input: InternalRow): Any = {
-    unwrap(
-      FunctionRegistry.invoke(method, function, conversionHelper
-        .convertIfNecessary(wrap(children.map(c => c.eval(input)), arguments, cached): _*): _*),
-      returnInspector)
+    val inputs = wrap(children.map(c => c.eval(input)), arguments, cached, inputDataTypes)
+    val ret = FunctionRegistry.invoke(
+      method,
+      function,
+      conversionHelper.convertIfNecessary(inputs : _*): _*)
+    unwrap(ret, returnInspector)
   }
 
   override def toString: String = {
@@ -135,47 +138,48 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
 }
 
 // Adapter from Catalyst ExpressionResult to Hive DeferredObject
-private[hive] class DeferredObjectAdapter(oi: ObjectInspector)
+private[hive] class DeferredObjectAdapter(oi: ObjectInspector, dataType: DataType)
   extends DeferredObject with HiveInspectors {
+
   private var func: () => Any = _
   def set(func: () => Any): Unit = {
     this.func = func
   }
   override def prepare(i: Int): Unit = {}
-  override def get(): AnyRef = wrap(func(), oi)
+  override def get(): AnyRef = wrap(func(), oi, dataType)
 }
 
 private[hive] case class HiveGenericUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
   extends Expression with HiveInspectors with CodegenFallback with Logging {
-  type UDFType = GenericUDF
+
+  override def nullable: Boolean = true
 
   override def deterministic: Boolean = isUDFDeterministic
 
-  override def nullable: Boolean = true
+  override def foldable: Boolean =
+    isUDFDeterministic && returnInspector.isInstanceOf[ConstantObjectInspector]
 
   @transient
-  lazy val function = funcWrapper.createFunction[UDFType]()
+  lazy val function = funcWrapper.createFunction[GenericUDF]()
 
   @transient
-  protected lazy val argumentInspectors = children.map(toInspector)
+  private lazy val argumentInspectors = children.map(toInspector)
 
   @transient
-  protected lazy val returnInspector = {
+  private lazy val returnInspector = {
     function.initializeAndFoldConstants(argumentInspectors.toArray)
   }
 
   @transient
-  protected lazy val isUDFDeterministic = {
+  private lazy val isUDFDeterministic = {
     val udfType = function.getClass.getAnnotation(classOf[HiveUDFType])
     udfType != null && udfType.deterministic()
   }
 
-  override def foldable: Boolean =
-    isUDFDeterministic && returnInspector.isInstanceOf[ConstantObjectInspector]
-
   @transient
-  protected lazy val deferedObjects =
-    argumentInspectors.map(new DeferredObjectAdapter(_)).toArray[DeferredObject]
+  private lazy val deferedObjects = argumentInspectors.zip(children).map { case (inspect, child) =>
+    new DeferredObjectAdapter(inspect, child.dataType)
+  }.toArray[DeferredObject]
 
   lazy val dataType: DataType = inspectorToDataType(returnInspector)
 
@@ -354,6 +358,9 @@ private[hive] case class HiveWindowFunction(
   // Output buffer.
   private var outputBuffer: Any = _
 
+  @transient
+  private lazy val inputDataTypes: Array[DataType] = children.map(_.dataType).toArray
+
   override def init(): Unit = {
     evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors)
   }
@@ -368,8 +375,13 @@ private[hive] case class HiveWindowFunction(
   }
 
   override def prepareInputParameters(input: InternalRow): AnyRef = {
-    wrap(inputProjection(input), inputInspectors, new Array[AnyRef](children.length))
+    wrap(
+      inputProjection(input),
+      inputInspectors,
+      new Array[AnyRef](children.length),
+      inputDataTypes)
   }
+
   // Add input parameters for a single row.
   override def update(input: AnyRef): Unit = {
     evaluator.iterate(hiveEvaluatorBuffer, input.asInstanceOf[Array[AnyRef]])
@@ -510,12 +522,15 @@ private[hive] case class HiveGenericUDTF(
     field => (inspectorToDataType(field.getFieldObjectInspector), true)
   }
 
+  @transient
+  private lazy val inputDataTypes: Array[DataType] = children.map(_.dataType).toArray
+
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
     outputInspector // Make sure initialized.
 
     val inputProjection = new InterpretedProjection(children)
 
-    function.process(wrap(inputProjection(input), inputInspectors, udtInput))
+    function.process(wrap(inputProjection(input), inputInspectors, udtInput, inputDataTypes))
     collector.collectRows()
   }
 
@@ -584,9 +599,12 @@ private[hive] case class HiveUDAFFunction(
   @transient
   protected lazy val cached = new Array[AnyRef](exprs.length)
 
+  @transient
+  private lazy val inputDataTypes: Array[DataType] = exprs.map(_.dataType).toArray
+
   def update(input: InternalRow): Unit = {
     val inputs = inputProjection(input)
-    function.iterate(buffer, wrap(inputs, inspectors, cached))
+    function.iterate(buffer, wrap(inputs, inspectors, cached, inputDataTypes))
   }
 }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 8bb498a06fc9e..0330013f5325e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -48,7 +48,11 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       ObjectInspectorOptions.JAVA).asInstanceOf[StructObjectInspector]
 
     val a = unwrap(state, soi).asInstanceOf[InternalRow]
-    val b = wrap(a, soi).asInstanceOf[UDAFPercentile.State]
+
+    val dt = new StructType()
+      .add("counts", MapType(LongType, LongType))
+      .add("percentiles", ArrayType(DoubleType))
+    val b = wrap(a, soi, dt).asInstanceOf[UDAFPercentile.State]
 
     val sfCounts = soi.getStructFieldRef("counts")
     val sfPercentiles = soi.getStructFieldRef("percentiles")
@@ -158,44 +162,45 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     val writableOIs = dataTypes.map(toWritableInspector)
     val nullRow = data.map(d => null)
 
-    checkValues(nullRow, nullRow.zip(writableOIs).map {
-      case (d, oi) => unwrap(wrap(d, oi), oi)
+    checkValues(nullRow, nullRow.zip(writableOIs).zip(dataTypes).map {
+      case ((d, oi), dt) => unwrap(wrap(d, oi, dt), oi)
     })
 
     // struct couldn't be constant, sweep it out
     val constantExprs = data.filter(!_.dataType.isInstanceOf[StructType])
+    val constantTypes = constantExprs.map(_.dataType)
     val constantData = constantExprs.map(_.eval())
     val constantNullData = constantData.map(_ => null)
     val constantWritableOIs = constantExprs.map(e => toWritableInspector(e.dataType))
     val constantNullWritableOIs =
       constantExprs.map(e => toInspector(Literal.create(null, e.dataType)))
 
-    checkValues(constantData, constantData.zip(constantWritableOIs).map {
-      case (d, oi) => unwrap(wrap(d, oi), oi)
+    checkValues(constantData, constantData.zip(constantWritableOIs).zip(constantTypes).map {
+      case ((d, oi), dt) => unwrap(wrap(d, oi, dt), oi)
     })
 
-    checkValues(constantNullData, constantData.zip(constantNullWritableOIs).map {
-      case (d, oi) => unwrap(wrap(d, oi), oi)
+    checkValues(constantNullData, constantData.zip(constantNullWritableOIs).zip(constantTypes).map {
+      case ((d, oi), dt) => unwrap(wrap(d, oi, dt), oi)
     })
 
-    checkValues(constantNullData, constantNullData.zip(constantWritableOIs).map {
-      case (d, oi) => unwrap(wrap(d, oi), oi)
+    checkValues(constantNullData, constantNullData.zip(constantWritableOIs).zip(constantTypes).map {
+      case ((d, oi), dt) => unwrap(wrap(d, oi, dt), oi)
     })
   }
 
   test("wrap / unwrap primitive writable object inspector") {
     val writableOIs = dataTypes.map(toWritableInspector)
 
-    checkValues(row, row.zip(writableOIs).map {
-      case (data, oi) => unwrap(wrap(data, oi), oi)
+    checkValues(row, row.zip(writableOIs).zip(dataTypes).map {
+      case ((data, oi), dt) => unwrap(wrap(data, oi, dt), oi)
     })
   }
 
   test("wrap / unwrap primitive java object inspector") {
     val ois = dataTypes.map(toInspector)
 
-    checkValues(row, row.zip(ois).map {
-      case (data, oi) => unwrap(wrap(data, oi), oi)
+    checkValues(row, row.zip(ois).zip(dataTypes).map {
+      case ((data, oi), dt) => unwrap(wrap(data, oi, dt), oi)
     })
   }
 
@@ -205,31 +210,33 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     })
     val inspector = toInspector(dt)
     checkValues(row,
-      unwrap(wrap(InternalRow.fromSeq(row), inspector), inspector).asInstanceOf[InternalRow])
-    checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt)))
+      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow])
+    checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
   }
 
   test("wrap / unwrap Array Type") {
     val dt = ArrayType(dataTypes(0))
 
     val d = row(0) :: row(0) :: Nil
-    checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt)))
-    checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt)))
+    checkValue(d, unwrap(wrap(d, toInspector(dt), dt), toInspector(dt)))
+    checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
     checkValue(d,
-      unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt))))
+      unwrap(wrap(d, toInspector(Literal.create(d, dt)), dt), toInspector(Literal.create(d, dt))))
     checkValue(d,
-      unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt))))
+      unwrap(wrap(null, toInspector(Literal.create(d, dt)), dt),
+        toInspector(Literal.create(d, dt))))
   }
 
   test("wrap / unwrap Map Type") {
     val dt = MapType(dataTypes(0), dataTypes(1))
 
     val d = Map(row(0) -> row(1))
-    checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt)))
-    checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt)))
+    checkValue(d, unwrap(wrap(d, toInspector(dt), dt), toInspector(dt)))
+    checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
     checkValue(d,
-      unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt))))
+      unwrap(wrap(d, toInspector(Literal.create(d, dt)), dt), toInspector(Literal.create(d, dt))))
     checkValue(d,
-      unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt))))
+      unwrap(wrap(null, toInspector(Literal.create(d, dt)), dt),
+        toInspector(Literal.create(d, dt))))
   }
 }

From fb5d43fb2529d78d55f1fe8d365191c946153640 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sun, 26 Jul 2015 10:29:22 -0700
Subject: [PATCH 0605/1454] [SPARK-9356][SQL]Remove the internal use of
 DecimalType.Unlimited

JIRA: https://issues.apache.org/jira/browse/SPARK-9356

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7671 from yjshen/deprecated_unlimit and squashes the following commits:

c707f56 [Yijie Shen] remove pattern matching in changePrecision
4a1823c [Yijie Shen] remove internal occurrence of Decimal.Unlimited
---
 .../spark/sql/catalyst/expressions/Cast.scala | 22 +++++++------------
 .../expressions/NullFunctionsSuite.scala      |  2 +-
 .../datasources/PartitioningUtils.scala       |  3 +--
 3 files changed, 10 insertions(+), 17 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index e5b83cd31bf0f..e208262da96dc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -507,20 +507,14 @@ case class Cast(child: Expression, dataType: DataType)
   }
 
   private[this] def changePrecision(d: String, decimalType: DecimalType,
-      evPrim: String, evNull: String): String = {
-    decimalType match {
-      case DecimalType.Unlimited =>
-        s"$evPrim = $d;"
-      case DecimalType.Fixed(precision, scale) =>
-        s"""
-          if ($d.changePrecision($precision, $scale)) {
-            $evPrim = $d;
-          } else {
-            $evNull = true;
-          }
-        """
-    }
-  }
+      evPrim: String, evNull: String): String =
+    s"""
+      if ($d.changePrecision(${decimalType.precision}, ${decimalType.scale})) {
+        $evPrim = $d;
+      } else {
+        $evNull = true;
+      }
+    """
 
   private[this] def castToDecimalCode(from: DataType, target: DecimalType): CastFunction = {
     from match {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
index 9efe44c83293d..ace6c15dc8418 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
@@ -92,7 +92,7 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     val nullOnly = Seq(Literal("x"),
       Literal.create(null, DoubleType),
-      Literal.create(null, DecimalType.Unlimited),
+      Literal.create(null, DecimalType.USER_DEFAULT),
       Literal(Float.MaxValue),
       Literal(false))
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
index 9d0fa894b9942..66dfcc308ceca 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
@@ -179,8 +179,7 @@ private[sql] object PartitioningUtils {
    * {{{
    *   NullType ->
    *   IntegerType -> LongType ->
-   *   DoubleType -> DecimalType.Unlimited ->
-   *   StringType
+   *   DoubleType -> StringType
    * }}}
    */
   private[sql] def resolvePartitions(

From 1cf19760d61a5a17bd175a906d34a2940141b76d Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Sun, 26 Jul 2015 13:03:13 -0700
Subject: [PATCH 0606/1454] [SPARK-9352] [SPARK-9353] Add tests for standalone
 scheduling code

This also fixes a small issue in the standalone Master that was uncovered by the new tests. For more detail, read the description of SPARK-9353.

Author: Andrew Or <andrew@databricks.com>

Closes #7668 from andrewor14/standalone-scheduling-tests and squashes the following commits:

d852faf [Andrew Or] Add tests + fix scheduling with memory limits
---
 .../apache/spark/deploy/master/Master.scala   |   8 +-
 .../spark/deploy/master/MasterSuite.scala     | 199 +++++++++++++++++-
 2 files changed, 202 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 029f94d1020be..51b3f0dead73e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -559,7 +559,7 @@ private[master] class Master(
    * allocated at a time, 12 cores from each worker would be assigned to each executor.
    * Since 12 < 16, no executors would launch [SPARK-8881].
    */
-  private[master] def scheduleExecutorsOnWorkers(
+  private def scheduleExecutorsOnWorkers(
       app: ApplicationInfo,
       usableWorkers: Array[WorkerInfo],
       spreadOutApps: Boolean): Array[Int] = {
@@ -585,7 +585,11 @@ private[master] class Master(
         while (keepScheduling && canLaunchExecutor(pos) && coresToAssign >= coresPerExecutor) {
           coresToAssign -= coresPerExecutor
           assignedCores(pos) += coresPerExecutor
-          assignedMemory(pos) += memoryPerExecutor
+          // If cores per executor is not set, we are assigning 1 core at a time
+          // without actually meaning to launch 1 executor for each core assigned
+          if (app.desc.coresPerExecutor.isDefined) {
+            assignedMemory(pos) += memoryPerExecutor
+          }
 
           // Spreading out an application means spreading out its executors across as
           // many workers as possible. If we are not spreading out, then we should keep
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index a8fbaf1d9da0a..4d7016d1e594b 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -25,14 +25,15 @@ import scala.language.postfixOps
 
 import org.json4s._
 import org.json4s.jackson.JsonMethods._
-import org.scalatest.Matchers
+import org.scalatest.{Matchers, PrivateMethodTester}
 import org.scalatest.concurrent.Eventually
 import other.supplier.{CustomPersistenceEngine, CustomRecoveryModeFactory}
 
-import org.apache.spark.{SparkConf, SparkFunSuite}
+import org.apache.spark.{SecurityManager, SparkConf, SparkFunSuite}
 import org.apache.spark.deploy._
+import org.apache.spark.rpc.RpcEnv
 
-class MasterSuite extends SparkFunSuite with Matchers with Eventually {
+class MasterSuite extends SparkFunSuite with Matchers with Eventually with PrivateMethodTester {
 
   test("can use a custom recovery mode factory") {
     val conf = new SparkConf(loadDefaults = false)
@@ -142,4 +143,196 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually {
     }
   }
 
+  test("basic scheduling - spread out") {
+    testBasicScheduling(spreadOut = true)
+  }
+
+  test("basic scheduling - no spread out") {
+    testBasicScheduling(spreadOut = false)
+  }
+
+  test("scheduling with max cores - spread out") {
+    testSchedulingWithMaxCores(spreadOut = true)
+  }
+
+  test("scheduling with max cores - no spread out") {
+    testSchedulingWithMaxCores(spreadOut = false)
+  }
+
+  test("scheduling with cores per executor - spread out") {
+    testSchedulingWithCoresPerExecutor(spreadOut = true)
+  }
+
+  test("scheduling with cores per executor - no spread out") {
+    testSchedulingWithCoresPerExecutor(spreadOut = false)
+  }
+
+  test("scheduling with cores per executor AND max cores - spread out") {
+    testSchedulingWithCoresPerExecutorAndMaxCores(spreadOut = true)
+  }
+
+  test("scheduling with cores per executor AND max cores - no spread out") {
+    testSchedulingWithCoresPerExecutorAndMaxCores(spreadOut = false)
+  }
+
+  private def testBasicScheduling(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo = makeAppInfo(1024)
+    val workerInfo = makeWorkerInfo(4096, 10)
+    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
+    val scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    assert(scheduledCores(0) === 10)
+    assert(scheduledCores(1) === 10)
+    assert(scheduledCores(2) === 10)
+  }
+
+  private def testSchedulingWithMaxCores(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo1 = makeAppInfo(1024, maxCores = Some(8))
+    val appInfo2 = makeAppInfo(1024, maxCores = Some(16))
+    val workerInfo = makeWorkerInfo(4096, 10)
+    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
+    var scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo1, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    // With spreading out, each worker should be assigned a few cores
+    if (spreadOut) {
+      assert(scheduledCores(0) === 3)
+      assert(scheduledCores(1) === 3)
+      assert(scheduledCores(2) === 2)
+    } else {
+      // Without spreading out, the cores should be concentrated on the first worker
+      assert(scheduledCores(0) === 8)
+      assert(scheduledCores(1) === 0)
+      assert(scheduledCores(2) === 0)
+    }
+    // Now test the same thing with max cores > cores per worker
+    scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo2, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    if (spreadOut) {
+      assert(scheduledCores(0) === 6)
+      assert(scheduledCores(1) === 5)
+      assert(scheduledCores(2) === 5)
+    } else {
+      // Without spreading out, the first worker should be fully booked,
+      // and the leftover cores should spill over to the second worker only.
+      assert(scheduledCores(0) === 10)
+      assert(scheduledCores(1) === 6)
+      assert(scheduledCores(2) === 0)
+    }
+  }
+
+  private def testSchedulingWithCoresPerExecutor(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo1 = makeAppInfo(1024, coresPerExecutor = Some(2))
+    val appInfo2 = makeAppInfo(256, coresPerExecutor = Some(2))
+    val appInfo3 = makeAppInfo(256, coresPerExecutor = Some(3))
+    val workerInfo = makeWorkerInfo(4096, 10)
+    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
+    // Each worker should end up with 4 executors with 2 cores each
+    // This should be 4 because of the memory restriction on each worker
+    var scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo1, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    assert(scheduledCores(0) === 8)
+    assert(scheduledCores(1) === 8)
+    assert(scheduledCores(2) === 8)
+    // Now test the same thing without running into the worker memory limit
+    // Each worker should now end up with 5 executors with 2 cores each
+    scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo2, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    assert(scheduledCores(0) === 10)
+    assert(scheduledCores(1) === 10)
+    assert(scheduledCores(2) === 10)
+    // Now test the same thing with a cores per executor that 10 is not divisible by
+    scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo3, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    assert(scheduledCores(0) === 9)
+    assert(scheduledCores(1) === 9)
+    assert(scheduledCores(2) === 9)
+  }
+
+  // Sorry for the long method name!
+  private def testSchedulingWithCoresPerExecutorAndMaxCores(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo1 = makeAppInfo(256, coresPerExecutor = Some(2), maxCores = Some(4))
+    val appInfo2 = makeAppInfo(256, coresPerExecutor = Some(2), maxCores = Some(20))
+    val appInfo3 = makeAppInfo(256, coresPerExecutor = Some(3), maxCores = Some(20))
+    val workerInfo = makeWorkerInfo(4096, 10)
+    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
+    // We should only launch two executors, each with exactly 2 cores
+    var scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo1, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    if (spreadOut) {
+      assert(scheduledCores(0) === 2)
+      assert(scheduledCores(1) === 2)
+      assert(scheduledCores(2) === 0)
+    } else {
+      assert(scheduledCores(0) === 4)
+      assert(scheduledCores(1) === 0)
+      assert(scheduledCores(2) === 0)
+    }
+    // Test max cores > number of cores per worker
+    scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo2, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    if (spreadOut) {
+      assert(scheduledCores(0) === 8)
+      assert(scheduledCores(1) === 6)
+      assert(scheduledCores(2) === 6)
+    } else {
+      assert(scheduledCores(0) === 10)
+      assert(scheduledCores(1) === 10)
+      assert(scheduledCores(2) === 0)
+    }
+    // Test max cores > number of cores per worker AND
+    // a cores per executor that is 10 is not divisible by
+    scheduledCores = master.invokePrivate(
+      _scheduleExecutorsOnWorkers(appInfo3, workerInfos, spreadOut))
+    assert(scheduledCores.length === 3)
+    if (spreadOut) {
+      assert(scheduledCores(0) === 6)
+      assert(scheduledCores(1) === 6)
+      assert(scheduledCores(2) === 6)
+    } else {
+      assert(scheduledCores(0) === 9)
+      assert(scheduledCores(1) === 9)
+      assert(scheduledCores(2) === 0)
+    }
+  }
+
+  // ===============================
+  // | Utility methods for testing |
+  // ===============================
+
+  private val _scheduleExecutorsOnWorkers = PrivateMethod[Array[Int]]('scheduleExecutorsOnWorkers)
+
+  private def makeMaster(conf: SparkConf = new SparkConf): Master = {
+    val securityMgr = new SecurityManager(conf)
+    val rpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 7077, conf, securityMgr)
+    val master = new Master(rpcEnv, rpcEnv.address, 8080, securityMgr, conf)
+    master
+  }
+
+  private def makeAppInfo(
+      memoryPerExecutorMb: Int,
+      coresPerExecutor: Option[Int] = None,
+      maxCores: Option[Int] = None): ApplicationInfo = {
+    val desc = new ApplicationDescription(
+      "test", maxCores, memoryPerExecutorMb, null, "", None, None, coresPerExecutor)
+    val appId = System.currentTimeMillis.toString
+    new ApplicationInfo(0, appId, desc, new Date, null, Int.MaxValue)
+  }
+
+  private def makeWorkerInfo(memoryMb: Int, cores: Int): WorkerInfo = {
+    val workerId = System.currentTimeMillis.toString
+    new WorkerInfo(workerId, "host", 100, cores, memoryMb, null, 101, "address")
+  }
+
 }

From 6b2baec04fa3d928f0ee84af8c2723ac03a4648c Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Sun, 26 Jul 2015 13:35:16 -0700
Subject: [PATCH 0607/1454] [SPARK-9326] Close lock file used for file
 downloads.

A lock file is used to ensure multiple executors running on the
same machine don't download the same file concurrently. Spark never
closes these lock files (releasing the lock does not close the
underlying file); this commit fixes that.

cc vanzin (looks like you've been involved in various other fixes surrounding these lock files)

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #7650 from kayousterhout/SPARK-9326 and squashes the following commits:

0401bd1 [Kay Ousterhout] Close lock file used for file downloads.
---
 core/src/main/scala/org/apache/spark/util/Utils.scala | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index c5816949cd360..c4012d0e83f7d 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -443,11 +443,11 @@ private[spark] object Utils extends Logging {
       val lockFileName = s"${url.hashCode}${timestamp}_lock"
       val localDir = new File(getLocalDir(conf))
       val lockFile = new File(localDir, lockFileName)
-      val raf = new RandomAccessFile(lockFile, "rw")
+      val lockFileChannel = new RandomAccessFile(lockFile, "rw").getChannel()
       // Only one executor entry.
       // The FileLock is only used to control synchronization for executors download file,
       // it's always safe regardless of lock type (mandatory or advisory).
-      val lock = raf.getChannel().lock()
+      val lock = lockFileChannel.lock()
       val cachedFile = new File(localDir, cachedFileName)
       try {
         if (!cachedFile.exists()) {
@@ -455,6 +455,7 @@ private[spark] object Utils extends Logging {
         }
       } finally {
         lock.release()
+        lockFileChannel.close()
       }
       copyFile(
         url,

From c025c3d0a1fdfbc45b64db9c871176b40b4a7b9b Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sun, 26 Jul 2015 16:49:19 -0700
Subject: [PATCH 0608/1454] [SPARK-9095] [SQL] Removes the old Parquet support

This PR removes the old Parquet support:

- Removes the old `ParquetRelation` together with related SQL configuration, plan nodes, strategies, utility classes, and test suites.

- Renames `ParquetRelation2` to `ParquetRelation`

- Renames `RowReadSupport` and `RowRecordMaterializer` to `CatalystReadSupport` and `CatalystRecordMaterializer` respectively, and moved them to separate files.

  This follows naming convention used in other Parquet data models implemented in parquet-mr. It should be easier for developers who are familiar with Parquet to follow.

There's still some other code that can be cleaned up. Especially `RowWriteSupport`. But I'd like to leave this part to SPARK-8848.

Author: Cheng Lian <lian@databricks.com>

Closes #7441 from liancheng/spark-9095 and squashes the following commits:

c7b6e38 [Cheng Lian] Removes WriteToFile
2d688d6 [Cheng Lian] Renames ParquetRelation2 to ParquetRelation
ca9e1b7 [Cheng Lian] Removes old Parquet support
---
 .../plans/logical/basicOperators.scala        |   6 -
 .../org/apache/spark/sql/DataFrame.scala      |   9 +-
 .../apache/spark/sql/DataFrameReader.scala    |   8 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |   6 -
 .../org/apache/spark/sql/SQLContext.scala     |   6 +-
 .../spark/sql/execution/SparkStrategies.scala |  58 +-
 .../sql/parquet/CatalystReadSupport.scala     | 153 ++++
 .../parquet/CatalystRecordMaterializer.scala  |  41 +
 .../sql/parquet/CatalystSchemaConverter.scala |   5 +
 .../spark/sql/parquet/ParquetConverter.scala  |   1 +
 .../spark/sql/parquet/ParquetRelation.scala   | 843 +++++++++++++++---
 .../sql/parquet/ParquetTableOperations.scala  | 492 ----------
 .../sql/parquet/ParquetTableSupport.scala     | 151 +---
 .../spark/sql/parquet/ParquetTypes.scala      |  42 +-
 .../apache/spark/sql/parquet/newParquet.scala | 732 ---------------
 .../sql/parquet/ParquetFilterSuite.scala      |  65 +-
 .../spark/sql/parquet/ParquetIOSuite.scala    |  37 +-
 .../ParquetPartitionDiscoverySuite.scala      |   2 +-
 .../spark/sql/parquet/ParquetQuerySuite.scala |  27 +-
 .../sql/parquet/ParquetSchemaSuite.scala      |  12 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 -
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  22 +-
 .../spark/sql/hive/HiveStrategies.scala       | 141 +--
 .../spark/sql/hive/HiveParquetSuite.scala     |  86 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  14 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |  54 +-
 .../apache/spark/sql/hive/parquetSuites.scala | 174 +---
 27 files changed, 1037 insertions(+), 2152 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 8e1a236e2988c..af68358daf5f1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -186,12 +186,6 @@ case class WithWindowDefinition(
   override def output: Seq[Attribute] = child.output
 }
 
-case class WriteToFile(
-    path: String,
-    child: LogicalPlan) extends UnaryNode {
-  override def output: Seq[Attribute] = child.output
-}
-
 /**
  * @param order  The ordering expressions
  * @param global True means global sorting apply for entire data set,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index fa942a1f8fd93..114ab91d10aa0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -139,8 +139,7 @@ class DataFrame private[sql](
     // happen right away to let these side effects take place eagerly.
     case _: Command |
          _: InsertIntoTable |
-         _: CreateTableUsingAsSelect |
-         _: WriteToFile =>
+         _: CreateTableUsingAsSelect =>
       LogicalRDD(queryExecution.analyzed.output, queryExecution.toRdd)(sqlContext)
     case _ =>
       queryExecution.analyzed
@@ -1615,11 +1614,7 @@ class DataFrame private[sql](
    */
   @deprecated("Use write.parquet(path)", "1.4.0")
   def saveAsParquetFile(path: String): Unit = {
-    if (sqlContext.conf.parquetUseDataSourceApi) {
-      write.format("parquet").mode(SaveMode.ErrorIfExists).save(path)
-    } else {
-      sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd
-    }
+    write.format("parquet").mode(SaveMode.ErrorIfExists).save(path)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index e9d782cdcd667..eb09807f9d9c2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -21,16 +21,16 @@ import java.util.Properties
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.{Logging, Partition}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
+import org.apache.spark.sql.execution.datasources.{LogicalRelation, ResolvedDataSource}
 import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation}
 import org.apache.spark.sql.json.JSONRelation
-import org.apache.spark.sql.parquet.ParquetRelation2
+import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.types.StructType
+import org.apache.spark.{Logging, Partition}
 
 /**
  * :: Experimental ::
@@ -259,7 +259,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
       }.toArray
 
       sqlContext.baseRelationToDataFrame(
-        new ParquetRelation2(
+        new ParquetRelation(
           globbedPaths.map(_.toString), None, None, extraOptions.toMap)(sqlContext))
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2a641b9d64a95..9b2dbd7442f5c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -276,10 +276,6 @@ private[spark] object SQLConf {
     defaultValue = Some(true),
     doc = "Enables Parquet filter push-down optimization when set to true.")
 
-  val PARQUET_USE_DATA_SOURCE_API = booleanConf("spark.sql.parquet.useDataSourceApi",
-    defaultValue = Some(true),
-    doc = "<TODO>")
-
   val PARQUET_FOLLOW_PARQUET_FORMAT_SPEC = booleanConf(
     key = "spark.sql.parquet.followParquetFormatSpec",
     defaultValue = Some(false),
@@ -456,8 +452,6 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def parquetFilterPushDown: Boolean = getConf(PARQUET_FILTER_PUSHDOWN_ENABLED)
 
-  private[spark] def parquetUseDataSourceApi: Boolean = getConf(PARQUET_USE_DATA_SOURCE_API)
-
   private[spark] def orcFilterPushDown: Boolean = getConf(ORC_FILTER_PUSHDOWN_ENABLED)
 
   private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITION_PATH)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 49bfe74b680af..0e25e06e99ab2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -870,7 +870,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
       LeftSemiJoin ::
       HashJoin ::
       InMemoryScans ::
-      ParquetOperations ::
       BasicOperators ::
       CartesianProduct ::
       BroadcastNestedLoopJoin :: Nil)
@@ -1115,11 +1114,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
   def parquetFile(paths: String*): DataFrame = {
     if (paths.isEmpty) {
       emptyDataFrame
-    } else if (conf.parquetUseDataSourceApi) {
-      read.parquet(paths : _*)
     } else {
-      DataFrame(this, parquet.ParquetRelation(
-        paths.mkString(","), Some(sparkContext.hadoopConfiguration), this))
+      read.parquet(paths : _*)
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index eb4be1900b153..e2c7e8006f3b1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -17,19 +17,18 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.sql.{SQLContext, Strategy, execution}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2}
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.{BroadcastHint, LogicalPlan}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation}
-import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsing, CreateTempTableUsing, DescribeCommand => LogicalDescribeCommand, _}
-import org.apache.spark.sql.parquet._
+import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand}
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.{SQLContext, Strategy, execution}
 
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   self: SQLContext#SparkPlanner =>
@@ -306,57 +305,6 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
-  object ParquetOperations extends Strategy {
-    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      // TODO: need to support writing to other types of files.  Unify the below code paths.
-      case logical.WriteToFile(path, child) =>
-        val relation =
-          ParquetRelation.create(path, child, sparkContext.hadoopConfiguration, sqlContext)
-        // Note: overwrite=false because otherwise the metadata we just created will be deleted
-        InsertIntoParquetTable(relation, planLater(child), overwrite = false) :: Nil
-      case logical.InsertIntoTable(
-          table: ParquetRelation, partition, child, overwrite, ifNotExists) =>
-        InsertIntoParquetTable(table, planLater(child), overwrite) :: Nil
-      case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) =>
-        val partitionColNames = relation.partitioningAttributes.map(_.name).toSet
-        val filtersToPush = filters.filter { pred =>
-            val referencedColNames = pred.references.map(_.name).toSet
-            referencedColNames.intersect(partitionColNames).isEmpty
-          }
-        val prunePushedDownFilters =
-          if (sqlContext.conf.parquetFilterPushDown) {
-            (predicates: Seq[Expression]) => {
-              // Note: filters cannot be pushed down to Parquet if they contain more complex
-              // expressions than simple "Attribute cmp Literal" comparisons. Here we remove all
-              // filters that have been pushed down. Note that a predicate such as "(A AND B) OR C"
-              // can result in "A OR C" being pushed down. Here we are conservative in the sense
-              // that even if "A" was pushed and we check for "A AND B" we still want to keep
-              // "A AND B" in the higher-level filter, not just "B".
-              predicates.map(p => p -> ParquetFilters.createFilter(p)).collect {
-                case (predicate, None) => predicate
-                // Filter needs to be applied above when it contains partitioning
-                // columns
-                case (predicate, _)
-                  if !predicate.references.map(_.name).toSet.intersect(partitionColNames).isEmpty =>
-                  predicate
-              }
-            }
-          } else {
-            identity[Seq[Expression]] _
-          }
-        pruneFilterProject(
-          projectList,
-          filters,
-          prunePushedDownFilters,
-          ParquetTableScan(
-            _,
-            relation,
-            if (sqlContext.conf.parquetFilterPushDown) filtersToPush else Nil)) :: Nil
-
-      case _ => Nil
-    }
-  }
-
   object InMemoryScans extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case PhysicalOperation(projectList, filters, mem: InMemoryRelation) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala
new file mode 100644
index 0000000000000..975fec101d9c2
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala
@@ -0,0 +1,153 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import java.util.{Map => JMap}
+
+import scala.collection.JavaConversions.{iterableAsScalaIterable, mapAsJavaMap, mapAsScalaMap}
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
+import org.apache.parquet.hadoop.api.{InitContext, ReadSupport}
+import org.apache.parquet.io.api.RecordMaterializer
+import org.apache.parquet.schema.MessageType
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types.StructType
+
+private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with Logging {
+  override def prepareForRead(
+      conf: Configuration,
+      keyValueMetaData: JMap[String, String],
+      fileSchema: MessageType,
+      readContext: ReadContext): RecordMaterializer[InternalRow] = {
+    log.debug(s"Preparing for read Parquet file with message type: $fileSchema")
+
+    val toCatalyst = new CatalystSchemaConverter(conf)
+    val parquetRequestedSchema = readContext.getRequestedSchema
+
+    val catalystRequestedSchema =
+      Option(readContext.getReadSupportMetadata).map(_.toMap).flatMap { metadata =>
+        metadata
+          // First tries to read requested schema, which may result from projections
+          .get(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
+          // If not available, tries to read Catalyst schema from file metadata.  It's only
+          // available if the target file is written by Spark SQL.
+          .orElse(metadata.get(CatalystReadSupport.SPARK_METADATA_KEY))
+      }.map(StructType.fromString).getOrElse {
+        logDebug("Catalyst schema not available, falling back to Parquet schema")
+        toCatalyst.convert(parquetRequestedSchema)
+      }
+
+    logDebug(s"Catalyst schema used to read Parquet files: $catalystRequestedSchema")
+    new CatalystRecordMaterializer(parquetRequestedSchema, catalystRequestedSchema)
+  }
+
+  override def init(context: InitContext): ReadContext = {
+    val conf = context.getConfiguration
+
+    // If the target file was written by Spark SQL, we should be able to find a serialized Catalyst
+    // schema of this file from its the metadata.
+    val maybeRowSchema = Option(conf.get(RowWriteSupport.SPARK_ROW_SCHEMA))
+
+    // Optional schema of requested columns, in the form of a string serialized from a Catalyst
+    // `StructType` containing all requested columns.
+    val maybeRequestedSchema = Option(conf.get(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA))
+
+    // Below we construct a Parquet schema containing all requested columns.  This schema tells
+    // Parquet which columns to read.
+    //
+    // If `maybeRequestedSchema` is defined, we assemble an equivalent Parquet schema.  Otherwise,
+    // we have to fallback to the full file schema which contains all columns in the file.
+    // Obviously this may waste IO bandwidth since it may read more columns than requested.
+    //
+    // Two things to note:
+    //
+    // 1. It's possible that some requested columns don't exist in the target Parquet file.  For
+    //    example, in the case of schema merging, the globally merged schema may contain extra
+    //    columns gathered from other Parquet files.  These columns will be simply filled with nulls
+    //    when actually reading the target Parquet file.
+    //
+    // 2. When `maybeRequestedSchema` is available, we can't simply convert the Catalyst schema to
+    //    Parquet schema using `CatalystSchemaConverter`, because the mapping is not unique due to
+    //    non-standard behaviors of some Parquet libraries/tools.  For example, a Parquet file
+    //    containing a single integer array field `f1` may have the following legacy 2-level
+    //    structure:
+    //
+    //      message root {
+    //        optional group f1 (LIST) {
+    //          required INT32 element;
+    //        }
+    //      }
+    //
+    //    while `CatalystSchemaConverter` may generate a standard 3-level structure:
+    //
+    //      message root {
+    //        optional group f1 (LIST) {
+    //          repeated group list {
+    //            required INT32 element;
+    //          }
+    //        }
+    //      }
+    //
+    //    Apparently, we can't use the 2nd schema to read the target Parquet file as they have
+    //    different physical structures.
+    val parquetRequestedSchema =
+      maybeRequestedSchema.fold(context.getFileSchema) { schemaString =>
+        val toParquet = new CatalystSchemaConverter(conf)
+        val fileSchema = context.getFileSchema.asGroupType()
+        val fileFieldNames = fileSchema.getFields.map(_.getName).toSet
+
+        StructType
+          // Deserializes the Catalyst schema of requested columns
+          .fromString(schemaString)
+          .map { field =>
+            if (fileFieldNames.contains(field.name)) {
+              // If the field exists in the target Parquet file, extracts the field type from the
+              // full file schema and makes a single-field Parquet schema
+              new MessageType("root", fileSchema.getType(field.name))
+            } else {
+              // Otherwise, just resorts to `CatalystSchemaConverter`
+              toParquet.convert(StructType(Array(field)))
+            }
+          }
+          // Merges all single-field Parquet schemas to form a complete schema for all requested
+          // columns.  Note that it's possible that no columns are requested at all (e.g., count
+          // some partition column of a partitioned Parquet table). That's why `fold` is used here
+          // and always fallback to an empty Parquet schema.
+          .fold(new MessageType("root")) {
+            _ union _
+          }
+      }
+
+    val metadata =
+      Map.empty[String, String] ++
+        maybeRequestedSchema.map(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA -> _) ++
+        maybeRowSchema.map(RowWriteSupport.SPARK_ROW_SCHEMA -> _)
+
+    logInfo(s"Going to read Parquet file with these requested columns: $parquetRequestedSchema")
+    new ReadContext(parquetRequestedSchema, metadata)
+  }
+}
+
+private[parquet] object CatalystReadSupport {
+  val SPARK_ROW_REQUESTED_SCHEMA = "org.apache.spark.sql.parquet.row.requested_schema"
+
+  val SPARK_METADATA_KEY = "org.apache.spark.sql.parquet.row.metadata"
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala
new file mode 100644
index 0000000000000..84f1dccfeb788
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import org.apache.parquet.io.api.{GroupConverter, RecordMaterializer}
+import org.apache.parquet.schema.MessageType
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A [[RecordMaterializer]] for Catalyst rows.
+ *
+ * @param parquetSchema Parquet schema of the records to be read
+ * @param catalystSchema Catalyst schema of the rows to be constructed
+ */
+private[parquet] class CatalystRecordMaterializer(
+    parquetSchema: MessageType, catalystSchema: StructType)
+  extends RecordMaterializer[InternalRow] {
+
+  private val rootConverter = new CatalystRowConverter(parquetSchema, catalystSchema, NoopUpdater)
+
+  override def getCurrentRecord: InternalRow = rootConverter.currentRow
+
+  override def getRootConverter: GroupConverter = rootConverter
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index 1d3a0d15d336e..e9ef01e2dba1b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -570,6 +570,11 @@ private[parquet] object CatalystSchemaConverter {
        """.stripMargin.split("\n").mkString(" "))
   }
 
+  def checkFieldNames(schema: StructType): StructType = {
+    schema.fieldNames.foreach(checkFieldName)
+    schema
+  }
+
   def analysisRequire(f: => Boolean, message: String): Unit = {
     if (!f) {
       throw new AnalysisException(message)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index be0a2029d233b..ea51650fe9039 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.parquet
 
 import org.apache.spark.sql.catalyst.InternalRow
 
+// TODO Removes this while fixing SPARK-8848
 private[sql] object CatalystConverter {
   // This is mostly Parquet convention (see, e.g., `ConversionPatterns`).
   // Note that "array" for the array elements is chosen by ParquetAvro.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 086559e9f7658..cc6fa2b88663f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -17,81 +17,720 @@
 
 package org.apache.spark.sql.parquet
 
-import java.io.IOException
+import java.net.URI
 import java.util.logging.{Level, Logger => JLogger}
+import java.util.{List => JList}
 
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.fs.permission.FsAction
+import scala.collection.JavaConversions._
+import scala.collection.mutable
+import scala.util.{Failure, Try}
+
+import com.google.common.base.Objects
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.io.Writable
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
+import org.apache.parquet.filter2.predicate.FilterApi
 import org.apache.parquet.hadoop.metadata.CompressionCodecName
-import org.apache.parquet.hadoop.{ParquetOutputCommitter, ParquetOutputFormat, ParquetRecordReader}
+import org.apache.parquet.hadoop.util.ContextUtil
+import org.apache.parquet.hadoop.{ParquetOutputCommitter, ParquetRecordReader, _}
 import org.apache.parquet.schema.MessageType
 import org.apache.parquet.{Log => ParquetLog}
 
-import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, UnresolvedException}
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap}
-import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics}
-import org.apache.spark.sql.types.StructType
-import org.apache.spark.sql.{DataFrame, SQLContext}
-import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.RDD._
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.{SqlNewHadoopPartition, SqlNewHadoopRDD}
+import org.apache.spark.sql.execution.datasources.PartitionSpec
+import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types.{DataType, StructType}
+import org.apache.spark.util.{SerializableConfiguration, Utils}
+
+
+private[sql] class DefaultSource extends HadoopFsRelationProvider {
+  override def createRelation(
+      sqlContext: SQLContext,
+      paths: Array[String],
+      schema: Option[StructType],
+      partitionColumns: Option[StructType],
+      parameters: Map[String, String]): HadoopFsRelation = {
+    new ParquetRelation(paths, schema, None, partitionColumns, parameters)(sqlContext)
+  }
+}
+
+// NOTE: This class is instantiated and used on executor side only, no need to be serializable.
+private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext)
+  extends OutputWriterInternal {
+
+  private val recordWriter: RecordWriter[Void, InternalRow] = {
+    val outputFormat = {
+      new ParquetOutputFormat[InternalRow]() {
+        // Here we override `getDefaultWorkFile` for two reasons:
+        //
+        //  1. To allow appending.  We need to generate unique output file names to avoid
+        //     overwriting existing files (either exist before the write job, or are just written
+        //     by other tasks within the same write job).
+        //
+        //  2. To allow dynamic partitioning.  Default `getDefaultWorkFile` uses
+        //     `FileOutputCommitter.getWorkPath()`, which points to the base directory of all
+        //     partitions in the case of dynamic partitioning.
+        override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
+          val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
+          val split = context.getTaskAttemptID.getTaskID.getId
+          new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
+        }
+      }
+    }
+
+    outputFormat.getRecordWriter(context)
+  }
+
+  override def writeInternal(row: InternalRow): Unit = recordWriter.write(null, row)
+
+  override def close(): Unit = recordWriter.close(context)
+}
+
+private[sql] class ParquetRelation(
+    override val paths: Array[String],
+    private val maybeDataSchema: Option[StructType],
+    // This is for metastore conversion.
+    private val maybePartitionSpec: Option[PartitionSpec],
+    override val userDefinedPartitionColumns: Option[StructType],
+    parameters: Map[String, String])(
+    val sqlContext: SQLContext)
+  extends HadoopFsRelation(maybePartitionSpec)
+  with Logging {
+
+  private[sql] def this(
+      paths: Array[String],
+      maybeDataSchema: Option[StructType],
+      maybePartitionSpec: Option[PartitionSpec],
+      parameters: Map[String, String])(
+      sqlContext: SQLContext) = {
+    this(
+      paths,
+      maybeDataSchema,
+      maybePartitionSpec,
+      maybePartitionSpec.map(_.partitionColumns),
+      parameters)(sqlContext)
+  }
+
+  // Should we merge schemas from all Parquet part-files?
+  private val shouldMergeSchemas =
+    parameters
+      .get(ParquetRelation.MERGE_SCHEMA)
+      .map(_.toBoolean)
+      .getOrElse(sqlContext.conf.getConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED))
+
+  private val maybeMetastoreSchema = parameters
+    .get(ParquetRelation.METASTORE_SCHEMA)
+    .map(DataType.fromJson(_).asInstanceOf[StructType])
+
+  private lazy val metadataCache: MetadataCache = {
+    val meta = new MetadataCache
+    meta.refresh()
+    meta
+  }
 
-/**
- * Relation that consists of data stored in a Parquet columnar format.
- *
- * Users should interact with parquet files though a [[DataFrame]], created by a [[SQLContext]]
- * instead of using this class directly.
- *
- * {{{
- *   val parquetRDD = sqlContext.parquetFile("path/to/parquet.file")
- * }}}
- *
- * @param path The path to the Parquet file.
- */
-private[sql] case class ParquetRelation(
-    path: String,
-    @transient conf: Option[Configuration],
-    @transient sqlContext: SQLContext,
-    partitioningAttributes: Seq[Attribute] = Nil)
-  extends LeafNode with MultiInstanceRelation {
-
-  /** Schema derived from ParquetFile */
-  def parquetSchema: MessageType =
-    ParquetTypesConverter
-      .readMetaData(new Path(path), conf)
-      .getFileMetaData
-      .getSchema
-
-  /** Attributes */
-  override val output =
-    partitioningAttributes ++
-    ParquetTypesConverter.readSchemaFromFile(
-      new Path(path.split(",").head),
-      conf,
-      sqlContext.conf.isParquetBinaryAsString,
-      sqlContext.conf.isParquetINT96AsTimestamp)
-  lazy val attributeMap = AttributeMap(output.map(o => o -> o))
-
-  override def newInstance(): this.type = {
-    ParquetRelation(path, conf, sqlContext).asInstanceOf[this.type]
-  }
-
-  // Equals must also take into account the output attributes so that we can distinguish between
-  // different instances of the same relation,
   override def equals(other: Any): Boolean = other match {
-    case p: ParquetRelation =>
-      p.path == path && p.output == output
+    case that: ParquetRelation =>
+      val schemaEquality = if (shouldMergeSchemas) {
+        this.shouldMergeSchemas == that.shouldMergeSchemas
+      } else {
+        this.dataSchema == that.dataSchema &&
+          this.schema == that.schema
+      }
+
+      this.paths.toSet == that.paths.toSet &&
+        schemaEquality &&
+        this.maybeDataSchema == that.maybeDataSchema &&
+        this.partitionColumns == that.partitionColumns
+
     case _ => false
   }
 
-  override def hashCode: Int = {
-    com.google.common.base.Objects.hashCode(path, output)
+  override def hashCode(): Int = {
+    if (shouldMergeSchemas) {
+      Objects.hashCode(
+        Boolean.box(shouldMergeSchemas),
+        paths.toSet,
+        maybeDataSchema,
+        partitionColumns)
+    } else {
+      Objects.hashCode(
+        Boolean.box(shouldMergeSchemas),
+        paths.toSet,
+        dataSchema,
+        schema,
+        maybeDataSchema,
+        partitionColumns)
+    }
+  }
+
+  /** Constraints on schema of dataframe to be stored. */
+  private def checkConstraints(schema: StructType): Unit = {
+    if (schema.fieldNames.length != schema.fieldNames.distinct.length) {
+      val duplicateColumns = schema.fieldNames.groupBy(identity).collect {
+        case (x, ys) if ys.length > 1 => "\"" + x + "\""
+      }.mkString(", ")
+      throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
+        s"cannot save to parquet format")
+    }
+  }
+
+  override def dataSchema: StructType = {
+    val schema = maybeDataSchema.getOrElse(metadataCache.dataSchema)
+    // check if schema satisfies the constraints
+    // before moving forward
+    checkConstraints(schema)
+    schema
+  }
+
+  override private[sql] def refresh(): Unit = {
+    super.refresh()
+    metadataCache.refresh()
+  }
+
+  // Parquet data source always uses Catalyst internal representations.
+  override val needConversion: Boolean = false
+
+  override def sizeInBytes: Long = metadataCache.dataStatuses.map(_.getLen).sum
+
+  override def prepareJobForWrite(job: Job): OutputWriterFactory = {
+    val conf = ContextUtil.getConfiguration(job)
+
+    val committerClass =
+      conf.getClass(
+        SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key,
+        classOf[ParquetOutputCommitter],
+        classOf[ParquetOutputCommitter])
+
+    if (conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key) == null) {
+      logInfo("Using default output committer for Parquet: " +
+        classOf[ParquetOutputCommitter].getCanonicalName)
+    } else {
+      logInfo("Using user defined output committer for Parquet: " + committerClass.getCanonicalName)
+    }
+
+    conf.setClass(
+      SQLConf.OUTPUT_COMMITTER_CLASS.key,
+      committerClass,
+      classOf[ParquetOutputCommitter])
+
+    // We're not really using `ParquetOutputFormat[Row]` for writing data here, because we override
+    // it in `ParquetOutputWriter` to support appending and dynamic partitioning.  The reason why
+    // we set it here is to setup the output committer class to `ParquetOutputCommitter`, which is
+    // bundled with `ParquetOutputFormat[Row]`.
+    job.setOutputFormatClass(classOf[ParquetOutputFormat[Row]])
+
+    // TODO There's no need to use two kinds of WriteSupport
+    // We should unify them. `SpecificMutableRow` can process both atomic (primitive) types and
+    // complex types.
+    val writeSupportClass =
+      if (dataSchema.map(_.dataType).forall(ParquetTypesConverter.isPrimitiveType)) {
+        classOf[MutableRowWriteSupport]
+      } else {
+        classOf[RowWriteSupport]
+      }
+
+    ParquetOutputFormat.setWriteSupportClass(job, writeSupportClass)
+    RowWriteSupport.setSchema(dataSchema.toAttributes, conf)
+
+    // Sets compression scheme
+    conf.set(
+      ParquetOutputFormat.COMPRESSION,
+      ParquetRelation
+        .shortParquetCompressionCodecNames
+        .getOrElse(
+          sqlContext.conf.parquetCompressionCodec.toUpperCase,
+          CompressionCodecName.UNCOMPRESSED).name())
+
+    new OutputWriterFactory {
+      override def newInstance(
+          path: String, dataSchema: StructType, context: TaskAttemptContext): OutputWriter = {
+        new ParquetOutputWriter(path, context)
+      }
+    }
+  }
+
+  override def buildScan(
+      requiredColumns: Array[String],
+      filters: Array[Filter],
+      inputFiles: Array[FileStatus],
+      broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = {
+    val useMetadataCache = sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA)
+    val parquetFilterPushDown = sqlContext.conf.parquetFilterPushDown
+    val assumeBinaryIsString = sqlContext.conf.isParquetBinaryAsString
+    val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
+    val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
+
+    // Create the function to set variable Parquet confs at both driver and executor side.
+    val initLocalJobFuncOpt =
+      ParquetRelation.initializeLocalJobFunc(
+        requiredColumns,
+        filters,
+        dataSchema,
+        useMetadataCache,
+        parquetFilterPushDown,
+        assumeBinaryIsString,
+        assumeInt96IsTimestamp,
+        followParquetFormatSpec) _
+
+    // Create the function to set input paths at the driver side.
+    val setInputPaths = ParquetRelation.initializeDriverSideJobFunc(inputFiles) _
+
+    Utils.withDummyCallSite(sqlContext.sparkContext) {
+      new SqlNewHadoopRDD(
+        sc = sqlContext.sparkContext,
+        broadcastedConf = broadcastedConf,
+        initDriverSideJobFuncOpt = Some(setInputPaths),
+        initLocalJobFuncOpt = Some(initLocalJobFuncOpt),
+        inputFormatClass = classOf[ParquetInputFormat[InternalRow]],
+        keyClass = classOf[Void],
+        valueClass = classOf[InternalRow]) {
+
+        val cacheMetadata = useMetadataCache
+
+        @transient val cachedStatuses = inputFiles.map { f =>
+          // In order to encode the authority of a Path containing special characters such as '/'
+          // (which does happen in some S3N credentials), we need to use the string returned by the
+          // URI of the path to create a new Path.
+          val pathWithEscapedAuthority = escapePathUserInfo(f.getPath)
+          new FileStatus(
+            f.getLen, f.isDir, f.getReplication, f.getBlockSize, f.getModificationTime,
+            f.getAccessTime, f.getPermission, f.getOwner, f.getGroup, pathWithEscapedAuthority)
+        }.toSeq
+
+        private def escapePathUserInfo(path: Path): Path = {
+          val uri = path.toUri
+          new Path(new URI(
+            uri.getScheme, uri.getRawUserInfo, uri.getHost, uri.getPort, uri.getPath,
+            uri.getQuery, uri.getFragment))
+        }
+
+        // Overridden so we can inject our own cached files statuses.
+        override def getPartitions: Array[SparkPartition] = {
+          val inputFormat = new ParquetInputFormat[InternalRow] {
+            override def listStatus(jobContext: JobContext): JList[FileStatus] = {
+              if (cacheMetadata) cachedStatuses else super.listStatus(jobContext)
+            }
+          }
+
+          val jobContext = newJobContext(getConf(isDriverSide = true), jobId)
+          val rawSplits = inputFormat.getSplits(jobContext)
+
+          Array.tabulate[SparkPartition](rawSplits.size) { i =>
+            new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
+          }
+        }
+      }.values.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
+    }
   }
 
-  // TODO: Use data from the footers.
-  override lazy val statistics = Statistics(sizeInBytes = sqlContext.conf.defaultSizeInBytes)
+  private class MetadataCache {
+    // `FileStatus` objects of all "_metadata" files.
+    private var metadataStatuses: Array[FileStatus] = _
+
+    // `FileStatus` objects of all "_common_metadata" files.
+    private var commonMetadataStatuses: Array[FileStatus] = _
+
+    // `FileStatus` objects of all data files (Parquet part-files).
+    var dataStatuses: Array[FileStatus] = _
+
+    // Schema of the actual Parquet files, without partition columns discovered from partition
+    // directory paths.
+    var dataSchema: StructType = null
+
+    // Schema of the whole table, including partition columns.
+    var schema: StructType = _
+
+    // Cached leaves
+    var cachedLeaves: Set[FileStatus] = null
+
+    /**
+     * Refreshes `FileStatus`es, footers, partition spec, and table schema.
+     */
+    def refresh(): Unit = {
+      val currentLeafStatuses = cachedLeafStatuses()
+
+      // Check if cachedLeafStatuses is changed or not
+      val leafStatusesChanged = (cachedLeaves == null) ||
+        !cachedLeaves.equals(currentLeafStatuses)
+
+      if (leafStatusesChanged) {
+        cachedLeaves = currentLeafStatuses.toIterator.toSet
+
+        // Lists `FileStatus`es of all leaf nodes (files) under all base directories.
+        val leaves = currentLeafStatuses.filter { f =>
+          isSummaryFile(f.getPath) ||
+            !(f.getPath.getName.startsWith("_") || f.getPath.getName.startsWith("."))
+        }.toArray
+
+        dataStatuses = leaves.filterNot(f => isSummaryFile(f.getPath))
+        metadataStatuses =
+          leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_METADATA_FILE)
+        commonMetadataStatuses =
+          leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE)
+
+        dataSchema = {
+          val dataSchema0 = maybeDataSchema
+            .orElse(readSchema())
+            .orElse(maybeMetastoreSchema)
+            .getOrElse(throw new AnalysisException(
+              s"Failed to discover schema of Parquet file(s) in the following location(s):\n" +
+                paths.mkString("\n\t")))
+
+          // If this Parquet relation is converted from a Hive Metastore table, must reconcile case
+          // case insensitivity issue and possible schema mismatch (probably caused by schema
+          // evolution).
+          maybeMetastoreSchema
+            .map(ParquetRelation.mergeMetastoreParquetSchema(_, dataSchema0))
+            .getOrElse(dataSchema0)
+        }
+      }
+    }
+
+    private def isSummaryFile(file: Path): Boolean = {
+      file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
+        file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
+    }
+
+    private def readSchema(): Option[StructType] = {
+      // Sees which file(s) we need to touch in order to figure out the schema.
+      //
+      // Always tries the summary files first if users don't require a merged schema.  In this case,
+      // "_common_metadata" is more preferable than "_metadata" because it doesn't contain row
+      // groups information, and could be much smaller for large Parquet files with lots of row
+      // groups.  If no summary file is available, falls back to some random part-file.
+      //
+      // NOTE: Metadata stored in the summary files are merged from all part-files.  However, for
+      // user defined key-value metadata (in which we store Spark SQL schema), Parquet doesn't know
+      // how to merge them correctly if some key is associated with different values in different
+      // part-files.  When this happens, Parquet simply gives up generating the summary file.  This
+      // implies that if a summary file presents, then:
+      //
+      //   1. Either all part-files have exactly the same Spark SQL schema, or
+      //   2. Some part-files don't contain Spark SQL schema in the key-value metadata at all (thus
+      //      their schemas may differ from each other).
+      //
+      // Here we tend to be pessimistic and take the second case into account.  Basically this means
+      // we can't trust the summary files if users require a merged schema, and must touch all part-
+      // files to do the merge.
+      val filesToTouch =
+        if (shouldMergeSchemas) {
+          // Also includes summary files, 'cause there might be empty partition directories.
+          (metadataStatuses ++ commonMetadataStatuses ++ dataStatuses).toSeq
+        } else {
+          // Tries any "_common_metadata" first. Parquet files written by old versions or Parquet
+          // don't have this.
+          commonMetadataStatuses.headOption
+            // Falls back to "_metadata"
+            .orElse(metadataStatuses.headOption)
+            // Summary file(s) not found, the Parquet file is either corrupted, or different part-
+            // files contain conflicting user defined metadata (two or more values are associated
+            // with a same key in different files).  In either case, we fall back to any of the
+            // first part-file, and just assume all schemas are consistent.
+            .orElse(dataStatuses.headOption)
+            .toSeq
+        }
+
+      assert(
+        filesToTouch.nonEmpty || maybeDataSchema.isDefined || maybeMetastoreSchema.isDefined,
+        "No predefined schema found, " +
+          s"and no Parquet data files or summary files found under ${paths.mkString(", ")}.")
+
+      ParquetRelation.mergeSchemasInParallel(filesToTouch, sqlContext)
+    }
+  }
 }
 
-private[sql] object ParquetRelation {
+private[sql] object ParquetRelation extends Logging {
+  // Whether we should merge schemas collected from all Parquet part-files.
+  private[sql] val MERGE_SCHEMA = "mergeSchema"
+
+  // Hive Metastore schema, used when converting Metastore Parquet tables.  This option is only used
+  // internally.
+  private[sql] val METASTORE_SCHEMA = "metastoreSchema"
+
+  /** This closure sets various Parquet configurations at both driver side and executor side. */
+  private[parquet] def initializeLocalJobFunc(
+      requiredColumns: Array[String],
+      filters: Array[Filter],
+      dataSchema: StructType,
+      useMetadataCache: Boolean,
+      parquetFilterPushDown: Boolean,
+      assumeBinaryIsString: Boolean,
+      assumeInt96IsTimestamp: Boolean,
+      followParquetFormatSpec: Boolean)(job: Job): Unit = {
+    val conf = job.getConfiguration
+    conf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[CatalystReadSupport].getName)
+
+    // Try to push down filters when filter push-down is enabled.
+    if (parquetFilterPushDown) {
+      filters
+        // Collects all converted Parquet filter predicates. Notice that not all predicates can be
+        // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap`
+        // is used here.
+        .flatMap(ParquetFilters.createFilter(dataSchema, _))
+        .reduceOption(FilterApi.and)
+        .foreach(ParquetInputFormat.setFilterPredicate(conf, _))
+    }
+
+    conf.set(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA, {
+      val requestedSchema = StructType(requiredColumns.map(dataSchema(_)))
+      CatalystSchemaConverter.checkFieldNames(requestedSchema).json
+    })
+
+    conf.set(
+      RowWriteSupport.SPARK_ROW_SCHEMA,
+      CatalystSchemaConverter.checkFieldNames(dataSchema).json)
+
+    // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata
+    conf.setBoolean(SQLConf.PARQUET_CACHE_METADATA.key, useMetadataCache)
+
+    // Sets flags for Parquet schema conversion
+    conf.setBoolean(SQLConf.PARQUET_BINARY_AS_STRING.key, assumeBinaryIsString)
+    conf.setBoolean(SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, assumeInt96IsTimestamp)
+    conf.setBoolean(SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key, followParquetFormatSpec)
+  }
+
+  /** This closure sets input paths at the driver side. */
+  private[parquet] def initializeDriverSideJobFunc(
+      inputFiles: Array[FileStatus])(job: Job): Unit = {
+    // We side the input paths at the driver side.
+    logInfo(s"Reading Parquet file(s) from ${inputFiles.map(_.getPath).mkString(", ")}")
+    if (inputFiles.nonEmpty) {
+      FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*)
+    }
+  }
+
+  private[parquet] def readSchema(
+      footers: Seq[Footer], sqlContext: SQLContext): Option[StructType] = {
+
+    def parseParquetSchema(schema: MessageType): StructType = {
+      val converter = new CatalystSchemaConverter(
+        sqlContext.conf.isParquetBinaryAsString,
+        sqlContext.conf.isParquetBinaryAsString,
+        sqlContext.conf.followParquetFormatSpec)
+
+      converter.convert(schema)
+    }
+
+    val seen = mutable.HashSet[String]()
+    val finalSchemas: Seq[StructType] = footers.flatMap { footer =>
+      val metadata = footer.getParquetMetadata.getFileMetaData
+      val serializedSchema = metadata
+        .getKeyValueMetaData
+        .toMap
+        .get(CatalystReadSupport.SPARK_METADATA_KEY)
+      if (serializedSchema.isEmpty) {
+        // Falls back to Parquet schema if no Spark SQL schema found.
+        Some(parseParquetSchema(metadata.getSchema))
+      } else if (!seen.contains(serializedSchema.get)) {
+        seen += serializedSchema.get
+
+        // Don't throw even if we failed to parse the serialized Spark schema. Just fallback to
+        // whatever is available.
+        Some(Try(DataType.fromJson(serializedSchema.get))
+          .recover { case _: Throwable =>
+            logInfo(
+              s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " +
+                "falling back to the deprecated DataType.fromCaseClassString parser.")
+            DataType.fromCaseClassString(serializedSchema.get)
+          }
+          .recover { case cause: Throwable =>
+            logWarning(
+              s"""Failed to parse serialized Spark schema in Parquet key-value metadata:
+                 |\t$serializedSchema
+               """.stripMargin,
+              cause)
+          }
+          .map(_.asInstanceOf[StructType])
+          .getOrElse {
+            // Falls back to Parquet schema if Spark SQL schema can't be parsed.
+            parseParquetSchema(metadata.getSchema)
+          })
+      } else {
+        None
+      }
+    }
+
+    finalSchemas.reduceOption { (left, right) =>
+      try left.merge(right) catch { case e: Throwable =>
+        throw new SparkException(s"Failed to merge incompatible schemas $left and $right", e)
+      }
+    }
+  }
+
+  /**
+   * Reconciles Hive Metastore case insensitivity issue and data type conflicts between Metastore
+   * schema and Parquet schema.
+   *
+   * Hive doesn't retain case information, while Parquet is case sensitive. On the other hand, the
+   * schema read from Parquet files may be incomplete (e.g. older versions of Parquet doesn't
+   * distinguish binary and string).  This method generates a correct schema by merging Metastore
+   * schema data types and Parquet schema field names.
+   */
+  private[parquet] def mergeMetastoreParquetSchema(
+      metastoreSchema: StructType,
+      parquetSchema: StructType): StructType = {
+    def schemaConflictMessage: String =
+      s"""Converting Hive Metastore Parquet, but detected conflicting schemas. Metastore schema:
+         |${metastoreSchema.prettyJson}
+         |
+         |Parquet schema:
+         |${parquetSchema.prettyJson}
+       """.stripMargin
+
+    val mergedParquetSchema = mergeMissingNullableFields(metastoreSchema, parquetSchema)
+
+    assert(metastoreSchema.size <= mergedParquetSchema.size, schemaConflictMessage)
+
+    val ordinalMap = metastoreSchema.zipWithIndex.map {
+      case (field, index) => field.name.toLowerCase -> index
+    }.toMap
+
+    val reorderedParquetSchema = mergedParquetSchema.sortBy(f =>
+      ordinalMap.getOrElse(f.name.toLowerCase, metastoreSchema.size + 1))
+
+    StructType(metastoreSchema.zip(reorderedParquetSchema).map {
+      // Uses Parquet field names but retains Metastore data types.
+      case (mSchema, pSchema) if mSchema.name.toLowerCase == pSchema.name.toLowerCase =>
+        mSchema.copy(name = pSchema.name)
+      case _ =>
+        throw new SparkException(schemaConflictMessage)
+    })
+  }
+
+  /**
+   * Returns the original schema from the Parquet file with any missing nullable fields from the
+   * Hive Metastore schema merged in.
+   *
+   * When constructing a DataFrame from a collection of structured data, the resulting object has
+   * a schema corresponding to the union of the fields present in each element of the collection.
+   * Spark SQL simply assigns a null value to any field that isn't present for a particular row.
+   * In some cases, it is possible that a given table partition stored as a Parquet file doesn't
+   * contain a particular nullable field in its schema despite that field being present in the
+   * table schema obtained from the Hive Metastore. This method returns a schema representing the
+   * Parquet file schema along with any additional nullable fields from the Metastore schema
+   * merged in.
+   */
+  private[parquet] def mergeMissingNullableFields(
+      metastoreSchema: StructType,
+      parquetSchema: StructType): StructType = {
+    val fieldMap = metastoreSchema.map(f => f.name.toLowerCase -> f).toMap
+    val missingFields = metastoreSchema
+      .map(_.name.toLowerCase)
+      .diff(parquetSchema.map(_.name.toLowerCase))
+      .map(fieldMap(_))
+      .filter(_.nullable)
+    StructType(parquetSchema ++ missingFields)
+  }
+
+  /**
+   * Figures out a merged Parquet schema with a distributed Spark job.
+   *
+   * Note that locality is not taken into consideration here because:
+   *
+   *  1. For a single Parquet part-file, in most cases the footer only resides in the last block of
+   *     that file.  Thus we only need to retrieve the location of the last block.  However, Hadoop
+   *     `FileSystem` only provides API to retrieve locations of all blocks, which can be
+   *     potentially expensive.
+   *
+   *  2. This optimization is mainly useful for S3, where file metadata operations can be pretty
+   *     slow.  And basically locality is not available when using S3 (you can't run computation on
+   *     S3 nodes).
+   */
+  def mergeSchemasInParallel(
+      filesToTouch: Seq[FileStatus], sqlContext: SQLContext): Option[StructType] = {
+    val assumeBinaryIsString = sqlContext.conf.isParquetBinaryAsString
+    val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
+    val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
+    val serializedConf = new SerializableConfiguration(sqlContext.sparkContext.hadoopConfiguration)
+
+    // HACK ALERT:
+    //
+    // Parquet requires `FileStatus`es to read footers.  Here we try to send cached `FileStatus`es
+    // to executor side to avoid fetching them again.  However, `FileStatus` is not `Serializable`
+    // but only `Writable`.  What makes it worth, for some reason, `FileStatus` doesn't play well
+    // with `SerializableWritable[T]` and always causes a weird `IllegalStateException`.  These
+    // facts virtually prevents us to serialize `FileStatus`es.
+    //
+    // Since Parquet only relies on path and length information of those `FileStatus`es to read
+    // footers, here we just extract them (which can be easily serialized), send them to executor
+    // side, and resemble fake `FileStatus`es there.
+    val partialFileStatusInfo = filesToTouch.map(f => (f.getPath.toString, f.getLen))
+
+    // Issues a Spark job to read Parquet schema in parallel.
+    val partiallyMergedSchemas =
+      sqlContext
+        .sparkContext
+        .parallelize(partialFileStatusInfo)
+        .mapPartitions { iterator =>
+          // Resembles fake `FileStatus`es with serialized path and length information.
+          val fakeFileStatuses = iterator.map { case (path, length) =>
+            new FileStatus(length, false, 0, 0, 0, 0, null, null, null, new Path(path))
+          }.toSeq
+
+          // Skips row group information since we only need the schema
+          val skipRowGroups = true
+
+          // Reads footers in multi-threaded manner within each task
+          val footers =
+            ParquetFileReader.readAllFootersInParallel(
+              serializedConf.value, fakeFileStatuses, skipRowGroups)
+
+          // Converter used to convert Parquet `MessageType` to Spark SQL `StructType`
+          val converter =
+            new CatalystSchemaConverter(
+              assumeBinaryIsString = assumeBinaryIsString,
+              assumeInt96IsTimestamp = assumeInt96IsTimestamp,
+              followParquetFormatSpec = followParquetFormatSpec)
+
+          footers.map { footer =>
+            ParquetRelation.readSchemaFromFooter(footer, converter)
+          }.reduceOption(_ merge _).iterator
+        }.collect()
+
+    partiallyMergedSchemas.reduceOption(_ merge _)
+  }
+
+  /**
+   * Reads Spark SQL schema from a Parquet footer.  If a valid serialized Spark SQL schema string
+   * can be found in the file metadata, returns the deserialized [[StructType]], otherwise, returns
+   * a [[StructType]] converted from the [[MessageType]] stored in this footer.
+   */
+  def readSchemaFromFooter(
+      footer: Footer, converter: CatalystSchemaConverter): StructType = {
+    val fileMetaData = footer.getParquetMetadata.getFileMetaData
+    fileMetaData
+      .getKeyValueMetaData
+      .toMap
+      .get(CatalystReadSupport.SPARK_METADATA_KEY)
+      .flatMap(deserializeSchemaString)
+      .getOrElse(converter.convert(fileMetaData.getSchema))
+  }
+
+  private def deserializeSchemaString(schemaString: String): Option[StructType] = {
+    // Tries to deserialize the schema string as JSON first, then falls back to the case class
+    // string parser (data generated by older versions of Spark SQL uses this format).
+    Try(DataType.fromJson(schemaString).asInstanceOf[StructType]).recover {
+      case _: Throwable =>
+        logInfo(
+          s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " +
+            "falling back to the deprecated DataType.fromCaseClassString parser.")
+        DataType.fromCaseClassString(schemaString).asInstanceOf[StructType]
+    }.recoverWith {
+      case cause: Throwable =>
+        logWarning(
+          "Failed to parse and ignored serialized Spark schema in " +
+            s"Parquet key-value metadata:\n\t$schemaString", cause)
+        Failure(cause)
+    }.toOption
+  }
 
   def enableLogForwarding() {
     // Note: the org.apache.parquet.Log class has a static initializer that
@@ -127,12 +766,6 @@ private[sql] object ParquetRelation {
     JLogger.getLogger(classOf[ParquetRecordReader[_]].getName).setLevel(Level.OFF)
   }
 
-  // The element type for the RDDs that this relation maps to.
-  type RowType = org.apache.spark.sql.catalyst.expressions.GenericMutableRow
-
-  // The compression type
-  type CompressionType = org.apache.parquet.hadoop.metadata.CompressionCodecName
-
   // The parquet compression short names
   val shortParquetCompressionCodecNames = Map(
     "NONE"         -> CompressionCodecName.UNCOMPRESSED,
@@ -140,82 +773,4 @@ private[sql] object ParquetRelation {
     "SNAPPY"       -> CompressionCodecName.SNAPPY,
     "GZIP"         -> CompressionCodecName.GZIP,
     "LZO"          -> CompressionCodecName.LZO)
-
-  /**
-   * Creates a new ParquetRelation and underlying Parquetfile for the given LogicalPlan. Note that
-   * this is used inside [[org.apache.spark.sql.execution.SparkStrategies SparkStrategies]] to
-   * create a resolved relation as a data sink for writing to a Parquetfile. The relation is empty
-   * but is initialized with ParquetMetadata and can be inserted into.
-   *
-   * @param pathString The directory the Parquetfile will be stored in.
-   * @param child The child node that will be used for extracting the schema.
-   * @param conf A configuration to be used.
-   * @return An empty ParquetRelation with inferred metadata.
-   */
-  def create(pathString: String,
-             child: LogicalPlan,
-             conf: Configuration,
-             sqlContext: SQLContext): ParquetRelation = {
-    if (!child.resolved) {
-      throw new UnresolvedException[LogicalPlan](
-        child,
-        "Attempt to create Parquet table from unresolved child (when schema is not available)")
-    }
-    createEmpty(pathString, child.output, false, conf, sqlContext)
-  }
-
-  /**
-   * Creates an empty ParquetRelation and underlying Parquetfile that only
-   * consists of the Metadata for the given schema.
-   *
-   * @param pathString The directory the Parquetfile will be stored in.
-   * @param attributes The schema of the relation.
-   * @param conf A configuration to be used.
-   * @return An empty ParquetRelation.
-   */
-  def createEmpty(pathString: String,
-                  attributes: Seq[Attribute],
-                  allowExisting: Boolean,
-                  conf: Configuration,
-                  sqlContext: SQLContext): ParquetRelation = {
-    val path = checkPath(pathString, allowExisting, conf)
-    conf.set(ParquetOutputFormat.COMPRESSION, shortParquetCompressionCodecNames.getOrElse(
-      sqlContext.conf.parquetCompressionCodec.toUpperCase, CompressionCodecName.UNCOMPRESSED)
-      .name())
-    ParquetRelation.enableLogForwarding()
-    // This is a hack. We always set nullable/containsNull/valueContainsNull to true
-    // for the schema of a parquet data.
-    val schema = StructType.fromAttributes(attributes).asNullable
-    val newAttributes = schema.toAttributes
-    ParquetTypesConverter.writeMetaData(newAttributes, path, conf)
-    new ParquetRelation(path.toString, Some(conf), sqlContext) {
-      override val output = newAttributes
-    }
-  }
-
-  private def checkPath(pathStr: String, allowExisting: Boolean, conf: Configuration): Path = {
-    if (pathStr == null) {
-      throw new IllegalArgumentException("Unable to create ParquetRelation: path is null")
-    }
-    val origPath = new Path(pathStr)
-    val fs = origPath.getFileSystem(conf)
-    if (fs == null) {
-      throw new IllegalArgumentException(
-        s"Unable to create ParquetRelation: incorrectly formatted path $pathStr")
-    }
-    val path = origPath.makeQualified(fs)
-    if (!allowExisting && fs.exists(path)) {
-      sys.error(s"File $pathStr already exists.")
-    }
-
-    if (fs.exists(path) &&
-        !fs.getFileStatus(path)
-        .getPermission
-        .getUserAction
-        .implies(FsAction.READ_WRITE)) {
-      throw new IOException(
-        s"Unable to create ParquetRelation: path $path not read-writable")
-    }
-    path
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
deleted file mode 100644
index 75cbbde4f1512..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ /dev/null
@@ -1,492 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.parquet
-
-import java.io.IOException
-import java.text.{NumberFormat, SimpleDateFormat}
-import java.util.concurrent.TimeUnit
-import java.util.Date
-
-import scala.collection.JavaConversions._
-import scala.util.Try
-
-import com.google.common.cache.CacheBuilder
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{BlockLocation, FileStatus, Path}
-import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter, FileOutputFormat => NewFileOutputFormat}
-import org.apache.parquet.hadoop._
-import org.apache.parquet.hadoop.api.ReadSupport
-import org.apache.parquet.hadoop.util.ContextUtil
-import org.apache.parquet.schema.MessageType
-
-import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.mapred.SparkHadoopMapRedUtil
-import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, _}
-import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
-import org.apache.spark.sql.types.StructType
-import org.apache.spark.{Logging, TaskContext}
-import org.apache.spark.util.SerializableConfiguration
-
-/**
- * :: DeveloperApi ::
- * Parquet table scan operator. Imports the file that backs the given
- * [[org.apache.spark.sql.parquet.ParquetRelation]] as a ``RDD[InternalRow]``.
- */
-private[sql] case class ParquetTableScan(
-    attributes: Seq[Attribute],
-    relation: ParquetRelation,
-    columnPruningPred: Seq[Expression])
-  extends LeafNode {
-
-  // The resolution of Parquet attributes is case sensitive, so we resolve the original attributes
-  // by exprId. note: output cannot be transient, see
-  // https://issues.apache.org/jira/browse/SPARK-1367
-  val output = attributes.map(relation.attributeMap)
-
-  // A mapping of ordinals partitionRow -> finalOutput.
-  val requestedPartitionOrdinals = {
-    val partitionAttributeOrdinals = AttributeMap(relation.partitioningAttributes.zipWithIndex)
-
-    attributes.zipWithIndex.flatMap {
-      case (attribute, finalOrdinal) =>
-        partitionAttributeOrdinals.get(attribute).map(_ -> finalOrdinal)
-    }
-  }.toArray
-
-  protected override def doExecute(): RDD[InternalRow] = {
-    import org.apache.parquet.filter2.compat.FilterCompat.FilterPredicateCompat
-
-    val sc = sqlContext.sparkContext
-    val job = new Job(sc.hadoopConfiguration)
-    ParquetInputFormat.setReadSupportClass(job, classOf[RowReadSupport])
-
-    val conf: Configuration = ContextUtil.getConfiguration(job)
-
-    relation.path.split(",").foreach { curPath =>
-      val qualifiedPath = {
-        val path = new Path(curPath)
-        path.getFileSystem(conf).makeQualified(path)
-      }
-      NewFileInputFormat.addInputPath(job, qualifiedPath)
-    }
-
-    // Store both requested and original schema in `Configuration`
-    conf.set(
-      RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
-      ParquetTypesConverter.convertToString(output))
-    conf.set(
-      RowWriteSupport.SPARK_ROW_SCHEMA,
-      ParquetTypesConverter.convertToString(relation.output))
-
-    // Store record filtering predicate in `Configuration`
-    // Note 1: the input format ignores all predicates that cannot be expressed
-    // as simple column predicate filters in Parquet. Here we just record
-    // the whole pruning predicate.
-    ParquetFilters
-      .createRecordFilter(columnPruningPred)
-      .map(_.asInstanceOf[FilterPredicateCompat].getFilterPredicate)
-      // Set this in configuration of ParquetInputFormat, needed for RowGroupFiltering
-      .foreach(ParquetInputFormat.setFilterPredicate(conf, _))
-
-    // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata
-    conf.setBoolean(
-      SQLConf.PARQUET_CACHE_METADATA.key,
-      sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA, true))
-
-    // Use task side metadata in parquet
-    conf.setBoolean(ParquetInputFormat.TASK_SIDE_METADATA, true)
-
-    val baseRDD =
-      new org.apache.spark.rdd.NewHadoopRDD(
-        sc,
-        classOf[FilteringParquetRowInputFormat],
-        classOf[Void],
-        classOf[InternalRow],
-        conf)
-
-    if (requestedPartitionOrdinals.nonEmpty) {
-      // This check is based on CatalystConverter.createRootConverter.
-      val primitiveRow = output.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType))
-
-      // Uses temporary variable to avoid the whole `ParquetTableScan` object being captured into
-      // the `mapPartitionsWithInputSplit` closure below.
-      val outputSize = output.size
-
-      baseRDD.mapPartitionsWithInputSplit { case (split, iter) =>
-        val partValue = "([^=]+)=([^=]+)".r
-        val partValues =
-          split.asInstanceOf[org.apache.parquet.hadoop.ParquetInputSplit]
-            .getPath
-            .toString
-            .split("/")
-            .flatMap {
-              case partValue(key, value) => Some(key -> value)
-              case _ => None
-            }.toMap
-
-        // Convert the partitioning attributes into the correct types
-        val partitionRowValues =
-          relation.partitioningAttributes
-            .map(a => Cast(Literal(partValues(a.name)), a.dataType).eval(EmptyRow))
-
-        if (primitiveRow) {
-          new Iterator[InternalRow] {
-            def hasNext: Boolean = iter.hasNext
-            def next(): InternalRow = {
-              // We are using CatalystPrimitiveRowConverter and it returns a SpecificMutableRow.
-              val row = iter.next()._2.asInstanceOf[SpecificMutableRow]
-
-              // Parquet will leave partitioning columns empty, so we fill them in here.
-              var i = 0
-              while (i < requestedPartitionOrdinals.length) {
-                row(requestedPartitionOrdinals(i)._2) =
-                  partitionRowValues(requestedPartitionOrdinals(i)._1)
-                i += 1
-              }
-              row
-            }
-          }
-        } else {
-          // Create a mutable row since we need to fill in values from partition columns.
-          val mutableRow = new GenericMutableRow(outputSize)
-          new Iterator[InternalRow] {
-            def hasNext: Boolean = iter.hasNext
-            def next(): InternalRow = {
-              // We are using CatalystGroupConverter and it returns a GenericRow.
-              // Since GenericRow is not mutable, we just cast it to a Row.
-              val row = iter.next()._2.asInstanceOf[InternalRow]
-
-              var i = 0
-              while (i < row.numFields) {
-                mutableRow(i) = row.genericGet(i)
-                i += 1
-              }
-              // Parquet will leave partitioning columns empty, so we fill them in here.
-              i = 0
-              while (i < requestedPartitionOrdinals.length) {
-                mutableRow(requestedPartitionOrdinals(i)._2) =
-                  partitionRowValues(requestedPartitionOrdinals(i)._1)
-                i += 1
-              }
-              mutableRow
-            }
-          }
-        }
-      }
-    } else {
-      baseRDD.map(_._2)
-    }
-  }
-
-  /**
-   * Applies a (candidate) projection.
-   *
-   * @param prunedAttributes The list of attributes to be used in the projection.
-   * @return Pruned TableScan.
-   */
-  def pruneColumns(prunedAttributes: Seq[Attribute]): ParquetTableScan = {
-    val success = validateProjection(prunedAttributes)
-    if (success) {
-      ParquetTableScan(prunedAttributes, relation, columnPruningPred)
-    } else {
-      sys.error("Warning: Could not validate Parquet schema projection in pruneColumns")
-    }
-  }
-
-  /**
-   * Evaluates a candidate projection by checking whether the candidate is a subtype
-   * of the original type.
-   *
-   * @param projection The candidate projection.
-   * @return True if the projection is valid, false otherwise.
-   */
-  private def validateProjection(projection: Seq[Attribute]): Boolean = {
-    val original: MessageType = relation.parquetSchema
-    val candidate: MessageType = ParquetTypesConverter.convertFromAttributes(projection)
-    Try(original.checkContains(candidate)).isSuccess
-  }
-}
-
-/**
- * :: DeveloperApi ::
- * Operator that acts as a sink for queries on RDDs and can be used to
- * store the output inside a directory of Parquet files. This operator
- * is similar to Hive's INSERT INTO TABLE operation in the sense that
- * one can choose to either overwrite or append to a directory. Note
- * that consecutive insertions to the same table must have compatible
- * (source) schemas.
- *
- * WARNING: EXPERIMENTAL! InsertIntoParquetTable with overwrite=false may
- * cause data corruption in the case that multiple users try to append to
- * the same table simultaneously. Inserting into a table that was
- * previously generated by other means (e.g., by creating an HDFS
- * directory and importing Parquet files generated by other tools) may
- * cause unpredicted behaviour and therefore results in a RuntimeException
- * (only detected via filename pattern so will not catch all cases).
- */
-@DeveloperApi
-private[sql] case class InsertIntoParquetTable(
-    relation: ParquetRelation,
-    child: SparkPlan,
-    overwrite: Boolean = false)
-  extends UnaryNode with SparkHadoopMapReduceUtil {
-
-  /**
-   * Inserts all rows into the Parquet file.
-   */
-  protected override def doExecute(): RDD[InternalRow] = {
-    // TODO: currently we do not check whether the "schema"s are compatible
-    // That means if one first creates a table and then INSERTs data with
-    // and incompatible schema the execution will fail. It would be nice
-    // to catch this early one, maybe having the planner validate the schema
-    // before calling execute().
-
-    val childRdd = child.execute()
-    assert(childRdd != null)
-
-    val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
-
-    val writeSupport =
-      if (child.output.map(_.dataType).forall(ParquetTypesConverter.isPrimitiveType)) {
-        log.debug("Initializing MutableRowWriteSupport")
-        classOf[org.apache.spark.sql.parquet.MutableRowWriteSupport]
-      } else {
-        classOf[org.apache.spark.sql.parquet.RowWriteSupport]
-      }
-
-    ParquetOutputFormat.setWriteSupportClass(job, writeSupport)
-
-    val conf = ContextUtil.getConfiguration(job)
-    // This is a hack. We always set nullable/containsNull/valueContainsNull to true
-    // for the schema of a parquet data.
-    val schema = StructType.fromAttributes(relation.output).asNullable
-    RowWriteSupport.setSchema(schema.toAttributes, conf)
-
-    val fspath = new Path(relation.path)
-    val fs = fspath.getFileSystem(conf)
-
-    if (overwrite) {
-      try {
-        fs.delete(fspath, true)
-      } catch {
-        case e: IOException =>
-          throw new IOException(
-            s"Unable to clear output directory ${fspath.toString} prior"
-              + s" to InsertIntoParquetTable:\n${e.toString}")
-      }
-    }
-    saveAsHadoopFile(childRdd, relation.path.toString, conf)
-
-    // We return the child RDD to allow chaining (alternatively, one could return nothing).
-    childRdd
-  }
-
-  override def output: Seq[Attribute] = child.output
-
-  /**
-   * Stores the given Row RDD as a Hadoop file.
-   *
-   * Note: We cannot use ``saveAsNewAPIHadoopFile`` from [[org.apache.spark.rdd.PairRDDFunctions]]
-   * together with [[org.apache.spark.util.MutablePair]] because ``PairRDDFunctions`` uses
-   * ``Tuple2`` and not ``Product2``. Also, we want to allow appending files to an existing
-   * directory and need to determine which was the largest written file index before starting to
-   * write.
-   *
-   * @param rdd The [[org.apache.spark.rdd.RDD]] to writer
-   * @param path The directory to write to.
-   * @param conf A [[org.apache.hadoop.conf.Configuration]].
-   */
-  private def saveAsHadoopFile(
-      rdd: RDD[InternalRow],
-      path: String,
-      conf: Configuration) {
-    val job = new Job(conf)
-    val keyType = classOf[Void]
-    job.setOutputKeyClass(keyType)
-    job.setOutputValueClass(classOf[InternalRow])
-    NewFileOutputFormat.setOutputPath(job, new Path(path))
-    val wrappedConf = new SerializableConfiguration(job.getConfiguration)
-    val formatter = new SimpleDateFormat("yyyyMMddHHmm")
-    val jobtrackerID = formatter.format(new Date())
-    val stageId = sqlContext.sparkContext.newRddId()
-
-    val taskIdOffset =
-      if (overwrite) {
-        1
-      } else {
-        FileSystemHelper
-          .findMaxTaskId(NewFileOutputFormat.getOutputPath(job).toString, job.getConfiguration) + 1
-      }
-
-    def writeShard(context: TaskContext, iter: Iterator[InternalRow]): Int = {
-      /* "reduce task" <split #> <attempt # = spark task #> */
-      val attemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = false, context.partitionId,
-        context.attemptNumber)
-      val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId)
-      val format = new AppendingParquetOutputFormat(taskIdOffset)
-      val committer = format.getOutputCommitter(hadoopContext)
-      committer.setupTask(hadoopContext)
-      val writer = format.getRecordWriter(hadoopContext)
-      try {
-        while (iter.hasNext) {
-          val row = iter.next()
-          writer.write(null, row)
-        }
-      } finally {
-        writer.close(hadoopContext)
-      }
-      SparkHadoopMapRedUtil.commitTask(committer, hadoopContext, context)
-      1
-    }
-    val jobFormat = new AppendingParquetOutputFormat(taskIdOffset)
-    /* apparently we need a TaskAttemptID to construct an OutputCommitter;
-     * however we're only going to use this local OutputCommitter for
-     * setupJob/commitJob, so we just use a dummy "map" task.
-     */
-    val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = true, 0, 0)
-    val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId)
-    val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext)
-    jobCommitter.setupJob(jobTaskContext)
-    sqlContext.sparkContext.runJob(rdd, writeShard _)
-    jobCommitter.commitJob(jobTaskContext)
-  }
-}
-
-/**
- * TODO: this will be able to append to directories it created itself, not necessarily
- * to imported ones.
- */
-private[parquet] class AppendingParquetOutputFormat(offset: Int)
-  extends org.apache.parquet.hadoop.ParquetOutputFormat[InternalRow] {
-  // override to accept existing directories as valid output directory
-  override def checkOutputSpecs(job: JobContext): Unit = {}
-  var committer: OutputCommitter = null
-
-  // override to choose output filename so not overwrite existing ones
-  override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-    val numfmt = NumberFormat.getInstance()
-    numfmt.setMinimumIntegerDigits(5)
-    numfmt.setGroupingUsed(false)
-
-    val taskId: TaskID = getTaskAttemptID(context).getTaskID
-    val partition: Int = taskId.getId
-    val filename = "part-r-" + numfmt.format(partition + offset) + ".parquet"
-    val committer: FileOutputCommitter =
-      getOutputCommitter(context).asInstanceOf[FileOutputCommitter]
-    new Path(committer.getWorkPath, filename)
-  }
-
-  // The TaskAttemptContext is a class in hadoop-1 but is an interface in hadoop-2.
-  // The signatures of the method TaskAttemptContext.getTaskAttemptID for the both versions
-  // are the same, so the method calls are source-compatible but NOT binary-compatible because
-  // the opcode of method call for class is INVOKEVIRTUAL and for interface is INVOKEINTERFACE.
-  private def getTaskAttemptID(context: TaskAttemptContext): TaskAttemptID = {
-    context.getClass.getMethod("getTaskAttemptID").invoke(context).asInstanceOf[TaskAttemptID]
-  }
-
-  // override to create output committer from configuration
-  override def getOutputCommitter(context: TaskAttemptContext): OutputCommitter = {
-    if (committer == null) {
-      val output = getOutputPath(context)
-      val cls = context.getConfiguration.getClass("spark.sql.parquet.output.committer.class",
-        classOf[ParquetOutputCommitter], classOf[ParquetOutputCommitter])
-      val ctor = cls.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext])
-      committer = ctor.newInstance(output, context).asInstanceOf[ParquetOutputCommitter]
-    }
-    committer
-  }
-
-  // FileOutputFormat.getOutputPath takes JobConf in hadoop-1 but JobContext in hadoop-2
-  private def getOutputPath(context: TaskAttemptContext): Path = {
-    context.getConfiguration().get("mapred.output.dir") match {
-      case null => null
-      case name => new Path(name)
-    }
-  }
-}
-
-// TODO Removes this class after removing old Parquet support code
-/**
- * We extend ParquetInputFormat in order to have more control over which
- * RecordFilter we want to use.
- */
-private[parquet] class FilteringParquetRowInputFormat
-  extends org.apache.parquet.hadoop.ParquetInputFormat[InternalRow] with Logging {
-
-  override def createRecordReader(
-      inputSplit: InputSplit,
-      taskAttemptContext: TaskAttemptContext): RecordReader[Void, InternalRow] = {
-
-    import org.apache.parquet.filter2.compat.FilterCompat.NoOpFilter
-
-    val readSupport: ReadSupport[InternalRow] = new RowReadSupport()
-
-    val filter = ParquetInputFormat.getFilter(ContextUtil.getConfiguration(taskAttemptContext))
-    if (!filter.isInstanceOf[NoOpFilter]) {
-      new ParquetRecordReader[InternalRow](
-        readSupport,
-        filter)
-    } else {
-      new ParquetRecordReader[InternalRow](readSupport)
-    }
-  }
-
-}
-
-private[parquet] object FileSystemHelper {
-  def listFiles(pathStr: String, conf: Configuration): Seq[Path] = {
-    val origPath = new Path(pathStr)
-    val fs = origPath.getFileSystem(conf)
-    if (fs == null) {
-      throw new IllegalArgumentException(
-        s"ParquetTableOperations: Path $origPath is incorrectly formatted")
-    }
-    val path = origPath.makeQualified(fs)
-    if (!fs.exists(path) || !fs.getFileStatus(path).isDir) {
-      throw new IllegalArgumentException(
-        s"ParquetTableOperations: path $path does not exist or is not a directory")
-    }
-    fs.globStatus(path)
-      .flatMap { status => if (status.isDir) fs.listStatus(status.getPath) else List(status) }
-      .map(_.getPath)
-  }
-
-    /**
-     * Finds the maximum taskid in the output file names at the given path.
-     */
-  def findMaxTaskId(pathStr: String, conf: Configuration): Int = {
-    val files = FileSystemHelper.listFiles(pathStr, conf)
-    // filename pattern is part-r-<int>.parquet
-    val nameP = new scala.util.matching.Regex("""part-.-(\d{1,}).*""", "taskid")
-    val hiddenFileP = new scala.util.matching.Regex("_.*")
-    files.map(_.getName).map {
-      case nameP(taskid) => taskid.toInt
-      case hiddenFileP() => 0
-      case other: String =>
-        sys.error("ERROR: attempting to append to set of Parquet files and found file" +
-          s"that does not match name pattern: $other")
-      case _ => 0
-    }.reduceOption(_ max _).getOrElse(0)
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 7b6a7f65d69db..fc9f61a636768 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -18,18 +18,13 @@
 package org.apache.spark.sql.parquet
 
 import java.nio.{ByteBuffer, ByteOrder}
-import java.util
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions._
-
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.column.ParquetProperties
 import org.apache.parquet.hadoop.ParquetOutputFormat
-import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
-import org.apache.parquet.hadoop.api.{InitContext, ReadSupport, WriteSupport}
+import org.apache.parquet.hadoop.api.WriteSupport
 import org.apache.parquet.io.api._
-import org.apache.parquet.schema.MessageType
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
@@ -38,147 +33,6 @@ import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
-/**
- * A [[RecordMaterializer]] for Catalyst rows.
- *
- * @param parquetSchema Parquet schema of the records to be read
- * @param catalystSchema Catalyst schema of the rows to be constructed
- */
-private[parquet] class RowRecordMaterializer(parquetSchema: MessageType, catalystSchema: StructType)
-  extends RecordMaterializer[InternalRow] {
-
-  private val rootConverter = new CatalystRowConverter(parquetSchema, catalystSchema, NoopUpdater)
-
-  override def getCurrentRecord: InternalRow = rootConverter.currentRow
-
-  override def getRootConverter: GroupConverter = rootConverter
-}
-
-private[parquet] class RowReadSupport extends ReadSupport[InternalRow] with Logging {
-  override def prepareForRead(
-      conf: Configuration,
-      keyValueMetaData: util.Map[String, String],
-      fileSchema: MessageType,
-      readContext: ReadContext): RecordMaterializer[InternalRow] = {
-    log.debug(s"Preparing for read Parquet file with message type: $fileSchema")
-
-    val toCatalyst = new CatalystSchemaConverter(conf)
-    val parquetRequestedSchema = readContext.getRequestedSchema
-
-    val catalystRequestedSchema =
-      Option(readContext.getReadSupportMetadata).map(_.toMap).flatMap { metadata =>
-        metadata
-          // First tries to read requested schema, which may result from projections
-          .get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
-          // If not available, tries to read Catalyst schema from file metadata.  It's only
-          // available if the target file is written by Spark SQL.
-          .orElse(metadata.get(RowReadSupport.SPARK_METADATA_KEY))
-      }.map(StructType.fromString).getOrElse {
-        logDebug("Catalyst schema not available, falling back to Parquet schema")
-        toCatalyst.convert(parquetRequestedSchema)
-      }
-
-    logDebug(s"Catalyst schema used to read Parquet files: $catalystRequestedSchema")
-    new RowRecordMaterializer(parquetRequestedSchema, catalystRequestedSchema)
-  }
-
-  override def init(context: InitContext): ReadContext = {
-    val conf = context.getConfiguration
-
-    // If the target file was written by Spark SQL, we should be able to find a serialized Catalyst
-    // schema of this file from its the metadata.
-    val maybeRowSchema = Option(conf.get(RowWriteSupport.SPARK_ROW_SCHEMA))
-
-    // Optional schema of requested columns, in the form of a string serialized from a Catalyst
-    // `StructType` containing all requested columns.
-    val maybeRequestedSchema = Option(conf.get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA))
-
-    // Below we construct a Parquet schema containing all requested columns.  This schema tells
-    // Parquet which columns to read.
-    //
-    // If `maybeRequestedSchema` is defined, we assemble an equivalent Parquet schema.  Otherwise,
-    // we have to fallback to the full file schema which contains all columns in the file.
-    // Obviously this may waste IO bandwidth since it may read more columns than requested.
-    //
-    // Two things to note:
-    //
-    // 1. It's possible that some requested columns don't exist in the target Parquet file.  For
-    //    example, in the case of schema merging, the globally merged schema may contain extra
-    //    columns gathered from other Parquet files.  These columns will be simply filled with nulls
-    //    when actually reading the target Parquet file.
-    //
-    // 2. When `maybeRequestedSchema` is available, we can't simply convert the Catalyst schema to
-    //    Parquet schema using `CatalystSchemaConverter`, because the mapping is not unique due to
-    //    non-standard behaviors of some Parquet libraries/tools.  For example, a Parquet file
-    //    containing a single integer array field `f1` may have the following legacy 2-level
-    //    structure:
-    //
-    //      message root {
-    //        optional group f1 (LIST) {
-    //          required INT32 element;
-    //        }
-    //      }
-    //
-    //    while `CatalystSchemaConverter` may generate a standard 3-level structure:
-    //
-    //      message root {
-    //        optional group f1 (LIST) {
-    //          repeated group list {
-    //            required INT32 element;
-    //          }
-    //        }
-    //      }
-    //
-    //    Apparently, we can't use the 2nd schema to read the target Parquet file as they have
-    //    different physical structures.
-    val parquetRequestedSchema =
-      maybeRequestedSchema.fold(context.getFileSchema) { schemaString =>
-        val toParquet = new CatalystSchemaConverter(conf)
-        val fileSchema = context.getFileSchema.asGroupType()
-        val fileFieldNames = fileSchema.getFields.map(_.getName).toSet
-
-        StructType
-          // Deserializes the Catalyst schema of requested columns
-          .fromString(schemaString)
-          .map { field =>
-            if (fileFieldNames.contains(field.name)) {
-              // If the field exists in the target Parquet file, extracts the field type from the
-              // full file schema and makes a single-field Parquet schema
-              new MessageType("root", fileSchema.getType(field.name))
-            } else {
-              // Otherwise, just resorts to `CatalystSchemaConverter`
-              toParquet.convert(StructType(Array(field)))
-            }
-          }
-          // Merges all single-field Parquet schemas to form a complete schema for all requested
-          // columns.  Note that it's possible that no columns are requested at all (e.g., count
-          // some partition column of a partitioned Parquet table). That's why `fold` is used here
-          // and always fallback to an empty Parquet schema.
-          .fold(new MessageType("root")) {
-            _ union _
-          }
-      }
-
-    val metadata =
-      Map.empty[String, String] ++
-        maybeRequestedSchema.map(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA -> _) ++
-        maybeRowSchema.map(RowWriteSupport.SPARK_ROW_SCHEMA -> _)
-
-    logInfo(s"Going to read Parquet file with these requested columns: $parquetRequestedSchema")
-    new ReadContext(parquetRequestedSchema, metadata)
-  }
-}
-
-private[parquet] object RowReadSupport {
-  val SPARK_ROW_REQUESTED_SCHEMA = "org.apache.spark.sql.parquet.row.requested_schema"
-  val SPARK_METADATA_KEY = "org.apache.spark.sql.parquet.row.metadata"
-
-  private def getRequestedSchema(configuration: Configuration): Seq[Attribute] = {
-    val schemaString = configuration.get(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
-    if (schemaString == null) null else ParquetTypesConverter.convertFromString(schemaString)
-  }
-}
-
 /**
  * A `parquet.hadoop.api.WriteSupport` for Row objects.
  */
@@ -190,7 +44,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
   override def init(configuration: Configuration): WriteSupport.WriteContext = {
     val origAttributesStr: String = configuration.get(RowWriteSupport.SPARK_ROW_SCHEMA)
     val metadata = new JHashMap[String, String]()
-    metadata.put(RowReadSupport.SPARK_METADATA_KEY, origAttributesStr)
+    metadata.put(CatalystReadSupport.SPARK_METADATA_KEY, origAttributesStr)
 
     if (attributes == null) {
       attributes = ParquetTypesConverter.convertFromString(origAttributesStr).toArray
@@ -443,4 +297,3 @@ private[parquet] object RowWriteSupport {
       ParquetProperties.WriterVersion.PARQUET_1_0.toString)
   }
 }
-
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index e748bd7857bd8..3854f5bd39fb1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -53,15 +53,6 @@ private[parquet] object ParquetTypesConverter extends Logging {
     length
   }
 
-  def convertToAttributes(
-      parquetSchema: MessageType,
-      isBinaryAsString: Boolean,
-      isInt96AsTimestamp: Boolean): Seq[Attribute] = {
-    val converter = new CatalystSchemaConverter(
-      isBinaryAsString, isInt96AsTimestamp, followParquetFormatSpec = false)
-    converter.convert(parquetSchema).toAttributes
-  }
-
   def convertFromAttributes(attributes: Seq[Attribute]): MessageType = {
     val converter = new CatalystSchemaConverter()
     converter.convert(StructType.fromAttributes(attributes))
@@ -103,7 +94,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
     }
     val extraMetadata = new java.util.HashMap[String, String]()
     extraMetadata.put(
-      RowReadSupport.SPARK_METADATA_KEY,
+      CatalystReadSupport.SPARK_METADATA_KEY,
       ParquetTypesConverter.convertToString(attributes))
     // TODO: add extra data, e.g., table name, date, etc.?
 
@@ -165,35 +156,4 @@ private[parquet] object ParquetTypesConverter extends Logging {
       .getOrElse(
         throw new IllegalArgumentException(s"Could not find Parquet metadata at path $path"))
   }
-
-  /**
-   * Reads in Parquet Metadata from the given path and tries to extract the schema
-   * (Catalyst attributes) from the application-specific key-value map. If this
-   * is empty it falls back to converting from the Parquet file schema which
-   * may lead to an upcast of types (e.g., {byte, short} to int).
-   *
-   * @param origPath The path at which we expect one (or more) Parquet files.
-   * @param conf The Hadoop configuration to use.
-   * @return A list of attributes that make up the schema.
-   */
-  def readSchemaFromFile(
-      origPath: Path,
-      conf: Option[Configuration],
-      isBinaryAsString: Boolean,
-      isInt96AsTimestamp: Boolean): Seq[Attribute] = {
-    val keyValueMetadata: java.util.Map[String, String] =
-      readMetaData(origPath, conf)
-        .getFileMetaData
-        .getKeyValueMetaData
-    if (keyValueMetadata.get(RowReadSupport.SPARK_METADATA_KEY) != null) {
-      convertFromString(keyValueMetadata.get(RowReadSupport.SPARK_METADATA_KEY))
-    } else {
-      val attributes = convertToAttributes(
-        readMetaData(origPath, conf).getFileMetaData.getSchema,
-        isBinaryAsString,
-        isInt96AsTimestamp)
-      log.info(s"Falling back to schema conversion from Parquet types; result: $attributes")
-      attributes
-    }
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
deleted file mode 100644
index 8ec228c2b25bc..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala
+++ /dev/null
@@ -1,732 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.parquet
-
-import java.net.URI
-import java.util.{List => JList}
-
-import scala.collection.JavaConversions._
-import scala.collection.mutable
-import scala.util.{Failure, Try}
-
-import com.google.common.base.Objects
-import org.apache.hadoop.fs.{FileStatus, Path}
-import org.apache.hadoop.io.Writable
-import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
-import org.apache.parquet.filter2.predicate.FilterApi
-import org.apache.parquet.hadoop._
-import org.apache.parquet.hadoop.metadata.CompressionCodecName
-import org.apache.parquet.hadoop.util.ContextUtil
-import org.apache.parquet.schema.MessageType
-
-import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
-import org.apache.spark.rdd.RDD._
-import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.execution.{SqlNewHadoopPartition, SqlNewHadoopRDD}
-import org.apache.spark.sql.execution.datasources.PartitionSpec
-import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.{DataType, StructType}
-import org.apache.spark.util.{SerializableConfiguration, Utils}
-
-
-private[sql] class DefaultSource extends HadoopFsRelationProvider {
-  override def createRelation(
-      sqlContext: SQLContext,
-      paths: Array[String],
-      schema: Option[StructType],
-      partitionColumns: Option[StructType],
-      parameters: Map[String, String]): HadoopFsRelation = {
-    new ParquetRelation2(paths, schema, None, partitionColumns, parameters)(sqlContext)
-  }
-}
-
-// NOTE: This class is instantiated and used on executor side only, no need to be serializable.
-private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext)
-  extends OutputWriterInternal {
-
-  private val recordWriter: RecordWriter[Void, InternalRow] = {
-    val outputFormat = {
-      new ParquetOutputFormat[InternalRow]() {
-        // Here we override `getDefaultWorkFile` for two reasons:
-        //
-        //  1. To allow appending.  We need to generate unique output file names to avoid
-        //     overwriting existing files (either exist before the write job, or are just written
-        //     by other tasks within the same write job).
-        //
-        //  2. To allow dynamic partitioning.  Default `getDefaultWorkFile` uses
-        //     `FileOutputCommitter.getWorkPath()`, which points to the base directory of all
-        //     partitions in the case of dynamic partitioning.
-        override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-          val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
-          val split = context.getTaskAttemptID.getTaskID.getId
-          new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
-        }
-      }
-    }
-
-    outputFormat.getRecordWriter(context)
-  }
-
-  override def writeInternal(row: InternalRow): Unit = recordWriter.write(null, row)
-
-  override def close(): Unit = recordWriter.close(context)
-}
-
-private[sql] class ParquetRelation2(
-    override val paths: Array[String],
-    private val maybeDataSchema: Option[StructType],
-    // This is for metastore conversion.
-    private val maybePartitionSpec: Option[PartitionSpec],
-    override val userDefinedPartitionColumns: Option[StructType],
-    parameters: Map[String, String])(
-    val sqlContext: SQLContext)
-  extends HadoopFsRelation(maybePartitionSpec)
-  with Logging {
-
-  private[sql] def this(
-      paths: Array[String],
-      maybeDataSchema: Option[StructType],
-      maybePartitionSpec: Option[PartitionSpec],
-      parameters: Map[String, String])(
-      sqlContext: SQLContext) = {
-    this(
-      paths,
-      maybeDataSchema,
-      maybePartitionSpec,
-      maybePartitionSpec.map(_.partitionColumns),
-      parameters)(sqlContext)
-  }
-
-  // Should we merge schemas from all Parquet part-files?
-  private val shouldMergeSchemas =
-    parameters
-      .get(ParquetRelation2.MERGE_SCHEMA)
-      .map(_.toBoolean)
-      .getOrElse(sqlContext.conf.getConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED))
-
-  private val maybeMetastoreSchema = parameters
-    .get(ParquetRelation2.METASTORE_SCHEMA)
-    .map(DataType.fromJson(_).asInstanceOf[StructType])
-
-  private lazy val metadataCache: MetadataCache = {
-    val meta = new MetadataCache
-    meta.refresh()
-    meta
-  }
-
-  override def equals(other: Any): Boolean = other match {
-    case that: ParquetRelation2 =>
-      val schemaEquality = if (shouldMergeSchemas) {
-        this.shouldMergeSchemas == that.shouldMergeSchemas
-      } else {
-        this.dataSchema == that.dataSchema &&
-          this.schema == that.schema
-      }
-
-      this.paths.toSet == that.paths.toSet &&
-        schemaEquality &&
-        this.maybeDataSchema == that.maybeDataSchema &&
-        this.partitionColumns == that.partitionColumns
-
-    case _ => false
-  }
-
-  override def hashCode(): Int = {
-    if (shouldMergeSchemas) {
-      Objects.hashCode(
-        Boolean.box(shouldMergeSchemas),
-        paths.toSet,
-        maybeDataSchema,
-        partitionColumns)
-    } else {
-      Objects.hashCode(
-        Boolean.box(shouldMergeSchemas),
-        paths.toSet,
-        dataSchema,
-        schema,
-        maybeDataSchema,
-        partitionColumns)
-    }
-  }
-
-  /** Constraints on schema of dataframe to be stored. */
-  private def checkConstraints(schema: StructType): Unit = {
-    if (schema.fieldNames.length != schema.fieldNames.distinct.length) {
-      val duplicateColumns = schema.fieldNames.groupBy(identity).collect {
-        case (x, ys) if ys.length > 1 => "\"" + x + "\""
-      }.mkString(", ")
-      throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
-        s"cannot save to parquet format")
-    }
-  }
-
-  override def dataSchema: StructType = {
-    val schema = maybeDataSchema.getOrElse(metadataCache.dataSchema)
-    // check if schema satisfies the constraints
-    // before moving forward
-    checkConstraints(schema)
-    schema
-  }
-
-  override private[sql] def refresh(): Unit = {
-    super.refresh()
-    metadataCache.refresh()
-  }
-
-  // Parquet data source always uses Catalyst internal representations.
-  override val needConversion: Boolean = false
-
-  override def sizeInBytes: Long = metadataCache.dataStatuses.map(_.getLen).sum
-
-  override def prepareJobForWrite(job: Job): OutputWriterFactory = {
-    val conf = ContextUtil.getConfiguration(job)
-
-    val committerClass =
-      conf.getClass(
-        SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key,
-        classOf[ParquetOutputCommitter],
-        classOf[ParquetOutputCommitter])
-
-    if (conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key) == null) {
-      logInfo("Using default output committer for Parquet: " +
-        classOf[ParquetOutputCommitter].getCanonicalName)
-    } else {
-      logInfo("Using user defined output committer for Parquet: " + committerClass.getCanonicalName)
-    }
-
-    conf.setClass(
-      SQLConf.OUTPUT_COMMITTER_CLASS.key,
-      committerClass,
-      classOf[ParquetOutputCommitter])
-
-    // We're not really using `ParquetOutputFormat[Row]` for writing data here, because we override
-    // it in `ParquetOutputWriter` to support appending and dynamic partitioning.  The reason why
-    // we set it here is to setup the output committer class to `ParquetOutputCommitter`, which is
-    // bundled with `ParquetOutputFormat[Row]`.
-    job.setOutputFormatClass(classOf[ParquetOutputFormat[Row]])
-
-    // TODO There's no need to use two kinds of WriteSupport
-    // We should unify them. `SpecificMutableRow` can process both atomic (primitive) types and
-    // complex types.
-    val writeSupportClass =
-      if (dataSchema.map(_.dataType).forall(ParquetTypesConverter.isPrimitiveType)) {
-        classOf[MutableRowWriteSupport]
-      } else {
-        classOf[RowWriteSupport]
-      }
-
-    ParquetOutputFormat.setWriteSupportClass(job, writeSupportClass)
-    RowWriteSupport.setSchema(dataSchema.toAttributes, conf)
-
-    // Sets compression scheme
-    conf.set(
-      ParquetOutputFormat.COMPRESSION,
-      ParquetRelation
-        .shortParquetCompressionCodecNames
-        .getOrElse(
-          sqlContext.conf.parquetCompressionCodec.toUpperCase,
-          CompressionCodecName.UNCOMPRESSED).name())
-
-    new OutputWriterFactory {
-      override def newInstance(
-          path: String, dataSchema: StructType, context: TaskAttemptContext): OutputWriter = {
-        new ParquetOutputWriter(path, context)
-      }
-    }
-  }
-
-  override def buildScan(
-      requiredColumns: Array[String],
-      filters: Array[Filter],
-      inputFiles: Array[FileStatus],
-      broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = {
-    val useMetadataCache = sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA)
-    val parquetFilterPushDown = sqlContext.conf.parquetFilterPushDown
-    val assumeBinaryIsString = sqlContext.conf.isParquetBinaryAsString
-    val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
-    val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
-
-    // Create the function to set variable Parquet confs at both driver and executor side.
-    val initLocalJobFuncOpt =
-      ParquetRelation2.initializeLocalJobFunc(
-        requiredColumns,
-        filters,
-        dataSchema,
-        useMetadataCache,
-        parquetFilterPushDown,
-        assumeBinaryIsString,
-        assumeInt96IsTimestamp,
-        followParquetFormatSpec) _
-
-    // Create the function to set input paths at the driver side.
-    val setInputPaths = ParquetRelation2.initializeDriverSideJobFunc(inputFiles) _
-
-    Utils.withDummyCallSite(sqlContext.sparkContext) {
-      new SqlNewHadoopRDD(
-        sc = sqlContext.sparkContext,
-        broadcastedConf = broadcastedConf,
-        initDriverSideJobFuncOpt = Some(setInputPaths),
-        initLocalJobFuncOpt = Some(initLocalJobFuncOpt),
-        inputFormatClass = classOf[ParquetInputFormat[InternalRow]],
-        keyClass = classOf[Void],
-        valueClass = classOf[InternalRow]) {
-
-        val cacheMetadata = useMetadataCache
-
-        @transient val cachedStatuses = inputFiles.map { f =>
-          // In order to encode the authority of a Path containing special characters such as '/'
-          // (which does happen in some S3N credentials), we need to use the string returned by the
-          // URI of the path to create a new Path.
-          val pathWithEscapedAuthority = escapePathUserInfo(f.getPath)
-          new FileStatus(
-            f.getLen, f.isDir, f.getReplication, f.getBlockSize, f.getModificationTime,
-            f.getAccessTime, f.getPermission, f.getOwner, f.getGroup, pathWithEscapedAuthority)
-        }.toSeq
-
-        private def escapePathUserInfo(path: Path): Path = {
-          val uri = path.toUri
-          new Path(new URI(
-            uri.getScheme, uri.getRawUserInfo, uri.getHost, uri.getPort, uri.getPath,
-            uri.getQuery, uri.getFragment))
-        }
-
-        // Overridden so we can inject our own cached files statuses.
-        override def getPartitions: Array[SparkPartition] = {
-          val inputFormat = new ParquetInputFormat[InternalRow] {
-            override def listStatus(jobContext: JobContext): JList[FileStatus] = {
-              if (cacheMetadata) cachedStatuses else super.listStatus(jobContext)
-            }
-          }
-
-          val jobContext = newJobContext(getConf(isDriverSide = true), jobId)
-          val rawSplits = inputFormat.getSplits(jobContext)
-
-          Array.tabulate[SparkPartition](rawSplits.size) { i =>
-            new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
-          }
-        }
-      }.values.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
-    }
-  }
-
-  private class MetadataCache {
-    // `FileStatus` objects of all "_metadata" files.
-    private var metadataStatuses: Array[FileStatus] = _
-
-    // `FileStatus` objects of all "_common_metadata" files.
-    private var commonMetadataStatuses: Array[FileStatus] = _
-
-    // `FileStatus` objects of all data files (Parquet part-files).
-    var dataStatuses: Array[FileStatus] = _
-
-    // Schema of the actual Parquet files, without partition columns discovered from partition
-    // directory paths.
-    var dataSchema: StructType = null
-
-    // Schema of the whole table, including partition columns.
-    var schema: StructType = _
-
-    // Cached leaves
-    var cachedLeaves: Set[FileStatus] = null
-
-    /**
-     * Refreshes `FileStatus`es, footers, partition spec, and table schema.
-     */
-    def refresh(): Unit = {
-      val currentLeafStatuses = cachedLeafStatuses()
-
-      // Check if cachedLeafStatuses is changed or not
-      val leafStatusesChanged = (cachedLeaves == null) ||
-        !cachedLeaves.equals(currentLeafStatuses)
-
-      if (leafStatusesChanged) {
-        cachedLeaves = currentLeafStatuses.toIterator.toSet
-
-        // Lists `FileStatus`es of all leaf nodes (files) under all base directories.
-        val leaves = currentLeafStatuses.filter { f =>
-          isSummaryFile(f.getPath) ||
-            !(f.getPath.getName.startsWith("_") || f.getPath.getName.startsWith("."))
-        }.toArray
-
-        dataStatuses = leaves.filterNot(f => isSummaryFile(f.getPath))
-        metadataStatuses =
-          leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_METADATA_FILE)
-        commonMetadataStatuses =
-          leaves.filter(_.getPath.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE)
-
-        dataSchema = {
-          val dataSchema0 = maybeDataSchema
-            .orElse(readSchema())
-            .orElse(maybeMetastoreSchema)
-            .getOrElse(throw new AnalysisException(
-              s"Failed to discover schema of Parquet file(s) in the following location(s):\n" +
-                paths.mkString("\n\t")))
-
-          // If this Parquet relation is converted from a Hive Metastore table, must reconcile case
-          // case insensitivity issue and possible schema mismatch (probably caused by schema
-          // evolution).
-          maybeMetastoreSchema
-            .map(ParquetRelation2.mergeMetastoreParquetSchema(_, dataSchema0))
-            .getOrElse(dataSchema0)
-        }
-      }
-    }
-
-    private def isSummaryFile(file: Path): Boolean = {
-      file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
-        file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
-    }
-
-    private def readSchema(): Option[StructType] = {
-      // Sees which file(s) we need to touch in order to figure out the schema.
-      //
-      // Always tries the summary files first if users don't require a merged schema.  In this case,
-      // "_common_metadata" is more preferable than "_metadata" because it doesn't contain row
-      // groups information, and could be much smaller for large Parquet files with lots of row
-      // groups.  If no summary file is available, falls back to some random part-file.
-      //
-      // NOTE: Metadata stored in the summary files are merged from all part-files.  However, for
-      // user defined key-value metadata (in which we store Spark SQL schema), Parquet doesn't know
-      // how to merge them correctly if some key is associated with different values in different
-      // part-files.  When this happens, Parquet simply gives up generating the summary file.  This
-      // implies that if a summary file presents, then:
-      //
-      //   1. Either all part-files have exactly the same Spark SQL schema, or
-      //   2. Some part-files don't contain Spark SQL schema in the key-value metadata at all (thus
-      //      their schemas may differ from each other).
-      //
-      // Here we tend to be pessimistic and take the second case into account.  Basically this means
-      // we can't trust the summary files if users require a merged schema, and must touch all part-
-      // files to do the merge.
-      val filesToTouch =
-        if (shouldMergeSchemas) {
-          // Also includes summary files, 'cause there might be empty partition directories.
-          (metadataStatuses ++ commonMetadataStatuses ++ dataStatuses).toSeq
-        } else {
-          // Tries any "_common_metadata" first. Parquet files written by old versions or Parquet
-          // don't have this.
-          commonMetadataStatuses.headOption
-            // Falls back to "_metadata"
-            .orElse(metadataStatuses.headOption)
-            // Summary file(s) not found, the Parquet file is either corrupted, or different part-
-            // files contain conflicting user defined metadata (two or more values are associated
-            // with a same key in different files).  In either case, we fall back to any of the
-            // first part-file, and just assume all schemas are consistent.
-            .orElse(dataStatuses.headOption)
-            .toSeq
-        }
-
-      assert(
-        filesToTouch.nonEmpty || maybeDataSchema.isDefined || maybeMetastoreSchema.isDefined,
-        "No predefined schema found, " +
-          s"and no Parquet data files or summary files found under ${paths.mkString(", ")}.")
-
-      ParquetRelation2.mergeSchemasInParallel(filesToTouch, sqlContext)
-    }
-  }
-}
-
-private[sql] object ParquetRelation2 extends Logging {
-  // Whether we should merge schemas collected from all Parquet part-files.
-  private[sql] val MERGE_SCHEMA = "mergeSchema"
-
-  // Hive Metastore schema, used when converting Metastore Parquet tables.  This option is only used
-  // internally.
-  private[sql] val METASTORE_SCHEMA = "metastoreSchema"
-
-  /** This closure sets various Parquet configurations at both driver side and executor side. */
-  private[parquet] def initializeLocalJobFunc(
-      requiredColumns: Array[String],
-      filters: Array[Filter],
-      dataSchema: StructType,
-      useMetadataCache: Boolean,
-      parquetFilterPushDown: Boolean,
-      assumeBinaryIsString: Boolean,
-      assumeInt96IsTimestamp: Boolean,
-      followParquetFormatSpec: Boolean)(job: Job): Unit = {
-    val conf = job.getConfiguration
-    conf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[RowReadSupport].getName)
-
-    // Try to push down filters when filter push-down is enabled.
-    if (parquetFilterPushDown) {
-      filters
-        // Collects all converted Parquet filter predicates. Notice that not all predicates can be
-        // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap`
-        // is used here.
-        .flatMap(ParquetFilters.createFilter(dataSchema, _))
-        .reduceOption(FilterApi.and)
-        .foreach(ParquetInputFormat.setFilterPredicate(conf, _))
-    }
-
-    conf.set(RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA, {
-      val requestedSchema = StructType(requiredColumns.map(dataSchema(_)))
-      ParquetTypesConverter.convertToString(requestedSchema.toAttributes)
-    })
-
-    conf.set(
-      RowWriteSupport.SPARK_ROW_SCHEMA,
-      ParquetTypesConverter.convertToString(dataSchema.toAttributes))
-
-    // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata
-    conf.setBoolean(SQLConf.PARQUET_CACHE_METADATA.key, useMetadataCache)
-
-    // Sets flags for Parquet schema conversion
-    conf.setBoolean(SQLConf.PARQUET_BINARY_AS_STRING.key, assumeBinaryIsString)
-    conf.setBoolean(SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, assumeInt96IsTimestamp)
-    conf.setBoolean(SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key, followParquetFormatSpec)
-  }
-
-  /** This closure sets input paths at the driver side. */
-  private[parquet] def initializeDriverSideJobFunc(
-      inputFiles: Array[FileStatus])(job: Job): Unit = {
-    // We side the input paths at the driver side.
-    logInfo(s"Reading Parquet file(s) from ${inputFiles.map(_.getPath).mkString(", ")}")
-    if (inputFiles.nonEmpty) {
-      FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*)
-    }
-  }
-
-  private[parquet] def readSchema(
-      footers: Seq[Footer], sqlContext: SQLContext): Option[StructType] = {
-
-    def parseParquetSchema(schema: MessageType): StructType = {
-      StructType.fromAttributes(
-        // TODO Really no need to use `Attribute` here, we only need to know the data type.
-        ParquetTypesConverter.convertToAttributes(
-          schema,
-          sqlContext.conf.isParquetBinaryAsString,
-          sqlContext.conf.isParquetINT96AsTimestamp))
-    }
-
-    val seen = mutable.HashSet[String]()
-    val finalSchemas: Seq[StructType] = footers.flatMap { footer =>
-      val metadata = footer.getParquetMetadata.getFileMetaData
-      val serializedSchema = metadata
-        .getKeyValueMetaData
-        .toMap
-        .get(RowReadSupport.SPARK_METADATA_KEY)
-      if (serializedSchema.isEmpty) {
-        // Falls back to Parquet schema if no Spark SQL schema found.
-        Some(parseParquetSchema(metadata.getSchema))
-      } else if (!seen.contains(serializedSchema.get)) {
-        seen += serializedSchema.get
-
-        // Don't throw even if we failed to parse the serialized Spark schema. Just fallback to
-        // whatever is available.
-        Some(Try(DataType.fromJson(serializedSchema.get))
-          .recover { case _: Throwable =>
-            logInfo(
-              s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " +
-                "falling back to the deprecated DataType.fromCaseClassString parser.")
-            DataType.fromCaseClassString(serializedSchema.get)
-          }
-          .recover { case cause: Throwable =>
-            logWarning(
-              s"""Failed to parse serialized Spark schema in Parquet key-value metadata:
-                 |\t$serializedSchema
-               """.stripMargin,
-              cause)
-          }
-          .map(_.asInstanceOf[StructType])
-          .getOrElse {
-            // Falls back to Parquet schema if Spark SQL schema can't be parsed.
-            parseParquetSchema(metadata.getSchema)
-          })
-      } else {
-        None
-      }
-    }
-
-    finalSchemas.reduceOption { (left, right) =>
-      try left.merge(right) catch { case e: Throwable =>
-        throw new SparkException(s"Failed to merge incompatible schemas $left and $right", e)
-      }
-    }
-  }
-
-  /**
-   * Reconciles Hive Metastore case insensitivity issue and data type conflicts between Metastore
-   * schema and Parquet schema.
-   *
-   * Hive doesn't retain case information, while Parquet is case sensitive. On the other hand, the
-   * schema read from Parquet files may be incomplete (e.g. older versions of Parquet doesn't
-   * distinguish binary and string).  This method generates a correct schema by merging Metastore
-   * schema data types and Parquet schema field names.
-   */
-  private[parquet] def mergeMetastoreParquetSchema(
-      metastoreSchema: StructType,
-      parquetSchema: StructType): StructType = {
-    def schemaConflictMessage: String =
-      s"""Converting Hive Metastore Parquet, but detected conflicting schemas. Metastore schema:
-         |${metastoreSchema.prettyJson}
-         |
-         |Parquet schema:
-         |${parquetSchema.prettyJson}
-       """.stripMargin
-
-    val mergedParquetSchema = mergeMissingNullableFields(metastoreSchema, parquetSchema)
-
-    assert(metastoreSchema.size <= mergedParquetSchema.size, schemaConflictMessage)
-
-    val ordinalMap = metastoreSchema.zipWithIndex.map {
-      case (field, index) => field.name.toLowerCase -> index
-    }.toMap
-
-    val reorderedParquetSchema = mergedParquetSchema.sortBy(f =>
-      ordinalMap.getOrElse(f.name.toLowerCase, metastoreSchema.size + 1))
-
-    StructType(metastoreSchema.zip(reorderedParquetSchema).map {
-      // Uses Parquet field names but retains Metastore data types.
-      case (mSchema, pSchema) if mSchema.name.toLowerCase == pSchema.name.toLowerCase =>
-        mSchema.copy(name = pSchema.name)
-      case _ =>
-        throw new SparkException(schemaConflictMessage)
-    })
-  }
-
-  /**
-   * Returns the original schema from the Parquet file with any missing nullable fields from the
-   * Hive Metastore schema merged in.
-   *
-   * When constructing a DataFrame from a collection of structured data, the resulting object has
-   * a schema corresponding to the union of the fields present in each element of the collection.
-   * Spark SQL simply assigns a null value to any field that isn't present for a particular row.
-   * In some cases, it is possible that a given table partition stored as a Parquet file doesn't
-   * contain a particular nullable field in its schema despite that field being present in the
-   * table schema obtained from the Hive Metastore. This method returns a schema representing the
-   * Parquet file schema along with any additional nullable fields from the Metastore schema
-   * merged in.
-   */
-  private[parquet] def mergeMissingNullableFields(
-      metastoreSchema: StructType,
-      parquetSchema: StructType): StructType = {
-    val fieldMap = metastoreSchema.map(f => f.name.toLowerCase -> f).toMap
-    val missingFields = metastoreSchema
-      .map(_.name.toLowerCase)
-      .diff(parquetSchema.map(_.name.toLowerCase))
-      .map(fieldMap(_))
-      .filter(_.nullable)
-    StructType(parquetSchema ++ missingFields)
-  }
-
-  /**
-   * Figures out a merged Parquet schema with a distributed Spark job.
-   *
-   * Note that locality is not taken into consideration here because:
-   *
-   *  1. For a single Parquet part-file, in most cases the footer only resides in the last block of
-   *     that file.  Thus we only need to retrieve the location of the last block.  However, Hadoop
-   *     `FileSystem` only provides API to retrieve locations of all blocks, which can be
-   *     potentially expensive.
-   *
-   *  2. This optimization is mainly useful for S3, where file metadata operations can be pretty
-   *     slow.  And basically locality is not available when using S3 (you can't run computation on
-   *     S3 nodes).
-   */
-  def mergeSchemasInParallel(
-      filesToTouch: Seq[FileStatus], sqlContext: SQLContext): Option[StructType] = {
-    val assumeBinaryIsString = sqlContext.conf.isParquetBinaryAsString
-    val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
-    val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
-    val serializedConf = new SerializableConfiguration(sqlContext.sparkContext.hadoopConfiguration)
-
-    // HACK ALERT:
-    //
-    // Parquet requires `FileStatus`es to read footers.  Here we try to send cached `FileStatus`es
-    // to executor side to avoid fetching them again.  However, `FileStatus` is not `Serializable`
-    // but only `Writable`.  What makes it worth, for some reason, `FileStatus` doesn't play well
-    // with `SerializableWritable[T]` and always causes a weird `IllegalStateException`.  These
-    // facts virtually prevents us to serialize `FileStatus`es.
-    //
-    // Since Parquet only relies on path and length information of those `FileStatus`es to read
-    // footers, here we just extract them (which can be easily serialized), send them to executor
-    // side, and resemble fake `FileStatus`es there.
-    val partialFileStatusInfo = filesToTouch.map(f => (f.getPath.toString, f.getLen))
-
-    // Issues a Spark job to read Parquet schema in parallel.
-    val partiallyMergedSchemas =
-      sqlContext
-        .sparkContext
-        .parallelize(partialFileStatusInfo)
-        .mapPartitions { iterator =>
-          // Resembles fake `FileStatus`es with serialized path and length information.
-          val fakeFileStatuses = iterator.map { case (path, length) =>
-            new FileStatus(length, false, 0, 0, 0, 0, null, null, null, new Path(path))
-          }.toSeq
-
-          // Skips row group information since we only need the schema
-          val skipRowGroups = true
-
-          // Reads footers in multi-threaded manner within each task
-          val footers =
-            ParquetFileReader.readAllFootersInParallel(
-              serializedConf.value, fakeFileStatuses, skipRowGroups)
-
-          // Converter used to convert Parquet `MessageType` to Spark SQL `StructType`
-          val converter =
-            new CatalystSchemaConverter(
-              assumeBinaryIsString = assumeBinaryIsString,
-              assumeInt96IsTimestamp = assumeInt96IsTimestamp,
-              followParquetFormatSpec = followParquetFormatSpec)
-
-          footers.map { footer =>
-            ParquetRelation2.readSchemaFromFooter(footer, converter)
-          }.reduceOption(_ merge _).iterator
-        }.collect()
-
-    partiallyMergedSchemas.reduceOption(_ merge _)
-  }
-
-  /**
-   * Reads Spark SQL schema from a Parquet footer.  If a valid serialized Spark SQL schema string
-   * can be found in the file metadata, returns the deserialized [[StructType]], otherwise, returns
-   * a [[StructType]] converted from the [[MessageType]] stored in this footer.
-   */
-  def readSchemaFromFooter(
-      footer: Footer, converter: CatalystSchemaConverter): StructType = {
-    val fileMetaData = footer.getParquetMetadata.getFileMetaData
-    fileMetaData
-      .getKeyValueMetaData
-      .toMap
-      .get(RowReadSupport.SPARK_METADATA_KEY)
-      .flatMap(deserializeSchemaString)
-      .getOrElse(converter.convert(fileMetaData.getSchema))
-  }
-
-  private def deserializeSchemaString(schemaString: String): Option[StructType] = {
-    // Tries to deserialize the schema string as JSON first, then falls back to the case class
-    // string parser (data generated by older versions of Spark SQL uses this format).
-    Try(DataType.fromJson(schemaString).asInstanceOf[StructType]).recover {
-      case _: Throwable =>
-        logInfo(
-          s"Serialized Spark schema in Parquet key-value metadata is not in JSON format, " +
-            "falling back to the deprecated DataType.fromCaseClassString parser.")
-        DataType.fromCaseClassString(schemaString).asInstanceOf[StructType]
-    }.recoverWith {
-      case cause: Throwable =>
-        logWarning(
-          "Failed to parse and ignored serialized Spark schema in " +
-            s"Parquet key-value metadata:\n\t$schemaString", cause)
-        Failure(cause)
-    }.toOption
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
index 23df102cd951d..b6a7c4fbddbdc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.parquet
 
-import org.scalatest.BeforeAndAfterAll
 import org.apache.parquet.filter2.predicate.Operators._
 import org.apache.parquet.filter2.predicate.{FilterPredicate, Operators}
 
@@ -40,7 +39,7 @@ import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
  * 2. `Tuple1(Option(x))` is used together with `AnyVal` types like `Int` to ensure the inferred
  *    data type is nullable.
  */
-class ParquetFilterSuiteBase extends QueryTest with ParquetTest {
+class ParquetFilterSuite extends QueryTest with ParquetTest {
   lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
 
   private def checkFilterPredicate(
@@ -56,17 +55,9 @@ class ParquetFilterSuiteBase extends QueryTest with ParquetTest {
         .select(output.map(e => Column(e)): _*)
         .where(Column(predicate))
 
-      val maybeAnalyzedPredicate = {
-        val forParquetTableScan = query.queryExecution.executedPlan.collect {
-          case plan: ParquetTableScan => plan.columnPruningPred
-        }.flatten.reduceOption(_ && _)
-
-        val forParquetDataSource = query.queryExecution.optimizedPlan.collect {
-          case PhysicalOperation(_, filters, LogicalRelation(_: ParquetRelation2)) => filters
-        }.flatten.reduceOption(_ && _)
-
-        forParquetTableScan.orElse(forParquetDataSource)
-      }
+      val maybeAnalyzedPredicate = query.queryExecution.optimizedPlan.collect {
+        case PhysicalOperation(_, filters, LogicalRelation(_: ParquetRelation)) => filters
+      }.flatten.reduceOption(_ && _)
 
       assert(maybeAnalyzedPredicate.isDefined)
       maybeAnalyzedPredicate.foreach { pred =>
@@ -98,7 +89,7 @@ class ParquetFilterSuiteBase extends QueryTest with ParquetTest {
       (predicate: Predicate, filterClass: Class[_ <: FilterPredicate], expected: Seq[Row])
       (implicit df: DataFrame): Unit = {
     def checkBinaryAnswer(df: DataFrame, expected: Seq[Row]) = {
-      assertResult(expected.map(_.getAs[Array[Byte]](0).mkString(",")).toSeq.sorted) {
+      assertResult(expected.map(_.getAs[Array[Byte]](0).mkString(",")).sorted) {
         df.map(_.getAs[Array[Byte]](0).mkString(",")).collect().toSeq.sorted
       }
     }
@@ -308,18 +299,6 @@ class ParquetFilterSuiteBase extends QueryTest with ParquetTest {
         '_1 < 2.b || '_1 > 3.b, classOf[Operators.Or], Seq(Row(1.b), Row(4.b)))
     }
   }
-}
-
-class ParquetDataSourceOnFilterSuite extends ParquetFilterSuiteBase with BeforeAndAfterAll {
-  lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi
-
-  override protected def beforeAll(): Unit = {
-    sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true)
-  }
-
-  override protected def afterAll(): Unit = {
-    sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
 
   test("SPARK-6554: don't push down predicates which reference partition columns") {
     import sqlContext.implicits._
@@ -338,37 +317,3 @@ class ParquetDataSourceOnFilterSuite extends ParquetFilterSuiteBase with BeforeA
     }
   }
 }
-
-class ParquetDataSourceOffFilterSuite extends ParquetFilterSuiteBase with BeforeAndAfterAll {
-  lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi
-
-  override protected def beforeAll(): Unit = {
-    sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false)
-  }
-
-  override protected def afterAll(): Unit = {
-    sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
-
-  test("SPARK-6742: don't push down predicates which reference partition columns") {
-    import sqlContext.implicits._
-
-    withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") {
-      withTempPath { dir =>
-        val path = s"${dir.getCanonicalPath}/part=1"
-        (1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(path)
-
-        // If the "part = 1" filter gets pushed down, this query will throw an exception since
-        // "part" is not a valid column in the actual Parquet file
-        val df = DataFrame(sqlContext, org.apache.spark.sql.parquet.ParquetRelation(
-          path,
-          Some(sqlContext.sparkContext.hadoopConfiguration), sqlContext,
-          Seq(AttributeReference("part", IntegerType, false)()) ))
-
-        checkAnswer(
-          df.filter("a = 1 or part = 1"),
-          (1 to 3).map(i => Row(1, i, i.toString)))
-      }
-    }
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
index 3a5b860484e86..b5314a3dd92e5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
@@ -32,7 +32,6 @@ import org.apache.parquet.hadoop.metadata.{CompressionCodecName, FileMetaData, P
 import org.apache.parquet.hadoop.{Footer, ParquetFileWriter, ParquetOutputCommitter, ParquetWriter}
 import org.apache.parquet.io.api.RecordConsumer
 import org.apache.parquet.schema.{MessageType, MessageTypeParser}
-import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkException
 import org.apache.spark.sql._
@@ -63,7 +62,7 @@ private[parquet] class TestGroupWriteSupport(schema: MessageType) extends WriteS
 /**
  * A test suite that tests basic Parquet I/O.
  */
-class ParquetIOSuiteBase extends QueryTest with ParquetTest {
+class ParquetIOSuite extends QueryTest with ParquetTest {
   lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
   import sqlContext.implicits._
 
@@ -357,7 +356,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest {
       """.stripMargin)
 
     withTempPath { location =>
-      val extraMetadata = Map(RowReadSupport.SPARK_METADATA_KEY -> sparkSchema.toString)
+      val extraMetadata = Map(CatalystReadSupport.SPARK_METADATA_KEY -> sparkSchema.toString)
       val fileMetadata = new FileMetaData(parquetSchema, extraMetadata, "Spark")
       val path = new Path(location.getCanonicalPath)
 
@@ -422,26 +421,6 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest {
       }
     }
   }
-}
-
-class BogusParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
-  extends ParquetOutputCommitter(outputPath, context) {
-
-  override def commitJob(jobContext: JobContext): Unit = {
-    sys.error("Intentional exception for testing purposes")
-  }
-}
-
-class ParquetDataSourceOnIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll {
-  private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi
-
-  override protected def beforeAll(): Unit = {
-    sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true)
-  }
-
-  override protected def afterAll(): Unit = {
-    sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API.key, originalConf.toString)
-  }
 
   test("SPARK-6330 regression test") {
     // In 1.3.0, save to fs other than file: without configuring core-site.xml would get:
@@ -456,14 +435,10 @@ class ParquetDataSourceOnIOSuite extends ParquetIOSuiteBase with BeforeAndAfterA
   }
 }
 
-class ParquetDataSourceOffIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll {
-  private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi
-
-  override protected def beforeAll(): Unit = {
-    sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false)
-  }
+class BogusParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
+  extends ParquetOutputCommitter(outputPath, context) {
 
-  override protected def afterAll(): Unit = {
-    sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
+  override def commitJob(jobContext: JobContext): Unit = {
+    sys.error("Intentional exception for testing purposes")
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
index 7f16b1125c7a5..2eef10189f11c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
@@ -467,7 +467,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest {
       (1 to 10).map(i => (i, i.toString)).toDF("a", "b").write.parquet(dir.getCanonicalPath)
       val queryExecution = sqlContext.read.parquet(dir.getCanonicalPath).queryExecution
       queryExecution.analyzed.collectFirst {
-        case LogicalRelation(relation: ParquetRelation2) =>
+        case LogicalRelation(relation: ParquetRelation) =>
           assert(relation.partitionSpec === PartitionSpec.emptySpec)
       }.getOrElse {
         fail(s"Expecting a ParquetRelation2, but got:\n$queryExecution")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 21007d95ed752..c037faf4cfd92 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.parquet
 
 import org.apache.hadoop.fs.Path
-import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{QueryTest, Row, SQLConf}
@@ -26,7 +25,7 @@ import org.apache.spark.sql.{QueryTest, Row, SQLConf}
 /**
  * A test suite that tests various Parquet queries.
  */
-class ParquetQuerySuiteBase extends QueryTest with ParquetTest {
+class ParquetQuerySuite extends QueryTest with ParquetTest {
   lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
   import sqlContext.sql
 
@@ -164,27 +163,3 @@ class ParquetQuerySuiteBase extends QueryTest with ParquetTest {
     }
   }
 }
-
-class ParquetDataSourceOnQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll {
-  private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi
-
-  override protected def beforeAll(): Unit = {
-    sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true)
-  }
-
-  override protected def afterAll(): Unit = {
-    sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
-}
-
-class ParquetDataSourceOffQuerySuite extends ParquetQuerySuiteBase with BeforeAndAfterAll {
-  private lazy val originalConf = sqlContext.conf.parquetUseDataSourceApi
-
-  override protected def beforeAll(): Unit = {
-    sqlContext.conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false)
-  }
-
-  override protected def afterAll(): Unit = {
-    sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
index fa629392674bd..4a0b3b60f419d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
@@ -378,7 +378,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
         StructField("lowerCase", StringType),
         StructField("UPPERCase", DoubleType, nullable = false)))) {
 
-      ParquetRelation2.mergeMetastoreParquetSchema(
+      ParquetRelation.mergeMetastoreParquetSchema(
         StructType(Seq(
           StructField("lowercase", StringType),
           StructField("uppercase", DoubleType, nullable = false))),
@@ -393,7 +393,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       StructType(Seq(
         StructField("UPPERCase", DoubleType, nullable = false)))) {
 
-      ParquetRelation2.mergeMetastoreParquetSchema(
+      ParquetRelation.mergeMetastoreParquetSchema(
         StructType(Seq(
           StructField("uppercase", DoubleType, nullable = false))),
 
@@ -404,7 +404,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
 
     // Metastore schema contains additional non-nullable fields.
     assert(intercept[Throwable] {
-      ParquetRelation2.mergeMetastoreParquetSchema(
+      ParquetRelation.mergeMetastoreParquetSchema(
         StructType(Seq(
           StructField("uppercase", DoubleType, nullable = false),
           StructField("lowerCase", BinaryType, nullable = false))),
@@ -415,7 +415,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
 
     // Conflicting non-nullable field names
     intercept[Throwable] {
-      ParquetRelation2.mergeMetastoreParquetSchema(
+      ParquetRelation.mergeMetastoreParquetSchema(
         StructType(Seq(StructField("lower", StringType, nullable = false))),
         StructType(Seq(StructField("lowerCase", BinaryType))))
     }
@@ -429,7 +429,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
         StructField("firstField", StringType, nullable = true),
         StructField("secondField", StringType, nullable = true),
         StructField("thirdfield", StringType, nullable = true)))) {
-      ParquetRelation2.mergeMetastoreParquetSchema(
+      ParquetRelation.mergeMetastoreParquetSchema(
         StructType(Seq(
           StructField("firstfield", StringType, nullable = true),
           StructField("secondfield", StringType, nullable = true),
@@ -442,7 +442,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     // Merge should fail if the Metastore contains any additional fields that are not
     // nullable.
     assert(intercept[Throwable] {
-      ParquetRelation2.mergeMetastoreParquetSchema(
+      ParquetRelation.mergeMetastoreParquetSchema(
         StructType(Seq(
           StructField("firstfield", StringType, nullable = true),
           StructField("secondfield", StringType, nullable = true),
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 4cdb83c5116f9..1b8edefef4093 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -444,9 +444,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
       HiveDDLStrategy,
       DDLStrategy,
       TakeOrderedAndProject,
-      ParquetOperations,
       InMemoryScans,
-      ParquetConversion, // Must be before HiveTableScans
       HiveTableScans,
       DataSinks,
       Scripts,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 0a2121c955871..262923531216f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -21,7 +21,6 @@ import scala.collection.JavaConversions._
 
 import com.google.common.base.Objects
 import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache}
-
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.common.StatsSetupConst
 import org.apache.hadoop.hive.metastore.Warehouse
@@ -30,7 +29,6 @@ import org.apache.hadoop.hive.ql.metadata._
 import org.apache.hadoop.hive.ql.plan.TableDesc
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{Catalog, MultiInstanceRelation, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions._
@@ -39,10 +37,11 @@ import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.execution.datasources
-import org.apache.spark.sql.execution.datasources.{Partition => ParquetPartition, PartitionSpec, CreateTableUsingAsSelect, ResolvedDataSource, LogicalRelation}
+import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
-import org.apache.spark.sql.parquet.ParquetRelation2
+import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
 
 
 private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: HiveContext)
@@ -260,8 +259,8 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     // serialize the Metastore schema to JSON and pass it as a data source option because of the
     // evil case insensitivity issue, which is reconciled within `ParquetRelation2`.
     val parquetOptions = Map(
-      ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json,
-      ParquetRelation2.MERGE_SCHEMA -> mergeSchema.toString)
+      ParquetRelation.METASTORE_SCHEMA -> metastoreSchema.json,
+      ParquetRelation.MERGE_SCHEMA -> mergeSchema.toString)
     val tableIdentifier =
       QualifiedTableName(metastoreRelation.databaseName, metastoreRelation.tableName)
 
@@ -272,7 +271,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         partitionSpecInMetastore: Option[PartitionSpec]): Option[LogicalRelation] = {
       cachedDataSourceTables.getIfPresent(tableIdentifier) match {
         case null => None // Cache miss
-        case logical@LogicalRelation(parquetRelation: ParquetRelation2) =>
+        case logical@LogicalRelation(parquetRelation: ParquetRelation) =>
           // If we have the same paths, same schema, and same partition spec,
           // we will use the cached Parquet Relation.
           val useCached =
@@ -317,7 +316,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       val cached = getCached(tableIdentifier, paths, metastoreSchema, Some(partitionSpec))
       val parquetRelation = cached.getOrElse {
         val created = LogicalRelation(
-          new ParquetRelation2(
+          new ParquetRelation(
             paths.toArray, None, Some(partitionSpec), parquetOptions)(hive))
         cachedDataSourceTables.put(tableIdentifier, created)
         created
@@ -330,7 +329,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       val cached = getCached(tableIdentifier, paths, metastoreSchema, None)
       val parquetRelation = cached.getOrElse {
         val created = LogicalRelation(
-          new ParquetRelation2(paths.toArray, None, None, parquetOptions)(hive))
+          new ParquetRelation(paths.toArray, None, None, parquetOptions)(hive))
         cachedDataSourceTables.put(tableIdentifier, created)
         created
       }
@@ -370,8 +369,6 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
   /**
    * When scanning or writing to non-partitioned Metastore Parquet tables, convert them to Parquet
    * data source relations for better performance.
-   *
-   * This rule can be considered as [[HiveStrategies.ParquetConversion]] done right.
    */
   object ParquetConversions extends Rule[LogicalPlan] {
     override def apply(plan: LogicalPlan): LogicalPlan = {
@@ -386,7 +383,6 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
             // Inserting into partitioned table is not supported in Parquet data source (yet).
             if !relation.hiveQlTable.isPartitioned &&
               hive.convertMetastoreParquet &&
-              conf.parquetUseDataSourceApi &&
               relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") =>
           val parquetRelation = convertToParquetRelation(relation)
           val attributedRewrites = relation.output.zip(parquetRelation.output)
@@ -397,7 +393,6 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
           // Inserting into partitioned table is not supported in Parquet data source (yet).
           if !relation.hiveQlTable.isPartitioned &&
             hive.convertMetastoreParquet &&
-            conf.parquetUseDataSourceApi &&
             relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") =>
           val parquetRelation = convertToParquetRelation(relation)
           val attributedRewrites = relation.output.zip(parquetRelation.output)
@@ -406,7 +401,6 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         // Read path
         case p @ PhysicalOperation(_, _, relation: MetastoreRelation)
             if hive.convertMetastoreParquet &&
-              conf.parquetUseDataSourceApi &&
               relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") =>
           val parquetRelation = convertToParquetRelation(relation)
           val attributedRewrites = relation.output.zip(parquetRelation.output)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index a22c3292eff94..cd6cd322c94ed 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -17,23 +17,14 @@
 
 package org.apache.spark.sql.hive
 
-import scala.collection.JavaConversions._
-
-import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.CatalystTypeConverters
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand, _}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsing, CreateTableUsingAsSelect, DescribeCommand}
+import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand, _}
 import org.apache.spark.sql.hive.execution._
-import org.apache.spark.sql.parquet.ParquetRelation
-import org.apache.spark.sql.types.StringType
 
 
 private[hive] trait HiveStrategies {
@@ -42,136 +33,6 @@ private[hive] trait HiveStrategies {
 
   val hiveContext: HiveContext
 
-  /**
-   * :: Experimental ::
-   * Finds table scans that would use the Hive SerDe and replaces them with our own native parquet
-   * table scan operator.
-   *
-   * TODO: Much of this logic is duplicated in HiveTableScan.  Ideally we would do some refactoring
-   * but since this is after the code freeze for 1.1 all logic is here to minimize disruption.
-   *
-   * Other issues:
-   *  - Much of this logic assumes case insensitive resolution.
-   */
-  @Experimental
-  object ParquetConversion extends Strategy {
-    implicit class LogicalPlanHacks(s: DataFrame) {
-      def lowerCase: DataFrame = DataFrame(s.sqlContext, s.logicalPlan)
-
-      def addPartitioningAttributes(attrs: Seq[Attribute]): DataFrame = {
-        // Don't add the partitioning key if its already present in the data.
-        if (attrs.map(_.name).toSet.subsetOf(s.logicalPlan.output.map(_.name).toSet)) {
-          s
-        } else {
-          DataFrame(
-            s.sqlContext,
-            s.logicalPlan transform {
-              case p: ParquetRelation => p.copy(partitioningAttributes = attrs)
-            })
-        }
-      }
-    }
-
-    implicit class PhysicalPlanHacks(originalPlan: SparkPlan) {
-      def fakeOutput(newOutput: Seq[Attribute]): OutputFaker =
-        OutputFaker(
-          originalPlan.output.map(a =>
-            newOutput.find(a.name.toLowerCase == _.name.toLowerCase)
-              .getOrElse(
-                sys.error(s"Can't find attribute $a to fake in set ${newOutput.mkString(",")}"))),
-          originalPlan)
-    }
-
-    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case PhysicalOperation(projectList, predicates, relation: MetastoreRelation)
-          if relation.tableDesc.getSerdeClassName.contains("Parquet") &&
-             hiveContext.convertMetastoreParquet &&
-             !hiveContext.conf.parquetUseDataSourceApi =>
-
-        // Filter out all predicates that only deal with partition keys
-        val partitionsKeys = AttributeSet(relation.partitionKeys)
-        val (pruningPredicates, otherPredicates) = predicates.partition {
-          _.references.subsetOf(partitionsKeys)
-        }
-
-        // We are going to throw the predicates and projection back at the whole optimization
-        // sequence so lets unresolve all the attributes, allowing them to be rebound to the
-        // matching parquet attributes.
-        val unresolvedOtherPredicates = Column(otherPredicates.map(_ transform {
-          case a: AttributeReference => UnresolvedAttribute(a.name)
-        }).reduceOption(And).getOrElse(Literal(true)))
-
-        val unresolvedProjection: Seq[Column] = projectList.map(_ transform {
-          case a: AttributeReference => UnresolvedAttribute(a.name)
-        }).map(Column(_))
-
-        try {
-          if (relation.hiveQlTable.isPartitioned) {
-            val rawPredicate = pruningPredicates.reduceOption(And).getOrElse(Literal(true))
-            // Translate the predicate so that it automatically casts the input values to the
-            // correct data types during evaluation.
-            val castedPredicate = rawPredicate transform {
-              case a: AttributeReference =>
-                val idx = relation.partitionKeys.indexWhere(a.exprId == _.exprId)
-                val key = relation.partitionKeys(idx)
-                Cast(BoundReference(idx, StringType, nullable = true), key.dataType)
-            }
-
-            val inputData = new GenericMutableRow(relation.partitionKeys.size)
-            val pruningCondition =
-              if (codegenEnabled) {
-                GeneratePredicate.generate(castedPredicate)
-              } else {
-                InterpretedPredicate.create(castedPredicate)
-              }
-
-            val partitions = relation.getHiveQlPartitions(pruningPredicates).filter { part =>
-              val partitionValues = part.getValues
-              var i = 0
-              while (i < partitionValues.size()) {
-                inputData(i) = CatalystTypeConverters.convertToCatalyst(partitionValues(i))
-                i += 1
-              }
-              pruningCondition(inputData)
-            }
-
-            val partitionLocations = partitions.map(_.getLocation)
-
-            if (partitionLocations.isEmpty) {
-              PhysicalRDD(plan.output, sparkContext.emptyRDD[InternalRow]) :: Nil
-            } else {
-              hiveContext
-                .read.parquet(partitionLocations: _*)
-                .addPartitioningAttributes(relation.partitionKeys)
-                .lowerCase
-                .where(unresolvedOtherPredicates)
-                .select(unresolvedProjection: _*)
-                .queryExecution
-                .executedPlan
-                .fakeOutput(projectList.map(_.toAttribute)) :: Nil
-            }
-
-          } else {
-            hiveContext
-              .read.parquet(relation.hiveQlTable.getDataLocation.toString)
-              .lowerCase
-              .where(unresolvedOtherPredicates)
-              .select(unresolvedProjection: _*)
-              .queryExecution
-              .executedPlan
-              .fakeOutput(projectList.map(_.toAttribute)) :: Nil
-          }
-        } catch {
-          // parquetFile will throw an exception when there is no data.
-          // TODO: Remove this hack for Spark 1.3.
-          case iae: java.lang.IllegalArgumentException
-              if iae.getMessage.contains("Can not create a Path from an empty string") =>
-            PhysicalRDD(plan.output, sparkContext.emptyRDD[InternalRow]) :: Nil
-        }
-      case _ => Nil
-    }
-  }
-
   object Scripts extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.ScriptTransformation(input, script, output, child, schema: HiveScriptIOSchema) =>
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
index af68615e8e9d6..a45c2d957278f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.parquet.ParquetTest
-import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.sql.{QueryTest, Row}
 
 case class Cases(lower: String, UPPER: String)
 
@@ -28,64 +28,54 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
 
   import sqlContext._
 
-  def run(prefix: String): Unit = {
-    test(s"$prefix: Case insensitive attribute names") {
-      withParquetTable((1 to 4).map(i => Cases(i.toString, i.toString)), "cases") {
-        val expected = (1 to 4).map(i => Row(i.toString))
-        checkAnswer(sql("SELECT upper FROM cases"), expected)
-        checkAnswer(sql("SELECT LOWER FROM cases"), expected)
-      }
+  test("Case insensitive attribute names") {
+    withParquetTable((1 to 4).map(i => Cases(i.toString, i.toString)), "cases") {
+      val expected = (1 to 4).map(i => Row(i.toString))
+      checkAnswer(sql("SELECT upper FROM cases"), expected)
+      checkAnswer(sql("SELECT LOWER FROM cases"), expected)
     }
+  }
 
-    test(s"$prefix: SELECT on Parquet table") {
-      val data = (1 to 4).map(i => (i, s"val_$i"))
-      withParquetTable(data, "t") {
-        checkAnswer(sql("SELECT * FROM t"), data.map(Row.fromTuple))
-      }
+  test("SELECT on Parquet table") {
+    val data = (1 to 4).map(i => (i, s"val_$i"))
+    withParquetTable(data, "t") {
+      checkAnswer(sql("SELECT * FROM t"), data.map(Row.fromTuple))
     }
+  }
 
-    test(s"$prefix: Simple column projection + filter on Parquet table") {
-      withParquetTable((1 to 4).map(i => (i % 2 == 0, i, s"val_$i")), "t") {
-        checkAnswer(
-          sql("SELECT `_1`, `_3` FROM t WHERE `_1` = true"),
-          Seq(Row(true, "val_2"), Row(true, "val_4")))
-      }
+  test("Simple column projection + filter on Parquet table") {
+    withParquetTable((1 to 4).map(i => (i % 2 == 0, i, s"val_$i")), "t") {
+      checkAnswer(
+        sql("SELECT `_1`, `_3` FROM t WHERE `_1` = true"),
+        Seq(Row(true, "val_2"), Row(true, "val_4")))
     }
+  }
 
-    test(s"$prefix: Converting Hive to Parquet Table via saveAsParquetFile") {
-      withTempPath { dir =>
-        sql("SELECT * FROM src").write.parquet(dir.getCanonicalPath)
-        read.parquet(dir.getCanonicalPath).registerTempTable("p")
-        withTempTable("p") {
-          checkAnswer(
-            sql("SELECT * FROM src ORDER BY key"),
-            sql("SELECT * from p ORDER BY key").collect().toSeq)
-        }
+  test("Converting Hive to Parquet Table via saveAsParquetFile") {
+    withTempPath { dir =>
+      sql("SELECT * FROM src").write.parquet(dir.getCanonicalPath)
+      read.parquet(dir.getCanonicalPath).registerTempTable("p")
+      withTempTable("p") {
+        checkAnswer(
+          sql("SELECT * FROM src ORDER BY key"),
+          sql("SELECT * from p ORDER BY key").collect().toSeq)
       }
     }
+  }
 
-    test(s"$prefix: INSERT OVERWRITE TABLE Parquet table") {
-      withParquetTable((1 to 10).map(i => (i, s"val_$i")), "t") {
-        withTempPath { file =>
-          sql("SELECT * FROM t LIMIT 1").write.parquet(file.getCanonicalPath)
-          read.parquet(file.getCanonicalPath).registerTempTable("p")
-          withTempTable("p") {
-            // let's do three overwrites for good measure
-            sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
-            sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
-            sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
-            checkAnswer(sql("SELECT * FROM p"), sql("SELECT * FROM t").collect().toSeq)
-          }
+  test("INSERT OVERWRITE TABLE Parquet table") {
+    withParquetTable((1 to 10).map(i => (i, s"val_$i")), "t") {
+      withTempPath { file =>
+        sql("SELECT * FROM t LIMIT 1").write.parquet(file.getCanonicalPath)
+        read.parquet(file.getCanonicalPath).registerTempTable("p")
+        withTempTable("p") {
+          // let's do three overwrites for good measure
+          sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
+          sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
+          sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
+          checkAnswer(sql("SELECT * FROM p"), sql("SELECT * FROM t").collect().toSeq)
         }
       }
     }
   }
-
-  withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API.key -> "true") {
-    run("Parquet data source enabled")
-  }
-
-  withSQLConf(SQLConf.PARQUET_USE_DATA_SOURCE_API.key -> "false") {
-    run("Parquet data source disabled")
-  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index e403f32efaf91..4fdf774ead75e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -21,10 +21,9 @@ import java.io.File
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapred.InvalidInputException
+import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.Logging
 import org.apache.spark.sql._
@@ -33,7 +32,7 @@ import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.parquet.ParquetRelation2
+import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
@@ -564,10 +563,7 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeA
   }
 
   test("scan a parquet table created through a CTAS statement") {
-    withSQLConf(
-      HiveContext.CONVERT_METASTORE_PARQUET.key -> "true",
-      SQLConf.PARQUET_USE_DATA_SOURCE_API.key -> "true") {
-
+    withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "true") {
       withTempTable("jt") {
         (1 to 10).map(i => i -> s"str$i").toDF("a", "b").registerTempTable("jt")
 
@@ -582,9 +578,9 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeA
             Row(3) :: Row(4) :: Nil)
 
           table("test_parquet_ctas").queryExecution.optimizedPlan match {
-            case LogicalRelation(p: ParquetRelation2) => // OK
+            case LogicalRelation(p: ParquetRelation) => // OK
             case _ =>
-              fail(s"test_parquet_ctas should have be converted to ${classOf[ParquetRelation2]}")
+              fail(s"test_parquet_ctas should have be converted to ${classOf[ParquetRelation]}")
           }
         }
       }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 03428265422e6..ff42fdefaa62a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -28,7 +28,8 @@ import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
-import org.apache.spark.sql.parquet.ParquetRelation2
+import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 
 case class Nested1(f1: Nested2)
@@ -61,7 +62,9 @@ class MyDialect extends DefaultParserDialect
  * Hive to generate them (in contrast to HiveQuerySuite).  Often this is because the query is
  * valid, but Hive currently cannot execute it.
  */
-class SQLQuerySuite extends QueryTest {
+class SQLQuerySuite extends QueryTest with SQLTestUtils {
+  override def sqlContext: SQLContext = TestHive
+
   test("SPARK-6835: udtf in lateral view") {
     val df = Seq((1, 1)).toDF("c1", "c2")
     df.registerTempTable("table1")
@@ -195,17 +198,17 @@ class SQLQuerySuite extends QueryTest {
     def checkRelation(tableName: String, isDataSourceParquet: Boolean): Unit = {
       val relation = EliminateSubQueries(catalog.lookupRelation(Seq(tableName)))
       relation match {
-        case LogicalRelation(r: ParquetRelation2) =>
+        case LogicalRelation(r: ParquetRelation) =>
           if (!isDataSourceParquet) {
             fail(
               s"${classOf[MetastoreRelation].getCanonicalName} is expected, but found " +
-              s"${ParquetRelation2.getClass.getCanonicalName}.")
+              s"${ParquetRelation.getClass.getCanonicalName}.")
           }
 
         case r: MetastoreRelation =>
           if (isDataSourceParquet) {
             fail(
-              s"${ParquetRelation2.getClass.getCanonicalName} is expected, but found " +
+              s"${ParquetRelation.getClass.getCanonicalName} is expected, but found " +
               s"${classOf[MetastoreRelation].getCanonicalName}.")
           }
       }
@@ -350,33 +353,26 @@ class SQLQuerySuite extends QueryTest {
       "serde_p1=p1", "serde_p2=p2", "tbl_p1=p11", "tbl_p2=p22", "MANAGED_TABLE"
     )
 
-    val origUseParquetDataSource = conf.parquetUseDataSourceApi
-    try {
-      setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false)
-      sql(
-        """CREATE TABLE ctas5
-          | STORED AS parquet AS
-          |   SELECT key, value
-          |   FROM src
-          |   ORDER BY key, value""".stripMargin).collect()
-
-      checkExistence(sql("DESC EXTENDED ctas5"), true,
-        "name:key", "type:string", "name:value", "ctas5",
-        "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat",
-        "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat",
-        "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe",
-        "MANAGED_TABLE"
-      )
-
-      val default = convertMetastoreParquet
-      // use the Hive SerDe for parquet tables
-      sql("set spark.sql.hive.convertMetastoreParquet = false")
+    sql(
+      """CREATE TABLE ctas5
+        | STORED AS parquet AS
+        |   SELECT key, value
+        |   FROM src
+        |   ORDER BY key, value""".stripMargin).collect()
+
+    checkExistence(sql("DESC EXTENDED ctas5"), true,
+      "name:key", "type:string", "name:value", "ctas5",
+      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat",
+      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat",
+      "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe",
+      "MANAGED_TABLE"
+    )
+
+    // use the Hive SerDe for parquet tables
+    withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
       checkAnswer(
         sql("SELECT key, value FROM ctas5 ORDER BY key, value"),
         sql("SELECT key, value FROM src ORDER BY key, value").collect().toSeq)
-      sql(s"set spark.sql.hive.convertMetastoreParquet = $default")
-    } finally {
-      setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, origUseParquetDataSource)
     }
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index 82a8daf8b4b09..f56fb96c52d37 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -22,13 +22,13 @@ import java.io.File
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql._
-import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD}
 import org.apache.spark.sql.execution.datasources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation}
+import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD}
 import org.apache.spark.sql.hive.execution.HiveTableScan
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.parquet.{ParquetRelation2, ParquetTableScan}
+import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
@@ -57,7 +57,7 @@ case class ParquetDataWithKeyAndComplexTypes(
  * A suite to test the automatic conversion of metastore tables with parquet data to use the
  * built in parquet support.
  */
-class ParquetMetastoreSuiteBase extends ParquetPartitioningTest {
+class ParquetMetastoreSuite extends ParquetPartitioningTest {
   override def beforeAll(): Unit = {
     super.beforeAll()
 
@@ -134,6 +134,19 @@ class ParquetMetastoreSuiteBase extends ParquetPartitioningTest {
       LOCATION '${partitionedTableDirWithKeyAndComplexTypes.getCanonicalPath}'
     """)
 
+    sql(
+      """
+        |create table test_parquet
+        |(
+        |  intField INT,
+        |  stringField STRING
+        |)
+        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+        |STORED AS
+        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+      """.stripMargin)
+
     (1 to 10).foreach { p =>
       sql(s"ALTER TABLE partitioned_parquet ADD PARTITION (p=$p)")
     }
@@ -166,6 +179,7 @@ class ParquetMetastoreSuiteBase extends ParquetPartitioningTest {
     sql("DROP TABLE normal_parquet")
     sql("DROP TABLE IF EXISTS jt")
     sql("DROP TABLE IF EXISTS jt_array")
+    sql("DROP TABLE IF EXISTS test_parquet")
     setConf(HiveContext.CONVERT_METASTORE_PARQUET, false)
   }
 
@@ -176,40 +190,9 @@ class ParquetMetastoreSuiteBase extends ParquetPartitioningTest {
       }.isEmpty)
     assert(
       sql("SELECT * FROM normal_parquet").queryExecution.executedPlan.collect {
-        case _: ParquetTableScan => true
         case _: PhysicalRDD => true
       }.nonEmpty)
   }
-}
-
-class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase {
-  val originalConf = conf.parquetUseDataSourceApi
-
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-
-    sql(
-      """
-        |create table test_parquet
-        |(
-        |  intField INT,
-        |  stringField STRING
-        |)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
-
-    conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true)
-  }
-
-  override def afterAll(): Unit = {
-    super.afterAll()
-    sql("DROP TABLE IF EXISTS test_parquet")
-
-    setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
 
   test("scan an empty parquet table") {
     checkAnswer(sql("SELECT count(*) FROM test_parquet"), Row(0))
@@ -292,10 +275,10 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase {
     )
 
     table("test_parquet_ctas").queryExecution.optimizedPlan match {
-      case LogicalRelation(_: ParquetRelation2) => // OK
+      case LogicalRelation(_: ParquetRelation) => // OK
       case _ => fail(
         "test_parquet_ctas should be converted to " +
-          s"${classOf[ParquetRelation2].getCanonicalName}")
+          s"${classOf[ParquetRelation].getCanonicalName}")
     }
 
     sql("DROP TABLE IF EXISTS test_parquet_ctas")
@@ -316,9 +299,9 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase {
 
     val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt")
     df.queryExecution.executedPlan match {
-      case ExecutedCommand(InsertIntoHadoopFsRelation(_: ParquetRelation2, _, _)) => // OK
+      case ExecutedCommand(InsertIntoHadoopFsRelation(_: ParquetRelation, _, _)) => // OK
       case o => fail("test_insert_parquet should be converted to a " +
-        s"${classOf[ParquetRelation2].getCanonicalName} and " +
+        s"${classOf[ParquetRelation].getCanonicalName} and " +
         s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan. " +
         s"However, found a ${o.toString} ")
     }
@@ -346,9 +329,9 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase {
 
     val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array")
     df.queryExecution.executedPlan match {
-      case ExecutedCommand(InsertIntoHadoopFsRelation(r: ParquetRelation2, _, _)) => // OK
+      case ExecutedCommand(InsertIntoHadoopFsRelation(r: ParquetRelation, _, _)) => // OK
       case o => fail("test_insert_parquet should be converted to a " +
-        s"${classOf[ParquetRelation2].getCanonicalName} and " +
+        s"${classOf[ParquetRelation].getCanonicalName} and " +
         s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." +
         s"However, found a ${o.toString} ")
     }
@@ -379,17 +362,17 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase {
 
     assertResult(2) {
       analyzed.collect {
-        case r @ LogicalRelation(_: ParquetRelation2) => r
+        case r @ LogicalRelation(_: ParquetRelation) => r
       }.size
     }
 
     sql("DROP TABLE ms_convert")
   }
 
-  def collectParquetRelation(df: DataFrame): ParquetRelation2 = {
+  def collectParquetRelation(df: DataFrame): ParquetRelation = {
     val plan = df.queryExecution.analyzed
     plan.collectFirst {
-      case LogicalRelation(r: ParquetRelation2) => r
+      case LogicalRelation(r: ParquetRelation) => r
     }.getOrElse {
       fail(s"Expecting a ParquetRelation2, but got:\n$plan")
     }
@@ -439,7 +422,7 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase {
       // Converted test_parquet should be cached.
       catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) match {
         case null => fail("Converted test_parquet should be cached in the cache.")
-        case logical @ LogicalRelation(parquetRelation: ParquetRelation2) => // OK
+        case logical @ LogicalRelation(parquetRelation: ParquetRelation) => // OK
         case other =>
           fail(
             "The cached test_parquet should be a Parquet Relation. " +
@@ -543,81 +526,10 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase {
   }
 }
 
-class ParquetDataSourceOffMetastoreSuite extends ParquetMetastoreSuiteBase {
-  val originalConf = conf.parquetUseDataSourceApi
-
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false)
-  }
-
-  override def afterAll(): Unit = {
-    super.afterAll()
-    setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
-
-  test("MetastoreRelation in InsertIntoTable will not be converted") {
-    sql(
-      """
-        |create table test_insert_parquet
-        |(
-        |  intField INT
-        |)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
-
-    val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt")
-    df.queryExecution.executedPlan match {
-      case insert: execution.InsertIntoHiveTable => // OK
-      case o => fail(s"The SparkPlan should be ${classOf[InsertIntoHiveTable].getCanonicalName}. " +
-        s"However, found ${o.toString}.")
-    }
-
-    checkAnswer(
-      sql("SELECT intField FROM test_insert_parquet WHERE test_insert_parquet.intField > 5"),
-      sql("SELECT a FROM jt WHERE jt.a > 5").collect()
-    )
-
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
-  }
-
-  // TODO: enable it after the fix of SPARK-5950.
-  ignore("MetastoreRelation in InsertIntoHiveTable will not be converted") {
-    sql(
-      """
-        |create table test_insert_parquet
-        |(
-        |  int_array array<int>
-        |)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
-
-    val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array")
-    df.queryExecution.executedPlan match {
-      case insert: execution.InsertIntoHiveTable => // OK
-      case o => fail(s"The SparkPlan should be ${classOf[InsertIntoHiveTable].getCanonicalName}. " +
-        s"However, found ${o.toString}.")
-    }
-
-    checkAnswer(
-      sql("SELECT int_array FROM test_insert_parquet"),
-      sql("SELECT a FROM jt_array").collect()
-    )
-
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
-  }
-}
-
 /**
  * A suite of tests for the Parquet support through the data sources API.
  */
-class ParquetSourceSuiteBase extends ParquetPartitioningTest {
+class ParquetSourceSuite extends ParquetPartitioningTest {
   override def beforeAll(): Unit = {
     super.beforeAll()
 
@@ -712,20 +624,6 @@ class ParquetSourceSuiteBase extends ParquetPartitioningTest {
       }
     }
   }
-}
-
-class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase {
-  val originalConf = conf.parquetUseDataSourceApi
-
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, true)
-  }
-
-  override def afterAll(): Unit = {
-    super.afterAll()
-    setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
 
   test("values in arrays and maps stored in parquet are always nullable") {
     val df = createDataFrame(Tuple2(Map(2 -> 3), Seq(4, 5, 6)) :: Nil).toDF("m", "a")
@@ -734,7 +632,7 @@ class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase {
     val expectedSchema1 =
       StructType(
         StructField("m", mapType1, nullable = true) ::
-        StructField("a", arrayType1, nullable = true) :: Nil)
+          StructField("a", arrayType1, nullable = true) :: Nil)
     assert(df.schema === expectedSchema1)
 
     df.write.format("parquet").saveAsTable("alwaysNullable")
@@ -772,20 +670,6 @@ class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase {
   }
 }
 
-class ParquetDataSourceOffSourceSuite extends ParquetSourceSuiteBase {
-  val originalConf = conf.parquetUseDataSourceApi
-
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, false)
-  }
-
-  override def afterAll(): Unit = {
-    super.afterAll()
-    setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf)
-  }
-}
-
 /**
  * A collection of tests for parquet data with various forms of partitioning.
  */

From 1efe97dc9ed31e3b8727b81be633b7e96dd3cd34 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Sun, 26 Jul 2015 18:34:19 -0700
Subject: [PATCH 0609/1454] [SPARK-8867][SQL] Support list / describe function
 usage

As Hive does, we need to list all of the registered UDF and its usage for user.

We add the annotation to describe a UDF, so we can get the literal description info while registering the UDF.
e.g.
```scala
ExpressionDescription(
    usage = "_FUNC_(expr) - Returns the absolute value of the numeric value",
    extended = """> SELECT _FUNC_('-1')
                  1""")
 case class Abs(child: Expression) extends UnaryArithmetic {
...
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7259 from chenghao-intel/desc_function and squashes the following commits:

cf29bba [Cheng Hao] fixing the code style issue
5193855 [Cheng Hao] Add more powerful parser for show functions
c645a6b [Cheng Hao] fix bug in unit test
78d40f1 [Cheng Hao] update the padding issue for usage
48ee4b3 [Cheng Hao] update as feedback
70eb4e9 [Cheng Hao] add show/describe function support
---
 .../expressions/ExpressionDescription.java    | 43 +++++++++++
 .../catalyst/expressions/ExpressionInfo.java  | 55 +++++++++++++
 .../catalyst/analysis/FunctionRegistry.scala  | 56 +++++++++++---
 .../sql/catalyst/expressions/arithmetic.scala |  3 +
 .../expressions/stringOperations.scala        |  6 ++
 .../sql/catalyst/plans/logical/commands.scala | 28 ++++++-
 .../org/apache/spark/sql/SparkSQLParser.scala | 28 ++++++-
 .../spark/sql/execution/SparkStrategies.scala |  5 ++
 .../apache/spark/sql/execution/commands.scala | 77 ++++++++++++++++++-
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 26 +++++++
 .../org/apache/spark/sql/hive/hiveUDFs.scala  | 28 ++++++-
 .../hive/execution/HiveComparisonTest.scala   |  6 +-
 .../sql/hive/execution/SQLQuerySuite.scala    | 48 +++++++++++-
 13 files changed, 389 insertions(+), 20 deletions(-)
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionDescription.java
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionInfo.java

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionDescription.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionDescription.java
new file mode 100644
index 0000000000000..9e10f27d59d55
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionDescription.java
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+import org.apache.spark.annotation.DeveloperApi;
+
+import java.lang.annotation.Retention;
+import java.lang.annotation.RetentionPolicy;
+
+/**
+ * ::DeveloperApi::
+
+ * A function description type which can be recognized by FunctionRegistry, and will be used to
+ * show the usage of the function in human language.
+ *
+ * `usage()` will be used for the function usage in brief way.
+ * `extended()` will be used for the function usage in verbose way, suppose
+ *              an example will be provided.
+ *
+ *  And we can refer the function name by `_FUNC_`, in `usage` and `extended`, as it's
+ *  registered in `FunctionRegistry`.
+ */
+@DeveloperApi
+@Retention(RetentionPolicy.RUNTIME)
+public @interface ExpressionDescription {
+    String usage() default "_FUNC_ is undocumented";
+    String extended() default "No example for _FUNC_.";
+}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionInfo.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionInfo.java
new file mode 100644
index 0000000000000..ba8e9cb4be28b
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/ExpressionInfo.java
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+/**
+ * Expression information, will be used to describe a expression.
+ */
+public class ExpressionInfo {
+    private String className;
+    private String usage;
+    private String name;
+    private String extended;
+
+    public String getClassName() {
+        return className;
+    }
+
+    public String getUsage() {
+        return usage;
+    }
+
+    public String getName() {
+        return name;
+    }
+
+    public String getExtended() {
+        return extended;
+    }
+
+    public ExpressionInfo(String className, String name, String usage, String extended) {
+        this.className = className;
+        this.name = name;
+        this.usage = usage;
+        this.extended = extended;
+    }
+
+    public ExpressionInfo(String className, String name) {
+        this(className, name, null, null);
+    }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 9c349838c28a1..aa05f448d12bc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -30,26 +30,44 @@ import org.apache.spark.sql.catalyst.util.StringKeyHashMap
 /** A catalog for looking up user defined functions, used by an [[Analyzer]]. */
 trait FunctionRegistry {
 
-  def registerFunction(name: String, builder: FunctionBuilder): Unit
+  final def registerFunction(name: String, builder: FunctionBuilder): Unit = {
+    registerFunction(name, new ExpressionInfo(builder.getClass.getCanonicalName, name), builder)
+  }
+
+  def registerFunction(name: String, info: ExpressionInfo, builder: FunctionBuilder): Unit
 
   @throws[AnalysisException]("If function does not exist")
   def lookupFunction(name: String, children: Seq[Expression]): Expression
+
+  /* List all of the registered function names. */
+  def listFunction(): Seq[String]
+
+  /* Get the class of the registered function by specified name. */
+  def lookupFunction(name: String): Option[ExpressionInfo]
 }
 
 class SimpleFunctionRegistry extends FunctionRegistry {
 
-  private val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive = false)
+  private val functionBuilders =
+    StringKeyHashMap[(ExpressionInfo, FunctionBuilder)](caseSensitive = false)
 
-  override def registerFunction(name: String, builder: FunctionBuilder): Unit = {
-    functionBuilders.put(name, builder)
+  override def registerFunction(name: String, info: ExpressionInfo, builder: FunctionBuilder)
+  : Unit = {
+    functionBuilders.put(name, (info, builder))
   }
 
   override def lookupFunction(name: String, children: Seq[Expression]): Expression = {
-    val func = functionBuilders.get(name).getOrElse {
+    val func = functionBuilders.get(name).map(_._2).getOrElse {
       throw new AnalysisException(s"undefined function $name")
     }
     func(children)
   }
+
+  override def listFunction(): Seq[String] = functionBuilders.iterator.map(_._1).toList.sorted
+
+  override def lookupFunction(name: String): Option[ExpressionInfo] = {
+    functionBuilders.get(name).map(_._1)
+  }
 }
 
 /**
@@ -57,13 +75,22 @@ class SimpleFunctionRegistry extends FunctionRegistry {
  * functions are already filled in and the analyzer needs only to resolve attribute references.
  */
 object EmptyFunctionRegistry extends FunctionRegistry {
-  override def registerFunction(name: String, builder: FunctionBuilder): Unit = {
+  override def registerFunction(name: String, info: ExpressionInfo, builder: FunctionBuilder)
+  : Unit = {
     throw new UnsupportedOperationException
   }
 
   override def lookupFunction(name: String, children: Seq[Expression]): Expression = {
     throw new UnsupportedOperationException
   }
+
+  override def listFunction(): Seq[String] = {
+    throw new UnsupportedOperationException
+  }
+
+  override def lookupFunction(name: String): Option[ExpressionInfo] = {
+    throw new UnsupportedOperationException
+  }
 }
 
 
@@ -71,7 +98,7 @@ object FunctionRegistry {
 
   type FunctionBuilder = Seq[Expression] => Expression
 
-  val expressions: Map[String, FunctionBuilder] = Map(
+  val expressions: Map[String, (ExpressionInfo, FunctionBuilder)] = Map(
     // misc non-aggregate functions
     expression[Abs]("abs"),
     expression[CreateArray]("array"),
@@ -205,13 +232,13 @@ object FunctionRegistry {
 
   val builtin: FunctionRegistry = {
     val fr = new SimpleFunctionRegistry
-    expressions.foreach { case (name, builder) => fr.registerFunction(name, builder) }
+    expressions.foreach { case (name, (info, builder)) => fr.registerFunction(name, info, builder) }
     fr
   }
 
   /** See usage above. */
   private def expression[T <: Expression](name: String)
-      (implicit tag: ClassTag[T]): (String, FunctionBuilder) = {
+      (implicit tag: ClassTag[T]): (String, (ExpressionInfo, FunctionBuilder)) = {
 
     // See if we can find a constructor that accepts Seq[Expression]
     val varargCtor = Try(tag.runtimeClass.getDeclaredConstructor(classOf[Seq[_]])).toOption
@@ -237,6 +264,15 @@ object FunctionRegistry {
         }
       }
     }
-    (name, builder)
+
+    val clazz = tag.runtimeClass
+    val df = clazz.getAnnotation(classOf[ExpressionDescription])
+    if (df != null) {
+      (name,
+        (new ExpressionInfo(clazz.getCanonicalName, name, df.usage(), df.extended()),
+        builder))
+    } else {
+      (name, (new ExpressionInfo(clazz.getCanonicalName, name), builder))
+    }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 7c254a8750a9f..b37f530ec6814 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -65,6 +65,9 @@ case class UnaryPositive(child: Expression) extends UnaryExpression with Expects
 /**
  * A function that get the absolute value of the numeric value.
  */
+@ExpressionDescription(
+  usage = "_FUNC_(expr) - Returns the absolute value of the numeric value",
+  extended = "> SELECT _FUNC_('-1');\n1")
 case class Abs(child: Expression)
   extends UnaryExpression with ExpectsInputTypes with CodegenFallback {
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index cf187ad5a0a9f..38b0fb37dee3b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -214,6 +214,9 @@ trait String2StringExpression extends ImplicitCastInputTypes {
 /**
  * A function that converts the characters of a string to uppercase.
  */
+@ExpressionDescription(
+  usage = "_FUNC_(str) - Returns str with all characters changed to uppercase",
+  extended = "> SELECT _FUNC_('SparkSql');\n 'SPARKSQL'")
 case class Upper(child: Expression)
   extends UnaryExpression with String2StringExpression {
 
@@ -227,6 +230,9 @@ case class Upper(child: Expression)
 /**
  * A function that converts the characters of a string to lowercase.
  */
+@ExpressionDescription(
+  usage = "_FUNC_(str) - Returns str with all characters changed to lowercase",
+  extended = "> SELECT _FUNC_('SparkSql');\n'sparksql'")
 case class Lower(child: Expression) extends UnaryExpression with String2StringExpression {
 
   override def convert(v: UTF8String): UTF8String = v.toLowerCase
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index 246f4d7e34d3d..e6621e0f50a9e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -17,7 +17,8 @@
 
 package org.apache.spark.sql.catalyst.plans.logical
 
-import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute}
+import org.apache.spark.sql.types.StringType
 
 /**
  * A logical node that represents a non-query command to be executed by the system.  For example,
@@ -25,3 +26,28 @@ import org.apache.spark.sql.catalyst.expressions.Attribute
  * eagerly executed.
  */
 trait Command
+
+/**
+ * Returned for the "DESCRIBE [EXTENDED] FUNCTION functionName" command.
+ * @param functionName The function to be described.
+ * @param isExtended True if "DESCRIBE EXTENDED" is used. Otherwise, false.
+ */
+private[sql] case class DescribeFunction(
+    functionName: String,
+    isExtended: Boolean) extends LogicalPlan with Command {
+
+  override def children: Seq[LogicalPlan] = Seq.empty
+  override val output: Seq[Attribute] = Seq(
+    AttributeReference("function_desc", StringType, nullable = false)())
+}
+
+/**
+ * Returned for the "SHOW FUNCTIONS" command, which will list all of the
+ * registered function list.
+ */
+private[sql] case class ShowFunctions(
+    db: Option[String], pattern: Option[String]) extends LogicalPlan with Command {
+  override def children: Seq[LogicalPlan] = Seq.empty
+  override val output: Seq[Attribute] = Seq(
+    AttributeReference("function", StringType, nullable = false)())
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala
index e59fa6e162900..ea8fce6ca9cf2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala
@@ -21,7 +21,7 @@ import scala.util.parsing.combinator.RegexParsers
 
 import org.apache.spark.sql.catalyst.AbstractSparkSQLParser
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.logical.{DescribeFunction, LogicalPlan, ShowFunctions}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.types.StringType
 
@@ -57,6 +57,10 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr
   protected val AS = Keyword("AS")
   protected val CACHE = Keyword("CACHE")
   protected val CLEAR = Keyword("CLEAR")
+  protected val DESCRIBE = Keyword("DESCRIBE")
+  protected val EXTENDED = Keyword("EXTENDED")
+  protected val FUNCTION = Keyword("FUNCTION")
+  protected val FUNCTIONS = Keyword("FUNCTIONS")
   protected val IN = Keyword("IN")
   protected val LAZY = Keyword("LAZY")
   protected val SET = Keyword("SET")
@@ -65,7 +69,8 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr
   protected val TABLES = Keyword("TABLES")
   protected val UNCACHE = Keyword("UNCACHE")
 
-  override protected lazy val start: Parser[LogicalPlan] = cache | uncache | set | show | others
+  override protected lazy val start: Parser[LogicalPlan] =
+    cache | uncache | set | show | desc | others
 
   private lazy val cache: Parser[LogicalPlan] =
     CACHE ~> LAZY.? ~ (TABLE ~> ident) ~ (AS ~> restInput).? ^^ {
@@ -85,9 +90,24 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr
       case input => SetCommandParser(input)
     }
 
+  // It can be the following patterns:
+  // SHOW FUNCTIONS;
+  // SHOW FUNCTIONS mydb.func1;
+  // SHOW FUNCTIONS func1;
+  // SHOW FUNCTIONS `mydb.a`.`func1.aa`;
   private lazy val show: Parser[LogicalPlan] =
-    SHOW ~> TABLES ~ (IN ~> ident).? ^^ {
-      case _ ~ dbName => ShowTablesCommand(dbName)
+    ( SHOW ~> TABLES ~ (IN ~> ident).? ^^ {
+        case _ ~ dbName => ShowTablesCommand(dbName)
+      }
+    | SHOW ~ FUNCTIONS ~> ((ident <~ ".").? ~ (ident | stringLit)).? ^^ {
+        case Some(f) => ShowFunctions(f._1, Some(f._2))
+        case None => ShowFunctions(None, None)
+      }
+    )
+
+  private lazy val desc: Parser[LogicalPlan] =
+    DESCRIBE ~ FUNCTION ~> EXTENDED.? ~ (ident | stringLit) ^^ {
+      case isExtended ~ functionName => DescribeFunction(functionName, isExtended.isDefined)
     }
 
   private lazy val others: Parser[LogicalPlan] =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index e2c7e8006f3b1..deeea3900c241 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -428,6 +428,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         ExecutedCommand(
           RunnableDescribeCommand(resultPlan, describe.output, isExtended)) :: Nil
 
+      case logical.ShowFunctions(db, pattern) => ExecutedCommand(ShowFunctions(db, pattern)) :: Nil
+
+      case logical.DescribeFunction(function, extended) =>
+        ExecutedCommand(DescribeFunction(function, extended)) :: Nil
+
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index bace3f8a9c8d4..6b83025d5a153 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -24,7 +24,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{ExpressionDescription, Expression, Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.types._
@@ -298,3 +298,78 @@ case class ShowTablesCommand(databaseName: Option[String]) extends RunnableComma
     rows
   }
 }
+
+/**
+ * A command for users to list all of the registered functions.
+ * The syntax of using this command in SQL is:
+ * {{{
+ *    SHOW FUNCTIONS
+ * }}}
+ * TODO currently we are simply ignore the db
+ */
+case class ShowFunctions(db: Option[String], pattern: Option[String]) extends RunnableCommand {
+  override val output: Seq[Attribute] = {
+    val schema = StructType(
+      StructField("function", StringType, nullable = false) :: Nil)
+
+    schema.toAttributes
+  }
+
+  override def run(sqlContext: SQLContext): Seq[Row] = pattern match {
+    case Some(p) =>
+      try {
+        val regex = java.util.regex.Pattern.compile(p)
+        sqlContext.functionRegistry.listFunction().filter(regex.matcher(_).matches()).map(Row(_))
+      } catch {
+        // probably will failed in the regex that user provided, then returns empty row.
+        case _: Throwable => Seq.empty[Row]
+      }
+    case None =>
+      sqlContext.functionRegistry.listFunction().map(Row(_))
+  }
+}
+
+/**
+ * A command for users to get the usage of a registered function.
+ * The syntax of using this command in SQL is
+ * {{{
+ *   DESCRIBE FUNCTION [EXTENDED] upper;
+ * }}}
+ */
+case class DescribeFunction(
+    functionName: String,
+    isExtended: Boolean) extends RunnableCommand {
+
+  override val output: Seq[Attribute] = {
+    val schema = StructType(
+      StructField("function_desc", StringType, nullable = false) :: Nil)
+
+    schema.toAttributes
+  }
+
+  private def replaceFunctionName(usage: String, functionName: String): String = {
+    if (usage == null) {
+      "To be added."
+    } else {
+      usage.replaceAll("_FUNC_", functionName)
+    }
+  }
+
+  override def run(sqlContext: SQLContext): Seq[Row] = {
+    sqlContext.functionRegistry.lookupFunction(functionName) match {
+      case Some(info) =>
+        val result =
+          Row(s"Function: ${info.getName}") ::
+          Row(s"Class: ${info.getClassName}") ::
+          Row(s"Usage: ${replaceFunctionName(info.getUsage(), info.getName)}") :: Nil
+
+        if (isExtended) {
+          result :+ Row(s"Extended Usage:\n${replaceFunctionName(info.getExtended, info.getName)}")
+        } else {
+          result
+        }
+
+      case None => Seq(Row(s"Function: $functionName is not found."))
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index cd386b7a3ecf9..8cef0b39f87dc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.scalatest.BeforeAndAfterAll
 
 import java.sql.Timestamp
@@ -58,6 +59,31 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     checkAnswer(queryCoalesce, Row("1") :: Nil)
   }
 
+  test("show functions") {
+    checkAnswer(sql("SHOW functions"), FunctionRegistry.builtin.listFunction().sorted.map(Row(_)))
+  }
+
+  test("describe functions") {
+    checkExistence(sql("describe function extended upper"), true,
+      "Function: upper",
+      "Class: org.apache.spark.sql.catalyst.expressions.Upper",
+      "Usage: upper(str) - Returns str with all characters changed to uppercase",
+      "Extended Usage:",
+      "> SELECT upper('SparkSql');",
+      "'SPARKSQL'")
+
+    checkExistence(sql("describe functioN Upper"), true,
+      "Function: upper",
+      "Class: org.apache.spark.sql.catalyst.expressions.Upper",
+      "Usage: upper(str) - Returns str with all characters changed to uppercase")
+
+    checkExistence(sql("describe functioN Upper"), false,
+      "Extended Usage")
+
+    checkExistence(sql("describe functioN abcadf"), true,
+      "Function: abcadf is not found.")
+  }
+
   test("SPARK-6743: no columns from cache") {
     Seq(
       (83, 0, 38),
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 54bf6bd67ff84..8732e9abf8d31 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -76,8 +76,32 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
     }
   }
 
-  override def registerFunction(name: String, builder: FunctionBuilder): Unit =
-    underlying.registerFunction(name, builder)
+  override def registerFunction(name: String, info: ExpressionInfo, builder: FunctionBuilder)
+  : Unit = underlying.registerFunction(name, info, builder)
+
+  /* List all of the registered function names. */
+  override def listFunction(): Seq[String] = {
+    val a = FunctionRegistry.getFunctionNames ++ underlying.listFunction()
+    a.toList.sorted
+  }
+
+  /* Get the class of the registered function by specified name. */
+  override def lookupFunction(name: String): Option[ExpressionInfo] = {
+    underlying.lookupFunction(name).orElse(
+    Try {
+      val info = FunctionRegistry.getFunctionInfo(name)
+      val annotation = info.getFunctionClass.getAnnotation(classOf[Description])
+      if (annotation != null) {
+        Some(new ExpressionInfo(
+          info.getFunctionClass.getCanonicalName,
+          annotation.name(),
+          annotation.value(),
+          annotation.extended()))
+      } else {
+        None
+      }
+    }.getOrElse(None))
+  }
 }
 
 private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, children: Seq[Expression])
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index efb04bf3d5097..638b9c810372a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -370,7 +370,11 @@ abstract class HiveComparisonTest
             // Check that the results match unless its an EXPLAIN query.
             val preparedHive = prepareAnswer(hiveQuery, hive)
 
-            if ((!hiveQuery.logical.isInstanceOf[ExplainCommand]) && preparedHive != catalyst) {
+            // We will ignore the ExplainCommand, ShowFunctions, DescribeFunction
+            if ((!hiveQuery.logical.isInstanceOf[ExplainCommand]) &&
+                (!hiveQuery.logical.isInstanceOf[ShowFunctions]) &&
+                (!hiveQuery.logical.isInstanceOf[DescribeFunction]) &&
+                preparedHive != catalyst) {
 
               val hivePrintOut = s"== HIVE - ${preparedHive.size} row(s) ==" +: preparedHive
               val catalystPrintOut = s"== CATALYST - ${catalyst.size} row(s) ==" +: catalyst
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index ff42fdefaa62a..013936377b24c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -19,9 +19,11 @@ package org.apache.spark.sql.hive.execution
 
 import java.sql.{Date, Timestamp}
 
+import scala.collection.JavaConversions._
+
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.DefaultParserDialect
-import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
+import org.apache.spark.sql.catalyst.analysis.{FunctionRegistry, EliminateSubQueries}
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.hive.test.TestHive
@@ -138,6 +140,50 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
       (1 to 6).map(_ => Row("CA", 20151)))
   }
 
+  test("show functions") {
+    val allFunctions =
+      (FunctionRegistry.builtin.listFunction().toSet[String] ++
+        org.apache.hadoop.hive.ql.exec.FunctionRegistry.getFunctionNames).toList.sorted
+    checkAnswer(sql("SHOW functions"), allFunctions.map(Row(_)))
+    checkAnswer(sql("SHOW functions abs"), Row("abs"))
+    checkAnswer(sql("SHOW functions 'abs'"), Row("abs"))
+    checkAnswer(sql("SHOW functions abc.abs"), Row("abs"))
+    checkAnswer(sql("SHOW functions `abc`.`abs`"), Row("abs"))
+    checkAnswer(sql("SHOW functions `abc`.`abs`"), Row("abs"))
+    checkAnswer(sql("SHOW functions `~`"), Row("~"))
+    checkAnswer(sql("SHOW functions `a function doens't exist`"), Nil)
+    checkAnswer(sql("SHOW functions `weekofyea.*`"), Row("weekofyear"))
+    // this probably will failed if we add more function with `sha` prefixing.
+    checkAnswer(sql("SHOW functions `sha.*`"), Row("sha") :: Row("sha1") :: Row("sha2") :: Nil)
+  }
+
+  test("describe functions") {
+    // The Spark SQL built-in functions
+    checkExistence(sql("describe function extended upper"), true,
+      "Function: upper",
+      "Class: org.apache.spark.sql.catalyst.expressions.Upper",
+      "Usage: upper(str) - Returns str with all characters changed to uppercase",
+      "Extended Usage:",
+      "> SELECT upper('SparkSql')",
+      "'SPARKSQL'")
+
+    checkExistence(sql("describe functioN Upper"), true,
+      "Function: upper",
+      "Class: org.apache.spark.sql.catalyst.expressions.Upper",
+      "Usage: upper(str) - Returns str with all characters changed to uppercase")
+
+    checkExistence(sql("describe functioN Upper"), false,
+      "Extended Usage")
+
+    checkExistence(sql("describe functioN abcadf"), true,
+      "Function: abcadf is not found.")
+
+    checkExistence(sql("describe functioN  `~`"), true,
+      "Function: ~",
+      "Class: org.apache.hadoop.hive.ql.udf.UDFOPBitNot",
+      "Usage: ~ n - Bitwise not")
+  }
+
   test("SPARK-5371: union with null and sum") {
     val df = Seq((1, 1)).toDF("c1", "c2")
     df.registerTempTable("table1")

From 945d8bcbf67032edd7bdd201cf9f88c75b3464f7 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Sun, 26 Jul 2015 22:13:37 -0700
Subject: [PATCH 0610/1454] [SPARK-9306] [SQL] Don't use SortMergeJoin when
 joining on unsortable columns

JIRA: https://issues.apache.org/jira/browse/SPARK-9306

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7645 from viirya/smj_unsortable and squashes the following commits:

a240707 [Liang-Chi Hsieh] Use forall instead of exists for readability.
55221fa [Liang-Chi Hsieh] Shouldn't use SortMergeJoin when joining on unsortable columns.
---
 .../sql/catalyst/planning/patterns.scala      |  2 +-
 .../spark/sql/execution/SparkStrategies.scala | 19 +++++++++++++++----
 .../org/apache/spark/sql/JoinSuite.scala      | 12 ++++++++++++
 3 files changed, 28 insertions(+), 5 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index b8e3b0d53a505..1e7b2a536ac12 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -184,7 +184,7 @@ object PartialAggregation {
  * A pattern that finds joins with equality conditions that can be evaluated using equi-join.
  */
 object ExtractEquiJoinKeys extends Logging with PredicateHelper {
-  /** (joinType, rightKeys, leftKeys, condition, leftChild, rightChild) */
+  /** (joinType, leftKeys, rightKeys, condition, leftChild, rightChild) */
   type ReturnType =
     (JoinType, Seq[Expression], Seq[Expression], Option[Expression], LogicalPlan, LogicalPlan)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index deeea3900c241..306bbfec624c0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -35,9 +35,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
   object LeftSemiJoin extends Strategy with PredicateHelper {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right)
-        if sqlContext.conf.autoBroadcastJoinThreshold > 0 &&
-          right.statistics.sizeInBytes <= sqlContext.conf.autoBroadcastJoinThreshold =>
+      case ExtractEquiJoinKeys(
+             LeftSemi, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         joins.BroadcastLeftSemiJoinHash(
           leftKeys, rightKeys, planLater(left), planLater(right), condition) :: Nil
       // Find left semi joins where at least some predicates can be evaluated by matching join keys
@@ -90,6 +89,18 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
     }
 
+    private[this] def isValidSort(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression]): Boolean = {
+      leftKeys.zip(rightKeys).forall { keys =>
+        (keys._1.dataType, keys._2.dataType) match {
+          case (l: AtomicType, r: AtomicType) => true
+          case (NullType, NullType) => true
+          case _ => false
+        }
+      }
+    }
+
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildRight)
@@ -100,7 +111,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       // If the sort merge join option is set, we want to use sort merge join prior to hashjoin
       // for now let's support inner join first, then add outer join
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-        if sqlContext.conf.sortMergeJoinEnabled =>
+        if sqlContext.conf.sortMergeJoinEnabled && isValidSort(leftKeys, rightKeys) =>
         val mergeJoin =
           joins.SortMergeJoin(leftKeys, rightKeys, planLater(left), planLater(right))
         condition.map(Filter(_, mergeJoin)).getOrElse(mergeJoin) :: Nil
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 8953889d1fae9..dfb2a7e099748 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -108,6 +108,18 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
     }
   }
 
+  test("SortMergeJoin shouldn't work on unsortable columns") {
+    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
+    try {
+      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+      Seq(
+        ("SELECT * FROM arrayData JOIN complexData ON data = a", classOf[ShuffledHashJoin])
+      ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
+    } finally {
+      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
+    }
+  }
+
   test("broadcasted hash join operator selection") {
     ctx.cacheManager.clearCache()
     ctx.sql("CACHE TABLE testData")

From aa80c64fcf9626b3720ee000a653db9266b74839 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 26 Jul 2015 23:01:04 -0700
Subject: [PATCH 0611/1454] [SPARK-9368][SQL] Support get(ordinal, dataType)
 generic getter in UnsafeRow.

Author: Reynold Xin <rxin@databricks.com>

Closes #7682 from rxin/unsaferow-generic-getter and squashes the following commits:

3063788 [Reynold Xin] Reset the change for real this time.
0f57c55 [Reynold Xin] Reset the changes in ExpressionEvalHelper.
fb6ca30 [Reynold Xin] Support BinaryType.
24a3e46 [Reynold Xin] Added support for DateType/TimestampType.
9989064 [Reynold Xin] JoinedRow.
11f80a3 [Reynold Xin] [SPARK-9368][SQL] Support get(ordinal, dataType) generic getter in UnsafeRow.
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 52 ++++++++++++++++++-
 .../spark/sql/catalyst/InternalRow.scala      |  4 +-
 .../sql/catalyst/expressions/Projection.scala |  2 +-
 .../expressions/SpecificMutableRow.scala      |  2 +-
 .../codegen/GenerateProjection.scala          |  2 +-
 .../spark/sql/catalyst/expressions/rows.scala |  4 +-
 6 files changed, 58 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 87e5a89c19658..0fb33dd5a15a0 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -24,7 +24,7 @@
 import java.util.HashSet;
 import java.util.Set;
 
-import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.*;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
@@ -235,6 +235,41 @@ public Object get(int ordinal) {
     throw new UnsupportedOperationException();
   }
 
+  @Override
+  public Object get(int ordinal, DataType dataType) {
+    if (dataType instanceof NullType) {
+      return null;
+    } else if (dataType instanceof BooleanType) {
+      return getBoolean(ordinal);
+    } else if (dataType instanceof ByteType) {
+      return getByte(ordinal);
+    } else if (dataType instanceof ShortType) {
+      return getShort(ordinal);
+    } else if (dataType instanceof IntegerType) {
+      return getInt(ordinal);
+    } else if (dataType instanceof LongType) {
+      return getLong(ordinal);
+    } else if (dataType instanceof FloatType) {
+      return getFloat(ordinal);
+    } else if (dataType instanceof DoubleType) {
+      return getDouble(ordinal);
+    } else if (dataType instanceof DecimalType) {
+      return getDecimal(ordinal);
+    } else if (dataType instanceof DateType) {
+      return getInt(ordinal);
+    } else if (dataType instanceof TimestampType) {
+      return getLong(ordinal);
+    } else if (dataType instanceof BinaryType) {
+      return getBinary(ordinal);
+    } else if (dataType instanceof StringType) {
+      return getUTF8String(ordinal);
+    } else if (dataType instanceof StructType) {
+      return getStruct(ordinal, ((StructType) dataType).size());
+    } else {
+      throw new UnsupportedOperationException("Unsupported data type " + dataType.simpleString());
+    }
+  }
+
   @Override
   public boolean isNullAt(int ordinal) {
     assertIndexIsValid(ordinal);
@@ -436,4 +471,19 @@ public String toString() {
   public boolean anyNull() {
     return BitSetMethods.anySet(baseObject, baseOffset, bitSetWidthInBytes / 8);
   }
+
+  /**
+   * Writes the content of this row into a memory address, identified by an object and an offset.
+   * The target memory address must already been allocated, and have enough space to hold all the
+   * bytes in this string.
+   */
+  public void writeToMemory(Object target, long targetOffset) {
+    PlatformDependent.copyMemory(
+      baseObject,
+      baseOffset,
+      target,
+      targetOffset,
+      sizeInBytes
+    );
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 385d9671386dc..ad3977281d1a9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -30,11 +30,11 @@ abstract class InternalRow extends Serializable {
 
   def numFields: Int
 
-  def get(ordinal: Int): Any
+  def get(ordinal: Int): Any = get(ordinal, null)
 
   def genericGet(ordinal: Int): Any = get(ordinal, null)
 
-  def get(ordinal: Int, dataType: DataType): Any = get(ordinal)
+  def get(ordinal: Int, dataType: DataType): Any
 
   def getAs[T](ordinal: Int, dataType: DataType): T = get(ordinal, dataType).asInstanceOf[T]
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index cc89d74146b34..27d6ff587ab71 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -198,7 +198,7 @@ class JoinedRow extends InternalRow {
     if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
   }
 
-  override def get(i: Int): Any =
+  override def get(i: Int, dataType: DataType): Any =
     if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 5953a093dc684..b877ce47c083f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -219,7 +219,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
     values(i).isNull = true
   }
 
-  override def get(i: Int): Any = values(i).boxed
+  override def get(i: Int, dataType: DataType): Any = values(i).boxed
 
   override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
     values(ordinal).boxed.asInstanceOf[InternalRow]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index a361b216eb472..35920147105ff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -183,7 +183,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        public Object get(int i) {
+        public Object get(int i, ${classOf[DataType].getName} dataType) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index daeabe8e90f1d..b7c4ece4a16fe 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -99,7 +99,7 @@ class GenericInternalRow(protected[sql] val values: Array[Any]) extends Internal
 
   override def numFields: Int = values.length
 
-  override def get(i: Int): Any = values(i)
+  override def get(i: Int, dataType: DataType): Any = values(i)
 
   override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
     values(ordinal).asInstanceOf[InternalRow]
@@ -130,7 +130,7 @@ class GenericMutableRow(val values: Array[Any]) extends MutableRow {
 
   override def numFields: Int = values.length
 
-  override def get(i: Int): Any = values(i)
+  override def get(i: Int, dataType: DataType): Any = values(i)
 
   override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
     values(ordinal).asInstanceOf[InternalRow]

From 4ffd3a1db5ecff653b02aa325786e734351c8bd2 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sun, 26 Jul 2015 23:58:03 -0700
Subject: [PATCH 0612/1454] [SPARK-9371][SQL] fix the support for special chars
 in column names for hive context

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7684 from cloud-fan/hive and squashes the following commits:

da21ffe [Wenchen Fan] fix the support for special chars in column names for hive context
---
 .../src/main/scala/org/apache/spark/sql/hive/HiveQl.scala | 6 +++---
 .../apache/spark/sql/hive/execution/SQLQuerySuite.scala   | 8 ++++++++
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 620b8a44d8a9b..2f79b0aad045c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -1321,11 +1321,11 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     /* Attribute References */
     case Token("TOK_TABLE_OR_COL",
            Token(name, Nil) :: Nil) =>
-      UnresolvedAttribute(cleanIdentifier(name))
+      UnresolvedAttribute.quoted(cleanIdentifier(name))
     case Token(".", qualifier :: Token(attr, Nil) :: Nil) =>
       nodeToExpr(qualifier) match {
-        case UnresolvedAttribute(qualifierName) =>
-          UnresolvedAttribute(qualifierName :+ cleanIdentifier(attr))
+        case UnresolvedAttribute(nameParts) =>
+          UnresolvedAttribute(nameParts :+ cleanIdentifier(attr))
         case other => UnresolvedExtractValue(other, Literal(attr))
       }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 013936377b24c..8371dd0716c06 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -1067,4 +1067,12 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
     )
     TestHive.dropTempTable("test_SPARK8588")
   }
+
+  test("SPARK-9371: fix the support for special chars in column names for hive context") {
+    TestHive.read.json(TestHive.sparkContext.makeRDD(
+      """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil))
+      .registerTempTable("t")
+
+    checkAnswer(sql("SELECT a.`c.b`, `b.$q`[0].`a@!.q`, `q.w`.`w.i&`[0] FROM t"), Row(1, 1, 1))
+  }
 }

From 72981bc8f0d421e2563e2543a8c16a8cc76ad3aa Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 27 Jul 2015 17:15:35 +0800
Subject: [PATCH 0613/1454] [SPARK-7943] [SPARK-8105] [SPARK-8435] [SPARK-8714]
 [SPARK-8561] Fixes multi-database support

This PR fixes a set of issues related to multi-database. A new data structure `TableIdentifier` is introduced to identify a table among multiple databases. We should stop using a single `String` (table name without database name), or `Seq[String]` (optional database name plus table name) to identify tables internally.

Author: Cheng Lian <lian@databricks.com>

Closes #7623 from liancheng/spark-8131-multi-db and squashes the following commits:

f3bcd4b [Cheng Lian] Addresses PR comments
e0eb76a [Cheng Lian] Fixes styling issues
41e2207 [Cheng Lian] Fixes multi-database support
d4d1ec2 [Cheng Lian] Adds multi-database test cases
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  14 ++
 .../spark/sql/catalyst/TableIdentifier.scala  |  31 ++++
 .../spark/sql/catalyst/analysis/Catalog.scala |   9 +-
 .../apache/spark/sql/DataFrameWriter.scala    |  83 ++++-----
 .../org/apache/spark/sql/SQLContext.scala     |   6 +-
 .../spark/sql/execution/datasources/ddl.scala |  15 +-
 .../spark/sql/parquet/ParquetTest.scala       |   4 +-
 .../apache/spark/sql/test/SQLTestUtils.scala  |  29 +++-
 .../apache/spark/sql/hive/HiveContext.scala   |   5 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  31 +++-
 .../spark/sql/hive/MultiDatabaseSuite.scala   | 159 ++++++++++++++++++
 .../apache/spark/sql/hive/orc/OrcTest.scala   |   7 +-
 12 files changed, 327 insertions(+), 66 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index c494e5d704213..b423f0fa04f69 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -48,6 +48,15 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     }
   }
 
+  def parseTableIdentifier(input: String): TableIdentifier = {
+    // Initialize the Keywords.
+    initLexical
+    phrase(tableIdentifier)(new lexical.Scanner(input)) match {
+      case Success(ident, _) => ident
+      case failureOrError => sys.error(failureOrError.toString)
+    }
+  }
+
   // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword`
   // properties via reflection the class in runtime for constructing the SqlLexical object
   protected val ALL = Keyword("ALL")
@@ -444,4 +453,9 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     (ident <~ ".") ~ ident ~ rep("." ~> ident) ^^ {
       case i1 ~ i2 ~ rest => UnresolvedAttribute(Seq(i1, i2) ++ rest)
     }
+
+  protected lazy val tableIdentifier: Parser[TableIdentifier] =
+    (ident <~ ".").? ~ ident ^^ {
+      case maybeDbName ~ tableName => TableIdentifier(tableName, maybeDbName)
+    }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
new file mode 100644
index 0000000000000..aebcdeb9d070f
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
@@ -0,0 +1,31 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst
+
+/**
+ * Identifies a `table` in `database`.  If `database` is not defined, the current database is used.
+ */
+private[sql] case class TableIdentifier(table: String, database: Option[String] = None) {
+  def withDatabase(database: String): TableIdentifier = this.copy(database = Some(database))
+
+  def toSeq: Seq[String] = database.toSeq :+ table
+
+  override def toString: String = toSeq.map("`" + _ + "`").mkString(".")
+
+  def unquotedString: String = toSeq.mkString(".")
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index 1541491608b24..5766e6a2dd51a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -23,8 +23,7 @@ import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.sql.catalyst.CatalystConf
-import org.apache.spark.sql.catalyst.EmptyConf
+import org.apache.spark.sql.catalyst.{TableIdentifier, CatalystConf, EmptyConf}
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery}
 
 /**
@@ -54,7 +53,7 @@ trait Catalog {
    */
   def getTables(databaseName: Option[String]): Seq[(String, Boolean)]
 
-  def refreshTable(databaseName: String, tableName: String): Unit
+  def refreshTable(tableIdent: TableIdentifier): Unit
 
   def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit
 
@@ -132,7 +131,7 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
     result
   }
 
-  override def refreshTable(databaseName: String, tableName: String): Unit = {
+  override def refreshTable(tableIdent: TableIdentifier): Unit = {
     throw new UnsupportedOperationException
   }
 }
@@ -241,7 +240,7 @@ object EmptyCatalog extends Catalog {
 
   override def unregisterAllTables(): Unit = {}
 
-  override def refreshTable(databaseName: String, tableName: String): Unit = {
+  override def refreshTable(tableIdent: TableIdentifier): Unit = {
     throw new UnsupportedOperationException
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 05da05d7b8050..7e3318cefe62c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.util.Properties
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.sql.catalyst.{SqlParser, TableIdentifier}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.plans.logical.InsertIntoTable
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, ResolvedDataSource}
@@ -159,15 +160,19 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    * @since 1.4.0
    */
   def insertInto(tableName: String): Unit = {
-    val partitions =
-      partitioningColumns.map(_.map(col => col -> (None: Option[String])).toMap)
-    val overwrite = (mode == SaveMode.Overwrite)
-    df.sqlContext.executePlan(InsertIntoTable(
-      UnresolvedRelation(Seq(tableName)),
-      partitions.getOrElse(Map.empty[String, Option[String]]),
-      df.logicalPlan,
-      overwrite,
-      ifNotExists = false)).toRdd
+    insertInto(new SqlParser().parseTableIdentifier(tableName))
+  }
+
+  private def insertInto(tableIdent: TableIdentifier): Unit = {
+    val partitions = partitioningColumns.map(_.map(col => col -> (None: Option[String])).toMap)
+    val overwrite = mode == SaveMode.Overwrite
+    df.sqlContext.executePlan(
+      InsertIntoTable(
+        UnresolvedRelation(tableIdent.toSeq),
+        partitions.getOrElse(Map.empty[String, Option[String]]),
+        df.logicalPlan,
+        overwrite,
+        ifNotExists = false)).toRdd
   }
 
   /**
@@ -183,35 +188,37 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    * @since 1.4.0
    */
   def saveAsTable(tableName: String): Unit = {
-    if (df.sqlContext.catalog.tableExists(tableName :: Nil) && mode != SaveMode.Overwrite) {
-      mode match {
-        case SaveMode.Ignore =>
-          // Do nothing
-
-        case SaveMode.ErrorIfExists =>
-          throw new AnalysisException(s"Table $tableName already exists.")
-
-        case SaveMode.Append =>
-          // If it is Append, we just ask insertInto to handle it. We will not use insertInto
-          // to handle saveAsTable with Overwrite because saveAsTable can change the schema of
-          // the table. But, insertInto with Overwrite requires the schema of data be the same
-          // the schema of the table.
-          insertInto(tableName)
-
-        case SaveMode.Overwrite =>
-          throw new UnsupportedOperationException("overwrite mode unsupported.")
-      }
-    } else {
-      val cmd =
-        CreateTableUsingAsSelect(
-          tableName,
-          source,
-          temporary = false,
-          partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]),
-          mode,
-          extraOptions.toMap,
-          df.logicalPlan)
-      df.sqlContext.executePlan(cmd).toRdd
+    saveAsTable(new SqlParser().parseTableIdentifier(tableName))
+  }
+
+  private def saveAsTable(tableIdent: TableIdentifier): Unit = {
+    val tableExists = df.sqlContext.catalog.tableExists(tableIdent.toSeq)
+
+    (tableExists, mode) match {
+      case (true, SaveMode.Ignore) =>
+        // Do nothing
+
+      case (true, SaveMode.ErrorIfExists) =>
+        throw new AnalysisException(s"Table $tableIdent already exists.")
+
+      case (true, SaveMode.Append) =>
+        // If it is Append, we just ask insertInto to handle it. We will not use insertInto
+        // to handle saveAsTable with Overwrite because saveAsTable can change the schema of
+        // the table. But, insertInto with Overwrite requires the schema of data be the same
+        // the schema of the table.
+        insertInto(tableIdent)
+
+      case _ =>
+        val cmd =
+          CreateTableUsingAsSelect(
+            tableIdent.unquotedString,
+            source,
+            temporary = false,
+            partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]),
+            mode,
+            extraOptions.toMap,
+            df.logicalPlan)
+        df.sqlContext.executePlan(cmd).toRdd
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 0e25e06e99ab2..dbb2a09846548 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -798,8 +798,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @group ddl_ops
    * @since 1.3.0
    */
-  def table(tableName: String): DataFrame =
-    DataFrame(this, catalog.lookupRelation(Seq(tableName)))
+  def table(tableName: String): DataFrame = {
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
+    DataFrame(this, catalog.lookupRelation(tableIdent.toSeq))
+  }
 
   /**
    * Returns a [[DataFrame]] containing names of existing tables in the current database.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index 1f2797ec5527a..e73b3704d4dfe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -21,16 +21,17 @@ import scala.language.{existentials, implicitConversions}
 import scala.util.matching.Regex
 
 import org.apache.hadoop.fs.Path
+
 import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SQLContext, SaveMode}
-import org.apache.spark.sql.catalyst.AbstractSparkSQLParser
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.{AbstractSparkSQLParser, TableIdentifier}
 import org.apache.spark.sql.execution.RunnableCommand
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SQLContext, SaveMode}
 import org.apache.spark.util.Utils
 
 /**
@@ -151,7 +152,7 @@ private[sql] class DDLParser(
   protected lazy val refreshTable: Parser[LogicalPlan] =
     REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ {
       case maybeDatabaseName ~ tableName =>
-        RefreshTable(maybeDatabaseName.getOrElse("default"), tableName)
+        RefreshTable(TableIdentifier(tableName, maybeDatabaseName))
     }
 
   protected lazy val options: Parser[Map[String, String]] =
@@ -442,16 +443,16 @@ private[sql] case class CreateTempTableUsingAsSelect(
   }
 }
 
-private[sql] case class RefreshTable(databaseName: String, tableName: String)
+private[sql] case class RefreshTable(tableIdent: TableIdentifier)
   extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
     // Refresh the given table's metadata first.
-    sqlContext.catalog.refreshTable(databaseName, tableName)
+    sqlContext.catalog.refreshTable(tableIdent)
 
     // If this table is cached as a InMemoryColumnarRelation, drop the original
     // cached version and make the new version cached lazily.
-    val logicalPlan = sqlContext.catalog.lookupRelation(Seq(databaseName, tableName))
+    val logicalPlan = sqlContext.catalog.lookupRelation(tableIdent.toSeq)
     // Use lookupCachedData directly since RefreshTable also takes databaseName.
     val isCached = sqlContext.cacheManager.lookupCachedData(logicalPlan).nonEmpty
     if (isCached) {
@@ -461,7 +462,7 @@ private[sql] case class RefreshTable(databaseName: String, tableName: String)
       // Uncache the logicalPlan.
       sqlContext.cacheManager.tryUncacheQuery(df, blocking = true)
       // Cache it again.
-      sqlContext.cacheManager.cacheQuery(df, Some(tableName))
+      sqlContext.cacheManager.cacheQuery(df, Some(tableIdent.table))
     }
 
     Seq.empty[Row]
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala
index eb15a1609f1d0..64e94056f209a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala
@@ -22,6 +22,7 @@ import java.io.File
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
+import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.{DataFrame, SaveMode}
 
@@ -32,8 +33,7 @@ import org.apache.spark.sql.{DataFrame, SaveMode}
  * convenient to use tuples rather than special case classes when writing test cases/suites.
  * Especially, `Tuple1.apply` can be used to easily wrap a single type/value.
  */
-private[sql] trait ParquetTest extends SQLTestUtils {
-
+private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
   /**
    * Writes `data` to a Parquet file, which is then passed to `f` and will be deleted after `f`
    * returns.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index fa01823e9417c..4c11acdab9ec0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -18,13 +18,15 @@
 package org.apache.spark.sql.test
 
 import java.io.File
+import java.util.UUID
 
 import scala.util.Try
 
+import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.util.Utils
 
-trait SQLTestUtils {
+trait SQLTestUtils { this: SparkFunSuite =>
   def sqlContext: SQLContext
 
   protected def configuration = sqlContext.sparkContext.hadoopConfiguration
@@ -87,4 +89,29 @@ trait SQLTestUtils {
       }
     }
   }
+
+  /**
+   * Creates a temporary database and switches current database to it before executing `f`.  This
+   * database is dropped after `f` returns.
+   */
+  protected def withTempDatabase(f: String => Unit): Unit = {
+    val dbName = s"db_${UUID.randomUUID().toString.replace('-', '_')}"
+
+    try {
+      sqlContext.sql(s"CREATE DATABASE $dbName")
+    } catch { case cause: Throwable =>
+      fail("Failed to create temporary database", cause)
+    }
+
+    try f(dbName) finally sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
+  }
+
+  /**
+   * Activates database `db` before executing `f`, then switches back to `default` database after
+   * `f` returns.
+   */
+  protected def activateDatabase(db: String)(f: => Unit): Unit = {
+    sqlContext.sql(s"USE $db")
+    try f finally sqlContext.sql(s"USE default")
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 1b8edefef4093..110f51a305861 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -40,7 +40,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
 import org.apache.spark.sql.SQLConf.SQLConfEntry
 import org.apache.spark.sql.SQLConf.SQLConfEntry._
-import org.apache.spark.sql.catalyst.ParserDialect
+import org.apache.spark.sql.catalyst.{TableIdentifier, ParserDialect}
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUDFs, SetCommand}
@@ -267,7 +267,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    * @since 1.3.0
    */
   def refreshTable(tableName: String): Unit = {
-    catalog.refreshTable(catalog.client.currentDatabase, tableName)
+    val tableIdent = TableIdentifier(tableName).withDatabase(catalog.client.currentDatabase)
+    catalog.refreshTable(tableIdent)
   }
 
   protected[hive] def invalidateTable(tableName: String): Unit = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 262923531216f..9c707a7a2eca1 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -29,13 +29,13 @@ import org.apache.hadoop.hive.ql.metadata._
 import org.apache.hadoop.hive.ql.plan.TableDesc
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.{Catalog, MultiInstanceRelation, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.catalyst.{InternalRow, SqlParser, TableIdentifier}
 import org.apache.spark.sql.execution.datasources
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
@@ -43,7 +43,6 @@ import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
 
-
 private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: HiveContext)
   extends Catalog with Logging {
 
@@ -115,7 +114,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     CacheBuilder.newBuilder().maximumSize(1000).build(cacheLoader)
   }
 
-  override def refreshTable(databaseName: String, tableName: String): Unit = {
+  override def refreshTable(tableIdent: TableIdentifier): Unit = {
     // refreshTable does not eagerly reload the cache. It just invalidate the cache.
     // Next time when we use the table, it will be populated in the cache.
     // Since we also cache ParquetRelations converted from Hive Parquet tables and
@@ -124,7 +123,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     // it is better at here to invalidate the cache to avoid confusing waring logs from the
     // cache loader (e.g. cannot find data source provider, which is only defined for
     // data source table.).
-    invalidateTable(databaseName, tableName)
+    invalidateTable(tableIdent.database.getOrElse(client.currentDatabase), tableIdent.table)
   }
 
   def invalidateTable(databaseName: String, tableName: String): Unit = {
@@ -144,7 +143,27 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       provider: String,
       options: Map[String, String],
       isExternal: Boolean): Unit = {
-    val (dbName, tblName) = processDatabaseAndTableName(client.currentDatabase, tableName)
+    createDataSourceTable(
+      new SqlParser().parseTableIdentifier(tableName),
+      userSpecifiedSchema,
+      partitionColumns,
+      provider,
+      options,
+      isExternal)
+  }
+
+  private def createDataSourceTable(
+      tableIdent: TableIdentifier,
+      userSpecifiedSchema: Option[StructType],
+      partitionColumns: Array[String],
+      provider: String,
+      options: Map[String, String],
+      isExternal: Boolean): Unit = {
+    val (dbName, tblName) = {
+      val database = tableIdent.database.getOrElse(client.currentDatabase)
+      processDatabaseAndTableName(database, tableIdent.table)
+    }
+
     val tableProperties = new scala.collection.mutable.HashMap[String, String]
     tableProperties.put("spark.sql.sources.provider", provider)
 
@@ -177,7 +196,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         // partitions when we load the table. However, if there are specified partition columns,
         // we simplily ignore them and provide a warning message..
         logWarning(
-          s"The schema and partitions of table $tableName will be inferred when it is loaded. " +
+          s"The schema and partitions of table $tableIdent will be inferred when it is loaded. " +
             s"Specified partition columns (${partitionColumns.mkString(",")}) will be ignored.")
       }
       Seq.empty[HiveColumn]
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
new file mode 100644
index 0000000000000..73852f13ad20d
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
@@ -0,0 +1,159 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.{QueryTest, SQLContext, SaveMode}
+
+class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
+  override val sqlContext: SQLContext = TestHive
+
+  import sqlContext.sql
+
+  private val df = sqlContext.range(10).coalesce(1)
+
+  test(s"saveAsTable() to non-default database - with USE - Overwrite") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        df.write.mode(SaveMode.Overwrite).saveAsTable("t")
+        assert(sqlContext.tableNames().contains("t"))
+        checkAnswer(sqlContext.table("t"), df)
+      }
+
+      assert(sqlContext.tableNames(db).contains("t"))
+      checkAnswer(sqlContext.table(s"$db.t"), df)
+    }
+  }
+
+  test(s"saveAsTable() to non-default database - without USE - Overwrite") {
+    withTempDatabase { db =>
+      df.write.mode(SaveMode.Overwrite).saveAsTable(s"$db.t")
+      assert(sqlContext.tableNames(db).contains("t"))
+      checkAnswer(sqlContext.table(s"$db.t"), df)
+    }
+  }
+
+  test(s"saveAsTable() to non-default database - with USE - Append") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        df.write.mode(SaveMode.Overwrite).saveAsTable("t")
+        df.write.mode(SaveMode.Append).saveAsTable("t")
+        assert(sqlContext.tableNames().contains("t"))
+        checkAnswer(sqlContext.table("t"), df.unionAll(df))
+      }
+
+      assert(sqlContext.tableNames(db).contains("t"))
+      checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+    }
+  }
+
+  test(s"saveAsTable() to non-default database - without USE - Append") {
+    withTempDatabase { db =>
+      df.write.mode(SaveMode.Overwrite).saveAsTable(s"$db.t")
+      df.write.mode(SaveMode.Append).saveAsTable(s"$db.t")
+      assert(sqlContext.tableNames(db).contains("t"))
+      checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+    }
+  }
+
+  test(s"insertInto() non-default database - with USE") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        df.write.mode(SaveMode.Overwrite).saveAsTable("t")
+        assert(sqlContext.tableNames().contains("t"))
+
+        df.write.insertInto(s"$db.t")
+        checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+      }
+    }
+  }
+
+  test(s"insertInto() non-default database - without USE") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        df.write.mode(SaveMode.Overwrite).saveAsTable("t")
+        assert(sqlContext.tableNames().contains("t"))
+      }
+
+      assert(sqlContext.tableNames(db).contains("t"))
+
+      df.write.insertInto(s"$db.t")
+      checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+    }
+  }
+
+  test("Looks up tables in non-default database") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        sql("CREATE TABLE t (key INT)")
+        checkAnswer(sqlContext.table("t"), sqlContext.emptyDataFrame)
+      }
+
+      checkAnswer(sqlContext.table(s"$db.t"), sqlContext.emptyDataFrame)
+    }
+  }
+
+  test("Drops a table in a non-default database") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        sql(s"CREATE TABLE t (key INT)")
+        assert(sqlContext.tableNames().contains("t"))
+        assert(!sqlContext.tableNames("default").contains("t"))
+      }
+
+      assert(!sqlContext.tableNames().contains("t"))
+      assert(sqlContext.tableNames(db).contains("t"))
+
+      activateDatabase(db) {
+        sql(s"DROP TABLE t")
+        assert(!sqlContext.tableNames().contains("t"))
+        assert(!sqlContext.tableNames("default").contains("t"))
+      }
+
+      assert(!sqlContext.tableNames().contains("t"))
+      assert(!sqlContext.tableNames(db).contains("t"))
+    }
+  }
+
+  test("Refreshes a table in a non-default database") {
+    import org.apache.spark.sql.functions.lit
+
+    withTempDatabase { db =>
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
+
+        activateDatabase(db) {
+          sql(
+            s"""CREATE EXTERNAL TABLE t (id BIGINT)
+               |PARTITIONED BY (p INT)
+               |STORED AS PARQUET
+               |LOCATION '$path'
+             """.stripMargin)
+
+          checkAnswer(sqlContext.table("t"), sqlContext.emptyDataFrame)
+
+          df.write.parquet(s"$path/p=1")
+          sql("ALTER TABLE t ADD PARTITION (p=1)")
+          sql("REFRESH TABLE t")
+          checkAnswer(sqlContext.table("t"), df.withColumn("p", lit(1)))
+        }
+      }
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
index 9d76d6503a3e6..145965388da01 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
@@ -22,14 +22,15 @@ import java.io.File
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SQLTestUtils
 
-private[sql] trait OrcTest extends SQLTestUtils {
+private[sql] trait OrcTest extends SQLTestUtils { this: SparkFunSuite =>
   lazy val sqlContext = org.apache.spark.sql.hive.test.TestHive
 
-  import sqlContext.sparkContext
   import sqlContext.implicits._
+  import sqlContext.sparkContext
 
   /**
    * Writes `data` to a Orc file, which is then passed to `f` and will be deleted after `f`

From 622838165756e9669cbf7af13eccbc719638f40b Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Mon, 27 Jul 2015 08:02:40 -0500
Subject: [PATCH 0614/1454] [SPARK-8405] [DOC] Add how to view logs on Web UI
 when yarn log aggregation is enabled

Some users may not be aware that the logs are available on Web UI even if Yarn log aggregation is enabled. Update the doc to make this clear and what need to be configured.

Author: Carson Wang <carson.wang@intel.com>

Closes #7463 from carsonwang/YarnLogDoc and squashes the following commits:

274c054 [Carson Wang] Minor text fix
74df3a1 [Carson Wang] address comments
5a95046 [Carson Wang] Update the text in the doc
e5775c1 [Carson Wang] Update doc about how to view the logs on Web UI when yarn log aggregation is enabled
---
 docs/running-on-yarn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index de22ab557cacf..cac08a91b97d9 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -68,9 +68,9 @@ In YARN terminology, executors and application masters run inside "containers".
 
     yarn logs -applicationId <app ID>
     
-will print out the contents of all log files from all containers from the given application. You can also view the container log files directly in HDFS using the HDFS shell or API. The directory where they are located can be found by looking at your YARN configs (`yarn.nodemanager.remote-app-log-dir` and `yarn.nodemanager.remote-app-log-dir-suffix`).
+will print out the contents of all log files from all containers from the given application. You can also view the container log files directly in HDFS using the HDFS shell or API. The directory where they are located can be found by looking at your YARN configs (`yarn.nodemanager.remote-app-log-dir` and `yarn.nodemanager.remote-app-log-dir-suffix`). The logs are also available on the Spark Web UI under the Executors Tab. You need to have both the Spark history server and the MapReduce history server running and configure `yarn.log.server.url` in `yarn-site.xml` properly. The log URL on the Spark history server UI will redirect you to the MapReduce history server to show the aggregated logs.
 
-When log aggregation isn't turned on, logs are retained locally on each machine under `YARN_APP_LOGS_DIR`, which is usually configured to `/tmp/logs` or `$HADOOP_HOME/logs/userlogs` depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID.
+When log aggregation isn't turned on, logs are retained locally on each machine under `YARN_APP_LOGS_DIR`, which is usually configured to `/tmp/logs` or `$HADOOP_HOME/logs/userlogs` depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory.  Subdirectories organize log files by application ID and container ID. The logs are also available on the Spark Web UI under the Executors Tab and doesn't require running the MapReduce history server.
 
 To review per-container launch environment, increase `yarn.nodemanager.delete.debug-delay-sec` to a
 large value (e.g. 36000), and then access the application cache through `yarn.nodemanager.local-dirs`

From aa19c696e25ebb07fd3df110cfcbcc69954ce335 Mon Sep 17 00:00:00 2001
From: Rene Treffer <treffer+github@measite.de>
Date: Mon, 27 Jul 2015 23:29:40 +0800
Subject: [PATCH 0615/1454] [SPARK-4176] [SQL] Supports decimal types with
 precision > 18 in Parquet

This PR is based on #6796 authored by rtreffer.

To support large decimal precisions (> 18), we do the following things in this PR:

1. Making `CatalystSchemaConverter` support large decimal precision

   Decimal types with large precision are always converted to fixed-length byte array.

2. Making `CatalystRowConverter` support reading decimal values with large precision

   When the precision is > 18, constructs `Decimal` values with an unscaled `BigInteger` rather than an unscaled `Long`.

3. Making `RowWriteSupport` support writing decimal values with large precision

   In this PR we always write decimals as fixed-length byte array, because Parquet write path hasn't been refactored to conform Parquet format spec (see SPARK-6774 & SPARK-8848).

Two follow-up tasks should be done in future PRs:

- [ ] Writing decimals as `INT32`, `INT64` when possible while fixing SPARK-8848
- [ ] Adding compatibility tests as part of SPARK-5463

Author: Cheng Lian <lian@databricks.com>

Closes #7455 from liancheng/spark-4176 and squashes the following commits:

a543d10 [Cheng Lian] Fixes errors introduced while rebasing
9e31cdf [Cheng Lian] Supports decimals with precision > 18 for Parquet
---
 .../sql/parquet/CatalystRowConverter.scala    | 25 +++++---
 .../sql/parquet/CatalystSchemaConverter.scala | 46 +++++++------
 .../sql/parquet/ParquetTableSupport.scala     | 64 +++++++++++++------
 .../spark/sql/parquet/ParquetIOSuite.scala    | 10 +--
 4 files changed, 85 insertions(+), 60 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
index b5e4263008f56..e00bd90edb3dd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.parquet
 
+import java.math.{BigDecimal, BigInteger}
 import java.nio.ByteOrder
 
 import scala.collection.JavaConversions._
@@ -263,17 +264,23 @@ private[parquet] class CatalystRowConverter(
       val scale = decimalType.scale
       val bytes = value.getBytes
 
-      var unscaled = 0L
-      var i = 0
+      if (precision <= 8) {
+        // Constructs a `Decimal` with an unscaled `Long` value if possible.
+        var unscaled = 0L
+        var i = 0
 
-      while (i < bytes.length) {
-        unscaled = (unscaled << 8) | (bytes(i) & 0xff)
-        i += 1
-      }
+        while (i < bytes.length) {
+          unscaled = (unscaled << 8) | (bytes(i) & 0xff)
+          i += 1
+        }
 
-      val bits = 8 * bytes.length
-      unscaled = (unscaled << (64 - bits)) >> (64 - bits)
-      Decimal(unscaled, precision, scale)
+        val bits = 8 * bytes.length
+        unscaled = (unscaled << (64 - bits)) >> (64 - bits)
+        Decimal(unscaled, precision, scale)
+      } else {
+        // Otherwise, resorts to an unscaled `BigInteger` instead.
+        Decimal(new BigDecimal(new BigInteger(bytes), scale), precision, scale)
+      }
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index e9ef01e2dba1b..d43ca95b4eea0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -387,24 +387,18 @@ private[parquet] class CatalystSchemaConverter(
       // =====================================
 
       // Spark 1.4.x and prior versions only support decimals with a maximum precision of 18 and
-      // always store decimals in fixed-length byte arrays.
-      case DecimalType.Fixed(precision, scale)
-        if precision <= maxPrecisionForBytes(8) && !followParquetFormatSpec =>
+      // always store decimals in fixed-length byte arrays.  To keep compatibility with these older
+      // versions, here we convert decimals with all precisions to `FIXED_LEN_BYTE_ARRAY` annotated
+      // by `DECIMAL`.
+      case DecimalType.Fixed(precision, scale) if !followParquetFormatSpec =>
         Types
           .primitive(FIXED_LEN_BYTE_ARRAY, repetition)
           .as(DECIMAL)
           .precision(precision)
           .scale(scale)
-          .length(minBytesForPrecision(precision))
+          .length(CatalystSchemaConverter.minBytesForPrecision(precision))
           .named(field.name)
 
-      case dec @ DecimalType() if !followParquetFormatSpec =>
-        throw new AnalysisException(
-          s"Data type $dec is not supported. " +
-            s"When ${SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key} is set to false," +
-            "decimal precision and scale must be specified, " +
-            "and precision must be less than or equal to 18.")
-
       // =====================================
       // Decimals (follow Parquet format spec)
       // =====================================
@@ -436,7 +430,7 @@ private[parquet] class CatalystSchemaConverter(
           .as(DECIMAL)
           .precision(precision)
           .scale(scale)
-          .length(minBytesForPrecision(precision))
+          .length(CatalystSchemaConverter.minBytesForPrecision(precision))
           .named(field.name)
 
       // ===================================================
@@ -548,15 +542,6 @@ private[parquet] class CatalystSchemaConverter(
         Math.pow(2, 8 * numBytes - 1) - 1)))  // max value stored in numBytes
       .asInstanceOf[Int]
   }
-
-  // Min byte counts needed to store decimals with various precisions
-  private val minBytesForPrecision: Array[Int] = Array.tabulate(38) { precision =>
-    var numBytes = 1
-    while (math.pow(2.0, 8 * numBytes - 1) < math.pow(10.0, precision)) {
-      numBytes += 1
-    }
-    numBytes
-  }
 }
 
 
@@ -580,4 +565,23 @@ private[parquet] object CatalystSchemaConverter {
       throw new AnalysisException(message)
     }
   }
+
+  private def computeMinBytesForPrecision(precision : Int) : Int = {
+    var numBytes = 1
+    while (math.pow(2.0, 8 * numBytes - 1) < math.pow(10.0, precision)) {
+      numBytes += 1
+    }
+    numBytes
+  }
+
+  private val MIN_BYTES_FOR_PRECISION = Array.tabulate[Int](39)(computeMinBytesForPrecision)
+
+  // Returns the minimum number of bytes needed to store a decimal with a given `precision`.
+  def minBytesForPrecision(precision : Int) : Int = {
+    if (precision < MIN_BYTES_FOR_PRECISION.length) {
+      MIN_BYTES_FOR_PRECISION(precision)
+    } else {
+      computeMinBytesForPrecision(precision)
+    }
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index fc9f61a636768..78ecfad1d57c6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.parquet
 
+import java.math.BigInteger
 import java.nio.{ByteBuffer, ByteOrder}
 import java.util.{HashMap => JHashMap}
 
@@ -114,11 +115,8 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
           Binary.fromByteArray(value.asInstanceOf[UTF8String].getBytes))
         case BinaryType => writer.addBinary(
           Binary.fromByteArray(value.asInstanceOf[Array[Byte]]))
-        case d: DecimalType =>
-          if (d.precision > 18) {
-            sys.error(s"Unsupported datatype $d, cannot write to consumer")
-          }
-          writeDecimal(value.asInstanceOf[Decimal], d.precision)
+        case DecimalType.Fixed(precision, _) =>
+          writeDecimal(value.asInstanceOf[Decimal], precision)
         case _ => sys.error(s"Do not know how to writer $schema to consumer")
       }
     }
@@ -199,20 +197,47 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
     writer.endGroup()
   }
 
-  // Scratch array used to write decimals as fixed-length binary
-  private[this] val scratchBytes = new Array[Byte](8)
+  // Scratch array used to write decimals as fixed-length byte array
+  private[this] var reusableDecimalBytes = new Array[Byte](16)
 
   private[parquet] def writeDecimal(decimal: Decimal, precision: Int): Unit = {
-    val numBytes = ParquetTypesConverter.BYTES_FOR_PRECISION(precision)
-    val unscaledLong = decimal.toUnscaledLong
-    var i = 0
-    var shift = 8 * (numBytes - 1)
-    while (i < numBytes) {
-      scratchBytes(i) = (unscaledLong >> shift).toByte
-      i += 1
-      shift -= 8
+    val numBytes = CatalystSchemaConverter.minBytesForPrecision(precision)
+
+    def longToBinary(unscaled: Long): Binary = {
+      var i = 0
+      var shift = 8 * (numBytes - 1)
+      while (i < numBytes) {
+        reusableDecimalBytes(i) = (unscaled >> shift).toByte
+        i += 1
+        shift -= 8
+      }
+      Binary.fromByteArray(reusableDecimalBytes, 0, numBytes)
     }
-    writer.addBinary(Binary.fromByteArray(scratchBytes, 0, numBytes))
+
+    def bigIntegerToBinary(unscaled: BigInteger): Binary = {
+      unscaled.toByteArray match {
+        case bytes if bytes.length == numBytes =>
+          Binary.fromByteArray(bytes)
+
+        case bytes if bytes.length <= reusableDecimalBytes.length =>
+          val signedByte = (if (bytes.head < 0) -1 else 0).toByte
+          java.util.Arrays.fill(reusableDecimalBytes, 0, numBytes - bytes.length, signedByte)
+          System.arraycopy(bytes, 0, reusableDecimalBytes, numBytes - bytes.length, bytes.length)
+          Binary.fromByteArray(reusableDecimalBytes, 0, numBytes)
+
+        case bytes =>
+          reusableDecimalBytes = new Array[Byte](bytes.length)
+          bigIntegerToBinary(unscaled)
+      }
+    }
+
+    val binary = if (numBytes <= 8) {
+      longToBinary(decimal.toUnscaledLong)
+    } else {
+      bigIntegerToBinary(decimal.toJavaBigDecimal.unscaledValue())
+    }
+
+    writer.addBinary(binary)
   }
 
   // array used to write Timestamp as Int96 (fixed-length binary)
@@ -268,11 +293,8 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
         writer.addBinary(Binary.fromByteArray(record.getUTF8String(index).getBytes))
       case BinaryType =>
         writer.addBinary(Binary.fromByteArray(record.getBinary(index)))
-      case d: DecimalType =>
-        if (d.precision > 18) {
-          sys.error(s"Unsupported datatype $d, cannot write to consumer")
-        }
-        writeDecimal(record.getDecimal(index), d.precision)
+      case DecimalType.Fixed(precision, _) =>
+        writeDecimal(record.getDecimal(index), precision)
       case _ => sys.error(s"Unsupported datatype $ctype, cannot write to consumer")
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
index b5314a3dd92e5..b415da5b8c136 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
@@ -106,21 +106,13 @@ class ParquetIOSuite extends QueryTest with ParquetTest {
         // Parquet doesn't allow column names with spaces, have to add an alias here
         .select($"_1" cast decimal as "dec")
 
-    for ((precision, scale) <- Seq((5, 2), (1, 0), (1, 1), (18, 10), (18, 17))) {
+    for ((precision, scale) <- Seq((5, 2), (1, 0), (1, 1), (18, 10), (18, 17), (19, 0), (38, 37))) {
       withTempPath { dir =>
         val data = makeDecimalRDD(DecimalType(precision, scale))
         data.write.parquet(dir.getCanonicalPath)
         checkAnswer(sqlContext.read.parquet(dir.getCanonicalPath), data.collect().toSeq)
       }
     }
-
-    // Decimals with precision above 18 are not yet supported
-    intercept[Throwable] {
-      withTempPath { dir =>
-        makeDecimalRDD(DecimalType(19, 10)).write.parquet(dir.getCanonicalPath)
-        sqlContext.read.parquet(dir.getCanonicalPath).collect()
-      }
-    }
   }
 
   test("date type") {

From 90006f3c51f8cf9535854246050e27bb76b043f0 Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Tue, 28 Jul 2015 01:33:31 +0900
Subject: [PATCH 0616/1454] Pregel example type fix

Pregel example to express single source shortest path from https://spark.apache.org/docs/latest/graphx-programming-guide.html#pregel-api does not work due to incorrect type. The reason is that `GraphGenerators.logNormalGraph` returns the graph with `Long` vertices. Fixing `val graph: Graph[Int, Double]` to `val graph: Graph[Long, Double]`.

Author: Alexander Ulanov <nashb@yandex.ru>

Closes #7695 from avulanov/SPARK-9380-pregel-doc and squashes the following commits:

c269429 [Alexander Ulanov] Pregel example type fix
---
 docs/graphx-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 3f10cb2dc3d2a..99f8c827f767f 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -800,7 +800,7 @@ import org.apache.spark.graphx._
 // Import random graph generation library
 import org.apache.spark.graphx.util.GraphGenerators
 // A graph with edge attributes containing distances
-val graph: Graph[Int, Double] =
+val graph: Graph[Long, Double] =
   GraphGenerators.logNormalGraph(sc, numVertices = 100).mapEdges(e => e.attr.toDouble)
 val sourceId: VertexId = 42 // The ultimate source
 // Initialize the graph such that all vertices except the root have distance infinity.

From ecad9d4346ec158746e61aebdf1590215a77f369 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 27 Jul 2015 09:34:49 -0700
Subject: [PATCH 0617/1454] [SPARK-9364] Fix array out of bounds and
 use-after-free bugs in UnsafeExternalSorter

This patch fixes two bugs in UnsafeExternalSorter and UnsafeExternalRowSorter:

- UnsafeExternalSorter does not properly update freeSpaceInCurrentPage, which can cause it to write past the end of memory pages and trigger segfaults.
- UnsafeExternalRowSorter has a use-after-free bug when returning the last row from an iterator.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7680 from JoshRosen/SPARK-9364 and squashes the following commits:

590f311 [Josh Rosen] null out row
f4cf91d [Josh Rosen] Fix use-after-free bug in UnsafeExternalRowSorter.
8abcf82 [Josh Rosen] Properly decrement freeSpaceInCurrentPage in UnsafeExternalSorter
---
 .../unsafe/sort/UnsafeExternalSorter.java     |  7 ++++++-
 .../sort/UnsafeExternalSorterSuite.java       | 19 +++++++++++++++++++
 .../execution/UnsafeExternalRowSorter.java    |  9 ++++++---
 3 files changed, 31 insertions(+), 4 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 4d6731ee60af3..80b03d7e99e2b 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -150,6 +150,11 @@ private long getMemoryUsage() {
     return sorter.getMemoryUsage() + (allocatedPages.size() * (long) PAGE_SIZE);
   }
 
+  @VisibleForTesting
+  public int getNumberOfAllocatedPages() {
+    return allocatedPages.size();
+  }
+
   public long freeMemory() {
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
@@ -257,7 +262,7 @@ public void insertRecord(
       currentPagePosition,
       lengthInBytes);
     currentPagePosition += lengthInBytes;
-
+    freeSpaceInCurrentPage -= totalSpaceRequired;
     sorter.insertRecord(recordAddress, prefix);
   }
 
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index ea8755e21eb68..0e391b751226d 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -199,4 +199,23 @@ public void testSortingEmptyArrays() throws Exception {
     }
   }
 
+  @Test
+  public void testFillingPage() throws Exception {
+    final UnsafeExternalSorter sorter = new UnsafeExternalSorter(
+      memoryManager,
+      shuffleMemoryManager,
+      blockManager,
+      taskContext,
+      recordComparator,
+      prefixComparator,
+      1024,
+      new SparkConf());
+
+    byte[] record = new byte[16];
+    while (sorter.getNumberOfAllocatedPages() < 2) {
+      sorter.insertRecord(record, PlatformDependent.BYTE_ARRAY_OFFSET, record.length, 0);
+    }
+    sorter.freeMemory();
+  }
+
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index be4ff400c4754..4c3f2c6557140 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -124,7 +124,7 @@ Iterator<InternalRow> sort() throws IOException {
       return new AbstractScalaRowIterator() {
 
         private final int numFields = schema.length();
-        private final UnsafeRow row = new UnsafeRow();
+        private UnsafeRow row = new UnsafeRow();
 
         @Override
         public boolean hasNext() {
@@ -141,10 +141,13 @@ public InternalRow next() {
               numFields,
               sortedIterator.getRecordLength());
             if (!hasNext()) {
-              row.copy(); // so that we don't have dangling pointers to freed page
+              UnsafeRow copy = row.copy(); // so that we don't have dangling pointers to freed page
+              row = null; // so that we don't keep references to the base object
               cleanupResources();
+              return copy;
+            } else {
+              return row;
             }
-            return row;
           } catch (IOException e) {
             cleanupResources();
             // Scala iterators don't declare any checked exceptions, so we need to use this hack

From c0b7df68f81c2a2a9c1065009fe75c278fa30499 Mon Sep 17 00:00:00 2001
From: Ryan Williams <ryan.blake.williams@gmail.com>
Date: Mon, 27 Jul 2015 12:54:08 -0500
Subject: [PATCH 0618/1454] [SPARK-9366] use task's stageAttemptId in TaskEnd
 event

Author: Ryan Williams <ryan.blake.williams@gmail.com>

Closes #7681 from ryan-williams/task-stage-attempt and squashes the following commits:

d6d5f0f [Ryan Williams] use task's stageAttemptId in TaskEnd event
---
 .../main/scala/org/apache/spark/scheduler/DAGScheduler.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 552dabcfa5139..b6a833bbb0833 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -927,7 +927,7 @@ class DAGScheduler(
     // The success case is dealt with separately below, since we need to compute accumulator
     // updates before posting.
     if (event.reason != Success) {
-      val attemptId = stageIdToStage.get(task.stageId).map(_.latestInfo.attemptId).getOrElse(-1)
+      val attemptId = task.stageAttemptId
       listenerBus.post(SparkListenerTaskEnd(stageId, attemptId, taskType, event.reason,
         event.taskInfo, event.taskMetrics))
     }

From e2f38167f8b5678ac45794eacb9c7bb9b951af82 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 27 Jul 2015 11:02:16 -0700
Subject: [PATCH 0619/1454] [SPARK-9376] [SQL] use a seed in
 RandomDataGeneratorSuite

Make this test deterministic, i.e. make sure this test can be passed no matter how many times we run it.

The origin implementation uses a random seed and gives a chance that we may break the null check assertion `assert(Iterator.fill(100)(generator()).contains(null))`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7691 from cloud-fan/seed and squashes the following commits:

eae7281 [Wenchen Fan] use a seed in RandomDataGeneratorSuite
---
 .../scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
index 677ba0a18040c..cccac7efa09e9 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGeneratorSuite.scala
@@ -32,7 +32,7 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
    */
   def testRandomDataGeneration(dataType: DataType, nullable: Boolean = true): Unit = {
     val toCatalyst = CatalystTypeConverters.createToCatalystConverter(dataType)
-    val generator = RandomDataGenerator.forType(dataType, nullable).getOrElse {
+    val generator = RandomDataGenerator.forType(dataType, nullable, Some(33)).getOrElse {
       fail(s"Random data generator was not defined for $dataType")
     }
     if (nullable) {

From 1f7b3d9dc7c2ed9d31f9083284cf900fd4c21e42 Mon Sep 17 00:00:00 2001
From: George Dittmar <georgedittmar@gmail.com>
Date: Mon, 27 Jul 2015 11:16:33 -0700
Subject: [PATCH 0620/1454] [SPARK-7423] [MLLIB] Modify ClassificationModel and
 Probabalistic model to use Vector.argmax

Use Vector.argmax call instead of converting to dense vector before calculating predictions.

Author: George Dittmar <georgedittmar@gmail.com>

Closes #7670 from GeorgeDittmar/sprk-7423 and squashes the following commits:

e796747 [George Dittmar] Changing ClassificationModel and ProbabilisticClassificationModel to use Vector.argmax instead of converting to DenseVector
---
 .../scala/org/apache/spark/ml/classification/Classifier.scala   | 2 +-
 .../spark/ml/classification/ProbabilisticClassifier.scala       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index 85c097bc64a4f..581d8fa7749be 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -156,5 +156,5 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
    * This may be overridden to support thresholds which favor particular labels.
    * @return  predicted label
    */
-  protected def raw2prediction(rawPrediction: Vector): Double = rawPrediction.toDense.argmax
+  protected def raw2prediction(rawPrediction: Vector): Double = rawPrediction.argmax
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index 38e832372698c..dad451108626d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -173,5 +173,5 @@ private[spark] abstract class ProbabilisticClassificationModel[
    * This may be overridden to support thresholds which favor particular labels.
    * @return  predicted label
    */
-  protected def probability2prediction(probability: Vector): Double = probability.toDense.argmax
+  protected def probability2prediction(probability: Vector): Double = probability.argmax
 }

From dd9ae7945ab65d353ed2b113e0c1a00a0533ffd6 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 27 Jul 2015 11:23:29 -0700
Subject: [PATCH 0621/1454] [SPARK-9351] [SQL] remove literals from grouping
 expressions in Aggregate

literals in grouping expressions have no effect at all, only make our grouping key bigger, so we should remove them in Optimizer.

I also make old and new aggregation code consistent about literals in grouping here. In old aggregation, actually literals in grouping are already removed but new aggregation is not. So I explicitly make it a rule in Optimizer.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7583 from cloud-fan/minor and squashes the following commits:

471adff [Wenchen Fan] add test
0839925 [Wenchen Fan] use transformDown when rewrite final result expressions
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 17 +++++++++--
 .../sql/catalyst/planning/patterns.scala      |  4 +--
 ...ite.scala => AggregateOptimizeSuite.scala} | 19 ++++++++++--
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 29 +++++++++++++++----
 4 files changed, 57 insertions(+), 12 deletions(-)
 rename sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/{ReplaceDistinctWithAggregateSuite.scala => AggregateOptimizeSuite.scala} (72%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index b59f800e7cc0f..813c62009666c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -36,8 +36,9 @@ object DefaultOptimizer extends Optimizer {
     // SubQueries are only needed for analysis and can be removed before execution.
     Batch("Remove SubQueries", FixedPoint(100),
       EliminateSubQueries) ::
-    Batch("Distinct", FixedPoint(100),
-      ReplaceDistinctWithAggregate) ::
+    Batch("Aggregate", FixedPoint(100),
+      ReplaceDistinctWithAggregate,
+      RemoveLiteralFromGroupExpressions) ::
     Batch("Operator Optimizations", FixedPoint(100),
       // Operator push down
       SetOperationPushDown,
@@ -799,3 +800,15 @@ object ReplaceDistinctWithAggregate extends Rule[LogicalPlan] {
     case Distinct(child) => Aggregate(child.output, child.output, child)
   }
 }
+
+/**
+ * Removes literals from group expressions in [[Aggregate]], as they have no effect to the result
+ * but only makes the grouping key bigger.
+ */
+object RemoveLiteralFromGroupExpressions extends Rule[LogicalPlan] {
+  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    case a @ Aggregate(grouping, _, _) =>
+      val newGrouping = grouping.filter(!_.foldable)
+      a.copy(groupingExpressions = newGrouping)
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 1e7b2a536ac12..b9ca712c1ee1c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -144,14 +144,14 @@ object PartialAggregation {
         // time. However some of them might be unnamed so we alias them allowing them to be
         // referenced in the second aggregation.
         val namedGroupingExpressions: Seq[(Expression, NamedExpression)] =
-          groupingExpressions.filter(!_.isInstanceOf[Literal]).map {
+          groupingExpressions.map {
             case n: NamedExpression => (n, n)
             case other => (other, Alias(other, "PartialGroup")())
           }
 
         // Replace aggregations with a new expression that computes the result from the already
         // computed partial evaluations and grouping values.
-        val rewrittenAggregateExpressions = aggregateExpressions.map(_.transformUp {
+        val rewrittenAggregateExpressions = aggregateExpressions.map(_.transformDown {
           case e: Expression if partialEvaluations.contains(new TreeNodeRef(e)) =>
             partialEvaluations(new TreeNodeRef(e)).finalEvaluation
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceDistinctWithAggregateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/AggregateOptimizeSuite.scala
similarity index 72%
rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceDistinctWithAggregateSuite.scala
rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/AggregateOptimizeSuite.scala
index df29a62ff0e15..2d080b95b1292 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceDistinctWithAggregateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/AggregateOptimizeSuite.scala
@@ -19,14 +19,17 @@ package org.apache.spark.sql.catalyst.optimizer
 
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions.Literal
 import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, Distinct, LocalRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 
-class ReplaceDistinctWithAggregateSuite extends PlanTest {
+class AggregateOptimizeSuite extends PlanTest {
 
   object Optimize extends RuleExecutor[LogicalPlan] {
-    val batches = Batch("ProjectCollapsing", Once, ReplaceDistinctWithAggregate) :: Nil
+    val batches = Batch("Aggregate", FixedPoint(100),
+      ReplaceDistinctWithAggregate,
+      RemoveLiteralFromGroupExpressions) :: Nil
   }
 
   test("replace distinct with aggregate") {
@@ -39,4 +42,16 @@ class ReplaceDistinctWithAggregateSuite extends PlanTest {
 
     comparePlans(optimized, correctAnswer)
   }
+
+  test("remove literals in grouping expression") {
+    val input = LocalRelation('a.int, 'b.int)
+
+    val query =
+      input.groupBy('a, Literal(1), Literal(1) + Literal(2))(sum('b))
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer = input.groupBy('a)(sum('b))
+
+    comparePlans(optimized, correctAnswer)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 8cef0b39f87dc..358e319476e83 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -463,12 +463,29 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
   }
 
   test("literal in agg grouping expressions") {
-    checkAnswer(
-      sql("SELECT a, count(1) FROM testData2 GROUP BY a, 1"),
-      Seq(Row(1, 2), Row(2, 2), Row(3, 2)))
-    checkAnswer(
-      sql("SELECT a, count(2) FROM testData2 GROUP BY a, 2"),
-      Seq(Row(1, 2), Row(2, 2), Row(3, 2)))
+    def literalInAggTest(): Unit = {
+      checkAnswer(
+        sql("SELECT a, count(1) FROM testData2 GROUP BY a, 1"),
+        Seq(Row(1, 2), Row(2, 2), Row(3, 2)))
+      checkAnswer(
+        sql("SELECT a, count(2) FROM testData2 GROUP BY a, 2"),
+        Seq(Row(1, 2), Row(2, 2), Row(3, 2)))
+
+      checkAnswer(
+        sql("SELECT a, 1, sum(b) FROM testData2 GROUP BY a, 1"),
+        sql("SELECT a, 1, sum(b) FROM testData2 GROUP BY a"))
+      checkAnswer(
+        sql("SELECT a, 1, sum(b) FROM testData2 GROUP BY a, 1 + 2"),
+        sql("SELECT a, 1, sum(b) FROM testData2 GROUP BY a"))
+      checkAnswer(
+        sql("SELECT 1, 2, sum(b) FROM testData2 GROUP BY 1, 2"),
+        sql("SELECT 1, 2, sum(b) FROM testData2"))
+    }
+
+    literalInAggTest()
+    withSQLConf(SQLConf.USE_SQL_AGGREGATE2.key -> "false") {
+      literalInAggTest()
+    }
   }
 
   test("aggregates with nulls") {

From 75438422c2cd90dca53f84879cddecfc2ee0e957 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 27 Jul 2015 11:28:22 -0700
Subject: [PATCH 0622/1454] [SPARK-9369][SQL] Support IntervalType in UnsafeRow

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7688 from cloud-fan/interval and squashes the following commits:

5b36b17 [Wenchen Fan] fix codegen
a99ed50 [Wenchen Fan] address comment
9e6d319 [Wenchen Fan] Support IntervalType in UnsafeRow
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 23 ++++++++++++++-----
 .../expressions/UnsafeRowWriters.java         | 19 ++++++++++++++-
 .../spark/sql/catalyst/InternalRow.scala      |  4 +++-
 .../catalyst/expressions/BoundAttribute.scala |  1 +
 .../spark/sql/catalyst/expressions/Cast.scala |  2 +-
 .../expressions/codegen/CodeGenerator.scala   |  7 +++---
 .../codegen/GenerateUnsafeProjection.scala    |  6 +++++
 .../expressions/ExpressionEvalHelper.scala    |  2 --
 8 files changed, 50 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 0fb33dd5a15a0..fb084dd13b620 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -29,6 +29,7 @@
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
+import org.apache.spark.unsafe.types.Interval;
 import org.apache.spark.unsafe.types.UTF8String;
 
 import static org.apache.spark.sql.types.DataTypes.*;
@@ -90,7 +91,8 @@ public static int calculateBitSetWidthInBytes(int numFields) {
     final Set<DataType> _readableFieldTypes = new HashSet<>(
       Arrays.asList(new DataType[]{
         StringType,
-        BinaryType
+        BinaryType,
+        IntervalType
       }));
     _readableFieldTypes.addAll(settableFieldTypes);
     readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes);
@@ -332,11 +334,6 @@ public UTF8String getUTF8String(int ordinal) {
     return isNullAt(ordinal) ? null : UTF8String.fromBytes(getBinary(ordinal));
   }
 
-  @Override
-  public String getString(int ordinal) {
-    return getUTF8String(ordinal).toString();
-  }
-
   @Override
   public byte[] getBinary(int ordinal) {
     if (isNullAt(ordinal)) {
@@ -358,6 +355,20 @@ public byte[] getBinary(int ordinal) {
     }
   }
 
+  @Override
+  public Interval getInterval(int ordinal) {
+    if (isNullAt(ordinal)) {
+      return null;
+    } else {
+      final long offsetAndSize = getLong(ordinal);
+      final int offset = (int) (offsetAndSize >> 32);
+      final int months = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+      final long microseconds =
+        PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset + 8);
+      return new Interval(months, microseconds);
+    }
+  }
+
   @Override
   public UnsafeRow getStruct(int ordinal, int numFields) {
     if (isNullAt(ordinal)) {
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 87521d1f23c99..0ba31d3b9b743 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -20,6 +20,7 @@
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
+import org.apache.spark.unsafe.types.Interval;
 import org.apache.spark.unsafe.types.UTF8String;
 
 /**
@@ -54,7 +55,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UTF8String in
     }
   }
 
-  /** Writer for bianry (byte array) type. */
+  /** Writer for binary (byte array) type. */
   public static class BinaryWriter {
 
     public static int getSize(byte[] input) {
@@ -80,4 +81,20 @@ public static int write(UnsafeRow target, int ordinal, int cursor, byte[] input)
     }
   }
 
+  /** Writer for interval type. */
+  public static class IntervalWriter {
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, Interval input) {
+      final long offset = target.getBaseOffset() + cursor;
+
+      // Write the months and microseconds fields of Interval to the variable length portion.
+      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset, input.months);
+      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset + 8, input.microseconds);
+
+      // Set the fixed length portion.
+      target.setLong(ordinal, ((long) cursor) << 32);
+      return 16;
+    }
+  }
+
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index ad3977281d1a9..9a11de3840ce2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.unsafe.types.{Interval, UTF8String}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -60,6 +60,8 @@ abstract class InternalRow extends Serializable {
 
   def getDecimal(ordinal: Int): Decimal = getAs[Decimal](ordinal, DecimalType.SYSTEM_DEFAULT)
 
+  def getInterval(ordinal: Int): Interval = getAs[Interval](ordinal, IntervalType)
+
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 6b5c450e3fb0a..41a877f214e55 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -48,6 +48,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
         case DoubleType => input.getDouble(ordinal)
         case StringType => input.getUTF8String(ordinal)
         case BinaryType => input.getBinary(ordinal)
+        case IntervalType => input.getInterval(ordinal)
         case t: StructType => input.getStruct(ordinal, t.size)
         case dataType => input.get(ordinal, dataType)
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index e208262da96dc..bd8b0177eb00e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -630,7 +630,7 @@ case class Cast(child: Expression, dataType: DataType)
   private[this] def castToIntervalCode(from: DataType): CastFunction = from match {
     case StringType =>
       (c, evPrim, evNull) =>
-        s"$evPrim = org.apache.spark.unsafe.types.Interval.fromString($c.toString());"
+        s"$evPrim = Interval.fromString($c.toString());"
   }
 
   private[this] def decimalToTimestampCode(d: String): String =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 2a1e288cb8377..2f02c90b1d5b3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -79,7 +79,6 @@ class CodeGenContext {
     mutableStates += ((javaType, variableName, initCode))
   }
 
-  final val intervalType: String = classOf[Interval].getName
   final val JAVA_BOOLEAN = "boolean"
   final val JAVA_BYTE = "byte"
   final val JAVA_SHORT = "short"
@@ -109,6 +108,7 @@ class CodeGenContext {
       case _ if isPrimitiveType(jt) => s"$row.get${primitiveTypeName(jt)}($ordinal)"
       case StringType => s"$row.getUTF8String($ordinal)"
       case BinaryType => s"$row.getBinary($ordinal)"
+      case IntervalType => s"$row.getInterval($ordinal)"
       case t: StructType => s"$row.getStruct($ordinal, ${t.size})"
       case _ => s"($jt)$row.get($ordinal)"
     }
@@ -150,7 +150,7 @@ class CodeGenContext {
     case dt: DecimalType => "Decimal"
     case BinaryType => "byte[]"
     case StringType => "UTF8String"
-    case IntervalType => intervalType
+    case IntervalType => "Interval"
     case _: StructType => "InternalRow"
     case _: ArrayType => s"scala.collection.Seq"
     case _: MapType => s"scala.collection.Map"
@@ -292,7 +292,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       classOf[InternalRow].getName,
       classOf[UnsafeRow].getName,
       classOf[UTF8String].getName,
-      classOf[Decimal].getName
+      classOf[Decimal].getName,
+      classOf[Interval].getName
     ))
     evaluator.setExtendedClass(classOf[GeneratedClass])
     try {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index afd0d9cfa1ddd..9d2161947b351 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -33,10 +33,12 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
   private val StringWriter = classOf[UnsafeRowWriters.UTF8StringWriter].getName
   private val BinaryWriter = classOf[UnsafeRowWriters.BinaryWriter].getName
+  private val IntervalWriter = classOf[UnsafeRowWriters.IntervalWriter].getName
 
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
     case t: AtomicType if !t.isInstanceOf[DecimalType] => true
+    case _: IntervalType => true
     case NullType => true
     case _ => false
   }
@@ -68,6 +70,8 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           s" + (${exprs(i).isNull} ? 0 : $StringWriter.getSize(${exprs(i).primitive}))"
         case BinaryType =>
           s" + (${exprs(i).isNull} ? 0 : $BinaryWriter.getSize(${exprs(i).primitive}))"
+        case IntervalType =>
+          s" + (${exprs(i).isNull} ? 0 : 16)"
         case _ => ""
       }
     }.mkString("")
@@ -80,6 +84,8 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           s"$cursorTerm += $StringWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
         case BinaryType =>
           s"$cursorTerm += $BinaryWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
+        case IntervalType =>
+          s"$cursorTerm += $IntervalWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
         case NullType => ""
         case _ =>
           throw new UnsupportedOperationException(s"Not supported DataType: ${e.dataType}")
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 8b0f90cf3a623..ab0cdc857c80e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -78,8 +78,6 @@ trait ExpressionEvalHelper {
       generator
     } catch {
       case e: Throwable =>
-        val ctx = new CodeGenContext
-        val evaluated = expression.gen(ctx)
         fail(
           s"""
             |Code generation of $expression failed:

From 85a50a6352b72c4619d010e29e3a76774dbc0c71 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 27 Jul 2015 12:25:34 -0700
Subject: [PATCH 0623/1454] [HOTFIX] Disable pylint since it is failing master.

---
 dev/lint-python | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/dev/lint-python b/dev/lint-python
index e02dff220eb87..53bccc1fab535 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -96,19 +96,19 @@ fi
 
 rm "$PEP8_REPORT_PATH"
 
-for to_be_checked in "$PATHS_TO_CHECK"
-do
-    pylint --rcfile="$SPARK_ROOT_DIR/pylintrc" $to_be_checked >> "$PYLINT_REPORT_PATH"
-done
-
-if [ "${PIPESTATUS[0]}" -ne 0 ]; then
-    lint_status=1
-    echo "Pylint checks failed."
-    cat "$PYLINT_REPORT_PATH"
-else
-    echo "Pylint checks passed."
-fi
-
-rm "$PYLINT_REPORT_PATH"
+# for to_be_checked in "$PATHS_TO_CHECK"
+# do
+#     pylint --rcfile="$SPARK_ROOT_DIR/pylintrc" $to_be_checked >> "$PYLINT_REPORT_PATH"
+# done
+
+# if [ "${PIPESTATUS[0]}" -ne 0 ]; then
+#     lint_status=1
+#     echo "Pylint checks failed."
+#     cat "$PYLINT_REPORT_PATH"
+# else
+#     echo "Pylint checks passed."
+# fi
+
+# rm "$PYLINT_REPORT_PATH"
 
 exit "$lint_status"

From fa84e4a7ba6eab476487185178a556e4f04e4199 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 27 Jul 2015 13:21:04 -0700
Subject: [PATCH 0624/1454] Closes #7690 since it has been merged into
 branch-1.4.


From 55946e76fd136958081f073c0c5e3ff8563d505b Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 27 Jul 2015 13:26:57 -0700
Subject: [PATCH 0625/1454] [SPARK-9349] [SQL] UDAF cleanup

https://issues.apache.org/jira/browse/SPARK-9349

With this PR, we only expose `UserDefinedAggregateFunction` (an abstract class) and `MutableAggregationBuffer` (an interface). Other internal wrappers and helper classes are moved to `org.apache.spark.sql.execution.aggregate` and marked as `private[sql]`.

Author: Yin Huai <yhuai@databricks.com>

Closes #7687 from yhuai/UDAF-cleanup and squashes the following commits:

db36542 [Yin Huai] Add comments to UDAF examples.
ae17f66 [Yin Huai] Address comments.
9c9fa5f [Yin Huai] UDAF cleanup.
---
 .../apache/spark/sql/UDAFRegistration.scala   |   3 +-
 .../aggregate/udaf.scala                      | 122 +++++-------------
 .../apache/spark/sql/expressions/udaf.scala   | 101 +++++++++++++++
 .../spark/sql/hive/aggregate/MyDoubleAvg.java |  34 ++++-
 .../spark/sql/hive/aggregate/MyDoubleSum.java |  28 +++-
 5 files changed, 187 insertions(+), 101 deletions(-)
 rename sql/core/src/main/scala/org/apache/spark/sql/{expressions => execution}/aggregate/udaf.scala (67%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
index 5b872f5e3eecd..0d4e30f29255e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
@@ -19,7 +19,8 @@ package org.apache.spark.sql
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.{Expression}
-import org.apache.spark.sql.expressions.aggregate.{ScalaUDAF, UserDefinedAggregateFunction}
+import org.apache.spark.sql.execution.aggregate.ScalaUDAF
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
 
 class UDAFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
similarity index 67%
rename from sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
index 4ada9eca7a035..073c45ae2f9f2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
@@ -15,87 +15,29 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.expressions.aggregate
+package org.apache.spark.sql.execution.aggregate
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
-import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
+import org.apache.spark.sql.catalyst.expressions.{MutableRow, InterpretedMutableProjection, AttributeReference, Expression}
 import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateFunction2
-import org.apache.spark.sql.types._
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
+import org.apache.spark.sql.types.{Metadata, StructField, StructType, DataType}
 
 /**
- * The abstract class for implementing user-defined aggregate function.
+ * A Mutable [[Row]] representing an mutable aggregation buffer.
  */
-abstract class UserDefinedAggregateFunction extends Serializable {
-
-  /**
-   * A [[StructType]] represents data types of input arguments of this aggregate function.
-   * For example, if a [[UserDefinedAggregateFunction]] expects two input arguments
-   * with type of [[DoubleType]] and [[LongType]], the returned [[StructType]] will look like
-   *
-   * ```
-   *   StructType(Seq(StructField("doubleInput", DoubleType), StructField("longInput", LongType)))
-   * ```
-   *
-   * The name of a field of this [[StructType]] is only used to identify the corresponding
-   * input argument. Users can choose names to identify the input arguments.
-   */
-  def inputSchema: StructType
-
-  /**
-   * A [[StructType]] represents data types of values in the aggregation buffer.
-   * For example, if a [[UserDefinedAggregateFunction]]'s buffer has two values
-   * (i.e. two intermediate values) with type of [[DoubleType]] and [[LongType]],
-   * the returned [[StructType]] will look like
-   *
-   * ```
-   *   StructType(Seq(StructField("doubleInput", DoubleType), StructField("longInput", LongType)))
-   * ```
-   *
-   * The name of a field of this [[StructType]] is only used to identify the corresponding
-   * buffer value. Users can choose names to identify the input arguments.
-   */
-  def bufferSchema: StructType
-
-  /**
-   * The [[DataType]] of the returned value of this [[UserDefinedAggregateFunction]].
-   */
-  def returnDataType: DataType
-
-  /** Indicates if this function is deterministic. */
-  def deterministic: Boolean
-
-  /**
-   *  Initializes the given aggregation buffer. Initial values set by this method should satisfy
-   *  the condition that when merging two buffers with initial values, the new buffer should
-   *  still store initial values.
-   */
-  def initialize(buffer: MutableAggregationBuffer): Unit
-
-  /** Updates the given aggregation buffer `buffer` with new input data from `input`. */
-  def update(buffer: MutableAggregationBuffer, input: Row): Unit
-
-  /** Merges two aggregation buffers and stores the updated buffer values back in `buffer1`. */
-  def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit
-
-  /**
-   * Calculates the final result of this [[UserDefinedAggregateFunction]] based on the given
-   * aggregation buffer.
-   */
-  def evaluate(buffer: Row): Any
-}
-
-private[sql] abstract class AggregationBuffer(
+private[sql] class MutableAggregationBufferImpl (
+    schema: StructType,
     toCatalystConverters: Array[Any => Any],
     toScalaConverters: Array[Any => Any],
-    bufferOffset: Int)
-  extends Row {
-
-  override def length: Int = toCatalystConverters.length
+    bufferOffset: Int,
+    var underlyingBuffer: MutableRow)
+  extends MutableAggregationBuffer {
 
-  protected val offsets: Array[Int] = {
+  private[this] val offsets: Array[Int] = {
     val newOffsets = new Array[Int](length)
     var i = 0
     while (i < newOffsets.length) {
@@ -104,18 +46,8 @@ private[sql] abstract class AggregationBuffer(
     }
     newOffsets
   }
-}
 
-/**
- * A Mutable [[Row]] representing an mutable aggregation buffer.
- */
-class MutableAggregationBuffer private[sql] (
-    schema: StructType,
-    toCatalystConverters: Array[Any => Any],
-    toScalaConverters: Array[Any => Any],
-    bufferOffset: Int,
-    var underlyingBuffer: MutableRow)
-  extends AggregationBuffer(toCatalystConverters, toScalaConverters, bufferOffset) {
+  override def length: Int = toCatalystConverters.length
 
   override def get(i: Int): Any = {
     if (i >= length || i < 0) {
@@ -133,8 +65,8 @@ class MutableAggregationBuffer private[sql] (
     underlyingBuffer.update(offsets(i), toCatalystConverters(i)(value))
   }
 
-  override def copy(): MutableAggregationBuffer = {
-    new MutableAggregationBuffer(
+  override def copy(): MutableAggregationBufferImpl = {
+    new MutableAggregationBufferImpl(
       schema,
       toCatalystConverters,
       toScalaConverters,
@@ -146,13 +78,25 @@ class MutableAggregationBuffer private[sql] (
 /**
  * A [[Row]] representing an immutable aggregation buffer.
  */
-class InputAggregationBuffer private[sql] (
+private[sql] class InputAggregationBuffer private[sql] (
     schema: StructType,
     toCatalystConverters: Array[Any => Any],
     toScalaConverters: Array[Any => Any],
     bufferOffset: Int,
     var underlyingInputBuffer: InternalRow)
-  extends AggregationBuffer(toCatalystConverters, toScalaConverters, bufferOffset) {
+  extends Row {
+
+  private[this] val offsets: Array[Int] = {
+    val newOffsets = new Array[Int](length)
+    var i = 0
+    while (i < newOffsets.length) {
+      newOffsets(i) = bufferOffset + i
+      i += 1
+    }
+    newOffsets
+  }
+
+  override def length: Int = toCatalystConverters.length
 
   override def get(i: Int): Any = {
     if (i >= length || i < 0) {
@@ -179,7 +123,7 @@ class InputAggregationBuffer private[sql] (
  * @param children
  * @param udaf
  */
-case class ScalaUDAF(
+private[sql] case class ScalaUDAF(
     children: Seq[Expression],
     udaf: UserDefinedAggregateFunction)
   extends AggregateFunction2 with Logging {
@@ -243,8 +187,8 @@ case class ScalaUDAF(
       bufferOffset,
       null)
 
-  lazy val mutableAggregateBuffer: MutableAggregationBuffer =
-    new MutableAggregationBuffer(
+  lazy val mutableAggregateBuffer: MutableAggregationBufferImpl =
+    new MutableAggregationBufferImpl(
       bufferSchema,
       bufferValuesToCatalystConverters,
       bufferValuesToScalaConverters,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
new file mode 100644
index 0000000000000..278dd438fab4a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
@@ -0,0 +1,101 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.expressions
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.types._
+import org.apache.spark.annotation.Experimental
+
+/**
+ * :: Experimental ::
+ * The abstract class for implementing user-defined aggregate functions.
+ */
+@Experimental
+abstract class UserDefinedAggregateFunction extends Serializable {
+
+  /**
+   * A [[StructType]] represents data types of input arguments of this aggregate function.
+   * For example, if a [[UserDefinedAggregateFunction]] expects two input arguments
+   * with type of [[DoubleType]] and [[LongType]], the returned [[StructType]] will look like
+   *
+   * ```
+   *   new StructType()
+   *    .add("doubleInput", DoubleType)
+   *    .add("longInput", LongType)
+   * ```
+   *
+   * The name of a field of this [[StructType]] is only used to identify the corresponding
+   * input argument. Users can choose names to identify the input arguments.
+   */
+  def inputSchema: StructType
+
+  /**
+   * A [[StructType]] represents data types of values in the aggregation buffer.
+   * For example, if a [[UserDefinedAggregateFunction]]'s buffer has two values
+   * (i.e. two intermediate values) with type of [[DoubleType]] and [[LongType]],
+   * the returned [[StructType]] will look like
+   *
+   * ```
+   *   new StructType()
+   *    .add("doubleInput", DoubleType)
+   *    .add("longInput", LongType)
+   * ```
+   *
+   * The name of a field of this [[StructType]] is only used to identify the corresponding
+   * buffer value. Users can choose names to identify the input arguments.
+   */
+  def bufferSchema: StructType
+
+  /**
+   * The [[DataType]] of the returned value of this [[UserDefinedAggregateFunction]].
+   */
+  def returnDataType: DataType
+
+  /** Indicates if this function is deterministic. */
+  def deterministic: Boolean
+
+  /**
+   *  Initializes the given aggregation buffer. Initial values set by this method should satisfy
+   *  the condition that when merging two buffers with initial values, the new buffer
+   *  still store initial values.
+   */
+  def initialize(buffer: MutableAggregationBuffer): Unit
+
+  /** Updates the given aggregation buffer `buffer` with new input data from `input`. */
+  def update(buffer: MutableAggregationBuffer, input: Row): Unit
+
+  /** Merges two aggregation buffers and stores the updated buffer values back to `buffer1`. */
+  def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit
+
+  /**
+   * Calculates the final result of this [[UserDefinedAggregateFunction]] based on the given
+   * aggregation buffer.
+   */
+  def evaluate(buffer: Row): Any
+}
+
+/**
+ * :: Experimental ::
+ * A [[Row]] representing an mutable aggregation buffer.
+ */
+@Experimental
+trait MutableAggregationBuffer extends Row {
+
+  /** Update the ith value of this buffer. */
+  def update(i: Int, value: Any): Unit
+}
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
index 5c9d0e97a99c6..a2247e3da1554 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
@@ -21,13 +21,18 @@
 import java.util.List;
 
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.expressions.aggregate.MutableAggregationBuffer;
-import org.apache.spark.sql.expressions.aggregate.UserDefinedAggregateFunction;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
 import org.apache.spark.sql.types.DataType;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
+/**
+ * An example {@link UserDefinedAggregateFunction} to calculate a special average value of a
+ * {@link org.apache.spark.sql.types.DoubleType} column. This special average value is the sum
+ * of the average value of input values and 100.0.
+ */
 public class MyDoubleAvg extends UserDefinedAggregateFunction {
 
   private StructType _inputDataType;
@@ -37,10 +42,13 @@ public class MyDoubleAvg extends UserDefinedAggregateFunction {
   private DataType _returnDataType;
 
   public MyDoubleAvg() {
-    List<StructField> inputfields = new ArrayList<StructField>();
-    inputfields.add(DataTypes.createStructField("inputDouble", DataTypes.DoubleType, true));
-    _inputDataType = DataTypes.createStructType(inputfields);
+    List<StructField> inputFields = new ArrayList<StructField>();
+    inputFields.add(DataTypes.createStructField("inputDouble", DataTypes.DoubleType, true));
+    _inputDataType = DataTypes.createStructType(inputFields);
 
+    // The buffer has two values, bufferSum for storing the current sum and
+    // bufferCount for storing the number of non-null input values that have been contribuetd
+    // to the current sum.
     List<StructField> bufferFields = new ArrayList<StructField>();
     bufferFields.add(DataTypes.createStructField("bufferSum", DataTypes.DoubleType, true));
     bufferFields.add(DataTypes.createStructField("bufferCount", DataTypes.LongType, true));
@@ -66,16 +74,23 @@ public MyDoubleAvg() {
   }
 
   @Override public void initialize(MutableAggregationBuffer buffer) {
+    // The initial value of the sum is null.
     buffer.update(0, null);
+    // The initial value of the count is 0.
     buffer.update(1, 0L);
   }
 
   @Override public void update(MutableAggregationBuffer buffer, Row input) {
+    // This input Row only has a single column storing the input value in Double.
+    // We only update the buffer when the input value is not null.
     if (!input.isNullAt(0)) {
+      // If the buffer value (the intermediate result of the sum) is still null,
+      // we set the input value to the buffer and set the bufferCount to 1.
       if (buffer.isNullAt(0)) {
         buffer.update(0, input.getDouble(0));
         buffer.update(1, 1L);
       } else {
+        // Otherwise, update the bufferSum and increment bufferCount.
         Double newValue = input.getDouble(0) + buffer.getDouble(0);
         buffer.update(0, newValue);
         buffer.update(1, buffer.getLong(1) + 1L);
@@ -84,11 +99,16 @@ public MyDoubleAvg() {
   }
 
   @Override public void merge(MutableAggregationBuffer buffer1, Row buffer2) {
+    // buffer1 and buffer2 have the same structure.
+    // We only update the buffer1 when the input buffer2's sum value is not null.
     if (!buffer2.isNullAt(0)) {
       if (buffer1.isNullAt(0)) {
+        // If the buffer value (intermediate result of the sum) is still null,
+        // we set the it as the input buffer's value.
         buffer1.update(0, buffer2.getDouble(0));
         buffer1.update(1, buffer2.getLong(1));
       } else {
+        // Otherwise, we update the bufferSum and bufferCount.
         Double newValue = buffer2.getDouble(0) + buffer1.getDouble(0);
         buffer1.update(0, newValue);
         buffer1.update(1, buffer1.getLong(1) + buffer2.getLong(1));
@@ -98,10 +118,12 @@ public MyDoubleAvg() {
 
   @Override public Object evaluate(Row buffer) {
     if (buffer.isNullAt(0)) {
+      // If the bufferSum is still null, we return null because this function has not got
+      // any input row.
       return null;
     } else {
+      // Otherwise, we calculate the special average value.
       return buffer.getDouble(0) / buffer.getLong(1) + 100.0;
     }
   }
 }
-
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
index 1d4587a27c787..da29e24d267dd 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
@@ -20,14 +20,18 @@
 import java.util.ArrayList;
 import java.util.List;
 
-import org.apache.spark.sql.expressions.aggregate.MutableAggregationBuffer;
-import org.apache.spark.sql.expressions.aggregate.UserDefinedAggregateFunction;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.sql.types.DataType;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.Row;
 
+/**
+ * An example {@link UserDefinedAggregateFunction} to calculate the sum of a
+ * {@link org.apache.spark.sql.types.DoubleType} column.
+ */
 public class MyDoubleSum extends UserDefinedAggregateFunction {
 
   private StructType _inputDataType;
@@ -37,9 +41,9 @@ public class MyDoubleSum extends UserDefinedAggregateFunction {
   private DataType _returnDataType;
 
   public MyDoubleSum() {
-    List<StructField> inputfields = new ArrayList<StructField>();
-    inputfields.add(DataTypes.createStructField("inputDouble", DataTypes.DoubleType, true));
-    _inputDataType = DataTypes.createStructType(inputfields);
+    List<StructField> inputFields = new ArrayList<StructField>();
+    inputFields.add(DataTypes.createStructField("inputDouble", DataTypes.DoubleType, true));
+    _inputDataType = DataTypes.createStructType(inputFields);
 
     List<StructField> bufferFields = new ArrayList<StructField>();
     bufferFields.add(DataTypes.createStructField("bufferDouble", DataTypes.DoubleType, true));
@@ -65,14 +69,20 @@ public MyDoubleSum() {
   }
 
   @Override public void initialize(MutableAggregationBuffer buffer) {
+    // The initial value of the sum is null.
     buffer.update(0, null);
   }
 
   @Override public void update(MutableAggregationBuffer buffer, Row input) {
+    // This input Row only has a single column storing the input value in Double.
+    // We only update the buffer when the input value is not null.
     if (!input.isNullAt(0)) {
       if (buffer.isNullAt(0)) {
+        // If the buffer value (the intermediate result of the sum) is still null,
+        // we set the input value to the buffer.
         buffer.update(0, input.getDouble(0));
       } else {
+        // Otherwise, we add the input value to the buffer value.
         Double newValue = input.getDouble(0) + buffer.getDouble(0);
         buffer.update(0, newValue);
       }
@@ -80,10 +90,16 @@ public MyDoubleSum() {
   }
 
   @Override public void merge(MutableAggregationBuffer buffer1, Row buffer2) {
+    // buffer1 and buffer2 have the same structure.
+    // We only update the buffer1 when the input buffer2's value is not null.
     if (!buffer2.isNullAt(0)) {
       if (buffer1.isNullAt(0)) {
+        // If the buffer value (intermediate result of the sum) is still null,
+        // we set the it as the input buffer's value.
         buffer1.update(0, buffer2.getDouble(0));
       } else {
+        // Otherwise, we add the input buffer's value (buffer1) to the mutable
+        // buffer's value (buffer2).
         Double newValue = buffer2.getDouble(0) + buffer1.getDouble(0);
         buffer1.update(0, newValue);
       }
@@ -92,8 +108,10 @@ public MyDoubleSum() {
 
   @Override public Object evaluate(Row buffer) {
     if (buffer.isNullAt(0)) {
+      // If the buffer value is still null, we return null.
       return null;
     } else {
+      // Otherwise, the intermediate sum is the final result.
       return buffer.getDouble(0);
     }
   }

From 8e7d2bee23dad1535846dae2dc31e35058db16cd Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 27 Jul 2015 13:28:03 -0700
Subject: [PATCH 0626/1454] [SPARK-9378] [SQL] Fixes test case "CTAS with
 serde"

This is a proper version of PR #7693 authored by viirya

The reason why "CTAS with serde" fails is that the `MetastoreRelation` gets converted to a Parquet data source relation by default.

Author: Cheng Lian <lian@databricks.com>

Closes #7700 from liancheng/spark-9378-fix-ctas-test and squashes the following commits:

4413af0 [Cheng Lian] Fixes test case "CTAS with serde"
---
 .../spark/sql/hive/execution/SQLQuerySuite.scala | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 8371dd0716c06..c4923d83e48f3 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -406,13 +406,15 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
         |   FROM src
         |   ORDER BY key, value""".stripMargin).collect()
 
-    checkExistence(sql("DESC EXTENDED ctas5"), true,
-      "name:key", "type:string", "name:value", "ctas5",
-      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat",
-      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat",
-      "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe",
-      "MANAGED_TABLE"
-    )
+    withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
+      checkExistence(sql("DESC EXTENDED ctas5"), true,
+        "name:key", "type:string", "name:value", "ctas5",
+        "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat",
+        "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat",
+        "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe",
+        "MANAGED_TABLE"
+      )
+    }
 
     // use the Hive SerDe for parquet tables
     withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {

From 3ab7525dceeb1c2f3c21efb1ee5a9c8bb0fd0c13 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 27 Jul 2015 13:40:50 -0700
Subject: [PATCH 0627/1454] [SPARK-9355][SQL] Remove InternalRow.get generic
 getter call in columnar cache code

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7673 from cloud-fan/row-generic-getter-columnar and squashes the following commits:

88b1170 [Wenchen Fan] fix style
eeae712 [Wenchen Fan] Remove Internal.get generic getter call in columnar cache code
---
 .../spark/sql/columnar/ColumnAccessor.scala   | 12 ++---
 .../spark/sql/columnar/ColumnBuilder.scala    | 18 +++----
 .../spark/sql/columnar/ColumnStats.scala      |  6 ++-
 .../spark/sql/columnar/ColumnType.scala       | 49 +++++++++++--------
 .../compression/CompressionScheme.scala       |  2 +-
 .../compression/compressionSchemes.scala      | 14 +++---
 .../spark/sql/columnar/ColumnStatsSuite.scala | 12 ++---
 .../spark/sql/columnar/ColumnTypeSuite.scala  | 30 ++++++------
 .../sql/columnar/ColumnarTestUtils.scala      | 18 +++----
 .../NullableColumnAccessorSuite.scala         | 18 +++----
 .../columnar/NullableColumnBuilderSuite.scala | 21 ++++----
 .../compression/BooleanBitSetSuite.scala      |  2 +-
 12 files changed, 107 insertions(+), 95 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
index 931469bed634a..4c29a093218a0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
@@ -41,9 +41,9 @@ private[sql] trait ColumnAccessor {
   protected def underlyingBuffer: ByteBuffer
 }
 
-private[sql] abstract class BasicColumnAccessor[T <: DataType, JvmType](
+private[sql] abstract class BasicColumnAccessor[JvmType](
     protected val buffer: ByteBuffer,
-    protected val columnType: ColumnType[T, JvmType])
+    protected val columnType: ColumnType[JvmType])
   extends ColumnAccessor {
 
   protected def initialize() {}
@@ -93,14 +93,14 @@ private[sql] class StringColumnAccessor(buffer: ByteBuffer)
   extends NativeColumnAccessor(buffer, STRING)
 
 private[sql] class BinaryColumnAccessor(buffer: ByteBuffer)
-  extends BasicColumnAccessor[BinaryType.type, Array[Byte]](buffer, BINARY)
+  extends BasicColumnAccessor[Array[Byte]](buffer, BINARY)
   with NullableColumnAccessor
 
 private[sql] class FixedDecimalColumnAccessor(buffer: ByteBuffer, precision: Int, scale: Int)
   extends NativeColumnAccessor(buffer, FIXED_DECIMAL(precision, scale))
 
-private[sql] class GenericColumnAccessor(buffer: ByteBuffer)
-  extends BasicColumnAccessor[DataType, Array[Byte]](buffer, GENERIC)
+private[sql] class GenericColumnAccessor(buffer: ByteBuffer, dataType: DataType)
+  extends BasicColumnAccessor[Array[Byte]](buffer, GENERIC(dataType))
   with NullableColumnAccessor
 
 private[sql] class DateColumnAccessor(buffer: ByteBuffer)
@@ -131,7 +131,7 @@ private[sql] object ColumnAccessor {
       case BinaryType => new BinaryColumnAccessor(dup)
       case DecimalType.Fixed(precision, scale) if precision < 19 =>
         new FixedDecimalColumnAccessor(dup, precision, scale)
-      case _ => new GenericColumnAccessor(dup)
+      case other => new GenericColumnAccessor(dup, other)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index 087c52239713d..454b7b91a63f5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -46,9 +46,9 @@ private[sql] trait ColumnBuilder {
   def build(): ByteBuffer
 }
 
-private[sql] class BasicColumnBuilder[T <: DataType, JvmType](
+private[sql] class BasicColumnBuilder[JvmType](
     val columnStats: ColumnStats,
-    val columnType: ColumnType[T, JvmType])
+    val columnType: ColumnType[JvmType])
   extends ColumnBuilder {
 
   protected var columnName: String = _
@@ -78,16 +78,16 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType](
   }
 }
 
-private[sql] abstract class ComplexColumnBuilder[T <: DataType, JvmType](
+private[sql] abstract class ComplexColumnBuilder[JvmType](
     columnStats: ColumnStats,
-    columnType: ColumnType[T, JvmType])
-  extends BasicColumnBuilder[T, JvmType](columnStats, columnType)
+    columnType: ColumnType[JvmType])
+  extends BasicColumnBuilder[JvmType](columnStats, columnType)
   with NullableColumnBuilder
 
 private[sql] abstract class NativeColumnBuilder[T <: AtomicType](
     override val columnStats: ColumnStats,
     override val columnType: NativeColumnType[T])
-  extends BasicColumnBuilder[T, T#InternalType](columnStats, columnType)
+  extends BasicColumnBuilder[T#InternalType](columnStats, columnType)
   with NullableColumnBuilder
   with AllCompressionSchemes
   with CompressibleColumnBuilder[T]
@@ -118,8 +118,8 @@ private[sql] class FixedDecimalColumnBuilder(
     FIXED_DECIMAL(precision, scale))
 
 // TODO (lian) Add support for array, struct and map
-private[sql] class GenericColumnBuilder
-  extends ComplexColumnBuilder(new GenericColumnStats, GENERIC)
+private[sql] class GenericColumnBuilder(dataType: DataType)
+  extends ComplexColumnBuilder(new GenericColumnStats(dataType), GENERIC(dataType))
 
 private[sql] class DateColumnBuilder extends NativeColumnBuilder(new DateColumnStats, DATE)
 
@@ -164,7 +164,7 @@ private[sql] object ColumnBuilder {
       case BinaryType => new BinaryColumnBuilder
       case DecimalType.Fixed(precision, scale) if precision < 19 =>
         new FixedDecimalColumnBuilder(precision, scale)
-      case _ => new GenericColumnBuilder
+      case other => new GenericColumnBuilder(other)
     }
 
     builder.initialize(initialSize, columnName, useCompression)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 7c63179af6470..32a84b2676e07 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -252,11 +252,13 @@ private[sql] class FixedDecimalColumnStats extends ColumnStats {
     InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
-private[sql] class GenericColumnStats extends ColumnStats {
+private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
+  val columnType = GENERIC(dataType)
+
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
     if (!row.isNullAt(ordinal)) {
-      sizeInBytes += GENERIC.actualSize(row, ordinal)
+      sizeInBytes += columnType.actualSize(row, ordinal)
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index c0ca52751b66c..2863f6c230a9d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -31,14 +31,18 @@ import org.apache.spark.unsafe.types.UTF8String
  * An abstract class that represents type of a column. Used to append/extract Java objects into/from
  * the underlying [[ByteBuffer]] of a column.
  *
- * @param typeId A unique ID representing the type.
- * @param defaultSize Default size in bytes for one element of type T (e.g. 4 for `Int`).
- * @tparam T Scala data type for the column.
  * @tparam JvmType Underlying Java type to represent the elements.
  */
-private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
-    val typeId: Int,
-    val defaultSize: Int) {
+private[sql] sealed abstract class ColumnType[JvmType] {
+
+  // The catalyst data type of this column.
+  def dataType: DataType
+
+  // A unique ID representing the type.
+  def typeId: Int
+
+  // Default size in bytes for one element of type T (e.g. 4 for `Int`).
+  def defaultSize: Int
 
   /**
    * Extracts a value out of the buffer at the buffer's current position.
@@ -90,7 +94,7 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
    * boxing/unboxing costs whenever possible.
    */
   def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
-    to(toOrdinal) = from.get(fromOrdinal)
+    to.update(toOrdinal, from.get(fromOrdinal, dataType))
   }
 
   /**
@@ -103,9 +107,9 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
 
 private[sql] abstract class NativeColumnType[T <: AtomicType](
     val dataType: T,
-    typeId: Int,
-    defaultSize: Int)
-  extends ColumnType[T, T#InternalType](typeId, defaultSize) {
+    val typeId: Int,
+    val defaultSize: Int)
+  extends ColumnType[T#InternalType] {
 
   /**
    * Scala TypeTag. Can be used to create primitive arrays and hash tables.
@@ -400,10 +404,10 @@ private[sql] object FIXED_DECIMAL {
   val defaultSize = 8
 }
 
-private[sql] sealed abstract class ByteArrayColumnType[T <: DataType](
-    typeId: Int,
-    defaultSize: Int)
-  extends ColumnType[T, Array[Byte]](typeId, defaultSize) {
+private[sql] sealed abstract class ByteArrayColumnType(
+    val typeId: Int,
+    val defaultSize: Int)
+  extends ColumnType[Array[Byte]] {
 
   override def actualSize(row: InternalRow, ordinal: Int): Int = {
     getField(row, ordinal).length + 4
@@ -421,9 +425,12 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType](
   }
 }
 
-private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](11, 16) {
+private[sql] object BINARY extends ByteArrayColumnType(11, 16) {
+
+  def dataType: DataType = BooleanType
+
   override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]): Unit = {
-    row(ordinal) = value
+    row.update(ordinal, value)
   }
 
   override def getField(row: InternalRow, ordinal: Int): Array[Byte] = {
@@ -434,18 +441,18 @@ private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](11, 16)
 // Used to process generic objects (all types other than those listed above). Objects should be
 // serialized first before appending to the column `ByteBuffer`, and is also extracted as serialized
 // byte array.
-private[sql] object GENERIC extends ByteArrayColumnType[DataType](12, 16) {
+private[sql] case class GENERIC(dataType: DataType) extends ByteArrayColumnType(12, 16) {
   override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]): Unit = {
-    row(ordinal) = SparkSqlSerializer.deserialize[Any](value)
+    row.update(ordinal, SparkSqlSerializer.deserialize[Any](value))
   }
 
   override def getField(row: InternalRow, ordinal: Int): Array[Byte] = {
-    SparkSqlSerializer.serialize(row.get(ordinal))
+    SparkSqlSerializer.serialize(row.get(ordinal, dataType))
   }
 }
 
 private[sql] object ColumnType {
-  def apply(dataType: DataType): ColumnType[_, _] = {
+  def apply(dataType: DataType): ColumnType[_] = {
     dataType match {
       case BooleanType => BOOLEAN
       case ByteType => BYTE
@@ -460,7 +467,7 @@ private[sql] object ColumnType {
       case BinaryType => BINARY
       case DecimalType.Fixed(precision, scale) if precision < 19 =>
         FIXED_DECIMAL(precision, scale)
-      case _ => GENERIC
+      case other => GENERIC(other)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala
index 4eaec6d853d4d..b1ef9b2ef7849 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala
@@ -46,7 +46,7 @@ private[sql] trait Decoder[T <: AtomicType] {
 private[sql] trait CompressionScheme {
   def typeId: Int
 
-  def supports(columnType: ColumnType[_, _]): Boolean
+  def supports(columnType: ColumnType[_]): Boolean
 
   def encoder[T <: AtomicType](columnType: NativeColumnType[T]): Encoder[T]
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
index 6150df6930b32..c91d960a0932b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
@@ -32,7 +32,7 @@ import org.apache.spark.util.Utils
 private[sql] case object PassThrough extends CompressionScheme {
   override val typeId = 0
 
-  override def supports(columnType: ColumnType[_, _]): Boolean = true
+  override def supports(columnType: ColumnType[_]): Boolean = true
 
   override def encoder[T <: AtomicType](columnType: NativeColumnType[T]): Encoder[T] = {
     new this.Encoder[T](columnType)
@@ -78,7 +78,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
     new this.Decoder(buffer, columnType)
   }
 
-  override def supports(columnType: ColumnType[_, _]): Boolean = columnType match {
+  override def supports(columnType: ColumnType[_]): Boolean = columnType match {
     case INT | LONG | SHORT | BYTE | STRING | BOOLEAN => true
     case _ => false
   }
@@ -128,7 +128,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
         while (from.hasRemaining) {
           columnType.extract(from, value, 0)
 
-          if (value.get(0) == currentValue.get(0)) {
+          if (value.get(0, columnType.dataType) == currentValue.get(0, columnType.dataType)) {
             currentRun += 1
           } else {
             // Writes current run
@@ -189,7 +189,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme {
     new this.Encoder[T](columnType)
   }
 
-  override def supports(columnType: ColumnType[_, _]): Boolean = columnType match {
+  override def supports(columnType: ColumnType[_]): Boolean = columnType match {
     case INT | LONG | STRING => true
     case _ => false
   }
@@ -304,7 +304,7 @@ private[sql] case object BooleanBitSet extends CompressionScheme {
     (new this.Encoder).asInstanceOf[compression.Encoder[T]]
   }
 
-  override def supports(columnType: ColumnType[_, _]): Boolean = columnType == BOOLEAN
+  override def supports(columnType: ColumnType[_]): Boolean = columnType == BOOLEAN
 
   class Encoder extends compression.Encoder[BooleanType.type] {
     private var _uncompressedSize = 0
@@ -392,7 +392,7 @@ private[sql] case object IntDelta extends CompressionScheme {
     (new Encoder).asInstanceOf[compression.Encoder[T]]
   }
 
-  override def supports(columnType: ColumnType[_, _]): Boolean = columnType == INT
+  override def supports(columnType: ColumnType[_]): Boolean = columnType == INT
 
   class Encoder extends compression.Encoder[IntegerType.type] {
     protected var _compressedSize: Int = 0
@@ -472,7 +472,7 @@ private[sql] case object LongDelta extends CompressionScheme {
     (new Encoder).asInstanceOf[compression.Encoder[T]]
   }
 
-  override def supports(columnType: ColumnType[_, _]): Boolean = columnType == LONG
+  override def supports(columnType: ColumnType[_]): Boolean = columnType == LONG
 
   class Encoder extends compression.Encoder[LongType.type] {
     protected var _compressedSize: Int = 0
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 31e7b0e72e510..4499a7207031d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -58,15 +58,15 @@ class ColumnStatsSuite extends SparkFunSuite {
       val rows = Seq.fill(10)(makeRandomRow(columnType)) ++ Seq.fill(10)(makeNullRow(1))
       rows.foreach(columnStats.gatherStats(_, 0))
 
-      val values = rows.take(10).map(_.get(0).asInstanceOf[T#InternalType])
+      val values = rows.take(10).map(_.get(0, columnType.dataType).asInstanceOf[T#InternalType])
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1))
-      assertResult(10, "Wrong null count")(stats.get(2))
-      assertResult(20, "Wrong row count")(stats.get(3))
-      assertResult(stats.get(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.genericGet(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.genericGet(1))
+      assertResult(10, "Wrong null count")(stats.genericGet(2))
+      assertResult(20, "Wrong row count")(stats.genericGet(3))
+      assertResult(stats.genericGet(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
index 4d46a657056e0..8f024690efd0d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
@@ -32,13 +32,15 @@ import org.apache.spark.unsafe.types.UTF8String
 
 
 class ColumnTypeSuite extends SparkFunSuite with Logging {
-  val DEFAULT_BUFFER_SIZE = 512
+  private val DEFAULT_BUFFER_SIZE = 512
+  private val MAP_GENERIC = GENERIC(MapType(IntegerType, StringType))
 
   test("defaultSize") {
     val checks = Map(
       BOOLEAN -> 1, BYTE -> 1, SHORT -> 2, INT -> 4, DATE -> 4,
       LONG -> 8, TIMESTAMP -> 8, FLOAT -> 4, DOUBLE -> 8,
-      STRING -> 8, BINARY -> 16, FIXED_DECIMAL(15, 10) -> 8, GENERIC -> 16)
+      STRING -> 8, BINARY -> 16, FIXED_DECIMAL(15, 10) -> 8,
+      MAP_GENERIC -> 16)
 
     checks.foreach { case (columnType, expectedSize) =>
       assertResult(expectedSize, s"Wrong defaultSize for $columnType") {
@@ -48,8 +50,8 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
   }
 
   test("actualSize") {
-    def checkActualSize[T <: DataType, JvmType](
-        columnType: ColumnType[T, JvmType],
+    def checkActualSize[JvmType](
+        columnType: ColumnType[JvmType],
         value: JvmType,
         expected: Int): Unit = {
 
@@ -74,7 +76,7 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
     checkActualSize(FIXED_DECIMAL(15, 10), Decimal(0, 15, 10), 8)
 
     val generic = Map(1 -> "a")
-    checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 8)
+    checkActualSize(MAP_GENERIC, SparkSqlSerializer.serialize(generic), 4 + 8)
   }
 
   testNativeColumnType(BOOLEAN)(
@@ -123,7 +125,7 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
       UTF8String.fromBytes(bytes)
     })
 
-  testColumnType[BinaryType.type, Array[Byte]](
+  testColumnType[Array[Byte]](
     BINARY,
     (buffer: ByteBuffer, bytes: Array[Byte]) => {
       buffer.putInt(bytes.length).put(bytes)
@@ -140,7 +142,7 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
     val obj = Map(1 -> "spark", 2 -> "sql")
     val serializedObj = SparkSqlSerializer.serialize(obj)
 
-    GENERIC.append(SparkSqlSerializer.serialize(obj), buffer)
+    MAP_GENERIC.append(SparkSqlSerializer.serialize(obj), buffer)
     buffer.rewind()
 
     val length = buffer.getInt()
@@ -157,7 +159,7 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
 
     assertResult(obj, "Deserialized object didn't equal to the original object") {
       buffer.rewind()
-      SparkSqlSerializer.deserialize(GENERIC.extract(buffer))
+      SparkSqlSerializer.deserialize(MAP_GENERIC.extract(buffer))
     }
   }
 
@@ -170,7 +172,7 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
     val obj = CustomClass(Int.MaxValue, Long.MaxValue)
     val serializedObj = serializer.serialize(obj).array()
 
-    GENERIC.append(serializer.serialize(obj).array(), buffer)
+    MAP_GENERIC.append(serializer.serialize(obj).array(), buffer)
     buffer.rewind()
 
     val length = buffer.getInt
@@ -192,7 +194,7 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
 
     assertResult(obj, "Custom deserialized object didn't equal the original object") {
       buffer.rewind()
-      serializer.deserialize(ByteBuffer.wrap(GENERIC.extract(buffer)))
+      serializer.deserialize(ByteBuffer.wrap(MAP_GENERIC.extract(buffer)))
     }
   }
 
@@ -201,11 +203,11 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
       (putter: (ByteBuffer, T#InternalType) => Unit,
       getter: (ByteBuffer) => T#InternalType): Unit = {
 
-    testColumnType[T, T#InternalType](columnType, putter, getter)
+    testColumnType[T#InternalType](columnType, putter, getter)
   }
 
-  def testColumnType[T <: DataType, JvmType](
-      columnType: ColumnType[T, JvmType],
+  def testColumnType[JvmType](
+      columnType: ColumnType[JvmType],
       putter: (ByteBuffer, JvmType) => Unit,
       getter: (ByteBuffer) => JvmType): Unit = {
 
@@ -262,7 +264,7 @@ class ColumnTypeSuite extends SparkFunSuite with Logging {
       }
     }
 
-    assertResult(GENERIC) {
+    assertResult(GENERIC(DecimalType(19, 0))) {
       ColumnType(DecimalType(19, 0))
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
index d9861339739c9..79bb7d072feb2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala
@@ -31,7 +31,7 @@ object ColumnarTestUtils {
     row
   }
 
-  def makeRandomValue[T <: DataType, JvmType](columnType: ColumnType[T, JvmType]): JvmType = {
+  def makeRandomValue[JvmType](columnType: ColumnType[JvmType]): JvmType = {
     def randomBytes(length: Int) = {
       val bytes = new Array[Byte](length)
       Random.nextBytes(bytes)
@@ -58,15 +58,15 @@ object ColumnarTestUtils {
   }
 
   def makeRandomValues(
-      head: ColumnType[_ <: DataType, _],
-      tail: ColumnType[_ <: DataType, _]*): Seq[Any] = makeRandomValues(Seq(head) ++ tail)
+      head: ColumnType[_],
+      tail: ColumnType[_]*): Seq[Any] = makeRandomValues(Seq(head) ++ tail)
 
-  def makeRandomValues(columnTypes: Seq[ColumnType[_ <: DataType, _]]): Seq[Any] = {
+  def makeRandomValues(columnTypes: Seq[ColumnType[_]]): Seq[Any] = {
     columnTypes.map(makeRandomValue(_))
   }
 
-  def makeUniqueRandomValues[T <: DataType, JvmType](
-      columnType: ColumnType[T, JvmType],
+  def makeUniqueRandomValues[JvmType](
+      columnType: ColumnType[JvmType],
       count: Int): Seq[JvmType] = {
 
     Iterator.iterate(HashSet.empty[JvmType]) { set =>
@@ -75,10 +75,10 @@ object ColumnarTestUtils {
   }
 
   def makeRandomRow(
-      head: ColumnType[_ <: DataType, _],
-      tail: ColumnType[_ <: DataType, _]*): InternalRow = makeRandomRow(Seq(head) ++ tail)
+      head: ColumnType[_],
+      tail: ColumnType[_]*): InternalRow = makeRandomRow(Seq(head) ++ tail)
 
-  def makeRandomRow(columnTypes: Seq[ColumnType[_ <: DataType, _]]): InternalRow = {
+  def makeRandomRow(columnTypes: Seq[ColumnType[_]]): InternalRow = {
     val row = new GenericMutableRow(columnTypes.length)
     makeRandomValues(columnTypes).zipWithIndex.foreach { case (value, index) =>
       row(index) = value
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
index d421f4d8d091e..f4f6c7649bfa8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
@@ -21,17 +21,17 @@ import java.nio.ByteBuffer
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
-import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.types.{StringType, ArrayType, DataType}
 
-class TestNullableColumnAccessor[T <: DataType, JvmType](
+class TestNullableColumnAccessor[JvmType](
     buffer: ByteBuffer,
-    columnType: ColumnType[T, JvmType])
+    columnType: ColumnType[JvmType])
   extends BasicColumnAccessor(buffer, columnType)
   with NullableColumnAccessor
 
 object TestNullableColumnAccessor {
-  def apply[T <: DataType, JvmType](buffer: ByteBuffer, columnType: ColumnType[T, JvmType])
-    : TestNullableColumnAccessor[T, JvmType] = {
+  def apply[JvmType](buffer: ByteBuffer, columnType: ColumnType[JvmType])
+    : TestNullableColumnAccessor[JvmType] = {
     // Skips the column type ID
     buffer.getInt()
     new TestNullableColumnAccessor(buffer, columnType)
@@ -43,13 +43,13 @@ class NullableColumnAccessorSuite extends SparkFunSuite {
 
   Seq(
     BOOLEAN, BYTE, SHORT, INT, DATE, LONG, TIMESTAMP, FLOAT, DOUBLE,
-    STRING, BINARY, FIXED_DECIMAL(15, 10), GENERIC)
+    STRING, BINARY, FIXED_DECIMAL(15, 10), GENERIC(ArrayType(StringType)))
     .foreach {
     testNullableColumnAccessor(_)
   }
 
-  def testNullableColumnAccessor[T <: DataType, JvmType](
-      columnType: ColumnType[T, JvmType]): Unit = {
+  def testNullableColumnAccessor[JvmType](
+      columnType: ColumnType[JvmType]): Unit = {
 
     val typeName = columnType.getClass.getSimpleName.stripSuffix("$")
     val nullRow = makeNullRow(1)
@@ -75,7 +75,7 @@ class NullableColumnAccessorSuite extends SparkFunSuite {
       (0 until 4).foreach { _ =>
         assert(accessor.hasNext)
         accessor.extractTo(row, 0)
-        assert(row.get(0) === randomRow.get(0))
+        assert(row.get(0, columnType.dataType) === randomRow.get(0, columnType.dataType))
 
         assert(accessor.hasNext)
         accessor.extractTo(row, 0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
index cd8bf75ff1752..241d09ea205e9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
@@ -21,13 +21,13 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.sql.types._
 
-class TestNullableColumnBuilder[T <: DataType, JvmType](columnType: ColumnType[T, JvmType])
-  extends BasicColumnBuilder[T, JvmType](new NoopColumnStats, columnType)
+class TestNullableColumnBuilder[JvmType](columnType: ColumnType[JvmType])
+  extends BasicColumnBuilder[JvmType](new NoopColumnStats, columnType)
   with NullableColumnBuilder
 
 object TestNullableColumnBuilder {
-  def apply[T <: DataType, JvmType](columnType: ColumnType[T, JvmType], initialSize: Int = 0)
-    : TestNullableColumnBuilder[T, JvmType] = {
+  def apply[JvmType](columnType: ColumnType[JvmType], initialSize: Int = 0)
+    : TestNullableColumnBuilder[JvmType] = {
     val builder = new TestNullableColumnBuilder(columnType)
     builder.initialize(initialSize)
     builder
@@ -39,13 +39,13 @@ class NullableColumnBuilderSuite extends SparkFunSuite {
 
   Seq(
     BOOLEAN, BYTE, SHORT, INT, DATE, LONG, TIMESTAMP, FLOAT, DOUBLE,
-    STRING, BINARY, FIXED_DECIMAL(15, 10), GENERIC)
+    STRING, BINARY, FIXED_DECIMAL(15, 10), GENERIC(ArrayType(StringType)))
     .foreach {
     testNullableColumnBuilder(_)
   }
 
-  def testNullableColumnBuilder[T <: DataType, JvmType](
-      columnType: ColumnType[T, JvmType]): Unit = {
+  def testNullableColumnBuilder[JvmType](
+      columnType: ColumnType[JvmType]): Unit = {
 
     val typeName = columnType.getClass.getSimpleName.stripSuffix("$")
 
@@ -92,13 +92,14 @@ class NullableColumnBuilderSuite extends SparkFunSuite {
 
       // For non-null values
       (0 until 4).foreach { _ =>
-        val actual = if (columnType == GENERIC) {
-          SparkSqlSerializer.deserialize[Any](GENERIC.extract(buffer))
+        val actual = if (columnType.isInstanceOf[GENERIC]) {
+          SparkSqlSerializer.deserialize[Any](columnType.extract(buffer).asInstanceOf[Array[Byte]])
         } else {
           columnType.extract(buffer)
         }
 
-        assert(actual === randomRow.get(0), "Extracted value didn't equal to the original one")
+        assert(actual === randomRow.get(0, columnType.dataType),
+          "Extracted value didn't equal to the original one")
       }
 
       assert(!buffer.hasRemaining)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
index 33092c83a1a1c..9a2948c59ba42 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
@@ -33,7 +33,7 @@ class BooleanBitSetSuite extends SparkFunSuite {
 
     val builder = TestCompressibleColumnBuilder(new NoopColumnStats, BOOLEAN, BooleanBitSet)
     val rows = Seq.fill[InternalRow](count)(makeRandomRow(BOOLEAN))
-    val values = rows.map(_.get(0))
+    val values = rows.map(_.getBoolean(0))
 
     rows.foreach(builder.appendFrom(_, 0))
     val buffer = builder.build()

From c1be9f309acad4d1b1908fa7800e7ef4f3e872ce Mon Sep 17 00:00:00 2001
From: Hari Shreedharan <hshreedharan@apache.org>
Date: Mon, 27 Jul 2015 15:16:46 -0700
Subject: [PATCH 0628/1454] =?UTF-8?q?[SPARK-8988]=20[YARN]=20Make=20sure?=
 =?UTF-8?q?=20driver=20log=20links=20appear=20in=20secure=20cluste?=
 =?UTF-8?q?=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…r mode.

The NodeReports API currently used does not work in secure mode since we do not get RM tokens. Instead this patch just uses environment vars exported by YARN to create the log links.

Author: Hari Shreedharan <hshreedharan@apache.org>

Closes #7624 from harishreedharan/driver-logs-env and squashes the following commits:

7368c7e [Hari Shreedharan] [SPARK-8988][YARN] Make sure driver log links appear in secure cluster mode.
---
 .../cluster/YarnClusterSchedulerBackend.scala | 71 +++++--------------
 1 file changed, 17 insertions(+), 54 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
index 33f580aaebdc0..1aed5a1675075 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
@@ -19,6 +19,8 @@ package org.apache.spark.scheduler.cluster
 
 import java.net.NetworkInterface
 
+import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
+
 import scala.collection.JavaConverters._
 
 import org.apache.hadoop.yarn.api.records.NodeState
@@ -64,68 +66,29 @@ private[spark] class YarnClusterSchedulerBackend(
     }
 
   override def getDriverLogUrls: Option[Map[String, String]] = {
-    var yarnClientOpt: Option[YarnClient] = None
     var driverLogs: Option[Map[String, String]] = None
     try {
       val yarnConf = new YarnConfiguration(sc.hadoopConfiguration)
       val containerId = YarnSparkHadoopUtil.get.getContainerId
-      yarnClientOpt = Some(YarnClient.createYarnClient())
-      yarnClientOpt.foreach { yarnClient =>
-        yarnClient.init(yarnConf)
-        yarnClient.start()
-
-        // For newer versions of YARN, we can find the HTTP address for a given node by getting a
-        // container report for a given container. But container reports came only in Hadoop 2.4,
-        // so we basically have to get the node reports for all nodes and find the one which runs
-        // this container. For that we have to compare the node's host against the current host.
-        // Since the host can have multiple addresses, we need to compare against all of them to
-        // find out if one matches.
-
-        // Get all the addresses of this node.
-        val addresses =
-          NetworkInterface.getNetworkInterfaces.asScala
-            .flatMap(_.getInetAddresses.asScala)
-            .toSeq
-
-        // Find a node report that matches one of the addresses
-        val nodeReport =
-          yarnClient.getNodeReports(NodeState.RUNNING).asScala.find { x =>
-            val host = x.getNodeId.getHost
-            addresses.exists { address =>
-              address.getHostAddress == host ||
-                address.getHostName == host ||
-                address.getCanonicalHostName == host
-            }
-          }
 
-        // Now that we have found the report for the Node Manager that the AM is running on, we
-        // can get the base HTTP address for the Node manager from the report.
-        // The format used for the logs for each container is well-known and can be constructed
-        // using the NM's HTTP address and the container ID.
-        // The NM may be running several containers, but we can build the URL for the AM using
-        // the AM's container ID, which we already know.
-        nodeReport.foreach { report =>
-          val httpAddress = report.getHttpAddress
-          // lookup appropriate http scheme for container log urls
-          val yarnHttpPolicy = yarnConf.get(
-            YarnConfiguration.YARN_HTTP_POLICY_KEY,
-            YarnConfiguration.YARN_HTTP_POLICY_DEFAULT
-          )
-          val user = Utils.getCurrentUserName()
-          val httpScheme = if (yarnHttpPolicy == "HTTPS_ONLY") "https://" else "http://"
-          val baseUrl = s"$httpScheme$httpAddress/node/containerlogs/$containerId/$user"
-          logDebug(s"Base URL for logs: $baseUrl")
-          driverLogs = Some(Map(
-            "stderr" -> s"$baseUrl/stderr?start=-4096",
-            "stdout" -> s"$baseUrl/stdout?start=-4096"))
-        }
-      }
+      val httpAddress = System.getenv(Environment.NM_HOST.name()) +
+        ":" + System.getenv(Environment.NM_HTTP_PORT.name())
+      // lookup appropriate http scheme for container log urls
+      val yarnHttpPolicy = yarnConf.get(
+        YarnConfiguration.YARN_HTTP_POLICY_KEY,
+        YarnConfiguration.YARN_HTTP_POLICY_DEFAULT
+      )
+      val user = Utils.getCurrentUserName()
+      val httpScheme = if (yarnHttpPolicy == "HTTPS_ONLY") "https://" else "http://"
+      val baseUrl = s"$httpScheme$httpAddress/node/containerlogs/$containerId/$user"
+      logDebug(s"Base URL for logs: $baseUrl")
+      driverLogs = Some(Map(
+        "stderr" -> s"$baseUrl/stderr?start=-4096",
+        "stdout" -> s"$baseUrl/stdout?start=-4096"))
     } catch {
       case e: Exception =>
-        logInfo("Node Report API is not available in the version of YARN being used, so AM" +
+        logInfo("Error while building AM log links, so AM" +
           " logs link will not appear in application UI", e)
-    } finally {
-      yarnClientOpt.foreach(_.close())
     }
     driverLogs
   }

From 2104931d7d726eda2c098e0f403c7f1533df8746 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 27 Jul 2015 15:18:48 -0700
Subject: [PATCH 0629/1454] [SPARK-9385] [HOT-FIX] [PYSPARK] Comment out Python
 style check

https://issues.apache.org/jira/browse/SPARK-9385

Comment out Python style check because of error shown in https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/3088/AMPLAB_JENKINS_BUILD_PROFILE=hadoop1.0,label=centos/console

Author: Yin Huai <yhuai@databricks.com>

Closes #7702 from yhuai/SPARK-9385 and squashes the following commits:

146e6ef [Yin Huai] Comment out Python style check because of error shown in https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/3088/AMPLAB_JENKINS_BUILD_PROFILE=hadoop1.0,label=centos/console
---
 dev/run-tests.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/dev/run-tests.py b/dev/run-tests.py
index 1f0d218514f92..d1cb66860b3f8 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -198,8 +198,9 @@ def run_scala_style_checks():
 
 
 def run_python_style_checks():
-    set_title_and_block("Running Python style checks", "BLOCK_PYTHON_STYLE")
-    run_cmd([os.path.join(SPARK_HOME, "dev", "lint-python")])
+    # set_title_and_block("Running Python style checks", "BLOCK_PYTHON_STYLE")
+    # run_cmd([os.path.join(SPARK_HOME, "dev", "lint-python")])
+    pass
 
 
 def build_spark_documentation():

From ab625956616664c2b4861781a578311da75a9ae4 Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Mon, 27 Jul 2015 15:46:35 -0700
Subject: [PATCH 0630/1454] [SPARK-4352] [YARN] [WIP] Incorporate locality
 preferences in dynamic allocation requests

Currently there's no locality preference for container request in YARN mode, this will affect the performance if fetching data remotely, so here proposed to add locality in Yarn dynamic allocation mode.

Ping sryza, please help to review, thanks a lot.

Author: jerryshao <saisai.shao@intel.com>

Closes #6394 from jerryshao/SPARK-4352 and squashes the following commits:

d45fecb [jerryshao] Add documents
6c3fe5c [jerryshao] Fix bug
8db6c0e [jerryshao] Further address the comments
2e2b2cb [jerryshao] Fix rebase compiling problem
ce5f096 [jerryshao] Fix style issue
7f7df95 [jerryshao] Fix rebase issue
9ca9e07 [jerryshao] Code refactor according to comments
d3e4236 [jerryshao] Further address the comments
5e7a593 [jerryshao] Fix bug introduced code rebase
9ca7783 [jerryshao] Style changes
08317f9 [jerryshao] code and comment refines
65b2423 [jerryshao] Further address the comments
a27c587 [jerryshao] address the comment
27faabc [jerryshao] redundant code remove
9ce06a1 [jerryshao] refactor the code
f5ba27b [jerryshao] Style fix
2c6cc8a [jerryshao] Fix bug and add unit tests
0757335 [jerryshao] Consider the distribution of existed containers to recalculate the new container requests
0ad66ff [jerryshao] Fix compile bugs
1c20381 [jerryshao] Minor fix
5ef2dc8 [jerryshao] Add docs and improve the code
3359814 [jerryshao] Fix rebase and test bugs
0398539 [jerryshao] reinitialize the new implementation
67596d6 [jerryshao] Still fix the code
654e1d2 [jerryshao] Fix some bugs
45b1c89 [jerryshao] Further polish the algorithm
dea0152 [jerryshao] Enable node locality information in YarnAllocator
74bbcc6 [jerryshao] Support node locality for dynamic allocation initial commit
---
 .../spark/ExecutorAllocationClient.scala      |  18 +-
 .../spark/ExecutorAllocationManager.scala     |  62 +++++-
 .../scala/org/apache/spark/SparkContext.scala |  25 ++-
 .../apache/spark/scheduler/DAGScheduler.scala |  26 ++-
 .../org/apache/spark/scheduler/Stage.scala    |   7 +-
 .../apache/spark/scheduler/StageInfo.scala    |  13 +-
 .../cluster/CoarseGrainedClusterMessage.scala |   6 +-
 .../CoarseGrainedSchedulerBackend.scala       |  32 ++-
 .../cluster/YarnSchedulerBackend.scala        |   3 +-
 .../ExecutorAllocationManagerSuite.scala      |  55 +++++-
 .../apache/spark/HeartbeatReceiverSuite.scala |   7 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |   5 +-
 ...yPreferredContainerPlacementStrategy.scala | 182 ++++++++++++++++++
 .../spark/deploy/yarn/YarnAllocator.scala     |  47 ++++-
 .../ContainerPlacementStrategySuite.scala     | 125 ++++++++++++
 .../deploy/yarn/YarnAllocatorSuite.scala      |  14 +-
 16 files changed, 578 insertions(+), 49 deletions(-)
 create mode 100644 yarn/src/main/scala/org/apache/spark/deploy/yarn/LocalityPreferredContainerPlacementStrategy.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/deploy/yarn/ContainerPlacementStrategySuite.scala

diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala
index 443830f8d03b6..842bfdbadc948 100644
--- a/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala
+++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationClient.scala
@@ -24,11 +24,23 @@ package org.apache.spark
 private[spark] trait ExecutorAllocationClient {
 
   /**
-   * Express a preference to the cluster manager for a given total number of executors.
-   * This can result in canceling pending requests or filing additional requests.
+   * Update the cluster manager on our scheduling needs. Three bits of information are included
+   * to help it make decisions.
+   * @param numExecutors The total number of executors we'd like to have. The cluster manager
+   *                     shouldn't kill any running executor to reach this number, but,
+   *                     if all existing executors were to die, this is the number of executors
+   *                     we'd want to be allocated.
+   * @param localityAwareTasks The number of tasks in all active stages that have a locality
+   *                           preferences. This includes running, pending, and completed tasks.
+   * @param hostToLocalTaskCount A map of hosts to the number of tasks from all active stages
+   *                             that would like to like to run on that host.
+   *                             This includes running, pending, and completed tasks.
    * @return whether the request is acknowledged by the cluster manager.
    */
-  private[spark] def requestTotalExecutors(numExecutors: Int): Boolean
+  private[spark] def requestTotalExecutors(
+      numExecutors: Int,
+      localityAwareTasks: Int,
+      hostToLocalTaskCount: Map[String, Int]): Boolean
 
   /**
    * Request an additional number of executors from the cluster manager.
diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
index 648bcfe28cad2..1877aaf2cac55 100644
--- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
+++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
@@ -161,6 +161,12 @@ private[spark] class ExecutorAllocationManager(
   //   (2) an executor idle timeout has elapsed.
   @volatile private var initializing: Boolean = true
 
+  // Number of locality aware tasks, used for executor placement.
+  private var localityAwareTasks = 0
+
+  // Host to possible task running on it, used for executor placement.
+  private var hostToLocalTaskCount: Map[String, Int] = Map.empty
+
   /**
    * Verify that the settings specified through the config are valid.
    * If not, throw an appropriate exception.
@@ -295,7 +301,7 @@ private[spark] class ExecutorAllocationManager(
 
       // If the new target has not changed, avoid sending a message to the cluster manager
       if (numExecutorsTarget < oldNumExecutorsTarget) {
-        client.requestTotalExecutors(numExecutorsTarget)
+        client.requestTotalExecutors(numExecutorsTarget, localityAwareTasks, hostToLocalTaskCount)
         logDebug(s"Lowering target number of executors to $numExecutorsTarget (previously " +
           s"$oldNumExecutorsTarget) because not all requested executors are actually needed")
       }
@@ -349,7 +355,8 @@ private[spark] class ExecutorAllocationManager(
       return 0
     }
 
-    val addRequestAcknowledged = testing || client.requestTotalExecutors(numExecutorsTarget)
+    val addRequestAcknowledged = testing ||
+      client.requestTotalExecutors(numExecutorsTarget, localityAwareTasks, hostToLocalTaskCount)
     if (addRequestAcknowledged) {
       val executorsString = "executor" + { if (delta > 1) "s" else "" }
       logInfo(s"Requesting $delta new $executorsString because tasks are backlogged" +
@@ -519,6 +526,12 @@ private[spark] class ExecutorAllocationManager(
     // Number of tasks currently running on the cluster.  Should be 0 when no stages are active.
     private var numRunningTasks: Int = _
 
+    // stageId to tuple (the number of task with locality preferences, a map where each pair is a
+    // node and the number of tasks that would like to be scheduled on that node) map,
+    // maintain the executor placement hints for each stage Id used by resource framework to better
+    // place the executors.
+    private val stageIdToExecutorPlacementHints = new mutable.HashMap[Int, (Int, Map[String, Int])]
+
     override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = {
       initializing = false
       val stageId = stageSubmitted.stageInfo.stageId
@@ -526,6 +539,24 @@ private[spark] class ExecutorAllocationManager(
       allocationManager.synchronized {
         stageIdToNumTasks(stageId) = numTasks
         allocationManager.onSchedulerBacklogged()
+
+        // Compute the number of tasks requested by the stage on each host
+        var numTasksPending = 0
+        val hostToLocalTaskCountPerStage = new mutable.HashMap[String, Int]()
+        stageSubmitted.stageInfo.taskLocalityPreferences.foreach { locality =>
+          if (!locality.isEmpty) {
+            numTasksPending += 1
+            locality.foreach { location =>
+              val count = hostToLocalTaskCountPerStage.getOrElse(location.host, 0) + 1
+              hostToLocalTaskCountPerStage(location.host) = count
+            }
+          }
+        }
+        stageIdToExecutorPlacementHints.put(stageId,
+          (numTasksPending, hostToLocalTaskCountPerStage.toMap))
+
+        // Update the executor placement hints
+        updateExecutorPlacementHints()
       }
     }
 
@@ -534,6 +565,10 @@ private[spark] class ExecutorAllocationManager(
       allocationManager.synchronized {
         stageIdToNumTasks -= stageId
         stageIdToTaskIndices -= stageId
+        stageIdToExecutorPlacementHints -= stageId
+
+        // Update the executor placement hints
+        updateExecutorPlacementHints()
 
         // If this is the last stage with pending tasks, mark the scheduler queue as empty
         // This is needed in case the stage is aborted for any reason
@@ -637,6 +672,29 @@ private[spark] class ExecutorAllocationManager(
     def isExecutorIdle(executorId: String): Boolean = {
       !executorIdToTaskIds.contains(executorId)
     }
+
+    /**
+     * Update the Executor placement hints (the number of tasks with locality preferences,
+     * a map where each pair is a node and the number of tasks that would like to be scheduled
+     * on that node).
+     *
+     * These hints are updated when stages arrive and complete, so are not up-to-date at task
+     * granularity within stages.
+     */
+    def updateExecutorPlacementHints(): Unit = {
+      var localityAwareTasks = 0
+      val localityToCount = new mutable.HashMap[String, Int]()
+      stageIdToExecutorPlacementHints.values.foreach { case (numTasksPending, localities) =>
+        localityAwareTasks += numTasksPending
+        localities.foreach { case (hostname, count) =>
+          val updatedCount = localityToCount.getOrElse(hostname, 0) + count
+          localityToCount(hostname) = updatedCount
+        }
+      }
+
+      allocationManager.localityAwareTasks = localityAwareTasks
+      allocationManager.hostToLocalTaskCount = localityToCount.toMap
+    }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 6a6b94a271cfc..ac6ac6c216767 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1382,16 +1382,29 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   }
 
   /**
-   * Express a preference to the cluster manager for a given total number of executors.
-   * This can result in canceling pending requests or filing additional requests.
-   * This is currently only supported in YARN mode. Return whether the request is received.
-   */
-  private[spark] override def requestTotalExecutors(numExecutors: Int): Boolean = {
+   * Update the cluster manager on our scheduling needs. Three bits of information are included
+   * to help it make decisions.
+   * @param numExecutors The total number of executors we'd like to have. The cluster manager
+   *                     shouldn't kill any running executor to reach this number, but,
+   *                     if all existing executors were to die, this is the number of executors
+   *                     we'd want to be allocated.
+   * @param localityAwareTasks The number of tasks in all active stages that have a locality
+   *                           preferences. This includes running, pending, and completed tasks.
+   * @param hostToLocalTaskCount A map of hosts to the number of tasks from all active stages
+   *                             that would like to like to run on that host.
+   *                             This includes running, pending, and completed tasks.
+   * @return whether the request is acknowledged by the cluster manager.
+   */
+  private[spark] override def requestTotalExecutors(
+      numExecutors: Int,
+      localityAwareTasks: Int,
+      hostToLocalTaskCount: scala.collection.immutable.Map[String, Int]
+    ): Boolean = {
     assert(supportDynamicAllocation,
       "Requesting executors is currently only supported in YARN and Mesos modes")
     schedulerBackend match {
       case b: CoarseGrainedSchedulerBackend =>
-        b.requestTotalExecutors(numExecutors)
+        b.requestTotalExecutors(numExecutors, localityAwareTasks, hostToLocalTaskCount)
       case _ =>
         logWarning("Requesting executors is only supported in coarse-grained mode")
         false
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index b6a833bbb0833..cdf6078421123 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -790,8 +790,28 @@ class DAGScheduler(
     // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
     // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
     // event.
-    stage.makeNewStageAttempt(partitionsToCompute.size)
     outputCommitCoordinator.stageStart(stage.id)
+    val taskIdToLocations = try {
+      stage match {
+        case s: ShuffleMapStage =>
+          partitionsToCompute.map { id => (id, getPreferredLocs(stage.rdd, id))}.toMap
+        case s: ResultStage =>
+          val job = s.resultOfJob.get
+          partitionsToCompute.map { id =>
+            val p = job.partitions(id)
+            (id, getPreferredLocs(stage.rdd, p))
+          }.toMap
+      }
+    } catch {
+      case NonFatal(e) =>
+        stage.makeNewStageAttempt(partitionsToCompute.size)
+        listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
+        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}")
+        runningStages -= stage
+        return
+    }
+
+    stage.makeNewStageAttempt(partitionsToCompute.size, taskIdToLocations.values.toSeq)
     listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
 
     // TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
@@ -830,7 +850,7 @@ class DAGScheduler(
       stage match {
         case stage: ShuffleMapStage =>
           partitionsToCompute.map { id =>
-            val locs = getPreferredLocs(stage.rdd, id)
+            val locs = taskIdToLocations(id)
             val part = stage.rdd.partitions(id)
             new ShuffleMapTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs)
           }
@@ -840,7 +860,7 @@ class DAGScheduler(
           partitionsToCompute.map { id =>
             val p: Int = job.partitions(id)
             val part = stage.rdd.partitions(p)
-            val locs = getPreferredLocs(stage.rdd, p)
+            val locs = taskIdToLocations(id)
             new ResultTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs, id)
           }
       }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index b86724de2cb73..40a333a3e06b2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -77,8 +77,11 @@ private[spark] abstract class Stage(
   private var _latestInfo: StageInfo = StageInfo.fromStage(this, nextAttemptId)
 
   /** Creates a new attempt for this stage by creating a new StageInfo with a new attempt ID. */
-  def makeNewStageAttempt(numPartitionsToCompute: Int): Unit = {
-    _latestInfo = StageInfo.fromStage(this, nextAttemptId, Some(numPartitionsToCompute))
+  def makeNewStageAttempt(
+      numPartitionsToCompute: Int,
+      taskLocalityPreferences: Seq[Seq[TaskLocation]] = Seq.empty): Unit = {
+    _latestInfo = StageInfo.fromStage(
+      this, nextAttemptId, Some(numPartitionsToCompute), taskLocalityPreferences)
     nextAttemptId += 1
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
index 5d2abbc67e9d9..24796c14300b1 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
@@ -34,7 +34,8 @@ class StageInfo(
     val numTasks: Int,
     val rddInfos: Seq[RDDInfo],
     val parentIds: Seq[Int],
-    val details: String) {
+    val details: String,
+    private[spark] val taskLocalityPreferences: Seq[Seq[TaskLocation]] = Seq.empty) {
   /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */
   var submissionTime: Option[Long] = None
   /** Time when all tasks in the stage completed or when the stage was cancelled. */
@@ -70,7 +71,12 @@ private[spark] object StageInfo {
    * shuffle dependencies. Therefore, all ancestor RDDs related to this Stage's RDD through a
    * sequence of narrow dependencies should also be associated with this Stage.
    */
-  def fromStage(stage: Stage, attemptId: Int, numTasks: Option[Int] = None): StageInfo = {
+  def fromStage(
+      stage: Stage,
+      attemptId: Int,
+      numTasks: Option[Int] = None,
+      taskLocalityPreferences: Seq[Seq[TaskLocation]] = Seq.empty
+    ): StageInfo = {
     val ancestorRddInfos = stage.rdd.getNarrowAncestors.map(RDDInfo.fromRdd)
     val rddInfos = Seq(RDDInfo.fromRdd(stage.rdd)) ++ ancestorRddInfos
     new StageInfo(
@@ -80,6 +86,7 @@ private[spark] object StageInfo {
       numTasks.getOrElse(stage.numTasks),
       rddInfos,
       stage.parents.map(_.id),
-      stage.details)
+      stage.details,
+      taskLocalityPreferences)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
index 4be1eda2e9291..06f5438433b6e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
@@ -86,7 +86,11 @@ private[spark] object CoarseGrainedClusterMessages {
 
   // Request executors by specifying the new total number of executors desired
   // This includes executors already pending or running
-  case class RequestExecutors(requestedTotal: Int) extends CoarseGrainedClusterMessage
+  case class RequestExecutors(
+      requestedTotal: Int,
+      localityAwareTasks: Int,
+      hostToLocalTaskCount: Map[String, Int])
+    extends CoarseGrainedClusterMessage
 
   case class KillExecutors(executorIds: Seq[String]) extends CoarseGrainedClusterMessage
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index c65b3e517773e..660702f6e6fd0 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -66,6 +66,12 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
   // Executors we have requested the cluster manager to kill that have not died yet
   private val executorsPendingToRemove = new HashSet[String]
 
+  // A map to store hostname with its possible task number running on it
+  protected var hostToLocalTaskCount: Map[String, Int] = Map.empty
+
+  // The number of pending tasks which is locality required
+  protected var localityAwareTasks = 0
+
   class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)])
     extends ThreadSafeRpcEndpoint with Logging {
 
@@ -339,6 +345,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
     logInfo(s"Requesting $numAdditionalExecutors additional executor(s) from the cluster manager")
     logDebug(s"Number of pending executors is now $numPendingExecutors")
+
     numPendingExecutors += numAdditionalExecutors
     // Account for executors pending to be added or removed
     val newTotal = numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size
@@ -346,16 +353,33 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
   }
 
   /**
-   * Express a preference to the cluster manager for a given total number of executors. This can
-   * result in canceling pending requests or filing additional requests.
-   * @return whether the request is acknowledged.
+   * Update the cluster manager on our scheduling needs. Three bits of information are included
+   * to help it make decisions.
+   * @param numExecutors The total number of executors we'd like to have. The cluster manager
+   *                     shouldn't kill any running executor to reach this number, but,
+   *                     if all existing executors were to die, this is the number of executors
+   *                     we'd want to be allocated.
+   * @param localityAwareTasks The number of tasks in all active stages that have a locality
+   *                           preferences. This includes running, pending, and completed tasks.
+   * @param hostToLocalTaskCount A map of hosts to the number of tasks from all active stages
+   *                             that would like to like to run on that host.
+   *                             This includes running, pending, and completed tasks.
+   * @return whether the request is acknowledged by the cluster manager.
    */
-  final override def requestTotalExecutors(numExecutors: Int): Boolean = synchronized {
+  final override def requestTotalExecutors(
+      numExecutors: Int,
+      localityAwareTasks: Int,
+      hostToLocalTaskCount: Map[String, Int]
+    ): Boolean = synchronized {
     if (numExecutors < 0) {
       throw new IllegalArgumentException(
         "Attempted to request a negative number of executor(s) " +
           s"$numExecutors from the cluster manager. Please specify a positive number!")
     }
+
+    this.localityAwareTasks = localityAwareTasks
+    this.hostToLocalTaskCount = hostToLocalTaskCount
+
     numPendingExecutors =
       math.max(numExecutors - numExistingExecutors + executorsPendingToRemove.size, 0)
     doRequestTotalExecutors(numExecutors)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
index bc67abb5df446..074282d1be37d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
@@ -53,7 +53,8 @@ private[spark] abstract class YarnSchedulerBackend(
    * This includes executors already pending or running.
    */
   override def doRequestTotalExecutors(requestedTotal: Int): Boolean = {
-    yarnSchedulerEndpoint.askWithRetry[Boolean](RequestExecutors(requestedTotal))
+    yarnSchedulerEndpoint.askWithRetry[Boolean](
+      RequestExecutors(requestedTotal, localityAwareTasks, hostToLocalTaskCount))
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
index 803e1831bb269..34caca892891c 100644
--- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
@@ -751,6 +751,42 @@ class ExecutorAllocationManagerSuite
     assert(numExecutorsTarget(manager) === 2)
   }
 
+  test("get pending task number and related locality preference") {
+    sc = createSparkContext(2, 5, 3)
+    val manager = sc.executorAllocationManager.get
+
+    val localityPreferences1 = Seq(
+      Seq(TaskLocation("host1"), TaskLocation("host2"), TaskLocation("host3")),
+      Seq(TaskLocation("host1"), TaskLocation("host2"), TaskLocation("host4")),
+      Seq(TaskLocation("host2"), TaskLocation("host3"), TaskLocation("host4")),
+      Seq.empty,
+      Seq.empty
+    )
+    val stageInfo1 = createStageInfo(1, 5, localityPreferences1)
+    sc.listenerBus.postToAll(SparkListenerStageSubmitted(stageInfo1))
+
+    assert(localityAwareTasks(manager) === 3)
+    assert(hostToLocalTaskCount(manager) ===
+      Map("host1" -> 2, "host2" -> 3, "host3" -> 2, "host4" -> 2))
+
+    val localityPreferences2 = Seq(
+      Seq(TaskLocation("host2"), TaskLocation("host3"), TaskLocation("host5")),
+      Seq(TaskLocation("host3"), TaskLocation("host4"), TaskLocation("host5")),
+      Seq.empty
+    )
+    val stageInfo2 = createStageInfo(2, 3, localityPreferences2)
+    sc.listenerBus.postToAll(SparkListenerStageSubmitted(stageInfo2))
+
+    assert(localityAwareTasks(manager) === 5)
+    assert(hostToLocalTaskCount(manager) ===
+      Map("host1" -> 2, "host2" -> 4, "host3" -> 4, "host4" -> 3, "host5" -> 2))
+
+    sc.listenerBus.postToAll(SparkListenerStageCompleted(stageInfo1))
+    assert(localityAwareTasks(manager) === 2)
+    assert(hostToLocalTaskCount(manager) ===
+      Map("host2" -> 1, "host3" -> 2, "host4" -> 1, "host5" -> 2))
+  }
+
   private def createSparkContext(
       minExecutors: Int = 1,
       maxExecutors: Int = 5,
@@ -784,8 +820,13 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester {
   private val sustainedSchedulerBacklogTimeout = 2L
   private val executorIdleTimeout = 3L
 
-  private def createStageInfo(stageId: Int, numTasks: Int): StageInfo = {
-    new StageInfo(stageId, 0, "name", numTasks, Seq.empty, Seq.empty, "no details")
+  private def createStageInfo(
+      stageId: Int,
+      numTasks: Int,
+      taskLocalityPreferences: Seq[Seq[TaskLocation]] = Seq.empty
+    ): StageInfo = {
+    new StageInfo(
+      stageId, 0, "name", numTasks, Seq.empty, Seq.empty, "no details", taskLocalityPreferences)
   }
 
   private def createTaskInfo(taskId: Int, taskIndex: Int, executorId: String): TaskInfo = {
@@ -815,6 +856,8 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester {
   private val _onSchedulerQueueEmpty = PrivateMethod[Unit]('onSchedulerQueueEmpty)
   private val _onExecutorIdle = PrivateMethod[Unit]('onExecutorIdle)
   private val _onExecutorBusy = PrivateMethod[Unit]('onExecutorBusy)
+  private val _localityAwareTasks = PrivateMethod[Int]('localityAwareTasks)
+  private val _hostToLocalTaskCount = PrivateMethod[Map[String, Int]]('hostToLocalTaskCount)
 
   private def numExecutorsToAdd(manager: ExecutorAllocationManager): Int = {
     manager invokePrivate _numExecutorsToAdd()
@@ -885,4 +928,12 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester {
   private def onExecutorBusy(manager: ExecutorAllocationManager, id: String): Unit = {
     manager invokePrivate _onExecutorBusy(id)
   }
+
+  private def localityAwareTasks(manager: ExecutorAllocationManager): Int = {
+    manager invokePrivate _localityAwareTasks()
+  }
+
+  private def hostToLocalTaskCount(manager: ExecutorAllocationManager): Map[String, Int] = {
+    manager invokePrivate _hostToLocalTaskCount()
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
index 5a2670e4d1cf0..139b8dc25f4b4 100644
--- a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
+++ b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala
@@ -182,7 +182,7 @@ class HeartbeatReceiverSuite
 
     // Adjust the target number of executors on the cluster manager side
     assert(fakeClusterManager.getTargetNumExecutors === 0)
-    sc.requestTotalExecutors(2)
+    sc.requestTotalExecutors(2, 0, Map.empty)
     assert(fakeClusterManager.getTargetNumExecutors === 2)
     assert(fakeClusterManager.getExecutorIdsToKill.isEmpty)
 
@@ -241,7 +241,8 @@ private class FakeSchedulerBackend(
   extends CoarseGrainedSchedulerBackend(scheduler, rpcEnv) {
 
   protected override def doRequestTotalExecutors(requestedTotal: Int): Boolean = {
-    clusterManagerEndpoint.askWithRetry[Boolean](RequestExecutors(requestedTotal))
+    clusterManagerEndpoint.askWithRetry[Boolean](
+      RequestExecutors(requestedTotal, localityAwareTasks, hostToLocalTaskCount))
   }
 
   protected override def doKillExecutors(executorIds: Seq[String]): Boolean = {
@@ -260,7 +261,7 @@ private class FakeClusterManager(override val rpcEnv: RpcEnv) extends RpcEndpoin
   def getExecutorIdsToKill: Set[String] = executorIdsToKill.toSet
 
   override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
-    case RequestExecutors(requestedTotal) =>
+    case RequestExecutors(requestedTotal, _, _) =>
       targetNumExecutors = requestedTotal
       context.reply(true)
     case KillExecutors(executorIds) =>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 83dafa4a125d2..44acc7374d024 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -555,11 +555,12 @@ private[spark] class ApplicationMaster(
     }
 
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
-      case RequestExecutors(requestedTotal) =>
+      case RequestExecutors(requestedTotal, localityAwareTasks, hostToLocalTaskCount) =>
         Option(allocator) match {
           case Some(a) =>
             allocatorLock.synchronized {
-              if (a.requestTotalExecutors(requestedTotal)) {
+              if (a.requestTotalExecutorsWithPreferredLocalities(requestedTotal,
+                localityAwareTasks, hostToLocalTaskCount)) {
                 allocatorLock.notifyAll()
               }
             }
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/LocalityPreferredContainerPlacementStrategy.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/LocalityPreferredContainerPlacementStrategy.scala
new file mode 100644
index 0000000000000..081780204e424
--- /dev/null
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/LocalityPreferredContainerPlacementStrategy.scala
@@ -0,0 +1,182 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import scala.collection.mutable.{ArrayBuffer, HashMap, Set}
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.yarn.api.records.{ContainerId, Resource}
+import org.apache.hadoop.yarn.util.RackResolver
+
+import org.apache.spark.SparkConf
+
+private[yarn] case class ContainerLocalityPreferences(nodes: Array[String], racks: Array[String])
+
+/**
+ * This strategy is calculating the optimal locality preferences of YARN containers by considering
+ * the node ratio of pending tasks, number of required cores/containers and and locality of current
+ * existing containers. The target of this algorithm is to maximize the number of tasks that
+ * would run locally.
+ *
+ * Consider a situation in which we have 20 tasks that require (host1, host2, host3)
+ * and 10 tasks that require (host1, host2, host4), besides each container has 2 cores
+ * and cpus per task is 1, so the required container number is 15,
+ * and host ratio is (host1: 30, host2: 30, host3: 20, host4: 10).
+ *
+ * 1. If requested container number (18) is more than the required container number (15):
+ *
+ * requests for 5 containers with nodes: (host1, host2, host3, host4)
+ * requests for 5 containers with nodes: (host1, host2, host3)
+ * requests for 5 containers with nodes: (host1, host2)
+ * requests for 3 containers with no locality preferences.
+ *
+ * The placement ratio is 3 : 3 : 2 : 1, and set the additional containers with no locality
+ * preferences.
+ *
+ * 2. If requested container number (10) is less than or equal to the required container number
+ * (15):
+ *
+ * requests for 4 containers with nodes: (host1, host2, host3, host4)
+ * requests for 3 containers with nodes: (host1, host2, host3)
+ * requests for 3 containers with nodes: (host1, host2)
+ *
+ * The placement ratio is 10 : 10 : 7 : 4, close to expected ratio (3 : 3 : 2 : 1)
+ *
+ * 3. If containers exist but none of them can match the requested localities,
+ * follow the method of 1 and 2.
+ *
+ * 4. If containers exist and some of them can match the requested localities.
+ * For example if we have 1 containers on each node (host1: 1, host2: 1: host3: 1, host4: 1),
+ * and the expected containers on each node would be (host1: 5, host2: 5, host3: 4, host4: 2),
+ * so the newly requested containers on each node would be updated to (host1: 4, host2: 4,
+ * host3: 3, host4: 1), 12 containers by total.
+ *
+ *   4.1 If requested container number (18) is more than newly required containers (12). Follow
+ *   method 1 with updated ratio 4 : 4 : 3 : 1.
+ *
+ *   4.2 If request container number (10) is more than newly required containers (12). Follow
+ *   method 2 with updated ratio 4 : 4 : 3 : 1.
+ *
+ * 5. If containers exist and existing localities can fully cover the requested localities.
+ * For example if we have 5 containers on each node (host1: 5, host2: 5, host3: 5, host4: 5),
+ * which could cover the current requested localities. This algorithm will allocate all the
+ * requested containers with no localities.
+ */
+private[yarn] class LocalityPreferredContainerPlacementStrategy(
+    val sparkConf: SparkConf,
+    val yarnConf: Configuration,
+    val resource: Resource) {
+
+  // Number of CPUs per task
+  private val CPUS_PER_TASK = sparkConf.getInt("spark.task.cpus", 1)
+
+  /**
+   * Calculate each container's node locality and rack locality
+   * @param numContainer number of containers to calculate
+   * @param numLocalityAwareTasks number of locality required tasks
+   * @param hostToLocalTaskCount a map to store the preferred hostname and possible task
+   *                             numbers running on it, used as hints for container allocation
+   * @return node localities and rack localities, each locality is an array of string,
+   *         the length of localities is the same as number of containers
+   */
+  def localityOfRequestedContainers(
+      numContainer: Int,
+      numLocalityAwareTasks: Int,
+      hostToLocalTaskCount: Map[String, Int],
+      allocatedHostToContainersMap: HashMap[String, Set[ContainerId]]
+    ): Array[ContainerLocalityPreferences] = {
+    val updatedHostToContainerCount = expectedHostToContainerCount(
+      numLocalityAwareTasks, hostToLocalTaskCount, allocatedHostToContainersMap)
+    val updatedLocalityAwareContainerNum = updatedHostToContainerCount.values.sum
+
+    // The number of containers to allocate, divided into two groups, one with preferred locality,
+    // and the other without locality preference.
+    val requiredLocalityFreeContainerNum =
+      math.max(0, numContainer - updatedLocalityAwareContainerNum)
+    val requiredLocalityAwareContainerNum = numContainer - requiredLocalityFreeContainerNum
+
+    val containerLocalityPreferences = ArrayBuffer[ContainerLocalityPreferences]()
+    if (requiredLocalityFreeContainerNum > 0) {
+      for (i <- 0 until requiredLocalityFreeContainerNum) {
+        containerLocalityPreferences += ContainerLocalityPreferences(
+          null.asInstanceOf[Array[String]], null.asInstanceOf[Array[String]])
+      }
+    }
+
+    if (requiredLocalityAwareContainerNum > 0) {
+      val largestRatio = updatedHostToContainerCount.values.max
+      // Round the ratio of preferred locality to the number of locality required container
+      // number, which is used for locality preferred host calculating.
+      var preferredLocalityRatio = updatedHostToContainerCount.mapValues { ratio =>
+        val adjustedRatio = ratio.toDouble * requiredLocalityAwareContainerNum / largestRatio
+        adjustedRatio.ceil.toInt
+      }
+
+      for (i <- 0 until requiredLocalityAwareContainerNum) {
+        // Only filter out the ratio which is larger than 0, which means the current host can
+        // still be allocated with new container request.
+        val hosts = preferredLocalityRatio.filter(_._2 > 0).keys.toArray
+        val racks = hosts.map { h =>
+          RackResolver.resolve(yarnConf, h).getNetworkLocation
+        }.toSet
+        containerLocalityPreferences += ContainerLocalityPreferences(hosts, racks.toArray)
+
+        // Minus 1 each time when the host is used. When the current ratio is 0,
+        // which means all the required ratio is satisfied, this host will not be allocated again.
+        preferredLocalityRatio = preferredLocalityRatio.mapValues(_ - 1)
+      }
+    }
+
+    containerLocalityPreferences.toArray
+  }
+
+  /**
+   * Calculate the number of executors need to satisfy the given number of pending tasks.
+   */
+  private def numExecutorsPending(numTasksPending: Int): Int = {
+    val coresPerExecutor = resource.getVirtualCores
+    (numTasksPending * CPUS_PER_TASK + coresPerExecutor - 1) / coresPerExecutor
+  }
+
+  /**
+   * Calculate the expected host to number of containers by considering with allocated containers.
+   * @param localityAwareTasks number of locality aware tasks
+   * @param hostToLocalTaskCount a map to store the preferred hostname and possible task
+   *                             numbers running on it, used as hints for container allocation
+   * @return a map with hostname as key and required number of containers on this host as value
+   */
+  private def expectedHostToContainerCount(
+      localityAwareTasks: Int,
+      hostToLocalTaskCount: Map[String, Int],
+      allocatedHostToContainersMap: HashMap[String, Set[ContainerId]]
+    ): Map[String, Int] = {
+    val totalLocalTaskNum = hostToLocalTaskCount.values.sum
+    hostToLocalTaskCount.map { case (host, count) =>
+      val expectedCount =
+        count.toDouble * numExecutorsPending(localityAwareTasks) / totalLocalTaskNum
+      val existedCount = allocatedHostToContainersMap.get(host)
+        .map(_.size)
+        .getOrElse(0)
+
+      // If existing container can not fully satisfy the expected number of container,
+      // the required container number is expected count minus existed count. Otherwise the
+      // required container number is 0.
+      (host, math.max(0, (expectedCount - existedCount).ceil.toInt))
+    }
+  }
+}
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 940873fbd046c..6c103394af098 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -96,7 +96,7 @@ private[yarn] class YarnAllocator(
   // Number of cores per executor.
   protected val executorCores = args.executorCores
   // Resource capability requested for each executors
-  private val resource = Resource.newInstance(executorMemory + memoryOverhead, executorCores)
+  private[yarn] val resource = Resource.newInstance(executorMemory + memoryOverhead, executorCores)
 
   private val launcherPool = new ThreadPoolExecutor(
     // max pool size of Integer.MAX_VALUE is ignored because we use an unbounded queue
@@ -127,6 +127,16 @@ private[yarn] class YarnAllocator(
     }
   }
 
+  // A map to store preferred hostname and possible task numbers running on it.
+  private var hostToLocalTaskCounts: Map[String, Int] = Map.empty
+
+  // Number of tasks that have locality preferences in active stages
+  private var numLocalityAwareTasks: Int = 0
+
+  // A container placement strategy based on pending tasks' locality preference
+  private[yarn] val containerPlacementStrategy =
+    new LocalityPreferredContainerPlacementStrategy(sparkConf, conf, resource)
+
   def getNumExecutorsRunning: Int = numExecutorsRunning
 
   def getNumExecutorsFailed: Int = numExecutorsFailed
@@ -146,10 +156,19 @@ private[yarn] class YarnAllocator(
    * Request as many executors from the ResourceManager as needed to reach the desired total. If
    * the requested total is smaller than the current number of running executors, no executors will
    * be killed.
-   *
+   * @param requestedTotal total number of containers requested
+   * @param localityAwareTasks number of locality aware tasks to be used as container placement hint
+   * @param hostToLocalTaskCount a map of preferred hostname to possible task counts to be used as
+   *                             container placement hint.
    * @return Whether the new requested total is different than the old value.
    */
-  def requestTotalExecutors(requestedTotal: Int): Boolean = synchronized {
+  def requestTotalExecutorsWithPreferredLocalities(
+      requestedTotal: Int,
+      localityAwareTasks: Int,
+      hostToLocalTaskCount: Map[String, Int]): Boolean = synchronized {
+    this.numLocalityAwareTasks = localityAwareTasks
+    this.hostToLocalTaskCounts = hostToLocalTaskCount
+
     if (requestedTotal != targetNumExecutors) {
       logInfo(s"Driver requested a total number of $requestedTotal executor(s).")
       targetNumExecutors = requestedTotal
@@ -221,12 +240,20 @@ private[yarn] class YarnAllocator(
     val numPendingAllocate = getNumPendingAllocate
     val missing = targetNumExecutors - numPendingAllocate - numExecutorsRunning
 
+    // TODO. Consider locality preferences of pending container requests.
+    // Since the last time we made container requests, stages have completed and been submitted,
+    // and that the localities at which we requested our pending executors
+    // no longer apply to our current needs. We should consider to remove all outstanding
+    // container requests and add requests anew each time to avoid this.
     if (missing > 0) {
       logInfo(s"Will request $missing executor containers, each with ${resource.getVirtualCores} " +
         s"cores and ${resource.getMemory} MB memory including $memoryOverhead MB overhead")
 
-      for (i <- 0 until missing) {
-        val request = createContainerRequest(resource)
+      val containerLocalityPreferences = containerPlacementStrategy.localityOfRequestedContainers(
+        missing, numLocalityAwareTasks, hostToLocalTaskCounts, allocatedHostToContainersMap)
+
+      for (locality <- containerLocalityPreferences) {
+        val request = createContainerRequest(resource, locality.nodes, locality.racks)
         amClient.addContainerRequest(request)
         val nodes = request.getNodes
         val hostStr = if (nodes == null || nodes.isEmpty) "Any" else nodes.last
@@ -249,11 +276,14 @@ private[yarn] class YarnAllocator(
    * Creates a container request, handling the reflection required to use YARN features that were
    * added in recent versions.
    */
-  private def createContainerRequest(resource: Resource): ContainerRequest = {
+  protected def createContainerRequest(
+      resource: Resource,
+      nodes: Array[String],
+      racks: Array[String]): ContainerRequest = {
     nodeLabelConstructor.map { constructor =>
-      constructor.newInstance(resource, null, null, RM_REQUEST_PRIORITY, true: java.lang.Boolean,
+      constructor.newInstance(resource, nodes, racks, RM_REQUEST_PRIORITY, true: java.lang.Boolean,
         labelExpression.orNull)
-    }.getOrElse(new ContainerRequest(resource, null, null, RM_REQUEST_PRIORITY))
+    }.getOrElse(new ContainerRequest(resource, nodes, racks, RM_REQUEST_PRIORITY))
   }
 
   /**
@@ -437,7 +467,6 @@ private[yarn] class YarnAllocator(
     releasedContainers.add(container.getId())
     amClient.releaseAssignedContainer(container.getId())
   }
-
 }
 
 private object YarnAllocator {
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ContainerPlacementStrategySuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ContainerPlacementStrategySuite.scala
new file mode 100644
index 0000000000000..b7fe4ccc67a38
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ContainerPlacementStrategySuite.scala
@@ -0,0 +1,125 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import org.scalatest.{BeforeAndAfterEach, Matchers}
+
+import org.apache.spark.SparkFunSuite
+
+class ContainerPlacementStrategySuite extends SparkFunSuite with Matchers with BeforeAndAfterEach {
+
+  private val yarnAllocatorSuite = new YarnAllocatorSuite
+  import yarnAllocatorSuite._
+
+  override def beforeEach() {
+    yarnAllocatorSuite.beforeEach()
+  }
+
+  override def afterEach() {
+    yarnAllocatorSuite.afterEach()
+  }
+
+  test("allocate locality preferred containers with enough resource and no matched existed " +
+    "containers") {
+    // 1. All the locations of current containers cannot satisfy the new requirements
+    // 2. Current requested container number can fully satisfy the pending tasks.
+
+    val handler = createAllocator(2)
+    handler.updateResourceRequests()
+    handler.handleAllocatedContainers(Array(createContainer("host1"), createContainer("host2")))
+
+    val localities = handler.containerPlacementStrategy.localityOfRequestedContainers(
+      3, 15, Map("host3" -> 15, "host4" -> 15, "host5" -> 10), handler.allocatedHostToContainersMap)
+
+    assert(localities.map(_.nodes) === Array(
+      Array("host3", "host4", "host5"),
+      Array("host3", "host4", "host5"),
+      Array("host3", "host4")))
+  }
+
+  test("allocate locality preferred containers with enough resource and partially matched " +
+    "containers") {
+    // 1. Parts of current containers' locations can satisfy the new requirements
+    // 2. Current requested container number can fully satisfy the pending tasks.
+
+    val handler = createAllocator(3)
+    handler.updateResourceRequests()
+    handler.handleAllocatedContainers(Array(
+      createContainer("host1"),
+      createContainer("host1"),
+      createContainer("host2")
+    ))
+
+    val localities = handler.containerPlacementStrategy.localityOfRequestedContainers(
+      3, 15, Map("host1" -> 15, "host2" -> 15, "host3" -> 10), handler.allocatedHostToContainersMap)
+
+    assert(localities.map(_.nodes) ===
+      Array(null, Array("host2", "host3"), Array("host2", "host3")))
+  }
+
+  test("allocate locality preferred containers with limited resource and partially matched " +
+    "containers") {
+    // 1. Parts of current containers' locations can satisfy the new requirements
+    // 2. Current requested container number cannot fully satisfy the pending tasks.
+
+    val handler = createAllocator(3)
+    handler.updateResourceRequests()
+    handler.handleAllocatedContainers(Array(
+      createContainer("host1"),
+      createContainer("host1"),
+      createContainer("host2")
+    ))
+
+    val localities = handler.containerPlacementStrategy.localityOfRequestedContainers(
+      1, 15, Map("host1" -> 15, "host2" -> 15, "host3" -> 10), handler.allocatedHostToContainersMap)
+
+    assert(localities.map(_.nodes) === Array(Array("host2", "host3")))
+  }
+
+  test("allocate locality preferred containers with fully matched containers") {
+    // Current containers' locations can fully satisfy the new requirements
+
+    val handler = createAllocator(5)
+    handler.updateResourceRequests()
+    handler.handleAllocatedContainers(Array(
+      createContainer("host1"),
+      createContainer("host1"),
+      createContainer("host2"),
+      createContainer("host2"),
+      createContainer("host3")
+    ))
+
+    val localities = handler.containerPlacementStrategy.localityOfRequestedContainers(
+      3, 15, Map("host1" -> 15, "host2" -> 15, "host3" -> 10), handler.allocatedHostToContainersMap)
+
+    assert(localities.map(_.nodes) === Array(null, null, null))
+  }
+
+  test("allocate containers with no locality preference") {
+    // Request new container without locality preference
+
+    val handler = createAllocator(2)
+    handler.updateResourceRequests()
+    handler.handleAllocatedContainers(Array(createContainer("host1"), createContainer("host2")))
+
+    val localities = handler.containerPlacementStrategy.localityOfRequestedContainers(
+      1, 0, Map.empty, handler.allocatedHostToContainersMap)
+
+    assert(localities.map(_.nodes) === Array(null))
+  }
+}
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
index 7509000771d94..37a789fcd375b 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
@@ -25,6 +25,7 @@ import org.apache.hadoop.net.DNSToSwitchMapping
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
+import org.scalatest.{BeforeAndAfterEach, Matchers}
 
 import org.apache.spark.{SecurityManager, SparkFunSuite}
 import org.apache.spark.SparkConf
@@ -32,8 +33,6 @@ import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
 import org.apache.spark.deploy.yarn.YarnAllocator._
 import org.apache.spark.scheduler.SplitInfo
 
-import org.scalatest.{BeforeAndAfterEach, Matchers}
-
 class MockResolver extends DNSToSwitchMapping {
 
   override def resolve(names: JList[String]): JList[String] = {
@@ -171,7 +170,7 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
     handler.getNumExecutorsRunning should be (0)
     handler.getNumPendingAllocate should be (4)
 
-    handler.requestTotalExecutors(3)
+    handler.requestTotalExecutorsWithPreferredLocalities(3, 0, Map.empty)
     handler.updateResourceRequests()
     handler.getNumPendingAllocate should be (3)
 
@@ -182,7 +181,7 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
     handler.allocatedContainerToHostMap.get(container.getId).get should be ("host1")
     handler.allocatedHostToContainersMap.get("host1").get should contain (container.getId)
 
-    handler.requestTotalExecutors(2)
+    handler.requestTotalExecutorsWithPreferredLocalities(2, 0, Map.empty)
     handler.updateResourceRequests()
     handler.getNumPendingAllocate should be (1)
   }
@@ -193,7 +192,7 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
     handler.getNumExecutorsRunning should be (0)
     handler.getNumPendingAllocate should be (4)
 
-    handler.requestTotalExecutors(3)
+    handler.requestTotalExecutorsWithPreferredLocalities(3, 0, Map.empty)
     handler.updateResourceRequests()
     handler.getNumPendingAllocate should be (3)
 
@@ -203,7 +202,7 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
 
     handler.getNumExecutorsRunning should be (2)
 
-    handler.requestTotalExecutors(1)
+    handler.requestTotalExecutorsWithPreferredLocalities(1, 0, Map.empty)
     handler.updateResourceRequests()
     handler.getNumPendingAllocate should be (0)
     handler.getNumExecutorsRunning should be (2)
@@ -219,7 +218,7 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
     val container2 = createContainer("host2")
     handler.handleAllocatedContainers(Array(container1, container2))
 
-    handler.requestTotalExecutors(1)
+    handler.requestTotalExecutorsWithPreferredLocalities(1, 0, Map.empty)
     handler.executorIdToContainer.keys.foreach { id => handler.killExecutor(id ) }
 
     val statuses = Seq(container1, container2).map { c =>
@@ -241,5 +240,4 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
     assert(vmemMsg.contains("5.8 GB of 4.2 GB virtual memory used."))
     assert(pmemMsg.contains("2.1 MB of 2 GB physical memory used."))
   }
-
 }

From dafe8d857dff4c61981476282cbfe11f5c008078 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 27 Jul 2015 15:49:42 -0700
Subject: [PATCH 0631/1454] [SPARK-9385] [PYSPARK] Enable PEP8 but disable
 installing pylint.

Instead of disabling all python style check, we should enable PEP8. So, this PR just comments out the part installing pylint.

Author: Yin Huai <yhuai@databricks.com>

Closes #7704 from yhuai/SPARK-9385 and squashes the following commits:

0056359 [Yin Huai] Enable PEP8 but disable installing pylint.
---
 dev/lint-python  | 30 +++++++++++++++---------------
 dev/run-tests.py |  5 ++---
 2 files changed, 17 insertions(+), 18 deletions(-)

diff --git a/dev/lint-python b/dev/lint-python
index 53bccc1fab535..575dbb0ae321b 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -58,21 +58,21 @@ export "PYTHONPATH=$SPARK_ROOT_DIR/dev/pylint"
 export "PYLINT_HOME=$PYTHONPATH"
 export "PATH=$PYTHONPATH:$PATH"
 
-if [ ! -d "$PYLINT_HOME" ]; then
-    mkdir "$PYLINT_HOME"
-    # Redirect the annoying pylint installation output.
-    easy_install -d "$PYLINT_HOME" pylint==1.4.4 &>> "$PYLINT_INSTALL_INFO"
-    easy_install_status="$?"
-
-    if [ "$easy_install_status" -ne 0 ]; then
-        echo "Unable to install pylint locally in \"$PYTHONPATH\"."
-        cat "$PYLINT_INSTALL_INFO"
-        exit "$easy_install_status"
-    fi
-
-    rm "$PYLINT_INSTALL_INFO"
-
-fi
+# if [ ! -d "$PYLINT_HOME" ]; then
+#     mkdir "$PYLINT_HOME"
+#     # Redirect the annoying pylint installation output.
+#     easy_install -d "$PYLINT_HOME" pylint==1.4.4 &>> "$PYLINT_INSTALL_INFO"
+#     easy_install_status="$?"
+#
+#     if [ "$easy_install_status" -ne 0 ]; then
+#         echo "Unable to install pylint locally in \"$PYTHONPATH\"."
+#         cat "$PYLINT_INSTALL_INFO"
+#         exit "$easy_install_status"
+#     fi
+#
+#     rm "$PYLINT_INSTALL_INFO"
+#
+# fi
 
 # There is no need to write this output to a file
 #+ first, but we do so so that the check status can
diff --git a/dev/run-tests.py b/dev/run-tests.py
index d1cb66860b3f8..1f0d218514f92 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -198,9 +198,8 @@ def run_scala_style_checks():
 
 
 def run_python_style_checks():
-    # set_title_and_block("Running Python style checks", "BLOCK_PYTHON_STYLE")
-    # run_cmd([os.path.join(SPARK_HOME, "dev", "lint-python")])
-    pass
+    set_title_and_block("Running Python style checks", "BLOCK_PYTHON_STYLE")
+    run_cmd([os.path.join(SPARK_HOME, "dev", "lint-python")])
 
 
 def build_spark_documentation():

From 8ddfa52c208bf329c2b2c8909c6be04301e36083 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Mon, 27 Jul 2015 17:17:49 -0700
Subject: [PATCH 0632/1454] [SPARK-9230] [ML] Support StringType features in
 RFormula

This adds StringType feature support via OneHotEncoder. As part of this task it was necessary to change RFormula to an Estimator, so that factor levels could be determined from the training dataset.

Not sure if I am using uids correctly here, would be good to get reviewer help on that.
cc mengxr

Umbrella design doc: https://docs.google.com/document/d/10NZNSEurN2EdWM31uFYsgayIPfCFHiuIu3pCWrUmP_c/edit#

Author: Eric Liang <ekl@databricks.com>

Closes #7574 from ericl/string-features and squashes the following commits:

f99131a [Eric Liang] comments
0bf3c26 [Eric Liang] update docs
c302a2c [Eric Liang] fix tests
9d1ac82 [Eric Liang] Merge remote-tracking branch 'upstream/master' into string-features
e713da3 [Eric Liang] comments
4d79193 [Eric Liang] revert to seq + distinct
169a085 [Eric Liang] tweak functional test
a230a47 [Eric Liang] Merge branch 'master' into string-features
72bd6f3 [Eric Liang] fix merge
d841cec [Eric Liang] Merge branch 'master' into string-features
5b2c4a2 [Eric Liang] Mon Jul 20 18:45:33 PDT 2015
b01c7c5 [Eric Liang] add test
8a637db [Eric Liang] encoder wip
a1d03f4 [Eric Liang] refactor into estimator
---
 R/pkg/inst/tests/test_mllib.R                 |   6 +-
 .../apache/spark/ml/feature/RFormula.scala    | 133 ++++++++++++++----
 .../ml/feature/RFormulaParserSuite.scala      |   1 +
 .../spark/ml/feature/RFormulaSuite.scala      |  64 +++++----
 4 files changed, 142 insertions(+), 62 deletions(-)

diff --git a/R/pkg/inst/tests/test_mllib.R b/R/pkg/inst/tests/test_mllib.R
index a492763344ae6..29152a11688a2 100644
--- a/R/pkg/inst/tests/test_mllib.R
+++ b/R/pkg/inst/tests/test_mllib.R
@@ -35,8 +35,8 @@ test_that("glm and predict", {
 
 test_that("predictions match with native glm", {
   training <- createDataFrame(sqlContext, iris)
-  model <- glm(Sepal_Width ~ Sepal_Length, data = training)
+  model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
   vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Sepal.Length, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-9), rVals - vals)
+  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
 })
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index f7b46efa10e90..0a95b1ee8de6e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -17,17 +17,34 @@
 
 package org.apache.spark.ml.feature
 
+import scala.collection.mutable.ArrayBuffer
 import scala.util.parsing.combinator.RegexParsers
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.{Estimator, Model, Transformer, Pipeline, PipelineModel, PipelineStage}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol}
 import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 
+/**
+ * Base trait for [[RFormula]] and [[RFormulaModel]].
+ */
+private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol {
+  /** @group getParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group getParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
+
+  protected def hasLabelCol(schema: StructType): Boolean = {
+    schema.map(_.name).contains($(labelCol))
+  }
+}
+
 /**
  * :: Experimental ::
  * Implements the transforms required for fitting a dataset against an R model formula. Currently
@@ -35,8 +52,7 @@ import org.apache.spark.sql.types._
  * docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
  */
 @Experimental
-class RFormula(override val uid: String)
-  extends Transformer with HasFeaturesCol with HasLabelCol {
+class RFormula(override val uid: String) extends Estimator[RFormulaModel] with RFormulaBase {
 
   def this() = this(Identifiable.randomUID("rFormula"))
 
@@ -62,19 +78,74 @@ class RFormula(override val uid: String)
   /** @group getParam */
   def getFormula: String = $(formula)
 
-  /** @group getParam */
-  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+  override def fit(dataset: DataFrame): RFormulaModel = {
+    require(parsedFormula.isDefined, "Must call setFormula() first.")
+    // StringType terms and terms representing interactions need to be encoded before assembly.
+    // TODO(ekl) add support for feature interactions
+    var encoderStages = ArrayBuffer[PipelineStage]()
+    var tempColumns = ArrayBuffer[String]()
+    val encodedTerms = parsedFormula.get.terms.map { term =>
+      dataset.schema(term) match {
+        case column if column.dataType == StringType =>
+          val indexCol = term + "_idx_" + uid
+          val encodedCol = term + "_onehot_" + uid
+          encoderStages += new StringIndexer().setInputCol(term).setOutputCol(indexCol)
+          encoderStages += new OneHotEncoder().setInputCol(indexCol).setOutputCol(encodedCol)
+          tempColumns += indexCol
+          tempColumns += encodedCol
+          encodedCol
+        case _ =>
+          term
+      }
+    }
+    encoderStages += new VectorAssembler(uid)
+      .setInputCols(encodedTerms.toArray)
+      .setOutputCol($(featuresCol))
+    encoderStages += new ColumnPruner(tempColumns.toSet)
+    val pipelineModel = new Pipeline(uid).setStages(encoderStages.toArray).fit(dataset)
+    copyValues(new RFormulaModel(uid, parsedFormula.get, pipelineModel).setParent(this))
+  }
 
-  /** @group getParam */
-  def setLabelCol(value: String): this.type = set(labelCol, value)
+  // optimistic schema; does not contain any ML attributes
+  override def transformSchema(schema: StructType): StructType = {
+    if (hasLabelCol(schema)) {
+      StructType(schema.fields :+ StructField($(featuresCol), new VectorUDT, true))
+    } else {
+      StructType(schema.fields :+ StructField($(featuresCol), new VectorUDT, true) :+
+        StructField($(labelCol), DoubleType, true))
+    }
+  }
+
+  override def copy(extra: ParamMap): RFormula = defaultCopy(extra)
+
+  override def toString: String = s"RFormula(${get(formula)})"
+}
+
+/**
+ * :: Experimental ::
+ * A fitted RFormula. Fitting is required to determine the factor levels of formula terms.
+ * @param parsedFormula a pre-parsed R formula.
+ * @param pipelineModel the fitted feature model, including factor to index mappings.
+ */
+@Experimental
+class RFormulaModel private[feature](
+    override val uid: String,
+    parsedFormula: ParsedRFormula,
+    pipelineModel: PipelineModel)
+  extends Model[RFormulaModel] with RFormulaBase {
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    checkCanTransform(dataset.schema)
+    transformLabel(pipelineModel.transform(dataset))
+  }
 
   override def transformSchema(schema: StructType): StructType = {
     checkCanTransform(schema)
-    val withFeatures = transformFeatures.transformSchema(schema)
+    val withFeatures = pipelineModel.transformSchema(schema)
     if (hasLabelCol(schema)) {
       withFeatures
-    } else if (schema.exists(_.name == parsedFormula.get.label)) {
-      val nullable = schema(parsedFormula.get.label).dataType match {
+    } else if (schema.exists(_.name == parsedFormula.label)) {
+      val nullable = schema(parsedFormula.label).dataType match {
         case _: NumericType | BooleanType => false
         case _ => true
       }
@@ -86,24 +157,19 @@ class RFormula(override val uid: String)
     }
   }
 
-  override def transform(dataset: DataFrame): DataFrame = {
-    checkCanTransform(dataset.schema)
-    transformLabel(transformFeatures.transform(dataset))
-  }
-
-  override def copy(extra: ParamMap): RFormula = defaultCopy(extra)
+  override def copy(extra: ParamMap): RFormulaModel = copyValues(
+    new RFormulaModel(uid, parsedFormula, pipelineModel))
 
-  override def toString: String = s"RFormula(${get(formula)})"
+  override def toString: String = s"RFormulaModel(${parsedFormula})"
 
   private def transformLabel(dataset: DataFrame): DataFrame = {
-    val labelName = parsedFormula.get.label
+    val labelName = parsedFormula.label
     if (hasLabelCol(dataset.schema)) {
       dataset
     } else if (dataset.schema.exists(_.name == labelName)) {
       dataset.schema(labelName).dataType match {
         case _: NumericType | BooleanType =>
           dataset.withColumn($(labelCol), dataset(labelName).cast(DoubleType))
-        // TODO(ekl) add support for string-type labels
         case other =>
           throw new IllegalArgumentException("Unsupported type for label: " + other)
       }
@@ -114,25 +180,32 @@ class RFormula(override val uid: String)
     }
   }
 
-  private def transformFeatures: Transformer = {
-    // TODO(ekl) add support for non-numeric features and feature interactions
-    new VectorAssembler(uid)
-      .setInputCols(parsedFormula.get.terms.toArray)
-      .setOutputCol($(featuresCol))
-  }
-
   private def checkCanTransform(schema: StructType) {
-    require(parsedFormula.isDefined, "Must call setFormula() first.")
     val columnNames = schema.map(_.name)
     require(!columnNames.contains($(featuresCol)), "Features column already exists.")
     require(
       !columnNames.contains($(labelCol)) || schema($(labelCol)).dataType == DoubleType,
       "Label column already exists and is not of type DoubleType.")
   }
+}
 
-  private def hasLabelCol(schema: StructType): Boolean = {
-    schema.map(_.name).contains($(labelCol))
+/**
+ * Utility transformer for removing temporary columns from a DataFrame.
+ * TODO(ekl) make this a public transformer
+ */
+private class ColumnPruner(columnsToPrune: Set[String]) extends Transformer {
+  override val uid = Identifiable.randomUID("columnPruner")
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val columnsToKeep = dataset.columns.filter(!columnsToPrune.contains(_))
+    dataset.select(columnsToKeep.map(dataset.col) : _*)
   }
+
+  override def transformSchema(schema: StructType): StructType = {
+    StructType(schema.fields.filter(col => !columnsToPrune.contains(col.name)))
+  }
+
+  override def copy(extra: ParamMap): ColumnPruner = defaultCopy(extra)
 }
 
 /**
@@ -149,7 +222,7 @@ private[ml] object RFormulaParser extends RegexParsers {
   def expr: Parser[List[String]] = term ~ rep("+" ~> term) ^^ { case a ~ list => a :: list }
 
   def formula: Parser[ParsedRFormula] =
-    (term ~ "~" ~ expr) ^^ { case r ~ "~" ~ t => ParsedRFormula(r, t) }
+    (term ~ "~" ~ expr) ^^ { case r ~ "~" ~ t => ParsedRFormula(r, t.distinct) }
 
   def parse(value: String): ParsedRFormula = parseAll(formula, value) match {
     case Success(result, _) => result
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
index c8d065f37a605..c4b45aee06384 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
@@ -28,6 +28,7 @@ class RFormulaParserSuite extends SparkFunSuite {
 
   test("parse simple formulas") {
     checkParse("y ~ x", "y", Seq("x"))
+    checkParse("y ~ x + x", "y", Seq("x"))
     checkParse("y ~   ._foo  ", "y", Seq("._foo"))
     checkParse("resp ~ A_VAR + B + c123", "resp", Seq("A_VAR", "B", "c123"))
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
index 79c4ccf02d4e0..8148c553e9051 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
@@ -31,72 +31,78 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext {
     val formula = new RFormula().setFormula("id ~ v1 + v2")
     val original = sqlContext.createDataFrame(
       Seq((0, 1.0, 3.0), (2, 2.0, 5.0))).toDF("id", "v1", "v2")
-    val result = formula.transform(original)
-    val resultSchema = formula.transformSchema(original.schema)
+    val model = formula.fit(original)
+    val result = model.transform(original)
+    val resultSchema = model.transformSchema(original.schema)
     val expected = sqlContext.createDataFrame(
       Seq(
-        (0, 1.0, 3.0, Vectors.dense(Array(1.0, 3.0)), 0.0),
-        (2, 2.0, 5.0, Vectors.dense(Array(2.0, 5.0)), 2.0))
+        (0, 1.0, 3.0, Vectors.dense(1.0, 3.0), 0.0),
+        (2, 2.0, 5.0, Vectors.dense(2.0, 5.0), 2.0))
       ).toDF("id", "v1", "v2", "features", "label")
     // TODO(ekl) make schema comparisons ignore metadata, to avoid .toString
     assert(result.schema.toString == resultSchema.toString)
     assert(resultSchema == expected.schema)
-    assert(result.collect().toSeq == expected.collect().toSeq)
+    assert(result.collect() === expected.collect())
   }
 
   test("features column already exists") {
     val formula = new RFormula().setFormula("y ~ x").setFeaturesCol("x")
     val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "y")
     intercept[IllegalArgumentException] {
-      formula.transformSchema(original.schema)
+      formula.fit(original)
     }
     intercept[IllegalArgumentException] {
-      formula.transform(original)
+      formula.fit(original)
     }
   }
 
   test("label column already exists") {
     val formula = new RFormula().setFormula("y ~ x").setLabelCol("y")
     val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "y")
-    val resultSchema = formula.transformSchema(original.schema)
+    val model = formula.fit(original)
+    val resultSchema = model.transformSchema(original.schema)
     assert(resultSchema.length == 3)
-    assert(resultSchema.toString == formula.transform(original).schema.toString)
+    assert(resultSchema.toString == model.transform(original).schema.toString)
   }
 
   test("label column already exists but is not double type") {
     val formula = new RFormula().setFormula("y ~ x").setLabelCol("y")
     val original = sqlContext.createDataFrame(Seq((0, 1), (2, 2))).toDF("x", "y")
+    val model = formula.fit(original)
     intercept[IllegalArgumentException] {
-      formula.transformSchema(original.schema)
+      model.transformSchema(original.schema)
     }
     intercept[IllegalArgumentException] {
-      formula.transform(original)
+      model.transform(original)
     }
   }
 
   test("allow missing label column for test datasets") {
     val formula = new RFormula().setFormula("y ~ x").setLabelCol("label")
     val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "_not_y")
-    val resultSchema = formula.transformSchema(original.schema)
+    val model = formula.fit(original)
+    val resultSchema = model.transformSchema(original.schema)
     assert(resultSchema.length == 3)
     assert(!resultSchema.exists(_.name == "label"))
-    assert(resultSchema.toString == formula.transform(original).schema.toString)
+    assert(resultSchema.toString == model.transform(original).schema.toString)
   }
 
-// TODO(ekl) enable after we implement string label support
-//  test("transform string label") {
-//    val formula = new RFormula().setFormula("name ~ id")
-//    val original = sqlContext.createDataFrame(
-//      Seq((1, "foo"), (2, "bar"), (3, "bar"))).toDF("id", "name")
-//    val result = formula.transform(original)
-//    val resultSchema = formula.transformSchema(original.schema)
-//    val expected = sqlContext.createDataFrame(
-//      Seq(
-//        (1, "foo", Vectors.dense(Array(1.0)), 1.0),
-//        (2, "bar", Vectors.dense(Array(2.0)), 0.0),
-//        (3, "bar", Vectors.dense(Array(3.0)), 0.0))
-//      ).toDF("id", "name", "features", "label")
-//    assert(result.schema.toString == resultSchema.toString)
-//    assert(result.collect().toSeq == expected.collect().toSeq)
-//  }
+  test("encodes string terms") {
+    val formula = new RFormula().setFormula("id ~ a + b")
+    val original = sqlContext.createDataFrame(
+      Seq((1, "foo", 4), (2, "bar", 4), (3, "bar", 5), (4, "baz", 5))
+    ).toDF("id", "a", "b")
+    val model = formula.fit(original)
+    val result = model.transform(original)
+    val resultSchema = model.transformSchema(original.schema)
+    val expected = sqlContext.createDataFrame(
+      Seq(
+        (1, "foo", 4, Vectors.dense(0.0, 1.0, 4.0), 1.0),
+        (2, "bar", 4, Vectors.dense(1.0, 0.0, 4.0), 2.0),
+        (3, "bar", 5, Vectors.dense(1.0, 0.0, 5.0), 3.0),
+        (4, "baz", 5, Vectors.dense(0.0, 0.0, 5.0), 4.0))
+      ).toDF("id", "a", "b", "features", "label")
+    assert(result.schema.toString == resultSchema.toString)
+    assert(result.collect() === expected.collect())
+  }
 }

From ce89ff477aea6def68265ed218f6105680755c9a Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 27 Jul 2015 17:32:34 -0700
Subject: [PATCH 0633/1454] [SPARK-9386] [SQL] Feature flag for metastore
 partition pruning

Since we have been seeing a lot of failures related to this new feature, lets put it behind a flag and turn it off by default.

Author: Michael Armbrust <michael@databricks.com>

Closes #7703 from marmbrus/optionalMetastorePruning and squashes the following commits:

6ad128c [Michael Armbrust] style
8447835 [Michael Armbrust] [SPARK-9386][SQL] Feature flag for metastore partition pruning
fd37b87 [Michael Armbrust] add config flag
---
 .../main/scala/org/apache/spark/sql/SQLConf.scala    |  7 +++++++
 .../apache/spark/sql/hive/HiveMetastoreCatalog.scala | 12 +++++++++++-
 .../spark/sql/hive/client/ClientInterface.scala      | 10 ++++------
 3 files changed, 22 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 9b2dbd7442f5c..40eba33f595ca 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -301,6 +301,11 @@ private[spark] object SQLConf {
     defaultValue = Some(true),
     doc = "<TODO>")
 
+  val HIVE_METASTORE_PARTITION_PRUNING = booleanConf("spark.sql.hive.metastorePartitionPruning",
+    defaultValue = Some(false),
+    doc = "When true, some predicates will be pushed down into the Hive metastore so that " +
+          "unmatching partitions can be eliminated earlier.")
+
   val COLUMN_NAME_OF_CORRUPT_RECORD = stringConf("spark.sql.columnNameOfCorruptRecord",
     defaultValue = Some("_corrupt_record"),
     doc = "<TODO>")
@@ -456,6 +461,8 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITION_PATH)
 
+  private[spark] def metastorePartitionPruning: Boolean = getConf(HIVE_METASTORE_PARTITION_PRUNING)
+
   private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT)
 
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 9c707a7a2eca1..3180c05445c9f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -678,8 +678,18 @@ private[hive] case class MetastoreRelation
     }
   )
 
+  // When metastore partition pruning is turned off, we cache the list of all partitions to
+  // mimic the behavior of Spark < 1.5
+  lazy val allPartitions = table.getAllPartitions
+
   def getHiveQlPartitions(predicates: Seq[Expression] = Nil): Seq[Partition] = {
-    table.getPartitions(predicates).map { p =>
+    val rawPartitions = if (sqlContext.conf.metastorePartitionPruning) {
+      table.getPartitions(predicates)
+    } else {
+      allPartitions
+    }
+
+    rawPartitions.map { p =>
       val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
       tPartition.setDbName(databaseName)
       tPartition.setTableName(tableName)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index 1656587d14835..d834b4e83e043 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -72,12 +72,10 @@ private[hive] case class HiveTable(
 
   def isPartitioned: Boolean = partitionColumns.nonEmpty
 
-  def getPartitions(predicates: Seq[Expression]): Seq[HivePartition] = {
-    predicates match {
-      case Nil => client.getAllPartitions(this)
-      case _ => client.getPartitionsByFilter(this, predicates)
-    }
-  }
+  def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this)
+
+  def getPartitions(predicates: Seq[Expression]): Seq[HivePartition] =
+    client.getPartitionsByFilter(this, predicates)
 
   // Hive does not support backticks when passing names to the client.
   def qualifiedName: String = s"$database.$name"

From daa1964b6098f79100def78451bda181b5c92198 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 27 Jul 2015 17:59:43 -0700
Subject: [PATCH 0634/1454] [SPARK-8882] [STREAMING] Add a new Receiver
 scheduling mechanism

The design doc: https://docs.google.com/document/d/1ZsoRvHjpISPrDmSjsGzuSu8UjwgbtmoCTzmhgTurHJw/edit?usp=sharing

Author: zsxwing <zsxwing@gmail.com>

Closes #7276 from zsxwing/receiver-scheduling and squashes the following commits:

137b257 [zsxwing] Add preferredNumExecutors to rescheduleReceiver
61a6c3f [zsxwing] Set state to ReceiverState.INACTIVE in deregisterReceiver
5e1fa48 [zsxwing] Fix the code style
7451498 [zsxwing] Move DummyReceiver back to ReceiverTrackerSuite
715ef9c [zsxwing] Rename: scheduledLocations -> scheduledExecutors; locations -> executors
05daf9c [zsxwing] Use receiverTrackingInfo.toReceiverInfo
1d6d7c8 [zsxwing] Merge branch 'master' into receiver-scheduling
8f93c8d [zsxwing] Use hostPort as the receiver location rather than host; fix comments and unit tests
59f8887 [zsxwing] Schedule all receivers at the same time when launching them
075e0a3 [zsxwing] Add receiver RDD name; use '!isTrackerStarted' instead
276a4ac [zsxwing] Remove "ReceiverLauncher" and move codes to "launchReceivers"
fab9a01 [zsxwing] Move methods back to the outer class
4e639c4 [zsxwing] Fix unintentional changes
f60d021 [zsxwing] Reorganize ReceiverTracker to use an event loop for lock free
105037e [zsxwing] Merge branch 'master' into receiver-scheduling
5fee132 [zsxwing] Update tha scheduling algorithm to avoid to keep restarting Receiver
9e242c8 [zsxwing] Remove the ScheduleReceiver message because we can refuse it when receiving RegisterReceiver
a9acfbf [zsxwing] Merge branch 'squash-pr-6294' into receiver-scheduling
881edb9 [zsxwing] ReceiverScheduler -> ReceiverSchedulingPolicy
e530bcc [zsxwing] [SPARK-5681][Streaming] Use a lock to eliminate the race condition when stopping receivers and registering receivers happen at the same time #6294
3b87e4a [zsxwing] Revert SparkContext.scala
a86850c [zsxwing] Remove submitAsyncJob and revert JobWaiter
f549595 [zsxwing] Add comments for the scheduling approach
9ecc08e [zsxwing] Fix comments and code style
28d1bee [zsxwing] Make 'host' protected; rescheduleReceiver -> getAllowedLocations
2c86a9e [zsxwing] Use tryFailure to support calling jobFailed multiple times
ca6fe35 [zsxwing] Add a test for Receiver.restart
27acd45 [zsxwing] Add unit tests for LoadBalanceReceiverSchedulerImplSuite
cc76142 [zsxwing] Add JobWaiter.toFuture to avoid blocking threads
d9a3e72 [zsxwing] Add a new Receiver scheduling mechanism
---
 .../receiver/ReceiverSupervisor.scala         |   4 +-
 .../receiver/ReceiverSupervisorImpl.scala     |   6 +-
 .../streaming/scheduler/ReceiverInfo.scala    |   1 -
 .../scheduler/ReceiverSchedulingPolicy.scala  | 171 +++++++
 .../streaming/scheduler/ReceiverTracker.scala | 468 +++++++++++-------
 .../scheduler/ReceiverTrackingInfo.scala      |  55 ++
 .../ReceiverSchedulingPolicySuite.scala       | 130 +++++
 .../scheduler/ReceiverTrackerSuite.scala      |  66 +--
 .../StreamingJobProgressListenerSuite.scala   |   6 +-
 9 files changed, 674 insertions(+), 233 deletions(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTrackingInfo.scala
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
index a7c220f426ecf..e98017a63756e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
@@ -24,9 +24,9 @@ import scala.collection.mutable.ArrayBuffer
 import scala.concurrent._
 import scala.util.control.NonFatal
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.{SparkEnv, Logging, SparkConf}
 import org.apache.spark.storage.StreamBlockId
-import org.apache.spark.util.ThreadUtils
+import org.apache.spark.util.{Utils, ThreadUtils}
 
 /**
  * Abstract class that is responsible for supervising a Receiver in the worker.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index 2f6841ee8879c..0d802f83549af 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -30,7 +30,7 @@ import org.apache.spark.storage.StreamBlockId
 import org.apache.spark.streaming.Time
 import org.apache.spark.streaming.scheduler._
 import org.apache.spark.streaming.util.WriteAheadLogUtils
-import org.apache.spark.util.{RpcUtils, Utils}
+import org.apache.spark.util.RpcUtils
 import org.apache.spark.{Logging, SparkEnv, SparkException}
 
 /**
@@ -46,6 +46,8 @@ private[streaming] class ReceiverSupervisorImpl(
     checkpointDirOption: Option[String]
   ) extends ReceiverSupervisor(receiver, env.conf) with Logging {
 
+  private val hostPort = SparkEnv.get.blockManager.blockManagerId.hostPort
+
   private val receivedBlockHandler: ReceivedBlockHandler = {
     if (WriteAheadLogUtils.enableReceiverLog(env.conf)) {
       if (checkpointDirOption.isEmpty) {
@@ -170,7 +172,7 @@ private[streaming] class ReceiverSupervisorImpl(
 
   override protected def onReceiverStart(): Boolean = {
     val msg = RegisterReceiver(
-      streamId, receiver.getClass.getSimpleName, Utils.localHostName(), endpoint)
+      streamId, receiver.getClass.getSimpleName, hostPort, endpoint)
     trackerEndpoint.askWithRetry[Boolean](msg)
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
index de85f24dd988d..59df892397fe0 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
@@ -28,7 +28,6 @@ import org.apache.spark.rpc.RpcEndpointRef
 case class ReceiverInfo(
     streamId: Int,
     name: String,
-    private[streaming] val endpoint: RpcEndpointRef,
     active: Boolean,
     location: String,
     lastErrorMessage: String = "",
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
new file mode 100644
index 0000000000000..ef5b687b5831a
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
@@ -0,0 +1,171 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import scala.collection.Map
+import scala.collection.mutable
+
+import org.apache.spark.streaming.receiver.Receiver
+
+private[streaming] class ReceiverSchedulingPolicy {
+
+  /**
+   * Try our best to schedule receivers with evenly distributed. However, if the
+   * `preferredLocation`s of receivers are not even, we may not be able to schedule them evenly
+   * because we have to respect them.
+   *
+   * Here is the approach to schedule executors:
+   * <ol>
+   *   <li>First, schedule all the receivers with preferred locations (hosts), evenly among the
+   *       executors running on those host.</li>
+   *   <li>Then, schedule all other receivers evenly among all the executors such that overall
+   *       distribution over all the receivers is even.</li>
+   * </ol>
+   *
+   * This method is called when we start to launch receivers at the first time.
+   */
+  def scheduleReceivers(
+      receivers: Seq[Receiver[_]], executors: Seq[String]): Map[Int, Seq[String]] = {
+    if (receivers.isEmpty) {
+      return Map.empty
+    }
+
+    if (executors.isEmpty) {
+      return receivers.map(_.streamId -> Seq.empty).toMap
+    }
+
+    val hostToExecutors = executors.groupBy(_.split(":")(0))
+    val scheduledExecutors = Array.fill(receivers.length)(new mutable.ArrayBuffer[String])
+    val numReceiversOnExecutor = mutable.HashMap[String, Int]()
+    // Set the initial value to 0
+    executors.foreach(e => numReceiversOnExecutor(e) = 0)
+
+    // Firstly, we need to respect "preferredLocation". So if a receiver has "preferredLocation",
+    // we need to make sure the "preferredLocation" is in the candidate scheduled executor list.
+    for (i <- 0 until receivers.length) {
+      // Note: preferredLocation is host but executors are host:port
+      receivers(i).preferredLocation.foreach { host =>
+        hostToExecutors.get(host) match {
+          case Some(executorsOnHost) =>
+            // preferredLocation is a known host. Select an executor that has the least receivers in
+            // this host
+            val leastScheduledExecutor =
+              executorsOnHost.minBy(executor => numReceiversOnExecutor(executor))
+            scheduledExecutors(i) += leastScheduledExecutor
+            numReceiversOnExecutor(leastScheduledExecutor) =
+              numReceiversOnExecutor(leastScheduledExecutor) + 1
+          case None =>
+            // preferredLocation is an unknown host.
+            // Note: There are two cases:
+            // 1. This executor is not up. But it may be up later.
+            // 2. This executor is dead, or it's not a host in the cluster.
+            // Currently, simply add host to the scheduled executors.
+            scheduledExecutors(i) += host
+        }
+      }
+    }
+
+    // For those receivers that don't have preferredLocation, make sure we assign at least one
+    // executor to them.
+    for (scheduledExecutorsForOneReceiver <- scheduledExecutors.filter(_.isEmpty)) {
+      // Select the executor that has the least receivers
+      val (leastScheduledExecutor, numReceivers) = numReceiversOnExecutor.minBy(_._2)
+      scheduledExecutorsForOneReceiver += leastScheduledExecutor
+      numReceiversOnExecutor(leastScheduledExecutor) = numReceivers + 1
+    }
+
+    // Assign idle executors to receivers that have less executors
+    val idleExecutors = numReceiversOnExecutor.filter(_._2 == 0).map(_._1)
+    for (executor <- idleExecutors) {
+      // Assign an idle executor to the receiver that has least candidate executors.
+      val leastScheduledExecutors = scheduledExecutors.minBy(_.size)
+      leastScheduledExecutors += executor
+    }
+
+    receivers.map(_.streamId).zip(scheduledExecutors).toMap
+  }
+
+  /**
+   * Return a list of candidate executors to run the receiver. If the list is empty, the caller can
+   * run this receiver in arbitrary executor. The caller can use `preferredNumExecutors` to require
+   * returning `preferredNumExecutors` executors if possible.
+   *
+   * This method tries to balance executors' load. Here is the approach to schedule executors
+   * for a receiver.
+   * <ol>
+   *   <li>
+   *     If preferredLocation is set, preferredLocation should be one of the candidate executors.
+   *   </li>
+   *   <li>
+   *     Every executor will be assigned to a weight according to the receivers running or
+   *     scheduling on it.
+   *     <ul>
+   *       <li>
+   *         If a receiver is running on an executor, it contributes 1.0 to the executor's weight.
+   *       </li>
+   *       <li>
+   *         If a receiver is scheduled to an executor but has not yet run, it contributes
+   *         `1.0 / #candidate_executors_of_this_receiver` to the executor's weight.</li>
+   *     </ul>
+   *     At last, if there are more than `preferredNumExecutors` idle executors (weight = 0),
+   *     returns all idle executors. Otherwise, we only return `preferredNumExecutors` best options
+   *     according to the weights.
+   *   </li>
+   * </ol>
+   *
+   * This method is called when a receiver is registering with ReceiverTracker or is restarting.
+   */
+  def rescheduleReceiver(
+      receiverId: Int,
+      preferredLocation: Option[String],
+      receiverTrackingInfoMap: Map[Int, ReceiverTrackingInfo],
+      executors: Seq[String],
+      preferredNumExecutors: Int = 3): Seq[String] = {
+    if (executors.isEmpty) {
+      return Seq.empty
+    }
+
+    // Always try to schedule to the preferred locations
+    val scheduledExecutors = mutable.Set[String]()
+    scheduledExecutors ++= preferredLocation
+
+    val executorWeights = receiverTrackingInfoMap.values.flatMap { receiverTrackingInfo =>
+      receiverTrackingInfo.state match {
+        case ReceiverState.INACTIVE => Nil
+        case ReceiverState.SCHEDULED =>
+          val scheduledExecutors = receiverTrackingInfo.scheduledExecutors.get
+          // The probability that a scheduled receiver will run in an executor is
+          // 1.0 / scheduledLocations.size
+          scheduledExecutors.map(location => location -> (1.0 / scheduledExecutors.size))
+        case ReceiverState.ACTIVE => Seq(receiverTrackingInfo.runningExecutor.get -> 1.0)
+      }
+    }.groupBy(_._1).mapValues(_.map(_._2).sum) // Sum weights for each executor
+
+    val idleExecutors = (executors.toSet -- executorWeights.keys).toSeq
+    if (idleExecutors.size >= preferredNumExecutors) {
+      // If there are more than `preferredNumExecutors` idle executors, return all of them
+      scheduledExecutors ++= idleExecutors
+    } else {
+      // If there are less than `preferredNumExecutors` idle executors, return 3 best options
+      scheduledExecutors ++= idleExecutors
+      val sortedExecutors = executorWeights.toSeq.sortBy(_._2).map(_._1)
+      scheduledExecutors ++= (idleExecutors ++ sortedExecutors).take(preferredNumExecutors)
+    }
+    scheduledExecutors.toSeq
+  }
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 9cc6ffcd12f61..6270137951b5a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -17,17 +17,27 @@
 
 package org.apache.spark.streaming.scheduler
 
-import scala.collection.mutable.{ArrayBuffer, HashMap, SynchronizedMap}
+import java.util.concurrent.{TimeUnit, CountDownLatch}
+
+import scala.collection.mutable.HashMap
+import scala.concurrent.ExecutionContext
 import scala.language.existentials
-import scala.math.max
+import scala.util.{Failure, Success}
 
 import org.apache.spark.streaming.util.WriteAheadLogUtils
-import org.apache.spark.{Logging, SparkEnv, SparkException}
+import org.apache.spark._
+import org.apache.spark.rdd.RDD
 import org.apache.spark.rpc._
 import org.apache.spark.streaming.{StreamingContext, Time}
-import org.apache.spark.streaming.receiver.{CleanupOldBlocks, Receiver, ReceiverSupervisorImpl,
-  StopReceiver, UpdateRateLimit}
-import org.apache.spark.util.SerializableConfiguration
+import org.apache.spark.streaming.receiver._
+import org.apache.spark.util.{ThreadUtils, SerializableConfiguration}
+
+
+/** Enumeration to identify current state of a Receiver */
+private[streaming] object ReceiverState extends Enumeration {
+  type ReceiverState = Value
+  val INACTIVE, SCHEDULED, ACTIVE = Value
+}
 
 /**
  * Messages used by the NetworkReceiver and the ReceiverTracker to communicate
@@ -37,7 +47,7 @@ private[streaming] sealed trait ReceiverTrackerMessage
 private[streaming] case class RegisterReceiver(
     streamId: Int,
     typ: String,
-    host: String,
+    hostPort: String,
     receiverEndpoint: RpcEndpointRef
   ) extends ReceiverTrackerMessage
 private[streaming] case class AddBlock(receivedBlockInfo: ReceivedBlockInfo)
@@ -46,7 +56,38 @@ private[streaming] case class ReportError(streamId: Int, message: String, error:
 private[streaming] case class DeregisterReceiver(streamId: Int, msg: String, error: String)
   extends ReceiverTrackerMessage
 
-private[streaming] case object StopAllReceivers extends ReceiverTrackerMessage
+/**
+ * Messages used by the driver and ReceiverTrackerEndpoint to communicate locally.
+ */
+private[streaming] sealed trait ReceiverTrackerLocalMessage
+
+/**
+ * This message will trigger ReceiverTrackerEndpoint to restart a Spark job for the receiver.
+ */
+private[streaming] case class RestartReceiver(receiver: Receiver[_])
+  extends ReceiverTrackerLocalMessage
+
+/**
+ * This message is sent to ReceiverTrackerEndpoint when we start to launch Spark jobs for receivers
+ * at the first time.
+ */
+private[streaming] case class StartAllReceivers(receiver: Seq[Receiver[_]])
+  extends ReceiverTrackerLocalMessage
+
+/**
+ * This message will trigger ReceiverTrackerEndpoint to send stop signals to all registered
+ * receivers.
+ */
+private[streaming] case object StopAllReceivers extends ReceiverTrackerLocalMessage
+
+/**
+ * A message used by ReceiverTracker to ask all receiver's ids still stored in
+ * ReceiverTrackerEndpoint.
+ */
+private[streaming] case object AllReceiverIds extends ReceiverTrackerLocalMessage
+
+private[streaming] case class UpdateReceiverRateLimit(streamUID: Int, newRate: Long)
+  extends ReceiverTrackerLocalMessage
 
 /**
  * This class manages the execution of the receivers of ReceiverInputDStreams. Instance of
@@ -60,8 +101,6 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
   private val receiverInputStreams = ssc.graph.getReceiverInputStreams()
   private val receiverInputStreamIds = receiverInputStreams.map { _.id }
-  private val receiverExecutor = new ReceiverLauncher()
-  private val receiverInfo = new HashMap[Int, ReceiverInfo] with SynchronizedMap[Int, ReceiverInfo]
   private val receivedBlockTracker = new ReceivedBlockTracker(
     ssc.sparkContext.conf,
     ssc.sparkContext.hadoopConfiguration,
@@ -86,6 +125,24 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
   // This not being null means the tracker has been started and not stopped
   private var endpoint: RpcEndpointRef = null
 
+  private val schedulingPolicy = new ReceiverSchedulingPolicy()
+
+  // Track the active receiver job number. When a receiver job exits ultimately, countDown will
+  // be called.
+  private val receiverJobExitLatch = new CountDownLatch(receiverInputStreams.size)
+
+  /**
+   * Track all receivers' information. The key is the receiver id, the value is the receiver info.
+   * It's only accessed in ReceiverTrackerEndpoint.
+   */
+  private val receiverTrackingInfos = new HashMap[Int, ReceiverTrackingInfo]
+
+  /**
+   * Store all preferred locations for all receivers. We need this information to schedule
+   * receivers. It's only accessed in ReceiverTrackerEndpoint.
+   */
+  private val receiverPreferredLocations = new HashMap[Int, Option[String]]
+
   /** Start the endpoint and receiver execution thread. */
   def start(): Unit = synchronized {
     if (isTrackerStarted) {
@@ -95,7 +152,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     if (!receiverInputStreams.isEmpty) {
       endpoint = ssc.env.rpcEnv.setupEndpoint(
         "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv))
-      if (!skipReceiverLaunch) receiverExecutor.start()
+      if (!skipReceiverLaunch) launchReceivers()
       logInfo("ReceiverTracker started")
       trackerState = Started
     }
@@ -112,20 +169,18 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
         // Wait for the Spark job that runs the receivers to be over
         // That is, for the receivers to quit gracefully.
-        receiverExecutor.awaitTermination(10000)
+        receiverJobExitLatch.await(10, TimeUnit.SECONDS)
 
         if (graceful) {
-          val pollTime = 100
           logInfo("Waiting for receiver job to terminate gracefully")
-          while (receiverInfo.nonEmpty || receiverExecutor.running) {
-            Thread.sleep(pollTime)
-          }
+          receiverJobExitLatch.await()
           logInfo("Waited for receiver job to terminate gracefully")
         }
 
         // Check if all the receivers have been deregistered or not
-        if (receiverInfo.nonEmpty) {
-          logWarning("Not all of the receivers have deregistered, " + receiverInfo)
+        val receivers = endpoint.askWithRetry[Seq[Int]](AllReceiverIds)
+        if (receivers.nonEmpty) {
+          logWarning("Not all of the receivers have deregistered, " + receivers)
         } else {
           logInfo("All of the receivers have deregistered successfully")
         }
@@ -154,9 +209,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
   /** Get the blocks allocated to the given batch and stream. */
   def getBlocksOfBatchAndStream(batchTime: Time, streamId: Int): Seq[ReceivedBlockInfo] = {
-    synchronized {
-      receivedBlockTracker.getBlocksOfBatchAndStream(batchTime, streamId)
-    }
+    receivedBlockTracker.getBlocksOfBatchAndStream(batchTime, streamId)
   }
 
   /**
@@ -170,8 +223,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     // Signal the receivers to delete old block data
     if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
       logInfo(s"Cleanup old received batch data: $cleanupThreshTime")
-      receiverInfo.values.flatMap { info => Option(info.endpoint) }
-        .foreach { _.send(CleanupOldBlocks(cleanupThreshTime)) }
+      endpoint.send(CleanupOldBlocks(cleanupThreshTime))
     }
   }
 
@@ -179,7 +231,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
   private def registerReceiver(
       streamId: Int,
       typ: String,
-      host: String,
+      hostPort: String,
       receiverEndpoint: RpcEndpointRef,
       senderAddress: RpcAddress
     ): Boolean = {
@@ -189,13 +241,20 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
     if (isTrackerStopping || isTrackerStopped) {
       false
+    } else if (!scheduleReceiver(streamId).contains(hostPort)) {
+      // Refuse it since it's scheduled to a wrong executor
+      false
     } else {
-      // "stopReceivers" won't happen at the same time because both "registerReceiver" and are
-      // called in the event loop. So here we can assume "stopReceivers" has not yet been called. If
-      // "stopReceivers" is called later, it should be able to see this receiver.
-      receiverInfo(streamId) = ReceiverInfo(
-        streamId, s"${typ}-${streamId}", receiverEndpoint, true, host)
-      listenerBus.post(StreamingListenerReceiverStarted(receiverInfo(streamId)))
+      val name = s"${typ}-${streamId}"
+      val receiverTrackingInfo = ReceiverTrackingInfo(
+        streamId,
+        ReceiverState.ACTIVE,
+        scheduledExecutors = None,
+        runningExecutor = Some(hostPort),
+        name = Some(name),
+        endpoint = Some(receiverEndpoint))
+      receiverTrackingInfos.put(streamId, receiverTrackingInfo)
+      listenerBus.post(StreamingListenerReceiverStarted(receiverTrackingInfo.toReceiverInfo))
       logInfo("Registered receiver for stream " + streamId + " from " + senderAddress)
       true
     }
@@ -203,21 +262,20 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
   /** Deregister a receiver */
   private def deregisterReceiver(streamId: Int, message: String, error: String) {
-    val newReceiverInfo = receiverInfo.get(streamId) match {
+    val lastErrorTime =
+      if (error == null || error == "") -1 else ssc.scheduler.clock.getTimeMillis()
+    val errorInfo = ReceiverErrorInfo(
+      lastErrorMessage = message, lastError = error, lastErrorTime = lastErrorTime)
+    val newReceiverTrackingInfo = receiverTrackingInfos.get(streamId) match {
       case Some(oldInfo) =>
-        val lastErrorTime =
-          if (error == null || error == "") -1 else ssc.scheduler.clock.getTimeMillis()
-        oldInfo.copy(endpoint = null, active = false, lastErrorMessage = message,
-          lastError = error, lastErrorTime = lastErrorTime)
+        oldInfo.copy(state = ReceiverState.INACTIVE, errorInfo = Some(errorInfo))
       case None =>
         logWarning("No prior receiver info")
-        val lastErrorTime =
-          if (error == null || error == "") -1 else ssc.scheduler.clock.getTimeMillis()
-        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message,
-          lastError = error, lastErrorTime = lastErrorTime)
+        ReceiverTrackingInfo(
+          streamId, ReceiverState.INACTIVE, None, None, None, None, Some(errorInfo))
     }
-    receiverInfo -= streamId
-    listenerBus.post(StreamingListenerReceiverStopped(newReceiverInfo))
+    receiverTrackingInfos -= streamId
+    listenerBus.post(StreamingListenerReceiverStopped(newReceiverTrackingInfo.toReceiverInfo))
     val messageWithError = if (error != null && !error.isEmpty) {
       s"$message - $error"
     } else {
@@ -228,9 +286,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
   /** Update a receiver's maximum ingestion rate */
   def sendRateUpdate(streamUID: Int, newRate: Long): Unit = {
-    for (info <- receiverInfo.get(streamUID); eP <- Option(info.endpoint)) {
-      eP.send(UpdateRateLimit(newRate))
-    }
+    endpoint.send(UpdateReceiverRateLimit(streamUID, newRate))
   }
 
   /** Add new blocks for the given stream */
@@ -240,16 +296,21 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
 
   /** Report error sent by a receiver */
   private def reportError(streamId: Int, message: String, error: String) {
-    val newReceiverInfo = receiverInfo.get(streamId) match {
+    val newReceiverTrackingInfo = receiverTrackingInfos.get(streamId) match {
       case Some(oldInfo) =>
-        oldInfo.copy(lastErrorMessage = message, lastError = error)
+        val errorInfo = ReceiverErrorInfo(lastErrorMessage = message, lastError = error,
+          lastErrorTime = oldInfo.errorInfo.map(_.lastErrorTime).getOrElse(-1L))
+        oldInfo.copy(errorInfo = Some(errorInfo))
       case None =>
         logWarning("No prior receiver info")
-        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message,
-          lastError = error, lastErrorTime = ssc.scheduler.clock.getTimeMillis())
+        val errorInfo = ReceiverErrorInfo(lastErrorMessage = message, lastError = error,
+          lastErrorTime = ssc.scheduler.clock.getTimeMillis())
+        ReceiverTrackingInfo(
+          streamId, ReceiverState.INACTIVE, None, None, None, None, Some(errorInfo))
     }
-    receiverInfo(streamId) = newReceiverInfo
-    listenerBus.post(StreamingListenerReceiverError(receiverInfo(streamId)))
+
+    receiverTrackingInfos(streamId) = newReceiverTrackingInfo
+    listenerBus.post(StreamingListenerReceiverError(newReceiverTrackingInfo.toReceiverInfo))
     val messageWithError = if (error != null && !error.isEmpty) {
       s"$message - $error"
     } else {
@@ -258,171 +319,242 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     logWarning(s"Error reported by receiver for stream $streamId: $messageWithError")
   }
 
+  private def scheduleReceiver(receiverId: Int): Seq[String] = {
+    val preferredLocation = receiverPreferredLocations.getOrElse(receiverId, None)
+    val scheduledExecutors = schedulingPolicy.rescheduleReceiver(
+      receiverId, preferredLocation, receiverTrackingInfos, getExecutors)
+    updateReceiverScheduledExecutors(receiverId, scheduledExecutors)
+    scheduledExecutors
+  }
+
+  private def updateReceiverScheduledExecutors(
+      receiverId: Int, scheduledExecutors: Seq[String]): Unit = {
+    val newReceiverTrackingInfo = receiverTrackingInfos.get(receiverId) match {
+      case Some(oldInfo) =>
+        oldInfo.copy(state = ReceiverState.SCHEDULED,
+          scheduledExecutors = Some(scheduledExecutors))
+      case None =>
+        ReceiverTrackingInfo(
+          receiverId,
+          ReceiverState.SCHEDULED,
+          Some(scheduledExecutors),
+          runningExecutor = None)
+    }
+    receiverTrackingInfos.put(receiverId, newReceiverTrackingInfo)
+  }
+
   /** Check if any blocks are left to be processed */
   def hasUnallocatedBlocks: Boolean = {
     receivedBlockTracker.hasUnallocatedReceivedBlocks
   }
 
+  /**
+   * Get the list of executors excluding driver
+   */
+  private def getExecutors: Seq[String] = {
+    if (ssc.sc.isLocal) {
+      Seq(ssc.sparkContext.env.blockManager.blockManagerId.hostPort)
+    } else {
+      ssc.sparkContext.env.blockManager.master.getMemoryStatus.filter { case (blockManagerId, _) =>
+        blockManagerId.executorId != SparkContext.DRIVER_IDENTIFIER // Ignore the driver location
+      }.map { case (blockManagerId, _) => blockManagerId.hostPort }.toSeq
+    }
+  }
+
+  /**
+   * Run the dummy Spark job to ensure that all slaves have registered. This avoids all the
+   * receivers to be scheduled on the same node.
+   *
+   * TODO Should poll the executor number and wait for executors according to
+   * "spark.scheduler.minRegisteredResourcesRatio" and
+   * "spark.scheduler.maxRegisteredResourcesWaitingTime" rather than running a dummy job.
+   */
+  private def runDummySparkJob(): Unit = {
+    if (!ssc.sparkContext.isLocal) {
+      ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
+    }
+    assert(getExecutors.nonEmpty)
+  }
+
+  /**
+   * Get the receivers from the ReceiverInputDStreams, distributes them to the
+   * worker nodes as a parallel collection, and runs them.
+   */
+  private def launchReceivers(): Unit = {
+    val receivers = receiverInputStreams.map(nis => {
+      val rcvr = nis.getReceiver()
+      rcvr.setReceiverId(nis.id)
+      rcvr
+    })
+
+    runDummySparkJob()
+
+    logInfo("Starting " + receivers.length + " receivers")
+    endpoint.send(StartAllReceivers(receivers))
+  }
+
+  /** Check if tracker has been marked for starting */
+  private def isTrackerStarted: Boolean = trackerState == Started
+
+  /** Check if tracker has been marked for stopping */
+  private def isTrackerStopping: Boolean = trackerState == Stopping
+
+  /** Check if tracker has been marked for stopped */
+  private def isTrackerStopped: Boolean = trackerState == Stopped
+
   /** RpcEndpoint to receive messages from the receivers. */
   private class ReceiverTrackerEndpoint(override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint {
 
+    // TODO Remove this thread pool after https://github.com/apache/spark/issues/7385 is merged
+    private val submitJobThreadPool = ExecutionContext.fromExecutorService(
+      ThreadUtils.newDaemonCachedThreadPool("submit-job-thead-pool"))
+
     override def receive: PartialFunction[Any, Unit] = {
+      // Local messages
+      case StartAllReceivers(receivers) =>
+        val scheduledExecutors = schedulingPolicy.scheduleReceivers(receivers, getExecutors)
+        for (receiver <- receivers) {
+          val executors = scheduledExecutors(receiver.streamId)
+          updateReceiverScheduledExecutors(receiver.streamId, executors)
+          receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
+          startReceiver(receiver, executors)
+        }
+      case RestartReceiver(receiver) =>
+        val scheduledExecutors = schedulingPolicy.rescheduleReceiver(
+          receiver.streamId,
+          receiver.preferredLocation,
+          receiverTrackingInfos,
+          getExecutors)
+        updateReceiverScheduledExecutors(receiver.streamId, scheduledExecutors)
+        startReceiver(receiver, scheduledExecutors)
+      case c: CleanupOldBlocks =>
+        receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c))
+      case UpdateReceiverRateLimit(streamUID, newRate) =>
+        for (info <- receiverTrackingInfos.get(streamUID); eP <- info.endpoint) {
+          eP.send(UpdateRateLimit(newRate))
+        }
+      // Remote messages
       case ReportError(streamId, message, error) =>
         reportError(streamId, message, error)
     }
 
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
-      case RegisterReceiver(streamId, typ, host, receiverEndpoint) =>
+      // Remote messages
+      case RegisterReceiver(streamId, typ, hostPort, receiverEndpoint) =>
         val successful =
-          registerReceiver(streamId, typ, host, receiverEndpoint, context.sender.address)
+          registerReceiver(streamId, typ, hostPort, receiverEndpoint, context.sender.address)
         context.reply(successful)
       case AddBlock(receivedBlockInfo) =>
         context.reply(addBlock(receivedBlockInfo))
       case DeregisterReceiver(streamId, message, error) =>
         deregisterReceiver(streamId, message, error)
         context.reply(true)
+      // Local messages
+      case AllReceiverIds =>
+        context.reply(receiverTrackingInfos.keys.toSeq)
       case StopAllReceivers =>
         assert(isTrackerStopping || isTrackerStopped)
         stopReceivers()
         context.reply(true)
     }
 
-    /** Send stop signal to the receivers. */
-    private def stopReceivers() {
-      // Signal the receivers to stop
-      receiverInfo.values.flatMap { info => Option(info.endpoint)}
-        .foreach { _.send(StopReceiver) }
-      logInfo("Sent stop signal to all " + receiverInfo.size + " receivers")
-    }
-  }
-
-  /** This thread class runs all the receivers on the cluster.  */
-  class ReceiverLauncher {
-    @transient val env = ssc.env
-    @volatile @transient var running = false
-    @transient val thread = new Thread() {
-      override def run() {
-        try {
-          SparkEnv.set(env)
-          startReceivers()
-        } catch {
-          case ie: InterruptedException => logInfo("ReceiverLauncher interrupted")
-        }
-      }
-    }
-
-    def start() {
-      thread.start()
-    }
-
     /**
-     * Get the list of executors excluding driver
-     */
-    private def getExecutors(ssc: StreamingContext): List[String] = {
-      val executors = ssc.sparkContext.getExecutorMemoryStatus.map(_._1.split(":")(0)).toList
-      val driver = ssc.sparkContext.getConf.get("spark.driver.host")
-      executors.diff(List(driver))
-    }
-
-    /** Set host location(s) for each receiver so as to distribute them over
-     * executors in a round-robin fashion taking into account preferredLocation if set
+     * Start a receiver along with its scheduled executors
      */
-    private[streaming] def scheduleReceivers(receivers: Seq[Receiver[_]],
-      executors: List[String]): Array[ArrayBuffer[String]] = {
-      val locations = new Array[ArrayBuffer[String]](receivers.length)
-      var i = 0
-      for (i <- 0 until receivers.length) {
-        locations(i) = new ArrayBuffer[String]()
-        if (receivers(i).preferredLocation.isDefined) {
-          locations(i) += receivers(i).preferredLocation.get
-        }
+    private def startReceiver(receiver: Receiver[_], scheduledExecutors: Seq[String]): Unit = {
+      val receiverId = receiver.streamId
+      if (!isTrackerStarted) {
+        onReceiverJobFinish(receiverId)
+        return
       }
-      var count = 0
-      for (i <- 0 until max(receivers.length, executors.length)) {
-        if (!receivers(i % receivers.length).preferredLocation.isDefined) {
-          locations(i % receivers.length) += executors(count)
-          count += 1
-          if (count == executors.length) {
-            count = 0
-          }
-        }
-      }
-      locations
-    }
-
-    /**
-     * Get the receivers from the ReceiverInputDStreams, distributes them to the
-     * worker nodes as a parallel collection, and runs them.
-     */
-    private def startReceivers() {
-      val receivers = receiverInputStreams.map(nis => {
-        val rcvr = nis.getReceiver()
-        rcvr.setReceiverId(nis.id)
-        rcvr
-      })
 
       val checkpointDirOption = Option(ssc.checkpointDir)
       val serializableHadoopConf =
         new SerializableConfiguration(ssc.sparkContext.hadoopConfiguration)
 
       // Function to start the receiver on the worker node
-      val startReceiver = (iterator: Iterator[Receiver[_]]) => {
-        if (!iterator.hasNext) {
-          throw new SparkException(
-            "Could not start receiver as object not found.")
-        }
-        val receiver = iterator.next()
-        val supervisor = new ReceiverSupervisorImpl(
-          receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
-        supervisor.start()
-        supervisor.awaitTermination()
-      }
-
-      // Run the dummy Spark job to ensure that all slaves have registered.
-      // This avoids all the receivers to be scheduled on the same node.
-      if (!ssc.sparkContext.isLocal) {
-        ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
-      }
+      val startReceiverFunc = new StartReceiverFunc(checkpointDirOption, serializableHadoopConf)
 
-      // Get the list of executors and schedule receivers
-      val executors = getExecutors(ssc)
-      val tempRDD =
-        if (!executors.isEmpty) {
-          val locations = scheduleReceivers(receivers, executors)
-          val roundRobinReceivers = (0 until receivers.length).map(i =>
-            (receivers(i), locations(i)))
-          ssc.sc.makeRDD[Receiver[_]](roundRobinReceivers)
+      // Create the RDD using the scheduledExecutors to run the receiver in a Spark job
+      val receiverRDD: RDD[Receiver[_]] =
+        if (scheduledExecutors.isEmpty) {
+          ssc.sc.makeRDD(Seq(receiver), 1)
         } else {
-          ssc.sc.makeRDD(receivers, receivers.size)
+          ssc.sc.makeRDD(Seq(receiver -> scheduledExecutors))
         }
+      receiverRDD.setName(s"Receiver $receiverId")
+      val future = ssc.sparkContext.submitJob[Receiver[_], Unit, Unit](
+        receiverRDD, startReceiverFunc, Seq(0), (_, _) => Unit, ())
+      // We will keep restarting the receiver job until ReceiverTracker is stopped
+      future.onComplete {
+        case Success(_) =>
+          if (!isTrackerStarted) {
+            onReceiverJobFinish(receiverId)
+          } else {
+            logInfo(s"Restarting Receiver $receiverId")
+            self.send(RestartReceiver(receiver))
+          }
+        case Failure(e) =>
+          if (!isTrackerStarted) {
+            onReceiverJobFinish(receiverId)
+          } else {
+            logError("Receiver has been stopped. Try to restart it.", e)
+            logInfo(s"Restarting Receiver $receiverId")
+            self.send(RestartReceiver(receiver))
+          }
+      }(submitJobThreadPool)
+      logInfo(s"Receiver ${receiver.streamId} started")
+    }
 
-      // Distribute the receivers and start them
-      logInfo("Starting " + receivers.length + " receivers")
-      running = true
-      try {
-        ssc.sparkContext.runJob(tempRDD, ssc.sparkContext.clean(startReceiver))
-        logInfo("All of the receivers have been terminated")
-      } finally {
-        running = false
-      }
+    override def onStop(): Unit = {
+      submitJobThreadPool.shutdownNow()
     }
 
     /**
-     * Wait until the Spark job that runs the receivers is terminated, or return when
-     * `milliseconds` elapses
+     * Call when a receiver is terminated. It means we won't restart its Spark job.
      */
-    def awaitTermination(milliseconds: Long): Unit = {
-      thread.join(milliseconds)
+    private def onReceiverJobFinish(receiverId: Int): Unit = {
+      receiverJobExitLatch.countDown()
+      receiverTrackingInfos.remove(receiverId).foreach { receiverTrackingInfo =>
+        if (receiverTrackingInfo.state == ReceiverState.ACTIVE) {
+          logWarning(s"Receiver $receiverId exited but didn't deregister")
+        }
+      }
     }
-  }
 
-  /** Check if tracker has been marked for starting */
-  private def isTrackerStarted(): Boolean = trackerState == Started
+    /** Send stop signal to the receivers. */
+    private def stopReceivers() {
+      receiverTrackingInfos.values.flatMap(_.endpoint).foreach { _.send(StopReceiver) }
+      logInfo("Sent stop signal to all " + receiverTrackingInfos.size + " receivers")
+    }
+  }
 
-  /** Check if tracker has been marked for stopping */
-  private def isTrackerStopping(): Boolean = trackerState == Stopping
+}
 
-  /** Check if tracker has been marked for stopped */
-  private def isTrackerStopped(): Boolean = trackerState == Stopped
+/**
+ * Function to start the receiver on the worker node. Use a class instead of closure to avoid
+ * the serialization issue.
+ */
+private class StartReceiverFunc(
+    checkpointDirOption: Option[String],
+    serializableHadoopConf: SerializableConfiguration)
+  extends (Iterator[Receiver[_]] => Unit) with Serializable {
+
+  override def apply(iterator: Iterator[Receiver[_]]): Unit = {
+    if (!iterator.hasNext) {
+      throw new SparkException(
+        "Could not start receiver as object not found.")
+    }
+    if (TaskContext.get().attemptNumber() == 0) {
+      val receiver = iterator.next()
+      assert(iterator.hasNext == false)
+      val supervisor = new ReceiverSupervisorImpl(
+        receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
+      supervisor.start()
+      supervisor.awaitTermination()
+    } else {
+      // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
+    }
+  }
 
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTrackingInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTrackingInfo.scala
new file mode 100644
index 0000000000000..043ff4d0ff054
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTrackingInfo.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.streaming.scheduler.ReceiverState._
+
+private[streaming] case class ReceiverErrorInfo(
+    lastErrorMessage: String = "", lastError: String = "", lastErrorTime: Long = -1L)
+
+/**
+ * Class having information about a receiver.
+ *
+ * @param receiverId the unique receiver id
+ * @param state the current Receiver state
+ * @param scheduledExecutors the scheduled executors provided by ReceiverSchedulingPolicy
+ * @param runningExecutor the running executor if the receiver is active
+ * @param name the receiver name
+ * @param endpoint the receiver endpoint. It can be used to send messages to the receiver
+ * @param errorInfo the receiver error information if it fails
+ */
+private[streaming] case class ReceiverTrackingInfo(
+    receiverId: Int,
+    state: ReceiverState,
+    scheduledExecutors: Option[Seq[String]],
+    runningExecutor: Option[String],
+    name: Option[String] = None,
+    endpoint: Option[RpcEndpointRef] = None,
+    errorInfo: Option[ReceiverErrorInfo] = None) {
+
+  def toReceiverInfo: ReceiverInfo = ReceiverInfo(
+    receiverId,
+    name.getOrElse(""),
+    state == ReceiverState.ACTIVE,
+    location = runningExecutor.getOrElse(""),
+    lastErrorMessage = errorInfo.map(_.lastErrorMessage).getOrElse(""),
+    lastError = errorInfo.map(_.lastError).getOrElse(""),
+    lastErrorTime = errorInfo.map(_.lastErrorTime).getOrElse(-1L)
+  )
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
new file mode 100644
index 0000000000000..93f920fdc71f1
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
@@ -0,0 +1,130 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import scala.collection.mutable
+
+import org.apache.spark.SparkFunSuite
+
+class ReceiverSchedulingPolicySuite extends SparkFunSuite {
+
+  val receiverSchedulingPolicy = new ReceiverSchedulingPolicy
+
+  test("rescheduleReceiver: empty executors") {
+    val scheduledExecutors =
+      receiverSchedulingPolicy.rescheduleReceiver(0, None, Map.empty, executors = Seq.empty)
+    assert(scheduledExecutors === Seq.empty)
+  }
+
+  test("rescheduleReceiver: receiver preferredLocation") {
+    val receiverTrackingInfoMap = Map(
+      0 -> ReceiverTrackingInfo(0, ReceiverState.INACTIVE, None, None))
+    val scheduledExecutors = receiverSchedulingPolicy.rescheduleReceiver(
+      0, Some("host1"), receiverTrackingInfoMap, executors = Seq("host2"))
+    assert(scheduledExecutors.toSet === Set("host1", "host2"))
+  }
+
+  test("rescheduleReceiver: return all idle executors if more than 3 idle executors") {
+    val executors = Seq("host1", "host2", "host3", "host4", "host5")
+    // host3 is idle
+    val receiverTrackingInfoMap = Map(
+      0 -> ReceiverTrackingInfo(0, ReceiverState.ACTIVE, None, Some("host1")))
+    val scheduledExecutors = receiverSchedulingPolicy.rescheduleReceiver(
+      1, None, receiverTrackingInfoMap, executors)
+    assert(scheduledExecutors.toSet === Set("host2", "host3", "host4", "host5"))
+  }
+
+  test("rescheduleReceiver: return 3 best options if less than 3 idle executors") {
+    val executors = Seq("host1", "host2", "host3", "host4", "host5")
+    // Weights: host1 = 1.5, host2 = 0.5, host3 = 1.0
+    // host4 and host5 are idle
+    val receiverTrackingInfoMap = Map(
+      0 -> ReceiverTrackingInfo(0, ReceiverState.ACTIVE, None, Some("host1")),
+      1 -> ReceiverTrackingInfo(1, ReceiverState.SCHEDULED, Some(Seq("host2", "host3")), None),
+      2 -> ReceiverTrackingInfo(1, ReceiverState.SCHEDULED, Some(Seq("host1", "host3")), None))
+    val scheduledExecutors = receiverSchedulingPolicy.rescheduleReceiver(
+      3, None, receiverTrackingInfoMap, executors)
+    assert(scheduledExecutors.toSet === Set("host2", "host4", "host5"))
+  }
+
+  test("scheduleReceivers: " +
+    "schedule receivers evenly when there are more receivers than executors") {
+    val receivers = (0 until 6).map(new DummyReceiver(_))
+    val executors = (10000 until 10003).map(port => s"localhost:${port}")
+    val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, executors)
+    val numReceiversOnExecutor = mutable.HashMap[String, Int]()
+    // There should be 2 receivers running on each executor and each receiver has one executor
+    scheduledExecutors.foreach { case (receiverId, executors) =>
+      assert(executors.size == 1)
+      numReceiversOnExecutor(executors(0)) = numReceiversOnExecutor.getOrElse(executors(0), 0) + 1
+    }
+    assert(numReceiversOnExecutor === executors.map(_ -> 2).toMap)
+  }
+
+
+  test("scheduleReceivers: " +
+    "schedule receivers evenly when there are more executors than receivers") {
+    val receivers = (0 until 3).map(new DummyReceiver(_))
+    val executors = (10000 until 10006).map(port => s"localhost:${port}")
+    val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, executors)
+    val numReceiversOnExecutor = mutable.HashMap[String, Int]()
+    // There should be 1 receiver running on each executor and each receiver has two executors
+    scheduledExecutors.foreach { case (receiverId, executors) =>
+      assert(executors.size == 2)
+      executors.foreach { l =>
+        numReceiversOnExecutor(l) = numReceiversOnExecutor.getOrElse(l, 0) + 1
+      }
+    }
+    assert(numReceiversOnExecutor === executors.map(_ -> 1).toMap)
+  }
+
+  test("scheduleReceivers: schedule receivers evenly when the preferredLocations are even") {
+    val receivers = (0 until 3).map(new DummyReceiver(_)) ++
+      (3 until 6).map(new DummyReceiver(_, Some("localhost")))
+    val executors = (10000 until 10003).map(port => s"localhost:${port}") ++
+      (10003 until 10006).map(port => s"localhost2:${port}")
+    val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, executors)
+    val numReceiversOnExecutor = mutable.HashMap[String, Int]()
+    // There should be 1 receiver running on each executor and each receiver has 1 executor
+    scheduledExecutors.foreach { case (receiverId, executors) =>
+      assert(executors.size == 1)
+      executors.foreach { l =>
+        numReceiversOnExecutor(l) = numReceiversOnExecutor.getOrElse(l, 0) + 1
+      }
+    }
+    assert(numReceiversOnExecutor === executors.map(_ -> 1).toMap)
+    // Make sure we schedule the receivers to their preferredLocations
+    val executorsForReceiversWithPreferredLocation =
+      scheduledExecutors.filter { case (receiverId, executors) => receiverId >= 3 }.flatMap(_._2)
+    // We can simply check the executor set because we only know each receiver only has 1 executor
+    assert(executorsForReceiversWithPreferredLocation.toSet ===
+      (10000 until 10003).map(port => s"localhost:${port}").toSet)
+  }
+
+  test("scheduleReceivers: return empty if no receiver") {
+    assert(receiverSchedulingPolicy.scheduleReceivers(Seq.empty, Seq("localhost:10000")).isEmpty)
+  }
+
+  test("scheduleReceivers: return empty scheduled executors if no executors") {
+    val receivers = (0 until 3).map(new DummyReceiver(_))
+    val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, Seq.empty)
+    scheduledExecutors.foreach { case (receiverId, executors) =>
+      assert(executors.isEmpty)
+    }
+  }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index aadb7231757b8..e2159bd4f225d 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -18,66 +18,18 @@
 package org.apache.spark.streaming.scheduler
 
 import org.scalatest.concurrent.Eventually._
-import org.scalatest.concurrent.Timeouts
 import org.scalatest.time.SpanSugar._
-import org.apache.spark.streaming._
+
 import org.apache.spark.SparkConf
-import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming._
 import org.apache.spark.streaming.receiver._
-import org.apache.spark.util.Utils
-import org.apache.spark.streaming.dstream.InputDStream
-import scala.reflect.ClassTag
 import org.apache.spark.streaming.dstream.ReceiverInputDStream
+import org.apache.spark.storage.StorageLevel
 
 /** Testsuite for receiver scheduling */
 class ReceiverTrackerSuite extends TestSuiteBase {
   val sparkConf = new SparkConf().setMaster("local[8]").setAppName("test")
   val ssc = new StreamingContext(sparkConf, Milliseconds(100))
-  val tracker = new ReceiverTracker(ssc)
-  val launcher = new tracker.ReceiverLauncher()
-  val executors: List[String] = List("0", "1", "2", "3")
-
-  test("receiver scheduling - all or none have preferred location") {
-
-    def parse(s: String): Array[Array[String]] = {
-      val outerSplit = s.split("\\|")
-      val loc = new Array[Array[String]](outerSplit.length)
-      var i = 0
-      for (i <- 0 until outerSplit.length) {
-        loc(i) = outerSplit(i).split("\\,")
-      }
-      loc
-    }
-
-    def testScheduler(numReceivers: Int, preferredLocation: Boolean, allocation: String) {
-      val receivers =
-        if (preferredLocation) {
-          Array.tabulate(numReceivers)(i => new DummyReceiver(host =
-            Some(((i + 1) % executors.length).toString)))
-        } else {
-          Array.tabulate(numReceivers)(_ => new DummyReceiver)
-        }
-      val locations = launcher.scheduleReceivers(receivers, executors)
-      val expectedLocations = parse(allocation)
-      assert(locations.deep === expectedLocations.deep)
-    }
-
-    testScheduler(numReceivers = 5, preferredLocation = false, allocation = "0|1|2|3|0")
-    testScheduler(numReceivers = 3, preferredLocation = false, allocation = "0,3|1|2")
-    testScheduler(numReceivers = 4, preferredLocation = true, allocation = "1|2|3|0")
-  }
-
-  test("receiver scheduling - some have preferred location") {
-    val numReceivers = 4;
-    val receivers: Seq[Receiver[_]] = Seq(new DummyReceiver(host = Some("1")),
-      new DummyReceiver, new DummyReceiver, new DummyReceiver)
-    val locations = launcher.scheduleReceivers(receivers, executors)
-    assert(locations(0)(0) === "1")
-    assert(locations(1)(0) === "0")
-    assert(locations(2)(0) === "1")
-    assert(locations(0).length === 1)
-    assert(locations(3).length === 1)
-  }
 
   test("Receiver tracker - propagates rate limit") {
     object ReceiverStartedWaiter extends StreamingListener {
@@ -134,19 +86,19 @@ private class RateLimitInputDStream(@transient ssc_ : StreamingContext)
  * @note It's necessary to be a top-level object, or else serialization would create another
  *       one on the executor side and we won't be able to read its rate limit.
  */
-private object SingletonDummyReceiver extends DummyReceiver
+private object SingletonDummyReceiver extends DummyReceiver(0)
 
 /**
  * Dummy receiver implementation
  */
-private class DummyReceiver(host: Option[String] = None)
+private class DummyReceiver(receiverId: Int, host: Option[String] = None)
   extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
 
-  def onStart() {
-  }
+  setReceiverId(receiverId)
 
-  def onStop() {
-  }
+  override def onStart(): Unit = {}
+
+  override def onStop(): Unit = {}
 
   override def preferredLocation: Option[String] = host
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
index 40dc1fb601bd0..0891309f956d2 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
@@ -119,20 +119,20 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     listener.numTotalReceivedRecords should be (600)
 
     // onReceiverStarted
-    val receiverInfoStarted = ReceiverInfo(0, "test", null, true, "localhost")
+    val receiverInfoStarted = ReceiverInfo(0, "test", true, "localhost")
     listener.onReceiverStarted(StreamingListenerReceiverStarted(receiverInfoStarted))
     listener.receiverInfo(0) should be (Some(receiverInfoStarted))
     listener.receiverInfo(1) should be (None)
 
     // onReceiverError
-    val receiverInfoError = ReceiverInfo(1, "test", null, true, "localhost")
+    val receiverInfoError = ReceiverInfo(1, "test", true, "localhost")
     listener.onReceiverError(StreamingListenerReceiverError(receiverInfoError))
     listener.receiverInfo(0) should be (Some(receiverInfoStarted))
     listener.receiverInfo(1) should be (Some(receiverInfoError))
     listener.receiverInfo(2) should be (None)
 
     // onReceiverStopped
-    val receiverInfoStopped = ReceiverInfo(2, "test", null, true, "localhost")
+    val receiverInfoStopped = ReceiverInfo(2, "test", true, "localhost")
     listener.onReceiverStopped(StreamingListenerReceiverStopped(receiverInfoStopped))
     listener.receiverInfo(0) should be (Some(receiverInfoStarted))
     listener.receiverInfo(1) should be (Some(receiverInfoError))

From 2e7f99a004f08a42e86f6f603e4ba35cb52561c4 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Mon, 27 Jul 2015 21:08:56 -0700
Subject: [PATCH 0635/1454] [SPARK-8195] [SPARK-8196] [SQL] udf next_day
 last_day

next_day, returns next certain dayofweek.
last_day, returns the last day of the month which given date belongs to.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #6986 from adrian-wang/udfnlday and squashes the following commits:

ef7e3da [Daoyuan Wang] fix
02b3426 [Daoyuan Wang] address 2 comments
dc69630 [Daoyuan Wang] address comments from rxin
8846086 [Daoyuan Wang] address comments from rxin
d09bcce [Daoyuan Wang] multi fix
1a9de3d [Daoyuan Wang] function next_day and last_day
---
 .../catalyst/analysis/FunctionRegistry.scala  |  4 +-
 .../expressions/datetimeFunctions.scala       | 72 +++++++++++++++++++
 .../sql/catalyst/util/DateTimeUtils.scala     | 46 ++++++++++++
 .../expressions/DateExpressionsSuite.scala    | 28 ++++++++
 .../org/apache/spark/sql/functions.scala      | 17 +++++
 .../apache/spark/sql/DateFunctionsSuite.scala | 22 ++++++
 6 files changed, 188 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index aa05f448d12bc..61ee6f6f71631 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -219,8 +219,10 @@ object FunctionRegistry {
     expression[DayOfYear]("dayofyear"),
     expression[DayOfMonth]("dayofmonth"),
     expression[Hour]("hour"),
-    expression[Month]("month"),
+    expression[LastDay]("last_day"),
     expression[Minute]("minute"),
+    expression[Month]("month"),
+    expression[NextDay]("next_day"),
     expression[Quarter]("quarter"),
     expression[Second]("second"),
     expression[WeekOfYear]("weekofyear"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index 9e55f0546e123..b00a1b26fa285 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -265,3 +265,75 @@ case class DateFormatClass(left: Expression, right: Expression) extends BinaryEx
     })
   }
 }
+
+/**
+ * Returns the last day of the month which the date belongs to.
+ */
+case class LastDay(startDate: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+  override def child: Expression = startDate
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = DateType
+
+  override def prettyName: String = "last_day"
+
+  override def nullSafeEval(date: Any): Any = {
+    val days = date.asInstanceOf[Int]
+    DateTimeUtils.getLastDayOfMonth(days)
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (sd) => {
+      s"$dtu.getLastDayOfMonth($sd)"
+    })
+  }
+}
+
+/**
+ * Returns the first date which is later than startDate and named as dayOfWeek.
+ * For example, NextDay(2015-07-27, Sunday) would return 2015-08-02, which is the first
+ * sunday later than 2015-07-27.
+ */
+case class NextDay(startDate: Expression, dayOfWeek: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = startDate
+  override def right: Expression = dayOfWeek
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType, StringType)
+
+  override def dataType: DataType = DateType
+
+  override def nullSafeEval(start: Any, dayOfW: Any): Any = {
+    val dow = DateTimeUtils.getDayOfWeekFromString(dayOfW.asInstanceOf[UTF8String])
+    if (dow == -1) {
+      null
+    } else {
+      val sd = start.asInstanceOf[Int]
+      DateTimeUtils.getNextDateForDayOfWeek(sd, dow)
+    }
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (sd, dowS) => {
+      val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+      val dow = ctx.freshName("dow")
+      val genDow = if (right.foldable) {
+        val dowVal = DateTimeUtils.getDayOfWeekFromString(
+          dayOfWeek.eval(InternalRow.empty).asInstanceOf[UTF8String])
+        s"int $dow = $dowVal;"
+      } else {
+        s"int $dow = $dtu.getDayOfWeekFromString($dowS);"
+      }
+      genDow + s"""
+        if ($dow == -1) {
+          ${ev.isNull} = true;
+        } else {
+          ${ev.primitive} = $dtu.getNextDateForDayOfWeek($sd, $dow);
+        }
+       """
+    })
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 07412e73b6a5b..2e28fb9af9b65 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -573,4 +573,50 @@ object DateTimeUtils {
       dayInYear - 334
     }
   }
+
+  /**
+   * Returns Day of week from String. Starting from Thursday, marked as 0.
+   * (Because 1970-01-01 is Thursday).
+   */
+  def getDayOfWeekFromString(string: UTF8String): Int = {
+    val dowString = string.toString.toUpperCase
+    dowString match {
+      case "SU" | "SUN" | "SUNDAY" => 3
+      case "MO" | "MON" | "MONDAY" => 4
+      case "TU" | "TUE" | "TUESDAY" => 5
+      case "WE" | "WED" | "WEDNESDAY" => 6
+      case "TH" | "THU" | "THURSDAY" => 0
+      case "FR" | "FRI" | "FRIDAY" => 1
+      case "SA" | "SAT" | "SATURDAY" => 2
+      case _ => -1
+    }
+  }
+
+  /**
+   * Returns the first date which is later than startDate and is of the given dayOfWeek.
+   * dayOfWeek is an integer ranges in [0, 6], and 0 is Thu, 1 is Fri, etc,.
+   */
+  def getNextDateForDayOfWeek(startDate: Int, dayOfWeek: Int): Int = {
+    startDate + 1 + ((dayOfWeek - 1 - startDate) % 7 + 7) % 7
+  }
+
+  /**
+   * number of days in a non-leap year.
+   */
+  private[this] val daysInNormalYear = Array(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)
+
+  /**
+   * Returns last day of the month for the given date. The date is expressed in days
+   * since 1.1.1970.
+   */
+  def getLastDayOfMonth(date: Int): Int = {
+    val dayOfMonth = getDayOfMonth(date)
+    val month = getMonth(date)
+    if (month == 2 && isLeapYear(getYear(date))) {
+      date + daysInNormalYear(month - 1) + 1 - dayOfMonth
+    } else {
+      date + daysInNormalYear(month - 1) - dayOfMonth
+    }
+  }
+
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index bdba6ce891386..4d2d33765a269 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -22,6 +22,7 @@ import java.text.SimpleDateFormat
 import java.util.Calendar
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types.{StringType, TimestampType, DateType}
 
 class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -246,4 +247,31 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
+  test("last_day") {
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-02-28"))), Date.valueOf("2015-02-28"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-03-27"))), Date.valueOf("2015-03-31"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-04-26"))), Date.valueOf("2015-04-30"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-05-25"))), Date.valueOf("2015-05-31"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-06-24"))), Date.valueOf("2015-06-30"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-07-23"))), Date.valueOf("2015-07-31"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-08-01"))), Date.valueOf("2015-08-31"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-09-02"))), Date.valueOf("2015-09-30"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-10-03"))), Date.valueOf("2015-10-31"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-11-04"))), Date.valueOf("2015-11-30"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2015-12-05"))), Date.valueOf("2015-12-31"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2016-01-06"))), Date.valueOf("2016-01-31"))
+    checkEvaluation(LastDay(Literal(Date.valueOf("2016-02-07"))), Date.valueOf("2016-02-29"))
+  }
+
+  test("next_day") {
+    checkEvaluation(
+      NextDay(Literal(Date.valueOf("2015-07-23")), Literal("Thu")),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-30")))
+    checkEvaluation(
+      NextDay(Literal(Date.valueOf("2015-07-23")), Literal("THURSDAY")),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-30")))
+    checkEvaluation(
+      NextDay(Literal(Date.valueOf("2015-07-23")), Literal("th")),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-30")))
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index cab3db609dd4b..d18558b510f0b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2032,6 +2032,13 @@ object functions {
    */
   def hour(columnName: String): Column = hour(Column(columnName))
 
+  /**
+   * Returns the last day of the month which the given date belongs to.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def last_day(e: Column): Column = LastDay(e.expr)
+
   /**
    * Extracts the minutes as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2046,6 +2053,16 @@ object functions {
    */
   def minute(columnName: String): Column = minute(Column(columnName))
 
+  /**
+   * Returns the first date which is later than given date sd and named as dow.
+   * For example, `next_day('2015-07-27', "Sunday")` would return 2015-08-02, which is the
+   * first Sunday later than 2015-07-27. The parameter dayOfWeek could be 2-letter, 3-letter,
+   * or full name of the day of the week (e.g. Mo, tue, FRIDAY).
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def next_day(sd: Column, dayOfWeek: String): Column = NextDay(sd.expr, lit(dayOfWeek).expr)
+
   /**
    * Extracts the seconds as an integer from a given date/timestamp/string.
    * @group datetime_funcs
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 9e80ae86920d9..ff1c7562dc4a6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -184,4 +184,26 @@ class DateFunctionsSuite extends QueryTest {
       Row(15, 15, 15))
   }
 
+  test("function last_day") {
+    val df1 = Seq((1, "2015-07-23"), (2, "2015-07-24")).toDF("i", "d")
+    val df2 = Seq((1, "2015-07-23 00:11:22"), (2, "2015-07-24 11:22:33")).toDF("i", "t")
+    checkAnswer(
+      df1.select(last_day(col("d"))),
+      Seq(Row(Date.valueOf("2015-07-31")), Row(Date.valueOf("2015-07-31"))))
+    checkAnswer(
+      df2.select(last_day(col("t"))),
+      Seq(Row(Date.valueOf("2015-07-31")), Row(Date.valueOf("2015-07-31"))))
+  }
+
+  test("function next_day") {
+    val df1 = Seq(("mon", "2015-07-23"), ("tuesday", "2015-07-20")).toDF("dow", "d")
+    val df2 = Seq(("th", "2015-07-23 00:11:22"), ("xx", "2015-07-24 11:22:33")).toDF("dow", "t")
+    checkAnswer(
+      df1.select(next_day(col("d"), "MONDAY")),
+      Seq(Row(Date.valueOf("2015-07-27")), Row(Date.valueOf("2015-07-27"))))
+    checkAnswer(
+      df2.select(next_day(col("t"), "th")),
+      Seq(Row(Date.valueOf("2015-07-30")), Row(null)))
+  }
+
 }

From 84da8792e2a99736edb6c94df7eda87915a8a476 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 27 Jul 2015 21:41:15 -0700
Subject: [PATCH 0636/1454] [SPARK-9395][SQL] Create a SpecializedGetters
 interface to track all the specialized getters.

As we are adding more and more specialized getters to more classes (coming soon ArrayData), this interface can help us prevent missing a method in some interfaces.

Author: Reynold Xin <rxin@databricks.com>

Closes #7713 from rxin/SpecializedGetters and squashes the following commits:

3b39be1 [Reynold Xin] Added override modifier.
567ba9c [Reynold Xin] [SPARK-9395][SQL] Create a SpecializedGetters interface to track all the specialized getters.
---
 .../expressions/SpecializedGetters.java       | 53 +++++++++++++++++++
 .../spark/sql/catalyst/InternalRow.scala      | 30 ++++++-----
 2 files changed, 69 insertions(+), 14 deletions(-)
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
new file mode 100644
index 0000000000000..5f28d52a94bd7
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
@@ -0,0 +1,53 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.types.Decimal;
+import org.apache.spark.unsafe.types.Interval;
+import org.apache.spark.unsafe.types.UTF8String;
+
+public interface SpecializedGetters {
+
+  boolean isNullAt(int ordinal);
+
+  boolean getBoolean(int ordinal);
+
+  byte getByte(int ordinal);
+
+  short getShort(int ordinal);
+
+  int getInt(int ordinal);
+
+  long getLong(int ordinal);
+
+  float getFloat(int ordinal);
+
+  double getDouble(int ordinal);
+
+  Decimal getDecimal(int ordinal);
+
+  UTF8String getUTF8String(int ordinal);
+
+  byte[] getBinary(int ordinal);
+
+  Interval getInterval(int ordinal);
+
+  InternalRow getStruct(int ordinal, int numFields);
+
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 9a11de3840ce2..e395a67434fa7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -26,7 +26,7 @@ import org.apache.spark.unsafe.types.{Interval, UTF8String}
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
  * internal types.
  */
-abstract class InternalRow extends Serializable {
+abstract class InternalRow extends Serializable with SpecializedGetters {
 
   def numFields: Int
 
@@ -38,29 +38,30 @@ abstract class InternalRow extends Serializable {
 
   def getAs[T](ordinal: Int, dataType: DataType): T = get(ordinal, dataType).asInstanceOf[T]
 
-  def isNullAt(ordinal: Int): Boolean = get(ordinal) == null
+  override def isNullAt(ordinal: Int): Boolean = get(ordinal) == null
 
-  def getBoolean(ordinal: Int): Boolean = getAs[Boolean](ordinal, BooleanType)
+  override def getBoolean(ordinal: Int): Boolean = getAs[Boolean](ordinal, BooleanType)
 
-  def getByte(ordinal: Int): Byte = getAs[Byte](ordinal, ByteType)
+  override def getByte(ordinal: Int): Byte = getAs[Byte](ordinal, ByteType)
 
-  def getShort(ordinal: Int): Short = getAs[Short](ordinal, ShortType)
+  override def getShort(ordinal: Int): Short = getAs[Short](ordinal, ShortType)
 
-  def getInt(ordinal: Int): Int = getAs[Int](ordinal, IntegerType)
+  override def getInt(ordinal: Int): Int = getAs[Int](ordinal, IntegerType)
 
-  def getLong(ordinal: Int): Long = getAs[Long](ordinal, LongType)
+  override def getLong(ordinal: Int): Long = getAs[Long](ordinal, LongType)
 
-  def getFloat(ordinal: Int): Float = getAs[Float](ordinal, FloatType)
+  override def getFloat(ordinal: Int): Float = getAs[Float](ordinal, FloatType)
 
-  def getDouble(ordinal: Int): Double = getAs[Double](ordinal, DoubleType)
+  override def getDouble(ordinal: Int): Double = getAs[Double](ordinal, DoubleType)
 
-  def getUTF8String(ordinal: Int): UTF8String = getAs[UTF8String](ordinal, StringType)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs[UTF8String](ordinal, StringType)
 
-  def getBinary(ordinal: Int): Array[Byte] = getAs[Array[Byte]](ordinal, BinaryType)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs[Array[Byte]](ordinal, BinaryType)
 
-  def getDecimal(ordinal: Int): Decimal = getAs[Decimal](ordinal, DecimalType.SYSTEM_DEFAULT)
+  override def getDecimal(ordinal: Int): Decimal =
+    getAs[Decimal](ordinal, DecimalType.SYSTEM_DEFAULT)
 
-  def getInterval(ordinal: Int): Interval = getAs[Interval](ordinal, IntervalType)
+  override def getInterval(ordinal: Int): Interval = getAs[Interval](ordinal, IntervalType)
 
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
@@ -71,7 +72,8 @@ abstract class InternalRow extends Serializable {
    * @param ordinal position to get the struct from.
    * @param numFields number of fields the struct type has
    */
-  def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs[InternalRow](ordinal, null)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow =
+    getAs[InternalRow](ordinal, null)
 
   override def toString: String = s"[${this.mkString(",")}]"
 

From 3bc7055e265ee5c75af8726579663cea0590f6c0 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 27 Jul 2015 22:04:54 -0700
Subject: [PATCH 0637/1454] Fixed a test failure.

---
 .../test/scala/org/apache/spark/sql/DateFunctionsSuite.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index ff1c7562dc4a6..001fcd035c82a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -203,7 +203,7 @@ class DateFunctionsSuite extends QueryTest {
       Seq(Row(Date.valueOf("2015-07-27")), Row(Date.valueOf("2015-07-27"))))
     checkAnswer(
       df2.select(next_day(col("t"), "th")),
-      Seq(Row(Date.valueOf("2015-07-30")), Row(null)))
+      Seq(Row(Date.valueOf("2015-07-30")), Row(Date.valueOf("2015-07-30"))))
   }
 
 }

From 63a492b931765b1edd66624421d503f1927825ec Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Mon, 27 Jul 2015 22:47:31 -0700
Subject: [PATCH 0638/1454] [SPARK-8828] [SQL] Revert SPARK-5680

JIRA: https://issues.apache.org/jira/browse/SPARK-8828

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7667 from yjshen/revert_combinesum_2 and squashes the following commits:

c37ccb1 [Yijie Shen] add test case
8377214 [Yijie Shen] revert spark.sql.useAggregate2 to its default value
e2305ac [Yijie Shen] fix bug - avg on decimal column
7cb0e95 [Yijie Shen] [wip] resolving bugs
1fadb5a [Yijie Shen] remove occurance
17c6248 [Yijie Shen] revert SPARK-5680
---
 .../sql/catalyst/expressions/aggregates.scala | 70 ++-----------------
 .../sql/execution/GeneratedAggregate.scala    | 41 +----------
 .../spark/sql/execution/SparkStrategies.scala |  2 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 31 ++++++++
 .../execution/HiveCompatibilitySuite.scala    |  1 -
 ..._format-0-eff4ef3c207d14d5121368f294697964 |  0
 ..._format-1-4a03c4328565c60ca99689239f07fb16 |  1 -
 7 files changed, 37 insertions(+), 109 deletions(-)
 delete mode 100644 sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964
 delete mode 100644 sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 42343d4d8d79c..5d4b349b1597a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -404,7 +404,7 @@ case class Average(child: Expression) extends UnaryExpression with PartialAggreg
 
         // partialSum already increase the precision by 10
         val castedSum = Cast(Sum(partialSum.toAttribute), partialSum.dataType)
-        val castedCount = Sum(partialCount.toAttribute)
+        val castedCount = Cast(Sum(partialCount.toAttribute), partialSum.dataType)
         SplitEvaluation(
           Cast(Divide(castedSum, castedCount), dataType),
           partialCount :: partialSum :: Nil)
@@ -490,13 +490,13 @@ case class Sum(child: Expression) extends UnaryExpression with PartialAggregate1
       case DecimalType.Fixed(_, _) =>
         val partialSum = Alias(Sum(child), "PartialSum")()
         SplitEvaluation(
-          Cast(CombineSum(partialSum.toAttribute), dataType),
+          Cast(Sum(partialSum.toAttribute), dataType),
           partialSum :: Nil)
 
       case _ =>
         val partialSum = Alias(Sum(child), "PartialSum")()
         SplitEvaluation(
-          CombineSum(partialSum.toAttribute),
+          Sum(partialSum.toAttribute),
           partialSum :: Nil)
     }
   }
@@ -522,8 +522,7 @@ case class SumFunction(expr: Expression, base: AggregateExpression1) extends Agg
 
   private val sum = MutableLiteral(null, calcType)
 
-  private val addFunction =
-    Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero))
+  private val addFunction = Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum))
 
   override def update(input: InternalRow): Unit = {
     sum.update(addFunction, input)
@@ -538,67 +537,6 @@ case class SumFunction(expr: Expression, base: AggregateExpression1) extends Agg
   }
 }
 
-/**
- * Sum should satisfy 3 cases:
- * 1) sum of all null values = zero
- * 2) sum for table column with no data = null
- * 3) sum of column with null and not null values = sum of not null values
- * Require separate CombineSum Expression and function as it has to distinguish "No data" case
- * versus "data equals null" case, while aggregating results and at each partial expression.i.e.,
- * Combining    PartitionLevel   InputData
- *                           <-- null
- * Zero     <-- Zero         <-- null
- *
- *          <-- null         <-- no data
- * null     <-- null         <-- no data
- */
-case class CombineSum(child: Expression) extends AggregateExpression1 {
-  def this() = this(null)
-
-  override def children: Seq[Expression] = child :: Nil
-  override def nullable: Boolean = true
-  override def dataType: DataType = child.dataType
-  override def toString: String = s"CombineSum($child)"
-  override def newInstance(): CombineSumFunction = new CombineSumFunction(child, this)
-}
-
-case class CombineSumFunction(expr: Expression, base: AggregateExpression1)
-  extends AggregateFunction1 {
-
-  def this() = this(null, null) // Required for serialization.
-
-  private val calcType =
-    expr.dataType match {
-      case DecimalType.Fixed(precision, scale) =>
-        DecimalType.bounded(precision + 10, scale)
-      case _ =>
-        expr.dataType
-    }
-
-  private val zero = Cast(Literal(0), calcType)
-
-  private val sum = MutableLiteral(null, calcType)
-
-  private val addFunction =
-    Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero))
-
-  override def update(input: InternalRow): Unit = {
-    val result = expr.eval(input)
-    // partial sum result can be null only when no input rows present
-    if(result != null) {
-      sum.update(addFunction, input)
-    }
-  }
-
-  override def eval(input: InternalRow): Any = {
-    expr.dataType match {
-      case DecimalType.Fixed(_, _) =>
-        Cast(sum, dataType).eval(null)
-      case _ => sum.eval(null)
-    }
-  }
-}
-
 case class SumDistinct(child: Expression) extends UnaryExpression with PartialAggregate1 {
 
   def this() = this(null)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 5ad4691a5ca07..1cd1420480f03 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -108,7 +108,7 @@ case class GeneratedAggregate(
           Add(
             Coalesce(currentSum :: zero :: Nil),
             Cast(expr, calcType)
-          ) :: currentSum :: zero :: Nil)
+          ) :: currentSum :: Nil)
         val result =
           expr.dataType match {
             case DecimalType.Fixed(_, _) =>
@@ -118,45 +118,6 @@ case class GeneratedAggregate(
 
         AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
 
-      case cs @ CombineSum(expr) =>
-        val calcType =
-          expr.dataType match {
-            case DecimalType.Fixed(p, s) =>
-              DecimalType.bounded(p + 10, s)
-            case _ =>
-              expr.dataType
-          }
-
-        val currentSum = AttributeReference("currentSum", calcType, nullable = true)()
-        val initialValue = Literal.create(null, calcType)
-
-        // Coalesce avoids double calculation...
-        // but really, common sub expression elimination would be better....
-        val zero = Cast(Literal(0), calcType)
-        // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its
-        // UnscaledValue will be null if and only if x is null; helps with Average on decimals
-        val actualExpr = expr match {
-          case UnscaledValue(e) => e
-          case _ => expr
-        }
-        // partial sum result can be null only when no input rows present
-        val updateFunction = If(
-          IsNotNull(actualExpr),
-          Coalesce(
-            Add(
-              Coalesce(currentSum :: zero :: Nil),
-              Cast(expr, calcType)) :: currentSum :: zero :: Nil),
-          currentSum)
-
-        val result =
-          expr.dataType match {
-            case DecimalType.Fixed(_, _) =>
-              Cast(currentSum, cs.dataType)
-            case _ => currentSum
-          }
-
-        AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
-
       case m @ Max(expr) =>
         val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)()
         val initialValue = Literal.create(null, expr.dataType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 306bbfec624c0..d88a02298c00d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -201,7 +201,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
 
     def canBeCodeGened(aggs: Seq[AggregateExpression1]): Boolean = aggs.forall {
-      case _: CombineSum | _: Sum | _: Count | _: Max | _: Min |  _: CombineSetsAndCount => true
+      case _: Sum | _: Count | _: Max | _: Min |  _: CombineSetsAndCount => true
       // The generated set implementation is pretty limited ATM.
       case CollectHashSet(exprs) if exprs.size == 1  &&
            Seq(IntegerType, LongType).contains(exprs.head.dataType) => true
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 358e319476e83..42724ed766af5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -227,6 +227,37 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Seq(Row("1"), Row("2")))
   }
 
+  test("SPARK-8828 sum should return null if all input values are null") {
+    withSQLConf(SQLConf.USE_SQL_AGGREGATE2.key -> "true") {
+      withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "true") {
+        checkAnswer(
+          sql("select sum(a), avg(a) from allNulls"),
+          Seq(Row(null, null))
+        )
+      }
+      withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "false") {
+        checkAnswer(
+          sql("select sum(a), avg(a) from allNulls"),
+          Seq(Row(null, null))
+        )
+      }
+    }
+    withSQLConf(SQLConf.USE_SQL_AGGREGATE2.key -> "false") {
+      withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "true") {
+        checkAnswer(
+          sql("select sum(a), avg(a) from allNulls"),
+          Seq(Row(null, null))
+        )
+      }
+      withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "false") {
+        checkAnswer(
+          sql("select sum(a), avg(a) from allNulls"),
+          Seq(Row(null, null))
+        )
+      }
+    }
+  }
+
   test("aggregation with codegen") {
     val originalValue = sqlContext.conf.codegenEnabled
     sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index b12b3838e615c..ec959cb2194b0 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -822,7 +822,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udaf_covar_pop",
     "udaf_covar_samp",
     "udaf_histogram_numeric",
-    "udaf_number_format",
     "udf2",
     "udf5",
     "udf6",
diff --git a/sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964 b/sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 b/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16
deleted file mode 100644
index c6f275a0db131..0000000000000
--- a/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16
+++ /dev/null
@@ -1 +0,0 @@
-0.0	NULL	NULL	NULL

From 60f08c7c8775c0462b74bc65b41397be6eb24b6d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 27 Jul 2015 22:51:15 -0700
Subject: [PATCH 0639/1454] [SPARK-9373][SQL] Support StructType in Tungsten
 projection

This pull request updates GenerateUnsafeProjection to support StructType. If an input struct type is backed already by an UnsafeRow, GenerateUnsafeProjection copies the bytes directly into its buffer space without any conversion. However, if the input is not an UnsafeRow, GenerateUnsafeProjection runs the code generated recursively to convert the input into an UnsafeRow and then copies it into the buffer space.

Also create a TungstenProject operator that projects data directly into UnsafeRow. Note that I'm not sure if this is the way we want to structure Unsafe+codegen operators, but we can defer that decision to follow-up pull requests.

Author: Reynold Xin <rxin@databricks.com>

Closes #7689 from rxin/tungsten-struct-type and squashes the following commits:

9162f42 [Reynold Xin] Support IntervalType in UnsafeRow's getter.
be9f377 [Reynold Xin] Fixed tests.
10c4b7c [Reynold Xin] Format generated code.
77e8d0e [Reynold Xin] Fixed NondeterministicSuite.
ac4951d [Reynold Xin] Yay.
ac203bf [Reynold Xin] More comments.
9f36216 [Reynold Xin] Updated comment.
6b781fe [Reynold Xin] Reset the change in DataFrameSuite.
525b95b [Reynold Xin] Merged with master, more documentation & test cases.
321859a [Reynold Xin] [SPARK-9373][SQL] Support StructType in Tungsten projection [WIP]
---
 .../sql/catalyst/expressions/UnsafeRow.java   |   2 +
 .../expressions/UnsafeRowWriters.java         |  48 +++++-
 .../catalyst/expressions/BoundAttribute.scala |   9 +-
 .../codegen/GenerateUnsafeProjection.scala    | 162 ++++++++++++++++--
 .../expressions/complexTypeCreator.scala      |  94 ++++++++--
 .../ArithmeticExpressionSuite.scala           |   2 +-
 .../expressions/BitwiseFunctionsSuite.scala   |  20 ++-
 .../expressions/ExpressionEvalHelper.scala    |  26 ++-
 .../spark/sql/execution/SparkStrategies.scala |   9 +-
 .../spark/sql/execution/basicOperators.scala  |  25 +++
 .../spark/sql/DataFrameTungstenSuite.scala    |  84 +++++++++
 .../expression/NondeterministicSuite.scala    |   2 +-
 12 files changed, 430 insertions(+), 53 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index fb084dd13b620..955fb4226fc0e 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -265,6 +265,8 @@ public Object get(int ordinal, DataType dataType) {
       return getBinary(ordinal);
     } else if (dataType instanceof StringType) {
       return getUTF8String(ordinal);
+    } else if (dataType instanceof IntervalType) {
+      return getInterval(ordinal);
     } else if (dataType instanceof StructType) {
       return getStruct(ordinal, ((StructType) dataType).size());
     } else {
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 0ba31d3b9b743..8fdd7399602d2 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
+import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
@@ -81,6 +82,52 @@ public static int write(UnsafeRow target, int ordinal, int cursor, byte[] input)
     }
   }
 
+  /**
+   * Writer for struct type where the struct field is backed by an {@link UnsafeRow}.
+   *
+   * We throw UnsupportedOperationException for inputs that are not backed by {@link UnsafeRow}.
+   * Non-UnsafeRow struct fields are handled directly in
+   * {@link org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection}
+   * by generating the Java code needed to convert them into UnsafeRow.
+   */
+  public static class StructWriter {
+    public static int getSize(InternalRow input) {
+      int numBytes = 0;
+      if (input instanceof UnsafeRow) {
+        numBytes = ((UnsafeRow) input).getSizeInBytes();
+      } else {
+        // This is handled directly in GenerateUnsafeProjection.
+        throw new UnsupportedOperationException();
+      }
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes);
+    }
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, InternalRow input) {
+      int numBytes = 0;
+      final long offset = target.getBaseOffset() + cursor;
+      if (input instanceof UnsafeRow) {
+        final UnsafeRow row = (UnsafeRow) input;
+        numBytes = row.getSizeInBytes();
+
+        // zero-out the padding bytes
+        if ((numBytes & 0x07) > 0) {
+          PlatformDependent.UNSAFE.putLong(
+            target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        }
+
+        // Write the string to the variable length portion.
+        row.writeToMemory(target.getBaseObject(), offset);
+
+        // Set the fixed length portion.
+        target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
+      } else {
+        // This is handled directly in GenerateUnsafeProjection.
+        throw new UnsupportedOperationException();
+      }
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes);
+    }
+  }
+
   /** Writer for interval type. */
   public static class IntervalWriter {
 
@@ -96,5 +143,4 @@ public static int write(UnsafeRow target, int ordinal, int cursor, Interval inpu
       return 16;
     }
   }
-
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 41a877f214e55..8304d4ccd47f7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -50,7 +50,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
         case BinaryType => input.getBinary(ordinal)
         case IntervalType => input.getInterval(ordinal)
         case t: StructType => input.getStruct(ordinal, t.size)
-        case dataType => input.get(ordinal, dataType)
+        case _ => input.get(ordinal, dataType)
       }
     }
   }
@@ -64,10 +64,11 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
   override def exprId: ExprId = throw new UnsupportedOperationException
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val javaType = ctx.javaType(dataType)
+    val value = ctx.getColumn("i", dataType, ordinal)
     s"""
-        boolean ${ev.isNull} = i.isNullAt($ordinal);
-        ${ctx.javaType(dataType)} ${ev.primitive} = ${ev.isNull} ?
-            ${ctx.defaultValue(dataType)} : (${ctx.getColumn("i", dataType, ordinal)});
+      boolean ${ev.isNull} = i.isNullAt($ordinal);
+      $javaType ${ev.primitive} = ${ev.isNull} ? ${ctx.defaultValue(dataType)} : ($value);
     """
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 9d2161947b351..3e87f7285847c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -34,11 +34,13 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   private val StringWriter = classOf[UnsafeRowWriters.UTF8StringWriter].getName
   private val BinaryWriter = classOf[UnsafeRowWriters.BinaryWriter].getName
   private val IntervalWriter = classOf[UnsafeRowWriters.IntervalWriter].getName
+  private val StructWriter = classOf[UnsafeRowWriters.StructWriter].getName
 
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
     case t: AtomicType if !t.isInstanceOf[DecimalType] => true
     case _: IntervalType => true
+    case t: StructType => t.toSeq.forall(field => canSupport(field.dataType))
     case NullType => true
     case _ => false
   }
@@ -55,15 +57,22 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val ret = ev.primitive
     ctx.addMutableState("UnsafeRow", ret, s"$ret = new UnsafeRow();")
-    val bufferTerm = ctx.freshName("buffer")
-    ctx.addMutableState("byte[]", bufferTerm, s"$bufferTerm = new byte[64];")
-    val cursorTerm = ctx.freshName("cursor")
-    val numBytesTerm = ctx.freshName("numBytes")
+    val buffer = ctx.freshName("buffer")
+    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
+    val cursor = ctx.freshName("cursor")
+    val numBytes = ctx.freshName("numBytes")
 
-    val exprs = expressions.map(_.gen(ctx))
+    val exprs = expressions.zipWithIndex.map { case (e, i) =>
+      e.dataType match {
+        case st: StructType =>
+          createCodeForStruct(ctx, e.gen(ctx), st)
+        case _ =>
+          e.gen(ctx)
+      }
+    }
     val allExprs = exprs.map(_.code).mkString("\n")
-    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
 
+    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
     val additionalSize = expressions.zipWithIndex.map { case (e, i) =>
       e.dataType match {
         case StringType =>
@@ -72,6 +81,8 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           s" + (${exprs(i).isNull} ? 0 : $BinaryWriter.getSize(${exprs(i).primitive}))"
         case IntervalType =>
           s" + (${exprs(i).isNull} ? 0 : 16)"
+        case _: StructType =>
+          s" + (${exprs(i).isNull} ? 0 : $StructWriter.getSize(${exprs(i).primitive}))"
         case _ => ""
       }
     }.mkString("")
@@ -81,11 +92,13 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         case dt if ctx.isPrimitiveType(dt) =>
           s"${ctx.setColumn(ret, dt, i, exprs(i).primitive)}"
         case StringType =>
-          s"$cursorTerm += $StringWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
+          s"$cursor += $StringWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
         case BinaryType =>
-          s"$cursorTerm += $BinaryWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
+          s"$cursor += $BinaryWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
         case IntervalType =>
-          s"$cursorTerm += $IntervalWriter.write($ret, $i, $cursorTerm, ${exprs(i).primitive})"
+          s"$cursor += $IntervalWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
+        case t: StructType =>
+          s"$cursor += $StructWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
         case NullType => ""
         case _ =>
           throw new UnsupportedOperationException(s"Not supported DataType: ${e.dataType}")
@@ -99,24 +112,139 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     s"""
       $allExprs
-      int $numBytesTerm = $fixedSize $additionalSize;
-      if ($numBytesTerm > $bufferTerm.length) {
-        $bufferTerm = new byte[$numBytesTerm];
+      int $numBytes = $fixedSize $additionalSize;
+      if ($numBytes > $buffer.length) {
+        $buffer = new byte[$numBytes];
       }
 
       $ret.pointTo(
-        $bufferTerm,
+        $buffer,
         org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
         ${expressions.size},
-        $numBytesTerm);
-      int $cursorTerm = $fixedSize;
-
+        $numBytes);
+      int $cursor = $fixedSize;
 
       $writers
       boolean ${ev.isNull} = false;
      """
   }
 
+  /**
+   * Generates the Java code to convert a struct (backed by InternalRow) to UnsafeRow.
+   *
+   * This function also handles nested structs by recursively generating the code to do conversion.
+   *
+   * @param ctx code generation context
+   * @param input the input struct, identified by a [[GeneratedExpressionCode]]
+   * @param schema schema of the struct field
+   */
+  // TODO: refactor createCode and this function to reduce code duplication.
+  private def createCodeForStruct(
+      ctx: CodeGenContext,
+      input: GeneratedExpressionCode,
+      schema: StructType): GeneratedExpressionCode = {
+
+    val isNull = input.isNull
+    val primitive = ctx.freshName("structConvert")
+    ctx.addMutableState("UnsafeRow", primitive, s"$primitive = new UnsafeRow();")
+    val buffer = ctx.freshName("buffer")
+    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
+    val cursor = ctx.freshName("cursor")
+
+    val exprs: Seq[GeneratedExpressionCode] = schema.map(_.dataType).zipWithIndex.map {
+      case (dt, i) => dt match {
+      case st: StructType =>
+        val nestedStructEv = GeneratedExpressionCode(
+          code = "",
+          isNull = s"${input.primitive}.isNullAt($i)",
+          primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
+        )
+        createCodeForStruct(ctx, nestedStructEv, st)
+      case _ =>
+        GeneratedExpressionCode(
+          code = "",
+          isNull = s"${input.primitive}.isNullAt($i)",
+          primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
+        )
+      }
+    }
+    val allExprs = exprs.map(_.code).mkString("\n")
+
+    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
+    val additionalSize = schema.toSeq.map(_.dataType).zip(exprs).map { case (dt, ev) =>
+      dt match {
+        case StringType =>
+          s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
+        case BinaryType =>
+          s" + (${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive}))"
+        case IntervalType =>
+          s" + (${ev.isNull} ? 0 : 16)"
+        case _: StructType =>
+          s" + (${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive}))"
+        case _ => ""
+      }
+    }.mkString("")
+
+    val writers = schema.toSeq.map(_.dataType).zip(exprs).zipWithIndex.map { case ((dt, ev), i) =>
+      val update = dt match {
+        case _ if ctx.isPrimitiveType(dt) =>
+          s"${ctx.setColumn(primitive, dt, i, exprs(i).primitive)}"
+        case StringType =>
+          s"$cursor += $StringWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
+        case BinaryType =>
+          s"$cursor += $BinaryWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
+        case IntervalType =>
+          s"$cursor += $IntervalWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
+        case t: StructType =>
+          s"$cursor += $StructWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
+        case NullType => ""
+        case _ =>
+          throw new UnsupportedOperationException(s"Not supported DataType: $dt")
+      }
+      s"""
+          if (${exprs(i).isNull}) {
+            $primitive.setNullAt($i);
+          } else {
+            $update;
+          }
+        """
+    }.mkString("\n          ")
+
+    // Note that we add a shortcut here for performance: if the input is already an UnsafeRow,
+    // just copy the bytes directly into our buffer space without running any conversion.
+    // We also had to use a hack to introduce a "tmp" variable, to avoid the Java compiler from
+    // complaining that a GenericMutableRow (generated by expressions) cannot be cast to UnsafeRow.
+    val tmp = ctx.freshName("tmp")
+    val numBytes = ctx.freshName("numBytes")
+    val code = s"""
+       |${input.code}
+       |if (!${input.isNull}) {
+       |  Object $tmp = (Object) ${input.primitive};
+       |  if ($tmp instanceof UnsafeRow) {
+       |    $primitive = (UnsafeRow) $tmp;
+       |  } else {
+       |    $allExprs
+       |
+       |    int $numBytes = $fixedSize $additionalSize;
+       |    if ($numBytes > $buffer.length) {
+       |      $buffer = new byte[$numBytes];
+       |    }
+       |
+       |    $primitive.pointTo(
+       |      $buffer,
+       |      org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
+       |      ${exprs.size},
+       |      $numBytes);
+       |    int $cursor = $fixedSize;
+       |
+       |    $writers
+       |  }
+       |}
+     """.stripMargin
+
+    GeneratedExpressionCode(code, isNull, primitive)
+  }
+
   protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
     in.map(ExpressionCanonicalizer.execute)
 
@@ -159,7 +287,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       }
       """
 
-    logDebug(s"code for ${expressions.mkString(",")}:\n$code")
+    logDebug(s"code for ${expressions.mkString(",")}:\n${CodeFormatter.format(code)}")
 
     val c = compile(code)
     c.generate(ctx.references.toArray).asInstanceOf[UnsafeProjection]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 119168fa59f15..d8c9087ff5380 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -104,18 +104,19 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
       children.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
-        if (${eval.isNull}) {
-          ${ev.primitive}.update($i, null);
-        } else {
-          ${ev.primitive}.update($i, ${eval.primitive});
-        }
-       """
+          if (${eval.isNull}) {
+            ${ev.primitive}.update($i, null);
+          } else {
+            ${ev.primitive}.update($i, ${eval.primitive});
+          }
+         """
       }.mkString("\n")
   }
 
   override def prettyName: String = "struct"
 }
 
+
 /**
  * Creates a struct with the given field names and values
  *
@@ -168,14 +169,83 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
       valExprs.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
-        if (${eval.isNull}) {
-          ${ev.primitive}.update($i, null);
-        } else {
-          ${ev.primitive}.update($i, ${eval.primitive});
-        }
-       """
+          if (${eval.isNull}) {
+            ${ev.primitive}.update($i, null);
+          } else {
+            ${ev.primitive}.update($i, ${eval.primitive});
+          }
+         """
       }.mkString("\n")
   }
 
   override def prettyName: String = "named_struct"
 }
+
+/**
+ * Returns a Row containing the evaluation of all children expressions. This is a variant that
+ * returns UnsafeRow directly. The unsafe projection operator replaces [[CreateStruct]] with
+ * this expression automatically at runtime.
+ */
+case class CreateStructUnsafe(children: Seq[Expression]) extends Expression {
+
+  override def foldable: Boolean = children.forall(_.foldable)
+
+  override lazy val resolved: Boolean = childrenResolved
+
+  override lazy val dataType: StructType = {
+    val fields = children.zipWithIndex.map { case (child, idx) =>
+      child match {
+        case ne: NamedExpression =>
+          StructField(ne.name, ne.dataType, ne.nullable, ne.metadata)
+        case _ =>
+          StructField(s"col${idx + 1}", child.dataType, child.nullable, Metadata.empty)
+      }
+    }
+    StructType(fields)
+  }
+
+  override def nullable: Boolean = false
+
+  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    GenerateUnsafeProjection.createCode(ctx, ev, children)
+  }
+
+  override def prettyName: String = "struct_unsafe"
+}
+
+
+/**
+ * Creates a struct with the given field names and values. This is a variant that returns
+ * UnsafeRow directly. The unsafe projection operator replaces [[CreateStruct]] with
+ * this expression automatically at runtime.
+ *
+ * @param children Seq(name1, val1, name2, val2, ...)
+ */
+case class CreateNamedStructUnsafe(children: Seq[Expression]) extends Expression {
+
+  private lazy val (nameExprs, valExprs) =
+    children.grouped(2).map { case Seq(name, value) => (name, value) }.toList.unzip
+
+  private lazy val names = nameExprs.map(_.eval(EmptyRow).toString)
+
+  override lazy val dataType: StructType = {
+    val fields = names.zip(valExprs).map { case (name, valExpr) =>
+      StructField(name, valExpr.dataType, valExpr.nullable, Metadata.empty)
+    }
+    StructType(fields)
+  }
+
+  override def foldable: Boolean = valExprs.forall(_.foldable)
+
+  override def nullable: Boolean = false
+
+  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    GenerateUnsafeProjection.createCode(ctx, ev, valExprs)
+  }
+
+  override def prettyName: String = "named_struct_unsafe"
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index e7e5231d32c9e..7773e098e0caa 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -170,6 +170,6 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Pmod(-7, 3), 2)
     checkEvaluation(Pmod(7.2D, 4.1D), 3.1000000000000005)
     checkEvaluation(Pmod(Decimal(0.7), Decimal(0.2)), Decimal(0.1))
-    checkEvaluation(Pmod(2L, Long.MaxValue), 2)
+    checkEvaluation(Pmod(2L, Long.MaxValue), 2L)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
index 648fbf5a4c30b..fa30fbe528479 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
@@ -30,8 +30,9 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(expr, expected)
     }
 
-    check(1.toByte, ~1.toByte)
-    check(1000.toShort, ~1000.toShort)
+    // Need the extra toByte even though IntelliJ thought it's not needed.
+    check(1.toByte, (~1.toByte).toByte)
+    check(1000.toShort, (~1000.toShort).toShort)
     check(1000000, ~1000000)
     check(123456789123L, ~123456789123L)
 
@@ -45,8 +46,9 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(expr, expected)
     }
 
-    check(1.toByte, 2.toByte, 1.toByte & 2.toByte)
-    check(1000.toShort, 2.toShort, 1000.toShort & 2.toShort)
+    // Need the extra toByte even though IntelliJ thought it's not needed.
+    check(1.toByte, 2.toByte, (1.toByte & 2.toByte).toByte)
+    check(1000.toShort, 2.toShort, (1000.toShort & 2.toShort).toShort)
     check(1000000, 4, 1000000 & 4)
     check(123456789123L, 5L, 123456789123L & 5L)
 
@@ -63,8 +65,9 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(expr, expected)
     }
 
-    check(1.toByte, 2.toByte, 1.toByte | 2.toByte)
-    check(1000.toShort, 2.toShort, 1000.toShort | 2.toShort)
+    // Need the extra toByte even though IntelliJ thought it's not needed.
+    check(1.toByte, 2.toByte, (1.toByte | 2.toByte).toByte)
+    check(1000.toShort, 2.toShort, (1000.toShort | 2.toShort).toShort)
     check(1000000, 4, 1000000 | 4)
     check(123456789123L, 5L, 123456789123L | 5L)
 
@@ -81,8 +84,9 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(expr, expected)
     }
 
-    check(1.toByte, 2.toByte, 1.toByte ^ 2.toByte)
-    check(1000.toShort, 2.toShort, 1000.toShort ^ 2.toShort)
+    // Need the extra toByte even though IntelliJ thought it's not needed.
+    check(1.toByte, 2.toByte, (1.toByte ^ 2.toByte).toByte)
+    check(1000.toShort, 2.toShort, (1000.toShort ^ 2.toShort).toShort)
     check(1000000, 4, 1000000 ^ 4)
     check(123456789123L, 5L, 123456789123L ^ 5L)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index ab0cdc857c80e..136368bf5b368 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -114,7 +114,7 @@ trait ExpressionEvalHelper {
     val actual = plan(inputRow).get(0, expression.dataType)
     if (!checkResult(actual, expected)) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
-      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input")
+      fail(s"Incorrect evaluation: $expression, actual: $actual, expected: $expected$input")
     }
   }
 
@@ -146,7 +146,8 @@ trait ExpressionEvalHelper {
 
     if (actual != expectedRow) {
       val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
-      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expectedRow$input")
+      fail("Incorrect Evaluation in codegen mode: " +
+        s"$expression, actual: $actual, expected: $expectedRow$input")
     }
     if (actual.copy() != expectedRow) {
       fail(s"Copy of generated Row is wrong: actual: ${actual.copy()}, expected: $expectedRow")
@@ -163,12 +164,21 @@ trait ExpressionEvalHelper {
       expression)
 
     val unsafeRow = plan(inputRow)
-    // UnsafeRow cannot be compared with GenericInternalRow directly
-    val actual = FromUnsafeProjection(expression.dataType :: Nil)(unsafeRow)
-    val expectedRow = InternalRow(expected)
-    if (actual != expectedRow) {
-      val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
-      fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expectedRow$input")
+    val input = if (inputRow == EmptyRow) "" else s", input: $inputRow"
+
+    if (expected == null) {
+      if (!unsafeRow.isNullAt(0)) {
+        val expectedRow = InternalRow(expected)
+        fail("Incorrect evaluation in unsafe mode: " +
+          s"$expression, actual: $unsafeRow, expected: $expectedRow$input")
+      }
+    } else {
+      val lit = InternalRow(expected)
+      val expectedRow = UnsafeProjection.create(Array(expression.dataType)).apply(lit)
+      if (unsafeRow != expectedRow) {
+        fail("Incorrect evaluation in unsafe mode: " +
+          s"$expression, actual: $unsafeRow, expected: $expectedRow$input")
+      }
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index d88a02298c00d..314b85f126dd2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -363,7 +363,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case logical.Sort(sortExprs, global, child) =>
         getSortOperator(sortExprs, global, planLater(child)):: Nil
       case logical.Project(projectList, child) =>
-        execution.Project(projectList, planLater(child)) :: Nil
+        // If unsafe mode is enabled and we support these data types in Unsafe, use the
+        // Tungsten project. Otherwise, use the normal project.
+        if (sqlContext.conf.unsafeEnabled &&
+          UnsafeProjection.canSupport(projectList) && UnsafeProjection.canSupport(child.schema)) {
+          execution.TungstenProject(projectList, planLater(child)) :: Nil
+        } else {
+          execution.Project(projectList, planLater(child)) :: Nil
+        }
       case logical.Filter(condition, child) =>
         execution.Filter(condition, planLater(child)) :: Nil
       case e @ logical.Expand(_, _, _, child) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index fe429d862a0a3..b02e60dc85cdd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -49,6 +49,31 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
 }
 
+
+/**
+ * A variant of [[Project]] that returns [[UnsafeRow]]s.
+ */
+case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
+
+  override def outputsUnsafeRows: Boolean = true
+  override def canProcessUnsafeRows: Boolean = true
+  override def canProcessSafeRows: Boolean = true
+
+  override def output: Seq[Attribute] = projectList.map(_.toAttribute)
+
+  protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
+    this.transformAllExpressions {
+      case CreateStruct(children) => CreateStructUnsafe(children)
+      case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
+    }
+    val project = UnsafeProjection.create(projectList, child.output)
+    iter.map(project)
+  }
+
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+}
+
+
 /**
  * :: DeveloperApi ::
  */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
new file mode 100644
index 0000000000000..bf8ef9a97bc60
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types._
+
+/**
+ * An end-to-end test suite specifically for testing Tungsten (Unsafe/CodeGen) mode.
+ *
+ * This is here for now so I can make sure Tungsten project is tested without refactoring existing
+ * end-to-end test infra. In the long run this should just go away.
+ */
+class DataFrameTungstenSuite extends QueryTest with SQLTestUtils {
+
+  override lazy val sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
+  import sqlContext.implicits._
+
+  test("test simple types") {
+    withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
+      val df = sqlContext.sparkContext.parallelize(Seq((1, 2))).toDF("a", "b")
+      assert(df.select(struct("a", "b")).first().getStruct(0) === Row(1, 2))
+    }
+  }
+
+  test("test struct type") {
+    withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
+      val struct = Row(1, 2L, 3.0F, 3.0)
+      val data = sqlContext.sparkContext.parallelize(Seq(Row(1, struct)))
+
+      val schema = new StructType()
+        .add("a", IntegerType)
+        .add("b",
+          new StructType()
+            .add("b1", IntegerType)
+            .add("b2", LongType)
+            .add("b3", FloatType)
+            .add("b4", DoubleType))
+
+      val df = sqlContext.createDataFrame(data, schema)
+      assert(df.select("b").first() === Row(struct))
+    }
+  }
+
+  test("test nested struct type") {
+    withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
+      val innerStruct = Row(1, "abcd")
+      val outerStruct = Row(1, 2L, 3.0F, 3.0, innerStruct, "efg")
+      val data = sqlContext.sparkContext.parallelize(Seq(Row(1, outerStruct)))
+
+      val schema = new StructType()
+        .add("a", IntegerType)
+        .add("b",
+          new StructType()
+            .add("b1", IntegerType)
+            .add("b2", LongType)
+            .add("b3", FloatType)
+            .add("b4", DoubleType)
+            .add("b5", new StructType()
+            .add("b5a", IntegerType)
+            .add("b5b", StringType))
+            .add("b6", StringType))
+
+      val df = sqlContext.createDataFrame(data, schema)
+      assert(df.select("b").first() === Row(outerStruct))
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
index 99e11fd64b2b9..1c5a2ed2c0a53 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
@@ -23,7 +23,7 @@ import org.apache.spark.sql.execution.expressions.{SparkPartitionID, Monotonical
 
 class NondeterministicSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("MonotonicallyIncreasingID") {
-    checkEvaluation(MonotonicallyIncreasingID(), 0)
+    checkEvaluation(MonotonicallyIncreasingID(), 0L)
   }
 
   test("SparkPartitionID") {

From 9c5612f4e197dec82a5eac9542896d6216a866b7 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Mon, 27 Jul 2015 23:02:23 -0700
Subject: [PATCH 0640/1454] [MINOR] [SQL] Support mutable expression unit test
 with codegen projection

This is actually contains 3 minor issues:
1) Enable the unit test(codegen) for mutable expressions (FormatNumber, Regexp_Replace/Regexp_Extract)
2) Use the `PlatformDependent.copyMemory` instead of the `System.arrayCopy`

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7566 from chenghao-intel/codegen_ut and squashes the following commits:

24f43ea [Cheng Hao] enable codegen for mutable expression & UTF8String performance
---
 .../expressions/stringOperations.scala        |  1 -
 .../spark/sql/StringFunctionsSuite.scala      | 34 ++++++++++++++-----
 .../apache/spark/unsafe/types/UTF8String.java | 32 ++++++++---------
 3 files changed, 41 insertions(+), 26 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 38b0fb37dee3b..edfffbc01c7b0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -777,7 +777,6 @@ case class Levenshtein(left: Expression, right: Expression) extends BinaryExpres
   override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType)
 
   override def dataType: DataType = IntegerType
-
   protected override def nullSafeEval(leftValue: Any, rightValue: Any): Any =
     leftValue.asInstanceOf[UTF8String].levenshteinDistance(rightValue.asInstanceOf[UTF8String])
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 0f9c986f649a1..8e0ea76d15881 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -57,19 +57,27 @@ class StringFunctionsSuite extends QueryTest {
   }
 
   test("string regex_replace / regex_extract") {
-    val df = Seq(("100-200", "")).toDF("a", "b")
+    val df = Seq(
+      ("100-200", "(\\d+)-(\\d+)", "300"),
+      ("100-200", "(\\d+)-(\\d+)", "400"),
+      ("100-200", "(\\d+)", "400")).toDF("a", "b", "c")
 
     checkAnswer(
       df.select(
         regexp_replace($"a", "(\\d+)", "num"),
         regexp_extract($"a", "(\\d+)-(\\d+)", 1)),
-      Row("num-num", "100"))
-
-    checkAnswer(
-      df.selectExpr(
-        "regexp_replace(a, '(\\d+)', 'num')",
-        "regexp_extract(a, '(\\d+)-(\\d+)', 2)"),
-      Row("num-num", "200"))
+      Row("num-num", "100") :: Row("num-num", "100") :: Row("num-num", "100") :: Nil)
+
+    // for testing the mutable state of the expression in code gen.
+    // This is a hack way to enable the codegen, thus the codegen is enable by default,
+    // it will still use the interpretProjection if projection followed by a LocalRelation,
+    // hence we add a filter operator.
+    // See the optimizer rule `ConvertToLocalRelation`
+    checkAnswer(
+      df.filter("isnotnull(a)").selectExpr(
+        "regexp_replace(a, b, c)",
+        "regexp_extract(a, b, 1)"),
+      Row("300", "100") :: Row("400", "100") :: Row("400-400", "100") :: Nil)
   }
 
   test("string ascii function") {
@@ -290,5 +298,15 @@ class StringFunctionsSuite extends QueryTest {
         df.selectExpr("format_number(e, g)"), // decimal type of the 2nd argument is unacceptable
         Row("5.0000"))
     }
+
+    // for testing the mutable state of the expression in code gen.
+    // This is a hack way to enable the codegen, thus the codegen is enable by default,
+    // it will still use the interpretProjection if projection follows by a LocalRelation,
+    // hence we add a filter operator.
+    // See the optimizer rule `ConvertToLocalRelation`
+    val df2 = Seq((5L, 4), (4L, 3), (3L, 2)).toDF("a", "b")
+    checkAnswer(
+      df2.filter("b>0").selectExpr("format_number(a, b)"),
+      Row("5.0000") :: Row("4.000") :: Row("3.00") :: Nil)
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 85381cf0ef425..3e1cc67dbf337 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -300,13 +300,13 @@ public UTF8String trimRight() {
   }
 
   public UTF8String reverse() {
-    byte[] bytes = getBytes();
-    byte[] result = new byte[bytes.length];
+    byte[] result = new byte[this.numBytes];
 
     int i = 0; // position in byte
     while (i < numBytes) {
       int len = numBytesForFirstByte(getByte(i));
-      System.arraycopy(bytes, i, result, result.length - i - len, len);
+      copyMemory(this.base, this.offset + i, result,
+              BYTE_ARRAY_OFFSET + result.length - i - len, len);
 
       i += len;
     }
@@ -316,11 +316,11 @@ public UTF8String reverse() {
 
   public UTF8String repeat(int times) {
     if (times <=0) {
-      return fromBytes(new byte[0]);
+      return EMPTY_UTF8;
     }
 
     byte[] newBytes = new byte[numBytes * times];
-    System.arraycopy(getBytes(), 0, newBytes, 0, numBytes);
+    copyMemory(this.base, this.offset, newBytes, BYTE_ARRAY_OFFSET, numBytes);
 
     int copied = 1;
     while (copied < times) {
@@ -385,16 +385,15 @@ public UTF8String rpad(int len, UTF8String pad) {
       UTF8String remain = pad.substring(0, spaces - padChars * count);
 
       byte[] data = new byte[this.numBytes + pad.numBytes * count + remain.numBytes];
-      System.arraycopy(getBytes(), 0, data, 0, this.numBytes);
+      copyMemory(this.base, this.offset, data, BYTE_ARRAY_OFFSET, this.numBytes);
       int offset = this.numBytes;
       int idx = 0;
-      byte[] padBytes = pad.getBytes();
       while (idx < count) {
-        System.arraycopy(padBytes, 0, data, offset, pad.numBytes);
+        copyMemory(pad.base, pad.offset, data, BYTE_ARRAY_OFFSET + offset, pad.numBytes);
         ++idx;
         offset += pad.numBytes;
       }
-      System.arraycopy(remain.getBytes(), 0, data, offset, remain.numBytes);
+      copyMemory(remain.base, remain.offset, data, BYTE_ARRAY_OFFSET + offset, remain.numBytes);
 
       return UTF8String.fromBytes(data);
     }
@@ -421,15 +420,14 @@ public UTF8String lpad(int len, UTF8String pad) {
 
       int offset = 0;
       int idx = 0;
-      byte[] padBytes = pad.getBytes();
       while (idx < count) {
-        System.arraycopy(padBytes, 0, data, offset, pad.numBytes);
+        copyMemory(pad.base, pad.offset, data, BYTE_ARRAY_OFFSET + offset, pad.numBytes);
         ++idx;
         offset += pad.numBytes;
       }
-      System.arraycopy(remain.getBytes(), 0, data, offset, remain.numBytes);
+      copyMemory(remain.base, remain.offset, data, BYTE_ARRAY_OFFSET + offset, remain.numBytes);
       offset += remain.numBytes;
-      System.arraycopy(getBytes(), 0, data, offset, numBytes());
+      copyMemory(this.base, this.offset, data, BYTE_ARRAY_OFFSET + offset, numBytes());
 
       return UTF8String.fromBytes(data);
     }
@@ -454,9 +452,9 @@ public static UTF8String concat(UTF8String... inputs) {
     int offset = 0;
     for (int i = 0; i < inputs.length; i++) {
       int len = inputs[i].numBytes;
-      PlatformDependent.copyMemory(
+      copyMemory(
         inputs[i].base, inputs[i].offset,
-        result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+        result, BYTE_ARRAY_OFFSET + offset,
         len);
       offset += len;
     }
@@ -494,7 +492,7 @@ public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
     for (int i = 0, j = 0; i < inputs.length; i++) {
       if (inputs[i] != null) {
         int len = inputs[i].numBytes;
-        PlatformDependent.copyMemory(
+        copyMemory(
           inputs[i].base, inputs[i].offset,
           result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
           len);
@@ -503,7 +501,7 @@ public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
         j++;
         // Add separator if this is not the last input.
         if (j < numInputs) {
-          PlatformDependent.copyMemory(
+          copyMemory(
             separator.base, separator.offset,
             result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
             separator.numBytes);

From d93ab93d673c5007a1edb90a424b451c91c8a285 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 27 Jul 2015 23:34:29 -0700
Subject: [PATCH 0641/1454] [SPARK-9335] [STREAMING] [TESTS] Make sure the test
 stream is deleted in KinesisBackedBlockRDDSuite

KinesisBackedBlockRDDSuite should make sure delete the stream.

Author: zsxwing <zsxwing@gmail.com>

Closes #7663 from zsxwing/fix-SPARK-9335 and squashes the following commits:

f0e9154 [zsxwing] Revert "[HOTFIX] - Disable Kinesis tests due to rate limits"
71a4552 [zsxwing] Make sure the test stream is deleted
---
 .../streaming/kinesis/KinesisBackedBlockRDDSuite.scala     | 7 +++++--
 .../spark/streaming/kinesis/KinesisStreamSuite.scala       | 4 ++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
index b2e2a4246dbd5..e81fb11e5959f 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.streaming.kinesis
 
-import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
+import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBlockId}
-import org.apache.spark.{SparkConf, SparkContext, SparkException, SparkFunSuite}
+import org.apache.spark.{SparkConf, SparkContext, SparkException}
 
 class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll {
 
@@ -65,6 +65,9 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
   }
 
   override def afterAll(): Unit = {
+    if (testUtils != null) {
+      testUtils.deleteStream()
+    }
     if (sc != null) {
       sc.stop()
     }
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
index 4992b041765e9..f9c952b9468bb 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -59,7 +59,7 @@ class KinesisStreamSuite extends KinesisFunSuite
     }
   }
 
-  ignore("KinesisUtils API") {
+  test("KinesisUtils API") {
     ssc = new StreamingContext(sc, Seconds(1))
     // Tests the API, does not actually test data receiving
     val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
@@ -83,7 +83,7 @@ class KinesisStreamSuite extends KinesisFunSuite
    * you must have AWS credentials available through the default AWS provider chain,
    * and you have to set the system environment variable RUN_KINESIS_TESTS=1 .
    */
-  ignore("basic operation") {
+  testIfEnabled("basic operation") {
     val kinesisTestUtils = new KinesisTestUtils()
     try {
       kinesisTestUtils.createStream()

From fc3bd96bc3e4a1a2a1eb9b982b3468abd137e395 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 27 Jul 2015 23:56:16 -0700
Subject: [PATCH 0642/1454] Closes #6836 since Round has already been
 implemented.


From 15724fac569258d2a149507d8c767d0de0ae8306 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 28 Jul 2015 00:52:26 -0700
Subject: [PATCH 0643/1454] [SPARK-9394][SQL] Handle parentheses in
 CodeFormatter.

Our CodeFormatter currently does not handle parentheses, and as a result in code dump, we see code formatted this way:

```
foo(
a,
b,
c)
```

With this patch, it is formatted this way:
```
foo(
  a,
  b,
  c)
```

Author: Reynold Xin <rxin@databricks.com>

Closes #7712 from rxin/codeformat-parentheses and squashes the following commits:

c2b1c5f [Reynold Xin] Took square bracket out
3cfb174 [Reynold Xin] Code review feedback.
91f5bb1 [Reynold Xin] [SPARK-9394][SQL] Handle parentheses in CodeFormatter.
---
 .../expressions/codegen/CodeFormatter.scala   |  8 ++---
 .../codegen/CodeFormatterSuite.scala          | 30 +++++++++++++++++++
 2 files changed, 34 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala
index 2087cc7f109bc..c98182c96b165 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatter.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions.codegen
 
 /**
- * An utility class that indents a block of code based on the curly braces.
- *
+ * An utility class that indents a block of code based on the curly braces and parentheses.
  * This is used to prettify generated code when in debug mode (or exceptions).
  *
  * Written by Matei Zaharia.
@@ -35,11 +34,12 @@ private class CodeFormatter {
   private var indentString = ""
 
   private def addLine(line: String): Unit = {
-    val indentChange = line.count(_ == '{') - line.count(_ == '}')
+    val indentChange =
+      line.count(c => "({".indexOf(c) >= 0) - line.count(c => ")}".indexOf(c) >= 0)
     val newIndentLevel = math.max(0, indentLevel + indentChange)
     // Lines starting with '}' should be de-indented even if they contain '{' after;
     // in addition, lines ending with ':' are typically labels
-    val thisLineIndent = if (line.startsWith("}") || line.endsWith(":")) {
+    val thisLineIndent = if (line.startsWith("}") || line.startsWith(")") || line.endsWith(":")) {
       " " * (indentSize * (indentLevel - 1))
     } else {
       indentString
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala
index 478702fea6146..46daa3eb8bf80 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeFormatterSuite.scala
@@ -73,4 +73,34 @@ class CodeFormatterSuite extends SparkFunSuite {
       |}
     """.stripMargin
   }
+
+  testCase("if else on the same line") {
+    """
+      |class A {
+      | if (c) {duh;} else {boo;}
+      |}
+    """.stripMargin
+  }{
+    """
+      |class A {
+      |  if (c) {duh;} else {boo;}
+      |}
+    """.stripMargin
+  }
+
+  testCase("function calls") {
+    """
+      |foo(
+      |a,
+      |b,
+      |c)
+    """.stripMargin
+  }{
+    """
+      |foo(
+      |  a,
+      |  b,
+      |  c)
+    """.stripMargin
+  }
 }

From ac8c549e2fa9ff3451deb4c3e49d151eeac18acc Mon Sep 17 00:00:00 2001
From: Kenichi Maehashi <webmaster@kenichimaehashi.com>
Date: Tue, 28 Jul 2015 15:57:21 +0100
Subject: [PATCH 0644/1454] [EC2] Cosmetic fix for usage of spark-ec2
 --ebs-vol-num option

The last line of the usage seems ugly.

```
$ spark-ec2 --help
<snip>
  --ebs-vol-num=EBS_VOL_NUM
                        Number of EBS volumes to attach to each node as
                        /vol[x]. The volumes will be deleted when the
                        instances terminate. Only possible on EBS-backed AMIs.
                        EBS volumes are only attached if --ebs-vol-size >
                        0.Only support up to 8 EBS volumes.
```

After applying this patch:

```
$ spark-ec2 --help
<snip>
  --ebs-vol-num=EBS_VOL_NUM
                        Number of EBS volumes to attach to each node as
                        /vol[x]. The volumes will be deleted when the
                        instances terminate. Only possible on EBS-backed AMIs.
                        EBS volumes are only attached if --ebs-vol-size > 0.
                        Only support up to 8 EBS volumes.
```

As this is a trivial thing I didn't create JIRA for this.

Author: Kenichi Maehashi <webmaster@kenichimaehashi.com>

Closes #7632 from kmaehashi/spark-ec2-cosmetic-fix and squashes the following commits:

526c118 [Kenichi Maehashi] cosmetic fix for spark-ec2 --ebs-vol-num option usage
---
 ec2/spark_ec2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 7c83d68e7993e..ccf922d9371fb 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -242,7 +242,7 @@ def parse_args():
         help="Number of EBS volumes to attach to each node as /vol[x]. " +
              "The volumes will be deleted when the instances terminate. " +
              "Only possible on EBS-backed AMIs. " +
-             "EBS volumes are only attached if --ebs-vol-size > 0." +
+             "EBS volumes are only attached if --ebs-vol-size > 0. " +
              "Only support up to 8 EBS volumes.")
     parser.add_option(
         "--placement-group", type="string", default=None,

From 4af622c855a32b1846242a6dd38b252ca30c8b82 Mon Sep 17 00:00:00 2001
From: vinodkc <vinod.kc.in@gmail.com>
Date: Tue, 28 Jul 2015 08:48:57 -0700
Subject: [PATCH 0645/1454] [SPARK-8919] [DOCUMENTATION, MLLIB] Added @since
 tags to mllib.recommendation

Author: vinodkc <vinod.kc.in@gmail.com>

Closes #7325 from vinodkc/add_since_mllib.recommendation and squashes the following commits:

93156f2 [vinodkc] Changed 0.8.0 to 0.9.1
c413350 [vinodkc] Added @since
---
 .../spark/mllib/recommendation/ALS.scala      | 10 +++++
 .../MatrixFactorizationModel.scala            | 38 ++++++++++++++++++-
 2 files changed, 47 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 93290e6508529..56c549ef99cb7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -26,6 +26,7 @@ import org.apache.spark.storage.StorageLevel
 
 /**
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
+ * @since 0.8.0
  */
 case class Rating(user: Int, product: Int, rating: Double)
 
@@ -254,6 +255,7 @@ class ALS private (
 
 /**
  * Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
+ * @since 0.8.0
  */
 object ALS {
   /**
@@ -269,6 +271,7 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param seed       random seed
+   * @since 0.9.1
    */
   def train(
       ratings: RDD[Rating],
@@ -293,6 +296,7 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
+   * @since 0.8.0
    */
   def train(
       ratings: RDD[Rating],
@@ -315,6 +319,7 @@ object ALS {
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
+   * @since 0.8.0
    */
   def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
     : MatrixFactorizationModel = {
@@ -331,6 +336,7 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
+   * @since 0.8.0
    */
   def train(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
@@ -351,6 +357,7 @@ object ALS {
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
    * @param seed       random seed
+   * @since 0.8.1
    */
   def trainImplicit(
       ratings: RDD[Rating],
@@ -377,6 +384,7 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
+   * @since 0.8.1
    */
   def trainImplicit(
       ratings: RDD[Rating],
@@ -401,6 +409,7 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param alpha      confidence parameter
+   * @since 0.8.1
    */
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
     : MatrixFactorizationModel = {
@@ -418,6 +427,7 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
+   * @since 0.8.1
    */
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 43d219a49cf4e..261ca9cef0c5b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -49,6 +49,7 @@ import org.apache.spark.storage.StorageLevel
  *                     the features computed for this user.
  * @param productFeatures RDD of tuples where each tuple represents the productId
  *                        and the features computed for this product.
+ * @since 0.8.0
  */
 class MatrixFactorizationModel(
     val rank: Int,
@@ -73,7 +74,9 @@ class MatrixFactorizationModel(
     }
   }
 
-  /** Predict the rating of one user for one product. */
+  /** Predict the rating of one user for one product.
+   * @since 0.8.0
+   */
   def predict(user: Int, product: Int): Double = {
     val userVector = userFeatures.lookup(user).head
     val productVector = productFeatures.lookup(product).head
@@ -111,6 +114,7 @@ class MatrixFactorizationModel(
    *
    * @param usersProducts  RDD of (user, product) pairs.
    * @return RDD of Ratings.
+   * @since 0.9.0
    */
   def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
     // Previously the partitions of ratings are only based on the given products.
@@ -142,6 +146,7 @@ class MatrixFactorizationModel(
 
   /**
    * Java-friendly version of [[MatrixFactorizationModel.predict]].
+   * @since 1.2.0
    */
   def predict(usersProducts: JavaPairRDD[JavaInteger, JavaInteger]): JavaRDD[Rating] = {
     predict(usersProducts.rdd.asInstanceOf[RDD[(Int, Int)]]).toJavaRDD()
@@ -157,6 +162,7 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the user. The score is an opaque value that indicates how strongly
    *  recommended the product is.
+   *  @since 1.1.0
    */
   def recommendProducts(user: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num)
@@ -173,6 +179,7 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the product. The score is an opaque value that indicates how strongly
    *  recommended the user is.
+   *  @since 1.1.0
    */
   def recommendUsers(product: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num)
@@ -180,6 +187,20 @@ class MatrixFactorizationModel(
 
   protected override val formatVersion: String = "1.0"
 
+  /**
+   * Save this model to the given path.
+   *
+   * This saves:
+   *  - human-readable (JSON) model metadata to path/metadata/
+   *  - Parquet formatted data to path/data/
+   *
+   * The model may be loaded using [[Loader.load]].
+   *
+   * @param sc  Spark context used to save model data.
+   * @param path  Path specifying the directory in which to save this model.
+   *              If the directory already exists, this method throws an exception.
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     MatrixFactorizationModel.SaveLoadV1_0.save(this, path)
   }
@@ -191,6 +212,7 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of
    * rating objects which contains the same userId, recommended productID and a "score" in the
    * rating field. Semantics of score is same as recommendProducts API
+   * @since 1.4.0
    */
   def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, userFeatures, productFeatures, num).map {
@@ -208,6 +230,7 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array
    * of rating objects which contains the recommended userId, same productID and a "score" in the
    * rating field. Semantics of score is same as recommendUsers API
+   * @since 1.4.0
    */
   def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, productFeatures, userFeatures, num).map {
@@ -218,6 +241,9 @@ class MatrixFactorizationModel(
   }
 }
 
+/**
+ * @since 1.3.0
+ */
 object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -292,6 +318,16 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
     }
   }
 
+  /**
+   * Load a model from the given path.
+   *
+   * The model should have been saved by [[Saveable.save]].
+   *
+   * @param sc  Spark context used for loading model files.
+   * @param path  Path specifying the directory to which the model was saved.
+   * @return  Model instance
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): MatrixFactorizationModel = {
     val (loadedClassName, formatVersion, _) = loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName

From 5a2330e546074013ef706ac09028626912ec5475 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 28 Jul 2015 09:42:35 -0700
Subject: [PATCH 0646/1454] [SPARK-9402][SQL] Remove CodegenFallback from Abs /
 FormatNumber.

Both expressions already implement code generation.

Author: Reynold Xin <rxin@databricks.com>

Closes #7723 from rxin/abs-formatnum and squashes the following commits:

31ed765 [Reynold Xin] [SPARK-9402][SQL] Remove CodegenFallback from Abs / FormatNumber.
---
 .../org/apache/spark/sql/catalyst/expressions/arithmetic.scala | 3 +--
 .../spark/sql/catalyst/expressions/stringOperations.scala      | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index b37f530ec6814..4ec866475f8b0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -68,8 +68,7 @@ case class UnaryPositive(child: Expression) extends UnaryExpression with Expects
 @ExpressionDescription(
   usage = "_FUNC_(expr) - Returns the absolute value of the numeric value",
   extended = "> SELECT _FUNC_('-1');\n1")
-case class Abs(child: Expression)
-  extends UnaryExpression with ExpectsInputTypes with CodegenFallback {
+case class Abs(child: Expression) extends UnaryExpression with ExpectsInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] = Seq(NumericType)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index edfffbc01c7b0..6db4e19c24ed5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -1139,7 +1139,7 @@ case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expressio
  * fractional part.
  */
 case class FormatNumber(x: Expression, d: Expression)
-  extends BinaryExpression with ExpectsInputTypes with CodegenFallback {
+  extends BinaryExpression with ExpectsInputTypes {
 
   override def left: Expression = x
   override def right: Expression = d

From c740bed17215a9608c9eb9d80ffdf0fcf72c3911 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 28 Jul 2015 09:43:12 -0700
Subject: [PATCH 0647/1454] [SPARK-9373][SQL] follow up for StructType support
 in Tungsten projection.

Author: Reynold Xin <rxin@databricks.com>

Closes #7720 from rxin/struct-followup and squashes the following commits:

d9757f5 [Reynold Xin] [SPARK-9373][SQL] follow up for StructType support in Tungsten projection.
---
 .../expressions/UnsafeRowWriters.java         |  6 +--
 .../codegen/GenerateUnsafeProjection.scala    | 40 +++++++++----------
 .../spark/sql/execution/SparkStrategies.scala |  3 +-
 3 files changed, 23 insertions(+), 26 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 8fdd7399602d2..32faad374015c 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -47,7 +47,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UTF8String in
           target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
-      // Write the string to the variable length portion.
+      // Write the bytes to the variable length portion.
       input.writeToMemory(target.getBaseObject(), offset);
 
       // Set the fixed length portion.
@@ -73,7 +73,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, byte[] input)
           target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
-      // Write the string to the variable length portion.
+      // Write the bytes to the variable length portion.
       ByteArray.writeToMemory(input, target.getBaseObject(), offset);
 
       // Set the fixed length portion.
@@ -115,7 +115,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, InternalRow i
             target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
         }
 
-        // Write the string to the variable length portion.
+        // Write the bytes to the variable length portion.
         row.writeToMemory(target.getBaseObject(), offset);
 
         // Set the fixed length portion.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 3e87f7285847c..9a4c00e86a3ec 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -62,14 +62,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     val cursor = ctx.freshName("cursor")
     val numBytes = ctx.freshName("numBytes")
 
-    val exprs = expressions.zipWithIndex.map { case (e, i) =>
-      e.dataType match {
-        case st: StructType =>
-          createCodeForStruct(ctx, e.gen(ctx), st)
-        case _ =>
-          e.gen(ctx)
-      }
-    }
+    val exprs = expressions.map { e => e.dataType match {
+      case st: StructType => createCodeForStruct(ctx, e.gen(ctx), st)
+      case _ => e.gen(ctx)
+    }}
     val allExprs = exprs.map(_.code).mkString("\n")
 
     val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
@@ -153,20 +149,20 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val exprs: Seq[GeneratedExpressionCode] = schema.map(_.dataType).zipWithIndex.map {
       case (dt, i) => dt match {
-      case st: StructType =>
-        val nestedStructEv = GeneratedExpressionCode(
-          code = "",
-          isNull = s"${input.primitive}.isNullAt($i)",
-          primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
-        )
-        createCodeForStruct(ctx, nestedStructEv, st)
-      case _ =>
-        GeneratedExpressionCode(
-          code = "",
-          isNull = s"${input.primitive}.isNullAt($i)",
-          primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
-        )
-      }
+        case st: StructType =>
+          val nestedStructEv = GeneratedExpressionCode(
+            code = "",
+            isNull = s"${input.primitive}.isNullAt($i)",
+            primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
+          )
+          createCodeForStruct(ctx, nestedStructEv, st)
+        case _ =>
+          GeneratedExpressionCode(
+            code = "",
+            isNull = s"${input.primitive}.isNullAt($i)",
+            primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
+          )
+        }
     }
     val allExprs = exprs.map(_.code).mkString("\n")
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 314b85f126dd2..f3ef066528ff8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -339,7 +339,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
      *               if necessary.
      */
     def getSortOperator(sortExprs: Seq[SortOrder], global: Boolean, child: SparkPlan): SparkPlan = {
-      if (sqlContext.conf.unsafeEnabled && UnsafeExternalSort.supportsSchema(child.schema)) {
+      if (sqlContext.conf.unsafeEnabled && sqlContext.conf.codegenEnabled &&
+        UnsafeExternalSort.supportsSchema(child.schema)) {
         execution.UnsafeExternalSort(sortExprs, global, child)
       } else if (sqlContext.conf.externalSortEnabled) {
         execution.ExternalSort(sortExprs, global, child)

From 9bbe0171cb434edb160fad30ea2d4221f525c919 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 28 Jul 2015 09:43:39 -0700
Subject: [PATCH 0648/1454] [SPARK-8196][SQL] Fix null handling & documentation
 for next_day.

The original patch didn't handle nulls correctly for next_day.

Author: Reynold Xin <rxin@databricks.com>

Closes #7718 from rxin/next_day and squashes the following commits:

616a425 [Reynold Xin] Merged DatetimeExpressionsSuite into DateFunctionsSuite.
faa78cf [Reynold Xin] Merged DatetimeFunctionsSuite into DateExpressionsSuite.
6c4fb6a [Reynold Xin] [SPARK-8196][SQL] Fix null handling & documentation for next_day.
---
 .../sql/catalyst/expressions/Expression.scala | 12 ++---
 .../expressions/datetimeFunctions.scala       | 46 ++++++++++-------
 .../sql/catalyst/expressions/literals.scala   |  2 +-
 .../sql/catalyst/util/DateTimeUtils.scala     |  2 +-
 .../expressions/DateExpressionsSuite.scala    | 43 +++++++++++++---
 .../expressions/DatetimeFunctionsSuite.scala  | 37 --------------
 .../expressions/ExpressionEvalHelper.scala    |  1 +
 .../expressions/NonFoldableLiteral.scala      | 50 +++++++++++++++++++
 .../org/apache/spark/sql/functions.scala      | 20 +++++---
 .../apache/spark/sql/DateFunctionsSuite.scala | 21 ++++++++
 .../spark/sql/DatetimeExpressionsSuite.scala  | 48 ------------------
 11 files changed, 158 insertions(+), 124 deletions(-)
 delete mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index cb4c3f24b2721..03e36c7871bcf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -355,9 +355,9 @@ abstract class BinaryExpression extends Expression {
    * @param f accepts two variable names and returns Java code to compute the output.
    */
   protected def defineCodeGen(
-    ctx: CodeGenContext,
-    ev: GeneratedExpressionCode,
-    f: (String, String) => String): String = {
+      ctx: CodeGenContext,
+      ev: GeneratedExpressionCode,
+      f: (String, String) => String): String = {
     nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
       s"${ev.primitive} = ${f(eval1, eval2)};"
     })
@@ -372,9 +372,9 @@ abstract class BinaryExpression extends Expression {
    *          and returns Java code to compute the output.
    */
   protected def nullSafeCodeGen(
-    ctx: CodeGenContext,
-    ev: GeneratedExpressionCode,
-    f: (String, String) => String): String = {
+      ctx: CodeGenContext,
+      ev: GeneratedExpressionCode,
+      f: (String, String) => String): String = {
     val eval1 = left.gen(ctx)
     val eval2 = right.gen(ctx)
     val resultCode = f(eval1.primitive, eval2.primitive)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index b00a1b26fa285..c37afc13f2d17 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -276,8 +276,6 @@ case class LastDay(startDate: Expression) extends UnaryExpression with ImplicitC
 
   override def dataType: DataType = DateType
 
-  override def prettyName: String = "last_day"
-
   override def nullSafeEval(date: Any): Any = {
     val days = date.asInstanceOf[Int]
     DateTimeUtils.getLastDayOfMonth(days)
@@ -289,12 +287,16 @@ case class LastDay(startDate: Expression) extends UnaryExpression with ImplicitC
       s"$dtu.getLastDayOfMonth($sd)"
     })
   }
+
+  override def prettyName: String = "last_day"
 }
 
 /**
  * Returns the first date which is later than startDate and named as dayOfWeek.
  * For example, NextDay(2015-07-27, Sunday) would return 2015-08-02, which is the first
- * sunday later than 2015-07-27.
+ * Sunday later than 2015-07-27.
+ *
+ * Allowed "dayOfWeek" is defined in [[DateTimeUtils.getDayOfWeekFromString]].
  */
 case class NextDay(startDate: Expression, dayOfWeek: Expression)
   extends BinaryExpression with ImplicitCastInputTypes {
@@ -318,22 +320,32 @@ case class NextDay(startDate: Expression, dayOfWeek: Expression)
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, (sd, dowS) => {
-      val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-      val dow = ctx.freshName("dow")
-      val genDow = if (right.foldable) {
-        val dowVal = DateTimeUtils.getDayOfWeekFromString(
-          dayOfWeek.eval(InternalRow.empty).asInstanceOf[UTF8String])
-        s"int $dow = $dowVal;"
-      } else {
-        s"int $dow = $dtu.getDayOfWeekFromString($dowS);"
-      }
-      genDow + s"""
-        if ($dow == -1) {
-          ${ev.isNull} = true;
+      val dateTimeUtilClass = DateTimeUtils.getClass.getName.stripSuffix("$")
+      val dayOfWeekTerm = ctx.freshName("dayOfWeek")
+      if (dayOfWeek.foldable) {
+        val input = dayOfWeek.eval().asInstanceOf[UTF8String]
+        if ((input eq null) || DateTimeUtils.getDayOfWeekFromString(input) == -1) {
+          s"""
+             |${ev.isNull} = true;
+           """.stripMargin
         } else {
-          ${ev.primitive} = $dtu.getNextDateForDayOfWeek($sd, $dow);
+          val dayOfWeekValue = DateTimeUtils.getDayOfWeekFromString(input)
+          s"""
+             |${ev.primitive} = $dateTimeUtilClass.getNextDateForDayOfWeek($sd, $dayOfWeekValue);
+           """.stripMargin
         }
-       """
+      } else {
+        s"""
+           |int $dayOfWeekTerm = $dateTimeUtilClass.getDayOfWeekFromString($dowS);
+           |if ($dayOfWeekTerm == -1) {
+           |  ${ev.isNull} = true;
+           |} else {
+           |  ${ev.primitive} = $dateTimeUtilClass.getNextDateForDayOfWeek($sd, $dayOfWeekTerm);
+           |}
+         """.stripMargin
+      }
     })
   }
+
+  override def prettyName: String = "next_day"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index 85060b7893556..064a1720c36e8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -118,7 +118,7 @@ case class Literal protected (value: Any, dataType: DataType)
             super.genCode(ctx, ev)
           } else {
             ev.isNull = "false"
-            ev.primitive = s"${value}"
+            ev.primitive = s"${value}D"
             ""
           }
         case ByteType | ShortType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 2e28fb9af9b65..8b0b80c26db17 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -575,7 +575,7 @@ object DateTimeUtils {
   }
 
   /**
-   * Returns Day of week from String. Starting from Thursday, marked as 0.
+   * Returns day of week from String. Starting from Thursday, marked as 0.
    * (Because 1970-01-01 is Thursday).
    */
   def getDayOfWeekFromString(string: UTF8String): Int = {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index 4d2d33765a269..30c5769424bd7 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -32,6 +32,19 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
   val ts = new Timestamp(sdf.parse("2013-11-08 13:10:15").getTime)
 
+  test("datetime function current_date") {
+    val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    val cd = CurrentDate().eval(EmptyRow).asInstanceOf[Int]
+    val d1 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    assert(d0 <= cd && cd <= d1 && d1 - d0 <= 1)
+  }
+
+  test("datetime function current_timestamp") {
+    val ct = DateTimeUtils.toJavaTimestamp(CurrentTimestamp().eval(EmptyRow).asInstanceOf[Long])
+    val t1 = System.currentTimeMillis()
+    assert(math.abs(t1 - ct.getTime) < 5000)
+  }
+
   test("DayOfYear") {
     val sdfDay = new SimpleDateFormat("D")
     (2002 to 2004).foreach { y =>
@@ -264,14 +277,28 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("next_day") {
+    def testNextDay(input: String, dayOfWeek: String, output: String): Unit = {
+      checkEvaluation(
+        NextDay(Literal(Date.valueOf(input)), NonFoldableLiteral(dayOfWeek)),
+        DateTimeUtils.fromJavaDate(Date.valueOf(output)))
+      checkEvaluation(
+        NextDay(Literal(Date.valueOf(input)), Literal(dayOfWeek)),
+        DateTimeUtils.fromJavaDate(Date.valueOf(output)))
+    }
+    testNextDay("2015-07-23", "Mon", "2015-07-27")
+    testNextDay("2015-07-23", "mo", "2015-07-27")
+    testNextDay("2015-07-23", "Tue", "2015-07-28")
+    testNextDay("2015-07-23", "tu", "2015-07-28")
+    testNextDay("2015-07-23", "we", "2015-07-29")
+    testNextDay("2015-07-23", "wed", "2015-07-29")
+    testNextDay("2015-07-23", "Thu", "2015-07-30")
+    testNextDay("2015-07-23", "TH", "2015-07-30")
+    testNextDay("2015-07-23", "Fri", "2015-07-24")
+    testNextDay("2015-07-23", "fr", "2015-07-24")
+
+    checkEvaluation(NextDay(Literal(Date.valueOf("2015-07-23")), Literal("xx")), null)
+    checkEvaluation(NextDay(Literal.create(null, DateType), Literal("xx")), null)
     checkEvaluation(
-      NextDay(Literal(Date.valueOf("2015-07-23")), Literal("Thu")),
-      DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-30")))
-    checkEvaluation(
-      NextDay(Literal(Date.valueOf("2015-07-23")), Literal("THURSDAY")),
-      DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-30")))
-    checkEvaluation(
-      NextDay(Literal(Date.valueOf("2015-07-23")), Literal("th")),
-      DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-30")))
+      NextDay(Literal(Date.valueOf("2015-07-23")), Literal.create(null, StringType)), null)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala
deleted file mode 100644
index 1618c24871c60..0000000000000
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DatetimeFunctionsSuite.scala
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.expressions
-
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
-
-class DatetimeFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
-  test("datetime function current_date") {
-    val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
-    val cd = CurrentDate().eval(EmptyRow).asInstanceOf[Int]
-    val d1 = DateTimeUtils.millisToDays(System.currentTimeMillis())
-    assert(d0 <= cd && cd <= d1 && d1 - d0 <= 1)
-  }
-
-  test("datetime function current_timestamp") {
-    val ct = DateTimeUtils.toJavaTimestamp(CurrentTimestamp().eval(EmptyRow).asInstanceOf[Long])
-    val t1 = System.currentTimeMillis()
-    assert(math.abs(t1 - ct.getTime) < 5000)
-  }
-
-}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 136368bf5b368..0c8611d5ddefa 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -82,6 +82,7 @@ trait ExpressionEvalHelper {
           s"""
             |Code generation of $expression failed:
             |$e
+            |${e.getStackTraceString}
           """.stripMargin)
     }
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala
new file mode 100644
index 0000000000000..0559fb80e7fce
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.types._
+
+
+/**
+ * A literal value that is not foldable. Used in expression codegen testing to test code path
+ * that behave differently based on foldable values.
+ */
+case class NonFoldableLiteral(value: Any, dataType: DataType)
+  extends LeafExpression with CodegenFallback {
+
+  override def foldable: Boolean = false
+  override def nullable: Boolean = true
+
+  override def toString: String = if (value != null) value.toString else "null"
+
+  override def eval(input: InternalRow): Any = value
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    Literal.create(value, dataType).genCode(ctx, ev)
+  }
+}
+
+
+object NonFoldableLiteral {
+  def apply(value: Any): NonFoldableLiteral = {
+    val lit = Literal(value)
+    NonFoldableLiteral(lit.value, lit.dataType)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index d18558b510f0b..cec61b66b157c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2033,7 +2033,10 @@ object functions {
   def hour(columnName: String): Column = hour(Column(columnName))
 
   /**
-   * Returns the last day of the month which the given date belongs to.
+   * Given a date column, returns the last day of the month which the given date belongs to.
+   * For example, input "2015-07-27" returns "2015-07-31" since July 31 is the last day of the
+   * month in July 2015.
+   *
    * @group datetime_funcs
    * @since 1.5.0
    */
@@ -2054,14 +2057,19 @@ object functions {
   def minute(columnName: String): Column = minute(Column(columnName))
 
   /**
-   * Returns the first date which is later than given date sd and named as dow.
-   * For example, `next_day('2015-07-27', "Sunday")` would return 2015-08-02, which is the
-   * first Sunday later than 2015-07-27. The parameter dayOfWeek could be 2-letter, 3-letter,
-   * or full name of the day of the week (e.g. Mo, tue, FRIDAY).
+   * Given a date column, returns the first date which is later than the value of the date column
+   * that is on the specified day of the week.
+   *
+   * For example, `next_day('2015-07-27', "Sunday")` returns 2015-08-02 because that is the first
+   * Sunday after 2015-07-27.
+   *
+   * Day of the week parameter is case insensitive, and accepts:
+   * "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".
+   *
    * @group datetime_funcs
    * @since 1.5.0
    */
-  def next_day(sd: Column, dayOfWeek: String): Column = NextDay(sd.expr, lit(dayOfWeek).expr)
+  def next_day(date: Column, dayOfWeek: String): Column = NextDay(date.expr, lit(dayOfWeek).expr)
 
   /**
    * Extracts the seconds as an integer from a given date/timestamp/string.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 001fcd035c82a..36820cbbc7e5e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.sql.{Timestamp, Date}
 import java.text.SimpleDateFormat
 
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.functions._
 
 class DateFunctionsSuite extends QueryTest {
@@ -27,6 +28,26 @@ class DateFunctionsSuite extends QueryTest {
 
   import ctx.implicits._
 
+  test("function current_date") {
+    val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
+    val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    val d1 = DateTimeUtils.fromJavaDate(df1.select(current_date()).collect().head.getDate(0))
+    val d2 = DateTimeUtils.fromJavaDate(
+      ctx.sql("""SELECT CURRENT_DATE()""").collect().head.getDate(0))
+    val d3 = DateTimeUtils.millisToDays(System.currentTimeMillis())
+    assert(d0 <= d1 && d1 <= d2 && d2 <= d3 && d3 - d0 <= 1)
+  }
+
+  test("function current_timestamp") {
+    val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
+    checkAnswer(df1.select(countDistinct(current_timestamp())), Row(1))
+    // Execution in one query should return the same value
+    checkAnswer(ctx.sql("""SELECT CURRENT_TIMESTAMP() = CURRENT_TIMESTAMP()"""),
+      Row(true))
+    assert(math.abs(ctx.sql("""SELECT CURRENT_TIMESTAMP()""").collect().head.getTimestamp(
+      0).getTime - System.currentTimeMillis()) < 5000)
+  }
+
   val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
   val sdfDate = new SimpleDateFormat("yyyy-MM-dd")
   val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala
deleted file mode 100644
index 44b915304533c..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/DatetimeExpressionsSuite.scala
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.functions._
-
-class DatetimeExpressionsSuite extends QueryTest {
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-
-  import ctx.implicits._
-
-  lazy val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
-
-  test("function current_date") {
-    val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
-    val d1 = DateTimeUtils.fromJavaDate(df1.select(current_date()).collect().head.getDate(0))
-    val d2 = DateTimeUtils.fromJavaDate(
-      ctx.sql("""SELECT CURRENT_DATE()""").collect().head.getDate(0))
-    val d3 = DateTimeUtils.millisToDays(System.currentTimeMillis())
-    assert(d0 <= d1 && d1 <= d2 && d2 <= d3 && d3 - d0 <= 1)
-  }
-
-  test("function current_timestamp") {
-    checkAnswer(df1.select(countDistinct(current_timestamp())), Row(1))
-    // Execution in one query should return the same value
-    checkAnswer(ctx.sql("""SELECT CURRENT_TIMESTAMP() = CURRENT_TIMESTAMP()"""),
-      Row(true))
-    assert(math.abs(ctx.sql("""SELECT CURRENT_TIMESTAMP()""").collect().head.getTimestamp(
-      0).getTime - System.currentTimeMillis()) < 5000)
-  }
-
-}

From 35ef853b3f9d955949c464e4a0d445147e0e9a07 Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Tue, 28 Jul 2015 10:12:09 -0700
Subject: [PATCH 0649/1454] [SPARK-9397] DataFrame should provide an API to
 find source data files if applicable

Certain applications would benefit from being able to inspect DataFrames that are straightforwardly produced by data sources that stem from files, and find out their source data. For example, one might want to display to a user the size of the data underlying a table, or to copy or mutate it.

This PR exposes an `inputFiles` method on DataFrame which attempts to discover the source data in a best-effort manner, by inspecting HadoopFsRelations and JSONRelations.

Author: Aaron Davidson <aaron@databricks.com>

Closes #7717 from aarondav/paths and squashes the following commits:

ff67430 [Aaron Davidson] inputFiles
0acd3ad [Aaron Davidson] [SPARK-9397] DataFrame should provide an API to find source data files if applicable
---
 .../org/apache/spark/sql/DataFrame.scala      | 20 +++++++++++++++++--
 .../org/apache/spark/sql/DataFrameSuite.scala | 20 +++++++++++++++++++
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  6 +++---
 3 files changed, 41 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 114ab91d10aa0..3ea0f9ed3bddd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -40,8 +40,9 @@ import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
 import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD}
-import org.apache.spark.sql.execution.datasources.CreateTableUsingAsSelect
-import org.apache.spark.sql.json.JacksonGenerator
+import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
+import org.apache.spark.sql.json.{JacksonGenerator, JSONRelation}
+import org.apache.spark.sql.sources.HadoopFsRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.Utils
@@ -1546,6 +1547,21 @@ class DataFrame private[sql](
     }
   }
 
+  /**
+   * Returns a best-effort snapshot of the files that compose this DataFrame. This method simply
+   * asks each constituent BaseRelation for its respective files and takes the union of all results.
+   * Depending on the source relations, this may not find all input files. Duplicates are removed.
+   */
+  def inputFiles: Array[String] = {
+    val files: Seq[String] = logicalPlan.collect {
+      case LogicalRelation(fsBasedRelation: HadoopFsRelation) =>
+        fsBasedRelation.paths.toSeq
+      case LogicalRelation(jsonRelation: JSONRelation) =>
+        jsonRelation.path.toSeq
+    }.flatten
+    files.toSet.toArray
+  }
+
   ////////////////////////////////////////////////////////////////////////////
   // for Python API
   ////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index f67f2c60c0e16..3151e071b19ea 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -23,7 +23,10 @@ import scala.language.postfixOps
 import scala.util.Random
 
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
+import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.json.JSONRelation
+import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SQLTestUtils}
 
@@ -491,6 +494,23 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     checkAnswer(df.select(df("key")), testData.select('key).collect().toSeq)
   }
 
+  test("inputFiles") {
+    val fakeRelation1 = new ParquetRelation(Array("/my/path", "/my/other/path"),
+      Some(testData.schema), None, Map.empty)(sqlContext)
+    val df1 = DataFrame(sqlContext, LogicalRelation(fakeRelation1))
+    assert(df1.inputFiles.toSet == fakeRelation1.paths.toSet)
+
+    val fakeRelation2 = new JSONRelation("/json/path", 1, Some(testData.schema), sqlContext)
+    val df2 = DataFrame(sqlContext, LogicalRelation(fakeRelation2))
+    assert(df2.inputFiles.toSet == fakeRelation2.path.toSet)
+
+    val unionDF = df1.unionAll(df2)
+    assert(unionDF.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.path)
+
+    val filtered = df1.filter("false").unionAll(df2.intersect(df2))
+    assert(filtered.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.path)
+  }
+
   ignore("show") {
     // This test case is intended ignored, but to make sure it compiles correctly
     testData.select($"*").show()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 3180c05445c9f..a8c9b4fa71b99 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -274,9 +274,9 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     val metastoreSchema = StructType.fromAttributes(metastoreRelation.output)
     val mergeSchema = hive.convertMetastoreParquetWithSchemaMerging
 
-    // NOTE: Instead of passing Metastore schema directly to `ParquetRelation2`, we have to
+    // NOTE: Instead of passing Metastore schema directly to `ParquetRelation`, we have to
     // serialize the Metastore schema to JSON and pass it as a data source option because of the
-    // evil case insensitivity issue, which is reconciled within `ParquetRelation2`.
+    // evil case insensitivity issue, which is reconciled within `ParquetRelation`.
     val parquetOptions = Map(
       ParquetRelation.METASTORE_SCHEMA -> metastoreSchema.json,
       ParquetRelation.MERGE_SCHEMA -> mergeSchema.toString)
@@ -290,7 +290,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         partitionSpecInMetastore: Option[PartitionSpec]): Option[LogicalRelation] = {
       cachedDataSourceTables.getIfPresent(tableIdentifier) match {
         case null => None // Cache miss
-        case logical@LogicalRelation(parquetRelation: ParquetRelation) =>
+        case logical @ LogicalRelation(parquetRelation: ParquetRelation) =>
           // If we have the same paths, same schema, and same partition spec,
           // we will use the cached Parquet Relation.
           val useCached =

From 614323406225a3522ee601935ce3052449614145 Mon Sep 17 00:00:00 2001
From: trestletech <jeff.allen@trestletechnology.net>
Date: Tue, 28 Jul 2015 10:45:19 -0700
Subject: [PATCH 0650/1454] Use vector-friendly comparison for packages
 argument.

Otherwise, `sparkR.init()` with multiple `sparkPackages` results in this warning:

```
Warning message:
In if (packages != "") { :
  the condition has length > 1 and only the first element will be used
```

Author: trestletech <jeff.allen@trestletechnology.net>

Closes #7701 from trestletech/compare-packages and squashes the following commits:

72c8b36 [trestletech] Correct function name.
c52db0e [trestletech] Added test for multiple packages.
3aab1a7 [trestletech] Use vector-friendly comparison for packages argument.
---
 R/pkg/R/client.R               | 2 +-
 R/pkg/inst/tests/test_client.R | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/client.R b/R/pkg/R/client.R
index 6f772158ddfe8..c811d1dac3bd5 100644
--- a/R/pkg/R/client.R
+++ b/R/pkg/R/client.R
@@ -48,7 +48,7 @@ generateSparkSubmitArgs <- function(args, sparkHome, jars, sparkSubmitOpts, pack
     jars <- paste("--jars", jars)
   }
 
-  if (packages != "") {
+  if (!identical(packages, "")) {
     packages <- paste("--packages", packages)
   }
 
diff --git a/R/pkg/inst/tests/test_client.R b/R/pkg/inst/tests/test_client.R
index 30b05c1a2afcd..8a20991f89af8 100644
--- a/R/pkg/inst/tests/test_client.R
+++ b/R/pkg/inst/tests/test_client.R
@@ -30,3 +30,7 @@ test_that("no package specified doesn't add packages flag", {
   expect_equal(gsub("[[:space:]]", "", args),
                "")
 })
+
+test_that("multiple packages don't produce a warning", {
+  expect_that(generateSparkSubmitArgs("", "", "", "", c("A", "B")), not(gives_warning()))
+})

From 31ec6a871eebd2377961c5195f9c2bff3a899fba Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 28 Jul 2015 11:48:56 -0700
Subject: [PATCH 0651/1454] [SPARK-9327] [DOCS] Fix documentation about
 classpath config options.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7651 from vanzin/SPARK-9327 and squashes the following commits:

2923e23 [Marcelo Vanzin] [SPARK-9327] [docs] Fix documentation about classpath config options.
---
 docs/configuration.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 200f3cd212e46..fd236137cb96e 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -203,7 +203,7 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.driver.extraClassPath</code></td>
   <td>(none)</td>
   <td>
-    Extra classpath entries to append to the classpath of the driver.
+    Extra classpath entries to prepend to the classpath of the driver.
 
     <br /><em>Note:</em> In client mode, this config must not be set through the <code>SparkConf</code>
     directly in your application, because the driver JVM has already started at that point.
@@ -250,7 +250,7 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.executor.extraClassPath</code></td>
   <td>(none)</td>
   <td>
-    Extra classpath entries to append to the classpath of executors. This exists primarily for 
+    Extra classpath entries to prepend to the classpath of executors. This exists primarily for 
     backwards-compatibility with older versions of Spark. Users typically should not need to set 
     this option.
   </td>

From 6cdcc21fe654ac0a2d0d72783eb10005fc513af6 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 28 Jul 2015 13:16:48 -0700
Subject: [PATCH 0652/1454] [SPARK-9196] [SQL] Ignore test
 DatetimeExpressionsSuite: function current_timestamp.

This test is flaky. https://issues.apache.org/jira/browse/SPARK-9196 will track the fix of it. For now, let's disable this test.

Author: Yin Huai <yhuai@databricks.com>

Closes #7727 from yhuai/SPARK-9196-ignore and squashes the following commits:

f92bded [Yin Huai] Ignore current_timestamp.
---
 .../test/scala/org/apache/spark/sql/DateFunctionsSuite.scala   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 36820cbbc7e5e..07eb6e4a8d8cd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -38,7 +38,8 @@ class DateFunctionsSuite extends QueryTest {
     assert(d0 <= d1 && d1 <= d2 && d2 <= d3 && d3 - d0 <= 1)
   }
 
-  test("function current_timestamp") {
+  // This is a bad test. SPARK-9196 will fix it and re-enable it.
+  ignore("function current_timestamp") {
     val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
     checkAnswer(df1.select(countDistinct(current_timestamp())), Row(1))
     // Execution in one query should return the same value

From 8d5bb5283c3cc9180ef34b05be4a715d83073b1e Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Tue, 28 Jul 2015 14:16:57 -0700
Subject: [PATCH 0653/1454] [SPARK-9391] [ML] Support minus, dot, and intercept
 operators in SparkR RFormula

Adds '.', '-', and intercept parsing to RFormula. Also splits RFormulaParser into a separate file.

Umbrella design doc here: https://docs.google.com/document/d/10NZNSEurN2EdWM31uFYsgayIPfCFHiuIu3pCWrUmP_c/edit?usp=sharing

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #7707 from ericl/string-features-2 and squashes the following commits:

8588625 [Eric Liang] exclude complex types for .
8106ffe [Eric Liang] comments
a9350bb [Eric Liang] s/var/val
9c50d4d [Eric Liang] Merge branch 'string-features' into string-features-2
581afb2 [Eric Liang] Merge branch 'master' into string-features
08ae539 [Eric Liang] Merge branch 'string-features' into string-features-2
f99131a [Eric Liang] comments
cecec43 [Eric Liang] Merge branch 'string-features' into string-features-2
0bf3c26 [Eric Liang] update docs
4592df2 [Eric Liang] intercept supports
7412a2e [Eric Liang] Fri Jul 24 14:56:51 PDT 2015
3cf848e [Eric Liang] fix the parser
0556c2b [Eric Liang] Merge branch 'string-features' into string-features-2
c302a2c [Eric Liang] fix tests
9d1ac82 [Eric Liang] Merge remote-tracking branch 'upstream/master' into string-features
e713da3 [Eric Liang] comments
cd231a9 [Eric Liang] Wed Jul 22 17:18:44 PDT 2015
4d79193 [Eric Liang] revert to seq + distinct
169a085 [Eric Liang] tweak functional test
a230a47 [Eric Liang] Merge branch 'master' into string-features
72bd6f3 [Eric Liang] fix merge
d841cec [Eric Liang] Merge branch 'master' into string-features
5b2c4a2 [Eric Liang] Mon Jul 20 18:45:33 PDT 2015
b01c7c5 [Eric Liang] add test
8a637db [Eric Liang] encoder wip
a1d03f4 [Eric Liang] refactor into estimator
---
 R/pkg/R/mllib.R                               |   2 +-
 R/pkg/inst/tests/test_mllib.R                 |   8 ++
 .../apache/spark/ml/feature/RFormula.scala    |  52 +++----
 .../spark/ml/feature/RFormulaParser.scala     | 129 ++++++++++++++++++
 .../apache/spark/ml/r/SparkRWrappers.scala    |  10 +-
 .../ml/feature/RFormulaParserSuite.scala      |  55 +++++++-
 6 files changed, 215 insertions(+), 41 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala

diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 258e354081fc1..6a8bacaa552c6 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -27,7 +27,7 @@ setClass("PipelineModel", representation(model = "jobj"))
 #' Fits a generalized linear model, similarly to R's glm(). Also see the glmnet package.
 #'
 #' @param formula A symbolic description of the model to be fitted. Currently only a few formula
-#'                operators are supported, including '~' and '+'.
+#'                operators are supported, including '~', '+', '-', and '.'.
 #' @param data DataFrame for training
 #' @param family Error distribution. "gaussian" -> linear regression, "binomial" -> logistic reg.
 #' @param lambda Regularization parameter
diff --git a/R/pkg/inst/tests/test_mllib.R b/R/pkg/inst/tests/test_mllib.R
index 29152a11688a2..3bef69324770a 100644
--- a/R/pkg/inst/tests/test_mllib.R
+++ b/R/pkg/inst/tests/test_mllib.R
@@ -40,3 +40,11 @@ test_that("predictions match with native glm", {
   rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
   expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
 })
+
+test_that("dot minus and intercept vs native glm", {
+  training <- createDataFrame(sqlContext, iris)
+  model <- glm(Sepal_Width ~ . - Species + 0, data = training)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+})
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index 0a95b1ee8de6e..0b428d278d908 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -78,13 +78,20 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
   /** @group getParam */
   def getFormula: String = $(formula)
 
+  /** Whether the formula specifies fitting an intercept. */
+  private[ml] def hasIntercept: Boolean = {
+    require(parsedFormula.isDefined, "Must call setFormula() first.")
+    parsedFormula.get.hasIntercept
+  }
+
   override def fit(dataset: DataFrame): RFormulaModel = {
     require(parsedFormula.isDefined, "Must call setFormula() first.")
+    val resolvedFormula = parsedFormula.get.resolve(dataset.schema)
     // StringType terms and terms representing interactions need to be encoded before assembly.
     // TODO(ekl) add support for feature interactions
-    var encoderStages = ArrayBuffer[PipelineStage]()
-    var tempColumns = ArrayBuffer[String]()
-    val encodedTerms = parsedFormula.get.terms.map { term =>
+    val encoderStages = ArrayBuffer[PipelineStage]()
+    val tempColumns = ArrayBuffer[String]()
+    val encodedTerms = resolvedFormula.terms.map { term =>
       dataset.schema(term) match {
         case column if column.dataType == StringType =>
           val indexCol = term + "_idx_" + uid
@@ -103,7 +110,7 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
       .setOutputCol($(featuresCol))
     encoderStages += new ColumnPruner(tempColumns.toSet)
     val pipelineModel = new Pipeline(uid).setStages(encoderStages.toArray).fit(dataset)
-    copyValues(new RFormulaModel(uid, parsedFormula.get, pipelineModel).setParent(this))
+    copyValues(new RFormulaModel(uid, resolvedFormula, pipelineModel).setParent(this))
   }
 
   // optimistic schema; does not contain any ML attributes
@@ -124,13 +131,13 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
 /**
  * :: Experimental ::
  * A fitted RFormula. Fitting is required to determine the factor levels of formula terms.
- * @param parsedFormula a pre-parsed R formula.
+ * @param resolvedFormula the fitted R formula.
  * @param pipelineModel the fitted feature model, including factor to index mappings.
  */
 @Experimental
 class RFormulaModel private[feature](
     override val uid: String,
-    parsedFormula: ParsedRFormula,
+    resolvedFormula: ResolvedRFormula,
     pipelineModel: PipelineModel)
   extends Model[RFormulaModel] with RFormulaBase {
 
@@ -144,8 +151,8 @@ class RFormulaModel private[feature](
     val withFeatures = pipelineModel.transformSchema(schema)
     if (hasLabelCol(schema)) {
       withFeatures
-    } else if (schema.exists(_.name == parsedFormula.label)) {
-      val nullable = schema(parsedFormula.label).dataType match {
+    } else if (schema.exists(_.name == resolvedFormula.label)) {
+      val nullable = schema(resolvedFormula.label).dataType match {
         case _: NumericType | BooleanType => false
         case _ => true
       }
@@ -158,12 +165,12 @@ class RFormulaModel private[feature](
   }
 
   override def copy(extra: ParamMap): RFormulaModel = copyValues(
-    new RFormulaModel(uid, parsedFormula, pipelineModel))
+    new RFormulaModel(uid, resolvedFormula, pipelineModel))
 
-  override def toString: String = s"RFormulaModel(${parsedFormula})"
+  override def toString: String = s"RFormulaModel(${resolvedFormula})"
 
   private def transformLabel(dataset: DataFrame): DataFrame = {
-    val labelName = parsedFormula.label
+    val labelName = resolvedFormula.label
     if (hasLabelCol(dataset.schema)) {
       dataset
     } else if (dataset.schema.exists(_.name == labelName)) {
@@ -207,26 +214,3 @@ private class ColumnPruner(columnsToPrune: Set[String]) extends Transformer {
 
   override def copy(extra: ParamMap): ColumnPruner = defaultCopy(extra)
 }
-
-/**
- * Represents a parsed R formula.
- */
-private[ml] case class ParsedRFormula(label: String, terms: Seq[String])
-
-/**
- * Limited implementation of R formula parsing. Currently supports: '~', '+'.
- */
-private[ml] object RFormulaParser extends RegexParsers {
-  def term: Parser[String] = "([a-zA-Z]|\\.[a-zA-Z_])[a-zA-Z0-9._]*".r
-
-  def expr: Parser[List[String]] = term ~ rep("+" ~> term) ^^ { case a ~ list => a :: list }
-
-  def formula: Parser[ParsedRFormula] =
-    (term ~ "~" ~ expr) ^^ { case r ~ "~" ~ t => ParsedRFormula(r, t.distinct) }
-
-  def parse(value: String): ParsedRFormula = parseAll(formula, value) match {
-    case Success(result, _) => result
-    case failure: NoSuccess => throw new IllegalArgumentException(
-      "Could not parse formula: " + value)
-  }
-}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala
new file mode 100644
index 0000000000000..1ca3b92a7d92a
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala
@@ -0,0 +1,129 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import scala.util.parsing.combinator.RegexParsers
+
+import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.sql.types._
+
+/**
+ * Represents a parsed R formula.
+ */
+private[ml] case class ParsedRFormula(label: ColumnRef, terms: Seq[Term]) {
+  /**
+   * Resolves formula terms into column names. A schema is necessary for inferring the meaning
+   * of the special '.' term. Duplicate terms will be removed during resolution.
+   */
+  def resolve(schema: StructType): ResolvedRFormula = {
+    var includedTerms = Seq[String]()
+    terms.foreach {
+      case Dot =>
+        includedTerms ++= simpleTypes(schema).filter(_ != label.value)
+      case ColumnRef(value) =>
+        includedTerms :+= value
+      case Deletion(term: Term) =>
+        term match {
+          case ColumnRef(value) =>
+            includedTerms = includedTerms.filter(_ != value)
+          case Dot =>
+            // e.g. "- .", which removes all first-order terms
+            val fromSchema = simpleTypes(schema)
+            includedTerms = includedTerms.filter(fromSchema.contains(_))
+          case _: Deletion =>
+            assert(false, "Deletion terms cannot be nested")
+          case _: Intercept =>
+        }
+      case _: Intercept =>
+    }
+    ResolvedRFormula(label.value, includedTerms.distinct)
+  }
+
+  /** Whether this formula specifies fitting with an intercept term. */
+  def hasIntercept: Boolean = {
+    var intercept = true
+    terms.foreach {
+      case Intercept(enabled) =>
+        intercept = enabled
+      case Deletion(Intercept(enabled)) =>
+        intercept = !enabled
+      case _ =>
+    }
+    intercept
+  }
+
+  // the dot operator excludes complex column types
+  private def simpleTypes(schema: StructType): Seq[String] = {
+    schema.fields.filter(_.dataType match {
+      case _: NumericType | StringType | BooleanType | _: VectorUDT => true
+      case _ => false
+    }).map(_.name)
+  }
+}
+
+/**
+ * Represents a fully evaluated and simplified R formula.
+ */
+private[ml] case class ResolvedRFormula(label: String, terms: Seq[String])
+
+/**
+ * R formula terms. See the R formula docs here for more information:
+ * http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+ */
+private[ml] sealed trait Term
+
+/* R formula reference to all available columns, e.g. "." in a formula */
+private[ml] case object Dot extends Term
+
+/* R formula reference to a column, e.g. "+ Species" in a formula */
+private[ml] case class ColumnRef(value: String) extends Term
+
+/* R formula intercept toggle, e.g. "+ 0" in a formula */
+private[ml] case class Intercept(enabled: Boolean) extends Term
+
+/* R formula deletion of a variable, e.g. "- Species" in a formula */
+private[ml] case class Deletion(term: Term) extends Term
+
+/**
+ * Limited implementation of R formula parsing. Currently supports: '~', '+', '-', '.'.
+ */
+private[ml] object RFormulaParser extends RegexParsers {
+  def intercept: Parser[Intercept] =
+    "([01])".r ^^ { case a => Intercept(a == "1") }
+
+  def columnRef: Parser[ColumnRef] =
+    "([a-zA-Z]|\\.[a-zA-Z_])[a-zA-Z0-9._]*".r ^^ { case a => ColumnRef(a) }
+
+  def term: Parser[Term] = intercept | columnRef | "\\.".r ^^ { case _ => Dot }
+
+  def terms: Parser[List[Term]] = (term ~ rep("+" ~ term | "-" ~ term)) ^^ {
+    case op ~ list => list.foldLeft(List(op)) {
+      case (left, "+" ~ right) => left ++ Seq(right)
+      case (left, "-" ~ right) => left ++ Seq(Deletion(right))
+    }
+  }
+
+  def formula: Parser[ParsedRFormula] =
+    (columnRef ~ "~" ~ terms) ^^ { case r ~ "~" ~ t => ParsedRFormula(r, t) }
+
+  def parse(value: String): ParsedRFormula = parseAll(formula, value) match {
+    case Success(result, _) => result
+    case failure: NoSuccess => throw new IllegalArgumentException(
+      "Could not parse formula: " + value)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
index 1ee080641e3e3..9f70592ccad7e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
@@ -32,8 +32,14 @@ private[r] object SparkRWrappers {
       alpha: Double): PipelineModel = {
     val formula = new RFormula().setFormula(value)
     val estimator = family match {
-      case "gaussian" => new LinearRegression().setRegParam(lambda).setElasticNetParam(alpha)
-      case "binomial" => new LogisticRegression().setRegParam(lambda).setElasticNetParam(alpha)
+      case "gaussian" => new LinearRegression()
+        .setRegParam(lambda)
+        .setElasticNetParam(alpha)
+        .setFitIntercept(formula.hasIntercept)
+      case "binomial" => new LogisticRegression()
+        .setRegParam(lambda)
+        .setElasticNetParam(alpha)
+        .setFitIntercept(formula.hasIntercept)
     }
     val pipeline = new Pipeline().setStages(Array(formula, estimator))
     pipeline.fit(df)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
index c4b45aee06384..436e66bab09b0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaParserSuite.scala
@@ -18,12 +18,17 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.types._
 
 class RFormulaParserSuite extends SparkFunSuite {
-  private def checkParse(formula: String, label: String, terms: Seq[String]) {
-    val parsed = RFormulaParser.parse(formula)
-    assert(parsed.label == label)
-    assert(parsed.terms == terms)
+  private def checkParse(
+      formula: String,
+      label: String,
+      terms: Seq[String],
+      schema: StructType = null) {
+    val resolved = RFormulaParser.parse(formula).resolve(schema)
+    assert(resolved.label == label)
+    assert(resolved.terms == terms)
   }
 
   test("parse simple formulas") {
@@ -32,4 +37,46 @@ class RFormulaParserSuite extends SparkFunSuite {
     checkParse("y ~   ._foo  ", "y", Seq("._foo"))
     checkParse("resp ~ A_VAR + B + c123", "resp", Seq("A_VAR", "B", "c123"))
   }
+
+  test("parse dot") {
+    val schema = (new StructType)
+      .add("a", "int", true)
+      .add("b", "long", false)
+      .add("c", "string", true)
+    checkParse("a ~ .", "a", Seq("b", "c"), schema)
+  }
+
+  test("parse deletion") {
+    val schema = (new StructType)
+      .add("a", "int", true)
+      .add("b", "long", false)
+      .add("c", "string", true)
+    checkParse("a ~ c - b", "a", Seq("c"), schema)
+  }
+
+  test("parse additions and deletions in order") {
+    val schema = (new StructType)
+      .add("a", "int", true)
+      .add("b", "long", false)
+      .add("c", "string", true)
+    checkParse("a ~ . - b + . - c", "a", Seq("b"), schema)
+  }
+
+  test("dot ignores complex column types") {
+    val schema = (new StructType)
+      .add("a", "int", true)
+      .add("b", "tinyint", false)
+      .add("c", "map<string, string>", true)
+    checkParse("a ~ .", "a", Seq("b"), schema)
+  }
+
+  test("parse intercept") {
+    assert(RFormulaParser.parse("a ~ b").hasIntercept)
+    assert(RFormulaParser.parse("a ~ b + 1").hasIntercept)
+    assert(RFormulaParser.parse("a ~ b - 0").hasIntercept)
+    assert(RFormulaParser.parse("a ~ b - 1 + 1").hasIntercept)
+    assert(!RFormulaParser.parse("a ~ b + 0").hasIntercept)
+    assert(!RFormulaParser.parse("a ~ b - 1").hasIntercept)
+    assert(!RFormulaParser.parse("a ~ b + 1 - 1").hasIntercept)
+  }
 }

From b88b868eb378bdb7459978842b5572a0b498f412 Mon Sep 17 00:00:00 2001
From: Joseph Batchik <josephbatchik@gmail.com>
Date: Tue, 28 Jul 2015 14:39:25 -0700
Subject: [PATCH 0654/1454] [SPARK-8003][SQL] Added virtual column support to
 Spark

Added virtual column support by adding a new resolution role to the query analyzer. Additional virtual columns can be added by adding case expressions to [the new rule](https://github.com/JDrit/spark/blob/virt_columns/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala#L1026) and my modifying the [logical plan](https://github.com/JDrit/spark/blob/virt_columns/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala#L216) to resolve them.

This also solves [SPARK-8003](https://issues.apache.org/jira/browse/SPARK-8003)

This allows you to perform queries such as:
```sql
select spark__partition__id, count(*) as c from table group by spark__partition__id;
```

Author: Joseph Batchik <josephbatchik@gmail.com>
Author: JD <jd@csh.rit.edu>

Closes #7478 from JDrit/virt_columns and squashes the following commits:

7932bf0 [Joseph Batchik] adding spark__partition__id to hive as well
f8a9c6c [Joseph Batchik] merging in master
e49da48 [JD] fixes for @rxin's suggestions
60e120b [JD] fixing test in merge
4bf8554 [JD] merging in master
c68bc0f [Joseph Batchik] Adding function register ability to SQLContext and adding a function for spark__partition__id()
---
 .../sql/catalyst/analysis/FunctionRegistry.scala    |  2 +-
 .../scala/org/apache/spark/sql/SQLContext.scala     | 11 ++++++++++-
 .../execution/expressions/SparkPartitionID.scala    |  2 +-
 .../main/scala/org/apache/spark/sql/functions.scala |  2 +-
 .../test/scala/org/apache/spark/sql/UDFSuite.scala  |  7 +++++++
 .../expression/NondeterministicSuite.scala          |  2 +-
 .../org/apache/spark/sql/hive/HiveContext.scala     | 13 +++++++++++--
 .../scala/org/apache/spark/sql/hive/UDFSuite.scala  |  9 ++++++++-
 8 files changed, 40 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 61ee6f6f71631..9b60943a1e147 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -239,7 +239,7 @@ object FunctionRegistry {
   }
 
   /** See usage above. */
-  private def expression[T <: Expression](name: String)
+  def expression[T <: Expression](name: String)
       (implicit tag: ClassTag[T]): (String, (ExpressionInfo, FunctionBuilder)) = {
 
     // See if we can find a constructor that accepts Seq[Expression]
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index dbb2a09846548..56cd8f22e7cf4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -31,6 +31,8 @@ import org.apache.spark.SparkContext
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.{expression => FunctionExpression, FunctionBuilder}
+import org.apache.spark.sql.execution.expressions.SparkPartitionID
 import org.apache.spark.sql.SQLConf.SQLConfEntry
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.errors.DialectException
@@ -140,7 +142,14 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   // TODO how to handle the temp function per user session?
   @transient
-  protected[sql] lazy val functionRegistry: FunctionRegistry = FunctionRegistry.builtin
+  protected[sql] lazy val functionRegistry: FunctionRegistry = {
+    val reg = FunctionRegistry.builtin
+    val extendedFunctions = List[(String, (ExpressionInfo, FunctionBuilder))](
+      FunctionExpression[SparkPartitionID]("spark__partition__id")
+    )
+    extendedFunctions.foreach { case(name, (info, fun)) => reg.registerFunction(name, info, fun) }
+    reg
+  }
 
   @transient
   protected[sql] lazy val analyzer: Analyzer =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
index 61ef079d89af5..98c8eab8372aa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.types.{IntegerType, DataType}
 /**
  * Expression that returns the current partition id of the Spark task.
  */
-private[sql] case object SparkPartitionID extends LeafExpression with Nondeterministic {
+private[sql] case class SparkPartitionID() extends LeafExpression with Nondeterministic {
 
   override def nullable: Boolean = false
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index cec61b66b157c..0148991512213 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -741,7 +741,7 @@ object functions {
    * @group normal_funcs
    * @since 1.4.0
    */
-  def sparkPartitionId(): Column = execution.expressions.SparkPartitionID
+  def sparkPartitionId(): Column = execution.expressions.SparkPartitionID()
 
   /**
    * Computes the square root of the specified float value.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index c1516b450cbd4..9b326c16350c8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -51,6 +51,13 @@ class UDFSuite extends QueryTest {
     df.selectExpr("count(distinct a)")
   }
 
+  test("SPARK-8003 spark__partition__id") {
+    val df = Seq((1, "Tearing down the walls that divide us")).toDF("id", "saying")
+    df.registerTempTable("tmp_table")
+    checkAnswer(ctx.sql("select spark__partition__id() from tmp_table").toDF(), Row(0))
+    ctx.dropTempTable("tmp_table")
+  }
+
   test("error reporting for incorrect number of arguments") {
     val df = ctx.emptyDataFrame
     val e = intercept[AnalysisException] {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
index 1c5a2ed2c0a53..b6e79ff9cc95d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
@@ -27,6 +27,6 @@ class NondeterministicSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("SparkPartitionID") {
-    checkEvaluation(SparkPartitionID, 0)
+    checkEvaluation(SparkPartitionID(), 0)
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 110f51a305861..8b35c1275f388 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -38,6 +38,9 @@ import org.apache.spark.Logging
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.{expression => FunctionExpression, FunctionBuilder}
+import org.apache.spark.sql.catalyst.expressions.ExpressionInfo
+import org.apache.spark.sql.execution.expressions.SparkPartitionID
 import org.apache.spark.sql.SQLConf.SQLConfEntry
 import org.apache.spark.sql.SQLConf.SQLConfEntry._
 import org.apache.spark.sql.catalyst.{TableIdentifier, ParserDialect}
@@ -372,8 +375,14 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
 
   // Note that HiveUDFs will be overridden by functions registered in this context.
   @transient
-  override protected[sql] lazy val functionRegistry: FunctionRegistry =
-    new HiveFunctionRegistry(FunctionRegistry.builtin)
+  override protected[sql] lazy val functionRegistry: FunctionRegistry = {
+    val reg = new HiveFunctionRegistry(FunctionRegistry.builtin)
+    val extendedFunctions = List[(String, (ExpressionInfo, FunctionBuilder))](
+      FunctionExpression[SparkPartitionID]("spark__partition__id")
+    )
+    extendedFunctions.foreach { case(name, (info, fun)) => reg.registerFunction(name, info, fun) }
+    reg
+  }
 
   /* An analyzer that uses the Hive metastore. */
   @transient
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
index 4056dee777574..9cea5d413c817 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
@@ -17,13 +17,14 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.{Row, QueryTest}
 
 case class FunctionResult(f1: String, f2: String)
 
 class UDFSuite extends QueryTest {
 
   private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
+  import ctx.implicits._
 
   test("UDF case insensitive") {
     ctx.udf.register("random0", () => { Math.random() })
@@ -33,4 +34,10 @@ class UDFSuite extends QueryTest {
     assert(ctx.sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
     assert(ctx.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5)
   }
+
+  test("SPARK-8003 spark__partition__id") {
+    val df = Seq((1, "Two Fiiiiive")).toDF("id", "saying")
+    ctx.registerDataFrameAsTable(df, "test_table")
+    checkAnswer(ctx.sql("select spark__partition__id() from test_table LIMIT 1").toDF(), Row(0))
+  }
 }

From 198d181dfb2c04102afe40680a4637d951e92c0b Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Tue, 28 Jul 2015 15:00:25 -0700
Subject: [PATCH 0655/1454] [SPARK-7105] [PYSPARK] [MLLIB] Support model
 save/load in GMM

This PR introduces save / load for GMM's in python API.

Also I refactored `GaussianMixtureModel` and inherited it from `JavaModelWrapper` with model being `GaussianMixtureModelWrapper`, a wrapper which provides convenience methods to `GaussianMixtureModel` (due to serialization and deserialization issues) and I moved the creation of gaussians to the scala backend.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7617 from MechCoder/python_gmm_save_load and squashes the following commits:

9c305aa [MechCoder] [SPARK-7105] [PySpark] [MLlib] Support model save/load in GMM
---
 .../python/GaussianMixtureModelWrapper.scala  | 53 +++++++++++++
 .../mllib/api/python/PythonMLLibAPI.scala     | 13 +---
 python/pyspark/mllib/clustering.py            | 75 +++++++++++++------
 python/pyspark/mllib/util.py                  |  6 ++
 4 files changed, 114 insertions(+), 33 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala
new file mode 100644
index 0000000000000..0ec88ef77d695
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala
@@ -0,0 +1,53 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.api.python
+
+import java.util.{List => JList}
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.linalg.{Vector, Vectors, Matrix}
+import org.apache.spark.mllib.clustering.GaussianMixtureModel
+
+/**
+  * Wrapper around GaussianMixtureModel to provide helper methods in Python
+  */
+private[python] class GaussianMixtureModelWrapper(model: GaussianMixtureModel) {
+  val weights: Vector = Vectors.dense(model.weights)
+  val k: Int = weights.size
+
+  /**
+    * Returns gaussians as a List of Vectors and Matrices corresponding each MultivariateGaussian
+    */
+  val gaussians: JList[Object] = {
+    val modelGaussians = model.gaussians
+    var i = 0
+    var mu = ArrayBuffer.empty[Vector]
+    var sigma = ArrayBuffer.empty[Matrix]
+    while (i < k) {
+      mu += modelGaussians(i).mu
+      sigma += modelGaussians(i).sigma
+      i += 1
+    }
+    List(mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava
+  }
+
+  def save(sc: SparkContext, path: String): Unit = model.save(sc, path)
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index fda8d5a0b048f..6f080d32bbf4d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -364,7 +364,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       seed: java.lang.Long,
       initialModelWeights: java.util.ArrayList[Double],
       initialModelMu: java.util.ArrayList[Vector],
-      initialModelSigma: java.util.ArrayList[Matrix]): JList[Object] = {
+      initialModelSigma: java.util.ArrayList[Matrix]): GaussianMixtureModelWrapper = {
     val gmmAlg = new GaussianMixture()
       .setK(k)
       .setConvergenceTol(convergenceTol)
@@ -382,16 +382,7 @@ private[python] class PythonMLLibAPI extends Serializable {
     if (seed != null) gmmAlg.setSeed(seed)
 
     try {
-      val model = gmmAlg.run(data.rdd.persist(StorageLevel.MEMORY_AND_DISK))
-      var wt = ArrayBuffer.empty[Double]
-      var mu = ArrayBuffer.empty[Vector]
-      var sigma = ArrayBuffer.empty[Matrix]
-      for (i <- 0 until model.k) {
-          wt += model.weights(i)
-          mu += model.gaussians(i).mu
-          sigma += model.gaussians(i).sigma
-      }
-      List(Vectors.dense(wt.toArray), mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava
+      new GaussianMixtureModelWrapper(gmmAlg.run(data.rdd.persist(StorageLevel.MEMORY_AND_DISK)))
     } finally {
       data.rdd.unpersist(blocking = false)
     }
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index 58ad99d46e23b..900ade248c386 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -152,11 +152,19 @@ def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||"
         return KMeansModel([c.toArray() for c in centers])
 
 
-class GaussianMixtureModel(object):
+@inherit_doc
+class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader):
+
+    """
+    .. note:: Experimental
 
-    """A clustering model derived from the Gaussian Mixture Model method.
+    A clustering model derived from the Gaussian Mixture Model method.
 
     >>> from pyspark.mllib.linalg import Vectors, DenseMatrix
+    >>> from numpy.testing import assert_equal
+    >>> from shutil import rmtree
+    >>> import os, tempfile
+
     >>> clusterdata_1 =  sc.parallelize(array([-0.1,-0.05,-0.01,-0.1,
     ...                                         0.9,0.8,0.75,0.935,
     ...                                        -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2))
@@ -169,6 +177,25 @@ class GaussianMixtureModel(object):
     True
     >>> labels[4]==labels[5]
     True
+
+    >>> path = tempfile.mkdtemp()
+    >>> model.save(sc, path)
+    >>> sameModel = GaussianMixtureModel.load(sc, path)
+    >>> assert_equal(model.weights, sameModel.weights)
+    >>> mus, sigmas = list(
+    ...     zip(*[(g.mu, g.sigma) for g in model.gaussians]))
+    >>> sameMus, sameSigmas = list(
+    ...     zip(*[(g.mu, g.sigma) for g in sameModel.gaussians]))
+    >>> mus == sameMus
+    True
+    >>> sigmas == sameSigmas
+    True
+    >>> from shutil import rmtree
+    >>> try:
+    ...     rmtree(path)
+    ... except OSError:
+    ...     pass
+
     >>> data =  array([-5.1971, -2.5359, -3.8220,
     ...                -5.2211, -5.0602,  4.7118,
     ...                 6.8989, 3.4592,  4.6322,
@@ -182,25 +209,15 @@ class GaussianMixtureModel(object):
     True
     >>> labels[3]==labels[4]
     True
-    >>> clusterdata_3 = sc.parallelize(data.reshape(15, 1))
-    >>> im = GaussianMixtureModel([0.5, 0.5],
-    ...      [MultivariateGaussian(Vectors.dense([-1.0]), DenseMatrix(1, 1, [1.0])),
-    ...      MultivariateGaussian(Vectors.dense([1.0]), DenseMatrix(1, 1, [1.0]))])
-    >>> model = GaussianMixture.train(clusterdata_3, 2, initialModel=im)
     """
 
-    def __init__(self, weights, gaussians):
-        self._weights = weights
-        self._gaussians = gaussians
-        self._k = len(self._weights)
-
     @property
     def weights(self):
         """
         Weights for each Gaussian distribution in the mixture, where weights[i] is
         the weight for Gaussian i, and weights.sum == 1.
         """
-        return self._weights
+        return array(self.call("weights"))
 
     @property
     def gaussians(self):
@@ -208,12 +225,14 @@ def gaussians(self):
         Array of MultivariateGaussian where gaussians[i] represents
         the Multivariate Gaussian (Normal) Distribution for Gaussian i.
         """
-        return self._gaussians
+        return [
+            MultivariateGaussian(gaussian[0], gaussian[1])
+            for gaussian in zip(*self.call("gaussians"))]
 
     @property
     def k(self):
         """Number of gaussians in mixture."""
-        return self._k
+        return len(self.weights)
 
     def predict(self, x):
         """
@@ -238,17 +257,30 @@ def predictSoft(self, x):
         :return:     membership_matrix. RDD of array of double values.
         """
         if isinstance(x, RDD):
-            means, sigmas = zip(*[(g.mu, g.sigma) for g in self._gaussians])
+            means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
             membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector),
-                                              _convert_to_vector(self._weights), means, sigmas)
+                                              _convert_to_vector(self.weights), means, sigmas)
             return membership_matrix.map(lambda x: pyarray.array('d', x))
         else:
             raise TypeError("x should be represented by an RDD, "
                             "but got %s." % type(x))
 
+    @classmethod
+    def load(cls, sc, path):
+        """Load the GaussianMixtureModel from disk.
+
+        :param sc: SparkContext
+        :param path: str, path to where the model is stored.
+        """
+        model = cls._load_java(sc, path)
+        wrapper = sc._jvm.GaussianMixtureModelWrapper(model)
+        return cls(wrapper)
+
 
 class GaussianMixture(object):
     """
+    .. note:: Experimental
+
     Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
 
     :param data:            RDD of data points
@@ -271,11 +303,10 @@ def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initia
             initialModelWeights = initialModel.weights
             initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
             initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
-        weight, mu, sigma = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
-                                          k, convergenceTol, maxIterations, seed,
-                                          initialModelWeights, initialModelMu, initialModelSigma)
-        mvg_obj = [MultivariateGaussian(mu[i], sigma[i]) for i in range(k)]
-        return GaussianMixtureModel(weight, mvg_obj)
+        java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
+                                   k, convergenceTol, maxIterations, seed,
+                                   initialModelWeights, initialModelMu, initialModelSigma)
+        return GaussianMixtureModel(java_model)
 
 
 class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader):
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 875d3b2d642c6..916de2d6fcdbd 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -21,7 +21,9 @@
 
 if sys.version > '3':
     xrange = range
+    basestring = str
 
+from pyspark import SparkContext
 from pyspark.mllib.common import callMLlibFunc, inherit_doc
 from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector
 
@@ -223,6 +225,10 @@ class JavaSaveable(Saveable):
     """
 
     def save(self, sc, path):
+        if not isinstance(sc, SparkContext):
+            raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
+        if not isinstance(path, basestring):
+            raise TypeError("path should be a basestring, got type %s" % type(path))
         self._java_model.save(sc._jsc.sc(), path)
 
 

From 21825529eae66293ec5d8638911303fa54944dd5 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 28 Jul 2015 15:56:19 -0700
Subject: [PATCH 0656/1454] [SPARK-9247] [SQL] Use BytesToBytesMap for
 broadcast join

This PR introduce BytesToBytesMap to UnsafeHashedRelation, use it in executor for better performance.

It serialize all the key and values from java HashMap, put them into a BytesToBytesMap while deserializing. All the values for a same key are stored continuous to have better memory locality.

This PR also address the comments for #7480 , do some clean up.

Author: Davies Liu <davies@databricks.com>

Closes #7592 from davies/unsafe_map2 and squashes the following commits:

42c578a [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_map2
fd09528 [Davies Liu] remove thread local cache and update docs
1c5ad8d [Davies Liu] fix test
5eb1b5a [Davies Liu] address comments in #7480
46f1f22 [Davies Liu] fix style
fc221e0 [Davies Liu] use BytesToBytesMap for broadcast join
---
 .../execution/joins/BroadcastHashJoin.scala   |   2 +-
 .../joins/BroadcastHashOuterJoin.scala        |   2 +-
 .../joins/BroadcastLeftSemiJoinHash.scala     |   6 +-
 .../joins/BroadcastNestedLoopJoin.scala       |  36 ++--
 .../spark/sql/execution/joins/HashJoin.scala  |  35 ++--
 .../sql/execution/joins/HashOuterJoin.scala   |  34 ++--
 .../sql/execution/joins/HashSemiJoin.scala    |  14 +-
 .../sql/execution/joins/HashedRelation.scala  | 166 ++++++++++++++----
 .../execution/joins/LeftSemiJoinHash.scala    |   2 +-
 .../execution/joins/ShuffledHashJoin.scala    |   2 +-
 .../joins/ShuffledHashOuterJoin.scala         |   8 +-
 .../execution/joins/HashedRelationSuite.scala |  28 +--
 12 files changed, 214 insertions(+), 121 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index abaa4a6ce86a2..624efc1b1d734 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -62,7 +62,7 @@ case class BroadcastHashJoin(
   private val broadcastFuture = future {
     // Note that we use .execute().collect() because we don't want to convert data to Scala types
     val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    val hashed = buildHashRelation(input.iterator)
+    val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.size)
     sparkContext.broadcast(hashed)
   }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index c9d1a880f4ef4..77e7fe71009b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -61,7 +61,7 @@ case class BroadcastHashOuterJoin(
   private val broadcastFuture = future {
     // Note that we use .execute().collect() because we don't want to convert data to Scala types
     val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    val hashed = buildHashRelation(input.iterator)
+    val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
     sparkContext.broadcast(hashed)
   }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index f71c0ce352904..a60593911f94f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -37,17 +37,17 @@ case class BroadcastLeftSemiJoinHash(
     condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
   protected override def doExecute(): RDD[InternalRow] = {
-    val buildIter = right.execute().map(_.copy()).collect().toIterator
+    val input = right.execute().map(_.copy()).collect()
 
     if (condition.isEmpty) {
-      val hashSet = buildKeyHashSet(buildIter)
+      val hashSet = buildKeyHashSet(input.toIterator)
       val broadcastedRelation = sparkContext.broadcast(hashSet)
 
       left.execute().mapPartitions { streamIter =>
         hashSemiJoin(streamIter, broadcastedRelation.value)
       }
     } else {
-      val hashRelation = buildHashRelation(buildIter)
+      val hashRelation = HashedRelation(input.toIterator, rightKeyGenerator, input.size)
       val broadcastedRelation = sparkContext.broadcast(hashRelation)
 
       left.execute().mapPartitions { streamIter =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 700636966f8be..83b726a8e2897 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -47,13 +47,11 @@ case class BroadcastNestedLoopJoin(
   override def outputsUnsafeRows: Boolean = left.outputsUnsafeRows || right.outputsUnsafeRows
   override def canProcessUnsafeRows: Boolean = true
 
-  @transient private[this] lazy val resultProjection: Projection = {
+  @transient private[this] lazy val resultProjection: InternalRow => InternalRow = {
     if (outputsUnsafeRows) {
       UnsafeProjection.create(schema)
     } else {
-      new Projection {
-        override def apply(r: InternalRow): InternalRow = r
-      }
+      identity[InternalRow]
     }
   }
 
@@ -96,7 +94,6 @@ case class BroadcastNestedLoopJoin(
         var streamRowMatched = false
 
         while (i < broadcastedRelation.value.size) {
-          // TODO: One bitset per partition instead of per row.
           val broadcastedRow = broadcastedRelation.value(i)
           buildSide match {
             case BuildRight if boundCondition(joinedRow(streamedRow, broadcastedRow)) =>
@@ -135,17 +132,26 @@ case class BroadcastNestedLoopJoin(
       val buf: CompactBuffer[InternalRow] = new CompactBuffer()
       var i = 0
       val rel = broadcastedRelation.value
-      while (i < rel.length) {
-        if (!allIncludedBroadcastTuples.contains(i)) {
-          (joinType, buildSide) match {
-            case (RightOuter | FullOuter, BuildRight) =>
-              buf += resultProjection(new JoinedRow(leftNulls, rel(i)))
-            case (LeftOuter | FullOuter, BuildLeft) =>
-              buf += resultProjection(new JoinedRow(rel(i), rightNulls))
-            case _ =>
+      (joinType, buildSide) match {
+        case (RightOuter | FullOuter, BuildRight) =>
+          val joinedRow = new JoinedRow
+          joinedRow.withLeft(leftNulls)
+          while (i < rel.length) {
+            if (!allIncludedBroadcastTuples.contains(i)) {
+              buf += resultProjection(joinedRow.withRight(rel(i))).copy()
+            }
+            i += 1
           }
-        }
-        i += 1
+        case (LeftOuter | FullOuter, BuildLeft) =>
+          val joinedRow = new JoinedRow
+          joinedRow.withRight(rightNulls)
+          while (i < rel.length) {
+            if (!allIncludedBroadcastTuples.contains(i)) {
+              buf += resultProjection(joinedRow.withLeft(rel(i))).copy()
+            }
+            i += 1
+          }
+        case _ =>
       }
       buf.toSeq
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 46ab5b0d1cc6d..6b3d1652923fd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.execution.joins
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkPlan
-import org.apache.spark.util.collection.CompactBuffer
 
 
 trait HashJoin {
@@ -44,16 +43,24 @@ trait HashJoin {
 
   override def output: Seq[Attribute] = left.output ++ right.output
 
-  protected[this] def supportUnsafe: Boolean = {
+  protected[this] def isUnsafeMode: Boolean = {
     (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
-  override def outputsUnsafeRows: Boolean = supportUnsafe
-  override def canProcessUnsafeRows: Boolean = supportUnsafe
+  override def outputsUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessSafeRows: Boolean = !isUnsafeMode
+
+  @transient protected lazy val buildSideKeyGenerator: Projection =
+    if (isUnsafeMode) {
+      UnsafeProjection.create(buildKeys, buildPlan.output)
+    } else {
+      newMutableProjection(buildKeys, buildPlan.output)()
+    }
 
   @transient protected lazy val streamSideKeyGenerator: Projection =
-    if (supportUnsafe) {
+    if (isUnsafeMode) {
       UnsafeProjection.create(streamedKeys, streamedPlan.output)
     } else {
       newMutableProjection(streamedKeys, streamedPlan.output)()
@@ -65,18 +72,16 @@ trait HashJoin {
   {
     new Iterator[InternalRow] {
       private[this] var currentStreamedRow: InternalRow = _
-      private[this] var currentHashMatches: CompactBuffer[InternalRow] = _
+      private[this] var currentHashMatches: Seq[InternalRow] = _
       private[this] var currentMatchPosition: Int = -1
 
       // Mutable per row objects.
       private[this] val joinRow = new JoinedRow
-      private[this] val resultProjection: Projection = {
-        if (supportUnsafe) {
+      private[this] val resultProjection: (InternalRow) => InternalRow = {
+        if (isUnsafeMode) {
           UnsafeProjection.create(self.schema)
         } else {
-          new Projection {
-            override def apply(r: InternalRow): InternalRow = r
-          }
+          identity[InternalRow]
         }
       }
 
@@ -122,12 +127,4 @@ trait HashJoin {
       }
     }
   }
-
-  protected[this] def buildHashRelation(buildIter: Iterator[InternalRow]): HashedRelation = {
-    if (supportUnsafe) {
-      UnsafeHashedRelation(buildIter, buildKeys, buildPlan)
-    } else {
-      HashedRelation(buildIter, newProjection(buildKeys, buildPlan.output))
-    }
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 6bf2f82954046..7e671e7914f1a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -75,30 +75,36 @@ trait HashOuterJoin {
         s"HashOuterJoin should not take $x as the JoinType")
   }
 
-  protected[this] def supportUnsafe: Boolean = {
+  protected[this] def isUnsafeMode: Boolean = {
     (self.codegenEnabled && joinType != FullOuter
       && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
-  override def outputsUnsafeRows: Boolean = supportUnsafe
-  override def canProcessUnsafeRows: Boolean = supportUnsafe
+  override def outputsUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessSafeRows: Boolean = !isUnsafeMode
 
-  protected[this] def streamedKeyGenerator(): Projection = {
-    if (supportUnsafe) {
+  @transient protected lazy val buildKeyGenerator: Projection =
+    if (isUnsafeMode) {
+      UnsafeProjection.create(buildKeys, buildPlan.output)
+    } else {
+      newMutableProjection(buildKeys, buildPlan.output)()
+    }
+
+  @transient protected[this] lazy val streamedKeyGenerator: Projection = {
+    if (isUnsafeMode) {
       UnsafeProjection.create(streamedKeys, streamedPlan.output)
     } else {
       newProjection(streamedKeys, streamedPlan.output)
     }
   }
 
-  @transient private[this] lazy val resultProjection: Projection = {
-    if (supportUnsafe) {
+  @transient private[this] lazy val resultProjection: InternalRow => InternalRow = {
+    if (isUnsafeMode) {
       UnsafeProjection.create(self.schema)
     } else {
-      new Projection {
-        override def apply(r: InternalRow): InternalRow = r
-      }
+      identity[InternalRow]
     }
   }
 
@@ -230,12 +236,4 @@ trait HashOuterJoin {
 
     hashTable
   }
-
-  protected[this] def buildHashRelation(buildIter: Iterator[InternalRow]): HashedRelation = {
-    if (supportUnsafe) {
-      UnsafeHashedRelation(buildIter, buildKeys, buildPlan)
-    } else {
-      HashedRelation(buildIter, newProjection(buildKeys, buildPlan.output))
-    }
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 7f49264d40354..97fde8f975bfd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -35,11 +35,13 @@ trait HashSemiJoin {
   protected[this] def supportUnsafe: Boolean = {
     (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
       && UnsafeProjection.canSupport(rightKeys)
-      && UnsafeProjection.canSupport(left.schema))
+      && UnsafeProjection.canSupport(left.schema)
+      && UnsafeProjection.canSupport(right.schema))
   }
 
-  override def outputsUnsafeRows: Boolean = right.outputsUnsafeRows
+  override def outputsUnsafeRows: Boolean = supportUnsafe
   override def canProcessUnsafeRows: Boolean = supportUnsafe
+  override def canProcessSafeRows: Boolean = !supportUnsafe
 
   @transient protected lazy val leftKeyGenerator: Projection =
     if (supportUnsafe) {
@@ -87,14 +89,6 @@ trait HashSemiJoin {
     })
   }
 
-  protected def buildHashRelation(buildIter: Iterator[InternalRow]): HashedRelation = {
-    if (supportUnsafe) {
-      UnsafeHashedRelation(buildIter, rightKeys, right)
-    } else {
-      HashedRelation(buildIter, newProjection(rightKeys, right.output))
-    }
-  }
-
   protected def hashSemiJoin(
       streamIter: Iterator[InternalRow],
       hashedRelation: HashedRelation): Iterator[InternalRow] = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 8d5731afd59b8..9c058f1f72fe4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -18,12 +18,15 @@
 package org.apache.spark.sql.execution.joins
 
 import java.io.{Externalizable, ObjectInput, ObjectOutput}
+import java.nio.ByteOrder
 import java.util.{HashMap => JavaHashMap}
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.execution.{SparkPlan, SparkSqlSerializer}
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.execution.SparkSqlSerializer
+import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.map.BytesToBytesMap
+import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.util.collection.CompactBuffer
 
 
@@ -32,7 +35,7 @@ import org.apache.spark.util.collection.CompactBuffer
  * object.
  */
 private[joins] sealed trait HashedRelation {
-  def get(key: InternalRow): CompactBuffer[InternalRow]
+  def get(key: InternalRow): Seq[InternalRow]
 
   // This is a helper method to implement Externalizable, and is used by
   // GeneralHashedRelation and UniqueKeyHashedRelation
@@ -59,9 +62,9 @@ private[joins] final class GeneralHashedRelation(
     private var hashTable: JavaHashMap[InternalRow, CompactBuffer[InternalRow]])
   extends HashedRelation with Externalizable {
 
-  def this() = this(null) // Needed for serialization
+  private def this() = this(null) // Needed for serialization
 
-  override def get(key: InternalRow): CompactBuffer[InternalRow] = hashTable.get(key)
+  override def get(key: InternalRow): Seq[InternalRow] = hashTable.get(key)
 
   override def writeExternal(out: ObjectOutput): Unit = {
     writeBytes(out, SparkSqlSerializer.serialize(hashTable))
@@ -81,9 +84,9 @@ private[joins]
 final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalRow, InternalRow])
   extends HashedRelation with Externalizable {
 
-  def this() = this(null) // Needed for serialization
+  private def this() = this(null) // Needed for serialization
 
-  override def get(key: InternalRow): CompactBuffer[InternalRow] = {
+  override def get(key: InternalRow): Seq[InternalRow] = {
     val v = hashTable.get(key)
     if (v eq null) null else CompactBuffer(v)
   }
@@ -109,6 +112,10 @@ private[joins] object HashedRelation {
       keyGenerator: Projection,
       sizeEstimate: Int = 64): HashedRelation = {
 
+    if (keyGenerator.isInstanceOf[UnsafeProjection]) {
+      return UnsafeHashedRelation(input, keyGenerator.asInstanceOf[UnsafeProjection], sizeEstimate)
+    }
+
     // TODO: Use Spark's HashMap implementation.
     val hashTable = new JavaHashMap[InternalRow, CompactBuffer[InternalRow]](sizeEstimate)
     var currentRow: InternalRow = null
@@ -149,31 +156,133 @@ private[joins] object HashedRelation {
   }
 }
 
-
 /**
- * A HashedRelation for UnsafeRow, which is backed by BytesToBytesMap that maps the key into a
- * sequence of values.
+ * A HashedRelation for UnsafeRow, which is backed by HashMap or BytesToBytesMap that maps the key
+ * into a sequence of values.
+ *
+ * When it's created, it uses HashMap. After it's serialized and deserialized, it switch to use
+ * BytesToBytesMap for better memory performance (multiple values for the same are stored as a
+ * continuous byte array.
  *
- * TODO(davies): use BytesToBytesMap
+ * It's serialized in the following format:
+ *  [number of keys]
+ *  [size of key] [size of all values in bytes] [key bytes] [bytes for all values]
+ *  ...
+ *
+ * All the values are serialized as following:
+ *   [number of fields] [number of bytes] [underlying bytes of UnsafeRow]
+ *   ...
  */
 private[joins] final class UnsafeHashedRelation(
     private var hashTable: JavaHashMap[UnsafeRow, CompactBuffer[UnsafeRow]])
   extends HashedRelation with Externalizable {
 
-  def this() = this(null)  // Needed for serialization
+  private[joins] def this() = this(null)  // Needed for serialization
+
+  // Use BytesToBytesMap in executor for better performance (it's created when deserialization)
+  @transient private[this] var binaryMap: BytesToBytesMap = _
 
-  override def get(key: InternalRow): CompactBuffer[InternalRow] = {
+  override def get(key: InternalRow): Seq[InternalRow] = {
     val unsafeKey = key.asInstanceOf[UnsafeRow]
-    // Thanks to type eraser
-    hashTable.get(unsafeKey).asInstanceOf[CompactBuffer[InternalRow]]
+
+    if (binaryMap != null) {
+      // Used in Broadcast join
+      val loc = binaryMap.lookup(unsafeKey.getBaseObject, unsafeKey.getBaseOffset,
+        unsafeKey.getSizeInBytes)
+      if (loc.isDefined) {
+        val buffer = CompactBuffer[UnsafeRow]()
+
+        val base = loc.getValueAddress.getBaseObject
+        var offset = loc.getValueAddress.getBaseOffset
+        val last = loc.getValueAddress.getBaseOffset + loc.getValueLength
+        while (offset < last) {
+          val numFields = PlatformDependent.UNSAFE.getInt(base, offset)
+          val sizeInBytes = PlatformDependent.UNSAFE.getInt(base, offset + 4)
+          offset += 8
+
+          val row = new UnsafeRow
+          row.pointTo(base, offset, numFields, sizeInBytes)
+          buffer += row
+          offset += sizeInBytes
+        }
+        buffer
+      } else {
+        null
+      }
+
+    } else {
+      // Use the JavaHashMap in Local mode or ShuffleHashJoin
+      hashTable.get(unsafeKey)
+    }
   }
 
   override def writeExternal(out: ObjectOutput): Unit = {
-    writeBytes(out, SparkSqlSerializer.serialize(hashTable))
+    out.writeInt(hashTable.size())
+
+    val iter = hashTable.entrySet().iterator()
+    while (iter.hasNext) {
+      val entry = iter.next()
+      val key = entry.getKey
+      val values = entry.getValue
+
+      // write all the values as single byte array
+      var totalSize = 0L
+      var i = 0
+      while (i < values.size) {
+        totalSize += values(i).getSizeInBytes + 4 + 4
+        i += 1
+      }
+      assert(totalSize < Integer.MAX_VALUE, "values are too big")
+
+      // [key size] [values size] [key bytes] [values bytes]
+      out.writeInt(key.getSizeInBytes)
+      out.writeInt(totalSize.toInt)
+      out.write(key.getBytes)
+      i = 0
+      while (i < values.size) {
+        // [num of fields] [num of bytes] [row bytes]
+        // write the integer in native order, so they can be read by UNSAFE.getInt()
+        if (ByteOrder.nativeOrder() == ByteOrder.BIG_ENDIAN) {
+          out.writeInt(values(i).numFields())
+          out.writeInt(values(i).getSizeInBytes)
+        } else {
+          out.writeInt(Integer.reverseBytes(values(i).numFields()))
+          out.writeInt(Integer.reverseBytes(values(i).getSizeInBytes))
+        }
+        out.write(values(i).getBytes)
+        i += 1
+      }
+    }
   }
 
   override def readExternal(in: ObjectInput): Unit = {
-    hashTable = SparkSqlSerializer.deserialize(readBytes(in))
+    val nKeys = in.readInt()
+    // This is used in Broadcast, shared by multiple tasks, so we use on-heap memory
+    val memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    binaryMap = new BytesToBytesMap(memoryManager, nKeys * 2) // reduce hash collision
+
+    var i = 0
+    var keyBuffer = new Array[Byte](1024)
+    var valuesBuffer = new Array[Byte](1024)
+    while (i < nKeys) {
+      val keySize = in.readInt()
+      val valuesSize = in.readInt()
+      if (keySize > keyBuffer.size) {
+        keyBuffer = new Array[Byte](keySize)
+      }
+      in.readFully(keyBuffer, 0, keySize)
+      if (valuesSize > valuesBuffer.size) {
+        valuesBuffer = new Array[Byte](valuesSize)
+      }
+      in.readFully(valuesBuffer, 0, valuesSize)
+
+      // put it into binary map
+      val loc = binaryMap.lookup(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize)
+      assert(!loc.isDefined, "Duplicated key found!")
+      loc.putNewKey(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize,
+        valuesBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, valuesSize)
+      i += 1
+    }
   }
 }
 
@@ -181,33 +290,14 @@ private[joins] object UnsafeHashedRelation {
 
   def apply(
       input: Iterator[InternalRow],
-      buildKeys: Seq[Expression],
-      buildPlan: SparkPlan,
-      sizeEstimate: Int = 64): HashedRelation = {
-    val boundedKeys = buildKeys.map(BindReferences.bindReference(_, buildPlan.output))
-    apply(input, boundedKeys, buildPlan.schema, sizeEstimate)
-  }
-
-  // Used for tests
-  def apply(
-      input: Iterator[InternalRow],
-      buildKeys: Seq[Expression],
-      rowSchema: StructType,
+      keyGenerator: UnsafeProjection,
       sizeEstimate: Int): HashedRelation = {
 
-    // TODO: Use BytesToBytesMap.
     val hashTable = new JavaHashMap[UnsafeRow, CompactBuffer[UnsafeRow]](sizeEstimate)
-    val toUnsafe = UnsafeProjection.create(rowSchema)
-    val keyGenerator = UnsafeProjection.create(buildKeys)
 
     // Create a mapping of buildKeys -> rows
     while (input.hasNext) {
-      val currentRow = input.next()
-      val unsafeRow = if (currentRow.isInstanceOf[UnsafeRow]) {
-        currentRow.asInstanceOf[UnsafeRow]
-      } else {
-        toUnsafe(currentRow)
-      }
+      val unsafeRow = input.next().asInstanceOf[UnsafeRow]
       val rowKey = keyGenerator(unsafeRow)
       if (!rowKey.anyNull) {
         val existingMatchList = hashTable.get(rowKey)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
index 874712a4e739f..26a664104d6fb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
@@ -46,7 +46,7 @@ case class LeftSemiJoinHash(
         val hashSet = buildKeyHashSet(buildIter)
         hashSemiJoin(streamIter, hashSet)
       } else {
-        val hashRelation = buildHashRelation(buildIter)
+        val hashRelation = HashedRelation(buildIter, rightKeyGenerator)
         hashSemiJoin(streamIter, hashRelation)
       }
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
index 948d0ccebceb0..5439e10a60b2a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
@@ -45,7 +45,7 @@ case class ShuffledHashJoin(
 
   protected override def doExecute(): RDD[InternalRow] = {
     buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
-      val hashed = buildHashRelation(buildIter)
+      val hashed = HashedRelation(buildIter, buildSideKeyGenerator)
       hashJoin(streamIter, hashed)
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index f54f1edd38ec8..d29b593207c4d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -50,8 +50,8 @@ case class ShuffledHashOuterJoin(
       // TODO this probably can be replaced by external sort (sort merged join?)
       joinType match {
         case LeftOuter =>
-          val hashed = buildHashRelation(rightIter)
-          val keyGenerator = streamedKeyGenerator()
+          val hashed = HashedRelation(rightIter, buildKeyGenerator)
+          val keyGenerator = streamedKeyGenerator
           leftIter.flatMap( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
@@ -59,8 +59,8 @@ case class ShuffledHashOuterJoin(
           })
 
         case RightOuter =>
-          val hashed = buildHashRelation(leftIter)
-          val keyGenerator = streamedKeyGenerator()
+          val hashed = HashedRelation(leftIter, buildKeyGenerator)
+          val keyGenerator = streamedKeyGenerator
           rightIter.flatMap ( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 9dd2220f0967e..8b1a9b21a96b9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -17,11 +17,12 @@
 
 package org.apache.spark.sql.execution.joins
 
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream, ObjectInputStream, ObjectOutputStream}
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.execution.SparkSqlSerializer
-import org.apache.spark.sql.types.{StructField, StructType, IntegerType}
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 import org.apache.spark.util.collection.CompactBuffer
 
 
@@ -64,27 +65,34 @@ class HashedRelationSuite extends SparkFunSuite {
   }
 
   test("UnsafeHashedRelation") {
+    val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
+    val toUnsafe = UnsafeProjection.create(schema)
+    val unsafeData = data.map(toUnsafe(_).copy()).toArray
+
     val buildKey = Seq(BoundReference(0, IntegerType, false))
-    val schema = StructType(StructField("a", IntegerType, true) :: Nil)
-    val hashed = UnsafeHashedRelation(data.iterator, buildKey, schema, 1)
+    val keyGenerator = UnsafeProjection.create(buildKey)
+    val hashed = UnsafeHashedRelation(unsafeData.iterator, keyGenerator, 1)
     assert(hashed.isInstanceOf[UnsafeHashedRelation])
 
-    val toUnsafeKey = UnsafeProjection.create(schema)
-    val unsafeData = data.map(toUnsafeKey(_).copy()).toArray
     assert(hashed.get(unsafeData(0)) === CompactBuffer[InternalRow](unsafeData(0)))
     assert(hashed.get(unsafeData(1)) === CompactBuffer[InternalRow](unsafeData(1)))
-    assert(hashed.get(toUnsafeKey(InternalRow(10))) === null)
+    assert(hashed.get(toUnsafe(InternalRow(10))) === null)
 
     val data2 = CompactBuffer[InternalRow](unsafeData(2).copy())
     data2 += unsafeData(2).copy()
     assert(hashed.get(unsafeData(2)) === data2)
 
-    val hashed2 = SparkSqlSerializer.deserialize(SparkSqlSerializer.serialize(hashed))
-      .asInstanceOf[UnsafeHashedRelation]
+    val os = new ByteArrayOutputStream()
+    val out = new ObjectOutputStream(os)
+    hashed.asInstanceOf[UnsafeHashedRelation].writeExternal(out)
+    out.flush()
+    val in = new ObjectInputStream(new ByteArrayInputStream(os.toByteArray))
+    val hashed2 = new UnsafeHashedRelation()
+    hashed2.readExternal(in)
     assert(hashed2.get(unsafeData(0)) === CompactBuffer[InternalRow](unsafeData(0)))
     assert(hashed2.get(unsafeData(1)) === CompactBuffer[InternalRow](unsafeData(1)))
-    assert(hashed2.get(toUnsafeKey(InternalRow(10))) === null)
+    assert(hashed2.get(toUnsafe(InternalRow(10))) === null)
     assert(hashed2.get(unsafeData(2)) === data2)
   }
 }

From 59b92add7cc9cca1eaf0c558edb7c4add66c284f Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 28 Jul 2015 16:04:48 -0700
Subject: [PATCH 0657/1454] [SPARK-9393] [SQL] Fix several error-handling bugs
 in ScriptTransform operator

SparkSQL's ScriptTransform operator has several serious bugs which make debugging fairly difficult:

- If exceptions are thrown in the writing thread then the child process will not be killed, leading to a deadlock because the reader thread will block while waiting for input that will never arrive.
- TaskContext is not propagated to the writer thread, which may cause errors in upstream pipelined operators.
- Exceptions which occur in the writer thread are not propagated to the main reader thread, which may cause upstream errors to be silently ignored instead of killing the job.  This can lead to silently incorrect query results.
- The writer thread is not a daemon thread, but it should be.

In addition, the code in this file is extremely messy:

- Lots of fields are nullable but the nullability isn't clearly explained.
- Many confusing variable names: for instance, there are variables named `ite` and `iterator` that are defined in the same scope.
- Some code was misindented.
- The `*serdeClass` variables are actually expected to be single-quoted strings, which is really confusing: I feel that this parsing / extraction should be performed in the analyzer, not in the operator itself.
- There were no unit tests for the operator itself, only end-to-end tests.

This pull request addresses these issues, borrowing some error-handling techniques from PySpark's PythonRDD.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7710 from JoshRosen/script-transform and squashes the following commits:

16c44e2 [Josh Rosen] Update some comments
983f200 [Josh Rosen] Use unescapeSQLString instead of stripQuotes
6a06a8c [Josh Rosen] Clean up handling of quotes in serde class name
494cde0 [Josh Rosen] Propagate TaskContext to writer thread
323bb2b [Josh Rosen] Fix error-swallowing bug
b31258d [Josh Rosen] Rename iterator variables to disambiguate.
88278de [Josh Rosen] Split ScriptTransformation writer thread into own class.
8b162b6 [Josh Rosen] Add failing test which demonstrates exception masking issue
4ee36a2 [Josh Rosen] Kill script transform subprocess when error occurs in input writer.
bd4c948 [Josh Rosen] Skip launching of external command for empty partitions.
b43e4ec [Josh Rosen] Clean up nullability in ScriptTransformation
fa18d26 [Josh Rosen] Add basic unit test for script transform with 'cat' command.
---
 .../spark/sql/execution/SparkPlanTest.scala   |  27 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |  10 +-
 .../hive/execution/ScriptTransformation.scala | 280 +++++++++++-------
 .../execution/ScriptTransformationSuite.scala | 123 ++++++++
 4 files changed, 317 insertions(+), 123 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index 6a8f394545816..f46855edfe0de 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -21,7 +21,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{DataFrame, DataFrameHolder, Row}
+import org.apache.spark.sql.{SQLContext, DataFrame, DataFrameHolder, Row}
 
 import scala.language.implicitConversions
 import scala.reflect.runtime.universe.TypeTag
@@ -33,11 +33,13 @@ import scala.util.control.NonFatal
  */
 class SparkPlanTest extends SparkFunSuite {
 
+  protected def sqlContext: SQLContext = TestSQLContext
+
   /**
    * Creates a DataFrame from a local Seq of Product.
    */
   implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = {
-    TestSQLContext.implicits.localSeqToDataFrameHolder(data)
+    sqlContext.implicits.localSeqToDataFrameHolder(data)
   }
 
   /**
@@ -98,7 +100,7 @@ class SparkPlanTest extends SparkFunSuite {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean = true): Unit = {
-    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers) match {
+    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -121,7 +123,8 @@ class SparkPlanTest extends SparkFunSuite {
       planFunction: SparkPlan => SparkPlan,
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean = true): Unit = {
-    SparkPlanTest.checkAnswer(input, planFunction, expectedPlanFunction, sortAnswers) match {
+    SparkPlanTest.checkAnswer(
+        input, planFunction, expectedPlanFunction, sortAnswers, sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -147,13 +150,14 @@ object SparkPlanTest {
       input: DataFrame,
       planFunction: SparkPlan => SparkPlan,
       expectedPlanFunction: SparkPlan => SparkPlan,
-      sortAnswers: Boolean): Option[String] = {
+      sortAnswers: Boolean,
+      sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.queryExecution.sparkPlan)
     val expectedOutputPlan = expectedPlanFunction(input.queryExecution.sparkPlan)
 
     val expectedAnswer: Seq[Row] = try {
-      executePlan(expectedOutputPlan)
+      executePlan(expectedOutputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -168,7 +172,7 @@ object SparkPlanTest {
     }
 
     val actualAnswer: Seq[Row] = try {
-      executePlan(outputPlan)
+      executePlan(outputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -207,12 +211,13 @@ object SparkPlanTest {
       input: Seq[DataFrame],
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
-      sortAnswers: Boolean): Option[String] = {
+      sortAnswers: Boolean,
+      sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.map(_.queryExecution.sparkPlan))
 
     val sparkAnswer: Seq[Row] = try {
-      executePlan(outputPlan)
+      executePlan(outputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -275,10 +280,10 @@ object SparkPlanTest {
     }
   }
 
-  private def executePlan(outputPlan: SparkPlan): Seq[Row] = {
+  private def executePlan(outputPlan: SparkPlan, sqlContext: SQLContext): Seq[Row] = {
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
-    val resolvedPlan = TestSQLContext.prepareForExecution.execute(
+    val resolvedPlan = sqlContext.prepareForExecution.execute(
       outputPlan transform {
         case plan: SparkPlan =>
           val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 2f79b0aad045c..e6df64d2642bc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -874,15 +874,15 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             }
 
             def matchSerDe(clause: Seq[ASTNode])
-              : (Seq[(String, String)], String, Seq[(String, String)]) = clause match {
+              : (Seq[(String, String)], Option[String], Seq[(String, String)]) = clause match {
               case Token("TOK_SERDEPROPS", propsClause) :: Nil =>
                 val rowFormat = propsClause.map {
                   case Token(name, Token(value, Nil) :: Nil) => (name, value)
                 }
-                (rowFormat, "", Nil)
+                (rowFormat, None, Nil)
 
               case Token("TOK_SERDENAME", Token(serdeClass, Nil) :: Nil) :: Nil =>
-                (Nil, serdeClass, Nil)
+                (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), Nil)
 
               case Token("TOK_SERDENAME", Token(serdeClass, Nil) ::
                 Token("TOK_TABLEPROPERTIES",
@@ -891,9 +891,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                   case Token("TOK_TABLEPROPERTY", Token(name, Nil) :: Token(value, Nil) :: Nil) =>
                     (name, value)
                 }
-                (Nil, serdeClass, serdeProps)
+                (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), serdeProps)
 
-              case Nil => (Nil, "", Nil)
+              case Nil => (Nil, None, Nil)
             }
 
             val (inRowFormat, inSerdeClass, inSerdeProps) = matchSerDe(inputSerdeClause)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 205e622195f09..741c705e2a253 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -17,15 +17,18 @@
 
 package org.apache.spark.sql.hive.execution
 
-import java.io.{BufferedReader, DataInputStream, DataOutputStream, EOFException, InputStreamReader}
+import java.io._
 import java.util.Properties
+import javax.annotation.Nullable
 
 import scala.collection.JavaConversions._
+import scala.util.control.NonFatal
 
 import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.serde2.AbstractSerDe
 import org.apache.hadoop.hive.serde2.objectinspector._
 
+import org.apache.spark.{TaskContext, Logging}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
@@ -56,21 +59,53 @@ case class ScriptTransformation(
   override def otherCopyArgs: Seq[HiveContext] = sc :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = {
-    child.execute().mapPartitions { iter =>
+    def processIterator(inputIterator: Iterator[InternalRow]): Iterator[InternalRow] = {
       val cmd = List("/bin/bash", "-c", script)
       val builder = new ProcessBuilder(cmd)
-      // We need to start threads connected to the process pipeline:
-      // 1) The error msg generated by the script process would be hidden.
-      // 2) If the error msg is too big to chock up the buffer, the input logic would be hung
+
       val proc = builder.start()
       val inputStream = proc.getInputStream
       val outputStream = proc.getOutputStream
       val errorStream = proc.getErrorStream
-      val reader = new BufferedReader(new InputStreamReader(inputStream))
 
-      val (outputSerde, outputSoi) = ioschema.initOutputSerDe(output)
+      // In order to avoid deadlocks, we need to consume the error output of the child process.
+      // To avoid issues caused by large error output, we use a circular buffer to limit the amount
+      // of error output that we retain. See SPARK-7862 for more discussion of the deadlock / hang
+      // that motivates this.
+      val stderrBuffer = new CircularBuffer(2048)
+      new RedirectThread(
+        errorStream,
+        stderrBuffer,
+        "Thread-ScriptTransformation-STDERR-Consumer").start()
+
+      val outputProjection = new InterpretedProjection(input, child.output)
+
+      // This nullability is a performance optimization in order to avoid an Option.foreach() call
+      // inside of a loop
+      @Nullable val (inputSerde, inputSoi) = ioschema.initInputSerDe(input).getOrElse((null, null))
+
+      // This new thread will consume the ScriptTransformation's input rows and write them to the
+      // external process. That process's output will be read by this current thread.
+      val writerThread = new ScriptTransformationWriterThread(
+        inputIterator,
+        outputProjection,
+        inputSerde,
+        inputSoi,
+        ioschema,
+        outputStream,
+        proc,
+        stderrBuffer,
+        TaskContext.get()
+      )
+
+      // This nullability is a performance optimization in order to avoid an Option.foreach() call
+      // inside of a loop
+      @Nullable val (outputSerde, outputSoi) = {
+        ioschema.initOutputSerDe(output).getOrElse((null, null))
+      }
 
-      val iterator: Iterator[InternalRow] = new Iterator[InternalRow] with HiveInspectors {
+      val reader = new BufferedReader(new InputStreamReader(inputStream))
+      val outputIterator: Iterator[InternalRow] = new Iterator[InternalRow] with HiveInspectors {
         var cacheRow: InternalRow = null
         var curLine: String = null
         var eof: Boolean = false
@@ -79,12 +114,26 @@ case class ScriptTransformation(
           if (outputSerde == null) {
             if (curLine == null) {
               curLine = reader.readLine()
-              curLine != null
+              if (curLine == null) {
+                if (writerThread.exception.isDefined) {
+                  throw writerThread.exception.get
+                }
+                false
+              } else {
+                true
+              }
             } else {
               true
             }
           } else {
-            !eof
+            if (eof) {
+              if (writerThread.exception.isDefined) {
+                throw writerThread.exception.get
+              }
+              false
+            } else {
+              true
+            }
           }
         }
 
@@ -110,11 +159,11 @@ case class ScriptTransformation(
               }
               i += 1
             })
-            return mutableRow
+            mutableRow
           } catch {
             case e: EOFException =>
               eof = true
-              return null
+              null
           }
         }
 
@@ -146,49 +195,83 @@ case class ScriptTransformation(
         }
       }
 
-      val (inputSerde, inputSoi) = ioschema.initInputSerDe(input)
-      val dataOutputStream = new DataOutputStream(outputStream)
-      val outputProjection = new InterpretedProjection(input, child.output)
+      writerThread.start()
 
-      // TODO make the 2048 configurable?
-      val stderrBuffer = new CircularBuffer(2048)
-      // Consume the error stream from the pipeline, otherwise it will be blocked if
-      // the pipeline is full.
-      new RedirectThread(errorStream, // input stream from the pipeline
-        stderrBuffer,                 // output to a circular buffer
-        "Thread-ScriptTransformation-STDERR-Consumer").start()
+      outputIterator
+    }
 
-      // Put the write(output to the pipeline) into a single thread
-      // and keep the collector as remain in the main thread.
-      // otherwise it will causes deadlock if the data size greater than
-      // the pipeline / buffer capacity.
-      new Thread(new Runnable() {
-        override def run(): Unit = {
-          Utils.tryWithSafeFinally {
-            iter
-              .map(outputProjection)
-              .foreach { row =>
-              if (inputSerde == null) {
-                val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
-                  ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
-
-                outputStream.write(data)
-              } else {
-                val writable = inputSerde.serialize(
-                  row.asInstanceOf[GenericInternalRow].values, inputSoi)
-                prepareWritable(writable).write(dataOutputStream)
-              }
-            }
-            outputStream.close()
-          } {
-            if (proc.waitFor() != 0) {
-              logError(stderrBuffer.toString) // log the stderr circular buffer
-            }
-          }
-        }
-      }, "Thread-ScriptTransformation-Feed").start()
+    child.execute().mapPartitions { iter =>
+      if (iter.hasNext) {
+        processIterator(iter)
+      } else {
+        // If the input iterator has no rows then do not launch the external script.
+        Iterator.empty
+      }
+    }
+  }
+}
 
-      iterator
+private class ScriptTransformationWriterThread(
+    iter: Iterator[InternalRow],
+    outputProjection: Projection,
+    @Nullable inputSerde: AbstractSerDe,
+    @Nullable inputSoi: ObjectInspector,
+    ioschema: HiveScriptIOSchema,
+    outputStream: OutputStream,
+    proc: Process,
+    stderrBuffer: CircularBuffer,
+    taskContext: TaskContext
+  ) extends Thread("Thread-ScriptTransformation-Feed") with Logging {
+
+  setDaemon(true)
+
+  @volatile private var _exception: Throwable = null
+
+  /** Contains the exception thrown while writing the parent iterator to the external process. */
+  def exception: Option[Throwable] = Option(_exception)
+
+  override def run(): Unit = Utils.logUncaughtExceptions {
+    TaskContext.setTaskContext(taskContext)
+
+    val dataOutputStream = new DataOutputStream(outputStream)
+
+    // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
+    // let's use a variable to record whether the `finally` block was hit due to an exception
+    var threwException: Boolean = true
+    try {
+      iter.map(outputProjection).foreach { row =>
+        if (inputSerde == null) {
+          val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
+            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
+          outputStream.write(data)
+        } else {
+          val writable = inputSerde.serialize(
+            row.asInstanceOf[GenericInternalRow].values, inputSoi)
+          prepareWritable(writable).write(dataOutputStream)
+        }
+      }
+      outputStream.close()
+      threwException = false
+    } catch {
+      case NonFatal(e) =>
+        // An error occurred while writing input, so kill the child process. According to the
+        // Javadoc this call will not throw an exception:
+        _exception = e
+        proc.destroy()
+        throw e
+    } finally {
+      try {
+        if (proc.waitFor() != 0) {
+          logError(stderrBuffer.toString) // log the stderr circular buffer
+        }
+      } catch {
+        case NonFatal(exceptionFromFinallyBlock) =>
+          if (!threwException) {
+            throw exceptionFromFinallyBlock
+          } else {
+            log.error("Exception in finally block", exceptionFromFinallyBlock)
+          }
+      }
     }
   }
 }
@@ -200,33 +283,43 @@ private[hive]
 case class HiveScriptIOSchema (
     inputRowFormat: Seq[(String, String)],
     outputRowFormat: Seq[(String, String)],
-    inputSerdeClass: String,
-    outputSerdeClass: String,
+    inputSerdeClass: Option[String],
+    outputSerdeClass: Option[String],
     inputSerdeProps: Seq[(String, String)],
     outputSerdeProps: Seq[(String, String)],
     schemaLess: Boolean) extends ScriptInputOutputSchema with HiveInspectors {
 
-  val defaultFormat = Map(("TOK_TABLEROWFORMATFIELD", "\t"),
-                          ("TOK_TABLEROWFORMATLINES", "\n"))
+  private val defaultFormat = Map(
+    ("TOK_TABLEROWFORMATFIELD", "\t"),
+    ("TOK_TABLEROWFORMATLINES", "\n")
+  )
 
   val inputRowFormatMap = inputRowFormat.toMap.withDefault((k) => defaultFormat(k))
   val outputRowFormatMap = outputRowFormat.toMap.withDefault((k) => defaultFormat(k))
 
 
-  def initInputSerDe(input: Seq[Expression]): (AbstractSerDe, ObjectInspector) = {
-    val (columns, columnTypes) = parseAttrs(input)
-    val serde = initSerDe(inputSerdeClass, columns, columnTypes, inputSerdeProps)
-    (serde, initInputSoi(serde, columns, columnTypes))
+  def initInputSerDe(input: Seq[Expression]): Option[(AbstractSerDe, ObjectInspector)] = {
+    inputSerdeClass.map { serdeClass =>
+      val (columns, columnTypes) = parseAttrs(input)
+      val serde = initSerDe(serdeClass, columns, columnTypes, inputSerdeProps)
+      val fieldObjectInspectors = columnTypes.map(toInspector)
+      val objectInspector = ObjectInspectorFactory
+        .getStandardStructObjectInspector(columns, fieldObjectInspectors)
+        .asInstanceOf[ObjectInspector]
+      (serde, objectInspector)
+    }
   }
 
-  def initOutputSerDe(output: Seq[Attribute]): (AbstractSerDe, StructObjectInspector) = {
-    val (columns, columnTypes) = parseAttrs(output)
-    val serde = initSerDe(outputSerdeClass, columns, columnTypes, outputSerdeProps)
-    (serde, initOutputputSoi(serde))
+  def initOutputSerDe(output: Seq[Attribute]): Option[(AbstractSerDe, StructObjectInspector)] = {
+    outputSerdeClass.map { serdeClass =>
+      val (columns, columnTypes) = parseAttrs(output)
+      val serde = initSerDe(serdeClass, columns, columnTypes, outputSerdeProps)
+      val structObjectInspector = serde.getObjectInspector().asInstanceOf[StructObjectInspector]
+      (serde, structObjectInspector)
+    }
   }
 
-  def parseAttrs(attrs: Seq[Expression]): (Seq[String], Seq[DataType]) = {
-
+  private def parseAttrs(attrs: Seq[Expression]): (Seq[String], Seq[DataType]) = {
     val columns = attrs.map {
       case aref: AttributeReference => aref.name
       case e: NamedExpression => e.name
@@ -242,52 +335,25 @@ case class HiveScriptIOSchema (
     (columns, columnTypes)
   }
 
-  def initSerDe(serdeClassName: String, columns: Seq[String],
-    columnTypes: Seq[DataType], serdeProps: Seq[(String, String)]): AbstractSerDe = {
+  private def initSerDe(
+      serdeClassName: String,
+      columns: Seq[String],
+      columnTypes: Seq[DataType],
+      serdeProps: Seq[(String, String)]): AbstractSerDe = {
 
-    val serde: AbstractSerDe = if (serdeClassName != "") {
-      val trimed_class = serdeClassName.split("'")(1)
-      Utils.classForName(trimed_class)
-        .newInstance.asInstanceOf[AbstractSerDe]
-    } else {
-      null
-    }
+    val serde = Utils.classForName(serdeClassName).newInstance.asInstanceOf[AbstractSerDe]
 
-    if (serde != null) {
-      val columnTypesNames = columnTypes.map(_.toTypeInfo.getTypeName()).mkString(",")
+    val columnTypesNames = columnTypes.map(_.toTypeInfo.getTypeName()).mkString(",")
 
-      var propsMap = serdeProps.map(kv => {
-        (kv._1.split("'")(1), kv._2.split("'")(1))
-      }).toMap + (serdeConstants.LIST_COLUMNS -> columns.mkString(","))
-      propsMap = propsMap + (serdeConstants.LIST_COLUMN_TYPES -> columnTypesNames)
+    var propsMap = serdeProps.map(kv => {
+      (kv._1.split("'")(1), kv._2.split("'")(1))
+    }).toMap + (serdeConstants.LIST_COLUMNS -> columns.mkString(","))
+    propsMap = propsMap + (serdeConstants.LIST_COLUMN_TYPES -> columnTypesNames)
 
-      val properties = new Properties()
-      properties.putAll(propsMap)
-      serde.initialize(null, properties)
-    }
+    val properties = new Properties()
+    properties.putAll(propsMap)
+    serde.initialize(null, properties)
 
     serde
   }
-
-  def initInputSoi(inputSerde: AbstractSerDe, columns: Seq[String], columnTypes: Seq[DataType])
-    : ObjectInspector = {
-
-    if (inputSerde != null) {
-      val fieldObjectInspectors = columnTypes.map(toInspector(_))
-      ObjectInspectorFactory
-        .getStandardStructObjectInspector(columns, fieldObjectInspectors)
-        .asInstanceOf[ObjectInspector]
-    } else {
-      null
-    }
-  }
-
-  def initOutputputSoi(outputSerde: AbstractSerDe): StructObjectInspector = {
-    if (outputSerde != null) {
-      outputSerde.getObjectInspector().asInstanceOf[StructObjectInspector]
-    } else {
-      null
-    }
-  }
 }
-
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
new file mode 100644
index 0000000000000..0875232aede3e
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
@@ -0,0 +1,123 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
+import org.scalatest.exceptions.TestFailedException
+
+import org.apache.spark.TaskContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
+import org.apache.spark.sql.execution.{UnaryNode, SparkPlan, SparkPlanTest}
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.types.StringType
+
+class ScriptTransformationSuite extends SparkPlanTest {
+
+  override def sqlContext: SQLContext = TestHive
+
+  private val noSerdeIOSchema = HiveScriptIOSchema(
+    inputRowFormat = Seq.empty,
+    outputRowFormat = Seq.empty,
+    inputSerdeClass = None,
+    outputSerdeClass = None,
+    inputSerdeProps = Seq.empty,
+    outputSerdeProps = Seq.empty,
+    schemaLess = false
+  )
+
+  private val serdeIOSchema = noSerdeIOSchema.copy(
+    inputSerdeClass = Some(classOf[LazySimpleSerDe].getCanonicalName),
+    outputSerdeClass = Some(classOf[LazySimpleSerDe].getCanonicalName)
+  )
+
+  test("cat without SerDe") {
+    val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+    checkAnswer(
+      rowsDf,
+      (child: SparkPlan) => new ScriptTransformation(
+        input = Seq(rowsDf.col("a").expr),
+        script = "cat",
+        output = Seq(AttributeReference("a", StringType)()),
+        child = child,
+        ioschema = noSerdeIOSchema
+      )(TestHive),
+      rowsDf.collect())
+  }
+
+  test("cat with LazySimpleSerDe") {
+    val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+    checkAnswer(
+      rowsDf,
+      (child: SparkPlan) => new ScriptTransformation(
+        input = Seq(rowsDf.col("a").expr),
+        script = "cat",
+        output = Seq(AttributeReference("a", StringType)()),
+        child = child,
+        ioschema = serdeIOSchema
+      )(TestHive),
+      rowsDf.collect())
+  }
+
+  test("script transformation should not swallow errors from upstream operators (no serde)") {
+    val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+    val e = intercept[TestFailedException] {
+      checkAnswer(
+        rowsDf,
+        (child: SparkPlan) => new ScriptTransformation(
+          input = Seq(rowsDf.col("a").expr),
+          script = "cat",
+          output = Seq(AttributeReference("a", StringType)()),
+          child = ExceptionInjectingOperator(child),
+          ioschema = noSerdeIOSchema
+        )(TestHive),
+        rowsDf.collect())
+    }
+    assert(e.getMessage().contains("intentional exception"))
+  }
+
+  test("script transformation should not swallow errors from upstream operators (with serde)") {
+    val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+    val e = intercept[TestFailedException] {
+      checkAnswer(
+        rowsDf,
+        (child: SparkPlan) => new ScriptTransformation(
+          input = Seq(rowsDf.col("a").expr),
+          script = "cat",
+          output = Seq(AttributeReference("a", StringType)()),
+          child = ExceptionInjectingOperator(child),
+          ioschema = serdeIOSchema
+        )(TestHive),
+        rowsDf.collect())
+    }
+    assert(e.getMessage().contains("intentional exception"))
+  }
+}
+
+private case class ExceptionInjectingOperator(child: SparkPlan) extends UnaryNode {
+  override protected def doExecute(): RDD[InternalRow] = {
+    child.execute().map { x =>
+      assert(TaskContext.get() != null) // Make sure that TaskContext is defined.
+      Thread.sleep(1000) // This sleep gives the external process time to start.
+      throw new IllegalArgumentException("intentional exception")
+    }
+  }
+  override def output: Seq[Attribute] = child.output
+}

From c5ed36953f840018f603dfde94fcb4651e5246ac Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 28 Jul 2015 16:41:56 -0700
Subject: [PATCH 0658/1454] [STREAMING] [HOTFIX] Ignore ReceiverTrackerSuite
 flaky test

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7738 from tdas/ReceiverTrackerSuite-hotfix and squashes the following commits:

00f0ee1 [Tathagata Das] ignore flaky test
---
 .../apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index e2159bd4f225d..b039233f36316 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -31,7 +31,7 @@ class ReceiverTrackerSuite extends TestSuiteBase {
   val sparkConf = new SparkConf().setMaster("local[8]").setAppName("test")
   val ssc = new StreamingContext(sparkConf, Milliseconds(100))
 
-  test("Receiver tracker - propagates rate limit") {
+  ignore("Receiver tracker - propagates rate limit") {
     object ReceiverStartedWaiter extends StreamingListener {
       @volatile
       var started = false

From b7f54119f86f916481aeccc67f07e77dc2a924c7 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 28 Jul 2015 17:03:59 -0700
Subject: [PATCH 0659/1454] [SPARK-9420][SQL] Move expressions in sql/core
 package to catalyst.

Since catalyst package already depends on Spark core, we can move those expressions
into catalyst, and simplify function registry.

This is a followup of #7478.

Author: Reynold Xin <rxin@databricks.com>

Closes #7735 from rxin/SPARK-8003 and squashes the following commits:

2ffbdc3 [Reynold Xin] [SPARK-8003][SQL] Move expressions in sql/core package to catalyst.
---
 .../sql/catalyst/analysis/Analyzer.scala      |  3 ++-
 .../catalyst/analysis/FunctionRegistry.scala  | 17 +++++++-------
 .../MonotonicallyIncreasingID.scala           |  3 +--
 .../expressions/SparkPartitionID.scala        |  3 +--
 .../expressions}/NondeterministicSuite.scala  |  4 +---
 .../org/apache/spark/sql/SQLContext.scala     | 11 +--------
 .../sql/execution/expressions/package.scala   | 23 -------------------
 .../org/apache/spark/sql/functions.scala      |  4 ++--
 .../scala/org/apache/spark/sql/UDFSuite.scala |  4 ++--
 .../apache/spark/sql/hive/HiveContext.scala   | 13 ++---------
 .../org/apache/spark/sql/hive/UDFSuite.scala  |  4 ++--
 11 files changed, 23 insertions(+), 66 deletions(-)
 rename sql/{core/src/main/scala/org/apache/spark/sql/execution => catalyst/src/main/scala/org/apache/spark/sql/catalyst}/expressions/MonotonicallyIncreasingID.scala (95%)
 rename sql/{core/src/main/scala/org/apache/spark/sql/execution => catalyst/src/main/scala/org/apache/spark/sql/catalyst}/expressions/SparkPartitionID.scala (93%)
 rename sql/{core/src/test/scala/org/apache/spark/sql/execution/expression => catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions}/NondeterministicSuite.scala (83%)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index a723e92114b32..a309ee35ee582 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions.aggregate.{Complete, AggregateExpression2, AggregateFunction2}
 import org.apache.spark.sql.catalyst.expressions._
@@ -25,7 +27,6 @@ import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.trees.TreeNodeRef
 import org.apache.spark.sql.catalyst.{SimpleCatalystConf, CatalystConf}
 import org.apache.spark.sql.types._
-import scala.collection.mutable.ArrayBuffer
 
 /**
  * A trivial [[Analyzer]] with an [[EmptyCatalog]] and [[EmptyFunctionRegistry]]. Used for testing
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 9b60943a1e147..372f80d4a8b16 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -161,13 +161,6 @@ object FunctionRegistry {
     expression[ToDegrees]("degrees"),
     expression[ToRadians]("radians"),
 
-    // misc functions
-    expression[Md5]("md5"),
-    expression[Sha2]("sha2"),
-    expression[Sha1]("sha1"),
-    expression[Sha1]("sha"),
-    expression[Crc32]("crc32"),
-
     // aggregate functions
     expression[Average]("avg"),
     expression[Count]("count"),
@@ -229,7 +222,15 @@ object FunctionRegistry {
     expression[Year]("year"),
 
     // collection functions
-    expression[Size]("size")
+    expression[Size]("size"),
+
+    // misc functions
+    expression[Crc32]("crc32"),
+    expression[Md5]("md5"),
+    expression[Sha1]("sha"),
+    expression[Sha1]("sha1"),
+    expression[Sha2]("sha2"),
+    expression[SparkPartitionID]("spark_partition_id")
   )
 
   val builtin: FunctionRegistry = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/MonotonicallyIncreasingID.scala
similarity index 95%
rename from sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/MonotonicallyIncreasingID.scala
index eca36b3274420..291b7a5bc3af5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/MonotonicallyIncreasingID.scala
@@ -15,11 +15,10 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.execution.expressions
+package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Nondeterministic, LeafExpression}
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types.{LongType, DataType}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SparkPartitionID.scala
similarity index 93%
rename from sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SparkPartitionID.scala
index 98c8eab8372aa..3f6480bbf0114 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SparkPartitionID.scala
@@ -15,11 +15,10 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.execution.expressions
+package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.TaskContext
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Nondeterministic, LeafExpression}
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types.{IntegerType, DataType}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NondeterministicSuite.scala
similarity index 83%
rename from sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
rename to sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NondeterministicSuite.scala
index b6e79ff9cc95d..82894822ab0f4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/expression/NondeterministicSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NondeterministicSuite.scala
@@ -15,11 +15,9 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.execution.expression
+package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.expressions. ExpressionEvalHelper
-import org.apache.spark.sql.execution.expressions.{SparkPartitionID, MonotonicallyIncreasingID}
 
 class NondeterministicSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("MonotonicallyIncreasingID") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 56cd8f22e7cf4..dbb2a09846548 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -31,8 +31,6 @@ import org.apache.spark.SparkContext
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.{expression => FunctionExpression, FunctionBuilder}
-import org.apache.spark.sql.execution.expressions.SparkPartitionID
 import org.apache.spark.sql.SQLConf.SQLConfEntry
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.errors.DialectException
@@ -142,14 +140,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   // TODO how to handle the temp function per user session?
   @transient
-  protected[sql] lazy val functionRegistry: FunctionRegistry = {
-    val reg = FunctionRegistry.builtin
-    val extendedFunctions = List[(String, (ExpressionInfo, FunctionBuilder))](
-      FunctionExpression[SparkPartitionID]("spark__partition__id")
-    )
-    extendedFunctions.foreach { case(name, (info, fun)) => reg.registerFunction(name, info, fun) }
-    reg
-  }
+  protected[sql] lazy val functionRegistry: FunctionRegistry = FunctionRegistry.builtin
 
   @transient
   protected[sql] lazy val analyzer: Analyzer =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala
deleted file mode 100644
index 568b7ac2c5987..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-/**
- * Package containing expressions that are specific to Spark runtime.
- */
-package object expressions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 0148991512213..4261a5e7cbeb5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -634,7 +634,7 @@ object functions {
    * @group normal_funcs
    * @since 1.4.0
    */
-  def monotonicallyIncreasingId(): Column = execution.expressions.MonotonicallyIncreasingID()
+  def monotonicallyIncreasingId(): Column = MonotonicallyIncreasingID()
 
   /**
    * Return an alternative value `r` if `l` is NaN.
@@ -741,7 +741,7 @@ object functions {
    * @group normal_funcs
    * @since 1.4.0
    */
-  def sparkPartitionId(): Column = execution.expressions.SparkPartitionID()
+  def sparkPartitionId(): Column = SparkPartitionID()
 
   /**
    * Computes the square root of the specified float value.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index 9b326c16350c8..d9c8b380ef146 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -51,10 +51,10 @@ class UDFSuite extends QueryTest {
     df.selectExpr("count(distinct a)")
   }
 
-  test("SPARK-8003 spark__partition__id") {
+  test("SPARK-8003 spark_partition_id") {
     val df = Seq((1, "Tearing down the walls that divide us")).toDF("id", "saying")
     df.registerTempTable("tmp_table")
-    checkAnswer(ctx.sql("select spark__partition__id() from tmp_table").toDF(), Row(0))
+    checkAnswer(ctx.sql("select spark_partition_id() from tmp_table").toDF(), Row(0))
     ctx.dropTempTable("tmp_table")
   }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 8b35c1275f388..110f51a305861 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -38,9 +38,6 @@ import org.apache.spark.Logging
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.{expression => FunctionExpression, FunctionBuilder}
-import org.apache.spark.sql.catalyst.expressions.ExpressionInfo
-import org.apache.spark.sql.execution.expressions.SparkPartitionID
 import org.apache.spark.sql.SQLConf.SQLConfEntry
 import org.apache.spark.sql.SQLConf.SQLConfEntry._
 import org.apache.spark.sql.catalyst.{TableIdentifier, ParserDialect}
@@ -375,14 +372,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
 
   // Note that HiveUDFs will be overridden by functions registered in this context.
   @transient
-  override protected[sql] lazy val functionRegistry: FunctionRegistry = {
-    val reg = new HiveFunctionRegistry(FunctionRegistry.builtin)
-    val extendedFunctions = List[(String, (ExpressionInfo, FunctionBuilder))](
-      FunctionExpression[SparkPartitionID]("spark__partition__id")
-    )
-    extendedFunctions.foreach { case(name, (info, fun)) => reg.registerFunction(name, info, fun) }
-    reg
-  }
+  override protected[sql] lazy val functionRegistry: FunctionRegistry =
+    new HiveFunctionRegistry(FunctionRegistry.builtin)
 
   /* An analyzer that uses the Hive metastore. */
   @transient
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
index 9cea5d413c817..37afc2142abf7 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
@@ -35,9 +35,9 @@ class UDFSuite extends QueryTest {
     assert(ctx.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5)
   }
 
-  test("SPARK-8003 spark__partition__id") {
+  test("SPARK-8003 spark_partition_id") {
     val df = Seq((1, "Two Fiiiiive")).toDF("id", "saying")
     ctx.registerDataFrameAsTable(df, "test_table")
-    checkAnswer(ctx.sql("select spark__partition__id() from test_table LIMIT 1").toDF(), Row(0))
+    checkAnswer(ctx.sql("select spark_partition_id() from test_table LIMIT 1").toDF(), Row(0))
   }
 }

From 6662ee21244067180c1bcef0b16107b2979fd933 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 28 Jul 2015 17:42:35 -0700
Subject: [PATCH 0660/1454] [SPARK-9418][SQL] Use sort-merge join as the
 default shuffle join.

Sort-merge join is more robust in Spark since sorting can be made using the Tungsten sort operator.

Author: Reynold Xin <rxin@databricks.com>

Closes #7733 from rxin/smj and squashes the following commits:

61e4d34 [Reynold Xin] Fixed test case.
5ffd731 [Reynold Xin] Fixed JoinSuite.
a137dc0 [Reynold Xin] [SPARK-9418][SQL] Use sort-merge join as the default shuffle join.
---
 .../src/main/scala/org/apache/spark/sql/SQLConf.scala     | 2 +-
 .../src/test/scala/org/apache/spark/sql/JoinSuite.scala   | 6 +++---
 ...bilitySuite.scala => HashJoinCompatibilitySuite.scala} | 8 ++++----
 .../scala/org/apache/spark/sql/hive/StatisticsSuite.scala | 2 +-
 4 files changed, 9 insertions(+), 9 deletions(-)
 rename sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/{SortMergeCompatibilitySuite.scala => HashJoinCompatibilitySuite.scala} (97%)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 40eba33f595ca..cdb0c7a1c07a7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -322,7 +322,7 @@ private[spark] object SQLConf {
       " memory.")
 
   val SORTMERGE_JOIN = booleanConf("spark.sql.planner.sortMergeJoin",
-    defaultValue = Some(false),
+    defaultValue = Some(true),
     doc = "When true, use sort merge join (as opposed to hash join) by default for large joins.")
 
   // This is only used for the thriftserver
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index dfb2a7e099748..666f26bf620e1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -79,9 +79,9 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       ("SELECT * FROM testData FULL OUTER JOIN testData2 WHERE key = 2", classOf[CartesianProduct]),
       ("SELECT * FROM testData JOIN testData2 WHERE key > a", classOf[CartesianProduct]),
       ("SELECT * FROM testData FULL OUTER JOIN testData2 WHERE key > a", classOf[CartesianProduct]),
-      ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[ShuffledHashJoin]),
-      ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[ShuffledHashJoin]),
-      ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[ShuffledHashJoin]),
+      ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]),
+      ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]),
+      ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin]),
       ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
       ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
         classOf[ShuffledHashOuterJoin]),
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala
similarity index 97%
rename from sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala
rename to sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala
index 1fe4fe9629c02..1a5ba20404c4e 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala
@@ -23,16 +23,16 @@ import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.hive.test.TestHive
 
 /**
- * Runs the test cases that are included in the hive distribution with sort merge join is true.
+ * Runs the test cases that are included in the hive distribution with hash joins.
  */
-class SortMergeCompatibilitySuite extends HiveCompatibilitySuite {
+class HashJoinCompatibilitySuite extends HiveCompatibilitySuite {
   override def beforeAll() {
     super.beforeAll()
-    TestHive.setConf(SQLConf.SORTMERGE_JOIN, true)
+    TestHive.setConf(SQLConf.SORTMERGE_JOIN, false)
   }
 
   override def afterAll() {
-    TestHive.setConf(SQLConf.SORTMERGE_JOIN, false)
+    TestHive.setConf(SQLConf.SORTMERGE_JOIN, true)
     super.afterAll()
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
index f067ea0d4fc75..bc72b0172a467 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -172,7 +172,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
         bhj = df.queryExecution.sparkPlan.collect { case j: BroadcastHashJoin => j }
         assert(bhj.isEmpty, "BroadcastHashJoin still planned even though it is switched off")
 
-        val shj = df.queryExecution.sparkPlan.collect { case j: ShuffledHashJoin => j }
+        val shj = df.queryExecution.sparkPlan.collect { case j: SortMergeJoin => j }
         assert(shj.size === 1,
           "ShuffledHashJoin should be planned when BroadcastHashJoin is turned off")
 

From e78ec1a8fabfe409c92c4904208f53dbdcfcf139 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 28 Jul 2015 17:51:58 -0700
Subject: [PATCH 0661/1454] [SPARK-9421] Fix null-handling bugs in
 UnsafeRow.getDouble, getFloat(), and get(ordinal, dataType)

UnsafeRow.getDouble and getFloat() return NaN when called on columns that are null, which is inconsistent with the behavior of other row classes (which is to return 0.0).

In addition, the generic get(ordinal, dataType) method should always return null for a null literal, but currently it handles nulls by calling the type-specific accessors.

This patch addresses both of these issues and adds a regression test.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7736 from JoshRosen/unsafe-row-null-fixes and squashes the following commits:

c8eb2ee [Josh Rosen] Fix test in UnsafeRowConverterSuite
6214682 [Josh Rosen] Fixes to null handling in UnsafeRow
---
 .../sql/catalyst/expressions/UnsafeRow.java     | 14 +++-----------
 .../expressions/UnsafeRowConverterSuite.scala   |  4 ++--
 .../org/apache/spark/sql/UnsafeRowSuite.scala   | 17 ++++++++++++++++-
 3 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 955fb4226fc0e..64a8edc34d681 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -239,7 +239,7 @@ public Object get(int ordinal) {
 
   @Override
   public Object get(int ordinal, DataType dataType) {
-    if (dataType instanceof NullType) {
+    if (isNullAt(ordinal) || dataType instanceof NullType) {
       return null;
     } else if (dataType instanceof BooleanType) {
       return getBoolean(ordinal);
@@ -313,21 +313,13 @@ public long getLong(int ordinal) {
   @Override
   public float getFloat(int ordinal) {
     assertIndexIsValid(ordinal);
-    if (isNullAt(ordinal)) {
-      return Float.NaN;
-    } else {
-      return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(ordinal));
-    }
+    return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public double getDouble(int ordinal) {
     assertIndexIsValid(ordinal);
-    if (isNullAt(ordinal)) {
-      return Float.NaN;
-    } else {
-      return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
-    }
+    return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 2834b54e8fb2e..b7bc17f89e82f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -146,8 +146,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(createdFromNull.getShort(3) === 0)
     assert(createdFromNull.getInt(4) === 0)
     assert(createdFromNull.getLong(5) === 0)
-    assert(java.lang.Float.isNaN(createdFromNull.getFloat(6)))
-    assert(java.lang.Double.isNaN(createdFromNull.getDouble(7)))
+    assert(createdFromNull.getFloat(6) === 0.0f)
+    assert(createdFromNull.getDouble(7) === 0.0d)
     assert(createdFromNull.getUTF8String(8) === null)
     assert(createdFromNull.getBinary(9) === null)
     // assert(createdFromNull.get(10) === null)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index ad3bb1744cb3c..e72a1bc6c4e20 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -22,7 +22,7 @@ import java.io.ByteArrayOutputStream
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
-import org.apache.spark.sql.types.{DataType, IntegerType, StringType}
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.memory.MemoryAllocator
 import org.apache.spark.unsafe.types.UTF8String
@@ -67,4 +67,19 @@ class UnsafeRowSuite extends SparkFunSuite {
 
     assert(bytesFromArrayBackedRow === bytesFromOffheapRow)
   }
+
+  test("calling getDouble() and getFloat() on null columns") {
+    val row = InternalRow.apply(null, null)
+    val unsafeRow = UnsafeProjection.create(Array[DataType](FloatType, DoubleType)).apply(row)
+    assert(unsafeRow.getFloat(0) === row.getFloat(0))
+    assert(unsafeRow.getDouble(1) === row.getDouble(1))
+  }
+
+  test("calling get(ordinal, datatype) on null columns") {
+    val row = InternalRow.apply(null)
+    val unsafeRow = UnsafeProjection.create(Array[DataType](NullType)).apply(row)
+    for (dataType <- DataTypeTestUtils.atomicTypes) {
+      assert(unsafeRow.get(0, dataType) === null)
+    }
+  }
 }

From 3744b7fd42e52011af60cc205fcb4e4b23b35c68 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 28 Jul 2015 19:01:25 -0700
Subject: [PATCH 0662/1454] [SPARK-9422] [SQL] Remove the placeholder
 attributes used in the aggregation buffers

https://issues.apache.org/jira/browse/SPARK-9422

Author: Yin Huai <yhuai@databricks.com>

Closes #7737 from yhuai/removePlaceHolder and squashes the following commits:

ec29b44 [Yin Huai]  Remove placeholder attributes.
---
 .../expressions/aggregate/interfaces.scala    |  27 ++-
 .../aggregate/aggregateOperators.scala        |   4 +-
 .../aggregate/sortBasedIterators.scala        | 209 +++++++-----------
 .../spark/sql/execution/aggregate/udaf.scala  |  17 +-
 .../spark/sql/execution/aggregate/utils.scala |   4 +-
 5 files changed, 121 insertions(+), 140 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
index 10bd19c8a840f..9fb7623172e78 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -103,9 +103,30 @@ abstract class AggregateFunction2
   final override def foldable: Boolean = false
 
   /**
-   * The offset of this function's buffer in the underlying buffer shared with other functions.
+   * The offset of this function's start buffer value in the
+   * underlying shared mutable aggregation buffer.
+   * For example, we have two aggregate functions `avg(x)` and `avg(y)`, which share
+   * the same aggregation buffer. In this shared buffer, the position of the first
+   * buffer value of `avg(x)` will be 0 and the position of the first buffer value of `avg(y)`
+   * will be 2.
    */
-  var bufferOffset: Int = 0
+  var mutableBufferOffset: Int = 0
+
+  /**
+   * The offset of this function's start buffer value in the
+   * underlying shared input aggregation buffer. An input aggregation buffer is used
+   * when we merge two aggregation buffers and it is basically the immutable one
+   * (we merge an input aggregation buffer and a mutable aggregation buffer and
+   * then store the new buffer values to the mutable aggregation buffer).
+   * Usually, an input aggregation buffer also contain extra elements like grouping
+   * keys at the beginning. So, mutableBufferOffset and inputBufferOffset are often
+   * different.
+   * For example, we have a grouping expression `key``, and two aggregate functions
+   * `avg(x)` and `avg(y)`. In this shared input aggregation buffer, the position of the first
+   * buffer value of `avg(x)` will be 1 and the position of the first buffer value of `avg(y)`
+   * will be 3 (position 0 is used for the value of key`).
+   */
+  var inputBufferOffset: Int = 0
 
   /** The schema of the aggregation buffer. */
   def bufferSchema: StructType
@@ -176,7 +197,7 @@ abstract class AlgebraicAggregate extends AggregateFunction2 with Serializable w
   override def initialize(buffer: MutableRow): Unit = {
     var i = 0
     while (i < bufferAttributes.size) {
-      buffer(i + bufferOffset) = initialValues(i).eval()
+      buffer(i + mutableBufferOffset) = initialValues(i).eval()
       i += 1
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
index 0c9082897f390..98538c462bc89 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
@@ -72,8 +72,10 @@ case class Aggregate2Sort(
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
     child.execute().mapPartitions { iter =>
       if (aggregateExpressions.length == 0) {
-        new GroupingIterator(
+        new FinalSortAggregationIterator(
           groupingExpressions,
+          Nil,
+          Nil,
           resultExpressions,
           newMutableProjection,
           child.output,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
index 1b89edafa8dad..2ca0cb82c1aab 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
@@ -41,7 +41,8 @@ private[sql] abstract class SortAggregationIterator(
   ///////////////////////////////////////////////////////////////////////////
 
   protected val aggregateFunctions: Array[AggregateFunction2] = {
-    var bufferOffset = initialBufferOffset
+    var mutableBufferOffset = 0
+    var inputBufferOffset: Int = initialInputBufferOffset
     val functions = new Array[AggregateFunction2](aggregateExpressions.length)
     var i = 0
     while (i < aggregateExpressions.length) {
@@ -54,13 +55,18 @@ private[sql] abstract class SortAggregationIterator(
           // function's children in the update method of this aggregate function.
           // Those eval calls require BoundReferences to work.
           BindReferences.bindReference(func, inputAttributes)
-        case _ => func
+        case _ =>
+          // We only need to set inputBufferOffset for aggregate functions with mode
+          // PartialMerge and Final.
+          func.inputBufferOffset = inputBufferOffset
+          inputBufferOffset += func.bufferSchema.length
+          func
       }
-      // Set bufferOffset for this function. It is important that setting bufferOffset
-      // happens after all potential bindReference operations because bindReference
-      // will create a new instance of the function.
-      funcWithBoundReferences.bufferOffset = bufferOffset
-      bufferOffset += funcWithBoundReferences.bufferSchema.length
+      // Set mutableBufferOffset for this function. It is important that setting
+      // mutableBufferOffset happens after all potential bindReference operations
+      // because bindReference will create a new instance of the function.
+      funcWithBoundReferences.mutableBufferOffset = mutableBufferOffset
+      mutableBufferOffset += funcWithBoundReferences.bufferSchema.length
       functions(i) = funcWithBoundReferences
       i += 1
     }
@@ -97,25 +103,24 @@ private[sql] abstract class SortAggregationIterator(
     // The number of elements of the underlying buffer of this operator.
     // All aggregate functions are sharing this underlying buffer and they find their
     // buffer values through bufferOffset.
-    var size = initialBufferOffset
-    var i = 0
-    while (i < aggregateFunctions.length) {
-      size += aggregateFunctions(i).bufferSchema.length
-      i += 1
-    }
-    new GenericMutableRow(size)
+    // var size = 0
+    // var i = 0
+    // while (i < aggregateFunctions.length) {
+    //  size += aggregateFunctions(i).bufferSchema.length
+    //  i += 1
+    // }
+    new GenericMutableRow(aggregateFunctions.map(_.bufferSchema.length).sum)
   }
 
   protected val joinedRow = new JoinedRow
 
-  protected val placeholderExpressions = Seq.fill(initialBufferOffset)(NoOp)
-
   // This projection is used to initialize buffer values for all AlgebraicAggregates.
   protected val algebraicInitialProjection = {
-    val initExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
+    val initExpressions = aggregateFunctions.flatMap {
       case ae: AlgebraicAggregate => ae.initialValues
       case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
     }
+
     newMutableProjection(initExpressions, Nil)().target(buffer)
   }
 
@@ -132,10 +137,6 @@ private[sql] abstract class SortAggregationIterator(
   // Indicates if we has new group of rows to process.
   protected var hasNewGroup: Boolean = true
 
-  ///////////////////////////////////////////////////////////////////////////
-  // Private methods
-  ///////////////////////////////////////////////////////////////////////////
-
   /** Initializes buffer values for all aggregate functions. */
   protected def initializeBuffer(): Unit = {
     algebraicInitialProjection(EmptyRow)
@@ -160,6 +161,10 @@ private[sql] abstract class SortAggregationIterator(
     }
   }
 
+  ///////////////////////////////////////////////////////////////////////////
+  // Private methods
+  ///////////////////////////////////////////////////////////////////////////
+
   /** Processes rows in the current group. It will stop when it find a new group. */
   private def processCurrentGroup(): Unit = {
     currentGroupingKey = nextGroupingKey
@@ -218,10 +223,13 @@ private[sql] abstract class SortAggregationIterator(
   // Methods that need to be implemented
   ///////////////////////////////////////////////////////////////////////////
 
-  protected def initialBufferOffset: Int
+  /** The initial input buffer offset for `inputBufferOffset` of an [[AggregateFunction2]]. */
+  protected def initialInputBufferOffset: Int
 
+  /** The function used to process an input row. */
   protected def processRow(row: InternalRow): Unit
 
+  /** The function used to generate the result row. */
   protected def generateOutput(): InternalRow
 
   ///////////////////////////////////////////////////////////////////////////
@@ -231,37 +239,6 @@ private[sql] abstract class SortAggregationIterator(
   initialize()
 }
 
-/**
- * An iterator only used to group input rows according to values of `groupingExpressions`.
- * It assumes that input rows are already grouped by values of `groupingExpressions`.
- */
-class GroupingIterator(
-    groupingExpressions: Seq[NamedExpression],
-    resultExpressions: Seq[NamedExpression],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    inputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow])
-  extends SortAggregationIterator(
-    groupingExpressions,
-    Nil,
-    newMutableProjection,
-    inputAttributes,
-    inputIter) {
-
-  private val resultProjection =
-    newMutableProjection(resultExpressions, groupingExpressions.map(_.toAttribute))()
-
-  override protected def initialBufferOffset: Int = 0
-
-  override protected def processRow(row: InternalRow): Unit = {
-    // Since we only do grouping, there is nothing to do at here.
-  }
-
-  override protected def generateOutput(): InternalRow = {
-    resultProjection(currentGroupingKey)
-  }
-}
-
 /**
  * An iterator used to do partial aggregations (for those aggregate functions with mode Partial).
  * It assumes that input rows are already grouped by values of `groupingExpressions`.
@@ -291,7 +268,7 @@ class PartialSortAggregationIterator(
     newMutableProjection(updateExpressions, bufferSchema ++ inputAttributes)().target(buffer)
   }
 
-  override protected def initialBufferOffset: Int = 0
+  override protected def initialInputBufferOffset: Int = 0
 
   override protected def processRow(row: InternalRow): Unit = {
     // Process all algebraic aggregate functions.
@@ -318,11 +295,7 @@ class PartialSortAggregationIterator(
  * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
  *
  * The format of its internal buffer is:
- * |placeholder1|...|placeholderN|aggregationBuffer1|...|aggregationBufferN|
- * Every placeholder is for a grouping expression.
- * The actual buffers are stored after placeholderN.
- * The reason that we have placeholders at here is to make our underlying buffer have the same
- * length with a input row.
+ * |aggregationBuffer1|...|aggregationBufferN|
  *
  * The format of its output rows is:
  * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
@@ -340,33 +313,21 @@ class PartialMergeSortAggregationIterator(
     inputAttributes,
     inputIter) {
 
-  private val placeholderAttributes =
-    Seq.fill(initialBufferOffset)(AttributeReference("placeholder", NullType)())
-
   // This projection is used to merge buffer values for all AlgebraicAggregates.
   private val algebraicMergeProjection = {
-    val bufferSchemata =
-      placeholderAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
-        placeholderAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
-    val mergeExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
+    val mergeInputSchema =
+      aggregateFunctions.flatMap(_.bufferAttributes) ++
+        groupingExpressions.map(_.toAttribute) ++
+        aggregateFunctions.flatMap(_.cloneBufferAttributes)
+    val mergeExpressions = aggregateFunctions.flatMap {
       case ae: AlgebraicAggregate => ae.mergeExpressions
       case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
     }
 
-    newMutableProjection(mergeExpressions, bufferSchemata)()
+    newMutableProjection(mergeExpressions, mergeInputSchema)()
   }
 
-  // This projection is used to extract aggregation buffers from the underlying buffer.
-  // We need it because the underlying buffer has placeholders at its beginning.
-  private val extractsBufferValues = {
-    val expressions = aggregateFunctions.flatMap {
-      case agg => agg.bufferAttributes
-    }
-
-    newMutableProjection(expressions, inputAttributes)()
-  }
-
-  override protected def initialBufferOffset: Int = groupingExpressions.length
+  override protected def initialInputBufferOffset: Int = groupingExpressions.length
 
   override protected def processRow(row: InternalRow): Unit = {
     // Process all algebraic aggregate functions.
@@ -381,7 +342,7 @@ class PartialMergeSortAggregationIterator(
 
   override protected def generateOutput(): InternalRow = {
     // We output grouping expressions and aggregation buffers.
-    joinedRow(currentGroupingKey, extractsBufferValues(buffer))
+    joinedRow(currentGroupingKey, buffer).copy()
   }
 }
 
@@ -393,11 +354,7 @@ class PartialMergeSortAggregationIterator(
  * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
  *
  * The format of its internal buffer is:
- * |placeholder1|...|placeholder N|aggregationBuffer1|...|aggregationBufferN|
- * Every placeholder is for a grouping expression.
- * The actual buffers are stored after placeholderN.
- * The reason that we have placeholders at here is to make our underlying buffer have the same
- * length with a input row.
+ * |aggregationBuffer1|...|aggregationBufferN|
  *
  * The format of its output rows is represented by the schema of `resultExpressions`.
  */
@@ -425,27 +382,23 @@ class FinalSortAggregationIterator(
     newMutableProjection(
       resultExpressions, groupingExpressions.map(_.toAttribute) ++ aggregateAttributes)()
 
-  private val offsetAttributes =
-    Seq.fill(initialBufferOffset)(AttributeReference("placeholder", NullType)())
-
   // This projection is used to merge buffer values for all AlgebraicAggregates.
   private val algebraicMergeProjection = {
-    val bufferSchemata =
-      offsetAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
-        offsetAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
-    val mergeExpressions = placeholderExpressions ++ aggregateFunctions.flatMap {
+    val mergeInputSchema =
+      aggregateFunctions.flatMap(_.bufferAttributes) ++
+        groupingExpressions.map(_.toAttribute) ++
+        aggregateFunctions.flatMap(_.cloneBufferAttributes)
+    val mergeExpressions = aggregateFunctions.flatMap {
       case ae: AlgebraicAggregate => ae.mergeExpressions
       case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
     }
 
-    newMutableProjection(mergeExpressions, bufferSchemata)()
+    newMutableProjection(mergeExpressions, mergeInputSchema)()
   }
 
   // This projection is used to evaluate all AlgebraicAggregates.
   private val algebraicEvalProjection = {
-    val bufferSchemata =
-      offsetAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
-        offsetAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
+    val bufferSchemata = aggregateFunctions.flatMap(_.bufferAttributes)
     val evalExpressions = aggregateFunctions.map {
       case ae: AlgebraicAggregate => ae.evaluateExpression
       case agg: AggregateFunction2 => NoOp
@@ -454,7 +407,7 @@ class FinalSortAggregationIterator(
     newMutableProjection(evalExpressions, bufferSchemata)()
   }
 
-  override protected def initialBufferOffset: Int = groupingExpressions.length
+  override protected def initialInputBufferOffset: Int = groupingExpressions.length
 
   override def initialize(): Unit = {
     if (inputIter.hasNext) {
@@ -471,7 +424,10 @@ class FinalSortAggregationIterator(
         // Right now, the buffer only contains initial buffer values. Because
         // merging two buffers with initial values will generate a row that
         // still store initial values. We set the currentRow as the copy of the current buffer.
-        val currentRow = buffer.copy()
+        // Because input aggregation buffer has initialInputBufferOffset extra values at the
+        // beginning, we create a dummy row for this part.
+        val currentRow =
+          joinedRow(new GenericInternalRow(initialInputBufferOffset), buffer).copy()
         nextGroupingKey = groupGenerator(currentRow).copy()
         firstRowInNextGroup = currentRow
       } else {
@@ -518,18 +474,15 @@ class FinalSortAggregationIterator(
  * Final mode.
  *
  * The format of its internal buffer is:
- * |placeholder1|...|placeholder(N+M)|aggregationBuffer1|...|aggregationBuffer(N+M)|
- * The first N placeholders represent slots of grouping expressions.
- * Then, next M placeholders represent slots of col1 to colM.
+ * |aggregationBuffer1|...|aggregationBuffer(N+M)|
  * For aggregation buffers, first N aggregation buffers are used by N aggregate functions with
  * mode Final. Then, the last M aggregation buffers are used by M aggregate functions with mode
- * Complete. The reason that we have placeholders at here is to make our underlying buffer
- * have the same length with a input row.
+ * Complete.
  *
  * The format of its output rows is represented by the schema of `resultExpressions`.
  */
 class FinalAndCompleteSortAggregationIterator(
-    override protected val initialBufferOffset: Int,
+    override protected val initialInputBufferOffset: Int,
     groupingExpressions: Seq[NamedExpression],
     finalAggregateExpressions: Seq[AggregateExpression2],
     finalAggregateAttributes: Seq[Attribute],
@@ -561,9 +514,6 @@ class FinalAndCompleteSortAggregationIterator(
     newMutableProjection(resultExpressions, inputSchema)()
   }
 
-  private val offsetAttributes =
-    Seq.fill(initialBufferOffset)(AttributeReference("placeholder", NullType)())
-
   // All aggregate functions with mode Final.
   private val finalAggregateFunctions: Array[AggregateFunction2] = {
     val functions = new Array[AggregateFunction2](finalAggregateExpressions.length)
@@ -601,38 +551,38 @@ class FinalAndCompleteSortAggregationIterator(
   // This projection is used to merge buffer values for all AlgebraicAggregates with mode
   // Final.
   private val finalAlgebraicMergeProjection = {
-    val numCompleteOffsetAttributes =
-      completeAggregateFunctions.map(_.bufferAttributes.length).sum
-    val completeOffsetAttributes =
-      Seq.fill(numCompleteOffsetAttributes)(AttributeReference("placeholder", NullType)())
-    val completeOffsetExpressions = Seq.fill(numCompleteOffsetAttributes)(NoOp)
-
-    val bufferSchemata =
-      offsetAttributes ++ finalAggregateFunctions.flatMap(_.bufferAttributes) ++
-        completeOffsetAttributes ++ offsetAttributes ++
-        finalAggregateFunctions.flatMap(_.cloneBufferAttributes) ++ completeOffsetAttributes
+    // The first initialInputBufferOffset values of the input aggregation buffer is
+    // for grouping expressions and distinct columns.
+    val groupingAttributesAndDistinctColumns = inputAttributes.take(initialInputBufferOffset)
+
+    val completeOffsetExpressions =
+      Seq.fill(completeAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
+
+    val mergeInputSchema =
+      finalAggregateFunctions.flatMap(_.bufferAttributes) ++
+        completeAggregateFunctions.flatMap(_.bufferAttributes) ++
+        groupingAttributesAndDistinctColumns ++
+        finalAggregateFunctions.flatMap(_.cloneBufferAttributes)
     val mergeExpressions =
-      placeholderExpressions ++ finalAggregateFunctions.flatMap {
+      finalAggregateFunctions.flatMap {
         case ae: AlgebraicAggregate => ae.mergeExpressions
         case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
       } ++ completeOffsetExpressions
-
-    newMutableProjection(mergeExpressions, bufferSchemata)()
+    newMutableProjection(mergeExpressions, mergeInputSchema)()
   }
 
   // This projection is used to update buffer values for all AlgebraicAggregates with mode
   // Complete.
   private val completeAlgebraicUpdateProjection = {
-    val numFinalOffsetAttributes = finalAggregateFunctions.map(_.bufferAttributes.length).sum
-    val finalOffsetAttributes =
-      Seq.fill(numFinalOffsetAttributes)(AttributeReference("placeholder", NullType)())
-    val finalOffsetExpressions = Seq.fill(numFinalOffsetAttributes)(NoOp)
+    // We do not touch buffer values of aggregate functions with the Final mode.
+    val finalOffsetExpressions =
+      Seq.fill(finalAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
 
     val bufferSchema =
-      offsetAttributes ++ finalOffsetAttributes ++
+      finalAggregateFunctions.flatMap(_.bufferAttributes) ++
         completeAggregateFunctions.flatMap(_.bufferAttributes)
     val updateExpressions =
-      placeholderExpressions ++ finalOffsetExpressions ++ completeAggregateFunctions.flatMap {
+      finalOffsetExpressions ++ completeAggregateFunctions.flatMap {
         case ae: AlgebraicAggregate => ae.updateExpressions
         case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
       }
@@ -641,9 +591,7 @@ class FinalAndCompleteSortAggregationIterator(
 
   // This projection is used to evaluate all AlgebraicAggregates.
   private val algebraicEvalProjection = {
-    val bufferSchemata =
-      offsetAttributes ++ aggregateFunctions.flatMap(_.bufferAttributes) ++
-        offsetAttributes ++ aggregateFunctions.flatMap(_.cloneBufferAttributes)
+    val bufferSchemata = aggregateFunctions.flatMap(_.bufferAttributes)
     val evalExpressions = aggregateFunctions.map {
       case ae: AlgebraicAggregate => ae.evaluateExpression
       case agg: AggregateFunction2 => NoOp
@@ -667,7 +615,10 @@ class FinalAndCompleteSortAggregationIterator(
         // Right now, the buffer only contains initial buffer values. Because
         // merging two buffers with initial values will generate a row that
         // still store initial values. We set the currentRow as the copy of the current buffer.
-        val currentRow = buffer.copy()
+        // Because input aggregation buffer has initialInputBufferOffset extra values at the
+        // beginning, we create a dummy row for this part.
+        val currentRow =
+          joinedRow(new GenericInternalRow(initialInputBufferOffset), buffer).copy()
         nextGroupingKey = groupGenerator(currentRow).copy()
         firstRowInNextGroup = currentRow
       } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
index 073c45ae2f9f2..cc54319171bdb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
@@ -184,7 +184,7 @@ private[sql] case class ScalaUDAF(
       bufferSchema,
       bufferValuesToCatalystConverters,
       bufferValuesToScalaConverters,
-      bufferOffset,
+      inputBufferOffset,
       null)
 
   lazy val mutableAggregateBuffer: MutableAggregationBufferImpl =
@@ -192,9 +192,16 @@ private[sql] case class ScalaUDAF(
       bufferSchema,
       bufferValuesToCatalystConverters,
       bufferValuesToScalaConverters,
-      bufferOffset,
+      mutableBufferOffset,
       null)
 
+  lazy val evalAggregateBuffer: InputAggregationBuffer =
+    new InputAggregationBuffer(
+      bufferSchema,
+      bufferValuesToCatalystConverters,
+      bufferValuesToScalaConverters,
+      mutableBufferOffset,
+      null)
 
   override def initialize(buffer: MutableRow): Unit = {
     mutableAggregateBuffer.underlyingBuffer = buffer
@@ -217,10 +224,10 @@ private[sql] case class ScalaUDAF(
     udaf.merge(mutableAggregateBuffer, inputAggregateBuffer)
   }
 
-  override def eval(buffer: InternalRow = null): Any = {
-    inputAggregateBuffer.underlyingInputBuffer = buffer
+  override def eval(buffer: InternalRow): Any = {
+    evalAggregateBuffer.underlyingInputBuffer = buffer
 
-    udaf.evaluate(inputAggregateBuffer)
+    udaf.evaluate(evalAggregateBuffer)
   }
 
   override def toString: String = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
index 5bbe6c162ff4b..6549c87752a7d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
@@ -292,8 +292,8 @@ object Utils {
         AggregateExpression2(aggregateFunction, PartialMerge, false)
     }
     val partialMergeAggregateAttributes =
-      partialMergeAggregateExpressions.map {
-        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+      partialMergeAggregateExpressions.flatMap { agg =>
+        agg.aggregateFunction.bufferAttributes
       }
     val partialMergeAggregate =
       Aggregate2Sort(

From 429b2f0df4ef97a3b94cead06a7eb51581eabb18 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 28 Jul 2015 21:37:50 -0700
Subject: [PATCH 0663/1454] [SPARK-8608][SPARK-8609][SPARK-9083][SQL] reset
 mutable states of nondeterministic expression before evaluation and fix
 PullOutNondeterministic

We will do local projection for LocalRelation, and thus reuse the same Expression object among multiply evaluations. We should reset the mutable states of Expression before evaluate it.

Fix `PullOutNondeterministic` rule to make it work for `Sort`.

Also got a chance to cleanup the dataframe test suite.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7674 from cloud-fan/show and squashes the following commits:

888934f [Wenchen Fan] fix sort
c0e93e8 [Wenchen Fan] local DataFrame with random columns should return same value when call `show`
---
 .../sql/catalyst/analysis/Analyzer.scala      |  15 +-
 .../sql/catalyst/expressions/Expression.scala |   8 +-
 .../sql/catalyst/expressions/Projection.scala |   4 +-
 .../sql/catalyst/expressions/predicates.scala |   2 +-
 .../sql/catalyst/analysis/AnalysisSuite.scala |  12 +-
 .../expressions/ExpressionEvalHelper.scala    |   2 +-
 .../org/apache/spark/sql/DataFrameSuite.scala | 153 +++++++++++-------
 7 files changed, 120 insertions(+), 76 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index a309ee35ee582..a6ea0cc0a83a8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -928,12 +928,17 @@ class Analyzer(
       // from LogicalPlan, currently we only do it for UnaryNode which has same output
       // schema with its child.
       case p: UnaryNode if p.output == p.child.output && p.expressions.exists(!_.deterministic) =>
-        val nondeterministicExprs = p.expressions.filterNot(_.deterministic).map { e =>
-          val ne = e match {
-            case n: NamedExpression => n
-            case _ => Alias(e, "_nondeterministic")()
+        val nondeterministicExprs = p.expressions.filterNot(_.deterministic).flatMap { expr =>
+          val leafNondeterministic = expr.collect {
+            case n: Nondeterministic => n
+          }
+          leafNondeterministic.map { e =>
+            val ne = e match {
+              case n: NamedExpression => n
+              case _ => Alias(e, "_nondeterministic")()
+            }
+            new TreeNodeRef(e) -> ne
           }
-          new TreeNodeRef(e) -> ne
         }.toMap
         val newPlan = p.transformExpressions { case e =>
           nondeterministicExprs.get(new TreeNodeRef(e)).map(_.toAttribute).getOrElse(e)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 03e36c7871bcf..8fc182607ce68 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -201,11 +201,9 @@ trait Nondeterministic extends Expression {
 
   private[this] var initialized = false
 
-  final def initialize(): Unit = {
-    if (!initialized) {
-      initInternal()
-      initialized = true
-    }
+  final def setInitialValues(): Unit = {
+    initInternal()
+    initialized = true
   }
 
   protected def initInternal(): Unit
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 27d6ff587ab71..b3beb7e28f208 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -32,7 +32,7 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection {
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
   expressions.foreach(_.foreach {
-    case n: Nondeterministic => n.initialize()
+    case n: Nondeterministic => n.setInitialValues()
     case _ =>
   })
 
@@ -63,7 +63,7 @@ case class InterpretedMutableProjection(expressions: Seq[Expression]) extends Mu
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
   expressions.foreach(_.foreach {
-    case n: Nondeterministic => n.initialize()
+    case n: Nondeterministic => n.setInitialValues()
     case _ =>
   })
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 5bfe1cad24a3e..ab7d3afce8f2e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -31,7 +31,7 @@ object InterpretedPredicate {
 
   def create(expression: Expression): (InternalRow => Boolean) = {
     expression.foreach {
-      case n: Nondeterministic => n.initialize()
+      case n: Nondeterministic => n.setInitialValues()
       case _ =>
     }
     (r: InternalRow) => expression.eval(r).asInstanceOf[Boolean]
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index ed645b618dc9b..4589facb49b76 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -153,7 +153,7 @@ class AnalysisSuite extends AnalysisTest {
     assert(pl(4).dataType == DoubleType)
   }
 
-  test("pull out nondeterministic expressions from unary LogicalPlan") {
+  test("pull out nondeterministic expressions from RepartitionByExpression") {
     val plan = RepartitionByExpression(Seq(Rand(33)), testRelation)
     val projected = Alias(Rand(33), "_nondeterministic")()
     val expected =
@@ -162,4 +162,14 @@ class AnalysisSuite extends AnalysisTest {
           Project(testRelation.output :+ projected, testRelation)))
     checkAnalysis(plan, expected)
   }
+
+  test("pull out nondeterministic expressions from Sort") {
+    val plan = Sort(Seq(SortOrder(Rand(33), Ascending)), false, testRelation)
+    val projected = Alias(Rand(33), "_nondeterministic")()
+    val expected =
+      Project(testRelation.output,
+        Sort(Seq(SortOrder(projected.toAttribute, Ascending)), false,
+          Project(testRelation.output :+ projected, testRelation)))
+    checkAnalysis(plan, expected)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 0c8611d5ddefa..3c05e5c3b833c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -65,7 +65,7 @@ trait ExpressionEvalHelper {
 
   protected def evaluate(expression: Expression, inputRow: InternalRow = EmptyRow): Any = {
     expression.foreach {
-      case n: Nondeterministic => n.initialize()
+      case n: Nondeterministic => n.setInitialValues()
       case _ =>
     }
     expression.eval(inputRow)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 3151e071b19ea..97beae2f85c50 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -33,33 +33,28 @@ import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SQLTestUtils}
 class DataFrameSuite extends QueryTest with SQLTestUtils {
   import org.apache.spark.sql.TestData._
 
-  lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-
-  def sqlContext: SQLContext = ctx
+  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
+  import sqlContext.implicits._
 
   test("analysis error should be eagerly reported") {
-    val oldSetting = ctx.conf.dataFrameEagerAnalysis
     // Eager analysis.
-    ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, true)
-
-    intercept[Exception] { testData.select('nonExistentName) }
-    intercept[Exception] {
-      testData.groupBy('key).agg(Map("nonExistentName" -> "sum"))
-    }
-    intercept[Exception] {
-      testData.groupBy("nonExistentName").agg(Map("key" -> "sum"))
-    }
-    intercept[Exception] {
-      testData.groupBy($"abcd").agg(Map("key" -> "sum"))
+    withSQLConf(SQLConf.DATAFRAME_EAGER_ANALYSIS.key -> "true") {
+      intercept[Exception] { testData.select('nonExistentName) }
+      intercept[Exception] {
+        testData.groupBy('key).agg(Map("nonExistentName" -> "sum"))
+      }
+      intercept[Exception] {
+        testData.groupBy("nonExistentName").agg(Map("key" -> "sum"))
+      }
+      intercept[Exception] {
+        testData.groupBy($"abcd").agg(Map("key" -> "sum"))
+      }
     }
 
     // No more eager analysis once the flag is turned off
-    ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, false)
-    testData.select('nonExistentName)
-
-    // Set the flag back to original value before this test.
-    ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting)
+    withSQLConf(SQLConf.DATAFRAME_EAGER_ANALYSIS.key -> "false") {
+      testData.select('nonExistentName)
+    }
   }
 
   test("dataframe toString") {
@@ -77,21 +72,18 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("invalid plan toString, debug mode") {
-    val oldSetting = ctx.conf.dataFrameEagerAnalysis
-    ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, true)
-
     // Turn on debug mode so we can see invalid query plans.
     import org.apache.spark.sql.execution.debug._
-    ctx.debug()
 
-    val badPlan = testData.select('badColumn)
+    withSQLConf(SQLConf.DATAFRAME_EAGER_ANALYSIS.key -> "true") {
+      sqlContext.debug()
 
-    assert(badPlan.toString contains badPlan.queryExecution.toString,
-      "toString on bad query plans should include the query execution but was:\n" +
-        badPlan.toString)
+      val badPlan = testData.select('badColumn)
 
-    // Set the flag back to original value before this test.
-    ctx.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting)
+      assert(badPlan.toString contains badPlan.queryExecution.toString,
+        "toString on bad query plans should include the query execution but was:\n" +
+          badPlan.toString)
+    }
   }
 
   test("access complex data") {
@@ -107,8 +99,8 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("empty data frame") {
-    assert(ctx.emptyDataFrame.columns.toSeq === Seq.empty[String])
-    assert(ctx.emptyDataFrame.count() === 0)
+    assert(sqlContext.emptyDataFrame.columns.toSeq === Seq.empty[String])
+    assert(sqlContext.emptyDataFrame.count() === 0)
   }
 
   test("head and take") {
@@ -344,7 +336,7 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("replace column using withColumn") {
-    val df2 = ctx.sparkContext.parallelize(Array(1, 2, 3)).toDF("x")
+    val df2 = sqlContext.sparkContext.parallelize(Array(1, 2, 3)).toDF("x")
     val df3 = df2.withColumn("x", df2("x") + 1)
     checkAnswer(
       df3.select("x"),
@@ -425,7 +417,7 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
 
   test("randomSplit") {
     val n = 600
-    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = sqlContext.sparkContext.parallelize(1 to n, 2).toDF("id")
     for (seed <- 1 to 5) {
       val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
       assert(splits.length == 3, "wrong number of splits")
@@ -519,7 +511,7 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
 
   test("showString: truncate = [true, false]") {
     val longString = Array.fill(21)("1").mkString
-    val df = ctx.sparkContext.parallelize(Seq("1", longString)).toDF()
+    val df = sqlContext.sparkContext.parallelize(Seq("1", longString)).toDF()
     val expectedAnswerForFalse = """+---------------------+
                                    ||_1                   |
                                    |+---------------------+
@@ -609,21 +601,17 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") {
-    val rowRDD = ctx.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0))))
+    val rowRDD = sqlContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0))))
     val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false)))
-    val df = ctx.createDataFrame(rowRDD, schema)
+    val df = sqlContext.createDataFrame(rowRDD, schema)
     df.rdd.collect()
   }
 
-  test("SPARK-6899") {
-    val originalValue = ctx.conf.codegenEnabled
-    ctx.setConf(SQLConf.CODEGEN_ENABLED, true)
-    try{
+  test("SPARK-6899: type should match when using codegen") {
+    withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "true") {
       checkAnswer(
         decimalData.agg(avg('a)),
         Row(new java.math.BigDecimal(2.0)))
-    } finally {
-      ctx.setConf(SQLConf.CODEGEN_ENABLED, originalValue)
     }
   }
 
@@ -635,14 +623,14 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-7551: support backticks for DataFrame attribute resolution") {
-    val df = ctx.read.json(ctx.sparkContext.makeRDD(
+    val df = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
       """{"a.b": {"c": {"d..e": {"f": 1}}}}""" :: Nil))
     checkAnswer(
       df.select(df("`a.b`.c.`d..e`.`f`")),
       Row(1)
     )
 
-    val df2 = ctx.read.json(ctx.sparkContext.makeRDD(
+    val df2 = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
       """{"a  b": {"c": {"d  e": {"f": 1}}}}""" :: Nil))
     checkAnswer(
       df2.select(df2("`a  b`.c.d  e.f")),
@@ -662,7 +650,7 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-7324 dropDuplicates") {
-    val testData = ctx.sparkContext.parallelize(
+    val testData = sqlContext.sparkContext.parallelize(
       (2, 1, 2) :: (1, 1, 1) ::
       (1, 2, 1) :: (2, 1, 2) ::
       (2, 2, 2) :: (2, 2, 1) ::
@@ -710,49 +698,49 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
 
   test("SPARK-7150 range api") {
     // numSlice is greater than length
-    val res1 = ctx.range(0, 10, 1, 15).select("id")
+    val res1 = sqlContext.range(0, 10, 1, 15).select("id")
     assert(res1.count == 10)
     assert(res1.agg(sum("id")).as("sumid").collect() === Seq(Row(45)))
 
-    val res2 = ctx.range(3, 15, 3, 2).select("id")
+    val res2 = sqlContext.range(3, 15, 3, 2).select("id")
     assert(res2.count == 4)
     assert(res2.agg(sum("id")).as("sumid").collect() === Seq(Row(30)))
 
-    val res3 = ctx.range(1, -2).select("id")
+    val res3 = sqlContext.range(1, -2).select("id")
     assert(res3.count == 0)
 
     // start is positive, end is negative, step is negative
-    val res4 = ctx.range(1, -2, -2, 6).select("id")
+    val res4 = sqlContext.range(1, -2, -2, 6).select("id")
     assert(res4.count == 2)
     assert(res4.agg(sum("id")).as("sumid").collect() === Seq(Row(0)))
 
     // start, end, step are negative
-    val res5 = ctx.range(-3, -8, -2, 1).select("id")
+    val res5 = sqlContext.range(-3, -8, -2, 1).select("id")
     assert(res5.count == 3)
     assert(res5.agg(sum("id")).as("sumid").collect() === Seq(Row(-15)))
 
     // start, end are negative, step is positive
-    val res6 = ctx.range(-8, -4, 2, 1).select("id")
+    val res6 = sqlContext.range(-8, -4, 2, 1).select("id")
     assert(res6.count == 2)
     assert(res6.agg(sum("id")).as("sumid").collect() === Seq(Row(-14)))
 
-    val res7 = ctx.range(-10, -9, -20, 1).select("id")
+    val res7 = sqlContext.range(-10, -9, -20, 1).select("id")
     assert(res7.count == 0)
 
-    val res8 = ctx.range(Long.MinValue, Long.MaxValue, Long.MaxValue, 100).select("id")
+    val res8 = sqlContext.range(Long.MinValue, Long.MaxValue, Long.MaxValue, 100).select("id")
     assert(res8.count == 3)
     assert(res8.agg(sum("id")).as("sumid").collect() === Seq(Row(-3)))
 
-    val res9 = ctx.range(Long.MaxValue, Long.MinValue, Long.MinValue, 100).select("id")
+    val res9 = sqlContext.range(Long.MaxValue, Long.MinValue, Long.MinValue, 100).select("id")
     assert(res9.count == 2)
     assert(res9.agg(sum("id")).as("sumid").collect() === Seq(Row(Long.MaxValue - 1)))
 
     // only end provided as argument
-    val res10 = ctx.range(10).select("id")
+    val res10 = sqlContext.range(10).select("id")
     assert(res10.count == 10)
     assert(res10.agg(sum("id")).as("sumid").collect() === Seq(Row(45)))
 
-    val res11 = ctx.range(-1).select("id")
+    val res11 = sqlContext.range(-1).select("id")
     assert(res11.count == 0)
   }
 
@@ -819,13 +807,13 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
 
       // pass case: parquet table (HadoopFsRelation)
       df.write.mode(SaveMode.Overwrite).parquet(tempParquetFile.getCanonicalPath)
-      val pdf = ctx.read.parquet(tempParquetFile.getCanonicalPath)
+      val pdf = sqlContext.read.parquet(tempParquetFile.getCanonicalPath)
       pdf.registerTempTable("parquet_base")
       insertion.write.insertInto("parquet_base")
 
       // pass case: json table (InsertableRelation)
       df.write.mode(SaveMode.Overwrite).json(tempJsonFile.getCanonicalPath)
-      val jdf = ctx.read.json(tempJsonFile.getCanonicalPath)
+      val jdf = sqlContext.read.json(tempJsonFile.getCanonicalPath)
       jdf.registerTempTable("json_base")
       insertion.write.mode(SaveMode.Overwrite).insertInto("json_base")
 
@@ -845,11 +833,54 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
       assert(e2.getMessage.contains("Inserting into an RDD-based table is not allowed."))
 
       // error case: insert into an OneRowRelation
-      new DataFrame(ctx, OneRowRelation).registerTempTable("one_row")
+      new DataFrame(sqlContext, OneRowRelation).registerTempTable("one_row")
       val e3 = intercept[AnalysisException] {
         insertion.write.insertInto("one_row")
       }
       assert(e3.getMessage.contains("Inserting into an RDD-based table is not allowed."))
     }
   }
+
+  test("SPARK-8608: call `show` on local DataFrame with random columns should return same value") {
+    // Make sure we can pass this test for both codegen mode and interpreted mode.
+    withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "true") {
+      val df = testData.select(rand(33))
+      assert(df.showString(5) == df.showString(5))
+    }
+
+    withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "false") {
+      val df = testData.select(rand(33))
+      assert(df.showString(5) == df.showString(5))
+    }
+
+    // We will reuse the same Expression object for LocalRelation.
+    val df = (1 to 10).map(Tuple1.apply).toDF().select(rand(33))
+    assert(df.showString(5) == df.showString(5))
+  }
+
+  test("SPARK-8609: local DataFrame with random columns should return same value after sort") {
+    // Make sure we can pass this test for both codegen mode and interpreted mode.
+    withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "true") {
+      checkAnswer(testData.sort(rand(33)), testData.sort(rand(33)))
+    }
+
+    withSQLConf(SQLConf.CODEGEN_ENABLED.key -> "false") {
+      checkAnswer(testData.sort(rand(33)), testData.sort(rand(33)))
+    }
+
+    // We will reuse the same Expression object for LocalRelation.
+    val df = (1 to 10).map(Tuple1.apply).toDF()
+    checkAnswer(df.sort(rand(33)), df.sort(rand(33)))
+  }
+
+  test("SPARK-9083: sort with non-deterministic expressions") {
+    import org.apache.spark.util.random.XORShiftRandom
+
+    val seed = 33
+    val df = (1 to 100).map(Tuple1.apply).toDF("i")
+    val random = new XORShiftRandom(seed)
+    val expected = (1 to 100).map(_ -> random.nextDouble()).sortBy(_._2).map(_._1)
+    val actual = df.sort(rand(seed)).collect().map(_.getInt(0))
+    assert(expected === actual)
+  }
 }

From ea49705bd4feb2f25e1b536f0b3ddcfc72a57101 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 28 Jul 2015 21:53:28 -0700
Subject: [PATCH 0664/1454] [SPARK-9419] ShuffleMemoryManager and MemoryStore
 should track memory on a per-task, not per-thread, basis

Spark's ShuffleMemoryManager and MemoryStore track memory on a per-thread basis, which causes problems in the handful of cases where we have tasks that use multiple threads. In PythonRDD, RRDD, ScriptTransformation, and PipedRDD we consume the input iterator in a separate thread in order to write it to an external process.  As a result, these RDD's input iterators are consumed in a different thread than the thread that created them, which can cause problems in our memory allocation tracking. For example, if allocations are performed in one thread but deallocations are performed in a separate thread then memory may be leaked or we may get errors complaining that more memory was allocated than was freed.

I think that the right way to fix this is to change our accounting to be performed on a per-task instead of per-thread basis.  Note that the current per-thread tracking has caused problems in the past; SPARK-3731 (#2668) fixes a memory leak in PythonRDD that was caused by this issue (that fix is no longer necessary as of this patch).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7734 from JoshRosen/memory-tracking-fixes and squashes the following commits:

b4b1702 [Josh Rosen] Propagate TaskContext to writer threads.
57c9b4e [Josh Rosen] Merge remote-tracking branch 'origin/master' into memory-tracking-fixes
ed25d3b [Josh Rosen] Address minor PR review comments
44f6497 [Josh Rosen] Fix long line.
7b0f04b [Josh Rosen] Fix ShuffleMemoryManagerSuite
f57f3f2 [Josh Rosen] More thread -> task changes
fa78ee8 [Josh Rosen] Move Executor's cleanup into Task so that TaskContext is defined when cleanup is performed
5e2f01e [Josh Rosen] Fix capitalization
1b0083b [Josh Rosen] Roll back fix in PySpark, which is no longer necessary
2e1e0f8 [Josh Rosen] Use TaskAttemptIds to track shuffle memory
c9e8e54 [Josh Rosen] Use TaskAttemptIds to track unroll memory
---
 .../apache/spark/api/python/PythonRDD.scala   |  6 +-
 .../scala/org/apache/spark/api/r/RRDD.scala   |  2 +
 .../org/apache/spark/executor/Executor.scala  |  4 -
 .../scala/org/apache/spark/rdd/PipedRDD.scala |  1 +
 .../org/apache/spark/scheduler/Task.scala     | 15 ++-
 .../spark/shuffle/ShuffleMemoryManager.scala  | 88 +++++++++--------
 .../apache/spark/storage/MemoryStore.scala    | 95 ++++++++++---------
 .../shuffle/ShuffleMemoryManagerSuite.scala   | 41 +++++---
 .../spark/storage/BlockManagerSuite.scala     | 84 ++++++++--------
 9 files changed, 184 insertions(+), 152 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 598953ac3bcc8..55e563ee968be 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -207,6 +207,7 @@ private[spark] class PythonRDD(
 
     override def run(): Unit = Utils.logUncaughtExceptions {
       try {
+        TaskContext.setTaskContext(context)
         val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize)
         val dataOut = new DataOutputStream(stream)
         // Partition index
@@ -263,11 +264,6 @@ private[spark] class PythonRDD(
           if (!worker.isClosed) {
             Utils.tryLog(worker.shutdownOutput())
           }
-      } finally {
-        // Release memory used by this thread for shuffles
-        env.shuffleMemoryManager.releaseMemoryForThisThread()
-        // Release memory used by this thread for unrolling blocks
-        env.blockManager.memoryStore.releaseUnrollMemoryForThisThread()
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
index 23a470d6afcae..1cf2824f862ee 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
@@ -112,6 +112,7 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag](
     partition: Int): Unit = {
 
     val env = SparkEnv.get
+    val taskContext = TaskContext.get()
     val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt
     val stream = new BufferedOutputStream(output, bufferSize)
 
@@ -119,6 +120,7 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag](
       override def run(): Unit = {
         try {
           SparkEnv.set(env)
+          TaskContext.setTaskContext(taskContext)
           val dataOut = new DataOutputStream(stream)
           dataOut.writeInt(partition)
 
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index e76664f1bd7b0..7bc7fce7ae8dd 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -313,10 +313,6 @@ private[spark] class Executor(
           }
 
       } finally {
-        // Release memory used by this thread for shuffles
-        env.shuffleMemoryManager.releaseMemoryForThisThread()
-        // Release memory used by this thread for unrolling blocks
-        env.blockManager.memoryStore.releaseUnrollMemoryForThisThread()
         runningTasks.remove(taskId)
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
index defdabf95ac4b..3bb9998e1db44 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
@@ -133,6 +133,7 @@ private[spark] class PipedRDD[T: ClassTag](
     // Start a thread to feed the process input from our parent's iterator
     new Thread("stdin writer for " + command) {
       override def run() {
+        TaskContext.setTaskContext(context)
         val out = new PrintWriter(proc.getOutputStream)
 
         // scalastyle:off println
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
index d11a00956a9a9..1978305cfefbd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
@@ -23,7 +23,7 @@ import java.nio.ByteBuffer
 import scala.collection.mutable.HashMap
 
 import org.apache.spark.metrics.MetricsSystem
-import org.apache.spark.{TaskContextImpl, TaskContext}
+import org.apache.spark.{SparkEnv, TaskContextImpl, TaskContext}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.serializer.SerializerInstance
 import org.apache.spark.unsafe.memory.TaskMemoryManager
@@ -86,7 +86,18 @@ private[spark] abstract class Task[T](
       (runTask(context), context.collectAccumulators())
     } finally {
       context.markTaskCompleted()
-      TaskContext.unset()
+      try {
+        Utils.tryLogNonFatalError {
+          // Release memory used by this thread for shuffles
+          SparkEnv.get.shuffleMemoryManager.releaseMemoryForThisTask()
+        }
+        Utils.tryLogNonFatalError {
+          // Release memory used by this thread for unrolling blocks
+          SparkEnv.get.blockManager.memoryStore.releaseUnrollMemoryForThisTask()
+        }
+      } finally {
+        TaskContext.unset()
+      }
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index 3bcc7178a3d8b..f038b722957b8 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -19,95 +19,101 @@ package org.apache.spark.shuffle
 
 import scala.collection.mutable
 
-import org.apache.spark.{Logging, SparkException, SparkConf}
+import org.apache.spark.{Logging, SparkException, SparkConf, TaskContext}
 
 /**
- * Allocates a pool of memory to task threads for use in shuffle operations. Each disk-spilling
+ * Allocates a pool of memory to tasks for use in shuffle operations. Each disk-spilling
  * collection (ExternalAppendOnlyMap or ExternalSorter) used by these tasks can acquire memory
  * from this pool and release it as it spills data out. When a task ends, all its memory will be
  * released by the Executor.
  *
- * This class tries to ensure that each thread gets a reasonable share of memory, instead of some
- * thread ramping up to a large amount first and then causing others to spill to disk repeatedly.
- * If there are N threads, it ensures that each thread can acquire at least 1 / 2N of the memory
+ * This class tries to ensure that each task gets a reasonable share of memory, instead of some
+ * task ramping up to a large amount first and then causing others to spill to disk repeatedly.
+ * If there are N tasks, it ensures that each tasks can acquire at least 1 / 2N of the memory
  * before it has to spill, and at most 1 / N. Because N varies dynamically, we keep track of the
- * set of active threads and redo the calculations of 1 / 2N and 1 / N in waiting threads whenever
+ * set of active tasks and redo the calculations of 1 / 2N and 1 / N in waiting tasks whenever
  * this set changes. This is all done by synchronizing access on "this" to mutate state and using
  * wait() and notifyAll() to signal changes.
  */
 private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
-  private val threadMemory = new mutable.HashMap[Long, Long]()  // threadId -> memory bytes
+  private val taskMemory = new mutable.HashMap[Long, Long]()  // taskAttemptId -> memory bytes
 
   def this(conf: SparkConf) = this(ShuffleMemoryManager.getMaxMemory(conf))
 
+  private def currentTaskAttemptId(): Long = {
+    // In case this is called on the driver, return an invalid task attempt id.
+    Option(TaskContext.get()).map(_.taskAttemptId()).getOrElse(-1L)
+  }
+
   /**
-   * Try to acquire up to numBytes memory for the current thread, and return the number of bytes
+   * Try to acquire up to numBytes memory for the current task, and return the number of bytes
    * obtained, or 0 if none can be allocated. This call may block until there is enough free memory
-   * in some situations, to make sure each thread has a chance to ramp up to at least 1 / 2N of the
-   * total memory pool (where N is the # of active threads) before it is forced to spill. This can
-   * happen if the number of threads increases but an older thread had a lot of memory already.
+   * in some situations, to make sure each task has a chance to ramp up to at least 1 / 2N of the
+   * total memory pool (where N is the # of active tasks) before it is forced to spill. This can
+   * happen if the number of tasks increases but an older task had a lot of memory already.
    */
   def tryToAcquire(numBytes: Long): Long = synchronized {
-    val threadId = Thread.currentThread().getId
+    val taskAttemptId = currentTaskAttemptId()
     assert(numBytes > 0, "invalid number of bytes requested: " + numBytes)
 
-    // Add this thread to the threadMemory map just so we can keep an accurate count of the number
-    // of active threads, to let other threads ramp down their memory in calls to tryToAcquire
-    if (!threadMemory.contains(threadId)) {
-      threadMemory(threadId) = 0L
-      notifyAll()  // Will later cause waiting threads to wake up and check numThreads again
+    // Add this task to the taskMemory map just so we can keep an accurate count of the number
+    // of active tasks, to let other tasks ramp down their memory in calls to tryToAcquire
+    if (!taskMemory.contains(taskAttemptId)) {
+      taskMemory(taskAttemptId) = 0L
+      notifyAll()  // Will later cause waiting tasks to wake up and check numThreads again
     }
 
     // Keep looping until we're either sure that we don't want to grant this request (because this
-    // thread would have more than 1 / numActiveThreads of the memory) or we have enough free
-    // memory to give it (we always let each thread get at least 1 / (2 * numActiveThreads)).
+    // task would have more than 1 / numActiveTasks of the memory) or we have enough free
+    // memory to give it (we always let each task get at least 1 / (2 * numActiveTasks)).
     while (true) {
-      val numActiveThreads = threadMemory.keys.size
-      val curMem = threadMemory(threadId)
-      val freeMemory = maxMemory - threadMemory.values.sum
+      val numActiveTasks = taskMemory.keys.size
+      val curMem = taskMemory(taskAttemptId)
+      val freeMemory = maxMemory - taskMemory.values.sum
 
-      // How much we can grant this thread; don't let it grow to more than 1 / numActiveThreads;
+      // How much we can grant this task; don't let it grow to more than 1 / numActiveTasks;
       // don't let it be negative
-      val maxToGrant = math.min(numBytes, math.max(0, (maxMemory / numActiveThreads) - curMem))
+      val maxToGrant = math.min(numBytes, math.max(0, (maxMemory / numActiveTasks) - curMem))
 
-      if (curMem < maxMemory / (2 * numActiveThreads)) {
-        // We want to let each thread get at least 1 / (2 * numActiveThreads) before blocking;
-        // if we can't give it this much now, wait for other threads to free up memory
-        // (this happens if older threads allocated lots of memory before N grew)
-        if (freeMemory >= math.min(maxToGrant, maxMemory / (2 * numActiveThreads) - curMem)) {
+      if (curMem < maxMemory / (2 * numActiveTasks)) {
+        // We want to let each task get at least 1 / (2 * numActiveTasks) before blocking;
+        // if we can't give it this much now, wait for other tasks to free up memory
+        // (this happens if older tasks allocated lots of memory before N grew)
+        if (freeMemory >= math.min(maxToGrant, maxMemory / (2 * numActiveTasks) - curMem)) {
           val toGrant = math.min(maxToGrant, freeMemory)
-          threadMemory(threadId) += toGrant
+          taskMemory(taskAttemptId) += toGrant
           return toGrant
         } else {
-          logInfo(s"Thread $threadId waiting for at least 1/2N of shuffle memory pool to be free")
+          logInfo(
+            s"Thread $taskAttemptId waiting for at least 1/2N of shuffle memory pool to be free")
           wait()
         }
       } else {
         // Only give it as much memory as is free, which might be none if it reached 1 / numThreads
         val toGrant = math.min(maxToGrant, freeMemory)
-        threadMemory(threadId) += toGrant
+        taskMemory(taskAttemptId) += toGrant
         return toGrant
       }
     }
     0L  // Never reached
   }
 
-  /** Release numBytes bytes for the current thread. */
+  /** Release numBytes bytes for the current task. */
   def release(numBytes: Long): Unit = synchronized {
-    val threadId = Thread.currentThread().getId
-    val curMem = threadMemory.getOrElse(threadId, 0L)
+    val taskAttemptId = currentTaskAttemptId()
+    val curMem = taskMemory.getOrElse(taskAttemptId, 0L)
     if (curMem < numBytes) {
       throw new SparkException(
-        s"Internal error: release called on ${numBytes} bytes but thread only has ${curMem}")
+        s"Internal error: release called on ${numBytes} bytes but task only has ${curMem}")
     }
-    threadMemory(threadId) -= numBytes
+    taskMemory(taskAttemptId) -= numBytes
     notifyAll()  // Notify waiters who locked "this" in tryToAcquire that memory has been freed
   }
 
-  /** Release all memory for the current thread and mark it as inactive (e.g. when a task ends). */
-  def releaseMemoryForThisThread(): Unit = synchronized {
-    val threadId = Thread.currentThread().getId
-    threadMemory.remove(threadId)
+  /** Release all memory for the current task and mark it as inactive (e.g. when a task ends). */
+  def releaseMemoryForThisTask(): Unit = synchronized {
+    val taskAttemptId = currentTaskAttemptId()
+    taskMemory.remove(taskAttemptId)
     notifyAll()  // Notify waiters who locked "this" in tryToAcquire that memory has been freed
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
index ed609772e6979..6f27f00307f8c 100644
--- a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala
@@ -23,6 +23,7 @@ import java.util.LinkedHashMap
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
+import org.apache.spark.TaskContext
 import org.apache.spark.util.{SizeEstimator, Utils}
 import org.apache.spark.util.collection.SizeTrackingVector
 
@@ -43,11 +44,11 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
   // Ensure only one thread is putting, and if necessary, dropping blocks at any given time
   private val accountingLock = new Object
 
-  // A mapping from thread ID to amount of memory used for unrolling a block (in bytes)
+  // A mapping from taskAttemptId to amount of memory used for unrolling a block (in bytes)
   // All accesses of this map are assumed to have manually synchronized on `accountingLock`
   private val unrollMemoryMap = mutable.HashMap[Long, Long]()
   // Same as `unrollMemoryMap`, but for pending unroll memory as defined below.
-  // Pending unroll memory refers to the intermediate memory occupied by a thread
+  // Pending unroll memory refers to the intermediate memory occupied by a task
   // after the unroll but before the actual putting of the block in the cache.
   // This chunk of memory is expected to be released *as soon as* we finish
   // caching the corresponding block as opposed to until after the task finishes.
@@ -250,21 +251,21 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     var elementsUnrolled = 0
     // Whether there is still enough memory for us to continue unrolling this block
     var keepUnrolling = true
-    // Initial per-thread memory to request for unrolling blocks (bytes). Exposed for testing.
+    // Initial per-task memory to request for unrolling blocks (bytes). Exposed for testing.
     val initialMemoryThreshold = unrollMemoryThreshold
     // How often to check whether we need to request more memory
     val memoryCheckPeriod = 16
-    // Memory currently reserved by this thread for this particular unrolling operation
+    // Memory currently reserved by this task for this particular unrolling operation
     var memoryThreshold = initialMemoryThreshold
     // Memory to request as a multiple of current vector size
     val memoryGrowthFactor = 1.5
-    // Previous unroll memory held by this thread, for releasing later (only at the very end)
-    val previousMemoryReserved = currentUnrollMemoryForThisThread
+    // Previous unroll memory held by this task, for releasing later (only at the very end)
+    val previousMemoryReserved = currentUnrollMemoryForThisTask
     // Underlying vector for unrolling the block
     var vector = new SizeTrackingVector[Any]
 
     // Request enough memory to begin unrolling
-    keepUnrolling = reserveUnrollMemoryForThisThread(initialMemoryThreshold)
+    keepUnrolling = reserveUnrollMemoryForThisTask(initialMemoryThreshold)
 
     if (!keepUnrolling) {
       logWarning(s"Failed to reserve initial memory threshold of " +
@@ -283,7 +284,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
             // Hold the accounting lock, in case another thread concurrently puts a block that
             // takes up the unrolling space we just ensured here
             accountingLock.synchronized {
-              if (!reserveUnrollMemoryForThisThread(amountToRequest)) {
+              if (!reserveUnrollMemoryForThisTask(amountToRequest)) {
                 // If the first request is not granted, try again after ensuring free space
                 // If there is still not enough space, give up and drop the partition
                 val spaceToEnsure = maxUnrollMemory - currentUnrollMemory
@@ -291,7 +292,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
                   val result = ensureFreeSpace(blockId, spaceToEnsure)
                   droppedBlocks ++= result.droppedBlocks
                 }
-                keepUnrolling = reserveUnrollMemoryForThisThread(amountToRequest)
+                keepUnrolling = reserveUnrollMemoryForThisTask(amountToRequest)
               }
             }
             // New threshold is currentSize * memoryGrowthFactor
@@ -317,9 +318,9 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
       // later when the task finishes.
       if (keepUnrolling) {
         accountingLock.synchronized {
-          val amountToRelease = currentUnrollMemoryForThisThread - previousMemoryReserved
-          releaseUnrollMemoryForThisThread(amountToRelease)
-          reservePendingUnrollMemoryForThisThread(amountToRelease)
+          val amountToRelease = currentUnrollMemoryForThisTask - previousMemoryReserved
+          releaseUnrollMemoryForThisTask(amountToRelease)
+          reservePendingUnrollMemoryForThisTask(amountToRelease)
         }
       }
     }
@@ -397,7 +398,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
         droppedBlockStatus.foreach { status => droppedBlocks += ((blockId, status)) }
       }
       // Release the unroll memory used because we no longer need the underlying Array
-      releasePendingUnrollMemoryForThisThread()
+      releasePendingUnrollMemoryForThisTask()
     }
     ResultWithDroppedBlocks(putSuccess, droppedBlocks)
   }
@@ -427,9 +428,9 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
 
     // Take into account the amount of memory currently occupied by unrolling blocks
     // and minus the pending unroll memory for that block on current thread.
-    val threadId = Thread.currentThread().getId
+    val taskAttemptId = currentTaskAttemptId()
     val actualFreeMemory = freeMemory - currentUnrollMemory +
-      pendingUnrollMemoryMap.getOrElse(threadId, 0L)
+      pendingUnrollMemoryMap.getOrElse(taskAttemptId, 0L)
 
     if (actualFreeMemory < space) {
       val rddToAdd = getRddId(blockIdToAdd)
@@ -455,7 +456,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
         logInfo(s"${selectedBlocks.size} blocks selected for dropping")
         for (blockId <- selectedBlocks) {
           val entry = entries.synchronized { entries.get(blockId) }
-          // This should never be null as only one thread should be dropping
+          // This should never be null as only one task should be dropping
           // blocks and removing entries. However the check is still here for
           // future safety.
           if (entry != null) {
@@ -482,79 +483,85 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     entries.synchronized { entries.containsKey(blockId) }
   }
 
+  private def currentTaskAttemptId(): Long = {
+    // In case this is called on the driver, return an invalid task attempt id.
+    Option(TaskContext.get()).map(_.taskAttemptId()).getOrElse(-1L)
+  }
+
   /**
-   * Reserve additional memory for unrolling blocks used by this thread.
+   * Reserve additional memory for unrolling blocks used by this task.
    * Return whether the request is granted.
    */
-  def reserveUnrollMemoryForThisThread(memory: Long): Boolean = {
+  def reserveUnrollMemoryForThisTask(memory: Long): Boolean = {
     accountingLock.synchronized {
       val granted = freeMemory > currentUnrollMemory + memory
       if (granted) {
-        val threadId = Thread.currentThread().getId
-        unrollMemoryMap(threadId) = unrollMemoryMap.getOrElse(threadId, 0L) + memory
+        val taskAttemptId = currentTaskAttemptId()
+        unrollMemoryMap(taskAttemptId) = unrollMemoryMap.getOrElse(taskAttemptId, 0L) + memory
       }
       granted
     }
   }
 
   /**
-   * Release memory used by this thread for unrolling blocks.
-   * If the amount is not specified, remove the current thread's allocation altogether.
+   * Release memory used by this task for unrolling blocks.
+   * If the amount is not specified, remove the current task's allocation altogether.
    */
-  def releaseUnrollMemoryForThisThread(memory: Long = -1L): Unit = {
-    val threadId = Thread.currentThread().getId
+  def releaseUnrollMemoryForThisTask(memory: Long = -1L): Unit = {
+    val taskAttemptId = currentTaskAttemptId()
     accountingLock.synchronized {
       if (memory < 0) {
-        unrollMemoryMap.remove(threadId)
+        unrollMemoryMap.remove(taskAttemptId)
       } else {
-        unrollMemoryMap(threadId) = unrollMemoryMap.getOrElse(threadId, memory) - memory
-        // If this thread claims no more unroll memory, release it completely
-        if (unrollMemoryMap(threadId) <= 0) {
-          unrollMemoryMap.remove(threadId)
+        unrollMemoryMap(taskAttemptId) = unrollMemoryMap.getOrElse(taskAttemptId, memory) - memory
+        // If this task claims no more unroll memory, release it completely
+        if (unrollMemoryMap(taskAttemptId) <= 0) {
+          unrollMemoryMap.remove(taskAttemptId)
         }
       }
     }
   }
 
   /**
-   * Reserve the unroll memory of current unroll successful block used by this thread
+   * Reserve the unroll memory of current unroll successful block used by this task
    * until actually put the block into memory entry.
    */
-  def reservePendingUnrollMemoryForThisThread(memory: Long): Unit = {
-    val threadId = Thread.currentThread().getId
+  def reservePendingUnrollMemoryForThisTask(memory: Long): Unit = {
+    val taskAttemptId = currentTaskAttemptId()
     accountingLock.synchronized {
-       pendingUnrollMemoryMap(threadId) = pendingUnrollMemoryMap.getOrElse(threadId, 0L) + memory
+       pendingUnrollMemoryMap(taskAttemptId) =
+         pendingUnrollMemoryMap.getOrElse(taskAttemptId, 0L) + memory
     }
   }
 
   /**
-   * Release pending unroll memory of current unroll successful block used by this thread
+   * Release pending unroll memory of current unroll successful block used by this task
    */
-  def releasePendingUnrollMemoryForThisThread(): Unit = {
-    val threadId = Thread.currentThread().getId
+  def releasePendingUnrollMemoryForThisTask(): Unit = {
+    val taskAttemptId = currentTaskAttemptId()
     accountingLock.synchronized {
-      pendingUnrollMemoryMap.remove(threadId)
+      pendingUnrollMemoryMap.remove(taskAttemptId)
     }
   }
 
   /**
-   * Return the amount of memory currently occupied for unrolling blocks across all threads.
+   * Return the amount of memory currently occupied for unrolling blocks across all tasks.
    */
   def currentUnrollMemory: Long = accountingLock.synchronized {
     unrollMemoryMap.values.sum + pendingUnrollMemoryMap.values.sum
   }
 
   /**
-   * Return the amount of memory currently occupied for unrolling blocks by this thread.
+   * Return the amount of memory currently occupied for unrolling blocks by this task.
    */
-  def currentUnrollMemoryForThisThread: Long = accountingLock.synchronized {
-    unrollMemoryMap.getOrElse(Thread.currentThread().getId, 0L)
+  def currentUnrollMemoryForThisTask: Long = accountingLock.synchronized {
+    unrollMemoryMap.getOrElse(currentTaskAttemptId(), 0L)
   }
 
   /**
-   * Return the number of threads currently unrolling blocks.
+   * Return the number of tasks currently unrolling blocks.
    */
-  def numThreadsUnrolling: Int = accountingLock.synchronized { unrollMemoryMap.keys.size }
+  def numTasksUnrolling: Int = accountingLock.synchronized { unrollMemoryMap.keys.size }
 
   /**
    * Log information about current memory usage.
@@ -566,7 +573,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
     logInfo(
       s"Memory use = ${Utils.bytesToString(blocksMemory)} (blocks) + " +
       s"${Utils.bytesToString(unrollMemory)} (scratch space shared across " +
-      s"$numThreadsUnrolling thread(s)) = ${Utils.bytesToString(totalMemory)}. " +
+      s"$numTasksUnrolling tasks(s)) = ${Utils.bytesToString(totalMemory)}. " +
       s"Storage limit = ${Utils.bytesToString(maxMemory)}."
     )
   }
diff --git a/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
index 96778c9ebafb1..f495b6a037958 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
@@ -17,26 +17,39 @@
 
 package org.apache.spark.shuffle
 
+import java.util.concurrent.CountDownLatch
+import java.util.concurrent.atomic.AtomicInteger
+
+import org.mockito.Mockito._
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.time.SpanSugar._
-import java.util.concurrent.atomic.AtomicBoolean
-import java.util.concurrent.CountDownLatch
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{SparkFunSuite, TaskContext}
 
 class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
+
+  val nextTaskAttemptId = new AtomicInteger()
+
   /** Launch a thread with the given body block and return it. */
   private def startThread(name: String)(body: => Unit): Thread = {
     val thread = new Thread("ShuffleMemorySuite " + name) {
       override def run() {
-        body
+        try {
+          val taskAttemptId = nextTaskAttemptId.getAndIncrement
+          val mockTaskContext = mock(classOf[TaskContext], RETURNS_SMART_NULLS)
+          when(mockTaskContext.taskAttemptId()).thenReturn(taskAttemptId)
+          TaskContext.setTaskContext(mockTaskContext)
+          body
+        } finally {
+          TaskContext.unset()
+        }
       }
     }
     thread.start()
     thread
   }
 
-  test("single thread requesting memory") {
+  test("single task requesting memory") {
     val manager = new ShuffleMemoryManager(1000L)
 
     assert(manager.tryToAcquire(100L) === 100L)
@@ -50,7 +63,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     assert(manager.tryToAcquire(300L) === 300L)
     assert(manager.tryToAcquire(300L) === 200L)
 
-    manager.releaseMemoryForThisThread()
+    manager.releaseMemoryForThisTask()
     assert(manager.tryToAcquire(1000L) === 1000L)
     assert(manager.tryToAcquire(100L) === 0L)
   }
@@ -107,8 +120,8 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
   }
 
 
-  test("threads cannot grow past 1 / N") {
-    // Two threads request 250 bytes first, wait for each other to get it, and then request
+  test("tasks cannot grow past 1 / N") {
+    // Two tasks request 250 bytes first, wait for each other to get it, and then request
     // 500 more; we should only grant 250 bytes to each of them on this second request
 
     val manager = new ShuffleMemoryManager(1000L)
@@ -158,7 +171,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     assert(state.t2Result2 === 250L)
   }
 
-  test("threads can block to get at least 1 / 2N memory") {
+  test("tasks can block to get at least 1 / 2N memory") {
     // t1 grabs 1000 bytes and then waits until t2 is ready to make a request. It sleeps
     // for a bit and releases 250 bytes, which should then be granted to t2. Further requests
     // by t2 will return false right away because it now has 1 / 2N of the memory.
@@ -224,7 +237,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     }
   }
 
-  test("releaseMemoryForThisThread") {
+  test("releaseMemoryForThisTask") {
     // t1 grabs 1000 bytes and then waits until t2 is ready to make a request. It sleeps
     // for a bit and releases all its memory. t2 should now be able to grab all the memory.
 
@@ -251,9 +264,9 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
         }
       }
       // Sleep a bit before releasing our memory; this is hacky but it would be difficult to make
-      // sure the other thread blocks for some time otherwise
+      // sure the other task blocks for some time otherwise
       Thread.sleep(300)
-      manager.releaseMemoryForThisThread()
+      manager.releaseMemoryForThisTask()
     }
 
     val t2 = startThread("t2") {
@@ -282,7 +295,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
       t2.join()
     }
 
-    // Both threads should've been able to acquire their memory; the second one will have waited
+    // Both tasks should've been able to acquire their memory; the second one will have waited
     // until the first one acquired 1000 bytes and then released all of it
     state.synchronized {
       assert(state.t1Result === 1000L, "t1 could not allocate memory")
@@ -293,7 +306,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     }
   }
 
-  test("threads should not be granted a negative size") {
+  test("tasks should not be granted a negative size") {
     val manager = new ShuffleMemoryManager(1000L)
     manager.tryToAcquire(700L)
 
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index bcee901f5dd5f..f480fd107a0c2 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -1004,32 +1004,32 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     store = makeBlockManager(12000)
     val memoryStore = store.memoryStore
     assert(memoryStore.currentUnrollMemory === 0)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     // Reserve
-    memoryStore.reserveUnrollMemoryForThisThread(100)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 100)
-    memoryStore.reserveUnrollMemoryForThisThread(200)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 300)
-    memoryStore.reserveUnrollMemoryForThisThread(500)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 800)
-    memoryStore.reserveUnrollMemoryForThisThread(1000000)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 800) // not granted
+    memoryStore.reserveUnrollMemoryForThisTask(100)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 100)
+    memoryStore.reserveUnrollMemoryForThisTask(200)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 300)
+    memoryStore.reserveUnrollMemoryForThisTask(500)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 800)
+    memoryStore.reserveUnrollMemoryForThisTask(1000000)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 800) // not granted
     // Release
-    memoryStore.releaseUnrollMemoryForThisThread(100)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 700)
-    memoryStore.releaseUnrollMemoryForThisThread(100)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 600)
+    memoryStore.releaseUnrollMemoryForThisTask(100)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 700)
+    memoryStore.releaseUnrollMemoryForThisTask(100)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 600)
     // Reserve again
-    memoryStore.reserveUnrollMemoryForThisThread(4400)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 5000)
-    memoryStore.reserveUnrollMemoryForThisThread(20000)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 5000) // not granted
+    memoryStore.reserveUnrollMemoryForThisTask(4400)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 5000)
+    memoryStore.reserveUnrollMemoryForThisTask(20000)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 5000) // not granted
     // Release again
-    memoryStore.releaseUnrollMemoryForThisThread(1000)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 4000)
-    memoryStore.releaseUnrollMemoryForThisThread() // release all
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    memoryStore.releaseUnrollMemoryForThisTask(1000)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 4000)
+    memoryStore.releaseUnrollMemoryForThisTask() // release all
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
   }
 
   /**
@@ -1060,24 +1060,24 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     val bigList = List.fill(40)(new Array[Byte](1000))
     val memoryStore = store.memoryStore
     val droppedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     // Unroll with all the space in the world. This should succeed and return an array.
     var unrollResult = memoryStore.unrollSafely("unroll", smallList.iterator, droppedBlocks)
     verifyUnroll(smallList.iterator, unrollResult, shouldBeArray = true)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
-    memoryStore.releasePendingUnrollMemoryForThisThread()
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
+    memoryStore.releasePendingUnrollMemoryForThisTask()
 
     // Unroll with not enough space. This should succeed after kicking out someBlock1.
     store.putIterator("someBlock1", smallList.iterator, StorageLevel.MEMORY_ONLY)
     store.putIterator("someBlock2", smallList.iterator, StorageLevel.MEMORY_ONLY)
     unrollResult = memoryStore.unrollSafely("unroll", smallList.iterator, droppedBlocks)
     verifyUnroll(smallList.iterator, unrollResult, shouldBeArray = true)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
     assert(droppedBlocks.size === 1)
     assert(droppedBlocks.head._1 === TestBlockId("someBlock1"))
     droppedBlocks.clear()
-    memoryStore.releasePendingUnrollMemoryForThisThread()
+    memoryStore.releasePendingUnrollMemoryForThisTask()
 
     // Unroll huge block with not enough space. Even after ensuring free space of 12000 * 0.4 =
     // 4800 bytes, there is still not enough room to unroll this block. This returns an iterator.
@@ -1085,7 +1085,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     store.putIterator("someBlock3", smallList.iterator, StorageLevel.MEMORY_ONLY)
     unrollResult = memoryStore.unrollSafely("unroll", bigList.iterator, droppedBlocks)
     verifyUnroll(bigList.iterator, unrollResult, shouldBeArray = false)
-    assert(memoryStore.currentUnrollMemoryForThisThread > 0) // we returned an iterator
+    assert(memoryStore.currentUnrollMemoryForThisTask > 0) // we returned an iterator
     assert(droppedBlocks.size === 1)
     assert(droppedBlocks.head._1 === TestBlockId("someBlock2"))
     droppedBlocks.clear()
@@ -1099,7 +1099,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     val bigList = List.fill(40)(new Array[Byte](1000))
     def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]]
     def bigIterator: Iterator[Any] = bigList.iterator.asInstanceOf[Iterator[Any]]
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     // Unroll with plenty of space. This should succeed and cache both blocks.
     val result1 = memoryStore.putIterator("b1", smallIterator, memOnly, returnValues = true)
@@ -1110,7 +1110,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     assert(result2.size > 0)
     assert(result1.data.isLeft) // unroll did not drop this block to disk
     assert(result2.data.isLeft)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     // Re-put these two blocks so block manager knows about them too. Otherwise, block manager
     // would not know how to drop them from memory later.
@@ -1126,7 +1126,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     assert(!memoryStore.contains("b1"))
     assert(memoryStore.contains("b2"))
     assert(memoryStore.contains("b3"))
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
     memoryStore.remove("b3")
     store.putIterator("b3", smallIterator, memOnly)
 
@@ -1138,7 +1138,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     assert(!memoryStore.contains("b2"))
     assert(memoryStore.contains("b3"))
     assert(!memoryStore.contains("b4"))
-    assert(memoryStore.currentUnrollMemoryForThisThread > 0) // we returned an iterator
+    assert(memoryStore.currentUnrollMemoryForThisTask > 0) // we returned an iterator
   }
 
   /**
@@ -1153,7 +1153,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     val bigList = List.fill(40)(new Array[Byte](1000))
     def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]]
     def bigIterator: Iterator[Any] = bigList.iterator.asInstanceOf[Iterator[Any]]
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     store.putIterator("b1", smallIterator, memAndDisk)
     store.putIterator("b2", smallIterator, memAndDisk)
@@ -1170,7 +1170,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     assert(!diskStore.contains("b3"))
     memoryStore.remove("b3")
     store.putIterator("b3", smallIterator, StorageLevel.MEMORY_ONLY)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     // Unroll huge block with not enough space. This should fail and drop the new block to disk
     // directly in addition to kicking out b2 in the process. Memory store should contain only
@@ -1186,7 +1186,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     assert(diskStore.contains("b2"))
     assert(!diskStore.contains("b3"))
     assert(diskStore.contains("b4"))
-    assert(memoryStore.currentUnrollMemoryForThisThread > 0) // we returned an iterator
+    assert(memoryStore.currentUnrollMemoryForThisTask > 0) // we returned an iterator
   }
 
   test("multiple unrolls by the same thread") {
@@ -1195,32 +1195,32 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     val memoryStore = store.memoryStore
     val smallList = List.fill(40)(new Array[Byte](100))
     def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]]
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     // All unroll memory used is released because unrollSafely returned an array
     memoryStore.putIterator("b1", smallIterator, memOnly, returnValues = true)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
     memoryStore.putIterator("b2", smallIterator, memOnly, returnValues = true)
-    assert(memoryStore.currentUnrollMemoryForThisThread === 0)
+    assert(memoryStore.currentUnrollMemoryForThisTask === 0)
 
     // Unroll memory is not released because unrollSafely returned an iterator
     // that still depends on the underlying vector used in the process
     memoryStore.putIterator("b3", smallIterator, memOnly, returnValues = true)
-    val unrollMemoryAfterB3 = memoryStore.currentUnrollMemoryForThisThread
+    val unrollMemoryAfterB3 = memoryStore.currentUnrollMemoryForThisTask
     assert(unrollMemoryAfterB3 > 0)
 
     // The unroll memory owned by this thread builds on top of its value after the previous unrolls
     memoryStore.putIterator("b4", smallIterator, memOnly, returnValues = true)
-    val unrollMemoryAfterB4 = memoryStore.currentUnrollMemoryForThisThread
+    val unrollMemoryAfterB4 = memoryStore.currentUnrollMemoryForThisTask
     assert(unrollMemoryAfterB4 > unrollMemoryAfterB3)
 
     // ... but only to a certain extent (until we run out of free space to grant new unroll memory)
     memoryStore.putIterator("b5", smallIterator, memOnly, returnValues = true)
-    val unrollMemoryAfterB5 = memoryStore.currentUnrollMemoryForThisThread
+    val unrollMemoryAfterB5 = memoryStore.currentUnrollMemoryForThisTask
     memoryStore.putIterator("b6", smallIterator, memOnly, returnValues = true)
-    val unrollMemoryAfterB6 = memoryStore.currentUnrollMemoryForThisThread
+    val unrollMemoryAfterB6 = memoryStore.currentUnrollMemoryForThisTask
     memoryStore.putIterator("b7", smallIterator, memOnly, returnValues = true)
-    val unrollMemoryAfterB7 = memoryStore.currentUnrollMemoryForThisThread
+    val unrollMemoryAfterB7 = memoryStore.currentUnrollMemoryForThisTask
     assert(unrollMemoryAfterB5 === unrollMemoryAfterB4)
     assert(unrollMemoryAfterB6 === unrollMemoryAfterB4)
     assert(unrollMemoryAfterB7 === unrollMemoryAfterB4)

From 6309b93467b06f27cd76d4662b51b47de100c677 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 28 Jul 2015 22:38:28 -0700
Subject: [PATCH 0665/1454] [SPARK-9398] [SQL] Datetime cleanup

JIRA: https://issues.apache.org/jira/browse/SPARK-9398

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7725 from yjshen/date_null_check and squashes the following commits:

b4eade1 [Yijie Shen] inline daysToMonthEnd
d09acc1 [Yijie Shen] implement getLastDayOfMonth to avoid repeated evaluation
d857ec3 [Yijie Shen] add null check in DateExpressionSuite
---
 .../expressions/datetimeFunctions.scala       | 45 ++++++-------------
 .../sql/catalyst/util/DateTimeUtils.scala     | 43 +++++++++++++-----
 .../expressions/DateExpressionsSuite.scala    |  2 +
 3 files changed, 47 insertions(+), 43 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index c37afc13f2d17..efecb771f2f5d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -74,9 +74,7 @@ case class Hour(child: Expression) extends UnaryExpression with ImplicitCastInpu
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
-      s"""$dtu.getHours($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getHours($c)")
   }
 }
 
@@ -92,9 +90,7 @@ case class Minute(child: Expression) extends UnaryExpression with ImplicitCastIn
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
-      s"""$dtu.getMinutes($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getMinutes($c)")
   }
 }
 
@@ -110,9 +106,7 @@ case class Second(child: Expression) extends UnaryExpression with ImplicitCastIn
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
-      s"""$dtu.getSeconds($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getSeconds($c)")
   }
 }
 
@@ -128,9 +122,7 @@ case class DayOfYear(child: Expression) extends UnaryExpression with ImplicitCas
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
-      s"""$dtu.getDayInYear($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getDayInYear($c)")
   }
 }
 
@@ -147,9 +139,7 @@ case class Year(child: Expression) extends UnaryExpression with ImplicitCastInpu
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, c =>
-      s"""$dtu.getYear($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getYear($c)")
   }
 }
 
@@ -165,9 +155,7 @@ case class Quarter(child: Expression) extends UnaryExpression with ImplicitCastI
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
-      s"""$dtu.getQuarter($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getQuarter($c)")
   }
 }
 
@@ -183,9 +171,7 @@ case class Month(child: Expression) extends UnaryExpression with ImplicitCastInp
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
-      s"""$dtu.getMonth($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getMonth($c)")
   }
 }
 
@@ -201,9 +187,7 @@ case class DayOfMonth(child: Expression) extends UnaryExpression with ImplicitCa
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (c) =>
-      s"""$dtu.getDayOfMonth($c)"""
-    )
+    defineCodeGen(ctx, ev, c => s"$dtu.getDayOfMonth($c)")
   }
 }
 
@@ -226,7 +210,7 @@ case class WeekOfYear(child: Expression) extends UnaryExpression with ImplicitCa
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, (time) => {
+    nullSafeCodeGen(ctx, ev, time => {
       val cal = classOf[Calendar].getName
       val c = ctx.freshName("cal")
       ctx.addMutableState(cal, c,
@@ -250,8 +234,6 @@ case class DateFormatClass(left: Expression, right: Expression) extends BinaryEx
 
   override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType, StringType)
 
-  override def prettyName: String = "date_format"
-
   override protected def nullSafeEval(timestamp: Any, format: Any): Any = {
     val sdf = new SimpleDateFormat(format.toString)
     UTF8String.fromString(sdf.format(new Date(timestamp.asInstanceOf[Long] / 1000)))
@@ -264,6 +246,8 @@ case class DateFormatClass(left: Expression, right: Expression) extends BinaryEx
           .format(new java.sql.Date($timestamp / 1000)))"""
     })
   }
+
+  override def prettyName: String = "date_format"
 }
 
 /**
@@ -277,15 +261,12 @@ case class LastDay(startDate: Expression) extends UnaryExpression with ImplicitC
   override def dataType: DataType = DateType
 
   override def nullSafeEval(date: Any): Any = {
-    val days = date.asInstanceOf[Int]
-    DateTimeUtils.getLastDayOfMonth(days)
+    DateTimeUtils.getLastDayOfMonth(date.asInstanceOf[Int])
   }
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
-    defineCodeGen(ctx, ev, (sd) => {
-      s"$dtu.getLastDayOfMonth($sd)"
-    })
+    defineCodeGen(ctx, ev, sd => s"$dtu.getLastDayOfMonth($sd)")
   }
 
   override def prettyName: String = "last_day"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 8b0b80c26db17..93966a503c27c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -600,23 +600,44 @@ object DateTimeUtils {
     startDate + 1 + ((dayOfWeek - 1 - startDate) % 7 + 7) % 7
   }
 
-  /**
-   * number of days in a non-leap year.
-   */
-  private[this] val daysInNormalYear = Array(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)
-
   /**
    * Returns last day of the month for the given date. The date is expressed in days
    * since 1.1.1970.
    */
   def getLastDayOfMonth(date: Int): Int = {
-    val dayOfMonth = getDayOfMonth(date)
-    val month = getMonth(date)
-    if (month == 2 && isLeapYear(getYear(date))) {
-      date + daysInNormalYear(month - 1) + 1 - dayOfMonth
+    var (year, dayInYear) = getYearAndDayInYear(date)
+    if (isLeapYear(year)) {
+      if (dayInYear > 31 && dayInYear <= 60) {
+        return date + (60 - dayInYear)
+      } else if (dayInYear > 60) {
+        dayInYear = dayInYear - 1
+      }
+    }
+    val lastDayOfMonthInYear = if (dayInYear <= 31) {
+      31
+    } else if (dayInYear <= 59) {
+      59
+    } else if (dayInYear <= 90) {
+      90
+    } else if (dayInYear <= 120) {
+      120
+    } else if (dayInYear <= 151) {
+      151
+    } else if (dayInYear <= 181) {
+      181
+    } else if (dayInYear <= 212) {
+      212
+    } else if (dayInYear <= 243) {
+      243
+    } else if (dayInYear <= 273) {
+      273
+    } else if (dayInYear <= 304) {
+      304
+    } else if (dayInYear <= 334) {
+      334
     } else {
-      date + daysInNormalYear(month - 1) - dayOfMonth
+      365
     }
+    date + (lastDayOfMonthInYear - dayInYear)
   }
-
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index 30c5769424bd7..aca8d6eb3500c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -106,6 +106,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkEvaluation(DayOfYear(Literal.create(null, DateType)), null)
   }
 
   test("Year") {
@@ -274,6 +275,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(LastDay(Literal(Date.valueOf("2015-12-05"))), Date.valueOf("2015-12-31"))
     checkEvaluation(LastDay(Literal(Date.valueOf("2016-01-06"))), Date.valueOf("2016-01-31"))
     checkEvaluation(LastDay(Literal(Date.valueOf("2016-02-07"))), Date.valueOf("2016-02-29"))
+    checkEvaluation(LastDay(Literal.create(null, DateType)), null)
   }
 
   test("next_day") {

From 15667a0afa5fb17f4cc6fbf32b2ddb573630f20a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 28 Jul 2015 22:51:08 -0700
Subject: [PATCH 0666/1454] [SPARK-9281] [SQL] use decimal or double when
 parsing SQL

Right now, we use double to parse all the float number in SQL. When it's used in expression together with DecimalType, it will turn the decimal into double as well. Also it will loss some precision when using double.

This PR change to parse float number to decimal or double, based on it's  using scientific notation or not, see https://msdn.microsoft.com/en-us/library/ms179899.aspx

This is a break change, should we doc it somewhere?

Author: Davies Liu <davies@databricks.com>

Closes #7642 from davies/parse_decimal and squashes the following commits:

1f576d9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into parse_decimal
5e142b6 [Davies Liu] fix scala style
eca99de [Davies Liu] fix tests
2afe702 [Davies Liu] Merge branch 'master' of github.com:apache/spark into parse_decimal
f4a320b [Davies Liu] Update SqlParser.scala
1c48e34 [Davies Liu] use decimal or double when parsing SQL
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 14 +++++-
 .../catalyst/analysis/HiveTypeCoercion.scala  | 50 ++++++++++++-------
 .../sql/catalyst/analysis/AnalysisSuite.scala |  4 +-
 .../spark/sql/MathExpressionsSuite.scala      |  3 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 14 +++---
 .../org/apache/spark/sql/json/JsonSuite.scala | 14 +++---
 6 files changed, 62 insertions(+), 37 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index b423f0fa04f69..e5f115f74bf3b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -332,8 +332,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
   protected lazy val numericLiteral: Parser[Literal] =
     ( integral  ^^ { case i => Literal(toNarrowestIntegerType(i)) }
     | sign.? ~ unsignedFloat ^^ {
-      // TODO(davies): some precisions may loss, we should create decimal literal
-      case s ~ f => Literal(BigDecimal(s.getOrElse("") + f).doubleValue())
+      case s ~ f => Literal(toDecimalOrDouble(s.getOrElse("") + f))
     }
     )
 
@@ -420,6 +419,17 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     }
   }
 
+  private def toDecimalOrDouble(value: String): Any = {
+    val decimal = BigDecimal(value)
+    // follow the behavior in MS SQL Server
+    // https://msdn.microsoft.com/en-us/library/ms179899.aspx
+    if (value.contains('E') || value.contains('e')) {
+      decimal.doubleValue()
+    } else {
+      decimal.underlying()
+    }
+  }
+
   protected lazy val baseExpression: Parser[Expression] =
     ( "*" ^^^ UnresolvedStar(None)
     | ident <~ "." ~ "*" ^^ { case tableName => UnresolvedStar(Option(tableName)) }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index e0527503442f0..ecc48986e35d8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -109,13 +109,35 @@ object HiveTypeCoercion {
    * Find the tightest common type of a set of types by continuously applying
    * `findTightestCommonTypeOfTwo` on these types.
    */
-  private def findTightestCommonType(types: Seq[DataType]) = {
+  private def findTightestCommonType(types: Seq[DataType]): Option[DataType] = {
     types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match {
       case None => None
       case Some(d) => findTightestCommonTypeOfTwo(d, c)
     })
   }
 
+  private def findWiderTypeForTwo(t1: DataType, t2: DataType): Option[DataType] = (t1, t2) match {
+    case (t1: DecimalType, t2: DecimalType) =>
+      Some(DecimalPrecision.widerDecimalType(t1, t2))
+    case (t: IntegralType, d: DecimalType) =>
+      Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
+    case (d: DecimalType, t: IntegralType) =>
+      Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
+    case (t: FractionalType, d: DecimalType) =>
+      Some(DoubleType)
+    case (d: DecimalType, t: FractionalType) =>
+      Some(DoubleType)
+    case _ =>
+      findTightestCommonTypeToString(t1, t2)
+  }
+
+  private def findWiderCommonType(types: Seq[DataType]) = {
+    types.foldLeft[Option[DataType]](Some(NullType))((r, c) => r match {
+      case Some(d) => findWiderTypeForTwo(d, c)
+      case None => None
+    })
+  }
+
   /**
    * Applies any changes to [[AttributeReference]] data types that are made by other rules to
    * instances higher in the query tree.
@@ -182,20 +204,7 @@ object HiveTypeCoercion {
 
       val castedTypes = left.output.zip(right.output).map {
         case (lhs, rhs) if lhs.dataType != rhs.dataType =>
-          (lhs.dataType, rhs.dataType) match {
-            case (t1: DecimalType, t2: DecimalType) =>
-              Some(DecimalPrecision.widerDecimalType(t1, t2))
-            case (t: IntegralType, d: DecimalType) =>
-              Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
-            case (d: DecimalType, t: IntegralType) =>
-              Some(DecimalPrecision.widerDecimalType(DecimalType.forType(t), d))
-            case (t: FractionalType, d: DecimalType) =>
-              Some(DoubleType)
-            case (d: DecimalType, t: FractionalType) =>
-              Some(DoubleType)
-            case _ =>
-              findTightestCommonTypeToString(lhs.dataType, rhs.dataType)
-          }
+          findWiderTypeForTwo(lhs.dataType, rhs.dataType)
         case other => None
       }
 
@@ -236,8 +245,13 @@ object HiveTypeCoercion {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
-      case a @ BinaryArithmetic(left @ StringType(), r) =>
-        a.makeCopy(Array(Cast(left, DoubleType), r))
+      case a @ BinaryArithmetic(left @ StringType(), right @ DecimalType.Expression(_, _)) =>
+        a.makeCopy(Array(Cast(left, DecimalType.SYSTEM_DEFAULT), right))
+      case a @ BinaryArithmetic(left @ DecimalType.Expression(_, _), right @ StringType()) =>
+        a.makeCopy(Array(left, Cast(right, DecimalType.SYSTEM_DEFAULT)))
+
+      case a @ BinaryArithmetic(left @ StringType(), right) =>
+        a.makeCopy(Array(Cast(left, DoubleType), right))
       case a @ BinaryArithmetic(left, right @ StringType()) =>
         a.makeCopy(Array(left, Cast(right, DoubleType)))
 
@@ -543,7 +557,7 @@ object HiveTypeCoercion {
       // compatible with every child column.
       case c @ Coalesce(es) if es.map(_.dataType).distinct.size > 1 =>
         val types = es.map(_.dataType)
-        findTightestCommonTypeAndPromoteToString(types) match {
+        findWiderCommonType(types) match {
           case Some(finalDataType) => Coalesce(es.map(Cast(_, finalDataType)))
           case None => c
         }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 4589facb49b76..221b4e92f086c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -145,11 +145,11 @@ class AnalysisSuite extends AnalysisTest {
         'e / 'e as 'div5))
     val pl = plan.asInstanceOf[Project].projectList
 
-    // StringType will be promoted into Double
     assert(pl(0).dataType == DoubleType)
     assert(pl(1).dataType == DoubleType)
     assert(pl(2).dataType == DoubleType)
-    assert(pl(3).dataType == DoubleType)
+    // StringType will be promoted into Decimal(38, 18)
+    assert(pl(3).dataType == DecimalType(38, 29))
     assert(pl(4).dataType == DoubleType)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 21256704a5b16..8cf2ef5957d8d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -216,7 +216,8 @@ class MathExpressionsSuite extends QueryTest {
     checkAnswer(
       ctx.sql(s"SELECT round($pi, -3), round($pi, -2), round($pi, -1), " +
         s"round($pi, 0), round($pi, 1), round($pi, 2), round($pi, 3)"),
-      Seq(Row(0.0, 0.0, 0.0, 3.0, 3.1, 3.14, 3.142))
+      Seq(Row(BigDecimal("0E3"), BigDecimal("0E2"), BigDecimal("0E1"), BigDecimal(3),
+        BigDecimal("3.1"), BigDecimal("3.14"), BigDecimal("3.142")))
     )
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 42724ed766af5..d13dde1cdc8b2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -368,7 +368,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(1))
     checkAnswer(
       sql("SELECT COALESCE(null, 1, 1.5)"),
-      Row(1.toDouble))
+      Row(BigDecimal(1)))
     checkAnswer(
       sql("SELECT COALESCE(null, null, null)"),
       Row(null))
@@ -1234,19 +1234,19 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
   test("Floating point number format") {
     checkAnswer(
-      sql("SELECT 0.3"), Row(0.3)
+      sql("SELECT 0.3"), Row(BigDecimal(0.3).underlying())
     )
 
     checkAnswer(
-      sql("SELECT -0.8"), Row(-0.8)
+      sql("SELECT -0.8"), Row(BigDecimal(-0.8).underlying())
     )
 
     checkAnswer(
-      sql("SELECT .5"), Row(0.5)
+      sql("SELECT .5"), Row(BigDecimal(0.5))
     )
 
     checkAnswer(
-      sql("SELECT -.18"), Row(-0.18)
+      sql("SELECT -.18"), Row(BigDecimal(-0.18))
     )
   }
 
@@ -1279,11 +1279,11 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     )
 
     checkAnswer(
-      sql("SELECT -5.2"), Row(-5.2)
+      sql("SELECT -5.2"), Row(BigDecimal(-5.2))
     )
 
     checkAnswer(
-      sql("SELECT +6.8"), Row(6.8)
+      sql("SELECT +6.8"), Row(BigDecimal(6.8))
     )
 
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 3ac312d6f4c50..f19f22fca7d54 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -422,14 +422,14 @@ class JsonSuite extends QueryTest with TestJsonData {
       Row(-89) :: Row(21474836370L) :: Row(21474836470L) :: Nil
     )
 
-    // Widening to DoubleType
+    // Widening to DecimalType
     checkAnswer(
       sql("select num_num_2 + 1.3 from jsonTable where num_num_2 > 1.1"),
-      Row(21474836472.2) ::
-        Row(92233720368547758071.3) :: Nil
+      Row(BigDecimal("21474836472.2")) ::
+        Row(BigDecimal("92233720368547758071.3")) :: Nil
     )
 
-    // Widening to DoubleType
+    // Widening to Double
     checkAnswer(
       sql("select num_num_3 + 1.2 from jsonTable where num_num_3 > 1.1"),
       Row(101.2) :: Row(21474836471.2) :: Nil
@@ -438,13 +438,13 @@ class JsonSuite extends QueryTest with TestJsonData {
     // Number and String conflict: resolve the type as number in this query.
     checkAnswer(
       sql("select num_str + 1.2 from jsonTable where num_str > 14"),
-      Row(92233720368547758071.2)
+      Row(BigDecimal("92233720368547758071.2"))
     )
 
     // Number and String conflict: resolve the type as number in this query.
     checkAnswer(
       sql("select num_str + 1.2 from jsonTable where num_str >= 92233720368547758060"),
-      Row(new java.math.BigDecimal("92233720368547758071.2").doubleValue)
+      Row(new java.math.BigDecimal("92233720368547758071.2"))
     )
 
     // String and Boolean conflict: resolve the type as string.
@@ -503,7 +503,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     // Number and String conflict: resolve the type as number in this query.
     checkAnswer(
       sql("select num_str + 1.2 from jsonTable where num_str > 13"),
-      Row(14.3) :: Row(92233720368547758071.2) :: Nil
+      Row(BigDecimal("14.3")) :: Row(BigDecimal("92233720368547758071.2")) :: Nil
     )
   }
 

From 708794e8aae2c66bd291bab4f12117c33b57840c Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 29 Jul 2015 00:08:45 -0700
Subject: [PATCH 0667/1454] [SPARK-9251][SQL] do not order by expressions which
 still need evaluation

as an offline discussion with rxin , it's weird to be computing stuff while doing sorting, we should only order by bound reference during execution.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7593 from cloud-fan/sort and squashes the following commits:

7b1bef7 [Wenchen Fan] add test
daf206d [Wenchen Fan] add more comments
289bee0 [Wenchen Fan] do not order by expressions which still need evaluation
---
 .../sql/catalyst/analysis/Analyzer.scala      | 58 +++++++++++++++++++
 .../sql/catalyst/expressions/random.scala     |  4 +-
 .../plans/logical/basicOperators.scala        | 13 +++--
 .../sql/catalyst/analysis/AnalysisSuite.scala | 36 ++++++++++--
 .../scala/org/apache/spark/sql/TestData.scala |  2 -
 5 files changed, 101 insertions(+), 12 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index a6ea0cc0a83a8..265f3d1e41765 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -79,6 +79,7 @@ class Analyzer(
       ExtractWindowExpressions ::
       GlobalAggregates ::
       UnresolvedHavingClauseAttributes ::
+      RemoveEvaluationFromSort ::
       HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
@@ -947,6 +948,63 @@ class Analyzer(
         Project(p.output, newPlan.withNewChildren(newChild :: Nil))
     }
   }
+
+  /**
+   * Removes all still-need-evaluate ordering expressions from sort and use an inner project to
+   * materialize them, finally use a outer project to project them away to keep the result same.
+   * Then we can make sure we only sort by [[AttributeReference]]s.
+   *
+   * As an example,
+   * {{{
+   *   Sort('a, 'b + 1,
+   *     Relation('a, 'b))
+   * }}}
+   * will be turned into:
+   * {{{
+   *   Project('a, 'b,
+   *     Sort('a, '_sortCondition,
+   *       Project('a, 'b, ('b + 1).as("_sortCondition"),
+   *         Relation('a, 'b))))
+   * }}}
+   */
+  object RemoveEvaluationFromSort extends Rule[LogicalPlan] {
+    private def hasAlias(expr: Expression) = {
+      expr.find {
+        case a: Alias => true
+        case _ => false
+      }.isDefined
+    }
+
+    override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      // The ordering expressions have no effect to the output schema of `Sort`,
+      // so `Alias`s in ordering expressions are unnecessary and we should remove them.
+      case s @ Sort(ordering, _, _) if ordering.exists(hasAlias) =>
+        val newOrdering = ordering.map(_.transformUp {
+          case Alias(child, _) => child
+        }.asInstanceOf[SortOrder])
+        s.copy(order = newOrdering)
+
+      case s @ Sort(ordering, global, child)
+        if s.expressions.forall(_.resolved) && s.childrenResolved && !s.hasNoEvaluation =>
+
+        val (ref, needEval) = ordering.partition(_.child.isInstanceOf[AttributeReference])
+
+        val namedExpr = needEval.map(_.child match {
+          case n: NamedExpression => n
+          case e => Alias(e, "_sortCondition")()
+        })
+
+        val newOrdering = ref ++ needEval.zip(namedExpr).map { case (order, ne) =>
+          order.copy(child = ne.toAttribute)
+        }
+
+        // Add still-need-evaluate ordering expressions into inner project and then project
+        // them away after the sort.
+        Project(child.output,
+          Sort(newOrdering, global,
+            Project(child.output ++ namedExpr, child)))
+    }
+  }
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
index 8f30519697a37..62d3d204ca872 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
@@ -66,7 +66,7 @@ case class Rand(seed: Long) extends RDG {
     val rngTerm = ctx.freshName("rng")
     val className = classOf[XORShiftRandom].getName
     ctx.addMutableState(className, rngTerm,
-      s"$rngTerm = new $className($seed + org.apache.spark.TaskContext.getPartitionId());")
+      s"$rngTerm = new $className(${seed}L + org.apache.spark.TaskContext.getPartitionId());")
     ev.isNull = "false"
     s"""
       final ${ctx.javaType(dataType)} ${ev.primitive} = $rngTerm.nextDouble();
@@ -89,7 +89,7 @@ case class Randn(seed: Long) extends RDG {
     val rngTerm = ctx.freshName("rng")
     val className = classOf[XORShiftRandom].getName
     ctx.addMutableState(className, rngTerm,
-      s"$rngTerm = new $className($seed + org.apache.spark.TaskContext.getPartitionId());")
+      s"$rngTerm = new $className(${seed}L + org.apache.spark.TaskContext.getPartitionId());")
     ev.isNull = "false"
     s"""
       final ${ctx.javaType(dataType)} ${ev.primitive} = $rngTerm.nextGaussian();
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index af68358daf5f1..ad5af19578f33 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -33,7 +33,7 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend
       }.nonEmpty
     )
 
-    !expressions.exists(!_.resolved) && childrenResolved && !hasSpecialExpressions
+    expressions.forall(_.resolved) && childrenResolved && !hasSpecialExpressions
   }
 }
 
@@ -67,7 +67,7 @@ case class Generate(
     generator.resolved &&
       childrenResolved &&
       generator.elementTypes.length == generatorOutput.length &&
-      !generatorOutput.exists(!_.resolved)
+      generatorOutput.forall(_.resolved)
   }
 
   // we don't want the gOutput to be taken as part of the expressions
@@ -187,7 +187,7 @@ case class WithWindowDefinition(
 }
 
 /**
- * @param order  The ordering expressions
+ * @param order  The ordering expressions, should all be [[AttributeReference]]
  * @param global True means global sorting apply for entire data set,
  *               False means sorting only apply within the partition.
  * @param child  Child logical plan
@@ -197,6 +197,11 @@ case class Sort(
     global: Boolean,
     child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
+
+  def hasNoEvaluation: Boolean = order.forall(_.child.isInstanceOf[AttributeReference])
+
+  override lazy val resolved: Boolean =
+    expressions.forall(_.resolved) && childrenResolved && hasNoEvaluation
 }
 
 case class Aggregate(
@@ -211,7 +216,7 @@ case class Aggregate(
       }.nonEmpty
     )
 
-    !expressions.exists(!_.resolved) && childrenResolved && !hasWindowExpressions
+    expressions.forall(_.resolved) && childrenResolved && !hasWindowExpressions
   }
 
   override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 221b4e92f086c..a86cefe941e8e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -165,11 +165,39 @@ class AnalysisSuite extends AnalysisTest {
 
   test("pull out nondeterministic expressions from Sort") {
     val plan = Sort(Seq(SortOrder(Rand(33), Ascending)), false, testRelation)
-    val projected = Alias(Rand(33), "_nondeterministic")()
+    val analyzed = caseSensitiveAnalyzer.execute(plan)
+    analyzed.transform {
+      case s: Sort if s.expressions.exists(!_.deterministic) =>
+        fail("nondeterministic expressions are not allowed in Sort")
+    }
+  }
+
+  test("remove still-need-evaluate ordering expressions from sort") {
+    val a = testRelation2.output(0)
+    val b = testRelation2.output(1)
+
+    def makeOrder(e: Expression): SortOrder = SortOrder(e, Ascending)
+
+    val noEvalOrdering = makeOrder(a)
+    val noEvalOrderingWithAlias = makeOrder(Alias(Alias(b, "name1")(), "name2")())
+
+    val needEvalExpr = Coalesce(Seq(a, Literal("1")))
+    val needEvalExpr2 = Coalesce(Seq(a, b))
+    val needEvalOrdering = makeOrder(needEvalExpr)
+    val needEvalOrdering2 = makeOrder(needEvalExpr2)
+
+    val plan = Sort(
+      Seq(noEvalOrdering, noEvalOrderingWithAlias, needEvalOrdering, needEvalOrdering2),
+      false, testRelation2)
+
+    val evaluatedOrdering = makeOrder(AttributeReference("_sortCondition", StringType)())
+    val materializedExprs = Seq(needEvalExpr, needEvalExpr2).map(e => Alias(e, "_sortCondition")())
+
     val expected =
-      Project(testRelation.output,
-        Sort(Seq(SortOrder(projected.toAttribute, Ascending)), false,
-          Project(testRelation.output :+ projected, testRelation)))
+      Project(testRelation2.output,
+        Sort(Seq(makeOrder(a), makeOrder(b), evaluatedOrdering, evaluatedOrdering), false,
+          Project(testRelation2.output ++ materializedExprs, testRelation2)))
+
     checkAnalysis(plan, expected)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index 207d7a352c7b3..e340f54850bcc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql
 
-import java.sql.Timestamp
-
 import org.apache.spark.sql.test.TestSQLContext.implicits._
 import org.apache.spark.sql.test._
 

From 97906944e133dec13068f16520b6abbcdc79e84f Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 29 Jul 2015 09:36:22 -0700
Subject: [PATCH 0668/1454] [SPARK-9127][SQL] Rand/Randn codegen fails with
 long seed.

Author: Reynold Xin <rxin@databricks.com>

Closes #7747 from rxin/SPARK-9127 and squashes the following commits:

e851418 [Reynold Xin] [SPARK-9127][SQL] Rand/Randn codegen fails with long seed.
---
 .../spark/sql/catalyst/expressions/RandomSuite.scala       | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
index 698c81ba24482..5db992654811a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
@@ -20,8 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import org.scalatest.Matchers._
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.DoubleType
 
 
 class RandomSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -30,4 +28,9 @@ class RandomSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkDoubleEvaluation(Rand(30), 0.7363714192755834 +- 0.001)
     checkDoubleEvaluation(Randn(30), 0.5181478766595276 +- 0.001)
   }
+
+  test("SPARK-9127 codegen with long seed") {
+    checkDoubleEvaluation(Rand(5419823303878592871L), 0.4061913198963727 +- 0.001)
+    checkDoubleEvaluation(Randn(5419823303878592871L), -0.24417152005343168 +- 0.001)
+  }
 }

From 069a4c414db4612d7bdb6f5615c1ba36998e5a49 Mon Sep 17 00:00:00 2001
From: Joseph Batchik <joseph.batchik@cloudera.com>
Date: Wed, 29 Jul 2015 14:02:32 -0500
Subject: [PATCH 0669/1454] [SPARK-746] [CORE] Added Avro Serialization to Kryo

Added a custom Kryo serializer for generic Avro records to reduce the network IO
involved during a shuffle. This compresses the schema and allows for users to
register their schemas ahead of time to further reduce traffic.

Currently Kryo tries to use its default serializer for generic Records, which will include
a lot of unneeded data in each record.

Author: Joseph Batchik <joseph.batchik@cloudera.com>
Author: Joseph Batchik <josephbatchik@gmail.com>

Closes #7004 from JDrit/Avro_serialization and squashes the following commits:

8158d51 [Joseph Batchik] updated per feedback
c0cf329 [Joseph Batchik] implemented @squito suggestion for SparkEnv
dd71efe [Joseph Batchik] fixed bug with serializing
1183a48 [Joseph Batchik] updated codec settings
fa9298b [Joseph Batchik] forgot a couple of fixes
c5fe794 [Joseph Batchik] implemented @squito suggestion
0f5471a [Joseph Batchik] implemented @squito suggestion to use a codec that is already in spark
6d1925c [Joseph Batchik] fixed to changes suggested by @squito
d421bf5 [Joseph Batchik] updated pom to removed versions
ab46d10 [Joseph Batchik] Changed Avro dependency to be similar to parent
f4ae251 [Joseph Batchik] fixed serialization error in that SparkConf cannot be serialized
2b545cc [Joseph Batchik] started working on fixes for pr
97fba62 [Joseph Batchik] Added a custom Kryo serializer for generic Avro records to reduce the network IO involved during a shuffle. This compresses the schema and allows for users to register their schemas ahead of time to further reduce traffic.
---
 core/pom.xml                                  |   5 +
 .../scala/org/apache/spark/SparkConf.scala    |  23 ++-
 .../serializer/GenericAvroSerializer.scala    | 150 ++++++++++++++++++
 .../spark/serializer/KryoSerializer.scala     |   6 +
 .../GenericAvroSerializerSuite.scala          |  84 ++++++++++
 5 files changed, 267 insertions(+), 1 deletion(-)
 create mode 100644 core/src/main/scala/org/apache/spark/serializer/GenericAvroSerializer.scala
 create mode 100644 core/src/test/scala/org/apache/spark/serializer/GenericAvroSerializerSuite.scala

diff --git a/core/pom.xml b/core/pom.xml
index 95f36eb348698..6fa87ec6a24af 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -34,6 +34,11 @@
   <name>Spark Project Core</name>
   <url>http://spark.apache.org/</url>
   <dependencies>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+    </dependency>
     <dependency>
       <groupId>com.google.guava</groupId>
       <artifactId>guava</artifactId>
diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 6cf36fbbd6254..4161792976c7b 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -18,11 +18,12 @@
 package org.apache.spark
 
 import java.util.concurrent.ConcurrentHashMap
-import java.util.concurrent.atomic.AtomicBoolean
 
 import scala.collection.JavaConverters._
 import scala.collection.mutable.LinkedHashSet
 
+import org.apache.avro.{SchemaNormalization, Schema}
+
 import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.util.Utils
 
@@ -161,6 +162,26 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     this
   }
 
+  private final val avroNamespace = "avro.schema."
+
+  /**
+   * Use Kryo serialization and register the given set of Avro schemas so that the generic
+   * record serializer can decrease network IO
+   */
+  def registerAvroSchemas(schemas: Schema*): SparkConf = {
+    for (schema <- schemas) {
+      set(avroNamespace + SchemaNormalization.parsingFingerprint64(schema), schema.toString)
+    }
+    this
+  }
+
+  /** Gets all the avro schemas in the configuration used in the generic Avro record serializer */
+  def getAvroSchema: Map[Long, String] = {
+    getAll.filter { case (k, v) => k.startsWith(avroNamespace) }
+      .map { case (k, v) => (k.substring(avroNamespace.length).toLong, v) }
+      .toMap
+  }
+
   /** Remove a parameter from the configuration */
   def remove(key: String): SparkConf = {
     settings.remove(key)
diff --git a/core/src/main/scala/org/apache/spark/serializer/GenericAvroSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/GenericAvroSerializer.scala
new file mode 100644
index 0000000000000..62f8aae7f2126
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/serializer/GenericAvroSerializer.scala
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.serializer
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+import java.nio.ByteBuffer
+
+import scala.collection.mutable
+
+import com.esotericsoftware.kryo.{Kryo, Serializer => KSerializer}
+import com.esotericsoftware.kryo.io.{Input => KryoInput, Output => KryoOutput}
+import org.apache.avro.{Schema, SchemaNormalization}
+import org.apache.avro.generic.{GenericData, GenericRecord}
+import org.apache.avro.io._
+import org.apache.commons.io.IOUtils
+
+import org.apache.spark.{SparkException, SparkEnv}
+import org.apache.spark.io.CompressionCodec
+
+/**
+ * Custom serializer used for generic Avro records. If the user registers the schemas
+ * ahead of time, then the schema's fingerprint will be sent with each message instead of the actual
+ * schema, as to reduce network IO.
+ * Actions like parsing or compressing schemas are computationally expensive so the serializer
+ * caches all previously seen values as to reduce the amount of work needed to do.
+ * @param schemas a map where the keys are unique IDs for Avro schemas and the values are the
+ *                string representation of the Avro schema, used to decrease the amount of data
+ *                that needs to be serialized.
+ */
+private[serializer] class GenericAvroSerializer(schemas: Map[Long, String])
+  extends KSerializer[GenericRecord] {
+
+  /** Used to reduce the amount of effort to compress the schema */
+  private val compressCache = new mutable.HashMap[Schema, Array[Byte]]()
+  private val decompressCache = new mutable.HashMap[ByteBuffer, Schema]()
+
+  /** Reuses the same datum reader/writer since the same schema will be used many times */
+  private val writerCache = new mutable.HashMap[Schema, DatumWriter[_]]()
+  private val readerCache = new mutable.HashMap[Schema, DatumReader[_]]()
+
+  /** Fingerprinting is very expensive so this alleviates most of the work */
+  private val fingerprintCache = new mutable.HashMap[Schema, Long]()
+  private val schemaCache = new mutable.HashMap[Long, Schema]()
+
+  // GenericAvroSerializer can't take a SparkConf in the constructor b/c then it would become
+  // a member of KryoSerializer, which would make KryoSerializer not Serializable.  We make
+  // the codec lazy here just b/c in some unit tests, we use a KryoSerializer w/out having
+  // the SparkEnv set (note those tests would fail if they tried to serialize avro data).
+  private lazy val codec = CompressionCodec.createCodec(SparkEnv.get.conf)
+
+  /**
+   * Used to compress Schemas when they are being sent over the wire.
+   * The compression results are memoized to reduce the compression time since the
+   * same schema is compressed many times over
+   */
+  def compress(schema: Schema): Array[Byte] = compressCache.getOrElseUpdate(schema, {
+    val bos = new ByteArrayOutputStream()
+    val out = codec.compressedOutputStream(bos)
+    out.write(schema.toString.getBytes("UTF-8"))
+    out.close()
+    bos.toByteArray
+  })
+
+  /**
+   * Decompresses the schema into the actual in-memory object. Keeps an internal cache of already
+   * seen values so to limit the number of times that decompression has to be done.
+   */
+  def decompress(schemaBytes: ByteBuffer): Schema = decompressCache.getOrElseUpdate(schemaBytes, {
+    val bis = new ByteArrayInputStream(schemaBytes.array())
+    val bytes = IOUtils.toByteArray(codec.compressedInputStream(bis))
+    new Schema.Parser().parse(new String(bytes, "UTF-8"))
+  })
+
+  /**
+   * Serializes a record to the given output stream. It caches a lot of the internal data as
+   * to not redo work
+   */
+  def serializeDatum[R <: GenericRecord](datum: R, output: KryoOutput): Unit = {
+    val encoder = EncoderFactory.get.binaryEncoder(output, null)
+    val schema = datum.getSchema
+    val fingerprint = fingerprintCache.getOrElseUpdate(schema, {
+      SchemaNormalization.parsingFingerprint64(schema)
+    })
+    schemas.get(fingerprint) match {
+      case Some(_) =>
+        output.writeBoolean(true)
+        output.writeLong(fingerprint)
+      case None =>
+        output.writeBoolean(false)
+        val compressedSchema = compress(schema)
+        output.writeInt(compressedSchema.length)
+        output.writeBytes(compressedSchema)
+    }
+
+    writerCache.getOrElseUpdate(schema, GenericData.get.createDatumWriter(schema))
+      .asInstanceOf[DatumWriter[R]]
+      .write(datum, encoder)
+    encoder.flush()
+  }
+
+  /**
+   * Deserializes generic records into their in-memory form. There is internal
+   * state to keep a cache of already seen schemas and datum readers.
+   */
+  def deserializeDatum(input: KryoInput): GenericRecord = {
+    val schema = {
+      if (input.readBoolean()) {
+        val fingerprint = input.readLong()
+        schemaCache.getOrElseUpdate(fingerprint, {
+          schemas.get(fingerprint) match {
+            case Some(s) => new Schema.Parser().parse(s)
+            case None =>
+              throw new SparkException(
+                "Error reading attempting to read avro data -- encountered an unknown " +
+                  s"fingerprint: $fingerprint, not sure what schema to use.  This could happen " +
+                  "if you registered additional schemas after starting your spark context.")
+          }
+        })
+      } else {
+        val length = input.readInt()
+        decompress(ByteBuffer.wrap(input.readBytes(length)))
+      }
+    }
+    val decoder = DecoderFactory.get.directBinaryDecoder(input, null)
+    readerCache.getOrElseUpdate(schema, GenericData.get.createDatumReader(schema))
+      .asInstanceOf[DatumReader[GenericRecord]]
+      .read(null, decoder)
+  }
+
+  override def write(kryo: Kryo, output: KryoOutput, datum: GenericRecord): Unit =
+    serializeDatum(datum, output)
+
+  override def read(kryo: Kryo, input: KryoInput, datumClass: Class[GenericRecord]): GenericRecord =
+    deserializeDatum(input)
+}
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 7cb6e080533ad..0ff7562e912ca 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -27,6 +27,7 @@ import com.esotericsoftware.kryo.{Kryo, KryoException}
 import com.esotericsoftware.kryo.io.{Input => KryoInput, Output => KryoOutput}
 import com.esotericsoftware.kryo.serializers.{JavaSerializer => KryoJavaSerializer}
 import com.twitter.chill.{AllScalaRegistrar, EmptyScalaKryoInstantiator}
+import org.apache.avro.generic.{GenericData, GenericRecord}
 import org.roaringbitmap.{ArrayContainer, BitmapContainer, RoaringArray, RoaringBitmap}
 
 import org.apache.spark._
@@ -73,6 +74,8 @@ class KryoSerializer(conf: SparkConf)
     .split(',')
     .filter(!_.isEmpty)
 
+  private val avroSchemas = conf.getAvroSchema
+
   def newKryoOutput(): KryoOutput = new KryoOutput(bufferSize, math.max(bufferSize, maxBufferSize))
 
   def newKryo(): Kryo = {
@@ -101,6 +104,9 @@ class KryoSerializer(conf: SparkConf)
     kryo.register(classOf[HttpBroadcast[_]], new KryoJavaSerializer())
     kryo.register(classOf[PythonBroadcast], new KryoJavaSerializer())
 
+    kryo.register(classOf[GenericRecord], new GenericAvroSerializer(avroSchemas))
+    kryo.register(classOf[GenericData.Record], new GenericAvroSerializer(avroSchemas))
+
     try {
       // scalastyle:off classforname
       // Use the default classloader when calling the user registrator.
diff --git a/core/src/test/scala/org/apache/spark/serializer/GenericAvroSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/GenericAvroSerializerSuite.scala
new file mode 100644
index 0000000000000..bc9f3708ed69d
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/serializer/GenericAvroSerializerSuite.scala
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.serializer
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+import java.nio.ByteBuffer
+
+import com.esotericsoftware.kryo.io.{Output, Input}
+import org.apache.avro.{SchemaBuilder, Schema}
+import org.apache.avro.generic.GenericData.Record
+
+import org.apache.spark.{SparkFunSuite, SharedSparkContext}
+
+class GenericAvroSerializerSuite extends SparkFunSuite with SharedSparkContext {
+  conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+
+  val schema : Schema = SchemaBuilder
+    .record("testRecord").fields()
+    .requiredString("data")
+    .endRecord()
+  val record = new Record(schema)
+  record.put("data", "test data")
+
+  test("schema compression and decompression") {
+    val genericSer = new GenericAvroSerializer(conf.getAvroSchema)
+    assert(schema === genericSer.decompress(ByteBuffer.wrap(genericSer.compress(schema))))
+  }
+
+  test("record serialization and deserialization") {
+    val genericSer = new GenericAvroSerializer(conf.getAvroSchema)
+
+    val outputStream = new ByteArrayOutputStream()
+    val output = new Output(outputStream)
+    genericSer.serializeDatum(record, output)
+    output.flush()
+    output.close()
+
+    val input = new Input(new ByteArrayInputStream(outputStream.toByteArray))
+    assert(genericSer.deserializeDatum(input) === record)
+  }
+
+  test("uses schema fingerprint to decrease message size") {
+    val genericSerFull = new GenericAvroSerializer(conf.getAvroSchema)
+
+    val output = new Output(new ByteArrayOutputStream())
+
+    val beginningNormalPosition = output.total()
+    genericSerFull.serializeDatum(record, output)
+    output.flush()
+    val normalLength = output.total - beginningNormalPosition
+
+    conf.registerAvroSchemas(schema)
+    val genericSerFinger = new GenericAvroSerializer(conf.getAvroSchema)
+    val beginningFingerprintPosition = output.total()
+    genericSerFinger.serializeDatum(record, output)
+    val fingerprintLength = output.total - beginningFingerprintPosition
+
+    assert(fingerprintLength < normalLength)
+  }
+
+  test("caches previously seen schemas") {
+    val genericSer = new GenericAvroSerializer(conf.getAvroSchema)
+    val compressedSchema = genericSer.compress(schema)
+    val decompressedScheam = genericSer.decompress(ByteBuffer.wrap(compressedSchema))
+
+    assert(compressedSchema.eq(genericSer.compress(schema)))
+    assert(decompressedScheam.eq(genericSer.decompress(ByteBuffer.wrap(compressedSchema))))
+  }
+}

From 819be46e5a73f2d19230354ebba30c58538590f5 Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Wed, 29 Jul 2015 13:47:37 -0700
Subject: [PATCH 0670/1454] [SPARK-8977] [STREAMING] Defines the RateEstimator
 interface, and impements the RateController
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Based on #7471.

- [x] add a test that exercises the publish path from driver to receiver
- [ ] remove Serializable from `RateController` and `RateEstimator`

Author: Iulian Dragos <jaguarul@gmail.com>
Author: François Garillot <francois@garillot.net>

Closes #7600 from dragos/topic/streaming-bp/rate-controller and squashes the following commits:

f168c94 [Iulian Dragos] Latest review round.
5125e60 [Iulian Dragos] Fix style.
a2eb3b9 [Iulian Dragos] Merge remote-tracking branch 'upstream/master' into topic/streaming-bp/rate-controller
475e346 [Iulian Dragos] Latest round of reviews.
e9fb45e [Iulian Dragos] - Add a test for checkpointing - fixed serialization for RateController.executionContext
715437a [Iulian Dragos] Review comments and added a `reset` call in ReceiverTrackerTest.
e57c66b [Iulian Dragos] Added a couple of tests for the full scenario from driver to receivers, with several rate updates.
b425d32 [Iulian Dragos] Removed DeveloperAPI, removed rateEstimator field, removed Noop rate estimator, changed logic for initialising rate estimator.
238cfc6 [Iulian Dragos] Merge remote-tracking branch 'upstream/master' into topic/streaming-bp/rate-controller
34a389d [Iulian Dragos] Various style changes and a first test for the rate controller.
d32ca36 [François Garillot] [SPARK-8977][Streaming] Defines the RateEstimator interface, and implements the ReceiverRateController
8941cf9 [Iulian Dragos] Renames and other nitpicks.
162d9e5 [Iulian Dragos] Use Reflection for accessing truly private `executor` method and use the listener bus to know when receivers have registered (`onStart` is called before receivers have registered, leading to flaky behavior).
210f495 [Iulian Dragos] Revert "Added a few tests that measure the receiver’s rate."
0c51959 [Iulian Dragos] Added a few tests that measure the receiver’s rate.
261a051 [Iulian Dragos] - removed field to hold the current rate limit in rate limiter - made rate limit a Long and default to Long.MaxValue (consequence of the above) - removed custom `waitUntil` and replaced it by `eventually`
cd1397d [Iulian Dragos] Add a test for the propagation of a new rate limit from driver to receivers.
6369b30 [Iulian Dragos] Merge pull request #15 from huitseeker/SPARK-8975
d15de42 [François Garillot] [SPARK-8975][Streaming] Adds Ratelimiter unit tests w.r.t. spark.streaming.receiver.maxRate
4721c7d [François Garillot] [SPARK-8975][Streaming] Add a mechanism to send a new rate from the driver to the block generator
---
 .../streaming/dstream/InputDStream.scala      |   7 +-
 .../dstream/ReceiverInputDStream.scala        |  26 ++++-
 .../streaming/scheduler/JobScheduler.scala    |   6 +
 .../streaming/scheduler/RateController.scala  |  90 +++++++++++++++
 .../scheduler/rate/RateEstimator.scala        |  59 ++++++++++
 .../spark/streaming/CheckpointSuite.scala     |  28 +++++
 .../scheduler/RateControllerSuite.scala       | 103 ++++++++++++++++++
 .../ReceiverSchedulingPolicySuite.scala       |  10 +-
 .../scheduler/ReceiverTrackerSuite.scala      |  41 +++++--
 9 files changed, 355 insertions(+), 15 deletions(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
index d58c99a8ff321..a6c4cd220e42f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
@@ -21,7 +21,9 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDDOperationScope
-import org.apache.spark.streaming.{Time, Duration, StreamingContext}
+import org.apache.spark.streaming.{Duration, StreamingContext, Time}
+import org.apache.spark.streaming.scheduler.RateController
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
 import org.apache.spark.util.Utils
 
 /**
@@ -47,6 +49,9 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext)
   /** This is an unique identifier for the input stream. */
   val id = ssc.getNewInputStreamId()
 
+  // Keep track of the freshest rate for this stream using the rateEstimator
+  protected[streaming] val rateController: Option[RateController] = None
+
   /** A human-readable name of this InputDStream */
   private[streaming] def name: String = {
     // e.g. FlumePollingDStream -> "Flume polling stream"
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index a50f0efc030ce..646a8c3530a62 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -21,10 +21,11 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.storage.BlockId
-import org.apache.spark.streaming._
+import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
 import org.apache.spark.streaming.receiver.Receiver
-import org.apache.spark.streaming.scheduler.StreamInputInfo
+import org.apache.spark.streaming.scheduler.{RateController, StreamInputInfo}
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
 import org.apache.spark.streaming.util.WriteAheadLogUtils
 
 /**
@@ -40,6 +41,17 @@ import org.apache.spark.streaming.util.WriteAheadLogUtils
 abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingContext)
   extends InputDStream[T](ssc_) {
 
+  /**
+   * Asynchronously maintains & sends new rate limits to the receiver through the receiver tracker.
+   */
+  override protected[streaming] val rateController: Option[RateController] = {
+    if (RateController.isBackPressureEnabled(ssc.conf)) {
+      RateEstimator.create(ssc.conf).map { new ReceiverRateController(id, _) }
+    } else {
+      None
+    }
+  }
+
   /**
    * Gets the receiver object that will be sent to the worker nodes
    * to receive data. This method needs to defined by any specific implementation
@@ -110,4 +122,14 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
     }
     Some(blockRDD)
   }
+
+  /**
+   * A RateController that sends the new rate to receivers, via the receiver tracker.
+   */
+  private[streaming] class ReceiverRateController(id: Int, estimator: RateEstimator)
+      extends RateController(id, estimator) {
+    override def publish(rate: Long): Unit =
+      ssc.scheduler.receiverTracker.sendRateUpdate(id, rate)
+  }
 }
+
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
index 4af9b6d3b56ab..58bdda7794bf2 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
@@ -66,6 +66,12 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
     }
     eventLoop.start()
 
+    // attach rate controllers of input streams to receive batch completion updates
+    for {
+      inputDStream <- ssc.graph.getInputStreams
+      rateController <- inputDStream.rateController
+    } ssc.addStreamingListener(rateController)
+
     listenerBus.start(ssc.sparkContext)
     receiverTracker = new ReceiverTracker(ssc)
     inputInfoTracker = new InputInfoTracker(ssc)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
new file mode 100644
index 0000000000000..882ca0676b6ad
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import java.io.ObjectInputStream
+import java.util.concurrent.atomic.AtomicLong
+
+import scala.concurrent.{ExecutionContext, Future}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
+import org.apache.spark.util.{ThreadUtils, Utils}
+
+/**
+ * A StreamingListener that receives batch completion updates, and maintains
+ * an estimate of the speed at which this stream should ingest messages,
+ * given an estimate computation from a `RateEstimator`
+ */
+private[streaming] abstract class RateController(val streamUID: Int, rateEstimator: RateEstimator)
+    extends StreamingListener with Serializable {
+
+  init()
+
+  protected def publish(rate: Long): Unit
+
+  @transient
+  implicit private var executionContext: ExecutionContext = _
+
+  @transient
+  private var rateLimit: AtomicLong = _
+
+  /**
+   * An initialization method called both from the constructor and Serialization code.
+   */
+  private def init() {
+    executionContext = ExecutionContext.fromExecutorService(
+      ThreadUtils.newDaemonSingleThreadExecutor("stream-rate-update"))
+    rateLimit = new AtomicLong(-1L)
+  }
+
+  private def readObject(ois: ObjectInputStream): Unit = Utils.tryOrIOException {
+    ois.defaultReadObject()
+    init()
+  }
+
+  /**
+   * Compute the new rate limit and publish it asynchronously.
+   */
+  private def computeAndPublish(time: Long, elems: Long, workDelay: Long, waitDelay: Long): Unit =
+    Future[Unit] {
+      val newRate = rateEstimator.compute(time, elems, workDelay, waitDelay)
+      newRate.foreach { s =>
+        rateLimit.set(s.toLong)
+        publish(getLatestRate())
+      }
+    }
+
+  def getLatestRate(): Long = rateLimit.get()
+
+  override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) {
+    val elements = batchCompleted.batchInfo.streamIdToInputInfo
+
+    for {
+      processingEnd <- batchCompleted.batchInfo.processingEndTime;
+      workDelay <- batchCompleted.batchInfo.processingDelay;
+      waitDelay <- batchCompleted.batchInfo.schedulingDelay;
+      elems <- elements.get(streamUID).map(_.numRecords)
+    } computeAndPublish(processingEnd, elems, workDelay, waitDelay)
+  }
+}
+
+object RateController {
+  def isBackPressureEnabled(conf: SparkConf): Boolean =
+    conf.getBoolean("spark.streaming.backpressure.enable", false)
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
new file mode 100644
index 0000000000000..a08685119e5d5
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler.rate
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkException
+
+/**
+ * A component that estimates the rate at wich an InputDStream should ingest
+ * elements, based on updates at every batch completion.
+ */
+private[streaming] trait RateEstimator extends Serializable {
+
+  /**
+   * Computes the number of elements the stream attached to this `RateEstimator`
+   * should ingest per second, given an update on the size and completion
+   * times of the latest batch.
+   *
+   * @param time The timetamp of the current batch interval that just finished
+   * @param elements The number of elements that were processed in this batch
+   * @param processingDelay The time in ms that took for the job to complete
+   * @param schedulingDelay The time in ms that the job spent in the scheduling queue
+   */
+  def compute(
+      time: Long,
+      elements: Long,
+      processingDelay: Long,
+      schedulingDelay: Long): Option[Double]
+}
+
+object RateEstimator {
+
+  /**
+   * Return a new RateEstimator based on the value of `spark.streaming.RateEstimator`.
+   *
+   * @return None if there is no configured estimator, otherwise an instance of RateEstimator
+   * @throws IllegalArgumentException if there is a configured RateEstimator that doesn't match any
+   *         known estimators.
+   */
+  def create(conf: SparkConf): Option[RateEstimator] =
+    conf.getOption("spark.streaming.backpressure.rateEstimator").map { estimator =>
+      throw new IllegalArgumentException(s"Unkown rate estimator: $estimator")
+    }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index d308ac05a54fe..67c2d900940ab 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -30,8 +30,10 @@ import org.apache.hadoop.io.{IntWritable, Text}
 import org.apache.hadoop.mapred.TextOutputFormat
 import org.apache.hadoop.mapreduce.lib.output.{TextOutputFormat => NewTextOutputFormat}
 import org.scalatest.concurrent.Eventually._
+import org.scalatest.time.SpanSugar._
 
 import org.apache.spark.streaming.dstream.{DStream, FileInputDStream}
+import org.apache.spark.streaming.scheduler.{RateLimitInputDStream, ConstantEstimator, SingletonTestRateReceiver}
 import org.apache.spark.util.{Clock, ManualClock, Utils}
 
 /**
@@ -391,6 +393,32 @@ class CheckpointSuite extends TestSuiteBase {
     testCheckpointedOperation(input, operation, output, 7)
   }
 
+  test("recovery maintains rate controller") {
+    ssc = new StreamingContext(conf, batchDuration)
+    ssc.checkpoint(checkpointDir)
+
+    val dstream = new RateLimitInputDStream(ssc) {
+      override val rateController =
+        Some(new ReceiverRateController(id, new ConstantEstimator(200.0)))
+    }
+    SingletonTestRateReceiver.reset()
+
+    val output = new TestOutputStreamWithPartitions(dstream.checkpoint(batchDuration * 2))
+    output.register()
+    runStreams(ssc, 5, 5)
+
+    SingletonTestRateReceiver.reset()
+    ssc = new StreamingContext(checkpointDir)
+    ssc.start()
+    val outputNew = advanceTimeWithRealDelay(ssc, 2)
+
+    eventually(timeout(5.seconds)) {
+      assert(dstream.getCurrentRateLimit === Some(200))
+    }
+    ssc.stop()
+    ssc = null
+  }
+
   // This tests whether file input stream remembers what files were seen before
   // the master failure and uses them again to process a large window operation.
   // It also tests whether batches, whose processing was incomplete due to the
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
new file mode 100644
index 0000000000000..921da773f6c11
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
@@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import scala.collection.mutable
+import scala.reflect.ClassTag
+import scala.util.control.NonFatal
+
+import org.scalatest.Matchers._
+import org.scalatest.concurrent.Eventually._
+import org.scalatest.time.SpanSugar._
+
+import org.apache.spark.streaming._
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
+
+class RateControllerSuite extends TestSuiteBase {
+
+  override def useManualClock: Boolean = false
+
+  test("rate controller publishes updates") {
+    val ssc = new StreamingContext(conf, batchDuration)
+    withStreamingContext(ssc) { ssc =>
+      val dstream = new RateLimitInputDStream(ssc)
+      dstream.register()
+      ssc.start()
+
+      eventually(timeout(10.seconds)) {
+        assert(dstream.publishCalls > 0)
+      }
+    }
+  }
+
+  test("publish rates reach receivers") {
+    val ssc = new StreamingContext(conf, batchDuration)
+    withStreamingContext(ssc) { ssc =>
+      val dstream = new RateLimitInputDStream(ssc) {
+        override val rateController =
+          Some(new ReceiverRateController(id, new ConstantEstimator(200.0)))
+      }
+      dstream.register()
+      SingletonTestRateReceiver.reset()
+      ssc.start()
+
+      eventually(timeout(10.seconds)) {
+        assert(dstream.getCurrentRateLimit === Some(200))
+      }
+    }
+  }
+
+  test("multiple publish rates reach receivers") {
+    val ssc = new StreamingContext(conf, batchDuration)
+    withStreamingContext(ssc) { ssc =>
+      val rates = Seq(100L, 200L, 300L)
+
+      val dstream = new RateLimitInputDStream(ssc) {
+        override val rateController =
+          Some(new ReceiverRateController(id, new ConstantEstimator(rates.map(_.toDouble): _*)))
+      }
+      SingletonTestRateReceiver.reset()
+      dstream.register()
+
+      val observedRates = mutable.HashSet.empty[Long]
+      ssc.start()
+
+      eventually(timeout(20.seconds)) {
+        dstream.getCurrentRateLimit.foreach(observedRates += _)
+        // Long.MaxValue (essentially, no rate limit) is the initial rate limit for any Receiver
+        observedRates should contain theSameElementsAs (rates :+ Long.MaxValue)
+      }
+    }
+  }
+}
+
+private[streaming] class ConstantEstimator(rates: Double*) extends RateEstimator {
+  private var idx: Int = 0
+
+  private def nextRate(): Double = {
+    val rate = rates(idx)
+    idx = (idx + 1) % rates.size
+    rate
+  }
+
+  def compute(
+      time: Long,
+      elements: Long,
+      processingDelay: Long,
+      schedulingDelay: Long): Option[Double] = Some(nextRate())
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
index 93f920fdc71f1..0418d776ecc9a 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
@@ -64,7 +64,7 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
 
   test("scheduleReceivers: " +
     "schedule receivers evenly when there are more receivers than executors") {
-    val receivers = (0 until 6).map(new DummyReceiver(_))
+    val receivers = (0 until 6).map(new RateTestReceiver(_))
     val executors = (10000 until 10003).map(port => s"localhost:${port}")
     val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, executors)
     val numReceiversOnExecutor = mutable.HashMap[String, Int]()
@@ -79,7 +79,7 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
 
   test("scheduleReceivers: " +
     "schedule receivers evenly when there are more executors than receivers") {
-    val receivers = (0 until 3).map(new DummyReceiver(_))
+    val receivers = (0 until 3).map(new RateTestReceiver(_))
     val executors = (10000 until 10006).map(port => s"localhost:${port}")
     val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, executors)
     val numReceiversOnExecutor = mutable.HashMap[String, Int]()
@@ -94,8 +94,8 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
   }
 
   test("scheduleReceivers: schedule receivers evenly when the preferredLocations are even") {
-    val receivers = (0 until 3).map(new DummyReceiver(_)) ++
-      (3 until 6).map(new DummyReceiver(_, Some("localhost")))
+    val receivers = (0 until 3).map(new RateTestReceiver(_)) ++
+      (3 until 6).map(new RateTestReceiver(_, Some("localhost")))
     val executors = (10000 until 10003).map(port => s"localhost:${port}") ++
       (10003 until 10006).map(port => s"localhost2:${port}")
     val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, executors)
@@ -121,7 +121,7 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
   }
 
   test("scheduleReceivers: return empty scheduled executors if no executors") {
-    val receivers = (0 until 3).map(new DummyReceiver(_))
+    val receivers = (0 until 3).map(new RateTestReceiver(_))
     val scheduledExecutors = receiverSchedulingPolicy.scheduleReceivers(receivers, Seq.empty)
     scheduledExecutors.foreach { case (receiverId, executors) =>
       assert(executors.isEmpty)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index b039233f36316..aff8b53f752fa 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -43,6 +43,7 @@ class ReceiverTrackerSuite extends TestSuiteBase {
 
     ssc.addStreamingListener(ReceiverStartedWaiter)
     ssc.scheduler.listenerBus.start(ssc.sc)
+    SingletonTestRateReceiver.reset()
 
     val newRateLimit = 100L
     val inputDStream = new RateLimitInputDStream(ssc)
@@ -62,36 +63,62 @@ class ReceiverTrackerSuite extends TestSuiteBase {
   }
 }
 
-/** An input DStream with a hard-coded receiver that gives access to internals for testing. */
-private class RateLimitInputDStream(@transient ssc_ : StreamingContext)
+/**
+ * An input DStream with a hard-coded receiver that gives access to internals for testing.
+ *
+ * @note Make sure to call {{{SingletonDummyReceiver.reset()}}} before using this in a test,
+ *       or otherwise you may get {{{NotSerializableException}}} when trying to serialize
+ *       the receiver.
+ * @see [[[SingletonDummyReceiver]]].
+ */
+private[streaming] class RateLimitInputDStream(@transient ssc_ : StreamingContext)
   extends ReceiverInputDStream[Int](ssc_) {
 
-  override def getReceiver(): DummyReceiver = SingletonDummyReceiver
+  override def getReceiver(): RateTestReceiver = SingletonTestRateReceiver
 
   def getCurrentRateLimit: Option[Long] = {
     invokeExecutorMethod.getCurrentRateLimit
   }
 
+  @volatile
+  var publishCalls = 0
+
+  override val rateController: Option[RateController] = {
+    Some(new RateController(id, new ConstantEstimator(100.0)) {
+      override def publish(rate: Long): Unit = {
+        publishCalls += 1
+      }
+    })
+  }
+
   private def invokeExecutorMethod: ReceiverSupervisor = {
     val c = classOf[Receiver[_]]
     val ex = c.getDeclaredMethod("executor")
     ex.setAccessible(true)
-    ex.invoke(SingletonDummyReceiver).asInstanceOf[ReceiverSupervisor]
+    ex.invoke(SingletonTestRateReceiver).asInstanceOf[ReceiverSupervisor]
   }
 }
 
 /**
- * A Receiver as an object so we can read its rate limit.
+ * A Receiver as an object so we can read its rate limit. Make sure to call `reset()` when
+ * reusing this receiver, otherwise a non-null `executor_` field will prevent it from being
+ * serialized when receivers are installed on executors.
  *
  * @note It's necessary to be a top-level object, or else serialization would create another
  *       one on the executor side and we won't be able to read its rate limit.
  */
-private object SingletonDummyReceiver extends DummyReceiver(0)
+private[streaming] object SingletonTestRateReceiver extends RateTestReceiver(0) {
+
+  /** Reset the object to be usable in another test. */
+  def reset(): Unit = {
+    executor_ = null
+  }
+}
 
 /**
  * Dummy receiver implementation
  */
-private class DummyReceiver(receiverId: Int, host: Option[String] = None)
+private[streaming] class RateTestReceiver(receiverId: Int, host: Option[String] = None)
   extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
 
   setReceiverId(receiverId)

From 5340dfaf94a3c54199f8cc3c78e11f61e34d0a67 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 29 Jul 2015 13:49:22 -0700
Subject: [PATCH 0671/1454] [SPARK-9430][SQL] Rename IntervalType to
 CalendarIntervalType.

We want to introduce a new IntervalType in 1.6 that is based on only the number of microseoncds,
so interval can be compared.

Renaming the existing IntervalType to CalendarIntervalType so we can do that in the future.

Author: Reynold Xin <rxin@databricks.com>

Closes #7745 from rxin/calendarintervaltype and squashes the following commits:

99f64e8 [Reynold Xin] One more line ...
13466c8 [Reynold Xin] Fixed tests.
e20f24e [Reynold Xin] [SPARK-9430][SQL] Rename IntervalType to CalendarIntervalType.
---
 .../expressions/SpecializedGetters.java       |   4 +-
 .../sql/catalyst/expressions/UnsafeRow.java   |  10 +-
 .../expressions/UnsafeRowWriters.java         |   4 +-
 .../org/apache/spark/sql/types/DataTypes.java |   4 +-
 .../spark/sql/catalyst/InternalRow.scala      |   5 +-
 .../apache/spark/sql/catalyst/SqlParser.scala |  16 +-
 .../catalyst/expressions/BoundAttribute.scala |   2 +-
 .../spark/sql/catalyst/expressions/Cast.scala |  12 +-
 .../sql/catalyst/expressions/arithmetic.scala |  20 +--
 .../expressions/codegen/CodeGenerator.scala   |   6 +-
 .../codegen/GenerateUnsafeProjection.scala    |  10 +-
 .../sql/catalyst/expressions/literals.scala   |   2 +-
 .../spark/sql/types/AbstractDataType.scala    |   2 +-
 ...lType.scala => CalendarIntervalType.scala} |  15 +-
 .../ExpressionTypeCheckingSuite.scala         |   7 +-
 .../analysis/HiveTypeCoercionSuite.scala      |   2 +-
 .../sql/catalyst/expressions/CastSuite.scala  |   9 +-
 .../spark/sql/execution/basicOperators.scala  | 131 ---------------
 .../spark/sql/execution/datasources/ddl.scala |   2 +-
 .../org/apache/spark/sql/execution/sort.scala | 159 ++++++++++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  20 +--
 .../{Interval.java => CalendarInterval.java}  |  24 +--
 .../spark/unsafe/types/IntervalSuite.java     |  72 ++++----
 23 files changed, 286 insertions(+), 252 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/types/{IntervalType.scala => CalendarIntervalType.scala} (64%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
 rename unsafe/src/main/java/org/apache/spark/unsafe/types/{Interval.java => CalendarInterval.java} (87%)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
index 5f28d52a94bd7..bc345dcd00e49 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
@@ -19,7 +19,7 @@
 
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.unsafe.types.Interval;
+import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 
 public interface SpecializedGetters {
@@ -46,7 +46,7 @@ public interface SpecializedGetters {
 
   byte[] getBinary(int ordinal);
 
-  Interval getInterval(int ordinal);
+  CalendarInterval getInterval(int ordinal);
 
   InternalRow getStruct(int ordinal, int numFields);
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 64a8edc34d681..6d684bac37573 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -29,7 +29,7 @@
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
-import org.apache.spark.unsafe.types.Interval;
+import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 
 import static org.apache.spark.sql.types.DataTypes.*;
@@ -92,7 +92,7 @@ public static int calculateBitSetWidthInBytes(int numFields) {
       Arrays.asList(new DataType[]{
         StringType,
         BinaryType,
-        IntervalType
+        CalendarIntervalType
       }));
     _readableFieldTypes.addAll(settableFieldTypes);
     readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes);
@@ -265,7 +265,7 @@ public Object get(int ordinal, DataType dataType) {
       return getBinary(ordinal);
     } else if (dataType instanceof StringType) {
       return getUTF8String(ordinal);
-    } else if (dataType instanceof IntervalType) {
+    } else if (dataType instanceof CalendarIntervalType) {
       return getInterval(ordinal);
     } else if (dataType instanceof StructType) {
       return getStruct(ordinal, ((StructType) dataType).size());
@@ -350,7 +350,7 @@ public byte[] getBinary(int ordinal) {
   }
 
   @Override
-  public Interval getInterval(int ordinal) {
+  public CalendarInterval getInterval(int ordinal) {
     if (isNullAt(ordinal)) {
       return null;
     } else {
@@ -359,7 +359,7 @@ public Interval getInterval(int ordinal) {
       final int months = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
       final long microseconds =
         PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset + 8);
-      return new Interval(months, microseconds);
+      return new CalendarInterval(months, microseconds);
     }
   }
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 32faad374015c..c3259e21c4a78 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -21,7 +21,7 @@
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
-import org.apache.spark.unsafe.types.Interval;
+import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 
 /**
@@ -131,7 +131,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, InternalRow i
   /** Writer for interval type. */
   public static class IntervalWriter {
 
-    public static int write(UnsafeRow target, int ordinal, int cursor, Interval input) {
+    public static int write(UnsafeRow target, int ordinal, int cursor, CalendarInterval input) {
       final long offset = target.getBaseOffset() + cursor;
 
       // Write the months and microseconds fields of Interval to the variable length portion.
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java b/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
index 5703de42393de..17659d7d960b0 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/types/DataTypes.java
@@ -50,9 +50,9 @@ public class DataTypes {
   public static final DataType TimestampType = TimestampType$.MODULE$;
 
   /**
-   * Gets the IntervalType object.
+   * Gets the CalendarIntervalType object.
    */
-  public static final DataType IntervalType = IntervalType$.MODULE$;
+  public static final DataType CalendarIntervalType = CalendarIntervalType$.MODULE$;
 
   /**
    * Gets the DoubleType object.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index e395a67434fa7..a5999e64ec554 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.{Interval, UTF8String}
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -61,7 +61,8 @@ abstract class InternalRow extends Serializable with SpecializedGetters {
   override def getDecimal(ordinal: Int): Decimal =
     getAs[Decimal](ordinal, DecimalType.SYSTEM_DEFAULT)
 
-  override def getInterval(ordinal: Int): Interval = getAs[Interval](ordinal, IntervalType)
+  override def getInterval(ordinal: Int): CalendarInterval =
+    getAs[CalendarInterval](ordinal, CalendarIntervalType)
 
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index e5f115f74bf3b..f2498861c9573 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -25,7 +25,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.Interval
+import org.apache.spark.unsafe.types.CalendarInterval
 
 /**
  * A very simple SQL parser.  Based loosely on:
@@ -365,32 +365,32 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
 
   protected lazy val millisecond: Parser[Long] =
     integral <~ intervalUnit("millisecond") ^^ {
-      case num => num.toLong * Interval.MICROS_PER_MILLI
+      case num => num.toLong * CalendarInterval.MICROS_PER_MILLI
     }
 
   protected lazy val second: Parser[Long] =
     integral <~ intervalUnit("second") ^^ {
-      case num => num.toLong * Interval.MICROS_PER_SECOND
+      case num => num.toLong * CalendarInterval.MICROS_PER_SECOND
     }
 
   protected lazy val minute: Parser[Long] =
     integral <~ intervalUnit("minute") ^^ {
-      case num => num.toLong * Interval.MICROS_PER_MINUTE
+      case num => num.toLong * CalendarInterval.MICROS_PER_MINUTE
     }
 
   protected lazy val hour: Parser[Long] =
     integral <~ intervalUnit("hour") ^^ {
-      case num => num.toLong * Interval.MICROS_PER_HOUR
+      case num => num.toLong * CalendarInterval.MICROS_PER_HOUR
     }
 
   protected lazy val day: Parser[Long] =
     integral <~ intervalUnit("day") ^^ {
-      case num => num.toLong * Interval.MICROS_PER_DAY
+      case num => num.toLong * CalendarInterval.MICROS_PER_DAY
     }
 
   protected lazy val week: Parser[Long] =
     integral <~ intervalUnit("week") ^^ {
-      case num => num.toLong * Interval.MICROS_PER_WEEK
+      case num => num.toLong * CalendarInterval.MICROS_PER_WEEK
     }
 
   protected lazy val intervalLiteral: Parser[Literal] =
@@ -406,7 +406,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
           val months = Seq(year, month).map(_.getOrElse(0)).sum
           val microseconds = Seq(week, day, hour, minute, second, millisecond, microsecond)
             .map(_.getOrElse(0L)).sum
-          Literal.create(new Interval(months, microseconds), IntervalType)
+          Literal.create(new CalendarInterval(months, microseconds), CalendarIntervalType)
       }
 
   private def toNarrowestIntegerType(value: String): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 8304d4ccd47f7..371681b5d494f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -48,7 +48,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
         case DoubleType => input.getDouble(ordinal)
         case StringType => input.getUTF8String(ordinal)
         case BinaryType => input.getBinary(ordinal)
-        case IntervalType => input.getInterval(ordinal)
+        case CalendarIntervalType => input.getInterval(ordinal)
         case t: StructType => input.getStruct(ordinal, t.size)
         case _ => input.get(ordinal, dataType)
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index bd8b0177eb00e..c6e8af27667ee 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.{Interval, UTF8String}
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 import scala.collection.mutable
 
@@ -55,7 +55,7 @@ object Cast {
 
     case (_, DateType) => true
 
-    case (StringType, IntervalType) => true
+    case (StringType, CalendarIntervalType) => true
 
     case (StringType, _: NumericType) => true
     case (BooleanType, _: NumericType) => true
@@ -225,7 +225,7 @@ case class Cast(child: Expression, dataType: DataType)
   // IntervalConverter
   private[this] def castToInterval(from: DataType): Any => Any = from match {
     case StringType =>
-      buildCast[UTF8String](_, s => Interval.fromString(s.toString))
+      buildCast[UTF8String](_, s => CalendarInterval.fromString(s.toString))
     case _ => _ => null
   }
 
@@ -398,7 +398,7 @@ case class Cast(child: Expression, dataType: DataType)
     case DateType => castToDate(from)
     case decimal: DecimalType => castToDecimal(from, decimal)
     case TimestampType => castToTimestamp(from)
-    case IntervalType => castToInterval(from)
+    case CalendarIntervalType => castToInterval(from)
     case BooleanType => castToBoolean(from)
     case ByteType => castToByte(from)
     case ShortType => castToShort(from)
@@ -438,7 +438,7 @@ case class Cast(child: Expression, dataType: DataType)
     case DateType => castToDateCode(from, ctx)
     case decimal: DecimalType => castToDecimalCode(from, decimal)
     case TimestampType => castToTimestampCode(from, ctx)
-    case IntervalType => castToIntervalCode(from)
+    case CalendarIntervalType => castToIntervalCode(from)
     case BooleanType => castToBooleanCode(from)
     case ByteType => castToByteCode(from)
     case ShortType => castToShortCode(from)
@@ -630,7 +630,7 @@ case class Cast(child: Expression, dataType: DataType)
   private[this] def castToIntervalCode(from: DataType): CastFunction = from match {
     case StringType =>
       (c, evPrim, evNull) =>
-        s"$evPrim = Interval.fromString($c.toString());"
+        s"$evPrim = CalendarInterval.fromString($c.toString());"
   }
 
   private[this] def decimalToTimestampCode(d: String): String =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 4ec866475f8b0..6f8f4dd230f12 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.Interval
+import org.apache.spark.unsafe.types.CalendarInterval
 
 
 case class UnaryMinus(child: Expression) extends UnaryExpression with ExpectsInputTypes {
@@ -37,12 +37,12 @@ case class UnaryMinus(child: Expression) extends UnaryExpression with ExpectsInp
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
     case dt: DecimalType => defineCodeGen(ctx, ev, c => s"$c.unary_$$minus()")
     case dt: NumericType => defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})(-($c))")
-    case dt: IntervalType => defineCodeGen(ctx, ev, c => s"$c.negate()")
+    case dt: CalendarIntervalType => defineCodeGen(ctx, ev, c => s"$c.negate()")
   }
 
   protected override def nullSafeEval(input: Any): Any = {
-    if (dataType.isInstanceOf[IntervalType]) {
-      input.asInstanceOf[Interval].negate()
+    if (dataType.isInstanceOf[CalendarIntervalType]) {
+      input.asInstanceOf[CalendarInterval].negate()
     } else {
       numeric.negate(input)
     }
@@ -121,8 +121,8 @@ case class Add(left: Expression, right: Expression) extends BinaryArithmetic {
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
-    if (dataType.isInstanceOf[IntervalType]) {
-      input1.asInstanceOf[Interval].add(input2.asInstanceOf[Interval])
+    if (dataType.isInstanceOf[CalendarIntervalType]) {
+      input1.asInstanceOf[CalendarInterval].add(input2.asInstanceOf[CalendarInterval])
     } else {
       numeric.plus(input1, input2)
     }
@@ -134,7 +134,7 @@ case class Add(left: Expression, right: Expression) extends BinaryArithmetic {
     case ByteType | ShortType =>
       defineCodeGen(ctx, ev,
         (eval1, eval2) => s"(${ctx.javaType(dataType)})($eval1 $symbol $eval2)")
-    case IntervalType =>
+    case CalendarIntervalType =>
       defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.add($eval2)")
     case _ =>
       defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1 $symbol $eval2")
@@ -150,8 +150,8 @@ case class Subtract(left: Expression, right: Expression) extends BinaryArithmeti
   private lazy val numeric = TypeUtils.getNumeric(dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = {
-    if (dataType.isInstanceOf[IntervalType]) {
-      input1.asInstanceOf[Interval].subtract(input2.asInstanceOf[Interval])
+    if (dataType.isInstanceOf[CalendarIntervalType]) {
+      input1.asInstanceOf[CalendarInterval].subtract(input2.asInstanceOf[CalendarInterval])
     } else {
       numeric.minus(input1, input2)
     }
@@ -163,7 +163,7 @@ case class Subtract(left: Expression, right: Expression) extends BinaryArithmeti
     case ByteType | ShortType =>
       defineCodeGen(ctx, ev,
         (eval1, eval2) => s"(${ctx.javaType(dataType)})($eval1 $symbol $eval2)")
-    case IntervalType =>
+    case CalendarIntervalType =>
       defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1.subtract($eval2)")
     case _ =>
       defineCodeGen(ctx, ev, (eval1, eval2) => s"$eval1 $symbol $eval2")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 2f02c90b1d5b3..092f4c9fb0bd2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -108,7 +108,7 @@ class CodeGenContext {
       case _ if isPrimitiveType(jt) => s"$row.get${primitiveTypeName(jt)}($ordinal)"
       case StringType => s"$row.getUTF8String($ordinal)"
       case BinaryType => s"$row.getBinary($ordinal)"
-      case IntervalType => s"$row.getInterval($ordinal)"
+      case CalendarIntervalType => s"$row.getInterval($ordinal)"
       case t: StructType => s"$row.getStruct($ordinal, ${t.size})"
       case _ => s"($jt)$row.get($ordinal)"
     }
@@ -150,7 +150,7 @@ class CodeGenContext {
     case dt: DecimalType => "Decimal"
     case BinaryType => "byte[]"
     case StringType => "UTF8String"
-    case IntervalType => "Interval"
+    case CalendarIntervalType => "CalendarInterval"
     case _: StructType => "InternalRow"
     case _: ArrayType => s"scala.collection.Seq"
     case _: MapType => s"scala.collection.Map"
@@ -293,7 +293,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       classOf[UnsafeRow].getName,
       classOf[UTF8String].getName,
       classOf[Decimal].getName,
-      classOf[Interval].getName
+      classOf[CalendarInterval].getName
     ))
     evaluator.setExtendedClass(classOf[GeneratedClass])
     try {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 9a4c00e86a3ec..dc725c28aaa27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -39,7 +39,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
     case t: AtomicType if !t.isInstanceOf[DecimalType] => true
-    case _: IntervalType => true
+    case _: CalendarIntervalType => true
     case t: StructType => t.toSeq.forall(field => canSupport(field.dataType))
     case NullType => true
     case _ => false
@@ -75,7 +75,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           s" + (${exprs(i).isNull} ? 0 : $StringWriter.getSize(${exprs(i).primitive}))"
         case BinaryType =>
           s" + (${exprs(i).isNull} ? 0 : $BinaryWriter.getSize(${exprs(i).primitive}))"
-        case IntervalType =>
+        case CalendarIntervalType =>
           s" + (${exprs(i).isNull} ? 0 : 16)"
         case _: StructType =>
           s" + (${exprs(i).isNull} ? 0 : $StructWriter.getSize(${exprs(i).primitive}))"
@@ -91,7 +91,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           s"$cursor += $StringWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
         case BinaryType =>
           s"$cursor += $BinaryWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
-        case IntervalType =>
+        case CalendarIntervalType =>
           s"$cursor += $IntervalWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
         case t: StructType =>
           s"$cursor += $StructWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
@@ -173,7 +173,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
         case BinaryType =>
           s" + (${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive}))"
-        case IntervalType =>
+        case CalendarIntervalType =>
           s" + (${ev.isNull} ? 0 : 16)"
         case _: StructType =>
           s" + (${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive}))"
@@ -189,7 +189,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           s"$cursor += $StringWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
         case BinaryType =>
           s"$cursor += $BinaryWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
-        case IntervalType =>
+        case CalendarIntervalType =>
           s"$cursor += $IntervalWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
         case t: StructType =>
           s"$cursor += $StructWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index 064a1720c36e8..34bad23802ba4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -42,7 +42,7 @@ object Literal {
     case t: Timestamp => Literal(DateTimeUtils.fromJavaTimestamp(t), TimestampType)
     case d: Date => Literal(DateTimeUtils.fromJavaDate(d), DateType)
     case a: Array[Byte] => Literal(a, BinaryType)
-    case i: Interval => Literal(i, IntervalType)
+    case i: CalendarInterval => Literal(i, CalendarIntervalType)
     case null => Literal(null, NullType)
     case _ =>
       throw new RuntimeException("Unsupported literal type " + v.getClass + " " + v)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index 40bf4b299c990..e0667c629486d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -95,7 +95,7 @@ private[sql] object TypeCollection {
    * Types that include numeric types and interval type. They are only used in unary_minus,
    * unary_positive, add and subtract operations.
    */
-  val NumericAndInterval = TypeCollection(NumericType, IntervalType)
+  val NumericAndInterval = TypeCollection(NumericType, CalendarIntervalType)
 
   def apply(types: AbstractDataType*): TypeCollection = new TypeCollection(types)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntervalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/CalendarIntervalType.scala
similarity index 64%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntervalType.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/types/CalendarIntervalType.scala
index 87c6e9e6e5e2c..3565f52c21f69 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntervalType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/CalendarIntervalType.scala
@@ -22,16 +22,19 @@ import org.apache.spark.annotation.DeveloperApi
 
 /**
  * :: DeveloperApi ::
- * The data type representing time intervals.
+ * The data type representing calendar time intervals. The calendar time interval is stored
+ * internally in two components: number of months the number of microseconds.
  *
- * Please use the singleton [[DataTypes.IntervalType]].
+ * Note that calendar intervals are not comparable.
+ *
+ * Please use the singleton [[DataTypes.CalendarIntervalType]].
  */
 @DeveloperApi
-class IntervalType private() extends DataType {
+class CalendarIntervalType private() extends DataType {
 
-  override def defaultSize: Int = 4096
+  override def defaultSize: Int = 16
 
-  private[spark] override def asNullable: IntervalType = this
+  private[spark] override def asNullable: CalendarIntervalType = this
 }
 
-case object IntervalType extends IntervalType
+case object CalendarIntervalType extends CalendarIntervalType
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index ad15136ee9a2f..8acd4c685e2bc 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -53,7 +53,7 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
   }
 
   test("check types for unary arithmetic") {
-    assertError(UnaryMinus('stringField), "type (numeric or interval)")
+    assertError(UnaryMinus('stringField), "type (numeric or calendarinterval)")
     assertError(Abs('stringField), "expected to be of type numeric")
     assertError(BitwiseNot('stringField), "expected to be of type integral")
   }
@@ -78,8 +78,9 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertErrorForDifferingTypes(MaxOf('intField, 'booleanField))
     assertErrorForDifferingTypes(MinOf('intField, 'booleanField))
 
-    assertError(Add('booleanField, 'booleanField), "accepts (numeric or interval) type")
-    assertError(Subtract('booleanField, 'booleanField), "accepts (numeric or interval) type")
+    assertError(Add('booleanField, 'booleanField), "accepts (numeric or calendarinterval) type")
+    assertError(Subtract('booleanField, 'booleanField),
+      "accepts (numeric or calendarinterval) type")
     assertError(Multiply('booleanField, 'booleanField), "accepts numeric type")
     assertError(Divide('booleanField, 'booleanField), "accepts numeric type")
     assertError(Remainder('booleanField, 'booleanField), "accepts numeric type")
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 4454d51b75877..1d9ee5ddf3a5a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -116,7 +116,7 @@ class HiveTypeCoercionSuite extends PlanTest {
     shouldNotCast(IntegerType, MapType)
     shouldNotCast(IntegerType, StructType)
 
-    shouldNotCast(IntervalType, StringType)
+    shouldNotCast(CalendarIntervalType, StringType)
 
     // Don't implicitly cast complex types to string.
     shouldNotCast(ArrayType(StringType), StringType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 408353cf70a49..0e0213be0f57b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -719,12 +719,13 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("case between string and interval") {
-    import org.apache.spark.unsafe.types.Interval
+    import org.apache.spark.unsafe.types.CalendarInterval
 
-    checkEvaluation(Cast(Literal("interval -3 month 7 hours"), IntervalType),
-      new Interval(-3, 7 * Interval.MICROS_PER_HOUR))
+    checkEvaluation(Cast(Literal("interval -3 month 7 hours"), CalendarIntervalType),
+      new CalendarInterval(-3, 7 * CalendarInterval.MICROS_PER_HOUR))
     checkEvaluation(Cast(Literal.create(
-      new Interval(15, -3 * Interval.MICROS_PER_DAY), IntervalType), StringType),
+      new CalendarInterval(15, -3 * CalendarInterval.MICROS_PER_DAY), CalendarIntervalType),
+      StringType),
       "interval 1 years 3 months -3 days")
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index b02e60dc85cdd..2294a670c735f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -220,137 +220,6 @@ case class TakeOrderedAndProject(
   override def outputOrdering: Seq[SortOrder] = sortOrder
 }
 
-/**
- * :: DeveloperApi ::
- * Performs a sort on-heap.
- * @param global when true performs a global sort of all partitions by shuffling the data first
- *               if necessary.
- */
-@DeveloperApi
-case class Sort(
-    sortOrder: Seq[SortOrder],
-    global: Boolean,
-    child: SparkPlan)
-  extends UnaryNode {
-  override def requiredChildDistribution: Seq[Distribution] =
-    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
-
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
-    child.execute().mapPartitions( { iterator =>
-      val ordering = newOrdering(sortOrder, child.output)
-      iterator.map(_.copy()).toArray.sorted(ordering).iterator
-    }, preservesPartitioning = true)
-  }
-
-  override def output: Seq[Attribute] = child.output
-
-  override def outputOrdering: Seq[SortOrder] = sortOrder
-}
-
-/**
- * :: DeveloperApi ::
- * Performs a sort, spilling to disk as needed.
- * @param global when true performs a global sort of all partitions by shuffling the data first
- *               if necessary.
- */
-@DeveloperApi
-case class ExternalSort(
-    sortOrder: Seq[SortOrder],
-    global: Boolean,
-    child: SparkPlan)
-  extends UnaryNode {
-
-  override def requiredChildDistribution: Seq[Distribution] =
-    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
-
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
-    child.execute().mapPartitions( { iterator =>
-      val ordering = newOrdering(sortOrder, child.output)
-      val sorter = new ExternalSorter[InternalRow, Null, InternalRow](ordering = Some(ordering))
-      sorter.insertAll(iterator.map(r => (r.copy, null)))
-      val baseIterator = sorter.iterator.map(_._1)
-      // TODO(marmbrus): The complex type signature below thwarts inference for no reason.
-      CompletionIterator[InternalRow, Iterator[InternalRow]](baseIterator, sorter.stop())
-    }, preservesPartitioning = true)
-  }
-
-  override def output: Seq[Attribute] = child.output
-
-  override def outputOrdering: Seq[SortOrder] = sortOrder
-}
-
-/**
- * :: DeveloperApi ::
- * Optimized version of [[ExternalSort]] that operates on binary data (implemented as part of
- * Project Tungsten).
- *
- * @param global when true performs a global sort of all partitions by shuffling the data first
- *               if necessary.
- * @param testSpillFrequency Method for configuring periodic spilling in unit tests. If set, will
- *                           spill every `frequency` records.
- */
-@DeveloperApi
-case class UnsafeExternalSort(
-    sortOrder: Seq[SortOrder],
-    global: Boolean,
-    child: SparkPlan,
-    testSpillFrequency: Int = 0)
-  extends UnaryNode {
-
-  private[this] val schema: StructType = child.schema
-
-  override def requiredChildDistribution: Seq[Distribution] =
-    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
-
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
-    assert(codegenEnabled, "UnsafeExternalSort requires code generation to be enabled")
-    def doSort(iterator: Iterator[InternalRow]): Iterator[InternalRow] = {
-      val ordering = newOrdering(sortOrder, child.output)
-      val boundSortExpression = BindReferences.bindReference(sortOrder.head, child.output)
-      // Hack until we generate separate comparator implementations for ascending vs. descending
-      // (or choose to codegen them):
-      val prefixComparator = {
-        val comp = SortPrefixUtils.getPrefixComparator(boundSortExpression)
-        if (sortOrder.head.direction == Descending) {
-          new PrefixComparator {
-            override def compare(p1: Long, p2: Long): Int = -1 * comp.compare(p1, p2)
-          }
-        } else {
-          comp
-        }
-      }
-      val prefixComputer = {
-        val prefixComputer = SortPrefixUtils.getPrefixComputer(boundSortExpression)
-        new UnsafeExternalRowSorter.PrefixComputer {
-          override def computePrefix(row: InternalRow): Long = prefixComputer(row)
-        }
-      }
-      val sorter = new UnsafeExternalRowSorter(schema, ordering, prefixComparator, prefixComputer)
-      if (testSpillFrequency > 0) {
-        sorter.setTestSpillFrequency(testSpillFrequency)
-      }
-      sorter.sort(iterator)
-    }
-    child.execute().mapPartitions(doSort, preservesPartitioning = true)
-  }
-
-  override def output: Seq[Attribute] = child.output
-
-  override def outputOrdering: Seq[SortOrder] = sortOrder
-
-  override def outputsUnsafeRows: Boolean = true
-}
-
-@DeveloperApi
-object UnsafeExternalSort {
-  /**
-   * Return true if UnsafeExternalSort can sort rows with the given schema, false otherwise.
-   */
-  def supportsSchema(schema: StructType): Boolean = {
-    UnsafeExternalRowSorter.supportsSchema(schema)
-  }
-}
-
 
 /**
  * :: DeveloperApi ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index e73b3704d4dfe..0cdb407ad57b9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -308,7 +308,7 @@ private[sql] object ResolvedDataSource {
       mode: SaveMode,
       options: Map[String, String],
       data: DataFrame): ResolvedDataSource = {
-    if (data.schema.map(_.dataType).exists(_.isInstanceOf[IntervalType])) {
+    if (data.schema.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
       throw new AnalysisException("Cannot save interval data type into external storage.")
     }
     val clazz: Class[_] = lookupDataSource(provider)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
new file mode 100644
index 0000000000000..f82208868c3e3
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -0,0 +1,159 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.errors._
+import org.apache.spark.sql.catalyst.expressions.{Descending, BindReferences, Attribute, SortOrder}
+import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, OrderedDistribution, Distribution}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.CompletionIterator
+import org.apache.spark.util.collection.ExternalSorter
+import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// This file defines various sort operators.
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+/**
+ * Performs a sort on-heap.
+ * @param global when true performs a global sort of all partitions by shuffling the data first
+ *               if necessary.
+ */
+case class Sort(
+    sortOrder: Seq[SortOrder],
+    global: Boolean,
+    child: SparkPlan)
+  extends UnaryNode {
+  override def requiredChildDistribution: Seq[Distribution] =
+    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
+    child.execute().mapPartitions( { iterator =>
+      val ordering = newOrdering(sortOrder, child.output)
+      iterator.map(_.copy()).toArray.sorted(ordering).iterator
+    }, preservesPartitioning = true)
+  }
+
+  override def output: Seq[Attribute] = child.output
+
+  override def outputOrdering: Seq[SortOrder] = sortOrder
+}
+
+/**
+ * Performs a sort, spilling to disk as needed.
+ * @param global when true performs a global sort of all partitions by shuffling the data first
+ *               if necessary.
+ */
+case class ExternalSort(
+    sortOrder: Seq[SortOrder],
+    global: Boolean,
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
+    child.execute().mapPartitions( { iterator =>
+      val ordering = newOrdering(sortOrder, child.output)
+      val sorter = new ExternalSorter[InternalRow, Null, InternalRow](ordering = Some(ordering))
+      sorter.insertAll(iterator.map(r => (r.copy(), null)))
+      val baseIterator = sorter.iterator.map(_._1)
+      // TODO(marmbrus): The complex type signature below thwarts inference for no reason.
+      CompletionIterator[InternalRow, Iterator[InternalRow]](baseIterator, sorter.stop())
+    }, preservesPartitioning = true)
+  }
+
+  override def output: Seq[Attribute] = child.output
+
+  override def outputOrdering: Seq[SortOrder] = sortOrder
+}
+
+/**
+ * Optimized version of [[ExternalSort]] that operates on binary data (implemented as part of
+ * Project Tungsten).
+ *
+ * @param global when true performs a global sort of all partitions by shuffling the data first
+ *               if necessary.
+ * @param testSpillFrequency Method for configuring periodic spilling in unit tests. If set, will
+ *                           spill every `frequency` records.
+ */
+case class UnsafeExternalSort(
+    sortOrder: Seq[SortOrder],
+    global: Boolean,
+    child: SparkPlan,
+    testSpillFrequency: Int = 0)
+  extends UnaryNode {
+
+  private[this] val schema: StructType = child.schema
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
+    assert(codegenEnabled, "UnsafeExternalSort requires code generation to be enabled")
+    def doSort(iterator: Iterator[InternalRow]): Iterator[InternalRow] = {
+      val ordering = newOrdering(sortOrder, child.output)
+      val boundSortExpression = BindReferences.bindReference(sortOrder.head, child.output)
+      // Hack until we generate separate comparator implementations for ascending vs. descending
+      // (or choose to codegen them):
+      val prefixComparator = {
+        val comp = SortPrefixUtils.getPrefixComparator(boundSortExpression)
+        if (sortOrder.head.direction == Descending) {
+          new PrefixComparator {
+            override def compare(p1: Long, p2: Long): Int = -1 * comp.compare(p1, p2)
+          }
+        } else {
+          comp
+        }
+      }
+      val prefixComputer = {
+        val prefixComputer = SortPrefixUtils.getPrefixComputer(boundSortExpression)
+        new UnsafeExternalRowSorter.PrefixComputer {
+          override def computePrefix(row: InternalRow): Long = prefixComputer(row)
+        }
+      }
+      val sorter = new UnsafeExternalRowSorter(schema, ordering, prefixComparator, prefixComputer)
+      if (testSpillFrequency > 0) {
+        sorter.setTestSpillFrequency(testSpillFrequency)
+      }
+      sorter.sort(iterator)
+    }
+    child.execute().mapPartitions(doSort, preservesPartitioning = true)
+  }
+
+  override def output: Seq[Attribute] = child.output
+
+  override def outputOrdering: Seq[SortOrder] = sortOrder
+
+  override def outputsUnsafeRows: Boolean = true
+}
+
+@DeveloperApi
+object UnsafeExternalSort {
+  /**
+   * Return true if UnsafeExternalSort can sort rows with the given schema, false otherwise.
+   */
+  def supportsSchema(schema: StructType): Boolean = {
+    UnsafeExternalRowSorter.supportsSchema(schema)
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index d13dde1cdc8b2..535011fe3db5b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1577,10 +1577,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
   }
 
   test("SPARK-8753: add interval type") {
-    import org.apache.spark.unsafe.types.Interval
+    import org.apache.spark.unsafe.types.CalendarInterval
 
     val df = sql("select interval 3 years -3 month 7 week 123 microseconds")
-    checkAnswer(df, Row(new Interval(12 * 3 - 3, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123 )))
+    checkAnswer(df, Row(new CalendarInterval(12 * 3 - 3, 7L * 1000 * 1000 * 3600 * 24 * 7 + 123 )))
     withTempPath(f => {
       // Currently we don't yet support saving out values of interval data type.
       val e = intercept[AnalysisException] {
@@ -1602,20 +1602,20 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
   }
 
   test("SPARK-8945: add and subtract expressions for interval type") {
-    import org.apache.spark.unsafe.types.Interval
-    import org.apache.spark.unsafe.types.Interval.MICROS_PER_WEEK
+    import org.apache.spark.unsafe.types.CalendarInterval
+    import org.apache.spark.unsafe.types.CalendarInterval.MICROS_PER_WEEK
 
     val df = sql("select interval 3 years -3 month 7 week 123 microseconds as i")
-    checkAnswer(df, Row(new Interval(12 * 3 - 3, 7L * MICROS_PER_WEEK + 123)))
+    checkAnswer(df, Row(new CalendarInterval(12 * 3 - 3, 7L * MICROS_PER_WEEK + 123)))
 
-    checkAnswer(df.select(df("i") + new Interval(2, 123)),
-      Row(new Interval(12 * 3 - 3 + 2, 7L * MICROS_PER_WEEK + 123 + 123)))
+    checkAnswer(df.select(df("i") + new CalendarInterval(2, 123)),
+      Row(new CalendarInterval(12 * 3 - 3 + 2, 7L * MICROS_PER_WEEK + 123 + 123)))
 
-    checkAnswer(df.select(df("i") - new Interval(2, 123)),
-      Row(new Interval(12 * 3 - 3 - 2, 7L * MICROS_PER_WEEK + 123 - 123)))
+    checkAnswer(df.select(df("i") - new CalendarInterval(2, 123)),
+      Row(new CalendarInterval(12 * 3 - 3 - 2, 7L * MICROS_PER_WEEK + 123 - 123)))
 
     // unary minus
     checkAnswer(df.select(-df("i")),
-      Row(new Interval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
+      Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
similarity index 87%
rename from unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
rename to unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
index 71b1a85a818ea..92a5e4f86f234 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/Interval.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
@@ -24,7 +24,7 @@
 /**
  * The internal representation of interval type.
  */
-public final class Interval implements Serializable {
+public final class CalendarInterval implements Serializable {
   public static final long MICROS_PER_MILLI = 1000L;
   public static final long MICROS_PER_SECOND = MICROS_PER_MILLI * 1000;
   public static final long MICROS_PER_MINUTE = MICROS_PER_SECOND * 60;
@@ -58,7 +58,7 @@ private static long toLong(String s) {
     }
   }
 
-  public static Interval fromString(String s) {
+  public static CalendarInterval fromString(String s) {
     if (s == null) {
       return null;
     }
@@ -75,40 +75,40 @@ public static Interval fromString(String s) {
       microseconds += toLong(m.group(7)) * MICROS_PER_SECOND;
       microseconds += toLong(m.group(8)) * MICROS_PER_MILLI;
       microseconds += toLong(m.group(9));
-      return new Interval((int) months, microseconds);
+      return new CalendarInterval((int) months, microseconds);
     }
   }
 
   public final int months;
   public final long microseconds;
 
-  public Interval(int months, long microseconds) {
+  public CalendarInterval(int months, long microseconds) {
     this.months = months;
     this.microseconds = microseconds;
   }
 
-  public Interval add(Interval that) {
+  public CalendarInterval add(CalendarInterval that) {
     int months = this.months + that.months;
     long microseconds = this.microseconds + that.microseconds;
-    return new Interval(months, microseconds);
+    return new CalendarInterval(months, microseconds);
   }
 
-  public Interval subtract(Interval that) {
+  public CalendarInterval subtract(CalendarInterval that) {
     int months = this.months - that.months;
     long microseconds = this.microseconds - that.microseconds;
-    return new Interval(months, microseconds);
+    return new CalendarInterval(months, microseconds);
   }
 
-  public Interval negate() {
-    return new Interval(-this.months, -this.microseconds);
+  public CalendarInterval negate() {
+    return new CalendarInterval(-this.months, -this.microseconds);
   }
 
   @Override
   public boolean equals(Object other) {
     if (this == other) return true;
-    if (other == null || !(other instanceof Interval)) return false;
+    if (other == null || !(other instanceof CalendarInterval)) return false;
 
-    Interval o = (Interval) other;
+    CalendarInterval o = (CalendarInterval) other;
     return this.months == o.months && this.microseconds == o.microseconds;
   }
 
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
index d29517cda66a3..e6733a7aae6f5 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
@@ -20,16 +20,16 @@
 import org.junit.Test;
 
 import static junit.framework.Assert.*;
-import static org.apache.spark.unsafe.types.Interval.*;
+import static org.apache.spark.unsafe.types.CalendarInterval.*;
 
 public class IntervalSuite {
 
   @Test
   public void equalsTest() {
-    Interval i1 = new Interval(3, 123);
-    Interval i2 = new Interval(3, 321);
-    Interval i3 = new Interval(1, 123);
-    Interval i4 = new Interval(3, 123);
+    CalendarInterval i1 = new CalendarInterval(3, 123);
+    CalendarInterval i2 = new CalendarInterval(3, 321);
+    CalendarInterval i3 = new CalendarInterval(1, 123);
+    CalendarInterval i4 = new CalendarInterval(3, 123);
 
     assertNotSame(i1, i2);
     assertNotSame(i1, i3);
@@ -39,21 +39,21 @@ public void equalsTest() {
 
   @Test
   public void toStringTest() {
-    Interval i;
+    CalendarInterval i;
 
-    i = new Interval(34, 0);
+    i = new CalendarInterval(34, 0);
     assertEquals(i.toString(), "interval 2 years 10 months");
 
-    i = new Interval(-34, 0);
+    i = new CalendarInterval(-34, 0);
     assertEquals(i.toString(), "interval -2 years -10 months");
 
-    i = new Interval(0, 3 * MICROS_PER_WEEK + 13 * MICROS_PER_HOUR + 123);
+    i = new CalendarInterval(0, 3 * MICROS_PER_WEEK + 13 * MICROS_PER_HOUR + 123);
     assertEquals(i.toString(), "interval 3 weeks 13 hours 123 microseconds");
 
-    i = new Interval(0, -3 * MICROS_PER_WEEK - 13 * MICROS_PER_HOUR - 123);
+    i = new CalendarInterval(0, -3 * MICROS_PER_WEEK - 13 * MICROS_PER_HOUR - 123);
     assertEquals(i.toString(), "interval -3 weeks -13 hours -123 microseconds");
 
-    i = new Interval(34, 3 * MICROS_PER_WEEK + 13 * MICROS_PER_HOUR + 123);
+    i = new CalendarInterval(34, 3 * MICROS_PER_WEEK + 13 * MICROS_PER_HOUR + 123);
     assertEquals(i.toString(), "interval 2 years 10 months 3 weeks 13 hours 123 microseconds");
   }
 
@@ -72,33 +72,33 @@ public void fromStringTest() {
     String input;
 
     input = "interval   -5  years  23   month";
-    Interval result = new Interval(-5 * 12 + 23, 0);
-    assertEquals(Interval.fromString(input), result);
+    CalendarInterval result = new CalendarInterval(-5 * 12 + 23, 0);
+    assertEquals(CalendarInterval.fromString(input), result);
 
     input = "interval   -5  years  23   month   ";
-    assertEquals(Interval.fromString(input), result);
+    assertEquals(CalendarInterval.fromString(input), result);
 
     input = "  interval   -5  years  23   month   ";
-    assertEquals(Interval.fromString(input), result);
+    assertEquals(CalendarInterval.fromString(input), result);
 
     // Error cases
     input = "interval   3month 1 hour";
-    assertEquals(Interval.fromString(input), null);
+    assertEquals(CalendarInterval.fromString(input), null);
 
     input = "interval 3 moth 1 hour";
-    assertEquals(Interval.fromString(input), null);
+    assertEquals(CalendarInterval.fromString(input), null);
 
     input = "interval";
-    assertEquals(Interval.fromString(input), null);
+    assertEquals(CalendarInterval.fromString(input), null);
 
     input = "int";
-    assertEquals(Interval.fromString(input), null);
+    assertEquals(CalendarInterval.fromString(input), null);
 
     input = "";
-    assertEquals(Interval.fromString(input), null);
+    assertEquals(CalendarInterval.fromString(input), null);
 
     input = null;
-    assertEquals(Interval.fromString(input), null);
+    assertEquals(CalendarInterval.fromString(input), null);
   }
 
   @Test
@@ -106,18 +106,18 @@ public void addTest() {
     String input = "interval 3 month 1 hour";
     String input2 = "interval 2 month 100 hour";
 
-    Interval interval = Interval.fromString(input);
-    Interval interval2 = Interval.fromString(input2);
+    CalendarInterval interval = CalendarInterval.fromString(input);
+    CalendarInterval interval2 = CalendarInterval.fromString(input2);
 
-    assertEquals(interval.add(interval2), new Interval(5, 101 * MICROS_PER_HOUR));
+    assertEquals(interval.add(interval2), new CalendarInterval(5, 101 * MICROS_PER_HOUR));
 
     input = "interval -10 month -81 hour";
     input2 = "interval 75 month 200 hour";
 
-    interval = Interval.fromString(input);
-    interval2 = Interval.fromString(input2);
+    interval = CalendarInterval.fromString(input);
+    interval2 = CalendarInterval.fromString(input2);
 
-    assertEquals(interval.add(interval2), new Interval(65, 119 * MICROS_PER_HOUR));
+    assertEquals(interval.add(interval2), new CalendarInterval(65, 119 * MICROS_PER_HOUR));
   }
 
   @Test
@@ -125,25 +125,25 @@ public void subtractTest() {
     String input = "interval 3 month 1 hour";
     String input2 = "interval 2 month 100 hour";
 
-    Interval interval = Interval.fromString(input);
-    Interval interval2 = Interval.fromString(input2);
+    CalendarInterval interval = CalendarInterval.fromString(input);
+    CalendarInterval interval2 = CalendarInterval.fromString(input2);
 
-    assertEquals(interval.subtract(interval2), new Interval(1, -99 * MICROS_PER_HOUR));
+    assertEquals(interval.subtract(interval2), new CalendarInterval(1, -99 * MICROS_PER_HOUR));
 
     input = "interval -10 month -81 hour";
     input2 = "interval 75 month 200 hour";
 
-    interval = Interval.fromString(input);
-    interval2 = Interval.fromString(input2);
+    interval = CalendarInterval.fromString(input);
+    interval2 = CalendarInterval.fromString(input2);
 
-    assertEquals(interval.subtract(interval2), new Interval(-85, -281 * MICROS_PER_HOUR));
+    assertEquals(interval.subtract(interval2), new CalendarInterval(-85, -281 * MICROS_PER_HOUR));
   }
 
   private void testSingleUnit(String unit, int number, int months, long microseconds) {
     String input1 = "interval " + number + " " + unit;
     String input2 = "interval " + number + " " + unit + "s";
-    Interval result = new Interval(months, microseconds);
-    assertEquals(Interval.fromString(input1), result);
-    assertEquals(Interval.fromString(input2), result);
+    CalendarInterval result = new CalendarInterval(months, microseconds);
+    assertEquals(CalendarInterval.fromString(input1), result);
+    assertEquals(CalendarInterval.fromString(input2), result);
   }
 }

From b715933fc69a49653abdb2fba0818dfc4f35d358 Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Wed, 29 Jul 2015 13:59:00 -0700
Subject: [PATCH 0672/1454] [SPARK-9436] [GRAPHX] Pregel simplification patch

Pregel code contains two consecutive joins:
```
g.vertices.innerJoin(messages)(vprog)
...
g = g.outerJoinVertices(newVerts)
{ (vid, old, newOpt) => newOpt.getOrElse(old) }
```
This can be simplified with one join. ankurdave proposed a patch based on our discussion in the mailing list: https://www.mail-archive.com/devspark.apache.org/msg10316.html

Author: Alexander Ulanov <nashb@yandex.ru>

Closes #7749 from avulanov/SPARK-9436-pregel and squashes the following commits:

8568e06 [Alexander Ulanov] Pregel simplification patch
---
 .../org/apache/spark/graphx/Pregel.scala      | 23 ++++++++-----------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
index cfcf7244eaed5..2ca60d51f8331 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
@@ -127,28 +127,25 @@ object Pregel extends Logging {
     var prevG: Graph[VD, ED] = null
     var i = 0
     while (activeMessages > 0 && i < maxIterations) {
-      // Receive the messages. Vertices that didn't get any messages do not appear in newVerts.
-      val newVerts = g.vertices.innerJoin(messages)(vprog).cache()
-      // Update the graph with the new vertices.
+      // Receive the messages and update the vertices.
       prevG = g
-      g = g.outerJoinVertices(newVerts) { (vid, old, newOpt) => newOpt.getOrElse(old) }
-      g.cache()
+      g = g.joinVertices(messages)(vprog).cache()
 
       val oldMessages = messages
-      // Send new messages. Vertices that didn't get any messages don't appear in newVerts, so don't
-      // get to send messages. We must cache messages so it can be materialized on the next line,
-      // allowing us to uncache the previous iteration.
-      messages = g.mapReduceTriplets(sendMsg, mergeMsg, Some((newVerts, activeDirection))).cache()
-      // The call to count() materializes `messages`, `newVerts`, and the vertices of `g`. This
-      // hides oldMessages (depended on by newVerts), newVerts (depended on by messages), and the
-      // vertices of prevG (depended on by newVerts, oldMessages, and the vertices of g).
+      // Send new messages, skipping edges where neither side received a message. We must cache
+      // messages so it can be materialized on the next line, allowing us to uncache the previous
+      // iteration.
+      messages = g.mapReduceTriplets(
+        sendMsg, mergeMsg, Some((oldMessages, activeDirection))).cache()
+      // The call to count() materializes `messages` and the vertices of `g`. This hides oldMessages
+      // (depended on by the vertices of g) and the vertices of prevG (depended on by oldMessages
+      // and the vertices of g).
       activeMessages = messages.count()
 
       logInfo("Pregel finished iteration " + i)
 
       // Unpersist the RDDs hidden by newly-materialized RDDs
       oldMessages.unpersist(blocking = false)
-      newVerts.unpersist(blocking = false)
       prevG.unpersistVertices(blocking = false)
       prevG.edges.unpersist(blocking = false)
       // count the iteration

From 1b0099fc62d02ff6216a76fbfe17a4ec5b2f3536 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 29 Jul 2015 16:00:30 -0700
Subject: [PATCH 0673/1454] [SPARK-9411] [SQL] Make Tungsten page sizes
 configurable

We need to make page sizes configurable so we can reduce them in unit tests and increase them in real production workloads.  These sizes are now controlled by a new configuration, `spark.buffer.pageSize`.  The new default is 64 megabytes.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7741 from JoshRosen/SPARK-9411 and squashes the following commits:

a43c4db [Josh Rosen] Fix pow
2c0eefc [Josh Rosen] Fix MAXIMUM_PAGE_SIZE_BYTES comment + value
bccfb51 [Josh Rosen] Lower page size to 4MB in TestHive
ba54d4b [Josh Rosen] Make UnsafeExternalSorter's page size configurable
0045aa2 [Josh Rosen] Make UnsafeShuffle's page size configurable
bc734f0 [Josh Rosen] Rename configuration
e614858 [Josh Rosen] Makes BytesToBytesMap page size configurable
---
 .../unsafe/UnsafeShuffleExternalSorter.java   | 35 +++++++++------
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |  5 +++
 .../unsafe/sort/UnsafeExternalSorter.java     | 30 +++++++------
 .../unsafe/UnsafeShuffleWriterSuite.java      |  6 +--
 .../UnsafeFixedWidthAggregationMap.java       |  5 ++-
 .../UnsafeFixedWidthAggregationMapSuite.scala |  6 ++-
 .../sql/execution/GeneratedAggregate.scala    |  4 +-
 .../sql/execution/joins/HashedRelation.scala  |  7 ++-
 .../apache/spark/sql/hive/test/TestHive.scala |  1 +
 .../spark/unsafe/map/BytesToBytesMap.java     | 43 ++++++++++++-------
 .../unsafe/memory/TaskMemoryManager.java      | 13 ++++--
 .../map/AbstractBytesToBytesMapSuite.java     | 22 +++++-----
 12 files changed, 112 insertions(+), 65 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 1d460432be9ff..1aa6ba4201261 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -59,14 +59,14 @@ final class UnsafeShuffleExternalSorter {
 
   private final Logger logger = LoggerFactory.getLogger(UnsafeShuffleExternalSorter.class);
 
-  private static final int PAGE_SIZE = PackedRecordPointer.MAXIMUM_PAGE_SIZE_BYTES;
   @VisibleForTesting
   static final int DISK_WRITE_BUFFER_SIZE = 1024 * 1024;
-  @VisibleForTesting
-  static final int MAX_RECORD_SIZE = PAGE_SIZE - 4;
 
   private final int initialSize;
   private final int numPartitions;
+  private final int pageSizeBytes;
+  @VisibleForTesting
+  final int maxRecordSizeBytes;
   private final TaskMemoryManager memoryManager;
   private final ShuffleMemoryManager shuffleMemoryManager;
   private final BlockManager blockManager;
@@ -109,7 +109,10 @@ public UnsafeShuffleExternalSorter(
     this.numPartitions = numPartitions;
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
     this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
-
+    this.pageSizeBytes = (int) Math.min(
+      PackedRecordPointer.MAXIMUM_PAGE_SIZE_BYTES,
+      conf.getSizeAsBytes("spark.buffer.pageSize", "64m"));
+    this.maxRecordSizeBytes = pageSizeBytes - 4;
     this.writeMetrics = writeMetrics;
     initializeForWriting();
   }
@@ -272,7 +275,11 @@ void spill() throws IOException {
   }
 
   private long getMemoryUsage() {
-    return sorter.getMemoryUsage() + (allocatedPages.size() * (long) PAGE_SIZE);
+    long totalPageSize = 0;
+    for (MemoryBlock page : allocatedPages) {
+      totalPageSize += page.size();
+    }
+    return sorter.getMemoryUsage() + totalPageSize;
   }
 
   private long freeMemory() {
@@ -346,23 +353,23 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
       // TODO: we should track metrics on the amount of space wasted when we roll over to a new page
       // without using the free space at the end of the current page. We should also do this for
       // BytesToBytesMap.
-      if (requiredSpace > PAGE_SIZE) {
+      if (requiredSpace > pageSizeBytes) {
         throw new IOException("Required space " + requiredSpace + " is greater than page size (" +
-          PAGE_SIZE + ")");
+          pageSizeBytes + ")");
       } else {
-        final long memoryAcquired = shuffleMemoryManager.tryToAcquire(PAGE_SIZE);
-        if (memoryAcquired < PAGE_SIZE) {
+        final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+        if (memoryAcquired < pageSizeBytes) {
           shuffleMemoryManager.release(memoryAcquired);
           spill();
-          final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(PAGE_SIZE);
-          if (memoryAcquiredAfterSpilling != PAGE_SIZE) {
+          final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+          if (memoryAcquiredAfterSpilling != pageSizeBytes) {
             shuffleMemoryManager.release(memoryAcquiredAfterSpilling);
-            throw new IOException("Unable to acquire " + PAGE_SIZE + " bytes of memory");
+            throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
           }
         }
-        currentPage = memoryManager.allocatePage(PAGE_SIZE);
+        currentPage = memoryManager.allocatePage(pageSizeBytes);
         currentPagePosition = currentPage.getBaseOffset();
-        freeSpaceInCurrentPage = PAGE_SIZE;
+        freeSpaceInCurrentPage = pageSizeBytes;
         allocatedPages.add(currentPage);
       }
     }
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index 764578b181422..d47d6fc9c2ac4 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -129,6 +129,11 @@ public UnsafeShuffleWriter(
     open();
   }
 
+  @VisibleForTesting
+  public int maxRecordSizeBytes() {
+    return sorter.maxRecordSizeBytes;
+  }
+
   /**
    * This convenience method should only be called in test code.
    */
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 80b03d7e99e2b..c21990f4e4778 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -41,10 +41,7 @@ public final class UnsafeExternalSorter {
 
   private final Logger logger = LoggerFactory.getLogger(UnsafeExternalSorter.class);
 
-  private static final int PAGE_SIZE = 1 << 27;  // 128 megabytes
-  @VisibleForTesting
-  static final int MAX_RECORD_SIZE = PAGE_SIZE - 4;
-
+  private final long pageSizeBytes;
   private final PrefixComparator prefixComparator;
   private final RecordComparator recordComparator;
   private final int initialSize;
@@ -91,6 +88,7 @@ public UnsafeExternalSorter(
     this.initialSize = initialSize;
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility for units
     this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
+    this.pageSizeBytes = conf.getSizeAsBytes("spark.buffer.pageSize", "64m");
     initializeForWriting();
   }
 
@@ -147,7 +145,11 @@ public void spill() throws IOException {
   }
 
   private long getMemoryUsage() {
-    return sorter.getMemoryUsage() + (allocatedPages.size() * (long) PAGE_SIZE);
+    long totalPageSize = 0;
+    for (MemoryBlock page : allocatedPages) {
+      totalPageSize += page.size();
+    }
+    return sorter.getMemoryUsage() + totalPageSize;
   }
 
   @VisibleForTesting
@@ -214,23 +216,23 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
       // TODO: we should track metrics on the amount of space wasted when we roll over to a new page
       // without using the free space at the end of the current page. We should also do this for
       // BytesToBytesMap.
-      if (requiredSpace > PAGE_SIZE) {
+      if (requiredSpace > pageSizeBytes) {
         throw new IOException("Required space " + requiredSpace + " is greater than page size (" +
-          PAGE_SIZE + ")");
+          pageSizeBytes + ")");
       } else {
-        final long memoryAcquired = shuffleMemoryManager.tryToAcquire(PAGE_SIZE);
-        if (memoryAcquired < PAGE_SIZE) {
+        final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+        if (memoryAcquired < pageSizeBytes) {
           shuffleMemoryManager.release(memoryAcquired);
           spill();
-          final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(PAGE_SIZE);
-          if (memoryAcquiredAfterSpilling != PAGE_SIZE) {
+          final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+          if (memoryAcquiredAfterSpilling != pageSizeBytes) {
             shuffleMemoryManager.release(memoryAcquiredAfterSpilling);
-            throw new IOException("Unable to acquire " + PAGE_SIZE + " bytes of memory");
+            throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
           }
         }
-        currentPage = memoryManager.allocatePage(PAGE_SIZE);
+        currentPage = memoryManager.allocatePage(pageSizeBytes);
         currentPagePosition = currentPage.getBaseOffset();
-        freeSpaceInCurrentPage = PAGE_SIZE;
+        freeSpaceInCurrentPage = pageSizeBytes;
         allocatedPages.add(currentPage);
       }
     }
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 10c3eedbf4b46..04fc09b323dbb 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -111,7 +111,7 @@ public void setUp() throws IOException {
     mergedOutputFile = File.createTempFile("mergedoutput", "", tempDir);
     partitionSizesInMergedFile = null;
     spillFilesCreated.clear();
-    conf = new SparkConf();
+    conf = new SparkConf().set("spark.buffer.pageSize", "128m");
     taskMetrics = new TaskMetrics();
 
     when(shuffleMemoryManager.tryToAcquire(anyLong())).then(returnsFirstArg());
@@ -512,12 +512,12 @@ public void close() { }
     writer.insertRecordIntoSorter(new Tuple2<Object, Object>(new byte[1], new byte[1]));
     writer.forceSorterToSpill();
     // We should be able to write a record that's right _at_ the max record size
-    final byte[] atMaxRecordSize = new byte[UnsafeShuffleExternalSorter.MAX_RECORD_SIZE];
+    final byte[] atMaxRecordSize = new byte[writer.maxRecordSizeBytes()];
     new Random(42).nextBytes(atMaxRecordSize);
     writer.insertRecordIntoSorter(new Tuple2<Object, Object>(new byte[0], atMaxRecordSize));
     writer.forceSorterToSpill();
     // Inserting a record that's larger than the max record size should fail:
-    final byte[] exceedsMaxRecordSize = new byte[UnsafeShuffleExternalSorter.MAX_RECORD_SIZE + 1];
+    final byte[] exceedsMaxRecordSize = new byte[writer.maxRecordSizeBytes() + 1];
     new Random(42).nextBytes(exceedsMaxRecordSize);
     Product2<Object, Object> hugeRecord =
       new Tuple2<Object, Object>(new byte[0], exceedsMaxRecordSize);
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
index 684de6e81d67c..03f4c3ed8e6bb 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
@@ -95,6 +95,7 @@ public static boolean supportsAggregationBufferSchema(StructType schema) {
    * @param groupingKeySchema the schema of the grouping key, used for row conversion.
    * @param memoryManager the memory manager used to allocate our Unsafe memory structures.
    * @param initialCapacity the initial capacity of the map (a sizing hint to avoid re-hashing).
+   * @param pageSizeBytes the data page size, in bytes; limits the maximum record size.
    * @param enablePerfMetrics if true, performance metrics will be recorded (has minor perf impact)
    */
   public UnsafeFixedWidthAggregationMap(
@@ -103,11 +104,13 @@ public UnsafeFixedWidthAggregationMap(
       StructType groupingKeySchema,
       TaskMemoryManager memoryManager,
       int initialCapacity,
+      long pageSizeBytes,
       boolean enablePerfMetrics) {
     this.aggregationBufferSchema = aggregationBufferSchema;
     this.groupingKeyProjection = UnsafeProjection.create(groupingKeySchema);
     this.groupingKeySchema = groupingKeySchema;
-    this.map = new BytesToBytesMap(memoryManager, initialCapacity, enablePerfMetrics);
+    this.map =
+      new BytesToBytesMap(memoryManager, initialCapacity, pageSizeBytes, enablePerfMetrics);
     this.enablePerfMetrics = enablePerfMetrics;
 
     // Initialize the buffer for aggregation value
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
index 48b7dc57451a3..6a907290f2dbe 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
@@ -39,6 +39,7 @@ class UnsafeFixedWidthAggregationMapSuite
   private val groupKeySchema = StructType(StructField("product", StringType) :: Nil)
   private val aggBufferSchema = StructType(StructField("salePrice", IntegerType) :: Nil)
   private def emptyAggregationBuffer: InternalRow = InternalRow(0)
+  private val PAGE_SIZE_BYTES: Long = 1L << 26; // 64 megabytes
 
   private var memoryManager: TaskMemoryManager = null
 
@@ -69,7 +70,8 @@ class UnsafeFixedWidthAggregationMapSuite
       aggBufferSchema,
       groupKeySchema,
       memoryManager,
-      1024, // initial capacity
+      1024, // initial capacity,
+      PAGE_SIZE_BYTES,
       false // disable perf metrics
     )
     assert(!map.iterator().hasNext)
@@ -83,6 +85,7 @@ class UnsafeFixedWidthAggregationMapSuite
       groupKeySchema,
       memoryManager,
       1024, // initial capacity
+      PAGE_SIZE_BYTES,
       false // disable perf metrics
     )
     val groupKey = InternalRow(UTF8String.fromString("cats"))
@@ -109,6 +112,7 @@ class UnsafeFixedWidthAggregationMapSuite
       groupKeySchema,
       memoryManager,
       128, // initial capacity
+      PAGE_SIZE_BYTES,
       false // disable perf metrics
     )
     val rand = new Random(42)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 1cd1420480f03..b85aada9d9d4c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.TaskContext
+import org.apache.spark.{SparkEnv, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -260,12 +260,14 @@ case class GeneratedAggregate(
       } else if (unsafeEnabled && schemaSupportsUnsafe) {
         assert(iter.hasNext, "There should be at least one row for this path")
         log.info("Using Unsafe-based aggregator")
+        val pageSizeBytes = SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
         val aggregationMap = new UnsafeFixedWidthAggregationMap(
           newAggregationBuffer(EmptyRow),
           aggregationBufferSchema,
           groupKeySchema,
           TaskContext.get.taskMemoryManager(),
           1024 * 16, // initial capacity
+          pageSizeBytes,
           false // disable tracking of performance metrics
         )
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 9c058f1f72fe4..7a507391316a9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -21,6 +21,7 @@ import java.io.{Externalizable, ObjectInput, ObjectOutput}
 import java.nio.ByteOrder
 import java.util.{HashMap => JavaHashMap}
 
+import org.apache.spark.{SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
@@ -259,7 +260,11 @@ private[joins] final class UnsafeHashedRelation(
     val nKeys = in.readInt()
     // This is used in Broadcast, shared by multiple tasks, so we use on-heap memory
     val memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
-    binaryMap = new BytesToBytesMap(memoryManager, nKeys * 2) // reduce hash collision
+    val pageSizeBytes = SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
+    binaryMap = new BytesToBytesMap(
+      memoryManager,
+      nKeys * 2, // reduce hash collision
+      pageSizeBytes)
 
     var i = 0
     var keyBuffer = new Array[Byte](1024)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 3662a4352f55d..7bbdef90cd6b9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -56,6 +56,7 @@ object TestHive
         .set("spark.sql.test", "")
         .set("spark.sql.hive.metastore.barrierPrefixes",
           "org.apache.spark.sql.hive.execution.PairSerDe")
+        .set("spark.buffer.pageSize", "4m")
         // SPARK-8910
         .set("spark.ui.enabled", "false")))
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index d0bde69cc1068..198e0684f32f8 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -74,12 +74,6 @@ public final class BytesToBytesMap {
    */
   private long pageCursor = 0;
 
-  /**
-   * The size of the data pages that hold key and value data. Map entries cannot span multiple
-   * pages, so this limits the maximum entry size.
-   */
-  private static final long PAGE_SIZE_BYTES = 1L << 26; // 64 megabytes
-
   /**
    * The maximum number of keys that BytesToBytesMap supports. The hash table has to be
    * power-of-2-sized and its backing Java array can contain at most (1 << 30) elements, since
@@ -117,6 +111,12 @@ public final class BytesToBytesMap {
 
   private final double loadFactor;
 
+  /**
+   * The size of the data pages that hold key and value data. Map entries cannot span multiple
+   * pages, so this limits the maximum entry size.
+   */
+  private final long pageSizeBytes;
+
   /**
    * Number of keys defined in the map.
    */
@@ -153,10 +153,12 @@ public BytesToBytesMap(
       TaskMemoryManager memoryManager,
       int initialCapacity,
       double loadFactor,
+      long pageSizeBytes,
       boolean enablePerfMetrics) {
     this.memoryManager = memoryManager;
     this.loadFactor = loadFactor;
     this.loc = new Location();
+    this.pageSizeBytes = pageSizeBytes;
     this.enablePerfMetrics = enablePerfMetrics;
     if (initialCapacity <= 0) {
       throw new IllegalArgumentException("Initial capacity must be greater than 0");
@@ -165,18 +167,26 @@ public BytesToBytesMap(
       throw new IllegalArgumentException(
         "Initial capacity " + initialCapacity + " exceeds maximum capacity of " + MAX_CAPACITY);
     }
+    if (pageSizeBytes > TaskMemoryManager.MAXIMUM_PAGE_SIZE_BYTES) {
+      throw new IllegalArgumentException("Page size " + pageSizeBytes + " cannot exceed " +
+        TaskMemoryManager.MAXIMUM_PAGE_SIZE_BYTES);
+    }
     allocate(initialCapacity);
   }
 
-  public BytesToBytesMap(TaskMemoryManager memoryManager, int initialCapacity) {
-    this(memoryManager, initialCapacity, 0.70, false);
+  public BytesToBytesMap(
+      TaskMemoryManager memoryManager,
+      int initialCapacity,
+      long pageSizeBytes) {
+    this(memoryManager, initialCapacity, 0.70, pageSizeBytes, false);
   }
 
   public BytesToBytesMap(
       TaskMemoryManager memoryManager,
       int initialCapacity,
+      long pageSizeBytes,
       boolean enablePerfMetrics) {
-    this(memoryManager, initialCapacity, 0.70, enablePerfMetrics);
+    this(memoryManager, initialCapacity, 0.70, pageSizeBytes, enablePerfMetrics);
   }
 
   /**
@@ -443,20 +453,20 @@ public void putNewKey(
       // must be stored in the same memory page.
       // (8 byte key length) (key) (8 byte value length) (value)
       final long requiredSize = 8 + keyLengthBytes + 8 + valueLengthBytes;
-      assert (requiredSize <= PAGE_SIZE_BYTES - 8); // Reserve 8 bytes for the end-of-page marker.
+      assert (requiredSize <= pageSizeBytes - 8); // Reserve 8 bytes for the end-of-page marker.
       size++;
       bitset.set(pos);
 
       // If there's not enough space in the current page, allocate a new page (8 bytes are reserved
       // for the end-of-page marker).
-      if (currentDataPage == null || PAGE_SIZE_BYTES - 8 - pageCursor < requiredSize) {
+      if (currentDataPage == null || pageSizeBytes - 8 - pageCursor < requiredSize) {
         if (currentDataPage != null) {
           // There wasn't enough space in the current page, so write an end-of-page marker:
           final Object pageBaseObject = currentDataPage.getBaseObject();
           final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor;
           PlatformDependent.UNSAFE.putLong(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
         }
-        MemoryBlock newPage = memoryManager.allocatePage(PAGE_SIZE_BYTES);
+        MemoryBlock newPage = memoryManager.allocatePage(pageSizeBytes);
         dataPages.add(newPage);
         pageCursor = 0;
         currentDataPage = newPage;
@@ -538,10 +548,11 @@ public void free() {
 
   /** Returns the total amount of memory, in bytes, consumed by this map's managed structures. */
   public long getTotalMemoryConsumption() {
-    return (
-      dataPages.size() * PAGE_SIZE_BYTES +
-      bitset.memoryBlock().size() +
-      longArray.memoryBlock().size());
+    long totalDataPagesSize = 0L;
+    for (MemoryBlock dataPage : dataPages) {
+      totalDataPagesSize += dataPage.size();
+    }
+    return totalDataPagesSize + bitset.memoryBlock().size() + longArray.memoryBlock().size();
   }
 
   /**
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
index 10881969dbc78..dd70df3b1f791 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
@@ -58,8 +58,13 @@ public class TaskMemoryManager {
   /** The number of entries in the page table. */
   private static final int PAGE_TABLE_SIZE = 1 << PAGE_NUMBER_BITS;
 
-  /** Maximum supported data page size */
-  private static final long MAXIMUM_PAGE_SIZE = (1L << OFFSET_BITS);
+  /**
+   * Maximum supported data page size (in bytes). In principle, the maximum addressable page size is
+   * (1L << OFFSET_BITS) bytes, which is 2+ petabytes. However, the on-heap allocator's maximum page
+   * size is limited by the maximum amount of data that can be stored in a  long[] array, which is
+   * (2^32 - 1) * 8 bytes (or 16 gigabytes). Therefore, we cap this at 16 gigabytes.
+   */
+  public static final long MAXIMUM_PAGE_SIZE_BYTES = ((1L << 31) - 1) * 8L;
 
   /** Bit mask for the lower 51 bits of a long. */
   private static final long MASK_LONG_LOWER_51_BITS = 0x7FFFFFFFFFFFFL;
@@ -110,9 +115,9 @@ public TaskMemoryManager(ExecutorMemoryManager executorMemoryManager) {
    * intended for allocating large blocks of memory that will be shared between operators.
    */
   public MemoryBlock allocatePage(long size) {
-    if (size > MAXIMUM_PAGE_SIZE) {
+    if (size > MAXIMUM_PAGE_SIZE_BYTES) {
       throw new IllegalArgumentException(
-        "Cannot allocate a page with more than " + MAXIMUM_PAGE_SIZE + " bytes");
+        "Cannot allocate a page with more than " + MAXIMUM_PAGE_SIZE_BYTES + " bytes");
     }
 
     final int pageNumber;
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index dae47e4bab0cb..0be94ad371255 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -43,6 +43,7 @@ public abstract class AbstractBytesToBytesMapSuite {
 
   private TaskMemoryManager memoryManager;
   private TaskMemoryManager sizeLimitedMemoryManager;
+  private final long PAGE_SIZE_BYTES = 1L << 26; // 64 megabytes
 
   @Before
   public void setup() {
@@ -110,7 +111,7 @@ private static boolean arrayEquals(
 
   @Test
   public void emptyMap() {
-    BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64);
+    BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64, PAGE_SIZE_BYTES);
     try {
       Assert.assertEquals(0, map.size());
       final int keyLengthInWords = 10;
@@ -125,7 +126,7 @@ public void emptyMap() {
 
   @Test
   public void setAndRetrieveAKey() {
-    BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64);
+    BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64, PAGE_SIZE_BYTES);
     final int recordLengthWords = 10;
     final int recordLengthBytes = recordLengthWords * 8;
     final byte[] keyData = getRandomByteArray(recordLengthWords);
@@ -177,7 +178,7 @@ public void setAndRetrieveAKey() {
   @Test
   public void iteratorTest() throws Exception {
     final int size = 4096;
-    BytesToBytesMap map = new BytesToBytesMap(memoryManager, size / 2);
+    BytesToBytesMap map = new BytesToBytesMap(memoryManager, size / 2, PAGE_SIZE_BYTES);
     try {
       for (long i = 0; i < size; i++) {
         final long[] value = new long[] { i };
@@ -235,7 +236,7 @@ public void iteratingOverDataPagesWithWastedSpace() throws Exception {
     final int NUM_ENTRIES = 1000 * 1000;
     final int KEY_LENGTH = 16;
     final int VALUE_LENGTH = 40;
-    final BytesToBytesMap map = new BytesToBytesMap(memoryManager, NUM_ENTRIES);
+    final BytesToBytesMap map = new BytesToBytesMap(memoryManager, NUM_ENTRIES, PAGE_SIZE_BYTES);
     // Each record will take 8 + 8 + 16 + 40 = 72 bytes of space in the data page. Our 64-megabyte
     // pages won't be evenly-divisible by records of this size, which will cause us to waste some
     // space at the end of the page. This is necessary in order for us to take the end-of-record
@@ -304,7 +305,7 @@ public void randomizedStressTest() {
     // Java arrays' hashCodes() aren't based on the arrays' contents, so we need to wrap arrays
     // into ByteBuffers in order to use them as keys here.
     final Map<ByteBuffer, byte[]> expected = new HashMap<ByteBuffer, byte[]>();
-    final BytesToBytesMap map = new BytesToBytesMap(memoryManager, size);
+    final BytesToBytesMap map = new BytesToBytesMap(memoryManager, size, PAGE_SIZE_BYTES);
 
     try {
       // Fill the map to 90% full so that we can trigger probing
@@ -353,14 +354,15 @@ public void randomizedStressTest() {
   @Test
   public void initialCapacityBoundsChecking() {
     try {
-      new BytesToBytesMap(sizeLimitedMemoryManager, 0);
+      new BytesToBytesMap(sizeLimitedMemoryManager, 0, PAGE_SIZE_BYTES);
       Assert.fail("Expected IllegalArgumentException to be thrown");
     } catch (IllegalArgumentException e) {
       // expected exception
     }
 
     try {
-      new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY + 1);
+      new BytesToBytesMap(
+        sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY + 1, PAGE_SIZE_BYTES);
       Assert.fail("Expected IllegalArgumentException to be thrown");
     } catch (IllegalArgumentException e) {
       // expected exception
@@ -368,15 +370,15 @@ public void initialCapacityBoundsChecking() {
 
    // Can allocate _at_ the max capacity
     BytesToBytesMap map =
-      new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY);
+      new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY, PAGE_SIZE_BYTES);
     map.free();
   }
 
   @Test
   public void resizingLargeMap() {
     // As long as a map's capacity is below the max, we should be able to resize up to the max
-    BytesToBytesMap map =
-      new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY - 64);
+    BytesToBytesMap map = new BytesToBytesMap(
+      sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY - 64, PAGE_SIZE_BYTES);
     map.growAndRehash();
     map.free();
   }

From 2cc212d56a1d50fe68d5816f71b27803de1f6389 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 29 Jul 2015 16:20:20 -0700
Subject: [PATCH 0674/1454] [SPARK-6793] [MLLIB] OnlineLDAOptimizer LDA
 perplexity

Implements `logPerplexity` in `OnlineLDAOptimizer`. Also refactors inference code into companion object to enable future reuse (e.g. `predict` method).

Author: Feynman Liang <fliang@databricks.com>

Closes #7705 from feynmanliang/SPARK-6793-perplexity and squashes the following commits:

6da2c99 [Feynman Liang] Remove get* from LDAModel public API
8381da6 [Feynman Liang] Code review comments
17f7000 [Feynman Liang] Documentation typo fixes
2f452a4 [Feynman Liang] Remove auxillary DistributedLDAModel constructor
a275914 [Feynman Liang] Prevent empty counts calls to variationalInference
06d02d9 [Feynman Liang] Remove deprecated LocalLDAModel constructor
afecb46 [Feynman Liang] Fix regression bug in sstats accumulator
5a327a0 [Feynman Liang] Code review quick fixes
998c03e [Feynman Liang] Fix style
1cbb67d [Feynman Liang] Fix access modifier bug
4362daa [Feynman Liang] Organize imports
4f171f7 [Feynman Liang] Fix indendation
2f049ce [Feynman Liang] Fix failing save/load tests
7415e96 [Feynman Liang] Pick changes from big PR
11e7c33 [Feynman Liang] Merge remote-tracking branch 'apache/master' into SPARK-6793-perplexity
f8adc48 [Feynman Liang] Add logPerplexity, refactor variationalBound into a method
cd521d6 [Feynman Liang] Refactor methods into companion class
7f62a55 [Feynman Liang] --amend
c62cb1e [Feynman Liang] Outer product for stats, revert Range slicing
aead650 [Feynman Liang] Range slice, in-place update, reduce transposes
---
 .../spark/mllib/clustering/LDAModel.scala     | 200 ++++++++++++++----
 .../spark/mllib/clustering/LDAOptimizer.scala | 138 +++++++-----
 .../spark/mllib/clustering/LDAUtils.scala     |  55 +++++
 .../spark/mllib/clustering/JavaLDASuite.java  |   6 +-
 .../spark/mllib/clustering/LDASuite.scala     |  53 ++++-
 5 files changed, 348 insertions(+), 104 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 31c1d520fd659..059b52ef20a98 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -17,10 +17,9 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM, normalize, sum => brzSum, DenseVector => BDV}
-
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, normalize, sum}
+import breeze.numerics.{exp, lgamma}
 import org.apache.hadoop.fs.Path
-
 import org.json4s.DefaultFormats
 import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
@@ -28,14 +27,13 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaPairRDD
-import org.apache.spark.graphx.{VertexId, Edge, EdgeContext, Graph}
-import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix, DenseVector}
-import org.apache.spark.mllib.util.{Saveable, Loader}
+import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
+import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
+import org.apache.spark.mllib.util.{Loader, Saveable}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{SQLContext, Row}
+import org.apache.spark.sql.{Row, SQLContext}
 import org.apache.spark.util.BoundedPriorityQueue
 
-
 /**
  * :: Experimental ::
  *
@@ -53,6 +51,31 @@ abstract class LDAModel private[clustering] extends Saveable {
   /** Vocabulary size (number of terms or terms in the vocabulary) */
   def vocabSize: Int
 
+  /**
+   * Concentration parameter (commonly named "alpha") for the prior placed on documents'
+   * distributions over topics ("theta").
+   *
+   * This is the parameter to a Dirichlet distribution.
+   */
+  def docConcentration: Vector
+
+  /**
+   * Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
+   * distributions over terms.
+   *
+   * This is the parameter to a symmetric Dirichlet distribution.
+   *
+   * Note: The topics' distributions over terms are called "beta" in the original LDA paper
+   * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
+   */
+  def topicConcentration: Double
+
+  /**
+  * Shape parameter for random initialization of variational parameter gamma.
+  * Used for variational inference for perplexity and other test-time computations.
+  */
+  protected def gammaShape: Double
+
   /**
    * Inferred topics, where each topic is represented by a distribution over terms.
    * This is a matrix of size vocabSize x k, where each column is a topic.
@@ -168,7 +191,10 @@ abstract class LDAModel private[clustering] extends Saveable {
  */
 @Experimental
 class LocalLDAModel private[clustering] (
-    private val topics: Matrix) extends LDAModel with Serializable {
+    val topics: Matrix,
+    override val docConcentration: Vector,
+    override val topicConcentration: Double,
+    override protected[clustering] val gammaShape: Double) extends LDAModel with Serializable {
 
   override def k: Int = topics.numCols
 
@@ -197,8 +223,82 @@ class LocalLDAModel private[clustering] (
   // TODO:
   // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ???
 
+  /**
+   * Calculate the log variational bound on perplexity. See Equation (16) in original Online
+   * LDA paper.
+   * @param documents test corpus to use for calculating perplexity
+   * @return the log perplexity per word
+   */
+  def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
+    val corpusWords = documents
+      .map { case (_, termCounts) => termCounts.toArray.sum }
+      .sum()
+    val batchVariationalBound = bound(documents, docConcentration,
+      topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k, vocabSize)
+    val perWordBound = batchVariationalBound / corpusWords
+
+    perWordBound
+  }
+
+  /**
+   * Estimate the variational likelihood bound of from `documents`:
+   *    log p(documents) >= E_q[log p(documents)] - E_q[log q(documents)]
+   * This bound is derived by decomposing the LDA model to:
+   *    log p(documents) = E_q[log p(documents)] - E_q[log q(documents)] + D(q|p)
+   * and noting that the KL-divergence D(q|p) >= 0. See Equation (16) in original Online LDA paper.
+   * @param documents a subset of the test corpus
+   * @param alpha document-topic Dirichlet prior parameters
+   * @param eta topic-word Dirichlet prior parameters
+   * @param lambda parameters for variational q(beta | lambda) topic-word distributions
+   * @param gammaShape shape parameter for random initialization of variational q(theta | gamma)
+   *                   topic mixture distributions
+   * @param k number of topics
+   * @param vocabSize number of unique terms in the entire test corpus
+   */
+  private def bound(
+      documents: RDD[(Long, Vector)],
+      alpha: Vector,
+      eta: Double,
+      lambda: BDM[Double],
+      gammaShape: Double,
+      k: Int,
+      vocabSize: Long): Double = {
+    val brzAlpha = alpha.toBreeze.toDenseVector
+    // transpose because dirichletExpectation normalizes by row and we need to normalize
+    // by topic (columns of lambda)
+    val Elogbeta = LDAUtils.dirichletExpectation(lambda.t).t
+
+    var score = documents.filter(_._2.numActives > 0).map { case (id: Long, termCounts: Vector) =>
+      var docScore = 0.0D
+      val (gammad: BDV[Double], _) = OnlineLDAOptimizer.variationalTopicInference(
+        termCounts, exp(Elogbeta), brzAlpha, gammaShape, k)
+      val Elogthetad: BDV[Double] = LDAUtils.dirichletExpectation(gammad)
+
+      // E[log p(doc | theta, beta)]
+      termCounts.foreachActive { case (idx, count) =>
+        docScore += LDAUtils.logSumExp(Elogthetad + Elogbeta(idx, ::).t)
+      }
+      // E[log p(theta | alpha) - log q(theta | gamma)]; assumes alpha is a vector
+      docScore += sum((brzAlpha - gammad) :* Elogthetad)
+      docScore += sum(lgamma(gammad) - lgamma(brzAlpha))
+      docScore += lgamma(sum(brzAlpha)) - lgamma(sum(gammad))
+
+      docScore
+    }.sum()
+
+    // E[log p(beta | eta) - log q (beta | lambda)]; assumes eta is a scalar
+    score += sum((eta - lambda) :* Elogbeta)
+    score += sum(lgamma(lambda) - lgamma(eta))
+
+    val sumEta = eta * vocabSize
+    score += sum(lgamma(sumEta) - lgamma(sum(lambda(::, breeze.linalg.*))))
+
+    score
+  }
+
 }
 
+
 @Experimental
 object LocalLDAModel extends Loader[LocalLDAModel] {
 
@@ -212,6 +312,8 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
     // as a Row in data.
     case class Data(topic: Vector, index: Int)
 
+    // TODO: explicitly save docConcentration, topicConcentration, and gammaShape for use in
+    // model.predict()
     def save(sc: SparkContext, path: String, topicsMatrix: Matrix): Unit = {
       val sqlContext = SQLContext.getOrCreate(sc)
       import sqlContext.implicits._
@@ -219,7 +321,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       val k = topicsMatrix.numCols
       val metadata = compact(render
         (("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~
-         ("k" -> k) ~ ("vocabSize" -> topicsMatrix.numRows)))
+          ("k" -> k) ~ ("vocabSize" -> topicsMatrix.numRows)))
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
 
       val topicsDenseMatrix = topicsMatrix.toBreeze.toDenseMatrix
@@ -243,7 +345,11 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       topics.foreach { case Row(vec: Vector, ind: Int) =>
         brzTopics(::, ind) := vec.toBreeze
       }
-      new LocalLDAModel(Matrices.fromBreeze(brzTopics))
+      val topicsMat = Matrices.fromBreeze(brzTopics)
+
+      // TODO: initialize with docConcentration, topicConcentration, and gammaShape after SPARK-9940
+      new LocalLDAModel(topicsMat,
+        Vectors.dense(Array.fill(topicsMat.numRows)(1.0 / topicsMat.numRows)), 1D, 100D)
     }
   }
 
@@ -259,8 +365,8 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
         SaveLoadV1_0.load(sc, path)
       case _ => throw new Exception(
         s"LocalLDAModel.load did not recognize model with (className, format version):" +
-        s"($loadedClassName, $loadedVersion).  Supported:\n" +
-        s"  ($classNameV1_0, 1.0)")
+          s"($loadedClassName, $loadedVersion).  Supported:\n" +
+          s"  ($classNameV1_0, 1.0)")
     }
 
     val topicsMatrix = model.topicsMatrix
@@ -268,7 +374,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       s"LocalLDAModel requires $expectedK topics, got ${topicsMatrix.numCols} topics")
     require(expectedVocabSize == topicsMatrix.numRows,
       s"LocalLDAModel requires $expectedVocabSize terms for each topic, " +
-      s"but got ${topicsMatrix.numRows}")
+        s"but got ${topicsMatrix.numRows}")
     model
   }
 }
@@ -282,28 +388,25 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
  * than the [[LocalLDAModel]].
  */
 @Experimental
-class DistributedLDAModel private (
+class DistributedLDAModel private[clustering] (
     private[clustering] val graph: Graph[LDA.TopicCounts, LDA.TokenCount],
     private[clustering] val globalTopicTotals: LDA.TopicCounts,
     val k: Int,
     val vocabSize: Int,
-    private[clustering] val docConcentration: Double,
-    private[clustering] val topicConcentration: Double,
+    override val docConcentration: Vector,
+    override val topicConcentration: Double,
+    override protected[clustering] val gammaShape: Double,
     private[spark] val iterationTimes: Array[Double]) extends LDAModel {
 
   import LDA._
 
-  private[clustering] def this(state: EMLDAOptimizer, iterationTimes: Array[Double]) = {
-    this(state.graph, state.globalTopicTotals, state.k, state.vocabSize, state.docConcentration,
-      state.topicConcentration, iterationTimes)
-  }
-
   /**
    * Convert model to a local model.
    * The local model stores the inferred topics but not the topic distributions for training
    * documents.
    */
-  def toLocal: LocalLDAModel = new LocalLDAModel(topicsMatrix)
+  def toLocal: LocalLDAModel = new LocalLDAModel(topicsMatrix, docConcentration, topicConcentration,
+    gammaShape)
 
   /**
    * Inferred topics, where each topic is represented by a distribution over terms.
@@ -375,8 +478,9 @@ class DistributedLDAModel private (
    *    hyperparameters.
    */
   lazy val logLikelihood: Double = {
-    val eta = topicConcentration
-    val alpha = docConcentration
+    // TODO: generalize this for asymmetric (non-scalar) alpha
+    val alpha = this.docConcentration(0) // To avoid closure capture of enclosing object
+    val eta = this.topicConcentration
     assert(eta > 1.0)
     assert(alpha > 1.0)
     val N_k = globalTopicTotals
@@ -400,8 +504,9 @@ class DistributedLDAModel private (
    *  log P(topics, topic distributions for docs | alpha, eta)
    */
   lazy val logPrior: Double = {
-    val eta = topicConcentration
-    val alpha = docConcentration
+    // TODO: generalize this for asymmetric (non-scalar) alpha
+    val alpha = this.docConcentration(0) // To avoid closure capture of enclosing object
+    val eta = this.topicConcentration
     // Term vertices: Compute phi_{wk}.  Use to compute prior log probability.
     // Doc vertex: Compute theta_{kj}.  Use to compute prior log probability.
     val N_k = globalTopicTotals
@@ -412,12 +517,12 @@ class DistributedLDAModel private (
           val N_wk = vertex._2
           val smoothed_N_wk: TopicCounts = N_wk + (eta - 1.0)
           val phi_wk: TopicCounts = smoothed_N_wk :/ smoothed_N_k
-          (eta - 1.0) * brzSum(phi_wk.map(math.log))
+          (eta - 1.0) * sum(phi_wk.map(math.log))
         } else {
           val N_kj = vertex._2
           val smoothed_N_kj: TopicCounts = N_kj + (alpha - 1.0)
           val theta_kj: TopicCounts = normalize(smoothed_N_kj, 1.0)
-          (alpha - 1.0) * brzSum(theta_kj.map(math.log))
+          (alpha - 1.0) * sum(theta_kj.map(math.log))
         }
     }
     graph.vertices.aggregate(0.0)(seqOp, _ + _)
@@ -448,7 +553,7 @@ class DistributedLDAModel private (
   override def save(sc: SparkContext, path: String): Unit = {
     DistributedLDAModel.SaveLoadV1_0.save(
       sc, path, graph, globalTopicTotals, k, vocabSize, docConcentration, topicConcentration,
-      iterationTimes)
+      iterationTimes, gammaShape)
   }
 }
 
@@ -478,17 +583,20 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
         globalTopicTotals: LDA.TopicCounts,
         k: Int,
         vocabSize: Int,
-        docConcentration: Double,
+        docConcentration: Vector,
         topicConcentration: Double,
-        iterationTimes: Array[Double]): Unit = {
+        iterationTimes: Array[Double],
+        gammaShape: Double): Unit = {
       val sqlContext = SQLContext.getOrCreate(sc)
       import sqlContext.implicits._
 
       val metadata = compact(render
         (("class" -> classNameV1_0) ~ ("version" -> thisFormatVersion) ~
-         ("k" -> k) ~ ("vocabSize" -> vocabSize) ~ ("docConcentration" -> docConcentration) ~
-         ("topicConcentration" -> topicConcentration) ~
-         ("iterationTimes" -> iterationTimes.toSeq)))
+          ("k" -> k) ~ ("vocabSize" -> vocabSize) ~
+          ("docConcentration" -> docConcentration.toArray.toSeq) ~
+          ("topicConcentration" -> topicConcentration) ~
+          ("iterationTimes" -> iterationTimes.toSeq) ~
+          ("gammaShape" -> gammaShape)))
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
 
       val newPath = new Path(Loader.dataPath(path), "globalTopicTotals").toUri.toString
@@ -510,9 +618,10 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
         sc: SparkContext,
         path: String,
         vocabSize: Int,
-        docConcentration: Double,
+        docConcentration: Vector,
         topicConcentration: Double,
-        iterationTimes: Array[Double]): DistributedLDAModel = {
+        iterationTimes: Array[Double],
+        gammaShape: Double): DistributedLDAModel = {
       val dataPath = new Path(Loader.dataPath(path), "globalTopicTotals").toUri.toString
       val vertexDataPath = new Path(Loader.dataPath(path), "topicCounts").toUri.toString
       val edgeDataPath = new Path(Loader.dataPath(path), "tokenCounts").toUri.toString
@@ -536,7 +645,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
       val graph: Graph[LDA.TopicCounts, LDA.TokenCount] = Graph(vertices, edges)
 
       new DistributedLDAModel(graph, globalTopicTotals, globalTopicTotals.length, vocabSize,
-        docConcentration, topicConcentration, iterationTimes)
+        docConcentration, topicConcentration, gammaShape, iterationTimes)
     }
 
   }
@@ -546,32 +655,35 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
     implicit val formats = DefaultFormats
     val expectedK = (metadata \ "k").extract[Int]
     val vocabSize = (metadata \ "vocabSize").extract[Int]
-    val docConcentration = (metadata \ "docConcentration").extract[Double]
+    val docConcentration =
+      Vectors.dense((metadata \ "docConcentration").extract[Seq[Double]].toArray)
     val topicConcentration = (metadata \ "topicConcentration").extract[Double]
     val iterationTimes = (metadata \ "iterationTimes").extract[Seq[Double]]
+    val gammaShape = (metadata \ "gammaShape").extract[Double]
     val classNameV1_0 = SaveLoadV1_0.classNameV1_0
 
     val model = (loadedClassName, loadedVersion) match {
       case (className, "1.0") if className == classNameV1_0 => {
-        DistributedLDAModel.SaveLoadV1_0.load(
-          sc, path, vocabSize, docConcentration, topicConcentration, iterationTimes.toArray)
+        DistributedLDAModel.SaveLoadV1_0.load(sc, path, vocabSize, docConcentration,
+          topicConcentration, iterationTimes.toArray, gammaShape)
       }
       case _ => throw new Exception(
         s"DistributedLDAModel.load did not recognize model with (className, format version):" +
-        s"($loadedClassName, $loadedVersion).  Supported: ($classNameV1_0, 1.0)")
+          s"($loadedClassName, $loadedVersion).  Supported: ($classNameV1_0, 1.0)")
     }
 
     require(model.vocabSize == vocabSize,
       s"DistributedLDAModel requires $vocabSize vocabSize, got ${model.vocabSize} vocabSize")
     require(model.docConcentration == docConcentration,
       s"DistributedLDAModel requires $docConcentration docConcentration, " +
-      s"got ${model.docConcentration} docConcentration")
+        s"got ${model.docConcentration} docConcentration")
     require(model.topicConcentration == topicConcentration,
       s"DistributedLDAModel requires $topicConcentration docConcentration, " +
-      s"got ${model.topicConcentration} docConcentration")
+        s"got ${model.topicConcentration} docConcentration")
     require(expectedK == model.k,
       s"DistributedLDAModel requires $expectedK topics, got ${model.k} topics")
     model
   }
 
 }
+
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index f4170a3d98dd8..7e75e7083acb5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import java.util.Random
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, normalize, sum}
-import breeze.numerics.{abs, digamma, exp}
+import breeze.numerics.{abs, exp}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
 import org.apache.spark.annotation.DeveloperApi
@@ -208,7 +208,11 @@ final class EMLDAOptimizer extends LDAOptimizer {
   override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
     require(graph != null, "graph is null, EMLDAOptimizer not initialized.")
     this.graphCheckpointer.deleteAllCheckpoints()
-    new DistributedLDAModel(this, iterationTimes)
+    // This assumes gammaShape = 100 in OnlineLDAOptimizer to ensure equivalence in LDAModel.toLocal
+    // conversion
+    new DistributedLDAModel(this.graph, this.globalTopicTotals, this.k, this.vocabSize,
+      Vectors.dense(Array.fill(this.k)(this.docConcentration)), this.topicConcentration,
+      100, iterationTimes)
   }
 }
 
@@ -385,71 +389,52 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     iteration += 1
     val k = this.k
     val vocabSize = this.vocabSize
-    val Elogbeta = dirichletExpectation(lambda).t
-    val expElogbeta = exp(Elogbeta)
+    val expElogbeta = exp(LDAUtils.dirichletExpectation(lambda)).t
     val alpha = this.alpha.toBreeze
     val gammaShape = this.gammaShape
 
-    val stats: RDD[BDM[Double]] = batch.mapPartitions { docs =>
-      val stat = BDM.zeros[Double](k, vocabSize)
-      docs.foreach { doc =>
-        val termCounts = doc._2
-        val (ids: List[Int], cts: Array[Double]) = termCounts match {
-          case v: DenseVector => ((0 until v.size).toList, v.values)
-          case v: SparseVector => (v.indices.toList, v.values)
-          case v => throw new IllegalArgumentException("Online LDA does not support vector type "
-            + v.getClass)
-        }
-        if (!ids.isEmpty) {
-
-          // Initialize the variational distribution q(theta|gamma) for the mini-batch
-          val gammad: BDV[Double] =
-            new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k) // K
-          val expElogthetad: BDV[Double] = exp(digamma(gammad) - digamma(sum(gammad))) // K
-          val expElogbetad: BDM[Double] = expElogbeta(ids, ::).toDenseMatrix // ids * K
-
-          val phinorm: BDV[Double] = expElogbetad * expElogthetad :+ 1e-100 // ids
-          var meanchange = 1D
-          val ctsVector = new BDV[Double](cts) // ids
-
-          // Iterate between gamma and phi until convergence
-          while (meanchange > 1e-3) {
-            val lastgamma = gammad.copy
-            //        K                  K * ids               ids
-            gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/ phinorm))) :+ alpha
-            expElogthetad := exp(digamma(gammad) - digamma(sum(gammad)))
-            phinorm := expElogbetad * expElogthetad :+ 1e-100
-            meanchange = sum(abs(gammad - lastgamma)) / k
-          }
+    val stats: RDD[(BDM[Double], List[BDV[Double]])] = batch.mapPartitions { docs =>
+      val nonEmptyDocs = docs.filter(_._2.numActives > 0)
 
-          stat(::, ids) := expElogthetad.asDenseMatrix.t * (ctsVector :/ phinorm).asDenseMatrix
+      val stat = BDM.zeros[Double](k, vocabSize)
+      var gammaPart = List[BDV[Double]]()
+      nonEmptyDocs.zipWithIndex.foreach { case ((_, termCounts: Vector), idx: Int) =>
+        val ids: List[Int] = termCounts match {
+          case v: DenseVector => (0 until v.size).toList
+          case v: SparseVector => v.indices.toList
         }
+        val (gammad, sstats) = OnlineLDAOptimizer.variationalTopicInference(
+          termCounts, expElogbeta, alpha, gammaShape, k)
+        stat(::, ids) := stat(::, ids).toDenseMatrix + sstats
+        gammaPart = gammad :: gammaPart
       }
-      Iterator(stat)
+      Iterator((stat, gammaPart))
     }
-
-    val statsSum: BDM[Double] = stats.reduce(_ += _)
+    val statsSum: BDM[Double] = stats.map(_._1).reduce(_ += _)
+    val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(
+      stats.map(_._2).reduce(_ ++ _).map(_.toDenseMatrix): _*)
     val batchResult = statsSum :* expElogbeta.t
 
     // Note that this is an optimization to avoid batch.count
-    update(batchResult, iteration, (miniBatchFraction * corpusSize).ceil.toInt)
+    updateLambda(batchResult, (miniBatchFraction * corpusSize).ceil.toInt)
     this
   }
 
-  override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
-    new LocalLDAModel(Matrices.fromBreeze(lambda).transpose)
-  }
-
   /**
    * Update lambda based on the batch submitted. batchSize can be different for each iteration.
    */
-  private[clustering] def update(stat: BDM[Double], iter: Int, batchSize: Int): Unit = {
+  private def updateLambda(stat: BDM[Double], batchSize: Int): Unit = {
     // weight of the mini-batch.
-    val weight = math.pow(getTau0 + iter, -getKappa)
+    val weight = rho()
 
     // Update lambda based on documents.
-    lambda = lambda * (1 - weight) +
-      (stat * (corpusSize.toDouble / batchSize.toDouble) + eta) * weight
+    lambda := (1 - weight) * lambda +
+      weight * (stat * (corpusSize.toDouble / batchSize.toDouble) + eta)
+  }
+
+  /** Calculates learning rate rho, which decays as a function of [[iteration]] */
+  private def rho(): Double = {
+    math.pow(getTau0 + this.iteration, -getKappa)
   }
 
   /**
@@ -463,15 +448,56 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     new BDM[Double](col, row, temp).t
   }
 
+  override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
+    new LocalLDAModel(Matrices.fromBreeze(lambda).transpose, alpha, eta, gammaShape)
+  }
+
+}
+
+/**
+ * Serializable companion object containing helper methods and shared code for
+ * [[OnlineLDAOptimizer]] and [[LocalLDAModel]].
+ */
+private[clustering] object OnlineLDAOptimizer {
   /**
-   * For theta ~ Dir(alpha), computes E[log(theta)] given alpha. Currently the implementation
-   * uses digamma which is accurate but expensive.
+   * Uses variational inference to infer the topic distribution `gammad` given the term counts
+   * for a document. `termCounts` must be non-empty, otherwise Breeze will throw a BLAS error.
+   *
+   * An optimization (Lee, Seung: Algorithms for non-negative matrix factorization, NIPS 2001)
+   * avoids explicit computation of variational parameter `phi`.
+   * @see [[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.7566]]
    */
-  private def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = {
-    val rowSum = sum(alpha(breeze.linalg.*, ::))
-    val digAlpha = digamma(alpha)
-    val digRowSum = digamma(rowSum)
-    val result = digAlpha(::, breeze.linalg.*) - digRowSum
-    result
+  private[clustering] def variationalTopicInference(
+      termCounts: Vector,
+      expElogbeta: BDM[Double],
+      alpha: breeze.linalg.Vector[Double],
+      gammaShape: Double,
+      k: Int): (BDV[Double], BDM[Double]) = {
+    val (ids: List[Int], cts: Array[Double]) = termCounts match {
+      case v: DenseVector => ((0 until v.size).toList, v.values)
+      case v: SparseVector => (v.indices.toList, v.values)
+    }
+    // Initialize the variational distribution q(theta|gamma) for the mini-batch
+    val gammad: BDV[Double] =
+      new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k)                   // K
+    val expElogthetad: BDV[Double] = exp(LDAUtils.dirichletExpectation(gammad))  // K
+    val expElogbetad = expElogbeta(ids, ::).toDenseMatrix                        // ids * K
+
+    val phinorm: BDV[Double] = expElogbetad * expElogthetad :+ 1e-100            // ids
+    var meanchange = 1D
+    val ctsVector = new BDV[Double](cts)                                         // ids
+
+    // Iterate between gamma and phi until convergence
+    while (meanchange > 1e-3) {
+      val lastgamma = gammad.copy
+      //        K                  K * ids               ids
+      gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/ phinorm))) :+ alpha
+      expElogthetad := exp(LDAUtils.dirichletExpectation(gammad))
+      phinorm := expElogbetad * expElogthetad :+ 1e-100
+      meanchange = sum(abs(gammad - lastgamma)) / k
+    }
+
+    val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector :/ phinorm).asDenseMatrix
+    (gammad, sstatsd)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
new file mode 100644
index 0000000000000..f7e5ce1665fe6
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.mllib.clustering
+
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, max, sum}
+import breeze.numerics._
+
+/**
+ * Utility methods for LDA.
+ */
+object LDAUtils {
+  /**
+   * Log Sum Exp with overflow protection using the identity:
+   * For any a: \log \sum_{n=1}^N \exp\{x_n\} = a + \log \sum_{n=1}^N \exp\{x_n - a\}
+   */
+  private[clustering] def logSumExp(x: BDV[Double]): Double = {
+    val a = max(x)
+    a + log(sum(exp(x :- a)))
+  }
+
+  /**
+   * For theta ~ Dir(alpha), computes E[log(theta)] given alpha. Currently the implementation
+   * uses [[breeze.numerics.digamma]] which is accurate but expensive.
+   */
+  private[clustering] def dirichletExpectation(alpha: BDV[Double]): BDV[Double] = {
+    digamma(alpha) - digamma(sum(alpha))
+  }
+
+  /**
+   * Computes [[dirichletExpectation()]] row-wise, assuming each row of alpha are
+   * Dirichlet parameters.
+   */
+  private[clustering] def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = {
+    val rowSum = sum(alpha(breeze.linalg.*, ::))
+    val digAlpha = digamma(alpha)
+    val digRowSum = digamma(rowSum)
+    val result = digAlpha(::, breeze.linalg.*) - digRowSum
+    result
+  }
+
+}
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index b48f190f599a2..d272a42c8576f 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -19,6 +19,7 @@
 
 import java.io.Serializable;
 import java.util.ArrayList;
+import java.util.Arrays;
 
 import scala.Tuple2;
 
@@ -59,7 +60,10 @@ public void tearDown() {
 
   @Test
   public void localLDAModel() {
-    LocalLDAModel model = new LocalLDAModel(LDASuite$.MODULE$.tinyTopics());
+    Matrix topics = LDASuite$.MODULE$.tinyTopics();
+    double[] topicConcentration = new double[topics.numRows()];
+    Arrays.fill(topicConcentration, 1.0D / topics.numRows());
+    LocalLDAModel model = new LocalLDAModel(topics, Vectors.dense(topicConcentration), 1D, 100D);
 
     // Check: basic parameters
     assertEquals(model.k(), tinyK);
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 376a87f0511b4..aa36336ebbee6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM}
+import breeze.linalg.{DenseMatrix => BDM, max, argmax}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.graphx.Edge
@@ -31,7 +31,8 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
   import LDASuite._
 
   test("LocalLDAModel") {
-    val model = new LocalLDAModel(tinyTopics)
+    val model = new LocalLDAModel(tinyTopics,
+      Vectors.dense(Array.fill(tinyTopics.numRows)(1.0 / tinyTopics.numRows)), 1D, 100D)
 
     // Check: basic parameters
     assert(model.k === tinyK)
@@ -235,6 +236,51 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("LocalLDAModel logPerplexity") {
+    val k = 2
+    val vocabSize = 6
+    val alpha = 0.01
+    val eta = 0.01
+    val gammaShape = 100
+    val topics = new DenseMatrix(numRows = vocabSize, numCols = k, values = Array(
+      1.86738052, 1.94056535, 1.89981687, 0.0833265, 0.07405918, 0.07940597,
+      0.15081551, 0.08637973, 0.12428538, 1.9474897, 1.94615165, 1.95204124))
+
+    def toydata: Array[(Long, Vector)] = Array(
+      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
+      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
+      Vectors.sparse(6, Array(4, 5), Array(1, 1))
+    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
+    val docs = sc.parallelize(toydata)
+
+
+    val ldaModel: LocalLDAModel = new LocalLDAModel(
+      topics, Vectors.dense(Array.fill(k)(alpha)), eta, gammaShape)
+
+    /* Verify results using gensim:
+       import numpy as np
+       from gensim import models
+       corpus = [
+          [(0, 1.0), (1, 1.0)],
+          [(1, 1.0), (2, 1.0)],
+          [(0, 1.0), (2, 1.0)],
+          [(3, 1.0), (4, 1.0)],
+          [(3, 1.0), (5, 1.0)],
+          [(4, 1.0), (5, 1.0)]]
+       np.random.seed(2345)
+       lda = models.ldamodel.LdaModel(
+          corpus=corpus, alpha=0.01, eta=0.01, num_topics=2, update_every=0, passes=100,
+          decay=0.51, offset=1024)
+       print(lda.log_perplexity(corpus))
+       > -3.69051285096
+     */
+
+    assert(ldaModel.logPerplexity(docs) ~== -3.690D relTol 1E-3D)
+  }
+
   test("OnlineLDAOptimizer with asymmetric prior") {
     def toydata: Array[(Long, Vector)] = Array(
       Vectors.sparse(6, Array(0, 1), Array(1, 1)),
@@ -287,7 +333,8 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("model save/load") {
     // Test for LocalLDAModel.
-    val localModel = new LocalLDAModel(tinyTopics)
+    val localModel = new LocalLDAModel(tinyTopics,
+      Vectors.dense(Array.fill(tinyTopics.numRows)(1.0 / tinyTopics.numRows)), 1D, 100D)
     val tempDir1 = Utils.createTempDir()
     val path1 = tempDir1.toURI.toString
 

From 86505962e6c9da1ee18c6a3533e169a22e4f1665 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 29 Jul 2015 16:49:02 -0700
Subject: [PATCH 0675/1454] [SPARK-9448][SQL] GenerateUnsafeProjection should
 not share expressions across instances.

We accidentally moved the list of expressions from the generated code instance to the class wrapper, and as a result, different threads are sharing the same set of expressions, which cause problems for expressions with mutable state.

This pull request fixed that problem, and also added unit tests for all codegen classes, except GeneratedOrdering (which will never need any expressions since sort now only accepts bound references.

Author: Reynold Xin <rxin@databricks.com>

Closes #7759 from rxin/SPARK-9448 and squashes the following commits:

c09b50f [Reynold Xin] [SPARK-9448][SQL] GenerateUnsafeProjection should not share expressions across instances.
---
 .../codegen/GenerateUnsafeProjection.scala    | 12 +--
 .../CodegenExpressionCachingSuite.scala       | 90 +++++++++++++++++++
 2 files changed, 96 insertions(+), 6 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index dc725c28aaa27..7be60114ce674 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -256,18 +256,18 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     eval.code = createCode(ctx, eval, expressions)
 
     val code = s"""
-      private $exprType[] expressions;
-
-      public Object generate($exprType[] expr) {
-        this.expressions = expr;
-        return new SpecificProjection();
+      public Object generate($exprType[] exprs) {
+        return new SpecificProjection(exprs);
       }
 
       class SpecificProjection extends ${classOf[UnsafeProjection].getName} {
 
+        private $exprType[] expressions;
+
         ${declareMutableStates(ctx)}
 
-        public SpecificProjection() {
+        public SpecificProjection($exprType[] expressions) {
+          this.expressions = expressions;
           ${initMutableStates(ctx)}
         }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala
new file mode 100644
index 0000000000000..866bf904e4a4c
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, LeafExpression}
+import org.apache.spark.sql.types.{BooleanType, DataType}
+
+/**
+ * A test suite that makes sure code generation handles expression internally states correctly.
+ */
+class CodegenExpressionCachingSuite extends SparkFunSuite {
+
+  test("GenerateUnsafeProjection") {
+    val expr1 = MutableExpression()
+    val instance1 = UnsafeProjection.create(Seq(expr1))
+    assert(instance1.apply(null).getBoolean(0) === false)
+
+    val expr2 = MutableExpression()
+    expr2.mutableState = true
+    val instance2 = UnsafeProjection.create(Seq(expr2))
+    assert(instance1.apply(null).getBoolean(0) === false)
+    assert(instance2.apply(null).getBoolean(0) === true)
+  }
+
+  test("GenerateProjection") {
+    val expr1 = MutableExpression()
+    val instance1 = GenerateProjection.generate(Seq(expr1))
+    assert(instance1.apply(null).getBoolean(0) === false)
+
+    val expr2 = MutableExpression()
+    expr2.mutableState = true
+    val instance2 = GenerateProjection.generate(Seq(expr2))
+    assert(instance1.apply(null).getBoolean(0) === false)
+    assert(instance2.apply(null).getBoolean(0) === true)
+  }
+
+  test("GenerateMutableProjection") {
+    val expr1 = MutableExpression()
+    val instance1 = GenerateMutableProjection.generate(Seq(expr1))()
+    assert(instance1.apply(null).getBoolean(0) === false)
+
+    val expr2 = MutableExpression()
+    expr2.mutableState = true
+    val instance2 = GenerateMutableProjection.generate(Seq(expr2))()
+    assert(instance1.apply(null).getBoolean(0) === false)
+    assert(instance2.apply(null).getBoolean(0) === true)
+  }
+
+  test("GeneratePredicate") {
+    val expr1 = MutableExpression()
+    val instance1 = GeneratePredicate.generate(expr1)
+    assert(instance1.apply(null) === false)
+
+    val expr2 = MutableExpression()
+    expr2.mutableState = true
+    val instance2 = GeneratePredicate.generate(expr2)
+    assert(instance1.apply(null) === false)
+    assert(instance2.apply(null) === true)
+  }
+
+}
+
+
+/**
+ * An expression with mutable state so we can change it freely in our test suite.
+ */
+case class MutableExpression() extends LeafExpression with CodegenFallback {
+  var mutableState: Boolean = false
+  override def eval(input: InternalRow): Any = mutableState
+
+  override def nullable: Boolean = false
+  override def dataType: DataType = BooleanType
+}

From 103d8cce78533b38b4f8060b30f7f455113bc6b5 Mon Sep 17 00:00:00 2001
From: Bimal Tandel <bimal@bimal-MBP.local>
Date: Wed, 29 Jul 2015 16:54:58 -0700
Subject: [PATCH 0676/1454] [SPARK-8921] [MLLIB] Add @since tags to mllib.stat

Author: Bimal Tandel <bimal@bimal-MBP.local>

Closes #7730 from BimalTandel/branch_spark_8921 and squashes the following commits:

3ea230a [Bimal Tandel] Spark 8921 add @since tags
---
 .../spark/mllib/stat/KernelDensity.scala      |  5 ++++
 .../stat/MultivariateOnlineSummarizer.scala   | 27 +++++++++++++++++++
 .../stat/MultivariateStatisticalSummary.scala |  9 +++++++
 .../apache/spark/mllib/stat/Statistics.scala  | 20 ++++++++++++--
 .../distribution/MultivariateGaussian.scala   |  9 +++++--
 5 files changed, 66 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
index 58a50f9c19f14..93a6753efd4d9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
@@ -37,6 +37,7 @@ import org.apache.spark.rdd.RDD
  *   .setBandwidth(3.0)
  * val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
  * }}}
+ * @since 1.4.0
  */
 @Experimental
 class KernelDensity extends Serializable {
@@ -51,6 +52,7 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the bandwidth (standard deviation) of the Gaussian kernel (default: `1.0`).
+   * @since 1.4.0
    */
   def setBandwidth(bandwidth: Double): this.type = {
     require(bandwidth > 0, s"Bandwidth must be positive, but got $bandwidth.")
@@ -60,6 +62,7 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the sample to use for density estimation.
+   * @since 1.4.0
    */
   def setSample(sample: RDD[Double]): this.type = {
     this.sample = sample
@@ -68,6 +71,7 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the sample to use for density estimation (for Java users).
+   * @since 1.4.0
    */
   def setSample(sample: JavaRDD[java.lang.Double]): this.type = {
     this.sample = sample.rdd.asInstanceOf[RDD[Double]]
@@ -76,6 +80,7 @@ class KernelDensity extends Serializable {
 
   /**
    * Estimates probability density function at the given array of points.
+   * @since 1.4.0
    */
   def estimate(points: Array[Double]): Array[Double] = {
     val sample = this.sample
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index d321cc554c1cc..62da9f2ef22a3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -33,6 +33,7 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
  * Reference: [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]]
  * Zero elements (including explicit zero values) are skipped when calling add(),
  * to have time complexity O(nnz) instead of O(n) for each column.
+ * @since 1.1.0
  */
 @DeveloperApi
 class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable {
@@ -52,6 +53,7 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    *
    * @param sample The sample in dense/sparse vector format to be added into this summarizer.
    * @return This MultivariateOnlineSummarizer object.
+   * @since 1.1.0
    */
   def add(sample: Vector): this.type = {
     if (n == 0) {
@@ -107,6 +109,7 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    *
    * @param other The other MultivariateOnlineSummarizer to be merged.
    * @return This MultivariateOnlineSummarizer object.
+   * @since 1.1.0
    */
   def merge(other: MultivariateOnlineSummarizer): this.type = {
     if (this.totalCnt != 0 && other.totalCnt != 0) {
@@ -149,6 +152,9 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     this
   }
 
+  /**
+   * @since 1.1.0
+   */
   override def mean: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -161,6 +167,9 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     Vectors.dense(realMean)
   }
 
+  /**
+   * @since 1.1.0
+   */
   override def variance: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -183,14 +192,23 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     Vectors.dense(realVariance)
   }
 
+  /**
+   * @since 1.1.0
+   */
   override def count: Long = totalCnt
 
+  /**
+   * @since 1.1.0
+   */
   override def numNonzeros: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
     Vectors.dense(nnz)
   }
 
+  /**
+   * @since 1.1.0
+   */
   override def max: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -202,6 +220,9 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     Vectors.dense(currMax)
   }
 
+  /**
+   * @since 1.1.0
+   */
   override def min: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -213,6 +234,9 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     Vectors.dense(currMin)
   }
 
+  /**
+   * @since 1.2.0
+   */
   override def normL2: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -227,6 +251,9 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     Vectors.dense(realMagnitude)
   }
 
+  /**
+   * @since 1.2.0
+   */
   override def normL1: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
index 6a364c93284af..3bb49f12289e1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
@@ -21,46 +21,55 @@ import org.apache.spark.mllib.linalg.Vector
 
 /**
  * Trait for multivariate statistical summary of a data matrix.
+ * @since 1.0.0
  */
 trait MultivariateStatisticalSummary {
 
   /**
    * Sample mean vector.
+   * @since 1.0.0
    */
   def mean: Vector
 
   /**
    * Sample variance vector. Should return a zero vector if the sample size is 1.
+   * @since 1.0.0
    */
   def variance: Vector
 
   /**
    * Sample size.
+   * @since 1.0.0
    */
   def count: Long
 
   /**
    * Number of nonzero elements (including explicitly presented zero values) in each column.
+   * @since 1.0.0
    */
   def numNonzeros: Vector
 
   /**
    * Maximum value of each column.
+   * @since 1.0.0
    */
   def max: Vector
 
   /**
    * Minimum value of each column.
+   * @since 1.0.0
    */
   def min: Vector
 
   /**
    * Euclidean magnitude of each column
+   * @since 1.2.0
    */
   def normL2: Vector
 
   /**
    * L1 norm of each column
+   * @since 1.2.0
    */
   def normL1: Vector
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index 90332028cfb3a..f84502919e381 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -32,6 +32,7 @@ import org.apache.spark.rdd.RDD
 /**
  * :: Experimental ::
  * API for statistical functions in MLlib.
+ * @since 1.1.0
  */
 @Experimental
 object Statistics {
@@ -41,6 +42,7 @@ object Statistics {
    *
    * @param X an RDD[Vector] for which column-wise summary statistics are to be computed.
    * @return [[MultivariateStatisticalSummary]] object containing column-wise summary statistics.
+   * @since 1.1.0
    */
   def colStats(X: RDD[Vector]): MultivariateStatisticalSummary = {
     new RowMatrix(X).computeColumnSummaryStatistics()
@@ -52,6 +54,7 @@ object Statistics {
    *
    * @param X an RDD[Vector] for which the correlation matrix is to be computed.
    * @return Pearson correlation matrix comparing columns in X.
+   * @since 1.1.0
    */
   def corr(X: RDD[Vector]): Matrix = Correlations.corrMatrix(X)
 
@@ -68,6 +71,7 @@ object Statistics {
    * @param method String specifying the method to use for computing correlation.
    *               Supported: `pearson` (default), `spearman`
    * @return Correlation matrix comparing columns in X.
+   * @since 1.1.0
    */
   def corr(X: RDD[Vector], method: String): Matrix = Correlations.corrMatrix(X, method)
 
@@ -81,10 +85,14 @@ object Statistics {
    * @param x RDD[Double] of the same cardinality as y.
    * @param y RDD[Double] of the same cardinality as x.
    * @return A Double containing the Pearson correlation between the two input RDD[Double]s
+   * @since 1.1.0
    */
   def corr(x: RDD[Double], y: RDD[Double]): Double = Correlations.corr(x, y)
 
-  /** Java-friendly version of [[corr()]] */
+  /**
+   * Java-friendly version of [[corr()]]
+   * @since 1.4.1
+   */
   def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double]): Double =
     corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]])
 
@@ -101,10 +109,14 @@ object Statistics {
    *               Supported: `pearson` (default), `spearman`
    * @return A Double containing the correlation between the two input RDD[Double]s using the
    *         specified method.
+   * @since 1.1.0
    */
   def corr(x: RDD[Double], y: RDD[Double], method: String): Double = Correlations.corr(x, y, method)
 
-  /** Java-friendly version of [[corr()]] */
+  /**
+   * Java-friendly version of [[corr()]]
+   * @since 1.4.1
+   */
   def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double], method: String): Double =
     corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]], method)
 
@@ -121,6 +133,7 @@ object Statistics {
    *                 `expected` is rescaled if the `expected` sum differs from the `observed` sum.
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
+   * @since 1.1.0
    */
   def chiSqTest(observed: Vector, expected: Vector): ChiSqTestResult = {
     ChiSqTest.chiSquared(observed, expected)
@@ -135,6 +148,7 @@ object Statistics {
    * @param observed Vector containing the observed categorical counts/relative frequencies.
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
+   * @since 1.1.0
    */
   def chiSqTest(observed: Vector): ChiSqTestResult = ChiSqTest.chiSquared(observed)
 
@@ -145,6 +159,7 @@ object Statistics {
    * @param observed The contingency matrix (containing either counts or relative frequencies).
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
+   * @since 1.1.0
    */
   def chiSqTest(observed: Matrix): ChiSqTestResult = ChiSqTest.chiSquaredMatrix(observed)
 
@@ -157,6 +172,7 @@ object Statistics {
    *             Real-valued features will be treated as categorical for each distinct value.
    * @return an array containing the ChiSquaredTestResult for every feature against the label.
    *         The order of the elements in the returned array reflects the order of input features.
+   * @since 1.1.0
    */
   def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = {
     ChiSqTest.chiSquaredFeatures(data)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
index cf51b24ff777f..9aa7763d7890d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
@@ -32,6 +32,7 @@ import org.apache.spark.mllib.util.MLUtils
  *
  * @param mu The mean vector of the distribution
  * @param sigma The covariance matrix of the distribution
+ * @since 1.3.0
  */
 @DeveloperApi
 class MultivariateGaussian (
@@ -60,12 +61,16 @@ class MultivariateGaussian (
    */
   private val (rootSigmaInv: DBM[Double], u: Double) = calculateCovarianceConstants
 
-  /** Returns density of this multivariate Gaussian at given point, x */
+  /** Returns density of this multivariate Gaussian at given point, x
+    * @since 1.3.0
+    */
   def pdf(x: Vector): Double = {
     pdf(x.toBreeze)
   }
 
-  /** Returns the log-density of this multivariate Gaussian at given point, x */
+  /** Returns the log-density of this multivariate Gaussian at given point, x
+    * @since 1.3.0
+    */
   def logpdf(x: Vector): Double = {
     logpdf(x.toBreeze)
   }

From 37c2d1927cebdd19a14c054f670cb0fb9a263586 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 29 Jul 2015 18:18:29 -0700
Subject: [PATCH 0677/1454] [SPARK-9016] [ML] make random forest classifiers
 implement classification trait

Implement the classification trait for RandomForestClassifiers. The plan is to use this in the future to providing thresholding for RandomForestClassifiers (as well as other classifiers that implement that trait).

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7432 from holdenk/SPARK-9016-make-random-forest-classifiers-implement-classification-trait and squashes the following commits:

bf22fa6 [Holden Karau] Add missing imports for testing suite
e948f0d [Holden Karau] Check the prediction generation from rawprediciton
25320c3 [Holden Karau] Don't supply numClasses when not needed, assert model classes are as expected
1a67e04 [Holden Karau] Use old decission tree stuff instead
673e0c3 [Holden Karau] Merge branch 'master' into SPARK-9016-make-random-forest-classifiers-implement-classification-trait
0d15b96 [Holden Karau] FIx typo
5eafad4 [Holden Karau] add a constructor for rootnode + num classes
fc6156f [Holden Karau] scala style fix
2597915 [Holden Karau] take num classes in constructor
3ccfe4a [Holden Karau] Merge in master, make pass numClasses through randomforest for training
222a10b [Holden Karau] Increase numtrees to 3 in the python test since before the two were equal and the argmax was selecting the last one
16aea1c [Holden Karau] Make tests match the new models
b454a02 [Holden Karau] Make the Tree classifiers extends the Classifier base class
77b4114 [Holden Karau] Import vectors lib
---
 .../RandomForestClassifier.scala              | 30 ++++++++++---------
 .../RandomForestClassifierSuite.scala         | 18 ++++++++---
 python/pyspark/ml/classification.py           |  4 +--
 3 files changed, 32 insertions(+), 20 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index fc0693f67cc2e..bc19bd6df894f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -25,7 +25,7 @@ import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
@@ -43,7 +43,7 @@ import org.apache.spark.sql.types.DoubleType
  */
 @Experimental
 final class RandomForestClassifier(override val uid: String)
-  extends Predictor[Vector, RandomForestClassifier, RandomForestClassificationModel]
+  extends Classifier[Vector, RandomForestClassifier, RandomForestClassificationModel]
   with RandomForestParams with TreeClassifierParams {
 
   def this() = this(Identifiable.randomUID("rfc"))
@@ -98,7 +98,7 @@ final class RandomForestClassifier(override val uid: String)
     val trees =
       RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
         .map(_.asInstanceOf[DecisionTreeClassificationModel])
-    new RandomForestClassificationModel(trees)
+    new RandomForestClassificationModel(trees, numClasses)
   }
 
   override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra)
@@ -125,8 +125,9 @@ object RandomForestClassifier {
 @Experimental
 final class RandomForestClassificationModel private[ml] (
     override val uid: String,
-    private val _trees: Array[DecisionTreeClassificationModel])
-  extends PredictionModel[Vector, RandomForestClassificationModel]
+    private val _trees: Array[DecisionTreeClassificationModel],
+    override val numClasses: Int)
+  extends ClassificationModel[Vector, RandomForestClassificationModel]
   with TreeEnsembleModel with Serializable {
 
   require(numTrees > 0, "RandomForestClassificationModel requires at least 1 tree.")
@@ -135,8 +136,8 @@ final class RandomForestClassificationModel private[ml] (
    * Construct a random forest classification model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeClassificationModel]) =
-    this(Identifiable.randomUID("rfc"), trees)
+  def this(trees: Array[DecisionTreeClassificationModel], numClasses: Int) =
+    this(Identifiable.randomUID("rfc"), trees, numClasses)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
@@ -153,20 +154,20 @@ final class RandomForestClassificationModel private[ml] (
     dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
   }
 
-  override protected def predict(features: Vector): Double = {
+  override protected def predictRaw(features: Vector): Vector = {
     // TODO: When we add a generic Bagging class, handle transform there: SPARK-7128
     // Classifies using majority votes.
     // Ignore the weights since all are 1.0 for now.
-    val votes = mutable.Map.empty[Int, Double]
+    val votes = new Array[Double](numClasses)
     _trees.view.foreach { tree =>
       val prediction = tree.rootNode.predict(features).toInt
-      votes(prediction) = votes.getOrElse(prediction, 0.0) + 1.0 // 1.0 = weight
+      votes(prediction) = votes(prediction) + 1.0 // 1.0 = weight
     }
-    votes.maxBy(_._2)._1
+    Vectors.dense(votes)
   }
 
   override def copy(extra: ParamMap): RandomForestClassificationModel = {
-    copyValues(new RandomForestClassificationModel(uid, _trees), extra)
+    copyValues(new RandomForestClassificationModel(uid, _trees, numClasses), extra)
   }
 
   override def toString: String = {
@@ -185,7 +186,8 @@ private[ml] object RandomForestClassificationModel {
   def fromOld(
       oldModel: OldRandomForestModel,
       parent: RandomForestClassifier,
-      categoricalFeatures: Map[Int, Int]): RandomForestClassificationModel = {
+      categoricalFeatures: Map[Int, Int],
+      numClasses: Int): RandomForestClassificationModel = {
     require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" +
       s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).")
     val newTrees = oldModel.trees.map { tree =>
@@ -193,6 +195,6 @@ private[ml] object RandomForestClassificationModel {
       DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures)
     }
     val uid = if (parent != null) parent.uid else Identifiable.randomUID("rfc")
-    new RandomForestClassificationModel(uid, newTrees)
+    new RandomForestClassificationModel(uid, newTrees, numClasses)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index 1b6b69c7dc71e..ab711c8e4b215 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -21,13 +21,13 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.LeafNode
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, Row}
 
 /**
  * Test suite for [[RandomForestClassifier]].
@@ -66,7 +66,7 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
   test("params") {
     ParamsSuite.checkParams(new RandomForestClassifier)
     val model = new RandomForestClassificationModel("rfc",
-      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0))))
+      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0))), 2)
     ParamsSuite.checkParams(model)
   }
 
@@ -167,9 +167,19 @@ private object RandomForestClassifierSuite {
     val newModel = rf.fit(newData)
     // Use parent from newTree since this is not checked anyways.
     val oldModelAsNew = RandomForestClassificationModel.fromOld(
-      oldModel, newModel.parent.asInstanceOf[RandomForestClassifier], categoricalFeatures)
+      oldModel, newModel.parent.asInstanceOf[RandomForestClassifier], categoricalFeatures,
+      numClasses)
     TreeTests.checkEqual(oldModelAsNew, newModel)
     assert(newModel.hasParent)
     assert(!newModel.trees.head.asInstanceOf[DecisionTreeClassificationModel].hasParent)
+    assert(newModel.numClasses == numClasses)
+    val results = newModel.transform(newData)
+    results.select("rawPrediction", "prediction").collect().foreach {
+      case Row(raw: Vector, prediction: Double) => {
+        assert(raw.size == numClasses)
+        val predFromRaw = raw.toArray.zipWithIndex.maxBy(_._1)._2
+        assert(predFromRaw == prediction)
+      }
+    }
   }
 }
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 89117e492846b..5a82bc286d1e8 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -299,9 +299,9 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
     >>> si_model = stringIndexer.fit(df)
     >>> td = si_model.transform(df)
-    >>> rf = RandomForestClassifier(numTrees=2, maxDepth=2, labelCol="indexed", seed=42)
+    >>> rf = RandomForestClassifier(numTrees=3, maxDepth=2, labelCol="indexed", seed=42)
     >>> model = rf.fit(td)
-    >>> allclose(model.treeWeights, [1.0, 1.0])
+    >>> allclose(model.treeWeights, [1.0, 1.0, 1.0])
     True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction

From 2a9fe4a4e7acbe4c9d3b6c6e61ff46d1472ee5f4 Mon Sep 17 00:00:00 2001
From: sethah <seth.hendrickson16@gmail.com>
Date: Wed, 29 Jul 2015 18:23:07 -0700
Subject: [PATCH 0678/1454] [SPARK-6129] [MLLIB] [DOCS] Added user guide for
 evaluation metrics

Author: sethah <seth.hendrickson16@gmail.com>

Closes #7655 from sethah/Working_on_6129 and squashes the following commits:

253db2d [sethah] removed number formatting from example code
b769cab [sethah] rewording threshold section
d5dad4d [sethah] adding some explanations of concepts to the eval metrics user guide
3a61ff9 [sethah] Removing unnecessary latex commands from metrics guide
c9dd058 [sethah] Cleaning up and formatting metrics user guide section
6f31c21 [sethah] All example code for metrics section done
98813fe [sethah] Most java and python example code added. Further latex formatting
53a24fc [sethah] Adding documentations of metrics for ML algorithms to user guide
---
 docs/mllib-evaluation-metrics.md | 1497 ++++++++++++++++++++++++++++++
 docs/mllib-guide.md              |    1 +
 2 files changed, 1498 insertions(+)
 create mode 100644 docs/mllib-evaluation-metrics.md

diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md
new file mode 100644
index 0000000000000..4ca0bb06b26a6
--- /dev/null
+++ b/docs/mllib-evaluation-metrics.md
@@ -0,0 +1,1497 @@
+---
+layout: global
+title: Evaluation Metrics - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Evaluation Metrics
+---
+
+* Table of contents
+{:toc}
+
+Spark's MLlib comes with a number of machine learning algorithms that can be used to learn from and make predictions
+on data. When these algorithms are applied to build machine learning models, there is a need to evaluate the performance
+of the model on some criteria, which depends on the application and its requirements. Spark's MLlib also provides a
+suite of metrics for the purpose of evaluating the performance of machine learning models.
+
+Specific machine learning algorithms fall under broader types of machine learning applications like classification,
+regression, clustering, etc. Each of these types have well established metrics for performance evaluation and those
+metrics that are currently available in Spark's MLlib are detailed in this section.
+
+## Classification model evaluation
+
+While there are many different types of classification algorithms, the evaluation of classification models all share
+similar principles. In a [supervised classification problem](https://en.wikipedia.org/wiki/Statistical_classification),
+there exists a true output and a model-generated predicted output for each data point. For this reason, the results for
+each data point can be assigned to one of four categories:
+
+* True Positive (TP) - label is positive and prediction is also positive
+* True Negative (TN) - label is negative and prediction is also negative
+* False Positive (FP) - label is negative but prediction is positive
+* False Negative (FN) - label is positive but prediction is negative
+
+These four numbers are the building blocks for most classifier evaluation metrics. A fundamental point when considering
+classifier evaluation is that pure accuracy (i.e. was the prediction correct or incorrect) is not generally a good metric. The
+reason for this is because a dataset may be highly unbalanced. For example, if a model is designed to predict fraud from
+a dataset where 95% of the data points are _not fraud_ and 5% of the data points are _fraud_, then a naive classifier
+that predicts _not fraud_, regardless of input, will be 95% accurate. For this reason, metrics like
+[precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall) are typically used because they take into
+account the *type* of error. In most applications there is some desired balance between precision and recall, which can
+be captured by combining the two into a single metric, called the [F-measure](https://en.wikipedia.org/wiki/F1_score).
+
+### Binary classification
+
+[Binary classifiers](https://en.wikipedia.org/wiki/Binary_classification) are used to separate the elements of a given
+dataset into one of two possible groups (e.g. fraud or not fraud) and is a special case of multiclass classification.
+Most binary classification metrics can be generalized to multiclass classification metrics.
+
+#### Threshold tuning
+
+It is import to understand that many classification models actually output a "score" (often times a probability) for
+each class, where a higher score indicates higher likelihood. In the binary case, the model may output a probability for
+each class: $P(Y=1|X)$ and $P(Y=0|X)$. Instead of simply taking the higher probability, there may be some cases where
+the model might need to be tuned so that it only predicts a class when the probability is very high (e.g. only block a
+credit card transaction if the model predicts fraud with >90% probability). Therefore, there is a prediction *threshold*
+which determines what the predicted class will be based on the probabilities that the model outputs.
+
+Tuning the prediction threshold will change the precision and recall of the model and is an important part of model
+optimization. In order to visualize how precision, recall, and other metrics change as a function of the threshold it is
+common practice to plot competing metrics against one another, parameterized by threshold. A P-R curve plots (precision,
+recall) points for different threshold values, while a
+[receiver operating characteristic](https://en.wikipedia.org/wiki/Receiver_operating_characteristic), or ROC, curve
+plots (recall, false positive rate) points.
+
+**Available metrics**
+
+<table class="table">
+  <thead>
+    <tr><th>Metric</th><th>Definition</th></tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Precision (Postive Predictive Value)</td>
+      <td>$PPV=\frac{TP}{TP + FP}$</td>
+    </tr>
+    <tr>
+      <td>Recall (True Positive Rate)</td>
+      <td>$TPR=\frac{TP}{P}=\frac{TP}{TP + FN}$</td>
+    </tr>
+    <tr>
+      <td>F-measure</td>
+      <td>$F(\beta) = \left(1 + \beta^2\right) \cdot \left(\frac{PPV \cdot TPR}
+          {\beta^2 \cdot PPV + TPR}\right)$</td>
+    </tr>
+    <tr>
+      <td>Receiver Operating Characteristic (ROC)</td>
+      <td>$FPR(T)=\int^\infty_{T} P_0(T)\,dT \\ TPR(T)=\int^\infty_{T} P_1(T)\,dT$</td>
+    </tr>
+    <tr>
+      <td>Area Under ROC Curve</td>
+      <td>$AUROC=\int^1_{0} \frac{TP}{P} d\left(\frac{FP}{N}\right)$</td>
+    </tr>
+    <tr>
+      <td>Area Under Precision-Recall Curve</td>
+      <td>$AUPRC=\int^1_{0} \frac{TP}{TP+FP} d\left(\frac{TP}{P}\right)$</td>
+    </tr>
+  </tbody>
+</table>
+
+
+**Examples**
+
+<div class="codetabs">
+The following code snippets illustrate how to load a sample dataset, train a binary classification algorithm on the
+data, and evaluate the performance of the algorithm by several binary evaluation metrics.
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLUtils
+
+// Load training data in LIBSVM format
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_binary_classification_data.txt")
+
+// Split data into training (60%) and test (40%)
+val Array(training, test) = data.randomSplit(Array(0.6, 0.4), seed = 11L)
+training.cache()
+
+// Run training algorithm to build the model
+val model = new LogisticRegressionWithLBFGS()
+  .setNumClasses(2)
+  .run(training)
+
+// Clear the prediction threshold so the model will return probabilities
+model.clearThreshold
+
+// Compute raw scores on the test set
+val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
+  val prediction = model.predict(features)
+  (prediction, label)
+}
+
+// Instantiate metrics object
+val metrics = new BinaryClassificationMetrics(predictionAndLabels)
+
+// Precision by threshold
+val precision = metrics.precisionByThreshold
+precision.foreach { case (t, p) =>
+    println(s"Threshold: $t, Precision: $p")
+}
+
+// Recall by threshold
+val recall = metrics.precisionByThreshold
+recall.foreach { case (t, r) =>
+    println(s"Threshold: $t, Recall: $r")
+}
+
+// Precision-Recall Curve
+val PRC = metrics.pr
+
+// F-measure
+val f1Score = metrics.fMeasureByThreshold
+f1Score.foreach { case (t, f) =>
+    println(s"Threshold: $t, F-score: $f, Beta = 1")
+}
+
+val beta = 0.5
+val fScore = metrics.fMeasureByThreshold(beta)
+f1Score.foreach { case (t, f) =>
+    println(s"Threshold: $t, F-score: $f, Beta = 0.5")
+}
+
+// AUPRC
+val auPRC = metrics.areaUnderPR
+println("Area under precision-recall curve = " + auPRC)
+
+// Compute thresholds used in ROC and PR curves
+val thresholds = precision.map(_._1)
+
+// ROC Curve
+val roc = metrics.roc
+
+// AUROC
+val auROC = metrics.areaUnderROC
+println("Area under ROC = " + auROC)
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS;
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class BinaryClassification {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("Binary Classification Metrics");
+    SparkContext sc = new SparkContext(conf);
+    String path = "data/mllib/sample_binary_classification_data.txt";
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+
+    // Split initial RDD into two... [60% training data, 40% testing data].
+    JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[] {0.6, 0.4}, 11L);
+    JavaRDD<LabeledPoint> training = splits[0].cache();
+    JavaRDD<LabeledPoint> test = splits[1];
+
+    // Run training algorithm to build the model.
+    final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
+      .setNumClasses(2)
+      .run(training.rdd());
+
+    // Clear the prediction threshold so the model will return probabilities
+    model.clearThreshold();
+
+    // Compute raw scores on the test set.
+    JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
+      new Function<LabeledPoint, Tuple2<Object, Object>>() {
+        public Tuple2<Object, Object> call(LabeledPoint p) {
+          Double prediction = model.predict(p.features());
+          return new Tuple2<Object, Object>(prediction, p.label());
+        }
+      }
+    );
+
+    // Get evaluation metrics.
+    BinaryClassificationMetrics metrics = new BinaryClassificationMetrics(predictionAndLabels.rdd());
+
+    // Precision by threshold
+    JavaRDD<Tuple2<Object, Object>> precision = metrics.precisionByThreshold().toJavaRDD();
+    System.out.println("Precision by threshold: " + precision.toArray());
+
+    // Recall by threshold
+    JavaRDD<Tuple2<Object, Object>> recall = metrics.recallByThreshold().toJavaRDD();
+    System.out.println("Recall by threshold: " + recall.toArray());
+
+    // F Score by threshold
+    JavaRDD<Tuple2<Object, Object>> f1Score = metrics.fMeasureByThreshold().toJavaRDD();
+    System.out.println("F1 Score by threshold: " + f1Score.toArray());
+
+    JavaRDD<Tuple2<Object, Object>> f2Score = metrics.fMeasureByThreshold(2.0).toJavaRDD();
+    System.out.println("F2 Score by threshold: " + f2Score.toArray());
+
+    // Precision-recall curve
+    JavaRDD<Tuple2<Object, Object>> prc = metrics.pr().toJavaRDD();
+    System.out.println("Precision-recall curve: " + prc.toArray());
+
+    // Thresholds
+    JavaRDD<Double> thresholds = precision.map(
+      new Function<Tuple2<Object, Object>, Double>() {
+        public Double call (Tuple2<Object, Object> t) {
+          return new Double(t._1().toString());
+        }
+      }
+    );
+
+    // ROC Curve
+    JavaRDD<Tuple2<Object, Object>> roc = metrics.roc().toJavaRDD();
+    System.out.println("ROC curve: " + roc.toArray());
+
+    // AUPRC
+    System.out.println("Area under precision-recall curve = " + metrics.areaUnderPR());
+
+    // AUROC
+    System.out.println("Area under ROC = " + metrics.areaUnderROC());
+
+    // Save and load model
+    model.save(sc, "myModelPath");
+    LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc, "myModelPath");
+  }
+}
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+from pyspark.mllib.classification import LogisticRegressionWithLBFGS
+from pyspark.mllib.evaluation import BinaryClassificationMetrics
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.util import MLUtils
+
+# Several of the methods available in scala are currently missing from pyspark
+
+# Load training data in LIBSVM format
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_binary_classification_data.txt")
+
+# Split data into training (60%) and test (40%)
+training, test = data.randomSplit([0.6, 0.4], seed = 11L)
+training.cache()
+
+# Run training algorithm to build the model
+model = LogisticRegressionWithLBFGS.train(training)
+
+# Compute raw scores on the test set
+predictionAndLabels = test.map(lambda lp: (float(model.predict(lp.features)), lp.label))
+
+# Instantiate metrics object
+metrics = BinaryClassificationMetrics(predictionAndLabels)
+
+# Area under precision-recall curve
+print "Area under PR = %s" % metrics.areaUnderPR
+
+# Area under ROC curve
+print "Area under ROC = %s" % metrics.areaUnderROC
+
+{% endhighlight %}
+
+</div>
+</div>
+
+
+### Multiclass classification
+
+A [multiclass classification](https://en.wikipedia.org/wiki/Multiclass_classification) describes a classification
+problem where there are $M \gt 2$ possible labels for each data point (the case where $M=2$ is the binary
+classification problem). For example, classifying handwriting samples to the digits 0 to 9, having 10 possible classes.
+
+For multiclass metrics, the notion of positives and negatives is slightly different. Predictions and labels can still
+be positive or negative, but they must be considered under the context of a particular class. Each label and prediction
+take on the value of one of the multiple classes and so they are said to be positive for their particular class and negative
+for all other classes. So, a true positive occurs whenever the prediction and the label match, while a true negative
+occurs when neither the prediction nor the label take on the value of a given class. By this convention, there can be
+multiple true negatives for a given data sample. The extension of false negatives and false positives from the former
+definitions of positive and negative labels is straightforward.
+
+#### Label based metrics
+
+Opposed to binary classification where there are only two possible labels, multiclass classification problems have many
+possible labels and so the concept of label-based metrics is introduced. Overall precision measures precision across all
+labels -  the number of times any class was predicted correctly (true positives) normalized by the number of data
+points. Precision by label considers only one class, and measures the number of time a specific label was predicted
+correctly normalized by the number of times that label appears in the output.
+
+**Available metrics**
+
+Define the class, or label, set as
+
+$$L = \{\ell_0, \ell_1, \ldots, \ell_{M-1} \} $$
+
+The true output vector $\mathbf{y}$ consists of $N$ elements
+
+$$\mathbf{y}_0, \mathbf{y}_1, \ldots, \mathbf{y}_{N-1} \in L $$
+
+A multiclass prediction algorithm generates a prediction vector $\hat{\mathbf{y}}$ of $N$ elements
+
+$$\hat{\mathbf{y}}_0, \hat{\mathbf{y}}_1, \ldots, \hat{\mathbf{y}}_{N-1} \in L $$
+
+For this section, a modified delta function $\hat{\delta}(x)$ will prove useful
+
+$$\hat{\delta}(x) = \begin{cases}1 & \text{if $x = 0$}, \\ 0 & \text{otherwise}.\end{cases}$$
+
+<table class="table">
+  <thead>
+    <tr><th>Metric</th><th>Definition</th></tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Confusion Matrix</td>
+      <td>
+        $C_{ij} = \sum_{k=0}^{N-1} \hat{\delta}(\mathbf{y}_k-\ell_i) \cdot \hat{\delta}(\hat{\mathbf{y}}_k - \ell_j)\\ \\
+         \left( \begin{array}{ccc}
+         \sum_{k=0}^{N-1} \hat{\delta}(\mathbf{y}_k-\ell_1) \cdot \hat{\delta}(\hat{\mathbf{y}}_k - \ell_1) & \ldots &
+         \sum_{k=0}^{N-1} \hat{\delta}(\mathbf{y}_k-\ell_1) \cdot \hat{\delta}(\hat{\mathbf{y}}_k - \ell_N) \\
+         \vdots & \ddots & \vdots \\
+         \sum_{k=0}^{N-1} \hat{\delta}(\mathbf{y}_k-\ell_N) \cdot \hat{\delta}(\hat{\mathbf{y}}_k - \ell_1) & \ldots &
+         \sum_{k=0}^{N-1} \hat{\delta}(\mathbf{y}_k-\ell_N) \cdot \hat{\delta}(\hat{\mathbf{y}}_k - \ell_N)
+         \end{array} \right)$
+      </td>
+    </tr>
+    <tr>
+      <td>Overall Precision</td>
+      <td>$PPV = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i -
+        \mathbf{y}_i\right)$</td>
+    </tr>
+    <tr>
+      <td>Overall Recall</td>
+      <td>$TPR = \frac{TP}{TP + FN} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i -
+        \mathbf{y}_i\right)$</td>
+    </tr>
+    <tr>
+      <td>Overall F1-measure</td>
+      <td>$F1 = 2 \cdot \left(\frac{PPV \cdot TPR}
+          {PPV + TPR}\right)$</td>
+    </tr>
+    <tr>
+      <td>Precision by label</td>
+      <td>$PPV(\ell) = \frac{TP}{TP + FP} =
+          \frac{\sum_{i=0}^{N-1} \hat{\delta}(\hat{\mathbf{y}}_i - \ell) \cdot \hat{\delta}(\mathbf{y}_i - \ell)}
+          {\sum_{i=0}^{N-1} \hat{\delta}(\hat{\mathbf{y}}_i - \ell)}$</td>
+    </tr>
+    <tr>
+      <td>Recall by label</td>
+      <td>$TPR(\ell)=\frac{TP}{P} =
+          \frac{\sum_{i=0}^{N-1} \hat{\delta}(\hat{\mathbf{y}}_i - \ell) \cdot \hat{\delta}(\mathbf{y}_i - \ell)}
+          {\sum_{i=0}^{N-1} \hat{\delta}(\mathbf{y}_i - \ell)}$</td>
+    </tr>
+    <tr>
+      <td>F-measure by label</td>
+      <td>$F(\beta, \ell) = \left(1 + \beta^2\right) \cdot \left(\frac{PPV(\ell) \cdot TPR(\ell)}
+          {\beta^2 \cdot PPV(\ell) + TPR(\ell)}\right)$</td>
+    </tr>
+    <tr>
+      <td>Weighted precision</td>
+      <td>$PPV_{w}= \frac{1}{N} \sum\nolimits_{\ell \in L} PPV(\ell)
+          \cdot \sum_{i=0}^{N-1} \hat{\delta}(\mathbf{y}_i-\ell)$</td>
+    </tr>
+    <tr>
+      <td>Weighted recall</td>
+      <td>$TPR_{w}= \frac{1}{N} \sum\nolimits_{\ell \in L} TPR(\ell)
+          \cdot \sum_{i=0}^{N-1} \hat{\delta}(\mathbf{y}_i-\ell)$</td>
+    </tr>
+    <tr>
+      <td>Weighted F-measure</td>
+      <td>$F_{w}(\beta)= \frac{1}{N} \sum\nolimits_{\ell \in L} F(\beta, \ell)
+          \cdot \sum_{i=0}^{N-1} \hat{\delta}(\mathbf{y}_i-\ell)$</td>
+    </tr>
+  </tbody>
+</table>
+
+**Examples**
+
+<div class="codetabs">
+The following code snippets illustrate how to load a sample dataset, train a multiclass classification algorithm on
+the data, and evaluate the performance of the algorithm by several multiclass classification evaluation metrics.
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLUtils
+
+// Load training data in LIBSVM format
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt")
+
+// Split data into training (60%) and test (40%)
+val Array(training, test) = data.randomSplit(Array(0.6, 0.4), seed = 11L)
+training.cache()
+
+// Run training algorithm to build the model
+val model = new LogisticRegressionWithLBFGS()
+  .setNumClasses(3)
+  .run(training)
+
+// Compute raw scores on the test set
+val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
+  val prediction = model.predict(features)
+  (prediction, label)
+}
+
+// Instantiate metrics object
+val metrics = new MulticlassMetrics(predictionAndLabels)
+
+// Confusion matrix
+println("Confusion matrix:")
+println(metrics.confusionMatrix)
+
+// Overall Statistics
+val precision = metrics.precision
+val recall = metrics.recall // same as true positive rate
+val f1Score = metrics.fMeasure
+println("Summary Statistics")
+println(s"Precision = $precision")
+println(s"Recall = $recall")
+println(s"F1 Score = $f1Score")
+
+// Precision by label
+val labels = metrics.labels
+labels.foreach { l =>
+    println(s"Precision($l) = " + metrics.precision(l))
+}
+
+// Recall by label
+labels.foreach { l =>
+    println(s"Recall($l) = " + metrics.recall(l))
+}
+
+// False positive rate by label
+labels.foreach { l =>
+    println(s"FPR($l) = " + metrics.falsePositiveRate(l))
+}
+
+// F-measure by label
+labels.foreach { l =>
+    println(s"F1-Score($l) = " + metrics.fMeasure(l))
+}
+
+// Weighted stats
+println(s"Weighted precision: ${metrics.weightedPrecision}")
+println(s"Weighted recall: ${metrics.weightedRecall}")
+println(s"Weighted F1 score: ${metrics.weightedFMeasure}")
+println(s"Weighted false positive rate: ${metrics.weightedFalsePositiveRate}")
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS;
+import org.apache.spark.mllib.evaluation.MulticlassMetrics;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+
+public class MulticlassClassification {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("Multiclass Classification Metrics");
+    SparkContext sc = new SparkContext(conf);
+    String path = "data/mllib/sample_multiclass_classification_data.txt";
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+
+    // Split initial RDD into two... [60% training data, 40% testing data].
+    JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[] {0.6, 0.4}, 11L);
+    JavaRDD<LabeledPoint> training = splits[0].cache();
+    JavaRDD<LabeledPoint> test = splits[1];
+
+    // Run training algorithm to build the model.
+    final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
+      .setNumClasses(3)
+      .run(training.rdd());
+
+    // Compute raw scores on the test set.
+    JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
+      new Function<LabeledPoint, Tuple2<Object, Object>>() {
+        public Tuple2<Object, Object> call(LabeledPoint p) {
+          Double prediction = model.predict(p.features());
+          return new Tuple2<Object, Object>(prediction, p.label());
+        }
+      }
+    );
+
+    // Get evaluation metrics.
+    MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd());
+
+    // Confusion matrix
+    Matrix confusion = metrics.confusionMatrix();
+    System.out.println("Confusion matrix: \n" + confusion);
+
+    // Overall statistics
+    System.out.println("Precision = " + metrics.precision());
+    System.out.println("Recall = " + metrics.recall());
+    System.out.println("F1 Score = " + metrics.fMeasure());
+
+    // Stats by labels
+    for (int i = 0; i < metrics.labels().length; i++) {
+        System.out.format("Class %f precision = %f\n", metrics.labels()[i], metrics.precision(metrics.labels()[i]));
+        System.out.format("Class %f recall = %f\n", metrics.labels()[i], metrics.recall(metrics.labels()[i]));
+        System.out.format("Class %f F1 score = %f\n", metrics.labels()[i], metrics.fMeasure(metrics.labels()[i]));
+    }
+
+    //Weighted stats
+    System.out.format("Weighted precision = %f\n", metrics.weightedPrecision());
+    System.out.format("Weighted recall = %f\n", metrics.weightedRecall());
+    System.out.format("Weighted F1 score = %f\n", metrics.weightedFMeasure());
+    System.out.format("Weighted false positive rate = %f\n", metrics.weightedFalsePositiveRate());
+
+    // Save and load model
+    model.save(sc, "myModelPath");
+    LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc, "myModelPath");
+  }
+}
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+from pyspark.mllib.classification import LogisticRegressionWithLBFGS
+from pyspark.mllib.util import MLUtils
+from pyspark.mllib.evaluation import MulticlassMetrics
+
+# Load training data in LIBSVM format
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt")
+
+# Split data into training (60%) and test (40%)
+training, test = data.randomSplit([0.6, 0.4], seed = 11L)
+training.cache()
+
+# Run training algorithm to build the model
+model = LogisticRegressionWithLBFGS.train(training, numClasses=3)
+
+# Compute raw scores on the test set
+predictionAndLabels = test.map(lambda lp: (float(model.predict(lp.features)), lp.label))
+
+# Instantiate metrics object
+metrics = MulticlassMetrics(predictionAndLabels)
+
+# Overall statistics
+precision = metrics.precision()
+recall = metrics.recall()
+f1Score = metrics.fMeasure()
+print "Summary Stats"
+print "Precision = %s" % precision
+print "Recall = %s" % recall
+print "F1 Score = %s" % f1Score
+
+# Statistics by class
+labels = data.map(lambda lp: lp.label).distinct().collect()
+for label in sorted(labels):
+    print "Class %s precision = %s" % (label, metrics.precision(label))
+    print "Class %s recall = %s" % (label, metrics.recall(label))
+    print "Class %s F1 Measure = %s" % (label, metrics.fMeasure(label, beta=1.0))
+
+# Weighted stats
+print "Weighted recall = %s" % metrics.weightedRecall
+print "Weighted precision = %s" % metrics.weightedPrecision
+print "Weighted F(1) Score = %s" % metrics.weightedFMeasure()
+print "Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5)
+print "Weighted false positive rate = %s" % metrics.weightedFalsePositiveRate
+{% endhighlight %}
+
+</div>
+</div>
+
+### Multilabel classification
+
+A [multilabel classification](https://en.wikipedia.org/wiki/Multi-label_classification) problem involves mapping
+each sample in a dataset to a set of class labels. In this type of classification problem, the labels are not
+mutually exclusive. For example, when classifying a set of news articles into topics, a single article might be both
+science and politics.
+
+Because the labels are not mutually exclusive, the predictions and true labels are now vectors of label *sets*, rather
+than vectors of labels. Multilabel metrics, therefore, extend the fundamental ideas of precision, recall, etc. to
+operations on sets. For example, a true positive for a given class now occurs when that class exists in the predicted
+set and it exists in the true label set, for a specific data point.
+
+**Available metrics**
+
+Here we define a set $D$ of $N$ documents
+
+$$D = \left\{d_0, d_1, ..., d_{N-1}\right\}$$
+
+Define $L_0, L_1, ..., L_{N-1}$ to be a family of label sets and $P_0, P_1, ..., P_{N-1}$
+to be a family of prediction sets where $L_i$ and $P_i$ are the label set and prediction set, respectively, that
+correspond to document $d_i$.
+
+The set of all unique labels is given by
+
+$$L = \bigcup_{k=0}^{N-1} L_k$$
+
+The following definition of indicator function $I_A(x)$ on a set $A$ will be necessary
+
+$$I_A(x) = \begin{cases}1 & \text{if $x \in A$}, \\ 0 & \text{otherwise}.\end{cases}$$
+
+<table class="table">
+  <thead>
+    <tr><th>Metric</th><th>Definition</th></tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Precision</td><td>$\frac{1}{N} \sum_{i=0}^{N-1} \frac{\left|P_i \cap L_i\right|}{\left|P_i\right|}$</td>
+    </tr>
+    <tr>
+      <td>Recall</td><td>$\frac{1}{N} \sum_{i=0}^{N-1} \frac{\left|L_i \cap P_i\right|}{\left|L_i\right|}$</td>
+    </tr>
+    <tr>
+      <td>Accuracy</td>
+      <td>
+        $\frac{1}{N} \sum_{i=0}^{N - 1} \frac{\left|L_i \cap P_i \right|}
+        {\left|L_i\right| + \left|P_i\right| - \left|L_i \cap P_i \right|}$
+      </td>
+    </tr>
+    <tr>
+      <td>Precision by label</td><td>$PPV(\ell)=\frac{TP}{TP + FP}=
+          \frac{\sum_{i=0}^{N-1} I_{P_i}(\ell) \cdot I_{L_i}(\ell)}
+          {\sum_{i=0}^{N-1} I_{P_i}(\ell)}$</td>
+    </tr>
+    <tr>
+      <td>Recall by label</td><td>$TPR(\ell)=\frac{TP}{P}=
+          \frac{\sum_{i=0}^{N-1} I_{P_i}(\ell) \cdot I_{L_i}(\ell)}
+          {\sum_{i=0}^{N-1} I_{L_i}(\ell)}$</td>
+    </tr>
+    <tr>
+      <td>F1-measure by label</td><td>$F1(\ell) = 2
+                            \cdot \left(\frac{PPV(\ell) \cdot TPR(\ell)}
+                            {PPV(\ell) + TPR(\ell)}\right)$</td>
+    </tr>
+    <tr>
+      <td>Hamming Loss</td>
+      <td>
+        $\frac{1}{N \cdot \left|L\right|} \sum_{i=0}^{N - 1} \left|L_i\right| + \left|P_i\right| - 2\left|L_i
+          \cap P_i\right|$
+      </td>
+    </tr>
+    <tr>
+      <td>Subset Accuracy</td>
+      <td>$\frac{1}{N} \sum_{i=0}^{N-1} I_{\{L_i\}}(P_i)$</td>
+    </tr>
+    <tr>
+      <td>F1 Measure</td>
+      <td>$\frac{1}{N} \sum_{i=0}^{N-1} 2 \frac{\left|P_i \cap L_i\right|}{\left|P_i\right| \cdot \left|L_i\right|}$</td>
+    </tr>
+    <tr>
+      <td>Micro precision</td>
+      <td>$\frac{TP}{TP + FP}=\frac{\sum_{i=0}^{N-1} \left|P_i \cap L_i\right|}
+          {\sum_{i=0}^{N-1} \left|P_i \cap L_i\right| + \sum_{i=0}^{N-1} \left|P_i - L_i\right|}$</td>
+    </tr>
+    <tr>
+      <td>Micro recall</td>
+      <td>$\frac{TP}{TP + FN}=\frac{\sum_{i=0}^{N-1} \left|P_i \cap L_i\right|}
+        {\sum_{i=0}^{N-1} \left|P_i \cap L_i\right| + \sum_{i=0}^{N-1} \left|L_i - P_i\right|}$</td>
+    </tr>
+    <tr>
+      <td>Micro F1 Measure</td>
+      <td>
+        $2 \cdot \frac{TP}{2 \cdot TP + FP + FN}=2 \cdot \frac{\sum_{i=0}^{N-1} \left|P_i \cap L_i\right|}{2 \cdot
+        \sum_{i=0}^{N-1} \left|P_i \cap L_i\right| + \sum_{i=0}^{N-1} \left|L_i - P_i\right| + \sum_{i=0}^{N-1}
+        \left|P_i - L_i\right|}$
+      </td>
+    </tr>
+  </tbody>
+</table>
+
+**Examples**
+
+The following code snippets illustrate how to evaluate the performance of a multilabel classifer. The examples
+use the fake prediction and label data for multilabel classification that is shown below.
+
+Document predictions:
+
+* doc 0 - predict 0, 1 - class 0, 2
+* doc 1 - predict 0, 2 - class 0, 1
+* doc 2 - predict none - class 0
+* doc 3 - predict 2 - class 2
+* doc 4 - predict 2, 0 - class 2, 0
+* doc 5 - predict 0, 1, 2 - class 0, 1
+* doc 6 - predict 1 - class 1, 2
+
+Predicted classes:
+
+* class 0 - doc 0, 1, 4, 5 (total 4)
+* class 1 - doc 0, 5, 6 (total 3)
+* class 2 - doc 1, 3, 4, 5 (total 4)
+
+True classes:
+
+* class 0 - doc 0, 1, 2, 4, 5 (total 5)
+* class 1 - doc 1, 5, 6 (total 3)
+* class 2 - doc 0, 3, 4, 6 (total 4)
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.mllib.evaluation.MultilabelMetrics
+import org.apache.spark.rdd.RDD;
+
+val scoreAndLabels: RDD[(Array[Double], Array[Double])] = sc.parallelize(
+  Seq((Array(0.0, 1.0), Array(0.0, 2.0)),
+    (Array(0.0, 2.0), Array(0.0, 1.0)),
+    (Array(), Array(0.0)),
+    (Array(2.0), Array(2.0)),
+    (Array(2.0, 0.0), Array(2.0, 0.0)),
+    (Array(0.0, 1.0, 2.0), Array(0.0, 1.0)),
+    (Array(1.0), Array(1.0, 2.0))), 2)
+
+// Instantiate metrics object
+val metrics = new MultilabelMetrics(scoreAndLabels)
+
+// Summary stats
+println(s"Recall = ${metrics.recall}")
+println(s"Precision = ${metrics.precision}")
+println(s"F1 measure = ${metrics.f1Measure}")
+println(s"Accuracy = ${metrics.accuracy}")
+
+// Individual label stats
+metrics.labels.foreach(label => println(s"Class $label precision = ${metrics.precision(label)}"))
+metrics.labels.foreach(label => println(s"Class $label recall = ${metrics.recall(label)}"))
+metrics.labels.foreach(label => println(s"Class $label F1-score = ${metrics.f1Measure(label)}"))
+
+// Micro stats
+println(s"Micro recall = ${metrics.microRecall}")
+println(s"Micro precision = ${metrics.microPrecision}")
+println(s"Micro F1 measure = ${metrics.microF1Measure}")
+
+// Hamming loss
+println(s"Hamming loss = ${metrics.hammingLoss}")
+
+// Subset accuracy
+println(s"Subset accuracy = ${metrics.subsetAccuracy}")
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.mllib.evaluation.MultilabelMetrics;
+import org.apache.spark.SparkConf;
+import java.util.Arrays;
+import java.util.List;
+
+public class MultilabelClassification {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("Multilabel Classification Metrics");
+    JavaSparkContext sc = new JavaSparkContext(conf);
+
+    List<Tuple2<double[], double[]>> data = Arrays.asList(
+        new Tuple2<double[], double[]>(new double[]{0.0, 1.0}, new double[]{0.0, 2.0}),
+        new Tuple2<double[], double[]>(new double[]{0.0, 2.0}, new double[]{0.0, 1.0}),
+        new Tuple2<double[], double[]>(new double[]{}, new double[]{0.0}),
+        new Tuple2<double[], double[]>(new double[]{2.0}, new double[]{2.0}),
+        new Tuple2<double[], double[]>(new double[]{2.0, 0.0}, new double[]{2.0, 0.0}),
+        new Tuple2<double[], double[]>(new double[]{0.0, 1.0, 2.0}, new double[]{0.0, 1.0}),
+        new Tuple2<double[], double[]>(new double[]{1.0}, new double[]{1.0, 2.0})
+        );
+    JavaRDD<Tuple2<double[], double[]>> scoreAndLabels = sc.parallelize(data);
+
+    // Instantiate metrics object
+    MultilabelMetrics metrics = new MultilabelMetrics(scoreAndLabels.rdd());
+
+    // Summary stats
+    System.out.format("Recall = %f\n", metrics.recall());
+    System.out.format("Precision = %f\n", metrics.precision());
+    System.out.format("F1 measure = %f\n", metrics.f1Measure());
+    System.out.format("Accuracy = %f\n", metrics.accuracy());
+
+    // Stats by labels
+    for (int i = 0; i < metrics.labels().length - 1; i++) {
+        System.out.format("Class %1.1f precision = %f\n", metrics.labels()[i], metrics.precision(metrics.labels()[i]));
+        System.out.format("Class %1.1f recall = %f\n", metrics.labels()[i], metrics.recall(metrics.labels()[i]));
+        System.out.format("Class %1.1f F1 score = %f\n", metrics.labels()[i], metrics.f1Measure(metrics.labels()[i]));
+    }
+
+    // Micro stats
+    System.out.format("Micro recall = %f\n", metrics.microRecall());
+    System.out.format("Micro precision = %f\n", metrics.microPrecision());
+    System.out.format("Micro F1 measure = %f\n", metrics.microF1Measure());
+
+    // Hamming loss
+    System.out.format("Hamming loss = %f\n", metrics.hammingLoss());
+
+    // Subset accuracy
+    System.out.format("Subset accuracy = %f\n", metrics.subsetAccuracy());
+
+  }
+}
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+from pyspark.mllib.evaluation import MultilabelMetrics
+
+scoreAndLabels = sc.parallelize([
+    ([0.0, 1.0], [0.0, 2.0]),
+    ([0.0, 2.0], [0.0, 1.0]),
+    ([], [0.0]),
+    ([2.0], [2.0]),
+    ([2.0, 0.0], [2.0, 0.0]),
+    ([0.0, 1.0, 2.0], [0.0, 1.0]),
+    ([1.0], [1.0, 2.0])])
+
+# Instantiate metrics object
+metrics = MultilabelMetrics(scoreAndLabels)
+
+# Summary stats
+print "Recall = %s" % metrics.recall()
+print "Precision = %s" % metrics.precision()
+print "F1 measure = %s" % metrics.f1Measure()
+print "Accuracy = %s" % metrics.accuracy
+
+# Individual label stats
+labels = scoreAndLabels.flatMap(lambda x: x[1]).distinct().collect()
+for label in labels:
+    print "Class %s precision = %s" % (label, metrics.precision(label))
+    print "Class %s recall = %s" % (label, metrics.recall(label))
+    print "Class %s F1 Measure = %s" % (label, metrics.f1Measure(label))
+
+# Micro stats
+print "Micro precision = %s" % metrics.microPrecision
+print "Micro recall = %s" % metrics.microRecall
+print "Micro F1 measure = %s" % metrics.microF1Measure
+
+# Hamming loss
+print "Hamming loss = %s" % metrics.hammingLoss
+
+# Subset accuracy
+print "Subset accuracy = %s" % metrics.subsetAccuracy
+
+{% endhighlight %}
+
+</div>
+</div>
+
+### Ranking systems
+
+The role of a ranking algorithm (often thought of as a [recommender system](https://en.wikipedia.org/wiki/Recommender_system))
+is to return to the user a set of relevant items or documents based on some training data. The definition of relevance
+may vary and is usually application specific. Ranking system metrics aim to quantify the effectiveness of these
+rankings or recommendations in various contexts. Some metrics compare a set of recommended documents to a ground truth
+set of relevant documents, while other metrics may incorporate numerical ratings explicitly.
+
+**Available metrics**
+
+A ranking system usually deals with a set of $M$ users
+
+$$U = \left\{u_0, u_1, ..., u_{M-1}\right\}$$
+
+Each user ($u_i$) having a set of $N$ ground truth relevant documents
+
+$$D_i = \left\{d_0, d_1, ..., d_{N-1}\right\}$$
+
+And a list of $Q$ recommended documents, in order of decreasing relevance
+
+$$R_i = \left[r_0, r_1, ..., r_{Q-1}\right]$$
+
+The goal of the ranking system is to produce the most relevant set of documents for each user. The relevance of the
+sets and the effectiveness of the algorithms can be measured using the metrics listed below.
+
+It is necessary to define a function which, provided a recommended document and a set of ground truth relevant
+documents, returns a relevance score for the recommended document.
+
+$$rel_D(r) = \begin{cases}1 & \text{if $r \in D$}, \\ 0 & \text{otherwise}.\end{cases}$$
+
+<table class="table">
+  <thead>
+    <tr><th>Metric</th><th>Definition</th><th>Notes</th></tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>
+        Precision at k
+      </td>
+      <td>
+        $p(k)=\frac{1}{M} \sum_{i=0}^{M-1} {\frac{1}{k} \sum_{j=0}^{\text{min}(\left|D\right|, k) - 1} rel_{D_i}(R_i(j))}$
+      </td>
+      <td>
+        <a href="https://en.wikipedia.org/wiki/Information_retrieval#Precision_at_K">Precision at k</a> is a measure of
+         how many of the first k recommended documents are in the set of true relevant documents averaged across all
+         users. In this metric, the order of the recommendations is not taken into account.
+      </td>
+    </tr>
+    <tr>
+      <td>Mean Average Precision</td>
+      <td>
+        $MAP=\frac{1}{M} \sum_{i=0}^{M-1} {\frac{1}{\left|D_i\right|} \sum_{j=0}^{Q-1} \frac{rel_{D_i}(R_i(j))}{j + 1}}$
+      </td>
+      <td>
+        <a href="https://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision">MAP</a> is a measure of how
+         many of the recommended documents are in the set of true relevant documents, where the
+        order of the recommendations is taken into account (i.e. penalty for highly relevant documents is higher).
+      </td>
+    </tr>
+    <tr>
+      <td>Normalized Discounted Cumulative Gain</td>
+      <td>
+        $NDCG(k)=\frac{1}{M} \sum_{i=0}^{M-1} {\frac{1}{IDCG(D_i, k)}\sum_{j=0}^{n-1}
+          \frac{rel_{D_i}(R_i(j))}{\text{ln}(j+1)}} \\
+        \text{Where} \\
+        \hspace{5 mm} n = \text{min}\left(\text{max}\left(|R_i|,|D_i|\right),k\right) \\
+        \hspace{5 mm} IDCG(D, k) = \sum_{j=0}^{\text{min}(\left|D\right|, k) - 1} \frac{1}{\text{ln}(j+1)}$
+      </td>
+      <td>
+        <a href="https://en.wikipedia.org/wiki/Information_retrieval#Discounted_cumulative_gain">NDCG at k</a> is a
+        measure of how many of the first k recommended documents are in the set of true relevant documents averaged
+        across all users. In contrast to precision at k, this metric takes into account the order of the recommendations
+        (documents are assumed to be in order of decreasing relevance).
+      </td>
+    </tr>
+  </tbody>
+</table>
+
+**Examples**
+
+The following code snippets illustrate how to load a sample dataset, train an alternating least squares recommendation
+model on the data, and evaluate the performance of the recommender by several ranking metrics. A brief summary of the
+methodology is provided below.
+
+MovieLens ratings are on a scale of 1-5:
+
+ * 5: Must see
+ * 4: Will enjoy
+ * 3: It's okay
+ * 2: Fairly bad
+ * 1: Awful
+
+So we should not recommend a movie if the predicted rating is less than 3.
+To map ratings to confidence scores, we use:
+
+ * 5 -> 2.5
+ * 4 -> 1.5
+ * 3 -> 0.5
+ * 2 -> -0.5
+ * 1 -> -1.5.
+
+This mappings means unobserved entries are generally between It's okay and Fairly bad. The semantics of 0 in this
+expanded world of non-positive weights are "the same as never having interacted at all."
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.mllib.evaluation.{RegressionMetrics, RankingMetrics}
+import org.apache.spark.mllib.recommendation.{ALS, Rating}
+
+// Read in the ratings data
+val ratings = sc.textFile("data/mllib/sample_movielens_data.txt").map { line =>
+  val fields = line.split("::")
+  Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble - 2.5)
+}.cache()
+
+// Map ratings to 1 or 0, 1 indicating a movie that should be recommended
+val binarizedRatings = ratings.map(r => Rating(r.user, r.product, if (r.rating > 0) 1.0 else 0.0)).cache()
+
+// Summarize ratings
+val numRatings = ratings.count()
+val numUsers = ratings.map(_.user).distinct().count()
+val numMovies = ratings.map(_.product).distinct().count()
+println(s"Got $numRatings ratings from $numUsers users on $numMovies movies.")
+
+// Build the model
+val numIterations = 10
+val rank = 10
+val lambda = 0.01
+val model = ALS.train(ratings, rank, numIterations, lambda)
+
+// Define a function to scale ratings from 0 to 1
+def scaledRating(r: Rating): Rating = {
+  val scaledRating = math.max(math.min(r.rating, 1.0), 0.0)
+  Rating(r.user, r.product, scaledRating)
+}
+
+// Get sorted top ten predictions for each user and then scale from [0, 1]
+val userRecommended = model.recommendProductsForUsers(10).map{ case (user, recs) =>
+  (user, recs.map(scaledRating))
+}
+
+// Assume that any movie a user rated 3 or higher (which maps to a 1) is a relevant document
+// Compare with top ten most relevant documents
+val userMovies = binarizedRatings.groupBy(_.user)
+val relevantDocuments = userMovies.join(userRecommended).map{ case (user, (actual, predictions)) =>
+  (predictions.map(_.product), actual.filter(_.rating > 0.0).map(_.product).toArray)
+}
+
+// Instantiate metrics object
+val metrics = new RankingMetrics(relevantDocuments)
+
+// Precision at K
+Array(1, 3, 5).foreach{ k =>
+  println(s"Precision at $k = ${metrics.precisionAt(k)}")
+}
+
+// Mean average precision
+println(s"Mean average precision = ${metrics.meanAveragePrecision}")
+
+// Normalized discounted cumulative gain
+Array(1, 3, 5).foreach{ k =>
+  println(s"NDCG at $k = ${metrics.ndcgAt(k)}")
+}
+
+// Get predictions for each data point
+val allPredictions = model.predict(ratings.map(r => (r.user, r.product))).map(r => ((r.user, r.product), r.rating))
+val allRatings = ratings.map(r => ((r.user, r.product), r.rating))
+val predictionsAndLabels = allPredictions.join(allRatings).map{ case ((user, product), (predicted, actual)) =>
+  (predicted, actual)
+}
+
+// Get the RMSE using regression metrics
+val regressionMetrics = new RegressionMetrics(predictionsAndLabels)
+println(s"RMSE = ${regressionMetrics.rootMeanSquaredError}")
+
+// R-squared
+println(s"R-squared = ${regressionMetrics.r2}")
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.function.Function;
+import java.util.*;
+import org.apache.spark.mllib.evaluation.RegressionMetrics;
+import org.apache.spark.mllib.evaluation.RankingMetrics;
+import org.apache.spark.mllib.recommendation.ALS;
+import org.apache.spark.mllib.recommendation.Rating;
+
+// Read in the ratings data
+public class Ranking {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("Ranking Metrics");
+    JavaSparkContext sc = new JavaSparkContext(conf);
+    String path = "data/mllib/sample_movielens_data.txt";
+    JavaRDD<String> data = sc.textFile(path);
+    JavaRDD<Rating> ratings = data.map(
+      new Function<String, Rating>() {
+        public Rating call(String line) {
+          String[] parts = line.split("::");
+          return new Rating(Integer.parseInt(parts[0]), Integer.parseInt(parts[1]), Double.parseDouble(parts[2]) - 2.5);
+        }
+      }
+    );
+    ratings.cache();
+
+    // Train an ALS model
+    final MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), 10, 10, 0.01);
+
+    // Get top 10 recommendations for every user and scale ratings from 0 to 1
+    JavaRDD<Tuple2<Object, Rating[]>> userRecs = model.recommendProductsForUsers(10).toJavaRDD();
+    JavaRDD<Tuple2<Object, Rating[]>> userRecsScaled = userRecs.map(
+      new Function<Tuple2<Object, Rating[]>, Tuple2<Object, Rating[]>>() {
+        public Tuple2<Object, Rating[]> call(Tuple2<Object, Rating[]> t) {
+          Rating[] scaledRatings = new Rating[t._2().length];
+          for (int i = 0; i < scaledRatings.length; i++) {
+            double newRating = Math.max(Math.min(t._2()[i].rating(), 1.0), 0.0);
+            scaledRatings[i] = new Rating(t._2()[i].user(), t._2()[i].product(), newRating);
+          }
+          return new Tuple2<Object, Rating[]>(t._1(), scaledRatings);
+        }
+      }
+    );
+    JavaPairRDD<Object, Rating[]> userRecommended = JavaPairRDD.fromJavaRDD(userRecsScaled);
+
+    // Map ratings to 1 or 0, 1 indicating a movie that should be recommended
+    JavaRDD<Rating> binarizedRatings = ratings.map(
+      new Function<Rating, Rating>() {
+        public Rating call(Rating r) {
+          double binaryRating;
+          if (r.rating() > 0.0) {
+            binaryRating = 1.0;
+          }
+          else {
+            binaryRating = 0.0;
+          }
+          return new Rating(r.user(), r.product(), binaryRating);
+        }
+      }
+    );
+
+    // Group ratings by common user
+    JavaPairRDD<Object, Iterable<Rating>> userMovies = binarizedRatings.groupBy(
+      new Function<Rating, Object>() {
+        public Object call(Rating r) {
+          return r.user();
+        }
+      }
+    );
+
+    // Get true relevant documents from all user ratings
+    JavaPairRDD<Object, List<Integer>> userMoviesList = userMovies.mapValues(
+      new Function<Iterable<Rating>, List<Integer>>() {
+        public List<Integer> call(Iterable<Rating> docs) {
+          List<Integer> products = new ArrayList<Integer>();
+          for (Rating r : docs) {
+            if (r.rating() > 0.0) {
+              products.add(r.product());
+            }
+          }
+          return products;
+        }
+      }
+    );
+
+    // Extract the product id from each recommendation
+    JavaPairRDD<Object, List<Integer>> userRecommendedList = userRecommended.mapValues(
+      new Function<Rating[], List<Integer>>() {
+        public List<Integer> call(Rating[] docs) {
+          List<Integer> products = new ArrayList<Integer>();
+          for (Rating r : docs) {
+            products.add(r.product());
+          }
+          return products;
+        }
+      }
+    );
+    JavaRDD<Tuple2<List<Integer>, List<Integer>>> relevantDocs = userMoviesList.join(userRecommendedList).values();
+
+    // Instantiate the metrics object
+    RankingMetrics metrics = RankingMetrics.of(relevantDocs);
+
+    // Precision and NDCG at k
+    Integer[] kVector = {1, 3, 5};
+    for (Integer k : kVector) {
+      System.out.format("Precision at %d = %f\n", k, metrics.precisionAt(k));
+      System.out.format("NDCG at %d = %f\n", k, metrics.ndcgAt(k));
+    }
+
+    // Mean average precision
+    System.out.format("Mean average precision = %f\n", metrics.meanAveragePrecision());
+
+    // Evaluate the model using numerical ratings and regression metrics
+    JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
+      new Function<Rating, Tuple2<Object, Object>>() {
+        public Tuple2<Object, Object> call(Rating r) {
+          return new Tuple2<Object, Object>(r.user(), r.product());
+        }
+      }
+    );
+    JavaPairRDD<Tuple2<Integer, Integer>, Object> predictions = JavaPairRDD.fromJavaRDD(
+      model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
+        new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Object>>() {
+          public Tuple2<Tuple2<Integer, Integer>, Object> call(Rating r){
+            return new Tuple2<Tuple2<Integer, Integer>, Object>(
+              new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
+          }
+        }
+    ));
+    JavaRDD<Tuple2<Object, Object>> ratesAndPreds =
+      JavaPairRDD.fromJavaRDD(ratings.map(
+        new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Object>>() {
+          public Tuple2<Tuple2<Integer, Integer>, Object> call(Rating r){
+            return new Tuple2<Tuple2<Integer, Integer>, Object>(
+              new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
+          }
+        }
+    )).join(predictions).values();
+
+    // Create regression metrics object
+    RegressionMetrics regressionMetrics = new RegressionMetrics(ratesAndPreds.rdd());
+
+    // Root mean squared error
+    System.out.format("RMSE = %f\n", regressionMetrics.rootMeanSquaredError());
+
+    // R-squared
+    System.out.format("R-squared = %f\n", regressionMetrics.r2());
+  }
+}
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+from pyspark.mllib.recommendation import ALS, Rating
+from pyspark.mllib.evaluation import RegressionMetrics, RankingMetrics
+
+#  Read in the ratings data
+lines = sc.textFile("data/mllib/sample_movielens_data.txt")
+
+def parseLine(line):
+    fields = line.split("::")
+    return Rating(int(fields[0]), int(fields[1]), float(fields[2]) - 2.5)
+ratings = lines.map(lambda r: parseLine(r))
+
+# Train a model on to predict user-product ratings
+model = ALS.train(ratings, 10, 10, 0.01)
+
+# Get predicted ratings on all existing user-product pairs
+testData = ratings.map(lambda p: (p.user, p.product))
+predictions = model.predictAll(testData).map(lambda r: ((r.user, r.product), r.rating))
+
+ratingsTuple = ratings.map(lambda r: ((r.user, r.product), r.rating))
+scoreAndLabels = predictions.join(ratingsTuple).map(lambda tup: tup[1])
+
+# Instantiate regression metrics to compare predicted and actual ratings
+metrics = RegressionMetrics(scoreAndLabels)
+
+# Root mean sqaured error
+print "RMSE = %s" % metrics.rootMeanSquaredError
+
+# R-squared
+print "R-squared = %s" % metrics.r2
+
+{% endhighlight %}
+
+</div>
+</div>
+
+## Regression model evaluation
+
+[Regression analysis](https://en.wikipedia.org/wiki/Regression_analysis) is used when predicting a continuous output
+variable from a number of independent variables.
+
+**Available metrics**
+
+<table class="table">
+  <thead>
+    <tr><th>Metric</th><th>Definition</th></tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Mean Squared Error (MSE)</td>
+      <td>$MSE = \frac{\sum_{i=0}^{N-1} (\mathbf{y}_i - \hat{\mathbf{y}}_i)^2}{N}$</td>
+    </tr>
+    <tr>
+      <td>Root Mean Squared Error (RMSE)</td>
+      <td>$RMSE = \sqrt{\frac{\sum_{i=0}^{N-1} (\mathbf{y}_i - \hat{\mathbf{y}}_i)^2}{N}}$</td>
+    </tr>
+    <tr>
+      <td>Mean Absoloute Error (MAE)</td>
+      <td>$MAE=\sum_{i=0}^{N-1} \left|\mathbf{y}_i - \hat{\mathbf{y}}_i\right|$</td>
+    </tr>
+    <tr>
+      <td>Coefficient of Determination $(R^2)$</td>
+      <td>$R^2=1 - \frac{MSE}{\text{VAR}(\mathbf{y}) \cdot (N-1)}=1-\frac{\sum_{i=0}^{N-1}
+        (\mathbf{y}_i - \hat{\mathbf{y}}_i)^2}{\sum_{i=0}^{N-1}(\mathbf{y}_i-\bar{\mathbf{y}})^2}$</td>
+    </tr>
+    <tr>
+      <td>Explained Variance</td>
+      <td>$1 - \frac{\text{VAR}(\mathbf{y} - \mathbf{\hat{y}})}{\text{VAR}(\mathbf{y})}$</td>
+    </tr>
+  </tbody>
+</table>
+
+**Examples**
+
+<div class="codetabs">
+The following code snippets illustrate how to load a sample dataset, train a linear regression algorithm on the data,
+and evaluate the performance of the algorithm by several regression metrics.
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.LinearRegressionModel
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.evaluation.RegressionMetrics
+import org.apache.spark.mllib.util.MLUtils
+
+// Load the data
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_linear_regression_data.txt").cache()
+
+// Build the model
+val numIterations = 100
+val model = LinearRegressionWithSGD.train(data, numIterations)
+
+// Get predictions
+val valuesAndPreds = data.map{ point =>
+  val prediction = model.predict(point.features)
+  (prediction, point.label)
+}
+
+// Instantiate metrics object
+val metrics = new RegressionMetrics(valuesAndPreds)
+
+// Squared error
+println(s"MSE = ${metrics.meanSquaredError}")
+println(s"RMSE = ${metrics.rootMeanSquaredError}")
+
+// R-squared
+println(s"R-squared = ${metrics.r2}")
+
+// Mean absolute error
+println(s"MAE = ${metrics.meanAbsoluteError}")
+
+// Explained variance
+println(s"Explained variance = ${metrics.explainedVariance}")
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.regression.LinearRegressionModel;
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD;
+import org.apache.spark.mllib.evaluation.RegressionMetrics;
+import org.apache.spark.SparkConf;
+
+public class LinearRegression {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("Linear Regression Example");
+    JavaSparkContext sc = new JavaSparkContext(conf);
+
+    // Load and parse the data
+    String path = "data/mllib/sample_linear_regression_data.txt";
+    JavaRDD<String> data = sc.textFile(path);
+    JavaRDD<LabeledPoint> parsedData = data.map(
+      new Function<String, LabeledPoint>() {
+        public LabeledPoint call(String line) {
+          String[] parts = line.split(" ");
+          double[] v = new double[parts.length - 1];
+          for (int i = 1; i < parts.length - 1; i++)
+            v[i - 1] = Double.parseDouble(parts[i].split(":")[1]);
+          return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
+        }
+      }
+    );
+    parsedData.cache();
+
+    // Building the model
+    int numIterations = 100;
+    final LinearRegressionModel model =
+      LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations);
+
+    // Evaluate model on training examples and compute training error
+    JavaRDD<Tuple2<Object, Object>> valuesAndPreds = parsedData.map(
+      new Function<LabeledPoint, Tuple2<Object, Object>>() {
+        public Tuple2<Object, Object> call(LabeledPoint point) {
+          double prediction = model.predict(point.features());
+          return new Tuple2<Object, Object>(prediction, point.label());
+        }
+      }
+    );
+
+    // Instantiate metrics object
+    RegressionMetrics metrics = new RegressionMetrics(valuesAndPreds.rdd());
+
+    // Squared error
+    System.out.format("MSE = %f\n", metrics.meanSquaredError());
+    System.out.format("RMSE = %f\n", metrics.rootMeanSquaredError());
+
+    // R-squared
+    System.out.format("R Squared = %f\n", metrics.r2());
+
+    // Mean absolute error
+    System.out.format("MAE = %f\n", metrics.meanAbsoluteError());
+
+    // Explained variance
+    System.out.format("Explained Variance = %f\n", metrics.explainedVariance());
+
+    // Save and load model
+    model.save(sc.sc(), "myModelPath");
+    LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(), "myModelPath");
+  }
+}
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD
+from pyspark.mllib.evaluation import RegressionMetrics
+from pyspark.mllib.linalg import DenseVector
+
+# Load and parse the data
+def parsePoint(line):
+    values = line.split()
+    return LabeledPoint(float(values[0]), DenseVector([float(x.split(':')[1]) for x in values[1:]]))
+
+data = sc.textFile("data/mllib/sample_linear_regression_data.txt")
+parsedData = data.map(parsePoint)
+
+# Build the model
+model = LinearRegressionWithSGD.train(parsedData)
+
+# Get predictions
+valuesAndPreds = parsedData.map(lambda p: (float(model.predict(p.features)), p.label))
+
+# Instantiate metrics object
+metrics = RegressionMetrics(valuesAndPreds)
+
+# Squared Error
+print "MSE = %s" % metrics.meanSquaredError
+print "RMSE = %s" % metrics.rootMeanSquaredError
+
+# R-squared
+print "R-squared = %s" % metrics.r2
+
+# Mean absolute error
+print "MAE = %s" % metrics.meanAbsoluteError
+
+# Explained variance
+print "Explained variance = %s" % metrics.explainedVariance
+
+{% endhighlight %}
+
+</div>
+</div>
\ No newline at end of file
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index d2d1cc93fe006..eea864eacf7c4 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -48,6 +48,7 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
   * FP-growth
+* [Evaluation Metrics](mllib-evaluation-metrics.html)
 * [Optimization (developer)](mllib-optimization.html)
   * stochastic gradient descent
   * limited-memory BFGS (L-BFGS)

From a200e64561c8803731578267df16906f6773cbea Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 29 Jul 2015 19:02:15 -0700
Subject: [PATCH 0679/1454] [SPARK-9440] [MLLIB] Add hyperparameters to
 LocalLDAModel save/load

jkbradley MechCoder

Resolves blocking issue for SPARK-6793. Please review after #7705 is merged.

Author: Feynman Liang <fliang@databricks.com>

Closes #7757 from feynmanliang/SPARK-9940-localSaveLoad and squashes the following commits:

d0d8cf4 [Feynman Liang] Fix thisClassName
0f30109 [Feynman Liang] Fix tests after changing LDAModel public API
dc61981 [Feynman Liang] Add hyperparams to LocalLDAModel save/load
---
 .../spark/mllib/clustering/LDAModel.scala     | 40 +++++++++++++------
 .../spark/mllib/clustering/LDASuite.scala     |  6 ++-
 2 files changed, 33 insertions(+), 13 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 059b52ef20a98..ece28848aa02c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -215,7 +215,8 @@ class LocalLDAModel private[clustering] (
   override protected def formatVersion = "1.0"
 
   override def save(sc: SparkContext, path: String): Unit = {
-    LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix)
+    LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix, docConcentration, topicConcentration,
+      gammaShape)
   }
   // TODO
   // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ???
@@ -312,16 +313,23 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
     // as a Row in data.
     case class Data(topic: Vector, index: Int)
 
-    // TODO: explicitly save docConcentration, topicConcentration, and gammaShape for use in
-    // model.predict()
-    def save(sc: SparkContext, path: String, topicsMatrix: Matrix): Unit = {
+    def save(
+        sc: SparkContext,
+        path: String,
+        topicsMatrix: Matrix,
+        docConcentration: Vector,
+        topicConcentration: Double,
+        gammaShape: Double): Unit = {
       val sqlContext = SQLContext.getOrCreate(sc)
       import sqlContext.implicits._
 
       val k = topicsMatrix.numCols
       val metadata = compact(render
         (("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~
-          ("k" -> k) ~ ("vocabSize" -> topicsMatrix.numRows)))
+          ("k" -> k) ~ ("vocabSize" -> topicsMatrix.numRows) ~
+          ("docConcentration" -> docConcentration.toArray.toSeq) ~
+          ("topicConcentration" -> topicConcentration) ~
+          ("gammaShape" -> gammaShape)))
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
 
       val topicsDenseMatrix = topicsMatrix.toBreeze.toDenseMatrix
@@ -331,7 +339,12 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       sc.parallelize(topics, 1).toDF().write.parquet(Loader.dataPath(path))
     }
 
-    def load(sc: SparkContext, path: String): LocalLDAModel = {
+    def load(
+        sc: SparkContext,
+        path: String,
+        docConcentration: Vector,
+        topicConcentration: Double,
+        gammaShape: Double): LocalLDAModel = {
       val dataPath = Loader.dataPath(path)
       val sqlContext = SQLContext.getOrCreate(sc)
       val dataFrame = sqlContext.read.parquet(dataPath)
@@ -348,8 +361,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       val topicsMat = Matrices.fromBreeze(brzTopics)
 
       // TODO: initialize with docConcentration, topicConcentration, and gammaShape after SPARK-9940
-      new LocalLDAModel(topicsMat,
-        Vectors.dense(Array.fill(topicsMat.numRows)(1.0 / topicsMat.numRows)), 1D, 100D)
+      new LocalLDAModel(topicsMat, docConcentration, topicConcentration, gammaShape)
     }
   }
 
@@ -358,11 +370,15 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
     implicit val formats = DefaultFormats
     val expectedK = (metadata \ "k").extract[Int]
     val expectedVocabSize = (metadata \ "vocabSize").extract[Int]
+    val docConcentration =
+      Vectors.dense((metadata \ "docConcentration").extract[Seq[Double]].toArray)
+    val topicConcentration = (metadata \ "topicConcentration").extract[Double]
+    val gammaShape = (metadata \ "gammaShape").extract[Double]
     val classNameV1_0 = SaveLoadV1_0.thisClassName
 
     val model = (loadedClassName, loadedVersion) match {
       case (className, "1.0") if className == classNameV1_0 =>
-        SaveLoadV1_0.load(sc, path)
+        SaveLoadV1_0.load(sc, path, docConcentration, topicConcentration, gammaShape)
       case _ => throw new Exception(
         s"LocalLDAModel.load did not recognize model with (className, format version):" +
           s"($loadedClassName, $loadedVersion).  Supported:\n" +
@@ -565,7 +581,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
     val thisFormatVersion = "1.0"
 
-    val classNameV1_0 = "org.apache.spark.mllib.clustering.DistributedLDAModel"
+    val thisClassName = "org.apache.spark.mllib.clustering.DistributedLDAModel"
 
     // Store globalTopicTotals as a Vector.
     case class Data(globalTopicTotals: Vector)
@@ -591,7 +607,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
       import sqlContext.implicits._
 
       val metadata = compact(render
-        (("class" -> classNameV1_0) ~ ("version" -> thisFormatVersion) ~
+        (("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~
           ("k" -> k) ~ ("vocabSize" -> vocabSize) ~
           ("docConcentration" -> docConcentration.toArray.toSeq) ~
           ("topicConcentration" -> topicConcentration) ~
@@ -660,7 +676,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
     val topicConcentration = (metadata \ "topicConcentration").extract[Double]
     val iterationTimes = (metadata \ "iterationTimes").extract[Seq[Double]]
     val gammaShape = (metadata \ "gammaShape").extract[Double]
-    val classNameV1_0 = SaveLoadV1_0.classNameV1_0
+    val classNameV1_0 = SaveLoadV1_0.thisClassName
 
     val model = (loadedClassName, loadedVersion) match {
       case (className, "1.0") if className == classNameV1_0 => {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index aa36336ebbee6..b91c7cefed22e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -334,7 +334,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
   test("model save/load") {
     // Test for LocalLDAModel.
     val localModel = new LocalLDAModel(tinyTopics,
-      Vectors.dense(Array.fill(tinyTopics.numRows)(1.0 / tinyTopics.numRows)), 1D, 100D)
+      Vectors.dense(Array.fill(tinyTopics.numRows)(0.01)), 0.5D, 10D)
     val tempDir1 = Utils.createTempDir()
     val path1 = tempDir1.toURI.toString
 
@@ -360,6 +360,9 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
       assert(samelocalModel.topicsMatrix === localModel.topicsMatrix)
       assert(samelocalModel.k === localModel.k)
       assert(samelocalModel.vocabSize === localModel.vocabSize)
+      assert(samelocalModel.docConcentration === localModel.docConcentration)
+      assert(samelocalModel.topicConcentration === localModel.topicConcentration)
+      assert(samelocalModel.gammaShape === localModel.gammaShape)
 
       val sameDistributedModel = DistributedLDAModel.load(sc, path2)
       assert(distributedModel.topicsMatrix === sameDistributedModel.topicsMatrix)
@@ -368,6 +371,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
       assert(distributedModel.iterationTimes === sameDistributedModel.iterationTimes)
       assert(distributedModel.docConcentration === sameDistributedModel.docConcentration)
       assert(distributedModel.topicConcentration === sameDistributedModel.topicConcentration)
+      assert(distributedModel.gammaShape === sameDistributedModel.gammaShape)
       assert(distributedModel.globalTopicTotals === sameDistributedModel.globalTopicTotals)
 
       val graph = distributedModel.graph

From 9514d874f0cf61f1eb4ec4f5f66e053119f769c9 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 29 Jul 2015 20:46:03 -0700
Subject: [PATCH 0680/1454] [SPARK-9458] Avoid object allocation in prefix
 generation.

In our existing sort prefix generation code, we use expression's eval method to generate the prefix, which results in object allocation for every prefix. We can use the specialized getters available on InternalRow directly to avoid the object allocation.

I also removed the FLOAT prefix, opting for converting float directly to double.

Author: Reynold Xin <rxin@databricks.com>

Closes #7763 from rxin/sort-prefix and squashes the following commits:

5dc2f06 [Reynold Xin] [SPARK-9458] Avoid object allocation in prefix generation.
---
 .../unsafe/sort/PrefixComparators.java        | 16 ------
 .../unsafe/sort/PrefixComparatorsSuite.scala  | 12 -----
 .../execution/UnsafeExternalRowSorter.java    |  2 +-
 .../spark/sql/execution/SortPrefixUtils.scala | 51 +++++++++----------
 .../spark/sql/execution/SparkStrategies.scala |  4 +-
 .../org/apache/spark/sql/execution/sort.scala |  5 +-
 .../execution/RowFormatConvertersSuite.scala  |  2 +-
 .../execution/UnsafeExternalSortSuite.scala   | 10 ++--
 8 files changed, 35 insertions(+), 67 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index bf1bc5dffba78..5624e067da2cc 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -31,7 +31,6 @@ private PrefixComparators() {}
 
   public static final StringPrefixComparator STRING = new StringPrefixComparator();
   public static final IntegralPrefixComparator INTEGRAL = new IntegralPrefixComparator();
-  public static final FloatPrefixComparator FLOAT = new FloatPrefixComparator();
   public static final DoublePrefixComparator DOUBLE = new DoublePrefixComparator();
 
   public static final class StringPrefixComparator extends PrefixComparator {
@@ -78,21 +77,6 @@ public int compare(long a, long b) {
     public final long NULL_PREFIX = Long.MIN_VALUE;
   }
 
-  public static final class FloatPrefixComparator extends PrefixComparator {
-    @Override
-    public int compare(long aPrefix, long bPrefix) {
-      float a = Float.intBitsToFloat((int) aPrefix);
-      float b = Float.intBitsToFloat((int) bPrefix);
-      return Utils.nanSafeCompareFloats(a, b);
-    }
-
-    public long computePrefix(float value) {
-      return Float.floatToIntBits(value) & 0xffffffffL;
-    }
-
-    public final long NULL_PREFIX = computePrefix(Float.NEGATIVE_INFINITY);
-  }
-
   public static final class DoublePrefixComparator extends PrefixComparator {
     @Override
     public int compare(long aPrefix, long bPrefix) {
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
index dc03e374b51db..28fe9259453a6 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -48,18 +48,6 @@ class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
     forAll { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
   }
 
-  test("float prefix comparator handles NaN properly") {
-    val nan1: Float = java.lang.Float.intBitsToFloat(0x7f800001)
-    val nan2: Float = java.lang.Float.intBitsToFloat(0x7fffffff)
-    assert(nan1.isNaN)
-    assert(nan2.isNaN)
-    val nan1Prefix = PrefixComparators.FLOAT.computePrefix(nan1)
-    val nan2Prefix = PrefixComparators.FLOAT.computePrefix(nan2)
-    assert(nan1Prefix === nan2Prefix)
-    val floatMaxPrefix = PrefixComparators.FLOAT.computePrefix(Float.MaxValue)
-    assert(PrefixComparators.FLOAT.compare(nan1Prefix, floatMaxPrefix) === 1)
-  }
-
   test("double prefix comparator handles NaNs properly") {
     val nan1: Double = java.lang.Double.longBitsToDouble(0x7ff0000000000001L)
     val nan2: Double = java.lang.Double.longBitsToDouble(0x7fffffffffffffffL)
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 4c3f2c6557140..8342833246f7d 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -121,7 +121,7 @@ Iterator<InternalRow> sort() throws IOException {
         // here in order to prevent memory leaks.
         cleanupResources();
       }
-      return new AbstractScalaRowIterator() {
+      return new AbstractScalaRowIterator<InternalRow>() {
 
         private final int numFields = schema.length();
         private UnsafeRow row = new UnsafeRow();
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 2dee3542d6101..050d27f1460fb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -19,7 +19,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.SortOrder
+import org.apache.spark.sql.catalyst.expressions.{BoundReference, SortOrder}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.collection.unsafe.sort.{PrefixComparators, PrefixComparator}
@@ -39,57 +39,54 @@ object SortPrefixUtils {
     sortOrder.dataType match {
       case StringType => PrefixComparators.STRING
       case BooleanType | ByteType | ShortType | IntegerType | LongType => PrefixComparators.INTEGRAL
-      case FloatType => PrefixComparators.FLOAT
-      case DoubleType => PrefixComparators.DOUBLE
+      case FloatType | DoubleType => PrefixComparators.DOUBLE
       case _ => NoOpPrefixComparator
     }
   }
 
   def getPrefixComputer(sortOrder: SortOrder): InternalRow => Long = {
+    val bound = sortOrder.child.asInstanceOf[BoundReference]
+    val pos = bound.ordinal
     sortOrder.dataType match {
-      case StringType => (row: InternalRow) => {
-        PrefixComparators.STRING.computePrefix(sortOrder.child.eval(row).asInstanceOf[UTF8String])
-      }
+      case StringType =>
+        (row: InternalRow) => {
+          PrefixComparators.STRING.computePrefix(row.getUTF8String(pos))
+        }
       case BooleanType =>
         (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else if (sortOrder.child.eval(row).asInstanceOf[Boolean]) 1
+          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else if (row.getBoolean(pos)) 1
           else 0
         }
       case ByteType =>
         (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Byte]
+          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getByte(pos)
         }
       case ShortType =>
         (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Short]
+          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getShort(pos)
         }
       case IntegerType =>
         (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Int]
+          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getInt(pos)
         }
       case LongType =>
         (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Long]
+          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getLong(pos)
         }
       case FloatType => (row: InternalRow) => {
-        val exprVal = sortOrder.child.eval(row)
-        if (exprVal == null) PrefixComparators.FLOAT.NULL_PREFIX
-        else PrefixComparators.FLOAT.computePrefix(sortOrder.child.eval(row).asInstanceOf[Float])
+        if (row.isNullAt(pos)) {
+          PrefixComparators.DOUBLE.NULL_PREFIX
+        } else {
+          PrefixComparators.DOUBLE.computePrefix(row.getFloat(pos).toDouble)
+        }
       }
       case DoubleType => (row: InternalRow) => {
-        val exprVal = sortOrder.child.eval(row)
-        if (exprVal == null) PrefixComparators.DOUBLE.NULL_PREFIX
-        else PrefixComparators.DOUBLE.computePrefix(sortOrder.child.eval(row).asInstanceOf[Double])
+        if (row.isNullAt(pos)) {
+          PrefixComparators.DOUBLE.NULL_PREFIX
+        } else {
+          PrefixComparators.DOUBLE.computePrefix(row.getDouble(pos))
+        }
       }
       case _ => (row: InternalRow) => 0L
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index f3ef066528ff8..4ab2c41f1b339 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -340,8 +340,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
      */
     def getSortOperator(sortExprs: Seq[SortOrder], global: Boolean, child: SparkPlan): SparkPlan = {
       if (sqlContext.conf.unsafeEnabled && sqlContext.conf.codegenEnabled &&
-        UnsafeExternalSort.supportsSchema(child.schema)) {
-        execution.UnsafeExternalSort(sortExprs, global, child)
+        TungstenSort.supportsSchema(child.schema)) {
+        execution.TungstenSort(sortExprs, global, child)
       } else if (sqlContext.conf.externalSortEnabled) {
         execution.ExternalSort(sortExprs, global, child)
       } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index f82208868c3e3..d0ad310062853 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -97,7 +97,7 @@ case class ExternalSort(
  * @param testSpillFrequency Method for configuring periodic spilling in unit tests. If set, will
  *                           spill every `frequency` records.
  */
-case class UnsafeExternalSort(
+case class TungstenSort(
     sortOrder: Seq[SortOrder],
     global: Boolean,
     child: SparkPlan,
@@ -110,7 +110,6 @@ case class UnsafeExternalSort(
     if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
-    assert(codegenEnabled, "UnsafeExternalSort requires code generation to be enabled")
     def doSort(iterator: Iterator[InternalRow]): Iterator[InternalRow] = {
       val ordering = newOrdering(sortOrder, child.output)
       val boundSortExpression = BindReferences.bindReference(sortOrder.head, child.output)
@@ -149,7 +148,7 @@ case class UnsafeExternalSort(
 }
 
 @DeveloperApi
-object UnsafeExternalSort {
+object TungstenSort {
   /**
    * Return true if UnsafeExternalSort can sort rows with the given schema, false otherwise.
    */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 7b75f755918c1..c458f95ca1ab3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -31,7 +31,7 @@ class RowFormatConvertersSuite extends SparkPlanTest {
 
   private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
   assert(!outputsSafe.outputsUnsafeRows)
-  private val outputsUnsafe = UnsafeExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  private val outputsUnsafe = TungstenSort(Nil, false, PhysicalRDD(Seq.empty, null))
   assert(outputsUnsafe.outputsUnsafeRows)
 
   test("planner should insert unsafe->safe conversions when required") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
index 7a4baa9e4a49d..9cabc4b90bf8e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
@@ -42,7 +42,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
     checkThatPlansAgree(
       (1 to 100).map(v => Tuple1(v)).toDF("a"),
-      (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
+      (child: SparkPlan) => Limit(10, TungstenSort('a.asc :: Nil, true, child)),
       (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
       sortAnswers = false
     )
@@ -53,7 +53,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     try {
       checkThatPlansAgree(
         (1 to 100).map(v => Tuple1(v)).toDF("a"),
-        (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
+        (child: SparkPlan) => Limit(10, TungstenSort('a.asc :: Nil, true, child)),
         (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
         sortAnswers = false
       )
@@ -68,7 +68,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     val stringLength = 1024 * 1024 * 2
     checkThatPlansAgree(
       Seq(Tuple1("a" * stringLength), Tuple1("b" * stringLength)).toDF("a").repartition(1),
-      UnsafeExternalSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 1),
+      TungstenSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 1),
       Sort(sortOrder, global = true, _: SparkPlan),
       sortAnswers = false
     )
@@ -88,11 +88,11 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
         TestSQLContext.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)
       )
-      assert(UnsafeExternalSort.supportsSchema(inputDf.schema))
+      assert(TungstenSort.supportsSchema(inputDf.schema))
       checkThatPlansAgree(
         inputDf,
         plan => ConvertToSafe(
-          UnsafeExternalSort(sortOrder, global = true, plan: SparkPlan, testSpillFrequency = 23)),
+          TungstenSort(sortOrder, global = true, plan: SparkPlan, testSpillFrequency = 23)),
         Sort(sortOrder, global = true, _: SparkPlan),
         sortAnswers = false
       )

From 07fd7d36471dfb823c1ce3e3a18464043affde18 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 29 Jul 2015 21:18:43 -0700
Subject: [PATCH 0681/1454] [SPARK-9460] Avoid byte array allocation in
 StringPrefixComparator.

As of today, StringPrefixComparator converts the long values back to byte arrays in order to compare them. This patch optimizes this to compare the longs directly, rather than turning the longs into byte arrays and comparing them byte by byte (unsigned).

This only works on little-endian architecture right now.

Author: Reynold Xin <rxin@databricks.com>

Closes #7765 from rxin/SPARK-9460 and squashes the following commits:

e4908cc [Reynold Xin] Stricter randomized tests.
4c8d094 [Reynold Xin] [SPARK-9460] Avoid byte array allocation in StringPrefixComparator.
---
 .../unsafe/sort/PrefixComparators.java        | 29 ++-----------------
 .../unsafe/sort/PrefixComparatorsSuite.scala  | 19 ++++++++----
 .../apache/spark/unsafe/types/UTF8String.java |  9 ++++++
 .../spark/unsafe/types/UTF8StringSuite.java   | 11 +++++++
 4 files changed, 36 insertions(+), 32 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index 5624e067da2cc..a9ee6042fec74 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -17,9 +17,7 @@
 
 package org.apache.spark.util.collection.unsafe.sort;
 
-import com.google.common.base.Charsets;
-import com.google.common.primitives.Longs;
-import com.google.common.primitives.UnsignedBytes;
+import com.google.common.primitives.UnsignedLongs;
 
 import org.apache.spark.annotation.Private;
 import org.apache.spark.unsafe.types.UTF8String;
@@ -36,32 +34,11 @@ private PrefixComparators() {}
   public static final class StringPrefixComparator extends PrefixComparator {
     @Override
     public int compare(long aPrefix, long bPrefix) {
-      // TODO: can done more efficiently
-      byte[] a = Longs.toByteArray(aPrefix);
-      byte[] b = Longs.toByteArray(bPrefix);
-      for (int i = 0; i < 8; i++) {
-        int c = UnsignedBytes.compare(a[i], b[i]);
-        if (c != 0) return c;
-      }
-      return 0;
-    }
-
-    public long computePrefix(byte[] bytes) {
-      if (bytes == null) {
-        return 0L;
-      } else {
-        byte[] padded = new byte[8];
-        System.arraycopy(bytes, 0, padded, 0, Math.min(bytes.length, 8));
-        return Longs.fromByteArray(padded);
-      }
-    }
-
-    public long computePrefix(String value) {
-      return value == null ? 0L : computePrefix(value.getBytes(Charsets.UTF_8));
+      return UnsignedLongs.compare(aPrefix, bPrefix);
     }
 
     public long computePrefix(UTF8String value) {
-      return value == null ? 0L : computePrefix(value.getBytes());
+      return value == null ? 0L : value.getPrefix();
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
index 28fe9259453a6..26b7a9e816d1e 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -17,22 +17,29 @@
 
 package org.apache.spark.util.collection.unsafe.sort
 
+import com.google.common.primitives.UnsignedBytes
 import org.scalatest.prop.PropertyChecks
-
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.unsafe.types.UTF8String
 
 class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
 
   test("String prefix comparator") {
 
     def testPrefixComparison(s1: String, s2: String): Unit = {
-      val s1Prefix = PrefixComparators.STRING.computePrefix(s1)
-      val s2Prefix = PrefixComparators.STRING.computePrefix(s2)
+      val utf8string1 = UTF8String.fromString(s1)
+      val utf8string2 = UTF8String.fromString(s2)
+      val s1Prefix = PrefixComparators.STRING.computePrefix(utf8string1)
+      val s2Prefix = PrefixComparators.STRING.computePrefix(utf8string2)
       val prefixComparisonResult = PrefixComparators.STRING.compare(s1Prefix, s2Prefix)
+
+      val cmp = UnsignedBytes.lexicographicalComparator().compare(
+        utf8string1.getBytes.take(8), utf8string2.getBytes.take(8))
+
       assert(
-        (prefixComparisonResult == 0) ||
-        (prefixComparisonResult < 0 && s1 < s2) ||
-        (prefixComparisonResult > 0 && s1 > s2))
+        (prefixComparisonResult == 0 && cmp == 0) ||
+        (prefixComparisonResult < 0 && s1.compareTo(s2) < 0) ||
+        (prefixComparisonResult > 0 && s1.compareTo(s2) > 0))
     }
 
     // scalastyle:off
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 3e1cc67dbf337..57522003ba2ba 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -137,6 +137,15 @@ public int numChars() {
     return len;
   }
 
+  /**
+   * Returns a 64-bit integer that can be used as the prefix used in sorting.
+   */
+  public long getPrefix() {
+    long p = PlatformDependent.UNSAFE.getLong(base, offset);
+    p = java.lang.Long.reverseBytes(p);
+    return p;
+  }
+
   /**
    * Returns the underline bytes, will be a copy of it if it's part of another array.
    */
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index e2a5628ff4d93..42e09e435a412 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -63,8 +63,19 @@ public void emptyStringTest() {
     assertEquals(0, EMPTY_UTF8.numBytes());
   }
 
+  @Test
+  public void prefix() {
+    assertTrue(fromString("a").getPrefix() - fromString("b").getPrefix() < 0);
+    assertTrue(fromString("ab").getPrefix() - fromString("b").getPrefix() < 0);
+    assertTrue(
+      fromString("abbbbbbbbbbbasdf").getPrefix() - fromString("bbbbbbbbbbbbasdf").getPrefix() < 0);
+    assertTrue(fromString("").getPrefix() - fromString("a").getPrefix() < 0);
+    assertTrue(fromString("你好").getPrefix() - fromString("世界").getPrefix() > 0);
+  }
+
   @Test
   public void compareTo() {
+    assertTrue(fromString("").compareTo(fromString("a")) < 0);
     assertTrue(fromString("abc").compareTo(fromString("ABC")) > 0);
     assertTrue(fromString("abc0").compareTo(fromString("abc")) > 0);
     assertTrue(fromString("abcabcabc").compareTo(fromString("abcabcabc")) == 0);

From 27850af5255352cebd933ed3cc3d82c9ff6e9b62 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 29 Jul 2015 21:24:47 -0700
Subject: [PATCH 0682/1454] [SPARK-9462][SQL] Initialize nondeterministic
 expressions in code gen fallback mode.

Author: Reynold Xin <rxin@databricks.com>

Closes #7767 from rxin/SPARK-9462 and squashes the following commits:

ef3e2d9 [Reynold Xin] Removed println
713ac3a [Reynold Xin] More unit tests.
bb5c334 [Reynold Xin] [SPARK-9462][SQL] Initialize nondeterministic expressions in code gen fallback mode.
---
 .../expressions/codegen/CodegenFallback.scala |  7 ++-
 .../CodegenExpressionCachingSuite.scala       | 46 +++++++++++++++++--
 2 files changed, 47 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
index 6b187f05604fd..3492d2c6189ed 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenFallback.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.catalyst.expressions.{Nondeterministic, Expression}
 
 /**
  * A trait that can be used to provide a fallback mode for expression code generation.
@@ -25,6 +25,11 @@ import org.apache.spark.sql.catalyst.expressions.Expression
 trait CodegenFallback extends Expression {
 
   protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    foreach {
+      case n: Nondeterministic => n.setInitialValues()
+      case _ =>
+    }
+
     ctx.references += this
     val objectTerm = ctx.freshName("obj")
     s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala
index 866bf904e4a4c..2d3f98dbbd3d1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodegenExpressionCachingSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, LeafExpression}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types.{BooleanType, DataType}
 
 /**
@@ -27,7 +27,32 @@ import org.apache.spark.sql.types.{BooleanType, DataType}
  */
 class CodegenExpressionCachingSuite extends SparkFunSuite {
 
-  test("GenerateUnsafeProjection") {
+  test("GenerateUnsafeProjection should initialize expressions") {
+    // Use an Add to wrap two of them together in case we only initialize the top level expressions.
+    val expr = And(NondeterministicExpression(), NondeterministicExpression())
+    val instance = UnsafeProjection.create(Seq(expr))
+    assert(instance.apply(null).getBoolean(0) === false)
+  }
+
+  test("GenerateProjection should initialize expressions") {
+    val expr = And(NondeterministicExpression(), NondeterministicExpression())
+    val instance = GenerateProjection.generate(Seq(expr))
+    assert(instance.apply(null).getBoolean(0) === false)
+  }
+
+  test("GenerateMutableProjection should initialize expressions") {
+    val expr = And(NondeterministicExpression(), NondeterministicExpression())
+    val instance = GenerateMutableProjection.generate(Seq(expr))()
+    assert(instance.apply(null).getBoolean(0) === false)
+  }
+
+  test("GeneratePredicate should initialize expressions") {
+    val expr = And(NondeterministicExpression(), NondeterministicExpression())
+    val instance = GeneratePredicate.generate(expr)
+    assert(instance.apply(null) === false)
+  }
+
+  test("GenerateUnsafeProjection should not share expression instances") {
     val expr1 = MutableExpression()
     val instance1 = UnsafeProjection.create(Seq(expr1))
     assert(instance1.apply(null).getBoolean(0) === false)
@@ -39,7 +64,7 @@ class CodegenExpressionCachingSuite extends SparkFunSuite {
     assert(instance2.apply(null).getBoolean(0) === true)
   }
 
-  test("GenerateProjection") {
+  test("GenerateProjection should not share expression instances") {
     val expr1 = MutableExpression()
     val instance1 = GenerateProjection.generate(Seq(expr1))
     assert(instance1.apply(null).getBoolean(0) === false)
@@ -51,7 +76,7 @@ class CodegenExpressionCachingSuite extends SparkFunSuite {
     assert(instance2.apply(null).getBoolean(0) === true)
   }
 
-  test("GenerateMutableProjection") {
+  test("GenerateMutableProjection should not share expression instances") {
     val expr1 = MutableExpression()
     val instance1 = GenerateMutableProjection.generate(Seq(expr1))()
     assert(instance1.apply(null).getBoolean(0) === false)
@@ -63,7 +88,7 @@ class CodegenExpressionCachingSuite extends SparkFunSuite {
     assert(instance2.apply(null).getBoolean(0) === true)
   }
 
-  test("GeneratePredicate") {
+  test("GeneratePredicate should not share expression instances") {
     val expr1 = MutableExpression()
     val instance1 = GeneratePredicate.generate(expr1)
     assert(instance1.apply(null) === false)
@@ -77,6 +102,17 @@ class CodegenExpressionCachingSuite extends SparkFunSuite {
 
 }
 
+/**
+ * An expression that's non-deterministic and doesn't support codegen.
+ */
+case class NondeterministicExpression()
+  extends LeafExpression with Nondeterministic with CodegenFallback {
+  override protected def initInternal(): Unit = { }
+  override protected def evalInternal(input: InternalRow): Any = false
+  override def nullable: Boolean = false
+  override def dataType: DataType = BooleanType
+}
+
 
 /**
  * An expression with mutable state so we can change it freely in our test suite.

From f5dd11339fc9a6d11350f63beeca7c14aec169b1 Mon Sep 17 00:00:00 2001
From: Alex Angelini <alex.louis.angelini@gmail.com>
Date: Wed, 29 Jul 2015 22:25:38 -0700
Subject: [PATCH 0683/1454] Fix reference to self.names in StructType

`names` is not defined in this context, I think you meant `self.names`.

davies

Author: Alex Angelini <alex.louis.angelini@gmail.com>

Closes #7766 from angelini/fix_struct_type_names and squashes the following commits:

01543a1 [Alex Angelini] Fix reference to self.names in StructType
---
 python/pyspark/sql/types.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index b97d50c945f24..8859308d66027 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -531,7 +531,7 @@ def toInternal(self, obj):
 
         if self._needSerializeFields:
             if isinstance(obj, dict):
-                return tuple(f.toInternal(obj.get(n)) for n, f in zip(names, self.fields))
+                return tuple(f.toInternal(obj.get(n)) for n, f in zip(self.names, self.fields))
             elif isinstance(obj, (tuple, list)):
                 return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
             else:

From e044705b4402f86d0557ecd146f3565388c7eeb4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 29 Jul 2015 22:30:49 -0700
Subject: [PATCH 0684/1454] [SPARK-9116] [SQL] [PYSPARK] support Python only
 UDT in __main__

Also we could create a Python UDT without having a Scala one, it's important for Python users.

cc mengxr JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #7453 from davies/class_in_main and squashes the following commits:

4dfd5e1 [Davies Liu] add tests for Python and Scala UDT
793d9b2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
dc65f19 [Davies Liu] address comment
a9a3c40 [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
a86e1fc [Davies Liu] fix serialization
ad528ba [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
63f52ef [Davies Liu] fix pylint check
655b8a9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
316a394 [Davies Liu] support Python UDT with UTF
0bcb3ef [Davies Liu] fix bug in mllib
de986d6 [Davies Liu] fix test
83d65ac [Davies Liu] fix bug in StructType
55bb86e [Davies Liu] support Python UDT in __main__ (without Scala one)
---
 pylintrc                                      |   2 +-
 python/pyspark/cloudpickle.py                 |  38 +++++-
 python/pyspark/shuffle.py                     |   2 +-
 python/pyspark/sql/context.py                 | 108 ++++++++++-------
 python/pyspark/sql/tests.py                   | 112 ++++++++++++++++--
 python/pyspark/sql/types.py                   |  78 ++++++------
 .../org/apache/spark/sql/types/DataType.scala |   9 ++
 .../spark/sql/types/UserDefinedType.scala     |  29 +++++
 .../spark/sql/execution/pythonUDFs.scala      |   1 -
 9 files changed, 286 insertions(+), 93 deletions(-)

diff --git a/pylintrc b/pylintrc
index 061775960393b..6a675770da69a 100644
--- a/pylintrc
+++ b/pylintrc
@@ -84,7 +84,7 @@ enable=
 # If you would like to improve the code quality of pyspark, remove any of these disabled errors
 # run ./dev/lint-python and see if the errors raised by pylint can be fixed.
 
-disable=invalid-name,missing-docstring,protected-access,unused-argument,no-member,unused-wildcard-import,redefined-builtin,too-many-arguments,unused-variable,too-few-public-methods,bad-continuation,duplicate-code,redefined-outer-name,too-many-ancestors,import-error,superfluous-parens,unused-import,line-too-long,no-name-in-module,unnecessary-lambda,import-self,no-self-use,unidiomatic-typecheck,fixme,too-many-locals,cyclic-import,too-many-branches,bare-except,wildcard-import,dangerous-default-value,broad-except,too-many-public-methods,deprecated-lambda,anomalous-backslash-in-string,too-many-lines,reimported,too-many-statements,bad-whitespace,unpacking-non-sequence,too-many-instance-attributes,abstract-method,old-style-class,global-statement,attribute-defined-outside-init,arguments-differ,undefined-all-variable,no-init,useless-else-on-loop,super-init-not-called,notimplemented-raised,too-many-return-statements,pointless-string-statement,global-variable-undefined,bad-classmethod-argument,too-many-format-args,parse-error,no-self-argument,pointless-statement,undefined-variable
+disable=invalid-name,missing-docstring,protected-access,unused-argument,no-member,unused-wildcard-import,redefined-builtin,too-many-arguments,unused-variable,too-few-public-methods,bad-continuation,duplicate-code,redefined-outer-name,too-many-ancestors,import-error,superfluous-parens,unused-import,line-too-long,no-name-in-module,unnecessary-lambda,import-self,no-self-use,unidiomatic-typecheck,fixme,too-many-locals,cyclic-import,too-many-branches,bare-except,wildcard-import,dangerous-default-value,broad-except,too-many-public-methods,deprecated-lambda,anomalous-backslash-in-string,too-many-lines,reimported,too-many-statements,bad-whitespace,unpacking-non-sequence,too-many-instance-attributes,abstract-method,old-style-class,global-statement,attribute-defined-outside-init,arguments-differ,undefined-all-variable,no-init,useless-else-on-loop,super-init-not-called,notimplemented-raised,too-many-return-statements,pointless-string-statement,global-variable-undefined,bad-classmethod-argument,too-many-format-args,parse-error,no-self-argument,pointless-statement,undefined-variable,undefined-loop-variable
 
 
 [REPORTS]
diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index 9ef93071d2e77..3b647985801b7 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -350,7 +350,26 @@ def save_global(self, obj, name=None, pack=struct.pack):
             if new_override:
                 d['__new__'] = obj.__new__
 
-            self.save_reduce(typ, (obj.__name__, obj.__bases__, d), obj=obj)
+            self.save(_load_class)
+            self.save_reduce(typ, (obj.__name__, obj.__bases__, {"__doc__": obj.__doc__}), obj=obj)
+            d.pop('__doc__', None)
+            # handle property and staticmethod
+            dd = {}
+            for k, v in d.items():
+                if isinstance(v, property):
+                    k = ('property', k)
+                    v = (v.fget, v.fset, v.fdel, v.__doc__)
+                elif isinstance(v, staticmethod) and hasattr(v, '__func__'):
+                    k = ('staticmethod', k)
+                    v = v.__func__
+                elif isinstance(v, classmethod) and hasattr(v, '__func__'):
+                    k = ('classmethod', k)
+                    v = v.__func__
+                dd[k] = v
+            self.save(dd)
+            self.write(pickle.TUPLE2)
+            self.write(pickle.REDUCE)
+
         else:
             raise pickle.PicklingError("Can't pickle %r" % obj)
 
@@ -708,6 +727,23 @@ def _make_skel_func(code, closures, base_globals = None):
                               None, None, closure)
 
 
+def _load_class(cls, d):
+    """
+    Loads additional properties into class `cls`.
+    """
+    for k, v in d.items():
+        if isinstance(k, tuple):
+            typ, k = k
+            if typ == 'property':
+                v = property(*v)
+            elif typ == 'staticmethod':
+                v = staticmethod(v)
+            elif typ == 'classmethod':
+                v = classmethod(v)
+        setattr(cls, k, v)
+    return cls
+
+
 """Constructors for 3rd party libraries
 Note: These can never be renamed due to client compatibility issues"""
 
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index 8fb71bac64a5e..b8118bdb7ca76 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -606,7 +606,7 @@ def _open_file(self):
         if not os.path.exists(d):
             os.makedirs(d)
         p = os.path.join(d, str(id(self)))
-        self._file = open(p, "wb+", 65536)
+        self._file = open(p, "w+b", 65536)
         self._ser = BatchedSerializer(CompressedSerializer(PickleSerializer()), 1024)
         os.unlink(p)
 
diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index abb6522dde7b0..917de24f3536b 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -277,6 +277,66 @@ def applySchema(self, rdd, schema):
 
         return self.createDataFrame(rdd, schema)
 
+    def _createFromRDD(self, rdd, schema, samplingRatio):
+        """
+        Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
+        """
+        if schema is None or isinstance(schema, (list, tuple)):
+            struct = self._inferSchema(rdd, samplingRatio)
+            converter = _create_converter(struct)
+            rdd = rdd.map(converter)
+            if isinstance(schema, (list, tuple)):
+                for i, name in enumerate(schema):
+                    struct.fields[i].name = name
+                    struct.names[i] = name
+            schema = struct
+
+        elif isinstance(schema, StructType):
+            # take the first few rows to verify schema
+            rows = rdd.take(10)
+            for row in rows:
+                _verify_type(row, schema)
+
+        else:
+            raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
+
+        # convert python objects to sql data
+        rdd = rdd.map(schema.toInternal)
+        return rdd, schema
+
+    def _createFromLocal(self, data, schema):
+        """
+        Create an RDD for DataFrame from an list or pandas.DataFrame, returns
+        the RDD and schema.
+        """
+        if has_pandas and isinstance(data, pandas.DataFrame):
+            if schema is None:
+                schema = [str(x) for x in data.columns]
+            data = [r.tolist() for r in data.to_records(index=False)]
+
+        # make sure data could consumed multiple times
+        if not isinstance(data, list):
+            data = list(data)
+
+        if schema is None or isinstance(schema, (list, tuple)):
+            struct = self._inferSchemaFromList(data)
+            if isinstance(schema, (list, tuple)):
+                for i, name in enumerate(schema):
+                    struct.fields[i].name = name
+                    struct.names[i] = name
+            schema = struct
+
+        elif isinstance(schema, StructType):
+            for row in data:
+                _verify_type(row, schema)
+
+        else:
+            raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
+
+        # convert python objects to sql data
+        data = [schema.toInternal(row) for row in data]
+        return self._sc.parallelize(data), schema
+
     @since(1.3)
     @ignore_unicode_prefix
     def createDataFrame(self, data, schema=None, samplingRatio=None):
@@ -340,49 +400,15 @@ def createDataFrame(self, data, schema=None, samplingRatio=None):
         if isinstance(data, DataFrame):
             raise TypeError("data is already a DataFrame")
 
-        if has_pandas and isinstance(data, pandas.DataFrame):
-            if schema is None:
-                schema = [str(x) for x in data.columns]
-            data = [r.tolist() for r in data.to_records(index=False)]
-
-        if not isinstance(data, RDD):
-            if not isinstance(data, list):
-                data = list(data)
-            try:
-                # data could be list, tuple, generator ...
-                rdd = self._sc.parallelize(data)
-            except Exception:
-                raise TypeError("cannot create an RDD from type: %s" % type(data))
+        if isinstance(data, RDD):
+            rdd, schema = self._createFromRDD(data, schema, samplingRatio)
         else:
-            rdd = data
-
-        if schema is None or isinstance(schema, (list, tuple)):
-            if isinstance(data, RDD):
-                struct = self._inferSchema(rdd, samplingRatio)
-            else:
-                struct = self._inferSchemaFromList(data)
-            if isinstance(schema, (list, tuple)):
-                for i, name in enumerate(schema):
-                    struct.fields[i].name = name
-            schema = struct
-            converter = _create_converter(schema)
-            rdd = rdd.map(converter)
-
-        elif isinstance(schema, StructType):
-            # take the first few rows to verify schema
-            rows = rdd.take(10)
-            for row in rows:
-                _verify_type(row, schema)
-
-        else:
-            raise TypeError("schema should be StructType or list or None")
-
-        # convert python objects to sql data
-        rdd = rdd.map(schema.toInternal)
-
+            rdd, schema = self._createFromLocal(data, schema)
         jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
-        df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
-        return DataFrame(df, self)
+        jdf = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
+        df = DataFrame(jdf, self)
+        df._schema = schema
+        return df
 
     @since(1.3)
     def registerDataFrameAsTable(self, df, tableName):
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 5aa6135dc1ee7..ebd3ea8db6a43 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -75,7 +75,7 @@ def sqlType(self):
 
     @classmethod
     def module(cls):
-        return 'pyspark.tests'
+        return 'pyspark.sql.tests'
 
     @classmethod
     def scalaUDT(cls):
@@ -106,10 +106,45 @@ def __str__(self):
         return "(%s,%s)" % (self.x, self.y)
 
     def __eq__(self, other):
-        return isinstance(other, ExamplePoint) and \
+        return isinstance(other, self.__class__) and \
             other.x == self.x and other.y == self.y
 
 
+class PythonOnlyUDT(UserDefinedType):
+    """
+    User-defined type (UDT) for ExamplePoint.
+    """
+
+    @classmethod
+    def sqlType(self):
+        return ArrayType(DoubleType(), False)
+
+    @classmethod
+    def module(cls):
+        return '__main__'
+
+    def serialize(self, obj):
+        return [obj.x, obj.y]
+
+    def deserialize(self, datum):
+        return PythonOnlyPoint(datum[0], datum[1])
+
+    @staticmethod
+    def foo():
+        pass
+
+    @property
+    def props(self):
+        return {}
+
+
+class PythonOnlyPoint(ExamplePoint):
+    """
+    An example class to demonstrate UDT in only Python
+    """
+    __UDT__ = PythonOnlyUDT()
+
+
 class DataTypeTests(unittest.TestCase):
     # regression test for SPARK-6055
     def test_data_type_eq(self):
@@ -395,10 +430,39 @@ def test_convert_row_to_dict(self):
         self.assertEqual(1, row.asDict()["l"][0].a)
         self.assertEqual(1.0, row.asDict()['d']['key'].c)
 
+    def test_udt(self):
+        from pyspark.sql.types import _parse_datatype_json_string, _infer_type, _verify_type
+        from pyspark.sql.tests import ExamplePointUDT, ExamplePoint
+
+        def check_datatype(datatype):
+            pickled = pickle.loads(pickle.dumps(datatype))
+            assert datatype == pickled
+            scala_datatype = self.sqlCtx._ssql_ctx.parseDataType(datatype.json())
+            python_datatype = _parse_datatype_json_string(scala_datatype.json())
+            assert datatype == python_datatype
+
+        check_datatype(ExamplePointUDT())
+        structtype_with_udt = StructType([StructField("label", DoubleType(), False),
+                                          StructField("point", ExamplePointUDT(), False)])
+        check_datatype(structtype_with_udt)
+        p = ExamplePoint(1.0, 2.0)
+        self.assertEqual(_infer_type(p), ExamplePointUDT())
+        _verify_type(ExamplePoint(1.0, 2.0), ExamplePointUDT())
+        self.assertRaises(ValueError, lambda: _verify_type([1.0, 2.0], ExamplePointUDT()))
+
+        check_datatype(PythonOnlyUDT())
+        structtype_with_udt = StructType([StructField("label", DoubleType(), False),
+                                          StructField("point", PythonOnlyUDT(), False)])
+        check_datatype(structtype_with_udt)
+        p = PythonOnlyPoint(1.0, 2.0)
+        self.assertEqual(_infer_type(p), PythonOnlyUDT())
+        _verify_type(PythonOnlyPoint(1.0, 2.0), PythonOnlyUDT())
+        self.assertRaises(ValueError, lambda: _verify_type([1.0, 2.0], PythonOnlyUDT()))
+
     def test_infer_schema_with_udt(self):
         from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
         row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
-        df = self.sc.parallelize([row]).toDF()
+        df = self.sqlCtx.createDataFrame([row])
         schema = df.schema
         field = [f for f in schema.fields if f.name == "point"][0]
         self.assertEqual(type(field.dataType), ExamplePointUDT)
@@ -406,36 +470,66 @@ def test_infer_schema_with_udt(self):
         point = self.sqlCtx.sql("SELECT point FROM labeled_point").head().point
         self.assertEqual(point, ExamplePoint(1.0, 2.0))
 
+        row = Row(label=1.0, point=PythonOnlyPoint(1.0, 2.0))
+        df = self.sqlCtx.createDataFrame([row])
+        schema = df.schema
+        field = [f for f in schema.fields if f.name == "point"][0]
+        self.assertEqual(type(field.dataType), PythonOnlyUDT)
+        df.registerTempTable("labeled_point")
+        point = self.sqlCtx.sql("SELECT point FROM labeled_point").head().point
+        self.assertEqual(point, PythonOnlyPoint(1.0, 2.0))
+
     def test_apply_schema_with_udt(self):
         from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
         row = (1.0, ExamplePoint(1.0, 2.0))
-        rdd = self.sc.parallelize([row])
         schema = StructType([StructField("label", DoubleType(), False),
                              StructField("point", ExamplePointUDT(), False)])
-        df = rdd.toDF(schema)
+        df = self.sqlCtx.createDataFrame([row], schema)
         point = df.head().point
         self.assertEquals(point, ExamplePoint(1.0, 2.0))
 
+        row = (1.0, PythonOnlyPoint(1.0, 2.0))
+        schema = StructType([StructField("label", DoubleType(), False),
+                             StructField("point", PythonOnlyUDT(), False)])
+        df = self.sqlCtx.createDataFrame([row], schema)
+        point = df.head().point
+        self.assertEquals(point, PythonOnlyPoint(1.0, 2.0))
+
     def test_udf_with_udt(self):
         from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
         row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
-        df = self.sc.parallelize([row]).toDF()
+        df = self.sqlCtx.createDataFrame([row])
         self.assertEqual(1.0, df.map(lambda r: r.point.x).first())
         udf = UserDefinedFunction(lambda p: p.y, DoubleType())
         self.assertEqual(2.0, df.select(udf(df.point)).first()[0])
         udf2 = UserDefinedFunction(lambda p: ExamplePoint(p.x + 1, p.y + 1), ExamplePointUDT())
         self.assertEqual(ExamplePoint(2.0, 3.0), df.select(udf2(df.point)).first()[0])
 
+        row = Row(label=1.0, point=PythonOnlyPoint(1.0, 2.0))
+        df = self.sqlCtx.createDataFrame([row])
+        self.assertEqual(1.0, df.map(lambda r: r.point.x).first())
+        udf = UserDefinedFunction(lambda p: p.y, DoubleType())
+        self.assertEqual(2.0, df.select(udf(df.point)).first()[0])
+        udf2 = UserDefinedFunction(lambda p: PythonOnlyPoint(p.x + 1, p.y + 1), PythonOnlyUDT())
+        self.assertEqual(PythonOnlyPoint(2.0, 3.0), df.select(udf2(df.point)).first()[0])
+
     def test_parquet_with_udt(self):
-        from pyspark.sql.tests import ExamplePoint
+        from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
         row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
-        df0 = self.sc.parallelize([row]).toDF()
+        df0 = self.sqlCtx.createDataFrame([row])
         output_dir = os.path.join(self.tempdir.name, "labeled_point")
-        df0.saveAsParquetFile(output_dir)
+        df0.write.parquet(output_dir)
         df1 = self.sqlCtx.parquetFile(output_dir)
         point = df1.head().point
         self.assertEquals(point, ExamplePoint(1.0, 2.0))
 
+        row = Row(label=1.0, point=PythonOnlyPoint(1.0, 2.0))
+        df0 = self.sqlCtx.createDataFrame([row])
+        df0.write.parquet(output_dir, mode='overwrite')
+        df1 = self.sqlCtx.parquetFile(output_dir)
+        point = df1.head().point
+        self.assertEquals(point, PythonOnlyPoint(1.0, 2.0))
+
     def test_column_operators(self):
         ci = self.df.key
         cs = self.df.value
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 8859308d66027..0976aea72c034 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -22,6 +22,7 @@
 import calendar
 import json
 import re
+import base64
 from array import array
 
 if sys.version >= "3":
@@ -31,6 +32,8 @@
 from py4j.protocol import register_input_converter
 from py4j.java_gateway import JavaClass
 
+from pyspark.serializers import CloudPickleSerializer
+
 __all__ = [
     "DataType", "NullType", "StringType", "BinaryType", "BooleanType", "DateType",
     "TimestampType", "DecimalType", "DoubleType", "FloatType", "ByteType", "IntegerType",
@@ -458,7 +461,7 @@ def __init__(self, fields=None):
             self.names = [f.name for f in fields]
             assert all(isinstance(f, StructField) for f in fields),\
                 "fields should be a list of StructField"
-        self._needSerializeFields = None
+        self._needSerializeAnyField = any(f.needConversion() for f in self.fields)
 
     def add(self, field, data_type=None, nullable=True, metadata=None):
         """
@@ -501,6 +504,7 @@ def add(self, field, data_type=None, nullable=True, metadata=None):
                 data_type_f = data_type
             self.fields.append(StructField(field, data_type_f, nullable, metadata))
             self.names.append(field)
+        self._needSerializeAnyField = any(f.needConversion() for f in self.fields)
         return self
 
     def simpleString(self):
@@ -526,10 +530,7 @@ def toInternal(self, obj):
         if obj is None:
             return
 
-        if self._needSerializeFields is None:
-            self._needSerializeFields = any(f.needConversion() for f in self.fields)
-
-        if self._needSerializeFields:
+        if self._needSerializeAnyField:
             if isinstance(obj, dict):
                 return tuple(f.toInternal(obj.get(n)) for n, f in zip(self.names, self.fields))
             elif isinstance(obj, (tuple, list)):
@@ -550,7 +551,10 @@ def fromInternal(self, obj):
         if isinstance(obj, Row):
             # it's already converted by pickler
             return obj
-        values = [f.dataType.fromInternal(v) for f, v in zip(self.fields, obj)]
+        if self._needSerializeAnyField:
+            values = [f.fromInternal(v) for f, v in zip(self.fields, obj)]
+        else:
+            values = obj
         return _create_row(self.names, values)
 
 
@@ -581,9 +585,10 @@ def module(cls):
     @classmethod
     def scalaUDT(cls):
         """
-        The class name of the paired Scala UDT.
+        The class name of the paired Scala UDT (could be '', if there
+        is no corresponding one).
         """
-        raise NotImplementedError("UDT must have a paired Scala UDT.")
+        return ''
 
     def needConversion(self):
         return True
@@ -622,22 +627,37 @@ def json(self):
         return json.dumps(self.jsonValue(), separators=(',', ':'), sort_keys=True)
 
     def jsonValue(self):
-        schema = {
-            "type": "udt",
-            "class": self.scalaUDT(),
-            "pyClass": "%s.%s" % (self.module(), type(self).__name__),
-            "sqlType": self.sqlType().jsonValue()
-        }
+        if self.scalaUDT():
+            assert self.module() != '__main__', 'UDT in __main__ cannot work with ScalaUDT'
+            schema = {
+                "type": "udt",
+                "class": self.scalaUDT(),
+                "pyClass": "%s.%s" % (self.module(), type(self).__name__),
+                "sqlType": self.sqlType().jsonValue()
+            }
+        else:
+            ser = CloudPickleSerializer()
+            b = ser.dumps(type(self))
+            schema = {
+                "type": "udt",
+                "pyClass": "%s.%s" % (self.module(), type(self).__name__),
+                "serializedClass": base64.b64encode(b).decode('utf8'),
+                "sqlType": self.sqlType().jsonValue()
+            }
         return schema
 
     @classmethod
     def fromJson(cls, json):
-        pyUDT = json["pyClass"]
+        pyUDT = str(json["pyClass"])
         split = pyUDT.rfind(".")
         pyModule = pyUDT[:split]
         pyClass = pyUDT[split+1:]
         m = __import__(pyModule, globals(), locals(), [pyClass])
-        UDT = getattr(m, pyClass)
+        if not hasattr(m, pyClass):
+            s = base64.b64decode(json['serializedClass'].encode('utf-8'))
+            UDT = CloudPickleSerializer().loads(s)
+        else:
+            UDT = getattr(m, pyClass)
         return UDT()
 
     def __eq__(self, other):
@@ -696,11 +716,6 @@ def _parse_datatype_json_string(json_string):
     >>> complex_maptype = MapType(complex_structtype,
     ...                           complex_arraytype, False)
     >>> check_datatype(complex_maptype)
-
-    >>> check_datatype(ExamplePointUDT())
-    >>> structtype_with_udt = StructType([StructField("label", DoubleType(), False),
-    ...                                   StructField("point", ExamplePointUDT(), False)])
-    >>> check_datatype(structtype_with_udt)
     """
     return _parse_datatype_json_value(json.loads(json_string))
 
@@ -752,10 +767,6 @@ def _parse_datatype_json_value(json_value):
 
 def _infer_type(obj):
     """Infer the DataType from obj
-
-    >>> p = ExamplePoint(1.0, 2.0)
-    >>> _infer_type(p)
-    ExamplePointUDT
     """
     if obj is None:
         return NullType()
@@ -1090,11 +1101,6 @@ def _verify_type(obj, dataType):
     Traceback (most recent call last):
         ...
     ValueError:...
-    >>> _verify_type(ExamplePoint(1.0, 2.0), ExamplePointUDT())
-    >>> _verify_type([1.0, 2.0], ExamplePointUDT()) # doctest: +IGNORE_EXCEPTION_DETAIL
-    Traceback (most recent call last):
-        ...
-    ValueError:...
     """
     # all objects are nullable
     if obj is None:
@@ -1259,18 +1265,12 @@ def convert(self, obj, gateway_client):
 def _test():
     import doctest
     from pyspark.context import SparkContext
-    # let doctest run in pyspark.sql.types, so DataTypes can be picklable
-    import pyspark.sql.types
-    from pyspark.sql import Row, SQLContext
-    from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
-    globs = pyspark.sql.types.__dict__.copy()
+    from pyspark.sql import SQLContext
+    globs = globals()
     sc = SparkContext('local[4]', 'PythonTest')
     globs['sc'] = sc
     globs['sqlContext'] = SQLContext(sc)
-    globs['ExamplePoint'] = ExamplePoint
-    globs['ExamplePointUDT'] = ExamplePointUDT
-    (failure_count, test_count) = doctest.testmod(
-        pyspark.sql.types, globs=globs, optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index 591fb26e67c4a..f4428c2e8b202 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -142,12 +142,21 @@ object DataType {
     ("type", JString("struct"))) =>
       StructType(fields.map(parseStructField))
 
+    // Scala/Java UDT
     case JSortedObject(
     ("class", JString(udtClass)),
     ("pyClass", _),
     ("sqlType", _),
     ("type", JString("udt"))) =>
       Utils.classForName(udtClass).newInstance().asInstanceOf[UserDefinedType[_]]
+
+    // Python UDT
+    case JSortedObject(
+    ("pyClass", JString(pyClass)),
+    ("serializedClass", JString(serialized)),
+    ("sqlType", v: JValue),
+    ("type", JString("udt"))) =>
+        new PythonUserDefinedType(parseDataType(v), pyClass, serialized)
   }
 
   private def parseStructField(json: JValue): StructField = json match {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala
index e47cfb4833bd8..4305903616bd9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala
@@ -45,6 +45,9 @@ abstract class UserDefinedType[UserType] extends DataType with Serializable {
   /** Paired Python UDT class, if exists. */
   def pyUDT: String = null
 
+  /** Serialized Python UDT class, if exists. */
+  def serializedPyClass: String = null
+
   /**
    * Convert the user type to a SQL datum
    *
@@ -82,3 +85,29 @@ abstract class UserDefinedType[UserType] extends DataType with Serializable {
   override private[sql] def acceptsType(dataType: DataType) =
     this.getClass == dataType.getClass
 }
+
+/**
+ * ::DeveloperApi::
+ * The user defined type in Python.
+ *
+ * Note: This can only be accessed via Python UDF, or accessed as serialized object.
+ */
+private[sql] class PythonUserDefinedType(
+    val sqlType: DataType,
+    override val pyUDT: String,
+    override val serializedPyClass: String) extends UserDefinedType[Any] {
+
+  /* The serialization is handled by UDT class in Python */
+  override def serialize(obj: Any): Any = obj
+  override def deserialize(datam: Any): Any = datam
+
+  /* There is no Java class for Python UDT */
+  override def userClass: java.lang.Class[Any] = null
+
+  override private[sql] def jsonValue: JValue = {
+    ("type" -> "udt") ~
+      ("pyClass" -> pyUDT) ~
+      ("serializedClass" -> serializedPyClass) ~
+      ("sqlType" -> sqlType.jsonValue)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index ec084a299649e..3c38916fd7504 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -267,7 +267,6 @@ object EvaluatePython {
           pickler.save(row.values(i))
           i += 1
         }
-        row.values.foreach(pickler.save)
         out.write(Opcodes.TUPLE)
         out.write(Opcodes.REDUCE)
       }

From 712465b68e50df7a2050b27528acda9f0d95ba1f Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 29 Jul 2015 22:51:06 -0700
Subject: [PATCH 0685/1454] HOTFIX: disable HashedRelationSuite.

---
 .../spark/sql/execution/joins/HashedRelationSuite.scala     | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 8b1a9b21a96b9..941f6d4f6a450 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -33,7 +33,7 @@ class HashedRelationSuite extends SparkFunSuite {
     override def apply(row: InternalRow): InternalRow = row
   }
 
-  test("GeneralHashedRelation") {
+  ignore("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
     val hashed = HashedRelation(data.iterator, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
@@ -47,7 +47,7 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed.get(data(2)) === data2)
   }
 
-  test("UniqueKeyHashedRelation") {
+  ignore("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
     val hashed = HashedRelation(data.iterator, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
@@ -64,7 +64,7 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(uniqHashed.getValue(InternalRow(10)) === null)
   }
 
-  test("UnsafeHashedRelation") {
+  ignore("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
     val toUnsafe = UnsafeProjection.create(schema)

From e127ec34d58ceb0a9d45748c2f2918786ba0a83d Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 29 Jul 2015 23:24:20 -0700
Subject: [PATCH 0686/1454] [SPARK-9428] [SQL] Add test cases for null inputs
 for expression unit tests

JIRA: https://issues.apache.org/jira/browse/SPARK-9428

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7748 from yjshen/string_cleanup and squashes the following commits:

e0c2b3d [Yijie Shen] update codegen in RegExpExtract and RegExpReplace
26614d2 [Yijie Shen] MathFunctionSuite
a402859 [Yijie Shen] complex_create, conditional and cast
6e4e608 [Yijie Shen] arithmetic and cast
52593c1 [Yijie Shen] null input test cases for StringExpressionSuite
---
 .../spark/sql/catalyst/expressions/Cast.scala | 12 ++--
 .../expressions/complexTypeCreator.scala      | 16 +++--
 .../catalyst/expressions/conditionals.scala   | 10 +--
 .../spark/sql/catalyst/expressions/math.scala | 14 ++---
 .../expressions/stringOperations.scala        | 11 ++--
 .../ExpressionTypeCheckingSuite.scala         |  7 ++-
 .../ArithmeticExpressionSuite.scala           |  3 +
 .../sql/catalyst/expressions/CastSuite.scala  | 52 ++++++++++++++-
 .../expressions/ComplexTypeSuite.scala        | 23 +++----
 .../ConditionalExpressionSuite.scala          |  4 ++
 .../expressions/MathFunctionsSuite.scala      | 63 ++++++++++---------
 .../catalyst/expressions/RandomSuite.scala    |  1 -
 .../expressions/StringExpressionsSuite.scala  | 26 ++++++++
 .../org/apache/spark/sql/functions.scala      |  6 +-
 14 files changed, 167 insertions(+), 81 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index c6e8af27667ee..8c01c13c9ccd5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -599,7 +599,7 @@ case class Cast(child: Expression, dataType: DataType)
           }
          """
     case BooleanType =>
-      (c, evPrim, evNull) => s"$evPrim = $c ? 1L : 0;"
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1L : 0L;"
     case _: IntegralType =>
       (c, evPrim, evNull) => s"$evPrim = ${longToTimeStampCode(c)};"
     case DateType =>
@@ -665,7 +665,7 @@ case class Cast(child: Expression, dataType: DataType)
           }
         """
     case BooleanType =>
-      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+      (c, evPrim, evNull) => s"$evPrim = $c ? (byte) 1 : (byte) 0;"
     case DateType =>
       (c, evPrim, evNull) => s"$evNull = true;"
     case TimestampType =>
@@ -687,7 +687,7 @@ case class Cast(child: Expression, dataType: DataType)
           }
         """
     case BooleanType =>
-      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+      (c, evPrim, evNull) => s"$evPrim = $c ? (short) 1 : (short) 0;"
     case DateType =>
       (c, evPrim, evNull) => s"$evNull = true;"
     case TimestampType =>
@@ -731,7 +731,7 @@ case class Cast(child: Expression, dataType: DataType)
           }
         """
     case BooleanType =>
-      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1L : 0L;"
     case DateType =>
       (c, evPrim, evNull) => s"$evNull = true;"
     case TimestampType =>
@@ -753,7 +753,7 @@ case class Cast(child: Expression, dataType: DataType)
           }
         """
     case BooleanType =>
-      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1.0f : 0.0f;"
     case DateType =>
       (c, evPrim, evNull) => s"$evNull = true;"
     case TimestampType =>
@@ -775,7 +775,7 @@ case class Cast(child: Expression, dataType: DataType)
           }
         """
     case BooleanType =>
-      (c, evPrim, evNull) => s"$evPrim = $c ? 1 : 0;"
+      (c, evPrim, evNull) => s"$evPrim = $c ? 1.0d : 0.0d;"
     case DateType =>
       (c, evPrim, evNull) => s"$evNull = true;"
     case TimestampType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index d8c9087ff5380..0517050a45109 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.unsafe.types.UTF8String
+
 import scala.collection.mutable
 
 import org.apache.spark.sql.catalyst.InternalRow
@@ -127,11 +129,12 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
   private lazy val (nameExprs, valExprs) =
     children.grouped(2).map { case Seq(name, value) => (name, value) }.toList.unzip
 
-  private lazy val names = nameExprs.map(_.eval(EmptyRow).toString)
+  private lazy val names = nameExprs.map(_.eval(EmptyRow))
 
   override lazy val dataType: StructType = {
     val fields = names.zip(valExprs).map { case (name, valExpr) =>
-      StructField(name, valExpr.dataType, valExpr.nullable, Metadata.empty)
+      StructField(name.asInstanceOf[UTF8String].toString,
+        valExpr.dataType, valExpr.nullable, Metadata.empty)
     }
     StructType(fields)
   }
@@ -144,14 +147,15 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
     if (children.size % 2 != 0) {
       TypeCheckResult.TypeCheckFailure(s"$prettyName expects an even number of arguments.")
     } else {
-      val invalidNames =
-        nameExprs.filterNot(e => e.foldable && e.dataType == StringType && !nullable)
+      val invalidNames = nameExprs.filterNot(e => e.foldable && e.dataType == StringType)
       if (invalidNames.nonEmpty) {
         TypeCheckResult.TypeCheckFailure(
-          s"Odd position only allow foldable and not-null StringType expressions, got :" +
+          s"Only foldable StringType expressions are allowed to appear at odd position , got :" +
             s" ${invalidNames.mkString(",")}")
-      } else {
+      } else if (names.forall(_ != null)){
         TypeCheckResult.TypeCheckSuccess
+      } else {
+        TypeCheckResult.TypeCheckFailure("Field name should not be null")
       }
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index 15b33da884dcb..961b1d8616801 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -315,7 +315,6 @@ case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseW
  * It takes at least 2 parameters, and returns null iff all parameters are null.
  */
 case class Least(children: Seq[Expression]) extends Expression {
-  require(children.length > 1, "LEAST requires at least 2 arguments, got " + children.length)
 
   override def nullable: Boolean = children.forall(_.nullable)
   override def foldable: Boolean = children.forall(_.foldable)
@@ -323,7 +322,9 @@ case class Least(children: Seq[Expression]) extends Expression {
   private lazy val ordering = TypeUtils.getOrdering(dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
-    if (children.map(_.dataType).distinct.count(_ != NullType) > 1) {
+    if (children.length <= 1) {
+      TypeCheckResult.TypeCheckFailure(s"LEAST requires at least 2 arguments")
+    } else if (children.map(_.dataType).distinct.count(_ != NullType) > 1) {
       TypeCheckResult.TypeCheckFailure(
         s"The expressions should all have the same type," +
           s" got LEAST (${children.map(_.dataType)}).")
@@ -369,7 +370,6 @@ case class Least(children: Seq[Expression]) extends Expression {
  * It takes at least 2 parameters, and returns null iff all parameters are null.
  */
 case class Greatest(children: Seq[Expression]) extends Expression {
-  require(children.length > 1, "GREATEST requires at least 2 arguments, got " + children.length)
 
   override def nullable: Boolean = children.forall(_.nullable)
   override def foldable: Boolean = children.forall(_.foldable)
@@ -377,7 +377,9 @@ case class Greatest(children: Seq[Expression]) extends Expression {
   private lazy val ordering = TypeUtils.getOrdering(dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
-    if (children.map(_.dataType).distinct.count(_ != NullType) > 1) {
+    if (children.length <= 1) {
+      TypeCheckResult.TypeCheckFailure(s"GREATEST requires at least 2 arguments")
+    } else if (children.map(_.dataType).distinct.count(_ != NullType) > 1) {
       TypeCheckResult.TypeCheckFailure(
         s"The expressions should all have the same type," +
           s" got GREATEST (${children.map(_.dataType)}).")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index 68cca0ad3d067..e6d807f6d897b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -646,19 +646,19 @@ case class Logarithm(left: Expression, right: Expression)
 /**
  * Round the `child`'s result to `scale` decimal place when `scale` >= 0
  * or round at integral part when `scale` < 0.
- * For example, round(31.415, 2) would eval to 31.42 and round(31.415, -1) would eval to 30.
+ * For example, round(31.415, 2) = 31.42 and round(31.415, -1) = 30.
  *
- * Child of IntegralType would eval to itself when `scale` >= 0.
- * Child of FractionalType whose value is NaN or Infinite would always eval to itself.
+ * Child of IntegralType would round to itself when `scale` >= 0.
+ * Child of FractionalType whose value is NaN or Infinite would always round to itself.
  *
- * Round's dataType would always equal to `child`'s dataType except for [[DecimalType.Fixed]],
- * which leads to scale update in DecimalType's [[PrecisionInfo]]
+ * Round's dataType would always equal to `child`'s dataType except for DecimalType,
+ * which would lead scale decrease from the origin DecimalType.
  *
  * @param child expr to be round, all [[NumericType]] is allowed as Input
  * @param scale new scale to be round to, this should be a constant int at runtime
  */
 case class Round(child: Expression, scale: Expression)
-  extends BinaryExpression with ExpectsInputTypes {
+  extends BinaryExpression with ImplicitCastInputTypes {
 
   import BigDecimal.RoundingMode.HALF_UP
 
@@ -838,6 +838,4 @@ case class Round(child: Expression, scale: Expression)
       """
     }
   }
-
-  override def prettyName: String = "round"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 6db4e19c24ed5..5b3a64a09679c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -22,7 +22,6 @@ import java.util.Locale
 import java.util.regex.{MatchResult, Pattern}
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -52,7 +51,7 @@ case class Concat(children: Seq[Expression]) extends Expression with ImplicitCas
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val evals = children.map(_.gen(ctx))
     val inputs = evals.map { eval =>
-      s"${eval.isNull} ? (UTF8String)null : ${eval.primitive}"
+      s"${eval.isNull} ? null : ${eval.primitive}"
     }.mkString(", ")
     evals.map(_.code).mkString("\n") + s"""
       boolean ${ev.isNull} = false;
@@ -1008,7 +1007,7 @@ case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expressio
 
     s"""
       ${evalSubject.code}
-      boolean ${ev.isNull} = ${evalSubject.isNull};
+      boolean ${ev.isNull} = true;
       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
       if (!${evalSubject.isNull}) {
         ${evalRegexp.code}
@@ -1103,9 +1102,9 @@ case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expressio
     val evalIdx = idx.gen(ctx)
 
     s"""
-      ${ctx.javaType(dataType)} ${ev.primitive} = null;
-      boolean ${ev.isNull} = true;
       ${evalSubject.code}
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      boolean ${ev.isNull} = true;
       if (!${evalSubject.isNull}) {
         ${evalRegexp.code}
         if (!${evalRegexp.isNull}) {
@@ -1117,7 +1116,7 @@ case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expressio
               ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
             }
             ${classOf[java.util.regex.Matcher].getCanonicalName} m =
-                                   ${termPattern}.matcher(${evalSubject.primitive}.toString());
+              ${termPattern}.matcher(${evalSubject.primitive}.toString());
             if (m.find()) {
               ${classOf[java.util.regex.MatchResult].getCanonicalName} mr = m.toMatchResult();
               ${ev.primitive} = ${classNameUTF8String}.fromString(mr.group(${evalIdx.primitive}));
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index 8acd4c685e2bc..a52e4cb4dfd9f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -167,10 +167,13 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
       CreateNamedStruct(Seq("a", "b", 2.0)), "even number of arguments")
     assertError(
       CreateNamedStruct(Seq(1, "a", "b", 2.0)),
-        "Odd position only allow foldable and not-null StringType expressions")
+        "Only foldable StringType expressions are allowed to appear at odd position")
     assertError(
       CreateNamedStruct(Seq('a.string.at(0), "a", "b", 2.0)),
-        "Odd position only allow foldable and not-null StringType expressions")
+        "Only foldable StringType expressions are allowed to appear at odd position")
+    assertError(
+      CreateNamedStruct(Seq(Literal.create(null, StringType), "a")),
+        "Field name should not be null")
   }
 
   test("check types for ROUND") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index 7773e098e0caa..d03b0fbbfb2b2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -116,9 +116,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
 
   test("Abs") {
     testNumericDataTypes { convert =>
+      val input = Literal(convert(1))
+      val dataType = input.dataType
       checkEvaluation(Abs(Literal(convert(0))), convert(0))
       checkEvaluation(Abs(Literal(convert(1))), convert(1))
       checkEvaluation(Abs(Literal(convert(-1))), convert(1))
+      checkEvaluation(Abs(Literal.create(null, dataType)), null)
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 0e0213be0f57b..a517da9872852 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -43,6 +43,42 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(cast(v, Literal(expected).dataType), expected)
   }
 
+  private def checkNullCast(from: DataType, to: DataType): Unit = {
+    checkEvaluation(Cast(Literal.create(null, from), to), null)
+  }
+
+  test("null cast") {
+    import DataTypeTestUtils._
+
+    // follow [[org.apache.spark.sql.catalyst.expressions.Cast.canCast]] logic
+    // to ensure we test every possible cast situation here
+    atomicTypes.zip(atomicTypes).foreach { case (from, to) =>
+      checkNullCast(from, to)
+    }
+
+    atomicTypes.foreach(dt => checkNullCast(NullType, dt))
+    atomicTypes.foreach(dt => checkNullCast(dt, StringType))
+    checkNullCast(StringType, BinaryType)
+    checkNullCast(StringType, BooleanType)
+    checkNullCast(DateType, BooleanType)
+    checkNullCast(TimestampType, BooleanType)
+    numericTypes.foreach(dt => checkNullCast(dt, BooleanType))
+
+    checkNullCast(StringType, TimestampType)
+    checkNullCast(BooleanType, TimestampType)
+    checkNullCast(DateType, TimestampType)
+    numericTypes.foreach(dt => checkNullCast(dt, TimestampType))
+
+    atomicTypes.foreach(dt => checkNullCast(dt, DateType))
+
+    checkNullCast(StringType, CalendarIntervalType)
+    numericTypes.foreach(dt => checkNullCast(StringType, dt))
+    numericTypes.foreach(dt => checkNullCast(BooleanType, dt))
+    numericTypes.foreach(dt => checkNullCast(DateType, dt))
+    numericTypes.foreach(dt => checkNullCast(TimestampType, dt))
+    for (from <- numericTypes; to <- numericTypes) checkNullCast(from, to)
+  }
+
   test("cast string to date") {
     var c = Calendar.getInstance()
     c.set(2015, 0, 1, 0, 0, 0)
@@ -69,8 +105,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("cast string to timestamp") {
-    checkEvaluation(Cast(Literal("123"), TimestampType),
-      null)
+    checkEvaluation(Cast(Literal("123"), TimestampType), null)
 
     var c = Calendar.getInstance()
     c.set(2015, 0, 1, 0, 0, 0)
@@ -473,6 +508,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     val array_notNull = Literal.create(Seq("123", "abc", ""),
       ArrayType(StringType, containsNull = false))
 
+    checkNullCast(ArrayType(StringType), ArrayType(IntegerType))
+
     {
       val ret = cast(array, ArrayType(IntegerType, containsNull = true))
       assert(ret.resolved === true)
@@ -526,6 +563,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       Map("a" -> "123", "b" -> "abc", "c" -> ""),
       MapType(StringType, StringType, valueContainsNull = false))
 
+    checkNullCast(MapType(StringType, IntegerType), MapType(StringType, StringType))
+
     {
       val ret = cast(map, MapType(StringType, IntegerType, valueContainsNull = true))
       assert(ret.resolved === true)
@@ -580,6 +619,14 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("cast from struct") {
+    checkNullCast(
+      StructType(Seq(
+        StructField("a", StringType),
+        StructField("b", IntegerType))),
+      StructType(Seq(
+        StructField("a", StringType),
+        StructField("b", StringType))))
+
     val struct = Literal.create(
       InternalRow(
         UTF8String.fromString("123"),
@@ -728,5 +775,4 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       StringType),
       "interval 1 years 3 months -3 days")
   }
-
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index fc842772f3480..5de5ddce975d8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -132,6 +132,7 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(CreateArray(intWithNull), intSeq :+ null, EmptyRow)
     checkEvaluation(CreateArray(longWithNull), longSeq :+ null, EmptyRow)
     checkEvaluation(CreateArray(strWithNull), strSeq :+ null, EmptyRow)
+    checkEvaluation(CreateArray(Literal.create(null, IntegerType) :: Nil), null :: Nil)
   }
 
   test("CreateStruct") {
@@ -139,26 +140,20 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
     val c1 = 'a.int.at(0)
     val c3 = 'c.int.at(2)
     checkEvaluation(CreateStruct(Seq(c1, c3)), create_row(1, 3), row)
+    checkEvaluation(CreateStruct(Literal.create(null, LongType) :: Nil), create_row(null))
   }
 
   test("CreateNamedStruct") {
-    val row = InternalRow(1, 2, 3)
+    val row = create_row(1, 2, 3)
     val c1 = 'a.int.at(0)
     val c3 = 'c.int.at(2)
-    checkEvaluation(CreateNamedStruct(Seq("a", c1, "b", c3)), InternalRow(1, 3), row)
-  }
-
-  test("CreateNamedStruct with literal field") {
-    val row = InternalRow(1, 2, 3)
-    val c1 = 'a.int.at(0)
+    checkEvaluation(CreateNamedStruct(Seq("a", c1, "b", c3)), create_row(1, 3), row)
     checkEvaluation(CreateNamedStruct(Seq("a", c1, "b", "y")),
-      InternalRow(1, UTF8String.fromString("y")), row)
-  }
-
-  test("CreateNamedStruct from all literal fields") {
-    checkEvaluation(
-      CreateNamedStruct(Seq("a", "x", "b", 2.0)),
-      InternalRow(UTF8String.fromString("x"), 2.0), InternalRow.empty)
+      create_row(1, UTF8String.fromString("y")), row)
+    checkEvaluation(CreateNamedStruct(Seq("a", "x", "b", 2.0)),
+      create_row(UTF8String.fromString("x"), 2.0))
+    checkEvaluation(CreateNamedStruct(Seq("a", Literal.create(null, IntegerType))),
+      create_row(null))
   }
 
   test("test dsl for complex type") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
index b31d6661c8c1c..d26bcdb2902ab 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
@@ -149,6 +149,8 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Least(Seq(c1, c2, Literal(-1))), -1, row)
     checkEvaluation(Least(Seq(c4, c5, c3, c3, Literal("a"))), "a", row)
 
+    val nullLiteral = Literal.create(null, IntegerType)
+    checkEvaluation(Least(Seq(nullLiteral, nullLiteral)), null)
     checkEvaluation(Least(Seq(Literal(null), Literal(null))), null, InternalRow.empty)
     checkEvaluation(Least(Seq(Literal(-1.0), Literal(2.5))), -1.0, InternalRow.empty)
     checkEvaluation(Least(Seq(Literal(-1), Literal(2))), -1, InternalRow.empty)
@@ -188,6 +190,8 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Greatest(Seq(c1, c2, Literal(2))), 2, row)
     checkEvaluation(Greatest(Seq(c4, c5, c3, Literal("ccc"))), "ccc", row)
 
+    val nullLiteral = Literal.create(null, IntegerType)
+    checkEvaluation(Greatest(Seq(nullLiteral, nullLiteral)), null)
     checkEvaluation(Greatest(Seq(Literal(null), Literal(null))), null, InternalRow.empty)
     checkEvaluation(Greatest(Seq(Literal(-1.0), Literal(2.5))), 2.5, InternalRow.empty)
     checkEvaluation(Greatest(Seq(Literal(-1), Literal(2))), 2, InternalRow.empty)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 21459a7c69838..9fcb548af6bbb 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -110,35 +110,17 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(c(Literal(1.0), Literal.create(null, DoubleType)), null, create_row(null))
   }
 
-  test("conv") {
-    checkEvaluation(Conv(Literal("3"), Literal(10), Literal(2)), "11")
-    checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(-16)), "-F")
-    checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(16)), "FFFFFFFFFFFFFFF1")
-    checkEvaluation(Conv(Literal("big"), Literal(36), Literal(16)), "3A48")
-    checkEvaluation(Conv(Literal.create(null, StringType), Literal(36), Literal(16)), null)
-    checkEvaluation(Conv(Literal("3"), Literal.create(null, IntegerType), Literal(16)), null)
-    checkEvaluation(
-      Conv(Literal("1234"), Literal(10), Literal(37)), null)
-    checkEvaluation(
-      Conv(Literal(""), Literal(10), Literal(16)), null)
-    checkEvaluation(
-      Conv(Literal("9223372036854775807"), Literal(36), Literal(16)), "FFFFFFFFFFFFFFFF")
-    // If there is an invalid digit in the number, the longest valid prefix should be converted.
-    checkEvaluation(
-      Conv(Literal("11abc"), Literal(10), Literal(16)), "B")
-  }
-
   private def checkNaN(
-      expression: Expression, inputRow: InternalRow = EmptyRow): Unit = {
+    expression: Expression, inputRow: InternalRow = EmptyRow): Unit = {
     checkNaNWithoutCodegen(expression, inputRow)
     checkNaNWithGeneratedProjection(expression, inputRow)
     checkNaNWithOptimization(expression, inputRow)
   }
 
   private def checkNaNWithoutCodegen(
-      expression: Expression,
-      expected: Any,
-      inputRow: InternalRow = EmptyRow): Unit = {
+    expression: Expression,
+    expected: Any,
+    inputRow: InternalRow = EmptyRow): Unit = {
     val actual = try evaluate(expression, inputRow) catch {
       case e: Exception => fail(s"Exception evaluating $expression", e)
     }
@@ -149,7 +131,6 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
-
   private def checkNaNWithGeneratedProjection(
     expression: Expression,
     inputRow: InternalRow = EmptyRow): Unit = {
@@ -172,6 +153,25 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkNaNWithoutCodegen(optimizedPlan.expressions.head, inputRow)
   }
 
+  test("conv") {
+    checkEvaluation(Conv(Literal("3"), Literal(10), Literal(2)), "11")
+    checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(-16)), "-F")
+    checkEvaluation(Conv(Literal("-15"), Literal(10), Literal(16)), "FFFFFFFFFFFFFFF1")
+    checkEvaluation(Conv(Literal("big"), Literal(36), Literal(16)), "3A48")
+    checkEvaluation(Conv(Literal.create(null, StringType), Literal(36), Literal(16)), null)
+    checkEvaluation(Conv(Literal("3"), Literal.create(null, IntegerType), Literal(16)), null)
+    checkEvaluation(Conv(Literal("3"), Literal(16), Literal.create(null, IntegerType)), null)
+    checkEvaluation(
+      Conv(Literal("1234"), Literal(10), Literal(37)), null)
+    checkEvaluation(
+      Conv(Literal(""), Literal(10), Literal(16)), null)
+    checkEvaluation(
+      Conv(Literal("9223372036854775807"), Literal(36), Literal(16)), "FFFFFFFFFFFFFFFF")
+    // If there is an invalid digit in the number, the longest valid prefix should be converted.
+    checkEvaluation(
+      Conv(Literal("11abc"), Literal(10), Literal(16)), "B")
+  }
+
   test("e") {
     testLeaf(EulerNumber, math.E)
   }
@@ -417,7 +417,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("round") {
-    val domain = -6 to 6
+    val scales = -6 to 6
     val doublePi: Double = math.Pi
     val shortPi: Short = 31415
     val intPi: Int = 314159265
@@ -437,17 +437,16 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       31415926535900000L, 31415926535898000L, 31415926535897900L, 31415926535897930L) ++
       Seq.fill(7)(31415926535897932L)
 
-    val bdResults: Seq[BigDecimal] = Seq(BigDecimal(3.0), BigDecimal(3.1), BigDecimal(3.14),
-      BigDecimal(3.142), BigDecimal(3.1416), BigDecimal(3.14159),
-      BigDecimal(3.141593), BigDecimal(3.1415927))
-
-    domain.zipWithIndex.foreach { case (scale, i) =>
+    scales.zipWithIndex.foreach { case (scale, i) =>
       checkEvaluation(Round(doublePi, scale), doubleResults(i), EmptyRow)
       checkEvaluation(Round(shortPi, scale), shortResults(i), EmptyRow)
       checkEvaluation(Round(intPi, scale), intResults(i), EmptyRow)
       checkEvaluation(Round(longPi, scale), longResults(i), EmptyRow)
     }
 
+    val bdResults: Seq[BigDecimal] = Seq(BigDecimal(3.0), BigDecimal(3.1), BigDecimal(3.14),
+      BigDecimal(3.142), BigDecimal(3.1416), BigDecimal(3.14159),
+      BigDecimal(3.141593), BigDecimal(3.1415927))
     // round_scale > current_scale would result in precision increase
     // and not allowed by o.a.s.s.types.Decimal.changePrecision, therefore null
     (0 to 7).foreach { i =>
@@ -456,5 +455,11 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     (8 to 10).foreach { scale =>
       checkEvaluation(Round(bdPi, scale), null, EmptyRow)
     }
+
+    DataTypeTestUtils.numericTypes.foreach { dataType =>
+      checkEvaluation(Round(Literal.create(null, dataType), Literal(2)), null)
+      checkEvaluation(Round(Literal.create(null, dataType),
+        Literal.create(null, IntegerType)), null)
+    }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
index 5db992654811a..4a644d136f09c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/RandomSuite.scala
@@ -21,7 +21,6 @@ import org.scalatest.Matchers._
 
 import org.apache.spark.SparkFunSuite
 
-
 class RandomSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("random") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 3d294fda5d103..07b952531ec2e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -348,6 +348,9 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(StringTrimLeft(s), "花花世界 ", create_row("  花花世界 "))
     checkEvaluation(StringTrim(s), "花花世界", create_row("  花花世界 "))
     // scalastyle:on
+    checkEvaluation(StringTrim(Literal.create(null, StringType)), null)
+    checkEvaluation(StringTrimLeft(Literal.create(null, StringType)), null)
+    checkEvaluation(StringTrimRight(Literal.create(null, StringType)), null)
   }
 
   test("FORMAT") {
@@ -391,6 +394,9 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val s3 = 'c.string.at(2)
     val s4 = 'd.int.at(3)
     val row1 = create_row("aaads", "aa", "zz", 1)
+    val row2 = create_row(null, "aa", "zz", 0)
+    val row3 = create_row("aaads", null, "zz", 0)
+    val row4 = create_row(null, null, null, 0)
 
     checkEvaluation(new StringLocate(Literal("aa"), Literal("aaads")), 1, row1)
     checkEvaluation(StringLocate(Literal("aa"), Literal("aaads"), Literal(1)), 2, row1)
@@ -402,6 +408,9 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(StringLocate(s2, s1, s4), 2, row1)
     checkEvaluation(new StringLocate(s3, s1), 0, row1)
     checkEvaluation(StringLocate(s3, s1, Literal.create(null, IntegerType)), 0, row1)
+    checkEvaluation(new StringLocate(s2, s1), null, row2)
+    checkEvaluation(new StringLocate(s2, s1), null, row3)
+    checkEvaluation(new StringLocate(s2, s1, Literal.create(null, IntegerType)), 0, row4)
   }
 
   test("LPAD/RPAD") {
@@ -448,6 +457,7 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val row1 = create_row("abccc")
     checkEvaluation(StringReverse(Literal("abccc")), "cccba", row1)
     checkEvaluation(StringReverse(s), "cccba", row1)
+    checkEvaluation(StringReverse(Literal.create(null, StringType)), null, row1)
   }
 
   test("SPACE") {
@@ -466,6 +476,9 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val row1 = create_row("100-200", "(\\d+)", "num")
     val row2 = create_row("100-200", "(\\d+)", "###")
     val row3 = create_row("100-200", "(-)", "###")
+    val row4 = create_row(null, "(\\d+)", "###")
+    val row5 = create_row("100-200", null, "###")
+    val row6 = create_row("100-200", "(-)", null)
 
     val s = 's.string.at(0)
     val p = 'p.string.at(1)
@@ -475,6 +488,9 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(expr, "num-num", row1)
     checkEvaluation(expr, "###-###", row2)
     checkEvaluation(expr, "100###200", row3)
+    checkEvaluation(expr, null, row4)
+    checkEvaluation(expr, null, row5)
+    checkEvaluation(expr, null, row6)
   }
 
   test("RegexExtract") {
@@ -482,6 +498,9 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val row2 = create_row("100-200", "(\\d+)-(\\d+)", 2)
     val row3 = create_row("100-200", "(\\d+).*", 1)
     val row4 = create_row("100-200", "([a-z])", 1)
+    val row5 = create_row(null, "([a-z])", 1)
+    val row6 = create_row("100-200", null, 1)
+    val row7 = create_row("100-200", "([a-z])", null)
 
     val s = 's.string.at(0)
     val p = 'p.string.at(1)
@@ -492,6 +511,9 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(expr, "200", row2)
     checkEvaluation(expr, "100", row3)
     checkEvaluation(expr, "", row4) // will not match anything, empty string get
+    checkEvaluation(expr, null, row5)
+    checkEvaluation(expr, null, row6)
+    checkEvaluation(expr, null, row7)
 
     val expr1 = new RegExpExtract(s, p)
     checkEvaluation(expr1, "100", row1)
@@ -501,11 +523,15 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val s1 = 'a.string.at(0)
     val s2 = 'b.string.at(1)
     val row1 = create_row("aa2bb3cc", "[1-9]+")
+    val row2 = create_row(null, "[1-9]+")
+    val row3 = create_row("aa2bb3cc", null)
 
     checkEvaluation(
       StringSplit(Literal("aa2bb3cc"), Literal("[1-9]+")), Seq("aa", "bb", "cc"), row1)
     checkEvaluation(
       StringSplit(s1, s2), Seq("aa", "bb", "cc"), row1)
+    checkEvaluation(StringSplit(s1, s2), null, row2)
+    checkEvaluation(StringSplit(s1, s2), null, row3)
   }
 
   test("length for string / binary") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4261a5e7cbeb5..4e68a88e7cda6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1423,7 +1423,8 @@ object functions {
   def round(columnName: String): Column = round(Column(columnName), 0)
 
   /**
-   * Returns the value of `e` rounded to `scale` decimal places.
+   * Round the value of `e` to `scale` decimal places if `scale` >= 0
+   * or at integral part when `scale` < 0.
    *
    * @group math_funcs
    * @since 1.5.0
@@ -1431,7 +1432,8 @@ object functions {
   def round(e: Column, scale: Int): Column = Round(e.expr, Literal(scale))
 
   /**
-   * Returns the value of the given column rounded to `scale` decimal places.
+   * Round the value of the given column to `scale` decimal places if `scale` >= 0
+   * or at integral part when `scale` < 0.
    *
    * @group math_funcs
    * @since 1.5.0

From 1221849f91739454b8e495889cba7498ba8beea7 Mon Sep 17 00:00:00 2001
From: Joseph Batchik <josephbatchik@gmail.com>
Date: Wed, 29 Jul 2015 23:35:55 -0700
Subject: [PATCH 0687/1454] [SPARK-8005][SQL] Input file name

Users can now get the file name of the partition being read in. A thread local variable is in `SQLNewHadoopRDD` and is set when the partition is computed. `SQLNewHadoopRDD` is moved to core so that the catalyst package can reach it.

This supports:

`df.select(inputFileName())`

and

`sqlContext.sql("select input_file_name() from table")`

Author: Joseph Batchik <josephbatchik@gmail.com>

Closes #7743 from JDrit/input_file_name and squashes the following commits:

abb8609 [Joseph Batchik] fixed failing test and changed the default value to be an empty string
d2f323d [Joseph Batchik] updates per review
102061f [Joseph Batchik] updates per review
75313f5 [Joseph Batchik] small fixes
c7f7b5a [Joseph Batchik] addeding input file name to Spark SQL
---
 .../apache/spark/rdd}/SqlNewHadoopRDD.scala   | 34 +++++++++++--
 .../catalyst/analysis/FunctionRegistry.scala  |  3 +-
 .../catalyst/expressions/InputFileName.scala  | 49 +++++++++++++++++++
 .../expressions/SparkPartitionID.scala        |  2 +
 .../expressions/NondeterministicSuite.scala   |  4 ++
 .../org/apache/spark/sql/functions.scala      |  9 ++++
 .../spark/sql/parquet/ParquetRelation.scala   |  3 +-
 .../spark/sql/ColumnExpressionSuite.scala     | 17 ++++++-
 .../scala/org/apache/spark/sql/UDFSuite.scala | 17 ++++++-
 .../org/apache/spark/sql/hive/UDFSuite.scala  |  6 ---
 10 files changed, 128 insertions(+), 16 deletions(-)
 rename {sql/core/src/main/scala/org/apache/spark/sql/execution => core/src/main/scala/org/apache/spark/rdd}/SqlNewHadoopRDD.scala (91%)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/InputFileName.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
similarity index 91%
rename from sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
rename to core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index 3d75b6a91def6..35e44cb59c1be 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -15,12 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.execution
+package org.apache.spark.rdd
 
 import java.text.SimpleDateFormat
 import java.util.Date
 
-import org.apache.spark.{Partition => SparkPartition, _}
+import scala.reflect.ClassTag
+
 import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
@@ -30,12 +31,12 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.{Partition => SparkPartition, _}
 import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD
-import org.apache.spark.rdd.{HadoopRDD, RDD}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
-import scala.reflect.ClassTag
 
 private[spark] class SqlNewHadoopPartition(
     rddId: Int,
@@ -62,7 +63,7 @@ private[spark] class SqlNewHadoopPartition(
  * changes based on [[org.apache.spark.rdd.HadoopRDD]]. In future, this functionality will be
  * folded into core.
  */
-private[sql] class SqlNewHadoopRDD[K, V](
+private[spark] class SqlNewHadoopRDD[K, V](
     @transient sc : SparkContext,
     broadcastedConf: Broadcast[SerializableConfiguration],
     @transient initDriverSideJobFuncOpt: Option[Job => Unit],
@@ -128,6 +129,12 @@ private[sql] class SqlNewHadoopRDD[K, V](
       val inputMetrics = context.taskMetrics
         .getInputMetricsForReadMethod(DataReadMethod.Hadoop)
 
+      // Sets the thread local variable for the file's name
+      split.serializableHadoopSplit.value match {
+        case fs: FileSplit => SqlNewHadoopRDD.setInputFileName(fs.getPath.toString)
+        case _ => SqlNewHadoopRDD.unsetInputFileName()
+      }
+
       // Find a function that will return the FileSystem bytes read by this thread. Do this before
       // creating RecordReader, because RecordReader's constructor might read some bytes
       val bytesReadCallback = inputMetrics.bytesReadCallback.orElse {
@@ -188,6 +195,8 @@ private[sql] class SqlNewHadoopRDD[K, V](
             reader.close()
             reader = null
 
+            SqlNewHadoopRDD.unsetInputFileName()
+
             if (bytesReadCallback.isDefined) {
               inputMetrics.updateBytesRead()
             } else if (split.serializableHadoopSplit.value.isInstanceOf[FileSplit] ||
@@ -250,6 +259,21 @@ private[sql] class SqlNewHadoopRDD[K, V](
 }
 
 private[spark] object SqlNewHadoopRDD {
+
+  /**
+   * The thread variable for the name of the current file being read. This is used by
+   * the InputFileName function in Spark SQL.
+   */
+  private[this] val inputFileName: ThreadLocal[UTF8String] = new ThreadLocal[UTF8String] {
+    override protected def initialValue(): UTF8String = UTF8String.fromString("")
+  }
+
+  def getInputFileName(): UTF8String = inputFileName.get()
+
+  private[spark] def setInputFileName(file: String) = inputFileName.set(UTF8String.fromString(file))
+
+  private[spark] def unsetInputFileName(): Unit = inputFileName.remove()
+
   /**
    * Analogous to [[org.apache.spark.rdd.MapPartitionsRDD]], but passes in an InputSplit to
    * the given function rather than the index of the partition.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 372f80d4a8b16..378df4f57d9e2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -230,7 +230,8 @@ object FunctionRegistry {
     expression[Sha1]("sha"),
     expression[Sha1]("sha1"),
     expression[Sha2]("sha2"),
-    expression[SparkPartitionID]("spark_partition_id")
+    expression[SparkPartitionID]("spark_partition_id"),
+    expression[InputFileName]("input_file_name")
   )
 
   val builtin: FunctionRegistry = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/InputFileName.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/InputFileName.scala
new file mode 100644
index 0000000000000..1e74f716955e3
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/InputFileName.scala
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.rdd.SqlNewHadoopRDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
+import org.apache.spark.sql.types.{DataType, StringType}
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * Expression that returns the name of the current file being read in using [[SqlNewHadoopRDD]]
+ */
+case class InputFileName() extends LeafExpression with Nondeterministic {
+
+  override def nullable: Boolean = true
+
+  override def dataType: DataType = StringType
+
+  override val prettyName = "INPUT_FILE_NAME"
+
+  override protected def initInternal(): Unit = {}
+
+  override protected def evalInternal(input: InternalRow): UTF8String = {
+    SqlNewHadoopRDD.getInputFileName()
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    ev.isNull = "false"
+    s"final ${ctx.javaType(dataType)} ${ev.primitive} = " +
+      "org.apache.spark.rdd.SqlNewHadoopRDD.getInputFileName();"
+  }
+
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SparkPartitionID.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SparkPartitionID.scala
index 3f6480bbf0114..4b1772a2deed5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SparkPartitionID.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SparkPartitionID.scala
@@ -34,6 +34,8 @@ private[sql] case class SparkPartitionID() extends LeafExpression with Nondeterm
 
   @transient private[this] var partitionId: Int = _
 
+  override val prettyName = "SPARK_PARTITION_ID"
+
   override protected def initInternal(): Unit = {
     partitionId = TaskContext.getPartitionId()
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NondeterministicSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NondeterministicSuite.scala
index 82894822ab0f4..bf1c930c0bd0b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NondeterministicSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NondeterministicSuite.scala
@@ -27,4 +27,8 @@ class NondeterministicSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("SparkPartitionID") {
     checkEvaluation(SparkPartitionID(), 0)
   }
+
+  test("InputFileName") {
+    checkEvaluation(InputFileName(), "")
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 4e68a88e7cda6..a2fece62f61f9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -743,6 +743,15 @@ object functions {
    */
   def sparkPartitionId(): Column = SparkPartitionID()
 
+  /**
+   * The file name of the current Spark task
+   *
+   * Note that this is indeterministic becuase it depends on what is currently being read in.
+   *
+   * @group normal_funcs
+   */
+  def inputFileName(): Column = InputFileName()
+
   /**
    * Computes the square root of the specified float value.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index cc6fa2b88663f..1a8176d8a80ab 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -39,11 +39,10 @@ import org.apache.parquet.{Log => ParquetLog}
 
 import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{SqlNewHadoopPartition, SqlNewHadoopRDD, RDD}
 import org.apache.spark.rdd.RDD._
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.execution.{SqlNewHadoopPartition, SqlNewHadoopRDD}
 import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{DataType, StructType}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 1f9f7118c3f04..5c1102410879a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -22,13 +22,16 @@ import org.scalatest.Matchers._
 import org.apache.spark.sql.execution.Project
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.test.SQLTestUtils
 
-class ColumnExpressionSuite extends QueryTest {
+class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
   import org.apache.spark.sql.TestData._
 
   private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
   import ctx.implicits._
 
+  override def sqlContext(): SQLContext = ctx
+
   test("alias") {
     val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList")
     assert(df.select(df("a").as("b")).columns.head === "b")
@@ -489,6 +492,18 @@ class ColumnExpressionSuite extends QueryTest {
     )
   }
 
+  test("InputFileName") {
+    withTempPath { dir =>
+      val data = sqlContext.sparkContext.parallelize(0 to 10).toDF("id")
+      data.write.parquet(dir.getCanonicalPath)
+      val answer = sqlContext.read.parquet(dir.getCanonicalPath).select(inputFileName())
+        .head.getString(0)
+      assert(answer.contains(dir.getCanonicalPath))
+
+      checkAnswer(data.select(inputFileName()).limit(1), Row(""))
+    }
+  }
+
   test("lift alias out of cast") {
     compareExpressions(
       col("1234").as("name").cast("int").expr,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index d9c8b380ef146..183dc3407b3ab 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -17,14 +17,17 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.test.SQLTestUtils
 
 case class FunctionResult(f1: String, f2: String)
 
-class UDFSuite extends QueryTest {
+class UDFSuite extends QueryTest with SQLTestUtils {
 
   private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
   import ctx.implicits._
 
+  override def sqlContext(): SQLContext = ctx
+
   test("built-in fixed arity expressions") {
     val df = ctx.emptyDataFrame
     df.selectExpr("rand()", "randn()", "rand(5)", "randn(50)")
@@ -58,6 +61,18 @@ class UDFSuite extends QueryTest {
     ctx.dropTempTable("tmp_table")
   }
 
+  test("SPARK-8005 input_file_name") {
+    withTempPath { dir =>
+      val data = ctx.sparkContext.parallelize(0 to 10, 2).toDF("id")
+      data.write.parquet(dir.getCanonicalPath)
+      ctx.read.parquet(dir.getCanonicalPath).registerTempTable("test_table")
+      val answer = ctx.sql("select input_file_name() from test_table").head().getString(0)
+      assert(answer.contains(dir.getCanonicalPath))
+      assert(ctx.sql("select input_file_name() from test_table").distinct().collect().length >= 2)
+      ctx.dropTempTable("test_table")
+    }
+  }
+
   test("error reporting for incorrect number of arguments") {
     val df = ctx.emptyDataFrame
     val e = intercept[AnalysisException] {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
index 37afc2142abf7..9b3ede43ee2d1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
@@ -34,10 +34,4 @@ class UDFSuite extends QueryTest {
     assert(ctx.sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
     assert(ctx.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5)
   }
-
-  test("SPARK-8003 spark_partition_id") {
-    val df = Seq((1, "Two Fiiiiive")).toDF("id", "saying")
-    ctx.registerDataFrameAsTable(df, "test_table")
-    checkAnswer(ctx.sql("select spark_partition_id() from test_table LIMIT 1").toDF(), Row(0))
-  }
 }

From 76f2e393a5fad0db8b56c4b8dad5ef686bf140a4 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 30 Jul 2015 00:46:36 -0700
Subject: [PATCH 0688/1454] [SPARK-9335] [TESTS] Enable Kinesis tests only when
 files in extras/kinesis-asl are changed

Author: zsxwing <zsxwing@gmail.com>

Closes #7711 from zsxwing/SPARK-9335-test and squashes the following commits:

c13ec2f [zsxwing] environs -> environ
69c2865 [zsxwing] Merge remote-tracking branch 'origin/master' into SPARK-9335-test
ef84a08 [zsxwing] Revert "Modify the Kinesis project to trigger ENABLE_KINESIS_TESTS"
f691028 [zsxwing] Modify the Kinesis project to trigger ENABLE_KINESIS_TESTS
7618205 [zsxwing] Enable Kinesis tests only when files in extras/kinesis-asl are changed
---
 dev/run-tests.py                | 16 ++++++++++++++++
 dev/sparktestsupport/modules.py | 14 ++++++++++++--
 2 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/dev/run-tests.py b/dev/run-tests.py
index 1f0d218514f92..29420da9aa956 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -85,6 +85,13 @@ def identify_changed_files_from_git_commits(patch_sha, target_branch=None, targe
     return [f for f in raw_output.split('\n') if f]
 
 
+def setup_test_environ(environ):
+    print("[info] Setup the following environment variables for tests: ")
+    for (k, v) in environ.items():
+        print("%s=%s" % (k, v))
+        os.environ[k] = v
+
+
 def determine_modules_to_test(changed_modules):
     """
     Given a set of modules that have changed, compute the transitive closure of those modules'
@@ -455,6 +462,15 @@ def main():
     print("[info] Found the following changed modules:",
           ", ".join(x.name for x in changed_modules))
 
+    # setup environment variables
+    # note - the 'root' module doesn't collect environment variables for all modules. Because the
+    # environment variables should not be set if a module is not changed, even if running the 'root'
+    # module. So here we should use changed_modules rather than test_modules.
+    test_environ = {}
+    for m in changed_modules:
+        test_environ.update(m.environ)
+    setup_test_environ(test_environ)
+
     test_modules = determine_modules_to_test(changed_modules)
 
     # license checks
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 3073d489bad4a..030d982e99106 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -29,7 +29,7 @@ class Module(object):
     changed.
     """
 
-    def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(),
+    def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(), environ={},
                  sbt_test_goals=(), python_test_goals=(), blacklisted_python_implementations=(),
                  should_run_r_tests=False):
         """
@@ -43,6 +43,8 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
             filename strings.
         :param build_profile_flags: A set of profile flags that should be passed to Maven or SBT in
             order to build and test this module (e.g. '-PprofileName').
+        :param environ: A dict of environment variables that should be set when files in this
+            module are changed.
         :param sbt_test_goals: A set of SBT test goals for testing this module.
         :param python_test_goals: A set of Python test goals for testing this module.
         :param blacklisted_python_implementations: A set of Python implementations that are not
@@ -55,6 +57,7 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         self.source_file_prefixes = source_file_regexes
         self.sbt_test_goals = sbt_test_goals
         self.build_profile_flags = build_profile_flags
+        self.environ = environ
         self.python_test_goals = python_test_goals
         self.blacklisted_python_implementations = blacklisted_python_implementations
         self.should_run_r_tests = should_run_r_tests
@@ -126,15 +129,22 @@ def contains_file(self, filename):
 )
 
 
+# Don't set the dependencies because changes in other modules should not trigger Kinesis tests.
+# Kinesis tests depends on external Amazon kinesis service. We should run these tests only when
+# files in streaming_kinesis_asl are changed, so that if Kinesis experiences an outage, we don't
+# fail other PRs.
 streaming_kinesis_asl = Module(
     name="kinesis-asl",
-    dependencies=[streaming],
+    dependencies=[],
     source_file_regexes=[
         "extras/kinesis-asl/",
     ],
     build_profile_flags=[
         "-Pkinesis-asl",
     ],
+    environ={
+        "ENABLE_KINESIS_TESTS": "1"
+    },
     sbt_test_goals=[
         "kinesis-asl/test",
     ]

From 4a8bb9d00d8181aff5f5183194d9aa2a65deacdf Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 30 Jul 2015 01:04:24 -0700
Subject: [PATCH 0689/1454] Revert "[SPARK-9458] Avoid object allocation in
 prefix generation."

This reverts commit 9514d874f0cf61f1eb4ec4f5f66e053119f769c9.
---
 .../unsafe/sort/PrefixComparators.java        | 16 ++++++
 .../unsafe/sort/PrefixComparatorsSuite.scala  | 12 +++++
 .../execution/UnsafeExternalRowSorter.java    |  2 +-
 .../spark/sql/execution/SortPrefixUtils.scala | 51 ++++++++++---------
 .../spark/sql/execution/SparkStrategies.scala |  4 +-
 .../org/apache/spark/sql/execution/sort.scala |  5 +-
 .../execution/RowFormatConvertersSuite.scala  |  2 +-
 .../execution/UnsafeExternalSortSuite.scala   | 10 ++--
 8 files changed, 67 insertions(+), 35 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index a9ee6042fec74..600aff7d15d8a 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -29,6 +29,7 @@ private PrefixComparators() {}
 
   public static final StringPrefixComparator STRING = new StringPrefixComparator();
   public static final IntegralPrefixComparator INTEGRAL = new IntegralPrefixComparator();
+  public static final FloatPrefixComparator FLOAT = new FloatPrefixComparator();
   public static final DoublePrefixComparator DOUBLE = new DoublePrefixComparator();
 
   public static final class StringPrefixComparator extends PrefixComparator {
@@ -54,6 +55,21 @@ public int compare(long a, long b) {
     public final long NULL_PREFIX = Long.MIN_VALUE;
   }
 
+  public static final class FloatPrefixComparator extends PrefixComparator {
+    @Override
+    public int compare(long aPrefix, long bPrefix) {
+      float a = Float.intBitsToFloat((int) aPrefix);
+      float b = Float.intBitsToFloat((int) bPrefix);
+      return Utils.nanSafeCompareFloats(a, b);
+    }
+
+    public long computePrefix(float value) {
+      return Float.floatToIntBits(value) & 0xffffffffL;
+    }
+
+    public final long NULL_PREFIX = computePrefix(Float.NEGATIVE_INFINITY);
+  }
+
   public static final class DoublePrefixComparator extends PrefixComparator {
     @Override
     public int compare(long aPrefix, long bPrefix) {
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
index 26b7a9e816d1e..cf53a8ad21c60 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -55,6 +55,18 @@ class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
     forAll { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
   }
 
+  test("float prefix comparator handles NaN properly") {
+    val nan1: Float = java.lang.Float.intBitsToFloat(0x7f800001)
+    val nan2: Float = java.lang.Float.intBitsToFloat(0x7fffffff)
+    assert(nan1.isNaN)
+    assert(nan2.isNaN)
+    val nan1Prefix = PrefixComparators.FLOAT.computePrefix(nan1)
+    val nan2Prefix = PrefixComparators.FLOAT.computePrefix(nan2)
+    assert(nan1Prefix === nan2Prefix)
+    val floatMaxPrefix = PrefixComparators.FLOAT.computePrefix(Float.MaxValue)
+    assert(PrefixComparators.FLOAT.compare(nan1Prefix, floatMaxPrefix) === 1)
+  }
+
   test("double prefix comparator handles NaNs properly") {
     val nan1: Double = java.lang.Double.longBitsToDouble(0x7ff0000000000001L)
     val nan2: Double = java.lang.Double.longBitsToDouble(0x7fffffffffffffffL)
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 8342833246f7d..4c3f2c6557140 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -121,7 +121,7 @@ Iterator<InternalRow> sort() throws IOException {
         // here in order to prevent memory leaks.
         cleanupResources();
       }
-      return new AbstractScalaRowIterator<InternalRow>() {
+      return new AbstractScalaRowIterator() {
 
         private final int numFields = schema.length();
         private UnsafeRow row = new UnsafeRow();
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 050d27f1460fb..2dee3542d6101 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -19,7 +19,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{BoundReference, SortOrder}
+import org.apache.spark.sql.catalyst.expressions.SortOrder
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.collection.unsafe.sort.{PrefixComparators, PrefixComparator}
@@ -39,54 +39,57 @@ object SortPrefixUtils {
     sortOrder.dataType match {
       case StringType => PrefixComparators.STRING
       case BooleanType | ByteType | ShortType | IntegerType | LongType => PrefixComparators.INTEGRAL
-      case FloatType | DoubleType => PrefixComparators.DOUBLE
+      case FloatType => PrefixComparators.FLOAT
+      case DoubleType => PrefixComparators.DOUBLE
       case _ => NoOpPrefixComparator
     }
   }
 
   def getPrefixComputer(sortOrder: SortOrder): InternalRow => Long = {
-    val bound = sortOrder.child.asInstanceOf[BoundReference]
-    val pos = bound.ordinal
     sortOrder.dataType match {
-      case StringType =>
-        (row: InternalRow) => {
-          PrefixComparators.STRING.computePrefix(row.getUTF8String(pos))
-        }
+      case StringType => (row: InternalRow) => {
+        PrefixComparators.STRING.computePrefix(sortOrder.child.eval(row).asInstanceOf[UTF8String])
+      }
       case BooleanType =>
         (row: InternalRow) => {
-          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else if (row.getBoolean(pos)) 1
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else if (sortOrder.child.eval(row).asInstanceOf[Boolean]) 1
           else 0
         }
       case ByteType =>
         (row: InternalRow) => {
-          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getByte(pos)
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Byte]
         }
       case ShortType =>
         (row: InternalRow) => {
-          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getShort(pos)
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Short]
         }
       case IntegerType =>
         (row: InternalRow) => {
-          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getInt(pos)
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Int]
         }
       case LongType =>
         (row: InternalRow) => {
-          if (row.isNullAt(pos)) PrefixComparators.INTEGRAL.NULL_PREFIX else row.getLong(pos)
+          val exprVal = sortOrder.child.eval(row)
+          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
+          else sortOrder.child.eval(row).asInstanceOf[Long]
         }
       case FloatType => (row: InternalRow) => {
-        if (row.isNullAt(pos)) {
-          PrefixComparators.DOUBLE.NULL_PREFIX
-        } else {
-          PrefixComparators.DOUBLE.computePrefix(row.getFloat(pos).toDouble)
-        }
+        val exprVal = sortOrder.child.eval(row)
+        if (exprVal == null) PrefixComparators.FLOAT.NULL_PREFIX
+        else PrefixComparators.FLOAT.computePrefix(sortOrder.child.eval(row).asInstanceOf[Float])
       }
       case DoubleType => (row: InternalRow) => {
-        if (row.isNullAt(pos)) {
-          PrefixComparators.DOUBLE.NULL_PREFIX
-        } else {
-          PrefixComparators.DOUBLE.computePrefix(row.getDouble(pos))
-        }
+        val exprVal = sortOrder.child.eval(row)
+        if (exprVal == null) PrefixComparators.DOUBLE.NULL_PREFIX
+        else PrefixComparators.DOUBLE.computePrefix(sortOrder.child.eval(row).asInstanceOf[Double])
       }
       case _ => (row: InternalRow) => 0L
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 4ab2c41f1b339..f3ef066528ff8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -340,8 +340,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
      */
     def getSortOperator(sortExprs: Seq[SortOrder], global: Boolean, child: SparkPlan): SparkPlan = {
       if (sqlContext.conf.unsafeEnabled && sqlContext.conf.codegenEnabled &&
-        TungstenSort.supportsSchema(child.schema)) {
-        execution.TungstenSort(sortExprs, global, child)
+        UnsafeExternalSort.supportsSchema(child.schema)) {
+        execution.UnsafeExternalSort(sortExprs, global, child)
       } else if (sqlContext.conf.externalSortEnabled) {
         execution.ExternalSort(sortExprs, global, child)
       } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index d0ad310062853..f82208868c3e3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -97,7 +97,7 @@ case class ExternalSort(
  * @param testSpillFrequency Method for configuring periodic spilling in unit tests. If set, will
  *                           spill every `frequency` records.
  */
-case class TungstenSort(
+case class UnsafeExternalSort(
     sortOrder: Seq[SortOrder],
     global: Boolean,
     child: SparkPlan,
@@ -110,6 +110,7 @@ case class TungstenSort(
     if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
+    assert(codegenEnabled, "UnsafeExternalSort requires code generation to be enabled")
     def doSort(iterator: Iterator[InternalRow]): Iterator[InternalRow] = {
       val ordering = newOrdering(sortOrder, child.output)
       val boundSortExpression = BindReferences.bindReference(sortOrder.head, child.output)
@@ -148,7 +149,7 @@ case class TungstenSort(
 }
 
 @DeveloperApi
-object TungstenSort {
+object UnsafeExternalSort {
   /**
    * Return true if UnsafeExternalSort can sort rows with the given schema, false otherwise.
    */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index c458f95ca1ab3..7b75f755918c1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -31,7 +31,7 @@ class RowFormatConvertersSuite extends SparkPlanTest {
 
   private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
   assert(!outputsSafe.outputsUnsafeRows)
-  private val outputsUnsafe = TungstenSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  private val outputsUnsafe = UnsafeExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
   assert(outputsUnsafe.outputsUnsafeRows)
 
   test("planner should insert unsafe->safe conversions when required") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
index 9cabc4b90bf8e..7a4baa9e4a49d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
@@ -42,7 +42,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
     checkThatPlansAgree(
       (1 to 100).map(v => Tuple1(v)).toDF("a"),
-      (child: SparkPlan) => Limit(10, TungstenSort('a.asc :: Nil, true, child)),
+      (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
       (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
       sortAnswers = false
     )
@@ -53,7 +53,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     try {
       checkThatPlansAgree(
         (1 to 100).map(v => Tuple1(v)).toDF("a"),
-        (child: SparkPlan) => Limit(10, TungstenSort('a.asc :: Nil, true, child)),
+        (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
         (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
         sortAnswers = false
       )
@@ -68,7 +68,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     val stringLength = 1024 * 1024 * 2
     checkThatPlansAgree(
       Seq(Tuple1("a" * stringLength), Tuple1("b" * stringLength)).toDF("a").repartition(1),
-      TungstenSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 1),
+      UnsafeExternalSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 1),
       Sort(sortOrder, global = true, _: SparkPlan),
       sortAnswers = false
     )
@@ -88,11 +88,11 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
         TestSQLContext.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)
       )
-      assert(TungstenSort.supportsSchema(inputDf.schema))
+      assert(UnsafeExternalSort.supportsSchema(inputDf.schema))
       checkThatPlansAgree(
         inputDf,
         plan => ConvertToSafe(
-          TungstenSort(sortOrder, global = true, plan: SparkPlan, testSpillFrequency = 23)),
+          UnsafeExternalSort(sortOrder, global = true, plan: SparkPlan, testSpillFrequency = 23)),
         Sort(sortOrder, global = true, _: SparkPlan),
         sortAnswers = false
       )

From 5ba2d44068b89fd8e81cfd24f49bf20d373f81b9 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 30 Jul 2015 01:21:39 -0700
Subject: [PATCH 0690/1454] Fix flaky HashedRelationSuite

SparkEnv might not have been set in local unit tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #7784 from rxin/HashedRelationSuite and squashes the following commits:

435d64b [Reynold Xin] Fix flaky HashedRelationSuite
---
 .../apache/spark/sql/execution/joins/HashedRelation.scala  | 7 +++++--
 .../spark/sql/execution/joins/HashedRelationSuite.scala    | 6 +++---
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 7a507391316a9..26dbc911e9521 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -21,7 +21,7 @@ import java.io.{Externalizable, ObjectInput, ObjectOutput}
 import java.nio.ByteOrder
 import java.util.{HashMap => JavaHashMap}
 
-import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.{SparkConf, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
@@ -260,7 +260,10 @@ private[joins] final class UnsafeHashedRelation(
     val nKeys = in.readInt()
     // This is used in Broadcast, shared by multiple tasks, so we use on-heap memory
     val memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
-    val pageSizeBytes = SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
+
+    val pageSizeBytes = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf())
+      .getSizeAsBytes("spark.buffer.pageSize", "64m")
+
     binaryMap = new BytesToBytesMap(
       memoryManager,
       nKeys * 2, // reduce hash collision
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 941f6d4f6a450..8b1a9b21a96b9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -33,7 +33,7 @@ class HashedRelationSuite extends SparkFunSuite {
     override def apply(row: InternalRow): InternalRow = row
   }
 
-  ignore("GeneralHashedRelation") {
+  test("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
     val hashed = HashedRelation(data.iterator, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
@@ -47,7 +47,7 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed.get(data(2)) === data2)
   }
 
-  ignore("UniqueKeyHashedRelation") {
+  test("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
     val hashed = HashedRelation(data.iterator, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
@@ -64,7 +64,7 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(uniqHashed.getValue(InternalRow(10)) === null)
   }
 
-  ignore("UnsafeHashedRelation") {
+  test("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
     val toUnsafe = UnsafeProjection.create(schema)

From 6175d6cfe795fbd88e3ee713fac375038a3993a8 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 30 Jul 2015 17:45:30 +0800
Subject: [PATCH 0691/1454] [SPARK-8838] [SQL] Add config to enable/disable
 merging part-files when merging parquet schema

JIRA: https://issues.apache.org/jira/browse/SPARK-8838

Currently all part-files are merged when merging parquet schema. However, in case there are many part-files and we can make sure that all the part-files have the same schema as their summary file. If so, we provide a configuration to disable merging part-files when merging parquet schema.

In short, we need to merge parquet schema because different summary files may contain different schema. But the part-files are confirmed to have the same schema with summary files.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7238 from viirya/option_partfile_merge and squashes the following commits:

71d5b5f [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into option_partfile_merge
8816f44 [Liang-Chi Hsieh] For comments.
dbc8e6b [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into option_partfile_merge
afc2fa1 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into option_partfile_merge
d4ed7e6 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into option_partfile_merge
df43027 [Liang-Chi Hsieh] Get dataStatuses' partitions based on all paths.
4eb2f00 [Liang-Chi Hsieh] Use given parameter.
ea8f6e5 [Liang-Chi Hsieh] Correct the code comments.
a57be0e [Liang-Chi Hsieh] Merge part-files if there are no summary files.
47df981 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into option_partfile_merge
4caf293 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into option_partfile_merge
0e734e0 [Liang-Chi Hsieh] Use correct API.
3b6be5b [Liang-Chi Hsieh] Fix key not found.
4bdd7e0 [Liang-Chi Hsieh] Don't read footer files if we can skip them.
8bbebcb [Liang-Chi Hsieh] Figure out how to test the config.
bbd4ce7 [Liang-Chi Hsieh] Add config to enable/disable merging part-files when merging parquet schema.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  7 +++++
 .../spark/sql/parquet/ParquetRelation.scala   | 19 ++++++++++++-
 .../spark/sql/parquet/ParquetQuerySuite.scala | 27 +++++++++++++++++++
 3 files changed, 52 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index cdb0c7a1c07a7..2564bbd2077bf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -247,6 +247,13 @@ private[spark] object SQLConf {
           "otherwise the schema is picked from the summary file or a random data file " +
           "if no summary file is available.")
 
+  val PARQUET_SCHEMA_RESPECT_SUMMARIES = booleanConf("spark.sql.parquet.respectSummaryFiles",
+    defaultValue = Some(false),
+    doc = "When true, we make assumption that all part-files of Parquet are consistent with " +
+          "summary files and we will ignore them when merging schema. Otherwise, if this is " +
+          "false, which is the default, we will merge all part-files. This should be considered " +
+          "as expert-only option, and shouldn't be enabled before knowing what it means exactly.")
+
   val PARQUET_BINARY_AS_STRING = booleanConf("spark.sql.parquet.binaryAsString",
     defaultValue = Some(false),
     doc = "Some other Parquet-producing systems, in particular Impala and older versions of " +
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 1a8176d8a80ab..b4337a48dbd80 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -124,6 +124,9 @@ private[sql] class ParquetRelation(
       .map(_.toBoolean)
       .getOrElse(sqlContext.conf.getConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED))
 
+  private val mergeRespectSummaries =
+    sqlContext.conf.getConf(SQLConf.PARQUET_SCHEMA_RESPECT_SUMMARIES)
+
   private val maybeMetastoreSchema = parameters
     .get(ParquetRelation.METASTORE_SCHEMA)
     .map(DataType.fromJson(_).asInstanceOf[StructType])
@@ -421,7 +424,21 @@ private[sql] class ParquetRelation(
       val filesToTouch =
         if (shouldMergeSchemas) {
           // Also includes summary files, 'cause there might be empty partition directories.
-          (metadataStatuses ++ commonMetadataStatuses ++ dataStatuses).toSeq
+
+          // If mergeRespectSummaries config is true, we assume that all part-files are the same for
+          // their schema with summary files, so we ignore them when merging schema.
+          // If the config is disabled, which is the default setting, we merge all part-files.
+          // In this mode, we only need to merge schemas contained in all those summary files.
+          // You should enable this configuration only if you are very sure that for the parquet
+          // part-files to read there are corresponding summary files containing correct schema.
+
+          val needMerged: Seq[FileStatus] =
+            if (mergeRespectSummaries) {
+              Seq()
+            } else {
+              dataStatuses
+            }
+          (metadataStatuses ++ commonMetadataStatuses ++ needMerged).toSeq
         } else {
           // Tries any "_common_metadata" first. Parquet files written by old versions or Parquet
           // don't have this.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index c037faf4cfd92..a95f70f2bba69 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.sql.parquet
 
+import java.io.File
+
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.util.Utils
 
 /**
  * A test suite that tests various Parquet queries.
@@ -123,6 +126,30 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
     }
   }
 
+  test("Enabling/disabling merging partfiles when merging parquet schema") {
+    def testSchemaMerging(expectedColumnNumber: Int): Unit = {
+      withTempDir { dir =>
+        val basePath = dir.getCanonicalPath
+        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        // delete summary files, so if we don't merge part-files, one column will not be included.
+        Utils.deleteRecursively(new File(basePath + "/foo=1/_metadata"))
+        Utils.deleteRecursively(new File(basePath + "/foo=1/_common_metadata"))
+        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
+      }
+    }
+
+    withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "true",
+      SQLConf.PARQUET_SCHEMA_RESPECT_SUMMARIES.key -> "true") {
+      testSchemaMerging(2)
+    }
+
+    withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "true",
+      SQLConf.PARQUET_SCHEMA_RESPECT_SUMMARIES.key -> "false") {
+      testSchemaMerging(3)
+    }
+  }
+
   test("Enabling/disabling schema merging") {
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>

From d31c618e3c8838f8198556876b9dcbbbf835f7b2 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Thu, 30 Jul 2015 07:49:10 -0700
Subject: [PATCH 0692/1454] [SPARK-7368] [MLLIB] Add QR decomposition for
 RowMatrix

jira: https://issues.apache.org/jira/browse/SPARK-7368
Add QR decomposition for RowMatrix.

I'm not sure what's the blueprint about the distributed Matrix from community and whether this will be a desirable feature , so I sent a prototype for discussion. I'll go on polish the code and provide ut and performance statistics if it's acceptable.

The implementation refers to the [paper: https://www.cs.purdue.edu/homes/dgleich/publications/Benson%202013%20-%20direct-tsqr.pdf]
Austin R. Benson, David F. Gleich, James Demmel. "Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures", 2013 IEEE International Conference on Big Data, which is a stable algorithm with good scalability.

Currently I tried it on a 400000 * 500 rowMatrix (16 partitions) and it can bring down the computation time from 8.8 mins (using breeze.linalg.qr.reduced)  to 2.6 mins on a 4 worker cluster. I think there will still be some room for performance improvement.

Any trial and suggestion is welcome.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #5909 from hhbyyh/qrDecomposition and squashes the following commits:

cec797b [Yuhao Yang] remove unnecessary qr
0fb1012 [Yuhao Yang] hierarchy R computing
3fbdb61 [Yuhao Yang] update qr to indirect and add ut
0d913d3 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
39213c3 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
c0fc0c7 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
39b0b22 [Yuhao Yang] initial draft for discussion
---
 .../linalg/SingularValueDecomposition.scala   |  8 ++++
 .../mllib/linalg/distributed/RowMatrix.scala  | 46 ++++++++++++++++++-
 .../linalg/distributed/RowMatrixSuite.scala   | 17 +++++++
 3 files changed, 70 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index 9669c364bad8f..b416d50a5631e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -25,3 +25,11 @@ import org.apache.spark.annotation.Experimental
  */
 @Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
+
+/**
+ * :: Experimental ::
+ * Represents QR factors.
+ */
+@Experimental
+case class QRDecomposition[UType, VType](Q: UType, R: VType)
+
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 1626da9c3d2ee..bfc90c9ef8527 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -22,7 +22,7 @@ import java.util.Arrays
 import scala.collection.mutable.ListBuffer
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, SparseVector => BSV, axpy => brzAxpy,
-  svd => brzSvd}
+  svd => brzSvd, MatrixSingularException, inv}
 import breeze.numerics.{sqrt => brzSqrt}
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
@@ -497,6 +497,50 @@ class RowMatrix(
     columnSimilaritiesDIMSUM(computeColumnSummaryStatistics().normL2.toArray, gamma)
   }
 
+  /**
+   * Compute QR decomposition for [[RowMatrix]]. The implementation is designed to optimize the QR
+   * decomposition (factorization) for the [[RowMatrix]] of a tall and skinny shape.
+   * Reference:
+   *  Paul G. Constantine, David F. Gleich. "Tall and skinny QR factorizations in MapReduce
+   *  architectures"  ([[http://dx.doi.org/10.1145/1996092.1996103]])
+   *
+   * @param computeQ whether to computeQ
+   * @return QRDecomposition(Q, R), Q = null if computeQ = false.
+   */
+  def tallSkinnyQR(computeQ: Boolean = false): QRDecomposition[RowMatrix, Matrix] = {
+    val col = numCols().toInt
+    // split rows horizontally into smaller matrices, and compute QR for each of them
+    val blockQRs = rows.glom().map { partRows =>
+      val bdm = BDM.zeros[Double](partRows.length, col)
+      var i = 0
+      partRows.foreach { row =>
+        bdm(i, ::) := row.toBreeze.t
+        i += 1
+      }
+      breeze.linalg.qr.reduced(bdm).r
+    }
+
+    // combine the R part from previous results vertically into a tall matrix
+    val combinedR = blockQRs.treeReduce{ (r1, r2) =>
+      val stackedR = BDM.vertcat(r1, r2)
+      breeze.linalg.qr.reduced(stackedR).r
+    }
+    val finalR = Matrices.fromBreeze(combinedR.toDenseMatrix)
+    val finalQ = if (computeQ) {
+      try {
+        val invR = inv(combinedR)
+        this.multiply(Matrices.fromBreeze(invR))
+      } catch {
+        case err: MatrixSingularException =>
+          logWarning("R is not invertible and return Q as null")
+          null
+      }
+    } else {
+      null
+    }
+    QRDecomposition(finalQ, finalR)
+  }
+
   /**
    * Find all similar columns using the DIMSUM sampling algorithm, described in two papers
    *
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
index b6cb53d0c743e..283ffec1d49d7 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
 
 import scala.util.Random
 
+import breeze.numerics.abs
 import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, norm => brzNorm, svd => brzSvd}
 
 import org.apache.spark.SparkFunSuite
@@ -238,6 +239,22 @@ class RowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
       }
     }
   }
+
+  test("QR Decomposition") {
+    for (mat <- Seq(denseMat, sparseMat)) {
+      val result = mat.tallSkinnyQR(true)
+      val expected = breeze.linalg.qr.reduced(mat.toBreeze())
+      val calcQ = result.Q
+      val calcR = result.R
+      assert(closeToZero(abs(expected.q) - abs(calcQ.toBreeze())))
+      assert(closeToZero(abs(expected.r) - abs(calcR.toBreeze.asInstanceOf[BDM[Double]])))
+      assert(closeToZero(calcQ.multiply(calcR).toBreeze - mat.toBreeze()))
+      // Decomposition without computing Q
+      val rOnly = mat.tallSkinnyQR(computeQ = false)
+      assert(rOnly.Q == null)
+      assert(closeToZero(abs(expected.r) - abs(rOnly.R.toBreeze.asInstanceOf[BDM[Double]])))
+    }
+  }
 }
 
 class RowMatrixClusterSuite extends SparkFunSuite with LocalClusterSparkContext {

From c5815930be46a89469440b7c61b59764fb67a54c Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Thu, 30 Jul 2015 07:56:15 -0700
Subject: [PATCH 0693/1454] [SPARK-5561] [MLLIB] Generalized
 PeriodicCheckpointer for RDDs and Graphs

PeriodicGraphCheckpointer was introduced for Latent Dirichlet Allocation (LDA), but it was meant to be generalized to work with Graphs, RDDs, and other data structures based on RDDs.  This PR generalizes it.

For those who are not familiar with the periodic checkpointer, it tries to automatically handle persisting/unpersisting and checkpointing/removing checkpoint files in a lineage of RDD-based objects.

I need it generalized to use with GradientBoostedTrees [https://issues.apache.org/jira/browse/SPARK-6684].  It should be useful for other iterative algorithms as well.

Changes I made:
* Copied PeriodicGraphCheckpointer to PeriodicCheckpointer.
* Within PeriodicCheckpointer, I created abstract methods for the basic operations (checkpoint, persist, etc.).
* The subclasses for Graphs and RDDs implement those abstract methods.
* I copied the test suite for the graph checkpointer and made tiny modifications to make it work for RDDs.

To review this PR, I recommend doing 2 diffs:
(1) diff between the old PeriodicGraphCheckpointer.scala and the new PeriodicCheckpointer.scala
(2) diff between the 2 test suites

CCing andrewor14 in case there are relevant changes to checkpointing.
CCing feynmanliang in case you're interested in learning about checkpointing.
CCing mengxr for final OK.
Thanks all!

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7728 from jkbradley/gbt-checkpoint and squashes the following commits:

d41902c [Joseph K. Bradley] Oops, forgot to update an extra time in the checkpointer tests, after the last commit. I'll fix that. I'll also make some of the checkpointer methods protected, which I should have done before.
32b23b8 [Joseph K. Bradley] fixed usage of checkpointer in lda
0b3dbc0 [Joseph K. Bradley] Changed checkpointer constructor not to take initial data.
568918c [Joseph K. Bradley] Generalized PeriodicGraphCheckpointer to PeriodicCheckpointer, with subclasses for RDDs and Graphs.
---
 .../spark/mllib/clustering/LDAOptimizer.scala |   6 +-
 .../mllib/impl/PeriodicCheckpointer.scala     | 154 ++++++++++++++++
 .../impl/PeriodicGraphCheckpointer.scala      | 105 ++---------
 .../mllib/impl/PeriodicRDDCheckpointer.scala  |  97 ++++++++++
 .../impl/PeriodicGraphCheckpointerSuite.scala |  16 +-
 .../impl/PeriodicRDDCheckpointerSuite.scala   | 173 ++++++++++++++++++
 6 files changed, 452 insertions(+), 99 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicCheckpointer.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointer.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointerSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 7e75e7083acb5..4b90fbdf0ce7e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -142,8 +142,8 @@ final class EMLDAOptimizer extends LDAOptimizer {
     this.k = k
     this.vocabSize = docs.take(1).head._2.size
     this.checkpointInterval = lda.getCheckpointInterval
-    this.graphCheckpointer = new
-      PeriodicGraphCheckpointer[TopicCounts, TokenCount](graph, checkpointInterval)
+    this.graphCheckpointer = new PeriodicGraphCheckpointer[TopicCounts, TokenCount](
+      checkpointInterval, graph.vertices.sparkContext)
     this.globalTopicTotals = computeGlobalTopicTotals()
     this
   }
@@ -188,7 +188,7 @@ final class EMLDAOptimizer extends LDAOptimizer {
     // Update the vertex descriptors with the new counts.
     val newGraph = GraphImpl.fromExistingRDDs(docTopicDistributions, graph.edges)
     graph = newGraph
-    graphCheckpointer.updateGraph(newGraph)
+    graphCheckpointer.update(newGraph)
     globalTopicTotals = computeGlobalTopicTotals()
     this
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicCheckpointer.scala b/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicCheckpointer.scala
new file mode 100644
index 0000000000000..72d3aabc9b1f4
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicCheckpointer.scala
@@ -0,0 +1,154 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.impl
+
+import scala.collection.mutable
+
+import org.apache.hadoop.fs.{Path, FileSystem}
+
+import org.apache.spark.{SparkContext, Logging}
+import org.apache.spark.storage.StorageLevel
+
+
+/**
+ * This abstraction helps with persisting and checkpointing RDDs and types derived from RDDs
+ * (such as Graphs and DataFrames).  In documentation, we use the phrase "Dataset" to refer to
+ * the distributed data type (RDD, Graph, etc.).
+ *
+ * Specifically, this abstraction automatically handles persisting and (optionally) checkpointing,
+ * as well as unpersisting and removing checkpoint files.
+ *
+ * Users should call update() when a new Dataset has been created,
+ * before the Dataset has been materialized.  After updating [[PeriodicCheckpointer]], users are
+ * responsible for materializing the Dataset to ensure that persisting and checkpointing actually
+ * occur.
+ *
+ * When update() is called, this does the following:
+ *  - Persist new Dataset (if not yet persisted), and put in queue of persisted Datasets.
+ *  - Unpersist Datasets from queue until there are at most 3 persisted Datasets.
+ *  - If using checkpointing and the checkpoint interval has been reached,
+ *     - Checkpoint the new Dataset, and put in a queue of checkpointed Datasets.
+ *     - Remove older checkpoints.
+ *
+ * WARNINGS:
+ *  - This class should NOT be copied (since copies may conflict on which Datasets should be
+ *    checkpointed).
+ *  - This class removes checkpoint files once later Datasets have been checkpointed.
+ *    However, references to the older Datasets will still return isCheckpointed = true.
+ *
+ * @param checkpointInterval  Datasets will be checkpointed at this interval
+ * @param sc  SparkContext for the Datasets given to this checkpointer
+ * @tparam T  Dataset type, such as RDD[Double]
+ */
+private[mllib] abstract class PeriodicCheckpointer[T](
+    val checkpointInterval: Int,
+    val sc: SparkContext) extends Logging {
+
+  /** FIFO queue of past checkpointed Datasets */
+  private val checkpointQueue = mutable.Queue[T]()
+
+  /** FIFO queue of past persisted Datasets */
+  private val persistedQueue = mutable.Queue[T]()
+
+  /** Number of times [[update()]] has been called */
+  private var updateCount = 0
+
+  /**
+   * Update with a new Dataset. Handle persistence and checkpointing as needed.
+   * Since this handles persistence and checkpointing, this should be called before the Dataset
+   * has been materialized.
+   *
+   * @param newData  New Dataset created from previous Datasets in the lineage.
+   */
+  def update(newData: T): Unit = {
+    persist(newData)
+    persistedQueue.enqueue(newData)
+    // We try to maintain 2 Datasets in persistedQueue to support the semantics of this class:
+    // Users should call [[update()]] when a new Dataset has been created,
+    // before the Dataset has been materialized.
+    while (persistedQueue.size > 3) {
+      val dataToUnpersist = persistedQueue.dequeue()
+      unpersist(dataToUnpersist)
+    }
+    updateCount += 1
+
+    // Handle checkpointing (after persisting)
+    if ((updateCount % checkpointInterval) == 0 && sc.getCheckpointDir.nonEmpty) {
+      // Add new checkpoint before removing old checkpoints.
+      checkpoint(newData)
+      checkpointQueue.enqueue(newData)
+      // Remove checkpoints before the latest one.
+      var canDelete = true
+      while (checkpointQueue.size > 1 && canDelete) {
+        // Delete the oldest checkpoint only if the next checkpoint exists.
+        if (isCheckpointed(checkpointQueue.head)) {
+          removeCheckpointFile()
+        } else {
+          canDelete = false
+        }
+      }
+    }
+  }
+
+  /** Checkpoint the Dataset */
+  protected def checkpoint(data: T): Unit
+
+  /** Return true iff the Dataset is checkpointed */
+  protected def isCheckpointed(data: T): Boolean
+
+  /**
+   * Persist the Dataset.
+   * Note: This should handle checking the current [[StorageLevel]] of the Dataset.
+   */
+  protected def persist(data: T): Unit
+
+  /** Unpersist the Dataset */
+  protected def unpersist(data: T): Unit
+
+  /** Get list of checkpoint files for this given Dataset */
+  protected def getCheckpointFiles(data: T): Iterable[String]
+
+  /**
+   * Call this at the end to delete any remaining checkpoint files.
+   */
+  def deleteAllCheckpoints(): Unit = {
+    while (checkpointQueue.nonEmpty) {
+      removeCheckpointFile()
+    }
+  }
+
+  /**
+   * Dequeue the oldest checkpointed Dataset, and remove its checkpoint files.
+   * This prints a warning but does not fail if the files cannot be removed.
+   */
+  private def removeCheckpointFile(): Unit = {
+    val old = checkpointQueue.dequeue()
+    // Since the old checkpoint is not deleted by Spark, we manually delete it.
+    val fs = FileSystem.get(sc.hadoopConfiguration)
+    getCheckpointFiles(old).foreach { checkpointFile =>
+      try {
+        fs.delete(new Path(checkpointFile), true)
+      } catch {
+        case e: Exception =>
+          logWarning("PeriodicCheckpointer could not remove old checkpoint file: " +
+            checkpointFile)
+      }
+    }
+  }
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointer.scala b/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointer.scala
index 6e5dd119dd653..11a059536c50c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointer.scala
@@ -17,11 +17,7 @@
 
 package org.apache.spark.mllib.impl
 
-import scala.collection.mutable
-
-import org.apache.hadoop.fs.{Path, FileSystem}
-
-import org.apache.spark.Logging
+import org.apache.spark.SparkContext
 import org.apache.spark.graphx.Graph
 import org.apache.spark.storage.StorageLevel
 
@@ -31,12 +27,12 @@ import org.apache.spark.storage.StorageLevel
  * Specifically, it automatically handles persisting and (optionally) checkpointing, as well as
  * unpersisting and removing checkpoint files.
  *
- * Users should call [[PeriodicGraphCheckpointer.updateGraph()]] when a new graph has been created,
+ * Users should call update() when a new graph has been created,
  * before the graph has been materialized.  After updating [[PeriodicGraphCheckpointer]], users are
  * responsible for materializing the graph to ensure that persisting and checkpointing actually
  * occur.
  *
- * When [[PeriodicGraphCheckpointer.updateGraph()]] is called, this does the following:
+ * When update() is called, this does the following:
  *  - Persist new graph (if not yet persisted), and put in queue of persisted graphs.
  *  - Unpersist graphs from queue until there are at most 3 persisted graphs.
  *  - If using checkpointing and the checkpoint interval has been reached,
@@ -52,7 +48,7 @@ import org.apache.spark.storage.StorageLevel
  * Example usage:
  * {{{
  *  val (graph1, graph2, graph3, ...) = ...
- *  val cp = new PeriodicGraphCheckpointer(graph1, dir, 2)
+ *  val cp = new PeriodicGraphCheckpointer(2, sc)
  *  graph1.vertices.count(); graph1.edges.count()
  *  // persisted: graph1
  *  cp.updateGraph(graph2)
@@ -73,99 +69,30 @@ import org.apache.spark.storage.StorageLevel
  *  // checkpointed: graph4
  * }}}
  *
- * @param currentGraph  Initial graph
  * @param checkpointInterval Graphs will be checkpointed at this interval
  * @tparam VD  Vertex descriptor type
  * @tparam ED  Edge descriptor type
  *
- * TODO: Generalize this for Graphs and RDDs, and move it out of MLlib.
+ * TODO: Move this out of MLlib?
  */
 private[mllib] class PeriodicGraphCheckpointer[VD, ED](
-    var currentGraph: Graph[VD, ED],
-    val checkpointInterval: Int) extends Logging {
-
-  /** FIFO queue of past checkpointed RDDs */
-  private val checkpointQueue = mutable.Queue[Graph[VD, ED]]()
-
-  /** FIFO queue of past persisted RDDs */
-  private val persistedQueue = mutable.Queue[Graph[VD, ED]]()
-
-  /** Number of times [[updateGraph()]] has been called */
-  private var updateCount = 0
-
-  /**
-   * Spark Context for the Graphs given to this checkpointer.
-   * NOTE: This code assumes that only one SparkContext is used for the given graphs.
-   */
-  private val sc = currentGraph.vertices.sparkContext
+    checkpointInterval: Int,
+    sc: SparkContext)
+  extends PeriodicCheckpointer[Graph[VD, ED]](checkpointInterval, sc) {
 
-  updateGraph(currentGraph)
+  override protected def checkpoint(data: Graph[VD, ED]): Unit = data.checkpoint()
 
-  /**
-   * Update [[currentGraph]] with a new graph. Handle persistence and checkpointing as needed.
-   * Since this handles persistence and checkpointing, this should be called before the graph
-   * has been materialized.
-   *
-   * @param newGraph  New graph created from previous graphs in the lineage.
-   */
-  def updateGraph(newGraph: Graph[VD, ED]): Unit = {
-    if (newGraph.vertices.getStorageLevel == StorageLevel.NONE) {
-      newGraph.persist()
-    }
-    persistedQueue.enqueue(newGraph)
-    // We try to maintain 2 Graphs in persistedQueue to support the semantics of this class:
-    // Users should call [[updateGraph()]] when a new graph has been created,
-    // before the graph has been materialized.
-    while (persistedQueue.size > 3) {
-      val graphToUnpersist = persistedQueue.dequeue()
-      graphToUnpersist.unpersist(blocking = false)
-    }
-    updateCount += 1
+  override protected def isCheckpointed(data: Graph[VD, ED]): Boolean = data.isCheckpointed
 
-    // Handle checkpointing (after persisting)
-    if ((updateCount % checkpointInterval) == 0 && sc.getCheckpointDir.nonEmpty) {
-      // Add new checkpoint before removing old checkpoints.
-      newGraph.checkpoint()
-      checkpointQueue.enqueue(newGraph)
-      // Remove checkpoints before the latest one.
-      var canDelete = true
-      while (checkpointQueue.size > 1 && canDelete) {
-        // Delete the oldest checkpoint only if the next checkpoint exists.
-        if (checkpointQueue.get(1).get.isCheckpointed) {
-          removeCheckpointFile()
-        } else {
-          canDelete = false
-        }
-      }
+  override protected def persist(data: Graph[VD, ED]): Unit = {
+    if (data.vertices.getStorageLevel == StorageLevel.NONE) {
+      data.persist()
     }
   }
 
-  /**
-   * Call this at the end to delete any remaining checkpoint files.
-   */
-  def deleteAllCheckpoints(): Unit = {
-    while (checkpointQueue.size > 0) {
-      removeCheckpointFile()
-    }
-  }
+  override protected def unpersist(data: Graph[VD, ED]): Unit = data.unpersist(blocking = false)
 
-  /**
-   * Dequeue the oldest checkpointed Graph, and remove its checkpoint files.
-   * This prints a warning but does not fail if the files cannot be removed.
-   */
-  private def removeCheckpointFile(): Unit = {
-    val old = checkpointQueue.dequeue()
-    // Since the old checkpoint is not deleted by Spark, we manually delete it.
-    val fs = FileSystem.get(sc.hadoopConfiguration)
-    old.getCheckpointFiles.foreach { checkpointFile =>
-      try {
-        fs.delete(new Path(checkpointFile), true)
-      } catch {
-        case e: Exception =>
-          logWarning("PeriodicGraphCheckpointer could not remove old checkpoint file: " +
-            checkpointFile)
-      }
-    }
+  override protected def getCheckpointFiles(data: Graph[VD, ED]): Iterable[String] = {
+    data.getCheckpointFiles
   }
-
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointer.scala b/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointer.scala
new file mode 100644
index 0000000000000..f31ed2aa90a64
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointer.scala
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.impl
+
+import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+
+/**
+ * This class helps with persisting and checkpointing RDDs.
+ * Specifically, it automatically handles persisting and (optionally) checkpointing, as well as
+ * unpersisting and removing checkpoint files.
+ *
+ * Users should call update() when a new RDD has been created,
+ * before the RDD has been materialized.  After updating [[PeriodicRDDCheckpointer]], users are
+ * responsible for materializing the RDD to ensure that persisting and checkpointing actually
+ * occur.
+ *
+ * When update() is called, this does the following:
+ *  - Persist new RDD (if not yet persisted), and put in queue of persisted RDDs.
+ *  - Unpersist RDDs from queue until there are at most 3 persisted RDDs.
+ *  - If using checkpointing and the checkpoint interval has been reached,
+ *     - Checkpoint the new RDD, and put in a queue of checkpointed RDDs.
+ *     - Remove older checkpoints.
+ *
+ * WARNINGS:
+ *  - This class should NOT be copied (since copies may conflict on which RDDs should be
+ *    checkpointed).
+ *  - This class removes checkpoint files once later RDDs have been checkpointed.
+ *    However, references to the older RDDs will still return isCheckpointed = true.
+ *
+ * Example usage:
+ * {{{
+ *  val (rdd1, rdd2, rdd3, ...) = ...
+ *  val cp = new PeriodicRDDCheckpointer(2, sc)
+ *  rdd1.count();
+ *  // persisted: rdd1
+ *  cp.update(rdd2)
+ *  rdd2.count();
+ *  // persisted: rdd1, rdd2
+ *  // checkpointed: rdd2
+ *  cp.update(rdd3)
+ *  rdd3.count();
+ *  // persisted: rdd1, rdd2, rdd3
+ *  // checkpointed: rdd2
+ *  cp.update(rdd4)
+ *  rdd4.count();
+ *  // persisted: rdd2, rdd3, rdd4
+ *  // checkpointed: rdd4
+ *  cp.update(rdd5)
+ *  rdd5.count();
+ *  // persisted: rdd3, rdd4, rdd5
+ *  // checkpointed: rdd4
+ * }}}
+ *
+ * @param checkpointInterval  RDDs will be checkpointed at this interval
+ * @tparam T  RDD element type
+ *
+ * TODO: Move this out of MLlib?
+ */
+private[mllib] class PeriodicRDDCheckpointer[T](
+    checkpointInterval: Int,
+    sc: SparkContext)
+  extends PeriodicCheckpointer[RDD[T]](checkpointInterval, sc) {
+
+  override protected def checkpoint(data: RDD[T]): Unit = data.checkpoint()
+
+  override protected def isCheckpointed(data: RDD[T]): Boolean = data.isCheckpointed
+
+  override protected def persist(data: RDD[T]): Unit = {
+    if (data.getStorageLevel == StorageLevel.NONE) {
+      data.persist()
+    }
+  }
+
+  override protected def unpersist(data: RDD[T]): Unit = data.unpersist(blocking = false)
+
+  override protected def getCheckpointFiles(data: RDD[T]): Iterable[String] = {
+    data.getCheckpointFile.map(x => x)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala
index d34888af2d73b..e331c75989187 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicGraphCheckpointerSuite.scala
@@ -30,20 +30,20 @@ class PeriodicGraphCheckpointerSuite extends SparkFunSuite with MLlibTestSparkCo
 
   import PeriodicGraphCheckpointerSuite._
 
-  // TODO: Do I need to call count() on the graphs' RDDs?
-
   test("Persisting") {
     var graphsToCheck = Seq.empty[GraphToCheck]
 
     val graph1 = createGraph(sc)
-    val checkpointer = new PeriodicGraphCheckpointer(graph1, 10)
+    val checkpointer =
+      new PeriodicGraphCheckpointer[Double, Double](10, graph1.vertices.sparkContext)
+    checkpointer.update(graph1)
     graphsToCheck = graphsToCheck :+ GraphToCheck(graph1, 1)
     checkPersistence(graphsToCheck, 1)
 
     var iteration = 2
     while (iteration < 9) {
       val graph = createGraph(sc)
-      checkpointer.updateGraph(graph)
+      checkpointer.update(graph)
       graphsToCheck = graphsToCheck :+ GraphToCheck(graph, iteration)
       checkPersistence(graphsToCheck, iteration)
       iteration += 1
@@ -57,7 +57,9 @@ class PeriodicGraphCheckpointerSuite extends SparkFunSuite with MLlibTestSparkCo
     var graphsToCheck = Seq.empty[GraphToCheck]
     sc.setCheckpointDir(path)
     val graph1 = createGraph(sc)
-    val checkpointer = new PeriodicGraphCheckpointer(graph1, checkpointInterval)
+    val checkpointer = new PeriodicGraphCheckpointer[Double, Double](
+      checkpointInterval, graph1.vertices.sparkContext)
+    checkpointer.update(graph1)
     graph1.edges.count()
     graph1.vertices.count()
     graphsToCheck = graphsToCheck :+ GraphToCheck(graph1, 1)
@@ -66,7 +68,7 @@ class PeriodicGraphCheckpointerSuite extends SparkFunSuite with MLlibTestSparkCo
     var iteration = 2
     while (iteration < 9) {
       val graph = createGraph(sc)
-      checkpointer.updateGraph(graph)
+      checkpointer.update(graph)
       graph.vertices.count()
       graph.edges.count()
       graphsToCheck = graphsToCheck :+ GraphToCheck(graph, iteration)
@@ -168,7 +170,7 @@ private object PeriodicGraphCheckpointerSuite {
       } else {
         // Graph should never be checkpointed
         assert(!graph.isCheckpointed, "Graph should never have been checkpointed")
-        assert(graph.getCheckpointFiles.length == 0, "Graph should not have any checkpoint files")
+        assert(graph.getCheckpointFiles.isEmpty, "Graph should not have any checkpoint files")
       }
     } catch {
       case e: AssertionError =>
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointerSuite.scala
new file mode 100644
index 0000000000000..b2a459a68b5fa
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointerSuite.scala
@@ -0,0 +1,173 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.impl
+
+import org.apache.hadoop.fs.{FileSystem, Path}
+
+import org.apache.spark.{SparkContext, SparkFunSuite}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.Utils
+
+
+class PeriodicRDDCheckpointerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  import PeriodicRDDCheckpointerSuite._
+
+  test("Persisting") {
+    var rddsToCheck = Seq.empty[RDDToCheck]
+
+    val rdd1 = createRDD(sc)
+    val checkpointer = new PeriodicRDDCheckpointer[Double](10, rdd1.sparkContext)
+    checkpointer.update(rdd1)
+    rddsToCheck = rddsToCheck :+ RDDToCheck(rdd1, 1)
+    checkPersistence(rddsToCheck, 1)
+
+    var iteration = 2
+    while (iteration < 9) {
+      val rdd = createRDD(sc)
+      checkpointer.update(rdd)
+      rddsToCheck = rddsToCheck :+ RDDToCheck(rdd, iteration)
+      checkPersistence(rddsToCheck, iteration)
+      iteration += 1
+    }
+  }
+
+  test("Checkpointing") {
+    val tempDir = Utils.createTempDir()
+    val path = tempDir.toURI.toString
+    val checkpointInterval = 2
+    var rddsToCheck = Seq.empty[RDDToCheck]
+    sc.setCheckpointDir(path)
+    val rdd1 = createRDD(sc)
+    val checkpointer = new PeriodicRDDCheckpointer[Double](checkpointInterval, rdd1.sparkContext)
+    checkpointer.update(rdd1)
+    rdd1.count()
+    rddsToCheck = rddsToCheck :+ RDDToCheck(rdd1, 1)
+    checkCheckpoint(rddsToCheck, 1, checkpointInterval)
+
+    var iteration = 2
+    while (iteration < 9) {
+      val rdd = createRDD(sc)
+      checkpointer.update(rdd)
+      rdd.count()
+      rddsToCheck = rddsToCheck :+ RDDToCheck(rdd, iteration)
+      checkCheckpoint(rddsToCheck, iteration, checkpointInterval)
+      iteration += 1
+    }
+
+    checkpointer.deleteAllCheckpoints()
+    rddsToCheck.foreach { rdd =>
+      confirmCheckpointRemoved(rdd.rdd)
+    }
+
+    Utils.deleteRecursively(tempDir)
+  }
+}
+
+private object PeriodicRDDCheckpointerSuite {
+
+  case class RDDToCheck(rdd: RDD[Double], gIndex: Int)
+
+  def createRDD(sc: SparkContext): RDD[Double] = {
+    sc.parallelize(Seq(0.0, 1.0, 2.0, 3.0))
+  }
+
+  def checkPersistence(rdds: Seq[RDDToCheck], iteration: Int): Unit = {
+    rdds.foreach { g =>
+      checkPersistence(g.rdd, g.gIndex, iteration)
+    }
+  }
+
+  /**
+   * Check storage level of rdd.
+   * @param gIndex  Index of rdd in order inserted into checkpointer (from 1).
+   * @param iteration  Total number of rdds inserted into checkpointer.
+   */
+  def checkPersistence(rdd: RDD[_], gIndex: Int, iteration: Int): Unit = {
+    try {
+      if (gIndex + 2 < iteration) {
+        assert(rdd.getStorageLevel == StorageLevel.NONE)
+      } else {
+        assert(rdd.getStorageLevel != StorageLevel.NONE)
+      }
+    } catch {
+      case _: AssertionError =>
+        throw new Exception(s"PeriodicRDDCheckpointerSuite.checkPersistence failed with:\n" +
+          s"\t gIndex = $gIndex\n" +
+          s"\t iteration = $iteration\n" +
+          s"\t rdd.getStorageLevel = ${rdd.getStorageLevel}\n")
+    }
+  }
+
+  def checkCheckpoint(rdds: Seq[RDDToCheck], iteration: Int, checkpointInterval: Int): Unit = {
+    rdds.reverse.foreach { g =>
+      checkCheckpoint(g.rdd, g.gIndex, iteration, checkpointInterval)
+    }
+  }
+
+  def confirmCheckpointRemoved(rdd: RDD[_]): Unit = {
+    // Note: We cannot check rdd.isCheckpointed since that value is never updated.
+    //       Instead, we check for the presence of the checkpoint files.
+    //       This test should continue to work even after this rdd.isCheckpointed issue
+    //       is fixed (though it can then be simplified and not look for the files).
+    val fs = FileSystem.get(rdd.sparkContext.hadoopConfiguration)
+    rdd.getCheckpointFile.foreach { checkpointFile =>
+      assert(!fs.exists(new Path(checkpointFile)), "RDD checkpoint file should have been removed")
+    }
+  }
+
+  /**
+   * Check checkpointed status of rdd.
+   * @param gIndex  Index of rdd in order inserted into checkpointer (from 1).
+   * @param iteration  Total number of rdds inserted into checkpointer.
+   */
+  def checkCheckpoint(
+      rdd: RDD[_],
+      gIndex: Int,
+      iteration: Int,
+      checkpointInterval: Int): Unit = {
+    try {
+      if (gIndex % checkpointInterval == 0) {
+        // We allow 2 checkpoint intervals since we perform an action (checkpointing a second rdd)
+        // only AFTER PeriodicRDDCheckpointer decides whether to remove the previous checkpoint.
+        if (iteration - 2 * checkpointInterval < gIndex && gIndex <= iteration) {
+          assert(rdd.isCheckpointed, "RDD should be checkpointed")
+          assert(rdd.getCheckpointFile.nonEmpty, "RDD should have 2 checkpoint files")
+        } else {
+          confirmCheckpointRemoved(rdd)
+        }
+      } else {
+        // RDD should never be checkpointed
+        assert(!rdd.isCheckpointed, "RDD should never have been checkpointed")
+        assert(rdd.getCheckpointFile.isEmpty, "RDD should not have any checkpoint files")
+      }
+    } catch {
+      case e: AssertionError =>
+        throw new Exception(s"PeriodicRDDCheckpointerSuite.checkCheckpoint failed with:\n" +
+          s"\t gIndex = $gIndex\n" +
+          s"\t iteration = $iteration\n" +
+          s"\t checkpointInterval = $checkpointInterval\n" +
+          s"\t rdd.isCheckpointed = ${rdd.isCheckpointed}\n" +
+          s"\t rdd.getCheckpointFile = ${rdd.getCheckpointFile.mkString(", ")}\n" +
+          s"  AssertionError message: ${e.getMessage}")
+    }
+  }
+
+}

From d212a314227dec26c0dbec8ed3422d0ec8f818f9 Mon Sep 17 00:00:00 2001
From: zhangjiajin <zhangjiajin@huawei.com>
Date: Thu, 30 Jul 2015 08:14:09 -0700
Subject: [PATCH 0694/1454] [SPARK-8998] [MLLIB] Distribute PrefixSpan
 computation for large projected databases

Continuation of work by zhangjiajin

Closes #7412

Author: zhangjiajin <zhangjiajin@huawei.com>
Author: Feynman Liang <fliang@databricks.com>
Author: zhang jiajin <zhangjiajin@huawei.com>

Closes #7783 from feynmanliang/SPARK-8998-improve-distributed and squashes the following commits:

a61943d [Feynman Liang] Collect small patterns to local
4ddf479 [Feynman Liang] Parallelize freqItemCounts
ad23aa9 [zhang jiajin] Merge pull request #1 from feynmanliang/SPARK-8998-collectBeforeLocal
87fa021 [Feynman Liang] Improve extend prefix readability
c2caa5c [Feynman Liang] Readability improvements and comments
1235cfc [Feynman Liang] Use Iterable[Array[_]] over Array[Array[_]] for database
da0091b [Feynman Liang] Use lists for prefixes to reuse data
cb2a4fc [Feynman Liang] Inline code for readability
01c9ae9 [Feynman Liang] Add getters
6e149fa [Feynman Liang] Fix splitPrefixSuffixPairs
64271b3 [zhangjiajin] Modified codes according to comments.
d2250b7 [zhangjiajin] remove minPatternsBeforeLocalProcessing, add maxSuffixesBeforeLocalProcessing.
b07e20c [zhangjiajin] Merge branch 'master' of https://github.com/apache/spark into CollectEnoughPrefixes
095aa3a [zhangjiajin] Modified the code according to the review comments.
baa2885 [zhangjiajin] Modified the code according to the review comments.
6560c69 [zhangjiajin] Add feature: Collect enough frequent prefixes before projection in PrefixeSpan
a8fde87 [zhangjiajin] Merge branch 'master' of https://github.com/apache/spark
4dd1c8a [zhangjiajin] initialize file before rebase.
078d410 [zhangjiajin] fix a scala style error.
22b0ef4 [zhangjiajin] Add feature: Collect enough frequent prefixes before projection in PrefixSpan.
ca9c4c8 [zhangjiajin] Modified the code according to the review comments.
574e56c [zhangjiajin] Add new object LocalPrefixSpan, and do some optimization.
ba5df34 [zhangjiajin] Fix a Scala style error.
4c60fb3 [zhangjiajin] Fix some Scala style errors.
1dd33ad [zhangjiajin] Modified the code according to the review comments.
89bc368 [zhangjiajin] Fixed a Scala style error.
a2eb14c [zhang jiajin] Delete PrefixspanSuite.scala
951fd42 [zhang jiajin] Delete Prefixspan.scala
575995f [zhangjiajin] Modified the code according to the review comments.
91fd7e6 [zhangjiajin] Add new algorithm PrefixSpan and test file.
---
 .../spark/mllib/fpm/LocalPrefixSpan.scala     |   6 +-
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 203 +++++++++++++-----
 .../spark/mllib/fpm/PrefixSpanSuite.scala     |  21 +-
 3 files changed, 161 insertions(+), 69 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
index 7ead6327486cc..0ea792081086d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
@@ -40,7 +40,7 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
       minCount: Long,
       maxPatternLength: Int,
       prefixes: List[Int],
-      database: Array[Array[Int]]): Iterator[(List[Int], Long)] = {
+      database: Iterable[Array[Int]]): Iterator[(List[Int], Long)] = {
     if (prefixes.length == maxPatternLength || database.isEmpty) return Iterator.empty
     val frequentItemAndCounts = getFreqItemAndCounts(minCount, database)
     val filteredDatabase = database.map(x => x.filter(frequentItemAndCounts.contains))
@@ -67,7 +67,7 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
     }
   }
 
-  def project(database: Array[Array[Int]], prefix: Int): Array[Array[Int]] = {
+  def project(database: Iterable[Array[Int]], prefix: Int): Iterable[Array[Int]] = {
     database
       .map(getSuffix(prefix, _))
       .filter(_.nonEmpty)
@@ -81,7 +81,7 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
    */
   private def getFreqItemAndCounts(
       minCount: Long,
-      database: Array[Array[Int]]): mutable.Map[Int, Long] = {
+      database: Iterable[Array[Int]]): mutable.Map[Int, Long] = {
     // TODO: use PrimitiveKeyOpenHashMap
     val counts = mutable.Map[Int, Long]().withDefaultValue(0L)
     database.foreach { sequence =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index 6f52db7b073ae..e6752332cdeeb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.mllib.fpm
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.RDD
@@ -43,28 +45,45 @@ class PrefixSpan private (
     private var minSupport: Double,
     private var maxPatternLength: Int) extends Logging with Serializable {
 
+  /**
+   * The maximum number of items allowed in a projected database before local processing. If a
+   * projected database exceeds this size, another iteration of distributed PrefixSpan is run.
+   */
+  // TODO: make configurable with a better default value, 10000 may be too small
+  private val maxLocalProjDBSize: Long = 10000
+
   /**
    * Constructs a default instance with default parameters
    * {minSupport: `0.1`, maxPatternLength: `10`}.
    */
   def this() = this(0.1, 10)
 
+  /**
+   * Get the minimal support (i.e. the frequency of occurrence before a pattern is considered
+   * frequent).
+   */
+  def getMinSupport: Double = this.minSupport
+
   /**
    * Sets the minimal support level (default: `0.1`).
    */
   def setMinSupport(minSupport: Double): this.type = {
-    require(minSupport >= 0 && minSupport <= 1,
-      "The minimum support value must be between 0 and 1, including 0 and 1.")
+    require(minSupport >= 0 && minSupport <= 1, "The minimum support value must be in [0, 1].")
     this.minSupport = minSupport
     this
   }
 
+  /**
+   * Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
+   */
+  def getMaxPatternLength: Double = this.maxPatternLength
+
   /**
    * Sets maximal pattern length (default: `10`).
    */
   def setMaxPatternLength(maxPatternLength: Int): this.type = {
-    require(maxPatternLength >= 1,
-      "The maximum pattern length value must be greater than 0.")
+    // TODO: support unbounded pattern length when maxPatternLength = 0
+    require(maxPatternLength >= 1, "The maximum pattern length value must be greater than 0.")
     this.maxPatternLength = maxPatternLength
     this
   }
@@ -78,81 +97,153 @@ class PrefixSpan private (
    *         the value of pair is the pattern's count.
    */
   def run(sequences: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
+    val sc = sequences.sparkContext
+
     if (sequences.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
     }
-    val minCount = getMinCount(sequences)
-    val lengthOnePatternsAndCounts =
-      getFreqItemAndCounts(minCount, sequences).collect()
-    val prefixAndProjectedDatabase = getPrefixAndProjectedDatabase(
-      lengthOnePatternsAndCounts.map(_._1), sequences)
-    val groupedProjectedDatabase = prefixAndProjectedDatabase
-      .map(x => (x._1.toSeq, x._2))
-      .groupByKey()
-      .map(x => (x._1.toArray, x._2.toArray))
-    val nextPatterns = getPatternsInLocal(minCount, groupedProjectedDatabase)
-    val lengthOnePatternsAndCountsRdd =
-      sequences.sparkContext.parallelize(
-        lengthOnePatternsAndCounts.map(x => (Array(x._1), x._2)))
-    val allPatterns = lengthOnePatternsAndCountsRdd ++ nextPatterns
-    allPatterns
+
+    // Convert min support to a min number of transactions for this dataset
+    val minCount = if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
+
+    // (Frequent items -> number of occurrences, all items here satisfy the `minSupport` threshold
+    val freqItemCounts = sequences
+      .flatMap(seq => seq.distinct.map(item => (item, 1L)))
+      .reduceByKey(_ + _)
+      .filter(_._2 >= minCount)
+      .collect()
+
+    // Pairs of (length 1 prefix, suffix consisting of frequent items)
+    val itemSuffixPairs = {
+      val freqItems = freqItemCounts.map(_._1).toSet
+      sequences.flatMap { seq =>
+        val filteredSeq = seq.filter(freqItems.contains(_))
+        freqItems.flatMap { item =>
+          val candidateSuffix = LocalPrefixSpan.getSuffix(item, filteredSeq)
+          candidateSuffix match {
+            case suffix if !suffix.isEmpty => Some((List(item), suffix))
+            case _ => None
+          }
+        }
+      }
+    }
+
+    // Accumulator for the computed results to be returned, initialized to the frequent items (i.e.
+    // frequent length-one prefixes)
+    var resultsAccumulator = freqItemCounts.map(x => (List(x._1), x._2))
+
+    // Remaining work to be locally and distributively processed respectfully
+    var (pairsForLocal, pairsForDistributed) = partitionByProjDBSize(itemSuffixPairs)
+
+    // Continue processing until no pairs for distributed processing remain (i.e. all prefixes have
+    // projected database sizes <= `maxLocalProjDBSize`)
+    while (pairsForDistributed.count() != 0) {
+      val (nextPatternAndCounts, nextPrefixSuffixPairs) =
+        extendPrefixes(minCount, pairsForDistributed)
+      pairsForDistributed.unpersist()
+      val (smallerPairsPart, largerPairsPart) = partitionByProjDBSize(nextPrefixSuffixPairs)
+      pairsForDistributed = largerPairsPart
+      pairsForDistributed.persist(StorageLevel.MEMORY_AND_DISK)
+      pairsForLocal ++= smallerPairsPart
+      resultsAccumulator ++= nextPatternAndCounts.collect()
+    }
+
+    // Process the small projected databases locally
+    val remainingResults = getPatternsInLocal(
+      minCount, sc.parallelize(pairsForLocal, 1).groupByKey())
+
+    (sc.parallelize(resultsAccumulator, 1) ++ remainingResults)
+      .map { case (pattern, count) => (pattern.toArray, count) }
   }
 
+
   /**
-   * Get the minimum count (sequences count * minSupport).
-   * @param sequences input data set, contains a set of sequences,
-   * @return minimum count,
+   * Partitions the prefix-suffix pairs by projected database size.
+   * @param prefixSuffixPairs prefix (length n) and suffix pairs,
+   * @return prefix-suffix pairs partitioned by whether their projected database size is <= or
+   *         greater than [[maxLocalProjDBSize]]
    */
-  private def getMinCount(sequences: RDD[Array[Int]]): Long = {
-    if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
+  private def partitionByProjDBSize(prefixSuffixPairs: RDD[(List[Int], Array[Int])])
+    : (Array[(List[Int], Array[Int])], RDD[(List[Int], Array[Int])]) = {
+    val prefixToSuffixSize = prefixSuffixPairs
+      .aggregateByKey(0)(
+        seqOp = { case (count, suffix) => count + suffix.length },
+        combOp = { _ + _ })
+    val smallPrefixes = prefixToSuffixSize
+      .filter(_._2 <= maxLocalProjDBSize)
+      .keys
+      .collect()
+      .toSet
+    val small = prefixSuffixPairs.filter { case (prefix, _) => smallPrefixes.contains(prefix) }
+    val large = prefixSuffixPairs.filter { case (prefix, _) => !smallPrefixes.contains(prefix) }
+    (small.collect(), large)
   }
 
   /**
-   * Generates frequent items by filtering the input data using minimal count level.
-   * @param minCount the absolute minimum count
-   * @param sequences original sequences data
-   * @return array of item and count pair
+   * Extends all prefixes by one item from their suffix and computes the resulting frequent prefixes
+   * and remaining work.
+   * @param minCount minimum count
+   * @param prefixSuffixPairs prefix (length N) and suffix pairs,
+   * @return (frequent length N+1 extended prefix, count) pairs and (frequent length N+1 extended
+   *         prefix, corresponding suffix) pairs.
    */
-  private def getFreqItemAndCounts(
+  private def extendPrefixes(
       minCount: Long,
-      sequences: RDD[Array[Int]]): RDD[(Int, Long)] = {
-    sequences.flatMap(_.distinct.map((_, 1L)))
+      prefixSuffixPairs: RDD[(List[Int], Array[Int])])
+    : (RDD[(List[Int], Long)], RDD[(List[Int], Array[Int])]) = {
+
+    // (length N prefix, item from suffix) pairs and their corresponding number of occurrences
+    // Every (prefix :+ suffix) is guaranteed to have support exceeding `minSupport`
+    val prefixItemPairAndCounts = prefixSuffixPairs
+      .flatMap { case (prefix, suffix) => suffix.distinct.map(y => ((prefix, y), 1L)) }
       .reduceByKey(_ + _)
       .filter(_._2 >= minCount)
-  }
 
-  /**
-   * Get the frequent prefixes' projected database.
-   * @param frequentPrefixes frequent prefixes
-   * @param sequences sequences data
-   * @return prefixes and projected database
-   */
-  private def getPrefixAndProjectedDatabase(
-      frequentPrefixes: Array[Int],
-      sequences: RDD[Array[Int]]): RDD[(Array[Int], Array[Int])] = {
-    val filteredSequences = sequences.map { p =>
-      p.filter (frequentPrefixes.contains(_) )
-    }
-    filteredSequences.flatMap { x =>
-      frequentPrefixes.map { y =>
-        val sub = LocalPrefixSpan.getSuffix(y, x)
-        (Array(y), sub)
-      }.filter(_._2.nonEmpty)
-    }
+    // Map from prefix to set of possible next items from suffix
+    val prefixToNextItems = prefixItemPairAndCounts
+      .keys
+      .groupByKey()
+      .mapValues(_.toSet)
+      .collect()
+      .toMap
+
+
+    // Frequent patterns with length N+1 and their corresponding counts
+    val extendedPrefixAndCounts = prefixItemPairAndCounts
+      .map { case ((prefix, item), count) => (item :: prefix, count) }
+
+    // Remaining work, all prefixes will have length N+1
+    val extendedPrefixAndSuffix = prefixSuffixPairs
+      .filter(x => prefixToNextItems.contains(x._1))
+      .flatMap { case (prefix, suffix) =>
+        val frequentNextItems = prefixToNextItems(prefix)
+        val filteredSuffix = suffix.filter(frequentNextItems.contains(_))
+        frequentNextItems.flatMap { item =>
+          LocalPrefixSpan.getSuffix(item, filteredSuffix) match {
+            case suffix if !suffix.isEmpty => Some(item :: prefix, suffix)
+            case _ => None
+          }
+        }
+      }
+
+    (extendedPrefixAndCounts, extendedPrefixAndSuffix)
   }
 
   /**
-   * calculate the patterns in local.
+   * Calculate the patterns in local.
    * @param minCount the absolute minimum count
-   * @param data patterns and projected sequences data data
+   * @param data prefixes and projected sequences data data
    * @return patterns
    */
   private def getPatternsInLocal(
       minCount: Long,
-      data: RDD[(Array[Int], Array[Array[Int]])]): RDD[(Array[Int], Long)] = {
-    data.flatMap { case (prefix, projDB) =>
-      LocalPrefixSpan.run(minCount, maxPatternLength, prefix.toList, projDB)
-        .map { case (pattern: List[Int], count: Long) => (pattern.toArray.reverse, count) }
+      data: RDD[(List[Int], Iterable[Array[Int]])]): RDD[(List[Int], Long)] = {
+    data.flatMap {
+      case (prefix, projDB) =>
+        LocalPrefixSpan.run(minCount, maxPatternLength, prefix.toList.reverse, projDB)
+          .map { case (pattern: List[Int], count: Long) =>
+          (pattern.reverse, count)
+        }
     }
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
index 9f107c89f6d80..6dd2dc926acc5 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -44,13 +44,6 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     val rdd = sc.parallelize(sequences, 2).cache()
 
-    def compareResult(
-        expectedValue: Array[(Array[Int], Long)],
-        actualValue: Array[(Array[Int], Long)]): Boolean = {
-      expectedValue.map(x => (x._1.toSeq, x._2)).toSet ==
-        actualValue.map(x => (x._1.toSeq, x._2)).toSet
-    }
-
     val prefixspan = new PrefixSpan()
       .setMinSupport(0.33)
       .setMaxPatternLength(50)
@@ -76,7 +69,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(4, 5), 2L),
       (Array(5), 3L)
     )
-    assert(compareResult(expectedValue1, result1.collect()))
+    assert(compareResults(expectedValue1, result1.collect()))
 
     prefixspan.setMinSupport(0.5).setMaxPatternLength(50)
     val result2 = prefixspan.run(rdd)
@@ -87,7 +80,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(4), 4L),
       (Array(5), 3L)
     )
-    assert(compareResult(expectedValue2, result2.collect()))
+    assert(compareResults(expectedValue2, result2.collect()))
 
     prefixspan.setMinSupport(0.33).setMaxPatternLength(2)
     val result3 = prefixspan.run(rdd)
@@ -107,6 +100,14 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(4, 5), 2L),
       (Array(5), 3L)
     )
-    assert(compareResult(expectedValue3, result3.collect()))
+    assert(compareResults(expectedValue3, result3.collect()))
+  }
+
+  private def compareResults(
+    expectedValue: Array[(Array[Int], Long)],
+    actualValue: Array[(Array[Int], Long)]): Boolean = {
+    expectedValue.map(x => (x._1.toSeq, x._2)).toSet ==
+      actualValue.map(x => (x._1.toSeq, x._2)).toSet
   }
+
 }

From 9c0501c5d04d83ca25ce433138bf64df6a14dc58 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Thu, 30 Jul 2015 08:20:52 -0700
Subject: [PATCH 0695/1454] [SPARK-] [MLLIB] minor fix on tokenizer doc

A trivial fix for the comments of RegexTokenizer.

Maybe this is too small, yet I just noticed it and think it can be quite misleading. I can create a jira if necessary.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7791 from hhbyyh/docFix and squashes the following commits:

cdf2542 [Yuhao Yang] minor fix on tokenizer doc
---
 .../src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
index 0b3af4747e693..248288ca73e99 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
@@ -50,7 +50,7 @@ class Tokenizer(override val uid: String) extends UnaryTransformer[String, Seq[S
 /**
  * :: Experimental ::
  * A regex based tokenizer that extracts tokens either by using the provided regex pattern to split
- * the text (default) or repeatedly matching the regex (if `gaps` is true).
+ * the text (default) or repeatedly matching the regex (if `gaps` is false).
  * Optional parameters also allow filtering tokens using a minimal length.
  * It returns an array of strings that can be empty.
  */

From a6e53a9c8b24326d1b6dca7a0e36ce6c643daa77 Mon Sep 17 00:00:00 2001
From: Meihua Wu <meihuawu@umich.edu>
Date: Thu, 30 Jul 2015 08:52:01 -0700
Subject: [PATCH 0696/1454] [SPARK-9225] [MLLIB] LDASuite needs unit tests for
 empty documents

Add unit tests for running LDA with empty documents.
Both EMLDAOptimizer and OnlineLDAOptimizer are tested.

feynmanliang

Author: Meihua Wu <meihuawu@umich.edu>

Closes #7620 from rotationsymmetry/SPARK-9225 and squashes the following commits:

3ed7c88 [Meihua Wu] Incorporate reviewer's further comments
f9432e8 [Meihua Wu] Incorporate reviewer's comments
8e1b9ec [Meihua Wu] Merge remote-tracking branch 'upstream/master' into SPARK-9225
ad55665 [Meihua Wu] Add unit tests for running LDA with empty documents
---
 .../spark/mllib/clustering/LDASuite.scala     | 40 +++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index b91c7cefed22e..61d2edfd9fb5f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -390,6 +390,46 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("EMLDAOptimizer with empty docs") {
+    val vocabSize = 6
+    val emptyDocsArray = Array.fill(6)(Vectors.sparse(vocabSize, Array.empty, Array.empty))
+    val emptyDocs = emptyDocsArray
+      .zipWithIndex.map { case (wordCounts, docId) =>
+        (docId.toLong, wordCounts)
+    }
+    val distributedEmptyDocs = sc.parallelize(emptyDocs, 2)
+
+    val op = new EMLDAOptimizer()
+    val lda = new LDA()
+      .setK(3)
+      .setMaxIterations(5)
+      .setSeed(12345)
+      .setOptimizer(op)
+
+    val model = lda.run(distributedEmptyDocs)
+    assert(model.vocabSize === vocabSize)
+  }
+
+  test("OnlineLDAOptimizer with empty docs") {
+    val vocabSize = 6
+    val emptyDocsArray = Array.fill(6)(Vectors.sparse(vocabSize, Array.empty, Array.empty))
+    val emptyDocs = emptyDocsArray
+      .zipWithIndex.map { case (wordCounts, docId) =>
+        (docId.toLong, wordCounts)
+    }
+    val distributedEmptyDocs = sc.parallelize(emptyDocs, 2)
+
+    val op = new OnlineLDAOptimizer()
+    val lda = new LDA()
+      .setK(3)
+      .setMaxIterations(5)
+      .setSeed(12345)
+      .setOptimizer(op)
+
+    val model = lda.run(distributedEmptyDocs)
+    assert(model.vocabSize === vocabSize)
+  }
+
 }
 
 private[clustering] object LDASuite {

From ed3cb1d21c73645c8f6e6ee08181f876fc192e41 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 30 Jul 2015 09:19:55 -0700
Subject: [PATCH 0697/1454] [SPARK-9277] [MLLIB] SparseVector constructor must
 throw an error when declared number of elements less than array length

Check that SparseVector size is at least as big as the number of indices/values provided. And add tests for constructor checks.

CC MechCoder jkbradley -- I am not sure if a change needs to also happen in the Python API? I didn't see it had any similar checks to begin with, but I don't know it well.

Author: Sean Owen <sowen@cloudera.com>

Closes #7794 from srowen/SPARK-9277 and squashes the following commits:

e8dc31e [Sean Owen] Fix scalastyle
6ffe34a [Sean Owen] Check that SparseVector size is at least as big as the number of indices/values provided. And add tests for constructor checks.
---
 .../org/apache/spark/mllib/linalg/Vectors.scala   |  2 ++
 .../apache/spark/mllib/linalg/VectorsSuite.scala  | 15 +++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 0cb28d78bec05..23c2c16d68d9a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -637,6 +637,8 @@ class SparseVector(
   require(indices.length == values.length, "Sparse vectors require that the dimension of the" +
     s" indices match the dimension of the values. You provided ${indices.length} indices and " +
     s" ${values.length} values.")
+  require(indices.length <= size, s"You provided ${indices.length} indices and values, " +
+    s"which exceeds the specified vector size ${size}.")
 
   override def toString: String =
     s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index 03be4119bdaca..1c37ea5123e82 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -57,6 +57,21 @@ class VectorsSuite extends SparkFunSuite with Logging {
     assert(vec.values === values)
   }
 
+  test("sparse vector construction with mismatched indices/values array") {
+    intercept[IllegalArgumentException] {
+      Vectors.sparse(4, Array(1, 2, 3), Array(3.0, 5.0, 7.0, 9.0))
+    }
+    intercept[IllegalArgumentException] {
+      Vectors.sparse(4, Array(1, 2, 3), Array(3.0, 5.0))
+    }
+  }
+
+  test("sparse vector construction with too many indices vs size") {
+    intercept[IllegalArgumentException] {
+      Vectors.sparse(3, Array(1, 2, 3, 4), Array(3.0, 5.0, 7.0, 9.0))
+    }
+  }
+
   test("dense to array") {
     val vec = Vectors.dense(arr).asInstanceOf[DenseVector]
     assert(vec.toArray.eq(arr))

From 81464f2a8243c6ae2a39bac7ebdc50d4f60af451 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 30 Jul 2015 09:45:17 -0700
Subject: [PATCH 0698/1454] [MINOR] [MLLIB] fix doc for RegexTokenizer

This is #7791 for Python. hhbyyh

Author: Xiangrui Meng <meng@databricks.com>

Closes #7798 from mengxr/regex-tok-py and squashes the following commits:

baa2dcd [Xiangrui Meng] fix doc for RegexTokenizer
---
 python/pyspark/ml/feature.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 86e654dd0779f..015e7a9d4900a 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -525,7 +525,7 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
     A regex based tokenizer that extracts tokens either by using the
     provided regex pattern (in Java dialect) to split the text
-    (default) or repeatedly matching the regex (if gaps is true).
+    (default) or repeatedly matching the regex (if gaps is false).
     Optional parameters also allow filtering tokens using a minimal
     length.
     It returns an array of strings that can be empty.

From 7492a33fdd074446c30c657d771a69932a00246d Mon Sep 17 00:00:00 2001
From: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Thu, 30 Jul 2015 10:00:27 -0700
Subject: [PATCH 0699/1454] [SPARK-9248] [SPARKR] Closing curly-braces should
 always be on their own line

### JIRA
[[SPARK-9248] Closing curly-braces should always be on their own line - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9248)

## The result of `dev/lint-r`
[The result of `dev/lint-r` for SPARK-9248 at the revistion:6175d6cfe795fbd88e3ee713fac375038a3993a8](https://gist.github.com/yu-iskw/96cadcea4ce664c41f81)

Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7795 from yu-iskw/SPARK-9248 and squashes the following commits:

c8eccd3 [Yuu ISHIKAWA] [SPARK-9248][SparkR] Closing curly-braces should always be on their own line
---
 R/pkg/R/generics.R               | 14 +++++++-------
 R/pkg/R/pairRDD.R                |  4 ++--
 R/pkg/R/sparkR.R                 |  9 ++++++---
 R/pkg/inst/tests/test_sparkSQL.R |  6 ++++--
 4 files changed, 19 insertions(+), 14 deletions(-)

diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 836e0175c391f..a3a121058e165 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -254,8 +254,10 @@ setGeneric("flatMapValues", function(X, FUN) { standardGeneric("flatMapValues")
 
 # @rdname intersection
 # @export
-setGeneric("intersection", function(x, other, numPartitions = 1) {
-  standardGeneric("intersection") })
+setGeneric("intersection",
+           function(x, other, numPartitions = 1) {
+             standardGeneric("intersection")
+           })
 
 # @rdname keys
 # @export
@@ -489,9 +491,7 @@ setGeneric("sample",
 #' @rdname sample
 #' @export
 setGeneric("sample_frac",
-           function(x, withReplacement, fraction, seed) {
-             standardGeneric("sample_frac")
-           })
+           function(x, withReplacement, fraction, seed) { standardGeneric("sample_frac") })
 
 #' @rdname saveAsParquetFile
 #' @export
@@ -553,8 +553,8 @@ setGeneric("withColumn", function(x, colName, col) { standardGeneric("withColumn
 
 #' @rdname withColumnRenamed
 #' @export
-setGeneric("withColumnRenamed", function(x, existingCol, newCol) {
-  standardGeneric("withColumnRenamed") })
+setGeneric("withColumnRenamed",
+           function(x, existingCol, newCol) { standardGeneric("withColumnRenamed") })
 
 
 ###################### Column Methods ##########################
diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R
index ebc6ff65e9d0f..83801d3209700 100644
--- a/R/pkg/R/pairRDD.R
+++ b/R/pkg/R/pairRDD.R
@@ -202,8 +202,8 @@ setMethod("partitionBy",
 
             packageNamesArr <- serialize(.sparkREnv$.packages,
                                          connection = NULL)
-            broadcastArr <- lapply(ls(.broadcastNames), function(name) {
-                                   get(name, .broadcastNames) })
+            broadcastArr <- lapply(ls(.broadcastNames),
+                                   function(name) { get(name, .broadcastNames) })
             jrdd <- getJRDD(x)
 
             # We create a PairwiseRRDD that extends RDD[(Int, Array[Byte])],
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 76c15875b50d5..e83104f116422 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -22,7 +22,8 @@
 connExists <- function(env) {
   tryCatch({
     exists(".sparkRCon", envir = env) && isOpen(env[[".sparkRCon"]])
-  }, error = function(err) {
+  },
+  error = function(err) {
     return(FALSE)
   })
 }
@@ -153,7 +154,8 @@ sparkR.init <- function(
   .sparkREnv$backendPort <- backendPort
   tryCatch({
     connectBackend("localhost", backendPort)
-  }, error = function(err) {
+  },
+  error = function(err) {
     stop("Failed to connect JVM\n")
   })
 
@@ -264,7 +266,8 @@ sparkRHive.init <- function(jsc = NULL) {
   ssc <- callJMethod(sc, "sc")
   hiveCtx <- tryCatch({
     newJObject("org.apache.spark.sql.hive.HiveContext", ssc)
-  }, error = function(err) {
+  },
+  error = function(err) {
     stop("Spark SQL is not built with Hive support")
   })
 
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 62fe48a5d6c7b..d5db97248c770 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -112,7 +112,8 @@ test_that("create DataFrame from RDD", {
   df <- jsonFile(sqlContext, jsonPathNa)
   hiveCtx <- tryCatch({
     newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc)
-  }, error = function(err) {
+  },
+  error = function(err) {
     skip("Hive is not build with SparkSQL, skipped")
   })
   sql(hiveCtx, "CREATE TABLE people (name string, age double, height float)")
@@ -602,7 +603,8 @@ test_that("write.df() as parquet file", {
 test_that("test HiveContext", {
   hiveCtx <- tryCatch({
     newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc)
-  }, error = function(err) {
+  },
+  error = function(err) {
     skip("Hive is not build with SparkSQL, skipped")
   })
   df <- createExternalTable(hiveCtx, "json", jsonPath, "json")

From c0cc0eaec67208c087a30c1b1f50c00b2c1ebf08 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 30 Jul 2015 10:04:30 -0700
Subject: [PATCH 0700/1454] [SPARK-9390][SQL] create a wrapper for array type

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7724 from cloud-fan/array-data and squashes the following commits:

d0408a1 [Wenchen Fan] fix python
661e608 [Wenchen Fan] rebase
f39256c [Wenchen Fan] fix hive...
6dbfa6f [Wenchen Fan] fix hive again...
8cb8842 [Wenchen Fan] remove element type parameter from getArray
43e9816 [Wenchen Fan] fix mllib
e719afc [Wenchen Fan] fix hive
4346290 [Wenchen Fan] address comment
d4a38da [Wenchen Fan] remove sizeInBytes and add license
7e283e2 [Wenchen Fan] create a wrapper for array type
---
 .../apache/spark/mllib/linalg/Matrices.scala  |  16 +--
 .../apache/spark/mllib/linalg/Vectors.scala   |  15 +--
 .../expressions/SpecializedGetters.java       |   2 +
 .../sql/catalyst/CatalystTypeConverters.scala |  29 +++--
 .../spark/sql/catalyst/InternalRow.scala      |   2 +
 .../catalyst/expressions/BoundAttribute.scala |   2 +-
 .../spark/sql/catalyst/expressions/Cast.scala |  39 ++++--
 .../expressions/codegen/CodeGenerator.scala   |  28 ++--
 .../codegen/GenerateUnsafeProjection.scala    |   4 +-
 .../expressions/collectionOperations.scala    |  10 +-
 .../expressions/complexTypeCreator.scala      |  20 ++-
 .../expressions/complexTypeExtractors.scala   |  59 ++++++---
 .../sql/catalyst/expressions/generators.scala |   4 +-
 .../expressions/stringOperations.scala        |  12 +-
 .../sql/catalyst/optimizer/Optimizer.scala    |   3 +-
 .../apache/spark/sql/types/ArrayData.scala    | 121 ++++++++++++++++++
 .../spark/sql/types/GenericArrayData.scala    |  59 +++++++++
 .../sql/catalyst/expressions/CastSuite.scala  |  21 ++-
 .../expressions/ComplexTypeSuite.scala        |   2 +-
 .../spark/sql/execution/debug/package.scala   |   4 +-
 .../spark/sql/execution/pythonUDFs.scala      |  19 ++-
 .../sql/execution/stat/FrequentItems.scala    |   4 +-
 .../apache/spark/sql/json/InferSchema.scala   |   2 +-
 .../apache/spark/sql/json/JacksonParser.scala |  30 +++--
 .../sql/parquet/CatalystRowConverter.scala    |   2 +-
 .../spark/sql/parquet/ParquetConverter.scala  |   3 +-
 .../sql/parquet/ParquetTableSupport.scala     |  12 +-
 .../apache/spark/sql/JavaDataFrameSuite.java  |   5 +-
 .../spark/sql/UserDefinedTypeSuite.scala      |   8 +-
 .../spark/sql/sources/TableScanSuite.scala    |  30 ++---
 .../spark/sql/hive/HiveInspectors.scala       |  28 ++--
 .../hive/execution/ScriptTransformation.scala |  12 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |   2 +-
 .../spark/sql/hive/HiveInspectorSuite.scala   |   2 +-
 34 files changed, 430 insertions(+), 181 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index d82ba2456df1a..88914fa875990 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -154,9 +154,9 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
         row.setByte(0, 0)
         row.setInt(1, sm.numRows)
         row.setInt(2, sm.numCols)
-        row.update(3, sm.colPtrs.toSeq)
-        row.update(4, sm.rowIndices.toSeq)
-        row.update(5, sm.values.toSeq)
+        row.update(3, new GenericArrayData(sm.colPtrs.map(_.asInstanceOf[Any])))
+        row.update(4, new GenericArrayData(sm.rowIndices.map(_.asInstanceOf[Any])))
+        row.update(5, new GenericArrayData(sm.values.map(_.asInstanceOf[Any])))
         row.setBoolean(6, sm.isTransposed)
 
       case dm: DenseMatrix =>
@@ -165,7 +165,7 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
         row.setInt(2, dm.numCols)
         row.setNullAt(3)
         row.setNullAt(4)
-        row.update(5, dm.values.toSeq)
+        row.update(5, new GenericArrayData(dm.values.map(_.asInstanceOf[Any])))
         row.setBoolean(6, dm.isTransposed)
     }
     row
@@ -179,14 +179,12 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
         val tpe = row.getByte(0)
         val numRows = row.getInt(1)
         val numCols = row.getInt(2)
-        val values = row.getAs[Seq[Double]](5, ArrayType(DoubleType, containsNull = false)).toArray
+        val values = row.getArray(5).toArray.map(_.asInstanceOf[Double])
         val isTransposed = row.getBoolean(6)
         tpe match {
           case 0 =>
-            val colPtrs =
-              row.getAs[Seq[Int]](3, ArrayType(IntegerType, containsNull = false)).toArray
-            val rowIndices =
-              row.getAs[Seq[Int]](4, ArrayType(IntegerType, containsNull = false)).toArray
+            val colPtrs = row.getArray(3).toArray.map(_.asInstanceOf[Int])
+            val rowIndices = row.getArray(4).toArray.map(_.asInstanceOf[Int])
             new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values, isTransposed)
           case 1 =>
             new DenseMatrix(numRows, numCols, values, isTransposed)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 23c2c16d68d9a..89a1818db0d1d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -187,15 +187,15 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
         val row = new GenericMutableRow(4)
         row.setByte(0, 0)
         row.setInt(1, size)
-        row.update(2, indices.toSeq)
-        row.update(3, values.toSeq)
+        row.update(2, new GenericArrayData(indices.map(_.asInstanceOf[Any])))
+        row.update(3, new GenericArrayData(values.map(_.asInstanceOf[Any])))
         row
       case DenseVector(values) =>
         val row = new GenericMutableRow(4)
         row.setByte(0, 1)
         row.setNullAt(1)
         row.setNullAt(2)
-        row.update(3, values.toSeq)
+        row.update(3, new GenericArrayData(values.map(_.asInstanceOf[Any])))
         row
     }
   }
@@ -209,14 +209,11 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
         tpe match {
           case 0 =>
             val size = row.getInt(1)
-            val indices =
-              row.getAs[Seq[Int]](2, ArrayType(IntegerType, containsNull = false)).toArray
-            val values =
-              row.getAs[Seq[Double]](3, ArrayType(DoubleType, containsNull = false)).toArray
+            val indices = row.getArray(2).toArray().map(_.asInstanceOf[Int])
+            val values = row.getArray(3).toArray().map(_.asInstanceOf[Double])
             new SparseVector(size, indices, values)
           case 1 =>
-            val values =
-              row.getAs[Seq[Double]](3, ArrayType(DoubleType, containsNull = false)).toArray
+            val values = row.getArray(3).toArray().map(_.asInstanceOf[Double])
             new DenseVector(values)
         }
     }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
index bc345dcd00e49..f7cea13688876 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions;
 
 import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.types.ArrayData;
 import org.apache.spark.sql.types.Decimal;
 import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
@@ -50,4 +51,5 @@ public interface SpecializedGetters {
 
   InternalRow getStruct(int ordinal, int numFields);
 
+  ArrayData getArray(int ordinal);
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index d1d89a1f48329..22452c0f201ef 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -55,7 +55,6 @@ object CatalystTypeConverters {
 
   private def isWholePrimitive(dt: DataType): Boolean = dt match {
     case dt if isPrimitive(dt) => true
-    case ArrayType(elementType, _) => isWholePrimitive(elementType)
     case MapType(keyType, valueType, _) => isWholePrimitive(keyType) && isWholePrimitive(valueType)
     case _ => false
   }
@@ -154,39 +153,41 @@ object CatalystTypeConverters {
 
   /** Converter for arrays, sequences, and Java iterables. */
   private case class ArrayConverter(
-      elementType: DataType) extends CatalystTypeConverter[Any, Seq[Any], Seq[Any]] {
+      elementType: DataType) extends CatalystTypeConverter[Any, Seq[Any], ArrayData] {
 
     private[this] val elementConverter = getConverterForType(elementType)
 
     private[this] val isNoChange = isWholePrimitive(elementType)
 
-    override def toCatalystImpl(scalaValue: Any): Seq[Any] = {
+    override def toCatalystImpl(scalaValue: Any): ArrayData = {
       scalaValue match {
-        case a: Array[_] => a.toSeq.map(elementConverter.toCatalyst)
-        case s: Seq[_] => s.map(elementConverter.toCatalyst)
+        case a: Array[_] =>
+          new GenericArrayData(a.map(elementConverter.toCatalyst))
+        case s: Seq[_] =>
+          new GenericArrayData(s.map(elementConverter.toCatalyst).toArray)
         case i: JavaIterable[_] =>
           val iter = i.iterator
-          var convertedIterable: List[Any] = List()
+          val convertedIterable = scala.collection.mutable.ArrayBuffer.empty[Any]
           while (iter.hasNext) {
             val item = iter.next()
-            convertedIterable :+= elementConverter.toCatalyst(item)
+            convertedIterable += elementConverter.toCatalyst(item)
           }
-          convertedIterable
+          new GenericArrayData(convertedIterable.toArray)
       }
     }
 
-    override def toScala(catalystValue: Seq[Any]): Seq[Any] = {
+    override def toScala(catalystValue: ArrayData): Seq[Any] = {
       if (catalystValue == null) {
         null
       } else if (isNoChange) {
-        catalystValue
+        catalystValue.toArray()
       } else {
-        catalystValue.map(elementConverter.toScala)
+        catalystValue.toArray().map(elementConverter.toScala)
       }
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Seq[Any] =
-      toScala(row.get(column, ArrayType(elementType)).asInstanceOf[Seq[Any]])
+      toScala(row.getArray(column))
   }
 
   private case class MapConverter(
@@ -402,9 +403,9 @@ object CatalystTypeConverters {
     case t: Timestamp => TimestampConverter.toCatalyst(t)
     case d: BigDecimal => BigDecimalConverter.toCatalyst(d)
     case d: JavaBigDecimal => BigDecimalConverter.toCatalyst(d)
-    case seq: Seq[Any] => seq.map(convertToCatalyst)
+    case seq: Seq[Any] => new GenericArrayData(seq.map(convertToCatalyst).toArray)
     case r: Row => InternalRow(r.toSeq.map(convertToCatalyst): _*)
-    case arr: Array[Any] => arr.map(convertToCatalyst)
+    case arr: Array[Any] => new GenericArrayData(arr.map(convertToCatalyst))
     case m: Map[_, _] =>
       m.map { case (k, v) => (convertToCatalyst(k), convertToCatalyst(v)) }.toMap
     case other => other
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index a5999e64ec554..486ba036548c8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -76,6 +76,8 @@ abstract class InternalRow extends Serializable with SpecializedGetters {
   override def getStruct(ordinal: Int, numFields: Int): InternalRow =
     getAs[InternalRow](ordinal, null)
 
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal, null)
+
   override def toString: String = s"[${this.mkString(",")}]"
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 371681b5d494f..45709c1c8f554 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -65,7 +65,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val javaType = ctx.javaType(dataType)
-    val value = ctx.getColumn("i", dataType, ordinal)
+    val value = ctx.getValue("i", dataType, ordinal.toString)
     s"""
       boolean ${ev.isNull} = i.isNullAt($ordinal);
       $javaType ${ev.primitive} = ${ev.isNull} ? ${ctx.defaultValue(dataType)} : ($value);
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 8c01c13c9ccd5..43be11c48ae7c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -363,7 +363,21 @@ case class Cast(child: Expression, dataType: DataType)
 
   private[this] def castArray(from: ArrayType, to: ArrayType): Any => Any = {
     val elementCast = cast(from.elementType, to.elementType)
-    buildCast[Seq[Any]](_, _.map(v => if (v == null) null else elementCast(v)))
+    // TODO: Could be faster?
+    buildCast[ArrayData](_, array => {
+      val length = array.numElements()
+      val values = new Array[Any](length)
+      var i = 0
+      while (i < length) {
+        if (array.isNullAt(i)) {
+          values(i) = null
+        } else {
+          values(i) = elementCast(array.get(i))
+        }
+        i += 1
+      }
+      new GenericArrayData(values)
+    })
   }
 
   private[this] def castMap(from: MapType, to: MapType): Any => Any = {
@@ -789,37 +803,36 @@ case class Cast(child: Expression, dataType: DataType)
   private[this] def castArrayCode(
       from: ArrayType, to: ArrayType, ctx: CodeGenContext): CastFunction = {
     val elementCast = nullSafeCastFunction(from.elementType, to.elementType, ctx)
-
-    val arraySeqClass = classOf[mutable.ArraySeq[Any]].getName
+    val arrayClass = classOf[GenericArrayData].getName
     val fromElementNull = ctx.freshName("feNull")
     val fromElementPrim = ctx.freshName("fePrim")
     val toElementNull = ctx.freshName("teNull")
     val toElementPrim = ctx.freshName("tePrim")
     val size = ctx.freshName("n")
     val j = ctx.freshName("j")
-    val result = ctx.freshName("result")
+    val values = ctx.freshName("values")
 
     (c, evPrim, evNull) =>
       s"""
-        final int $size = $c.size();
-        final $arraySeqClass<Object> $result = new $arraySeqClass<Object>($size);
+        final int $size = $c.numElements();
+        final Object[] $values = new Object[$size];
         for (int $j = 0; $j < $size; $j ++) {
-          if ($c.apply($j) == null) {
-            $result.update($j, null);
+          if ($c.isNullAt($j)) {
+            $values[$j] = null;
           } else {
             boolean $fromElementNull = false;
             ${ctx.javaType(from.elementType)} $fromElementPrim =
-              (${ctx.boxedType(from.elementType)}) $c.apply($j);
+              ${ctx.getValue(c, from.elementType, j)};
             ${castCode(ctx, fromElementPrim,
               fromElementNull, toElementPrim, toElementNull, to.elementType, elementCast)}
             if ($toElementNull) {
-              $result.update($j, null);
+              $values[$j] = null;
             } else {
-              $result.update($j, $toElementPrim);
+              $values[$j] = $toElementPrim;
             }
           }
         }
-        $evPrim = $result;
+        $evPrim = new $arrayClass($values);
       """
   }
 
@@ -891,7 +904,7 @@ case class Cast(child: Expression, dataType: DataType)
           $result.setNullAt($i);
         } else {
           $fromType $fromFieldPrim =
-            ${ctx.getColumn(tmpRow, from.fields(i).dataType, i)};
+            ${ctx.getValue(tmpRow, from.fields(i).dataType, i.toString)};
           ${castCode(ctx, fromFieldPrim,
             fromFieldNull, toFieldPrim, toFieldNull, to.fields(i).dataType, cast)}
           if ($toFieldNull) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 092f4c9fb0bd2..c39e0df6fae2a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -100,17 +100,18 @@ class CodeGenContext {
   }
 
   /**
-   * Returns the code to access a column in Row for a given DataType.
+   * Returns the code to access a value in `SpecializedGetters` for a given DataType.
    */
-  def getColumn(row: String, dataType: DataType, ordinal: Int): String = {
+  def getValue(getter: String, dataType: DataType, ordinal: String): String = {
     val jt = javaType(dataType)
     dataType match {
-      case _ if isPrimitiveType(jt) => s"$row.get${primitiveTypeName(jt)}($ordinal)"
-      case StringType => s"$row.getUTF8String($ordinal)"
-      case BinaryType => s"$row.getBinary($ordinal)"
-      case CalendarIntervalType => s"$row.getInterval($ordinal)"
-      case t: StructType => s"$row.getStruct($ordinal, ${t.size})"
-      case _ => s"($jt)$row.get($ordinal)"
+      case _ if isPrimitiveType(jt) => s"$getter.get${primitiveTypeName(jt)}($ordinal)"
+      case StringType => s"$getter.getUTF8String($ordinal)"
+      case BinaryType => s"$getter.getBinary($ordinal)"
+      case CalendarIntervalType => s"$getter.getInterval($ordinal)"
+      case t: StructType => s"$getter.getStruct($ordinal, ${t.size})"
+      case a: ArrayType => s"$getter.getArray($ordinal)"
+      case _ => s"($jt)$getter.get($ordinal)" // todo: remove generic getter.
     }
   }
 
@@ -152,8 +153,8 @@ class CodeGenContext {
     case StringType => "UTF8String"
     case CalendarIntervalType => "CalendarInterval"
     case _: StructType => "InternalRow"
-    case _: ArrayType => s"scala.collection.Seq"
-    case _: MapType => s"scala.collection.Map"
+    case _: ArrayType => "ArrayData"
+    case _: MapType => "scala.collection.Map"
     case dt: OpenHashSetUDT if dt.elementType == IntegerType => classOf[IntegerHashSet].getName
     case dt: OpenHashSetUDT if dt.elementType == LongType => classOf[LongHashSet].getName
     case _ => "Object"
@@ -214,7 +215,9 @@ class CodeGenContext {
     case dt: DataType if isPrimitiveType(dt) => s"($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)"
     case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)"
     case NullType => "0"
-    case other => s"$c1.compare($c2)"
+    case other if other.isInstanceOf[AtomicType] => s"$c1.compare($c2)"
+    case _ => throw new IllegalArgumentException(
+      "cannot generate compare code for un-comparable type")
   }
 
   /**
@@ -293,7 +296,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       classOf[UnsafeRow].getName,
       classOf[UTF8String].getName,
       classOf[Decimal].getName,
-      classOf[CalendarInterval].getName
+      classOf[CalendarInterval].getName,
+      classOf[ArrayData].getName
     ))
     evaluator.setExtendedClass(classOf[GeneratedClass])
     try {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 7be60114ce674..a662357fb6cf9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -153,14 +153,14 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           val nestedStructEv = GeneratedExpressionCode(
             code = "",
             isNull = s"${input.primitive}.isNullAt($i)",
-            primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
+            primitive = s"${ctx.getValue(input.primitive, dt, i.toString)}"
           )
           createCodeForStruct(ctx, nestedStructEv, st)
         case _ =>
           GeneratedExpressionCode(
             code = "",
             isNull = s"${input.primitive}.isNullAt($i)",
-            primitive = s"${ctx.getColumn(input.primitive, dt, i)}"
+            primitive = s"${ctx.getValue(input.primitive, dt, i.toString)}"
           )
         }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 2d92dcf23a86e..1a00dbc254de1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -27,11 +27,15 @@ case class Size(child: Expression) extends UnaryExpression with ExpectsInputType
   override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(ArrayType, MapType))
 
   override def nullSafeEval(value: Any): Int = child.dataType match {
-    case ArrayType(_, _) => value.asInstanceOf[Seq[Any]].size
-    case MapType(_, _, _) => value.asInstanceOf[Map[Any, Any]].size
+    case _: ArrayType => value.asInstanceOf[ArrayData].numElements()
+    case _: MapType => value.asInstanceOf[Map[Any, Any]].size
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).size();")
+    val sizeCall = child.dataType match {
+      case _: ArrayType => "numElements()"
+      case _: MapType => "size()"
+    }
+    nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).$sizeCall;")
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 0517050a45109..a145dfb4bbf08 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -18,12 +18,9 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.unsafe.types.UTF8String
-
-import scala.collection.mutable
-
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GeneratedExpressionCode, CodeGenContext}
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
@@ -46,25 +43,26 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
   override def nullable: Boolean = false
 
   override def eval(input: InternalRow): Any = {
-    children.map(_.eval(input))
+    new GenericArrayData(children.map(_.eval(input)).toArray)
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val arraySeqClass = classOf[mutable.ArraySeq[Any]].getName
+    val arrayClass = classOf[GenericArrayData].getName
     s"""
-      boolean ${ev.isNull} = false;
-      $arraySeqClass<Object> ${ev.primitive} = new $arraySeqClass<Object>(${children.size});
+      final boolean ${ev.isNull} = false;
+      final Object[] values = new Object[${children.size}];
     """ +
       children.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
           if (${eval.isNull}) {
-            ${ev.primitive}.update($i, null);
+            values[$i] = null;
           } else {
-            ${ev.primitive}.update($i, ${eval.primitive});
+            values[$i] = ${eval.primitive};
           }
          """
-      }.mkString("\n")
+      }.mkString("\n") +
+      s"final ${ctx.javaType(dataType)} ${ev.primitive} = new $arrayClass(values);"
   }
 
   override def prettyName: String = "array"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
index 6331a9eb603ca..99393c9c76ab6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
@@ -57,7 +57,8 @@ object ExtractValue {
       case (ArrayType(StructType(fields), containsNull), NonNullLiteral(v, StringType)) =>
         val fieldName = v.toString
         val ordinal = findField(fields, fieldName, resolver)
-        GetArrayStructFields(child, fields(ordinal).copy(name = fieldName), ordinal, containsNull)
+        GetArrayStructFields(child, fields(ordinal).copy(name = fieldName),
+          ordinal, fields.length, containsNull)
 
       case (_: ArrayType, _) if extraction.dataType.isInstanceOf[IntegralType] =>
         GetArrayItem(child, extraction)
@@ -118,7 +119,7 @@ case class GetStructField(child: Expression, field: StructField, ordinal: Int)
         if ($eval.isNullAt($ordinal)) {
           ${ev.isNull} = true;
         } else {
-          ${ev.primitive} = ${ctx.getColumn(eval, dataType, ordinal)};
+          ${ev.primitive} = ${ctx.getValue(eval, dataType, ordinal.toString)};
         }
       """
     })
@@ -134,6 +135,7 @@ case class GetArrayStructFields(
     child: Expression,
     field: StructField,
     ordinal: Int,
+    numFields: Int,
     containsNull: Boolean) extends UnaryExpression {
 
   override def dataType: DataType = ArrayType(field.dataType, containsNull)
@@ -141,26 +143,45 @@ case class GetArrayStructFields(
   override def toString: String = s"$child.${field.name}"
 
   protected override def nullSafeEval(input: Any): Any = {
-    input.asInstanceOf[Seq[InternalRow]].map { row =>
-      if (row == null) null else row.get(ordinal, field.dataType)
+    val array = input.asInstanceOf[ArrayData]
+    val length = array.numElements()
+    val result = new Array[Any](length)
+    var i = 0
+    while (i < length) {
+      if (array.isNullAt(i)) {
+        result(i) = null
+      } else {
+        val row = array.getStruct(i, numFields)
+        if (row.isNullAt(ordinal)) {
+          result(i) = null
+        } else {
+          result(i) = row.get(ordinal, field.dataType)
+        }
+      }
+      i += 1
     }
+    new GenericArrayData(result)
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val arraySeqClass = "scala.collection.mutable.ArraySeq"
-    // TODO: consider using Array[_] for ArrayType child to avoid
-    // boxing of primitives
+    val arrayClass = classOf[GenericArrayData].getName
     nullSafeCodeGen(ctx, ev, eval => {
       s"""
-        final int n = $eval.size();
-        final $arraySeqClass<Object> values = new $arraySeqClass<Object>(n);
+        final int n = $eval.numElements();
+        final Object[] values = new Object[n];
         for (int j = 0; j < n; j++) {
-          InternalRow row = (InternalRow) $eval.apply(j);
-          if (row != null && !row.isNullAt($ordinal)) {
-            values.update(j, ${ctx.getColumn("row", field.dataType, ordinal)});
+          if ($eval.isNullAt(j)) {
+            values[j] = null;
+          } else {
+            final InternalRow row = $eval.getStruct(j, $numFields);
+            if (row.isNullAt($ordinal)) {
+              values[j] = null;
+            } else {
+              values[j] = ${ctx.getValue("row", field.dataType, ordinal.toString)};
+            }
           }
         }
-        ${ev.primitive} = (${ctx.javaType(dataType)}) values;
+        ${ev.primitive} = new $arrayClass(values);
       """
     })
   }
@@ -186,23 +207,23 @@ case class GetArrayItem(child: Expression, ordinal: Expression) extends BinaryEx
   protected override def nullSafeEval(value: Any, ordinal: Any): Any = {
     // TODO: consider using Array[_] for ArrayType child to avoid
     // boxing of primitives
-    val baseValue = value.asInstanceOf[Seq[_]]
+    val baseValue = value.asInstanceOf[ArrayData]
     val index = ordinal.asInstanceOf[Number].intValue()
-    if (index >= baseValue.size || index < 0) {
+    if (index >= baseValue.numElements() || index < 0) {
       null
     } else {
-      baseValue(index)
+      baseValue.get(index)
     }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
       s"""
-        final int index = (int)$eval2;
-        if (index >= $eval1.size() || index < 0) {
+        final int index = (int) $eval2;
+        if (index >= $eval1.numElements() || index < 0) {
           ${ev.isNull} = true;
         } else {
-          ${ev.primitive} = (${ctx.boxedType(dataType)})$eval1.apply(index);
+          ${ev.primitive} = ${ctx.getValue(eval1, dataType, "index")};
         }
       """
     })
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index 2dbcf2830f876..8064235c64ef9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -121,8 +121,8 @@ case class Explode(child: Expression) extends UnaryExpression with Generator wit
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
     child.dataType match {
       case ArrayType(_, _) =>
-        val inputArray = child.eval(input).asInstanceOf[Seq[Any]]
-        if (inputArray == null) Nil else inputArray.map(v => InternalRow(v))
+        val inputArray = child.eval(input).asInstanceOf[ArrayData]
+        if (inputArray == null) Nil else inputArray.toArray().map(v => InternalRow(v))
       case MapType(_, _, _) =>
         val inputMap = child.eval(input).asInstanceOf[Map[Any, Any]]
         if (inputMap == null) Nil
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 5b3a64a09679c..79c0ca56a8e79 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -92,7 +92,7 @@ case class ConcatWs(children: Seq[Expression])
     val flatInputs = children.flatMap { child =>
       child.eval(input) match {
         case s: UTF8String => Iterator(s)
-        case arr: Seq[_] => arr.asInstanceOf[Seq[UTF8String]]
+        case arr: ArrayData => arr.toArray().map(_.asInstanceOf[UTF8String])
         case null => Iterator(null.asInstanceOf[UTF8String])
       }
     }
@@ -105,7 +105,7 @@ case class ConcatWs(children: Seq[Expression])
       val evals = children.map(_.gen(ctx))
 
       val inputs = evals.map { eval =>
-        s"${eval.isNull} ? (UTF8String)null : ${eval.primitive}"
+        s"${eval.isNull} ? (UTF8String) null : ${eval.primitive}"
       }.mkString(", ")
 
       evals.map(_.code).mkString("\n") + s"""
@@ -665,13 +665,15 @@ case class StringSplit(str: Expression, pattern: Expression)
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
 
   override def nullSafeEval(string: Any, regex: Any): Any = {
-    string.asInstanceOf[UTF8String].split(regex.asInstanceOf[UTF8String], -1).toSeq
+    val strings = string.asInstanceOf[UTF8String].split(regex.asInstanceOf[UTF8String], -1)
+    new GenericArrayData(strings.asInstanceOf[Array[Any]])
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val arrayClass = classOf[GenericArrayData].getName
     nullSafeCodeGen(ctx, ev, (str, pattern) =>
-      s"""${ev.primitive} = scala.collection.JavaConversions.asScalaBuffer(
-            java.util.Arrays.asList($str.split($pattern, -1)));""")
+      // Array in java is covariant, so we don't need to cast UTF8String[] to Object[].
+      s"""${ev.primitive} = new $arrayClass($str.split($pattern, -1));""")
   }
 
   override def prettyName: String = "split"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 813c62009666c..29d706dcb39a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -312,7 +312,8 @@ object NullPropagation extends Rule[LogicalPlan] {
       case e @ GetMapValue(Literal(null, _), _) => Literal.create(null, e.dataType)
       case e @ GetMapValue(_, Literal(null, _)) => Literal.create(null, e.dataType)
       case e @ GetStructField(Literal(null, _), _, _) => Literal.create(null, e.dataType)
-      case e @ GetArrayStructFields(Literal(null, _), _, _, _) => Literal.create(null, e.dataType)
+      case e @ GetArrayStructFields(Literal(null, _), _, _, _, _) =>
+        Literal.create(null, e.dataType)
       case e @ EqualNullSafe(Literal(null, _), r) => IsNull(r)
       case e @ EqualNullSafe(l, Literal(null, _)) => IsNull(l)
       case e @ Count(expr) if !expr.nullable => Count(Literal(1))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
new file mode 100644
index 0000000000000..14a7285877622
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
@@ -0,0 +1,121 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types
+
+import org.apache.spark.sql.catalyst.expressions.SpecializedGetters
+
+abstract class ArrayData extends SpecializedGetters with Serializable {
+  // todo: remove this after we handle all types.(map type need special getter)
+  def get(ordinal: Int): Any
+
+  def numElements(): Int
+
+  // todo: need a more efficient way to iterate array type.
+  def toArray(): Array[Any] = {
+    val n = numElements()
+    val values = new Array[Any](n)
+    var i = 0
+    while (i < n) {
+      if (isNullAt(i)) {
+        values(i) = null
+      } else {
+        values(i) = get(i)
+      }
+      i += 1
+    }
+    values
+  }
+
+  override def toString(): String = toArray.mkString("[", ",", "]")
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[ArrayData]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[ArrayData]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numElements()
+    if (len != other.numElements()) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = get(i)
+        val o2 = other.get(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numElements()
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          get(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
new file mode 100644
index 0000000000000..7992ba947c069
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.unsafe.types.{UTF8String, CalendarInterval}
+
+class GenericArrayData(array: Array[Any]) extends ArrayData {
+  private def getAs[T](ordinal: Int) = get(ordinal).asInstanceOf[T]
+
+  override def toArray(): Array[Any] = array
+
+  override def get(ordinal: Int): Any = array(ordinal)
+
+  override def isNullAt(ordinal: Int): Boolean = get(ordinal) == null
+
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+
+  override def getDecimal(ordinal: Int): Decimal = getAs(ordinal)
+
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+
+  override def numElements(): Int = array.length
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index a517da9872852..4f35b653d73c0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -21,6 +21,7 @@ import java.sql.{Timestamp, Date}
 import java.util.{TimeZone, Calendar}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
@@ -730,13 +731,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("complex casting") {
     val complex = Literal.create(
-      InternalRow(
-        Seq(UTF8String.fromString("123"), UTF8String.fromString("abc"), UTF8String.fromString("")),
-        Map(
-          UTF8String.fromString("a") -> UTF8String.fromString("123"),
-          UTF8String.fromString("b") -> UTF8String.fromString("abc"),
-          UTF8String.fromString("c") -> UTF8String.fromString("")),
-        InternalRow(0)),
+      Row(
+        Seq("123", "abc", ""),
+        Map("a" ->"123", "b" -> "abc", "c" -> ""),
+        Row(0)),
       StructType(Seq(
         StructField("a",
           ArrayType(StringType, containsNull = false), nullable = true),
@@ -756,13 +754,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
           StructField("l", LongType, nullable = true)))))))
 
     assert(ret.resolved === true)
-    checkEvaluation(ret, InternalRow(
+    checkEvaluation(ret, Row(
       Seq(123, null, null),
-      Map(
-        UTF8String.fromString("a") -> true,
-        UTF8String.fromString("b") -> true,
-        UTF8String.fromString("c") -> false),
-      InternalRow(0L)))
+      Map("a" -> true, "b" -> true, "c" -> false),
+      Row(0L)))
   }
 
   test("case between string and interval") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index 5de5ddce975d8..3fa246b69d1f1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -110,7 +110,7 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
       expr.dataType match {
         case ArrayType(StructType(fields), containsNull) =>
           val field = fields.find(_.name == fieldName).get
-          GetArrayStructFields(expr, field, fields.indexOf(field), containsNull)
+          GetArrayStructFields(expr, field, fields.indexOf(field), fields.length, containsNull)
       }
     }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index aeeb0e45270dd..f26f41fb75d57 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -158,8 +158,8 @@ package object debug {
 
       case (row: InternalRow, StructType(fields)) =>
         row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
-      case (s: Seq[_], ArrayType(elemType, _)) =>
-        s.foreach(typeCheck(_, elemType))
+      case (a: ArrayData, ArrayType(elemType, _)) =>
+        a.toArray().foreach(typeCheck(_, elemType))
       case (m: Map[_, _], MapType(keyType, valueType, _)) =>
         m.keys.foreach(typeCheck(_, keyType))
         m.values.foreach(typeCheck(_, valueType))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 3c38916fd7504..ef1c6e57dc08a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -134,8 +134,19 @@ object EvaluatePython {
       }
       new GenericInternalRowWithSchema(values, struct)
 
-    case (seq: Seq[Any], array: ArrayType) =>
-      seq.map(x => toJava(x, array.elementType)).asJava
+    case (a: ArrayData, array: ArrayType) =>
+      val length = a.numElements()
+      val values = new java.util.ArrayList[Any](length)
+      var i = 0
+      while (i < length) {
+        if (a.isNullAt(i)) {
+          values.add(null)
+        } else {
+          values.add(toJava(a.get(i), array.elementType))
+        }
+        i += 1
+      }
+      values
 
     case (obj: Map[_, _], mt: MapType) => obj.map {
       case (k, v) => (toJava(k, mt.keyType), toJava(v, mt.valueType))
@@ -190,10 +201,10 @@ object EvaluatePython {
     case (c, BinaryType) if c.getClass.isArray && c.getClass.getComponentType.getName == "byte" => c
 
     case (c: java.util.List[_], ArrayType(elementType, _)) =>
-      c.map { e => fromJava(e, elementType)}.toSeq
+      new GenericArrayData(c.map { e => fromJava(e, elementType)}.toArray)
 
     case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
-      c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType)).toSeq
+      new GenericArrayData(c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType)))
 
     case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) => c.map {
       case (key, value) => (fromJava(key, keyType), fromJava(value, valueType))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
index 78da2840dad69..9329148aa233c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.{Map => MutableMap}
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
-import org.apache.spark.sql.types.{DataType, ArrayType, StructField, StructType}
+import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, DataFrame}
 
 private[sql] object FrequentItems extends Logging {
@@ -110,7 +110,7 @@ private[sql] object FrequentItems extends Logging {
         baseCounts
       }
     )
-    val justItems = freqItems.map(m => m.baseMap.keys.toSeq)
+    val justItems = freqItems.map(m => m.baseMap.keys.toArray).map(new GenericArrayData(_))
     val resultRow = InternalRow(justItems : _*)
     // append frequent Items to the column name for easy debugging
     val outputCols = colInfo.map { v =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
index 0eb3b04007f8d..04ab5e2217882 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
@@ -125,7 +125,7 @@ private[sql] object InferSchema {
    * Convert NullType to StringType and remove StructTypes with no fields
    */
   private def canonicalizeType: DataType => Option[DataType] = {
-    case at@ArrayType(elementType, _) =>
+    case at @ ArrayType(elementType, _) =>
       for {
         canonicalType <- canonicalizeType(elementType)
       } yield {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index 381e7ed54428f..1c309f8794ef3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -110,8 +110,13 @@ private[sql] object JacksonParser {
       case (START_OBJECT, st: StructType) =>
         convertObject(factory, parser, st)
 
+      case (START_ARRAY, st: StructType) =>
+        // SPARK-3308: support reading top level JSON arrays and take every element
+        // in such an array as a row
+        convertArray(factory, parser, st)
+
       case (START_ARRAY, ArrayType(st, _)) =>
-        convertList(factory, parser, st)
+        convertArray(factory, parser, st)
 
       case (START_OBJECT, ArrayType(st, _)) =>
         // the business end of SPARK-3308:
@@ -165,16 +170,16 @@ private[sql] object JacksonParser {
     builder.result()
   }
 
-  private def convertList(
+  private def convertArray(
       factory: JsonFactory,
       parser: JsonParser,
-      schema: DataType): Seq[Any] = {
-    val builder = Seq.newBuilder[Any]
+      elementType: DataType): ArrayData = {
+    val values = scala.collection.mutable.ArrayBuffer.empty[Any]
     while (nextUntil(parser, JsonToken.END_ARRAY)) {
-      builder += convertField(factory, parser, schema)
+      values += convertField(factory, parser, elementType)
     }
 
-    builder.result()
+    new GenericArrayData(values.toArray)
   }
 
   private def parseJson(
@@ -201,12 +206,15 @@ private[sql] object JacksonParser {
           val parser = factory.createParser(record)
           parser.nextToken()
 
-          // to support both object and arrays (see SPARK-3308) we'll start
-          // by converting the StructType schema to an ArrayType and let
-          // convertField wrap an object into a single value array when necessary.
-          convertField(factory, parser, ArrayType(schema)) match {
+          convertField(factory, parser, schema) match {
             case null => failedRecord(record)
-            case list: Seq[InternalRow @unchecked] => list
+            case row: InternalRow => row :: Nil
+            case array: ArrayData =>
+              if (array.numElements() == 0) {
+                Nil
+              } else {
+                array.toArray().map(_.asInstanceOf[InternalRow])
+              }
             case _ =>
               sys.error(
                 s"Failed to parse record $record. Please make sure that each line of the file " +
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
index e00bd90edb3dd..172db8362afb6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
@@ -325,7 +325,7 @@ private[parquet] class CatalystRowConverter(
 
     override def getConverter(fieldIndex: Int): Converter = elementConverter
 
-    override def end(): Unit = updater.set(currentArray)
+    override def end(): Unit = updater.set(new GenericArrayData(currentArray.toArray))
 
     // NOTE: We can't reuse the mutable `ArrayBuffer` here and must instantiate a new buffer for the
     // next value.  `Row.copy()` only copies row cells, it doesn't do deep copy to objects stored
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index ea51650fe9039..2332a36468dbc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.parquet
 
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types.ArrayData
 
 // TODO Removes this while fixing SPARK-8848
 private[sql] object CatalystConverter {
@@ -32,7 +33,7 @@ private[sql] object CatalystConverter {
   val MAP_SCHEMA_NAME = "map"
 
   // TODO: consider using Array[T] for arrays to avoid boxing of primitive types
-  type ArrayScalaType[T] = Seq[T]
+  type ArrayScalaType[T] = ArrayData
   type StructScalaType[T] = InternalRow
   type MapScalaType[K, V] = Map[K, V]
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 78ecfad1d57c6..79dd16b7b0c39 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -146,15 +146,15 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
       array: CatalystConverter.ArrayScalaType[_]): Unit = {
     val elementType = schema.elementType
     writer.startGroup()
-    if (array.size > 0) {
+    if (array.numElements() > 0) {
       if (schema.containsNull) {
         writer.startField(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME, 0)
         var i = 0
-        while (i < array.size) {
+        while (i < array.numElements()) {
           writer.startGroup()
-          if (array(i) != null) {
+          if (!array.isNullAt(i)) {
             writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
-            writeValue(elementType, array(i))
+            writeValue(elementType, array.get(i))
             writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
           }
           writer.endGroup()
@@ -164,8 +164,8 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
       } else {
         writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
         var i = 0
-        while (i < array.size) {
-          writeValue(elementType, array(i))
+        while (i < array.numElements()) {
+          writeValue(elementType, array.get(i))
           i = i + 1
         }
         writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 72c42f4fe376b..9e61d06f4036e 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -30,7 +30,6 @@
 
 import scala.collection.JavaConversions;
 import scala.collection.Seq;
-import scala.collection.mutable.Buffer;
 
 import java.io.Serializable;
 import java.util.Arrays;
@@ -168,10 +167,10 @@ public void testCreateDataFrameFromJavaBeans() {
     for (int i = 0; i < result.length(); i++) {
       Assert.assertEquals(bean.getB()[i], result.apply(i));
     }
-    Buffer<Integer> outputBuffer = (Buffer<Integer>) first.getJavaMap(2).get("hello");
+    Seq<Integer> outputBuffer = (Seq<Integer>) first.getJavaMap(2).get("hello");
     Assert.assertArrayEquals(
       bean.getC().get("hello"),
-      Ints.toArray(JavaConversions.bufferAsJavaList(outputBuffer)));
+      Ints.toArray(JavaConversions.seqAsJavaList(outputBuffer)));
     Seq<String> d = first.getAs(3);
     Assert.assertEquals(bean.getD().size(), d.length());
     for (int i = 0; i < d.length(); i++) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index 45c9f06941c10..77ed4a9c0d5ae 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -47,17 +47,17 @@ private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] {
 
   override def sqlType: DataType = ArrayType(DoubleType, containsNull = false)
 
-  override def serialize(obj: Any): Seq[Double] = {
+  override def serialize(obj: Any): ArrayData = {
     obj match {
       case features: MyDenseVector =>
-        features.data.toSeq
+        new GenericArrayData(features.data.map(_.asInstanceOf[Any]))
     }
   }
 
   override def deserialize(datum: Any): MyDenseVector = {
     datum match {
-      case data: Seq[_] =>
-        new MyDenseVector(data.asInstanceOf[Seq[Double]].toArray)
+      case data: ArrayData =>
+        new MyDenseVector(data.toArray.map(_.asInstanceOf[Double]))
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index 5e189c3563ca8..cfb03ff485b7c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -67,12 +67,12 @@ case class AllDataTypesScan(
 
   override def schema: StructType = userSpecifiedSchema
 
-  override def needConversion: Boolean = false
+  override def needConversion: Boolean = true
 
   override def buildScan(): RDD[Row] = {
     sqlContext.sparkContext.parallelize(from to to).map { i =>
-      InternalRow(
-        UTF8String.fromString(s"str_$i"),
+      Row(
+        s"str_$i",
         s"str_$i".getBytes(),
         i % 2 == 0,
         i.toByte,
@@ -81,19 +81,19 @@ case class AllDataTypesScan(
         i.toLong,
         i.toFloat,
         i.toDouble,
-        Decimal(new java.math.BigDecimal(i)),
-        Decimal(new java.math.BigDecimal(i)),
-        DateTimeUtils.fromJavaDate(new Date(1970, 1, 1)),
-        DateTimeUtils.fromJavaTimestamp(new Timestamp(20000 + i)),
-        UTF8String.fromString(s"varchar_$i"),
+        new java.math.BigDecimal(i),
+        new java.math.BigDecimal(i),
+        new Date(1970, 1, 1),
+        new Timestamp(20000 + i),
+        s"varchar_$i",
         Seq(i, i + 1),
-        Seq(Map(UTF8String.fromString(s"str_$i") -> InternalRow(i.toLong))),
-        Map(i -> UTF8String.fromString(i.toString)),
-        Map(Map(UTF8String.fromString(s"str_$i") -> i.toFloat) -> InternalRow(i.toLong)),
-        InternalRow(i, UTF8String.fromString(i.toString)),
-        InternalRow(Seq(UTF8String.fromString(s"str_$i"), UTF8String.fromString(s"str_${i + 1}")),
-          InternalRow(Seq(DateTimeUtils.fromJavaDate(new Date(1970, 1, i + 1))))))
-    }.asInstanceOf[RDD[Row]]
+        Seq(Map(s"str_$i" -> Row(i.toLong))),
+        Map(i -> i.toString),
+        Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)),
+        Row(i, i.toString),
+          Row(Seq(s"str_$i", s"str_${i + 1}"),
+            Row(Seq(new Date(1970, 1, i + 1)))))
+    }
   }
 }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index f467500259c91..5926ef9aa388b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -52,9 +52,8 @@ import scala.collection.JavaConversions._
  *     java.sql.Timestamp
  *  Complex Types =>
  *    Map: scala.collection.immutable.Map
- *    List: scala.collection.immutable.Seq
- *    Struct:
- *           [[org.apache.spark.sql.catalyst.InternalRow]]
+ *    List: [[org.apache.spark.sql.types.ArrayData]]
+ *    Struct: [[org.apache.spark.sql.catalyst.InternalRow]]
  *    Union: NOT SUPPORTED YET
  *  The Complex types plays as a container, which can hold arbitrary data types.
  *
@@ -297,7 +296,10 @@ private[hive] trait HiveInspectors {
       }.toMap
     case li: StandardConstantListObjectInspector =>
       // take the value from the list inspector object, rather than the input data
-      li.getWritableConstantValue.map(unwrap(_, li.getListElementObjectInspector)).toSeq
+      val values = li.getWritableConstantValue
+        .map(unwrap(_, li.getListElementObjectInspector))
+        .toArray
+      new GenericArrayData(values)
     // if the value is null, we don't care about the object inspector type
     case _ if data == null => null
     case poi: VoidObjectInspector => null // always be null for void object inspector
@@ -339,7 +341,10 @@ private[hive] trait HiveInspectors {
     }
     case li: ListObjectInspector =>
       Option(li.getList(data))
-        .map(_.map(unwrap(_, li.getListElementObjectInspector)).toSeq)
+        .map { l =>
+          val values = l.map(unwrap(_, li.getListElementObjectInspector)).toArray
+          new GenericArrayData(values)
+        }
         .orNull
     case mi: MapObjectInspector =>
       Option(mi.getMap(data)).map(
@@ -391,7 +396,13 @@ private[hive] trait HiveInspectors {
 
     case loi: ListObjectInspector =>
       val wrapper = wrapperFor(loi.getListElementObjectInspector)
-      (o: Any) => if (o != null) seqAsJavaList(o.asInstanceOf[Seq[_]].map(wrapper)) else null
+      (o: Any) => {
+        if (o != null) {
+          seqAsJavaList(o.asInstanceOf[ArrayData].toArray().map(wrapper))
+        } else {
+          null
+        }
+      }
 
     case moi: MapObjectInspector =>
       // The Predef.Map is scala.collection.immutable.Map.
@@ -520,7 +531,7 @@ private[hive] trait HiveInspectors {
     case x: ListObjectInspector =>
       val list = new java.util.ArrayList[Object]
       val tpe = dataType.asInstanceOf[ArrayType].elementType
-      a.asInstanceOf[Seq[_]].foreach {
+      a.asInstanceOf[ArrayData].toArray().foreach {
         v => list.add(wrap(v, x.getListElementObjectInspector, tpe))
       }
       list
@@ -634,7 +645,8 @@ private[hive] trait HiveInspectors {
         ObjectInspectorFactory.getStandardConstantListObjectInspector(listObjectInspector, null)
       } else {
         val list = new java.util.ArrayList[Object]()
-        value.asInstanceOf[Seq[_]].foreach(v => list.add(wrap(v, listObjectInspector, dt)))
+        value.asInstanceOf[ArrayData].toArray()
+          .foreach(v => list.add(wrap(v, listObjectInspector, dt)))
         ObjectInspectorFactory.getStandardConstantListObjectInspector(listObjectInspector, list)
       }
     case Literal(value, MapType(keyType, valueType, _)) =>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 741c705e2a253..7e3342cc84c0e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -176,13 +176,13 @@ case class ScriptTransformation(
             val prevLine = curLine
             curLine = reader.readLine()
             if (!ioschema.schemaLess) {
-              new GenericInternalRow(CatalystTypeConverters.convertToCatalyst(
-                prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD")))
-                .asInstanceOf[Array[Any]])
+              new GenericInternalRow(
+                prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+                  .map(CatalystTypeConverters.convertToCatalyst))
             } else {
-              new GenericInternalRow(CatalystTypeConverters.convertToCatalyst(
-                prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2))
-                .asInstanceOf[Array[Any]])
+              new GenericInternalRow(
+                prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2)
+                  .map(CatalystTypeConverters.convertToCatalyst))
             }
           } else {
             val ret = deserialize()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 8732e9abf8d31..4a13022eddf60 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -431,7 +431,7 @@ private[hive] case class HiveWindowFunction(
       // if pivotResult is true, we will get a Seq having the same size with the size
       // of the window frame. At here, we will return the result at the position of
       // index in the output buffer.
-      outputBuffer.asInstanceOf[Seq[Any]].get(index)
+      outputBuffer.asInstanceOf[ArrayData].get(index)
     }
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 0330013f5325e..f719f2e06ab63 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -217,7 +217,7 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
   test("wrap / unwrap Array Type") {
     val dt = ArrayType(dataTypes(0))
 
-    val d = row(0) :: row(0) :: Nil
+    val d = new GenericArrayData(Array(row(0), row(0)))
     checkValue(d, unwrap(wrap(d, toInspector(dt), dt), toInspector(dt)))
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
     checkValue(d,

From 7bbf02f0bddefd19985372af79e906a38bc528b6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Garillot?= <francois@garillot.net>
Date: Thu, 30 Jul 2015 18:14:08 +0100
Subject: [PATCH 0701/1454] [SPARK-9267] [CORE] Retire stringify(Partial)?Value
 from Accumulators
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

cc srowen

Author: François Garillot <francois@garillot.net>

Closes #7678 from huitseeker/master and squashes the following commits:

5e99f57 [François Garillot] [SPARK-9267][Core] Retire stringify(Partial)?Value from Accumulators
---
 core/src/main/scala/org/apache/spark/Accumulators.scala     | 3 ---
 .../scala/org/apache/spark/scheduler/DAGScheduler.scala     | 6 ++----
 2 files changed, 2 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 2f4fcac890eef..eb75f26718e19 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -341,7 +341,4 @@ private[spark] object Accumulators extends Logging {
     }
   }
 
-  def stringifyPartialValue(partialValue: Any): String = "%s".format(partialValue)
-
-  def stringifyValue(value: Any): String = "%s".format(value)
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index cdf6078421123..c4fa277c21254 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -916,11 +916,9 @@ class DAGScheduler(
           // To avoid UI cruft, ignore cases where value wasn't updated
           if (acc.name.isDefined && partialValue != acc.zero) {
             val name = acc.name.get
-            val stringPartialValue = Accumulators.stringifyPartialValue(partialValue)
-            val stringValue = Accumulators.stringifyValue(acc.value)
-            stage.latestInfo.accumulables(id) = AccumulableInfo(id, name, stringValue)
+            stage.latestInfo.accumulables(id) = AccumulableInfo(id, name, s"${acc.value}")
             event.taskInfo.accumulables +=
-              AccumulableInfo(id, name, Some(stringPartialValue), stringValue)
+              AccumulableInfo(id, name, Some(s"$partialValue"), s"${acc.value}")
           }
         }
       } catch {

From 5363ed71568c3e7c082146d654a9c669d692d894 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 30 Jul 2015 10:30:37 -0700
Subject: [PATCH 0702/1454] [SPARK-9361] [SQL] Refactor new aggregation code to
 reduce the times of checking compatibility

JIRA: https://issues.apache.org/jira/browse/SPARK-9361

Currently, we call `aggregate.Utils.tryConvert` in many places to check it the logical.Aggregate can be run with new aggregation. But looks like `aggregate.Utils.tryConvert` will cost considerable time to run. We should only call `tryConvert` once and keep it value in `logical.Aggregate` and reuse it.

In `org.apache.spark.sql.execution.aggregate.Utils`, the codes involving with `tryConvert` should be moved to catalyst because it actually doesn't deal with execution details.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7677 from viirya/refactor_aggregate and squashes the following commits:

babea30 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into refactor_aggregate
9a589d7 [Liang-Chi Hsieh] Fix scala style.
0a91329 [Liang-Chi Hsieh] Refactor new aggregation code to reduce the times to call tryConvert.
---
 .../expressions/aggregate/interfaces.scala    |   4 +-
 .../expressions/aggregate/utils.scala         | 167 ++++++++++++++++++
 .../plans/logical/basicOperators.scala        |   3 +
 .../spark/sql/execution/SparkStrategies.scala |  34 ++--
 .../spark/sql/execution/aggregate/utils.scala | 144 ---------------
 5 files changed, 188 insertions(+), 164 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
index 9fb7623172e78..d08f553cefe8c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -42,7 +42,7 @@ private[sql] case object Partial extends AggregateMode
 private[sql] case object PartialMerge extends AggregateMode
 
 /**
- * An [[AggregateFunction2]] with [[PartialMerge]] mode is used to merge aggregation buffers
+ * An [[AggregateFunction2]] with [[Final]] mode is used to merge aggregation buffers
  * containing intermediate results for this function and then generate final result.
  * This function updates the given aggregation buffer by merging multiple aggregation buffers.
  * When it has processed all input rows, the final result of this function is returned.
@@ -50,7 +50,7 @@ private[sql] case object PartialMerge extends AggregateMode
 private[sql] case object Final extends AggregateMode
 
 /**
- * An [[AggregateFunction2]] with [[Partial]] mode is used to evaluate this function directly
+ * An [[AggregateFunction2]] with [[Complete]] mode is used to evaluate this function directly
  * from original input rows without any partial aggregation.
  * This function updates the given aggregation buffer with the original input of this
  * function. When it has processed all input rows, the final result of this function is returned.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
new file mode 100644
index 0000000000000..4a43318a95490
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
@@ -0,0 +1,167 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.aggregate
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan}
+import org.apache.spark.sql.types.{StructType, MapType, ArrayType}
+
+/**
+ * Utility functions used by the query planner to convert our plan to new aggregation code path.
+ */
+object Utils {
+  // Right now, we do not support complex types in the grouping key schema.
+  private def supportsGroupingKeySchema(aggregate: Aggregate): Boolean = {
+    val hasComplexTypes = aggregate.groupingExpressions.map(_.dataType).exists {
+      case array: ArrayType => true
+      case map: MapType => true
+      case struct: StructType => true
+      case _ => false
+    }
+
+    !hasComplexTypes
+  }
+
+  private def doConvert(plan: LogicalPlan): Option[Aggregate] = plan match {
+    case p: Aggregate if supportsGroupingKeySchema(p) =>
+      val converted = p.transformExpressionsDown {
+        case expressions.Average(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Average(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Count(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Count(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        // We do not support multiple COUNT DISTINCT columns for now.
+        case expressions.CountDistinct(children) if children.length == 1 =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Count(children.head),
+            mode = aggregate.Complete,
+            isDistinct = true)
+
+        case expressions.First(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.First(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Last(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Last(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Max(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Max(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Min(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Min(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.Sum(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Sum(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.SumDistinct(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Sum(child),
+            mode = aggregate.Complete,
+            isDistinct = true)
+      }
+      // Check if there is any expressions.AggregateExpression1 left.
+      // If so, we cannot convert this plan.
+      val hasAggregateExpression1 = converted.aggregateExpressions.exists { expr =>
+        // For every expressions, check if it contains AggregateExpression1.
+        expr.find {
+          case agg: expressions.AggregateExpression1 => true
+          case other => false
+        }.isDefined
+      }
+
+      // Check if there are multiple distinct columns.
+      val aggregateExpressions = converted.aggregateExpressions.flatMap { expr =>
+        expr.collect {
+          case agg: AggregateExpression2 => agg
+        }
+      }.toSet.toSeq
+      val functionsWithDistinct = aggregateExpressions.filter(_.isDistinct)
+      val hasMultipleDistinctColumnSets =
+        if (functionsWithDistinct.map(_.aggregateFunction.children).distinct.length > 1) {
+          true
+        } else {
+          false
+        }
+
+      if (!hasAggregateExpression1 && !hasMultipleDistinctColumnSets) Some(converted) else None
+
+    case other => None
+  }
+
+  def checkInvalidAggregateFunction2(aggregate: Aggregate): Unit = {
+    // If the plan cannot be converted, we will do a final round check to see if the original
+    // logical.Aggregate contains both AggregateExpression1 and AggregateExpression2. If so,
+    // we need to throw an exception.
+    val aggregateFunction2s = aggregate.aggregateExpressions.flatMap { expr =>
+      expr.collect {
+        case agg: AggregateExpression2 => agg.aggregateFunction
+      }
+    }.distinct
+    if (aggregateFunction2s.nonEmpty) {
+      // For functions implemented based on the new interface, prepare a list of function names.
+      val invalidFunctions = {
+        if (aggregateFunction2s.length > 1) {
+          s"${aggregateFunction2s.tail.map(_.nodeName).mkString(",")} " +
+            s"and ${aggregateFunction2s.head.nodeName} are"
+        } else {
+          s"${aggregateFunction2s.head.nodeName} is"
+        }
+      }
+      val errorMessage =
+        s"${invalidFunctions} implemented based on the new Aggregate Function " +
+          s"interface and it cannot be used with functions implemented based on " +
+          s"the old Aggregate Function interface."
+      throw new AnalysisException(errorMessage)
+    }
+  }
+
+  def tryConvert(plan: LogicalPlan): Option[Aggregate] = plan match {
+    case p: Aggregate =>
+      val converted = doConvert(p)
+      if (converted.isDefined) {
+        converted
+      } else {
+        checkInvalidAggregateFunction2(p)
+        None
+      }
+    case other => None
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index ad5af19578f33..a67f8de6b733a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.Utils
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashSet
@@ -219,6 +220,8 @@ case class Aggregate(
     expressions.forall(_.resolved) && childrenResolved && !hasWindowExpressions
   }
 
+  lazy val newAggregation: Option[Aggregate] = Utils.tryConvert(this)
+
   override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute)
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index f3ef066528ff8..52a9b02d373c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
+import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2, Utils}
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.{BroadcastHint, LogicalPlan}
@@ -193,11 +193,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case _ => Nil
     }
 
-    def canBeConvertedToNewAggregation(plan: LogicalPlan): Boolean = {
-      aggregate.Utils.tryConvert(
-        plan,
-        sqlContext.conf.useSqlAggregate2,
-        sqlContext.conf.codegenEnabled).isDefined
+    def canBeConvertedToNewAggregation(plan: LogicalPlan): Boolean = plan match {
+      case a: logical.Aggregate =>
+        if (sqlContext.conf.useSqlAggregate2 && sqlContext.conf.codegenEnabled) {
+          a.newAggregation.isDefined
+        } else {
+          Utils.checkInvalidAggregateFunction2(a)
+          false
+        }
+      case _ => false
     }
 
     def canBeCodeGened(aggs: Seq[AggregateExpression1]): Boolean = aggs.forall {
@@ -217,12 +221,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
    */
   object Aggregation extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case p: logical.Aggregate =>
-        val converted =
-          aggregate.Utils.tryConvert(
-            p,
-            sqlContext.conf.useSqlAggregate2,
-            sqlContext.conf.codegenEnabled)
+      case p: logical.Aggregate if sqlContext.conf.useSqlAggregate2 &&
+          sqlContext.conf.codegenEnabled =>
+        val converted = p.newAggregation
         converted match {
           case None => Nil // Cannot convert to new aggregation code path.
           case Some(logical.Aggregate(groupingExpressions, resultExpressions, child)) =>
@@ -377,17 +378,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case e @ logical.Expand(_, _, _, child) =>
         execution.Expand(e.projections, e.output, planLater(child)) :: Nil
       case a @ logical.Aggregate(group, agg, child) => {
-        val useNewAggregation =
-          aggregate.Utils.tryConvert(
-            a,
-            sqlContext.conf.useSqlAggregate2,
-            sqlContext.conf.codegenEnabled).isDefined
-        if (useNewAggregation) {
+        val useNewAggregation = sqlContext.conf.useSqlAggregate2 && sqlContext.conf.codegenEnabled
+        if (useNewAggregation && a.newAggregation.isDefined) {
           // If this logical.Aggregate can be planned to use new aggregation code path
           // (i.e. it can be planned by the Strategy Aggregation), we will not use the old
           // aggregation code path.
           Nil
         } else {
+          Utils.checkInvalidAggregateFunction2(a)
           execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
         }
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
index 6549c87752a7d..03635baae4a5f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
@@ -29,150 +29,6 @@ import org.apache.spark.sql.types.{StructType, MapType, ArrayType}
  * Utility functions used by the query planner to convert our plan to new aggregation code path.
  */
 object Utils {
-  // Right now, we do not support complex types in the grouping key schema.
-  private def supportsGroupingKeySchema(aggregate: Aggregate): Boolean = {
-    val hasComplexTypes = aggregate.groupingExpressions.map(_.dataType).exists {
-      case array: ArrayType => true
-      case map: MapType => true
-      case struct: StructType => true
-      case _ => false
-    }
-
-    !hasComplexTypes
-  }
-
-  private def tryConvert(plan: LogicalPlan): Option[Aggregate] = plan match {
-    case p: Aggregate if supportsGroupingKeySchema(p) =>
-      val converted = p.transformExpressionsDown {
-        case expressions.Average(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Average(child),
-            mode = aggregate.Complete,
-            isDistinct = false)
-
-        case expressions.Count(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Count(child),
-            mode = aggregate.Complete,
-            isDistinct = false)
-
-        // We do not support multiple COUNT DISTINCT columns for now.
-        case expressions.CountDistinct(children) if children.length == 1 =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Count(children.head),
-            mode = aggregate.Complete,
-            isDistinct = true)
-
-        case expressions.First(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.First(child),
-            mode = aggregate.Complete,
-            isDistinct = false)
-
-        case expressions.Last(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Last(child),
-            mode = aggregate.Complete,
-            isDistinct = false)
-
-        case expressions.Max(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Max(child),
-            mode = aggregate.Complete,
-            isDistinct = false)
-
-        case expressions.Min(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Min(child),
-            mode = aggregate.Complete,
-            isDistinct = false)
-
-        case expressions.Sum(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Sum(child),
-            mode = aggregate.Complete,
-            isDistinct = false)
-
-        case expressions.SumDistinct(child) =>
-          aggregate.AggregateExpression2(
-            aggregateFunction = aggregate.Sum(child),
-            mode = aggregate.Complete,
-            isDistinct = true)
-      }
-      // Check if there is any expressions.AggregateExpression1 left.
-      // If so, we cannot convert this plan.
-      val hasAggregateExpression1 = converted.aggregateExpressions.exists { expr =>
-        // For every expressions, check if it contains AggregateExpression1.
-        expr.find {
-          case agg: expressions.AggregateExpression1 => true
-          case other => false
-        }.isDefined
-      }
-
-      // Check if there are multiple distinct columns.
-      val aggregateExpressions = converted.aggregateExpressions.flatMap { expr =>
-        expr.collect {
-          case agg: AggregateExpression2 => agg
-        }
-      }.toSet.toSeq
-      val functionsWithDistinct = aggregateExpressions.filter(_.isDistinct)
-      val hasMultipleDistinctColumnSets =
-        if (functionsWithDistinct.map(_.aggregateFunction.children).distinct.length > 1) {
-          true
-        } else {
-          false
-        }
-
-      if (!hasAggregateExpression1 && !hasMultipleDistinctColumnSets) Some(converted) else None
-
-    case other => None
-  }
-
-  private def checkInvalidAggregateFunction2(aggregate: Aggregate): Unit = {
-    // If the plan cannot be converted, we will do a final round check to if the original
-    // logical.Aggregate contains both AggregateExpression1 and AggregateExpression2. If so,
-    // we need to throw an exception.
-    val aggregateFunction2s = aggregate.aggregateExpressions.flatMap { expr =>
-      expr.collect {
-        case agg: AggregateExpression2 => agg.aggregateFunction
-      }
-    }.distinct
-    if (aggregateFunction2s.nonEmpty) {
-      // For functions implemented based on the new interface, prepare a list of function names.
-      val invalidFunctions = {
-        if (aggregateFunction2s.length > 1) {
-          s"${aggregateFunction2s.tail.map(_.nodeName).mkString(",")} " +
-            s"and ${aggregateFunction2s.head.nodeName} are"
-        } else {
-          s"${aggregateFunction2s.head.nodeName} is"
-        }
-      }
-      val errorMessage =
-        s"${invalidFunctions} implemented based on the new Aggregate Function " +
-          s"interface and it cannot be used with functions implemented based on " +
-          s"the old Aggregate Function interface."
-      throw new AnalysisException(errorMessage)
-    }
-  }
-
-  def tryConvert(
-      plan: LogicalPlan,
-      useNewAggregation: Boolean,
-      codeGenEnabled: Boolean): Option[Aggregate] = plan match {
-    case p: Aggregate if useNewAggregation && codeGenEnabled =>
-      val converted = tryConvert(p)
-      if (converted.isDefined) {
-        converted
-      } else {
-        checkInvalidAggregateFunction2(p)
-        None
-      }
-    case p: Aggregate =>
-      checkInvalidAggregateFunction2(p)
-      None
-    case other => None
-  }
-
   def planAggregateWithoutDistinct(
       groupingExpressions: Seq[Expression],
       aggregateExpressions: Seq[AggregateExpression2],

From e53534655d6198e5b8a507010d26c7b4c4e7f1fd Mon Sep 17 00:00:00 2001
From: Mridul Muralidharan <mridulm@yahoo-inc.com>
Date: Thu, 30 Jul 2015 10:37:53 -0700
Subject: [PATCH 0703/1454] [SPARK-8297] [YARN] Scheduler backend is not
 notified in case node fails in YARN

This change adds code to notify the scheduler backend when a container dies in YARN.

Author: Mridul Muralidharan <mridulm@yahoo-inc.com>
Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7431 from vanzin/SPARK-8297 and squashes the following commits:

471e4a0 [Marcelo Vanzin] Fix unit test after merge.
d4adf4e [Marcelo Vanzin] Merge branch 'master' into SPARK-8297
3b262e8 [Marcelo Vanzin] Merge branch 'master' into SPARK-8297
537da6f [Marcelo Vanzin] Make an expected log less scary.
04dc112 [Marcelo Vanzin] Use driver <-> AM communication to send "remove executor" request.
8855b97 [Marcelo Vanzin] Merge remote-tracking branch 'mridul/fix_yarn_scheduler_bug' into SPARK-8297
687790f [Mridul Muralidharan] Merge branch 'fix_yarn_scheduler_bug' of github.com:mridulm/spark into fix_yarn_scheduler_bug
e1b0067 [Mridul Muralidharan] Fix failing testcase, fix merge issue from our 1.3 -> master
9218fcc [Mridul Muralidharan] Fix failing testcase
362d64a [Mridul Muralidharan] Merge branch 'fix_yarn_scheduler_bug' of github.com:mridulm/spark into fix_yarn_scheduler_bug
62ad0cc [Mridul Muralidharan] Merge branch 'fix_yarn_scheduler_bug' of github.com:mridulm/spark into fix_yarn_scheduler_bug
bbf8811 [Mridul Muralidharan] Merge branch 'fix_yarn_scheduler_bug' of github.com:mridulm/spark into fix_yarn_scheduler_bug
9ee1307 [Mridul Muralidharan] Fix SPARK-8297
a3a0f01 [Mridul Muralidharan] Fix SPARK-8297
---
 .../CoarseGrainedSchedulerBackend.scala       |  2 +-
 .../cluster/YarnSchedulerBackend.scala        |  2 ++
 .../spark/deploy/yarn/ApplicationMaster.scala | 22 +++++++++----
 .../spark/deploy/yarn/YarnAllocator.scala     | 32 +++++++++++++++----
 .../spark/deploy/yarn/YarnRMClient.scala      |  5 ++-
 .../deploy/yarn/YarnAllocatorSuite.scala      | 29 +++++++++++++++++
 6 files changed, 77 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 660702f6e6fd0..bd89160af4ffa 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -241,7 +241,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
           scheduler.executorLost(executorId, SlaveLost(reason))
           listenerBus.post(
             SparkListenerExecutorRemoved(System.currentTimeMillis(), executorId, reason))
-        case None => logError(s"Asked to remove non-existent executor $executorId")
+        case None => logInfo(s"Asked to remove non-existent executor $executorId")
       }
     }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
index 074282d1be37d..044f6288fabdd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
@@ -109,6 +109,8 @@ private[spark] abstract class YarnSchedulerBackend(
       case AddWebUIFilter(filterName, filterParams, proxyBase) =>
         addWebUIFilter(filterName, filterParams, proxyBase)
 
+      case RemoveExecutor(executorId, reason) =>
+        removeExecutor(executorId, reason)
     }
 
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 44acc7374d024..1d67b3ebb51b7 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -229,7 +229,11 @@ private[spark] class ApplicationMaster(
     sparkContextRef.compareAndSet(sc, null)
   }
 
-  private def registerAM(_rpcEnv: RpcEnv, uiAddress: String, securityMgr: SecurityManager) = {
+  private def registerAM(
+      _rpcEnv: RpcEnv,
+      driverRef: RpcEndpointRef,
+      uiAddress: String,
+      securityMgr: SecurityManager) = {
     val sc = sparkContextRef.get()
 
     val appId = client.getAttemptId().getApplicationId().toString()
@@ -246,6 +250,7 @@ private[spark] class ApplicationMaster(
         RpcAddress(_sparkConf.get("spark.driver.host"), _sparkConf.get("spark.driver.port").toInt),
         CoarseGrainedSchedulerBackend.ENDPOINT_NAME)
     allocator = client.register(driverUrl,
+      driverRef,
       yarnConf,
       _sparkConf,
       if (sc != null) sc.preferredNodeLocationData else Map(),
@@ -262,17 +267,20 @@ private[spark] class ApplicationMaster(
    *
    * In cluster mode, the AM and the driver belong to same process
    * so the AMEndpoint need not monitor lifecycle of the driver.
+   *
+   * @return A reference to the driver's RPC endpoint.
    */
   private def runAMEndpoint(
       host: String,
       port: String,
-      isClusterMode: Boolean): Unit = {
+      isClusterMode: Boolean): RpcEndpointRef = {
     val driverEndpoint = rpcEnv.setupEndpointRef(
       SparkEnv.driverActorSystemName,
       RpcAddress(host, port.toInt),
       YarnSchedulerBackend.ENDPOINT_NAME)
     amEndpoint =
       rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpoint, isClusterMode))
+    driverEndpoint
   }
 
   private def runDriver(securityMgr: SecurityManager): Unit = {
@@ -290,11 +298,11 @@ private[spark] class ApplicationMaster(
         "Timed out waiting for SparkContext.")
     } else {
       rpcEnv = sc.env.rpcEnv
-      runAMEndpoint(
+      val driverRef = runAMEndpoint(
         sc.getConf.get("spark.driver.host"),
         sc.getConf.get("spark.driver.port"),
         isClusterMode = true)
-      registerAM(rpcEnv, sc.ui.map(_.appUIAddress).getOrElse(""), securityMgr)
+      registerAM(rpcEnv, driverRef, sc.ui.map(_.appUIAddress).getOrElse(""), securityMgr)
       userClassThread.join()
     }
   }
@@ -302,9 +310,9 @@ private[spark] class ApplicationMaster(
   private def runExecutorLauncher(securityMgr: SecurityManager): Unit = {
     val port = sparkConf.getInt("spark.yarn.am.port", 0)
     rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, port, sparkConf, securityMgr)
-    waitForSparkDriver()
+    val driverRef = waitForSparkDriver()
     addAmIpFilter()
-    registerAM(rpcEnv, sparkConf.get("spark.driver.appUIAddress", ""), securityMgr)
+    registerAM(rpcEnv, driverRef, sparkConf.get("spark.driver.appUIAddress", ""), securityMgr)
 
     // In client mode the actor will stop the reporter thread.
     reporterThread.join()
@@ -428,7 +436,7 @@ private[spark] class ApplicationMaster(
     }
   }
 
-  private def waitForSparkDriver(): Unit = {
+  private def waitForSparkDriver(): RpcEndpointRef = {
     logInfo("Waiting for Spark driver to be reachable.")
     var driverUp = false
     val hostport = args.userArgs(0)
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 6c103394af098..59caa787b6e20 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -36,6 +36,9 @@ import org.apache.log4j.{Level, Logger}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
+import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
 
 /**
  * YarnAllocator is charged with requesting containers from the YARN ResourceManager and deciding
@@ -52,6 +55,7 @@ import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
  */
 private[yarn] class YarnAllocator(
     driverUrl: String,
+    driverRef: RpcEndpointRef,
     conf: Configuration,
     sparkConf: SparkConf,
     amClient: AMRMClient[ContainerRequest],
@@ -88,6 +92,9 @@ private[yarn] class YarnAllocator(
   // Visible for testing.
   private[yarn] val executorIdToContainer = new HashMap[String, Container]
 
+  private var numUnexpectedContainerRelease = 0L
+  private val containerIdToExecutorId = new HashMap[ContainerId, String]
+
   // Executor memory in MB.
   protected val executorMemory = args.executorMemory
   // Additional memory overhead.
@@ -184,6 +191,7 @@ private[yarn] class YarnAllocator(
   def killExecutor(executorId: String): Unit = synchronized {
     if (executorIdToContainer.contains(executorId)) {
       val container = executorIdToContainer.remove(executorId).get
+      containerIdToExecutorId.remove(container.getId)
       internalReleaseContainer(container)
       numExecutorsRunning -= 1
     } else {
@@ -383,6 +391,7 @@ private[yarn] class YarnAllocator(
 
       logInfo("Launching container %s for on host %s".format(containerId, executorHostname))
       executorIdToContainer(executorId) = container
+      containerIdToExecutorId(container.getId) = executorId
 
       val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
         new HashSet[ContainerId])
@@ -413,12 +422,8 @@ private[yarn] class YarnAllocator(
   private[yarn] def processCompletedContainers(completedContainers: Seq[ContainerStatus]): Unit = {
     for (completedContainer <- completedContainers) {
       val containerId = completedContainer.getContainerId
-
-      if (releasedContainers.contains(containerId)) {
-        // Already marked the container for release, so remove it from
-        // `releasedContainers`.
-        releasedContainers.remove(containerId)
-      } else {
+      val alreadyReleased = releasedContainers.remove(containerId)
+      if (!alreadyReleased) {
         // Decrement the number of executors running. The next iteration of
         // the ApplicationMaster's reporting thread will take care of allocating.
         numExecutorsRunning -= 1
@@ -460,6 +465,18 @@ private[yarn] class YarnAllocator(
 
         allocatedContainerToHostMap.remove(containerId)
       }
+
+      containerIdToExecutorId.remove(containerId).foreach { eid =>
+        executorIdToContainer.remove(eid)
+
+        if (!alreadyReleased) {
+          // The executor could have gone away (like no route to host, node failure, etc)
+          // Notify backend about the failure of the executor
+          numUnexpectedContainerRelease += 1
+          driverRef.send(RemoveExecutor(eid,
+            s"Yarn deallocated the executor $eid (container $containerId)"))
+        }
+      }
     }
   }
 
@@ -467,6 +484,9 @@ private[yarn] class YarnAllocator(
     releasedContainers.add(container.getId())
     amClient.releaseAssignedContainer(container.getId())
   }
+
+  private[yarn] def getNumUnexpectedContainerRelease = numUnexpectedContainerRelease
+
 }
 
 private object YarnAllocator {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
index 7f533ee55e8bb..4999f9c06210a 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
@@ -33,6 +33,7 @@ import org.apache.hadoop.yarn.util.ConverterUtils
 import org.apache.hadoop.yarn.webapp.util.WebAppUtils
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.scheduler.SplitInfo
 import org.apache.spark.util.Utils
 
@@ -56,6 +57,7 @@ private[spark] class YarnRMClient(args: ApplicationMasterArguments) extends Logg
    */
   def register(
       driverUrl: String,
+      driverRef: RpcEndpointRef,
       conf: YarnConfiguration,
       sparkConf: SparkConf,
       preferredNodeLocations: Map[String, Set[SplitInfo]],
@@ -73,7 +75,8 @@ private[spark] class YarnRMClient(args: ApplicationMasterArguments) extends Logg
       amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress)
       registered = true
     }
-    new YarnAllocator(driverUrl, conf, sparkConf, amClient, getAttemptId(), args, securityMgr)
+    new YarnAllocator(driverUrl, driverRef, conf, sparkConf, amClient, getAttemptId(), args,
+      securityMgr)
   }
 
   /**
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
index 37a789fcd375b..58318bf9bcc08 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
@@ -27,10 +27,14 @@ import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
 import org.scalatest.{BeforeAndAfterEach, Matchers}
 
+import org.scalatest.{BeforeAndAfterEach, Matchers}
+import org.mockito.Mockito._
+
 import org.apache.spark.{SecurityManager, SparkFunSuite}
 import org.apache.spark.SparkConf
 import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
 import org.apache.spark.deploy.yarn.YarnAllocator._
+import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.scheduler.SplitInfo
 
 class MockResolver extends DNSToSwitchMapping {
@@ -90,6 +94,7 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
       "--class", "SomeClass")
     new YarnAllocator(
       "not used",
+      mock(classOf[RpcEndpointRef]),
       conf,
       sparkConf,
       rmClient,
@@ -230,6 +235,30 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
     handler.getNumPendingAllocate should be (1)
   }
 
+  test("lost executor removed from backend") {
+    val handler = createAllocator(4)
+    handler.updateResourceRequests()
+    handler.getNumExecutorsRunning should be (0)
+    handler.getNumPendingAllocate should be (4)
+
+    val container1 = createContainer("host1")
+    val container2 = createContainer("host2")
+    handler.handleAllocatedContainers(Array(container1, container2))
+
+    handler.requestTotalExecutorsWithPreferredLocalities(2, 0, Map())
+
+    val statuses = Seq(container1, container2).map { c =>
+      ContainerStatus.newInstance(c.getId(), ContainerState.COMPLETE, "Failed", -1)
+    }
+    handler.updateResourceRequests()
+    handler.processCompletedContainers(statuses.toSeq)
+    handler.updateResourceRequests()
+    handler.getNumExecutorsRunning should be (0)
+    handler.getNumPendingAllocate should be (2)
+    handler.getNumExecutorsFailed should be (2)
+    handler.getNumUnexpectedContainerRelease should be (2)
+  }
+
   test("memory exceeded diagnostic regexes") {
     val diagnostics =
       "Container [pid=12465,containerID=container_1412887393566_0003_01_000002] is running " +

From ab78b1d2a6ce26833ea3878a63921efd805a3737 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 30 Jul 2015 10:40:04 -0700
Subject: [PATCH 0704/1454] [SPARK-9388] [YARN] Make executor info log messages
 easier to read.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7706 from vanzin/SPARK-9388 and squashes the following commits:

028b990 [Marcelo Vanzin] Single log statement.
3c5fb6a [Marcelo Vanzin] YARN not Yarn.
5bcd7a0 [Marcelo Vanzin] [SPARK-9388] [yarn] Make executor info log messages easier to read.
---
 .../scala/org/apache/spark/deploy/yarn/Client.scala |  2 +-
 .../apache/spark/deploy/yarn/ExecutorRunnable.scala | 13 ++++++++++---
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index bc28ce5eeae72..4ac3397f1ad28 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -767,7 +767,7 @@ private[spark] class Client(
     amContainer.setCommands(printableCommands)
 
     logDebug("===============================================================================")
-    logDebug("Yarn AM launch context:")
+    logDebug("YARN AM launch context:")
     logDebug(s"    user class: ${Option(args.userClass).getOrElse("N/A")}")
     logDebug("    env:")
     launchEnv.foreach { case (k, v) => logDebug(s"        $k -> $v") }
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 78e27fb7f3337..52580deb372c2 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -86,10 +86,17 @@ class ExecutorRunnable(
     val commands = prepareCommand(masterAddress, slaveId, hostname, executorMemory, executorCores,
       appId, localResources)
 
-    logInfo(s"Setting up executor with environment: $env")
-    logInfo("Setting up executor with commands: " + commands)
-    ctx.setCommands(commands)
+    logInfo(s"""
+      |===============================================================================
+      |YARN executor launch context:
+      |  env:
+      |${env.map { case (k, v) => s"    $k -> $v\n" }.mkString}
+      |  command:
+      |    ${commands.mkString(" ")}
+      |===============================================================================
+      """.stripMargin)
 
+    ctx.setCommands(commands)
     ctx.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr))
 
     // If external shuffle service is enabled, register with the Yarn shuffle service already

From 520ec0ff9db75267f627dc4615b2316a1a3d44d7 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Thu, 30 Jul 2015 10:45:32 -0700
Subject: [PATCH 0705/1454] [SPARK-8850] [SQL] Enable Unsafe mode by default

This pull request enables Unsafe mode by default in Spark SQL. In order to do this, we had to fix a number of small issues:

**List of fixed blockers**:

- [x] Make some default buffer sizes configurable so that HiveCompatibilitySuite can run properly (#7741).
- [x] Memory leak on grouped aggregation of empty input (fixed by #7560 to fix this)
- [x] Update planner to also check whether codegen is enabled before planning unsafe operators.
- [x] Investigate failing HiveThriftBinaryServerSuite test.  This turns out to be caused by a ClassCastException that occurs when Exchange tries to apply an interpreted RowOrdering to an UnsafeRow when range partitioning an RDD.  This could be fixed by #7408, but a shorter-term fix is to just skip the Unsafe exchange path when RangePartitioner is used.
- [x] Memory leak exceptions masking exceptions that actually caused tasks to fail (will be fixed by #7603).
- [x]  ~~https://issues.apache.org/jira/browse/SPARK-9162, to implement code generation for ScalaUDF.  This is necessary for `UDFSuite` to pass.  For now, I've just ignored this test in order to try to find other problems while we wait for a fix.~~ This is no longer necessary as of #7682.
- [x] Memory leaks from Limit after UnsafeExternalSort cause the memory leak detector to fail tests. This is a huge problem in the HiveCompatibilitySuite (fixed by f4ac642a4e5b2a7931c5e04e086bb10e263b1db6).
- [x] Tests in `AggregationQuerySuite` are failing due to NaN-handling issues in UnsafeRow, which were fixed in #7736.
- [x] `org.apache.spark.sql.ColumnExpressionSuite.rand` needs to be updated so that the planner check also matches `TungstenProject`.
- [x] After having lowered the buffer sizes to 4MB so that most of HiveCompatibilitySuite runs:
  - [x] Wrong answer in `join_1to1` (fixed by #7680)
  - [x] Wrong answer in `join_nulls` (fixed by #7680)
  - [x] Managed memory OOM / leak in `lateral_view`
  - [x] Seems to hang indefinitely in `partcols1`.  This might be a deadlock in script transformation or a bug in error-handling code? The hang was fixed by #7710.
  - [x] Error while freeing memory in `partcols1`: will be fixed by #7734.
- [x] After fixing the `partcols1` hang, it appears that a number of later tests have issues as well.
- [x] Fix thread-safety bug in codegen fallback expression evaluation (#7759).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7564 from JoshRosen/unsafe-by-default and squashes the following commits:

83c0c56 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-by-default
f4cc859 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-by-default
963f567 [Josh Rosen] Reduce buffer size for R tests
d6986de [Josh Rosen] Lower page size in PySpark tests
013b9da [Josh Rosen] Also match TungstenProject in checkNumProjects
5d0b2d3 [Josh Rosen] Add task completion callback to avoid leak in limit after sort
ea250da [Josh Rosen] Disable unsafe Exchange path when RangePartitioning is used
715517b [Josh Rosen] Enable Unsafe by default
---
 R/run-tests.sh                                |  2 +-
 .../unsafe/sort/UnsafeExternalSorter.java     | 14 +++++++++++++
 python/pyspark/java_gateway.py                |  6 +++++-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  2 +-
 .../apache/spark/sql/execution/Exchange.scala |  7 ++++++-
 .../spark/sql/ColumnExpressionSuite.scala     |  3 ++-
 .../execution/UnsafeExternalSortSuite.scala   | 20 +------------------
 7 files changed, 30 insertions(+), 24 deletions(-)

diff --git a/R/run-tests.sh b/R/run-tests.sh
index e82ad0ba2cd06..18a1e13bdc655 100755
--- a/R/run-tests.sh
+++ b/R/run-tests.sh
@@ -23,7 +23,7 @@ FAILED=0
 LOGFILE=$FWDIR/unit-tests.out
 rm -f $LOGFILE
 
-SPARK_TESTING=1 $FWDIR/../bin/sparkR --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE
+SPARK_TESTING=1 $FWDIR/../bin/sparkR --conf spark.buffer.pageSize=4m --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE
 FAILED=$((PIPESTATUS[0]||$FAILED))
 
 if [[ $FAILED != 0 ]]; then
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index c21990f4e4778..866e0b4151577 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -20,6 +20,9 @@
 import java.io.IOException;
 import java.util.LinkedList;
 
+import scala.runtime.AbstractFunction0;
+import scala.runtime.BoxedUnit;
+
 import com.google.common.annotations.VisibleForTesting;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -90,6 +93,17 @@ public UnsafeExternalSorter(
     this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
     this.pageSizeBytes = conf.getSizeAsBytes("spark.buffer.pageSize", "64m");
     initializeForWriting();
+
+    // Register a cleanup task with TaskContext to ensure that memory is guaranteed to be freed at
+    // the end of the task. This is necessary to avoid memory leaks in when the downstream operator
+    // does not fully consume the sorter's output (e.g. sort followed by limit).
+    taskContext.addOnCompleteCallback(new AbstractFunction0<BoxedUnit>() {
+      @Override
+      public BoxedUnit apply() {
+        freeMemory();
+        return null;
+      }
+    });
   }
 
   // TODO: metrics tracking + integration with shuffle write metrics
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 90cd342a6cf7f..60be85e53e2aa 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -52,7 +52,11 @@ def launch_gateway():
         script = "./bin/spark-submit.cmd" if on_windows else "./bin/spark-submit"
         submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "pyspark-shell")
         if os.environ.get("SPARK_TESTING"):
-            submit_args = "--conf spark.ui.enabled=false " + submit_args
+            submit_args = ' '.join([
+                "--conf spark.ui.enabled=false",
+                "--conf spark.buffer.pageSize=4mb",
+                submit_args
+            ])
         command = [os.path.join(SPARK_HOME, script)] + shlex.split(submit_args)
 
         # Start a socket that will be used by PythonGatewayServer to communicate its port to us
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2564bbd2077bf..6644e85d4a037 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -229,7 +229,7 @@ private[spark] object SQLConf {
       " a specific query.")
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
-    defaultValue = Some(false),
+    defaultValue = Some(true),
     doc = "When true, use the new optimized Tungsten physical execution backend.")
 
   val DIALECT = stringConf(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 41a0c519ba527..70e5031fb63c0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -47,7 +47,12 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
   override def canProcessSafeRows: Boolean = true
 
-  override def canProcessUnsafeRows: Boolean = true
+  override def canProcessUnsafeRows: Boolean = {
+    // Do not use the Unsafe path if we are using a RangePartitioning, since this may lead to
+    // an interpreted RowOrdering being applied to an UnsafeRow, which will lead to
+    // ClassCastExceptions at runtime. This check can be removed after SPARK-9054 is fixed.
+    !newPartitioning.isInstanceOf[RangePartitioning]
+  }
 
   /**
    * Determines whether records must be defensively copied before being sent to the shuffle.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 5c1102410879a..eb64684ae0fd9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql
 
 import org.scalatest.Matchers._
 
-import org.apache.spark.sql.execution.Project
+import org.apache.spark.sql.execution.{Project, TungstenProject}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.test.SQLTestUtils
@@ -538,6 +538,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     def checkNumProjects(df: DataFrame, expectedNumProjects: Int): Unit = {
       val projects = df.queryExecution.executedPlan.collect {
         case project: Project => project
+        case tungstenProject: TungstenProject => tungstenProject
       }
       assert(projects.size === expectedNumProjects)
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
index 7a4baa9e4a49d..138636b0c65b8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
@@ -36,10 +36,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
   }
 
-  ignore("sort followed by limit should not leak memory") {
-    // TODO: this test is going to fail until we implement a proper iterator interface
-    // with a close() method.
-    TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
+  test("sort followed by limit") {
     checkThatPlansAgree(
       (1 to 100).map(v => Tuple1(v)).toDF("a"),
       (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
@@ -48,21 +45,6 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     )
   }
 
-  test("sort followed by limit") {
-    TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
-    try {
-      checkThatPlansAgree(
-        (1 to 100).map(v => Tuple1(v)).toDF("a"),
-        (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
-        (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
-        sortAnswers = false
-      )
-    } finally {
-      TestSQLContext.sparkContext.conf.set("spark.unsafe.exceptionOnMemoryLeak", "false")
-
-    }
-  }
-
   test("sorting does not crash for large inputs") {
     val sortOrder = 'a.asc :: Nil
     val stringLength = 1024 * 1024 * 2

From 06b6a074fb224b3fe23922bdc89fc5f7c2ffaaf6 Mon Sep 17 00:00:00 2001
From: Imran Rashid <irashid@cloudera.com>
Date: Thu, 30 Jul 2015 10:46:26 -0700
Subject: [PATCH 0706/1454] [SPARK-9437] [CORE] avoid overflow in SizeEstimator

https://issues.apache.org/jira/browse/SPARK-9437

Author: Imran Rashid <irashid@cloudera.com>

Closes #7750 from squito/SPARK-9437_size_estimator_overflow and squashes the following commits:

29493f1 [Imran Rashid] prevent another potential overflow
bc1cb82 [Imran Rashid] avoid overflow
---
 .../main/scala/org/apache/spark/util/SizeEstimator.scala    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
index 7d84468f62ab1..14b1f2a17e707 100644
--- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
+++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala
@@ -217,10 +217,10 @@ object SizeEstimator extends Logging {
     var arrSize: Long = alignSize(objectSize + INT_SIZE)
 
     if (elementClass.isPrimitive) {
-      arrSize += alignSize(length * primitiveSize(elementClass))
+      arrSize += alignSize(length.toLong * primitiveSize(elementClass))
       state.size += arrSize
     } else {
-      arrSize += alignSize(length * pointerSize)
+      arrSize += alignSize(length.toLong * pointerSize)
       state.size += arrSize
 
       if (length <= ARRAY_SIZE_FOR_SAMPLING) {
@@ -336,7 +336,7 @@ object SizeEstimator extends Logging {
     // hg.openjdk.java.net/jdk8/jdk8/hotspot/file/tip/src/share/vm/classfile/classFileParser.cpp
     var alignedSize = shellSize
     for (size <- fieldSizes if sizeCount(size) > 0) {
-      val count = sizeCount(size)
+      val count = sizeCount(size).toLong
       // If there are internal gaps, smaller field can fit in.
       alignedSize = math.max(alignedSize, alignSizeUp(shellSize, size) + size * count)
       shellSize += size * count

From 6d94bf6ac10ac851636c62439f8f2737f3526a2a Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Thu, 30 Jul 2015 11:13:15 -0700
Subject: [PATCH 0707/1454] [SPARK-8174] [SPARK-8175] [SQL] function
 unix_timestamp, from_unixtime

unix_timestamp(): long
Gets current Unix timestamp in seconds.

unix_timestamp(string|date): long
Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds), using the default timezone and the default locale, return null if fail: unix_timestamp('2009-03-20 11:30:01') = 1237573801

unix_timestamp(string date, string pattern): long
Convert time string with given pattern (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html]) to Unix time stamp (in seconds), return null if fail: unix_timestamp('2009-03-20', 'yyyy-MM-dd') = 1237532400.

from_unixtime(bigint unixtime[, string format]): string
Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string representing the timestamp of that moment in the current system time zone in the format of "1970-01-01 00:00:00".

Jira:
https://issues.apache.org/jira/browse/SPARK-8174
https://issues.apache.org/jira/browse/SPARK-8175

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #7644 from adrian-wang/udfunixtime and squashes the following commits:

2fe20c4 [Daoyuan Wang] util.Date
ea2ec16 [Daoyuan Wang] use util.Date for better performance
a2cf929 [Daoyuan Wang] doc return null instead of 0
f6f070a [Daoyuan Wang] address comments from davies
6a4cbb3 [Daoyuan Wang] temp
56ded53 [Daoyuan Wang] rebase and address comments
14a8b37 [Daoyuan Wang] function unix_timestamp, from_unixtime
---
 .../catalyst/analysis/FunctionRegistry.scala  |   2 +
 .../expressions/datetimeFunctions.scala       | 219 +++++++++++++++++-
 .../expressions/DateExpressionsSuite.scala    |  59 ++++-
 .../org/apache/spark/sql/functions.scala      |  42 ++++
 .../apache/spark/sql/DateFunctionsSuite.scala |  56 +++++
 5 files changed, 374 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 378df4f57d9e2..d663f12bc6d0d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -211,6 +211,7 @@ object FunctionRegistry {
     expression[DayOfMonth]("day"),
     expression[DayOfYear]("dayofyear"),
     expression[DayOfMonth]("dayofmonth"),
+    expression[FromUnixTime]("from_unixtime"),
     expression[Hour]("hour"),
     expression[LastDay]("last_day"),
     expression[Minute]("minute"),
@@ -218,6 +219,7 @@ object FunctionRegistry {
     expression[NextDay]("next_day"),
     expression[Quarter]("quarter"),
     expression[Second]("second"),
+    expression[UnixTimestamp]("unix_timestamp"),
     expression[WeekOfYear]("weekofyear"),
     expression[Year]("year"),
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index efecb771f2f5d..a5e6249e438d2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import java.sql.Date
 import java.text.SimpleDateFormat
 import java.util.{Calendar, TimeZone}
 
@@ -28,6 +27,8 @@ import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
+import scala.util.Try
+
 /**
  * Returns the current date at the start of query evaluation.
  * All calls of current_date within the same query return the same value.
@@ -236,20 +237,232 @@ case class DateFormatClass(left: Expression, right: Expression) extends BinaryEx
 
   override protected def nullSafeEval(timestamp: Any, format: Any): Any = {
     val sdf = new SimpleDateFormat(format.toString)
-    UTF8String.fromString(sdf.format(new Date(timestamp.asInstanceOf[Long] / 1000)))
+    UTF8String.fromString(sdf.format(new java.util.Date(timestamp.asInstanceOf[Long] / 1000)))
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val sdf = classOf[SimpleDateFormat].getName
     defineCodeGen(ctx, ev, (timestamp, format) => {
       s"""UTF8String.fromString((new $sdf($format.toString()))
-          .format(new java.sql.Date($timestamp / 1000)))"""
+          .format(new java.util.Date($timestamp / 1000)))"""
     })
   }
 
   override def prettyName: String = "date_format"
 }
 
+/**
+ * Converts time string with given pattern
+ * (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html])
+ * to Unix time stamp (in seconds), returns null if fail.
+ * Note that hive Language Manual says it returns 0 if fail, but in fact it returns null.
+ * If the second parameter is missing, use "yyyy-MM-dd HH:mm:ss".
+ * If no parameters provided, the first parameter will be current_timestamp.
+ * If the first parameter is a Date or Timestamp instead of String, we will ignore the
+ * second parameter.
+ */
+case class UnixTimestamp(timeExp: Expression, format: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  override def left: Expression = timeExp
+  override def right: Expression = format
+
+  def this(time: Expression) = {
+    this(time, Literal("yyyy-MM-dd HH:mm:ss"))
+  }
+
+  def this() = {
+    this(CurrentTimestamp())
+  }
+
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(StringType, DateType, TimestampType), StringType)
+
+  override def dataType: DataType = LongType
+
+  private lazy val constFormat: UTF8String = right.eval().asInstanceOf[UTF8String]
+
+  override def eval(input: InternalRow): Any = {
+    val t = left.eval(input)
+    if (t == null) {
+      null
+    } else {
+      left.dataType match {
+        case DateType =>
+          DateTimeUtils.daysToMillis(t.asInstanceOf[Int]) / 1000L
+        case TimestampType =>
+          t.asInstanceOf[Long] / 1000000L
+        case StringType if right.foldable =>
+          if (constFormat != null) {
+            Try(new SimpleDateFormat(constFormat.toString).parse(
+              t.asInstanceOf[UTF8String].toString).getTime / 1000L).getOrElse(null)
+          } else {
+            null
+          }
+        case StringType =>
+          val f = format.eval(input)
+          if (f == null) {
+            null
+          } else {
+            val formatString = f.asInstanceOf[UTF8String].toString
+            Try(new SimpleDateFormat(formatString).parse(
+              t.asInstanceOf[UTF8String].toString).getTime / 1000L).getOrElse(null)
+          }
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    left.dataType match {
+      case StringType if right.foldable =>
+        val sdf = classOf[SimpleDateFormat].getName
+        val fString = if (constFormat == null) null else constFormat.toString
+        val formatter = ctx.freshName("formatter")
+        if (fString == null) {
+          s"""
+            boolean ${ev.isNull} = true;
+            ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          """
+        } else {
+          val eval1 = left.gen(ctx)
+          s"""
+            ${eval1.code}
+            boolean ${ev.isNull} = ${eval1.isNull};
+            ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+            if (!${ev.isNull}) {
+              try {
+                $sdf $formatter = new $sdf("$fString");
+                ${ev.primitive} =
+                  $formatter.parse(${eval1.primitive}.toString()).getTime() / 1000L;
+              } catch (java.lang.Throwable e) {
+                ${ev.isNull} = true;
+              }
+            }
+          """
+        }
+      case StringType =>
+        val sdf = classOf[SimpleDateFormat].getName
+        nullSafeCodeGen(ctx, ev, (string, format) => {
+          s"""
+            try {
+              ${ev.primitive} =
+                (new $sdf($format.toString())).parse($string.toString()).getTime() / 1000L;
+            } catch (java.lang.Throwable e) {
+              ${ev.isNull} = true;
+            }
+          """
+        })
+      case TimestampType =>
+        val eval1 = left.gen(ctx)
+        s"""
+          ${eval1.code}
+          boolean ${ev.isNull} = ${eval1.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = ${eval1.primitive} / 1000000L;
+          }
+        """
+      case DateType =>
+        val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+        val eval1 = left.gen(ctx)
+        s"""
+          ${eval1.code}
+          boolean ${ev.isNull} = ${eval1.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $dtu.daysToMillis(${eval1.primitive}) / 1000L;
+          }
+        """
+    }
+  }
+}
+
+/**
+ * Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
+ * representing the timestamp of that moment in the current system time zone in the given
+ * format. If the format is missing, using format like "1970-01-01 00:00:00".
+ * Note that hive Language Manual says it returns 0 if fail, but in fact it returns null.
+ */
+case class FromUnixTime(sec: Expression, format: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = sec
+  override def right: Expression = format
+
+  def this(unix: Expression) = {
+    this(unix, Literal("yyyy-MM-dd HH:mm:ss"))
+  }
+
+  override def dataType: DataType = StringType
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(LongType, StringType)
+
+  private lazy val constFormat: UTF8String = right.eval().asInstanceOf[UTF8String]
+
+  override def eval(input: InternalRow): Any = {
+    val time = left.eval(input)
+    if (time == null) {
+      null
+    } else {
+      if (format.foldable) {
+        if (constFormat == null) {
+          null
+        } else {
+          Try(UTF8String.fromString(new SimpleDateFormat(constFormat.toString).format(
+            new java.util.Date(time.asInstanceOf[Long] * 1000L)))).getOrElse(null)
+        }
+      } else {
+        val f = format.eval(input)
+        if (f == null) {
+          null
+        } else {
+          Try(UTF8String.fromString(new SimpleDateFormat(
+            f.asInstanceOf[UTF8String].toString).format(new java.util.Date(
+              time.asInstanceOf[Long] * 1000L)))).getOrElse(null)
+        }
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val sdf = classOf[SimpleDateFormat].getName
+    if (format.foldable) {
+      if (constFormat == null) {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      } else {
+        val t = left.gen(ctx)
+        s"""
+          ${t.code}
+          boolean ${ev.isNull} = ${t.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            try {
+              ${ev.primitive} = UTF8String.fromString(new $sdf("${constFormat.toString}").format(
+                new java.util.Date(${t.primitive} * 1000L)));
+            } catch (java.lang.Throwable e) {
+              ${ev.isNull} = true;
+            }
+          }
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (seconds, f) => {
+        s"""
+        try {
+          ${ev.primitive} = UTF8String.fromString((new $sdf($f.toString())).format(
+            new java.util.Date($seconds * 1000L)));
+        } catch (java.lang.Throwable e) {
+          ${ev.isNull} = true;
+        }""".stripMargin
+      })
+    }
+  }
+
+}
+
 /**
  * Returns the last day of the month which the date belongs to.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index aca8d6eb3500c..e1387f945ffa4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -22,8 +22,9 @@ import java.text.SimpleDateFormat
 import java.util.Calendar
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.types.{StringType, TimestampType, DateType}
+import org.apache.spark.sql.types._
 
 class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -303,4 +304,60 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       NextDay(Literal(Date.valueOf("2015-07-23")), Literal.create(null, StringType)), null)
   }
+
+  test("from_unixtime") {
+    val sdf1 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+    val fmt2 = "yyyy-MM-dd HH:mm:ss.SSS"
+    val sdf2 = new SimpleDateFormat(fmt2)
+    checkEvaluation(
+      FromUnixTime(Literal(0L), Literal("yyyy-MM-dd HH:mm:ss")), sdf1.format(new Timestamp(0)))
+    checkEvaluation(FromUnixTime(
+      Literal(1000L), Literal("yyyy-MM-dd HH:mm:ss")), sdf1.format(new Timestamp(1000000)))
+    checkEvaluation(
+      FromUnixTime(Literal(-1000L), Literal(fmt2)), sdf2.format(new Timestamp(-1000000)))
+    checkEvaluation(
+      FromUnixTime(Literal.create(null, LongType), Literal.create(null, StringType)), null)
+    checkEvaluation(
+      FromUnixTime(Literal.create(null, LongType), Literal("yyyy-MM-dd HH:mm:ss")), null)
+    checkEvaluation(FromUnixTime(Literal(1000L), Literal.create(null, StringType)), null)
+    checkEvaluation(
+      FromUnixTime(Literal(0L), Literal("not a valid format")), null)
+  }
+
+  test("unix_timestamp") {
+    val sdf1 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+    val fmt2 = "yyyy-MM-dd HH:mm:ss.SSS"
+    val sdf2 = new SimpleDateFormat(fmt2)
+    val fmt3 = "yy-MM-dd"
+    val sdf3 = new SimpleDateFormat(fmt3)
+    val date1 = Date.valueOf("2015-07-24")
+    checkEvaluation(
+      UnixTimestamp(Literal(sdf1.format(new Timestamp(0))), Literal("yyyy-MM-dd HH:mm:ss")), 0L)
+    checkEvaluation(UnixTimestamp(
+      Literal(sdf1.format(new Timestamp(1000000))), Literal("yyyy-MM-dd HH:mm:ss")), 1000L)
+    checkEvaluation(
+      UnixTimestamp(Literal(new Timestamp(1000000)), Literal("yyyy-MM-dd HH:mm:ss")), 1000L)
+    checkEvaluation(
+      UnixTimestamp(Literal(date1), Literal("yyyy-MM-dd HH:mm:ss")),
+      DateTimeUtils.daysToMillis(DateTimeUtils.fromJavaDate(date1)) / 1000L)
+    checkEvaluation(
+      UnixTimestamp(Literal(sdf2.format(new Timestamp(-1000000))), Literal(fmt2)), -1000L)
+    checkEvaluation(UnixTimestamp(
+      Literal(sdf3.format(Date.valueOf("2015-07-24"))), Literal(fmt3)),
+      DateTimeUtils.daysToMillis(DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-24"))) / 1000L)
+    val t1 = UnixTimestamp(
+      CurrentTimestamp(), Literal("yyyy-MM-dd HH:mm:ss")).eval().asInstanceOf[Long]
+    val t2 = UnixTimestamp(
+      CurrentTimestamp(), Literal("yyyy-MM-dd HH:mm:ss")).eval().asInstanceOf[Long]
+    assert(t2 - t1 <= 1)
+    checkEvaluation(
+      UnixTimestamp(Literal.create(null, DateType), Literal.create(null, StringType)), null)
+    checkEvaluation(
+      UnixTimestamp(Literal.create(null, DateType), Literal("yyyy-MM-dd HH:mm:ss")), null)
+    checkEvaluation(UnixTimestamp(
+      Literal(date1), Literal.create(null, StringType)), date1.getTime / 1000L)
+    checkEvaluation(
+      UnixTimestamp(Literal("2015-07-24"), Literal("not a valid format")), null)
+  }
+
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index a2fece62f61f9..3f440e062eb96 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2110,6 +2110,48 @@ object functions {
    */
   def weekofyear(columnName: String): Column = weekofyear(Column(columnName))
 
+  /**
+   * Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
+   * representing the timestamp of that moment in the current system time zone in the given
+   * format.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def from_unixtime(ut: Column): Column = FromUnixTime(ut.expr, Literal("yyyy-MM-dd HH:mm:ss"))
+
+  /**
+   * Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
+   * representing the timestamp of that moment in the current system time zone in the given
+   * format.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def from_unixtime(ut: Column, f: String): Column = FromUnixTime(ut.expr, Literal(f))
+
+  /**
+   * Gets current Unix timestamp in seconds.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def unix_timestamp(): Column = UnixTimestamp(CurrentTimestamp(), Literal("yyyy-MM-dd HH:mm:ss"))
+
+  /**
+   * Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds),
+   * using the default timezone and the default locale, return null if fail.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def unix_timestamp(s: Column): Column = UnixTimestamp(s.expr, Literal("yyyy-MM-dd HH:mm:ss"))
+
+  /**
+   * Convert time string with given pattern
+   * (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html])
+   * to Unix time stamp (in seconds), return null if fail.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def unix_timestamp(s: Column, p: String): Column = UnixTimestamp(s.expr, Literal(p))
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   // Collection functions
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 07eb6e4a8d8cd..df4cb57ac5b21 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -228,4 +228,60 @@ class DateFunctionsSuite extends QueryTest {
       Seq(Row(Date.valueOf("2015-07-30")), Row(Date.valueOf("2015-07-30"))))
   }
 
+  test("from_unixtime") {
+    val sdf1 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
+    val fmt2 = "yyyy-MM-dd HH:mm:ss.SSS"
+    val sdf2 = new SimpleDateFormat(fmt2)
+    val fmt3 = "yy-MM-dd HH-mm-ss"
+    val sdf3 = new SimpleDateFormat(fmt3)
+    val df = Seq((1000, "yyyy-MM-dd HH:mm:ss.SSS"), (-1000, "yy-MM-dd HH-mm-ss")).toDF("a", "b")
+    checkAnswer(
+      df.select(from_unixtime(col("a"))),
+      Seq(Row(sdf1.format(new Timestamp(1000000))), Row(sdf1.format(new Timestamp(-1000000)))))
+    checkAnswer(
+      df.select(from_unixtime(col("a"), fmt2)),
+      Seq(Row(sdf2.format(new Timestamp(1000000))), Row(sdf2.format(new Timestamp(-1000000)))))
+    checkAnswer(
+      df.select(from_unixtime(col("a"), fmt3)),
+      Seq(Row(sdf3.format(new Timestamp(1000000))), Row(sdf3.format(new Timestamp(-1000000)))))
+    checkAnswer(
+      df.selectExpr("from_unixtime(a)"),
+      Seq(Row(sdf1.format(new Timestamp(1000000))), Row(sdf1.format(new Timestamp(-1000000)))))
+    checkAnswer(
+      df.selectExpr(s"from_unixtime(a, '$fmt2')"),
+      Seq(Row(sdf2.format(new Timestamp(1000000))), Row(sdf2.format(new Timestamp(-1000000)))))
+    checkAnswer(
+      df.selectExpr(s"from_unixtime(a, '$fmt3')"),
+      Seq(Row(sdf3.format(new Timestamp(1000000))), Row(sdf3.format(new Timestamp(-1000000)))))
+  }
+
+  test("unix_timestamp") {
+    val date1 = Date.valueOf("2015-07-24")
+    val date2 = Date.valueOf("2015-07-25")
+    val ts1 = Timestamp.valueOf("2015-07-24 10:00:00.3")
+    val ts2 = Timestamp.valueOf("2015-07-25 02:02:02.2")
+    val s1 = "2015/07/24 10:00:00.5"
+    val s2 = "2015/07/25 02:02:02.6"
+    val ss1 = "2015-07-24 10:00:00"
+    val ss2 = "2015-07-25 02:02:02"
+    val fmt = "yyyy/MM/dd HH:mm:ss.S"
+    val df = Seq((date1, ts1, s1, ss1), (date2, ts2, s2, ss2)).toDF("d", "ts", "s", "ss")
+    checkAnswer(df.select(unix_timestamp(col("ts"))), Seq(
+      Row(ts1.getTime / 1000L), Row(ts2.getTime / 1000L)))
+    checkAnswer(df.select(unix_timestamp(col("ss"))), Seq(
+      Row(ts1.getTime / 1000L), Row(ts2.getTime / 1000L)))
+    checkAnswer(df.select(unix_timestamp(col("d"), fmt)), Seq(
+      Row(date1.getTime / 1000L), Row(date2.getTime / 1000L)))
+    checkAnswer(df.select(unix_timestamp(col("s"), fmt)), Seq(
+      Row(ts1.getTime / 1000L), Row(ts2.getTime / 1000L)))
+    checkAnswer(df.selectExpr("unix_timestamp(ts)"), Seq(
+      Row(ts1.getTime / 1000L), Row(ts2.getTime / 1000L)))
+    checkAnswer(df.selectExpr("unix_timestamp(ss)"), Seq(
+      Row(ts1.getTime / 1000L), Row(ts2.getTime / 1000L)))
+    checkAnswer(df.selectExpr(s"unix_timestamp(d, '$fmt')"), Seq(
+      Row(date1.getTime / 1000L), Row(date2.getTime / 1000L)))
+    checkAnswer(df.selectExpr(s"unix_timestamp(s, '$fmt')"), Seq(
+      Row(ts1.getTime / 1000L), Row(ts2.getTime / 1000L)))
+  }
+
 }

From a20e743fb863de809863652931bc982aac2d1f86 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 30 Jul 2015 13:09:43 -0700
Subject: [PATCH 0708/1454] [SPARK-9460] Fix prefix generation for UTF8String.

Previously we could be getting garbage data if the number of bytes is 0, or on JVMs that are 4 byte aligned, or when compressedoops is on.

Author: Reynold Xin <rxin@databricks.com>

Closes #7789 from rxin/utf8string and squashes the following commits:

86ffa3e [Reynold Xin] Mask out data outside of valid range.
4d647ed [Reynold Xin] Mask out data.
c6e8794 [Reynold Xin] [SPARK-9460] Fix prefix generation for UTF8String.
---
 .../apache/spark/unsafe/types/UTF8String.java | 36 +++++++++++++++++--
 .../spark/unsafe/types/UTF8StringSuite.java   |  8 +++++
 2 files changed, 41 insertions(+), 3 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 57522003ba2ba..c38953f65d7d7 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -65,6 +65,19 @@ public static UTF8String fromBytes(byte[] bytes) {
     }
   }
 
+  /**
+   * Creates an UTF8String from byte array, which should be encoded in UTF-8.
+   *
+   * Note: `bytes` will be hold by returned UTF8String.
+   */
+  public static UTF8String fromBytes(byte[] bytes, int offset, int numBytes) {
+    if (bytes != null) {
+      return new UTF8String(bytes, BYTE_ARRAY_OFFSET + offset, numBytes);
+    } else {
+      return null;
+    }
+  }
+
   /**
    * Creates an UTF8String from String.
    */
@@ -89,10 +102,10 @@ public static UTF8String blankString(int length) {
     return fromBytes(spaces);
   }
 
-  protected UTF8String(Object base, long offset, int size) {
+  protected UTF8String(Object base, long offset, int numBytes) {
     this.base = base;
     this.offset = offset;
-    this.numBytes = size;
+    this.numBytes = numBytes;
   }
 
   /**
@@ -141,7 +154,24 @@ public int numChars() {
    * Returns a 64-bit integer that can be used as the prefix used in sorting.
    */
   public long getPrefix() {
-    long p = PlatformDependent.UNSAFE.getLong(base, offset);
+    // Since JVMs are either 4-byte aligned or 8-byte aligned, we check the size of the string.
+    // If size is 0, just return 0.
+    // If size is between 0 and 4 (inclusive), assume data is 4-byte aligned under the hood and
+    // use a getInt to fetch the prefix.
+    // If size is greater than 4, assume we have at least 8 bytes of data to fetch.
+    // After getting the data, we use a mask to mask out data that is not part of the string.
+    long p;
+    if (numBytes >= 8) {
+      p = PlatformDependent.UNSAFE.getLong(base, offset);
+    } else  if (numBytes > 4) {
+      p = PlatformDependent.UNSAFE.getLong(base, offset);
+      p = p & ((1L << numBytes * 8) - 1);
+    } else if (numBytes > 0) {
+      p = (long) PlatformDependent.UNSAFE.getInt(base, offset);
+      p = p & ((1L << numBytes * 8) - 1);
+    } else {
+      p = 0;
+    }
     p = java.lang.Long.reverseBytes(p);
     return p;
   }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 42e09e435a412..f2cc19ca6b172 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -71,6 +71,14 @@ public void prefix() {
       fromString("abbbbbbbbbbbasdf").getPrefix() - fromString("bbbbbbbbbbbbasdf").getPrefix() < 0);
     assertTrue(fromString("").getPrefix() - fromString("a").getPrefix() < 0);
     assertTrue(fromString("你好").getPrefix() - fromString("世界").getPrefix() > 0);
+
+    byte[] buf1 = {1, 2, 3, 4, 5, 6, 7, 8, 9};
+    byte[] buf2 = {1, 2, 3};
+    UTF8String str1 = UTF8String.fromBytes(buf1, 0, 3);
+    UTF8String str2 = UTF8String.fromBytes(buf1, 0, 8);
+    UTF8String str3 = UTF8String.fromBytes(buf2);
+    assertTrue(str1.getPrefix() - str2.getPrefix() < 0);
+    assertEquals(str1.getPrefix(), str3.getPrefix());
   }
 
   @Test

From d8cfd531c7c50c9b00ab546be458f44f84c386ae Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 30 Jul 2015 13:17:54 -0700
Subject: [PATCH 0709/1454] [SPARK-5567] [MLLIB] Add predict method to
 LocalLDAModel

jkbradley hhbyyh

Adds `topicDistributions` to LocalLDAModel. Please review after #7757 is merged.

Author: Feynman Liang <fliang@databricks.com>

Closes #7760 from feynmanliang/SPARK-5567-predict-in-LDA and squashes the following commits:

0ad1134 [Feynman Liang] Remove println
27b3877 [Feynman Liang] Code review fixes
6bfb87c [Feynman Liang] Remove extra newline
476f788 [Feynman Liang] Fix checks and doc for variationalInference
061780c [Feynman Liang] Code review cleanup
3be2947 [Feynman Liang] Rename topicDistribution -> topicDistributions
2a821a6 [Feynman Liang] Add predict methods to LocalLDAModel
---
 .../spark/mllib/clustering/LDAModel.scala     | 42 +++++++++++--
 .../spark/mllib/clustering/LDAOptimizer.scala |  5 +-
 .../spark/mllib/clustering/LDASuite.scala     | 63 +++++++++++++++++++
 3 files changed, 102 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index ece28848aa02c..6cfad3fbbdb87 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -186,7 +186,6 @@ abstract class LDAModel private[clustering] extends Saveable {
  * This model stores only the inferred topics.
  * It may be used for computing topics for new documents, but it may give less accurate answers
  * than the [[DistributedLDAModel]].
- *
  * @param topics Inferred topics (vocabSize x k matrix).
  */
 @Experimental
@@ -221,9 +220,6 @@ class LocalLDAModel private[clustering] (
   // TODO
   // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ???
 
-  // TODO:
-  // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ???
-
   /**
    * Calculate the log variational bound on perplexity. See Equation (16) in original Online
    * LDA paper.
@@ -269,7 +265,7 @@ class LocalLDAModel private[clustering] (
     // by topic (columns of lambda)
     val Elogbeta = LDAUtils.dirichletExpectation(lambda.t).t
 
-    var score = documents.filter(_._2.numActives > 0).map { case (id: Long, termCounts: Vector) =>
+    var score = documents.filter(_._2.numNonzeros > 0).map { case (id: Long, termCounts: Vector) =>
       var docScore = 0.0D
       val (gammad: BDV[Double], _) = OnlineLDAOptimizer.variationalTopicInference(
         termCounts, exp(Elogbeta), brzAlpha, gammaShape, k)
@@ -277,7 +273,7 @@ class LocalLDAModel private[clustering] (
 
       // E[log p(doc | theta, beta)]
       termCounts.foreachActive { case (idx, count) =>
-        docScore += LDAUtils.logSumExp(Elogthetad + Elogbeta(idx, ::).t)
+        docScore += count * LDAUtils.logSumExp(Elogthetad + Elogbeta(idx, ::).t)
       }
       // E[log p(theta | alpha) - log q(theta | gamma)]; assumes alpha is a vector
       docScore += sum((brzAlpha - gammad) :* Elogthetad)
@@ -297,6 +293,40 @@ class LocalLDAModel private[clustering] (
     score
   }
 
+  /**
+   * Predicts the topic mixture distribution for each document (often called "theta" in the
+   * literature).  Returns a vector of zeros for an empty document.
+   *
+   * This uses a variational approximation following Hoffman et al. (2010), where the approximate
+   * distribution is called "gamma."  Technically, this method returns this approximation "gamma"
+   * for each document.
+   * @param documents documents to predict topic mixture distributions for
+   * @return An RDD of (document ID, topic mixture distribution for document)
+   */
+  // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
+  def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = {
+    // Double transpose because dirichletExpectation normalizes by row and we need to normalize
+    // by topic (columns of lambda)
+    val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.toBreeze.toDenseMatrix.t).t)
+    val docConcentrationBrz = this.docConcentration.toBreeze
+    val gammaShape = this.gammaShape
+    val k = this.k
+
+    documents.map { case (id: Long, termCounts: Vector) =>
+      if (termCounts.numNonzeros == 0) {
+         (id, Vectors.zeros(k))
+      } else {
+        val (gamma, _) = OnlineLDAOptimizer.variationalTopicInference(
+          termCounts,
+          expElogbeta,
+          docConcentrationBrz,
+          gammaShape,
+          k)
+        (id, Vectors.dense(normalize(gamma, 1.0).toArray))
+      }
+    }
+  }
+
 }
 
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 4b90fbdf0ce7e..9dbec41efeada 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -394,7 +394,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     val gammaShape = this.gammaShape
 
     val stats: RDD[(BDM[Double], List[BDV[Double]])] = batch.mapPartitions { docs =>
-      val nonEmptyDocs = docs.filter(_._2.numActives > 0)
+      val nonEmptyDocs = docs.filter(_._2.numNonzeros > 0)
 
       val stat = BDM.zeros[Double](k, vocabSize)
       var gammaPart = List[BDV[Double]]()
@@ -461,7 +461,8 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 private[clustering] object OnlineLDAOptimizer {
   /**
    * Uses variational inference to infer the topic distribution `gammad` given the term counts
-   * for a document. `termCounts` must be non-empty, otherwise Breeze will throw a BLAS error.
+   * for a document. `termCounts` must contain at least one non-zero entry, otherwise Breeze will
+   * throw a BLAS error.
    *
    * An optimization (Lee, Seung: Algorithms for non-negative matrix factorization, NIPS 2001)
    * avoids explicit computation of variational parameter `phi`.
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 61d2edfd9fb5f..d74482d3a7598 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -242,6 +242,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     val alpha = 0.01
     val eta = 0.01
     val gammaShape = 100
+    // obtained from LDA model trained in gensim, see below
     val topics = new DenseMatrix(numRows = vocabSize, numCols = k, values = Array(
       1.86738052, 1.94056535, 1.89981687, 0.0833265, 0.07405918, 0.07940597,
       0.15081551, 0.08637973, 0.12428538, 1.9474897, 1.94615165, 1.95204124))
@@ -281,6 +282,68 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(ldaModel.logPerplexity(docs) ~== -3.690D relTol 1E-3D)
   }
 
+  test("LocalLDAModel predict") {
+    val k = 2
+    val vocabSize = 6
+    val alpha = 0.01
+    val eta = 0.01
+    val gammaShape = 100
+    // obtained from LDA model trained in gensim, see below
+    val topics = new DenseMatrix(numRows = vocabSize, numCols = k, values = Array(
+      1.86738052, 1.94056535, 1.89981687, 0.0833265, 0.07405918, 0.07940597,
+      0.15081551, 0.08637973, 0.12428538, 1.9474897, 1.94615165, 1.95204124))
+
+    def toydata: Array[(Long, Vector)] = Array(
+      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
+      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
+      Vectors.sparse(6, Array(4, 5), Array(1, 1))
+    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
+    val docs = sc.parallelize(toydata)
+
+    val ldaModel: LocalLDAModel = new LocalLDAModel(
+      topics, Vectors.dense(Array.fill(k)(alpha)), eta, gammaShape)
+
+    /* Verify results using gensim:
+       import numpy as np
+       from gensim import models
+       corpus = [
+          [(0, 1.0), (1, 1.0)],
+          [(1, 1.0), (2, 1.0)],
+          [(0, 1.0), (2, 1.0)],
+          [(3, 1.0), (4, 1.0)],
+          [(3, 1.0), (5, 1.0)],
+          [(4, 1.0), (5, 1.0)]]
+       np.random.seed(2345)
+       lda = models.ldamodel.LdaModel(
+          corpus=corpus, alpha=0.01, eta=0.01, num_topics=2, update_every=0, passes=100,
+          decay=0.51, offset=1024)
+       print(list(lda.get_document_topics(corpus)))
+       > [[(0, 0.99504950495049516)], [(0, 0.99504950495049516)],
+       > [(0, 0.99504950495049516)], [(1, 0.99504950495049516)],
+       > [(1, 0.99504950495049516)], [(1, 0.99504950495049516)]]
+     */
+
+    val expectedPredictions = List(
+      (0, 0.99504), (0, 0.99504),
+      (0, 0.99504), (1, 0.99504),
+      (1, 0.99504), (1, 0.99504))
+
+    val actualPredictions = ldaModel.topicDistributions(docs).map { case (id, topics) =>
+        // convert results to expectedPredictions format, which only has highest probability topic
+        val topicsBz = topics.toBreeze.toDenseVector
+        (id, (argmax(topicsBz), max(topicsBz)))
+      }.sortByKey()
+      .values
+      .collect()
+
+    expectedPredictions.zip(actualPredictions).forall { case (expected, actual) =>
+      expected._1 === actual._1 && (expected._2 ~== actual._2 relTol 1E-3D)
+    }
+  }
+
   test("OnlineLDAOptimizer with asymmetric prior") {
     def toydata: Array[(Long, Vector)] = Array(
       Vectors.sparse(6, Array(0, 1), Array(1, 1)),

From 1abf7dc16ca1ba1777fe874c8b81fe6f2b0a6de5 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Thu, 30 Jul 2015 13:21:46 -0700
Subject: [PATCH 0710/1454] [SPARK-8186] [SPARK-8187] [SPARK-8194] [SPARK-8198]
 [SPARK-9133] [SPARK-9290] [SQL] functions: date_add, date_sub, add_months,
 months_between, time-interval calculation

This PR is based on #7589 , thanks to adrian-wang

Added SQL function date_add, date_sub, add_months, month_between, also add a rule for
add/subtract of date/timestamp and interval.

Closes #7589

cc rxin

Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Davies Liu <davies@databricks.com>

Closes #7754 from davies/date_add and squashes the following commits:

e8c633a [Davies Liu] Merge branch 'master' of github.com:apache/spark into date_add
9e8e085 [Davies Liu] Merge branch 'master' of github.com:apache/spark into date_add
6224ce4 [Davies Liu] fix conclict
bd18cd4 [Davies Liu] Merge branch 'master' of github.com:apache/spark into date_add
e47ff2c [Davies Liu] add python api, fix date functions
01943d0 [Davies Liu] Merge branch 'master' into date_add
522e91a [Daoyuan Wang] fix
e8a639a [Daoyuan Wang] fix
42df486 [Daoyuan Wang] fix style
87c4b77 [Daoyuan Wang] function add_months, months_between and some fixes
1a68e03 [Daoyuan Wang] poc of time interval calculation
c506661 [Daoyuan Wang] function date_add , date_sub
---
 python/pyspark/sql/functions.py               |  76 ++++++-
 .../catalyst/analysis/FunctionRegistry.scala  |   4 +
 .../catalyst/analysis/HiveTypeCoercion.scala  |  22 ++
 .../expressions/datetimeFunctions.scala       | 155 ++++++++++++-
 .../sql/catalyst/util/DateTimeUtils.scala     | 139 ++++++++++++
 .../analysis/HiveTypeCoercionSuite.scala      |  30 +++
 .../expressions/DateExpressionsSuite.scala    | 176 +++++++++------
 .../catalyst/util/DateTimeUtilsSuite.scala    | 205 +++++++++++-------
 .../org/apache/spark/sql/functions.scala      |  29 +++
 .../apache/spark/sql/DateFunctionsSuite.scala | 117 ++++++++++
 10 files changed, 791 insertions(+), 162 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index d930f7db25d25..a7295e25f0aa5 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -59,7 +59,7 @@
 __all__ += ['lag', 'lead', 'ntile']
 
 __all__ += [
-    'date_format',
+    'date_format', 'date_add', 'date_sub', 'add_months', 'months_between',
     'year', 'quarter', 'month', 'hour', 'minute', 'second',
     'dayofmonth', 'dayofyear', 'weekofyear']
 
@@ -716,7 +716,7 @@ def date_format(dateCol, format):
     [Row(date=u'04/08/2015')]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.date_format(dateCol, format))
+    return Column(sc._jvm.functions.date_format(_to_java_column(dateCol), format))
 
 
 @since(1.5)
@@ -729,7 +729,7 @@ def year(col):
     [Row(year=2015)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.year(col))
+    return Column(sc._jvm.functions.year(_to_java_column(col)))
 
 
 @since(1.5)
@@ -742,7 +742,7 @@ def quarter(col):
     [Row(quarter=2)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.quarter(col))
+    return Column(sc._jvm.functions.quarter(_to_java_column(col)))
 
 
 @since(1.5)
@@ -755,7 +755,7 @@ def month(col):
     [Row(month=4)]
    """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.month(col))
+    return Column(sc._jvm.functions.month(_to_java_column(col)))
 
 
 @since(1.5)
@@ -768,7 +768,7 @@ def dayofmonth(col):
     [Row(day=8)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.dayofmonth(col))
+    return Column(sc._jvm.functions.dayofmonth(_to_java_column(col)))
 
 
 @since(1.5)
@@ -781,7 +781,7 @@ def dayofyear(col):
     [Row(day=98)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.dayofyear(col))
+    return Column(sc._jvm.functions.dayofyear(_to_java_column(col)))
 
 
 @since(1.5)
@@ -794,7 +794,7 @@ def hour(col):
     [Row(hour=13)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.hour(col))
+    return Column(sc._jvm.functions.hour(_to_java_column(col)))
 
 
 @since(1.5)
@@ -807,7 +807,7 @@ def minute(col):
     [Row(minute=8)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.minute(col))
+    return Column(sc._jvm.functions.minute(_to_java_column(col)))
 
 
 @since(1.5)
@@ -820,7 +820,7 @@ def second(col):
     [Row(second=15)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.second(col))
+    return Column(sc._jvm.functions.second(_to_java_column(col)))
 
 
 @since(1.5)
@@ -829,11 +829,63 @@ def weekofyear(col):
     Extract the week number of a given date as integer.
 
     >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])
-    >>> df.select(weekofyear('a').alias('week')).collect()
+    >>> df.select(weekofyear(df.a).alias('week')).collect()
     [Row(week=15)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.weekofyear(col))
+    return Column(sc._jvm.functions.weekofyear(_to_java_column(col)))
+
+
+@since(1.5)
+def date_add(start, days):
+    """
+    Returns the date that is `days` days after `start`
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['d'])
+    >>> df.select(date_add(df.d, 1).alias('d')).collect()
+    [Row(d=datetime.date(2015, 4, 9))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.date_add(_to_java_column(start), days))
+
+
+@since(1.5)
+def date_sub(start, days):
+    """
+    Returns the date that is `days` days before `start`
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['d'])
+    >>> df.select(date_sub(df.d, 1).alias('d')).collect()
+    [Row(d=datetime.date(2015, 4, 7))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.date_sub(_to_java_column(start), days))
+
+
+@since(1.5)
+def add_months(start, months):
+    """
+    Returns the date that is `months` months after `start`
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['d'])
+    >>> df.select(add_months(df.d, 1).alias('d')).collect()
+    [Row(d=datetime.date(2015, 5, 8))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.add_months(_to_java_column(start), months))
+
+
+@since(1.5)
+def months_between(date1, date2):
+    """
+    Returns the number of months between date1 and date2.
+
+    >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00', '1996-10-30')], ['t', 'd'])
+    >>> df.select(months_between(df.t, df.d).alias('months')).collect()
+    [Row(months=3.9495967...)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.months_between(_to_java_column(date1), _to_java_column(date2)))
 
 
 @since(1.5)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index d663f12bc6d0d..6c7c481fab8db 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -205,9 +205,12 @@ object FunctionRegistry {
     expression[Upper]("upper"),
 
     // datetime functions
+    expression[AddMonths]("add_months"),
     expression[CurrentDate]("current_date"),
     expression[CurrentTimestamp]("current_timestamp"),
+    expression[DateAdd]("date_add"),
     expression[DateFormatClass]("date_format"),
+    expression[DateSub]("date_sub"),
     expression[DayOfMonth]("day"),
     expression[DayOfYear]("dayofyear"),
     expression[DayOfMonth]("dayofmonth"),
@@ -216,6 +219,7 @@ object FunctionRegistry {
     expression[LastDay]("last_day"),
     expression[Minute]("minute"),
     expression[Month]("month"),
+    expression[MonthsBetween]("months_between"),
     expression[NextDay]("next_day"),
     expression[Quarter]("quarter"),
     expression[Second]("second"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index ecc48986e35d8..603afc4032a37 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -47,6 +47,7 @@ object HiveTypeCoercion {
       Division ::
       PropagateTypes ::
       ImplicitTypeCasts ::
+      DateTimeOperations ::
       Nil
 
   // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
@@ -638,6 +639,27 @@ object HiveTypeCoercion {
     }
   }
 
+  /**
+   * Turns Add/Subtract of DateType/TimestampType/StringType and CalendarIntervalType
+   * to TimeAdd/TimeSub
+   */
+  object DateTimeOperations extends Rule[LogicalPlan] {
+
+    private val acceptedTypes = Seq(DateType, TimestampType, StringType)
+
+    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+      // Skip nodes who's children have not been resolved yet.
+      case e if !e.childrenResolved => e
+
+      case Add(l @ CalendarIntervalType(), r) if acceptedTypes.contains(r.dataType) =>
+        Cast(TimeAdd(r, l), r.dataType)
+      case Add(l, r @ CalendarIntervalType()) if acceptedTypes.contains(l.dataType) =>
+        Cast(TimeAdd(l, r), l.dataType)
+      case Subtract(l, r @ CalendarIntervalType()) if acceptedTypes.contains(l.dataType) =>
+        Cast(TimeSub(l, r), l.dataType)
+    }
+  }
+
   /**
    * Casts types according to the expected input types for [[Expression]]s.
    */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index a5e6249e438d2..9795673ee0664 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -25,7 +25,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 import scala.util.Try
 
@@ -63,6 +63,53 @@ case class CurrentTimestamp() extends LeafExpression with CodegenFallback {
   }
 }
 
+/**
+ * Adds a number of days to startdate.
+ */
+case class DateAdd(startDate: Expression, days: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = startDate
+  override def right: Expression = days
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType, IntegerType)
+
+  override def dataType: DataType = DateType
+
+  override def nullSafeEval(start: Any, d: Any): Any = {
+    start.asInstanceOf[Int] + d.asInstanceOf[Int]
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (sd, d) => {
+      s"""${ev.primitive} = $sd + $d;"""
+    })
+  }
+}
+
+/**
+ * Subtracts a number of days to startdate.
+ */
+case class DateSub(startDate: Expression, days: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+  override def left: Expression = startDate
+  override def right: Expression = days
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType, IntegerType)
+
+  override def dataType: DataType = DateType
+
+  override def nullSafeEval(start: Any, d: Any): Any = {
+    start.asInstanceOf[Int] - d.asInstanceOf[Int]
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (sd, d) => {
+      s"""${ev.primitive} = $sd - $d;"""
+    })
+  }
+}
+
 case class Hour(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
 
   override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType)
@@ -543,3 +590,109 @@ case class NextDay(startDate: Expression, dayOfWeek: Expression)
 
   override def prettyName: String = "next_day"
 }
+
+/**
+ * Adds an interval to timestamp.
+ */
+case class TimeAdd(start: Expression, interval: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = start
+  override def right: Expression = interval
+
+  override def toString: String = s"$left + $right"
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType, CalendarIntervalType)
+
+  override def dataType: DataType = TimestampType
+
+  override def nullSafeEval(start: Any, interval: Any): Any = {
+    val itvl = interval.asInstanceOf[CalendarInterval]
+    DateTimeUtils.timestampAddInterval(
+      start.asInstanceOf[Long], itvl.months, itvl.microseconds)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (sd, i) => {
+      s"""$dtu.timestampAddInterval($sd, $i.months, $i.microseconds)"""
+    })
+  }
+}
+
+/**
+ * Subtracts an interval from timestamp.
+ */
+case class TimeSub(start: Expression, interval: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = start
+  override def right: Expression = interval
+
+  override def toString: String = s"$left - $right"
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType, CalendarIntervalType)
+
+  override def dataType: DataType = TimestampType
+
+  override def nullSafeEval(start: Any, interval: Any): Any = {
+    val itvl = interval.asInstanceOf[CalendarInterval]
+    DateTimeUtils.timestampAddInterval(
+      start.asInstanceOf[Long], 0 - itvl.months, 0 - itvl.microseconds)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (sd, i) => {
+      s"""$dtu.timestampAddInterval($sd, 0 - $i.months, 0 - $i.microseconds)"""
+    })
+  }
+}
+
+/**
+ * Returns the date that is num_months after start_date.
+ */
+case class AddMonths(startDate: Expression, numMonths: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = startDate
+  override def right: Expression = numMonths
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType, IntegerType)
+
+  override def dataType: DataType = DateType
+
+  override def nullSafeEval(start: Any, months: Any): Any = {
+    DateTimeUtils.dateAddMonths(start.asInstanceOf[Int], months.asInstanceOf[Int])
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (sd, m) => {
+      s"""$dtu.dateAddMonths($sd, $m)"""
+    })
+  }
+}
+
+/**
+ * Returns number of months between dates date1 and date2.
+ */
+case class MonthsBetween(date1: Expression, date2: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = date1
+  override def right: Expression = date2
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType, TimestampType)
+
+  override def dataType: DataType = DoubleType
+
+  override def nullSafeEval(t1: Any, t2: Any): Any = {
+    DateTimeUtils.monthsBetween(t1.asInstanceOf[Long], t2.asInstanceOf[Long])
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    defineCodeGen(ctx, ev, (l, r) => {
+      s"""$dtu.monthsBetween($l, $r)"""
+    })
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 93966a503c27c..53abdf6618eac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -45,6 +45,7 @@ object DateTimeUtils {
   final val to2001 = -11323
 
   // this is year -17999, calculation: 50 * daysIn400Year
+  final val YearZero = -17999
   final val toYearZero = to2001 + 7304850
 
   @transient lazy val defaultTimeZone = TimeZone.getDefault
@@ -575,6 +576,144 @@ object DateTimeUtils {
   }
 
   /**
+   * The number of days for each month (not leap year)
+   */
+  private val monthDays = Array(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)
+
+  /**
+   * Returns the date value for the first day of the given month.
+   * The month is expressed in months since year zero (17999 BC), starting from 0.
+   */
+  private def firstDayOfMonth(absoluteMonth: Int): Int = {
+    val absoluteYear = absoluteMonth / 12
+    var monthInYear = absoluteMonth - absoluteYear * 12
+    var date = getDateFromYear(absoluteYear)
+    if (monthInYear >= 2 && isLeapYear(absoluteYear + YearZero)) {
+      date += 1
+    }
+    while (monthInYear > 0) {
+      date += monthDays(monthInYear - 1)
+      monthInYear -= 1
+    }
+    date
+  }
+
+  /**
+   * Returns the date value for January 1 of the given year.
+   * The year is expressed in years since year zero (17999 BC), starting from 0.
+   */
+  private def getDateFromYear(absoluteYear: Int): Int = {
+    val absoluteDays = (absoluteYear * 365 + absoluteYear / 400 - absoluteYear / 100
+      + absoluteYear / 4)
+    absoluteDays - toYearZero
+  }
+
+  /**
+   * Add date and year-month interval.
+   * Returns a date value, expressed in days since 1.1.1970.
+   */
+  def dateAddMonths(days: Int, months: Int): Int = {
+    val absoluteMonth = (getYear(days) - YearZero) * 12 + getMonth(days) - 1 + months
+    val currentMonthInYear = absoluteMonth % 12
+    val currentYear = absoluteMonth / 12
+    val leapDay = if (currentMonthInYear == 1 && isLeapYear(currentYear + YearZero)) 1 else 0
+    val lastDayOfMonth = monthDays(currentMonthInYear) + leapDay
+
+    val dayOfMonth = getDayOfMonth(days)
+    val currentDayInMonth = if (getDayOfMonth(days + 1) == 1 || dayOfMonth >= lastDayOfMonth) {
+      // last day of the month
+      lastDayOfMonth
+    } else {
+      dayOfMonth
+    }
+    firstDayOfMonth(absoluteMonth) + currentDayInMonth - 1
+  }
+
+  /**
+   * Add timestamp and full interval.
+   * Returns a timestamp value, expressed in microseconds since 1.1.1970 00:00:00.
+   */
+  def timestampAddInterval(start: Long, months: Int, microseconds: Long): Long = {
+    val days = millisToDays(start / 1000L)
+    val newDays = dateAddMonths(days, months)
+    daysToMillis(newDays) * 1000L + start - daysToMillis(days) * 1000L + microseconds
+  }
+
+  /**
+   * Returns the last dayInMonth in the month it belongs to. The date is expressed
+   * in days since 1.1.1970. the return value starts from 1.
+   */
+  private def getLastDayInMonthOfMonth(date: Int): Int = {
+    var (year, dayInYear) = getYearAndDayInYear(date)
+    if (isLeapYear(year)) {
+      if (dayInYear > 31 && dayInYear <= 60) {
+        return 29
+      } else if (dayInYear > 60) {
+        dayInYear = dayInYear - 1
+      }
+    }
+    if (dayInYear <= 31) {
+      31
+    } else if (dayInYear <= 59) {
+      28
+    } else if (dayInYear <= 90) {
+      31
+    } else if (dayInYear <= 120) {
+      30
+    } else if (dayInYear <= 151) {
+      31
+    } else if (dayInYear <= 181) {
+      30
+    } else if (dayInYear <= 212) {
+      31
+    } else if (dayInYear <= 243) {
+      31
+    } else if (dayInYear <= 273) {
+      30
+    } else if (dayInYear <= 304) {
+      31
+    } else if (dayInYear <= 334) {
+      30
+    } else {
+      31
+    }
+  }
+
+  /**
+   * Returns number of months between time1 and time2. time1 and time2 are expressed in
+   * microseconds since 1.1.1970.
+   *
+   * If time1 and time2 having the same day of month, or both are the last day of month,
+   * it returns an integer (time under a day will be ignored).
+   *
+   * Otherwise, the difference is calculated based on 31 days per month, and rounding to
+   * 8 digits.
+   */
+  def monthsBetween(time1: Long, time2: Long): Double = {
+    val millis1 = time1 / 1000L
+    val millis2 = time2 / 1000L
+    val date1 = millisToDays(millis1)
+    val date2 = millisToDays(millis2)
+    // TODO(davies): get year, month, dayOfMonth from single function
+    val dayInMonth1 = getDayOfMonth(date1)
+    val dayInMonth2 = getDayOfMonth(date2)
+    val months1 = getYear(date1) * 12 + getMonth(date1)
+    val months2 = getYear(date2) * 12 + getMonth(date2)
+
+    if (dayInMonth1 == dayInMonth2 || (dayInMonth1 == getLastDayInMonthOfMonth(date1)
+      && dayInMonth2 == getLastDayInMonthOfMonth(date2))) {
+      return (months1 - months2).toDouble
+    }
+    // milliseconds is enough for 8 digits precision on the right side
+    val timeInDay1 = millis1 - daysToMillis(date1)
+    val timeInDay2 = millis2 - daysToMillis(date2)
+    val timesBetween = (timeInDay1 - timeInDay2).toDouble / MILLIS_PER_DAY
+    val diff = (months1 - months2).toDouble + (dayInMonth1 - dayInMonth2 + timesBetween) / 31.0
+    // rounding to 8 digits
+    math.round(diff * 1e8) / 1e8
+  }
+
+  /*
    * Returns day of week from String. Starting from Thursday, marked as 0.
    * (Because 1970-01-01 is Thursday).
    */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 1d9ee5ddf3a5a..70608771dd110 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -17,12 +17,15 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import java.sql.Timestamp
+
 import org.apache.spark.sql.catalyst.plans.PlanTest
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
 
 class HiveTypeCoercionSuite extends PlanTest {
 
@@ -400,6 +403,33 @@ class HiveTypeCoercionSuite extends PlanTest {
     }
   }
 
+  test("rule for date/timestamp operations") {
+    val dateTimeOperations = HiveTypeCoercion.DateTimeOperations
+    val date = Literal(new java.sql.Date(0L))
+    val timestamp = Literal(new Timestamp(0L))
+    val interval = Literal(new CalendarInterval(0, 0))
+    val str = Literal("2015-01-01")
+
+    ruleTest(dateTimeOperations, Add(date, interval), Cast(TimeAdd(date, interval), DateType))
+    ruleTest(dateTimeOperations, Add(interval, date), Cast(TimeAdd(date, interval), DateType))
+    ruleTest(dateTimeOperations, Add(timestamp, interval),
+      Cast(TimeAdd(timestamp, interval), TimestampType))
+    ruleTest(dateTimeOperations, Add(interval, timestamp),
+      Cast(TimeAdd(timestamp, interval), TimestampType))
+    ruleTest(dateTimeOperations, Add(str, interval), Cast(TimeAdd(str, interval), StringType))
+    ruleTest(dateTimeOperations, Add(interval, str), Cast(TimeAdd(str, interval), StringType))
+
+    ruleTest(dateTimeOperations, Subtract(date, interval), Cast(TimeSub(date, interval), DateType))
+    ruleTest(dateTimeOperations, Subtract(timestamp, interval),
+      Cast(TimeSub(timestamp, interval), TimestampType))
+    ruleTest(dateTimeOperations, Subtract(str, interval), Cast(TimeSub(str, interval), StringType))
+
+    // interval operations should not be effected
+    ruleTest(dateTimeOperations, Add(interval, interval), Add(interval, interval))
+    ruleTest(dateTimeOperations, Subtract(interval, interval), Subtract(interval, interval))
+  }
+
+
   /**
    * There are rules that need to not fire before child expressions get resolved.
    * We use this test to make sure those rules do not fire early.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index e1387f945ffa4..fd1d6c1d25497 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -22,8 +22,8 @@ import java.text.SimpleDateFormat
 import java.util.Calendar
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.unsafe.types.CalendarInterval
 import org.apache.spark.sql.types._
 
 class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -48,56 +48,8 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("DayOfYear") {
     val sdfDay = new SimpleDateFormat("D")
-    (2002 to 2004).foreach { y =>
-      (0 to 11).foreach { m =>
-        (0 to 5).foreach { i =>
-          val c = Calendar.getInstance()
-          c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
-            sdfDay.format(c.getTime).toInt)
-        }
-      }
-    }
-
     (1998 to 2002).foreach { y =>
-      (0 to 11).foreach { m =>
-        (0 to 5).foreach { i =>
-          val c = Calendar.getInstance()
-          c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
-            sdfDay.format(c.getTime).toInt)
-        }
-      }
-    }
-
-    (1969 to 1970).foreach { y =>
-      (0 to 11).foreach { m =>
-        (0 to 5).foreach { i =>
-          val c = Calendar.getInstance()
-          c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
-            sdfDay.format(c.getTime).toInt)
-        }
-      }
-    }
-
-    (2402 to 2404).foreach { y =>
-      (0 to 11).foreach { m =>
-        (0 to 5).foreach { i =>
-          val c = Calendar.getInstance()
-          c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
-            sdfDay.format(c.getTime).toInt)
-        }
-      }
-    }
-
-    (2398 to 2402).foreach { y =>
-      (0 to 11).foreach { m =>
+      (0 to 3).foreach { m =>
         (0 to 5).foreach { i =>
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
@@ -117,7 +69,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Year(Cast(Literal(ts), DateType)), 2013)
 
     val c = Calendar.getInstance()
-    (2000 to 2010).foreach { y =>
+    (2000 to 2002).foreach { y =>
       (0 to 11 by 11).foreach { m =>
         c.set(y, m, 28)
         (0 to 5 * 24).foreach { i =>
@@ -155,20 +107,8 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Month(Cast(Literal(ts), DateType)), 11)
 
     (2003 to 2004).foreach { y =>
-      (0 to 11).foreach { m =>
-        (0 to 5 * 24).foreach { i =>
-          val c = Calendar.getInstance()
-          c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.HOUR_OF_DAY, i)
-          checkEvaluation(Month(Literal(new Date(c.getTimeInMillis))),
-            c.get(Calendar.MONTH) + 1)
-        }
-      }
-    }
-
-    (1999 to 2000).foreach { y =>
-      (0 to 11).foreach { m =>
-        (0 to 5 * 24).foreach { i =>
+      (0 to 3).foreach { m =>
+        (0 to 2 * 24).foreach { i =>
           val c = Calendar.getInstance()
           c.set(y, m, 28, 0, 0, 0)
           c.add(Calendar.HOUR_OF_DAY, i)
@@ -262,6 +202,112 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
+  test("date_add") {
+    checkEvaluation(
+      DateAdd(Literal(Date.valueOf("2016-02-28")), Literal(1)),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2016-02-29")))
+    checkEvaluation(
+      DateAdd(Literal(Date.valueOf("2016-02-28")), Literal(-365)),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2015-02-28")))
+    checkEvaluation(DateAdd(Literal.create(null, DateType), Literal(1)), null)
+    checkEvaluation(DateAdd(Literal(Date.valueOf("2016-02-28")), Literal.create(null, IntegerType)),
+      null)
+    checkEvaluation(DateAdd(Literal.create(null, DateType), Literal.create(null, IntegerType)),
+      null)
+  }
+
+  test("date_sub") {
+    checkEvaluation(
+      DateSub(Literal(Date.valueOf("2015-01-01")), Literal(1)),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2014-12-31")))
+    checkEvaluation(
+      DateSub(Literal(Date.valueOf("2015-01-01")), Literal(-1)),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2015-01-02")))
+    checkEvaluation(DateSub(Literal.create(null, DateType), Literal(1)), null)
+    checkEvaluation(DateSub(Literal(Date.valueOf("2016-02-28")), Literal.create(null, IntegerType)),
+      null)
+    checkEvaluation(DateSub(Literal.create(null, DateType), Literal.create(null, IntegerType)),
+      null)
+  }
+
+  test("time_add") {
+    checkEvaluation(
+      TimeAdd(Literal(Timestamp.valueOf("2016-01-29 10:00:00")),
+        Literal(new CalendarInterval(1, 123000L))),
+      DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2016-02-29 10:00:00.123")))
+
+    checkEvaluation(
+      TimeAdd(Literal.create(null, TimestampType), Literal(new CalendarInterval(1, 123000L))),
+      null)
+    checkEvaluation(
+      TimeAdd(Literal(Timestamp.valueOf("2016-01-29 10:00:00")),
+        Literal.create(null, CalendarIntervalType)),
+      null)
+    checkEvaluation(
+      TimeAdd(Literal.create(null, TimestampType), Literal.create(null, CalendarIntervalType)),
+      null)
+  }
+
+  test("time_sub") {
+    checkEvaluation(
+      TimeSub(Literal(Timestamp.valueOf("2016-03-31 10:00:00")),
+        Literal(new CalendarInterval(1, 0))),
+      DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2016-02-29 10:00:00")))
+    checkEvaluation(
+      TimeSub(
+        Literal(Timestamp.valueOf("2016-03-30 00:00:01")),
+        Literal(new CalendarInterval(1, 2000000.toLong))),
+      DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2016-02-28 23:59:59")))
+
+    checkEvaluation(
+      TimeSub(Literal.create(null, TimestampType), Literal(new CalendarInterval(1, 123000L))),
+      null)
+    checkEvaluation(
+      TimeSub(Literal(Timestamp.valueOf("2016-01-29 10:00:00")),
+        Literal.create(null, CalendarIntervalType)),
+      null)
+    checkEvaluation(
+      TimeSub(Literal.create(null, TimestampType), Literal.create(null, CalendarIntervalType)),
+      null)
+  }
+
+  test("add_months") {
+    checkEvaluation(AddMonths(Literal(Date.valueOf("2015-01-30")), Literal(1)),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2015-02-28")))
+    checkEvaluation(AddMonths(Literal(Date.valueOf("2016-03-30")), Literal(-1)),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2016-02-29")))
+    checkEvaluation(
+      AddMonths(Literal(Date.valueOf("2015-01-30")), Literal.create(null, IntegerType)),
+      null)
+    checkEvaluation(AddMonths(Literal.create(null, DateType), Literal(1)), null)
+    checkEvaluation(AddMonths(Literal.create(null, DateType), Literal.create(null, IntegerType)),
+      null)
+  }
+
+  test("months_between") {
+    checkEvaluation(
+      MonthsBetween(Literal(Timestamp.valueOf("1997-02-28 10:30:00")),
+        Literal(Timestamp.valueOf("1996-10-30 00:00:00"))),
+      3.94959677)
+    checkEvaluation(
+      MonthsBetween(Literal(Timestamp.valueOf("2015-01-30 11:52:00")),
+        Literal(Timestamp.valueOf("2015-01-30 11:50:00"))),
+      0.0)
+    checkEvaluation(
+      MonthsBetween(Literal(Timestamp.valueOf("2015-01-31 00:00:00")),
+        Literal(Timestamp.valueOf("2015-03-31 22:00:00"))),
+      -2.0)
+    checkEvaluation(
+      MonthsBetween(Literal(Timestamp.valueOf("2015-03-31 22:00:00")),
+        Literal(Timestamp.valueOf("2015-02-28 00:00:00"))),
+      1.0)
+    val t = Literal(Timestamp.valueOf("2015-03-31 22:00:00"))
+    val tnull = Literal.create(null, TimestampType)
+    checkEvaluation(MonthsBetween(t, tnull), null)
+    checkEvaluation(MonthsBetween(tnull, t), null)
+    checkEvaluation(MonthsBetween(tnull, tnull), null)
+  }
+
   test("last_day") {
     checkEvaluation(LastDay(Literal(Date.valueOf("2015-02-28"))), Date.valueOf("2015-02-28"))
     checkEvaluation(LastDay(Literal(Date.valueOf("2015-03-27"))), Date.valueOf("2015-03-31"))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index fab9eb9cd4c9f..60d2bcfe13757 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -19,47 +19,48 @@ package org.apache.spark.sql.catalyst.util
 
 import java.sql.{Date, Timestamp}
 import java.text.SimpleDateFormat
-import java.util.{TimeZone, Calendar}
+import java.util.{Calendar, TimeZone}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.sql.catalyst.util.DateTimeUtils._
 
 class DateTimeUtilsSuite extends SparkFunSuite {
 
   private[this] def getInUTCDays(timestamp: Long): Int = {
     val tz = TimeZone.getDefault
-    ((timestamp + tz.getOffset(timestamp)) / DateTimeUtils.MILLIS_PER_DAY).toInt
+    ((timestamp + tz.getOffset(timestamp)) / MILLIS_PER_DAY).toInt
   }
 
   test("timestamp and us") {
     val now = new Timestamp(System.currentTimeMillis())
     now.setNanos(1000)
-    val ns = DateTimeUtils.fromJavaTimestamp(now)
+    val ns = fromJavaTimestamp(now)
     assert(ns % 1000000L === 1)
-    assert(DateTimeUtils.toJavaTimestamp(ns) === now)
+    assert(toJavaTimestamp(ns) === now)
 
     List(-111111111111L, -1L, 0, 1L, 111111111111L).foreach { t =>
-      val ts = DateTimeUtils.toJavaTimestamp(t)
-      assert(DateTimeUtils.fromJavaTimestamp(ts) === t)
-      assert(DateTimeUtils.toJavaTimestamp(DateTimeUtils.fromJavaTimestamp(ts)) === ts)
+      val ts = toJavaTimestamp(t)
+      assert(fromJavaTimestamp(ts) === t)
+      assert(toJavaTimestamp(fromJavaTimestamp(ts)) === ts)
     }
   }
 
   test("us and julian day") {
-    val (d, ns) = DateTimeUtils.toJulianDay(0)
-    assert(d === DateTimeUtils.JULIAN_DAY_OF_EPOCH)
-    assert(ns === DateTimeUtils.SECONDS_PER_DAY / 2 * DateTimeUtils.NANOS_PER_SECOND)
-    assert(DateTimeUtils.fromJulianDay(d, ns) == 0L)
+    val (d, ns) = toJulianDay(0)
+    assert(d === JULIAN_DAY_OF_EPOCH)
+    assert(ns === SECONDS_PER_DAY / 2 * NANOS_PER_SECOND)
+    assert(fromJulianDay(d, ns) == 0L)
 
     val t = new Timestamp(61394778610000L) // (2015, 6, 11, 10, 10, 10, 100)
-    val (d1, ns1) = DateTimeUtils.toJulianDay(DateTimeUtils.fromJavaTimestamp(t))
-    val t2 = DateTimeUtils.toJavaTimestamp(DateTimeUtils.fromJulianDay(d1, ns1))
+    val (d1, ns1) = toJulianDay(fromJavaTimestamp(t))
+    val t2 = toJavaTimestamp(fromJulianDay(d1, ns1))
     assert(t.equals(t2))
   }
 
   test("SPARK-6785: java date conversion before and after epoch") {
     def checkFromToJavaDate(d1: Date): Unit = {
-      val d2 = DateTimeUtils.toJavaDate(DateTimeUtils.fromJavaDate(d1))
+      val d2 = toJavaDate(fromJavaDate(d1))
       assert(d2.toString === d1.toString)
     }
 
@@ -95,157 +96,156 @@ class DateTimeUtilsSuite extends SparkFunSuite {
   }
 
   test("string to date") {
-    import DateTimeUtils.millisToDays
 
     var c = Calendar.getInstance()
     c.set(2015, 0, 28, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-01-28")).get ===
+    assert(stringToDate(UTF8String.fromString("2015-01-28")).get ===
       millisToDays(c.getTimeInMillis))
     c.set(2015, 0, 1, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015")).get ===
+    assert(stringToDate(UTF8String.fromString("2015")).get ===
       millisToDays(c.getTimeInMillis))
     c = Calendar.getInstance()
     c.set(2015, 2, 1, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03")).get ===
+    assert(stringToDate(UTF8String.fromString("2015-03")).get ===
       millisToDays(c.getTimeInMillis))
     c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18")).get ===
+    assert(stringToDate(UTF8String.fromString("2015-03-18")).get ===
       millisToDays(c.getTimeInMillis))
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18 ")).get ===
+    assert(stringToDate(UTF8String.fromString("2015-03-18 ")).get ===
       millisToDays(c.getTimeInMillis))
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18 123142")).get ===
+    assert(stringToDate(UTF8String.fromString("2015-03-18 123142")).get ===
       millisToDays(c.getTimeInMillis))
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18T123123")).get ===
+    assert(stringToDate(UTF8String.fromString("2015-03-18T123123")).get ===
       millisToDays(c.getTimeInMillis))
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18T")).get ===
+    assert(stringToDate(UTF8String.fromString("2015-03-18T")).get ===
       millisToDays(c.getTimeInMillis))
 
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-03-18X")).isEmpty)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015/03/18")).isEmpty)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015.03.18")).isEmpty)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("20150318")).isEmpty)
-    assert(DateTimeUtils.stringToDate(UTF8String.fromString("2015-031-8")).isEmpty)
+    assert(stringToDate(UTF8String.fromString("2015-03-18X")).isEmpty)
+    assert(stringToDate(UTF8String.fromString("2015/03/18")).isEmpty)
+    assert(stringToDate(UTF8String.fromString("2015.03.18")).isEmpty)
+    assert(stringToDate(UTF8String.fromString("20150318")).isEmpty)
+    assert(stringToDate(UTF8String.fromString("2015-031-8")).isEmpty)
   }
 
   test("string to timestamp") {
     var c = Calendar.getInstance()
     c.set(1969, 11, 31, 16, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("1969-12-31 16:00:00")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("1969-12-31 16:00:00")).get ===
       c.getTimeInMillis * 1000)
     c.set(2015, 0, 1, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015")).get ===
       c.getTimeInMillis * 1000)
     c = Calendar.getInstance()
     c.set(2015, 2, 1, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03")).get ===
       c.getTimeInMillis * 1000)
     c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18")).get ===
       c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 ")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18 ")).get ===
       c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18T")).get ===
       c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance()
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 12:03:17")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18 12:03:17")).get ===
       c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17")).get ===
       c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT-13:53"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17-13:53")).get === c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17Z")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17Z")).get ===
       c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 12:03:17Z")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18 12:03:17Z")).get ===
       c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT-01:00"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17-1:0")).get ===
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18T12:03:17-1:0")).get ===
       c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17-01:00")).get === c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17+07:30")).get === c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:03"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17+07:03")).get === c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance()
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18 12:03:17.123")).get === c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.123")).get === c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 456)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.456Z")).get  === c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18 12:03:17.456Z")).get  === c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT-01:00"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.123-1:0")).get  === c.getTimeInMillis * 1000)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.123-01:00")).get ===  c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.123+07:30")).get ===  c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.123+07:30")).get === c.getTimeInMillis * 1000)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.123121+7:30")).get ===
         c.getTimeInMillis * 1000 + 121)
 
     c = Calendar.getInstance(TimeZone.getTimeZone("GMT+07:30"))
     c.set(2015, 2, 18, 12, 3, 17)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03:17.12312+7:30")).get ===
         c.getTimeInMillis * 1000 + 120)
 
@@ -254,7 +254,7 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     c.set(Calendar.MINUTE, 12)
     c.set(Calendar.SECOND, 15)
     c.set(Calendar.MILLISECOND, 0)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("18:12:15")).get ===
       c.getTimeInMillis * 1000)
 
@@ -263,7 +263,7 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     c.set(Calendar.MINUTE, 12)
     c.set(Calendar.SECOND, 15)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("T18:12:15.12312+7:30")).get ===
       c.getTimeInMillis * 1000 + 120)
 
@@ -272,93 +272,130 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     c.set(Calendar.MINUTE, 12)
     c.set(Calendar.SECOND, 15)
     c.set(Calendar.MILLISECOND, 123)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("18:12:15.12312+7:30")).get ===
       c.getTimeInMillis * 1000 + 120)
 
     c = Calendar.getInstance()
     c.set(2011, 4, 6, 7, 8, 9)
     c.set(Calendar.MILLISECOND, 100)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2011-05-06 07:08:09.1000")).get === c.getTimeInMillis * 1000)
 
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("238")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18 123142")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18T123123")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-03-18X")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015/03/18")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015.03.18")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("20150318")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(UTF8String.fromString("2015-031-8")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(UTF8String.fromString("238")).isEmpty)
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18 123142")).isEmpty)
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18T123123")).isEmpty)
+    assert(stringToTimestamp(UTF8String.fromString("2015-03-18X")).isEmpty)
+    assert(stringToTimestamp(UTF8String.fromString("2015/03/18")).isEmpty)
+    assert(stringToTimestamp(UTF8String.fromString("2015.03.18")).isEmpty)
+    assert(stringToTimestamp(UTF8String.fromString("20150318")).isEmpty)
+    assert(stringToTimestamp(UTF8String.fromString("2015-031-8")).isEmpty)
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03.17-20:0")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03.17-0:70")).isEmpty)
-    assert(DateTimeUtils.stringToTimestamp(
+    assert(stringToTimestamp(
       UTF8String.fromString("2015-03-18T12:03.17-1:0:0")).isEmpty)
   }
 
   test("hours") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 13, 2, 11)
-    assert(DateTimeUtils.getHours(c.getTimeInMillis * 1000) === 13)
+    assert(getHours(c.getTimeInMillis * 1000) === 13)
     c.set(2015, 12, 8, 2, 7, 9)
-    assert(DateTimeUtils.getHours(c.getTimeInMillis * 1000) === 2)
+    assert(getHours(c.getTimeInMillis * 1000) === 2)
   }
 
   test("minutes") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 13, 2, 11)
-    assert(DateTimeUtils.getMinutes(c.getTimeInMillis * 1000) === 2)
+    assert(getMinutes(c.getTimeInMillis * 1000) === 2)
     c.set(2015, 2, 8, 2, 7, 9)
-    assert(DateTimeUtils.getMinutes(c.getTimeInMillis * 1000) === 7)
+    assert(getMinutes(c.getTimeInMillis * 1000) === 7)
   }
 
   test("seconds") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 13, 2, 11)
-    assert(DateTimeUtils.getSeconds(c.getTimeInMillis * 1000) === 11)
+    assert(getSeconds(c.getTimeInMillis * 1000) === 11)
     c.set(2015, 2, 8, 2, 7, 9)
-    assert(DateTimeUtils.getSeconds(c.getTimeInMillis * 1000) === 9)
+    assert(getSeconds(c.getTimeInMillis * 1000) === 9)
   }
 
   test("get day in year") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
-    assert(DateTimeUtils.getDayInYear(getInUTCDays(c.getTimeInMillis)) === 77)
+    assert(getDayInYear(getInUTCDays(c.getTimeInMillis)) === 77)
     c.set(2012, 2, 18, 0, 0, 0)
-    assert(DateTimeUtils.getDayInYear(getInUTCDays(c.getTimeInMillis)) === 78)
+    assert(getDayInYear(getInUTCDays(c.getTimeInMillis)) === 78)
   }
 
   test("get year") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
-    assert(DateTimeUtils.getYear(getInUTCDays(c.getTimeInMillis)) === 2015)
+    assert(getYear(getInUTCDays(c.getTimeInMillis)) === 2015)
     c.set(2012, 2, 18, 0, 0, 0)
-    assert(DateTimeUtils.getYear(getInUTCDays(c.getTimeInMillis)) === 2012)
+    assert(getYear(getInUTCDays(c.getTimeInMillis)) === 2012)
   }
 
   test("get quarter") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
-    assert(DateTimeUtils.getQuarter(getInUTCDays(c.getTimeInMillis)) === 1)
+    assert(getQuarter(getInUTCDays(c.getTimeInMillis)) === 1)
     c.set(2012, 11, 18, 0, 0, 0)
-    assert(DateTimeUtils.getQuarter(getInUTCDays(c.getTimeInMillis)) === 4)
+    assert(getQuarter(getInUTCDays(c.getTimeInMillis)) === 4)
   }
 
   test("get month") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
-    assert(DateTimeUtils.getMonth(getInUTCDays(c.getTimeInMillis)) === 3)
+    assert(getMonth(getInUTCDays(c.getTimeInMillis)) === 3)
     c.set(2012, 11, 18, 0, 0, 0)
-    assert(DateTimeUtils.getMonth(getInUTCDays(c.getTimeInMillis)) === 12)
+    assert(getMonth(getInUTCDays(c.getTimeInMillis)) === 12)
   }
 
   test("get day of month") {
     val c = Calendar.getInstance()
     c.set(2015, 2, 18, 0, 0, 0)
-    assert(DateTimeUtils.getDayOfMonth(getInUTCDays(c.getTimeInMillis)) === 18)
+    assert(getDayOfMonth(getInUTCDays(c.getTimeInMillis)) === 18)
     c.set(2012, 11, 24, 0, 0, 0)
-    assert(DateTimeUtils.getDayOfMonth(getInUTCDays(c.getTimeInMillis)) === 24)
+    assert(getDayOfMonth(getInUTCDays(c.getTimeInMillis)) === 24)
+  }
+
+  test("date add months") {
+    val c1 = Calendar.getInstance()
+    c1.set(1997, 1, 28, 10, 30, 0)
+    val days1 = millisToDays(c1.getTimeInMillis)
+    val c2 = Calendar.getInstance()
+    c2.set(2000, 1, 29)
+    assert(dateAddMonths(days1, 36) === millisToDays(c2.getTimeInMillis))
+    c2.set(1996, 0, 31)
+    assert(dateAddMonths(days1, -13) === millisToDays(c2.getTimeInMillis))
+  }
+
+  test("timestamp add months") {
+    val c1 = Calendar.getInstance()
+    c1.set(1997, 1, 28, 10, 30, 0)
+    c1.set(Calendar.MILLISECOND, 0)
+    val ts1 = c1.getTimeInMillis * 1000L
+    val c2 = Calendar.getInstance()
+    c2.set(2000, 1, 29, 10, 30, 0)
+    c2.set(Calendar.MILLISECOND, 123)
+    val ts2 = c2.getTimeInMillis * 1000L
+    assert(timestampAddInterval(ts1, 36, 123000) === ts2)
+  }
+
+  test("monthsBetween") {
+    val c1 = Calendar.getInstance()
+    c1.set(1997, 1, 28, 10, 30, 0)
+    val c2 = Calendar.getInstance()
+    c2.set(1996, 9, 30, 0, 0, 0)
+    assert(monthsBetween(c1.getTimeInMillis * 1000L, c2.getTimeInMillis * 1000L) === 3.94959677)
+    c2.set(2000, 1, 28, 0, 0, 0)
+    assert(monthsBetween(c1.getTimeInMillis * 1000L, c2.getTimeInMillis * 1000L) === -36)
+    c2.set(2000, 1, 29, 0, 0, 0)
+    assert(monthsBetween(c1.getTimeInMillis * 1000L, c2.getTimeInMillis * 1000L) === -36)
+    c2.set(1996, 2, 31, 0, 0, 0)
+    assert(monthsBetween(c1.getTimeInMillis * 1000L, c2.getTimeInMillis * 1000L) === 11)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 3f440e062eb96..168894d66117d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1927,6 +1927,14 @@ object functions {
   // DateTime functions
   //////////////////////////////////////////////////////////////////////////////////////////////
 
+  /**
+   * Returns the date that is numMonths after startDate.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def add_months(startDate: Column, numMonths: Int): Column =
+    AddMonths(startDate.expr, Literal(numMonths))
+
   /**
    * Converts a date/timestamp/string to a value of string in the format specified by the date
    * format given by the second argument.
@@ -1959,6 +1967,20 @@ object functions {
   def date_format(dateColumnName: String, format: String): Column =
     date_format(Column(dateColumnName), format)
 
+  /**
+   * Returns the date that is `days` days after `start`
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def date_add(start: Column, days: Int): Column = DateAdd(start.expr, Literal(days))
+
+  /**
+   * Returns the date that is `days` days before `start`
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def date_sub(start: Column, days: Int): Column = DateSub(start.expr, Literal(days))
+
   /**
    * Extracts the year as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2067,6 +2089,13 @@ object functions {
    */
   def minute(columnName: String): Column = minute(Column(columnName))
 
+  /*
+   * Returns number of months between dates `date1` and `date2`.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def months_between(date1: Column, date2: Column): Column = MonthsBetween(date1.expr, date2.expr)
+
   /**
    * Given a date column, returns the first date which is later than the value of the date column
    * that is on the specified day of the week.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index df4cb57ac5b21..b7267c413165a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -22,6 +22,7 @@ import java.text.SimpleDateFormat
 
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.functions._
+import org.apache.spark.unsafe.types.CalendarInterval
 
 class DateFunctionsSuite extends QueryTest {
   private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
@@ -206,6 +207,122 @@ class DateFunctionsSuite extends QueryTest {
       Row(15, 15, 15))
   }
 
+  test("function date_add") {
+    val st1 = "2015-06-01 12:34:56"
+    val st2 = "2015-06-02 12:34:56"
+    val t1 = Timestamp.valueOf(st1)
+    val t2 = Timestamp.valueOf(st2)
+    val s1 = "2015-06-01"
+    val s2 = "2015-06-02"
+    val d1 = Date.valueOf(s1)
+    val d2 = Date.valueOf(s2)
+    val df = Seq((t1, d1, s1, st1), (t2, d2, s2, st2)).toDF("t", "d", "s", "ss")
+    checkAnswer(
+      df.select(date_add(col("d"), 1)),
+      Seq(Row(Date.valueOf("2015-06-02")), Row(Date.valueOf("2015-06-03"))))
+    checkAnswer(
+      df.select(date_add(col("t"), 3)),
+      Seq(Row(Date.valueOf("2015-06-04")), Row(Date.valueOf("2015-06-05"))))
+    checkAnswer(
+      df.select(date_add(col("s"), 5)),
+      Seq(Row(Date.valueOf("2015-06-06")), Row(Date.valueOf("2015-06-07"))))
+    checkAnswer(
+      df.select(date_add(col("ss"), 7)),
+      Seq(Row(Date.valueOf("2015-06-08")), Row(Date.valueOf("2015-06-09"))))
+
+    checkAnswer(df.selectExpr("DATE_ADD(null, 1)"), Seq(Row(null), Row(null)))
+    checkAnswer(
+      df.selectExpr("""DATE_ADD(d, 1)"""),
+      Seq(Row(Date.valueOf("2015-06-02")), Row(Date.valueOf("2015-06-03"))))
+  }
+
+  test("function date_sub") {
+    val st1 = "2015-06-01 12:34:56"
+    val st2 = "2015-06-02 12:34:56"
+    val t1 = Timestamp.valueOf(st1)
+    val t2 = Timestamp.valueOf(st2)
+    val s1 = "2015-06-01"
+    val s2 = "2015-06-02"
+    val d1 = Date.valueOf(s1)
+    val d2 = Date.valueOf(s2)
+    val df = Seq((t1, d1, s1, st1), (t2, d2, s2, st2)).toDF("t", "d", "s", "ss")
+    checkAnswer(
+      df.select(date_sub(col("d"), 1)),
+      Seq(Row(Date.valueOf("2015-05-31")), Row(Date.valueOf("2015-06-01"))))
+    checkAnswer(
+      df.select(date_sub(col("t"), 1)),
+      Seq(Row(Date.valueOf("2015-05-31")), Row(Date.valueOf("2015-06-01"))))
+    checkAnswer(
+      df.select(date_sub(col("s"), 1)),
+      Seq(Row(Date.valueOf("2015-05-31")), Row(Date.valueOf("2015-06-01"))))
+    checkAnswer(
+      df.select(date_sub(col("ss"), 1)),
+      Seq(Row(Date.valueOf("2015-05-31")), Row(Date.valueOf("2015-06-01"))))
+    checkAnswer(
+      df.select(date_sub(lit(null), 1)).limit(1), Row(null))
+
+    checkAnswer(df.selectExpr("""DATE_SUB(d, null)"""), Seq(Row(null), Row(null)))
+    checkAnswer(
+      df.selectExpr("""DATE_SUB(d, 1)"""),
+      Seq(Row(Date.valueOf("2015-05-31")), Row(Date.valueOf("2015-06-01"))))
+  }
+
+  test("time_add") {
+    val t1 = Timestamp.valueOf("2015-07-31 23:59:59")
+    val t2 = Timestamp.valueOf("2015-12-31 00:00:00")
+    val d1 = Date.valueOf("2015-07-31")
+    val d2 = Date.valueOf("2015-12-31")
+    val i = new CalendarInterval(2, 2000000L)
+    val df = Seq((1, t1, d1), (3, t2, d2)).toDF("n", "t", "d")
+    checkAnswer(
+      df.selectExpr(s"d + $i"),
+      Seq(Row(Date.valueOf("2015-09-30")), Row(Date.valueOf("2016-02-29"))))
+    checkAnswer(
+      df.selectExpr(s"t + $i"),
+      Seq(Row(Timestamp.valueOf("2015-10-01 00:00:01")),
+        Row(Timestamp.valueOf("2016-02-29 00:00:02"))))
+  }
+
+  test("time_sub") {
+    val t1 = Timestamp.valueOf("2015-10-01 00:00:01")
+    val t2 = Timestamp.valueOf("2016-02-29 00:00:02")
+    val d1 = Date.valueOf("2015-09-30")
+    val d2 = Date.valueOf("2016-02-29")
+    val i = new CalendarInterval(2, 2000000L)
+    val df = Seq((1, t1, d1), (3, t2, d2)).toDF("n", "t", "d")
+    checkAnswer(
+      df.selectExpr(s"d - $i"),
+      Seq(Row(Date.valueOf("2015-07-30")), Row(Date.valueOf("2015-12-30"))))
+    checkAnswer(
+      df.selectExpr(s"t - $i"),
+      Seq(Row(Timestamp.valueOf("2015-07-31 23:59:59")),
+        Row(Timestamp.valueOf("2015-12-31 00:00:00"))))
+  }
+
+  test("function add_months") {
+    val d1 = Date.valueOf("2015-08-31")
+    val d2 = Date.valueOf("2015-02-28")
+    val df = Seq((1, d1), (2, d2)).toDF("n", "d")
+    checkAnswer(
+      df.select(add_months(col("d"), 1)),
+      Seq(Row(Date.valueOf("2015-09-30")), Row(Date.valueOf("2015-03-31"))))
+    checkAnswer(
+      df.selectExpr("add_months(d, -1)"),
+      Seq(Row(Date.valueOf("2015-07-31")), Row(Date.valueOf("2015-01-31"))))
+  }
+
+  test("function months_between") {
+    val d1 = Date.valueOf("2015-07-31")
+    val d2 = Date.valueOf("2015-02-16")
+    val t1 = Timestamp.valueOf("2014-09-30 23:30:00")
+    val t2 = Timestamp.valueOf("2015-09-16 12:00:00")
+    val s1 = "2014-09-15 11:30:00"
+    val s2 = "2015-10-01 00:00:00"
+    val df = Seq((t1, d1, s1), (t2, d2, s2)).toDF("t", "d", "s")
+    checkAnswer(df.select(months_between(col("t"), col("d"))), Seq(Row(-10.0), Row(7.0)))
+    checkAnswer(df.selectExpr("months_between(t, s)"), Seq(Row(0.5), Row(-0.5)))
+  }
+
   test("function last_day") {
     val df1 = Seq((1, "2015-07-23"), (2, "2015-07-24")).toDF("i", "d")
     val df2 = Seq((1, "2015-07-23 00:11:22"), (2, "2015-07-24 11:22:33")).toDF("i", "t")

From 89cda69ecd5ef942a68ad13fc4e1f4184010f087 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 30 Jul 2015 14:08:59 -0700
Subject: [PATCH 0711/1454] [SPARK-9454] Change LDASuite tests to use vector
 comparisons

jkbradley Changes the current hacky string-comparison for vector compares.

Author: Feynman Liang <fliang@databricks.com>

Closes #7775 from feynmanliang/SPARK-9454-ldasuite-vector-compare and squashes the following commits:

bd91a82 [Feynman Liang] Remove println
905c76e [Feynman Liang] Fix string compare in distributed EM
2f24c13 [Feynman Liang] Improve LDASuite tests
---
 .../spark/mllib/clustering/LDASuite.scala     | 33 ++++++++-----------
 1 file changed, 14 insertions(+), 19 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index d74482d3a7598..c43e1e575c09c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -83,21 +83,14 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(model.topicsMatrix === localModel.topicsMatrix)
 
     // Check: topic summaries
-    //  The odd decimal formatting and sorting is a hack to do a robust comparison.
-    val roundedTopicSummary = model.describeTopics().map { case (terms, termWeights) =>
-      // cut values to 3 digits after the decimal place
-      terms.zip(termWeights).map { case (term, weight) =>
-        ("%.3f".format(weight).toDouble, term.toInt)
-      }
-    }.sortBy(_.mkString(""))
-    val roundedLocalTopicSummary = localModel.describeTopics().map { case (terms, termWeights) =>
-      // cut values to 3 digits after the decimal place
-      terms.zip(termWeights).map { case (term, weight) =>
-        ("%.3f".format(weight).toDouble, term.toInt)
-      }
-    }.sortBy(_.mkString(""))
-    roundedTopicSummary.zip(roundedLocalTopicSummary).foreach { case (t1, t2) =>
-      assert(t1 === t2)
+    val topicSummary = model.describeTopics().map { case (terms, termWeights) =>
+      Vectors.sparse(tinyVocabSize, terms, termWeights)
+    }.sortBy(_.toString)
+    val localTopicSummary = localModel.describeTopics().map { case (terms, termWeights) =>
+      Vectors.sparse(tinyVocabSize, terms, termWeights)
+    }.sortBy(_.toString)
+    topicSummary.zip(localTopicSummary).foreach { case (topics, topicsLocal) =>
+      assert(topics ~== topicsLocal absTol 0.01)
     }
 
     // Check: per-doc topic distributions
@@ -197,10 +190,12 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
     // verify the result, Note this generate the identical result as
     // [[https://github.com/Blei-Lab/onlineldavb]]
-    val topic1 = op.getLambda(0, ::).inner.toArray.map("%.4f".format(_)).mkString(", ")
-    val topic2 = op.getLambda(1, ::).inner.toArray.map("%.4f".format(_)).mkString(", ")
-    assert("1.1101, 1.2076, 1.3050, 0.8899, 0.7924, 0.6950" == topic1)
-    assert("0.8899, 0.7924, 0.6950, 1.1101, 1.2076, 1.3050" == topic2)
+    val topic1: Vector = Vectors.fromBreeze(op.getLambda(0, ::).t)
+    val topic2: Vector = Vectors.fromBreeze(op.getLambda(1, ::).t)
+    val expectedTopic1 = Vectors.dense(1.1101, 1.2076, 1.3050, 0.8899, 0.7924, 0.6950)
+    val expectedTopic2 = Vectors.dense(0.8899, 0.7924, 0.6950, 1.1101, 1.2076, 1.3050)
+    assert(topic1 ~== expectedTopic1 absTol 0.01)
+    assert(topic2 ~== expectedTopic2 absTol 0.01)
   }
 
   test("OnlineLDAOptimizer with toy data") {

From 0dbd6963d589a8f6ad344273f3da7df680ada515 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 30 Jul 2015 15:39:46 -0700
Subject: [PATCH 0712/1454] [SPARK-9479] [STREAMING] [TESTS] Fix
 ReceiverTrackerSuite failure for maven build and other potential test
 failures in Streaming

See https://issues.apache.org/jira/browse/SPARK-9479 for the failure cause.

The PR includes the following changes:
1. Make ReceiverTrackerSuite create StreamingContext in the test body.
2. Fix places that don't stop StreamingContext. I verified no SparkContext was stopped in the shutdown hook locally after this fix.
3. Fix an issue that `ReceiverTracker.endpoint` may be null.
4. Make sure stopping SparkContext in non-main thread won't fail other tests.

Author: zsxwing <zsxwing@gmail.com>

Closes #7797 from zsxwing/fix-ReceiverTrackerSuite and squashes the following commits:

3a4bb98 [zsxwing] Fix another potential NPE
d7497df [zsxwing] Fix ReceiverTrackerSuite; make sure StreamingContext in tests is closed
---
 .../StreamingLogisticRegressionSuite.scala    | 21 +++++--
 .../clustering/StreamingKMeansSuite.scala     | 17 ++++--
 .../StreamingLinearRegressionSuite.scala      | 21 +++++--
 .../streaming/scheduler/ReceiverTracker.scala | 12 +++-
 .../apache/spark/streaming/JavaAPISuite.java  |  1 +
 .../streaming/BasicOperationsSuite.scala      | 58 ++++++++++---------
 .../spark/streaming/InputStreamsSuite.scala   | 38 ++++++------
 .../spark/streaming/MasterFailureTest.scala   |  8 ++-
 .../streaming/StreamingContextSuite.scala     | 22 +++++--
 .../streaming/StreamingListenerSuite.scala    | 13 ++++-
 .../scheduler/ReceiverTrackerSuite.scala      | 56 +++++++++---------
 .../StreamingJobProgressListenerSuite.scala   | 19 ++++--
 12 files changed, 183 insertions(+), 103 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala
index fd653296c9d97..d7b291d5a6330 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala
@@ -24,13 +24,22 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.streaming.dstream.DStream
-import org.apache.spark.streaming.TestSuiteBase
+import org.apache.spark.streaming.{StreamingContext, TestSuiteBase}
 
 class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase {
 
   // use longer wait time to ensure job completion
   override def maxWaitTimeMillis: Int = 30000
 
+  var ssc: StreamingContext = _
+
+  override def afterFunction() {
+    super.afterFunction()
+    if (ssc != null) {
+      ssc.stop()
+    }
+  }
+
   // Test if we can accurately learn B for Y = logistic(BX) on streaming data
   test("parameter accuracy") {
 
@@ -50,7 +59,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase
     }
 
     // apply model training to input stream
-    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
       model.trainOn(inputDStream)
       inputDStream.count()
     })
@@ -84,7 +93,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase
 
     // apply model training to input stream, storing the intermediate results
     // (we add a count to ensure the result is a DStream)
-    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
       model.trainOn(inputDStream)
       inputDStream.foreachRDD(x => history.append(math.abs(model.latestModel().weights(0) - B)))
       inputDStream.count()
@@ -118,7 +127,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase
     }
 
     // apply model predictions to test stream
-    val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
       model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
     })
 
@@ -147,7 +156,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase
     }
 
     // train and predict
-    val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
       model.trainOn(inputDStream)
       model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
     })
@@ -167,7 +176,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase
       .setNumIterations(10)
     val numBatches = 10
     val emptyInput = Seq.empty[Seq[LabeledPoint]]
-    val ssc = setupStreams(emptyInput,
+    ssc = setupStreams(emptyInput,
       (inputDStream: DStream[LabeledPoint]) => {
         model.trainOn(inputDStream)
         model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala
index ac01622b8a089..3645d29dccdb2 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.TestingUtils._
-import org.apache.spark.streaming.TestSuiteBase
+import org.apache.spark.streaming.{StreamingContext, TestSuiteBase}
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.util.random.XORShiftRandom
 
@@ -28,6 +28,15 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase {
 
   override def maxWaitTimeMillis: Int = 30000
 
+  var ssc: StreamingContext = _
+
+  override def afterFunction() {
+    super.afterFunction()
+    if (ssc != null) {
+      ssc.stop()
+    }
+  }
+
   test("accuracy for single center and equivalence to grand average") {
     // set parameters
     val numBatches = 10
@@ -46,7 +55,7 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase {
     val (input, centers) = StreamingKMeansDataGenerator(numPoints, numBatches, k, d, r, 42)
 
     // setup and run the model training
-    val ssc = setupStreams(input, (inputDStream: DStream[Vector]) => {
+    ssc = setupStreams(input, (inputDStream: DStream[Vector]) => {
       model.trainOn(inputDStream)
       inputDStream.count()
     })
@@ -82,7 +91,7 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase {
     val (input, centers) = StreamingKMeansDataGenerator(numPoints, numBatches, k, d, r, 42)
 
     // setup and run the model training
-    val ssc = setupStreams(input, (inputDStream: DStream[Vector]) => {
+    ssc = setupStreams(input, (inputDStream: DStream[Vector]) => {
       kMeans.trainOn(inputDStream)
       inputDStream.count()
     })
@@ -114,7 +123,7 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase {
       StreamingKMeansDataGenerator(numPoints, numBatches, k, d, r, 42, Array(Vectors.dense(0.0)))
 
     // setup and run the model training
-    val ssc = setupStreams(input, (inputDStream: DStream[Vector]) => {
+    ssc = setupStreams(input, (inputDStream: DStream[Vector]) => {
       kMeans.trainOn(inputDStream)
       inputDStream.count()
     })
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
index a2a4c5f6b8b70..34c07ed170816 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
@@ -22,14 +22,23 @@ import scala.collection.mutable.ArrayBuffer
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.LinearDataGenerator
+import org.apache.spark.streaming.{StreamingContext, TestSuiteBase}
 import org.apache.spark.streaming.dstream.DStream
-import org.apache.spark.streaming.TestSuiteBase
 
 class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase {
 
   // use longer wait time to ensure job completion
   override def maxWaitTimeMillis: Int = 20000
 
+  var ssc: StreamingContext = _
+
+  override def afterFunction() {
+    super.afterFunction()
+    if (ssc != null) {
+      ssc.stop()
+    }
+  }
+
   // Assert that two values are equal within tolerance epsilon
   def assertEqual(v1: Double, v2: Double, epsilon: Double) {
     def errorMessage = v1.toString + " did not equal " + v2.toString
@@ -62,7 +71,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase {
     }
 
     // apply model training to input stream
-    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
       model.trainOn(inputDStream)
       inputDStream.count()
     })
@@ -98,7 +107,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase {
 
     // apply model training to input stream, storing the intermediate results
     // (we add a count to ensure the result is a DStream)
-    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
       model.trainOn(inputDStream)
       inputDStream.foreachRDD(x => history.append(math.abs(model.latestModel().weights(0) - 10.0)))
       inputDStream.count()
@@ -129,7 +138,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase {
     }
 
     // apply model predictions to test stream
-    val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
       model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
     })
     // collect the output as (true, estimated) tuples
@@ -156,7 +165,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase {
     }
 
     // train and predict
-    val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
+    ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
       model.trainOn(inputDStream)
       model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
     })
@@ -177,7 +186,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase {
     val numBatches = 10
     val nPoints = 100
     val emptyInput = Seq.empty[Seq[LabeledPoint]]
-    val ssc = setupStreams(emptyInput,
+    ssc = setupStreams(emptyInput,
       (inputDStream: DStream[LabeledPoint]) => {
         model.trainOn(inputDStream)
         model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 6270137951b5a..e076fb5ea174b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -223,7 +223,11 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     // Signal the receivers to delete old block data
     if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
       logInfo(s"Cleanup old received batch data: $cleanupThreshTime")
-      endpoint.send(CleanupOldBlocks(cleanupThreshTime))
+      synchronized {
+        if (isTrackerStarted) {
+          endpoint.send(CleanupOldBlocks(cleanupThreshTime))
+        }
+      }
     }
   }
 
@@ -285,8 +289,10 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
   }
 
   /** Update a receiver's maximum ingestion rate */
-  def sendRateUpdate(streamUID: Int, newRate: Long): Unit = {
-    endpoint.send(UpdateReceiverRateLimit(streamUID, newRate))
+  def sendRateUpdate(streamUID: Int, newRate: Long): Unit = synchronized {
+    if (isTrackerStarted) {
+      endpoint.send(UpdateReceiverRateLimit(streamUID, newRate))
+    }
   }
 
   /** Add new blocks for the given stream */
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
index a34f23475804a..e0718f73aa13f 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
@@ -1735,6 +1735,7 @@ public Integer call(String s) throws Exception {
   @SuppressWarnings("unchecked")
   @Test
   public void testContextGetOrCreate() throws InterruptedException {
+    ssc.stop();
 
     final SparkConf conf = new SparkConf()
         .setMaster("local[2]")
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
index 08faeaa58f419..255376807c957 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
@@ -81,39 +81,41 @@ class BasicOperationsSuite extends TestSuiteBase {
   test("repartition (more partitions)") {
     val input = Seq(1 to 100, 101 to 200, 201 to 300)
     val operation = (r: DStream[Int]) => r.repartition(5)
-    val ssc = setupStreams(input, operation, 2)
-    val output = runStreamsWithPartitions(ssc, 3, 3)
-    assert(output.size === 3)
-    val first = output(0)
-    val second = output(1)
-    val third = output(2)
-
-    assert(first.size === 5)
-    assert(second.size === 5)
-    assert(third.size === 5)
-
-    assert(first.flatten.toSet.equals((1 to 100).toSet) )
-    assert(second.flatten.toSet.equals((101 to 200).toSet))
-    assert(third.flatten.toSet.equals((201 to 300).toSet))
+    withStreamingContext(setupStreams(input, operation, 2)) { ssc =>
+      val output = runStreamsWithPartitions(ssc, 3, 3)
+      assert(output.size === 3)
+      val first = output(0)
+      val second = output(1)
+      val third = output(2)
+
+      assert(first.size === 5)
+      assert(second.size === 5)
+      assert(third.size === 5)
+
+      assert(first.flatten.toSet.equals((1 to 100).toSet))
+      assert(second.flatten.toSet.equals((101 to 200).toSet))
+      assert(third.flatten.toSet.equals((201 to 300).toSet))
+    }
   }
 
   test("repartition (fewer partitions)") {
     val input = Seq(1 to 100, 101 to 200, 201 to 300)
     val operation = (r: DStream[Int]) => r.repartition(2)
-    val ssc = setupStreams(input, operation, 5)
-    val output = runStreamsWithPartitions(ssc, 3, 3)
-    assert(output.size === 3)
-    val first = output(0)
-    val second = output(1)
-    val third = output(2)
-
-    assert(first.size === 2)
-    assert(second.size === 2)
-    assert(third.size === 2)
-
-    assert(first.flatten.toSet.equals((1 to 100).toSet))
-    assert(second.flatten.toSet.equals( (101 to 200).toSet))
-    assert(third.flatten.toSet.equals((201 to 300).toSet))
+    withStreamingContext(setupStreams(input, operation, 5)) { ssc =>
+      val output = runStreamsWithPartitions(ssc, 3, 3)
+      assert(output.size === 3)
+      val first = output(0)
+      val second = output(1)
+      val third = output(2)
+
+      assert(first.size === 2)
+      assert(second.size === 2)
+      assert(third.size === 2)
+
+      assert(first.flatten.toSet.equals((1 to 100).toSet))
+      assert(second.flatten.toSet.equals((101 to 200).toSet))
+      assert(third.flatten.toSet.equals((201 to 300).toSet))
+    }
   }
 
   test("groupByKey") {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index b74d67c63a788..ec2852d9a0206 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -325,27 +325,31 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
   }
 
   test("test track the number of input stream") {
-    val ssc = new StreamingContext(conf, batchDuration)
+    withStreamingContext(new StreamingContext(conf, batchDuration)) { ssc =>
 
-    class TestInputDStream extends InputDStream[String](ssc) {
-      def start() { }
-      def stop() { }
-      def compute(validTime: Time): Option[RDD[String]] = None
-    }
+      class TestInputDStream extends InputDStream[String](ssc) {
+        def start() {}
 
-    class TestReceiverInputDStream extends ReceiverInputDStream[String](ssc) {
-      def getReceiver: Receiver[String] = null
-    }
+        def stop() {}
+
+        def compute(validTime: Time): Option[RDD[String]] = None
+      }
+
+      class TestReceiverInputDStream extends ReceiverInputDStream[String](ssc) {
+        def getReceiver: Receiver[String] = null
+      }
 
-    // Register input streams
-    val receiverInputStreams = Array(new TestReceiverInputDStream, new TestReceiverInputDStream)
-    val inputStreams = Array(new TestInputDStream, new TestInputDStream, new TestInputDStream)
+      // Register input streams
+      val receiverInputStreams = Array(new TestReceiverInputDStream, new TestReceiverInputDStream)
+      val inputStreams = Array(new TestInputDStream, new TestInputDStream, new TestInputDStream)
 
-    assert(ssc.graph.getInputStreams().length == receiverInputStreams.length + inputStreams.length)
-    assert(ssc.graph.getReceiverInputStreams().length == receiverInputStreams.length)
-    assert(ssc.graph.getReceiverInputStreams() === receiverInputStreams)
-    assert(ssc.graph.getInputStreams().map(_.id) === Array.tabulate(5)(i => i))
-    assert(receiverInputStreams.map(_.id) === Array(0, 1))
+      assert(ssc.graph.getInputStreams().length ==
+        receiverInputStreams.length + inputStreams.length)
+      assert(ssc.graph.getReceiverInputStreams().length == receiverInputStreams.length)
+      assert(ssc.graph.getReceiverInputStreams() === receiverInputStreams)
+      assert(ssc.graph.getInputStreams().map(_.id) === Array.tabulate(5)(i => i))
+      assert(receiverInputStreams.map(_.id) === Array(0, 1))
+    }
   }
 
   def testFileStream(newFilesOnly: Boolean) {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala b/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala
index 6e9d4431090a2..0e64b57e0ffd8 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala
@@ -244,7 +244,13 @@ object MasterFailureTest extends Logging {
       } catch {
         case e: Exception => logError("Error running streaming context", e)
       }
-      if (killingThread.isAlive) killingThread.interrupt()
+      if (killingThread.isAlive) {
+        killingThread.interrupt()
+        // SparkContext.stop will set SparkEnv.env to null. We need to make sure SparkContext is
+        // stopped before running the next test. Otherwise, it's possible that we set SparkEnv.env
+        // to null after the next test creates the new SparkContext and fail the test.
+        killingThread.join()
+      }
       ssc.stop()
 
       logInfo("Has been killed = " + killed)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 4bba9691f8aa5..84a5fbb3d95eb 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -120,7 +120,7 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
 
     val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName)
     myConf.set("spark.streaming.checkpoint.directory", checkpointDirectory)
-    val ssc = new StreamingContext(myConf, batchDuration)
+    ssc = new StreamingContext(myConf, batchDuration)
     assert(ssc.checkpointDir != null)
   }
 
@@ -369,16 +369,22 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
     }
     assert(exception.isInstanceOf[TestFailedDueToTimeoutException], "Did not wait for stop")
 
+    var t: Thread = null
     // test whether wait exits if context is stopped
     failAfter(10000 millis) { // 10 seconds because spark takes a long time to shutdown
-      new Thread() {
+      t = new Thread() {
         override def run() {
           Thread.sleep(500)
           ssc.stop()
         }
-      }.start()
+      }
+      t.start()
       ssc.awaitTermination()
     }
+    // SparkContext.stop will set SparkEnv.env to null. We need to make sure SparkContext is stopped
+    // before running the next test. Otherwise, it's possible that we set SparkEnv.env to null after
+    // the next test creates the new SparkContext and fail the test.
+    t.join()
   }
 
   test("awaitTermination after stop") {
@@ -430,16 +436,22 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
       assert(ssc.awaitTerminationOrTimeout(500) === false)
     }
 
+    var t: Thread = null
     // test whether awaitTerminationOrTimeout() return true if context is stopped
     failAfter(10000 millis) { // 10 seconds because spark takes a long time to shutdown
-      new Thread() {
+      t = new Thread() {
         override def run() {
           Thread.sleep(500)
           ssc.stop()
         }
-      }.start()
+      }
+      t.start()
       assert(ssc.awaitTerminationOrTimeout(10000) === true)
     }
+    // SparkContext.stop will set SparkEnv.env to null. We need to make sure SparkContext is stopped
+    // before running the next test. Otherwise, it's possible that we set SparkEnv.env to null after
+    // the next test creates the new SparkContext and fail the test.
+    t.join()
   }
 
   test("getOrCreate") {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
index 4bc1dd4a30fc4..d840c349bbbc4 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
@@ -36,13 +36,22 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers {
   val input = (1 to 4).map(Seq(_)).toSeq
   val operation = (d: DStream[Int]) => d.map(x => x)
 
+  var ssc: StreamingContext = _
+
+  override def afterFunction() {
+    super.afterFunction()
+    if (ssc != null) {
+      ssc.stop()
+    }
+  }
+
   // To make sure that the processing start and end times in collected
   // information are different for successive batches
   override def batchDuration: Duration = Milliseconds(100)
   override def actuallyWait: Boolean = true
 
   test("batch info reporting") {
-    val ssc = setupStreams(input, operation)
+    ssc = setupStreams(input, operation)
     val collector = new BatchInfoCollector
     ssc.addStreamingListener(collector)
     runStreams(ssc, input.size, input.size)
@@ -107,7 +116,7 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers {
   }
 
   test("receiver info reporting") {
-    val ssc = new StreamingContext("local[2]", "test", Milliseconds(1000))
+    ssc = new StreamingContext("local[2]", "test", Milliseconds(1000))
     val inputStream = ssc.receiverStream(new StreamingListenerSuiteReceiver)
     inputStream.foreachRDD(_.count)
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index aff8b53f752fa..afad5f16dbc71 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -29,36 +29,40 @@ import org.apache.spark.storage.StorageLevel
 /** Testsuite for receiver scheduling */
 class ReceiverTrackerSuite extends TestSuiteBase {
   val sparkConf = new SparkConf().setMaster("local[8]").setAppName("test")
-  val ssc = new StreamingContext(sparkConf, Milliseconds(100))
 
-  ignore("Receiver tracker - propagates rate limit") {
-    object ReceiverStartedWaiter extends StreamingListener {
-      @volatile
-      var started = false
+  test("Receiver tracker - propagates rate limit") {
+    withStreamingContext(new StreamingContext(sparkConf, Milliseconds(100))) { ssc =>
+      object ReceiverStartedWaiter extends StreamingListener {
+        @volatile
+        var started = false
 
-      override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = {
-        started = true
+        override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = {
+          started = true
+        }
       }
-    }
-
-    ssc.addStreamingListener(ReceiverStartedWaiter)
-    ssc.scheduler.listenerBus.start(ssc.sc)
-    SingletonTestRateReceiver.reset()
-
-    val newRateLimit = 100L
-    val inputDStream = new RateLimitInputDStream(ssc)
-    val tracker = new ReceiverTracker(ssc)
-    tracker.start()
 
-    // we wait until the Receiver has registered with the tracker,
-    // otherwise our rate update is lost
-    eventually(timeout(5 seconds)) {
-      assert(ReceiverStartedWaiter.started)
-    }
-    tracker.sendRateUpdate(inputDStream.id, newRateLimit)
-    // this is an async message, we need to wait a bit for it to be processed
-    eventually(timeout(3 seconds)) {
-      assert(inputDStream.getCurrentRateLimit.get === newRateLimit)
+      ssc.addStreamingListener(ReceiverStartedWaiter)
+      ssc.scheduler.listenerBus.start(ssc.sc)
+      SingletonTestRateReceiver.reset()
+
+      val newRateLimit = 100L
+      val inputDStream = new RateLimitInputDStream(ssc)
+      val tracker = new ReceiverTracker(ssc)
+      tracker.start()
+      try {
+        // we wait until the Receiver has registered with the tracker,
+        // otherwise our rate update is lost
+        eventually(timeout(5 seconds)) {
+          assert(ReceiverStartedWaiter.started)
+        }
+        tracker.sendRateUpdate(inputDStream.id, newRateLimit)
+        // this is an async message, we need to wait a bit for it to be processed
+        eventually(timeout(3 seconds)) {
+          assert(inputDStream.getCurrentRateLimit.get === newRateLimit)
+        }
+      } finally {
+        tracker.stop(false)
+      }
     }
   }
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
index 0891309f956d2..995f1197ccdfd 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
@@ -22,15 +22,24 @@ import java.util.Properties
 import org.scalatest.Matchers
 
 import org.apache.spark.scheduler.SparkListenerJobStart
+import org.apache.spark.streaming._
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.streaming.scheduler._
-import org.apache.spark.streaming.{Duration, Time, Milliseconds, TestSuiteBase}
 
 class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
 
   val input = (1 to 4).map(Seq(_)).toSeq
   val operation = (d: DStream[Int]) => d.map(x => x)
 
+  var ssc: StreamingContext = _
+
+  override def afterFunction() {
+    super.afterFunction()
+    if (ssc != null) {
+      ssc.stop()
+    }
+  }
+
   private def createJobStart(
       batchTime: Time, outputOpId: Int, jobId: Int): SparkListenerJobStart = {
     val properties = new Properties()
@@ -46,7 +55,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
 
   test("onBatchSubmitted, onBatchStarted, onBatchCompleted, " +
     "onReceiverStarted, onReceiverError, onReceiverStopped") {
-    val ssc = setupStreams(input, operation)
+    ssc = setupStreams(input, operation)
     val listener = new StreamingJobProgressListener(ssc)
 
     val streamIdToInputInfo = Map(
@@ -141,7 +150,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
   }
 
   test("Remove the old completed batches when exceeding the limit") {
-    val ssc = setupStreams(input, operation)
+    ssc = setupStreams(input, operation)
     val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
     val listener = new StreamingJobProgressListener(ssc)
 
@@ -158,7 +167,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
   }
 
   test("out-of-order onJobStart and onBatchXXX") {
-    val ssc = setupStreams(input, operation)
+    ssc = setupStreams(input, operation)
     val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
     val listener = new StreamingJobProgressListener(ssc)
 
@@ -209,7 +218,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
   }
 
   test("detect memory leak") {
-    val ssc = setupStreams(input, operation)
+    ssc = setupStreams(input, operation)
     val listener = new StreamingJobProgressListener(ssc)
 
     val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)

From 7f7a319c4ce07f07a6bd68100cf0a4f1da66269e Mon Sep 17 00:00:00 2001
From: martinzapletal <zapletal-martin@email.cz>
Date: Thu, 30 Jul 2015 15:57:14 -0700
Subject: [PATCH 0713/1454] [SPARK-8671] [ML] Added isotonic regression to the
 pipeline API.

Author: martinzapletal <zapletal-martin@email.cz>

Closes #7517 from zapletal-martin/SPARK-8671-isotonic-regression-api and squashes the following commits:

8c435c1 [martinzapletal] Review https://github.com/apache/spark/pull/7517 feedback update.
bebbb86 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8671-isotonic-regression-api
b68efc0 [martinzapletal] Added tests for param validation.
07c12bd [martinzapletal] Comments and refactoring.
834fcf7 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8671-isotonic-regression-api
b611fee [martinzapletal] SPARK-8671. Added first version of isotonic regression to pipeline API
---
 .../ml/regression/IsotonicRegression.scala    | 144 +++++++++++++++++
 .../regression/IsotonicRegressionSuite.scala  | 148 ++++++++++++++++++
 2 files changed, 292 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
new file mode 100644
index 0000000000000..4ece8cf8cf0b6
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -0,0 +1,144 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.regression
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.PredictorParams
+import org.apache.spark.ml.param.{Param, ParamMap, BooleanParam}
+import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
+import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression}
+import org.apache.spark.mllib.regression.{IsotonicRegressionModel => MLlibIsotonicRegressionModel}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types.{DoubleType, DataType}
+import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Params for isotonic regression.
+ */
+private[regression] trait IsotonicRegressionParams extends PredictorParams {
+
+  /**
+   * Param for weight column name.
+   * TODO: Move weightCol to sharedParams.
+   *
+   * @group param
+   */
+  final val weightCol: Param[String] =
+    new Param[String](this, "weightCol", "weight column name")
+
+  /** @group getParam */
+  final def getWeightCol: String = $(weightCol)
+
+  /**
+   * Param for isotonic parameter.
+   * Isotonic (increasing) or antitonic (decreasing) sequence.
+   * @group param
+   */
+  final val isotonic: BooleanParam =
+    new BooleanParam(this, "isotonic", "isotonic (increasing) or antitonic (decreasing) sequence")
+
+  /** @group getParam */
+  final def getIsotonicParam: Boolean = $(isotonic)
+}
+
+/**
+ * :: Experimental ::
+ * Isotonic regression.
+ *
+ * Currently implemented using parallelized pool adjacent violators algorithm.
+ * Only univariate (single feature) algorithm supported.
+ *
+ * Uses [[org.apache.spark.mllib.regression.IsotonicRegression]].
+ */
+@Experimental
+class IsotonicRegression(override val uid: String)
+  extends Regressor[Double, IsotonicRegression, IsotonicRegressionModel]
+  with IsotonicRegressionParams {
+
+  def this() = this(Identifiable.randomUID("isoReg"))
+
+  /**
+   * Set the isotonic parameter.
+   * Default is true.
+   * @group setParam
+   */
+  def setIsotonicParam(value: Boolean): this.type = set(isotonic, value)
+  setDefault(isotonic -> true)
+
+  /**
+   * Set weight column param.
+   * Default is weight.
+   * @group setParam
+   */
+  def setWeightParam(value: String): this.type = set(weightCol, value)
+  setDefault(weightCol -> "weight")
+
+  override private[ml] def featuresDataType: DataType = DoubleType
+
+  override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra)
+
+  private[this] def extractWeightedLabeledPoints(
+      dataset: DataFrame): RDD[(Double, Double, Double)] = {
+
+    dataset.select($(labelCol), $(featuresCol), $(weightCol))
+      .map { case Row(label: Double, features: Double, weights: Double) =>
+        (label, features, weights)
+      }
+  }
+
+  override protected def train(dataset: DataFrame): IsotonicRegressionModel = {
+    SchemaUtils.checkColumnType(dataset.schema, $(weightCol), DoubleType)
+    // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
+    val instances = extractWeightedLabeledPoints(dataset)
+    val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
+    if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
+
+    val isotonicRegression = new MLlibIsotonicRegression().setIsotonic($(isotonic))
+    val parentModel = isotonicRegression.run(instances)
+
+    new IsotonicRegressionModel(uid, parentModel)
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by IsotonicRegression.
+ * Predicts using a piecewise linear function.
+ *
+ * For detailed rules see [[org.apache.spark.mllib.regression.IsotonicRegressionModel.predict()]].
+ *
+ * @param parentModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]]
+ *                    model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]].
+ */
+class IsotonicRegressionModel private[ml] (
+    override val uid: String,
+    private[ml] val parentModel: MLlibIsotonicRegressionModel)
+  extends RegressionModel[Double, IsotonicRegressionModel]
+  with IsotonicRegressionParams {
+
+  override def featuresDataType: DataType = DoubleType
+
+  override protected def predict(features: Double): Double = {
+    parentModel.predict(features)
+  }
+
+  override def copy(extra: ParamMap): IsotonicRegressionModel = {
+    copyValues(new IsotonicRegressionModel(uid, parentModel), extra)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
new file mode 100644
index 0000000000000..66e4b170bae80
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
@@ -0,0 +1,148 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.regression
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+import org.apache.spark.sql.{DataFrame, Row}
+
+class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
+  private val schema = StructType(
+    Array(
+      StructField("label", DoubleType),
+      StructField("features", DoubleType),
+      StructField("weight", DoubleType)))
+
+  private val predictionSchema = StructType(Array(StructField("features", DoubleType)))
+
+  private def generateIsotonicInput(labels: Seq[Double]): DataFrame = {
+    val data = Seq.tabulate(labels.size)(i => Row(labels(i), i.toDouble, 1d))
+    val parallelData = sc.parallelize(data)
+
+    sqlContext.createDataFrame(parallelData, schema)
+  }
+
+  private def generatePredictionInput(features: Seq[Double]): DataFrame = {
+    val data = Seq.tabulate(features.size)(i => Row(features(i)))
+
+    val parallelData = sc.parallelize(data)
+    sqlContext.createDataFrame(parallelData, predictionSchema)
+  }
+
+  test("isotonic regression predictions") {
+    val dataset = generateIsotonicInput(Seq(1, 2, 3, 1, 6, 17, 16, 17, 18))
+    val trainer = new IsotonicRegression().setIsotonicParam(true)
+
+    val model = trainer.fit(dataset)
+
+    val predictions = model
+      .transform(dataset)
+      .select("prediction").map {
+        case Row(pred) => pred
+      }.collect()
+
+    assert(predictions === Array(1, 2, 2, 2, 6, 16.5, 16.5, 17, 18))
+
+    assert(model.parentModel.boundaries === Array(0, 1, 3, 4, 5, 6, 7, 8))
+    assert(model.parentModel.predictions === Array(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0))
+    assert(model.parentModel.isotonic)
+  }
+
+  test("antitonic regression predictions") {
+    val dataset = generateIsotonicInput(Seq(7, 5, 3, 5, 1))
+    val trainer = new IsotonicRegression().setIsotonicParam(false)
+
+    val model = trainer.fit(dataset)
+    val features = generatePredictionInput(Seq(-2.0, -1.0, 0.5, 0.75, 1.0, 2.0, 9.0))
+
+    val predictions = model
+      .transform(features)
+      .select("prediction").map {
+        case Row(pred) => pred
+      }.collect()
+
+    assert(predictions === Array(7, 7, 6, 5.5, 5, 4, 1))
+  }
+
+  test("params validation") {
+    val dataset = generateIsotonicInput(Seq(1, 2, 3))
+    val ir = new IsotonicRegression
+    ParamsSuite.checkParams(ir)
+    val model = ir.fit(dataset)
+    ParamsSuite.checkParams(model)
+  }
+
+  test("default params") {
+    val dataset = generateIsotonicInput(Seq(1, 2, 3))
+    val ir = new IsotonicRegression()
+    assert(ir.getLabelCol === "label")
+    assert(ir.getFeaturesCol === "features")
+    assert(ir.getWeightCol === "weight")
+    assert(ir.getPredictionCol === "prediction")
+    assert(ir.getIsotonicParam === true)
+
+    val model = ir.fit(dataset)
+    model.transform(dataset)
+      .select("label", "features", "prediction", "weight")
+      .collect()
+
+    assert(model.getLabelCol === "label")
+    assert(model.getFeaturesCol === "features")
+    assert(model.getWeightCol === "weight")
+    assert(model.getPredictionCol === "prediction")
+    assert(model.getIsotonicParam === true)
+    assert(model.hasParent)
+  }
+
+  test("set parameters") {
+    val isotonicRegression = new IsotonicRegression()
+      .setIsotonicParam(false)
+      .setWeightParam("w")
+      .setFeaturesCol("f")
+      .setLabelCol("l")
+      .setPredictionCol("p")
+
+    assert(isotonicRegression.getIsotonicParam === false)
+    assert(isotonicRegression.getWeightCol === "w")
+    assert(isotonicRegression.getFeaturesCol === "f")
+    assert(isotonicRegression.getLabelCol === "l")
+    assert(isotonicRegression.getPredictionCol === "p")
+  }
+
+  test("missing column") {
+    val dataset = generateIsotonicInput(Seq(1, 2, 3))
+
+    intercept[IllegalArgumentException] {
+      new IsotonicRegression().setWeightParam("w").fit(dataset)
+    }
+
+    intercept[IllegalArgumentException] {
+      new IsotonicRegression().setFeaturesCol("f").fit(dataset)
+    }
+
+    intercept[IllegalArgumentException] {
+      new IsotonicRegression().setLabelCol("l").fit(dataset)
+    }
+
+    intercept[IllegalArgumentException] {
+      new IsotonicRegression().fit(dataset).setFeaturesCol("f").transform(dataset)
+    }
+  }
+}

From be7be6d4c7d978c20e601d1f5f56ecb3479814cb Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Thu, 30 Jul 2015 16:04:23 -0700
Subject: [PATCH 0714/1454] [SPARK-6684] [MLLIB] [ML] Add checkpointing to GBTs

Add checkpointing to GradientBoostedTrees, GBTClassifier, GBTRegressor

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7804 from jkbradley/gbt-checkpoint3 and squashes the following commits:

3fbd7ba [Joseph K. Bradley] tiny fix
b3e160c [Joseph K. Bradley] unset checkpoint dir after test
9cc3a04 [Joseph K. Bradley] added checkpointing to GBTs
---
 .../spark/mllib/clustering/LDAOptimizer.scala |  1 +
 .../mllib/tree/GradientBoostedTrees.scala     | 48 +++++------
 .../tree/configuration/BoostingStrategy.scala |  3 +-
 .../classification/GBTClassifierSuite.scala   | 20 +++++
 .../ml/regression/GBTRegressorSuite.scala     | 20 ++++-
 .../tree/GradientBoostedTreesSuite.scala      | 79 +++++++++++--------
 6 files changed, 114 insertions(+), 57 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 9dbec41efeada..d6f8b29a43dfd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -144,6 +144,7 @@ final class EMLDAOptimizer extends LDAOptimizer {
     this.checkpointInterval = lda.getCheckpointInterval
     this.graphCheckpointer = new PeriodicGraphCheckpointer[TopicCounts, TokenCount](
       checkpointInterval, graph.vertices.sparkContext)
+    this.graphCheckpointer.update(this.graph)
     this.globalTopicTotals = computeGlobalTopicTotals()
     this
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index a835f96d5d0e3..9ce6faa137c41 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -20,6 +20,7 @@ package org.apache.spark.mllib.tree
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.mllib.impl.PeriodicRDDCheckpointer
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.BoostingStrategy
 import org.apache.spark.mllib.tree.configuration.Algo._
@@ -184,22 +185,28 @@ object GradientBoostedTrees extends Logging {
       false
     }
 
+    // Prepare periodic checkpointers
+    val predErrorCheckpointer = new PeriodicRDDCheckpointer[(Double, Double)](
+      treeStrategy.getCheckpointInterval, input.sparkContext)
+    val validatePredErrorCheckpointer = new PeriodicRDDCheckpointer[(Double, Double)](
+      treeStrategy.getCheckpointInterval, input.sparkContext)
+
     timer.stop("init")
 
     logDebug("##########")
     logDebug("Building tree 0")
     logDebug("##########")
-    var data = input
 
     // Initialize tree
     timer.start("building tree 0")
-    val firstTreeModel = new DecisionTree(treeStrategy).run(data)
+    val firstTreeModel = new DecisionTree(treeStrategy).run(input)
     val firstTreeWeight = 1.0
     baseLearners(0) = firstTreeModel
     baseLearnerWeights(0) = firstTreeWeight
 
     var predError: RDD[(Double, Double)] = GradientBoostedTreesModel.
       computeInitialPredictionAndError(input, firstTreeWeight, firstTreeModel, loss)
+    predErrorCheckpointer.update(predError)
     logDebug("error of gbt = " + predError.values.mean())
 
     // Note: A model of type regression is used since we require raw prediction
@@ -207,35 +214,34 @@ object GradientBoostedTrees extends Logging {
 
     var validatePredError: RDD[(Double, Double)] = GradientBoostedTreesModel.
       computeInitialPredictionAndError(validationInput, firstTreeWeight, firstTreeModel, loss)
+    if (validate) validatePredErrorCheckpointer.update(validatePredError)
     var bestValidateError = if (validate) validatePredError.values.mean() else 0.0
     var bestM = 1
 
-    // pseudo-residual for second iteration
-    data = predError.zip(input).map { case ((pred, _), point) =>
-      LabeledPoint(-loss.gradient(pred, point.label), point.features)
-    }
-
     var m = 1
-    while (m < numIterations) {
+    var doneLearning = false
+    while (m < numIterations && !doneLearning) {
+      // Update data with pseudo-residuals
+      val data = predError.zip(input).map { case ((pred, _), point) =>
+        LabeledPoint(-loss.gradient(pred, point.label), point.features)
+      }
+
       timer.start(s"building tree $m")
       logDebug("###################################################")
       logDebug("Gradient boosting tree iteration " + m)
       logDebug("###################################################")
       val model = new DecisionTree(treeStrategy).run(data)
       timer.stop(s"building tree $m")
-      // Create partial model
+      // Update partial model
       baseLearners(m) = model
       // Note: The setting of baseLearnerWeights is incorrect for losses other than SquaredError.
       //       Technically, the weight should be optimized for the particular loss.
       //       However, the behavior should be reasonable, though not optimal.
       baseLearnerWeights(m) = learningRate
-      // Note: A model of type regression is used since we require raw prediction
-      val partialModel = new GradientBoostedTreesModel(
-        Regression, baseLearners.slice(0, m + 1),
-        baseLearnerWeights.slice(0, m + 1))
 
       predError = GradientBoostedTreesModel.updatePredictionError(
         input, predError, baseLearnerWeights(m), baseLearners(m), loss)
+      predErrorCheckpointer.update(predError)
       logDebug("error of gbt = " + predError.values.mean())
 
       if (validate) {
@@ -246,21 +252,15 @@ object GradientBoostedTrees extends Logging {
 
         validatePredError = GradientBoostedTreesModel.updatePredictionError(
           validationInput, validatePredError, baseLearnerWeights(m), baseLearners(m), loss)
+        validatePredErrorCheckpointer.update(validatePredError)
         val currentValidateError = validatePredError.values.mean()
         if (bestValidateError - currentValidateError < validationTol) {
-          return new GradientBoostedTreesModel(
-            boostingStrategy.treeStrategy.algo,
-            baseLearners.slice(0, bestM),
-            baseLearnerWeights.slice(0, bestM))
+          doneLearning = true
         } else if (currentValidateError < bestValidateError) {
-            bestValidateError = currentValidateError
-            bestM = m + 1
+          bestValidateError = currentValidateError
+          bestM = m + 1
         }
       }
-      // Update data with pseudo-residuals
-      data = predError.zip(input).map { case ((pred, _), point) =>
-        LabeledPoint(-loss.gradient(pred, point.label), point.features)
-      }
       m += 1
     }
 
@@ -269,6 +269,8 @@ object GradientBoostedTrees extends Logging {
     logInfo("Internal timing for DecisionTree:")
     logInfo(s"$timer")
 
+    predErrorCheckpointer.deleteAllCheckpoints()
+    validatePredErrorCheckpointer.deleteAllCheckpoints()
     if (persistedInput) input.unpersist()
 
     if (validate) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 2d6b01524ff3d..9fd30c9b56319 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -36,7 +36,8 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  *                     learning rate should be between in the interval (0, 1]
  * @param validationTol Useful when runWithValidation is used. If the error rate on the
  *                      validation input between two iterations is less than the validationTol
- *                      then stop. Ignored when [[run]] is used.
+ *                      then stop.  Ignored when
+ *                      [[org.apache.spark.mllib.tree.GradientBoostedTrees.run()]] is used.
  */
 @Experimental
 case class BoostingStrategy(
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index 82c345491bb3c..a7bc77965fefd 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -28,6 +28,7 @@ import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.util.Utils
 
 
 /**
@@ -76,6 +77,25 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("Checkpointing") {
+    val tempDir = Utils.createTempDir()
+    val path = tempDir.toURI.toString
+    sc.setCheckpointDir(path)
+
+    val categoricalFeatures = Map.empty[Int, Int]
+    val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2)
+    val gbt = new GBTClassifier()
+      .setMaxDepth(2)
+      .setLossType("logistic")
+      .setMaxIter(5)
+      .setStepSize(0.1)
+      .setCheckpointInterval(2)
+    val model = gbt.fit(df)
+
+    sc.checkpointDir = None
+    Utils.deleteRecursively(tempDir)
+  }
+
   // TODO: Reinstate test once runWithValidation is implemented   SPARK-7132
   /*
   test("runWithValidation stops early and performs better on a validation dataset") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index 9682edcd9ba84..dbdce0c9dea54 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -25,7 +25,8 @@ import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees =>
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.util.Utils
 
 
 /**
@@ -88,6 +89,23 @@ class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(predictions.min() < -1)
   }
 
+  test("Checkpointing") {
+    val tempDir = Utils.createTempDir()
+    val path = tempDir.toURI.toString
+    sc.setCheckpointDir(path)
+
+    val df = sqlContext.createDataFrame(data)
+    val gbt = new GBTRegressor()
+      .setMaxDepth(2)
+      .setMaxIter(5)
+      .setStepSize(0.1)
+      .setCheckpointInterval(2)
+    val model = gbt.fit(df)
+
+    sc.checkpointDir = None
+    Utils.deleteRecursively(tempDir)
+  }
+
   // TODO: Reinstate test once runWithValidation is implemented  SPARK-7132
   /*
   test("runWithValidation stops early and performs better on a validation dataset") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
index 2521b3342181a..6fc9e8df621df 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
@@ -166,43 +166,58 @@ class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext
 
     val algos = Array(Regression, Regression, Classification)
     val losses = Array(SquaredError, AbsoluteError, LogLoss)
-    (algos zip losses) map {
-      case (algo, loss) => {
-        val treeStrategy = new Strategy(algo = algo, impurity = Variance, maxDepth = 2,
-          categoricalFeaturesInfo = Map.empty)
-        val boostingStrategy =
-          new BoostingStrategy(treeStrategy, loss, numIterations, validationTol = 0.0)
-        val gbtValidate = new GradientBoostedTrees(boostingStrategy)
-          .runWithValidation(trainRdd, validateRdd)
-        val numTrees = gbtValidate.numTrees
-        assert(numTrees !== numIterations)
-
-        // Test that it performs better on the validation dataset.
-        val gbt = new GradientBoostedTrees(boostingStrategy).run(trainRdd)
-        val (errorWithoutValidation, errorWithValidation) = {
-          if (algo == Classification) {
-            val remappedRdd = validateRdd.map(x => new LabeledPoint(2 * x.label - 1, x.features))
-            (loss.computeError(gbt, remappedRdd), loss.computeError(gbtValidate, remappedRdd))
-          } else {
-            (loss.computeError(gbt, validateRdd), loss.computeError(gbtValidate, validateRdd))
-          }
-        }
-        assert(errorWithValidation <= errorWithoutValidation)
-
-        // Test that results from evaluateEachIteration comply with runWithValidation.
-        // Note that convergenceTol is set to 0.0
-        val evaluationArray = gbt.evaluateEachIteration(validateRdd, loss)
-        assert(evaluationArray.length === numIterations)
-        assert(evaluationArray(numTrees) > evaluationArray(numTrees - 1))
-        var i = 1
-        while (i < numTrees) {
-          assert(evaluationArray(i) <= evaluationArray(i - 1))
-          i += 1
+    algos.zip(losses).foreach { case (algo, loss) =>
+      val treeStrategy = new Strategy(algo = algo, impurity = Variance, maxDepth = 2,
+        categoricalFeaturesInfo = Map.empty)
+      val boostingStrategy =
+        new BoostingStrategy(treeStrategy, loss, numIterations, validationTol = 0.0)
+      val gbtValidate = new GradientBoostedTrees(boostingStrategy)
+        .runWithValidation(trainRdd, validateRdd)
+      val numTrees = gbtValidate.numTrees
+      assert(numTrees !== numIterations)
+
+      // Test that it performs better on the validation dataset.
+      val gbt = new GradientBoostedTrees(boostingStrategy).run(trainRdd)
+      val (errorWithoutValidation, errorWithValidation) = {
+        if (algo == Classification) {
+          val remappedRdd = validateRdd.map(x => new LabeledPoint(2 * x.label - 1, x.features))
+          (loss.computeError(gbt, remappedRdd), loss.computeError(gbtValidate, remappedRdd))
+        } else {
+          (loss.computeError(gbt, validateRdd), loss.computeError(gbtValidate, validateRdd))
         }
       }
+      assert(errorWithValidation <= errorWithoutValidation)
+
+      // Test that results from evaluateEachIteration comply with runWithValidation.
+      // Note that convergenceTol is set to 0.0
+      val evaluationArray = gbt.evaluateEachIteration(validateRdd, loss)
+      assert(evaluationArray.length === numIterations)
+      assert(evaluationArray(numTrees) > evaluationArray(numTrees - 1))
+      var i = 1
+      while (i < numTrees) {
+        assert(evaluationArray(i) <= evaluationArray(i - 1))
+        i += 1
+      }
     }
   }
 
+  test("Checkpointing") {
+    val tempDir = Utils.createTempDir()
+    val path = tempDir.toURI.toString
+    sc.setCheckpointDir(path)
+
+    val rdd = sc.parallelize(GradientBoostedTreesSuite.data, 2)
+
+    val treeStrategy = new Strategy(algo = Regression, impurity = Variance, maxDepth = 2,
+      categoricalFeaturesInfo = Map.empty, checkpointInterval = 2)
+    val boostingStrategy = new BoostingStrategy(treeStrategy, SquaredError, 5, 0.1)
+
+    val gbt = GradientBoostedTrees.train(rdd, boostingStrategy)
+
+    sc.checkpointDir = None
+    Utils.deleteRecursively(tempDir)
+  }
+
 }
 
 private object GradientBoostedTreesSuite {

From e7905a9395c1a002f50bab29e16a729e14d4ed6f Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Thu, 30 Jul 2015 16:15:43 -0700
Subject: [PATCH 0715/1454] [SPARK-9463] [ML] Expose model coefficients with
 names in SparkR RFormula

Preview:

```
> summary(m)
            features coefficients
1        (Intercept)    1.6765001
2       Sepal_Length    0.3498801
3 Species.versicolor   -0.9833885
4  Species.virginica   -1.0075104

```

Design doc from umbrella task: https://docs.google.com/document/d/10NZNSEurN2EdWM31uFYsgayIPfCFHiuIu3pCWrUmP_c/edit

cc mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #7771 from ericl/summary and squashes the following commits:

ccd54c3 [Eric Liang] second pass
a5ca93b [Eric Liang] comments
2772111 [Eric Liang] clean up
70483ef [Eric Liang] fix test
7c247d4 [Eric Liang] Merge branch 'master' into summary
3c55024 [Eric Liang] working
8c539aa [Eric Liang] first pass
---
 R/pkg/NAMESPACE                               |  3 ++-
 R/pkg/R/mllib.R                               | 26 ++++++++++++++++++
 R/pkg/inst/tests/test_mllib.R                 | 11 ++++++++
 .../spark/ml/feature/OneHotEncoder.scala      | 12 ++++-----
 .../apache/spark/ml/feature/RFormula.scala    | 12 ++++++++-
 .../apache/spark/ml/r/SparkRWrappers.scala    | 27 +++++++++++++++++--
 .../ml/regression/LinearRegression.scala      |  8 ++++--
 .../spark/ml/feature/OneHotEncoderSuite.scala |  8 +++---
 .../spark/ml/feature/RFormulaSuite.scala      | 18 +++++++++++++
 9 files changed, 108 insertions(+), 17 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 7f7a8a2e4de24..a329e14f25aeb 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -12,7 +12,8 @@ export("print.jobj")
 
 # MLlib integration
 exportMethods("glm",
-              "predict")
+              "predict",
+              "summary")
 
 # Job group lifecycle management methods
 export("setJobGroup",
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 6a8bacaa552c6..efddcc1d8d71c 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -71,3 +71,29 @@ setMethod("predict", signature(object = "PipelineModel"),
           function(object, newData) {
             return(dataFrame(callJMethod(object@model, "transform", newData@sdf)))
           })
+
+#' Get the summary of a model
+#'
+#' Returns the summary of a model produced by glm(), similarly to R's summary().
+#'
+#' @param model A fitted MLlib model
+#' @return a list with a 'coefficient' component, which is the matrix of coefficients. See
+#'         summary.glm for more information.
+#' @rdname glm
+#' @export
+#' @examples
+#'\dontrun{
+#' model <- glm(y ~ x, trainingData)
+#' summary(model)
+#'}
+setMethod("summary", signature(object = "PipelineModel"),
+          function(object) {
+            features <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
+                                   "getModelFeatures", object@model)
+            weights <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
+                                   "getModelWeights", object@model)
+            coefficients <- as.matrix(unlist(weights))
+            colnames(coefficients) <- c("Estimate")
+            rownames(coefficients) <- unlist(features)
+            return(list(coefficients = coefficients))
+          })
diff --git a/R/pkg/inst/tests/test_mllib.R b/R/pkg/inst/tests/test_mllib.R
index 3bef69324770a..f272de78ad4a6 100644
--- a/R/pkg/inst/tests/test_mllib.R
+++ b/R/pkg/inst/tests/test_mllib.R
@@ -48,3 +48,14 @@ test_that("dot minus and intercept vs native glm", {
   rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
   expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
 })
+
+test_that("summary coefficients match with native glm", {
+  training <- createDataFrame(sqlContext, iris)
+  stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training))
+  coefs <- as.vector(stats$coefficients)
+  rCoefs <- as.vector(coef(glm(Sepal.Width ~ Sepal.Length + Species, data = iris)))
+  expect_true(all(abs(rCoefs - coefs) < 1e-6))
+  expect_true(all(
+    as.character(stats$features) ==
+    c("(Intercept)", "Sepal_Length", "Species__versicolor", "Species__virginica")))
+})
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
index 3825942795645..9c60d4084ec46 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
@@ -66,7 +66,6 @@ class OneHotEncoder(override val uid: String) extends Transformer
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
   override def transformSchema(schema: StructType): StructType = {
-    val is = "_is_"
     val inputColName = $(inputCol)
     val outputColName = $(outputCol)
 
@@ -79,17 +78,17 @@ class OneHotEncoder(override val uid: String) extends Transformer
     val outputAttrNames: Option[Array[String]] = inputAttr match {
       case nominal: NominalAttribute =>
         if (nominal.values.isDefined) {
-          nominal.values.map(_.map(v => inputColName + is + v))
+          nominal.values
         } else if (nominal.numValues.isDefined) {
-          nominal.numValues.map(n => Array.tabulate(n)(i => inputColName + is + i))
+          nominal.numValues.map(n => Array.tabulate(n)(_.toString))
         } else {
           None
         }
       case binary: BinaryAttribute =>
         if (binary.values.isDefined) {
-          binary.values.map(_.map(v => inputColName + is + v))
+          binary.values
         } else {
-          Some(Array.tabulate(2)(i => inputColName + is + i))
+          Some(Array.tabulate(2)(_.toString))
         }
       case _: NumericAttribute =>
         throw new RuntimeException(
@@ -123,7 +122,6 @@ class OneHotEncoder(override val uid: String) extends Transformer
 
   override def transform(dataset: DataFrame): DataFrame = {
     // schema transformation
-    val is = "_is_"
     val inputColName = $(inputCol)
     val outputColName = $(outputCol)
     val shouldDropLast = $(dropLast)
@@ -142,7 +140,7 @@ class OneHotEncoder(override val uid: String) extends Transformer
             math.max(m0, m1)
           }
         ).toInt + 1
-      val outputAttrNames = Array.tabulate(numAttrs)(i => inputColName + is + i)
+      val outputAttrNames = Array.tabulate(numAttrs)(_.toString)
       val filtered = if (shouldDropLast) outputAttrNames.dropRight(1) else outputAttrNames
       val outputAttrs: Array[Attribute] =
         filtered.map(name => BinaryAttribute.defaultAttr.withName(name))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index 0b428d278d908..d1726917e4517 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.feature
 
+import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 import scala.util.parsing.combinator.RegexParsers
 
@@ -91,11 +92,20 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
     // TODO(ekl) add support for feature interactions
     val encoderStages = ArrayBuffer[PipelineStage]()
     val tempColumns = ArrayBuffer[String]()
+    val takenNames = mutable.Set(dataset.columns: _*)
     val encodedTerms = resolvedFormula.terms.map { term =>
       dataset.schema(term) match {
         case column if column.dataType == StringType =>
           val indexCol = term + "_idx_" + uid
-          val encodedCol = term + "_onehot_" + uid
+          val encodedCol = {
+            var tmp = term
+            while (takenNames.contains(tmp)) {
+              tmp += "_"
+            }
+            tmp
+          }
+          takenNames.add(indexCol)
+          takenNames.add(encodedCol)
           encoderStages += new StringIndexer().setInputCol(term).setOutputCol(indexCol)
           encoderStages += new OneHotEncoder().setInputCol(indexCol).setOutputCol(encodedCol)
           tempColumns += indexCol
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
index 9f70592ccad7e..f5a022c31ed90 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.ml.api.r
 
+import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.feature.RFormula
-import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
+import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel}
 import org.apache.spark.ml.{Pipeline, PipelineModel}
 import org.apache.spark.sql.DataFrame
 
@@ -44,4 +45,26 @@ private[r] object SparkRWrappers {
     val pipeline = new Pipeline().setStages(Array(formula, estimator))
     pipeline.fit(df)
   }
+
+  def getModelWeights(model: PipelineModel): Array[Double] = {
+    model.stages.last match {
+      case m: LinearRegressionModel =>
+        Array(m.intercept) ++ m.weights.toArray
+      case _: LogisticRegressionModel =>
+        throw new UnsupportedOperationException(
+          "No weights available for LogisticRegressionModel")  // SPARK-9492
+    }
+  }
+
+  def getModelFeatures(model: PipelineModel): Array[String] = {
+    model.stages.last match {
+      case m: LinearRegressionModel =>
+        val attrs = AttributeGroup.fromStructField(
+          m.summary.predictions.schema(m.summary.featuresCol))
+        Array("(Intercept)") ++ attrs.attributes.get.map(_.name.get)
+      case _: LogisticRegressionModel =>
+        throw new UnsupportedOperationException(
+          "No features names available for LogisticRegressionModel")  // SPARK-9492
+    }
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 89718e0f3e15a..3b85ba001b128 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -36,6 +36,7 @@ import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.StructField
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.StatCounter
 
@@ -146,9 +147,10 @@ class LinearRegression(override val uid: String)
 
       val model = new LinearRegressionModel(uid, weights, intercept)
       val trainingSummary = new LinearRegressionTrainingSummary(
-        model.transform(dataset).select($(predictionCol), $(labelCol)),
+        model.transform(dataset),
         $(predictionCol),
         $(labelCol),
+        $(featuresCol),
         Array(0D))
       return copyValues(model.setSummary(trainingSummary))
     }
@@ -221,9 +223,10 @@ class LinearRegression(override val uid: String)
 
     val model = copyValues(new LinearRegressionModel(uid, weights, intercept))
     val trainingSummary = new LinearRegressionTrainingSummary(
-      model.transform(dataset).select($(predictionCol), $(labelCol)),
+      model.transform(dataset),
       $(predictionCol),
       $(labelCol),
+      $(featuresCol),
       objectiveHistory)
     model.setSummary(trainingSummary)
   }
@@ -300,6 +303,7 @@ class LinearRegressionTrainingSummary private[regression] (
     predictions: DataFrame,
     predictionCol: String,
     labelCol: String,
+    val featuresCol: String,
     val objectiveHistory: Array[Double])
   extends LinearRegressionSummary(predictions, predictionCol, labelCol) {
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
index 65846a846b7b4..321eeb843941c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
@@ -86,8 +86,8 @@ class OneHotEncoderSuite extends SparkFunSuite with MLlibTestSparkContext {
     val output = encoder.transform(df)
     val group = AttributeGroup.fromStructField(output.schema("encoded"))
     assert(group.size === 2)
-    assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("size_is_small").withIndex(0))
-    assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("size_is_medium").withIndex(1))
+    assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("small").withIndex(0))
+    assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("medium").withIndex(1))
   }
 
   test("input column without ML attribute") {
@@ -98,7 +98,7 @@ class OneHotEncoderSuite extends SparkFunSuite with MLlibTestSparkContext {
     val output = encoder.transform(df)
     val group = AttributeGroup.fromStructField(output.schema("encoded"))
     assert(group.size === 2)
-    assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("index_is_0").withIndex(0))
-    assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("index_is_1").withIndex(1))
+    assert(group.getAttr(0) === BinaryAttribute.defaultAttr.withName("0").withIndex(0))
+    assert(group.getAttr(1) === BinaryAttribute.defaultAttr.withName("1").withIndex(1))
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
index 8148c553e9051..6aed3243afce8 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -105,4 +106,21 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(result.schema.toString == resultSchema.toString)
     assert(result.collect() === expected.collect())
   }
+
+  test("attribute generation") {
+    val formula = new RFormula().setFormula("id ~ a + b")
+    val original = sqlContext.createDataFrame(
+      Seq((1, "foo", 4), (2, "bar", 4), (3, "bar", 5), (4, "baz", 5))
+    ).toDF("id", "a", "b")
+    val model = formula.fit(original)
+    val result = model.transform(original)
+    val attrs = AttributeGroup.fromStructField(result.schema("features"))
+    val expectedAttrs = new AttributeGroup(
+      "features",
+      Array(
+        new BinaryAttribute(Some("a__bar"), Some(1)),
+        new BinaryAttribute(Some("a__foo"), Some(2)),
+        new NumericAttribute(Some("b"), Some(3))))
+    assert(attrs === expectedAttrs)
+  }
 }

From 157840d1b14502a4f25cff53633c927998c6ada1 Mon Sep 17 00:00:00 2001
From: Hossein <hossein@databricks.com>
Date: Thu, 30 Jul 2015 16:16:17 -0700
Subject: [PATCH 0716/1454] [SPARK-8742] [SPARKR] Improve SparkR error messages
 for DataFrame API

This patch improves SparkR error message reporting, especially with DataFrame API. When there is a user error (e.g., malformed SQL query), the message of the cause is sent back through the RPC and the R client reads it and returns it back to user.

cc shivaram

Author: Hossein <hossein@databricks.com>

Closes #7742 from falaki/SPARK-8742 and squashes the following commits:

4f643c9 [Hossein] Not logging exceptions in RBackendHandler
4a8005c [Hossein] Returning stack track of causing exception from RBackendHandler
5cf17f0 [Hossein] Adding unit test for error messages from SQLContext
2af75d5 [Hossein] Reading error message in case of failure and stoping with that message
f479c99 [Hossein] Wrting exception cause message in JVM
---
 R/pkg/R/backend.R                                      |  4 +++-
 R/pkg/inst/tests/test_sparkSQL.R                       |  5 +++++
 .../scala/org/apache/spark/api/r/RBackendHandler.scala | 10 ++++++++--
 3 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/R/pkg/R/backend.R b/R/pkg/R/backend.R
index 2fb6fae55f28c..49162838b8d1a 100644
--- a/R/pkg/R/backend.R
+++ b/R/pkg/R/backend.R
@@ -110,6 +110,8 @@ invokeJava <- function(isStatic, objId, methodName, ...) {
 
   # TODO: check the status code to output error information
   returnStatus <- readInt(conn)
-  stopifnot(returnStatus == 0)
+  if (returnStatus != 0) {
+    stop(readString(conn))
+  }
   readObject(conn)
 }
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index d5db97248c770..61c8a7ec7d837 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -1002,6 +1002,11 @@ test_that("crosstab() on a DataFrame", {
   expect_identical(expected, ordered)
 })
 
+test_that("SQL error message is returned from JVM", {
+  retError <- tryCatch(sql(sqlContext, "select * from blah"), error = function(e) e)
+  expect_equal(grepl("Table Not Found: blah", retError), TRUE)
+})
+
 unlink(parquetPath)
 unlink(jsonPath)
 unlink(jsonPathNa)
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index a5de10fe89c42..14dac4ed28ce3 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -69,8 +69,11 @@ private[r] class RBackendHandler(server: RBackend)
             case e: Exception =>
               logError(s"Removing $objId failed", e)
               writeInt(dos, -1)
+              writeString(dos, s"Removing $objId failed: ${e.getMessage}")
           }
-        case _ => dos.writeInt(-1)
+        case _ =>
+          dos.writeInt(-1)
+          writeString(dos, s"Error: unknown method $methodName")
       }
     } else {
       handleMethodCall(isStatic, objId, methodName, numArgs, dis, dos)
@@ -146,8 +149,11 @@ private[r] class RBackendHandler(server: RBackend)
       }
     } catch {
       case e: Exception =>
-        logError(s"$methodName on $objId failed", e)
+        logError(s"$methodName on $objId failed")
         writeInt(dos, -1)
+        // Writing the error message of the cause for the exception. This will be returned
+        // to user in the R process.
+        writeString(dos, Utils.exceptionString(e.getCause))
     }
   }
 

From 04c8409107710fc9a625ee513d68c149745539f3 Mon Sep 17 00:00:00 2001
From: Calvin Jia <jia.calvin@gmail.com>
Date: Thu, 30 Jul 2015 16:32:40 -0700
Subject: [PATCH 0717/1454] [SPARK-9199] [CORE] Update Tachyon dependency from
 0.6.4 -> 0.7.0

No new dependencies are added. The exclusion changes are due to the change in tachyon-client 0.7.0's project structure.

There is no client side API change in Tachyon 0.7.0 so no code changes are required.

Author: Calvin Jia <jia.calvin@gmail.com>

Closes #7577 from calvinjia/SPARK-9199 and squashes the following commits:

4e81e40 [Calvin Jia] Update Tachyon dependency from 0.6.4 -> 0.7.0
---
 core/pom.xml         | 34 +++++-----------------------------
 make-distribution.sh |  2 +-
 2 files changed, 6 insertions(+), 30 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 6fa87ec6a24af..202678779150b 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -286,7 +286,7 @@
     <dependency>
       <groupId>org.tachyonproject</groupId>
       <artifactId>tachyon-client</artifactId>
-      <version>0.6.4</version>
+      <version>0.7.0</version>
       <exclusions>
         <exclusion>
           <groupId>org.apache.hadoop</groupId>
@@ -297,36 +297,12 @@
           <artifactId>curator-recipes</artifactId>
         </exclusion>
         <exclusion>
-          <groupId>org.eclipse.jetty</groupId>
-          <artifactId>jetty-jsp</artifactId>
+          <groupId>org.tachyonproject</groupId>
+          <artifactId>tachyon-underfs-glusterfs</artifactId>
         </exclusion>
         <exclusion>
-          <groupId>org.eclipse.jetty</groupId>
-          <artifactId>jetty-webapp</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.eclipse.jetty</groupId>
-          <artifactId>jetty-server</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.eclipse.jetty</groupId>
-          <artifactId>jetty-servlet</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>junit</groupId>
-          <artifactId>junit</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.powermock</groupId>
-          <artifactId>powermock-module-junit4</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.powermock</groupId>
-          <artifactId>powermock-api-mockito</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.apache.curator</groupId>
-          <artifactId>curator-test</artifactId>
+          <groupId>org.tachyonproject</groupId>
+          <artifactId>tachyon-underfs-s3</artifactId>
         </exclusion>
       </exclusions>
     </dependency>
diff --git a/make-distribution.sh b/make-distribution.sh
index cac7032bb2e87..4789b0e09cc8a 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -33,7 +33,7 @@ SPARK_HOME="$(cd "`dirname "$0"`"; pwd)"
 DISTDIR="$SPARK_HOME/dist"
 
 SPARK_TACHYON=false
-TACHYON_VERSION="0.6.4"
+TACHYON_VERSION="0.7.0"
 TACHYON_TGZ="tachyon-${TACHYON_VERSION}-bin.tar.gz"
 TACHYON_URL="https://github.com/amplab/tachyon/releases/download/v${TACHYON_VERSION}/${TACHYON_TGZ}"
 

From 1afdeb7b458f86e2641f062fb9ddc00e9c5c7531 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 30 Jul 2015 16:44:02 -0700
Subject: [PATCH 0718/1454] [STREAMING] [TEST] [HOTFIX] Fixed Kinesis test to
 not throw weird errors when Kinesis tests are enabled without AWS keys

If Kinesis tests are enabled by env ENABLE_KINESIS_TESTS = 1 but no AWS credentials are found, the desired behavior is the fail the test using with
```
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kinesis.KinesisBackedBlockRDDSuite *** ABORTED *** (3 seconds, 5 milliseconds)
[info]   java.lang.Exception: Kinesis tests enabled, but could get not AWS credentials
```

Instead KinesisStreamSuite fails with

```
[info] - basic operation *** FAILED *** (3 seconds, 35 milliseconds)
[info]   java.lang.IllegalArgumentException: requirement failed: Stream not yet created, call createStream() to create one
[info]   at scala.Predef$.require(Predef.scala:233)
[info]   at org.apache.spark.streaming.kinesis.KinesisTestUtils.streamName(KinesisTestUtils.scala:77)
[info]   at org.apache.spark.streaming.kinesis.KinesisTestUtils$$anonfun$deleteStream$1.apply(KinesisTestUtils.scala:150)
[info]   at org.apache.spark.streaming.kinesis.KinesisTestUtils$$anonfun$deleteStream$1.apply(KinesisTestUtils.scala:150)
[info]   at org.apache.spark.Logging$class.logWarning(Logging.scala:71)
[info]   at org.apache.spark.streaming.kinesis.KinesisTestUtils.logWarning(KinesisTestUtils.scala:39)
[info]   at org.apache.spark.streaming.kinesis.KinesisTestUtils.deleteStream(KinesisTestUtils.scala:150)
[info]   at org.apache.spark.streaming.kinesis.KinesisStreamSuite$$anonfun$3.apply$mcV$sp(KinesisStreamSuite.scala:111)
[info]   at org.apache.spark.streaming.kinesis.KinesisStreamSuite$$anonfun$3.apply(KinesisStreamSuite.scala:86)
[info]   at org.apache.spark.streaming.kinesis.KinesisStreamSuite$$anonfun$3.apply(KinesisStreamSuite.scala:86)
```
This is because attempting to delete a non-existent Kinesis stream throws uncaught exception. This PR fixes it.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7809 from tdas/kinesis-test-hotfix and squashes the following commits:

7c372e6 [Tathagata Das] Fixed test
---
 .../streaming/kinesis/KinesisTestUtils.scala  | 27 ++++++++++---------
 .../kinesis/KinesisStreamSuite.scala          |  4 +--
 2 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index 0ff1b7ed0fd90..ca39358b75cb6 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -53,6 +53,8 @@ private class KinesisTestUtils(
 
   @volatile
   private var streamCreated = false
+
+  @volatile
   private var _streamName: String = _
 
   private lazy val kinesisClient = {
@@ -115,21 +117,9 @@ private class KinesisTestUtils(
     shardIdToSeqNumbers.toMap
   }
 
-  def describeStream(streamNameToDescribe: String = streamName): Option[StreamDescription] = {
-    try {
-      val describeStreamRequest = new DescribeStreamRequest().withStreamName(streamNameToDescribe)
-      val desc = kinesisClient.describeStream(describeStreamRequest).getStreamDescription()
-      Some(desc)
-    } catch {
-      case rnfe: ResourceNotFoundException =>
-        None
-    }
-  }
-
   def deleteStream(): Unit = {
     try {
-      if (describeStream().nonEmpty) {
-        val deleteStreamRequest = new DeleteStreamRequest()
+      if (streamCreated) {
         kinesisClient.deleteStream(streamName)
       }
     } catch {
@@ -149,6 +139,17 @@ private class KinesisTestUtils(
     }
   }
 
+  private def describeStream(streamNameToDescribe: String): Option[StreamDescription] = {
+    try {
+      val describeStreamRequest = new DescribeStreamRequest().withStreamName(streamNameToDescribe)
+      val desc = kinesisClient.describeStream(describeStreamRequest).getStreamDescription()
+      Some(desc)
+    } catch {
+      case rnfe: ResourceNotFoundException =>
+        None
+    }
+  }
+
   private def findNonExistentStreamName(): String = {
     var testStreamName: String = null
     do {
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
index f9c952b9468bb..b88c9c6478d56 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -88,11 +88,11 @@ class KinesisStreamSuite extends KinesisFunSuite
     try {
       kinesisTestUtils.createStream()
       ssc = new StreamingContext(sc, Seconds(1))
-      val aWSCredentials = KinesisTestUtils.getAWSCredentials()
+      val awsCredentials = KinesisTestUtils.getAWSCredentials()
       val stream = KinesisUtils.createStream(ssc, kinesisAppName, kinesisTestUtils.streamName,
         kinesisTestUtils.endpointUrl, kinesisTestUtils.regionName, InitialPositionInStream.LATEST,
         Seconds(10), StorageLevel.MEMORY_ONLY,
-        aWSCredentials.getAWSAccessKeyId, aWSCredentials.getAWSSecretKey)
+        awsCredentials.getAWSAccessKeyId, awsCredentials.getAWSSecretKey)
 
       val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
       stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>

From ca71cc8c8b2d64b7756ae697c06876cd18b536dc Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 30 Jul 2015 16:57:38 -0700
Subject: [PATCH 0719/1454] [SPARK-9408] [PYSPARK] [MLLIB] Refactor linalg.py
 to /linalg

This is based on MechCoder 's PR https://github.com/apache/spark/pull/7731. Hopefully it could pass tests. MechCoder I tried to make minimal changes. If this passes Jenkins, we can merge this one first and then try to move `__init__.py` to `local.py` in a separate PR.

Closes #7731

Author: Xiangrui Meng <meng@databricks.com>

Closes #7746 from mengxr/SPARK-9408 and squashes the following commits:

0e05a3b [Xiangrui Meng] merge master
1135551 [Xiangrui Meng] add a comment for str(...)
c48cae0 [Xiangrui Meng] update tests
173a805 [Xiangrui Meng] move linalg.py to linalg/__init__.py
---
 dev/sparktestsupport/modules.py                        | 2 +-
 python/pyspark/mllib/{linalg.py => linalg/__init__.py} | 0
 python/pyspark/sql/types.py                            | 2 +-
 3 files changed, 2 insertions(+), 2 deletions(-)
 rename python/pyspark/mllib/{linalg.py => linalg/__init__.py} (100%)

diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 030d982e99106..44600cb9523c1 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -323,7 +323,7 @@ def contains_file(self, filename):
         "pyspark.mllib.evaluation",
         "pyspark.mllib.feature",
         "pyspark.mllib.fpm",
-        "pyspark.mllib.linalg",
+        "pyspark.mllib.linalg.__init__",
         "pyspark.mllib.random",
         "pyspark.mllib.recommendation",
         "pyspark.mllib.regression",
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg/__init__.py
similarity index 100%
rename from python/pyspark/mllib/linalg.py
rename to python/pyspark/mllib/linalg/__init__.py
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 0976aea72c034..6f74b7162f7cc 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -648,7 +648,7 @@ def jsonValue(self):
 
     @classmethod
     def fromJson(cls, json):
-        pyUDT = str(json["pyClass"])
+        pyUDT = str(json["pyClass"])  # convert unicode to str
         split = pyUDT.rfind(".")
         pyModule = pyUDT[:split]
         pyClass = pyUDT[split+1:]

From df32669514afc0223ecdeca30fbfbe0b40baef3a Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 30 Jul 2015 17:16:03 -0700
Subject: [PATCH 0720/1454] [SPARK-7157][SQL] add sampleBy to DataFrame

This was previously committed but then reverted due to test failures (see #6769).

Author: Xiangrui Meng <meng@databricks.com>

Closes #7755 from rxin/SPARK-7157 and squashes the following commits:

fbf9044 [Xiangrui Meng] fix python test
542bd37 [Xiangrui Meng] update test
604fe6d [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-7157
f051afd [Xiangrui Meng] use udf instead of building expression
f4e9425 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-7157
8fb990b [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-7157
103beb3 [Xiangrui Meng] add Java-friendly sampleBy
991f26f [Xiangrui Meng] fix seed
4a14834 [Xiangrui Meng] move sampleBy to stat
832f7cc [Xiangrui Meng] add sampleBy to DataFrame
---
 python/pyspark/sql/dataframe.py               | 41 ++++++++++++++++++
 .../spark/sql/DataFrameStatFunctions.scala    | 42 +++++++++++++++++++
 .../apache/spark/sql/JavaDataFrameSuite.java  |  9 ++++
 .../apache/spark/sql/DataFrameStatSuite.scala | 12 +++++-
 4 files changed, 102 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index d76e051bd73a1..0f3480c239187 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -441,6 +441,42 @@ def sample(self, withReplacement, fraction, seed=None):
         rdd = self._jdf.sample(withReplacement, fraction, long(seed))
         return DataFrame(rdd, self.sql_ctx)
 
+    @since(1.5)
+    def sampleBy(self, col, fractions, seed=None):
+        """
+        Returns a stratified sample without replacement based on the
+        fraction given on each stratum.
+
+        :param col: column that defines strata
+        :param fractions:
+            sampling fraction for each stratum. If a stratum is not
+            specified, we treat its fraction as zero.
+        :param seed: random seed
+        :return: a new DataFrame that represents the stratified sample
+
+        >>> from pyspark.sql.functions import col
+        >>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key"))
+        >>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
+        >>> sampled.groupBy("key").count().orderBy("key").show()
+        +---+-----+
+        |key|count|
+        +---+-----+
+        |  0|    3|
+        |  1|    8|
+        +---+-----+
+
+        """
+        if not isinstance(col, str):
+            raise ValueError("col must be a string, but got %r" % type(col))
+        if not isinstance(fractions, dict):
+            raise ValueError("fractions must be a dict but got %r" % type(fractions))
+        for k, v in fractions.items():
+            if not isinstance(k, (float, int, long, basestring)):
+                raise ValueError("key must be float, int, long, or string, but got %r" % type(k))
+            fractions[k] = float(v)
+        seed = seed if seed is not None else random.randint(0, sys.maxsize)
+        return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx)
+
     @since(1.4)
     def randomSplit(self, weights, seed=None):
         """Randomly splits this :class:`DataFrame` with the provided weights.
@@ -1314,6 +1350,11 @@ def freqItems(self, cols, support=None):
 
     freqItems.__doc__ = DataFrame.freqItems.__doc__
 
+    def sampleBy(self, col, fractions, seed=None):
+        return self.df.sampleBy(col, fractions, seed)
+
+    sampleBy.__doc__ = DataFrame.sampleBy.__doc__
+
 
 def _test():
     import doctest
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index 4ec58082e7aef..2e68e358f2f1f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -17,6 +17,10 @@
 
 package org.apache.spark.sql
 
+import java.{util => ju, lang => jl}
+
+import scala.collection.JavaConverters._
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.execution.stat._
 
@@ -166,4 +170,42 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
   def freqItems(cols: Seq[String]): DataFrame = {
     FrequentItems.singlePassFreqItems(df, cols, 0.01)
   }
+
+  /**
+   * Returns a stratified sample without replacement based on the fraction given on each stratum.
+   * @param col column that defines strata
+   * @param fractions sampling fraction for each stratum. If a stratum is not specified, we treat
+   *                  its fraction as zero.
+   * @param seed random seed
+   * @tparam T stratum type
+   * @return a new [[DataFrame]] that represents the stratified sample
+   *
+   * @since 1.5.0
+   */
+  def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+    require(fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+      s"Fractions must be in [0, 1], but got $fractions.")
+    import org.apache.spark.sql.functions.{rand, udf}
+    val c = Column(col)
+    val r = rand(seed)
+    val f = udf { (stratum: Any, x: Double) =>
+      x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+    }
+    df.filter(f(c, r))
+  }
+
+  /**
+   * Returns a stratified sample without replacement based on the fraction given on each stratum.
+   * @param col column that defines strata
+   * @param fractions sampling fraction for each stratum. If a stratum is not specified, we treat
+   *                  its fraction as zero.
+   * @param seed random seed
+   * @tparam T stratum type
+   * @return a new [[DataFrame]] that represents the stratified sample
+   *
+   * @since 1.5.0
+   */
+  def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+    sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+  }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 9e61d06f4036e..2c669bb59a0b5 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -226,4 +226,13 @@ public void testCovariance() {
     Double result = df.stat().cov("a", "b");
     Assert.assertTrue(Math.abs(result) < 1e-6);
   }
+
+  @Test
+  public void testSampleBy() {
+    DataFrame df = context.range(0, 100).select(col("id").mod(3).as("key"));
+    DataFrame sampled = df.stat().<Integer>sampleBy("key", ImmutableMap.of(0, 0.1, 1, 0.2), 0L);
+    Row[] actual = sampled.groupBy("key").count().orderBy("key").collect();
+    Row[] expected = new Row[] {RowFactory.create(0, 5), RowFactory.create(1, 8)};
+    Assert.assertArrayEquals(expected, actual);
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 7ba4ba73e0cc9..07a675e64f527 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -21,9 +21,9 @@ import java.util.Random
 
 import org.scalatest.Matchers._
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.functions.col
 
-class DataFrameStatSuite extends SparkFunSuite  {
+class DataFrameStatSuite extends QueryTest {
 
   private val sqlCtx = org.apache.spark.sql.test.TestSQLContext
   import sqlCtx.implicits._
@@ -130,4 +130,12 @@ class DataFrameStatSuite extends SparkFunSuite  {
     val items2 = singleColResults.collect().head
     items2.getSeq[Double](0) should contain (-1.0)
   }
+
+  test("sampleBy") {
+    val df = sqlCtx.range(0, 100).select((col("id") % 3).as("key"))
+    val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L)
+    checkAnswer(
+      sampled.groupBy("key").count().orderBy("key"),
+      Seq(Row(0, 5), Row(1, 8)))
+  }
 }

From e7a0976e991f75a7bda99509e2b040daab965ae6 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 30 Jul 2015 17:17:27 -0700
Subject: [PATCH 0721/1454] [SPARK-9458][SPARK-9469][SQL] Code generate prefix
 computation in sorting & moves unsafe conversion out of TungstenSort.

Author: Reynold Xin <rxin@databricks.com>

Closes #7803 from rxin/SPARK-9458 and squashes the following commits:

5b032dc [Reynold Xin] Fix string.
b670dbb [Reynold Xin] [SPARK-9458][SPARK-9469][SQL] Code generate prefix computation in sorting & moves unsafe conversion out of TungstenSort.
---
 .../unsafe/sort/PrefixComparators.java        | 49 ++++++++------
 .../unsafe/sort/PrefixComparatorsSuite.scala  | 22 ++-----
 .../execution/UnsafeExternalRowSorter.java    | 27 ++++----
 .../sql/catalyst/expressions/SortOrder.scala  | 44 ++++++++++++-
 .../spark/sql/execution/SortPrefixUtils.scala | 64 +++----------------
 .../spark/sql/execution/SparkStrategies.scala |  4 +-
 .../sql/execution/joins/HashedRelation.scala  |  4 +-
 .../org/apache/spark/sql/execution/sort.scala | 64 ++++++++-----------
 .../execution/RowFormatConvertersSuite.scala  | 11 ++--
 ...ortSuite.scala => TungstenSortSuite.scala} | 10 +--
 10 files changed, 138 insertions(+), 161 deletions(-)
 rename sql/core/src/test/scala/org/apache/spark/sql/execution/{UnsafeExternalSortSuite.scala => TungstenSortSuite.scala} (87%)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index 600aff7d15d8a..4d7e5b3dfba6e 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -28,9 +28,11 @@ public class PrefixComparators {
   private PrefixComparators() {}
 
   public static final StringPrefixComparator STRING = new StringPrefixComparator();
-  public static final IntegralPrefixComparator INTEGRAL = new IntegralPrefixComparator();
-  public static final FloatPrefixComparator FLOAT = new FloatPrefixComparator();
+  public static final StringPrefixComparatorDesc STRING_DESC = new StringPrefixComparatorDesc();
+  public static final LongPrefixComparator LONG = new LongPrefixComparator();
+  public static final LongPrefixComparatorDesc LONG_DESC = new LongPrefixComparatorDesc();
   public static final DoublePrefixComparator DOUBLE = new DoublePrefixComparator();
+  public static final DoublePrefixComparatorDesc DOUBLE_DESC = new DoublePrefixComparatorDesc();
 
   public static final class StringPrefixComparator extends PrefixComparator {
     @Override
@@ -38,50 +40,55 @@ public int compare(long aPrefix, long bPrefix) {
       return UnsignedLongs.compare(aPrefix, bPrefix);
     }
 
-    public long computePrefix(UTF8String value) {
+    public static long computePrefix(UTF8String value) {
       return value == null ? 0L : value.getPrefix();
     }
   }
 
-  /**
-   * Prefix comparator for all integral types (boolean, byte, short, int, long).
-   */
-  public static final class IntegralPrefixComparator extends PrefixComparator {
+  public static final class StringPrefixComparatorDesc extends PrefixComparator {
+    @Override
+    public int compare(long bPrefix, long aPrefix) {
+      return UnsignedLongs.compare(aPrefix, bPrefix);
+    }
+  }
+
+  public static final class LongPrefixComparator extends PrefixComparator {
     @Override
     public int compare(long a, long b) {
       return (a < b) ? -1 : (a > b) ? 1 : 0;
     }
+  }
 
-    public final long NULL_PREFIX = Long.MIN_VALUE;
+  public static final class LongPrefixComparatorDesc extends PrefixComparator {
+    @Override
+    public int compare(long b, long a) {
+      return (a < b) ? -1 : (a > b) ? 1 : 0;
+    }
   }
 
-  public static final class FloatPrefixComparator extends PrefixComparator {
+  public static final class DoublePrefixComparator extends PrefixComparator {
     @Override
     public int compare(long aPrefix, long bPrefix) {
-      float a = Float.intBitsToFloat((int) aPrefix);
-      float b = Float.intBitsToFloat((int) bPrefix);
-      return Utils.nanSafeCompareFloats(a, b);
+      double a = Double.longBitsToDouble(aPrefix);
+      double b = Double.longBitsToDouble(bPrefix);
+      return Utils.nanSafeCompareDoubles(a, b);
     }
 
-    public long computePrefix(float value) {
-      return Float.floatToIntBits(value) & 0xffffffffL;
+    public static long computePrefix(double value) {
+      return Double.doubleToLongBits(value);
     }
-
-    public final long NULL_PREFIX = computePrefix(Float.NEGATIVE_INFINITY);
   }
 
-  public static final class DoublePrefixComparator extends PrefixComparator {
+  public static final class DoublePrefixComparatorDesc extends PrefixComparator {
     @Override
-    public int compare(long aPrefix, long bPrefix) {
+    public int compare(long bPrefix, long aPrefix) {
       double a = Double.longBitsToDouble(aPrefix);
       double b = Double.longBitsToDouble(bPrefix);
       return Utils.nanSafeCompareDoubles(a, b);
     }
 
-    public long computePrefix(double value) {
+    public static long computePrefix(double value) {
       return Double.doubleToLongBits(value);
     }
-
-    public final long NULL_PREFIX = computePrefix(Double.NEGATIVE_INFINITY);
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
index cf53a8ad21c60..26a2e96edaaa2 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -29,8 +29,8 @@ class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
     def testPrefixComparison(s1: String, s2: String): Unit = {
       val utf8string1 = UTF8String.fromString(s1)
       val utf8string2 = UTF8String.fromString(s2)
-      val s1Prefix = PrefixComparators.STRING.computePrefix(utf8string1)
-      val s2Prefix = PrefixComparators.STRING.computePrefix(utf8string2)
+      val s1Prefix = PrefixComparators.StringPrefixComparator.computePrefix(utf8string1)
+      val s2Prefix = PrefixComparators.StringPrefixComparator.computePrefix(utf8string2)
       val prefixComparisonResult = PrefixComparators.STRING.compare(s1Prefix, s2Prefix)
 
       val cmp = UnsignedBytes.lexicographicalComparator().compare(
@@ -55,27 +55,15 @@ class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
     forAll { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
   }
 
-  test("float prefix comparator handles NaN properly") {
-    val nan1: Float = java.lang.Float.intBitsToFloat(0x7f800001)
-    val nan2: Float = java.lang.Float.intBitsToFloat(0x7fffffff)
-    assert(nan1.isNaN)
-    assert(nan2.isNaN)
-    val nan1Prefix = PrefixComparators.FLOAT.computePrefix(nan1)
-    val nan2Prefix = PrefixComparators.FLOAT.computePrefix(nan2)
-    assert(nan1Prefix === nan2Prefix)
-    val floatMaxPrefix = PrefixComparators.FLOAT.computePrefix(Float.MaxValue)
-    assert(PrefixComparators.FLOAT.compare(nan1Prefix, floatMaxPrefix) === 1)
-  }
-
   test("double prefix comparator handles NaNs properly") {
     val nan1: Double = java.lang.Double.longBitsToDouble(0x7ff0000000000001L)
     val nan2: Double = java.lang.Double.longBitsToDouble(0x7fffffffffffffffL)
     assert(nan1.isNaN)
     assert(nan2.isNaN)
-    val nan1Prefix = PrefixComparators.DOUBLE.computePrefix(nan1)
-    val nan2Prefix = PrefixComparators.DOUBLE.computePrefix(nan2)
+    val nan1Prefix = PrefixComparators.DoublePrefixComparator.computePrefix(nan1)
+    val nan2Prefix = PrefixComparators.DoublePrefixComparator.computePrefix(nan2)
     assert(nan1Prefix === nan2Prefix)
-    val doubleMaxPrefix = PrefixComparators.DOUBLE.computePrefix(Double.MaxValue)
+    val doubleMaxPrefix = PrefixComparators.DoublePrefixComparator.computePrefix(Double.MaxValue)
     assert(PrefixComparators.DOUBLE.compare(nan1Prefix, doubleMaxPrefix) === 1)
   }
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 4c3f2c6557140..68c49feae938e 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -48,7 +48,6 @@ final class UnsafeExternalRowSorter {
   private long numRowsInserted = 0;
 
   private final StructType schema;
-  private final UnsafeProjection unsafeProjection;
   private final PrefixComputer prefixComputer;
   private final UnsafeExternalSorter sorter;
 
@@ -62,7 +61,6 @@ public UnsafeExternalRowSorter(
       PrefixComparator prefixComparator,
       PrefixComputer prefixComputer) throws IOException {
     this.schema = schema;
-    this.unsafeProjection = UnsafeProjection.create(schema);
     this.prefixComputer = prefixComputer;
     final SparkEnv sparkEnv = SparkEnv.get();
     final TaskContext taskContext = TaskContext.get();
@@ -88,13 +86,12 @@ void setTestSpillFrequency(int frequency) {
   }
 
   @VisibleForTesting
-  void insertRow(InternalRow row) throws IOException {
-    UnsafeRow unsafeRow = unsafeProjection.apply(row);
+  void insertRow(UnsafeRow row) throws IOException {
     final long prefix = prefixComputer.computePrefix(row);
     sorter.insertRecord(
-      unsafeRow.getBaseObject(),
-      unsafeRow.getBaseOffset(),
-      unsafeRow.getSizeInBytes(),
+      row.getBaseObject(),
+      row.getBaseOffset(),
+      row.getSizeInBytes(),
       prefix
     );
     numRowsInserted++;
@@ -113,7 +110,7 @@ private void cleanupResources() {
   }
 
   @VisibleForTesting
-  Iterator<InternalRow> sort() throws IOException {
+  Iterator<UnsafeRow> sort() throws IOException {
     try {
       final UnsafeSorterIterator sortedIterator = sorter.getSortedIterator();
       if (!sortedIterator.hasNext()) {
@@ -121,7 +118,7 @@ Iterator<InternalRow> sort() throws IOException {
         // here in order to prevent memory leaks.
         cleanupResources();
       }
-      return new AbstractScalaRowIterator() {
+      return new AbstractScalaRowIterator<UnsafeRow>() {
 
         private final int numFields = schema.length();
         private UnsafeRow row = new UnsafeRow();
@@ -132,7 +129,7 @@ public boolean hasNext() {
         }
 
         @Override
-        public InternalRow next() {
+        public UnsafeRow next() {
           try {
             sortedIterator.loadNext();
             row.pointTo(
@@ -164,11 +161,11 @@ public InternalRow next() {
   }
 
 
-  public Iterator<InternalRow> sort(Iterator<InternalRow> inputIterator) throws IOException {
-      while (inputIterator.hasNext()) {
-        insertRow(inputIterator.next());
-      }
-      return sort();
+  public Iterator<UnsafeRow> sort(Iterator<UnsafeRow> inputIterator) throws IOException {
+    while (inputIterator.hasNext()) {
+      insertRow(inputIterator.next());
+    }
+    return sort();
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index 3f436c0eb893c..9fe877f10fa08 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -17,7 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
+import org.apache.spark.sql.types._
+import org.apache.spark.util.collection.unsafe.sort.PrefixComparators.DoublePrefixComparator
 
 abstract sealed class SortDirection
 case object Ascending extends SortDirection
@@ -37,4 +40,43 @@ case class SortOrder(child: Expression, direction: SortDirection)
   override def nullable: Boolean = child.nullable
 
   override def toString: String = s"$child ${if (direction == Ascending) "ASC" else "DESC"}"
+
+  def isAscending: Boolean = direction == Ascending
+}
+
+/**
+ * An expression to generate a 64-bit long prefix used in sorting.
+ */
+case class SortPrefix(child: SortOrder) extends UnaryExpression {
+
+  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val childCode = child.child.gen(ctx)
+    val input = childCode.primitive
+    val DoublePrefixCmp = classOf[DoublePrefixComparator].getName
+
+    val (nullValue: Long, prefixCode: String) = child.child.dataType match {
+      case BooleanType =>
+        (Long.MinValue, s"$input ? 1L : 0L")
+      case _: IntegralType =>
+        (Long.MinValue, s"(long) $input")
+      case FloatType | DoubleType =>
+        (DoublePrefixComparator.computePrefix(Double.NegativeInfinity),
+          s"$DoublePrefixCmp.computePrefix((double)$input)")
+      case StringType => (0L, s"$input.getPrefix()")
+      case _ => (0L, "0L")
+    }
+
+    childCode.code +
+    s"""
+      |long ${ev.primitive} = ${nullValue}L;
+      |boolean ${ev.isNull} = false;
+      |if (!${childCode.isNull}) {
+      |  ${ev.primitive} = $prefixCode;
+      |}
+    """.stripMargin
+  }
+
+  override def dataType: DataType = LongType
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 2dee3542d6101..a2145b185ce90 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -18,10 +18,8 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.SortOrder
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.collection.unsafe.sort.{PrefixComparators, PrefixComparator}
 
 
@@ -37,61 +35,15 @@ object SortPrefixUtils {
 
   def getPrefixComparator(sortOrder: SortOrder): PrefixComparator = {
     sortOrder.dataType match {
-      case StringType => PrefixComparators.STRING
-      case BooleanType | ByteType | ShortType | IntegerType | LongType => PrefixComparators.INTEGRAL
-      case FloatType => PrefixComparators.FLOAT
-      case DoubleType => PrefixComparators.DOUBLE
+      case StringType if sortOrder.isAscending => PrefixComparators.STRING
+      case StringType if !sortOrder.isAscending => PrefixComparators.STRING_DESC
+      case BooleanType | ByteType | ShortType | IntegerType | LongType if sortOrder.isAscending =>
+        PrefixComparators.LONG
+      case BooleanType | ByteType | ShortType | IntegerType | LongType if !sortOrder.isAscending =>
+        PrefixComparators.LONG_DESC
+      case FloatType | DoubleType if sortOrder.isAscending => PrefixComparators.DOUBLE
+      case FloatType | DoubleType if !sortOrder.isAscending => PrefixComparators.DOUBLE_DESC
       case _ => NoOpPrefixComparator
     }
   }
-
-  def getPrefixComputer(sortOrder: SortOrder): InternalRow => Long = {
-    sortOrder.dataType match {
-      case StringType => (row: InternalRow) => {
-        PrefixComparators.STRING.computePrefix(sortOrder.child.eval(row).asInstanceOf[UTF8String])
-      }
-      case BooleanType =>
-        (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else if (sortOrder.child.eval(row).asInstanceOf[Boolean]) 1
-          else 0
-        }
-      case ByteType =>
-        (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Byte]
-        }
-      case ShortType =>
-        (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Short]
-        }
-      case IntegerType =>
-        (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Int]
-        }
-      case LongType =>
-        (row: InternalRow) => {
-          val exprVal = sortOrder.child.eval(row)
-          if (exprVal == null) PrefixComparators.INTEGRAL.NULL_PREFIX
-          else sortOrder.child.eval(row).asInstanceOf[Long]
-        }
-      case FloatType => (row: InternalRow) => {
-        val exprVal = sortOrder.child.eval(row)
-        if (exprVal == null) PrefixComparators.FLOAT.NULL_PREFIX
-        else PrefixComparators.FLOAT.computePrefix(sortOrder.child.eval(row).asInstanceOf[Float])
-      }
-      case DoubleType => (row: InternalRow) => {
-        val exprVal = sortOrder.child.eval(row)
-        if (exprVal == null) PrefixComparators.DOUBLE.NULL_PREFIX
-        else PrefixComparators.DOUBLE.computePrefix(sortOrder.child.eval(row).asInstanceOf[Double])
-      }
-      case _ => (row: InternalRow) => 0L
-    }
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 52a9b02d373c7..03d24a88d4ecd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -341,8 +341,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
      */
     def getSortOperator(sortExprs: Seq[SortOrder], global: Boolean, child: SparkPlan): SparkPlan = {
       if (sqlContext.conf.unsafeEnabled && sqlContext.conf.codegenEnabled &&
-        UnsafeExternalSort.supportsSchema(child.schema)) {
-        execution.UnsafeExternalSort(sortExprs, global, child)
+        TungstenSort.supportsSchema(child.schema)) {
+        execution.TungstenSort(sortExprs, global, child)
       } else if (sqlContext.conf.externalSortEnabled) {
         execution.ExternalSort(sortExprs, global, child)
       } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 26dbc911e9521..f88a45f48aee9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -229,7 +229,7 @@ private[joins] final class UnsafeHashedRelation(
       // write all the values as single byte array
       var totalSize = 0L
       var i = 0
-      while (i < values.size) {
+      while (i < values.length) {
         totalSize += values(i).getSizeInBytes + 4 + 4
         i += 1
       }
@@ -240,7 +240,7 @@ private[joins] final class UnsafeHashedRelation(
       out.writeInt(totalSize.toInt)
       out.write(key.getBytes)
       i = 0
-      while (i < values.size) {
+      while (i < values.length) {
         // [num of fields] [num of bytes] [row bytes]
         // write the integer in native order, so they can be read by UNSAFE.getInt()
         if (ByteOrder.nativeOrder() == ByteOrder.BIG_ENDIAN) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index f82208868c3e3..6d903ab23c57f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -17,16 +17,14 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
-import org.apache.spark.sql.catalyst.expressions.{Descending, BindReferences, Attribute, SortOrder}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, OrderedDistribution, Distribution}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.CompletionIterator
 import org.apache.spark.util.collection.ExternalSorter
-import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 // This file defines various sort operators.
@@ -97,59 +95,53 @@ case class ExternalSort(
  * @param testSpillFrequency Method for configuring periodic spilling in unit tests. If set, will
  *                           spill every `frequency` records.
  */
-case class UnsafeExternalSort(
+case class TungstenSort(
     sortOrder: Seq[SortOrder],
     global: Boolean,
     child: SparkPlan,
     testSpillFrequency: Int = 0)
   extends UnaryNode {
 
-  private[this] val schema: StructType = child.schema
+  override def outputsUnsafeRows: Boolean = true
+  override def canProcessUnsafeRows: Boolean = true
+  override def canProcessSafeRows: Boolean = false
+
+  override def output: Seq[Attribute] = child.output
+
+  override def outputOrdering: Seq[SortOrder] = sortOrder
 
   override def requiredChildDistribution: Seq[Distribution] =
     if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil
 
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "sort") {
-    assert(codegenEnabled, "UnsafeExternalSort requires code generation to be enabled")
-    def doSort(iterator: Iterator[InternalRow]): Iterator[InternalRow] = {
-      val ordering = newOrdering(sortOrder, child.output)
-      val boundSortExpression = BindReferences.bindReference(sortOrder.head, child.output)
-      // Hack until we generate separate comparator implementations for ascending vs. descending
-      // (or choose to codegen them):
-      val prefixComparator = {
-        val comp = SortPrefixUtils.getPrefixComparator(boundSortExpression)
-        if (sortOrder.head.direction == Descending) {
-          new PrefixComparator {
-            override def compare(p1: Long, p2: Long): Int = -1 * comp.compare(p1, p2)
-          }
-        } else {
-          comp
-        }
-      }
-      val prefixComputer = {
-        val prefixComputer = SortPrefixUtils.getPrefixComputer(boundSortExpression)
-        new UnsafeExternalRowSorter.PrefixComputer {
-          override def computePrefix(row: InternalRow): Long = prefixComputer(row)
+  protected override def doExecute(): RDD[InternalRow] = {
+    val schema = child.schema
+    val childOutput = child.output
+    child.execute().mapPartitions({ iter =>
+      val ordering = newOrdering(sortOrder, childOutput)
+
+      // The comparator for comparing prefix
+      val boundSortExpression = BindReferences.bindReference(sortOrder.head, childOutput)
+      val prefixComparator = SortPrefixUtils.getPrefixComparator(boundSortExpression)
+
+      // The generator for prefix
+      val prefixProjection = UnsafeProjection.create(Seq(SortPrefix(boundSortExpression)))
+      val prefixComputer = new UnsafeExternalRowSorter.PrefixComputer {
+        override def computePrefix(row: InternalRow): Long = {
+          prefixProjection.apply(row).getLong(0)
         }
       }
+
       val sorter = new UnsafeExternalRowSorter(schema, ordering, prefixComparator, prefixComputer)
       if (testSpillFrequency > 0) {
         sorter.setTestSpillFrequency(testSpillFrequency)
       }
-      sorter.sort(iterator)
-    }
-    child.execute().mapPartitions(doSort, preservesPartitioning = true)
+      sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]])
+    }, preservesPartitioning = true)
   }
 
-  override def output: Seq[Attribute] = child.output
-
-  override def outputOrdering: Seq[SortOrder] = sortOrder
-
-  override def outputsUnsafeRows: Boolean = true
 }
 
-@DeveloperApi
-object UnsafeExternalSort {
+object TungstenSort {
   /**
    * Return true if UnsafeExternalSort can sort rows with the given schema, false otherwise.
    */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 7b75f755918c1..707cd9c6d939b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.CatalystTypeConverters
-import org.apache.spark.sql.catalyst.expressions.IsNull
+import org.apache.spark.sql.catalyst.expressions.{Literal, IsNull}
 import org.apache.spark.sql.test.TestSQLContext
 
 class RowFormatConvertersSuite extends SparkPlanTest {
@@ -31,7 +30,7 @@ class RowFormatConvertersSuite extends SparkPlanTest {
 
   private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
   assert(!outputsSafe.outputsUnsafeRows)
-  private val outputsUnsafe = UnsafeExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  private val outputsUnsafe = TungstenSort(Nil, false, PhysicalRDD(Seq.empty, null))
   assert(outputsUnsafe.outputsUnsafeRows)
 
   test("planner should insert unsafe->safe conversions when required") {
@@ -41,14 +40,14 @@ class RowFormatConvertersSuite extends SparkPlanTest {
   }
 
   test("filter can process unsafe rows") {
-    val plan = Filter(IsNull(null), outputsUnsafe)
+    val plan = Filter(IsNull(IsNull(Literal(1))), outputsUnsafe)
     val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
-    assert(getConverters(preparedPlan).isEmpty)
+    assert(getConverters(preparedPlan).size === 1)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("filter can process safe rows") {
-    val plan = Filter(IsNull(null), outputsSafe)
+    val plan = Filter(IsNull(IsNull(Literal(1))), outputsSafe)
     val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).isEmpty)
     assert(!preparedPlan.outputsUnsafeRows)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
similarity index 87%
rename from sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index 138636b0c65b8..450963547c798 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeExternalSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types._
 
-class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
+class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
 
   override def beforeAll(): Unit = {
     TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
@@ -39,7 +39,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
   test("sort followed by limit") {
     checkThatPlansAgree(
       (1 to 100).map(v => Tuple1(v)).toDF("a"),
-      (child: SparkPlan) => Limit(10, UnsafeExternalSort('a.asc :: Nil, true, child)),
+      (child: SparkPlan) => Limit(10, TungstenSort('a.asc :: Nil, true, child)),
       (child: SparkPlan) => Limit(10, Sort('a.asc :: Nil, global = true, child)),
       sortAnswers = false
     )
@@ -50,7 +50,7 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     val stringLength = 1024 * 1024 * 2
     checkThatPlansAgree(
       Seq(Tuple1("a" * stringLength), Tuple1("b" * stringLength)).toDF("a").repartition(1),
-      UnsafeExternalSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 1),
+      TungstenSort(sortOrder, global = true, _: SparkPlan, testSpillFrequency = 1),
       Sort(sortOrder, global = true, _: SparkPlan),
       sortAnswers = false
     )
@@ -70,11 +70,11 @@ class UnsafeExternalSortSuite extends SparkPlanTest with BeforeAndAfterAll {
         TestSQLContext.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)
       )
-      assert(UnsafeExternalSort.supportsSchema(inputDf.schema))
+      assert(TungstenSort.supportsSchema(inputDf.schema))
       checkThatPlansAgree(
         inputDf,
         plan => ConvertToSafe(
-          UnsafeExternalSort(sortOrder, global = true, plan: SparkPlan, testSpillFrequency = 23)),
+          TungstenSort(sortOrder, global = true, plan: SparkPlan, testSpillFrequency = 23)),
         Sort(sortOrder, global = true, _: SparkPlan),
         sortAnswers = false
       )

From 0b1a464b6e061580a75b99a91b042069d76bbbfd Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 30 Jul 2015 17:18:32 -0700
Subject: [PATCH 0722/1454] [SPARK-9425] [SQL] support DecimalType in UnsafeRow

This PR brings the support of DecimalType in UnsafeRow, for precision <= 18, it's settable, otherwise it's not settable.

Author: Davies Liu <davies@databricks.com>

Closes #7758 from davies/unsafe_decimal and squashes the following commits:

478b1ba [Davies Liu] address comments
536314c [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_decimal
7c2e77a [Davies Liu] fix JoinedRow
76d6fa4 [Davies Liu] fix tests
99d3151 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_decimal
d49c6ae [Davies Liu] support DecimalType in UnsafeRow
---
 .../expressions/SpecializedGetters.java       |   2 +-
 .../UnsafeFixedWidthAggregationMap.java       |  22 ++--
 .../sql/catalyst/expressions/UnsafeRow.java   |  53 +++++---
 .../expressions/UnsafeRowWriters.java         |  42 +++++++
 .../sql/catalyst/CatalystTypeConverters.scala |   9 +-
 .../spark/sql/catalyst/InternalRow.scala      |   4 +-
 .../sql/catalyst/expressions/Projection.scala |   7 +-
 .../expressions/codegen/CodeGenerator.scala   |   9 +-
 .../codegen/GenerateUnsafeProjection.scala    | 115 ++++++++++--------
 .../spark/sql/catalyst/expressions/rows.scala |   3 +-
 .../org/apache/spark/sql/types/Decimal.scala  |   6 +-
 .../spark/sql/types/GenericArrayData.scala    |   2 +-
 .../sql/catalyst/expressions/CastSuite.scala  |   5 +-
 .../expressions/DateExpressionsSuite.scala    |   2 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala |   8 +-
 .../expressions/UnsafeRowConverterSuite.scala |  17 +--
 .../spark/sql/columnar/ColumnBuilder.scala    |   2 +-
 .../spark/sql/columnar/ColumnStats.scala      |   4 +-
 .../spark/sql/columnar/ColumnType.scala       |   2 +-
 .../sql/execution/GeneratedAggregate.scala    |   2 +-
 .../sql/execution/SparkSqlSerializer2.scala   |   2 +-
 .../sql/parquet/ParquetTableSupport.scala     |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  40 +++++-
 23 files changed, 237 insertions(+), 125 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
index f7cea13688876..e3d3ba7a9ccc0 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
@@ -41,7 +41,7 @@ public interface SpecializedGetters {
 
   double getDouble(int ordinal);
 
-  Decimal getDecimal(int ordinal);
+  Decimal getDecimal(int ordinal, int precision, int scale);
 
   UTF8String getUTF8String(int ordinal);
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
index 03f4c3ed8e6bb..f3b462778dc10 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
@@ -20,6 +20,8 @@
 import java.util.Iterator;
 
 import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.types.Decimal;
+import org.apache.spark.sql.types.DecimalType;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.PlatformDependent;
@@ -61,26 +63,18 @@ public final class UnsafeFixedWidthAggregationMap {
 
   private final boolean enablePerfMetrics;
 
-  /**
-   * @return true if UnsafeFixedWidthAggregationMap supports grouping keys with the given schema,
-   *         false otherwise.
-   */
-  public static boolean supportsGroupKeySchema(StructType schema) {
-    for (StructField field: schema.fields()) {
-      if (!UnsafeRow.readableFieldTypes.contains(field.dataType())) {
-        return false;
-      }
-    }
-    return true;
-  }
-
   /**
    * @return true if UnsafeFixedWidthAggregationMap supports aggregation buffers with the given
    *         schema, false otherwise.
    */
   public static boolean supportsAggregationBufferSchema(StructType schema) {
     for (StructField field: schema.fields()) {
-      if (!UnsafeRow.settableFieldTypes.contains(field.dataType())) {
+      if (field.dataType() instanceof DecimalType) {
+        DecimalType dt = (DecimalType) field.dataType();
+        if (dt.precision() > Decimal.MAX_LONG_DIGITS()) {
+          return false;
+        }
+      } else if (!UnsafeRow.settableFieldTypes.contains(field.dataType())) {
         return false;
       }
     }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 6d684bac37573..e7088edced1a1 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -19,6 +19,8 @@
 
 import java.io.IOException;
 import java.io.OutputStream;
+import java.math.BigDecimal;
+import java.math.BigInteger;
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.HashSet;
@@ -65,12 +67,7 @@ public static int calculateBitSetWidthInBytes(int numFields) {
    */
   public static final Set<DataType> settableFieldTypes;
 
-  /**
-   * Fields types can be read(but not set (e.g. set() will throw UnsupportedOperationException).
-   */
-  public static final Set<DataType> readableFieldTypes;
-
-  // TODO: support DecimalType
+  // DecimalType(precision <= 18) is settable
   static {
     settableFieldTypes = Collections.unmodifiableSet(
       new HashSet<>(
@@ -86,16 +83,6 @@ public static int calculateBitSetWidthInBytes(int numFields) {
           DateType,
           TimestampType
         })));
-
-    // We support get() on a superset of the types for which we support set():
-    final Set<DataType> _readableFieldTypes = new HashSet<>(
-      Arrays.asList(new DataType[]{
-        StringType,
-        BinaryType,
-        CalendarIntervalType
-      }));
-    _readableFieldTypes.addAll(settableFieldTypes);
-    readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes);
   }
 
   //////////////////////////////////////////////////////////////////////////////
@@ -232,6 +219,21 @@ public void setFloat(int ordinal, float value) {
     PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
+  @Override
+  public void setDecimal(int ordinal, Decimal value, int precision) {
+    assertIndexIsValid(ordinal);
+    if (value == null) {
+      setNullAt(ordinal);
+    } else {
+      if (precision <= Decimal.MAX_LONG_DIGITS()) {
+        setLong(ordinal, value.toUnscaledLong());
+      } else {
+        // TODO(davies): support update decimal (hold a bounded space even it's null)
+        throw new UnsupportedOperationException();
+      }
+    }
+  }
+
   @Override
   public Object get(int ordinal) {
     throw new UnsupportedOperationException();
@@ -256,7 +258,8 @@ public Object get(int ordinal, DataType dataType) {
     } else if (dataType instanceof DoubleType) {
       return getDouble(ordinal);
     } else if (dataType instanceof DecimalType) {
-      return getDecimal(ordinal);
+      DecimalType dt = (DecimalType) dataType;
+      return getDecimal(ordinal, dt.precision(), dt.scale());
     } else if (dataType instanceof DateType) {
       return getInt(ordinal);
     } else if (dataType instanceof TimestampType) {
@@ -322,6 +325,22 @@ public double getDouble(int ordinal) {
     return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
+  @Override
+  public Decimal getDecimal(int ordinal, int precision, int scale) {
+    assertIndexIsValid(ordinal);
+    if (isNullAt(ordinal)) {
+      return null;
+    }
+    if (precision <= Decimal.MAX_LONG_DIGITS()) {
+      return Decimal.apply(getLong(ordinal), precision, scale);
+    } else {
+      byte[] bytes = getBinary(ordinal);
+      BigInteger bigInteger = new BigInteger(bytes);
+      BigDecimal javaDecimal = new BigDecimal(bigInteger, scale);
+      return Decimal.apply(new scala.math.BigDecimal(javaDecimal), precision, scale);
+    }
+  }
+
   @Override
   public UTF8String getUTF8String(int ordinal) {
     assertIndexIsValid(ordinal);
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index c3259e21c4a78..f43a285cd6cad 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions;
 
 import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.types.Decimal;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
@@ -30,6 +31,47 @@
  */
 public class UnsafeRowWriters {
 
+  /** Writer for Decimal with precision under 18. */
+  public static class CompactDecimalWriter {
+
+    public static int getSize(Decimal input) {
+      return 0;
+    }
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input) {
+      target.setLong(ordinal, input.toUnscaledLong());
+      return 0;
+    }
+  }
+
+  /** Writer for Decimal with precision larger than 18. */
+  public static class DecimalWriter {
+
+    public static int getSize(Decimal input) {
+      // bounded size
+      return 16;
+    }
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input) {
+      final long offset = target.getBaseOffset() + cursor;
+      final byte[] bytes = input.toJavaBigDecimal().unscaledValue().toByteArray();
+      final int numBytes = bytes.length;
+      assert(numBytes <= 16);
+
+      // zero-out the bytes
+      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset, 0L);
+      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset + 8, 0L);
+
+      // Write the bytes to the variable length portion.
+      PlatformDependent.copyMemory(bytes, PlatformDependent.BYTE_ARRAY_OFFSET,
+        target.getBaseObject(), offset, numBytes);
+
+      // Set the fixed length portion.
+      target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
+      return 16;
+    }
+  }
+
   /** Writer for UTF8String. */
   public static class UTF8StringWriter {
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 22452c0f201ef..7ca20fe97fbef 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -68,7 +68,7 @@ object CatalystTypeConverters {
       case StringType => StringConverter
       case DateType => DateConverter
       case TimestampType => TimestampConverter
-      case dt: DecimalType => BigDecimalConverter
+      case dt: DecimalType => new DecimalConverter(dt)
       case BooleanType => BooleanConverter
       case ByteType => ByteConverter
       case ShortType => ShortConverter
@@ -306,7 +306,8 @@ object CatalystTypeConverters {
       DateTimeUtils.toJavaTimestamp(row.getLong(column))
   }
 
-  private object BigDecimalConverter extends CatalystTypeConverter[Any, JavaBigDecimal, Decimal] {
+  private class DecimalConverter(dataType: DecimalType)
+    extends CatalystTypeConverter[Any, JavaBigDecimal, Decimal] {
     override def toCatalystImpl(scalaValue: Any): Decimal = scalaValue match {
       case d: BigDecimal => Decimal(d)
       case d: JavaBigDecimal => Decimal(d)
@@ -314,9 +315,11 @@ object CatalystTypeConverters {
     }
     override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal
     override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal =
-      row.getDecimal(column).toJavaBigDecimal
+      row.getDecimal(column, dataType.precision, dataType.scale).toJavaBigDecimal
   }
 
+  private object BigDecimalConverter extends DecimalConverter(DecimalType.SYSTEM_DEFAULT)
+
   private abstract class PrimitiveConverter[T] extends CatalystTypeConverter[T, Any, Any] {
     final override def toScala(catalystValue: Any): Any = catalystValue
     final override def toCatalystImpl(scalaValue: T): Any = scalaValue
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 486ba036548c8..b19bf4386b0ba 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -58,8 +58,8 @@ abstract class InternalRow extends Serializable with SpecializedGetters {
 
   override def getBinary(ordinal: Int): Array[Byte] = getAs[Array[Byte]](ordinal, BinaryType)
 
-  override def getDecimal(ordinal: Int): Decimal =
-    getAs[Decimal](ordinal, DecimalType.SYSTEM_DEFAULT)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal =
+    getAs[Decimal](ordinal, DecimalType(precision, scale))
 
   override def getInterval(ordinal: Int): CalendarInterval =
     getAs[CalendarInterval](ordinal, CalendarIntervalType)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index b3beb7e28f208..7c7664e4c1a91 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GenerateMutableProjection}
-import org.apache.spark.sql.types.{StructType, DataType}
+import org.apache.spark.sql.types.{Decimal, StructType, DataType}
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -225,6 +225,11 @@ class JoinedRow extends InternalRow {
   override def getFloat(i: Int): Float =
     if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
 
+  override def getDecimal(i: Int, precision: Int, scale: Int): Decimal = {
+    if (i < row1.numFields) row1.getDecimal(i, precision, scale)
+    else row2.getDecimal(i - row1.numFields, precision, scale)
+  }
+
   override def getStruct(i: Int, numFields: Int): InternalRow = {
     if (i < row1.numFields) {
       row1.getStruct(i, numFields)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index c39e0df6fae2a..60e2863f7bbb0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -106,6 +106,7 @@ class CodeGenContext {
     val jt = javaType(dataType)
     dataType match {
       case _ if isPrimitiveType(jt) => s"$getter.get${primitiveTypeName(jt)}($ordinal)"
+      case t: DecimalType => s"$getter.getDecimal($ordinal, ${t.precision}, ${t.scale})"
       case StringType => s"$getter.getUTF8String($ordinal)"
       case BinaryType => s"$getter.getBinary($ordinal)"
       case CalendarIntervalType => s"$getter.getInterval($ordinal)"
@@ -120,10 +121,10 @@ class CodeGenContext {
    */
   def setColumn(row: String, dataType: DataType, ordinal: Int, value: String): String = {
     val jt = javaType(dataType)
-    if (isPrimitiveType(jt)) {
-      s"$row.set${primitiveTypeName(jt)}($ordinal, $value)"
-    } else {
-      s"$row.update($ordinal, $value)"
+    dataType match {
+      case _ if isPrimitiveType(jt) => s"$row.set${primitiveTypeName(jt)}($ordinal, $value)"
+      case t: DecimalType => s"$row.setDecimal($ordinal, $value, ${t.precision})"
+      case _ => s"$row.update($ordinal, $value)"
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index a662357fb6cf9..1d223986d9441 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -35,6 +35,8 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   private val BinaryWriter = classOf[UnsafeRowWriters.BinaryWriter].getName
   private val IntervalWriter = classOf[UnsafeRowWriters.IntervalWriter].getName
   private val StructWriter = classOf[UnsafeRowWriters.StructWriter].getName
+  private val CompactDecimalWriter = classOf[UnsafeRowWriters.CompactDecimalWriter].getName
+  private val DecimalWriter = classOf[UnsafeRowWriters.DecimalWriter].getName
 
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
@@ -42,9 +44,64 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     case _: CalendarIntervalType => true
     case t: StructType => t.toSeq.forall(field => canSupport(field.dataType))
     case NullType => true
+    case t: DecimalType => true
     case _ => false
   }
 
+  def genAdditionalSize(dt: DataType, ev: GeneratedExpressionCode): String = dt match {
+    case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
+      s" + (${ev.isNull} ? 0 : $DecimalWriter.getSize(${ev.primitive}))"
+    case StringType =>
+      s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
+    case BinaryType =>
+      s" + (${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive}))"
+    case CalendarIntervalType =>
+      s" + (${ev.isNull} ? 0 : 16)"
+    case _: StructType =>
+      s" + (${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive}))"
+    case _ => ""
+  }
+
+  def genFieldWriter(
+      ctx: CodeGenContext,
+      fieldType: DataType,
+      ev: GeneratedExpressionCode,
+      primitive: String,
+      index: Int,
+      cursor: String): String = fieldType match {
+    case _ if ctx.isPrimitiveType(fieldType) =>
+      s"${ctx.setColumn(primitive, fieldType, index, ev.primitive)}"
+    case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
+      s"""
+       // make sure Decimal object has the same scale as DecimalType
+       if (${ev.primitive}.changePrecision(${t.precision}, ${t.scale})) {
+         $CompactDecimalWriter.write($primitive, $index, $cursor, ${ev.primitive});
+       } else {
+         $primitive.setNullAt($index);
+       }
+       """
+    case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
+      s"""
+       // make sure Decimal object has the same scale as DecimalType
+       if (${ev.primitive}.changePrecision(${t.precision}, ${t.scale})) {
+         $cursor += $DecimalWriter.write($primitive, $index, $cursor, ${ev.primitive});
+       } else {
+         $primitive.setNullAt($index);
+       }
+       """
+    case StringType =>
+      s"$cursor += $StringWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+    case BinaryType =>
+      s"$cursor += $BinaryWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+    case CalendarIntervalType =>
+      s"$cursor += $IntervalWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+    case t: StructType =>
+      s"$cursor += $StructWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+    case NullType => ""
+    case _ =>
+      throw new UnsupportedOperationException(s"Not supported DataType: $fieldType")
+  }
+
   /**
    * Generates the code to create an [[UnsafeRow]] object based on the input expressions.
    * @param ctx context for code generation
@@ -69,36 +126,12 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     val allExprs = exprs.map(_.code).mkString("\n")
 
     val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
-    val additionalSize = expressions.zipWithIndex.map { case (e, i) =>
-      e.dataType match {
-        case StringType =>
-          s" + (${exprs(i).isNull} ? 0 : $StringWriter.getSize(${exprs(i).primitive}))"
-        case BinaryType =>
-          s" + (${exprs(i).isNull} ? 0 : $BinaryWriter.getSize(${exprs(i).primitive}))"
-        case CalendarIntervalType =>
-          s" + (${exprs(i).isNull} ? 0 : 16)"
-        case _: StructType =>
-          s" + (${exprs(i).isNull} ? 0 : $StructWriter.getSize(${exprs(i).primitive}))"
-        case _ => ""
-      }
+    val additionalSize = expressions.zipWithIndex.map {
+      case (e, i) => genAdditionalSize(e.dataType, exprs(i))
     }.mkString("")
 
     val writers = expressions.zipWithIndex.map { case (e, i) =>
-      val update = e.dataType match {
-        case dt if ctx.isPrimitiveType(dt) =>
-          s"${ctx.setColumn(ret, dt, i, exprs(i).primitive)}"
-        case StringType =>
-          s"$cursor += $StringWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
-        case BinaryType =>
-          s"$cursor += $BinaryWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
-        case CalendarIntervalType =>
-          s"$cursor += $IntervalWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
-        case t: StructType =>
-          s"$cursor += $StructWriter.write($ret, $i, $cursor, ${exprs(i).primitive})"
-        case NullType => ""
-        case _ =>
-          throw new UnsupportedOperationException(s"Not supported DataType: ${e.dataType}")
-      }
+      val update = genFieldWriter(ctx, e.dataType, exprs(i), ret, i, cursor)
       s"""if (${exprs(i).isNull}) {
             $ret.setNullAt($i);
           } else {
@@ -168,35 +201,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
     val additionalSize = schema.toSeq.map(_.dataType).zip(exprs).map { case (dt, ev) =>
-      dt match {
-        case StringType =>
-          s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
-        case BinaryType =>
-          s" + (${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive}))"
-        case CalendarIntervalType =>
-          s" + (${ev.isNull} ? 0 : 16)"
-        case _: StructType =>
-          s" + (${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive}))"
-        case _ => ""
-      }
+      genAdditionalSize(dt, ev)
     }.mkString("")
 
     val writers = schema.toSeq.map(_.dataType).zip(exprs).zipWithIndex.map { case ((dt, ev), i) =>
-      val update = dt match {
-        case _ if ctx.isPrimitiveType(dt) =>
-          s"${ctx.setColumn(primitive, dt, i, exprs(i).primitive)}"
-        case StringType =>
-          s"$cursor += $StringWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
-        case BinaryType =>
-          s"$cursor += $BinaryWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
-        case CalendarIntervalType =>
-          s"$cursor += $IntervalWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
-        case t: StructType =>
-          s"$cursor += $StructWriter.write($primitive, $i, $cursor, ${exprs(i).primitive})"
-        case NullType => ""
-        case _ =>
-          throw new UnsupportedOperationException(s"Not supported DataType: $dt")
-      }
+      val update = genFieldWriter(ctx, dt, ev, primitive, i, cursor)
       s"""
           if (${exprs(i).isNull}) {
             $primitive.setNullAt($i);
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index b7c4ece4a16fe..df6ea586c87ba 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.{DataType, StructType, AtomicType}
+import org.apache.spark.sql.types.{Decimal, DataType, StructType, AtomicType}
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -39,6 +39,7 @@ abstract class MutableRow extends InternalRow {
   def setShort(i: Int, value: Short): Unit = { update(i, value) }
   def setByte(i: Int, value: Byte): Unit = { update(i, value) }
   def setFloat(i: Int, value: Float): Unit = { update(i, value) }
+  def setDecimal(i: Int, value: Decimal, precision: Int) { update(i, value) }
   def setString(i: Int, value: String): Unit = {
     update(i, UTF8String.fromString(value))
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index bc689810bc292..c0155eeb450a6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -188,6 +188,10 @@ final class Decimal extends Ordered[Decimal] with Serializable {
    * @return true if successful, false if overflow would occur
    */
   def changePrecision(precision: Int, scale: Int): Boolean = {
+    // fast path for UnsafeProjection
+    if (precision == this.precision && scale == this.scale) {
+      return true
+    }
     // First, update our longVal if we can, or transfer over to using a BigDecimal
     if (decimalVal.eq(null)) {
       if (scale < _scale) {
@@ -224,7 +228,7 @@ final class Decimal extends Ordered[Decimal] with Serializable {
       decimalVal = newVal
     } else {
       // We're still using Longs, but we should check whether we match the new precision
-      val p = POW_10(math.min(_precision, MAX_LONG_DIGITS))
+      val p = POW_10(math.min(precision, MAX_LONG_DIGITS))
       if (longVal <= -p || longVal >= p) {
         // Note that we shouldn't have been able to fix this by switching to BigDecimal
         return false
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
index 7992ba947c069..35ace673fb3da 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
@@ -43,7 +43,7 @@ class GenericArrayData(array: Array[Any]) extends ArrayData {
 
   override def getDouble(ordinal: Int): Double = getAs(ordinal)
 
-  override def getDecimal(ordinal: Int): Decimal = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
 
   override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 4f35b653d73c0..1ad70733eae03 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -242,10 +242,9 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(cast(123L, DecimalType.USER_DEFAULT), Decimal(123))
     checkEvaluation(cast(123L, DecimalType(3, 0)), Decimal(123))
-    checkEvaluation(cast(123L, DecimalType(3, 1)), Decimal(123.0))
+    checkEvaluation(cast(123L, DecimalType(3, 1)), null)
 
-    // TODO: Fix the following bug and re-enable it.
-    // checkEvaluation(cast(123L, DecimalType(2, 0)), null)
+    checkEvaluation(cast(123L, DecimalType(2, 0)), null)
   }
 
   test("cast from boolean") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index fd1d6c1d25497..887e43621a941 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import java.sql.{Timestamp, Date}
+import java.sql.{Date, Timestamp}
 import java.text.SimpleDateFormat
 import java.util.Calendar
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
index 6a907290f2dbe..c6b4c729de2f9 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
@@ -55,13 +55,13 @@ class UnsafeFixedWidthAggregationMapSuite
   }
 
   test("supported schemas") {
+    assert(supportsAggregationBufferSchema(
+      StructType(StructField("x", DecimalType.USER_DEFAULT) :: Nil)))
+    assert(!supportsAggregationBufferSchema(
+      StructType(StructField("x", DecimalType.SYSTEM_DEFAULT) :: Nil)))
     assert(!supportsAggregationBufferSchema(StructType(StructField("x", StringType) :: Nil)))
-    assert(supportsGroupKeySchema(StructType(StructField("x", StringType) :: Nil)))
-
     assert(
       !supportsAggregationBufferSchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil)))
-    assert(
-      !supportsGroupKeySchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil)))
   }
 
   test("empty map") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index b7bc17f89e82f..a0e1701339ea7 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -46,7 +46,6 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(unsafeRow.getLong(1) === 1)
     assert(unsafeRow.getInt(2) === 2)
 
-    // We can copy UnsafeRows as long as they don't reference ObjectPools
     val unsafeRowCopy = unsafeRow.copy()
     assert(unsafeRowCopy.getLong(0) === 0)
     assert(unsafeRowCopy.getLong(1) === 1)
@@ -122,8 +121,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       FloatType,
       DoubleType,
       StringType,
-      BinaryType
-      // DecimalType.Default,
+      BinaryType,
+      DecimalType.USER_DEFAULT
       // ArrayType(IntegerType)
     )
     val converter = UnsafeProjection.create(fieldTypes)
@@ -150,7 +149,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(createdFromNull.getDouble(7) === 0.0d)
     assert(createdFromNull.getUTF8String(8) === null)
     assert(createdFromNull.getBinary(9) === null)
-    // assert(createdFromNull.get(10) === null)
+    assert(createdFromNull.getDecimal(10, 10, 0) === null)
     // assert(createdFromNull.get(11) === null)
 
     // If we have an UnsafeRow with columns that are initially non-null and we null out those
@@ -168,7 +167,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       r.setDouble(7, 700)
       r.update(8, UTF8String.fromString("hello"))
       r.update(9, "world".getBytes)
-      // r.update(10, Decimal(10))
+      r.setDecimal(10, Decimal(10), 10)
       // r.update(11, Array(11))
       r
     }
@@ -184,7 +183,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.getDouble(7) === rowWithNoNullColumns.getDouble(7))
     assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
     assert(setToNullAfterCreation.getBinary(9) === rowWithNoNullColumns.getBinary(9))
-    // assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
+    assert(setToNullAfterCreation.getDecimal(10, 10, 0) ===
+      rowWithNoNullColumns.getDecimal(10, 10, 0))
     // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
 
     for (i <- fieldTypes.indices) {
@@ -203,7 +203,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     setToNullAfterCreation.setDouble(7, 700)
     // setToNullAfterCreation.update(8, UTF8String.fromString("hello"))
     // setToNullAfterCreation.update(9, "world".getBytes)
-    // setToNullAfterCreation.update(10, Decimal(10))
+    setToNullAfterCreation.setDecimal(10, Decimal(10), 10)
     // setToNullAfterCreation.update(11, Array(11))
 
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
@@ -216,7 +216,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.getDouble(7) === rowWithNoNullColumns.getDouble(7))
     // assert(setToNullAfterCreation.getString(8) === rowWithNoNullColumns.getString(8))
     // assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
-    // assert(setToNullAfterCreation.get(10) === rowWithNoNullColumns.get(10))
+    assert(setToNullAfterCreation.getDecimal(10, 10, 0) ===
+      rowWithNoNullColumns.getDecimal(10, 10, 0))
     // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index 454b7b91a63f5..1620fc401ba6e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -114,7 +114,7 @@ private[sql] class FixedDecimalColumnBuilder(
     precision: Int,
     scale: Int)
   extends NativeColumnBuilder(
-    new FixedDecimalColumnStats,
+    new FixedDecimalColumnStats(precision, scale),
     FIXED_DECIMAL(precision, scale))
 
 // TODO (lian) Add support for array, struct and map
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 32a84b2676e07..af1a8ecca9b57 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -234,14 +234,14 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     InternalRow(null, null, nullCount, count, sizeInBytes)
 }
 
-private[sql] class FixedDecimalColumnStats extends ColumnStats {
+private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends ColumnStats {
   protected var upper: Decimal = null
   protected var lower: Decimal = null
 
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = {
     super.gatherStats(row, ordinal)
     if (!row.isNullAt(ordinal)) {
-      val value = row.getDecimal(ordinal)
+      val value = row.getDecimal(ordinal, precision, scale)
       if (upper == null || value.compareTo(upper) > 0) upper = value
       if (lower == null || value.compareTo(lower) < 0) lower = value
       sizeInBytes += FIXED_DECIMAL.defaultSize
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 2863f6c230a9d..30f8fe320db3d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -392,7 +392,7 @@ private[sql] case class FIXED_DECIMAL(precision: Int, scale: Int)
   }
 
   override def getField(row: InternalRow, ordinal: Int): Decimal = {
-    row.getDecimal(ordinal)
+    row.getDecimal(ordinal, precision, scale)
   }
 
   override def setField(row: MutableRow, ordinal: Int, value: Decimal): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index b85aada9d9d4c..d851eae3fcc71 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -202,7 +202,7 @@ case class GeneratedAggregate(
 
     val schemaSupportsUnsafe: Boolean = {
       UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
-        UnsafeFixedWidthAggregationMap.supportsGroupKeySchema(groupKeySchema)
+        UnsafeProjection.canSupport(groupKeySchema)
     }
 
     child.execute().mapPartitions { iter =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index c808442a4849b..e5bbd0aaed0a5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -298,7 +298,7 @@ private[sql] object SparkSqlSerializer2 {
                 out.writeByte(NULL)
               } else {
                 out.writeByte(NOT_NULL)
-                val value = row.getDecimal(i)
+                val value = row.getDecimal(i, decimal.precision, decimal.scale)
                 val javaBigDecimal = value.toJavaBigDecimal
                 // First, write out the unscaled value.
                 val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 79dd16b7b0c39..ec8da38a3d427 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -293,8 +293,8 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport {
         writer.addBinary(Binary.fromByteArray(record.getUTF8String(index).getBytes))
       case BinaryType =>
         writer.addBinary(Binary.fromByteArray(record.getBinary(index)))
-      case DecimalType.Fixed(precision, _) =>
-        writeDecimal(record.getDecimal(index), precision)
+      case DecimalType.Fixed(precision, scale) =>
+        writeDecimal(record.getDecimal(index, precision, scale), precision)
       case _ => sys.error(s"Unsupported datatype $ctype, cannot write to consumer")
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 4499a7207031d..66014ddca0596 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -34,8 +34,7 @@ class ColumnStatsSuite extends SparkFunSuite {
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
     InternalRow(Double.MaxValue, Double.MinValue, 0))
   testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
-  testColumnStats(classOf[FixedDecimalColumnStats],
-    FIXED_DECIMAL(15, 10), InternalRow(null, null, 0))
+  testDecimalColumnStats(InternalRow(null, null, 0))
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
@@ -52,7 +51,7 @@ class ColumnStatsSuite extends SparkFunSuite {
     }
 
     test(s"$columnStatsName: non-empty") {
-      import ColumnarTestUtils._
+      import org.apache.spark.sql.columnar.ColumnarTestUtils._
 
       val columnStats = columnStatsClass.newInstance()
       val rows = Seq.fill(10)(makeRandomRow(columnType)) ++ Seq.fill(10)(makeNullRow(1))
@@ -73,4 +72,39 @@ class ColumnStatsSuite extends SparkFunSuite {
       }
     }
   }
+
+  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](initialStatistics: InternalRow) {
+
+    val columnStatsName = classOf[FixedDecimalColumnStats].getSimpleName
+    val columnType = FIXED_DECIMAL(15, 10)
+
+    test(s"$columnStatsName: empty") {
+      val columnStats = new FixedDecimalColumnStats(15, 10)
+      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+        case (actual, expected) => assert(actual === expected)
+      }
+    }
+
+    test(s"$columnStatsName: non-empty") {
+      import org.apache.spark.sql.columnar.ColumnarTestUtils._
+
+      val columnStats = new FixedDecimalColumnStats(15, 10)
+      val rows = Seq.fill(10)(makeRandomRow(columnType)) ++ Seq.fill(10)(makeNullRow(1))
+      rows.foreach(columnStats.gatherStats(_, 0))
+
+      val values = rows.take(10).map(_.get(0, columnType.dataType).asInstanceOf[T#InternalType])
+      val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
+      val stats = columnStats.collectedStatistics
+
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.genericGet(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.genericGet(1))
+      assertResult(10, "Wrong null count")(stats.genericGet(2))
+      assertResult(20, "Wrong row count")(stats.genericGet(3))
+      assertResult(stats.genericGet(4), "Wrong size in bytes") {
+        rows.map { row =>
+          if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
+        }.sum
+      }
+    }
+  }
 }

From 351eda0e2fd47c183c4298469970032097ad07a0 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 30 Jul 2015 17:22:51 -0700
Subject: [PATCH 0723/1454] [SPARK-6319][SQL] Throw AnalysisException when
 using BinaryType on Join and Aggregate

JIRA: https://issues.apache.org/jira/browse/SPARK-6319

Spark SQL uses plain byte arrays to represent binary values. However, the arrays are compared by reference rather than by values. Thus, we should not use BinaryType on Join and Aggregate in current implementation.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7787 from viirya/agg_no_binary_type and squashes the following commits:

4f76cac [Liang-Chi Hsieh] Throw AnalysisException when using BinaryType on Join and Aggregate.
---
 .../sql/catalyst/analysis/CheckAnalysis.scala | 20 +++++++++++++++++++
 .../spark/sql/DataFrameAggregateSuite.scala   | 11 +++++++++-
 .../org/apache/spark/sql/JoinSuite.scala      |  9 +++++++++
 3 files changed, 39 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index a373714832962..0ebc3d180a780 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -87,6 +87,18 @@ trait CheckAnalysis {
               s"join condition '${condition.prettyString}' " +
                 s"of type ${condition.dataType.simpleString} is not a boolean.")
 
+          case j @ Join(_, _, _, Some(condition)) =>
+            def checkValidJoinConditionExprs(expr: Expression): Unit = expr match {
+              case p: Predicate =>
+                p.asInstanceOf[Expression].children.foreach(checkValidJoinConditionExprs)
+              case e if e.dataType.isInstanceOf[BinaryType] =>
+                failAnalysis(s"expression ${e.prettyString} in join condition " +
+                  s"'${condition.prettyString}' can't be binary type.")
+              case _ => // OK
+            }
+
+            checkValidJoinConditionExprs(condition)
+
           case Aggregate(groupingExprs, aggregateExprs, child) =>
             def checkValidAggregateExpression(expr: Expression): Unit = expr match {
               case _: AggregateExpression => // OK
@@ -100,7 +112,15 @@ trait CheckAnalysis {
               case e => e.children.foreach(checkValidAggregateExpression)
             }
 
+            def checkValidGroupingExprs(expr: Expression): Unit = expr.dataType match {
+              case BinaryType =>
+                failAnalysis(s"grouping expression '${expr.prettyString}' in aggregate can " +
+                  s"not be binary type.")
+              case _ => // OK
+            }
+
             aggregateExprs.foreach(checkValidAggregateExpression)
+            aggregateExprs.foreach(checkValidGroupingExprs)
 
           case Sort(orders, _, _) =>
             orders.foreach { order =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index b26d3ab253a1d..228ece8065151 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql
 
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.DecimalType
+import org.apache.spark.sql.types.{BinaryType, DecimalType}
 
 
 class DataFrameAggregateSuite extends QueryTest {
@@ -191,4 +191,13 @@ class DataFrameAggregateSuite extends QueryTest {
       Row(null))
   }
 
+  test("aggregation can't work on binary type") {
+    val df = Seq(1, 1, 2, 2).map(i => Tuple1(i.toString)).toDF("c").select($"c" cast BinaryType)
+    intercept[AnalysisException] {
+      df.groupBy("c").agg(count("*"))
+    }
+    intercept[AnalysisException] {
+      df.distinct
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 666f26bf620e1..27c08f64649ee 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -22,6 +22,7 @@ import org.scalatest.BeforeAndAfterEach
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.execution.joins._
+import org.apache.spark.sql.types.BinaryType
 
 
 class JoinSuite extends QueryTest with BeforeAndAfterEach {
@@ -489,4 +490,12 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
         Row(3, 2) :: Nil)
 
   }
+
+  test("Join can't work on binary type") {
+    val left = Seq(1, 1, 2, 2).map(i => Tuple1(i.toString)).toDF("c").select($"c" cast BinaryType)
+    val right = Seq(1, 1, 2, 2).map(i => Tuple1(i.toString)).toDF("d").select($"d" cast BinaryType)
+    intercept[AnalysisException] {
+      left.join(right, ($"left.N" === $"right.N"), "full")
+    }
+  }
 }

From 65fa4181c35135080870c1e4c1f904ada3a8cf59 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 30 Jul 2015 17:26:18 -0700
Subject: [PATCH 0724/1454] [SPARK-9077] [MLLIB] Improve error message for
 decision trees when numExamples < maxCategoriesPerFeature

Improve error message when number of examples is less than arity of high-arity categorical feature

CC jkbradley is this about what you had in mind? I know it's a starter, but was on my list to close out in the short term.

Author: Sean Owen <sowen@cloudera.com>

Closes #7800 from srowen/SPARK-9077 and squashes the following commits:

b8f6cdb [Sean Owen] Improve error message when number of examples is less than arity of high-arity categorical feature
---
 .../spark/mllib/tree/impl/DecisionTreeMetadata.scala      | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index 380291ac22bd3..9fe264656ede7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -128,9 +128,13 @@ private[spark] object DecisionTreeMetadata extends Logging {
     // based on the number of training examples.
     if (strategy.categoricalFeaturesInfo.nonEmpty) {
       val maxCategoriesPerFeature = strategy.categoricalFeaturesInfo.values.max
+      val maxCategory =
+        strategy.categoricalFeaturesInfo.find(_._2 == maxCategoriesPerFeature).get._1
       require(maxCategoriesPerFeature <= maxPossibleBins,
-        s"DecisionTree requires maxBins (= $maxPossibleBins) >= max categories " +
-          s"in categorical features (= $maxCategoriesPerFeature)")
+        s"DecisionTree requires maxBins (= $maxPossibleBins) to be at least as large as the " +
+        s"number of values in each categorical feature, but categorical feature $maxCategory " +
+        s"has $maxCategoriesPerFeature values. Considering remove this and other categorical " +
+        "features with a large number of values, or add more training examples.")
     }
 
     val unorderedFeatures = new mutable.HashSet[Int]()

From 3c66ff727d4b47220e1ff363cea215189ed64f36 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Thu, 30 Jul 2015 17:38:48 -0700
Subject: [PATCH 0725/1454] [SPARK-9489] Remove unnecessary compatibility and
 requirements checks from Exchange

While reviewing yhuai's patch for SPARK-2205 (#7773), I noticed that Exchange's `compatible` check may be incorrectly returning `false` in many cases.  As far as I know, this is not actually a problem because the `compatible`, `meetsRequirements`, and `needsAnySort` checks are serving only as short-circuit performance optimizations that are not necessary for correctness.

In order to reduce code complexity, I think that we should remove these checks and unconditionally rewrite the operator's children.  This should be safe because we rewrite the tree in a single bottom-up pass.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7807 from JoshRosen/SPARK-9489 and squashes the following commits:

9d76ce9 [Josh Rosen] [SPARK-9489] Remove compatibleWith, meetsRequirements, and needsAnySort checks from Exchange
---
 .../plans/physical/partitioning.scala         | 35 ---------
 .../apache/spark/sql/execution/Exchange.scala | 76 +++++--------------
 2 files changed, 17 insertions(+), 94 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index 2dcfa19fec383..f4d1dbaf28efe 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -86,14 +86,6 @@ sealed trait Partitioning {
    */
   def satisfies(required: Distribution): Boolean
 
-  /**
-   * Returns true iff all distribution guarantees made by this partitioning can also be made
-   * for the `other` specified partitioning.
-   * For example, two [[HashPartitioning HashPartitioning]]s are
-   * only compatible if the `numPartitions` of them is the same.
-   */
-  def compatibleWith(other: Partitioning): Boolean
-
   /** Returns the expressions that are used to key the partitioning. */
   def keyExpressions: Seq[Expression]
 }
@@ -104,11 +96,6 @@ case class UnknownPartitioning(numPartitions: Int) extends Partitioning {
     case _ => false
   }
 
-  override def compatibleWith(other: Partitioning): Boolean = other match {
-    case UnknownPartitioning(_) => true
-    case _ => false
-  }
-
   override def keyExpressions: Seq[Expression] = Nil
 }
 
@@ -117,11 +104,6 @@ case object SinglePartition extends Partitioning {
 
   override def satisfies(required: Distribution): Boolean = true
 
-  override def compatibleWith(other: Partitioning): Boolean = other match {
-    case SinglePartition => true
-    case _ => false
-  }
-
   override def keyExpressions: Seq[Expression] = Nil
 }
 
@@ -130,11 +112,6 @@ case object BroadcastPartitioning extends Partitioning {
 
   override def satisfies(required: Distribution): Boolean = true
 
-  override def compatibleWith(other: Partitioning): Boolean = other match {
-    case SinglePartition => true
-    case _ => false
-  }
-
   override def keyExpressions: Seq[Expression] = Nil
 }
 
@@ -159,12 +136,6 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
     case _ => false
   }
 
-  override def compatibleWith(other: Partitioning): Boolean = other match {
-    case BroadcastPartitioning => true
-    case h: HashPartitioning if h == this => true
-    case _ => false
-  }
-
   override def keyExpressions: Seq[Expression] = expressions
 }
 
@@ -199,11 +170,5 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
     case _ => false
   }
 
-  override def compatibleWith(other: Partitioning): Boolean = other match {
-    case BroadcastPartitioning => true
-    case r: RangePartitioning if r == this => true
-    case _ => false
-  }
-
   override def keyExpressions: Seq[Expression] = ordering.map(_.child)
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 70e5031fb63c0..6bd57f010a990 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -202,41 +202,6 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
 
   def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
     case operator: SparkPlan =>
-      // True iff every child's outputPartitioning satisfies the corresponding
-      // required data distribution.
-      def meetsRequirements: Boolean =
-        operator.requiredChildDistribution.zip(operator.children).forall {
-          case (required, child) =>
-            val valid = child.outputPartitioning.satisfies(required)
-            logDebug(
-              s"${if (valid) "Valid" else "Invalid"} distribution," +
-                s"required: $required current: ${child.outputPartitioning}")
-            valid
-        }
-
-      // True iff any of the children are incorrectly sorted.
-      def needsAnySort: Boolean =
-        operator.requiredChildOrdering.zip(operator.children).exists {
-          case (required, child) => required.nonEmpty && required != child.outputOrdering
-        }
-
-      // True iff outputPartitionings of children are compatible with each other.
-      // It is possible that every child satisfies its required data distribution
-      // but two children have incompatible outputPartitionings. For example,
-      // A dataset is range partitioned by "a.asc" (RangePartitioning) and another
-      // dataset is hash partitioned by "a" (HashPartitioning). Tuples in these two
-      // datasets are both clustered by "a", but these two outputPartitionings are not
-      // compatible.
-      // TODO: ASSUMES TRANSITIVITY?
-      def compatible: Boolean =
-        operator.children
-          .map(_.outputPartitioning)
-          .sliding(2)
-          .forall {
-            case Seq(a) => true
-            case Seq(a, b) => a.compatibleWith(b)
-          }
-
       // Adds Exchange or Sort operators as required
       def addOperatorsIfNecessary(
           partitioning: Partitioning,
@@ -269,33 +234,26 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
         addSortIfNecessary(addShuffleIfNecessary(child))
       }
 
-      if (meetsRequirements && compatible && !needsAnySort) {
-        operator
-      } else {
-        // At least one child does not satisfies its required data distribution or
-        // at least one child's outputPartitioning is not compatible with another child's
-        // outputPartitioning. In this case, we need to add Exchange operators.
-        val requirements =
-          (operator.requiredChildDistribution, operator.requiredChildOrdering, operator.children)
+      val requirements =
+        (operator.requiredChildDistribution, operator.requiredChildOrdering, operator.children)
 
-        val fixedChildren = requirements.zipped.map {
-          case (AllTuples, rowOrdering, child) =>
-            addOperatorsIfNecessary(SinglePartition, rowOrdering, child)
-          case (ClusteredDistribution(clustering), rowOrdering, child) =>
-            addOperatorsIfNecessary(HashPartitioning(clustering, numPartitions), rowOrdering, child)
-          case (OrderedDistribution(ordering), rowOrdering, child) =>
-            addOperatorsIfNecessary(RangePartitioning(ordering, numPartitions), rowOrdering, child)
+      val fixedChildren = requirements.zipped.map {
+        case (AllTuples, rowOrdering, child) =>
+          addOperatorsIfNecessary(SinglePartition, rowOrdering, child)
+        case (ClusteredDistribution(clustering), rowOrdering, child) =>
+          addOperatorsIfNecessary(HashPartitioning(clustering, numPartitions), rowOrdering, child)
+        case (OrderedDistribution(ordering), rowOrdering, child) =>
+          addOperatorsIfNecessary(RangePartitioning(ordering, numPartitions), rowOrdering, child)
 
-          case (UnspecifiedDistribution, Seq(), child) =>
-            child
-          case (UnspecifiedDistribution, rowOrdering, child) =>
-            sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
+        case (UnspecifiedDistribution, Seq(), child) =>
+          child
+        case (UnspecifiedDistribution, rowOrdering, child) =>
+          sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
 
-          case (dist, ordering, _) =>
-            sys.error(s"Don't know how to ensure $dist with ordering $ordering")
-        }
-
-        operator.withNewChildren(fixedChildren)
+        case (dist, ordering, _) =>
+          sys.error(s"Don't know how to ensure $dist with ordering $ordering")
       }
+
+      operator.withNewChildren(fixedChildren)
   }
 }

From 9307f5653d19a6a2fda355a675ca9ea97e35611b Mon Sep 17 00:00:00 2001
From: cody koeninger <cody@koeninger.org>
Date: Thu, 30 Jul 2015 17:44:20 -0700
Subject: [PATCH 0726/1454] [SPARK-9472] [STREAMING] consistent hadoop
 configuration, streaming only

Author: cody koeninger <cody@koeninger.org>

Closes #7772 from koeninger/streaming-hadoop-config and squashes the following commits:

5267284 [cody koeninger] [SPARK-4229][Streaming] consistent hadoop configuration, streaming only
---
 .../main/scala/org/apache/spark/streaming/Checkpoint.scala | 3 ++-
 .../org/apache/spark/streaming/StreamingContext.scala      | 7 ++++---
 .../apache/spark/streaming/api/java/JavaPairDStream.scala  | 2 +-
 .../spark/streaming/api/java/JavaStreamingContext.scala    | 3 ++-
 4 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 65d4e933bf8e9..2780d5b6adbcf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -25,6 +25,7 @@ import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark.{SparkException, SparkConf, Logging}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.util.{MetadataCleaner, Utils}
 import org.apache.spark.streaming.scheduler.JobGenerator
@@ -100,7 +101,7 @@ object Checkpoint extends Logging {
     }
 
     val path = new Path(checkpointDir)
-    val fs = fsOption.getOrElse(path.getFileSystem(new Configuration()))
+    val fs = fsOption.getOrElse(path.getFileSystem(SparkHadoopUtil.get.conf))
     if (fs.exists(path)) {
       val statuses = fs.listStatus(path)
       if (statuses != null) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 92438f1b1fbf7..177e710ace54b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -34,6 +34,7 @@ import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 
 import org.apache.spark._
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.input.FixedLengthBinaryInputFormat
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.serializer.SerializationDebugger
@@ -110,7 +111,7 @@ class StreamingContext private[streaming] (
    * Recreate a StreamingContext from a checkpoint file.
    * @param path Path to the directory that was specified as the checkpoint directory
    */
-  def this(path: String) = this(path, new Configuration)
+  def this(path: String) = this(path, SparkHadoopUtil.get.conf)
 
   /**
    * Recreate a StreamingContext from a checkpoint file using an existing SparkContext.
@@ -803,7 +804,7 @@ object StreamingContext extends Logging {
   def getActiveOrCreate(
       checkpointPath: String,
       creatingFunc: () => StreamingContext,
-      hadoopConf: Configuration = new Configuration(),
+      hadoopConf: Configuration = SparkHadoopUtil.get.conf,
       createOnError: Boolean = false
     ): StreamingContext = {
     ACTIVATION_LOCK.synchronized {
@@ -828,7 +829,7 @@ object StreamingContext extends Logging {
   def getOrCreate(
       checkpointPath: String,
       creatingFunc: () => StreamingContext,
-      hadoopConf: Configuration = new Configuration(),
+      hadoopConf: Configuration = SparkHadoopUtil.get.conf,
       createOnError: Boolean = false
     ): StreamingContext = {
     val checkpointOption = CheckpointReader.read(
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
index 959ac9c177f81..26383e420101e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
@@ -788,7 +788,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
       keyClass: Class[_],
       valueClass: Class[_],
       outputFormatClass: Class[F],
-      conf: Configuration = new Configuration) {
+      conf: Configuration = dstream.context.sparkContext.hadoopConfiguration) {
     dstream.saveAsNewAPIHadoopFiles(prefix, suffix, keyClass, valueClass, outputFormatClass, conf)
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 40deb6d7ea79a..35cc3ce5cf468 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -33,6 +33,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext}
 import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2}
 import org.apache.spark.api.java.function.{Function0 => JFunction0}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
@@ -136,7 +137,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
    * Recreate a JavaStreamingContext from a checkpoint file.
    * @param path Path to the directory that was specified as the checkpoint directory
    */
-  def this(path: String) = this(new StreamingContext(path, new Configuration))
+  def this(path: String) = this(new StreamingContext(path, SparkHadoopUtil.get.conf))
 
   /**
    * Re-creates a JavaStreamingContext from a checkpoint file.

From 83670fc9e6fc9c7a6ae68dfdd3f9335ea72f4ab0 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Thu, 30 Jul 2015 19:22:38 -0700
Subject: [PATCH 0727/1454] [SPARK-8176] [SPARK-8197] [SQL] function to_date/
 trunc

This PR is based on #6988 , thanks to adrian-wang .

This brings two SQL functions: to_date() and trunc().

Closes #6988

Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Davies Liu <davies@databricks.com>

Closes #7805 from davies/to_date and squashes the following commits:

2c7beba [Davies Liu] Merge branch 'master' of github.com:apache/spark into to_date
310dd55 [Daoyuan Wang] remove dup test in rebase
980b092 [Daoyuan Wang] resolve rebase conflict
a476c5a [Daoyuan Wang] address comments from davies
d44ea5f [Daoyuan Wang] function to_date, trunc
---
 python/pyspark/sql/functions.py               | 30 +++++++
 .../catalyst/analysis/FunctionRegistry.scala  |  2 +
 .../expressions/datetimeFunctions.scala       | 88 ++++++++++++++++++-
 .../sql/catalyst/util/DateTimeUtils.scala     | 34 +++++++
 .../expressions/DateExpressionsSuite.scala    | 29 +++++-
 .../expressions/NonFoldableLiteral.scala      |  4 +
 .../org/apache/spark/sql/functions.scala      | 16 ++++
 .../apache/spark/sql/DateFunctionsSuite.scala | 44 ++++++++++
 8 files changed, 245 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index a7295e25f0aa5..8024a8de07c98 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -888,6 +888,36 @@ def months_between(date1, date2):
     return Column(sc._jvm.functions.months_between(_to_java_column(date1), _to_java_column(date2)))
 
 
+@since(1.5)
+def to_date(col):
+    """
+    Converts the column of StringType or TimestampType into DateType.
+
+    >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
+    >>> df.select(to_date(df.t).alias('date')).collect()
+    [Row(date=datetime.date(1997, 2, 28))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.to_date(_to_java_column(col)))
+
+
+@since(1.5)
+def trunc(date, format):
+    """
+    Returns date truncated to the unit specified by the format.
+
+    :param format: 'year', 'YYYY', 'yy' or 'month', 'mon', 'mm'
+
+    >>> df = sqlContext.createDataFrame([('1997-02-28',)], ['d'])
+    >>> df.select(trunc(df.d, 'year').alias('year')).collect()
+    [Row(year=datetime.date(1997, 1, 1))]
+    >>> df.select(trunc(df.d, 'mon').alias('month')).collect()
+    [Row(month=datetime.date(1997, 2, 1))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.trunc(_to_java_column(date), format))
+
+
 @since(1.5)
 def size(col):
     """
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 6c7c481fab8db..1bf7204a2515c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -223,6 +223,8 @@ object FunctionRegistry {
     expression[NextDay]("next_day"),
     expression[Quarter]("quarter"),
     expression[Second]("second"),
+    expression[ToDate]("to_date"),
+    expression[TruncDate]("trunc"),
     expression[UnixTimestamp]("unix_timestamp"),
     expression[WeekOfYear]("weekofyear"),
     expression[Year]("year"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index 9795673ee0664..6e7613340c032 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -507,7 +507,6 @@ case class FromUnixTime(sec: Expression, format: Expression)
       })
     }
   }
-
 }
 
 /**
@@ -696,3 +695,90 @@ case class MonthsBetween(date1: Expression, date2: Expression)
     })
   }
 }
+
+/**
+ * Returns the date part of a timestamp or string.
+ */
+case class ToDate(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  // Implicit casting of spark will accept string in both date and timestamp format, as
+  // well as TimestampType.
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType)
+
+  override def dataType: DataType = DateType
+
+  override def eval(input: InternalRow): Any = child.eval(input)
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, d => d)
+  }
+}
+
+/*
+ * Returns date truncated to the unit specified by the format.
+ */
+case class TruncDate(date: Expression, format: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+  override def left: Expression = date
+  override def right: Expression = format
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType, StringType)
+  override def dataType: DataType = DateType
+  override def prettyName: String = "trunc"
+
+  lazy val minItemConst = DateTimeUtils.parseTruncLevel(format.eval().asInstanceOf[UTF8String])
+
+  override def eval(input: InternalRow): Any = {
+    val minItem = if (format.foldable) {
+      minItemConst
+    } else {
+      DateTimeUtils.parseTruncLevel(format.eval().asInstanceOf[UTF8String])
+    }
+    if (minItem == -1) {
+      // unknown format
+      null
+    } else {
+      val d = date.eval(input)
+      if (d == null) {
+        null
+      } else {
+        DateTimeUtils.truncDate(d.asInstanceOf[Int], minItem)
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+
+    if (format.foldable) {
+      if (minItemConst == -1) {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      } else {
+        val d = date.gen(ctx)
+        s"""
+          ${d.code}
+          boolean ${ev.isNull} = ${d.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $dtu.truncDate(${d.primitive}, $minItemConst);
+          }
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (dateVal, fmt) => {
+        val form = ctx.freshName("form")
+        s"""
+          int $form = $dtu.parseTruncLevel($fmt);
+          if ($form == -1) {
+            ${ev.isNull} = true;
+          } else {
+            ${ev.primitive} = $dtu.truncDate($dateVal, $form);
+          }
+        """
+      })
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 53abdf6618eac..5a7c25b8d508d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -779,4 +779,38 @@ object DateTimeUtils {
     }
     date + (lastDayOfMonthInYear - dayInYear)
   }
+
+  private val TRUNC_TO_YEAR = 1
+  private val TRUNC_TO_MONTH = 2
+  private val TRUNC_INVALID = -1
+
+  /**
+   * Returns the trunc date from original date and trunc level.
+   * Trunc level should be generated using `parseTruncLevel()`, should only be 1 or 2.
+   */
+  def truncDate(d: Int, level: Int): Int = {
+    if (level == TRUNC_TO_YEAR) {
+      d - DateTimeUtils.getDayInYear(d) + 1
+    } else if (level == TRUNC_TO_MONTH) {
+      d - DateTimeUtils.getDayOfMonth(d) + 1
+    } else {
+      throw new Exception(s"Invalid trunc level: $level")
+    }
+  }
+
+  /**
+   * Returns the truncate level, could be TRUNC_YEAR, TRUNC_MONTH, or TRUNC_INVALID,
+   * TRUNC_INVALID means unsupported truncate level.
+   */
+  def parseTruncLevel(format: UTF8String): Int = {
+    if (format == null) {
+      TRUNC_INVALID
+    } else {
+      format.toString.toUpperCase match {
+        case "YEAR" | "YYYY" | "YY" => TRUNC_TO_YEAR
+        case "MON" | "MONTH" | "MM" => TRUNC_TO_MONTH
+        case _ => TRUNC_INVALID
+      }
+    }
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index 887e43621a941..6c15c05da3094 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -351,6 +351,34 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       NextDay(Literal(Date.valueOf("2015-07-23")), Literal.create(null, StringType)), null)
   }
 
+  test("function to_date") {
+    checkEvaluation(
+      ToDate(Literal(Date.valueOf("2015-07-22"))),
+      DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-22")))
+    checkEvaluation(ToDate(Literal.create(null, DateType)), null)
+  }
+
+  test("function trunc") {
+    def testTrunc(input: Date, fmt: String, expected: Date): Unit = {
+      checkEvaluation(TruncDate(Literal.create(input, DateType), Literal.create(fmt, StringType)),
+        expected)
+      checkEvaluation(
+        TruncDate(Literal.create(input, DateType), NonFoldableLiteral.create(fmt, StringType)),
+        expected)
+    }
+    val date = Date.valueOf("2015-07-22")
+    Seq("yyyy", "YYYY", "year", "YEAR", "yy", "YY").foreach{ fmt =>
+      testTrunc(date, fmt, Date.valueOf("2015-01-01"))
+    }
+    Seq("month", "MONTH", "mon", "MON", "mm", "MM").foreach { fmt =>
+      testTrunc(date, fmt, Date.valueOf("2015-07-01"))
+    }
+    testTrunc(date, "DD", null)
+    testTrunc(date, null, null)
+    testTrunc(null, "MON", null)
+    testTrunc(null, null, null)
+  }
+
   test("from_unixtime") {
     val sdf1 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
     val fmt2 = "yyyy-MM-dd HH:mm:ss.SSS"
@@ -405,5 +433,4 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       UnixTimestamp(Literal("2015-07-24"), Literal("not a valid format")), null)
   }
-
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala
index 0559fb80e7fce..31ecf4a9e810a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NonFoldableLiteral.scala
@@ -47,4 +47,8 @@ object NonFoldableLiteral {
     val lit = Literal(value)
     NonFoldableLiteral(lit.value, lit.dataType)
   }
+  def create(value: Any, dataType: DataType): NonFoldableLiteral = {
+    val lit = Literal.create(value, dataType)
+    NonFoldableLiteral(lit.value, lit.dataType)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 168894d66117d..46dc4605a5ccb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2181,6 +2181,22 @@ object functions {
    */
   def unix_timestamp(s: Column, p: String): Column = UnixTimestamp(s.expr, Literal(p))
 
+  /*
+   * Converts the column into DateType.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def to_date(e: Column): Column = ToDate(e.expr)
+
+  /**
+   * Returns date truncated to the unit specified by the format.
+   *
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def trunc(date: Column, format: String): Column = TruncDate(date.expr, Literal(format))
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   // Collection functions
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index b7267c413165a..8c596fad74ee4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -345,6 +345,50 @@ class DateFunctionsSuite extends QueryTest {
       Seq(Row(Date.valueOf("2015-07-30")), Row(Date.valueOf("2015-07-30"))))
   }
 
+  test("function to_date") {
+    val d1 = Date.valueOf("2015-07-22")
+    val d2 = Date.valueOf("2015-07-01")
+    val t1 = Timestamp.valueOf("2015-07-22 10:00:00")
+    val t2 = Timestamp.valueOf("2014-12-31 23:59:59")
+    val s1 = "2015-07-22 10:00:00"
+    val s2 = "2014-12-31"
+    val df = Seq((d1, t1, s1), (d2, t2, s2)).toDF("d", "t", "s")
+
+    checkAnswer(
+      df.select(to_date(col("t"))),
+      Seq(Row(Date.valueOf("2015-07-22")), Row(Date.valueOf("2014-12-31"))))
+    checkAnswer(
+      df.select(to_date(col("d"))),
+      Seq(Row(Date.valueOf("2015-07-22")), Row(Date.valueOf("2015-07-01"))))
+    checkAnswer(
+      df.select(to_date(col("s"))),
+      Seq(Row(Date.valueOf("2015-07-22")), Row(Date.valueOf("2014-12-31"))))
+
+    checkAnswer(
+      df.selectExpr("to_date(t)"),
+      Seq(Row(Date.valueOf("2015-07-22")), Row(Date.valueOf("2014-12-31"))))
+    checkAnswer(
+      df.selectExpr("to_date(d)"),
+      Seq(Row(Date.valueOf("2015-07-22")), Row(Date.valueOf("2015-07-01"))))
+    checkAnswer(
+      df.selectExpr("to_date(s)"),
+      Seq(Row(Date.valueOf("2015-07-22")), Row(Date.valueOf("2014-12-31"))))
+  }
+
+  test("function trunc") {
+    val df = Seq(
+      (1, Timestamp.valueOf("2015-07-22 10:00:00")),
+      (2, Timestamp.valueOf("2014-12-31 00:00:00"))).toDF("i", "t")
+
+    checkAnswer(
+      df.select(trunc(col("t"), "YY")),
+      Seq(Row(Date.valueOf("2015-01-01")), Row(Date.valueOf("2014-01-01"))))
+
+    checkAnswer(
+      df.selectExpr("trunc(t, 'Month')"),
+      Seq(Row(Date.valueOf("2015-07-01")), Row(Date.valueOf("2014-12-01"))))
+  }
+
   test("from_unixtime") {
     val sdf1 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
     val fmt2 = "yyyy-MM-dd HH:mm:ss.SSS"

From 4e5919bfb47a58bcbda90ae01c1bed2128ded983 Mon Sep 17 00:00:00 2001
From: Ram Sriharsha <rsriharsha@hw11853.local>
Date: Thu, 30 Jul 2015 23:02:11 -0700
Subject: [PATCH 0728/1454] [SPARK-7690] [ML] Multiclass classification
 Evaluator

Multiclass Classification Evaluator for ML Pipelines. F1 score, precision, recall, weighted precision and weighted recall are supported as available metrics.

Author: Ram Sriharsha <rsriharsha@hw11853.local>

Closes #7475 from harsha2010/SPARK-7690 and squashes the following commits:

9bf4ec7 [Ram Sriharsha] fix indentation
3f09a85 [Ram Sriharsha] cleanup doc
16115ae [Ram Sriharsha] code review fixes
032d2a3 [Ram Sriharsha] fix test
eec9865 [Ram Sriharsha] Fix Python Indentation
1dbeffd [Ram Sriharsha] Merge branch 'master' into SPARK-7690
68cea85 [Ram Sriharsha] Merge branch 'master' into SPARK-7690
54c03de [Ram Sriharsha] [SPARK-7690][ml][WIP] Multiclass Evaluator for ML Pipeline
---
 .../MulticlassClassificationEvaluator.scala   | 85 +++++++++++++++++++
 ...lticlassClassificationEvaluatorSuite.scala | 28 ++++++
 python/pyspark/ml/evaluation.py               | 66 ++++++++++++++
 3 files changed, 179 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
new file mode 100644
index 0000000000000..44f779c1908d7
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.evaluation
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.{ParamMap, ParamValidators, Param}
+import org.apache.spark.ml.param.shared.{HasLabelCol, HasPredictionCol}
+import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.sql.types.DoubleType
+
+/**
+ * :: Experimental ::
+ * Evaluator for multiclass classification, which expects two input columns: score and label.
+ */
+@Experimental
+class MulticlassClassificationEvaluator (override val uid: String)
+  extends Evaluator with HasPredictionCol with HasLabelCol {
+
+  def this() = this(Identifiable.randomUID("mcEval"))
+
+  /**
+   * param for metric name in evaluation (supports `"f1"` (default), `"precision"`, `"recall"`,
+   * `"weightedPrecision"`, `"weightedRecall"`)
+   * @group param
+   */
+  val metricName: Param[String] = {
+    val allowedParams = ParamValidators.inArray(Array("f1", "precision",
+      "recall", "weightedPrecision", "weightedRecall"))
+    new Param(this, "metricName", "metric name in evaluation " +
+      "(f1|precision|recall|weightedPrecision|weightedRecall)", allowedParams)
+  }
+
+  /** @group getParam */
+  def getMetricName: String = $(metricName)
+
+  /** @group setParam */
+  def setMetricName(value: String): this.type = set(metricName, value)
+
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
+
+  /** @group setParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
+
+  setDefault(metricName -> "f1")
+
+  override def evaluate(dataset: DataFrame): Double = {
+    val schema = dataset.schema
+    SchemaUtils.checkColumnType(schema, $(predictionCol), DoubleType)
+    SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
+
+    val predictionAndLabels = dataset.select($(predictionCol), $(labelCol))
+      .map { case Row(prediction: Double, label: Double) =>
+      (prediction, label)
+    }
+    val metrics = new MulticlassMetrics(predictionAndLabels)
+    val metric = $(metricName) match {
+      case "f1" => metrics.weightedFMeasure
+      case "precision" => metrics.precision
+      case "recall" => metrics.recall
+      case "weightedPrecision" => metrics.weightedPrecision
+      case "weightedRecall" => metrics.weightedRecall
+    }
+    metric
+  }
+
+  override def copy(extra: ParamMap): MulticlassClassificationEvaluator = defaultCopy(extra)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala
new file mode 100644
index 0000000000000..6d8412b0b3701
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.evaluation
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+
+class MulticlassClassificationEvaluatorSuite extends SparkFunSuite {
+
+  test("params") {
+    ParamsSuite.checkParams(new MulticlassClassificationEvaluator)
+  }
+}
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 595593a7f2cde..06e809352225b 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -214,6 +214,72 @@ def setParams(self, predictionCol="prediction", labelCol="label",
         kwargs = self.setParams._input_kwargs
         return self._set(**kwargs)
 
+
+@inherit_doc
+class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
+    """
+    Evaluator for Multiclass Classification, which expects two input
+    columns: prediction and label.
+    >>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
+    ...     (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
+    >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["prediction", "label"])
+    ...
+    >>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction")
+    >>> evaluator.evaluate(dataset)
+    0.66...
+    >>> evaluator.evaluate(dataset, {evaluator.metricName: "precision"})
+    0.66...
+    >>> evaluator.evaluate(dataset, {evaluator.metricName: "recall"})
+    0.66...
+    """
+    # a placeholder to make it appear in the generated doc
+    metricName = Param(Params._dummy(), "metricName",
+                       "metric name in evaluation "
+                       "(f1|precision|recall|weightedPrecision|weightedRecall)")
+
+    @keyword_only
+    def __init__(self, predictionCol="prediction", labelCol="label",
+                 metricName="f1"):
+        """
+        __init__(self, predictionCol="prediction", labelCol="label", \
+                 metricName="f1")
+        """
+        super(MulticlassClassificationEvaluator, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator", self.uid)
+        # param for metric name in evaluation (f1|precision|recall|weightedPrecision|weightedRecall)
+        self.metricName = Param(self, "metricName",
+                                "metric name in evaluation"
+                                " (f1|precision|recall|weightedPrecision|weightedRecall)")
+        self._setDefault(predictionCol="prediction", labelCol="label",
+                         metricName="f1")
+        kwargs = self.__init__._input_kwargs
+        self._set(**kwargs)
+
+    def setMetricName(self, value):
+        """
+        Sets the value of :py:attr:`metricName`.
+        """
+        self._paramMap[self.metricName] = value
+        return self
+
+    def getMetricName(self):
+        """
+        Gets the value of metricName or its default value.
+        """
+        return self.getOrDefault(self.metricName)
+
+    @keyword_only
+    def setParams(self, predictionCol="prediction", labelCol="label",
+                  metricName="f1"):
+        """
+        setParams(self, predictionCol="prediction", labelCol="label", \
+                  metricName="f1")
+        Sets params for multiclass classification evaluator.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

From 69b62f76fced18efa35a107c9be4bc22eba72878 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 30 Jul 2015 23:03:48 -0700
Subject: [PATCH 0729/1454] [SPARK-9214] [ML] [PySpark] support ml.NaiveBayes
 for Python

support ml.NaiveBayes for Python

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7568 from yanboliang/spark-9214 and squashes the following commits:

5ee3fd6 [Yanbo Liang] fix typos
3ecd046 [Yanbo Liang] fix typos
f9c94d1 [Yanbo Liang] change lambda_ to smoothing and fix other issues
180452a [Yanbo Liang] fix typos
7dda1f4 [Yanbo Liang] support ml.NaiveBayes for Python
---
 .../spark/ml/classification/NaiveBayes.scala  |  10 +-
 .../classification/JavaNaiveBayesSuite.java   |   4 +-
 .../ml/classification/NaiveBayesSuite.scala   |   6 +-
 python/pyspark/ml/classification.py           | 116 +++++++++++++++++-
 4 files changed, 125 insertions(+), 11 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 1f547e4a98af7..5be35fe209291 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -38,11 +38,11 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
    * (default = 1.0).
    * @group param
    */
-  final val lambda: DoubleParam = new DoubleParam(this, "lambda", "The smoothing parameter.",
+  final val smoothing: DoubleParam = new DoubleParam(this, "smoothing", "The smoothing parameter.",
     ParamValidators.gtEq(0))
 
   /** @group getParam */
-  final def getLambda: Double = $(lambda)
+  final def getSmoothing: Double = $(smoothing)
 
   /**
    * The model type which is a string (case-sensitive).
@@ -79,8 +79,8 @@ class NaiveBayes(override val uid: String)
    * Default is 1.0.
    * @group setParam
    */
-  def setLambda(value: Double): this.type = set(lambda, value)
-  setDefault(lambda -> 1.0)
+  def setSmoothing(value: Double): this.type = set(smoothing, value)
+  setDefault(smoothing -> 1.0)
 
   /**
    * Set the model type using a string (case-sensitive).
@@ -92,7 +92,7 @@ class NaiveBayes(override val uid: String)
 
   override protected def train(dataset: DataFrame): NaiveBayesModel = {
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
-    val oldModel = OldNaiveBayes.train(oldDataset, $(lambda), $(modelType))
+    val oldModel = OldNaiveBayes.train(oldDataset, $(smoothing), $(modelType))
     NaiveBayesModel.fromOld(oldModel, this)
   }
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
index 09a9fba0c19cf..a700c9cddb206 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -68,7 +68,7 @@ public void naiveBayesDefaultParams() {
     assert(nb.getLabelCol() == "label");
     assert(nb.getFeaturesCol() == "features");
     assert(nb.getPredictionCol() == "prediction");
-    assert(nb.getLambda() == 1.0);
+    assert(nb.getSmoothing() == 1.0);
     assert(nb.getModelType() == "multinomial");
   }
 
@@ -89,7 +89,7 @@ public void testNaiveBayes() {
     });
 
     DataFrame dataset = jsql.createDataFrame(jrdd, schema);
-    NaiveBayes nb = new NaiveBayes().setLambda(0.5).setModelType("multinomial");
+    NaiveBayes nb = new NaiveBayes().setSmoothing(0.5).setModelType("multinomial");
     NaiveBayesModel model = nb.fit(dataset);
 
     DataFrame predictionAndLabels = model.transform(dataset).select("prediction", "label");
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index 76381a2741296..264bde3703c5f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -58,7 +58,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(nb.getLabelCol === "label")
     assert(nb.getFeaturesCol === "features")
     assert(nb.getPredictionCol === "prediction")
-    assert(nb.getLambda === 1.0)
+    assert(nb.getSmoothing === 1.0)
     assert(nb.getModelType === "multinomial")
   }
 
@@ -75,7 +75,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     val testDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
       piArray, thetaArray, nPoints, 42, "multinomial"))
-    val nb = new NaiveBayes().setLambda(1.0).setModelType("multinomial")
+    val nb = new NaiveBayes().setSmoothing(1.0).setModelType("multinomial")
     val model = nb.fit(testDataset)
 
     validateModelFit(pi, theta, model)
@@ -101,7 +101,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     val testDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
       piArray, thetaArray, nPoints, 45, "bernoulli"))
-    val nb = new NaiveBayes().setLambda(1.0).setModelType("bernoulli")
+    val nb = new NaiveBayes().setSmoothing(1.0).setModelType("bernoulli")
     val model = nb.fit(testDataset)
 
     validateModelFit(pi, theta, model)
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 5a82bc286d1e8..93ffcd40949b3 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -25,7 +25,8 @@
 
 __all__ = ['LogisticRegression', 'LogisticRegressionModel', 'DecisionTreeClassifier',
            'DecisionTreeClassificationModel', 'GBTClassifier', 'GBTClassificationModel',
-           'RandomForestClassifier', 'RandomForestClassificationModel']
+           'RandomForestClassifier', 'RandomForestClassificationModel', 'NaiveBayes',
+           'NaiveBayesModel']
 
 
 @inherit_doc
@@ -576,6 +577,119 @@ class GBTClassificationModel(TreeEnsembleModels):
     """
 
 
+@inherit_doc
+class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
+    """
+    Naive Bayes Classifiers.
+
+    >>> from pyspark.sql import Row
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([
+    ...     Row(label=0.0, features=Vectors.dense([0.0, 0.0])),
+    ...     Row(label=0.0, features=Vectors.dense([0.0, 1.0])),
+    ...     Row(label=1.0, features=Vectors.dense([1.0, 0.0]))])
+    >>> nb = NaiveBayes(smoothing=1.0, modelType="multinomial")
+    >>> model = nb.fit(df)
+    >>> model.pi
+    DenseVector([-0.51..., -0.91...])
+    >>> model.theta
+    DenseMatrix(2, 2, [-1.09..., -0.40..., -0.40..., -1.09...], 1)
+    >>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
+    >>> model.transform(test0).head().prediction
+    1.0
+    >>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
+    >>> model.transform(test1).head().prediction
+    1.0
+    """
+
+    # a placeholder to make it appear in the generated doc
+    smoothing = Param(Params._dummy(), "smoothing", "The smoothing parameter, should be >= 0, " +
+                      "default is 1.0")
+    modelType = Param(Params._dummy(), "modelType", "The model type which is a string " +
+                      "(case-sensitive). Supported options: multinomial (default) and bernoulli.")
+
+    @keyword_only
+    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 smoothing=1.0, modelType="multinomial"):
+        """
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 smoothing=1.0, modelType="multinomial")
+        """
+        super(NaiveBayes, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.NaiveBayes", self.uid)
+        #: param for the smoothing parameter.
+        self.smoothing = Param(self, "smoothing", "The smoothing parameter, should be >= 0, " +
+                               "default is 1.0")
+        #: param for the model type.
+        self.modelType = Param(self, "modelType", "The model type which is a string " +
+                               "(case-sensitive). Supported options: multinomial (default) " +
+                               "and bernoulli.")
+        self._setDefault(smoothing=1.0, modelType="multinomial")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  smoothing=1.0, modelType="multinomial"):
+        """
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  smoothing=1.0, modelType="multinomial")
+        Sets params for Naive Bayes.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def _create_model(self, java_model):
+        return NaiveBayesModel(java_model)
+
+    def setSmoothing(self, value):
+        """
+        Sets the value of :py:attr:`smoothing`.
+        """
+        self._paramMap[self.smoothing] = value
+        return self
+
+    def getSmoothing(self):
+        """
+        Gets the value of smoothing or its default value.
+        """
+        return self.getOrDefault(self.smoothing)
+
+    def setModelType(self, value):
+        """
+        Sets the value of :py:attr:`modelType`.
+        """
+        self._paramMap[self.modelType] = value
+        return self
+
+    def getModelType(self):
+        """
+        Gets the value of modelType or its default value.
+        """
+        return self.getOrDefault(self.modelType)
+
+
+class NaiveBayesModel(JavaModel):
+    """
+    Model fitted by NaiveBayes.
+    """
+
+    @property
+    def pi(self):
+        """
+        log of class priors.
+        """
+        return self._call_java("pi")
+
+    @property
+    def theta(self):
+        """
+        log of class conditional probabilities.
+        """
+        return self._call_java("theta")
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

From 0244170b66476abc4a39ed609a852f1a6fa455e7 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 30 Jul 2015 23:05:58 -0700
Subject: [PATCH 0730/1454] [SPARK-9152][SQL] Implement code generation for
 Like and RLike

JIRA: https://issues.apache.org/jira/browse/SPARK-9152

This PR implements code generation for `Like` and `RLike`.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7561 from viirya/like_rlike_codegen and squashes the following commits:

fe5641b [Liang-Chi Hsieh] Add test for NonFoldableLiteral.
ccd1b43 [Liang-Chi Hsieh] For comments.
0086723 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into like_rlike_codegen
50df9a8 [Liang-Chi Hsieh] Use nullSafeCodeGen.
8092a68 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into like_rlike_codegen
696d451 [Liang-Chi Hsieh] Check expression foldable.
48e5536 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into like_rlike_codegen
aea58e0 [Liang-Chi Hsieh] For comments.
46d946f [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into like_rlike_codegen
a0fb76e [Liang-Chi Hsieh] For comments.
6cffe3c [Liang-Chi Hsieh] For comments.
69f0fb6 [Liang-Chi Hsieh] Add code generation for Like and RLike.
---
 .../expressions/stringOperations.scala        | 105 ++++++++++++++----
 .../spark/sql/catalyst/util/StringUtils.scala |  47 ++++++++
 .../expressions/StringExpressionsSuite.scala  |  16 +++
 .../sql/catalyst/util/StringUtilsSuite.scala  |  34 ++++++
 4 files changed, 180 insertions(+), 22 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/StringUtilsSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 79c0ca56a8e79..99a62343f138d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -21,8 +21,11 @@ import java.text.DecimalFormat
 import java.util.Locale
 import java.util.regex.{MatchResult, Pattern}
 
+import org.apache.commons.lang3.StringEscapeUtils
+
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.catalyst.util.StringUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -160,32 +163,51 @@ trait StringRegexExpression extends ImplicitCastInputTypes {
 case class Like(left: Expression, right: Expression)
   extends BinaryExpression with StringRegexExpression with CodegenFallback {
 
-  // replace the _ with .{1} exactly match 1 time of any character
-  // replace the % with .*, match 0 or more times with any character
-  override def escape(v: String): String =
-    if (!v.isEmpty) {
-      "(?s)" + (' ' +: v.init).zip(v).flatMap {
-        case (prev, '\\') => ""
-        case ('\\', c) =>
-          c match {
-            case '_' => "_"
-            case '%' => "%"
-            case _ => Pattern.quote("\\" + c)
-          }
-        case (prev, c) =>
-          c match {
-            case '_' => "."
-            case '%' => ".*"
-            case _ => Pattern.quote(Character.toString(c))
-          }
-      }.mkString
-    } else {
-      v
-    }
+  override def escape(v: String): String = StringUtils.escapeLikeRegex(v)
 
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
 
   override def toString: String = s"$left LIKE $right"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val patternClass = classOf[Pattern].getName
+    val escapeFunc = StringUtils.getClass.getName.stripSuffix("$") + ".escapeLikeRegex"
+    val pattern = ctx.freshName("pattern")
+
+    if (right.foldable) {
+      val rVal = right.eval()
+      if (rVal != null) {
+        val regexStr =
+          StringEscapeUtils.escapeJava(escape(rVal.asInstanceOf[UTF8String].toString()))
+        ctx.addMutableState(patternClass, pattern,
+          s"""$pattern = ${patternClass}.compile("$regexStr");""")
+
+        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
+        val eval = left.gen(ctx)
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = ${eval.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).matches();
+          }
+        """
+      } else {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+        s"""
+          String rightStr = ${eval2}.toString();
+          ${patternClass} $pattern = ${patternClass}.compile($escapeFunc(rightStr));
+          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).matches();
+        """
+      })
+    }
+  }
 }
 
 
@@ -195,6 +217,45 @@ case class RLike(left: Expression, right: Expression)
   override def escape(v: String): String = v
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
   override def toString: String = s"$left RLIKE $right"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val patternClass = classOf[Pattern].getName
+    val pattern = ctx.freshName("pattern")
+
+    if (right.foldable) {
+      val rVal = right.eval()
+      if (rVal != null) {
+        val regexStr =
+          StringEscapeUtils.escapeJava(rVal.asInstanceOf[UTF8String].toString())
+        ctx.addMutableState(patternClass, pattern,
+          s"""$pattern = ${patternClass}.compile("$regexStr");""")
+
+        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
+        val eval = left.gen(ctx)
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = ${eval.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).find(0);
+          }
+        """
+      } else {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+        s"""
+          String rightStr = ${eval2}.toString();
+          ${patternClass} $pattern = ${patternClass}.compile(rightStr);
+          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).find(0);
+        """
+      })
+    }
+  }
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
new file mode 100644
index 0000000000000..9ddfb3a0d3759
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util
+
+import java.util.regex.Pattern
+
+object StringUtils {
+
+  // replace the _ with .{1} exactly match 1 time of any character
+  // replace the % with .*, match 0 or more times with any character
+  def escapeLikeRegex(v: String): String = {
+    if (!v.isEmpty) {
+      "(?s)" + (' ' +: v.init).zip(v).flatMap {
+        case (prev, '\\') => ""
+        case ('\\', c) =>
+          c match {
+            case '_' => "_"
+            case '%' => "%"
+            case _ => Pattern.quote("\\" + c)
+          }
+        case (prev, c) =>
+          c match {
+            case '_' => "."
+            case '%' => ".*"
+            case _ => Pattern.quote(Character.toString(c))
+          }
+      }.mkString
+    } else {
+      v
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 07b952531ec2e..3ecd0d374c46b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -191,6 +191,15 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, StringType).like("a"), null)
     checkEvaluation(Literal.create("a", StringType).like(Literal.create(null, StringType)), null)
     checkEvaluation(Literal.create(null, StringType).like(Literal.create(null, StringType)), null)
+    checkEvaluation(
+      Literal.create("a", StringType).like(NonFoldableLiteral.create("a", StringType)), true)
+    checkEvaluation(
+      Literal.create("a", StringType).like(NonFoldableLiteral.create(null, StringType)), null)
+    checkEvaluation(
+      Literal.create(null, StringType).like(NonFoldableLiteral.create("a", StringType)), null)
+    checkEvaluation(
+      Literal.create(null, StringType).like(NonFoldableLiteral.create(null, StringType)), null)
+
     checkEvaluation("abdef" like "abdef", true)
     checkEvaluation("a_%b" like "a\\__b", true)
     checkEvaluation("addb" like "a_%b", true)
@@ -232,6 +241,13 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, StringType) rlike "abdef", null)
     checkEvaluation("abdef" rlike Literal.create(null, StringType), null)
     checkEvaluation(Literal.create(null, StringType) rlike Literal.create(null, StringType), null)
+    checkEvaluation("abdef" rlike NonFoldableLiteral.create("abdef", StringType), true)
+    checkEvaluation("abdef" rlike NonFoldableLiteral.create(null, StringType), null)
+    checkEvaluation(
+      Literal.create(null, StringType) rlike NonFoldableLiteral.create("abdef", StringType), null)
+    checkEvaluation(
+      Literal.create(null, StringType) rlike NonFoldableLiteral.create(null, StringType), null)
+
     checkEvaluation("abdef" rlike "abdef", true)
     checkEvaluation("abbbbc" rlike "a.*c", true)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/StringUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/StringUtilsSuite.scala
new file mode 100644
index 0000000000000..d6f273f9e568a
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/StringUtilsSuite.scala
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.util.StringUtils._
+
+class StringUtilsSuite extends SparkFunSuite {
+
+  test("escapeLikeRegex") {
+    assert(escapeLikeRegex("abdef") === "(?s)\\Qa\\E\\Qb\\E\\Qd\\E\\Qe\\E\\Qf\\E")
+    assert(escapeLikeRegex("a\\__b") === "(?s)\\Qa\\E_.\\Qb\\E")
+    assert(escapeLikeRegex("a_%b") === "(?s)\\Qa\\E..*\\Qb\\E")
+    assert(escapeLikeRegex("a%\\%b") === "(?s)\\Qa\\E.*%\\Qb\\E")
+    assert(escapeLikeRegex("a%") === "(?s)\\Qa\\E.*")
+    assert(escapeLikeRegex("**") === "(?s)\\Q*\\E\\Q*\\E")
+    assert(escapeLikeRegex("a_b") === "(?s)\\Qa\\E.\\Qb\\E")
+  }
+}

From a3a85d73da053c8e2830759fbc68b734081fa4f3 Mon Sep 17 00:00:00 2001
From: WangTaoTheTonic <wangtao111@huawei.com>
Date: Thu, 30 Jul 2015 23:50:06 -0700
Subject: [PATCH 0731/1454] [SPARK-9496][SQL]do not print the password in
 config

https://issues.apache.org/jira/browse/SPARK-9496

We better do not print the password in log.

Author: WangTaoTheTonic <wangtao111@huawei.com>

Closes #7815 from WangTaoTheTonic/master and squashes the following commits:

c7a5145 [WangTaoTheTonic] do not print the password in config
---
 .../org/apache/spark/sql/hive/client/ClientWrapper.scala    | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 8adda54754230..6e0912da5862d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -91,7 +91,11 @@ private[hive] class ClientWrapper(
         // this action explicit.
         initialConf.setClassLoader(initClassLoader)
         config.foreach { case (k, v) =>
-          logDebug(s"Hive Config: $k=$v")
+          if (k.toLowerCase.contains("password")) {
+            logDebug(s"Hive Config: $k=xxx")
+          } else {
+            logDebug(s"Hive Config: $k=$v")
+          }
           initialConf.set(k, v)
         }
         val newState = new SessionState(initialConf)

From 6bba7509a932aa4d39266df2d15b1370b7aabbec Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 31 Jul 2015 08:28:05 -0700
Subject: [PATCH 0732/1454] [SPARK-9500] add TernaryExpression to simplify
 ternary expressions

There lots of duplicated code in ternary expressions, create a TernaryExpression for them to reduce duplicated code.

cc chenghao-intel

Author: Davies Liu <davies@databricks.com>

Closes #7816 from davies/ternary and squashes the following commits:

ed2bf76 [Davies Liu] add TernaryExpression
---
 .../sql/catalyst/expressions/Expression.scala |  85 +++++
 .../expressions/codegen/CodeGenerator.scala   |   2 +-
 .../spark/sql/catalyst/expressions/math.scala |  66 +---
 .../expressions/stringOperations.scala        | 356 +++++-------------
 4 files changed, 183 insertions(+), 326 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 8fc182607ce68..2842b3ec5a0c8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -432,3 +432,88 @@ abstract class BinaryOperator extends BinaryExpression with ExpectsInputTypes {
 private[sql] object BinaryOperator {
   def unapply(e: BinaryOperator): Option[(Expression, Expression)] = Some((e.left, e.right))
 }
+
+/**
+ * An expression with three inputs and one output. The output is by default evaluated to null
+ * if any input is evaluated to null.
+ */
+abstract class TernaryExpression extends Expression {
+
+  override def foldable: Boolean = children.forall(_.foldable)
+
+  override def nullable: Boolean = children.exists(_.nullable)
+
+  /**
+   * Default behavior of evaluation according to the default nullability of BinaryExpression.
+   * If subclass of BinaryExpression override nullable, probably should also override this.
+   */
+  override def eval(input: InternalRow): Any = {
+    val exprs = children
+    val value1 = exprs(0).eval(input)
+    if (value1 != null) {
+      val value2 = exprs(1).eval(input)
+      if (value2 != null) {
+        val value3 = exprs(2).eval(input)
+        if (value3 != null) {
+          return nullSafeEval(value1, value2, value3)
+        }
+      }
+    }
+    null
+  }
+
+  /**
+   * Called by default [[eval]] implementation.  If subclass of BinaryExpression keep the default
+   * nullability, they can override this method to save null-check code.  If we need full control
+   * of evaluation process, we should override [[eval]].
+   */
+  protected def nullSafeEval(input1: Any, input2: Any, input3: Any): Any =
+    sys.error(s"BinaryExpressions must override either eval or nullSafeEval")
+
+  /**
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
+   *
+   * @param f accepts two variable names and returns Java code to compute the output.
+   */
+  protected def defineCodeGen(
+    ctx: CodeGenContext,
+    ev: GeneratedExpressionCode,
+    f: (String, String, String) => String): String = {
+    nullSafeCodeGen(ctx, ev, (eval1, eval2, eval3) => {
+      s"${ev.primitive} = ${f(eval1, eval2, eval3)};"
+    })
+  }
+
+  /**
+   * Short hand for generating binary evaluation code.
+   * If either of the sub-expressions is null, the result of this computation
+   * is assumed to be null.
+   *
+   * @param f function that accepts the 2 non-null evaluation result names of children
+   *          and returns Java code to compute the output.
+   */
+  protected def nullSafeCodeGen(
+    ctx: CodeGenContext,
+    ev: GeneratedExpressionCode,
+    f: (String, String, String) => String): String = {
+    val evals = children.map(_.gen(ctx))
+    val resultCode = f(evals(0).primitive, evals(1).primitive, evals(2).primitive)
+    s"""
+      ${evals(0).code}
+      boolean ${ev.isNull} = true;
+      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+      if (!${evals(0).isNull}) {
+        ${evals(1).code}
+        if (!${evals(1).isNull}) {
+          ${evals(2).code}
+          if (!${evals(2).isNull}) {
+            ${ev.isNull} = false;  // resultCode could change nullability
+            $resultCode
+          }
+        }
+      }
+    """
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 60e2863f7bbb0..e50ec27fc2eb6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -305,7 +305,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       evaluator.cook(code)
     } catch {
       case e: Exception =>
-        val msg = "failed to compile:\n " + CodeFormatter.format(code)
+        val msg = s"failed to compile: $e\n" + CodeFormatter.format(code)
         logError(msg, e)
         throw new Exception(msg, e)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
index e6d807f6d897b..15ceb9193a8c5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
@@ -165,69 +165,29 @@ case class Cosh(child: Expression) extends UnaryMathExpression(math.cosh, "COSH"
  * @param toBaseExpr to which base
  */
 case class Conv(numExpr: Expression, fromBaseExpr: Expression, toBaseExpr: Expression)
-  extends Expression with ImplicitCastInputTypes {
-
-  override def foldable: Boolean = numExpr.foldable && fromBaseExpr.foldable && toBaseExpr.foldable
-
-  override def nullable: Boolean = numExpr.nullable || fromBaseExpr.nullable || toBaseExpr.nullable
+  extends TernaryExpression with ImplicitCastInputTypes {
 
   override def children: Seq[Expression] = Seq(numExpr, fromBaseExpr, toBaseExpr)
-
   override def inputTypes: Seq[AbstractDataType] = Seq(StringType, IntegerType, IntegerType)
-
   override def dataType: DataType = StringType
 
-  /** Returns the result of evaluating this expression on a given input Row */
-  override def eval(input: InternalRow): Any = {
-    val num = numExpr.eval(input)
-    if (num != null) {
-      val fromBase = fromBaseExpr.eval(input)
-      if (fromBase != null) {
-        val toBase = toBaseExpr.eval(input)
-        if (toBase != null) {
-          NumberConverter.convert(
-            num.asInstanceOf[UTF8String].getBytes,
-            fromBase.asInstanceOf[Int],
-            toBase.asInstanceOf[Int])
-        } else {
-          null
-        }
-      } else {
-        null
-      }
-    } else {
-      null
-    }
+  override def nullSafeEval(num: Any, fromBase: Any, toBase: Any): Any = {
+    NumberConverter.convert(
+      num.asInstanceOf[UTF8String].getBytes,
+      fromBase.asInstanceOf[Int],
+      toBase.asInstanceOf[Int])
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val numGen = numExpr.gen(ctx)
-    val from = fromBaseExpr.gen(ctx)
-    val to = toBaseExpr.gen(ctx)
-
     val numconv = NumberConverter.getClass.getName.stripSuffix("$")
-    s"""
-       ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-       ${numGen.code}
-       boolean ${ev.isNull} = ${numGen.isNull};
-       if (!${ev.isNull}) {
-         ${from.code}
-         if (!${from.isNull}) {
-           ${to.code}
-           if (!${to.isNull}) {
-             ${ev.primitive} = $numconv.convert(${numGen.primitive}.getBytes(),
-               ${from.primitive}, ${to.primitive});
-             if (${ev.primitive} == null) {
-               ${ev.isNull} = true;
-             }
-           } else {
-             ${ev.isNull} = true;
-           }
-         } else {
-           ${ev.isNull} = true;
-         }
+    nullSafeCodeGen(ctx, ev, (num, from, to) =>
+      s"""
+       ${ev.primitive} = $numconv.convert($num.getBytes(), $from, $to);
+       if (${ev.primitive} == null) {
+         ${ev.isNull} = true;
        }
-     """
+       """
+    )
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 99a62343f138d..684eac12bd6f0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -426,15 +426,13 @@ case class StringInstr(str: Expression, substr: Expression)
  * in given string after position pos.
  */
 case class StringLocate(substr: Expression, str: Expression, start: Expression)
-  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+  extends TernaryExpression with ImplicitCastInputTypes with CodegenFallback {
 
   def this(substr: Expression, str: Expression) = {
     this(substr, str, Literal(0))
   }
 
   override def children: Seq[Expression] = substr :: str :: start :: Nil
-  override def foldable: Boolean = children.forall(_.foldable)
-  override def nullable: Boolean = substr.nullable || str.nullable
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType, IntegerType)
 
@@ -467,60 +465,18 @@ case class StringLocate(substr: Expression, str: Expression, start: Expression)
  * Returns str, left-padded with pad to a length of len.
  */
 case class StringLPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends TernaryExpression with ImplicitCastInputTypes {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
-  override def foldable: Boolean = children.forall(_.foldable)
-  override def nullable: Boolean = children.exists(_.nullable)
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val s = str.eval(input)
-    if (s == null) {
-      null
-    } else {
-      val l = len.eval(input)
-      if (l == null) {
-        null
-      } else {
-        val p = pad.eval(input)
-        if (p == null) {
-          null
-        } else {
-          val len = l.asInstanceOf[Int]
-          val str = s.asInstanceOf[UTF8String]
-          val pad = p.asInstanceOf[UTF8String]
-
-          str.lpad(len, pad)
-        }
-      }
-    }
+  override def nullSafeEval(str: Any, len: Any, pad: Any): Any = {
+    str.asInstanceOf[UTF8String].lpad(len.asInstanceOf[Int], pad.asInstanceOf[UTF8String])
   }
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val lenGen = len.gen(ctx)
-    val strGen = str.gen(ctx)
-    val padGen = pad.gen(ctx)
-
-    s"""
-      ${lenGen.code}
-      boolean ${ev.isNull} = ${lenGen.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${strGen.code}
-        if (!${strGen.isNull}) {
-          ${padGen.code}
-          if (!${padGen.isNull}) {
-            ${ev.primitive} = ${strGen.primitive}.lpad(${lenGen.primitive}, ${padGen.primitive});
-          } else {
-            ${ev.isNull} = true;
-          }
-        } else {
-          ${ev.isNull} = true;
-        }
-      }
-     """
+    defineCodeGen(ctx, ev, (str, len, pad) => s"$str.lpad($len, $pad)")
   }
 
   override def prettyName: String = "lpad"
@@ -530,60 +486,18 @@ case class StringLPad(str: Expression, len: Expression, pad: Expression)
  * Returns str, right-padded with pad to a length of len.
  */
 case class StringRPad(str: Expression, len: Expression, pad: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends TernaryExpression with ImplicitCastInputTypes {
 
   override def children: Seq[Expression] = str :: len :: pad :: Nil
-  override def foldable: Boolean = children.forall(_.foldable)
-  override def nullable: Boolean = children.exists(_.nullable)
   override def dataType: DataType = StringType
   override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, StringType)
 
-  override def eval(input: InternalRow): Any = {
-    val s = str.eval(input)
-    if (s == null) {
-      null
-    } else {
-      val l = len.eval(input)
-      if (l == null) {
-        null
-      } else {
-        val p = pad.eval(input)
-        if (p == null) {
-          null
-        } else {
-          val len = l.asInstanceOf[Int]
-          val str = s.asInstanceOf[UTF8String]
-          val pad = p.asInstanceOf[UTF8String]
-
-          str.rpad(len, pad)
-        }
-      }
-    }
+  override def nullSafeEval(str: Any, len: Any, pad: Any): Any = {
+    str.asInstanceOf[UTF8String].rpad(len.asInstanceOf[Int], pad.asInstanceOf[UTF8String])
   }
 
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val lenGen = len.gen(ctx)
-    val strGen = str.gen(ctx)
-    val padGen = pad.gen(ctx)
-
-    s"""
-      ${lenGen.code}
-      boolean ${ev.isNull} = ${lenGen.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${strGen.code}
-        if (!${strGen.isNull}) {
-          ${padGen.code}
-          if (!${padGen.isNull}) {
-            ${ev.primitive} = ${strGen.primitive}.rpad(${lenGen.primitive}, ${padGen.primitive});
-          } else {
-            ${ev.isNull} = true;
-          }
-        } else {
-          ${ev.isNull} = true;
-        }
-      }
-     """
+    defineCodeGen(ctx, ev, (str, len, pad) => s"$str.rpad($len, $pad)")
   }
 
   override def prettyName: String = "rpad"
@@ -745,68 +659,24 @@ case class StringSplit(str: Expression, pattern: Expression)
  * Defined for String and Binary types.
  */
 case class Substring(str: Expression, pos: Expression, len: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends TernaryExpression with ImplicitCastInputTypes {
 
   def this(str: Expression, pos: Expression) = {
     this(str, pos, Literal(Integer.MAX_VALUE))
   }
 
-  override def foldable: Boolean = str.foldable && pos.foldable && len.foldable
-  override def nullable: Boolean = str.nullable || pos.nullable || len.nullable
-
   override def dataType: DataType = StringType
 
   override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, IntegerType)
 
   override def children: Seq[Expression] = str :: pos :: len :: Nil
 
-  override def eval(input: InternalRow): Any = {
-    val stringEval = str.eval(input)
-    if (stringEval != null) {
-      val posEval = pos.eval(input)
-      if (posEval != null) {
-        val lenEval = len.eval(input)
-        if (lenEval != null) {
-          stringEval.asInstanceOf[UTF8String]
-            .substringSQL(posEval.asInstanceOf[Int], lenEval.asInstanceOf[Int])
-        } else {
-          null
-        }
-      } else {
-        null
-      }
-    } else {
-      null
-    }
+  override def nullSafeEval(string: Any, pos: Any, len: Any): Any = {
+    string.asInstanceOf[UTF8String].substringSQL(pos.asInstanceOf[Int], len.asInstanceOf[Int])
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val strGen = str.gen(ctx)
-    val posGen = pos.gen(ctx)
-    val lenGen = len.gen(ctx)
-
-    val start = ctx.freshName("start")
-    val end = ctx.freshName("end")
-
-    s"""
-      ${strGen.code}
-      boolean ${ev.isNull} = ${strGen.isNull};
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${ev.isNull}) {
-        ${posGen.code}
-        if (!${posGen.isNull}) {
-          ${lenGen.code}
-          if (!${lenGen.isNull}) {
-            ${ev.primitive} = ${strGen.primitive}
-              .substringSQL(${posGen.primitive}, ${lenGen.primitive});
-          } else {
-            ${ev.isNull} = true;
-          }
-        } else {
-          ${ev.isNull} = true;
-        }
-      }
-     """
+    defineCodeGen(ctx, ev, (str, pos, len) => s"$str.substringSQL($pos, $len)")
   }
 }
 
@@ -986,7 +856,7 @@ case class Encode(value: Expression, charset: Expression)
  * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
  */
 case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends TernaryExpression with ImplicitCastInputTypes {
 
   // last regex in string, we will update the pattern iff regexp value changed.
   @transient private var lastRegex: UTF8String = _
@@ -998,40 +868,26 @@ case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expressio
   // result buffer write by Matcher
   @transient private val result: StringBuffer = new StringBuffer
 
-  override def nullable: Boolean = subject.nullable || regexp.nullable || rep.nullable
-  override def foldable: Boolean = subject.foldable && regexp.foldable && rep.foldable
-
-  override def eval(input: InternalRow): Any = {
-    val s = subject.eval(input)
-    if (null != s) {
-      val p = regexp.eval(input)
-      if (null != p) {
-        val r = rep.eval(input)
-        if (null != r) {
-          if (!p.equals(lastRegex)) {
-            // regex value changed
-            lastRegex = p.asInstanceOf[UTF8String]
-            pattern = Pattern.compile(lastRegex.toString)
-          }
-          if (!r.equals(lastReplacementInUTF8)) {
-            // replacement string changed
-            lastReplacementInUTF8 = r.asInstanceOf[UTF8String]
-            lastReplacement = lastReplacementInUTF8.toString
-          }
-          val m = pattern.matcher(s.toString())
-          result.delete(0, result.length())
-
-          while (m.find) {
-            m.appendReplacement(result, lastReplacement)
-          }
-          m.appendTail(result)
+  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
+    if (!p.equals(lastRegex)) {
+      // regex value changed
+      lastRegex = p.asInstanceOf[UTF8String]
+      pattern = Pattern.compile(lastRegex.toString)
+    }
+    if (!r.equals(lastReplacementInUTF8)) {
+      // replacement string changed
+      lastReplacementInUTF8 = r.asInstanceOf[UTF8String]
+      lastReplacement = lastReplacementInUTF8.toString
+    }
+    val m = pattern.matcher(s.toString())
+    result.delete(0, result.length())
 
-          return UTF8String.fromString(result.toString)
-        }
-      }
+    while (m.find) {
+      m.appendReplacement(result, lastReplacement)
     }
+    m.appendTail(result)
 
-    null
+    UTF8String.fromString(result.toString)
   }
 
   override def dataType: DataType = StringType
@@ -1048,59 +904,43 @@ case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expressio
 
     val termResult = ctx.freshName("result")
 
-    val classNameUTF8String = classOf[UTF8String].getCanonicalName
     val classNamePattern = classOf[Pattern].getCanonicalName
-    val classNameString = classOf[java.lang.String].getCanonicalName
     val classNameStringBuffer = classOf[java.lang.StringBuffer].getCanonicalName
 
-    ctx.addMutableState(classNameUTF8String,
+    ctx.addMutableState("UTF8String",
       termLastRegex, s"${termLastRegex} = null;")
     ctx.addMutableState(classNamePattern,
       termPattern, s"${termPattern} = null;")
-    ctx.addMutableState(classNameString,
+    ctx.addMutableState("String",
       termLastReplacement, s"${termLastReplacement} = null;")
-    ctx.addMutableState(classNameUTF8String,
+    ctx.addMutableState("UTF8String",
       termLastReplacementInUTF8, s"${termLastReplacementInUTF8} = null;")
     ctx.addMutableState(classNameStringBuffer,
       termResult, s"${termResult} = new $classNameStringBuffer();")
 
-    val evalSubject = subject.gen(ctx)
-    val evalRegexp = regexp.gen(ctx)
-    val evalRep = rep.gen(ctx)
-
+    nullSafeCodeGen(ctx, ev, (subject, regexp, rep) => {
     s"""
-      ${evalSubject.code}
-      boolean ${ev.isNull} = true;
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      if (!${evalSubject.isNull}) {
-        ${evalRegexp.code}
-        if (!${evalRegexp.isNull}) {
-          ${evalRep.code}
-          if (!${evalRep.isNull}) {
-            if (!${evalRegexp.primitive}.equals(${termLastRegex})) {
-              // regex value changed
-              ${termLastRegex} = ${evalRegexp.primitive};
-              ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
-            }
-            if (!${evalRep.primitive}.equals(${termLastReplacementInUTF8})) {
-              // replacement string changed
-              ${termLastReplacementInUTF8} = ${evalRep.primitive};
-              ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
-            }
-            ${termResult}.delete(0, ${termResult}.length());
-            ${classOf[java.util.regex.Matcher].getCanonicalName} m =
-                                   ${termPattern}.matcher(${evalSubject.primitive}.toString());
+      if (!$regexp.equals(${termLastRegex})) {
+        // regex value changed
+        ${termLastRegex} = $regexp;
+        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
+      }
+      if (!$rep.equals(${termLastReplacementInUTF8})) {
+        // replacement string changed
+        ${termLastReplacementInUTF8} = $rep;
+        ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
+      }
+      ${termResult}.delete(0, ${termResult}.length());
+      java.util.regex.Matcher m = ${termPattern}.matcher($subject.toString());
 
-            while (m.find()) {
-              m.appendReplacement(${termResult}, ${termLastReplacement});
-            }
-            m.appendTail(${termResult});
-            ${ev.primitive} = ${classNameUTF8String}.fromString(${termResult}.toString());
-            ${ev.isNull} = false;
-          }
-        }
+      while (m.find()) {
+        m.appendReplacement(${termResult}, ${termLastReplacement});
       }
+      m.appendTail(${termResult});
+      ${ev.primitive} = UTF8String.fromString(${termResult}.toString());
+      ${ev.isNull} = false;
     """
+    })
   }
 }
 
@@ -1110,7 +950,7 @@ case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expressio
  * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
  */
 case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expression)
-  extends Expression with ImplicitCastInputTypes {
+  extends TernaryExpression with ImplicitCastInputTypes {
   def this(s: Expression, r: Expression) = this(s, r, Literal(1))
 
   // last regex in string, we will update the pattern iff regexp value changed.
@@ -1118,32 +958,19 @@ case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expressio
   // last regex pattern, we cache it for performance concern
   @transient private var pattern: Pattern = _
 
-  override def nullable: Boolean = subject.nullable || regexp.nullable || idx.nullable
-  override def foldable: Boolean = subject.foldable && regexp.foldable && idx.foldable
-
-  override def eval(input: InternalRow): Any = {
-    val s = subject.eval(input)
-    if (null != s) {
-      val p = regexp.eval(input)
-      if (null != p) {
-        val r = idx.eval(input)
-        if (null != r) {
-          if (!p.equals(lastRegex)) {
-            // regex value changed
-            lastRegex = p.asInstanceOf[UTF8String]
-            pattern = Pattern.compile(lastRegex.toString)
-          }
-          val m = pattern.matcher(s.toString())
-          if (m.find) {
-            val mr: MatchResult = m.toMatchResult
-            return UTF8String.fromString(mr.group(r.asInstanceOf[Int]))
-          }
-          return UTF8String.EMPTY_UTF8
-        }
-      }
+  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
+    if (!p.equals(lastRegex)) {
+      // regex value changed
+      lastRegex = p.asInstanceOf[UTF8String]
+      pattern = Pattern.compile(lastRegex.toString)
+    }
+    val m = pattern.matcher(s.toString())
+    if (m.find) {
+      val mr: MatchResult = m.toMatchResult
+      UTF8String.fromString(mr.group(r.asInstanceOf[Int]))
+    } else {
+      UTF8String.EMPTY_UTF8
     }
-
-    null
   }
 
   override def dataType: DataType = StringType
@@ -1154,44 +981,29 @@ case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expressio
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val termLastRegex = ctx.freshName("lastRegex")
     val termPattern = ctx.freshName("pattern")
-    val classNameUTF8String = classOf[UTF8String].getCanonicalName
     val classNamePattern = classOf[Pattern].getCanonicalName
 
-    ctx.addMutableState(classNameUTF8String, termLastRegex, s"${termLastRegex} = null;")
+    ctx.addMutableState("UTF8String", termLastRegex, s"${termLastRegex} = null;")
     ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
 
-    val evalSubject = subject.gen(ctx)
-    val evalRegexp = regexp.gen(ctx)
-    val evalIdx = idx.gen(ctx)
-
-    s"""
-      ${evalSubject.code}
-      ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-      boolean ${ev.isNull} = true;
-      if (!${evalSubject.isNull}) {
-        ${evalRegexp.code}
-        if (!${evalRegexp.isNull}) {
-          ${evalIdx.code}
-          if (!${evalIdx.isNull}) {
-            if (!${evalRegexp.primitive}.equals(${termLastRegex})) {
-              // regex value changed
-              ${termLastRegex} = ${evalRegexp.primitive};
-              ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
-            }
-            ${classOf[java.util.regex.Matcher].getCanonicalName} m =
-              ${termPattern}.matcher(${evalSubject.primitive}.toString());
-            if (m.find()) {
-              ${classOf[java.util.regex.MatchResult].getCanonicalName} mr = m.toMatchResult();
-              ${ev.primitive} = ${classNameUTF8String}.fromString(mr.group(${evalIdx.primitive}));
-              ${ev.isNull} = false;
-            } else {
-              ${ev.primitive} = ${classNameUTF8String}.EMPTY_UTF8;
-              ${ev.isNull} = false;
-            }
-          }
-        }
+    nullSafeCodeGen(ctx, ev, (subject, regexp, idx) => {
+      s"""
+      if (!$regexp.equals(${termLastRegex})) {
+        // regex value changed
+        ${termLastRegex} = $regexp;
+        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
       }
-    """
+      java.util.regex.Matcher m =
+        ${termPattern}.matcher($subject.toString());
+      if (m.find()) {
+        java.util.regex.MatchResult mr = m.toMatchResult();
+        ${ev.primitive} = UTF8String.fromString(mr.group($idx));
+        ${ev.isNull} = false;
+      } else {
+        ${ev.primitive} = UTF8String.EMPTY_UTF8;
+        ${ev.isNull} = false;
+      }"""
+    })
   }
 }
 

From fc0e57e5aba82a3f227fef05a843283e2ec893fc Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Fri, 31 Jul 2015 09:33:38 -0700
Subject: [PATCH 0733/1454] [SPARK-9053] [SPARKR] Fix spaces around parens,
 infix operators etc.

### JIRA
[[SPARK-9053] Fix spaces around parens, infix operators etc. - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9053)

### The Result of `lint-r`
[The result of lint-r at the rivision:a4c83cb1e4b066cd60264b6572fd3e51d160d26a](https://gist.github.com/yu-iskw/d253d7f8ef351f86443d)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7584 from yu-iskw/SPARK-9053 and squashes the following commits:

613170f [Yu ISHIKAWA] Ignore a warning about a space before a left parentheses
ede61e1 [Yu ISHIKAWA] Ignores two warnings about a space before a left parentheses. TODO: After updating `lintr`, we will remove the ignores
de3e0db [Yu ISHIKAWA] Add '## nolint start' & '## nolint end' statement to ignore infix space warnings
e233ea8 [Yu ISHIKAWA] [SPARK-9053][SparkR] Fix spaces around parens, infix operators etc.
---
 R/pkg/R/DataFrame.R                     | 4 ++++
 R/pkg/R/RDD.R                           | 7 +++++--
 R/pkg/R/column.R                        | 2 +-
 R/pkg/R/context.R                       | 2 +-
 R/pkg/R/pairRDD.R                       | 2 +-
 R/pkg/R/utils.R                         | 4 ++--
 R/pkg/inst/tests/test_binary_function.R | 2 +-
 R/pkg/inst/tests/test_rdd.R             | 6 +++---
 R/pkg/inst/tests/test_sparkSQL.R        | 4 +++-
 9 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index f4c93d3c7dd67..b31ad3729e09b 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1322,9 +1322,11 @@ setMethod("write.df",
                                     "org.apache.spark.sql.parquet")
             }
             allModes <- c("append", "overwrite", "error", "ignore")
+            # nolint start
             if (!(mode %in% allModes)) {
               stop('mode should be one of "append", "overwrite", "error", "ignore"')
             }
+            # nolint end
             jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode)
             options <- varargsToEnv(...)
             if (!is.null(path)) {
@@ -1384,9 +1386,11 @@ setMethod("saveAsTable",
                                     "org.apache.spark.sql.parquet")
             }
             allModes <- c("append", "overwrite", "error", "ignore")
+            # nolint start
             if (!(mode %in% allModes)) {
               stop('mode should be one of "append", "overwrite", "error", "ignore"')
             }
+            # nolint end
             jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode)
             options <- varargsToEnv(...)
             callJMethod(df@sdf, "saveAsTable", tableName, source, jmode, options)
diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R
index d2d096709245d..2a013b3dbb968 100644
--- a/R/pkg/R/RDD.R
+++ b/R/pkg/R/RDD.R
@@ -85,7 +85,9 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val)
 
   isPipelinable <- function(rdd) {
     e <- rdd@env
+    # nolint start
     !(e$isCached || e$isCheckpointed)
+    # nolint end
   }
 
   if (!inherits(prev, "PipelinedRDD") || !isPipelinable(prev)) {
@@ -97,7 +99,8 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val)
     # prev_serializedMode is used during the delayed computation of JRDD in getJRDD
   } else {
     pipelinedFunc <- function(partIndex, part) {
-      func(partIndex, prev@func(partIndex, part))
+      f <- prev@func
+      func(partIndex, f(partIndex, part))
     }
     .Object@func <- cleanClosure(pipelinedFunc)
     .Object@prev_jrdd <- prev@prev_jrdd # maintain the pipeline
@@ -841,7 +844,7 @@ setMethod("sampleRDD",
                 if (withReplacement) {
                   count <- rpois(1, fraction)
                   if (count > 0) {
-                    res[(len + 1):(len + count)] <- rep(list(elem), count)
+                    res[ (len + 1) : (len + count) ] <- rep(list(elem), count)
                     len <- len + count
                   }
                 } else {
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 2892e1416cc65..eeaf9f193b728 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -65,7 +65,7 @@ functions <- c("min", "max", "sum", "avg", "mean", "count", "abs", "sqrt",
                "acos", "asin", "atan", "cbrt", "ceiling", "cos", "cosh", "exp",
                "expm1", "floor", "log", "log10", "log1p", "rint", "sign",
                "sin", "sinh", "tan", "tanh", "toDegrees", "toRadians")
-binary_mathfunctions<- c("atan2", "hypot")
+binary_mathfunctions <- c("atan2", "hypot")
 
 createOperator <- function(op) {
   setMethod(op,
diff --git a/R/pkg/R/context.R b/R/pkg/R/context.R
index 43be9c904fdf6..720990e1c6087 100644
--- a/R/pkg/R/context.R
+++ b/R/pkg/R/context.R
@@ -121,7 +121,7 @@ parallelize <- function(sc, coll, numSlices = 1) {
     numSlices <- length(coll)
 
   sliceLen <- ceiling(length(coll) / numSlices)
-  slices <- split(coll, rep(1:(numSlices + 1), each = sliceLen)[1:length(coll)])
+  slices <- split(coll, rep(1: (numSlices + 1), each = sliceLen)[1:length(coll)])
 
   # Serialize each slice: obtain a list of raws, or a list of lists (slices) of
   # 2-tuples of raws
diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R
index 83801d3209700..199c3fd6ab1b2 100644
--- a/R/pkg/R/pairRDD.R
+++ b/R/pkg/R/pairRDD.R
@@ -879,7 +879,7 @@ setMethod("sampleByKey",
                   if (withReplacement) {
                     count <- rpois(1, frac)
                     if (count > 0) {
-                      res[(len + 1):(len + count)] <- rep(list(elem), count)
+                      res[ (len + 1) : (len + count) ] <- rep(list(elem), count)
                       len <- len + count
                     }
                   } else {
diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 3f45589a50443..4f9f4d9cad2a8 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -32,7 +32,7 @@ convertJListToRList <- function(jList, flatten, logicalUpperBound = NULL,
   }
 
   results <- if (arrSize > 0) {
-    lapply(0:(arrSize - 1),
+    lapply(0 : (arrSize - 1),
           function(index) {
             obj <- callJMethod(jList, "get", as.integer(index))
 
@@ -572,7 +572,7 @@ mergePartitions <- function(rdd, zip) {
           keys <- list()
         }
         if (lengthOfValues > 1) {
-          values <- part[(lengthOfKeys + 1) : (len - 1)]
+          values <- part[ (lengthOfKeys + 1) : (len - 1) ]
         } else {
           values <- list()
         }
diff --git a/R/pkg/inst/tests/test_binary_function.R b/R/pkg/inst/tests/test_binary_function.R
index dca0657c57e0d..f054ac9a87d61 100644
--- a/R/pkg/inst/tests/test_binary_function.R
+++ b/R/pkg/inst/tests/test_binary_function.R
@@ -40,7 +40,7 @@ test_that("union on two RDDs", {
   expect_equal(actual, c(as.list(nums), mockFile))
   expect_equal(getSerializedMode(union.rdd), "byte")
 
-  rdd<- map(text.rdd, function(x) {x})
+  rdd <- map(text.rdd, function(x) {x})
   union.rdd <- unionRDD(rdd, text.rdd)
   actual <- collect(union.rdd)
   expect_equal(actual, as.list(c(mockFile, mockFile)))
diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R
index 6c3aaab8c711e..71aed2bb9d6a8 100644
--- a/R/pkg/inst/tests/test_rdd.R
+++ b/R/pkg/inst/tests/test_rdd.R
@@ -250,7 +250,7 @@ test_that("flatMapValues() on pairwise RDDs", {
   expect_equal(actual, list(list(1,1), list(1,2), list(2,3), list(2,4)))
 
   # Generate x to x+1 for every value
-  actual <- collect(flatMapValues(intRdd, function(x) { x:(x + 1) }))
+  actual <- collect(flatMapValues(intRdd, function(x) { x: (x + 1) }))
   expect_equal(actual,
                list(list(1L, -1), list(1L, 0), list(2L, 100), list(2L, 101),
                     list(2L, 1), list(2L, 2), list(1L, 200), list(1L, 201)))
@@ -293,7 +293,7 @@ test_that("sumRDD() on RDDs", {
 })
 
 test_that("keyBy on RDDs", {
-  func <- function(x) { x*x }
+  func <- function(x) { x * x }
   keys <- keyBy(rdd, func)
   actual <- collect(keys)
   expect_equal(actual, lapply(nums, function(x) { list(func(x), x) }))
@@ -311,7 +311,7 @@ test_that("repartition/coalesce on RDDs", {
   r2 <- repartition(rdd, 6)
   expect_equal(numPartitions(r2), 6L)
   count <- length(collectPartition(r2, 0L))
-  expect_true(count >=0 && count <= 4)
+  expect_true(count >= 0 && count <= 4)
 
   # coalesce
   r3 <- coalesce(rdd, 1)
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 61c8a7ec7d837..aca41aa6dcf24 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -666,10 +666,12 @@ test_that("column binary mathfunctions", {
   expect_equal(collect(select(df, atan2(df$a, df$b)))[2, "ATAN2(a, b)"], atan2(2, 6))
   expect_equal(collect(select(df, atan2(df$a, df$b)))[3, "ATAN2(a, b)"], atan2(3, 7))
   expect_equal(collect(select(df, atan2(df$a, df$b)))[4, "ATAN2(a, b)"], atan2(4, 8))
+  ## nolint start
   expect_equal(collect(select(df, hypot(df$a, df$b)))[1, "HYPOT(a, b)"], sqrt(1^2 + 5^2))
   expect_equal(collect(select(df, hypot(df$a, df$b)))[2, "HYPOT(a, b)"], sqrt(2^2 + 6^2))
   expect_equal(collect(select(df, hypot(df$a, df$b)))[3, "HYPOT(a, b)"], sqrt(3^2 + 7^2))
   expect_equal(collect(select(df, hypot(df$a, df$b)))[4, "HYPOT(a, b)"], sqrt(4^2 + 8^2))
+  ## nolint end
 })
 
 test_that("string operators", {
@@ -876,7 +878,7 @@ test_that("parquetFile works with multiple input paths", {
   write.df(df, parquetPath2, "parquet", mode="overwrite")
   parquetDF <- parquetFile(sqlContext, parquetPath, parquetPath2)
   expect_is(parquetDF, "DataFrame")
-  expect_equal(count(parquetDF), count(df)*2)
+  expect_equal(count(parquetDF), count(df) * 2)
 })
 
 test_that("describe() on a DataFrame", {

From 04a49edfdb606c01fa4f8ae6e730ec4f9bd0cb6d Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 31 Jul 2015 09:34:10 -0700
Subject: [PATCH 0734/1454] [SPARK-9497] [SPARK-9509] [CORE] Use ask instead of
 askWithRetry

`RpcEndpointRef.askWithRetry` throws `SparkException` rather than `TimeoutException`. Use ask to replace it because we don't need to retry here.

Author: zsxwing <zsxwing@gmail.com>

Closes #7824 from zsxwing/SPARK-9497 and squashes the following commits:

7bfc2b4 [zsxwing] Use ask instead of askWithRetry
---
 .../scala/org/apache/spark/deploy/client/AppClient.scala     | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index 79b251e7e62fe..a659abf70395d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -27,7 +27,7 @@ import org.apache.spark.deploy.{ApplicationDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.master.Master
 import org.apache.spark.rpc._
-import org.apache.spark.util.{ThreadUtils, Utils}
+import org.apache.spark.util.{RpcUtils, ThreadUtils, Utils}
 
 /**
  * Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL,
@@ -248,7 +248,8 @@ private[spark] class AppClient(
   def stop() {
     if (endpoint != null) {
       try {
-        endpoint.askWithRetry[Boolean](StopAppClient)
+        val timeout = RpcUtils.askRpcTimeout(conf)
+        timeout.awaitResult(endpoint.ask[Boolean](StopAppClient))
       } catch {
         case e: TimeoutException =>
           logInfo("Stop request to Master timed out; it may already be shut down.")

From 27ae851ce16082775ffbcb5b8fc6bdbe65dc70fc Mon Sep 17 00:00:00 2001
From: tedyu <yuzhihong@gmail.com>
Date: Fri, 31 Jul 2015 18:16:55 +0100
Subject: [PATCH 0735/1454] [SPARK-9446] Clear Active SparkContext in stop()
 method

In thread 'stopped SparkContext remaining active' on mailing list, Andres observed the following in driver log:
```
15/07/29 15:17:09 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster has disassociated: <address removed>
15/07/29 15:17:09 INFO YarnClientSchedulerBackend: Shutting down all executors
Exception in thread "Yarn application state monitor" org.apache.spark.SparkException: Error asking standalone scheduler to shut down executors
        at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:261)
        at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stop(CoarseGrainedSchedulerBackend.scala:266)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.stop(YarnClientSchedulerBackend.scala:158)
        at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:416)
        at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1411)
        at org.apache.spark.SparkContext.stop(SparkContext.scala:1644)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$$anon$1.run(YarnClientSchedulerBackend.scala:139)
Caused by: java.lang.InterruptedException
        at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1325)
        at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
        at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
        at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
        at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
        at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
        at scala.concurrent.Await$.result(package.scala:190)15/07/29 15:17:09 INFO YarnClientSchedulerBackend: Asking each executor to shut down

        at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:102)
        at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:78)
        at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:257)
        ... 6 more
```
Effect of the above exception is that a stopped SparkContext is returned to user since SparkContext.clearActiveContext() is not called.

Author: tedyu <yuzhihong@gmail.com>

Closes #7756 from tedyu/master and squashes the following commits:

7339ff2 [tedyu] Move null assignment out of tryLogNonFatalError block
6e02cd9 [tedyu] Use Utils.tryLogNonFatalError to guard resource release
f5fb519 [tedyu] Clear Active SparkContext in stop() method using finally
---
 .../scala/org/apache/spark/SparkContext.scala | 50 ++++++++++++++-----
 1 file changed, 37 insertions(+), 13 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index ac6ac6c216767..2d8aa25d81daa 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1689,33 +1689,57 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       Utils.removeShutdownHook(_shutdownHookRef)
     }
 
-    postApplicationEnd()
-    _ui.foreach(_.stop())
+    Utils.tryLogNonFatalError {
+      postApplicationEnd()
+    }
+    Utils.tryLogNonFatalError {
+      _ui.foreach(_.stop())
+    }
     if (env != null) {
-      env.metricsSystem.report()
+      Utils.tryLogNonFatalError {
+        env.metricsSystem.report()
+      }
     }
     if (metadataCleaner != null) {
-      metadataCleaner.cancel()
+      Utils.tryLogNonFatalError {
+        metadataCleaner.cancel()
+      }
+    }
+    Utils.tryLogNonFatalError {
+      _cleaner.foreach(_.stop())
+    }
+    Utils.tryLogNonFatalError {
+      _executorAllocationManager.foreach(_.stop())
     }
-    _cleaner.foreach(_.stop())
-    _executorAllocationManager.foreach(_.stop())
     if (_dagScheduler != null) {
-      _dagScheduler.stop()
+      Utils.tryLogNonFatalError {
+        _dagScheduler.stop()
+      }
       _dagScheduler = null
     }
     if (_listenerBusStarted) {
-      listenerBus.stop()
-      _listenerBusStarted = false
+      Utils.tryLogNonFatalError {
+        listenerBus.stop()
+        _listenerBusStarted = false
+      }
+    }
+    Utils.tryLogNonFatalError {
+      _eventLogger.foreach(_.stop())
     }
-    _eventLogger.foreach(_.stop())
     if (env != null && _heartbeatReceiver != null) {
-      env.rpcEnv.stop(_heartbeatReceiver)
+      Utils.tryLogNonFatalError {
+        env.rpcEnv.stop(_heartbeatReceiver)
+      }
+    }
+    Utils.tryLogNonFatalError {
+      _progressBar.foreach(_.stop())
     }
-    _progressBar.foreach(_.stop())
     _taskScheduler = null
     // TODO: Cache.stop()?
     if (_env != null) {
-      _env.stop()
+      Utils.tryLogNonFatalError {
+        _env.stop()
+      }
       SparkEnv.set(null)
     }
     SparkContext.clearActiveContext()

From 0024da9157ba12ec84883a78441fa6835c1d0042 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 31 Jul 2015 11:07:34 -0700
Subject: [PATCH 0736/1454] [SQL] address comments for to_date/trunc

This PR address the comments in #7805

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7817 from davies/trunc and squashes the following commits:

f729d5f [Davies Liu] rollback
cb7f7832 [Davies Liu] genCode() is protected
31e52ef [Davies Liu] fix style
ed1edc7 [Davies Liu] address comments for #7805
---
 .../catalyst/expressions/datetimeFunctions.scala  | 15 ++++++++-------
 .../spark/sql/catalyst/util/DateTimeUtils.scala   |  3 ++-
 .../expressions/ExpressionEvalHelper.scala        |  4 +---
 .../scala/org/apache/spark/sql/functions.scala    |  3 +++
 4 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index 6e7613340c032..07dea5b470b5f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -726,15 +726,16 @@ case class TruncDate(date: Expression, format: Expression)
   override def dataType: DataType = DateType
   override def prettyName: String = "trunc"
 
-  lazy val minItemConst = DateTimeUtils.parseTruncLevel(format.eval().asInstanceOf[UTF8String])
+  private lazy val truncLevel: Int =
+    DateTimeUtils.parseTruncLevel(format.eval().asInstanceOf[UTF8String])
 
   override def eval(input: InternalRow): Any = {
-    val minItem = if (format.foldable) {
-      minItemConst
+    val level = if (format.foldable) {
+      truncLevel
     } else {
       DateTimeUtils.parseTruncLevel(format.eval().asInstanceOf[UTF8String])
     }
-    if (minItem == -1) {
+    if (level == -1) {
       // unknown format
       null
     } else {
@@ -742,7 +743,7 @@ case class TruncDate(date: Expression, format: Expression)
       if (d == null) {
         null
       } else {
-        DateTimeUtils.truncDate(d.asInstanceOf[Int], minItem)
+        DateTimeUtils.truncDate(d.asInstanceOf[Int], level)
       }
     }
   }
@@ -751,7 +752,7 @@ case class TruncDate(date: Expression, format: Expression)
     val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
 
     if (format.foldable) {
-      if (minItemConst == -1) {
+      if (truncLevel == -1) {
         s"""
           boolean ${ev.isNull} = true;
           ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
@@ -763,7 +764,7 @@ case class TruncDate(date: Expression, format: Expression)
           boolean ${ev.isNull} = ${d.isNull};
           ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
           if (!${ev.isNull}) {
-            ${ev.primitive} = $dtu.truncDate(${d.primitive}, $minItemConst);
+            ${ev.primitive} = $dtu.truncDate(${d.primitive}, $truncLevel);
           }
         """
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 5a7c25b8d508d..032ed8a56a50e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -794,7 +794,8 @@ object DateTimeUtils {
     } else if (level == TRUNC_TO_MONTH) {
       d - DateTimeUtils.getDayOfMonth(d) + 1
     } else {
-      throw new Exception(s"Invalid trunc level: $level")
+      // caller make sure that this should never be reached
+      sys.error(s"Invalid trunc level: $level")
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 3c05e5c3b833c..a41185b4d8754 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -18,11 +18,9 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.scalactic.TripleEqualsSupport.Spread
-import org.scalatest.Matchers._
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 46dc4605a5ccb..5d82a5eadd94d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2192,6 +2192,9 @@ object functions {
   /**
    * Returns date truncated to the unit specified by the format.
    *
+   * @param format: 'year', 'yyyy', 'yy' for truncate by year,
+   *               or 'month', 'mon', 'mm' for truncate by month
+   *
    * @group datetime_funcs
    * @since 1.5.0
    */

From 6add4eddb39e7748a87da3e921ea3c7881d30a82 Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Fri, 31 Jul 2015 11:22:40 -0700
Subject: [PATCH 0737/1454] [SPARK-9471] [ML] Multilayer Perceptron

This pull request contains the following feature for ML:
   - Multilayer Perceptron classifier

This implementation is based on our initial pull request with bgreeven: https://github.com/apache/spark/pull/1290 and inspired by very insightful suggestions from mengxr and witgo (I would like to thank all other people from the mentioned thread for useful discussions). The original code was extensively tested and benchmarked. Since then, I've addressed two main requirements that prevented the code from merging into the main branch:
   - Extensible interface, so it will be easy to implement new types of networks
     - Main building blocks are traits `Layer` and `LayerModel`. They are used for constructing layers of ANN. New layers can be added by extending the `Layer` and `LayerModel` traits. These traits are private in this release in order to save path to improve them based on community feedback
     - Back propagation is implemented in general form, so there is no need to change it (optimization algorithm) when new layers are implemented
   - Speed and scalability: this implementation has to be comparable in terms of speed to the state of the art single node implementations.
     - The developed benchmark for large ANN shows that the proposed code is on par with C++ CPU implementation and scales nicely with the number of workers. Details can be found here: https://github.com/avulanov/ann-benchmark

   - DBN and RBM by witgo https://github.com/witgo/spark/tree/ann-interface-gemm-dbn
   - Dropout https://github.com/avulanov/spark/tree/ann-interface-gemm

mengxr and dbtsai kindly agreed to perform code review.

Author: Alexander Ulanov <nashb@yandex.ru>
Author: Bert Greevenbosch <opensrc@bertgreevenbosch.nl>

Closes #7621 from avulanov/SPARK-2352-ann and squashes the following commits:

4806b6f [Alexander Ulanov] Addressing reviewers comments.
a7e7951 [Alexander Ulanov] Default blockSize: 100. Added documentation to blockSize parameter and DataStacker class
f69bb3d [Alexander Ulanov] Addressing reviewers comments.
374bea6 [Alexander Ulanov] Moving ANN to ML package. GradientDescent constructor is now spark private.
43b0ae2 [Alexander Ulanov] Addressing reviewers comments. Adding multiclass test.
9d18469 [Alexander Ulanov] Addressing reviewers comments: unnecessary copy of data in predict
35125ab [Alexander Ulanov] Style fix in tests
e191301 [Alexander Ulanov] Apache header
a226133 [Alexander Ulanov] Multilayer Perceptron regressor and classifier
---
 .../org/apache/spark/ml/ann/BreezeUtil.scala  |  63 ++
 .../scala/org/apache/spark/ml/ann/Layer.scala | 882 ++++++++++++++++++
 .../MultilayerPerceptronClassifier.scala      | 193 ++++
 .../org/apache/spark/ml/param/params.scala    |   5 +
 .../mllib/optimization/GradientDescent.scala  |   2 +-
 .../org/apache/spark/ml/ann/ANNSuite.scala    |  91 ++
 .../MultilayerPerceptronClassifierSuite.scala |  91 ++
 7 files changed, 1326 insertions(+), 1 deletion(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala
new file mode 100644
index 0000000000000..7429f9d652ac5
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.ann
+
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV}
+import com.github.fommil.netlib.BLAS.{getInstance => NativeBLAS}
+
+/**
+ * In-place DGEMM and DGEMV for Breeze
+ */
+private[ann] object BreezeUtil {
+
+  // TODO: switch to MLlib BLAS interface
+  private def transposeString(a: BDM[Double]): String = if (a.isTranspose) "T" else "N"
+
+  /**
+   * DGEMM: C := alpha * A * B + beta * C
+   * @param alpha alpha
+   * @param a A
+   * @param b B
+   * @param beta beta
+   * @param c C
+   */
+  def dgemm(alpha: Double, a: BDM[Double], b: BDM[Double], beta: Double, c: BDM[Double]): Unit = {
+    // TODO: add code if matrices isTranspose!!!
+    require(a.cols == b.rows, "A & B Dimension mismatch!")
+    require(a.rows == c.rows, "A & C Dimension mismatch!")
+    require(b.cols == c.cols, "A & C Dimension mismatch!")
+    NativeBLAS.dgemm(transposeString(a), transposeString(b), c.rows, c.cols, a.cols,
+      alpha, a.data, a.offset, a.majorStride, b.data, b.offset, b.majorStride,
+      beta, c.data, c.offset, c.rows)
+  }
+
+  /**
+   * DGEMV: y := alpha * A * x + beta * y
+   * @param alpha alpha
+   * @param a A
+   * @param x x
+   * @param beta beta
+   * @param y y
+   */
+  def dgemv(alpha: Double, a: BDM[Double], x: BDV[Double], beta: Double, y: BDV[Double]): Unit = {
+    require(a.cols == x.length, "A & b Dimension mismatch!")
+    NativeBLAS.dgemv(transposeString(a), a.rows, a.cols,
+      alpha, a.data, a.offset, a.majorStride, x.data, x.offset, x.stride,
+      beta, y.data, y.offset, y.stride)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
new file mode 100644
index 0000000000000..b5258ff348477
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
@@ -0,0 +1,882 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.ann
+
+import breeze.linalg.{*, DenseMatrix => BDM, DenseVector => BDV, Vector => BV, axpy => Baxpy,
+  sum => Bsum}
+import breeze.numerics.{log => Blog, sigmoid => Bsigmoid}
+
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.optimization._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.random.XORShiftRandom
+
+/**
+ * Trait that holds Layer properties, that are needed to instantiate it.
+ * Implements Layer instantiation.
+ *
+ */
+private[ann] trait Layer extends Serializable {
+  /**
+   * Returns the instance of the layer based on weights provided
+   * @param weights vector with layer weights
+   * @param position position of weights in the vector
+   * @return the layer model
+   */
+  def getInstance(weights: Vector, position: Int): LayerModel
+
+  /**
+   * Returns the instance of the layer with random generated weights
+   * @param seed seed
+   * @return the layer model
+   */
+  def getInstance(seed: Long): LayerModel
+}
+
+/**
+ * Trait that holds Layer weights (or parameters).
+ * Implements functions needed for forward propagation, computing delta and gradient.
+ * Can return weights in Vector format.
+ */
+private[ann] trait LayerModel extends Serializable {
+  /**
+   * number of weights
+   */
+  val size: Int
+
+  /**
+   * Evaluates the data (process the data through the layer)
+   * @param data data
+   * @return processed data
+   */
+  def eval(data: BDM[Double]): BDM[Double]
+
+  /**
+   * Computes the delta for back propagation
+   * @param nextDelta delta of the next layer
+   * @param input input data
+   * @return delta
+   */
+  def prevDelta(nextDelta: BDM[Double], input: BDM[Double]): BDM[Double]
+
+  /**
+   * Computes the gradient
+   * @param delta delta for this layer
+   * @param input input data
+   * @return gradient
+   */
+  def grad(delta: BDM[Double], input: BDM[Double]): Array[Double]
+
+  /**
+   * Returns weights for the layer in a single vector
+   * @return layer weights
+   */
+  def weights(): Vector
+}
+
+/**
+ * Layer properties of affine transformations, that is y=A*x+b
+ * @param numIn number of inputs
+ * @param numOut number of outputs
+ */
+private[ann] class AffineLayer(val numIn: Int, val numOut: Int) extends Layer {
+
+  override def getInstance(weights: Vector, position: Int): LayerModel = {
+    AffineLayerModel(this, weights, position)
+  }
+
+  override def getInstance(seed: Long = 11L): LayerModel = {
+    AffineLayerModel(this, seed)
+  }
+}
+
+/**
+ * Model of Affine layer y=A*x+b
+ * @param w weights (matrix A)
+ * @param b bias (vector b)
+ */
+private[ann] class AffineLayerModel private(w: BDM[Double], b: BDV[Double]) extends LayerModel {
+  val size = w.size + b.length
+  val gwb = new Array[Double](size)
+  private lazy val gw: BDM[Double] = new BDM[Double](w.rows, w.cols, gwb)
+  private lazy val gb: BDV[Double] = new BDV[Double](gwb, w.size)
+  private var z: BDM[Double] = null
+  private var d: BDM[Double] = null
+  private var ones: BDV[Double] = null
+
+  override def eval(data: BDM[Double]): BDM[Double] = {
+    if (z == null || z.cols != data.cols) z = new BDM[Double](w.rows, data.cols)
+    z(::, *) := b
+    BreezeUtil.dgemm(1.0, w, data, 1.0, z)
+    z
+  }
+
+  override def prevDelta(nextDelta: BDM[Double], input: BDM[Double]): BDM[Double] = {
+    if (d == null || d.cols != nextDelta.cols) d = new BDM[Double](w.cols, nextDelta.cols)
+    BreezeUtil.dgemm(1.0, w.t, nextDelta, 0.0, d)
+    d
+  }
+
+  override def grad(delta: BDM[Double], input: BDM[Double]): Array[Double] = {
+    BreezeUtil.dgemm(1.0 / input.cols, delta, input.t, 0.0, gw)
+    if (ones == null || ones.length != delta.cols) ones = BDV.ones[Double](delta.cols)
+    BreezeUtil.dgemv(1.0 / input.cols, delta, ones, 0.0, gb)
+    gwb
+  }
+
+  override def weights(): Vector = AffineLayerModel.roll(w, b)
+}
+
+/**
+ * Fabric for Affine layer models
+ */
+private[ann] object AffineLayerModel {
+
+  /**
+   * Creates a model of Affine layer
+   * @param layer layer properties
+   * @param weights vector with weights
+   * @param position position of weights in the vector
+   * @return model of Affine layer
+   */
+  def apply(layer: AffineLayer, weights: Vector, position: Int): AffineLayerModel = {
+    val (w, b) = unroll(weights, position, layer.numIn, layer.numOut)
+    new AffineLayerModel(w, b)
+  }
+
+  /**
+   * Creates a model of Affine layer
+   * @param layer layer properties
+   * @param seed seed
+   * @return model of Affine layer
+   */
+  def apply(layer: AffineLayer, seed: Long): AffineLayerModel = {
+    val (w, b) = randomWeights(layer.numIn, layer.numOut, seed)
+    new AffineLayerModel(w, b)
+  }
+
+  /**
+   * Unrolls the weights from the vector
+   * @param weights vector with weights
+   * @param position position of weights for this layer
+   * @param numIn number of layer inputs
+   * @param numOut number of layer outputs
+   * @return matrix A and vector b
+   */
+  def unroll(
+    weights: Vector,
+    position: Int,
+    numIn: Int,
+    numOut: Int): (BDM[Double], BDV[Double]) = {
+    val weightsCopy = weights.toArray
+    // TODO: the array is not copied to BDMs, make sure this is OK!
+    val a = new BDM[Double](numOut, numIn, weightsCopy, position)
+    val b = new BDV[Double](weightsCopy, position + (numOut * numIn), 1, numOut)
+    (a, b)
+  }
+
+  /**
+   * Roll the layer weights into a vector
+   * @param a matrix A
+   * @param b vector b
+   * @return vector of weights
+   */
+  def roll(a: BDM[Double], b: BDV[Double]): Vector = {
+    val result = new Array[Double](a.size + b.length)
+    // TODO: make sure that we need to copy!
+    System.arraycopy(a.toArray, 0, result, 0, a.size)
+    System.arraycopy(b.toArray, 0, result, a.size, b.length)
+    Vectors.dense(result)
+  }
+
+  /**
+   * Generate random weights for the layer
+   * @param numIn number of inputs
+   * @param numOut number of outputs
+   * @param seed seed
+   * @return (matrix A, vector b)
+   */
+  def randomWeights(numIn: Int, numOut: Int, seed: Long = 11L): (BDM[Double], BDV[Double]) = {
+    val rand: XORShiftRandom = new XORShiftRandom(seed)
+    val weights = BDM.fill[Double](numOut, numIn){ (rand.nextDouble * 4.8 - 2.4) / numIn }
+    val bias = BDV.fill[Double](numOut){ (rand.nextDouble * 4.8 - 2.4) / numIn }
+    (weights, bias)
+  }
+}
+
+/**
+ * Trait for functions and their derivatives for functional layers
+ */
+private[ann] trait ActivationFunction extends Serializable {
+
+  /**
+   * Implements a function
+   * @param x input data
+   * @param y output data
+   */
+  def eval(x: BDM[Double], y: BDM[Double]): Unit
+
+  /**
+   * Implements a derivative of a function (needed for the back propagation)
+   * @param x input data
+   * @param y output data
+   */
+  def derivative(x: BDM[Double], y: BDM[Double]): Unit
+
+  /**
+   * Implements a cross entropy error of a function.
+   * Needed if the functional layer that contains this function is the output layer
+   * of the network.
+   * @param target target output
+   * @param output computed output
+   * @param result intermediate result
+   * @return cross-entropy
+   */
+  def crossEntropy(target: BDM[Double], output: BDM[Double], result: BDM[Double]): Double
+
+  /**
+   * Implements a mean squared error of a function
+   * @param target target output
+   * @param output computed output
+   * @param result intermediate result
+   * @return mean squared error
+   */
+  def squared(target: BDM[Double], output: BDM[Double], result: BDM[Double]): Double
+}
+
+/**
+ * Implements in-place application of functions
+ */
+private[ann] object ActivationFunction {
+
+  def apply(x: BDM[Double], y: BDM[Double], func: Double => Double): Unit = {
+    var i = 0
+    while (i < x.rows) {
+      var j = 0
+      while (j < x.cols) {
+        y(i, j) = func(x(i, j))
+        j += 1
+      }
+      i += 1
+    }
+  }
+
+  def apply(
+    x1: BDM[Double],
+    x2: BDM[Double],
+    y: BDM[Double],
+    func: (Double, Double) => Double): Unit = {
+    var i = 0
+    while (i < x1.rows) {
+      var j = 0
+      while (j < x1.cols) {
+        y(i, j) = func(x1(i, j), x2(i, j))
+        j += 1
+      }
+      i += 1
+    }
+  }
+}
+
+/**
+ * Implements SoftMax activation function
+ */
+private[ann] class SoftmaxFunction extends ActivationFunction {
+  override def eval(x: BDM[Double], y: BDM[Double]): Unit = {
+    var j = 0
+    // find max value to make sure later that exponent is computable
+    while (j < x.cols) {
+      var i = 0
+      var max = Double.MinValue
+      while (i < x.rows) {
+        if (x(i, j) > max) {
+          max = x(i, j)
+        }
+        i += 1
+      }
+      var sum = 0.0
+      i = 0
+      while (i < x.rows) {
+        val res = Math.exp(x(i, j) - max)
+        y(i, j) = res
+        sum += res
+        i += 1
+      }
+      i = 0
+      while (i < x.rows) {
+        y(i, j) /= sum
+        i += 1
+      }
+      j += 1
+    }
+  }
+
+  override def crossEntropy(
+    output: BDM[Double],
+    target: BDM[Double],
+    result: BDM[Double]): Double = {
+    def m(o: Double, t: Double): Double = o - t
+    ActivationFunction(output, target, result, m)
+    -Bsum( target :* Blog(output)) / output.cols
+  }
+
+  override def derivative(x: BDM[Double], y: BDM[Double]): Unit = {
+    def sd(z: Double): Double = (1 - z) * z
+    ActivationFunction(x, y, sd)
+  }
+
+  override def squared(output: BDM[Double], target: BDM[Double], result: BDM[Double]): Double = {
+    throw new UnsupportedOperationException("Sorry, squared error is not defined for SoftMax.")
+  }
+}
+
+/**
+ * Implements Sigmoid activation function
+ */
+private[ann] class SigmoidFunction extends ActivationFunction {
+  override def eval(x: BDM[Double], y: BDM[Double]): Unit = {
+    def s(z: Double): Double = Bsigmoid(z)
+    ActivationFunction(x, y, s)
+  }
+
+  override def crossEntropy(
+    output: BDM[Double],
+    target: BDM[Double],
+    result: BDM[Double]): Double = {
+    def m(o: Double, t: Double): Double = o - t
+    ActivationFunction(output, target, result, m)
+    -Bsum(target :* Blog(output)) / output.cols
+  }
+
+  override def derivative(x: BDM[Double], y: BDM[Double]): Unit = {
+    def sd(z: Double): Double = (1 - z) * z
+    ActivationFunction(x, y, sd)
+  }
+
+  override def squared(output: BDM[Double], target: BDM[Double], result: BDM[Double]): Double = {
+    // TODO: make it readable
+    def m(o: Double, t: Double): Double = (o - t)
+    ActivationFunction(output, target, result, m)
+    val e = Bsum(result :* result) / 2 / output.cols
+    def m2(x: Double, o: Double) = x * (o - o * o)
+    ActivationFunction(result, output, result, m2)
+    e
+  }
+}
+
+/**
+ * Functional layer properties, y = f(x)
+ * @param activationFunction activation function
+ */
+private[ann] class FunctionalLayer (val activationFunction: ActivationFunction) extends Layer {
+  override def getInstance(weights: Vector, position: Int): LayerModel = getInstance(0L)
+
+  override def getInstance(seed: Long): LayerModel =
+    FunctionalLayerModel(this)
+}
+
+/**
+ * Functional layer model. Holds no weights.
+ * @param activationFunction activation function
+ */
+private[ann] class FunctionalLayerModel private (val activationFunction: ActivationFunction)
+  extends LayerModel {
+  val size = 0
+  // matrices for in-place computations
+  // outputs
+  private var f: BDM[Double] = null
+  // delta
+  private var d: BDM[Double] = null
+  // matrix for error computation
+  private var e: BDM[Double] = null
+  // delta gradient
+  private lazy val dg = new Array[Double](0)
+
+  override def eval(data: BDM[Double]): BDM[Double] = {
+    if (f == null || f.cols != data.cols) f = new BDM[Double](data.rows, data.cols)
+    activationFunction.eval(data, f)
+    f
+  }
+
+  override def prevDelta(nextDelta: BDM[Double], input: BDM[Double]): BDM[Double] = {
+    if (d == null || d.cols != nextDelta.cols) d = new BDM[Double](nextDelta.rows, nextDelta.cols)
+    activationFunction.derivative(input, d)
+    d :*= nextDelta
+    d
+  }
+
+  override def grad(delta: BDM[Double], input: BDM[Double]): Array[Double] = dg
+
+  override def weights(): Vector = Vectors.dense(new Array[Double](0))
+
+  def crossEntropy(output: BDM[Double], target: BDM[Double]): (BDM[Double], Double) = {
+    if (e == null || e.cols != output.cols) e = new BDM[Double](output.rows, output.cols)
+    val error = activationFunction.crossEntropy(output, target, e)
+    (e, error)
+  }
+
+  def squared(output: BDM[Double], target: BDM[Double]): (BDM[Double], Double) = {
+    if (e == null || e.cols != output.cols) e = new BDM[Double](output.rows, output.cols)
+    val error = activationFunction.squared(output, target, e)
+    (e, error)
+  }
+
+  def error(output: BDM[Double], target: BDM[Double]): (BDM[Double], Double) = {
+    // TODO: allow user pick error
+    activationFunction match {
+      case sigmoid: SigmoidFunction => squared(output, target)
+      case softmax: SoftmaxFunction => crossEntropy(output, target)
+    }
+  }
+}
+
+/**
+ * Fabric of functional layer models
+ */
+private[ann] object FunctionalLayerModel {
+  def apply(layer: FunctionalLayer): FunctionalLayerModel =
+    new FunctionalLayerModel(layer.activationFunction)
+}
+
+/**
+ * Trait for the artificial neural network (ANN) topology properties
+ */
+private[ann] trait Topology extends Serializable{
+  def getInstance(weights: Vector): TopologyModel
+  def getInstance(seed: Long): TopologyModel
+}
+
+/**
+ * Trait for ANN topology model
+ */
+private[ann] trait TopologyModel extends Serializable{
+  /**
+   * Forward propagation
+   * @param data input data
+   * @return array of outputs for each of the layers
+   */
+  def forward(data: BDM[Double]): Array[BDM[Double]]
+
+  /**
+   * Prediction of the model
+   * @param data input data
+   * @return prediction
+   */
+  def predict(data: Vector): Vector
+
+  /**
+   * Computes gradient for the network
+   * @param data input data
+   * @param target target output
+   * @param cumGradient cumulative gradient
+   * @param blockSize block size
+   * @return error
+   */
+  def computeGradient(data: BDM[Double], target: BDM[Double], cumGradient: Vector,
+                      blockSize: Int): Double
+
+  /**
+   * Returns the weights of the ANN
+   * @return weights
+   */
+  def weights(): Vector
+}
+
+/**
+ * Feed forward ANN
+ * @param layers
+ */
+private[ann] class FeedForwardTopology private(val layers: Array[Layer]) extends Topology {
+  override def getInstance(weights: Vector): TopologyModel = FeedForwardModel(this, weights)
+
+  override def getInstance(seed: Long): TopologyModel = FeedForwardModel(this, seed)
+}
+
+/**
+ * Factory for some of the frequently-used topologies
+ */
+private[ml] object FeedForwardTopology {
+  /**
+   * Creates a feed forward topology from the array of layers
+   * @param layers array of layers
+   * @return feed forward topology
+   */
+  def apply(layers: Array[Layer]): FeedForwardTopology = {
+    new FeedForwardTopology(layers)
+  }
+
+  /**
+   * Creates a multi-layer perceptron
+   * @param layerSizes sizes of layers including input and output size
+   * @param softmax wether to use SoftMax or Sigmoid function for an output layer.
+   *                Softmax is default
+   * @return multilayer perceptron topology
+   */
+  def multiLayerPerceptron(layerSizes: Array[Int], softmax: Boolean = true): FeedForwardTopology = {
+    val layers = new Array[Layer]((layerSizes.length - 1) * 2)
+    for(i <- 0 until layerSizes.length - 1){
+      layers(i * 2) = new AffineLayer(layerSizes(i), layerSizes(i + 1))
+      layers(i * 2 + 1) =
+        if (softmax && i == layerSizes.length - 2) {
+          new FunctionalLayer(new SoftmaxFunction())
+        } else {
+          new FunctionalLayer(new SigmoidFunction())
+        }
+    }
+    FeedForwardTopology(layers)
+  }
+}
+
+/**
+ * Model of Feed Forward Neural Network.
+ * Implements forward, gradient computation and can return weights in vector format.
+ * @param layerModels models of layers
+ * @param topology topology of the network
+ */
+private[ml] class FeedForwardModel private(
+    val layerModels: Array[LayerModel],
+    val topology: FeedForwardTopology) extends TopologyModel {
+  override def forward(data: BDM[Double]): Array[BDM[Double]] = {
+    val outputs = new Array[BDM[Double]](layerModels.length)
+    outputs(0) = layerModels(0).eval(data)
+    for (i <- 1 until layerModels.length) {
+      outputs(i) = layerModels(i).eval(outputs(i-1))
+    }
+    outputs
+  }
+
+  override def computeGradient(
+    data: BDM[Double],
+    target: BDM[Double],
+    cumGradient: Vector,
+    realBatchSize: Int): Double = {
+    val outputs = forward(data)
+    val deltas = new Array[BDM[Double]](layerModels.length)
+    val L = layerModels.length - 1
+    val (newE, newError) = layerModels.last match {
+      case flm: FunctionalLayerModel => flm.error(outputs.last, target)
+      case _ =>
+        throw new UnsupportedOperationException("Non-functional layer not supported at the top")
+    }
+    deltas(L) = new BDM[Double](0, 0)
+    deltas(L - 1) = newE
+    for (i <- (L - 2) to (0, -1)) {
+      deltas(i) = layerModels(i + 1).prevDelta(deltas(i + 1), outputs(i + 1))
+    }
+    val grads = new Array[Array[Double]](layerModels.length)
+    for (i <- 0 until layerModels.length) {
+      val input = if (i==0) data else outputs(i - 1)
+      grads(i) = layerModels(i).grad(deltas(i), input)
+    }
+    // update cumGradient
+    val cumGradientArray = cumGradient.toArray
+    var offset = 0
+    // TODO: extract roll
+    for (i <- 0 until grads.length) {
+      val gradArray = grads(i)
+      var k = 0
+      while (k < gradArray.length) {
+        cumGradientArray(offset + k) += gradArray(k)
+        k += 1
+      }
+      offset += gradArray.length
+    }
+    newError
+  }
+
+  // TODO: do we really need to copy the weights? they should be read-only
+  override def weights(): Vector = {
+    // TODO: extract roll
+    var size = 0
+    for (i <- 0 until layerModels.length) {
+      size += layerModels(i).size
+    }
+    val array = new Array[Double](size)
+    var offset = 0
+    for (i <- 0 until layerModels.length) {
+      val layerWeights = layerModels(i).weights().toArray
+      System.arraycopy(layerWeights, 0, array, offset, layerWeights.length)
+      offset += layerWeights.length
+    }
+    Vectors.dense(array)
+  }
+
+  override def predict(data: Vector): Vector = {
+    val size = data.size
+    val result = forward(new BDM[Double](size, 1, data.toArray))
+    Vectors.dense(result.last.toArray)
+  }
+}
+
+/**
+ * Fabric for feed forward ANN models
+ */
+private[ann] object FeedForwardModel {
+
+  /**
+   * Creates a model from a topology and weights
+   * @param topology topology
+   * @param weights weights
+   * @return model
+   */
+  def apply(topology: FeedForwardTopology, weights: Vector): FeedForwardModel = {
+    val layers = topology.layers
+    val layerModels = new Array[LayerModel](layers.length)
+    var offset = 0
+    for (i <- 0 until layers.length) {
+      layerModels(i) = layers(i).getInstance(weights, offset)
+      offset += layerModels(i).size
+    }
+    new FeedForwardModel(layerModels, topology)
+  }
+
+  /**
+   * Creates a model given a topology and seed
+   * @param topology topology
+   * @param seed seed for generating the weights
+   * @return model
+   */
+  def apply(topology: FeedForwardTopology, seed: Long = 11L): FeedForwardModel = {
+    val layers = topology.layers
+    val layerModels = new Array[LayerModel](layers.length)
+    var offset = 0
+    for(i <- 0 until layers.length){
+      layerModels(i) = layers(i).getInstance(seed)
+      offset += layerModels(i).size
+    }
+    new FeedForwardModel(layerModels, topology)
+  }
+}
+
+/**
+ * Neural network gradient. Does nothing but calling Model's gradient
+ * @param topology topology
+ * @param dataStacker data stacker
+ */
+private[ann] class ANNGradient(topology: Topology, dataStacker: DataStacker) extends Gradient {
+
+  override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = {
+    val gradient = Vectors.zeros(weights.size)
+    val loss = compute(data, label, weights, gradient)
+    (gradient, loss)
+  }
+
+  override def compute(
+    data: Vector,
+    label: Double,
+    weights: Vector,
+    cumGradient: Vector): Double = {
+    val (input, target, realBatchSize) = dataStacker.unstack(data)
+    val model = topology.getInstance(weights)
+    model.computeGradient(input, target, cumGradient, realBatchSize)
+  }
+}
+
+/**
+ * Stacks pairs of training samples (input, output) in one vector allowing them to pass
+ * through Optimizer/Gradient interfaces. If stackSize is more than one, makes blocks
+ * or matrices of inputs and outputs and then stack them in one vector.
+ * This can be used for further batch computations after unstacking.
+ * @param stackSize stack size
+ * @param inputSize size of the input vectors
+ * @param outputSize size of the output vectors
+ */
+private[ann] class DataStacker(stackSize: Int, inputSize: Int, outputSize: Int)
+  extends Serializable {
+
+  /**
+   * Stacks the data
+   * @param data RDD of vector pairs
+   * @return RDD of double (always zero) and vector that contains the stacked vectors
+   */
+  def stack(data: RDD[(Vector, Vector)]): RDD[(Double, Vector)] = {
+    val stackedData = if (stackSize == 1) {
+      data.map { v =>
+        (0.0,
+          Vectors.fromBreeze(BDV.vertcat(
+            v._1.toBreeze.toDenseVector,
+            v._2.toBreeze.toDenseVector))
+          ) }
+    } else {
+      data.mapPartitions { it =>
+        it.grouped(stackSize).map { seq =>
+          val size = seq.size
+          val bigVector = new Array[Double](inputSize * size + outputSize * size)
+          var i = 0
+          seq.foreach { case (in, out) =>
+            System.arraycopy(in.toArray, 0, bigVector, i * inputSize, inputSize)
+            System.arraycopy(out.toArray, 0, bigVector,
+              inputSize * size + i * outputSize, outputSize)
+            i += 1
+          }
+          (0.0, Vectors.dense(bigVector))
+        }
+      }
+    }
+    stackedData
+  }
+
+  /**
+   * Unstack the stacked vectors into matrices for batch operations
+   * @param data stacked vector
+   * @return pair of matrices holding input and output data and the real stack size
+   */
+  def unstack(data: Vector): (BDM[Double], BDM[Double], Int) = {
+    val arrData = data.toArray
+    val realStackSize = arrData.length / (inputSize + outputSize)
+    val input = new BDM(inputSize, realStackSize, arrData)
+    val target = new BDM(outputSize, realStackSize, arrData, inputSize * realStackSize)
+    (input, target, realStackSize)
+  }
+}
+
+/**
+ * Simple updater
+ */
+private[ann] class ANNUpdater extends Updater {
+
+  override def compute(
+    weightsOld: Vector,
+    gradient: Vector,
+    stepSize: Double,
+    iter: Int,
+    regParam: Double): (Vector, Double) = {
+    val thisIterStepSize = stepSize
+    val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
+    Baxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
+    (Vectors.fromBreeze(brzWeights), 0)
+  }
+}
+
+/**
+ * MLlib-style trainer class that trains a network given the data and topology
+ * @param topology topology of ANN
+ * @param inputSize input size
+ * @param outputSize output size
+ */
+private[ml] class FeedForwardTrainer(
+    topology: Topology,
+    val inputSize: Int,
+    val outputSize: Int) extends Serializable {
+
+  // TODO: what if we need to pass random seed?
+  private var _weights = topology.getInstance(11L).weights()
+  private var _stackSize = 128
+  private var dataStacker = new DataStacker(_stackSize, inputSize, outputSize)
+  private var _gradient: Gradient = new ANNGradient(topology, dataStacker)
+  private var _updater: Updater = new ANNUpdater()
+  private var optimizer: Optimizer = LBFGSOptimizer.setConvergenceTol(1e-4).setNumIterations(100)
+
+  /**
+   * Returns weights
+   * @return weights
+   */
+  def getWeights: Vector = _weights
+
+  /**
+   * Sets weights
+   * @param value weights
+   * @return trainer
+   */
+  def setWeights(value: Vector): FeedForwardTrainer = {
+    _weights = value
+    this
+  }
+
+  /**
+   * Sets the stack size
+   * @param value stack size
+   * @return trainer
+   */
+  def setStackSize(value: Int): FeedForwardTrainer = {
+    _stackSize = value
+    dataStacker = new DataStacker(value, inputSize, outputSize)
+    this
+  }
+
+  /**
+   * Sets the SGD optimizer
+   * @return SGD optimizer
+   */
+  def SGDOptimizer: GradientDescent = {
+    val sgd = new GradientDescent(_gradient, _updater)
+    optimizer = sgd
+    sgd
+  }
+
+  /**
+   * Sets the LBFGS optimizer
+   * @return LBGS optimizer
+   */
+  def LBFGSOptimizer: LBFGS = {
+    val lbfgs = new LBFGS(_gradient, _updater)
+    optimizer = lbfgs
+    lbfgs
+  }
+
+  /**
+   * Sets the updater
+   * @param value updater
+   * @return trainer
+   */
+  def setUpdater(value: Updater): FeedForwardTrainer = {
+    _updater = value
+    updateUpdater(value)
+    this
+  }
+
+  /**
+   * Sets the gradient
+   * @param value gradient
+   * @return trainer
+   */
+  def setGradient(value: Gradient): FeedForwardTrainer = {
+    _gradient = value
+    updateGradient(value)
+    this
+  }
+
+  private[this] def updateGradient(gradient: Gradient): Unit = {
+    optimizer match {
+      case lbfgs: LBFGS => lbfgs.setGradient(gradient)
+      case sgd: GradientDescent => sgd.setGradient(gradient)
+      case other => throw new UnsupportedOperationException(
+        s"Only LBFGS and GradientDescent are supported but got ${other.getClass}.")
+    }
+  }
+
+  private[this] def updateUpdater(updater: Updater): Unit = {
+    optimizer match {
+      case lbfgs: LBFGS => lbfgs.setUpdater(updater)
+      case sgd: GradientDescent => sgd.setUpdater(updater)
+      case other => throw new UnsupportedOperationException(
+        s"Only LBFGS and GradientDescent are supported but got ${other.getClass}.")
+    }
+  }
+
+  /**
+   * Trains the ANN
+   * @param data RDD of input and output vector pairs
+   * @return model
+   */
+  def train(data: RDD[(Vector, Vector)]): TopologyModel = {
+    val newWeights = optimizer.optimize(dataStacker.stack(data), getWeights)
+    topology.getInstance(newWeights)
+  }
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
new file mode 100644
index 0000000000000..8cd2103d7d5e6
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -0,0 +1,193 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.shared.{HasTol, HasMaxIter, HasSeed}
+import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
+import org.apache.spark.ml.param.{IntParam, ParamValidators, IntArrayParam, ParamMap}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.ml.ann.{FeedForwardTrainer, FeedForwardTopology}
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.sql.DataFrame
+
+/** Params for Multilayer Perceptron. */
+private[ml] trait MultilayerPerceptronParams extends PredictorParams
+  with HasSeed with HasMaxIter with HasTol {
+  /**
+   * Layer sizes including input size and output size.
+   * @group param
+   */
+  final val layers: IntArrayParam = new IntArrayParam(this, "layers",
+    "Sizes of layers from input layer to output layer" +
+      " E.g., Array(780, 100, 10) means 780 inputs, " +
+      "one hidden layer with 100 neurons and output layer of 10 neurons.",
+    // TODO: how to check ALSO that all elements are greater than 0?
+    ParamValidators.arrayLengthGt(1)
+  )
+
+  /** @group setParam */
+  def setLayers(value: Array[Int]): this.type = set(layers, value)
+
+  /** @group getParam */
+  final def getLayers: Array[Int] = $(layers)
+
+  /**
+   * Block size for stacking input data in matrices to speed up the computation.
+   * Data is stacked within partitions. If block size is more than remaining data in
+   * a partition then it is adjusted to the size of this data.
+   * Recommended size is between 10 and 1000.
+   * @group expertParam
+   */
+  final val blockSize: IntParam = new IntParam(this, "blockSize",
+    "Block size for stacking input data in matrices. Data is stacked within partitions." +
+      " If block size is more than remaining data in a partition then " +
+      "it is adjusted to the size of this data. Recommended size is between 10 and 1000",
+    ParamValidators.gt(0))
+
+  /** @group setParam */
+  def setBlockSize(value: Int): this.type = set(blockSize, value)
+
+  /** @group getParam */
+  final def getBlockSize: Int = $(blockSize)
+
+  /**
+   * Set the maximum number of iterations.
+   * Default is 100.
+   * @group setParam
+   */
+  def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+  /**
+   * Set the convergence tolerance of iterations.
+   * Smaller value will lead to higher accuracy with the cost of more iterations.
+   * Default is 1E-4.
+   * @group setParam
+   */
+  def setTol(value: Double): this.type = set(tol, value)
+
+  /**
+   * Set the seed for weights initialization.
+   * @group setParam
+   */
+  def setSeed(value: Long): this.type = set(seed, value)
+
+  setDefault(maxIter -> 100, tol -> 1e-4, layers -> Array(1, 1), blockSize -> 128)
+}
+
+/** Label to vector converter. */
+private object LabelConverter {
+  // TODO: Use OneHotEncoder instead
+  /**
+   * Encodes a label as a vector.
+   * Returns a vector of given length with zeroes at all positions
+   * and value 1.0 at the position that corresponds to the label.
+   *
+   * @param labeledPoint labeled point
+   * @param labelCount total number of labels
+   * @return pair of features and vector encoding of a label
+   */
+  def encodeLabeledPoint(labeledPoint: LabeledPoint, labelCount: Int): (Vector, Vector) = {
+    val output = Array.fill(labelCount)(0.0)
+    output(labeledPoint.label.toInt) = 1.0
+    (labeledPoint.features, Vectors.dense(output))
+  }
+
+  /**
+   * Converts a vector to a label.
+   * Returns the position of the maximal element of a vector.
+   *
+   * @param output label encoded with a vector
+   * @return label
+   */
+  def decodeLabel(output: Vector): Double = {
+    output.argmax.toDouble
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Classifier trainer based on the Multilayer Perceptron.
+ * Each layer has sigmoid activation function, output layer has softmax.
+ * Number of inputs has to be equal to the size of feature vectors.
+ * Number of outputs has to be equal to the total number of labels.
+ *
+ */
+@Experimental
+class MultilayerPerceptronClassifier(override val uid: String)
+  extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassifierModel]
+  with MultilayerPerceptronParams {
+
+  def this() = this(Identifiable.randomUID("mlpc"))
+
+  override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra)
+
+  /**
+   * Train a model using the given dataset and parameters.
+   * Developers can implement this instead of [[fit()]] to avoid dealing with schema validation
+   * and copying parameters into the model.
+   *
+   * @param dataset Training dataset
+   * @return Fitted model
+   */
+  override protected def train(dataset: DataFrame): MultilayerPerceptronClassifierModel = {
+    val myLayers = $(layers)
+    val labels = myLayers.last
+    val lpData = extractLabeledPoints(dataset)
+    val data = lpData.map(lp => LabelConverter.encodeLabeledPoint(lp, labels))
+    val topology = FeedForwardTopology.multiLayerPerceptron(myLayers, true)
+    val FeedForwardTrainer = new FeedForwardTrainer(topology, myLayers(0), myLayers.last)
+    FeedForwardTrainer.LBFGSOptimizer.setConvergenceTol($(tol)).setNumIterations($(maxIter))
+    FeedForwardTrainer.setStackSize($(blockSize))
+    val mlpModel = FeedForwardTrainer.train(data)
+    new MultilayerPerceptronClassifierModel(uid, myLayers, mlpModel.weights())
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Classifier model based on the Multilayer Perceptron.
+ * Each layer has sigmoid activation function, output layer has softmax.
+ * @param uid uid
+ * @param layers array of layer sizes including input and output layers
+ * @param weights vector of initial weights for the model that consists of the weights of layers
+ * @return prediction model
+ */
+@Experimental
+class MultilayerPerceptronClassifierModel private[ml] (
+    override val uid: String,
+    layers: Array[Int],
+    weights: Vector)
+  extends PredictionModel[Vector, MultilayerPerceptronClassifierModel]
+  with Serializable {
+
+  private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).getInstance(weights)
+
+  /**
+   * Predict label for the given features.
+   * This internal method is used to implement [[transform()]] and output [[predictionCol]].
+   */
+  override protected def predict(features: Vector): Double = {
+    LabelConverter.decodeLabel(mlpModel.predict(features))
+  }
+
+  override def copy(extra: ParamMap): MultilayerPerceptronClassifierModel = {
+    copyValues(new MultilayerPerceptronClassifierModel(uid, layers, weights), extra)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 954aa17e26a02..d68f5ff0053c9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -166,6 +166,11 @@ object ParamValidators {
   def inArray[T](allowed: java.util.List[T]): T => Boolean = { (value: T) =>
     allowed.contains(value)
   }
+
+  /** Check that the array length is greater than lowerBound. */
+  def arrayLengthGt[T](lowerBound: Double): Array[T] => Boolean = { (value: Array[T]) =>
+    value.length > lowerBound
+  }
 }
 
 // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ...
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index ab7611fd077ef..8f0d1e4aa010a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -32,7 +32,7 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
  * @param gradient Gradient function to be used.
  * @param updater Updater to be used to update weights after every iteration.
  */
-class GradientDescent private[mllib] (private var gradient: Gradient, private var updater: Updater)
+class GradientDescent private[spark] (private var gradient: Gradient, private var updater: Updater)
   extends Optimizer with Logging {
 
   private var stepSize: Double = 1.0
diff --git a/mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala
new file mode 100644
index 0000000000000..1292e57d7c01a
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.ann
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+
+
+class ANNSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  // TODO: test for weights comparison with Weka MLP
+  test("ANN with Sigmoid learns XOR function with LBFGS optimizer") {
+    val inputs = Array(
+      Array(0.0, 0.0),
+      Array(0.0, 1.0),
+      Array(1.0, 0.0),
+      Array(1.0, 1.0)
+    )
+    val outputs = Array(0.0, 1.0, 1.0, 0.0)
+    val data = inputs.zip(outputs).map { case (features, label) =>
+      (Vectors.dense(features), Vectors.dense(label))
+    }
+    val rddData = sc.parallelize(data, 1)
+    val hiddenLayersTopology = Array(5)
+    val dataSample = rddData.first()
+    val layerSizes = dataSample._1.size +: hiddenLayersTopology :+ dataSample._2.size
+    val topology = FeedForwardTopology.multiLayerPerceptron(layerSizes, false)
+    val initialWeights = FeedForwardModel(topology, 23124).weights()
+    val trainer = new FeedForwardTrainer(topology, 2, 1)
+    trainer.setWeights(initialWeights)
+    trainer.LBFGSOptimizer.setNumIterations(20)
+    val model = trainer.train(rddData)
+    val predictionAndLabels = rddData.map { case (input, label) =>
+      (model.predict(input)(0), label(0))
+    }.collect()
+    predictionAndLabels.foreach { case (p, l) =>
+      assert(math.round(p) === l)
+    }
+  }
+
+  test("ANN with SoftMax learns XOR function with 2-bit output and batch GD optimizer") {
+    val inputs = Array(
+      Array(0.0, 0.0),
+      Array(0.0, 1.0),
+      Array(1.0, 0.0),
+      Array(1.0, 1.0)
+    )
+    val outputs = Array(
+      Array(1.0, 0.0),
+      Array(0.0, 1.0),
+      Array(0.0, 1.0),
+      Array(1.0, 0.0)
+    )
+    val data = inputs.zip(outputs).map { case (features, label) =>
+      (Vectors.dense(features), Vectors.dense(label))
+    }
+    val rddData = sc.parallelize(data, 1)
+    val hiddenLayersTopology = Array(5)
+    val dataSample = rddData.first()
+    val layerSizes = dataSample._1.size +: hiddenLayersTopology :+ dataSample._2.size
+    val topology = FeedForwardTopology.multiLayerPerceptron(layerSizes, false)
+    val initialWeights = FeedForwardModel(topology, 23124).weights()
+    val trainer = new FeedForwardTrainer(topology, 2, 2)
+    trainer.SGDOptimizer.setNumIterations(2000)
+    trainer.setWeights(initialWeights)
+    val model = trainer.train(rddData)
+    val predictionAndLabels = rddData.map { case (input, label) =>
+      (model.predict(input), label)
+    }.collect()
+    predictionAndLabels.foreach { case (p, l) =>
+      assert(p ~== l absTol 0.5)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala
new file mode 100644
index 0000000000000..ddc948f65df45
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.classification.LogisticRegressionSuite._
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.sql.Row
+
+class MultilayerPerceptronClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("XOR function learning as binary classification problem with two outputs.") {
+    val dataFrame = sqlContext.createDataFrame(Seq(
+        (Vectors.dense(0.0, 0.0), 0.0),
+        (Vectors.dense(0.0, 1.0), 1.0),
+        (Vectors.dense(1.0, 0.0), 1.0),
+        (Vectors.dense(1.0, 1.0), 0.0))
+    ).toDF("features", "label")
+    val layers = Array[Int](2, 5, 2)
+    val trainer = new MultilayerPerceptronClassifier()
+      .setLayers(layers)
+      .setBlockSize(1)
+      .setSeed(11L)
+      .setMaxIter(100)
+    val model = trainer.fit(dataFrame)
+    val result = model.transform(dataFrame)
+    val predictionAndLabels = result.select("prediction", "label").collect()
+    predictionAndLabels.foreach { case Row(p: Double, l: Double) =>
+      assert(p == l)
+    }
+  }
+
+  // TODO: implement a more rigorous test
+  test("3 class classification with 2 hidden layers") {
+    val nPoints = 1000
+
+    // The following weights are taken from OneVsRestSuite.scala
+    // they represent 3-class iris dataset
+    val weights = Array(
+      -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
+      -0.16624, -0.84355, -0.048509, -0.301789, 4.170682)
+
+    val xMean = Array(5.843, 3.057, 3.758, 1.199)
+    val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
+    val rdd = sc.parallelize(generateMultinomialLogisticInput(
+      weights, xMean, xVariance, true, nPoints, 42), 2)
+    val dataFrame = sqlContext.createDataFrame(rdd).toDF("label", "features")
+    val numClasses = 3
+    val numIterations = 100
+    val layers = Array[Int](4, 5, 4, numClasses)
+    val trainer = new MultilayerPerceptronClassifier()
+      .setLayers(layers)
+      .setBlockSize(1)
+      .setSeed(11L)
+      .setMaxIter(numIterations)
+    val model = trainer.fit(dataFrame)
+    val mlpPredictionAndLabels = model.transform(dataFrame).select("prediction", "label")
+      .map { case Row(p: Double, l: Double) => (p, l) }
+    // train multinomial logistic regression
+    val lr = new LogisticRegressionWithLBFGS()
+      .setIntercept(true)
+      .setNumClasses(numClasses)
+    lr.optimizer.setRegParam(0.0)
+      .setNumIterations(numIterations)
+    val lrModel = lr.run(rdd)
+    val lrPredictionAndLabels = lrModel.predict(rdd.map(_.features)).zip(rdd.map(_.label))
+    // MLP's predictions should not differ a lot from LR's.
+    val lrMetrics = new MulticlassMetrics(lrPredictionAndLabels)
+    val mlpMetrics = new MulticlassMetrics(mlpPredictionAndLabels)
+    assert(mlpMetrics.confusionMatrix ~== lrMetrics.confusionMatrix absTol 100)
+  }
+}

From 4011a947154d97a9ffb5a71f077481a12534d36b Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Fri, 31 Jul 2015 11:50:15 -0700
Subject: [PATCH 0738/1454] [SPARK-9231] [MLLIB] DistributedLDAModel method for
 top topics per document

jira: https://issues.apache.org/jira/browse/SPARK-9231

Helper method in DistributedLDAModel of this form:
```
/**
 * For each document, return the top k weighted topics for that document.
 * return RDD of (doc ID, topic indices, topic weights)
 */
def topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])]
```

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7785 from hhbyyh/topTopicsPerdoc and squashes the following commits:

30ad153 [Yuhao Yang] small fix
fd24580 [Yuhao Yang] add topTopics per document to DistributedLDAModel
---
 .../spark/mllib/clustering/LDAModel.scala     | 19 ++++++++++++++++++-
 .../spark/mllib/clustering/LDASuite.scala     | 13 ++++++++++++-
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 6cfad3fbbdb87..82281a0daf008 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, normalize, sum}
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argtopk, normalize, sum}
 import breeze.numerics.{exp, lgamma}
 import org.apache.hadoop.fs.Path
 import org.json4s.DefaultFormats
@@ -591,6 +591,23 @@ class DistributedLDAModel private[clustering] (
     JavaPairRDD.fromRDD(topicDistributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
   }
 
+  /**
+   * For each document, return the top k weighted topics for that document and their weights.
+   * @return RDD of (doc ID, topic indices, topic weights)
+   */
+  def topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])] = {
+    graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
+      val topIndices = argtopk(topicCounts, k)
+      val sumCounts = sum(topicCounts)
+      val weights = if (sumCounts != 0) {
+        topicCounts(topIndices) / sumCounts
+      } else {
+        topicCounts(topIndices)
+      }
+      (docID.toLong, topIndices.toArray, weights.toArray)
+    }
+  }
+
   // TODO:
   // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ???
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index c43e1e575c09c..695ee3b82efc5 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM, max, argmax}
+import breeze.linalg.{DenseMatrix => BDM, argtopk, max, argmax}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.graphx.Edge
@@ -108,6 +108,17 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
       assert(topicDistribution.toArray.sum ~== 1.0 absTol 1e-5)
     }
 
+    val top2TopicsPerDoc = model.topTopicsPerDocument(2).map(t => (t._1, (t._2, t._3)))
+    model.topicDistributions.join(top2TopicsPerDoc).collect().foreach {
+      case (docId, (topicDistribution, (indices, weights))) =>
+        assert(indices.length == 2)
+        assert(weights.length == 2)
+        val bdvTopicDist = topicDistribution.toBreeze
+        val top2Indices = argtopk(bdvTopicDist, 2)
+        assert(top2Indices.toArray === indices)
+        assert(bdvTopicDist(top2Indices).toArray === weights)
+    }
+
     // Check: log probabilities
     assert(model.logLikelihood < 0.0)
     assert(model.logPrior < 0.0)

From e8bdcdeabb2df139a656f86686cdb53c891b1f4b Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 31 Jul 2015 11:56:52 -0700
Subject: [PATCH 0739/1454] [SPARK-6885] [ML] decision tree support predict
 class probabilities

Decision tree support predict class probabilities.
Implement the prediction probabilities function referred the old DecisionTree API and the [sklean API](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/tree.py#L593).
I make the DecisionTreeClassificationModel inherit from ProbabilisticClassificationModel, make the predictRaw to return the raw counts vector and make raw2probabilityInPlace/predictProbability return the probabilities for each prediction.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7694 from yanboliang/spark-6885 and squashes the following commits:

08d5b7f [Yanbo Liang] fix ImpurityStats null parameters and raw2probabilityInPlace sum = 0 issue
2174278 [Yanbo Liang] solve merge conflicts
7e90ba8 [Yanbo Liang] fix typos
33ae183 [Yanbo Liang] fix annotation
ff043d3 [Yanbo Liang] raw2probabilityInPlace should operate in-place
c32d6ce [Yanbo Liang] optimize calculateImpurityStats function again
6167fb0 [Yanbo Liang] optimize calculateImpurityStats function
fbbe2ec [Yanbo Liang] eliminate duplicated struct and code
beb1634 [Yanbo Liang] try to eliminate impurityStats for each LearningNode
99e8943 [Yanbo Liang] code optimization
5ec3323 [Yanbo Liang] implement InformationGainAndImpurityStats
227c91b [Yanbo Liang] refactor LearningNode to store ImpurityCalculator
d746ffc [Yanbo Liang] decision tree support predict class probabilities
---
 .../DecisionTreeClassifier.scala              |  40 ++++--
 .../ml/classification/GBTClassifier.scala     |   2 +-
 .../RandomForestClassifier.scala              |   2 +-
 .../ml/regression/DecisionTreeRegressor.scala |   2 +-
 .../spark/ml/regression/GBTRegressor.scala    |   2 +-
 .../ml/regression/RandomForestRegressor.scala |   2 +-
 .../scala/org/apache/spark/ml/tree/Node.scala |  80 ++++++-----
 .../spark/ml/tree/impl/RandomForest.scala     | 126 ++++++++----------
 .../spark/mllib/tree/impurity/Entropy.scala   |   2 +-
 .../spark/mllib/tree/impurity/Gini.scala      |   2 +-
 .../spark/mllib/tree/impurity/Impurity.scala  |   2 +-
 .../spark/mllib/tree/impurity/Variance.scala  |   2 +-
 .../tree/model/InformationGainStats.scala     |  61 ++++++++-
 .../DecisionTreeClassifierSuite.scala         |  30 ++++-
 .../classification/GBTClassifierSuite.scala   |   2 +-
 .../RandomForestClassifierSuite.scala         |   2 +-
 16 files changed, 229 insertions(+), 130 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 36fe1bd40469c..f27cfd0331419 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -18,12 +18,11 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
@@ -39,7 +38,7 @@ import org.apache.spark.sql.DataFrame
  */
 @Experimental
 final class DecisionTreeClassifier(override val uid: String)
-  extends Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
+  extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
   with DecisionTreeParams with TreeClassifierParams {
 
   def this() = this(Identifiable.randomUID("dtc"))
@@ -106,8 +105,9 @@ object DecisionTreeClassifier {
 @Experimental
 final class DecisionTreeClassificationModel private[ml] (
     override val uid: String,
-    override val rootNode: Node)
-  extends PredictionModel[Vector, DecisionTreeClassificationModel]
+    override val rootNode: Node,
+    override val numClasses: Int)
+  extends ProbabilisticClassificationModel[Vector, DecisionTreeClassificationModel]
   with DecisionTreeModel with Serializable {
 
   require(rootNode != null,
@@ -117,14 +117,36 @@ final class DecisionTreeClassificationModel private[ml] (
    * Construct a decision tree classification model.
    * @param rootNode  Root node of tree, with other nodes attached.
    */
-  def this(rootNode: Node) = this(Identifiable.randomUID("dtc"), rootNode)
+  def this(rootNode: Node, numClasses: Int) =
+    this(Identifiable.randomUID("dtc"), rootNode, numClasses)
 
   override protected def predict(features: Vector): Double = {
-    rootNode.predict(features)
+    rootNode.predictImpl(features).prediction
+  }
+
+  override protected def predictRaw(features: Vector): Vector = {
+    Vectors.dense(rootNode.predictImpl(features).impurityStats.stats.clone())
+  }
+
+  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
+    rawPrediction match {
+      case dv: DenseVector =>
+        var i = 0
+        val size = dv.size
+        val sum = dv.values.sum
+        while (i < size) {
+          dv.values(i) = if (sum != 0) dv.values(i) / sum else 0.0
+          i += 1
+        }
+        dv
+      case sv: SparseVector =>
+        throw new RuntimeException("Unexpected error in DecisionTreeClassificationModel:" +
+          " raw2probabilityInPlace encountered SparseVector")
+    }
   }
 
   override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
-    copyValues(new DecisionTreeClassificationModel(uid, rootNode), extra)
+    copyValues(new DecisionTreeClassificationModel(uid, rootNode, numClasses), extra)
   }
 
   override def toString: String = {
@@ -149,6 +171,6 @@ private[ml] object DecisionTreeClassificationModel {
         s" DecisionTreeClassificationModel (new API).  Algo is: ${oldModel.algo}")
     val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures)
     val uid = if (parent != null) parent.uid else Identifiable.randomUID("dtc")
-    new DecisionTreeClassificationModel(uid, rootNode)
+    new DecisionTreeClassificationModel(uid, rootNode, -1)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index eb0b1a0a405fc..c3891a9599262 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -190,7 +190,7 @@ final class GBTClassificationModel(
   override protected def predict(features: Vector): Double = {
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
-    val treePredictions = _trees.map(_.rootNode.predict(features))
+    val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
     val prediction = blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
     if (prediction > 0.0) 1.0 else 0.0
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index bc19bd6df894f..0c7eb4a662fdb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -160,7 +160,7 @@ final class RandomForestClassificationModel private[ml] (
     // Ignore the weights since all are 1.0 for now.
     val votes = new Array[Double](numClasses)
     _trees.view.foreach { tree =>
-      val prediction = tree.rootNode.predict(features).toInt
+      val prediction = tree.rootNode.predictImpl(features).prediction.toInt
       votes(prediction) = votes(prediction) + 1.0 // 1.0 = weight
     }
     Vectors.dense(votes)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 6f3340c2f02be..4d30e4b5548aa 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -110,7 +110,7 @@ final class DecisionTreeRegressionModel private[ml] (
   def this(rootNode: Node) = this(Identifiable.randomUID("dtr"), rootNode)
 
   override protected def predict(features: Vector): Double = {
-    rootNode.predict(features)
+    rootNode.predictImpl(features).prediction
   }
 
   override def copy(extra: ParamMap): DecisionTreeRegressionModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index e38dc73ee0ba7..5633bc320273a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -180,7 +180,7 @@ final class GBTRegressionModel(
   override protected def predict(features: Vector): Double = {
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
-    val treePredictions = _trees.map(_.rootNode.predict(features))
+    val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
     blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 506a878c2553b..17fb1ad5e15d4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -143,7 +143,7 @@ final class RandomForestRegressionModel private[ml] (
     // TODO: When we add a generic Bagging class, handle transform there.  SPARK-7128
     // Predict average of tree predictions.
     // Ignore the weights since all are 1.0 for now.
-    _trees.map(_.rootNode.predict(features)).sum / numTrees
+    _trees.map(_.rootNode.predictImpl(features).prediction).sum / numTrees
   }
 
   override def copy(extra: ParamMap): RandomForestRegressionModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
index bbc2427ca7d3d..8879352a600a9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
@@ -19,8 +19,9 @@ package org.apache.spark.ml.tree
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 import org.apache.spark.mllib.tree.model.{InformationGainStats => OldInformationGainStats,
-  Node => OldNode, Predict => OldPredict}
+  Node => OldNode, Predict => OldPredict, ImpurityStats}
 
 /**
  * :: DeveloperApi ::
@@ -38,8 +39,15 @@ sealed abstract class Node extends Serializable {
   /** Impurity measure at this node (for training data) */
   def impurity: Double
 
+  /**
+   * Statistics aggregated from training data at this node, used to compute prediction, impurity,
+   * and probabilities.
+   * For classification, the array of class counts must be normalized to a probability distribution.
+   */
+  private[tree] def impurityStats: ImpurityCalculator
+
   /** Recursive prediction helper method */
-  private[ml] def predict(features: Vector): Double = prediction
+  private[ml] def predictImpl(features: Vector): LeafNode
 
   /**
    * Get the number of nodes in tree below this node, including leaf nodes.
@@ -75,7 +83,8 @@ private[ml] object Node {
     if (oldNode.isLeaf) {
       // TODO: Once the implementation has been moved to this API, then include sufficient
       //       statistics here.
-      new LeafNode(prediction = oldNode.predict.predict, impurity = oldNode.impurity)
+      new LeafNode(prediction = oldNode.predict.predict,
+        impurity = oldNode.impurity, impurityStats = null)
     } else {
       val gain = if (oldNode.stats.nonEmpty) {
         oldNode.stats.get.gain
@@ -85,7 +94,7 @@ private[ml] object Node {
       new InternalNode(prediction = oldNode.predict.predict, impurity = oldNode.impurity,
         gain = gain, leftChild = fromOld(oldNode.leftNode.get, categoricalFeatures),
         rightChild = fromOld(oldNode.rightNode.get, categoricalFeatures),
-        split = Split.fromOld(oldNode.split.get, categoricalFeatures))
+        split = Split.fromOld(oldNode.split.get, categoricalFeatures), impurityStats = null)
     }
   }
 }
@@ -99,11 +108,13 @@ private[ml] object Node {
 @DeveloperApi
 final class LeafNode private[ml] (
     override val prediction: Double,
-    override val impurity: Double) extends Node {
+    override val impurity: Double,
+    override val impurityStats: ImpurityCalculator) extends Node {
 
-  override def toString: String = s"LeafNode(prediction = $prediction, impurity = $impurity)"
+  override def toString: String =
+    s"LeafNode(prediction = $prediction, impurity = $impurity)"
 
-  override private[ml] def predict(features: Vector): Double = prediction
+  override private[ml] def predictImpl(features: Vector): LeafNode = this
 
   override private[tree] def numDescendants: Int = 0
 
@@ -115,9 +126,8 @@ final class LeafNode private[ml] (
   override private[tree] def subtreeDepth: Int = 0
 
   override private[ml] def toOld(id: Int): OldNode = {
-    // NOTE: We do NOT store 'prob' in the new API currently.
-    new OldNode(id, new OldPredict(prediction, prob = 0.0), impurity, isLeaf = true,
-      None, None, None, None)
+    new OldNode(id, new OldPredict(prediction, prob = impurityStats.prob(prediction)),
+      impurity, isLeaf = true, None, None, None, None)
   }
 }
 
@@ -139,17 +149,18 @@ final class InternalNode private[ml] (
     val gain: Double,
     val leftChild: Node,
     val rightChild: Node,
-    val split: Split) extends Node {
+    val split: Split,
+    override val impurityStats: ImpurityCalculator) extends Node {
 
   override def toString: String = {
     s"InternalNode(prediction = $prediction, impurity = $impurity, split = $split)"
   }
 
-  override private[ml] def predict(features: Vector): Double = {
+  override private[ml] def predictImpl(features: Vector): LeafNode = {
     if (split.shouldGoLeft(features)) {
-      leftChild.predict(features)
+      leftChild.predictImpl(features)
     } else {
-      rightChild.predict(features)
+      rightChild.predictImpl(features)
     }
   }
 
@@ -172,9 +183,8 @@ final class InternalNode private[ml] (
   override private[ml] def toOld(id: Int): OldNode = {
     assert(id.toLong * 2 < Int.MaxValue, "Decision Tree could not be converted from new to old API"
       + " since the old API does not support deep trees.")
-    // NOTE: We do NOT store 'prob' in the new API currently.
-    new OldNode(id, new OldPredict(prediction, prob = 0.0), impurity, isLeaf = false,
-      Some(split.toOld), Some(leftChild.toOld(OldNode.leftChildIndex(id))),
+    new OldNode(id, new OldPredict(prediction, prob = impurityStats.prob(prediction)), impurity,
+      isLeaf = false, Some(split.toOld), Some(leftChild.toOld(OldNode.leftChildIndex(id))),
       Some(rightChild.toOld(OldNode.rightChildIndex(id))),
       Some(new OldInformationGainStats(gain, impurity, leftChild.impurity, rightChild.impurity,
         new OldPredict(leftChild.prediction, prob = 0.0),
@@ -223,36 +233,36 @@ private object InternalNode {
  *
  * @param id  We currently use the same indexing as the old implementation in
  *            [[org.apache.spark.mllib.tree.model.Node]], but this will change later.
- * @param predictionStats  Predicted label + class probability (for classification).
- *                         We will later modify this to store aggregate statistics for labels
- *                         to provide all class probabilities (for classification) and maybe a
- *                         distribution (for regression).
  * @param isLeaf  Indicates whether this node will definitely be a leaf in the learned tree,
  *                so that we do not need to consider splitting it further.
- * @param stats  Old structure for storing stats about information gain, prediction, etc.
- *               This is legacy and will be modified in the future.
+ * @param stats  Impurity statistics for this node.
  */
 private[tree] class LearningNode(
     var id: Int,
-    var predictionStats: OldPredict,
-    var impurity: Double,
     var leftChild: Option[LearningNode],
     var rightChild: Option[LearningNode],
     var split: Option[Split],
     var isLeaf: Boolean,
-    var stats: Option[OldInformationGainStats]) extends Serializable {
+    var stats: ImpurityStats) extends Serializable {
 
   /**
    * Convert this [[LearningNode]] to a regular [[Node]], and recurse on any children.
    */
   def toNode: Node = {
     if (leftChild.nonEmpty) {
-      assert(rightChild.nonEmpty && split.nonEmpty && stats.nonEmpty,
+      assert(rightChild.nonEmpty && split.nonEmpty && stats != null,
         "Unknown error during Decision Tree learning.  Could not convert LearningNode to Node.")
-      new InternalNode(predictionStats.predict, impurity, stats.get.gain,
-        leftChild.get.toNode, rightChild.get.toNode, split.get)
+      new InternalNode(stats.impurityCalculator.predict, stats.impurity, stats.gain,
+        leftChild.get.toNode, rightChild.get.toNode, split.get, stats.impurityCalculator)
     } else {
-      new LeafNode(predictionStats.predict, impurity)
+      if (stats.valid) {
+        new LeafNode(stats.impurityCalculator.predict, stats.impurity,
+          stats.impurityCalculator)
+      } else {
+        // Here we want to keep same behavior with the old mllib.DecisionTreeModel
+        new LeafNode(stats.impurityCalculator.predict, -1.0, stats.impurityCalculator)
+      }
+
     }
   }
 
@@ -263,16 +273,14 @@ private[tree] object LearningNode {
   /** Create a node with some of its fields set. */
   def apply(
       id: Int,
-      predictionStats: OldPredict,
-      impurity: Double,
-      isLeaf: Boolean): LearningNode = {
-    new LearningNode(id, predictionStats, impurity, None, None, None, false, None)
+      isLeaf: Boolean,
+      stats: ImpurityStats): LearningNode = {
+    new LearningNode(id, None, None, None, false, stats)
   }
 
   /** Create an empty node with the given node index.  Values must be set later on. */
   def emptyNode(nodeIndex: Int): LearningNode = {
-    new LearningNode(nodeIndex, new OldPredict(Double.NaN, Double.NaN), Double.NaN,
-      None, None, None, false, None)
+    new LearningNode(nodeIndex, None, None, None, false, null)
   }
 
   // The below indexing methods were copied from spark.mllib.tree.model.Node
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
index 15b56bd844bad..a8b90d9d266a1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
@@ -31,7 +31,7 @@ import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => O
 import org.apache.spark.mllib.tree.impl.{BaggedPoint, DTStatsAggregator, DecisionTreeMetadata,
   TimeTracker}
 import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
-import org.apache.spark.mllib.tree.model.{InformationGainStats, Predict}
+import org.apache.spark.mllib.tree.model.ImpurityStats
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
@@ -180,13 +180,17 @@ private[ml] object RandomForest extends Logging {
     parentUID match {
       case Some(uid) =>
         if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map(rootNode => new DecisionTreeClassificationModel(uid, rootNode.toNode))
+          topNodes.map { rootNode =>
+            new DecisionTreeClassificationModel(uid, rootNode.toNode, strategy.getNumClasses)
+          }
         } else {
           topNodes.map(rootNode => new DecisionTreeRegressionModel(uid, rootNode.toNode))
         }
       case None =>
         if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map(rootNode => new DecisionTreeClassificationModel(rootNode.toNode))
+          topNodes.map { rootNode =>
+            new DecisionTreeClassificationModel(rootNode.toNode, strategy.getNumClasses)
+          }
         } else {
           topNodes.map(rootNode => new DecisionTreeRegressionModel(rootNode.toNode))
         }
@@ -549,9 +553,9 @@ private[ml] object RandomForest extends Logging {
         }
 
         // find best split for each node
-        val (split: Split, stats: InformationGainStats, predict: Predict) =
+        val (split: Split, stats: ImpurityStats) =
           binsToBestSplit(aggStats, splits, featuresForNode, nodes(nodeIndex))
-        (nodeIndex, (split, stats, predict))
+        (nodeIndex, (split, stats))
     }.collectAsMap()
 
     timer.stop("chooseSplits")
@@ -568,17 +572,15 @@ private[ml] object RandomForest extends Logging {
         val nodeIndex = node.id
         val nodeInfo = treeToNodeToIndexInfo(treeIndex)(nodeIndex)
         val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val (split: Split, stats: InformationGainStats, predict: Predict) =
+        val (split: Split, stats: ImpurityStats) =
           nodeToBestSplits(aggNodeIndex)
         logDebug("best split = " + split)
 
         // Extract info for this node.  Create children if not leaf.
         val isLeaf =
           (stats.gain <= 0) || (LearningNode.indexToLevel(nodeIndex) == metadata.maxDepth)
-        node.predictionStats = predict
         node.isLeaf = isLeaf
-        node.stats = Some(stats)
-        node.impurity = stats.impurity
+        node.stats = stats
         logDebug("Node = " + node)
 
         if (!isLeaf) {
@@ -587,9 +589,9 @@ private[ml] object RandomForest extends Logging {
           val leftChildIsLeaf = childIsLeaf || (stats.leftImpurity == 0.0)
           val rightChildIsLeaf = childIsLeaf || (stats.rightImpurity == 0.0)
           node.leftChild = Some(LearningNode(LearningNode.leftChildIndex(nodeIndex),
-            stats.leftPredict, stats.leftImpurity, leftChildIsLeaf))
+            leftChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.leftImpurityCalculator)))
           node.rightChild = Some(LearningNode(LearningNode.rightChildIndex(nodeIndex),
-            stats.rightPredict, stats.rightImpurity, rightChildIsLeaf))
+            rightChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.rightImpurityCalculator)))
 
           if (nodeIdCache.nonEmpty) {
             val nodeIndexUpdater = NodeIndexUpdater(
@@ -621,28 +623,44 @@ private[ml] object RandomForest extends Logging {
   }
 
   /**
-   * Calculate the information gain for a given (feature, split) based upon left/right aggregates.
+   * Calculate the impurity statistics for a give (feature, split) based upon left/right aggregates.
+   * @param stats the recycle impurity statistics for this feature's all splits,
+   *              only 'impurity' and 'impurityCalculator' are valid between each iteration
    * @param leftImpurityCalculator left node aggregates for this (feature, split)
    * @param rightImpurityCalculator right node aggregate for this (feature, split)
-   * @return information gain and statistics for split
+   * @param metadata learning and dataset metadata for DecisionTree
+   * @return Impurity statistics for this (feature, split)
    */
-  private def calculateGainForSplit(
+  private def calculateImpurityStats(
+      stats: ImpurityStats,
       leftImpurityCalculator: ImpurityCalculator,
       rightImpurityCalculator: ImpurityCalculator,
-      metadata: DecisionTreeMetadata,
-      impurity: Double): InformationGainStats = {
+      metadata: DecisionTreeMetadata): ImpurityStats = {
+
+    val parentImpurityCalculator: ImpurityCalculator = if (stats == null) {
+      leftImpurityCalculator.copy.add(rightImpurityCalculator)
+    } else {
+      stats.impurityCalculator
+    }
+
+    val impurity: Double = if (stats == null) {
+      parentImpurityCalculator.calculate()
+    } else {
+      stats.impurity
+    }
+
     val leftCount = leftImpurityCalculator.count
     val rightCount = rightImpurityCalculator.count
 
+    val totalCount = leftCount + rightCount
+
     // If left child or right child doesn't satisfy minimum instances per node,
     // then this split is invalid, return invalid information gain stats.
     if ((leftCount < metadata.minInstancesPerNode) ||
       (rightCount < metadata.minInstancesPerNode)) {
-      return InformationGainStats.invalidInformationGainStats
+      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
     }
 
-    val totalCount = leftCount + rightCount
-
     val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
     val rightImpurity = rightImpurityCalculator.calculate()
 
@@ -654,39 +672,11 @@ private[ml] object RandomForest extends Logging {
     // if information gain doesn't satisfy minimum information gain,
     // then this split is invalid, return invalid information gain stats.
     if (gain < metadata.minInfoGain) {
-      return InformationGainStats.invalidInformationGainStats
+      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
     }
 
-    // calculate left and right predict
-    val leftPredict = calculatePredict(leftImpurityCalculator)
-    val rightPredict = calculatePredict(rightImpurityCalculator)
-
-    new InformationGainStats(gain, impurity, leftImpurity, rightImpurity,
-      leftPredict, rightPredict)
-  }
-
-  private def calculatePredict(impurityCalculator: ImpurityCalculator): Predict = {
-    val predict = impurityCalculator.predict
-    val prob = impurityCalculator.prob(predict)
-    new Predict(predict, prob)
-  }
-
-  /**
-   * Calculate predict value for current node, given stats of any split.
-   * Note that this function is called only once for each node.
-   * @param leftImpurityCalculator left node aggregates for a split
-   * @param rightImpurityCalculator right node aggregates for a split
-   * @return predict value and impurity for current node
-   */
-  private def calculatePredictImpurity(
-      leftImpurityCalculator: ImpurityCalculator,
-      rightImpurityCalculator: ImpurityCalculator): (Predict, Double) = {
-    val parentNodeAgg = leftImpurityCalculator.copy
-    parentNodeAgg.add(rightImpurityCalculator)
-    val predict = calculatePredict(parentNodeAgg)
-    val impurity = parentNodeAgg.calculate()
-
-    (predict, impurity)
+    new ImpurityStats(gain, impurity, parentImpurityCalculator,
+      leftImpurityCalculator, rightImpurityCalculator)
   }
 
   /**
@@ -698,14 +688,14 @@ private[ml] object RandomForest extends Logging {
       binAggregates: DTStatsAggregator,
       splits: Array[Array[Split]],
       featuresForNode: Option[Array[Int]],
-      node: LearningNode): (Split, InformationGainStats, Predict) = {
+      node: LearningNode): (Split, ImpurityStats) = {
 
-    // Calculate prediction and impurity if current node is top node
+    // Calculate InformationGain and ImpurityStats if current node is top node
     val level = LearningNode.indexToLevel(node.id)
-    var predictionAndImpurity: Option[(Predict, Double)] = if (level == 0) {
-      None
+    var gainAndImpurityStats: ImpurityStats = if (level ==0) {
+      null
     } else {
-      Some((node.predictionStats, node.impurity))
+      node.stats
     }
 
     // For each (feature, split), calculate the gain, and select the best (feature, split).
@@ -734,11 +724,9 @@ private[ml] object RandomForest extends Logging {
               val rightChildStats =
                 binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
               rightChildStats.subtract(leftChildStats)
-              predictionAndImpurity = Some(predictionAndImpurity.getOrElse(
-                calculatePredictImpurity(leftChildStats, rightChildStats)))
-              val gainStats = calculateGainForSplit(leftChildStats,
-                rightChildStats, binAggregates.metadata, predictionAndImpurity.get._2)
-              (splitIdx, gainStats)
+              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
+                leftChildStats, rightChildStats, binAggregates.metadata)
+              (splitIdx, gainAndImpurityStats)
             }.maxBy(_._2.gain)
           (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
         } else if (binAggregates.metadata.isUnordered(featureIndex)) {
@@ -750,11 +738,9 @@ private[ml] object RandomForest extends Logging {
               val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
               val rightChildStats =
                 binAggregates.getImpurityCalculator(rightChildOffset, splitIndex)
-              predictionAndImpurity = Some(predictionAndImpurity.getOrElse(
-                calculatePredictImpurity(leftChildStats, rightChildStats)))
-              val gainStats = calculateGainForSplit(leftChildStats,
-                rightChildStats, binAggregates.metadata, predictionAndImpurity.get._2)
-              (splitIndex, gainStats)
+              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
+                leftChildStats, rightChildStats, binAggregates.metadata)
+              (splitIndex, gainAndImpurityStats)
             }.maxBy(_._2.gain)
           (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
         } else {
@@ -825,11 +811,9 @@ private[ml] object RandomForest extends Logging {
               val rightChildStats =
                 binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
               rightChildStats.subtract(leftChildStats)
-              predictionAndImpurity = Some(predictionAndImpurity.getOrElse(
-                calculatePredictImpurity(leftChildStats, rightChildStats)))
-              val gainStats = calculateGainForSplit(leftChildStats,
-                rightChildStats, binAggregates.metadata, predictionAndImpurity.get._2)
-              (splitIndex, gainStats)
+              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
+                leftChildStats, rightChildStats, binAggregates.metadata)
+              (splitIndex, gainAndImpurityStats)
             }.maxBy(_._2.gain)
           val categoriesForSplit =
             categoriesSortedByCentroid.map(_._1.toDouble).slice(0, bestFeatureSplitIndex + 1)
@@ -839,7 +823,7 @@ private[ml] object RandomForest extends Logging {
         }
       }.maxBy(_._2.gain)
 
-    (bestSplit, bestSplitStats, predictionAndImpurity.get._1)
+    (bestSplit, bestSplitStats)
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
index 5ac10f3fd32dd..0768204c33914 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -118,7 +118,7 @@ private[tree] class EntropyAggregator(numClasses: Int)
  * (node, feature, bin).
  * @param stats  Array of sufficient statistics for a (node, feature, bin).
  */
-private[tree] class EntropyCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
+private[spark] class EntropyCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
 
   /**
    * Make a deep copy of this [[ImpurityCalculator]].
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
index 19d318203c344..d0077db6832e3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -114,7 +114,7 @@ private[tree] class GiniAggregator(numClasses: Int)
  * (node, feature, bin).
  * @param stats  Array of sufficient statistics for a (node, feature, bin).
  */
-private[tree] class GiniCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
+private[spark] class GiniCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
 
   /**
    * Make a deep copy of this [[ImpurityCalculator]].
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index 578749d85a4e6..86cee7e430b0a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -95,7 +95,7 @@ private[spark] abstract class ImpurityAggregator(val statsSize: Int) extends Ser
  * (node, feature, bin).
  * @param stats  Array of sufficient statistics for a (node, feature, bin).
  */
-private[spark] abstract class ImpurityCalculator(val stats: Array[Double]) {
+private[spark] abstract class ImpurityCalculator(val stats: Array[Double]) extends Serializable {
 
   /**
    * Make a deep copy of this [[ImpurityCalculator]].
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
index 7104a7fa4dd4c..04d0cd24e6632 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -98,7 +98,7 @@ private[tree] class VarianceAggregator()
  * (node, feature, bin).
  * @param stats  Array of sufficient statistics for a (node, feature, bin).
  */
-private[tree] class VarianceCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
+private[spark] class VarianceCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
 
   require(stats.size == 3,
     s"VarianceCalculator requires sufficient statistics array stats to be of length 3," +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index dc9e0f9f51ffb..508bf9c1bdb47 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.tree.model
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 
 /**
  * :: DeveloperApi ::
@@ -66,7 +67,6 @@ class InformationGainStats(
   }
 }
 
-
 private[spark] object InformationGainStats {
   /**
    * An [[org.apache.spark.mllib.tree.model.InformationGainStats]] object to
@@ -76,3 +76,62 @@ private[spark] object InformationGainStats {
   val invalidInformationGainStats = new InformationGainStats(Double.MinValue, -1.0, -1.0, -1.0,
     new Predict(0.0, 0.0), new Predict(0.0, 0.0))
 }
+
+/**
+ * :: DeveloperApi ::
+ * Impurity statistics for each split
+ * @param gain information gain value
+ * @param impurity current node impurity
+ * @param impurityCalculator impurity statistics for current node
+ * @param leftImpurityCalculator impurity statistics for left child node
+ * @param rightImpurityCalculator impurity statistics for right child node
+ * @param valid whether the current split satisfies minimum info gain or
+ *              minimum number of instances per node
+ */
+@DeveloperApi
+private[spark] class ImpurityStats(
+    val gain: Double,
+    val impurity: Double,
+    val impurityCalculator: ImpurityCalculator,
+    val leftImpurityCalculator: ImpurityCalculator,
+    val rightImpurityCalculator: ImpurityCalculator,
+    val valid: Boolean = true) extends Serializable {
+
+  override def toString: String = {
+    s"gain = $gain, impurity = $impurity, left impurity = $leftImpurity, " +
+      s"right impurity = $rightImpurity"
+  }
+
+  def leftImpurity: Double = if (leftImpurityCalculator != null) {
+    leftImpurityCalculator.calculate()
+  } else {
+    -1.0
+  }
+
+  def rightImpurity: Double = if (rightImpurityCalculator != null) {
+    rightImpurityCalculator.calculate()
+  } else {
+    -1.0
+  }
+}
+
+private[spark] object ImpurityStats {
+
+  /**
+   * Return an [[org.apache.spark.mllib.tree.model.ImpurityStats]] object to
+   * denote that current split doesn't satisfies minimum info gain or
+   * minimum number of instances per node.
+   */
+  def getInvalidImpurityStats(impurityCalculator: ImpurityCalculator): ImpurityStats = {
+    new ImpurityStats(Double.MinValue, impurityCalculator.calculate(),
+      impurityCalculator, null, null, false)
+  }
+
+  /**
+   * Return an [[org.apache.spark.mllib.tree.model.ImpurityStats]] object
+   * that only 'impurity' and 'impurityCalculator' are defined.
+   */
+  def getEmptyImpurityStats(impurityCalculator: ImpurityCalculator): ImpurityStats = {
+    new ImpurityStats(Double.NaN, impurityCalculator.calculate(), impurityCalculator, null, null)
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
index 73b4805c4c597..c7bbf1ce07a23 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
@@ -21,12 +21,13 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.LeafNode
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, DecisionTreeSuite => OldDecisionTreeSuite}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.Row
 
 class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
 
@@ -57,7 +58,7 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
 
   test("params") {
     ParamsSuite.checkParams(new DecisionTreeClassifier)
-    val model = new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0))
+    val model = new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2)
     ParamsSuite.checkParams(model)
   }
 
@@ -231,6 +232,31 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses)
   }
 
+  test("predictRaw and predictProbability") {
+    val rdd = continuousDataPointsForMulticlassRDD
+    val dt = new DecisionTreeClassifier()
+      .setImpurity("Gini")
+      .setMaxDepth(4)
+      .setMaxBins(100)
+    val categoricalFeatures = Map(0 -> 3)
+    val numClasses = 3
+
+    val newData: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
+    val newTree = dt.fit(newData)
+
+    val predictions = newTree.transform(newData)
+      .select(newTree.getPredictionCol, newTree.getRawPredictionCol, newTree.getProbabilityCol)
+      .collect()
+
+    predictions.foreach { case Row(pred: Double, rawPred: Vector, probPred: Vector) =>
+      assert(pred === rawPred.argmax,
+        s"Expected prediction $pred but calculated ${rawPred.argmax} from rawPrediction.")
+      val sum = rawPred.toArray.sum
+      assert(Vectors.dense(rawPred.toArray.map(_ / sum)) === probPred,
+        "probability prediction mismatch")
+    }
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index a7bc77965fefd..d4b5896c12c06 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -58,7 +58,7 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
   test("params") {
     ParamsSuite.checkParams(new GBTClassifier)
     val model = new GBTClassificationModel("gbtc",
-      Array(new DecisionTreeRegressionModel("dtr", new LeafNode(0.0, 0.0))),
+      Array(new DecisionTreeRegressionModel("dtr", new LeafNode(0.0, 0.0, null))),
       Array(1.0))
     ParamsSuite.checkParams(model)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index ab711c8e4b215..dbb2577c6204d 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -66,7 +66,7 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
   test("params") {
     ParamsSuite.checkParams(new RandomForestClassifier)
     val model = new RandomForestClassificationModel("rfc",
-      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0))), 2)
+      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2)), 2)
     ParamsSuite.checkParams(model)
   }
 

From 0a1d2ca42c8b31d6b0e70163795f0185d4622f87 Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Fri, 31 Jul 2015 12:04:03 -0700
Subject: [PATCH 0740/1454] [SPARK-8979] Add a PID based rate estimator
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Based on #7600

/cc tdas

Author: Iulian Dragos <jaguarul@gmail.com>
Author: François Garillot <francois@garillot.net>

Closes #7648 from dragos/topic/streaming-bp/pid and squashes the following commits:

aa5b097 [Iulian Dragos] Add more comments, made all PID constant parameters positive, a couple more tests.
93b74f8 [Iulian Dragos] Better explanation of historicalError.
7975b0c [Iulian Dragos] Add configuration for PID.
26cfd78 [Iulian Dragos] A couple of variable renames.
d0bdf7c [Iulian Dragos] Update to latest version of the code, various style and name improvements.
d58b845 [François Garillot] [SPARK-8979][Streaming] Implements a PIDRateEstimator
---
 .../dstream/ReceiverInputDStream.scala        |   2 +-
 .../scheduler/rate/PIDRateEstimator.scala     | 124 ++++++++++++++++
 .../scheduler/rate/RateEstimator.scala        |  18 ++-
 .../rate/PIDRateEstimatorSuite.scala          | 137 ++++++++++++++++++
 4 files changed, 276 insertions(+), 5 deletions(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index 646a8c3530a62..670ef8d296a0b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -46,7 +46,7 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
    */
   override protected[streaming] val rateController: Option[RateController] = {
     if (RateController.isBackPressureEnabled(ssc.conf)) {
-      RateEstimator.create(ssc.conf).map { new ReceiverRateController(id, _) }
+      Some(new ReceiverRateController(id, RateEstimator.create(ssc.conf, ssc.graph.batchDuration)))
     } else {
       None
     }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
new file mode 100644
index 0000000000000..6ae56a68ad88c
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
@@ -0,0 +1,124 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler.rate
+
+/**
+ * Implements a proportional-integral-derivative (PID) controller which acts on
+ * the speed of ingestion of elements into Spark Streaming. A PID controller works
+ * by calculating an '''error''' between a measured output and a desired value. In the
+ * case of Spark Streaming the error is the difference between the measured processing
+ * rate (number of elements/processing delay) and the previous rate.
+ *
+ * @see https://en.wikipedia.org/wiki/PID_controller
+ *
+ * @param batchDurationMillis the batch duration, in milliseconds
+ * @param proportional how much the correction should depend on the current
+ *        error. This term usually provides the bulk of correction and should be positive or zero.
+ *        A value too large would make the controller overshoot the setpoint, while a small value
+ *        would make the controller too insensitive. The default value is 1.
+ * @param integral how much the correction should depend on the accumulation
+ *        of past errors. This value should be positive or 0. This term accelerates the movement
+ *        towards the desired value, but a large value may lead to overshooting. The default value
+ *        is 0.2.
+ * @param derivative how much the correction should depend on a prediction
+ *        of future errors, based on current rate of change. This value should be positive or 0.
+ *        This term is not used very often, as it impacts stability of the system. The default
+ *        value is 0.
+ */
+private[streaming] class PIDRateEstimator(
+    batchIntervalMillis: Long,
+    proportional: Double = 1D,
+    integral: Double = .2D,
+    derivative: Double = 0D)
+  extends RateEstimator {
+
+  private var firstRun: Boolean = true
+  private var latestTime: Long = -1L
+  private var latestRate: Double = -1D
+  private var latestError: Double = -1L
+
+  require(
+    batchIntervalMillis > 0,
+    s"Specified batch interval $batchIntervalMillis in PIDRateEstimator is invalid.")
+  require(
+    proportional >= 0,
+    s"Proportional term $proportional in PIDRateEstimator should be >= 0.")
+  require(
+    integral >= 0,
+    s"Integral term $integral in PIDRateEstimator should be >= 0.")
+  require(
+    derivative >= 0,
+    s"Derivative term $derivative in PIDRateEstimator should be >= 0.")
+
+
+  def compute(time: Long, // in milliseconds
+      numElements: Long,
+      processingDelay: Long, // in milliseconds
+      schedulingDelay: Long // in milliseconds
+    ): Option[Double] = {
+
+    this.synchronized {
+      if (time > latestTime && processingDelay > 0 && batchIntervalMillis > 0) {
+
+        // in seconds, should be close to batchDuration
+        val delaySinceUpdate = (time - latestTime).toDouble / 1000
+
+        // in elements/second
+        val processingRate = numElements.toDouble / processingDelay * 1000
+
+        // In our system `error` is the difference between the desired rate and the measured rate
+        // based on the latest batch information. We consider the desired rate to be latest rate,
+        // which is what this estimator calculated for the previous batch.
+        // in elements/second
+        val error = latestRate - processingRate
+
+        // The error integral, based on schedulingDelay as an indicator for accumulated errors.
+        // A scheduling delay s corresponds to s * processingRate overflowing elements. Those
+        // are elements that couldn't be processed in previous batches, leading to this delay.
+        // In the following, we assume the processingRate didn't change too much.
+        // From the number of overflowing elements we can calculate the rate at which they would be
+        // processed by dividing it by the batch interval. This rate is our "historical" error,
+        // or integral part, since if we subtracted this rate from the previous "calculated rate",
+        // there wouldn't have been any overflowing elements, and the scheduling delay would have
+        // been zero.
+        // (in elements/second)
+        val historicalError = schedulingDelay.toDouble * processingRate / batchIntervalMillis
+
+        // in elements/(second ^ 2)
+        val dError = (error - latestError) / delaySinceUpdate
+
+        val newRate = (latestRate - proportional * error -
+                                    integral * historicalError -
+                                    derivative * dError).max(0.0)
+        latestTime = time
+        if (firstRun) {
+          latestRate = processingRate
+          latestError = 0D
+          firstRun = false
+
+          None
+        } else {
+          latestRate = newRate
+          latestError = error
+
+          Some(newRate)
+        }
+      } else None
+    }
+  }
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
index a08685119e5d5..17ccebc1ed41b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
@@ -19,6 +19,7 @@ package org.apache.spark.streaming.scheduler.rate
 
 import org.apache.spark.SparkConf
 import org.apache.spark.SparkException
+import org.apache.spark.streaming.Duration
 
 /**
  * A component that estimates the rate at wich an InputDStream should ingest
@@ -48,12 +49,21 @@ object RateEstimator {
   /**
    * Return a new RateEstimator based on the value of `spark.streaming.RateEstimator`.
    *
-   * @return None if there is no configured estimator, otherwise an instance of RateEstimator
+   * The only known estimator right now is `pid`.
+   *
+   * @return An instance of RateEstimator
    * @throws IllegalArgumentException if there is a configured RateEstimator that doesn't match any
    *         known estimators.
    */
-  def create(conf: SparkConf): Option[RateEstimator] =
-    conf.getOption("spark.streaming.backpressure.rateEstimator").map { estimator =>
-      throw new IllegalArgumentException(s"Unkown rate estimator: $estimator")
+  def create(conf: SparkConf, batchInterval: Duration): RateEstimator =
+    conf.get("spark.streaming.backpressure.rateEstimator", "pid") match {
+      case "pid" =>
+        val proportional = conf.getDouble("spark.streaming.backpressure.pid.proportional", 1.0)
+        val integral = conf.getDouble("spark.streaming.backpressure.pid.integral", 0.2)
+        val derived = conf.getDouble("spark.streaming.backpressure.pid.derived", 0.0)
+        new PIDRateEstimator(batchInterval.milliseconds, proportional, integral, derived)
+
+      case estimator =>
+        throw new IllegalArgumentException(s"Unkown rate estimator: $estimator")
     }
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
new file mode 100644
index 0000000000000..97c32d8f2d59e
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
@@ -0,0 +1,137 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.scheduler.rate
+
+import scala.util.Random
+
+import org.scalatest.Inspectors.forAll
+import org.scalatest.Matchers
+
+import org.apache.spark.{SparkConf, SparkFunSuite}
+import org.apache.spark.streaming.Seconds
+
+class PIDRateEstimatorSuite extends SparkFunSuite with Matchers {
+
+  test("the right estimator is created") {
+    val conf = new SparkConf
+    conf.set("spark.streaming.backpressure.rateEstimator", "pid")
+    val pid = RateEstimator.create(conf, Seconds(1))
+    pid.getClass should equal(classOf[PIDRateEstimator])
+  }
+
+  test("estimator checks ranges") {
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(0, 1, 2, 3)
+    }
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(100, -1, 2, 3)
+    }
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(100, 0, -1, 3)
+    }
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(100, 0, 0, -1)
+    }
+  }
+
+  private def createDefaultEstimator: PIDRateEstimator = {
+    new PIDRateEstimator(20, 1D, 0D, 0D)
+  }
+
+  test("first bound is None") {
+    val p = createDefaultEstimator
+    p.compute(0, 10, 10, 0) should equal(None)
+  }
+
+  test("second bound is rate") {
+    val p = createDefaultEstimator
+    p.compute(0, 10, 10, 0)
+    // 1000 elements / s
+    p.compute(10, 10, 10, 0) should equal(Some(1000))
+  }
+
+  test("works even with no time between updates") {
+    val p = createDefaultEstimator
+    p.compute(0, 10, 10, 0)
+    p.compute(10, 10, 10, 0)
+    p.compute(10, 10, 10, 0) should equal(None)
+  }
+
+  test("bound is never negative") {
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+    // prepare a series of batch updates, one every 20ms, 0 processed elements, 2ms of processing
+    // this might point the estimator to try and decrease the bound, but we test it never
+    // goes below zero, which would be nonsensical.
+    val times = List.tabulate(50)(x => x * 20) // every 20ms
+    val elements = List.fill(50)(0) // no processing
+    val proc = List.fill(50)(20) // 20ms of processing
+    val sched = List.fill(50)(100) // strictly positive accumulation
+    val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
+    res.head should equal(None)
+    res.tail should equal(List.fill(49)(Some(0D)))
+  }
+
+  test("with no accumulated or positive error, |I| > 0, follow the processing speed") {
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+    // prepare a series of batch updates, one every 20ms with an increasing number of processed
+    // elements in each batch, but constant processing time, and no accumulated error. Even though
+    // the integral part is non-zero, the estimated rate should follow only the proportional term
+    val times = List.tabulate(50)(x => x * 20) // every 20ms
+    val elements = List.tabulate(50)(x => x * 20) // increasing
+    val proc = List.fill(50)(20) // 20ms of processing
+    val sched = List.fill(50)(0)
+    val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
+    res.head should equal(None)
+    res.tail should equal(List.tabulate(50)(x => Some(x * 1000D)).tail)
+  }
+
+  test("with no accumulated but some positive error, |I| > 0, follow the processing speed") {
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+    // prepare a series of batch updates, one every 20ms with an decreasing number of processed
+    // elements in each batch, but constant processing time, and no accumulated error. Even though
+    // the integral part is non-zero, the estimated rate should follow only the proportional term,
+    // asking for less and less elements
+    val times = List.tabulate(50)(x => x * 20) // every 20ms
+    val elements = List.tabulate(50)(x => (50 - x) * 20) // decreasing
+    val proc = List.fill(50)(20) // 20ms of processing
+    val sched = List.fill(50)(0)
+    val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
+    res.head should equal(None)
+    res.tail should equal(List.tabulate(50)(x => Some((50 - x) * 1000D)).tail)
+  }
+
+  test("with some accumulated and some positive error, |I| > 0, stay below the processing speed") {
+    val p = new PIDRateEstimator(20, 1D, .01D, 0D)
+    val times = List.tabulate(50)(x => x * 20) // every 20ms
+    val rng = new Random()
+    val elements = List.tabulate(50)(x => rng.nextInt(1000))
+    val procDelayMs = 20
+    val proc = List.fill(50)(procDelayMs) // 20ms of processing
+    val sched = List.tabulate(50)(x => rng.nextInt(19)) // random wait
+    val speeds = elements map ((x) => x.toDouble / procDelayMs * 1000)
+
+    val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
+    res.head should equal(None)
+    forAll(List.range(1, 50)) { (n) =>
+      res(n) should not be None
+      if (res(n).get > 0 && sched(n) > 0) {
+        res(n).get should be < speeds(n)
+      }
+    }
+  }
+}

From 39ab199a3f735b7658ab3331d3e2fb03441aec13 Mon Sep 17 00:00:00 2001
From: Herman van Hovell <hvanhovell@questtec.nl>
Date: Fri, 31 Jul 2015 12:07:18 -0700
Subject: [PATCH 0741/1454] [SPARK-8640] [SQL] Enable Processing of Multiple
 Window Frames in a Single Window Operator

This PR enables the processing of multiple window frames in a single window operator. This should improve the performance of processing multiple window expressions wich share partition by/order by clauses, because it will be more efficient with respect to memory use and group processing.

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #7515 from hvanhovell/SPARK-8640 and squashes the following commits:

f0e1c21 [Herman van Hovell] Changed Window Logical/Physical plans to use partition by/order by specs directly instead of using WindowSpec.
e1711c2 [Herman van Hovell] Enabled the processing of multiple window frames in a single Window operator.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 12 +++++++-----
 .../plans/logical/basicOperators.scala        |  3 ++-
 .../spark/sql/execution/SparkStrategies.scala |  5 +++--
 .../apache/spark/sql/execution/Window.scala   | 19 ++++++++++---------
 .../sql/hive/execution/HivePlanTest.scala     | 18 ++++++++++++++++++
 5 files changed, 40 insertions(+), 17 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 265f3d1e41765..51d910b258647 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -347,7 +347,7 @@ class Analyzer(
             val newOutput = oldVersion.generatorOutput.map(_.newInstance())
             (oldVersion, oldVersion.copy(generatorOutput = newOutput))
 
-          case oldVersion @ Window(_, windowExpressions, _, child)
+          case oldVersion @ Window(_, windowExpressions, _, _, child)
               if AttributeSet(windowExpressions.map(_.toAttribute)).intersect(conflictingAttributes)
                 .nonEmpty =>
             (oldVersion, oldVersion.copy(windowExpressions = newAliases(windowExpressions)))
@@ -825,7 +825,7 @@ class Analyzer(
         }.asInstanceOf[NamedExpression]
       }
 
-      // Second, we group extractedWindowExprBuffer based on their Window Spec.
+      // Second, we group extractedWindowExprBuffer based on their Partition and Order Specs.
       val groupedWindowExpressions = extractedWindowExprBuffer.groupBy { expr =>
         val distinctWindowSpec = expr.collect {
           case window: WindowExpression => window.windowSpec
@@ -841,7 +841,8 @@ class Analyzer(
           failAnalysis(s"$expr has multiple Window Specifications ($distinctWindowSpec)." +
             s"Please file a bug report with this error message, stack trace, and the query.")
         } else {
-          distinctWindowSpec.head
+          val spec = distinctWindowSpec.head
+          (spec.partitionSpec, spec.orderSpec)
         }
       }.toSeq
 
@@ -850,9 +851,10 @@ class Analyzer(
       var currentChild = child
       var i = 0
       while (i < groupedWindowExpressions.size) {
-        val (windowSpec, windowExpressions) = groupedWindowExpressions(i)
+        val ((partitionSpec, orderSpec), windowExpressions) = groupedWindowExpressions(i)
         // Set currentChild to the newly created Window operator.
-        currentChild = Window(currentChild.output, windowExpressions, windowSpec, currentChild)
+        currentChild = Window(currentChild.output, windowExpressions,
+          partitionSpec, orderSpec, currentChild)
 
         // Move to next Window Spec.
         i += 1
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index a67f8de6b733a..aacfc86ab0e49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -228,7 +228,8 @@ case class Aggregate(
 case class Window(
     projectList: Seq[Attribute],
     windowExpressions: Seq[NamedExpression],
-    windowSpec: WindowSpecDefinition,
+    partitionSpec: Seq[Expression],
+    orderSpec: Seq[SortOrder],
     child: LogicalPlan) extends UnaryNode {
 
   override def output: Seq[Attribute] =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 03d24a88d4ecd..4aff52d992e6b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -389,8 +389,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
         }
       }
-      case logical.Window(projectList, windowExpressions, spec, child) =>
-        execution.Window(projectList, windowExpressions, spec, planLater(child)) :: Nil
+      case logical.Window(projectList, windowExprs, partitionSpec, orderSpec, child) =>
+        execution.Window(
+          projectList, windowExprs, partitionSpec, orderSpec, planLater(child)) :: Nil
       case logical.Sample(lb, ub, withReplacement, seed, child) =>
         execution.Sample(lb, ub, withReplacement, seed, planLater(child)) :: Nil
       case logical.LocalRelation(output, data) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
index 91c8a02e2b5bc..fe9f2c7028171 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -80,23 +80,24 @@ import scala.collection.mutable
 case class Window(
     projectList: Seq[Attribute],
     windowExpression: Seq[NamedExpression],
-    windowSpec: WindowSpecDefinition,
+    partitionSpec: Seq[Expression],
+    orderSpec: Seq[SortOrder],
     child: SparkPlan)
   extends UnaryNode {
 
   override def output: Seq[Attribute] = projectList ++ windowExpression.map(_.toAttribute)
 
   override def requiredChildDistribution: Seq[Distribution] = {
-    if (windowSpec.partitionSpec.isEmpty) {
+    if (partitionSpec.isEmpty) {
       // Only show warning when the number of bytes is larger than 100 MB?
       logWarning("No Partition Defined for Window operation! Moving all data to a single "
         + "partition, this can cause serious performance degradation.")
       AllTuples :: Nil
-    } else ClusteredDistribution(windowSpec.partitionSpec) :: Nil
+    } else ClusteredDistribution(partitionSpec) :: Nil
   }
 
   override def requiredChildOrdering: Seq[Seq[SortOrder]] =
-    Seq(windowSpec.partitionSpec.map(SortOrder(_, Ascending)) ++ windowSpec.orderSpec)
+    Seq(partitionSpec.map(SortOrder(_, Ascending)) ++ orderSpec)
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
 
@@ -115,12 +116,12 @@ case class Window(
       case RangeFrame =>
         val (exprs, current, bound) = if (offset == 0) {
           // Use the entire order expression when the offset is 0.
-          val exprs = windowSpec.orderSpec.map(_.child)
+          val exprs = orderSpec.map(_.child)
           val projection = newMutableProjection(exprs, child.output)
-          (windowSpec.orderSpec, projection(), projection())
-        } else if (windowSpec.orderSpec.size == 1) {
+          (orderSpec, projection(), projection())
+        } else if (orderSpec.size == 1) {
           // Use only the first order expression when the offset is non-null.
-          val sortExpr = windowSpec.orderSpec.head
+          val sortExpr = orderSpec.head
           val expr = sortExpr.child
           // Create the projection which returns the current 'value'.
           val current = newMutableProjection(expr :: Nil, child.output)()
@@ -250,7 +251,7 @@ case class Window(
 
         // Get all relevant projections.
         val result = createResultProjection(unboundExpressions)
-        val grouping = newProjection(windowSpec.partitionSpec, child.output)
+        val grouping = newProjection(partitionSpec, child.output)
 
         // Manage the stream and the grouping.
         var nextRow: InternalRow = EmptyRow
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
index bdb53ddf59c19..ba56a8a6b689c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
@@ -17,7 +17,10 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.catalyst.plans.logical
+import org.apache.spark.sql.expressions.Window
 import org.apache.spark.sql.hive.test.TestHive
 
 class HivePlanTest extends QueryTest {
@@ -31,4 +34,19 @@ class HivePlanTest extends QueryTest {
 
     comparePlans(optimized, correctAnswer)
   }
+
+  test("window expressions sharing the same partition by and order by clause") {
+    val df = Seq.empty[(Int, String, Int, Int)].toDF("id", "grp", "seq", "val")
+    val window = Window.
+      partitionBy($"grp").
+      orderBy($"val")
+    val query = df.select(
+      $"id",
+      sum($"val").over(window.rowsBetween(-1, 1)),
+      sum($"val").over(window.rangeBetween(-1, 1))
+    )
+    val plan = query.queryExecution.analyzed
+    assert(plan.collect{ case w: logical.Window => w }.size === 1,
+      "Should have only 1 Window operator.")
+  }
 }

From 3afc1de89cb4de9f8ea74003dd1e6b5b006d06f0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 31 Jul 2015 12:09:48 -0700
Subject: [PATCH 0742/1454] [SPARK-8564] [STREAMING] Add the Python API for
 Kinesis

This PR adds the Python API for Kinesis, including a Python example and a simple unit test.

Author: zsxwing <zsxwing@gmail.com>

Closes #6955 from zsxwing/kinesis-python and squashes the following commits:

e42e471 [zsxwing] Merge branch 'master' into kinesis-python
455f7ea [zsxwing] Remove streaming_kinesis_asl_assembly module and simply add the source folder to streaming_kinesis_asl module
32e6451 [zsxwing] Merge remote-tracking branch 'origin/master' into kinesis-python
5082d28 [zsxwing] Fix the syntax error for Python 2.6
fca416b [zsxwing] Fix wrong comparison
96670ff [zsxwing] Fix the compilation error after merging master
756a128 [zsxwing] Merge branch 'master' into kinesis-python
6c37395 [zsxwing] Print stack trace for debug
7c5cfb0 [zsxwing] RUN_KINESIS_TESTS -> ENABLE_KINESIS_TESTS
cc9d071 [zsxwing] Fix the python test errors
466b425 [zsxwing] Add python tests for Kinesis
e33d505 [zsxwing] Merge remote-tracking branch 'origin/master' into kinesis-python
3da2601 [zsxwing] Fix the kinesis folder
687446b [zsxwing] Fix the error message and the maven output path
add2beb [zsxwing] Merge branch 'master' into kinesis-python
4957c0b [zsxwing] Add the Python API for Kinesis
---
 dev/run-tests.py                              |   3 +-
 dev/sparktestsupport/modules.py               |   9 +-
 docs/streaming-kinesis-integration.md         |  19 +++
 extras/kinesis-asl-assembly/pom.xml           | 103 ++++++++++++++++
 .../streaming/kinesis_wordcount_asl.py        |  81 +++++++++++++
 .../streaming/kinesis/KinesisTestUtils.scala  |  19 ++-
 .../streaming/kinesis/KinesisUtils.scala      |  78 +++++++++---
 pom.xml                                       |   1 +
 project/SparkBuild.scala                      |   6 +-
 python/pyspark/streaming/kinesis.py           | 112 ++++++++++++++++++
 python/pyspark/streaming/tests.py             |  86 +++++++++++++-
 11 files changed, 492 insertions(+), 25 deletions(-)
 create mode 100644 extras/kinesis-asl-assembly/pom.xml
 create mode 100644 extras/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py
 create mode 100644 python/pyspark/streaming/kinesis.py

diff --git a/dev/run-tests.py b/dev/run-tests.py
index 29420da9aa956..b6d181418f027 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -301,7 +301,8 @@ def build_spark_sbt(hadoop_version):
     sbt_goals = ["package",
                  "assembly/assembly",
                  "streaming-kafka-assembly/assembly",
-                 "streaming-flume-assembly/assembly"]
+                 "streaming-flume-assembly/assembly",
+                 "streaming-kinesis-asl-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
     print("[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments: ",
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 44600cb9523c1..956dc81b62e93 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -138,6 +138,7 @@ def contains_file(self, filename):
     dependencies=[],
     source_file_regexes=[
         "extras/kinesis-asl/",
+        "extras/kinesis-asl-assembly/",
     ],
     build_profile_flags=[
         "-Pkinesis-asl",
@@ -300,7 +301,13 @@ def contains_file(self, filename):
 
 pyspark_streaming = Module(
     name="pyspark-streaming",
-    dependencies=[pyspark_core, streaming, streaming_kafka, streaming_flume_assembly],
+    dependencies=[
+        pyspark_core,
+        streaming,
+        streaming_kafka,
+        streaming_flume_assembly,
+        streaming_kinesis_asl
+    ],
     source_file_regexes=[
         "python/pyspark/streaming"
     ],
diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md
index aa9749afbc867..a7bcaec6fcd84 100644
--- a/docs/streaming-kinesis-integration.md
+++ b/docs/streaming-kinesis-integration.md
@@ -51,6 +51,17 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 	See the [API docs](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisUtils.html)
 	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java). Refer to the next subsection for instructions to run the example.
 
+	</div>
+	<div data-lang="python" markdown="1">
+		from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream
+
+		kinesisStream = KinesisUtils.createStream(
+			streamingContext, [Kinesis app name], [Kinesis stream name], [endpoint URL],
+			[region name], [initial position], [checkpoint interval], StorageLevel.MEMORY_AND_DISK_2)
+
+	See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.kinesis.KinesisUtils)
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py). Refer to the next subsection for instructions to run the example.
+
 	</div>
 	</div>
 
@@ -135,6 +146,14 @@ To run the example,
 
         bin/run-example streaming.JavaKinesisWordCountASL [Kinesis app name] [Kinesis stream name] [endpoint URL]
 
+	</div>
+	<div data-lang="python" markdown="1">
+
+        bin/spark-submit --jars extras/kinesis-asl/target/scala-*/\
+            spark-streaming-kinesis-asl-assembly_*.jar \
+            extras/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py \
+            [Kinesis app name] [Kinesis stream name] [endpoint URL] [region name]
+
 	</div>
 	</div>
 
diff --git a/extras/kinesis-asl-assembly/pom.xml b/extras/kinesis-asl-assembly/pom.xml
new file mode 100644
index 0000000000000..70d2c9c58f54e
--- /dev/null
+++ b/extras/kinesis-asl-assembly/pom.xml
@@ -0,0 +1,103 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent_2.10</artifactId>
+    <version>1.5.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-kinesis-asl-assembly_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project Kinesis Assembly</name>
+  <url>http://spark.apache.org/</url>
+
+  <properties>
+    <sbt.project.name>streaming-kinesis-asl-assembly</sbt.project.name>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-kinesis-asl_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <scope>provided</scope>
+    </dependency>
+  </dependencies>
+
+  <build>
+  <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+  <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+  <plugins>
+    <plugin>
+      <groupId>org.apache.maven.plugins</groupId>
+      <artifactId>maven-shade-plugin</artifactId>
+      <configuration>
+        <shadedArtifactAttached>false</shadedArtifactAttached>
+        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-kinesis-asl-assembly-${project.version}.jar</outputFile>
+        <artifactSet>
+          <includes>
+            <include>*:*</include>
+          </includes>
+        </artifactSet>
+        <filters>
+          <filter>
+            <artifact>*:*</artifact>
+            <excludes>
+              <exclude>META-INF/*.SF</exclude>
+              <exclude>META-INF/*.DSA</exclude>
+              <exclude>META-INF/*.RSA</exclude>
+            </excludes>
+          </filter>
+        </filters>
+      </configuration>
+      <executions>
+        <execution>
+          <phase>package</phase>
+          <goals>
+            <goal>shade</goal>
+          </goals>
+          <configuration>
+            <transformers>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                <resource>reference.conf</resource>
+              </transformer>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
+                <resource>log4j.properties</resource>
+              </transformer>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer"/>
+              <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/>
+            </transformers>
+          </configuration>
+        </execution>
+      </executions>
+    </plugin>
+  </plugins>
+</build>
+</project>
+
diff --git a/extras/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py b/extras/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py
new file mode 100644
index 0000000000000..f428f64da3c42
--- /dev/null
+++ b/extras/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py
@@ -0,0 +1,81 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+  Consumes messages from a Amazon Kinesis streams and does wordcount.
+
+  This example spins up 1 Kinesis Receiver per shard for the given stream.
+  It then starts pulling from the last checkpointed sequence number of the given stream.
+
+  Usage: kinesis_wordcount_asl.py <app-name> <stream-name> <endpoint-url> <region-name>
+    <app-name> is the name of the consumer app, used to track the read data in DynamoDB
+    <stream-name> name of the Kinesis stream (ie. mySparkStream)
+    <endpoint-url> endpoint of the Kinesis service
+      (e.g. https://kinesis.us-east-1.amazonaws.com)
+
+
+  Example:
+      # export AWS keys if necessary
+      $ export AWS_ACCESS_KEY_ID=<your-access-key>
+      $ export AWS_SECRET_KEY=<your-secret-key>
+
+      # run the example
+      $ bin/spark-submit -jar extras/kinesis-asl/target/scala-*/\
+        spark-streaming-kinesis-asl-assembly_*.jar \
+        extras/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py \
+        myAppName mySparkStream https://kinesis.us-east-1.amazonaws.com
+
+  There is a companion helper class called KinesisWordProducerASL which puts dummy data
+  onto the Kinesis stream.
+
+  This code uses the DefaultAWSCredentialsProviderChain to find credentials
+  in the following order:
+      Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY
+      Java System Properties - aws.accessKeyId and aws.secretKey
+      Credential profiles file - default location (~/.aws/credentials) shared by all AWS SDKs
+      Instance profile credentials - delivered through the Amazon EC2 metadata service
+  For more information, see
+      http://docs.aws.amazon.com/AWSSdkDocsJava/latest/DeveloperGuide/credentials.html
+
+  See http://spark.apache.org/docs/latest/streaming-kinesis-integration.html for more details on
+  the Kinesis Spark Streaming integration.
+"""
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream
+
+if __name__ == "__main__":
+    if len(sys.argv) != 5:
+        print(
+            "Usage: kinesis_wordcount_asl.py <app-name> <stream-name> <endpoint-url> <region-name>",
+            file=sys.stderr)
+        sys.exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingKinesisWordCountAsl")
+    ssc = StreamingContext(sc, 1)
+    appName, streamName, endpointUrl, regionName = sys.argv[1:]
+    lines = KinesisUtils.createStream(
+        ssc, appName, streamName, endpointUrl, regionName, InitialPositionInStream.LATEST, 2)
+    counts = lines.flatMap(lambda line: line.split(" ")) \
+        .map(lambda word: (word, 1)) \
+        .reduceByKey(lambda a, b: a+b)
+    counts.pprint()
+
+    ssc.start()
+    ssc.awaitTermination()
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index ca39358b75cb6..255ac27f793ba 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -36,9 +36,15 @@ import org.apache.spark.Logging
 /**
  * Shared utility methods for performing Kinesis tests that actually transfer data
  */
-private class KinesisTestUtils(
-    val endpointUrl: String = "https://kinesis.us-west-2.amazonaws.com",
-    _regionName: String = "") extends Logging {
+private class KinesisTestUtils(val endpointUrl: String, _regionName: String) extends Logging {
+
+  def this() {
+    this("https://kinesis.us-west-2.amazonaws.com", "")
+  }
+
+  def this(endpointUrl: String) {
+    this(endpointUrl, "")
+  }
 
   val regionName = if (_regionName.length == 0) {
     RegionUtils.getRegionByEndpoint(endpointUrl).getName()
@@ -117,6 +123,13 @@ private class KinesisTestUtils(
     shardIdToSeqNumbers.toMap
   }
 
+  /**
+   * Expose a Python friendly API.
+   */
+  def pushData(testData: java.util.List[Int]): Unit = {
+    pushData(scala.collection.JavaConversions.asScalaBuffer(testData))
+  }
+
   def deleteStream(): Unit = {
     try {
       if (streamCreated) {
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
index e5acab50181e1..7dab17eba8483 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
@@ -86,19 +86,19 @@ object KinesisUtils {
    * @param endpointUrl  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
    * @param regionName   Name of region used by the Kinesis Client Library (KCL) to update
    *                     DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
-   * @param awsAccessKeyId  AWS AccessKeyId (if null, will use DefaultAWSCredentialsProviderChain)
-   * @param awsSecretKey  AWS SecretKey (if null, will use DefaultAWSCredentialsProviderChain)
-   * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
-   *                            See the Kinesis Spark Streaming documentation for more
-   *                            details on the different types of checkpoints.
    * @param initialPositionInStream  In the absence of Kinesis checkpoint info, this is the
    *                                 worker's initial starting position in the stream.
    *                                 The values are either the beginning of the stream
    *                                 per Kinesis' limit of 24 hours
    *                                 (InitialPositionInStream.TRIM_HORIZON) or
    *                                 the tip of the stream (InitialPositionInStream.LATEST).
+   * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
+   *                            See the Kinesis Spark Streaming documentation for more
+   *                            details on the different types of checkpoints.
    * @param storageLevel Storage level to use for storing the received objects.
    *                     StorageLevel.MEMORY_AND_DISK_2 is recommended.
+   * @param awsAccessKeyId  AWS AccessKeyId (if null, will use DefaultAWSCredentialsProviderChain)
+   * @param awsSecretKey  AWS SecretKey (if null, will use DefaultAWSCredentialsProviderChain)
    */
   def createStream(
       ssc: StreamingContext,
@@ -130,7 +130,7 @@ object KinesisUtils {
    * - The Kinesis application name used by the Kinesis Client Library (KCL) will be the app name in
    *   [[org.apache.spark.SparkConf]].
    *
-   * @param ssc Java StreamingContext object
+   * @param ssc StreamingContext object
    * @param streamName   Kinesis stream name
    * @param endpointUrl  Endpoint url of Kinesis service
    *                     (e.g., https://kinesis.us-east-1.amazonaws.com)
@@ -175,15 +175,15 @@ object KinesisUtils {
    * @param endpointUrl  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
    * @param regionName   Name of region used by the Kinesis Client Library (KCL) to update
    *                     DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
-   * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
-   *                            See the Kinesis Spark Streaming documentation for more
-   *                            details on the different types of checkpoints.
    * @param initialPositionInStream  In the absence of Kinesis checkpoint info, this is the
    *                                 worker's initial starting position in the stream.
    *                                 The values are either the beginning of the stream
    *                                 per Kinesis' limit of 24 hours
    *                                 (InitialPositionInStream.TRIM_HORIZON) or
    *                                 the tip of the stream (InitialPositionInStream.LATEST).
+   * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
+   *                            See the Kinesis Spark Streaming documentation for more
+   *                            details on the different types of checkpoints.
    * @param storageLevel Storage level to use for storing the received objects.
    *                     StorageLevel.MEMORY_AND_DISK_2 is recommended.
    */
@@ -206,8 +206,8 @@ object KinesisUtils {
    * This uses the Kinesis Client Library (KCL) to pull messages from Kinesis.
    *
    * Note:
-   *  The given AWS credentials will get saved in DStream checkpoints if checkpointing
-   *  is enabled. Make sure that your checkpoint directory is secure.
+   * The given AWS credentials will get saved in DStream checkpoints if checkpointing
+   * is enabled. Make sure that your checkpoint directory is secure.
    *
    * @param jssc Java StreamingContext object
    * @param kinesisAppName  Kinesis application name used by the Kinesis Client Library
@@ -216,19 +216,19 @@ object KinesisUtils {
    * @param endpointUrl  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
    * @param regionName   Name of region used by the Kinesis Client Library (KCL) to update
    *                     DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
-   * @param awsAccessKeyId  AWS AccessKeyId (if null, will use DefaultAWSCredentialsProviderChain)
-   * @param awsSecretKey  AWS SecretKey (if null, will use DefaultAWSCredentialsProviderChain)
-   * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
-   *                            See the Kinesis Spark Streaming documentation for more
-   *                            details on the different types of checkpoints.
    * @param initialPositionInStream  In the absence of Kinesis checkpoint info, this is the
    *                                 worker's initial starting position in the stream.
    *                                 The values are either the beginning of the stream
    *                                 per Kinesis' limit of 24 hours
    *                                 (InitialPositionInStream.TRIM_HORIZON) or
    *                                 the tip of the stream (InitialPositionInStream.LATEST).
+   * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
+   *                            See the Kinesis Spark Streaming documentation for more
+   *                            details on the different types of checkpoints.
    * @param storageLevel Storage level to use for storing the received objects.
    *                     StorageLevel.MEMORY_AND_DISK_2 is recommended.
+   * @param awsAccessKeyId  AWS AccessKeyId (if null, will use DefaultAWSCredentialsProviderChain)
+   * @param awsSecretKey  AWS SecretKey (if null, will use DefaultAWSCredentialsProviderChain)
    */
   def createStream(
       jssc: JavaStreamingContext,
@@ -297,3 +297,49 @@ object KinesisUtils {
     }
   }
 }
+
+/**
+ * This is a helper class that wraps the methods in KinesisUtils into more Python-friendly class and
+ * function so that it can be easily instantiated and called from Python's KinesisUtils.
+ */
+private class KinesisUtilsPythonHelper {
+
+  def getInitialPositionInStream(initialPositionInStream: Int): InitialPositionInStream = {
+    initialPositionInStream match {
+      case 0 => InitialPositionInStream.LATEST
+      case 1 => InitialPositionInStream.TRIM_HORIZON
+      case _ => throw new IllegalArgumentException(
+        "Illegal InitialPositionInStream. Please use " +
+          "InitialPositionInStream.LATEST or InitialPositionInStream.TRIM_HORIZON")
+    }
+  }
+
+  def createStream(
+      jssc: JavaStreamingContext,
+      kinesisAppName: String,
+      streamName: String,
+      endpointUrl: String,
+      regionName: String,
+      initialPositionInStream: Int,
+      checkpointInterval: Duration,
+      storageLevel: StorageLevel,
+      awsAccessKeyId: String,
+      awsSecretKey: String
+      ): JavaReceiverInputDStream[Array[Byte]] = {
+    if (awsAccessKeyId == null && awsSecretKey != null) {
+      throw new IllegalArgumentException("awsSecretKey is set but awsAccessKeyId is null")
+    }
+    if (awsAccessKeyId != null && awsSecretKey == null) {
+      throw new IllegalArgumentException("awsAccessKeyId is set but awsSecretKey is null")
+    }
+    if (awsAccessKeyId == null && awsSecretKey == null) {
+      KinesisUtils.createStream(jssc, kinesisAppName, streamName, endpointUrl, regionName,
+        getInitialPositionInStream(initialPositionInStream), checkpointInterval, storageLevel)
+    } else {
+      KinesisUtils.createStream(jssc, kinesisAppName, streamName, endpointUrl, regionName,
+        getInitialPositionInStream(initialPositionInStream), checkpointInterval, storageLevel,
+        awsAccessKeyId, awsSecretKey)
+    }
+  }
+
+}
diff --git a/pom.xml b/pom.xml
index 35fc8c44bc1b0..e351c7c19df96 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1642,6 +1642,7 @@
       <id>kinesis-asl</id>
       <modules>
         <module>extras/kinesis-asl</module>
+        <module>extras/kinesis-asl-assembly</module>
       </modules>
     </profile>
 
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 61a05d375d99e..9a33baa7c6ce1 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -45,8 +45,8 @@ object BuildCommons {
     sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
     "kinesis-asl").map(ProjectRef(buildLocation, _))
 
-  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly) =
-    Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly")
+  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly, streamingKinesisAslAssembly) =
+    Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly", "streaming-kinesis-asl-assembly")
       .map(ProjectRef(buildLocation, _))
 
   val tools = ProjectRef(buildLocation, "tools")
@@ -382,7 +382,7 @@ object Assembly {
         .getOrElse(SbtPomKeys.effectivePom.value.getProperties.get("hadoop.version").asInstanceOf[String])
     },
     jarName in assembly <<= (version, moduleName, hadoopVersion) map { (v, mName, hv) =>
-      if (mName.contains("streaming-flume-assembly") || mName.contains("streaming-kafka-assembly")) {
+      if (mName.contains("streaming-flume-assembly") || mName.contains("streaming-kafka-assembly") || mName.contains("streaming-kinesis-asl-assembly")) {
         // This must match the same name used in maven (see external/kafka-assembly/pom.xml)
         s"${mName}-${v}.jar"
       } else {
diff --git a/python/pyspark/streaming/kinesis.py b/python/pyspark/streaming/kinesis.py
new file mode 100644
index 0000000000000..bcfe2703fecf9
--- /dev/null
+++ b/python/pyspark/streaming/kinesis.py
@@ -0,0 +1,112 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from py4j.java_gateway import Py4JJavaError
+
+from pyspark.serializers import PairDeserializer, NoOpSerializer
+from pyspark.storagelevel import StorageLevel
+from pyspark.streaming import DStream
+
+__all__ = ['KinesisUtils', 'InitialPositionInStream', 'utf8_decoder']
+
+
+def utf8_decoder(s):
+    """ Decode the unicode as UTF-8 """
+    return s and s.decode('utf-8')
+
+
+class KinesisUtils(object):
+
+    @staticmethod
+    def createStream(ssc, kinesisAppName, streamName, endpointUrl, regionName,
+                     initialPositionInStream, checkpointInterval,
+                     storageLevel=StorageLevel.MEMORY_AND_DISK_2,
+                     awsAccessKeyId=None, awsSecretKey=None, decoder=utf8_decoder):
+        """
+        Create an input stream that pulls messages from a Kinesis stream. This uses the
+        Kinesis Client Library (KCL) to pull messages from Kinesis.
+
+        Note: The given AWS credentials will get saved in DStream checkpoints if checkpointing is
+        enabled. Make sure that your checkpoint directory is secure.
+
+        :param ssc:  StreamingContext object
+        :param kinesisAppName:  Kinesis application name used by the Kinesis Client Library (KCL) to
+                                update DynamoDB
+        :param streamName:  Kinesis stream name
+        :param endpointUrl:  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
+        :param regionName:  Name of region used by the Kinesis Client Library (KCL) to update
+                            DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
+        :param initialPositionInStream:  In the absence of Kinesis checkpoint info, this is the
+                                         worker's initial starting position in the stream. The
+                                         values are either the beginning of the stream per Kinesis'
+                                         limit of 24 hours (InitialPositionInStream.TRIM_HORIZON) or
+                                         the tip of the stream (InitialPositionInStream.LATEST).
+        :param checkpointInterval:  Checkpoint interval for Kinesis checkpointing. See the Kinesis
+                                    Spark Streaming documentation for more details on the different
+                                    types of checkpoints.
+        :param storageLevel:  Storage level to use for storing the received objects (default is
+                              StorageLevel.MEMORY_AND_DISK_2)
+        :param awsAccessKeyId:  AWS AccessKeyId (default is None. If None, will use
+                                DefaultAWSCredentialsProviderChain)
+        :param awsSecretKey:  AWS SecretKey (default is None. If None, will use
+                              DefaultAWSCredentialsProviderChain)
+        :param decoder:  A function used to decode value (default is utf8_decoder)
+        :return: A DStream object
+        """
+        jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
+        jduration = ssc._jduration(checkpointInterval)
+
+        try:
+            # Use KinesisUtilsPythonHelper to access Scala's KinesisUtils
+            helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\
+                .loadClass("org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper")
+            helper = helperClass.newInstance()
+            jstream = helper.createStream(ssc._jssc, kinesisAppName, streamName, endpointUrl,
+                                          regionName, initialPositionInStream, jduration, jlevel,
+                                          awsAccessKeyId, awsSecretKey)
+        except Py4JJavaError as e:
+            if 'ClassNotFoundException' in str(e.java_exception):
+                KinesisUtils._printErrorMsg(ssc.sparkContext)
+            raise e
+        stream = DStream(jstream, ssc, NoOpSerializer())
+        return stream.map(lambda v: decoder(v))
+
+    @staticmethod
+    def _printErrorMsg(sc):
+        print("""
+________________________________________________________________________________________________
+
+  Spark Streaming's Kinesis libraries not found in class path. Try one of the following.
+
+  1. Include the Kinesis library and its dependencies with in the
+     spark-submit command as
+
+     $ bin/spark-submit --packages org.apache.spark:spark-streaming-kinesis-asl:%s ...
+
+  2. Download the JAR of the artifact from Maven Central http://search.maven.org/,
+     Group Id = org.apache.spark, Artifact Id = spark-streaming-kinesis-asl-assembly, Version = %s.
+     Then, include the jar in the spark-submit command as
+
+     $ bin/spark-submit --jars <spark-streaming-kinesis-asl-assembly.jar> ...
+
+________________________________________________________________________________________________
+
+""" % (sc.version, sc.version))
+
+
+class InitialPositionInStream(object):
+    LATEST, TRIM_HORIZON = (0, 1)
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 4ecae1e4bf282..5cd544b2144ef 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -36,9 +36,11 @@
     import unittest
 
 from pyspark.context import SparkConf, SparkContext, RDD
+from pyspark.storagelevel import StorageLevel
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.kafka import Broker, KafkaUtils, OffsetRange, TopicAndPartition
 from pyspark.streaming.flume import FlumeUtils
+from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream
 
 
 class PySparkStreamingTestCase(unittest.TestCase):
@@ -891,6 +893,67 @@ def test_flume_polling_multiple_hosts(self):
         self._testMultipleTimes(self._testFlumePollingMultipleHosts)
 
 
+class KinesisStreamTests(PySparkStreamingTestCase):
+
+    def test_kinesis_stream_api(self):
+        # Don't start the StreamingContext because we cannot test it in Jenkins
+        kinesisStream1 = KinesisUtils.createStream(
+            self.ssc, "myAppNam", "mySparkStream",
+            "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+            InitialPositionInStream.LATEST, 2, StorageLevel.MEMORY_AND_DISK_2)
+        kinesisStream2 = KinesisUtils.createStream(
+            self.ssc, "myAppNam", "mySparkStream",
+            "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+            InitialPositionInStream.LATEST, 2, StorageLevel.MEMORY_AND_DISK_2,
+            "awsAccessKey", "awsSecretKey")
+
+    def test_kinesis_stream(self):
+        if os.environ.get('ENABLE_KINESIS_TESTS') != '1':
+            print("Skip test_kinesis_stream")
+            return
+
+        import random
+        kinesisAppName = ("KinesisStreamTests-%d" % abs(random.randint(0, 10000000)))
+        kinesisTestUtilsClz = \
+            self.sc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+                .loadClass("org.apache.spark.streaming.kinesis.KinesisTestUtils")
+        kinesisTestUtils = kinesisTestUtilsClz.newInstance()
+        try:
+            kinesisTestUtils.createStream()
+            aWSCredentials = kinesisTestUtils.getAWSCredentials()
+            stream = KinesisUtils.createStream(
+                self.ssc, kinesisAppName, kinesisTestUtils.streamName(),
+                kinesisTestUtils.endpointUrl(), kinesisTestUtils.regionName(),
+                InitialPositionInStream.LATEST, 10, StorageLevel.MEMORY_ONLY,
+                aWSCredentials.getAWSAccessKeyId(), aWSCredentials.getAWSSecretKey())
+
+            outputBuffer = []
+
+            def get_output(_, rdd):
+                for e in rdd.collect():
+                    outputBuffer.append(e)
+
+            stream.foreachRDD(get_output)
+            self.ssc.start()
+
+            testData = [i for i in range(1, 11)]
+            expectedOutput = set([str(i) for i in testData])
+            start_time = time.time()
+            while time.time() - start_time < 120:
+                kinesisTestUtils.pushData(testData)
+                if expectedOutput == set(outputBuffer):
+                    break
+                time.sleep(10)
+            self.assertEqual(expectedOutput, set(outputBuffer))
+        except:
+            import traceback
+            traceback.print_exc()
+            raise
+        finally:
+            kinesisTestUtils.deleteStream()
+            kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
+
+
 def search_kafka_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     kafka_assembly_dir = os.path.join(SPARK_HOME, "external/kafka-assembly")
@@ -926,10 +989,31 @@ def search_flume_assembly_jar():
     else:
         return jars[0]
 
+
+def search_kinesis_asl_assembly_jar():
+    SPARK_HOME = os.environ["SPARK_HOME"]
+    kinesis_asl_assembly_dir = os.path.join(SPARK_HOME, "extras/kinesis-asl-assembly")
+    jars = glob.glob(
+        os.path.join(kinesis_asl_assembly_dir,
+                     "target/scala-*/spark-streaming-kinesis-asl-assembly-*.jar"))
+    if not jars:
+        raise Exception(
+            ("Failed to find Spark Streaming Kinesis ASL assembly jar in %s. " %
+             kinesis_asl_assembly_dir) + "You need to build Spark with "
+            "'build/sbt -Pkinesis-asl assembly/assembly streaming-kinesis-asl-assembly/assembly' "
+            "or 'build/mvn -Pkinesis-asl package' before running this test")
+    elif len(jars) > 1:
+        raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs in %s; please "
+                         "remove all but one") % kinesis_asl_assembly_dir)
+    else:
+        return jars[0]
+
+
 if __name__ == "__main__":
     kafka_assembly_jar = search_kafka_assembly_jar()
     flume_assembly_jar = search_flume_assembly_jar()
-    jars = "%s,%s" % (kafka_assembly_jar, flume_assembly_jar)
+    kinesis_asl_assembly_jar = search_kinesis_asl_assembly_jar()
+    jars = "%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar)
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
     unittest.main()

From d04634701413410938a133358fe1d9fbc077645e Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 31 Jul 2015 12:10:55 -0700
Subject: [PATCH 0743/1454] [SPARK-9504] [STREAMING] [TESTS] Use eventually to
 fix the flaky test

The previous code uses `ssc.awaitTerminationOrTimeout(500)`. Since nobody will stop it during `awaitTerminationOrTimeout`, it's just like `sleep(500)`. In a super overloaded Jenkins worker, the receiver may be not able to start in 500 milliseconds. Verified this in the log of https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/39149/ There is no log about starting the receiver before this failure. That's why `assert(runningCount > 0)` failed.

This PR replaces `awaitTerminationOrTimeout` with `eventually` which should be more reliable.

Author: zsxwing <zsxwing@gmail.com>

Closes #7823 from zsxwing/SPARK-9504 and squashes the following commits:

7af66a6 [zsxwing] Remove wrong assertion
5ba2c99 [zsxwing] Use eventually to fix the flaky test
---
 .../apache/spark/streaming/StreamingContextSuite.scala | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 84a5fbb3d95eb..b7db280f63588 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -261,7 +261,7 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
     for (i <- 1 to 4) {
       logInfo("==================================\n\n\n")
       ssc = new StreamingContext(sc, Milliseconds(100))
-      var runningCount = 0
+      @volatile var runningCount = 0
       TestReceiver.counter.set(1)
       val input = ssc.receiverStream(new TestReceiver)
       input.count().foreachRDD { rdd =>
@@ -270,14 +270,14 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
         logInfo("Count = " + count + ", Running count = " + runningCount)
       }
       ssc.start()
-      ssc.awaitTerminationOrTimeout(500)
+      eventually(timeout(10.seconds), interval(10.millis)) {
+        assert(runningCount > 0)
+      }
       ssc.stop(stopSparkContext = false, stopGracefully = true)
       logInfo("Running count = " + runningCount)
       logInfo("TestReceiver.counter = " + TestReceiver.counter.get())
-      assert(runningCount > 0)
       assert(
-        (TestReceiver.counter.get() == runningCount + 1) ||
-          (TestReceiver.counter.get() == runningCount + 2),
+        TestReceiver.counter.get() == runningCount + 1,
         "Received records = " + TestReceiver.counter.get() + ", " +
           "processed records = " + runningCount
       )

From a8340fa7df17e3f0a3658f8b8045ab840845a72a Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Fri, 31 Jul 2015 12:12:22 -0700
Subject: [PATCH 0744/1454] [SPARK-9481] Add logLikelihood to LocalLDAModel

jkbradley Exposes `bound` (variational log likelihood bound) through public API as `logLikelihood`. Also adds unit tests, some DRYing of `LDASuite`, and includes unit tests mentioned in #7760

Author: Feynman Liang <fliang@databricks.com>

Closes #7801 from feynmanliang/SPARK-9481-logLikelihood and squashes the following commits:

6d1b2c9 [Feynman Liang] Negate perplexity definition
5f62b20 [Feynman Liang] Add logLikelihood
---
 .../spark/mllib/clustering/LDAModel.scala     |  20 ++-
 .../spark/mllib/clustering/LDASuite.scala     | 129 +++++++++---------
 2 files changed, 78 insertions(+), 71 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 82281a0daf008..ff7035d2246c2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -217,22 +217,28 @@ class LocalLDAModel private[clustering] (
     LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix, docConcentration, topicConcentration,
       gammaShape)
   }
-  // TODO
-  // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ???
+
+  // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
+  /**
+   * Calculates a lower bound on the log likelihood of the entire corpus.
+   * @param documents test corpus to use for calculating log likelihood
+   * @return variational lower bound on the log likelihood of the entire corpus
+   */
+  def logLikelihood(documents: RDD[(Long, Vector)]): Double = bound(documents,
+    docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
+    vocabSize)
 
   /**
-   * Calculate the log variational bound on perplexity. See Equation (16) in original Online
+   * Calculate an upper bound bound on perplexity. See Equation (16) in original Online
    * LDA paper.
    * @param documents test corpus to use for calculating perplexity
-   * @return the log perplexity per word
+   * @return variational upper bound on log perplexity per word
    */
   def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
     val corpusWords = documents
       .map { case (_, termCounts) => termCounts.toArray.sum }
       .sum()
-    val batchVariationalBound = bound(documents, docConcentration,
-      topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k, vocabSize)
-    val perWordBound = batchVariationalBound / corpusWords
+    val perWordBound = -logLikelihood(documents) / corpusWords
 
     perWordBound
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 695ee3b82efc5..79d2a1cafd1fa 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -210,16 +210,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("OnlineLDAOptimizer with toy data") {
-    def toydata: Array[(Long, Vector)] = Array(
-      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
-      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
-      Vectors.sparse(6, Array(4, 5), Array(1, 1))
-    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
-
-    val docs = sc.parallelize(toydata)
+    val docs = sc.parallelize(toyData)
     val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
       .setGammaShape(1e10)
     val lda = new LDA().setK(2)
@@ -242,30 +233,45 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
-  test("LocalLDAModel logPerplexity") {
-    val k = 2
-    val vocabSize = 6
-    val alpha = 0.01
-    val eta = 0.01
-    val gammaShape = 100
-    // obtained from LDA model trained in gensim, see below
-    val topics = new DenseMatrix(numRows = vocabSize, numCols = k, values = Array(
-      1.86738052, 1.94056535, 1.89981687, 0.0833265, 0.07405918, 0.07940597,
-      0.15081551, 0.08637973, 0.12428538, 1.9474897, 1.94615165, 1.95204124))
+  test("LocalLDAModel logLikelihood") {
+    val ldaModel: LocalLDAModel = toyModel
 
-    def toydata: Array[(Long, Vector)] = Array(
-      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
-      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
-      Vectors.sparse(6, Array(4, 5), Array(1, 1))
-    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
-    val docs = sc.parallelize(toydata)
+    val docsSingleWord = sc.parallelize(Array(Vectors.sparse(6, Array(0), Array(1)))
+      .zipWithIndex
+      .map { case (wordCounts, docId) => (docId.toLong, wordCounts) })
+    val docsRepeatedWord = sc.parallelize(Array(Vectors.sparse(6, Array(0), Array(5)))
+      .zipWithIndex
+      .map { case (wordCounts, docId) => (docId.toLong, wordCounts) })
 
+    /* Verify results using gensim:
+       import numpy as np
+       from gensim import models
+       corpus = [
+          [(0, 1.0), (1, 1.0)],
+          [(1, 1.0), (2, 1.0)],
+          [(0, 1.0), (2, 1.0)],
+          [(3, 1.0), (4, 1.0)],
+          [(3, 1.0), (5, 1.0)],
+          [(4, 1.0), (5, 1.0)]]
+       np.random.seed(2345)
+       lda = models.ldamodel.LdaModel(
+          corpus=corpus, alpha=0.01, eta=0.01, num_topics=2, update_every=0, passes=100,
+          decay=0.51, offset=1024)
+       docsSingleWord = [[(0, 1.0)]]
+       docsRepeatedWord = [[(0, 5.0)]]
+       print(lda.bound(docsSingleWord))
+       > -25.9706969833
+       print(lda.bound(docsRepeatedWord))
+       > -31.4413908227
+     */
 
-    val ldaModel: LocalLDAModel = new LocalLDAModel(
-      topics, Vectors.dense(Array.fill(k)(alpha)), eta, gammaShape)
+    assert(ldaModel.logLikelihood(docsSingleWord) ~== -25.971 relTol 1E-3D)
+    assert(ldaModel.logLikelihood(docsRepeatedWord) ~== -31.441  relTol 1E-3D)
+  }
+
+  test("LocalLDAModel logPerplexity") {
+    val docs = sc.parallelize(toyData)
+    val ldaModel: LocalLDAModel = toyModel
 
     /* Verify results using gensim:
        import numpy as np
@@ -285,32 +291,13 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
        > -3.69051285096
      */
 
-    assert(ldaModel.logPerplexity(docs) ~== -3.690D relTol 1E-3D)
+    // Gensim's definition of perplexity is negative our (and Stanford NLP's) definition
+    assert(ldaModel.logPerplexity(docs) ~== 3.690D relTol 1E-3D)
   }
 
   test("LocalLDAModel predict") {
-    val k = 2
-    val vocabSize = 6
-    val alpha = 0.01
-    val eta = 0.01
-    val gammaShape = 100
-    // obtained from LDA model trained in gensim, see below
-    val topics = new DenseMatrix(numRows = vocabSize, numCols = k, values = Array(
-      1.86738052, 1.94056535, 1.89981687, 0.0833265, 0.07405918, 0.07940597,
-      0.15081551, 0.08637973, 0.12428538, 1.9474897, 1.94615165, 1.95204124))
-
-    def toydata: Array[(Long, Vector)] = Array(
-      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
-      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
-      Vectors.sparse(6, Array(4, 5), Array(1, 1))
-    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
-    val docs = sc.parallelize(toydata)
-
-    val ldaModel: LocalLDAModel = new LocalLDAModel(
-      topics, Vectors.dense(Array.fill(k)(alpha)), eta, gammaShape)
+    val docs = sc.parallelize(toyData)
+    val ldaModel: LocalLDAModel = toyModel
 
     /* Verify results using gensim:
        import numpy as np
@@ -351,16 +338,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("OnlineLDAOptimizer with asymmetric prior") {
-    def toydata: Array[(Long, Vector)] = Array(
-      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
-      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
-      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
-      Vectors.sparse(6, Array(4, 5), Array(1, 1))
-    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
-
-    val docs = sc.parallelize(toydata)
+    val docs = sc.parallelize(toyData)
     val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
       .setGammaShape(1e10)
     val lda = new LDA().setK(2)
@@ -531,4 +509,27 @@ private[clustering] object LDASuite {
   def getNonEmptyDoc(corpus: Array[(Long, Vector)]): Array[(Long, Vector)] = corpus.filter {
     case (_, wc: Vector) => Vectors.norm(wc, p = 1.0) != 0.0
   }
+
+  def toyData: Array[(Long, Vector)] = Array(
+    Vectors.sparse(6, Array(0, 1), Array(1, 1)),
+    Vectors.sparse(6, Array(1, 2), Array(1, 1)),
+    Vectors.sparse(6, Array(0, 2), Array(1, 1)),
+    Vectors.sparse(6, Array(3, 4), Array(1, 1)),
+    Vectors.sparse(6, Array(3, 5), Array(1, 1)),
+    Vectors.sparse(6, Array(4, 5), Array(1, 1))
+  ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
+
+  def toyModel: LocalLDAModel = {
+    val k = 2
+    val vocabSize = 6
+    val alpha = 0.01
+    val eta = 0.01
+    val gammaShape = 100
+    val topics = new DenseMatrix(numRows = vocabSize, numCols = k, values = Array(
+      1.86738052, 1.94056535, 1.89981687, 0.0833265, 0.07405918, 0.07940597,
+      0.15081551, 0.08637973, 0.12428538, 1.9474897, 1.94615165, 1.95204124))
+    val ldaModel: LocalLDAModel = new LocalLDAModel(
+      topics, Vectors.dense(Array.fill(k)(alpha)), eta, gammaShape)
+    ldaModel
+  }
 }

From c0686668ae6a92b6bb4801a55c3b78aedbee816a Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Fri, 31 Jul 2015 20:27:00 +0100
Subject: [PATCH 0745/1454] [SPARK-9202] capping maximum number of
 executor&driver information kept in Worker

https://issues.apache.org/jira/browse/SPARK-9202

Author: CodingCat <zhunansjtu@gmail.com>

Closes #7714 from CodingCat/SPARK-9202 and squashes the following commits:

23977fb [CodingCat] add comments about why we don't synchronize finishedExecutors & finishedDrivers
dc9772d [CodingCat] addressing the comments
e125241 [CodingCat] stylistic fix
80bfe52 [CodingCat] fix JsonProtocolSuite
d7d9485 [CodingCat] styistic fix and respect insert ordering
031755f [CodingCat] add license info & stylistic fix
c3b5361 [CodingCat] test cases and docs
c557b3a [CodingCat] applications are fine
9cac751 [CodingCat] application is fine...
ad87ed7 [CodingCat] trimFinishedExecutorsAndDrivers
---
 .../apache/spark/deploy/worker/Worker.scala   | 124 ++++++++++------
 .../spark/deploy/worker/ui/WorkerWebUI.scala  |   4 +-
 .../apache/spark/deploy/DeployTestUtils.scala |  89 ++++++++++++
 .../spark/deploy/JsonProtocolSuite.scala      |  59 ++------
 .../spark/deploy/worker/WorkerSuite.scala     | 133 +++++++++++++++++-
 docs/configuration.md                         |  14 ++
 6 files changed, 329 insertions(+), 94 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/deploy/DeployTestUtils.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 82e9578bbcba5..0276c24f85368 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -25,7 +25,7 @@ import java.util.concurrent._
 import java.util.concurrent.{Future => JFuture, ScheduledFuture => JScheduledFuture}
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable.{HashMap, HashSet}
+import scala.collection.mutable.{HashMap, HashSet, LinkedHashMap}
 import scala.concurrent.ExecutionContext
 import scala.util.Random
 import scala.util.control.NonFatal
@@ -115,13 +115,18 @@ private[worker] class Worker(
     }
 
   var workDir: File = null
-  val finishedExecutors = new HashMap[String, ExecutorRunner]
+  val finishedExecutors = new LinkedHashMap[String, ExecutorRunner]
   val drivers = new HashMap[String, DriverRunner]
   val executors = new HashMap[String, ExecutorRunner]
-  val finishedDrivers = new HashMap[String, DriverRunner]
+  val finishedDrivers = new LinkedHashMap[String, DriverRunner]
   val appDirectories = new HashMap[String, Seq[String]]
   val finishedApps = new HashSet[String]
 
+  val retainedExecutors = conf.getInt("spark.worker.ui.retainedExecutors",
+    WorkerWebUI.DEFAULT_RETAINED_EXECUTORS)
+  val retainedDrivers = conf.getInt("spark.worker.ui.retainedDrivers",
+    WorkerWebUI.DEFAULT_RETAINED_DRIVERS)
+
   // The shuffle service is not actually started unless configured.
   private val shuffleService = new ExternalShuffleService(conf, securityMgr)
 
@@ -461,25 +466,7 @@ private[worker] class Worker(
       }
 
     case executorStateChanged @ ExecutorStateChanged(appId, execId, state, message, exitStatus) =>
-      sendToMaster(executorStateChanged)
-      val fullId = appId + "/" + execId
-      if (ExecutorState.isFinished(state)) {
-        executors.get(fullId) match {
-          case Some(executor) =>
-            logInfo("Executor " + fullId + " finished with state " + state +
-              message.map(" message " + _).getOrElse("") +
-              exitStatus.map(" exitStatus " + _).getOrElse(""))
-            executors -= fullId
-            finishedExecutors(fullId) = executor
-            coresUsed -= executor.cores
-            memoryUsed -= executor.memory
-          case None =>
-            logInfo("Unknown Executor " + fullId + " finished with state " + state +
-              message.map(" message " + _).getOrElse("") +
-              exitStatus.map(" exitStatus " + _).getOrElse(""))
-        }
-        maybeCleanupApplication(appId)
-      }
+      handleExecutorStateChanged(executorStateChanged)
 
     case KillExecutor(masterUrl, appId, execId) =>
       if (masterUrl != activeMasterUrl) {
@@ -523,24 +510,8 @@ private[worker] class Worker(
       }
     }
 
-    case driverStageChanged @ DriverStateChanged(driverId, state, exception) => {
-      state match {
-        case DriverState.ERROR =>
-          logWarning(s"Driver $driverId failed with unrecoverable exception: ${exception.get}")
-        case DriverState.FAILED =>
-          logWarning(s"Driver $driverId exited with failure")
-        case DriverState.FINISHED =>
-          logInfo(s"Driver $driverId exited successfully")
-        case DriverState.KILLED =>
-          logInfo(s"Driver $driverId was killed by user")
-        case _ =>
-          logDebug(s"Driver $driverId changed state to $state")
-      }
-      sendToMaster(driverStageChanged)
-      val driver = drivers.remove(driverId).get
-      finishedDrivers(driverId) = driver
-      memoryUsed -= driver.driverDesc.mem
-      coresUsed -= driver.driverDesc.cores
+    case driverStateChanged @ DriverStateChanged(driverId, state, exception) => {
+      handleDriverStateChanged(driverStateChanged)
     }
 
     case ReregisterWithMaster =>
@@ -614,6 +585,78 @@ private[worker] class Worker(
     webUi.stop()
     metricsSystem.stop()
   }
+
+  private def trimFinishedExecutorsIfNecessary(): Unit = {
+    // do not need to protect with locks since both WorkerPage and Restful server get data through
+    // thread-safe RpcEndPoint
+    if (finishedExecutors.size > retainedExecutors) {
+      finishedExecutors.take(math.max(finishedExecutors.size / 10, 1)).foreach {
+        case (executorId, _) => finishedExecutors.remove(executorId)
+      }
+    }
+  }
+
+  private def trimFinishedDriversIfNecessary(): Unit = {
+    // do not need to protect with locks since both WorkerPage and Restful server get data through
+    // thread-safe RpcEndPoint
+    if (finishedDrivers.size > retainedDrivers) {
+      finishedDrivers.take(math.max(finishedDrivers.size / 10, 1)).foreach {
+        case (driverId, _) => finishedDrivers.remove(driverId)
+      }
+    }
+  }
+
+  private[worker] def handleDriverStateChanged(driverStateChanged: DriverStateChanged): Unit = {
+    val driverId = driverStateChanged.driverId
+    val exception = driverStateChanged.exception
+    val state = driverStateChanged.state
+    state match {
+      case DriverState.ERROR =>
+        logWarning(s"Driver $driverId failed with unrecoverable exception: ${exception.get}")
+      case DriverState.FAILED =>
+        logWarning(s"Driver $driverId exited with failure")
+      case DriverState.FINISHED =>
+        logInfo(s"Driver $driverId exited successfully")
+      case DriverState.KILLED =>
+        logInfo(s"Driver $driverId was killed by user")
+      case _ =>
+        logDebug(s"Driver $driverId changed state to $state")
+    }
+    sendToMaster(driverStateChanged)
+    val driver = drivers.remove(driverId).get
+    finishedDrivers(driverId) = driver
+    trimFinishedDriversIfNecessary()
+    memoryUsed -= driver.driverDesc.mem
+    coresUsed -= driver.driverDesc.cores
+  }
+
+  private[worker] def handleExecutorStateChanged(executorStateChanged: ExecutorStateChanged):
+    Unit = {
+    sendToMaster(executorStateChanged)
+    val state = executorStateChanged.state
+    if (ExecutorState.isFinished(state)) {
+      val appId = executorStateChanged.appId
+      val fullId = appId + "/" + executorStateChanged.execId
+      val message = executorStateChanged.message
+      val exitStatus = executorStateChanged.exitStatus
+      executors.get(fullId) match {
+        case Some(executor) =>
+          logInfo("Executor " + fullId + " finished with state " + state +
+            message.map(" message " + _).getOrElse("") +
+            exitStatus.map(" exitStatus " + _).getOrElse(""))
+          executors -= fullId
+          finishedExecutors(fullId) = executor
+          trimFinishedExecutorsIfNecessary()
+          coresUsed -= executor.cores
+          memoryUsed -= executor.memory
+        case None =>
+          logInfo("Unknown Executor " + fullId + " finished with state " + state +
+            message.map(" message " + _).getOrElse("") +
+            exitStatus.map(" exitStatus " + _).getOrElse(""))
+      }
+      maybeCleanupApplication(appId)
+    }
+  }
 }
 
 private[deploy] object Worker extends Logging {
@@ -669,5 +712,4 @@ private[deploy] object Worker extends Logging {
       cmd
     }
   }
-
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
index 334a5b10142aa..709a27233598c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
@@ -53,6 +53,8 @@ class WorkerWebUI(
   }
 }
 
-private[ui] object WorkerWebUI {
+private[worker] object WorkerWebUI {
   val STATIC_RESOURCE_BASE = SparkUI.STATIC_RESOURCE_DIR
+  val DEFAULT_RETAINED_DRIVERS = 1000
+  val DEFAULT_RETAINED_EXECUTORS = 1000
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/DeployTestUtils.scala b/core/src/test/scala/org/apache/spark/deploy/DeployTestUtils.scala
new file mode 100644
index 0000000000000..967aa0976f0ce
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/deploy/DeployTestUtils.scala
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.io.File
+import java.util.Date
+
+import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, WorkerInfo}
+import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner}
+import org.apache.spark.{SecurityManager, SparkConf}
+
+private[deploy] object DeployTestUtils {
+  def createAppDesc(): ApplicationDescription = {
+    val cmd = new Command("mainClass", List("arg1", "arg2"), Map(), Seq(), Seq(), Seq())
+    new ApplicationDescription("name", Some(4), 1234, cmd, "appUiUrl")
+  }
+
+  def createAppInfo() : ApplicationInfo = {
+    val appInfo = new ApplicationInfo(JsonConstants.appInfoStartTime,
+      "id", createAppDesc(), JsonConstants.submitDate, null, Int.MaxValue)
+    appInfo.endTime = JsonConstants.currTimeInMillis
+    appInfo
+  }
+
+  def createDriverCommand(): Command = new Command(
+    "org.apache.spark.FakeClass", Seq("some arg --and-some options -g foo"),
+    Map(("K1", "V1"), ("K2", "V2")), Seq("cp1", "cp2"), Seq("lp1", "lp2"), Seq("-Dfoo")
+  )
+
+  def createDriverDesc(): DriverDescription =
+    new DriverDescription("hdfs://some-dir/some.jar", 100, 3, false, createDriverCommand())
+
+  def createDriverInfo(): DriverInfo = new DriverInfo(3, "driver-3",
+    createDriverDesc(), new Date())
+
+  def createWorkerInfo(): WorkerInfo = {
+    val workerInfo = new WorkerInfo("id", "host", 8080, 4, 1234, null, 80, "publicAddress")
+    workerInfo.lastHeartbeat = JsonConstants.currTimeInMillis
+    workerInfo
+  }
+
+  def createExecutorRunner(execId: Int): ExecutorRunner = {
+    new ExecutorRunner(
+      "appId",
+      execId,
+      createAppDesc(),
+      4,
+      1234,
+      null,
+      "workerId",
+      "host",
+      123,
+      "publicAddress",
+      new File("sparkHome"),
+      new File("workDir"),
+      "akka://worker",
+      new SparkConf,
+      Seq("localDir"),
+      ExecutorState.RUNNING)
+  }
+
+  def createDriverRunner(driverId: String): DriverRunner = {
+    val conf = new SparkConf()
+    new DriverRunner(
+      conf,
+      driverId,
+      new File("workDir"),
+      new File("sparkHome"),
+      createDriverDesc(),
+      null,
+      "akka://worker",
+      new SecurityManager(conf))
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
index 08529e0ef2806..0a9f128a3a6b6 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.deploy
 
-import java.io.File
 import java.util.Date
 
 import com.fasterxml.jackson.core.JsonParseException
@@ -25,12 +24,14 @@ import org.json4s._
 import org.json4s.jackson.JsonMethods
 
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse}
-import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo}
-import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner}
-import org.apache.spark.{JsonTestUtils, SecurityManager, SparkConf, SparkFunSuite}
+import org.apache.spark.deploy.master.{ApplicationInfo, RecoveryState}
+import org.apache.spark.deploy.worker.ExecutorRunner
+import org.apache.spark.{JsonTestUtils, SparkFunSuite}
 
 class JsonProtocolSuite extends SparkFunSuite with JsonTestUtils {
 
+  import org.apache.spark.deploy.DeployTestUtils._
+
   test("writeApplicationInfo") {
     val output = JsonProtocol.writeApplicationInfo(createAppInfo())
     assertValidJson(output)
@@ -50,7 +51,7 @@ class JsonProtocolSuite extends SparkFunSuite with JsonTestUtils {
   }
 
   test("writeExecutorRunner") {
-    val output = JsonProtocol.writeExecutorRunner(createExecutorRunner())
+    val output = JsonProtocol.writeExecutorRunner(createExecutorRunner(123))
     assertValidJson(output)
     assertValidDataInJson(output, JsonMethods.parse(JsonConstants.executorRunnerJsonStr))
   }
@@ -77,9 +78,10 @@ class JsonProtocolSuite extends SparkFunSuite with JsonTestUtils {
 
   test("writeWorkerState") {
     val executors = List[ExecutorRunner]()
-    val finishedExecutors = List[ExecutorRunner](createExecutorRunner(), createExecutorRunner())
-    val drivers = List(createDriverRunner())
-    val finishedDrivers = List(createDriverRunner(), createDriverRunner())
+    val finishedExecutors = List[ExecutorRunner](createExecutorRunner(123),
+      createExecutorRunner(123))
+    val drivers = List(createDriverRunner("driverId"))
+    val finishedDrivers = List(createDriverRunner("driverId"), createDriverRunner("driverId"))
     val stateResponse = new WorkerStateResponse("host", 8080, "workerId", executors,
       finishedExecutors, drivers, finishedDrivers, "masterUrl", 4, 1234, 4, 1234, "masterWebUiUrl")
     val output = JsonProtocol.writeWorkerState(stateResponse)
@@ -87,47 +89,6 @@ class JsonProtocolSuite extends SparkFunSuite with JsonTestUtils {
     assertValidDataInJson(output, JsonMethods.parse(JsonConstants.workerStateJsonStr))
   }
 
-  def createAppDesc(): ApplicationDescription = {
-    val cmd = new Command("mainClass", List("arg1", "arg2"), Map(), Seq(), Seq(), Seq())
-    new ApplicationDescription("name", Some(4), 1234, cmd, "appUiUrl")
-  }
-
-  def createAppInfo() : ApplicationInfo = {
-    val appInfo = new ApplicationInfo(JsonConstants.appInfoStartTime,
-      "id", createAppDesc(), JsonConstants.submitDate, null, Int.MaxValue)
-    appInfo.endTime = JsonConstants.currTimeInMillis
-    appInfo
-  }
-
-  def createDriverCommand(): Command = new Command(
-    "org.apache.spark.FakeClass", Seq("some arg --and-some options -g foo"),
-    Map(("K1", "V1"), ("K2", "V2")), Seq("cp1", "cp2"), Seq("lp1", "lp2"), Seq("-Dfoo")
-  )
-
-  def createDriverDesc(): DriverDescription =
-    new DriverDescription("hdfs://some-dir/some.jar", 100, 3, false, createDriverCommand())
-
-  def createDriverInfo(): DriverInfo = new DriverInfo(3, "driver-3",
-    createDriverDesc(), new Date())
-
-  def createWorkerInfo(): WorkerInfo = {
-    val workerInfo = new WorkerInfo("id", "host", 8080, 4, 1234, null, 80, "publicAddress")
-    workerInfo.lastHeartbeat = JsonConstants.currTimeInMillis
-    workerInfo
-  }
-
-  def createExecutorRunner(): ExecutorRunner = {
-    new ExecutorRunner("appId", 123, createAppDesc(), 4, 1234, null, "workerId", "host", 123,
-      "publicAddress", new File("sparkHome"), new File("workDir"), "akka://worker",
-      new SparkConf, Seq("localDir"), ExecutorState.RUNNING)
-  }
-
-  def createDriverRunner(): DriverRunner = {
-    val conf = new SparkConf()
-    new DriverRunner(conf, "driverId", new File("workDir"), new File("sparkHome"),
-      createDriverDesc(), null, "akka://worker", new SecurityManager(conf))
-  }
-
   def assertValidJson(json: JValue) {
     try {
       JsonMethods.parse(JsonMethods.compact(json))
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala
index 0f4d3b28d09df..faed4bdc68447 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala
@@ -17,13 +17,18 @@
 
 package org.apache.spark.deploy.worker
 
-import org.apache.spark.{SparkConf, SparkFunSuite}
-import org.apache.spark.deploy.Command
-
 import org.scalatest.Matchers
 
+import org.apache.spark.deploy.DeployMessages.{DriverStateChanged, ExecutorStateChanged}
+import org.apache.spark.deploy.master.DriverState
+import org.apache.spark.deploy.{Command, ExecutorState}
+import org.apache.spark.rpc.{RpcAddress, RpcEnv}
+import org.apache.spark.{SecurityManager, SparkConf, SparkFunSuite}
+
 class WorkerSuite extends SparkFunSuite with Matchers {
 
+  import org.apache.spark.deploy.DeployTestUtils._
+
   def cmd(javaOpts: String*): Command = {
     Command("", Seq.empty, Map.empty, Seq.empty, Seq.empty, Seq(javaOpts : _*))
   }
@@ -56,4 +61,126 @@ class WorkerSuite extends SparkFunSuite with Matchers {
           "-Dspark.ssl.useNodeLocalConf=true", "-Dspark.ssl.opt1=y", "-Dspark.ssl.opt2=z")
 
   }
+
+  test("test clearing of finishedExecutors (small number of executors)") {
+    val conf = new SparkConf()
+    conf.set("spark.worker.ui.retainedExecutors", 2.toString)
+    val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
+    val worker = new Worker(rpcEnv, 50000, 20, 1234 * 5, Array.fill(1)(RpcAddress("1.2.3.4", 1234)),
+      "sparkWorker1", "Worker", "/tmp", conf, new SecurityManager(conf))
+    // initialize workers
+    for (i <- 0 until 5) {
+      worker.executors += s"app1/$i" -> createExecutorRunner(i)
+    }
+    // initialize ExecutorStateChanged Message
+    worker.handleExecutorStateChanged(
+      ExecutorStateChanged("app1", 0, ExecutorState.EXITED, None, None))
+    assert(worker.finishedExecutors.size === 1)
+    assert(worker.executors.size === 4)
+    for (i <- 1 until 5) {
+      worker.handleExecutorStateChanged(
+        ExecutorStateChanged("app1", i, ExecutorState.EXITED, None, None))
+      assert(worker.finishedExecutors.size === 2)
+      if (i > 1) {
+        assert(!worker.finishedExecutors.contains(s"app1/${i - 2}"))
+      }
+      assert(worker.executors.size === 4 - i)
+    }
+  }
+
+  test("test clearing of finishedExecutors (more executors)") {
+    val conf = new SparkConf()
+    conf.set("spark.worker.ui.retainedExecutors", 30.toString)
+    val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
+    val worker = new Worker(rpcEnv, 50000, 20, 1234 * 5, Array.fill(1)(RpcAddress("1.2.3.4", 1234)),
+      "sparkWorker1", "Worker", "/tmp", conf, new SecurityManager(conf))
+    // initialize workers
+    for (i <- 0 until 50) {
+      worker.executors += s"app1/$i" -> createExecutorRunner(i)
+    }
+    // initialize ExecutorStateChanged Message
+    worker.handleExecutorStateChanged(
+      ExecutorStateChanged("app1", 0, ExecutorState.EXITED, None, None))
+    assert(worker.finishedExecutors.size === 1)
+    assert(worker.executors.size === 49)
+    for (i <- 1 until 50) {
+      val expectedValue = {
+        if (worker.finishedExecutors.size < 30) {
+          worker.finishedExecutors.size + 1
+        } else {
+          28
+        }
+      }
+      worker.handleExecutorStateChanged(
+        ExecutorStateChanged("app1", i, ExecutorState.EXITED, None, None))
+      if (expectedValue == 28) {
+        for (j <- i - 30 until i - 27) {
+          assert(!worker.finishedExecutors.contains(s"app1/$j"))
+        }
+      }
+      assert(worker.executors.size === 49 - i)
+      assert(worker.finishedExecutors.size === expectedValue)
+    }
+  }
+
+  test("test clearing of finishedDrivers (small number of drivers)") {
+    val conf = new SparkConf()
+    conf.set("spark.worker.ui.retainedDrivers", 2.toString)
+    val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
+    val worker = new Worker(rpcEnv, 50000, 20, 1234 * 5, Array.fill(1)(RpcAddress("1.2.3.4", 1234)),
+      "sparkWorker1", "Worker", "/tmp", conf, new SecurityManager(conf))
+    // initialize workers
+    for (i <- 0 until 5) {
+      val driverId = s"driverId-$i"
+      worker.drivers += driverId -> createDriverRunner(driverId)
+    }
+    // initialize DriverStateChanged Message
+    worker.handleDriverStateChanged(DriverStateChanged("driverId-0", DriverState.FINISHED, None))
+    assert(worker.drivers.size === 4)
+    assert(worker.finishedDrivers.size === 1)
+    for (i <- 1 until 5) {
+      val driverId = s"driverId-$i"
+      worker.handleDriverStateChanged(DriverStateChanged(driverId, DriverState.FINISHED, None))
+      if (i > 1) {
+        assert(!worker.finishedDrivers.contains(s"driverId-${i - 2}"))
+      }
+      assert(worker.drivers.size === 4 - i)
+      assert(worker.finishedDrivers.size === 2)
+    }
+  }
+
+  test("test clearing of finishedDrivers (more drivers)") {
+    val conf = new SparkConf()
+    conf.set("spark.worker.ui.retainedDrivers", 30.toString)
+    val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
+    val worker = new Worker(rpcEnv, 50000, 20, 1234 * 5, Array.fill(1)(RpcAddress("1.2.3.4", 1234)),
+      "sparkWorker1", "Worker", "/tmp", conf, new SecurityManager(conf))
+    // initialize workers
+    for (i <- 0 until 50) {
+      val driverId = s"driverId-$i"
+      worker.drivers += driverId -> createDriverRunner(driverId)
+    }
+    // initialize DriverStateChanged Message
+    worker.handleDriverStateChanged(DriverStateChanged("driverId-0", DriverState.FINISHED, None))
+    assert(worker.finishedDrivers.size === 1)
+    assert(worker.drivers.size === 49)
+    for (i <- 1 until 50) {
+      val expectedValue = {
+        if (worker.finishedDrivers.size < 30) {
+          worker.finishedDrivers.size + 1
+        } else {
+          28
+        }
+      }
+      val driverId = s"driverId-$i"
+      worker.handleDriverStateChanged(DriverStateChanged(driverId, DriverState.FINISHED, None))
+      if (expectedValue == 28) {
+        for (j <- i - 30 until i - 27) {
+          assert(!worker.finishedDrivers.contains(s"driverId-$j"))
+        }
+      }
+      assert(worker.drivers.size === 49 - i)
+      assert(worker.finishedDrivers.size === expectedValue)
+    }
+  }
 }
diff --git a/docs/configuration.md b/docs/configuration.md
index fd236137cb96e..24b606356a149 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -557,6 +557,20 @@ Apart from these, the following properties are also available, and may be useful
     collecting.
   </td>
 </tr>
+<tr>
+  <td><code>spark.worker.ui.retainedExecutors</code></td>
+  <td>1000</td>
+  <td>
+    How many finished executors the Spark UI and status APIs remember before garbage collecting.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.worker.ui.retainedDrivers</code></td>
+  <td>1000</td>
+  <td>
+    How many finished drivers the Spark UI and status APIs remember before garbage collecting.
+  </td>
+</tr>
 </table>
 
 #### Compression and Serialization

From 3c0d2e55210735e0df2f8febb5f63c224af230e3 Mon Sep 17 00:00:00 2001
From: Meihua Wu <meihuawu@umich.edu>
Date: Fri, 31 Jul 2015 13:01:10 -0700
Subject: [PATCH 0746/1454] [SPARK-9246] [MLLIB] DistributedLDAModel predict
 top docs per topic

Add topDocumentsPerTopic to DistributedLDAModel.

Add ScalaDoc and unit tests.

Author: Meihua Wu <meihuawu@umich.edu>

Closes #7769 from rotationsymmetry/SPARK-9246 and squashes the following commits:

1029e79c [Meihua Wu] clean up code comments
a023b82 [Meihua Wu] Update tests to use Long for doc index.
91e5998 [Meihua Wu] Use Long for doc index.
b9f70cf [Meihua Wu] Revise topDocumentsPerTopic
26ff3f6 [Meihua Wu] Add topDocumentsPerTopic, scala doc and unit tests
---
 .../spark/mllib/clustering/LDAModel.scala     | 37 +++++++++++++++++++
 .../spark/mllib/clustering/LDASuite.scala     | 22 +++++++++++
 2 files changed, 59 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index ff7035d2246c2..0cdac84eeb591 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -516,6 +516,43 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
+  /**
+   * Return the top documents for each topic
+   *
+   * This is approximate; it may not return exactly the top-weighted documents for each topic.
+   * To get a more precise set of top documents, increase maxDocumentsPerTopic.
+   *
+   * @param maxDocumentsPerTopic  Maximum number of documents to collect for each topic.
+   * @return  Array over topics.  Each element represent as a pair of matching arrays:
+   *          (IDs for the documents, weights of the topic in these documents).
+   *          For each topic, documents are sorted in order of decreasing topic weights.
+   */
+  def topDocumentsPerTopic(maxDocumentsPerTopic: Int): Array[(Array[Long], Array[Double])] = {
+    val numTopics = k
+    val topicsInQueues: Array[BoundedPriorityQueue[(Double, Long)]] =
+      topicDistributions.mapPartitions { docVertices =>
+        // For this partition, collect the most common docs for each topic in queues:
+        //  queues(topic) = queue of (doc topic, doc ID).
+        val queues =
+          Array.fill(numTopics)(new BoundedPriorityQueue[(Double, Long)](maxDocumentsPerTopic))
+        for ((docId, docTopics) <- docVertices) {
+          var topic = 0
+          while (topic < numTopics) {
+            queues(topic) += (docTopics(topic) -> docId)
+            topic += 1
+          }
+        }
+        Iterator(queues)
+      }.treeReduce { (q1, q2) =>
+        q1.zip(q2).foreach { case (a, b) => a ++= b }
+        q1
+      }
+    topicsInQueues.map { q =>
+      val (docTopics, docs) = q.toArray.sortBy(-_._1).unzip
+      (docs.toArray, docTopics.toArray)
+    }
+  }
+
   // TODO
   // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ???
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 79d2a1cafd1fa..f2b94707fd0ff 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -122,6 +122,28 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     // Check: log probabilities
     assert(model.logLikelihood < 0.0)
     assert(model.logPrior < 0.0)
+
+    // Check: topDocumentsPerTopic
+    // Compare it with top documents per topic derived from topicDistributions
+    val topDocsByTopicDistributions = { n: Int =>
+      Range(0, k).map { topic =>
+        val (doc, docWeights) = topicDistributions.sortBy(-_._2(topic)).take(n).unzip
+        (doc.toArray, docWeights.map(_(topic)).toArray)
+      }.toArray
+    }
+
+    // Top 3 documents per topic
+    model.topDocumentsPerTopic(3).zip(topDocsByTopicDistributions(3)).foreach {case (t1, t2) =>
+      assert(t1._1 === t2._1)
+      assert(t1._2 === t2._2)
+    }
+
+    // All documents per topic
+    val q = tinyCorpus.length
+    model.topDocumentsPerTopic(q).zip(topDocsByTopicDistributions(q)).foreach {case (t1, t2) =>
+      assert(t1._1 === t2._1)
+      assert(t1._2 === t2._2)
+    }
   }
 
   test("vertex indexing") {

From 060c79aab58efd4ce7353a1b00534de0d9e1de0b Mon Sep 17 00:00:00 2001
From: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.local>
Date: Fri, 31 Jul 2015 13:08:55 -0700
Subject: [PATCH 0747/1454] [SPARK-9056] [STREAMING] Rename configuration
 `spark.streaming.minRememberDuration` to
 `spark.streaming.fileStream.minRememberDuration`

Rename configuration `spark.streaming.minRememberDuration` to `spark.streaming.fileStream.minRememberDuration`

Author: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.local>
Author: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.Samavihome>

Closes #7740 from sabhyankar/spark_branch_9056 and squashes the following commits:

d5b2f1f [Sameer Abhyankar] Correct deprecated version to 1.5
1268133 [Sameer Abhyankar] Add {} and indentation
ddf9844 [Sameer Abhyankar] Change 4 space indentation to 2 space indentation
1819b5f [Sameer Abhyankar] Use spark.streaming.fileStream.minRememberDuration property in lieu of spark.streaming.minRememberDuration
---
 core/src/main/scala/org/apache/spark/SparkConf.scala        | 4 +++-
 .../apache/spark/streaming/dstream/FileInputDStream.scala   | 6 ++++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 4161792976c7b..08bab4bf2739f 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -548,7 +548,9 @@ private[spark] object SparkConf extends Logging {
     "spark.rpc.askTimeout" -> Seq(
       AlternateConfig("spark.akka.askTimeout", "1.4")),
     "spark.rpc.lookupTimeout" -> Seq(
-      AlternateConfig("spark.akka.lookupTimeout", "1.4"))
+      AlternateConfig("spark.akka.lookupTimeout", "1.4")),
+    "spark.streaming.fileStream.minRememberDuration" -> Seq(
+      AlternateConfig("spark.streaming.minRememberDuration", "1.5"))
     )
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
index dd4da9d9ca6a2..c358f5b5bd70b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
@@ -86,8 +86,10 @@ class FileInputDStream[K, V, F <: NewInputFormat[K, V]](
    * Files with mod times older than this "window" of remembering will be ignored. So if new
    * files are visible within this window, then the file will get selected in the next batch.
    */
-  private val minRememberDurationS =
-    Seconds(ssc.conf.getTimeAsSeconds("spark.streaming.minRememberDuration", "60s"))
+  private val minRememberDurationS = {
+    Seconds(ssc.conf.getTimeAsSeconds("spark.streaming.fileStream.minRememberDuration",
+      ssc.conf.get("spark.streaming.minRememberDuration", "60s")))
+  }
 
   // This is a def so that it works during checkpoint recovery:
   private def clock = ssc.scheduler.clock

From fbef566a107b47e5fddde0ea65b8587d5039062d Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 31 Jul 2015 13:11:42 -0700
Subject: [PATCH 0748/1454] [SPARK-9308] [ML] ml.NaiveBayesModel support
 predicting class probabilities

Make NaiveBayesModel support predicting class probabilities, inherit from ProbabilisticClassificationModel.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7672 from yanboliang/spark-9308 and squashes the following commits:

25e224c [Yanbo Liang] raw2probabilityInPlace should operate in-place
3ee56d6 [Yanbo Liang] change predictRaw and raw2probabilityInPlace
c07e7a2 [Yanbo Liang] ml.NaiveBayesModel support predicting class probabilities
---
 .../spark/ml/classification/NaiveBayes.scala  | 65 ++++++++++++++-----
 .../ml/classification/NaiveBayesSuite.scala   | 54 ++++++++++++++-
 2 files changed, 101 insertions(+), 18 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 5be35fe209291..b46b676204e0e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -69,7 +69,7 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
  * The input feature values must be nonnegative.
  */
 class NaiveBayes(override val uid: String)
-  extends Predictor[Vector, NaiveBayes, NaiveBayesModel]
+  extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel]
   with NaiveBayesParams {
 
   def this() = this(Identifiable.randomUID("nb"))
@@ -106,7 +106,7 @@ class NaiveBayesModel private[ml] (
     override val uid: String,
     val pi: Vector,
     val theta: Matrix)
-  extends PredictionModel[Vector, NaiveBayesModel] with NaiveBayesParams {
+  extends ProbabilisticClassificationModel[Vector, NaiveBayesModel] with NaiveBayesParams {
 
   import OldNaiveBayes.{Bernoulli, Multinomial}
 
@@ -129,29 +129,62 @@ class NaiveBayesModel private[ml] (
       throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
   }
 
-  override protected def predict(features: Vector): Double = {
+  override val numClasses: Int = pi.size
+
+  private def multinomialCalculation(features: Vector) = {
+    val prob = theta.multiply(features)
+    BLAS.axpy(1.0, pi, prob)
+    prob
+  }
+
+  private def bernoulliCalculation(features: Vector) = {
+    features.foreachActive((_, value) =>
+      if (value != 0.0 && value != 1.0) {
+        throw new SparkException(
+          s"Bernoulli naive Bayes requires 0 or 1 feature values but found $features.")
+      }
+    )
+    val prob = thetaMinusNegTheta.get.multiply(features)
+    BLAS.axpy(1.0, pi, prob)
+    BLAS.axpy(1.0, negThetaSum.get, prob)
+    prob
+  }
+
+  override protected def predictRaw(features: Vector): Vector = {
     $(modelType) match {
       case Multinomial =>
-        val prob = theta.multiply(features)
-        BLAS.axpy(1.0, pi, prob)
-        prob.argmax
+        multinomialCalculation(features)
       case Bernoulli =>
-        features.foreachActive{ (index, value) =>
-          if (value != 0.0 && value != 1.0) {
-            throw new SparkException(
-              s"Bernoulli naive Bayes requires 0 or 1 feature values but found $features")
-          }
-        }
-        val prob = thetaMinusNegTheta.get.multiply(features)
-        BLAS.axpy(1.0, pi, prob)
-        BLAS.axpy(1.0, negThetaSum.get, prob)
-        prob.argmax
+        bernoulliCalculation(features)
       case _ =>
         // This should never happen.
         throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
     }
   }
 
+  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
+    rawPrediction match {
+      case dv: DenseVector =>
+        var i = 0
+        val size = dv.size
+        val maxLog = dv.values.max
+        while (i < size) {
+          dv.values(i) = math.exp(dv.values(i) - maxLog)
+          i += 1
+        }
+        val probSum = dv.values.sum
+        i = 0
+        while (i < size) {
+          dv.values(i) = dv.values(i) / probSum
+          i += 1
+        }
+        dv
+      case sv: SparseVector =>
+        throw new RuntimeException("Unexpected error in NaiveBayesModel:" +
+          " raw2probabilityInPlace encountered SparseVector")
+    }
+  }
+
   override def copy(extra: ParamMap): NaiveBayesModel = {
     copyValues(new NaiveBayesModel(uid, pi, theta).setParent(this.parent), extra)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index 264bde3703c5f..aea3d9b694490 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -17,8 +17,11 @@
 
 package org.apache.spark.ml.classification
 
+import breeze.linalg.{Vector => BV}
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.classification.NaiveBayes
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -28,6 +31,8 @@ import org.apache.spark.sql.Row
 
 class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
+  import NaiveBayes.{Multinomial, Bernoulli}
+
   def validatePrediction(predictionAndLabels: DataFrame): Unit = {
     val numOfErrorPredictions = predictionAndLabels.collect().count {
       case Row(prediction: Double, label: Double) =>
@@ -46,6 +51,43 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(model.theta.map(math.exp) ~== thetaData.map(math.exp) absTol 0.05, "theta mismatch")
   }
 
+  def expectedMultinomialProbabilities(model: NaiveBayesModel, feature: Vector): Vector = {
+    val logClassProbs: BV[Double] = model.pi.toBreeze + model.theta.multiply(feature).toBreeze
+    val classProbs = logClassProbs.toArray.map(math.exp)
+    val classProbsSum = classProbs.sum
+    Vectors.dense(classProbs.map(_ / classProbsSum))
+  }
+
+  def expectedBernoulliProbabilities(model: NaiveBayesModel, feature: Vector): Vector = {
+    val negThetaMatrix = model.theta.map(v => math.log(1.0 - math.exp(v)))
+    val negFeature = Vectors.dense(feature.toArray.map(v => 1.0 - v))
+    val piTheta: BV[Double] = model.pi.toBreeze + model.theta.multiply(feature).toBreeze
+    val logClassProbs: BV[Double] = piTheta + negThetaMatrix.multiply(negFeature).toBreeze
+    val classProbs = logClassProbs.toArray.map(math.exp)
+    val classProbsSum = classProbs.sum
+    Vectors.dense(classProbs.map(_ / classProbsSum))
+  }
+
+  def validateProbabilities(
+      featureAndProbabilities: DataFrame,
+      model: NaiveBayesModel,
+      modelType: String): Unit = {
+    featureAndProbabilities.collect().foreach {
+      case Row(features: Vector, probability: Vector) => {
+        assert(probability.toArray.sum ~== 1.0 relTol 1.0e-10)
+        val expected = modelType match {
+          case Multinomial =>
+            expectedMultinomialProbabilities(model, features)
+          case Bernoulli =>
+            expectedBernoulliProbabilities(model, features)
+          case _ =>
+            throw new UnknownError(s"Invalid modelType: $modelType.")
+        }
+        assert(probability ~== expected relTol 1.0e-10)
+      }
+    }
+  }
+
   test("params") {
     ParamsSuite.checkParams(new NaiveBayes)
     val model = new NaiveBayesModel("nb", pi = Vectors.dense(Array(0.2, 0.8)),
@@ -83,9 +125,13 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     val validationDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
       piArray, thetaArray, nPoints, 17, "multinomial"))
-    val predictionAndLabels = model.transform(validationDataset).select("prediction", "label")
 
+    val predictionAndLabels = model.transform(validationDataset).select("prediction", "label")
     validatePrediction(predictionAndLabels)
+
+    val featureAndProbabilities = model.transform(validationDataset)
+      .select("features", "probability")
+    validateProbabilities(featureAndProbabilities, model, "multinomial")
   }
 
   test("Naive Bayes Bernoulli") {
@@ -109,8 +155,12 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     val validationDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
       piArray, thetaArray, nPoints, 20, "bernoulli"))
-    val predictionAndLabels = model.transform(validationDataset).select("prediction", "label")
 
+    val predictionAndLabels = model.transform(validationDataset).select("prediction", "label")
     validatePrediction(predictionAndLabels)
+
+    val featureAndProbabilities = model.transform(validationDataset)
+      .select("features", "probability")
+    validateProbabilities(featureAndProbabilities, model, "bernoulli")
   }
 }

From 815c8245f47e61226a04e2e02f508457b5e9e536 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 31 Jul 2015 13:45:12 -0700
Subject: [PATCH 0749/1454] [SPARK-9466] [SQL] Increate two timeouts in
 CliSuite.

Hopefully this can resolve the flakiness of this suite.

JIRA: https://issues.apache.org/jira/browse/SPARK-9466

Author: Yin Huai <yhuai@databricks.com>

Closes #7777 from yhuai/SPARK-9466 and squashes the following commits:

e0e3a86 [Yin Huai] Increate the timeout.
---
 .../org/apache/spark/sql/hive/thriftserver/CliSuite.scala     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
index 13b0c5951dddc..df80d04b40801 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -137,7 +137,7 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
   }
 
   test("Single command with --database") {
-    runCliWithin(1.minute)(
+    runCliWithin(2.minute)(
       "CREATE DATABASE hive_test_db;"
         -> "OK",
       "USE hive_test_db;"
@@ -148,7 +148,7 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
         -> "Time taken: "
     )
 
-    runCliWithin(1.minute, Seq("--database", "hive_test_db", "-e", "SHOW TABLES;"))(
+    runCliWithin(2.minute, Seq("--database", "hive_test_db", "-e", "SHOW TABLES;"))(
       ""
         -> "OK",
       ""

From 873ab0f9692d8ea6220abdb8d9200041068372a8 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Fri, 31 Jul 2015 13:45:28 -0700
Subject: [PATCH 0750/1454] [SPARK-9490] [DOCS] [MLLIB] MLlib evaluation
 metrics guide example python code uses deprecated print statement

Use print(x) not print x for Python 3 in eval examples
CC sethah mengxr -- just wanted to close this out before 1.5

Author: Sean Owen <sowen@cloudera.com>

Closes #7822 from srowen/SPARK-9490 and squashes the following commits:

01abeba [Sean Owen] Change "print x" to "print(x)" in the rest of the docs too
bd7f7fb [Sean Owen] Use print(x) not print x for Python 3 in eval examples
---
 docs/ml-guide.md                    |  2 +-
 docs/mllib-evaluation-metrics.md    | 66 ++++++++++++++---------------
 docs/mllib-feature-extraction.md    |  2 +-
 docs/mllib-statistics.md            | 20 ++++-----
 docs/quick-start.md                 |  2 +-
 docs/sql-programming-guide.md       |  6 +--
 docs/streaming-programming-guide.md |  2 +-
 7 files changed, 50 insertions(+), 50 deletions(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 8c46adf256a9a..b6ca50e98db02 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -561,7 +561,7 @@ test = sc.parallelize([(4L, "spark i j k"),
 prediction = model.transform(test)
 selected = prediction.select("id", "text", "prediction")
 for row in selected.collect():
-    print row
+    print(row)
 
 sc.stop()
 {% endhighlight %}
diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md
index 4ca0bb06b26a6..7066d5c97418c 100644
--- a/docs/mllib-evaluation-metrics.md
+++ b/docs/mllib-evaluation-metrics.md
@@ -302,10 +302,10 @@ predictionAndLabels = test.map(lambda lp: (float(model.predict(lp.features)), lp
 metrics = BinaryClassificationMetrics(predictionAndLabels)
 
 # Area under precision-recall curve
-print "Area under PR = %s" % metrics.areaUnderPR
+print("Area under PR = %s" % metrics.areaUnderPR)
 
 # Area under ROC curve
-print "Area under ROC = %s" % metrics.areaUnderROC
+print("Area under ROC = %s" % metrics.areaUnderROC)
 
 {% endhighlight %}
 
@@ -606,24 +606,24 @@ metrics = MulticlassMetrics(predictionAndLabels)
 precision = metrics.precision()
 recall = metrics.recall()
 f1Score = metrics.fMeasure()
-print "Summary Stats"
-print "Precision = %s" % precision
-print "Recall = %s" % recall
-print "F1 Score = %s" % f1Score
+print("Summary Stats")
+print("Precision = %s" % precision)
+print("Recall = %s" % recall)
+print("F1 Score = %s" % f1Score)
 
 # Statistics by class
 labels = data.map(lambda lp: lp.label).distinct().collect()
 for label in sorted(labels):
-    print "Class %s precision = %s" % (label, metrics.precision(label))
-    print "Class %s recall = %s" % (label, metrics.recall(label))
-    print "Class %s F1 Measure = %s" % (label, metrics.fMeasure(label, beta=1.0))
+    print("Class %s precision = %s" % (label, metrics.precision(label)))
+    print("Class %s recall = %s" % (label, metrics.recall(label)))
+    print("Class %s F1 Measure = %s" % (label, metrics.fMeasure(label, beta=1.0)))
 
 # Weighted stats
-print "Weighted recall = %s" % metrics.weightedRecall
-print "Weighted precision = %s" % metrics.weightedPrecision
-print "Weighted F(1) Score = %s" % metrics.weightedFMeasure()
-print "Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5)
-print "Weighted false positive rate = %s" % metrics.weightedFalsePositiveRate
+print("Weighted recall = %s" % metrics.weightedRecall)
+print("Weighted precision = %s" % metrics.weightedPrecision)
+print("Weighted F(1) Score = %s" % metrics.weightedFMeasure())
+print("Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5))
+print("Weighted false positive rate = %s" % metrics.weightedFalsePositiveRate)
 {% endhighlight %}
 
 </div>
@@ -881,28 +881,28 @@ scoreAndLabels = sc.parallelize([
 metrics = MultilabelMetrics(scoreAndLabels)
 
 # Summary stats
-print "Recall = %s" % metrics.recall()
-print "Precision = %s" % metrics.precision()
-print "F1 measure = %s" % metrics.f1Measure()
-print "Accuracy = %s" % metrics.accuracy
+print("Recall = %s" % metrics.recall())
+print("Precision = %s" % metrics.precision())
+print("F1 measure = %s" % metrics.f1Measure())
+print("Accuracy = %s" % metrics.accuracy)
 
 # Individual label stats
 labels = scoreAndLabels.flatMap(lambda x: x[1]).distinct().collect()
 for label in labels:
-    print "Class %s precision = %s" % (label, metrics.precision(label))
-    print "Class %s recall = %s" % (label, metrics.recall(label))
-    print "Class %s F1 Measure = %s" % (label, metrics.f1Measure(label))
+    print("Class %s precision = %s" % (label, metrics.precision(label)))
+    print("Class %s recall = %s" % (label, metrics.recall(label)))
+    print("Class %s F1 Measure = %s" % (label, metrics.f1Measure(label)))
 
 # Micro stats
-print "Micro precision = %s" % metrics.microPrecision
-print "Micro recall = %s" % metrics.microRecall
-print "Micro F1 measure = %s" % metrics.microF1Measure
+print("Micro precision = %s" % metrics.microPrecision)
+print("Micro recall = %s" % metrics.microRecall)
+print("Micro F1 measure = %s" % metrics.microF1Measure)
 
 # Hamming loss
-print "Hamming loss = %s" % metrics.hammingLoss
+print("Hamming loss = %s" % metrics.hammingLoss)
 
 # Subset accuracy
-print "Subset accuracy = %s" % metrics.subsetAccuracy
+print("Subset accuracy = %s" % metrics.subsetAccuracy)
 
 {% endhighlight %}
 
@@ -1283,10 +1283,10 @@ scoreAndLabels = predictions.join(ratingsTuple).map(lambda tup: tup[1])
 metrics = RegressionMetrics(scoreAndLabels)
 
 # Root mean sqaured error
-print "RMSE = %s" % metrics.rootMeanSquaredError
+print("RMSE = %s" % metrics.rootMeanSquaredError)
 
 # R-squared
-print "R-squared = %s" % metrics.r2
+print("R-squared = %s" % metrics.r2)
 
 {% endhighlight %}
 
@@ -1479,17 +1479,17 @@ valuesAndPreds = parsedData.map(lambda p: (float(model.predict(p.features)), p.l
 metrics = RegressionMetrics(valuesAndPreds)
 
 # Squared Error
-print "MSE = %s" % metrics.meanSquaredError
-print "RMSE = %s" % metrics.rootMeanSquaredError
+print("MSE = %s" % metrics.meanSquaredError)
+print("RMSE = %s" % metrics.rootMeanSquaredError)
 
 # R-squared
-print "R-squared = %s" % metrics.r2
+print("R-squared = %s" % metrics.r2)
 
 # Mean absolute error
-print "MAE = %s" % metrics.meanAbsoluteError
+print("MAE = %s" % metrics.meanAbsoluteError)
 
 # Explained variance
-print "Explained variance = %s" % metrics.explainedVariance
+print("Explained variance = %s" % metrics.explainedVariance)
 
 {% endhighlight %}
 
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index a69e41e2a1936..de86aba2ae627 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -221,7 +221,7 @@ model = word2vec.fit(inp)
 synonyms = model.findSynonyms('china', 40)
 
 for word, cosine_distance in synonyms:
-    print "{}: {}".format(word, cosine_distance)
+    print("{}: {}".format(word, cosine_distance))
 {% endhighlight %}
 </div>
 </div>
diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index de5d6485f9b5f..be04d0b4b53a8 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -95,9 +95,9 @@ mat = ... # an RDD of Vectors
 
 # Compute column summary statistics.
 summary = Statistics.colStats(mat)
-print summary.mean()
-print summary.variance()
-print summary.numNonzeros()
+print(summary.mean())
+print(summary.variance())
+print(summary.numNonzeros())
 
 {% endhighlight %}
 </div>
@@ -183,12 +183,12 @@ seriesY = ... # must have the same number of partitions and cardinality as serie
 
 # Compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a 
 # method is not specified, Pearson's method will be used by default. 
-print Statistics.corr(seriesX, seriesY, method="pearson")
+print(Statistics.corr(seriesX, seriesY, method="pearson"))
 
 data = ... # an RDD of Vectors
 # calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method.
 # If a method is not specified, Pearson's method will be used by default. 
-print Statistics.corr(data, method="pearson")
+print(Statistics.corr(data, method="pearson"))
 
 {% endhighlight %}
 </div>
@@ -398,14 +398,14 @@ vec = Vectors.dense(...) # a vector composed of the frequencies of events
 # compute the goodness of fit. If a second vector to test against is not supplied as a parameter,
 # the test runs against a uniform distribution.
 goodnessOfFitTestResult = Statistics.chiSqTest(vec)
-print goodnessOfFitTestResult # summary of the test including the p-value, degrees of freedom,
-                              # test statistic, the method used, and the null hypothesis.
+print(goodnessOfFitTestResult) # summary of the test including the p-value, degrees of freedom,
+                               # test statistic, the method used, and the null hypothesis.
 
 mat = Matrices.dense(...) # a contingency matrix
 
 # conduct Pearson's independence test on the input contingency matrix
 independenceTestResult = Statistics.chiSqTest(mat)
-print independenceTestResult  # summary of the test including the p-value, degrees of freedom...
+print(independenceTestResult)  # summary of the test including the p-value, degrees of freedom...
 
 obs = sc.parallelize(...)  # LabeledPoint(feature, label) .
 
@@ -415,8 +415,8 @@ obs = sc.parallelize(...)  # LabeledPoint(feature, label) .
 featureTestResults = Statistics.chiSqTest(obs)
 
 for i, result in enumerate(featureTestResults):
-    print "Column $d:" % (i + 1)
-    print result
+    print("Column $d:" % (i + 1))
+    print(result)
 {% endhighlight %}
 </div>
 
diff --git a/docs/quick-start.md b/docs/quick-start.md
index bb39e4111f244..ce2cc9d2169cd 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -406,7 +406,7 @@ logData = sc.textFile(logFile).cache()
 numAs = logData.filter(lambda s: 'a' in s).count()
 numBs = logData.filter(lambda s: 'b' in s).count()
 
-print "Lines with a: %i, lines with b: %i" % (numAs, numBs)
+print("Lines with a: %i, lines with b: %i" % (numAs, numBs))
 {% endhighlight %}
 
 
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 95945eb7fc8a0..d31baa080cbce 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -570,7 +570,7 @@ teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 1
 # The results of SQL queries are RDDs and support all the normal RDD operations.
 teenNames = teenagers.map(lambda p: "Name: " + p.name)
 for teenName in teenNames.collect():
-  print teenName
+  print(teenName)
 {% endhighlight %}
 
 </div>
@@ -752,7 +752,7 @@ results = sqlContext.sql("SELECT name FROM people")
 # The results of SQL queries are RDDs and support all the normal RDD operations.
 names = results.map(lambda p: "Name: " + p.name)
 for name in names.collect():
-  print name
+  print(name)
 {% endhighlight %}
 
 </div>
@@ -1006,7 +1006,7 @@ parquetFile.registerTempTable("parquetFile");
 teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
 teenNames = teenagers.map(lambda p: "Name: " + p.name)
 for teenName in teenNames.collect():
-  print teenName
+  print(teenName)
 {% endhighlight %}
 
 </div>
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 2f3013b533eb0..4663b3f14c527 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1525,7 +1525,7 @@ def getSqlContextInstance(sparkContext):
 words = ... # DStream of strings
 
 def process(time, rdd):
-    print "========= %s =========" % str(time)
+    print("========= %s =========" % str(time))
     try:
         # Get the singleton instance of SQLContext
         sqlContext = getSqlContextInstance(rdd.context)

From 6e5fd613ea4b9aa0ab485ba681277a51a4367168 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Fri, 31 Jul 2015 21:51:55 +0100
Subject: [PATCH 0751/1454] [SPARK-9507] [BUILD] Remove dependency reduced POM
 hack now that shade plugin is updated

Update to shade plugin 2.4.1, which removes the need for the dependency-reduced-POM workaround and the 'release' profile. Fix management of shade plugin version so children inherit it; bump assembly plugin version while here

See https://issues.apache.org/jira/browse/SPARK-8819

I verified that `mvn clean package -DskipTests` works with Maven 3.3.3.

pwendell are you up for trying this for the 1.5.0 release?

Author: Sean Owen <sowen@cloudera.com>

Closes #7826 from srowen/SPARK-9507 and squashes the following commits:

e0b0fd2 [Sean Owen] Update to shade plugin 2.4.1, which removes the need for the dependency-reduced-POM workaround and the 'release' profile. Fix management of shade plugin version so children inherit it; bump assembly plugin version while here
---
 dev/create-release/create-release.sh |  4 ++--
 pom.xml                              | 33 +++++-----------------------
 2 files changed, 8 insertions(+), 29 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 86a7a4068c40e..4311c8c9e4ca6 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -118,13 +118,13 @@ if [[ ! "$@" =~ --skip-publish ]]; then
 
   rm -rf $SPARK_REPO
 
-  build/mvn -DskipTests -Pyarn -Phive -Prelease\
+  build/mvn -DskipTests -Pyarn -Phive \
     -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
   ./dev/change-scala-version.sh 2.11
   
-  build/mvn -DskipTests -Pyarn -Phive -Prelease\
+  build/mvn -DskipTests -Pyarn -Phive \
     -Dscala-2.11 -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     clean install
 
diff --git a/pom.xml b/pom.xml
index e351c7c19df96..1371a1b6bd9f1 100644
--- a/pom.xml
+++ b/pom.xml
@@ -160,9 +160,6 @@
     <fasterxml.jackson.version>2.4.4</fasterxml.jackson.version>
     <snappy.version>1.1.1.7</snappy.version>
     <netlib.java.version>1.1.2</netlib.java.version>
-    <!-- For maven shade plugin (see SPARK-8819) -->
-    <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
-
     <test.java.home>${java.home}</test.java.home>
 
     <!--
@@ -1376,7 +1373,12 @@
         <plugin>
           <groupId>org.apache.maven.plugins</groupId>
           <artifactId>maven-assembly-plugin</artifactId>
-          <version>2.5.3</version>
+          <version>2.5.5</version>
+        </plugin>
+        <plugin>
+          <groupId>org.apache.maven.plugins</groupId>
+          <artifactId>maven-shade-plugin</artifactId>
+          <version>2.4.1</version>
         </plugin>
         <plugin>
           <groupId>org.apache.maven.plugins</groupId>
@@ -1470,11 +1472,8 @@
       <plugin>
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-shade-plugin</artifactId>
-        <version>2.3</version>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <!-- Work around MSHADE-148. See SPARK-8819. -->
-          <createDependencyReducedPom>${create.dependency.reduced.pom}</createDependencyReducedPom>
           <artifactSet>
             <includes>
               <!-- At a minimum we must include this to force effective pom generation -->
@@ -1836,26 +1835,6 @@
       </properties>
     </profile>
 
-    <profile>
-      <!--
-          Use this profile only for making Spark releases. Note that due to SPARK-8819,
-          you must use maven version 3.2.x or before to avoid running into MSHADE-148.
-      -->
-      <id>release</id>
-      <properties>
-        <!--
-            The maven shade plugin has a bug where enabling the `createDependencyReducedPom`
-            property causes maven to go into an infinite loop (MSHADE-148). This is only an
-            issue for the Spark build if the maven version is 3.3.x or newer (SPARK-8819).
-
-            However, since disabling this property has the side effect of not resolving
-            variables in the released pom files (SPARK-8781), we need to enable this during
-            releases.
-        -->
-        <create.dependency.reduced.pom>true</create.dependency.reduced.pom>
-      </properties>
-    </profile>
-
     <!--
       These empty profiles are available in some sub-modules. Declare them here so that
       maven does not complain when they're provided on the command line for a sub-module

From 82f47b811607a1eeeecba437fe0ffc15d4e5f9ec Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Fri, 31 Jul 2015 14:02:44 -0700
Subject: [PATCH 0752/1454] [SPARK-9510] [SPARKR] Remaining SparkR style fixes

With the change in this patch, I get no more warnings from `./dev/lint-r` in my machine

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7834 from shivaram/sparkr-style-fixes and squashes the following commits:

716cd8e [Shivaram Venkataraman] Remaining SparkR style fixes
---
 R/pkg/R/RDD.R                    | 6 +++---
 R/pkg/inst/tests/test_sparkSQL.R | 4 +++-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R
index 2a013b3dbb968..051e441d4e063 100644
--- a/R/pkg/R/RDD.R
+++ b/R/pkg/R/RDD.R
@@ -1264,12 +1264,12 @@ setMethod("pipeRDD",
           signature(x = "RDD", command = "character"),
           function(x, command, env = list()) {
             func <- function(part) {
-              trim.trailing.func <- function(x) {
+              trim_trailing_func <- function(x) {
                 sub("[\r\n]*$", "", toString(x))
               }
-              input <- unlist(lapply(part, trim.trailing.func))
+              input <- unlist(lapply(part, trim_trailing_func))
               res <- system2(command, stdout = TRUE, input = input, env = env)
-              lapply(res, trim.trailing.func)
+              lapply(res, trim_trailing_func)
             }
             lapplyPartition(x, func)
           })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index aca41aa6dcf24..25f697314f803 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -128,7 +128,9 @@ test_that("create DataFrame from RDD", {
   expect_equal(dtypes(df2), list(c("name", "string"), c("age", "int"), c("height", "float")))
   expect_equal(collect(where(df2, df2$name == "Bob")), c("Bob", 16, 176.5))
 
-  localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18), height=c(164.10, 181.4, 173.7))
+  localDF <- data.frame(name=c("John", "Smith", "Sarah"),
+                        age=c(19, 23, 18),
+                        height=c(164.10, 181.4, 173.7))
   df <- createDataFrame(sqlContext, localDF, schema)
   expect_is(df, "DataFrame")
   expect_equal(count(df), 3)

From 710c2b5dd2dc6b8d947303ad8dfae4539b63fe11 Mon Sep 17 00:00:00 2001
From: Hossein <hossein@databricks.com>
Date: Fri, 31 Jul 2015 14:07:41 -0700
Subject: [PATCH 0753/1454] [SPARK-9324] [SPARK-9322] [SPARK-9321] [SPARKR]
 Some aliases for R-like functions in DataFrames

Adds following aliases:
* unique (distinct)
* rbind (unionAll): accepts many DataFrames
* nrow (count)
* ncol
* dim
* names (columns): along with the replacement function to change names

Author: Hossein <hossein@databricks.com>

Closes #7764 from falaki/sparkR-alias and squashes the following commits:

56016f5 [Hossein] Updated R documentation
5e4a4d0 [Hossein] Removed extra code
f51cbef [Hossein] Merge branch 'master' into sparkR-alias
c1b88bd [Hossein] Moved setGeneric and other comments applied
d9307f8 [Hossein] Added tests
b5aa988 [Hossein] Added dim, ncol, nrow, names, rbind, and unique functions to DataFrames
---
 R/pkg/NAMESPACE                  |  6 +++
 R/pkg/R/DataFrame.R              | 90 ++++++++++++++++++++++++++++++++
 R/pkg/R/generics.R               |  4 ++
 R/pkg/inst/tests/test_sparkSQL.R | 22 ++++++--
 4 files changed, 119 insertions(+), 3 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index a329e14f25aeb..ff116cb1fbde2 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -29,6 +29,7 @@ exportMethods("arrange",
               "count",
               "crosstab",
               "describe",
+              "dim",
               "distinct",
               "dropna",
               "dtypes",
@@ -45,11 +46,15 @@ exportMethods("arrange",
               "isLocal",
               "join",
               "limit",
+              "names",
+              "ncol",
+              "nrow",
               "orderBy",
               "mutate",
               "names",
               "persist",
               "printSchema",
+              "rbind",
               "registerTempTable",
               "rename",
               "repartition",
@@ -66,6 +71,7 @@ exportMethods("arrange",
               "summarize",
               "take",
               "unionAll",
+              "unique",
               "unpersist",
               "where",
               "withColumn",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index b31ad3729e09b..b4065d2944bdc 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -255,6 +255,16 @@ setMethod("names",
             columns(x)
           })
 
+#' @rdname columns
+setMethod("names<-",
+          signature(x = "DataFrame"),
+          function(x, value) {
+            if (!is.null(value)) {
+              sdf <- callJMethod(x@sdf, "toDF", listToSeq(as.list(value)))
+              dataFrame(sdf)
+            }
+          })
+
 #' Register Temporary Table
 #'
 #' Registers a DataFrame as a Temporary Table in the SQLContext
@@ -473,6 +483,18 @@ setMethod("distinct",
             dataFrame(sdf)
           })
 
+#' @title Distinct rows in a DataFrame
+#
+#' @description Returns a new DataFrame containing distinct rows in this DataFrame
+#'
+#' @rdname unique
+#' @aliases unique
+setMethod("unique",
+          signature(x = "DataFrame"),
+          function(x) {
+            distinct(x)
+          })
+
 #' Sample
 #'
 #' Return a sampled subset of this DataFrame using a random seed.
@@ -534,6 +556,58 @@ setMethod("count",
             callJMethod(x@sdf, "count")
           })
 
+#' @title Number of rows for a DataFrame
+#' @description Returns number of rows in a DataFrames
+#'
+#' @name nrow
+#'
+#' @rdname nrow
+#' @aliases count
+setMethod("nrow",
+          signature(x = "DataFrame"),
+          function(x) {
+            count(x)
+          })
+
+#' Returns the number of columns in a DataFrame
+#'
+#' @param x a SparkSQL DataFrame
+#'
+#' @rdname ncol
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' path <- "path/to/file.json"
+#' df <- jsonFile(sqlContext, path)
+#' ncol(df)
+#' }
+setMethod("ncol",
+          signature(x = "DataFrame"),
+          function(x) {
+            length(columns(x))
+          })
+
+#' Returns the dimentions (number of rows and columns) of a DataFrame
+#' @param x a SparkSQL DataFrame
+#'
+#' @rdname dim
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' path <- "path/to/file.json"
+#' df <- jsonFile(sqlContext, path)
+#' dim(df)
+#' }
+setMethod("dim",
+          signature(x = "DataFrame"),
+          function(x) {
+            c(count(x), ncol(x))
+          })
+
 #' Collects all the elements of a Spark DataFrame and coerces them into an R data.frame.
 #'
 #' @param x A SparkSQL DataFrame
@@ -1231,6 +1305,22 @@ setMethod("unionAll",
             dataFrame(unioned)
           })
 
+#' @title Union two or more DataFrames
+#
+#' @description Returns a new DataFrame containing rows of all parameters.
+#
+#' @rdname rbind
+#' @aliases unionAll
+setMethod("rbind",
+          signature(... = "DataFrame"),
+          function(x, ..., deparse.level = 1) {
+            if (nargs() == 3) {
+              unionAll(x, ...)
+            } else {
+              unionAll(x, Recall(..., deparse.level = 1))
+            }
+          })
+
 #' Intersect
 #'
 #' Return a new DataFrame containing rows only in both this DataFrame
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index a3a121058e165..71d1e348c4efb 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -669,3 +669,7 @@ setGeneric("upper", function(x) { standardGeneric("upper") })
 #' @rdname glm
 #' @export
 setGeneric("glm")
+
+#' @rdname rbind
+#' @export
+setGeneric("rbind", signature = "...")
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 25f697314f803..9faee8d59c3af 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -88,6 +88,9 @@ test_that("create DataFrame from RDD", {
   df <- createDataFrame(sqlContext, rdd, list("a", "b"))
   expect_is(df, "DataFrame")
   expect_equal(count(df), 10)
+  expect_equal(nrow(df), 10)
+  expect_equal(ncol(df), 2)
+  expect_equal(dim(df), c(10, 2))
   expect_equal(columns(df), c("a", "b"))
   expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
 
@@ -491,7 +494,7 @@ test_that("head() and first() return the correct data", {
   expect_equal(nrow(testFirst), 1)
 })
 
-test_that("distinct() on DataFrames", {
+test_that("distinct() and unique on DataFrames", {
   lines <- c("{\"name\":\"Michael\"}",
              "{\"name\":\"Andy\", \"age\":30}",
              "{\"name\":\"Justin\", \"age\":19}",
@@ -503,6 +506,10 @@ test_that("distinct() on DataFrames", {
   uniques <- distinct(df)
   expect_is(uniques, "DataFrame")
   expect_equal(count(uniques), 3)
+
+  uniques2 <- unique(df)
+  expect_is(uniques2, "DataFrame")
+  expect_equal(count(uniques2), 3)
 })
 
 test_that("sample on a DataFrame", {
@@ -815,7 +822,7 @@ test_that("isLocal()", {
   expect_false(isLocal(df))
 })
 
-test_that("unionAll(), except(), and intersect() on a DataFrame", {
+test_that("unionAll(), rbind(), except(), and intersect() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
 
   lines <- c("{\"name\":\"Bob\", \"age\":24}",
@@ -830,6 +837,11 @@ test_that("unionAll(), except(), and intersect() on a DataFrame", {
   expect_equal(count(unioned), 6)
   expect_equal(first(unioned)$name, "Michael")
 
+  unioned2 <- arrange(rbind(unioned, df, df2), df$age)
+  expect_is(unioned2, "DataFrame")
+  expect_equal(count(unioned2), 12)
+  expect_equal(first(unioned2)$name, "Michael")
+
   excepted <- arrange(except(df, df2), desc(df$age))
   expect_is(unioned, "DataFrame")
   expect_equal(count(excepted), 2)
@@ -853,7 +865,7 @@ test_that("withColumn() and withColumnRenamed()", {
   expect_equal(columns(newDF2)[1], "newerAge")
 })
 
-test_that("mutate() and rename()", {
+test_that("mutate(), rename() and names()", {
   df <- jsonFile(sqlContext, jsonPath)
   newDF <- mutate(df, newAge = df$age + 2)
   expect_equal(length(columns(newDF)), 3)
@@ -863,6 +875,10 @@ test_that("mutate() and rename()", {
   newDF2 <- rename(df, newerAge = df$age)
   expect_equal(length(columns(newDF2)), 2)
   expect_equal(columns(newDF2)[1], "newerAge")
+
+  names(newDF2) <- c("newerName", "evenNewerAge")
+  expect_equal(length(names(newDF2)), 2)
+  expect_equal(names(newDF2)[1], "newerName")
 })
 
 test_that("write.df() on DataFrame and works with parquetFile", {

From 3fc0cb92001798167a14c1377362a3335397dd4c Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 31 Jul 2015 14:13:06 -0700
Subject: [PATCH 0754/1454] [SPARK-9233] [SQL] Enable code-gen in window
 function unit tests

Since code-gen is enabled by default, it is better to run window function tests with code-gen.

https://issues.apache.org/jira/browse/SPARK-9233

Author: Yin Huai <yhuai@databricks.com>

Closes #7832 from yhuai/SPARK-9233 and squashes the following commits:

4e4e4cc [Yin Huai] style
ca80e07 [Yin Huai] Test window function with codegen.
---
 .../sql/catalyst/analysis/Analyzer.scala      |  9 ++++-
 .../HiveWindowFunctionQuerySuite.scala        | 38 +++----------------
 2 files changed, 12 insertions(+), 35 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 51d910b258647..f5daba1543da9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -853,8 +853,13 @@ class Analyzer(
       while (i < groupedWindowExpressions.size) {
         val ((partitionSpec, orderSpec), windowExpressions) = groupedWindowExpressions(i)
         // Set currentChild to the newly created Window operator.
-        currentChild = Window(currentChild.output, windowExpressions,
-          partitionSpec, orderSpec, currentChild)
+        currentChild =
+          Window(
+            currentChild.output,
+            windowExpressions,
+            partitionSpec,
+            orderSpec,
+            currentChild)
 
         // Move to next Window Spec.
         i += 1
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
index 24a758f53170a..92bb9e6d73af1 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
@@ -32,7 +32,7 @@ import org.apache.spark.util.Utils
  * for different tests and there are a few properties needed to let Hive generate golden
  * files, every `createQueryTest` calls should explicitly set `reset` to `false`.
  */
-abstract class HiveWindowFunctionQueryBaseSuite extends HiveComparisonTest with BeforeAndAfter {
+class HiveWindowFunctionQuerySuite extends HiveComparisonTest with BeforeAndAfter {
   private val originalTimeZone = TimeZone.getDefault
   private val originalLocale = Locale.getDefault
   private val testTempDir = Utils.createTempDir()
@@ -759,21 +759,7 @@ abstract class HiveWindowFunctionQueryBaseSuite extends HiveComparisonTest with
     """.stripMargin, reset = false)
 }
 
-class HiveWindowFunctionQueryWithoutCodeGenSuite extends HiveWindowFunctionQueryBaseSuite {
-  var originalCodegenEnabled: Boolean = _
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    originalCodegenEnabled = conf.codegenEnabled
-    sql("set spark.sql.codegen=false")
-  }
-
-  override def afterAll(): Unit = {
-    sql(s"set spark.sql.codegen=$originalCodegenEnabled")
-    super.afterAll()
-  }
-}
-
-abstract class HiveWindowFunctionQueryFileBaseSuite
+class HiveWindowFunctionQueryFileSuite
   extends HiveCompatibilitySuite with BeforeAndAfter {
   private val originalTimeZone = TimeZone.getDefault
   private val originalLocale = Locale.getDefault
@@ -789,11 +775,11 @@ abstract class HiveWindowFunctionQueryFileBaseSuite
     // The following settings are used for generating golden files with Hive.
     // We have to use kryo to correctly let Hive serialize plans with window functions.
     // This is used to generate golden files.
-    sql("set hive.plan.serialization.format=kryo")
+    // sql("set hive.plan.serialization.format=kryo")
     // Explicitly set fs to local fs.
-    sql(s"set fs.default.name=file://$testTempDir/")
+    // sql(s"set fs.default.name=file://$testTempDir/")
     // Ask Hive to run jobs in-process as a single map and reduce task.
-    sql("set mapred.job.tracker=local")
+    // sql("set mapred.job.tracker=local")
   }
 
   override def afterAll() {
@@ -838,17 +824,3 @@ abstract class HiveWindowFunctionQueryFileBaseSuite
     case (name, _) => realWhiteList.contains(name)
   }
 }
-
-class HiveWindowFunctionQueryFileWithoutCodeGenSuite extends HiveWindowFunctionQueryFileBaseSuite {
-  var originalCodegenEnabled: Boolean = _
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    originalCodegenEnabled = conf.codegenEnabled
-    sql("set spark.sql.codegen=false")
-  }
-
-  override def afterAll(): Unit = {
-    sql(s"set spark.sql.codegen=$originalCodegenEnabled")
-    super.afterAll()
-  }
-}

From 4d5a6e7b60b315968973e2298eeee5eb174ec721 Mon Sep 17 00:00:00 2001
From: HuJiayin <jiayin.hu@intel.com>
Date: Fri, 31 Jul 2015 16:05:26 -0700
Subject: [PATCH 0755/1454]  [SPARK-8271][SQL]string function: soundex

This PR brings SQL function soundex(), see https://issues.apache.org/jira/browse/HIVE-9738

It's based on #7115 , thanks to HuJiayin

Author: HuJiayin <jiayin.hu@intel.com>
Author: Davies Liu <davies@databricks.com>

Closes #7812 from davies/soundex and squashes the following commits:

fa75941 [Davies Liu] Merge branch 'master' of github.com:apache/spark into soundex
a4bd6d8 [Davies Liu] fix soundex
2538908 [HuJiayin] add codegen soundex
d15d329 [HuJiayin] add back ut
ded1a14 [HuJiayin] Merge branch 'master' of https://github.com/apache/spark
e2dec2c [HuJiayin] support soundex rebase code
---
 python/pyspark/sql/functions.py               | 17 ++++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/stringOperations.scala        | 16 ++++++
 .../expressions/StringExpressionsSuite.scala  | 28 ++++++++++
 .../org/apache/spark/sql/functions.scala      |  8 +++
 .../spark/sql/StringFunctionsSuite.scala      |  9 ++++
 .../apache/spark/unsafe/types/UTF8String.java | 53 +++++++++++++++++++
 .../spark/unsafe/types/UTF8StringSuite.java   | 48 +++++++++++++++++
 8 files changed, 180 insertions(+)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 8024a8de07c98..bb9926ce8ca85 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -63,6 +63,8 @@
     'year', 'quarter', 'month', 'hour', 'minute', 'second',
     'dayofmonth', 'dayofyear', 'weekofyear']
 
+__all__ += ['soundex']
+
 
 def _create_function(name, doc=""):
     """ Create a function for aggregator by name"""
@@ -922,6 +924,7 @@ def trunc(date, format):
 def size(col):
     """
     Collection function: returns the length of the array or map stored in the column.
+
     :param col: name of column or expression
 
     >>> df = sqlContext.createDataFrame([([1, 2, 3],),([1],),([],)], ['data'])
@@ -932,6 +935,20 @@ def size(col):
     return Column(sc._jvm.functions.size(_to_java_column(col)))
 
 
+@since
+@ignore_unicode_prefix
+def soundex(col):
+    """
+    Returns the SoundEx encoding for a string
+
+    >>> df = sqlContext.createDataFrame([("Peters",),("Uhrbach",)], ['name'])
+    >>> df.select(soundex(df.name).alias("soundex")).collect()
+    [Row(soundex=u'P362'), Row(soundex=u'U612')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.size(_to_java_column(col)))
+
+
 class UserDefinedFunction(object):
     """
     User defined function in Python
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 1bf7204a2515c..3f61a9af1f075 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -194,6 +194,7 @@ object FunctionRegistry {
     expression[StringRepeat]("repeat"),
     expression[StringReverse]("reverse"),
     expression[StringTrimRight]("rtrim"),
+    expression[SoundEx]("soundex"),
     expression[StringSpace]("space"),
     expression[StringSplit]("split"),
     expression[Substring]("substr"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 684eac12bd6f0..160e72f384a9b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -718,6 +718,22 @@ case class Levenshtein(left: Expression, right: Expression) extends BinaryExpres
   }
 }
 
+/**
+ * A function that return soundex code of the given string expression.
+ */
+case class SoundEx(child: Expression) extends UnaryExpression with ExpectsInputTypes {
+
+  override def dataType: DataType = StringType
+
+  override def inputTypes: Seq[DataType] = Seq(StringType)
+
+  override def nullSafeEval(input: Any): Any = input.asInstanceOf[UTF8String].soundex()
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, c => s"$c.soundex()")
+  }
+}
+
 /**
  * Returns the numeric value of the first character of str.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 3ecd0d374c46b..fb72fe1714153 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -347,6 +347,34 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     // scalastyle:on
   }
 
+  test("soundex unit test") {
+    checkEvaluation(SoundEx(Literal("ZIN")), "Z500")
+    checkEvaluation(SoundEx(Literal("SU")), "S000")
+    checkEvaluation(SoundEx(Literal("")), "")
+    checkEvaluation(SoundEx(Literal.create(null, StringType)), null)
+
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    checkEvaluation(SoundEx(Literal("测试")), "测试")
+    checkEvaluation(SoundEx(Literal("Tschüss")), "T220")
+    // scalastyle:on
+    checkEvaluation(SoundEx(Literal("zZ")), "Z000", create_row("s8"))
+    checkEvaluation(SoundEx(Literal("RAGSSEEESSSVEEWE")), "R221")
+    checkEvaluation(SoundEx(Literal("Ashcraft")), "A261")
+    checkEvaluation(SoundEx(Literal("Aswcraft")), "A261")
+    checkEvaluation(SoundEx(Literal("Tymczak")), "T522")
+    checkEvaluation(SoundEx(Literal("Pfister")), "P236")
+    checkEvaluation(SoundEx(Literal("Miller")), "M460")
+    checkEvaluation(SoundEx(Literal("Peterson")), "P362")
+    checkEvaluation(SoundEx(Literal("Peters")), "P362")
+    checkEvaluation(SoundEx(Literal("Auerbach")), "A612")
+    checkEvaluation(SoundEx(Literal("Uhrbach")), "U612")
+    checkEvaluation(SoundEx(Literal("Moskowitz")), "M232")
+    checkEvaluation(SoundEx(Literal("Moskovitz")), "M213")
+    checkEvaluation(SoundEx(Literal("relyheewsgeessg")), "R422")
+    checkEvaluation(SoundEx(Literal("!!")), "!!")
+  }
+
   test("TRIM/LTRIM/RTRIM") {
     val s = 'a.string.at(0)
     checkEvaluation(StringTrim(Literal(" aa  ")), "aa", create_row(" abdef "))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 5d82a5eadd94d..89ffa9c50d6c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1902,6 +1902,14 @@ object functions {
     StringRepeat(str.expr, lit(n).expr)
   }
 
+  /**
+   * * Return the soundex code for the specified expression.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def soundex(e: Column): Column = SoundEx(e.expr)
+
   /**
    * Splits str around pattern (pattern is a regular expression).
    * NOTE: pattern is a string represent the regular expression.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 8e0ea76d15881..b7f073cccb6ac 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -142,6 +142,15 @@ class StringFunctionsSuite extends QueryTest {
       Row("aa123cc"))
   }
 
+  test("soundex function") {
+    val df = Seq(("MARY", "SU")).toDF("l", "r")
+    checkAnswer(
+      df.select(soundex($"l"), soundex($"r")), Row("M600", "S000"))
+
+    checkAnswer(
+      df.selectExpr("SoundEx(l)", "SoundEx(r)"), Row("M600", "S000"))
+  }
+
   test("string instr function") {
     val df = Seq(("aaads", "aa", "zz")).toDF("a", "b", "c")
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index c38953f65d7d7..9d4998fd48a38 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -680,4 +680,57 @@ public int hashCode() {
     }
     return result;
   }
+
+  /**
+   * Soundex mapping table
+   */
+  private static final byte[] US_ENGLISH_MAPPING = {'0', '1', '2', '3', '0', '1', '2', '7',
+    '0', '2', '2', '4', '5', '5', '0', '1', '2', '6', '2', '3', '0', '1', '7', '2', '0', '2'};
+
+  /**
+   * Encodes a string into a Soundex value. Soundex is an encoding used to relate similar names,
+   * but can also be used as a general purpose scheme to find word with similar phonemes.
+   * https://en.wikipedia.org/wiki/Soundex
+   */
+  public UTF8String soundex() {
+    if (numBytes == 0) {
+      return EMPTY_UTF8;
+    }
+
+    byte b = getByte(0);
+    if ('a' <= b && b <= 'z') {
+      b -= 32;
+    } else if (b < 'A' || 'Z' < b) {
+      // first character must be a letter
+      return this;
+    }
+    byte sx[] = {'0', '0', '0', '0'};
+    sx[0] = b;
+    int sxi = 1;
+    int idx = b - 'A';
+    byte lastCode = US_ENGLISH_MAPPING[idx];
+
+    for (int i = 1; i < numBytes; i++) {
+      b = getByte(i);
+      if ('a' <= b && b <= 'z') {
+        b -= 32;
+      } else if (b < 'A' || 'Z' < b) {
+        // not a letter, skip it
+        lastCode = '0';
+        continue;
+      }
+      idx = b - 'A';
+      byte code = US_ENGLISH_MAPPING[idx];
+      if (code == '7') {
+        // ignore it
+      } else {
+        if (code != '0' && code != lastCode) {
+          sx[sxi++] = code;
+          if (sxi > 3) break;
+        }
+        lastCode = code;
+      }
+    }
+    return UTF8String.fromBytes(sx);
+  }
 }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index f2cc19ca6b172..c565210872322 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -341,4 +341,52 @@ public void createBlankString() {
     assertEquals(fromString("   "), blankString(3));
     assertEquals(fromString(""), blankString(0));
   }
+
+  @Test
+  public void soundex() {
+    assertEquals(fromString("Robert").soundex(), fromString("R163"));
+    assertEquals(fromString("Rupert").soundex(), fromString("R163"));
+    assertEquals(fromString("Rubin").soundex(), fromString("R150"));
+    assertEquals(fromString("Ashcraft").soundex(), fromString("A261"));
+    assertEquals(fromString("Ashcroft").soundex(), fromString("A261"));
+    assertEquals(fromString("Burroughs").soundex(), fromString("B620"));
+    assertEquals(fromString("Burrows").soundex(), fromString("B620"));
+    assertEquals(fromString("Ekzampul").soundex(), fromString("E251"));
+    assertEquals(fromString("Example").soundex(), fromString("E251"));
+    assertEquals(fromString("Ellery").soundex(), fromString("E460"));
+    assertEquals(fromString("Euler").soundex(), fromString("E460"));
+    assertEquals(fromString("Ghosh").soundex(), fromString("G200"));
+    assertEquals(fromString("Gauss").soundex(), fromString("G200"));
+    assertEquals(fromString("Gutierrez").soundex(), fromString("G362"));
+    assertEquals(fromString("Heilbronn").soundex(), fromString("H416"));
+    assertEquals(fromString("Hilbert").soundex(), fromString("H416"));
+    assertEquals(fromString("Jackson").soundex(), fromString("J250"));
+    assertEquals(fromString("Kant").soundex(), fromString("K530"));
+    assertEquals(fromString("Knuth").soundex(), fromString("K530"));
+    assertEquals(fromString("Lee").soundex(), fromString("L000"));
+    assertEquals(fromString("Lukasiewicz").soundex(), fromString("L222"));
+    assertEquals(fromString("Lissajous").soundex(), fromString("L222"));
+    assertEquals(fromString("Ladd").soundex(), fromString("L300"));
+    assertEquals(fromString("Lloyd").soundex(), fromString("L300"));
+    assertEquals(fromString("Moses").soundex(), fromString("M220"));
+    assertEquals(fromString("O'Hara").soundex(), fromString("O600"));
+    assertEquals(fromString("Pfister").soundex(), fromString("P236"));
+    assertEquals(fromString("Rubin").soundex(), fromString("R150"));
+    assertEquals(fromString("Robert").soundex(), fromString("R163"));
+    assertEquals(fromString("Rupert").soundex(), fromString("R163"));
+    assertEquals(fromString("Soundex").soundex(), fromString("S532"));
+    assertEquals(fromString("Sownteks").soundex(), fromString("S532"));
+    assertEquals(fromString("Tymczak").soundex(), fromString("T522"));
+    assertEquals(fromString("VanDeusen").soundex(), fromString("V532"));
+    assertEquals(fromString("Washington").soundex(), fromString("W252"));
+    assertEquals(fromString("Wheaton").soundex(), fromString("W350"));
+
+    assertEquals(fromString("a").soundex(), fromString("A000"));
+    assertEquals(fromString("ab").soundex(), fromString("A100"));
+    assertEquals(fromString("abc").soundex(), fromString("A120"));
+    assertEquals(fromString("abcd").soundex(), fromString("A123"));
+    assertEquals(fromString("").soundex(), fromString(""));
+    assertEquals(fromString("123").soundex(), fromString("123"));
+    assertEquals(fromString("世界千世").soundex(), fromString("世界千世"));
+  }
 }

From f51fd6fbb4d9822502f98b312251e317d757bc3a Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Fri, 31 Jul 2015 18:36:22 -0700
Subject: [PATCH 0756/1454] [SPARK-8936] [MLLIB] OnlineLDA document-topic
 Dirichlet hyperparameter optimization

Adds `alpha` (document-topic Dirichlet parameter) hyperparameter optimization to `OnlineLDAOptimizer` following Huang: Maximum Likelihood Estimation of Dirichlet Distribution Parameters. Also introduces a private `setSampleWithReplacement` to `OnlineLDAOptimizer` for unit testing purposes.

Author: Feynman Liang <fliang@databricks.com>

Closes #7836 from feynmanliang/SPARK-8936-alpha-optimize and squashes the following commits:

4bef484 [Feynman Liang] Documentation improvements
c3c6c1d [Feynman Liang] Fix docs
151e859 [Feynman Liang] Fix style
fa77518 [Feynman Liang] Hyperparameter optimization
---
 .../spark/mllib/clustering/LDAOptimizer.scala | 75 ++++++++++++++++---
 .../spark/mllib/clustering/LDASuite.scala     | 34 +++++++++
 2 files changed, 99 insertions(+), 10 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index d6f8b29a43dfd..b0e14cb8296a6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -19,8 +19,8 @@ package org.apache.spark.mllib.clustering
 
 import java.util.Random
 
-import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, normalize, sum}
-import breeze.numerics.{abs, exp}
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, all, normalize, sum}
+import breeze.numerics.{trigamma, abs, exp}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
 import org.apache.spark.annotation.DeveloperApi
@@ -239,22 +239,26 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /** alias for docConcentration */
   private var alpha: Vector = Vectors.dense(0)
 
-  /** (private[clustering] for debugging)  Get docConcentration */
+  /** (for debugging)  Get docConcentration */
   private[clustering] def getAlpha: Vector = alpha
 
   /** alias for topicConcentration */
   private var eta: Double = 0
 
-  /** (private[clustering] for debugging)  Get topicConcentration */
+  /** (for debugging)  Get topicConcentration */
   private[clustering] def getEta: Double = eta
 
   private var randomGenerator: java.util.Random = null
 
+  /** (for debugging) Whether to sample mini-batches with replacement. (default = true) */
+  private var sampleWithReplacement: Boolean = true
+
   // Online LDA specific parameters
   // Learning rate is: (tau0 + t)^{-kappa}
   private var tau0: Double = 1024
   private var kappa: Double = 0.51
   private var miniBatchFraction: Double = 0.05
+  private var optimizeAlpha: Boolean = false
 
   // internal data structure
   private var docs: RDD[(Long, Vector)] = null
@@ -262,7 +266,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /** Dirichlet parameter for the posterior over topics */
   private var lambda: BDM[Double] = null
 
-  /** (private[clustering] for debugging) Get parameter for topics */
+  /** (for debugging) Get parameter for topics */
   private[clustering] def getLambda: BDM[Double] = lambda
 
   /** Current iteration (count of invocations of [[next()]]) */
@@ -325,7 +329,22 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   }
 
   /**
-   * (private[clustering])
+   * Optimize alpha, indicates whether alpha (Dirichlet parameter for document-topic distribution)
+   * will be optimized during training.
+   */
+  def getOptimzeAlpha: Boolean = this.optimizeAlpha
+
+  /**
+   * Sets whether to optimize alpha parameter during training.
+   *
+   * Default: false
+   */
+  def setOptimzeAlpha(optimizeAlpha: Boolean): this.type = {
+    this.optimizeAlpha = optimizeAlpha
+    this
+  }
+
+  /**
    * Set the Dirichlet parameter for the posterior over topics.
    * This is only used for testing now. In the future, it can help support training stop/resume.
    */
@@ -335,7 +354,6 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   }
 
   /**
-   * (private[clustering])
    * Used for random initialization of the variational parameters.
    * Larger value produces values closer to 1.0.
    * This is only used for testing currently.
@@ -345,6 +363,15 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     this
   }
 
+  /**
+   * Sets whether to sample mini-batches with or without replacement. (default = true)
+   * This is only used for testing currently.
+   */
+  private[clustering] def setSampleWithReplacement(replace: Boolean): this.type = {
+    this.sampleWithReplacement = replace
+    this
+  }
+
   override private[clustering] def initialize(
       docs: RDD[(Long, Vector)],
       lda: LDA): OnlineLDAOptimizer = {
@@ -376,7 +403,8 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   }
 
   override private[clustering] def next(): OnlineLDAOptimizer = {
-    val batch = docs.sample(withReplacement = true, miniBatchFraction, randomGenerator.nextLong())
+    val batch = docs.sample(withReplacement = sampleWithReplacement, miniBatchFraction,
+      randomGenerator.nextLong())
     if (batch.isEmpty()) return this
     submitMiniBatch(batch)
   }
@@ -418,6 +446,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
     // Note that this is an optimization to avoid batch.count
     updateLambda(batchResult, (miniBatchFraction * corpusSize).ceil.toInt)
+    if (optimizeAlpha) updateAlpha(gammat)
     this
   }
 
@@ -433,13 +462,39 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
       weight * (stat * (corpusSize.toDouble / batchSize.toDouble) + eta)
   }
 
-  /** Calculates learning rate rho, which decays as a function of [[iteration]] */
+  /**
+   * Update alpha based on `gammat`, the inferred topic distributions for documents in the
+   * current mini-batch. Uses Newton-Rhapson method.
+   * @see Section 3.3, Huang: Maximum Likelihood Estimation of Dirichlet Distribution Parameters
+   *      (http://jonathan-huang.org/research/dirichlet/dirichlet.pdf)
+   */
+  private def updateAlpha(gammat: BDM[Double]): Unit = {
+    val weight = rho()
+    val N = gammat.rows.toDouble
+    val alpha = this.alpha.toBreeze.toDenseVector
+    val logphat: BDM[Double] = sum(LDAUtils.dirichletExpectation(gammat)(::, breeze.linalg.*)) / N
+    val gradf = N * (-LDAUtils.dirichletExpectation(alpha) + logphat.toDenseVector)
+
+    val c = N * trigamma(sum(alpha))
+    val q = -N * trigamma(alpha)
+    val b = sum(gradf / q) / (1D / c + sum(1D / q))
+
+    val dalpha = -(gradf - b) / q
+
+    if (all((weight * dalpha + alpha) :> 0D)) {
+      alpha :+= weight * dalpha
+      this.alpha = Vectors.dense(alpha.toArray)
+    }
+  }
+
+
+  /** Calculate learning rate rho for the current [[iteration]]. */
   private def rho(): Double = {
     math.pow(getTau0 + this.iteration, -getKappa)
   }
 
   /**
-   * Get a random matrix to initialize lambda
+   * Get a random matrix to initialize lambda.
    */
   private def getGammaMatrix(row: Int, col: Int): BDM[Double] = {
     val randBasis = new RandBasis(new org.apache.commons.math3.random.MersenneTwister(
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index f2b94707fd0ff..fdc2554ab853e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -400,6 +400,40 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("OnlineLDAOptimizer alpha hyperparameter optimization") {
+    val k = 2
+    val docs = sc.parallelize(toyData)
+    val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
+      .setGammaShape(100).setOptimzeAlpha(true).setSampleWithReplacement(false)
+    val lda = new LDA().setK(k)
+      .setDocConcentration(1D / k)
+      .setTopicConcentration(0.01)
+      .setMaxIterations(100)
+      .setOptimizer(op)
+      .setSeed(12345)
+    val ldaModel: LocalLDAModel = lda.run(docs).asInstanceOf[LocalLDAModel]
+
+    /* Verify the results with gensim:
+      import numpy as np
+      from gensim import models
+      corpus = [
+       [(0, 1.0), (1, 1.0)],
+       [(1, 1.0), (2, 1.0)],
+       [(0, 1.0), (2, 1.0)],
+       [(3, 1.0), (4, 1.0)],
+       [(3, 1.0), (5, 1.0)],
+       [(4, 1.0), (5, 1.0)]]
+      np.random.seed(2345)
+      lda = models.ldamodel.LdaModel(
+         corpus=corpus, alpha='auto', eta=0.01, num_topics=2, update_every=0, passes=100,
+         decay=0.51, offset=1024)
+      print(lda.alpha)
+      > [ 0.42582646  0.43511073]
+     */
+
+    assert(ldaModel.docConcentration ~== Vectors.dense(0.42582646, 0.43511073) absTol 0.05)
+  }
+
   test("model save/load") {
     // Test for LocalLDAModel.
     val localModel = new LocalLDAModel(tinyTopics,

From 8cb415a4b9bc1f82127ccce4a5579d433f4e8f83 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 31 Jul 2015 19:19:27 -0700
Subject: [PATCH 0757/1454] [SPARK-9451] [SQL] Support entries larger than
 default page size in BytesToBytesMap & integrate with ShuffleMemoryManager

This patch adds support for entries larger than the default page size in BytesToBytesMap.  These large rows are handled by allocating special overflow pages to hold individual entries.

In addition, this patch integrates BytesToBytesMap with the ShuffleMemoryManager:

- Move BytesToBytesMap from `unsafe` to `core` so that it can import `ShuffleMemoryManager`.
- Before allocating new data pages, ask the ShuffleMemoryManager to reserve the memory:
  - `putNewKey()` now returns a boolean to indicate whether the insert succeeded or failed due to a lack of memory.  The caller can use this value to respond to the memory pressure (e.g. by spilling).
- `UnsafeFixedWidthAggregationMap. getAggregationBuffer()` now returns `null` to signal failure due to a lack of memory.
- Updated all uses of these classes to handle these error conditions.
- Added new tests for allocating large records and for allocations which fail due to memory pressure.
- Extended the `afterAll()` test teardown methods to detect ShuffleMemoryManager leaks.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7762 from JoshRosen/large-rows and squashes the following commits:

ae7bc56 [Josh Rosen] Fix compilation
82fc657 [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-rows
34ab943 [Josh Rosen] Remove semi
31a525a [Josh Rosen] Integrate BytesToBytesMap with ShuffleMemoryManager.
626b33c [Josh Rosen] Move code to sql/core and spark/core packages so that ShuffleMemoryManager can be integrated
ec4484c [Josh Rosen] Move BytesToBytesMap from unsafe package to core.
642ed69 [Josh Rosen] Rename size to numElements
bea1152 [Josh Rosen] Add basic test.
2cd3570 [Josh Rosen] Remove accidental duplicated code
07ff9ef [Josh Rosen] Basic support for large rows in BytesToBytesMap.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 170 ++++++++++-----
 .../unsafe/map/HashMapGrowthStrategy.java     |   0
 .../spark/shuffle/ShuffleMemoryManager.scala  |   8 +-
 .../map/AbstractBytesToBytesMapSuite.java     | 204 ++++++++++++++----
 .../map/BytesToBytesMapOffHeapSuite.java      |   0
 .../map/BytesToBytesMapOnHeapSuite.java       |   0
 .../UnsafeFixedWidthAggregationMap.java       |  25 ++-
 .../sql/execution/GeneratedAggregate.scala    |   6 +
 .../sql/execution/joins/HashedRelation.scala  |  27 ++-
 .../UnsafeFixedWidthAggregationMapSuite.scala |  36 ++--
 .../spark/unsafe/memory/MemoryBlock.java      |   2 +-
 .../unsafe/memory/TaskMemoryManager.java      |   1 +
 12 files changed, 353 insertions(+), 126 deletions(-)
 rename {unsafe => core}/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java (78%)
 rename {unsafe => core}/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java (100%)
 rename {unsafe => core}/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java (64%)
 rename {unsafe => core}/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java (100%)
 rename {unsafe => core}/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java (100%)
 rename sql/{catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions => core/src/main/java/org/apache/spark/sql/execution}/UnsafeFixedWidthAggregationMap.java (89%)
 rename sql/{catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions => core/src/test/scala/org/apache/spark/sql/execution}/UnsafeFixedWidthAggregationMapSuite.scala (78%)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
similarity index 78%
rename from unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
rename to core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 198e0684f32f8..0f42950e6ed8b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.unsafe.map;
 
+import java.io.IOException;
 import java.lang.Override;
 import java.lang.UnsupportedOperationException;
 import java.util.Iterator;
@@ -24,7 +25,10 @@
 import java.util.List;
 
 import com.google.common.annotations.VisibleForTesting;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
+import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.unsafe.*;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.array.LongArray;
@@ -45,6 +49,8 @@
  */
 public final class BytesToBytesMap {
 
+  private final Logger logger = LoggerFactory.getLogger(BytesToBytesMap.class);
+
   private static final Murmur3_x86_32 HASHER = new Murmur3_x86_32(0);
 
   private static final HashMapGrowthStrategy growthStrategy = HashMapGrowthStrategy.DOUBLING;
@@ -54,7 +60,9 @@ public final class BytesToBytesMap {
    */
   private static final int END_OF_PAGE_MARKER = -1;
 
-  private final TaskMemoryManager memoryManager;
+  private final TaskMemoryManager taskMemoryManager;
+
+  private final ShuffleMemoryManager shuffleMemoryManager;
 
   /**
    * A linked list for tracking all allocated data pages so that we can free all of our memory.
@@ -120,7 +128,7 @@ public final class BytesToBytesMap {
   /**
    * Number of keys defined in the map.
    */
-  private int size;
+  private int numElements;
 
   /**
    * The map will be expanded once the number of keys exceeds this threshold.
@@ -150,12 +158,14 @@ public final class BytesToBytesMap {
   private long numHashCollisions = 0;
 
   public BytesToBytesMap(
-      TaskMemoryManager memoryManager,
+      TaskMemoryManager taskMemoryManager,
+      ShuffleMemoryManager shuffleMemoryManager,
       int initialCapacity,
       double loadFactor,
       long pageSizeBytes,
       boolean enablePerfMetrics) {
-    this.memoryManager = memoryManager;
+    this.taskMemoryManager = taskMemoryManager;
+    this.shuffleMemoryManager = shuffleMemoryManager;
     this.loadFactor = loadFactor;
     this.loc = new Location();
     this.pageSizeBytes = pageSizeBytes;
@@ -175,24 +185,32 @@ public BytesToBytesMap(
   }
 
   public BytesToBytesMap(
-      TaskMemoryManager memoryManager,
+      TaskMemoryManager taskMemoryManager,
+      ShuffleMemoryManager shuffleMemoryManager,
       int initialCapacity,
       long pageSizeBytes) {
-    this(memoryManager, initialCapacity, 0.70, pageSizeBytes, false);
+    this(taskMemoryManager, shuffleMemoryManager, initialCapacity, 0.70, pageSizeBytes, false);
   }
 
   public BytesToBytesMap(
-      TaskMemoryManager memoryManager,
+      TaskMemoryManager taskMemoryManager,
+      ShuffleMemoryManager shuffleMemoryManager,
       int initialCapacity,
       long pageSizeBytes,
       boolean enablePerfMetrics) {
-    this(memoryManager, initialCapacity, 0.70, pageSizeBytes, enablePerfMetrics);
+    this(
+      taskMemoryManager,
+      shuffleMemoryManager,
+      initialCapacity,
+      0.70,
+      pageSizeBytes,
+      enablePerfMetrics);
   }
 
   /**
    * Returns the number of keys defined in the map.
    */
-  public int size() { return size; }
+  public int numElements() { return numElements; }
 
   private static final class BytesToBytesMapIterator implements Iterator<Location> {
 
@@ -252,7 +270,7 @@ public void remove() {
    * `lookup()`, the behavior of the returned iterator is undefined.
    */
   public Iterator<Location> iterator() {
-    return new BytesToBytesMapIterator(size, dataPages.iterator(), loc);
+    return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc);
   }
 
   /**
@@ -330,7 +348,8 @@ public final class Location {
 
     private void updateAddressesAndSizes(long fullKeyAddress) {
       updateAddressesAndSizes(
-        memoryManager.getPage(fullKeyAddress), memoryManager.getOffsetInPage(fullKeyAddress));
+        taskMemoryManager.getPage(fullKeyAddress),
+        taskMemoryManager.getOffsetInPage(fullKeyAddress));
     }
 
     private void updateAddressesAndSizes(Object page, long keyOffsetInPage) {
@@ -411,7 +430,8 @@ public int getValueLength() {
     /**
      * Store a new key and value. This method may only be called once for a given key; if you want
      * to update the value associated with a key, then you can directly manipulate the bytes stored
-     * at the value address.
+     * at the value address. The return value indicates whether the put succeeded or whether it
+     * failed because additional memory could not be acquired.
      * <p>
      * It is only valid to call this method immediately after calling `lookup()` using the same key.
      * </p>
@@ -428,14 +448,19 @@ public int getValueLength() {
      * <pre>
      *   Location loc = map.lookup(keyBaseObject, keyBaseOffset, keyLengthInBytes);
      *   if (!loc.isDefined()) {
-     *     loc.putNewKey(keyBaseObject, keyBaseOffset, keyLengthInBytes, ...)
+     *     if (!loc.putNewKey(keyBaseObject, keyBaseOffset, keyLengthInBytes, ...)) {
+     *       // handle failure to grow map (by spilling, for example)
+     *     }
      *   }
      * </pre>
      * <p>
      * Unspecified behavior if the key is not defined.
      * </p>
+     *
+     * @return true if the put() was successful and false if the put() failed because memory could
+     *         not be acquired.
      */
-    public void putNewKey(
+    public boolean putNewKey(
         Object keyBaseObject,
         long keyBaseOffset,
         int keyLengthBytes,
@@ -445,63 +470,110 @@ public void putNewKey(
       assert (!isDefined) : "Can only set value once for a key";
       assert (keyLengthBytes % 8 == 0);
       assert (valueLengthBytes % 8 == 0);
-      if (size == MAX_CAPACITY) {
+      if (numElements == MAX_CAPACITY) {
         throw new IllegalStateException("BytesToBytesMap has reached maximum capacity");
       }
+
       // Here, we'll copy the data into our data pages. Because we only store a relative offset from
       // the key address instead of storing the absolute address of the value, the key and value
       // must be stored in the same memory page.
       // (8 byte key length) (key) (8 byte value length) (value)
       final long requiredSize = 8 + keyLengthBytes + 8 + valueLengthBytes;
-      assert (requiredSize <= pageSizeBytes - 8); // Reserve 8 bytes for the end-of-page marker.
-      size++;
-      bitset.set(pos);
 
-      // If there's not enough space in the current page, allocate a new page (8 bytes are reserved
-      // for the end-of-page marker).
-      if (currentDataPage == null || pageSizeBytes - 8 - pageCursor < requiredSize) {
+      // --- Figure out where to insert the new record ---------------------------------------------
+
+      final MemoryBlock dataPage;
+      final Object dataPageBaseObject;
+      final long dataPageInsertOffset;
+      boolean useOverflowPage = requiredSize > pageSizeBytes - 8;
+      if (useOverflowPage) {
+        // The record is larger than the page size, so allocate a special overflow page just to hold
+        // that record.
+        final long memoryRequested = requiredSize + 8;
+        final long memoryGranted = shuffleMemoryManager.tryToAcquire(memoryRequested);
+        if (memoryGranted != memoryRequested) {
+          shuffleMemoryManager.release(memoryGranted);
+          logger.debug("Failed to acquire {} bytes of memory", memoryRequested);
+          return false;
+        }
+        MemoryBlock overflowPage = taskMemoryManager.allocatePage(memoryRequested);
+        dataPages.add(overflowPage);
+        dataPage = overflowPage;
+        dataPageBaseObject = overflowPage.getBaseObject();
+        dataPageInsertOffset = overflowPage.getBaseOffset();
+      } else if (currentDataPage == null || pageSizeBytes - 8 - pageCursor < requiredSize) {
+        // The record can fit in a data page, but either we have not allocated any pages yet or
+        // the current page does not have enough space.
         if (currentDataPage != null) {
           // There wasn't enough space in the current page, so write an end-of-page marker:
           final Object pageBaseObject = currentDataPage.getBaseObject();
           final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor;
           PlatformDependent.UNSAFE.putLong(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
         }
-        MemoryBlock newPage = memoryManager.allocatePage(pageSizeBytes);
+        final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+        if (memoryGranted != pageSizeBytes) {
+          shuffleMemoryManager.release(memoryGranted);
+          logger.debug("Failed to acquire {} bytes of memory", pageSizeBytes);
+          return false;
+        }
+        MemoryBlock newPage = taskMemoryManager.allocatePage(pageSizeBytes);
         dataPages.add(newPage);
         pageCursor = 0;
         currentDataPage = newPage;
+        dataPage = currentDataPage;
+        dataPageBaseObject = currentDataPage.getBaseObject();
+        dataPageInsertOffset = currentDataPage.getBaseOffset();
+      } else {
+        // There is enough space in the current data page.
+        dataPage = currentDataPage;
+        dataPageBaseObject = currentDataPage.getBaseObject();
+        dataPageInsertOffset = currentDataPage.getBaseOffset() + pageCursor;
       }
 
+      // --- Append the key and value data to the current data page --------------------------------
+
+      long insertCursor = dataPageInsertOffset;
+
       // Compute all of our offsets up-front:
-      final Object pageBaseObject = currentDataPage.getBaseObject();
-      final long pageBaseOffset = currentDataPage.getBaseOffset();
-      final long keySizeOffsetInPage = pageBaseOffset + pageCursor;
-      pageCursor += 8; // word used to store the key size
-      final long keyDataOffsetInPage = pageBaseOffset + pageCursor;
-      pageCursor += keyLengthBytes;
-      final long valueSizeOffsetInPage = pageBaseOffset + pageCursor;
-      pageCursor += 8; // word used to store the value size
-      final long valueDataOffsetInPage = pageBaseOffset + pageCursor;
-      pageCursor += valueLengthBytes;
+      final long keySizeOffsetInPage = insertCursor;
+      insertCursor += 8; // word used to store the key size
+      final long keyDataOffsetInPage = insertCursor;
+      insertCursor += keyLengthBytes;
+      final long valueSizeOffsetInPage = insertCursor;
+      insertCursor += 8; // word used to store the value size
+      final long valueDataOffsetInPage = insertCursor;
+      insertCursor += valueLengthBytes; // word used to store the value size
 
       // Copy the key
-      PlatformDependent.UNSAFE.putLong(pageBaseObject, keySizeOffsetInPage, keyLengthBytes);
+      PlatformDependent.UNSAFE.putLong(dataPageBaseObject, keySizeOffsetInPage, keyLengthBytes);
       PlatformDependent.copyMemory(
-        keyBaseObject, keyBaseOffset, pageBaseObject, keyDataOffsetInPage, keyLengthBytes);
+        keyBaseObject, keyBaseOffset, dataPageBaseObject, keyDataOffsetInPage, keyLengthBytes);
       // Copy the value
-      PlatformDependent.UNSAFE.putLong(pageBaseObject, valueSizeOffsetInPage, valueLengthBytes);
-      PlatformDependent.copyMemory(
-        valueBaseObject, valueBaseOffset, pageBaseObject, valueDataOffsetInPage, valueLengthBytes);
+      PlatformDependent.UNSAFE.putLong(dataPageBaseObject, valueSizeOffsetInPage, valueLengthBytes);
+      PlatformDependent.copyMemory(valueBaseObject, valueBaseOffset, dataPageBaseObject,
+        valueDataOffsetInPage, valueLengthBytes);
+
+      // --- Update bookeeping data structures -----------------------------------------------------
+
+      if (useOverflowPage) {
+        // Store the end-of-page marker at the end of the data page
+        PlatformDependent.UNSAFE.putLong(dataPageBaseObject, insertCursor, END_OF_PAGE_MARKER);
+      } else {
+        pageCursor += requiredSize;
+      }
 
-      final long storedKeyAddress = memoryManager.encodePageNumberAndOffset(
-        currentDataPage, keySizeOffsetInPage);
+      numElements++;
+      bitset.set(pos);
+      final long storedKeyAddress = taskMemoryManager.encodePageNumberAndOffset(
+        dataPage, keySizeOffsetInPage);
       longArray.set(pos * 2, storedKeyAddress);
       longArray.set(pos * 2 + 1, keyHashcode);
       updateAddressesAndSizes(storedKeyAddress);
       isDefined = true;
-      if (size > growthThreshold && longArray.size() < MAX_CAPACITY) {
+      if (numElements > growthThreshold && longArray.size() < MAX_CAPACITY) {
         growAndRehash();
       }
+      return true;
     }
   }
 
@@ -516,7 +588,7 @@ private void allocate(int capacity) {
     // The capacity needs to be divisible by 64 so that our bit set can be sized properly
     capacity = Math.max((int) Math.min(MAX_CAPACITY, nextPowerOf2(capacity)), 64);
     assert (capacity <= MAX_CAPACITY);
-    longArray = new LongArray(memoryManager.allocate(capacity * 8L * 2));
+    longArray = new LongArray(MemoryBlock.fromLongArray(new long[capacity * 2]));
     bitset = new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64]));
 
     this.growthThreshold = (int) (capacity * loadFactor);
@@ -530,18 +602,14 @@ private void allocate(int capacity) {
    * This method is idempotent.
    */
   public void free() {
-    if (longArray != null) {
-      memoryManager.free(longArray.memoryBlock());
-      longArray = null;
-    }
-    if (bitset != null) {
-      // The bitset's heap memory isn't managed by a memory manager, so no need to free it here.
-      bitset = null;
-    }
+    longArray = null;
+    bitset = null;
     Iterator<MemoryBlock> dataPagesIterator = dataPages.iterator();
     while (dataPagesIterator.hasNext()) {
-      memoryManager.freePage(dataPagesIterator.next());
+      MemoryBlock dataPage = dataPagesIterator.next();
       dataPagesIterator.remove();
+      taskMemoryManager.freePage(dataPage);
+      shuffleMemoryManager.release(dataPage.size());
     }
     assert(dataPages.isEmpty());
   }
@@ -628,8 +696,6 @@ void growAndRehash() {
       }
     }
 
-    // Deallocate the old data structures.
-    memoryManager.free(oldLongArray.memoryBlock());
     if (enablePerfMetrics) {
       timeSpentResizingNs += System.nanoTime() - resizeStartTime;
     }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java b/core/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java
similarity index 100%
rename from unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java
rename to core/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index f038b722957b8..00c1e078a441c 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -85,7 +85,7 @@ private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
           return toGrant
         } else {
           logInfo(
-            s"Thread $taskAttemptId waiting for at least 1/2N of shuffle memory pool to be free")
+            s"TID $taskAttemptId waiting for at least 1/2N of shuffle memory pool to be free")
           wait()
         }
       } else {
@@ -116,6 +116,12 @@ private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
     taskMemory.remove(taskAttemptId)
     notifyAll()  // Notify waiters who locked "this" in tryToAcquire that memory has been freed
   }
+
+  /** Returns the memory consumption, in bytes, for the current task */
+  def getMemoryConsumptionForThisTask(): Long = synchronized {
+    val taskAttemptId = currentTaskAttemptId()
+    taskMemory.getOrElse(taskAttemptId, 0L)
+  }
 }
 
 private object ShuffleMemoryManager {
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
similarity index 64%
rename from unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
rename to core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 0be94ad371255..60f483acbcb80 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -21,15 +21,14 @@
 import java.nio.ByteBuffer;
 import java.util.*;
 
-import org.junit.After;
-import org.junit.Assert;
-import org.junit.Before;
-import org.junit.Test;
+import org.junit.*;
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
+import static org.hamcrest.Matchers.greaterThan;
 import static org.mockito.AdditionalMatchers.geq;
 import static org.mockito.Mockito.*;
 
+import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.memory.*;
 import org.apache.spark.unsafe.PlatformDependent;
@@ -41,32 +40,39 @@ public abstract class AbstractBytesToBytesMapSuite {
 
   private final Random rand = new Random(42);
 
-  private TaskMemoryManager memoryManager;
-  private TaskMemoryManager sizeLimitedMemoryManager;
+  private ShuffleMemoryManager shuffleMemoryManager;
+  private TaskMemoryManager taskMemoryManager;
+  private TaskMemoryManager sizeLimitedTaskMemoryManager;
   private final long PAGE_SIZE_BYTES = 1L << 26; // 64 megabytes
 
   @Before
   public void setup() {
-    memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(getMemoryAllocator()));
+    shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
+    taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(getMemoryAllocator()));
     // Mocked memory manager for tests that check the maximum array size, since actually allocating
     // such large arrays will cause us to run out of memory in our tests.
-    sizeLimitedMemoryManager = spy(memoryManager);
-    when(sizeLimitedMemoryManager.allocate(geq(1L << 20))).thenAnswer(new Answer<MemoryBlock>() {
-      @Override
-      public MemoryBlock answer(InvocationOnMock invocation) throws Throwable {
-        if (((Long) invocation.getArguments()[0] / 8) > Integer.MAX_VALUE) {
-          throw new OutOfMemoryError("Requested array size exceeds VM limit");
+    sizeLimitedTaskMemoryManager = mock(TaskMemoryManager.class);
+    when(sizeLimitedTaskMemoryManager.allocate(geq(1L << 20))).thenAnswer(
+      new Answer<MemoryBlock>() {
+        @Override
+        public MemoryBlock answer(InvocationOnMock invocation) throws Throwable {
+          if (((Long) invocation.getArguments()[0] / 8) > Integer.MAX_VALUE) {
+            throw new OutOfMemoryError("Requested array size exceeds VM limit");
+          }
+          return new MemoryBlock(null, 0, (Long) invocation.getArguments()[0]);
         }
-        return memoryManager.allocate(1L << 20);
       }
-    });
+    );
   }
 
   @After
   public void tearDown() {
-    if (memoryManager != null) {
-      memoryManager.cleanUpAllAllocatedMemory();
-      memoryManager = null;
+    if (taskMemoryManager != null) {
+      long leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask();
+      Assert.assertEquals(0, taskMemoryManager.cleanUpAllAllocatedMemory());
+      Assert.assertEquals(0, leakedShuffleMemory);
+      shuffleMemoryManager = null;
+      taskMemoryManager = null;
     }
   }
 
@@ -85,7 +91,7 @@ private static byte[] getByteArray(MemoryLocation loc, int size) {
   }
 
   private byte[] getRandomByteArray(int numWords) {
-    Assert.assertTrue(numWords > 0);
+    Assert.assertTrue(numWords >= 0);
     final int lengthInBytes = numWords * 8;
     final byte[] bytes = new byte[lengthInBytes];
     rand.nextBytes(bytes);
@@ -111,9 +117,10 @@ private static boolean arrayEquals(
 
   @Test
   public void emptyMap() {
-    BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64, PAGE_SIZE_BYTES);
+    BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, 64, PAGE_SIZE_BYTES);
     try {
-      Assert.assertEquals(0, map.size());
+      Assert.assertEquals(0, map.numElements());
       final int keyLengthInWords = 10;
       final int keyLengthInBytes = keyLengthInWords * 8;
       final byte[] key = getRandomByteArray(keyLengthInWords);
@@ -126,7 +133,8 @@ public void emptyMap() {
 
   @Test
   public void setAndRetrieveAKey() {
-    BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64, PAGE_SIZE_BYTES);
+    BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, 64, PAGE_SIZE_BYTES);
     final int recordLengthWords = 10;
     final int recordLengthBytes = recordLengthWords * 8;
     final byte[] keyData = getRandomByteArray(recordLengthWords);
@@ -135,14 +143,14 @@ public void setAndRetrieveAKey() {
       final BytesToBytesMap.Location loc =
         map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes);
       Assert.assertFalse(loc.isDefined());
-      loc.putNewKey(
+      Assert.assertTrue(loc.putNewKey(
         keyData,
         BYTE_ARRAY_OFFSET,
         recordLengthBytes,
         valueData,
         BYTE_ARRAY_OFFSET,
         recordLengthBytes
-      );
+      ));
       // After storing the key and value, the other location methods should return results that
       // reflect the result of this store without us having to call lookup() again on the same key.
       Assert.assertEquals(recordLengthBytes, loc.getKeyLength());
@@ -158,14 +166,14 @@ public void setAndRetrieveAKey() {
       Assert.assertArrayEquals(valueData, getByteArray(loc.getValueAddress(), recordLengthBytes));
 
       try {
-        loc.putNewKey(
+        Assert.assertTrue(loc.putNewKey(
           keyData,
           BYTE_ARRAY_OFFSET,
           recordLengthBytes,
           valueData,
           BYTE_ARRAY_OFFSET,
           recordLengthBytes
-        );
+        ));
         Assert.fail("Should not be able to set a new value for a key");
       } catch (AssertionError e) {
         // Expected exception; do nothing.
@@ -178,7 +186,8 @@ public void setAndRetrieveAKey() {
   @Test
   public void iteratorTest() throws Exception {
     final int size = 4096;
-    BytesToBytesMap map = new BytesToBytesMap(memoryManager, size / 2, PAGE_SIZE_BYTES);
+    BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, size / 2, PAGE_SIZE_BYTES);
     try {
       for (long i = 0; i < size; i++) {
         final long[] value = new long[] { i };
@@ -187,23 +196,23 @@ public void iteratorTest() throws Exception {
         Assert.assertFalse(loc.isDefined());
         // Ensure that we store some zero-length keys
         if (i % 5 == 0) {
-          loc.putNewKey(
+          Assert.assertTrue(loc.putNewKey(
             null,
             PlatformDependent.LONG_ARRAY_OFFSET,
             0,
             value,
             PlatformDependent.LONG_ARRAY_OFFSET,
             8
-          );
+          ));
         } else {
-          loc.putNewKey(
+          Assert.assertTrue(loc.putNewKey(
             value,
             PlatformDependent.LONG_ARRAY_OFFSET,
             8,
             value,
             PlatformDependent.LONG_ARRAY_OFFSET,
             8
-          );
+          ));
         }
       }
       final java.util.BitSet valuesSeen = new java.util.BitSet(size);
@@ -236,7 +245,8 @@ public void iteratingOverDataPagesWithWastedSpace() throws Exception {
     final int NUM_ENTRIES = 1000 * 1000;
     final int KEY_LENGTH = 16;
     final int VALUE_LENGTH = 40;
-    final BytesToBytesMap map = new BytesToBytesMap(memoryManager, NUM_ENTRIES, PAGE_SIZE_BYTES);
+    final BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, NUM_ENTRIES, PAGE_SIZE_BYTES);
     // Each record will take 8 + 8 + 16 + 40 = 72 bytes of space in the data page. Our 64-megabyte
     // pages won't be evenly-divisible by records of this size, which will cause us to waste some
     // space at the end of the page. This is necessary in order for us to take the end-of-record
@@ -251,14 +261,14 @@ public void iteratingOverDataPagesWithWastedSpace() throws Exception {
           KEY_LENGTH
         );
         Assert.assertFalse(loc.isDefined());
-        loc.putNewKey(
+        Assert.assertTrue(loc.putNewKey(
           key,
           LONG_ARRAY_OFFSET,
           KEY_LENGTH,
           value,
           LONG_ARRAY_OFFSET,
           VALUE_LENGTH
-        );
+        ));
       }
       Assert.assertEquals(2, map.getNumDataPages());
 
@@ -305,7 +315,8 @@ public void randomizedStressTest() {
     // Java arrays' hashCodes() aren't based on the arrays' contents, so we need to wrap arrays
     // into ByteBuffers in order to use them as keys here.
     final Map<ByteBuffer, byte[]> expected = new HashMap<ByteBuffer, byte[]>();
-    final BytesToBytesMap map = new BytesToBytesMap(memoryManager, size, PAGE_SIZE_BYTES);
+    final BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, size, PAGE_SIZE_BYTES);
 
     try {
       // Fill the map to 90% full so that we can trigger probing
@@ -320,14 +331,14 @@ public void randomizedStressTest() {
             key.length
           );
           Assert.assertFalse(loc.isDefined());
-          loc.putNewKey(
+          Assert.assertTrue(loc.putNewKey(
             key,
             BYTE_ARRAY_OFFSET,
             key.length,
             value,
             BYTE_ARRAY_OFFSET,
             value.length
-          );
+          ));
           // After calling putNewKey, the following should be true, even before calling
           // lookup():
           Assert.assertTrue(loc.isDefined());
@@ -351,10 +362,102 @@ public void randomizedStressTest() {
     }
   }
 
+  @Test
+  public void randomizedTestWithRecordsLargerThanPageSize() {
+    final long pageSizeBytes = 128;
+    final BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, 64, pageSizeBytes);
+    // Java arrays' hashCodes() aren't based on the arrays' contents, so we need to wrap arrays
+    // into ByteBuffers in order to use them as keys here.
+    final Map<ByteBuffer, byte[]> expected = new HashMap<ByteBuffer, byte[]>();
+    try {
+      for (int i = 0; i < 1000; i++) {
+        final byte[] key = getRandomByteArray(rand.nextInt(128));
+        final byte[] value = getRandomByteArray(rand.nextInt(128));
+        if (!expected.containsKey(ByteBuffer.wrap(key))) {
+          expected.put(ByteBuffer.wrap(key), value);
+          final BytesToBytesMap.Location loc = map.lookup(
+            key,
+            BYTE_ARRAY_OFFSET,
+            key.length
+          );
+          Assert.assertFalse(loc.isDefined());
+          Assert.assertTrue(loc.putNewKey(
+            key,
+            BYTE_ARRAY_OFFSET,
+            key.length,
+            value,
+            BYTE_ARRAY_OFFSET,
+            value.length
+          ));
+          // After calling putNewKey, the following should be true, even before calling
+          // lookup():
+          Assert.assertTrue(loc.isDefined());
+          Assert.assertEquals(key.length, loc.getKeyLength());
+          Assert.assertEquals(value.length, loc.getValueLength());
+          Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), key.length));
+          Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), value.length));
+        }
+      }
+      for (Map.Entry<ByteBuffer, byte[]> entry : expected.entrySet()) {
+        final byte[] key = entry.getKey().array();
+        final byte[] value = entry.getValue();
+        final BytesToBytesMap.Location loc = map.lookup(key, BYTE_ARRAY_OFFSET, key.length);
+        Assert.assertTrue(loc.isDefined());
+        Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), loc.getKeyLength()));
+        Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), loc.getValueLength()));
+      }
+    } finally {
+      map.free();
+    }
+  }
+
+  @Test
+  public void failureToAllocateFirstPage() {
+    shuffleMemoryManager = new ShuffleMemoryManager(1024);
+    BytesToBytesMap map =
+      new BytesToBytesMap(taskMemoryManager, shuffleMemoryManager, 1, PAGE_SIZE_BYTES);
+    try {
+      final long[] emptyArray = new long[0];
+      final BytesToBytesMap.Location loc =
+        map.lookup(emptyArray, PlatformDependent.LONG_ARRAY_OFFSET, 0);
+      Assert.assertFalse(loc.isDefined());
+      Assert.assertFalse(loc.putNewKey(
+        emptyArray, LONG_ARRAY_OFFSET, 0,
+        emptyArray, LONG_ARRAY_OFFSET, 0
+      ));
+    } finally {
+      map.free();
+    }
+  }
+
+
+  @Test
+  public void failureToGrow() {
+    shuffleMemoryManager = new ShuffleMemoryManager(1024 * 10);
+    BytesToBytesMap map = new BytesToBytesMap(taskMemoryManager, shuffleMemoryManager, 1, 1024);
+    try {
+      boolean success = true;
+      int i;
+      for (i = 0; i < 1024; i++) {
+        final long[] arr = new long[]{i};
+        final BytesToBytesMap.Location loc = map.lookup(arr, PlatformDependent.LONG_ARRAY_OFFSET, 8);
+        success = loc.putNewKey(arr, LONG_ARRAY_OFFSET, 8, arr, LONG_ARRAY_OFFSET, 8);
+        if (!success) {
+          break;
+        }
+      }
+      Assert.assertThat(i, greaterThan(0));
+      Assert.assertFalse(success);
+    } finally {
+      map.free();
+    }
+  }
+
   @Test
   public void initialCapacityBoundsChecking() {
     try {
-      new BytesToBytesMap(sizeLimitedMemoryManager, 0, PAGE_SIZE_BYTES);
+      new BytesToBytesMap(sizeLimitedTaskMemoryManager, shuffleMemoryManager, 0, PAGE_SIZE_BYTES);
       Assert.fail("Expected IllegalArgumentException to be thrown");
     } catch (IllegalArgumentException e) {
       // expected exception
@@ -362,23 +465,34 @@ public void initialCapacityBoundsChecking() {
 
     try {
       new BytesToBytesMap(
-        sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY + 1, PAGE_SIZE_BYTES);
+        sizeLimitedTaskMemoryManager,
+        shuffleMemoryManager,
+        BytesToBytesMap.MAX_CAPACITY + 1,
+        PAGE_SIZE_BYTES);
       Assert.fail("Expected IllegalArgumentException to be thrown");
     } catch (IllegalArgumentException e) {
       // expected exception
     }
 
-   // Can allocate _at_ the max capacity
-    BytesToBytesMap map =
-      new BytesToBytesMap(sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY, PAGE_SIZE_BYTES);
-    map.free();
+    // Ignored because this can OOM now that we allocate the long array w/o a TaskMemoryManager
+    // Can allocate _at_ the max capacity
+    //    BytesToBytesMap map = new BytesToBytesMap(
+    //      sizeLimitedTaskMemoryManager,
+    //      shuffleMemoryManager,
+    //      BytesToBytesMap.MAX_CAPACITY,
+    //      PAGE_SIZE_BYTES);
+    //    map.free();
   }
 
-  @Test
+  // Ignored because this can OOM now that we allocate the long array w/o a TaskMemoryManager
+  @Ignore
   public void resizingLargeMap() {
     // As long as a map's capacity is below the max, we should be able to resize up to the max
     BytesToBytesMap map = new BytesToBytesMap(
-      sizeLimitedMemoryManager, BytesToBytesMap.MAX_CAPACITY - 64, PAGE_SIZE_BYTES);
+      sizeLimitedTaskMemoryManager,
+      shuffleMemoryManager,
+      BytesToBytesMap.MAX_CAPACITY - 64,
+      PAGE_SIZE_BYTES);
     map.growAndRehash();
     map.free();
   }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java
similarity index 100%
rename from unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java
rename to core/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java
similarity index 100%
rename from unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java
rename to core/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
similarity index 89%
rename from sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
rename to sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index f3b462778dc10..66012e3c94d27 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -15,11 +15,15 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.catalyst.expressions;
+package org.apache.spark.sql.execution;
 
+import java.io.IOException;
 import java.util.Iterator;
 
+import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
 import org.apache.spark.sql.types.Decimal;
 import org.apache.spark.sql.types.DecimalType;
 import org.apache.spark.sql.types.StructField;
@@ -87,7 +91,9 @@ public static boolean supportsAggregationBufferSchema(StructType schema) {
    * @param emptyAggregationBuffer the default value for new keys (a "zero" of the agg. function)
    * @param aggregationBufferSchema the schema of the aggregation buffer, used for row conversion.
    * @param groupingKeySchema the schema of the grouping key, used for row conversion.
-   * @param memoryManager the memory manager used to allocate our Unsafe memory structures.
+   * @param taskMemoryManager the memory manager used to allocate our Unsafe memory structures.
+   * @param shuffleMemoryManager the shuffle memory manager, for coordinating our memory usage with
+   *                             other tasks.
    * @param initialCapacity the initial capacity of the map (a sizing hint to avoid re-hashing).
    * @param pageSizeBytes the data page size, in bytes; limits the maximum record size.
    * @param enablePerfMetrics if true, performance metrics will be recorded (has minor perf impact)
@@ -96,15 +102,16 @@ public UnsafeFixedWidthAggregationMap(
       InternalRow emptyAggregationBuffer,
       StructType aggregationBufferSchema,
       StructType groupingKeySchema,
-      TaskMemoryManager memoryManager,
+      TaskMemoryManager taskMemoryManager,
+      ShuffleMemoryManager shuffleMemoryManager,
       int initialCapacity,
       long pageSizeBytes,
       boolean enablePerfMetrics) {
     this.aggregationBufferSchema = aggregationBufferSchema;
     this.groupingKeyProjection = UnsafeProjection.create(groupingKeySchema);
     this.groupingKeySchema = groupingKeySchema;
-    this.map =
-      new BytesToBytesMap(memoryManager, initialCapacity, pageSizeBytes, enablePerfMetrics);
+    this.map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, initialCapacity, pageSizeBytes, enablePerfMetrics);
     this.enablePerfMetrics = enablePerfMetrics;
 
     // Initialize the buffer for aggregation value
@@ -116,7 +123,8 @@ public UnsafeFixedWidthAggregationMap(
 
   /**
    * Return the aggregation buffer for the current group. For efficiency, all calls to this method
-   * return the same object.
+   * return the same object. If additional memory could not be allocated, then this method will
+   * signal an error by returning null.
    */
   public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     final UnsafeRow unsafeGroupingKeyRow = this.groupingKeyProjection.apply(groupingKey);
@@ -129,7 +137,7 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     if (!loc.isDefined()) {
       // This is the first time that we've seen this grouping key, so we'll insert a copy of the
       // empty aggregation buffer into the map:
-      loc.putNewKey(
+      boolean putSucceeded = loc.putNewKey(
         unsafeGroupingKeyRow.getBaseObject(),
         unsafeGroupingKeyRow.getBaseOffset(),
         unsafeGroupingKeyRow.getSizeInBytes(),
@@ -137,6 +145,9 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
         PlatformDependent.BYTE_ARRAY_OFFSET,
         emptyAggregationBuffer.length
       );
+      if (!putSucceeded) {
+        return null;
+      }
     }
 
     // Reset the pointer to point to the value that we just stored or looked up:
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index d851eae3fcc71..469de6ca8e101 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.execution
 
+import java.io.IOException
+
 import org.apache.spark.{SparkEnv, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
@@ -266,6 +268,7 @@ case class GeneratedAggregate(
           aggregationBufferSchema,
           groupKeySchema,
           TaskContext.get.taskMemoryManager(),
+          SparkEnv.get.shuffleMemoryManager,
           1024 * 16, // initial capacity
           pageSizeBytes,
           false // disable tracking of performance metrics
@@ -275,6 +278,9 @@ case class GeneratedAggregate(
           val currentRow: InternalRow = iter.next()
           val groupKey: InternalRow = groupProjection(currentRow)
           val aggregationBuffer = aggregationMap.getAggregationBuffer(groupKey)
+          if (aggregationBuffer == null) {
+            throw new IOException("Could not allocate memory to grow aggregation buffer")
+          }
           updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow))
         }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index f88a45f48aee9..cc8bbfd2f8943 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -17,10 +17,11 @@
 
 package org.apache.spark.sql.execution.joins
 
-import java.io.{Externalizable, ObjectInput, ObjectOutput}
+import java.io.{IOException, Externalizable, ObjectInput, ObjectOutput}
 import java.nio.ByteOrder
 import java.util.{HashMap => JavaHashMap}
 
+import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.{SparkConf, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -28,6 +29,7 @@ import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
+import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.CompactBuffer
 
 
@@ -217,7 +219,7 @@ private[joins] final class UnsafeHashedRelation(
     }
   }
 
-  override def writeExternal(out: ObjectOutput): Unit = {
+  override def writeExternal(out: ObjectOutput): Unit = Utils.tryOrIOException {
     out.writeInt(hashTable.size())
 
     val iter = hashTable.entrySet().iterator()
@@ -256,16 +258,26 @@ private[joins] final class UnsafeHashedRelation(
     }
   }
 
-  override def readExternal(in: ObjectInput): Unit = {
+  override def readExternal(in: ObjectInput): Unit = Utils.tryOrIOException {
     val nKeys = in.readInt()
     // This is used in Broadcast, shared by multiple tasks, so we use on-heap memory
-    val memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    val taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+
+    // Dummy shuffle memory manager which always grants all memory allocation requests.
+    // We use this because it doesn't make sense count shared broadcast variables' memory usage
+    // towards individual tasks' quotas. In the future, we should devise a better way of handling
+    // this.
+    val shuffleMemoryManager = new ShuffleMemoryManager(new SparkConf()) {
+      override def tryToAcquire(numBytes: Long): Long = numBytes
+      override def release(numBytes: Long): Unit = {}
+    }
 
     val pageSizeBytes = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf())
       .getSizeAsBytes("spark.buffer.pageSize", "64m")
 
     binaryMap = new BytesToBytesMap(
-      memoryManager,
+      taskMemoryManager,
+      shuffleMemoryManager,
       nKeys * 2, // reduce hash collision
       pageSizeBytes)
 
@@ -287,8 +299,11 @@ private[joins] final class UnsafeHashedRelation(
       // put it into binary map
       val loc = binaryMap.lookup(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize)
       assert(!loc.isDefined, "Duplicated key found!")
-      loc.putNewKey(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize,
+      val putSuceeded = loc.putNewKey(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize,
         valuesBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, valuesSize)
+      if (!putSuceeded) {
+        throw new IOException("Could not allocate memory to grow BytesToBytesMap")
+      }
       i += 1
     }
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
similarity index 78%
rename from sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index c6b4c729de2f9..79fd52dacda52 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -15,17 +15,18 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.catalyst.expressions
+package org.apache.spark.sql.execution
+
+import org.scalatest.{BeforeAndAfterEach, Matchers}
 
 import scala.collection.JavaConverters._
 import scala.util.Random
 
-import org.scalatest.{BeforeAndAfterEach, Matchers}
-
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, TaskMemoryManager, MemoryAllocator}
+import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.unsafe.types.UTF8String
 
 
@@ -41,16 +42,20 @@ class UnsafeFixedWidthAggregationMapSuite
   private def emptyAggregationBuffer: InternalRow = InternalRow(0)
   private val PAGE_SIZE_BYTES: Long = 1L << 26; // 64 megabytes
 
-  private var memoryManager: TaskMemoryManager = null
+  private var taskMemoryManager: TaskMemoryManager = null
+  private var shuffleMemoryManager: ShuffleMemoryManager = null
 
   override def beforeEach(): Unit = {
-    memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    shuffleMemoryManager = new ShuffleMemoryManager(Long.MaxValue)
   }
 
   override def afterEach(): Unit = {
-    if (memoryManager != null) {
-      memoryManager.cleanUpAllAllocatedMemory()
-      memoryManager = null
+    if (taskMemoryManager != null) {
+      val leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask
+      assert(taskMemoryManager.cleanUpAllAllocatedMemory() === 0)
+      assert(leakedShuffleMemory === 0)
+      taskMemoryManager = null
     }
   }
 
@@ -69,7 +74,8 @@ class UnsafeFixedWidthAggregationMapSuite
       emptyAggregationBuffer,
       aggBufferSchema,
       groupKeySchema,
-      memoryManager,
+      taskMemoryManager,
+      shuffleMemoryManager,
       1024, // initial capacity,
       PAGE_SIZE_BYTES,
       false // disable perf metrics
@@ -83,7 +89,8 @@ class UnsafeFixedWidthAggregationMapSuite
       emptyAggregationBuffer,
       aggBufferSchema,
       groupKeySchema,
-      memoryManager,
+      taskMemoryManager,
+      shuffleMemoryManager,
       1024, // initial capacity
       PAGE_SIZE_BYTES,
       false // disable perf metrics
@@ -91,7 +98,7 @@ class UnsafeFixedWidthAggregationMapSuite
     val groupKey = InternalRow(UTF8String.fromString("cats"))
 
     // Looking up a key stores a zero-entry in the map (like Python Counters or DefaultDicts)
-    map.getAggregationBuffer(groupKey)
+    assert(map.getAggregationBuffer(groupKey) != null)
     val iter = map.iterator()
     val entry = iter.next()
     assert(!iter.hasNext)
@@ -110,7 +117,8 @@ class UnsafeFixedWidthAggregationMapSuite
       emptyAggregationBuffer,
       aggBufferSchema,
       groupKeySchema,
-      memoryManager,
+      taskMemoryManager,
+      shuffleMemoryManager,
       128, // initial capacity
       PAGE_SIZE_BYTES,
       false // disable perf metrics
@@ -118,7 +126,7 @@ class UnsafeFixedWidthAggregationMapSuite
     val rand = new Random(42)
     val groupKeys: Set[String] = Seq.fill(512)(rand.nextString(1024)).toSet
     groupKeys.foreach { keyString =>
-      map.getAggregationBuffer(InternalRow(UTF8String.fromString(keyString)))
+      assert(map.getAggregationBuffer(InternalRow(UTF8String.fromString(keyString))) != null)
     }
     val seenKeys: Set[String] = map.iterator().asScala.map { entry =>
       entry.key.getString(0)
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
index 3dc82d8c2eb39..91be46ba21ff8 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
@@ -34,7 +34,7 @@ public class MemoryBlock extends MemoryLocation {
    */
   int pageNumber = -1;
 
-  MemoryBlock(@Nullable Object obj, long offset, long length) {
+  public MemoryBlock(@Nullable Object obj, long offset, long length) {
     super(obj, offset);
     this.length = length;
   }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
index dd70df3b1f791..358bb37250158 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
@@ -164,6 +164,7 @@ public void freePage(MemoryBlock page) {
    * top-level Javadoc for more details).
    */
   public MemoryBlock allocate(long size) throws OutOfMemoryError {
+    assert(size > 0) : "Size must be positive, but got " + size;
     final MemoryBlock memory = executorMemoryManager.allocate(size);
     allocatedNonPageMemory.add(memory);
     return memory;

From 712f5b7a9ab52c26e3d086629633950ec2fb7afc Mon Sep 17 00:00:00 2001
From: Hossein <hossein@databricks.com>
Date: Fri, 31 Jul 2015 19:24:00 -0700
Subject: [PATCH 0758/1454] [SPARK-9318] [SPARK-9320] [SPARKR] Aliases for
 merge and summary functions on DataFrames

This PR adds synonyms for ```merge``` and ```summary``` in SparkR DataFrame API.

cc shivaram

Author: Hossein <hossein@databricks.com>

Closes #7806 from falaki/SPARK-9320 and squashes the following commits:

72600f7 [Hossein] Updated docs
92a6e75 [Hossein] Fixed merge generic signature issue
4c2b051 [Hossein] Fixing naming with mllib summary
0f3a64c [Hossein] Added ... to generic for merge
30fbaf8 [Hossein] Merged master
ae1a4cf [Hossein] Merge branch 'master' into SPARK-9320
e8eb86f [Hossein] Add a generic for merge
fc01f2d [Hossein] Added unit test
8d92012 [Hossein] Added merge as an alias for join
5b8bedc [Hossein] Added unit test
632693d [Hossein] Added summary as an alias for describe for DataFrame
---
 R/pkg/NAMESPACE                  |  2 ++
 R/pkg/R/DataFrame.R              | 22 ++++++++++++++++++++++
 R/pkg/R/generics.R               |  8 ++++++++
 R/pkg/R/mllib.R                  |  8 ++++----
 R/pkg/inst/tests/test_sparkSQL.R | 14 ++++++++++++--
 5 files changed, 48 insertions(+), 6 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index ff116cb1fbde2..b2d92bdf4840e 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -46,6 +46,7 @@ exportMethods("arrange",
               "isLocal",
               "join",
               "limit",
+              "merge",
               "names",
               "ncol",
               "nrow",
@@ -69,6 +70,7 @@ exportMethods("arrange",
               "show",
               "showDF",
               "summarize",
+              "summary",
               "take",
               "unionAll",
               "unique",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index b4065d2944bdc..895603235011e 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1279,6 +1279,15 @@ setMethod("join",
             dataFrame(sdf)
           })
 
+#' rdname merge
+#' aliases join
+setMethod("merge",
+          signature(x = "DataFrame", y = "DataFrame"),
+          function(x, y, joinExpr = NULL, joinType = NULL, ...) {
+            join(x, y, joinExpr, joinType)
+          })
+
+
 #' UnionAll
 #'
 #' Return a new DataFrame containing the union of rows in this DataFrame
@@ -1524,6 +1533,19 @@ setMethod("describe",
             dataFrame(sdf)
           })
 
+#' @title Summary
+#'
+#' @description Computes statistics for numeric columns of the DataFrame
+#'
+#' @rdname summary
+#' @aliases describe
+setMethod("summary",
+          signature(x = "DataFrame"),
+          function(x) {
+            describe(x)
+          })
+
+
 #' dropna
 #'
 #' Returns a new DataFrame omitting rows with null values.
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 71d1e348c4efb..c43b947129e87 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -461,6 +461,10 @@ setGeneric("isLocal", function(x) { standardGeneric("isLocal") })
 #' @export
 setGeneric("limit", function(x, num) {standardGeneric("limit") })
 
+#' rdname merge
+#' @export
+setGeneric("merge")
+
 #' @rdname withColumn
 #' @export
 setGeneric("mutate", function(x, ...) {standardGeneric("mutate") })
@@ -531,6 +535,10 @@ setGeneric("showDF", function(x,...) { standardGeneric("showDF") })
 #' @export
 setGeneric("summarize", function(x,...) { standardGeneric("summarize") })
 
+##' rdname summary
+##' @export
+setGeneric("summary", function(x, ...) { standardGeneric("summary") })
+
 # @rdname tojson
 # @export
 setGeneric("toJSON", function(x) { standardGeneric("toJSON") })
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index efddcc1d8d71c..b524d1fd87496 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -86,12 +86,12 @@ setMethod("predict", signature(object = "PipelineModel"),
 #' model <- glm(y ~ x, trainingData)
 #' summary(model)
 #'}
-setMethod("summary", signature(object = "PipelineModel"),
-          function(object) {
+setMethod("summary", signature(x = "PipelineModel"),
+          function(x, ...) {
             features <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
-                                   "getModelFeatures", object@model)
+                                   "getModelFeatures", x@model)
             weights <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
-                                   "getModelWeights", object@model)
+                                   "getModelWeights", x@model)
             coefficients <- as.matrix(unlist(weights))
             colnames(coefficients) <- c("Estimate")
             rownames(coefficients) <- unlist(features)
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 9faee8d59c3af..7377fc8f1ca9c 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -765,7 +765,7 @@ test_that("filter() on a DataFrame", {
   expect_equal(count(filtered6), 2)
 })
 
-test_that("join() on a DataFrame", {
+test_that("join() and merge() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
 
   mockLines2 <- c("{\"name\":\"Michael\", \"test\": \"yes\"}",
@@ -794,6 +794,12 @@ test_that("join() on a DataFrame", {
   expect_equal(names(joined4), c("newAge", "name", "test"))
   expect_equal(count(joined4), 4)
   expect_equal(collect(orderBy(joined4, joined4$name))$newAge[3], 24)
+
+  merged <- select(merge(df, df2, df$name == df2$name, "outer"),
+                   alias(df$age + 5, "newAge"), df$name, df2$test)
+  expect_equal(names(merged), c("newAge", "name", "test"))
+  expect_equal(count(merged), 4)
+  expect_equal(collect(orderBy(merged, joined4$name))$newAge[3], 24)
 })
 
 test_that("toJSON() returns an RDD of the correct values", {
@@ -899,7 +905,7 @@ test_that("parquetFile works with multiple input paths", {
   expect_equal(count(parquetDF), count(df) * 2)
 })
 
-test_that("describe() on a DataFrame", {
+test_that("describe() and summarize() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   stats <- describe(df, "age")
   expect_equal(collect(stats)[1, "summary"], "count")
@@ -908,6 +914,10 @@ test_that("describe() on a DataFrame", {
   stats <- describe(df)
   expect_equal(collect(stats)[4, "name"], "Andy")
   expect_equal(collect(stats)[5, "age"], "30")
+
+  stats2 <- summary(df)
+  expect_equal(collect(stats2)[4, "name"], "Andy")
+  expect_equal(collect(stats2)[5, "age"], "30")
 })
 
 test_that("dropna() on a DataFrame", {

From 03377d2522776267a07b7d6ae9bddf79a4e0f516 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 31 Jul 2015 21:09:00 -0700
Subject: [PATCH 0759/1454] [SPARK-9358][SQL] Code generation for UnsafeRow
 joiner.

This patch creates a code generated unsafe row concatenator that can be used to concatenate/join two UnsafeRows into a single UnsafeRow.

Since it is inherently hard to test these low level stuff, the test suites employ randomized testing heavily in order to guarantee correctness.

Author: Reynold Xin <rxin@databricks.com>

Closes #7821 from rxin/rowconcat and squashes the following commits:

8717f35 [Reynold Xin] Rebase and code review.
72c5d8e [Reynold Xin] Fixed a bug.
a84ed2e [Reynold Xin] Fixed offset.
40c3fb2 [Reynold Xin] Reset random data generator.
f0913aa [Reynold Xin] Test fixes.
6687b6f [Reynold Xin] Updated documentation.
00354b9 [Reynold Xin] Support concat data as well.
e9a4347 [Reynold Xin] Updated.
6269f96 [Reynold Xin] Fixed a bug .
0f89716 [Reynold Xin] [SPARK-9358][SQL][WIP] Code generation for UnsafeRow concat.
---
 .../sql/catalyst/expressions/UnsafeRow.java   |  19 ++
 .../expressions/codegen/CodeGenerator.scala   |   2 +
 .../codegen/GenerateUnsafeProjection.scala    |   6 +-
 .../codegen/GenerateUnsafeRowJoiner.scala     | 241 ++++++++++++++++++
 .../spark/sql/RandomDataGenerator.scala       |  15 +-
 .../GenerateUnsafeRowJoinerBitsetSuite.scala  | 147 +++++++++++
 .../GenerateUnsafeRowJoinerSuite.scala        | 114 +++++++++
 .../UnsafeFixedWidthAggregationMap.java       |   7 +-
 .../sql/execution/TungstenSortSuite.scala     |   3 +
 9 files changed, 544 insertions(+), 10 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index e7088edced1a1..24dc80b1a7932 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -85,6 +85,14 @@ public static int calculateBitSetWidthInBytes(int numFields) {
         })));
   }
 
+  public static boolean isFixedLength(DataType dt) {
+    if (dt instanceof DecimalType) {
+      return ((DecimalType) dt).precision() < Decimal.MAX_LONG_DIGITS();
+    } else {
+      return settableFieldTypes.contains(dt);
+    }
+  }
+
   //////////////////////////////////////////////////////////////////////////////
   // Private fields and methods
   //////////////////////////////////////////////////////////////////////////////
@@ -144,6 +152,17 @@ public void pointTo(Object baseObject, long baseOffset, int numFields, int sizeI
     this.sizeInBytes = sizeInBytes;
   }
 
+  /**
+   * Update this UnsafeRow to point to the underlying byte array.
+   *
+   * @param buf byte array to point to
+   * @param numFields the number of fields in this row
+   * @param sizeInBytes the number of bytes valid in the byte array
+   */
+  public void pointTo(byte[] buf, int numFields, int sizeInBytes) {
+    pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
+  }
+
   private void assertIndexIsValid(int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     assert index < numFields : "index (" + index + ") should < " + numFields;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index e50ec27fc2eb6..36f4e9c6be570 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -27,6 +27,7 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.PlatformDependent
 import org.apache.spark.unsafe.types._
 
 
@@ -293,6 +294,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
     val evaluator = new ClassBodyEvaluator()
     evaluator.setParentClassLoader(getClass.getClassLoader)
     evaluator.setDefaultImports(Array(
+      classOf[PlatformDependent].getName,
       classOf[InternalRow].getName,
       classOf[UnsafeRow].getName,
       classOf[UTF8String].getName,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 1d223986d9441..6c99086046683 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -266,16 +266,16 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val code = s"""
       public Object generate($exprType[] exprs) {
-        return new SpecificProjection(exprs);
+        return new SpecificUnsafeProjection(exprs);
       }
 
-      class SpecificProjection extends ${classOf[UnsafeProjection].getName} {
+      class SpecificUnsafeProjection extends ${classOf[UnsafeProjection].getName} {
 
         private $exprType[] expressions;
 
         ${declareMutableStates(ctx)}
 
-        public SpecificProjection($exprType[] expressions) {
+        public SpecificUnsafeProjection($exprType[] expressions) {
           this.expressions = expressions;
           ${initMutableStates(ctx)}
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
new file mode 100644
index 0000000000000..645eb48d5a51b
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
@@ -0,0 +1,241 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, Attribute}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.unsafe.PlatformDependent
+
+
+abstract class UnsafeRowJoiner {
+  def join(row1: UnsafeRow, row2: UnsafeRow): UnsafeRow
+}
+
+
+/**
+ * A code generator for concatenating two [[UnsafeRow]]s into a single [[UnsafeRow]].
+ *
+ * The high level algorithm is:
+ *
+ * 1. Concatenate the two bitsets together into a single one, taking padding into account.
+ * 2. Move fixed-length data.
+ * 3. Move variable-length data.
+ * 4. Update the offset position (i.e. the upper 32 bits in the fixed length part) for all
+ *    variable-length data.
+ */
+object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), UnsafeRowJoiner] {
+
+  def dump(word: Long): String = {
+    Seq.tabulate(64) { i => if ((word >> i) % 2 == 0) "0" else "1" }.reverse.mkString
+  }
+
+  override protected def create(in: (StructType, StructType)): UnsafeRowJoiner = {
+    create(in._1, in._2)
+  }
+
+  override protected def canonicalize(in: (StructType, StructType)): (StructType, StructType) = in
+
+  override protected def bind(in: (StructType, StructType), inputSchema: Seq[Attribute])
+    : (StructType, StructType) = {
+    in
+  }
+
+  def create(schema1: StructType, schema2: StructType): UnsafeRowJoiner = {
+    val ctx = newCodeGenContext()
+    val offset = PlatformDependent.BYTE_ARRAY_OFFSET
+
+    val bitset1Words = (schema1.size + 63) / 64
+    val bitset2Words = (schema2.size + 63) / 64
+    val outputBitsetWords = (schema1.size + schema2.size + 63) / 64
+    val bitset1Remainder = schema1.size % 64
+    val bitset2Remainder = schema2.size % 64
+
+    // The number of words we can reduce when we concat two rows together.
+    // The only reduction comes from merging the bitset portion of the two rows, saving 1 word.
+    val sizeReduction = bitset1Words + bitset2Words - outputBitsetWords
+
+    // --------------------- copy bitset from row 1 ----------------------- //
+    val copyBitset1 = Seq.tabulate(bitset1Words) { i =>
+      s"""
+         |PlatformDependent.UNSAFE.putLong(buf, ${offset + i * 8},
+         |  PlatformDependent.UNSAFE.getLong(obj1, ${offset + i * 8}));
+       """.stripMargin
+    }.mkString
+
+
+    // --------------------- copy bitset from row 2 ----------------------- //
+    var copyBitset2 = ""
+    if (bitset1Remainder == 0) {
+      copyBitset2 += Seq.tabulate(bitset2Words) { i =>
+        s"""
+           |PlatformDependent.UNSAFE.putLong(buf, ${offset + (bitset1Words + i) * 8},
+           |  PlatformDependent.UNSAFE.getLong(obj2, ${offset + i * 8}));
+         """.stripMargin
+      }.mkString
+    } else {
+      copyBitset2 = Seq.tabulate(bitset2Words) { i =>
+        s"""
+           |long bs2w$i = PlatformDependent.UNSAFE.getLong(obj2, ${offset + i * 8});
+           |long bs2w${i}p1 = (bs2w$i << $bitset1Remainder) & ~((1L << $bitset1Remainder) - 1);
+           |long bs2w${i}p2 = (bs2w$i >>> ${64 - bitset1Remainder});
+         """.stripMargin
+      }.mkString
+
+      copyBitset2 += Seq.tabulate(bitset2Words) { i =>
+        val currentOffset = offset + (bitset1Words + i - 1) * 8
+        if (i == 0) {
+          if (bitset1Words > 0) {
+            s"""
+               |PlatformDependent.UNSAFE.putLong(buf, $currentOffset,
+               |  bs2w${i}p1 | PlatformDependent.UNSAFE.getLong(obj1, $currentOffset));
+            """.stripMargin
+          } else {
+            s"""
+               |PlatformDependent.UNSAFE.putLong(buf, $currentOffset + 8, bs2w${i}p1);
+            """.stripMargin
+          }
+        } else {
+          s"""
+             |PlatformDependent.UNSAFE.putLong(buf, $currentOffset, bs2w${i}p1 | bs2w${i - 1}p2);
+          """.stripMargin
+        }
+      }.mkString("\n")
+
+      if (bitset2Words > 0 &&
+        (bitset2Remainder == 0 || bitset2Remainder > (64 - bitset1Remainder))) {
+        val lastWord = bitset2Words - 1
+        copyBitset2 +=
+          s"""
+             |PlatformDependent.UNSAFE.putLong(buf, ${offset + (outputBitsetWords - 1) * 8},
+             |  bs2w${lastWord}p2);
+          """.stripMargin
+      }
+    }
+
+    // --------------------- copy fixed length portion from row 1 ----------------------- //
+    var cursor = offset + outputBitsetWords * 8
+    val copyFixedLengthRow1 = s"""
+       |// Copy fixed length data for row1
+       |PlatformDependent.copyMemory(
+       |  obj1, offset1 + ${bitset1Words * 8},
+       |  buf, $cursor,
+       |  ${schema1.size * 8});
+     """.stripMargin
+    cursor += schema1.size * 8
+
+    // --------------------- copy fixed length portion from row 2 ----------------------- //
+    val copyFixedLengthRow2 = s"""
+       |// Copy fixed length data for row2
+       |PlatformDependent.copyMemory(
+       |  obj2, offset2 + ${bitset2Words * 8},
+       |  buf, $cursor,
+       |  ${schema2.size * 8});
+     """.stripMargin
+    cursor += schema2.size * 8
+
+    // --------------------- copy variable length portion from row 1 ----------------------- //
+    val copyVariableLengthRow1 = s"""
+       |// Copy variable length data for row1
+       |long numBytesBitsetAndFixedRow1 = ${(bitset1Words + schema1.size) * 8};
+       |long numBytesVariableRow1 = row1.getSizeInBytes() - numBytesBitsetAndFixedRow1;
+       |PlatformDependent.copyMemory(
+       |  obj1, offset1 + ${(bitset1Words + schema1.size) * 8},
+       |  buf, $cursor,
+       |  numBytesVariableRow1);
+     """.stripMargin
+
+    // --------------------- copy variable length portion from row 2 ----------------------- //
+    val copyVariableLengthRow2 = s"""
+       |// Copy variable length data for row2
+       |long numBytesBitsetAndFixedRow2 = ${(bitset2Words + schema2.size) * 8};
+       |long numBytesVariableRow2 = row2.getSizeInBytes() - numBytesBitsetAndFixedRow2;
+       |PlatformDependent.copyMemory(
+       |  obj2, offset2 + ${(bitset2Words + schema2.size) * 8},
+       |  buf, $cursor + numBytesVariableRow1,
+       |  numBytesVariableRow2);
+     """.stripMargin
+
+    // ------------- update fixed length data for variable length data type  --------------- //
+    val updateOffset = (schema1 ++ schema2).zipWithIndex.map { case (field, i) =>
+      // Skip fixed length data types, and only generate code for variable length data
+      if (UnsafeRow.isFixedLength(field.dataType)) {
+        ""
+      } else {
+        // Number of bytes to increase for the offset. Note that since in UnsafeRow we store the
+        // offset in the upper 32 bit of the words, we can just shift the offset to the left by
+        // 32 and increment that amount in place.
+        val shift =
+          if (i < schema1.size) {
+            s"${(outputBitsetWords - bitset1Words + schema2.size) * 8}L"
+          } else {
+            s"${(outputBitsetWords - bitset2Words + schema1.size) * 8}L + numBytesVariableRow1"
+          }
+        val cursor = offset + outputBitsetWords * 8 + i * 8
+        s"""
+           |PlatformDependent.UNSAFE.putLong(buf, $cursor,
+           |  PlatformDependent.UNSAFE.getLong(buf, $cursor) + ($shift << 32));
+         """.stripMargin
+      }
+    }.mkString
+
+    // ------------------------ Finally, put everything together  --------------------------- //
+    val code = s"""
+       |public Object generate($exprType[] exprs) {
+       |  return new SpecificUnsafeRowJoiner();
+       |}
+       |
+       |class SpecificUnsafeRowJoiner extends ${classOf[UnsafeRowJoiner].getName} {
+       |  private byte[] buf = new byte[64];
+       |  private UnsafeRow out = new UnsafeRow();
+       |
+       |  public UnsafeRow join(UnsafeRow row1, UnsafeRow row2) {
+       |    // row1: ${schema1.size} fields, $bitset1Words words in bitset
+       |    // row2: ${schema2.size}, $bitset2Words words in bitset
+       |    // output: ${schema1.size + schema2.size} fields, $outputBitsetWords words in bitset
+       |    final int sizeInBytes = row1.getSizeInBytes() + row2.getSizeInBytes();
+       |    if (sizeInBytes > buf.length) {
+       |      buf = new byte[sizeInBytes];
+       |    }
+       |
+       |    final Object obj1 = row1.getBaseObject();
+       |    final long offset1 = row1.getBaseOffset();
+       |    final Object obj2 = row2.getBaseObject();
+       |    final long offset2 = row2.getBaseOffset();
+       |
+       |    $copyBitset1
+       |    $copyBitset2
+       |    $copyFixedLengthRow1
+       |    $copyFixedLengthRow2
+       |    $copyVariableLengthRow1
+       |    $copyVariableLengthRow2
+       |    $updateOffset
+       |
+       |    out.pointTo(buf, ${schema1.size + schema2.size}, sizeInBytes - $sizeReduction);
+       |
+       |    return out;
+       |  }
+       |}
+     """.stripMargin
+
+    logDebug(s"SpecificUnsafeRowJoiner($schema1, $schema2):\n${CodeFormatter.format(code)}")
+    // println(CodeFormatter.format(code))
+
+    val c = compile(code)
+    c.generate(Array.empty).asInstanceOf[UnsafeRowJoiner]
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
index 75ae29d690770..81267dc915c10 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -65,6 +65,19 @@ object RandomDataGenerator {
     Some(f)
   }
 
+  /**
+   * Returns a randomly generated schema, based on the given accepted types.
+   *
+   * @param numFields the number of fields in this schema
+   * @param acceptedTypes types to draw from.
+   */
+  def randomSchema(numFields: Int, acceptedTypes: Seq[DataType]): StructType = {
+    StructType(Seq.tabulate(numFields) { i =>
+      val dt = acceptedTypes(Random.nextInt(acceptedTypes.size))
+      StructField("col_" + i, dt, nullable = true)
+    })
+  }
+
   /**
    * Returns a function which generates random values for the given [[DataType]], or `None` if no
    * random data generator is defined for that data type. The generated values will use an external
@@ -94,7 +107,7 @@ object RandomDataGenerator {
       case DateType => Some(() => new java.sql.Date(rand.nextInt()))
       case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
       case DecimalType.Fixed(precision, scale) => Some(
-        () => BigDecimal.apply(rand.nextLong, rand.nextInt, new MathContext(precision)))
+        () => BigDecimal.apply(rand.nextLong(), rand.nextInt(), new MathContext(precision)))
       case DoubleType => randomNumeric[Double](
         rand, r => longBitsToDouble(r.nextLong()), Seq(Double.MinValue, Double.MinPositiveValue,
           Double.MaxValue, Double.PositiveInfinity, Double.NegativeInfinity, Double.NaN, 0.0))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
new file mode 100644
index 0000000000000..76d9d991ed0dc
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
@@ -0,0 +1,147 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import scala.util.Random
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
+import org.apache.spark.sql.types._
+
+/**
+ * A test suite for the bitset portion of the row concatenation.
+ */
+class GenerateUnsafeRowJoinerBitsetSuite extends SparkFunSuite {
+
+  test("bitset concat: boundary size 0, 0") {
+    testBitsets(0, 0)
+  }
+
+  test("bitset concat: boundary size 0, 64") {
+    testBitsets(0, 64)
+  }
+
+  test("bitset concat: boundary size 64, 0") {
+    testBitsets(64, 0)
+  }
+
+  test("bitset concat: boundary size 64, 64") {
+    testBitsets(64, 64)
+  }
+
+  test("bitset concat: boundary size 0, 128") {
+    testBitsets(0, 128)
+  }
+
+  test("bitset concat: boundary size 128, 0") {
+    testBitsets(128, 0)
+  }
+
+  test("bitset concat: boundary size 128, 128") {
+    testBitsets(128, 128)
+  }
+
+  test("bitset concat: single word bitsets") {
+    testBitsets(10, 5)
+  }
+
+  test("bitset concat: first bitset larger than a word") {
+    testBitsets(67, 5)
+  }
+
+  test("bitset concat: second bitset larger than a word") {
+    testBitsets(6, 67)
+  }
+
+  test("bitset concat: no reduction in bitset size") {
+    testBitsets(33, 34)
+  }
+
+  test("bitset concat: two words") {
+    testBitsets(120, 95)
+  }
+
+  test("bitset concat: bitset 65, 128") {
+    testBitsets(65, 128)
+  }
+
+  test("bitset concat: randomized tests") {
+    for (i <- 1 until 20) {
+      val numFields1 = Random.nextInt(1000)
+      val numFields2 = Random.nextInt(1000)
+      testBitsetsOnce(numFields1, numFields2)
+    }
+  }
+
+  private def createUnsafeRow(numFields: Int): UnsafeRow = {
+    val row = new UnsafeRow
+    val sizeInBytes = numFields * 8 + ((numFields + 63) / 64) * 8
+    val buf = new Array[Byte](sizeInBytes)
+    row.pointTo(buf, numFields, sizeInBytes)
+    row
+  }
+
+  private def testBitsets(numFields1: Int, numFields2: Int): Unit = {
+    for (i <- 0 until 5) {
+      testBitsetsOnce(numFields1, numFields2)
+    }
+  }
+
+  private def testBitsetsOnce(numFields1: Int, numFields2: Int): Unit = {
+    info(s"num fields: $numFields1 and $numFields2")
+    val schema1 = StructType(Seq.tabulate(numFields1) { i => StructField(s"a_$i", IntegerType) })
+    val schema2 = StructType(Seq.tabulate(numFields2) { i => StructField(s"b_$i", IntegerType) })
+
+    val row1 = createUnsafeRow(numFields1)
+    val row2 = createUnsafeRow(numFields2)
+
+    if (numFields1 > 0) {
+      for (i <- 0 until Random.nextInt(numFields1)) {
+        row1.setNullAt(Random.nextInt(numFields1))
+      }
+    }
+    if (numFields2 > 0) {
+      for (i <- 0 until Random.nextInt(numFields2)) {
+        row2.setNullAt(Random.nextInt(numFields2))
+      }
+    }
+
+    val concater = GenerateUnsafeRowJoiner.create(schema1, schema2)
+    val output = concater.join(row1, row2)
+
+    def dumpDebug(): String = {
+      val set1 = Seq.tabulate(numFields1) { i => if (row1.isNullAt(i)) "1" else "0" }
+      val set2 = Seq.tabulate(numFields2) { i => if (row2.isNullAt(i)) "1" else "0" }
+      val out = Seq.tabulate(numFields1 + numFields2) { i => if (output.isNullAt(i)) "1" else "0" }
+
+      s"""
+         |input1: ${set1.mkString}
+         |input2: ${set2.mkString}
+         |output: ${out.mkString}
+       """.stripMargin
+    }
+
+    for (i <- 0 until (numFields1 + numFields2)) {
+      if (i < numFields1) {
+        assert(output.isNullAt(i) === row1.isNullAt(i), dumpDebug())
+      } else {
+        assert(output.isNullAt(i) === row2.isNullAt(i - numFields1), dumpDebug())
+      }
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala
new file mode 100644
index 0000000000000..59729e7646beb
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerSuite.scala
@@ -0,0 +1,114 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import scala.util.Random
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.UnsafeProjection
+import org.apache.spark.sql.types._
+
+/**
+ * Test suite for [[GenerateUnsafeRowJoiner]].
+ *
+ * There is also a separate [[GenerateUnsafeRowJoinerBitsetSuite]] that tests specifically
+ * concatenation for the bitset portion, since that is the hardest one to get right.
+ */
+class GenerateUnsafeRowJoinerSuite extends SparkFunSuite {
+
+  private val fixed = Seq(IntegerType)
+  private val variable = Seq(IntegerType, StringType)
+
+  test("simple fixed width types") {
+    testConcat(0, 0, fixed)
+    testConcat(0, 1, fixed)
+    testConcat(1, 0, fixed)
+    testConcat(64, 0, fixed)
+    testConcat(0, 64, fixed)
+    testConcat(64, 64, fixed)
+  }
+
+  test("randomized fix width types") {
+    for (i <- 0 until 20) {
+      testConcatOnce(Random.nextInt(100), Random.nextInt(100), fixed)
+    }
+  }
+
+  test("simple variable width types") {
+    testConcat(0, 0, variable)
+    testConcat(0, 1, variable)
+    testConcat(1, 0, variable)
+    testConcat(64, 0, variable)
+    testConcat(0, 64, variable)
+    testConcat(64, 64, variable)
+  }
+
+  test("randomized variable width types") {
+    for (i <- 0 until 10) {
+      testConcatOnce(Random.nextInt(100), Random.nextInt(100), variable)
+    }
+  }
+
+  private def testConcat(numFields1: Int, numFields2: Int, candidateTypes: Seq[DataType]): Unit = {
+    for (i <- 0 until 10) {
+      testConcatOnce(numFields1, numFields2, candidateTypes)
+    }
+  }
+
+  private def testConcatOnce(numFields1: Int, numFields2: Int, candidateTypes: Seq[DataType]) {
+    info(s"schema size $numFields1, $numFields2")
+    val schema1 = RandomDataGenerator.randomSchema(numFields1, candidateTypes)
+    val schema2 = RandomDataGenerator.randomSchema(numFields2, candidateTypes)
+
+    // Create the converters needed to convert from external row to internal row and to UnsafeRows.
+    val internalConverter1 = CatalystTypeConverters.createToCatalystConverter(schema1)
+    val internalConverter2 = CatalystTypeConverters.createToCatalystConverter(schema2)
+    val converter1 = UnsafeProjection.create(schema1)
+    val converter2 = UnsafeProjection.create(schema2)
+
+    // Create the input rows, convert them into UnsafeRows.
+    val extRow1 = RandomDataGenerator.forType(schema1, nullable = false).get.apply()
+    val extRow2 = RandomDataGenerator.forType(schema2, nullable = false).get.apply()
+    val row1 = converter1.apply(internalConverter1.apply(extRow1).asInstanceOf[InternalRow])
+    val row2 = converter2.apply(internalConverter2.apply(extRow2).asInstanceOf[InternalRow])
+
+    // Run the joiner.
+    val mergedSchema = StructType(schema1 ++ schema2)
+    val concater = GenerateUnsafeRowJoiner.create(schema1, schema2)
+    val output = concater.join(row1, row2)
+
+    // Test everything equals ...
+    for (i <- mergedSchema.indices) {
+      if (i < schema1.size) {
+        assert(output.isNullAt(i) === row1.isNullAt(i))
+        if (!output.isNullAt(i)) {
+          assert(output.get(i, mergedSchema(i).dataType) === row1.get(i, mergedSchema(i).dataType))
+        }
+      } else {
+        assert(output.isNullAt(i) === row2.isNullAt(i - schema1.size))
+        if (!output.isNullAt(i)) {
+          assert(output.get(i, mergedSchema(i).dataType) ===
+            row2.get(i - schema1.size, mergedSchema(i).dataType))
+        }
+      }
+    }
+  }
+
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 66012e3c94d27..08a98cdd94a4c 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -73,12 +73,7 @@ public final class UnsafeFixedWidthAggregationMap {
    */
   public static boolean supportsAggregationBufferSchema(StructType schema) {
     for (StructField field: schema.fields()) {
-      if (field.dataType() instanceof DecimalType) {
-        DecimalType dt = (DecimalType) field.dataType();
-        if (dt.precision() > Decimal.MAX_LONG_DIGITS()) {
-          return false;
-        }
-      } else if (!UnsafeRow.settableFieldTypes.contains(field.dataType())) {
+      if (!UnsafeRow.isFixedLength(field.dataType())) {
         return false;
       }
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index 450963547c798..b3f821e0cdd37 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -26,6 +26,9 @@ import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types._
 
+/**
+ * A test suite that generates randomized data to test the [[TungstenSort]] operator.
+ */
 class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
 
   override def beforeAll(): Unit = {

From 6996bd2e81bf6597dcda499d9a9a80927a43e30f Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Fri, 31 Jul 2015 21:18:01 -0700
Subject: [PATCH 0760/1454] [SPARK-8264][SQL]add substring_index function

This PR is based on #7533 , thanks to zhichao-li

Closes #7533

Author: zhichao.li <zhichao.li@intel.com>
Author: Davies Liu <davies@databricks.com>

Closes #7843 from davies/str_index and squashes the following commits:

391347b [Davies Liu] add python api
3ce7802 [Davies Liu] fix substringIndex
f2d29a1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into str_index
515519b [zhichao.li] add foldable and remove null checking
9546991 [zhichao.li] scala style
67c253a [zhichao.li] hide some apis and clean code
b19b013 [zhichao.li] add codegen and clean code
ac863e9 [zhichao.li] reduce the calling of numChars
12e108f [zhichao.li] refine unittest
d92951b [zhichao.li] add lastIndexOf
52d7b03 [zhichao.li] add substring_index function
---
 python/pyspark/sql/functions.py               | 19 +++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/stringOperations.scala        | 25 ++++++
 .../expressions/StringExpressionsSuite.scala  | 31 +++++++
 .../org/apache/spark/sql/functions.scala      | 12 ++-
 .../spark/sql/StringFunctionsSuite.scala      | 57 +++++++++++++
 .../apache/spark/unsafe/types/UTF8String.java | 80 ++++++++++++++++++-
 .../spark/unsafe/types/UTF8StringSuite.java   | 38 +++++++++
 8 files changed, 261 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index bb9926ce8ca85..89a2a5ceaa9bf 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -920,6 +920,25 @@ def trunc(date, format):
     return Column(sc._jvm.functions.trunc(_to_java_column(date), format))
 
 
+@since(1.5)
+@ignore_unicode_prefix
+def substring_index(str, delim, count):
+    """
+    Returns the substring from string str before count occurrences of the delimiter delim.
+    If count is positive, everything the left of the final delimiter (counting from left) is
+    returned. If count is negative, every to the right of the final delimiter (counting from the
+    right) is returned. substring_index performs a case-sensitive match when searching for delim.
+
+    >>> df = sqlContext.createDataFrame([('a.b.c.d',)], ['s'])
+    >>> df.select(substring_index(df.s, '.', 2).alias('s')).collect()
+    [Row(s=u'a.b')]
+    >>> df.select(substring_index(df.s, '.', -3).alias('s')).collect()
+    [Row(s=u'b.c.d')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.substring_index(_to_java_column(str), delim, count))
+
+
 @since(1.5)
 def size(col):
     """
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 3f61a9af1f075..ee44cbcba68e7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -199,6 +199,7 @@ object FunctionRegistry {
     expression[StringSplit]("split"),
     expression[Substring]("substr"),
     expression[Substring]("substring"),
+    expression[SubstringIndex]("substring_index"),
     expression[StringTrim]("trim"),
     expression[UnBase64]("unbase64"),
     expression[Upper]("ucase"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 160e72f384a9b..5dd387a418a1d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -421,6 +421,31 @@ case class StringInstr(str: Expression, substr: Expression)
   }
 }
 
+/**
+ * Returns the substring from string str before count occurrences of the delimiter delim.
+ * If count is positive, everything the left of the final delimiter (counting from left) is
+ * returned. If count is negative, every to the right of the final delimiter (counting from the
+ * right) is returned. substring_index performs a case-sensitive match when searching for delim.
+ */
+case class SubstringIndex(strExpr: Expression, delimExpr: Expression, countExpr: Expression)
+ extends TernaryExpression with ImplicitCastInputTypes {
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType, IntegerType)
+  override def children: Seq[Expression] = Seq(strExpr, delimExpr, countExpr)
+  override def prettyName: String = "substring_index"
+
+  override def nullSafeEval(str: Any, delim: Any, count: Any): Any = {
+    str.asInstanceOf[UTF8String].subStringIndex(
+      delim.asInstanceOf[UTF8String],
+      count.asInstanceOf[Int])
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, (str, delim, count) => s"$str.subStringIndex($delim, $count)")
+  }
+}
+
 /**
  * A function that returns the position of the first occurrence of substr
  * in given string after position pos.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index fb72fe1714153..ad87ab36fd645 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 
 class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -187,6 +188,36 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(s.substring(0), "example", row)
   }
 
+  test("string substring_index function") {
+    checkEvaluation(
+      SubstringIndex(Literal("www.apache.org"), Literal("."), Literal(3)), "www.apache.org")
+    checkEvaluation(
+      SubstringIndex(Literal("www.apache.org"), Literal("."), Literal(2)), "www.apache")
+    checkEvaluation(
+      SubstringIndex(Literal("www.apache.org"), Literal("."), Literal(1)), "www")
+    checkEvaluation(
+      SubstringIndex(Literal("www.apache.org"), Literal("."), Literal(0)), "")
+    checkEvaluation(
+      SubstringIndex(Literal("www.apache.org"), Literal("."), Literal(-3)), "www.apache.org")
+    checkEvaluation(
+      SubstringIndex(Literal("www.apache.org"), Literal("."), Literal(-2)), "apache.org")
+    checkEvaluation(
+      SubstringIndex(Literal("www.apache.org"), Literal("."), Literal(-1)), "org")
+    checkEvaluation(
+      SubstringIndex(Literal(""), Literal("."), Literal(-2)), "")
+    checkEvaluation(
+      SubstringIndex(Literal.create(null, StringType), Literal("."), Literal(-2)), null)
+    checkEvaluation(SubstringIndex(
+        Literal("www.apache.org"), Literal.create(null, StringType), Literal(-2)), null)
+    // non ascii chars
+    // scalastyle:off
+    checkEvaluation(
+      SubstringIndex(Literal("大千世界大千世界"), Literal( "千"), Literal(2)), "大千世界大")
+    // scalastyle:on
+    checkEvaluation(
+      SubstringIndex(Literal("www||apache||org"), Literal( "||"), Literal(2)), "www||apache")
+  }
+
   test("LIKE literal Regular Expression") {
     checkEvaluation(Literal.create(null, StringType).like("a"), null)
     checkEvaluation(Literal.create("a", StringType).like(Literal.create(null, StringType)), null)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 89ffa9c50d6c7..57bb00a7417af 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1788,8 +1788,18 @@ object functions {
   def instr(str: Column, substring: String): Column = StringInstr(str.expr, lit(substring).expr)
 
   /**
-   * Locate the position of the first occurrence of substr in a string column.
+   * Returns the substring from string str before count occurrences of the delimiter delim.
+   * If count is positive, everything the left of the final delimiter (counting from left) is
+   * returned. If count is negative, every to the right of the final delimiter (counting from the
+   * right) is returned. substring_index performs a case-sensitive match when searching for delim.
    *
+   * @group string_funcs
+   */
+  def substring_index(str: Column, delim: String, count: Int): Column =
+    SubstringIndex(str.expr, lit(delim).expr, lit(count).expr)
+
+  /**
+   * Locate the position of the first occurrence of substr.
    * NOTE: The position is not zero based, but 1 based index, returns 0 if substr
    * could not be found in str.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index b7f073cccb6ac..628da95298aa9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -163,6 +163,63 @@ class StringFunctionsSuite extends QueryTest {
       Row(1))
   }
 
+  test("string substring_index function") {
+    val df = Seq(("www.apache.org", ".", "zz")).toDF("a", "b", "c")
+    checkAnswer(
+      df.select(substring_index($"a", ".", 3)),
+      Row("www.apache.org"))
+    checkAnswer(
+      df.select(substring_index($"a", ".", 2)),
+      Row("www.apache"))
+    checkAnswer(
+      df.select(substring_index($"a", ".", 1)),
+      Row("www"))
+    checkAnswer(
+      df.select(substring_index($"a", ".", 0)),
+      Row(""))
+    checkAnswer(
+      df.select(substring_index(lit("www.apache.org"), ".", -1)),
+      Row("org"))
+    checkAnswer(
+      df.select(substring_index(lit("www.apache.org"), ".", -2)),
+      Row("apache.org"))
+    checkAnswer(
+      df.select(substring_index(lit("www.apache.org"), ".", -3)),
+      Row("www.apache.org"))
+    // str is empty string
+    checkAnswer(
+      df.select(substring_index(lit(""), ".", 1)),
+      Row(""))
+    // empty string delim
+    checkAnswer(
+      df.select(substring_index(lit("www.apache.org"), "", 1)),
+      Row(""))
+    // delim does not exist in str
+    checkAnswer(
+      df.select(substring_index(lit("www.apache.org"), "#", 1)),
+      Row("www.apache.org"))
+    // delim is 2 chars
+    checkAnswer(
+      df.select(substring_index(lit("www||apache||org"), "||", 2)),
+      Row("www||apache"))
+    checkAnswer(
+      df.select(substring_index(lit("www||apache||org"), "||", -2)),
+      Row("apache||org"))
+    // null
+    checkAnswer(
+      df.select(substring_index(lit(null), "||", 2)),
+      Row(null))
+    checkAnswer(
+      df.select(substring_index(lit("www.apache.org"), null, 2)),
+      Row(null))
+    // non ascii chars
+    // scalastyle:off
+    checkAnswer(
+      df.selectExpr("""substring_index("大千世界大千世界", "千", 2)"""),
+      Row("大千世界大"))
+    // scalastyle:on
+  }
+
   test("string locate function") {
     val df = Seq(("aaads", "aa", "zz", 1)).toDF("a", "b", "c", "d")
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 9d4998fd48a38..2561c1c2a1932 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -198,7 +198,7 @@ public byte[] getBytes() {
    */
   public UTF8String substring(final int start, final int until) {
     if (until <= start || start >= numBytes) {
-      return fromBytes(new byte[0]);
+      return UTF8String.EMPTY_UTF8;
     }
 
     int i = 0;
@@ -406,6 +406,84 @@ public int indexOf(UTF8String v, int start) {
     return -1;
   }
 
+  /**
+   * Find the `str` from left to right.
+   */
+  private int find(UTF8String str, int start) {
+    assert (str.numBytes > 0);
+    while (start <= numBytes - str.numBytes) {
+      if (ByteArrayMethods.arrayEquals(base, offset + start, str.base, str.offset, str.numBytes)) {
+        return start;
+      }
+      start += 1;
+    }
+    return -1;
+  }
+
+  /**
+   * Find the `str` from right to left.
+   */
+  private int rfind(UTF8String str, int start) {
+    assert (str.numBytes > 0);
+    while (start >= 0) {
+      if (ByteArrayMethods.arrayEquals(base, offset + start, str.base, str.offset, str.numBytes)) {
+        return start;
+      }
+      start -= 1;
+    }
+    return -1;
+  }
+
+  /**
+   * Returns the substring from string str before count occurrences of the delimiter delim.
+   * If count is positive, everything the left of the final delimiter (counting from left) is
+   * returned. If count is negative, every to the right of the final delimiter (counting from the
+   * right) is returned. subStringIndex performs a case-sensitive match when searching for delim.
+   */
+  public UTF8String subStringIndex(UTF8String delim, int count) {
+    if (delim.numBytes == 0 || count == 0) {
+      return EMPTY_UTF8;
+    }
+    if (count > 0) {
+      int idx = -1;
+      while (count > 0) {
+        idx = find(delim, idx + 1);
+        if (idx >= 0) {
+          count --;
+        } else {
+          // can not find enough delim
+          return this;
+        }
+      }
+      if (idx == 0) {
+        return EMPTY_UTF8;
+      }
+      byte[] bytes = new byte[idx];
+      copyMemory(base, offset, bytes, BYTE_ARRAY_OFFSET, idx);
+      return fromBytes(bytes);
+
+    } else {
+      int idx = numBytes - delim.numBytes + 1;
+      count = -count;
+      while (count > 0) {
+        idx = rfind(delim, idx - 1);
+        if (idx >= 0) {
+          count --;
+        } else {
+          // can not find enough delim
+          return this;
+        }
+      }
+      if (idx + delim.numBytes == numBytes) {
+        return EMPTY_UTF8;
+      }
+      int size = numBytes - delim.numBytes - idx;
+      byte[] bytes = new byte[size];
+      copyMemory(base, offset + idx + delim.numBytes, bytes, BYTE_ARRAY_OFFSET, size);
+      return fromBytes(bytes);
+    }
+  }
+
   /**
    * Returns str, right-padded with pad to a length of len
    * For example:
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index c565210872322..43eed7063298d 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -240,6 +240,44 @@ public void indexOf() {
     assertEquals(3, fromString("数据砖头").indexOf(fromString("头"), 0));
   }
 
+  @Test
+  public void substring_index() {
+    assertEquals(fromString("www.apache.org"),
+      fromString("www.apache.org").subStringIndex(fromString("."), 3));
+    assertEquals(fromString("www.apache"),
+      fromString("www.apache.org").subStringIndex(fromString("."), 2));
+    assertEquals(fromString("www"),
+      fromString("www.apache.org").subStringIndex(fromString("."), 1));
+    assertEquals(fromString(""),
+      fromString("www.apache.org").subStringIndex(fromString("."), 0));
+    assertEquals(fromString("org"),
+      fromString("www.apache.org").subStringIndex(fromString("."), -1));
+    assertEquals(fromString("apache.org"),
+      fromString("www.apache.org").subStringIndex(fromString("."), -2));
+    assertEquals(fromString("www.apache.org"),
+      fromString("www.apache.org").subStringIndex(fromString("."), -3));
+    // str is empty string
+    assertEquals(fromString(""),
+      fromString("").subStringIndex(fromString("."), 1));
+    // empty string delim
+    assertEquals(fromString(""),
+      fromString("www.apache.org").subStringIndex(fromString(""), 1));
+    // delim does not exist in str
+    assertEquals(fromString("www.apache.org"),
+      fromString("www.apache.org").subStringIndex(fromString("#"), 2));
+    // delim is 2 chars
+    assertEquals(fromString("www||apache"),
+      fromString("www||apache||org").subStringIndex(fromString("||"), 2));
+    assertEquals(fromString("apache||org"),
+      fromString("www||apache||org").subStringIndex(fromString("||"), -2));
+    // non ascii chars
+    assertEquals(fromString("大千世界大"),
+      fromString("大千世界大千世界").subStringIndex(fromString("千"), 2));
+    // overlapped delim
+    assertEquals(fromString("||"), fromString("||||||").subStringIndex(fromString("|||"), 3));
+    assertEquals(fromString("|||"), fromString("||||||").subStringIndex(fromString("|||"), -4));
+  }
+
   @Test
   public void reverse() {
     assertEquals(fromString("olleh"), fromString("hello").reverse());

From 14f263448471f182123fc84619559df90e7ae52c Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 31 Jul 2015 21:19:23 -0700
Subject: [PATCH 0761/1454] [SPARK-9464][SQL] Property checks for UTF8String

This PR is based on the original work by JoshRosen in #7780, which adds ScalaCheck property-based tests for UTF8String.

Author: Josh Rosen <joshrosen@databricks.com>
Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7830 from yjshen/utf8-property-checks and squashes the following commits:

593da3a [Yijie Shen] resolve comments
c0800e6 [Yijie Shen] Finish all todos in suite
52f51a0 [Josh Rosen] Add some more failing tests
49ed0697 [Josh Rosen] Rename suite
9209c64 [Josh Rosen] UTF8String Property Checks.
---
 unsafe/pom.xml                                |  10 +
 .../apache/spark/unsafe/types/UTF8String.java |  19 +-
 .../spark/unsafe/types/UTF8StringSuite.java   |  13 +-
 .../types/UTF8StringPropertyCheckSuite.scala  | 249 ++++++++++++++++++
 4 files changed, 280 insertions(+), 11 deletions(-)
 create mode 100644 unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala

diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index 33782c6c66f90..89475ee3cf5a1 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -70,6 +70,16 @@
       <artifactId>mockito-core</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.commons</groupId>
+      <artifactId>commons-lang3</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 2561c1c2a1932..f6dafe94c617a 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -301,10 +301,9 @@ public UTF8String trim() {
     int s = 0;
     int e = this.numBytes - 1;
     // skip all of the space (0x20) in the left side
-    while (s < this.numBytes && getByte(s) == 0x20) s++;
+    while (s < this.numBytes && getByte(s) <= 0x20 && getByte(s) >= 0x00) s++;
     // skip all of the space (0x20) in the right side
-    while (e >= 0 && getByte(e) == 0x20) e--;
-
+    while (e >= 0 && getByte(e) <= 0x20 && getByte(e) >= 0x00) e--;
     if (s > e) {
       // empty string
       return UTF8String.fromBytes(new byte[0]);
@@ -316,7 +315,7 @@ public UTF8String trim() {
   public UTF8String trimLeft() {
     int s = 0;
     // skip all of the space (0x20) in the left side
-    while (s < this.numBytes && getByte(s) == 0x20) s++;
+    while (s < this.numBytes && getByte(s) <= 0x20 && getByte(s) >= 0x00) s++;
     if (s == this.numBytes) {
       // empty string
       return UTF8String.fromBytes(new byte[0]);
@@ -328,7 +327,7 @@ public UTF8String trimLeft() {
   public UTF8String trimRight() {
     int e = numBytes - 1;
     // skip all of the space (0x20) in the right side
-    while (e >= 0 && getByte(e) == 0x20) e--;
+    while (e >= 0 && getByte(e) <= 0x20 && getByte(e) >= 0x00) e--;
 
     if (e < 0) {
       // empty string
@@ -354,7 +353,7 @@ public UTF8String reverse() {
   }
 
   public UTF8String repeat(int times) {
-    if (times <=0) {
+    if (times <= 0) {
       return EMPTY_UTF8;
     }
 
@@ -492,7 +491,7 @@ public UTF8String subStringIndex(UTF8String delim, int count) {
    */
   public UTF8String rpad(int len, UTF8String pad) {
     int spaces = len - this.numChars(); // number of char need to pad
-    if (spaces <= 0) {
+    if (spaces <= 0 || pad.numBytes() == 0) {
       // no padding at all, return the substring of the current string
       return substring(0, len);
     } else {
@@ -507,7 +506,7 @@ public UTF8String rpad(int len, UTF8String pad) {
       int idx = 0;
       while (idx < count) {
         copyMemory(pad.base, pad.offset, data, BYTE_ARRAY_OFFSET + offset, pad.numBytes);
-        ++idx;
+        ++ idx;
         offset += pad.numBytes;
       }
       copyMemory(remain.base, remain.offset, data, BYTE_ARRAY_OFFSET + offset, remain.numBytes);
@@ -524,7 +523,7 @@ public UTF8String rpad(int len, UTF8String pad) {
    */
   public UTF8String lpad(int len, UTF8String pad) {
     int spaces = len - this.numChars(); // number of char need to pad
-    if (spaces <= 0) {
+    if (spaces <= 0 || pad.numBytes() == 0) {
       // no padding at all, return the substring of the current string
       return substring(0, len);
     } else {
@@ -539,7 +538,7 @@ public UTF8String lpad(int len, UTF8String pad) {
       int idx = 0;
       while (idx < count) {
         copyMemory(pad.base, pad.offset, data, BYTE_ARRAY_OFFSET + offset, pad.numBytes);
-        ++idx;
+        ++ idx;
         offset += pad.numBytes;
       }
       copyMemory(remain.base, remain.offset, data, BYTE_ARRAY_OFFSET + offset, remain.numBytes);
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 43eed7063298d..ed50cdcb29af8 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -309,7 +309,6 @@ public void pad() {
     assertEquals(fromString("hello?????"), fromString("hello").rpad(10, fromString("?????")));
     assertEquals(fromString("???????"), EMPTY_UTF8.rpad(7, fromString("?????")));
 
-
     assertEquals(fromString("数据砖"), fromString("数据砖头").lpad(3, fromString("????")));
     assertEquals(fromString("?数据砖头"), fromString("数据砖头").lpad(5, fromString("????")));
     assertEquals(fromString("??数据砖头"), fromString("数据砖头").lpad(6, fromString("????")));
@@ -327,6 +326,18 @@ public void pad() {
     assertEquals(
       fromString("数据砖头孙行者孙行者孙行"),
       fromString("数据砖头").rpad(12, fromString("孙行者")));
+
+    assertEquals(EMPTY_UTF8, fromString("数据砖头").lpad(-10, fromString("孙行者")));
+    assertEquals(EMPTY_UTF8, fromString("数据砖头").lpad(-10, EMPTY_UTF8));
+    assertEquals(fromString("数据砖头"), fromString("数据砖头").lpad(5, EMPTY_UTF8));
+    assertEquals(fromString("数据砖"), fromString("数据砖头").lpad(3, EMPTY_UTF8));
+    assertEquals(EMPTY_UTF8, EMPTY_UTF8.lpad(3, EMPTY_UTF8));
+
+    assertEquals(EMPTY_UTF8, fromString("数据砖头").rpad(-10, fromString("孙行者")));
+    assertEquals(EMPTY_UTF8, fromString("数据砖头").rpad(-10, EMPTY_UTF8));
+    assertEquals(fromString("数据砖头"), fromString("数据砖头").rpad(5, EMPTY_UTF8));
+    assertEquals(fromString("数据砖"), fromString("数据砖头").rpad(3, EMPTY_UTF8));
+    assertEquals(EMPTY_UTF8, EMPTY_UTF8.rpad(3, EMPTY_UTF8));
   }
 
   @Test
diff --git a/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala b/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala
new file mode 100644
index 0000000000000..12a002befa0ac
--- /dev/null
+++ b/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala
@@ -0,0 +1,249 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.unsafe.types
+
+import org.apache.commons.lang3.StringUtils
+
+import org.scalacheck.{Arbitrary, Gen}
+import org.scalatest.prop.GeneratorDrivenPropertyChecks
+// scalastyle:off
+import org.scalatest.{FunSuite, Matchers}
+
+import org.apache.spark.unsafe.types.UTF8String.{fromString => toUTF8}
+
+/**
+ * This TestSuite utilize ScalaCheck to generate randomized inputs for UTF8String testing.
+ */
+class UTF8StringPropertyCheckSuite extends FunSuite with GeneratorDrivenPropertyChecks with Matchers {
+// scalastyle:on
+
+  test("toString") {
+    forAll { (s: String) =>
+      assert(toUTF8(s).toString() === s)
+    }
+  }
+
+  test("numChars") {
+    forAll { (s: String) =>
+      assert(toUTF8(s).numChars() === s.length)
+    }
+  }
+
+  test("startsWith") {
+    forAll { (s: String) =>
+      val utf8 = toUTF8(s)
+      assert(utf8.startsWith(utf8))
+      for (i <- 1 to s.length) {
+        assert(utf8.startsWith(toUTF8(s.dropRight(i))))
+      }
+    }
+  }
+
+  test("endsWith") {
+    forAll { (s: String) =>
+      val utf8 = toUTF8(s)
+      assert(utf8.endsWith(utf8))
+      for (i <- 1 to s.length) {
+        assert(utf8.endsWith(toUTF8(s.drop(i))))
+      }
+    }
+  }
+
+  test("toUpperCase") {
+    forAll { (s: String) =>
+      assert(toUTF8(s).toUpperCase === toUTF8(s.toUpperCase))
+    }
+  }
+
+  test("toLowerCase") {
+    forAll { (s: String) =>
+      assert(toUTF8(s).toLowerCase === toUTF8(s.toLowerCase))
+    }
+  }
+
+  test("compare") {
+    forAll { (s1: String, s2: String) =>
+      assert(Math.signum(toUTF8(s1).compareTo(toUTF8(s2))) === Math.signum(s1.compareTo(s2)))
+    }
+  }
+
+  test("substring") {
+    forAll { (s: String) =>
+      for (start <- 0 to s.length; end <- 0 to s.length; if start <= end) {
+        assert(toUTF8(s).substring(start, end).toString === s.substring(start, end))
+      }
+    }
+  }
+
+  test("contains") {
+    forAll { (s: String) =>
+      for (start <- 0 to s.length; end <- 0 to s.length; if start <= end) {
+        val substring = s.substring(start, end)
+        assert(toUTF8(s).contains(toUTF8(substring)) === s.contains(substring))
+      }
+    }
+  }
+
+  val whitespaceChar: Gen[Char] = Gen.choose(0x00, 0x20).map(_.toChar)
+  val whitespaceString: Gen[String] = Gen.listOf(whitespaceChar).map(_.mkString)
+  val randomString: Gen[String] = Arbitrary.arbString.arbitrary
+
+  test("trim, trimLeft, trimRight") {
+    // lTrim and rTrim are both modified from java.lang.String.trim
+    def lTrim(s: String): String = {
+      var st = 0
+      val array: Array[Char] = s.toCharArray
+      while ((st < s.length) && (array(st) <= ' ')) {
+        st += 1
+      }
+      if (st > 0) s.substring(st, s.length) else s
+    }
+    def rTrim(s: String): String = {
+      var len = s.length
+      val array: Array[Char] = s.toCharArray
+      while ((len > 0) && (array(len - 1) <= ' ')) {
+        len -= 1
+      }
+      if (len < s.length) s.substring(0, len) else s
+    }
+
+    forAll(
+        whitespaceString,
+        randomString,
+        whitespaceString
+    ) { (start: String, middle: String, end: String) =>
+      val s = start + middle + end
+      assert(toUTF8(s).trim() === toUTF8(s.trim()))
+      assert(toUTF8(s).trimLeft() === toUTF8(lTrim(s)))
+      assert(toUTF8(s).trimRight() === toUTF8(rTrim(s)))
+    }
+  }
+
+  test("reverse") {
+    forAll { (s: String) =>
+      assert(toUTF8(s).reverse === toUTF8(s.reverse))
+    }
+  }
+
+  test("indexOf") {
+    forAll { (s: String) =>
+      for (start <- 0 to s.length; end <- 0 to s.length; if start <= end) {
+        val substring = s.substring(start, end)
+        assert(toUTF8(s).indexOf(toUTF8(substring), 0) === s.indexOf(substring))
+      }
+    }
+  }
+
+  val randomInt = Gen.choose(-100, 100)
+
+  test("repeat") {
+    def repeat(str: String, times: Int): String = {
+      if (times > 0) str * times else ""
+    }
+    // ScalaCheck always generating too large repeat times which might hang the test forever.
+    forAll(randomString, randomInt) { (s: String, times: Int) =>
+      assert(toUTF8(s).repeat(times) === toUTF8(repeat(s, times)))
+    }
+  }
+
+  test("lpad, rpad") {
+    def padding(origin: String, pad: String, length: Int, isLPad: Boolean): String = {
+      if (length <= 0) return ""
+      if (length <= origin.length) {
+        if (length <= 0) "" else origin.substring(0, length)
+      } else {
+        if (pad.length == 0) return origin
+        val toPad = length - origin.length
+        val partPad = if (toPad % pad.length == 0) "" else pad.substring(0, toPad % pad.length)
+        if (isLPad) {
+          pad * (toPad / pad.length) + partPad + origin
+        } else {
+          origin + pad * (toPad / pad.length) + partPad
+        }
+      }
+    }
+
+    forAll (
+      randomString,
+      randomString,
+      randomInt
+    ) { (s: String, pad: String, length: Int) =>
+      assert(toUTF8(s).lpad(length, toUTF8(pad)) ===
+        toUTF8(padding(s, pad, length, true)))
+      assert(toUTF8(s).rpad(length, toUTF8(pad)) ===
+        toUTF8(padding(s, pad, length, false)))
+    }
+  }
+
+  val nullalbeSeq = Gen.listOf(Gen.oneOf[String](null: String, randomString))
+
+  test("concat") {
+    def concat(orgin: Seq[String]): String =
+      if (orgin.exists(_ == null)) null else orgin.mkString
+
+    forAll { (inputs: Seq[String]) =>
+      assert(UTF8String.concat(inputs.map(toUTF8): _*) === toUTF8(inputs.mkString))
+    }
+    forAll (nullalbeSeq) { (inputs: Seq[String]) =>
+      assert(UTF8String.concat(inputs.map(toUTF8): _*) === toUTF8(concat(inputs)))
+    }
+  }
+
+  test("concatWs") {
+    def concatWs(sep: String, inputs: Seq[String]): String = {
+      if (sep == null) return null
+      inputs.filter(_ != null).mkString(sep)
+    }
+
+    forAll { (sep: String, inputs: Seq[String]) =>
+      assert(UTF8String.concatWs(toUTF8(sep), inputs.map(toUTF8): _*) ===
+        toUTF8(inputs.mkString(sep)))
+    }
+    forAll(randomString, nullalbeSeq) {(sep: String, inputs: Seq[String]) =>
+      assert(UTF8String.concatWs(toUTF8(sep), inputs.map(toUTF8): _*) ===
+        toUTF8(concatWs(sep, inputs)))
+    }
+  }
+
+  // TODO: enable this when we find a proper way to generate valid patterns
+  ignore("split") {
+    forAll { (s: String, pattern: String, limit: Int) =>
+      assert(toUTF8(s).split(toUTF8(pattern), limit) ===
+        s.split(pattern, limit).map(toUTF8(_)))
+    }
+  }
+
+  test("levenshteinDistance") {
+    forAll { (one: String, another: String) =>
+      assert(toUTF8(one).levenshteinDistance(toUTF8(another)) ===
+        StringUtils.getLevenshteinDistance(one, another))
+    }
+  }
+
+  test("hashCode") {
+    forAll { (s: String) =>
+      assert(toUTF8(s).hashCode() === toUTF8(s).hashCode())
+    }
+  }
+
+  test("equals") {
+    forAll { (one: String, another: String) =>
+      assert(toUTF8(one).equals(toUTF8(another)) === one.equals(another))
+    }
+  }
+}

From 3320b0ba262159c0c7209ce39b353c93c597077d Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Fri, 31 Jul 2015 22:26:30 -0700
Subject: [PATCH 0762/1454] [SPARK-9415][SQL] Throw AnalysisException when
 using MapType on Join and Aggregate

JIRA: https://issues.apache.org/jira/browse/SPARK-9415

Following up #7787. We shouldn't use MapType as grouping keys and join keys too.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7819 from viirya/map_join_groupby and squashes the following commits:

005ee0c [Liang-Chi Hsieh] For comments.
7463398 [Liang-Chi Hsieh] MapType can't be used as join keys, grouping keys.
---
 .../sql/catalyst/analysis/CheckAnalysis.scala | 16 +++--
 .../analysis/AnalysisErrorSuite.scala         | 68 ++++++++++++++++++-
 .../spark/sql/DataFrameAggregateSuite.scala   | 10 ---
 .../org/apache/spark/sql/JoinSuite.scala      |  8 ---
 4 files changed, 77 insertions(+), 25 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 0ebc3d180a780..364569d8f0b40 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -92,8 +92,11 @@ trait CheckAnalysis {
               case p: Predicate =>
                 p.asInstanceOf[Expression].children.foreach(checkValidJoinConditionExprs)
               case e if e.dataType.isInstanceOf[BinaryType] =>
-                failAnalysis(s"expression ${e.prettyString} in join condition " +
-                  s"'${condition.prettyString}' can't be binary type.")
+                failAnalysis(s"binary type expression ${e.prettyString} cannot be used " +
+                  "in join conditions")
+              case e if e.dataType.isInstanceOf[MapType] =>
+                failAnalysis(s"map type expression ${e.prettyString} cannot be used " +
+                  "in join conditions")
               case _ => // OK
             }
 
@@ -114,13 +117,16 @@ trait CheckAnalysis {
 
             def checkValidGroupingExprs(expr: Expression): Unit = expr.dataType match {
               case BinaryType =>
-                failAnalysis(s"grouping expression '${expr.prettyString}' in aggregate can " +
-                  s"not be binary type.")
+                failAnalysis(s"binary type expression ${expr.prettyString} cannot be used " +
+                  "in grouping expression")
+              case m: MapType =>
+                failAnalysis(s"map type expression ${expr.prettyString} cannot be used " +
+                  "in grouping expression")
               case _ => // OK
             }
 
             aggregateExprs.foreach(checkValidAggregateExpression)
-            aggregateExprs.foreach(checkValidGroupingExprs)
+            groupingExprs.foreach(checkValidGroupingExprs)
 
           case Sort(orders, _, _) =>
             orders.foreach { order =>
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 2588df98246dd..aa19cdce31ec6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -181,7 +181,71 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
     val error = intercept[AnalysisException] {
       SimpleAnalyzer.checkAnalysis(join)
     }
-    error.message.contains("Failure when resolving conflicting references in Join")
-    error.message.contains("Conflicting attributes")
+    assert(error.message.contains("Failure when resolving conflicting references in Join"))
+    assert(error.message.contains("Conflicting attributes"))
+  }
+
+  test("aggregation can't work on binary and map types") {
+    val plan =
+      Aggregate(
+        AttributeReference("a", BinaryType)(exprId = ExprId(2)) :: Nil,
+        Alias(Sum(AttributeReference("b", IntegerType)(exprId = ExprId(1))), "c")() :: Nil,
+        LocalRelation(
+          AttributeReference("a", BinaryType)(exprId = ExprId(2)),
+          AttributeReference("b", IntegerType)(exprId = ExprId(1))))
+
+    val error = intercept[AnalysisException] {
+      caseSensitiveAnalyze(plan)
+    }
+    assert(error.message.contains("binary type expression a cannot be used in grouping expression"))
+
+    val plan2 =
+      Aggregate(
+        AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)) :: Nil,
+        Alias(Sum(AttributeReference("b", IntegerType)(exprId = ExprId(1))), "c")() :: Nil,
+        LocalRelation(
+          AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)),
+          AttributeReference("b", IntegerType)(exprId = ExprId(1))))
+
+    val error2 = intercept[AnalysisException] {
+      caseSensitiveAnalyze(plan2)
+    }
+    assert(error2.message.contains("map type expression a cannot be used in grouping expression"))
+  }
+
+  test("Join can't work on binary and map types") {
+    val plan =
+      Join(
+        LocalRelation(
+          AttributeReference("a", BinaryType)(exprId = ExprId(2)),
+          AttributeReference("b", IntegerType)(exprId = ExprId(1))),
+        LocalRelation(
+          AttributeReference("c", BinaryType)(exprId = ExprId(4)),
+          AttributeReference("d", IntegerType)(exprId = ExprId(3))),
+        Inner,
+        Some(EqualTo(AttributeReference("a", BinaryType)(exprId = ExprId(2)),
+          AttributeReference("c", BinaryType)(exprId = ExprId(4)))))
+
+    val error = intercept[AnalysisException] {
+      caseSensitiveAnalyze(plan)
+    }
+    assert(error.message.contains("binary type expression a cannot be used in join conditions"))
+
+    val plan2 =
+      Join(
+        LocalRelation(
+          AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)),
+          AttributeReference("b", IntegerType)(exprId = ExprId(1))),
+        LocalRelation(
+          AttributeReference("c", MapType(IntegerType, StringType))(exprId = ExprId(4)),
+          AttributeReference("d", IntegerType)(exprId = ExprId(3))),
+        Inner,
+        Some(EqualTo(AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)),
+          AttributeReference("c", MapType(IntegerType, StringType))(exprId = ExprId(4)))))
+
+    val error2 = intercept[AnalysisException] {
+      caseSensitiveAnalyze(plan2)
+    }
+    assert(error2.message.contains("map type expression a cannot be used in join conditions"))
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index 228ece8065151..f9cff7440a76e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -190,14 +190,4 @@ class DataFrameAggregateSuite extends QueryTest {
       emptyTableData.agg(sumDistinct('a)),
       Row(null))
   }
-
-  test("aggregation can't work on binary type") {
-    val df = Seq(1, 1, 2, 2).map(i => Tuple1(i.toString)).toDF("c").select($"c" cast BinaryType)
-    intercept[AnalysisException] {
-      df.groupBy("c").agg(count("*"))
-    }
-    intercept[AnalysisException] {
-      df.distinct
-    }
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 27c08f64649ee..5bef1d8966031 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -490,12 +490,4 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
         Row(3, 2) :: Nil)
 
   }
-
-  test("Join can't work on binary type") {
-    val left = Seq(1, 1, 2, 2).map(i => Tuple1(i.toString)).toDF("c").select($"c" cast BinaryType)
-    val right = Seq(1, 1, 2, 2).map(i => Tuple1(i.toString)).toDF("d").select($"d" cast BinaryType)
-    intercept[AnalysisException] {
-      left.join(right, ($"left.N" === $"right.N"), "full")
-    }
-  }
 }

From 67ad4e21fc68336b0ad6f9a363fb5ebb51f592bf Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 31 Jul 2015 23:11:22 -0700
Subject: [PATCH 0763/1454] [SPARK-8232] [SQL] Add sort_array support

Add expression `sort_array` support.

Author: Cheng Hao <hao.cheng@intel.com>

This patch had conflicts when merged, resolved by
Committer: Davies Liu <davies.liu@gmail.com>

Closes #7581 from chenghao-intel/sort_array and squashes the following commits:

664c960 [Cheng Hao] update the sort_array by using the ArrayData
276d2d5 [Cheng Hao] add empty line
0edab9c [Cheng Hao] Add asending/descending support for sort_array
80fc0f8 [Cheng Hao] Add type checking
a42b678 [Cheng Hao] Add sort_array support
---
 python/pyspark/sql/functions.py               | 20 +++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/collectionOperations.scala    | 80 ++++++++++++++++++-
 .../CollectionFunctionsSuite.scala            | 22 +++++
 .../org/apache/spark/sql/functions.scala      | 19 ++++-
 .../spark/sql/DataFrameFunctionsSuite.scala   | 51 +++++++++++-
 6 files changed, 186 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 89a2a5ceaa9bf..fb542e6cff81a 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -51,6 +51,7 @@
     'sha1',
     'sha2',
     'size',
+    'sort_array',
     'sparkPartitionId',
     'struct',
     'udf',
@@ -570,8 +571,10 @@ def length(col):
 def format_number(col, d):
     """Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
        and returns the result as a string.
+
     :param col: the column name of the numeric value to be formatted
     :param d: the N decimal places
+
     >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
     [Row(v=u'5.0000')]
     """
@@ -968,6 +971,23 @@ def soundex(col):
     return Column(sc._jvm.functions.size(_to_java_column(col)))
 
 
+@since(1.5)
+def sort_array(col, asc=True):
+    """
+    Collection function: sorts the input array for the given column in ascending order.
+
+    :param col: name of column or expression
+
+    >>> df = sqlContext.createDataFrame([([2, 1, 3],),([1],),([],)], ['data'])
+    >>> df.select(sort_array(df.data).alias('r')).collect()
+    [Row(r=[1, 2, 3]), Row(r=[1]), Row(r=[])]
+    >>> df.select(sort_array(df.data, asc=False).alias('r')).collect()
+    [Row(r=[3, 2, 1]), Row(r=[1]), Row(r=[])]
+     """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.sort_array(_to_java_column(col), asc))
+
+
 class UserDefinedFunction(object):
     """
     User defined function in Python
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index ee44cbcba68e7..6e144518bb009 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -233,6 +233,7 @@ object FunctionRegistry {
 
     // collection functions
     expression[Size]("size"),
+    expression[SortArray]("sort_array"),
 
     // misc functions
     expression[Crc32]("crc32"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 1a00dbc254de1..0a530596a98c6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -16,7 +16,10 @@
  */
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import java.util.Comparator
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 
 /**
@@ -39,3 +42,78 @@ case class Size(child: Expression) extends UnaryExpression with ExpectsInputType
     nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).$sizeCall;")
   }
 }
+
+/**
+ * Sorts the input array in ascending / descending order according to the natural ordering of
+ * the array elements and returns it.
+ */
+case class SortArray(base: Expression, ascendingOrder: Expression)
+  extends BinaryExpression with ExpectsInputTypes with CodegenFallback {
+
+  def this(e: Expression) = this(e, Literal(true))
+
+  override def left: Expression = base
+  override def right: Expression = ascendingOrder
+  override def dataType: DataType = base.dataType
+  override def inputTypes: Seq[AbstractDataType] = Seq(ArrayType, BooleanType)
+
+  override def checkInputDataTypes(): TypeCheckResult = base.dataType match {
+    case _ @ ArrayType(n: AtomicType, _) => TypeCheckResult.TypeCheckSuccess
+    case _ @ ArrayType(n, _) => TypeCheckResult.TypeCheckFailure(
+                    s"Type $n is not the AtomicType, we can not perform the ordering operations")
+    case other =>
+      TypeCheckResult.TypeCheckFailure(s"ArrayType(AtomicType) is expected, but we got $other")
+  }
+
+  @transient
+  private lazy val lt = {
+    val ordering = base.dataType match {
+      case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
+    }
+
+    new Comparator[Any]() {
+      override def compare(o1: Any, o2: Any): Int = {
+        if (o1 == null && o2 == null) {
+          0
+        } else if (o1 == null) {
+          -1
+        } else if (o2 == null) {
+          1
+        } else {
+          ordering.compare(o1, o2)
+        }
+      }
+    }
+  }
+
+  @transient
+  private lazy val gt = {
+    val ordering = base.dataType match {
+      case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
+    }
+
+    new Comparator[Any]() {
+      override def compare(o1: Any, o2: Any): Int = {
+        if (o1 == null && o2 == null) {
+          0
+        } else if (o1 == null) {
+          1
+        } else if (o2 == null) {
+          -1
+        } else {
+          -ordering.compare(o1, o2)
+        }
+      }
+    }
+  }
+
+  override def nullSafeEval(array: Any, ascending: Any): Any = {
+    val data = array.asInstanceOf[ArrayData].toArray().asInstanceOf[Array[AnyRef]]
+    java.util.Arrays.sort(
+      data,
+      if (ascending.asInstanceOf[Boolean]) lt else gt)
+    new GenericArrayData(data.asInstanceOf[Array[Any]])
+  }
+
+  override def prettyName: String = "sort_array"
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
index 28c41b57169f9..2c7e85c446ec6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -43,4 +43,26 @@ class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, MapType(StringType, StringType)), null)
     checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
   }
+
+  test("Sort Array") {
+    val a0 = Literal.create(Seq(2, 1, 3), ArrayType(IntegerType))
+    val a1 = Literal.create(Seq[Integer](), ArrayType(IntegerType))
+    val a2 = Literal.create(Seq("b", "a"), ArrayType(StringType))
+    val a3 = Literal.create(Seq("b", null, "a"), ArrayType(StringType))
+
+    checkEvaluation(new SortArray(a0), Seq(1, 2, 3))
+    checkEvaluation(new SortArray(a1), Seq[Integer]())
+    checkEvaluation(new SortArray(a2), Seq("a", "b"))
+    checkEvaluation(new SortArray(a3), Seq(null, "a", "b"))
+    checkEvaluation(SortArray(a0, Literal(true)), Seq(1, 2, 3))
+    checkEvaluation(SortArray(a1, Literal(true)), Seq[Integer]())
+    checkEvaluation(SortArray(a2, Literal(true)), Seq("a", "b"))
+    checkEvaluation(new SortArray(a3, Literal(true)), Seq(null, "a", "b"))
+    checkEvaluation(SortArray(a0, Literal(false)), Seq(3, 2, 1))
+    checkEvaluation(SortArray(a1, Literal(false)), Seq[Integer]())
+    checkEvaluation(SortArray(a2, Literal(false)), Seq("b", "a"))
+    checkEvaluation(new SortArray(a3, Literal(false)), Seq("b", "a", null))
+
+    checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 57bb00a7417af..3c9421f5cd14b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2223,19 +2223,30 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Returns length of array or map
+   * Returns length of array or map.
+   *
    * @group collection_funcs
    * @since 1.5.0
    */
-  def size(columnName: String): Column = size(Column(columnName))
+  def size(e: Column): Column = Size(e.expr)
 
   /**
-   * Returns length of array or map
+   * Sorts the input array for the given column in ascending order,
+   * according to the natural ordering of the array elements.
+   *
    * @group collection_funcs
    * @since 1.5.0
    */
-  def size(column: Column): Column = Size(column.expr)
+  def sort_array(e: Column): Column = sort_array(e, true)
 
+  /**
+   * Sorts the input array for the given column in ascending / descending order,
+   * according to the natural ordering of the array elements.
+   *
+   * @group collection_funcs
+   * @since 1.5.0
+   */
+  def sort_array(e: Column, asc: Boolean): Column = SortArray(e.expr, lit(asc).expr)
 
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 1baec5d37699d..46921d14256b9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -267,6 +267,53 @@ class DataFrameFunctionsSuite extends QueryTest {
     )
   }
 
+  test("sort_array function") {
+    val df = Seq(
+      (Array[Int](2, 1, 3), Array("b", "c", "a")),
+      (Array[Int](), Array[String]()),
+      (null, null)
+    ).toDF("a", "b")
+    checkAnswer(
+      df.select(sort_array($"a"), sort_array($"b")),
+      Seq(
+        Row(Seq(1, 2, 3), Seq("a", "b", "c")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+    checkAnswer(
+      df.select(sort_array($"a", false), sort_array($"b", false)),
+      Seq(
+        Row(Seq(3, 2, 1), Seq("c", "b", "a")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+    checkAnswer(
+      df.selectExpr("sort_array(a)", "sort_array(b)"),
+      Seq(
+        Row(Seq(1, 2, 3), Seq("a", "b", "c")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+    checkAnswer(
+      df.selectExpr("sort_array(a, true)", "sort_array(b, false)"),
+      Seq(
+        Row(Seq(1, 2, 3), Seq("c", "b", "a")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+
+    val df2 = Seq((Array[Array[Int]](Array(2)), "x")).toDF("a", "b")
+    assert(intercept[AnalysisException] {
+      df2.selectExpr("sort_array(a)").collect()
+    }.getMessage().contains("Type ArrayType(IntegerType,false) is not the AtomicType, " +
+      "we can not perform the ordering operations"))
+
+    val df3 = Seq(("xxx", "x")).toDF("a", "b")
+    assert(intercept[AnalysisException] {
+      df3.selectExpr("sort_array(a)").collect()
+    }.getMessage().contains("ArrayType(AtomicType) is expected, but we got StringType"))
+  }
+
   test("array size function") {
     val df = Seq(
       (Array[Int](1, 2), "x"),
@@ -274,7 +321,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       (Array[Int](1, 2, 3), "z")
     ).toDF("a", "b")
     checkAnswer(
-      df.select(size("a")),
+      df.select(size($"a")),
       Seq(Row(2), Row(0), Row(3))
     )
     checkAnswer(
@@ -290,7 +337,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       (Map[Int, Int](1 -> 1, 2 -> 2, 3 -> 3), "z")
     ).toDF("a", "b")
     checkAnswer(
-      df.select(size("a")),
+      df.select(size($"a")),
       Seq(Row(2), Row(0), Row(3))
     )
     checkAnswer(

From d90f2cf7a2a1d1e69f9ab385f35f62d4091b5302 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 31 Jul 2015 23:55:16 -0700
Subject: [PATCH 0764/1454] [SPARK-9517][SQL] BytesToBytesMap should encode
 data the same way as UnsafeExternalSorter

BytesToBytesMap current encodes key/value data in the following format:
```
8B key length, key data, 8B value length, value data
```

UnsafeExternalSorter, on the other hand, encodes data this way:
```
4B record length, data
```

As a result, we cannot pass records encoded by BytesToBytesMap directly into UnsafeExternalSorter for sorting. However, if we rearrange data slightly, we can then pass the key/value records directly into UnsafeExternalSorter:
```
4B key+value length, 4B key length, key data, value data
```

Author: Reynold Xin <rxin@databricks.com>

Closes #7845 from rxin/kvsort-rebase and squashes the following commits:

5716b59 [Reynold Xin] Fixed test.
2e62ccb [Reynold Xin] Updated BytesToBytesMap's data encoding to put the key first.
a51b641 [Reynold Xin] Added a KV sorter interface.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 58 ++++++++-------
 .../unsafe/sort/UnsafeExternalSorter.java     | 15 ++++
 .../map/AbstractBytesToBytesMapSuite.java     |  6 +-
 .../sql/execution/UnsafeKeyValueSorter.java   | 30 ++++++++
 .../UnsafeFixedWidthAggregationMap.java       | 73 +++++++++----------
 .../sql/execution/GeneratedAggregate.scala    | 27 ++++---
 .../UnsafeFixedWidthAggregationMapSuite.scala | 27 ++++---
 .../org/apache/spark/unsafe/KVIterator.java   | 29 ++++++++
 8 files changed, 175 insertions(+), 90 deletions(-)
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeKeyValueSorter.java
 create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 0f42950e6ed8b..481375f493a50 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -17,7 +17,6 @@
 
 package org.apache.spark.unsafe.map;
 
-import java.io.IOException;
 import java.lang.Override;
 import java.lang.UnsupportedOperationException;
 import java.util.Iterator;
@@ -212,7 +211,7 @@ public BytesToBytesMap(
    */
   public int numElements() { return numElements; }
 
-  private static final class BytesToBytesMapIterator implements Iterator<Location> {
+  public static final class BytesToBytesMapIterator implements Iterator<Location> {
 
     private final int numRecords;
     private final Iterator<MemoryBlock> dataPagesIterator;
@@ -222,7 +221,8 @@ private static final class BytesToBytesMapIterator implements Iterator<Location>
     private Object pageBaseObject;
     private long offsetInPage;
 
-    BytesToBytesMapIterator(int numRecords, Iterator<MemoryBlock> dataPagesIterator, Location loc) {
+    private BytesToBytesMapIterator(
+        int numRecords, Iterator<MemoryBlock> dataPagesIterator, Location loc) {
       this.numRecords = numRecords;
       this.dataPagesIterator = dataPagesIterator;
       this.loc = loc;
@@ -244,13 +244,13 @@ public boolean hasNext() {
 
     @Override
     public Location next() {
-      int keyLength = (int) PlatformDependent.UNSAFE.getLong(pageBaseObject, offsetInPage);
-      if (keyLength == END_OF_PAGE_MARKER) {
+      int totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
+      if (totalLength == END_OF_PAGE_MARKER) {
         advanceToNextPage();
-        keyLength = (int) PlatformDependent.UNSAFE.getLong(pageBaseObject, offsetInPage);
+        totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
       }
       loc.with(pageBaseObject, offsetInPage);
-      offsetInPage += 8 + 8 + keyLength + loc.getValueLength();
+      offsetInPage += 8 + totalLength;
       currentRecordNumber++;
       return loc;
     }
@@ -269,7 +269,7 @@ public void remove() {
    * If any other lookups or operations are performed on this map while iterating over it, including
    * `lookup()`, the behavior of the returned iterator is undefined.
    */
-  public Iterator<Location> iterator() {
+  public BytesToBytesMapIterator iterator() {
     return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc);
   }
 
@@ -352,15 +352,18 @@ private void updateAddressesAndSizes(long fullKeyAddress) {
         taskMemoryManager.getOffsetInPage(fullKeyAddress));
     }
 
-    private void updateAddressesAndSizes(Object page, long keyOffsetInPage) {
-        long position = keyOffsetInPage;
-        keyLength = (int) PlatformDependent.UNSAFE.getLong(page, position);
-        position += 8; // word used to store the key size
-        keyMemoryLocation.setObjAndOffset(page, position);
-        position += keyLength;
-        valueLength = (int) PlatformDependent.UNSAFE.getLong(page, position);
-        position += 8; // word used to store the key size
-        valueMemoryLocation.setObjAndOffset(page, position);
+    private void updateAddressesAndSizes(final Object page, final long keyOffsetInPage) {
+      long position = keyOffsetInPage;
+      final int totalLength = PlatformDependent.UNSAFE.getInt(page, position);
+      position += 4;
+      keyLength = PlatformDependent.UNSAFE.getInt(page, position);
+      position += 4;
+      valueLength = totalLength - keyLength;
+
+      keyMemoryLocation.setObjAndOffset(page, position);
+
+      position += keyLength;
+      valueMemoryLocation.setObjAndOffset(page, position);
     }
 
     Location with(int pos, int keyHashcode, boolean isDefined) {
@@ -478,7 +481,7 @@ public boolean putNewKey(
       // the key address instead of storing the absolute address of the value, the key and value
       // must be stored in the same memory page.
       // (8 byte key length) (key) (8 byte value length) (value)
-      final long requiredSize = 8 + keyLengthBytes + 8 + valueLengthBytes;
+      final long requiredSize = 8 + keyLengthBytes + valueLengthBytes;
 
       // --- Figure out where to insert the new record ---------------------------------------------
 
@@ -508,7 +511,7 @@ public boolean putNewKey(
           // There wasn't enough space in the current page, so write an end-of-page marker:
           final Object pageBaseObject = currentDataPage.getBaseObject();
           final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor;
-          PlatformDependent.UNSAFE.putLong(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
+          PlatformDependent.UNSAFE.putInt(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
         }
         final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
         if (memoryGranted != pageSizeBytes) {
@@ -535,21 +538,22 @@ public boolean putNewKey(
       long insertCursor = dataPageInsertOffset;
 
       // Compute all of our offsets up-front:
-      final long keySizeOffsetInPage = insertCursor;
-      insertCursor += 8; // word used to store the key size
+      final long totalLengthOffset = insertCursor;
+      insertCursor += 4;
+      final long keyLengthOffset = insertCursor;
+      insertCursor += 4;
       final long keyDataOffsetInPage = insertCursor;
       insertCursor += keyLengthBytes;
-      final long valueSizeOffsetInPage = insertCursor;
-      insertCursor += 8; // word used to store the value size
       final long valueDataOffsetInPage = insertCursor;
       insertCursor += valueLengthBytes; // word used to store the value size
 
+      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, totalLengthOffset,
+        keyLengthBytes + valueLengthBytes);
+      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, keyLengthOffset, keyLengthBytes);
       // Copy the key
-      PlatformDependent.UNSAFE.putLong(dataPageBaseObject, keySizeOffsetInPage, keyLengthBytes);
       PlatformDependent.copyMemory(
         keyBaseObject, keyBaseOffset, dataPageBaseObject, keyDataOffsetInPage, keyLengthBytes);
       // Copy the value
-      PlatformDependent.UNSAFE.putLong(dataPageBaseObject, valueSizeOffsetInPage, valueLengthBytes);
       PlatformDependent.copyMemory(valueBaseObject, valueBaseOffset, dataPageBaseObject,
         valueDataOffsetInPage, valueLengthBytes);
 
@@ -557,7 +561,7 @@ public boolean putNewKey(
 
       if (useOverflowPage) {
         // Store the end-of-page marker at the end of the data page
-        PlatformDependent.UNSAFE.putLong(dataPageBaseObject, insertCursor, END_OF_PAGE_MARKER);
+        PlatformDependent.UNSAFE.putInt(dataPageBaseObject, insertCursor, END_OF_PAGE_MARKER);
       } else {
         pageCursor += requiredSize;
       }
@@ -565,7 +569,7 @@ public boolean putNewKey(
       numElements++;
       bitset.set(pos);
       final long storedKeyAddress = taskMemoryManager.encodePageNumberAndOffset(
-        dataPage, keySizeOffsetInPage);
+        dataPage, totalLengthOffset);
       longArray.set(pos * 2, storedKeyAddress);
       longArray.set(pos * 2 + 1, keyHashcode);
       updateAddressesAndSizes(storedKeyAddress);
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 866e0b4151577..c05f2c332eee3 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -282,6 +282,21 @@ public void insertRecord(
     sorter.insertRecord(recordAddress, prefix);
   }
 
+  /**
+   * Write a record to the sorter. The record is broken down into two different parts, and
+   *
+   */
+  public void insertRecord(
+      Object recordBaseObject1,
+      long recordBaseOffset1,
+      int lengthInBytes1,
+      Object recordBaseObject2,
+      long recordBaseOffset2,
+      int lengthInBytes2,
+      long prefix) throws IOException {
+
+  }
+
   public UnsafeSorterIterator getSortedIterator() throws IOException {
     final UnsafeSorterIterator inMemoryIterator = sorter.getSortedIterator();
     int numIteratorsToMerge = spillWriters.size() + (inMemoryIterator.hasNext() ? 1 : 0);
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 60f483acbcb80..70f8ca4d21345 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -243,17 +243,17 @@ public void iteratorTest() throws Exception {
   @Test
   public void iteratingOverDataPagesWithWastedSpace() throws Exception {
     final int NUM_ENTRIES = 1000 * 1000;
-    final int KEY_LENGTH = 16;
+    final int KEY_LENGTH = 24;
     final int VALUE_LENGTH = 40;
     final BytesToBytesMap map = new BytesToBytesMap(
       taskMemoryManager, shuffleMemoryManager, NUM_ENTRIES, PAGE_SIZE_BYTES);
-    // Each record will take 8 + 8 + 16 + 40 = 72 bytes of space in the data page. Our 64-megabyte
+    // Each record will take 8 + 24 + 40 = 72 bytes of space in the data page. Our 64-megabyte
     // pages won't be evenly-divisible by records of this size, which will cause us to waste some
     // space at the end of the page. This is necessary in order for us to take the end-of-record
     // handling branch in iterator().
     try {
       for (int i = 0; i < NUM_ENTRIES; i++) {
-        final long[] key = new long[] { i, i };  // 2 * 8 = 16 bytes
+        final long[] key = new long[] { i, i, i };  // 3 * 8 = 24 bytes
         final long[] value = new long[] { i, i, i, i, i }; // 5 * 8 = 40 bytes
         final BytesToBytesMap.Location loc = map.lookup(
           key,
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeKeyValueSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeKeyValueSorter.java
new file mode 100644
index 0000000000000..59c774da74acf
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeKeyValueSorter.java
@@ -0,0 +1,30 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution;
+
+import java.io.IOException;
+
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
+import org.apache.spark.unsafe.KVIterator;
+
+public abstract class UnsafeKeyValueSorter {
+
+  public abstract void insert(UnsafeRow key, UnsafeRow value);
+
+  public abstract KVIterator<UnsafeRow, UnsafeRow> sort() throws IOException;
+}
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 08a98cdd94a4c..c18b6dea6b2e1 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -17,9 +17,6 @@
 
 package org.apache.spark.sql.execution;
 
-import java.io.IOException;
-import java.util.Iterator;
-
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
@@ -28,6 +25,7 @@
 import org.apache.spark.sql.types.DecimalType;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
+import org.apache.spark.unsafe.KVIterator;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
 import org.apache.spark.unsafe.memory.MemoryLocation;
@@ -156,54 +154,55 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     return currentAggregationBuffer;
   }
 
-  /**
-   * Mutable pair object returned by {@link UnsafeFixedWidthAggregationMap#iterator()}.
-   */
-  public static class MapEntry {
-    private MapEntry() { };
-    public final UnsafeRow key = new UnsafeRow();
-    public final UnsafeRow value = new UnsafeRow();
-  }
-
   /**
    * Returns an iterator over the keys and values in this map.
    *
    * For efficiency, each call returns the same object.
    */
-  public Iterator<MapEntry> iterator() {
-    return new Iterator<MapEntry>() {
+  public KVIterator<UnsafeRow, UnsafeRow> iterator() {
+    return new KVIterator<UnsafeRow, UnsafeRow>() {
+
+      private final BytesToBytesMap.BytesToBytesMapIterator mapLocationIterator = map.iterator();
+      private final UnsafeRow key = new UnsafeRow();
+      private final UnsafeRow value = new UnsafeRow();
 
-      private final MapEntry entry = new MapEntry();
-      private final Iterator<BytesToBytesMap.Location> mapLocationIterator = map.iterator();
+      @Override
+      public boolean next() {
+        if (mapLocationIterator.hasNext()) {
+          final BytesToBytesMap.Location loc = mapLocationIterator.next();
+          final MemoryLocation keyAddress = loc.getKeyAddress();
+          final MemoryLocation valueAddress = loc.getValueAddress();
+          key.pointTo(
+            keyAddress.getBaseObject(),
+            keyAddress.getBaseOffset(),
+            groupingKeySchema.length(),
+            loc.getKeyLength()
+          );
+          value.pointTo(
+            valueAddress.getBaseObject(),
+            valueAddress.getBaseOffset(),
+            aggregationBufferSchema.length(),
+            loc.getValueLength()
+          );
+          return true;
+        } else {
+          return false;
+        }
+      }
 
       @Override
-      public boolean hasNext() {
-        return mapLocationIterator.hasNext();
+      public UnsafeRow getKey() {
+        return key;
       }
 
       @Override
-      public MapEntry next() {
-        final BytesToBytesMap.Location loc = mapLocationIterator.next();
-        final MemoryLocation keyAddress = loc.getKeyAddress();
-        final MemoryLocation valueAddress = loc.getValueAddress();
-        entry.key.pointTo(
-          keyAddress.getBaseObject(),
-          keyAddress.getBaseOffset(),
-          groupingKeySchema.length(),
-          loc.getKeyLength()
-        );
-        entry.value.pointTo(
-          valueAddress.getBaseObject(),
-          valueAddress.getBaseOffset(),
-          aggregationBufferSchema.length(),
-          loc.getValueLength()
-        );
-        return entry;
+      public UnsafeRow getValue() {
+        return value;
       }
 
       @Override
-      public void remove() {
-        throw new UnsupportedOperationException();
+      public void close() {
+        // Do nothing.
       }
     };
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 469de6ca8e101..cd87b8deba0c2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -287,21 +287,26 @@ case class GeneratedAggregate(
         new Iterator[InternalRow] {
           private[this] val mapIterator = aggregationMap.iterator()
           private[this] val resultProjection = resultProjectionBuilder()
+          private[this] var _hasNext = mapIterator.next()
 
-          def hasNext: Boolean = mapIterator.hasNext
+          def hasNext: Boolean = _hasNext
 
           def next(): InternalRow = {
-            val entry = mapIterator.next()
-            val result = resultProjection(joinedRow(entry.key, entry.value))
-            if (hasNext) {
-              result
+            if (_hasNext) {
+              val result = resultProjection(joinedRow(mapIterator.getKey, mapIterator.getValue))
+              _hasNext = mapIterator.next()
+              if (_hasNext) {
+                result
+              } else {
+                // This is the last element in the iterator, so let's free the buffer. Before we do,
+                // though, we need to make a defensive copy of the result so that we don't return an
+                // object that might contain dangling pointers to the freed memory
+                val resultCopy = result.copy()
+                aggregationMap.free()
+                resultCopy
+              }
             } else {
-              // This is the last element in the iterator, so let's free the buffer. Before we do,
-              // though, we need to make a defensive copy of the result so that we don't return an
-              // object that might contain dangling pointers to the freed memory
-              val resultCopy = result.copy()
-              aggregationMap.free()
-              resultCopy
+              throw new java.util.NoSuchElementException
             }
           }
         }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index 79fd52dacda52..6a2c51ca88ac3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution
 import org.scalatest.{BeforeAndAfterEach, Matchers}
 
 import scala.collection.JavaConverters._
+import scala.collection.mutable
 import scala.util.Random
 
 import org.apache.spark.SparkFunSuite
@@ -52,7 +53,7 @@ class UnsafeFixedWidthAggregationMapSuite
 
   override def afterEach(): Unit = {
     if (taskMemoryManager != null) {
-      val leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask
+      val leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask()
       assert(taskMemoryManager.cleanUpAllAllocatedMemory() === 0)
       assert(leakedShuffleMemory === 0)
       taskMemoryManager = null
@@ -80,7 +81,7 @@ class UnsafeFixedWidthAggregationMapSuite
       PAGE_SIZE_BYTES,
       false // disable perf metrics
     )
-    assert(!map.iterator().hasNext)
+    assert(!map.iterator().next())
     map.free()
   }
 
@@ -100,13 +101,13 @@ class UnsafeFixedWidthAggregationMapSuite
     // Looking up a key stores a zero-entry in the map (like Python Counters or DefaultDicts)
     assert(map.getAggregationBuffer(groupKey) != null)
     val iter = map.iterator()
-    val entry = iter.next()
-    assert(!iter.hasNext)
-    entry.key.getString(0) should be ("cats")
-    entry.value.getInt(0) should be (0)
+    assert(iter.next())
+    iter.getKey.getString(0) should be ("cats")
+    iter.getValue.getInt(0) should be (0)
+    assert(!iter.next())
 
     // Modifications to rows retrieved from the map should update the values in the map
-    entry.value.setInt(0, 42)
+    iter.getValue.setInt(0, 42)
     map.getAggregationBuffer(groupKey).getInt(0) should be (42)
 
     map.free()
@@ -128,12 +129,14 @@ class UnsafeFixedWidthAggregationMapSuite
     groupKeys.foreach { keyString =>
       assert(map.getAggregationBuffer(InternalRow(UTF8String.fromString(keyString))) != null)
     }
-    val seenKeys: Set[String] = map.iterator().asScala.map { entry =>
-      entry.key.getString(0)
-    }.toSet
-    seenKeys.size should be (groupKeys.size)
-    seenKeys should be (groupKeys)
 
+    val seenKeys = new mutable.HashSet[String]
+    val iter = map.iterator()
+    while (iter.next()) {
+      seenKeys += iter.getKey.getString(0)
+    }
+    assert(seenKeys.size === groupKeys.size)
+    assert(seenKeys === groupKeys)
     map.free()
   }
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java b/unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java
new file mode 100644
index 0000000000000..fb163401c0d27
--- /dev/null
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.unsafe;
+
+public abstract class KVIterator<K, V> {
+
+  public abstract boolean next();
+
+  public abstract K getKey();
+
+  public abstract V getValue();
+
+  public abstract void close();
+}

From 1d59a4162bf5142af270ed7f4b3eab42870c87b7 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 1 Aug 2015 00:17:15 -0700
Subject: [PATCH 0765/1454] [SPARK-9480][SQL] add MapData and cleanup internal
 row stuff

This PR adds a `MapData` as internal representation of map type in Spark SQL, and provides a default implementation with just 2 `ArrayData`.

After that, we have specialized getters for all internal type, so I removed generic getter in `ArrayData` and added specialized `toArray` for it.
Also did some refactor and cleanup for `InternalRow` and its subclasses.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7799 from cloud-fan/map-data and squashes the following commits:

77d482f [Wenchen Fan] fix python
e8f6682 [Wenchen Fan] skip MapData equality check in HiveInspectorSuite
40cc9db [Wenchen Fan] add toString
6e06ec9 [Wenchen Fan] some more cleanup
a90aca1 [Wenchen Fan] add MapData
---
 .../apache/spark/mllib/linalg/Matrices.scala  |   6 +-
 .../apache/spark/mllib/linalg/Vectors.scala   |   6 +-
 .../expressions/SpecializedGetters.java       |   6 +
 .../sql/catalyst/expressions/UnsafeRow.java   |  12 +-
 .../sql/catalyst/CatalystTypeConverters.scala |  79 +++++----
 .../spark/sql/catalyst/InternalRow.scala      | 117 ++++---------
 .../catalyst/expressions/BoundAttribute.scala |   3 +
 .../spark/sql/catalyst/expressions/Cast.scala | 101 +++++-------
 .../GenericSpecializedGetters.scala           |  61 +++++++
 .../sql/catalyst/expressions/Projection.scala |  69 ++++----
 .../expressions/SpecificMutableRow.scala      |  13 +-
 .../expressions/codegen/CodeGenerator.scala   |  11 +-
 .../codegen/GenerateProjection.scala          |   2 +-
 .../expressions/collectionOperations.scala    |   8 +-
 .../expressions/complexTypeExtractors.scala   |  81 ++++++---
 .../sql/catalyst/expressions/generators.scala |  29 +++-
 .../spark/sql/catalyst/expressions/rows.scala |  44 ++---
 .../expressions/stringOperations.scala        |   2 +-
 .../spark/sql/types/ArrayBasedMapData.scala   |  51 ++++++
 .../apache/spark/sql/types/ArrayData.scala    | 155 +++++++++---------
 .../spark/sql/types/GenericArrayData.scala    | 116 +++++++++----
 .../org/apache/spark/sql/types/MapData.scala  |  38 +++++
 .../expressions/ComplexTypeSuite.scala        |   4 -
 .../expressions/UnsafeRowConverterSuite.scala |   2 +-
 .../spark/sql/execution/debug/package.scala   |  14 +-
 .../spark/sql/execution/pythonUDFs.scala      |  31 ++--
 .../apache/spark/sql/json/JacksonParser.scala |  21 ++-
 .../sql/parquet/CatalystRowConverter.scala    |  16 +-
 .../spark/sql/parquet/ParquetConverter.scala  |   8 +-
 .../sql/parquet/ParquetTableSupport.scala     |  23 +--
 .../org/apache/spark/sql/DataFrameSuite.scala |   1 +
 .../scala/org/apache/spark/sql/RowSuite.scala |   4 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  34 ++--
 .../scala/org/apache/spark/sql/TestData.scala |   4 +-
 .../spark/sql/UserDefinedTypeSuite.scala      |   2 +-
 .../spark/sql/hive/HiveInspectors.scala       |  89 ++++++----
 .../hive/execution/InsertIntoHiveTable.scala  |   4 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |   4 +-
 .../spark/sql/hive/orc/OrcRelation.scala      |   7 +-
 .../spark/sql/hive/HiveInspectorSuite.scala   |   4 +-
 40 files changed, 750 insertions(+), 532 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 88914fa875990..1c858348bf20e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -179,12 +179,12 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
         val tpe = row.getByte(0)
         val numRows = row.getInt(1)
         val numCols = row.getInt(2)
-        val values = row.getArray(5).toArray.map(_.asInstanceOf[Double])
+        val values = row.getArray(5).toDoubleArray()
         val isTransposed = row.getBoolean(6)
         tpe match {
           case 0 =>
-            val colPtrs = row.getArray(3).toArray.map(_.asInstanceOf[Int])
-            val rowIndices = row.getArray(4).toArray.map(_.asInstanceOf[Int])
+            val colPtrs = row.getArray(3).toIntArray()
+            val rowIndices = row.getArray(4).toIntArray()
             new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values, isTransposed)
           case 1 =>
             new DenseMatrix(numRows, numCols, values, isTransposed)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 89a1818db0d1d..96d1f48ba2ba3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -209,11 +209,11 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
         tpe match {
           case 0 =>
             val size = row.getInt(1)
-            val indices = row.getArray(2).toArray().map(_.asInstanceOf[Int])
-            val values = row.getArray(3).toArray().map(_.asInstanceOf[Double])
+            val indices = row.getArray(2).toIntArray()
+            val values = row.getArray(3).toDoubleArray()
             new SparseVector(size, indices, values)
           case 1 =>
-            val values = row.getArray(3).toArray().map(_.asInstanceOf[Double])
+            val values = row.getArray(3).toDoubleArray()
             new DenseVector(values)
         }
     }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
index e3d3ba7a9ccc0..8f1027f3164c8 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/SpecializedGetters.java
@@ -19,7 +19,9 @@
 
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.ArrayData;
+import org.apache.spark.sql.types.DataType;
 import org.apache.spark.sql.types.Decimal;
+import org.apache.spark.sql.types.MapData;
 import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 
@@ -52,4 +54,8 @@ public interface SpecializedGetters {
   InternalRow getStruct(int ordinal, int numFields);
 
   ArrayData getArray(int ordinal);
+
+  MapData getMap(int ordinal);
+
+  Object get(int ordinal, DataType dataType);
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 24dc80b1a7932..5a19aa8920c2f 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -118,6 +118,11 @@ private long getFieldOffset(int ordinal) {
     return baseOffset + bitSetWidthInBytes + ordinal * 8L;
   }
 
+  private void assertIndexIsValid(int index) {
+    assert index >= 0 : "index (" + index + ") should >= 0";
+    assert index < numFields : "index (" + index + ") should < " + numFields;
+  }
+
   //////////////////////////////////////////////////////////////////////////////
   // Public methods
   //////////////////////////////////////////////////////////////////////////////
@@ -163,11 +168,6 @@ public void pointTo(byte[] buf, int numFields, int sizeInBytes) {
     pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
   }
 
-  private void assertIndexIsValid(int index) {
-    assert index >= 0 : "index (" + index + ") should >= 0";
-    assert index < numFields : "index (" + index + ") should < " + numFields;
-  }
-
   @Override
   public void setNullAt(int i) {
     assertIndexIsValid(i);
@@ -254,7 +254,7 @@ public void setDecimal(int ordinal, Decimal value, int precision) {
   }
 
   @Override
-  public Object get(int ordinal) {
+  public Object genericGet(int ordinal) {
     throw new UnsupportedOperationException();
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 7ca20fe97fbef..c666864e43abd 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -23,7 +23,6 @@ import java.sql.{Date, Timestamp}
 import java.util.{Map => JavaMap}
 import javax.annotation.Nullable
 
-import scala.collection.mutable.HashMap
 import scala.language.existentials
 
 import org.apache.spark.sql.Row
@@ -53,12 +52,6 @@ object CatalystTypeConverters {
     }
   }
 
-  private def isWholePrimitive(dt: DataType): Boolean = dt match {
-    case dt if isPrimitive(dt) => true
-    case MapType(keyType, valueType, _) => isWholePrimitive(keyType) && isWholePrimitive(valueType)
-    case _ => false
-  }
-
   private def getConverterForType(dataType: DataType): CatalystTypeConverter[Any, Any, Any] = {
     val converter = dataType match {
       case udt: UserDefinedType[_] => UDTConverter(udt)
@@ -157,8 +150,6 @@ object CatalystTypeConverters {
 
     private[this] val elementConverter = getConverterForType(elementType)
 
-    private[this] val isNoChange = isWholePrimitive(elementType)
-
     override def toCatalystImpl(scalaValue: Any): ArrayData = {
       scalaValue match {
         case a: Array[_] =>
@@ -179,10 +170,14 @@ object CatalystTypeConverters {
     override def toScala(catalystValue: ArrayData): Seq[Any] = {
       if (catalystValue == null) {
         null
-      } else if (isNoChange) {
-        catalystValue.toArray()
+      } else if (isPrimitive(elementType)) {
+        catalystValue.toArray[Any](elementType)
       } else {
-        catalystValue.toArray().map(elementConverter.toScala)
+        val result = new Array[Any](catalystValue.numElements())
+        catalystValue.foreach(elementType, (i, e) => {
+          result(i) = elementConverter.toScala(e)
+        })
+        result
       }
     }
 
@@ -193,44 +188,58 @@ object CatalystTypeConverters {
   private case class MapConverter(
       keyType: DataType,
       valueType: DataType)
-    extends CatalystTypeConverter[Any, Map[Any, Any], Map[Any, Any]] {
+    extends CatalystTypeConverter[Any, Map[Any, Any], MapData] {
 
     private[this] val keyConverter = getConverterForType(keyType)
     private[this] val valueConverter = getConverterForType(valueType)
 
-    private[this] val isNoChange = isWholePrimitive(keyType) && isWholePrimitive(valueType)
-
-    override def toCatalystImpl(scalaValue: Any): Map[Any, Any] = scalaValue match {
+    override def toCatalystImpl(scalaValue: Any): MapData = scalaValue match {
       case m: Map[_, _] =>
-        m.map { case (k, v) =>
-          keyConverter.toCatalyst(k) -> valueConverter.toCatalyst(v)
+        val length = m.size
+        val convertedKeys = new Array[Any](length)
+        val convertedValues = new Array[Any](length)
+
+        var i = 0
+        for ((key, value) <- m) {
+          convertedKeys(i) = keyConverter.toCatalyst(key)
+          convertedValues(i) = valueConverter.toCatalyst(value)
+          i += 1
         }
+        ArrayBasedMapData(convertedKeys, convertedValues)
 
       case jmap: JavaMap[_, _] =>
+        val length = jmap.size()
+        val convertedKeys = new Array[Any](length)
+        val convertedValues = new Array[Any](length)
+
+        var i = 0
         val iter = jmap.entrySet.iterator
-        val convertedMap: HashMap[Any, Any] = HashMap()
         while (iter.hasNext) {
           val entry = iter.next()
-          val key = keyConverter.toCatalyst(entry.getKey)
-          convertedMap(key) = valueConverter.toCatalyst(entry.getValue)
+          convertedKeys(i) = keyConverter.toCatalyst(entry.getKey)
+          convertedValues(i) = valueConverter.toCatalyst(entry.getValue)
+          i += 1
         }
-        convertedMap
+        ArrayBasedMapData(convertedKeys, convertedValues)
     }
 
-    override def toScala(catalystValue: Map[Any, Any]): Map[Any, Any] = {
+    override def toScala(catalystValue: MapData): Map[Any, Any] = {
       if (catalystValue == null) {
         null
-      } else if (isNoChange) {
-        catalystValue
       } else {
-        catalystValue.map { case (k, v) =>
-          keyConverter.toScala(k) -> valueConverter.toScala(v)
-        }
+        val keys = catalystValue.keyArray().toArray[Any](keyType)
+        val values = catalystValue.valueArray().toArray[Any](valueType)
+        val convertedKeys =
+          if (isPrimitive(keyType)) keys else keys.map(keyConverter.toScala)
+        val convertedValues =
+          if (isPrimitive(valueType)) values else values.map(valueConverter.toScala)
+
+        convertedKeys.zip(convertedValues).toMap
       }
     }
 
     override def toScalaImpl(row: InternalRow, column: Int): Map[Any, Any] =
-      toScala(row.get(column, MapType(keyType, valueType)).asInstanceOf[Map[Any, Any]])
+      toScala(row.getMap(column))
   }
 
   private case class StructConverter(
@@ -410,7 +419,17 @@ object CatalystTypeConverters {
     case r: Row => InternalRow(r.toSeq.map(convertToCatalyst): _*)
     case arr: Array[Any] => new GenericArrayData(arr.map(convertToCatalyst))
     case m: Map[_, _] =>
-      m.map { case (k, v) => (convertToCatalyst(k), convertToCatalyst(v)) }.toMap
+      val length = m.size
+      val convertedKeys = new Array[Any](length)
+      val convertedValues = new Array[Any](length)
+
+      var i = 0
+      for ((key, value) <- m) {
+        convertedKeys(i) = convertToCatalyst(key)
+        convertedValues(i) = convertToCatalyst(value)
+        i += 1
+      }
+      ArrayBasedMapData(convertedKeys, convertedValues)
     case other => other
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index b19bf4386b0ba..7656d054dc361 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -19,71 +19,25 @@ package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
  * internal types.
  */
-abstract class InternalRow extends Serializable with SpecializedGetters {
+// todo: make InternalRow just extends SpecializedGetters, remove generic getter
+abstract class InternalRow extends GenericSpecializedGetters with Serializable {
 
   def numFields: Int
 
-  def get(ordinal: Int): Any = get(ordinal, null)
-
-  def genericGet(ordinal: Int): Any = get(ordinal, null)
-
-  def get(ordinal: Int, dataType: DataType): Any
-
-  def getAs[T](ordinal: Int, dataType: DataType): T = get(ordinal, dataType).asInstanceOf[T]
-
-  override def isNullAt(ordinal: Int): Boolean = get(ordinal) == null
-
-  override def getBoolean(ordinal: Int): Boolean = getAs[Boolean](ordinal, BooleanType)
-
-  override def getByte(ordinal: Int): Byte = getAs[Byte](ordinal, ByteType)
-
-  override def getShort(ordinal: Int): Short = getAs[Short](ordinal, ShortType)
-
-  override def getInt(ordinal: Int): Int = getAs[Int](ordinal, IntegerType)
-
-  override def getLong(ordinal: Int): Long = getAs[Long](ordinal, LongType)
-
-  override def getFloat(ordinal: Int): Float = getAs[Float](ordinal, FloatType)
-
-  override def getDouble(ordinal: Int): Double = getAs[Double](ordinal, DoubleType)
-
-  override def getUTF8String(ordinal: Int): UTF8String = getAs[UTF8String](ordinal, StringType)
-
-  override def getBinary(ordinal: Int): Array[Byte] = getAs[Array[Byte]](ordinal, BinaryType)
-
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal =
-    getAs[Decimal](ordinal, DecimalType(precision, scale))
-
-  override def getInterval(ordinal: Int): CalendarInterval =
-    getAs[CalendarInterval](ordinal, CalendarIntervalType)
-
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
-  /**
-   * Returns a struct from ordinal position.
-   *
-   * @param ordinal position to get the struct from.
-   * @param numFields number of fields the struct type has
-   */
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow =
-    getAs[InternalRow](ordinal, null)
-
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal, null)
-
-  override def toString: String = s"[${this.mkString(",")}]"
+  override def toString: String = mkString("[", ",", "]")
 
   /**
    * Make a copy of the current [[InternalRow]] object.
    */
-  def copy(): InternalRow = this
+  def copy(): InternalRow
 
   /** Returns true if there are any NULL values in this row. */
   def anyNull: Boolean = {
@@ -117,8 +71,8 @@ abstract class InternalRow extends Serializable with SpecializedGetters {
         return false
       }
       if (!isNullAt(i)) {
-        val o1 = get(i)
-        val o2 = other.get(i)
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
         o1 match {
           case b1: Array[Byte] =>
             if (!o2.isInstanceOf[Array[Byte]] ||
@@ -143,34 +97,6 @@ abstract class InternalRow extends Serializable with SpecializedGetters {
     true
   }
 
-  /* ---------------------- utility methods for Scala ---------------------- */
-
-  /**
-   * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
-   */
-  def toSeq: Seq[Any] = {
-    val n = numFields
-    val values = new Array[Any](n)
-    var i = 0
-    while (i < n) {
-      values.update(i, get(i))
-      i += 1
-    }
-    values.toSeq
-  }
-
-  /** Displays all elements of this sequence in a string (without a separator). */
-  def mkString: String = toSeq.mkString
-
-  /** Displays all elements of this sequence in a string using a separator string. */
-  def mkString(sep: String): String = toSeq.mkString(sep)
-
-  /**
-   * Displays all elements of this traversable or iterator in a string using
-   * start, end, and separator strings.
-   */
-  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
-
   // Custom hashCode function that matches the efficient code generated version.
   override def hashCode: Int = {
     var result: Int = 37
@@ -181,7 +107,7 @@ abstract class InternalRow extends Serializable with SpecializedGetters {
         if (isNullAt(i)) {
           0
         } else {
-          get(i) match {
+          genericGet(i) match {
             case b: Boolean => if (b) 0 else 1
             case b: Byte => b.toInt
             case s: Short => s.toInt
@@ -200,6 +126,35 @@ abstract class InternalRow extends Serializable with SpecializedGetters {
     }
     result
   }
+
+  /* ---------------------- utility methods for Scala ---------------------- */
+
+  /**
+   * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
+   */
+  // todo: remove this as it needs the generic getter
+  def toSeq: Seq[Any] = {
+    val n = numFields
+    val values = new Array[Any](n)
+    var i = 0
+    while (i < n) {
+      values.update(i, genericGet(i))
+      i += 1
+    }
+    values
+  }
+
+  /** Displays all elements of this sequence in a string (without a separator). */
+  def mkString: String = toSeq.mkString
+
+  /** Displays all elements of this sequence in a string using a separator string. */
+  def mkString(sep: String): String = toSeq.mkString(sep)
+
+  /**
+   * Displays all elements of this traversable or iterator in a string using
+   * start, end, and separator strings.
+   */
+  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 45709c1c8f554..473b9b787058c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -49,7 +49,10 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
         case StringType => input.getUTF8String(ordinal)
         case BinaryType => input.getBinary(ordinal)
         case CalendarIntervalType => input.getInterval(ordinal)
+        case t: DecimalType => input.getDecimal(ordinal, t.precision, t.scale)
         case t: StructType => input.getStruct(ordinal, t.size)
+        case _: ArrayType => input.getArray(ordinal)
+        case _: MapType => input.getMap(ordinal)
         case _ => input.get(ordinal, dataType)
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 43be11c48ae7c..88429bb84b1e3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -361,30 +361,29 @@ case class Cast(child: Expression, dataType: DataType)
       b => x.numeric.asInstanceOf[Numeric[Any]].toFloat(b)
   }
 
-  private[this] def castArray(from: ArrayType, to: ArrayType): Any => Any = {
-    val elementCast = cast(from.elementType, to.elementType)
+  private[this] def castArray(fromType: DataType, toType: DataType): Any => Any = {
+    val elementCast = cast(fromType, toType)
     // TODO: Could be faster?
     buildCast[ArrayData](_, array => {
-      val length = array.numElements()
-      val values = new Array[Any](length)
-      var i = 0
-      while (i < length) {
-        if (array.isNullAt(i)) {
+      val values = new Array[Any](array.numElements())
+      array.foreach(fromType, (i, e) => {
+        if (e == null) {
           values(i) = null
         } else {
-          values(i) = elementCast(array.get(i))
+          values(i) = elementCast(e)
         }
-        i += 1
-      }
+      })
       new GenericArrayData(values)
     })
   }
 
   private[this] def castMap(from: MapType, to: MapType): Any => Any = {
-    val keyCast = cast(from.keyType, to.keyType)
-    val valueCast = cast(from.valueType, to.valueType)
-    buildCast[Map[Any, Any]](_, _.map {
-      case (key, value) => (keyCast(key), if (value == null) null else valueCast(value))
+    val keyCast = castArray(from.keyType, to.keyType)
+    val valueCast = castArray(from.valueType, to.valueType)
+    buildCast[MapData](_, map => {
+      val keys = keyCast(map.keyArray()).asInstanceOf[ArrayData]
+      val values = valueCast(map.valueArray()).asInstanceOf[ArrayData]
+      new ArrayBasedMapData(keys, values)
     })
   }
 
@@ -420,7 +419,7 @@ case class Cast(child: Expression, dataType: DataType)
     case FloatType => castToFloat(from)
     case LongType => castToLong(from)
     case DoubleType => castToDouble(from)
-    case array: ArrayType => castArray(from.asInstanceOf[ArrayType], array)
+    case array: ArrayType => castArray(from.asInstanceOf[ArrayType].elementType, array.elementType)
     case map: MapType => castMap(from.asInstanceOf[MapType], map)
     case struct: StructType => castStruct(from.asInstanceOf[StructType], struct)
   }
@@ -461,7 +460,8 @@ case class Cast(child: Expression, dataType: DataType)
     case LongType => castToLongCode(from)
     case DoubleType => castToDoubleCode(from)
 
-    case array: ArrayType => castArrayCode(from.asInstanceOf[ArrayType], array, ctx)
+    case array: ArrayType =>
+      castArrayCode(from.asInstanceOf[ArrayType].elementType, array.elementType, ctx)
     case map: MapType => castMapCode(from.asInstanceOf[MapType], map, ctx)
     case struct: StructType => castStructCode(from.asInstanceOf[StructType], struct, ctx)
   }
@@ -801,8 +801,8 @@ case class Cast(child: Expression, dataType: DataType)
   }
 
   private[this] def castArrayCode(
-      from: ArrayType, to: ArrayType, ctx: CodeGenContext): CastFunction = {
-    val elementCast = nullSafeCastFunction(from.elementType, to.elementType, ctx)
+      fromType: DataType, toType: DataType, ctx: CodeGenContext): CastFunction = {
+    val elementCast = nullSafeCastFunction(fromType, toType, ctx)
     val arrayClass = classOf[GenericArrayData].getName
     val fromElementNull = ctx.freshName("feNull")
     val fromElementPrim = ctx.freshName("fePrim")
@@ -821,10 +821,10 @@ case class Cast(child: Expression, dataType: DataType)
             $values[$j] = null;
           } else {
             boolean $fromElementNull = false;
-            ${ctx.javaType(from.elementType)} $fromElementPrim =
-              ${ctx.getValue(c, from.elementType, j)};
+            ${ctx.javaType(fromType)} $fromElementPrim =
+              ${ctx.getValue(c, fromType, j)};
             ${castCode(ctx, fromElementPrim,
-              fromElementNull, toElementPrim, toElementNull, to.elementType, elementCast)}
+              fromElementNull, toElementPrim, toElementNull, toType, elementCast)}
             if ($toElementNull) {
               $values[$j] = null;
             } else {
@@ -837,48 +837,29 @@ case class Cast(child: Expression, dataType: DataType)
   }
 
   private[this] def castMapCode(from: MapType, to: MapType, ctx: CodeGenContext): CastFunction = {
-    val keyCast = nullSafeCastFunction(from.keyType, to.keyType, ctx)
-    val valueCast = nullSafeCastFunction(from.valueType, to.valueType, ctx)
-
-    val hashMapClass = classOf[mutable.HashMap[Any, Any]].getName
-    val fromKeyPrim = ctx.freshName("fkp")
-    val fromKeyNull = ctx.freshName("fkn")
-    val fromValuePrim = ctx.freshName("fvp")
-    val fromValueNull = ctx.freshName("fvn")
-    val toKeyPrim = ctx.freshName("tkp")
-    val toKeyNull = ctx.freshName("tkn")
-    val toValuePrim = ctx.freshName("tvp")
-    val toValueNull = ctx.freshName("tvn")
-    val result = ctx.freshName("result")
+    val keysCast = castArrayCode(from.keyType, to.keyType, ctx)
+    val valuesCast = castArrayCode(from.valueType, to.valueType, ctx)
+
+    val mapClass = classOf[ArrayBasedMapData].getName
+
+    val keys = ctx.freshName("keys")
+    val convertedKeys = ctx.freshName("convertedKeys")
+    val convertedKeysNull = ctx.freshName("convertedKeysNull")
+
+    val values = ctx.freshName("values")
+    val convertedValues = ctx.freshName("convertedValues")
+    val convertedValuesNull = ctx.freshName("convertedValuesNull")
 
     (c, evPrim, evNull) =>
       s"""
-        final $hashMapClass $result = new $hashMapClass();
-        scala.collection.Iterator iter = $c.iterator();
-        while (iter.hasNext()) {
-          scala.Tuple2 kv = (scala.Tuple2) iter.next();
-          boolean $fromKeyNull = false;
-          ${ctx.javaType(from.keyType)} $fromKeyPrim =
-            (${ctx.boxedType(from.keyType)}) kv._1();
-          ${castCode(ctx, fromKeyPrim,
-            fromKeyNull, toKeyPrim, toKeyNull, to.keyType, keyCast)}
-
-          boolean $fromValueNull = kv._2() == null;
-          if ($fromValueNull) {
-            $result.put($toKeyPrim, null);
-          } else {
-            ${ctx.javaType(from.valueType)} $fromValuePrim =
-              (${ctx.boxedType(from.valueType)}) kv._2();
-            ${castCode(ctx, fromValuePrim,
-              fromValueNull, toValuePrim, toValueNull, to.valueType, valueCast)}
-            if ($toValueNull) {
-              $result.put($toKeyPrim, null);
-            } else {
-              $result.put($toKeyPrim, $toValuePrim);
-            }
-          }
-        }
-        $evPrim = $result;
+        final ArrayData $keys = $c.keyArray();
+        final ArrayData $values = $c.valueArray();
+        ${castCode(ctx, keys, "false",
+          convertedKeys, convertedKeysNull, ArrayType(to.keyType), keysCast)}
+        ${castCode(ctx, values, "false",
+          convertedValues, convertedValuesNull, ArrayType(to.valueType), valuesCast)}
+
+        $evPrim = new $mapClass($convertedKeys, $convertedValues);
       """
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala
new file mode 100644
index 0000000000000..6e957928e02a8
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
+
+trait GenericSpecializedGetters extends SpecializedGetters {
+
+  def genericGet(ordinal: Int): Any
+
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+
+  override def get(ordinal: Int, elementType: DataType): AnyRef = getAs(ordinal)
+
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 7c7664e4c1a91..d79325aea87e3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -19,8 +19,8 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GenerateMutableProjection}
-import org.apache.spark.sql.types.{Decimal, StructType, DataType}
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * A [[Projection]] that is calculated by calling the `eval` of each of the specified expressions.
@@ -190,45 +190,55 @@ class JoinedRow extends InternalRow {
 
   override def numFields: Int = row1.numFields + row2.numFields
 
-  override def getUTF8String(i: Int): UTF8String = {
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-  }
-
-  override def getBinary(i: Int): Array[Byte] = {
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-  }
-
-  override def get(i: Int, dataType: DataType): Any =
-    if (i < row1.numFields) row1.get(i) else row2.get(i - row1.numFields)
+  override def genericGet(i: Int): Any =
+    if (i < row1.numFields) row1.genericGet(i) else row2.genericGet(i - row1.numFields)
 
   override def isNullAt(i: Int): Boolean =
     if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
 
+  override def getBoolean(i: Int): Boolean =
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
+
+  override def getByte(i: Int): Byte =
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
+
+  override def getShort(i: Int): Short =
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
+
   override def getInt(i: Int): Int =
     if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
 
   override def getLong(i: Int): Long =
     if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
 
+  override def getFloat(i: Int): Float =
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
+
   override def getDouble(i: Int): Double =
     if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
 
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
+  override def getDecimal(i: Int, precision: Int, scale: Int): Decimal = {
+    if (i < row1.numFields) {
+      row1.getDecimal(i, precision, scale)
+    } else {
+      row2.getDecimal(i - row1.numFields, precision, scale)
+    }
+  }
 
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
+  override def getUTF8String(i: Int): UTF8String =
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
 
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
+  override def getBinary(i: Int): Array[Byte] =
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
 
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
+  override def getArray(i: Int): ArrayData =
+    if (i < row1.numFields) row1.getArray(i) else row2.getArray(i - row1.numFields)
 
-  override def getDecimal(i: Int, precision: Int, scale: Int): Decimal = {
-    if (i < row1.numFields) row1.getDecimal(i, precision, scale)
-    else row2.getDecimal(i - row1.numFields, precision, scale)
-  }
+  override def getInterval(i: Int): CalendarInterval =
+    if (i < row1.numFields) row1.getInterval(i) else row2.getInterval(i - row1.numFields)
+
+  override def getMap(i: Int): MapData =
+    if (i < row1.numFields) row1.getMap(i) else row2.getMap(i - row1.numFields)
 
   override def getStruct(i: Int, numFields: Int): InternalRow = {
     if (i < row1.numFields) {
@@ -239,14 +249,9 @@ class JoinedRow extends InternalRow {
   }
 
   override def copy(): InternalRow = {
-    val totalSize = row1.numFields + row2.numFields
-    val copiedValues = new Array[Any](totalSize)
-    var i = 0
-    while(i < totalSize) {
-      copiedValues(i) = get(i)
-      i += 1
-    }
-    new GenericInternalRow(copiedValues)
+    val copy1 = row1.copy()
+    val copy2 = row2.copy()
+    new JoinedRow(copy1, copy2)
   }
 
   override def toString: String = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index b877ce47c083f..d149a5b179b63 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -213,18 +213,12 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
 
   override def numFields: Int = values.length
 
-  override def toSeq: Seq[Any] = values.map(_.boxed).toSeq
+  override def toSeq: Seq[Any] = values.map(_.boxed)
 
   override def setNullAt(i: Int): Unit = {
     values(i).isNull = true
   }
 
-  override def get(i: Int, dataType: DataType): Any = values(i).boxed
-
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
-    values(ordinal).boxed.asInstanceOf[InternalRow]
-  }
-
   override def isNullAt(i: Int): Boolean = values(i).isNull
 
   override def copy(): InternalRow = {
@@ -238,6 +232,8 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
     new GenericInternalRow(newValues)
   }
 
+  override def genericGet(i: Int): Any = values(i).boxed
+
   override def update(ordinal: Int, value: Any) {
     if (value == null) {
       setNullAt(ordinal)
@@ -246,9 +242,6 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
     }
   }
 
-  override def setString(ordinal: Int, value: String): Unit =
-    update(ordinal, UTF8String.fromString(value))
-
   override def setInt(ordinal: Int, value: Int): Unit = {
     val currentValue = values(ordinal).asInstanceOf[MutableInt]
     currentValue.isNull = false
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 36f4e9c6be570..fc7cfee989666 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -112,8 +112,10 @@ class CodeGenContext {
       case BinaryType => s"$getter.getBinary($ordinal)"
       case CalendarIntervalType => s"$getter.getInterval($ordinal)"
       case t: StructType => s"$getter.getStruct($ordinal, ${t.size})"
-      case a: ArrayType => s"$getter.getArray($ordinal)"
-      case _ => s"($jt)$getter.get($ordinal)" // todo: remove generic getter.
+      case _: ArrayType => s"$getter.getArray($ordinal)"
+      case _: MapType => s"$getter.getMap($ordinal)"
+      case NullType => "null"
+      case _ => s"($jt)$getter.get($ordinal, null)"
     }
   }
 
@@ -156,7 +158,7 @@ class CodeGenContext {
     case CalendarIntervalType => "CalendarInterval"
     case _: StructType => "InternalRow"
     case _: ArrayType => "ArrayData"
-    case _: MapType => "scala.collection.Map"
+    case _: MapType => "MapData"
     case dt: OpenHashSetUDT if dt.elementType == IntegerType => classOf[IntegerHashSet].getName
     case dt: OpenHashSetUDT if dt.elementType == LongType => classOf[LongHashSet].getName
     case _ => "Object"
@@ -300,7 +302,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       classOf[UTF8String].getName,
       classOf[Decimal].getName,
       classOf[CalendarInterval].getName,
-      classOf[ArrayData].getName
+      classOf[ArrayData].getName,
+      classOf[MapData].getName
     ))
     evaluator.setExtendedClass(classOf[GeneratedClass])
     try {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 35920147105ff..6f9acda071a62 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -183,7 +183,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        public Object get(int i, ${classOf[DataType].getName} dataType) {
+        public Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 0a530596a98c6..1156797b2c37d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -31,15 +31,11 @@ case class Size(child: Expression) extends UnaryExpression with ExpectsInputType
 
   override def nullSafeEval(value: Any): Int = child.dataType match {
     case _: ArrayType => value.asInstanceOf[ArrayData].numElements()
-    case _: MapType => value.asInstanceOf[Map[Any, Any]].size
+    case _: MapType => value.asInstanceOf[MapData].numElements()
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val sizeCall = child.dataType match {
-      case _: ArrayType => "numElements()"
-      case _: MapType => "size()"
-    }
-    nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).$sizeCall;")
+    nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).numElements();")
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
index 99393c9c76ab6..9927da21b052e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import scala.collection.Map
-
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis._
@@ -41,7 +39,7 @@ object ExtractValue {
    *    Struct      |   Literal String   |        GetStructField
    * Array[Struct]  |   Literal String   |     GetArrayStructFields
    *    Array       |   Integral type    |         GetArrayItem
-   *     Map        |      Any type      |         GetMapValue
+   *     Map        |   map key type     |         GetMapValue
    */
   def apply(
       child: Expression,
@@ -60,18 +58,14 @@ object ExtractValue {
         GetArrayStructFields(child, fields(ordinal).copy(name = fieldName),
           ordinal, fields.length, containsNull)
 
-      case (_: ArrayType, _) if extraction.dataType.isInstanceOf[IntegralType] =>
-        GetArrayItem(child, extraction)
+      case (_: ArrayType, _) => GetArrayItem(child, extraction)
 
-      case (_: MapType, _) =>
-        GetMapValue(child, extraction)
+      case (MapType(kt, _, _), _) => GetMapValue(child, extraction)
 
       case (otherType, _) =>
         val errorMsg = otherType match {
-          case StructType(_) | ArrayType(StructType(_), _) =>
+          case StructType(_) =>
             s"Field name should be String Literal, but it's $extraction"
-          case _: ArrayType =>
-            s"Array index should be integral type, but it's ${extraction.dataType}"
           case other =>
             s"Can't extract value from $child"
         }
@@ -190,9 +184,13 @@ case class GetArrayStructFields(
 /**
  * Returns the field at `ordinal` in the Array `child`.
  *
- * No need to do type checking since it is handled by [[ExtractValue]].
+ * We need to do type checking here as `ordinal` expression maybe unresolved.
  */
-case class GetArrayItem(child: Expression, ordinal: Expression) extends BinaryExpression {
+case class GetArrayItem(child: Expression, ordinal: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  // We have done type checking for child in `ExtractValue`, so only need to check the `ordinal`.
+  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType, IntegralType)
 
   override def toString: String = s"$child[$ordinal]"
 
@@ -205,14 +203,12 @@ case class GetArrayItem(child: Expression, ordinal: Expression) extends BinaryEx
   override def dataType: DataType = child.dataType.asInstanceOf[ArrayType].elementType
 
   protected override def nullSafeEval(value: Any, ordinal: Any): Any = {
-    // TODO: consider using Array[_] for ArrayType child to avoid
-    // boxing of primitives
     val baseValue = value.asInstanceOf[ArrayData]
     val index = ordinal.asInstanceOf[Number].intValue()
     if (index >= baseValue.numElements() || index < 0) {
       null
     } else {
-      baseValue.get(index)
+      baseValue.get(index, dataType)
     }
   }
 
@@ -233,9 +229,15 @@ case class GetArrayItem(child: Expression, ordinal: Expression) extends BinaryEx
 /**
  * Returns the value of key `key` in Map `child`.
  *
- * No need to do type checking since it is handled by [[ExtractValue]].
+ * We need to do type checking here as `key` expression maybe unresolved.
  */
-case class GetMapValue(child: Expression, key: Expression) extends BinaryExpression {
+case class GetMapValue(child: Expression, key: Expression)
+  extends BinaryExpression with ExpectsInputTypes {
+
+  private def keyType = child.dataType.asInstanceOf[MapType].keyType
+
+  // We have done type checking for child in `ExtractValue`, so only need to check the `key`.
+  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType, keyType)
 
   override def toString: String = s"$child[$key]"
 
@@ -247,16 +249,53 @@ case class GetMapValue(child: Expression, key: Expression) extends BinaryExpress
 
   override def dataType: DataType = child.dataType.asInstanceOf[MapType].valueType
 
+  // todo: current search is O(n), improve it.
   protected override def nullSafeEval(value: Any, ordinal: Any): Any = {
-    val baseValue = value.asInstanceOf[Map[Any, _]]
-    baseValue.get(ordinal).orNull
+    val map = value.asInstanceOf[MapData]
+    val length = map.numElements()
+    val keys = map.keyArray()
+
+    var i = 0
+    var found = false
+    while (i < length && !found) {
+      if (keys.get(i, keyType) == ordinal) {
+        found = true
+      } else {
+        i += 1
+      }
+    }
+
+    if (!found) {
+      null
+    } else {
+      map.valueArray().get(i, dataType)
+    }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val index = ctx.freshName("index")
+    val length = ctx.freshName("length")
+    val keys = ctx.freshName("keys")
+    val found = ctx.freshName("found")
+    val key = ctx.freshName("key")
     nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
       s"""
-        if ($eval1.contains($eval2)) {
-          ${ev.primitive} = (${ctx.boxedType(dataType)})$eval1.apply($eval2);
+        final int $length = $eval1.numElements();
+        final ArrayData $keys = $eval1.keyArray();
+
+        int $index = 0;
+        boolean $found = false;
+        while ($index < $length && !$found) {
+          final ${ctx.javaType(keyType)} $key = ${ctx.getValue(keys, keyType, index)};
+          if (${ctx.genEqual(keyType, key, eval2)}) {
+            $found = true;
+          } else {
+            $index++;
+          }
+        }
+
+        if ($found) {
+          ${ev.primitive} = ${ctx.getValue(eval1 + ".valueArray()", dataType, index)};
         } else {
           ${ev.isNull} = true;
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index 8064235c64ef9..d474853355e5b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -120,13 +120,30 @@ case class Explode(child: Expression) extends UnaryExpression with Generator wit
 
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
     child.dataType match {
-      case ArrayType(_, _) =>
+      case ArrayType(et, _) =>
         val inputArray = child.eval(input).asInstanceOf[ArrayData]
-        if (inputArray == null) Nil else inputArray.toArray().map(v => InternalRow(v))
-      case MapType(_, _, _) =>
-        val inputMap = child.eval(input).asInstanceOf[Map[Any, Any]]
-        if (inputMap == null) Nil
-        else inputMap.map { case (k, v) => InternalRow(k, v) }
+        if (inputArray == null) {
+          Nil
+        } else {
+          val rows = new Array[InternalRow](inputArray.numElements())
+          inputArray.foreach(et, (i, e) => {
+            rows(i) = InternalRow(e)
+          })
+          rows
+        }
+      case MapType(kt, vt, _) =>
+        val inputMap = child.eval(input).asInstanceOf[MapData]
+        if (inputMap == null) {
+          Nil
+        } else {
+          val rows = new Array[InternalRow](inputMap.numElements())
+          var i = 0
+          inputMap.foreach(kt, vt, (k, v) => {
+            rows(i) = InternalRow(k, v)
+            i += 1
+          })
+          rows
+        }
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index df6ea586c87ba..73f6b7a5500e6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -32,28 +32,14 @@ abstract class MutableRow extends InternalRow {
   def update(i: Int, value: Any)
 
   // default implementation (slow)
-  def setInt(i: Int, value: Int): Unit = { update(i, value) }
-  def setLong(i: Int, value: Long): Unit = { update(i, value) }
-  def setDouble(i: Int, value: Double): Unit = { update(i, value) }
   def setBoolean(i: Int, value: Boolean): Unit = { update(i, value) }
-  def setShort(i: Int, value: Short): Unit = { update(i, value) }
   def setByte(i: Int, value: Byte): Unit = { update(i, value) }
+  def setShort(i: Int, value: Short): Unit = { update(i, value) }
+  def setInt(i: Int, value: Int): Unit = { update(i, value) }
+  def setLong(i: Int, value: Long): Unit = { update(i, value) }
   def setFloat(i: Int, value: Float): Unit = { update(i, value) }
+  def setDouble(i: Int, value: Double): Unit = { update(i, value) }
   def setDecimal(i: Int, value: Decimal, precision: Int) { update(i, value) }
-  def setString(i: Int, value: String): Unit = {
-    update(i, UTF8String.fromString(value))
-  }
-
-  override def copy(): InternalRow = {
-    val n = numFields
-    val arr = new Array[Any](n)
-    var i = 0
-    while (i < n) {
-      arr(i) = get(i)
-      i += 1
-    }
-    new GenericInternalRow(arr)
-  }
 }
 
 /**
@@ -96,17 +82,13 @@ class GenericInternalRow(protected[sql] val values: Array[Any]) extends Internal
 
   def this(size: Int) = this(new Array[Any](size))
 
-  override def toSeq: Seq[Any] = values.toSeq
-
-  override def numFields: Int = values.length
+  override def genericGet(ordinal: Int): Any = values(ordinal)
 
-  override def get(i: Int, dataType: DataType): Any = values(i)
+  override def toSeq: Seq[Any] = values
 
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
-    values(ordinal).asInstanceOf[InternalRow]
-  }
+  override def numFields: Int = values.length
 
-  override def copy(): InternalRow = this
+  override def copy(): InternalRow = new GenericInternalRow(values.clone())
 }
 
 /**
@@ -127,15 +109,11 @@ class GenericMutableRow(val values: Array[Any]) extends MutableRow {
 
   def this(size: Int) = this(new Array[Any](size))
 
-  override def toSeq: Seq[Any] = values.toSeq
-
-  override def numFields: Int = values.length
+  override def genericGet(ordinal: Int): Any = values(ordinal)
 
-  override def get(i: Int, dataType: DataType): Any = values(i)
+  override def toSeq: Seq[Any] = values
 
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = {
-    values(ordinal).asInstanceOf[InternalRow]
-  }
+  override def numFields: Int = values.length
 
   override def setNullAt(i: Int): Unit = { values(i) = null}
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 5dd387a418a1d..3ce5d6a9c7e02 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -95,7 +95,7 @@ case class ConcatWs(children: Seq[Expression])
     val flatInputs = children.flatMap { child =>
       child.eval(input) match {
         case s: UTF8String => Iterator(s)
-        case arr: ArrayData => arr.toArray().map(_.asInstanceOf[UTF8String])
+        case arr: ArrayData => arr.toArray[UTF8String](StringType)
         case null => Iterator(null.asInstanceOf[UTF8String])
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala
new file mode 100644
index 0000000000000..db4876355daec
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala
@@ -0,0 +1,51 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types
+
+class ArrayBasedMapData(val keyArray: ArrayData, val valueArray: ArrayData) extends MapData {
+  require(keyArray.numElements() == valueArray.numElements())
+
+  override def numElements(): Int = keyArray.numElements()
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[ArrayBasedMapData]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[ArrayBasedMapData]
+    if (other eq null) {
+      return false
+    }
+
+    this.keyArray == other.keyArray && this.valueArray == other.valueArray
+  }
+
+  override def hashCode: Int = {
+    keyArray.hashCode() * 37 + valueArray.hashCode()
+  }
+
+  override def toString(): String = {
+    s"keys: $keyArray\nvalues: $valueArray"
+  }
+}
+
+object ArrayBasedMapData {
+  def apply(keys: Array[Any], values: Array[Any]): ArrayBasedMapData = {
+    new ArrayBasedMapData(new GenericArrayData(keys), new GenericArrayData(values))
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
index 14a7285877622..c99fc233255e5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
@@ -20,102 +20,111 @@ package org.apache.spark.sql.types
 import org.apache.spark.sql.catalyst.expressions.SpecializedGetters
 
 abstract class ArrayData extends SpecializedGetters with Serializable {
-  // todo: remove this after we handle all types.(map type need special getter)
-  def get(ordinal: Int): Any
-
   def numElements(): Int
 
-  // todo: need a more efficient way to iterate array type.
-  def toArray(): Array[Any] = {
-    val n = numElements()
-    val values = new Array[Any](n)
+  def toBooleanArray(): Array[Boolean] = {
+    val size = numElements()
+    val values = new Array[Boolean](size)
     var i = 0
-    while (i < n) {
-      if (isNullAt(i)) {
-        values(i) = null
-      } else {
-        values(i) = get(i)
-      }
+    while (i < size) {
+      values(i) = getBoolean(i)
       i += 1
     }
     values
   }
 
-  override def toString(): String = toArray.mkString("[", ",", "]")
+  def toByteArray(): Array[Byte] = {
+    val size = numElements()
+    val values = new Array[Byte](size)
+    var i = 0
+    while (i < size) {
+      values(i) = getByte(i)
+      i += 1
+    }
+    values
+  }
 
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[ArrayData]) {
-      return false
+  def toShortArray(): Array[Short] = {
+    val size = numElements()
+    val values = new Array[Short](size)
+    var i = 0
+    while (i < size) {
+      values(i) = getShort(i)
+      i += 1
     }
+    values
+  }
 
-    val other = o.asInstanceOf[ArrayData]
-    if (other eq null) {
-      return false
+  def toIntArray(): Array[Int] = {
+    val size = numElements()
+    val values = new Array[Int](size)
+    var i = 0
+    while (i < size) {
+      values(i) = getInt(i)
+      i += 1
     }
+    values
+  }
 
-    val len = numElements()
-    if (len != other.numElements()) {
-      return false
+  def toLongArray(): Array[Long] = {
+    val size = numElements()
+    val values = new Array[Long](size)
+    var i = 0
+    while (i < size) {
+      values(i) = getLong(i)
+      i += 1
     }
+    values
+  }
 
+  def toFloatArray(): Array[Float] = {
+    val size = numElements()
+    val values = new Array[Float](size)
     var i = 0
-    while (i < len) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = get(i)
-        val o2 = other.get(i)
-        o1 match {
-          case b1: Array[Byte] =>
-            if (!o2.isInstanceOf[Array[Byte]] ||
-              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-              return false
-            }
-          case f1: Float if java.lang.Float.isNaN(f1) =>
-            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
-              return false
-            }
-          case d1: Double if java.lang.Double.isNaN(d1) =>
-            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
-              return false
-            }
-          case _ => if (o1 != o2) {
-            return false
-          }
-        }
+    while (i < size) {
+      values(i) = getFloat(i)
+      i += 1
+    }
+    values
+  }
+
+  def toDoubleArray(): Array[Double] = {
+    val size = numElements()
+    val values = new Array[Double](size)
+    var i = 0
+    while (i < size) {
+      values(i) = getDouble(i)
+      i += 1
+    }
+    values
+  }
+
+  def toArray[T](elementType: DataType): Array[T] = {
+    val size = numElements()
+    val values = new Array[Any](size)
+    var i = 0
+    while (i < size) {
+      if (isNullAt(i)) {
+        values(i) = null
+      } else {
+        values(i) = get(i, elementType)
       }
       i += 1
     }
-    true
+    values.asInstanceOf[Array[T]]
   }
 
-  override def hashCode: Int = {
-    var result: Int = 37
+  // todo: specialize this.
+  def foreach(elementType: DataType, f: (Int, Any) => Unit): Unit = {
+    val size = numElements()
     var i = 0
-    val len = numElements()
-    while (i < len) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          get(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case a: Array[Byte] => java.util.Arrays.hashCode(a)
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
+    while (i < size) {
+      if (isNullAt(i)) {
+        f(i, null)
+      } else {
+        f(i, get(i, elementType))
+      }
       i += 1
     }
-    result
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
index 35ace673fb3da..b3e75f8bad502 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
@@ -17,43 +17,91 @@
 
 package org.apache.spark.sql.types
 
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.unsafe.types.{UTF8String, CalendarInterval}
+import org.apache.spark.sql.catalyst.expressions.GenericSpecializedGetters
 
-class GenericArrayData(array: Array[Any]) extends ArrayData {
-  private def getAs[T](ordinal: Int) = get(ordinal).asInstanceOf[T]
+class GenericArrayData(array: Array[Any]) extends ArrayData with GenericSpecializedGetters {
 
-  override def toArray(): Array[Any] = array
+  override def genericGet(ordinal: Int): Any = array(ordinal)
 
-  override def get(ordinal: Int): Any = array(ordinal)
-
-  override def isNullAt(ordinal: Int): Boolean = get(ordinal) == null
-
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def toArray[T](elementType: DataType): Array[T] = array.asInstanceOf[Array[T]]
 
   override def numElements(): Int = array.length
+
+  override def toString(): String = array.mkString("[", ",", "]")
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[GenericArrayData]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[GenericArrayData]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numElements()
+    if (len != other.numElements()) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numElements()
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          genericGet(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala
new file mode 100644
index 0000000000000..5514c3cd8546a
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.types
+
+abstract class MapData extends Serializable {
+
+  def numElements(): Int
+
+  def keyArray(): ArrayData
+
+  def valueArray(): ArrayData
+
+  def foreach(keyType: DataType, valueType: DataType, f: (Any, Any) => Unit): Unit = {
+    val length = numElements()
+    val keys = keyArray()
+    val values = valueArray()
+    var i = 0
+    while (i < length) {
+      f(keys.get(i, keyType), values.get(i, valueType))
+      i += 1
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
index 3fa246b69d1f1..e60990aeb423f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
@@ -171,8 +171,6 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("error message of ExtractValue") {
     val structType = StructType(StructField("a", StringType, true) :: Nil)
-    val arrayStructType = ArrayType(structType)
-    val arrayType = ArrayType(StringType)
     val otherType = StringType
 
     def checkErrorMessage(
@@ -189,8 +187,6 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
 
     checkErrorMessage(structType, IntegerType, "Field name should be String Literal")
-    checkErrorMessage(arrayStructType, BooleanType, "Field name should be String Literal")
-    checkErrorMessage(arrayType, StringType, "Array index should be integral type")
     checkErrorMessage(otherType, StringType, "Can't extract value from")
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index a0e1701339ea7..44f845620a109 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -87,7 +87,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
     val row = new SpecificMutableRow(fieldTypes)
     row.setLong(0, 0)
-    row.setString(1, "Hello")
+    row.update(1, UTF8String.fromString("Hello"))
     row.update(2, DateTimeUtils.fromJavaDate(Date.valueOf("1970-01-01")))
     row.update(3, DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2015-05-08 08:10:25")))
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index f26f41fb75d57..c37007f1eece7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -159,10 +159,16 @@ package object debug {
       case (row: InternalRow, StructType(fields)) =>
         row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
       case (a: ArrayData, ArrayType(elemType, _)) =>
-        a.toArray().foreach(typeCheck(_, elemType))
-      case (m: Map[_, _], MapType(keyType, valueType, _)) =>
-        m.keys.foreach(typeCheck(_, keyType))
-        m.values.foreach(typeCheck(_, valueType))
+        a.foreach(elemType, (_, e) => {
+          typeCheck(e, elemType)
+        })
+      case (m: MapData, MapType(keyType, valueType, _)) =>
+        m.keyArray().foreach(keyType, (_, e) => {
+          typeCheck(e, keyType)
+        })
+        m.valueArray().foreach(valueType, (_, e) => {
+          typeCheck(e, valueType)
+        })
 
       case (_: Long, LongType) =>
       case (_: Int, IntegerType) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index ef1c6e57dc08a..aade2e769ccd6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -135,22 +135,18 @@ object EvaluatePython {
       new GenericInternalRowWithSchema(values, struct)
 
     case (a: ArrayData, array: ArrayType) =>
-      val length = a.numElements()
-      val values = new java.util.ArrayList[Any](length)
-      var i = 0
-      while (i < length) {
-        if (a.isNullAt(i)) {
-          values.add(null)
-        } else {
-          values.add(toJava(a.get(i), array.elementType))
-        }
-        i += 1
-      }
+      val values = new java.util.ArrayList[Any](a.numElements())
+      a.foreach(array.elementType, (_, e) => {
+        values.add(toJava(e, array.elementType))
+      })
       values
 
-    case (obj: Map[_, _], mt: MapType) => obj.map {
-      case (k, v) => (toJava(k, mt.keyType), toJava(v, mt.valueType))
-    }.asJava
+    case (map: MapData, mt: MapType) =>
+      val jmap = new java.util.HashMap[Any, Any](map.numElements())
+      map.foreach(mt.keyType, mt.valueType, (k, v) => {
+        jmap.put(toJava(k, mt.keyType), toJava(v, mt.valueType))
+      })
+      jmap
 
     case (ud, udt: UserDefinedType[_]) => toJava(ud, udt.sqlType)
 
@@ -206,9 +202,10 @@ object EvaluatePython {
     case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
       new GenericArrayData(c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType)))
 
-    case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) => c.map {
-      case (key, value) => (fromJava(key, keyType), fromJava(value, valueType))
-    }.toMap
+    case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) =>
+      val keys = c.keysIterator.map(fromJava(_, keyType)).toArray
+      val values = c.valuesIterator.map(fromJava(_, valueType)).toArray
+      ArrayBasedMapData(keys, values)
 
     case (c, StructType(fields)) if c.getClass.isArray =>
       new GenericInternalRow(c.asInstanceOf[Array[_]].zip(fields).map {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index 1c309f8794ef3..bf0448ee9645d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -19,10 +19,10 @@ package org.apache.spark.sql.json
 
 import java.io.ByteArrayOutputStream
 
-import scala.collection.Map
-
 import com.fasterxml.jackson.core._
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -31,7 +31,6 @@ import org.apache.spark.sql.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
-
 private[sql] object JacksonParser {
   def apply(
       json: RDD[String],
@@ -160,21 +159,21 @@ private[sql] object JacksonParser {
   private def convertMap(
       factory: JsonFactory,
       parser: JsonParser,
-      valueType: DataType): Map[UTF8String, Any] = {
-    val builder = Map.newBuilder[UTF8String, Any]
+      valueType: DataType): MapData = {
+    val keys = ArrayBuffer.empty[UTF8String]
+    val values = ArrayBuffer.empty[Any]
     while (nextUntil(parser, JsonToken.END_OBJECT)) {
-      builder +=
-        UTF8String.fromString(parser.getCurrentName) -> convertField(factory, parser, valueType)
+      keys += UTF8String.fromString(parser.getCurrentName)
+      values += convertField(factory, parser, valueType)
     }
-
-    builder.result()
+    ArrayBasedMapData(keys.toArray, values.toArray)
   }
 
   private def convertArray(
       factory: JsonFactory,
       parser: JsonParser,
       elementType: DataType): ArrayData = {
-    val values = scala.collection.mutable.ArrayBuffer.empty[Any]
+    val values = ArrayBuffer.empty[Any]
     while (nextUntil(parser, JsonToken.END_ARRAY)) {
       values += convertField(factory, parser, elementType)
     }
@@ -213,7 +212,7 @@ private[sql] object JacksonParser {
               if (array.numElements() == 0) {
                 Nil
               } else {
-                array.toArray().map(_.asInstanceOf[InternalRow])
+                array.toArray[InternalRow](schema)
               }
             case _ =>
               sys.error(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
index 172db8362afb6..6938b071065cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
@@ -385,7 +385,8 @@ private[parquet] class CatalystRowConverter(
       updater: ParentContainerUpdater)
     extends GroupConverter {
 
-    private var currentMap: mutable.Map[Any, Any] = _
+    private var currentKeys: ArrayBuffer[Any] = _
+    private var currentValues: ArrayBuffer[Any] = _
 
     private val keyValueConverter = {
       val repeatedType = parquetType.getType(0).asGroupType()
@@ -398,12 +399,16 @@ private[parquet] class CatalystRowConverter(
 
     override def getConverter(fieldIndex: Int): Converter = keyValueConverter
 
-    override def end(): Unit = updater.set(currentMap)
+    override def end(): Unit =
+      updater.set(ArrayBasedMapData(currentKeys.toArray, currentValues.toArray))
 
     // NOTE: We can't reuse the mutable Map here and must instantiate a new `Map` for the next
     // value.  `Row.copy()` only copies row cells, it doesn't do deep copy to objects stored in row
     // cells.
-    override def start(): Unit = currentMap = mutable.Map.empty[Any, Any]
+    override def start(): Unit = {
+      currentKeys = ArrayBuffer.empty[Any]
+      currentValues = ArrayBuffer.empty[Any]
+    }
 
     /** Parquet converter for key-value pairs within the map. */
     private final class KeyValueConverter(
@@ -430,7 +435,10 @@ private[parquet] class CatalystRowConverter(
 
       override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
 
-      override def end(): Unit = currentMap(currentKey) = currentValue
+      override def end(): Unit = {
+        currentKeys += currentKey
+        currentValues += currentValue
+      }
 
       override def start(): Unit = {
         currentKey = null
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index 2332a36468dbc..6ed3580af0729 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.parquet
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.ArrayData
+import org.apache.spark.sql.types.{MapData, ArrayData}
 
 // TODO Removes this while fixing SPARK-8848
 private[sql] object CatalystConverter {
@@ -33,7 +33,7 @@ private[sql] object CatalystConverter {
   val MAP_SCHEMA_NAME = "map"
 
   // TODO: consider using Array[T] for arrays to avoid boxing of primitive types
-  type ArrayScalaType[T] = ArrayData
-  type StructScalaType[T] = InternalRow
-  type MapScalaType[K, V] = Map[K, V]
+  type ArrayScalaType = ArrayData
+  type StructScalaType = InternalRow
+  type MapScalaType = MapData
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index ec8da38a3d427..9cd0250f9c510 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -88,13 +88,13 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
         case t: UserDefinedType[_] => writeValue(t.sqlType, value)
         case t @ ArrayType(_, _) => writeArray(
           t,
-          value.asInstanceOf[CatalystConverter.ArrayScalaType[_]])
+          value.asInstanceOf[CatalystConverter.ArrayScalaType])
         case t @ MapType(_, _, _) => writeMap(
           t,
-          value.asInstanceOf[CatalystConverter.MapScalaType[_, _]])
+          value.asInstanceOf[CatalystConverter.MapScalaType])
         case t @ StructType(_) => writeStruct(
           t,
-          value.asInstanceOf[CatalystConverter.StructScalaType[_]])
+          value.asInstanceOf[CatalystConverter.StructScalaType])
         case _ => writePrimitive(schema.asInstanceOf[AtomicType], value)
       }
     }
@@ -124,7 +124,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
 
   private[parquet] def writeStruct(
       schema: StructType,
-      struct: CatalystConverter.StructScalaType[_]): Unit = {
+      struct: CatalystConverter.StructScalaType): Unit = {
     if (struct != null) {
       val fields = schema.fields.toArray
       writer.startGroup()
@@ -143,7 +143,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
 
   private[parquet] def writeArray(
       schema: ArrayType,
-      array: CatalystConverter.ArrayScalaType[_]): Unit = {
+      array: CatalystConverter.ArrayScalaType): Unit = {
     val elementType = schema.elementType
     writer.startGroup()
     if (array.numElements() > 0) {
@@ -154,7 +154,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
           writer.startGroup()
           if (!array.isNullAt(i)) {
             writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
-            writeValue(elementType, array.get(i))
+            writeValue(elementType, array.get(i, elementType))
             writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
           }
           writer.endGroup()
@@ -165,7 +165,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
         writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
         var i = 0
         while (i < array.numElements()) {
-          writeValue(elementType, array.get(i))
+          writeValue(elementType, array.get(i, elementType))
           i = i + 1
         }
         writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
@@ -176,11 +176,12 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
 
   private[parquet] def writeMap(
       schema: MapType,
-      map: CatalystConverter.MapScalaType[_, _]): Unit = {
+      map: CatalystConverter.MapScalaType): Unit = {
     writer.startGroup()
-    if (map.size > 0) {
+    val length = map.numElements()
+    if (length > 0) {
       writer.startField(CatalystConverter.MAP_SCHEMA_NAME, 0)
-      for ((key, value) <- map) {
+      map.foreach(schema.keyType, schema.valueType, (key, value) => {
         writer.startGroup()
         writer.startField(CatalystConverter.MAP_KEY_SCHEMA_NAME, 0)
         writeValue(schema.keyType, key)
@@ -191,7 +192,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
           writer.endField(CatalystConverter.MAP_VALUE_SCHEMA_NAME, 1)
         }
         writer.endGroup()
-      }
+      })
       writer.endField(CatalystConverter.MAP_SCHEMA_NAME, 0)
     }
     writer.endGroup()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 97beae2f85c50..aef940a526675 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -620,6 +620,7 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     assert(complexData.filter(complexData("m")("1") === 1).count() == 1)
     assert(complexData.filter(complexData("s")("key") === 1).count() == 1)
     assert(complexData.filter(complexData("m")(complexData("s")("value")) === 1).count() == 1)
+    assert(complexData.filter(complexData("a")(complexData("s")("key")) === 1).count() == 1)
   }
 
   test("SPARK-7551: support backticks for DataFrame attribute resolution") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 01b7c21e84159..8a679c7865d6a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.sql
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.execution.SparkSqlSerializer
-
 import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, SpecificMutableRow}
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 class RowSuite extends SparkFunSuite {
 
@@ -31,7 +31,7 @@ class RowSuite extends SparkFunSuite {
   test("create row") {
     val expected = new GenericMutableRow(4)
     expected.setInt(0, 2147483647)
-    expected.setString(1, "this is a string")
+    expected.update(1, UTF8String.fromString("this is a string"))
     expected.setBoolean(2, false)
     expected.setNullAt(3)
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 535011fe3db5b..51fe9d9d98bf3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -581,42 +581,28 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
   }
 
   test("sorting") {
-    val before = sqlContext.conf.externalSortEnabled
-    sqlContext.setConf(SQLConf.EXTERNAL_SORT, false)
-    sortTest()
-    sqlContext.setConf(SQLConf.EXTERNAL_SORT, before)
+    withSQLConf(SQLConf.EXTERNAL_SORT.key -> "false") {
+      sortTest()
+    }
   }
 
   test("external sorting") {
-    val before = sqlContext.conf.externalSortEnabled
-    sqlContext.setConf(SQLConf.EXTERNAL_SORT, true)
-    sortTest()
-    sqlContext.setConf(SQLConf.EXTERNAL_SORT, before)
+    withSQLConf(SQLConf.EXTERNAL_SORT.key -> "true") {
+      sortTest()
+    }
   }
 
   test("SPARK-6927 sorting with codegen on") {
-    val externalbefore = sqlContext.conf.externalSortEnabled
-    val codegenbefore = sqlContext.conf.codegenEnabled
-    sqlContext.setConf(SQLConf.EXTERNAL_SORT, false)
-    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
-    try{
+    withSQLConf(SQLConf.EXTERNAL_SORT.key -> "false",
+      SQLConf.CODEGEN_ENABLED.key -> "true") {
       sortTest()
-    } finally {
-      sqlContext.setConf(SQLConf.EXTERNAL_SORT, externalbefore)
-      sqlContext.setConf(SQLConf.CODEGEN_ENABLED, codegenbefore)
     }
   }
 
   test("SPARK-6927 external sorting with codegen on") {
-    val externalbefore = sqlContext.conf.externalSortEnabled
-    val codegenbefore = sqlContext.conf.codegenEnabled
-    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
-    sqlContext.setConf(SQLConf.EXTERNAL_SORT, true)
-    try {
+    withSQLConf(SQLConf.EXTERNAL_SORT.key -> "true",
+      SQLConf.CODEGEN_ENABLED.key -> "true") {
       sortTest()
-    } finally {
-      sqlContext.setConf(SQLConf.EXTERNAL_SORT, externalbefore)
-      sqlContext.setConf(SQLConf.CODEGEN_ENABLED, codegenbefore)
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index e340f54850bcc..bd9729c431f30 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -190,8 +190,8 @@ object TestData {
   case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean)
   val complexData =
     TestSQLContext.sparkContext.parallelize(
-      ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1), true)
-        :: ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2), false)
+      ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true)
+        :: ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false)
         :: Nil).toDF()
   complexData.registerTempTable("complexData")
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index 77ed4a9c0d5ae..f29935224e5b6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -57,7 +57,7 @@ private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] {
   override def deserialize(datum: Any): MyDenseVector = {
     datum match {
       case data: ArrayData =>
-        new MyDenseVector(data.toArray.map(_.asInstanceOf[Double]))
+        new MyDenseVector(data.toDoubleArray())
     }
   }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 5926ef9aa388b..39d798d072aeb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -51,7 +51,7 @@ import scala.collection.JavaConversions._
  *     java.sql.Date
  *     java.sql.Timestamp
  *  Complex Types =>
- *    Map: scala.collection.immutable.Map
+ *    Map: [[org.apache.spark.sql.types.MapData]]
  *    List: [[org.apache.spark.sql.types.ArrayData]]
  *    Struct: [[org.apache.spark.sql.catalyst.InternalRow]]
  *    Union: NOT SUPPORTED YET
@@ -290,10 +290,10 @@ private[hive] trait HiveInspectors {
       DateTimeUtils.fromJavaDate(poi.getWritableConstantValue.get())
     case mi: StandardConstantMapObjectInspector =>
       // take the value from the map inspector object, rather than the input data
-      mi.getWritableConstantValue.map { case (k, v) =>
-        (unwrap(k, mi.getMapKeyObjectInspector),
-          unwrap(v, mi.getMapValueObjectInspector))
-      }.toMap
+      val map = mi.getWritableConstantValue
+      val keys = map.keysIterator.map(unwrap(_, mi.getMapKeyObjectInspector)).toArray
+      val values = map.valuesIterator.map(unwrap(_, mi.getMapValueObjectInspector)).toArray
+      ArrayBasedMapData(keys, values)
     case li: StandardConstantListObjectInspector =>
       // take the value from the list inspector object, rather than the input data
       val values = li.getWritableConstantValue
@@ -347,12 +347,14 @@ private[hive] trait HiveInspectors {
         }
         .orNull
     case mi: MapObjectInspector =>
-      Option(mi.getMap(data)).map(
-        _.map {
-          case (k, v) =>
-            (unwrap(k, mi.getMapKeyObjectInspector),
-              unwrap(v, mi.getMapValueObjectInspector))
-        }.toMap).orNull
+      val map = mi.getMap(data)
+      if (map == null) {
+        null
+      } else {
+        val keys = map.keysIterator.map(unwrap(_, mi.getMapKeyObjectInspector)).toArray
+        val values = map.valuesIterator.map(unwrap(_, mi.getMapValueObjectInspector)).toArray
+        ArrayBasedMapData(keys, values)
+      }
     // currently, hive doesn't provide the ConstantStructObjectInspector
     case si: StructObjectInspector =>
       val allRefs = si.getAllStructFieldRefs
@@ -365,7 +367,7 @@ private[hive] trait HiveInspectors {
    * Wraps with Hive types based on object inspector.
    * TODO: Consolidate all hive OI/data interface code.
    */
-  protected def wrapperFor(oi: ObjectInspector): Any => Any = oi match {
+  protected def wrapperFor(oi: ObjectInspector, dataType: DataType): Any => Any = oi match {
     case _: JavaHiveVarcharObjectInspector =>
       (o: Any) =>
         val s = o.asInstanceOf[UTF8String].toString
@@ -381,7 +383,10 @@ private[hive] trait HiveInspectors {
       (o: Any) => DateTimeUtils.toJavaTimestamp(o.asInstanceOf[Long])
 
     case soi: StandardStructObjectInspector =>
-      val wrappers = soi.getAllStructFieldRefs.map(ref => wrapperFor(ref.getFieldObjectInspector))
+      val schema = dataType.asInstanceOf[StructType]
+      val wrappers = soi.getAllStructFieldRefs.zip(schema.fields).map { case (ref, field) =>
+        wrapperFor(ref.getFieldObjectInspector, field.dataType)
+      }
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
@@ -395,27 +400,34 @@ private[hive] trait HiveInspectors {
       }
 
     case loi: ListObjectInspector =>
-      val wrapper = wrapperFor(loi.getListElementObjectInspector)
+      val elementType = dataType.asInstanceOf[ArrayType].elementType
+      val wrapper = wrapperFor(loi.getListElementObjectInspector, elementType)
       (o: Any) => {
         if (o != null) {
-          seqAsJavaList(o.asInstanceOf[ArrayData].toArray().map(wrapper))
+          val array = o.asInstanceOf[ArrayData]
+          val values = new java.util.ArrayList[Any](array.numElements())
+          array.foreach(elementType, (_, e) => {
+            values.add(wrapper(e))
+          })
+          values
         } else {
           null
         }
       }
 
     case moi: MapObjectInspector =>
-      // The Predef.Map is scala.collection.immutable.Map.
-      // Since the map values can be mutable, we explicitly import scala.collection.Map at here.
-      import scala.collection.Map
+      val mt = dataType.asInstanceOf[MapType]
+      val keyWrapper = wrapperFor(moi.getMapKeyObjectInspector, mt.keyType)
+      val valueWrapper = wrapperFor(moi.getMapValueObjectInspector, mt.valueType)
 
-      val keyWrapper = wrapperFor(moi.getMapKeyObjectInspector)
-      val valueWrapper = wrapperFor(moi.getMapValueObjectInspector)
       (o: Any) => {
         if (o != null) {
-          mapAsJavaMap(o.asInstanceOf[Map[_, _]].map { case (key, value) =>
-            keyWrapper(key) -> valueWrapper(value)
+          val map = o.asInstanceOf[MapData]
+          val jmap = new java.util.HashMap[Any, Any](map.numElements())
+          map.foreach(mt.keyType, mt.valueType, (k, v) => {
+            jmap.put(keyWrapper(k), valueWrapper(v))
           })
+          jmap
         } else {
           null
         }
@@ -531,18 +543,21 @@ private[hive] trait HiveInspectors {
     case x: ListObjectInspector =>
       val list = new java.util.ArrayList[Object]
       val tpe = dataType.asInstanceOf[ArrayType].elementType
-      a.asInstanceOf[ArrayData].toArray().foreach {
-        v => list.add(wrap(v, x.getListElementObjectInspector, tpe))
-      }
+      a.asInstanceOf[ArrayData].foreach(tpe, (_, e) => {
+        list.add(wrap(e, x.getListElementObjectInspector, tpe))
+      })
       list
     case x: MapObjectInspector =>
       val keyType = dataType.asInstanceOf[MapType].keyType
       val valueType = dataType.asInstanceOf[MapType].valueType
+      val map = a.asInstanceOf[MapData]
+
       // Some UDFs seem to assume we pass in a HashMap.
-      val hashMap = new java.util.HashMap[AnyRef, AnyRef]()
-      hashMap.putAll(a.asInstanceOf[Map[_, _]].map { case (k, v) =>
-        wrap(k, x.getMapKeyObjectInspector, keyType) ->
-          wrap(v, x.getMapValueObjectInspector, valueType)
+      val hashMap = new java.util.HashMap[Any, Any](map.numElements())
+
+      map.foreach(keyType, valueType, (k, v) => {
+        hashMap.put(wrap(k, x.getMapKeyObjectInspector, keyType),
+          wrap(v, x.getMapValueObjectInspector, valueType))
       })
 
       hashMap
@@ -645,8 +660,9 @@ private[hive] trait HiveInspectors {
         ObjectInspectorFactory.getStandardConstantListObjectInspector(listObjectInspector, null)
       } else {
         val list = new java.util.ArrayList[Object]()
-        value.asInstanceOf[ArrayData].toArray()
-          .foreach(v => list.add(wrap(v, listObjectInspector, dt)))
+        value.asInstanceOf[ArrayData].foreach(dt, (_, e) => {
+          list.add(wrap(e, listObjectInspector, dt))
+        })
         ObjectInspectorFactory.getStandardConstantListObjectInspector(listObjectInspector, list)
       }
     case Literal(value, MapType(keyType, valueType, _)) =>
@@ -655,11 +671,14 @@ private[hive] trait HiveInspectors {
       if (value == null) {
         ObjectInspectorFactory.getStandardConstantMapObjectInspector(keyOI, valueOI, null)
       } else {
-        val map = new java.util.HashMap[Object, Object]()
-        value.asInstanceOf[Map[_, _]].foreach (entry => {
-          map.put(wrap(entry._1, keyOI, keyType), wrap(entry._2, valueOI, valueType))
+        val map = value.asInstanceOf[MapData]
+        val jmap = new java.util.HashMap[Any, Any](map.numElements())
+
+        map.foreach(keyType, valueType, (k, v) => {
+          jmap.put(wrap(k, keyOI, keyType), wrap(v, valueOI, valueType))
         })
-        ObjectInspectorFactory.getStandardConstantMapObjectInspector(keyOI, valueOI, map)
+
+        ObjectInspectorFactory.getStandardConstantMapObjectInspector(keyOI, valueOI, jmap)
       }
     // We will enumerate all of the possible constant expressions, throw exception if we missed
     case Literal(_, dt) => sys.error(s"Hive doesn't support the constant type [$dt].")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index e4944caeff924..40a6a32156687 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -95,9 +95,9 @@ case class InsertIntoHiveTable(
         .asInstanceOf[StructObjectInspector]
 
       val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector).toArray
-      val wrappers = fieldOIs.map(wrapperFor)
-      val outputData = new Array[Any](fieldOIs.length)
       val dataTypes: Array[DataType] = child.output.map(_.dataType).toArray
+      val wrappers = fieldOIs.zip(dataTypes).map { case (f, dt) => wrapperFor(f, dt)}
+      val outputData = new Array[Any](fieldOIs.length)
 
       writerContainer.executorSideSetup(context.stageId, context.partitionId, context.attemptNumber)
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 4a13022eddf60..abe5c69003130 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -428,10 +428,10 @@ private[hive] case class HiveWindowFunction(
       // if pivotResult is false, we will get a single value for all rows in the frame.
       outputBuffer
     } else {
-      // if pivotResult is true, we will get a Seq having the same size with the size
+      // if pivotResult is true, we will get a ArrayData having the same size with the size
       // of the window frame. At here, we will return the result at the position of
       // index in the output buffer.
-      outputBuffer.asInstanceOf[ArrayData].get(index)
+      outputBuffer.asInstanceOf[ArrayData].get(index, dataType)
     }
   }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 924f4d37ce21f..6fa599734892b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -95,9 +95,10 @@ private[orc] class OrcOutputWriter(
   private val reusableOutputBuffer = new Array[Any](dataSchema.length)
 
   // Used to convert Catalyst values into Hadoop `Writable`s.
-  private val wrappers = structOI.getAllStructFieldRefs.map { ref =>
-    wrapperFor(ref.getFieldObjectInspector)
-  }.toArray
+  private val wrappers = structOI.getAllStructFieldRefs.zip(dataSchema.fields.map(_.dataType))
+    .map { case (ref, dt) =>
+      wrapperFor(ref.getFieldObjectInspector, dt)
+    }.toArray
 
   // `OrcRecordWriter.close()` creates an empty file if no rows are written at all.  We use this
   // flag to decide whether `OrcRecordWriter.close()` needs to be called.
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index f719f2e06ab63..99e95fb921301 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -147,6 +147,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       case (r1: Array[Byte], r2: Array[Byte])
         if r1 != null && r2 != null && r1.length == r2.length =>
         r1.zip(r2).foreach { case (b1, b2) => assert(b1 === b2) }
+      // We don't support equality & ordering for map type, so skip it.
+      case (r1: MapData, r2: MapData) =>
       case (r1, r2) => assert(r1 === r2)
     }
   }
@@ -230,7 +232,7 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
   test("wrap / unwrap Map Type") {
     val dt = MapType(dataTypes(0), dataTypes(1))
 
-    val d = Map(row(0) -> row(1))
+    val d = ArrayBasedMapData(Array(row(0)), Array(row(1)))
     checkValue(d, unwrap(wrap(d, toInspector(dt), dt), toInspector(dt)))
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
     checkValue(d,

From 60ea7ab4bbfaea29a6cdf4e0e71ddc56afd04de6 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sat, 1 Aug 2015 00:41:15 -0700
Subject: [PATCH 0766/1454] Revert "[SPARK-8232] [SQL] Add sort_array support"

This reverts commit 67ad4e21fc68336b0ad6f9a363fb5ebb51f592bf.
---
 python/pyspark/sql/functions.py               | 20 -----
 .../catalyst/analysis/FunctionRegistry.scala  |  1 -
 .../expressions/collectionOperations.scala    | 80 +------------------
 .../CollectionFunctionsSuite.scala            | 22 -----
 .../org/apache/spark/sql/functions.scala      | 19 +----
 .../spark/sql/DataFrameFunctionsSuite.scala   | 51 +-----------
 6 files changed, 7 insertions(+), 186 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index fb542e6cff81a..89a2a5ceaa9bf 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -51,7 +51,6 @@
     'sha1',
     'sha2',
     'size',
-    'sort_array',
     'sparkPartitionId',
     'struct',
     'udf',
@@ -571,10 +570,8 @@ def length(col):
 def format_number(col, d):
     """Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
        and returns the result as a string.
-
     :param col: the column name of the numeric value to be formatted
     :param d: the N decimal places
-
     >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
     [Row(v=u'5.0000')]
     """
@@ -971,23 +968,6 @@ def soundex(col):
     return Column(sc._jvm.functions.size(_to_java_column(col)))
 
 
-@since(1.5)
-def sort_array(col, asc=True):
-    """
-    Collection function: sorts the input array for the given column in ascending order.
-
-    :param col: name of column or expression
-
-    >>> df = sqlContext.createDataFrame([([2, 1, 3],),([1],),([],)], ['data'])
-    >>> df.select(sort_array(df.data).alias('r')).collect()
-    [Row(r=[1, 2, 3]), Row(r=[1]), Row(r=[])]
-    >>> df.select(sort_array(df.data, asc=False).alias('r')).collect()
-    [Row(r=[3, 2, 1]), Row(r=[1]), Row(r=[])]
-     """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.sort_array(_to_java_column(col), asc))
-
-
 class UserDefinedFunction(object):
     """
     User defined function in Python
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 6e144518bb009..ee44cbcba68e7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -233,7 +233,6 @@ object FunctionRegistry {
 
     // collection functions
     expression[Size]("size"),
-    expression[SortArray]("sort_array"),
 
     // misc functions
     expression[Crc32]("crc32"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 1156797b2c37d..015bbb658266d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -16,10 +16,7 @@
  */
 package org.apache.spark.sql.catalyst.expressions
 
-import java.util.Comparator
-
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 
 /**
@@ -38,78 +35,3 @@ case class Size(child: Expression) extends UnaryExpression with ExpectsInputType
     nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).numElements();")
   }
 }
-
-/**
- * Sorts the input array in ascending / descending order according to the natural ordering of
- * the array elements and returns it.
- */
-case class SortArray(base: Expression, ascendingOrder: Expression)
-  extends BinaryExpression with ExpectsInputTypes with CodegenFallback {
-
-  def this(e: Expression) = this(e, Literal(true))
-
-  override def left: Expression = base
-  override def right: Expression = ascendingOrder
-  override def dataType: DataType = base.dataType
-  override def inputTypes: Seq[AbstractDataType] = Seq(ArrayType, BooleanType)
-
-  override def checkInputDataTypes(): TypeCheckResult = base.dataType match {
-    case _ @ ArrayType(n: AtomicType, _) => TypeCheckResult.TypeCheckSuccess
-    case _ @ ArrayType(n, _) => TypeCheckResult.TypeCheckFailure(
-                    s"Type $n is not the AtomicType, we can not perform the ordering operations")
-    case other =>
-      TypeCheckResult.TypeCheckFailure(s"ArrayType(AtomicType) is expected, but we got $other")
-  }
-
-  @transient
-  private lazy val lt = {
-    val ordering = base.dataType match {
-      case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
-    }
-
-    new Comparator[Any]() {
-      override def compare(o1: Any, o2: Any): Int = {
-        if (o1 == null && o2 == null) {
-          0
-        } else if (o1 == null) {
-          -1
-        } else if (o2 == null) {
-          1
-        } else {
-          ordering.compare(o1, o2)
-        }
-      }
-    }
-  }
-
-  @transient
-  private lazy val gt = {
-    val ordering = base.dataType match {
-      case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
-    }
-
-    new Comparator[Any]() {
-      override def compare(o1: Any, o2: Any): Int = {
-        if (o1 == null && o2 == null) {
-          0
-        } else if (o1 == null) {
-          1
-        } else if (o2 == null) {
-          -1
-        } else {
-          -ordering.compare(o1, o2)
-        }
-      }
-    }
-  }
-
-  override def nullSafeEval(array: Any, ascending: Any): Any = {
-    val data = array.asInstanceOf[ArrayData].toArray().asInstanceOf[Array[AnyRef]]
-    java.util.Arrays.sort(
-      data,
-      if (ascending.asInstanceOf[Boolean]) lt else gt)
-    new GenericArrayData(data.asInstanceOf[Array[Any]])
-  }
-
-  override def prettyName: String = "sort_array"
-}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
index 2c7e85c446ec6..28c41b57169f9 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -43,26 +43,4 @@ class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, MapType(StringType, StringType)), null)
     checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
   }
-
-  test("Sort Array") {
-    val a0 = Literal.create(Seq(2, 1, 3), ArrayType(IntegerType))
-    val a1 = Literal.create(Seq[Integer](), ArrayType(IntegerType))
-    val a2 = Literal.create(Seq("b", "a"), ArrayType(StringType))
-    val a3 = Literal.create(Seq("b", null, "a"), ArrayType(StringType))
-
-    checkEvaluation(new SortArray(a0), Seq(1, 2, 3))
-    checkEvaluation(new SortArray(a1), Seq[Integer]())
-    checkEvaluation(new SortArray(a2), Seq("a", "b"))
-    checkEvaluation(new SortArray(a3), Seq(null, "a", "b"))
-    checkEvaluation(SortArray(a0, Literal(true)), Seq(1, 2, 3))
-    checkEvaluation(SortArray(a1, Literal(true)), Seq[Integer]())
-    checkEvaluation(SortArray(a2, Literal(true)), Seq("a", "b"))
-    checkEvaluation(new SortArray(a3, Literal(true)), Seq(null, "a", "b"))
-    checkEvaluation(SortArray(a0, Literal(false)), Seq(3, 2, 1))
-    checkEvaluation(SortArray(a1, Literal(false)), Seq[Integer]())
-    checkEvaluation(SortArray(a2, Literal(false)), Seq("b", "a"))
-    checkEvaluation(new SortArray(a3, Literal(false)), Seq("b", "a", null))
-
-    checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 3c9421f5cd14b..57bb00a7417af 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2223,30 +2223,19 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Returns length of array or map.
-   *
+   * Returns length of array or map
    * @group collection_funcs
    * @since 1.5.0
    */
-  def size(e: Column): Column = Size(e.expr)
+  def size(columnName: String): Column = size(Column(columnName))
 
   /**
-   * Sorts the input array for the given column in ascending order,
-   * according to the natural ordering of the array elements.
-   *
+   * Returns length of array or map
    * @group collection_funcs
    * @since 1.5.0
    */
-  def sort_array(e: Column): Column = sort_array(e, true)
+  def size(column: Column): Column = Size(column.expr)
 
-  /**
-   * Sorts the input array for the given column in ascending / descending order,
-   * according to the natural ordering of the array elements.
-   *
-   * @group collection_funcs
-   * @since 1.5.0
-   */
-  def sort_array(e: Column, asc: Boolean): Column = SortArray(e.expr, lit(asc).expr)
 
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 46921d14256b9..1baec5d37699d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -267,53 +267,6 @@ class DataFrameFunctionsSuite extends QueryTest {
     )
   }
 
-  test("sort_array function") {
-    val df = Seq(
-      (Array[Int](2, 1, 3), Array("b", "c", "a")),
-      (Array[Int](), Array[String]()),
-      (null, null)
-    ).toDF("a", "b")
-    checkAnswer(
-      df.select(sort_array($"a"), sort_array($"b")),
-      Seq(
-        Row(Seq(1, 2, 3), Seq("a", "b", "c")),
-        Row(Seq[Int](), Seq[String]()),
-        Row(null, null))
-    )
-    checkAnswer(
-      df.select(sort_array($"a", false), sort_array($"b", false)),
-      Seq(
-        Row(Seq(3, 2, 1), Seq("c", "b", "a")),
-        Row(Seq[Int](), Seq[String]()),
-        Row(null, null))
-    )
-    checkAnswer(
-      df.selectExpr("sort_array(a)", "sort_array(b)"),
-      Seq(
-        Row(Seq(1, 2, 3), Seq("a", "b", "c")),
-        Row(Seq[Int](), Seq[String]()),
-        Row(null, null))
-    )
-    checkAnswer(
-      df.selectExpr("sort_array(a, true)", "sort_array(b, false)"),
-      Seq(
-        Row(Seq(1, 2, 3), Seq("c", "b", "a")),
-        Row(Seq[Int](), Seq[String]()),
-        Row(null, null))
-    )
-
-    val df2 = Seq((Array[Array[Int]](Array(2)), "x")).toDF("a", "b")
-    assert(intercept[AnalysisException] {
-      df2.selectExpr("sort_array(a)").collect()
-    }.getMessage().contains("Type ArrayType(IntegerType,false) is not the AtomicType, " +
-      "we can not perform the ordering operations"))
-
-    val df3 = Seq(("xxx", "x")).toDF("a", "b")
-    assert(intercept[AnalysisException] {
-      df3.selectExpr("sort_array(a)").collect()
-    }.getMessage().contains("ArrayType(AtomicType) is expected, but we got StringType"))
-  }
-
   test("array size function") {
     val df = Seq(
       (Array[Int](1, 2), "x"),
@@ -321,7 +274,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       (Array[Int](1, 2, 3), "z")
     ).toDF("a", "b")
     checkAnswer(
-      df.select(size($"a")),
+      df.select(size("a")),
       Seq(Row(2), Row(0), Row(3))
     )
     checkAnswer(
@@ -337,7 +290,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       (Map[Int, Int](1 -> 1, 2 -> 2, 3 -> 3), "z")
     ).toDF("a", "b")
     checkAnswer(
-      df.select(size($"a")),
+      df.select(size("a")),
       Seq(Row(2), Row(0), Row(3))
     )
     checkAnswer(

From 65038973a17904e0e04d453799ec108af240fbab Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Sat, 1 Aug 2015 01:09:38 -0700
Subject: [PATCH 0767/1454] [SPARK-7446] [MLLIB] Add inverse transform for
 string indexer

It is useful to convert the encoded indices back to their string representation for result inspection. We can add a function which creates an inverse transformation.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #6339 from holdenk/SPARK-7446-inverse-transform-for-string-indexer and squashes the following commits:

7cdf915 [Holden Karau] scala style comment fix
b9cffb6 [Holden Karau] Update the labels param to have the metadata note
6a38edb [Holden Karau] Setting the default needs to come after the value gets defined
9e241d8 [Holden Karau] use Array.empty
21c8cfa [Holden Karau] Merge branch 'master' into SPARK-7446-inverse-transform-for-string-indexer
64dd3a3 [Holden Karau] Merge branch 'master' into SPARK-7446-inverse-transform-for-string-indexer
4f06c59 [Holden Karau] Fix comment styles, use empty array as the default, etc.
a60c0e3 [Holden Karau] CR feedback (remove old constructor, add a note about use of setLabels)
1987b95 [Holden Karau] Use default copy
71e8d66 [Holden Karau] Make labels a local param for StringIndexerInverse
8450d0b [Holden Karau] Use the labels param in StringIndexerInverse
7464019 [Holden Karau] Add a labels param
868b1a9 [Holden Karau] Update scaladoc since we don't have labelsCol anymore
5aa38bf [Holden Karau] Add an inverse test using only meta data, pass labels when calling inverse method
f3e0c64 [Holden Karau] CR feedback
ebed932 [Holden Karau] Add Experimental tag and some scaladocs. Also don't require that the inputCol has the metadata on it, instead have the labelsCol specified when creating the inverse.
03ebf95 [Holden Karau] Add explicit type for invert function
ecc65e0 [Holden Karau] Read the metadata correctly, use the array, pass the test
a42d773 [Holden Karau] Fix test to supply cols as per new invert method
16cc3c3 [Holden Karau] Add an invert method
d4bcb20 [Holden Karau] Make the inverse string indexer into a transformer (still needs test updates but compiles)
e8bf3ad [Holden Karau] Merge branch 'master' into SPARK-7446-inverse-transform-for-string-indexer
c3fdee1 [Holden Karau] Some WIP refactoring based on jkbradley's CR feedback. Definite work-in-progress
557bef8 [Holden Karau] Instead of using a private inverse transform, add an invert function so we can use it in a pipeline
88779c1 [Holden Karau] fix long line
78b28c1 [Holden Karau] Finish reverse part and add a test :)
bb16a6a [Holden Karau] Some progress
---
 .../spark/ml/feature/StringIndexer.scala      | 108 +++++++++++++++++-
 .../spark/ml/feature/StringIndexerSuite.scala |  13 +++
 2 files changed, 118 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index bf7be363b8224..ebfa972532358 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -20,13 +20,14 @@ package org.apache.spark.ml.feature
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{Estimator, Model}
-import org.apache.spark.ml.attribute.NominalAttribute
+import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{NumericType, StringType, StructType}
+import org.apache.spark.sql.types.{DoubleType, NumericType, StringType, StructType}
 import org.apache.spark.util.collection.OpenHashMap
 
 /**
@@ -151,4 +152,105 @@ class StringIndexerModel private[ml] (
     val copied = new StringIndexerModel(uid, labels)
     copyValues(copied, extra)
   }
+
+  /**
+   * Return a model to perform the inverse transformation.
+   * Note: By default we keep the original columns during this transformation, so the inverse
+   * should only be used on new columns such as predicted labels.
+   */
+  def invert(inputCol: String, outputCol: String): StringIndexerInverse = {
+    new StringIndexerInverse()
+      .setInputCol(inputCol)
+      .setOutputCol(outputCol)
+      .setLabels(labels)
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Transform a provided column back to the original input types using either the metadata
+ * on the input column, or if provided using the labels supplied by the user.
+ * Note: By default we keep the original columns during this transformation,
+ * so the inverse should only be used on new columns such as predicted labels.
+ */
+@Experimental
+class StringIndexerInverse private[ml] (
+  override val uid: String) extends Transformer
+    with HasInputCol with HasOutputCol {
+
+  def this() =
+    this(Identifiable.randomUID("strIdxInv"))
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /**
+   * Optional labels to be provided by the user, if not supplied column
+   * metadata is read for labels. The default value is an empty array,
+   * but the empty array is ignored and column metadata used instead.
+   * @group setParam
+   */
+  def setLabels(value: Array[String]): this.type = set(labels, value)
+
+  /**
+   * Param for array of labels.
+   * Optional labels to be provided by the user, if not supplied column
+   * metadata is read for labels.
+   * @group param
+   */
+  final val labels: StringArrayParam = new StringArrayParam(this, "labels",
+    "array of labels, if not provided metadata from inputCol is used instead.")
+  setDefault(labels, Array.empty[String])
+
+  /**
+   * Optional labels to be provided by the user, if not supplied column
+   * metadata is read for labels.
+   * @group getParam
+   */
+  final def getLabels: Array[String] = $(labels)
+
+  /** Transform the schema for the inverse transformation */
+  override def transformSchema(schema: StructType): StructType = {
+    val inputColName = $(inputCol)
+    val inputDataType = schema(inputColName).dataType
+    require(inputDataType.isInstanceOf[NumericType],
+      s"The input column $inputColName must be a numeric type, " +
+        s"but got $inputDataType.")
+    val inputFields = schema.fields
+    val outputColName = $(outputCol)
+    require(inputFields.forall(_.name != outputColName),
+      s"Output column $outputColName already exists.")
+    val attr = NominalAttribute.defaultAttr.withName($(outputCol))
+    val outputFields = inputFields :+ attr.toStructField()
+    StructType(outputFields)
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val inputColSchema = dataset.schema($(inputCol))
+    // If the labels array is empty use column metadata
+    val values = if ($(labels).isEmpty) {
+      Attribute.fromStructField(inputColSchema)
+        .asInstanceOf[NominalAttribute].values.get
+    } else {
+      $(labels)
+    }
+    val indexer = udf { index: Double =>
+      val idx = index.toInt
+      if (0 <= idx && idx < values.size) {
+        values(idx)
+      } else {
+        throw new SparkException(s"Unseen index: $index ??")
+      }
+    }
+    val outputColName = $(outputCol)
+    dataset.select(col("*"),
+      indexer(dataset($(inputCol)).cast(DoubleType)).as(outputColName))
+  }
+
+  override def copy(extra: ParamMap): StringIndexerInverse = {
+    defaultCopy(extra)
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 99f82bea42688..d0295a0fe2fc1 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -47,6 +47,19 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
     // a -> 0, b -> 2, c -> 1
     val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0))
     assert(output === expected)
+    // convert reverse our transform
+    val reversed = indexer.invert("labelIndex", "label2")
+      .transform(transformed)
+      .select("id", "label2")
+    assert(df.collect().map(r => (r.getInt(0), r.getString(1))).toSet ===
+      reversed.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
+    // Check invert using only metadata
+    val inverse2 = new StringIndexerInverse()
+      .setInputCol("labelIndex")
+      .setOutputCol("label2")
+    val reversed2 = inverse2.transform(transformed).select("id", "label2")
+    assert(df.collect().map(r => (r.getInt(0), r.getString(1))).toSet ===
+      reversed2.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
   }
 
   test("StringIndexer with a numeric input column") {

From d2a9b66f6c0de89d6d16370af1c77c7f51b11d3e Mon Sep 17 00:00:00 2001
From: zhangjiajin <zhangjiajin@huawei.com>
Date: Sat, 1 Aug 2015 01:56:27 -0700
Subject: [PATCH 0768/1454] [SPARK-8999] [MLLIB] PrefixSpan non-temporal
 sequences

mengxr Extends PrefixSpan to non-temporal itemsets. Continues work by zhangjiajin

 * Internal API uses List[Set[Int]] which is likely not efficient; will need to refactor during QA

Closes #7646

Author: zhangjiajin <zhangjiajin@huawei.com>
Author: Feynman Liang <fliang@databricks.com>
Author: zhang jiajin <zhangjiajin@huawei.com>

Closes #7818 from feynmanliang/SPARK-8999-nonTemporal and squashes the following commits:

4ded81d [Feynman Liang] Replace all filters to filter nonempty
350e67e [Feynman Liang] Code review feedback
03156ca [Feynman Liang] Fix tests, drop delimiters at boundaries of sequences
d1fe0ed [Feynman Liang] Remove comments
86ca4e5 [Feynman Liang] Fix style
7c7bf39 [Feynman Liang] Fixed itemSet sequences
6073b10 [Feynman Liang] Basic itemset functionality, failing test
1a7fb48 [Feynman Liang] Add delimiter to results
5db00aa [Feynman Liang] Working for items, not itemsets
6787716 [Feynman Liang] Working on temporal sequences
f1114b9 [Feynman Liang] Add -1 delimiter
00fe756 [Feynman Liang] Reset base files for rebase
f486dcd [zhangjiajin] change maxLocalProjDBSize and fix a bug (remove -3 from frequent items).
60a0b76 [zhangjiajin] fixed a scala style error.
740c203 [zhangjiajin] fixed a scala style error.
5785cb8 [zhangjiajin] support non-temporal sequence
a5d649d [zhangjiajin] restore original version
09dc409 [zhangjiajin] Merge branch 'master' of https://github.com/apache/spark into multiItems_2
ae8c02d [zhangjiajin] Fixed some Scala style errors.
216ab0c [zhangjiajin] Support non-temporal sequence in PrefixSpan
b572f54 [zhangjiajin] initialize file before rebase.
f06772f [zhangjiajin] fix a scala style error.
a7e50d4 [zhangjiajin] Add feature: Collect enough frequent prefixes before projection in PrefixSpan.
c1d13d0 [zhang jiajin] Delete PrefixspanSuite.scala
d9d8137 [zhang jiajin] Delete Prefixspan.scala
c6ceb63 [zhangjiajin] Add new algorithm PrefixSpan and test file.
---
 .../spark/mllib/fpm/LocalPrefixSpan.scala     |  46 ++--
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 111 +++++---
 .../spark/mllib/fpm/PrefixSpanSuite.scala     | 237 +++++++++++++++---
 3 files changed, 302 insertions(+), 92 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
index 0ea792081086d..ccebf951c850d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
@@ -25,7 +25,7 @@ import org.apache.spark.Logging
  * Calculate all patterns of a projected database in local.
  */
 private[fpm] object LocalPrefixSpan extends Logging with Serializable {
-
+  import PrefixSpan._
   /**
    * Calculate all patterns of a projected database.
    * @param minCount minimum count
@@ -39,12 +39,19 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
   def run(
       minCount: Long,
       maxPatternLength: Int,
-      prefixes: List[Int],
-      database: Iterable[Array[Int]]): Iterator[(List[Int], Long)] = {
-    if (prefixes.length == maxPatternLength || database.isEmpty) return Iterator.empty
-    val frequentItemAndCounts = getFreqItemAndCounts(minCount, database)
-    val filteredDatabase = database.map(x => x.filter(frequentItemAndCounts.contains))
-    frequentItemAndCounts.iterator.flatMap { case (item, count) =>
+      prefixes: List[Set[Int]],
+      database: Iterable[List[Set[Int]]]): Iterator[(List[Set[Int]], Long)] = {
+    if (prefixes.length == maxPatternLength || database.isEmpty) {
+      return Iterator.empty
+    }
+    val freqItemSetsAndCounts = getFreqItemAndCounts(minCount, database)
+    val freqItems = freqItemSetsAndCounts.keys.flatten.toSet
+    val filteredDatabase = database.map { suffix =>
+      suffix
+        .map(item => freqItems.intersect(item))
+        .filter(_.nonEmpty)
+    }
+    freqItemSetsAndCounts.iterator.flatMap { case (item, count) =>
       val newPrefixes = item :: prefixes
       val newProjected = project(filteredDatabase, item)
       Iterator.single((newPrefixes, count)) ++
@@ -54,20 +61,23 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
 
   /**
    * Calculate suffix sequence immediately after the first occurrence of an item.
-   * @param item item to get suffix after
+   * @param item itemset to get suffix after
    * @param sequence sequence to extract suffix from
    * @return suffix sequence
    */
-  def getSuffix(item: Int, sequence: Array[Int]): Array[Int] = {
-    val index = sequence.indexOf(item)
+  def getSuffix(item: Set[Int], sequence: List[Set[Int]]): List[Set[Int]] = {
+    val itemsetSeq = sequence
+    val index = itemsetSeq.indexWhere(item.subsetOf(_))
     if (index == -1) {
-      Array()
+      List()
     } else {
-      sequence.drop(index + 1)
+      itemsetSeq.drop(index + 1)
     }
   }
 
-  def project(database: Iterable[Array[Int]], prefix: Int): Iterable[Array[Int]] = {
+  def project(
+      database: Iterable[List[Set[Int]]],
+      prefix: Set[Int]): Iterable[List[Set[Int]]] = {
     database
       .map(getSuffix(prefix, _))
       .filter(_.nonEmpty)
@@ -81,14 +91,16 @@ private[fpm] object LocalPrefixSpan extends Logging with Serializable {
    */
   private def getFreqItemAndCounts(
       minCount: Long,
-      database: Iterable[Array[Int]]): mutable.Map[Int, Long] = {
+      database: Iterable[List[Set[Int]]]): Map[Set[Int], Long] = {
     // TODO: use PrimitiveKeyOpenHashMap
-    val counts = mutable.Map[Int, Long]().withDefaultValue(0L)
+    val counts = mutable.Map[Set[Int], Long]().withDefaultValue(0L)
     database.foreach { sequence =>
-      sequence.distinct.foreach { item =>
+      sequence.flatMap(nonemptySubsets(_)).distinct.foreach { item =>
         counts(item) += 1L
       }
     }
-    counts.filter(_._2 >= minCount)
+    counts
+      .filter { case (_, count) => count >= minCount }
+      .toMap
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index e6752332cdeeb..22b4ddb8b3495 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.fpm
 
-import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable.ArrayBuilder
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
@@ -44,13 +44,14 @@ import org.apache.spark.storage.StorageLevel
 class PrefixSpan private (
     private var minSupport: Double,
     private var maxPatternLength: Int) extends Logging with Serializable {
+  import PrefixSpan._
 
   /**
    * The maximum number of items allowed in a projected database before local processing. If a
    * projected database exceeds this size, another iteration of distributed PrefixSpan is run.
    */
-  // TODO: make configurable with a better default value, 10000 may be too small
-  private val maxLocalProjDBSize: Long = 10000
+  // TODO: make configurable with a better default value
+  private val maxLocalProjDBSize: Long = 32000000L
 
   /**
    * Constructs a default instance with default parameters
@@ -90,35 +91,41 @@ class PrefixSpan private (
 
   /**
    * Find the complete set of sequential patterns in the input sequences.
-   * @param sequences input data set, contains a set of sequences,
-   *                  a sequence is an ordered list of elements.
+   * @param data ordered sequences of itemsets. Items are represented by non-negative integers.
+   *                  Each itemset has one or more items and is delimited by [[DELIMITER]].
    * @return a set of sequential pattern pairs,
    *         the key of pair is pattern (a list of elements),
    *         the value of pair is the pattern's count.
    */
-  def run(sequences: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
-    val sc = sequences.sparkContext
+  // TODO: generalize to arbitrary item-types and use mapping to Ints for internal algorithm
+  def run(data: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
+    val sc = data.sparkContext
 
-    if (sequences.getStorageLevel == StorageLevel.NONE) {
+    if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
     }
 
+    // Use List[Set[Item]] for internal computation
+    val sequences = data.map { seq => splitSequence(seq.toList) }
+
     // Convert min support to a min number of transactions for this dataset
     val minCount = if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
 
     // (Frequent items -> number of occurrences, all items here satisfy the `minSupport` threshold
     val freqItemCounts = sequences
-      .flatMap(seq => seq.distinct.map(item => (item, 1L)))
+      .flatMap(seq => seq.flatMap(nonemptySubsets(_)).distinct.map(item => (item, 1L)))
       .reduceByKey(_ + _)
-      .filter(_._2 >= minCount)
+      .filter { case (item, count) => (count >= minCount) }
       .collect()
+      .toMap
 
     // Pairs of (length 1 prefix, suffix consisting of frequent items)
     val itemSuffixPairs = {
-      val freqItems = freqItemCounts.map(_._1).toSet
+      val freqItemSets = freqItemCounts.keys.toSet
+      val freqItems = freqItemSets.flatten
       sequences.flatMap { seq =>
-        val filteredSeq = seq.filter(freqItems.contains(_))
-        freqItems.flatMap { item =>
+        val filteredSeq = seq.map(item => freqItems.intersect(item)).filter(_.nonEmpty)
+        freqItemSets.flatMap { item =>
           val candidateSuffix = LocalPrefixSpan.getSuffix(item, filteredSeq)
           candidateSuffix match {
             case suffix if !suffix.isEmpty => Some((List(item), suffix))
@@ -130,14 +137,15 @@ class PrefixSpan private (
 
     // Accumulator for the computed results to be returned, initialized to the frequent items (i.e.
     // frequent length-one prefixes)
-    var resultsAccumulator = freqItemCounts.map(x => (List(x._1), x._2))
+    var resultsAccumulator = freqItemCounts.map { case (item, count) => (List(item), count) }.toList
 
     // Remaining work to be locally and distributively processed respectfully
     var (pairsForLocal, pairsForDistributed) = partitionByProjDBSize(itemSuffixPairs)
 
     // Continue processing until no pairs for distributed processing remain (i.e. all prefixes have
-    // projected database sizes <= `maxLocalProjDBSize`)
-    while (pairsForDistributed.count() != 0) {
+    // projected database sizes <= `maxLocalProjDBSize`) or `maxPatternLength` is reached
+    var patternLength = 1
+    while (pairsForDistributed.count() != 0 && patternLength < maxPatternLength) {
       val (nextPatternAndCounts, nextPrefixSuffixPairs) =
         extendPrefixes(minCount, pairsForDistributed)
       pairsForDistributed.unpersist()
@@ -146,6 +154,7 @@ class PrefixSpan private (
       pairsForDistributed.persist(StorageLevel.MEMORY_AND_DISK)
       pairsForLocal ++= smallerPairsPart
       resultsAccumulator ++= nextPatternAndCounts.collect()
+      patternLength += 1 // pattern length grows one per iteration
     }
 
     // Process the small projected databases locally
@@ -153,7 +162,7 @@ class PrefixSpan private (
       minCount, sc.parallelize(pairsForLocal, 1).groupByKey())
 
     (sc.parallelize(resultsAccumulator, 1) ++ remainingResults)
-      .map { case (pattern, count) => (pattern.toArray, count) }
+      .map { case (pattern, count) => (flattenSequence(pattern.reverse).toArray, count) }
   }
 
 
@@ -163,8 +172,8 @@ class PrefixSpan private (
    * @return prefix-suffix pairs partitioned by whether their projected database size is <= or
    *         greater than [[maxLocalProjDBSize]]
    */
-  private def partitionByProjDBSize(prefixSuffixPairs: RDD[(List[Int], Array[Int])])
-    : (Array[(List[Int], Array[Int])], RDD[(List[Int], Array[Int])]) = {
+  private def partitionByProjDBSize(prefixSuffixPairs: RDD[(List[Set[Int]], List[Set[Int]])])
+    : (List[(List[Set[Int]], List[Set[Int]])], RDD[(List[Set[Int]], List[Set[Int]])]) = {
     val prefixToSuffixSize = prefixSuffixPairs
       .aggregateByKey(0)(
         seqOp = { case (count, suffix) => count + suffix.length },
@@ -176,12 +185,12 @@ class PrefixSpan private (
       .toSet
     val small = prefixSuffixPairs.filter { case (prefix, _) => smallPrefixes.contains(prefix) }
     val large = prefixSuffixPairs.filter { case (prefix, _) => !smallPrefixes.contains(prefix) }
-    (small.collect(), large)
+    (small.collect().toList, large)
   }
 
   /**
-   * Extends all prefixes by one item from their suffix and computes the resulting frequent prefixes
-   * and remaining work.
+   * Extends all prefixes by one itemset from their suffix and computes the resulting frequent
+   * prefixes and remaining work.
    * @param minCount minimum count
    * @param prefixSuffixPairs prefix (length N) and suffix pairs,
    * @return (frequent length N+1 extended prefix, count) pairs and (frequent length N+1 extended
@@ -189,15 +198,16 @@ class PrefixSpan private (
    */
   private def extendPrefixes(
       minCount: Long,
-      prefixSuffixPairs: RDD[(List[Int], Array[Int])])
-    : (RDD[(List[Int], Long)], RDD[(List[Int], Array[Int])]) = {
+      prefixSuffixPairs: RDD[(List[Set[Int]], List[Set[Int]])])
+    : (RDD[(List[Set[Int]], Long)], RDD[(List[Set[Int]], List[Set[Int]])]) = {
 
-    // (length N prefix, item from suffix) pairs and their corresponding number of occurrences
+    // (length N prefix, itemset from suffix) pairs and their corresponding number of occurrences
     // Every (prefix :+ suffix) is guaranteed to have support exceeding `minSupport`
     val prefixItemPairAndCounts = prefixSuffixPairs
-      .flatMap { case (prefix, suffix) => suffix.distinct.map(y => ((prefix, y), 1L)) }
+      .flatMap { case (prefix, suffix) =>
+      suffix.flatMap(nonemptySubsets(_)).distinct.map(y => ((prefix, y), 1L)) }
       .reduceByKey(_ + _)
-      .filter(_._2 >= minCount)
+      .filter { case (item, count) => (count >= minCount) }
 
     // Map from prefix to set of possible next items from suffix
     val prefixToNextItems = prefixItemPairAndCounts
@@ -207,7 +217,6 @@ class PrefixSpan private (
       .collect()
       .toMap
 
-
     // Frequent patterns with length N+1 and their corresponding counts
     val extendedPrefixAndCounts = prefixItemPairAndCounts
       .map { case ((prefix, item), count) => (item :: prefix, count) }
@@ -216,9 +225,12 @@ class PrefixSpan private (
     val extendedPrefixAndSuffix = prefixSuffixPairs
       .filter(x => prefixToNextItems.contains(x._1))
       .flatMap { case (prefix, suffix) =>
-        val frequentNextItems = prefixToNextItems(prefix)
-        val filteredSuffix = suffix.filter(frequentNextItems.contains(_))
-        frequentNextItems.flatMap { item =>
+        val frequentNextItemSets = prefixToNextItems(prefix)
+        val frequentNextItems = frequentNextItemSets.flatten
+        val filteredSuffix = suffix
+          .map(item => frequentNextItems.intersect(item))
+          .filter(_.nonEmpty)
+        frequentNextItemSets.flatMap { item =>
           LocalPrefixSpan.getSuffix(item, filteredSuffix) match {
             case suffix if !suffix.isEmpty => Some(item :: prefix, suffix)
             case _ => None
@@ -237,13 +249,38 @@ class PrefixSpan private (
    */
   private def getPatternsInLocal(
       minCount: Long,
-      data: RDD[(List[Int], Iterable[Array[Int]])]): RDD[(List[Int], Long)] = {
+      data: RDD[(List[Set[Int]], Iterable[List[Set[Int]]])]): RDD[(List[Set[Int]], Long)] = {
     data.flatMap {
-      case (prefix, projDB) =>
-        LocalPrefixSpan.run(minCount, maxPatternLength, prefix.toList.reverse, projDB)
-          .map { case (pattern: List[Int], count: Long) =>
-          (pattern.reverse, count)
-        }
+      case (prefix, projDB) => LocalPrefixSpan.run(minCount, maxPatternLength, prefix, projDB)
+    }
+  }
+
+}
+
+private[fpm] object PrefixSpan {
+  private[fpm] val DELIMITER = -1
+
+  /** Splits a sequence of itemsets delimited by [[DELIMITER]]. */
+  private[fpm] def splitSequence(sequence: List[Int]): List[Set[Int]] = {
+    sequence.span(_ != DELIMITER) match {
+      case (x, xs) if xs.length > 1 => x.toSet :: splitSequence(xs.tail)
+      case (x, xs) => List(x.toSet)
+    }
+  }
+
+  /** Flattens a sequence of itemsets into an Array, inserting[[DELIMITER]] between itemsets. */
+  private[fpm] def flattenSequence(sequence: List[Set[Int]]): List[Int] = {
+    val builder = ArrayBuilder.make[Int]()
+    for (itemSet <- sequence) {
+      builder += DELIMITER
+      builder ++= itemSet.toSeq.sorted
     }
+    builder.result().toList.drop(1) // drop trailing delimiter
+  }
+
+  /** Returns an iterator over all non-empty subsets of `itemSet` */
+  private[fpm] def nonemptySubsets(itemSet: Set[Int]): Iterator[Set[Int]] = {
+    // TODO: improve complexity by using partial prefixes, considering one item at a time
+    itemSet.subsets.filter(_ != Set.empty[Int])
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
index 6dd2dc926acc5..457f32670fd4e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -21,7 +21,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
 
-  test("PrefixSpan using Integer type") {
+  test("PrefixSpan using Integer type, singleton itemsets") {
 
     /*
       library("arulesSequences")
@@ -35,12 +35,12 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     */
 
     val sequences = Array(
-      Array(1, 3, 4, 5),
-      Array(2, 3, 1),
-      Array(2, 4, 1),
-      Array(3, 1, 3, 4, 5),
-      Array(3, 4, 4, 3),
-      Array(6, 5, 3))
+      Array(1, -1, 3, -1, 4, -1, 5),
+      Array(2, -1, 3, -1, 1),
+      Array(2, -1, 4, -1, 1),
+      Array(3, -1, 1, -1, 3, -1, 4, -1, 5),
+      Array(3, -1, 4, -1, 4, -1, 3),
+      Array(6, -1, 5, -1, 3))
 
     val rdd = sc.parallelize(sequences, 2).cache()
 
@@ -50,64 +50,225 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     val result1 = prefixspan.run(rdd)
     val expectedValue1 = Array(
       (Array(1), 4L),
-      (Array(1, 3), 2L),
-      (Array(1, 3, 4), 2L),
-      (Array(1, 3, 4, 5), 2L),
-      (Array(1, 3, 5), 2L),
-      (Array(1, 4), 2L),
-      (Array(1, 4, 5), 2L),
-      (Array(1, 5), 2L),
+      (Array(1, -1, 3), 2L),
+      (Array(1, -1, 3, -1, 4), 2L),
+      (Array(1, -1, 3, -1, 4, -1, 5), 2L),
+      (Array(1, -1, 3, -1, 5), 2L),
+      (Array(1, -1, 4), 2L),
+      (Array(1, -1, 4, -1, 5), 2L),
+      (Array(1, -1, 5), 2L),
       (Array(2), 2L),
-      (Array(2, 1), 2L),
+      (Array(2, -1, 1), 2L),
       (Array(3), 5L),
-      (Array(3, 1), 2L),
-      (Array(3, 3), 2L),
-      (Array(3, 4), 3L),
-      (Array(3, 4, 5), 2L),
-      (Array(3, 5), 2L),
+      (Array(3, -1, 1), 2L),
+      (Array(3, -1, 3), 2L),
+      (Array(3, -1, 4), 3L),
+      (Array(3, -1, 4, -1, 5), 2L),
+      (Array(3, -1, 5), 2L),
       (Array(4), 4L),
-      (Array(4, 5), 2L),
+      (Array(4, -1, 5), 2L),
       (Array(5), 3L)
     )
-    assert(compareResults(expectedValue1, result1.collect()))
+    compareResults(expectedValue1, result1.collect())
 
     prefixspan.setMinSupport(0.5).setMaxPatternLength(50)
     val result2 = prefixspan.run(rdd)
     val expectedValue2 = Array(
       (Array(1), 4L),
       (Array(3), 5L),
-      (Array(3, 4), 3L),
+      (Array(3, -1, 4), 3L),
       (Array(4), 4L),
       (Array(5), 3L)
     )
-    assert(compareResults(expectedValue2, result2.collect()))
+    compareResults(expectedValue2, result2.collect())
 
     prefixspan.setMinSupport(0.33).setMaxPatternLength(2)
     val result3 = prefixspan.run(rdd)
     val expectedValue3 = Array(
       (Array(1), 4L),
-      (Array(1, 3), 2L),
-      (Array(1, 4), 2L),
-      (Array(1, 5), 2L),
-      (Array(2, 1), 2L),
+      (Array(1, -1, 3), 2L),
+      (Array(1, -1, 4), 2L),
+      (Array(1, -1, 5), 2L),
+      (Array(2, -1, 1), 2L),
       (Array(2), 2L),
       (Array(3), 5L),
-      (Array(3, 1), 2L),
-      (Array(3, 3), 2L),
-      (Array(3, 4), 3L),
-      (Array(3, 5), 2L),
+      (Array(3, -1, 1), 2L),
+      (Array(3, -1, 3), 2L),
+      (Array(3, -1, 4), 3L),
+      (Array(3, -1, 5), 2L),
       (Array(4), 4L),
-      (Array(4, 5), 2L),
+      (Array(4, -1, 5), 2L),
       (Array(5), 3L)
     )
-    assert(compareResults(expectedValue3, result3.collect()))
+    compareResults(expectedValue3, result3.collect())
+  }
+
+  test("PrefixSpan using Integer type, variable-size itemsets") {
+    val sequences = Array(
+      Array(1, -1, 1, 2, 3, -1, 1, 3, -1, 4, -1, 3, 6),
+      Array(1, 4, -1, 3, -1, 2, 3, -1, 1, 5),
+      Array(5, 6, -1, 1, 2, -1, 4, 6, -1, 3, -1, 2),
+      Array(5, -1, 7, -1, 1, 6, -1, 3, -1, 2, -1, 3))
+    val rdd = sc.parallelize(sequences, 2).cache()
+    val prefixspan = new PrefixSpan().setMinSupport(0.5).setMaxPatternLength(5)
+    val result = prefixspan.run(rdd)
+
+    /*
+      To verify results, create file "prefixSpanSeqs" with content
+      (format = (transactionID, idxInTransaction, numItemsinItemset, itemset)):
+        1 1 1 1
+        1 2 3 1 2 3
+        1 3 2 1 3
+        1 4 1 4
+        1 5 2 3 6
+        2 1 2 1 4
+        2 2 1 3
+        2 3 2 2 3
+        2 4 2 1 5
+        3 1 2 5 6
+        3 2 2 1 2
+        3 3 2 4 6
+        3 4 1 3
+        3 5 1 2
+        4 1 1 5
+        4 2 1 7
+        4 3 2 1 6
+        4 4 1 3
+        4 5 1 2
+        4 6 1 3
+      In R, run:
+        library("arulesSequences")
+        prefixSpanSeqs = read_baskets("prefixSpanSeqs", info = c("sequenceID","eventID","SIZE"))
+        freqItemSeq = cspade(prefixSpanSeqs,
+                             parameter = list(support = 0.5, maxlen = 5 ))
+        resSeq = as(freqItemSeq, "data.frame")
+        resSeq
+
+                    sequence support
+        1              <{1}>    1.00
+        2              <{2}>    1.00
+        3              <{3}>    1.00
+        4              <{4}>    0.75
+        5              <{5}>    0.75
+        6              <{6}>    0.75
+        7          <{1},{6}>    0.50
+        8          <{2},{6}>    0.50
+        9          <{5},{6}>    0.50
+        10       <{1,2},{6}>    0.50
+        11         <{1},{4}>    0.50
+        12         <{2},{4}>    0.50
+        13       <{1,2},{4}>    0.50
+        14         <{1},{3}>    1.00
+        15         <{2},{3}>    0.75
+        16           <{2,3}>    0.50
+        17         <{3},{3}>    0.75
+        18         <{4},{3}>    0.75
+        19         <{5},{3}>    0.50
+        20         <{6},{3}>    0.50
+        21     <{5},{6},{3}>    0.50
+        22     <{6},{2},{3}>    0.50
+        23     <{5},{2},{3}>    0.50
+        24     <{5},{1},{3}>    0.50
+        25     <{2},{4},{3}>    0.50
+        26     <{1},{4},{3}>    0.50
+        27   <{1,2},{4},{3}>    0.50
+        28     <{1},{3},{3}>    0.75
+        29       <{1,2},{3}>    0.50
+        30     <{1},{2},{3}>    0.50
+        31       <{1},{2,3}>    0.50
+        32         <{1},{2}>    1.00
+        33           <{1,2}>    0.50
+        34         <{3},{2}>    0.75
+        35         <{4},{2}>    0.50
+        36         <{5},{2}>    0.50
+        37         <{6},{2}>    0.50
+        38     <{5},{6},{2}>    0.50
+        39     <{6},{3},{2}>    0.50
+        40     <{5},{3},{2}>    0.50
+        41     <{5},{1},{2}>    0.50
+        42     <{4},{3},{2}>    0.50
+        43     <{1},{3},{2}>    0.75
+        44 <{5},{6},{3},{2}>    0.50
+        45 <{5},{1},{3},{2}>    0.50
+        46         <{1},{1}>    0.50
+        47         <{2},{1}>    0.50
+        48         <{3},{1}>    0.50
+        49         <{5},{1}>    0.50
+        50       <{2,3},{1}>    0.50
+        51     <{1},{3},{1}>    0.50
+        52   <{1},{2,3},{1}>    0.50
+        53     <{1},{2},{1}>    0.50
+     */
+    val expectedValue = Array(
+      (Array(1), 4L),
+      (Array(2), 4L),
+      (Array(3), 4L),
+      (Array(4), 3L),
+      (Array(5), 3L),
+      (Array(6), 3L),
+      (Array(1, -1, 6), 2L),
+      (Array(2, -1, 6), 2L),
+      (Array(5, -1, 6), 2L),
+      (Array(1, 2, -1, 6), 2L),
+      (Array(1, -1, 4), 2L),
+      (Array(2, -1, 4), 2L),
+      (Array(1, 2, -1, 4), 2L),
+      (Array(1, -1, 3), 4L),
+      (Array(2, -1, 3), 3L),
+      (Array(2, 3), 2L),
+      (Array(3, -1, 3), 3L),
+      (Array(4, -1, 3), 3L),
+      (Array(5, -1, 3), 2L),
+      (Array(6, -1, 3), 2L),
+      (Array(5, -1, 6, -1, 3), 2L),
+      (Array(6, -1, 2, -1, 3), 2L),
+      (Array(5, -1, 2, -1, 3), 2L),
+      (Array(5, -1, 1, -1, 3), 2L),
+      (Array(2, -1, 4, -1, 3), 2L),
+      (Array(1, -1, 4, -1, 3), 2L),
+      (Array(1, 2, -1, 4, -1, 3), 2L),
+      (Array(1, -1, 3, -1, 3), 3L),
+      (Array(1, 2, -1, 3), 2L),
+      (Array(1, -1, 2, -1, 3), 2L),
+      (Array(1, -1, 2, 3), 2L),
+      (Array(1, -1, 2), 4L),
+      (Array(1, 2), 2L),
+      (Array(3, -1, 2), 3L),
+      (Array(4, -1, 2), 2L),
+      (Array(5, -1, 2), 2L),
+      (Array(6, -1, 2), 2L),
+      (Array(5, -1, 6, -1, 2), 2L),
+      (Array(6, -1, 3, -1, 2), 2L),
+      (Array(5, -1, 3, -1, 2), 2L),
+      (Array(5, -1, 1, -1, 2), 2L),
+      (Array(4, -1, 3, -1, 2), 2L),
+      (Array(1, -1, 3, -1, 2), 3L),
+      (Array(5, -1, 6, -1, 3, -1, 2), 2L),
+      (Array(5, -1, 1, -1, 3, -1, 2), 2L),
+      (Array(1, -1, 1), 2L),
+      (Array(2, -1, 1), 2L),
+      (Array(3, -1, 1), 2L),
+      (Array(5, -1, 1), 2L),
+      (Array(2, 3, -1, 1), 2L),
+      (Array(1, -1, 3, -1, 1), 2L),
+      (Array(1, -1, 2, 3, -1, 1), 2L),
+      (Array(1, -1, 2, -1, 1), 2L))
+
+    compareResults(expectedValue, result.collect())
   }
 
   private def compareResults(
-    expectedValue: Array[(Array[Int], Long)],
-    actualValue: Array[(Array[Int], Long)]): Boolean = {
-    expectedValue.map(x => (x._1.toSeq, x._2)).toSet ==
-      actualValue.map(x => (x._1.toSeq, x._2)).toSet
+      expectedValue: Array[(Array[Int], Long)],
+      actualValue: Array[(Array[Int], Long)]): Unit = {
+    val expectedSet = expectedValue.map(x => (x._1.toSeq, x._2)).toSet
+    val actualSet = actualValue.map(x => (x._1.toSeq, x._2)).toSet
+    assert(expectedSet === actualSet)
+  }
+
+  private def insertDelimiter(sequence: Array[Int]): Array[Int] = {
+    sequence.zip(Seq.fill(sequence.length)(PrefixSpan.DELIMITER)).map { case (a, b) =>
+      List(a, b)
+    }.flatten
   }
 
 }

From 8765665015ef47a23e00f7d01d4d280c31bb236d Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Sat, 1 Aug 2015 02:31:28 -0700
Subject: [PATCH 0769/1454] [SPARK-8169] [ML] Add StopWordsRemover as a
 transformer

jira: https://issues.apache.org/jira/browse/SPARK-8169

stop words: http://en.wikipedia.org/wiki/Stop_words

StopWordsRemover takes a string array column and outputs a string array column with all defined stop words removed. The transformer should also come with a standard set of stop words as default.

Currently I used a minimum stop words set since on some [case](http://nlp.stanford.edu/IR-book/html/htmledition/dropping-common-terms-stop-words-1.html), small set of stop words is preferred.
ASCII char has been tested, Yet I cannot check it in due to style check.

Further thought,
1. Maybe I should use OpenHashSet. Is it recommended?
2. Currently I leave the null in input array untouched, i.e. Array(null, null) => Array(null, null).
3. If the current stop words set looks too limited, any suggestion for replacement? We can have something similar to the one in [SKlearn](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/stop_words.py).

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #6742 from hhbyyh/stopwords and squashes the following commits:

fa959d8 [Yuhao Yang] separating udf
f190217 [Yuhao Yang] replace default list and other small fix
04403ab [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into stopwords
b3aa957 [Yuhao Yang] add stopWordsRemover
---
 .../spark/ml/feature/StopWordsRemover.scala   | 155 ++++++++++++++++++
 .../ml/feature/StopWordsRemoverSuite.scala    |  80 +++++++++
 2 files changed, 235 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
new file mode 100644
index 0000000000000..3cc41424460f2
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
@@ -0,0 +1,155 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.param.{ParamMap, BooleanParam, Param}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.types.{StringType, StructField, ArrayType, StructType}
+import org.apache.spark.sql.functions.{col, udf}
+
+/**
+ * stop words list
+ */
+private object StopWords {
+
+  /**
+   * Use the same default stopwords list as scikit-learn.
+   * The original list can be found from "Glasgow Information Retrieval Group"
+   * [[http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words]]
+   */
+  val EnglishStopWords = Array( "a", "about", "above", "across", "after", "afterwards", "again",
+    "against", "all", "almost", "alone", "along", "already", "also", "although", "always",
+    "am", "among", "amongst", "amoungst", "amount", "an", "and", "another",
+    "any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are",
+    "around", "as", "at", "back", "be", "became", "because", "become",
+    "becomes", "becoming", "been", "before", "beforehand", "behind", "being",
+    "below", "beside", "besides", "between", "beyond", "bill", "both",
+    "bottom", "but", "by", "call", "can", "cannot", "cant", "co", "con",
+    "could", "couldnt", "cry", "de", "describe", "detail", "do", "done",
+    "down", "due", "during", "each", "eg", "eight", "either", "eleven", "else",
+    "elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone",
+    "everything", "everywhere", "except", "few", "fifteen", "fify", "fill",
+    "find", "fire", "first", "five", "for", "former", "formerly", "forty",
+    "found", "four", "from", "front", "full", "further", "get", "give", "go",
+    "had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter",
+    "hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his",
+    "how", "however", "hundred", "i", "ie", "if", "in", "inc", "indeed",
+    "interest", "into", "is", "it", "its", "itself", "keep", "last", "latter",
+    "latterly", "least", "less", "ltd", "made", "many", "may", "me",
+    "meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly",
+    "move", "much", "must", "my", "myself", "name", "namely", "neither",
+    "never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone",
+    "nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on",
+    "once", "one", "only", "onto", "or", "other", "others", "otherwise", "our",
+    "ours", "ourselves", "out", "over", "own", "part", "per", "perhaps",
+    "please", "put", "rather", "re", "same", "see", "seem", "seemed",
+    "seeming", "seems", "serious", "several", "she", "should", "show", "side",
+    "since", "sincere", "six", "sixty", "so", "some", "somehow", "someone",
+    "something", "sometime", "sometimes", "somewhere", "still", "such",
+    "system", "take", "ten", "than", "that", "the", "their", "them",
+    "themselves", "then", "thence", "there", "thereafter", "thereby",
+    "therefore", "therein", "thereupon", "these", "they", "thick", "thin",
+    "third", "this", "those", "though", "three", "through", "throughout",
+    "thru", "thus", "to", "together", "too", "top", "toward", "towards",
+    "twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us",
+    "very", "via", "was", "we", "well", "were", "what", "whatever", "when",
+    "whence", "whenever", "where", "whereafter", "whereas", "whereby",
+    "wherein", "whereupon", "wherever", "whether", "which", "while", "whither",
+    "who", "whoever", "whole", "whom", "whose", "why", "will", "with",
+    "within", "without", "would", "yet", "you", "your", "yours", "yourself", "yourselves")
+}
+
+/**
+ * :: Experimental ::
+ * A feature transformer that filters out stop words from input.
+ * Note: null values from input array are preserved unless adding null to stopWords explicitly.
+ * @see [[http://en.wikipedia.org/wiki/Stop_words]]
+ */
+@Experimental
+class StopWordsRemover(override val uid: String)
+  extends Transformer with HasInputCol with HasOutputCol {
+
+  def this() = this(Identifiable.randomUID("stopWords"))
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /**
+   * the stop words set to be filtered out
+   * @group param
+   */
+  val stopWords: Param[Array[String]] = new Param(this, "stopWords", "stop words")
+
+  /** @group setParam */
+  def setStopWords(value: Array[String]): this.type = set(stopWords, value)
+
+  /** @group getParam */
+  def getStopWords: Array[String] = $(stopWords)
+
+  /**
+   * whether to do a case sensitive comparison over the stop words
+   * @group param
+   */
+  val caseSensitive: BooleanParam = new BooleanParam(this, "caseSensitive",
+    "whether to do case-sensitive comparison during filtering")
+
+  /** @group setParam */
+  def setCaseSensitive(value: Boolean): this.type = set(caseSensitive, value)
+
+  /** @group getParam */
+  def getCaseSensitive: Boolean = $(caseSensitive)
+
+  setDefault(stopWords -> StopWords.EnglishStopWords, caseSensitive -> false)
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val outputSchema = transformSchema(dataset.schema)
+    val t = if ($(caseSensitive)) {
+        val stopWordsSet = $(stopWords).toSet
+        udf { terms: Seq[String] =>
+          terms.filter(s => !stopWordsSet.contains(s))
+        }
+      } else {
+        val toLower = (s: String) => if (s != null) s.toLowerCase else s
+        val lowerStopWords = $(stopWords).map(toLower(_)).toSet
+        udf { terms: Seq[String] =>
+          terms.filter(s => !lowerStopWords.contains(toLower(s)))
+        }
+    }
+
+    val metadata = outputSchema($(outputCol)).metadata
+    dataset.select(col("*"), t(col($(inputCol))).as($(outputCol), metadata))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    val inputType = schema($(inputCol)).dataType
+    require(inputType.sameType(ArrayType(StringType)),
+      s"Input type must be ArrayType(StringType) but got $inputType.")
+    val outputFields = schema.fields :+
+      StructField($(outputCol), inputType, schema($(inputCol)).nullable)
+    StructType(outputFields)
+  }
+
+  override def copy(extra: ParamMap): StopWordsRemover = defaultCopy(extra)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
new file mode 100644
index 0000000000000..f01306f89cb5f
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, Row}
+
+object StopWordsRemoverSuite extends SparkFunSuite {
+  def testStopWordsRemover(t: StopWordsRemover, dataset: DataFrame): Unit = {
+    t.transform(dataset)
+      .select("filtered", "expected")
+      .collect()
+      .foreach { case Row(tokens, wantedTokens) =>
+        assert(tokens === wantedTokens)
+    }
+  }
+}
+
+class StopWordsRemoverSuite extends SparkFunSuite with MLlibTestSparkContext {
+  import StopWordsRemoverSuite._
+
+  test("StopWordsRemover default") {
+    val remover = new StopWordsRemover()
+      .setInputCol("raw")
+      .setOutputCol("filtered")
+    val dataSet = sqlContext.createDataFrame(Seq(
+      (Seq("test", "test"), Seq("test", "test")),
+      (Seq("a", "b", "c", "d"), Seq("b", "c", "d")),
+      (Seq("a", "the", "an"), Seq()),
+      (Seq("A", "The", "AN"), Seq()),
+      (Seq(null), Seq(null)),
+      (Seq(), Seq())
+    )).toDF("raw", "expected")
+
+    testStopWordsRemover(remover, dataSet)
+  }
+
+  test("StopWordsRemover case sensitive") {
+    val remover = new StopWordsRemover()
+      .setInputCol("raw")
+      .setOutputCol("filtered")
+      .setCaseSensitive(true)
+    val dataSet = sqlContext.createDataFrame(Seq(
+      (Seq("A"), Seq("A")),
+      (Seq("The", "the"), Seq("The"))
+    )).toDF("raw", "expected")
+
+    testStopWordsRemover(remover, dataSet)
+  }
+
+  test("StopWordsRemover with additional words") {
+    val stopWords = StopWords.EnglishStopWords ++ Array("python", "scala")
+    val remover = new StopWordsRemover()
+      .setInputCol("raw")
+      .setOutputCol("filtered")
+      .setStopWords(stopWords)
+    val dataSet = sqlContext.createDataFrame(Seq(
+      (Seq("python", "scala", "a"), Seq()),
+      (Seq("Python", "Scala", "swift"), Seq("swift"))
+    )).toDF("raw", "expected")
+
+    testStopWordsRemover(remover, dataSet)
+  }
+}

From cf6c9ca32a89422e25007d333bc8714d9b0ae6d8 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Sat, 1 Aug 2015 08:32:29 -0700
Subject: [PATCH 0770/1454] [SPARK-8232] [SQL] Add sort_array support

This PR is based on #7581 , just fix the conflict.

Author: Cheng Hao <hao.cheng@intel.com>
Author: Davies Liu <davies@databricks.com>

Closes #7851 from davies/sort_array and squashes the following commits:

a80ef66 [Davies Liu] fix conflict
7cfda65 [Davies Liu] Merge branch 'master' of github.com:apache/spark into sort_array
664c960 [Cheng Hao] update the sort_array by using the ArrayData
276d2d5 [Cheng Hao] add empty line
0edab9c [Cheng Hao] Add asending/descending support for sort_array
80fc0f8 [Cheng Hao] Add type checking
a42b678 [Cheng Hao] Add sort_array support
---
 python/pyspark/sql/functions.py               | 20 +++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/collectionOperations.scala    | 81 ++++++++++++++++++-
 .../CollectionFunctionsSuite.scala            | 22 +++++
 .../org/apache/spark/sql/functions.scala      | 19 ++++-
 .../spark/sql/DataFrameFunctionsSuite.scala   | 51 +++++++++++-
 6 files changed, 187 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 89a2a5ceaa9bf..81dc7d832eaf9 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -51,6 +51,7 @@
     'sha1',
     'sha2',
     'size',
+    'sort_array',
     'sparkPartitionId',
     'struct',
     'udf',
@@ -570,8 +571,10 @@ def length(col):
 def format_number(col, d):
     """Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
        and returns the result as a string.
+
     :param col: the column name of the numeric value to be formatted
     :param d: the N decimal places
+
     >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
     [Row(v=u'5.0000')]
     """
@@ -954,6 +957,23 @@ def size(col):
     return Column(sc._jvm.functions.size(_to_java_column(col)))
 
 
+@since(1.5)
+def sort_array(col, asc=True):
+    """
+    Collection function: sorts the input array for the given column in ascending order.
+
+    :param col: name of column or expression
+
+    >>> df = sqlContext.createDataFrame([([2, 1, 3],),([1],),([],)], ['data'])
+    >>> df.select(sort_array(df.data).alias('r')).collect()
+    [Row(r=[1, 2, 3]), Row(r=[1]), Row(r=[])]
+    >>> df.select(sort_array(df.data, asc=False).alias('r')).collect()
+    [Row(r=[3, 2, 1]), Row(r=[1]), Row(r=[])]
+     """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.sort_array(_to_java_column(col), asc))
+
+
 @since
 @ignore_unicode_prefix
 def soundex(col):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index ee44cbcba68e7..6e144518bb009 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -233,6 +233,7 @@ object FunctionRegistry {
 
     // collection functions
     expression[Size]("size"),
+    expression[SortArray]("sort_array"),
 
     // misc functions
     expression[Crc32]("crc32"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 015bbb658266d..80b8da23e880b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -16,7 +16,10 @@
  */
 package org.apache.spark.sql.catalyst.expressions
 
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
+import java.util.Comparator
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 
 /**
@@ -35,3 +38,79 @@ case class Size(child: Expression) extends UnaryExpression with ExpectsInputType
     nullSafeCodeGen(ctx, ev, c => s"${ev.primitive} = ($c).numElements();")
   }
 }
+
+/**
+ * Sorts the input array in ascending / descending order according to the natural ordering of
+ * the array elements and returns it.
+ */
+case class SortArray(base: Expression, ascendingOrder: Expression)
+  extends BinaryExpression with ExpectsInputTypes with CodegenFallback {
+
+  def this(e: Expression) = this(e, Literal(true))
+
+  override def left: Expression = base
+  override def right: Expression = ascendingOrder
+  override def dataType: DataType = base.dataType
+  override def inputTypes: Seq[AbstractDataType] = Seq(ArrayType, BooleanType)
+
+  override def checkInputDataTypes(): TypeCheckResult = base.dataType match {
+    case _ @ ArrayType(n: AtomicType, _) => TypeCheckResult.TypeCheckSuccess
+    case _ @ ArrayType(n, _) => TypeCheckResult.TypeCheckFailure(
+                    s"Type $n is not the AtomicType, we can not perform the ordering operations")
+    case other =>
+      TypeCheckResult.TypeCheckFailure(s"ArrayType(AtomicType) is expected, but we got $other")
+  }
+
+  @transient
+  private lazy val lt = {
+    val ordering = base.dataType match {
+      case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
+    }
+
+    new Comparator[Any]() {
+      override def compare(o1: Any, o2: Any): Int = {
+        if (o1 == null && o2 == null) {
+          0
+        } else if (o1 == null) {
+          -1
+        } else if (o2 == null) {
+          1
+        } else {
+          ordering.compare(o1, o2)
+        }
+      }
+    }
+  }
+
+  @transient
+  private lazy val gt = {
+    val ordering = base.dataType match {
+      case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
+    }
+
+    new Comparator[Any]() {
+      override def compare(o1: Any, o2: Any): Int = {
+        if (o1 == null && o2 == null) {
+          0
+        } else if (o1 == null) {
+          1
+        } else if (o2 == null) {
+          -1
+        } else {
+          -ordering.compare(o1, o2)
+        }
+      }
+    }
+  }
+
+  override def nullSafeEval(array: Any, ascending: Any): Any = {
+    val elementType = base.dataType.asInstanceOf[ArrayType].elementType
+    val data = array.asInstanceOf[ArrayData].toArray[AnyRef](elementType)
+    java.util.Arrays.sort(
+      data,
+      if (ascending.asInstanceOf[Boolean]) lt else gt)
+    new GenericArrayData(data.asInstanceOf[Array[Any]])
+  }
+
+  override def prettyName: String = "sort_array"
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
index 28c41b57169f9..2c7e85c446ec6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -43,4 +43,26 @@ class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, MapType(StringType, StringType)), null)
     checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
   }
+
+  test("Sort Array") {
+    val a0 = Literal.create(Seq(2, 1, 3), ArrayType(IntegerType))
+    val a1 = Literal.create(Seq[Integer](), ArrayType(IntegerType))
+    val a2 = Literal.create(Seq("b", "a"), ArrayType(StringType))
+    val a3 = Literal.create(Seq("b", null, "a"), ArrayType(StringType))
+
+    checkEvaluation(new SortArray(a0), Seq(1, 2, 3))
+    checkEvaluation(new SortArray(a1), Seq[Integer]())
+    checkEvaluation(new SortArray(a2), Seq("a", "b"))
+    checkEvaluation(new SortArray(a3), Seq(null, "a", "b"))
+    checkEvaluation(SortArray(a0, Literal(true)), Seq(1, 2, 3))
+    checkEvaluation(SortArray(a1, Literal(true)), Seq[Integer]())
+    checkEvaluation(SortArray(a2, Literal(true)), Seq("a", "b"))
+    checkEvaluation(new SortArray(a3, Literal(true)), Seq(null, "a", "b"))
+    checkEvaluation(SortArray(a0, Literal(false)), Seq(3, 2, 1))
+    checkEvaluation(SortArray(a1, Literal(false)), Seq[Integer]())
+    checkEvaluation(SortArray(a2, Literal(false)), Seq("b", "a"))
+    checkEvaluation(new SortArray(a3, Literal(false)), Seq("b", "a", null))
+
+    checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 57bb00a7417af..3c9421f5cd14b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2223,19 +2223,30 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Returns length of array or map
+   * Returns length of array or map.
+   *
    * @group collection_funcs
    * @since 1.5.0
    */
-  def size(columnName: String): Column = size(Column(columnName))
+  def size(e: Column): Column = Size(e.expr)
 
   /**
-   * Returns length of array or map
+   * Sorts the input array for the given column in ascending order,
+   * according to the natural ordering of the array elements.
+   *
    * @group collection_funcs
    * @since 1.5.0
    */
-  def size(column: Column): Column = Size(column.expr)
+  def sort_array(e: Column): Column = sort_array(e, true)
 
+  /**
+   * Sorts the input array for the given column in ascending / descending order,
+   * according to the natural ordering of the array elements.
+   *
+   * @group collection_funcs
+   * @since 1.5.0
+   */
+  def sort_array(e: Column, asc: Boolean): Column = SortArray(e.expr, lit(asc).expr)
 
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 1baec5d37699d..46921d14256b9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -267,6 +267,53 @@ class DataFrameFunctionsSuite extends QueryTest {
     )
   }
 
+  test("sort_array function") {
+    val df = Seq(
+      (Array[Int](2, 1, 3), Array("b", "c", "a")),
+      (Array[Int](), Array[String]()),
+      (null, null)
+    ).toDF("a", "b")
+    checkAnswer(
+      df.select(sort_array($"a"), sort_array($"b")),
+      Seq(
+        Row(Seq(1, 2, 3), Seq("a", "b", "c")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+    checkAnswer(
+      df.select(sort_array($"a", false), sort_array($"b", false)),
+      Seq(
+        Row(Seq(3, 2, 1), Seq("c", "b", "a")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+    checkAnswer(
+      df.selectExpr("sort_array(a)", "sort_array(b)"),
+      Seq(
+        Row(Seq(1, 2, 3), Seq("a", "b", "c")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+    checkAnswer(
+      df.selectExpr("sort_array(a, true)", "sort_array(b, false)"),
+      Seq(
+        Row(Seq(1, 2, 3), Seq("c", "b", "a")),
+        Row(Seq[Int](), Seq[String]()),
+        Row(null, null))
+    )
+
+    val df2 = Seq((Array[Array[Int]](Array(2)), "x")).toDF("a", "b")
+    assert(intercept[AnalysisException] {
+      df2.selectExpr("sort_array(a)").collect()
+    }.getMessage().contains("Type ArrayType(IntegerType,false) is not the AtomicType, " +
+      "we can not perform the ordering operations"))
+
+    val df3 = Seq(("xxx", "x")).toDF("a", "b")
+    assert(intercept[AnalysisException] {
+      df3.selectExpr("sort_array(a)").collect()
+    }.getMessage().contains("ArrayType(AtomicType) is expected, but we got StringType"))
+  }
+
   test("array size function") {
     val df = Seq(
       (Array[Int](1, 2), "x"),
@@ -274,7 +321,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       (Array[Int](1, 2, 3), "z")
     ).toDF("a", "b")
     checkAnswer(
-      df.select(size("a")),
+      df.select(size($"a")),
       Seq(Row(2), Row(0), Row(3))
     )
     checkAnswer(
@@ -290,7 +337,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       (Map[Int, Int](1 -> 1, 2 -> 2, 3 -> 3), "z")
     ).toDF("a", "b")
     checkAnswer(
-      df.select(size("a")),
+      df.select(size($"a")),
       Seq(Row(2), Row(0), Row(3))
     )
     checkAnswer(

From c5166f7a69faeaa8a41a774c73c1ed4d4c2cf0ce Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Sat, 1 Aug 2015 08:48:46 -0700
Subject: [PATCH 0771/1454] [SPARK-8263] [SQL] substr/substring should also
 support binary type

This is based on #7641, thanks to zhichao-li

Closes #7641

Author: zhichao.li <zhichao.li@intel.com>
Author: Davies Liu <davies@databricks.com>

Closes #7848 from davies/substr and squashes the following commits:

461b709 [Davies Liu] remove bytearry from tests
b45377a [Davies Liu] Merge branch 'master' of github.com:apache/spark into substr
01d795e [zhichao.li] scala style
99aa130 [zhichao.li] add substring to dataframe
4f68bfe [zhichao.li] add binary type support for substring
---
 python/pyspark/sql/functions.py               | 18 ++++++-
 .../expressions/stringOperations.scala        | 51 +++++++++++++++++--
 .../expressions/StringExpressionsSuite.scala  | 15 +++++-
 .../org/apache/spark/sql/functions.scala      | 11 ++++
 .../spark/sql/StringFunctionsSuite.scala      | 10 ++++
 .../apache/spark/unsafe/types/UTF8String.java | 17 ++++---
 6 files changed, 109 insertions(+), 13 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 81dc7d832eaf9..96975f54ff695 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -64,7 +64,7 @@
     'year', 'quarter', 'month', 'hour', 'minute', 'second',
     'dayofmonth', 'dayofyear', 'weekofyear']
 
-__all__ += ['soundex']
+__all__ += ['soundex', 'substring', 'substring_index']
 
 
 def _create_function(name, doc=""):
@@ -923,6 +923,22 @@ def trunc(date, format):
     return Column(sc._jvm.functions.trunc(_to_java_column(date), format))
 
 
+@since(1.5)
+@ignore_unicode_prefix
+def substring(str, pos, len):
+    """
+    Substring starts at `pos` and is of length `len` when str is String type or
+    returns the slice of byte array that starts at `pos` in byte and is of length `len`
+    when str is Binary type
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(substring(df.s, 1, 2).alias('s')).collect()
+    [Row(s=u'ab')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.substring(_to_java_column(str), pos, len))
+
+
 @since(1.5)
 @ignore_unicode_prefix
 def substring_index(str, delim, count):
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 3ce5d6a9c7e02..4d78c55497a34 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.text.DecimalFormat
+import java.util.Arrays
 import java.util.Locale
 import java.util.regex.{MatchResult, Pattern}
 
@@ -679,6 +680,34 @@ case class StringSplit(str: Expression, pattern: Expression)
   override def prettyName: String = "split"
 }
 
+object Substring {
+  def subStringBinarySQL(bytes: Array[Byte], pos: Int, len: Int): Array[Byte] = {
+    if (pos > bytes.length) {
+      return Array[Byte]()
+    }
+
+    var start = if (pos > 0) {
+      pos - 1
+    } else if (pos < 0) {
+      bytes.length + pos
+    } else {
+      0
+    }
+
+    val end = if ((bytes.length - start) < len) {
+      bytes.length
+    } else {
+      start + len
+    }
+
+    start = Math.max(start, 0)  // underflow
+    if (start < end) {
+      Arrays.copyOfRange(bytes, start, end)
+    } else {
+      Array[Byte]()
+    }
+  }
+}
 /**
  * A function that takes a substring of its first argument starting at a given position.
  * Defined for String and Binary types.
@@ -690,18 +719,31 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
     this(str, pos, Literal(Integer.MAX_VALUE))
   }
 
-  override def dataType: DataType = StringType
+  override def dataType: DataType = str.dataType
 
-  override def inputTypes: Seq[DataType] = Seq(StringType, IntegerType, IntegerType)
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(StringType, BinaryType), IntegerType, IntegerType)
 
   override def children: Seq[Expression] = str :: pos :: len :: Nil
 
   override def nullSafeEval(string: Any, pos: Any, len: Any): Any = {
-    string.asInstanceOf[UTF8String].substringSQL(pos.asInstanceOf[Int], len.asInstanceOf[Int])
+    str.dataType match {
+      case StringType => string.asInstanceOf[UTF8String]
+        .substringSQL(pos.asInstanceOf[Int], len.asInstanceOf[Int])
+      case BinaryType => Substring.subStringBinarySQL(string.asInstanceOf[Array[Byte]],
+        pos.asInstanceOf[Int], len.asInstanceOf[Int])
+    }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    defineCodeGen(ctx, ev, (str, pos, len) => s"$str.substringSQL($pos, $len)")
+
+    val cls = classOf[Substring].getName
+    defineCodeGen(ctx, ev, (string, pos, len) => {
+      str.dataType match {
+        case StringType => s"$string.substringSQL($pos, $len)"
+        case BinaryType => s"$cls.subStringBinarySQL($string, $pos, $len)"
+      }
+    })
   }
 }
 
@@ -1161,4 +1203,3 @@ case class FormatNumber(x: Expression, d: Expression)
 
   override def prettyName: String = "format_number"
 }
-
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index ad87ab36fd645..89c1e33420f09 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 
 
 class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -186,6 +185,20 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(s.substr(0), "example", row)
     checkEvaluation(s.substring(0, 2), "ex", row)
     checkEvaluation(s.substring(0), "example", row)
+
+    val bytes = Array[Byte](1, 2, 3, 4)
+    checkEvaluation(Substring(bytes, 0, 2), Array[Byte](1, 2))
+    checkEvaluation(Substring(bytes, 1, 2), Array[Byte](1, 2))
+    checkEvaluation(Substring(bytes, 2, 2), Array[Byte](2, 3))
+    checkEvaluation(Substring(bytes, 3, 2), Array[Byte](3, 4))
+    checkEvaluation(Substring(bytes, 4, 2), Array[Byte](4))
+    checkEvaluation(Substring(bytes, 8, 2), Array[Byte]())
+    checkEvaluation(Substring(bytes, -1, 2), Array[Byte](4))
+    checkEvaluation(Substring(bytes, -2, 2), Array[Byte](3, 4))
+    checkEvaluation(Substring(bytes, -3, 2), Array[Byte](2, 3))
+    checkEvaluation(Substring(bytes, -4, 2), Array[Byte](1, 2))
+    checkEvaluation(Substring(bytes, -5, 2), Array[Byte](1))
+    checkEvaluation(Substring(bytes, -8, 2), Array[Byte]())
   }
 
   test("string substring_index function") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 3c9421f5cd14b..babfe2187999c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1725,6 +1725,17 @@ object functions {
    */
   def format_number(x: Column, d: Int): Column = FormatNumber(x.expr, lit(d).expr)
 
+  /**
+   * Substring starts at `pos` and is of length `len` when str is String type or
+   * returns the slice of byte array that starts at `pos` in byte and is of length `len`
+   * when str is Binary type
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def substring(str: Column, pos: Int, len: Int): Column =
+    Substring(str.expr, lit(pos).expr, lit(len).expr)
+
   /**
    * Computes the Levenshtein distance of the two given string columns.
    * @group string_funcs
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 628da95298aa9..f40233db0aebd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -103,6 +103,16 @@ class StringFunctionsSuite extends QueryTest {
       Row("AQIDBA==", bytes))
   }
 
+  test("string / binary substring function") {
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    val df = Seq(("1世3", Array[Byte](1, 2, 3, 4))).toDF("a", "b")
+    checkAnswer(df.select(substring($"a", 1, 2)), Row("1世"))
+    checkAnswer(df.select(substring($"b", 2, 2)), Row(Array[Byte](2,3)))
+    checkAnswer(df.selectExpr("substring(a, 1, 2)"), Row("1世"))
+    // scalastyle:on
+  }
+
   test("string encode/decode function") {
     val bytes = Array[Byte](-27, -92, -89, -27, -115, -125, -28, -72, -106, -25, -107, -116)
     // scalastyle:off
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index f6dafe94c617a..208503d2fd5dc 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -198,7 +198,7 @@ public byte[] getBytes() {
    */
   public UTF8String substring(final int start, final int until) {
     if (until <= start || start >= numBytes) {
-      return UTF8String.EMPTY_UTF8;
+      return EMPTY_UTF8;
     }
 
     int i = 0;
@@ -214,9 +214,13 @@ public UTF8String substring(final int start, final int until) {
       c += 1;
     }
 
-    byte[] bytes = new byte[i - j];
-    copyMemory(base, offset + j, bytes, BYTE_ARRAY_OFFSET, i - j);
-    return fromBytes(bytes);
+    if (i > j) {
+      byte[] bytes = new byte[i - j];
+      copyMemory(base, offset + j, bytes, BYTE_ARRAY_OFFSET, i - j);
+      return fromBytes(bytes);
+    } else {
+      return EMPTY_UTF8;
+    }
   }
 
   public UTF8String substringSQL(int pos, int length) {
@@ -226,8 +230,9 @@ public UTF8String substringSQL(int pos, int length) {
     // refers to element i-1 in the sequence. If a start index i is less than 0, it refers
     // to the -ith element before the end of the sequence. If a start index i is 0, it
     // refers to the first element.
-    int start = (pos > 0) ? pos -1 : ((pos < 0) ? numChars() + pos : 0);
-    int end = (length == Integer.MAX_VALUE) ? Integer.MAX_VALUE : start + length;
+    int len = numChars();
+    int start = (pos > 0) ? pos -1 : ((pos < 0) ? len + pos : 0);
+    int end = (length == Integer.MAX_VALUE) ? len : start + length;
     return substring(start, end);
   }
 

From 6688ba6e68e342201b81ea09cc2c6ba216f90f3e Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Sat, 1 Aug 2015 11:57:14 -0700
Subject: [PATCH 0772/1454] [SPARK-4751] Dynamic allocation in standalone mode

Dynamic allocation is a feature that allows a Spark application to scale the number of executors up and down dynamically based on the workload. Support was first introduced in YARN since 1.2, and then extended to Mesos coarse-grained mode recently. Today, it is finally supported in standalone mode as well!

I tested this locally and it works as expected. This is WIP because unit tests are coming.

Author: Andrew Or <andrew@databricks.com>

Closes #7532 from andrewor14/standalone-da and squashes the following commits:

b3c1736 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
879e928 [Andrew Or] Add end-to-end tests for standalone dynamic allocation
accc8f6 [Andrew Or] Address comments
ee686a8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
c0a2c02 [Andrew Or] Fix build after merge conflict
24149eb [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
2e762d6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
6832bd7 [Andrew Or] Add tests for scheduling with executor limit
a82e907 [Andrew Or] Fix comments
0a8be79 [Andrew Or] Simplify logic by removing the worker blacklist
b7742af [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
2eb5f3f [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
1334e9a [Andrew Or] Fix MiMa
32abe44 [Andrew Or] Fix style
58cb06f [Andrew Or] Privatize worker blacklist for cleanliness
42ac215 [Andrew Or] Clean up comments and rewrite code for readability
49702d1 [Andrew Or] Clean up shuffle files after application exits
80047aa [Andrew Or] First working implementation
---
 .../org/apache/spark/HeartbeatReceiver.scala  |  16 +-
 .../scala/org/apache/spark/SparkContext.scala |  27 +-
 .../apache/spark/deploy/DeployMessage.scala   |   4 +
 .../spark/deploy/ExternalShuffleService.scala |   5 +
 .../spark/deploy/client/AppClient.scala       |  45 +++
 .../spark/deploy/master/ApplicationInfo.scala |  13 +-
 .../apache/spark/deploy/master/Master.scala   | 144 ++++++-
 .../apache/spark/deploy/worker/Worker.scala   |  13 +-
 .../CoarseGrainedSchedulerBackend.scala       |   5 +-
 .../cluster/SparkDeploySchedulerBackend.scala |  28 ++
 .../StandaloneDynamicAllocationSuite.scala    | 363 ++++++++++++++++++
 .../spark/deploy/master/MasterSuite.scala     | 256 ++++++------
 project/MimaExcludes.scala                    |   4 +
 13 files changed, 753 insertions(+), 170 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
index 43dd4a170731d..ee60d697d8799 100644
--- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
+++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala
@@ -177,16 +177,14 @@ private[spark] class HeartbeatReceiver(sc: SparkContext, clock: Clock)
           s"${now - lastSeenMs} ms exceeds timeout $executorTimeoutMs ms")
         scheduler.executorLost(executorId, SlaveLost("Executor heartbeat " +
           s"timed out after ${now - lastSeenMs} ms"))
-        if (sc.supportDynamicAllocation) {
           // Asynchronously kill the executor to avoid blocking the current thread
-          killExecutorThread.submit(new Runnable {
-            override def run(): Unit = Utils.tryLogNonFatalError {
-              // Note: we want to get an executor back after expiring this one,
-              // so do not simply call `sc.killExecutor` here (SPARK-8119)
-              sc.killAndReplaceExecutor(executorId)
-            }
-          })
-        }
+        killExecutorThread.submit(new Runnable {
+          override def run(): Unit = Utils.tryLogNonFatalError {
+            // Note: we want to get an executor back after expiring this one,
+            // so do not simply call `sc.killExecutor` here (SPARK-8119)
+            sc.killAndReplaceExecutor(executorId)
+          }
+        })
         executorLastSeen.remove(executorId)
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 2d8aa25d81daa..a1c66ef4fc5ea 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -531,8 +531,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     val dynamicAllocationEnabled = _conf.getBoolean("spark.dynamicAllocation.enabled", false)
     _executorAllocationManager =
       if (dynamicAllocationEnabled) {
-        assert(supportDynamicAllocation,
-          "Dynamic allocation of executors is currently only supported in YARN and Mesos mode")
         Some(new ExecutorAllocationManager(this, listenerBus, _conf))
       } else {
         None
@@ -1361,17 +1359,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     postEnvironmentUpdate()
   }
 
-  /**
-   * Return whether dynamically adjusting the amount of resources allocated to
-   * this application is supported. This is currently only available for YARN
-   * and Mesos coarse-grained mode.
-   */
-  private[spark] def supportDynamicAllocation: Boolean = {
-    (master.contains("yarn")
-      || master.contains("mesos")
-      || _conf.getBoolean("spark.dynamicAllocation.testing", false))
-  }
-
   /**
    * :: DeveloperApi ::
    * Register a listener to receive up-calls from events that happen during execution.
@@ -1400,8 +1387,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       localityAwareTasks: Int,
       hostToLocalTaskCount: scala.collection.immutable.Map[String, Int]
     ): Boolean = {
-    assert(supportDynamicAllocation,
-      "Requesting executors is currently only supported in YARN and Mesos modes")
     schedulerBackend match {
       case b: CoarseGrainedSchedulerBackend =>
         b.requestTotalExecutors(numExecutors, localityAwareTasks, hostToLocalTaskCount)
@@ -1414,12 +1399,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   /**
    * :: DeveloperApi ::
    * Request an additional number of executors from the cluster manager.
-   * This is currently only supported in YARN mode. Return whether the request is received.
+   * @return whether the request is received.
    */
   @DeveloperApi
   override def requestExecutors(numAdditionalExecutors: Int): Boolean = {
-    assert(supportDynamicAllocation,
-      "Requesting executors is currently only supported in YARN and Mesos modes")
     schedulerBackend match {
       case b: CoarseGrainedSchedulerBackend =>
         b.requestExecutors(numAdditionalExecutors)
@@ -1438,12 +1421,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * through this method with new ones, it should follow up explicitly with a call to
    * {{SparkContext#requestExecutors}}.
    *
-   * This is currently only supported in YARN mode. Return whether the request is received.
+   * @return whether the request is received.
    */
   @DeveloperApi
   override def killExecutors(executorIds: Seq[String]): Boolean = {
-    assert(supportDynamicAllocation,
-      "Killing executors is currently only supported in YARN and Mesos modes")
     schedulerBackend match {
       case b: CoarseGrainedSchedulerBackend =>
         b.killExecutors(executorIds)
@@ -1462,7 +1443,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * through this method with a new one, it should follow up explicitly with a call to
    * {{SparkContext#requestExecutors}}.
    *
-   * This is currently only supported in YARN mode. Return whether the request is received.
+   * @return whether the request is received.
    */
   @DeveloperApi
   override def killExecutor(executorId: String): Boolean = super.killExecutor(executorId)
@@ -1479,7 +1460,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * can steal the window of opportunity and acquire this application's resources in the
    * mean time.
    *
-   * This is currently only supported in YARN mode. Return whether the request is received.
+   * @return whether the request is received.
    */
   private[spark] def killAndReplaceExecutor(executorId: String): Boolean = {
     schedulerBackend match {
diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala
index 12727de9b4cf3..d8084a57658ad 100644
--- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala
@@ -107,6 +107,10 @@ private[deploy] object DeployMessages {
 
   case class MasterChangeAcknowledged(appId: String)
 
+  case class RequestExecutors(appId: String, requestedTotal: Int)
+
+  case class KillExecutors(appId: String, executorIds: Seq[String])
+
   // Master to AppClient
 
   case class RegisteredApplication(appId: String, master: RpcEndpointRef) extends DeployMessage
diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
index 09973a0a2c998..4089c3e771fa8 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
@@ -70,6 +70,11 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana
     server = transportContext.createServer(port, bootstraps)
   }
 
+  /** Clean up all shuffle files associated with an application that has exited. */
+  def applicationRemoved(appId: String): Unit = {
+    blockHandler.applicationRemoved(appId, true /* cleanupLocalDirs */)
+  }
+
   def stop() {
     if (server != null) {
       server.close()
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index a659abf70395d..7576a2985ee7b 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -197,6 +197,22 @@ private[spark] class AppClient(
         sendToMaster(UnregisterApplication(appId))
         context.reply(true)
         stop()
+
+      case r: RequestExecutors =>
+        master match {
+          case Some(m) => context.reply(m.askWithRetry[Boolean](r))
+          case None =>
+            logWarning("Attempted to request executors before registering with Master.")
+            context.reply(false)
+        }
+
+      case k: KillExecutors =>
+        master match {
+          case Some(m) => context.reply(m.askWithRetry[Boolean](k))
+          case None =>
+            logWarning("Attempted to kill executors before registering with Master.")
+            context.reply(false)
+        }
     }
 
     override def onDisconnected(address: RpcAddress): Unit = {
@@ -257,4 +273,33 @@ private[spark] class AppClient(
       endpoint = null
     }
   }
+
+  /**
+   * Request executors from the Master by specifying the total number desired,
+   * including existing pending and running executors.
+   *
+   * @return whether the request is acknowledged.
+   */
+  def requestTotalExecutors(requestedTotal: Int): Boolean = {
+    if (endpoint != null && appId != null) {
+      endpoint.askWithRetry[Boolean](RequestExecutors(appId, requestedTotal))
+    } else {
+      logWarning("Attempted to request executors before driver fully initialized.")
+      false
+    }
+  }
+
+  /**
+   * Kill the given list of executors through the Master.
+   * @return whether the kill request is acknowledged.
+   */
+  def killExecutors(executorIds: Seq[String]): Boolean = {
+    if (endpoint != null && appId != null) {
+      endpoint.askWithRetry[Boolean](KillExecutors(appId, executorIds))
+    } else {
+      logWarning("Attempted to kill executors before driver fully initialized.")
+      false
+    }
+  }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
index aa54ed9360f36..b40d20f9f7868 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
@@ -22,7 +22,6 @@ import java.util.Date
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.deploy.ApplicationDescription
 import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.util.Utils
@@ -43,6 +42,11 @@ private[spark] class ApplicationInfo(
   @transient var endTime: Long = _
   @transient var appSource: ApplicationSource = _
 
+  // A cap on the number of executors this application can have at any given time.
+  // By default, this is infinite. Only after the first allocation request is issued by the
+  // application will this be set to a finite value. This is used for dynamic allocation.
+  @transient private[master] var executorLimit: Int = _
+
   @transient private var nextExecutorId: Int = _
 
   init()
@@ -60,6 +64,7 @@ private[spark] class ApplicationInfo(
     appSource = new ApplicationSource(this)
     nextExecutorId = 0
     removedExecutors = new ArrayBuffer[ExecutorDesc]
+    executorLimit = Integer.MAX_VALUE
   }
 
   private def newExecutorId(useID: Option[Int] = None): Int = {
@@ -116,6 +121,12 @@ private[spark] class ApplicationInfo(
     state != ApplicationState.WAITING && state != ApplicationState.RUNNING
   }
 
+  /**
+   * Return the limit on the number of executors this application can have.
+   * For testing only.
+   */
+  private[deploy] def getExecutorLimit: Int = executorLimit
+
   def duration: Long = {
     if (endTime != -1) {
       endTime - startTime
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 51b3f0dead73e..e38e437fe1c5a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -45,7 +45,7 @@ import org.apache.spark.serializer.{JavaSerializer, Serializer}
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{ThreadUtils, SignalLogger, Utils}
 
-private[master] class Master(
+private[deploy] class Master(
     override val rpcEnv: RpcEnv,
     address: RpcAddress,
     webUiPort: Int,
@@ -468,6 +468,13 @@ private[master] class Master(
     case BoundPortsRequest => {
       context.reply(BoundPortsResponse(address.port, webUi.boundPort, restServerBoundPort))
     }
+
+    case RequestExecutors(appId, requestedTotal) =>
+      context.reply(handleRequestExecutors(appId, requestedTotal))
+
+    case KillExecutors(appId, executorIds) =>
+      val formattedExecutorIds = formatExecutorIds(executorIds)
+      context.reply(handleKillExecutors(appId, formattedExecutorIds))
   }
 
   override def onDisconnected(address: RpcAddress): Unit = {
@@ -563,32 +570,49 @@ private[master] class Master(
       app: ApplicationInfo,
       usableWorkers: Array[WorkerInfo],
       spreadOutApps: Boolean): Array[Int] = {
-    // If the number of cores per executor is not specified, then we can just schedule
-    // 1 core at a time since we expect a single executor to be launched on each worker
-    val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(1)
+    val coresPerExecutor = app.desc.coresPerExecutor
+    val minCoresPerExecutor = coresPerExecutor.getOrElse(1)
+    val oneExecutorPerWorker = coresPerExecutor.isEmpty
     val memoryPerExecutor = app.desc.memoryPerExecutorMB
     val numUsable = usableWorkers.length
     val assignedCores = new Array[Int](numUsable) // Number of cores to give to each worker
-    val assignedMemory = new Array[Int](numUsable) // Amount of memory to give to each worker
+    val assignedExecutors = new Array[Int](numUsable) // Number of new executors on each worker
     var coresToAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
-    var freeWorkers = (0 until numUsable).toIndexedSeq
 
+    /** Return whether the specified worker can launch an executor for this app. */
     def canLaunchExecutor(pos: Int): Boolean = {
-      usableWorkers(pos).coresFree - assignedCores(pos) >= coresPerExecutor &&
-      usableWorkers(pos).memoryFree - assignedMemory(pos) >= memoryPerExecutor
+      // If we allow multiple executors per worker, then we can always launch new executors.
+      // Otherwise, we may have already started assigning cores to the executor on this worker.
+      val launchingNewExecutor = !oneExecutorPerWorker || assignedExecutors(pos) == 0
+      val underLimit =
+        if (launchingNewExecutor) {
+          assignedExecutors.sum + app.executors.size < app.executorLimit
+        } else {
+          true
+        }
+      val assignedMemory = assignedExecutors(pos) * memoryPerExecutor
+      usableWorkers(pos).memoryFree - assignedMemory >= memoryPerExecutor &&
+      usableWorkers(pos).coresFree - assignedCores(pos) >= minCoresPerExecutor &&
+      coresToAssign >= minCoresPerExecutor &&
+      underLimit
     }
 
-    while (coresToAssign >= coresPerExecutor && freeWorkers.nonEmpty) {
-      freeWorkers = freeWorkers.filter(canLaunchExecutor)
+    // Keep launching executors until no more workers can accommodate any
+    // more executors, or if we have reached this application's limits
+    var freeWorkers = (0 until numUsable).filter(canLaunchExecutor)
+    while (freeWorkers.nonEmpty) {
       freeWorkers.foreach { pos =>
         var keepScheduling = true
-        while (keepScheduling && canLaunchExecutor(pos) && coresToAssign >= coresPerExecutor) {
-          coresToAssign -= coresPerExecutor
-          assignedCores(pos) += coresPerExecutor
-          // If cores per executor is not set, we are assigning 1 core at a time
-          // without actually meaning to launch 1 executor for each core assigned
-          if (app.desc.coresPerExecutor.isDefined) {
-            assignedMemory(pos) += memoryPerExecutor
+        while (keepScheduling && canLaunchExecutor(pos)) {
+          coresToAssign -= minCoresPerExecutor
+          assignedCores(pos) += minCoresPerExecutor
+
+          // If we are launching one executor per worker, then every iteration assigns 1 core
+          // to the executor. Otherwise, every iteration assigns cores to a new executor.
+          if (oneExecutorPerWorker) {
+            assignedExecutors(pos) = 1
+          } else {
+            assignedExecutors(pos) += 1
           }
 
           // Spreading out an application means spreading out its executors across as
@@ -600,6 +624,7 @@ private[master] class Master(
           }
         }
       }
+      freeWorkers = freeWorkers.filter(canLaunchExecutor)
     }
     assignedCores
   }
@@ -785,9 +810,7 @@ private[master] class Master(
       rebuildSparkUI(app)
 
       for (exec <- app.executors.values) {
-        exec.worker.removeExecutor(exec)
-        exec.worker.endpoint.send(KillExecutor(masterUrl, exec.application.id, exec.id))
-        exec.state = ExecutorState.KILLED
+        killExecutor(exec)
       }
       app.markFinished(state)
       if (state != ApplicationState.FINISHED) {
@@ -803,6 +826,87 @@ private[master] class Master(
     }
   }
 
+  /**
+   * Handle a request to set the target number of executors for this application.
+   *
+   * If the executor limit is adjusted upwards, new executors will be launched provided
+   * that there are workers with sufficient resources. If it is adjusted downwards, however,
+   * we do not kill existing executors until we explicitly receive a kill request.
+   *
+   * @return whether the application has previously registered with this Master.
+   */
+  private def handleRequestExecutors(appId: String, requestedTotal: Int): Boolean = {
+    idToApp.get(appId) match {
+      case Some(appInfo) =>
+        logInfo(s"Application $appId requested to set total executors to $requestedTotal.")
+        appInfo.executorLimit = requestedTotal
+        schedule()
+        true
+      case None =>
+        logWarning(s"Unknown application $appId requested $requestedTotal total executors.")
+        false
+    }
+  }
+
+  /**
+   * Handle a kill request from the given application.
+   *
+   * This method assumes the executor limit has already been adjusted downwards through
+   * a separate [[RequestExecutors]] message, such that we do not launch new executors
+   * immediately after the old ones are removed.
+   *
+   * @return whether the application has previously registered with this Master.
+   */
+  private def handleKillExecutors(appId: String, executorIds: Seq[Int]): Boolean = {
+    idToApp.get(appId) match {
+      case Some(appInfo) =>
+        logInfo(s"Application $appId requests to kill executors: " + executorIds.mkString(", "))
+        val (known, unknown) = executorIds.partition(appInfo.executors.contains)
+        known.foreach { executorId =>
+          val desc = appInfo.executors(executorId)
+          appInfo.removeExecutor(desc)
+          killExecutor(desc)
+        }
+        if (unknown.nonEmpty) {
+          logWarning(s"Application $appId attempted to kill non-existent executors: "
+            + unknown.mkString(", "))
+        }
+        schedule()
+        true
+      case None =>
+        logWarning(s"Unregistered application $appId requested us to kill executors!")
+        false
+    }
+  }
+
+  /**
+   * Cast the given executor IDs to integers and filter out the ones that fail.
+   *
+   * All executors IDs should be integers since we launched these executors. However,
+   * the kill interface on the driver side accepts arbitrary strings, so we need to
+   * handle non-integer executor IDs just to be safe.
+   */
+  private def formatExecutorIds(executorIds: Seq[String]): Seq[Int] = {
+    executorIds.flatMap { executorId =>
+      try {
+        Some(executorId.toInt)
+      } catch {
+        case e: NumberFormatException =>
+          logError(s"Encountered executor with a non-integer ID: $executorId. Ignoring")
+          None
+      }
+    }
+  }
+
+  /**
+   * Ask the worker on which the specified executor is launched to kill the executor.
+   */
+  private def killExecutor(exec: ExecutorDesc): Unit = {
+    exec.worker.removeExecutor(exec)
+    exec.worker.endpoint.send(KillExecutor(masterUrl, exec.application.id, exec.id))
+    exec.state = ExecutorState.KILLED
+  }
+
   /**
    * Rebuild a new SparkUI from the given application's event logs.
    * Return the UI if successful, else None
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 0276c24f85368..c82a7ccab54dc 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -40,7 +40,7 @@ import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.rpc._
 import org.apache.spark.util.{ThreadUtils, SignalLogger, Utils}
 
-private[worker] class Worker(
+private[deploy] class Worker(
     override val rpcEnv: RpcEnv,
     webUiPort: Int,
     cores: Int,
@@ -553,6 +553,7 @@ private[worker] class Worker(
           Utils.deleteRecursively(new File(dir))
         }
       }
+      shuffleService.applicationRemoved(id)
     }
   }
 
@@ -660,6 +661,9 @@ private[worker] class Worker(
 }
 
 private[deploy] object Worker extends Logging {
+  val SYSTEM_NAME = "sparkWorker"
+  val ENDPOINT_NAME = "Worker"
+
   def main(argStrings: Array[String]) {
     SignalLogger.register(log)
     val conf = new SparkConf
@@ -681,13 +685,12 @@ private[deploy] object Worker extends Logging {
       conf: SparkConf = new SparkConf): RpcEnv = {
 
     // The LocalSparkCluster runs multiple local sparkWorkerX actor systems
-    val systemName = "sparkWorker" + workerNumber.map(_.toString).getOrElse("")
-    val actorName = "Worker"
+    val systemName = SYSTEM_NAME + workerNumber.map(_.toString).getOrElse("")
     val securityMgr = new SecurityManager(conf)
     val rpcEnv = RpcEnv.create(systemName, host, port, conf, securityMgr)
     val masterAddresses = masterUrls.map(RpcAddress.fromSparkURL(_))
-    rpcEnv.setupEndpoint(actorName, new Worker(rpcEnv, webUiPort, cores, memory, masterAddresses,
-      systemName, actorName, workDir, conf, securityMgr))
+    rpcEnv.setupEndpoint(ENDPOINT_NAME, new Worker(rpcEnv, webUiPort, cores, memory,
+      masterAddresses, systemName, ENDPOINT_NAME, workDir, conf, securityMgr))
     rpcEnv
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index bd89160af4ffa..6acf8a9a5e9b4 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -134,7 +134,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
           context.reply(RegisterExecutorFailed("Duplicate executor ID: " + executorId))
         } else {
           logInfo("Registered executor: " + executorRef + " with ID " + executorId)
-          context.reply(RegisteredExecutor)
           addressToExecutorId(executorRef.address) = executorId
           totalCoreCount.addAndGet(cores)
           totalRegisteredExecutors.addAndGet(1)
@@ -149,6 +148,8 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
               logDebug(s"Decremented number of pending executors ($numPendingExecutors left)")
             }
           }
+          // Note: some tests expect the reply to come after we put the executor in the map
+          context.reply(RegisteredExecutor)
           listenerBus.post(
             SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data))
           makeOffers()
@@ -435,7 +436,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
 
   /**
    * Kill the given list of executors through the cluster manager.
-   * Return whether the kill request is acknowledged.
+   * @return whether the kill request is acknowledged.
    */
   protected def doKillExecutors(executorIds: Seq[String]): Boolean = false
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 687ae9620460f..bbe51b4a09a22 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -152,6 +152,34 @@ private[spark] class SparkDeploySchedulerBackend(
       super.applicationId
     }
 
+  /**
+   * Request executors from the Master by specifying the total number desired,
+   * including existing pending and running executors.
+   *
+   * @return whether the request is acknowledged.
+   */
+  protected override def doRequestTotalExecutors(requestedTotal: Int): Boolean = {
+    Option(client) match {
+      case Some(c) => c.requestTotalExecutors(requestedTotal)
+      case None =>
+        logWarning("Attempted to request executors before driver fully initialized.")
+        false
+    }
+  }
+
+  /**
+   * Kill the given list of executors through the Master.
+   * @return whether the kill request is acknowledged.
+   */
+  protected override def doKillExecutors(executorIds: Seq[String]): Boolean = {
+    Option(client) match {
+      case Some(c) => c.killExecutors(executorIds)
+      case None =>
+        logWarning("Attempted to kill executors before driver fully initialized.")
+        false
+    }
+  }
+
   private def waitForRegistration() = {
     registrationBarrier.acquire()
   }
diff --git a/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
new file mode 100644
index 0000000000000..08c41a897a861
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
@@ -0,0 +1,363 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import org.mockito.Mockito.{mock, when}
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark._
+import org.apache.spark.deploy.master.Master
+import org.apache.spark.deploy.worker.Worker
+import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcEnv}
+import org.apache.spark.scheduler.cluster._
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor
+
+/**
+ * End-to-end tests for dynamic allocation in standalone mode.
+ */
+class StandaloneDynamicAllocationSuite
+  extends SparkFunSuite
+  with LocalSparkContext
+  with BeforeAndAfterAll {
+
+  private val numWorkers = 2
+  private val conf = new SparkConf()
+  private val securityManager = new SecurityManager(conf)
+
+  private var masterRpcEnv: RpcEnv = null
+  private var workerRpcEnvs: Seq[RpcEnv] = null
+  private var master: Master = null
+  private var workers: Seq[Worker] = null
+
+  /**
+   * Start the local cluster.
+   * Note: local-cluster mode is insufficient because we want a reference to the Master.
+   */
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    masterRpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 0, conf, securityManager)
+    workerRpcEnvs = (0 until numWorkers).map { i =>
+      RpcEnv.create(Worker.SYSTEM_NAME + i, "localhost", 0, conf, securityManager)
+    }
+    master = makeMaster()
+    workers = makeWorkers(10, 2048)
+  }
+
+  override def afterAll(): Unit = {
+    masterRpcEnv.shutdown()
+    workerRpcEnvs.foreach(_.shutdown())
+    master.stop()
+    workers.foreach(_.stop())
+    masterRpcEnv = null
+    workerRpcEnvs = null
+    master = null
+    workers = null
+    super.afterAll()
+  }
+
+  test("dynamic allocation default behavior") {
+    sc = new SparkContext(appConf)
+    val appId = sc.applicationId
+    assert(master.apps.size === 1)
+    assert(master.apps.head.id === appId)
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === Int.MaxValue)
+    // kill all executors
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request 1
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.getExecutorLimit === 1)
+    // request 1 more
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === 2)
+    // request 1 more; this one won't go through
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === 3)
+    // kill all existing executors; we should end up with 3 - 2 = 1 executor
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.getExecutorLimit === 1)
+    // kill all executors again; this time we'll have 1 - 1 = 0 executors left
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request many more; this increases the limit well beyond the cluster capacity
+    assert(sc.requestExecutors(1000))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === 1000)
+  }
+
+  test("dynamic allocation with max cores <= cores per worker") {
+    sc = new SparkContext(appConf.set("spark.cores.max", "8"))
+    val appId = sc.applicationId
+    assert(master.apps.size === 1)
+    assert(master.apps.head.id === appId)
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.executors.values.map(_.cores).toArray === Array(4, 4))
+    assert(master.apps.head.getExecutorLimit === Int.MaxValue)
+    // kill all executors
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request 1
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.executors.values.head.cores === 8)
+    assert(master.apps.head.getExecutorLimit === 1)
+    // request 1 more; this one won't go through because we're already at max cores.
+    // This highlights a limitation of using dynamic allocation with max cores WITHOUT
+    // setting cores per executor: once an application scales down and then scales back
+    // up, its executors may not be spread out anymore!
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.getExecutorLimit === 2)
+    // request 1 more; this one also won't go through for the same reason
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.getExecutorLimit === 3)
+    // kill all existing executors; we should end up with 3 - 1 = 2 executor
+    // Note: we scheduled these executors together, so their cores should be evenly distributed
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.executors.values.map(_.cores).toArray === Array(4, 4))
+    assert(master.apps.head.getExecutorLimit === 2)
+    // kill all executors again; this time we'll have 1 - 1 = 0 executors left
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request many more; this increases the limit well beyond the cluster capacity
+    assert(sc.requestExecutors(1000))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.executors.values.map(_.cores).toArray === Array(4, 4))
+    assert(master.apps.head.getExecutorLimit === 1000)
+  }
+
+  test("dynamic allocation with max cores > cores per worker") {
+    sc = new SparkContext(appConf.set("spark.cores.max", "16"))
+    val appId = sc.applicationId
+    assert(master.apps.size === 1)
+    assert(master.apps.head.id === appId)
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.executors.values.map(_.cores).toArray === Array(8, 8))
+    assert(master.apps.head.getExecutorLimit === Int.MaxValue)
+    // kill all executors
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request 1
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.executors.values.head.cores === 10)
+    assert(master.apps.head.getExecutorLimit === 1)
+    // request 1 more
+    // Note: the cores are not evenly distributed because we scheduled these executors 1 by 1
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.executors.values.map(_.cores).toSet === Set(10, 6))
+    assert(master.apps.head.getExecutorLimit === 2)
+    // request 1 more; this one won't go through
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === 3)
+    // kill all existing executors; we should end up with 3 - 2 = 1 executor
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.executors.values.head.cores === 10)
+    assert(master.apps.head.getExecutorLimit === 1)
+    // kill all executors again; this time we'll have 1 - 1 = 0 executors left
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request many more; this increases the limit well beyond the cluster capacity
+    assert(sc.requestExecutors(1000))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.executors.values.map(_.cores).toArray === Array(8, 8))
+    assert(master.apps.head.getExecutorLimit === 1000)
+  }
+
+  test("dynamic allocation with cores per executor") {
+    sc = new SparkContext(appConf.set("spark.executor.cores", "2"))
+    val appId = sc.applicationId
+    assert(master.apps.size === 1)
+    assert(master.apps.head.id === appId)
+    assert(master.apps.head.executors.size === 10) // 20 cores total
+    assert(master.apps.head.getExecutorLimit === Int.MaxValue)
+    // kill all executors
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request 1
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.getExecutorLimit === 1)
+    // request 3 more
+    assert(sc.requestExecutors(3))
+    assert(master.apps.head.executors.size === 4)
+    assert(master.apps.head.getExecutorLimit === 4)
+    // request 10 more; only 6 will go through
+    assert(sc.requestExecutors(10))
+    assert(master.apps.head.executors.size === 10)
+    assert(master.apps.head.getExecutorLimit === 14)
+    // kill 2 executors; we should get 2 back immediately
+    assert(killNExecutors(sc, 2))
+    assert(master.apps.head.executors.size === 10)
+    assert(master.apps.head.getExecutorLimit === 12)
+    // kill 4 executors; we should end up with 12 - 4 = 8 executors
+    assert(killNExecutors(sc, 4))
+    assert(master.apps.head.executors.size === 8)
+    assert(master.apps.head.getExecutorLimit === 8)
+    // kill all executors; this time we'll have 8 - 8 = 0 executors left
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request many more; this increases the limit well beyond the cluster capacity
+    assert(sc.requestExecutors(1000))
+    assert(master.apps.head.executors.size === 10)
+    assert(master.apps.head.getExecutorLimit === 1000)
+  }
+
+  test("dynamic allocation with cores per executor AND max cores") {
+    sc = new SparkContext(appConf
+      .set("spark.executor.cores", "2")
+      .set("spark.cores.max", "8"))
+    val appId = sc.applicationId
+    assert(master.apps.size === 1)
+    assert(master.apps.head.id === appId)
+    assert(master.apps.head.executors.size === 4) // 8 cores total
+    assert(master.apps.head.getExecutorLimit === Int.MaxValue)
+    // kill all executors
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request 1
+    assert(sc.requestExecutors(1))
+    assert(master.apps.head.executors.size === 1)
+    assert(master.apps.head.getExecutorLimit === 1)
+    // request 3 more
+    assert(sc.requestExecutors(3))
+    assert(master.apps.head.executors.size === 4)
+    assert(master.apps.head.getExecutorLimit === 4)
+    // request 10 more; none will go through
+    assert(sc.requestExecutors(10))
+    assert(master.apps.head.executors.size === 4)
+    assert(master.apps.head.getExecutorLimit === 14)
+    // kill all executors; 4 executors will be launched immediately
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 4)
+    assert(master.apps.head.getExecutorLimit === 10)
+    // ... and again
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 4)
+    assert(master.apps.head.getExecutorLimit === 6)
+    // ... and again; now we end up with 6 - 4 = 2 executors left
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === 2)
+    // ... and again; this time we have 2 - 2 = 0 executors left
+    assert(killAllExecutors(sc))
+    assert(master.apps.head.executors.size === 0)
+    assert(master.apps.head.getExecutorLimit === 0)
+    // request many more; this increases the limit well beyond the cluster capacity
+    assert(sc.requestExecutors(1000))
+    assert(master.apps.head.executors.size === 4)
+    assert(master.apps.head.getExecutorLimit === 1000)
+  }
+
+  // ===============================
+  // | Utility methods for testing |
+  // ===============================
+
+  /** Return a SparkConf for applications that want to talk to our Master. */
+  private def appConf: SparkConf = {
+    new SparkConf()
+      .setMaster(masterRpcEnv.address.toSparkURL)
+      .setAppName("test")
+      .set("spark.executor.memory", "256m")
+  }
+
+  /** Make a master to which our application will send executor requests. */
+  private def makeMaster(): Master = {
+    val master = new Master(masterRpcEnv, masterRpcEnv.address, 0, securityManager, conf)
+    masterRpcEnv.setupEndpoint(Master.ENDPOINT_NAME, master)
+    master
+  }
+
+  /** Make a few workers that talk to our master. */
+  private def makeWorkers(cores: Int, memory: Int): Seq[Worker] = {
+    (0 until numWorkers).map { i =>
+      val rpcEnv = workerRpcEnvs(i)
+      val worker = new Worker(rpcEnv, 0, cores, memory, Array(masterRpcEnv.address),
+        Worker.SYSTEM_NAME + i, Worker.ENDPOINT_NAME, null, conf, securityManager)
+      rpcEnv.setupEndpoint(Worker.ENDPOINT_NAME, worker)
+      worker
+    }
+  }
+
+  /** Kill all executors belonging to this application. */
+  private def killAllExecutors(sc: SparkContext): Boolean = {
+    killNExecutors(sc, Int.MaxValue)
+  }
+
+  /** Kill N executors belonging to this application. */
+  private def killNExecutors(sc: SparkContext, n: Int): Boolean = {
+    syncExecutors(sc)
+    sc.killExecutors(getExecutorIds(sc).take(n))
+  }
+
+  /**
+   * Return a list of executor IDs belonging to this application.
+   *
+   * Note that we must use the executor IDs according to the Master, which has the most
+   * updated view. We cannot rely on the executor IDs according to the driver because we
+   * don't wait for executors to register. Otherwise the tests will take much longer to run.
+   */
+  private def getExecutorIds(sc: SparkContext): Seq[String] = {
+    assert(master.idToApp.contains(sc.applicationId))
+    master.idToApp(sc.applicationId).executors.keys.map(_.toString).toSeq
+  }
+
+  /**
+   * Sync executor IDs between the driver and the Master.
+   *
+   * This allows us to avoid waiting for new executors to register with the driver before
+   * we submit a request to kill them. This must be called before each kill request.
+   */
+  private def syncExecutors(sc: SparkContext): Unit = {
+    val driverExecutors = sc.getExecutorStorageStatus
+      .map(_.blockManagerId.executorId)
+      .filter { _ != SparkContext.DRIVER_IDENTIFIER}
+    val masterExecutors = getExecutorIds(sc)
+    val missingExecutors = masterExecutors.toSet.diff(driverExecutors.toSet).toSeq.sorted
+    missingExecutors.foreach { id =>
+      // Fake an executor registration so the driver knows about us
+      val port = System.currentTimeMillis % 65536
+      val endpointRef = mock(classOf[RpcEndpointRef])
+      val mockAddress = mock(classOf[RpcAddress])
+      when(endpointRef.address).thenReturn(mockAddress)
+      val message = RegisterExecutor(id, endpointRef, s"localhost:$port", 10, Map.empty)
+      val backend = sc.schedulerBackend.asInstanceOf[CoarseGrainedSchedulerBackend]
+      backend.driverEndpoint.askWithRetry[CoarseGrainedClusterMessage](message)
+    }
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 4d7016d1e594b..30780a0da7f8d 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -120,7 +120,7 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     CustomRecoveryModeFactory.instantiationAttempts should be > instantiationAttempts
   }
 
-  test("Master & worker web ui available") {
+  test("master/worker web ui available") {
     implicit val formats = org.json4s.DefaultFormats
     val conf = new SparkConf()
     val localCluster = new LocalSparkCluster(2, 2, 512, conf)
@@ -144,174 +144,202 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
   }
 
   test("basic scheduling - spread out") {
-    testBasicScheduling(spreadOut = true)
+    basicScheduling(spreadOut = true)
   }
 
   test("basic scheduling - no spread out") {
-    testBasicScheduling(spreadOut = false)
+    basicScheduling(spreadOut = false)
   }
 
   test("scheduling with max cores - spread out") {
-    testSchedulingWithMaxCores(spreadOut = true)
+    schedulingWithMaxCores(spreadOut = true)
   }
 
   test("scheduling with max cores - no spread out") {
-    testSchedulingWithMaxCores(spreadOut = false)
+    schedulingWithMaxCores(spreadOut = false)
   }
 
   test("scheduling with cores per executor - spread out") {
-    testSchedulingWithCoresPerExecutor(spreadOut = true)
+    schedulingWithCoresPerExecutor(spreadOut = true)
   }
 
   test("scheduling with cores per executor - no spread out") {
-    testSchedulingWithCoresPerExecutor(spreadOut = false)
+    schedulingWithCoresPerExecutor(spreadOut = false)
   }
 
   test("scheduling with cores per executor AND max cores - spread out") {
-    testSchedulingWithCoresPerExecutorAndMaxCores(spreadOut = true)
+    schedulingWithCoresPerExecutorAndMaxCores(spreadOut = true)
   }
 
   test("scheduling with cores per executor AND max cores - no spread out") {
-    testSchedulingWithCoresPerExecutorAndMaxCores(spreadOut = false)
+    schedulingWithCoresPerExecutorAndMaxCores(spreadOut = false)
   }
 
-  private def testBasicScheduling(spreadOut: Boolean): Unit = {
+  test("scheduling with executor limit - spread out") {
+    schedulingWithExecutorLimit(spreadOut = true)
+  }
+
+  test("scheduling with executor limit - no spread out") {
+    schedulingWithExecutorLimit(spreadOut = false)
+  }
+
+  test("scheduling with executor limit AND max cores - spread out") {
+    schedulingWithExecutorLimitAndMaxCores(spreadOut = true)
+  }
+
+  test("scheduling with executor limit AND max cores - no spread out") {
+    schedulingWithExecutorLimitAndMaxCores(spreadOut = false)
+  }
+
+  test("scheduling with executor limit AND cores per executor - spread out") {
+    schedulingWithExecutorLimitAndCoresPerExecutor(spreadOut = true)
+  }
+
+  test("scheduling with executor limit AND cores per executor - no spread out") {
+    schedulingWithExecutorLimitAndCoresPerExecutor(spreadOut = false)
+  }
+
+  test("scheduling with executor limit AND cores per executor AND max cores - spread out") {
+    schedulingWithEverything(spreadOut = true)
+  }
+
+  test("scheduling with executor limit AND cores per executor AND max cores - no spread out") {
+    schedulingWithEverything(spreadOut = false)
+  }
+
+  private def basicScheduling(spreadOut: Boolean): Unit = {
     val master = makeMaster()
     val appInfo = makeAppInfo(1024)
-    val workerInfo = makeWorkerInfo(4096, 10)
-    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
-    val scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
-    assert(scheduledCores(0) === 10)
-    assert(scheduledCores(1) === 10)
-    assert(scheduledCores(2) === 10)
+    val scheduledCores = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    assert(scheduledCores === Array(10, 10, 10))
   }
 
-  private def testSchedulingWithMaxCores(spreadOut: Boolean): Unit = {
+  private def schedulingWithMaxCores(spreadOut: Boolean): Unit = {
     val master = makeMaster()
     val appInfo1 = makeAppInfo(1024, maxCores = Some(8))
     val appInfo2 = makeAppInfo(1024, maxCores = Some(16))
-    val workerInfo = makeWorkerInfo(4096, 10)
-    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
-    var scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo1, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
-    // With spreading out, each worker should be assigned a few cores
-    if (spreadOut) {
-      assert(scheduledCores(0) === 3)
-      assert(scheduledCores(1) === 3)
-      assert(scheduledCores(2) === 2)
-    } else {
-      // Without spreading out, the cores should be concentrated on the first worker
-      assert(scheduledCores(0) === 8)
-      assert(scheduledCores(1) === 0)
-      assert(scheduledCores(2) === 0)
-    }
-    // Now test the same thing with max cores > cores per worker
-    scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo2, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
+    val scheduledCores1 = scheduleExecutorsOnWorkers(master, appInfo1, workerInfos, spreadOut)
+    val scheduledCores2 = scheduleExecutorsOnWorkers(master, appInfo2, workerInfos, spreadOut)
     if (spreadOut) {
-      assert(scheduledCores(0) === 6)
-      assert(scheduledCores(1) === 5)
-      assert(scheduledCores(2) === 5)
+      assert(scheduledCores1 === Array(3, 3, 2))
+      assert(scheduledCores2 === Array(6, 5, 5))
     } else {
-      // Without spreading out, the first worker should be fully booked,
-      // and the leftover cores should spill over to the second worker only.
-      assert(scheduledCores(0) === 10)
-      assert(scheduledCores(1) === 6)
-      assert(scheduledCores(2) === 0)
+      assert(scheduledCores1 === Array(8, 0, 0))
+      assert(scheduledCores2 === Array(10, 6, 0))
     }
   }
 
-  private def testSchedulingWithCoresPerExecutor(spreadOut: Boolean): Unit = {
+  private def schedulingWithCoresPerExecutor(spreadOut: Boolean): Unit = {
     val master = makeMaster()
     val appInfo1 = makeAppInfo(1024, coresPerExecutor = Some(2))
     val appInfo2 = makeAppInfo(256, coresPerExecutor = Some(2))
     val appInfo3 = makeAppInfo(256, coresPerExecutor = Some(3))
-    val workerInfo = makeWorkerInfo(4096, 10)
-    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
-    // Each worker should end up with 4 executors with 2 cores each
-    // This should be 4 because of the memory restriction on each worker
-    var scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo1, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
-    assert(scheduledCores(0) === 8)
-    assert(scheduledCores(1) === 8)
-    assert(scheduledCores(2) === 8)
-    // Now test the same thing without running into the worker memory limit
-    // Each worker should now end up with 5 executors with 2 cores each
-    scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo2, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
-    assert(scheduledCores(0) === 10)
-    assert(scheduledCores(1) === 10)
-    assert(scheduledCores(2) === 10)
-    // Now test the same thing with a cores per executor that 10 is not divisible by
-    scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo3, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
-    assert(scheduledCores(0) === 9)
-    assert(scheduledCores(1) === 9)
-    assert(scheduledCores(2) === 9)
+    val scheduledCores1 = scheduleExecutorsOnWorkers(master, appInfo1, workerInfos, spreadOut)
+    val scheduledCores2 = scheduleExecutorsOnWorkers(master, appInfo2, workerInfos, spreadOut)
+    val scheduledCores3 = scheduleExecutorsOnWorkers(master, appInfo3, workerInfos, spreadOut)
+    assert(scheduledCores1 === Array(8, 8, 8)) // 4 * 2 because of memory limits
+    assert(scheduledCores2 === Array(10, 10, 10)) // 5 * 2
+    assert(scheduledCores3 === Array(9, 9, 9)) // 3 * 3
   }
 
   // Sorry for the long method name!
-  private def testSchedulingWithCoresPerExecutorAndMaxCores(spreadOut: Boolean): Unit = {
+  private def schedulingWithCoresPerExecutorAndMaxCores(spreadOut: Boolean): Unit = {
     val master = makeMaster()
     val appInfo1 = makeAppInfo(256, coresPerExecutor = Some(2), maxCores = Some(4))
     val appInfo2 = makeAppInfo(256, coresPerExecutor = Some(2), maxCores = Some(20))
     val appInfo3 = makeAppInfo(256, coresPerExecutor = Some(3), maxCores = Some(20))
-    val workerInfo = makeWorkerInfo(4096, 10)
-    val workerInfos = Array(workerInfo, workerInfo, workerInfo)
-    // We should only launch two executors, each with exactly 2 cores
-    var scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo1, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
+    val scheduledCores1 = scheduleExecutorsOnWorkers(master, appInfo1, workerInfos, spreadOut)
+    val scheduledCores2 = scheduleExecutorsOnWorkers(master, appInfo2, workerInfos, spreadOut)
+    val scheduledCores3 = scheduleExecutorsOnWorkers(master, appInfo3, workerInfos, spreadOut)
+    if (spreadOut) {
+      assert(scheduledCores1 === Array(2, 2, 0))
+      assert(scheduledCores2 === Array(8, 6, 6))
+      assert(scheduledCores3 === Array(6, 6, 6))
+    } else {
+      assert(scheduledCores1 === Array(4, 0, 0))
+      assert(scheduledCores2 === Array(10, 10, 0))
+      assert(scheduledCores3 === Array(9, 9, 0))
+    }
+  }
+
+  private def schedulingWithExecutorLimit(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo = makeAppInfo(256)
+    appInfo.executorLimit = 0
+    val scheduledCores1 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 2
+    val scheduledCores2 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 5
+    val scheduledCores3 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    assert(scheduledCores1 === Array(0, 0, 0))
+    assert(scheduledCores2 === Array(10, 10, 0))
+    assert(scheduledCores3 === Array(10, 10, 10))
+  }
+
+  private def schedulingWithExecutorLimitAndMaxCores(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo = makeAppInfo(256, maxCores = Some(16))
+    appInfo.executorLimit = 0
+    val scheduledCores1 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 2
+    val scheduledCores2 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 5
+    val scheduledCores3 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    assert(scheduledCores1 === Array(0, 0, 0))
     if (spreadOut) {
-      assert(scheduledCores(0) === 2)
-      assert(scheduledCores(1) === 2)
-      assert(scheduledCores(2) === 0)
+      assert(scheduledCores2 === Array(8, 8, 0))
+      assert(scheduledCores3 === Array(6, 5, 5))
     } else {
-      assert(scheduledCores(0) === 4)
-      assert(scheduledCores(1) === 0)
-      assert(scheduledCores(2) === 0)
+      assert(scheduledCores2 === Array(10, 6, 0))
+      assert(scheduledCores3 === Array(10, 6, 0))
     }
-    // Test max cores > number of cores per worker
-    scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo2, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
+  }
+
+  private def schedulingWithExecutorLimitAndCoresPerExecutor(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo = makeAppInfo(256, coresPerExecutor = Some(4))
+    appInfo.executorLimit = 0
+    val scheduledCores1 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 2
+    val scheduledCores2 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 5
+    val scheduledCores3 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    assert(scheduledCores1 === Array(0, 0, 0))
     if (spreadOut) {
-      assert(scheduledCores(0) === 8)
-      assert(scheduledCores(1) === 6)
-      assert(scheduledCores(2) === 6)
+      assert(scheduledCores2 === Array(4, 4, 0))
     } else {
-      assert(scheduledCores(0) === 10)
-      assert(scheduledCores(1) === 10)
-      assert(scheduledCores(2) === 0)
+      assert(scheduledCores2 === Array(8, 0, 0))
     }
-    // Test max cores > number of cores per worker AND
-    // a cores per executor that is 10 is not divisible by
-    scheduledCores = master.invokePrivate(
-      _scheduleExecutorsOnWorkers(appInfo3, workerInfos, spreadOut))
-    assert(scheduledCores.length === 3)
+    assert(scheduledCores3 === Array(8, 8, 4))
+  }
+
+  // Everything being: executor limit + cores per executor + max cores
+  private def schedulingWithEverything(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo = makeAppInfo(256, coresPerExecutor = Some(4), maxCores = Some(18))
+    appInfo.executorLimit = 0
+    val scheduledCores1 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 2
+    val scheduledCores2 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    appInfo.executorLimit = 5
+    val scheduledCores3 = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    assert(scheduledCores1 === Array(0, 0, 0))
     if (spreadOut) {
-      assert(scheduledCores(0) === 6)
-      assert(scheduledCores(1) === 6)
-      assert(scheduledCores(2) === 6)
+      assert(scheduledCores2 === Array(4, 4, 0))
+      assert(scheduledCores3 === Array(8, 4, 4))
     } else {
-      assert(scheduledCores(0) === 9)
-      assert(scheduledCores(1) === 9)
-      assert(scheduledCores(2) === 0)
+      assert(scheduledCores2 === Array(8, 0, 0))
+      assert(scheduledCores3 === Array(8, 8, 0))
     }
   }
 
-  // ===============================
-  // | Utility methods for testing |
-  // ===============================
+  // ==========================================
+  // | Utility methods and fields for testing |
+  // ==========================================
 
   private val _scheduleExecutorsOnWorkers = PrivateMethod[Array[Int]]('scheduleExecutorsOnWorkers)
+  private val workerInfo = makeWorkerInfo(4096, 10)
+  private val workerInfos = Array(workerInfo, workerInfo, workerInfo)
 
   private def makeMaster(conf: SparkConf = new SparkConf): Master = {
     val securityMgr = new SecurityManager(conf)
@@ -335,4 +363,12 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     new WorkerInfo(workerId, "host", 100, cores, memoryMb, null, 101, "address")
   }
 
+  private def scheduleExecutorsOnWorkers(
+      master: Master,
+      appInfo: ApplicationInfo,
+      workerInfos: Array[WorkerInfo],
+      spreadOut: Boolean): Array[Int] = {
+    master.invokePrivate(_scheduleExecutorsOnWorkers(appInfo, workerInfos, spreadOut))
+  }
+
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index fa36629c37a35..f9384c4c3c9d6 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -151,6 +151,10 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException")
+          ) ++ Seq(
+            // SPARK-4751 Dynamic allocation for standalone mode
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.SparkContext.supportDynamicAllocation")
           )
 
         case v if v.startsWith("1.4") =>

From df733cbeae7a53826e89574af5463fa018329a22 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Sat, 1 Aug 2015 13:06:50 -0700
Subject: [PATCH 0773/1454] [SPARK-9491] Avoid fetching HBase tokens when not
 needed.

Look at HBase's configuration to make sure it's configured for
Kerberos. If the HBase configuration is missing, or if HBase is
configured for non-kerberos authentication, then skip getting
tokens.

Reference: http://hbase.apache.org/book.html#security.prerequisites

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7810 from vanzin/SPARK-9491 and squashes the following commits:

a57c776 [Marcelo Vanzin] [SPARK-9491] Avoid fetching HBase tokens when not needed.
---
 .../scala/org/apache/spark/deploy/yarn/Client.scala   | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 4ac3397f1ad28..fc11bbf97e2ec 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -1295,11 +1295,12 @@ object Client extends Logging {
 
         logDebug("Attempting to fetch HBase security token.")
 
-        val hbaseConf = confCreate.invoke(null, conf)
-        val token = obtainToken.invoke(null, hbaseConf).asInstanceOf[Token[TokenIdentifier]]
-        credentials.addToken(token.getService, token)
-
-        logInfo("Added HBase security token to credentials.")
+        val hbaseConf = confCreate.invoke(null, conf).asInstanceOf[Configuration]
+        if ("kerberos" == hbaseConf.get("hbase.security.authentication")) {
+          val token = obtainToken.invoke(null, hbaseConf).asInstanceOf[Token[TokenIdentifier]]
+          credentials.addToken(token.getService, token)
+          logInfo("Added HBase security token to credentials.")
+        }
       } catch {
         case e: java.lang.NoSuchMethodException =>
           logInfo("HBase Method not found: " + e)

From 3d1535d48822281953de1e8447de86fad728412a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sat, 1 Aug 2015 13:20:26 -0700
Subject: [PATCH 0774/1454] [SPARK-9520] [SQL] Support in-place sort in
 UnsafeFixedWidthAggregationMap

This pull request adds a sortedIterator method to UnsafeFixedWidthAggregationMap that sorts its data in-place by the grouping key.

This is needed so we can fallback to external sorting for aggregation.

Author: Reynold Xin <rxin@databricks.com>

Closes #7849 from rxin/bytes2bytes-sorting and squashes the following commits:

75018c6 [Reynold Xin] Updated documentation.
81a8694 [Reynold Xin] [SPARK-9520][SQL] Support in-place sort in UnsafeFixedWidthAggregationMap.
---
 .../spark/unsafe/map/BytesToBytesMap.java     |  41 +++++--
 .../sql/catalyst/expressions/Projection.scala |   2 +
 .../codegen/GenerateOrdering.scala            |  12 ++-
 .../UnsafeFixedWidthAggregationMap.java       | 100 +++++++++++++++++-
 .../spark/sql/execution/SortPrefixUtils.scala |  18 +++-
 .../UnsafeFixedWidthAggregationMapSuite.scala |  34 ++++++
 .../org/apache/spark/unsafe/KVIterator.java   |   4 +-
 7 files changed, 196 insertions(+), 15 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 481375f493a50..cf222b72727d8 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -23,6 +23,8 @@
 import java.util.LinkedList;
 import java.util.List;
 
+import javax.annotation.Nullable;
+
 import com.google.common.annotations.VisibleForTesting;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -217,6 +219,7 @@ public static final class BytesToBytesMapIterator implements Iterator<Location>
     private final Iterator<MemoryBlock> dataPagesIterator;
     private final Location loc;
 
+    private MemoryBlock currentPage;
     private int currentRecordNumber = 0;
     private Object pageBaseObject;
     private long offsetInPage;
@@ -232,7 +235,7 @@ private BytesToBytesMapIterator(
     }
 
     private void advanceToNextPage() {
-      final MemoryBlock currentPage = dataPagesIterator.next();
+      currentPage = dataPagesIterator.next();
       pageBaseObject = currentPage.getBaseObject();
       offsetInPage = currentPage.getBaseOffset();
     }
@@ -249,7 +252,7 @@ public Location next() {
         advanceToNextPage();
         totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
       }
-      loc.with(pageBaseObject, offsetInPage);
+      loc.with(currentPage, offsetInPage);
       offsetInPage += 8 + totalLength;
       currentRecordNumber++;
       return loc;
@@ -346,14 +349,19 @@ public final class Location {
     private int keyLength;
     private int valueLength;
 
+    /**
+     * Memory page containing the record. Only set if created by {@link BytesToBytesMap#iterator()}.
+     */
+    @Nullable private MemoryBlock memoryPage;
+
     private void updateAddressesAndSizes(long fullKeyAddress) {
       updateAddressesAndSizes(
         taskMemoryManager.getPage(fullKeyAddress),
         taskMemoryManager.getOffsetInPage(fullKeyAddress));
     }
 
-    private void updateAddressesAndSizes(final Object page, final long keyOffsetInPage) {
-      long position = keyOffsetInPage;
+    private void updateAddressesAndSizes(final Object page, final long offsetInPage) {
+      long position = offsetInPage;
       final int totalLength = PlatformDependent.UNSAFE.getInt(page, position);
       position += 4;
       keyLength = PlatformDependent.UNSAFE.getInt(page, position);
@@ -366,7 +374,7 @@ private void updateAddressesAndSizes(final Object page, final long keyOffsetInPa
       valueMemoryLocation.setObjAndOffset(page, position);
     }
 
-    Location with(int pos, int keyHashcode, boolean isDefined) {
+    private Location with(int pos, int keyHashcode, boolean isDefined) {
       this.pos = pos;
       this.isDefined = isDefined;
       this.keyHashcode = keyHashcode;
@@ -377,12 +385,21 @@ Location with(int pos, int keyHashcode, boolean isDefined) {
       return this;
     }
 
-    Location with(Object page, long keyOffsetInPage) {
+    private Location with(MemoryBlock page, long offsetInPage) {
       this.isDefined = true;
-      updateAddressesAndSizes(page, keyOffsetInPage);
+      this.memoryPage = page;
+      updateAddressesAndSizes(page.getBaseObject(), offsetInPage);
       return this;
     }
 
+    /**
+     * Returns the memory page that contains the current record.
+     * This is only valid if this is returned by {@link BytesToBytesMap#iterator()}.
+     */
+    public MemoryBlock getMemoryPage() {
+      return this.memoryPage;
+    }
+
     /**
      * Returns true if the key is defined at this position, and false otherwise.
      */
@@ -538,7 +555,7 @@ public boolean putNewKey(
       long insertCursor = dataPageInsertOffset;
 
       // Compute all of our offsets up-front:
-      final long totalLengthOffset = insertCursor;
+      final long recordOffset = insertCursor;
       insertCursor += 4;
       final long keyLengthOffset = insertCursor;
       insertCursor += 4;
@@ -547,7 +564,7 @@ public boolean putNewKey(
       final long valueDataOffsetInPage = insertCursor;
       insertCursor += valueLengthBytes; // word used to store the value size
 
-      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, totalLengthOffset,
+      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, recordOffset,
         keyLengthBytes + valueLengthBytes);
       PlatformDependent.UNSAFE.putInt(dataPageBaseObject, keyLengthOffset, keyLengthBytes);
       // Copy the key
@@ -569,7 +586,7 @@ public boolean putNewKey(
       numElements++;
       bitset.set(pos);
       final long storedKeyAddress = taskMemoryManager.encodePageNumberAndOffset(
-        dataPage, totalLengthOffset);
+        dataPage, recordOffset);
       longArray.set(pos * 2, storedKeyAddress);
       longArray.set(pos * 2 + 1, keyHashcode);
       updateAddressesAndSizes(storedKeyAddress);
@@ -618,6 +635,10 @@ public void free() {
     assert(dataPages.isEmpty());
   }
 
+  public TaskMemoryManager getTaskMemoryManager() {
+    return taskMemoryManager;
+  }
+
   /** Returns the total amount of memory, in bytes, consumed by this map's managed structures. */
   public long getTotalMemoryConsumption() {
     long totalDataPagesSize = 0L;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index d79325aea87e3..000be70f1768f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -125,6 +125,8 @@ object UnsafeProjection {
     GenerateUnsafeProjection.generate(exprs)
   }
 
+  def create(expr: Expression): UnsafeProjection = create(Seq(expr))
+
   /**
    * Returns an UnsafeProjection for given sequence of Expressions, which will be bound to
    * `inputSchema`.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index dbd4616d281c8..cc848aa1993ea 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -21,6 +21,7 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.Private
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.StructType
 
 /**
  * Inherits some default implementation for Java from `Ordering[Row]`
@@ -43,7 +44,16 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
   protected def bind(in: Seq[SortOrder], inputSchema: Seq[Attribute]): Seq[SortOrder] =
     in.map(BindReferences.bindReference(_, inputSchema))
 
-  protected def create(ordering: Seq[SortOrder]): Ordering[InternalRow] = {
+  /**
+   * Creates a code gen ordering for sorting this schema, in ascending order.
+   */
+  def create(schema: StructType): BaseOrdering = {
+    create(schema.zipWithIndex.map { case (field, ordinal) =>
+      SortOrder(BoundReference(ordinal, field.dataType, nullable = true), Ascending)
+    })
+  }
+
+  protected def create(ordering: Seq[SortOrder]): BaseOrdering = {
     val ctx = newCodeGenContext()
 
     val comparisons = ordering.map { order =>
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index c18b6dea6b2e1..a0a8dd51540d2 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -17,19 +17,26 @@
 
 package org.apache.spark.sql.execution;
 
+import java.io.IOException;
+
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
-import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.sql.types.DecimalType;
+import org.apache.spark.sql.catalyst.expressions.codegen.BaseOrdering;
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.KVIterator;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
+import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.MemoryLocation;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
+import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
+import org.apache.spark.util.collection.unsafe.sort.RecordComparator;
+import org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter;
+import org.apache.spark.util.collection.unsafe.sort.UnsafeSorterIterator;
 
 /**
  * Unsafe-based HashMap for performing aggregations where the aggregated values are fixed-width.
@@ -225,4 +232,93 @@ public void printPerfMetrics() {
     System.out.println("Total memory consumption (bytes): " + map.getTotalMemoryConsumption());
   }
 
+  /**
+   * Sorts the key, value data in this map in place, and return them as an iterator.
+   *
+   * The only memory that is allocated is the address/prefix array, 16 bytes per record.
+   */
+  public KVIterator<UnsafeRow, UnsafeRow> sortedIterator() {
+    int numElements = map.numElements();
+    final int numKeyFields = groupingKeySchema.size();
+    TaskMemoryManager memoryManager = map.getTaskMemoryManager();
+
+    UnsafeExternalRowSorter.PrefixComputer prefixComp =
+      SortPrefixUtils.createPrefixGenerator(groupingKeySchema);
+    PrefixComparator prefixComparator = SortPrefixUtils.getPrefixComparator(groupingKeySchema);
+
+    final BaseOrdering ordering = GenerateOrdering.create(groupingKeySchema);
+    RecordComparator recordComparator = new RecordComparator() {
+      private final UnsafeRow row1 = new UnsafeRow();
+      private final UnsafeRow row2 = new UnsafeRow();
+
+      @Override
+      public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff2) {
+        row1.pointTo(baseObj1, baseOff1 + 4, numKeyFields, -1);
+        row2.pointTo(baseObj2, baseOff2 + 4, numKeyFields, -1);
+        return ordering.compare(row1, row2);
+      }
+    };
+
+    // Insert the records into the in-memory sorter.
+    final UnsafeInMemorySorter sorter = new UnsafeInMemorySorter(
+      memoryManager, recordComparator, prefixComparator, numElements);
+
+    BytesToBytesMap.BytesToBytesMapIterator iter = map.iterator();
+    UnsafeRow row = new UnsafeRow();
+    while (iter.hasNext()) {
+      final BytesToBytesMap.Location loc = iter.next();
+      final Object baseObject = loc.getKeyAddress().getBaseObject();
+      final long baseOffset = loc.getKeyAddress().getBaseOffset();
+
+      // Get encoded memory address
+      MemoryBlock page = loc.getMemoryPage();
+      long address = memoryManager.encodePageNumberAndOffset(page, baseOffset - 8);
+
+      // Compute prefix
+      row.pointTo(baseObject, baseOffset, numKeyFields, loc.getKeyLength());
+      final long prefix = prefixComp.computePrefix(row);
+
+      sorter.insertRecord(address, prefix);
+    }
+
+    // Return the sorted result as an iterator.
+    return new KVIterator<UnsafeRow, UnsafeRow>() {
+
+      private UnsafeSorterIterator sortedIterator = sorter.getSortedIterator();
+      private final UnsafeRow key = new UnsafeRow();
+      private final UnsafeRow value = new UnsafeRow();
+      private int numValueFields = aggregationBufferSchema.size();
+
+      @Override
+      public boolean next() throws IOException {
+        if (sortedIterator.hasNext()) {
+          sortedIterator.loadNext();
+          Object baseObj = sortedIterator.getBaseObject();
+          long recordOffset = sortedIterator.getBaseOffset();
+          int recordLen = sortedIterator.getRecordLength();
+          int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
+          key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
+          value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, recordLen - keyLen);
+          return true;
+        } else {
+          return false;
+        }
+      }
+
+      @Override
+      public UnsafeRow getKey() {
+        return key;
+      }
+
+      @Override
+      public UnsafeRow getValue() {
+        return value;
+      }
+
+      @Override
+      public void close() {
+        // Do nothing
+      }
+    };
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index a2145b185ce90..17d4166af555a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -18,7 +18,8 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.sql.catalyst.expressions.SortOrder
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.unsafe.sort.{PrefixComparators, PrefixComparator}
 
@@ -46,4 +47,19 @@ object SortPrefixUtils {
       case _ => NoOpPrefixComparator
     }
   }
+
+  def getPrefixComparator(schema: StructType): PrefixComparator = {
+    val field = schema.head
+    getPrefixComparator(SortOrder(BoundReference(0, field.dataType, field.nullable), Ascending))
+  }
+
+  def createPrefixGenerator(schema: StructType): UnsafeExternalRowSorter.PrefixComputer = {
+    val boundReference = BoundReference(0, schema.head.dataType, nullable = true)
+    val prefixProjection = UnsafeProjection.create(SortPrefix(SortOrder(boundReference, Ascending)))
+    new UnsafeExternalRowSorter.PrefixComputer {
+      override def computePrefix(row: InternalRow): Long = {
+        prefixProjection.apply(row).getLong(0)
+      }
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index 6a2c51ca88ac3..098bdd0017a73 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -140,4 +140,38 @@ class UnsafeFixedWidthAggregationMapSuite
     map.free()
   }
 
+  test("test sorting") {
+    val map = new UnsafeFixedWidthAggregationMap(
+      emptyAggregationBuffer,
+      aggBufferSchema,
+      groupKeySchema,
+      taskMemoryManager,
+      shuffleMemoryManager,
+      128, // initial capacity
+      PAGE_SIZE_BYTES,
+      false // disable perf metrics
+    )
+
+    val rand = new Random(42)
+    val groupKeys: Set[String] = Seq.fill(512) {
+      Seq.fill(rand.nextInt(100))(rand.nextPrintableChar()).mkString
+    }.toSet
+    groupKeys.foreach { keyString =>
+      val buf = map.getAggregationBuffer(InternalRow(UTF8String.fromString(keyString)))
+      buf.setInt(0, keyString.length)
+      assert(buf != null)
+    }
+
+    val out = new scala.collection.mutable.ArrayBuffer[String]
+    val iter = map.sortedIterator()
+    while (iter.next()) {
+      assert(iter.getKey.getString(0).length === iter.getValue.getInt(0))
+      out += iter.getKey.getString(0)
+    }
+
+    assert(out === groupKeys.toSeq.sorted)
+
+    map.free()
+  }
+
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java b/unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java
index fb163401c0d27..5c9d5d9a38318 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/KVIterator.java
@@ -17,9 +17,11 @@
 
 package org.apache.spark.unsafe;
 
+import java.io.IOException;
+
 public abstract class KVIterator<K, V> {
 
-  public abstract boolean next();
+  public abstract boolean next() throws IOException;
 
   public abstract K getKey();
 

From 84a6982b35d87483bdf70ef4423cc4c8e0c3feb1 Mon Sep 17 00:00:00 2001
From: Meihua Wu <meihuawu@umich.edu>
Date: Sat, 1 Aug 2015 17:13:28 -0700
Subject: [PATCH 0775/1454] [SPARK-9530] [MLLIB] ScalaDoc should not indicate
 LDAModel.describeTopics and DistributedLDAModel.topDocumentsPerTopic as
 approximate

Remove ScalaDoc that suggests describeTopics and topDocumentsPerTopic are approximate.

cc jkbradley

Author: Meihua Wu <meihuawu@umich.edu>

Closes #7858 from rotationsymmetry/SPARK-9530 and squashes the following commits:

b574923 [Meihua Wu] Remove ScalaDoc that suggests describeTopics and topDocumentsPerTopic are approximate.
---
 .../scala/org/apache/spark/mllib/clustering/LDAModel.scala | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 0cdac84eeb591..6af90d7287ff8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -86,10 +86,6 @@ abstract class LDAModel private[clustering] extends Saveable {
   /**
    * Return the topics described by weighted terms.
    *
-   * This limits the number of terms per topic.
-   * This is approximate; it may not return exactly the top-weighted terms for each topic.
-   * To get a more precise set of top terms, increase maxTermsPerTopic.
-   *
    * @param maxTermsPerTopic  Maximum number of terms to collect for each topic.
    * @return  Array over topics.  Each topic is represented as a pair of matching arrays:
    *          (term indices, term weights in topic).
@@ -519,9 +515,6 @@ class DistributedLDAModel private[clustering] (
   /**
    * Return the top documents for each topic
    *
-   * This is approximate; it may not return exactly the top-weighted documents for each topic.
-   * To get a more precise set of top documents, increase maxDocumentsPerTopic.
-   *
    * @param maxDocumentsPerTopic  Maximum number of documents to collect for each topic.
    * @return  Array over topics.  Each element represent as a pair of matching arrays:
    *          (IDs for the documents, weights of the topic in these documents).

From 5d9e33d9a2633e45082ac395a64646364f22f4c4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 1 Aug 2015 18:22:46 -0700
Subject: [PATCH 0776/1454] [SPARK-9495] prefix of DateType/TimestampType

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7856 from davies/sort_improve and squashes the following commits:

5fc81bd [Davies Liu] support DateType/TimestampType
---
 .../apache/spark/sql/catalyst/expressions/SortOrder.scala   | 2 ++
 .../org/apache/spark/sql/execution/SortPrefixUtils.scala    | 6 ++++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index 9fe877f10fa08..afecf881c7440 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -61,6 +61,8 @@ case class SortPrefix(child: SortOrder) extends UnaryExpression {
         (Long.MinValue, s"$input ? 1L : 0L")
       case _: IntegralType =>
         (Long.MinValue, s"(long) $input")
+      case DateType | TimestampType =>
+        (Long.MinValue, s"(long) $input")
       case FloatType | DoubleType =>
         (DoublePrefixComparator.computePrefix(Double.NegativeInfinity),
           s"$DoublePrefixCmp.computePrefix((double)$input)")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 17d4166af555a..676656518f4c0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -38,9 +38,11 @@ object SortPrefixUtils {
     sortOrder.dataType match {
       case StringType if sortOrder.isAscending => PrefixComparators.STRING
       case StringType if !sortOrder.isAscending => PrefixComparators.STRING_DESC
-      case BooleanType | ByteType | ShortType | IntegerType | LongType if sortOrder.isAscending =>
+      case BooleanType | ByteType | ShortType | IntegerType | LongType | DateType | TimestampType
+          if sortOrder.isAscending =>
         PrefixComparators.LONG
-      case BooleanType | ByteType | ShortType | IntegerType | LongType if !sortOrder.isAscending =>
+      case BooleanType | ByteType | ShortType | IntegerType | LongType | DateType | TimestampType
+        if !sortOrder.isAscending =>
         PrefixComparators.LONG_DESC
       case FloatType | DoubleType if sortOrder.isAscending => PrefixComparators.DOUBLE
       case FloatType | DoubleType if !sortOrder.isAscending => PrefixComparators.DOUBLE_DESC

From 00cd92f32f17ca57d47aa2dcc716eb707aaee799 Mon Sep 17 00:00:00 2001
From: HuJiayin <jiayin.hu@intel.com>
Date: Sat, 1 Aug 2015 21:44:57 -0700
Subject: [PATCH 0777/1454] [SPARK-8269] [SQL] string function: initcap

This PR is based on #7208 , thanks to HuJiayin

Closes #7208

Author: HuJiayin <jiayin.hu@intel.com>
Author: Davies Liu <davies@databricks.com>

Closes #7850 from davies/initcap and squashes the following commits:

54472e9 [Davies Liu] fix python test
17ffe51 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap
ca46390 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap
3a906e4 [Davies Liu] implement title case in UTF8String
8b2506a [HuJiayin] Update functions.py
2cd43e5 [HuJiayin] fix python style check
b616c0e [HuJiayin] add python api
1f5a0ef [HuJiayin] add codegen
7e0c604 [HuJiayin] Merge branch 'master' of https://github.com/apache/spark into initcap
6a0b958 [HuJiayin] add column
c79482d [HuJiayin] support soundex
7ce416b [HuJiayin] support initcap rebase code
---
 python/pyspark/sql/functions.py               | 12 +++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/stringOperations.scala        | 17 ++++
 .../expressions/StringExpressionsSuite.scala  | 12 +++
 .../org/apache/spark/sql/functions.scala      |  9 ++
 .../spark/sql/StringFunctionsSuite.scala      |  9 ++
 .../apache/spark/unsafe/types/UTF8String.java | 88 +++++++++++++++++++
 .../spark/unsafe/types/UTF8StringSuite.java   |  8 ++
 8 files changed, 156 insertions(+)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 96975f54ff695..a73ecc7d93361 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -958,6 +958,18 @@ def substring_index(str, delim, count):
     return Column(sc._jvm.functions.substring_index(_to_java_column(str), delim, count))
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def initcap(col):
+    """Translate the first letter of each word to upper case in the sentence.
+
+    >>> sqlContext.createDataFrame([('ab cd',)], ['a']).select(initcap("a").alias('v')).collect()
+    [Row(v=u'Ab Cd')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.initcap(_to_java_column(col)))
+
+
 @since(1.5)
 def size(col):
     """
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 6e144518bb009..8fafd7778ae1f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -178,6 +178,7 @@ object FunctionRegistry {
     expression[Encode]("encode"),
     expression[Decode]("decode"),
     expression[FormatNumber]("format_number"),
+    expression[InitCap]("initcap"),
     expression[Lower]("lcase"),
     expression[Lower]("lower"),
     expression[Length]("length"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 4d78c55497a34..80c64e5689374 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -597,6 +597,23 @@ case class FormatString(children: Expression*) extends Expression with ImplicitC
   override def prettyName: String = "format_string"
 }
 
+/**
+ * Returns string, with the first letter of each word in uppercase.
+ * Words are delimited by whitespace.
+ */
+case class InitCap(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[DataType] = Seq(StringType)
+  override def dataType: DataType = StringType
+
+  override def nullSafeEval(string: Any): Any = {
+    string.asInstanceOf[UTF8String].toTitleCase
+  }
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, str => s"$str.toTitleCase()")
+  }
+}
+
 /**
  * Returns the string which repeat the given string value n times.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 89c1e33420f09..906be701beed7 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -377,6 +377,18 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Decode(b, Literal.create(null, StringType)), null, create_row(null))
   }
 
+  test("initcap unit test") {
+    checkEvaluation(InitCap(Literal.create(null, StringType)), null)
+    checkEvaluation(InitCap(Literal("a b")), "A B")
+    checkEvaluation(InitCap(Literal(" a")), " A")
+    checkEvaluation(InitCap(Literal("the test")), "The Test")
+    // scalastyle:off
+    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
+    checkEvaluation(InitCap(Literal("世界")), "世界")
+    // scalastyle:on
+  }
+
+
   test("Levenshtein distance") {
     checkEvaluation(Levenshtein(Literal.create(null, StringType), Literal("")), null)
     checkEvaluation(Levenshtein(Literal(""), Literal.create(null, StringType)), null)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index babfe2187999c..818aa109f3993 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1786,6 +1786,15 @@ object functions {
     FormatString((lit(format) +: arguments).map(_.expr): _*)
   }
 
+  /**
+   * Returns string, with the first letter of each word in uppercase.
+   * Words are delimited by whitespace.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def initcap(e: Column): Column = InitCap(e.expr)
+
   /**
    * Locate the position of the first occurrence of substr column in the given string.
    * Returns null if either of the arguments are null.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index f40233db0aebd..1c1be0c3cc1df 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -315,6 +315,15 @@ class StringFunctionsSuite extends QueryTest {
     }
   }
 
+  test("initcap function") {
+    val df = Seq(("ab", "a B")).toDF("l", "r")
+    checkAnswer(
+      df.select(initcap($"l"), initcap($"r")), Row("Ab", "A B"))
+
+    checkAnswer(
+      df.selectExpr("InitCap(l)", "InitCap(r)"), Row("Ab", "A B"))
+  }
+
   test("number format function") {
     val tuple =
       ("aa", 1.asInstanceOf[Byte], 2.asInstanceOf[Short],
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 208503d2fd5dc..213dc761bba48 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -279,6 +279,29 @@ public boolean endsWith(final UTF8String suffix) {
    * Returns the upper case of this string
    */
   public UTF8String toUpperCase() {
+    if (numBytes == 0) {
+      return EMPTY_UTF8;
+    }
+
+    byte[] bytes = new byte[numBytes];
+    bytes[0] = (byte) Character.toTitleCase(getByte(0));
+    for (int i = 0; i < numBytes; i++) {
+      byte b = getByte(i);
+      if (numBytesForFirstByte(b) != 1) {
+        // fallback
+        return toUpperCaseSlow();
+      }
+      int upper = Character.toUpperCase((int) b);
+      if (upper > 127) {
+        // fallback
+        return toUpperCaseSlow();
+      }
+      bytes[i] = (byte) upper;
+    }
+    return fromBytes(bytes);
+  }
+
+  private UTF8String toUpperCaseSlow() {
     return fromString(toString().toUpperCase());
   }
 
@@ -286,9 +309,74 @@ public UTF8String toUpperCase() {
    * Returns the lower case of this string
    */
   public UTF8String toLowerCase() {
+    if (numBytes == 0) {
+      return EMPTY_UTF8;
+    }
+
+    byte[] bytes = new byte[numBytes];
+    bytes[0] = (byte) Character.toTitleCase(getByte(0));
+    for (int i = 0; i < numBytes; i++) {
+      byte b = getByte(i);
+      if (numBytesForFirstByte(b) != 1) {
+        // fallback
+        return toLowerCaseSlow();
+      }
+      int lower = Character.toLowerCase((int) b);
+      if (lower > 127) {
+        // fallback
+        return toLowerCaseSlow();
+      }
+      bytes[i] = (byte) lower;
+    }
+    return fromBytes(bytes);
+  }
+
+  private UTF8String toLowerCaseSlow() {
     return fromString(toString().toLowerCase());
   }
 
+  /**
+   * Returns the title case of this string, that could be used as title.
+   */
+  public UTF8String toTitleCase() {
+    if (numBytes == 0) {
+      return EMPTY_UTF8;
+    }
+
+    byte[] bytes = new byte[numBytes];
+    for (int i = 0; i < numBytes; i++) {
+      byte b = getByte(i);
+      if (i == 0 || getByte(i - 1) == ' ') {
+        if (numBytesForFirstByte(b) != 1) {
+          // fallback
+          return toTitleCaseSlow();
+        }
+        int upper = Character.toTitleCase(b);
+        if (upper > 127) {
+          // fallback
+          return toTitleCaseSlow();
+        }
+        bytes[i] = (byte) upper;
+      } else {
+        bytes[i] = b;
+      }
+    }
+    return fromBytes(bytes);
+  }
+
+  private UTF8String toTitleCaseSlow() {
+    StringBuffer sb = new StringBuffer();
+    String s = toString();
+    sb.append(s);
+    sb.setCharAt(0, Character.toTitleCase(sb.charAt(0)));
+    for (int i = 1; i < s.length(); i++) {
+      if (sb.charAt(i - 1) == ' ') {
+        sb.setCharAt(i, Character.toTitleCase(sb.charAt(i)));
+      }
+    }
+    return fromString(sb.toString());
+  }
+
   /**
    * Copy the bytes from the current UTF8String, and make a new UTF8String.
    * @param start the start position of the current UTF8String in bytes.
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index ed50cdcb29af8..9b3190f8f0c3f 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -114,6 +114,14 @@ public void upperAndLower() {
     testUpperandLower("大千世界 数据砖头", "大千世界 数据砖头");
   }
 
+  @Test
+  public void titleCase() {
+    assertEquals(fromString(""), fromString("").toTitleCase());
+    assertEquals(fromString("Ab Bc Cd"), fromString("ab bc cd").toTitleCase());
+    assertEquals(fromString("Ѐ Ё Ђ Ѻ Ώ Ề"), fromString("ѐ ё ђ ѻ ώ ề").toTitleCase());
+    assertEquals(fromString("大千世界 数据砖头"), fromString("大千世界 数据砖头").toTitleCase());
+  }
+
   @Test
   public void concatTest() {
     assertEquals(EMPTY_UTF8, concat());

From c1b0cbd762d78bedca0ab564cf9ca0970b7b99d2 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 1 Aug 2015 21:46:46 -0700
Subject: [PATCH 0778/1454] [SPARK-8185] [SPARK-8188] [SPARK-8191] [SQL]
 function datediff, to_utc_timestamp, from_utc_timestamp

This PR is based on #7643 , thanks to adrian-wang

Author: Davies Liu <davies@databricks.com>
Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #7847 from davies/datediff and squashes the following commits:

74333d7 [Davies Liu] fix bug
22d8a8c [Davies Liu] optimize
85cdd21 [Davies Liu] remove unnecessary tests
241d90c [Davies Liu] Merge branch 'master' of github.com:apache/spark into datediff
e9dc0f5 [Davies Liu] fix datediff/to_utc_timestamp/from_utc_timestamp
c360447 [Daoyuan Wang] function datediff, to_utc_timestamp, from_utc_timestamp (commits merged)
---
 .../catalyst/analysis/FunctionRegistry.scala  |   3 +
 .../expressions/datetimeFunctions.scala       | 116 +++++++++++++++++-
 .../sql/catalyst/util/DateTimeUtils.scala     |  20 +++
 .../expressions/DateExpressionsSuite.scala    |  72 +++++++++--
 .../catalyst/util/DateTimeUtilsSuite.scala    |  22 ++++
 .../org/apache/spark/sql/functions.scala      |  22 ++++
 .../apache/spark/sql/DateFunctionsSuite.scala |  47 +++++++
 .../spark/sql/StringFunctionsSuite.scala      |  52 +-------
 .../execution/HiveCompatibilitySuite.scala    |   4 +-
 9 files changed, 297 insertions(+), 61 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 8fafd7778ae1f..bc0846646174a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -211,6 +211,7 @@ object FunctionRegistry {
     expression[AddMonths]("add_months"),
     expression[CurrentDate]("current_date"),
     expression[CurrentTimestamp]("current_timestamp"),
+    expression[DateDiff]("datediff"),
     expression[DateAdd]("date_add"),
     expression[DateFormatClass]("date_format"),
     expression[DateSub]("date_sub"),
@@ -218,6 +219,7 @@ object FunctionRegistry {
     expression[DayOfYear]("dayofyear"),
     expression[DayOfMonth]("dayofmonth"),
     expression[FromUnixTime]("from_unixtime"),
+    expression[FromUTCTimestamp]("from_utc_timestamp"),
     expression[Hour]("hour"),
     expression[LastDay]("last_day"),
     expression[Minute]("minute"),
@@ -227,6 +229,7 @@ object FunctionRegistry {
     expression[Quarter]("quarter"),
     expression[Second]("second"),
     expression[ToDate]("to_date"),
+    expression[ToUTCTimestamp]("to_utc_timestamp"),
     expression[TruncDate]("trunc"),
     expression[UnixTimestamp]("unix_timestamp"),
     expression[WeekOfYear]("weekofyear"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
index 07dea5b470b5f..32dc9b76821bf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
@@ -618,6 +618,53 @@ case class TimeAdd(start: Expression, interval: Expression)
   }
 }
 
+/**
+ * Assumes given timestamp is UTC and converts to given timezone.
+ */
+case class FromUTCTimestamp(left: Expression, right: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType, StringType)
+  override def dataType: DataType = TimestampType
+  override def prettyName: String = "from_utc_timestamp"
+
+  override def nullSafeEval(time: Any, timezone: Any): Any = {
+    DateTimeUtils.fromUTCTime(time.asInstanceOf[Long],
+      timezone.asInstanceOf[UTF8String].toString)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    if (right.foldable) {
+      val tz = right.eval()
+      if (tz == null) {
+        s"""
+           |boolean ${ev.isNull} = true;
+           |long ${ev.primitive} = 0;
+         """.stripMargin
+      } else {
+        val tzTerm = ctx.freshName("tz")
+        val tzClass = classOf[TimeZone].getName
+        ctx.addMutableState(tzClass, tzTerm, s"""$tzTerm = $tzClass.getTimeZone("$tz");""")
+        val eval = left.gen(ctx)
+        s"""
+           |${eval.code}
+           |boolean ${ev.isNull} = ${eval.isNull};
+           |long ${ev.primitive} = 0;
+           |if (!${ev.isNull}) {
+           |  ${ev.primitive} = ${eval.primitive} +
+           |   ${tzTerm}.getOffset(${eval.primitive} / 1000) * 1000L;
+           |}
+         """.stripMargin
+      }
+    } else {
+      defineCodeGen(ctx, ev, (timestamp, format) => {
+        s"""$dtu.fromUTCTime($timestamp, $format.toString())"""
+      })
+    }
+  }
+}
+
 /**
  * Subtracts an interval from timestamp.
  */
@@ -696,6 +743,53 @@ case class MonthsBetween(date1: Expression, date2: Expression)
   }
 }
 
+/**
+ * Assumes given timestamp is in given timezone and converts to UTC.
+ */
+case class ToUTCTimestamp(left: Expression, right: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(TimestampType, StringType)
+  override def dataType: DataType = TimestampType
+  override def prettyName: String = "to_utc_timestamp"
+
+  override def nullSafeEval(time: Any, timezone: Any): Any = {
+    DateTimeUtils.toUTCTime(time.asInstanceOf[Long],
+      timezone.asInstanceOf[UTF8String].toString)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val dtu = DateTimeUtils.getClass.getName.stripSuffix("$")
+    if (right.foldable) {
+      val tz = right.eval()
+      if (tz == null) {
+        s"""
+           |boolean ${ev.isNull} = true;
+           |long ${ev.primitive} = 0;
+         """.stripMargin
+      } else {
+        val tzTerm = ctx.freshName("tz")
+        val tzClass = classOf[TimeZone].getName
+        ctx.addMutableState(tzClass, tzTerm, s"""$tzTerm = $tzClass.getTimeZone("$tz");""")
+        val eval = left.gen(ctx)
+        s"""
+           |${eval.code}
+           |boolean ${ev.isNull} = ${eval.isNull};
+           |long ${ev.primitive} = 0;
+           |if (!${ev.isNull}) {
+           |  ${ev.primitive} = ${eval.primitive} -
+           |   ${tzTerm}.getOffset(${eval.primitive} / 1000) * 1000L;
+           |}
+         """.stripMargin
+      }
+    } else {
+      defineCodeGen(ctx, ev, (timestamp, format) => {
+        s"""$dtu.toUTCTime($timestamp, $format.toString())"""
+      })
+    }
+  }
+}
+
 /**
  * Returns the date part of a timestamp or string.
  */
@@ -714,7 +808,7 @@ case class ToDate(child: Expression) extends UnaryExpression with ImplicitCastIn
   }
 }
 
-/*
+/**
  * Returns date truncated to the unit specified by the format.
  */
 case class TruncDate(date: Expression, format: Expression)
@@ -783,3 +877,23 @@ case class TruncDate(date: Expression, format: Expression)
     }
   }
 }
+
+/**
+ * Returns the number of days from startDate to endDate.
+ */
+case class DateDiff(endDate: Expression, startDate: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = endDate
+  override def right: Expression = startDate
+  override def inputTypes: Seq[AbstractDataType] = Seq(DateType, DateType)
+  override def dataType: DataType = IntegerType
+
+  override def nullSafeEval(end: Any, start: Any): Any = {
+    end.asInstanceOf[Int] - start.asInstanceOf[Int]
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    defineCodeGen(ctx, ev, (end, start) => s"$end - $start")
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 032ed8a56a50e..6a98f4d9c54bc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -814,4 +814,24 @@ object DateTimeUtils {
       }
     }
   }
+
+  /**
+   * Returns a timestamp of given timezone from utc timestamp, with the same string
+   * representation in their timezone.
+   */
+  def fromUTCTime(time: Long, timeZone: String): Long = {
+    val tz = TimeZone.getTimeZone(timeZone)
+    val offset = tz.getOffset(time / 1000L)
+    time + offset * 1000L
+  }
+
+  /**
+   * Returns a utc timestamp from a given timestamp from a given timezone, with the same
+   * string representation in their timezone.
+   */
+  def toUTCTime(time: Long, timeZone: String): Long = {
+    val tz = TimeZone.getTimeZone(timeZone)
+    val offset = tz.getOffset(time / 1000L)
+    time - offset * 1000L
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index 6c15c05da3094..3bff8e012a763 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -23,8 +23,8 @@ import java.util.Calendar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.unsafe.types.CalendarInterval
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
 
 class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -48,15 +48,13 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("DayOfYear") {
     val sdfDay = new SimpleDateFormat("D")
-    (1998 to 2002).foreach { y =>
-      (0 to 3).foreach { m =>
-        (0 to 5).foreach { i =>
-          val c = Calendar.getInstance()
-          c.set(y, m, 28, 0, 0, 0)
-          c.add(Calendar.DATE, i)
-          checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
-            sdfDay.format(c.getTime).toInt)
-        }
+    (0 to 3).foreach { m =>
+      (0 to 5).foreach { i =>
+        val c = Calendar.getInstance()
+        c.set(2000, m, 28, 0, 0, 0)
+        c.add(Calendar.DATE, i)
+        checkEvaluation(DayOfYear(Literal(new Date(c.getTimeInMillis))),
+          sdfDay.format(c.getTime).toInt)
       }
     }
     checkEvaluation(DayOfYear(Literal.create(null, DateType)), null)
@@ -433,4 +431,58 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       UnixTimestamp(Literal("2015-07-24"), Literal("not a valid format")), null)
   }
+
+  test("datediff") {
+    checkEvaluation(
+      DateDiff(Literal(Date.valueOf("2015-07-24")), Literal(Date.valueOf("2015-07-21"))), 3)
+    checkEvaluation(
+      DateDiff(Literal(Date.valueOf("2015-07-21")), Literal(Date.valueOf("2015-07-24"))), -3)
+    checkEvaluation(DateDiff(Literal.create(null, DateType), Literal(Date.valueOf("2015-07-24"))),
+      null)
+    checkEvaluation(DateDiff(Literal(Date.valueOf("2015-07-24")), Literal.create(null, DateType)),
+      null)
+    checkEvaluation(
+      DateDiff(Literal.create(null, DateType), Literal.create(null, DateType)),
+      null)
+  }
+
+  test("to_utc_timestamp") {
+    def test(t: String, tz: String, expected: String): Unit = {
+      checkEvaluation(
+        ToUTCTimestamp(
+          Literal.create(if (t != null) Timestamp.valueOf(t) else null, TimestampType),
+          Literal.create(tz, StringType)),
+        if (expected != null) Timestamp.valueOf(expected) else null)
+      checkEvaluation(
+        ToUTCTimestamp(
+          Literal.create(if (t != null) Timestamp.valueOf(t) else null, TimestampType),
+          NonFoldableLiteral.create(tz, StringType)),
+        if (expected != null) Timestamp.valueOf(expected) else null)
+    }
+    test("2015-07-24 00:00:00", "PST", "2015-07-24 07:00:00")
+    test("2015-01-24 00:00:00", "PST", "2015-01-24 08:00:00")
+    test(null, "UTC", null)
+    test("2015-07-24 00:00:00", null, null)
+    test(null, null, null)
+  }
+
+  test("from_utc_timestamp") {
+    def test(t: String, tz: String, expected: String): Unit = {
+      checkEvaluation(
+        FromUTCTimestamp(
+          Literal.create(if (t != null) Timestamp.valueOf(t) else null, TimestampType),
+          Literal.create(tz, StringType)),
+        if (expected != null) Timestamp.valueOf(expected) else null)
+      checkEvaluation(
+        FromUTCTimestamp(
+          Literal.create(if (t != null) Timestamp.valueOf(t) else null, TimestampType),
+          NonFoldableLiteral.create(tz, StringType)),
+        if (expected != null) Timestamp.valueOf(expected) else null)
+    }
+    test("2015-07-24 00:00:00", "PST", "2015-07-23 17:00:00")
+    test("2015-01-24 00:00:00", "PST", "2015-01-23 16:00:00")
+    test(null, "UTC", null)
+    test("2015-07-24 00:00:00", null, null)
+    test(null, null, null)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 60d2bcfe13757..d18fa4df13355 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -398,4 +398,26 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     c2.set(1996, 2, 31, 0, 0, 0)
     assert(monthsBetween(c1.getTimeInMillis * 1000L, c2.getTimeInMillis * 1000L) === 11)
   }
+
+  test("from UTC timestamp") {
+    def test(utc: String, tz: String, expected: String): Unit = {
+      assert(toJavaTimestamp(fromUTCTime(fromJavaTimestamp(Timestamp.valueOf(utc)), tz)).toString
+        === expected)
+    }
+    test("2011-12-25 09:00:00.123456", "UTC", "2011-12-25 09:00:00.123456")
+    test("2011-12-25 09:00:00.123456", "JST", "2011-12-25 18:00:00.123456")
+    test("2011-12-25 09:00:00.123456", "PST", "2011-12-25 01:00:00.123456")
+    test("2011-12-25 09:00:00.123456", "Asia/Shanghai", "2011-12-25 17:00:00.123456")
+  }
+
+  test("to UTC timestamp") {
+    def test(utc: String, tz: String, expected: String): Unit = {
+      assert(toJavaTimestamp(toUTCTime(fromJavaTimestamp(Timestamp.valueOf(utc)), tz)).toString
+        === expected)
+    }
+    test("2011-12-25 09:00:00.123456", "UTC", "2011-12-25 09:00:00.123456")
+    test("2011-12-25 18:00:00.123456", "JST", "2011-12-25 09:00:00.123456")
+    test("2011-12-25 01:00:00.123456", "PST", "2011-12-25 09:00:00.123456")
+    test("2011-12-25 17:00:00.123456", "Asia/Shanghai", "2011-12-25 09:00:00.123456")
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 818aa109f3993..197cd3de61972 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2019,6 +2019,13 @@ object functions {
    */
   def date_sub(start: Column, days: Int): Column = DateSub(start.expr, Literal(days))
 
+  /**
+   * Returns the number of days from `start` to `end`.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def datediff(end: Column, start: Column): Column = DateDiff(end.expr, start.expr)
+
   /**
    * Extracts the year as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2238,6 +2245,21 @@ object functions {
    */
   def trunc(date: Column, format: String): Column = TruncDate(date.expr, Literal(format))
 
+  /**
+   * Assumes given timestamp is UTC and converts to given timezone.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def from_utc_timestamp(ts: Column, tz: String): Column =
+    FromUTCTimestamp(ts.expr, Literal(tz).expr)
+
+  /**
+   * Assumes given timestamp is in given timezone and converts to UTC.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def to_utc_timestamp(ts: Column, tz: String): Column = ToUTCTimestamp(ts.expr, Literal(tz).expr)
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   // Collection functions
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 8c596fad74ee4..0850f5cf772ed 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -445,4 +445,51 @@ class DateFunctionsSuite extends QueryTest {
       Row(ts1.getTime / 1000L), Row(ts2.getTime / 1000L)))
   }
 
+  test("datediff") {
+    val df = Seq(
+      (Date.valueOf("2015-07-24"), Timestamp.valueOf("2015-07-24 01:00:00"),
+        "2015-07-23", "2015-07-23 03:00:00"),
+      (Date.valueOf("2015-07-25"), Timestamp.valueOf("2015-07-25 02:00:00"),
+        "2015-07-24", "2015-07-24 04:00:00")
+    ).toDF("a", "b", "c", "d")
+    checkAnswer(df.select(datediff(col("a"), col("b"))), Seq(Row(0), Row(0)))
+    checkAnswer(df.select(datediff(col("a"), col("c"))), Seq(Row(1), Row(1)))
+    checkAnswer(df.select(datediff(col("d"), col("b"))), Seq(Row(-1), Row(-1)))
+    checkAnswer(df.selectExpr("datediff(a, d)"), Seq(Row(1), Row(1)))
+  }
+
+  test("from_utc_timestamp") {
+    val df = Seq(
+      (Timestamp.valueOf("2015-07-24 00:00:00"), "2015-07-24 00:00:00"),
+      (Timestamp.valueOf("2015-07-25 00:00:00"), "2015-07-25 00:00:00")
+    ).toDF("a", "b")
+    checkAnswer(
+      df.select(from_utc_timestamp(col("a"), "PST")),
+      Seq(
+        Row(Timestamp.valueOf("2015-07-23 17:00:00")),
+        Row(Timestamp.valueOf("2015-07-24 17:00:00"))))
+    checkAnswer(
+      df.select(from_utc_timestamp(col("b"), "PST")),
+      Seq(
+        Row(Timestamp.valueOf("2015-07-23 17:00:00")),
+        Row(Timestamp.valueOf("2015-07-24 17:00:00"))))
+  }
+
+  test("to_utc_timestamp") {
+    val df = Seq(
+      (Timestamp.valueOf("2015-07-24 00:00:00"), "2015-07-24 00:00:00"),
+      (Timestamp.valueOf("2015-07-25 00:00:00"), "2015-07-25 00:00:00")
+    ).toDF("a", "b")
+    checkAnswer(
+      df.select(to_utc_timestamp(col("a"), "PST")),
+      Seq(
+        Row(Timestamp.valueOf("2015-07-24 07:00:00")),
+        Row(Timestamp.valueOf("2015-07-25 07:00:00"))))
+    checkAnswer(
+      df.select(to_utc_timestamp(col("b"), "PST")),
+      Seq(
+        Row(Timestamp.valueOf("2015-07-24 07:00:00")),
+        Row(Timestamp.valueOf("2015-07-25 07:00:00"))))
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index 1c1be0c3cc1df..ab5da6ee79f1b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -175,59 +175,13 @@ class StringFunctionsSuite extends QueryTest {
 
   test("string substring_index function") {
     val df = Seq(("www.apache.org", ".", "zz")).toDF("a", "b", "c")
-    checkAnswer(
-      df.select(substring_index($"a", ".", 3)),
-      Row("www.apache.org"))
     checkAnswer(
       df.select(substring_index($"a", ".", 2)),
       Row("www.apache"))
     checkAnswer(
-      df.select(substring_index($"a", ".", 1)),
-      Row("www"))
-    checkAnswer(
-      df.select(substring_index($"a", ".", 0)),
-      Row(""))
-    checkAnswer(
-      df.select(substring_index(lit("www.apache.org"), ".", -1)),
-      Row("org"))
-    checkAnswer(
-      df.select(substring_index(lit("www.apache.org"), ".", -2)),
-      Row("apache.org"))
-    checkAnswer(
-      df.select(substring_index(lit("www.apache.org"), ".", -3)),
-      Row("www.apache.org"))
-    // str is empty string
-    checkAnswer(
-      df.select(substring_index(lit(""), ".", 1)),
-      Row(""))
-    // empty string delim
-    checkAnswer(
-      df.select(substring_index(lit("www.apache.org"), "", 1)),
-      Row(""))
-    // delim does not exist in str
-    checkAnswer(
-      df.select(substring_index(lit("www.apache.org"), "#", 1)),
-      Row("www.apache.org"))
-    // delim is 2 chars
-    checkAnswer(
-      df.select(substring_index(lit("www||apache||org"), "||", 2)),
-      Row("www||apache"))
-    checkAnswer(
-      df.select(substring_index(lit("www||apache||org"), "||", -2)),
-      Row("apache||org"))
-    // null
-    checkAnswer(
-      df.select(substring_index(lit(null), "||", 2)),
-      Row(null))
-    checkAnswer(
-      df.select(substring_index(lit("www.apache.org"), null, 2)),
-      Row(null))
-    // non ascii chars
-    // scalastyle:off
-    checkAnswer(
-      df.selectExpr("""substring_index("大千世界大千世界", "千", 2)"""),
-      Row("大千世界大"))
-    // scalastyle:on
+      df.selectExpr("substring_index(a, '.', 2)"),
+      Row("www.apache")
+    )
   }
 
   test("string locate function") {
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index ec959cb2194b0..53d5b22b527b2 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -263,6 +263,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "timestamp_2",
     "timestamp_udf",
 
+    // Hive returns string from UTC formatted timestamp, spark returns timestamp type
+    "date_udf",
+
     // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
     "udf7"
   )
@@ -397,7 +400,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "date_comparison",
     "date_join1",
     "date_serde",
-    "date_udf",
     "decimal_1",
     "decimal_4",
     "decimal_join",

From 57084e0c7c318912208ee31c52d61c14eeddd8f4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 1 Aug 2015 21:50:42 -0700
Subject: [PATCH 0779/1454] [SPARK-9459] [SQL] use generated
 FromUnsafeProjection to do deep copy for UTF8String and struct

When accessing a column in UnsafeRow, it's good to avoid the copy, then we should do deep copy when turn the UnsafeRow into generic Row, this PR brings generated FromUnsafeProjection to do that.

This PR also fix the expressions that cache the UTF8String, which should also copy it.

Author: Davies Liu <davies@databricks.com>

Closes #7840 from davies/avoid_copy and squashes the following commits:

230c8a1 [Davies Liu] address comment
fd797c9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into avoid_copy
e095dd0 [Davies Liu] rollback rename
8ef5b0b [Davies Liu] copy String in Columnar
81360b8 [Davies Liu] fix class name
9aecb88 [Davies Liu] use FromUnsafeProjection to do deep copy for UTF8String and struct
---
 .../sql/catalyst/expressions/UnsafeRow.java   |   8 +-
 .../sql/catalyst/expressions/Projection.scala |  33 ++--
 .../expressions/codegen/CodeGenerator.scala   |   2 +
 .../codegen/GenerateSafeProjection.scala      | 147 ++++++++++++++++++
 .../expressions/stringOperations.scala        |  12 +-
 .../expressions/CodeGenerationSuite.scala     |  45 +++++-
 .../spark/sql/columnar/ColumnType.scala       |  10 +-
 .../apache/spark/unsafe/types/UTF8String.java |  13 +-
 8 files changed, 243 insertions(+), 27 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 5a19aa8920c2f..1b475b2492747 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -363,15 +363,19 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
   @Override
   public UTF8String getUTF8String(int ordinal) {
     assertIndexIsValid(ordinal);
-    return isNullAt(ordinal) ? null : UTF8String.fromBytes(getBinary(ordinal));
+    if (isNullAt(ordinal)) return null;
+    final long offsetAndSize = getLong(ordinal);
+    final int offset = (int) (offsetAndSize >> 32);
+    final int size = (int) (offsetAndSize & ((1L << 32) - 1));
+    return UTF8String.fromAddress(baseObject, baseOffset + offset, size);
   }
 
   @Override
   public byte[] getBinary(int ordinal) {
+    assertIndexIsValid(ordinal);
     if (isNullAt(ordinal)) {
       return null;
     } else {
-      assertIndexIsValid(ordinal);
       final long offsetAndSize = getLong(ordinal);
       final int offset = (int) (offsetAndSize >> 32);
       final int size = (int) (offsetAndSize & ((1L << 32) - 1));
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 000be70f1768f..83129dc12dff6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateUnsafeProjection, GenerateMutableProjection}
-import org.apache.spark.sql.types._
+import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateSafeProjection, GenerateUnsafeProjection}
+import org.apache.spark.sql.types.{DataType, Decimal, StructType, _}
 import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
@@ -114,8 +114,7 @@ object UnsafeProjection {
    * Returns an UnsafeProjection for given Array of DataTypes.
    */
   def create(fields: Array[DataType]): UnsafeProjection = {
-    val exprs = fields.zipWithIndex.map(x => new BoundReference(x._2, x._1, true))
-    create(exprs)
+    create(fields.zipWithIndex.map(x => new BoundReference(x._2, x._1, true)))
   }
 
   /**
@@ -139,19 +138,27 @@ object UnsafeProjection {
 /**
  * A projection that could turn UnsafeRow into GenericInternalRow
  */
-case class FromUnsafeProjection(fields: Seq[DataType]) extends Projection {
+object FromUnsafeProjection {
 
-  def this(schema: StructType) = this(schema.fields.map(_.dataType))
-
-  private[this] val expressions = fields.zipWithIndex.map { case (dt, idx) =>
-    new BoundReference(idx, dt, true)
+  /**
+   * Returns an Projection for given StructType.
+   */
+  def apply(schema: StructType): Projection = {
+    apply(schema.fields.map(_.dataType))
   }
 
-  @transient private[this] lazy val generatedProj =
-    GenerateMutableProjection.generate(expressions)()
+  /**
+   * Returns an UnsafeProjection for given Array of DataTypes.
+   */
+  def apply(fields: Seq[DataType]): Projection = {
+    create(fields.zipWithIndex.map(x => new BoundReference(x._2, x._1, true)))
+  }
 
-  override def apply(input: InternalRow): InternalRow = {
-    generatedProj(input)
+  /**
+   * Returns an Projection for given sequence of Expressions (bounded).
+   */
+  private def create(exprs: Seq[Expression]): Projection = {
+    GenerateSafeProjection.generate(exprs)
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index fc7cfee989666..3177e6b750840 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -127,6 +127,8 @@ class CodeGenContext {
     dataType match {
       case _ if isPrimitiveType(jt) => s"$row.set${primitiveTypeName(jt)}($ordinal, $value)"
       case t: DecimalType => s"$row.setDecimal($ordinal, $value, ${t.precision})"
+      // The UTF8String may came from UnsafeRow, otherwise clone is cheap (re-use the bytes)
+      case StringType => s"$row.update($ordinal, $value.clone())"
       case _ => s"$row.update($ordinal, $value)"
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
new file mode 100644
index 0000000000000..f06ffc5449e76
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
@@ -0,0 +1,147 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
+import org.apache.spark.sql.types.{StringType, StructType, DataType}
+
+
+/**
+ * Generates byte code that produces a [[MutableRow]] object that can update itself based on a new
+ * input [[InternalRow]] for a fixed set of [[Expression Expressions]].
+ */
+object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection] {
+
+  protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
+    in.map(ExpressionCanonicalizer.execute)
+
+  protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
+    in.map(BindReferences.bindReference(_, inputSchema))
+
+  private def genUpdater(
+      ctx: CodeGenContext,
+      setter: String,
+      dataType: DataType,
+      ordinal: Int,
+      value: String): String = {
+    dataType match {
+      case struct: StructType =>
+        val rowTerm = ctx.freshName("row")
+        val updates = struct.map(_.dataType).zipWithIndex.map { case (dt, i) =>
+          val colTerm = ctx.freshName("col")
+          s"""
+            if ($value.isNullAt($i)) {
+              $rowTerm.setNullAt($i);
+            } else {
+              ${ctx.javaType(dt)} $colTerm = ${ctx.getValue(value, dt, s"$i")};
+              ${genUpdater(ctx, rowTerm, dt, i, colTerm)};
+            }
+           """
+        }.mkString("\n")
+        s"""
+          $genericMutableRowType $rowTerm = new $genericMutableRowType(${struct.fields.length});
+          $updates
+          $setter.update($ordinal, $rowTerm.copy());
+        """
+      case _ =>
+        ctx.setColumn(setter, dataType, ordinal, value)
+    }
+  }
+
+  protected def create(expressions: Seq[Expression]): Projection = {
+    val ctx = newCodeGenContext()
+    val projectionCode = expressions.zipWithIndex.map {
+      case (NoOp, _) => ""
+      case (e, i) =>
+        val evaluationCode = e.gen(ctx)
+        evaluationCode.code +
+          s"""
+            if (${evaluationCode.isNull}) {
+              mutableRow.setNullAt($i);
+            } else {
+              ${genUpdater(ctx, "mutableRow", e.dataType, i, evaluationCode.primitive)};
+            }
+          """
+    }
+    // collect projections into blocks as function has 64kb codesize limit in JVM
+    val projectionBlocks = new ArrayBuffer[String]()
+    val blockBuilder = new StringBuilder()
+    for (projection <- projectionCode) {
+      if (blockBuilder.length > 16 * 1000) {
+        projectionBlocks.append(blockBuilder.toString())
+        blockBuilder.clear()
+      }
+      blockBuilder.append(projection)
+    }
+    projectionBlocks.append(blockBuilder.toString())
+
+    val (projectionFuns, projectionCalls) = {
+      // inline it if we have only one block
+      if (projectionBlocks.length == 1) {
+        ("", projectionBlocks.head)
+      } else {
+        (
+          projectionBlocks.zipWithIndex.map { case (body, i) =>
+            s"""
+               |private void apply$i(InternalRow i) {
+               |  $body
+               |}
+             """.stripMargin
+          }.mkString,
+          projectionBlocks.indices.map(i => s"apply$i(i);").mkString("\n")
+          )
+      }
+    }
+
+    val code = s"""
+      public Object generate($exprType[] expr) {
+        return new SpecificSafeProjection(expr);
+      }
+
+      class SpecificSafeProjection extends ${classOf[BaseProjection].getName} {
+
+        private $exprType[] expressions;
+        private $mutableRowType mutableRow;
+        ${declareMutableStates(ctx)}
+
+        public SpecificSafeProjection($exprType[] expr) {
+          expressions = expr;
+          mutableRow = new $genericMutableRowType(${expressions.size});
+          ${initMutableStates(ctx)}
+        }
+
+        $projectionFuns
+
+        public Object apply(Object _i) {
+          InternalRow i = (InternalRow) _i;
+          $projectionCalls
+
+          return mutableRow;
+        }
+      }
+    """
+
+    logDebug(s"code for ${expressions.mkString(",")}:\n${CodeFormatter.format(code)}")
+
+    val c = compile(code)
+    c.generate(ctx.references.toArray).asInstanceOf[Projection]
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 80c64e5689374..56225290cd6b1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -971,12 +971,12 @@ case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expressio
   override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
     if (!p.equals(lastRegex)) {
       // regex value changed
-      lastRegex = p.asInstanceOf[UTF8String]
+      lastRegex = p.asInstanceOf[UTF8String].clone()
       pattern = Pattern.compile(lastRegex.toString)
     }
     if (!r.equals(lastReplacementInUTF8)) {
       // replacement string changed
-      lastReplacementInUTF8 = r.asInstanceOf[UTF8String]
+      lastReplacementInUTF8 = r.asInstanceOf[UTF8String].clone()
       lastReplacement = lastReplacementInUTF8.toString
     }
     val m = pattern.matcher(s.toString())
@@ -1022,12 +1022,12 @@ case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expressio
     s"""
       if (!$regexp.equals(${termLastRegex})) {
         // regex value changed
-        ${termLastRegex} = $regexp;
+        ${termLastRegex} = $regexp.clone();
         ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
       }
       if (!$rep.equals(${termLastReplacementInUTF8})) {
         // replacement string changed
-        ${termLastReplacementInUTF8} = $rep;
+        ${termLastReplacementInUTF8} = $rep.clone();
         ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
       }
       ${termResult}.delete(0, ${termResult}.length());
@@ -1061,7 +1061,7 @@ case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expressio
   override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
     if (!p.equals(lastRegex)) {
       // regex value changed
-      lastRegex = p.asInstanceOf[UTF8String]
+      lastRegex = p.asInstanceOf[UTF8String].clone()
       pattern = Pattern.compile(lastRegex.toString)
     }
     val m = pattern.matcher(s.toString())
@@ -1090,7 +1090,7 @@ case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expressio
       s"""
       if (!$regexp.equals(${termLastRegex})) {
         // regex value changed
-        ${termLastRegex} = $regexp;
+        ${termLastRegex} = $regexp.clone();
         ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
       }
       java.util.regex.Matcher m =
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index f4fbc49677ca3..cc82f7c3f5a73 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -20,11 +20,12 @@ package org.apache.spark.sql.catalyst.expressions
 import scala.math._
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.{Row, RandomDataGenerator}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.types.{DataTypeTestUtils, NullType, StructField, StructType}
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 /**
  * Additional tests for code generation.
@@ -93,4 +94,44 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
       fail(s"Incorrect Evaluation: expressions: $expressions, actual: $actual, expected: $expected")
     }
   }
+
+  test("test generated safe and unsafe projection") {
+    val schema = new StructType(Array(
+      StructField("a", StringType, true),
+      StructField("b", IntegerType, true),
+      StructField("c", new StructType(Array(
+        StructField("aa", StringType, true),
+        StructField("bb", IntegerType, true)
+      )), true),
+      StructField("d", new StructType(Array(
+        StructField("a", new StructType(Array(
+          StructField("b", StringType, true),
+          StructField("", IntegerType, true)
+        )), true)
+      )), true)
+    ))
+    val row = Row("a", 1, Row("b", 2), Row(Row("c", 3)))
+    val lit = Literal.create(row, schema)
+    val internalRow = lit.value.asInstanceOf[InternalRow]
+
+    val unsafeProj = UnsafeProjection.create(schema)
+    val unsafeRow: UnsafeRow = unsafeProj(internalRow)
+    assert(unsafeRow.getUTF8String(0) === UTF8String.fromString("a"))
+    assert(unsafeRow.getInt(1) === 1)
+    assert(unsafeRow.getStruct(2, 2).getUTF8String(0) === UTF8String.fromString("b"))
+    assert(unsafeRow.getStruct(2, 2).getInt(1) === 2)
+    assert(unsafeRow.getStruct(3, 1).getStruct(0, 2).getUTF8String(0) ===
+      UTF8String.fromString("c"))
+    assert(unsafeRow.getStruct(3, 1).getStruct(0, 2).getInt(1) === 3)
+
+    val fromUnsafe = FromUnsafeProjection(schema)
+    val internalRow2 = fromUnsafe(unsafeRow)
+    assert(internalRow === internalRow2)
+
+    // update unsafeRow should not affect internalRow2
+    unsafeRow.setInt(1, 10)
+    unsafeRow.getStruct(2, 2).setInt(1, 10)
+    unsafeRow.getStruct(3, 1).getStruct(0, 2).setInt(1, 4)
+    assert(internalRow === internalRow2)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 30f8fe320db3d..531a8244d55d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -329,7 +329,7 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
   }
 
   override def setField(row: MutableRow, ordinal: Int, value: UTF8String): Unit = {
-    row.update(ordinal, value)
+    row.update(ordinal, value.clone())
   }
 
   override def getField(row: InternalRow, ordinal: Int): UTF8String = {
@@ -337,7 +337,7 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
   }
 
   override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
-    to.update(toOrdinal, from.getUTF8String(fromOrdinal))
+    setField(to, toOrdinal, getField(from, fromOrdinal))
   }
 }
 
@@ -396,7 +396,11 @@ private[sql] case class FIXED_DECIMAL(precision: Int, scale: Int)
   }
 
   override def setField(row: MutableRow, ordinal: Int, value: Decimal): Unit = {
-    row(ordinal) = value
+    row.setDecimal(ordinal, value, precision)
+  }
+
+  override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
+    setField(to, toOrdinal, getField(from, fromOrdinal))
   }
 }
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 213dc761bba48..916825d007cc8 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -78,6 +78,17 @@ public static UTF8String fromBytes(byte[] bytes, int offset, int numBytes) {
     }
   }
 
+  /**
+   * Creates an UTF8String from given address (base and offset) and length.
+   */
+  public static UTF8String fromAddress(Object base, long offset, int numBytes) {
+    if (base != null) {
+      return new UTF8String(base, offset, numBytes);
+    } else {
+      return null;
+    }
+  }
+
   /**
    * Creates an UTF8String from String.
    */
@@ -437,7 +448,7 @@ public UTF8String reverse() {
     while (i < numBytes) {
       int len = numBytesForFirstByte(getByte(i));
       copyMemory(this.base, this.offset + i, result,
-              BYTE_ARRAY_OFFSET + result.length - i - len, len);
+        BYTE_ARRAY_OFFSET + result.length - i - len, len);
 
       i += len;
     }

From 28d944e86d066eb4c651dd803f0b022605ed644e Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Sat, 1 Aug 2015 23:11:25 -0700
Subject: [PATCH 0780/1454] [SPARK-9000] [MLLIB] Support generic item types in
 PrefixSpan

mengxr Please review after #7818 merges and master is rebased.

Continues work by rikima

Closes #7400

Author: Feynman Liang <fliang@databricks.com>
Author: masaki rikitoku <rikima3132@gmail.com>

Closes #7837 from feynmanliang/SPARK-7400-genericItems and squashes the following commits:

8b2c756 [Feynman Liang] Remove orig
92443c8 [Feynman Liang] Style fixes
42c6349 [Feynman Liang] Style fix
14e67fc [Feynman Liang] Generic prefixSpan itemtypes
b3b21e0 [Feynman Liang] Initial support for generic itemtype in public api
b86e0d5 [masaki rikitoku] modify to support generic item type
---
 .../apache/spark/mllib/fpm/PrefixSpan.scala   |  40 ++++++-
 .../spark/mllib/fpm/PrefixSpanSuite.scala     | 104 ++++++++++++++++--
 2 files changed, 132 insertions(+), 12 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index 22b4ddb8b3495..c1761c3642a5d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.fpm
 
 import scala.collection.mutable.ArrayBuilder
+import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
@@ -90,15 +91,44 @@ class PrefixSpan private (
   }
 
   /**
-   * Find the complete set of sequential patterns in the input sequences.
+   * Find the complete set of sequential patterns in the input sequences of itemsets.
+   * @param data ordered sequences of itemsets.
+   * @return (sequential itemset pattern, count) tuples
+   */
+  def run[Item: ClassTag](data: RDD[Array[Array[Item]]]): RDD[(Array[Array[Item]], Long)] = {
+    val itemToInt = data.aggregate(Set[Item]())(
+      seqOp = { (uniqItems, item) => uniqItems ++ item.flatten.toSet },
+      combOp = { _ ++ _ }
+    ).zipWithIndex.toMap
+    val intToItem = Map() ++ (itemToInt.map { case (k, v) => (v, k) })
+
+    val dataInternalRepr = data.map { seq =>
+      seq.map(itemset => itemset.map(itemToInt)).reduce((a, b) => a ++ (DELIMITER +: b))
+    }
+    val results = run(dataInternalRepr)
+
+    def toPublicRepr(pattern: Iterable[Int]): List[Array[Item]] = {
+      pattern.span(_ != DELIMITER) match {
+        case (x, xs) if xs.size > 1 => x.map(intToItem).toArray :: toPublicRepr(xs.tail)
+        case (x, xs) => List(x.map(intToItem).toArray)
+      }
+    }
+    results.map { case (seq: Array[Int], count: Long) =>
+      (toPublicRepr(seq).toArray, count)
+    }
+  }
+
+  /**
+   * Find the complete set of sequential patterns in the input sequences. This method utilizes
+   * the internal representation of itemsets as Array[Int] where each itemset is represented by
+   * a contiguous sequence of non-negative integers and delimiters represented by [[DELIMITER]].
    * @param data ordered sequences of itemsets. Items are represented by non-negative integers.
-   *                  Each itemset has one or more items and is delimited by [[DELIMITER]].
+   *             Each itemset has one or more items and is delimited by [[DELIMITER]].
    * @return a set of sequential pattern pairs,
    *         the key of pair is pattern (a list of elements),
    *         the value of pair is the pattern's count.
    */
-  // TODO: generalize to arbitrary item-types and use mapping to Ints for internal algorithm
-  def run(data: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
+  private[fpm] def run(data: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
     val sc = data.sparkContext
 
     if (data.getStorageLevel == StorageLevel.NONE) {
@@ -260,7 +290,7 @@ class PrefixSpan private (
 private[fpm] object PrefixSpan {
   private[fpm] val DELIMITER = -1
 
-  /** Splits a sequence of itemsets delimited by [[DELIMITER]]. */
+  /** Splits an array of itemsets delimited by [[DELIMITER]]. */
   private[fpm] def splitSequence(sequence: List[Int]): List[Set[Int]] = {
     sequence.span(_ != DELIMITER) match {
       case (x, xs) if xs.length > 1 => x.toSet :: splitSequence(xs.tail)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
index 457f32670fd4e..d87f61e385795 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -21,7 +21,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
 
-  test("PrefixSpan using Integer type, singleton itemsets") {
+  test("PrefixSpan internal (integer seq, -1 delim) run, singleton itemsets") {
 
     /*
       library("arulesSequences")
@@ -69,7 +69,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(4, -1, 5), 2L),
       (Array(5), 3L)
     )
-    compareResults(expectedValue1, result1.collect())
+    compareInternalResults(expectedValue1, result1.collect())
 
     prefixspan.setMinSupport(0.5).setMaxPatternLength(50)
     val result2 = prefixspan.run(rdd)
@@ -80,7 +80,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(4), 4L),
       (Array(5), 3L)
     )
-    compareResults(expectedValue2, result2.collect())
+    compareInternalResults(expectedValue2, result2.collect())
 
     prefixspan.setMinSupport(0.33).setMaxPatternLength(2)
     val result3 = prefixspan.run(rdd)
@@ -100,10 +100,10 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(4, -1, 5), 2L),
       (Array(5), 3L)
     )
-    compareResults(expectedValue3, result3.collect())
+    compareInternalResults(expectedValue3, result3.collect())
   }
 
-  test("PrefixSpan using Integer type, variable-size itemsets") {
+  test("PrefixSpan internal (integer seq, -1 delim) run, variable-size itemsets") {
     val sequences = Array(
       Array(1, -1, 1, 2, 3, -1, 1, 3, -1, 4, -1, 3, 6),
       Array(1, 4, -1, 3, -1, 2, 3, -1, 1, 5),
@@ -254,10 +254,100 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(1, -1, 2, 3, -1, 1), 2L),
       (Array(1, -1, 2, -1, 1), 2L))
 
-    compareResults(expectedValue, result.collect())
+    compareInternalResults(expectedValue, result.collect())
   }
 
-  private def compareResults(
+  test("PrefixSpan Integer type, variable-size itemsets") {
+    val sequences = Seq(
+      Array(Array(1, 2), Array(3)),
+      Array(Array(1), Array(3, 2), Array(1, 2)),
+      Array(Array(1, 2), Array(5)),
+      Array(Array(6)))
+    val rdd = sc.parallelize(sequences, 2).cache()
+
+    val prefixspan = new PrefixSpan()
+      .setMinSupport(0.5)
+      .setMaxPatternLength(5)
+
+    /*
+      To verify results, create file "prefixSpanSeqs2" with content
+      (format = (transactionID, idxInTransaction, numItemsinItemset, itemset)):
+        1 1 2 1 2
+        1 2 1 3
+        2 1 1 1
+        2 2 2 3 2
+        2 3 2 1 2
+        3 1 2 1 2
+        3 2 1 5
+        4 1 1 6
+      In R, run:
+        library("arulesSequences")
+        prefixSpanSeqs = read_baskets("prefixSpanSeqs", info = c("sequenceID","eventID","SIZE"))
+        freqItemSeq = cspade(prefixSpanSeqs,
+                             parameter = 0.5, maxlen = 5 ))
+        resSeq = as(freqItemSeq, "data.frame")
+        resSeq
+
+           sequence support
+        1     <{1}>    0.75
+        2     <{2}>    0.75
+        3     <{3}>    0.50
+        4 <{1},{3}>    0.50
+        5   <{1,2}>    0.75
+     */
+
+    val result = prefixspan.run(rdd)
+    val expected = Array(
+      (Array(Array(1)), 3L),
+      (Array(Array(2)), 3L),
+      (Array(Array(3)), 2L),
+      (Array(Array(1), Array(3)), 2L),
+      (Array(Array(1, 2)), 3L)
+    )
+    compareResults(expected, result.collect())
+  }
+
+  test("PrefixSpan String type, variable-size itemsets") {
+    // This is the same test as "PrefixSpan Int type, variable-size itemsets" except
+    // mapped to Strings
+    val intToString = (1 to 6).zip(Seq("a", "b", "c", "d", "e", "f")).toMap
+    val sequences = Seq(
+      Array(Array(1, 2), Array(3)),
+      Array(Array(1), Array(3, 2), Array(1, 2)),
+      Array(Array(1, 2), Array(5)),
+      Array(Array(6))).map(seq => seq.map(itemSet => itemSet.map(intToString)))
+    val rdd = sc.parallelize(sequences, 2).cache()
+
+    val prefixspan = new PrefixSpan()
+      .setMinSupport(0.5)
+      .setMaxPatternLength(5)
+
+    val result = prefixspan.run(rdd)
+    val expected = Array(
+      (Array(Array(1)), 3L),
+      (Array(Array(2)), 3L),
+      (Array(Array(3)), 2L),
+      (Array(Array(1), Array(3)), 2L),
+      (Array(Array(1, 2)), 3L)
+    ).map { case (pattern, count) =>
+      (pattern.map(itemSet => itemSet.map(intToString)), count)
+    }
+    compareResults(expected, result.collect())
+  }
+
+  private def compareResults[Item](
+      expectedValue: Array[(Array[Array[Item]], Long)],
+      actualValue: Array[(Array[Array[Item]], Long)]): Unit = {
+    val expectedSet = expectedValue.map { case (pattern: Array[Array[Item]], count: Long) =>
+      (pattern.map(itemSet => itemSet.toSet).toSeq, count)
+    }.toSet
+    val actualSet = actualValue.map { case (pattern: Array[Array[Item]], count: Long) =>
+      (pattern.map(itemSet => itemSet.toSet).toSeq, count)
+    }.toSet
+    assert(expectedSet === actualSet)
+  }
+
+  private def compareInternalResults(
       expectedValue: Array[(Array[Int], Long)],
       actualValue: Array[(Array[Int], Long)]): Unit = {
     val expectedSet = expectedValue.map(x => (x._1.toSeq, x._2)).toSet

From 16b928c5436b9b500d25b49bf3670bc50ddafbf9 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 1 Aug 2015 23:36:06 -0700
Subject: [PATCH 0781/1454] [SPARK-9529] [SQL] improve TungstenSort on
 DecimalType

Generate prefix for DecimalType, fix the random generator of decimal

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #7857 from davies/sort_decimal and squashes the following commits:

2433959 [Davies Liu] Merge branch 'master' of github.com:apache/spark into sort_decimal
de24253 [Davies Liu] fix style
0a54c1a [Davies Liu] sort decimal
---
 .../sql/catalyst/expressions/SortOrder.scala  | 13 ++++++++++++
 .../spark/sql/RandomDataGenerator.scala       |  5 ++++-
 .../spark/sql/types/DataTypeTestUtils.scala   |  3 ++-
 .../spark/sql/execution/SortPrefixUtils.scala | 20 +++++++++----------
 .../sql/execution/TungstenSortSuite.scala     |  3 +--
 5 files changed, 30 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index afecf881c7440..5eb5b0d176fc1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -67,6 +67,19 @@ case class SortPrefix(child: SortOrder) extends UnaryExpression {
         (DoublePrefixComparator.computePrefix(Double.NegativeInfinity),
           s"$DoublePrefixCmp.computePrefix((double)$input)")
       case StringType => (0L, s"$input.getPrefix()")
+      case dt: DecimalType if dt.precision - dt.scale <= Decimal.MAX_LONG_DIGITS =>
+        val prefix = if (dt.precision <= Decimal.MAX_LONG_DIGITS) {
+          s"$input.toUnscaledLong()"
+        } else {
+          // reduce the scale to fit in a long
+          val p = Decimal.MAX_LONG_DIGITS
+          val s = p - (dt.precision - dt.scale)
+          s"$input.changePrecision($p, $s) ? $input.toUnscaledLong() : ${Long.MinValue}L"
+        }
+        (Long.MinValue, prefix)
+      case dt: DecimalType =>
+        (DoublePrefixComparator.computePrefix(Double.NegativeInfinity),
+          s"$DoublePrefixCmp.computePrefix($input.toDouble())")
       case _ => (0L, "0L")
     }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
index 81267dc915c10..ea1fd23d0dbce 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -107,7 +107,10 @@ object RandomDataGenerator {
       case DateType => Some(() => new java.sql.Date(rand.nextInt()))
       case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
       case DecimalType.Fixed(precision, scale) => Some(
-        () => BigDecimal.apply(rand.nextLong(), rand.nextInt(), new MathContext(precision)))
+        () => BigDecimal.apply(
+          rand.nextLong() % math.pow(10, precision).toLong,
+          scale,
+          new MathContext(precision)))
       case DoubleType => randomNumeric[Double](
         rand, r => longBitsToDouble(r.nextLong()), Seq(Double.MinValue, Double.MinPositiveValue,
           Double.MaxValue, Double.PositiveInfinity, Double.NegativeInfinity, Double.NaN, 0.0))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
index 0ee9ddac815b8..417df006ab7c2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
@@ -34,8 +34,9 @@ object DataTypeTestUtils {
    * decimal types.
    */
   val fractionalTypes: Set[FractionalType] = Set(
+    DecimalType.USER_DEFAULT,
+    DecimalType(20, 5),
     DecimalType.SYSTEM_DEFAULT,
-    DecimalType(2, 1),
     DoubleType,
     FloatType
   )
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 676656518f4c0..2e870ec8ae965 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -36,16 +36,16 @@ object SortPrefixUtils {
 
   def getPrefixComparator(sortOrder: SortOrder): PrefixComparator = {
     sortOrder.dataType match {
-      case StringType if sortOrder.isAscending => PrefixComparators.STRING
-      case StringType if !sortOrder.isAscending => PrefixComparators.STRING_DESC
-      case BooleanType | ByteType | ShortType | IntegerType | LongType | DateType | TimestampType
-          if sortOrder.isAscending =>
-        PrefixComparators.LONG
-      case BooleanType | ByteType | ShortType | IntegerType | LongType | DateType | TimestampType
-        if !sortOrder.isAscending =>
-        PrefixComparators.LONG_DESC
-      case FloatType | DoubleType if sortOrder.isAscending => PrefixComparators.DOUBLE
-      case FloatType | DoubleType if !sortOrder.isAscending => PrefixComparators.DOUBLE_DESC
+      case StringType =>
+        if (sortOrder.isAscending) PrefixComparators.STRING else PrefixComparators.STRING_DESC
+      case BooleanType | ByteType | ShortType | IntegerType | LongType | DateType | TimestampType =>
+        if (sortOrder.isAscending) PrefixComparators.LONG else PrefixComparators.LONG_DESC
+      case dt: DecimalType if dt.precision - dt.scale <= Decimal.MAX_LONG_DIGITS =>
+        if (sortOrder.isAscending) PrefixComparators.LONG else PrefixComparators.LONG_DESC
+      case FloatType | DoubleType =>
+        if (sortOrder.isAscending) PrefixComparators.DOUBLE else PrefixComparators.DOUBLE_DESC
+      case dt: DecimalType =>
+        if (sortOrder.isAscending) PrefixComparators.DOUBLE else PrefixComparators.DOUBLE_DESC
       case _ => NoOpPrefixComparator
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index b3f821e0cdd37..c7949848513cf 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -61,8 +61,7 @@ class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
 
   // Test sorting on different data types
   for (
-    dataType <- DataTypeTestUtils.atomicTypes ++ Set(NullType)
-    if !dataType.isInstanceOf[DecimalType]; // We don't have an unsafe representation for decimals
+    dataType <- DataTypeTestUtils.atomicTypes ++ Set(NullType);
     nullable <- Seq(true, false);
     sortOrder <- Seq('a.asc :: Nil, 'a.desc :: Nil);
     randomDataGenerator <- RandomDataGenerator.forType(dataType, nullable)

From 9d1c0252690ffac7174e79c3159a9a46a966418a Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 2 Aug 2015 08:56:35 +0100
Subject: [PATCH 0782/1454] [SPARK-9521] [BUILD] Require Maven 3.3.3+ in the
 build

Enforce Maven 3.3.3+ in the build. (Also update the scala compiler plugin while we're at it.)

Author: Sean Owen <sowen@cloudera.com>

Closes #7852 from srowen/SPARK-9521 and squashes the following commits:

3093039 [Sean Owen] Enforce Maven 3.3.3+ in the build. (Also update the scala compiler plugin while we're at it.)
---
 pom.xml | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/pom.xml b/pom.xml
index 1371a1b6bd9f1..be0dac953abf7 100644
--- a/pom.xml
+++ b/pom.xml
@@ -59,7 +59,7 @@
   </issueManagement>
 
   <prerequisites>
-    <maven>3.0.4</maven>
+    <maven>${maven.version}</maven>
   </prerequisites>
 
   <mailingLists>
@@ -118,6 +118,7 @@
     <akka.group>com.typesafe.akka</akka.group>
     <akka.version>2.3.11</akka.version>
     <java.version>1.7</java.version>
+    <maven.version>3.3.3</maven.version>
     <sbt.project.name>spark</sbt.project.name>
     <mesos.version>0.21.1</mesos.version>
     <mesos.classifier>shaded-protobuf</mesos.classifier>
@@ -1153,7 +1154,7 @@
               <configuration>
                 <rules>
                   <requireMavenVersion>
-                    <version>3.0.4</version>
+                    <version>${maven.version}</version>
                   </requireMavenVersion>
                   <requireJavaVersion>
                     <version>${java.version}</version>
@@ -1171,7 +1172,7 @@
         <plugin>
           <groupId>net.alchim31.maven</groupId>
           <artifactId>scala-maven-plugin</artifactId>
-          <version>3.2.0</version>
+          <version>3.2.2</version>
           <executions>
             <execution>
               <id>eclipse-add-source</id>

From 244016a95c43ce6db422378e85a9d527bfe59bf1 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Sun, 2 Aug 2015 09:00:32 +0100
Subject: [PATCH 0783/1454] [SPARK-9149] [ML] [EXAMPLES] Add an example of
 spark.ml KMeans

[SPARK-9149] Add an example of spark.ml KMeans - ASF JIRA https://issues.apache.org/jira/browse/SPARK-9149

jkbradley Should we support other data formats, such as TSV or CSV. I have implemented these examples which support only space separated file which is same as the example for `spark.mllib`'s `KMeans`.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7697 from yu-iskw/SPARK-9149 and squashes the following commits:

7137bad [Yu ISHIKAWA] Fix the typo
56b9da2 [Yu ISHIKAWA] Fix the place of the wrong import statment
554e574 [Yu ISHIKAWA] Change the way to format input data in KMeansExample
e7a948a [Yu ISHIKAWA] Import spark.ml.clustering.KMeans
1901e0c [Yu ISHIKAWA] Change how to initialize an array for a DataFrame schema
d8043f5 [Yu ISHIKAWA] Return a value directly
d81bf55 [Yu ISHIKAWA] Fix a typo and its access specifiers
3e0862d [Yu ISHIKAWA] Make KMeansExample more simple
51ce9c1 [Yu ISHIKAWA] Make JavaKMeansExample more simple
a5a01e0 [Yu ISHIKAWA] Fix a Javadoc about the command to execute the example
b09ec13 [Yu ISHIKAWA] [SPARK-9149][ML][Examples] Add an example of spark.ml KMeans
---
 .../spark/examples/ml/JavaKMeansExample.java  | 97 +++++++++++++++++++
 examples/src/main/python/ml/kmeans_example.py | 71 ++++++++++++++
 .../spark/examples/ml/KMeansExample.scala     | 73 ++++++++++++++
 3 files changed, 241 insertions(+)
 create mode 100644 examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
 create mode 100644 examples/src/main/python/ml/kmeans_example.py
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
new file mode 100644
index 0000000000000..be2bf0c7b465c
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.ml;
+
+import java.util.regex.Pattern;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.ml.clustering.KMeansModel;
+import org.apache.spark.ml.clustering.KMeans;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.catalyst.expressions.GenericRow;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+
+/**
+ * An example demonstrating a k-means clustering.
+ * Run with
+ * <pre>
+ * bin/run-example ml.JavaSimpleParamsExample <file> <k>
+ * </pre>
+ */
+public class JavaKMeansExample {
+
+  private static class ParsePoint implements Function<String, Row> {
+    private static final Pattern separator = Pattern.compile(" ");
+
+    @Override
+    public Row call(String line) {
+      String[] tok = separator.split(line);
+      double[] point = new double[tok.length];
+      for (int i = 0; i < tok.length; ++i) {
+        point[i] = Double.parseDouble(tok[i]);
+      }
+      Vector[] points = {Vectors.dense(point)};
+      return new GenericRow(points);
+    }
+  }
+
+  public static void main(String[] args) {
+    if (args.length != 2) {
+      System.err.println("Usage: ml.JavaKMeansExample <file> <k>");
+      System.exit(1);
+    }
+    String inputFile = args[0];
+    int k = Integer.parseInt(args[1]);
+
+    // Parses the arguments
+    SparkConf conf = new SparkConf().setAppName("JavaKMeansExample");
+    JavaSparkContext jsc = new JavaSparkContext(conf);
+    SQLContext sqlContext = new SQLContext(jsc);
+
+    // Loads data
+    JavaRDD<Row> points = jsc.textFile(inputFile).map(new ParsePoint());
+    StructField[] fields = {new StructField("features", new VectorUDT(), false, Metadata.empty())};
+    StructType schema = new StructType(fields);
+    DataFrame dataset = sqlContext.createDataFrame(points, schema);
+
+    // Trains a k-means model
+    KMeans kmeans = new KMeans()
+      .setK(k);
+    KMeansModel model = kmeans.fit(dataset);
+
+    // Shows the result
+    Vector[] centers = model.clusterCenters();
+    System.out.println("Cluster Centers: ");
+    for (Vector center: centers) {
+      System.out.println(center);
+    }
+
+    jsc.stop();
+  }
+}
diff --git a/examples/src/main/python/ml/kmeans_example.py b/examples/src/main/python/ml/kmeans_example.py
new file mode 100644
index 0000000000000..150dadd42f33e
--- /dev/null
+++ b/examples/src/main/python/ml/kmeans_example.py
@@ -0,0 +1,71 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+import sys
+import re
+
+import numpy as np
+from pyspark import SparkContext
+from pyspark.ml.clustering import KMeans, KMeansModel
+from pyspark.mllib.linalg import VectorUDT, _convert_to_vector
+from pyspark.sql import SQLContext
+from pyspark.sql.types import Row, StructField, StructType
+
+"""
+A simple example demonstrating a k-means clustering.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/kmeans_example.py <input> <k>
+
+This example requires NumPy (http://www.numpy.org/).
+"""
+
+
+def parseVector(line):
+    array = np.array([float(x) for x in line.split(' ')])
+    return _convert_to_vector(array)
+
+
+if __name__ == "__main__":
+
+    FEATURES_COL = "features"
+
+    if len(sys.argv) != 3:
+        print("Usage: kmeans_example.py <file> <k>", file=sys.stderr)
+        exit(-1)
+    path = sys.argv[1]
+    k = sys.argv[2]
+
+    sc = SparkContext(appName="PythonKMeansExample")
+    sqlContext = SQLContext(sc)
+
+    lines = sc.textFile(path)
+    data = lines.map(parseVector)
+    row_rdd = data.map(lambda x: Row(x))
+    schema = StructType([StructField(FEATURES_COL, VectorUDT(), False)])
+    df = sqlContext.createDataFrame(row_rdd, schema)
+
+    kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol(FEATURES_COL)
+    model = kmeans.fit(df)
+    centers = model.clusterCenters()
+
+    print("Cluster Centers: ")
+    for center in centers:
+        print(center)
+
+    sc.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
new file mode 100644
index 0000000000000..5ce38462d1181
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.ml
+
+import org.apache.spark.{SparkContext, SparkConf}
+import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.ml.clustering.KMeans
+import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.types.{StructField, StructType}
+
+
+/**
+ * An example demonstrating a k-means clustering.
+ * Run with
+ * {{{
+ * bin/run-example ml.KMeansExample <file> <k>
+ * }}}
+ */
+object KMeansExample {
+
+  final val FEATURES_COL = "features"
+
+  def main(args: Array[String]): Unit = {
+    if (args.length != 2) {
+      // scalastyle:off println
+      System.err.println("Usage: ml.KMeansExample <file> <k>")
+      // scalastyle:on println
+      System.exit(1)
+    }
+    val input = args(0)
+    val k = args(1).toInt
+
+    // Creates a Spark context and a SQL context
+    val conf = new SparkConf().setAppName(s"${this.getClass.getSimpleName}")
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+
+    // Loads data
+    val rowRDD = sc.textFile(input).filter(_.nonEmpty)
+      .map(_.split(" ").map(_.toDouble)).map(Vectors.dense).map(Row(_))
+    val schema = StructType(Array(StructField(FEATURES_COL, new VectorUDT, false)))
+    val dataset = sqlContext.createDataFrame(rowRDD, schema)
+
+    // Trains a k-means model
+    val kmeans = new KMeans()
+      .setK(k)
+      .setFeaturesCol(FEATURES_COL)
+    val model = kmeans.fit(dataset)
+
+    // Shows the result
+    // scalastyle:off println
+    println("Final Centers: ")
+    model.clusterCenters.foreach(println)
+    // scalastyle:on println
+
+    sc.stop()
+  }
+}

From 8eafa2aeb6c1b465cfdb99f04c2137fc3eac0c01 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 2 Aug 2015 11:36:11 -0700
Subject: [PATCH 0784/1454] [SPARK-9208][SQL] Sort DataFrame functions
 alphabetically.

Author: Reynold Xin <rxin@databricks.com>

Closes #7861 from rxin/api-audit and squashes the following commits:

7200256 [Reynold Xin] [SPARK-9208][SQL] Sort DataFrame functions alphabetically.
---
 .../org/apache/spark/sql/functions.scala      | 640 ++++++++----------
 .../apache/spark/sql/DateFunctionsSuite.scala |  20 +-
 2 files changed, 294 insertions(+), 366 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 197cd3de61972..35958299076ce 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -69,15 +69,6 @@ object functions {
    */
   def column(colName: String): Column = Column(colName)
 
-  /**
-   * Convert a number in string format from one base to another.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def conv(num: Column, fromBase: Int, toBase: Int): Column =
-    Conv(num.expr, lit(fromBase).expr, lit(toBase).expr)
-
   /**
    * Creates a [[Column]] of literal value.
    *
@@ -132,36 +123,54 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Aggregate function: returns the sum of all values in the expression.
+   * Aggregate function: returns the approximate number of distinct items in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def sum(e: Column): Column = Sum(e.expr)
+  def approxCountDistinct(e: Column): Column = ApproxCountDistinct(e.expr)
 
   /**
-   * Aggregate function: returns the sum of all values in the given column.
+   * Aggregate function: returns the approximate number of distinct items in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def sum(columnName: String): Column = sum(Column(columnName))
+  def approxCountDistinct(columnName: String): Column = approxCountDistinct(column(columnName))
 
   /**
-   * Aggregate function: returns the sum of distinct values in the expression.
+   * Aggregate function: returns the approximate number of distinct items in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def sumDistinct(e: Column): Column = SumDistinct(e.expr)
+  def approxCountDistinct(e: Column, rsd: Double): Column = ApproxCountDistinct(e.expr, rsd)
 
   /**
-   * Aggregate function: returns the sum of distinct values in the expression.
+   * Aggregate function: returns the approximate number of distinct items in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def sumDistinct(columnName: String): Column = sumDistinct(Column(columnName))
+  def approxCountDistinct(columnName: String, rsd: Double): Column = {
+    approxCountDistinct(Column(columnName), rsd)
+  }
+
+  /**
+   * Aggregate function: returns the average of the values in a group.
+   *
+   * @group agg_funcs
+   * @since 1.3.0
+   */
+  def avg(e: Column): Column = Average(e.expr)
+
+  /**
+   * Aggregate function: returns the average of the values in a group.
+   *
+   * @group agg_funcs
+   * @since 1.3.0
+   */
+  def avg(columnName: String): Column = avg(Column(columnName))
 
   /**
    * Aggregate function: returns the number of items in a group.
@@ -204,140 +213,158 @@ object functions {
     countDistinct(Column(columnName), columnNames.map(Column.apply) : _*)
 
   /**
-   * Aggregate function: returns the approximate number of distinct items in a group.
+   * Aggregate function: returns the first value in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def approxCountDistinct(e: Column): Column = ApproxCountDistinct(e.expr)
+  def first(e: Column): Column = First(e.expr)
 
   /**
-   * Aggregate function: returns the approximate number of distinct items in a group.
+   * Aggregate function: returns the first value of a column in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def approxCountDistinct(columnName: String): Column = approxCountDistinct(column(columnName))
+  def first(columnName: String): Column = first(Column(columnName))
 
   /**
-   * Aggregate function: returns the approximate number of distinct items in a group.
+   * Aggregate function: returns the last value in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def approxCountDistinct(e: Column, rsd: Double): Column = ApproxCountDistinct(e.expr, rsd)
+  def last(e: Column): Column = Last(e.expr)
 
   /**
-   * Aggregate function: returns the approximate number of distinct items in a group.
+   * Aggregate function: returns the last value of the column in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def approxCountDistinct(columnName: String, rsd: Double): Column = {
-    approxCountDistinct(Column(columnName), rsd)
-  }
+  def last(columnName: String): Column = last(Column(columnName))
 
   /**
-   * Aggregate function: returns the average of the values in a group.
+   * Aggregate function: returns the maximum value of the expression in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def avg(e: Column): Column = Average(e.expr)
+  def max(e: Column): Column = Max(e.expr)
 
   /**
-   * Aggregate function: returns the average of the values in a group.
+   * Aggregate function: returns the maximum value of the column in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def avg(columnName: String): Column = avg(Column(columnName))
+  def max(columnName: String): Column = max(Column(columnName))
 
   /**
-   * Aggregate function: returns the first value in a group.
+   * Aggregate function: returns the average of the values in a group.
+   * Alias for avg.
    *
    * @group agg_funcs
-   * @since 1.3.0
+   * @since 1.4.0
    */
-  def first(e: Column): Column = First(e.expr)
+  def mean(e: Column): Column = avg(e)
 
   /**
-   * Aggregate function: returns the first value of a column in a group.
+   * Aggregate function: returns the average of the values in a group.
+   * Alias for avg.
    *
    * @group agg_funcs
-   * @since 1.3.0
+   * @since 1.4.0
    */
-  def first(columnName: String): Column = first(Column(columnName))
+  def mean(columnName: String): Column = avg(columnName)
 
   /**
-   * Aggregate function: returns the last value in a group.
+   * Aggregate function: returns the minimum value of the expression in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def last(e: Column): Column = Last(e.expr)
+  def min(e: Column): Column = Min(e.expr)
 
   /**
-   * Aggregate function: returns the last value of the column in a group.
+   * Aggregate function: returns the minimum value of the column in a group.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def last(columnName: String): Column = last(Column(columnName))
+  def min(columnName: String): Column = min(Column(columnName))
 
   /**
-   * Aggregate function: returns the average of the values in a group.
-   * Alias for avg.
+   * Aggregate function: returns the sum of all values in the expression.
    *
    * @group agg_funcs
-   * @since 1.4.0
+   * @since 1.3.0
    */
-  def mean(e: Column): Column = avg(e)
+  def sum(e: Column): Column = Sum(e.expr)
 
   /**
-   * Aggregate function: returns the average of the values in a group.
-   * Alias for avg.
+   * Aggregate function: returns the sum of all values in the given column.
    *
    * @group agg_funcs
-   * @since 1.4.0
+   * @since 1.3.0
    */
-  def mean(columnName: String): Column = avg(columnName)
+  def sum(columnName: String): Column = sum(Column(columnName))
 
   /**
-   * Aggregate function: returns the minimum value of the expression in a group.
+   * Aggregate function: returns the sum of distinct values in the expression.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def min(e: Column): Column = Min(e.expr)
+  def sumDistinct(e: Column): Column = SumDistinct(e.expr)
 
   /**
-   * Aggregate function: returns the minimum value of the column in a group.
+   * Aggregate function: returns the sum of distinct values in the expression.
    *
    * @group agg_funcs
    * @since 1.3.0
    */
-  def min(columnName: String): Column = min(Column(columnName))
+  def sumDistinct(columnName: String): Column = sumDistinct(Column(columnName))
+
+  //////////////////////////////////////////////////////////////////////////////////////////////
+  // Window functions
+  //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Aggregate function: returns the maximum value of the expression in a group.
+   * Window function: returns the cumulative distribution of values within a window partition,
+   * i.e. the fraction of rows that are below the current row.
    *
-   * @group agg_funcs
-   * @since 1.3.0
+   * {{{
+   *   N = total number of rows in the partition
+   *   cumeDist(x) = number of values before (and including) x / N
+   * }}}
+   *
+   *
+   * This is equivalent to the CUME_DIST function in SQL.
+   *
+   * @group window_funcs
+   * @since 1.4.0
    */
-  def max(e: Column): Column = Max(e.expr)
+  def cumeDist(): Column = {
+    UnresolvedWindowFunction("cume_dist", Nil)
+  }
 
   /**
-   * Aggregate function: returns the maximum value of the column in a group.
+   * Window function: returns the rank of rows within a window partition, without any gaps.
    *
-   * @group agg_funcs
-   * @since 1.3.0
+   * The difference between rank and denseRank is that denseRank leaves no gaps in ranking
+   * sequence when there are ties. That is, if you were ranking a competition using denseRank
+   * and had three people tie for second place, you would say that all three were in second
+   * place and that the next person came in third.
+   *
+   * This is equivalent to the DENSE_RANK function in SQL.
+   *
+   * @group window_funcs
+   * @since 1.4.0
    */
-  def max(columnName: String): Column = max(Column(columnName))
-
-  //////////////////////////////////////////////////////////////////////////////////////////////
-  // Window functions
-  //////////////////////////////////////////////////////////////////////////////////////////////
+  def denseRank(): Column = {
+    UnresolvedWindowFunction("dense_rank", Nil)
+  }
 
   /**
    * Window function: returns the value that is `offset` rows before the current row, and
@@ -466,32 +493,20 @@ object functions {
   }
 
   /**
-   * Window function: returns a sequential number starting at 1 within a window partition.
-   *
-   * This is equivalent to the ROW_NUMBER function in SQL.
-   *
-   * @group window_funcs
-   * @since 1.4.0
-   */
-  def rowNumber(): Column = {
-    UnresolvedWindowFunction("row_number", Nil)
-  }
-
-  /**
-   * Window function: returns the rank of rows within a window partition, without any gaps.
+   * Window function: returns the relative rank (i.e. percentile) of rows within a window partition.
    *
-   * The difference between rank and denseRank is that denseRank leaves no gaps in ranking
-   * sequence when there are ties. That is, if you were ranking a competition using denseRank
-   * and had three people tie for second place, you would say that all three were in second
-   * place and that the next person came in third.
+   * This is computed by:
+   * {{{
+   *   (rank of row in its partition - 1) / (number of rows in the partition - 1)
+   * }}}
    *
-   * This is equivalent to the DENSE_RANK function in SQL.
+   * This is equivalent to the PERCENT_RANK function in SQL.
    *
    * @group window_funcs
    * @since 1.4.0
    */
-  def denseRank(): Column = {
-    UnresolvedWindowFunction("dense_rank", Nil)
+  def percentRank(): Column = {
+    UnresolvedWindowFunction("percent_rank", Nil)
   }
 
   /**
@@ -512,39 +527,15 @@ object functions {
   }
 
   /**
-   * Window function: returns the cumulative distribution of values within a window partition,
-   * i.e. the fraction of rows that are below the current row.
-   *
-   * {{{
-   *   N = total number of rows in the partition
-   *   cumeDist(x) = number of values before (and including) x / N
-   * }}}
-   *
-   *
-   * This is equivalent to the CUME_DIST function in SQL.
-   *
-   * @group window_funcs
-   * @since 1.4.0
-   */
-  def cumeDist(): Column = {
-    UnresolvedWindowFunction("cume_dist", Nil)
-  }
-
-  /**
-   * Window function: returns the relative rank (i.e. percentile) of rows within a window partition.
-   *
-   * This is computed by:
-   * {{{
-   *   (rank of row in its partition - 1) / (number of rows in the partition - 1)
-   * }}}
+   * Window function: returns a sequential number starting at 1 within a window partition.
    *
-   * This is equivalent to the PERCENT_RANK function in SQL.
+   * This is equivalent to the ROW_NUMBER function in SQL.
    *
    * @group window_funcs
    * @since 1.4.0
    */
-  def percentRank(): Column = {
-    UnresolvedWindowFunction("percent_rank", Nil)
+  def rowNumber(): Column = {
+    UnresolvedWindowFunction("row_number", Nil)
   }
 
   //////////////////////////////////////////////////////////////////////////////////////////////
@@ -595,10 +586,10 @@ object functions {
   }
 
   /**
-   * Returns the first column that is not null and not NaN.
-   * {{{
-   *   df.select(coalesce(df("a"), df("b")))
-   * }}}
+   * Returns the first column that is not null, or null if all inputs are null.
+   *
+   * For example, `coalesce(a, b, c)` will return a if a is not null,
+   * or b if a is null and b is not null, or c if both a and b are null but c is not null.
    *
    * @group normal_funcs
    * @since 1.3.0
@@ -607,9 +598,11 @@ object functions {
   def coalesce(e: Column*): Column = Coalesce(e.map(_.expr))
 
   /**
-   * Creates a new row for each element in the given array or map column.
+   * Creates a string column for the file name of the current Spark task.
+   *
+   * @group normal_funcs
    */
-  def explode(e: Column): Column = Explode(e.expr)
+  def inputFileName(): Column = InputFileName()
 
   /**
    * Return true iff the column is NaN.
@@ -637,13 +630,14 @@ object functions {
   def monotonicallyIncreasingId(): Column = MonotonicallyIncreasingID()
 
   /**
-   * Return an alternative value `r` if `l` is NaN.
-   * This function is useful for mapping NaN values to null.
+   * Returns col1 if it is not NaN, or col2 if col1 is NaN.
+   *
+   * Both inputs should be floating point columns (DoubleType or FloatType).
    *
    * @group normal_funcs
    * @since 1.5.0
    */
-  def nanvl(l: Column, r: Column): Column = NaNvl(l.expr, r.expr)
+  def nanvl(col1: Column, col2: Column): Column = NaNvl(col1.expr, col2.expr)
 
   /**
    * Unary minus, i.e. negate the expression.
@@ -676,31 +670,6 @@ object functions {
    */
   def not(e: Column): Column = !e
 
-  /**
-   * Evaluates a list of conditions and returns one of multiple possible result expressions.
-   * If otherwise is not defined at the end, null is returned for unmatched conditions.
-   *
-   * {{{
-   *   // Example: encoding gender string column into integer.
-   *
-   *   // Scala:
-   *   people.select(when(people("gender") === "male", 0)
-   *     .when(people("gender") === "female", 1)
-   *     .otherwise(2))
-   *
-   *   // Java:
-   *   people.select(when(col("gender").equalTo("male"), 0)
-   *     .when(col("gender").equalTo("female"), 1)
-   *     .otherwise(2))
-   * }}}
-   *
-   * @group normal_funcs
-   * @since 1.4.0
-   */
-  def when(condition: Column, value: Any): Column = {
-    CaseWhen(Seq(condition.expr, lit(value).expr))
-  }
-
   /**
    * Generate a random column with i.i.d. samples from U[0.0, 1.0].
    *
@@ -744,19 +713,10 @@ object functions {
   def sparkPartitionId(): Column = SparkPartitionID()
 
   /**
-   * The file name of the current Spark task
-   *
-   * Note that this is indeterministic becuase it depends on what is currently being read in.
+   * Computes the square root of the specified float value.
    *
-   * @group normal_funcs
-   */
-  def inputFileName(): Column = InputFileName()
-
-  /**
-   * Computes the square root of the specified float value.
-   *
-   * @group math_funcs
-   * @since 1.3.0
+   * @group math_funcs
+   * @since 1.3.0
    */
   def sqrt(e: Column): Column = Sqrt(e.expr)
 
@@ -793,6 +753,31 @@ object functions {
     struct((colName +: colNames).map(col) : _*)
   }
 
+  /**
+   * Evaluates a list of conditions and returns one of multiple possible result expressions.
+   * If otherwise is not defined at the end, null is returned for unmatched conditions.
+   *
+   * {{{
+   *   // Example: encoding gender string column into integer.
+   *
+   *   // Scala:
+   *   people.select(when(people("gender") === "male", 0)
+   *     .when(people("gender") === "female", 1)
+   *     .otherwise(2))
+   *
+   *   // Java:
+   *   people.select(when(col("gender").equalTo("male"), 0)
+   *     .when(col("gender").equalTo("female"), 1)
+   *     .otherwise(2))
+   * }}}
+   *
+   * @group normal_funcs
+   * @since 1.4.0
+   */
+  def when(condition: Column, value: Any): Column = {
+    CaseWhen(Seq(condition.expr, lit(value).expr))
+  }
+
   /**
    * Computes bitwise NOT.
    *
@@ -992,6 +977,15 @@ object functions {
    */
   def ceil(columnName: String): Column = ceil(Column(columnName))
 
+  /**
+   * Convert a number in a string column from one base to another.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
+  def conv(num: Column, fromBase: Int, toBase: Int): Column =
+    Conv(num.expr, lit(fromBase).expr, lit(toBase).expr)
+
   /**
    * Computes the cosine of the given value.
    *
@@ -1024,22 +1018,6 @@ object functions {
    */
   def cosh(columnName: String): Column = cosh(Column(columnName))
 
-  /**
-   * Returns the current date.
-   *
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def current_date(): Column = CurrentDate()
-
-  /**
-   * Returns the current timestamp.
-   *
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def current_timestamp(): Column = CurrentTimestamp()
-
   /**
    * Computes the exponential of the given value.
    *
@@ -1671,109 +1649,75 @@ object functions {
   //////////////////////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Concatenates input strings together into a single string.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  @scala.annotation.varargs
-  def concat(exprs: Column*): Column = Concat(exprs.map(_.expr))
-
-  /**
-   * Concatenates input strings together into a single string, using the given separator.
+   * Computes the numeric value of the first character of the string column, and returns the
+   * result as a int column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  @scala.annotation.varargs
-  def concat_ws(sep: String, exprs: Column*): Column = {
-    ConcatWs(Literal.create(sep, StringType) +: exprs.map(_.expr))
-  }
+  def ascii(e: Column): Column = Ascii(e.expr)
 
   /**
-   * Computes the length of a given string / binary value.
+   * Computes the BASE64 encoding of a binary column and returns it as a string column.
+   * This is the reverse of unbase64.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def length(e: Column): Column = Length(e.expr)
-
-  /**
-   * Converts a string expression to lower case.
-   *
-   * @group string_funcs
-   * @since 1.3.0
-   */
-  def lower(e: Column): Column = Lower(e.expr)
-
-  /**
-   * Converts a string expression to upper case.
-   *
-   * @group string_funcs
-   * @since 1.3.0
-   */
-  def upper(e: Column): Column = Upper(e.expr)
+  def base64(e: Column): Column = Base64(e.expr)
 
   /**
-   * Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
-   * and returns the result as a string.
-   * If d is 0, the result has no decimal point or fractional part.
-   * If d < 0, the result will be null.
+   * Concatenates multiple input string columns together into a single string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def format_number(x: Column, d: Int): Column = FormatNumber(x.expr, lit(d).expr)
+  @scala.annotation.varargs
+  def concat(exprs: Column*): Column = Concat(exprs.map(_.expr))
 
   /**
-   * Substring starts at `pos` and is of length `len` when str is String type or
-   * returns the slice of byte array that starts at `pos` in byte and is of length `len`
-   * when str is Binary type
+   * Concatenates multiple input string columns together into a single string column,
+   * using the given separator.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def substring(str: Column, pos: Int, len: Int): Column =
-    Substring(str.expr, lit(pos).expr, lit(len).expr)
-
-  /**
-   * Computes the Levenshtein distance of the two given string columns.
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def levenshtein(l: Column, r: Column): Column = Levenshtein(l.expr, r.expr)
+  @scala.annotation.varargs
+  def concat_ws(sep: String, exprs: Column*): Column = {
+    ConcatWs(Literal.create(sep, StringType) +: exprs.map(_.expr))
+  }
 
   /**
-   * Computes the numeric value of the first character of the specified string column.
+   * Computes the first argument into a string from a binary using the provided character set
+   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+   * If either argument is null, the result will also be null.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def ascii(e: Column): Column = Ascii(e.expr)
+  def decode(value: Column, charset: String): Column = Decode(value.expr, lit(charset).expr)
 
   /**
-   * Trim the spaces from both ends for the specified string column.
+   * Computes the first argument into a binary from a string using the provided character set
+   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+   * If either argument is null, the result will also be null.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def trim(e: Column): Column = StringTrim(e.expr)
+  def encode(value: Column, charset: String): Column = Encode(value.expr, lit(charset).expr)
 
   /**
-   * Trim the spaces from left end for the specified string value.
+   * Formats numeric column x to a format like '#,###,###.##', rounded to d decimal places,
+   * and returns the result as a string column.
    *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def ltrim(e: Column): Column = StringTrimLeft(e.expr)
-
-  /**
-   * Trim the spaces from right end for the specified string value.
+   * If d is 0, the result has no decimal point or fractional part.
+   * If d < 0, the result will be null.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def rtrim(e: Column): Column = StringTrimRight(e.expr)
+  def format_number(x: Column, d: Int): Column = FormatNumber(x.expr, lit(d).expr)
 
   /**
    * Formats the arguments in printf-style and returns the result as a string column.
@@ -1787,9 +1731,11 @@ object functions {
   }
 
   /**
-   * Returns string, with the first letter of each word in uppercase.
+   * Returns a new string column by converting the first letter of each word to uppercase.
    * Words are delimited by whitespace.
    *
+   * For example, "hello world" will become "Hello World".
+   *
    * @group string_funcs
    * @since 1.5.0
    */
@@ -1808,15 +1754,27 @@ object functions {
   def instr(str: Column, substring: String): Column = StringInstr(str.expr, lit(substring).expr)
 
   /**
-   * Returns the substring from string str before count occurrences of the delimiter delim.
-   * If count is positive, everything the left of the final delimiter (counting from left) is
-   * returned. If count is negative, every to the right of the final delimiter (counting from the
-   * right) is returned. substring_index performs a case-sensitive match when searching for delim.
+   * Computes the length of a given string or binary column.
    *
    * @group string_funcs
+   * @since 1.5.0
    */
-  def substring_index(str: Column, delim: String, count: Int): Column =
-    SubstringIndex(str.expr, lit(delim).expr, lit(count).expr)
+  def length(e: Column): Column = Length(e.expr)
+
+  /**
+   * Converts a string column to lower case.
+   *
+   * @group string_funcs
+   * @since 1.3.0
+   */
+  def lower(e: Column): Column = Lower(e.expr)
+
+  /**
+   * Computes the Levenshtein distance of the two given string columns.
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def levenshtein(l: Column, r: Column): Column = Levenshtein(l.expr, r.expr)
 
   /**
    * Locate the position of the first occurrence of substr.
@@ -1830,6 +1788,14 @@ object functions {
     new StringLocate(lit(substr).expr, str.expr)
   }
 
+  /**
+   * Trim the spaces from left end for the specified string value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def ltrim(e: Column): Column = StringTrimLeft(e.expr)
+
   /**
    * Locate the position of the first occurrence of substr in a string column, after position pos.
    *
@@ -1843,6 +1809,15 @@ object functions {
     StringLocate(lit(substr).expr, str.expr, lit(pos).expr)
   }
 
+  /**
+   * Left-pad the string column with
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def lpad(str: Column, len: Int, pad: String): Column = {
+    StringLPad(str.expr, lit(len).expr, lit(pad).expr)
+  }
 
   /**
    * Extract a specific(idx) group identified by a java regex, from the specified string column.
@@ -1865,101 +1840,108 @@ object functions {
   }
 
   /**
-   * Computes the BASE64 encoding of a binary column and returns it as a string column.
-   * This is the reverse of unbase64.
+   * Decodes a BASE64 encoded string column and returns it as a binary column.
+   * This is the reverse of base64.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def base64(e: Column): Column = Base64(e.expr)
+  def unbase64(e: Column): Column = UnBase64(e.expr)
 
   /**
-   * Decodes a BASE64 encoded string column and returns it as a binary column.
-   * This is the reverse of base64.
+   * Right-padded with pad to a length of len.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def unbase64(e: Column): Column = UnBase64(e.expr)
+  def rpad(str: Column, len: Int, pad: String): Column = {
+    StringRPad(str.expr, lit(len).expr, lit(pad).expr)
+  }
 
   /**
-   * Left-padded with pad to a length of len.
+   * Repeats a string column n times, and returns it as a new string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def lpad(str: Column, len: Int, pad: String): Column = {
-    StringLPad(str.expr, lit(len).expr, lit(pad).expr)
+  def repeat(str: Column, n: Int): Column = {
+    StringRepeat(str.expr, lit(n).expr)
   }
 
   /**
-   * Computes the first argument into a binary from a string using the provided character set
-   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
-   * If either argument is null, the result will also be null.
+   * Reverses the string column and returns it as a new string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def encode(value: Column, charset: String): Column = Encode(value.expr, lit(charset).expr)
+  def reverse(str: Column): Column = {
+    StringReverse(str.expr)
+  }
 
   /**
-   * Computes the first argument into a string from a binary using the provided character set
-   * (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
-   * If either argument is null, the result will also be null.
+   * Trim the spaces from right end for the specified string value.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def decode(value: Column, charset: String): Column = Decode(value.expr, lit(charset).expr)
+  def rtrim(e: Column): Column = StringTrimRight(e.expr)
 
   /**
-   * Right-padded with pad to a length of len.
+   * * Return the soundex code for the specified expression.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def rpad(str: Column, len: Int, pad: String): Column = {
-    StringRPad(str.expr, lit(len).expr, lit(pad).expr)
-  }
+  def soundex(e: Column): Column = SoundEx(e.expr)
 
   /**
-   * Repeats a string column n times, and returns it as a new string column.
+   * Splits str around pattern (pattern is a regular expression).
+   * NOTE: pattern is a string represent the regular expression.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def repeat(str: Column, n: Int): Column = {
-    StringRepeat(str.expr, lit(n).expr)
+  def split(str: Column, pattern: String): Column = {
+    StringSplit(str.expr, lit(pattern).expr)
   }
 
   /**
-   * * Return the soundex code for the specified expression.
+   * Substring starts at `pos` and is of length `len` when str is String type or
+   * returns the slice of byte array that starts at `pos` in byte and is of length `len`
+   * when str is Binary type
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def soundex(e: Column): Column = SoundEx(e.expr)
+  def substring(str: Column, pos: Int, len: Int): Column =
+    Substring(str.expr, lit(pos).expr, lit(len).expr)
 
   /**
-   * Splits str around pattern (pattern is a regular expression).
-   * NOTE: pattern is a string represent the regular expression.
+   * Returns the substring from string str before count occurrences of the delimiter delim.
+   * If count is positive, everything the left of the final delimiter (counting from left) is
+   * returned. If count is negative, every to the right of the final delimiter (counting from the
+   * right) is returned. substring_index performs a case-sensitive match when searching for delim.
    *
    * @group string_funcs
-   * @since 1.5.0
    */
-  def split(str: Column, pattern: String): Column = {
-    StringSplit(str.expr, lit(pattern).expr)
-  }
+  def substring_index(str: Column, delim: String, count: Int): Column =
+    SubstringIndex(str.expr, lit(delim).expr, lit(count).expr)
 
   /**
-   * Reversed the string for the specified value.
+   * Trim the spaces from both ends for the specified string column.
    *
    * @group string_funcs
    * @since 1.5.0
    */
-  def reverse(str: Column): Column = {
-    StringReverse(str.expr)
-  }
+  def trim(e: Column): Column = StringTrim(e.expr)
+
+  /**
+   * Converts a string column to upper case.
+   *
+   * @group string_funcs
+   * @since 1.3.0
+   */
+  def upper(e: Column): Column = Upper(e.expr)
 
   //////////////////////////////////////////////////////////////////////////////////////////////
   // DateTime functions
@@ -1967,6 +1949,7 @@ object functions {
 
   /**
    * Returns the date that is numMonths after startDate.
+   *
    * @group datetime_funcs
    * @since 1.5.0
    */
@@ -1974,20 +1957,20 @@ object functions {
     AddMonths(startDate.expr, Literal(numMonths))
 
   /**
-   * Converts a date/timestamp/string to a value of string in the format specified by the date
-   * format given by the second argument.
-   *
-   * A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
-   * pattern letters of [[java.text.SimpleDateFormat]] can be used.
+   * Returns the current date as a date column.
    *
-   * NOTE: Use when ever possible specialized functions like [[year]]. These benefit from a
-   * specialized implementation.
+   * @group datetime_funcs
+   * @since 1.5.0
+   */
+  def current_date(): Column = CurrentDate()
+
+  /**
+   * Returns the current timestamp as a timestamp column.
    *
    * @group datetime_funcs
    * @since 1.5.0
    */
-  def date_format(dateExpr: Column, format: String): Column =
-    DateFormatClass(dateExpr.expr, Literal(format))
+  def current_timestamp(): Column = CurrentTimestamp()
 
   /**
    * Converts a date/timestamp/string to a value of string in the format specified by the date
@@ -2002,8 +1985,8 @@ object functions {
    * @group datetime_funcs
    * @since 1.5.0
    */
-  def date_format(dateColumnName: String, format: String): Column =
-    date_format(Column(dateColumnName), format)
+  def date_format(dateExpr: Column, format: String): Column =
+    DateFormatClass(dateExpr.expr, Literal(format))
 
   /**
    * Returns the date that is `days` days after `start`
@@ -2033,13 +2016,6 @@ object functions {
    */
   def year(e: Column): Column = Year(e.expr)
 
-  /**
-   * Extracts the year as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def year(columnName: String): Column = year(Column(columnName))
-
   /**
    * Extracts the quarter as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2047,13 +2023,6 @@ object functions {
    */
   def quarter(e: Column): Column = Quarter(e.expr)
 
-  /**
-   * Extracts the quarter as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def quarter(columnName: String): Column = quarter(Column(columnName))
-
   /**
    * Extracts the month as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2061,13 +2030,6 @@ object functions {
    */
   def month(e: Column): Column = Month(e.expr)
 
-  /**
-   * Extracts the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def month(columnName: String): Column = month(Column(columnName))
-
   /**
    * Extracts the day of the month as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2075,13 +2037,6 @@ object functions {
    */
   def dayofmonth(e: Column): Column = DayOfMonth(e.expr)
 
-  /**
-   * Extracts the day of the month as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def dayofmonth(columnName: String): Column = dayofmonth(Column(columnName))
-
   /**
    * Extracts the day of the year as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2089,13 +2044,6 @@ object functions {
    */
   def dayofyear(e: Column): Column = DayOfYear(e.expr)
 
-  /**
-   * Extracts the day of the year as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def dayofyear(columnName: String): Column = dayofyear(Column(columnName))
-
   /**
    * Extracts the hours as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2103,13 +2051,6 @@ object functions {
    */
   def hour(e: Column): Column = Hour(e.expr)
 
-  /**
-   * Extracts the hours as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def hour(columnName: String): Column = hour(Column(columnName))
-
   /**
    * Given a date column, returns the last day of the month which the given date belongs to.
    * For example, input "2015-07-27" returns "2015-07-31" since July 31 is the last day of the
@@ -2127,13 +2068,6 @@ object functions {
    */
   def minute(e: Column): Column = Minute(e.expr)
 
-  /**
-   * Extracts the minutes as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def minute(columnName: String): Column = minute(Column(columnName))
-
   /*
    * Returns number of months between dates `date1` and `date2`.
    * @group datetime_funcs
@@ -2163,13 +2097,6 @@ object functions {
    */
   def second(e: Column): Column = Second(e.expr)
 
-  /**
-   * Extracts the seconds as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def second(columnName: String): Column = second(Column(columnName))
-
   /**
    * Extracts the week number as an integer from a given date/timestamp/string.
    * @group datetime_funcs
@@ -2177,13 +2104,6 @@ object functions {
    */
   def weekofyear(e: Column): Column = WeekOfYear(e.expr)
 
-  /**
-   * Extracts the week number as an integer from a given date/timestamp/string.
-   * @group datetime_funcs
-   * @since 1.5.0
-   */
-  def weekofyear(columnName: String): Column = weekofyear(Column(columnName))
-
   /**
    * Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
    * representing the timestamp of that moment in the current system time zone in the given
@@ -2226,7 +2146,7 @@ object functions {
    */
   def unix_timestamp(s: Column, p: String): Column = UnixTimestamp(s.expr, Literal(p))
 
-  /*
+  /**
    * Converts the column into DateType.
    *
    * @group datetime_funcs
@@ -2264,6 +2184,14 @@ object functions {
   // Collection functions
   //////////////////////////////////////////////////////////////////////////////////////////////
 
+  /**
+   * Creates a new row for each element in the given array or map column.
+   *
+   * @group collection_funcs
+   * @since 1.3.0
+   */
+  def explode(e: Column): Column = Explode(e.expr)
+
   /**
    * Returns length of array or map.
    *
@@ -2279,7 +2207,7 @@ object functions {
    * @group collection_funcs
    * @since 1.5.0
    */
-  def sort_array(e: Column): Column = sort_array(e, true)
+  def sort_array(e: Column): Column = sort_array(e, asc = true)
 
   /**
    * Sorts the input array for the given column in ascending / descending order,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 0850f5cf772ed..17897caf952a3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -89,7 +89,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(date_format("a", "y"), date_format("b", "y"), date_format("c", "y")),
+      df.select(date_format($"a", "y"), date_format($"b", "y"), date_format($"c", "y")),
       Row("2015", "2015", "2013"))
 
     checkAnswer(
@@ -101,7 +101,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(year("a"), year("b"), year("c")),
+      df.select(year($"a"), year($"b"), year($"c")),
       Row(2015, 2015, 2013))
 
     checkAnswer(
@@ -115,7 +115,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(quarter("a"), quarter("b"), quarter("c")),
+      df.select(quarter($"a"), quarter($"b"), quarter($"c")),
       Row(2, 2, 4))
 
     checkAnswer(
@@ -127,7 +127,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(month("a"), month("b"), month("c")),
+      df.select(month($"a"), month($"b"), month($"c")),
       Row(4, 4, 4))
 
     checkAnswer(
@@ -139,7 +139,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(dayofmonth("a"), dayofmonth("b"), dayofmonth("c")),
+      df.select(dayofmonth($"a"), dayofmonth($"b"), dayofmonth($"c")),
       Row(8, 8, 8))
 
     checkAnswer(
@@ -151,7 +151,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(dayofyear("a"), dayofyear("b"), dayofyear("c")),
+      df.select(dayofyear($"a"), dayofyear($"b"), dayofyear($"c")),
       Row(98, 98, 98))
 
     checkAnswer(
@@ -163,7 +163,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(hour("a"), hour("b"), hour("c")),
+      df.select(hour($"a"), hour($"b"), hour($"c")),
       Row(0, 13, 13))
 
     checkAnswer(
@@ -175,7 +175,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(minute("a"), minute("b"), minute("c")),
+      df.select(minute($"a"), minute($"b"), minute($"c")),
       Row(0, 10, 10))
 
     checkAnswer(
@@ -187,7 +187,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdf.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(second("a"), second("b"), second("c")),
+      df.select(second($"a"), second($"b"), second($"c")),
       Row(0, 15, 15))
 
     checkAnswer(
@@ -199,7 +199,7 @@ class DateFunctionsSuite extends QueryTest {
     val df = Seq((d, sdfDate.format(d), ts)).toDF("a", "b", "c")
 
     checkAnswer(
-      df.select(weekofyear("a"), weekofyear("b"), weekofyear("c")),
+      df.select(weekofyear($"a"), weekofyear($"b"), weekofyear($"c")),
       Row(15, 15, 15))
 
     checkAnswer(

From 66924ffa6bdb8e0df1b90b789cb7ad443377e729 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sun, 2 Aug 2015 11:50:17 -0700
Subject: [PATCH 0785/1454] [SPARK-9527] [MLLIB] add PrefixSpanModel and make
 PrefixSpan Java friendly

1. Use `PrefixSpanModel` to wrap the frequent sequences.
2. Define `FreqSequence` to wrap each frequent sequence, which contains a Java-friendly method `javaSequence`
3. Overload `run` for Java users.
4. Added a unit test in Java to check Java compatibility.

zhangjiajin feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #7869 from mengxr/SPARK-9527 and squashes the following commits:

4345594 [Xiangrui Meng] add PrefixSpanModel and make PrefixSpan Java friendly
---
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 52 ++++++++++++--
 .../spark/mllib/fpm/JavaPrefixSpanSuite.java  | 67 +++++++++++++++++++
 .../spark/mllib/fpm/PrefixSpanSuite.scala     |  8 +--
 3 files changed, 118 insertions(+), 9 deletions(-)
 create mode 100644 mllib/src/test/java/org/apache/spark/mllib/fpm/JavaPrefixSpanSuite.java

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index c1761c3642a5d..9eaf733fada22 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -17,11 +17,16 @@
 
 package org.apache.spark.mllib.fpm
 
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuilder
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
@@ -93,9 +98,9 @@ class PrefixSpan private (
   /**
    * Find the complete set of sequential patterns in the input sequences of itemsets.
    * @param data ordered sequences of itemsets.
-   * @return (sequential itemset pattern, count) tuples
+   * @return a [[PrefixSpanModel]] that contains the frequent sequences
    */
-  def run[Item: ClassTag](data: RDD[Array[Array[Item]]]): RDD[(Array[Array[Item]], Long)] = {
+  def run[Item: ClassTag](data: RDD[Array[Array[Item]]]): PrefixSpanModel[Item] = {
     val itemToInt = data.aggregate(Set[Item]())(
       seqOp = { (uniqItems, item) => uniqItems ++ item.flatten.toSet },
       combOp = { _ ++ _ }
@@ -113,9 +118,25 @@ class PrefixSpan private (
         case (x, xs) => List(x.map(intToItem).toArray)
       }
     }
-    results.map { case (seq: Array[Int], count: Long) =>
-      (toPublicRepr(seq).toArray, count)
+    val freqSequences = results.map { case (seq: Array[Int], count: Long) =>
+      new FreqSequence[Item](toPublicRepr(seq).toArray, count)
     }
+    new PrefixSpanModel[Item](freqSequences)
+  }
+
+  /**
+   * A Java-friendly version of [[run()]] that reads sequences from a [[JavaRDD]] and returns
+   * frequent sequences in a [[PrefixSpanModel]].
+   * @param data ordered sequences of itemsets stored as Java Iterable of Iterables
+   * @tparam Item item type
+   * @tparam Itemset itemset type, which is an Iterable of Items
+   * @tparam Sequence sequence type, which is an Iterable of Itemsets
+   * @return a [[PrefixSpanModel]] that contains the frequent sequences
+   */
+  def run[Item, Itemset <: jl.Iterable[Item], Sequence <: jl.Iterable[Itemset]](
+      data: JavaRDD[Sequence]): PrefixSpanModel[Item] = {
+    implicit val tag = fakeClassTag[Item]
+    run(data.rdd.map(_.asScala.map(_.asScala.toArray).toArray))
   }
 
   /**
@@ -287,7 +308,7 @@ class PrefixSpan private (
 
 }
 
-private[fpm] object PrefixSpan {
+object PrefixSpan {
   private[fpm] val DELIMITER = -1
 
   /** Splits an array of itemsets delimited by [[DELIMITER]]. */
@@ -313,4 +334,25 @@ private[fpm] object PrefixSpan {
     // TODO: improve complexity by using partial prefixes, considering one item at a time
     itemSet.subsets.filter(_ != Set.empty[Int])
   }
+
+  /**
+   * Represents a frequence sequence.
+   * @param sequence a sequence of itemsets stored as an Array of Arrays
+   * @param freq frequency
+   * @tparam Item item type
+   */
+  class FreqSequence[Item](val sequence: Array[Array[Item]], val freq: Long) extends Serializable {
+    /**
+     * Returns sequence as a Java List of lists for Java users.
+     */
+    def javaSequence: ju.List[ju.List[Item]] = sequence.map(_.toList.asJava).toList.asJava
+  }
 }
+
+/**
+ * Model fitted by [[PrefixSpan]]
+ * @param freqSequences frequent sequences
+ * @tparam Item item type
+ */
+class PrefixSpanModel[Item](val freqSequences: RDD[PrefixSpan.FreqSequence[Item]])
+  extends Serializable
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaPrefixSpanSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaPrefixSpanSuite.java
new file mode 100644
index 0000000000000..34daf5fbde80f
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaPrefixSpanSuite.java
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.fpm;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence;
+
+public class JavaPrefixSpanSuite {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaPrefixSpan");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void runPrefixSpan() {
+    JavaRDD<List<List<Integer>>> sequences = sc.parallelize(Arrays.asList(
+      Arrays.asList(Arrays.asList(1, 2), Arrays.asList(3)),
+      Arrays.asList(Arrays.asList(1), Arrays.asList(3, 2), Arrays.asList(1, 2)),
+      Arrays.asList(Arrays.asList(1, 2), Arrays.asList(5)),
+      Arrays.asList(Arrays.asList(6))
+    ), 2);
+    PrefixSpan prefixSpan = new PrefixSpan()
+      .setMinSupport(0.5)
+      .setMaxPatternLength(5);
+    PrefixSpanModel<Integer> model = prefixSpan.run(sequences);
+    JavaRDD<FreqSequence<Integer>> freqSeqs = model.freqSequences().toJavaRDD();
+    List<FreqSequence<Integer>> localFreqSeqs = freqSeqs.collect();
+    Assert.assertEquals(5, localFreqSeqs.size());
+    // Check that each frequent sequence could be materialized.
+    for (PrefixSpan.FreqSequence<Integer> freqSeq: localFreqSeqs) {
+      List<List<Integer>> seq = freqSeq.javaSequence();
+      long freq = freqSeq.freq();
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
index d87f61e385795..0ae48d62cc6b5 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -296,7 +296,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
         5   <{1,2}>    0.75
      */
 
-    val result = prefixspan.run(rdd)
+    val model = prefixspan.run(rdd)
     val expected = Array(
       (Array(Array(1)), 3L),
       (Array(Array(2)), 3L),
@@ -304,7 +304,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(Array(1), Array(3)), 2L),
       (Array(Array(1, 2)), 3L)
     )
-    compareResults(expected, result.collect())
+    compareResults(expected, model.freqSequences.collect().map(x => (x.sequence, x.freq)))
   }
 
   test("PrefixSpan String type, variable-size itemsets") {
@@ -322,7 +322,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setMinSupport(0.5)
       .setMaxPatternLength(5)
 
-    val result = prefixspan.run(rdd)
+    val model = prefixspan.run(rdd)
     val expected = Array(
       (Array(Array(1)), 3L),
       (Array(Array(2)), 3L),
@@ -332,7 +332,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     ).map { case (pattern, count) =>
       (pattern.map(itemSet => itemSet.map(intToString)), count)
     }
-    compareResults(expected, result.collect())
+    compareResults(expected, model.freqSequences.collect().map(x => (x.sequence, x.freq)))
   }
 
   private def compareResults[Item](

From 2e981b7bfa9dec93fdcf25f3e7220cd6aaba744f Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 2 Aug 2015 12:32:14 -0700
Subject: [PATCH 0786/1454] [SPARK-9531] [SQL]
 UnsafeFixedWidthAggregationMap.destructAndCreateExternalSorter

This pull request adds a destructAndCreateExternalSorter method to UnsafeFixedWidthAggregationMap. The new method does the following:

1. Creates a new external sorter UnsafeKVExternalSorter
2. Adds all the data into an in-memory sorter, sorts them
3. Spills the sorted in-memory data to disk

This method can be used to fallback to sort-based aggregation when under memory pressure.

The pull request also includes accounting fixes from JoshRosen.

TODOs (that can be done in follow-up PRs)
- [x] Address Josh's feedbacks from #7849
- [x] More documentation and test cases
- [x] Make sure we are doing memory accounting correctly with test cases (e.g. did we release the memory in BytesToBytesMap twice?)
- [ ] Look harder at possible memory leaks and exception handling
- [ ] Randomized tester for the KV sorter as well as the aggregation map

Author: Reynold Xin <rxin@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>

Closes #7860 from rxin/kvsorter and squashes the following commits:

986a58c [Reynold Xin] Bug fix.
599317c [Reynold Xin] Style fix and slightly more compact code.
fe7bd4e [Reynold Xin] Bug fixes.
fd71bef [Reynold Xin] Merge remote-tracking branch 'josh/large-records-in-sql-sorter' into kvsorter-with-josh-fix
3efae38 [Reynold Xin] More fixes and documentation.
45f1b09 [Josh Rosen] Ensure that spill files are cleaned up
f6a9bd3 [Reynold Xin] Josh feedback.
9be8139 [Reynold Xin] Remove testSpillFrequency.
7cbe759 [Reynold Xin] [SPARK-9531][SQL] UnsafeFixedWidthAggregationMap.destructAndCreateExternalSorter.
ae4a8af [Josh Rosen] Detect leaked unsafe memory in UnsafeExternalSorterSuite.
52f9b06 [Josh Rosen] Detect ShuffleMemoryManager leaks in UnsafeExternalSorter.
---
 .../spark/unsafe/map/BytesToBytesMap.java     |  32 ++-
 .../unsafe/sort/UnsafeExternalSorter.java     | 197 +++++++++++----
 .../unsafe/sort/UnsafeInMemorySorter.java     |   4 +
 .../unsafe/sort/UnsafeSorterSpillReader.java  |   3 +
 .../unsafe/sort/UnsafeSorterSpillWriter.java  |   4 +
 .../map/AbstractBytesToBytesMapSuite.java     |   7 +-
 .../sort/UnsafeExternalSorterSuite.java       |  65 +++--
 .../sql/catalyst/expressions/UnsafeRow.java   |   3 +-
 .../execution/UnsafeExternalRowSorter.java    |   9 +-
 sql/core/pom.xml                              |   5 +
 .../UnsafeFixedWidthAggregationMap.java       | 103 +-------
 .../sql/execution/UnsafeKVExternalSorter.java | 236 ++++++++++++++++++
 .../spark/sql/execution/SortPrefixUtils.scala |  33 ++-
 .../org/apache/spark/sql/execution/sort.scala |   4 +-
 .../execution/TestShuffleMemoryManager.scala  |  51 ++++
 .../UnsafeFixedWidthAggregationMapSuite.scala | 124 ++++++---
 .../UnsafeKVExternalSorterSuite.scala         | 158 ++++++++++++
 17 files changed, 823 insertions(+), 215 deletions(-)
 create mode 100644 sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index cf222b72727d8..01a66084e918e 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -39,14 +39,22 @@
 
 /**
  * An append-only hash map where keys and values are contiguous regions of bytes.
- * <p>
+ *
  * This is backed by a power-of-2-sized hash table, using quadratic probing with triangular numbers,
  * which is guaranteed to exhaust the space.
- * <p>
+ *
  * The map can support up to 2^29 keys. If the key cardinality is higher than this, you should
  * probably be using sorting instead of hashing for better cache locality.
- * <p>
- * This class is not thread safe.
+ *
+ * The key and values under the hood are stored together, in the following format:
+ *   Bytes 0 to 4: len(k) (key length in bytes) + len(v) (value length in bytes) + 4
+ *   Bytes 4 to 8: len(k)
+ *   Bytes 8 to 8 + len(k): key data
+ *   Bytes 8 + len(k) to 8 + len(k) + len(v): value data
+ *
+ * This means that the first four bytes store the entire record (key + value) length. This format
+ * is consistent with {@link org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter},
+ * so we can pass records from this map directly into the sorter to sort records in place.
  */
 public final class BytesToBytesMap {
 
@@ -253,7 +261,7 @@ public Location next() {
         totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
       }
       loc.with(currentPage, offsetInPage);
-      offsetInPage += 8 + totalLength;
+      offsetInPage += 4 + totalLength;
       currentRecordNumber++;
       return loc;
     }
@@ -366,7 +374,7 @@ private void updateAddressesAndSizes(final Object page, final long offsetInPage)
       position += 4;
       keyLength = PlatformDependent.UNSAFE.getInt(page, position);
       position += 4;
-      valueLength = totalLength - keyLength;
+      valueLength = totalLength - keyLength - 4;
 
       keyMemoryLocation.setObjAndOffset(page, position);
 
@@ -565,7 +573,7 @@ public boolean putNewKey(
       insertCursor += valueLengthBytes; // word used to store the value size
 
       PlatformDependent.UNSAFE.putInt(dataPageBaseObject, recordOffset,
-        keyLengthBytes + valueLengthBytes);
+        keyLengthBytes + valueLengthBytes + 4);
       PlatformDependent.UNSAFE.putInt(dataPageBaseObject, keyLengthOffset, keyLengthBytes);
       // Copy the key
       PlatformDependent.copyMemory(
@@ -620,7 +628,7 @@ private void allocate(int capacity) {
    * Free all allocated memory associated with this map, including the storage for keys and values
    * as well as the hash map array itself.
    *
-   * This method is idempotent.
+   * This method is idempotent and can be called multiple times.
    */
   public void free() {
     longArray = null;
@@ -639,6 +647,14 @@ public TaskMemoryManager getTaskMemoryManager() {
     return taskMemoryManager;
   }
 
+  public ShuffleMemoryManager getShuffleMemoryManager() {
+    return shuffleMemoryManager;
+  }
+
+  public long getPageSizeBytes() {
+    return pageSizeBytes;
+  }
+
   /** Returns the total amount of memory, in bytes, consumed by this map's managed structures. */
   public long getTotalMemoryConsumption() {
     long totalDataPagesSize = 0L;
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index c05f2c332eee3..b984301cbbf2b 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -17,9 +17,12 @@
 
 package org.apache.spark.util.collection.unsafe.sort;
 
+import java.io.File;
 import java.io.IOException;
 import java.util.LinkedList;
 
+import javax.annotation.Nullable;
+
 import scala.runtime.AbstractFunction0;
 import scala.runtime.BoxedUnit;
 
@@ -27,7 +30,6 @@
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import org.apache.spark.SparkConf;
 import org.apache.spark.TaskContext;
 import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
@@ -48,7 +50,7 @@ public final class UnsafeExternalSorter {
   private final PrefixComparator prefixComparator;
   private final RecordComparator recordComparator;
   private final int initialSize;
-  private final TaskMemoryManager memoryManager;
+  private final TaskMemoryManager taskMemoryManager;
   private final ShuffleMemoryManager shuffleMemoryManager;
   private final BlockManager blockManager;
   private final TaskContext taskContext;
@@ -63,26 +65,57 @@ public final class UnsafeExternalSorter {
    * this might not be necessary if we maintained a pool of re-usable pages in the TaskMemoryManager
    * itself).
    */
-  private final LinkedList<MemoryBlock> allocatedPages = new LinkedList<MemoryBlock>();
+  private final LinkedList<MemoryBlock> allocatedPages = new LinkedList<>();
+
+  private final LinkedList<UnsafeSorterSpillWriter> spillWriters = new LinkedList<>();
 
   // These variables are reset after spilling:
-  private UnsafeInMemorySorter sorter;
+  private UnsafeInMemorySorter inMemSorter;
+  // Whether the in-mem sorter is created internally, or passed in from outside.
+  // If it is passed in from outside, we shouldn't release the in-mem sorter's memory.
+  private boolean isInMemSorterExternal = false;
   private MemoryBlock currentPage = null;
   private long currentPagePosition = -1;
   private long freeSpaceInCurrentPage = 0;
 
-  private final LinkedList<UnsafeSorterSpillWriter> spillWriters = new LinkedList<>();
+  public static UnsafeExternalSorter createWithExistingInMemorySorter(
+      TaskMemoryManager taskMemoryManager,
+      ShuffleMemoryManager shuffleMemoryManager,
+      BlockManager blockManager,
+      TaskContext taskContext,
+      RecordComparator recordComparator,
+      PrefixComparator prefixComparator,
+      int initialSize,
+      long pageSizeBytes,
+      UnsafeInMemorySorter inMemorySorter) throws IOException {
+    return new UnsafeExternalSorter(taskMemoryManager, shuffleMemoryManager, blockManager,
+      taskContext, recordComparator, prefixComparator, initialSize, pageSizeBytes, inMemorySorter);
+  }
 
-  public UnsafeExternalSorter(
-      TaskMemoryManager memoryManager,
+  public static UnsafeExternalSorter create(
+      TaskMemoryManager taskMemoryManager,
       ShuffleMemoryManager shuffleMemoryManager,
       BlockManager blockManager,
       TaskContext taskContext,
       RecordComparator recordComparator,
       PrefixComparator prefixComparator,
       int initialSize,
-      SparkConf conf) throws IOException {
-    this.memoryManager = memoryManager;
+      long pageSizeBytes) throws IOException {
+    return new UnsafeExternalSorter(taskMemoryManager, shuffleMemoryManager, blockManager,
+      taskContext, recordComparator, prefixComparator, initialSize, pageSizeBytes, null);
+  }
+
+  private UnsafeExternalSorter(
+      TaskMemoryManager taskMemoryManager,
+      ShuffleMemoryManager shuffleMemoryManager,
+      BlockManager blockManager,
+      TaskContext taskContext,
+      RecordComparator recordComparator,
+      PrefixComparator prefixComparator,
+      int initialSize,
+      long pageSizeBytes,
+      @Nullable UnsafeInMemorySorter existingInMemorySorter) throws IOException {
+    this.taskMemoryManager = taskMemoryManager;
     this.shuffleMemoryManager = shuffleMemoryManager;
     this.blockManager = blockManager;
     this.taskContext = taskContext;
@@ -90,9 +123,18 @@ public UnsafeExternalSorter(
     this.prefixComparator = prefixComparator;
     this.initialSize = initialSize;
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility for units
-    this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
-    this.pageSizeBytes = conf.getSizeAsBytes("spark.buffer.pageSize", "64m");
-    initializeForWriting();
+    // this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
+    this.fileBufferSizeBytes = 32 * 1024;
+    // this.pageSizeBytes = conf.getSizeAsBytes("spark.buffer.pageSize", "64m");
+    this.pageSizeBytes = pageSizeBytes;
+    this.writeMetrics = new ShuffleWriteMetrics();
+
+    if (existingInMemorySorter == null) {
+      initializeForWriting();
+    } else {
+      this.isInMemSorterExternal = true;
+      this.inMemSorter = existingInMemorySorter;
+    }
 
     // Register a cleanup task with TaskContext to ensure that memory is guaranteed to be freed at
     // the end of the task. This is necessary to avoid memory leaks in when the downstream operator
@@ -100,6 +142,7 @@ public UnsafeExternalSorter(
     taskContext.addOnCompleteCallback(new AbstractFunction0<BoxedUnit>() {
       @Override
       public BoxedUnit apply() {
+        deleteSpillFiles();
         freeMemory();
         return null;
       }
@@ -114,22 +157,31 @@ public BoxedUnit apply() {
    */
   private void initializeForWriting() throws IOException {
     this.writeMetrics = new ShuffleWriteMetrics();
-    // TODO: move this sizing calculation logic into a static method of sorter:
-    final long memoryRequested = initialSize * 8L * 2;
-    final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryRequested);
-    if (memoryAcquired != memoryRequested) {
+    final long pointerArrayMemory =
+      UnsafeInMemorySorter.getMemoryRequirementsForPointerArray(initialSize);
+    final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pointerArrayMemory);
+    if (memoryAcquired != pointerArrayMemory) {
       shuffleMemoryManager.release(memoryAcquired);
-      throw new IOException("Could not acquire " + memoryRequested + " bytes of memory");
+      throw new IOException("Could not acquire " + pointerArrayMemory + " bytes of memory");
     }
 
-    this.sorter =
-      new UnsafeInMemorySorter(memoryManager, recordComparator, prefixComparator, initialSize);
+    this.inMemSorter =
+      new UnsafeInMemorySorter(taskMemoryManager, recordComparator, prefixComparator, initialSize);
+    this.isInMemSorterExternal = false;
   }
 
   /**
-   * Sort and spill the current records in response to memory pressure.
+   * Marks the current page as no-more-space-available, and as a result, either allocate a
+   * new page or spill when we see the next record.
    */
   @VisibleForTesting
+  public void closeCurrentPage() {
+    freeSpaceInCurrentPage = 0;
+  }
+
+  /**
+   * Sort and spill the current records in response to memory pressure.
+   */
   public void spill() throws IOException {
     logger.info("Thread {} spilling sort data of {} to disk ({} {} so far)",
       Thread.currentThread().getId(),
@@ -139,9 +191,9 @@ public void spill() throws IOException {
 
     final UnsafeSorterSpillWriter spillWriter =
       new UnsafeSorterSpillWriter(blockManager, fileBufferSizeBytes, writeMetrics,
-        sorter.numRecords());
+        inMemSorter.numRecords());
     spillWriters.add(spillWriter);
-    final UnsafeSorterIterator sortedRecords = sorter.getSortedIterator();
+    final UnsafeSorterIterator sortedRecords = inMemSorter.getSortedIterator();
     while (sortedRecords.hasNext()) {
       sortedRecords.loadNext();
       final Object baseObject = sortedRecords.getBaseObject();
@@ -150,20 +202,24 @@ public void spill() throws IOException {
       spillWriter.write(baseObject, baseOffset, recordLength, sortedRecords.getKeyPrefix());
     }
     spillWriter.close();
-    final long sorterMemoryUsage = sorter.getMemoryUsage();
-    sorter = null;
-    shuffleMemoryManager.release(sorterMemoryUsage);
     final long spillSize = freeMemory();
+    // Note that this is more-or-less going to be a multiple of the page size, so wasted space in
+    // pages will currently be counted as memory spilled even though that space isn't actually
+    // written to disk. This also counts the space needed to store the sorter's pointer array.
     taskContext.taskMetrics().incMemoryBytesSpilled(spillSize);
     initializeForWriting();
   }
 
+  /**
+   * Return the total memory usage of this sorter, including the data pages and the sorter's pointer
+   * array.
+   */
   private long getMemoryUsage() {
     long totalPageSize = 0;
     for (MemoryBlock page : allocatedPages) {
       totalPageSize += page.size();
     }
-    return sorter.getMemoryUsage() + totalPageSize;
+    return inMemSorter.getMemoryUsage() + totalPageSize;
   }
 
   @VisibleForTesting
@@ -171,13 +227,26 @@ public int getNumberOfAllocatedPages() {
     return allocatedPages.size();
   }
 
+  /**
+   * Free this sorter's in-memory data structures, including its data pages and pointer array.
+   *
+   * @return the number of bytes freed.
+   */
   public long freeMemory() {
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
-      memoryManager.freePage(block);
+      taskMemoryManager.freePage(block);
       shuffleMemoryManager.release(block.size());
       memoryFreed += block.size();
     }
+    if (inMemSorter != null) {
+      if (!isInMemSorterExternal) {
+        long sorterMemoryUsage = inMemSorter.getMemoryUsage();
+        memoryFreed += sorterMemoryUsage;
+        shuffleMemoryManager.release(sorterMemoryUsage);
+      }
+      inMemSorter = null;
+    }
     allocatedPages.clear();
     currentPage = null;
     currentPagePosition = -1;
@@ -185,6 +254,20 @@ public long freeMemory() {
     return memoryFreed;
   }
 
+  /**
+   * Deletes any spill files created by this sorter.
+   */
+  public void deleteSpillFiles() {
+    for (UnsafeSorterSpillWriter spill : spillWriters) {
+      File file = spill.getFile();
+      if (file != null && file.exists()) {
+        if (!file.delete()) {
+          logger.error("Was unable to delete spill file {}", file.getAbsolutePath());
+        };
+      }
+    }
+  }
+
   /**
    * Checks whether there is enough space to insert a new record into the sorter.
    *
@@ -195,7 +278,7 @@ public long freeMemory() {
    */
   private boolean haveSpaceForRecord(int requiredSpace) {
     assert (requiredSpace > 0);
-    return (sorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
+    return (inMemSorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
   }
 
   /**
@@ -210,16 +293,16 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
     // TODO: merge these steps to first calculate total memory requirements for this insert,
     // then try to acquire; no point in acquiring sort buffer only to spill due to no space in the
     // data page.
-    if (!sorter.hasSpaceForAnotherRecord()) {
+    if (!inMemSorter.hasSpaceForAnotherRecord()) {
       logger.debug("Attempting to expand sort pointer array");
-      final long oldPointerArrayMemoryUsage = sorter.getMemoryUsage();
+      final long oldPointerArrayMemoryUsage = inMemSorter.getMemoryUsage();
       final long memoryToGrowPointerArray = oldPointerArrayMemoryUsage * 2;
       final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryToGrowPointerArray);
       if (memoryAcquired < memoryToGrowPointerArray) {
         shuffleMemoryManager.release(memoryAcquired);
         spill();
       } else {
-        sorter.expandPointerArray();
+        inMemSorter.expandPointerArray();
         shuffleMemoryManager.release(oldPointerArrayMemoryUsage);
       }
     }
@@ -236,7 +319,9 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
       } else {
         final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
         if (memoryAcquired < pageSizeBytes) {
-          shuffleMemoryManager.release(memoryAcquired);
+          if (memoryAcquired > 0) {
+            shuffleMemoryManager.release(memoryAcquired);
+          }
           spill();
           final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
           if (memoryAcquiredAfterSpilling != pageSizeBytes) {
@@ -244,7 +329,7 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
             throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
           }
         }
-        currentPage = memoryManager.allocatePage(pageSizeBytes);
+        currentPage = taskMemoryManager.allocatePage(pageSizeBytes);
         currentPagePosition = currentPage.getBaseOffset();
         freeSpaceInCurrentPage = pageSizeBytes;
         allocatedPages.add(currentPage);
@@ -267,7 +352,7 @@ public void insertRecord(
     }
 
     final long recordAddress =
-      memoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
+      taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
     final Object dataPageBaseObject = currentPage.getBaseObject();
     PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, lengthInBytes);
     currentPagePosition += 4;
@@ -279,26 +364,48 @@ public void insertRecord(
       lengthInBytes);
     currentPagePosition += lengthInBytes;
     freeSpaceInCurrentPage -= totalSpaceRequired;
-    sorter.insertRecord(recordAddress, prefix);
+    inMemSorter.insertRecord(recordAddress, prefix);
   }
 
   /**
-   * Write a record to the sorter. The record is broken down into two different parts, and
+   * Write a key-value record to the sorter. The key and value will be put together in-memory,
+   * using the following format:
    *
+   * record length (4 bytes), key length (4 bytes), key data, value data
+   *
+   * record length = key length + value length + 4
    */
-  public void insertRecord(
-      Object recordBaseObject1,
-      long recordBaseOffset1,
-      int lengthInBytes1,
-      Object recordBaseObject2,
-      long recordBaseOffset2,
-      int lengthInBytes2,
-      long prefix) throws IOException {
+  public void insertKVRecord(
+      Object keyBaseObj, long keyOffset, int keyLen,
+      Object valueBaseObj, long valueOffset, int valueLen, long prefix) throws IOException {
+    final int totalSpaceRequired = keyLen + valueLen + 4 + 4;
+    if (!haveSpaceForRecord(totalSpaceRequired)) {
+      allocateSpaceForRecord(totalSpaceRequired);
+    }
+
+    final long recordAddress =
+      taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
+    final Object dataPageBaseObject = currentPage.getBaseObject();
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, keyLen + valueLen + 4);
+    currentPagePosition += 4;
 
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, keyLen);
+    currentPagePosition += 4;
+
+    PlatformDependent.copyMemory(
+      keyBaseObj, keyOffset, dataPageBaseObject, currentPagePosition, keyLen);
+    currentPagePosition += keyLen;
+
+    PlatformDependent.copyMemory(
+      valueBaseObj, valueOffset, dataPageBaseObject, currentPagePosition, valueLen);
+    currentPagePosition += valueLen;
+
+    freeSpaceInCurrentPage -= totalSpaceRequired;
+    inMemSorter.insertRecord(recordAddress, prefix);
   }
 
   public UnsafeSorterIterator getSortedIterator() throws IOException {
-    final UnsafeSorterIterator inMemoryIterator = sorter.getSortedIterator();
+    final UnsafeSorterIterator inMemoryIterator = inMemSorter.getSortedIterator();
     int numIteratorsToMerge = spillWriters.size() + (inMemoryIterator.hasNext() ? 1 : 0);
     if (spillWriters.isEmpty()) {
       return inMemoryIterator;
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
index fc34ad9cff369..3131465391900 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
@@ -100,6 +100,10 @@ public long getMemoryUsage() {
     return pointerArray.length * 8L;
   }
 
+  static long getMemoryRequirementsForPointerArray(long numEntries) {
+    return numEntries * 2L * 8L;
+  }
+
   public boolean hasSpaceForAnotherRecord() {
     return pointerArrayInsertPosition + 2 < pointerArray.length;
   }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
index 29e9e0f30f934..ca1ccedc93c8e 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
@@ -31,6 +31,7 @@
  */
 final class UnsafeSorterSpillReader extends UnsafeSorterIterator {
 
+  private final File file;
   private InputStream in;
   private DataInputStream din;
 
@@ -48,6 +49,7 @@ public UnsafeSorterSpillReader(
       File file,
       BlockId blockId) throws IOException {
     assert (file.length() > 0);
+    this.file = file;
     final BufferedInputStream bs = new BufferedInputStream(new FileInputStream(file));
     this.in = blockManager.wrapForCompression(blockId, bs);
     this.din = new DataInputStream(this.in);
@@ -71,6 +73,7 @@ public void loadNext() throws IOException {
     numRecordsRemaining--;
     if (numRecordsRemaining == 0) {
       in.close();
+      file.delete();
       in = null;
       din = null;
     }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
index 71eed29563d4a..44cf6c756d7c3 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
@@ -140,6 +140,10 @@ public void close() throws IOException {
     writeBuffer = null;
   }
 
+  public File getFile() {
+    return file;
+  }
+
   public UnsafeSorterSpillReader getReader(BlockManager blockManager) throws IOException {
     return new UnsafeSorterSpillReader(blockManager, file, blockId);
   }
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 70f8ca4d21345..dbb7c662d7871 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -67,12 +67,11 @@ public MemoryBlock answer(InvocationOnMock invocation) throws Throwable {
 
   @After
   public void tearDown() {
-    if (taskMemoryManager != null) {
+    Assert.assertEquals(0L, taskMemoryManager.cleanUpAllAllocatedMemory());
+    if (shuffleMemoryManager != null) {
       long leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask();
-      Assert.assertEquals(0, taskMemoryManager.cleanUpAllAllocatedMemory());
-      Assert.assertEquals(0, leakedShuffleMemory);
       shuffleMemoryManager = null;
-      taskMemoryManager = null;
+      Assert.assertEquals(0L, leakedShuffleMemory);
     }
   }
 
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 0e391b751226d..52fa8bcd57e79 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -20,12 +20,14 @@
 import java.io.File;
 import java.io.InputStream;
 import java.io.OutputStream;
+import java.util.LinkedList;
 import java.util.UUID;
 
 import scala.Tuple2;
 import scala.Tuple2$;
 import scala.runtime.AbstractFunction1;
 
+import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
 import org.mockito.Mock;
@@ -33,7 +35,6 @@
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
 import static org.junit.Assert.*;
-import static org.mockito.AdditionalAnswers.returnsFirstArg;
 import static org.mockito.AdditionalAnswers.returnsSecondArg;
 import static org.mockito.Answers.RETURNS_SMART_NULLS;
 import static org.mockito.Mockito.*;
@@ -53,7 +54,8 @@
 
 public class UnsafeExternalSorterSuite {
 
-  final TaskMemoryManager memoryManager =
+  final LinkedList<File> spillFilesCreated = new LinkedList<File>();
+  final TaskMemoryManager taskMemoryManager =
     new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP));
   // Use integer comparison for comparing prefixes (which are partition ids, in this case)
   final PrefixComparator prefixComparator = new PrefixComparator() {
@@ -75,13 +77,15 @@ public int compare(
     }
   };
 
-  @Mock(answer = RETURNS_SMART_NULLS) ShuffleMemoryManager shuffleMemoryManager;
+  ShuffleMemoryManager shuffleMemoryManager;
   @Mock(answer = RETURNS_SMART_NULLS) BlockManager blockManager;
   @Mock(answer = RETURNS_SMART_NULLS) DiskBlockManager diskBlockManager;
   @Mock(answer = RETURNS_SMART_NULLS) TaskContext taskContext;
 
   File tempDir;
 
+  private final long pageSizeBytes = new SparkConf().getSizeAsBytes("spark.buffer.pageSize", "64m");
+
   private static final class CompressStream extends AbstractFunction1<OutputStream, OutputStream> {
     @Override
     public OutputStream apply(OutputStream stream) {
@@ -93,15 +97,17 @@ public OutputStream apply(OutputStream stream) {
   public void setUp() {
     MockitoAnnotations.initMocks(this);
     tempDir = new File(Utils.createTempDir$default$1());
+    shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
+    spillFilesCreated.clear();
     taskContext = mock(TaskContext.class);
     when(taskContext.taskMetrics()).thenReturn(new TaskMetrics());
-    when(shuffleMemoryManager.tryToAcquire(anyLong())).then(returnsFirstArg());
     when(blockManager.diskBlockManager()).thenReturn(diskBlockManager);
     when(diskBlockManager.createTempLocalBlock()).thenAnswer(new Answer<Tuple2<TempLocalBlockId, File>>() {
       @Override
       public Tuple2<TempLocalBlockId, File> answer(InvocationOnMock invocationOnMock) throws Throwable {
         TempLocalBlockId blockId = new TempLocalBlockId(UUID.randomUUID());
         File file = File.createTempFile("spillFile", ".spill", tempDir);
+        spillFilesCreated.add(file);
         return Tuple2$.MODULE$.apply(blockId, file);
       }
     });
@@ -130,6 +136,24 @@ public DiskBlockObjectWriter answer(InvocationOnMock invocationOnMock) throws Th
       .then(returnsSecondArg());
   }
 
+  @After
+  public void tearDown() {
+    long leakedUnsafeMemory = taskMemoryManager.cleanUpAllAllocatedMemory();
+    if (shuffleMemoryManager != null) {
+      long leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask();
+      shuffleMemoryManager = null;
+      assertEquals(0L, leakedShuffleMemory);
+    }
+    assertEquals(0, leakedUnsafeMemory);
+  }
+
+  private void assertSpillFilesWereCleanedUp() {
+    for (File spillFile : spillFilesCreated) {
+      assertFalse("Spill file " + spillFile.getPath() + " was not cleaned up",
+        spillFile.exists());
+    }
+  }
+
   private static void insertNumber(UnsafeExternalSorter sorter, int value) throws Exception {
     final int[] arr = new int[] { value };
     sorter.insertRecord(arr, PlatformDependent.INT_ARRAY_OFFSET, 4, value);
@@ -138,15 +162,15 @@ private static void insertNumber(UnsafeExternalSorter sorter, int value) throws
   @Test
   public void testSortingOnlyByPrefix() throws Exception {
 
-    final UnsafeExternalSorter sorter = new UnsafeExternalSorter(
-      memoryManager,
+    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
+      taskMemoryManager,
       shuffleMemoryManager,
       blockManager,
       taskContext,
       recordComparator,
       prefixComparator,
-      1024,
-      new SparkConf());
+      /* initialSize */ 1024,
+      pageSizeBytes);
 
     insertNumber(sorter, 5);
     insertNumber(sorter, 1);
@@ -165,22 +189,22 @@ public void testSortingOnlyByPrefix() throws Exception {
       // TODO: read rest of value.
     }
 
-    // TODO: test for cleanup:
-    // assert(tempDir.isEmpty)
+    sorter.freeMemory();
+    assertSpillFilesWereCleanedUp();
   }
 
   @Test
   public void testSortingEmptyArrays() throws Exception {
 
-    final UnsafeExternalSorter sorter = new UnsafeExternalSorter(
-      memoryManager,
+    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
+      taskMemoryManager,
       shuffleMemoryManager,
       blockManager,
       taskContext,
       recordComparator,
       prefixComparator,
-      1024,
-      new SparkConf());
+      /* initialSize */ 1024,
+      pageSizeBytes);
 
     sorter.insertRecord(null, 0, 0, 0);
     sorter.insertRecord(null, 0, 0, 0);
@@ -197,25 +221,30 @@ public void testSortingEmptyArrays() throws Exception {
       assertEquals(0, iter.getKeyPrefix());
       assertEquals(0, iter.getRecordLength());
     }
+
+    sorter.freeMemory();
+    assertSpillFilesWereCleanedUp();
   }
 
   @Test
   public void testFillingPage() throws Exception {
-    final UnsafeExternalSorter sorter = new UnsafeExternalSorter(
-      memoryManager,
+
+    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
+      taskMemoryManager,
       shuffleMemoryManager,
       blockManager,
       taskContext,
       recordComparator,
       prefixComparator,
-      1024,
-      new SparkConf());
+      /* initialSize */ 1024,
+      pageSizeBytes);
 
     byte[] record = new byte[16];
     while (sorter.getNumberOfAllocatedPages() < 2) {
       sorter.insertRecord(record, PlatformDependent.BYTE_ARRAY_OFFSET, record.length, 0);
     }
     sorter.freeMemory();
+    assertSpillFilesWereCleanedUp();
   }
 
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 1b475b2492747..b4fc0b7b705ec 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -507,7 +507,8 @@ public byte[] getBytes() {
   public String toString() {
     StringBuilder build = new StringBuilder("[");
     for (int i = 0; i < sizeInBytes; i += 8) {
-      build.append(PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + i));
+      build.append(java.lang.Long.toHexString(
+        PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + i)));
       build.append(',');
     }
     build.append(']');
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 68c49feae938e..5e4c6232c9471 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -59,20 +59,21 @@ public UnsafeExternalRowSorter(
       StructType schema,
       Ordering<InternalRow> ordering,
       PrefixComparator prefixComparator,
-      PrefixComputer prefixComputer) throws IOException {
+      PrefixComputer prefixComputer,
+      long pageSizeBytes) throws IOException {
     this.schema = schema;
     this.prefixComputer = prefixComputer;
     final SparkEnv sparkEnv = SparkEnv.get();
     final TaskContext taskContext = TaskContext.get();
-    sorter = new UnsafeExternalSorter(
+    sorter = UnsafeExternalSorter.create(
       taskContext.taskMemoryManager(),
       sparkEnv.shuffleMemoryManager(),
       sparkEnv.blockManager(),
       taskContext,
       new RowComparator(ordering, schema.length()),
       prefixComparator,
-      4096,
-      sparkEnv.conf()
+      /* initialSize */ 4096,
+      pageSizeBytes
     );
   }
 
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index be0966641b5c4..349007789f634 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -106,6 +106,11 @@
       <artifactId>parquet-avro</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.mockito</groupId>
+      <artifactId>mockito-core</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index a0a8dd51540d2..9e2c9334a7bee 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -19,24 +19,18 @@
 
 import java.io.IOException;
 
+import org.apache.spark.SparkEnv;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
-import org.apache.spark.sql.catalyst.expressions.codegen.BaseOrdering;
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.KVIterator;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
-import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.MemoryLocation;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
-import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
-import org.apache.spark.util.collection.unsafe.sort.RecordComparator;
-import org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter;
-import org.apache.spark.util.collection.unsafe.sort.UnsafeSorterIterator;
 
 /**
  * Unsafe-based HashMap for performing aggregations where the aggregated values are fixed-width.
@@ -215,7 +209,7 @@ public void close() {
   }
 
   /**
-   * Free the unsafe memory associated with this map.
+   * Free the memory associated with this map. This is idempotent and can be called multiple times.
    */
   public void free() {
     map.free();
@@ -233,92 +227,17 @@ public void printPerfMetrics() {
   }
 
   /**
-   * Sorts the key, value data in this map in place, and return them as an iterator.
+   * Sorts the map's records in place, spill them to disk, and returns an [[UnsafeKVExternalSorter]]
+   * that can be used to insert more records to do external sorting.
    *
    * The only memory that is allocated is the address/prefix array, 16 bytes per record.
+   *
+   * Note that this destroys the map, and as a result, the map cannot be used anymore after this.
    */
-  public KVIterator<UnsafeRow, UnsafeRow> sortedIterator() {
-    int numElements = map.numElements();
-    final int numKeyFields = groupingKeySchema.size();
-    TaskMemoryManager memoryManager = map.getTaskMemoryManager();
-
-    UnsafeExternalRowSorter.PrefixComputer prefixComp =
-      SortPrefixUtils.createPrefixGenerator(groupingKeySchema);
-    PrefixComparator prefixComparator = SortPrefixUtils.getPrefixComparator(groupingKeySchema);
-
-    final BaseOrdering ordering = GenerateOrdering.create(groupingKeySchema);
-    RecordComparator recordComparator = new RecordComparator() {
-      private final UnsafeRow row1 = new UnsafeRow();
-      private final UnsafeRow row2 = new UnsafeRow();
-
-      @Override
-      public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff2) {
-        row1.pointTo(baseObj1, baseOff1 + 4, numKeyFields, -1);
-        row2.pointTo(baseObj2, baseOff2 + 4, numKeyFields, -1);
-        return ordering.compare(row1, row2);
-      }
-    };
-
-    // Insert the records into the in-memory sorter.
-    final UnsafeInMemorySorter sorter = new UnsafeInMemorySorter(
-      memoryManager, recordComparator, prefixComparator, numElements);
-
-    BytesToBytesMap.BytesToBytesMapIterator iter = map.iterator();
-    UnsafeRow row = new UnsafeRow();
-    while (iter.hasNext()) {
-      final BytesToBytesMap.Location loc = iter.next();
-      final Object baseObject = loc.getKeyAddress().getBaseObject();
-      final long baseOffset = loc.getKeyAddress().getBaseOffset();
-
-      // Get encoded memory address
-      MemoryBlock page = loc.getMemoryPage();
-      long address = memoryManager.encodePageNumberAndOffset(page, baseOffset - 8);
-
-      // Compute prefix
-      row.pointTo(baseObject, baseOffset, numKeyFields, loc.getKeyLength());
-      final long prefix = prefixComp.computePrefix(row);
-
-      sorter.insertRecord(address, prefix);
-    }
-
-    // Return the sorted result as an iterator.
-    return new KVIterator<UnsafeRow, UnsafeRow>() {
-
-      private UnsafeSorterIterator sortedIterator = sorter.getSortedIterator();
-      private final UnsafeRow key = new UnsafeRow();
-      private final UnsafeRow value = new UnsafeRow();
-      private int numValueFields = aggregationBufferSchema.size();
-
-      @Override
-      public boolean next() throws IOException {
-        if (sortedIterator.hasNext()) {
-          sortedIterator.loadNext();
-          Object baseObj = sortedIterator.getBaseObject();
-          long recordOffset = sortedIterator.getBaseOffset();
-          int recordLen = sortedIterator.getRecordLength();
-          int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
-          key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
-          value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, recordLen - keyLen);
-          return true;
-        } else {
-          return false;
-        }
-      }
-
-      @Override
-      public UnsafeRow getKey() {
-        return key;
-      }
-
-      @Override
-      public UnsafeRow getValue() {
-        return value;
-      }
-
-      @Override
-      public void close() {
-        // Do nothing
-      }
-    };
+  public UnsafeKVExternalSorter destructAndCreateExternalSorter() throws IOException {
+    UnsafeKVExternalSorter sorter = new UnsafeKVExternalSorter(
+      groupingKeySchema, aggregationBufferSchema,
+      SparkEnv.get().blockManager(), map.getShuffleMemoryManager(), map.getPageSizeBytes(), map);
+    return sorter;
   }
 }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
new file mode 100644
index 0000000000000..f6b017686306b
--- /dev/null
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -0,0 +1,236 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution;
+
+import java.io.IOException;
+
+import javax.annotation.Nullable;
+
+import com.google.common.annotations.VisibleForTesting;
+
+import org.apache.spark.TaskContext;
+import org.apache.spark.shuffle.ShuffleMemoryManager;
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
+import org.apache.spark.sql.catalyst.expressions.codegen.BaseOrdering;
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.storage.BlockManager;
+import org.apache.spark.unsafe.KVIterator;
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.map.BytesToBytesMap;
+import org.apache.spark.unsafe.memory.MemoryBlock;
+import org.apache.spark.unsafe.memory.TaskMemoryManager;
+import org.apache.spark.util.collection.unsafe.sort.*;
+
+/**
+ * A class for performing external sorting on key-value records. Both key and value are UnsafeRows.
+ *
+ * Note that this class allows optionally passing in a {@link BytesToBytesMap} directly in order
+ * to perform in-place sorting of records in the map.
+ */
+public final class UnsafeKVExternalSorter {
+
+  private final StructType keySchema;
+  private final StructType valueSchema;
+  private final UnsafeExternalRowSorter.PrefixComputer prefixComputer;
+  private final UnsafeExternalSorter sorter;
+
+  public UnsafeKVExternalSorter(StructType keySchema, StructType valueSchema,
+      BlockManager blockManager, ShuffleMemoryManager shuffleMemoryManager, long pageSizeBytes)
+    throws IOException {
+    this(keySchema, valueSchema, blockManager, shuffleMemoryManager, pageSizeBytes, null);
+  }
+
+  public UnsafeKVExternalSorter(StructType keySchema, StructType valueSchema,
+      BlockManager blockManager, ShuffleMemoryManager shuffleMemoryManager, long pageSizeBytes,
+      @Nullable BytesToBytesMap map) throws IOException {
+    this.keySchema = keySchema;
+    this.valueSchema = valueSchema;
+    final TaskContext taskContext = TaskContext.get();
+
+    prefixComputer = SortPrefixUtils.createPrefixGenerator(keySchema);
+    PrefixComparator prefixComparator = SortPrefixUtils.getPrefixComparator(keySchema);
+    BaseOrdering ordering = GenerateOrdering.create(keySchema);
+    KVComparator recordComparator = new KVComparator(ordering, keySchema.length());
+
+    TaskMemoryManager taskMemoryManager = taskContext.taskMemoryManager();
+
+    if (map == null) {
+      sorter = UnsafeExternalSorter.create(
+        taskMemoryManager,
+        shuffleMemoryManager,
+        blockManager,
+        taskContext,
+        recordComparator,
+        prefixComparator,
+        /* initialSize */ 4096,
+        pageSizeBytes);
+    } else {
+      // Insert the records into the in-memory sorter.
+      final UnsafeInMemorySorter inMemSorter = new UnsafeInMemorySorter(
+        taskMemoryManager, recordComparator, prefixComparator, map.numElements());
+
+      final int numKeyFields = keySchema.size();
+      BytesToBytesMap.BytesToBytesMapIterator iter = map.iterator();
+      UnsafeRow row = new UnsafeRow();
+      while (iter.hasNext()) {
+        final BytesToBytesMap.Location loc = iter.next();
+        final Object baseObject = loc.getKeyAddress().getBaseObject();
+        final long baseOffset = loc.getKeyAddress().getBaseOffset();
+
+        // Get encoded memory address
+        // baseObject + baseOffset point to the beginning of the key data in the map, but that
+        // the KV-pair's length data is stored in the word immediately before that address
+        MemoryBlock page = loc.getMemoryPage();
+        long address = taskMemoryManager.encodePageNumberAndOffset(page, baseOffset - 8);
+
+        // Compute prefix
+        row.pointTo(baseObject, baseOffset, numKeyFields, loc.getKeyLength());
+        final long prefix = prefixComputer.computePrefix(row);
+
+        inMemSorter.insertRecord(address, prefix);
+      }
+
+      sorter = UnsafeExternalSorter.createWithExistingInMemorySorter(
+        taskContext.taskMemoryManager(),
+        shuffleMemoryManager,
+        blockManager,
+        taskContext,
+        new KVComparator(ordering, keySchema.length()),
+        prefixComparator,
+        /* initialSize */ 4096,
+        pageSizeBytes,
+        inMemSorter);
+
+      sorter.spill();
+      map.free();
+    }
+  }
+
+  /**
+   * Inserts a key-value record into the sorter. If the sorter no longer has enough memory to hold
+   * the record, the sorter sorts the existing records in-memory, writes them out as partially
+   * sorted runs, and then reallocates memory to hold the new record.
+   */
+  public void insertKV(UnsafeRow key, UnsafeRow value) throws IOException {
+    final long prefix = prefixComputer.computePrefix(key);
+    sorter.insertKVRecord(
+      key.getBaseObject(), key.getBaseOffset(), key.getSizeInBytes(),
+      value.getBaseObject(), value.getBaseOffset(), value.getSizeInBytes(), prefix);
+  }
+
+  public KVIterator<UnsafeRow, UnsafeRow> sortedIterator() throws IOException {
+    try {
+      final UnsafeSorterIterator underlying = sorter.getSortedIterator();
+      if (!underlying.hasNext()) {
+        // Since we won't ever call next() on an empty iterator, we need to clean up resources
+        // here in order to prevent memory leaks.
+        cleanupResources();
+      }
+
+      return new KVIterator<UnsafeRow, UnsafeRow>() {
+        private UnsafeRow key = new UnsafeRow();
+        private UnsafeRow value = new UnsafeRow();
+        private int numKeyFields = keySchema.size();
+        private int numValueFields = valueSchema.size();
+
+        @Override
+        public boolean next() throws IOException {
+          try {
+            if (underlying.hasNext()) {
+              underlying.loadNext();
+
+              Object baseObj = underlying.getBaseObject();
+              long recordOffset = underlying.getBaseOffset();
+              int recordLen = underlying.getRecordLength();
+
+              // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
+              int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
+              int valueLen = recordLen - keyLen - 4;
+
+              key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
+              value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
+
+              return true;
+            } else {
+              key = null;
+              value = null;
+              cleanupResources();
+              return false;
+            }
+          } catch (IOException e) {
+            cleanupResources();
+            throw e;
+          }
+        }
+
+        @Override
+        public UnsafeRow getKey() {
+          return key;
+        }
+
+        @Override
+        public UnsafeRow getValue() {
+          return value;
+        }
+
+        @Override
+        public void close() {
+          cleanupResources();
+        }
+      };
+    } catch (IOException e) {
+      cleanupResources();
+      throw e;
+    }
+  }
+
+  /**
+   * Marks the current page as no-more-space-available, and as a result, either allocate a
+   * new page or spill when we see the next record.
+   */
+  @VisibleForTesting
+  void closeCurrentPage() {
+    sorter.closeCurrentPage();
+  }
+
+  private void cleanupResources() {
+    sorter.freeMemory();
+  }
+
+  private static final class KVComparator extends RecordComparator {
+    private final BaseOrdering ordering;
+    private final UnsafeRow row1 = new UnsafeRow();
+    private final UnsafeRow row2 = new UnsafeRow();
+    private final int numKeyFields;
+
+    public KVComparator(BaseOrdering ordering, int numKeyFields) {
+      this.numKeyFields = numKeyFields;
+      this.ordering = ordering;
+    }
+
+    @Override
+    public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff2) {
+      // Note that since ordering doesn't need the total length of the record, we just pass -1
+      // into the row.
+      row1.pointTo(baseObj1, baseOff1 + 4, numKeyFields, -1);
+      row2.pointTo(baseObj2, baseOff2 + 4, numKeyFields, -1);
+      return ordering.compare(row1, row2);
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 2e870ec8ae965..49adf215379cf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -50,17 +50,36 @@ object SortPrefixUtils {
     }
   }
 
+  /**
+   * Creates the prefix comparator for the first field in the given schema, in ascending order.
+   */
   def getPrefixComparator(schema: StructType): PrefixComparator = {
-    val field = schema.head
-    getPrefixComparator(SortOrder(BoundReference(0, field.dataType, field.nullable), Ascending))
+    if (schema.nonEmpty) {
+      val field = schema.head
+      getPrefixComparator(SortOrder(BoundReference(0, field.dataType, field.nullable), Ascending))
+    } else {
+      new PrefixComparator {
+        override def compare(prefix1: Long, prefix2: Long): Int = 0
+      }
+    }
   }
 
+  /**
+   * Creates the prefix computer for the first field in the given schema, in ascending order.
+   */
   def createPrefixGenerator(schema: StructType): UnsafeExternalRowSorter.PrefixComputer = {
-    val boundReference = BoundReference(0, schema.head.dataType, nullable = true)
-    val prefixProjection = UnsafeProjection.create(SortPrefix(SortOrder(boundReference, Ascending)))
-    new UnsafeExternalRowSorter.PrefixComputer {
-      override def computePrefix(row: InternalRow): Long = {
-        prefixProjection.apply(row).getLong(0)
+    if (schema.nonEmpty) {
+      val boundReference = BoundReference(0, schema.head.dataType, nullable = true)
+      val prefixProjection = UnsafeProjection.create(
+        SortPrefix(SortOrder(boundReference, Ascending)))
+      new UnsafeExternalRowSorter.PrefixComputer {
+        override def computePrefix(row: InternalRow): Long = {
+          prefixProjection.apply(row).getLong(0)
+        }
+      }
+    } else {
+      new UnsafeExternalRowSorter.PrefixComputer {
+        override def computePrefix(row: InternalRow): Long = 0
       }
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index 6d903ab23c57f..92cf328c76cbc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -116,6 +116,7 @@ case class TungstenSort(
   protected override def doExecute(): RDD[InternalRow] = {
     val schema = child.schema
     val childOutput = child.output
+    val pageSize = sparkContext.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
     child.execute().mapPartitions({ iter =>
       val ordering = newOrdering(sortOrder, childOutput)
 
@@ -131,7 +132,8 @@ case class TungstenSort(
         }
       }
 
-      val sorter = new UnsafeExternalRowSorter(schema, ordering, prefixComparator, prefixComputer)
+      val sorter = new UnsafeExternalRowSorter(
+        schema, ordering, prefixComparator, prefixComputer, pageSize)
       if (testSpillFrequency > 0) {
         sorter.setTestSpillFrequency(testSpillFrequency)
       }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
new file mode 100644
index 0000000000000..53de2d0f0771f
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
@@ -0,0 +1,51 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.shuffle.ShuffleMemoryManager
+
+/**
+ * A [[ShuffleMemoryManager]] that can be controlled to run out of memory.
+ */
+class TestShuffleMemoryManager extends ShuffleMemoryManager(Long.MaxValue) {
+  private var oom = false
+
+  override def tryToAcquire(numBytes: Long): Long = {
+    if (oom) {
+      oom = false
+      0
+    } else {
+      // Uncomment the following to trace memory allocations.
+      // println(s"tryToAcquire $numBytes in " +
+      //   Thread.currentThread().getStackTrace.mkString("", "\n  -", ""))
+      val acquired = super.tryToAcquire(numBytes)
+      acquired
+    }
+  }
+
+  override def release(numBytes: Long): Unit = {
+    // Uncomment the following to trace memory releases.
+    // println(s"release $numBytes in " +
+    //   Thread.currentThread().getStackTrace.mkString("", "\n  -", ""))
+    super.release(numBytes)
+  }
+
+  def markAsOutOfMemory(): Unit = {
+    oom = true
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index 098bdd0017a73..4c94b3307d47b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -17,24 +17,26 @@
 
 package org.apache.spark.sql.execution
 
-import org.scalatest.{BeforeAndAfterEach, Matchers}
-
-import scala.collection.JavaConverters._
+import scala.util.control.NonFatal
 import scala.collection.mutable
-import scala.util.Random
+import scala.util.{Try, Random}
+
+import org.scalatest.Matchers
 
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.shuffle.ShuffleMemoryManager
+import org.apache.spark.sql.catalyst.expressions.UnsafeProjection
+import org.apache.spark.{TaskContextImpl, TaskContext, SparkFunSuite}
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.unsafe.types.UTF8String
 
-
-class UnsafeFixedWidthAggregationMapSuite
-  extends SparkFunSuite
-  with Matchers
-  with BeforeAndAfterEach {
+/**
+ * Test suite for [[UnsafeFixedWidthAggregationMap]].
+ *
+ * Use [[testWithMemoryLeakDetection]] rather than [[test]] to construct test cases.
+ */
+class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
 
   import UnsafeFixedWidthAggregationMap._
 
@@ -44,23 +46,40 @@ class UnsafeFixedWidthAggregationMapSuite
   private val PAGE_SIZE_BYTES: Long = 1L << 26; // 64 megabytes
 
   private var taskMemoryManager: TaskMemoryManager = null
-  private var shuffleMemoryManager: ShuffleMemoryManager = null
+  private var shuffleMemoryManager: TestShuffleMemoryManager = null
+
+  def testWithMemoryLeakDetection(name: String)(f: => Unit) {
+    def cleanup(): Unit = {
+      if (taskMemoryManager != null) {
+        val leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask()
+        assert(taskMemoryManager.cleanUpAllAllocatedMemory() === 0)
+        assert(leakedShuffleMemory === 0)
+        taskMemoryManager = null
+      }
+    }
 
-  override def beforeEach(): Unit = {
-    taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
-    shuffleMemoryManager = new ShuffleMemoryManager(Long.MaxValue)
+    test(name) {
+      taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+      shuffleMemoryManager = new TestShuffleMemoryManager
+      try {
+        f
+      } catch {
+        case NonFatal(e) =>
+          Try(cleanup())
+          throw e
+      }
+      cleanup()
+    }
   }
 
-  override def afterEach(): Unit = {
-    if (taskMemoryManager != null) {
-      val leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask()
-      assert(taskMemoryManager.cleanUpAllAllocatedMemory() === 0)
-      assert(leakedShuffleMemory === 0)
-      taskMemoryManager = null
-    }
+  private def randomStrings(n: Int): Seq[String] = {
+    val rand = new Random(42)
+    Seq.fill(512) {
+      Seq.fill(rand.nextInt(100))(rand.nextPrintableChar()).mkString
+    }.distinct
   }
 
-  test("supported schemas") {
+  testWithMemoryLeakDetection("supported schemas") {
     assert(supportsAggregationBufferSchema(
       StructType(StructField("x", DecimalType.USER_DEFAULT) :: Nil)))
     assert(!supportsAggregationBufferSchema(
@@ -70,7 +89,7 @@ class UnsafeFixedWidthAggregationMapSuite
       !supportsAggregationBufferSchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil)))
   }
 
-  test("empty map") {
+  testWithMemoryLeakDetection("empty map") {
     val map = new UnsafeFixedWidthAggregationMap(
       emptyAggregationBuffer,
       aggBufferSchema,
@@ -85,7 +104,7 @@ class UnsafeFixedWidthAggregationMapSuite
     map.free()
   }
 
-  test("updating values for a single key") {
+  testWithMemoryLeakDetection("updating values for a single key") {
     val map = new UnsafeFixedWidthAggregationMap(
       emptyAggregationBuffer,
       aggBufferSchema,
@@ -113,7 +132,7 @@ class UnsafeFixedWidthAggregationMapSuite
     map.free()
   }
 
-  test("inserting large random keys") {
+  testWithMemoryLeakDetection("inserting large random keys") {
     val map = new UnsafeFixedWidthAggregationMap(
       emptyAggregationBuffer,
       aggBufferSchema,
@@ -140,7 +159,21 @@ class UnsafeFixedWidthAggregationMapSuite
     map.free()
   }
 
-  test("test sorting") {
+  testWithMemoryLeakDetection("test external sorting") {
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    TaskContext.setTaskContext(new TaskContextImpl(
+      stageId = 0,
+      partitionId = 0,
+      taskAttemptId = 0,
+      attemptNumber = 0,
+      taskMemoryManager = taskMemoryManager,
+      metricsSystem = null))
+
+    // Memory consumption in the beginning of the task.
+    val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
+
     val map = new UnsafeFixedWidthAggregationMap(
       emptyAggregationBuffer,
       aggBufferSchema,
@@ -152,26 +185,47 @@ class UnsafeFixedWidthAggregationMapSuite
       false // disable perf metrics
     )
 
-    val rand = new Random(42)
-    val groupKeys: Set[String] = Seq.fill(512) {
-      Seq.fill(rand.nextInt(100))(rand.nextPrintableChar()).mkString
-    }.toSet
-    groupKeys.foreach { keyString =>
+    val keys = randomStrings(1024).take(512)
+    keys.foreach { keyString =>
       val buf = map.getAggregationBuffer(InternalRow(UTF8String.fromString(keyString)))
       buf.setInt(0, keyString.length)
       assert(buf != null)
     }
 
+    // Convert the map into a sorter
+    val sorter = map.destructAndCreateExternalSorter()
+
+    withClue(s"destructAndCreateExternalSorter should release memory used by the map") {
+      // 4096 * 16 is the initial size allocated for the pointer/prefix array in the in-mem sorter.
+      assert(shuffleMemoryManager.getMemoryConsumptionForThisTask() ===
+        initialMemoryConsumption + 4096 * 16)
+    }
+
+    // Add more keys to the sorter and make sure the results come out sorted.
+    val additionalKeys = randomStrings(1024)
+    val keyConverter = UnsafeProjection.create(groupKeySchema)
+    val valueConverter = UnsafeProjection.create(aggBufferSchema)
+
+    additionalKeys.zipWithIndex.foreach { case (str, i) =>
+      val k = InternalRow(UTF8String.fromString(str))
+      val v = InternalRow(str.length)
+      sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+
+      if ((i % 100) == 0) {
+        shuffleMemoryManager.markAsOutOfMemory()
+        sorter.closeCurrentPage()
+      }
+    }
+
     val out = new scala.collection.mutable.ArrayBuffer[String]
-    val iter = map.sortedIterator()
+    val iter = sorter.sortedIterator()
     while (iter.next()) {
       assert(iter.getKey.getString(0).length === iter.getValue.getInt(0))
       out += iter.getKey.getString(0)
     }
 
-    assert(out === groupKeys.toSeq.sorted)
+    assert(out === (keys ++ additionalKeys).sorted)
 
     map.free()
   }
-
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
new file mode 100644
index 0000000000000..5d214d7bfc388
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -0,0 +1,158 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import scala.util.Random
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{RowOrdering, UnsafeProjection}
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark._
+
+class UnsafeKVExternalSorterSuite extends SparkFunSuite {
+
+  test("sorting string key and int int value") {
+
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    val shuffleMemMgr = new TestShuffleMemoryManager
+
+    TaskContext.setTaskContext(new TaskContextImpl(
+      stageId = 0,
+      partitionId = 0,
+      taskAttemptId = 0,
+      attemptNumber = 0,
+      taskMemoryManager = taskMemMgr,
+      metricsSystem = null))
+
+    val keySchema = new StructType().add("a", StringType)
+    val valueSchema = new StructType().add("b", IntegerType).add("c", IntegerType)
+    val sorter = new UnsafeKVExternalSorter(
+      keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr,
+      16 * 1024)
+
+    val keyConverter = UnsafeProjection.create(keySchema)
+    val valueConverter = UnsafeProjection.create(valueSchema)
+
+    val rand = new Random(42)
+    val data = null +: Seq.fill[String](10) {
+      Seq.fill(rand.nextInt(100))(rand.nextPrintableChar()).mkString
+    }
+
+    val inputRows = data.map { str =>
+      keyConverter.apply(InternalRow(UTF8String.fromString(str))).copy()
+    }
+
+    var i = 0
+    data.foreach { str =>
+      if (str != null) {
+        val k = InternalRow(UTF8String.fromString(str))
+        val v = InternalRow(str.length, str.length + 1)
+        sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+      } else {
+        val k = InternalRow(UTF8String.fromString(str))
+        val v = InternalRow(-1, -2)
+        sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+      }
+
+      if ((i % 100) == 0) {
+        shuffleMemMgr.markAsOutOfMemory()
+        sorter.closeCurrentPage()
+      }
+      i += 1
+    }
+
+    val out = new scala.collection.mutable.ArrayBuffer[InternalRow]
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      if (iter.getKey.getUTF8String(0) == null) {
+        withClue(s"for null key") {
+          assert(-1 === iter.getValue.getInt(0))
+          assert(-2 === iter.getValue.getInt(1))
+        }
+      } else {
+        val key = iter.getKey.getString(0)
+        withClue(s"for key $key") {
+          assert(key.length === iter.getValue.getInt(0))
+          assert(key.length + 1 === iter.getValue.getInt(1))
+        }
+      }
+      out += iter.getKey.copy()
+    }
+
+    assert(out === inputRows.sorted(RowOrdering.forSchema(keySchema.map(_.dataType))))
+  }
+
+  test("sorting arbitrary string data") {
+
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    val shuffleMemMgr = new TestShuffleMemoryManager
+
+    TaskContext.setTaskContext(new TaskContextImpl(
+      stageId = 0,
+      partitionId = 0,
+      taskAttemptId = 0,
+      attemptNumber = 0,
+      taskMemoryManager = taskMemMgr,
+      metricsSystem = null))
+
+    val keySchema = new StructType().add("a", StringType)
+    val valueSchema = new StructType().add("b", IntegerType)
+    val sorter = new UnsafeKVExternalSorter(
+      keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr,
+      16 * 1024)
+
+    val keyConverter = UnsafeProjection.create(keySchema)
+    val valueConverter = UnsafeProjection.create(valueSchema)
+
+    val rand = new Random(42)
+    val data = Seq.fill(512) {
+      Seq.fill(rand.nextInt(100))(rand.nextPrintableChar()).mkString
+    }
+
+    var i = 0
+    data.foreach { str =>
+      val k = InternalRow(UTF8String.fromString(str))
+      val v = InternalRow(str.length)
+      sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+
+      if ((i % 100) == 0) {
+        shuffleMemMgr.markAsOutOfMemory()
+        sorter.closeCurrentPage()
+      }
+      i += 1
+    }
+
+    val out = new scala.collection.mutable.ArrayBuffer[String]
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      assert(iter.getKey.getString(0).length === iter.getValue.getInt(0))
+      out += iter.getKey.getString(0)
+    }
+
+    assert(out === data.sorted)
+  }
+}

From 0722f43316fc7ed0c1308b0f9d6d15f0c22ed56f Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Sun, 2 Aug 2015 17:53:44 -0700
Subject: [PATCH 0787/1454] [SPARK-7937][SQL] Support comparison on StructType

This brings #6519 up-to-date with master branch.

Closes #6519.

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #7877 from rxin/sort-struct and squashes the following commits:

4968231 [Reynold Xin] Minor fixes.
2537813 [Reynold Xin] Merge branch 'compare_named_struct' of github.com:viirya/spark-1 into sort-struct
d2ba8ad [Liang-Chi Hsieh] Remove unused import.
3a3f40e [Liang-Chi Hsieh] Don't need to add compare to InternalRow because we can use RowOrdering.
dae6aad [Liang-Chi Hsieh] Fix nested struct.
d5349c7 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into compare_named_struct
43d4354 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into compare_named_struct
1f66196 [Liang-Chi Hsieh] Reuse RowOrdering and GenerateOrdering.
f8b2e9c [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into compare_named_struct
1187a65 [Liang-Chi Hsieh] Fix scala style.
9d67f68 [Liang-Chi Hsieh] Fix wrongly merging.
8f4d775 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into compare_named_struct
94b27d5 [Liang-Chi Hsieh] Remove test for error on complex type comparison.
2071693 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into compare_named_struct
3c142e4 [Liang-Chi Hsieh] Fix scala style.
cf58dc3 [Liang-Chi Hsieh] Use checkAnswer.
f651b8d [Liang-Chi Hsieh] Remove Either and move orderings to BinaryComparison to reuse it.
b6e1009 [Liang-Chi Hsieh] Fix scala style.
3922b54 [Liang-Chi Hsieh] Support ordering on named_struct.
---
 .../expressions/codegen/CodeGenerator.scala   | 31 ++++++++++++-
 .../codegen/GenerateMutableProjection.scala   |  1 +
 .../codegen/GenerateOrdering.scala            | 23 +++++++++-
 .../codegen/GeneratePredicate.scala           |  2 +
 .../codegen/GenerateProjection.scala          |  1 +
 .../codegen/GenerateUnsafeProjection.scala    |  1 +
 .../spark/sql/catalyst/expressions/rows.scala |  4 ++
 .../spark/sql/catalyst/util/TypeUtils.scala   | 23 +++++++---
 .../apache/spark/sql/types/StructType.scala   | 17 +++++++-
 .../ExpressionTypeCheckingSuite.scala         |  4 +-
 .../sql/hive/execution/HiveUDFSuite.scala     | 43 ++++++++++++++++++-
 11 files changed, 135 insertions(+), 15 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 3177e6b750840..3c91227d06080 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -80,6 +80,16 @@ class CodeGenContext {
     mutableStates += ((javaType, variableName, initCode))
   }
 
+  /**
+   * Holding all the functions those will be added into generated class.
+   */
+  val addedFuntions: mutable.Map[String, String] =
+    mutable.Map.empty[String, String]
+
+  def addNewFunction(funcName: String, funcCode: String): Unit = {
+    addedFuntions += ((funcName, funcCode))
+  }
+
   final val JAVA_BOOLEAN = "boolean"
   final val JAVA_BYTE = "byte"
   final val JAVA_SHORT = "short"
@@ -221,6 +231,19 @@ class CodeGenContext {
     case dt: DataType if isPrimitiveType(dt) => s"($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)"
     case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)"
     case NullType => "0"
+    case schema: StructType if schema.supportOrdering(schema) =>
+      val comparisons = GenerateOrdering.genComparisons(this, schema)
+      val compareFunc = freshName("compareStruct")
+      val funcCode: String =
+        s"""
+          public int $compareFunc(InternalRow a, InternalRow b) {
+            InternalRow i = null;
+            $comparisons
+            return 0;
+          }
+        """
+      addNewFunction(compareFunc, funcCode)
+      s"this.$compareFunc($c1, $c2)"
     case other if other.isInstanceOf[AtomicType] => s"$c1.compare($c2)"
     case _ => throw new IllegalArgumentException(
       "cannot generate compare code for un-comparable type")
@@ -262,11 +285,15 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   protected def declareMutableStates(ctx: CodeGenContext): String = {
     ctx.mutableStates.map { case (javaType, variableName, _) =>
       s"private $javaType $variableName;"
-    }.mkString("\n      ")
+    }.mkString
   }
 
   protected def initMutableStates(ctx: CodeGenContext): String = {
-    ctx.mutableStates.map(_._3).mkString("\n        ")
+    ctx.mutableStates.map(_._3).mkString
+  }
+
+  protected def declareAddedFunctions(ctx: CodeGenContext): String = {
+    ctx.addedFuntions.map { case (funcName, funcCode) => funcCode }.mkString
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index 825031a4faf5e..e4a8fc24dac2f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -92,6 +92,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
         private $exprType[] expressions;
         private $mutableRowType mutableRow;
         ${declareMutableStates(ctx)}
+        ${declareAddedFunctions(ctx)}
 
         public SpecificProjection($exprType[] expr) {
           expressions = expr;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index cc848aa1993ea..4da91ed8d7752 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -53,9 +53,21 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
     })
   }
 
-  protected def create(ordering: Seq[SortOrder]): BaseOrdering = {
-    val ctx = newCodeGenContext()
+  /**
+   * Generates the code for comparing a struct type according to its natural ordering
+   * (i.e. ascending order by field 1, then field 2, ..., then field n.
+   */
+  def genComparisons(ctx: CodeGenContext, schema: StructType): String = {
+    val ordering = schema.fields.map(_.dataType).zipWithIndex.map {
+      case(dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
+    }
+    genComparisons(ctx, ordering)
+  }
 
+  /**
+   * Generates the code for ordering based on the given order.
+   */
+  def genComparisons(ctx: CodeGenContext, ordering: Seq[SortOrder]): String = {
     val comparisons = ordering.map { order =>
       val eval = order.child.gen(ctx)
       val asc = order.direction == Ascending
@@ -94,6 +106,12 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
           }
       """
     }.mkString("\n")
+    comparisons
+  }
+
+  protected def create(ordering: Seq[SortOrder]): BaseOrdering = {
+    val ctx = newCodeGenContext()
+    val comparisons = genComparisons(ctx, ordering)
     val code = s"""
       public SpecificOrdering generate($exprType[] expr) {
         return new SpecificOrdering(expr);
@@ -103,6 +121,7 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
 
         private $exprType[] expressions;
         ${declareMutableStates(ctx)}
+        ${declareAddedFunctions(ctx)}
 
         public SpecificOrdering($exprType[] expr) {
           expressions = expr;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
index dfd593fb7c064..c7e718a526420 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala
@@ -48,6 +48,8 @@ object GeneratePredicate extends CodeGenerator[Expression, (InternalRow) => Bool
       class SpecificPredicate extends ${classOf[Predicate].getName} {
         private final $exprType[] expressions;
         ${declareMutableStates(ctx)}
+        ${declareAddedFunctions(ctx)}
+
         public SpecificPredicate($exprType[] expr) {
           expressions = expr;
           ${initMutableStates(ctx)}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 6f9acda071a62..1572b2b99ab66 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -159,6 +159,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
     class SpecificProjection extends ${classOf[BaseProjection].getName} {
       private $exprType[] expressions;
       ${declareMutableStates(ctx)}
+      ${declareAddedFunctions(ctx)}
 
       public SpecificProjection($exprType[] expr) {
         expressions = expr;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 6c99086046683..934ec3f75c63f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -274,6 +274,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         private $exprType[] expressions;
 
         ${declareMutableStates(ctx)}
+        ${declareAddedFunctions(ctx)}
 
         public SpecificUnsafeProjection($exprType[] expressions) {
           this.expressions = expressions;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 73f6b7a5500e6..7e1031c755f83 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -145,6 +145,10 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
             n.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
           case n: AtomicType if order.direction == Descending =>
             n.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
+          case s: StructType if order.direction == Ascending =>
+            s.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
+          case s: StructType if order.direction == Descending =>
+            s.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
           case other => sys.error(s"Type $other does not support ordered operations")
         }
         if (comparison != 0) return comparison
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
index 0103ddcf9cfb7..2f50d40fe25ac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
@@ -33,11 +33,18 @@ object TypeUtils {
   }
 
   def checkForOrderingExpr(t: DataType, caller: String): TypeCheckResult = {
-    if (t.isInstanceOf[AtomicType] || t == NullType) {
-      TypeCheckResult.TypeCheckSuccess
-    } else {
-      TypeCheckResult.TypeCheckFailure(s"$caller accepts non-complex types, not $t")
+    t match {
+      case i: AtomicType => TypeCheckResult.TypeCheckSuccess
+      case n: NullType => TypeCheckResult.TypeCheckSuccess
+      case s: StructType =>
+        if (s.supportOrdering(s)) {
+          TypeCheckResult.TypeCheckSuccess
+        } else {
+          TypeCheckResult.TypeCheckFailure(s"Fields in $s do not support ordering")
+        }
+      case other => TypeCheckResult.TypeCheckFailure(s"$t doesn't support ordering on $caller")
     }
+
   }
 
   def checkForSameTypeInputExpr(types: Seq[DataType], caller: String): TypeCheckResult = {
@@ -52,8 +59,12 @@ object TypeUtils {
   def getNumeric(t: DataType): Numeric[Any] =
     t.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]]
 
-  def getOrdering(t: DataType): Ordering[Any] =
-    t.asInstanceOf[AtomicType].ordering.asInstanceOf[Ordering[Any]]
+  def getOrdering(t: DataType): Ordering[Any] = {
+    t match {
+      case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]]
+      case s: StructType => s.ordering.asInstanceOf[Ordering[Any]]
+    }
+  }
 
   def compareBinary(x: Array[Byte], y: Array[Byte]): Int = {
     for (i <- 0 until x.length; if i < y.length) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 2ef97a427c37e..2f23144858198 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute}
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute, RowOrdering}
 
 
 /**
@@ -300,8 +300,21 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
 
     StructType(newFields)
   }
-}
 
+  private[sql] val ordering = RowOrdering.forSchema(this.fields.map(_.dataType))
+
+  private[sql] def supportOrdering(s: StructType): Boolean = {
+    s.fields.forall { f =>
+      if (f.dataType.isInstanceOf[AtomicType]) {
+        true
+      } else if (f.dataType.isInstanceOf[StructType]) {
+        supportOrdering(f.dataType.asInstanceOf[StructType])
+      } else {
+        false
+      }
+    }
+  }
+}
 
 object StructType extends AbstractDataType {
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index a52e4cb4dfd9f..8f616ae9d29c3 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -145,8 +145,8 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertSuccess(SumDistinct('stringField))
     assertSuccess(Average('stringField))
 
-    assertError(Min('complexField), "function min accepts non-complex type")
-    assertError(Max('complexField), "function max accepts non-complex type")
+    assertError(Min('complexField), "doesn't support ordering on function min")
+    assertError(Max('complexField), "doesn't support ordering on function max")
     assertError(Sum('booleanField), "function sum accepts numeric type")
     assertError(SumDistinct('booleanField), "function sumDistinct accepts numeric type")
     assertError(Average('booleanField), "function average accepts numeric type")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 1bde5922b5278..7069afc9f7da2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectIn
 import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectInspectorFactory}
 import org.apache.hadoop.hive.serde2.{AbstractSerDe, SerDeStats}
 import org.apache.hadoop.io.Writable
-import org.apache.spark.sql.{AnalysisException, QueryTest, Row}
+import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.hive.test.TestHive
 
 import org.apache.spark.util.Utils
@@ -93,6 +93,47 @@ class HiveUDFSuite extends QueryTest {
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDF")
   }
 
+  test("Max/Min on named_struct") {
+    def testOrderInStruct(): Unit = {
+      checkAnswer(sql(
+        """
+          |SELECT max(named_struct(
+          |           "key", key,
+          |           "value", value)).value FROM src
+        """.stripMargin), Seq(Row("val_498")))
+      checkAnswer(sql(
+        """
+          |SELECT min(named_struct(
+          |           "key", key,
+          |           "value", value)).value FROM src
+        """.stripMargin), Seq(Row("val_0")))
+
+      // nested struct cases
+      checkAnswer(sql(
+        """
+          |SELECT max(named_struct(
+          |           "key", named_struct(
+                              "key", key,
+                              "value", value),
+          |           "value", value)).value FROM src
+        """.stripMargin), Seq(Row("val_498")))
+      checkAnswer(sql(
+        """
+          |SELECT min(named_struct(
+          |           "key", named_struct(
+                             "key", key,
+                             "value", value),
+          |           "value", value)).value FROM src
+        """.stripMargin), Seq(Row("val_0")))
+    }
+    val codegenDefault = TestHive.getConf(SQLConf.CODEGEN_ENABLED)
+    TestHive.setConf(SQLConf.CODEGEN_ENABLED, true)
+    testOrderInStruct()
+    TestHive.setConf(SQLConf.CODEGEN_ENABLED, false)
+    testOrderInStruct()
+    TestHive.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
+  }
+
   test("SPARK-6409 UDAFAverage test") {
     sql(s"CREATE TEMPORARY FUNCTION test_avg AS '${classOf[GenericUDAFAverage].getName}'")
     checkAnswer(

From 9d03ad910b853c1e0dcb946b05d48ab36a1ac533 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 2 Aug 2015 17:54:30 -0700
Subject: [PATCH 0788/1454] [SPARK-9543][SQL] Add randomized testing for
 UnsafeKVExternalSorter.

The detailed approach is documented in UnsafeKVExternalSorterSuite.testKVSorter(), working as follows:

1. Create input by generating data randomly based on the given key/value schema (which is also randomly drawn from a list of candidate types)
2. Run UnsafeKVExternalSorter on the generated data
3. Collect the output from the sorter, and make sure the keys are sorted in ascending order
4. Sort the input by both key and value, and sort the sorter output also by both key and value. Compare the sorted input and sorted output together to make sure all the key/values match.
5. Check memory allocation to make sure there is no memory leak.

There is also a spill flag. When set to true, the sorter will spill probabilistically roughly every 100 records.

Author: Reynold Xin <rxin@databricks.com>

Closes #7873 from rxin/kvsorter-randomized-test and squashes the following commits:

a08c251 [Reynold Xin] Resource cleanup.
0488b5c [Reynold Xin] [SPARK-9543][SQL] Add randomized testing for UnsafeKVExternalSorter.
---
 .../spark/sql/RandomDataGenerator.scala       |   6 +
 .../UnsafeFixedWidthAggregationMapSuite.scala |  18 +-
 .../UnsafeKVExternalSorterSuite.scala         | 222 +++++++++---------
 ...lSuite.java => CalendarIntervalSuite.java} |   2 +-
 4 files changed, 126 insertions(+), 122 deletions(-)
 rename unsafe/src/test/java/org/apache/spark/unsafe/types/{IntervalSuite.java => CalendarIntervalSuite.java} (99%)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
index ea1fd23d0dbce..11e0c120f4072 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -24,6 +24,7 @@ import java.math.MathContext
 import scala.util.Random
 
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
 
 /**
  * Random data generators for Spark SQL DataTypes. These generators do not generate uniformly random
@@ -106,6 +107,11 @@ object RandomDataGenerator {
       case BooleanType => Some(() => rand.nextBoolean())
       case DateType => Some(() => new java.sql.Date(rand.nextInt()))
       case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
+      case CalendarIntervalType => Some(() => {
+        val months = rand.nextInt(1000)
+        val ns = rand.nextLong()
+        new CalendarInterval(months, ns)
+      })
       case DecimalType.Fixed(precision, scale) => Some(
         () => BigDecimal.apply(
           rand.nextLong() % math.pow(10, precision).toLong,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index 4c94b3307d47b..7c591f6143b9e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -56,11 +56,21 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
         assert(leakedShuffleMemory === 0)
         taskMemoryManager = null
       }
+      TaskContext.unset()
     }
 
     test(name) {
       taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
       shuffleMemoryManager = new TestShuffleMemoryManager
+
+      TaskContext.setTaskContext(new TaskContextImpl(
+        stageId = 0,
+        partitionId = 0,
+        taskAttemptId = Random.nextInt(10000),
+        attemptNumber = 0,
+        taskMemoryManager = taskMemoryManager,
+        metricsSystem = null))
+
       try {
         f
       } catch {
@@ -163,14 +173,6 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
     // Calling this make sure we have block manager and everything else setup.
     TestSQLContext
 
-    TaskContext.setTaskContext(new TaskContextImpl(
-      stageId = 0,
-      partitionId = 0,
-      taskAttemptId = 0,
-      attemptNumber = 0,
-      taskMemoryManager = taskMemoryManager,
-      metricsSystem = null))
-
     // Memory consumption in the beginning of the task.
     val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index 5d214d7bfc388..0282b25b9dd50 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -19,140 +19,136 @@ package org.apache.spark.sql.execution
 
 import scala.util.Random
 
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{RowOrdering, UnsafeProjection}
+import org.apache.spark._
+import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, RowOrdering, UnsafeProjection}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
-import org.apache.spark.unsafe.types.UTF8String
-import org.apache.spark._
 
+/**
+ * Test suite for [[UnsafeKVExternalSorter]], with randomly generated test data.
+ */
 class UnsafeKVExternalSorterSuite extends SparkFunSuite {
 
-  test("sorting string key and int int value") {
-
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
+  private val keyTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
+  private val valueTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
 
-    val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
-    val shuffleMemMgr = new TestShuffleMemoryManager
+  testKVSorter(new StructType, new StructType, spill = true)
+  testKVSorter(new StructType().add("c1", IntegerType), new StructType, spill = true)
+  testKVSorter(new StructType, new StructType().add("c1", IntegerType), spill = true)
 
-    TaskContext.setTaskContext(new TaskContextImpl(
-      stageId = 0,
-      partitionId = 0,
-      taskAttemptId = 0,
-      attemptNumber = 0,
-      taskMemoryManager = taskMemMgr,
-      metricsSystem = null))
-
-    val keySchema = new StructType().add("a", StringType)
-    val valueSchema = new StructType().add("b", IntegerType).add("c", IntegerType)
-    val sorter = new UnsafeKVExternalSorter(
-      keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr,
-      16 * 1024)
-
-    val keyConverter = UnsafeProjection.create(keySchema)
-    val valueConverter = UnsafeProjection.create(valueSchema)
+  private val rand = new Random(42)
+  for (i <- 0 until 6) {
+    val keySchema = RandomDataGenerator.randomSchema(rand.nextInt(10) + 1, keyTypes)
+    val valueSchema = RandomDataGenerator.randomSchema(rand.nextInt(10) + 1, valueTypes)
+    testKVSorter(keySchema, valueSchema, spill = i > 3)
+  }
 
-    val rand = new Random(42)
-    val data = null +: Seq.fill[String](10) {
-      Seq.fill(rand.nextInt(100))(rand.nextPrintableChar()).mkString
-    }
+  /**
+   * Create a test case using randomly generated data for the given key and value schema.
+   *
+   * The approach works as follows:
+   *
+   * - Create input by randomly generating data based on the given schema
+   * - Run [[UnsafeKVExternalSorter]] on the generated data
+   * - Collect the output from the sorter, and make sure the keys are sorted in ascending order
+   * - Sort the input by both key and value, and sort the sorter output also by both key and value.
+   *   Compare the sorted input and sorted output together to make sure all the key/values match.
+   *
+   * If spill is set to true, the sorter will spill probabilistically roughly every 100 records.
+   */
+  private def testKVSorter(keySchema: StructType, valueSchema: StructType, spill: Boolean): Unit = {
+
+    val keySchemaStr = keySchema.map(_.dataType.simpleString).mkString("[", ",", "]")
+    val valueSchemaStr = valueSchema.map(_.dataType.simpleString).mkString("[", ",", "]")
+
+    test(s"kv sorting key schema $keySchemaStr and value schema $valueSchemaStr") {
+      // Calling this make sure we have block manager and everything else setup.
+      TestSQLContext
+
+      val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+      val shuffleMemMgr = new TestShuffleMemoryManager
+      TaskContext.setTaskContext(new TaskContextImpl(
+        stageId = 0,
+        partitionId = 0,
+        taskAttemptId = 98456,
+        attemptNumber = 0,
+        taskMemoryManager = taskMemMgr,
+        metricsSystem = null))
+
+      // Create the data converters
+      val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
+      val vExternalConverter = CatalystTypeConverters.createToCatalystConverter(valueSchema)
+      val kConverter = UnsafeProjection.create(keySchema)
+      val vConverter = UnsafeProjection.create(valueSchema)
+
+      val keyDataGen = RandomDataGenerator.forType(keySchema, nullable = false).get
+      val valueDataGen = RandomDataGenerator.forType(valueSchema, nullable = false).get
+
+      val input = Seq.fill(1024) {
+        val k = kConverter(kExternalConverter.apply(keyDataGen.apply()).asInstanceOf[InternalRow])
+        val v = vConverter(vExternalConverter.apply(valueDataGen.apply()).asInstanceOf[InternalRow])
+        (k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
+      }
 
-    val inputRows = data.map { str =>
-      keyConverter.apply(InternalRow(UTF8String.fromString(str))).copy()
-    }
+      val sorter = new UnsafeKVExternalSorter(
+        keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr, 16 * 1024 * 1024)
 
-    var i = 0
-    data.foreach { str =>
-      if (str != null) {
-        val k = InternalRow(UTF8String.fromString(str))
-        val v = InternalRow(str.length, str.length + 1)
-        sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
-      } else {
-        val k = InternalRow(UTF8String.fromString(str))
-        val v = InternalRow(-1, -2)
-        sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+      // Insert generated keys and values into the sorter
+      input.foreach { case (k, v) =>
+        sorter.insertKV(k.asInstanceOf[UnsafeRow], v.asInstanceOf[UnsafeRow])
+        // 1% chance we will spill
+        if (rand.nextDouble() < 0.01 && spill) {
+          shuffleMemMgr.markAsOutOfMemory()
+          sorter.closeCurrentPage()
+        }
       }
 
-      if ((i % 100) == 0) {
-        shuffleMemMgr.markAsOutOfMemory()
-        sorter.closeCurrentPage()
+      // Collect the sorted output
+      val out = new scala.collection.mutable.ArrayBuffer[(InternalRow, InternalRow)]
+      val iter = sorter.sortedIterator()
+      while (iter.next()) {
+        out += Tuple2(iter.getKey.copy(), iter.getValue.copy())
       }
-      i += 1
-    }
 
-    val out = new scala.collection.mutable.ArrayBuffer[InternalRow]
-    val iter = sorter.sortedIterator()
-    while (iter.next()) {
-      if (iter.getKey.getUTF8String(0) == null) {
-        withClue(s"for null key") {
-          assert(-1 === iter.getValue.getInt(0))
-          assert(-2 === iter.getValue.getInt(1))
-        }
-      } else {
-        val key = iter.getKey.getString(0)
-        withClue(s"for key $key") {
-          assert(key.length === iter.getValue.getInt(0))
-          assert(key.length + 1 === iter.getValue.getInt(1))
+      val keyOrdering = RowOrdering.forSchema(keySchema.map(_.dataType))
+      val valueOrdering = RowOrdering.forSchema(valueSchema.map(_.dataType))
+      val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
+        override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
+          keyOrdering.compare(x._1, y._1) match {
+            case 0 => valueOrdering.compare(x._2, y._2)
+            case cmp => cmp
+          }
         }
       }
-      out += iter.getKey.copy()
-    }
 
-    assert(out === inputRows.sorted(RowOrdering.forSchema(keySchema.map(_.dataType))))
-  }
-
-  test("sorting arbitrary string data") {
-
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
-
-    val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
-    val shuffleMemMgr = new TestShuffleMemoryManager
-
-    TaskContext.setTaskContext(new TaskContextImpl(
-      stageId = 0,
-      partitionId = 0,
-      taskAttemptId = 0,
-      attemptNumber = 0,
-      taskMemoryManager = taskMemMgr,
-      metricsSystem = null))
-
-    val keySchema = new StructType().add("a", StringType)
-    val valueSchema = new StructType().add("b", IntegerType)
-    val sorter = new UnsafeKVExternalSorter(
-      keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr,
-      16 * 1024)
-
-    val keyConverter = UnsafeProjection.create(keySchema)
-    val valueConverter = UnsafeProjection.create(valueSchema)
-
-    val rand = new Random(42)
-    val data = Seq.fill(512) {
-      Seq.fill(rand.nextInt(100))(rand.nextPrintableChar()).mkString
-    }
+      // Testing to make sure output from the sorter is sorted by key
+      var prevK: InternalRow = null
+      out.zipWithIndex.foreach { case ((k, v), i) =>
+        if (prevK != null) {
+          assert(keyOrdering.compare(prevK, k) <= 0,
+            s"""
+               |key is not in sorted order:
+               |previous key: $prevK
+               |current key : $k
+               """.stripMargin)
+        }
+        prevK = k
+      }
 
-    var i = 0
-    data.foreach { str =>
-      val k = InternalRow(UTF8String.fromString(str))
-      val v = InternalRow(str.length)
-      sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+      // Testing to make sure the key/value in output matches input
+      assert(out.sorted(kvOrdering) === input.sorted(kvOrdering))
 
-      if ((i % 100) == 0) {
-        shuffleMemMgr.markAsOutOfMemory()
-        sorter.closeCurrentPage()
+      // Make sure there is no memory leak
+      val leakedUnsafeMemory: Long = taskMemMgr.cleanUpAllAllocatedMemory
+      if (shuffleMemMgr != null) {
+        val leakedShuffleMemory: Long = shuffleMemMgr.getMemoryConsumptionForThisTask()
+        assert(0L === leakedShuffleMemory)
       }
-      i += 1
+      assert(0 === leakedUnsafeMemory)
+      TaskContext.unset()
     }
-
-    val out = new scala.collection.mutable.ArrayBuffer[String]
-    val iter = sorter.sortedIterator()
-    while (iter.next()) {
-      assert(iter.getKey.getString(0).length === iter.getValue.getInt(0))
-      out += iter.getKey.getString(0)
-    }
-
-    assert(out === data.sorted)
   }
 }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
similarity index 99%
rename from unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
rename to unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
index e6733a7aae6f5..6274b92b47dd4 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/IntervalSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
@@ -22,7 +22,7 @@
 import static junit.framework.Assert.*;
 import static org.apache.spark.unsafe.types.CalendarInterval.*;
 
-public class IntervalSuite {
+public class CalendarIntervalSuite {
 
   @Test
   public void equalsTest() {

From 536d2adc12c88ff67b5dde3dec998bcacef50fba Mon Sep 17 00:00:00 2001
From: KaiXinXiaoLei <huleilei1@huawei.com>
Date: Sun, 2 Aug 2015 20:04:21 -0700
Subject: [PATCH 0789/1454] [SPARK-9535][SQL][DOCS] Modify document for
 codegen.

#7142 made codegen enabled by default so let's modify the corresponding documents.

Closes #7142

Author: KaiXinXiaoLei <huleilei1@huawei.com>
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7863 from sarutak/SPARK-9535 and squashes the following commits:

0884424 [Kousuke Saruta] Removed a line which mentioned about the effect of codegen enabled
3c11af0 [Kousuke Saruta] Merge branch 'sqlconfig' of https://github.com/KaiXinXiaoLei/spark into SPARK-9535
4ee531d [KaiXinXiaoLei] delete space
4cfd11d [KaiXinXiaoLei] change spark.sql.planner.externalSort
d624cf8 [KaiXinXiaoLei] sql config is wrong
---
 docs/sql-programming-guide.md | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index d31baa080cbce..3ea77e82422fb 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1885,11 +1885,10 @@ that these options will be deprecated in future release as more optimizations ar
   </tr>
   <tr>
     <td><code>spark.sql.codegen</code></td>
-    <td>false</td>
+    <td>true</td>
     <td>
       When true, code will be dynamically generated at runtime for expression evaluation in a specific
-      query.  For some queries with complicated expression this option can lead to significant speed-ups.
-      However, for simple queries this can actually slow down query execution.
+      query. For some queries with complicated expression this option can lead to significant speed-ups.
     </td>
   </tr>
   <tr>
@@ -1901,7 +1900,7 @@ that these options will be deprecated in future release as more optimizations ar
   </tr>
   <tr>
     <td><code>spark.sql.planner.externalSort</code></td>
-    <td>false</td>
+    <td>true</td>
     <td>
       When true, performs sorts spilling to disk as needed otherwise sort each partition in memory.
     </td>

From 30e89111d673776a6b59b11cdb29ab8713ba6f7c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 2 Aug 2015 20:12:03 -0700
Subject: [PATCH 0790/1454] [SPARK-9546][SQL] Centralize orderable data type
 checking.

This pull request creates two isOrderable functions in RowOrdering that can be used to check whether a data type or a sequence of expressions can be used in sorting.

Author: Reynold Xin <rxin@databricks.com>

Closes #7880 from rxin/SPARK-9546 and squashes the following commits:

f9e322d [Reynold Xin] Fixed tests.
0439b43 [Reynold Xin] [SPARK-9546][SQL] Centralize orderable data type checking.
---
 .../sql/catalyst/analysis/CheckAnalysis.scala |  8 +-
 .../expressions/ExpectsInputTypes.scala       |  4 +-
 .../sql/catalyst/expressions/Expression.scala |  2 +-
 .../catalyst/expressions/RowOrdering.scala    | 93 +++++++++++++++++++
 .../sql/catalyst/expressions/SortOrder.scala  |  9 ++
 .../expressions/codegen/CodeGenerator.scala   | 12 ++-
 .../codegen/GenerateOrdering.scala            |  2 -
 .../expressions/collectionOperations.scala    | 21 +++--
 .../spark/sql/catalyst/expressions/rows.scala | 44 ---------
 .../spark/sql/catalyst/util/TypeUtils.scala   | 27 +++---
 .../apache/spark/sql/types/StructType.scala   | 12 ---
 .../analysis/AnalysisErrorSuite.scala         | 14 +--
 .../ExpressionTypeCheckingSuite.scala         | 50 +++++-----
 .../spark/sql/execution/SparkStrategies.scala | 14 +--
 .../spark/sql/DataFrameFunctionsSuite.scala   |  5 +-
 15 files changed, 173 insertions(+), 144 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 364569d8f0b40..187b238045f85 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -130,11 +130,9 @@ trait CheckAnalysis {
 
           case Sort(orders, _, _) =>
             orders.foreach { order =>
-              order.dataType match {
-                case t: AtomicType => // OK
-                case NullType => // OK
-                case t =>
-                  failAnalysis(s"Sorting is not supported for columns of type ${t.simpleString}")
+              if (!RowOrdering.isOrderable(order.dataType)) {
+                failAnalysis(
+                  s"sorting is not supported for columns of type ${order.dataType.simpleString}")
               }
             }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
index abe6457747550..2dcbd4eb15031 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExpectsInputTypes.scala
@@ -44,8 +44,8 @@ trait ExpectsInputTypes extends Expression {
   override def checkInputDataTypes(): TypeCheckResult = {
     val mismatches = children.zip(inputTypes).zipWithIndex.collect {
       case ((child, expected), idx) if !expected.acceptsType(child.dataType) =>
-        s"argument ${idx + 1} is expected to be of type ${expected.simpleString}, " +
-          s"however, '${child.prettyString}' is of type ${child.dataType.simpleString}."
+        s"argument ${idx + 1} requires ${expected.simpleString} type, " +
+          s"however, '${child.prettyString}' is of ${child.dataType.simpleString} type."
     }
 
     if (mismatches.isEmpty) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 2842b3ec5a0c8..ef2fc2e8c29d4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -420,7 +420,7 @@ abstract class BinaryOperator extends BinaryExpression with ExpectsInputTypes {
       TypeCheckResult.TypeCheckFailure(s"differing types in '$prettyString' " +
         s"(${left.dataType.simpleString} and ${right.dataType.simpleString}).")
     } else if (!inputType.acceptsType(left.dataType)) {
-      TypeCheckResult.TypeCheckFailure(s"'$prettyString' accepts ${inputType.simpleString} type," +
+      TypeCheckResult.TypeCheckFailure(s"'$prettyString' requires ${inputType.simpleString} type," +
         s" not ${left.dataType.simpleString}")
     } else {
       TypeCheckResult.TypeCheckSuccess
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala
new file mode 100644
index 0000000000000..873f5324c573e
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types._
+
+
+/**
+ * An interpreted row ordering comparator.
+ */
+class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
+
+  def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
+    this(ordering.map(BindReferences.bindReference(_, inputSchema)))
+
+  def compare(a: InternalRow, b: InternalRow): Int = {
+    var i = 0
+    while (i < ordering.size) {
+      val order = ordering(i)
+      val left = order.child.eval(a)
+      val right = order.child.eval(b)
+
+      if (left == null && right == null) {
+        // Both null, continue looking.
+      } else if (left == null) {
+        return if (order.direction == Ascending) -1 else 1
+      } else if (right == null) {
+        return if (order.direction == Ascending) 1 else -1
+      } else {
+        val comparison = order.dataType match {
+          case dt: AtomicType if order.direction == Ascending =>
+            dt.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
+          case dt: AtomicType if order.direction == Descending =>
+            dt.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
+          case s: StructType if order.direction == Ascending =>
+            s.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
+          case s: StructType if order.direction == Descending =>
+            s.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
+          case other =>
+            throw new IllegalArgumentException(s"Type $other does not support ordered operations")
+        }
+        if (comparison != 0) {
+          return comparison
+        }
+      }
+      i += 1
+    }
+    return 0
+  }
+}
+
+object RowOrdering {
+
+  /**
+   * Returns true iff the data type can be ordered (i.e. can be sorted).
+   */
+  def isOrderable(dataType: DataType): Boolean = dataType match {
+    case NullType => true
+    case dt: AtomicType => true
+    case struct: StructType => struct.fields.forall(f => isOrderable(f.dataType))
+    case _ => false
+  }
+
+  /**
+   * Returns true iff outputs from the expressions can be ordered.
+   */
+  def isOrderable(exprs: Seq[Expression]): Boolean = exprs.forall(e => isOrderable(e.dataType))
+
+  /**
+   * Creates a [[RowOrdering]] for the given schema, in natural ascending order.
+   */
+  def forSchema(dataTypes: Seq[DataType]): RowOrdering = {
+    new RowOrdering(dataTypes.zipWithIndex.map {
+      case (dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
+    })
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index 5eb5b0d176fc1..f6a872ba446eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparators.DoublePrefixComparator
@@ -36,6 +37,14 @@ case class SortOrder(child: Expression, direction: SortDirection)
   /** Sort order is not foldable because we don't have an eval for it. */
   override def foldable: Boolean = false
 
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (RowOrdering.isOrderable(dataType)) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(s"cannot sort data type ${dataType.simpleString}")
+    }
+  }
+
   override def dataType: DataType = child.dataType
   override def nullable: Boolean = child.nullable
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 3c91227d06080..03ec4b4b4ec55 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -220,7 +220,11 @@ class CodeGenContext {
   }
 
   /**
-   * Generates code for compare expression in Java.
+   * Generates code for comparing two expressions.
+   *
+   * @param dataType data type of the expressions
+   * @param c1 name of the variable of expression 1's output
+   * @param c2 name of the variable of expression 2's output
    */
   def genComp(dataType: DataType, c1: String, c2: String): String = dataType match {
     // java boolean doesn't support > or < operator
@@ -231,7 +235,7 @@ class CodeGenContext {
     case dt: DataType if isPrimitiveType(dt) => s"($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)"
     case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)"
     case NullType => "0"
-    case schema: StructType if schema.supportOrdering(schema) =>
+    case schema: StructType =>
       val comparisons = GenerateOrdering.genComparisons(this, schema)
       val compareFunc = freshName("compareStruct")
       val funcCode: String =
@@ -245,8 +249,8 @@ class CodeGenContext {
       addNewFunction(compareFunc, funcCode)
       s"this.$compareFunc($c1, $c2)"
     case other if other.isInstanceOf[AtomicType] => s"$c1.compare($c2)"
-    case _ => throw new IllegalArgumentException(
-      "cannot generate compare code for un-comparable type")
+    case _ =>
+      throw new IllegalArgumentException("cannot generate compare code for un-comparable type")
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
index 4da91ed8d7752..42be394c3bf5c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Private
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types.StructType
@@ -26,7 +25,6 @@ import org.apache.spark.sql.types.StructType
 /**
  * Inherits some default implementation for Java from `Ordering[Row]`
  */
-@Private
 class BaseOrdering extends Ordering[InternalRow] {
   def compare(a: InternalRow, b: InternalRow): Int = {
     throw new UnsupportedOperationException
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 80b8da23e880b..6ccb56578f790 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -20,6 +20,7 @@ import java.util.Comparator
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
 /**
@@ -54,15 +55,17 @@ case class SortArray(base: Expression, ascendingOrder: Expression)
   override def inputTypes: Seq[AbstractDataType] = Seq(ArrayType, BooleanType)
 
   override def checkInputDataTypes(): TypeCheckResult = base.dataType match {
-    case _ @ ArrayType(n: AtomicType, _) => TypeCheckResult.TypeCheckSuccess
-    case _ @ ArrayType(n, _) => TypeCheckResult.TypeCheckFailure(
-                    s"Type $n is not the AtomicType, we can not perform the ordering operations")
-    case other =>
-      TypeCheckResult.TypeCheckFailure(s"ArrayType(AtomicType) is expected, but we got $other")
+    case ArrayType(dt, _) if RowOrdering.isOrderable(dt) =>
+      TypeCheckResult.TypeCheckSuccess
+    case ArrayType(dt, _) =>
+      TypeCheckResult.TypeCheckFailure(
+        s"$prettyName does not support sorting array of type ${dt.simpleString}")
+    case _ =>
+      TypeCheckResult.TypeCheckFailure(s"$prettyName only supports array input.")
   }
 
   @transient
-  private lazy val lt = {
+  private lazy val lt: Comparator[Any] = {
     val ordering = base.dataType match {
       case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
     }
@@ -83,7 +86,7 @@ case class SortArray(base: Expression, ascendingOrder: Expression)
   }
 
   @transient
-  private lazy val gt = {
+  private lazy val gt: Comparator[Any] = {
     val ordering = base.dataType match {
       case _ @ ArrayType(n: AtomicType, _) => n.ordering.asInstanceOf[Ordering[Any]]
     }
@@ -106,9 +109,7 @@ case class SortArray(base: Expression, ascendingOrder: Expression)
   override def nullSafeEval(array: Any, ascending: Any): Any = {
     val elementType = base.dataType.asInstanceOf[ArrayType].elementType
     val data = array.asInstanceOf[ArrayData].toArray[AnyRef](elementType)
-    java.util.Arrays.sort(
-      data,
-      if (ascending.asInstanceOf[Boolean]) lt else gt)
+    java.util.Arrays.sort(data, if (ascending.asInstanceOf[Boolean]) lt else gt)
     new GenericArrayData(data.asInstanceOf[Array[Any]])
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 7e1031c755f83..d04434b953e41 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -121,47 +121,3 @@ class GenericMutableRow(val values: Array[Any]) extends MutableRow {
 
   override def copy(): InternalRow = new GenericInternalRow(values.clone())
 }
-
-class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
-  def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
-    this(ordering.map(BindReferences.bindReference(_, inputSchema)))
-
-  def compare(a: InternalRow, b: InternalRow): Int = {
-    var i = 0
-    while (i < ordering.size) {
-      val order = ordering(i)
-      val left = order.child.eval(a)
-      val right = order.child.eval(b)
-
-      if (left == null && right == null) {
-        // Both null, continue looking.
-      } else if (left == null) {
-        return if (order.direction == Ascending) -1 else 1
-      } else if (right == null) {
-        return if (order.direction == Ascending) 1 else -1
-      } else {
-        val comparison = order.dataType match {
-          case n: AtomicType if order.direction == Ascending =>
-            n.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
-          case n: AtomicType if order.direction == Descending =>
-            n.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
-          case s: StructType if order.direction == Ascending =>
-            s.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
-          case s: StructType if order.direction == Descending =>
-            s.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
-          case other => sys.error(s"Type $other does not support ordered operations")
-        }
-        if (comparison != 0) return comparison
-      }
-      i += 1
-    }
-    return 0
-  }
-}
-
-object RowOrdering {
-  def forSchema(dataTypes: Seq[DataType]): RowOrdering =
-    new RowOrdering(dataTypes.zipWithIndex.map {
-      case(dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
-    })
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
index 2f50d40fe25ac..0b41f92c6193c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
@@ -18,39 +18,34 @@
 package org.apache.spark.sql.catalyst.util
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.RowOrdering
 import org.apache.spark.sql.types._
 
 /**
  * Helper functions to check for valid data types.
  */
 object TypeUtils {
-  def checkForNumericExpr(t: DataType, caller: String): TypeCheckResult = {
-    if (t.isInstanceOf[NumericType] || t == NullType) {
+  def checkForNumericExpr(dt: DataType, caller: String): TypeCheckResult = {
+    if (dt.isInstanceOf[NumericType] || dt == NullType) {
       TypeCheckResult.TypeCheckSuccess
     } else {
-      TypeCheckResult.TypeCheckFailure(s"$caller accepts numeric types, not $t")
+      TypeCheckResult.TypeCheckFailure(s"$caller requires numeric types, not $dt")
     }
   }
 
-  def checkForOrderingExpr(t: DataType, caller: String): TypeCheckResult = {
-    t match {
-      case i: AtomicType => TypeCheckResult.TypeCheckSuccess
-      case n: NullType => TypeCheckResult.TypeCheckSuccess
-      case s: StructType =>
-        if (s.supportOrdering(s)) {
-          TypeCheckResult.TypeCheckSuccess
-        } else {
-          TypeCheckResult.TypeCheckFailure(s"Fields in $s do not support ordering")
-        }
-      case other => TypeCheckResult.TypeCheckFailure(s"$t doesn't support ordering on $caller")
+  def checkForOrderingExpr(dt: DataType, caller: String): TypeCheckResult = {
+    if (RowOrdering.isOrderable(dt)) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(s"$caller does not support ordering on type $dt")
     }
-
   }
 
   def checkForSameTypeInputExpr(types: Seq[DataType], caller: String): TypeCheckResult = {
     if (types.distinct.size > 1) {
       TypeCheckResult.TypeCheckFailure(
-        s"input to $caller should all be the same type, but it's ${types.mkString("[", ", ", "]")}")
+        s"input to $caller should all be the same type, but it's " +
+          types.map(_.simpleString).mkString("[", ", ", "]"))
     } else {
       TypeCheckResult.TypeCheckSuccess
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 2f23144858198..6928707f7bf6e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -302,18 +302,6 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
   }
 
   private[sql] val ordering = RowOrdering.forSchema(this.fields.map(_.dataType))
-
-  private[sql] def supportOrdering(s: StructType): Boolean = {
-    s.fields.forall { f =>
-      if (f.dataType.isInstanceOf[AtomicType]) {
-        true
-      } else if (f.dataType.isInstanceOf[StructType]) {
-        supportOrdering(f.dataType.asInstanceOf[StructType])
-      } else {
-        false
-      }
-    }
-  }
 }
 
 object StructType extends AbstractDataType {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index aa19cdce31ec6..26935c6e3b24f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -68,22 +68,22 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
   errorTest(
     "single invalid type, single arg",
     testRelation.select(TestFunction(dateLit :: Nil, IntegerType :: Nil).as('a)),
-    "cannot resolve" :: "testfunction" :: "argument 1" :: "expected to be of type int" ::
-    "'null' is of type date" ::Nil)
+    "cannot resolve" :: "testfunction" :: "argument 1" :: "requires int type" ::
+    "'null' is of date type" ::Nil)
 
   errorTest(
     "single invalid type, second arg",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, DateType :: IntegerType :: Nil).as('a)),
-    "cannot resolve" :: "testfunction" :: "argument 2" :: "expected to be of type int" ::
-    "'null' is of type date" ::Nil)
+    "cannot resolve" :: "testfunction" :: "argument 2" :: "requires int type" ::
+    "'null' is of date type" ::Nil)
 
   errorTest(
     "multiple invalid type",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, IntegerType :: IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 1" :: "argument 2" ::
-    "expected to be of type int" :: "'null' is of type date" ::Nil)
+    "requires int type" :: "'null' is of date type" ::Nil)
 
   errorTest(
     "unresolved window function",
@@ -111,12 +111,12 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
   errorTest(
     "bad casts",
     testRelation.select(Literal(1).cast(BinaryType).as('badCast)),
-    "cannot cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil)
+  "cannot cast" :: Literal(1).dataType.simpleString :: BinaryType.simpleString :: Nil)
 
   errorTest(
     "sorting by unsupported column types",
     listRelation.orderBy('list.asc),
-    "sorting" :: "type" :: "array<int>" :: Nil)
+    "sort" :: "type" :: "array<int>" :: Nil)
 
   errorTest(
     "non-boolean filters",
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index 8f616ae9d29c3..c9bcc68f02030 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -53,9 +53,9 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
   }
 
   test("check types for unary arithmetic") {
-    assertError(UnaryMinus('stringField), "type (numeric or calendarinterval)")
-    assertError(Abs('stringField), "expected to be of type numeric")
-    assertError(BitwiseNot('stringField), "expected to be of type integral")
+    assertError(UnaryMinus('stringField), "(numeric or calendarinterval) type")
+    assertError(Abs('stringField), "requires numeric type")
+    assertError(BitwiseNot('stringField), "requires integral type")
   }
 
   test("check types for binary arithmetic") {
@@ -78,21 +78,21 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertErrorForDifferingTypes(MaxOf('intField, 'booleanField))
     assertErrorForDifferingTypes(MinOf('intField, 'booleanField))
 
-    assertError(Add('booleanField, 'booleanField), "accepts (numeric or calendarinterval) type")
+    assertError(Add('booleanField, 'booleanField), "requires (numeric or calendarinterval) type")
     assertError(Subtract('booleanField, 'booleanField),
-      "accepts (numeric or calendarinterval) type")
-    assertError(Multiply('booleanField, 'booleanField), "accepts numeric type")
-    assertError(Divide('booleanField, 'booleanField), "accepts numeric type")
-    assertError(Remainder('booleanField, 'booleanField), "accepts numeric type")
+      "requires (numeric or calendarinterval) type")
+    assertError(Multiply('booleanField, 'booleanField), "requires numeric type")
+    assertError(Divide('booleanField, 'booleanField), "requires numeric type")
+    assertError(Remainder('booleanField, 'booleanField), "requires numeric type")
 
-    assertError(BitwiseAnd('booleanField, 'booleanField), "accepts integral type")
-    assertError(BitwiseOr('booleanField, 'booleanField), "accepts integral type")
-    assertError(BitwiseXor('booleanField, 'booleanField), "accepts integral type")
+    assertError(BitwiseAnd('booleanField, 'booleanField), "requires integral type")
+    assertError(BitwiseOr('booleanField, 'booleanField), "requires integral type")
+    assertError(BitwiseXor('booleanField, 'booleanField), "requires integral type")
 
     assertError(MaxOf('complexField, 'complexField),
-      s"accepts ${TypeCollection.Ordered.simpleString} type")
+      s"requires ${TypeCollection.Ordered.simpleString} type")
     assertError(MinOf('complexField, 'complexField),
-      s"accepts ${TypeCollection.Ordered.simpleString} type")
+      s"requires ${TypeCollection.Ordered.simpleString} type")
   }
 
   test("check types for predicates") {
@@ -116,13 +116,13 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertErrorForDifferingTypes(GreaterThanOrEqual('intField, 'booleanField))
 
     assertError(LessThan('complexField, 'complexField),
-      s"accepts ${TypeCollection.Ordered.simpleString} type")
+      s"requires ${TypeCollection.Ordered.simpleString} type")
     assertError(LessThanOrEqual('complexField, 'complexField),
-      s"accepts ${TypeCollection.Ordered.simpleString} type")
+      s"requires ${TypeCollection.Ordered.simpleString} type")
     assertError(GreaterThan('complexField, 'complexField),
-      s"accepts ${TypeCollection.Ordered.simpleString} type")
+      s"requires ${TypeCollection.Ordered.simpleString} type")
     assertError(GreaterThanOrEqual('complexField, 'complexField),
-      s"accepts ${TypeCollection.Ordered.simpleString} type")
+      s"requires ${TypeCollection.Ordered.simpleString} type")
 
     assertError(If('intField, 'stringField, 'stringField),
       "type of predicate expression in If should be boolean")
@@ -145,11 +145,11 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertSuccess(SumDistinct('stringField))
     assertSuccess(Average('stringField))
 
-    assertError(Min('complexField), "doesn't support ordering on function min")
-    assertError(Max('complexField), "doesn't support ordering on function max")
-    assertError(Sum('booleanField), "function sum accepts numeric type")
-    assertError(SumDistinct('booleanField), "function sumDistinct accepts numeric type")
-    assertError(Average('booleanField), "function average accepts numeric type")
+    assertError(Min('complexField), "min does not support ordering on type")
+    assertError(Max('complexField), "max does not support ordering on type")
+    assertError(Sum('booleanField), "function sum requires numeric type")
+    assertError(SumDistinct('booleanField), "function sumDistinct requires numeric type")
+    assertError(Average('booleanField), "function average requires numeric type")
   }
 
   test("check types for others") {
@@ -181,8 +181,8 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertSuccess(Round('intField, Literal(1)))
 
     assertError(Round('intField, 'intField), "Only foldable Expression is allowed")
-    assertError(Round('intField, 'booleanField), "expected to be of type int")
-    assertError(Round('intField, 'complexField), "expected to be of type int")
-    assertError(Round('booleanField, 'intField), "expected to be of type numeric")
+    assertError(Round('intField, 'booleanField), "requires int type")
+    assertError(Round('intField, 'complexField), "requires int type")
+    assertError(Round('booleanField, 'intField), "requires numeric type")
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 4aff52d992e6b..952ba7d45c13e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -89,18 +89,6 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
     }
 
-    private[this] def isValidSort(
-        leftKeys: Seq[Expression],
-        rightKeys: Seq[Expression]): Boolean = {
-      leftKeys.zip(rightKeys).forall { keys =>
-        (keys._1.dataType, keys._2.dataType) match {
-          case (l: AtomicType, r: AtomicType) => true
-          case (NullType, NullType) => true
-          case _ => false
-        }
-      }
-    }
-
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildRight)
@@ -111,7 +99,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       // If the sort merge join option is set, we want to use sort merge join prior to hashjoin
       // for now let's support inner join first, then add outer join
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-        if sqlContext.conf.sortMergeJoinEnabled && isValidSort(leftKeys, rightKeys) =>
+        if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
         val mergeJoin =
           joins.SortMergeJoin(leftKeys, rightKeys, planLater(left), planLater(right))
         condition.map(Filter(_, mergeJoin)).getOrElse(mergeJoin) :: Nil
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 46921d14256b9..431dcf7382f16 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -305,13 +305,12 @@ class DataFrameFunctionsSuite extends QueryTest {
     val df2 = Seq((Array[Array[Int]](Array(2)), "x")).toDF("a", "b")
     assert(intercept[AnalysisException] {
       df2.selectExpr("sort_array(a)").collect()
-    }.getMessage().contains("Type ArrayType(IntegerType,false) is not the AtomicType, " +
-      "we can not perform the ordering operations"))
+    }.getMessage().contains("does not support sorting array of type array<int>"))
 
     val df3 = Seq(("xxx", "x")).toDF("a", "b")
     assert(intercept[AnalysisException] {
       df3.selectExpr("sort_array(a)").collect()
-    }.getMessage().contains("ArrayType(AtomicType) is expected, but we got StringType"))
+    }.getMessage().contains("only supports array input"))
   }
 
   test("array size function") {

From 114ff926fcd078697c1111279b5cf6173b515865 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Sun, 2 Aug 2015 20:44:23 -0700
Subject: [PATCH 0791/1454] [SPARK-2205] [SQL] Avoid unnecessary exchange
 operators in multi-way joins

This PR adds `PartitioningCollection`, which is used to represent the `outputPartitioning` for SparkPlans with multiple children (e.g. `ShuffledHashJoin`). So, a `SparkPlan` can have multiple descriptions of its partitioning schemes. Taking `ShuffledHashJoin` as an example, it has two descriptions of its partitioning schemes, i.e. `left.outputPartitioning` and `right.outputPartitioning`. So when we have a query like `select * from t1 join t2 on (t1.x = t2.x) join t3 on (t2.x = t3.x)` will only have three Exchange operators (when shuffled joins are needed) instead of four.

The code in this PR was authored by yhuai; I'm opening this PR to factor out this change from #7685, a larger pull request which contains two other optimizations.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7773)
<!-- Reviewable:end -->

Author: Yin Huai <yhuai@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>

Closes #7773 from JoshRosen/multi-way-join-planning-improvements and squashes the following commits:

5c45924 [Josh Rosen] Merge remote-tracking branch 'origin/master' into multi-way-join-planning-improvements
cd8269b [Josh Rosen] Refactor test to use SQLTestUtils
2963857 [Yin Huai] Revert unnecessary SqlConf change.
73913f7 [Yin Huai] Add comments and test. Also, revert the change in ShuffledHashOuterJoin for now.
4a99204 [Josh Rosen] Delete unrelated expression change
884ab95 [Josh Rosen] Carve out only SPARK-2205 changes.
247e5fa [Josh Rosen] Merge remote-tracking branch 'origin/master' into multi-way-join-planning-improvements
c57a954 [Yin Huai] Bug fix.
d3d2e64 [Yin Huai] First round of cleanup.
f9516b0 [Yin Huai] Style
c6667e7 [Yin Huai] Add PartitioningCollection.
e616d3b [Yin Huai] wip
7c2d2d8 [Yin Huai] Bug fix and refactoring.
69bb072 [Yin Huai] Introduce NullSafeHashPartitioning and NullUnsafePartitioning.
d5b84c3 [Yin Huai] Do not add unnessary filters.
2201129 [Yin Huai] Filter out rows that will not be joined in equal joins early.
---
 .../plans/physical/partitioning.scala         | 87 ++++++++++++++++---
 .../sql/catalyst/DistributionSuite.scala      |  2 +-
 .../apache/spark/sql/execution/Exchange.scala |  2 +-
 .../joins/BroadcastHashOuterJoin.scala        |  4 +-
 .../sql/execution/joins/HashOuterJoin.scala   |  9 --
 .../execution/joins/LeftSemiJoinHash.scala    |  6 +-
 .../execution/joins/ShuffledHashJoin.scala    |  7 +-
 .../joins/ShuffledHashOuterJoin.scala         | 10 ++-
 .../sql/execution/joins/SortMergeJoin.scala   |  3 +-
 .../spark/sql/execution/PlannerSuite.scala    | 49 ++++++++++-
 10 files changed, 148 insertions(+), 31 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index f4d1dbaf28efe..ec659ce789c27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -60,8 +60,9 @@ case class ClusteredDistribution(clustering: Seq[Expression]) extends Distributi
 /**
  * Represents data where tuples have been ordered according to the `ordering`
  * [[Expression Expressions]].  This is a strictly stronger guarantee than
- * [[ClusteredDistribution]] as an ordering will ensure that tuples that share the same value for
- * the ordering expressions are contiguous and will never be split across partitions.
+ * [[ClusteredDistribution]] as an ordering will ensure that tuples that share the
+ * same value for the ordering expressions are contiguous and will never be split across
+ * partitions.
  */
 case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution {
   require(
@@ -86,8 +87,12 @@ sealed trait Partitioning {
    */
   def satisfies(required: Distribution): Boolean
 
-  /** Returns the expressions that are used to key the partitioning. */
-  def keyExpressions: Seq[Expression]
+  /**
+   * Returns true iff we can say that the partitioning scheme of this [[Partitioning]]
+   * guarantees the same partitioning scheme described by `other`.
+   */
+  // TODO: Add an example once we have the `nullSafe` concept.
+  def guarantees(other: Partitioning): Boolean
 }
 
 case class UnknownPartitioning(numPartitions: Int) extends Partitioning {
@@ -96,7 +101,7 @@ case class UnknownPartitioning(numPartitions: Int) extends Partitioning {
     case _ => false
   }
 
-  override def keyExpressions: Seq[Expression] = Nil
+  override def guarantees(other: Partitioning): Boolean = false
 }
 
 case object SinglePartition extends Partitioning {
@@ -104,7 +109,10 @@ case object SinglePartition extends Partitioning {
 
   override def satisfies(required: Distribution): Boolean = true
 
-  override def keyExpressions: Seq[Expression] = Nil
+  override def guarantees(other: Partitioning): Boolean = other match {
+    case SinglePartition => true
+    case _ => false
+  }
 }
 
 case object BroadcastPartitioning extends Partitioning {
@@ -112,7 +120,10 @@ case object BroadcastPartitioning extends Partitioning {
 
   override def satisfies(required: Distribution): Boolean = true
 
-  override def keyExpressions: Seq[Expression] = Nil
+  override def guarantees(other: Partitioning): Boolean = other match {
+    case BroadcastPartitioning => true
+    case _ => false
+  }
 }
 
 /**
@@ -127,7 +138,7 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
   override def nullable: Boolean = false
   override def dataType: DataType = IntegerType
 
-  private[this] lazy val clusteringSet = expressions.toSet
+  lazy val clusteringSet = expressions.toSet
 
   override def satisfies(required: Distribution): Boolean = required match {
     case UnspecifiedDistribution => true
@@ -136,7 +147,11 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
     case _ => false
   }
 
-  override def keyExpressions: Seq[Expression] = expressions
+  override def guarantees(other: Partitioning): Boolean = other match {
+    case o: HashPartitioning =>
+      this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
+    case _ => false
+  }
 }
 
 /**
@@ -170,5 +185,57 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
     case _ => false
   }
 
-  override def keyExpressions: Seq[Expression] = ordering.map(_.child)
+  override def guarantees(other: Partitioning): Boolean = other match {
+    case o: RangePartitioning => this == o
+    case _ => false
+  }
+}
+
+/**
+ * A collection of [[Partitioning]]s that can be used to describe the partitioning
+ * scheme of the output of a physical operator. It is usually used for an operator
+ * that has multiple children. In this case, a [[Partitioning]] in this collection
+ * describes how this operator's output is partitioned based on expressions from
+ * a child. For example, for a Join operator on two tables `A` and `B`
+ * with a join condition `A.key1 = B.key2`, assuming we use HashPartitioning schema,
+ * there are two [[Partitioning]]s can be used to describe how the output of
+ * this Join operator is partitioned, which are `HashPartitioning(A.key1)` and
+ * `HashPartitioning(B.key2)`. It is also worth noting that `partitionings`
+ * in this collection do not need to be equivalent, which is useful for
+ * Outer Join operators.
+ */
+case class PartitioningCollection(partitionings: Seq[Partitioning])
+  extends Expression with Partitioning with Unevaluable {
+
+  require(
+    partitionings.map(_.numPartitions).distinct.length == 1,
+    s"PartitioningCollection requires all of its partitionings have the same numPartitions.")
+
+  override def children: Seq[Expression] = partitionings.collect {
+    case expr: Expression => expr
+  }
+
+  override def nullable: Boolean = false
+
+  override def dataType: DataType = IntegerType
+
+  override val numPartitions = partitionings.map(_.numPartitions).distinct.head
+
+  /**
+   * Returns true if any `partitioning` of this collection satisfies the given
+   * [[Distribution]].
+   */
+  override def satisfies(required: Distribution): Boolean =
+    partitionings.exists(_.satisfies(required))
+
+  /**
+   * Returns true if any `partitioning` of this collection guarantees
+   * the given [[Partitioning]].
+   */
+  override def guarantees(other: Partitioning): Boolean =
+    partitionings.exists(_.guarantees(other))
+
+  override def toString: String = {
+    partitionings.map(_.toString).mkString("(", " or ", ")")
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala
index c046dbf4dc2c9..827f7ce692712 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala
@@ -42,7 +42,7 @@ class DistributionSuite extends SparkFunSuite {
     }
   }
 
-  test("HashPartitioning is the output partitioning") {
+  test("HashPartitioning (with nullSafe = true) is the output partitioning") {
     // Cases which do not need an exchange between two data properties.
     checkSatisfied(
       HashPartitioning(Seq('a, 'b, 'c), 10),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 6bd57f010a990..05b009d1935bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -209,7 +209,7 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
           child: SparkPlan): SparkPlan = {
 
         def addShuffleIfNecessary(child: SparkPlan): SparkPlan = {
-          if (child.outputPartitioning != partitioning) {
+          if (!child.outputPartitioning.guarantees(partitioning)) {
             Exchange(partitioning, child)
           } else {
             child
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index 77e7fe71009b7..309716a0efcc0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -24,7 +24,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{Distribution, UnspecifiedDistribution}
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 import org.apache.spark.util.ThreadUtils
@@ -57,6 +57,8 @@ case class BroadcastHashOuterJoin(
   override def requiredChildDistribution: Seq[Distribution] =
     UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
 
+  override def outputPartitioning: Partitioning = streamedPlan.outputPartitioning
+
   @transient
   private val broadcastFuture = future {
     // Note that we use .execute().collect() because we don't want to convert data to Scala types
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 7e671e7914f1a..a323aea4ea2c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -22,7 +22,6 @@ import java.util.{HashMap => JavaHashMap}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning}
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.util.collection.CompactBuffer
@@ -38,14 +37,6 @@ trait HashOuterJoin {
   val left: SparkPlan
   val right: SparkPlan
 
-  override def outputPartitioning: Partitioning = joinType match {
-    case LeftOuter => left.outputPartitioning
-    case RightOuter => right.outputPartitioning
-    case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
-    case x =>
-      throw new IllegalArgumentException(s"HashOuterJoin should not take $x as the JoinType")
-  }
-
   override def output: Seq[Attribute] = {
     joinType match {
       case LeftOuter =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
index 26a664104d6fb..68ccd34d8ed9b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
@@ -21,7 +21,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.ClusteredDistribution
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, ClusteredDistribution}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 
 /**
@@ -37,7 +37,9 @@ case class LeftSemiJoinHash(
     right: SparkPlan,
     condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
-  override def requiredChildDistribution: Seq[ClusteredDistribution] =
+  override def outputPartitioning: Partitioning = left.outputPartitioning
+
+  override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
index 5439e10a60b2a..fc6efe87bceb5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
@@ -21,7 +21,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
-import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning}
+import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 
 /**
@@ -38,9 +38,10 @@ case class ShuffledHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
-  override def outputPartitioning: Partitioning = left.outputPartitioning
+  override def outputPartitioning: Partitioning =
+    PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
 
-  override def requiredChildDistribution: Seq[ClusteredDistribution] =
+  override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index d29b593207c4d..eee8ad800f98e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -23,7 +23,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{Distribution, ClusteredDistribution}
+import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 
@@ -44,6 +44,14 @@ case class ShuffledHashOuterJoin(
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
+  override def outputPartitioning: Partitioning = joinType match {
+    case LeftOuter => left.outputPartitioning
+    case RightOuter => right.outputPartitioning
+    case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
+    case x =>
+      throw new IllegalArgumentException(s"HashOuterJoin should not take $x as the JoinType")
+  }
+
   protected override def doExecute(): RDD[InternalRow] = {
     val joinedRow = new JoinedRow()
     left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index bb18b5403f8e8..41be78afd37e6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -40,7 +40,8 @@ case class SortMergeJoin(
 
   override def output: Seq[Attribute] = left.output ++ right.output
 
-  override def outputPartitioning: Partitioning = left.outputPartitioning
+  override def outputPartitioning: Partitioning =
+    PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
 
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 845ce669f0b33..18b0e54dc7c53 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -23,14 +23,18 @@ import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin}
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
 import org.apache.spark.sql.test.TestSQLContext._
 import org.apache.spark.sql.test.TestSQLContext.implicits._
 import org.apache.spark.sql.test.TestSQLContext.planner._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Row, SQLConf, execution}
+import org.apache.spark.sql.{SQLContext, Row, SQLConf, execution}
 
 
-class PlannerSuite extends SparkFunSuite {
+class PlannerSuite extends SparkFunSuite with SQLTestUtils {
+
+  override def sqlContext: SQLContext = TestSQLContext
+
   private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
     val plannedOption = HashAggregation(query).headOption.orElse(Aggregation(query).headOption)
     val planned =
@@ -157,4 +161,45 @@ class PlannerSuite extends SparkFunSuite {
     val planned = planner.TakeOrderedAndProject(query)
     assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
   }
+
+  test("PartitioningCollection") {
+    withTempTable("normal", "small", "tiny") {
+      testData.registerTempTable("normal")
+      testData.limit(10).registerTempTable("small")
+      testData.limit(3).registerTempTable("tiny")
+
+      // Disable broadcast join
+      withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") {
+        {
+          val numExchanges = sql(
+            """
+              |SELECT *
+              |FROM
+              |  normal JOIN small ON (normal.key = small.key)
+              |  JOIN tiny ON (small.key = tiny.key)
+            """.stripMargin
+          ).queryExecution.executedPlan.collect {
+            case exchange: Exchange => exchange
+          }.length
+          assert(numExchanges === 3)
+        }
+
+        {
+          // This second query joins on different keys:
+          val numExchanges = sql(
+            """
+              |SELECT *
+              |FROM
+              |  normal JOIN small ON (normal.key = small.key)
+              |  JOIN tiny ON (normal.key = tiny.key)
+            """.stripMargin
+          ).queryExecution.executedPlan.collect {
+            case exchange: Exchange => exchange
+          }.length
+          assert(numExchanges === 3)
+        }
+
+      }
+    }
+  }
 }

From 4cdd8ecd66769316e8593da7790b84cd867968cd Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Sun, 2 Aug 2015 22:19:27 -0700
Subject: [PATCH 0792/1454] [SPARK-9536] [SPARK-9537] [SPARK-9538] [ML]
 [PYSPARK] ml.classification support raw and probability prediction for
 PySpark

Make the following ml.classification class support raw and probability prediction for PySpark:
```scala
NaiveBayesModel
DecisionTreeClassifierModel
LogisticRegressionModel
```

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7866 from yanboliang/spark-9536-9537 and squashes the following commits:

2934dab [Yanbo Liang] ml.NaiveBayes, ml.DecisionTreeClassifier and ml.LogisticRegression support probability prediction
---
 python/pyspark/ml/classification.py | 61 ++++++++++++++++++++---------
 1 file changed, 43 insertions(+), 18 deletions(-)

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 93ffcd40949b3..b5814f76de000 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -31,7 +31,7 @@
 
 @inherit_doc
 class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                         HasRegParam, HasTol, HasProbabilityCol):
+                         HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
     """
     Logistic regression.
 
@@ -42,13 +42,18 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
     ...     Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF()
     >>> lr = LogisticRegression(maxIter=5, regParam=0.01)
     >>> model = lr.fit(df)
-    >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
-    >>> model.transform(test0).head().prediction
-    0.0
     >>> model.weights
     DenseVector([5.5...])
     >>> model.intercept
     -2.68...
+    >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
+    >>> result = model.transform(test0).head()
+    >>> result.prediction
+    0.0
+    >>> result.probability
+    DenseVector([0.99..., 0.00...])
+    >>> result.rawPrediction
+    DenseVector([8.22..., -8.22...])
     >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
     >>> model.transform(test1).head().prediction
     1.0
@@ -70,11 +75,11 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 threshold=0.5, probabilityCol="probability"):
+                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, probabilityCol="probability")
+                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -98,11 +103,11 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  threshold=0.5, probabilityCol="probability"):
+                  threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, probabilityCol="probability")
+                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
         Sets params for logistic regression.
         """
         kwargs = self.setParams._input_kwargs
@@ -187,7 +192,8 @@ class GBTParams(object):
 
 @inherit_doc
 class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
-                             DecisionTreeParams, HasCheckpointInterval):
+                             HasProbabilityCol, HasRawPredictionCol, DecisionTreeParams,
+                             HasCheckpointInterval):
     """
     `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
     learning algorithm for classification.
@@ -209,8 +215,13 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> model.depth
     1
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
-    >>> model.transform(test0).head().prediction
+    >>> result = model.transform(test0).head()
+    >>> result.prediction
     0.0
+    >>> result.probability
+    DenseVector([1.0, 0.0])
+    >>> result.rawPrediction
+    DenseVector([1.0, 0.0])
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     1.0
@@ -223,10 +234,12 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 probabilityCol="probability", rawPredictionCol="rawPrediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
         """
@@ -246,11 +259,13 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  probabilityCol="probability", rawPredictionCol="rawPrediction",
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                   impurity="gini"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  probabilityCol="probability", rawPredictionCol="rawPrediction", \
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
         Sets params for the DecisionTreeClassifier.
@@ -578,7 +593,8 @@ class GBTClassificationModel(TreeEnsembleModels):
 
 
 @inherit_doc
-class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
+class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol,
+                 HasRawPredictionCol):
     """
     Naive Bayes Classifiers.
 
@@ -595,8 +611,13 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
     >>> model.theta
     DenseMatrix(2, 2, [-1.09..., -0.40..., -0.40..., -1.09...], 1)
     >>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
-    >>> model.transform(test0).head().prediction
+    >>> result = model.transform(test0).head()
+    >>> result.prediction
     1.0
+    >>> result.probability
+    DenseVector([0.42..., 0.57...])
+    >>> result.rawPrediction
+    DenseVector([-1.60..., -1.32...])
     >>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
     >>> model.transform(test1).head().prediction
     1.0
@@ -610,10 +631,12 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 smoothing=1.0, modelType="multinomial"):
+                 probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
+                 modelType="multinomial"):
         """
-        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 smoothing=1.0, modelType="multinomial")
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
+                 modelType="multinomial")
         """
         super(NaiveBayes, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -631,10 +654,12 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  smoothing=1.0, modelType="multinomial"):
+                  probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
+                  modelType="multinomial"):
         """
-        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  smoothing=1.0, modelType="multinomial")
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
+                  modelType="multinomial")
         Sets params for Naive Bayes.
         """
         kwargs = self.setParams._input_kwargs

From 687c8c37150f4c93f8e57d86bb56321a4891286b Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Sun, 2 Aug 2015 23:32:09 -0700
Subject: [PATCH 0793/1454] [SPARK-9372] [SQL] Filter nulls in join keys

This PR adds an optimization rule, `FilterNullsInJoinKey`, to add `Filter` before join operators to filter out rows having null values for join keys.

This optimization is guarded by a new SQL conf, `spark.sql.advancedOptimization`.

The code in this PR was authored by yhuai; I'm opening this PR to factor out this change from #7685, a larger pull request which contains two other optimizations.

Author: Yin Huai <yhuai@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>

Closes #7768 from JoshRosen/filter-nulls-in-join-key and squashes the following commits:

c02fc3f [Yin Huai] Address Josh's comments.
0a8e096 [Yin Huai] Update comments.
ea7d5a6 [Yin Huai] Make sure we do not keep adding filters.
be88760 [Yin Huai] Make it clear that FilterNullsInJoinKeySuite.scala is used to test FilterNullsInJoinKey.
8bb39ad [Yin Huai] Fix non-deterministic tests.
303236b [Josh Rosen] Revert changes that are unrelated to null join key filtering
40eeece [Josh Rosen] Merge remote-tracking branch 'origin/master' into filter-nulls-in-join-key
c57a954 [Yin Huai] Bug fix.
d3d2e64 [Yin Huai] First round of cleanup.
f9516b0 [Yin Huai] Style
c6667e7 [Yin Huai] Add PartitioningCollection.
e616d3b [Yin Huai] wip
7c2d2d8 [Yin Huai] Bug fix and refactoring.
69bb072 [Yin Huai] Introduce NullSafeHashPartitioning and NullUnsafePartitioning.
d5b84c3 [Yin Huai] Do not add unnessary filters.
2201129 [Yin Huai] Filter out rows that will not be joined in equal joins early.
---
 .../catalyst/expressions/nullFunctions.scala  |  48 +++-
 .../sql/catalyst/optimizer/Optimizer.scala    |  64 +++--
 .../plans/logical/basicOperators.scala        |  32 ++-
 .../expressions/ExpressionEvalHelper.scala    |   4 +-
 .../expressions/MathFunctionsSuite.scala      |   3 +-
 .../expressions/NullFunctionsSuite.scala      |  49 +++-
 .../spark/sql/DataFrameNaFunctions.scala      |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |   6 +
 .../org/apache/spark/sql/SQLContext.scala     |   5 +-
 .../extendedOperatorOptimizations.scala       | 160 ++++++++++++
 .../optimizer/FilterNullsInJoinKeySuite.scala | 236 ++++++++++++++++++
 11 files changed, 572 insertions(+), 37 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index 287718fab7f0d..d58c4756938c7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -210,14 +210,58 @@ case class IsNotNull(child: Expression) extends UnaryExpression with Predicate {
   }
 }
 
+/**
+ * A predicate that is evaluated to be true if there are at least `n` null values.
+ */
+case class AtLeastNNulls(n: Int, children: Seq[Expression]) extends Predicate {
+  override def nullable: Boolean = false
+  override def foldable: Boolean = children.forall(_.foldable)
+  override def toString: String = s"AtLeastNNulls($n, ${children.mkString(",")})"
+
+  private[this] val childrenArray = children.toArray
+
+  override def eval(input: InternalRow): Boolean = {
+    var numNulls = 0
+    var i = 0
+    while (i < childrenArray.length && numNulls < n) {
+      val evalC = childrenArray(i).eval(input)
+      if (evalC == null) {
+        numNulls += 1
+      }
+      i += 1
+    }
+    numNulls >= n
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val numNulls = ctx.freshName("numNulls")
+    val code = children.map { e =>
+      val eval = e.gen(ctx)
+      s"""
+        if ($numNulls < $n) {
+          ${eval.code}
+          if (${eval.isNull}) {
+            $numNulls += 1;
+          }
+        }
+      """
+    }.mkString("\n")
+    s"""
+      int $numNulls = 0;
+      $code
+      boolean ${ev.isNull} = false;
+      boolean ${ev.primitive} = $numNulls >= $n;
+     """
+  }
+}
 
 /**
  * A predicate that is evaluated to be true if there are at least `n` non-null and non-NaN values.
  */
-case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate {
+case class AtLeastNNonNullNans(n: Int, children: Seq[Expression]) extends Predicate {
   override def nullable: Boolean = false
   override def foldable: Boolean = children.forall(_.foldable)
-  override def toString: String = s"AtLeastNNulls(n, ${children.mkString(",")})"
+  override def toString: String = s"AtLeastNNonNullNans($n, ${children.mkString(",")})"
 
   private[this] val childrenArray = children.toArray
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 29d706dcb39a7..e4b6294dc7b8e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -31,8 +31,14 @@ import org.apache.spark.sql.types._
 
 abstract class Optimizer extends RuleExecutor[LogicalPlan]
 
-object DefaultOptimizer extends Optimizer {
-  val batches =
+class DefaultOptimizer extends Optimizer {
+
+  /**
+   * Override to provide additional rules for the "Operator Optimizations" batch.
+   */
+  val extendedOperatorOptimizationRules: Seq[Rule[LogicalPlan]] = Nil
+
+  lazy val batches =
     // SubQueries are only needed for analysis and can be removed before execution.
     Batch("Remove SubQueries", FixedPoint(100),
       EliminateSubQueries) ::
@@ -41,26 +47,27 @@ object DefaultOptimizer extends Optimizer {
       RemoveLiteralFromGroupExpressions) ::
     Batch("Operator Optimizations", FixedPoint(100),
       // Operator push down
-      SetOperationPushDown,
-      SamplePushDown,
-      PushPredicateThroughJoin,
-      PushPredicateThroughProject,
-      PushPredicateThroughGenerate,
-      ColumnPruning,
+      SetOperationPushDown ::
+      SamplePushDown ::
+      PushPredicateThroughJoin ::
+      PushPredicateThroughProject ::
+      PushPredicateThroughGenerate ::
+      ColumnPruning ::
       // Operator combine
-      ProjectCollapsing,
-      CombineFilters,
-      CombineLimits,
+      ProjectCollapsing ::
+      CombineFilters ::
+      CombineLimits ::
       // Constant folding
-      NullPropagation,
-      OptimizeIn,
-      ConstantFolding,
-      LikeSimplification,
-      BooleanSimplification,
-      RemovePositive,
-      SimplifyFilters,
-      SimplifyCasts,
-      SimplifyCaseConversionExpressions) ::
+      NullPropagation ::
+      OptimizeIn ::
+      ConstantFolding ::
+      LikeSimplification ::
+      BooleanSimplification ::
+      RemovePositive ::
+      SimplifyFilters ::
+      SimplifyCasts ::
+      SimplifyCaseConversionExpressions ::
+      extendedOperatorOptimizationRules.toList : _*) ::
     Batch("Decimal Optimizations", FixedPoint(100),
       DecimalAggregates) ::
     Batch("LocalRelation", FixedPoint(100),
@@ -222,12 +229,18 @@ object ColumnPruning extends Rule[LogicalPlan] {
   }
 
   /** Applies a projection only when the child is producing unnecessary attributes */
-  private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) =
+  private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) = {
     if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
-      Project(allReferences.filter(c.outputSet.contains).toSeq, c)
+      // We need to preserve the nullability of c's output.
+      // So, we first create a outputMap and if a reference is from the output of
+      // c, we use that output attribute from c.
+      val outputMap = AttributeMap(c.output.map(attr => (attr, attr)))
+      val projectList = allReferences.filter(outputMap.contains).map(outputMap).toSeq
+      Project(projectList, c)
     } else {
       c
     }
+  }
 }
 
 /**
@@ -517,6 +530,13 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
  */
 object CombineFilters extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    case Filter(Not(AtLeastNNulls(1, e1)), Filter(Not(AtLeastNNulls(1, e2)), grandChild)) =>
+      // If we are combining two expressions Not(AtLeastNNulls(1, e1)) and
+      // Not(AtLeastNNulls(1, e2))
+      // (this is used to make sure there is no null in the result of e1 and e2 and
+      // they are added by FilterNullsInJoinKey optimziation rule), we can
+      // just create a Not(AtLeastNNulls(1, (e1 ++ e2).distinct)).
+      Filter(Not(AtLeastNNulls(1, (e1 ++ e2).distinct)), grandChild)
     case ff @ Filter(fc, nf @ Filter(nc, grandChild)) => Filter(And(nc, fc), grandChild)
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index aacfc86ab0e49..54b5f49772664 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -86,7 +86,37 @@ case class Generate(
 }
 
 case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode {
-  override def output: Seq[Attribute] = child.output
+  /**
+   * Indicates if `atLeastNNulls` is used to check if atLeastNNulls.children
+   * have at least one null value and atLeastNNulls.children are all attributes.
+   */
+  private def isAtLeastOneNullOutputAttributes(atLeastNNulls: AtLeastNNulls): Boolean = {
+    val expressions = atLeastNNulls.children
+    val n = atLeastNNulls.n
+    if (n != 1) {
+      // AtLeastNNulls is not used to check if atLeastNNulls.children have
+      // at least one null value.
+      false
+    } else {
+      // AtLeastNNulls is used to check if atLeastNNulls.children have
+      // at least one null value. We need to make sure all atLeastNNulls.children
+      // are attributes.
+      expressions.forall(_.isInstanceOf[Attribute])
+    }
+  }
+
+  override def output: Seq[Attribute] = condition match {
+    case Not(a: AtLeastNNulls) if isAtLeastOneNullOutputAttributes(a) =>
+      // The condition is used to make sure that there is no null value in
+      // a.children.
+      val nonNullableAttributes = AttributeSet(a.children.asInstanceOf[Seq[Attribute]])
+      child.output.map {
+        case attr if nonNullableAttributes.contains(attr) =>
+          attr.withNullability(false)
+        case attr => attr
+      }
+    case _ => child.output
+  }
 }
 
 case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index a41185b4d8754..3e55151298741 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -31,6 +31,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 trait ExpressionEvalHelper {
   self: SparkFunSuite =>
 
+  protected val defaultOptimizer = new DefaultOptimizer
+
   protected def create_row(values: Any*): InternalRow = {
     InternalRow.fromSeq(values.map(CatalystTypeConverters.convertToCatalyst))
   }
@@ -186,7 +188,7 @@ trait ExpressionEvalHelper {
       expected: Any,
       inputRow: InternalRow = EmptyRow): Unit = {
     val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation)
-    val optimizedPlan = DefaultOptimizer.execute(plan)
+    val optimizedPlan = defaultOptimizer.execute(plan)
     checkEvaluationWithoutCodegen(optimizedPlan.expressions.head, expected, inputRow)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 9fcb548af6bbb..649a5b44dc036 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -23,7 +23,6 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateProjection, GenerateMutableProjection}
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 import org.apache.spark.sql.types._
 
@@ -149,7 +148,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     expression: Expression,
     inputRow: InternalRow = EmptyRow): Unit = {
     val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation)
-    val optimizedPlan = DefaultOptimizer.execute(plan)
+    val optimizedPlan = defaultOptimizer.execute(plan)
     checkNaNWithoutCodegen(optimizedPlan.expressions.head, inputRow)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
index ace6c15dc8418..bf197124d8dbc 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
@@ -77,7 +77,7 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
-  test("AtLeastNNonNulls") {
+  test("AtLeastNNonNullNans") {
     val mix = Seq(Literal("x"),
       Literal.create(null, StringType),
       Literal.create(null, DoubleType),
@@ -96,11 +96,46 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       Literal(Float.MaxValue),
       Literal(false))
 
-    checkEvaluation(AtLeastNNonNulls(2, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNulls(3, mix), false, EmptyRow)
-    checkEvaluation(AtLeastNNonNulls(3, nanOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNulls(4, nanOnly), false, EmptyRow)
-    checkEvaluation(AtLeastNNonNulls(3, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNulls(4, nullOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(0, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(2, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(3, mix), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(0, nanOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(3, nanOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(4, nanOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(0, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(3, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNullNans(4, nullOnly), false, EmptyRow)
+  }
+
+  test("AtLeastNNull") {
+    val mix = Seq(Literal("x"),
+      Literal.create(null, StringType),
+      Literal.create(null, DoubleType),
+      Literal(Double.NaN),
+      Literal(5f))
+
+    val nanOnly = Seq(Literal("x"),
+      Literal(10.0),
+      Literal(Float.NaN),
+      Literal(math.log(-2)),
+      Literal(Double.MaxValue))
+
+    val nullOnly = Seq(Literal("x"),
+      Literal.create(null, DoubleType),
+      Literal.create(null, DecimalType.USER_DEFAULT),
+      Literal(Float.MaxValue),
+      Literal(false))
+
+    checkEvaluation(AtLeastNNulls(0, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNulls(1, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNulls(2, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNulls(3, mix), false, EmptyRow)
+    checkEvaluation(AtLeastNNulls(0, nanOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNulls(1, nanOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNulls(2, nanOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNulls(0, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNulls(1, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNulls(2, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNulls(3, nullOnly), false, EmptyRow)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index a4fd4cf3b330b..ea85f0657a726 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -122,7 +122,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = {
     // Filtering condition:
     // only keep the row if it has at least `minNonNulls` non-null and non-NaN values.
-    val predicate = AtLeastNNonNulls(minNonNulls, cols.map(name => df.resolve(name)))
+    val predicate = AtLeastNNonNullNans(minNonNulls, cols.map(name => df.resolve(name)))
     df.filter(Column(predicate))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 6644e85d4a037..387960c4b482b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -413,6 +413,10 @@ private[spark] object SQLConf {
     "spark.sql.useSerializer2",
     defaultValue = Some(true), isPublic = false)
 
+  val ADVANCED_SQL_OPTIMIZATION = booleanConf(
+    "spark.sql.advancedOptimization",
+    defaultValue = Some(true), isPublic = false)
+
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
@@ -484,6 +488,8 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
 
+  private[spark] def advancedSqlOptimizations: Boolean = getConf(ADVANCED_SQL_OPTIMIZATION)
+
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
 
   private[spark] def defaultSizeInBytes: Long =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index dbb2a09846548..31e2b508d485e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -41,6 +41,7 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
+import org.apache.spark.sql.optimizer.FilterNullsInJoinKey
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -156,7 +157,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
     }
 
   @transient
-  protected[sql] lazy val optimizer: Optimizer = DefaultOptimizer
+  protected[sql] lazy val optimizer: Optimizer = new DefaultOptimizer {
+    override val extendedOperatorOptimizationRules = FilterNullsInJoinKey(self) :: Nil
+  }
 
   @transient
   protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala b/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
new file mode 100644
index 0000000000000..5a4dde5756964
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.optimizer
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.{Inner, LeftOuter, RightOuter, LeftSemi}
+import org.apache.spark.sql.catalyst.plans.logical.{Project, Filter, Join, LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.Rule
+
+/**
+ * An optimization rule used to insert Filters to filter out rows whose equal join keys
+ * have at least one null values. For this kind of rows, they will not contribute to
+ * the join results of equal joins because a null does not equal another null. We can
+ * filter them out before shuffling join input rows. For example, we have two tables
+ *
+ * table1(key String, value Int)
+ * "str1"|1
+ * null  |2
+ *
+ * table2(key String, value Int)
+ * "str1"|3
+ * null  |4
+ *
+ * For a inner equal join, the result will be
+ * "str1"|1|"str1"|3
+ *
+ * those two rows having null as the value of key will not contribute to the result.
+ * So, we can filter them out early.
+ *
+ * This optimization rule can be disabled by setting spark.sql.advancedOptimization to false.
+ *
+ */
+case class FilterNullsInJoinKey(
+    sqlContext: SQLContext)
+  extends Rule[LogicalPlan] {
+
+  /**
+   * Checks if we need to add a Filter operator. We will add a Filter when
+   * there is any attribute in `keys` whose corresponding attribute of `keys`
+   * in `plan.output` is still nullable (`nullable` field is `true`).
+   */
+  private def needsFilter(keys: Seq[Expression], plan: LogicalPlan): Boolean = {
+    val keyAttributeSet = AttributeSet(keys.filter(_.isInstanceOf[Attribute]))
+    plan.output.filter(keyAttributeSet.contains).exists(_.nullable)
+  }
+
+  /**
+   * Adds a Filter operator to make sure that every attribute in `keys` is non-nullable.
+   */
+  private def addFilterIfNecessary(
+      keys: Seq[Expression],
+      child: LogicalPlan): LogicalPlan = {
+    // We get all attributes from keys.
+    val attributes = keys.filter(_.isInstanceOf[Attribute])
+
+    // Then, we create a Filter to make sure these attributes are non-nullable.
+    val filter =
+      if (attributes.nonEmpty) {
+        Filter(Not(AtLeastNNulls(1, attributes)), child)
+      } else {
+        child
+      }
+
+    filter
+  }
+
+  /**
+   * We reconstruct the join condition.
+   */
+  private def reconstructJoinCondition(
+      leftKeys: Seq[Expression],
+      rightKeys: Seq[Expression],
+      otherPredicate: Option[Expression]): Expression = {
+    // First, we rewrite the equal condition part. When we extract those keys,
+    // we use splitConjunctivePredicates. So, it is safe to use .reduce(And).
+    val rewrittenEqualJoinCondition = leftKeys.zip(rightKeys).map {
+      case (l, r) => EqualTo(l, r)
+    }.reduce(And)
+
+    // Then, we add otherPredicate. When we extract those equal condition part,
+    // we use splitConjunctivePredicates. So, it is safe to use
+    // And(rewrittenEqualJoinCondition, c).
+    val rewrittenJoinCondition = otherPredicate
+      .map(c => And(rewrittenEqualJoinCondition, c))
+      .getOrElse(rewrittenEqualJoinCondition)
+
+    rewrittenJoinCondition
+  }
+
+  def apply(plan: LogicalPlan): LogicalPlan = {
+    if (!sqlContext.conf.advancedSqlOptimizations) {
+      plan
+    } else {
+      plan transform {
+        case join: Join => join match {
+          // For a inner join having equal join condition part, we can add filters
+          // to both sides of the join operator.
+          case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
+            if needsFilter(leftKeys, left) || needsFilter(rightKeys, right) =>
+            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
+            val withRightFilter = addFilterIfNecessary(rightKeys, right)
+            val rewrittenJoinCondition =
+              reconstructJoinCondition(leftKeys, rightKeys, condition)
+
+            Join(withLeftFilter, withRightFilter, Inner, Some(rewrittenJoinCondition))
+
+          // For a left outer join having equal join condition part, we can add a filter
+          // to the right side of the join operator.
+          case ExtractEquiJoinKeys(LeftOuter, leftKeys, rightKeys, condition, left, right)
+            if needsFilter(rightKeys, right) =>
+            val withRightFilter = addFilterIfNecessary(rightKeys, right)
+            val rewrittenJoinCondition =
+              reconstructJoinCondition(leftKeys, rightKeys, condition)
+
+            Join(left, withRightFilter, LeftOuter, Some(rewrittenJoinCondition))
+
+          // For a right outer join having equal join condition part, we can add a filter
+          // to the left side of the join operator.
+          case ExtractEquiJoinKeys(RightOuter, leftKeys, rightKeys, condition, left, right)
+            if needsFilter(leftKeys, left) =>
+            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
+            val rewrittenJoinCondition =
+              reconstructJoinCondition(leftKeys, rightKeys, condition)
+
+            Join(withLeftFilter, right, RightOuter, Some(rewrittenJoinCondition))
+
+          // For a left semi join having equal join condition part, we can add filters
+          // to both sides of the join operator.
+          case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right)
+            if needsFilter(leftKeys, left) || needsFilter(rightKeys, right) =>
+            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
+            val withRightFilter = addFilterIfNecessary(rightKeys, right)
+            val rewrittenJoinCondition =
+              reconstructJoinCondition(leftKeys, rightKeys, condition)
+
+            Join(withLeftFilter, withRightFilter, LeftSemi, Some(rewrittenJoinCondition))
+
+          case other => other
+        }
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala
new file mode 100644
index 0000000000000..f98e4acafbf2c
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala
@@ -0,0 +1,236 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.optimizer
+
+import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions.{Not, AtLeastNNulls}
+import org.apache.spark.sql.catalyst.optimizer._
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LocalRelation, LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.RuleExecutor
+import org.apache.spark.sql.test.TestSQLContext
+
+/** This is the test suite for FilterNullsInJoinKey optimization rule. */
+class FilterNullsInJoinKeySuite extends PlanTest {
+
+  // We add predicate pushdown rules at here to make sure we do not
+  // create redundant Filter operators. Also, because the attribute ordering of
+  // the Project operator added by ColumnPruning may be not deterministic
+  // (the ordering may depend on the testing environment),
+  // we first construct the plan with expected Filter operators and then
+  // run the optimizer to add the the Project for column pruning.
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches =
+      Batch("Subqueries", Once,
+        EliminateSubQueries) ::
+      Batch("Operator Optimizations", FixedPoint(100),
+        FilterNullsInJoinKey(TestSQLContext), // This is the rule we test in this suite.
+        CombineFilters,
+        PushPredicateThroughProject,
+        BooleanSimplification,
+        PushPredicateThroughJoin,
+        PushPredicateThroughGenerate,
+        ColumnPruning,
+        ProjectCollapsing) :: Nil
+  }
+
+  val leftRelation = LocalRelation('a.int, 'b.int, 'c.int, 'd.int)
+
+  val rightRelation = LocalRelation('e.int, 'f.int, 'g.int, 'h.int)
+
+  test("inner join") {
+    val joinCondition =
+      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
+
+    val joinedPlan =
+      leftRelation
+        .join(rightRelation, Inner, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    val optimized = Optimize.execute(joinedPlan.analyze)
+
+    // For an inner join, FilterNullsInJoinKey add filter to both side.
+    val correctLeft =
+      leftRelation
+        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
+
+    val correctRight =
+      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
+
+    val correctAnswer =
+      correctLeft
+        .join(correctRight, Inner, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
+  }
+
+  test("make sure we do not keep adding filters") {
+    val thirdRelation = LocalRelation('i.int, 'j.int, 'k.int, 'l.int)
+    val joinedPlan =
+      leftRelation
+        .join(rightRelation, Inner, Some('a === 'e))
+        .join(thirdRelation, Inner, Some('b === 'i && 'a === 'j))
+
+    val optimized = Optimize.execute(joinedPlan.analyze)
+    val conditions = optimized.collect {
+      case Filter(condition @ Not(AtLeastNNulls(1, exprs)), _) => exprs
+    }
+
+    // Make sure that we have three Not(AtLeastNNulls(1, exprs)) for those three tables.
+    assert(conditions.length === 3)
+
+    // Make sure attribtues are indeed a, b, e, i, and j.
+    assert(
+      conditions.flatMap(exprs => exprs).toSet ===
+        joinedPlan.select('a, 'b, 'e, 'i, 'j).analyze.output.toSet)
+  }
+
+  test("inner join (partially optimized)") {
+    val joinCondition =
+      ('a + 2 === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
+
+    val joinedPlan =
+      leftRelation
+        .join(rightRelation, Inner, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    val optimized = Optimize.execute(joinedPlan.analyze)
+
+    // We cannot extract attribute from the left join key.
+    val correctRight =
+      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
+
+    val correctAnswer =
+      leftRelation
+        .join(correctRight, Inner, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
+  }
+
+  test("inner join (not optimized)") {
+    val nonOptimizedJoinConditions =
+      Some('c - 100 + 'd === 'g + 1 - 'h) ::
+        Some('d > 'h || 'c === 'g) ::
+        Some('d + 'g + 'c > 'd - 'h) :: Nil
+
+    nonOptimizedJoinConditions.foreach { joinCondition =>
+      val joinedPlan =
+        leftRelation
+          .join(rightRelation.select('f, 'g, 'h), Inner, joinCondition)
+          .select('a, 'c, 'f, 'd, 'h, 'g)
+
+      val optimized = Optimize.execute(joinedPlan.analyze)
+
+      comparePlans(optimized, Optimize.execute(joinedPlan.analyze))
+    }
+  }
+
+  test("left outer join") {
+    val joinCondition =
+      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
+
+    val joinedPlan =
+      leftRelation
+        .join(rightRelation, LeftOuter, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    val optimized = Optimize.execute(joinedPlan.analyze)
+
+    // For a left outer join, FilterNullsInJoinKey add filter to the right side.
+    val correctRight =
+      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
+
+    val correctAnswer =
+      leftRelation
+        .join(correctRight, LeftOuter, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
+  }
+
+  test("right outer join") {
+    val joinCondition =
+      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
+
+    val joinedPlan =
+      leftRelation
+        .join(rightRelation, RightOuter, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    val optimized = Optimize.execute(joinedPlan.analyze)
+
+    // For a right outer join, FilterNullsInJoinKey add filter to the left side.
+    val correctLeft =
+      leftRelation
+        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
+
+    val correctAnswer =
+      correctLeft
+        .join(rightRelation, RightOuter, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+
+    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
+  }
+
+  test("full outer join") {
+    val joinCondition =
+      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
+
+    val joinedPlan =
+      leftRelation
+        .join(rightRelation, FullOuter, Some(joinCondition))
+        .select('a, 'f, 'd, 'h)
+
+    // FilterNullsInJoinKey does not fire for a full outer join.
+    val optimized = Optimize.execute(joinedPlan.analyze)
+
+    comparePlans(optimized, Optimize.execute(joinedPlan.analyze))
+  }
+
+  test("left semi join") {
+    val joinCondition =
+      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
+
+    val joinedPlan =
+      leftRelation
+        .join(rightRelation, LeftSemi, Some(joinCondition))
+        .select('a, 'd)
+
+    val optimized = Optimize.execute(joinedPlan.analyze)
+
+    // For a left semi join, FilterNullsInJoinKey add filter to both side.
+    val correctLeft =
+      leftRelation
+        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
+
+    val correctRight =
+      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
+
+    val correctAnswer =
+      correctLeft
+        .join(correctRight, LeftSemi, Some(joinCondition))
+        .select('a, 'd)
+
+    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
+  }
+}

From 608353c8e8e50461fafff91a2c885dca8af3aaa8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sun, 2 Aug 2015 23:41:16 -0700
Subject: [PATCH 0794/1454] [SPARK-9404][SPARK-9542][SQL] unsafe array data and
 map data

This PR adds a UnsafeArrayData, current we encode it in this way:

first 4 bytes is the # elements
then each 4 byte is the start offset of the element, unless it is negative, in which case the element is null.
followed by the elements themselves

an example:  [10, 11, 12, 13, null, 14] will be encoded as:
5, 28, 32, 36, 40, -44, 44, 10, 11, 12, 13, 14

Note that, when we read a UnsafeArrayData from bytes, we can read the first 4 bytes as numElements and take the rest(first 4 bytes skipped) as value region.

unsafe map data just use 2 unsafe array data, first 4 bytes is # of elements, second 4 bytes is numBytes of key array, the follows key array data and value array data.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7752 from cloud-fan/unsafe-array and squashes the following commits:

3269bd7 [Wenchen Fan] fix a bug
6445289 [Wenchen Fan] add unit tests
49adf26 [Wenchen Fan] add unsafe map
20d1039 [Wenchen Fan] add comments and unsafe converter
821b8db [Wenchen Fan] add unsafe array
---
 .../catalyst/expressions/UnsafeArrayData.java | 333 ++++++++++++++++++
 .../catalyst/expressions/UnsafeMapData.java   |  66 ++++
 .../catalyst/expressions/UnsafeReaders.java   |  48 +++
 .../sql/catalyst/expressions/UnsafeRow.java   |  34 +-
 .../expressions/UnsafeRowWriters.java         |  71 ++++
 .../catalyst/expressions/UnsafeWriters.java   | 208 +++++++++++
 .../sql/catalyst/expressions/FromUnsafe.scala |  67 ++++
 .../sql/catalyst/expressions/Projection.scala |  10 +-
 .../expressions/codegen/CodeGenerator.scala   |   4 +-
 .../codegen/GenerateUnsafeProjection.scala    | 327 ++++++++++++++++-
 .../spark/sql/types/ArrayBasedMapData.scala   |  15 +-
 .../apache/spark/sql/types/ArrayData.scala    |  14 +-
 .../spark/sql/types/GenericArrayData.scala    |  10 +-
 .../org/apache/spark/sql/types/MapData.scala  |   2 +
 .../expressions/UnsafeRowConverterSuite.scala | 114 +++++-
 .../apache/spark/unsafe/types/UTF8String.java |   3 +
 16 files changed, 1295 insertions(+), 31 deletions(-)
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
 create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
new file mode 100644
index 0000000000000..0374846d71674
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
@@ -0,0 +1,333 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.types.*;
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
+import org.apache.spark.unsafe.hash.Murmur3_x86_32;
+import org.apache.spark.unsafe.types.CalendarInterval;
+import org.apache.spark.unsafe.types.UTF8String;
+
+/**
+ * An Unsafe implementation of Array which is backed by raw memory instead of Java objects.
+ *
+ * Each tuple has two parts: [offsets] [values]
+ *
+ * In the `offsets` region, we store 4 bytes per element, represents the start address of this
+ * element in `values` region. We can get the length of this element by subtracting next offset.
+ * Note that offset can by negative which means this element is null.
+ *
+ * In the `values` region, we store the content of elements. As we can get length info, so elements
+ * can be variable-length.
+ *
+ * Note that when we write out this array, we should write out the `numElements` at first 4 bytes,
+ * then follows content. When we read in an array, we should read first 4 bytes as `numElements`
+ * and take the rest as content.
+ *
+ * Instances of `UnsafeArrayData` act as pointers to row data stored in this format.
+ */
+// todo: there is a lof of duplicated code between UnsafeRow and UnsafeArrayData.
+public class UnsafeArrayData extends ArrayData {
+
+  private Object baseObject;
+  private long baseOffset;
+
+  // The number of elements in this array
+  private int numElements;
+
+  // The size of this array's backing data, in bytes
+  private int sizeInBytes;
+
+  private int getElementOffset(int ordinal) {
+    return PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + ordinal * 4L);
+  }
+
+  private int getElementSize(int offset, int ordinal) {
+    if (ordinal == numElements - 1) {
+      return sizeInBytes - offset;
+    } else {
+      return Math.abs(getElementOffset(ordinal + 1)) - offset;
+    }
+  }
+
+  private void assertIndexIsValid(int ordinal) {
+    assert ordinal >= 0 : "ordinal (" + ordinal + ") should >= 0";
+    assert ordinal < numElements : "ordinal (" + ordinal + ") should < " + numElements;
+  }
+
+  /**
+   * Construct a new UnsafeArrayData. The resulting UnsafeArrayData won't be usable until
+   * `pointTo()` has been called, since the value returned by this constructor is equivalent
+   * to a null pointer.
+   */
+  public UnsafeArrayData() { }
+
+  public Object getBaseObject() { return baseObject; }
+  public long getBaseOffset() { return baseOffset; }
+  public int getSizeInBytes() { return sizeInBytes; }
+
+  @Override
+  public int numElements() { return numElements; }
+
+  /**
+   * Update this UnsafeArrayData to point to different backing data.
+   *
+   * @param baseObject the base object
+   * @param baseOffset the offset within the base object
+   * @param sizeInBytes the size of this row's backing data, in bytes
+   */
+  public void pointTo(Object baseObject, long baseOffset, int numElements, int sizeInBytes) {
+    assert numElements >= 0 : "numElements (" + numElements + ") should >= 0";
+    this.numElements = numElements;
+    this.baseObject = baseObject;
+    this.baseOffset = baseOffset;
+    this.sizeInBytes = sizeInBytes;
+  }
+
+  @Override
+  public boolean isNullAt(int ordinal) {
+    assertIndexIsValid(ordinal);
+    return getElementOffset(ordinal) < 0;
+  }
+
+  @Override
+  public Object get(int ordinal, DataType dataType) {
+    if (isNullAt(ordinal) || dataType instanceof NullType) {
+      return null;
+    } else if (dataType instanceof BooleanType) {
+      return getBoolean(ordinal);
+    } else if (dataType instanceof ByteType) {
+      return getByte(ordinal);
+    } else if (dataType instanceof ShortType) {
+      return getShort(ordinal);
+    } else if (dataType instanceof IntegerType) {
+      return getInt(ordinal);
+    } else if (dataType instanceof LongType) {
+      return getLong(ordinal);
+    } else if (dataType instanceof FloatType) {
+      return getFloat(ordinal);
+    } else if (dataType instanceof DoubleType) {
+      return getDouble(ordinal);
+    } else if (dataType instanceof DecimalType) {
+      DecimalType dt = (DecimalType) dataType;
+      return getDecimal(ordinal, dt.precision(), dt.scale());
+    } else if (dataType instanceof DateType) {
+      return getInt(ordinal);
+    } else if (dataType instanceof TimestampType) {
+      return getLong(ordinal);
+    } else if (dataType instanceof BinaryType) {
+      return getBinary(ordinal);
+    } else if (dataType instanceof StringType) {
+      return getUTF8String(ordinal);
+    } else if (dataType instanceof CalendarIntervalType) {
+      return getInterval(ordinal);
+    } else if (dataType instanceof StructType) {
+      return getStruct(ordinal, ((StructType) dataType).size());
+    } else if (dataType instanceof ArrayType) {
+      return getArray(ordinal);
+    } else if (dataType instanceof MapType) {
+      return getMap(ordinal);
+    } else {
+      throw new UnsupportedOperationException("Unsupported data type " + dataType.simpleString());
+    }
+  }
+
+  @Override
+  public boolean getBoolean(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return false;
+    return PlatformDependent.UNSAFE.getBoolean(baseObject, baseOffset + offset);
+  }
+
+  @Override
+  public byte getByte(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return 0;
+    return PlatformDependent.UNSAFE.getByte(baseObject, baseOffset + offset);
+  }
+
+  @Override
+  public short getShort(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return 0;
+    return PlatformDependent.UNSAFE.getShort(baseObject, baseOffset + offset);
+  }
+
+  @Override
+  public int getInt(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return 0;
+    return PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + offset);
+  }
+
+  @Override
+  public long getLong(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return 0;
+    return PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+  }
+
+  @Override
+  public float getFloat(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return 0;
+    return PlatformDependent.UNSAFE.getFloat(baseObject, baseOffset + offset);
+  }
+
+  @Override
+  public double getDouble(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return 0;
+    return PlatformDependent.UNSAFE.getDouble(baseObject, baseOffset + offset);
+  }
+
+  @Override
+  public Decimal getDecimal(int ordinal, int precision, int scale) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return null;
+
+    if (precision <= Decimal.MAX_LONG_DIGITS()) {
+      final long value = PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+      return Decimal.apply(value, precision, scale);
+    } else {
+      final byte[] bytes = getBinary(ordinal);
+      final BigInteger bigInteger = new BigInteger(bytes);
+      final BigDecimal javaDecimal = new BigDecimal(bigInteger, scale);
+      return Decimal.apply(new scala.math.BigDecimal(javaDecimal), precision, scale);
+    }
+  }
+
+  @Override
+  public UTF8String getUTF8String(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return null;
+    final int size = getElementSize(offset, ordinal);
+    return UTF8String.fromAddress(baseObject, baseOffset + offset, size);
+  }
+
+  @Override
+  public byte[] getBinary(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return null;
+    final int size = getElementSize(offset, ordinal);
+    final byte[] bytes = new byte[size];
+    PlatformDependent.copyMemory(
+      baseObject,
+      baseOffset + offset,
+      bytes,
+      PlatformDependent.BYTE_ARRAY_OFFSET,
+      size);
+    return bytes;
+  }
+
+  @Override
+  public CalendarInterval getInterval(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return null;
+    final int months = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+    final long microseconds =
+      PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset + 8);
+    return new CalendarInterval(months, microseconds);
+  }
+
+  @Override
+  public InternalRow getStruct(int ordinal, int numFields) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return null;
+    final int size = getElementSize(offset, ordinal);
+    final UnsafeRow row = new UnsafeRow();
+    row.pointTo(baseObject, baseOffset + offset, numFields, size);
+    return row;
+  }
+
+  @Override
+  public ArrayData getArray(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return null;
+    final int size = getElementSize(offset, ordinal);
+    return UnsafeReaders.readArray(baseObject, baseOffset + offset, size);
+  }
+
+  @Override
+  public MapData getMap(int ordinal) {
+    assertIndexIsValid(ordinal);
+    final int offset = getElementOffset(ordinal);
+    if (offset < 0) return null;
+    final int size = getElementSize(offset, ordinal);
+    return UnsafeReaders.readMap(baseObject, baseOffset + offset, size);
+  }
+
+  @Override
+  public int hashCode() {
+    return Murmur3_x86_32.hashUnsafeWords(baseObject, baseOffset, sizeInBytes, 42);
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other instanceof UnsafeArrayData) {
+      UnsafeArrayData o = (UnsafeArrayData) other;
+      return (sizeInBytes == o.sizeInBytes) &&
+        ByteArrayMethods.arrayEquals(baseObject, baseOffset, o.baseObject, o.baseOffset,
+          sizeInBytes);
+    }
+    return false;
+  }
+
+  public void writeToMemory(Object target, long targetOffset) {
+    PlatformDependent.copyMemory(
+      baseObject,
+      baseOffset,
+      target,
+      targetOffset,
+      sizeInBytes
+    );
+  }
+
+  @Override
+  public UnsafeArrayData copy() {
+    UnsafeArrayData arrayCopy = new UnsafeArrayData();
+    final byte[] arrayDataCopy = new byte[sizeInBytes];
+    PlatformDependent.copyMemory(
+      baseObject,
+      baseOffset,
+      arrayDataCopy,
+      PlatformDependent.BYTE_ARRAY_OFFSET,
+      sizeInBytes
+    );
+    arrayCopy.pointTo(arrayDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numElements, sizeInBytes);
+    return arrayCopy;
+  }
+}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java
new file mode 100644
index 0000000000000..46216054ab38b
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+import org.apache.spark.sql.types.ArrayData;
+import org.apache.spark.sql.types.MapData;
+
+/**
+ * An Unsafe implementation of Map which is backed by raw memory instead of Java objects.
+ *
+ * Currently we just use 2 UnsafeArrayData to represent UnsafeMapData.
+ */
+public class UnsafeMapData extends MapData {
+
+  public final UnsafeArrayData keys;
+  public final UnsafeArrayData values;
+  // The number of elements in this array
+  private int numElements;
+  // The size of this array's backing data, in bytes
+  private int sizeInBytes;
+
+  public int getSizeInBytes() { return sizeInBytes; }
+
+  public UnsafeMapData(UnsafeArrayData keys, UnsafeArrayData values) {
+    assert keys.numElements() == values.numElements();
+    this.sizeInBytes = keys.getSizeInBytes() + values.getSizeInBytes();
+    this.numElements = keys.numElements();
+    this.keys = keys;
+    this.values = values;
+  }
+
+  @Override
+  public int numElements() {
+    return numElements;
+  }
+
+  @Override
+  public ArrayData keyArray() {
+    return keys;
+  }
+
+  @Override
+  public ArrayData valueArray() {
+    return values;
+  }
+
+  @Override
+  public UnsafeMapData copy() {
+    return new UnsafeMapData(keys.copy(), values.copy());
+  }
+}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
new file mode 100644
index 0000000000000..b521b703389d3
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+import org.apache.spark.unsafe.PlatformDependent;
+
+public class UnsafeReaders {
+
+  public static UnsafeArrayData readArray(Object baseObject, long baseOffset, int numBytes) {
+    // Read the number of elements from first 4 bytes.
+    final int numElements = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset);
+    final UnsafeArrayData array = new UnsafeArrayData();
+    // Skip the first 4 bytes.
+    array.pointTo(baseObject, baseOffset + 4, numElements, numBytes - 4);
+    return array;
+  }
+
+  public static UnsafeMapData readMap(Object baseObject, long baseOffset, int numBytes) {
+    // Read the number of elements from first 4 bytes.
+    final int numElements = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset);
+    // Read the numBytes of key array in second 4 bytes.
+    final int keyArraySize = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + 4);
+    final int valueArraySize = numBytes - 8 - keyArraySize;
+
+    final UnsafeArrayData keyArray = new UnsafeArrayData();
+    keyArray.pointTo(baseObject, baseOffset + 8, numElements, keyArraySize);
+
+    final UnsafeArrayData valueArray = new UnsafeArrayData();
+    valueArray.pointTo(baseObject, baseOffset + 8 + keyArraySize, numElements, valueArraySize);
+
+    return new UnsafeMapData(keyArray, valueArray);
+  }
+}
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index b4fc0b7b705ec..c5d42d73a43a4 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -291,6 +291,10 @@ public Object get(int ordinal, DataType dataType) {
       return getInterval(ordinal);
     } else if (dataType instanceof StructType) {
       return getStruct(ordinal, ((StructType) dataType).size());
+    } else if (dataType instanceof ArrayType) {
+      return getArray(ordinal);
+    } else if (dataType instanceof MapType) {
+      return getMap(ordinal);
     } else {
       throw new UnsupportedOperationException("Unsupported data type " + dataType.simpleString());
     }
@@ -346,7 +350,6 @@ public double getDouble(int ordinal) {
 
   @Override
   public Decimal getDecimal(int ordinal, int precision, int scale) {
-    assertIndexIsValid(ordinal);
     if (isNullAt(ordinal)) {
       return null;
     }
@@ -362,7 +365,6 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
 
   @Override
   public UTF8String getUTF8String(int ordinal) {
-    assertIndexIsValid(ordinal);
     if (isNullAt(ordinal)) return null;
     final long offsetAndSize = getLong(ordinal);
     final int offset = (int) (offsetAndSize >> 32);
@@ -372,7 +374,6 @@ public UTF8String getUTF8String(int ordinal) {
 
   @Override
   public byte[] getBinary(int ordinal) {
-    assertIndexIsValid(ordinal);
     if (isNullAt(ordinal)) {
       return null;
     } else {
@@ -410,7 +411,6 @@ public UnsafeRow getStruct(int ordinal, int numFields) {
     if (isNullAt(ordinal)) {
       return null;
     } else {
-      assertIndexIsValid(ordinal);
       final long offsetAndSize = getLong(ordinal);
       final int offset = (int) (offsetAndSize >> 32);
       final int size = (int) (offsetAndSize & ((1L << 32) - 1));
@@ -420,11 +420,33 @@ public UnsafeRow getStruct(int ordinal, int numFields) {
     }
   }
 
+  @Override
+  public ArrayData getArray(int ordinal) {
+    if (isNullAt(ordinal)) {
+      return null;
+    } else {
+      final long offsetAndSize = getLong(ordinal);
+      final int offset = (int) (offsetAndSize >> 32);
+      final int size = (int) (offsetAndSize & ((1L << 32) - 1));
+      return UnsafeReaders.readArray(baseObject, baseOffset + offset, size);
+    }
+  }
+
+  @Override
+  public MapData getMap(int ordinal) {
+    if (isNullAt(ordinal)) {
+      return null;
+    } else {
+      final long offsetAndSize = getLong(ordinal);
+      final int offset = (int) (offsetAndSize >> 32);
+      final int size = (int) (offsetAndSize & ((1L << 32) - 1));
+      return UnsafeReaders.readMap(baseObject, baseOffset + offset, size);
+    }
+  }
+
   /**
    * Copies this row, returning a self-contained UnsafeRow that stores its data in an internal
    * byte array rather than referencing data stored in a data page.
-   * <p>
-   * This method is only supported on UnsafeRows that do not use ObjectPools.
    */
   @Override
   public UnsafeRow copy() {
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index f43a285cd6cad..31928731545da 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -19,6 +19,7 @@
 
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.Decimal;
+import org.apache.spark.sql.types.MapData;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
@@ -185,4 +186,74 @@ public static int write(UnsafeRow target, int ordinal, int cursor, CalendarInter
       return 16;
     }
   }
+
+  public static class ArrayWriter {
+
+    public static int getSize(UnsafeArrayData input) {
+      // we need extra 4 bytes the store the number of elements in this array.
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(input.getSizeInBytes() + 4);
+    }
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, UnsafeArrayData input) {
+      final int numBytes = input.getSizeInBytes() + 4;
+      final long offset = target.getBaseOffset() + cursor;
+
+      // write the number of elements into first 4 bytes.
+      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset, input.numElements());
+
+      // zero-out the padding bytes
+      if ((numBytes & 0x07) > 0) {
+        PlatformDependent.UNSAFE.putLong(
+          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+      }
+
+      // Write the bytes to the variable length portion.
+      input.writeToMemory(target.getBaseObject(), offset + 4);
+
+      // Set the fixed length portion.
+      target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
+
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes);
+    }
+  }
+
+  public static class MapWriter {
+
+    public static int getSize(UnsafeMapData input) {
+      // we need extra 8 bytes to store number of elements and numBytes of key array.
+      final int sizeInBytes = 4 + 4 + input.getSizeInBytes();
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(sizeInBytes);
+    }
+
+    public static int write(UnsafeRow target, int ordinal, int cursor, UnsafeMapData input) {
+      final long offset = target.getBaseOffset() + cursor;
+      final UnsafeArrayData keyArray = input.keys;
+      final UnsafeArrayData valueArray = input.values;
+      final int keysNumBytes = keyArray.getSizeInBytes();
+      final int valuesNumBytes = valueArray.getSizeInBytes();
+      final int numBytes = 4 + 4 + keysNumBytes + valuesNumBytes;
+
+      // write the number of elements into first 4 bytes.
+      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset, input.numElements());
+      // write the numBytes of key array into second 4 bytes.
+      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset + 4, keysNumBytes);
+
+      // zero-out the padding bytes
+      if ((numBytes & 0x07) > 0) {
+        PlatformDependent.UNSAFE.putLong(
+          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+      }
+
+      // Write the bytes of key array to the variable length portion.
+      keyArray.writeToMemory(target.getBaseObject(), offset + 8);
+
+      // Write the bytes of value array to the variable length portion.
+      valueArray.writeToMemory(target.getBaseObject(), offset + 8 + keysNumBytes);
+
+      // Set the fixed length portion.
+      target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
+
+      return ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes);
+    }
+  }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
new file mode 100644
index 0000000000000..0e8e405d055de
--- /dev/null
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
@@ -0,0 +1,208 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions;
+
+import org.apache.spark.sql.types.Decimal;
+import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
+import org.apache.spark.unsafe.types.CalendarInterval;
+import org.apache.spark.unsafe.types.UTF8String;
+
+/**
+ * A set of helper methods to write data into the variable length portion.
+ */
+public class UnsafeWriters {
+  public static void writeToMemory(
+      Object inputObject,
+      long inputOffset,
+      Object targetObject,
+      long targetOffset,
+      int numBytes) {
+
+    // zero-out the padding bytes
+//    if ((numBytes & 0x07) > 0) {
+//      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + ((numBytes >> 3) << 3), 0L);
+//    }
+
+    // Write the UnsafeData to the target memory.
+    PlatformDependent.copyMemory(
+      inputObject,
+      inputOffset,
+      targetObject,
+      targetOffset,
+      numBytes
+    );
+  }
+
+  public static int getRoundedSize(int size) {
+    //return ByteArrayMethods.roundNumberOfBytesToNearestWord(size);
+    // todo: do word alignment
+    return size;
+  }
+
+  /** Writer for Decimal with precision larger than 18. */
+  public static class DecimalWriter {
+
+    public static int getSize(Decimal input) {
+      return 16;
+    }
+
+    public static int write(Object targetObject, long targetOffset, Decimal input) {
+      final byte[] bytes = input.toJavaBigDecimal().unscaledValue().toByteArray();
+      final int numBytes = bytes.length;
+      assert(numBytes <= 16);
+
+      // zero-out the bytes
+      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset, 0L);
+      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + 8, 0L);
+
+      // Write the bytes to the variable length portion.
+      PlatformDependent.copyMemory(bytes,
+        PlatformDependent.BYTE_ARRAY_OFFSET,
+        targetObject,
+        targetOffset,
+        numBytes);
+
+      return 16;
+    }
+  }
+
+  /** Writer for UTF8String. */
+  public static class UTF8StringWriter {
+
+    public static int getSize(UTF8String input) {
+      return getRoundedSize(input.numBytes());
+    }
+
+    public static int write(Object targetObject, long targetOffset, UTF8String input) {
+      final int numBytes = input.numBytes();
+
+      // Write the bytes to the variable length portion.
+      writeToMemory(input.getBaseObject(), input.getBaseOffset(),
+        targetObject, targetOffset, numBytes);
+
+      return getRoundedSize(numBytes);
+    }
+  }
+
+  /** Writer for binary (byte array) type. */
+  public static class BinaryWriter {
+
+    public static int getSize(byte[] input) {
+      return getRoundedSize(input.length);
+    }
+
+    public static int write(Object targetObject, long targetOffset, byte[] input) {
+      final int numBytes = input.length;
+
+      // Write the bytes to the variable length portion.
+      writeToMemory(input, PlatformDependent.BYTE_ARRAY_OFFSET,
+        targetObject, targetOffset, numBytes);
+
+      return getRoundedSize(numBytes);
+    }
+  }
+
+  /** Writer for UnsafeRow. */
+  public static class StructWriter {
+
+    public static int getSize(UnsafeRow input) {
+      return getRoundedSize(input.getSizeInBytes());
+    }
+
+    public static int write(Object targetObject, long targetOffset, UnsafeRow input) {
+      final int numBytes = input.getSizeInBytes();
+
+      // Write the bytes to the variable length portion.
+      writeToMemory(input.getBaseObject(), input.getBaseOffset(),
+        targetObject, targetOffset, numBytes);
+
+      return getRoundedSize(numBytes);
+    }
+  }
+
+  /** Writer for interval type. */
+  public static class IntervalWriter {
+
+    public static int getSize(UnsafeRow input) {
+      return 16;
+    }
+
+    public static int write(Object targetObject, long targetOffset, CalendarInterval input) {
+
+      // Write the months and microseconds fields of Interval to the variable length portion.
+      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset, input.months);
+      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + 8, input.microseconds);
+
+      return 16;
+    }
+  }
+
+  /** Writer for UnsafeArrayData. */
+  public static class ArrayWriter {
+
+    public static int getSize(UnsafeArrayData input) {
+      // we need extra 4 bytes the store the number of elements in this array.
+      return getRoundedSize(input.getSizeInBytes() + 4);
+    }
+
+    public static int write(Object targetObject, long targetOffset, UnsafeArrayData input) {
+      final int numBytes = input.getSizeInBytes();
+
+      // write the number of elements into first 4 bytes.
+      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset, input.numElements());
+
+      // Write the bytes to the variable length portion.
+      writeToMemory(input.getBaseObject(), input.getBaseOffset(),
+        targetObject, targetOffset + 4, numBytes);
+
+      return getRoundedSize(numBytes + 4);
+    }
+  }
+
+  public static class MapWriter {
+
+    public static int getSize(UnsafeMapData input) {
+      // we need extra 8 bytes to store number of elements and numBytes of key array.
+      return getRoundedSize(4 + 4 + input.getSizeInBytes());
+    }
+
+    public static int write(Object targetObject, long targetOffset, UnsafeMapData input) {
+      final UnsafeArrayData keyArray = input.keys;
+      final UnsafeArrayData valueArray = input.values;
+      final int keysNumBytes = keyArray.getSizeInBytes();
+      final int valuesNumBytes = valueArray.getSizeInBytes();
+      final int numBytes = 4 + 4 + keysNumBytes + valuesNumBytes;
+
+      // write the number of elements into first 4 bytes.
+      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset, input.numElements());
+      // write the numBytes of key array into second 4 bytes.
+      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset + 4, keysNumBytes);
+
+      // Write the bytes of key array to the variable length portion.
+      writeToMemory(keyArray.getBaseObject(), keyArray.getBaseOffset(),
+        targetObject, targetOffset + 8, keysNumBytes);
+
+      // Write the bytes of value array to the variable length portion.
+      writeToMemory(valueArray.getBaseObject(), valueArray.getBaseOffset(),
+        targetObject, targetOffset + 8 + keysNumBytes, valuesNumBytes);
+
+      return getRoundedSize(numBytes);
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
new file mode 100644
index 0000000000000..3caf0fb3410c4
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.types._
+
+case class FromUnsafe(child: Expression) extends UnaryExpression
+  with ExpectsInputTypes with CodegenFallback {
+
+  override def inputTypes: Seq[AbstractDataType] =
+    Seq(TypeCollection(ArrayType, StructType, MapType))
+
+  override def dataType: DataType = child.dataType
+
+  private def convert(value: Any, dt: DataType): Any = dt match {
+    case StructType(fields) =>
+      val row = value.asInstanceOf[UnsafeRow]
+      val result = new Array[Any](fields.length)
+      fields.map(_.dataType).zipWithIndex.foreach { case (dt, i) =>
+        if (!row.isNullAt(i)) {
+          result(i) = convert(row.get(i, dt), dt)
+        }
+      }
+      new GenericInternalRow(result)
+
+    case ArrayType(elementType, _) =>
+      val array = value.asInstanceOf[UnsafeArrayData]
+      val length = array.numElements()
+      val result = new Array[Any](length)
+      var i = 0
+      while (i < length) {
+        if (!array.isNullAt(i)) {
+          result(i) = convert(array.get(i, elementType), elementType)
+        }
+        i += 1
+      }
+      new GenericArrayData(result)
+
+    case MapType(kt, vt, _) =>
+      val map = value.asInstanceOf[UnsafeMapData]
+      val safeKeyArray = convert(map.keys, ArrayType(kt)).asInstanceOf[GenericArrayData]
+      val safeValueArray = convert(map.values, ArrayType(vt)).asInstanceOf[GenericArrayData]
+      new ArrayBasedMapData(safeKeyArray, safeValueArray)
+
+    case _ => value
+  }
+
+  override def nullSafeEval(input: Any): Any = {
+    convert(input, dataType)
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 83129dc12dff6..79649741025a3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -151,7 +151,15 @@ object FromUnsafeProjection {
    * Returns an UnsafeProjection for given Array of DataTypes.
    */
   def apply(fields: Seq[DataType]): Projection = {
-    create(fields.zipWithIndex.map(x => new BoundReference(x._2, x._1, true)))
+    create(fields.zipWithIndex.map(x => {
+      val b = new BoundReference(x._2, x._1, true)
+      // todo: this is quite slow, maybe remove this whole projection after remove generic getter of
+      // InternalRow?
+      b.dataType match {
+        case _: StructType | _: ArrayType | _: MapType => FromUnsafe(b)
+        case _ => b
+      }
+    }))
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 03ec4b4b4ec55..7b41c9a3f3b8e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -336,7 +336,9 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
       classOf[Decimal].getName,
       classOf[CalendarInterval].getName,
       classOf[ArrayData].getName,
-      classOf[MapData].getName
+      classOf[UnsafeArrayData].getName,
+      classOf[MapData].getName,
+      classOf[UnsafeMapData].getName
     ))
     evaluator.setExtendedClass(classOf[GeneratedClass])
     try {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 934ec3f75c63f..fc3ecf5451426 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.PlatformDependent
 
 /**
  * Generates a [[Projection]] that returns an [[UnsafeRow]].
@@ -37,14 +38,19 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   private val StructWriter = classOf[UnsafeRowWriters.StructWriter].getName
   private val CompactDecimalWriter = classOf[UnsafeRowWriters.CompactDecimalWriter].getName
   private val DecimalWriter = classOf[UnsafeRowWriters.DecimalWriter].getName
+  private val ArrayWriter = classOf[UnsafeRowWriters.ArrayWriter].getName
+  private val MapWriter = classOf[UnsafeRowWriters.MapWriter].getName
+
+  private val PlatformDependent = classOf[PlatformDependent].getName
 
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
-    case t: AtomicType if !t.isInstanceOf[DecimalType] => true
+    case t: AtomicType => true
     case _: CalendarIntervalType => true
     case t: StructType => t.toSeq.forall(field => canSupport(field.dataType))
     case NullType => true
-    case t: DecimalType => true
+    case t: ArrayType if canSupport(t.elementType) => true
+    case MapType(kt, vt, _) if canSupport(kt) && canSupport(vt) => true
     case _ => false
   }
 
@@ -59,6 +65,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       s" + (${ev.isNull} ? 0 : 16)"
     case _: StructType =>
       s" + (${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive}))"
+    case _: ArrayType =>
+      s" + (${ev.isNull} ? 0 : $ArrayWriter.getSize(${ev.primitive}))"
+    case _: MapType =>
+      s" + (${ev.isNull} ? 0 : $MapWriter.getSize(${ev.primitive}))"
     case _ => ""
   }
 
@@ -95,8 +105,12 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       s"$cursor += $BinaryWriter.write($primitive, $index, $cursor, ${ev.primitive})"
     case CalendarIntervalType =>
       s"$cursor += $IntervalWriter.write($primitive, $index, $cursor, ${ev.primitive})"
-    case t: StructType =>
+    case _: StructType =>
       s"$cursor += $StructWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+    case _: ArrayType =>
+      s"$cursor += $ArrayWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+    case _: MapType =>
+      s"$cursor += $MapWriter.write($primitive, $index, $cursor, ${ev.primitive})"
     case NullType => ""
     case _ =>
       throw new UnsupportedOperationException(s"Not supported DataType: $fieldType")
@@ -148,7 +162,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
       $ret.pointTo(
         $buffer,
-        org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
+        $PlatformDependent.BYTE_ARRAY_OFFSET,
         ${expressions.size},
         $numBytes);
       int $cursor = $fixedSize;
@@ -237,7 +251,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
        |
        |    $primitive.pointTo(
        |      $buffer,
-       |      org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET,
+       |      $PlatformDependent.BYTE_ARRAY_OFFSET,
        |      ${exprs.size},
        |      $numBytes);
        |    int $cursor = $fixedSize;
@@ -250,6 +264,303 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     GeneratedExpressionCode(code, isNull, primitive)
   }
 
+  /**
+   * Generates the Java code to convert a struct (backed by InternalRow) to UnsafeRow.
+   *
+   * @param ctx code generation context
+   * @param inputs could be the codes for expressions or input struct fields.
+   * @param inputTypes types of the inputs
+   */
+  private def createCodeForStruct2(
+      ctx: CodeGenContext,
+      inputs: Seq[GeneratedExpressionCode],
+      inputTypes: Seq[DataType]): GeneratedExpressionCode = {
+
+    val output = ctx.freshName("convertedStruct")
+    ctx.addMutableState("UnsafeRow", output, s"$output = new UnsafeRow();")
+    val buffer = ctx.freshName("buffer")
+    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
+    val numBytes = ctx.freshName("numBytes")
+    val cursor = ctx.freshName("cursor")
+
+    val convertedFields = inputTypes.zip(inputs).map { case (dt, input) =>
+      createConvertCode(ctx, input, dt)
+    }
+
+    val fixedSize = 8 * inputTypes.length + UnsafeRow.calculateBitSetWidthInBytes(inputTypes.length)
+    val additionalSize = inputTypes.zip(convertedFields).map { case (dt, ev) =>
+      genAdditionalSize(dt, ev)
+    }.mkString("")
+
+    val fieldWriters = inputTypes.zip(convertedFields).zipWithIndex.map { case ((dt, ev), i) =>
+      val update = genFieldWriter(ctx, dt, ev, output, i, cursor)
+      s"""
+        if (${ev.isNull}) {
+          $output.setNullAt($i);
+        } else {
+          $update;
+        }
+      """
+    }.mkString("\n")
+
+    val code = s"""
+      ${convertedFields.map(_.code).mkString("\n")}
+
+      final int $numBytes = $fixedSize $additionalSize;
+      if ($numBytes > $buffer.length) {
+        $buffer = new byte[$numBytes];
+      }
+
+      $output.pointTo(
+        $buffer,
+        $PlatformDependent.BYTE_ARRAY_OFFSET,
+        ${inputTypes.length},
+        $numBytes);
+
+      int $cursor = $fixedSize;
+
+      $fieldWriters
+      """
+    GeneratedExpressionCode(code, "false", output)
+  }
+
+  private def getWriter(dt: DataType) = dt match {
+    case StringType => classOf[UnsafeWriters.UTF8StringWriter].getName
+    case BinaryType => classOf[UnsafeWriters.BinaryWriter].getName
+    case CalendarIntervalType => classOf[UnsafeWriters.IntervalWriter].getName
+    case _: StructType => classOf[UnsafeWriters.StructWriter].getName
+    case _: ArrayType => classOf[UnsafeWriters.ArrayWriter].getName
+    case _: MapType => classOf[UnsafeWriters.MapWriter].getName
+    case _: DecimalType => classOf[UnsafeWriters.DecimalWriter].getName
+  }
+
+  private def createCodeForArray(
+      ctx: CodeGenContext,
+      input: GeneratedExpressionCode,
+      elementType: DataType): GeneratedExpressionCode = {
+    val output = ctx.freshName("convertedArray")
+    ctx.addMutableState("UnsafeArrayData", output, s"$output = new UnsafeArrayData();")
+    val buffer = ctx.freshName("buffer")
+    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
+    val outputIsNull = ctx.freshName("isNull")
+    val tmp = ctx.freshName("tmp")
+    val numElements = ctx.freshName("numElements")
+    val fixedSize = ctx.freshName("fixedSize")
+    val numBytes = ctx.freshName("numBytes")
+    val elements = ctx.freshName("elements")
+    val cursor = ctx.freshName("cursor")
+    val index = ctx.freshName("index")
+
+    val element = GeneratedExpressionCode(
+      code = "",
+      isNull = s"$tmp.isNullAt($index)",
+      primitive = s"${ctx.getValue(tmp, elementType, index)}"
+    )
+    val convertedElement: GeneratedExpressionCode = createConvertCode(ctx, element, elementType)
+
+    // go through the input array to calculate how many bytes we need.
+    val calculateNumBytes = elementType match {
+      case _ if (ctx.isPrimitiveType(elementType)) =>
+        // Should we do word align?
+        val elementSize = elementType.defaultSize
+        s"""
+          $numBytes += $elementSize * $numElements;
+        """
+      case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
+        s"""
+          $numBytes += 8 * $numElements;
+        """
+      case _ =>
+        val writer = getWriter(elementType)
+        val elementSize = s"$writer.getSize($elements[$index])"
+        val unsafeType = elementType match {
+          case _: StructType => "UnsafeRow"
+          case _: ArrayType => "UnsafeArrayData"
+          case _: MapType => "UnsafeMapData"
+          case _ => ctx.javaType(elementType)
+        }
+        val copy = elementType match {
+          // We reuse the buffer during conversion, need copy it before process next element.
+          case _: StructType | _: ArrayType | _: MapType => ".copy()"
+          case _ => ""
+        }
+
+        s"""
+          final $unsafeType[] $elements = new $unsafeType[$numElements];
+          for (int $index = 0; $index < $numElements; $index++) {
+            ${convertedElement.code}
+            if (!${convertedElement.isNull}) {
+              $elements[$index] = ${convertedElement.primitive}$copy;
+              $numBytes += $elementSize;
+            }
+          }
+        """
+    }
+
+    val writeElement = elementType match {
+      case _ if (ctx.isPrimitiveType(elementType)) =>
+        // Should we do word align?
+        val elementSize = elementType.defaultSize
+        s"""
+          $PlatformDependent.UNSAFE.put${ctx.primitiveTypeName(elementType)}(
+            $buffer,
+            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            ${convertedElement.primitive});
+          $cursor += $elementSize;
+        """
+      case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
+        s"""
+          $PlatformDependent.UNSAFE.putLong(
+            $buffer,
+            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            ${convertedElement.primitive}.toUnscaledLong());
+          $cursor += 8;
+        """
+      case _ =>
+        val writer = getWriter(elementType)
+        s"""
+          $cursor += $writer.write(
+            $buffer,
+            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            $elements[$index]);
+        """
+    }
+
+    val checkNull = elementType match {
+      case _ if ctx.isPrimitiveType(elementType) => s"${convertedElement.isNull}"
+      case t: DecimalType => s"$elements[$index] == null" +
+        s" || !$elements[$index].changePrecision(${t.precision}, ${t.scale})"
+      case _ => s"$elements[$index] == null"
+    }
+
+    val code = s"""
+      ${input.code}
+      final boolean $outputIsNull = ${input.isNull};
+      if (!$outputIsNull) {
+        final ArrayData $tmp = ${input.primitive};
+        if ($tmp instanceof UnsafeArrayData) {
+          $output = (UnsafeArrayData) $tmp;
+        } else {
+          final int $numElements = $tmp.numElements();
+          final int $fixedSize = 4 * $numElements;
+          int $numBytes = $fixedSize;
+
+          $calculateNumBytes
+
+          if ($numBytes > $buffer.length) {
+            $buffer = new byte[$numBytes];
+          }
+
+          int $cursor = $fixedSize;
+          for (int $index = 0; $index < $numElements; $index++) {
+            if ($checkNull) {
+              // If element is null, write the negative value address into offset region.
+              $PlatformDependent.UNSAFE.putInt(
+                $buffer,
+                $PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
+                -$cursor);
+            } else {
+              $PlatformDependent.UNSAFE.putInt(
+                $buffer,
+                $PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
+                $cursor);
+
+              $writeElement
+            }
+          }
+
+          $output.pointTo(
+            $buffer,
+            $PlatformDependent.BYTE_ARRAY_OFFSET,
+            $numElements,
+            $numBytes);
+        }
+      }
+      """
+    GeneratedExpressionCode(code, outputIsNull, output)
+  }
+
+  private def createCodeForMap(
+      ctx: CodeGenContext,
+      input: GeneratedExpressionCode,
+      keyType: DataType,
+      valueType: DataType): GeneratedExpressionCode = {
+    val output = ctx.freshName("convertedMap")
+    val outputIsNull = ctx.freshName("isNull")
+    val tmp = ctx.freshName("tmp")
+
+    val keyArray = GeneratedExpressionCode(
+      code = "",
+      isNull = "false",
+      primitive = s"$tmp.keyArray()"
+    )
+    val valueArray = GeneratedExpressionCode(
+      code = "",
+      isNull = "false",
+      primitive = s"$tmp.valueArray()"
+    )
+    val convertedKeys: GeneratedExpressionCode = createCodeForArray(ctx, keyArray, keyType)
+    val convertedValues: GeneratedExpressionCode = createCodeForArray(ctx, valueArray, valueType)
+
+    val code = s"""
+      ${input.code}
+      final boolean $outputIsNull = ${input.isNull};
+      UnsafeMapData $output = null;
+      if (!$outputIsNull) {
+        final MapData $tmp = ${input.primitive};
+        if ($tmp instanceof UnsafeMapData) {
+          $output = (UnsafeMapData) $tmp;
+        } else {
+          ${convertedKeys.code}
+          ${convertedValues.code}
+          $output = new UnsafeMapData(${convertedKeys.primitive}, ${convertedValues.primitive});
+        }
+      }
+      """
+    GeneratedExpressionCode(code, outputIsNull, output)
+  }
+
+  /**
+   * Generates the java code to convert a data to its unsafe version.
+   */
+  private def createConvertCode(
+      ctx: CodeGenContext,
+      input: GeneratedExpressionCode,
+      dataType: DataType): GeneratedExpressionCode = dataType match {
+    case t: StructType =>
+      val output = ctx.freshName("convertedStruct")
+      val outputIsNull = ctx.freshName("isNull")
+      val tmp = ctx.freshName("tmp")
+      val fieldTypes = t.fields.map(_.dataType)
+      val fieldEvals = fieldTypes.zipWithIndex.map { case (dt, i) =>
+        val getFieldCode = ctx.getValue(tmp, dt, i.toString)
+        val fieldIsNull = s"$tmp.isNullAt($i)"
+        GeneratedExpressionCode("", fieldIsNull, getFieldCode)
+      }
+      val converter = createCodeForStruct2(ctx, fieldEvals, fieldTypes)
+      val code = s"""
+        ${input.code}
+         UnsafeRow $output = null;
+         final boolean $outputIsNull = ${input.isNull};
+         if (!$outputIsNull) {
+           final InternalRow $tmp = ${input.primitive};
+           if ($tmp instanceof UnsafeRow) {
+             $output = (UnsafeRow) $tmp;
+           } else {
+             ${converter.code}
+             $output = ${converter.primitive};
+           }
+         }
+        """
+      GeneratedExpressionCode(code, outputIsNull, output)
+
+    case ArrayType(elementType, _) => createCodeForArray(ctx, input, elementType)
+
+    case MapType(kt, vt, _) => createCodeForMap(ctx, input, kt, vt)
+
+    case _ => input
+  }
+
   protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
     in.map(ExpressionCanonicalizer.execute)
 
@@ -259,10 +570,8 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   protected def create(expressions: Seq[Expression]): UnsafeProjection = {
     val ctx = newCodeGenContext()
 
-    val isNull = ctx.freshName("retIsNull")
-    val primitive = ctx.freshName("retValue")
-    val eval = GeneratedExpressionCode("", isNull, primitive)
-    eval.code = createCode(ctx, eval, expressions)
+    val exprEvals = expressions.map(e => e.gen(ctx))
+    val eval = createCodeForStruct2(ctx, exprEvals, expressions.map(_.dataType))
 
     val code = s"""
       public Object generate($exprType[] exprs) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala
index db4876355daec..f6fa021adee95 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayBasedMapData.scala
@@ -22,6 +22,9 @@ class ArrayBasedMapData(val keyArray: ArrayData, val valueArray: ArrayData) exte
 
   override def numElements(): Int = keyArray.numElements()
 
+  override def copy(): MapData = new ArrayBasedMapData(keyArray.copy(), valueArray.copy())
+
+  // We need to check equality of map type in tests.
   override def equals(o: Any): Boolean = {
     if (!o.isInstanceOf[ArrayBasedMapData]) {
       return false
@@ -32,15 +35,15 @@ class ArrayBasedMapData(val keyArray: ArrayData, val valueArray: ArrayData) exte
       return false
     }
 
-    this.keyArray == other.keyArray && this.valueArray == other.valueArray
+    ArrayBasedMapData.toScalaMap(this) == ArrayBasedMapData.toScalaMap(other)
   }
 
   override def hashCode: Int = {
-    keyArray.hashCode() * 37 + valueArray.hashCode()
+    ArrayBasedMapData.toScalaMap(this).hashCode()
   }
 
   override def toString(): String = {
-    s"keys: $keyArray\nvalues: $valueArray"
+    s"keys: $keyArray, values: $valueArray"
   }
 }
 
@@ -48,4 +51,10 @@ object ArrayBasedMapData {
   def apply(keys: Array[Any], values: Array[Any]): ArrayBasedMapData = {
     new ArrayBasedMapData(new GenericArrayData(keys), new GenericArrayData(values))
   }
+
+  def toScalaMap(map: ArrayBasedMapData): Map[Any, Any] = {
+    val keys = map.keyArray.asInstanceOf[GenericArrayData].array
+    val values = map.valueArray.asInstanceOf[GenericArrayData].array
+    keys.zip(values).toMap
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
index c99fc233255e5..642c56f12ded1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayData.scala
@@ -17,11 +17,15 @@
 
 package org.apache.spark.sql.types
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.sql.catalyst.expressions.SpecializedGetters
 
 abstract class ArrayData extends SpecializedGetters with Serializable {
   def numElements(): Int
 
+  def copy(): ArrayData
+
   def toBooleanArray(): Array[Boolean] = {
     val size = numElements()
     val values = new Array[Boolean](size)
@@ -99,19 +103,19 @@ abstract class ArrayData extends SpecializedGetters with Serializable {
     values
   }
 
-  def toArray[T](elementType: DataType): Array[T] = {
+  def toArray[T: ClassTag](elementType: DataType): Array[T] = {
     val size = numElements()
-    val values = new Array[Any](size)
+    val values = new Array[T](size)
     var i = 0
     while (i < size) {
       if (isNullAt(i)) {
-        values(i) = null
+        values(i) = null.asInstanceOf[T]
       } else {
-        values(i) = get(i, elementType)
+        values(i) = get(i, elementType).asInstanceOf[T]
       }
       i += 1
     }
-    values.asInstanceOf[Array[T]]
+    values
   }
 
   // todo: specialize this.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
index b3e75f8bad502..b314acdfe3644 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
@@ -17,13 +17,19 @@
 
 package org.apache.spark.sql.types
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.sql.catalyst.expressions.GenericSpecializedGetters
 
-class GenericArrayData(array: Array[Any]) extends ArrayData with GenericSpecializedGetters {
+class GenericArrayData(private[sql] val array: Array[Any])
+  extends ArrayData with GenericSpecializedGetters {
 
   override def genericGet(ordinal: Int): Any = array(ordinal)
 
-  override def toArray[T](elementType: DataType): Array[T] = array.asInstanceOf[Array[T]]
+  override def copy(): ArrayData = new GenericArrayData(array.clone())
+
+  // todo: Array is invariant in scala, maybe use toSeq instead?
+  override def toArray[T: ClassTag](elementType: DataType): Array[T] = array.map(_.asInstanceOf[T])
 
   override def numElements(): Int = array.length
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala
index 5514c3cd8546a..f50969f0f0b79 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapData.scala
@@ -25,6 +25,8 @@ abstract class MapData extends Serializable {
 
   def valueArray(): ArrayData
 
+  def copy(): MapData
+
   def foreach(keyType: DataType, valueType: DataType, f: (Any, Any) => Unit): Unit = {
     val length = numElements()
     val keys = keyArray()
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 44f845620a109..59491c5ba160e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -31,6 +31,8 @@ import org.apache.spark.unsafe.types.UTF8String
 
 class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
+  private def roundedSize(size: Int) = ByteArrayMethods.roundNumberOfBytesToNearestWord(size)
+
   test("basic conversion with only primitive types") {
     val fieldTypes: Array[DataType] = Array(LongType, LongType, IntegerType)
     val converter = UnsafeProjection.create(fieldTypes)
@@ -73,8 +75,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
 
     val unsafeRow: UnsafeRow = converter.apply(row)
     assert(unsafeRow.getSizeInBytes === 8 + (8 * 3) +
-      ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length) +
-      ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length))
+      roundedSize("Hello".getBytes.length) +
+      roundedSize("World".getBytes.length))
 
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
@@ -92,8 +94,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     row.update(3, DateTimeUtils.fromJavaTimestamp(Timestamp.valueOf("2015-05-08 08:10:25")))
 
     val unsafeRow: UnsafeRow = converter.apply(row)
-    assert(unsafeRow.getSizeInBytes === 8 + (8 * 4) +
-      ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length))
+    assert(unsafeRow.getSizeInBytes === 8 + (8 * 4) + roundedSize("Hello".getBytes.length))
 
     assert(unsafeRow.getLong(0) === 0)
     assert(unsafeRow.getString(1) === "Hello")
@@ -172,6 +173,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       r
     }
 
+    // todo: we reuse the UnsafeRow in projection, so these tests are meaningless.
     val setToNullAfterCreation = converter.apply(rowWithNoNullColumns)
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
     assert(setToNullAfterCreation.getBoolean(1) === rowWithNoNullColumns.getBoolean(1))
@@ -235,4 +237,108 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     val converter = UnsafeProjection.create(fieldTypes)
     assert(converter.apply(row1).getBytes === converter.apply(row2).getBytes)
   }
+
+  test("basic conversion with array type") {
+    val fieldTypes: Array[DataType] = Array(
+      ArrayType(LongType),
+      ArrayType(ArrayType(LongType))
+    )
+    val converter = UnsafeProjection.create(fieldTypes)
+
+    val array1 = new GenericArrayData(Array[Any](1L, 2L))
+    val array2 = new GenericArrayData(Array[Any](new GenericArrayData(Array[Any](3L, 4L))))
+    val row = new GenericMutableRow(fieldTypes.length)
+    row.update(0, array1)
+    row.update(1, array2)
+
+    val unsafeRow: UnsafeRow = converter.apply(row)
+    assert(unsafeRow.numFields() == 2)
+
+    val unsafeArray1 = unsafeRow.getArray(0).asInstanceOf[UnsafeArrayData]
+    assert(unsafeArray1.getSizeInBytes == 4 * 2 + 8 * 2)
+    assert(unsafeArray1.numElements() == 2)
+    assert(unsafeArray1.getLong(0) == 1L)
+    assert(unsafeArray1.getLong(1) == 2L)
+
+    val unsafeArray2 = unsafeRow.getArray(1).asInstanceOf[UnsafeArrayData]
+    assert(unsafeArray2.numElements() == 1)
+
+    val nestedArray = unsafeArray2.getArray(0).asInstanceOf[UnsafeArrayData]
+    assert(nestedArray.getSizeInBytes == 4 * 2 + 8 * 2)
+    assert(nestedArray.numElements() == 2)
+    assert(nestedArray.getLong(0) == 3L)
+    assert(nestedArray.getLong(1) == 4L)
+
+    assert(unsafeArray2.getSizeInBytes == 4 + 4 + nestedArray.getSizeInBytes)
+
+    val array1Size = roundedSize(4 + unsafeArray1.getSizeInBytes)
+    val array2Size = roundedSize(4 + unsafeArray2.getSizeInBytes)
+    assert(unsafeRow.getSizeInBytes == 8 + 8 * 2 + array1Size + array2Size)
+  }
+
+  test("basic conversion with map type") {
+    def createArray(values: Any*): ArrayData = new GenericArrayData(values.toArray)
+
+    def testIntLongMap(map: UnsafeMapData, keys: Array[Int], values: Array[Long]): Unit = {
+      val numElements = keys.length
+      assert(map.numElements() == numElements)
+
+      val keyArray = map.keys
+      assert(keyArray.getSizeInBytes == 4 * numElements + 4 * numElements)
+      assert(keyArray.numElements() == numElements)
+      keys.zipWithIndex.foreach { case (key, i) =>
+        assert(keyArray.getInt(i) == key)
+      }
+
+      val valueArray = map.values
+      assert(valueArray.getSizeInBytes == 4 * numElements + 8 * numElements)
+      assert(valueArray.numElements() == numElements)
+      values.zipWithIndex.foreach { case (value, i) =>
+        assert(valueArray.getLong(i) == value)
+      }
+
+      assert(map.getSizeInBytes == keyArray.getSizeInBytes + valueArray.getSizeInBytes)
+    }
+
+    val fieldTypes: Array[DataType] = Array(
+      MapType(IntegerType, LongType),
+      MapType(IntegerType, MapType(IntegerType, LongType))
+    )
+    val converter = UnsafeProjection.create(fieldTypes)
+
+    val map1 = new ArrayBasedMapData(createArray(1, 2), createArray(3L, 4L))
+
+    val innerMap = new ArrayBasedMapData(createArray(5, 6), createArray(7L, 8L))
+    val map2 = new ArrayBasedMapData(createArray(9), createArray(innerMap))
+
+    val row = new GenericMutableRow(fieldTypes.length)
+    row.update(0, map1)
+    row.update(1, map2)
+
+    val unsafeRow: UnsafeRow = converter.apply(row)
+    assert(unsafeRow.numFields() == 2)
+
+    val unsafeMap1 = unsafeRow.getMap(0).asInstanceOf[UnsafeMapData]
+    testIntLongMap(unsafeMap1, Array(1, 2), Array(3L, 4L))
+
+    val unsafeMap2 = unsafeRow.getMap(1).asInstanceOf[UnsafeMapData]
+    assert(unsafeMap2.numElements() == 1)
+
+    val keyArray = unsafeMap2.keys
+    assert(keyArray.getSizeInBytes == 4 + 4)
+    assert(keyArray.numElements() == 1)
+    assert(keyArray.getInt(0) == 9)
+
+    val valueArray = unsafeMap2.values
+    assert(valueArray.numElements() == 1)
+    val nestedMap = valueArray.getMap(0).asInstanceOf[UnsafeMapData]
+    testIntLongMap(nestedMap, Array(5, 6), Array(7L, 8L))
+    assert(valueArray.getSizeInBytes == 4 + 8 + nestedMap.getSizeInBytes)
+
+    assert(unsafeMap2.getSizeInBytes == keyArray.getSizeInBytes + valueArray.getSizeInBytes)
+
+    val map1Size = roundedSize(8 + unsafeMap1.getSizeInBytes)
+    val map2Size = roundedSize(8 + unsafeMap2.getSizeInBytes)
+    assert(unsafeRow.getSizeInBytes == 8 + 8 * 2 + map1Size + map2Size)
+  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 916825d007cc8..f6c9b87778f8f 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -43,6 +43,9 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
   private final long offset;
   private final int numBytes;
 
+  public Object getBaseObject() { return base; }
+  public long getBaseOffset() { return offset; }
+
   private static int[] bytesOfCodePointInUTF8 = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
     3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,

From 98d6d9c7a996f5456eb2653bb96985a1a05f4ce1 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Mon, 3 Aug 2015 00:15:24 -0700
Subject: [PATCH 0795/1454] [SPARK-9549][SQL] fix bugs in expressions

JIRA: https://issues.apache.org/jira/browse/SPARK-9549

This PR fix the following bugs:
1.  `UnaryMinus`'s codegen version would fail to compile when the input is `Long.MinValue`
2.  `BinaryComparison` would fail to compile in codegen mode when comparing Boolean types.
3.  `AddMonth` would fail if passed a huge negative month, which would lead accessing negative index of `monthDays` array.
4.  `Nanvl` with different type operands.

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7882 from yjshen/minor_bug_fix and squashes the following commits:

41bbd2c [Yijie Shen] fix bug in Nanvl type coercion
3dee204 [Yijie Shen] address comments
4fa5de0 [Yijie Shen] fix bugs in expressions
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  5 ++
 .../sql/catalyst/expressions/arithmetic.scala |  9 ++-
 .../sql/catalyst/expressions/predicates.scala |  1 +
 .../sql/catalyst/util/DateTimeUtils.scala     |  7 ++-
 .../analysis/HiveTypeCoercionSuite.scala      | 12 ++++
 .../ArithmeticExpressionSuite.scala           |  6 +-
 .../expressions/DateExpressionsSuite.scala    |  2 +
 .../catalyst/expressions/PredicateSuite.scala | 62 +++++++++----------
 .../spark/sql/ColumnExpressionSuite.scala     | 18 +++---
 9 files changed, 79 insertions(+), 43 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 603afc4032a37..422d423747026 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -562,6 +562,11 @@ object HiveTypeCoercion {
           case Some(finalDataType) => Coalesce(es.map(Cast(_, finalDataType)))
           case None => c
         }
+
+      case NaNvl(l, r) if l.dataType == DoubleType && r.dataType == FloatType =>
+        NaNvl(l, Cast(r, DoubleType))
+      case NaNvl(l, r) if l.dataType == FloatType && r.dataType == DoubleType =>
+        NaNvl(Cast(l, DoubleType), r)
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 6f8f4dd230f12..0891b55494710 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -36,7 +36,14 @@ case class UnaryMinus(child: Expression) extends UnaryExpression with ExpectsInp
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = dataType match {
     case dt: DecimalType => defineCodeGen(ctx, ev, c => s"$c.unary_$$minus()")
-    case dt: NumericType => defineCodeGen(ctx, ev, c => s"(${ctx.javaType(dt)})(-($c))")
+    case dt: NumericType => nullSafeCodeGen(ctx, ev, eval => {
+      val originValue = ctx.freshName("origin")
+      // codegen would fail to compile if we just write (-($c))
+      // for example, we could not write --9223372036854775808L in code
+      s"""
+        ${ctx.javaType(dt)} $originValue = (${ctx.javaType(dt)})($eval);
+        ${ev.primitive} = (${ctx.javaType(dt)})(-($originValue));
+      """})
     case dt: CalendarIntervalType => defineCodeGen(ctx, ev, c => s"$c.negate()")
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index ab7d3afce8f2e..b69bbabee7e81 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -227,6 +227,7 @@ abstract class BinaryComparison extends BinaryOperator with Predicate {
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     if (ctx.isPrimitiveType(left.dataType)
+        && left.dataType != BooleanType // java boolean doesn't support > or < operator
         && left.dataType != FloatType
         && left.dataType != DoubleType) {
       // faster version
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 6a98f4d9c54bc..f645eb5f7bb01 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -614,8 +614,9 @@ object DateTimeUtils {
    */
   def dateAddMonths(days: Int, months: Int): Int = {
     val absoluteMonth = (getYear(days) - YearZero) * 12 + getMonth(days) - 1 + months
-    val currentMonthInYear = absoluteMonth % 12
-    val currentYear = absoluteMonth / 12
+    val nonNegativeMonth = if (absoluteMonth >= 0) absoluteMonth else 0
+    val currentMonthInYear = nonNegativeMonth % 12
+    val currentYear = nonNegativeMonth / 12
     val leapDay = if (currentMonthInYear == 1 && isLeapYear(currentYear + YearZero)) 1 else 0
     val lastDayOfMonth = monthDays(currentMonthInYear) + leapDay
 
@@ -626,7 +627,7 @@ object DateTimeUtils {
     } else {
       dayOfMonth
     }
-    firstDayOfMonth(absoluteMonth) + currentDayInMonth - 1
+    firstDayOfMonth(nonNegativeMonth) + currentDayInMonth - 1
   }
 
   /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index 70608771dd110..cbdf453f600ab 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -251,6 +251,18 @@ class HiveTypeCoercionSuite extends PlanTest {
         :: Nil))
   }
 
+  test("nanvl casts") {
+    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+      NaNvl(Literal.create(1.0, FloatType), Literal.create(1.0, DoubleType)),
+      NaNvl(Cast(Literal.create(1.0, FloatType), DoubleType), Literal.create(1.0, DoubleType)))
+    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+      NaNvl(Literal.create(1.0, DoubleType), Literal.create(1.0, FloatType)),
+      NaNvl(Literal.create(1.0, DoubleType), Cast(Literal.create(1.0, FloatType), DoubleType)))
+    ruleTest(HiveTypeCoercion.FunctionArgumentConversion,
+      NaNvl(Literal.create(1.0, DoubleType), Literal.create(1.0, DoubleType)),
+      NaNvl(Literal.create(1.0, DoubleType), Literal.create(1.0, DoubleType)))
+  }
+
   test("type coercion for If") {
     val rule = HiveTypeCoercion.IfCoercion
     ruleTest(rule,
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index d03b0fbbfb2b2..0bae8fe2fd8aa 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.Decimal
+import org.apache.spark.sql.types._
 
 class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
 
@@ -56,6 +56,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(UnaryMinus(input), convert(-1))
       checkEvaluation(UnaryMinus(Literal.create(null, dataType)), null)
     }
+    checkEvaluation(UnaryMinus(Literal(Long.MinValue)), Long.MinValue)
+    checkEvaluation(UnaryMinus(Literal(Int.MinValue)), Int.MinValue)
+    checkEvaluation(UnaryMinus(Literal(Short.MinValue)), Short.MinValue)
+    checkEvaluation(UnaryMinus(Literal(Byte.MinValue)), Byte.MinValue)
   }
 
   test("- (Minus)") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index 3bff8e012a763..e6e8790e90926 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -280,6 +280,8 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(AddMonths(Literal.create(null, DateType), Literal(1)), null)
     checkEvaluation(AddMonths(Literal.create(null, DateType), Literal.create(null, IntegerType)),
       null)
+    checkEvaluation(
+      AddMonths(Literal(Date.valueOf("2015-01-30")), Literal(Int.MinValue)), -7293498)
   }
 
   test("months_between") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 0bc2812a5dc83..d7eb13c50b134 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -136,60 +136,60 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true)
   }
 
-  private val smallValues = Seq(1, Decimal(1), Array(1.toByte), "a", 0f, 0d).map(Literal(_))
+  private val smallValues = Seq(1, Decimal(1), Array(1.toByte), "a", 0f, 0d, false).map(Literal(_))
   private val largeValues =
-    Seq(2, Decimal(2), Array(2.toByte), "b", Float.NaN, Double.NaN).map(Literal(_))
+    Seq(2, Decimal(2), Array(2.toByte), "b", Float.NaN, Double.NaN, true).map(Literal(_))
 
   private val equalValues1 =
-    Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN).map(Literal(_))
+    Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN, true).map(Literal(_))
   private val equalValues2 =
-    Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN).map(Literal(_))
+    Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN, true).map(Literal(_))
 
-  test("BinaryComparison: <") {
+  test("BinaryComparison: lessThan") {
     for (i <- 0 until smallValues.length) {
-      checkEvaluation(smallValues(i) < largeValues(i), true)
-      checkEvaluation(equalValues1(i) < equalValues2(i), false)
-      checkEvaluation(largeValues(i) < smallValues(i), false)
+      checkEvaluation(LessThan(smallValues(i), largeValues(i)), true)
+      checkEvaluation(LessThan(equalValues1(i), equalValues2(i)), false)
+      checkEvaluation(LessThan(largeValues(i), smallValues(i)), false)
     }
   }
 
-  test("BinaryComparison: <=") {
+  test("BinaryComparison: LessThanOrEqual") {
     for (i <- 0 until smallValues.length) {
-      checkEvaluation(smallValues(i) <= largeValues(i), true)
-      checkEvaluation(equalValues1(i) <= equalValues2(i), true)
-      checkEvaluation(largeValues(i) <= smallValues(i), false)
+      checkEvaluation(LessThanOrEqual(smallValues(i), largeValues(i)), true)
+      checkEvaluation(LessThanOrEqual(equalValues1(i), equalValues2(i)), true)
+      checkEvaluation(LessThanOrEqual(largeValues(i), smallValues(i)), false)
     }
   }
 
-  test("BinaryComparison: >") {
+  test("BinaryComparison: GreaterThan") {
     for (i <- 0 until smallValues.length) {
-      checkEvaluation(smallValues(i) > largeValues(i), false)
-      checkEvaluation(equalValues1(i) > equalValues2(i), false)
-      checkEvaluation(largeValues(i) > smallValues(i), true)
+      checkEvaluation(GreaterThan(smallValues(i), largeValues(i)), false)
+      checkEvaluation(GreaterThan(equalValues1(i), equalValues2(i)), false)
+      checkEvaluation(GreaterThan(largeValues(i), smallValues(i)), true)
     }
   }
 
-  test("BinaryComparison: >=") {
+  test("BinaryComparison: GreaterThanOrEqual") {
     for (i <- 0 until smallValues.length) {
-      checkEvaluation(smallValues(i) >= largeValues(i), false)
-      checkEvaluation(equalValues1(i) >= equalValues2(i), true)
-      checkEvaluation(largeValues(i) >= smallValues(i), true)
+      checkEvaluation(GreaterThanOrEqual(smallValues(i), largeValues(i)), false)
+      checkEvaluation(GreaterThanOrEqual(equalValues1(i), equalValues2(i)), true)
+      checkEvaluation(GreaterThanOrEqual(largeValues(i), smallValues(i)), true)
     }
   }
 
-  test("BinaryComparison: ===") {
+  test("BinaryComparison: EqualTo") {
     for (i <- 0 until smallValues.length) {
-      checkEvaluation(smallValues(i) === largeValues(i), false)
-      checkEvaluation(equalValues1(i) === equalValues2(i), true)
-      checkEvaluation(largeValues(i) === smallValues(i), false)
+      checkEvaluation(EqualTo(smallValues(i), largeValues(i)), false)
+      checkEvaluation(EqualTo(equalValues1(i), equalValues2(i)), true)
+      checkEvaluation(EqualTo(largeValues(i), smallValues(i)), false)
     }
   }
 
-  test("BinaryComparison: <=>") {
+  test("BinaryComparison: EqualNullSafe") {
     for (i <- 0 until smallValues.length) {
-      checkEvaluation(smallValues(i) <=> largeValues(i), false)
-      checkEvaluation(equalValues1(i) <=> equalValues2(i), true)
-      checkEvaluation(largeValues(i) <=> smallValues(i), false)
+      checkEvaluation(EqualNullSafe(smallValues(i), largeValues(i)), false)
+      checkEvaluation(EqualNullSafe(equalValues1(i), equalValues2(i)), true)
+      checkEvaluation(EqualNullSafe(largeValues(i), smallValues(i)), false)
     }
   }
 
@@ -209,8 +209,8 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     nullTest(GreaterThanOrEqual)
     nullTest(EqualTo)
 
-    checkEvaluation(normalInt <=> nullInt, false)
-    checkEvaluation(nullInt <=> normalInt, false)
-    checkEvaluation(nullInt <=> nullInt, true)
+    checkEvaluation(EqualNullSafe(normalInt, nullInt), false)
+    checkEvaluation(EqualNullSafe(nullInt, normalInt), false)
+    checkEvaluation(EqualNullSafe(nullInt, nullInt), true)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index eb64684ae0fd9..35ca0b4c7cc21 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -227,20 +227,24 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
 
   test("nanvl") {
     val testData = ctx.createDataFrame(ctx.sparkContext.parallelize(
-      Row(null, 3.0, Double.NaN, Double.PositiveInfinity) :: Nil),
+      Row(null, 3.0, Double.NaN, Double.PositiveInfinity, 1.0f, 4) :: Nil),
       StructType(Seq(StructField("a", DoubleType), StructField("b", DoubleType),
-        StructField("c", DoubleType), StructField("d", DoubleType))))
+        StructField("c", DoubleType), StructField("d", DoubleType),
+        StructField("e", FloatType), StructField("f", IntegerType))))
 
     checkAnswer(
       testData.select(
-        nanvl($"a", lit(5)), nanvl($"b", lit(10)),
-        nanvl($"c", lit(null).cast(DoubleType)), nanvl($"d", lit(10))),
-      Row(null, 3.0, null, Double.PositiveInfinity)
+        nanvl($"a", lit(5)), nanvl($"b", lit(10)), nanvl(lit(10), $"b"),
+        nanvl($"c", lit(null).cast(DoubleType)), nanvl($"d", lit(10)),
+        nanvl($"b", $"e"), nanvl($"e", $"f")),
+      Row(null, 3.0, 10.0, null, Double.PositiveInfinity, 3.0, 1.0)
     )
     testData.registerTempTable("t")
     checkAnswer(
-      ctx.sql("select nanvl(a, 5), nanvl(b, 10), nanvl(c, null), nanvl(d, 10) from t"),
-      Row(null, 3.0, null, Double.PositiveInfinity)
+      ctx.sql(
+        "select nanvl(a, 5), nanvl(b, 10), nanvl(10, b), nanvl(c, null), nanvl(d, 10), " +
+          " nanvl(b, e), nanvl(e, f) from t"),
+      Row(null, 3.0, 10.0, null, Double.PositiveInfinity, 3.0, 1.0)
     )
   }
 

From 1ebd41b141a95ec264bd2dd50f0fe24cd459035d Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 3 Aug 2015 00:23:08 -0700
Subject: [PATCH 0796/1454] [SPARK-9240] [SQL] Hybrid aggregate operator using
 unsafe row

This PR adds a base aggregation iterator `AggregationIterator`, which is used to create `SortBasedAggregationIterator` (for sort-based aggregation) and `UnsafeHybridAggregationIterator` (first it tries hash-based aggregation and falls back to the sort-based aggregation (using external sorter) if we cannot allocate memory for the map). With these two iterators, we will not need existing iterators and I am removing those. Also, we can use a single physical `Aggregate` operator and it internally determines what iterators to used.

https://issues.apache.org/jira/browse/SPARK-9240

Author: Yin Huai <yhuai@databricks.com>

Closes #7813 from yhuai/AggregateOperator and squashes the following commits:

e317e2b [Yin Huai] Remove unnecessary change.
74d93c5 [Yin Huai] Merge remote-tracking branch 'upstream/master' into AggregateOperator
ba6afbc [Yin Huai] Add a little bit more comments.
c9cf3b6 [Yin Huai] update
0f1b06f [Yin Huai] Remove unnecessary code.
21fd15f [Yin Huai] Remove unnecessary change.
964f88b [Yin Huai] Implement fallback strategy.
b1ea5cf [Yin Huai] wip
7fcbd87 [Yin Huai] Add a flag to control what iterator to use.
533d5b2 [Yin Huai] Prepare for fallback!
33b7022 [Yin Huai] wip
bd9282b [Yin Huai] UDAFs now supports UnsafeRow.
f52ee53 [Yin Huai] wip
3171f44 [Yin Huai] wip
d2c45a0 [Yin Huai] wip
f60cc83 [Yin Huai] Also check input schema.
af32210 [Yin Huai] Check iter.hasNext before we create an iterator because the constructor of the iterato will read at least one row from a non-empty input iter.
299008c [Yin Huai] First round cleanup.
3915bac [Yin Huai] Create a base iterator class for aggregation iterators and add the initial version of the hybrid iterator.
---
 .../expressions/aggregate/interfaces.scala    |  19 +-
 .../sql/execution/aggregate/Aggregate.scala   | 182 +++++
 .../aggregate/AggregationIterator.scala       | 490 +++++++++++++
 .../SortBasedAggregationIterator.scala        | 236 +++++++
 .../UnsafeHybridAggregationIterator.scala     | 398 +++++++++++
 .../aggregate/aggregateOperators.scala        | 175 -----
 .../aggregate/sortBasedIterators.scala        | 664 ------------------
 .../spark/sql/execution/aggregate/udaf.scala  | 269 ++++++-
 .../spark/sql/execution/aggregate/utils.scala |  99 +--
 .../spark/sql/execution/basicOperators.scala  |   1 -
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  10 +-
 .../execution/SparkSqlSerializer2Suite.scala  |   9 +-
 .../execution/AggregationQuerySuite.scala     | 118 ++--
 13 files changed, 1697 insertions(+), 973 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggregationIterator.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
index d08f553cefe8c..4abfdfe87d5e9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -110,7 +110,11 @@ abstract class AggregateFunction2
    * buffer value of `avg(x)` will be 0 and the position of the first buffer value of `avg(y)`
    * will be 2.
    */
-  var mutableBufferOffset: Int = 0
+  protected var mutableBufferOffset: Int = 0
+
+  def withNewMutableBufferOffset(newMutableBufferOffset: Int): Unit = {
+    mutableBufferOffset = newMutableBufferOffset
+  }
 
   /**
    * The offset of this function's start buffer value in the
@@ -126,7 +130,11 @@ abstract class AggregateFunction2
    * buffer value of `avg(x)` will be 1 and the position of the first buffer value of `avg(y)`
    * will be 3 (position 0 is used for the value of key`).
    */
-  var inputBufferOffset: Int = 0
+  protected var inputBufferOffset: Int = 0
+
+  def withNewInputBufferOffset(newInputBufferOffset: Int): Unit = {
+    inputBufferOffset = newInputBufferOffset
+  }
 
   /** The schema of the aggregation buffer. */
   def bufferSchema: StructType
@@ -195,11 +203,8 @@ abstract class AlgebraicAggregate extends AggregateFunction2 with Serializable w
   override def bufferSchema: StructType = StructType.fromAttributes(bufferAttributes)
 
   override def initialize(buffer: MutableRow): Unit = {
-    var i = 0
-    while (i < bufferAttributes.size) {
-      buffer(i + mutableBufferOffset) = initialValues(i).eval()
-      i += 1
-    }
+    throw new UnsupportedOperationException(
+      "AlgebraicAggregate's initialize should not be called directly")
   }
 
   override final def update(buffer: MutableRow, input: InternalRow): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
new file mode 100644
index 0000000000000..cf568dc048674
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
@@ -0,0 +1,182 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.errors._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
+import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan, UnaryNode}
+import org.apache.spark.sql.types.StructType
+
+/**
+ * An Aggregate Operator used to evaluate [[AggregateFunction2]]. Based on the data types
+ * of the grouping expressions and aggregate functions, it determines if it uses
+ * sort-based aggregation and hybrid (hash-based with sort-based as the fallback) to
+ * process input rows.
+ */
+case class Aggregate(
+    requiredChildDistributionExpressions: Option[Seq[Expression]],
+    groupingExpressions: Seq[NamedExpression],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    nonCompleteAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+
+  private[this] val allAggregateExpressions =
+    nonCompleteAggregateExpressions ++ completeAggregateExpressions
+
+  private[this] val hasNonAlgebricAggregateFunctions =
+    !allAggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate])
+
+  // Use the hybrid iterator if (1) unsafe is enabled, (2) the schemata of
+  // grouping key and aggregation buffer is supported; and (3) all
+  // aggregate functions are algebraic.
+  private[this] val supportsHybridIterator: Boolean = {
+    val aggregationBufferSchema: StructType =
+      StructType.fromAttributes(
+        allAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes))
+    val groupKeySchema: StructType =
+      StructType.fromAttributes(groupingExpressions.map(_.toAttribute))
+
+    val schemaSupportsUnsafe: Boolean =
+      UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
+        UnsafeProjection.canSupport(groupKeySchema)
+
+    // TODO: Use the hybrid iterator for non-algebric aggregate functions.
+    sqlContext.conf.unsafeEnabled && schemaSupportsUnsafe && !hasNonAlgebricAggregateFunctions
+  }
+
+  // We need to use sorted input if we have grouping expressions, and
+  // we cannot use the hybrid iterator or the hybrid is disabled.
+  private[this] val requiresSortedInput: Boolean = {
+    groupingExpressions.nonEmpty && !supportsHybridIterator
+  }
+
+  override def canProcessUnsafeRows: Boolean = !hasNonAlgebricAggregateFunctions
+
+  // If result expressions' data types are all fixed length, we generate unsafe rows
+  // (We have this requirement instead of check the result of UnsafeProjection.canSupport
+  // is because we use a mutable projection to generate the result).
+  override def outputsUnsafeRows: Boolean = {
+    // resultExpressions.map(_.dataType).forall(UnsafeRow.isFixedLength)
+    // TODO: Supports generating UnsafeRows. We can just re-enable the line above and fix
+    // any issue we get.
+    false
+  }
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  override def requiredChildDistribution: List[Distribution] = {
+    requiredChildDistributionExpressions match {
+      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
+      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
+      case None => UnspecifiedDistribution :: Nil
+    }
+  }
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
+    if (requiresSortedInput) {
+      // TODO: We should not sort the input rows if they are just in reversed order.
+      groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
+    } else {
+      Seq.fill(children.size)(Nil)
+    }
+  }
+
+  override def outputOrdering: Seq[SortOrder] = {
+    if (requiresSortedInput) {
+      // It is possible that the child.outputOrdering starts with the required
+      // ordering expressions (e.g. we require [a] as the sort expression and the
+      // child's outputOrdering is [a, b]). We can only guarantee the output rows
+      // are sorted by values of groupingExpressions.
+      groupingExpressions.map(SortOrder(_, Ascending))
+    } else {
+      Nil
+    }
+  }
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    child.execute().mapPartitions { iter =>
+      // Because the constructor of an aggregation iterator will read at least the first row,
+      // we need to get the value of iter.hasNext first.
+      val hasInput = iter.hasNext
+      val useHybridIterator =
+        hasInput &&
+          supportsHybridIterator &&
+          groupingExpressions.nonEmpty
+      if (useHybridIterator) {
+        UnsafeHybridAggregationIterator.createFromInputIterator(
+          groupingExpressions,
+          nonCompleteAggregateExpressions,
+          nonCompleteAggregateAttributes,
+          completeAggregateExpressions,
+          completeAggregateAttributes,
+          initialInputBufferOffset,
+          resultExpressions,
+          newMutableProjection _,
+          child.output,
+          iter,
+          outputsUnsafeRows)
+      } else {
+        if (!hasInput && groupingExpressions.nonEmpty) {
+          // This is a grouped aggregate and the input iterator is empty,
+          // so return an empty iterator.
+          Iterator[InternalRow]()
+        } else {
+          val outputIter = SortBasedAggregationIterator.createFromInputIterator(
+            groupingExpressions,
+            nonCompleteAggregateExpressions,
+            nonCompleteAggregateAttributes,
+            completeAggregateExpressions,
+            completeAggregateAttributes,
+            initialInputBufferOffset,
+            resultExpressions,
+            newMutableProjection _ ,
+            newProjection _,
+            child.output,
+            iter,
+            outputsUnsafeRows)
+          if (!hasInput && groupingExpressions.isEmpty) {
+            // There is no input and there is no grouping expressions.
+            // We need to output a single row as the output.
+            Iterator[InternalRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
+          } else {
+            outputIter
+          }
+        }
+      }
+    }
+  }
+
+  override def simpleString: String = {
+    val iterator = if (supportsHybridIterator && groupingExpressions.nonEmpty) {
+      classOf[UnsafeHybridAggregationIterator].getSimpleName
+    } else {
+      classOf[SortBasedAggregationIterator].getSimpleName
+    }
+
+    s"""NewAggregate with $iterator ${groupingExpressions} ${allAggregateExpressions}"""
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggregationIterator.scala
new file mode 100644
index 0000000000000..abca373b0c4f9
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggregationIterator.scala
@@ -0,0 +1,490 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.unsafe.KVIterator
+
+import scala.collection.mutable.ArrayBuffer
+
+/**
+ * The base class of [[SortBasedAggregationIterator]] and [[UnsafeHybridAggregationIterator]].
+ * It mainly contains two parts:
+ * 1. It initializes aggregate functions.
+ * 2. It creates two functions, `processRow` and `generateOutput` based on [[AggregateMode]] of
+ *    its aggregate functions. `processRow` is the function to handle an input. `generateOutput`
+ *    is used to generate result.
+ */
+abstract class AggregationIterator(
+    groupingKeyAttributes: Seq[Attribute],
+    valueAttributes: Seq[Attribute],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    nonCompleteAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    outputsUnsafeRows: Boolean)
+  extends Iterator[InternalRow] with Logging {
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Initializing functions.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // An Seq of all AggregateExpressions.
+  // It is important that all AggregateExpressions with the mode Partial, PartialMerge or Final
+  // are at the beginning of the allAggregateExpressions.
+  protected val allAggregateExpressions =
+    nonCompleteAggregateExpressions ++ completeAggregateExpressions
+
+  require(
+    allAggregateExpressions.map(_.mode).distinct.length <= 2,
+    s"$allAggregateExpressions are not supported becuase they have more than 2 distinct modes.")
+
+  /**
+   * The distinct modes of AggregateExpressions. Right now, we can handle the following mode:
+   *  - Partial-only: all AggregateExpressions have the mode of Partial;
+   *  - PartialMerge-only: all AggregateExpressions have the mode of PartialMerge);
+   *  - Final-only: all AggregateExpressions have the mode of Final;
+   *  - Final-Complete: some AggregateExpressions have the mode of Final and
+   *    others have the mode of Complete;
+   *  - Complete-only: nonCompleteAggregateExpressions is empty and we have AggregateExpressions
+   *    with mode Complete in completeAggregateExpressions; and
+   *  - Grouping-only: there is no AggregateExpression.
+   */
+  protected val aggregationMode: (Option[AggregateMode], Option[AggregateMode]) =
+    nonCompleteAggregateExpressions.map(_.mode).distinct.headOption ->
+      completeAggregateExpressions.map(_.mode).distinct.headOption
+
+  // Initialize all AggregateFunctions by binding references if necessary,
+  // and set inputBufferOffset and mutableBufferOffset.
+  protected val allAggregateFunctions: Array[AggregateFunction2] = {
+    var mutableBufferOffset = 0
+    var inputBufferOffset: Int = initialInputBufferOffset
+    val functions = new Array[AggregateFunction2](allAggregateExpressions.length)
+    var i = 0
+    while (i < allAggregateExpressions.length) {
+      val func = allAggregateExpressions(i).aggregateFunction
+      val funcWithBoundReferences = allAggregateExpressions(i).mode match {
+        case Partial | Complete if !func.isInstanceOf[AlgebraicAggregate] =>
+          // We need to create BoundReferences if the function is not an
+          // AlgebraicAggregate (it does not support code-gen) and the mode of
+          // this function is Partial or Complete because we will call eval of this
+          // function's children in the update method of this aggregate function.
+          // Those eval calls require BoundReferences to work.
+          BindReferences.bindReference(func, valueAttributes)
+        case _ =>
+          // We only need to set inputBufferOffset for aggregate functions with mode
+          // PartialMerge and Final.
+          func.withNewInputBufferOffset(inputBufferOffset)
+          inputBufferOffset += func.bufferSchema.length
+          func
+      }
+      // Set mutableBufferOffset for this function. It is important that setting
+      // mutableBufferOffset happens after all potential bindReference operations
+      // because bindReference will create a new instance of the function.
+      funcWithBoundReferences.withNewMutableBufferOffset(mutableBufferOffset)
+      mutableBufferOffset += funcWithBoundReferences.bufferSchema.length
+      functions(i) = funcWithBoundReferences
+      i += 1
+    }
+    functions
+  }
+
+  // Positions of those non-algebraic aggregate functions in allAggregateFunctions.
+  // For example, we have func1, func2, func3, func4 in aggregateFunctions, and
+  // func2 and func3 are non-algebraic aggregate functions.
+  // nonAlgebraicAggregateFunctionPositions will be [1, 2].
+  private[this] val allNonAlgebraicAggregateFunctionPositions: Array[Int] = {
+    val positions = new ArrayBuffer[Int]()
+    var i = 0
+    while (i < allAggregateFunctions.length) {
+      allAggregateFunctions(i) match {
+        case agg: AlgebraicAggregate =>
+        case _ => positions += i
+      }
+      i += 1
+    }
+    positions.toArray
+  }
+
+  // All AggregateFunctions functions with mode Partial, PartialMerge, or Final.
+  private[this] val nonCompleteAggregateFunctions: Array[AggregateFunction2] =
+    allAggregateFunctions.take(nonCompleteAggregateExpressions.length)
+
+  // All non-algebraic aggregate functions with mode Partial, PartialMerge, or Final.
+  private[this] val nonCompleteNonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
+    nonCompleteAggregateFunctions.collect {
+      case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
+    }
+
+  // The projection used to initialize buffer values for all AlgebraicAggregates.
+  private[this] val algebraicInitialProjection = {
+    val initExpressions = allAggregateFunctions.flatMap {
+      case ae: AlgebraicAggregate => ae.initialValues
+      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+    }
+    newMutableProjection(initExpressions, Nil)()
+  }
+
+  // All non-Algebraic AggregateFunctions.
+  private[this] val allNonAlgebraicAggregateFunctions =
+    allNonAlgebraicAggregateFunctionPositions.map(allAggregateFunctions)
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Methods and fields used by sub-classes.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // Initializing functions used to process a row.
+  protected val processRow: (MutableRow, InternalRow) => Unit = {
+    val rowToBeProcessed = new JoinedRow
+    val aggregationBufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
+    aggregationMode match {
+      // Partial-only
+      case (Some(Partial), None) =>
+        val updateExpressions = nonCompleteAggregateFunctions.flatMap {
+          case ae: AlgebraicAggregate => ae.updateExpressions
+          case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+        }
+        val algebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferSchema ++ valueAttributes)()
+
+        (currentBuffer: MutableRow, row: InternalRow) => {
+          algebraicUpdateProjection.target(currentBuffer)
+          // Process all algebraic aggregate functions.
+          algebraicUpdateProjection(rowToBeProcessed(currentBuffer, row))
+          // Process all non-algebraic aggregate functions.
+          var i = 0
+          while (i < nonCompleteNonAlgebraicAggregateFunctions.length) {
+            nonCompleteNonAlgebraicAggregateFunctions(i).update(currentBuffer, row)
+            i += 1
+          }
+        }
+
+      // PartialMerge-only or Final-only
+      case (Some(PartialMerge), None) | (Some(Final), None) =>
+        val inputAggregationBufferSchema = if (initialInputBufferOffset == 0) {
+          // If initialInputBufferOffset, the input value does not contain
+          // grouping keys.
+          // This part is pretty hacky.
+          allAggregateFunctions.flatMap(_.cloneBufferAttributes).toSeq
+        } else {
+          groupingKeyAttributes ++ allAggregateFunctions.flatMap(_.cloneBufferAttributes)
+        }
+        // val inputAggregationBufferSchema =
+        //  groupingKeyAttributes ++
+        //    allAggregateFunctions.flatMap(_.cloneBufferAttributes)
+        val mergeExpressions = nonCompleteAggregateFunctions.flatMap {
+          case ae: AlgebraicAggregate => ae.mergeExpressions
+          case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+        }
+        // This projection is used to merge buffer values for all AlgebraicAggregates.
+        val algebraicMergeProjection =
+          newMutableProjection(
+            mergeExpressions,
+            aggregationBufferSchema ++ inputAggregationBufferSchema)()
+
+        (currentBuffer: MutableRow, row: InternalRow) => {
+          // Process all algebraic aggregate functions.
+          algebraicMergeProjection.target(currentBuffer)(rowToBeProcessed(currentBuffer, row))
+          // Process all non-algebraic aggregate functions.
+          var i = 0
+          while (i < nonCompleteNonAlgebraicAggregateFunctions.length) {
+            nonCompleteNonAlgebraicAggregateFunctions(i).merge(currentBuffer, row)
+            i += 1
+          }
+        }
+
+      // Final-Complete
+      case (Some(Final), Some(Complete)) =>
+        val completeAggregateFunctions: Array[AggregateFunction2] =
+          allAggregateFunctions.takeRight(completeAggregateExpressions.length)
+        // All non-algebraic aggregate functions with mode Complete.
+        val completeNonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
+          completeAggregateFunctions.collect {
+            case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
+          }
+
+        // The first initialInputBufferOffset values of the input aggregation buffer is
+        // for grouping expressions and distinct columns.
+        val groupingAttributesAndDistinctColumns = valueAttributes.take(initialInputBufferOffset)
+
+        val completeOffsetExpressions =
+          Seq.fill(completeAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
+        // We do not touch buffer values of aggregate functions with the Final mode.
+        val finalOffsetExpressions =
+          Seq.fill(nonCompleteAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
+
+        val mergeInputSchema =
+          aggregationBufferSchema ++
+            groupingAttributesAndDistinctColumns ++
+            nonCompleteAggregateFunctions.flatMap(_.cloneBufferAttributes)
+        val mergeExpressions =
+          nonCompleteAggregateFunctions.flatMap {
+            case ae: AlgebraicAggregate => ae.mergeExpressions
+            case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+          } ++ completeOffsetExpressions
+        val finalAlgebraicMergeProjection =
+          newMutableProjection(mergeExpressions, mergeInputSchema)()
+
+        val updateExpressions =
+          finalOffsetExpressions ++ completeAggregateFunctions.flatMap {
+            case ae: AlgebraicAggregate => ae.updateExpressions
+            case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+          }
+        val completeAlgebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferSchema ++ valueAttributes)()
+
+        (currentBuffer: MutableRow, row: InternalRow) => {
+          val input = rowToBeProcessed(currentBuffer, row)
+          // For all aggregate functions with mode Complete, update buffers.
+          completeAlgebraicUpdateProjection.target(currentBuffer)(input)
+          var i = 0
+          while (i < completeNonAlgebraicAggregateFunctions.length) {
+            completeNonAlgebraicAggregateFunctions(i).update(currentBuffer, row)
+            i += 1
+          }
+
+          // For all aggregate functions with mode Final, merge buffers.
+          finalAlgebraicMergeProjection.target(currentBuffer)(input)
+          i = 0
+          while (i < nonCompleteNonAlgebraicAggregateFunctions.length) {
+            nonCompleteNonAlgebraicAggregateFunctions(i).merge(currentBuffer, row)
+            i += 1
+          }
+        }
+
+      // Complete-only
+      case (None, Some(Complete)) =>
+        val completeAggregateFunctions: Array[AggregateFunction2] =
+          allAggregateFunctions.takeRight(completeAggregateExpressions.length)
+        // All non-algebraic aggregate functions with mode Complete.
+        val completeNonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
+          completeAggregateFunctions.collect {
+            case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
+          }
+
+        val updateExpressions =
+          completeAggregateFunctions.flatMap {
+            case ae: AlgebraicAggregate => ae.updateExpressions
+            case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
+          }
+        val completeAlgebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferSchema ++ valueAttributes)()
+
+        (currentBuffer: MutableRow, row: InternalRow) => {
+          val input = rowToBeProcessed(currentBuffer, row)
+          // For all aggregate functions with mode Complete, update buffers.
+          completeAlgebraicUpdateProjection.target(currentBuffer)(input)
+          var i = 0
+          while (i < completeNonAlgebraicAggregateFunctions.length) {
+            completeNonAlgebraicAggregateFunctions(i).update(currentBuffer, row)
+            i += 1
+          }
+        }
+
+      // Grouping only.
+      case (None, None) => (currentBuffer: MutableRow, row: InternalRow) => {}
+
+      case other =>
+        sys.error(
+          s"Could not evaluate ${nonCompleteAggregateExpressions} because we do not " +
+            s"support evaluate modes $other in this iterator.")
+    }
+  }
+
+  // Initializing the function used to generate the output row.
+  protected val generateOutput: (InternalRow, MutableRow) => InternalRow = {
+    val rowToBeEvaluated = new JoinedRow
+    val safeOutoutRow = new GenericMutableRow(resultExpressions.length)
+    val mutableOutput = if (outputsUnsafeRows) {
+      UnsafeProjection.create(resultExpressions.map(_.dataType).toArray).apply(safeOutoutRow)
+    } else {
+      safeOutoutRow
+    }
+
+    aggregationMode match {
+      // Partial-only or PartialMerge-only: every output row is basically the values of
+      // the grouping expressions and the corresponding aggregation buffer.
+      case (Some(Partial), None) | (Some(PartialMerge), None) =>
+        // Because we cannot copy a joinedRow containing a UnsafeRow (UnsafeRow does not
+        // support generic getter), we create a mutable projection to output the
+        // JoinedRow(currentGroupingKey, currentBuffer)
+        val bufferSchema = nonCompleteAggregateFunctions.flatMap(_.bufferAttributes)
+        val resultProjection =
+          newMutableProjection(
+            groupingKeyAttributes ++ bufferSchema,
+            groupingKeyAttributes ++ bufferSchema)()
+        resultProjection.target(mutableOutput)
+
+        (currentGroupingKey: InternalRow, currentBuffer: MutableRow) => {
+          resultProjection(rowToBeEvaluated(currentGroupingKey, currentBuffer))
+          // rowToBeEvaluated(currentGroupingKey, currentBuffer)
+        }
+
+      // Final-only, Complete-only and Final-Complete: every output row contains values representing
+      // resultExpressions.
+      case (Some(Final), None) | (Some(Final) | None, Some(Complete)) =>
+        val bufferSchemata =
+          allAggregateFunctions.flatMap(_.bufferAttributes)
+        val evalExpressions = allAggregateFunctions.map {
+          case ae: AlgebraicAggregate => ae.evaluateExpression
+          case agg: AggregateFunction2 => NoOp
+        }
+        val algebraicEvalProjection = newMutableProjection(evalExpressions, bufferSchemata)()
+        val aggregateResultSchema = nonCompleteAggregateAttributes ++ completeAggregateAttributes
+        // TODO: Use unsafe row.
+        val aggregateResult = new GenericMutableRow(aggregateResultSchema.length)
+        val resultProjection =
+          newMutableProjection(
+            resultExpressions, groupingKeyAttributes ++ aggregateResultSchema)()
+        resultProjection.target(mutableOutput)
+
+        (currentGroupingKey: InternalRow, currentBuffer: MutableRow) => {
+          // Generate results for all algebraic aggregate functions.
+          algebraicEvalProjection.target(aggregateResult)(currentBuffer)
+          // Generate results for all non-algebraic aggregate functions.
+          var i = 0
+          while (i < allNonAlgebraicAggregateFunctions.length) {
+            aggregateResult.update(
+              allNonAlgebraicAggregateFunctionPositions(i),
+              allNonAlgebraicAggregateFunctions(i).eval(currentBuffer))
+            i += 1
+          }
+          resultProjection(rowToBeEvaluated(currentGroupingKey, aggregateResult))
+        }
+
+      // Grouping-only: we only output values of grouping expressions.
+      case (None, None) =>
+        val resultProjection =
+          newMutableProjection(resultExpressions, groupingKeyAttributes)()
+        resultProjection.target(mutableOutput)
+
+        (currentGroupingKey: InternalRow, currentBuffer: MutableRow) => {
+          resultProjection(currentGroupingKey)
+        }
+
+      case other =>
+        sys.error(
+          s"Could not evaluate ${nonCompleteAggregateExpressions} because we do not " +
+            s"support evaluate modes $other in this iterator.")
+    }
+  }
+
+  /** Initializes buffer values for all aggregate functions. */
+  protected def initializeBuffer(buffer: MutableRow): Unit = {
+    algebraicInitialProjection.target(buffer)(EmptyRow)
+    var i = 0
+    while (i < allNonAlgebraicAggregateFunctions.length) {
+      allNonAlgebraicAggregateFunctions(i).initialize(buffer)
+      i += 1
+    }
+  }
+
+  /**
+   * Creates a new aggregation buffer and initializes buffer values
+   * for all aggregate functions.
+   */
+  protected def newBuffer: MutableRow
+}
+
+object AggregationIterator {
+  def kvIterator(
+    groupingExpressions: Seq[NamedExpression],
+    newProjection: (Seq[Expression], Seq[Attribute]) => Projection,
+    inputAttributes: Seq[Attribute],
+    inputIter: Iterator[InternalRow]): KVIterator[InternalRow, InternalRow] = {
+    new KVIterator[InternalRow, InternalRow] {
+      private[this] val groupingKeyGenerator = newProjection(groupingExpressions, inputAttributes)
+
+      private[this] var groupingKey: InternalRow = _
+
+      private[this] var value: InternalRow = _
+
+      override def next(): Boolean = {
+        if (inputIter.hasNext) {
+          // Read the next input row.
+          val inputRow = inputIter.next()
+          // Get groupingKey based on groupingExpressions.
+          groupingKey = groupingKeyGenerator(inputRow)
+          // The value is the inputRow.
+          value = inputRow
+          true
+        } else {
+          false
+        }
+      }
+
+      override def getKey(): InternalRow = {
+        groupingKey
+      }
+
+      override def getValue(): InternalRow = {
+        value
+      }
+
+      override def close(): Unit = {
+        // Do nothing
+      }
+    }
+  }
+
+  def unsafeKVIterator(
+      groupingExpressions: Seq[NamedExpression],
+      inputAttributes: Seq[Attribute],
+      inputIter: Iterator[InternalRow]): KVIterator[UnsafeRow, InternalRow] = {
+    new KVIterator[UnsafeRow, InternalRow] {
+      private[this] val groupingKeyGenerator =
+        UnsafeProjection.create(groupingExpressions, inputAttributes)
+
+      private[this] var groupingKey: UnsafeRow = _
+
+      private[this] var value: InternalRow = _
+
+      override def next(): Boolean = {
+        if (inputIter.hasNext) {
+          // Read the next input row.
+          val inputRow = inputIter.next()
+          // Get groupingKey based on groupingExpressions.
+          groupingKey = groupingKeyGenerator.apply(inputRow)
+          // The value is the inputRow.
+          value = inputRow
+          true
+        } else {
+          false
+        }
+      }
+
+      override def getKey(): UnsafeRow = {
+        groupingKey
+      }
+
+      override def getValue(): InternalRow = {
+        value
+      }
+
+      override def close(): Unit = {
+        // Do nothing
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
new file mode 100644
index 0000000000000..78bcee16c9d00
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
@@ -0,0 +1,236 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2, AggregateFunction2}
+import org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.unsafe.KVIterator
+
+/**
+ * An iterator used to evaluate [[AggregateFunction2]]. It assumes the input rows have been
+ * sorted by values of [[groupingKeyAttributes]].
+ */
+class SortBasedAggregationIterator(
+    groupingKeyAttributes: Seq[Attribute],
+    valueAttributes: Seq[Attribute],
+    inputKVIterator: KVIterator[InternalRow, InternalRow],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    nonCompleteAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    outputsUnsafeRows: Boolean)
+  extends AggregationIterator(
+    groupingKeyAttributes,
+    valueAttributes,
+    nonCompleteAggregateExpressions,
+    nonCompleteAggregateAttributes,
+    completeAggregateExpressions,
+    completeAggregateAttributes,
+    initialInputBufferOffset,
+    resultExpressions,
+    newMutableProjection,
+    outputsUnsafeRows) {
+
+  override protected def newBuffer: MutableRow = {
+    val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val bufferRowSize: Int = bufferSchema.length
+
+    val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
+    val useUnsafeBuffer = bufferSchema.map(_.dataType).forall(UnsafeRow.isFixedLength)
+
+    val buffer = if (useUnsafeBuffer) {
+      val unsafeProjection =
+        UnsafeProjection.create(bufferSchema.map(_.dataType))
+      unsafeProjection.apply(genericMutableBuffer)
+    } else {
+      genericMutableBuffer
+    }
+    initializeBuffer(buffer)
+    buffer
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Mutable states for sort based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // The partition key of the current partition.
+  private[this] var currentGroupingKey: InternalRow = _
+
+  // The partition key of next partition.
+  private[this] var nextGroupingKey: InternalRow = _
+
+  // The first row of next partition.
+  private[this] var firstRowInNextGroup: InternalRow = _
+
+  // Indicates if we has new group of rows from the sorted input iterator
+  private[this] var sortedInputHasNewGroup: Boolean = false
+
+  // The aggregation buffer used by the sort-based aggregation.
+  private[this] val sortBasedAggregationBuffer: MutableRow = newBuffer
+
+  /** Processes rows in the current group. It will stop when it find a new group. */
+  protected def processCurrentSortedGroup(): Unit = {
+    currentGroupingKey = nextGroupingKey
+    // Now, we will start to find all rows belonging to this group.
+    // We create a variable to track if we see the next group.
+    var findNextPartition = false
+    // firstRowInNextGroup is the first row of this group. We first process it.
+    processRow(sortBasedAggregationBuffer, firstRowInNextGroup)
+
+    // The search will stop when we see the next group or there is no
+    // input row left in the iter.
+    var hasNext = inputKVIterator.next()
+    while (!findNextPartition && hasNext) {
+      // Get the grouping key.
+      val groupingKey = inputKVIterator.getKey
+      val currentRow = inputKVIterator.getValue
+
+      // Check if the current row belongs the current input row.
+      if (currentGroupingKey == groupingKey) {
+        processRow(sortBasedAggregationBuffer, currentRow)
+
+        hasNext = inputKVIterator.next()
+      } else {
+        // We find a new group.
+        findNextPartition = true
+        nextGroupingKey = groupingKey.copy()
+        firstRowInNextGroup = currentRow.copy()
+      }
+    }
+    // We have not seen a new group. It means that there is no new row in the input
+    // iter. The current group is the last group of the iter.
+    if (!findNextPartition) {
+      sortedInputHasNewGroup = false
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Iterator's public methods
+  ///////////////////////////////////////////////////////////////////////////
+
+  override final def hasNext: Boolean = sortedInputHasNewGroup
+
+  override final def next(): InternalRow = {
+    if (hasNext) {
+      // Process the current group.
+      processCurrentSortedGroup()
+      // Generate output row for the current group.
+      val outputRow = generateOutput(currentGroupingKey, sortBasedAggregationBuffer)
+      // Initialize buffer values for the next group.
+      initializeBuffer(sortBasedAggregationBuffer)
+
+      outputRow
+    } else {
+      // no more result
+      throw new NoSuchElementException
+    }
+  }
+
+  protected def initialize(): Unit = {
+    if (inputKVIterator.next()) {
+      initializeBuffer(sortBasedAggregationBuffer)
+
+      nextGroupingKey = inputKVIterator.getKey().copy()
+      firstRowInNextGroup = inputKVIterator.getValue().copy()
+
+      sortedInputHasNewGroup = true
+    } else {
+      // This inputIter is empty.
+      sortedInputHasNewGroup = false
+    }
+  }
+
+  initialize()
+
+  def outputForEmptyGroupingKeyWithoutInput(): InternalRow = {
+    initializeBuffer(sortBasedAggregationBuffer)
+    generateOutput(new GenericInternalRow(0), sortBasedAggregationBuffer)
+  }
+}
+
+object SortBasedAggregationIterator {
+  // scalastyle:off
+  def createFromInputIterator(
+      groupingExprs: Seq[NamedExpression],
+      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+      nonCompleteAggregateAttributes: Seq[Attribute],
+      completeAggregateExpressions: Seq[AggregateExpression2],
+      completeAggregateAttributes: Seq[Attribute],
+      initialInputBufferOffset: Int,
+      resultExpressions: Seq[NamedExpression],
+      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+      newProjection: (Seq[Expression], Seq[Attribute]) => Projection,
+      inputAttributes: Seq[Attribute],
+      inputIter: Iterator[InternalRow],
+      outputsUnsafeRows: Boolean): SortBasedAggregationIterator = {
+    val kvIterator = if (UnsafeProjection.canSupport(groupingExprs)) {
+      AggregationIterator.unsafeKVIterator(
+        groupingExprs,
+        inputAttributes,
+        inputIter).asInstanceOf[KVIterator[InternalRow, InternalRow]]
+    } else {
+      AggregationIterator.kvIterator(groupingExprs, newProjection, inputAttributes, inputIter)
+    }
+
+    new SortBasedAggregationIterator(
+      groupingExprs.map(_.toAttribute),
+      inputAttributes,
+      kvIterator,
+      nonCompleteAggregateExpressions,
+      nonCompleteAggregateAttributes,
+      completeAggregateExpressions,
+      completeAggregateAttributes,
+      initialInputBufferOffset,
+      resultExpressions,
+      newMutableProjection,
+      outputsUnsafeRows)
+  }
+
+  def createFromKVIterator(
+      groupingKeyAttributes: Seq[Attribute],
+      valueAttributes: Seq[Attribute],
+      inputKVIterator: KVIterator[InternalRow, InternalRow],
+      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+      nonCompleteAggregateAttributes: Seq[Attribute],
+      completeAggregateExpressions: Seq[AggregateExpression2],
+      completeAggregateAttributes: Seq[Attribute],
+      initialInputBufferOffset: Int,
+      resultExpressions: Seq[NamedExpression],
+      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+      outputsUnsafeRows: Boolean): SortBasedAggregationIterator = {
+    new SortBasedAggregationIterator(
+      groupingKeyAttributes,
+      valueAttributes,
+      inputKVIterator,
+      nonCompleteAggregateExpressions,
+      nonCompleteAggregateAttributes,
+      completeAggregateExpressions,
+      completeAggregateAttributes,
+      initialInputBufferOffset,
+      resultExpressions,
+      newMutableProjection,
+      outputsUnsafeRows)
+  }
+  // scalastyle:on
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
new file mode 100644
index 0000000000000..37d34eb7ccf09
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
@@ -0,0 +1,398 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.sql.execution.{UnsafeKeyValueSorter, UnsafeFixedWidthAggregationMap}
+import org.apache.spark.unsafe.KVIterator
+import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.types.StructType
+
+/**
+ * An iterator used to evaluate [[AggregateFunction2]].
+ * It first tries to use in-memory hash-based aggregation. If we cannot allocate more
+ * space for the hash map, we spill the sorted map entries, free the map, and then
+ * switch to sort-based aggregation.
+ */
+class UnsafeHybridAggregationIterator(
+    groupingKeyAttributes: Seq[Attribute],
+    valueAttributes: Seq[Attribute],
+    inputKVIterator: KVIterator[UnsafeRow, InternalRow],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    nonCompleteAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    outputsUnsafeRows: Boolean)
+  extends AggregationIterator(
+    groupingKeyAttributes,
+    valueAttributes,
+    nonCompleteAggregateExpressions,
+    nonCompleteAggregateAttributes,
+    completeAggregateExpressions,
+    completeAggregateAttributes,
+    initialInputBufferOffset,
+    resultExpressions,
+    newMutableProjection,
+    outputsUnsafeRows) {
+
+  require(groupingKeyAttributes.nonEmpty)
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Unsafe Aggregation buffers
+  ///////////////////////////////////////////////////////////////////////////
+
+  // This is the Unsafe Aggregation Map used to store all buffers.
+  private[this] val buffers = new UnsafeFixedWidthAggregationMap(
+    newBuffer,
+    StructType.fromAttributes(allAggregateFunctions.flatMap(_.bufferAttributes)),
+    StructType.fromAttributes(groupingKeyAttributes),
+    TaskContext.get.taskMemoryManager(),
+    SparkEnv.get.shuffleMemoryManager,
+    1024 * 16, // initial capacity
+    SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m"),
+    false // disable tracking of performance metrics
+  )
+
+  override protected def newBuffer: UnsafeRow = {
+    val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val bufferRowSize: Int = bufferSchema.length
+
+    val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
+    val unsafeProjection =
+      UnsafeProjection.create(bufferSchema.map(_.dataType))
+    val buffer = unsafeProjection.apply(genericMutableBuffer)
+    initializeBuffer(buffer)
+    buffer
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Methods and variables related to switching to sort-based aggregation
+  ///////////////////////////////////////////////////////////////////////////
+  private[this] var sortBased = false
+
+  private[this] var sortBasedAggregationIterator: SortBasedAggregationIterator = _
+
+  // The value part of the input KV iterator is used to store original input values of
+  // aggregate functions, we need to convert them to aggregation buffers.
+  private def processOriginalInput(
+      firstKey: UnsafeRow,
+      firstValue: InternalRow): KVIterator[UnsafeRow, UnsafeRow] = {
+    new KVIterator[UnsafeRow, UnsafeRow] {
+      private[this] var isFirstRow = true
+
+      private[this] var groupingKey: UnsafeRow = _
+
+      private[this] val buffer: UnsafeRow = newBuffer
+
+      override def next(): Boolean = {
+        initializeBuffer(buffer)
+        if (isFirstRow) {
+          isFirstRow = false
+          groupingKey = firstKey
+          processRow(buffer, firstValue)
+
+          true
+        } else if (inputKVIterator.next()) {
+          groupingKey = inputKVIterator.getKey()
+          val value = inputKVIterator.getValue()
+          processRow(buffer, value)
+
+          true
+        } else {
+          false
+        }
+      }
+
+      override def getKey(): UnsafeRow = {
+        groupingKey
+      }
+
+      override def getValue(): UnsafeRow = {
+        buffer
+      }
+
+      override def close(): Unit = {
+        // Do nothing.
+      }
+    }
+  }
+
+  // The value of the input KV Iterator has the format of groupingExprs + aggregation buffer.
+  // We need to project the aggregation buffer out.
+  private def projectInputBufferToUnsafe(
+      firstKey: UnsafeRow,
+      firstValue: InternalRow): KVIterator[UnsafeRow, UnsafeRow] = {
+    new KVIterator[UnsafeRow, UnsafeRow] {
+      private[this] var isFirstRow = true
+
+      private[this] var groupingKey: UnsafeRow = _
+
+      private[this] val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
+
+      private[this] val value: UnsafeRow = {
+        val genericMutableRow = new GenericMutableRow(bufferSchema.length)
+        UnsafeProjection.create(bufferSchema.map(_.dataType)).apply(genericMutableRow)
+      }
+
+      private[this] val projectInputBuffer = {
+        newMutableProjection(bufferSchema, valueAttributes)().target(value)
+      }
+
+      override def next(): Boolean = {
+        if (isFirstRow) {
+          isFirstRow = false
+          groupingKey = firstKey
+          projectInputBuffer(firstValue)
+
+          true
+        } else if (inputKVIterator.next()) {
+          groupingKey = inputKVIterator.getKey()
+          projectInputBuffer(inputKVIterator.getValue())
+
+          true
+        } else {
+          false
+        }
+      }
+
+      override def getKey(): UnsafeRow = {
+        groupingKey
+      }
+
+      override def getValue(): UnsafeRow = {
+        value
+      }
+
+      override def close(): Unit = {
+        // Do nothing.
+      }
+    }
+  }
+
+  /**
+   * We need to fall back to sort based aggregation because we do not have enough memory
+   * for our in-memory hash map (i.e. `buffers`).
+   */
+  private def switchToSortBasedAggregation(
+      currentGroupingKey: UnsafeRow,
+      currentRow: InternalRow): Unit = {
+    logInfo("falling back to sort based aggregation.")
+
+    // Step 1: Get the ExternalSorter containing entries of the map.
+    val externalSorter = buffers.destructAndCreateExternalSorter()
+
+    // Step 2: Free the memory used by the map.
+    buffers.free()
+
+    // Step 3: If we have aggregate function with mode Partial or Complete,
+    // we need to process them to get aggregation buffer.
+    // So, later in the sort-based aggregation iterator, we can do merge.
+    // If aggregate functions are with mode Final and PartialMerge,
+    // we just need to project the aggregation buffer from the input.
+    val needsProcess = aggregationMode match {
+      case (Some(Partial), None) => true
+      case (None, Some(Complete)) => true
+      case (Some(Final), Some(Complete)) => true
+      case _ => false
+    }
+
+    val processedIterator = if (needsProcess) {
+      processOriginalInput(currentGroupingKey, currentRow)
+    } else {
+      // The input value's format is groupingExprs + buffer.
+      // We need to project the buffer part out.
+      projectInputBufferToUnsafe(currentGroupingKey, currentRow)
+    }
+
+    // Step 4: Redirect processedIterator to externalSorter.
+    while (processedIterator.next()) {
+      externalSorter.insertKV(processedIterator.getKey(), processedIterator.getValue())
+    }
+
+    // Step 5: Get the sorted iterator from the externalSorter.
+    val sortedKVIterator: KVIterator[UnsafeRow, UnsafeRow] = externalSorter.sortedIterator()
+
+    // Step 6: We now create a SortBasedAggregationIterator based on sortedKVIterator.
+    // For a aggregate function with mode Partial, its mode in the SortBasedAggregationIterator
+    // will be PartialMerge. For a aggregate function with mode Complete,
+    // its mode in the SortBasedAggregationIterator will be Final.
+    val newNonCompleteAggregateExpressions = allAggregateExpressions.map {
+        case AggregateExpression2(func, Partial, isDistinct) =>
+          AggregateExpression2(func, PartialMerge, isDistinct)
+        case AggregateExpression2(func, Complete, isDistinct) =>
+          AggregateExpression2(func, Final, isDistinct)
+        case other => other
+      }
+    val newNonCompleteAggregateAttributes =
+      nonCompleteAggregateAttributes ++ completeAggregateAttributes
+
+    val newValueAttributes =
+      allAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
+
+    sortBasedAggregationIterator = SortBasedAggregationIterator.createFromKVIterator(
+      groupingKeyAttributes = groupingKeyAttributes,
+      valueAttributes = newValueAttributes,
+      inputKVIterator = sortedKVIterator.asInstanceOf[KVIterator[InternalRow, InternalRow]],
+      nonCompleteAggregateExpressions = newNonCompleteAggregateExpressions,
+      nonCompleteAggregateAttributes = newNonCompleteAggregateAttributes,
+      completeAggregateExpressions = Nil,
+      completeAggregateAttributes = Nil,
+      initialInputBufferOffset = 0,
+      resultExpressions = resultExpressions,
+      newMutableProjection = newMutableProjection,
+      outputsUnsafeRows = outputsUnsafeRows)
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Methods used to initialize this iterator.
+  ///////////////////////////////////////////////////////////////////////////
+
+  /** Starts to read input rows and falls back to sort-based aggregation if necessary. */
+  protected def initialize(): Unit = {
+    var hasNext = inputKVIterator.next()
+    while (!sortBased && hasNext) {
+      val groupingKey = inputKVIterator.getKey()
+      val currentRow = inputKVIterator.getValue()
+      val buffer = buffers.getAggregationBuffer(groupingKey)
+      if (buffer == null) {
+        // buffer == null means that we could not allocate more memory.
+        // Now, we need to spill the map and switch to sort-based aggregation.
+        switchToSortBasedAggregation(groupingKey, currentRow)
+        sortBased = true
+      } else {
+        processRow(buffer, currentRow)
+        hasNext = inputKVIterator.next()
+      }
+    }
+  }
+
+  // This is the starting point of this iterator.
+  initialize()
+
+  // Creates the iterator for the Hash Aggregation Map after we have populated
+  // contents of that map.
+  private[this] val aggregationBufferMapIterator = buffers.iterator()
+
+  private[this] var _mapIteratorHasNext = false
+
+  // Pre-load the first key-value pair from the map to make hasNext idempotent.
+  if (!sortBased) {
+    _mapIteratorHasNext = aggregationBufferMapIterator.next()
+    // If the map is empty, we just free it.
+    if (!_mapIteratorHasNext) {
+      buffers.free()
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Iterator's public methods
+  ///////////////////////////////////////////////////////////////////////////
+
+  override final def hasNext: Boolean = {
+    (sortBased && sortBasedAggregationIterator.hasNext) || (!sortBased && _mapIteratorHasNext)
+  }
+
+
+  override final def next(): InternalRow = {
+    if (hasNext) {
+      if (sortBased) {
+        sortBasedAggregationIterator.next()
+      } else {
+        // We did not fall back to the sort-based aggregation.
+        val result =
+          generateOutput(
+            aggregationBufferMapIterator.getKey,
+            aggregationBufferMapIterator.getValue)
+        // Pre-load next key-value pair form aggregationBufferMapIterator.
+        _mapIteratorHasNext = aggregationBufferMapIterator.next()
+
+        if (!_mapIteratorHasNext) {
+          val resultCopy = result.copy()
+          buffers.free()
+          resultCopy
+        } else {
+          result
+        }
+      }
+    } else {
+      // no more result
+      throw new NoSuchElementException
+    }
+  }
+}
+
+object UnsafeHybridAggregationIterator {
+  // scalastyle:off
+  def createFromInputIterator(
+      groupingExprs: Seq[NamedExpression],
+      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+      nonCompleteAggregateAttributes: Seq[Attribute],
+      completeAggregateExpressions: Seq[AggregateExpression2],
+      completeAggregateAttributes: Seq[Attribute],
+      initialInputBufferOffset: Int,
+      resultExpressions: Seq[NamedExpression],
+      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+      inputAttributes: Seq[Attribute],
+      inputIter: Iterator[InternalRow],
+      outputsUnsafeRows: Boolean): UnsafeHybridAggregationIterator = {
+    new UnsafeHybridAggregationIterator(
+      groupingExprs.map(_.toAttribute),
+      inputAttributes,
+      AggregationIterator.unsafeKVIterator(groupingExprs, inputAttributes, inputIter),
+      nonCompleteAggregateExpressions,
+      nonCompleteAggregateAttributes,
+      completeAggregateExpressions,
+      completeAggregateAttributes,
+      initialInputBufferOffset,
+      resultExpressions,
+      newMutableProjection,
+      outputsUnsafeRows)
+  }
+
+  def createFromKVIterator(
+      groupingKeyAttributes: Seq[Attribute],
+      valueAttributes: Seq[Attribute],
+      inputKVIterator: KVIterator[UnsafeRow, InternalRow],
+      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+      nonCompleteAggregateAttributes: Seq[Attribute],
+      completeAggregateExpressions: Seq[AggregateExpression2],
+      completeAggregateAttributes: Seq[Attribute],
+      initialInputBufferOffset: Int,
+      resultExpressions: Seq[NamedExpression],
+      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+      outputsUnsafeRows: Boolean): UnsafeHybridAggregationIterator = {
+    new UnsafeHybridAggregationIterator(
+      groupingKeyAttributes,
+      valueAttributes,
+      inputKVIterator,
+      nonCompleteAggregateExpressions,
+      nonCompleteAggregateAttributes,
+      completeAggregateExpressions,
+      completeAggregateAttributes,
+      initialInputBufferOffset,
+      resultExpressions,
+      newMutableProjection,
+      outputsUnsafeRows)
+  }
+  // scalastyle:on
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
deleted file mode 100644
index 98538c462bc89..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/aggregateOperators.scala
+++ /dev/null
@@ -1,175 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.aggregate
-
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.errors._
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, UnspecifiedDistribution}
-import org.apache.spark.sql.execution.{SparkPlan, UnaryNode}
-
-case class Aggregate2Sort(
-    requiredChildDistributionExpressions: Option[Seq[Expression]],
-    groupingExpressions: Seq[NamedExpression],
-    aggregateExpressions: Seq[AggregateExpression2],
-    aggregateAttributes: Seq[Attribute],
-    resultExpressions: Seq[NamedExpression],
-    child: SparkPlan)
-  extends UnaryNode {
-
-  override def canProcessUnsafeRows: Boolean = true
-
-  override def references: AttributeSet = {
-    val referencesInResults =
-      AttributeSet(resultExpressions.flatMap(_.references)) -- AttributeSet(aggregateAttributes)
-
-    AttributeSet(
-      groupingExpressions.flatMap(_.references) ++
-      aggregateExpressions.flatMap(_.references) ++
-      referencesInResults)
-  }
-
-  override def requiredChildDistribution: List[Distribution] = {
-    requiredChildDistributionExpressions match {
-      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
-      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
-      case None => UnspecifiedDistribution :: Nil
-    }
-  }
-
-  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
-    // TODO: We should not sort the input rows if they are just in reversed order.
-    groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
-  }
-
-  override def outputOrdering: Seq[SortOrder] = {
-    // It is possible that the child.outputOrdering starts with the required
-    // ordering expressions (e.g. we require [a] as the sort expression and the
-    // child's outputOrdering is [a, b]). We can only guarantee the output rows
-    // are sorted by values of groupingExpressions.
-    groupingExpressions.map(SortOrder(_, Ascending))
-  }
-
-  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
-
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
-    child.execute().mapPartitions { iter =>
-      if (aggregateExpressions.length == 0) {
-        new FinalSortAggregationIterator(
-          groupingExpressions,
-          Nil,
-          Nil,
-          resultExpressions,
-          newMutableProjection,
-          child.output,
-          iter)
-      } else {
-        val aggregationIterator: SortAggregationIterator = {
-          aggregateExpressions.map(_.mode).distinct.toList match {
-            case Partial :: Nil =>
-              new PartialSortAggregationIterator(
-                groupingExpressions,
-                aggregateExpressions,
-                newMutableProjection,
-                child.output,
-                iter)
-            case PartialMerge :: Nil =>
-              new PartialMergeSortAggregationIterator(
-                groupingExpressions,
-                aggregateExpressions,
-                newMutableProjection,
-                child.output,
-                iter)
-            case Final :: Nil =>
-              new FinalSortAggregationIterator(
-                groupingExpressions,
-                aggregateExpressions,
-                aggregateAttributes,
-                resultExpressions,
-                newMutableProjection,
-                child.output,
-                iter)
-            case other =>
-              sys.error(
-                s"Could not evaluate ${aggregateExpressions} because we do not support evaluate " +
-                  s"modes $other in this operator.")
-          }
-        }
-
-        aggregationIterator
-      }
-    }
-  }
-}
-
-case class FinalAndCompleteAggregate2Sort(
-    previousGroupingExpressions: Seq[NamedExpression],
-    groupingExpressions: Seq[NamedExpression],
-    finalAggregateExpressions: Seq[AggregateExpression2],
-    finalAggregateAttributes: Seq[Attribute],
-    completeAggregateExpressions: Seq[AggregateExpression2],
-    completeAggregateAttributes: Seq[Attribute],
-    resultExpressions: Seq[NamedExpression],
-    child: SparkPlan)
-  extends UnaryNode {
-  override def references: AttributeSet = {
-    val referencesInResults =
-      AttributeSet(resultExpressions.flatMap(_.references)) --
-        AttributeSet(finalAggregateExpressions) --
-        AttributeSet(completeAggregateExpressions)
-
-    AttributeSet(
-      groupingExpressions.flatMap(_.references) ++
-        finalAggregateExpressions.flatMap(_.references) ++
-        completeAggregateExpressions.flatMap(_.references) ++
-        referencesInResults)
-  }
-
-  override def requiredChildDistribution: List[Distribution] = {
-    if (groupingExpressions.isEmpty) {
-      AllTuples :: Nil
-    } else {
-      ClusteredDistribution(groupingExpressions) :: Nil
-    }
-  }
-
-  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
-    groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
-
-  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
-
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
-    child.execute().mapPartitions { iter =>
-
-      new FinalAndCompleteSortAggregationIterator(
-        previousGroupingExpressions.length,
-        groupingExpressions,
-        finalAggregateExpressions,
-        finalAggregateAttributes,
-        completeAggregateExpressions,
-        completeAggregateAttributes,
-        resultExpressions,
-        newMutableProjection,
-        child.output,
-        iter)
-    }
-  }
-
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
deleted file mode 100644
index 2ca0cb82c1aab..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/sortBasedIterators.scala
+++ /dev/null
@@ -1,664 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.aggregate
-
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.types.NullType
-
-import scala.collection.mutable.ArrayBuffer
-
-/**
- * An iterator used to evaluate aggregate functions. It assumes that input rows
- * are already grouped by values of `groupingExpressions`.
- */
-private[sql] abstract class SortAggregationIterator(
-    groupingExpressions: Seq[NamedExpression],
-    aggregateExpressions: Seq[AggregateExpression2],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    inputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow])
-  extends Iterator[InternalRow] {
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Static fields for this iterator
-  ///////////////////////////////////////////////////////////////////////////
-
-  protected val aggregateFunctions: Array[AggregateFunction2] = {
-    var mutableBufferOffset = 0
-    var inputBufferOffset: Int = initialInputBufferOffset
-    val functions = new Array[AggregateFunction2](aggregateExpressions.length)
-    var i = 0
-    while (i < aggregateExpressions.length) {
-      val func = aggregateExpressions(i).aggregateFunction
-      val funcWithBoundReferences = aggregateExpressions(i).mode match {
-        case Partial | Complete if !func.isInstanceOf[AlgebraicAggregate] =>
-          // We need to create BoundReferences if the function is not an
-          // AlgebraicAggregate (it does not support code-gen) and the mode of
-          // this function is Partial or Complete because we will call eval of this
-          // function's children in the update method of this aggregate function.
-          // Those eval calls require BoundReferences to work.
-          BindReferences.bindReference(func, inputAttributes)
-        case _ =>
-          // We only need to set inputBufferOffset for aggregate functions with mode
-          // PartialMerge and Final.
-          func.inputBufferOffset = inputBufferOffset
-          inputBufferOffset += func.bufferSchema.length
-          func
-      }
-      // Set mutableBufferOffset for this function. It is important that setting
-      // mutableBufferOffset happens after all potential bindReference operations
-      // because bindReference will create a new instance of the function.
-      funcWithBoundReferences.mutableBufferOffset = mutableBufferOffset
-      mutableBufferOffset += funcWithBoundReferences.bufferSchema.length
-      functions(i) = funcWithBoundReferences
-      i += 1
-    }
-    functions
-  }
-
-  // Positions of those non-algebraic aggregate functions in aggregateFunctions.
-  // For example, we have func1, func2, func3, func4 in aggregateFunctions, and
-  // func2 and func3 are non-algebraic aggregate functions.
-  // nonAlgebraicAggregateFunctionPositions will be [1, 2].
-  protected val nonAlgebraicAggregateFunctionPositions: Array[Int] = {
-    val positions = new ArrayBuffer[Int]()
-    var i = 0
-    while (i < aggregateFunctions.length) {
-      aggregateFunctions(i) match {
-        case agg: AlgebraicAggregate =>
-        case _ => positions += i
-      }
-      i += 1
-    }
-    positions.toArray
-  }
-
-  // All non-algebraic aggregate functions.
-  protected val nonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
-    nonAlgebraicAggregateFunctionPositions.map(aggregateFunctions)
-
-  // This is used to project expressions for the grouping expressions.
-  protected val groupGenerator =
-    newMutableProjection(groupingExpressions, inputAttributes)()
-
-  // The underlying buffer shared by all aggregate functions.
-  protected val buffer: MutableRow = {
-    // The number of elements of the underlying buffer of this operator.
-    // All aggregate functions are sharing this underlying buffer and they find their
-    // buffer values through bufferOffset.
-    // var size = 0
-    // var i = 0
-    // while (i < aggregateFunctions.length) {
-    //  size += aggregateFunctions(i).bufferSchema.length
-    //  i += 1
-    // }
-    new GenericMutableRow(aggregateFunctions.map(_.bufferSchema.length).sum)
-  }
-
-  protected val joinedRow = new JoinedRow
-
-  // This projection is used to initialize buffer values for all AlgebraicAggregates.
-  protected val algebraicInitialProjection = {
-    val initExpressions = aggregateFunctions.flatMap {
-      case ae: AlgebraicAggregate => ae.initialValues
-      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
-    }
-
-    newMutableProjection(initExpressions, Nil)().target(buffer)
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Mutable states
-  ///////////////////////////////////////////////////////////////////////////
-
-  // The partition key of the current partition.
-  protected var currentGroupingKey: InternalRow = _
-  // The partition key of next partition.
-  protected var nextGroupingKey: InternalRow = _
-  // The first row of next partition.
-  protected var firstRowInNextGroup: InternalRow = _
-  // Indicates if we has new group of rows to process.
-  protected var hasNewGroup: Boolean = true
-
-  /** Initializes buffer values for all aggregate functions. */
-  protected def initializeBuffer(): Unit = {
-    algebraicInitialProjection(EmptyRow)
-    var i = 0
-    while (i < nonAlgebraicAggregateFunctions.length) {
-      nonAlgebraicAggregateFunctions(i).initialize(buffer)
-      i += 1
-    }
-  }
-
-  protected def initialize(): Unit = {
-    if (inputIter.hasNext) {
-      initializeBuffer()
-      val currentRow = inputIter.next().copy()
-      // partitionGenerator is a mutable projection. Since we need to track nextGroupingKey,
-      // we are making a copy at here.
-      nextGroupingKey = groupGenerator(currentRow).copy()
-      firstRowInNextGroup = currentRow
-    } else {
-      // This iter is an empty one.
-      hasNewGroup = false
-    }
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Private methods
-  ///////////////////////////////////////////////////////////////////////////
-
-  /** Processes rows in the current group. It will stop when it find a new group. */
-  private def processCurrentGroup(): Unit = {
-    currentGroupingKey = nextGroupingKey
-    // Now, we will start to find all rows belonging to this group.
-    // We create a variable to track if we see the next group.
-    var findNextPartition = false
-    // firstRowInNextGroup is the first row of this group. We first process it.
-    processRow(firstRowInNextGroup)
-    // The search will stop when we see the next group or there is no
-    // input row left in the iter.
-    while (inputIter.hasNext && !findNextPartition) {
-      val currentRow = inputIter.next()
-      // Get the grouping key based on the grouping expressions.
-      // For the below compare method, we do not need to make a copy of groupingKey.
-      val groupingKey = groupGenerator(currentRow)
-      // Check if the current row belongs the current input row.
-      if (currentGroupingKey == groupingKey) {
-        processRow(currentRow)
-      } else {
-        // We find a new group.
-        findNextPartition = true
-        nextGroupingKey = groupingKey.copy()
-        firstRowInNextGroup = currentRow.copy()
-      }
-    }
-    // We have not seen a new group. It means that there is no new row in the input
-    // iter. The current group is the last group of the iter.
-    if (!findNextPartition) {
-      hasNewGroup = false
-    }
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Public methods
-  ///////////////////////////////////////////////////////////////////////////
-
-  override final def hasNext: Boolean = hasNewGroup
-
-  override final def next(): InternalRow = {
-    if (hasNext) {
-      // Process the current group.
-      processCurrentGroup()
-      // Generate output row for the current group.
-      val outputRow = generateOutput()
-      // Initilize buffer values for the next group.
-      initializeBuffer()
-
-      outputRow
-    } else {
-      // no more result
-      throw new NoSuchElementException
-    }
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Methods that need to be implemented
-  ///////////////////////////////////////////////////////////////////////////
-
-  /** The initial input buffer offset for `inputBufferOffset` of an [[AggregateFunction2]]. */
-  protected def initialInputBufferOffset: Int
-
-  /** The function used to process an input row. */
-  protected def processRow(row: InternalRow): Unit
-
-  /** The function used to generate the result row. */
-  protected def generateOutput(): InternalRow
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Initialize this iterator
-  ///////////////////////////////////////////////////////////////////////////
-
-  initialize()
-}
-
-/**
- * An iterator used to do partial aggregations (for those aggregate functions with mode Partial).
- * It assumes that input rows are already grouped by values of `groupingExpressions`.
- * The format of its output rows is:
- * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
- */
-class PartialSortAggregationIterator(
-    groupingExpressions: Seq[NamedExpression],
-    aggregateExpressions: Seq[AggregateExpression2],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    inputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow])
-  extends SortAggregationIterator(
-    groupingExpressions,
-    aggregateExpressions,
-    newMutableProjection,
-    inputAttributes,
-    inputIter) {
-
-  // This projection is used to update buffer values for all AlgebraicAggregates.
-  private val algebraicUpdateProjection = {
-    val bufferSchema = aggregateFunctions.flatMap(_.bufferAttributes)
-    val updateExpressions = aggregateFunctions.flatMap {
-      case ae: AlgebraicAggregate => ae.updateExpressions
-      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
-    }
-    newMutableProjection(updateExpressions, bufferSchema ++ inputAttributes)().target(buffer)
-  }
-
-  override protected def initialInputBufferOffset: Int = 0
-
-  override protected def processRow(row: InternalRow): Unit = {
-    // Process all algebraic aggregate functions.
-    algebraicUpdateProjection(joinedRow(buffer, row))
-    // Process all non-algebraic aggregate functions.
-    var i = 0
-    while (i < nonAlgebraicAggregateFunctions.length) {
-      nonAlgebraicAggregateFunctions(i).update(buffer, row)
-      i += 1
-    }
-  }
-
-  override protected def generateOutput(): InternalRow = {
-    // We just output the grouping expressions and the underlying buffer.
-    joinedRow(currentGroupingKey, buffer).copy()
-  }
-}
-
-/**
- * An iterator used to do partial merge aggregations (for those aggregate functions with mode
- * PartialMerge). It assumes that input rows are already grouped by values of
- * `groupingExpressions`.
- * The format of its input rows is:
- * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
- *
- * The format of its internal buffer is:
- * |aggregationBuffer1|...|aggregationBufferN|
- *
- * The format of its output rows is:
- * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
- */
-class PartialMergeSortAggregationIterator(
-    groupingExpressions: Seq[NamedExpression],
-    aggregateExpressions: Seq[AggregateExpression2],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    inputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow])
-  extends SortAggregationIterator(
-    groupingExpressions,
-    aggregateExpressions,
-    newMutableProjection,
-    inputAttributes,
-    inputIter) {
-
-  // This projection is used to merge buffer values for all AlgebraicAggregates.
-  private val algebraicMergeProjection = {
-    val mergeInputSchema =
-      aggregateFunctions.flatMap(_.bufferAttributes) ++
-        groupingExpressions.map(_.toAttribute) ++
-        aggregateFunctions.flatMap(_.cloneBufferAttributes)
-    val mergeExpressions = aggregateFunctions.flatMap {
-      case ae: AlgebraicAggregate => ae.mergeExpressions
-      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
-    }
-
-    newMutableProjection(mergeExpressions, mergeInputSchema)()
-  }
-
-  override protected def initialInputBufferOffset: Int = groupingExpressions.length
-
-  override protected def processRow(row: InternalRow): Unit = {
-    // Process all algebraic aggregate functions.
-    algebraicMergeProjection.target(buffer)(joinedRow(buffer, row))
-    // Process all non-algebraic aggregate functions.
-    var i = 0
-    while (i < nonAlgebraicAggregateFunctions.length) {
-      nonAlgebraicAggregateFunctions(i).merge(buffer, row)
-      i += 1
-    }
-  }
-
-  override protected def generateOutput(): InternalRow = {
-    // We output grouping expressions and aggregation buffers.
-    joinedRow(currentGroupingKey, buffer).copy()
-  }
-}
-
-/**
- * An iterator used to do final aggregations (for those aggregate functions with mode
- * Final). It assumes that input rows are already grouped by values of
- * `groupingExpressions`.
- * The format of its input rows is:
- * |groupingExpr1|...|groupingExprN|aggregationBuffer1|...|aggregationBufferN|
- *
- * The format of its internal buffer is:
- * |aggregationBuffer1|...|aggregationBufferN|
- *
- * The format of its output rows is represented by the schema of `resultExpressions`.
- */
-class FinalSortAggregationIterator(
-    groupingExpressions: Seq[NamedExpression],
-    aggregateExpressions: Seq[AggregateExpression2],
-    aggregateAttributes: Seq[Attribute],
-    resultExpressions: Seq[NamedExpression],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    inputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow])
-  extends SortAggregationIterator(
-    groupingExpressions,
-    aggregateExpressions,
-    newMutableProjection,
-    inputAttributes,
-    inputIter) {
-
-  // The result of aggregate functions.
-  private val aggregateResult: MutableRow = new GenericMutableRow(aggregateAttributes.length)
-
-  // The projection used to generate the output rows of this operator.
-  // This is only used when we are generating final results of aggregate functions.
-  private val resultProjection =
-    newMutableProjection(
-      resultExpressions, groupingExpressions.map(_.toAttribute) ++ aggregateAttributes)()
-
-  // This projection is used to merge buffer values for all AlgebraicAggregates.
-  private val algebraicMergeProjection = {
-    val mergeInputSchema =
-      aggregateFunctions.flatMap(_.bufferAttributes) ++
-        groupingExpressions.map(_.toAttribute) ++
-        aggregateFunctions.flatMap(_.cloneBufferAttributes)
-    val mergeExpressions = aggregateFunctions.flatMap {
-      case ae: AlgebraicAggregate => ae.mergeExpressions
-      case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
-    }
-
-    newMutableProjection(mergeExpressions, mergeInputSchema)()
-  }
-
-  // This projection is used to evaluate all AlgebraicAggregates.
-  private val algebraicEvalProjection = {
-    val bufferSchemata = aggregateFunctions.flatMap(_.bufferAttributes)
-    val evalExpressions = aggregateFunctions.map {
-      case ae: AlgebraicAggregate => ae.evaluateExpression
-      case agg: AggregateFunction2 => NoOp
-    }
-
-    newMutableProjection(evalExpressions, bufferSchemata)()
-  }
-
-  override protected def initialInputBufferOffset: Int = groupingExpressions.length
-
-  override def initialize(): Unit = {
-    if (inputIter.hasNext) {
-      initializeBuffer()
-      val currentRow = inputIter.next().copy()
-      // partitionGenerator is a mutable projection. Since we need to track nextGroupingKey,
-      // we are making a copy at here.
-      nextGroupingKey = groupGenerator(currentRow).copy()
-      firstRowInNextGroup = currentRow
-    } else {
-      if (groupingExpressions.isEmpty) {
-        // If there is no grouping expression, we need to generate a single row as the output.
-        initializeBuffer()
-        // Right now, the buffer only contains initial buffer values. Because
-        // merging two buffers with initial values will generate a row that
-        // still store initial values. We set the currentRow as the copy of the current buffer.
-        // Because input aggregation buffer has initialInputBufferOffset extra values at the
-        // beginning, we create a dummy row for this part.
-        val currentRow =
-          joinedRow(new GenericInternalRow(initialInputBufferOffset), buffer).copy()
-        nextGroupingKey = groupGenerator(currentRow).copy()
-        firstRowInNextGroup = currentRow
-      } else {
-        // This iter is an empty one.
-        hasNewGroup = false
-      }
-    }
-  }
-
-  override protected def processRow(row: InternalRow): Unit = {
-    // Process all algebraic aggregate functions.
-    algebraicMergeProjection.target(buffer)(joinedRow(buffer, row))
-    // Process all non-algebraic aggregate functions.
-    var i = 0
-    while (i < nonAlgebraicAggregateFunctions.length) {
-      nonAlgebraicAggregateFunctions(i).merge(buffer, row)
-      i += 1
-    }
-  }
-
-  override protected def generateOutput(): InternalRow = {
-    // Generate results for all algebraic aggregate functions.
-    algebraicEvalProjection.target(aggregateResult)(buffer)
-    // Generate results for all non-algebraic aggregate functions.
-    var i = 0
-    while (i < nonAlgebraicAggregateFunctions.length) {
-      aggregateResult.update(
-        nonAlgebraicAggregateFunctionPositions(i),
-        nonAlgebraicAggregateFunctions(i).eval(buffer))
-      i += 1
-    }
-    resultProjection(joinedRow(currentGroupingKey, aggregateResult))
-  }
-}
-
-/**
- * An iterator used to do both final aggregations (for those aggregate functions with mode
- * Final) and complete aggregations (for those aggregate functions with mode Complete).
- * It assumes that input rows are already grouped by values of `groupingExpressions`.
- * The format of its input rows is:
- * |groupingExpr1|...|groupingExprN|col1|...|colM|aggregationBuffer1|...|aggregationBufferN|
- * col1 to colM are columns used by aggregate functions with Complete mode.
- * aggregationBuffer1 to aggregationBufferN are buffers used by aggregate functions with
- * Final mode.
- *
- * The format of its internal buffer is:
- * |aggregationBuffer1|...|aggregationBuffer(N+M)|
- * For aggregation buffers, first N aggregation buffers are used by N aggregate functions with
- * mode Final. Then, the last M aggregation buffers are used by M aggregate functions with mode
- * Complete.
- *
- * The format of its output rows is represented by the schema of `resultExpressions`.
- */
-class FinalAndCompleteSortAggregationIterator(
-    override protected val initialInputBufferOffset: Int,
-    groupingExpressions: Seq[NamedExpression],
-    finalAggregateExpressions: Seq[AggregateExpression2],
-    finalAggregateAttributes: Seq[Attribute],
-    completeAggregateExpressions: Seq[AggregateExpression2],
-    completeAggregateAttributes: Seq[Attribute],
-    resultExpressions: Seq[NamedExpression],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    inputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow])
-  extends SortAggregationIterator(
-    groupingExpressions,
-    // TODO: document the ordering
-    finalAggregateExpressions ++ completeAggregateExpressions,
-    newMutableProjection,
-    inputAttributes,
-    inputIter) {
-
-  // The result of aggregate functions.
-  private val aggregateResult: MutableRow =
-    new GenericMutableRow(completeAggregateAttributes.length + finalAggregateAttributes.length)
-
-  // The projection used to generate the output rows of this operator.
-  // This is only used when we are generating final results of aggregate functions.
-  private val resultProjection = {
-    val inputSchema =
-      groupingExpressions.map(_.toAttribute) ++
-        finalAggregateAttributes ++
-        completeAggregateAttributes
-    newMutableProjection(resultExpressions, inputSchema)()
-  }
-
-  // All aggregate functions with mode Final.
-  private val finalAggregateFunctions: Array[AggregateFunction2] = {
-    val functions = new Array[AggregateFunction2](finalAggregateExpressions.length)
-    var i = 0
-    while (i < finalAggregateExpressions.length) {
-      functions(i) = aggregateFunctions(i)
-      i += 1
-    }
-    functions
-  }
-
-  // All non-algebraic aggregate functions with mode Final.
-  private val finalNonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
-    finalAggregateFunctions.collect {
-      case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
-    }
-
-  // All aggregate functions with mode Complete.
-  private val completeAggregateFunctions: Array[AggregateFunction2] = {
-    val functions = new Array[AggregateFunction2](completeAggregateExpressions.length)
-    var i = 0
-    while (i < completeAggregateExpressions.length) {
-      functions(i) = aggregateFunctions(finalAggregateFunctions.length + i)
-      i += 1
-    }
-    functions
-  }
-
-  // All non-algebraic aggregate functions with mode Complete.
-  private val completeNonAlgebraicAggregateFunctions: Array[AggregateFunction2] =
-    completeAggregateFunctions.collect {
-      case func: AggregateFunction2 if !func.isInstanceOf[AlgebraicAggregate] => func
-    }
-
-  // This projection is used to merge buffer values for all AlgebraicAggregates with mode
-  // Final.
-  private val finalAlgebraicMergeProjection = {
-    // The first initialInputBufferOffset values of the input aggregation buffer is
-    // for grouping expressions and distinct columns.
-    val groupingAttributesAndDistinctColumns = inputAttributes.take(initialInputBufferOffset)
-
-    val completeOffsetExpressions =
-      Seq.fill(completeAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
-
-    val mergeInputSchema =
-      finalAggregateFunctions.flatMap(_.bufferAttributes) ++
-        completeAggregateFunctions.flatMap(_.bufferAttributes) ++
-        groupingAttributesAndDistinctColumns ++
-        finalAggregateFunctions.flatMap(_.cloneBufferAttributes)
-    val mergeExpressions =
-      finalAggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.mergeExpressions
-        case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
-      } ++ completeOffsetExpressions
-    newMutableProjection(mergeExpressions, mergeInputSchema)()
-  }
-
-  // This projection is used to update buffer values for all AlgebraicAggregates with mode
-  // Complete.
-  private val completeAlgebraicUpdateProjection = {
-    // We do not touch buffer values of aggregate functions with the Final mode.
-    val finalOffsetExpressions =
-      Seq.fill(finalAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
-
-    val bufferSchema =
-      finalAggregateFunctions.flatMap(_.bufferAttributes) ++
-        completeAggregateFunctions.flatMap(_.bufferAttributes)
-    val updateExpressions =
-      finalOffsetExpressions ++ completeAggregateFunctions.flatMap {
-        case ae: AlgebraicAggregate => ae.updateExpressions
-        case agg: AggregateFunction2 => Seq.fill(agg.bufferAttributes.length)(NoOp)
-      }
-    newMutableProjection(updateExpressions, bufferSchema ++ inputAttributes)().target(buffer)
-  }
-
-  // This projection is used to evaluate all AlgebraicAggregates.
-  private val algebraicEvalProjection = {
-    val bufferSchemata = aggregateFunctions.flatMap(_.bufferAttributes)
-    val evalExpressions = aggregateFunctions.map {
-      case ae: AlgebraicAggregate => ae.evaluateExpression
-      case agg: AggregateFunction2 => NoOp
-    }
-
-    newMutableProjection(evalExpressions, bufferSchemata)()
-  }
-
-  override def initialize(): Unit = {
-    if (inputIter.hasNext) {
-      initializeBuffer()
-      val currentRow = inputIter.next().copy()
-      // partitionGenerator is a mutable projection. Since we need to track nextGroupingKey,
-      // we are making a copy at here.
-      nextGroupingKey = groupGenerator(currentRow).copy()
-      firstRowInNextGroup = currentRow
-    } else {
-      if (groupingExpressions.isEmpty) {
-        // If there is no grouping expression, we need to generate a single row as the output.
-        initializeBuffer()
-        // Right now, the buffer only contains initial buffer values. Because
-        // merging two buffers with initial values will generate a row that
-        // still store initial values. We set the currentRow as the copy of the current buffer.
-        // Because input aggregation buffer has initialInputBufferOffset extra values at the
-        // beginning, we create a dummy row for this part.
-        val currentRow =
-          joinedRow(new GenericInternalRow(initialInputBufferOffset), buffer).copy()
-        nextGroupingKey = groupGenerator(currentRow).copy()
-        firstRowInNextGroup = currentRow
-      } else {
-        // This iter is an empty one.
-        hasNewGroup = false
-      }
-    }
-  }
-
-  override protected def processRow(row: InternalRow): Unit = {
-    val input = joinedRow(buffer, row)
-    // For all aggregate functions with mode Complete, update buffers.
-    completeAlgebraicUpdateProjection(input)
-    var i = 0
-    while (i < completeNonAlgebraicAggregateFunctions.length) {
-      completeNonAlgebraicAggregateFunctions(i).update(buffer, row)
-      i += 1
-    }
-
-    // For all aggregate functions with mode Final, merge buffers.
-    finalAlgebraicMergeProjection.target(buffer)(input)
-    i = 0
-    while (i < finalNonAlgebraicAggregateFunctions.length) {
-      finalNonAlgebraicAggregateFunctions(i).merge(buffer, row)
-      i += 1
-    }
-  }
-
-  override protected def generateOutput(): InternalRow = {
-    // Generate results for all algebraic aggregate functions.
-    algebraicEvalProjection.target(aggregateResult)(buffer)
-    // Generate results for all non-algebraic aggregate functions.
-    var i = 0
-    while (i < nonAlgebraicAggregateFunctions.length) {
-      aggregateResult.update(
-        nonAlgebraicAggregateFunctionPositions(i),
-        nonAlgebraicAggregateFunctions(i).eval(buffer))
-      i += 1
-    }
-
-    resultProjection(joinedRow(currentGroupingKey, aggregateResult))
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
index cc54319171bdb..5fafc916bfa0b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
@@ -24,7 +24,154 @@ import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjecti
 import org.apache.spark.sql.catalyst.expressions.{MutableRow, InterpretedMutableProjection, AttributeReference, Expression}
 import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateFunction2
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
-import org.apache.spark.sql.types.{Metadata, StructField, StructType, DataType}
+import org.apache.spark.sql.types._
+
+/**
+ * A helper trait used to create specialized setter and getter for types supported by
+ * [[org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap]]'s buffer.
+ * (see UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema).
+ */
+sealed trait BufferSetterGetterUtils {
+
+  def createGetters(schema: StructType): Array[(InternalRow, Int) => Any] = {
+    val dataTypes = schema.fields.map(_.dataType)
+    val getters = new Array[(InternalRow, Int) => Any](dataTypes.length)
+
+    var i = 0
+    while (i < getters.length) {
+      getters(i) = dataTypes(i) match {
+        case BooleanType =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getBoolean(ordinal)
+
+        case ByteType =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getByte(ordinal)
+
+        case ShortType =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getShort(ordinal)
+
+        case IntegerType =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getInt(ordinal)
+
+        case LongType =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getLong(ordinal)
+
+        case FloatType =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getFloat(ordinal)
+
+        case DoubleType =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getDouble(ordinal)
+
+        case dt: DecimalType =>
+          val precision = dt.precision
+          val scale = dt.scale
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.getDecimal(ordinal, precision, scale)
+
+        case other =>
+          (row: InternalRow, ordinal: Int) =>
+            if (row.isNullAt(ordinal)) null else row.get(ordinal, other)
+      }
+
+      i += 1
+    }
+
+    getters
+  }
+
+  def createSetters(schema: StructType): Array[((MutableRow, Int, Any) => Unit)] = {
+    val dataTypes = schema.fields.map(_.dataType)
+    val setters = new Array[(MutableRow, Int, Any) => Unit](dataTypes.length)
+
+    var i = 0
+    while (i < setters.length) {
+      setters(i) = dataTypes(i) match {
+        case b: BooleanType =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setBoolean(ordinal, value.asInstanceOf[Boolean])
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case ByteType =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setByte(ordinal, value.asInstanceOf[Byte])
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case ShortType =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setShort(ordinal, value.asInstanceOf[Short])
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case IntegerType =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setInt(ordinal, value.asInstanceOf[Int])
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case LongType =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setLong(ordinal, value.asInstanceOf[Long])
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case FloatType =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setFloat(ordinal, value.asInstanceOf[Float])
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case DoubleType =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setDouble(ordinal, value.asInstanceOf[Double])
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case dt: DecimalType =>
+          val precision = dt.precision
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.setDecimal(ordinal, value.asInstanceOf[Decimal], precision)
+            } else {
+              row.setNullAt(ordinal)
+            }
+
+        case other =>
+          (row: MutableRow, ordinal: Int, value: Any) =>
+            if (value != null) {
+              row.update(ordinal, value)
+            } else {
+              row.setNullAt(ordinal)
+            }
+      }
+
+      i += 1
+    }
+
+    setters
+  }
+}
 
 /**
  * A Mutable [[Row]] representing an mutable aggregation buffer.
@@ -35,7 +182,7 @@ private[sql] class MutableAggregationBufferImpl (
     toScalaConverters: Array[Any => Any],
     bufferOffset: Int,
     var underlyingBuffer: MutableRow)
-  extends MutableAggregationBuffer {
+  extends MutableAggregationBuffer with BufferSetterGetterUtils {
 
   private[this] val offsets: Array[Int] = {
     val newOffsets = new Array[Int](length)
@@ -47,6 +194,10 @@ private[sql] class MutableAggregationBufferImpl (
     newOffsets
   }
 
+  private[this] val bufferValueGetters = createGetters(schema)
+
+  private[this] val bufferValueSetters = createSetters(schema)
+
   override def length: Int = toCatalystConverters.length
 
   override def get(i: Int): Any = {
@@ -54,7 +205,7 @@ private[sql] class MutableAggregationBufferImpl (
       throw new IllegalArgumentException(
         s"Could not access ${i}th value in this buffer because it only has $length values.")
     }
-    toScalaConverters(i)(underlyingBuffer.get(offsets(i), schema(i).dataType))
+    toScalaConverters(i)(bufferValueGetters(i)(underlyingBuffer, offsets(i)))
   }
 
   def update(i: Int, value: Any): Unit = {
@@ -62,7 +213,15 @@ private[sql] class MutableAggregationBufferImpl (
       throw new IllegalArgumentException(
         s"Could not update ${i}th value in this buffer because it only has $length values.")
     }
-    underlyingBuffer.update(offsets(i), toCatalystConverters(i)(value))
+
+    bufferValueSetters(i)(underlyingBuffer, offsets(i), toCatalystConverters(i)(value))
+  }
+
+  // Because get method call specialized getter based on the schema, we cannot use the
+  // default implementation of the isNullAt (which is get(i) == null).
+  // We have to override it to call isNullAt of the underlyingBuffer.
+  override def isNullAt(i: Int): Boolean = {
+    underlyingBuffer.isNullAt(offsets(i))
   }
 
   override def copy(): MutableAggregationBufferImpl = {
@@ -84,7 +243,7 @@ private[sql] class InputAggregationBuffer private[sql] (
     toScalaConverters: Array[Any => Any],
     bufferOffset: Int,
     var underlyingInputBuffer: InternalRow)
-  extends Row {
+  extends Row with BufferSetterGetterUtils {
 
   private[this] val offsets: Array[Int] = {
     val newOffsets = new Array[Int](length)
@@ -96,6 +255,10 @@ private[sql] class InputAggregationBuffer private[sql] (
     newOffsets
   }
 
+  private[this] val bufferValueGetters = createGetters(schema)
+
+  def getBufferOffset: Int = bufferOffset
+
   override def length: Int = toCatalystConverters.length
 
   override def get(i: Int): Any = {
@@ -103,8 +266,14 @@ private[sql] class InputAggregationBuffer private[sql] (
       throw new IllegalArgumentException(
         s"Could not access ${i}th value in this buffer because it only has $length values.")
     }
-    // TODO: Use buffer schema to avoid using generic getter.
-    toScalaConverters(i)(underlyingInputBuffer.get(offsets(i), schema(i).dataType))
+    toScalaConverters(i)(bufferValueGetters(i)(underlyingInputBuffer, offsets(i)))
+  }
+
+  // Because get method call specialized getter based on the schema, we cannot use the
+  // default implementation of the isNullAt (which is get(i) == null).
+  // We have to override it to call isNullAt of the underlyingInputBuffer.
+  override def isNullAt(i: Int): Boolean = {
+    underlyingInputBuffer.isNullAt(offsets(i))
   }
 
   override def copy(): InputAggregationBuffer = {
@@ -147,7 +316,7 @@ private[sql] case class ScalaUDAF(
 
   override lazy val cloneBufferAttributes = bufferAttributes.map(_.newInstance())
 
-  val childrenSchema: StructType = {
+  private[this] val childrenSchema: StructType = {
     val inputFields = children.zipWithIndex.map {
       case (child, index) =>
         StructField(s"input$index", child.dataType, child.nullable, Metadata.empty)
@@ -155,7 +324,7 @@ private[sql] case class ScalaUDAF(
     StructType(inputFields)
   }
 
-  lazy val inputProjection = {
+  private lazy val inputProjection = {
     val inputAttributes = childrenSchema.toAttributes
     log.debug(
       s"Creating MutableProj: $children, inputSchema: $inputAttributes.")
@@ -168,40 +337,68 @@ private[sql] case class ScalaUDAF(
     }
   }
 
-  val inputToScalaConverters: Any => Any =
+  private[this] val inputToScalaConverters: Any => Any =
     CatalystTypeConverters.createToScalaConverter(childrenSchema)
 
-  val bufferValuesToCatalystConverters: Array[Any => Any] = bufferSchema.fields.map { field =>
-    CatalystTypeConverters.createToCatalystConverter(field.dataType)
+  private[this] val bufferValuesToCatalystConverters: Array[Any => Any] = {
+    bufferSchema.fields.map { field =>
+      CatalystTypeConverters.createToCatalystConverter(field.dataType)
+    }
   }
 
-  val bufferValuesToScalaConverters: Array[Any => Any] = bufferSchema.fields.map { field =>
-    CatalystTypeConverters.createToScalaConverter(field.dataType)
+  private[this] val bufferValuesToScalaConverters: Array[Any => Any] = {
+    bufferSchema.fields.map { field =>
+      CatalystTypeConverters.createToScalaConverter(field.dataType)
+    }
   }
 
-  lazy val inputAggregateBuffer: InputAggregationBuffer =
-    new InputAggregationBuffer(
-      bufferSchema,
-      bufferValuesToCatalystConverters,
-      bufferValuesToScalaConverters,
-      inputBufferOffset,
-      null)
-
-  lazy val mutableAggregateBuffer: MutableAggregationBufferImpl =
-    new MutableAggregationBufferImpl(
-      bufferSchema,
-      bufferValuesToCatalystConverters,
-      bufferValuesToScalaConverters,
-      mutableBufferOffset,
-      null)
+  // This buffer is only used at executor side.
+  private[this] var inputAggregateBuffer: InputAggregationBuffer = null
+
+  // This buffer is only used at executor side.
+  private[this] var mutableAggregateBuffer: MutableAggregationBufferImpl = null
+
+  // This buffer is only used at executor side.
+  private[this] var evalAggregateBuffer: InputAggregationBuffer = null
+
+  /**
+   * Sets the inputBufferOffset to newInputBufferOffset and then create a new instance of
+   * `inputAggregateBuffer` based on this new inputBufferOffset.
+   */
+  override def withNewInputBufferOffset(newInputBufferOffset: Int): Unit = {
+    super.withNewInputBufferOffset(newInputBufferOffset)
+    // inputBufferOffset has been updated.
+    inputAggregateBuffer =
+      new InputAggregationBuffer(
+        bufferSchema,
+        bufferValuesToCatalystConverters,
+        bufferValuesToScalaConverters,
+        inputBufferOffset,
+        null)
+  }
 
-  lazy val evalAggregateBuffer: InputAggregationBuffer =
-    new InputAggregationBuffer(
-      bufferSchema,
-      bufferValuesToCatalystConverters,
-      bufferValuesToScalaConverters,
-      mutableBufferOffset,
-      null)
+  /**
+   * Sets the mutableBufferOffset to newMutableBufferOffset and then create a new instance of
+   * `mutableAggregateBuffer` and `evalAggregateBuffer` based on this new mutableBufferOffset.
+   */
+  override def withNewMutableBufferOffset(newMutableBufferOffset: Int): Unit = {
+    super.withNewMutableBufferOffset(newMutableBufferOffset)
+    // mutableBufferOffset has been updated.
+    mutableAggregateBuffer =
+      new MutableAggregationBufferImpl(
+        bufferSchema,
+        bufferValuesToCatalystConverters,
+        bufferValuesToScalaConverters,
+        mutableBufferOffset,
+        null)
+    evalAggregateBuffer =
+      new InputAggregationBuffer(
+        bufferSchema,
+        bufferValuesToCatalystConverters,
+        bufferValuesToScalaConverters,
+        mutableBufferOffset,
+        null)
+  }
 
   override def initialize(buffer: MutableRow): Unit = {
     mutableAggregateBuffer.underlyingBuffer = buffer
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
index 03635baae4a5f..960be08f84d94 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
@@ -17,13 +17,9 @@
 
 package org.apache.spark.sql.execution.aggregate
 
-import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan}
 import org.apache.spark.sql.execution.SparkPlan
-import org.apache.spark.sql.types.{StructType, MapType, ArrayType}
 
 /**
  * Utility functions used by the query planner to convert our plan to new aggregation code path.
@@ -52,13 +48,16 @@ object Utils {
       agg.aggregateFunction.bufferAttributes
     }
     val partialAggregate =
-      Aggregate2Sort(
-        None: Option[Seq[Expression]],
-        namedGroupingExpressions.map(_._2),
-        partialAggregateExpressions,
-        partialAggregateAttributes,
-        namedGroupingAttributes ++ partialAggregateAttributes,
-        child)
+      Aggregate(
+        requiredChildDistributionExpressions = None: Option[Seq[Expression]],
+        groupingExpressions = namedGroupingExpressions.map(_._2),
+        nonCompleteAggregateExpressions = partialAggregateExpressions,
+        nonCompleteAggregateAttributes = partialAggregateAttributes,
+        completeAggregateExpressions = Nil,
+        completeAggregateAttributes = Nil,
+        initialInputBufferOffset = 0,
+        resultExpressions = namedGroupingAttributes ++ partialAggregateAttributes,
+        child = child)
 
     // 2. Create an Aggregate Operator for final aggregations.
     val finalAggregateExpressions = aggregateExpressions.map(_.copy(mode = Final))
@@ -78,13 +77,17 @@ object Utils {
           }.getOrElse(expression)
       }.asInstanceOf[NamedExpression]
     }
-    val finalAggregate = Aggregate2Sort(
-      Some(namedGroupingAttributes),
-      namedGroupingAttributes,
-      finalAggregateExpressions,
-      finalAggregateAttributes,
-      rewrittenResultExpressions,
-      partialAggregate)
+    val finalAggregate =
+      Aggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes,
+        nonCompleteAggregateExpressions = finalAggregateExpressions,
+        nonCompleteAggregateAttributes = finalAggregateAttributes,
+        completeAggregateExpressions = Nil,
+        completeAggregateAttributes = Nil,
+        initialInputBufferOffset = namedGroupingAttributes.length,
+        resultExpressions = rewrittenResultExpressions,
+        child = partialAggregate)
 
     finalAggregate :: Nil
   }
@@ -133,14 +136,21 @@ object Utils {
     val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
       agg.aggregateFunction.bufferAttributes
     }
+    val partialAggregateGroupingExpressions =
+      (namedGroupingExpressions ++ namedDistinctColumnExpressions).map(_._2)
+    val partialAggregateResult =
+      namedGroupingAttributes ++ distinctColumnAttributes ++ partialAggregateAttributes
     val partialAggregate =
-      Aggregate2Sort(
-        None: Option[Seq[Expression]],
-        (namedGroupingExpressions ++ namedDistinctColumnExpressions).map(_._2),
-        partialAggregateExpressions,
-        partialAggregateAttributes,
-        namedGroupingAttributes ++ distinctColumnAttributes ++ partialAggregateAttributes,
-        child)
+      Aggregate(
+        requiredChildDistributionExpressions = None: Option[Seq[Expression]],
+        groupingExpressions = partialAggregateGroupingExpressions,
+        nonCompleteAggregateExpressions = partialAggregateExpressions,
+        nonCompleteAggregateAttributes = partialAggregateAttributes,
+        completeAggregateExpressions = Nil,
+        completeAggregateAttributes = Nil,
+        initialInputBufferOffset = 0,
+        resultExpressions = partialAggregateResult,
+        child = child)
 
     // 2. Create an Aggregate Operator for partial merge aggregations.
     val partialMergeAggregateExpressions = functionsWithoutDistinct.map {
@@ -151,14 +161,19 @@ object Utils {
       partialMergeAggregateExpressions.flatMap { agg =>
         agg.aggregateFunction.bufferAttributes
       }
+    val partialMergeAggregateResult =
+      namedGroupingAttributes ++ distinctColumnAttributes ++ partialMergeAggregateAttributes
     val partialMergeAggregate =
-      Aggregate2Sort(
-        Some(namedGroupingAttributes),
-        namedGroupingAttributes ++ distinctColumnAttributes,
-        partialMergeAggregateExpressions,
-        partialMergeAggregateAttributes,
-        namedGroupingAttributes ++ distinctColumnAttributes ++ partialMergeAggregateAttributes,
-        partialAggregate)
+      Aggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes ++ distinctColumnAttributes,
+        nonCompleteAggregateExpressions = partialMergeAggregateExpressions,
+        nonCompleteAggregateAttributes = partialMergeAggregateAttributes,
+        completeAggregateExpressions = Nil,
+        completeAggregateAttributes = Nil,
+        initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
+        resultExpressions = partialMergeAggregateResult,
+        child = partialAggregate)
 
     // 3. Create an Aggregate Operator for partial merge aggregations.
     val finalAggregateExpressions = functionsWithoutDistinct.map {
@@ -199,15 +214,17 @@ object Utils {
           }.getOrElse(expression)
       }.asInstanceOf[NamedExpression]
     }
-    val finalAndCompleteAggregate = FinalAndCompleteAggregate2Sort(
-      namedGroupingAttributes ++ distinctColumnAttributes,
-      namedGroupingAttributes,
-      finalAggregateExpressions,
-      finalAggregateAttributes,
-      completeAggregateExpressions,
-      completeAggregateAttributes,
-      rewrittenResultExpressions,
-      partialMergeAggregate)
+    val finalAndCompleteAggregate =
+      Aggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes,
+        nonCompleteAggregateExpressions = finalAggregateExpressions,
+        nonCompleteAggregateAttributes = finalAggregateAttributes,
+        completeAggregateExpressions = completeAggregateExpressions,
+        completeAggregateAttributes = completeAggregateAttributes,
+        initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
+        resultExpressions = rewrittenResultExpressions,
+        child = partialMergeAggregate)
 
     finalAndCompleteAggregate :: Nil
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 2294a670c735f..5a1b000e89875 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -220,7 +220,6 @@ case class TakeOrderedAndProject(
   override def outputOrdering: Seq[SortOrder] = sortOrder
 }
 
-
 /**
  * :: DeveloperApi ::
  * Return a new RDD that has exactly `numPartitions` partitions.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 51fe9d9d98bf3..bbadc202a4f06 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -17,14 +17,14 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
-import org.scalatest.BeforeAndAfterAll
-
 import java.sql.Timestamp
 
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
-import org.apache.spark.sql.execution.aggregate.Aggregate2Sort
+import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.execution.GeneratedAggregate
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.TestData._
@@ -273,7 +273,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       var hasGeneratedAgg = false
       df.queryExecution.executedPlan.foreach {
         case generatedAgg: GeneratedAggregate => hasGeneratedAgg = true
-        case newAggregate: Aggregate2Sort => hasGeneratedAgg = true
+        case newAggregate: aggregate.Aggregate => hasGeneratedAgg = true
         case _ =>
       }
       if (!hasGeneratedAgg) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
index 54f82f89ed18a..7978ed57a937e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
@@ -138,7 +138,14 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll
           s"Expected $expectedSerializerClass as the serializer of Exchange. " +
           s"However, the serializer was not set."
         val serializer = dependency.serializer.getOrElse(fail(serializerNotSetMessage))
-        assert(serializer.getClass === expectedSerializerClass)
+        val isExpectedSerializer =
+          serializer.getClass == expectedSerializerClass ||
+            serializer.getClass == classOf[UnsafeRowSerializer]
+        val wrongSerializerErrorMessage =
+          s"Expected ${expectedSerializerClass.getCanonicalName} or " +
+            s"${classOf[UnsafeRowSerializer].getCanonicalName}. But " +
+            s"${serializer.getClass.getCanonicalName} is used."
+        assert(isExpectedSerializer, wrongSerializerErrorMessage)
       case _ => // Ignore other nodes.
     }
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 0375eb79add95..6f0db27775e4d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.execution.aggregate.Aggregate2Sort
+import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import org.apache.spark.sql.{AnalysisException, QueryTest, Row}
+import org.apache.spark.sql.{SQLConf, AnalysisException, QueryTest, Row}
 import org.scalatest.BeforeAndAfterAll
 import test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
 
-class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
+abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
 
   override val sqlContext = TestHive
   import sqlContext.implicits._
@@ -34,7 +34,7 @@ class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAf
 
   override def beforeAll(): Unit = {
     originalUseAggregate2 = sqlContext.conf.useSqlAggregate2
-    sqlContext.sql("set spark.sql.useAggregate2=true")
+    sqlContext.setConf(SQLConf.USE_SQL_AGGREGATE2.key, "true")
     val data1 = Seq[(Integer, Integer)](
       (1, 10),
       (null, -60),
@@ -81,7 +81,7 @@ class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAf
     sqlContext.sql("DROP TABLE IF EXISTS agg1")
     sqlContext.sql("DROP TABLE IF EXISTS agg2")
     sqlContext.dropTempTable("emptyTable")
-    sqlContext.sql(s"set spark.sql.useAggregate2=$originalUseAggregate2")
+    sqlContext.setConf(SQLConf.USE_SQL_AGGREGATE2.key, originalUseAggregate2.toString)
   }
 
   test("empty table") {
@@ -454,54 +454,86 @@ class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAf
   }
 
   test("error handling") {
-    sqlContext.sql(s"set spark.sql.useAggregate2=false")
-    var errorMessage = intercept[AnalysisException] {
-      sqlContext.sql(
-        """
-          |SELECT
-          |  key,
-          |  sum(value + 1.5 * key),
-          |  mydoublesum(value),
-          |  mydoubleavg(value)
-          |FROM agg1
-          |GROUP BY key
-        """.stripMargin).collect()
-    }.getMessage
-    assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
+    withSQLConf("spark.sql.useAggregate2" -> "false") {
+      val errorMessage = intercept[AnalysisException] {
+        sqlContext.sql(
+          """
+            |SELECT
+            |  key,
+            |  sum(value + 1.5 * key),
+            |  mydoublesum(value),
+            |  mydoubleavg(value)
+            |FROM agg1
+            |GROUP BY key
+          """.stripMargin).collect()
+      }.getMessage
+      assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
+    }
 
     // TODO: once we support Hive UDAF in the new interface,
     // we can remove the following two tests.
-    sqlContext.sql(s"set spark.sql.useAggregate2=true")
-    errorMessage = intercept[AnalysisException] {
-      sqlContext.sql(
+    withSQLConf("spark.sql.useAggregate2" -> "true") {
+      val errorMessage = intercept[AnalysisException] {
+        sqlContext.sql(
+          """
+            |SELECT
+            |  key,
+            |  mydoublesum(value + 1.5 * key),
+            |  stddev_samp(value)
+            |FROM agg1
+            |GROUP BY key
+          """.stripMargin).collect()
+      }.getMessage
+      assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
+
+      // This will fall back to the old aggregate
+      val newAggregateOperators = sqlContext.sql(
         """
           |SELECT
           |  key,
-          |  mydoublesum(value + 1.5 * key),
+          |  sum(value + 1.5 * key),
           |  stddev_samp(value)
           |FROM agg1
           |GROUP BY key
-        """.stripMargin).collect()
-    }.getMessage
-    assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
-
-    // This will fall back to the old aggregate
-    val newAggregateOperators = sqlContext.sql(
-      """
-        |SELECT
-        |  key,
-        |  sum(value + 1.5 * key),
-        |  stddev_samp(value)
-        |FROM agg1
-        |GROUP BY key
-      """.stripMargin).queryExecution.executedPlan.collect {
-      case agg: Aggregate2Sort => agg
+        """.stripMargin).queryExecution.executedPlan.collect {
+        case agg: aggregate.Aggregate => agg
+      }
+      val message =
+        "We should fallback to the old aggregation code path if " +
+          "there is any aggregate function that cannot be converted to the new interface."
+      assert(newAggregateOperators.isEmpty, message)
     }
-    val message =
-      "We should fallback to the old aggregation code path if there is any aggregate function " +
-        "that cannot be converted to the new interface."
-    assert(newAggregateOperators.isEmpty, message)
+  }
+}
+
+class SortBasedAggregationQuerySuite extends AggregationQuerySuite {
 
-    sqlContext.sql(s"set spark.sql.useAggregate2=true")
+  var originalUnsafeEnabled: Boolean = _
+
+  override def beforeAll(): Unit = {
+    originalUnsafeEnabled = sqlContext.conf.unsafeEnabled
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, "false")
+    super.beforeAll()
+  }
+
+  override def afterAll(): Unit = {
+    super.afterAll()
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, originalUnsafeEnabled.toString)
+  }
+}
+
+class TungstenAggregationQuerySuite extends AggregationQuerySuite {
+
+  var originalUnsafeEnabled: Boolean = _
+
+  override def beforeAll(): Unit = {
+    originalUnsafeEnabled = sqlContext.conf.unsafeEnabled
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, "true")
+    super.beforeAll()
+  }
+
+  override def afterAll(): Unit = {
+    super.afterAll()
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, originalUnsafeEnabled.toString)
   }
 }

From 95dccc63350c45045f038bab9f8a5080b4e1f8cc Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Mon, 3 Aug 2015 01:55:58 -0700
Subject: [PATCH 0797/1454] [SPARK-8873] [MESOS] Clean up shuffle files if
 external shuffle service is used

This patch builds directly on #7820, which is largely written by tnachen. The only addition is one commit for cleaning up the code. There should be no functional differences between this and #7820.

Author: Timothy Chen <tnachen@gmail.com>
Author: Andrew Or <andrew@databricks.com>

Closes #7881 from andrewor14/tim-cleanup-mesos-shuffle and squashes the following commits:

8894f7d [Andrew Or] Clean up code
2a5fa10 [Andrew Or] Merge branch 'mesos_shuffle_clean' of github.com:tnachen/spark into tim-cleanup-mesos-shuffle
fadff89 [Timothy Chen] Address comments.
e4d0f1d [Timothy Chen] Clean up external shuffle data on driver exit with Mesos.
---
 .../scala/org/apache/spark/SparkContext.scala |   2 +-
 .../spark/deploy/ExternalShuffleService.scala |  17 ++-
 .../mesos/MesosExternalShuffleService.scala   | 107 ++++++++++++++++++
 .../org/apache/spark/rpc/RpcEndpoint.scala    |   6 +-
 .../mesos/CoarseMesosSchedulerBackend.scala   |  52 ++++++++-
 .../CoarseMesosSchedulerBackendSuite.scala    |   5 +-
 .../launcher/SparkClassCommandBuilder.java    |   3 +-
 .../spark/network/client/TransportClient.java |   5 +
 .../shuffle/ExternalShuffleBlockHandler.java  |   6 +
 .../shuffle/ExternalShuffleClient.java        |  12 +-
 .../mesos/MesosExternalShuffleClient.java     |  72 ++++++++++++
 .../protocol/BlockTransferMessage.java        |   4 +-
 .../protocol/mesos/RegisterDriver.java        |  60 ++++++++++
 sbin/start-mesos-shuffle-service.sh           |  35 ++++++
 sbin/stop-mesos-shuffle-service.sh            |  25 ++++
 15 files changed, 394 insertions(+), 17 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
 create mode 100644 network/shuffle/src/main/java/org/apache/spark/network/shuffle/mesos/MesosExternalShuffleClient.java
 create mode 100644 network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
 create mode 100755 sbin/start-mesos-shuffle-service.sh
 create mode 100755 sbin/stop-mesos-shuffle-service.sh

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index a1c66ef4fc5ea..6f336a7c299ab 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -2658,7 +2658,7 @@ object SparkContext extends Logging {
         val coarseGrained = sc.conf.getBoolean("spark.mesos.coarse", false)
         val url = mesosUrl.stripPrefix("mesos://") // strip scheme from raw Mesos URLs
         val backend = if (coarseGrained) {
-          new CoarseMesosSchedulerBackend(scheduler, sc, url)
+          new CoarseMesosSchedulerBackend(scheduler, sc, url, sc.env.securityManager)
         } else {
           new MesosSchedulerBackend(scheduler, sc, url)
         }
diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
index 4089c3e771fa8..20a9faa1784b7 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
@@ -27,6 +27,7 @@ import org.apache.spark.network.netty.SparkTransportConf
 import org.apache.spark.network.sasl.SaslServerBootstrap
 import org.apache.spark.network.server.TransportServer
 import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler
+import org.apache.spark.network.util.TransportConf
 import org.apache.spark.util.Utils
 
 /**
@@ -45,11 +46,16 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana
   private val useSasl: Boolean = securityManager.isAuthenticationEnabled()
 
   private val transportConf = SparkTransportConf.fromSparkConf(sparkConf, numUsableCores = 0)
-  private val blockHandler = new ExternalShuffleBlockHandler(transportConf)
+  private val blockHandler = newShuffleBlockHandler(transportConf)
   private val transportContext: TransportContext = new TransportContext(transportConf, blockHandler)
 
   private var server: TransportServer = _
 
+  /** Create a new shuffle block handler. Factored out for subclasses to override. */
+  protected def newShuffleBlockHandler(conf: TransportConf): ExternalShuffleBlockHandler = {
+    new ExternalShuffleBlockHandler(conf)
+  }
+
   /** Starts the external shuffle service if the user has configured us to. */
   def startIfEnabled() {
     if (enabled) {
@@ -93,6 +99,13 @@ object ExternalShuffleService extends Logging {
   private val barrier = new CountDownLatch(1)
 
   def main(args: Array[String]): Unit = {
+    main(args, (conf: SparkConf, sm: SecurityManager) => new ExternalShuffleService(conf, sm))
+  }
+
+  /** A helper main method that allows the caller to call this with a custom shuffle service. */
+  private[spark] def main(
+      args: Array[String],
+      newShuffleService: (SparkConf, SecurityManager) => ExternalShuffleService): Unit = {
     val sparkConf = new SparkConf
     Utils.loadDefaultSparkProperties(sparkConf)
     val securityManager = new SecurityManager(sparkConf)
@@ -100,7 +113,7 @@ object ExternalShuffleService extends Logging {
     // we override this value since this service is started from the command line
     // and we assume the user really wants it to be running
     sparkConf.set("spark.shuffle.service.enabled", "true")
-    server = new ExternalShuffleService(sparkConf, securityManager)
+    server = newShuffleService(sparkConf, securityManager)
     server.start()
 
     installShutdownHook()
diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
new file mode 100644
index 0000000000000..061857476a8a0
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.mesos
+
+import java.net.SocketAddress
+
+import scala.collection.mutable
+
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.deploy.ExternalShuffleService
+import org.apache.spark.network.client.{RpcResponseCallback, TransportClient}
+import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler
+import org.apache.spark.network.shuffle.protocol.BlockTransferMessage
+import org.apache.spark.network.shuffle.protocol.mesos.RegisterDriver
+import org.apache.spark.network.util.TransportConf
+
+/**
+ * An RPC endpoint that receives registration requests from Spark drivers running on Mesos.
+ * It detects driver termination and calls the cleanup callback to [[ExternalShuffleService]].
+ */
+private[mesos] class MesosExternalShuffleBlockHandler(transportConf: TransportConf)
+  extends ExternalShuffleBlockHandler(transportConf) with Logging {
+
+  // Stores a map of driver socket addresses to app ids
+  private val connectedApps = new mutable.HashMap[SocketAddress, String]
+
+  protected override def handleMessage(
+      message: BlockTransferMessage,
+      client: TransportClient,
+      callback: RpcResponseCallback): Unit = {
+    message match {
+      case RegisterDriverParam(appId) =>
+        val address = client.getSocketAddress
+        logDebug(s"Received registration request from app $appId (remote address $address).")
+        if (connectedApps.contains(address)) {
+          val existingAppId = connectedApps(address)
+          if (!existingAppId.equals(appId)) {
+            logError(s"A new app '$appId' has connected to existing address $address, " +
+              s"removing previously registered app '$existingAppId'.")
+            applicationRemoved(existingAppId, true)
+          }
+        }
+        connectedApps(address) = appId
+        callback.onSuccess(new Array[Byte](0))
+      case _ => super.handleMessage(message, client, callback)
+    }
+  }
+
+  /**
+   * On connection termination, clean up shuffle files written by the associated application.
+   */
+  override def connectionTerminated(client: TransportClient): Unit = {
+    val address = client.getSocketAddress
+    if (connectedApps.contains(address)) {
+      val appId = connectedApps(address)
+      logInfo(s"Application $appId disconnected (address was $address).")
+      applicationRemoved(appId, true /* cleanupLocalDirs */)
+      connectedApps.remove(address)
+    } else {
+      logWarning(s"Unknown $address disconnected.")
+    }
+  }
+
+  /** An extractor object for matching [[RegisterDriver]] message. */
+  private object RegisterDriverParam {
+    def unapply(r: RegisterDriver): Option[String] = Some(r.getAppId)
+  }
+}
+
+/**
+ * A wrapper of [[ExternalShuffleService]] that provides an additional endpoint for drivers
+ * to associate with. This allows the shuffle service to detect when a driver is terminated
+ * and can clean up the associated shuffle files.
+ */
+private[mesos] class MesosExternalShuffleService(conf: SparkConf, securityManager: SecurityManager)
+  extends ExternalShuffleService(conf, securityManager) {
+
+  protected override def newShuffleBlockHandler(
+      conf: TransportConf): ExternalShuffleBlockHandler = {
+    new MesosExternalShuffleBlockHandler(conf)
+  }
+}
+
+private[spark] object MesosExternalShuffleService extends Logging {
+
+  def main(args: Array[String]): Unit = {
+    ExternalShuffleService.main(args,
+      (conf: SparkConf, sm: SecurityManager) => new MesosExternalShuffleService(conf, sm))
+  }
+}
+
+
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala
index d2b2baef1d8c4..dfcbc51cdf616 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala
@@ -47,11 +47,11 @@ private[spark] trait ThreadSafeRpcEndpoint extends RpcEndpoint
  *
  * It is guaranteed that `onStart`, `receive` and `onStop` will be called in sequence.
  *
- * The lift-cycle will be:
+ * The life-cycle of an endpoint is:
  *
- * constructor onStart receive* onStop
+ * constructor -> onStart -> receive* -> onStop
  *
- * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use
+ * Note: `receive` can be called concurrently. If you want `receive` to be thread-safe, please use
  * [[ThreadSafeRpcEndpoint]]
  *
  * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index b7fde0d9b3265..15a0915708c7c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -26,12 +26,15 @@ import scala.collection.mutable.{HashMap, HashSet}
 
 import com.google.common.collect.HashBiMap
 import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _}
-import org.apache.mesos.{Scheduler => MScheduler, _}
+import org.apache.mesos.{Scheduler => MScheduler, SchedulerDriver}
+
+import org.apache.spark.{SecurityManager, SparkContext, SparkEnv, SparkException, TaskState}
+import org.apache.spark.network.netty.SparkTransportConf
+import org.apache.spark.network.shuffle.mesos.MesosExternalShuffleClient
 import org.apache.spark.rpc.RpcAddress
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
-import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState}
 
 /**
  * A SchedulerBackend that runs tasks on Mesos, but uses "coarse-grained" tasks, where it holds
@@ -46,7 +49,8 @@ import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState}
 private[spark] class CoarseMesosSchedulerBackend(
     scheduler: TaskSchedulerImpl,
     sc: SparkContext,
-    master: String)
+    master: String,
+    securityManager: SecurityManager)
   extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv)
   with MScheduler
   with MesosSchedulerUtils {
@@ -56,12 +60,19 @@ private[spark] class CoarseMesosSchedulerBackend(
   // Maximum number of cores to acquire (TODO: we'll need more flexible controls here)
   val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt
 
+  // If shuffle service is enabled, the Spark driver will register with the shuffle service.
+  // This is for cleaning up shuffle files reliably.
+  private val shuffleServiceEnabled = conf.getBoolean("spark.shuffle.service.enabled", false)
+
   // Cores we have acquired with each Mesos task ID
   val coresByTaskId = new HashMap[Int, Int]
   var totalCoresAcquired = 0
 
   val slaveIdsWithExecutors = new HashSet[String]
 
+  // Maping from slave Id to hostname
+  private val slaveIdToHost = new HashMap[String, String]
+
   val taskIdToSlaveId: HashBiMap[Int, String] = HashBiMap.create[Int, String]
   // How many times tasks on each slave failed
   val failuresBySlaveId: HashMap[String, Int] = new HashMap[String, Int]
@@ -90,6 +101,19 @@ private[spark] class CoarseMesosSchedulerBackend(
   private val slaveOfferConstraints =
     parseConstraintString(sc.conf.get("spark.mesos.constraints", ""))
 
+  // A client for talking to the external shuffle service, if it is a
+  private val mesosExternalShuffleClient: Option[MesosExternalShuffleClient] = {
+    if (shuffleServiceEnabled) {
+      Some(new MesosExternalShuffleClient(
+        SparkTransportConf.fromSparkConf(conf),
+        securityManager,
+        securityManager.isAuthenticationEnabled(),
+        securityManager.isSaslEncryptionEnabled()))
+    } else {
+      None
+    }
+  }
+
   var nextMesosTaskId = 0
 
   @volatile var appId: String = _
@@ -188,6 +212,7 @@ private[spark] class CoarseMesosSchedulerBackend(
 
   override def registered(d: SchedulerDriver, frameworkId: FrameworkID, masterInfo: MasterInfo) {
     appId = frameworkId.getValue
+    mesosExternalShuffleClient.foreach(_.init(appId))
     logInfo("Registered as framework ID " + appId)
     markRegistered()
   }
@@ -244,6 +269,7 @@ private[spark] class CoarseMesosSchedulerBackend(
 
           // accept the offer and launch the task
           logDebug(s"Accepting offer: $id with attributes: $offerAttributes mem: $mem cpu: $cpus")
+          slaveIdToHost(offer.getSlaveId.getValue) = offer.getHostname
           d.launchTasks(
             Collections.singleton(offer.getId),
             Collections.singleton(taskBuilder.build()), filters)
@@ -261,7 +287,27 @@ private[spark] class CoarseMesosSchedulerBackend(
     val taskId = status.getTaskId.getValue.toInt
     val state = status.getState
     logInfo(s"Mesos task $taskId is now $state")
+    val slaveId: String = status.getSlaveId.getValue
     stateLock.synchronized {
+      // If the shuffle service is enabled, have the driver register with each one of the
+      // shuffle services. This allows the shuffle services to clean up state associated with
+      // this application when the driver exits. There is currently not a great way to detect
+      // this through Mesos, since the shuffle services are set up independently.
+      if (TaskState.fromMesos(state).equals(TaskState.RUNNING) &&
+          slaveIdToHost.contains(slaveId) &&
+          shuffleServiceEnabled) {
+        assume(mesosExternalShuffleClient.isDefined,
+          "External shuffle client was not instantiated even though shuffle service is enabled.")
+        // TODO: Remove this and allow the MesosExternalShuffleService to detect
+        // framework termination when new Mesos Framework HTTP API is available.
+        val externalShufflePort = conf.getInt("spark.shuffle.service.port", 7337)
+        val hostname = slaveIdToHost.remove(slaveId).get
+        logDebug(s"Connecting to shuffle service on slave $slaveId, " +
+            s"host $hostname, port $externalShufflePort for app ${conf.getAppId}")
+        mesosExternalShuffleClient.get
+          .registerDriverWithShuffleService(hostname, externalShufflePort)
+      }
+
       if (TaskState.isFinished(TaskState.fromMesos(state))) {
         val slaveId = taskIdToSlaveId(taskId)
         slaveIdsWithExecutors -= slaveId
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
index 4b504df7b8851..525ee0d3bdc5a 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackendSuite.scala
@@ -30,7 +30,7 @@ import org.scalatest.mock.MockitoSugar
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.scheduler.TaskSchedulerImpl
-import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SecurityManager, SparkFunSuite}
 
 class CoarseMesosSchedulerBackendSuite extends SparkFunSuite
     with LocalSparkContext
@@ -59,7 +59,8 @@ class CoarseMesosSchedulerBackendSuite extends SparkFunSuite
   private def createSchedulerBackend(
       taskScheduler: TaskSchedulerImpl,
       driver: SchedulerDriver): CoarseMesosSchedulerBackend = {
-    val backend = new CoarseMesosSchedulerBackend(taskScheduler, sc, "master") {
+    val securityManager = mock[SecurityManager]
+    val backend = new CoarseMesosSchedulerBackend(taskScheduler, sc, "master", securityManager) {
       override protected def createSchedulerDriver(
         masterUrl: String,
         scheduler: Scheduler,
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
index de85720febf23..5f95e2c74f902 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
@@ -69,7 +69,8 @@ public List<String> buildCommand(Map<String, String> env) throws IOException {
     } else if (className.equals("org.apache.spark.executor.MesosExecutorBackend")) {
       javaOptsKeys.add("SPARK_EXECUTOR_OPTS");
       memKey = "SPARK_EXECUTOR_MEMORY";
-    } else if (className.equals("org.apache.spark.deploy.ExternalShuffleService")) {
+    } else if (className.equals("org.apache.spark.deploy.ExternalShuffleService") ||
+        className.equals("org.apache.spark.deploy.mesos.MesosExternalShuffleService")) {
       javaOptsKeys.add("SPARK_DAEMON_JAVA_OPTS");
       javaOptsKeys.add("SPARK_SHUFFLE_OPTS");
       memKey = "SPARK_DAEMON_MEMORY";
diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java b/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
index 37f2e34ceb24d..e8e7f06247d3e 100644
--- a/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
+++ b/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
@@ -19,6 +19,7 @@
 
 import java.io.Closeable;
 import java.io.IOException;
+import java.net.SocketAddress;
 import java.util.UUID;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.TimeUnit;
@@ -79,6 +80,10 @@ public boolean isActive() {
     return channel.isOpen() || channel.isActive();
   }
 
+  public SocketAddress getSocketAddress() {
+    return channel.remoteAddress();
+  }
+
   /**
    * Requests a single chunk from the remote side, from the pre-negotiated streamId.
    *
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
index e4faaf8854fc7..db9dc4f17cee9 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
@@ -65,7 +65,13 @@ public ExternalShuffleBlockHandler(TransportConf conf) {
   @Override
   public void receive(TransportClient client, byte[] message, RpcResponseCallback callback) {
     BlockTransferMessage msgObj = BlockTransferMessage.Decoder.fromByteArray(message);
+    handleMessage(msgObj, client, callback);
+  }
 
+  protected void handleMessage(
+      BlockTransferMessage msgObj,
+      TransportClient client,
+      RpcResponseCallback callback) {
     if (msgObj instanceof OpenBlocks) {
       OpenBlocks msg = (OpenBlocks) msgObj;
       List<ManagedBuffer> blocks = Lists.newArrayList();
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java
index 612bce571a493..ea6d248d66be3 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java
@@ -50,8 +50,8 @@ public class ExternalShuffleClient extends ShuffleClient {
   private final boolean saslEncryptionEnabled;
   private final SecretKeyHolder secretKeyHolder;
 
-  private TransportClientFactory clientFactory;
-  private String appId;
+  protected TransportClientFactory clientFactory;
+  protected String appId;
 
   /**
    * Creates an external shuffle client, with SASL optionally enabled. If SASL is not enabled,
@@ -71,6 +71,10 @@ public ExternalShuffleClient(
     this.saslEncryptionEnabled = saslEncryptionEnabled;
   }
 
+  protected void checkInit() {
+    assert appId != null : "Called before init()";
+  }
+
   @Override
   public void init(String appId) {
     this.appId = appId;
@@ -89,7 +93,7 @@ public void fetchBlocks(
       final String execId,
       String[] blockIds,
       BlockFetchingListener listener) {
-    assert appId != null : "Called before init()";
+    checkInit();
     logger.debug("External shuffle fetch from {}:{} (executor id {})", host, port, execId);
     try {
       RetryingBlockFetcher.BlockFetchStarter blockFetchStarter =
@@ -132,7 +136,7 @@ public void registerWithShuffleServer(
       int port,
       String execId,
       ExecutorShuffleInfo executorInfo) throws IOException {
-    assert appId != null : "Called before init()";
+    checkInit();
     TransportClient client = clientFactory.createClient(host, port);
     byte[] registerMessage = new RegisterExecutor(appId, execId, executorInfo).toByteArray();
     client.sendRpcSync(registerMessage, 5000 /* timeoutMs */);
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/mesos/MesosExternalShuffleClient.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/mesos/MesosExternalShuffleClient.java
new file mode 100644
index 0000000000000..7543b6be4f2a1
--- /dev/null
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/mesos/MesosExternalShuffleClient.java
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.shuffle.mesos;
+
+import java.io.IOException;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import org.apache.spark.network.client.RpcResponseCallback;
+import org.apache.spark.network.client.TransportClient;
+import org.apache.spark.network.sasl.SecretKeyHolder;
+import org.apache.spark.network.shuffle.ExternalShuffleClient;
+import org.apache.spark.network.shuffle.protocol.mesos.RegisterDriver;
+import org.apache.spark.network.util.TransportConf;
+
+/**
+ * A client for talking to the external shuffle service in Mesos coarse-grained mode.
+ *
+ * This is used by the Spark driver to register with each external shuffle service on the cluster.
+ * The reason why the driver has to talk to the service is for cleaning up shuffle files reliably
+ * after the application exits. Mesos does not provide a great alternative to do this, so Spark
+ * has to detect this itself.
+ */
+public class MesosExternalShuffleClient extends ExternalShuffleClient {
+  private final Logger logger = LoggerFactory.getLogger(MesosExternalShuffleClient.class);
+
+  /**
+   * Creates an Mesos external shuffle client that wraps the {@link ExternalShuffleClient}.
+   * Please refer to docs on {@link ExternalShuffleClient} for more information.
+   */
+  public MesosExternalShuffleClient(
+      TransportConf conf,
+      SecretKeyHolder secretKeyHolder,
+      boolean saslEnabled,
+      boolean saslEncryptionEnabled) {
+    super(conf, secretKeyHolder, saslEnabled, saslEncryptionEnabled);
+  }
+
+  public void registerDriverWithShuffleService(String host, int port) throws IOException {
+    checkInit();
+    byte[] registerDriver = new RegisterDriver(appId).toByteArray();
+    TransportClient client = clientFactory.createClient(host, port);
+    client.sendRpc(registerDriver, new RpcResponseCallback() {
+      @Override
+      public void onSuccess(byte[] response) {
+        logger.info("Successfully registered app " + appId + " with external shuffle service.");
+      }
+
+      @Override
+      public void onFailure(Throwable e) {
+        logger.warn("Unable to register app " + appId + " with external shuffle service. " +
+          "Please manually remove shuffle data after driver exit. Error: " + e);
+      }
+    });
+  }
+}
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/BlockTransferMessage.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/BlockTransferMessage.java
index 6c1210b33268a..fcb52363e632c 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/BlockTransferMessage.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/BlockTransferMessage.java
@@ -21,6 +21,7 @@
 import io.netty.buffer.Unpooled;
 
 import org.apache.spark.network.protocol.Encodable;
+import org.apache.spark.network.shuffle.protocol.mesos.RegisterDriver;
 
 /**
  * Messages handled by the {@link org.apache.spark.network.shuffle.ExternalShuffleBlockHandler}, or
@@ -37,7 +38,7 @@ public abstract class BlockTransferMessage implements Encodable {
 
   /** Preceding every serialized message is its type, which allows us to deserialize it. */
   public static enum Type {
-    OPEN_BLOCKS(0), UPLOAD_BLOCK(1), REGISTER_EXECUTOR(2), STREAM_HANDLE(3);
+    OPEN_BLOCKS(0), UPLOAD_BLOCK(1), REGISTER_EXECUTOR(2), STREAM_HANDLE(3), REGISTER_DRIVER(4);
 
     private final byte id;
 
@@ -60,6 +61,7 @@ public static BlockTransferMessage fromByteArray(byte[] msg) {
         case 1: return UploadBlock.decode(buf);
         case 2: return RegisterExecutor.decode(buf);
         case 3: return StreamHandle.decode(buf);
+        case 4: return RegisterDriver.decode(buf);
         default: throw new IllegalArgumentException("Unknown message type: " + type);
       }
     }
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
new file mode 100644
index 0000000000000..1c28fc1dff246
--- /dev/null
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.shuffle.protocol.mesos;
+
+import com.google.common.base.Objects;
+import io.netty.buffer.ByteBuf;
+
+import org.apache.spark.network.protocol.Encoders;
+import org.apache.spark.network.shuffle.protocol.BlockTransferMessage;
+
+/**
+ * A message sent from the driver to register with the MesosExternalShuffleService.
+ */
+public class RegisterDriver extends BlockTransferMessage {
+  private final String appId;
+
+  public RegisterDriver(String appId) {
+    this.appId = appId;
+  }
+
+  public String getAppId() { return appId; }
+
+  @Override
+  protected Type type() { return Type.REGISTER_DRIVER; }
+
+  @Override
+  public int encodedLength() {
+    return Encoders.Strings.encodedLength(appId);
+  }
+
+  @Override
+  public void encode(ByteBuf buf) {
+    Encoders.Strings.encode(buf, appId);
+  }
+
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(appId);
+  }
+
+  public static RegisterDriver decode(ByteBuf buf) {
+    String appId = Encoders.Strings.decode(buf);
+    return new RegisterDriver(appId);
+  }
+}
diff --git a/sbin/start-mesos-shuffle-service.sh b/sbin/start-mesos-shuffle-service.sh
new file mode 100755
index 0000000000000..64580762c5dc4
--- /dev/null
+++ b/sbin/start-mesos-shuffle-service.sh
@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Starts the Mesos external shuffle server on the machine this script is executed on.
+# The Mesos external shuffle service detects when an application exits and automatically
+# cleans up its shuffle files.
+#
+# Usage: start-mesos-shuffle-server.sh
+#
+# Use the SPARK_SHUFFLE_OPTS environment variable to set shuffle service configuration.
+#
+
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
+
+. "$sbin/spark-config.sh"
+. "$SPARK_PREFIX/bin/load-spark-env.sh"
+
+exec "$sbin"/spark-daemon.sh start org.apache.spark.deploy.mesos.MesosExternalShuffleService 1
diff --git a/sbin/stop-mesos-shuffle-service.sh b/sbin/stop-mesos-shuffle-service.sh
new file mode 100755
index 0000000000000..0e965d5ec5886
--- /dev/null
+++ b/sbin/stop-mesos-shuffle-service.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Stops the Mesos external shuffle service on the machine this script is executed on.
+
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
+
+"$sbin"/spark-daemon.sh stop org.apache.spark.deploy.mesos.MesosExternalShuffleService 1

From 137f47865df6e98ab70ae5ba30dc4d441fb41166 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 3 Aug 2015 04:21:15 -0700
Subject: [PATCH 0798/1454] [SPARK-9551][SQL] add a cheap version of copy for
 UnsafeRow to reuse a copy buffer

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7885 from cloud-fan/cheap-copy and squashes the following commits:

0900ca1 [Wenchen Fan] replace == with ===
73f4ada [Wenchen Fan] add tests
07b865a [Wenchen Fan] add a cheap version of copy
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 32 ++++++++++++++++
 .../org/apache/spark/sql/UnsafeRowSuite.scala | 38 +++++++++++++++++++
 2 files changed, 70 insertions(+)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index c5d42d73a43a4..f4230cfaba375 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -463,6 +463,38 @@ public UnsafeRow copy() {
     return rowCopy;
   }
 
+  /**
+   * Creates an empty UnsafeRow from a byte array with specified numBytes and numFields.
+   * The returned row is invalid until we call copyFrom on it.
+   */
+  public static UnsafeRow createFromByteArray(int numBytes, int numFields) {
+    final UnsafeRow row = new UnsafeRow();
+    row.pointTo(new byte[numBytes], numFields, numBytes);
+    return row;
+  }
+
+  /**
+   * Copies the input UnsafeRow to this UnsafeRow, and resize the underlying byte[] when the
+   * input row is larger than this row.
+   */
+  public void copyFrom(UnsafeRow row) {
+    // copyFrom is only available for UnsafeRow created from byte array.
+    assert (baseObject instanceof byte[]) && baseOffset == PlatformDependent.BYTE_ARRAY_OFFSET;
+    if (row.sizeInBytes > this.sizeInBytes) {
+      // resize the underlying byte[] if it's not large enough.
+      this.baseObject = new byte[row.sizeInBytes];
+    }
+    PlatformDependent.copyMemory(
+      row.baseObject,
+      row.baseOffset,
+      this.baseObject,
+      this.baseOffset,
+      row.sizeInBytes
+    );
+    // update the sizeInBytes.
+    this.sizeInBytes = row.sizeInBytes;
+  }
+
   /**
    * Write this UnsafeRow's underlying bytes to the given OutputStream.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index e72a1bc6c4e20..c5faaa663e749 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -82,4 +82,42 @@ class UnsafeRowSuite extends SparkFunSuite {
       assert(unsafeRow.get(0, dataType) === null)
     }
   }
+
+  test("createFromByteArray and copyFrom") {
+    val row = InternalRow(1, UTF8String.fromString("abc"))
+    val converter = UnsafeProjection.create(Array[DataType](IntegerType, StringType))
+    val unsafeRow = converter.apply(row)
+
+    val emptyRow = UnsafeRow.createFromByteArray(64, 2)
+    val buffer = emptyRow.getBaseObject
+
+    emptyRow.copyFrom(unsafeRow)
+    assert(emptyRow.getSizeInBytes() === unsafeRow.getSizeInBytes)
+    assert(emptyRow.getInt(0) === unsafeRow.getInt(0))
+    assert(emptyRow.getUTF8String(1) === unsafeRow.getUTF8String(1))
+    // make sure we reuse the buffer.
+    assert(emptyRow.getBaseObject === buffer)
+
+    // make sure we really copied the input row.
+    unsafeRow.setInt(0, 2)
+    assert(emptyRow.getInt(0) === 1)
+
+    val longString = UTF8String.fromString((1 to 100).map(_ => "abc").reduce(_ + _))
+    val row2 = InternalRow(3, longString)
+    val unsafeRow2 = converter.apply(row2)
+
+    // make sure we can resize.
+    emptyRow.copyFrom(unsafeRow2)
+    assert(emptyRow.getSizeInBytes() === unsafeRow2.getSizeInBytes)
+    assert(emptyRow.getInt(0) === 3)
+    assert(emptyRow.getUTF8String(1) === longString)
+    // make sure we really resized.
+    assert(emptyRow.getBaseObject != buffer)
+
+    // make sure we can still handle small rows after resize.
+    emptyRow.copyFrom(unsafeRow)
+    assert(emptyRow.getSizeInBytes() === unsafeRow.getSizeInBytes)
+    assert(emptyRow.getInt(0) === unsafeRow.getInt(0))
+    assert(emptyRow.getUTF8String(1) === unsafeRow.getUTF8String(1))
+  }
 }

From 191bf2689d127a9dd328b9cc517362fd51eaed3d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 3 Aug 2015 04:23:26 -0700
Subject: [PATCH 0799/1454] [SPARK-9518] [SQL] cleanup generated
 UnsafeRowJoiner and fix bug

Currently, when copy the bitsets, we didn't consider that the row1 may not sit in the beginning of byte array.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7892 from davies/clean_join and squashes the following commits:

14cce9e [Davies Liu] cleanup generated UnsafeRowJoiner and fix bug
---
 .../codegen/GenerateUnsafeRowJoiner.scala     | 102 ++++++------------
 .../GenerateUnsafeRowJoinerBitsetSuite.scala  |   7 +-
 2 files changed, 37 insertions(+), 72 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
index 645eb48d5a51b..5f8a6f8871722 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
@@ -40,10 +40,6 @@ abstract class UnsafeRowJoiner {
  */
 object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), UnsafeRowJoiner] {
 
-  def dump(word: Long): String = {
-    Seq.tabulate(64) { i => if ((word >> i) % 2 == 0) "0" else "1" }.reverse.mkString
-  }
-
   override protected def create(in: (StructType, StructType)): UnsafeRowJoiner = {
     create(in._1, in._2)
   }
@@ -56,76 +52,45 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
   }
 
   def create(schema1: StructType, schema2: StructType): UnsafeRowJoiner = {
-    val ctx = newCodeGenContext()
     val offset = PlatformDependent.BYTE_ARRAY_OFFSET
+    val getLong = "PlatformDependent.UNSAFE.getLong"
+    val putLong = "PlatformDependent.UNSAFE.putLong"
 
     val bitset1Words = (schema1.size + 63) / 64
     val bitset2Words = (schema2.size + 63) / 64
     val outputBitsetWords = (schema1.size + schema2.size + 63) / 64
     val bitset1Remainder = schema1.size % 64
-    val bitset2Remainder = schema2.size % 64
 
     // The number of words we can reduce when we concat two rows together.
     // The only reduction comes from merging the bitset portion of the two rows, saving 1 word.
     val sizeReduction = bitset1Words + bitset2Words - outputBitsetWords
 
-    // --------------------- copy bitset from row 1 ----------------------- //
-    val copyBitset1 = Seq.tabulate(bitset1Words) { i =>
-      s"""
-         |PlatformDependent.UNSAFE.putLong(buf, ${offset + i * 8},
-         |  PlatformDependent.UNSAFE.getLong(obj1, ${offset + i * 8}));
-       """.stripMargin
-    }.mkString
-
-
-    // --------------------- copy bitset from row 2 ----------------------- //
-    var copyBitset2 = ""
-    if (bitset1Remainder == 0) {
-      copyBitset2 += Seq.tabulate(bitset2Words) { i =>
-        s"""
-           |PlatformDependent.UNSAFE.putLong(buf, ${offset + (bitset1Words + i) * 8},
-           |  PlatformDependent.UNSAFE.getLong(obj2, ${offset + i * 8}));
-         """.stripMargin
-      }.mkString
-    } else {
-      copyBitset2 = Seq.tabulate(bitset2Words) { i =>
-        s"""
-           |long bs2w$i = PlatformDependent.UNSAFE.getLong(obj2, ${offset + i * 8});
-           |long bs2w${i}p1 = (bs2w$i << $bitset1Remainder) & ~((1L << $bitset1Remainder) - 1);
-           |long bs2w${i}p2 = (bs2w$i >>> ${64 - bitset1Remainder});
-         """.stripMargin
-      }.mkString
-
-      copyBitset2 += Seq.tabulate(bitset2Words) { i =>
-        val currentOffset = offset + (bitset1Words + i - 1) * 8
-        if (i == 0) {
-          if (bitset1Words > 0) {
-            s"""
-               |PlatformDependent.UNSAFE.putLong(buf, $currentOffset,
-               |  bs2w${i}p1 | PlatformDependent.UNSAFE.getLong(obj1, $currentOffset));
-            """.stripMargin
-          } else {
-            s"""
-               |PlatformDependent.UNSAFE.putLong(buf, $currentOffset + 8, bs2w${i}p1);
-            """.stripMargin
-          }
+    // --------------------- copy bitset from row 1 and row 2 --------------------------- //
+    val copyBitset = Seq.tabulate(outputBitsetWords) { i =>
+      val bits = if (bitset1Remainder > 0) {
+        if (i < bitset1Words - 1) {
+          s"$getLong(obj1, offset1 + ${i * 8})"
+        } else if (i == bitset1Words - 1) {
+          // combine last work of bitset1 and first word of bitset2
+          s"$getLong(obj1, offset1 + ${i * 8}) | ($getLong(obj2, offset2) << $bitset1Remainder)"
+        } else if (i - bitset1Words < bitset2Words - 1) {
+          // combine next two words of bitset2
+          s"($getLong(obj2, offset2 + ${(i - bitset1Words) * 8}) >>> (64 - $bitset1Remainder))" +
+            s"| ($getLong(obj2, offset2 + ${(i - bitset1Words + 1) * 8}) << $bitset1Remainder)"
+        } else {
+          // last word of bitset2
+          s"$getLong(obj2, offset2 + ${(i - bitset1Words) * 8}) >>> (64 - $bitset1Remainder)"
+        }
+      } else {
+        // they are aligned by word
+        if (i < bitset1Words) {
+          s"$getLong(obj1, offset1 + ${i * 8})"
         } else {
-          s"""
-             |PlatformDependent.UNSAFE.putLong(buf, $currentOffset, bs2w${i}p1 | bs2w${i - 1}p2);
-          """.stripMargin
+          s"$getLong(obj2, offset2 + ${(i - bitset1Words) * 8})"
         }
-      }.mkString("\n")
-
-      if (bitset2Words > 0 &&
-        (bitset2Remainder == 0 || bitset2Remainder > (64 - bitset1Remainder))) {
-        val lastWord = bitset2Words - 1
-        copyBitset2 +=
-          s"""
-             |PlatformDependent.UNSAFE.putLong(buf, ${offset + (outputBitsetWords - 1) * 8},
-             |  bs2w${lastWord}p2);
-          """.stripMargin
       }
-    }
+      s"$putLong(buf, ${offset + i * 8}, $bits);"
+    }.mkString("\n")
 
     // --------------------- copy fixed length portion from row 1 ----------------------- //
     var cursor = offset + outputBitsetWords * 8
@@ -149,10 +114,10 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     cursor += schema2.size * 8
 
     // --------------------- copy variable length portion from row 1 ----------------------- //
+    val numBytesBitsetAndFixedRow1 = (bitset1Words + schema1.size) * 8
     val copyVariableLengthRow1 = s"""
        |// Copy variable length data for row1
-       |long numBytesBitsetAndFixedRow1 = ${(bitset1Words + schema1.size) * 8};
-       |long numBytesVariableRow1 = row1.getSizeInBytes() - numBytesBitsetAndFixedRow1;
+       |long numBytesVariableRow1 = row1.getSizeInBytes() - $numBytesBitsetAndFixedRow1;
        |PlatformDependent.copyMemory(
        |  obj1, offset1 + ${(bitset1Words + schema1.size) * 8},
        |  buf, $cursor,
@@ -160,10 +125,10 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
      """.stripMargin
 
     // --------------------- copy variable length portion from row 2 ----------------------- //
+    val numBytesBitsetAndFixedRow2 = (bitset2Words + schema2.size) * 8
     val copyVariableLengthRow2 = s"""
        |// Copy variable length data for row2
-       |long numBytesBitsetAndFixedRow2 = ${(bitset2Words + schema2.size) * 8};
-       |long numBytesVariableRow2 = row2.getSizeInBytes() - numBytesBitsetAndFixedRow2;
+       |long numBytesVariableRow2 = row2.getSizeInBytes() - $numBytesBitsetAndFixedRow2;
        |PlatformDependent.copyMemory(
        |  obj2, offset2 + ${(bitset2Words + schema2.size) * 8},
        |  buf, $cursor + numBytesVariableRow1,
@@ -183,12 +148,11 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
           if (i < schema1.size) {
             s"${(outputBitsetWords - bitset1Words + schema2.size) * 8}L"
           } else {
-            s"${(outputBitsetWords - bitset2Words + schema1.size) * 8}L + numBytesVariableRow1"
+            s"(${(outputBitsetWords - bitset2Words + schema1.size) * 8}L + numBytesVariableRow1)"
           }
         val cursor = offset + outputBitsetWords * 8 + i * 8
         s"""
-           |PlatformDependent.UNSAFE.putLong(buf, $cursor,
-           |  PlatformDependent.UNSAFE.getLong(buf, $cursor) + ($shift << 32));
+           |$putLong(buf, $cursor, $getLong(buf, $cursor) + ($shift << 32));
          """.stripMargin
       }
     }.mkString
@@ -217,8 +181,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
        |    final Object obj2 = row2.getBaseObject();
        |    final long offset2 = row2.getBaseOffset();
        |
-       |    $copyBitset1
-       |    $copyBitset2
+       |    $copyBitset
        |    $copyFixedLengthRow1
        |    $copyFixedLengthRow2
        |    $copyVariableLengthRow1
@@ -233,7 +196,6 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
      """.stripMargin
 
     logDebug(s"SpecificUnsafeRowJoiner($schema1, $schema2):\n${CodeFormatter.format(code)}")
-    // println(CodeFormatter.format(code))
 
     val c = compile(code)
     c.generate(Array.empty).asInstanceOf[UnsafeRowJoiner]
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
index 76d9d991ed0dc..718a2acc8281d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
@@ -22,6 +22,7 @@ import scala.util.Random
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.PlatformDependent
 
 /**
  * A test suite for the bitset portion of the row concatenation.
@@ -91,8 +92,9 @@ class GenerateUnsafeRowJoinerBitsetSuite extends SparkFunSuite {
   private def createUnsafeRow(numFields: Int): UnsafeRow = {
     val row = new UnsafeRow
     val sizeInBytes = numFields * 8 + ((numFields + 63) / 64) * 8
-    val buf = new Array[Byte](sizeInBytes)
-    row.pointTo(buf, numFields, sizeInBytes)
+    val offset = numFields * 8
+    val buf = new Array[Byte](sizeInBytes + offset)
+    row.pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET + offset, numFields, sizeInBytes)
     row
   }
 
@@ -133,6 +135,7 @@ class GenerateUnsafeRowJoinerBitsetSuite extends SparkFunSuite {
          |input1: ${set1.mkString}
          |input2: ${set2.mkString}
          |output: ${out.mkString}
+         |expect: ${set1.mkString}${set2.mkString}
        """.stripMargin
     }
 

From 8be198c86935001907727fd16577231ff776125b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 3 Aug 2015 04:26:18 -0700
Subject: [PATCH 0800/1454] Two minor comments from code review on 191bf2689.

---
 .../catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala  | 2 +-
 .../codegen/GenerateUnsafeRowJoinerBitsetSuite.scala            | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
index 5f8a6f8871722..30b51dd83fa9a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
@@ -76,7 +76,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
         } else if (i - bitset1Words < bitset2Words - 1) {
           // combine next two words of bitset2
           s"($getLong(obj2, offset2 + ${(i - bitset1Words) * 8}) >>> (64 - $bitset1Remainder))" +
-            s"| ($getLong(obj2, offset2 + ${(i - bitset1Words + 1) * 8}) << $bitset1Remainder)"
+            s" | ($getLong(obj2, offset2 + ${(i - bitset1Words + 1) * 8}) << $bitset1Remainder)"
         } else {
           // last word of bitset2
           s"$getLong(obj2, offset2 + ${(i - bitset1Words) * 8}) >>> (64 - $bitset1Remainder)"
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
index 718a2acc8281d..aff1bee99faad 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
@@ -92,6 +92,8 @@ class GenerateUnsafeRowJoinerBitsetSuite extends SparkFunSuite {
   private def createUnsafeRow(numFields: Int): UnsafeRow = {
     val row = new UnsafeRow
     val sizeInBytes = numFields * 8 + ((numFields + 63) / 64) * 8
+    // Allocate a larger buffer than needed and point the UnsafeRow to somewhere in the middle.
+    // This way we can test the joiner when the input UnsafeRows are not the entire arrays.
     val offset = numFields * 8
     val buf = new Array[Byte](sizeInBytes + offset)
     row.pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET + offset, numFields, sizeInBytes)

From 69f5a7c934ac553ed52c00679b800bcffe83c1d6 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Mon, 3 Aug 2015 10:46:34 -0700
Subject: [PATCH 0801/1454] [SPARK-9528] [ML] Changed RandomForestClassifier to
 extend ProbabilisticClassifier

RandomForestClassifier now outputs rawPrediction based on tree probabilities, plus probability column computed from normalized rawPrediction.

CC: holdenk

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7859 from jkbradley/rf-prob and squashes the following commits:

6c28f51 [Joseph K. Bradley] Changed RandomForestClassifier to extend ProbabilisticClassifier
---
 .../DecisionTreeClassifier.scala              |  8 +---
 .../ProbabilisticClassifier.scala             | 27 +++++++++++++-
 .../RandomForestClassifier.scala              | 37 +++++++++++++------
 .../RandomForestClassifierSuite.scala         | 36 ++++++++++++++----
 4 files changed, 81 insertions(+), 27 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index f27cfd0331419..f2b992f8ba249 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -131,13 +131,7 @@ final class DecisionTreeClassificationModel private[ml] (
   override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
     rawPrediction match {
       case dv: DenseVector =>
-        var i = 0
-        val size = dv.size
-        val sum = dv.values.sum
-        while (i < size) {
-          dv.values(i) = if (sum != 0) dv.values(i) / sum else 0.0
-          i += 1
-        }
+        ProbabilisticClassificationModel.normalizeToProbabilitiesInPlace(dv)
         dv
       case sv: SparseVector =>
         throw new RuntimeException("Unexpected error in DecisionTreeClassificationModel:" +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index dad451108626d..f9c9c2371f5cd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.classification
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DoubleType, DataType, StructType}
@@ -175,3 +175,28 @@ private[spark] abstract class ProbabilisticClassificationModel[
    */
   protected def probability2prediction(probability: Vector): Double = probability.argmax
 }
+
+private[ml] object ProbabilisticClassificationModel {
+
+  /**
+   * Normalize a vector of raw predictions to be a multinomial probability vector, in place.
+   *
+   * The input raw predictions should be >= 0.
+   * The output vector sums to 1, unless the input vector is all-0 (in which case the output is
+   * all-0 too).
+   *
+   * NOTE: This is NOT applicable to all models, only ones which effectively use class
+   *       instance counts for raw predictions.
+   */
+  def normalizeToProbabilitiesInPlace(v: DenseVector): Unit = {
+    val sum = v.values.sum
+    if (sum != 0) {
+      var i = 0
+      val size = v.size
+      while (i < size) {
+        v.values(i) /= sum
+        i += 1
+      }
+    }
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 0c7eb4a662fdb..56e80cc8fe6e1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -17,22 +17,19 @@
 
 package org.apache.spark.ml.classification
 
-import scala.collection.mutable
-
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.tree.impl.RandomForest
-import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.DoubleType
+
 
 /**
  * :: Experimental ::
@@ -43,7 +40,7 @@ import org.apache.spark.sql.types.DoubleType
  */
 @Experimental
 final class RandomForestClassifier(override val uid: String)
-  extends Classifier[Vector, RandomForestClassifier, RandomForestClassificationModel]
+  extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel]
   with RandomForestParams with TreeClassifierParams {
 
   def this() = this(Identifiable.randomUID("rfc"))
@@ -127,7 +124,7 @@ final class RandomForestClassificationModel private[ml] (
     override val uid: String,
     private val _trees: Array[DecisionTreeClassificationModel],
     override val numClasses: Int)
-  extends ClassificationModel[Vector, RandomForestClassificationModel]
+  extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel]
   with TreeEnsembleModel with Serializable {
 
   require(numTrees > 0, "RandomForestClassificationModel requires at least 1 tree.")
@@ -157,15 +154,33 @@ final class RandomForestClassificationModel private[ml] (
   override protected def predictRaw(features: Vector): Vector = {
     // TODO: When we add a generic Bagging class, handle transform there: SPARK-7128
     // Classifies using majority votes.
-    // Ignore the weights since all are 1.0 for now.
-    val votes = new Array[Double](numClasses)
+    // Ignore the tree weights since all are 1.0 for now.
+    val votes = Array.fill[Double](numClasses)(0.0)
     _trees.view.foreach { tree =>
-      val prediction = tree.rootNode.predictImpl(features).prediction.toInt
-      votes(prediction) = votes(prediction) + 1.0 // 1.0 = weight
+      val classCounts: Array[Double] = tree.rootNode.predictImpl(features).impurityStats.stats
+      val total = classCounts.sum
+      if (total != 0) {
+        var i = 0
+        while (i < numClasses) {
+          votes(i) += classCounts(i) / total
+          i += 1
+        }
+      }
     }
     Vectors.dense(votes)
   }
 
+  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
+    rawPrediction match {
+      case dv: DenseVector =>
+        ProbabilisticClassificationModel.normalizeToProbabilitiesInPlace(dv)
+        dv
+      case sv: SparseVector =>
+        throw new RuntimeException("Unexpected error in RandomForestClassificationModel:" +
+          " raw2probabilityInPlace encountered SparseVector")
+    }
+  }
+
   override def copy(extra: ParamMap): RandomForestClassificationModel = {
     copyValues(new RandomForestClassificationModel(uid, _trees, numClasses), extra)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index dbb2577c6204d..edf848b21a905 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -26,6 +26,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Row}
 
@@ -121,6 +122,33 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     compareAPIs(rdd, rf2, categoricalFeatures, numClasses)
   }
 
+  test("predictRaw and predictProbability") {
+    val rdd = orderedLabeledPoints5_20
+    val rf = new RandomForestClassifier()
+      .setImpurity("Gini")
+      .setMaxDepth(3)
+      .setNumTrees(3)
+      .setSeed(123)
+    val categoricalFeatures = Map.empty[Int, Int]
+    val numClasses = 2
+
+    val df: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
+    val model = rf.fit(df)
+
+    val predictions = model.transform(df)
+      .select(rf.getPredictionCol, rf.getRawPredictionCol, rf.getProbabilityCol)
+      .collect()
+
+    predictions.foreach { case Row(pred: Double, rawPred: Vector, probPred: Vector) =>
+      assert(pred === rawPred.argmax,
+        s"Expected prediction $pred but calculated ${rawPred.argmax} from rawPrediction.")
+      val sum = rawPred.toArray.sum
+      assert(Vectors.dense(rawPred.toArray.map(_ / sum)) === probPred,
+        "probability prediction mismatch")
+      assert(probPred.toArray.sum ~== 1.0 relTol 1E-5)
+    }
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
@@ -173,13 +201,5 @@ private object RandomForestClassifierSuite {
     assert(newModel.hasParent)
     assert(!newModel.trees.head.asInstanceOf[DecisionTreeClassificationModel].hasParent)
     assert(newModel.numClasses == numClasses)
-    val results = newModel.transform(newData)
-    results.select("rawPrediction", "prediction").collect().foreach {
-      case Row(raw: Vector, prediction: Double) => {
-        assert(raw.size == numClasses)
-        val predFromRaw = raw.toArray.zipWithIndex.maxBy(_._1)._2
-        assert(predFromRaw == prediction)
-      }
-    }
   }
 }

From b41a32718d615b304efba146bf97be0229779b01 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 3 Aug 2015 10:58:37 -0700
Subject: [PATCH 0802/1454] [SPARK-1855] Local checkpointing

Certain use cases of Spark involve RDDs with long lineages that must be truncated periodically (e.g. GraphX). The existing way of doing it is through `rdd.checkpoint()`, which is expensive because it writes to HDFS. This patch provides an alternative to truncate lineages cheaply *without providing the same level of fault tolerance*.

**Local checkpointing** writes checkpointed data to the local file system through the block manager. It is much faster than replicating to a reliable storage and provides the same semantics as long as executors do not fail. It is accessible through a new operator `rdd.localCheckpoint()` and leaves the old one unchanged. Users may even decide to combine the two and call the reliable one less frequently.

The bulk of this patch involves refactoring the checkpointing interface to accept custom implementations of checkpointing. [Design doc](https://issues.apache.org/jira/secure/attachment/12741708/SPARK-7292-design.pdf).

Author: Andrew Or <andrew@databricks.com>

Closes #7279 from andrewor14/local-checkpoint and squashes the following commits:

729600f [Andrew Or] Oops, fix tests
34bc059 [Andrew Or] Avoid computing all partitions in local checkpoint
e43bbb6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
3be5aea [Andrew Or] Address comments
bf846a6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
ab003a3 [Andrew Or] Fix compile
c2e111b [Andrew Or] Address comments
33f167a [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
e908a42 [Andrew Or] Fix tests
f5be0f3 [Andrew Or] Use MEMORY_AND_DISK as the default local checkpoint level
a92657d [Andrew Or] Update a few comments
e58e3e3 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
4eb6eb1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
1bbe154 [Andrew Or] Simplify LocalCheckpointRDD
48a9996 [Andrew Or] Avoid traversing dependency tree + rewrite tests
62aba3f [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
db70dc2 [Andrew Or] Express local checkpointing through caching the original RDD
87d43c6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
c449b38 [Andrew Or] Fix style
4a182f3 [Andrew Or] Add fine-grained tests for local checkpointing
53b363b [Andrew Or] Rename a few more awkwardly named methods (minor)
e4cf071 [Andrew Or] Simplify LocalCheckpointRDD + docs + clean ups
4880deb [Andrew Or] Fix style
d096c67 [Andrew Or] Fix mima
172cb66 [Andrew Or] Fix mima?
e53d964 [Andrew Or] Fix style
56831c5 [Andrew Or] Add a few warnings and clear exception messages
2e59646 [Andrew Or] Add local checkpoint clean up tests
4dbbab1 [Andrew Or] Refactor CheckpointSuite to test local checkpointing
4514dc9 [Andrew Or] Clean local checkpoint files through RDD cleanups
0477eec [Andrew Or] Rename a few methods with awkward names (minor)
2e902e5 [Andrew Or] First implementation of local checkpointing
8447454 [Andrew Or] Fix tests
4ac1896 [Andrew Or] Refactor checkpoint interface for modularity
---
 .../org/apache/spark/ContextCleaner.scala     |   9 +-
 .../scala/org/apache/spark/SparkContext.scala |   2 +-
 .../scala/org/apache/spark/TaskContext.scala  |   8 +
 .../org/apache/spark/rdd/CheckpointRDD.scala  | 153 +-------
 .../apache/spark/rdd/LocalCheckpointRDD.scala |  67 ++++
 .../spark/rdd/LocalRDDCheckpointData.scala    |  83 +++++
 .../main/scala/org/apache/spark/rdd/RDD.scala | 128 +++++--
 .../apache/spark/rdd/RDDCheckpointData.scala  | 106 ++----
 .../spark/rdd/ReliableCheckpointRDD.scala     | 172 +++++++++
 .../spark/rdd/ReliableRDDCheckpointData.scala | 108 ++++++
 .../org/apache/spark/CheckpointSuite.scala    | 164 +++++----
 .../apache/spark/ContextCleanerSuite.scala    |  61 +++-
 .../spark/rdd/LocalCheckpointSuite.scala      | 330 ++++++++++++++++++
 project/MimaExcludes.scala                    |   9 +-
 14 files changed, 1085 insertions(+), 315 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
 create mode 100644 core/src/test/scala/org/apache/spark/rdd/LocalCheckpointSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
index 37198d887b07b..d23c1533db758 100644
--- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
@@ -22,7 +22,7 @@ import java.lang.ref.{ReferenceQueue, WeakReference}
 import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
 
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.{RDDCheckpointData, RDD}
+import org.apache.spark.rdd.{RDD, ReliableRDDCheckpointData}
 import org.apache.spark.util.Utils
 
 /**
@@ -231,11 +231,14 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
     }
   }
 
-  /** Perform checkpoint cleanup. */
+  /**
+   * Clean up checkpoint files written to a reliable storage.
+   * Locally checkpointed files are cleaned up separately through RDD cleanups.
+   */
   def doCleanCheckpoint(rddId: Int): Unit = {
     try {
       logDebug("Cleaning rdd checkpoint data " + rddId)
-      RDDCheckpointData.clearRDDCheckpointData(sc, rddId)
+      ReliableRDDCheckpointData.cleanCheckpoint(sc, rddId)
       listeners.foreach(_.checkpointCleaned(rddId))
       logInfo("Cleaned rdd checkpoint data " + rddId)
     }
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 6f336a7c299ab..4380cf45cc1b0 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1192,7 +1192,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   }
 
   protected[spark] def checkpointFile[T: ClassTag](path: String): RDD[T] = withScope {
-    new CheckpointRDD[T](this, path)
+    new ReliableCheckpointRDD[T](this, path)
   }
 
   /** Build the union of a list of RDDs. */
diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index b48836d5c8897..5d2c551d58514 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -59,6 +59,14 @@ object TaskContext {
    * Unset the thread local TaskContext. Internal to Spark.
    */
   protected[spark] def unset(): Unit = taskContext.remove()
+
+  /**
+   * Return an empty task context that is not actually used.
+   * Internal use only.
+   */
+  private[spark] def empty(): TaskContext = {
+    new TaskContextImpl(0, 0, 0, 0, null, null)
+  }
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
index e17bd47905d7a..72fe215dae73e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
@@ -17,156 +17,31 @@
 
 package org.apache.spark.rdd
 
-import java.io.IOException
-
 import scala.reflect.ClassTag
 
-import org.apache.hadoop.fs.Path
-
-import org.apache.spark._
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.{Partition, SparkContext, TaskContext}
 
+/**
+ * An RDD partition used to recover checkpointed data.
+ */
 private[spark] class CheckpointRDDPartition(val index: Int) extends Partition
 
 /**
- * This RDD represents a RDD checkpoint file (similar to HadoopRDD).
+ * An RDD that recovers checkpointed data from storage.
  */
-private[spark]
-class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String)
+private[spark] abstract class CheckpointRDD[T: ClassTag](@transient sc: SparkContext)
   extends RDD[T](sc, Nil) {
 
-  private val broadcastedConf = sc.broadcast(new SerializableConfiguration(sc.hadoopConfiguration))
-
-  @transient private val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration)
-
-  override def getCheckpointFile: Option[String] = Some(checkpointPath)
-
-  override def getPartitions: Array[Partition] = {
-    val cpath = new Path(checkpointPath)
-    val numPartitions =
-    // listStatus can throw exception if path does not exist.
-    if (fs.exists(cpath)) {
-      val dirContents = fs.listStatus(cpath).map(_.getPath)
-      val partitionFiles = dirContents.filter(_.getName.startsWith("part-")).map(_.toString).sorted
-      val numPart = partitionFiles.length
-      if (numPart > 0 && (! partitionFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) ||
-          ! partitionFiles(numPart-1).endsWith(CheckpointRDD.splitIdToFile(numPart-1)))) {
-        throw new SparkException("Invalid checkpoint directory: " + checkpointPath)
-      }
-      numPart
-    } else 0
-
-    Array.tabulate(numPartitions)(i => new CheckpointRDDPartition(i))
-  }
-
-  override def getPreferredLocations(split: Partition): Seq[String] = {
-    val status = fs.getFileStatus(new Path(checkpointPath,
-      CheckpointRDD.splitIdToFile(split.index)))
-    val locations = fs.getFileBlockLocations(status, 0, status.getLen)
-    locations.headOption.toList.flatMap(_.getHosts).filter(_ != "localhost")
-  }
-
-  override def compute(split: Partition, context: TaskContext): Iterator[T] = {
-    val file = new Path(checkpointPath, CheckpointRDD.splitIdToFile(split.index))
-    CheckpointRDD.readFromFile(file, broadcastedConf, context)
-  }
-
   // CheckpointRDD should not be checkpointed again
-  override def checkpoint(): Unit = { }
   override def doCheckpoint(): Unit = { }
-}
-
-private[spark] object CheckpointRDD extends Logging {
-  def splitIdToFile(splitId: Int): String = {
-    "part-%05d".format(splitId)
-  }
-
-  def writeToFile[T: ClassTag](
-      path: String,
-      broadcastedConf: Broadcast[SerializableConfiguration],
-      blockSize: Int = -1
-    )(ctx: TaskContext, iterator: Iterator[T]) {
-    val env = SparkEnv.get
-    val outputDir = new Path(path)
-    val fs = outputDir.getFileSystem(broadcastedConf.value.value)
-
-    val finalOutputName = splitIdToFile(ctx.partitionId)
-    val finalOutputPath = new Path(outputDir, finalOutputName)
-    val tempOutputPath =
-      new Path(outputDir, "." + finalOutputName + "-attempt-" + ctx.attemptNumber)
-
-    if (fs.exists(tempOutputPath)) {
-      throw new IOException("Checkpoint failed: temporary path " +
-        tempOutputPath + " already exists")
-    }
-    val bufferSize = env.conf.getInt("spark.buffer.size", 65536)
-
-    val fileOutputStream = if (blockSize < 0) {
-      fs.create(tempOutputPath, false, bufferSize)
-    } else {
-      // This is mainly for testing purpose
-      fs.create(tempOutputPath, false, bufferSize, fs.getDefaultReplication, blockSize)
-    }
-    val serializer = env.serializer.newInstance()
-    val serializeStream = serializer.serializeStream(fileOutputStream)
-    Utils.tryWithSafeFinally {
-      serializeStream.writeAll(iterator)
-    } {
-      serializeStream.close()
-    }
-
-    if (!fs.rename(tempOutputPath, finalOutputPath)) {
-      if (!fs.exists(finalOutputPath)) {
-        logInfo("Deleting tempOutputPath " + tempOutputPath)
-        fs.delete(tempOutputPath, false)
-        throw new IOException("Checkpoint failed: failed to save output of task: "
-          + ctx.attemptNumber + " and final output path does not exist")
-      } else {
-        // Some other copy of this task must've finished before us and renamed it
-        logInfo("Final output path " + finalOutputPath + " already exists; not overwriting it")
-        fs.delete(tempOutputPath, false)
-      }
-    }
-  }
-
-  def readFromFile[T](
-      path: Path,
-      broadcastedConf: Broadcast[SerializableConfiguration],
-      context: TaskContext
-    ): Iterator[T] = {
-    val env = SparkEnv.get
-    val fs = path.getFileSystem(broadcastedConf.value.value)
-    val bufferSize = env.conf.getInt("spark.buffer.size", 65536)
-    val fileInputStream = fs.open(path, bufferSize)
-    val serializer = env.serializer.newInstance()
-    val deserializeStream = serializer.deserializeStream(fileInputStream)
-
-    // Register an on-task-completion callback to close the input stream.
-    context.addTaskCompletionListener(context => deserializeStream.close())
-
-    deserializeStream.asIterator.asInstanceOf[Iterator[T]]
-  }
+  override def checkpoint(): Unit = { }
+  override def localCheckpoint(): this.type = this
 
-  // Test whether CheckpointRDD generate expected number of partitions despite
-  // each split file having multiple blocks. This needs to be run on a
-  // cluster (mesos or standalone) using HDFS.
-  def main(args: Array[String]) {
-    import org.apache.spark._
+  // Note: There is a bug in MiMa that complains about `AbstractMethodProblem`s in the
+  // base [[org.apache.spark.rdd.RDD]] class if we do not override the following methods.
+  // scalastyle:off
+  protected override def getPartitions: Array[Partition] = ???
+  override def compute(p: Partition, tc: TaskContext): Iterator[T] = ???
+  // scalastyle:on
 
-    val Array(cluster, hdfsPath) = args
-    val env = SparkEnv.get
-    val sc = new SparkContext(cluster, "CheckpointRDD Test")
-    val rdd = sc.makeRDD(1 to 10, 10).flatMap(x => 1 to 10000)
-    val path = new Path(hdfsPath, "temp")
-    val conf = SparkHadoopUtil.get.newConfiguration(new SparkConf())
-    val fs = path.getFileSystem(conf)
-    val broadcastedConf = sc.broadcast(new SerializableConfiguration(conf))
-    sc.runJob(rdd, CheckpointRDD.writeToFile[Int](path.toString, broadcastedConf, 1024) _)
-    val cpRDD = new CheckpointRDD[Int](sc, path.toString)
-    assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same")
-    assert(cpRDD.collect.toList == rdd.collect.toList, "Data of partitions not the same")
-    fs.delete(path, true)
-  }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
new file mode 100644
index 0000000000000..daa5779d688cc
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.{Partition, SparkContext, SparkEnv, SparkException, TaskContext}
+import org.apache.spark.storage.RDDBlockId
+
+/**
+ * A dummy CheckpointRDD that exists to provide informative error messages during failures.
+ *
+ * This is simply a placeholder because the original checkpointed RDD is expected to be
+ * fully cached. Only if an executor fails or if the user explicitly unpersists the original
+ * RDD will Spark ever attempt to compute this CheckpointRDD. When this happens, however,
+ * we must provide an informative error message.
+ *
+ * @param sc the active SparkContext
+ * @param rddId the ID of the checkpointed RDD
+ * @param numPartitions the number of partitions in the checkpointed RDD
+ */
+private[spark] class LocalCheckpointRDD[T: ClassTag](
+    @transient sc: SparkContext,
+    rddId: Int,
+    numPartitions: Int)
+  extends CheckpointRDD[T](sc) {
+
+  def this(rdd: RDD[T]) {
+    this(rdd.context, rdd.id, rdd.partitions.size)
+  }
+
+  protected override def getPartitions: Array[Partition] = {
+    (0 until numPartitions).toArray.map { i => new CheckpointRDDPartition(i) }
+  }
+
+  /**
+   * Throw an exception indicating that the relevant block is not found.
+   *
+   * This should only be called if the original RDD is explicitly unpersisted or if an
+   * executor is lost. Under normal circumstances, however, the original RDD (our child)
+   * is expected to be fully cached and so all partitions should already be computed and
+   * available in the block storage.
+   */
+  override def compute(partition: Partition, context: TaskContext): Iterator[T] = {
+    throw new SparkException(
+      s"Checkpoint block ${RDDBlockId(rddId, partition.index)} not found! Either the executor " +
+      s"that originally checkpointed this partition is no longer alive, or the original RDD is " +
+      s"unpersisted. If this problem persists, you may consider using `rdd.checkpoint()` " +
+      s"instead, which is slower than local checkpointing but more fault-tolerant.")
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
new file mode 100644
index 0000000000000..d6fad896845f6
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
@@ -0,0 +1,83 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.{Logging, SparkEnv, SparkException, TaskContext}
+import org.apache.spark.storage.{RDDBlockId, StorageLevel}
+import org.apache.spark.util.Utils
+
+/**
+ * An implementation of checkpointing implemented on top of Spark's caching layer.
+ *
+ * Local checkpointing trades off fault tolerance for performance by skipping the expensive
+ * step of saving the RDD data to a reliable and fault-tolerant storage. Instead, the data
+ * is written to the local, ephemeral block storage that lives in each executor. This is useful
+ * for use cases where RDDs build up long lineages that need to be truncated often (e.g. GraphX).
+ */
+private[spark] class LocalRDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+  extends RDDCheckpointData[T](rdd) with Logging {
+
+  /**
+   * Ensure the RDD is fully cached so the partitions can be recovered later.
+   */
+  protected override def doCheckpoint(): CheckpointRDD[T] = {
+    val level = rdd.getStorageLevel
+
+    // Assume storage level uses disk; otherwise memory eviction may cause data loss
+    assume(level.useDisk, s"Storage level $level is not appropriate for local checkpointing")
+
+    // Not all actions compute all partitions of the RDD (e.g. take). For correctness, we
+    // must cache any missing partitions. TODO: avoid running another job here (SPARK-8582).
+    val action = (tc: TaskContext, iterator: Iterator[T]) => Utils.getIteratorSize(iterator)
+    val missingPartitionIndices = rdd.partitions.map(_.index).filter { i =>
+      !SparkEnv.get.blockManager.master.contains(RDDBlockId(rdd.id, i))
+    }
+    if (missingPartitionIndices.nonEmpty) {
+      rdd.sparkContext.runJob(rdd, action, missingPartitionIndices)
+    }
+
+    new LocalCheckpointRDD[T](rdd)
+  }
+
+}
+
+private[spark] object LocalRDDCheckpointData {
+
+  val DEFAULT_STORAGE_LEVEL = StorageLevel.MEMORY_AND_DISK
+
+  /**
+   * Transform the specified storage level to one that uses disk.
+   *
+   * This guarantees that the RDD can be recomputed multiple times correctly as long as
+   * executors do not fail. Otherwise, if the RDD is cached in memory only, for instance,
+   * the checkpoint data will be lost if the relevant block is evicted from memory.
+   *
+   * This method is idempotent.
+   */
+  def transformStorageLevel(level: StorageLevel): StorageLevel = {
+    // If this RDD is to be cached off-heap, fail fast since we cannot provide any
+    // correctness guarantees about subsequent computations after the first one
+    if (level.useOffHeap) {
+      throw new SparkException("Local checkpointing is not compatible with off-heap caching.")
+    }
+
+    StorageLevel(useDisk = true, level.useMemory, level.deserialized, level.replication)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 6d61d227382d7..081c721f23687 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -149,23 +149,43 @@ abstract class RDD[T: ClassTag](
   }
 
   /**
-   * Set this RDD's storage level to persist its values across operations after the first time
-   * it is computed. This can only be used to assign a new storage level if the RDD does not
-   * have a storage level set yet..
+   * Mark this RDD for persisting using the specified level.
+   *
+   * @param newLevel the target storage level
+   * @param allowOverride whether to override any existing level with the new one
    */
-  def persist(newLevel: StorageLevel): this.type = {
+  private def persist(newLevel: StorageLevel, allowOverride: Boolean): this.type = {
     // TODO: Handle changes of StorageLevel
-    if (storageLevel != StorageLevel.NONE && newLevel != storageLevel) {
+    if (storageLevel != StorageLevel.NONE && newLevel != storageLevel && !allowOverride) {
       throw new UnsupportedOperationException(
         "Cannot change storage level of an RDD after it was already assigned a level")
     }
-    sc.persistRDD(this)
-    // Register the RDD with the ContextCleaner for automatic GC-based cleanup
-    sc.cleaner.foreach(_.registerRDDForCleanup(this))
+    // If this is the first time this RDD is marked for persisting, register it
+    // with the SparkContext for cleanups and accounting. Do this only once.
+    if (storageLevel == StorageLevel.NONE) {
+      sc.cleaner.foreach(_.registerRDDForCleanup(this))
+      sc.persistRDD(this)
+    }
     storageLevel = newLevel
     this
   }
 
+  /**
+   * Set this RDD's storage level to persist its values across operations after the first time
+   * it is computed. This can only be used to assign a new storage level if the RDD does not
+   * have a storage level set yet. Local checkpointing is an exception.
+   */
+  def persist(newLevel: StorageLevel): this.type = {
+    if (isLocallyCheckpointed) {
+      // This means the user previously called localCheckpoint(), which should have already
+      // marked this RDD for persisting. Here we should override the old storage level with
+      // one that is explicitly requested by the user (after adapting it to use disk).
+      persist(LocalRDDCheckpointData.transformStorageLevel(newLevel), allowOverride = true)
+    } else {
+      persist(newLevel, allowOverride = false)
+    }
+  }
+
   /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
   def persist(): this.type = persist(StorageLevel.MEMORY_ONLY)
 
@@ -1448,33 +1468,99 @@ abstract class RDD[T: ClassTag](
 
   /**
    * Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint
-   * directory set with SparkContext.setCheckpointDir() and all references to its parent
+   * directory set with `SparkContext#setCheckpointDir` and all references to its parent
    * RDDs will be removed. This function must be called before any job has been
    * executed on this RDD. It is strongly recommended that this RDD is persisted in
    * memory, otherwise saving it on a file will require recomputation.
    */
-  def checkpoint(): Unit = {
+  def checkpoint(): Unit = RDDCheckpointData.synchronized {
+    // NOTE: we use a global lock here due to complexities downstream with ensuring
+    // children RDD partitions point to the correct parent partitions. In the future
+    // we should revisit this consideration.
     if (context.checkpointDir.isEmpty) {
       throw new SparkException("Checkpoint directory has not been set in the SparkContext")
     } else if (checkpointData.isEmpty) {
-      // NOTE: we use a global lock here due to complexities downstream with ensuring
-      // children RDD partitions point to the correct parent partitions. In the future
-      // we should revisit this consideration.
-      RDDCheckpointData.synchronized {
-        checkpointData = Some(new RDDCheckpointData(this))
-      }
+      checkpointData = Some(new ReliableRDDCheckpointData(this))
+    }
+  }
+
+  /**
+   * Mark this RDD for local checkpointing using Spark's existing caching layer.
+   *
+   * This method is for users who wish to truncate RDD lineages while skipping the expensive
+   * step of replicating the materialized data in a reliable distributed file system. This is
+   * useful for RDDs with long lineages that need to be truncated periodically (e.g. GraphX).
+   *
+   * Local checkpointing sacrifices fault-tolerance for performance. In particular, checkpointed
+   * data is written to ephemeral local storage in the executors instead of to a reliable,
+   * fault-tolerant storage. The effect is that if an executor fails during the computation,
+   * the checkpointed data may no longer be accessible, causing an irrecoverable job failure.
+   *
+   * This is NOT safe to use with dynamic allocation, which removes executors along
+   * with their cached blocks. If you must use both features, you are advised to set
+   * `spark.dynamicAllocation.cachedExecutorIdleTimeout` to a high value.
+   *
+   * The checkpoint directory set through `SparkContext#setCheckpointDir` is not used.
+   */
+  def localCheckpoint(): this.type = RDDCheckpointData.synchronized {
+    if (conf.getBoolean("spark.dynamicAllocation.enabled", false) &&
+        conf.contains("spark.dynamicAllocation.cachedExecutorIdleTimeout")) {
+      logWarning("Local checkpointing is NOT safe to use with dynamic allocation, " +
+        "which removes executors along with their cached blocks. If you must use both " +
+        "features, you are advised to set `spark.dynamicAllocation.cachedExecutorIdleTimeout` " +
+        "to a high value. E.g. If you plan to use the RDD for 1 hour, set the timeout to " +
+        "at least 1 hour.")
+    }
+
+    // Note: At this point we do not actually know whether the user will call persist() on
+    // this RDD later, so we must explicitly call it here ourselves to ensure the cached
+    // blocks are registered for cleanup later in the SparkContext.
+    //
+    // If, however, the user has already called persist() on this RDD, then we must adapt
+    // the storage level he/she specified to one that is appropriate for local checkpointing
+    // (i.e. uses disk) to guarantee correctness.
+
+    if (storageLevel == StorageLevel.NONE) {
+      persist(LocalRDDCheckpointData.DEFAULT_STORAGE_LEVEL)
+    } else {
+      persist(LocalRDDCheckpointData.transformStorageLevel(storageLevel), allowOverride = true)
     }
+
+    checkpointData match {
+      case Some(reliable: ReliableRDDCheckpointData[_]) => logWarning(
+        "RDD was already marked for reliable checkpointing: overriding with local checkpoint.")
+      case _ =>
+    }
+    checkpointData = Some(new LocalRDDCheckpointData(this))
+    this
   }
 
   /**
-   * Return whether this RDD has been checkpointed or not
+   * Return whether this RDD is marked for checkpointing, either reliably or locally.
    */
   def isCheckpointed: Boolean = checkpointData.exists(_.isCheckpointed)
 
   /**
-   * Gets the name of the file to which this RDD was checkpointed
+   * Return whether this RDD is marked for local checkpointing.
+   * Exposed for testing.
    */
-  def getCheckpointFile: Option[String] = checkpointData.flatMap(_.getCheckpointFile)
+  private[rdd] def isLocallyCheckpointed: Boolean = {
+    checkpointData match {
+      case Some(_: LocalRDDCheckpointData[T]) => true
+      case _ => false
+    }
+  }
+
+  /**
+   * Gets the name of the directory to which this RDD was checkpointed.
+   * This is not defined if the RDD is checkpointed locally.
+   */
+  def getCheckpointFile: Option[String] = {
+    checkpointData match {
+      case Some(reliable: ReliableRDDCheckpointData[T]) => reliable.getCheckpointDir
+      case _ => None
+    }
+  }
 
   // =======================================================================
   // Other internal methods and fields
@@ -1545,7 +1631,7 @@ abstract class RDD[T: ClassTag](
       if (!doCheckpointCalled) {
         doCheckpointCalled = true
         if (checkpointData.isDefined) {
-          checkpointData.get.doCheckpoint()
+          checkpointData.get.checkpoint()
         } else {
           dependencies.foreach(_.rdd.doCheckpoint())
         }
@@ -1557,7 +1643,7 @@ abstract class RDD[T: ClassTag](
    * Changes the dependencies of this RDD from its original parents to a new RDD (`newRDD`)
    * created from the checkpoint file, and forget its old dependencies and partitions.
    */
-  private[spark] def markCheckpointed(checkpointRDD: RDD[_]) {
+  private[spark] def markCheckpointed(): Unit = {
     clearDependencies()
     partitions_ = null
     deps = null    // Forget the constructor argument for dependencies too
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index 4f954363bed8e..0e43520870c0a 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -19,10 +19,7 @@ package org.apache.spark.rdd
 
 import scala.reflect.ClassTag
 
-import org.apache.hadoop.fs.Path
-
-import org.apache.spark._
-import org.apache.spark.util.SerializableConfiguration
+import org.apache.spark.Partition
 
 /**
  * Enumeration to manage state transitions of an RDD through checkpointing
@@ -39,39 +36,31 @@ private[spark] object CheckpointState extends Enumeration {
  * as well as, manages the post-checkpoint state by providing the updated partitions,
  * iterator and preferred locations of the checkpointed RDD.
  */
-private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
-  extends Logging with Serializable {
+private[spark] abstract class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+  extends Serializable {
 
   import CheckpointState._
 
   // The checkpoint state of the associated RDD.
-  private var cpState = Initialized
-
-  // The file to which the associated RDD has been checkpointed to
-  private var cpFile: Option[String] = None
+  protected var cpState = Initialized
 
-  // The CheckpointRDD created from the checkpoint file, that is, the new parent the associated RDD.
-  // This is defined if and only if `cpState` is `Checkpointed`.
+  // The RDD that contains our checkpointed data
   private var cpRDD: Option[CheckpointRDD[T]] = None
 
   // TODO: are we sure we need to use a global lock in the following methods?
 
-  // Is the RDD already checkpointed
+  /**
+   * Return whether the checkpoint data for this RDD is already persisted.
+   */
   def isCheckpointed: Boolean = RDDCheckpointData.synchronized {
     cpState == Checkpointed
   }
 
-  // Get the file to which this RDD was checkpointed to as an Option
-  def getCheckpointFile: Option[String] = RDDCheckpointData.synchronized {
-    cpFile
-  }
-
   /**
-   * Materialize this RDD and write its content to a reliable DFS.
+   * Materialize this RDD and persist its content.
    * This is called immediately after the first action invoked on this RDD has completed.
    */
-  def doCheckpoint(): Unit = {
-
+  final def checkpoint(): Unit = {
     // Guard against multiple threads checkpointing the same RDD by
     // atomically flipping the state of this RDDCheckpointData
     RDDCheckpointData.synchronized {
@@ -82,64 +71,41 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
       }
     }
 
-    // Create the output path for the checkpoint
-    val path = RDDCheckpointData.rddCheckpointDataPath(rdd.context, rdd.id).get
-    val fs = path.getFileSystem(rdd.context.hadoopConfiguration)
-    if (!fs.mkdirs(path)) {
-      throw new SparkException(s"Failed to create checkpoint path $path")
-    }
-
-    // Save to file, and reload it as an RDD
-    val broadcastedConf = rdd.context.broadcast(
-      new SerializableConfiguration(rdd.context.hadoopConfiguration))
-    val newRDD = new CheckpointRDD[T](rdd.context, path.toString)
-    if (rdd.conf.getBoolean("spark.cleaner.referenceTracking.cleanCheckpoints", false)) {
-      rdd.context.cleaner.foreach { cleaner =>
-        cleaner.registerRDDCheckpointDataForCleanup(newRDD, rdd.id)
-      }
-    }
-
-    // TODO: This is expensive because it computes the RDD again unnecessarily (SPARK-8582)
-    rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _)
-    if (newRDD.partitions.length != rdd.partitions.length) {
-      throw new SparkException(
-        "Checkpoint RDD " + newRDD + "(" + newRDD.partitions.length + ") has different " +
-          "number of partitions than original RDD " + rdd + "(" + rdd.partitions.length + ")")
-    }
+    val newRDD = doCheckpoint()
 
-    // Change the dependencies and partitions of the RDD
+    // Update our state and truncate the RDD lineage
     RDDCheckpointData.synchronized {
-      cpFile = Some(path.toString)
       cpRDD = Some(newRDD)
-      rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and partitions
       cpState = Checkpointed
+      rdd.markCheckpointed()
     }
-    logInfo(s"Done checkpointing RDD ${rdd.id} to $path, new parent is RDD ${newRDD.id}")
-  }
-
-  def getPartitions: Array[Partition] = RDDCheckpointData.synchronized {
-    cpRDD.get.partitions
   }
 
-  def checkpointRDD: Option[CheckpointRDD[T]] = RDDCheckpointData.synchronized {
-    cpRDD
-  }
-}
+  /**
+   * Materialize this RDD and persist its content.
+   *
+   * Subclasses should override this method to define custom checkpointing behavior.
+   * @return the checkpoint RDD created in the process.
+   */
+  protected def doCheckpoint(): CheckpointRDD[T]
 
-private[spark] object RDDCheckpointData {
+  /**
+   * Return the RDD that contains our checkpointed data.
+   * This is only defined if the checkpoint state is `Checkpointed`.
+   */
+  def checkpointRDD: Option[CheckpointRDD[T]] = RDDCheckpointData.synchronized { cpRDD }
 
-  /** Return the path of the directory to which this RDD's checkpoint data is written. */
-  def rddCheckpointDataPath(sc: SparkContext, rddId: Int): Option[Path] = {
-    sc.checkpointDir.map { dir => new Path(dir, s"rdd-$rddId") }
+  /**
+   * Return the partitions of the resulting checkpoint RDD.
+   * For tests only.
+   */
+  def getPartitions: Array[Partition] = RDDCheckpointData.synchronized {
+    cpRDD.map(_.partitions).getOrElse { Array.empty }
   }
 
-  /** Clean up the files associated with the checkpoint data for this RDD. */
-  def clearRDDCheckpointData(sc: SparkContext, rddId: Int): Unit = {
-    rddCheckpointDataPath(sc, rddId).foreach { path =>
-      val fs = path.getFileSystem(sc.hadoopConfiguration)
-      if (fs.exists(path)) {
-        fs.delete(path, true)
-      }
-    }
-  }
 }
+
+/**
+ * Global lock for synchronizing checkpoint operations.
+ */
+private[spark] object RDDCheckpointData
diff --git a/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
new file mode 100644
index 0000000000000..35d8b0bfd18c5
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
@@ -0,0 +1,172 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import java.io.IOException
+
+import scala.reflect.ClassTag
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark._
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.util.{SerializableConfiguration, Utils}
+
+/**
+ * An RDD that reads from checkpoint files previously written to reliable storage.
+ */
+private[spark] class ReliableCheckpointRDD[T: ClassTag](
+    @transient sc: SparkContext,
+    val checkpointPath: String)
+  extends CheckpointRDD[T](sc) {
+
+  @transient private val hadoopConf = sc.hadoopConfiguration
+  @transient private val cpath = new Path(checkpointPath)
+  @transient private val fs = cpath.getFileSystem(hadoopConf)
+  private val broadcastedConf = sc.broadcast(new SerializableConfiguration(hadoopConf))
+
+  // Fail fast if checkpoint directory does not exist
+  require(fs.exists(cpath), s"Checkpoint directory does not exist: $checkpointPath")
+
+  /**
+   * Return the path of the checkpoint directory this RDD reads data from.
+   */
+  override def getCheckpointFile: Option[String] = Some(checkpointPath)
+
+  /**
+   * Return partitions described by the files in the checkpoint directory.
+   *
+   * Since the original RDD may belong to a prior application, there is no way to know a
+   * priori the number of partitions to expect. This method assumes that the original set of
+   * checkpoint files are fully preserved in a reliable storage across application lifespans.
+   */
+  protected override def getPartitions: Array[Partition] = {
+    // listStatus can throw exception if path does not exist.
+    val inputFiles = fs.listStatus(cpath)
+      .map(_.getPath)
+      .filter(_.getName.startsWith("part-"))
+      .sortBy(_.toString)
+    // Fail fast if input files are invalid
+    inputFiles.zipWithIndex.foreach { case (path, i) =>
+      if (!path.toString.endsWith(ReliableCheckpointRDD.checkpointFileName(i))) {
+        throw new SparkException(s"Invalid checkpoint file: $path")
+      }
+    }
+    Array.tabulate(inputFiles.length)(i => new CheckpointRDDPartition(i))
+  }
+
+  /**
+   * Return the locations of the checkpoint file associated with the given partition.
+   */
+  protected override def getPreferredLocations(split: Partition): Seq[String] = {
+    val status = fs.getFileStatus(
+      new Path(checkpointPath, ReliableCheckpointRDD.checkpointFileName(split.index)))
+    val locations = fs.getFileBlockLocations(status, 0, status.getLen)
+    locations.headOption.toList.flatMap(_.getHosts).filter(_ != "localhost")
+  }
+
+  /**
+   * Read the content of the checkpoint file associated with the given partition.
+   */
+  override def compute(split: Partition, context: TaskContext): Iterator[T] = {
+    val file = new Path(checkpointPath, ReliableCheckpointRDD.checkpointFileName(split.index))
+    ReliableCheckpointRDD.readCheckpointFile(file, broadcastedConf, context)
+  }
+
+}
+
+private[spark] object ReliableCheckpointRDD extends Logging {
+
+  /**
+   * Return the checkpoint file name for the given partition.
+   */
+  private def checkpointFileName(partitionIndex: Int): String = {
+    "part-%05d".format(partitionIndex)
+  }
+
+  /**
+   * Write this partition's values to a checkpoint file.
+   */
+  def writeCheckpointFile[T: ClassTag](
+      path: String,
+      broadcastedConf: Broadcast[SerializableConfiguration],
+      blockSize: Int = -1)(ctx: TaskContext, iterator: Iterator[T]) {
+    val env = SparkEnv.get
+    val outputDir = new Path(path)
+    val fs = outputDir.getFileSystem(broadcastedConf.value.value)
+
+    val finalOutputName = ReliableCheckpointRDD.checkpointFileName(ctx.partitionId())
+    val finalOutputPath = new Path(outputDir, finalOutputName)
+    val tempOutputPath =
+      new Path(outputDir, s".$finalOutputName-attempt-${ctx.attemptNumber()}")
+
+    if (fs.exists(tempOutputPath)) {
+      throw new IOException(s"Checkpoint failed: temporary path $tempOutputPath already exists")
+    }
+    val bufferSize = env.conf.getInt("spark.buffer.size", 65536)
+
+    val fileOutputStream = if (blockSize < 0) {
+      fs.create(tempOutputPath, false, bufferSize)
+    } else {
+      // This is mainly for testing purpose
+      fs.create(tempOutputPath, false, bufferSize, fs.getDefaultReplication, blockSize)
+    }
+    val serializer = env.serializer.newInstance()
+    val serializeStream = serializer.serializeStream(fileOutputStream)
+    Utils.tryWithSafeFinally {
+      serializeStream.writeAll(iterator)
+    } {
+      serializeStream.close()
+    }
+
+    if (!fs.rename(tempOutputPath, finalOutputPath)) {
+      if (!fs.exists(finalOutputPath)) {
+        logInfo(s"Deleting tempOutputPath $tempOutputPath")
+        fs.delete(tempOutputPath, false)
+        throw new IOException("Checkpoint failed: failed to save output of task: " +
+          s"${ctx.attemptNumber()} and final output path does not exist: $finalOutputPath")
+      } else {
+        // Some other copy of this task must've finished before us and renamed it
+        logInfo(s"Final output path $finalOutputPath already exists; not overwriting it")
+        fs.delete(tempOutputPath, false)
+      }
+    }
+  }
+
+  /**
+   * Read the content of the specified checkpoint file.
+   */
+  def readCheckpointFile[T](
+      path: Path,
+      broadcastedConf: Broadcast[SerializableConfiguration],
+      context: TaskContext): Iterator[T] = {
+    val env = SparkEnv.get
+    val fs = path.getFileSystem(broadcastedConf.value.value)
+    val bufferSize = env.conf.getInt("spark.buffer.size", 65536)
+    val fileInputStream = fs.open(path, bufferSize)
+    val serializer = env.serializer.newInstance()
+    val deserializeStream = serializer.deserializeStream(fileInputStream)
+
+    // Register an on-task-completion callback to close the input stream.
+    context.addTaskCompletionListener(context => deserializeStream.close())
+
+    deserializeStream.asIterator.asInstanceOf[Iterator[T]]
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
new file mode 100644
index 0000000000000..1df8eef5ff2b9
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.reflect.ClassTag
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark._
+import org.apache.spark.util.SerializableConfiguration
+
+/**
+ * An implementation of checkpointing that writes the RDD data to reliable storage.
+ * This allows drivers to be restarted on failure with previously computed state.
+ */
+private[spark] class ReliableRDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+  extends RDDCheckpointData[T](rdd) with Logging {
+
+  // The directory to which the associated RDD has been checkpointed to
+  // This is assumed to be a non-local path that points to some reliable storage
+  private val cpDir: String =
+    ReliableRDDCheckpointData.checkpointPath(rdd.context, rdd.id)
+      .map(_.toString)
+      .getOrElse { throw new SparkException("Checkpoint dir must be specified.") }
+
+  /**
+   * Return the directory to which this RDD was checkpointed.
+   * If the RDD is not checkpointed yet, return None.
+   */
+  def getCheckpointDir: Option[String] = RDDCheckpointData.synchronized {
+    if (isCheckpointed) {
+      Some(cpDir.toString)
+    } else {
+      None
+    }
+  }
+
+  /**
+   * Materialize this RDD and write its content to a reliable DFS.
+   * This is called immediately after the first action invoked on this RDD has completed.
+   */
+  protected override def doCheckpoint(): CheckpointRDD[T] = {
+
+    // Create the output path for the checkpoint
+    val path = new Path(cpDir)
+    val fs = path.getFileSystem(rdd.context.hadoopConfiguration)
+    if (!fs.mkdirs(path)) {
+      throw new SparkException(s"Failed to create checkpoint path $cpDir")
+    }
+
+    // Save to file, and reload it as an RDD
+    val broadcastedConf = rdd.context.broadcast(
+      new SerializableConfiguration(rdd.context.hadoopConfiguration))
+    // TODO: This is expensive because it computes the RDD again unnecessarily (SPARK-8582)
+    rdd.context.runJob(rdd, ReliableCheckpointRDD.writeCheckpointFile[T](cpDir, broadcastedConf) _)
+    val newRDD = new ReliableCheckpointRDD[T](rdd.context, cpDir)
+    if (newRDD.partitions.length != rdd.partitions.length) {
+      throw new SparkException(
+        s"Checkpoint RDD $newRDD(${newRDD.partitions.length}) has different " +
+          s"number of partitions from original RDD $rdd(${rdd.partitions.length})")
+    }
+
+    // Optionally clean our checkpoint files if the reference is out of scope
+    if (rdd.conf.getBoolean("spark.cleaner.referenceTracking.cleanCheckpoints", false)) {
+      rdd.context.cleaner.foreach { cleaner =>
+        cleaner.registerRDDCheckpointDataForCleanup(newRDD, rdd.id)
+      }
+    }
+
+    logInfo(s"Done checkpointing RDD ${rdd.id} to $cpDir, new parent is RDD ${newRDD.id}")
+
+    newRDD
+  }
+
+}
+
+private[spark] object ReliableRDDCheckpointData {
+
+  /** Return the path of the directory to which this RDD's checkpoint data is written. */
+  def checkpointPath(sc: SparkContext, rddId: Int): Option[Path] = {
+    sc.checkpointDir.map { dir => new Path(dir, s"rdd-$rddId") }
+  }
+
+  /** Clean up the files associated with the checkpoint data for this RDD. */
+  def cleanCheckpoint(sc: SparkContext, rddId: Int): Unit = {
+    checkpointPath(sc, rddId).foreach { path =>
+      val fs = path.getFileSystem(sc.hadoopConfiguration)
+      if (fs.exists(path)) {
+        fs.delete(path, true)
+      }
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index cc50e6d79a3e2..d343bb95cb68c 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -25,11 +25,15 @@ import org.apache.spark.rdd._
 import org.apache.spark.storage.{BlockId, StorageLevel, TestBlockId}
 import org.apache.spark.util.Utils
 
+/**
+ * Test suite for end-to-end checkpointing functionality.
+ * This tests both reliable checkpoints and local checkpoints.
+ */
 class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging {
-  var checkpointDir: File = _
-  val partitioner = new HashPartitioner(2)
+  private var checkpointDir: File = _
+  private val partitioner = new HashPartitioner(2)
 
-  override def beforeEach() {
+  override def beforeEach(): Unit = {
     super.beforeEach()
     checkpointDir = File.createTempFile("temp", "", Utils.createTempDir())
     checkpointDir.delete()
@@ -37,40 +41,43 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     sc.setCheckpointDir(checkpointDir.toString)
   }
 
-  override def afterEach() {
+  override def afterEach(): Unit = {
     super.afterEach()
     Utils.deleteRecursively(checkpointDir)
   }
 
-  test("basic checkpointing") {
+  runTest("basic checkpointing") { reliableCheckpoint: Boolean =>
     val parCollection = sc.makeRDD(1 to 4)
     val flatMappedRDD = parCollection.flatMap(x => 1 to x)
-    flatMappedRDD.checkpoint()
+    checkpoint(flatMappedRDD, reliableCheckpoint)
     assert(flatMappedRDD.dependencies.head.rdd === parCollection)
     val result = flatMappedRDD.collect()
     assert(flatMappedRDD.dependencies.head.rdd != parCollection)
     assert(flatMappedRDD.collect() === result)
   }
 
-  test("RDDs with one-to-one dependencies") {
-    testRDD(_.map(x => x.toString))
-    testRDD(_.flatMap(x => 1 to x))
-    testRDD(_.filter(_ % 2 == 0))
-    testRDD(_.sample(false, 0.5, 0))
-    testRDD(_.glom())
-    testRDD(_.mapPartitions(_.map(_.toString)))
-    testRDD(_.map(x => (x % 2, 1)).reduceByKey(_ + _).mapValues(_.toString))
-    testRDD(_.map(x => (x % 2, 1)).reduceByKey(_ + _).flatMapValues(x => 1 to x))
-    testRDD(_.pipe(Seq("cat")))
+  runTest("RDDs with one-to-one dependencies") { reliableCheckpoint: Boolean =>
+    testRDD(_.map(x => x.toString), reliableCheckpoint)
+    testRDD(_.flatMap(x => 1 to x), reliableCheckpoint)
+    testRDD(_.filter(_ % 2 == 0), reliableCheckpoint)
+    testRDD(_.sample(false, 0.5, 0), reliableCheckpoint)
+    testRDD(_.glom(), reliableCheckpoint)
+    testRDD(_.mapPartitions(_.map(_.toString)), reliableCheckpoint)
+    testRDD(_.map(x => (x % 2, 1)).reduceByKey(_ + _).mapValues(_.toString), reliableCheckpoint)
+    testRDD(_.map(x => (x % 2, 1)).reduceByKey(_ + _).flatMapValues(x => 1 to x),
+      reliableCheckpoint)
+    testRDD(_.pipe(Seq("cat")), reliableCheckpoint)
   }
 
-  test("ParallelCollection") {
+  runTest("ParallelCollectionRDD") { reliableCheckpoint: Boolean =>
     val parCollection = sc.makeRDD(1 to 4, 2)
     val numPartitions = parCollection.partitions.size
-    parCollection.checkpoint()
+    checkpoint(parCollection, reliableCheckpoint)
     assert(parCollection.dependencies === Nil)
     val result = parCollection.collect()
-    assert(sc.checkpointFile[Int](parCollection.getCheckpointFile.get).collect() === result)
+    if (reliableCheckpoint) {
+      assert(sc.checkpointFile[Int](parCollection.getCheckpointFile.get).collect() === result)
+    }
     assert(parCollection.dependencies != Nil)
     assert(parCollection.partitions.length === numPartitions)
     assert(parCollection.partitions.toList ===
@@ -78,44 +85,46 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     assert(parCollection.collect() === result)
   }
 
-  test("BlockRDD") {
+  runTest("BlockRDD") { reliableCheckpoint: Boolean =>
     val blockId = TestBlockId("id")
     val blockManager = SparkEnv.get.blockManager
     blockManager.putSingle(blockId, "test", StorageLevel.MEMORY_ONLY)
     val blockRDD = new BlockRDD[String](sc, Array(blockId))
     val numPartitions = blockRDD.partitions.size
-    blockRDD.checkpoint()
+    checkpoint(blockRDD, reliableCheckpoint)
     val result = blockRDD.collect()
-    assert(sc.checkpointFile[String](blockRDD.getCheckpointFile.get).collect() === result)
+    if (reliableCheckpoint) {
+      assert(sc.checkpointFile[String](blockRDD.getCheckpointFile.get).collect() === result)
+    }
     assert(blockRDD.dependencies != Nil)
     assert(blockRDD.partitions.length === numPartitions)
     assert(blockRDD.partitions.toList === blockRDD.checkpointData.get.getPartitions.toList)
     assert(blockRDD.collect() === result)
   }
 
-  test("ShuffledRDD") {
+  runTest("ShuffleRDD") { reliableCheckpoint: Boolean =>
     testRDD(rdd => {
       // Creating ShuffledRDD directly as PairRDDFunctions.combineByKey produces a MapPartitionedRDD
       new ShuffledRDD[Int, Int, Int](rdd.map(x => (x % 2, 1)), partitioner)
-    })
+    }, reliableCheckpoint)
   }
 
-  test("UnionRDD") {
+  runTest("UnionRDD") { reliableCheckpoint: Boolean =>
     def otherRDD: RDD[Int] = sc.makeRDD(1 to 10, 1)
-    testRDD(_.union(otherRDD))
-    testRDDPartitions(_.union(otherRDD))
+    testRDD(_.union(otherRDD), reliableCheckpoint)
+    testRDDPartitions(_.union(otherRDD), reliableCheckpoint)
   }
 
-  test("CartesianRDD") {
+  runTest("CartesianRDD") { reliableCheckpoint: Boolean =>
     def otherRDD: RDD[Int] = sc.makeRDD(1 to 10, 1)
-    testRDD(new CartesianRDD(sc, _, otherRDD))
-    testRDDPartitions(new CartesianRDD(sc, _, otherRDD))
+    testRDD(new CartesianRDD(sc, _, otherRDD), reliableCheckpoint)
+    testRDDPartitions(new CartesianRDD(sc, _, otherRDD), reliableCheckpoint)
 
     // Test that the CartesianRDD updates parent partitions (CartesianRDD.s1/s2) after
     // the parent RDD has been checkpointed and parent partitions have been changed.
     // Note that this test is very specific to the current implementation of CartesianRDD.
     val ones = sc.makeRDD(1 to 100, 10).map(x => x)
-    ones.checkpoint() // checkpoint that MappedRDD
+    checkpoint(ones, reliableCheckpoint) // checkpoint that MappedRDD
     val cartesian = new CartesianRDD(sc, ones, ones)
     val splitBeforeCheckpoint =
       serializeDeserialize(cartesian.partitions.head.asInstanceOf[CartesianPartition])
@@ -129,16 +138,16 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     )
   }
 
-  test("CoalescedRDD") {
-    testRDD(_.coalesce(2))
-    testRDDPartitions(_.coalesce(2))
+  runTest("CoalescedRDD") { reliableCheckpoint: Boolean =>
+    testRDD(_.coalesce(2), reliableCheckpoint)
+    testRDDPartitions(_.coalesce(2), reliableCheckpoint)
 
     // Test that the CoalescedRDDPartition updates parent partitions (CoalescedRDDPartition.parents)
     // after the parent RDD has been checkpointed and parent partitions have been changed.
     // Note that this test is very specific to the current implementation of
     // CoalescedRDDPartitions.
     val ones = sc.makeRDD(1 to 100, 10).map(x => x)
-    ones.checkpoint() // checkpoint that MappedRDD
+    checkpoint(ones, reliableCheckpoint) // checkpoint that MappedRDD
     val coalesced = new CoalescedRDD(ones, 2)
     val splitBeforeCheckpoint =
       serializeDeserialize(coalesced.partitions.head.asInstanceOf[CoalescedRDDPartition])
@@ -151,7 +160,7 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     )
   }
 
-  test("CoGroupedRDD") {
+  runTest("CoGroupedRDD") { reliableCheckpoint: Boolean =>
     val longLineageRDD1 = generateFatPairRDD()
 
     // Collect the RDD as sequences instead of arrays to enable equality tests in testRDD
@@ -160,26 +169,26 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
 
     testRDD(rdd => {
       CheckpointSuite.cogroup(longLineageRDD1, rdd.map(x => (x % 2, 1)), partitioner)
-    }, seqCollectFunc)
+    }, reliableCheckpoint, seqCollectFunc)
 
     val longLineageRDD2 = generateFatPairRDD()
     testRDDPartitions(rdd => {
       CheckpointSuite.cogroup(
         longLineageRDD2, sc.makeRDD(1 to 2, 2).map(x => (x % 2, 1)), partitioner)
-    }, seqCollectFunc)
+    }, reliableCheckpoint, seqCollectFunc)
   }
 
-  test("ZippedPartitionsRDD") {
-    testRDD(rdd => rdd.zip(rdd.map(x => x)))
-    testRDDPartitions(rdd => rdd.zip(rdd.map(x => x)))
+  runTest("ZippedPartitionsRDD") { reliableCheckpoint: Boolean =>
+    testRDD(rdd => rdd.zip(rdd.map(x => x)), reliableCheckpoint)
+    testRDDPartitions(rdd => rdd.zip(rdd.map(x => x)), reliableCheckpoint)
 
     // Test that ZippedPartitionsRDD updates parent partitions after parent RDDs have
     // been checkpointed and parent partitions have been changed.
     // Note that this test is very specific to the implementation of ZippedPartitionsRDD.
     val rdd = generateFatRDD()
     val zippedRDD = rdd.zip(rdd.map(x => x)).asInstanceOf[ZippedPartitionsRDD2[_, _, _]]
-    zippedRDD.rdd1.checkpoint()
-    zippedRDD.rdd2.checkpoint()
+    checkpoint(zippedRDD.rdd1, reliableCheckpoint)
+    checkpoint(zippedRDD.rdd2, reliableCheckpoint)
     val partitionBeforeCheckpoint =
       serializeDeserialize(zippedRDD.partitions.head.asInstanceOf[ZippedPartitionsPartition])
     zippedRDD.count()
@@ -194,27 +203,27 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     )
   }
 
-  test("PartitionerAwareUnionRDD") {
+  runTest("PartitionerAwareUnionRDD") { reliableCheckpoint: Boolean =>
     testRDD(rdd => {
       new PartitionerAwareUnionRDD[(Int, Int)](sc, Array(
         generateFatPairRDD(),
         rdd.map(x => (x % 2, 1)).reduceByKey(partitioner, _ + _)
       ))
-    })
+    }, reliableCheckpoint)
 
     testRDDPartitions(rdd => {
       new PartitionerAwareUnionRDD[(Int, Int)](sc, Array(
         generateFatPairRDD(),
         rdd.map(x => (x % 2, 1)).reduceByKey(partitioner, _ + _)
       ))
-    })
+    }, reliableCheckpoint)
 
     // Test that the PartitionerAwareUnionRDD updates parent partitions
     // (PartitionerAwareUnionRDD.parents) after the parent RDD has been checkpointed and parent
     // partitions have been changed. Note that this test is very specific to the current
     // implementation of PartitionerAwareUnionRDD.
     val pairRDD = generateFatPairRDD()
-    pairRDD.checkpoint()
+    checkpoint(pairRDD, reliableCheckpoint)
     val unionRDD = new PartitionerAwareUnionRDD(sc, Array(pairRDD))
     val partitionBeforeCheckpoint = serializeDeserialize(
       unionRDD.partitions.head.asInstanceOf[PartitionerAwareUnionRDDPartition])
@@ -228,17 +237,34 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     )
   }
 
-  test("CheckpointRDD with zero partitions") {
+  runTest("CheckpointRDD with zero partitions") { reliableCheckpoint: Boolean =>
     val rdd = new BlockRDD[Int](sc, Array[BlockId]())
     assert(rdd.partitions.size === 0)
     assert(rdd.isCheckpointed === false)
-    rdd.checkpoint()
+    checkpoint(rdd, reliableCheckpoint)
     assert(rdd.count() === 0)
     assert(rdd.isCheckpointed === true)
     assert(rdd.partitions.size === 0)
   }
 
-  def defaultCollectFunc[T](rdd: RDD[T]): Any = rdd.collect()
+  // Utility test methods
+
+  /** Checkpoint the RDD either locally or reliably. */
+  private def checkpoint(rdd: RDD[_], reliableCheckpoint: Boolean): Unit = {
+    if (reliableCheckpoint) {
+      rdd.checkpoint()
+    } else {
+      rdd.localCheckpoint()
+    }
+  }
+
+  /** Run a test twice, once for local checkpointing and once for reliable checkpointing. */
+  private def runTest(name: String)(body: Boolean => Unit): Unit = {
+    test(name + " [reliable checkpoint]")(body(true))
+    test(name + " [local checkpoint]")(body(false))
+  }
+
+  private def defaultCollectFunc[T](rdd: RDD[T]): Any = rdd.collect()
 
   /**
    * Test checkpointing of the RDD generated by the given operation. It tests whether the
@@ -246,11 +272,14 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
    * on all RDDs that have a parent RDD (i.e., do not call on ParallelCollection, BlockRDD, etc.).
    *
    * @param op an operation to run on the RDD
+   * @param reliableCheckpoint if true, use reliable checkpoints, otherwise use local checkpoints
    * @param collectFunc a function for collecting the values in the RDD, in case there are
    *   non-comparable types like arrays that we want to convert to something that supports ==
    */
-  def testRDD[U: ClassTag](op: (RDD[Int]) => RDD[U],
-      collectFunc: RDD[U] => Any = defaultCollectFunc[U] _) {
+  private def testRDD[U: ClassTag](
+      op: (RDD[Int]) => RDD[U],
+      reliableCheckpoint: Boolean,
+      collectFunc: RDD[U] => Any = defaultCollectFunc[U] _): Unit = {
     // Generate the final RDD using given RDD operation
     val baseRDD = generateFatRDD()
     val operatedRDD = op(baseRDD)
@@ -267,14 +296,16 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     // Find serialized sizes before and after the checkpoint
     logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString)
     val (rddSizeBeforeCheckpoint, partitionSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD)
-    operatedRDD.checkpoint()
+    checkpoint(operatedRDD, reliableCheckpoint)
     val result = collectFunc(operatedRDD)
     operatedRDD.collect() // force re-initialization of post-checkpoint lazy variables
     val (rddSizeAfterCheckpoint, partitionSizeAfterCheckpoint) = getSerializedSizes(operatedRDD)
     logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString)
 
     // Test whether the checkpoint file has been created
-    assert(collectFunc(sc.checkpointFile[U](operatedRDD.getCheckpointFile.get)) === result)
+    if (reliableCheckpoint) {
+      assert(collectFunc(sc.checkpointFile[U](operatedRDD.getCheckpointFile.get)) === result)
+    }
 
     // Test whether dependencies have been changed from its earlier parent RDD
     assert(operatedRDD.dependencies.head.rdd != parentRDD)
@@ -310,11 +341,14 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
    * partitions (i.e., do not call it on simple RDD like MappedRDD).
    *
    * @param op an operation to run on the RDD
+   * @param reliableCheckpoint if true, use reliable checkpoints, otherwise use local checkpoints
    * @param collectFunc a function for collecting the values in the RDD, in case there are
    *   non-comparable types like arrays that we want to convert to something that supports ==
    */
-  def testRDDPartitions[U: ClassTag](op: (RDD[Int]) => RDD[U],
-       collectFunc: RDD[U] => Any = defaultCollectFunc[U] _) {
+  private def testRDDPartitions[U: ClassTag](
+      op: (RDD[Int]) => RDD[U],
+      reliableCheckpoint: Boolean,
+      collectFunc: RDD[U] => Any = defaultCollectFunc[U] _): Unit = {
     // Generate the final RDD using given RDD operation
     val baseRDD = generateFatRDD()
     val operatedRDD = op(baseRDD)
@@ -328,7 +362,10 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
     // Find serialized sizes before and after the checkpoint
     logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString)
     val (rddSizeBeforeCheckpoint, partitionSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD)
-    parentRDDs.foreach(_.checkpoint())  // checkpoint the parent RDD, not the generated one
+    // checkpoint the parent RDD, not the generated one
+    parentRDDs.foreach { rdd =>
+      checkpoint(rdd, reliableCheckpoint)
+    }
     val result = collectFunc(operatedRDD)  // force checkpointing
     operatedRDD.collect() // force re-initialization of post-checkpoint lazy variables
     val (rddSizeAfterCheckpoint, partitionSizeAfterCheckpoint) = getSerializedSizes(operatedRDD)
@@ -350,7 +387,7 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
   /**
    * Generate an RDD such that both the RDD and its partitions have large size.
    */
-  def generateFatRDD(): RDD[Int] = {
+  private def generateFatRDD(): RDD[Int] = {
     new FatRDD(sc.makeRDD(1 to 100, 4)).map(x => x)
   }
 
@@ -358,7 +395,7 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
    * Generate an pair RDD (with partitioner) such that both the RDD and its partitions
    * have large size.
    */
-  def generateFatPairRDD(): RDD[(Int, Int)] = {
+  private def generateFatPairRDD(): RDD[(Int, Int)] = {
     new FatPairRDD(sc.makeRDD(1 to 100, 4), partitioner).mapValues(x => x)
   }
 
@@ -366,7 +403,7 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
    * Get serialized sizes of the RDD and its partitions, in order to test whether the size shrinks
    * upon checkpointing. Ignores the checkpointData field, which may grow when we checkpoint.
    */
-  def getSerializedSizes(rdd: RDD[_]): (Int, Int) = {
+  private def getSerializedSizes(rdd: RDD[_]): (Int, Int) = {
     val rddSize = Utils.serialize(rdd).size
     val rddCpDataSize = Utils.serialize(rdd.checkpointData).size
     val rddPartitionSize = Utils.serialize(rdd.partitions).size
@@ -394,7 +431,7 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
    * contents after deserialization (e.g., the contents of an RDD split after
    * it is sent to a slave along with a task)
    */
-  def serializeDeserialize[T](obj: T): T = {
+  private def serializeDeserialize[T](obj: T): T = {
     val bytes = Utils.serialize(obj)
     Utils.deserialize[T](bytes)
   }
@@ -402,10 +439,11 @@ class CheckpointSuite extends SparkFunSuite with LocalSparkContext with Logging
   /**
    * Recursively force the initialization of the all members of an RDD and it parents.
    */
-  def initializeRdd(rdd: RDD[_]) {
+  private def initializeRdd(rdd: RDD[_]): Unit = {
     rdd.partitions // forces the
-    rdd.dependencies.map(_.rdd).foreach(initializeRdd(_))
+    rdd.dependencies.map(_.rdd).foreach(initializeRdd)
   }
+
 }
 
 /** RDD partition that has large serialized size. */
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 26858ef2774fc..0c14bef7befd8 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -24,12 +24,11 @@ import scala.language.existentials
 import scala.util.Random
 
 import org.scalatest.BeforeAndAfter
-import org.scalatest.concurrent.{PatienceConfiguration, Eventually}
+import org.scalatest.concurrent.PatienceConfiguration
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.SparkContext._
-import org.apache.spark.rdd.{RDDCheckpointData, RDD}
+import org.apache.spark.rdd.{ReliableRDDCheckpointData, RDD}
 import org.apache.spark.storage._
 import org.apache.spark.shuffle.hash.HashShuffleManager
 import org.apache.spark.shuffle.sort.SortShuffleManager
@@ -52,6 +51,7 @@ abstract class ContextCleanerSuiteBase(val shuffleManager: Class[_] = classOf[Ha
     .setAppName("ContextCleanerSuite")
     .set("spark.cleaner.referenceTracking.blocking", "true")
     .set("spark.cleaner.referenceTracking.blocking.shuffle", "true")
+    .set("spark.cleaner.referenceTracking.cleanCheckpoints", "true")
     .set("spark.shuffle.manager", shuffleManager.getName)
 
   before {
@@ -209,11 +209,11 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase {
     postGCTester.assertCleanup()
   }
 
-  test("automatically cleanup checkpoint") {
+  test("automatically cleanup normal checkpoint") {
     val checkpointDir = java.io.File.createTempFile("temp", "")
     checkpointDir.deleteOnExit()
     checkpointDir.delete()
-    var rdd = newPairRDD
+    var rdd = newPairRDD()
     sc.setCheckpointDir(checkpointDir.toString)
     rdd.checkpoint()
     rdd.cache()
@@ -221,23 +221,26 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase {
     var rddId = rdd.id
 
     // Confirm the checkpoint directory exists
-    assert(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).isDefined)
-    val path = RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get
+    assert(ReliableRDDCheckpointData.checkpointPath(sc, rddId).isDefined)
+    val path = ReliableRDDCheckpointData.checkpointPath(sc, rddId).get
     val fs = path.getFileSystem(sc.hadoopConfiguration)
     assert(fs.exists(path))
 
     // the checkpoint is not cleaned by default (without the configuration set)
-    var postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil, Nil)
+    var postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil, Seq(rddId))
     rdd = null // Make RDD out of scope, ok if collected earlier
     runGC()
     postGCTester.assertCleanup()
-    assert(fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get))
+    assert(!fs.exists(ReliableRDDCheckpointData.checkpointPath(sc, rddId).get))
 
+    // Verify that checkpoints are NOT cleaned up if the config is not enabled
     sc.stop()
-    val conf = new SparkConf().setMaster("local[2]").setAppName("cleanupCheckpoint").
-      set("spark.cleaner.referenceTracking.cleanCheckpoints", "true")
+    val conf = new SparkConf()
+      .setMaster("local[2]")
+      .setAppName("cleanupCheckpoint")
+      .set("spark.cleaner.referenceTracking.cleanCheckpoints", "false")
     sc = new SparkContext(conf)
-    rdd = newPairRDD
+    rdd = newPairRDD()
     sc.setCheckpointDir(checkpointDir.toString)
     rdd.checkpoint()
     rdd.cache()
@@ -245,17 +248,40 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase {
     rddId = rdd.id
 
     // Confirm the checkpoint directory exists
-    assert(fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get))
+    assert(fs.exists(ReliableRDDCheckpointData.checkpointPath(sc, rddId).get))
 
     // Reference rdd to defeat any early collection by the JVM
     rdd.count()
 
     // Test that GC causes checkpoint data cleanup after dereferencing the RDD
-    postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil, Seq(rddId))
+    postGCTester = new CleanerTester(sc, Seq(rddId))
     rdd = null // Make RDD out of scope
     runGC()
     postGCTester.assertCleanup()
-    assert(!fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get))
+    assert(fs.exists(ReliableRDDCheckpointData.checkpointPath(sc, rddId).get))
+  }
+
+  test("automatically clean up local checkpoint") {
+    // Note that this test is similar to the RDD cleanup
+    // test because the same underlying mechanism is used!
+    var rdd = newPairRDD().localCheckpoint()
+    assert(rdd.checkpointData.isDefined)
+    assert(rdd.checkpointData.get.checkpointRDD.isEmpty)
+    rdd.count()
+    assert(rdd.checkpointData.get.checkpointRDD.isDefined)
+
+    // Test that GC does not cause checkpoint cleanup due to a strong reference
+    val preGCTester = new CleanerTester(sc, rddIds = Seq(rdd.id))
+    runGC()
+    intercept[Exception] {
+      preGCTester.assertCleanup()(timeout(1000 millis))
+    }
+
+    // Test that RDD going out of scope does cause the checkpoint blocks to be cleaned up
+    val postGCTester = new CleanerTester(sc, rddIds = Seq(rdd.id))
+    rdd = null
+    runGC()
+    postGCTester.assertCleanup()
   }
 
   test("automatically cleanup RDD + shuffle + broadcast") {
@@ -408,7 +434,10 @@ class SortShuffleContextCleanerSuite extends ContextCleanerSuiteBase(classOf[Sor
 }
 
 
-/** Class to test whether RDDs, shuffles, etc. have been successfully cleaned. */
+/**
+ * Class to test whether RDDs, shuffles, etc. have been successfully cleaned.
+ * The checkpoint here refers only to normal (reliable) checkpoints, not local checkpoints.
+ */
 class CleanerTester(
     sc: SparkContext,
     rddIds: Seq[Int] = Seq.empty,
diff --git a/core/src/test/scala/org/apache/spark/rdd/LocalCheckpointSuite.scala b/core/src/test/scala/org/apache/spark/rdd/LocalCheckpointSuite.scala
new file mode 100644
index 0000000000000..5103eb74b2457
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/rdd/LocalCheckpointSuite.scala
@@ -0,0 +1,330 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import org.apache.spark.{SparkException, SparkContext, LocalSparkContext, SparkFunSuite}
+
+import org.mockito.Mockito.spy
+import org.apache.spark.storage.{RDDBlockId, StorageLevel}
+
+/**
+ * Fine-grained tests for local checkpointing.
+ * For end-to-end tests, see CheckpointSuite.
+ */
+class LocalCheckpointSuite extends SparkFunSuite with LocalSparkContext {
+
+  override def beforeEach(): Unit = {
+    sc = new SparkContext("local[2]", "test")
+  }
+
+  test("transform storage level") {
+    val transform = LocalRDDCheckpointData.transformStorageLevel _
+    assert(transform(StorageLevel.NONE) === StorageLevel.DISK_ONLY)
+    assert(transform(StorageLevel.MEMORY_ONLY) === StorageLevel.MEMORY_AND_DISK)
+    assert(transform(StorageLevel.MEMORY_ONLY_SER) === StorageLevel.MEMORY_AND_DISK_SER)
+    assert(transform(StorageLevel.MEMORY_ONLY_2) === StorageLevel.MEMORY_AND_DISK_2)
+    assert(transform(StorageLevel.MEMORY_ONLY_SER_2) === StorageLevel.MEMORY_AND_DISK_SER_2)
+    assert(transform(StorageLevel.DISK_ONLY) === StorageLevel.DISK_ONLY)
+    assert(transform(StorageLevel.DISK_ONLY_2) === StorageLevel.DISK_ONLY_2)
+    assert(transform(StorageLevel.MEMORY_AND_DISK) === StorageLevel.MEMORY_AND_DISK)
+    assert(transform(StorageLevel.MEMORY_AND_DISK_SER) === StorageLevel.MEMORY_AND_DISK_SER)
+    assert(transform(StorageLevel.MEMORY_AND_DISK_2) === StorageLevel.MEMORY_AND_DISK_2)
+    assert(transform(StorageLevel.MEMORY_AND_DISK_SER_2) === StorageLevel.MEMORY_AND_DISK_SER_2)
+    // Off-heap is not supported and Spark should fail fast
+    intercept[SparkException] {
+      transform(StorageLevel.OFF_HEAP)
+    }
+  }
+
+  test("basic lineage truncation") {
+    val numPartitions = 4
+    val parallelRdd = sc.parallelize(1 to 100, numPartitions)
+    val mappedRdd = parallelRdd.map { i => i + 1 }
+    val filteredRdd = mappedRdd.filter { i => i % 2 == 0 }
+    val expectedPartitionIndices = (0 until numPartitions).toArray
+    assert(filteredRdd.checkpointData.isEmpty)
+    assert(filteredRdd.getStorageLevel === StorageLevel.NONE)
+    assert(filteredRdd.partitions.map(_.index) === expectedPartitionIndices)
+    assert(filteredRdd.dependencies.size === 1)
+    assert(filteredRdd.dependencies.head.rdd === mappedRdd)
+    assert(mappedRdd.dependencies.size === 1)
+    assert(mappedRdd.dependencies.head.rdd === parallelRdd)
+    assert(parallelRdd.dependencies.size === 0)
+
+    // Mark the RDD for local checkpointing
+    filteredRdd.localCheckpoint()
+    assert(filteredRdd.checkpointData.isDefined)
+    assert(!filteredRdd.checkpointData.get.isCheckpointed)
+    assert(!filteredRdd.checkpointData.get.checkpointRDD.isDefined)
+    assert(filteredRdd.getStorageLevel === LocalRDDCheckpointData.DEFAULT_STORAGE_LEVEL)
+
+    // After an action, the lineage is truncated
+    val result = filteredRdd.collect()
+    assert(filteredRdd.checkpointData.get.isCheckpointed)
+    assert(filteredRdd.checkpointData.get.checkpointRDD.isDefined)
+    val checkpointRdd = filteredRdd.checkpointData.flatMap(_.checkpointRDD).get
+    assert(filteredRdd.dependencies.size === 1)
+    assert(filteredRdd.dependencies.head.rdd === checkpointRdd)
+    assert(filteredRdd.partitions.map(_.index) === expectedPartitionIndices)
+    assert(checkpointRdd.partitions.map(_.index) === expectedPartitionIndices)
+
+    // Recomputation should yield the same result
+    assert(filteredRdd.collect() === result)
+    assert(filteredRdd.collect() === result)
+  }
+
+  test("basic lineage truncation - caching before checkpointing") {
+    testBasicLineageTruncationWithCaching(
+      newRdd.persist(StorageLevel.MEMORY_ONLY).localCheckpoint(),
+      StorageLevel.MEMORY_AND_DISK)
+  }
+
+  test("basic lineage truncation - caching after checkpointing") {
+    testBasicLineageTruncationWithCaching(
+      newRdd.localCheckpoint().persist(StorageLevel.MEMORY_ONLY),
+      StorageLevel.MEMORY_AND_DISK)
+  }
+
+  test("indirect lineage truncation") {
+    testIndirectLineageTruncation(
+      newRdd.localCheckpoint(),
+      LocalRDDCheckpointData.DEFAULT_STORAGE_LEVEL)
+  }
+
+  test("indirect lineage truncation - caching before checkpointing") {
+    testIndirectLineageTruncation(
+      newRdd.persist(StorageLevel.MEMORY_ONLY).localCheckpoint(),
+      StorageLevel.MEMORY_AND_DISK)
+  }
+
+  test("indirect lineage truncation - caching after checkpointing") {
+    testIndirectLineageTruncation(
+      newRdd.localCheckpoint().persist(StorageLevel.MEMORY_ONLY),
+      StorageLevel.MEMORY_AND_DISK)
+  }
+
+  test("checkpoint without draining iterator") {
+    testWithoutDrainingIterator(
+      newSortedRdd.localCheckpoint(),
+      LocalRDDCheckpointData.DEFAULT_STORAGE_LEVEL,
+      50)
+  }
+
+  test("checkpoint without draining iterator - caching before checkpointing") {
+    testWithoutDrainingIterator(
+      newSortedRdd.persist(StorageLevel.MEMORY_ONLY).localCheckpoint(),
+      StorageLevel.MEMORY_AND_DISK,
+      50)
+  }
+
+  test("checkpoint without draining iterator - caching after checkpointing") {
+    testWithoutDrainingIterator(
+      newSortedRdd.localCheckpoint().persist(StorageLevel.MEMORY_ONLY),
+      StorageLevel.MEMORY_AND_DISK,
+      50)
+  }
+
+  test("checkpoint blocks exist") {
+    testCheckpointBlocksExist(
+      newRdd.localCheckpoint(),
+      LocalRDDCheckpointData.DEFAULT_STORAGE_LEVEL)
+  }
+
+  test("checkpoint blocks exist - caching before checkpointing") {
+    testCheckpointBlocksExist(
+      newRdd.persist(StorageLevel.MEMORY_ONLY).localCheckpoint(),
+      StorageLevel.MEMORY_AND_DISK)
+  }
+
+  test("checkpoint blocks exist - caching after checkpointing") {
+    testCheckpointBlocksExist(
+      newRdd.localCheckpoint().persist(StorageLevel.MEMORY_ONLY),
+      StorageLevel.MEMORY_AND_DISK)
+  }
+
+  test("missing checkpoint block fails with informative message") {
+    val rdd = newRdd.localCheckpoint()
+    val numPartitions = rdd.partitions.size
+    val partitionIndices = rdd.partitions.map(_.index)
+    val bmm = sc.env.blockManager.master
+
+    // After an action, the blocks should be found somewhere in the cache
+    rdd.collect()
+    partitionIndices.foreach { i =>
+      assert(bmm.contains(RDDBlockId(rdd.id, i)))
+    }
+
+    // Remove one of the blocks to simulate executor failure
+    // Collecting the RDD should now fail with an informative exception
+    val blockId = RDDBlockId(rdd.id, numPartitions - 1)
+    bmm.removeBlock(blockId)
+    try {
+      rdd.collect()
+      fail("Collect should have failed if local checkpoint block is removed...")
+    } catch {
+      case se: SparkException =>
+        assert(se.getMessage.contains(s"Checkpoint block $blockId not found"))
+        assert(se.getMessage.contains("rdd.checkpoint()")) // suggest an alternative
+        assert(se.getMessage.contains("fault-tolerant")) // justify the alternative
+    }
+  }
+
+  /**
+   * Helper method to create a simple RDD.
+   */
+  private def newRdd: RDD[Int] = {
+    sc.parallelize(1 to 100, 4)
+      .map { i => i + 1 }
+      .filter { i => i % 2 == 0 }
+  }
+
+  /**
+   * Helper method to create a simple sorted RDD.
+   */
+  private def newSortedRdd: RDD[Int] = newRdd.sortBy(identity)
+
+  /**
+   * Helper method to test basic lineage truncation with caching.
+   *
+   * @param rdd an RDD that is both marked for caching and local checkpointing
+   */
+  private def testBasicLineageTruncationWithCaching[T](
+      rdd: RDD[T],
+      targetStorageLevel: StorageLevel): Unit = {
+    require(targetStorageLevel !== StorageLevel.NONE)
+    require(rdd.getStorageLevel !== StorageLevel.NONE)
+    require(rdd.isLocallyCheckpointed)
+    val result = rdd.collect()
+    assert(rdd.getStorageLevel === targetStorageLevel)
+    assert(rdd.checkpointData.isDefined)
+    assert(rdd.checkpointData.get.isCheckpointed)
+    assert(rdd.checkpointData.get.checkpointRDD.isDefined)
+    assert(rdd.dependencies.head.rdd === rdd.checkpointData.get.checkpointRDD.get)
+    assert(rdd.collect() === result)
+    assert(rdd.collect() === result)
+  }
+
+  /**
+   * Helper method to test indirect lineage truncation.
+   *
+   * Indirect lineage truncation here means the action is called on one of the
+   * checkpointed RDD's descendants, but not on the checkpointed RDD itself.
+   *
+   * @param rdd a locally checkpointed RDD
+   */
+  private def testIndirectLineageTruncation[T](
+      rdd: RDD[T],
+      targetStorageLevel: StorageLevel): Unit = {
+    require(targetStorageLevel !== StorageLevel.NONE)
+    require(rdd.isLocallyCheckpointed)
+    val rdd1 = rdd.map { i => i + "1" }
+    val rdd2 = rdd1.map { i => i + "2" }
+    val rdd3 = rdd2.map { i => i + "3" }
+    val rddDependencies = rdd.dependencies
+    val rdd1Dependencies = rdd1.dependencies
+    val rdd2Dependencies = rdd2.dependencies
+    val rdd3Dependencies = rdd3.dependencies
+    assert(rdd1Dependencies.size === 1)
+    assert(rdd1Dependencies.head.rdd === rdd)
+    assert(rdd2Dependencies.size === 1)
+    assert(rdd2Dependencies.head.rdd === rdd1)
+    assert(rdd3Dependencies.size === 1)
+    assert(rdd3Dependencies.head.rdd === rdd2)
+
+    // Only the locally checkpointed RDD should have special storage level
+    assert(rdd.getStorageLevel === targetStorageLevel)
+    assert(rdd1.getStorageLevel === StorageLevel.NONE)
+    assert(rdd2.getStorageLevel === StorageLevel.NONE)
+    assert(rdd3.getStorageLevel === StorageLevel.NONE)
+
+    // After an action, only the dependencies of the checkpointed RDD changes
+    val result = rdd3.collect()
+    assert(rdd.dependencies !== rddDependencies)
+    assert(rdd1.dependencies === rdd1Dependencies)
+    assert(rdd2.dependencies === rdd2Dependencies)
+    assert(rdd3.dependencies === rdd3Dependencies)
+    assert(rdd3.collect() === result)
+    assert(rdd3.collect() === result)
+  }
+
+  /**
+   * Helper method to test checkpointing without fully draining the iterator.
+   *
+   * Not all RDD actions fully consume the iterator. As a result, a subset of the partitions
+   * may not be cached. However, since we want to truncate the lineage safely, we explicitly
+   * ensure that *all* partitions are fully cached. This method asserts this behavior.
+   *
+   * @param rdd a locally checkpointed RDD
+   */
+  private def testWithoutDrainingIterator[T](
+      rdd: RDD[T],
+      targetStorageLevel: StorageLevel,
+      targetCount: Int): Unit = {
+    require(targetCount > 0)
+    require(targetStorageLevel !== StorageLevel.NONE)
+    require(rdd.isLocallyCheckpointed)
+
+    // This does not drain the iterator, but checkpointing should still work
+    val first = rdd.first()
+    assert(rdd.count() === targetCount)
+    assert(rdd.count() === targetCount)
+    assert(rdd.first() === first)
+    assert(rdd.first() === first)
+
+    // Test the same thing by calling actions on a descendant instead
+    val rdd1 = rdd.repartition(10)
+    val rdd2 = rdd1.repartition(100)
+    val rdd3 = rdd2.repartition(1000)
+    val first2 = rdd3.first()
+    assert(rdd3.count() === targetCount)
+    assert(rdd3.count() === targetCount)
+    assert(rdd3.first() === first2)
+    assert(rdd3.first() === first2)
+    assert(rdd.getStorageLevel === targetStorageLevel)
+    assert(rdd1.getStorageLevel === StorageLevel.NONE)
+    assert(rdd2.getStorageLevel === StorageLevel.NONE)
+    assert(rdd3.getStorageLevel === StorageLevel.NONE)
+  }
+
+  /**
+   * Helper method to test whether the checkpoint blocks are found in the cache.
+   *
+   * @param rdd a locally checkpointed RDD
+   */
+  private def testCheckpointBlocksExist[T](
+      rdd: RDD[T],
+      targetStorageLevel: StorageLevel): Unit = {
+    val bmm = sc.env.blockManager.master
+    val partitionIndices = rdd.partitions.map(_.index)
+
+    // The blocks should not exist before the action
+    partitionIndices.foreach { i =>
+      assert(!bmm.contains(RDDBlockId(rdd.id, i)))
+    }
+
+    // After an action, the blocks should be found in the cache with the expected level
+    rdd.collect()
+    partitionIndices.foreach { i =>
+      val blockId = RDDBlockId(rdd.id, i)
+      val status = bmm.getBlockStatus(blockId)
+      assert(status.nonEmpty)
+      assert(status.values.head.storageLevel === targetStorageLevel)
+    }
+  }
+
+}
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index f9384c4c3c9d6..280aac931915d 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -80,8 +80,13 @@ object MimaExcludes {
               "org.apache.spark.mllib.linalg.Matrix.numActives")
           ) ++ Seq(
             // SPARK-8914 Remove RDDApi
-            ProblemFilters.exclude[MissingClassProblem](
-            "org.apache.spark.sql.RDDApi")
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.RDDApi")
+          ) ++ Seq(
+            // SPARK-7292 Provide operator to truncate lineage cheaply
+            ProblemFilters.exclude[AbstractClassProblem](
+              "org.apache.spark.rdd.RDDCheckpointData"),
+            ProblemFilters.exclude[AbstractClassProblem](
+              "org.apache.spark.rdd.CheckpointRDD")
           ) ++ Seq(
             // SPARK-8701 Add input metadata in the batch page.
             ProblemFilters.exclude[MissingClassProblem](

From dfe7bd168d9bcf8c53f993f459ab473d893457b0 Mon Sep 17 00:00:00 2001
From: Joseph Batchik <joseph.batchik@cloudera.com>
Date: Mon, 3 Aug 2015 11:17:38 -0700
Subject: [PATCH 0803/1454] [SPARK-9511] [SQL] Fixed Table Name Parsing

The issue was that the tokenizer was parsing "1one" into the numeric 1 using the code on line 110. I added another case to accept strings that start with a number and then have a letter somewhere else in it as well.

Author: Joseph Batchik <joseph.batchik@cloudera.com>

Closes #7844 from JDrit/parse_error and squashes the following commits:

b8ca12f [Joseph Batchik] fixed parsing issue by adding another case
---
 .../spark/sql/catalyst/AbstractSparkSQLParser.scala    |  2 ++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala     | 10 ++++++++++
 2 files changed, 12 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
index d494ae7b71d16..5898a5f93f381 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
@@ -104,6 +104,8 @@ class SqlLexical extends StdLexical {
   override lazy val token: Parser[Token] =
     ( identChar ~ (identChar | digit).* ^^
       { case first ~ rest => processIdent((first :: rest).mkString) }
+    | digit.* ~ identChar ~ (identChar | digit).* ^^
+      { case first ~ middle ~ rest => processIdent((first ++ (middle :: rest)).mkString) }
     | rep1(digit) ~ ('.' ~> digit.*).? ^^ {
         case i ~ None => NumericLit(i.mkString)
         case i ~ Some(d) => FloatLit(i.mkString + "." + d.mkString)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index bbadc202a4f06..f1abae0720058 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1604,4 +1604,14 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     checkAnswer(df.select(-df("i")),
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
+
+  test("SPARK-9511: error with table starting with number") {
+    val df = sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
+      .toDF("num", "str")
+    df.registerTempTable("1one")
+
+    checkAnswer(sqlContext.sql("select count(num) from 1one"), Row(10))
+
+    sqlContext.dropTempTable("1one")
+  }
 }

From 7a9d09f0bb472a1671d3457e1f7108f4c2eb4121 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 3 Aug 2015 11:22:02 -0700
Subject: [PATCH 0804/1454] [SQL][minor] Simplify
 UnsafeRow.calculateBitSetWidthInBytes.

Author: Reynold Xin <rxin@databricks.com>

Closes #7897 from rxin/calculateBitSetWidthInBytes and squashes the following commits:

2e73b3a [Reynold Xin] [SQL][minor] Simplify UnsafeRow.calculateBitSetWidthInBytes.
---
 .../spark/sql/catalyst/expressions/UnsafeRow.java      |  2 +-
 .../scala/org/apache/spark/sql/UnsafeRowSuite.scala    | 10 ++++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index f4230cfaba375..e6750fce4fa80 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -59,7 +59,7 @@ public final class UnsafeRow extends MutableRow {
   //////////////////////////////////////////////////////////////////////////////
 
   public static int calculateBitSetWidthInBytes(int numFields) {
-    return ((numFields / 64) + (numFields % 64 == 0 ? 0 : 1)) * 8;
+    return ((numFields + 63)/ 64) * 8;
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index c5faaa663e749..89bad1bfdab0a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -28,6 +28,16 @@ import org.apache.spark.unsafe.memory.MemoryAllocator
 import org.apache.spark.unsafe.types.UTF8String
 
 class UnsafeRowSuite extends SparkFunSuite {
+
+  test("bitset width calculation") {
+    assert(UnsafeRow.calculateBitSetWidthInBytes(0) === 0)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(1) === 8)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(32) === 8)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(64) === 8)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(65) === 16)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(128) === 16)
+  }
+
   test("writeToStream") {
     val row = InternalRow.apply(UTF8String.fromString("hello"), UTF8String.fromString("world"), 123)
     val arrayBackedUnsafeRow: UnsafeRow =

From 703e44bff19f4c394f6f9bff1ce9152cdc68c51e Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 3 Aug 2015 12:06:58 -0700
Subject: [PATCH 0805/1454] [SPARK-9554] [SQL] Enables in-memory partition
 pruning by default

Author: Cheng Lian <lian@databricks.com>

Closes #7895 from liancheng/spark-9554/enable-in-memory-partition-pruning and squashes the following commits:

67c403e [Cheng Lian] Enables in-memory partition pruning by default
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 387960c4b482b..41ba1c7fe0574 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -200,7 +200,7 @@ private[spark] object SQLConf {
 
   val IN_MEMORY_PARTITION_PRUNING =
     booleanConf("spark.sql.inMemoryColumnarStorage.partitionPruning",
-      defaultValue = Some(false),
+      defaultValue = Some(true),
       doc = "When true, enable partition pruning for in-memory columnar tables.",
       isPublic = false)
 

From ff9169a002f1b75231fd25b7d04157a912503038 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Mon, 3 Aug 2015 12:17:46 -0700
Subject: [PATCH 0806/1454] [SPARK-5133] [ML] Added featureImportance to
 RandomForestClassifier and Regressor

Added featureImportance to RandomForestClassifier and Regressor.

This follows the scikit-learn implementation here: [https://github.com/scikit-learn/scikit-learn/blob/a95203b249c1cf392f86d001ad999e29b2392739/sklearn/tree/_tree.pyx#L3341]

CC: yanboliang  Would you mind taking a look?  Thanks!

Author: Joseph K. Bradley <joseph@databricks.com>
Author: Feynman Liang <fliang@databricks.com>

Closes #7838 from jkbradley/dt-feature-importance and squashes the following commits:

72a167a [Joseph K. Bradley] fixed unit test
86cea5f [Joseph K. Bradley] Modified RF featuresImportances to return Vector instead of Map
5aa74f0 [Joseph K. Bradley] finally fixed unit test for real
33df5db [Joseph K. Bradley] fix unit test
42a2d3b [Joseph K. Bradley] fix unit test
fe94e72 [Joseph K. Bradley] modified feature importance unit tests
cc693ee [Feynman Liang] Add classifier tests
79a6f87 [Feynman Liang] Compare dense vectors in test
21d01fc [Feynman Liang] Added failing SKLearn test
ac0b254 [Joseph K. Bradley] Added featureImportance to RandomForestClassifier/Regressor.  Need to add unit tests
---
 .../RandomForestClassifier.scala              |  30 ++++-
 .../ml/regression/RandomForestRegressor.scala |  33 +++++-
 .../scala/org/apache/spark/ml/tree/Node.scala |  19 +++-
 .../spark/ml/tree/impl/RandomForest.scala     |  92 +++++++++++++++
 .../org/apache/spark/ml/tree/treeModels.scala |   6 +
 .../JavaRandomForestClassifierSuite.java      |   2 +
 .../JavaRandomForestRegressorSuite.java       |   2 +
 .../RandomForestClassifierSuite.scala         |  31 ++++-
 .../org/apache/spark/ml/impl/TreeTests.scala  |  18 +++
 .../RandomForestRegressorSuite.scala          |  27 ++++-
 .../ml/tree/impl/RandomForestSuite.scala      | 107 ++++++++++++++++++
 11 files changed, 351 insertions(+), 16 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 56e80cc8fe6e1..b59826a59499a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -95,7 +95,8 @@ final class RandomForestClassifier(override val uid: String)
     val trees =
       RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
         .map(_.asInstanceOf[DecisionTreeClassificationModel])
-    new RandomForestClassificationModel(trees, numClasses)
+    val numFeatures = oldDataset.first().features.size
+    new RandomForestClassificationModel(trees, numFeatures, numClasses)
   }
 
   override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra)
@@ -118,11 +119,13 @@ object RandomForestClassifier {
  * features.
  * @param _trees  Decision trees in the ensemble.
  *               Warning: These have null parents.
+ * @param numFeatures  Number of features used by this model
  */
 @Experimental
 final class RandomForestClassificationModel private[ml] (
     override val uid: String,
     private val _trees: Array[DecisionTreeClassificationModel],
+    val numFeatures: Int,
     override val numClasses: Int)
   extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel]
   with TreeEnsembleModel with Serializable {
@@ -133,8 +136,8 @@ final class RandomForestClassificationModel private[ml] (
    * Construct a random forest classification model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeClassificationModel], numClasses: Int) =
-    this(Identifiable.randomUID("rfc"), trees, numClasses)
+  def this(trees: Array[DecisionTreeClassificationModel], numFeatures: Int, numClasses: Int) =
+    this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
@@ -182,13 +185,30 @@ final class RandomForestClassificationModel private[ml] (
   }
 
   override def copy(extra: ParamMap): RandomForestClassificationModel = {
-    copyValues(new RandomForestClassificationModel(uid, _trees, numClasses), extra)
+    copyValues(new RandomForestClassificationModel(uid, _trees, numFeatures, numClasses), extra)
   }
 
   override def toString: String = {
     s"RandomForestClassificationModel with $numTrees trees"
   }
 
+  /**
+   * Estimate of the importance of each feature.
+   *
+   * This generalizes the idea of "Gini" importance to other losses,
+   * following the explanation of Gini importance from "Random Forests" documentation
+   * by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
+   *
+   * This feature importance is calculated as follows:
+   *  - Average over trees:
+   *     - importance(feature j) = sum (over nodes which split on feature j) of the gain,
+   *       where gain is scaled by the number of instances passing through node
+   *     - Normalize importances for tree based on total number of training instances used
+   *       to build tree.
+   *  - Normalize feature importance vector to sum to 1.
+   */
+  lazy val featureImportances: Vector = RandomForest.featureImportances(trees, numFeatures)
+
   /** (private[ml]) Convert to a model in the old API */
   private[ml] def toOld: OldRandomForestModel = {
     new OldRandomForestModel(OldAlgo.Classification, _trees.map(_.toOld))
@@ -210,6 +230,6 @@ private[ml] object RandomForestClassificationModel {
       DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures)
     }
     val uid = if (parent != null) parent.uid else Identifiable.randomUID("rfc")
-    new RandomForestClassificationModel(uid, newTrees, numClasses)
+    new RandomForestClassificationModel(uid, newTrees, -1, numClasses)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 17fb1ad5e15d4..1ee43c8725732 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -30,7 +30,7 @@ import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestMo
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.DoubleType
+
 
 /**
  * :: Experimental ::
@@ -87,7 +87,8 @@ final class RandomForestRegressor(override val uid: String)
     val trees =
       RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
         .map(_.asInstanceOf[DecisionTreeRegressionModel])
-    new RandomForestRegressionModel(trees)
+    val numFeatures = oldDataset.first().features.size
+    new RandomForestRegressionModel(trees, numFeatures)
   }
 
   override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra)
@@ -108,11 +109,13 @@ object RandomForestRegressor {
  * [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]] model for regression.
  * It supports both continuous and categorical features.
  * @param _trees  Decision trees in the ensemble.
+ * @param numFeatures  Number of features used by this model
  */
 @Experimental
 final class RandomForestRegressionModel private[ml] (
     override val uid: String,
-    private val _trees: Array[DecisionTreeRegressionModel])
+    private val _trees: Array[DecisionTreeRegressionModel],
+    val numFeatures: Int)
   extends PredictionModel[Vector, RandomForestRegressionModel]
   with TreeEnsembleModel with Serializable {
 
@@ -122,7 +125,8 @@ final class RandomForestRegressionModel private[ml] (
    * Construct a random forest regression model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeRegressionModel]) = this(Identifiable.randomUID("rfr"), trees)
+  def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
+    this(Identifiable.randomUID("rfr"), trees, numFeatures)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
@@ -147,13 +151,30 @@ final class RandomForestRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): RandomForestRegressionModel = {
-    copyValues(new RandomForestRegressionModel(uid, _trees), extra)
+    copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra)
   }
 
   override def toString: String = {
     s"RandomForestRegressionModel with $numTrees trees"
   }
 
+  /**
+   * Estimate of the importance of each feature.
+   *
+   * This generalizes the idea of "Gini" importance to other losses,
+   * following the explanation of Gini importance from "Random Forests" documentation
+   * by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
+   *
+   * This feature importance is calculated as follows:
+   *  - Average over trees:
+   *     - importance(feature j) = sum (over nodes which split on feature j) of the gain,
+   *       where gain is scaled by the number of instances passing through node
+   *     - Normalize importances for tree based on total number of training instances used
+   *       to build tree.
+   *  - Normalize feature importance vector to sum to 1.
+   */
+  lazy val featureImportances: Vector = RandomForest.featureImportances(trees, numFeatures)
+
   /** (private[ml]) Convert to a model in the old API */
   private[ml] def toOld: OldRandomForestModel = {
     new OldRandomForestModel(OldAlgo.Regression, _trees.map(_.toOld))
@@ -173,6 +194,6 @@ private[ml] object RandomForestRegressionModel {
       // parent for each tree is null since there is no good way to set this.
       DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
     }
-    new RandomForestRegressionModel(parent.uid, newTrees)
+    new RandomForestRegressionModel(parent.uid, newTrees, -1)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
index 8879352a600a9..cd24931293903 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
@@ -44,7 +44,7 @@ sealed abstract class Node extends Serializable {
    * and probabilities.
    * For classification, the array of class counts must be normalized to a probability distribution.
    */
-  private[tree] def impurityStats: ImpurityCalculator
+  private[ml] def impurityStats: ImpurityCalculator
 
   /** Recursive prediction helper method */
   private[ml] def predictImpl(features: Vector): LeafNode
@@ -72,6 +72,12 @@ sealed abstract class Node extends Serializable {
    * @param id  Node ID using old format IDs
    */
   private[ml] def toOld(id: Int): OldNode
+
+  /**
+   * Trace down the tree, and return the largest feature index used in any split.
+   * @return  Max feature index used in a split, or -1 if there are no splits (single leaf node).
+   */
+  private[ml] def maxSplitFeatureIndex(): Int
 }
 
 private[ml] object Node {
@@ -109,7 +115,7 @@ private[ml] object Node {
 final class LeafNode private[ml] (
     override val prediction: Double,
     override val impurity: Double,
-    override val impurityStats: ImpurityCalculator) extends Node {
+    override private[ml] val impurityStats: ImpurityCalculator) extends Node {
 
   override def toString: String =
     s"LeafNode(prediction = $prediction, impurity = $impurity)"
@@ -129,6 +135,8 @@ final class LeafNode private[ml] (
     new OldNode(id, new OldPredict(prediction, prob = impurityStats.prob(prediction)),
       impurity, isLeaf = true, None, None, None, None)
   }
+
+  override private[ml] def maxSplitFeatureIndex(): Int = -1
 }
 
 /**
@@ -150,7 +158,7 @@ final class InternalNode private[ml] (
     val leftChild: Node,
     val rightChild: Node,
     val split: Split,
-    override val impurityStats: ImpurityCalculator) extends Node {
+    override private[ml] val impurityStats: ImpurityCalculator) extends Node {
 
   override def toString: String = {
     s"InternalNode(prediction = $prediction, impurity = $impurity, split = $split)"
@@ -190,6 +198,11 @@ final class InternalNode private[ml] (
         new OldPredict(leftChild.prediction, prob = 0.0),
         new OldPredict(rightChild.prediction, prob = 0.0))))
   }
+
+  override private[ml] def maxSplitFeatureIndex(): Int = {
+    math.max(split.featureIndex,
+      math.max(leftChild.maxSplitFeatureIndex(), rightChild.maxSplitFeatureIndex()))
+  }
 }
 
 private object InternalNode {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
index a8b90d9d266a1..4ac51a475474a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
@@ -26,6 +26,7 @@ import org.apache.spark.Logging
 import org.apache.spark.ml.classification.DecisionTreeClassificationModel
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impl.{BaggedPoint, DTStatsAggregator, DecisionTreeMetadata,
@@ -34,6 +35,7 @@ import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 import org.apache.spark.mllib.tree.model.ImpurityStats
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.collection.OpenHashMap
 import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
 
 
@@ -1113,4 +1115,94 @@ private[ml] object RandomForest extends Logging {
     }
   }
 
+  /**
+   * Given a Random Forest model, compute the importance of each feature.
+   * This generalizes the idea of "Gini" importance to other losses,
+   * following the explanation of Gini importance from "Random Forests" documentation
+   * by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
+   *
+   * This feature importance is calculated as follows:
+   *  - Average over trees:
+   *     - importance(feature j) = sum (over nodes which split on feature j) of the gain,
+   *       where gain is scaled by the number of instances passing through node
+   *     - Normalize importances for tree based on total number of training instances used
+   *       to build tree.
+   *  - Normalize feature importance vector to sum to 1.
+   *
+   * Note: This should not be used with Gradient-Boosted Trees.  It only makes sense for
+   *       independently trained trees.
+   * @param trees  Unweighted forest of trees
+   * @param numFeatures  Number of features in model (even if not all are explicitly used by
+   *                     the model).
+   *                     If -1, then numFeatures is set based on the max feature index in all trees.
+   * @return  Feature importance values, of length numFeatures.
+   */
+  private[ml] def featureImportances(trees: Array[DecisionTreeModel], numFeatures: Int): Vector = {
+    val totalImportances = new OpenHashMap[Int, Double]()
+    trees.foreach { tree =>
+      // Aggregate feature importance vector for this tree
+      val importances = new OpenHashMap[Int, Double]()
+      computeFeatureImportance(tree.rootNode, importances)
+      // Normalize importance vector for this tree, and add it to total.
+      // TODO: In the future, also support normalizing by tree.rootNode.impurityStats.count?
+      val treeNorm = importances.map(_._2).sum
+      if (treeNorm != 0) {
+        importances.foreach { case (idx, impt) =>
+          val normImpt = impt / treeNorm
+          totalImportances.changeValue(idx, normImpt, _ + normImpt)
+        }
+      }
+    }
+    // Normalize importances
+    normalizeMapValues(totalImportances)
+    // Construct vector
+    val d = if (numFeatures != -1) {
+      numFeatures
+    } else {
+      // Find max feature index used in trees
+      val maxFeatureIndex = trees.map(_.maxSplitFeatureIndex()).max
+      maxFeatureIndex + 1
+    }
+    if (d == 0) {
+      assert(totalImportances.size == 0, s"Unknown error in computing RandomForest feature" +
+        s" importance: No splits in forest, but some non-zero importances.")
+    }
+    val (indices, values) = totalImportances.iterator.toSeq.sortBy(_._1).unzip
+    Vectors.sparse(d, indices.toArray, values.toArray)
+  }
+
+  /**
+   * Recursive method for computing feature importances for one tree.
+   * This walks down the tree, adding to the importance of 1 feature at each node.
+   * @param node  Current node in recursion
+   * @param importances  Aggregate feature importances, modified by this method
+   */
+  private[impl] def computeFeatureImportance(
+      node: Node,
+      importances: OpenHashMap[Int, Double]): Unit = {
+    node match {
+      case n: InternalNode =>
+        val feature = n.split.featureIndex
+        val scaledGain = n.gain * n.impurityStats.count
+        importances.changeValue(feature, scaledGain, _ + scaledGain)
+        computeFeatureImportance(n.leftChild, importances)
+        computeFeatureImportance(n.rightChild, importances)
+      case n: LeafNode =>
+        // do nothing
+    }
+  }
+
+  /**
+   * Normalize the values of this map to sum to 1, in place.
+   * If all values are 0, this method does nothing.
+   * @param map  Map with non-negative values.
+   */
+  private[impl] def normalizeMapValues(map: OpenHashMap[Int, Double]): Unit = {
+    val total = map.map(_._2).sum
+    if (total != 0) {
+      val keys = map.iterator.map(_._1).toArray
+      keys.foreach { key => map.changeValue(key, 0.0, _ / total) }
+    }
+  }
+
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
index 22873909c33fa..b77191156f68f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
@@ -53,6 +53,12 @@ private[ml] trait DecisionTreeModel {
     val header = toString + "\n"
     header + rootNode.subtreeToString(2)
   }
+
+  /**
+   * Trace down the tree, and return the largest feature index used in any split.
+   * @return  Max feature index used in a split, or -1 if there are no splits (single leaf node).
+   */
+  private[ml] def maxSplitFeatureIndex(): Int = rootNode.maxSplitFeatureIndex()
 }
 
 /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
index 32d0b3856b7e2..a66a1e12927be 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
@@ -29,6 +29,7 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.impl.TreeTests;
 import org.apache.spark.mllib.classification.LogisticRegressionSuite;
+import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
 
@@ -85,6 +86,7 @@ public void runDT() {
     model.toDebugString();
     model.trees();
     model.treeWeights();
+    Vector importances = model.featureImportances();
 
     /*
     // TODO: Add test once save/load are implemented.  SPARK-6725
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
index e306ebadfe7cf..a00ce5e249c34 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
@@ -29,6 +29,7 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.classification.LogisticRegressionSuite;
 import org.apache.spark.ml.impl.TreeTests;
+import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
 
@@ -85,6 +86,7 @@ public void runDT() {
     model.toDebugString();
     model.trees();
     model.treeWeights();
+    Vector importances = model.featureImportances();
 
     /*
     // TODO: Add test once save/load are implemented.   SPARK-6725
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index edf848b21a905..6ca4b5aa5fde8 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -67,7 +67,7 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
   test("params") {
     ParamsSuite.checkParams(new RandomForestClassifier)
     val model = new RandomForestClassificationModel("rfc",
-      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2)), 2)
+      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2)), 2, 2)
     ParamsSuite.checkParams(model)
   }
 
@@ -149,6 +149,35 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     }
   }
 
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests of feature importance
+  /////////////////////////////////////////////////////////////////////////////
+  test("Feature importance with toy data") {
+    val numClasses = 2
+    val rf = new RandomForestClassifier()
+      .setImpurity("Gini")
+      .setMaxDepth(3)
+      .setNumTrees(3)
+      .setFeatureSubsetStrategy("all")
+      .setSubsamplingRate(1.0)
+      .setSeed(123)
+
+    // In this data, feature 1 is very important.
+    val data: RDD[LabeledPoint] = sc.parallelize(Seq(
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 1)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 1, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0)),
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0))
+    ))
+    val categoricalFeatures = Map.empty[Int, Int]
+    val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)
+
+    val importances = rf.fit(df).featureImportances
+    val mostImportantFeature = importances.argmax
+    assert(mostImportantFeature === 1)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
diff --git a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala
index 778abcba22c10..460849c79f04f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala
@@ -124,4 +124,22 @@ private[ml] object TreeTests extends SparkFunSuite {
         "checkEqual failed since the two tree ensembles were not identical")
     }
   }
+
+  /**
+   * Helper method for constructing a tree for testing.
+   * Given left, right children, construct a parent node.
+   * @param split  Split for parent node
+   * @return  Parent node with children attached
+   */
+  def buildParentNode(left: Node, right: Node, split: Split): Node = {
+    val leftImp = left.impurityStats
+    val rightImp = right.impurityStats
+    val parentImp = leftImp.copy.add(rightImp)
+    val leftWeight = leftImp.count / parentImp.count.toDouble
+    val rightWeight = rightImp.count / parentImp.count.toDouble
+    val gain = parentImp.calculate() -
+      (leftWeight * leftImp.calculate() + rightWeight * rightImp.calculate())
+    val pred = parentImp.predict
+    new InternalNode(pred, parentImp.calculate(), gain, left, right, split, parentImp)
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
index b24ecaa57c89b..992ce9562434e 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
@@ -26,7 +27,6 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * Test suite for [[RandomForestRegressor]].
  */
@@ -71,6 +71,31 @@ class RandomForestRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
     regressionTestWithContinuousFeatures(rf)
   }
 
+  test("Feature importance with toy data") {
+    val rf = new RandomForestRegressor()
+      .setImpurity("variance")
+      .setMaxDepth(3)
+      .setNumTrees(3)
+      .setFeatureSubsetStrategy("all")
+      .setSubsamplingRate(1.0)
+      .setSeed(123)
+
+    // In this data, feature 1 is very important.
+    val data: RDD[LabeledPoint] = sc.parallelize(Seq(
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 1)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 1, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0)),
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0))
+    ))
+    val categoricalFeatures = Map.empty[Int, Int]
+    val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0)
+
+    val importances = rf.fit(df).featureImportances
+    val mostImportantFeature = importances.argmax
+    assert(mostImportantFeature === 1)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala
new file mode 100644
index 0000000000000..dc852795c7f62
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.tree.{ContinuousSplit, DecisionTreeModel, LeafNode, Node}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.tree.impurity.GiniCalculator
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.util.collection.OpenHashMap
+
+/**
+ * Test suite for [[RandomForest]].
+ */
+class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  import RandomForestSuite.mapToVec
+
+  test("computeFeatureImportance, featureImportances") {
+    /* Build tree for testing, with this structure:
+          grandParent
+      left2       parent
+                left  right
+     */
+    val leftImp = new GiniCalculator(Array(3.0, 2.0, 1.0))
+    val left = new LeafNode(0.0, leftImp.calculate(), leftImp)
+
+    val rightImp = new GiniCalculator(Array(1.0, 2.0, 5.0))
+    val right = new LeafNode(2.0, rightImp.calculate(), rightImp)
+
+    val parent = TreeTests.buildParentNode(left, right, new ContinuousSplit(0, 0.5))
+    val parentImp = parent.impurityStats
+
+    val left2Imp = new GiniCalculator(Array(1.0, 6.0, 1.0))
+    val left2 = new LeafNode(0.0, left2Imp.calculate(), left2Imp)
+
+    val grandParent = TreeTests.buildParentNode(left2, parent, new ContinuousSplit(1, 1.0))
+    val grandImp = grandParent.impurityStats
+
+    // Test feature importance computed at different subtrees.
+    def testNode(node: Node, expected: Map[Int, Double]): Unit = {
+      val map = new OpenHashMap[Int, Double]()
+      RandomForest.computeFeatureImportance(node, map)
+      assert(mapToVec(map.toMap) ~== mapToVec(expected) relTol 0.01)
+    }
+
+    // Leaf node
+    testNode(left, Map.empty[Int, Double])
+
+    // Internal node with 2 leaf children
+    val feature0importance = parentImp.calculate() * parentImp.count -
+      (leftImp.calculate() * leftImp.count + rightImp.calculate() * rightImp.count)
+    testNode(parent, Map(0 -> feature0importance))
+
+    // Full tree
+    val feature1importance = grandImp.calculate() * grandImp.count -
+      (left2Imp.calculate() * left2Imp.count + parentImp.calculate() * parentImp.count)
+    testNode(grandParent, Map(0 -> feature0importance, 1 -> feature1importance))
+
+    // Forest consisting of (full tree) + (internal node with 2 leafs)
+    val trees = Array(parent, grandParent).map { root =>
+      new DecisionTreeClassificationModel(root, numClasses = 3).asInstanceOf[DecisionTreeModel]
+    }
+    val importances: Vector = RandomForest.featureImportances(trees, 2)
+    val tree2norm = feature0importance + feature1importance
+    val expected = Vectors.dense((1.0 + feature0importance / tree2norm) / 2.0,
+      (feature1importance / tree2norm) / 2.0)
+    assert(importances ~== expected relTol 0.01)
+  }
+
+  test("normalizeMapValues") {
+    val map = new OpenHashMap[Int, Double]()
+    map(0) = 1.0
+    map(2) = 2.0
+    RandomForest.normalizeMapValues(map)
+    val expected = Map(0 -> 1.0 / 3.0, 2 -> 2.0 / 3.0)
+    assert(mapToVec(map.toMap) ~== mapToVec(expected) relTol 0.01)
+  }
+
+}
+
+private object RandomForestSuite {
+
+  def mapToVec(map: Map[Int, Double]): Vector = {
+    val size = (map.keys.toSeq :+ 0).max + 1
+    val (indices, values) = map.toSeq.sortBy(_._1).unzip
+    Vectors.sparse(size, indices.toArray, values.toArray)
+  }
+}

From ba1c4e138de2ea84b55def4eed2bd363e60aea4d Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 3 Aug 2015 12:53:44 -0700
Subject: [PATCH 0807/1454] [SPARK-9558][DOCS]Update docs to follow the
 increase of memory defaults.

Now the memory defaults of master and slave in Standalone mode and History Server is 1g, not 512m. So let's update docs.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7896 from sarutak/update-doc-for-daemon-memory and squashes the following commits:

a77626c [Kousuke Saruta] Fix docs to follow the update of increase of memory defaults
---
 conf/spark-env.sh.template | 1 +
 docs/monitoring.md         | 2 +-
 docs/spark-standalone.md   | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index 192d3ae091134..c05fe381a36a7 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -38,6 +38,7 @@
 # - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
 # - SPARK_WORKER_DIR, to set the working directory of worker processes
 # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
+# - SPARK_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g).
 # - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
 # - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y")
 # - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
diff --git a/docs/monitoring.md b/docs/monitoring.md
index bcf885fe4e681..cedceb2958023 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -48,7 +48,7 @@ follows:
   <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
-    <td>Memory to allocate to the history server (default: 512m).</td>
+    <td>Memory to allocate to the history server (default: 1g).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 4f71fbc086cd0..2fe9ec3542b28 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -152,7 +152,7 @@ You can optionally configure the cluster further by setting environment variable
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
-    <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m).</td>
+    <td>Memory to allocate to the Spark master and worker daemons themselves (default: 1g).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>

From 8ca287ebbd58985a568341b08040d0efa9d3641a Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 3 Aug 2015 13:58:00 -0700
Subject: [PATCH 0808/1454] [SPARK-9191] [ML] [Doc] Add ml.PCA user guide and
 code examples

Add ml.PCA user guide document and code examples for Scala/Java/Python.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7522 from yanboliang/ml-pca-md and squashes the following commits:

60dec05 [Yanbo Liang] address comments
f992abe [Yanbo Liang] Add ml.PCA doc and examples
---
 docs/ml-features.md | 86 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 54068debe2159..fa0ad1f00ab12 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -461,6 +461,92 @@ for binarized_feature, in binarizedFeatures.collect():
 </div>
 </div>
 
+## PCA
+
+[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. A [PCA](api/scala/index.html#org.apache.spark.ml.feature.PCA) class trains a model to project vectors to a low-dimensional space using PCA. The example below shows how to project 5-dimensional feature vectors into 3-dimensional principal components.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+See the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.feature.PCA) for API details.
+{% highlight scala %}
+import org.apache.spark.ml.feature.PCA
+import org.apache.spark.mllib.linalg.Vectors
+
+val data = Array(
+  Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
+  Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
+  Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
+)
+val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
+val pca = new PCA()
+  .setInputCol("features")
+  .setOutputCol("pcaFeatures")
+  .setK(3)
+  .fit(df)
+val pcaDF = pca.transform(df)
+val result = pcaDF.select("pcaFeatures")
+result.show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+See the [Java API documentation](api/java/org/apache/spark/ml/feature/PCA.html) for API details.
+{% highlight java %}
+import com.google.common.collect.Lists;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.feature.PCA
+import org.apache.spark.ml.feature.PCAModel
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+JavaSparkContext jsc = ...
+SQLContext jsql = ...
+JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+  RowFactory.create(Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0})),
+  RowFactory.create(Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0)),
+  RowFactory.create(Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))
+));
+StructType schema = new StructType(new StructField[] {
+  new StructField("features", new VectorUDT(), false, Metadata.empty()),
+});
+DataFrame df = jsql.createDataFrame(data, schema);
+PCAModel pca = new PCA()
+  .setInputCol("features")
+  .setOutputCol("pcaFeatures")
+  .setK(3)
+  .fit(df);
+DataFrame result = pca.transform(df).select("pcaFeatures");
+result.show();
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+See the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.feature.PCA) for API details.
+{% highlight python %}
+from pyspark.ml.feature import PCA
+from pyspark.mllib.linalg import Vectors
+
+data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
+  (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
+  (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
+df = sqlContext.createDataFrame(data,["features"])
+pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures")
+model = pca.fit(df)
+result = model.transform(df).select("pcaFeatures")
+result.show(truncate=False)
+{% endhighlight %}
+</div>
+</div>
+
 ## PolynomialExpansion
 
 [Polynomial expansion](http://en.wikipedia.org/wiki/Polynomial_expansion) is the process of expanding your features into a polynomial space, which is formulated by an n-degree combination of original dimensions. A [PolynomialExpansion](api/scala/index.html#org.apache.spark.ml.feature.PolynomialExpansion) class provides this functionality.  The example below shows how to expand your features into a 3-degree polynomial space.

From e4765a46833baff1dd7465c4cf50e947de7e8f21 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 3 Aug 2015 13:59:35 -0700
Subject: [PATCH 0809/1454] [SPARK-9544] [MLLIB] add Python API for RFormula

Add Python API for RFormula. Similar to other feature transformers in Python. This is just a thin wrapper over the Scala implementation. ericl MechCoder

Author: Xiangrui Meng <meng@databricks.com>

Closes #7879 from mengxr/SPARK-9544 and squashes the following commits:

3d5ff03 [Xiangrui Meng] add an doctest for . and -
5e969a5 [Xiangrui Meng] fix pydoc
1cd41f8 [Xiangrui Meng] organize imports
3c18b10 [Xiangrui Meng] add Python API for RFormula
---
 .../apache/spark/ml/feature/RFormula.scala    | 21 ++---
 python/pyspark/ml/feature.py                  | 85 ++++++++++++++++++-
 2 files changed, 91 insertions(+), 15 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index d1726917e4517..d5360c9217ea9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -19,16 +19,14 @@ package org.apache.spark.ml.feature
 
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
-import scala.util.parsing.combinator.RegexParsers
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.{Estimator, Model, Transformer, Pipeline, PipelineModel, PipelineStage}
+import org.apache.spark.ml.{Estimator, Model, Pipeline, PipelineModel, PipelineStage, Transformer}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.sql.DataFrame
-import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 
 /**
@@ -63,31 +61,26 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
    */
   val formula: Param[String] = new Param(this, "formula", "R model formula")
 
-  private var parsedFormula: Option[ParsedRFormula] = None
-
   /**
    * Sets the formula to use for this transformer. Must be called before use.
    * @group setParam
    * @param value an R formula in string form (e.g. "y ~ x + z")
    */
-  def setFormula(value: String): this.type = {
-    parsedFormula = Some(RFormulaParser.parse(value))
-    set(formula, value)
-    this
-  }
+  def setFormula(value: String): this.type = set(formula, value)
 
   /** @group getParam */
   def getFormula: String = $(formula)
 
   /** Whether the formula specifies fitting an intercept. */
   private[ml] def hasIntercept: Boolean = {
-    require(parsedFormula.isDefined, "Must call setFormula() first.")
-    parsedFormula.get.hasIntercept
+    require(isDefined(formula), "Formula must be defined first.")
+    RFormulaParser.parse($(formula)).hasIntercept
   }
 
   override def fit(dataset: DataFrame): RFormulaModel = {
-    require(parsedFormula.isDefined, "Must call setFormula() first.")
-    val resolvedFormula = parsedFormula.get.resolve(dataset.schema)
+    require(isDefined(formula), "Formula must be defined first.")
+    val parsedFormula = RFormulaParser.parse($(formula))
+    val resolvedFormula = parsedFormula.resolve(dataset.schema)
     // StringType terms and terms representing interactions need to be encoded before assembly.
     // TODO(ekl) add support for feature interactions
     val encoderStages = ArrayBuffer[PipelineStage]()
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 015e7a9d4900a..3f04c41ac5ab6 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -24,7 +24,7 @@
 __all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
            'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
            'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
-           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel']
+           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc
@@ -1110,6 +1110,89 @@ class PCAModel(JavaModel):
     """
 
 
+@inherit_doc
+class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol):
+    """
+    .. note:: Experimental
+
+    Implements the transforms required for fitting a dataset against an
+    R model formula. Currently we support a limited subset of the R
+    operators, including '~', '+', '-', and '.'. Also see the R formula
+    docs:
+    http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+
+    >>> df = sqlContext.createDataFrame([
+    ...     (1.0, 1.0, "a"),
+    ...     (0.0, 2.0, "b"),
+    ...     (0.0, 0.0, "a")
+    ... ], ["y", "x", "s"])
+    >>> rf = RFormula(formula="y ~ x + s")
+    >>> rf.fit(df).transform(df).show()
+    +---+---+---+---------+-----+
+    |  y|  x|  s| features|label|
+    +---+---+---+---------+-----+
+    |1.0|1.0|  a|[1.0,1.0]|  1.0|
+    |0.0|2.0|  b|[2.0,0.0]|  0.0|
+    |0.0|0.0|  a|[0.0,1.0]|  0.0|
+    +---+---+---+---------+-----+
+    ...
+    >>> rf.fit(df, {rf.formula: "y ~ . - s"}).transform(df).show()
+    +---+---+---+--------+-----+
+    |  y|  x|  s|features|label|
+    +---+---+---+--------+-----+
+    |1.0|1.0|  a|   [1.0]|  1.0|
+    |0.0|2.0|  b|   [2.0]|  0.0|
+    |0.0|0.0|  a|   [0.0]|  0.0|
+    +---+---+---+--------+-----+
+    ...
+    """
+
+    # a placeholder to make it appear in the generated doc
+    formula = Param(Params._dummy(), "formula", "R model formula")
+
+    @keyword_only
+    def __init__(self, formula=None, featuresCol="features", labelCol="label"):
+        """
+        __init__(self, formula=None, featuresCol="features", labelCol="label")
+        """
+        super(RFormula, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RFormula", self.uid)
+        self.formula = Param(self, "formula", "R model formula")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, formula=None, featuresCol="features", labelCol="label"):
+        """
+        setParams(self, formula=None, featuresCol="features", labelCol="label")
+        Sets params for RFormula.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setFormula(self, value):
+        """
+        Sets the value of :py:attr:`formula`.
+        """
+        self._paramMap[self.formula] = value
+        return self
+
+    def getFormula(self):
+        """
+        Gets the value of :py:attr:`formula`.
+        """
+        return self.getOrDefault(self.formula)
+
+    def _create_model(self, java_model):
+        return RFormulaModel(java_model)
+
+
+class RFormulaModel(JavaModel):
+    """
+    Model fitted by :py:class:`RFormula`.
+    """
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

From 702aa9d7fb16c98a50e046edfd76b8a7861d0391 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 3 Aug 2015 14:22:07 -0700
Subject: [PATCH 0810/1454] [SPARK-8735] [SQL] Expose memory usage for
 shuffles, joins and aggregations

This patch exposes the memory used by internal data structures on the SparkUI. This tracks memory used by all spilling operations and SQL operators backed by Tungsten, e.g. `BroadcastHashJoin`, `ExternalSort`, `GeneratedAggregate` etc. The metric exposed is "peak execution memory", which broadly refers to the peak in-memory sizes of each of these data structure.

A separate patch will extend this by linking the new information to the SQL operators themselves.

<img width="950" alt="screen shot 2015-07-29 at 7 43 17 pm" src="https://cloud.githubusercontent.com/assets/2133137/8974776/b90fc980-362a-11e5-9e2b-842da75b1641.png">
<img width="802" alt="screen shot 2015-07-29 at 7 43 05 pm" src="https://cloud.githubusercontent.com/assets/2133137/8974777/baa76492-362a-11e5-9b77-e364a6a6b64e.png">

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7770)
<!-- Reviewable:end -->

Author: Andrew Or <andrew@databricks.com>

Closes #7770 from andrewor14/expose-memory-metrics and squashes the following commits:

9abecb9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
f5b0d68 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
d7df332 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
8eefbc5 [Andrew Or] Fix non-failing tests
9de2a12 [Andrew Or] Fix tests due to another logical merge conflict
876bfa4 [Andrew Or] Fix failing test after logical merge conflict
361a359 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
40b4802 [Andrew Or] Fix style?
d0fef87 [Andrew Or] Fix tests?
b3b92f6 [Andrew Or] Address comments
0625d73 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
c00a197 [Andrew Or] Fix potential NPEs
10da1cd [Andrew Or] Fix compile
17f4c2d [Andrew Or] Fix compile?
a87b4d0 [Andrew Or] Fix compile?
d70874d [Andrew Or] Fix test compile + address comments
2840b7d [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
6aa2f7a [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
b889a68 [Andrew Or] Minor changes: comments, spacing, style
663a303 [Andrew Or] UnsafeShuffleWriter: update peak memory before close
d090a94 [Andrew Or] Fix style
2480d84 [Andrew Or] Expand test coverage
5f1235b [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
1ecf678 [Andrew Or] Minor changes: comments, style, unused imports
0b6926c [Andrew Or] Oops
111a05e [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
a7a39a5 [Andrew Or] Strengthen presence check for accumulator
a919eb7 [Andrew Or] Add tests for unsafe shuffle writer
23c845d [Andrew Or] Add tests for SQL operators
a757550 [Andrew Or] Address comments
b5c51c1 [Andrew Or] Re-enable test in JavaAPISuite
5107691 [Andrew Or] Add tests for internal accumulators
59231e4 [Andrew Or] Fix tests
9528d09 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
5b5e6f3 [Andrew Or] Add peak execution memory to summary table + tooltip
92b4b6b [Andrew Or] Display peak execution memory on the UI
eee5437 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
d9b9015 [Andrew Or] Track execution memory in unsafe shuffles
770ee54 [Andrew Or] Track execution memory in broadcast joins
9c605a4 [Andrew Or] Track execution memory in GeneratedAggregate
9e824f2 [Andrew Or] Add back execution memory tracking for *ExternalSort
4ef4cb1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
e6c3e2f [Andrew Or] Move internal accumulators creation to Stage
a417592 [Andrew Or] Expose memory metrics in UnsafeExternalSorter
3c4f042 [Andrew Or] Track memory usage in ExternalAppendOnlyMap / ExternalSorter
bd7ab3f [Andrew Or] Add internal accumulators to TaskContext
---
 .../unsafe/UnsafeShuffleExternalSorter.java   |  27 ++-
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |  38 +++-
 .../spark/unsafe/map/BytesToBytesMap.java     |   8 +-
 .../unsafe/sort/UnsafeExternalSorter.java     |  29 ++-
 .../org/apache/spark/ui/static/webui.css      |   2 +-
 .../scala/org/apache/spark/Accumulators.scala |  60 +++++-
 .../scala/org/apache/spark/Aggregator.scala   |  24 +--
 .../scala/org/apache/spark/TaskContext.scala  |  13 +-
 .../org/apache/spark/TaskContextImpl.scala    |   8 +
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |   9 +-
 .../spark/scheduler/AccumulableInfo.scala     |   9 +-
 .../apache/spark/scheduler/DAGScheduler.scala |  28 ++-
 .../apache/spark/scheduler/ResultTask.scala   |   6 +-
 .../spark/scheduler/ShuffleMapTask.scala      |  10 +-
 .../org/apache/spark/scheduler/Stage.scala    |  16 ++
 .../org/apache/spark/scheduler/Task.scala     |  18 +-
 .../shuffle/hash/HashShuffleReader.scala      |   8 +-
 .../scala/org/apache/spark/ui/ToolTips.scala  |   7 +
 .../org/apache/spark/ui/jobs/StagePage.scala  | 140 +++++++++----
 .../spark/ui/jobs/TaskDetailsClassNames.scala |   1 +
 .../collection/ExternalAppendOnlyMap.scala    |  13 +-
 .../util/collection/ExternalSorter.scala      |  20 +-
 .../java/org/apache/spark/JavaAPISuite.java   |   3 +-
 .../unsafe/UnsafeShuffleWriterSuite.java      |  54 +++++
 .../map/AbstractBytesToBytesMapSuite.java     |  39 ++++
 .../sort/UnsafeExternalSorterSuite.java       |  46 +++++
 .../org/apache/spark/AccumulatorSuite.scala   | 193 +++++++++++++++++-
 .../org/apache/spark/CacheManagerSuite.scala  |  10 +-
 .../org/apache/spark/rdd/PipedRDDSuite.scala  |   2 +-
 .../org/apache/spark/scheduler/FakeTask.scala |   6 +-
 .../scheduler/NotSerializableFakeTask.scala   |   2 +-
 .../spark/scheduler/TaskContextSuite.scala    |   7 +-
 .../spark/scheduler/TaskSetManagerSuite.scala |   2 +-
 .../shuffle/hash/HashShuffleReaderSuite.scala |   2 +-
 .../ShuffleBlockFetcherIteratorSuite.scala    |   8 +-
 .../org/apache/spark/ui/StagePageSuite.scala  |  76 +++++++
 .../ExternalAppendOnlyMapSuite.scala          |  15 ++
 .../util/collection/ExternalSorterSuite.scala |  14 +-
 .../execution/UnsafeExternalRowSorter.java    |   7 +
 .../UnsafeFixedWidthAggregationMap.java       |   8 +
 .../sql/execution/GeneratedAggregate.scala    |  11 +-
 .../execution/joins/BroadcastHashJoin.scala   |  10 +-
 .../joins/BroadcastHashOuterJoin.scala        |   8 +
 .../joins/BroadcastLeftSemiJoinHash.scala     |  10 +-
 .../sql/execution/joins/HashedRelation.scala  |  22 +-
 .../org/apache/spark/sql/execution/sort.scala |  12 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  60 ++++--
 .../sql/execution/TungstenSortSuite.scala     |  12 ++
 .../UnsafeFixedWidthAggregationMapSuite.scala |   3 +-
 .../UnsafeKVExternalSorterSuite.scala         |   3 +-
 .../execution/joins/BroadcastJoinSuite.scala  |  94 +++++++++
 51 files changed, 1070 insertions(+), 163 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 1aa6ba4201261..bf4eaa59ff589 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -20,6 +20,7 @@
 import java.io.File;
 import java.io.IOException;
 import java.util.LinkedList;
+import javax.annotation.Nullable;
 
 import scala.Tuple2;
 
@@ -86,9 +87,12 @@ final class UnsafeShuffleExternalSorter {
 
   private final LinkedList<SpillInfo> spills = new LinkedList<SpillInfo>();
 
+  /** Peak memory used by this sorter so far, in bytes. **/
+  private long peakMemoryUsedBytes;
+
   // These variables are reset after spilling:
-  private UnsafeShuffleInMemorySorter sorter;
-  private MemoryBlock currentPage = null;
+  @Nullable private UnsafeShuffleInMemorySorter sorter;
+  @Nullable private MemoryBlock currentPage = null;
   private long currentPagePosition = -1;
   private long freeSpaceInCurrentPage = 0;
 
@@ -106,6 +110,7 @@ public UnsafeShuffleExternalSorter(
     this.blockManager = blockManager;
     this.taskContext = taskContext;
     this.initialSize = initialSize;
+    this.peakMemoryUsedBytes = initialSize;
     this.numPartitions = numPartitions;
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
     this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
@@ -279,10 +284,26 @@ private long getMemoryUsage() {
     for (MemoryBlock page : allocatedPages) {
       totalPageSize += page.size();
     }
-    return sorter.getMemoryUsage() + totalPageSize;
+    return ((sorter == null) ? 0 : sorter.getMemoryUsage()) + totalPageSize;
+  }
+
+  private void updatePeakMemoryUsed() {
+    long mem = getMemoryUsage();
+    if (mem > peakMemoryUsedBytes) {
+      peakMemoryUsedBytes = mem;
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
   }
 
   private long freeMemory() {
+    updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
       memoryManager.freePage(block);
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index d47d6fc9c2ac4..6e2eeb37c86f1 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -27,6 +27,7 @@
 import scala.collection.JavaConversions;
 import scala.reflect.ClassTag;
 import scala.reflect.ClassTag$;
+import scala.collection.immutable.Map;
 
 import com.google.common.annotations.VisibleForTesting;
 import com.google.common.io.ByteStreams;
@@ -78,8 +79,9 @@ public class UnsafeShuffleWriter<K, V> extends ShuffleWriter<K, V> {
   private final SparkConf sparkConf;
   private final boolean transferToEnabled;
 
-  private MapStatus mapStatus = null;
-  private UnsafeShuffleExternalSorter sorter = null;
+  @Nullable private MapStatus mapStatus;
+  @Nullable private UnsafeShuffleExternalSorter sorter;
+  private long peakMemoryUsedBytes = 0;
 
   /** Subclass of ByteArrayOutputStream that exposes `buf` directly. */
   private static final class MyByteArrayOutputStream extends ByteArrayOutputStream {
@@ -131,9 +133,28 @@ public UnsafeShuffleWriter(
 
   @VisibleForTesting
   public int maxRecordSizeBytes() {
+    assert(sorter != null);
     return sorter.maxRecordSizeBytes;
   }
 
+  private void updatePeakMemoryUsed() {
+    // sorter can be null if this writer is closed
+    if (sorter != null) {
+      long mem = sorter.getPeakMemoryUsedBytes();
+      if (mem > peakMemoryUsedBytes) {
+        peakMemoryUsedBytes = mem;
+      }
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
+  }
+
   /**
    * This convenience method should only be called in test code.
    */
@@ -144,7 +165,7 @@ public void write(Iterator<Product2<K, V>> records) throws IOException {
 
   @Override
   public void write(scala.collection.Iterator<Product2<K, V>> records) throws IOException {
-    // Keep track of success so we know if we ecountered an exception
+    // Keep track of success so we know if we encountered an exception
     // We do this rather than a standard try/catch/re-throw to handle
     // generic throwables.
     boolean success = false;
@@ -189,6 +210,8 @@ private void open() throws IOException {
 
   @VisibleForTesting
   void closeAndWriteOutput() throws IOException {
+    assert(sorter != null);
+    updatePeakMemoryUsed();
     serBuffer = null;
     serOutputStream = null;
     final SpillInfo[] spills = sorter.closeAndGetSpills();
@@ -209,6 +232,7 @@ void closeAndWriteOutput() throws IOException {
 
   @VisibleForTesting
   void insertRecordIntoSorter(Product2<K, V> record) throws IOException {
+    assert(sorter != null);
     final K key = record._1();
     final int partitionId = partitioner.getPartition(key);
     serBuffer.reset();
@@ -431,6 +455,14 @@ private long[] mergeSpillsWithTransferTo(SpillInfo[] spills, File outputFile) th
   @Override
   public Option<MapStatus> stop(boolean success) {
     try {
+      // Update task metrics from accumulators (null in UnsafeShuffleWriterSuite)
+      Map<String, Accumulator<Object>> internalAccumulators =
+        taskContext.internalMetricsToAccumulators();
+      if (internalAccumulators != null) {
+        internalAccumulators.apply(InternalAccumulator.PEAK_EXECUTION_MEMORY())
+          .add(getPeakMemoryUsedBytes());
+      }
+
       if (stopping) {
         return Option.apply(null);
       } else {
diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 01a66084e918e..20347433e16b2 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -505,7 +505,7 @@ public boolean putNewKey(
       // Here, we'll copy the data into our data pages. Because we only store a relative offset from
       // the key address instead of storing the absolute address of the value, the key and value
       // must be stored in the same memory page.
-      // (8 byte key length) (key) (8 byte value length) (value)
+      // (8 byte key length) (key) (value)
       final long requiredSize = 8 + keyLengthBytes + valueLengthBytes;
 
       // --- Figure out where to insert the new record ---------------------------------------------
@@ -655,7 +655,10 @@ public long getPageSizeBytes() {
     return pageSizeBytes;
   }
 
-  /** Returns the total amount of memory, in bytes, consumed by this map's managed structures. */
+  /**
+   * Returns the total amount of memory, in bytes, consumed by this map's managed structures.
+   * Note that this is also the peak memory used by this map, since the map is append-only.
+   */
   public long getTotalMemoryConsumption() {
     long totalDataPagesSize = 0L;
     for (MemoryBlock dataPage : dataPages) {
@@ -674,7 +677,6 @@ public long getTimeSpentResizingNs() {
     return timeSpentResizingNs;
   }
 
-
   /**
    * Returns the average number of probes per key lookup.
    */
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index b984301cbbf2b..bf5f965a9d8dc 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -70,13 +70,14 @@ public final class UnsafeExternalSorter {
   private final LinkedList<UnsafeSorterSpillWriter> spillWriters = new LinkedList<>();
 
   // These variables are reset after spilling:
-  private UnsafeInMemorySorter inMemSorter;
+  @Nullable private UnsafeInMemorySorter inMemSorter;
   // Whether the in-mem sorter is created internally, or passed in from outside.
   // If it is passed in from outside, we shouldn't release the in-mem sorter's memory.
   private boolean isInMemSorterExternal = false;
   private MemoryBlock currentPage = null;
   private long currentPagePosition = -1;
   private long freeSpaceInCurrentPage = 0;
+  private long peakMemoryUsedBytes = 0;
 
   public static UnsafeExternalSorter createWithExistingInMemorySorter(
       TaskMemoryManager taskMemoryManager,
@@ -183,6 +184,7 @@ public void closeCurrentPage() {
    * Sort and spill the current records in response to memory pressure.
    */
   public void spill() throws IOException {
+    assert(inMemSorter != null);
     logger.info("Thread {} spilling sort data of {} to disk ({} {} so far)",
       Thread.currentThread().getId(),
       Utils.bytesToString(getMemoryUsage()),
@@ -219,7 +221,22 @@ private long getMemoryUsage() {
     for (MemoryBlock page : allocatedPages) {
       totalPageSize += page.size();
     }
-    return inMemSorter.getMemoryUsage() + totalPageSize;
+    return ((inMemSorter == null) ? 0 : inMemSorter.getMemoryUsage()) + totalPageSize;
+  }
+
+  private void updatePeakMemoryUsed() {
+    long mem = getMemoryUsage();
+    if (mem > peakMemoryUsedBytes) {
+      peakMemoryUsedBytes = mem;
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
   }
 
   @VisibleForTesting
@@ -233,6 +250,7 @@ public int getNumberOfAllocatedPages() {
    * @return the number of bytes freed.
    */
   public long freeMemory() {
+    updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
       taskMemoryManager.freePage(block);
@@ -277,7 +295,8 @@ public void deleteSpillFiles() {
    * @return true if the record can be inserted without requiring more allocations, false otherwise.
    */
   private boolean haveSpaceForRecord(int requiredSpace) {
-    assert (requiredSpace > 0);
+    assert(requiredSpace > 0);
+    assert(inMemSorter != null);
     return (inMemSorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
   }
 
@@ -290,6 +309,7 @@ private boolean haveSpaceForRecord(int requiredSpace) {
    *                      the record size.
    */
   private void allocateSpaceForRecord(int requiredSpace) throws IOException {
+    assert(inMemSorter != null);
     // TODO: merge these steps to first calculate total memory requirements for this insert,
     // then try to acquire; no point in acquiring sort buffer only to spill due to no space in the
     // data page.
@@ -350,6 +370,7 @@ public void insertRecord(
     if (!haveSpaceForRecord(totalSpaceRequired)) {
       allocateSpaceForRecord(totalSpaceRequired);
     }
+    assert(inMemSorter != null);
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
@@ -382,6 +403,7 @@ public void insertKVRecord(
     if (!haveSpaceForRecord(totalSpaceRequired)) {
       allocateSpaceForRecord(totalSpaceRequired);
     }
+    assert(inMemSorter != null);
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
@@ -405,6 +427,7 @@ public void insertKVRecord(
   }
 
   public UnsafeSorterIterator getSortedIterator() throws IOException {
+    assert(inMemSorter != null);
     final UnsafeSorterIterator inMemoryIterator = inMemSorter.getSortedIterator();
     int numIteratorsToMerge = spillWriters.size() + (inMemoryIterator.hasNext() ? 1 : 0);
     if (spillWriters.isEmpty()) {
diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index b1cef47042247..648cd1b104802 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -207,7 +207,7 @@ span.additional-metric-title {
 /* Hide all additional metrics by default. This is done here rather than using JavaScript to
  * avoid slow page loads for stage pages with large numbers (e.g., thousands) of tasks. */
 .scheduler_delay, .deserialization_time, .fetch_wait_time, .shuffle_read_remote,
-.serialization_time, .getting_result_time {
+.serialization_time, .getting_result_time, .peak_execution_memory {
   display: none;
 }
 
diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index eb75f26718e19..b6a0119c696fd 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -152,8 +152,14 @@ class Accumulable[R, T] private[spark] (
     in.defaultReadObject()
     value_ = zero
     deserialized = true
-    val taskContext = TaskContext.get()
-    taskContext.registerAccumulator(this)
+    // Automatically register the accumulator when it is deserialized with the task closure.
+    // Note that internal accumulators are deserialized before the TaskContext is created and
+    // are registered in the TaskContext constructor.
+    if (!isInternal) {
+      val taskContext = TaskContext.get()
+      assume(taskContext != null, "Task context was null when deserializing user accumulators")
+      taskContext.registerAccumulator(this)
+    }
   }
 
   override def toString: String = if (value_ == null) "null" else value_.toString
@@ -248,10 +254,20 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa
  * @param param helper object defining how to add elements of type `T`
  * @tparam T result type
  */
-class Accumulator[T](@transient initialValue: T, param: AccumulatorParam[T], name: Option[String])
-  extends Accumulable[T, T](initialValue, param, name) {
+class Accumulator[T] private[spark] (
+    @transient initialValue: T,
+    param: AccumulatorParam[T],
+    name: Option[String],
+    internal: Boolean)
+  extends Accumulable[T, T](initialValue, param, name, internal) {
+
+  def this(initialValue: T, param: AccumulatorParam[T], name: Option[String]) = {
+    this(initialValue, param, name, false)
+  }
 
-  def this(initialValue: T, param: AccumulatorParam[T]) = this(initialValue, param, None)
+  def this(initialValue: T, param: AccumulatorParam[T]) = {
+    this(initialValue, param, None, false)
+  }
 }
 
 /**
@@ -342,3 +358,37 @@ private[spark] object Accumulators extends Logging {
   }
 
 }
+
+private[spark] object InternalAccumulator {
+  val PEAK_EXECUTION_MEMORY = "peakExecutionMemory"
+  val TEST_ACCUMULATOR = "testAccumulator"
+
+  // For testing only.
+  // This needs to be a def since we don't want to reuse the same accumulator across stages.
+  private def maybeTestAccumulator: Option[Accumulator[Long]] = {
+    if (sys.props.contains("spark.testing")) {
+      Some(new Accumulator(
+        0L, AccumulatorParam.LongAccumulatorParam, Some(TEST_ACCUMULATOR), internal = true))
+    } else {
+      None
+    }
+  }
+
+  /**
+   * Accumulators for tracking internal metrics.
+   *
+   * These accumulators are created with the stage such that all tasks in the stage will
+   * add to the same set of accumulators. We do this to report the distribution of accumulator
+   * values across all tasks within each stage.
+   */
+  def create(): Seq[Accumulator[Long]] = {
+    Seq(
+      // Execution memory refers to the memory used by internal data structures created
+      // during shuffles, aggregations and joins. The value of this accumulator should be
+      // approximately the sum of the peak sizes across all such data structures created
+      // in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
+      new Accumulator(
+        0L, AccumulatorParam.LongAccumulatorParam, Some(PEAK_EXECUTION_MEMORY), internal = true)
+    ) ++ maybeTestAccumulator.toSeq
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala
index ceeb58075d345..289aab9bd9e51 100644
--- a/core/src/main/scala/org/apache/spark/Aggregator.scala
+++ b/core/src/main/scala/org/apache/spark/Aggregator.scala
@@ -58,12 +58,7 @@ case class Aggregator[K, V, C] (
     } else {
       val combiners = new ExternalAppendOnlyMap[K, V, C](createCombiner, mergeValue, mergeCombiners)
       combiners.insertAll(iter)
-      // Update task metrics if context is not null
-      // TODO: Make context non optional in a future release
-      Option(context).foreach { c =>
-        c.taskMetrics.incMemoryBytesSpilled(combiners.memoryBytesSpilled)
-        c.taskMetrics.incDiskBytesSpilled(combiners.diskBytesSpilled)
-      }
+      updateMetrics(context, combiners)
       combiners.iterator
     }
   }
@@ -89,13 +84,18 @@ case class Aggregator[K, V, C] (
     } else {
       val combiners = new ExternalAppendOnlyMap[K, C, C](identity, mergeCombiners, mergeCombiners)
       combiners.insertAll(iter)
-      // Update task metrics if context is not null
-      // TODO: Make context non-optional in a future release
-      Option(context).foreach { c =>
-        c.taskMetrics.incMemoryBytesSpilled(combiners.memoryBytesSpilled)
-        c.taskMetrics.incDiskBytesSpilled(combiners.diskBytesSpilled)
-      }
+      updateMetrics(context, combiners)
       combiners.iterator
     }
   }
+
+  /** Update task metrics after populating the external map. */
+  private def updateMetrics(context: TaskContext, map: ExternalAppendOnlyMap[_, _, _]): Unit = {
+    Option(context).foreach { c =>
+      c.taskMetrics().incMemoryBytesSpilled(map.memoryBytesSpilled)
+      c.taskMetrics().incDiskBytesSpilled(map.diskBytesSpilled)
+      c.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(map.peakMemoryUsedBytes)
+    }
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index 5d2c551d58514..63cca80b2d734 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -61,12 +61,12 @@ object TaskContext {
   protected[spark] def unset(): Unit = taskContext.remove()
 
   /**
-   * Return an empty task context that is not actually used.
-   * Internal use only.
+   * An empty task context that does not represent an actual task.
    */
-  private[spark] def empty(): TaskContext = {
-    new TaskContextImpl(0, 0, 0, 0, null, null)
+  private[spark] def empty(): TaskContextImpl = {
+    new TaskContextImpl(0, 0, 0, 0, null, null, Seq.empty)
   }
+
 }
 
 
@@ -187,4 +187,9 @@ abstract class TaskContext extends Serializable {
    * accumulator id and the value of the Map is the latest accumulator local value.
    */
   private[spark] def collectAccumulators(): Map[Long, Any]
+
+  /**
+   * Accumulators for tracking internal metrics indexed by the name.
+   */
+  private[spark] val internalMetricsToAccumulators: Map[String, Accumulator[Long]]
 }
diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
index 9ee168ae016f8..5df94c6d3a103 100644
--- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
@@ -32,6 +32,7 @@ private[spark] class TaskContextImpl(
     override val attemptNumber: Int,
     override val taskMemoryManager: TaskMemoryManager,
     @transient private val metricsSystem: MetricsSystem,
+    internalAccumulators: Seq[Accumulator[Long]],
     val runningLocally: Boolean = false,
     val taskMetrics: TaskMetrics = TaskMetrics.empty)
   extends TaskContext
@@ -114,4 +115,11 @@ private[spark] class TaskContextImpl(
   private[spark] override def collectAccumulators(): Map[Long, Any] = synchronized {
     accumulators.mapValues(_.localValue).toMap
   }
+
+  private[spark] override val internalMetricsToAccumulators: Map[String, Accumulator[Long]] = {
+    // Explicitly register internal accumulators here because these are
+    // not captured in the task closure and are already deserialized
+    internalAccumulators.foreach(registerAccumulator)
+    internalAccumulators.map { a => (a.name.get, a) }.toMap
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 130b58882d8ee..9c617fc719cb5 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -23,8 +23,7 @@ import java.io.{IOException, ObjectOutputStream}
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.{InterruptibleIterator, Partition, Partitioner, SparkEnv, TaskContext}
-import org.apache.spark.{Dependency, OneToOneDependency, ShuffleDependency}
+import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.util.collection.{ExternalAppendOnlyMap, AppendOnlyMap, CompactBuffer}
 import org.apache.spark.util.Utils
@@ -169,8 +168,10 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
       for ((it, depNum) <- rddIterators) {
         map.insertAll(it.map(pair => (pair._1, new CoGroupValue(pair._2, depNum))))
       }
-      context.taskMetrics.incMemoryBytesSpilled(map.memoryBytesSpilled)
-      context.taskMetrics.incDiskBytesSpilled(map.diskBytesSpilled)
+      context.taskMetrics().incMemoryBytesSpilled(map.memoryBytesSpilled)
+      context.taskMetrics().incDiskBytesSpilled(map.diskBytesSpilled)
+      context.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(map.peakMemoryUsedBytes)
       new InterruptibleIterator(context,
         map.iterator.asInstanceOf[Iterator[(K, Array[Iterable[_]])]])
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
index e0edd7d4ae968..11d123eec43ca 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
@@ -24,11 +24,12 @@ import org.apache.spark.annotation.DeveloperApi
  * Information about an [[org.apache.spark.Accumulable]] modified during a task or stage.
  */
 @DeveloperApi
-class AccumulableInfo (
+class AccumulableInfo private[spark] (
     val id: Long,
     val name: String,
     val update: Option[String], // represents a partial update within a task
-    val value: String) {
+    val value: String,
+    val internal: Boolean) {
 
   override def equals(other: Any): Boolean = other match {
     case acc: AccumulableInfo =>
@@ -40,10 +41,10 @@ class AccumulableInfo (
 
 object AccumulableInfo {
   def apply(id: Long, name: String, update: Option[String], value: String): AccumulableInfo = {
-    new AccumulableInfo(id, name, update, value)
+    new AccumulableInfo(id, name, update, value, internal = false)
   }
 
   def apply(id: Long, name: String, value: String): AccumulableInfo = {
-    new AccumulableInfo(id, name, None, value)
+    new AccumulableInfo(id, name, None, value, internal = false)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index c4fa277c21254..bb489c6b6e98f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -773,16 +773,26 @@ class DAGScheduler(
     stage.pendingTasks.clear()
 
     // First figure out the indexes of partition ids to compute.
-    val partitionsToCompute: Seq[Int] = {
+    val (allPartitions: Seq[Int], partitionsToCompute: Seq[Int]) = {
       stage match {
         case stage: ShuffleMapStage =>
-          (0 until stage.numPartitions).filter(id => stage.outputLocs(id).isEmpty)
+          val allPartitions = 0 until stage.numPartitions
+          val filteredPartitions = allPartitions.filter { id => stage.outputLocs(id).isEmpty }
+          (allPartitions, filteredPartitions)
         case stage: ResultStage =>
           val job = stage.resultOfJob.get
-          (0 until job.numPartitions).filter(id => !job.finished(id))
+          val allPartitions = 0 until job.numPartitions
+          val filteredPartitions = allPartitions.filter { id => !job.finished(id) }
+          (allPartitions, filteredPartitions)
       }
     }
 
+    // Reset internal accumulators only if this stage is not partially submitted
+    // Otherwise, we may override existing accumulator values from some tasks
+    if (allPartitions == partitionsToCompute) {
+      stage.resetInternalAccumulators()
+    }
+
     val properties = jobIdToActiveJob.get(stage.firstJobId).map(_.properties).orNull
 
     runningStages += stage
@@ -852,7 +862,8 @@ class DAGScheduler(
           partitionsToCompute.map { id =>
             val locs = taskIdToLocations(id)
             val part = stage.rdd.partitions(id)
-            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs)
+            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
+              taskBinary, part, locs, stage.internalAccumulators)
           }
 
         case stage: ResultStage =>
@@ -861,7 +872,8 @@ class DAGScheduler(
             val p: Int = job.partitions(id)
             val part = stage.rdd.partitions(p)
             val locs = taskIdToLocations(id)
-            new ResultTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs, id)
+            new ResultTask(stage.id, stage.latestInfo.attemptId,
+              taskBinary, part, locs, id, stage.internalAccumulators)
           }
       }
     } catch {
@@ -916,9 +928,11 @@ class DAGScheduler(
           // To avoid UI cruft, ignore cases where value wasn't updated
           if (acc.name.isDefined && partialValue != acc.zero) {
             val name = acc.name.get
-            stage.latestInfo.accumulables(id) = AccumulableInfo(id, name, s"${acc.value}")
+            val value = s"${acc.value}"
+            stage.latestInfo.accumulables(id) =
+              new AccumulableInfo(id, name, None, value, acc.isInternal)
             event.taskInfo.accumulables +=
-              AccumulableInfo(id, name, Some(s"$partialValue"), s"${acc.value}")
+              new AccumulableInfo(id, name, Some(s"$partialValue"), value, acc.isInternal)
           }
         }
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index 9c2606e278c54..c4dc080e2b22b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -45,8 +45,10 @@ private[spark] class ResultTask[T, U](
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
     @transient locs: Seq[TaskLocation],
-    val outputId: Int)
-  extends Task[U](stageId, stageAttemptId, partition.index) with Serializable {
+    val outputId: Int,
+    internalAccumulators: Seq[Accumulator[Long]])
+  extends Task[U](stageId, stageAttemptId, partition.index, internalAccumulators)
+  with Serializable {
 
   @transient private[this] val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 14c8c00961487..f478f9982afef 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -43,12 +43,14 @@ private[spark] class ShuffleMapTask(
     stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
-    @transient private var locs: Seq[TaskLocation])
-  extends Task[MapStatus](stageId, stageAttemptId, partition.index) with Logging {
+    @transient private var locs: Seq[TaskLocation],
+    internalAccumulators: Seq[Accumulator[Long]])
+  extends Task[MapStatus](stageId, stageAttemptId, partition.index, internalAccumulators)
+  with Logging {
 
   /** A constructor used only in test suites. This does not require passing in an RDD. */
   def this(partitionId: Int) {
-    this(0, 0, null, new Partition { override def index: Int = 0 }, null)
+    this(0, 0, null, new Partition { override def index: Int = 0 }, null, null)
   }
 
   @transient private val preferredLocs: Seq[TaskLocation] = {
@@ -69,7 +71,7 @@ private[spark] class ShuffleMapTask(
       val manager = SparkEnv.get.shuffleManager
       writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
       writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
-      return writer.stop(success = true).get
+      writer.stop(success = true).get
     } catch {
       case e: Exception =>
         try {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 40a333a3e06b2..de05ee256dbfc 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -68,6 +68,22 @@ private[spark] abstract class Stage(
   val name = callSite.shortForm
   val details = callSite.longForm
 
+  private var _internalAccumulators: Seq[Accumulator[Long]] = Seq.empty
+
+  /** Internal accumulators shared across all tasks in this stage. */
+  def internalAccumulators: Seq[Accumulator[Long]] = _internalAccumulators
+
+  /**
+   * Re-initialize the internal accumulators associated with this stage.
+   *
+   * This is called every time the stage is submitted, *except* when a subset of tasks
+   * belonging to this stage has already finished. Otherwise, reinitializing the internal
+   * accumulators here again will override partial values from the finished tasks.
+   */
+  def resetInternalAccumulators(): Unit = {
+    _internalAccumulators = InternalAccumulator.create()
+  }
+
   /**
    * Pointer to the [StageInfo] object for the most recent attempt. This needs to be initialized
    * here, before any attempts have actually been created, because the DAGScheduler uses this
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
index 1978305cfefbd..9edf9f048f9fd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
@@ -23,7 +23,7 @@ import java.nio.ByteBuffer
 import scala.collection.mutable.HashMap
 
 import org.apache.spark.metrics.MetricsSystem
-import org.apache.spark.{SparkEnv, TaskContextImpl, TaskContext}
+import org.apache.spark.{Accumulator, SparkEnv, TaskContextImpl, TaskContext}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.serializer.SerializerInstance
 import org.apache.spark.unsafe.memory.TaskMemoryManager
@@ -47,7 +47,8 @@ import org.apache.spark.util.Utils
 private[spark] abstract class Task[T](
     val stageId: Int,
     val stageAttemptId: Int,
-    var partitionId: Int) extends Serializable {
+    val partitionId: Int,
+    internalAccumulators: Seq[Accumulator[Long]]) extends Serializable {
 
   /**
    * The key of the Map is the accumulator id and the value of the Map is the latest accumulator
@@ -68,12 +69,13 @@ private[spark] abstract class Task[T](
     metricsSystem: MetricsSystem)
   : (T, AccumulatorUpdates) = {
     context = new TaskContextImpl(
-      stageId = stageId,
-      partitionId = partitionId,
-      taskAttemptId = taskAttemptId,
-      attemptNumber = attemptNumber,
-      taskMemoryManager = taskMemoryManager,
-      metricsSystem = metricsSystem,
+      stageId,
+      partitionId,
+      taskAttemptId,
+      attemptNumber,
+      taskMemoryManager,
+      metricsSystem,
+      internalAccumulators,
       runningLocally = false)
     TaskContext.setTaskContext(context)
     context.taskMetrics.setHostname(Utils.localHostName())
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index de79fa56f017b..0c8f08f0f3b1b 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.shuffle.hash
 
-import org.apache.spark.{InterruptibleIterator, Logging, MapOutputTracker, SparkEnv, TaskContext}
+import org.apache.spark._
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
 import org.apache.spark.storage.{BlockManager, ShuffleBlockFetcherIterator}
@@ -100,8 +100,10 @@ private[spark] class HashShuffleReader[K, C](
         // the ExternalSorter won't spill to disk.
         val sorter = new ExternalSorter[K, C, C](ordering = Some(keyOrd), serializer = Some(ser))
         sorter.insertAll(aggregatedIter)
-        context.taskMetrics.incMemoryBytesSpilled(sorter.memoryBytesSpilled)
-        context.taskMetrics.incDiskBytesSpilled(sorter.diskBytesSpilled)
+        context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
+        context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
+        context.internalMetricsToAccumulators(
+          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.peakMemoryUsedBytes)
         sorter.iterator
       case None =>
         aggregatedIter
diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
index e2d25e36365fa..cb122eaed83d1 100644
--- a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
+++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
@@ -62,6 +62,13 @@ private[spark] object ToolTips {
     """Time that the executor spent paused for Java garbage collection while the task was
        running."""
 
+  val PEAK_EXECUTION_MEMORY =
+    """Execution memory refers to the memory used by internal data structures created during
+       shuffles, aggregations and joins when Tungsten is enabled. The value of this accumulator
+       should be approximately the sum of the peak sizes across all such data structures created
+       in this task. For SQL jobs, this only tracks all unsafe operators, broadcast joins, and
+       external sort."""
+
   val JOB_TIMELINE =
     """Shows when jobs started and ended and when executors joined or left. Drag to scroll.
        Click Enable Zooming and use mouse wheel to zoom in/out."""
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index cf04b5e59239b..3954c3d1ef894 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -26,6 +26,7 @@ import scala.xml.{Elem, Node, Unparsed}
 
 import org.apache.commons.lang3.StringEscapeUtils
 
+import org.apache.spark.{InternalAccumulator, SparkConf}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 import org.apache.spark.ui._
@@ -67,6 +68,8 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
   // if we find that it's okay.
   private val MAX_TIMELINE_TASKS = parent.conf.getInt("spark.ui.timeline.tasks.maximum", 1000)
 
+  private val displayPeakExecutionMemory =
+    parent.conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
 
   def render(request: HttpServletRequest): Seq[Node] = {
     progressListener.synchronized {
@@ -114,10 +117,11 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
 
       val stageData = stageDataOption.get
       val tasks = stageData.taskData.values.toSeq.sortBy(_.taskInfo.launchTime)
-
       val numCompleted = tasks.count(_.taskInfo.finished)
-      val accumulables = progressListener.stageIdToData((stageId, stageAttemptId)).accumulables
-      val hasAccumulators = accumulables.size > 0
+
+      val allAccumulables = progressListener.stageIdToData((stageId, stageAttemptId)).accumulables
+      val externalAccumulables = allAccumulables.values.filter { acc => !acc.internal }
+      val hasAccumulators = externalAccumulables.size > 0
 
       val summary =
         <div>
@@ -221,6 +225,15 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
                   <span class="additional-metric-title">Getting Result Time</span>
                 </span>
               </li>
+              {if (displayPeakExecutionMemory) {
+                <li>
+                  <span data-toggle="tooltip"
+                        title={ToolTips.PEAK_EXECUTION_MEMORY} data-placement="right">
+                    <input type="checkbox" name={TaskDetailsClassNames.PEAK_EXECUTION_MEMORY}/>
+                    <span class="additional-metric-title">Peak Execution Memory</span>
+                  </span>
+                </li>
+              }}
             </ul>
           </div>
         </div>
@@ -241,11 +254,12 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val accumulableTable = UIUtils.listingTable(
         accumulableHeaders,
         accumulableRow,
-        accumulables.values.toSeq)
+        externalAccumulables.toSeq)
 
       val currentTime = System.currentTimeMillis()
       val (taskTable, taskTableHTML) = try {
         val _taskTable = new TaskPagedTable(
+          parent.conf,
           UIUtils.prependBaseUri(parent.basePath) +
             s"/stages/stage?id=${stageId}&attempt=${stageAttemptId}",
           tasks,
@@ -294,12 +308,14 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         else {
           def getDistributionQuantiles(data: Seq[Double]): IndexedSeq[Double] =
             Distribution(data).get.getQuantiles()
-
           def getFormattedTimeQuantiles(times: Seq[Double]): Seq[Node] = {
             getDistributionQuantiles(times).map { millis =>
               <td>{UIUtils.formatDuration(millis.toLong)}</td>
             }
           }
+          def getFormattedSizeQuantiles(data: Seq[Double]): Seq[Elem] = {
+            getDistributionQuantiles(data).map(d => <td>{Utils.bytesToString(d.toLong)}</td>)
+          }
 
           val deserializationTimes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.executorDeserializeTime.toDouble
@@ -349,6 +365,23 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
               </span>
             </td> +:
             getFormattedTimeQuantiles(gettingResultTimes)
+
+          val peakExecutionMemory = validTasks.map { case TaskUIData(info, _, _) =>
+            info.accumulables
+              .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
+              .map { acc => acc.value.toLong }
+              .getOrElse(0L)
+              .toDouble
+          }
+          val peakExecutionMemoryQuantiles = {
+            <td>
+              <span data-toggle="tooltip"
+                    title={ToolTips.PEAK_EXECUTION_MEMORY} data-placement="right">
+                Peak Execution Memory
+              </span>
+            </td> +: getFormattedSizeQuantiles(peakExecutionMemory)
+          }
+
           // The scheduler delay includes the network delay to send the task to the worker
           // machine and to send back the result (but not the time to fetch the task result,
           // if it needed to be fetched from the block manager on the worker).
@@ -359,10 +392,6 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
             title={ToolTips.SCHEDULER_DELAY} data-placement="right">Scheduler Delay</span></td>
           val schedulerDelayQuantiles = schedulerDelayTitle +:
             getFormattedTimeQuantiles(schedulerDelays)
-
-          def getFormattedSizeQuantiles(data: Seq[Double]): Seq[Elem] =
-            getDistributionQuantiles(data).map(d => <td>{Utils.bytesToString(d.toLong)}</td>)
-
           def getFormattedSizeQuantilesWithRecords(data: Seq[Double], records: Seq[Double])
             : Seq[Elem] = {
             val recordDist = getDistributionQuantiles(records).iterator
@@ -466,6 +495,13 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
               {serializationQuantiles}
             </tr>,
             <tr class={TaskDetailsClassNames.GETTING_RESULT_TIME}>{gettingResultQuantiles}</tr>,
+            if (displayPeakExecutionMemory) {
+              <tr class={TaskDetailsClassNames.PEAK_EXECUTION_MEMORY}>
+                {peakExecutionMemoryQuantiles}
+              </tr>
+            } else {
+              Nil
+            },
             if (stageData.hasInput) <tr>{inputQuantiles}</tr> else Nil,
             if (stageData.hasOutput) <tr>{outputQuantiles}</tr> else Nil,
             if (stageData.hasShuffleRead) {
@@ -499,7 +535,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val executorTable = new ExecutorTable(stageId, stageAttemptId, parent)
 
       val maybeAccumulableTable: Seq[Node] =
-        if (accumulables.size > 0) { <h4>Accumulators</h4> ++ accumulableTable } else Seq()
+        if (hasAccumulators) { <h4>Accumulators</h4> ++ accumulableTable } else Seq()
 
       val content =
         summary ++
@@ -750,29 +786,30 @@ private[ui] case class TaskTableRowBytesSpilledData(
  * Contains all data that needs for sorting and generating HTML. Using this one rather than
  * TaskUIData to avoid creating duplicate contents during sorting the data.
  */
-private[ui] case class TaskTableRowData(
-    index: Int,
-    taskId: Long,
-    attempt: Int,
-    speculative: Boolean,
-    status: String,
-    taskLocality: String,
-    executorIdAndHost: String,
-    launchTime: Long,
-    duration: Long,
-    formatDuration: String,
-    schedulerDelay: Long,
-    taskDeserializationTime: Long,
-    gcTime: Long,
-    serializationTime: Long,
-    gettingResultTime: Long,
-    accumulators: Option[String], // HTML
-    input: Option[TaskTableRowInputData],
-    output: Option[TaskTableRowOutputData],
-    shuffleRead: Option[TaskTableRowShuffleReadData],
-    shuffleWrite: Option[TaskTableRowShuffleWriteData],
-    bytesSpilled: Option[TaskTableRowBytesSpilledData],
-    error: String)
+private[ui] class TaskTableRowData(
+    val index: Int,
+    val taskId: Long,
+    val attempt: Int,
+    val speculative: Boolean,
+    val status: String,
+    val taskLocality: String,
+    val executorIdAndHost: String,
+    val launchTime: Long,
+    val duration: Long,
+    val formatDuration: String,
+    val schedulerDelay: Long,
+    val taskDeserializationTime: Long,
+    val gcTime: Long,
+    val serializationTime: Long,
+    val gettingResultTime: Long,
+    val peakExecutionMemoryUsed: Long,
+    val accumulators: Option[String], // HTML
+    val input: Option[TaskTableRowInputData],
+    val output: Option[TaskTableRowOutputData],
+    val shuffleRead: Option[TaskTableRowShuffleReadData],
+    val shuffleWrite: Option[TaskTableRowShuffleWriteData],
+    val bytesSpilled: Option[TaskTableRowBytesSpilledData],
+    val error: String)
 
 private[ui] class TaskDataSource(
     tasks: Seq[TaskUIData],
@@ -816,10 +853,15 @@ private[ui] class TaskDataSource(
     val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L)
     val gettingResultTime = getGettingResultTime(info, currentTime)
 
-    val maybeAccumulators = info.accumulables
-    val accumulatorsReadable = maybeAccumulators.map { acc =>
+    val (taskInternalAccumulables, taskExternalAccumulables) =
+      info.accumulables.partition(_.internal)
+    val externalAccumulableReadable = taskExternalAccumulables.map { acc =>
       StringEscapeUtils.escapeHtml4(s"${acc.name}: ${acc.update.get}")
     }
+    val peakExecutionMemoryUsed = taskInternalAccumulables
+      .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
+      .map { acc => acc.value.toLong }
+      .getOrElse(0L)
 
     val maybeInput = metrics.flatMap(_.inputMetrics)
     val inputSortable = maybeInput.map(_.bytesRead).getOrElse(0L)
@@ -923,7 +965,7 @@ private[ui] class TaskDataSource(
         None
       }
 
-    TaskTableRowData(
+    new TaskTableRowData(
       info.index,
       info.taskId,
       info.attempt,
@@ -939,7 +981,8 @@ private[ui] class TaskDataSource(
       gcTime,
       serializationTime,
       gettingResultTime,
-      if (hasAccumulators) Some(accumulatorsReadable.mkString("<br/>")) else None,
+      peakExecutionMemoryUsed,
+      if (hasAccumulators) Some(externalAccumulableReadable.mkString("<br/>")) else None,
       input,
       output,
       shuffleRead,
@@ -1006,6 +1049,10 @@ private[ui] class TaskDataSource(
         override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
           Ordering.Long.compare(x.gettingResultTime, y.gettingResultTime)
       }
+      case "Peak Execution Memory" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.peakExecutionMemoryUsed, y.peakExecutionMemoryUsed)
+      }
       case "Accumulators" =>
         if (hasAccumulators) {
           new Ordering[TaskTableRowData] {
@@ -1132,6 +1179,7 @@ private[ui] class TaskDataSource(
 }
 
 private[ui] class TaskPagedTable(
+    conf: SparkConf,
     basePath: String,
     data: Seq[TaskUIData],
     hasAccumulators: Boolean,
@@ -1143,7 +1191,11 @@ private[ui] class TaskPagedTable(
     currentTime: Long,
     pageSize: Int,
     sortColumn: String,
-    desc: Boolean) extends PagedTable[TaskTableRowData]{
+    desc: Boolean) extends PagedTable[TaskTableRowData] {
+
+  // We only track peak memory used for unsafe operators
+  private val displayPeakExecutionMemory =
+    conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
 
   override def tableId: String = ""
 
@@ -1195,6 +1247,13 @@ private[ui] class TaskPagedTable(
         ("GC Time", ""),
         ("Result Serialization Time", TaskDetailsClassNames.RESULT_SERIALIZATION_TIME),
         ("Getting Result Time", TaskDetailsClassNames.GETTING_RESULT_TIME)) ++
+        {
+          if (displayPeakExecutionMemory) {
+            Seq(("Peak Execution Memory", TaskDetailsClassNames.PEAK_EXECUTION_MEMORY))
+          } else {
+            Nil
+          }
+        } ++
         {if (hasAccumulators) Seq(("Accumulators", "")) else Nil} ++
         {if (hasInput) Seq(("Input Size / Records", "")) else Nil} ++
         {if (hasOutput) Seq(("Output Size / Records", "")) else Nil} ++
@@ -1271,6 +1330,11 @@ private[ui] class TaskPagedTable(
       <td class={TaskDetailsClassNames.GETTING_RESULT_TIME}>
         {UIUtils.formatDuration(task.gettingResultTime)}
       </td>
+      {if (displayPeakExecutionMemory) {
+        <td class={TaskDetailsClassNames.PEAK_EXECUTION_MEMORY}>
+          {Utils.bytesToString(task.peakExecutionMemoryUsed)}
+        </td>
+      }}
       {if (task.accumulators.nonEmpty) {
         <td>{Unparsed(task.accumulators.get)}</td>
       }}
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala b/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala
index 9bf67db8acde1..d2dfc5a32915c 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala
@@ -31,4 +31,5 @@ private[spark] object TaskDetailsClassNames {
   val SHUFFLE_READ_REMOTE_SIZE = "shuffle_read_remote"
   val RESULT_SERIALIZATION_TIME = "serialization_time"
   val GETTING_RESULT_TIME = "getting_result_time"
+  val PEAK_EXECUTION_MEMORY = "peak_execution_memory"
 }
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index d166037351c31..f929b12606f0a 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -89,6 +89,7 @@ class ExternalAppendOnlyMap[K, V, C](
 
   // Number of bytes spilled in total
   private var _diskBytesSpilled = 0L
+  def diskBytesSpilled: Long = _diskBytesSpilled
 
   // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
   private val fileBufferSize =
@@ -97,6 +98,10 @@ class ExternalAppendOnlyMap[K, V, C](
   // Write metrics for current spill
   private var curWriteMetrics: ShuffleWriteMetrics = _
 
+  // Peak size of the in-memory map observed so far, in bytes
+  private var _peakMemoryUsedBytes: Long = 0L
+  def peakMemoryUsedBytes: Long = _peakMemoryUsedBytes
+
   private val keyComparator = new HashComparator[K]
   private val ser = serializer.newInstance()
 
@@ -126,7 +131,11 @@ class ExternalAppendOnlyMap[K, V, C](
 
     while (entries.hasNext) {
       curEntry = entries.next()
-      if (maybeSpill(currentMap, currentMap.estimateSize())) {
+      val estimatedSize = currentMap.estimateSize()
+      if (estimatedSize > _peakMemoryUsedBytes) {
+        _peakMemoryUsedBytes = estimatedSize
+      }
+      if (maybeSpill(currentMap, estimatedSize)) {
         currentMap = new SizeTrackingAppendOnlyMap[K, C]
       }
       currentMap.changeValue(curEntry._1, update)
@@ -207,8 +216,6 @@ class ExternalAppendOnlyMap[K, V, C](
     spilledMaps.append(new DiskMapIterator(file, blockId, batchSizes))
   }
 
-  def diskBytesSpilled: Long = _diskBytesSpilled
-
   /**
    * Return an iterator that merges the in-memory map with the spilled maps.
    * If no spill has occurred, simply return the in-memory map's iterator.
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index ba7ec834d622d..19287edbaf166 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -152,6 +152,9 @@ private[spark] class ExternalSorter[K, V, C](
   private var _diskBytesSpilled = 0L
   def diskBytesSpilled: Long = _diskBytesSpilled
 
+  // Peak size of the in-memory data structure observed so far, in bytes
+  private var _peakMemoryUsedBytes: Long = 0L
+  def peakMemoryUsedBytes: Long = _peakMemoryUsedBytes
 
   // A comparator for keys K that orders them within a partition to allow aggregation or sorting.
   // Can be a partial ordering by hash code if a total ordering is not provided through by the
@@ -224,15 +227,22 @@ private[spark] class ExternalSorter[K, V, C](
       return
     }
 
+    var estimatedSize = 0L
     if (usingMap) {
-      if (maybeSpill(map, map.estimateSize())) {
+      estimatedSize = map.estimateSize()
+      if (maybeSpill(map, estimatedSize)) {
         map = new PartitionedAppendOnlyMap[K, C]
       }
     } else {
-      if (maybeSpill(buffer, buffer.estimateSize())) {
+      estimatedSize = buffer.estimateSize()
+      if (maybeSpill(buffer, estimatedSize)) {
         buffer = newBuffer()
       }
     }
+
+    if (estimatedSize > _peakMemoryUsedBytes) {
+      _peakMemoryUsedBytes = estimatedSize
+    }
   }
 
   /**
@@ -684,8 +694,10 @@ private[spark] class ExternalSorter[K, V, C](
       }
     }
 
-    context.taskMetrics.incMemoryBytesSpilled(memoryBytesSpilled)
-    context.taskMetrics.incDiskBytesSpilled(diskBytesSpilled)
+    context.taskMetrics().incMemoryBytesSpilled(memoryBytesSpilled)
+    context.taskMetrics().incDiskBytesSpilled(diskBytesSpilled)
+    context.internalMetricsToAccumulators(
+      InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemoryUsedBytes)
 
     lengths
   }
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index e948ca33471a4..ffe4b4baffb2a 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -51,7 +51,6 @@
 
 import org.apache.spark.api.java.*;
 import org.apache.spark.api.java.function.*;
-import org.apache.spark.executor.TaskMetrics;
 import org.apache.spark.input.PortableDataStream;
 import org.apache.spark.partial.BoundedDouble;
 import org.apache.spark.partial.PartialResult;
@@ -1011,7 +1010,7 @@ public void persist() {
   @Test
   public void iterator() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2);
-    TaskContext context = new TaskContextImpl(0, 0, 0L, 0, null, null, false, new TaskMetrics());
+    TaskContext context = TaskContext$.MODULE$.empty();
     Assert.assertEquals(1, rdd.iterator(rdd.partitions().get(0), context).next().intValue());
   }
 
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 04fc09b323dbb..98c32bbc298d7 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -190,6 +190,7 @@ public Tuple2<TempShuffleBlockId, File> answer(
       });
 
     when(taskContext.taskMetrics()).thenReturn(taskMetrics);
+    when(taskContext.internalMetricsToAccumulators()).thenReturn(null);
 
     when(shuffleDep.serializer()).thenReturn(Option.<Serializer>apply(serializer));
     when(shuffleDep.partitioner()).thenReturn(hashPartitioner);
@@ -542,4 +543,57 @@ public void spillFilesAreDeletedWhenStoppingAfterError() throws IOException {
     writer.stop(false);
     assertSpillFilesWereCleanedUp();
   }
+
+  @Test
+  public void testPeakMemoryUsed() throws Exception {
+    final long recordLengthBytes = 8;
+    final long pageSizeBytes = 256;
+    final long numRecordsPerPage = pageSizeBytes / recordLengthBytes;
+    final SparkConf conf = new SparkConf().set("spark.buffer.pageSize", pageSizeBytes + "b");
+    final UnsafeShuffleWriter<Object, Object> writer =
+      new UnsafeShuffleWriter<Object, Object>(
+        blockManager,
+        shuffleBlockResolver,
+        taskMemoryManager,
+        shuffleMemoryManager,
+        new UnsafeShuffleHandle<Object, Object>(0, 1, shuffleDep),
+        0, // map id
+        taskContext,
+        conf);
+
+    // Peak memory should be monotonically increasing. More specifically, every time
+    // we allocate a new page it should increase by exactly the size of the page.
+    long previousPeakMemory = writer.getPeakMemoryUsedBytes();
+    long newPeakMemory;
+    try {
+      for (int i = 0; i < numRecordsPerPage * 10; i++) {
+        writer.insertRecordIntoSorter(new Tuple2<Object, Object>(1, 1));
+        newPeakMemory = writer.getPeakMemoryUsedBytes();
+        if (i % numRecordsPerPage == 0) {
+          // We allocated a new page for this record, so peak memory should change
+          assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
+        } else {
+          assertEquals(previousPeakMemory, newPeakMemory);
+        }
+        previousPeakMemory = newPeakMemory;
+      }
+
+      // Spilling should not change peak memory
+      writer.forceSorterToSpill();
+      newPeakMemory = writer.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+      for (int i = 0; i < numRecordsPerPage; i++) {
+        writer.insertRecordIntoSorter(new Tuple2<Object, Object>(1, 1));
+      }
+      newPeakMemory = writer.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+
+      // Closing the writer should not change peak memory
+      writer.closeAndWriteOutput();
+      newPeakMemory = writer.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+    } finally {
+      writer.stop(false);
+    }
+  }
 }
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index dbb7c662d7871..0e23a64fb74bb 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -25,6 +25,7 @@
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
 import static org.hamcrest.Matchers.greaterThan;
+import static org.junit.Assert.*;
 import static org.mockito.AdditionalMatchers.geq;
 import static org.mockito.Mockito.*;
 
@@ -495,4 +496,42 @@ public void resizingLargeMap() {
     map.growAndRehash();
     map.free();
   }
+
+  @Test
+  public void testTotalMemoryConsumption() {
+    final long recordLengthBytes = 24;
+    final long pageSizeBytes = 256 + 8; // 8 bytes for end-of-page marker
+    final long numRecordsPerPage = (pageSizeBytes - 8) / recordLengthBytes;
+    final BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, 1024, pageSizeBytes);
+
+    // Since BytesToBytesMap is append-only, we expect the total memory consumption to be
+    // monotonically increasing. More specifically, every time we allocate a new page it
+    // should increase by exactly the size of the page. In this regard, the memory usage
+    // at any given time is also the peak memory used.
+    long previousMemory = map.getTotalMemoryConsumption();
+    long newMemory;
+    try {
+      for (long i = 0; i < numRecordsPerPage * 10; i++) {
+        final long[] value = new long[]{i};
+        map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8).putNewKey(
+          value,
+          PlatformDependent.LONG_ARRAY_OFFSET,
+          8,
+          value,
+          PlatformDependent.LONG_ARRAY_OFFSET,
+          8);
+        newMemory = map.getTotalMemoryConsumption();
+        if (i % numRecordsPerPage == 0) {
+          // We allocated a new page for this record, so peak memory should change
+          assertEquals(previousMemory + pageSizeBytes, newMemory);
+        } else {
+          assertEquals(previousMemory, newMemory);
+        }
+        previousMemory = newMemory;
+      }
+    } finally {
+      map.free();
+    }
+  }
 }
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 52fa8bcd57e79..c11949d57a0ea 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -247,4 +247,50 @@ public void testFillingPage() throws Exception {
     assertSpillFilesWereCleanedUp();
   }
 
+  @Test
+  public void testPeakMemoryUsed() throws Exception {
+    final long recordLengthBytes = 8;
+    final long pageSizeBytes = 256;
+    final long numRecordsPerPage = pageSizeBytes / recordLengthBytes;
+    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
+      taskMemoryManager,
+      shuffleMemoryManager,
+      blockManager,
+      taskContext,
+      recordComparator,
+      prefixComparator,
+      1024,
+      pageSizeBytes);
+
+    // Peak memory should be monotonically increasing. More specifically, every time
+    // we allocate a new page it should increase by exactly the size of the page.
+    long previousPeakMemory = sorter.getPeakMemoryUsedBytes();
+    long newPeakMemory;
+    try {
+      for (int i = 0; i < numRecordsPerPage * 10; i++) {
+        insertNumber(sorter, i);
+        newPeakMemory = sorter.getPeakMemoryUsedBytes();
+        if (i % numRecordsPerPage == 0) {
+          // We allocated a new page for this record, so peak memory should change
+          assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
+        } else {
+          assertEquals(previousPeakMemory, newPeakMemory);
+        }
+        previousPeakMemory = newPeakMemory;
+      }
+
+      // Spilling should not change peak memory
+      sorter.spill();
+      newPeakMemory = sorter.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+      for (int i = 0; i < numRecordsPerPage; i++) {
+        insertNumber(sorter, i);
+      }
+      newPeakMemory = sorter.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+    } finally {
+      sorter.freeMemory();
+    }
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index e942d6579b2fd..48f549575f4d1 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -18,13 +18,17 @@
 package org.apache.spark
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 import scala.ref.WeakReference
 
 import org.scalatest.Matchers
+import org.scalatest.exceptions.TestFailedException
 
+import org.apache.spark.scheduler._
 
-class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContext {
 
+class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContext {
+  import InternalAccumulator._
 
   implicit def setAccum[A]: AccumulableParam[mutable.Set[A], A] =
     new AccumulableParam[mutable.Set[A], A] {
@@ -155,4 +159,191 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     assert(!Accumulators.originals.get(accId).isDefined)
   }
 
+  test("internal accumulators in TaskContext") {
+    val accums = InternalAccumulator.create()
+    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null, accums)
+    val internalMetricsToAccums = taskContext.internalMetricsToAccumulators
+    val collectedInternalAccums = taskContext.collectInternalAccumulators()
+    val collectedAccums = taskContext.collectAccumulators()
+    assert(internalMetricsToAccums.size > 0)
+    assert(internalMetricsToAccums.values.forall(_.isInternal))
+    assert(internalMetricsToAccums.contains(TEST_ACCUMULATOR))
+    val testAccum = internalMetricsToAccums(TEST_ACCUMULATOR)
+    assert(collectedInternalAccums.size === internalMetricsToAccums.size)
+    assert(collectedInternalAccums.size === collectedAccums.size)
+    assert(collectedInternalAccums.contains(testAccum.id))
+    assert(collectedAccums.contains(testAccum.id))
+  }
+
+  test("internal accumulators in a stage") {
+    val listener = new SaveInfoListener
+    val numPartitions = 10
+    sc = new SparkContext("local", "test")
+    sc.addSparkListener(listener)
+    // Have each task add 1 to the internal accumulator
+    sc.parallelize(1 to 100, numPartitions).mapPartitions { iter =>
+      TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
+      iter
+    }.count()
+    val stageInfos = listener.getCompletedStageInfos
+    val taskInfos = listener.getCompletedTaskInfos
+    assert(stageInfos.size === 1)
+    assert(taskInfos.size === numPartitions)
+    // The accumulator values should be merged in the stage
+    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+    assert(stageAccum.value.toLong === numPartitions)
+    // The accumulator should be updated locally on each task
+    val taskAccumValues = taskInfos.map { taskInfo =>
+      val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+      assert(taskAccum.update.isDefined)
+      assert(taskAccum.update.get.toLong === 1)
+      taskAccum.value.toLong
+    }
+    // Each task should keep track of the partial value on the way, i.e. 1, 2, ... numPartitions
+    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+  }
+
+  test("internal accumulators in multiple stages") {
+    val listener = new SaveInfoListener
+    val numPartitions = 10
+    sc = new SparkContext("local", "test")
+    sc.addSparkListener(listener)
+    // Each stage creates its own set of internal accumulators so the
+    // values for the same metric should not be mixed up across stages
+    sc.parallelize(1 to 100, numPartitions)
+      .map { i => (i, i) }
+      .mapPartitions { iter =>
+        TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
+        iter
+      }
+      .reduceByKey { case (x, y) => x + y }
+      .mapPartitions { iter =>
+        TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 10
+        iter
+      }
+      .repartition(numPartitions * 2)
+      .mapPartitions { iter =>
+        TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 100
+        iter
+      }
+      .count()
+    // We ran 3 stages, and the accumulator values should be distinct
+    val stageInfos = listener.getCompletedStageInfos
+    assert(stageInfos.size === 3)
+    val firstStageAccum = findAccumulableInfo(stageInfos(0).accumulables.values, TEST_ACCUMULATOR)
+    val secondStageAccum = findAccumulableInfo(stageInfos(1).accumulables.values, TEST_ACCUMULATOR)
+    val thirdStageAccum = findAccumulableInfo(stageInfos(2).accumulables.values, TEST_ACCUMULATOR)
+    assert(firstStageAccum.value.toLong === numPartitions)
+    assert(secondStageAccum.value.toLong === numPartitions * 10)
+    assert(thirdStageAccum.value.toLong === numPartitions * 2 * 100)
+  }
+
+  test("internal accumulators in fully resubmitted stages") {
+    testInternalAccumulatorsWithFailedTasks((i: Int) => true) // fail all tasks
+  }
+
+  test("internal accumulators in partially resubmitted stages") {
+    testInternalAccumulatorsWithFailedTasks((i: Int) => i % 2 == 0) // fail a subset
+  }
+
+  /**
+   * Return the accumulable info that matches the specified name.
+   */
+  private def findAccumulableInfo(
+      accums: Iterable[AccumulableInfo],
+      name: String): AccumulableInfo = {
+    accums.find { a => a.name == name }.getOrElse {
+      throw new TestFailedException(s"internal accumulator '$name' not found", 0)
+    }
+  }
+
+  /**
+   * Test whether internal accumulators are merged properly if some tasks fail.
+   */
+  private def testInternalAccumulatorsWithFailedTasks(failCondition: (Int => Boolean)): Unit = {
+    val listener = new SaveInfoListener
+    val numPartitions = 10
+    val numFailedPartitions = (0 until numPartitions).count(failCondition)
+    // This says use 1 core and retry tasks up to 2 times
+    sc = new SparkContext("local[1, 2]", "test")
+    sc.addSparkListener(listener)
+    sc.parallelize(1 to 100, numPartitions).mapPartitionsWithIndex { case (i, iter) =>
+      val taskContext = TaskContext.get()
+      taskContext.internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
+      // Fail the first attempts of a subset of the tasks
+      if (failCondition(i) && taskContext.attemptNumber() == 0) {
+        throw new Exception("Failing a task intentionally.")
+      }
+      iter
+    }.count()
+    val stageInfos = listener.getCompletedStageInfos
+    val taskInfos = listener.getCompletedTaskInfos
+    assert(stageInfos.size === 1)
+    assert(taskInfos.size === numPartitions + numFailedPartitions)
+    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+    // We should not double count values in the merged accumulator
+    assert(stageAccum.value.toLong === numPartitions)
+    val taskAccumValues = taskInfos.flatMap { taskInfo =>
+      if (!taskInfo.failed) {
+        // If a task succeeded, its update value should always be 1
+        val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+        assert(taskAccum.update.isDefined)
+        assert(taskAccum.update.get.toLong === 1)
+        Some(taskAccum.value.toLong)
+      } else {
+        // If a task failed, we should not get its accumulator values
+        assert(taskInfo.accumulables.isEmpty)
+        None
+      }
+    }
+    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+  }
+
+}
+
+private[spark] object AccumulatorSuite {
+
+  /**
+   * Run one or more Spark jobs and verify that the peak execution memory accumulator
+   * is updated afterwards.
+   */
+  def verifyPeakExecutionMemorySet(
+      sc: SparkContext,
+      testName: String)(testBody: => Unit): Unit = {
+    val listener = new SaveInfoListener
+    sc.addSparkListener(listener)
+    // Verify that the accumulator does not already exist
+    sc.parallelize(1 to 10).count()
+    val accums = listener.getCompletedStageInfos.flatMap(_.accumulables.values)
+    assert(!accums.exists(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY))
+    testBody
+    // Verify that peak execution memory is updated
+    val accum = listener.getCompletedStageInfos
+      .flatMap(_.accumulables.values)
+      .find(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY)
+      .getOrElse {
+        throw new TestFailedException(
+          s"peak execution memory accumulator not set in '$testName'", 0)
+      }
+    assert(accum.value.toLong > 0)
+  }
+}
+
+/**
+ * A simple listener that keeps track of the TaskInfos and StageInfos of all completed jobs.
+ */
+private class SaveInfoListener extends SparkListener {
+  private val completedStageInfos: ArrayBuffer[StageInfo] = new ArrayBuffer[StageInfo]
+  private val completedTaskInfos: ArrayBuffer[TaskInfo] = new ArrayBuffer[TaskInfo]
+
+  def getCompletedStageInfos: Seq[StageInfo] = completedStageInfos.toArray.toSeq
+  def getCompletedTaskInfos: Seq[TaskInfo] = completedTaskInfos.toArray.toSeq
+
+  override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
+    completedStageInfos += stageCompleted.stageInfo
+  }
+
+  override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
+    completedTaskInfos += taskEnd.taskInfo
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
index 618a5fb24710f..cb8bd04e496a7 100644
--- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
@@ -21,7 +21,7 @@ import org.mockito.Mockito._
 import org.scalatest.BeforeAndAfter
 import org.scalatest.mock.MockitoSugar
 
-import org.apache.spark.executor.DataReadMethod
+import org.apache.spark.executor.{DataReadMethod, TaskMetrics}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage._
 
@@ -65,7 +65,7 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     // in blockManager.put is a losing battle. You have been warned.
     blockManager = sc.env.blockManager
     cacheManager = sc.env.cacheManager
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     val computeValue = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     val getValue = blockManager.get(RDDBlockId(rdd.id, split.index))
     assert(computeValue.toList === List(1, 2, 3, 4))
@@ -77,7 +77,7 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12)
     when(blockManager.get(RDDBlockId(0, 0))).thenReturn(Some(result))
 
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     assert(value.toList === List(5, 6, 7))
   }
@@ -86,14 +86,14 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     // Local computation should not persist the resulting value, so don't expect a put().
     when(blockManager.get(RDDBlockId(0, 0))).thenReturn(None)
 
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null, true)
+    val context = new TaskContextImpl(0, 0, 0, 0, null, null, Seq.empty, runningLocally = true)
     val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     assert(value.toList === List(1, 2, 3, 4))
   }
 
   test("verify task metrics updated correctly") {
     cacheManager = sc.env.cacheManager
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     cacheManager.getOrCompute(rdd3, split, context, StorageLevel.MEMORY_ONLY)
     assert(context.taskMetrics.updatedBlocks.getOrElse(Seq()).size === 2)
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
index 3e8816a4c65be..5f73ec8675966 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
@@ -175,7 +175,7 @@ class PipedRDDSuite extends SparkFunSuite with SharedSparkContext {
       }
       val hadoopPart1 = generateFakeHadoopPartition()
       val pipedRdd = new PipedRDD(nums, "printenv " + varName)
-      val tContext = new TaskContextImpl(0, 0, 0, 0, null, null)
+      val tContext = TaskContext.empty()
       val rddIter = pipedRdd.compute(hadoopPart1, tContext)
       val arr = rddIter.toArray
       assert(arr(0) == "/some/path")
diff --git a/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
index b3ca150195a5f..f7e16af9d3a92 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
@@ -19,9 +19,11 @@ package org.apache.spark.scheduler
 
 import org.apache.spark.TaskContext
 
-class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId, 0, 0) {
+class FakeTask(
+    stageId: Int,
+    prefLocs: Seq[TaskLocation] = Nil)
+  extends Task[Int](stageId, 0, 0, Seq.empty) {
   override def runTask(context: TaskContext): Int = 0
-
   override def preferredLocations: Seq[TaskLocation] = prefLocs
 }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
index 383855caefa2f..f33324792495b 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
@@ -25,7 +25,7 @@ import org.apache.spark.TaskContext
  * A Task implementation that fails to serialize.
  */
 private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int)
-  extends Task[Array[Byte]](stageId, 0, 0) {
+  extends Task[Array[Byte]](stageId, 0, 0, Seq.empty) {
 
   override def runTask(context: TaskContext): Array[Byte] = Array.empty[Byte]
   override def preferredLocations: Seq[TaskLocation] = Seq[TaskLocation]()
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
index 9201d1e1f328b..450ab7b9fe92b 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
@@ -57,8 +57,9 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark
     }
     val closureSerializer = SparkEnv.get.closureSerializer.newInstance()
     val func = (c: TaskContext, i: Iterator[String]) => i.next()
-    val task = new ResultTask[String, String](0, 0,
-      sc.broadcast(closureSerializer.serialize((rdd, func)).array), rdd.partitions(0), Seq(), 0)
+    val taskBinary = sc.broadcast(closureSerializer.serialize((rdd, func)).array)
+    val task = new ResultTask[String, String](
+      0, 0, taskBinary, rdd.partitions(0), Seq.empty, 0, Seq.empty)
     intercept[RuntimeException] {
       task.run(0, 0, null)
     }
@@ -66,7 +67,7 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark
   }
 
   test("all TaskCompletionListeners should be called even if some fail") {
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     val listener = mock(classOf[TaskCompletionListener])
     context.addTaskCompletionListener(_ => throw new Exception("blah"))
     context.addTaskCompletionListener(listener)
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index 3abb99c4b2b54..f7cc4bb61d574 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -136,7 +136,7 @@ class FakeTaskScheduler(sc: SparkContext, liveExecutors: (String, String)* /* ex
 /**
  * A Task implementation that results in a large serialized task.
  */
-class LargeTask(stageId: Int) extends Task[Array[Byte]](stageId, 0, 0) {
+class LargeTask(stageId: Int) extends Task[Array[Byte]](stageId, 0, 0, Seq.empty) {
   val randomBuffer = new Array[Byte](TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024)
   val random = new Random(0)
   random.nextBytes(randomBuffer)
diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
index db718ecabbdb9..05b3afef5b839 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
@@ -138,7 +138,7 @@ class HashShuffleReaderSuite extends SparkFunSuite with LocalSparkContext {
       shuffleHandle,
       reduceId,
       reduceId + 1,
-      new TaskContextImpl(0, 0, 0, 0, null, null),
+      TaskContext.empty(),
       blockManager,
       mapOutputTracker)
 
diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
index cf8bd8ae69625..828153bdbfc44 100644
--- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
@@ -29,7 +29,7 @@ import org.mockito.invocation.InvocationOnMock
 import org.mockito.stubbing.Answer
 import org.scalatest.PrivateMethodTester
 
-import org.apache.spark.{SparkFunSuite, TaskContextImpl}
+import org.apache.spark.{SparkFunSuite, TaskContext}
 import org.apache.spark.network._
 import org.apache.spark.network.buffer.ManagedBuffer
 import org.apache.spark.network.shuffle.BlockFetchingListener
@@ -95,7 +95,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     )
 
     val iterator = new ShuffleBlockFetcherIterator(
-      new TaskContextImpl(0, 0, 0, 0, null, null),
+      TaskContext.empty(),
       transfer,
       blockManager,
       blocksByAddress,
@@ -165,7 +165,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq))
 
-    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val taskContext = TaskContext.empty()
     val iterator = new ShuffleBlockFetcherIterator(
       taskContext,
       transfer,
@@ -227,7 +227,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq))
 
-    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val taskContext = TaskContext.empty()
     val iterator = new ShuffleBlockFetcherIterator(
       taskContext,
       transfer,
diff --git a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
new file mode 100644
index 0000000000000..98f9314f31dff
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import org.mockito.Mockito.{mock, when, RETURNS_SMART_NULLS}
+
+import org.apache.spark.{LocalSparkContext, SparkConf, SparkFunSuite, Success}
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler._
+import org.apache.spark.ui.jobs.{JobProgressListener, StagePage, StagesTab}
+import org.apache.spark.ui.scope.RDDOperationGraphListener
+
+class StagePageSuite extends SparkFunSuite with LocalSparkContext {
+
+  test("peak execution memory only displayed if unsafe is enabled") {
+    val unsafeConf = "spark.sql.unsafe.enabled"
+    val conf = new SparkConf().set(unsafeConf, "true")
+    val html = renderStagePage(conf).toString().toLowerCase
+    val targetString = "peak execution memory"
+    assert(html.contains(targetString))
+    // Disable unsafe and make sure it's not there
+    val conf2 = new SparkConf().set(unsafeConf, "false")
+    val html2 = renderStagePage(conf2).toString().toLowerCase
+    assert(!html2.contains(targetString))
+  }
+
+  /**
+   * Render a stage page started with the given conf and return the HTML.
+   * This also runs a dummy stage to populate the page with useful content.
+   */
+  private def renderStagePage(conf: SparkConf): Seq[Node] = {
+    val jobListener = new JobProgressListener(conf)
+    val graphListener = new RDDOperationGraphListener(conf)
+    val tab = mock(classOf[StagesTab], RETURNS_SMART_NULLS)
+    val request = mock(classOf[HttpServletRequest])
+    when(tab.conf).thenReturn(conf)
+    when(tab.progressListener).thenReturn(jobListener)
+    when(tab.operationGraphListener).thenReturn(graphListener)
+    when(tab.appName).thenReturn("testing")
+    when(tab.headerTabs).thenReturn(Seq.empty)
+    when(request.getParameter("id")).thenReturn("0")
+    when(request.getParameter("attempt")).thenReturn("0")
+    val page = new StagePage(tab)
+
+    // Simulate a stage in job progress listener
+    val stageInfo = new StageInfo(0, 0, "dummy", 1, Seq.empty, Seq.empty, "details")
+    val taskInfo = new TaskInfo(0, 0, 0, 0, "0", "localhost", TaskLocality.ANY, false)
+    jobListener.onStageSubmitted(SparkListenerStageSubmitted(stageInfo))
+    jobListener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo))
+    taskInfo.markSuccessful()
+    jobListener.onTaskEnd(
+      SparkListenerTaskEnd(0, 0, "result", Success, taskInfo, TaskMetrics.empty))
+    jobListener.onStageCompleted(SparkListenerStageCompleted(stageInfo))
+    page.render(request)
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index 9c362f0de7076..12e9bafcc92c1 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -399,4 +399,19 @@ class ExternalAppendOnlyMapSuite extends SparkFunSuite with LocalSparkContext {
     sc.stop()
   }
 
+  test("external aggregation updates peak execution memory") {
+    val conf = createSparkConf(loadDefaults = false)
+      .set("spark.shuffle.memoryFraction", "0.001")
+      .set("spark.shuffle.manager", "hash") // make sure we're not also using ExternalSorter
+    sc = new SparkContext("local", "test", conf)
+    // No spilling
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external map without spilling") {
+      sc.parallelize(1 to 10, 2).map { i => (i, i) }.reduceByKey(_ + _).count()
+    }
+    // With spilling
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external map with spilling") {
+      sc.parallelize(1 to 1000 * 1000, 2).map { i => (i, i) }.reduceByKey(_ + _).count()
+    }
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
index 986cd8623d145..bdb0f4d507a7e 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
@@ -692,7 +692,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
     sortWithoutBreakingSortingContracts(createSparkConf(true, false))
   }
 
-  def sortWithoutBreakingSortingContracts(conf: SparkConf) {
+  private def sortWithoutBreakingSortingContracts(conf: SparkConf) {
     conf.set("spark.shuffle.memoryFraction", "0.01")
     conf.set("spark.shuffle.manager", "sort")
     sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
@@ -743,5 +743,15 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
     }
 
     sorter2.stop()
- }
+  }
+
+  test("sorting updates peak execution memory") {
+    val conf = createSparkConf(loadDefaults = false, kryo = false)
+      .set("spark.shuffle.manager", "sort")
+    sc = new SparkContext("local", "test", conf)
+    // Avoid aggregating here to make sure we're not also using ExternalAppendOnlyMap
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external sorter") {
+      sc.parallelize(1 to 1000, 2).repartition(100).count()
+    }
+  }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 5e4c6232c9471..193906d24790e 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -106,6 +106,13 @@ void spill() throws IOException {
     sorter.spill();
   }
 
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsage() {
+    return sorter.getPeakMemoryUsedBytes();
+  }
+
   private void cleanupResources() {
     sorter.freeMemory();
   }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 9e2c9334a7bee..43d06ce9bdfa3 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -208,6 +208,14 @@ public void close() {
     };
   }
 
+  /**
+   * The memory used by this map's managed structures, in bytes.
+   * Note that this is also the peak memory used by this map, since the map is append-only.
+   */
+  public long getMemoryUsage() {
+    return map.getTotalMemoryConsumption();
+  }
+
   /**
    * Free the memory associated with this map. This is idempotent and can be called multiple times.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index cd87b8deba0c2..bf4905dc1eef9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution
 
 import java.io.IOException
 
-import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.{InternalAccumulator, SparkEnv, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -263,11 +263,12 @@ case class GeneratedAggregate(
         assert(iter.hasNext, "There should be at least one row for this path")
         log.info("Using Unsafe-based aggregator")
         val pageSizeBytes = SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
+        val taskContext = TaskContext.get()
         val aggregationMap = new UnsafeFixedWidthAggregationMap(
           newAggregationBuffer(EmptyRow),
           aggregationBufferSchema,
           groupKeySchema,
-          TaskContext.get.taskMemoryManager(),
+          taskContext.taskMemoryManager(),
           SparkEnv.get.shuffleMemoryManager,
           1024 * 16, // initial capacity
           pageSizeBytes,
@@ -284,6 +285,10 @@ case class GeneratedAggregate(
           updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow))
         }
 
+        // Record memory used in the process
+        taskContext.internalMetricsToAccumulators(
+          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(aggregationMap.getMemoryUsage)
+
         new Iterator[InternalRow] {
           private[this] val mapIterator = aggregationMap.iterator()
           private[this] val resultProjection = resultProjectionBuilder()
@@ -300,7 +305,7 @@ case class GeneratedAggregate(
               } else {
                 // This is the last element in the iterator, so let's free the buffer. Before we do,
                 // though, we need to make a defensive copy of the result so that we don't return an
-                // object that might contain dangling pointers to the freed memory
+                // object that might contain dangling pointers to the freed memory.
                 val resultCopy = result.copy()
                 aggregationMap.free()
                 resultCopy
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index 624efc1b1d734..e73e2523a777f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -70,7 +71,14 @@ case class BroadcastHashJoin(
     val broadcastRelation = Await.result(broadcastFuture, timeout)
 
     streamedPlan.execute().mapPartitions { streamedIter =>
-      hashJoin(streamedIter, broadcastRelation.value)
+      val hashedRelation = broadcastRelation.value
+      hashedRelation match {
+        case unsafe: UnsafeHashedRelation =>
+          TaskContext.get().internalMetricsToAccumulators(
+            InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
+        case _ =>
+      }
+      hashJoin(streamedIter, hashedRelation)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index 309716a0efcc0..c35e439cc9deb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -75,6 +76,13 @@ case class BroadcastHashOuterJoin(
       val hashTable = broadcastRelation.value
       val keyGenerator = streamedKeyGenerator
 
+      hashTable match {
+        case unsafe: UnsafeHashedRelation =>
+          TaskContext.get().internalMetricsToAccumulators(
+            InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
+        case _ =>
+      }
+
       joinType match {
         case LeftOuter =>
           streamedIter.flatMap(currentRow => {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index a60593911f94f..5bd06fbdca605 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -51,7 +52,14 @@ case class BroadcastLeftSemiJoinHash(
       val broadcastedRelation = sparkContext.broadcast(hashRelation)
 
       left.execute().mapPartitions { streamIter =>
-        hashSemiJoin(streamIter, broadcastedRelation.value)
+        val hashedRelation = broadcastedRelation.value
+        hashedRelation match {
+          case unsafe: UnsafeHashedRelation =>
+            TaskContext.get().internalMetricsToAccumulators(
+              InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
+          case _ =>
+        }
+        hashSemiJoin(streamIter, hashedRelation)
       }
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index cc8bbfd2f8943..58b4236f7b5b5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -183,8 +183,27 @@ private[joins] final class UnsafeHashedRelation(
   private[joins] def this() = this(null)  // Needed for serialization
 
   // Use BytesToBytesMap in executor for better performance (it's created when deserialization)
+  // This is used in broadcast joins and distributed mode only
   @transient private[this] var binaryMap: BytesToBytesMap = _
 
+  /**
+   * Return the size of the unsafe map on the executors.
+   *
+   * For broadcast joins, this hashed relation is bigger on the driver because it is
+   * represented as a Java hash map there. While serializing the map to the executors,
+   * however, we rehash the contents in a binary map to reduce the memory footprint on
+   * the executors.
+   *
+   * For non-broadcast joins or in local mode, return 0.
+   */
+  def getUnsafeSize: Long = {
+    if (binaryMap != null) {
+      binaryMap.getTotalMemoryConsumption
+    } else {
+      0
+    }
+  }
+
   override def get(key: InternalRow): Seq[InternalRow] = {
     val unsafeKey = key.asInstanceOf[UnsafeRow]
 
@@ -214,7 +233,7 @@ private[joins] final class UnsafeHashedRelation(
       }
 
     } else {
-      // Use the JavaHashMap in Local mode or ShuffleHashJoin
+      // Use the Java HashMap in local mode or for non-broadcast joins (e.g. ShuffleHashJoin)
       hashTable.get(unsafeKey)
     }
   }
@@ -316,6 +335,7 @@ private[joins] object UnsafeHashedRelation {
       keyGenerator: UnsafeProjection,
       sizeEstimate: Int): HashedRelation = {
 
+    // Use a Java hash table here because unsafe maps expect fixed size records
     val hashTable = new JavaHashMap[UnsafeRow, CompactBuffer[UnsafeRow]](sizeEstimate)
 
     // Create a mapping of buildKeys -> rows
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index 92cf328c76cbc..3192b6ebe9075 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
@@ -76,6 +77,11 @@ case class ExternalSort(
       val sorter = new ExternalSorter[InternalRow, Null, InternalRow](ordering = Some(ordering))
       sorter.insertAll(iterator.map(r => (r.copy(), null)))
       val baseIterator = sorter.iterator.map(_._1)
+      val context = TaskContext.get()
+      context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
+      context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
+      context.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.peakMemoryUsedBytes)
       // TODO(marmbrus): The complex type signature below thwarts inference for no reason.
       CompletionIterator[InternalRow, Iterator[InternalRow]](baseIterator, sorter.stop())
     }, preservesPartitioning = true)
@@ -137,7 +143,11 @@ case class TungstenSort(
       if (testSpillFrequency > 0) {
         sorter.setTestSpillFrequency(testSpillFrequency)
       }
-      sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]])
+      val sortedIterator = sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]])
+      val taskContext = TaskContext.get()
+      taskContext.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.getPeakMemoryUsage)
+      sortedIterator
     }, preservesPartitioning = true)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index f1abae0720058..29dfcf2575227 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -21,6 +21,7 @@ import java.sql.Timestamp
 
 import org.scalatest.BeforeAndAfterAll
 
+import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
@@ -258,6 +259,23 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     }
   }
 
+  private def testCodeGen(sqlText: String, expectedResults: Seq[Row]): Unit = {
+    val df = sql(sqlText)
+    // First, check if we have GeneratedAggregate.
+    val hasGeneratedAgg = df.queryExecution.executedPlan
+      .collect { case _: GeneratedAggregate | _: aggregate.Aggregate => true }
+      .nonEmpty
+    if (!hasGeneratedAgg) {
+      fail(
+        s"""
+           |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan.
+           |${df.queryExecution.simpleString}
+         """.stripMargin)
+    }
+    // Then, check results.
+    checkAnswer(df, expectedResults)
+  }
+
   test("aggregation with codegen") {
     val originalValue = sqlContext.conf.codegenEnabled
     sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
@@ -267,26 +285,6 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       .unionAll(sqlContext.table("testData"))
       .registerTempTable("testData3x")
 
-    def testCodeGen(sqlText: String, expectedResults: Seq[Row]): Unit = {
-      val df = sql(sqlText)
-      // First, check if we have GeneratedAggregate.
-      var hasGeneratedAgg = false
-      df.queryExecution.executedPlan.foreach {
-        case generatedAgg: GeneratedAggregate => hasGeneratedAgg = true
-        case newAggregate: aggregate.Aggregate => hasGeneratedAgg = true
-        case _ =>
-      }
-      if (!hasGeneratedAgg) {
-        fail(
-          s"""
-             |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan.
-             |${df.queryExecution.simpleString}
-           """.stripMargin)
-      }
-      // Then, check results.
-      checkAnswer(df, expectedResults)
-    }
-
     try {
       // Just to group rows.
       testCodeGen(
@@ -1605,6 +1603,28 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 
+  test("aggregation with codegen updates peak execution memory") {
+    withSQLConf(
+        (SQLConf.CODEGEN_ENABLED.key, "true"),
+        (SQLConf.USE_SQL_AGGREGATE2.key, "false")) {
+      val sc = sqlContext.sparkContext
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "aggregation with codegen") {
+        testCodeGen(
+          "SELECT key, count(value) FROM testData GROUP BY key",
+          (1 to 100).map(i => Row(i, 1)))
+      }
+    }
+  }
+
+  test("external sorting updates peak execution memory") {
+    withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
+      val sc = sqlContext.sparkContext
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external sort") {
+        sortTest()
+      }
+    }
+  }
+
   test("SPARK-9511: error with table starting with number") {
     val df = sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
       .toDF("num", "str")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index c7949848513cf..88bce0e319f9e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -21,6 +21,7 @@ import scala.util.Random
 
 import org.scalatest.BeforeAndAfterAll
 
+import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.{RandomDataGenerator, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.test.TestSQLContext
@@ -59,6 +60,17 @@ class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     )
   }
 
+  test("sorting updates peak execution memory") {
+    val sc = TestSQLContext.sparkContext
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "unsafe external sort") {
+      checkThatPlansAgree(
+        (1 to 100).map(v => Tuple1(v)).toDF("a"),
+        (child: SparkPlan) => TungstenSort('a.asc :: Nil, true, child),
+        (child: SparkPlan) => Sort('a.asc :: Nil, global = true, child),
+        sortAnswers = false)
+    }
+  }
+
   // Test sorting on different data types
   for (
     dataType <- DataTypeTestUtils.atomicTypes ++ Set(NullType);
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index 7c591f6143b9e..ef827b0fe9b5b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -69,7 +69,8 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
         taskAttemptId = Random.nextInt(10000),
         attemptNumber = 0,
         taskMemoryManager = taskMemoryManager,
-        metricsSystem = null))
+        metricsSystem = null,
+        internalAccumulators = Seq.empty))
 
       try {
         f
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index 0282b25b9dd50..601a5a07ad002 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -76,7 +76,8 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
         taskAttemptId = 98456,
         attemptNumber = 0,
         taskMemoryManager = taskMemMgr,
-        metricsSystem = null))
+        metricsSystem = null,
+        internalAccumulators = Seq.empty))
 
       // Create the data converters
       val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
new file mode 100644
index 0000000000000..0554e11d252ba
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
@@ -0,0 +1,94 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TODO: uncomment the test here! It is currently failing due to
+// bad interaction with org.apache.spark.sql.test.TestSQLContext.
+
+// scalastyle:off
+//package org.apache.spark.sql.execution.joins
+//
+//import scala.reflect.ClassTag
+//
+//import org.scalatest.BeforeAndAfterAll
+//
+//import org.apache.spark.{AccumulatorSuite, SparkConf, SparkContext}
+//import org.apache.spark.sql.functions._
+//import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
+//
+///**
+// * Test various broadcast join operators with unsafe enabled.
+// *
+// * This needs to be its own suite because [[org.apache.spark.sql.test.TestSQLContext]] runs
+// * in local mode, but for tests in this suite we need to run Spark in local-cluster mode.
+// * In particular, the use of [[org.apache.spark.unsafe.map.BytesToBytesMap]] in
+// * [[org.apache.spark.sql.execution.joins.UnsafeHashedRelation]] is not triggered without
+// * serializing the hashed relation, which does not happen in local mode.
+// */
+//class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
+//  private var sc: SparkContext = null
+//  private var sqlContext: SQLContext = null
+//
+//  /**
+//   * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
+//   */
+//  override def beforeAll(): Unit = {
+//    super.beforeAll()
+//    val conf = new SparkConf()
+//      .setMaster("local-cluster[2,1,1024]")
+//      .setAppName("testing")
+//    sc = new SparkContext(conf)
+//    sqlContext = new SQLContext(sc)
+//    sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
+//    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
+//  }
+//
+//  override def afterAll(): Unit = {
+//    sc.stop()
+//    sc = null
+//    sqlContext = null
+//  }
+//
+//  /**
+//   * Test whether the specified broadcast join updates the peak execution memory accumulator.
+//   */
+//  private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
+//    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
+//      val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
+//      val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
+//      // Comparison at the end is for broadcast left semi join
+//      val joinExpression = df1("key") === df2("key") && df1("value") > df2("value")
+//      val df3 = df1.join(broadcast(df2), joinExpression, joinType)
+//      val plan = df3.queryExecution.executedPlan
+//      assert(plan.collect { case p: T => p }.size === 1)
+//      plan.executeCollect()
+//    }
+//  }
+//
+//  test("unsafe broadcast hash join updates peak execution memory") {
+//    testBroadcastJoin[BroadcastHashJoin]("unsafe broadcast hash join", "inner")
+//  }
+//
+//  test("unsafe broadcast hash outer join updates peak execution memory") {
+//    testBroadcastJoin[BroadcastHashOuterJoin]("unsafe broadcast hash outer join", "left_outer")
+//  }
+//
+//  test("unsafe broadcast left semi join updates peak execution memory") {
+//    testBroadcastJoin[BroadcastLeftSemiJoinHash]("unsafe broadcast left semi join", "leftsemi")
+//  }
+//
+//}
+// scalastyle:on

From b2e4b85d2db0320e9cbfaf5a5542f749f1f11cf4 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 3 Aug 2015 14:51:15 -0700
Subject: [PATCH 0811/1454] Revert "[SPARK-9372] [SQL] Filter nulls in join
 keys"

This reverts commit 687c8c37150f4c93f8e57d86bb56321a4891286b.
---
 .../catalyst/expressions/nullFunctions.scala  |  48 +---
 .../sql/catalyst/optimizer/Optimizer.scala    |  64 ++---
 .../plans/logical/basicOperators.scala        |  32 +--
 .../expressions/ExpressionEvalHelper.scala    |   4 +-
 .../expressions/MathFunctionsSuite.scala      |   3 +-
 .../expressions/NullFunctionsSuite.scala      |  49 +---
 .../spark/sql/DataFrameNaFunctions.scala      |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |   6 -
 .../org/apache/spark/sql/SQLContext.scala     |   5 +-
 .../extendedOperatorOptimizations.scala       | 160 ------------
 .../optimizer/FilterNullsInJoinKeySuite.scala | 236 ------------------
 11 files changed, 37 insertions(+), 572 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index d58c4756938c7..287718fab7f0d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -210,58 +210,14 @@ case class IsNotNull(child: Expression) extends UnaryExpression with Predicate {
   }
 }
 
-/**
- * A predicate that is evaluated to be true if there are at least `n` null values.
- */
-case class AtLeastNNulls(n: Int, children: Seq[Expression]) extends Predicate {
-  override def nullable: Boolean = false
-  override def foldable: Boolean = children.forall(_.foldable)
-  override def toString: String = s"AtLeastNNulls($n, ${children.mkString(",")})"
-
-  private[this] val childrenArray = children.toArray
-
-  override def eval(input: InternalRow): Boolean = {
-    var numNulls = 0
-    var i = 0
-    while (i < childrenArray.length && numNulls < n) {
-      val evalC = childrenArray(i).eval(input)
-      if (evalC == null) {
-        numNulls += 1
-      }
-      i += 1
-    }
-    numNulls >= n
-  }
-
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val numNulls = ctx.freshName("numNulls")
-    val code = children.map { e =>
-      val eval = e.gen(ctx)
-      s"""
-        if ($numNulls < $n) {
-          ${eval.code}
-          if (${eval.isNull}) {
-            $numNulls += 1;
-          }
-        }
-      """
-    }.mkString("\n")
-    s"""
-      int $numNulls = 0;
-      $code
-      boolean ${ev.isNull} = false;
-      boolean ${ev.primitive} = $numNulls >= $n;
-     """
-  }
-}
 
 /**
  * A predicate that is evaluated to be true if there are at least `n` non-null and non-NaN values.
  */
-case class AtLeastNNonNullNans(n: Int, children: Seq[Expression]) extends Predicate {
+case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate {
   override def nullable: Boolean = false
   override def foldable: Boolean = children.forall(_.foldable)
-  override def toString: String = s"AtLeastNNonNullNans($n, ${children.mkString(",")})"
+  override def toString: String = s"AtLeastNNulls(n, ${children.mkString(",")})"
 
   private[this] val childrenArray = children.toArray
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index e4b6294dc7b8e..29d706dcb39a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -31,14 +31,8 @@ import org.apache.spark.sql.types._
 
 abstract class Optimizer extends RuleExecutor[LogicalPlan]
 
-class DefaultOptimizer extends Optimizer {
-
-  /**
-   * Override to provide additional rules for the "Operator Optimizations" batch.
-   */
-  val extendedOperatorOptimizationRules: Seq[Rule[LogicalPlan]] = Nil
-
-  lazy val batches =
+object DefaultOptimizer extends Optimizer {
+  val batches =
     // SubQueries are only needed for analysis and can be removed before execution.
     Batch("Remove SubQueries", FixedPoint(100),
       EliminateSubQueries) ::
@@ -47,27 +41,26 @@ class DefaultOptimizer extends Optimizer {
       RemoveLiteralFromGroupExpressions) ::
     Batch("Operator Optimizations", FixedPoint(100),
       // Operator push down
-      SetOperationPushDown ::
-      SamplePushDown ::
-      PushPredicateThroughJoin ::
-      PushPredicateThroughProject ::
-      PushPredicateThroughGenerate ::
-      ColumnPruning ::
+      SetOperationPushDown,
+      SamplePushDown,
+      PushPredicateThroughJoin,
+      PushPredicateThroughProject,
+      PushPredicateThroughGenerate,
+      ColumnPruning,
       // Operator combine
-      ProjectCollapsing ::
-      CombineFilters ::
-      CombineLimits ::
+      ProjectCollapsing,
+      CombineFilters,
+      CombineLimits,
       // Constant folding
-      NullPropagation ::
-      OptimizeIn ::
-      ConstantFolding ::
-      LikeSimplification ::
-      BooleanSimplification ::
-      RemovePositive ::
-      SimplifyFilters ::
-      SimplifyCasts ::
-      SimplifyCaseConversionExpressions ::
-      extendedOperatorOptimizationRules.toList : _*) ::
+      NullPropagation,
+      OptimizeIn,
+      ConstantFolding,
+      LikeSimplification,
+      BooleanSimplification,
+      RemovePositive,
+      SimplifyFilters,
+      SimplifyCasts,
+      SimplifyCaseConversionExpressions) ::
     Batch("Decimal Optimizations", FixedPoint(100),
       DecimalAggregates) ::
     Batch("LocalRelation", FixedPoint(100),
@@ -229,18 +222,12 @@ object ColumnPruning extends Rule[LogicalPlan] {
   }
 
   /** Applies a projection only when the child is producing unnecessary attributes */
-  private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) = {
+  private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) =
     if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
-      // We need to preserve the nullability of c's output.
-      // So, we first create a outputMap and if a reference is from the output of
-      // c, we use that output attribute from c.
-      val outputMap = AttributeMap(c.output.map(attr => (attr, attr)))
-      val projectList = allReferences.filter(outputMap.contains).map(outputMap).toSeq
-      Project(projectList, c)
+      Project(allReferences.filter(c.outputSet.contains).toSeq, c)
     } else {
       c
     }
-  }
 }
 
 /**
@@ -530,13 +517,6 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
  */
 object CombineFilters extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-    case Filter(Not(AtLeastNNulls(1, e1)), Filter(Not(AtLeastNNulls(1, e2)), grandChild)) =>
-      // If we are combining two expressions Not(AtLeastNNulls(1, e1)) and
-      // Not(AtLeastNNulls(1, e2))
-      // (this is used to make sure there is no null in the result of e1 and e2 and
-      // they are added by FilterNullsInJoinKey optimziation rule), we can
-      // just create a Not(AtLeastNNulls(1, (e1 ++ e2).distinct)).
-      Filter(Not(AtLeastNNulls(1, (e1 ++ e2).distinct)), grandChild)
     case ff @ Filter(fc, nf @ Filter(nc, grandChild)) => Filter(And(nc, fc), grandChild)
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 54b5f49772664..aacfc86ab0e49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -86,37 +86,7 @@ case class Generate(
 }
 
 case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode {
-  /**
-   * Indicates if `atLeastNNulls` is used to check if atLeastNNulls.children
-   * have at least one null value and atLeastNNulls.children are all attributes.
-   */
-  private def isAtLeastOneNullOutputAttributes(atLeastNNulls: AtLeastNNulls): Boolean = {
-    val expressions = atLeastNNulls.children
-    val n = atLeastNNulls.n
-    if (n != 1) {
-      // AtLeastNNulls is not used to check if atLeastNNulls.children have
-      // at least one null value.
-      false
-    } else {
-      // AtLeastNNulls is used to check if atLeastNNulls.children have
-      // at least one null value. We need to make sure all atLeastNNulls.children
-      // are attributes.
-      expressions.forall(_.isInstanceOf[Attribute])
-    }
-  }
-
-  override def output: Seq[Attribute] = condition match {
-    case Not(a: AtLeastNNulls) if isAtLeastOneNullOutputAttributes(a) =>
-      // The condition is used to make sure that there is no null value in
-      // a.children.
-      val nonNullableAttributes = AttributeSet(a.children.asInstanceOf[Seq[Attribute]])
-      child.output.map {
-        case attr if nonNullableAttributes.contains(attr) =>
-          attr.withNullability(false)
-        case attr => attr
-      }
-    case _ => child.output
-  }
+  override def output: Seq[Attribute] = child.output
 }
 
 case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 3e55151298741..a41185b4d8754 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -31,8 +31,6 @@ import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 trait ExpressionEvalHelper {
   self: SparkFunSuite =>
 
-  protected val defaultOptimizer = new DefaultOptimizer
-
   protected def create_row(values: Any*): InternalRow = {
     InternalRow.fromSeq(values.map(CatalystTypeConverters.convertToCatalyst))
   }
@@ -188,7 +186,7 @@ trait ExpressionEvalHelper {
       expected: Any,
       inputRow: InternalRow = EmptyRow): Unit = {
     val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation)
-    val optimizedPlan = defaultOptimizer.execute(plan)
+    val optimizedPlan = DefaultOptimizer.execute(plan)
     checkEvaluationWithoutCodegen(optimizedPlan.expressions.head, expected, inputRow)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 649a5b44dc036..9fcb548af6bbb 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -23,6 +23,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateProjection, GenerateMutableProjection}
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 import org.apache.spark.sql.types._
 
@@ -148,7 +149,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     expression: Expression,
     inputRow: InternalRow = EmptyRow): Unit = {
     val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation)
-    val optimizedPlan = defaultOptimizer.execute(plan)
+    val optimizedPlan = DefaultOptimizer.execute(plan)
     checkNaNWithoutCodegen(optimizedPlan.expressions.head, inputRow)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
index bf197124d8dbc..ace6c15dc8418 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
@@ -77,7 +77,7 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
-  test("AtLeastNNonNullNans") {
+  test("AtLeastNNonNulls") {
     val mix = Seq(Literal("x"),
       Literal.create(null, StringType),
       Literal.create(null, DoubleType),
@@ -96,46 +96,11 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       Literal(Float.MaxValue),
       Literal(false))
 
-    checkEvaluation(AtLeastNNonNullNans(0, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(2, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(3, mix), false, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(0, nanOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(3, nanOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(4, nanOnly), false, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(0, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(3, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(4, nullOnly), false, EmptyRow)
-  }
-
-  test("AtLeastNNull") {
-    val mix = Seq(Literal("x"),
-      Literal.create(null, StringType),
-      Literal.create(null, DoubleType),
-      Literal(Double.NaN),
-      Literal(5f))
-
-    val nanOnly = Seq(Literal("x"),
-      Literal(10.0),
-      Literal(Float.NaN),
-      Literal(math.log(-2)),
-      Literal(Double.MaxValue))
-
-    val nullOnly = Seq(Literal("x"),
-      Literal.create(null, DoubleType),
-      Literal.create(null, DecimalType.USER_DEFAULT),
-      Literal(Float.MaxValue),
-      Literal(false))
-
-    checkEvaluation(AtLeastNNulls(0, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(1, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(2, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(3, mix), false, EmptyRow)
-    checkEvaluation(AtLeastNNulls(0, nanOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(1, nanOnly), false, EmptyRow)
-    checkEvaluation(AtLeastNNulls(2, nanOnly), false, EmptyRow)
-    checkEvaluation(AtLeastNNulls(0, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(1, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(2, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(3, nullOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(2, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, mix), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, nanOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(4, nanOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(4, nullOnly), false, EmptyRow)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index ea85f0657a726..a4fd4cf3b330b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -122,7 +122,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = {
     // Filtering condition:
     // only keep the row if it has at least `minNonNulls` non-null and non-NaN values.
-    val predicate = AtLeastNNonNullNans(minNonNulls, cols.map(name => df.resolve(name)))
+    val predicate = AtLeastNNonNulls(minNonNulls, cols.map(name => df.resolve(name)))
     df.filter(Column(predicate))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 41ba1c7fe0574..f836122b3e0e4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -413,10 +413,6 @@ private[spark] object SQLConf {
     "spark.sql.useSerializer2",
     defaultValue = Some(true), isPublic = false)
 
-  val ADVANCED_SQL_OPTIMIZATION = booleanConf(
-    "spark.sql.advancedOptimization",
-    defaultValue = Some(true), isPublic = false)
-
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
@@ -488,8 +484,6 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
 
-  private[spark] def advancedSqlOptimizations: Boolean = getConf(ADVANCED_SQL_OPTIMIZATION)
-
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
 
   private[spark] def defaultSizeInBytes: Long =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 31e2b508d485e..dbb2a09846548 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -41,7 +41,6 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
-import org.apache.spark.sql.optimizer.FilterNullsInJoinKey
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -157,9 +156,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     }
 
   @transient
-  protected[sql] lazy val optimizer: Optimizer = new DefaultOptimizer {
-    override val extendedOperatorOptimizationRules = FilterNullsInJoinKey(self) :: Nil
-  }
+  protected[sql] lazy val optimizer: Optimizer = DefaultOptimizer
 
   @transient
   protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala b/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
deleted file mode 100644
index 5a4dde5756964..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.optimizer
-
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
-import org.apache.spark.sql.catalyst.plans.{Inner, LeftOuter, RightOuter, LeftSemi}
-import org.apache.spark.sql.catalyst.plans.logical.{Project, Filter, Join, LogicalPlan}
-import org.apache.spark.sql.catalyst.rules.Rule
-
-/**
- * An optimization rule used to insert Filters to filter out rows whose equal join keys
- * have at least one null values. For this kind of rows, they will not contribute to
- * the join results of equal joins because a null does not equal another null. We can
- * filter them out before shuffling join input rows. For example, we have two tables
- *
- * table1(key String, value Int)
- * "str1"|1
- * null  |2
- *
- * table2(key String, value Int)
- * "str1"|3
- * null  |4
- *
- * For a inner equal join, the result will be
- * "str1"|1|"str1"|3
- *
- * those two rows having null as the value of key will not contribute to the result.
- * So, we can filter them out early.
- *
- * This optimization rule can be disabled by setting spark.sql.advancedOptimization to false.
- *
- */
-case class FilterNullsInJoinKey(
-    sqlContext: SQLContext)
-  extends Rule[LogicalPlan] {
-
-  /**
-   * Checks if we need to add a Filter operator. We will add a Filter when
-   * there is any attribute in `keys` whose corresponding attribute of `keys`
-   * in `plan.output` is still nullable (`nullable` field is `true`).
-   */
-  private def needsFilter(keys: Seq[Expression], plan: LogicalPlan): Boolean = {
-    val keyAttributeSet = AttributeSet(keys.filter(_.isInstanceOf[Attribute]))
-    plan.output.filter(keyAttributeSet.contains).exists(_.nullable)
-  }
-
-  /**
-   * Adds a Filter operator to make sure that every attribute in `keys` is non-nullable.
-   */
-  private def addFilterIfNecessary(
-      keys: Seq[Expression],
-      child: LogicalPlan): LogicalPlan = {
-    // We get all attributes from keys.
-    val attributes = keys.filter(_.isInstanceOf[Attribute])
-
-    // Then, we create a Filter to make sure these attributes are non-nullable.
-    val filter =
-      if (attributes.nonEmpty) {
-        Filter(Not(AtLeastNNulls(1, attributes)), child)
-      } else {
-        child
-      }
-
-    filter
-  }
-
-  /**
-   * We reconstruct the join condition.
-   */
-  private def reconstructJoinCondition(
-      leftKeys: Seq[Expression],
-      rightKeys: Seq[Expression],
-      otherPredicate: Option[Expression]): Expression = {
-    // First, we rewrite the equal condition part. When we extract those keys,
-    // we use splitConjunctivePredicates. So, it is safe to use .reduce(And).
-    val rewrittenEqualJoinCondition = leftKeys.zip(rightKeys).map {
-      case (l, r) => EqualTo(l, r)
-    }.reduce(And)
-
-    // Then, we add otherPredicate. When we extract those equal condition part,
-    // we use splitConjunctivePredicates. So, it is safe to use
-    // And(rewrittenEqualJoinCondition, c).
-    val rewrittenJoinCondition = otherPredicate
-      .map(c => And(rewrittenEqualJoinCondition, c))
-      .getOrElse(rewrittenEqualJoinCondition)
-
-    rewrittenJoinCondition
-  }
-
-  def apply(plan: LogicalPlan): LogicalPlan = {
-    if (!sqlContext.conf.advancedSqlOptimizations) {
-      plan
-    } else {
-      plan transform {
-        case join: Join => join match {
-          // For a inner join having equal join condition part, we can add filters
-          // to both sides of the join operator.
-          case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(leftKeys, left) || needsFilter(rightKeys, right) =>
-            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
-            val withRightFilter = addFilterIfNecessary(rightKeys, right)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(withLeftFilter, withRightFilter, Inner, Some(rewrittenJoinCondition))
-
-          // For a left outer join having equal join condition part, we can add a filter
-          // to the right side of the join operator.
-          case ExtractEquiJoinKeys(LeftOuter, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(rightKeys, right) =>
-            val withRightFilter = addFilterIfNecessary(rightKeys, right)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(left, withRightFilter, LeftOuter, Some(rewrittenJoinCondition))
-
-          // For a right outer join having equal join condition part, we can add a filter
-          // to the left side of the join operator.
-          case ExtractEquiJoinKeys(RightOuter, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(leftKeys, left) =>
-            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(withLeftFilter, right, RightOuter, Some(rewrittenJoinCondition))
-
-          // For a left semi join having equal join condition part, we can add filters
-          // to both sides of the join operator.
-          case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(leftKeys, left) || needsFilter(rightKeys, right) =>
-            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
-            val withRightFilter = addFilterIfNecessary(rightKeys, right)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(withLeftFilter, withRightFilter, LeftSemi, Some(rewrittenJoinCondition))
-
-          case other => other
-        }
-      }
-    }
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala
deleted file mode 100644
index f98e4acafbf2c..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala
+++ /dev/null
@@ -1,236 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.optimizer
-
-import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
-import org.apache.spark.sql.catalyst.dsl.plans._
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.expressions.{Not, AtLeastNNulls}
-import org.apache.spark.sql.catalyst.optimizer._
-import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.{Filter, LocalRelation, LogicalPlan}
-import org.apache.spark.sql.catalyst.rules.RuleExecutor
-import org.apache.spark.sql.test.TestSQLContext
-
-/** This is the test suite for FilterNullsInJoinKey optimization rule. */
-class FilterNullsInJoinKeySuite extends PlanTest {
-
-  // We add predicate pushdown rules at here to make sure we do not
-  // create redundant Filter operators. Also, because the attribute ordering of
-  // the Project operator added by ColumnPruning may be not deterministic
-  // (the ordering may depend on the testing environment),
-  // we first construct the plan with expected Filter operators and then
-  // run the optimizer to add the the Project for column pruning.
-  object Optimize extends RuleExecutor[LogicalPlan] {
-    val batches =
-      Batch("Subqueries", Once,
-        EliminateSubQueries) ::
-      Batch("Operator Optimizations", FixedPoint(100),
-        FilterNullsInJoinKey(TestSQLContext), // This is the rule we test in this suite.
-        CombineFilters,
-        PushPredicateThroughProject,
-        BooleanSimplification,
-        PushPredicateThroughJoin,
-        PushPredicateThroughGenerate,
-        ColumnPruning,
-        ProjectCollapsing) :: Nil
-  }
-
-  val leftRelation = LocalRelation('a.int, 'b.int, 'c.int, 'd.int)
-
-  val rightRelation = LocalRelation('e.int, 'f.int, 'g.int, 'h.int)
-
-  test("inner join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For an inner join, FilterNullsInJoinKey add filter to both side.
-    val correctLeft =
-      leftRelation
-        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
-
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      correctLeft
-        .join(correctRight, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("make sure we do not keep adding filters") {
-    val thirdRelation = LocalRelation('i.int, 'j.int, 'k.int, 'l.int)
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, Inner, Some('a === 'e))
-        .join(thirdRelation, Inner, Some('b === 'i && 'a === 'j))
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-    val conditions = optimized.collect {
-      case Filter(condition @ Not(AtLeastNNulls(1, exprs)), _) => exprs
-    }
-
-    // Make sure that we have three Not(AtLeastNNulls(1, exprs)) for those three tables.
-    assert(conditions.length === 3)
-
-    // Make sure attribtues are indeed a, b, e, i, and j.
-    assert(
-      conditions.flatMap(exprs => exprs).toSet ===
-        joinedPlan.select('a, 'b, 'e, 'i, 'j).analyze.output.toSet)
-  }
-
-  test("inner join (partially optimized)") {
-    val joinCondition =
-      ('a + 2 === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // We cannot extract attribute from the left join key.
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      leftRelation
-        .join(correctRight, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("inner join (not optimized)") {
-    val nonOptimizedJoinConditions =
-      Some('c - 100 + 'd === 'g + 1 - 'h) ::
-        Some('d > 'h || 'c === 'g) ::
-        Some('d + 'g + 'c > 'd - 'h) :: Nil
-
-    nonOptimizedJoinConditions.foreach { joinCondition =>
-      val joinedPlan =
-        leftRelation
-          .join(rightRelation.select('f, 'g, 'h), Inner, joinCondition)
-          .select('a, 'c, 'f, 'd, 'h, 'g)
-
-      val optimized = Optimize.execute(joinedPlan.analyze)
-
-      comparePlans(optimized, Optimize.execute(joinedPlan.analyze))
-    }
-  }
-
-  test("left outer join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, LeftOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For a left outer join, FilterNullsInJoinKey add filter to the right side.
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      leftRelation
-        .join(correctRight, LeftOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("right outer join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, RightOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For a right outer join, FilterNullsInJoinKey add filter to the left side.
-    val correctLeft =
-      leftRelation
-        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
-
-    val correctAnswer =
-      correctLeft
-        .join(rightRelation, RightOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("full outer join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, FullOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    // FilterNullsInJoinKey does not fire for a full outer join.
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    comparePlans(optimized, Optimize.execute(joinedPlan.analyze))
-  }
-
-  test("left semi join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, LeftSemi, Some(joinCondition))
-        .select('a, 'd)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For a left semi join, FilterNullsInJoinKey add filter to both side.
-    val correctLeft =
-      leftRelation
-        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
-
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      correctLeft
-        .join(correctRight, LeftSemi, Some(joinCondition))
-        .select('a, 'd)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-}

From a2409d1c8e8ddec04b529ac6f6a12b5993f0eeda Mon Sep 17 00:00:00 2001
From: Steve Loughran <stevel@hortonworks.com>
Date: Mon, 3 Aug 2015 15:24:34 -0700
Subject: [PATCH 0812/1454] [SPARK-8064] [SQL] Build against Hive 1.2.1

Cherry picked the parts of the initial SPARK-8064 WiP branch needed to get sql/hive to compile against hive 1.2.1. That's the ASF release packaged under org.apache.hive, not any fork.

Tests not run yet: that's what the machines are for

Author: Steve Loughran <stevel@hortonworks.com>
Author: Cheng Lian <lian@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Author: Patrick Wendell <patrick@databricks.com>

Closes #7191 from steveloughran/stevel/feature/SPARK-8064-hive-1.2-002 and squashes the following commits:

7556d85 [Cheng Lian] Updates .q files and corresponding golden files
ef4af62 [Steve Loughran] Merge commit '6a92bb09f46a04d6cd8c41bdba3ecb727ebb9030' into stevel/feature/SPARK-8064-hive-1.2-002
6a92bb0 [Cheng Lian] Overrides HiveConf time vars
dcbb391 [Cheng Lian] Adds com.twitter:parquet-hadoop-bundle:1.6.0 for Hive Parquet SerDe
0bbe475 [Steve Loughran] SPARK-8064 scalastyle rejects the standard Hadoop ASF license header...
fdf759b [Steve Loughran] SPARK-8064 classpath dependency suite to be in sync with shading in final (?) hive-exec spark
7a6c727 [Steve Loughran] SPARK-8064 switch to second staging repo of the spark-hive artifacts. This one has the protobuf-shaded hive-exec jar
376c003 [Steve Loughran] SPARK-8064 purge duplicate protobuf declaration
2c74697 [Steve Loughran] SPARK-8064 switch to the protobuf shaded hive-exec jar with tests to chase it down
cc44020 [Steve Loughran] SPARK-8064 remove hadoop.version from runtest.py, as profile will fix that automatically.
6901fa9 [Steve Loughran] SPARK-8064 explicit protobuf import
da310dc [Michael Armbrust] Fixes for Hive tests.
a775a75 [Steve Loughran] SPARK-8064 cherry-pick-incomplete
7404f34 [Patrick Wendell] Add spark-hive staging repo
832c164 [Steve Loughran] SPARK-8064 try to supress compiler warnings on Complex.java pasted-thrift-code
312c0d4 [Steve Loughran] SPARK-8064  maven/ivy dependency purge; calcite declaration needed
fa5ae7b [Steve Loughran] HIVE-8064 fix up hive-thriftserver dependencies and cut back on evicted references in the hive- packages; this keeps mvn and ivy resolution compatible, as the reconciliation policy is "by hand"
c188048 [Steve Loughran] SPARK-8064 manage the Hive depencencies to that -things that aren't needed are excluded -sql/hive built with ivy is in sync with the maven reconciliation policy, rather than latest-first
4c8be8d [Cheng Lian] WIP: Partial fix for Thrift server and CLI tests
314eb3c [Steve Loughran] SPARK-8064 deprecation warning  noise in one of the tests
17b0341 [Steve Loughran] SPARK-8064 IDE-hinted cleanups of Complex.java to reduce compiler warnings. It's all autogenerated code, so still ugly.
d029b92 [Steve Loughran] SPARK-8064 rely on unescaping to have already taken place, so go straight to map of serde options
23eca7e [Steve Loughran] HIVE-8064 handle raw and escaped property tokens
54d9b06 [Steve Loughran] SPARK-8064 fix compilation regression surfacing from rebase
0b12d5f [Steve Loughran] HIVE-8064 use subset of hive complex type whose types deserialize
fce73b6 [Steve Loughran] SPARK-8064 poms rely implicitly on the version of kryo chill provides
fd3aa5d [Steve Loughran] SPARK-8064 version of hive to d/l from ivy is 1.2.1
dc73ece [Steve Loughran] SPARK-8064 revert to master's determinstic pushdown strategy
d3c1e4a [Steve Loughran] SPARK-8064 purge UnionType
051cc21 [Steve Loughran] SPARK-8064 switch to an unshaded version of hive-exec-core, which must have been built with Kryo 2.21. This currently looks for a (locally built) version 1.2.1.spark
6684c60 [Steve Loughran] SPARK-8064 ignore RTE raised in blocking process.exitValue() call
e6121e5 [Steve Loughran] SPARK-8064 address review comments
aa43dc6 [Steve Loughran] SPARK-8064  more robust teardown on JavaMetastoreDatasourcesSuite
f2bff01 [Steve Loughran] SPARK-8064 better takeup of asynchronously caught error text
8b1ef38 [Steve Loughran] SPARK-8064: on failures executing spark-submit in HiveSparkSubmitSuite, print command line and all logged output.
5a9ce6b [Steve Loughran] SPARK-8064 add explicit reason for kv split failure, rather than array OOB. *does not address the issue*
642b63a [Steve Loughran] SPARK-8064 reinstate something cut briefly during rebasing
97194dc [Steve Loughran] SPARK-8064 add extra logging to the YarnClusterSuite classpath test. There should be no reason why this is failing on jenkins, but as it is (and presumably its CP-related), improve the logging including any exception raised.
335357f [Steve Loughran] SPARK-8064 fail fast on thrive process spawning tests on exit codes and/or error string patterns seen in log.
3ed872f [Steve Loughran] SPARK-8064 rename field double to  dbl
bca55e5 [Steve Loughran] SPARK-8064 missed one of the `date` escapes
41d6479 [Steve Loughran] SPARK-8064 wrap tests with withTable() calls to avoid table-exists exceptions
2bc29a4 [Steve Loughran] SPARK-8064 ParquetSuites to escape `date` field name
1ab9bc4 [Steve Loughran] SPARK-8064 TestHive to use sered2.thrift.test.Complex
bf3a249 [Steve Loughran] SPARK-8064: more resubmit than fix; tighten startup timeout to 60s. Still no obvious reason why jersey server code in spark-assembly isn't being picked up -it hasn't been shaded
c829b8f [Steve Loughran] SPARK-8064: reinstate yarn-rm-server dependencies to hive-exec to ensure that jersey server is on classpath on hadoop versions < 2.6
0b0f738 [Steve Loughran] SPARK-8064: thrift server startup to fail fast on any exception in the main thread
13abaf1 [Steve Loughran] SPARK-8064 Hive compatibilty tests sin sync with explain/show output from Hive 1.2.1
d14d5ea [Steve Loughran] SPARK-8064: DATE is now a predicate; you can't use it as a field in select ops
26eef1c [Steve Loughran] SPARK-8064: HIVE-9039 renamed TOK_UNION => TOK_UNIONALL while adding TOK_UNIONDISTINCT
3d64523 [Steve Loughran] SPARK-8064 improve diagns on uknown token; fix scalastyle failure
d0360f6 [Steve Loughran] SPARK-8064: delicate merge in of the branch vanzin/hive-1.1
1126e5a [Steve Loughran] SPARK-8064: name of unrecognized file format wasn't appearing in error text
8cb09c4 [Steve Loughran] SPARK-8064: test resilience/assertion improvements. Independent of the rest of the work; can be backported to earlier versions
dec12cb [Steve Loughran] SPARK-8064: when a CLI suite test fails include the full output text in the raised exception; this ensures that the stdout/stderr is included in jenkins reports, so it becomes possible to diagnose the cause.
463a670 [Steve Loughran] SPARK-8064 run-tests.py adds a hadoop-2.6 profile, and changes info messages to say "w/Hive 1.2.1" in console output
2531099 [Steve Loughran] SPARK-8064 successful attempt to get rid of pentaho as a transitive dependency of hive-exec
1d59100 [Steve Loughran] SPARK-8064 (unsuccessful) attempt to get rid of pentaho as a transitive dependency of hive-exec
75733fc [Steve Loughran] SPARK-8064 change thrift binary startup message to "Starting ThriftBinaryCLIService on port"
3ebc279 [Steve Loughran] SPARK-8064 move strings used to check for http/bin thrift services up into constants
c80979d [Steve Loughran] SPARK-8064: SparkSQLCLIDriver drops remote mode support. CLISuite Tests pass instead of timing out: undetected regression?
27e8370 [Steve Loughran] SPARK-8064 fix some style & IDE warnings
00e50d6 [Steve Loughran] SPARK-8064 stop excluding hive shims from dependency (commented out , for now)
cb4f142 [Steve Loughran] SPARK-8054 cut pentaho dependency from calcite
f7aa9cb [Steve Loughran] SPARK-8064 everything compiles with some commenting and moving of classes into a hive package
6c310b4 [Steve Loughran] SPARK-8064 subclass  Hive ServerOptionsProcessor to make it public again
f61a675 [Steve Loughran] SPARK-8064 thrift server switched to Hive 1.2.1, though it doesn't compile everywhere
4890b9d [Steve Loughran] SPARK-8064, build against Hive 1.2.1
---
 core/pom.xml                                  |   20 -
 dev/run-tests.py                              |    7 +-
 pom.xml                                       |  654 +++++++++-
 sbin/spark-daemon.sh                          |    2 +-
 sql/catalyst/pom.xml                          |    1 -
 .../parquet/ParquetCompatibilityTest.scala    |   13 +-
 sql/hive-thriftserver/pom.xml                 |   22 +-
 .../HiveServerServerOptionsProcessor.scala    |   37 +
 .../hive/thriftserver/HiveThriftServer2.scala |   27 +-
 .../SparkExecuteStatementOperation.scala      |    9 +-
 .../hive/thriftserver/SparkSQLCLIDriver.scala |   56 +-
 .../thriftserver/SparkSQLCLIService.scala     |   13 +-
 .../thriftserver/SparkSQLSessionManager.scala |   11 +-
 .../sql/hive/thriftserver/CliSuite.scala      |   75 +-
 .../HiveThriftServer2Suites.scala             |   40 +-
 .../execution/HiveCompatibilitySuite.scala    |   29 +-
 sql/hive/pom.xml                              |   92 +-
 .../apache/spark/sql/hive/HiveContext.scala   |  114 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |    5 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |   97 +-
 .../org/apache/spark/sql/hive/HiveShim.scala  |   15 +-
 .../sql/hive/client/ClientInterface.scala     |    4 +
 .../spark/sql/hive/client/ClientWrapper.scala |    5 +-
 .../spark/sql/hive/client/HiveShim.scala      |    2 +-
 .../hive/client/IsolatedClientLoader.scala    |    2 +-
 .../spark/sql/hive/client/package.scala       |    2 +-
 .../hive/execution/InsertIntoHiveTable.scala  |    2 +-
 .../hive/execution/ScriptTransformation.scala |    6 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |    2 +-
 .../spark/sql/hive/hiveWriterContainers.scala |    2 +-
 .../spark/sql/hive/orc/OrcFilters.scala       |    6 +-
 .../apache/spark/sql/hive/test/TestHive.scala |   36 +-
 .../apache/spark/sql/hive/test/Complex.java   | 1139 +++++++++++++++++
 .../hive/JavaMetastoreDataSourcesSuite.java   |    6 +-
 ...perator-0-ee7f6a60a9792041b85b18cda56429bf |    1 +
 ..._string-1-db089ff46f9826c7883198adacdfad59 |    6 +-
 ...tar_by-5-41d474f5e6d7c61c36f74b4bec4e9e44} |    0
 ...e_alter-3-2a91d52719cf4552ebeb867204552a26 |    2 +-
 ...b_table-4-b585371b624cbab2616a49f553a870a0 |    2 +-
 ...limited-1-2a91d52719cf4552ebeb867204552a26 |    2 +-
 ...e_serde-1-2a91d52719cf4552ebeb867204552a26 |    2 +-
 ...nctions-0-45a7762c39f1b0f26f076220e2764043 |   21 +
 ...perties-1-be4adb893c7f946ebd76a648ce3cc1ae |    2 +-
 ...date_add-1-efb60fcbd6d78ad35257fb1ec39ace2 |    4 +-
 ...ate_sub-1-7efeb74367835ade71e5e42b22f8ced4 |    4 +-
 ...atediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5 |    2 +-
 ...udf_day-0-c4c503756384ff1220222d84fd25e756 |    2 +-
 .../udf_day-1-87168babe1110fe4c38269843414ca4 |   11 +-
 ...ofmonth-0-7b2caf942528656555cf19c261a18502 |    2 +-
 ...ofmonth-1-ca24d07102ad264d79ff30c64a73a7e8 |   11 +-
 .../udf_if-0-b7ffa85b5785cccef2af1b285348cc2c |    2 +-
 .../udf_if-1-30cf7f51f92b5684e556deff3032d49a |    2 +-
 .../udf_if-1-b7ffa85b5785cccef2af1b285348cc2c |    2 +-
 .../udf_if-2-30cf7f51f92b5684e556deff3032d49a |    2 +-
 ..._minute-0-9a38997c1f41f4afe00faa0abc471aee |    2 +-
 ..._minute-1-16995573ac4f4a1b047ad6ee88699e48 |    8 +-
 ...f_month-0-9a38997c1f41f4afe00faa0abc471aee |    2 +-
 ...f_month-1-16995573ac4f4a1b047ad6ee88699e48 |    8 +-
 ...udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027 |    2 +-
 ..._stddev-1-18e1d598820013453fad45852e1a303d |    2 +-
 ...union3-0-99620f72f0282904846a596ca5b3e46c} |    0
 ...union3-2-90ca96ea59fd45cf0af8c020ae77c908} |    0
 ...union3-3-72b149ccaef751bcfe55d5ca37cb5fd7} |    0
 .../clientpositive/parenthesis_star_by.q      |    2 +-
 .../src/test/queries/clientpositive/union3.q  |   11 +-
 .../sql/hive/ClasspathDependenciesSuite.scala |  110 ++
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |   29 +-
 .../sql/hive/InsertIntoHiveTableSuite.scala   |    7 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |    9 +
 .../spark/sql/hive/StatisticsSuite.scala      |    3 +
 .../spark/sql/hive/client/VersionsSuite.scala |    6 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |   89 +-
 .../sql/hive/execution/PruningSuite.scala     |    8 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |  140 +-
 .../hive/orc/OrcHadoopFsRelationSuite.scala   |    8 +-
 .../hive/orc/OrcPartitionDiscoverySuite.scala |    3 +-
 .../apache/spark/sql/hive/parquetSuites.scala |  327 ++---
 yarn/pom.xml                                  |   10 -
 .../spark/deploy/yarn/YarnClusterSuite.scala  |   24 +-
 79 files changed, 2861 insertions(+), 584 deletions(-)
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java
 create mode 100644 sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf
 rename sql/hive/src/test/resources/golden/{parenthesis_star_by-5-6888c7f7894910538d82eefa23443189 => parenthesis_star_by-5-41d474f5e6d7c61c36f74b4bec4e9e44} (100%)
 rename sql/hive/src/test/resources/golden/{union3-0-6a8a35102de1b0b88c6721a704eb174d => union3-0-99620f72f0282904846a596ca5b3e46c} (100%)
 rename sql/hive/src/test/resources/golden/{union3-2-2a1dcd937f117f1955a169592b96d5f9 => union3-2-90ca96ea59fd45cf0af8c020ae77c908} (100%)
 rename sql/hive/src/test/resources/golden/{union3-3-8fc63f8edb2969a63cd4485f1867ba97 => union3-3-72b149ccaef751bcfe55d5ca37cb5fd7} (100%)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala

diff --git a/core/pom.xml b/core/pom.xml
index 202678779150b..0e53a79fd2235 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -46,30 +46,10 @@
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>chill_${scala.binary.version}</artifactId>
-      <exclusions>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm-commons</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>chill-java</artifactId>
-      <exclusions>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm-commons</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.apache.hadoop</groupId>
diff --git a/dev/run-tests.py b/dev/run-tests.py
index b6d181418f027..d1852b95bb292 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -273,6 +273,7 @@ def get_hadoop_profiles(hadoop_version):
         "hadoop2.0": ["-Phadoop-1", "-Dhadoop.version=2.0.0-mr1-cdh4.1.1"],
         "hadoop2.2": ["-Pyarn", "-Phadoop-2.2"],
         "hadoop2.3": ["-Pyarn", "-Phadoop-2.3", "-Dhadoop.version=2.3.0"],
+        "hadoop2.6": ["-Pyarn", "-Phadoop-2.6"],
     }
 
     if hadoop_version in sbt_maven_hadoop_profiles:
@@ -289,7 +290,7 @@ def build_spark_maven(hadoop_version):
     mvn_goals = ["clean", "package", "-DskipTests"]
     profiles_and_goals = build_profiles + mvn_goals
 
-    print("[info] Building Spark (w/Hive 0.13.1) using Maven with these arguments: ",
+    print("[info] Building Spark (w/Hive 1.2.1) using Maven with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_maven(profiles_and_goals)
@@ -305,14 +306,14 @@ def build_spark_sbt(hadoop_version):
                  "streaming-kinesis-asl-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
-    print("[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments: ",
+    print("[info] Building Spark (w/Hive 1.2.1) using SBT with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_sbt(profiles_and_goals)
 
 
 def build_apache_spark(build_tool, hadoop_version):
-    """Will build Spark against Hive v0.13.1 given the passed in build tool (either `sbt` or
+    """Will build Spark against Hive v1.2.1 given the passed in build tool (either `sbt` or
     `maven`). Defaults to using `sbt`."""
 
     set_title_and_block("Building Spark", "BLOCK_BUILD")
diff --git a/pom.xml b/pom.xml
index be0dac953abf7..a958cec867eae 100644
--- a/pom.xml
+++ b/pom.xml
@@ -134,11 +134,12 @@
     <curator.version>2.4.0</curator.version>
     <hive.group>org.spark-project.hive</hive.group>
     <!-- Version used in Maven Hive dependency -->
-    <hive.version>0.13.1a</hive.version>
+    <hive.version>1.2.1.spark</hive.version>
     <!-- Version used for internal directory structure -->
-    <hive.version.short>0.13.1</hive.version.short>
+    <hive.version.short>1.2.1</hive.version.short>
     <derby.version>10.10.1.1</derby.version>
     <parquet.version>1.7.0</parquet.version>
+    <hive.parquet.version>1.6.0</hive.parquet.version>
     <jblas.version>1.2.4</jblas.version>
     <jetty.version>8.1.14.v20131031</jetty.version>
     <orbit.version>3.0.0.v201112011016</orbit.version>
@@ -151,7 +152,10 @@
     <jets3t.version>0.7.1</jets3t.version>
     <aws.java.sdk.version>1.9.16</aws.java.sdk.version>
     <aws.kinesis.client.version>1.2.1</aws.kinesis.client.version>
+    <!--  org.apache.httpcomponents/httpclient-->
     <commons.httpclient.version>4.3.2</commons.httpclient.version>
+    <!--  commons-httpclient/commons-httpclient-->
+    <httpclient.classic.version>3.1</httpclient.classic.version>
     <commons.math3.version>3.4.1</commons.math3.version>
     <scala.version>2.10.4</scala.version>
     <scala.binary.version>2.10</scala.binary.version>
@@ -161,6 +165,23 @@
     <fasterxml.jackson.version>2.4.4</fasterxml.jackson.version>
     <snappy.version>1.1.1.7</snappy.version>
     <netlib.java.version>1.1.2</netlib.java.version>
+    <calcite.version>1.2.0-incubating</calcite.version>
+    <commons-codec.version>1.10</commons-codec.version>
+    <!-- org.apache.commons/commons-lang/-->
+    <commons-lang2.version>2.6</commons-lang2.version>
+    <!-- org.apache.commons/commons-lang3/-->
+    <commons-lang3.version>3.3.2</commons-lang3.version>
+    <datanucleus-core.version>3.2.10</datanucleus-core.version>
+    <janino.version>2.7.8</janino.version>
+    <jersey.version>1.9</jersey.version>
+    <joda.version>2.5</joda.version>
+    <jodd.version>3.5.2</jodd.version>
+    <jsr305.version>1.3.9</jsr305.version>
+    <libthrift.version>0.9.2</libthrift.version>
+
+    <!-- For maven shade plugin (see SPARK-8819) -->
+    <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
+
     <test.java.home>${java.home}</test.java.home>
 
     <!--
@@ -188,7 +209,6 @@
     <MaxPermGen>512m</MaxPermGen>
     <CodeCacheSize>512m</CodeCacheSize>
   </properties>
-
   <repositories>
     <repository>
       <id>central</id>
@@ -246,6 +266,14 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <repository>
+      <id>spark-hive-staging</id>
+      <name>Staging Repo for Hive 1.2.1 (Spark Version)</name>
+      <url>https://oss.sonatype.org/content/repositories/orgspark-project-1113</url>
+      <releases>
+        <enabled>true</enabled>
+      </releases>
+    </repository>
     <repository>
       <id>mapr-repo</id>
       <name>MapR Repository</name>
@@ -257,12 +285,13 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <!-- returning unauthorized on some operations -->
     <repository>
       <id>spring-releases</id>
       <name>Spring Release Repository</name>
       <url>https://repo.spring.io/libs-release</url>
       <releases>
-        <enabled>true</enabled>
+        <enabled>false</enabled>
       </releases>
       <snapshots>
         <enabled>false</enabled>
@@ -402,12 +431,17 @@
       <dependency>
         <groupId>org.apache.commons</groupId>
         <artifactId>commons-lang3</artifactId>
-        <version>3.3.2</version>
+        <version>${commons-lang3.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.commons</groupId>
+        <artifactId>commons-lang</artifactId>
+        <version>${commons-lang2.version}</version>
       </dependency>
       <dependency>
         <groupId>commons-codec</groupId>
         <artifactId>commons-codec</artifactId>
-        <version>1.10</version>
+        <version>${commons-codec.version}</version>
       </dependency>
       <dependency>
         <groupId>org.apache.commons</groupId>
@@ -422,7 +456,12 @@
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
         <artifactId>jsr305</artifactId>
-        <version>1.3.9</version>
+        <version>${jsr305.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>commons-httpclient</groupId>
+        <artifactId>commons-httpclient</artifactId>
+        <version>${httpclient.classic.version}</version>
       </dependency>
       <dependency>
         <groupId>org.apache.httpcomponents</groupId>
@@ -439,6 +478,16 @@
         <artifactId>selenium-java</artifactId>
         <version>2.42.2</version>
         <scope>test</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>io.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <!-- Added for selenium only, and should match its dependent version: -->
       <dependency>
@@ -624,15 +673,26 @@
       <dependency>
         <groupId>com.sun.jersey</groupId>
         <artifactId>jersey-server</artifactId>
-        <version>1.9</version>
+        <version>${jersey.version}</version>
         <scope>${hadoop.deps.scope}</scope>
       </dependency>
       <dependency>
         <groupId>com.sun.jersey</groupId>
         <artifactId>jersey-core</artifactId>
-        <version>1.9</version>
+        <version>${jersey.version}</version>
         <scope>${hadoop.deps.scope}</scope>
       </dependency>
+      <dependency>
+        <groupId>com.sun.jersey</groupId>
+        <artifactId>jersey-json</artifactId>
+        <version>${jersey.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>stax</groupId>
+            <artifactId>stax-api</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
       <dependency>
         <groupId>org.scala-lang</groupId>
         <artifactId>scala-compiler</artifactId>
@@ -1022,58 +1082,499 @@
         <artifactId>hive-beeline</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-exec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-jdbc</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-service</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-cli</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-exec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-jdbc</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-serde</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-service</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <dependency>
         <groupId>${hive.group}</groupId>
-        <artifactId>hive-exec</artifactId>
+        <artifactId>hive-common</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
         <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.ant</groupId>
+            <artifactId>ant</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>commons-logging</groupId>
             <artifactId>commons-logging</artifactId>
           </exclusion>
+        </exclusions>
+      </dependency>
+
+      <dependency>
+        <groupId>${hive.group}</groupId>
+        <artifactId>hive-exec</artifactId>
+<!--
+        <classifier>core</classifier>
+-->
+        <version>${hive.version}</version>
+        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+
+          <!-- pull this in when needed; the explicit definition culls the surplis-->
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-ant</artifactId>
+          </exclusion>
+          <!-- break the loop -->
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>spark-client</artifactId>
+          </exclusion>
+
+          <!-- excluded dependencies & transitive.
+           Some may be needed to be explicitly included-->
+          <exclusion>
+            <groupId>ant</groupId>
+            <artifactId>ant</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.ant</groupId>
+            <artifactId>ant</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>com.esotericsoftware.kryo</groupId>
             <artifactId>kryo</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>commons-codec</groupId>
+            <artifactId>commons-codec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-httpclient</groupId>
+            <artifactId>commons-httpclient</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>org.apache.avro</groupId>
             <artifactId>avro-mapred</artifactId>
           </exclusion>
+          <!--  this is needed and must be explicitly included later-->
+          <exclusion>
+            <groupId>org.apache.calcite</groupId>
+            <artifactId>calcite-core</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>apache-curator</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-client</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-jdbc</artifactId>
         <version>${hive.version}</version>
-        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-serde</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-service</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpclient</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpcore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
+
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-metastore</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-serde</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
+
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-serde</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
         <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-codec</groupId>
+            <artifactId>commons-codec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.code.findbugs</groupId>
+            <artifactId>jsr305</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.avro</groupId>
+            <artifactId>avro</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>commons-logging</groupId>
             <artifactId>commons-logging</artifactId>
           </exclusion>
+        </exclusions>
+      </dependency>
+
+      <dependency>
+        <groupId>${hive.group}</groupId>
+        <artifactId>hive-service</artifactId>
+        <version>${hive.version}</version>
+        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-exec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-codec</groupId>
+            <artifactId>commons-codec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-recipes</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+
+      <!-- hive shims pulls in hive 0.23 and a transitive dependency of the Hadoop version
+        Hive was built against. This dependency cuts out the YARN/hadoop dependency, which
+        is needed by Hive to submit work to a YARN cluster.-->
+      <dependency>
+        <groupId>${hive.group}</groupId>
+        <artifactId>hive-shims</artifactId>
+        <version>${hive.version}</version>
+        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.hadoop</groupId>
+            <artifactId>hadoop-yarn-server-resourcemanager</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>commons-logging</groupId>
-            <artifactId>commons-logging-api</artifactId>
+            <artifactId>commons-logging</artifactId>
           </exclusion>
         </exclusions>
       </dependency>
@@ -1095,6 +1596,12 @@
         <version>${parquet.version}</version>
         <scope>${parquet.test.deps.scope}</scope>
       </dependency>
+      <dependency>
+        <groupId>com.twitter</groupId>
+        <artifactId>parquet-hadoop-bundle</artifactId>
+        <version>${hive.parquet.version}</version>
+        <scope>runtime</scope>
+      </dependency>
       <dependency>
         <groupId>org.apache.flume</groupId>
         <artifactId>flume-ng-core</artifactId>
@@ -1135,6 +1642,125 @@
           </exclusion>
         </exclusions>
       </dependency>
+      <dependency>
+        <groupId>org.apache.calcite</groupId>
+        <artifactId>calcite-core</artifactId>
+        <version>${calcite.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-annotations</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-core</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-databind</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.code.findbugs</groupId>
+            <artifactId>jsr305</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.codehaus.janino</groupId>
+            <artifactId>janino</artifactId>
+          </exclusion>
+          <!-- hsqldb interferes with the use of derby as the default db
+            in hive's use of datanucleus.
+          -->
+          <exclusion>
+            <groupId>org.hsqldb</groupId>
+            <artifactId>hsqldb</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.pentaho</groupId>
+            <artifactId>pentaho-aggdesigner-algorithm</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.calcite</groupId>
+        <artifactId>calcite-avatica</artifactId>
+        <version>${calcite.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-annotations</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-core</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-databind</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.codehaus.janino</groupId>
+        <artifactId>janino</artifactId>
+        <version>${janino.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>joda-time</groupId>
+        <artifactId>joda-time</artifactId>
+        <version>${joda.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.jodd</groupId>
+        <artifactId>jodd-core</artifactId>
+        <version>${jodd.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.datanucleus</groupId>
+        <artifactId>datanucleus-core</artifactId>
+        <version>${datanucleus-core.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.thrift</groupId>
+        <artifactId>libthrift</artifactId>
+        <version>${libthrift.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpclient</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpcore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.thrift</groupId>
+        <artifactId>libfb303</artifactId>
+        <version>${libthrift.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpclient</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpcore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
     </dependencies>
   </dependencyManagement>
 
@@ -1271,6 +1897,8 @@
               <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
               <spark.driver.allowMultipleContexts>true</spark.driver.allowMultipleContexts>
               <spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
+              <!-- Needed by sql/hive tests. -->
+              <test.src.tables>src</test.src.tables>
             </systemProperties>
             <failIfNoTests>false</failIfNoTests>
           </configuration>
@@ -1305,6 +1933,8 @@
               <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
               <spark.driver.allowMultipleContexts>true</spark.driver.allowMultipleContexts>
               <spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
+              <!-- Needed by sql/hive tests. -->
+              <test.src.tables>__not_used__</test.src.tables>
             </systemProperties>
           </configuration>
           <executions>
diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh
index de762acc8fa0e..0fbe795822fbf 100755
--- a/sbin/spark-daemon.sh
+++ b/sbin/spark-daemon.sh
@@ -29,7 +29,7 @@
 #   SPARK_NICENESS The scheduling priority for daemons. Defaults to 0.
 ##
 
-usage="Usage: spark-daemon.sh [--config <conf-dir>] (start|stop|status) <spark-command> <spark-instance-number> <args...>"
+usage="Usage: spark-daemon.sh [--config <conf-dir>] (start|stop|submit|status) <spark-command> <spark-instance-number> <args...>"
 
 # if no args specified, show usage
 if [ $# -le 1 ]; then
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index f4b1cc3a4ffe7..75ab575dfde83 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -66,7 +66,6 @@
     <dependency>
       <groupId>org.codehaus.janino</groupId>
       <artifactId>janino</artifactId>
-      <version>2.7.8</version>
     </dependency>
   </dependencies>
   <build>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
index b4cdfd9e98f6f..57478931cd509 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
@@ -31,6 +31,14 @@ import org.apache.spark.util.Utils
 abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with BeforeAndAfterAll {
   protected var parquetStore: File = _
 
+  /**
+   * Optional path to a staging subdirectory which may be created during query processing
+   * (Hive does this).
+   * Parquet files under this directory will be ignored in [[readParquetSchema()]]
+   * @return an optional staging directory to ignore when scanning for parquet files.
+   */
+  protected def stagingDir: Option[String] = None
+
   override protected def beforeAll(): Unit = {
     parquetStore = Utils.createTempDir(namePrefix = "parquet-compat_")
     parquetStore.delete()
@@ -43,7 +51,10 @@ abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with
   def readParquetSchema(path: String): MessageType = {
     val fsPath = new Path(path)
     val fs = fsPath.getFileSystem(configuration)
-    val parquetFiles = fs.listStatus(fsPath).toSeq.filterNot(_.getPath.getName.startsWith("_"))
+    val parquetFiles = fs.listStatus(fsPath).toSeq.filterNot { status =>
+      status.getPath.getName.startsWith("_") ||
+        stagingDir.map(status.getPath.getName.startsWith).getOrElse(false)
+    }
     val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
     footers.head.getParquetMetadata.getFileMetaData.getSchema
   }
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index 73e6ccdb1eaf8..2dfbcb2425a37 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -60,21 +60,31 @@
       <groupId>${hive.group}</groupId>
       <artifactId>hive-jdbc</artifactId>
     </dependency>
+    <dependency>
+      <groupId>${hive.group}</groupId>
+      <artifactId>hive-service</artifactId>
+    </dependency>
     <dependency>
       <groupId>${hive.group}</groupId>
       <artifactId>hive-beeline</artifactId>
     </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-json</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+    </dependency>
     <!-- Added for selenium: -->
     <dependency>
       <groupId>org.seleniumhq.selenium</groupId>
       <artifactId>selenium-java</artifactId>
       <scope>test</scope>
-      <exclusions>
-        <exclusion>
-          <groupId>io.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
   </dependencies>
   <build>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala b/sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala
new file mode 100644
index 0000000000000..2228f651e2387
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.hive.service.server
+
+import org.apache.hive.service.server.HiveServer2.{StartOptionExecutor, ServerOptionsProcessor}
+
+/**
+ * Class to upgrade a package-private class to public, and
+ * implement a `process()` operation consistent with
+ * the behavior of older Hive versions
+ * @param serverName name of the hive server
+ */
+private[apache] class HiveServerServerOptionsProcessor(serverName: String)
+    extends ServerOptionsProcessor(serverName) {
+
+  def process(args: Array[String]): Boolean = {
+    // A parse failure automatically triggers a system exit
+    val response = super.parse(args)
+    val executor = response.getServerOptionsExecutor()
+    // return true if the parsed option was to start the service
+    executor.isInstanceOf[StartOptionExecutor]
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index b7db80d93f852..9c047347cb58d 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
+import java.util.Locale
+import java.util.concurrent.atomic.AtomicBoolean
+
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -24,7 +27,7 @@ import org.apache.commons.logging.LogFactory
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hive.service.cli.thrift.{ThriftBinaryCLIService, ThriftHttpCLIService}
-import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
+import org.apache.hive.service.server.{HiveServerServerOptionsProcessor, HiveServer2}
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationEnd, SparkListenerJobStart}
@@ -65,7 +68,7 @@ object HiveThriftServer2 extends Logging {
   }
 
   def main(args: Array[String]) {
-    val optionsProcessor = new ServerOptionsProcessor("HiveThriftServer2")
+    val optionsProcessor = new HiveServerServerOptionsProcessor("HiveThriftServer2")
     if (!optionsProcessor.process(args)) {
       System.exit(-1)
     }
@@ -241,9 +244,12 @@ object HiveThriftServer2 extends Logging {
 private[hive] class HiveThriftServer2(hiveContext: HiveContext)
   extends HiveServer2
   with ReflectedCompositeService {
+  // state is tracked internally so that the server only attempts to shut down if it successfully
+  // started, and then once only.
+  private val started = new AtomicBoolean(false)
 
   override def init(hiveConf: HiveConf) {
-    val sparkSqlCliService = new SparkSQLCLIService(hiveContext)
+    val sparkSqlCliService = new SparkSQLCLIService(this, hiveContext)
     setSuperField(this, "cliService", sparkSqlCliService)
     addService(sparkSqlCliService)
 
@@ -259,8 +265,19 @@ private[hive] class HiveThriftServer2(hiveContext: HiveContext)
   }
 
   private def isHTTPTransportMode(hiveConf: HiveConf): Boolean = {
-    val transportMode: String = hiveConf.getVar(ConfVars.HIVE_SERVER2_TRANSPORT_MODE)
-    transportMode.equalsIgnoreCase("http")
+    val transportMode = hiveConf.getVar(ConfVars.HIVE_SERVER2_TRANSPORT_MODE)
+    transportMode.toLowerCase(Locale.ENGLISH).equals("http")
+  }
+
+
+  override def start(): Unit = {
+    super.start()
+    started.set(true)
   }
 
+  override def stop(): Unit = {
+    if (started.getAndSet(false)) {
+       super.stop()
+    }
+  }
 }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
index e8758887ff3a2..833bf62d47d07 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
@@ -32,8 +32,7 @@ import org.apache.hive.service.cli._
 import org.apache.hadoop.hive.ql.metadata.Hive
 import org.apache.hadoop.hive.ql.metadata.HiveException
 import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hadoop.hive.shims.ShimLoader
-import org.apache.hadoop.security.UserGroupInformation
+import org.apache.hadoop.hive.shims.Utils
 import org.apache.hive.service.cli.operation.ExecuteStatementOperation
 import org.apache.hive.service.cli.session.HiveSession
 
@@ -146,7 +145,7 @@ private[hive] class SparkExecuteStatementOperation(
     } else {
       val parentSessionState = SessionState.get()
       val hiveConf = getConfigForOperation()
-      val sparkServiceUGI = ShimLoader.getHadoopShims.getUGIForConf(hiveConf)
+      val sparkServiceUGI = Utils.getUGI()
       val sessionHive = getCurrentHive()
       val currentSqlSession = hiveContext.currentSession
 
@@ -174,7 +173,7 @@ private[hive] class SparkExecuteStatementOperation(
           }
 
           try {
-            ShimLoader.getHadoopShims().doAs(sparkServiceUGI, doAsAction)
+            sparkServiceUGI.doAs(doAsAction)
           } catch {
             case e: Exception =>
               setOperationException(new HiveSQLException(e))
@@ -201,7 +200,7 @@ private[hive] class SparkExecuteStatementOperation(
     }
   }
 
-  private def runInternal(): Unit = {
+  override def runInternal(): Unit = {
     statementId = UUID.randomUUID().toString
     logInfo(s"Running query '$statement' with $statementId")
     setState(OperationState.RUNNING)
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index f66a17b20915f..d3886142b388d 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -20,9 +20,10 @@ package org.apache.spark.sql.hive.thriftserver
 import scala.collection.JavaConversions._
 
 import java.io._
-import java.util.{ArrayList => JArrayList}
+import java.util.{ArrayList => JArrayList, Locale}
 
-import jline.{ConsoleReader, History}
+import jline.console.ConsoleReader
+import jline.console.history.FileHistory
 
 import org.apache.commons.lang3.StringUtils
 import org.apache.commons.logging.LogFactory
@@ -40,6 +41,10 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.util.Utils
 
+/**
+ * This code doesn't support remote connections in Hive 1.2+, as the underlying CliDriver
+ * has dropped its support.
+ */
 private[hive] object SparkSQLCLIDriver extends Logging {
   private var prompt = "spark-sql"
   private var continuedPrompt = "".padTo(prompt.length, ' ')
@@ -111,16 +116,9 @@ private[hive] object SparkSQLCLIDriver extends Logging {
     // Clean up after we exit
     Utils.addShutdownHook { () => SparkSQLEnv.stop() }
 
+    val remoteMode = isRemoteMode(sessionState)
     // "-h" option has been passed, so connect to Hive thrift server.
-    if (sessionState.getHost != null) {
-      sessionState.connect()
-      if (sessionState.isRemoteMode) {
-        prompt = s"[${sessionState.getHost}:${sessionState.getPort}]" + prompt
-        continuedPrompt = "".padTo(prompt.length, ' ')
-      }
-    }
-
-    if (!sessionState.isRemoteMode) {
+    if (!remoteMode) {
       // Hadoop-20 and above - we need to augment classpath using hiveconf
       // components.
       // See also: code in ExecDriver.java
@@ -131,6 +129,9 @@ private[hive] object SparkSQLCLIDriver extends Logging {
       }
       conf.setClassLoader(loader)
       Thread.currentThread().setContextClassLoader(loader)
+    } else {
+      // Hive 1.2 + not supported in CLI
+      throw new RuntimeException("Remote operations not supported")
     }
 
     val cli = new SparkSQLCLIDriver
@@ -171,14 +172,14 @@ private[hive] object SparkSQLCLIDriver extends Logging {
     val reader = new ConsoleReader()
     reader.setBellEnabled(false)
     // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
-    CliDriver.getCommandCompletor.foreach((e) => reader.addCompletor(e))
+    CliDriver.getCommandCompleter.foreach((e) => reader.addCompleter(e))
 
     val historyDirectory = System.getProperty("user.home")
 
     try {
       if (new File(historyDirectory).exists()) {
         val historyFile = historyDirectory + File.separator + ".hivehistory"
-        reader.setHistory(new History(new File(historyFile)))
+        reader.setHistory(new FileHistory(new File(historyFile)))
       } else {
         logWarning("WARNING: Directory for Hive history file: " + historyDirectory +
                            " does not exist.   History will not be available during this session.")
@@ -190,10 +191,14 @@ private[hive] object SparkSQLCLIDriver extends Logging {
         logWarning(e.getMessage)
     }
 
+    // TODO: missing
+/*
     val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
     clientTransportTSocketField.setAccessible(true)
 
     transport = clientTransportTSocketField.get(sessionState).asInstanceOf[TSocket]
+*/
+    transport = null
 
     var ret = 0
     var prefix = ""
@@ -230,6 +235,13 @@ private[hive] object SparkSQLCLIDriver extends Logging {
 
     System.exit(ret)
   }
+
+
+  def isRemoteMode(state: CliSessionState): Boolean = {
+    //    sessionState.isRemoteMode
+    state.isHiveServerQuery
+  }
+
 }
 
 private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
@@ -239,25 +251,33 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
 
   private val console = new SessionState.LogHelper(LOG)
 
+  private val isRemoteMode = {
+    SparkSQLCLIDriver.isRemoteMode(sessionState)
+  }
+
   private val conf: Configuration =
     if (sessionState != null) sessionState.getConf else new Configuration()
 
   // Force initializing SparkSQLEnv. This is put here but not object SparkSQLCliDriver
   // because the Hive unit tests do not go through the main() code path.
-  if (!sessionState.isRemoteMode) {
+  if (!isRemoteMode) {
     SparkSQLEnv.init()
+  } else {
+    // Hive 1.2 + not supported in CLI
+    throw new RuntimeException("Remote operations not supported")
   }
 
   override def processCmd(cmd: String): Int = {
     val cmd_trimmed: String = cmd.trim()
+    val cmd_lower = cmd_trimmed.toLowerCase(Locale.ENGLISH)
     val tokens: Array[String] = cmd_trimmed.split("\\s+")
     val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
-    if (cmd_trimmed.toLowerCase.equals("quit") ||
-      cmd_trimmed.toLowerCase.equals("exit") ||
-      tokens(0).equalsIgnoreCase("source") ||
+    if (cmd_lower.equals("quit") ||
+      cmd_lower.equals("exit") ||
+      tokens(0).toLowerCase(Locale.ENGLISH).equals("source") ||
       cmd_trimmed.startsWith("!") ||
       tokens(0).toLowerCase.equals("list") ||
-      sessionState.isRemoteMode) {
+      isRemoteMode) {
       val start = System.currentTimeMillis()
       super.processCmd(cmd)
       val end = System.currentTimeMillis()
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
index 41f647d5f8c5a..644165acf70a7 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
@@ -23,11 +23,12 @@ import javax.security.auth.login.LoginException
 
 import org.apache.commons.logging.Log
 import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.hadoop.hive.shims.Utils
 import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hive.service.Service.STATE
 import org.apache.hive.service.auth.HiveAuthFactory
 import org.apache.hive.service.cli._
+import org.apache.hive.service.server.HiveServer2
 import org.apache.hive.service.{AbstractService, Service, ServiceException}
 
 import org.apache.spark.sql.hive.HiveContext
@@ -35,22 +36,22 @@ import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 
 import scala.collection.JavaConversions._
 
-private[hive] class SparkSQLCLIService(hiveContext: HiveContext)
-  extends CLIService
+private[hive] class SparkSQLCLIService(hiveServer: HiveServer2, hiveContext: HiveContext)
+  extends CLIService(hiveServer)
   with ReflectedCompositeService {
 
   override def init(hiveConf: HiveConf) {
     setSuperField(this, "hiveConf", hiveConf)
 
-    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveContext)
+    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveServer, hiveContext)
     setSuperField(this, "sessionManager", sparkSqlSessionManager)
     addService(sparkSqlSessionManager)
     var sparkServiceUGI: UserGroupInformation = null
 
-    if (ShimLoader.getHadoopShims.isSecurityEnabled) {
+    if (UserGroupInformation.isSecurityEnabled) {
       try {
         HiveAuthFactory.loginFromKeytab(hiveConf)
-        sparkServiceUGI = ShimLoader.getHadoopShims.getUGIForConf(hiveConf)
+        sparkServiceUGI = Utils.getUGI()
         setSuperField(this, "serviceUGI", sparkServiceUGI)
       } catch {
         case e @ (_: IOException | _: LoginException) =>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
index 2d5ee68002286..92ac0ec3fca29 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
@@ -25,14 +25,15 @@ import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hive.service.cli.SessionHandle
 import org.apache.hive.service.cli.session.SessionManager
 import org.apache.hive.service.cli.thrift.TProtocolVersion
+import org.apache.hive.service.server.HiveServer2
 
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager
 
 
-private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
-  extends SessionManager
+private[hive] class SparkSQLSessionManager(hiveServer: HiveServer2, hiveContext: HiveContext)
+  extends SessionManager(hiveServer)
   with ReflectedCompositeService {
 
   private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext)
@@ -55,12 +56,14 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
       protocol: TProtocolVersion,
       username: String,
       passwd: String,
+      ipAddress: String,
       sessionConf: java.util.Map[String, String],
       withImpersonation: Boolean,
       delegationToken: String): SessionHandle = {
     hiveContext.openSession()
-    val sessionHandle = super.openSession(
-      protocol, username, passwd, sessionConf, withImpersonation, delegationToken)
+    val sessionHandle =
+      super.openSession(protocol, username, passwd, ipAddress, sessionConf, withImpersonation,
+          delegationToken)
     val session = super.getSession(sessionHandle)
     HiveThriftServer2.listener.onSessionCreated(
       session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername)
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
index df80d04b40801..121b3e077f71f 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -23,6 +23,7 @@ import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
 import scala.concurrent.{Await, Promise}
 import scala.sys.process.{Process, ProcessLogger}
+import scala.util.Failure
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.scalatest.BeforeAndAfter
@@ -37,31 +38,46 @@ import org.apache.spark.util.Utils
 class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
   val warehousePath = Utils.createTempDir()
   val metastorePath = Utils.createTempDir()
+  val scratchDirPath = Utils.createTempDir()
 
   before {
-      warehousePath.delete()
-      metastorePath.delete()
+    warehousePath.delete()
+    metastorePath.delete()
+    scratchDirPath.delete()
   }
 
   after {
-      warehousePath.delete()
-      metastorePath.delete()
+    warehousePath.delete()
+    metastorePath.delete()
+    scratchDirPath.delete()
   }
 
+  /**
+   * Run a CLI operation and expect all the queries and expected answers to be returned.
+   * @param timeout maximum time for the commands to complete
+   * @param extraArgs any extra arguments
+   * @param errorResponses a sequence of strings whose presence in the stdout of the forked process
+   *                       is taken as an immediate error condition. That is: if a line beginning
+   *                       with one of these strings is found, fail the test immediately.
+   *                       The default value is `Seq("Error:")`
+   *
+   * @param queriesAndExpectedAnswers one or more tupes of query + answer
+   */
   def runCliWithin(
       timeout: FiniteDuration,
-      extraArgs: Seq[String] = Seq.empty)(
+      extraArgs: Seq[String] = Seq.empty,
+      errorResponses: Seq[String] = Seq("Error:"))(
       queriesAndExpectedAnswers: (String, String)*): Unit = {
 
     val (queries, expectedAnswers) = queriesAndExpectedAnswers.unzip
-    val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator)
-
     val command = {
+      val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator)
       val jdbcUrl = s"jdbc:derby:;databaseName=$metastorePath;create=true"
       s"""$cliScript
          |  --master local
          |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$jdbcUrl
          |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath
+         |  --hiveconf ${ConfVars.SCRATCHDIR}=$scratchDirPath
        """.stripMargin.split("\\s+").toSeq ++ extraArgs
     }
 
@@ -81,6 +97,12 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
         if (next == expectedAnswers.size) {
           foundAllExpectedAnswers.trySuccess(())
         }
+      } else {
+        errorResponses.foreach( r => {
+          if (line.startsWith(r)) {
+            foundAllExpectedAnswers.tryFailure(
+              new RuntimeException(s"Failed with error line '$line'"))
+          }})
       }
     }
 
@@ -88,16 +110,44 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
     val process = (Process(command, None) #< queryStream).run(
       ProcessLogger(captureOutput("stdout"), captureOutput("stderr")))
 
+    // catch the output value
+    class exitCodeCatcher extends Runnable {
+      var exitValue = 0
+
+      override def run(): Unit = {
+        try {
+          exitValue = process.exitValue()
+        } catch {
+          case rte: RuntimeException =>
+            // ignored as it will get triggered when the process gets destroyed
+            logDebug("Ignoring exception while waiting for exit code", rte)
+        }
+        if (exitValue != 0) {
+          // process exited: fail fast
+          foundAllExpectedAnswers.tryFailure(
+            new RuntimeException(s"Failed with exit code $exitValue"))
+        }
+      }
+    }
+    // spin off the code catche thread. No attempt is made to kill this
+    // as it will exit once the launched process terminates.
+    val codeCatcherThread = new Thread(new exitCodeCatcher())
+    codeCatcherThread.start()
+
     try {
-      Await.result(foundAllExpectedAnswers.future, timeout)
+      Await.ready(foundAllExpectedAnswers.future, timeout)
+      foundAllExpectedAnswers.future.value match {
+        case Some(Failure(t)) => throw t
+        case _ =>
+      }
     } catch { case cause: Throwable =>
-      logError(
+      val message =
         s"""
            |=======================
            |CliSuite failure output
            |=======================
            |Spark SQL CLI command line: ${command.mkString(" ")}
-           |
+           |Exception: $cause
            |Executed query $next "${queries(next)}",
            |But failed to capture expected output "${expectedAnswers(next)}" within $timeout.
            |
@@ -105,8 +155,9 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
            |===========================
            |End CliSuite failure output
            |===========================
-         """.stripMargin, cause)
-      throw cause
+         """.stripMargin
+      logError(message, cause)
+      fail(message, cause)
     } finally {
       process.destroy()
     }
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 39b31523e07cb..8374629b5d45a 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.hive.thriftserver
 
 import java.io.File
 import java.net.URL
-import java.nio.charset.StandardCharsets
 import java.sql.{Date, DriverManager, SQLException, Statement}
 
 import scala.collection.mutable.ArrayBuffer
@@ -492,7 +491,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
         new File(s"$tempLog4jConf/log4j.properties"),
         UTF_8)
 
-      tempLog4jConf + File.pathSeparator + sys.props("java.class.path")
+      tempLog4jConf // + File.pathSeparator + sys.props("java.class.path")
     }
 
     s"""$startScript
@@ -508,6 +507,20 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
      """.stripMargin.split("\\s+").toSeq
   }
 
+  /**
+   * String to scan for when looking for the the thrift binary endpoint running.
+   * This can change across Hive versions.
+   */
+  val THRIFT_BINARY_SERVICE_LIVE = "Starting ThriftBinaryCLIService on port"
+
+  /**
+   * String to scan for when looking for the the thrift HTTP endpoint running.
+   * This can change across Hive versions.
+   */
+  val THRIFT_HTTP_SERVICE_LIVE = "Started ThriftHttpCLIService in http"
+
+  val SERVER_STARTUP_TIMEOUT = 1.minute
+
   private def startThriftServer(port: Int, attempt: Int) = {
     warehousePath = Utils.createTempDir()
     warehousePath.delete()
@@ -545,23 +558,26 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
 
     // Ensures that the following "tail" command won't fail.
     logPath.createNewFile()
+    val successLines = Seq(THRIFT_BINARY_SERVICE_LIVE, THRIFT_HTTP_SERVICE_LIVE)
+    val failureLines = Seq("HiveServer2 is stopped", "Exception in thread", "Error:")
     logTailingProcess =
       // Using "-n +0" to make sure all lines in the log file are checked.
       Process(s"/usr/bin/env tail -n +0 -f ${logPath.getCanonicalPath}").run(ProcessLogger(
         (line: String) => {
           diagnosisBuffer += line
-
-          if (line.contains("ThriftBinaryCLIService listening on") ||
-              line.contains("Started ThriftHttpCLIService in http")) {
-            serverStarted.trySuccess(())
-          } else if (line.contains("HiveServer2 is stopped")) {
-            // This log line appears when the server fails to start and terminates gracefully (e.g.
-            // because of port contention).
-            serverStarted.tryFailure(new RuntimeException("Failed to start HiveThriftServer2"))
-          }
+          successLines.foreach(r => {
+            if (line.contains(r)) {
+              serverStarted.trySuccess(())
+            }
+          })
+          failureLines.foreach(r => {
+            if (line.contains(r)) {
+              serverStarted.tryFailure(new RuntimeException(s"Failed with output '$line'"))
+            }
+          })
         }))
 
-    Await.result(serverStarted.future, 2.minute)
+    Await.result(serverStarted.future, SERVER_STARTUP_TIMEOUT)
   }
 
   private def stopThriftServer(): Unit = {
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 53d5b22b527b2..c46a4a4b0be54 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -267,7 +267,34 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "date_udf",
 
     // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
-    "udf7"
+    "udf7",
+
+    // Trivial changes to DDL output
+    "compute_stats_empty_table",
+    "compute_stats_long",
+    "create_view_translate",
+    "show_create_table_serde",
+    "show_tblproperties",
+
+    // Odd changes to output
+    "merge4",
+
+    // Thift is broken...
+    "inputddl8",
+
+    // Hive changed ordering of ddl:
+    "varchar_union1",
+
+    // Parser changes in Hive 1.2
+    "input25",
+    "input26",
+
+    // Uses invalid table name
+    "innerjoin",
+
+    // classpath problems
+    "compute_stats.*",
+    "udf_bitmap_.*"
   )
 
   /**
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index b00f320318be0..be1607476e254 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -36,6 +36,11 @@
   </properties>
 
   <dependencies>
+    <!-- Added for Hive Parquet SerDe -->
+    <dependency>
+      <groupId>com.twitter</groupId>
+      <artifactId>parquet-hadoop-bundle</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
@@ -53,32 +58,42 @@
       <artifactId>spark-sql_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+<!--
     <dependency>
-      <groupId>${hive.group}</groupId>
-      <artifactId>hive-metastore</artifactId>
+      <groupId>com.google.guava</groupId>
+      <artifactId>guava</artifactId>
     </dependency>
     <dependency>
-      <groupId>commons-httpclient</groupId>
-      <artifactId>commons-httpclient</artifactId>
-      <version>3.1</version>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <version>${protobuf.version}</version>
     </dependency>
     <dependency>
       <groupId>${hive.group}</groupId>
-      <artifactId>hive-exec</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>org.apache.httpcomponents</groupId>
-      <artifactId>httpclient</artifactId>
-      <version>${commons.httpclient.version}</version>
+      <artifactId>hive-common</artifactId>
     </dependency>
+-->
     <dependency>
-      <groupId>org.codehaus.jackson</groupId>
-      <artifactId>jackson-mapper-asl</artifactId>
+      <groupId>${hive.group}</groupId>
+      <artifactId>hive-exec</artifactId>
+<!--
+      <classifier>core</classifier>
+-->
     </dependency>
     <dependency>
       <groupId>${hive.group}</groupId>
-      <artifactId>hive-serde</artifactId>
+      <artifactId>hive-metastore</artifactId>
     </dependency>
+    <!--
+        <dependency>
+          <groupId>${hive.group}</groupId>
+          <artifactId>hive-serde</artifactId>
+        </dependency>
+        <dependency>
+          <groupId>${hive.group}</groupId>
+          <artifactId>hive-shims</artifactId>
+        </dependency>
+    -->
     <!-- hive-serde already depends on avro, but this brings in customized config of avro deps from parent -->
     <dependency>
       <groupId>org.apache.avro</groupId>
@@ -91,6 +106,55 @@
       <artifactId>avro-mapred</artifactId>
       <classifier>${avro.mapred.classifier}</classifier>
     </dependency>
+    <dependency>
+      <groupId>commons-httpclient</groupId>
+      <artifactId>commons-httpclient</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.calcite</groupId>
+      <artifactId>calcite-avatica</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.calcite</groupId>
+      <artifactId>calcite-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.httpcomponents</groupId>
+      <artifactId>httpclient</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.codehaus.jackson</groupId>
+      <artifactId>jackson-mapper-asl</artifactId>
+    </dependency>
+    <!-- transitive dependencies of hive-exec-core doesn't declare -->
+    <dependency>
+      <groupId>commons-codec</groupId>
+      <artifactId>commons-codec</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>joda-time</groupId>
+      <artifactId>joda-time</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.jodd</groupId>
+      <artifactId>jodd-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.google.code.findbugs</groupId>
+      <artifactId>jsr305</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.datanucleus</groupId>
+      <artifactId>datanucleus-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.thrift</groupId>
+      <artifactId>libthrift</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.thrift</groupId>
+      <artifactId>libfb303</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.scalacheck</groupId>
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 110f51a305861..567d7fa12ff14 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -20,15 +20,18 @@ package org.apache.spark.sql.hive
 import java.io.File
 import java.net.{URL, URLClassLoader}
 import java.sql.Timestamp
+import java.util.concurrent.TimeUnit
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.HashMap
 import scala.language.implicitConversions
+import scala.concurrent.duration._
 
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.hive.common.StatsSetupConst
 import org.apache.hadoop.hive.common.`type`.HiveDecimal
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.metadata.Table
 import org.apache.hadoop.hive.ql.parse.VariableSubstitution
 import org.apache.hadoop.hive.ql.session.SessionState
@@ -164,6 +167,16 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
   }
   SessionState.setCurrentSessionState(executionHive.state)
 
+  /**
+   * Overrides default Hive configurations to avoid breaking changes to Spark SQL users.
+   *  - allow SQL11 keywords to be used as identifiers
+   */
+  private[sql] def defaultOverides() = {
+    setConf(ConfVars.HIVE_SUPPORT_SQL11_RESERVED_KEYWORDS.varname, "false")
+  }
+
+  defaultOverides()
+
   /**
    * The copy of the Hive client that is used to retrieve metadata from the Hive MetaStore.
    * The version of the Hive client that is used here must match the metastore that is configured
@@ -252,6 +265,10 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
   }
 
   protected[sql] override def parseSql(sql: String): LogicalPlan = {
+    var state = SessionState.get()
+    if (state == null) {
+      SessionState.setCurrentSessionState(tlSession.get().asInstanceOf[SQLSession].sessionState)
+    }
     super.parseSql(substitutor.substitute(hiveconf, sql))
   }
 
@@ -298,10 +315,21 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
         // Can we use fs.getContentSummary in future?
         // Seems fs.getContentSummary returns wrong table size on Jenkins. So we use
         // countFileSize to count the table size.
+        val stagingDir = metadataHive.getConf(HiveConf.ConfVars.STAGINGDIR.varname,
+          HiveConf.ConfVars.STAGINGDIR.defaultStrVal)
+
         def calculateTableSize(fs: FileSystem, path: Path): Long = {
           val fileStatus = fs.getFileStatus(path)
           val size = if (fileStatus.isDir) {
-            fs.listStatus(path).map(status => calculateTableSize(fs, status.getPath)).sum
+            fs.listStatus(path)
+              .map { status =>
+                if (!status.getPath().getName().startsWith(stagingDir)) {
+                  calculateTableSize(fs, status.getPath)
+                } else {
+                  0L
+                }
+              }
+              .sum
           } else {
             fileStatus.getLen
           }
@@ -398,7 +426,58 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
   }
 
   /** Overridden by child classes that need to set configuration before the client init. */
-  protected def configure(): Map[String, String] = Map.empty
+  protected def configure(): Map[String, String] = {
+    // Hive 0.14.0 introduces timeout operations in HiveConf, and changes default values of a bunch
+    // of time `ConfVar`s by adding time suffixes (`s`, `ms`, and `d` etc.).  This breaks backwards-
+    // compatibility when users are trying to connecting to a Hive metastore of lower version,
+    // because these options are expected to be integral values in lower versions of Hive.
+    //
+    // Here we enumerate all time `ConfVar`s and convert their values to numeric strings according
+    // to their output time units.
+    Seq(
+      ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_CLIENT_SOCKET_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_CLIENT_SOCKET_LIFETIME -> TimeUnit.SECONDS,
+      ConfVars.HMSHANDLERINTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.METASTORE_EVENT_DB_LISTENER_TTL -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_EVENT_CLEAN_FREQ -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_EVENT_EXPIRY_DURATION -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_AGGREGATE_STATS_CACHE_TTL -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_WRITER_WAIT -> TimeUnit.MILLISECONDS,
+      ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_READER_WAIT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVES_AUTO_PROGRESS_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_LOG_INCREMENTAL_PLAN_PROGRESS_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_STATS_JDBC_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_STATS_RETRIES_WAIT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_LOCK_SLEEP_BETWEEN_RETRIES -> TimeUnit.SECONDS,
+      ConfVars.HIVE_ZOOKEEPER_SESSION_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_ZOOKEEPER_CONNECTION_BASESLEEPTIME -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_TXN_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_COMPACTOR_WORKER_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_COMPACTOR_CHECK_INTERVAL -> TimeUnit.SECONDS,
+      ConfVars.HIVE_COMPACTOR_CLEANER_RUN_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_HTTP_MAX_IDLE_TIME -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_HTTP_WORKER_KEEPALIVE_TIME -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_HTTP_COOKIE_MAX_AGE -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_LOGIN_BEBACKOFF_SLOT_LENGTH -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_LOGIN_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_WORKER_KEEPALIVE_TIME -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_ASYNC_EXEC_SHUTDOWN_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_ASYNC_EXEC_KEEPALIVE_TIME -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_LONG_POLLING_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_SESSION_CHECK_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_IDLE_SESSION_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_IDLE_OPERATION_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.SERVER_READ_SOCKET_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_LOCALIZE_RESOURCE_WAIT_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.SPARK_CLIENT_FUTURE_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.SPARK_JOB_MONITOR_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.SPARK_RPC_CLIENT_CONNECT_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.SPARK_RPC_CLIENT_HANDSHAKE_TIMEOUT -> TimeUnit.MILLISECONDS
+    ).map { case (confVar, unit) =>
+      confVar.varname -> hiveconf.getTimeVar(confVar, unit).toString
+    }.toMap
+  }
 
   protected[hive] class SQLSession extends super.SQLSession {
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
@@ -515,19 +594,23 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
 
 private[hive] object HiveContext {
   /** The version of hive used internally by Spark SQL. */
-  val hiveExecutionVersion: String = "0.13.1"
+  val hiveExecutionVersion: String = "1.2.1"
 
   val HIVE_METASTORE_VERSION: String = "spark.sql.hive.metastore.version"
   val HIVE_METASTORE_JARS = stringConf("spark.sql.hive.metastore.jars",
     defaultValue = Some("builtin"),
-    doc = "Location of the jars that should be used to instantiate the HiveMetastoreClient. This" +
-      " property can be one of three options: " +
-      "1. \"builtin\" Use Hive 0.13.1, which is bundled with the Spark assembly jar when " +
-      "<code>-Phive</code> is enabled. When this option is chosen, " +
-      "spark.sql.hive.metastore.version must be either <code>0.13.1</code> or not defined. " +
-      "2. \"maven\" Use Hive jars of specified version downloaded from Maven repositories." +
-      "3. A classpath in the standard format for both Hive and Hadoop.")
-
+    doc = s"""
+      | Location of the jars that should be used to instantiate the HiveMetastoreClient.
+      | This property can be one of three options: "
+      | 1. "builtin"
+      |   Use Hive ${hiveExecutionVersion}, which is bundled with the Spark assembly jar when
+      |   <code>-Phive</code> is enabled. When this option is chosen,
+      |   <code>spark.sql.hive.metastore.version</code> must be either
+      |   <code>${hiveExecutionVersion}</code> or not defined.
+      | 2. "maven"
+      |   Use Hive jars of specified version downloaded from Maven repositories.
+      | 3. A classpath in the standard format for both Hive and Hadoop.
+    """.stripMargin)
   val CONVERT_METASTORE_PARQUET = booleanConf("spark.sql.hive.convertMetastoreParquet",
     defaultValue = Some(true),
     doc = "When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of " +
@@ -566,17 +649,18 @@ private[hive] object HiveContext {
   /** Constructs a configuration for hive, where the metastore is located in a temp directory. */
   def newTemporaryConfiguration(): Map[String, String] = {
     val tempDir = Utils.createTempDir()
-    val localMetastore = new File(tempDir, "metastore").getAbsolutePath
+    val localMetastore = new File(tempDir, "metastore")
     val propMap: HashMap[String, String] = HashMap()
     // We have to mask all properties in hive-site.xml that relates to metastore data source
     // as we used a local metastore here.
     HiveConf.ConfVars.values().foreach { confvar =>
       if (confvar.varname.contains("datanucleus") || confvar.varname.contains("jdo")) {
-        propMap.put(confvar.varname, confvar.defaultVal)
+        propMap.put(confvar.varname, confvar.getDefaultExpr())
       }
     }
-    propMap.put("javax.jdo.option.ConnectionURL",
-      s"jdbc:derby:;databaseName=$localMetastore;create=true")
+    propMap.put(HiveConf.ConfVars.METASTOREWAREHOUSE.varname, localMetastore.toURI.toString)
+    propMap.put(HiveConf.ConfVars.METASTORECONNECTURLKEY.varname,
+      s"jdbc:derby:;databaseName=${localMetastore.getAbsolutePath};create=true")
     propMap.put("datanucleus.rdbms.datastoreAdapterClassName",
       "org.datanucleus.store.rdbms.adapter.DerbyAdapter")
     propMap.toMap
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index a8c9b4fa71b99..16c186627f6cc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -649,11 +649,12 @@ private[hive] case class MetastoreRelation
     table.outputFormat.foreach(sd.setOutputFormat)
 
     val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
-    sd.setSerdeInfo(serdeInfo)
     table.serde.foreach(serdeInfo.setSerializationLib)
+    sd.setSerdeInfo(serdeInfo)
+
     val serdeParameters = new java.util.HashMap[String, String]()
-    serdeInfo.setParameters(serdeParameters)
     table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+    serdeInfo.setParameters(serdeParameters)
 
     new Table(tTable)
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index e6df64d2642bc..e2fdfc6163a00 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.hive
 
 import java.sql.Date
+import java.util.Locale
 
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -80,6 +81,7 @@ private[hive] object HiveQl extends Logging {
     "TOK_ALTERDATABASE_PROPERTIES",
     "TOK_ALTERINDEX_PROPERTIES",
     "TOK_ALTERINDEX_REBUILD",
+    "TOK_ALTERTABLE",
     "TOK_ALTERTABLE_ADDCOLS",
     "TOK_ALTERTABLE_ADDPARTS",
     "TOK_ALTERTABLE_ALTERPARTS",
@@ -94,6 +96,7 @@ private[hive] object HiveQl extends Logging {
     "TOK_ALTERTABLE_SKEWED",
     "TOK_ALTERTABLE_TOUCH",
     "TOK_ALTERTABLE_UNARCHIVE",
+    "TOK_ALTERVIEW",
     "TOK_ALTERVIEW_ADDPARTS",
     "TOK_ALTERVIEW_AS",
     "TOK_ALTERVIEW_DROPPARTS",
@@ -248,7 +251,7 @@ private[hive] object HiveQl extends Logging {
      * Otherwise, there will be Null pointer exception,
      * when retrieving properties form HiveConf.
      */
-    val hContext = new Context(hiveConf)
+    val hContext = new Context(SessionState.get().getConf())
     val node = ParseUtils.findRootNonNullToken((new ParseDriver).parse(sql, hContext))
     hContext.clear()
     node
@@ -577,12 +580,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             "TOK_TABLESKEWED", // Skewed by
             "TOK_TABLEROWFORMAT",
             "TOK_TABLESERIALIZER",
-            "TOK_FILEFORMAT_GENERIC", // For file formats not natively supported by Hive.
-            "TOK_TBLSEQUENCEFILE", // Stored as SequenceFile
-            "TOK_TBLTEXTFILE", // Stored as TextFile
-            "TOK_TBLRCFILE", // Stored as RCFile
-            "TOK_TBLORCFILE", // Stored as ORC File
-            "TOK_TBLPARQUETFILE", // Stored as PARQUET
+            "TOK_FILEFORMAT_GENERIC",
             "TOK_TABLEFILEFORMAT", // User-provided InputFormat and OutputFormat
             "TOK_STORAGEHANDLER", // Storage handler
             "TOK_TABLELOCATION",
@@ -706,36 +704,51 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             tableDesc = tableDesc.copy(serdeProperties = tableDesc.serdeProperties ++ serdeParams)
           }
         case Token("TOK_FILEFORMAT_GENERIC", child :: Nil) =>
-          throw new SemanticException(
-            "Unrecognized file format in STORED AS clause:${child.getText}")
+          child.getText().toLowerCase(Locale.ENGLISH) match {
+            case "orc" =>
+              tableDesc = tableDesc.copy(
+                inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
+                outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde"))
+              }
 
-        case Token("TOK_TBLRCFILE", Nil) =>
-          tableDesc = tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"))
-          if (tableDesc.serde.isEmpty) {
-            tableDesc = tableDesc.copy(
-              serde = Option("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe"))
-          }
+            case "parquet" =>
+              tableDesc = tableDesc.copy(
+                inputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
+                outputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"))
+              }
 
-        case Token("TOK_TBLORCFILE", Nil) =>
-          tableDesc = tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"))
-          if (tableDesc.serde.isEmpty) {
-            tableDesc = tableDesc.copy(
-              serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde"))
-          }
+            case "rcfile" =>
+              tableDesc = tableDesc.copy(
+                inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
+                outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe"))
+              }
 
-        case Token("TOK_TBLPARQUETFILE", Nil) =>
-          tableDesc = tableDesc.copy(
-            inputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
-            outputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"))
-          if (tableDesc.serde.isEmpty) {
-            tableDesc = tableDesc.copy(
-              serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"))
+            case "textfile" =>
+              tableDesc = tableDesc.copy(
+                inputFormat =
+                  Option("org.apache.hadoop.mapred.TextInputFormat"),
+                outputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat"))
+
+            case "sequencefile" =>
+              tableDesc = tableDesc.copy(
+                inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
+                outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat"))
+
+            case _ =>
+              throw new SemanticException(
+                s"Unrecognized file format in STORED AS clause: ${child.getText}")
           }
 
         case Token("TOK_TABLESERIALIZER",
@@ -751,7 +764,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         case Token("TOK_TABLEPROPERTIES", list :: Nil) =>
           tableDesc = tableDesc.copy(properties = tableDesc.properties ++ getProperties(list))
-        case list @ Token("TOK_TABLEFILEFORMAT", _) =>
+        case list @ Token("TOK_TABLEFILEFORMAT", children) =>
           tableDesc = tableDesc.copy(
             inputFormat =
               Option(BaseSemanticAnalyzer.unescapeSQLString(list.getChild(0).getText)),
@@ -889,7 +902,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 Token("TOK_TABLEPROPLIST", propsClause) :: Nil) :: Nil) :: Nil =>
                 val serdeProps = propsClause.map {
                   case Token("TOK_TABLEPROPERTY", Token(name, Nil) :: Token(value, Nil) :: Nil) =>
-                    (name, value)
+                    (BaseSemanticAnalyzer.unescapeSQLString(name),
+                      BaseSemanticAnalyzer.unescapeSQLString(value))
                 }
                 (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), serdeProps)
 
@@ -1037,10 +1051,11 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       // return With plan if there is CTE
       cteRelations.map(With(query, _)).getOrElse(query)
 
-    case Token("TOK_UNION", left :: right :: Nil) => Union(nodeToPlan(left), nodeToPlan(right))
+    // HIVE-9039 renamed TOK_UNION => TOK_UNIONALL while adding TOK_UNIONDISTINCT
+    case Token("TOK_UNIONALL", left :: right :: Nil) => Union(nodeToPlan(left), nodeToPlan(right))
 
     case a: ASTNode =>
-      throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
+      throw new NotImplementedError(s"No parse rules for $node:\n ${dumpTree(a).toString} ")
   }
 
   val allJoinTokens = "(TOK_.*JOIN)".r
@@ -1251,7 +1266,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite, true)
 
     case a: ASTNode =>
-      throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
+      throw new NotImplementedError(s"No parse rules for ${a.getName}:" +
+          s"\n ${dumpTree(a).toString} ")
   }
 
   protected def selExprNodeToExpr(node: Node): Option[Expression] = node match {
@@ -1274,7 +1290,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     case Token("TOK_HINTLIST", _) => None
 
     case a: ASTNode =>
-      throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
+      throw new NotImplementedError(s"No parse rules for ${a.getName }:" +
+          s"\n ${dumpTree(a).toString } ")
   }
 
   protected val escapedIdentifier = "`([^`]+)`".r
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
index a357bb39ca7fd..267074f3ad102 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -20,6 +20,8 @@ package org.apache.spark.sql.hive
 import java.io.{InputStream, OutputStream}
 import java.rmi.server.UID
 
+import org.apache.avro.Schema
+
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 import scala.language.implicitConversions
@@ -33,7 +35,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.ql.exec.{UDF, Utilities}
 import org.apache.hadoop.hive.ql.plan.{FileSinkDesc, TableDesc}
 import org.apache.hadoop.hive.serde2.ColumnProjectionUtils
-import org.apache.hadoop.hive.serde2.avro.AvroGenericRecordWritable
+import org.apache.hadoop.hive.serde2.avro.{AvroGenericRecordWritable, AvroSerdeUtils}
 import org.apache.hadoop.hive.serde2.objectinspector.primitive.HiveDecimalObjectInspector
 import org.apache.hadoop.io.Writable
 
@@ -82,10 +84,19 @@ private[hive] object HiveShim {
    * Bug introduced in hive-0.13. AvroGenericRecordWritable has a member recordReaderID that
    * is needed to initialize before serialization.
    */
-  def prepareWritable(w: Writable): Writable = {
+  def prepareWritable(w: Writable, serDeProps: Seq[(String, String)]): Writable = {
     w match {
       case w: AvroGenericRecordWritable =>
         w.setRecordReaderID(new UID())
+        // In Hive 1.1, the record's schema may need to be initialized manually or a NPE will
+        // be thrown.
+        if (w.getFileSchema() == null) {
+          serDeProps
+            .find(_._1 == AvroSerdeUtils.AvroTableProperties.SCHEMA_LITERAL.getPropName())
+            .foreach { kv =>
+              w.setFileSchema(new Schema.Parser().parse(kv._2))
+            }
+        }
       case _ =>
     }
     w
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index d834b4e83e043..a82e152dcda2c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -87,6 +87,10 @@ private[hive] case class HiveTable(
  * shared classes.
  */
 private[hive] trait ClientInterface {
+
+  /** Returns the configuration for the given key in the current session. */
+  def getConf(key: String, defaultValue: String): String
+
   /**
    * Runs a HiveQL command using Hive, returning the results as a list of strings.  Each row will
    * result in one string.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 6e0912da5862d..dc372be0e5a37 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -38,7 +38,6 @@ import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.execution.QueryExecutionException
 import org.apache.spark.util.{CircularBuffer, Utils}
 
-
 /**
  * A class that wraps the HiveClient and converts its responses to externally visible classes.
  * Note that this class is typically loaded with an internal classloader for each instantiation,
@@ -115,6 +114,10 @@ private[hive] class ClientWrapper(
   /** Returns the configuration for the current session. */
   def conf: HiveConf = SessionState.get().getConf
 
+  override def getConf(key: String, defaultValue: String): String = {
+    conf.get(key, defaultValue)
+  }
+
   // TODO: should be a def?s
   // When we create this val client, the HiveConf of it (conf) is the one associated with state.
   @GuardedBy("this")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 956997e5f9dce..6e826ce552204 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -512,7 +512,7 @@ private[client] class Shim_v1_2 extends Shim_v1_1 {
       listBucketingEnabled: Boolean): Unit = {
     loadDynamicPartitionsMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean,
       numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean, JBoolean.FALSE,
-      0: JLong)
+      0L: JLong)
   }
 
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index 97fb98199991b..f58bc7d7a0af4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -55,7 +55,7 @@ private[hive] object IsolatedClientLoader {
     case "14" | "0.14" | "0.14.0" => hive.v14
     case "1.0" | "1.0.0" => hive.v1_0
     case "1.1" | "1.1.0" => hive.v1_1
-    case "1.2" | "1.2.0" => hive.v1_2
+    case "1.2" | "1.2.0" | "1.2.1" => hive.v1_2
   }
 
   private def downloadVersion(version: HiveVersion, ivyPath: Option[String]): Seq[URL] = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
index b48082fe4b363..0503691a44249 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
@@ -56,7 +56,7 @@ package object client {
         "net.hydromatic:linq4j",
         "net.hydromatic:quidem"))
 
-    case object v1_2 extends HiveVersion("1.2.0",
+    case object v1_2 extends HiveVersion("1.2.1",
       exclusions = Seq("eigenbase:eigenbase-properties",
         "org.apache.curator:*",
         "org.pentaho:pentaho-aggdesigner-algorithm",
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 40a6a32156687..12c667e6e92da 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -129,7 +129,7 @@ case class InsertIntoHiveTable(
     // instances within the closure, since Serializer is not serializable while TableDesc is.
     val tableDesc = table.tableDesc
     val tableLocation = table.hiveQlTable.getDataLocation
-    val tmpLocation = hiveContext.getExternalTmpPath(tableLocation.toUri)
+    val tmpLocation = hiveContext.getExternalTmpPath(tableLocation)
     val fileSinkConf = new FileSinkDesc(tmpLocation.toString, tableDesc, false)
     val isCompressed = sc.hiveconf.getBoolean(
       ConfVars.COMPRESSRESULT.varname, ConfVars.COMPRESSRESULT.defaultBoolVal)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 7e3342cc84c0e..fbb86406f40cb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -247,7 +247,7 @@ private class ScriptTransformationWriterThread(
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
-          prepareWritable(writable).write(dataOutputStream)
+          prepareWritable(writable, ioschema.outputSerdeProps).write(dataOutputStream)
         }
       }
       outputStream.close()
@@ -345,9 +345,7 @@ case class HiveScriptIOSchema (
 
     val columnTypesNames = columnTypes.map(_.toTypeInfo.getTypeName()).mkString(",")
 
-    var propsMap = serdeProps.map(kv => {
-      (kv._1.split("'")(1), kv._2.split("'")(1))
-    }).toMap + (serdeConstants.LIST_COLUMNS -> columns.mkString(","))
+    var propsMap = serdeProps.toMap + (serdeConstants.LIST_COLUMNS -> columns.mkString(","))
     propsMap = propsMap + (serdeConstants.LIST_COLUMN_TYPES -> columnTypesNames)
 
     val properties = new Properties()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index abe5c69003130..8a86a87368f29 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -249,7 +249,7 @@ private[spark] object ResolveHiveWindowFunction extends Rule[LogicalPlan] {
           // Get the class of this function.
           // In Hive 0.12, there is no windowFunctionInfo.getFunctionClass. So, we use
           // windowFunctionInfo.getfInfo().getFunctionClass for both Hive 0.13 and Hive 0.13.1.
-          val functionClass = windowFunctionInfo.getfInfo().getFunctionClass
+          val functionClass = windowFunctionInfo.getFunctionClass()
           val newChildren =
             // Rank(), DENSE_RANK(), CUME_DIST(), and PERCENT_RANK() do not take explicit
             // input parameters and requires implicit parameters, which
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 8850e060d2a73..684ea1d137b49 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -171,7 +171,7 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
   import SparkHiveDynamicPartitionWriterContainer._
 
   private val defaultPartName = jobConf.get(
-    ConfVars.DEFAULTPARTITIONNAME.varname, ConfVars.DEFAULTPARTITIONNAME.defaultVal)
+    ConfVars.DEFAULTPARTITIONNAME.varname, ConfVars.DEFAULTPARTITIONNAME.defaultStrVal)
 
   @transient private var writers: mutable.HashMap[String, FileSinkOperator.RecordWriter] = _
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
index ddd5d24717add..86142e5d66f37 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive.orc
 
 import org.apache.hadoop.hive.common.`type`.{HiveChar, HiveDecimal, HiveVarchar}
-import org.apache.hadoop.hive.ql.io.sarg.SearchArgument
+import org.apache.hadoop.hive.ql.io.sarg.{SearchArgumentFactory, SearchArgument}
 import org.apache.hadoop.hive.ql.io.sarg.SearchArgument.Builder
 import org.apache.hadoop.hive.serde2.io.DateWritable
 
@@ -33,13 +33,13 @@ import org.apache.spark.sql.sources._
 private[orc] object OrcFilters extends Logging {
   def createFilter(expr: Array[Filter]): Option[SearchArgument] = {
     expr.reduceOption(And).flatMap { conjunction =>
-      val builder = SearchArgument.FACTORY.newBuilder()
+      val builder = SearchArgumentFactory.newBuilder()
       buildSearchArgument(conjunction, builder).map(_.build())
     }
   }
 
   private def buildSearchArgument(expression: Filter, builder: Builder): Option[Builder] = {
-    def newBuilder = SearchArgument.FACTORY.newBuilder()
+    def newBuilder = SearchArgumentFactory.newBuilder()
 
     def isSearchableLiteral(value: Any): Boolean = value match {
       // These are types recognized by the `SearchArgumentImpl.BuilderImpl.boxLiteral()` method.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 7bbdef90cd6b9..8d0bf46e8fad7 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -20,29 +20,25 @@ package org.apache.spark.sql.hive.test
 import java.io.File
 import java.util.{Set => JavaSet}
 
-import org.apache.hadoop.hive.conf.HiveConf
+import scala.collection.mutable
+import scala.language.implicitConversions
+
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.exec.FunctionRegistry
 import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat}
-import org.apache.hadoop.hive.ql.metadata.Table
-import org.apache.hadoop.hive.ql.parse.VariableSubstitution
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
 import org.apache.hadoop.hive.serde2.avro.AvroSerDe
 
-import org.apache.spark.sql.catalyst.CatalystConf
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.CacheTableCommand
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.execution.HiveNativeCommand
-import org.apache.spark.sql.SQLConf
 import org.apache.spark.util.Utils
 import org.apache.spark.{SparkConf, SparkContext}
 
-import scala.collection.mutable
-import scala.language.implicitConversions
-
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 
@@ -83,15 +79,25 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
 
   hiveconf.set("hive.plan.serialization.format", "javaXML")
 
-  lazy val warehousePath = Utils.createTempDir()
+  lazy val warehousePath = Utils.createTempDir(namePrefix = "warehouse-")
+
+  lazy val scratchDirPath = {
+    val dir = Utils.createTempDir(namePrefix = "scratch-")
+    dir.delete()
+    dir
+  }
 
   private lazy val temporaryConfig = newTemporaryConfiguration()
 
   /** Sets up the system initially or after a RESET command */
-  protected override def configure(): Map[String, String] =
-    temporaryConfig ++ Map(
-      ConfVars.METASTOREWAREHOUSE.varname -> warehousePath.toString,
-      ConfVars.METASTORE_INTEGER_JDO_PUSHDOWN.varname -> "true")
+  protected override def configure(): Map[String, String] = {
+    super.configure() ++ temporaryConfig ++ Map(
+      ConfVars.METASTOREWAREHOUSE.varname -> warehousePath.toURI.toString,
+      ConfVars.METASTORE_INTEGER_JDO_PUSHDOWN.varname -> "true",
+      ConfVars.SCRATCHDIR.varname -> scratchDirPath.toURI.toString,
+      ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY.varname -> "1"
+    )
+  }
 
   val testTempDir = Utils.createTempDir()
 
@@ -244,7 +250,6 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
     }),
     TestTable("src_thrift", () => {
       import org.apache.hadoop.hive.serde2.thrift.ThriftDeserializer
-      import org.apache.hadoop.hive.serde2.thrift.test.Complex
       import org.apache.hadoop.mapred.{SequenceFileInputFormat, SequenceFileOutputFormat}
       import org.apache.thrift.protocol.TBinaryProtocol
 
@@ -253,7 +258,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
          |CREATE TABLE src_thrift(fake INT)
          |ROW FORMAT SERDE '${classOf[ThriftDeserializer].getName}'
          |WITH SERDEPROPERTIES(
-         |  'serialization.class'='${classOf[Complex].getName}',
+         |  'serialization.class'='org.apache.spark.sql.hive.test.Complex',
          |  'serialization.format'='${classOf[TBinaryProtocol].getName}'
          |)
          |STORED AS
@@ -437,6 +442,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         case (k, v) =>
           metadataHive.runSqlHive(s"SET $k=$v")
       }
+      defaultOverides()
 
       runSqlHive("USE default")
 
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java
new file mode 100644
index 0000000000000..e010112bb9327
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java
@@ -0,0 +1,1139 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.sql.hive.test;
+
+import org.apache.commons.lang.builder.HashCodeBuilder;
+import org.apache.thrift.scheme.IScheme;
+import org.apache.thrift.scheme.SchemeFactory;
+import org.apache.thrift.scheme.StandardScheme;
+
+import org.apache.hadoop.hive.serde2.thrift.test.IntString;
+import org.apache.thrift.scheme.TupleScheme;
+import org.apache.thrift.protocol.TTupleProtocol;
+import org.apache.thrift.EncodingUtils;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Map;
+import java.util.HashMap;
+import java.util.EnumMap;
+import java.util.EnumSet;
+import java.util.Collections;
+import java.util.BitSet;
+
+/**
+ * This is a fork of Hive 0.13's org/apache/hadoop/hive/serde2/thrift/test/Complex.java, which
+ * does not contain union fields that are not supported by Spark SQL.
+ */
+
+@SuppressWarnings({"ALL", "unchecked"})
+public class Complex implements org.apache.thrift.TBase<Complex, Complex._Fields>, java.io.Serializable, Cloneable {
+  private static final org.apache.thrift.protocol.TStruct STRUCT_DESC = new org.apache.thrift.protocol.TStruct("Complex");
+
+  private static final org.apache.thrift.protocol.TField AINT_FIELD_DESC = new org.apache.thrift.protocol.TField("aint", org.apache.thrift.protocol.TType.I32, (short)1);
+  private static final org.apache.thrift.protocol.TField A_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("aString", org.apache.thrift.protocol.TType.STRING, (short)2);
+  private static final org.apache.thrift.protocol.TField LINT_FIELD_DESC = new org.apache.thrift.protocol.TField("lint", org.apache.thrift.protocol.TType.LIST, (short)3);
+  private static final org.apache.thrift.protocol.TField L_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("lString", org.apache.thrift.protocol.TType.LIST, (short)4);
+  private static final org.apache.thrift.protocol.TField LINT_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("lintString", org.apache.thrift.protocol.TType.LIST, (short)5);
+  private static final org.apache.thrift.protocol.TField M_STRING_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("mStringString", org.apache.thrift.protocol.TType.MAP, (short)6);
+
+  private static final Map<Class<? extends IScheme>, SchemeFactory> schemes = new HashMap<Class<? extends IScheme>, SchemeFactory>();
+  static {
+    schemes.put(StandardScheme.class, new ComplexStandardSchemeFactory());
+    schemes.put(TupleScheme.class, new ComplexTupleSchemeFactory());
+  }
+
+  private int aint; // required
+  private String aString; // required
+  private List<Integer> lint; // required
+  private List<String> lString; // required
+  private List<IntString> lintString; // required
+  private Map<String,String> mStringString; // required
+
+  /** The set of fields this struct contains, along with convenience methods for finding and manipulating them. */
+  public enum _Fields implements org.apache.thrift.TFieldIdEnum {
+    AINT((short)1, "aint"),
+    A_STRING((short)2, "aString"),
+    LINT((short)3, "lint"),
+    L_STRING((short)4, "lString"),
+    LINT_STRING((short)5, "lintString"),
+    M_STRING_STRING((short)6, "mStringString");
+
+    private static final Map<String, _Fields> byName = new HashMap<String, _Fields>();
+
+    static {
+      for (_Fields field : EnumSet.allOf(_Fields.class)) {
+        byName.put(field.getFieldName(), field);
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, or null if its not found.
+     */
+    public static _Fields findByThriftId(int fieldId) {
+      switch(fieldId) {
+        case 1: // AINT
+          return AINT;
+        case 2: // A_STRING
+          return A_STRING;
+        case 3: // LINT
+          return LINT;
+        case 4: // L_STRING
+          return L_STRING;
+        case 5: // LINT_STRING
+          return LINT_STRING;
+        case 6: // M_STRING_STRING
+          return M_STRING_STRING;
+        default:
+          return null;
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, throwing an exception
+     * if it is not found.
+     */
+    public static _Fields findByThriftIdOrThrow(int fieldId) {
+      _Fields fields = findByThriftId(fieldId);
+      if (fields == null) throw new IllegalArgumentException("Field " + fieldId + " doesn't exist!");
+      return fields;
+    }
+
+    /**
+     * Find the _Fields constant that matches name, or null if its not found.
+     */
+    public static _Fields findByName(String name) {
+      return byName.get(name);
+    }
+
+    private final short _thriftId;
+    private final String _fieldName;
+
+    _Fields(short thriftId, String fieldName) {
+      _thriftId = thriftId;
+      _fieldName = fieldName;
+    }
+
+    public short getThriftFieldId() {
+      return _thriftId;
+    }
+
+    public String getFieldName() {
+      return _fieldName;
+    }
+  }
+
+  // isset id assignments
+  private static final int __AINT_ISSET_ID = 0;
+  private byte __isset_bitfield = 0;
+  public static final Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> metaDataMap;
+  static {
+    Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> tmpMap = new EnumMap<_Fields, org.apache.thrift.meta_data.FieldMetaData>(_Fields.class);
+    tmpMap.put(_Fields.AINT, new org.apache.thrift.meta_data.FieldMetaData("aint", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32)));
+    tmpMap.put(_Fields.A_STRING, new org.apache.thrift.meta_data.FieldMetaData("aString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
+    tmpMap.put(_Fields.LINT, new org.apache.thrift.meta_data.FieldMetaData("lint", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST,
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32))));
+    tmpMap.put(_Fields.L_STRING, new org.apache.thrift.meta_data.FieldMetaData("lString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST,
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
+    tmpMap.put(_Fields.LINT_STRING, new org.apache.thrift.meta_data.FieldMetaData("lintString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST,
+            new org.apache.thrift.meta_data.StructMetaData(org.apache.thrift.protocol.TType.STRUCT, IntString.class))));
+    tmpMap.put(_Fields.M_STRING_STRING, new org.apache.thrift.meta_data.FieldMetaData("mStringString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.MapMetaData(org.apache.thrift.protocol.TType.MAP,
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING),
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
+    metaDataMap = Collections.unmodifiableMap(tmpMap);
+    org.apache.thrift.meta_data.FieldMetaData.addStructMetaDataMap(Complex.class, metaDataMap);
+  }
+
+  public Complex() {
+  }
+
+  public Complex(
+    int aint,
+    String aString,
+    List<Integer> lint,
+    List<String> lString,
+    List<IntString> lintString,
+    Map<String,String> mStringString)
+  {
+    this();
+    this.aint = aint;
+    setAintIsSet(true);
+    this.aString = aString;
+    this.lint = lint;
+    this.lString = lString;
+    this.lintString = lintString;
+    this.mStringString = mStringString;
+  }
+
+  /**
+   * Performs a deep copy on <i>other</i>.
+   */
+  public Complex(Complex other) {
+    __isset_bitfield = other.__isset_bitfield;
+    this.aint = other.aint;
+    if (other.isSetAString()) {
+      this.aString = other.aString;
+    }
+    if (other.isSetLint()) {
+      List<Integer> __this__lint = new ArrayList<Integer>();
+      for (Integer other_element : other.lint) {
+        __this__lint.add(other_element);
+      }
+      this.lint = __this__lint;
+    }
+    if (other.isSetLString()) {
+      List<String> __this__lString = new ArrayList<String>();
+      for (String other_element : other.lString) {
+        __this__lString.add(other_element);
+      }
+      this.lString = __this__lString;
+    }
+    if (other.isSetLintString()) {
+      List<IntString> __this__lintString = new ArrayList<IntString>();
+      for (IntString other_element : other.lintString) {
+        __this__lintString.add(new IntString(other_element));
+      }
+      this.lintString = __this__lintString;
+    }
+    if (other.isSetMStringString()) {
+      Map<String,String> __this__mStringString = new HashMap<String,String>();
+      for (Map.Entry<String, String> other_element : other.mStringString.entrySet()) {
+
+        String other_element_key = other_element.getKey();
+        String other_element_value = other_element.getValue();
+
+        String __this__mStringString_copy_key = other_element_key;
+
+        String __this__mStringString_copy_value = other_element_value;
+
+        __this__mStringString.put(__this__mStringString_copy_key, __this__mStringString_copy_value);
+      }
+      this.mStringString = __this__mStringString;
+    }
+  }
+
+  public Complex deepCopy() {
+    return new Complex(this);
+  }
+
+  @Override
+  public void clear() {
+    setAintIsSet(false);
+    this.aint = 0;
+    this.aString = null;
+    this.lint = null;
+    this.lString = null;
+    this.lintString = null;
+    this.mStringString = null;
+  }
+
+  public int getAint() {
+    return this.aint;
+  }
+
+  public void setAint(int aint) {
+    this.aint = aint;
+    setAintIsSet(true);
+  }
+
+  public void unsetAint() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __AINT_ISSET_ID);
+  }
+
+  /** Returns true if field aint is set (has been assigned a value) and false otherwise */
+  public boolean isSetAint() {
+    return EncodingUtils.testBit(__isset_bitfield, __AINT_ISSET_ID);
+  }
+
+  public void setAintIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __AINT_ISSET_ID, value);
+  }
+
+  public String getAString() {
+    return this.aString;
+  }
+
+  public void setAString(String aString) {
+    this.aString = aString;
+  }
+
+  public void unsetAString() {
+    this.aString = null;
+  }
+
+  /** Returns true if field aString is set (has been assigned a value) and false otherwise */
+  public boolean isSetAString() {
+    return this.aString != null;
+  }
+
+  public void setAStringIsSet(boolean value) {
+    if (!value) {
+      this.aString = null;
+    }
+  }
+
+  public int getLintSize() {
+    return (this.lint == null) ? 0 : this.lint.size();
+  }
+
+  public java.util.Iterator<Integer> getLintIterator() {
+    return (this.lint == null) ? null : this.lint.iterator();
+  }
+
+  public void addToLint(int elem) {
+    if (this.lint == null) {
+      this.lint = new ArrayList<>();
+    }
+    this.lint.add(elem);
+  }
+
+  public List<Integer> getLint() {
+    return this.lint;
+  }
+
+  public void setLint(List<Integer> lint) {
+    this.lint = lint;
+  }
+
+  public void unsetLint() {
+    this.lint = null;
+  }
+
+  /** Returns true if field lint is set (has been assigned a value) and false otherwise */
+  public boolean isSetLint() {
+    return this.lint != null;
+  }
+
+  public void setLintIsSet(boolean value) {
+    if (!value) {
+      this.lint = null;
+    }
+  }
+
+  public int getLStringSize() {
+    return (this.lString == null) ? 0 : this.lString.size();
+  }
+
+  public java.util.Iterator<String> getLStringIterator() {
+    return (this.lString == null) ? null : this.lString.iterator();
+  }
+
+  public void addToLString(String elem) {
+    if (this.lString == null) {
+      this.lString = new ArrayList<String>();
+    }
+    this.lString.add(elem);
+  }
+
+  public List<String> getLString() {
+    return this.lString;
+  }
+
+  public void setLString(List<String> lString) {
+    this.lString = lString;
+  }
+
+  public void unsetLString() {
+    this.lString = null;
+  }
+
+  /** Returns true if field lString is set (has been assigned a value) and false otherwise */
+  public boolean isSetLString() {
+    return this.lString != null;
+  }
+
+  public void setLStringIsSet(boolean value) {
+    if (!value) {
+      this.lString = null;
+    }
+  }
+
+  public int getLintStringSize() {
+    return (this.lintString == null) ? 0 : this.lintString.size();
+  }
+
+  public java.util.Iterator<IntString> getLintStringIterator() {
+    return (this.lintString == null) ? null : this.lintString.iterator();
+  }
+
+  public void addToLintString(IntString elem) {
+    if (this.lintString == null) {
+      this.lintString = new ArrayList<>();
+    }
+    this.lintString.add(elem);
+  }
+
+  public List<IntString> getLintString() {
+    return this.lintString;
+  }
+
+  public void setLintString(List<IntString> lintString) {
+    this.lintString = lintString;
+  }
+
+  public void unsetLintString() {
+    this.lintString = null;
+  }
+
+  /** Returns true if field lintString is set (has been assigned a value) and false otherwise */
+  public boolean isSetLintString() {
+    return this.lintString != null;
+  }
+
+  public void setLintStringIsSet(boolean value) {
+    if (!value) {
+      this.lintString = null;
+    }
+  }
+
+  public int getMStringStringSize() {
+    return (this.mStringString == null) ? 0 : this.mStringString.size();
+  }
+
+  public void putToMStringString(String key, String val) {
+    if (this.mStringString == null) {
+      this.mStringString = new HashMap<String,String>();
+    }
+    this.mStringString.put(key, val);
+  }
+
+  public Map<String,String> getMStringString() {
+    return this.mStringString;
+  }
+
+  public void setMStringString(Map<String,String> mStringString) {
+    this.mStringString = mStringString;
+  }
+
+  public void unsetMStringString() {
+    this.mStringString = null;
+  }
+
+  /** Returns true if field mStringString is set (has been assigned a value) and false otherwise */
+  public boolean isSetMStringString() {
+    return this.mStringString != null;
+  }
+
+  public void setMStringStringIsSet(boolean value) {
+    if (!value) {
+      this.mStringString = null;
+    }
+  }
+
+  public void setFieldValue(_Fields field, Object value) {
+    switch (field) {
+    case AINT:
+      if (value == null) {
+        unsetAint();
+      } else {
+        setAint((Integer)value);
+      }
+      break;
+
+    case A_STRING:
+      if (value == null) {
+        unsetAString();
+      } else {
+        setAString((String)value);
+      }
+      break;
+
+    case LINT:
+      if (value == null) {
+        unsetLint();
+      } else {
+        setLint((List<Integer>)value);
+      }
+      break;
+
+    case L_STRING:
+      if (value == null) {
+        unsetLString();
+      } else {
+        setLString((List<String>)value);
+      }
+      break;
+
+    case LINT_STRING:
+      if (value == null) {
+        unsetLintString();
+      } else {
+        setLintString((List<IntString>)value);
+      }
+      break;
+
+    case M_STRING_STRING:
+      if (value == null) {
+        unsetMStringString();
+      } else {
+        setMStringString((Map<String,String>)value);
+      }
+      break;
+
+    }
+  }
+
+  public Object getFieldValue(_Fields field) {
+    switch (field) {
+    case AINT:
+      return Integer.valueOf(getAint());
+
+    case A_STRING:
+      return getAString();
+
+    case LINT:
+      return getLint();
+
+    case L_STRING:
+      return getLString();
+
+    case LINT_STRING:
+      return getLintString();
+
+    case M_STRING_STRING:
+      return getMStringString();
+
+    }
+    throw new IllegalStateException();
+  }
+
+  /** Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise */
+  public boolean isSet(_Fields field) {
+    if (field == null) {
+      throw new IllegalArgumentException();
+    }
+
+    switch (field) {
+    case AINT:
+      return isSetAint();
+    case A_STRING:
+      return isSetAString();
+    case LINT:
+      return isSetLint();
+    case L_STRING:
+      return isSetLString();
+    case LINT_STRING:
+      return isSetLintString();
+    case M_STRING_STRING:
+      return isSetMStringString();
+    }
+    throw new IllegalStateException();
+  }
+
+  @Override
+  public boolean equals(Object that) {
+    if (that == null)
+      return false;
+    if (that instanceof Complex)
+      return this.equals((Complex)that);
+    return false;
+  }
+
+  public boolean equals(Complex that) {
+    if (that == null)
+      return false;
+
+    boolean this_present_aint = true;
+    boolean that_present_aint = true;
+    if (this_present_aint || that_present_aint) {
+      if (!(this_present_aint && that_present_aint))
+        return false;
+      if (this.aint != that.aint)
+        return false;
+    }
+
+    boolean this_present_aString = true && this.isSetAString();
+    boolean that_present_aString = true && that.isSetAString();
+    if (this_present_aString || that_present_aString) {
+      if (!(this_present_aString && that_present_aString))
+        return false;
+      if (!this.aString.equals(that.aString))
+        return false;
+    }
+
+    boolean this_present_lint = true && this.isSetLint();
+    boolean that_present_lint = true && that.isSetLint();
+    if (this_present_lint || that_present_lint) {
+      if (!(this_present_lint && that_present_lint))
+        return false;
+      if (!this.lint.equals(that.lint))
+        return false;
+    }
+
+    boolean this_present_lString = true && this.isSetLString();
+    boolean that_present_lString = true && that.isSetLString();
+    if (this_present_lString || that_present_lString) {
+      if (!(this_present_lString && that_present_lString))
+        return false;
+      if (!this.lString.equals(that.lString))
+        return false;
+    }
+
+    boolean this_present_lintString = true && this.isSetLintString();
+    boolean that_present_lintString = true && that.isSetLintString();
+    if (this_present_lintString || that_present_lintString) {
+      if (!(this_present_lintString && that_present_lintString))
+        return false;
+      if (!this.lintString.equals(that.lintString))
+        return false;
+    }
+
+    boolean this_present_mStringString = true && this.isSetMStringString();
+    boolean that_present_mStringString = true && that.isSetMStringString();
+    if (this_present_mStringString || that_present_mStringString) {
+      if (!(this_present_mStringString && that_present_mStringString))
+        return false;
+      if (!this.mStringString.equals(that.mStringString))
+        return false;
+    }
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    HashCodeBuilder builder = new HashCodeBuilder();
+
+    boolean present_aint = true;
+    builder.append(present_aint);
+    if (present_aint)
+      builder.append(aint);
+
+    boolean present_aString = true && (isSetAString());
+    builder.append(present_aString);
+    if (present_aString)
+      builder.append(aString);
+
+    boolean present_lint = true && (isSetLint());
+    builder.append(present_lint);
+    if (present_lint)
+      builder.append(lint);
+
+    boolean present_lString = true && (isSetLString());
+    builder.append(present_lString);
+    if (present_lString)
+      builder.append(lString);
+
+    boolean present_lintString = true && (isSetLintString());
+    builder.append(present_lintString);
+    if (present_lintString)
+      builder.append(lintString);
+
+    boolean present_mStringString = true && (isSetMStringString());
+    builder.append(present_mStringString);
+    if (present_mStringString)
+      builder.append(mStringString);
+
+    return builder.toHashCode();
+  }
+
+  public int compareTo(Complex other) {
+    if (!getClass().equals(other.getClass())) {
+      return getClass().getName().compareTo(other.getClass().getName());
+    }
+
+    int lastComparison = 0;
+    Complex typedOther = (Complex)other;
+
+    lastComparison = Boolean.valueOf(isSetAint()).compareTo(typedOther.isSetAint());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetAint()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.aint, typedOther.aint);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetAString()).compareTo(typedOther.isSetAString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetAString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.aString, typedOther.aString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetLint()).compareTo(typedOther.isSetLint());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetLint()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.lint, typedOther.lint);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetLString()).compareTo(typedOther.isSetLString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetLString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.lString, typedOther.lString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetLintString()).compareTo(typedOther.isSetLintString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetLintString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.lintString, typedOther.lintString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMStringString()).compareTo(typedOther.isSetMStringString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMStringString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.mStringString, typedOther.mStringString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    return 0;
+  }
+
+  public _Fields fieldForId(int fieldId) {
+    return _Fields.findByThriftId(fieldId);
+  }
+
+  public void read(org.apache.thrift.protocol.TProtocol iprot) throws org.apache.thrift.TException {
+    schemes.get(iprot.getScheme()).getScheme().read(iprot, this);
+  }
+
+  public void write(org.apache.thrift.protocol.TProtocol oprot) throws org.apache.thrift.TException {
+    schemes.get(oprot.getScheme()).getScheme().write(oprot, this);
+  }
+
+  @Override
+  public String toString() {
+    StringBuilder sb = new StringBuilder("Complex(");
+    boolean first = true;
+
+    sb.append("aint:");
+    sb.append(this.aint);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("aString:");
+    if (this.aString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.aString);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("lint:");
+    if (this.lint == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.lint);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("lString:");
+    if (this.lString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.lString);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("lintString:");
+    if (this.lintString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.lintString);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("mStringString:");
+    if (this.mStringString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.mStringString);
+    }
+    first = false;
+    sb.append(")");
+    return sb.toString();
+  }
+
+  public void validate() throws org.apache.thrift.TException {
+    // check for required fields
+    // check for sub-struct validity
+  }
+
+  private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException {
+    try {
+      write(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(out)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, ClassNotFoundException {
+    try {
+      // it doesn't seem like you should have to do this, but java serialization is wacky, and doesn't call the default constructor.
+      __isset_bitfield = 0;
+      read(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(in)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private static class ComplexStandardSchemeFactory implements SchemeFactory {
+    public ComplexStandardScheme getScheme() {
+      return new ComplexStandardScheme();
+    }
+  }
+
+  private static class ComplexStandardScheme extends StandardScheme<Complex> {
+
+    public void read(org.apache.thrift.protocol.TProtocol iprot, Complex struct) throws org.apache.thrift.TException {
+      org.apache.thrift.protocol.TField schemeField;
+      iprot.readStructBegin();
+      while (true)
+      {
+        schemeField = iprot.readFieldBegin();
+        if (schemeField.type == org.apache.thrift.protocol.TType.STOP) {
+          break;
+        }
+        switch (schemeField.id) {
+          case 1: // AINT
+            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
+              struct.aint = iprot.readI32();
+              struct.setAintIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 2: // A_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
+              struct.aString = iprot.readString();
+              struct.setAStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 3: // LINT
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list0 = iprot.readListBegin();
+                struct.lint = new ArrayList<Integer>(_list0.size);
+                for (int _i1 = 0; _i1 < _list0.size; ++_i1)
+                {
+                  int _elem2; // required
+                  _elem2 = iprot.readI32();
+                  struct.lint.add(_elem2);
+                }
+                iprot.readListEnd();
+              }
+              struct.setLintIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 4: // L_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list3 = iprot.readListBegin();
+                struct.lString = new ArrayList<String>(_list3.size);
+                for (int _i4 = 0; _i4 < _list3.size; ++_i4)
+                {
+                  String _elem5; // required
+                  _elem5 = iprot.readString();
+                  struct.lString.add(_elem5);
+                }
+                iprot.readListEnd();
+              }
+              struct.setLStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 5: // LINT_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list6 = iprot.readListBegin();
+                struct.lintString = new ArrayList<IntString>(_list6.size);
+                for (int _i7 = 0; _i7 < _list6.size; ++_i7)
+                {
+                  IntString _elem8; // required
+                  _elem8 = new IntString();
+                  _elem8.read(iprot);
+                  struct.lintString.add(_elem8);
+                }
+                iprot.readListEnd();
+              }
+              struct.setLintStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 6: // M_STRING_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.MAP) {
+              {
+                org.apache.thrift.protocol.TMap _map9 = iprot.readMapBegin();
+                struct.mStringString = new HashMap<String,String>(2*_map9.size);
+                for (int _i10 = 0; _i10 < _map9.size; ++_i10)
+                {
+                  String _key11; // required
+                  String _val12; // required
+                  _key11 = iprot.readString();
+                  _val12 = iprot.readString();
+                  struct.mStringString.put(_key11, _val12);
+                }
+                iprot.readMapEnd();
+              }
+              struct.setMStringStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          default:
+            org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+        }
+        iprot.readFieldEnd();
+      }
+      iprot.readStructEnd();
+      struct.validate();
+    }
+
+    public void write(org.apache.thrift.protocol.TProtocol oprot, Complex struct) throws org.apache.thrift.TException {
+      struct.validate();
+
+      oprot.writeStructBegin(STRUCT_DESC);
+      oprot.writeFieldBegin(AINT_FIELD_DESC);
+      oprot.writeI32(struct.aint);
+      oprot.writeFieldEnd();
+      if (struct.aString != null) {
+        oprot.writeFieldBegin(A_STRING_FIELD_DESC);
+        oprot.writeString(struct.aString);
+        oprot.writeFieldEnd();
+      }
+      if (struct.lint != null) {
+        oprot.writeFieldBegin(LINT_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, struct.lint.size()));
+          for (int _iter13 : struct.lint)
+          {
+            oprot.writeI32(_iter13);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.lString != null) {
+        oprot.writeFieldBegin(L_STRING_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, struct.lString.size()));
+          for (String _iter14 : struct.lString)
+          {
+            oprot.writeString(_iter14);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.lintString != null) {
+        oprot.writeFieldBegin(LINT_STRING_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, struct.lintString.size()));
+          for (IntString _iter15 : struct.lintString)
+          {
+            _iter15.write(oprot);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.mStringString != null) {
+        oprot.writeFieldBegin(M_STRING_STRING_FIELD_DESC);
+        {
+          oprot.writeMapBegin(new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.STRING, org.apache.thrift.protocol.TType.STRING, struct.mStringString.size()));
+          for (Map.Entry<String, String> _iter16 : struct.mStringString.entrySet())
+          {
+            oprot.writeString(_iter16.getKey());
+            oprot.writeString(_iter16.getValue());
+          }
+          oprot.writeMapEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      oprot.writeFieldStop();
+      oprot.writeStructEnd();
+    }
+
+  }
+
+  private static class ComplexTupleSchemeFactory implements SchemeFactory {
+    public ComplexTupleScheme getScheme() {
+      return new ComplexTupleScheme();
+    }
+  }
+
+  private static class ComplexTupleScheme extends TupleScheme<Complex> {
+
+    @Override
+    public void write(org.apache.thrift.protocol.TProtocol prot, Complex struct) throws org.apache.thrift.TException {
+      TTupleProtocol oprot = (TTupleProtocol) prot;
+      BitSet optionals = new BitSet();
+      if (struct.isSetAint()) {
+        optionals.set(0);
+      }
+      if (struct.isSetAString()) {
+        optionals.set(1);
+      }
+      if (struct.isSetLint()) {
+        optionals.set(2);
+      }
+      if (struct.isSetLString()) {
+        optionals.set(3);
+      }
+      if (struct.isSetLintString()) {
+        optionals.set(4);
+      }
+      if (struct.isSetMStringString()) {
+        optionals.set(5);
+      }
+      oprot.writeBitSet(optionals, 6);
+      if (struct.isSetAint()) {
+        oprot.writeI32(struct.aint);
+      }
+      if (struct.isSetAString()) {
+        oprot.writeString(struct.aString);
+      }
+      if (struct.isSetLint()) {
+        {
+          oprot.writeI32(struct.lint.size());
+          for (int _iter17 : struct.lint)
+          {
+            oprot.writeI32(_iter17);
+          }
+        }
+      }
+      if (struct.isSetLString()) {
+        {
+          oprot.writeI32(struct.lString.size());
+          for (String _iter18 : struct.lString)
+          {
+            oprot.writeString(_iter18);
+          }
+        }
+      }
+      if (struct.isSetLintString()) {
+        {
+          oprot.writeI32(struct.lintString.size());
+          for (IntString _iter19 : struct.lintString)
+          {
+            _iter19.write(oprot);
+          }
+        }
+      }
+      if (struct.isSetMStringString()) {
+        {
+          oprot.writeI32(struct.mStringString.size());
+          for (Map.Entry<String, String> _iter20 : struct.mStringString.entrySet())
+          {
+            oprot.writeString(_iter20.getKey());
+            oprot.writeString(_iter20.getValue());
+          }
+        }
+      }
+    }
+
+    @Override
+    public void read(org.apache.thrift.protocol.TProtocol prot, Complex struct) throws org.apache.thrift.TException {
+      TTupleProtocol iprot = (TTupleProtocol) prot;
+      BitSet incoming = iprot.readBitSet(6);
+      if (incoming.get(0)) {
+        struct.aint = iprot.readI32();
+        struct.setAintIsSet(true);
+      }
+      if (incoming.get(1)) {
+        struct.aString = iprot.readString();
+        struct.setAStringIsSet(true);
+      }
+      if (incoming.get(2)) {
+        {
+          org.apache.thrift.protocol.TList _list21 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, iprot.readI32());
+          struct.lint = new ArrayList<Integer>(_list21.size);
+          for (int _i22 = 0; _i22 < _list21.size; ++_i22)
+          {
+            int _elem23; // required
+            _elem23 = iprot.readI32();
+            struct.lint.add(_elem23);
+          }
+        }
+        struct.setLintIsSet(true);
+      }
+      if (incoming.get(3)) {
+        {
+          org.apache.thrift.protocol.TList _list24 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, iprot.readI32());
+          struct.lString = new ArrayList<String>(_list24.size);
+          for (int _i25 = 0; _i25 < _list24.size; ++_i25)
+          {
+            String _elem26; // required
+            _elem26 = iprot.readString();
+            struct.lString.add(_elem26);
+          }
+        }
+        struct.setLStringIsSet(true);
+      }
+      if (incoming.get(4)) {
+        {
+          org.apache.thrift.protocol.TList _list27 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, iprot.readI32());
+          struct.lintString = new ArrayList<IntString>(_list27.size);
+          for (int _i28 = 0; _i28 < _list27.size; ++_i28)
+          {
+            IntString _elem29; // required
+            _elem29 = new IntString();
+            _elem29.read(iprot);
+            struct.lintString.add(_elem29);
+          }
+        }
+        struct.setLintStringIsSet(true);
+      }
+      if (incoming.get(5)) {
+        {
+          org.apache.thrift.protocol.TMap _map30 = new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.STRING, org.apache.thrift.protocol.TType.STRING, iprot.readI32());
+          struct.mStringString = new HashMap<String,String>(2*_map30.size);
+          for (int _i31 = 0; _i31 < _map30.size; ++_i31)
+          {
+            String _key32; // required
+            String _val33; // required
+            _key32 = iprot.readString();
+            _val33 = iprot.readString();
+            struct.mStringString.put(_key32, _val33);
+          }
+        }
+        struct.setMStringStringIsSet(true);
+      }
+    }
+  }
+
+}
+
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
index 64d1ce92931eb..15c2c3deb0d83 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
@@ -90,8 +90,10 @@ public void setUp() throws IOException {
   @After
   public void tearDown() throws IOException {
     // Clean up tables.
-    sqlContext.sql("DROP TABLE IF EXISTS javaSavedTable");
-    sqlContext.sql("DROP TABLE IF EXISTS externalTable");
+    if (sqlContext != null) {
+      sqlContext.sql("DROP TABLE IF EXISTS javaSavedTable");
+      sqlContext.sql("DROP TABLE IF EXISTS externalTable");
+    }
   }
 
   @Test
diff --git a/sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf b/sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59 b/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59
index d35bf9093ca9c..2383bef940973 100644
--- a/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59
+++ b/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59
@@ -15,9 +15,9 @@ my_enum_structlist_map	map<string,array<struct<my_string:string,my_enum:string,o
 my_stringlist       	array<string>       	from deserializer   
 my_structlist       	array<struct<my_string:string,my_enum:string,optionals:struct<>>>	from deserializer   
 my_enumlist         	array<string>       	from deserializer   
-my_stringset        	struct<>            	from deserializer   
-my_enumset          	struct<>            	from deserializer   
-my_structset        	struct<>            	from deserializer   
+my_stringset        	array<string>       	from deserializer   
+my_enumset          	array<string>       	from deserializer   
+my_structset        	array<struct<my_string:string,my_enum:string,optionals:struct<>>>	from deserializer   
 optionals           	struct<>            	from deserializer   
 b                   	string              	                    
 	 	 
diff --git a/sql/hive/src/test/resources/golden/parenthesis_star_by-5-6888c7f7894910538d82eefa23443189 b/sql/hive/src/test/resources/golden/parenthesis_star_by-5-41d474f5e6d7c61c36f74b4bec4e9e44
similarity index 100%
rename from sql/hive/src/test/resources/golden/parenthesis_star_by-5-6888c7f7894910538d82eefa23443189
rename to sql/hive/src/test/resources/golden/parenthesis_star_by-5-41d474f5e6d7c61c36f74b4bec4e9e44
diff --git a/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26 b/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26
index 501bb6ab32f25..7bb2c0ab43984 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26
+++ b/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_showcrt1`(
+CREATE TABLE `tmp_showcrt1`(
   `key` smallint, 
   `value` float)
 COMMENT 'temporary table'
diff --git a/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0 b/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0
index 90f8415a1c6be..3cc1a57ee3a47 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0
+++ b/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_feng.tmp_showcrt`(
+CREATE TABLE `tmp_feng.tmp_showcrt`(
   `key` string, 
   `value` int)
 ROW FORMAT SERDE 
diff --git a/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26 b/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26
index 4ee22e5230316..b51c71a71f91c 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26
+++ b/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_showcrt1`(
+CREATE TABLE `tmp_showcrt1`(
   `key` int, 
   `value` string, 
   `newvalue` bigint)
diff --git a/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26 b/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26
index 6fda2570b53f1..29189e1d860a4 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26
+++ b/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_showcrt1`(
+CREATE TABLE `tmp_showcrt1`(
   `key` int, 
   `value` string, 
   `newvalue` bigint)
diff --git a/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043 b/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043
index 3049cd6243ad8..1b283db3e7744 100644
--- a/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043
+++ b/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043
@@ -17,6 +17,7 @@
 ^
 abs
 acos
+add_months
 and
 array
 array_contains
@@ -29,6 +30,7 @@ base64
 between
 bin
 case
+cbrt
 ceil
 ceiling
 coalesce
@@ -47,7 +49,11 @@ covar_samp
 create_union
 cume_dist
 current_database
+current_date
+current_timestamp
+current_user
 date_add
+date_format
 date_sub
 datediff
 day
@@ -65,6 +71,7 @@ ewah_bitmap_empty
 ewah_bitmap_or
 exp
 explode
+factorial
 field
 find_in_set
 first_value
@@ -73,6 +80,7 @@ format_number
 from_unixtime
 from_utc_timestamp
 get_json_object
+greatest
 hash
 hex
 histogram_numeric
@@ -81,6 +89,7 @@ if
 in
 in_file
 index
+initcap
 inline
 instr
 isnotnull
@@ -88,10 +97,13 @@ isnull
 java_method
 json_tuple
 lag
+last_day
 last_value
 lcase
 lead
+least
 length
+levenshtein
 like
 ln
 locate
@@ -109,11 +121,15 @@ max
 min
 minute
 month
+months_between
 named_struct
 negative
+next_day
 ngrams
 noop
+noopstreaming
 noopwithmap
+noopwithmapstreaming
 not
 ntile
 nvl
@@ -147,10 +163,14 @@ rpad
 rtrim
 second
 sentences
+shiftleft
+shiftright
+shiftrightunsigned
 sign
 sin
 size
 sort_array
+soundex
 space
 split
 sqrt
@@ -170,6 +190,7 @@ to_unix_timestamp
 to_utc_timestamp
 translate
 trim
+trunc
 ucase
 unbase64
 unhex
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae b/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
index 0f6cc6f44f1f7..fdf701f962800 100644
--- a/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
+++ b/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
@@ -1 +1 @@
-Table tmpfoo does not have property: bar	 
+Table default.tmpfoo does not have property: bar	 
diff --git a/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2 b/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2
index 3c91e138d7bd5..d8ec084f0b2b0 100644
--- a/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2
+++ b/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2
@@ -1,5 +1,5 @@
 date_add(start_date, num_days) - Returns the date that is num_days after start_date.
 start_date is a string in the format 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'. num_days is a number. The time part of start_date is ignored.
 Example:
-   > SELECT date_add('2009-30-07', 1) FROM src LIMIT 1;
-  '2009-31-07'
+   > SELECT date_add('2009-07-30', 1) FROM src LIMIT 1;
+  '2009-07-31'
diff --git a/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4 b/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4
index 29d663f35c586..169c500036255 100644
--- a/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4
+++ b/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4
@@ -1,5 +1,5 @@
 date_sub(start_date, num_days) - Returns the date that is num_days before start_date.
 start_date is a string in the format 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'. num_days is a number. The time part of start_date is ignored.
 Example:
-   > SELECT date_sub('2009-30-07', 1) FROM src LIMIT 1;
-  '2009-29-07'
+   > SELECT date_sub('2009-07-30', 1) FROM src LIMIT 1;
+  '2009-07-29'
diff --git a/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5 b/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5
index 7ccaee7ad3bd4..42197f7ad3e51 100644
--- a/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5
+++ b/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5
@@ -1,5 +1,5 @@
 datediff(date1, date2) - Returns the number of days between date1 and date2
 date1 and date2 are strings in the format 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'. The time parts are ignored.If date1 is earlier than date2, the result is negative.
 Example:
-   > SELECT datediff('2009-30-07', '2009-31-07') FROM src LIMIT 1;
+   > SELECT datediff('2009-07-30', '2009-07-31') FROM src LIMIT 1;
   1
diff --git a/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756 b/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756
index d4017178b4e6b..09703d10eab7a 100644
--- a/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756
+++ b/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756
@@ -1 +1 @@
-day(date) - Returns the date of the month of date
+day(param) - Returns the day of the month of date/timestamp, or day component of interval
diff --git a/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4 b/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4
index 6135aafa50860..7c0ec1dc3be59 100644
--- a/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4
+++ b/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4
@@ -1,6 +1,9 @@
-day(date) - Returns the date of the month of date
+day(param) - Returns the day of the month of date/timestamp, or day component of interval
 Synonyms: dayofmonth
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
-Example:
-   > SELECT day('2009-30-07', 1) FROM src LIMIT 1;
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
+2. A date value
+3. A timestamp value
+4. A day-time interval valueExample:
+   > SELECT day('2009-07-30') FROM src LIMIT 1;
   30
diff --git a/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502 b/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502
index 47a7018d9d5ac..c37eb0ec2e969 100644
--- a/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502
+++ b/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502
@@ -1 +1 @@
-dayofmonth(date) - Returns the date of the month of date
+dayofmonth(param) - Returns the day of the month of date/timestamp, or day component of interval
diff --git a/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8 b/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8
index d9490e20a3b6d..9e931f649914b 100644
--- a/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8
+++ b/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8
@@ -1,6 +1,9 @@
-dayofmonth(date) - Returns the date of the month of date
+dayofmonth(param) - Returns the day of the month of date/timestamp, or day component of interval
 Synonyms: day
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
-Example:
-   > SELECT dayofmonth('2009-30-07', 1) FROM src LIMIT 1;
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
+2. A date value
+3. A timestamp value
+4. A day-time interval valueExample:
+   > SELECT dayofmonth('2009-07-30') FROM src LIMIT 1;
   30
diff --git a/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c b/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c
+++ b/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a b/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a
+++ b/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c b/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c
+++ b/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a b/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a
+++ b/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee b/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee
index 231e4f382566d..06650592f8d3c 100644
--- a/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee
+++ b/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee
@@ -1 +1 @@
-minute(date) - Returns the minute of date
+minute(param) - Returns the minute component of the string/timestamp/interval
diff --git a/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48 b/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48
index ea842ea174ae4..08ddc19b84d82 100644
--- a/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48
+++ b/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48
@@ -1,6 +1,8 @@
-minute(date) - Returns the minute of date
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
-Example:
+minute(param) - Returns the minute component of the string/timestamp/interval
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
+2. A timestamp value
+3. A day-time interval valueExample:
    > SELECT minute('2009-07-30 12:58:59') FROM src LIMIT 1;
   58
   > SELECT minute('12:58:59') FROM src LIMIT 1;
diff --git a/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee b/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee
index 231e4f382566d..06650592f8d3c 100644
--- a/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee
+++ b/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee
@@ -1 +1 @@
-minute(date) - Returns the minute of date
+minute(param) - Returns the minute component of the string/timestamp/interval
diff --git a/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48 b/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48
index ea842ea174ae4..08ddc19b84d82 100644
--- a/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48
+++ b/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48
@@ -1,6 +1,8 @@
-minute(date) - Returns the minute of date
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
-Example:
+minute(param) - Returns the minute component of the string/timestamp/interval
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
+2. A timestamp value
+3. A day-time interval valueExample:
    > SELECT minute('2009-07-30 12:58:59') FROM src LIMIT 1;
   58
   > SELECT minute('12:58:59') FROM src LIMIT 1;
diff --git a/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027 b/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027
index d54ebfbd6fb1a..a529b107ff216 100644
--- a/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027
+++ b/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027
@@ -1,2 +1,2 @@
 std(x) - Returns the standard deviation of a set of numbers
-Synonyms: stddev_pop, stddev
+Synonyms: stddev, stddev_pop
diff --git a/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d b/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d
index 5f674788180e8..ac3176a382547 100644
--- a/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d
+++ b/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d
@@ -1,2 +1,2 @@
 stddev(x) - Returns the standard deviation of a set of numbers
-Synonyms: stddev_pop, std
+Synonyms: std, stddev_pop
diff --git a/sql/hive/src/test/resources/golden/union3-0-6a8a35102de1b0b88c6721a704eb174d b/sql/hive/src/test/resources/golden/union3-0-99620f72f0282904846a596ca5b3e46c
similarity index 100%
rename from sql/hive/src/test/resources/golden/union3-0-6a8a35102de1b0b88c6721a704eb174d
rename to sql/hive/src/test/resources/golden/union3-0-99620f72f0282904846a596ca5b3e46c
diff --git a/sql/hive/src/test/resources/golden/union3-2-2a1dcd937f117f1955a169592b96d5f9 b/sql/hive/src/test/resources/golden/union3-2-90ca96ea59fd45cf0af8c020ae77c908
similarity index 100%
rename from sql/hive/src/test/resources/golden/union3-2-2a1dcd937f117f1955a169592b96d5f9
rename to sql/hive/src/test/resources/golden/union3-2-90ca96ea59fd45cf0af8c020ae77c908
diff --git a/sql/hive/src/test/resources/golden/union3-3-8fc63f8edb2969a63cd4485f1867ba97 b/sql/hive/src/test/resources/golden/union3-3-72b149ccaef751bcfe55d5ca37cb5fd7
similarity index 100%
rename from sql/hive/src/test/resources/golden/union3-3-8fc63f8edb2969a63cd4485f1867ba97
rename to sql/hive/src/test/resources/golden/union3-3-72b149ccaef751bcfe55d5ca37cb5fd7
diff --git a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q
index 9e036c1a91d3b..e911fbf2d2c5c 100644
--- a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q
+++ b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q
@@ -5,6 +5,6 @@ SELECT * FROM (SELECT key, value FROM src DISTRIBUTE BY key, value)t ORDER BY ke
 
 
 SELECT key, value FROM src CLUSTER BY (key, value);
-SELECT key, value FROM src ORDER BY (key ASC, value ASC);
+SELECT key, value FROM src ORDER BY key ASC, value ASC;
 SELECT key, value FROM src SORT BY (key, value);
 SELECT * FROM (SELECT key, value FROM src DISTRIBUTE BY (key, value))t ORDER BY key, value;
diff --git a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q
index b26a2e2799f7a..a989800cbf851 100644
--- a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q
+++ b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q
@@ -1,42 +1,41 @@
+-- SORT_QUERY_RESULTS
 
 explain
 SELECT *
 FROM (
   SELECT 1 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 2 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 3 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
   UNION ALL
   SELECT 4 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
+  CLUSTER BY id
 ) a;
 
 
 
 CREATE TABLE union_out (id int);
 
-insert overwrite table union_out 
+insert overwrite table union_out
 SELECT *
 FROM (
   SELECT 1 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 2 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 3 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
   UNION ALL
   SELECT 4 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
+  CLUSTER BY id
 ) a;
 
-select * from union_out cluster by id;
+select * from union_out;
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala
new file mode 100644
index 0000000000000..34b2edb44b033
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala
@@ -0,0 +1,110 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import java.net.URL
+
+import org.apache.spark.SparkFunSuite
+
+/**
+ * Verify that some classes load and that others are not found on the classpath.
+ *
+ *
+ * This is used to detect classpath and shading conflict, especially between
+ * Spark's required Kryo version and that which can be found in some Hive versions.
+ */
+class ClasspathDependenciesSuite extends SparkFunSuite {
+  private val classloader = this.getClass.getClassLoader
+
+  private def assertLoads(classname: String): Unit = {
+    val resourceURL: URL = Option(findResource(classname)).getOrElse {
+      fail(s"Class $classname not found as ${resourceName(classname)}")
+    }
+
+    logInfo(s"Class $classname at $resourceURL")
+    classloader.loadClass(classname)
+  }
+
+  private def assertLoads(classes: String*): Unit = {
+    classes.foreach(assertLoads)
+  }
+
+  private def findResource(classname: String): URL = {
+    val resource = resourceName(classname)
+    classloader.getResource(resource)
+  }
+
+  private def resourceName(classname: String): String = {
+    classname.replace(".", "/") + ".class"
+  }
+
+  private def assertClassNotFound(classname: String): Unit = {
+    Option(findResource(classname)).foreach { resourceURL =>
+      fail(s"Class $classname found at $resourceURL")
+    }
+
+    intercept[ClassNotFoundException] {
+      classloader.loadClass(classname)
+    }
+  }
+
+  private def assertClassNotFound(classes: String*): Unit = {
+    classes.foreach(assertClassNotFound)
+  }
+
+  private val KRYO = "com.esotericsoftware.kryo.Kryo"
+
+  private val SPARK_HIVE = "org.apache.hive."
+  private val SPARK_SHADED = "org.spark-project.hive.shaded."
+
+  test("shaded Protobuf") {
+    assertLoads(SPARK_SHADED + "com.google.protobuf.ServiceException")
+  }
+
+  test("hive-common") {
+    assertLoads("org.apache.hadoop.hive.conf.HiveConf")
+  }
+
+  test("hive-exec") {
+    assertLoads("org.apache.hadoop.hive.ql.CommandNeedRetryException")
+  }
+
+  private val STD_INSTANTIATOR = "org.objenesis.strategy.StdInstantiatorStrategy"
+
+  test("unshaded kryo") {
+    assertLoads(KRYO, STD_INSTANTIATOR)
+  }
+
+  test("Forbidden Dependencies") {
+    assertClassNotFound(
+      SPARK_HIVE + KRYO,
+      SPARK_SHADED + KRYO,
+      "org.apache.hive." + KRYO,
+      "com.esotericsoftware.shaded." + STD_INSTANTIATOR,
+      SPARK_HIVE + "com.esotericsoftware.shaded." + STD_INSTANTIATOR,
+      "org.apache.hive.com.esotericsoftware.shaded." + STD_INSTANTIATOR
+    )
+  }
+
+  test("parquet-hadoop-bundle") {
+    assertLoads(
+      "parquet.hadoop.ParquetOutputFormat",
+      "parquet.hadoop.ParquetInputFormat"
+    )
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 72b35959a491b..b8d41065d3f02 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -19,8 +19,11 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
+import scala.collection.mutable.ArrayBuffer
 import scala.sys.process.{ProcessLogger, Process}
 
+import org.scalatest.exceptions.TestFailedDueToTimeoutException
+
 import org.apache.spark._
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
 import org.apache.spark.util.{ResetSystemProperties, Utils}
@@ -84,23 +87,39 @@ class HiveSparkSubmitSuite
   // This is copied from org.apache.spark.deploy.SparkSubmitSuite
   private def runSparkSubmit(args: Seq[String]): Unit = {
     val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
+    val history = ArrayBuffer.empty[String]
+    val commands = Seq("./bin/spark-submit") ++ args
+    val commandLine = commands.mkString("'", "' '", "'")
     val process = Process(
-      Seq("./bin/spark-submit") ++ args,
+      commands,
       new File(sparkHome),
       "SPARK_TESTING" -> "1",
       "SPARK_HOME" -> sparkHome
     ).run(ProcessLogger(
       // scalastyle:off println
-      (line: String) => { println(s"out> $line") },
-      (line: String) => { println(s"err> $line") }
+      (line: String) => { println(s"stdout> $line"); history += s"out> $line"},
+      (line: String) => { println(s"stderr> $line"); history += s"err> $line" }
       // scalastyle:on println
     ))
 
     try {
-      val exitCode = failAfter(180 seconds) { process.exitValue() }
+      val exitCode = failAfter(180.seconds) { process.exitValue() }
       if (exitCode != 0) {
-        fail(s"Process returned with exit code $exitCode. See the log4j logs for more detail.")
+        // include logs in output. Note that logging is async and may not have completed
+        // at the time this exception is raised
+        Thread.sleep(1000)
+        val historyLog = history.mkString("\n")
+        fail(s"$commandLine returned with exit code $exitCode." +
+            s" See the log4j logs for more detail." +
+            s"\n$historyLog")
       }
+    } catch {
+      case to: TestFailedDueToTimeoutException =>
+        val historyLog = history.mkString("\n")
+        fail(s"Timeout of $commandLine" +
+            s" See the log4j logs for more detail." +
+            s"\n$historyLog", to)
+        case t: Throwable => throw t
     } finally {
       // Ensure we still kill the process in case it timed out
       process.destroy()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
index 508695919e9a7..d33e81227db88 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
+import org.apache.hadoop.hive.conf.HiveConf
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.execution.QueryExecutionException
@@ -113,6 +114,8 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("SPARK-4203:random partition directory order") {
     sql("CREATE TABLE tmp_table (key int, value string)")
     val tmpDir = Utils.createTempDir()
+    val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
+
     sql(
       s"""
          |CREATE TABLE table_with_partition(c1 string)
@@ -145,7 +148,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
       """.stripMargin)
     def listFolders(path: File, acc: List[String]): List[List[String]] = {
       val dir = path.listFiles()
-      val folders = dir.filter(_.isDirectory).toList
+      val folders = dir.filter { e => e.isDirectory && !e.getName().startsWith(stagingDir) }.toList
       if (folders.isEmpty) {
         List(acc.reverse)
       } else {
@@ -158,7 +161,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
       "p1=a"::"p2=b"::"p3=c"::"p4=c"::"p5=1"::Nil ,
       "p1=a"::"p2=b"::"p3=c"::"p4=c"::"p5=4"::Nil
     )
-    assert(listFolders(tmpDir, List()).sortBy(_.toString()) == expected.sortBy(_.toString))
+    assert(listFolders(tmpDir, List()).sortBy(_.toString()) === expected.sortBy(_.toString))
     sql("DROP TABLE table_with_partition")
     sql("DROP TABLE tmp_table")
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index bb5f1febe9ad4..f00d3754c364a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import org.apache.hadoop.hive.conf.HiveConf
+
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.parquet.ParquetCompatibilityTest
 import org.apache.spark.sql.{Row, SQLConf, SQLContext}
@@ -26,6 +28,13 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
 
   override val sqlContext: SQLContext = TestHive
 
+  /**
+   * Set the staging directory (and hence path to ignore Parquet files under)
+   * to that set by [[HiveConf.ConfVars.STAGINGDIR]].
+   */
+  override val stagingDir: Option[String] =
+    Some(new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR))
+
   override protected def beforeAll(): Unit = {
     super.beforeAll()
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
index bc72b0172a467..e4fec7e2c8a2a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -54,6 +54,9 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
       }
     }
 
+    // Ensure session state is initialized.
+    ctx.parseSql("use default")
+
     assertAnalyzeCommand(
       "ANALYZE TABLE Table1 COMPUTE STATISTICS",
       classOf[HiveNativeCommand])
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index 3eb127e23d486..f0bb77092c0cf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.hive.client
 
 import java.io.File
 
+import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
@@ -48,7 +49,9 @@ class VersionsSuite extends SparkFunSuite with Logging {
   }
 
   test("success sanity check") {
-    val badClient = IsolatedClientLoader.forVersion("13", buildConf(), ivyPath).client
+    val badClient = IsolatedClientLoader.forVersion(HiveContext.hiveExecutionVersion,
+      buildConf(),
+      ivyPath).client
     val db = new HiveDatabase("default", "")
     badClient.createDatabase(db)
   }
@@ -91,6 +94,7 @@ class VersionsSuite extends SparkFunSuite with Logging {
   versions.foreach { version =>
     test(s"$version: create client") {
       client = null
+      System.gc() // Hack to avoid SEGV on some JVM versions.
       client = IsolatedClientLoader.forVersion(version, buildConf(), ivyPath).client
     }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 11a843becce69..a7cfac51cc097 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -52,14 +52,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
     // Add Locale setting
     Locale.setDefault(Locale.US)
-    sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
-    // The function source code can be found at:
-    // https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF
-    sql(
-      """
-        |CREATE TEMPORARY FUNCTION udtf_count2
-        |AS 'org.apache.spark.sql.hive.execution.GenericUDTFCount2'
-      """.stripMargin)
   }
 
   override def afterAll() {
@@ -69,15 +61,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     sql("DROP TEMPORARY FUNCTION udtf_count2")
   }
 
-  createQueryTest("Test UDTF.close in Lateral Views",
-     """
-       |SELECT key, cc
-       |FROM src LATERAL VIEW udtf_count2(value) dd AS cc
-     """.stripMargin, false) // false mean we have to keep the temp function in registry
-
-  createQueryTest("Test UDTF.close in SELECT",
-     "SELECT udtf_count2(a) FROM (SELECT 1 AS a FROM src LIMIT 3) table", false)
-
   test("SPARK-4908: concurrent hive native commands") {
     (1 to 100).par.map { _ =>
       sql("USE default")
@@ -176,8 +159,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
   createQueryTest("! operator",
     """
       |SELECT a FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 2 AS a FROM src LIMIT 1) table
+      |  SELECT 1 AS a UNION ALL SELECT 2 AS a) t
       |WHERE !(a>1)
     """.stripMargin)
 
@@ -229,71 +211,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     |FROM src LIMIT 1;
   """.stripMargin)
 
-  createQueryTest("count distinct 0 values",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 'a' AS a FROM src LIMIT 0) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value strings",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 'a' AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 'b' AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1 AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 2 values",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 2 AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 2 values including null",
-    """
-      |SELECT COUNT(DISTINCT a, 1) FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT null AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value + null",
-  """
-    |SELECT COUNT(DISTINCT a) FROM (
-    |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-    |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-    |  SELECT null AS a FROM src LIMIT 1) table
-  """.stripMargin)
-
-  createQueryTest("count distinct 1 value long",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1L AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 2 values long",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 2L AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value + null long",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT null AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
   createQueryTest("null case",
     "SELECT case when(true) then 1 else null end FROM src LIMIT 1")
 
@@ -674,7 +591,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     sql(
       """
         |SELECT a FROM (
-        |  SELECT 1 AS a FROM src LIMIT 1 ) table
+        |  SELECT 1 AS a FROM src LIMIT 1 ) t
         |WHERE abs(20141202) is not null
       """.stripMargin).collect()
   }
@@ -987,7 +904,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
         .zip(parts)
         .map { case (k, v) =>
           if (v == "NULL") {
-            s"$k=${ConfVars.DEFAULTPARTITIONNAME.defaultVal}"
+            s"$k=${ConfVars.DEFAULTPARTITIONNAME.defaultStrVal}"
           } else {
             s"$k=$v"
           }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index e83a7dc77e329..3bf8f3ac20480 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -82,16 +82,16 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
     Seq.empty)
 
   createPruningTest("Column pruning - non-trivial top project with aliases",
-    "SELECT c1 * 2 AS double FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3",
-    Seq("double"),
+    "SELECT c1 * 2 AS dbl FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3",
+    Seq("dbl"),
     Seq("key"),
     Seq.empty)
 
   // Partition pruning tests
 
   createPruningTest("Partition pruning - non-partitioned, non-trivial project",
-    "SELECT key * 2 AS double FROM src WHERE value IS NOT NULL",
-    Seq("double"),
+    "SELECT key * 2 AS dbl FROM src WHERE value IS NOT NULL",
+    Seq("dbl"),
     Seq("key", "value"),
     Seq.empty)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index c4923d83e48f3..95c1da6e9796c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -67,6 +67,25 @@ class MyDialect extends DefaultParserDialect
 class SQLQuerySuite extends QueryTest with SQLTestUtils {
   override def sqlContext: SQLContext = TestHive
 
+  test("UDTF") {
+    sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
+    // The function source code can be found at:
+    // https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF
+    sql(
+      """
+        |CREATE TEMPORARY FUNCTION udtf_count2
+        |AS 'org.apache.spark.sql.hive.execution.GenericUDTFCount2'
+      """.stripMargin)
+
+    checkAnswer(
+      sql("SELECT key, cc FROM src LATERAL VIEW udtf_count2(value) dd AS cc"),
+      Row(97, 500) :: Row(97, 500) :: Nil)
+
+    checkAnswer(
+      sql("SELECT udtf_count2(a) FROM (SELECT 1 AS a FROM src LIMIT 3) t"),
+      Row(3) :: Row(3) :: Nil)
+  }
+
   test("SPARK-6835: udtf in lateral view") {
     val df = Seq((1, 1)).toDF("c1", "c2")
     df.registerTempTable("table1")
@@ -264,47 +283,51 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
     setConf(HiveContext.CONVERT_CTAS, true)
 
-    sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    var message = intercept[AnalysisException] {
+    try {
       sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    }.getMessage
-    assert(message.contains("ctas1 already exists"))
-    checkRelation("ctas1", true)
-    sql("DROP TABLE ctas1")
-
-    // Specifying database name for query can be converted to data source write path
-    // is not allowed right now.
-    message = intercept[AnalysisException] {
-      sql("CREATE TABLE default.ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    }.getMessage
-    assert(
-      message.contains("Cannot specify database name in a CTAS statement"),
-      "When spark.sql.hive.convertCTAS is true, we should not allow " +
-      "database name specified.")
-
-    sql("CREATE TABLE ctas1 stored as textfile AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", true)
-    sql("DROP TABLE ctas1")
-
-    sql(
-      "CREATE TABLE ctas1 stored as sequencefile AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", true)
-    sql("DROP TABLE ctas1")
-
-    sql("CREATE TABLE ctas1 stored as rcfile AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", false)
-    sql("DROP TABLE ctas1")
-
-    sql("CREATE TABLE ctas1 stored as orc AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", false)
-    sql("DROP TABLE ctas1")
-
-    sql("CREATE TABLE ctas1 stored as parquet AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", false)
-    sql("DROP TABLE ctas1")
-
-    setConf(HiveContext.CONVERT_CTAS, originalConf)
+      sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
+      var message = intercept[AnalysisException] {
+        sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
+      }.getMessage
+      assert(message.contains("ctas1 already exists"))
+      checkRelation("ctas1", true)
+      sql("DROP TABLE ctas1")
+
+      // Specifying database name for query can be converted to data source write path
+      // is not allowed right now.
+      message = intercept[AnalysisException] {
+        sql("CREATE TABLE default.ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
+      }.getMessage
+      assert(
+        message.contains("Cannot specify database name in a CTAS statement"),
+        "When spark.sql.hive.convertCTAS is true, we should not allow " +
+            "database name specified.")
+
+      sql("CREATE TABLE ctas1 stored as textfile" +
+          " AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", true)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as sequencefile" +
+            " AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", true)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as rcfile AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", false)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as orc AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", false)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as parquet AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", false)
+      sql("DROP TABLE ctas1")
+    } finally {
+      setConf(HiveContext.CONVERT_CTAS, originalConf)
+      sql("DROP TABLE IF EXISTS ctas1")
+    }
   }
 
   test("SQL Dialect Switching") {
@@ -670,22 +693,25 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
     val originalConf = convertCTAS
     setConf(HiveContext.CONVERT_CTAS, false)
 
-    sql("CREATE TABLE explodeTest (key bigInt)")
-    table("explodeTest").queryExecution.analyzed match {
-      case metastoreRelation: MetastoreRelation => // OK
-      case _ =>
-        fail("To correctly test the fix of SPARK-5875, explodeTest should be a MetastoreRelation")
-    }
+    try {
+      sql("CREATE TABLE explodeTest (key bigInt)")
+      table("explodeTest").queryExecution.analyzed match {
+        case metastoreRelation: MetastoreRelation => // OK
+        case _ =>
+          fail("To correctly test the fix of SPARK-5875, explodeTest should be a MetastoreRelation")
+      }
 
-    sql(s"INSERT OVERWRITE TABLE explodeTest SELECT explode(a) AS val FROM data")
-    checkAnswer(
-      sql("SELECT key from explodeTest"),
-      (1 to 5).flatMap(i => Row(i) :: Row(i + 1) :: Nil)
-    )
+      sql(s"INSERT OVERWRITE TABLE explodeTest SELECT explode(a) AS val FROM data")
+      checkAnswer(
+        sql("SELECT key from explodeTest"),
+        (1 to 5).flatMap(i => Row(i) :: Row(i + 1) :: Nil)
+      )
 
-    sql("DROP TABLE explodeTest")
-    dropTempTable("data")
-    setConf(HiveContext.CONVERT_CTAS, originalConf)
+      sql("DROP TABLE explodeTest")
+      dropTempTable("data")
+    } finally {
+      setConf(HiveContext.CONVERT_CTAS, originalConf)
+    }
   }
 
   test("sanity test for SPARK-6618") {
@@ -1058,12 +1084,12 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   test("SPARK-8588 HiveTypeCoercion.inConversion fires too early") {
     val df =
       TestHive.createDataFrame(Seq((1, "2014-01-01"), (2, "2015-01-01"), (3, "2016-01-01")))
-    df.toDF("id", "date").registerTempTable("test_SPARK8588")
+    df.toDF("id", "datef").registerTempTable("test_SPARK8588")
     checkAnswer(
       TestHive.sql(
         """
-          |select id, concat(year(date))
-          |from test_SPARK8588 where concat(year(date), ' year') in ('2015 year', '2014 year')
+          |select id, concat(year(datef))
+          |from test_SPARK8588 where concat(year(datef), ' year') in ('2015 year', '2014 year')
         """.stripMargin),
       Row(1, "2014") :: Row(2, "2015") :: Nil
     )
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
index af3f468aaa5e9..deec0048d24b8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
@@ -48,11 +48,9 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        load(
-          source = dataSourceName,
-          options = Map(
-            "path" -> file.getCanonicalPath,
-            "dataSchema" -> dataSchemaWithPartition.json)))
+        read.options(Map(
+          "path" -> file.getCanonicalPath,
+          "dataSchema" -> dataSchemaWithPartition.json)).format(dataSourceName).load())
     }
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
index d463e8fd626f9..a46ca9a2c9706 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
@@ -31,7 +31,6 @@ import org.scalatest.BeforeAndAfterAll
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
-
 // The data where the partitioning key exists only in the directory structure.
 case class OrcParData(intField: Int, stringField: String)
 
@@ -40,7 +39,7 @@ case class OrcParDataWithKey(intField: Int, pi: Int, stringField: String, ps: St
 
 // TODO This test suite duplicates ParquetPartitionDiscoverySuite a lot
 class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
-  val defaultPartitionName = ConfVars.DEFAULTPARTITIONNAME.defaultVal
+  val defaultPartitionName = ConfVars.DEFAULTPARTITIONNAME.defaultStrVal
 
   def withTempDir(f: File => Unit): Unit = {
     val dir = Utils.createTempDir().getCanonicalFile
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index f56fb96c52d37..c4bc60086f6e1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -60,7 +60,14 @@ case class ParquetDataWithKeyAndComplexTypes(
 class ParquetMetastoreSuite extends ParquetPartitioningTest {
   override def beforeAll(): Unit = {
     super.beforeAll()
-
+    dropTables("partitioned_parquet",
+      "partitioned_parquet_with_key",
+      "partitioned_parquet_with_complextypes",
+      "partitioned_parquet_with_key_and_complextypes",
+      "normal_parquet",
+      "jt",
+      "jt_array",
+      "test_parquet")
     sql(s"""
       create external table partitioned_parquet
       (
@@ -172,14 +179,14 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
   }
 
   override def afterAll(): Unit = {
-    sql("DROP TABLE partitioned_parquet")
-    sql("DROP TABLE partitioned_parquet_with_key")
-    sql("DROP TABLE partitioned_parquet_with_complextypes")
-    sql("DROP TABLE partitioned_parquet_with_key_and_complextypes")
-    sql("DROP TABLE normal_parquet")
-    sql("DROP TABLE IF EXISTS jt")
-    sql("DROP TABLE IF EXISTS jt_array")
-    sql("DROP TABLE IF EXISTS test_parquet")
+    dropTables("partitioned_parquet",
+      "partitioned_parquet_with_key",
+      "partitioned_parquet_with_complextypes",
+      "partitioned_parquet_with_key_and_complextypes",
+      "normal_parquet",
+      "jt",
+      "jt_array",
+       "test_parquet")
     setConf(HiveContext.CONVERT_METASTORE_PARQUET, false)
   }
 
@@ -203,6 +210,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
   }
 
   test("insert into an empty parquet table") {
+    dropTables("test_insert_parquet")
     sql(
       """
         |create table test_insert_parquet
@@ -228,7 +236,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
       sql(s"SELECT intField, stringField FROM test_insert_parquet WHERE intField > 2"),
       Row(3, "str3") :: Row(4, "str4") :: Nil
     )
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
+    dropTables("test_insert_parquet")
 
     // Create it again.
     sql(
@@ -255,118 +263,118 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
       sql(s"SELECT intField, stringField FROM test_insert_parquet"),
       (1 to 10).map(i => Row(i, s"str$i")) ++ (1 to 4).map(i => Row(i, s"str$i"))
     )
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
+    dropTables("test_insert_parquet")
   }
 
   test("scan a parquet table created through a CTAS statement") {
-    sql(
-      """
-        |create table test_parquet_ctas ROW FORMAT
-        |SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-        |AS select * from jt
-      """.stripMargin)
+    withTable("test_parquet_ctas") {
+      sql(
+        """
+          |create table test_parquet_ctas ROW FORMAT
+          |SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+          |AS select * from jt
+        """.stripMargin)
 
-    checkAnswer(
-      sql(s"SELECT a, b FROM test_parquet_ctas WHERE a = 1"),
-      Seq(Row(1, "str1"))
-    )
+      checkAnswer(
+        sql(s"SELECT a, b FROM test_parquet_ctas WHERE a = 1"),
+        Seq(Row(1, "str1"))
+      )
 
-    table("test_parquet_ctas").queryExecution.optimizedPlan match {
-      case LogicalRelation(_: ParquetRelation) => // OK
-      case _ => fail(
-        "test_parquet_ctas should be converted to " +
-          s"${classOf[ParquetRelation].getCanonicalName}")
+      table("test_parquet_ctas").queryExecution.optimizedPlan match {
+        case LogicalRelation(_: ParquetRelation) => // OK
+        case _ => fail(
+          "test_parquet_ctas should be converted to " +
+              s"${classOf[ParquetRelation].getCanonicalName }")
+      }
     }
-
-    sql("DROP TABLE IF EXISTS test_parquet_ctas")
   }
 
   test("MetastoreRelation in InsertIntoTable will be converted") {
-    sql(
-      """
-        |create table test_insert_parquet
-        |(
-        |  intField INT
-        |)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
+    withTable("test_insert_parquet") {
+      sql(
+        """
+          |create table test_insert_parquet
+          |(
+          |  intField INT
+          |)
+          |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+        """.stripMargin)
+
+      val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt")
+      df.queryExecution.executedPlan match {
+        case ExecutedCommand(InsertIntoHadoopFsRelation(_: ParquetRelation, _, _)) => // OK
+        case o => fail("test_insert_parquet should be converted to a " +
+          s"${classOf[ParquetRelation].getCanonicalName} and " +
+          s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan. " +
+          s"However, found a ${o.toString} ")
+      }
 
-    val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt")
-    df.queryExecution.executedPlan match {
-      case ExecutedCommand(InsertIntoHadoopFsRelation(_: ParquetRelation, _, _)) => // OK
-      case o => fail("test_insert_parquet should be converted to a " +
-        s"${classOf[ParquetRelation].getCanonicalName} and " +
-        s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan. " +
-        s"However, found a ${o.toString} ")
+      checkAnswer(
+        sql("SELECT intField FROM test_insert_parquet WHERE test_insert_parquet.intField > 5"),
+        sql("SELECT a FROM jt WHERE jt.a > 5").collect()
+      )
     }
-
-    checkAnswer(
-      sql("SELECT intField FROM test_insert_parquet WHERE test_insert_parquet.intField > 5"),
-      sql("SELECT a FROM jt WHERE jt.a > 5").collect()
-    )
-
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
   }
 
   test("MetastoreRelation in InsertIntoHiveTable will be converted") {
-    sql(
-      """
-        |create table test_insert_parquet
-        |(
-        |  int_array array<int>
-        |)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
+    withTable("test_insert_parquet") {
+      sql(
+        """
+          |create table test_insert_parquet
+          |(
+          |  int_array array<int>
+          |)
+          |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+        """.stripMargin)
+
+      val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array")
+      df.queryExecution.executedPlan match {
+        case ExecutedCommand(InsertIntoHadoopFsRelation(r: ParquetRelation, _, _)) => // OK
+        case o => fail("test_insert_parquet should be converted to a " +
+          s"${classOf[ParquetRelation].getCanonicalName} and " +
+          s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." +
+          s"However, found a ${o.toString} ")
+      }
 
-    val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array")
-    df.queryExecution.executedPlan match {
-      case ExecutedCommand(InsertIntoHadoopFsRelation(r: ParquetRelation, _, _)) => // OK
-      case o => fail("test_insert_parquet should be converted to a " +
-        s"${classOf[ParquetRelation].getCanonicalName} and " +
-        s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." +
-        s"However, found a ${o.toString} ")
+      checkAnswer(
+        sql("SELECT int_array FROM test_insert_parquet"),
+        sql("SELECT a FROM jt_array").collect()
+      )
     }
-
-    checkAnswer(
-      sql("SELECT int_array FROM test_insert_parquet"),
-      sql("SELECT a FROM jt_array").collect()
-    )
-
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
   }
 
   test("SPARK-6450 regression test") {
-    sql(
-      """CREATE TABLE IF NOT EXISTS ms_convert (key INT)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
+    withTable("ms_convert") {
+      sql(
+        """CREATE TABLE IF NOT EXISTS ms_convert (key INT)
+          |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+        """.stripMargin)
+
+      // This shouldn't throw AnalysisException
+      val analyzed = sql(
+        """SELECT key FROM ms_convert
+          |UNION ALL
+          |SELECT key FROM ms_convert
+        """.stripMargin).queryExecution.analyzed
 
-    // This shouldn't throw AnalysisException
-    val analyzed = sql(
-      """SELECT key FROM ms_convert
-        |UNION ALL
-        |SELECT key FROM ms_convert
-      """.stripMargin).queryExecution.analyzed
-
-    assertResult(2) {
-      analyzed.collect {
-        case r @ LogicalRelation(_: ParquetRelation) => r
-      }.size
+      assertResult(2) {
+        analyzed.collect {
+          case r@LogicalRelation(_: ParquetRelation) => r
+        }.size
+      }
     }
-
-    sql("DROP TABLE ms_convert")
   }
 
   def collectParquetRelation(df: DataFrame): ParquetRelation = {
@@ -379,42 +387,42 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
   }
 
   test("SPARK-7749: non-partitioned metastore Parquet table lookup should use cached relation") {
-    sql(
-      s"""CREATE TABLE nonPartitioned (
-         |  key INT,
-         |  value STRING
-         |)
-         |STORED AS PARQUET
-       """.stripMargin)
-
-    // First lookup fills the cache
-    val r1 = collectParquetRelation(table("nonPartitioned"))
-    // Second lookup should reuse the cache
-    val r2 = collectParquetRelation(table("nonPartitioned"))
-    // They should be the same instance
-    assert(r1 eq r2)
-
-    sql("DROP TABLE nonPartitioned")
+    withTable("nonPartitioned") {
+      sql(
+        s"""CREATE TABLE nonPartitioned (
+           |  key INT,
+           |  value STRING
+           |)
+           |STORED AS PARQUET
+         """.stripMargin)
+
+      // First lookup fills the cache
+      val r1 = collectParquetRelation(table("nonPartitioned"))
+      // Second lookup should reuse the cache
+      val r2 = collectParquetRelation(table("nonPartitioned"))
+      // They should be the same instance
+      assert(r1 eq r2)
+    }
   }
 
   test("SPARK-7749: partitioned metastore Parquet table lookup should use cached relation") {
-    sql(
-      s"""CREATE TABLE partitioned (
-         |  key INT,
-         |  value STRING
-         |)
-         |PARTITIONED BY (part INT)
-         |STORED AS PARQUET
+    withTable("partitioned") {
+      sql(
+        s"""CREATE TABLE partitioned (
+           | key INT,
+           | value STRING
+           |)
+           |PARTITIONED BY (part INT)
+           |STORED AS PARQUET
        """.stripMargin)
 
-    // First lookup fills the cache
-    val r1 = collectParquetRelation(table("partitioned"))
-    // Second lookup should reuse the cache
-    val r2 = collectParquetRelation(table("partitioned"))
-    // They should be the same instance
-    assert(r1 eq r2)
-
-    sql("DROP TABLE partitioned")
+      // First lookup fills the cache
+      val r1 = collectParquetRelation(table("partitioned"))
+      // Second lookup should reuse the cache
+      val r2 = collectParquetRelation(table("partitioned"))
+      // They should be the same instance
+      assert(r1 eq r2)
+    }
   }
 
   test("Caching converted data source Parquet Relations") {
@@ -430,8 +438,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
       }
     }
 
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
-    sql("DROP TABLE IF EXISTS test_parquet_partitioned_cache_test")
+    dropTables("test_insert_parquet", "test_parquet_partitioned_cache_test")
 
     sql(
       """
@@ -479,7 +486,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
         |  intField INT,
         |  stringField STRING
         |)
-        |PARTITIONED BY (date string)
+        |PARTITIONED BY (`date` string)
         |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
         |STORED AS
         |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
@@ -491,7 +498,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     sql(
       """
         |INSERT INTO TABLE test_parquet_partitioned_cache_test
-        |PARTITION (date='2015-04-01')
+        |PARTITION (`date`='2015-04-01')
         |select a, b from jt
       """.stripMargin)
     // Right now, insert into a partitioned Parquet is not supported in data source Parquet.
@@ -500,7 +507,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     sql(
       """
         |INSERT INTO TABLE test_parquet_partitioned_cache_test
-        |PARTITION (date='2015-04-02')
+        |PARTITION (`date`='2015-04-02')
         |select a, b from jt
       """.stripMargin)
     assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null)
@@ -510,7 +517,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     checkCached(tableIdentifier)
     // Make sure we can read the data.
     checkAnswer(
-      sql("select STRINGField, date, intField from test_parquet_partitioned_cache_test"),
+      sql("select STRINGField, `date`, intField from test_parquet_partitioned_cache_test"),
       sql(
         """
           |select b, '2015-04-01', a FROM jt
@@ -521,8 +528,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     invalidateTable("test_parquet_partitioned_cache_test")
     assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null)
 
-    sql("DROP TABLE test_insert_parquet")
-    sql("DROP TABLE test_parquet_partitioned_cache_test")
+    dropTables("test_insert_parquet", "test_parquet_partitioned_cache_test")
   }
 }
 
@@ -532,6 +538,11 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
 class ParquetSourceSuite extends ParquetPartitioningTest {
   override def beforeAll(): Unit = {
     super.beforeAll()
+    dropTables("partitioned_parquet",
+      "partitioned_parquet_with_key",
+      "partitioned_parquet_with_complextypes",
+      "partitioned_parquet_with_key_and_complextypes",
+      "normal_parquet")
 
     sql( s"""
       create temporary table partitioned_parquet
@@ -635,22 +646,22 @@ class ParquetSourceSuite extends ParquetPartitioningTest {
           StructField("a", arrayType1, nullable = true) :: Nil)
     assert(df.schema === expectedSchema1)
 
-    df.write.format("parquet").saveAsTable("alwaysNullable")
+    withTable("alwaysNullable") {
+      df.write.format("parquet").saveAsTable("alwaysNullable")
 
-    val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = true)
-    val arrayType2 = ArrayType(IntegerType, containsNull = true)
-    val expectedSchema2 =
-      StructType(
-        StructField("m", mapType2, nullable = true) ::
-          StructField("a", arrayType2, nullable = true) :: Nil)
+      val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = true)
+      val arrayType2 = ArrayType(IntegerType, containsNull = true)
+      val expectedSchema2 =
+        StructType(
+          StructField("m", mapType2, nullable = true) ::
+              StructField("a", arrayType2, nullable = true) :: Nil)
 
-    assert(table("alwaysNullable").schema === expectedSchema2)
-
-    checkAnswer(
-      sql("SELECT m, a FROM alwaysNullable"),
-      Row(Map(2 -> 3), Seq(4, 5, 6)))
+      assert(table("alwaysNullable").schema === expectedSchema2)
 
-    sql("DROP TABLE alwaysNullable")
+      checkAnswer(
+        sql("SELECT m, a FROM alwaysNullable"),
+        Row(Map(2 -> 3), Seq(4, 5, 6)))
+    }
   }
 
   test("Aggregation attribute names can't contain special chars \" ,;{}()\\n\\t=\"") {
@@ -738,6 +749,16 @@ abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with
     partitionedTableDirWithKeyAndComplexTypes.delete()
   }
 
+  /**
+   * Drop named tables if they exist
+   * @param tableNames tables to drop
+   */
+  def dropTables(tableNames: String*): Unit = {
+    tableNames.foreach { name =>
+      sql(s"DROP TABLE IF EXISTS $name")
+    }
+  }
+
   Seq(
     "partitioned_parquet",
     "partitioned_parquet_with_key",
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 2aeed98285aa8..49360c48256ea 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -30,7 +30,6 @@
   <name>Spark Project YARN</name>
   <properties>
     <sbt.project.name>yarn</sbt.project.name>
-    <jersey.version>1.9</jersey.version>
   </properties>
 
   <dependencies>
@@ -125,25 +124,16 @@
      <dependency>
        <groupId>com.sun.jersey</groupId>
        <artifactId>jersey-core</artifactId>
-       <version>${jersey.version}</version>
        <scope>test</scope>
      </dependency>
      <dependency>
        <groupId>com.sun.jersey</groupId>
        <artifactId>jersey-json</artifactId>
-       <version>${jersey.version}</version>
        <scope>test</scope>
-       <exclusions>
-        <exclusion>
-          <groupId>stax</groupId>
-          <artifactId>stax-api</artifactId>
-        </exclusion>
-       </exclusions>
      </dependency>
      <dependency>
        <groupId>com.sun.jersey</groupId>
        <artifactId>jersey-server</artifactId>
-       <version>${jersey.version}</version>
        <scope>test</scope>
      </dependency>
   </dependencies>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 547863d9a0739..eb6e1fd370620 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -384,19 +384,29 @@ private object YarnClusterDriver extends Logging with Matchers {
 
 }
 
-private object YarnClasspathTest {
+private object YarnClasspathTest extends Logging {
+
+  var exitCode = 0
+
+  def error(m: String, ex: Throwable = null): Unit = {
+    logError(m, ex)
+    // scalastyle:off println
+    System.out.println(m)
+    if (ex != null) {
+      ex.printStackTrace(System.out)
+    }
+    // scalastyle:on println
+  }
 
   def main(args: Array[String]): Unit = {
     if (args.length != 2) {
-      // scalastyle:off println
-      System.err.println(
+      error(
         s"""
         |Invalid command line: ${args.mkString(" ")}
         |
         |Usage: YarnClasspathTest [driver result file] [executor result file]
         """.stripMargin)
       // scalastyle:on println
-      System.exit(1)
     }
 
     readResource(args(0))
@@ -406,6 +416,7 @@ private object YarnClasspathTest {
     } finally {
       sc.stop()
     }
+    System.exit(exitCode)
   }
 
   private def readResource(resultPath: String): Unit = {
@@ -415,6 +426,11 @@ private object YarnClasspathTest {
       val resource = ccl.getResourceAsStream("test.resource")
       val bytes = ByteStreams.toByteArray(resource)
       result = new String(bytes, 0, bytes.length, UTF_8)
+    } catch {
+      case t: Throwable =>
+        error(s"loading test.resource to $resultPath", t)
+        // set the exit code if not yet set
+        exitCode = 2
     } finally {
       Files.write(result, new File(resultPath), UTF_8)
     }

From 13675c742a71cbdc8324701c3694775ce1dd5c62 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Mon, 3 Aug 2015 16:44:25 -0700
Subject: [PATCH 0813/1454] [SPARK-8874] [ML] Add missing methods in Word2Vec

Add missing methods

1. getVectors
2. findSynonyms

to W2Vec scala and python API

mengxr

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7263 from MechCoder/missing_methods_w2vec and squashes the following commits:

149d5ca [MechCoder] minor doc
69d91b7 [MechCoder] [SPARK-8874] [ML] Add missing methods in Word2Vec
---
 .../apache/spark/ml/feature/Word2Vec.scala    | 38 +++++++++++-
 .../spark/ml/feature/Word2VecSuite.scala      | 62 +++++++++++++++++++
 2 files changed, 99 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 6ea6590956300..b4f46cef798dd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -18,15 +18,17 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.SparkContext
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.mllib.linalg.{VectorUDT, Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.types._
 
 /**
@@ -146,6 +148,40 @@ class Word2VecModel private[ml] (
     wordVectors: feature.Word2VecModel)
   extends Model[Word2VecModel] with Word2VecBase {
 
+
+  /**
+   * Returns a dataframe with two fields, "word" and "vector", with "word" being a String and
+   * and the vector the DenseVector that it is mapped to.
+   */
+  val getVectors: DataFrame = {
+    val sc = SparkContext.getOrCreate()
+    val sqlContext = SQLContext.getOrCreate(sc)
+    import sqlContext.implicits._
+    val wordVec = wordVectors.getVectors.mapValues(vec => Vectors.dense(vec.map(_.toDouble)))
+    sc.parallelize(wordVec.toSeq).toDF("word", "vector")
+  }
+
+  /**
+   * Find "num" number of words closest in similarity to the given word.
+   * Returns a dataframe with the words and the cosine similarities between the
+   * synonyms and the given word.
+   */
+  def findSynonyms(word: String, num: Int): DataFrame = {
+    findSynonyms(wordVectors.transform(word), num)
+  }
+
+  /**
+   * Find "num" number of words closest to similarity to the given vector representation
+   * of the word. Returns a dataframe with the words and the cosine similarities between the
+   * synonyms and the given word vector.
+   */
+  def findSynonyms(word: Vector, num: Int): DataFrame = {
+    val sc = SparkContext.getOrCreate()
+    val sqlContext = SQLContext.getOrCreate(sc)
+    import sqlContext.implicits._
+    sc.parallelize(wordVectors.findSynonyms(word, num)).toDF("word", "similarity")
+  }
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
index aa6ce533fd885..adcda0e623b25 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
@@ -67,5 +67,67 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(vector1 ~== vector2 absTol 1E-5, "Transformed vector is different with expected.")
     }
   }
+
+  test("getVectors") {
+
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    val sentence = "a b " * 100 + "a c " * 10
+    val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
+
+    val codes = Map(
+      "a" -> Array(-0.2811822295188904, -0.6356269121170044, -0.3020961284637451),
+      "b" -> Array(1.0309048891067505, -1.29472815990448, 0.22276712954044342),
+      "c" -> Array(-0.08456747233867645, 0.5137411952018738, 0.11731560528278351)
+    )
+    val expectedVectors = codes.toSeq.sortBy(_._1).map { case (w, v) => Vectors.dense(v) }
+
+    val docDF = doc.zip(doc).toDF("text", "alsotext")
+
+    val model = new Word2Vec()
+      .setVectorSize(3)
+      .setInputCol("text")
+      .setOutputCol("result")
+      .setSeed(42L)
+      .fit(docDF)
+
+    val realVectors = model.getVectors.sort("word").select("vector").map {
+      case Row(v: Vector) => v
+    }.collect()
+
+    realVectors.zip(expectedVectors).foreach {
+      case (real, expected) =>
+        assert(real ~== expected absTol 1E-5, "Actual vector is different from expected.")
+    }
+  }
+
+  test("findSynonyms") {
+
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    val sentence = "a b " * 100 + "a c " * 10
+    val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
+    val docDF = doc.zip(doc).toDF("text", "alsotext")
+
+    val model = new Word2Vec()
+      .setVectorSize(3)
+      .setInputCol("text")
+      .setOutputCol("result")
+      .setSeed(42L)
+      .fit(docDF)
+
+    val expectedSimilarity = Array(0.2789285076917586, -0.6336972059851644)
+    val (synonyms, similarity) = model.findSynonyms("a", 2).map {
+      case Row(w: String, sim: Double) => (w, sim)
+    }.collect().unzip
+
+    assert(synonyms.toArray === Array("b", "c"))
+    expectedSimilarity.zip(similarity).map {
+      case (expected, actual) => assert(math.abs((expected - actual) / expected) < 1E-5)
+    }
+
+  }
 }
 

From 7abaaad5b169520fbf7299808b2bafde089a16a2 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Mon, 3 Aug 2015 17:00:59 -0700
Subject: [PATCH 0814/1454] Add a prerequisites section for building docs

This puts all the install commands that need to be run in one section instead of being spread over many paragraphs

cc rxin

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7912 from shivaram/docs-setup-readme and squashes the following commits:

cf7a204 [Shivaram Venkataraman] Add a prerequisites section for building docs
---
 docs/README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/docs/README.md b/docs/README.md
index d7652e921f7df..50209896f986c 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -8,6 +8,16 @@ Read on to learn more about viewing documentation in plain text (i.e., markdown)
 documentation yourself. Why build it yourself? So that you have the docs that corresponds to
 whichever version of Spark you currently have checked out of revision control.
 
+## Prerequisites
+The Spark documenation build uses a number of tools to build HTML docs and API docs in Scala, Python
+and R. To get started you can run the following commands
+
+    $ sudo gem install jekyll
+    $ sudo gem install jekyll-redirect-from
+    $ sudo pip install Pygments
+    $ Rscript -e 'install.packages(c("knitr", "devtools"), repos="http://cran.stat.ucla.edu/")'
+
+
 ## Generating the Documentation HTML
 
 We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as

From b79b4f5f2251ed7efeec1f4b26e45a8ea6b85a6a Mon Sep 17 00:00:00 2001
From: Matthew Brandyberry <mbrandy@us.ibm.com>
Date: Mon, 3 Aug 2015 17:36:56 -0700
Subject: [PATCH 0815/1454] [SPARK-9483] Fix UTF8String.getPrefix for
 big-endian.

Previous code assumed little-endian.

Author: Matthew Brandyberry <mbrandy@us.ibm.com>

Closes #7902 from mtbrandy/SPARK-9483 and squashes the following commits:

ec31df8 [Matthew Brandyberry] [SPARK-9483] Changes from review comments.
17d54c6 [Matthew Brandyberry] [SPARK-9483] Fix UTF8String.getPrefix for big-endian.
---
 .../apache/spark/unsafe/types/UTF8String.java | 40 ++++++++++++++-----
 1 file changed, 30 insertions(+), 10 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index f6c9b87778f8f..d80bd57bd2048 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -20,6 +20,7 @@
 import javax.annotation.Nonnull;
 import java.io.Serializable;
 import java.io.UnsupportedEncodingException;
+import java.nio.ByteOrder;
 import java.util.Arrays;
 
 import org.apache.spark.unsafe.PlatformDependent;
@@ -53,6 +54,8 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
     5, 5, 5, 5,
     6, 6};
 
+  private static ByteOrder byteOrder = ByteOrder.nativeOrder();
+
   public static final UTF8String EMPTY_UTF8 = UTF8String.fromString("");
 
   /**
@@ -175,18 +178,35 @@ public long getPrefix() {
     // If size is greater than 4, assume we have at least 8 bytes of data to fetch.
     // After getting the data, we use a mask to mask out data that is not part of the string.
     long p;
-    if (numBytes >= 8) {
-      p = PlatformDependent.UNSAFE.getLong(base, offset);
-    } else  if (numBytes > 4) {
-      p = PlatformDependent.UNSAFE.getLong(base, offset);
-      p = p & ((1L << numBytes * 8) - 1);
-    } else if (numBytes > 0) {
-      p = (long) PlatformDependent.UNSAFE.getInt(base, offset);
-      p = p & ((1L << numBytes * 8) - 1);
+    long mask = 0;
+    if (byteOrder == ByteOrder.LITTLE_ENDIAN) {
+      if (numBytes >= 8) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+      } else if (numBytes > 4) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else if (numBytes > 0) {
+        p = (long) PlatformDependent.UNSAFE.getInt(base, offset);
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else {
+        p = 0;
+      }
+      p = java.lang.Long.reverseBytes(p);
     } else {
-      p = 0;
+      // byteOrder == ByteOrder.BIG_ENDIAN
+      if (numBytes >= 8) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+      } else if (numBytes > 4) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else if (numBytes > 0) {
+        p = ((long) PlatformDependent.UNSAFE.getInt(base, offset)) << 32;
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else {
+        p = 0;
+      }
     }
-    p = java.lang.Long.reverseBytes(p);
+    p &= ~mask;
     return p;
   }
 

From 1633d0a2612d94151f620c919425026150e69ae1 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Mon, 3 Aug 2015 17:42:03 -0700
Subject: [PATCH 0816/1454] [SPARK-9263] Added flags to exclude dependencies
 when using --packages

While the functionality is there to exclude packages, there are no flags that allow users to exclude dependencies, in case of dependency conflicts. We should provide users with a flag to add dependency exclusions in case the packages are not resolved properly (or not available due to licensing).

The flag I added was --packages-exclude, but I'm open on renaming it. I also added property flags in case people would like to use a conf file to provide dependencies, which is possible if there is a long list of dependencies or exclusions.

cc andrewor14 vanzin pwendell

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7599 from brkyvz/packages-exclusions and squashes the following commits:

636f410 [Burak Yavuz] addressed nits
6e54ede [Burak Yavuz] is this the culprit
b5e508e [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into packages-exclusions
154f5db [Burak Yavuz] addressed initial comments
1536d7a [Burak Yavuz] Added flags to exclude packages using --packages-exclude
---
 .../org/apache/spark/deploy/SparkSubmit.scala | 29 +++++++++---------
 .../spark/deploy/SparkSubmitArguments.scala   | 11 +++++++
 .../spark/deploy/SparkSubmitUtilsSuite.scala  | 30 +++++++++++++++++++
 .../launcher/SparkSubmitOptionParser.java     |  2 ++
 4 files changed, 57 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 0b39ee8fe3ba0..31185c8e77def 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -24,6 +24,7 @@ import java.security.PrivilegedExceptionAction
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
 
+import org.apache.commons.lang3.StringUtils
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.security.UserGroupInformation
 import org.apache.ivy.Ivy
@@ -37,6 +38,7 @@ import org.apache.ivy.core.settings.IvySettings
 import org.apache.ivy.plugins.matcher.GlobPatternMatcher
 import org.apache.ivy.plugins.repository.file.FileRepository
 import org.apache.ivy.plugins.resolver.{FileSystemResolver, ChainResolver, IBiblioResolver}
+
 import org.apache.spark.api.r.RUtils
 import org.apache.spark.SPARK_VERSION
 import org.apache.spark.deploy.rest._
@@ -275,21 +277,18 @@ object SparkSubmit {
 
     // Resolve maven dependencies if there are any and add classpath to jars. Add them to py-files
     // too for packages that include Python code
-    val resolvedMavenCoordinates =
-      SparkSubmitUtils.resolveMavenCoordinates(
-        args.packages, Option(args.repositories), Option(args.ivyRepoPath))
-    if (!resolvedMavenCoordinates.trim.isEmpty) {
-      if (args.jars == null || args.jars.trim.isEmpty) {
-        args.jars = resolvedMavenCoordinates
+    val exclusions: Seq[String] =
+      if (!StringUtils.isBlank(args.packagesExclusions)) {
+        args.packagesExclusions.split(",")
       } else {
-        args.jars += s",$resolvedMavenCoordinates"
+        Nil
       }
+    val resolvedMavenCoordinates = SparkSubmitUtils.resolveMavenCoordinates(args.packages,
+      Some(args.repositories), Some(args.ivyRepoPath), exclusions = exclusions)
+    if (!StringUtils.isBlank(resolvedMavenCoordinates)) {
+      args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates)
       if (args.isPython) {
-        if (args.pyFiles == null || args.pyFiles.trim.isEmpty) {
-          args.pyFiles = resolvedMavenCoordinates
-        } else {
-          args.pyFiles += s",$resolvedMavenCoordinates"
-        }
+        args.pyFiles = mergeFileLists(args.pyFiles, resolvedMavenCoordinates)
       }
     }
 
@@ -736,7 +735,7 @@ object SparkSubmit {
    * no files, into a single comma-separated string.
    */
   private def mergeFileLists(lists: String*): String = {
-    val merged = lists.filter(_ != null)
+    val merged = lists.filterNot(StringUtils.isBlank)
                       .flatMap(_.split(","))
                       .mkString(",")
     if (merged == "") null else merged
@@ -938,7 +937,7 @@ private[spark] object SparkSubmitUtils {
         // are supplied to spark-submit
         val alternateIvyCache = ivyPath.getOrElse("")
         val packagesDirectory: File =
-          if (alternateIvyCache.trim.isEmpty) {
+          if (alternateIvyCache == null || alternateIvyCache.trim.isEmpty) {
             new File(ivySettings.getDefaultIvyUserDir, "jars")
           } else {
             ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache))
@@ -1010,7 +1009,7 @@ private[spark] object SparkSubmitUtils {
     }
   }
 
-  private def createExclusion(
+  private[deploy] def createExclusion(
       coords: String,
       ivySettings: IvySettings,
       ivyConfName: String): ExcludeRule = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index b3710073e330c..44852ce4e84ac 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -59,6 +59,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
   var packages: String = null
   var repositories: String = null
   var ivyRepoPath: String = null
+  var packagesExclusions: String = null
   var verbose: Boolean = false
   var isPython: Boolean = false
   var pyFiles: String = null
@@ -172,6 +173,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
     name = Option(name).orElse(sparkProperties.get("spark.app.name")).orNull
     jars = Option(jars).orElse(sparkProperties.get("spark.jars")).orNull
     ivyRepoPath = sparkProperties.get("spark.jars.ivy").orNull
+    packages = Option(packages).orElse(sparkProperties.get("spark.jars.packages")).orNull
+    packagesExclusions = Option(packagesExclusions)
+      .orElse(sparkProperties.get("spark.jars.excludes")).orNull
     deployMode = Option(deployMode).orElse(env.get("DEPLOY_MODE")).orNull
     numExecutors = Option(numExecutors)
       .getOrElse(sparkProperties.get("spark.executor.instances").orNull)
@@ -299,6 +303,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
     |  childArgs               [${childArgs.mkString(" ")}]
     |  jars                    $jars
     |  packages                $packages
+    |  packagesExclusions      $packagesExclusions
     |  repositories            $repositories
     |  verbose                 $verbose
     |
@@ -391,6 +396,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
       case PACKAGES =>
         packages = value
 
+      case PACKAGES_EXCLUDE =>
+        packagesExclusions = value
+
       case REPOSITORIES =>
         repositories = value
 
@@ -482,6 +490,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
         |                              maven repo, then maven central and any additional remote
         |                              repositories given by --repositories. The format for the
         |                              coordinates should be groupId:artifactId:version.
+        |  --exclude-packages          Comma-separated list of groupId:artifactId, to exclude while
+        |                              resolving the dependencies provided in --packages to avoid
+        |                              dependency conflicts.
         |  --repositories              Comma-separated list of additional remote repositories to
         |                              search for the maven coordinates given with --packages.
         |  --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
index 01ece1a10f46d..63c346c1b8908 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
@@ -95,6 +95,25 @@ class SparkSubmitUtilsSuite extends SparkFunSuite with BeforeAndAfterAll {
     assert(md.getDependencies.length === 2)
   }
 
+  test("excludes works correctly") {
+    val md = SparkSubmitUtils.getModuleDescriptor
+    val excludes = Seq("a:b", "c:d")
+    excludes.foreach { e =>
+      md.addExcludeRule(SparkSubmitUtils.createExclusion(e + ":*", new IvySettings, "default"))
+    }
+    val rules = md.getAllExcludeRules
+    assert(rules.length === 2)
+    val rule1 = rules(0).getId.getModuleId
+    assert(rule1.getOrganisation === "a")
+    assert(rule1.getName === "b")
+    val rule2 = rules(1).getId.getModuleId
+    assert(rule2.getOrganisation === "c")
+    assert(rule2.getName === "d")
+    intercept[IllegalArgumentException] {
+      SparkSubmitUtils.createExclusion("e:f:g:h", new IvySettings, "default")
+    }
+  }
+
   test("ivy path works correctly") {
     val md = SparkSubmitUtils.getModuleDescriptor
     val artifacts = for (i <- 0 until 3) yield new MDArtifact(md, s"jar-$i", "jar", "jar")
@@ -168,4 +187,15 @@ class SparkSubmitUtilsSuite extends SparkFunSuite with BeforeAndAfterAll {
       assert(files.indexOf(main.artifactId) >= 0, "Did not return artifact")
     }
   }
+
+  test("exclude dependencies end to end") {
+    val main = new MavenCoordinate("my.great.lib", "mylib", "0.1")
+    val dep = "my.great.dep:mydep:0.5"
+    IvyTestUtils.withRepository(main, Some(dep), None) { repo =>
+      val files = SparkSubmitUtils.resolveMavenCoordinates(main.toString,
+        Some(repo), None, Seq("my.great.dep:mydep"), isTest = true)
+      assert(files.indexOf(main.artifactId) >= 0, "Did not return artifact")
+      assert(files.indexOf("my.great.dep") < 0, "Returned excluded artifact")
+    }
+  }
 }
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
index b88bba883ac65..5779eb3fc0f78 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
@@ -51,6 +51,7 @@ class SparkSubmitOptionParser {
   protected final String MASTER = "--master";
   protected final String NAME = "--name";
   protected final String PACKAGES = "--packages";
+  protected final String PACKAGES_EXCLUDE = "--exclude-packages";
   protected final String PROPERTIES_FILE = "--properties-file";
   protected final String PROXY_USER = "--proxy-user";
   protected final String PY_FILES = "--py-files";
@@ -105,6 +106,7 @@ class SparkSubmitOptionParser {
     { NAME },
     { NUM_EXECUTORS },
     { PACKAGES },
+    { PACKAGES_EXCLUDE },
     { PRINCIPAL },
     { PROPERTIES_FILE },
     { PROXY_USER },

From 3b0e44490aebfba30afc147e4a34a63439d985c6 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Mon, 3 Aug 2015 18:20:40 -0700
Subject: [PATCH 0817/1454] [SPARK-8416] highlight and topping the executor
 threads in thread dumping page

https://issues.apache.org/jira/browse/SPARK-8416

To facilitate debugging, I made this patch with three changes:

* render the executor-thread and non executor-thread entries with different background colors

* put the executor threads on the top of the list

* sort the threads alphabetically

Author: CodingCat <zhunansjtu@gmail.com>

Closes #7808 from CodingCat/SPARK-8416 and squashes the following commits:

34fc708 [CodingCat] fix className
d7b79dd [CodingCat] lowercase threadName
d032882 [CodingCat] sort alphabetically and change the css class name
f0513b1 [CodingCat] change the color & group threads by name
2da6e06 [CodingCat] small fix
3fc9f36 [CodingCat] define classes in webui.css
8ee125e [CodingCat] highlight and put on top the executor threads in thread dumping page
---
 .../org/apache/spark/ui/static/webui.css      |  8 +++++++
 .../ui/exec/ExecutorThreadDumpPage.scala      | 24 ++++++++++++++++---
 2 files changed, 29 insertions(+), 3 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index 648cd1b104802..04f3070d25b4a 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -224,3 +224,11 @@ span.additional-metric-title {
 a.expandbutton {
   cursor: pointer;
 }
+
+.executor-thread {
+  background: #E6E6E6;
+}
+
+.non-executor-thread {
+  background: #FAFAFA;
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala
index f0ae95bb8c812..b0a2cb4aa4d4b 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala
@@ -49,11 +49,29 @@ private[ui] class ExecutorThreadDumpPage(parent: ExecutorsTab) extends WebUIPage
     val maybeThreadDump = sc.get.getExecutorThreadDump(executorId)
 
     val content = maybeThreadDump.map { threadDump =>
-      val dumpRows = threadDump.map { thread =>
+      val dumpRows = threadDump.sortWith {
+        case (threadTrace1, threadTrace2) => {
+          val v1 = if (threadTrace1.threadName.contains("Executor task launch")) 1 else 0
+          val v2 = if (threadTrace2.threadName.contains("Executor task launch")) 1 else 0
+          if (v1 == v2) {
+            threadTrace1.threadName.toLowerCase < threadTrace2.threadName.toLowerCase
+          } else {
+            v1 > v2
+          }
+        }
+      }.map { thread =>
+        val threadName = thread.threadName
+        val className = "accordion-heading " + {
+          if (threadName.contains("Executor task launch")) {
+            "executor-thread"
+          } else {
+            "non-executor-thread"
+          }
+        }
         <div class="accordion-group">
-          <div class="accordion-heading" onclick="$(this).next().toggleClass('hidden')">
+          <div class={className} onclick="$(this).next().toggleClass('hidden')">
             <a class="accordion-toggle">
-              Thread {thread.threadId}: {thread.threadName} ({thread.threadState})
+              Thread {thread.threadId}: {threadName} ({thread.threadState})
             </a>
           </div>
           <div class="accordion-body hidden">

From 5eb89f67e323dcf9fa3d5b30f9b5cb8f10ca1e8c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 3 Aug 2015 18:47:02 -0700
Subject: [PATCH 0818/1454] [SPARK-9577][SQL] Surface concrete iterator types
 in various sort classes.

We often return abstract iterator types in various sort-related classes (e.g. UnsafeKVExternalSorter). It is actually better to return a more concrete type, so the callsite uses that type and JIT can inline the iterator calls.

Author: Reynold Xin <rxin@databricks.com>

Closes #7911 from rxin/surface-concrete-type and squashes the following commits:

0422add [Reynold Xin] [SPARK-9577][SQL] Surface concrete iterator types in various sort classes.
---
 .../unsafe/sort/UnsafeExternalSorter.java     |   2 +-
 .../unsafe/sort/UnsafeInMemorySorter.java     |   6 +-
 .../sql/execution/UnsafeKVExternalSorter.java | 112 +++++++++---------
 .../UnsafeHybridAggregationIterator.scala     |  30 +----
 4 files changed, 65 insertions(+), 85 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index bf5f965a9d8dc..dec7fcfa0ddc1 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -428,7 +428,7 @@ public void insertKVRecord(
 
   public UnsafeSorterIterator getSortedIterator() throws IOException {
     assert(inMemSorter != null);
-    final UnsafeSorterIterator inMemoryIterator = inMemSorter.getSortedIterator();
+    final UnsafeInMemorySorter.SortedIterator inMemoryIterator = inMemSorter.getSortedIterator();
     int numIteratorsToMerge = spillWriters.size() + (inMemoryIterator.hasNext() ? 1 : 0);
     if (spillWriters.isEmpty()) {
       return inMemoryIterator;
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
index 3131465391900..1e4b8a116e11a 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
@@ -133,7 +133,7 @@ public void insertRecord(long recordPointer, long keyPrefix) {
     pointerArrayInsertPosition++;
   }
 
-  private static final class SortedIterator extends UnsafeSorterIterator {
+  public static final class SortedIterator extends UnsafeSorterIterator {
 
     private final TaskMemoryManager memoryManager;
     private final int sortBufferInsertPosition;
@@ -144,7 +144,7 @@ private static final class SortedIterator extends UnsafeSorterIterator {
     private long keyPrefix;
     private int recordLength;
 
-    SortedIterator(
+    private SortedIterator(
         TaskMemoryManager memoryManager,
         int sortBufferInsertPosition,
         long[] sortBuffer) {
@@ -186,7 +186,7 @@ public void loadNext() {
    * Return an iterator over record pointers in sorted order. For efficiency, all calls to
    * {@code next()} will return the same mutable object.
    */
-  public UnsafeSorterIterator getSortedIterator() {
+  public SortedIterator getSortedIterator() {
     sorter.sort(pointerArray, 0, pointerArrayInsertPosition / 2, sortComparator);
     return new SortedIterator(memoryManager, pointerArrayInsertPosition, pointerArray);
   }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index f6b017686306b..312ec8ea0dd9d 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -134,7 +134,7 @@ public void insertKV(UnsafeRow key, UnsafeRow value) throws IOException {
       value.getBaseObject(), value.getBaseOffset(), value.getSizeInBytes(), prefix);
   }
 
-  public KVIterator<UnsafeRow, UnsafeRow> sortedIterator() throws IOException {
+  public KVSorterIterator sortedIterator() throws IOException {
     try {
       final UnsafeSorterIterator underlying = sorter.getSortedIterator();
       if (!underlying.hasNext()) {
@@ -142,58 +142,7 @@ public KVIterator<UnsafeRow, UnsafeRow> sortedIterator() throws IOException {
         // here in order to prevent memory leaks.
         cleanupResources();
       }
-
-      return new KVIterator<UnsafeRow, UnsafeRow>() {
-        private UnsafeRow key = new UnsafeRow();
-        private UnsafeRow value = new UnsafeRow();
-        private int numKeyFields = keySchema.size();
-        private int numValueFields = valueSchema.size();
-
-        @Override
-        public boolean next() throws IOException {
-          try {
-            if (underlying.hasNext()) {
-              underlying.loadNext();
-
-              Object baseObj = underlying.getBaseObject();
-              long recordOffset = underlying.getBaseOffset();
-              int recordLen = underlying.getRecordLength();
-
-              // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
-              int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
-              int valueLen = recordLen - keyLen - 4;
-
-              key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
-              value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
-
-              return true;
-            } else {
-              key = null;
-              value = null;
-              cleanupResources();
-              return false;
-            }
-          } catch (IOException e) {
-            cleanupResources();
-            throw e;
-          }
-        }
-
-        @Override
-        public UnsafeRow getKey() {
-          return key;
-        }
-
-        @Override
-        public UnsafeRow getValue() {
-          return value;
-        }
-
-        @Override
-        public void close() {
-          cleanupResources();
-        }
-      };
+      return new KVSorterIterator(underlying);
     } catch (IOException e) {
       cleanupResources();
       throw e;
@@ -233,4 +182,61 @@ public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff
       return ordering.compare(row1, row2);
     }
   }
+
+  public class KVSorterIterator extends KVIterator<UnsafeRow, UnsafeRow> {
+    private UnsafeRow key = new UnsafeRow();
+    private UnsafeRow value = new UnsafeRow();
+    private final int numKeyFields = keySchema.size();
+    private final int numValueFields = valueSchema.size();
+    private final UnsafeSorterIterator underlying;
+
+    private KVSorterIterator(UnsafeSorterIterator underlying) {
+      this.underlying = underlying;
+    }
+
+    @Override
+    public boolean next() throws IOException {
+      try {
+        if (underlying.hasNext()) {
+          underlying.loadNext();
+
+          Object baseObj = underlying.getBaseObject();
+          long recordOffset = underlying.getBaseOffset();
+          int recordLen = underlying.getRecordLength();
+
+          // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
+          int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
+          int valueLen = recordLen - keyLen - 4;
+
+          key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
+          value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
+
+          return true;
+        } else {
+          key = null;
+          value = null;
+          cleanupResources();
+          return false;
+        }
+      } catch (IOException e) {
+        cleanupResources();
+        throw e;
+      }
+    }
+
+    @Override
+    public UnsafeRow getKey() {
+      return key;
+    }
+
+    @Override
+    public UnsafeRow getValue() {
+      return value;
+    }
+
+    @Override
+    public void close() {
+      cleanupResources();
+    }
+  };
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
index 37d34eb7ccf09..b465787fe8cbd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
@@ -17,12 +17,12 @@
 
 package org.apache.spark.sql.execution.aggregate
 
-import org.apache.spark.sql.execution.{UnsafeKeyValueSorter, UnsafeFixedWidthAggregationMap}
 import org.apache.spark.unsafe.KVIterator
 import org.apache.spark.{SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
 import org.apache.spark.sql.types.StructType
 
 /**
@@ -230,7 +230,7 @@ class UnsafeHybridAggregationIterator(
     }
 
     // Step 5: Get the sorted iterator from the externalSorter.
-    val sortedKVIterator: KVIterator[UnsafeRow, UnsafeRow] = externalSorter.sortedIterator()
+    val sortedKVIterator: UnsafeKVExternalSorter#KVSorterIterator = externalSorter.sortedIterator()
 
     // Step 6: We now create a SortBasedAggregationIterator based on sortedKVIterator.
     // For a aggregate function with mode Partial, its mode in the SortBasedAggregationIterator
@@ -368,31 +368,5 @@ object UnsafeHybridAggregationIterator {
       newMutableProjection,
       outputsUnsafeRows)
   }
-
-  def createFromKVIterator(
-      groupingKeyAttributes: Seq[Attribute],
-      valueAttributes: Seq[Attribute],
-      inputKVIterator: KVIterator[UnsafeRow, InternalRow],
-      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-      nonCompleteAggregateAttributes: Seq[Attribute],
-      completeAggregateExpressions: Seq[AggregateExpression2],
-      completeAggregateAttributes: Seq[Attribute],
-      initialInputBufferOffset: Int,
-      resultExpressions: Seq[NamedExpression],
-      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-      outputsUnsafeRows: Boolean): UnsafeHybridAggregationIterator = {
-    new UnsafeHybridAggregationIterator(
-      groupingKeyAttributes,
-      valueAttributes,
-      inputKVIterator,
-      nonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes,
-      completeAggregateExpressions,
-      completeAggregateAttributes,
-      initialInputBufferOffset,
-      resultExpressions,
-      newMutableProjection,
-      outputsUnsafeRows)
-  }
   // scalastyle:on
 }

From 0afa6fbf525723e97c6dacfdba3ad1762637ffa9 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 4 Aug 2015 13:48:22 +0900
Subject: [PATCH 0819/1454] [SPARK-9521] [DOCS] Addendum. Require Maven 3.3.3+
 in the build

Follow on for #7852: Building Spark doc needs to refer to new Maven requirement too

Author: Sean Owen <sowen@cloudera.com>

Closes #7905 from srowen/SPARK-9521.2 and squashes the following commits:

73285df [Sean Owen] Follow on for #7852: Building Spark doc needs to refer to new Maven requirement too
---
 docs/building-spark.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/building-spark.md b/docs/building-spark.md
index a5da3b39502e2..f133eb96d9a21 100644
--- a/docs/building-spark.md
+++ b/docs/building-spark.md
@@ -7,7 +7,8 @@ redirect_from: "building-with-maven.html"
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-Building Spark using Maven requires Maven 3.0.4 or newer and Java 7+.
+Building Spark using Maven requires Maven 3.3.3 or newer and Java 7+.
+The Spark build can supply a suitable Maven binary; see below.
 
 # Building with `build/mvn`
 

From 9e952ecbce670e9b532a1c664a4d03b66e404112 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Mon, 3 Aug 2015 23:07:32 -0700
Subject: [PATCH 0820/1454] [SPARK-3190] [GRAPHX] Fix VertexRDD.count()
 overflow regression

SPARK-3190 was originally fixed by 96df92906978c5f58e0cc8ff5eebe5b35a08be3b, but a5ef58113667ff73562ce6db381cff96a0b354b0 introduced a regression during refactoring. This commit fixes the regression.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #7923 from ankurdave/SPARK-3190-reopening and squashes the following commits:

a3e1b23 [Ankur Dave] Fix VertexRDD.count() overflow regression
---
 .../main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala
index 33ac7b0ed6095..7f4e7e9d79d6b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala
@@ -87,7 +87,7 @@ class VertexRDDImpl[VD] private[graphx] (
 
   /** The number of vertices in the RDD. */
   override def count(): Long = {
-    partitionsRDD.map(_.size).reduce(_ + _)
+    partitionsRDD.map(_.size.toLong).reduce(_ + _)
   }
 
   override private[graphx] def mapVertexPartitions[VD2: ClassTag](

From 76d74090d60f74412bd45487e8db6aff2e8343a2 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 4 Aug 2015 12:02:26 +0100
Subject: [PATCH 0821/1454] [SPARK-9534] [BUILD] Enable javac lint for scalac
 parity; fix a lot of build warnings, 1.5.0 edition

Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.

I'll explain several of the changes inline in comments.

Author: Sean Owen <sowen@cloudera.com>

Closes #7862 from srowen/SPARK-9534 and squashes the following commits:

ea51618 [Sean Owen] Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.
---
 .../JavaSparkContextVarargsWorkaround.java    | 19 +++---
 .../spark/storage/TachyonBlockManager.scala   |  9 ++-
 .../master/PersistenceEngineSuite.scala       | 13 ++--
 .../mesos/MesosSchedulerUtilsSuite.scala      |  3 +
 .../examples/ml/JavaOneVsRestExample.java     |  1 +
 .../JavaStatefulNetworkWordCount.java         |  4 +-
 .../kafka/JavaDirectKafkaStreamSuite.java     |  2 +-
 .../evaluation/JavaRankingMetricsSuite.java   | 14 ++---
 .../ml/classification/NaiveBayesSuite.scala   |  4 +-
 .../network/protocol/ChunkFetchFailure.java   |  5 ++
 .../network/protocol/ChunkFetchRequest.java   |  5 ++
 .../network/protocol/ChunkFetchSuccess.java   |  5 ++
 .../spark/network/protocol/RpcFailure.java    |  5 ++
 .../spark/network/protocol/RpcRequest.java    |  5 ++
 .../spark/network/protocol/RpcResponse.java   |  5 ++
 .../spark/network/TestManagedBuffer.java      |  5 ++
 .../spark/network/sasl/SparkSaslSuite.java    | 16 ++---
 .../ExternalShuffleBlockHandlerSuite.java     |  6 +-
 .../shuffle/RetryingBlockFetcherSuite.java    | 47 +++++++-------
 pom.xml                                       |  4 ++
 .../apache/spark/sql/JavaDataFrameSuite.java  |  1 +
 .../spark/sql/sources/TableScanSuite.scala    | 16 +++--
 .../hive/client/IsolatedClientLoader.scala    |  2 +-
 .../spark/sql/hive/execution/commands.scala   |  2 +-
 .../spark/sql/hive/JavaDataFrameSuite.java    |  2 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |  4 +-
 .../streaming/scheduler/JobScheduler.scala    |  4 +-
 .../streaming/JavaWriteAheadLogSuite.java     | 62 ++++++++++---------
 .../spark/streaming/UISeleniumSuite.scala     |  4 +-
 29 files changed, 167 insertions(+), 107 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java b/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java
index 2090efd3b9990..d4c42b38ac224 100644
--- a/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java
+++ b/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java
@@ -23,11 +23,13 @@
 // See
 // http://scala-programming-language.1934581.n4.nabble.com/Workaround-for-implementing-java-varargs-in-2-7-2-final-tp1944767p1944772.html
 abstract class JavaSparkContextVarargsWorkaround {
-  public <T> JavaRDD<T> union(JavaRDD<T>... rdds) {
+
+  @SafeVarargs
+  public final <T> JavaRDD<T> union(JavaRDD<T>... rdds) {
     if (rdds.length == 0) {
       throw new IllegalArgumentException("Union called on empty list");
     }
-    ArrayList<JavaRDD<T>> rest = new ArrayList<JavaRDD<T>>(rdds.length - 1);
+    List<JavaRDD<T>> rest = new ArrayList<>(rdds.length - 1);
     for (int i = 1; i < rdds.length; i++) {
       rest.add(rdds[i]);
     }
@@ -38,18 +40,19 @@ public JavaDoubleRDD union(JavaDoubleRDD... rdds) {
     if (rdds.length == 0) {
       throw new IllegalArgumentException("Union called on empty list");
     }
-    ArrayList<JavaDoubleRDD> rest = new ArrayList<JavaDoubleRDD>(rdds.length - 1);
+    List<JavaDoubleRDD> rest = new ArrayList<>(rdds.length - 1);
     for (int i = 1; i < rdds.length; i++) {
       rest.add(rdds[i]);
     }
     return union(rdds[0], rest);
   }
 
-  public <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V>... rdds) {
+  @SafeVarargs
+  public final <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V>... rdds) {
     if (rdds.length == 0) {
       throw new IllegalArgumentException("Union called on empty list");
     }
-    ArrayList<JavaPairRDD<K, V>> rest = new ArrayList<JavaPairRDD<K, V>>(rdds.length - 1);
+    List<JavaPairRDD<K, V>> rest = new ArrayList<>(rdds.length - 1);
     for (int i = 1; i < rdds.length; i++) {
       rest.add(rdds[i]);
     }
@@ -57,7 +60,7 @@ public <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V>... rdds) {
   }
 
   // These methods take separate "first" and "rest" elements to avoid having the same type erasure
-  abstract public <T> JavaRDD<T> union(JavaRDD<T> first, List<JavaRDD<T>> rest);
-  abstract public JavaDoubleRDD union(JavaDoubleRDD first, List<JavaDoubleRDD> rest);
-  abstract public <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V> first, List<JavaPairRDD<K, V>> rest);
+  public abstract <T> JavaRDD<T> union(JavaRDD<T> first, List<JavaRDD<T>> rest);
+  public abstract JavaDoubleRDD union(JavaDoubleRDD first, List<JavaDoubleRDD> rest);
+  public abstract <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V> first, List<JavaPairRDD<K, V>> rest);
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
index b53c86e89a273..ebad5bc5ab28d 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
@@ -27,9 +27,10 @@ import scala.util.control.NonFatal
 import com.google.common.io.ByteStreams
 
 import tachyon.client.{ReadType, WriteType, TachyonFS, TachyonFile}
+import tachyon.conf.TachyonConf
 import tachyon.TachyonURI
 
-import org.apache.spark.{SparkException, SparkConf, Logging}
+import org.apache.spark.Logging
 import org.apache.spark.executor.ExecutorExitCode
 import org.apache.spark.util.Utils
 
@@ -60,7 +61,11 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log
 
     rootDirs = s"$storeDir/$appFolderName/$executorId"
     master = blockManager.conf.get(ExternalBlockStore.MASTER_URL, "tachyon://localhost:19998")
-    client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null
+    client = if (master != null && master != "") {
+      TachyonFS.get(new TachyonURI(master), new TachyonConf())
+    } else {
+      null
+    }
     // original implementation call System.exit, we change it to run without extblkstore support
     if (client == null) {
       logError("Failed to connect to the Tachyon as the master address is not configured")
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
index 11e87bd1dd8eb..34775577de8a3 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
@@ -73,11 +73,11 @@ class PersistenceEngineSuite extends SparkFunSuite {
     assert(persistenceEngine.read[String]("test_").isEmpty)
 
     // Test deserializing objects that contain RpcEndpointRef
-    val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
+    val testRpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
     try {
       // Create a real endpoint so that we can test RpcEndpointRef deserialization
-      val workerEndpoint = rpcEnv.setupEndpoint("worker", new RpcEndpoint {
-        override val rpcEnv: RpcEnv = rpcEnv
+      val workerEndpoint = testRpcEnv.setupEndpoint("worker", new RpcEndpoint {
+        override val rpcEnv: RpcEnv = testRpcEnv
       })
 
       val workerToPersist = new WorkerInfo(
@@ -93,7 +93,8 @@ class PersistenceEngineSuite extends SparkFunSuite {
 
       persistenceEngine.addWorker(workerToPersist)
 
-      val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData(rpcEnv)
+      val (storedApps, storedDrivers, storedWorkers) =
+        persistenceEngine.readPersistedData(testRpcEnv)
 
       assert(storedApps.isEmpty)
       assert(storedDrivers.isEmpty)
@@ -110,8 +111,8 @@ class PersistenceEngineSuite extends SparkFunSuite {
       assert(workerToPersist.webUiPort === recoveryWorkerInfo.webUiPort)
       assert(workerToPersist.publicAddress === recoveryWorkerInfo.publicAddress)
     } finally {
-      rpcEnv.shutdown()
-      rpcEnv.awaitTermination()
+      testRpcEnv.shutdown()
+      testRpcEnv.awaitTermination()
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
index b354914b6ffd0..2eb43b7313381 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.scheduler.cluster.mesos
 
+import scala.language.reflectiveCalls
+
 import org.apache.mesos.Protos.Value
 import org.mockito.Mockito._
 import org.scalatest._
 import org.scalatest.mock.MockitoSugar
+
 import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
 
 class MesosSchedulerUtilsSuite extends SparkFunSuite with Matchers with MockitoSugar {
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
index 75063dbf800d8..e7f2f6f615070 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
@@ -178,6 +178,7 @@ private static Params parse(String[] args) {
     return params;
   }
 
+  @SuppressWarnings("static")
   private static Options generateCommandlineOptions() {
     Option input = OptionBuilder.withArgName("input")
       .hasArg()
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
index dbf2ef02d7b76..02f58f48b07ab 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
@@ -85,7 +85,7 @@ public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
     @SuppressWarnings("unchecked")
     List<Tuple2<String, Integer>> tuples = Arrays.asList(new Tuple2<String, Integer>("hello", 1),
             new Tuple2<String, Integer>("world", 1));
-    JavaPairRDD<String, Integer> initialRDD = ssc.sc().parallelizePairs(tuples);
+    JavaPairRDD<String, Integer> initialRDD = ssc.sparkContext().parallelizePairs(tuples);
 
     JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
             args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER_2);
@@ -107,7 +107,7 @@ public Tuple2<String, Integer> call(String s) {
 
     // This will give a Dstream made of state (which is the cumulative count of the words)
     JavaPairDStream<String, Integer> stateDstream = wordsDstream.updateStateByKey(updateFunction,
-            new HashPartitioner(ssc.sc().defaultParallelism()), initialRDD);
+            new HashPartitioner(ssc.sparkContext().defaultParallelism()), initialRDD);
 
     stateDstream.print();
     ssc.start();
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
index 02cd24a35906f..9db07d0507fea 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
@@ -70,7 +70,7 @@ public void testKafkaStream() throws InterruptedException {
     final String topic1 = "topic1";
     final String topic2 = "topic2";
     // hold a reference to the current offset ranges, so it can be used downstream
-    final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference();
+    final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference<>();
 
     String[] topic1data = createTopicAndSendData(topic1);
     String[] topic2data = createTopicAndSendData(topic2);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java
index effc8a1a6dabc..fa4d334801ce4 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java
@@ -18,12 +18,12 @@
 package org.apache.spark.mllib.evaluation;
 
 import java.io.Serializable;
-import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
 
 import scala.Tuple2;
 import scala.Tuple2$;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -34,18 +34,18 @@
 
 public class JavaRankingMetricsSuite implements Serializable {
   private transient JavaSparkContext sc;
-  private transient JavaRDD<Tuple2<ArrayList<Integer>, ArrayList<Integer>>> predictionAndLabels;
+  private transient JavaRDD<Tuple2<List<Integer>, List<Integer>>> predictionAndLabels;
 
   @Before
   public void setUp() {
     sc = new JavaSparkContext("local", "JavaRankingMetricsSuite");
-    predictionAndLabels = sc.parallelize(Lists.newArrayList(
+    predictionAndLabels = sc.parallelize(Arrays.asList(
       Tuple2$.MODULE$.apply(
-        Lists.newArrayList(1, 6, 2, 7, 8, 3, 9, 10, 4, 5), Lists.newArrayList(1, 2, 3, 4, 5)),
+        Arrays.asList(1, 6, 2, 7, 8, 3, 9, 10, 4, 5), Arrays.asList(1, 2, 3, 4, 5)),
       Tuple2$.MODULE$.apply(
-        Lists.newArrayList(4, 1, 5, 6, 2, 7, 3, 8, 9, 10), Lists.newArrayList(1, 2, 3)),
+          Arrays.asList(4, 1, 5, 6, 2, 7, 3, 8, 9, 10), Arrays.asList(1, 2, 3)),
       Tuple2$.MODULE$.apply(
-        Lists.newArrayList(1, 2, 3, 4, 5), Lists.<Integer>newArrayList())), 2);
+          Arrays.asList(1, 2, 3, 4, 5), Arrays.<Integer>asList())), 2);
   }
 
   @After
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index aea3d9b694490..98bc9511163e7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -21,7 +21,7 @@ import breeze.linalg.{Vector => BV}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
-import org.apache.spark.mllib.classification.NaiveBayes
+import org.apache.spark.mllib.classification.NaiveBayes.{Multinomial, Bernoulli}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -31,8 +31,6 @@ import org.apache.spark.sql.Row
 
 class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
-  import NaiveBayes.{Multinomial, Bernoulli}
-
   def validatePrediction(predictionAndLabels: DataFrame): Unit = {
     val numOfErrorPredictions = predictionAndLabels.collect().count {
       case Row(prediction: Double, label: Double) =>
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java
index f76bb49e874fc..f0363830b61ac 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java
@@ -52,6 +52,11 @@ public static ChunkFetchFailure decode(ByteBuf buf) {
     return new ChunkFetchFailure(streamChunkId, errorString);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(streamChunkId, errorString);
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ChunkFetchFailure) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java
index 980947cf13f6b..5a173af54f618 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java
@@ -48,6 +48,11 @@ public static ChunkFetchRequest decode(ByteBuf buf) {
     return new ChunkFetchRequest(StreamChunkId.decode(buf));
   }
 
+  @Override
+  public int hashCode() {
+    return streamChunkId.hashCode();
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ChunkFetchRequest) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java
index ff4936470c697..c962fb7ecf76d 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java
@@ -61,6 +61,11 @@ public static ChunkFetchSuccess decode(ByteBuf buf) {
     return new ChunkFetchSuccess(streamChunkId, managedBuf);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(streamChunkId, buffer);
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ChunkFetchSuccess) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java b/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java
index 6b991375fc486..2dfc7876ba328 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java
@@ -50,6 +50,11 @@ public static RpcFailure decode(ByteBuf buf) {
     return new RpcFailure(requestId, errorString);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(requestId, errorString);
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof RpcFailure) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java b/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java
index cdee0b0e0316b..745039db742fa 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java
@@ -59,6 +59,11 @@ public static RpcRequest decode(ByteBuf buf) {
     return new RpcRequest(requestId, message);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(requestId, Arrays.hashCode(message));
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof RpcRequest) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java b/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java
index 0a62e09a8115c..1671cd444f039 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java
@@ -50,6 +50,11 @@ public static RpcResponse decode(ByteBuf buf) {
     return new RpcResponse(requestId, response);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(requestId, Arrays.hashCode(response));
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof RpcResponse) {
diff --git a/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java b/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java
index 38113a918f795..83c90f9eff2b1 100644
--- a/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java
+++ b/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java
@@ -80,6 +80,11 @@ public Object convertToNetty() throws IOException {
     return underlying.convertToNetty();
   }
 
+  @Override
+  public int hashCode() {
+    return underlying.hashCode();
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ManagedBuffer) {
diff --git a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java
index be6632bb8cf49..8104004847a24 100644
--- a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java
+++ b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java
@@ -17,11 +17,11 @@
 
 package org.apache.spark.network.sasl;
 
-import static com.google.common.base.Charsets.UTF_8;
 import static org.junit.Assert.*;
 import static org.mockito.Mockito.*;
 
 import java.io.File;
+import java.nio.charset.StandardCharsets;
 import java.util.Arrays;
 import java.util.List;
 import java.util.Random;
@@ -138,8 +138,8 @@ private void testBasicSasl(boolean encrypt) throws Exception {
         public Void answer(InvocationOnMock invocation) {
           byte[] message = (byte[]) invocation.getArguments()[1];
           RpcResponseCallback cb = (RpcResponseCallback) invocation.getArguments()[2];
-          assertEquals("Ping", new String(message, UTF_8));
-          cb.onSuccess("Pong".getBytes(UTF_8));
+          assertEquals("Ping", new String(message, StandardCharsets.UTF_8));
+          cb.onSuccess("Pong".getBytes(StandardCharsets.UTF_8));
           return null;
         }
       })
@@ -148,8 +148,9 @@ public Void answer(InvocationOnMock invocation) {
 
     SaslTestCtx ctx = new SaslTestCtx(rpcHandler, encrypt, false);
     try {
-      byte[] response = ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10));
-      assertEquals("Pong", new String(response, UTF_8));
+      byte[] response = ctx.client.sendRpcSync("Ping".getBytes(StandardCharsets.UTF_8),
+                                               TimeUnit.SECONDS.toMillis(10));
+      assertEquals("Pong", new String(response, StandardCharsets.UTF_8));
     } finally {
       ctx.close();
     }
@@ -235,7 +236,7 @@ public void testFileRegionEncryption() throws Exception {
     final String blockSizeConf = "spark.network.sasl.maxEncryptedBlockSize";
     System.setProperty(blockSizeConf, "1k");
 
-    final AtomicReference<ManagedBuffer> response = new AtomicReference();
+    final AtomicReference<ManagedBuffer> response = new AtomicReference<>();
     final File file = File.createTempFile("sasltest", ".txt");
     SaslTestCtx ctx = null;
     try {
@@ -321,7 +322,8 @@ public void testDataEncryptionIsActuallyEnabled() throws Exception {
     SaslTestCtx ctx = null;
     try {
       ctx = new SaslTestCtx(mock(RpcHandler.class), true, true);
-      ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10));
+      ctx.client.sendRpcSync("Ping".getBytes(StandardCharsets.UTF_8),
+                             TimeUnit.SECONDS.toMillis(10));
       fail("Should have failed to send RPC to server.");
     } catch (Exception e) {
       assertFalse(e.getCause() instanceof TimeoutException);
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
index 73374cdc77a23..1d197497b7c8f 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
@@ -90,9 +90,11 @@ public void testOpenShuffleBlocks() {
       (StreamHandle) BlockTransferMessage.Decoder.fromByteArray(response.getValue());
     assertEquals(2, handle.numChunks);
 
-    ArgumentCaptor<Iterator> stream = ArgumentCaptor.forClass(Iterator.class);
+    @SuppressWarnings("unchecked")
+    ArgumentCaptor<Iterator<ManagedBuffer>> stream = (ArgumentCaptor<Iterator<ManagedBuffer>>)
+        (ArgumentCaptor<?>) ArgumentCaptor.forClass(Iterator.class);
     verify(streamManager, times(1)).registerStream(stream.capture());
-    Iterator<ManagedBuffer> buffers = (Iterator<ManagedBuffer>) stream.getValue();
+    Iterator<ManagedBuffer> buffers = stream.getValue();
     assertEquals(block0Marker, buffers.next());
     assertEquals(block1Marker, buffers.next());
     assertFalse(buffers.hasNext());
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java
index 1ad0d72ae5ec5..06e46f9241094 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java
@@ -20,7 +20,9 @@
 
 import java.io.IOException;
 import java.nio.ByteBuffer;
+import java.util.Arrays;
 import java.util.LinkedHashSet;
+import java.util.List;
 import java.util.Map;
 
 import com.google.common.collect.ImmutableMap;
@@ -67,13 +69,13 @@ public void afterEach() {
   public void testNoFailures() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // Immediately return both blocks successfully.
       ImmutableMap.<String, Object>builder()
         .put("b0", block0)
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+      );
 
     performInteractions(interactions, listener);
 
@@ -86,13 +88,13 @@ public void testNoFailures() throws IOException {
   public void testUnrecoverableFailure() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0 throws a non-IOException error, so it will be failed without retry.
       ImmutableMap.<String, Object>builder()
         .put("b0", new RuntimeException("Ouch!"))
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -105,7 +107,7 @@ public void testUnrecoverableFailure() throws IOException {
   public void testSingleIOExceptionOnFirst() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // IOException will cause a retry. Since b0 fails, we will retry both.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException("Connection failed or something"))
@@ -114,8 +116,8 @@ public void testSingleIOExceptionOnFirst() throws IOException {
       ImmutableMap.<String, Object>builder()
         .put("b0", block0)
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -128,7 +130,7 @@ public void testSingleIOExceptionOnFirst() throws IOException {
   public void testSingleIOExceptionOnSecond() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // IOException will cause a retry. Since b1 fails, we will not retry b0.
       ImmutableMap.<String, Object>builder()
         .put("b0", block0)
@@ -136,8 +138,8 @@ public void testSingleIOExceptionOnSecond() throws IOException {
         .build(),
       ImmutableMap.<String, Object>builder()
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -150,7 +152,7 @@ public void testSingleIOExceptionOnSecond() throws IOException {
   public void testTwoIOExceptions() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0's IOException will trigger retry, b1's will be ignored.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException())
@@ -164,8 +166,8 @@ public void testTwoIOExceptions() throws IOException {
       // b1 returns successfully within 2 retries.
       ImmutableMap.<String, Object>builder()
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -178,7 +180,7 @@ public void testTwoIOExceptions() throws IOException {
   public void testThreeIOExceptions() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0's IOException will trigger retry, b1's will be ignored.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException())
@@ -196,8 +198,8 @@ public void testThreeIOExceptions() throws IOException {
       // This is not reached -- b1 has failed.
       ImmutableMap.<String, Object>builder()
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -210,7 +212,7 @@ public void testThreeIOExceptions() throws IOException {
   public void testRetryAndUnrecoverable() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0's IOException will trigger retry, subsequent messages will be ignored.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException())
@@ -226,8 +228,8 @@ public void testRetryAndUnrecoverable() throws IOException {
       // b2 succeeds in its last retry.
       ImmutableMap.<String, Object>builder()
         .put("b2", block2)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -248,7 +250,8 @@ public void testRetryAndUnrecoverable() throws IOException {
    * subset of the original blocks in a second interaction.
    */
   @SuppressWarnings("unchecked")
-  private void performInteractions(final Map[] interactions, BlockFetchingListener listener)
+  private static void performInteractions(List<? extends Map<String, Object>> interactions,
+                                          BlockFetchingListener listener)
     throws IOException {
 
     TransportConf conf = new TransportConf(new SystemPropertyConfigProvider());
diff --git a/pom.xml b/pom.xml
index a958cec867eae..b4ee3ccb0bff5 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1849,6 +1849,7 @@
               <javacArg>${java.version}</javacArg>
               <javacArg>-target</javacArg>
               <javacArg>${java.version}</javacArg>
+              <javacArg>-Xlint:all,-serial,-path</javacArg>
             </javacArgs>
           </configuration>
         </plugin>
@@ -1862,6 +1863,9 @@
             <encoding>UTF-8</encoding>
             <maxmem>1024m</maxmem>
             <fork>true</fork>
+            <compilerArgs>
+              <arg>-Xlint:all,-serial,-path</arg>
+            </compilerArgs>
           </configuration>
         </plugin>
         <!-- Surefire runs all Java tests -->
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 2c669bb59a0b5..7302361ab9fdb 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -167,6 +167,7 @@ public void testCreateDataFrameFromJavaBeans() {
     for (int i = 0; i < result.length(); i++) {
       Assert.assertEquals(bean.getB()[i], result.apply(i));
     }
+    @SuppressWarnings("unchecked")
     Seq<Integer> outputBuffer = (Seq<Integer>) first.getJavaMap(2).get("hello");
     Assert.assertArrayEquals(
       bean.getC().get("hello"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index cfb03ff485b7c..e34e0956d1fdd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql.sources
 
+import java.nio.charset.StandardCharsets
 import java.sql.{Date, Timestamp}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 
 class DefaultSource extends SimpleScanSource
 
@@ -73,7 +71,7 @@ case class AllDataTypesScan(
     sqlContext.sparkContext.parallelize(from to to).map { i =>
       Row(
         s"str_$i",
-        s"str_$i".getBytes(),
+        s"str_$i".getBytes(StandardCharsets.UTF_8),
         i % 2 == 0,
         i.toByte,
         i.toShort,
@@ -83,7 +81,7 @@ case class AllDataTypesScan(
         i.toDouble,
         new java.math.BigDecimal(i),
         new java.math.BigDecimal(i),
-        new Date(1970, 1, 1),
+        Date.valueOf("1970-01-01"),
         new Timestamp(20000 + i),
         s"varchar_$i",
         Seq(i, i + 1),
@@ -92,7 +90,7 @@ case class AllDataTypesScan(
         Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)),
         Row(i, i.toString),
           Row(Seq(s"str_$i", s"str_${i + 1}"),
-            Row(Seq(new Date(1970, 1, i + 1)))))
+            Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))))
     }
   }
 }
@@ -113,7 +111,7 @@ class TableScanSuite extends DataSourceTest {
       i.toDouble,
       new java.math.BigDecimal(i),
       new java.math.BigDecimal(i),
-      new Date(1970, 1, 1),
+      Date.valueOf("1970-01-01"),
       new Timestamp(20000 + i),
       s"varchar_$i",
       Seq(i, i + 1),
@@ -121,7 +119,7 @@ class TableScanSuite extends DataSourceTest {
       Map(i -> i.toString),
       Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)),
       Row(i, i.toString),
-      Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date(1970, 1, i + 1)))))
+      Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))))
   }.toSeq
 
   before {
@@ -280,7 +278,7 @@ class TableScanSuite extends DataSourceTest {
 
   sqlTest(
     "SELECT structFieldComplex.Value.`value_(2)` FROM tableWithSchema",
-    (1 to 10).map(i => Row(Seq(new Date(1970, 1, i + 1)))).toSeq)
+    (1 to 10).map(i => Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))).toSeq)
 
   test("Caching")  {
     // Cached Query Execution
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index f58bc7d7a0af4..a7d5a991948d9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -77,7 +77,7 @@ private[hive] object IsolatedClientLoader {
     // TODO: Remove copy logic.
     val tempDir = Utils.createTempDir(namePrefix = s"hive-${version}")
     allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir))
-    tempDir.listFiles().map(_.toURL)
+    tempDir.listFiles().map(_.toURI.toURL)
   }
 
   private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[URL]]
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index a47f9a4feb21b..05a78930afe3d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -88,7 +88,7 @@ case class AddJar(path: String) extends RunnableCommand {
     val currentClassLoader = Utils.getContextOrSparkClassLoader
 
     // Add jar to current context
-    val jarURL = new java.io.File(path).toURL
+    val jarURL = new java.io.File(path).toURI.toURL
     val newClassLoader = new java.net.URLClassLoader(Array(jarURL), currentClassLoader)
     Thread.currentThread.setContextClassLoader(newClassLoader)
     // We need to explicitly set the class loader associated with the conf in executionHive's
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 741a3cd31c603..613b2bcc80e37 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -54,7 +54,7 @@ public void setUp() throws IOException {
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"key\":" + i + ", \"value\":\"str" + i + "\"}");
     }
-    df = hc.jsonRDD(sc.parallelize(jsonObjects));
+    df = hc.read().json(sc.parallelize(jsonObjects));
     df.registerTempTable("window_table");
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 95c1da6e9796c..fb41451803634 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -660,7 +660,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
   test("resolve udtf in projection #2") {
     val rdd = sparkContext.makeRDD((1 to 2).map(i => s"""{"a":[$i, ${i + 1}]}"""))
-    jsonRDD(rdd).registerTempTable("data")
+    read.json(rdd).registerTempTable("data")
     checkAnswer(sql("SELECT explode(map(1, 1)) FROM data LIMIT 1"), Row(1, 1) :: Nil)
     checkAnswer(sql("SELECT explode(map(1, 1)) as (k1, k2) FROM data LIMIT 1"), Row(1, 1) :: Nil)
     intercept[AnalysisException] {
@@ -675,7 +675,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   // TGF with non-TGF in project is allowed in Spark SQL, but not in Hive
   test("TGF with non-TGF in projection") {
     val rdd = sparkContext.makeRDD( """{"a": "1", "b":"1"}""" :: Nil)
-    jsonRDD(rdd).registerTempTable("data")
+    read.json(rdd).registerTempTable("data")
     checkAnswer(
       sql("SELECT explode(map(a, b)) as (k1, k2), a, b FROM data"),
       Row("1", "1", "1", "1") :: Nil)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
index 58bdda7794bf2..7e735562dca33 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
@@ -40,7 +40,9 @@ private[scheduler] case class ErrorReported(msg: String, e: Throwable) extends J
 private[streaming]
 class JobScheduler(val ssc: StreamingContext) extends Logging {
 
-  private val jobSets = new ConcurrentHashMap[Time, JobSet]
+  // Use of ConcurrentHashMap.keySet later causes an odd runtime problem due to Java 7/8 diff
+  // https://gist.github.com/AlainODea/1375759b8720a3f9f094
+  private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]
   private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
   private val jobExecutor = Executors.newFixedThreadPool(numConcurrentJobs)
   private val jobGenerator = new JobGenerator(this)
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java
index 50e8f9fc159c8..175b8a496b4e5 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java
@@ -17,13 +17,15 @@
 
 package org.apache.spark.streaming;
 
+import java.nio.charset.StandardCharsets;
 import java.util.ArrayList;
 import java.nio.ByteBuffer;
 import java.util.Arrays;
-import java.util.Collection;
+import java.util.Iterator;
+import java.util.List;
 
-import org.apache.commons.collections.CollectionUtils;
-import org.apache.commons.collections.Transformer;
+import com.google.common.base.Function;
+import com.google.common.collect.Iterators;
 import org.apache.spark.SparkConf;
 import org.apache.spark.streaming.util.WriteAheadLog;
 import org.apache.spark.streaming.util.WriteAheadLogRecordHandle;
@@ -32,40 +34,40 @@
 import org.junit.Test;
 import org.junit.Assert;
 
-class JavaWriteAheadLogSuiteHandle extends WriteAheadLogRecordHandle {
-  int index = -1;
-  public JavaWriteAheadLogSuiteHandle(int idx) {
-    index = idx;
-  }
-}
-
 public class JavaWriteAheadLogSuite extends WriteAheadLog {
 
-  class Record {
+  static class JavaWriteAheadLogSuiteHandle extends WriteAheadLogRecordHandle {
+    int index = -1;
+    JavaWriteAheadLogSuiteHandle(int idx) {
+      index = idx;
+    }
+  }
+
+  static class Record {
     long time;
     int index;
     ByteBuffer buffer;
 
-    public Record(long tym, int idx, ByteBuffer buf) {
+    Record(long tym, int idx, ByteBuffer buf) {
       index = idx;
       time = tym;
       buffer = buf;
     }
   }
   private int index = -1;
-  private ArrayList<Record> records = new ArrayList<Record>();
+  private final List<Record> records = new ArrayList<>();
 
 
   // Methods for WriteAheadLog
   @Override
-  public WriteAheadLogRecordHandle write(java.nio.ByteBuffer record, long time) {
+  public WriteAheadLogRecordHandle write(ByteBuffer record, long time) {
     index += 1;
-    records.add(new org.apache.spark.streaming.JavaWriteAheadLogSuite.Record(time, index, record));
+    records.add(new Record(time, index, record));
     return new JavaWriteAheadLogSuiteHandle(index);
   }
 
   @Override
-  public java.nio.ByteBuffer read(WriteAheadLogRecordHandle handle) {
+  public ByteBuffer read(WriteAheadLogRecordHandle handle) {
     if (handle instanceof JavaWriteAheadLogSuiteHandle) {
       int reqdIndex = ((JavaWriteAheadLogSuiteHandle) handle).index;
       for (Record record: records) {
@@ -78,14 +80,13 @@ public java.nio.ByteBuffer read(WriteAheadLogRecordHandle handle) {
   }
 
   @Override
-  public java.util.Iterator<java.nio.ByteBuffer> readAll() {
-    Collection<ByteBuffer> buffers = CollectionUtils.collect(records, new Transformer() {
+  public Iterator<ByteBuffer> readAll() {
+    return Iterators.transform(records.iterator(), new Function<Record,ByteBuffer>() {
       @Override
-      public Object transform(Object input) {
-        return ((Record) input).buffer;
+      public ByteBuffer apply(Record input) {
+        return input.buffer;
       }
     });
-    return buffers.iterator();
   }
 
   @Override
@@ -110,20 +111,21 @@ public void testCustomWAL() {
     WriteAheadLog wal = WriteAheadLogUtils.createLogForDriver(conf, null, null);
 
     String data1 = "data1";
-    WriteAheadLogRecordHandle handle = wal.write(ByteBuffer.wrap(data1.getBytes()), 1234);
+    WriteAheadLogRecordHandle handle =
+        wal.write(ByteBuffer.wrap(data1.getBytes(StandardCharsets.UTF_8)), 1234);
     Assert.assertTrue(handle instanceof JavaWriteAheadLogSuiteHandle);
-    Assert.assertTrue(new String(wal.read(handle).array()).equals(data1));
+    Assert.assertEquals(new String(wal.read(handle).array(), StandardCharsets.UTF_8), data1);
 
-    wal.write(ByteBuffer.wrap("data2".getBytes()), 1235);
-    wal.write(ByteBuffer.wrap("data3".getBytes()), 1236);
-    wal.write(ByteBuffer.wrap("data4".getBytes()), 1237);
+    wal.write(ByteBuffer.wrap("data2".getBytes(StandardCharsets.UTF_8)), 1235);
+    wal.write(ByteBuffer.wrap("data3".getBytes(StandardCharsets.UTF_8)), 1236);
+    wal.write(ByteBuffer.wrap("data4".getBytes(StandardCharsets.UTF_8)), 1237);
     wal.clean(1236, false);
 
-    java.util.Iterator<java.nio.ByteBuffer> dataIterator = wal.readAll();
-    ArrayList<String> readData = new ArrayList<String>();
+    Iterator<ByteBuffer> dataIterator = wal.readAll();
+    List<String> readData = new ArrayList<>();
     while (dataIterator.hasNext()) {
-      readData.add(new String(dataIterator.next().array()));
+      readData.add(new String(dataIterator.next().array(), StandardCharsets.UTF_8));
     }
-    Assert.assertTrue(readData.equals(Arrays.asList("data3", "data4")));
+    Assert.assertEquals(readData, Arrays.asList("data3", "data4"));
   }
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
index a08578680cff9..068a6cb0e8fa4 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
@@ -100,8 +100,8 @@ class UISeleniumSuite
         // Check stat table
         val statTableHeaders = findAll(cssSelector("#stat-table th")).map(_.text).toSeq
         statTableHeaders.exists(
-          _.matches("Timelines \\(Last \\d+ batches, \\d+ active, \\d+ completed\\)")) should be
-          (true)
+          _.matches("Timelines \\(Last \\d+ batches, \\d+ active, \\d+ completed\\)")
+        ) should be (true)
         statTableHeaders should contain ("Histograms")
 
         val statTableCells = findAll(cssSelector("#stat-table td")).map(_.text).toSeq

From b211cbc7369af5eb2cb65d93c4c57c4db7143f47 Mon Sep 17 00:00:00 2001
From: tedyu <yuzhihong@gmail.com>
Date: Tue, 4 Aug 2015 12:22:53 +0100
Subject: [PATCH 0822/1454] [SPARK-8064] [BUILD] Follow-up. Undo change from
 SPARK-9507 that was accidentally reverted

This PR removes the dependency reduced POM hack brought back by #7191

Author: tedyu <yuzhihong@gmail.com>

Closes #7919 from tedyu/master and squashes the following commits:

1bfbd7b [tedyu] [BUILD] Remove dependency reduced POM hack
---
 pom.xml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pom.xml b/pom.xml
index b4ee3ccb0bff5..2bcc55b040a26 100644
--- a/pom.xml
+++ b/pom.xml
@@ -179,9 +179,6 @@
     <jsr305.version>1.3.9</jsr305.version>
     <libthrift.version>0.9.2</libthrift.version>
 
-    <!-- For maven shade plugin (see SPARK-8819) -->
-    <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
-
     <test.java.home>${java.home}</test.java.home>
 
     <!--

From cb7fa0aa93dae5a25a8e7e387dbd6b55a5a23fb0 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Tue, 4 Aug 2015 22:12:30 +0900
Subject: [PATCH 0823/1454] [SPARK-2016] [WEBUI] RDD partition table pagination
 for the RDD Page

Add pagination for the RDD page to avoid unresponsive UI when the number of the RDD partitions is large.
Before:
![rddpagebefore](https://cloud.githubusercontent.com/assets/9278199/8951533/3d9add54-3601-11e5-99d0-5653b473c49b.png)
After:
![rddpageafter](https://cloud.githubusercontent.com/assets/9278199/8951536/439d66e0-3601-11e5-9cee-1b380fe6620d.png)

Author: Carson Wang <carson.wang@intel.com>

Closes #7692 from carsonwang/SPARK-2016 and squashes the following commits:

03c7168 [Carson Wang] Fix style issues
612c18c [Carson Wang] RDD partition table pagination for the RDD Page
---
 .../org/apache/spark/ui/PagedTable.scala      |  16 +-
 .../org/apache/spark/ui/jobs/StagePage.scala  |  10 +-
 .../org/apache/spark/ui/storage/RDDPage.scala | 228 +++++++++++++++---
 3 files changed, 209 insertions(+), 45 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/PagedTable.scala b/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
index 17d7b39c2d951..6e2375477a688 100644
--- a/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
@@ -159,9 +159,9 @@ private[ui] trait PagedTable[T] {
       // "goButtonJsFuncName"
       val formJs =
         s"""$$(function(){
-          |  $$( "#form-task-page" ).submit(function(event) {
-          |    var page = $$("#form-task-page-no").val()
-          |    var pageSize = $$("#form-task-page-size").val()
+          |  $$( "#form-$tableId-page" ).submit(function(event) {
+          |    var page = $$("#form-$tableId-page-no").val()
+          |    var pageSize = $$("#form-$tableId-page-size").val()
           |    pageSize = pageSize ? pageSize: 100;
           |    if (page != "") {
           |      ${goButtonJsFuncName}(page, pageSize);
@@ -173,12 +173,14 @@ private[ui] trait PagedTable[T] {
 
       <div>
         <div>
-          <form id="form-task-page" class="form-inline pull-right" style="margin-bottom: 0px;">
+          <form id={s"form-$tableId-page"}
+                class="form-inline pull-right" style="margin-bottom: 0px;">
             <label>{totalPages} Pages. Jump to</label>
-            <input type="text" id="form-task-page-no" value={page.toString} class="span1" />
+            <input type="text" id={s"form-$tableId-page-no"} value={page.toString} class="span1" />
             <label>. Show </label>
-            <input type="text" id="form-task-page-size" value={pageSize.toString} class="span1" />
-            <label>tasks in a page.</label>
+            <input type="text"
+                   id={s"form-$tableId-page-size"} value={pageSize.toString} class="span1" />
+            <label>items in a page.</label>
             <button type="submit" class="btn">Go</button>
           </form>
         </div>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 3954c3d1ef894..0c94204df6530 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -988,8 +988,7 @@ private[ui] class TaskDataSource(
       shuffleRead,
       shuffleWrite,
       bytesSpilled,
-      errorMessage.getOrElse("")
-    )
+      errorMessage.getOrElse(""))
   }
 
   /**
@@ -1197,7 +1196,7 @@ private[ui] class TaskPagedTable(
   private val displayPeakExecutionMemory =
     conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
 
-  override def tableId: String = ""
+  override def tableId: String = "task-table"
 
   override def tableCssClass: String = "table table-bordered table-condensed table-striped"
 
@@ -1212,8 +1211,7 @@ private[ui] class TaskPagedTable(
     currentTime,
     pageSize,
     sortColumn,
-    desc
-  )
+    desc)
 
   override def pageLink(page: Int): String = {
     val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
@@ -1277,7 +1275,7 @@ private[ui] class TaskPagedTable(
         Seq(("Errors", ""))
 
     if (!taskHeadersAndCssClasses.map(_._1).contains(sortColumn)) {
-      new IllegalArgumentException(s"Unknown column: $sortColumn")
+      throw new IllegalArgumentException(s"Unknown column: $sortColumn")
     }
 
     val headerRow: Seq[Node] = {
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
index 36943978ff594..fd6cc3ed759b3 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.ui.storage
 
+import java.net.URLEncoder
 import javax.servlet.http.HttpServletRequest
 
-import scala.xml.Node
+import scala.xml.{Node, Unparsed}
 
 import org.apache.spark.status.api.v1.{AllRDDResource, RDDDataDistribution, RDDPartitionInfo}
-import org.apache.spark.ui.{UIUtils, WebUIPage}
+import org.apache.spark.ui.{PagedDataSource, PagedTable, UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
 /** Page showing storage details for a given RDD */
@@ -32,6 +33,17 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
   def render(request: HttpServletRequest): Seq[Node] = {
     val parameterId = request.getParameter("id")
     require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
+
+    val parameterBlockPage = request.getParameter("block.page")
+    val parameterBlockSortColumn = request.getParameter("block.sort")
+    val parameterBlockSortDesc = request.getParameter("block.desc")
+    val parameterBlockPageSize = request.getParameter("block.pageSize")
+
+    val blockPage = Option(parameterBlockPage).map(_.toInt).getOrElse(1)
+    val blockSortColumn = Option(parameterBlockSortColumn).getOrElse("Block Name")
+    val blockSortDesc = Option(parameterBlockSortDesc).map(_.toBoolean).getOrElse(false)
+    val blockPageSize = Option(parameterBlockPageSize).map(_.toInt).getOrElse(100)
+
     val rddId = parameterId.toInt
     val rddStorageInfo = AllRDDResource.getRDDStorageInfo(rddId, listener, includeDetails = true)
       .getOrElse {
@@ -44,8 +56,34 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
       rddStorageInfo.dataDistribution.get, id = Some("rdd-storage-by-worker-table"))
 
     // Block table
-    val blockTable = UIUtils.listingTable(blockHeader, blockRow, rddStorageInfo.partitions.get,
-      id = Some("rdd-storage-by-block-table"))
+    val (blockTable, blockTableHTML) = try {
+      val _blockTable = new BlockPagedTable(
+        UIUtils.prependBaseUri(parent.basePath) + s"/storage/rdd/?id=${rddId}",
+        rddStorageInfo.partitions.get,
+        blockPageSize,
+        blockSortColumn,
+        blockSortDesc)
+      (_blockTable, _blockTable.table(blockPage))
+    } catch {
+      case e @ (_ : IllegalArgumentException | _ : IndexOutOfBoundsException) =>
+        (null, <div class="alert alert-error">{e.getMessage}</div>)
+    }
+
+    val jsForScrollingDownToBlockTable =
+      <script>
+        {
+          Unparsed {
+            """
+              |$(function() {
+              |  if (/.*&block.sort=.*$/.test(location.search)) {
+              |    var topOffset = $("#blocks-section").offset().top;
+              |    $("html,body").animate({scrollTop: topOffset}, 200);
+              |  }
+              |});
+            """.stripMargin
+          }
+        }
+      </script>
 
     val content =
       <div class="row-fluid">
@@ -85,11 +123,11 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
         </div>
       </div>
 
-      <div class="row-fluid">
-        <div class="span12">
-          <h4> {rddStorageInfo.partitions.map(_.size).getOrElse(0)} Partitions </h4>
-          {blockTable}
-        </div>
+      <div>
+        <h4 id="blocks-section">
+          {rddStorageInfo.partitions.map(_.size).getOrElse(0)} Partitions
+        </h4>
+        {blockTableHTML ++ jsForScrollingDownToBlockTable}
       </div>;
 
     UIUtils.headerSparkPage("RDD Storage Info for " + rddStorageInfo.name, content, parent)
@@ -101,14 +139,6 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
     "Memory Usage",
     "Disk Usage")
 
-  /** Header fields for the block table */
-  private def blockHeader = Seq(
-    "Block Name",
-    "Storage Level",
-    "Size in Memory",
-    "Size on Disk",
-    "Executors")
-
   /** Render an HTML row representing a worker */
   private def workerRow(worker: RDDDataDistribution): Seq[Node] = {
     <tr>
@@ -120,23 +150,157 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
       <td>{Utils.bytesToString(worker.diskUsed)}</td>
     </tr>
   }
+}
+
+private[ui] case class BlockTableRowData(
+    blockName: String,
+    storageLevel: String,
+    memoryUsed: Long,
+    diskUsed: Long,
+    executors: String)
+
+private[ui] class BlockDataSource(
+    rddPartitions: Seq[RDDPartitionInfo],
+    pageSize: Int,
+    sortColumn: String,
+    desc: Boolean) extends PagedDataSource[BlockTableRowData](pageSize) {
+
+  private val data = rddPartitions.map(blockRow).sorted(ordering(sortColumn, desc))
+
+  override def dataSize: Int = data.size
+
+  override def sliceData(from: Int, to: Int): Seq[BlockTableRowData] = {
+    data.slice(from, to)
+  }
+
+  private def blockRow(rddPartition: RDDPartitionInfo): BlockTableRowData = {
+    BlockTableRowData(
+      rddPartition.blockName,
+      rddPartition.storageLevel,
+      rddPartition.memoryUsed,
+      rddPartition.diskUsed,
+      rddPartition.executors.mkString(" "))
+  }
+
+  /**
+   * Return Ordering according to sortColumn and desc
+   */
+  private def ordering(sortColumn: String, desc: Boolean): Ordering[BlockTableRowData] = {
+    val ordering = sortColumn match {
+      case "Block Name" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.String.compare(x.blockName, y.blockName)
+      }
+      case "Storage Level" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.String.compare(x.storageLevel, y.storageLevel)
+      }
+      case "Size in Memory" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.Long.compare(x.memoryUsed, y.memoryUsed)
+      }
+      case "Size on Disk" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.Long.compare(x.diskUsed, y.diskUsed)
+      }
+      case "Executors" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.String.compare(x.executors, y.executors)
+      }
+      case unknownColumn => throw new IllegalArgumentException(s"Unknown column: $unknownColumn")
+    }
+    if (desc) {
+      ordering.reverse
+    } else {
+      ordering
+    }
+  }
+}
+
+private[ui] class BlockPagedTable(
+    basePath: String,
+    rddPartitions: Seq[RDDPartitionInfo],
+    pageSize: Int,
+    sortColumn: String,
+    desc: Boolean) extends PagedTable[BlockTableRowData] {
+
+  override def tableId: String = "rdd-storage-by-block-table"
+
+  override def tableCssClass: String = "table table-bordered table-condensed table-striped"
+
+  override val dataSource: BlockDataSource = new BlockDataSource(
+    rddPartitions,
+    pageSize,
+    sortColumn,
+    desc)
+
+  override def pageLink(page: Int): String = {
+    val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
+    s"${basePath}&block.page=$page&block.sort=${encodedSortColumn}&block.desc=${desc}" +
+      s"&block.pageSize=${pageSize}"
+  }
+
+  override def goButtonJavascriptFunction: (String, String) = {
+    val jsFuncName = "goToBlockPage"
+    val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
+    val jsFunc = s"""
+      |currentBlockPageSize = ${pageSize}
+      |function goToBlockPage(page, pageSize) {
+      |  // Set page to 1 if the page size changes
+      |  page = pageSize == currentBlockPageSize ? page : 1;
+      |  var url = "${basePath}&block.sort=${encodedSortColumn}&block.desc=${desc}" +
+      |    "&block.page=" + page + "&block.pageSize=" + pageSize;
+      |  window.location.href = url;
+      |}
+     """.stripMargin
+    (jsFuncName, jsFunc)
+  }
 
-  /** Render an HTML row representing a block */
-  private def blockRow(row: RDDPartitionInfo): Seq[Node] = {
+  override def headers: Seq[Node] = {
+    val blockHeaders = Seq(
+      "Block Name",
+      "Storage Level",
+      "Size in Memory",
+      "Size on Disk",
+      "Executors")
+
+    if (!blockHeaders.contains(sortColumn)) {
+      throw new IllegalArgumentException(s"Unknown column: $sortColumn")
+    }
+
+    val headerRow: Seq[Node] = {
+      blockHeaders.map { header =>
+        if (header == sortColumn) {
+          val headerLink =
+            s"$basePath&block.sort=${URLEncoder.encode(header, "UTF-8")}&block.desc=${!desc}" +
+              s"&block.pageSize=${pageSize}"
+          val js = Unparsed(s"window.location.href='${headerLink}'")
+          val arrow = if (desc) "&#x25BE;" else "&#x25B4;" // UP or DOWN
+          <th onclick={js} style="cursor: pointer;">
+            {header}
+            <span>&nbsp;{Unparsed(arrow)}</span>
+          </th>
+        } else {
+          val headerLink =
+            s"$basePath&block.sort=${URLEncoder.encode(header, "UTF-8")}" +
+              s"&block.pageSize=${pageSize}"
+          val js = Unparsed(s"window.location.href='${headerLink}'")
+          <th onclick={js} style="cursor: pointer;">
+            {header}
+          </th>
+        }
+      }
+    }
+    <thead>{headerRow}</thead>
+  }
+
+  override def row(block: BlockTableRowData): Seq[Node] = {
     <tr>
-      <td>{row.blockName}</td>
-      <td>
-        {row.storageLevel}
-      </td>
-      <td sorttable_customkey={row.memoryUsed.toString}>
-        {Utils.bytesToString(row.memoryUsed)}
-      </td>
-      <td sorttable_customkey={row.diskUsed.toString}>
-        {Utils.bytesToString(row.diskUsed)}
-      </td>
-      <td>
-        {row.executors.map(l => <span>{l}<br/></span>)}
-      </td>
+      <td>{block.blockName}</td>
+      <td>{block.storageLevel}</td>
+      <td>{Utils.bytesToString(block.memoryUsed)}</td>
+      <td>{Utils.bytesToString(block.diskUsed)}</td>
+      <td>{block.executors}</td>
     </tr>
   }
 }

From d702d53732b44e8242448ce5302738bd130717d8 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 4 Aug 2015 22:19:11 +0900
Subject: [PATCH 0824/1454] [SPARK-9583] [BUILD] Do not print mvn debug
 messages to stdout.

This allows build/mvn to be used by make-distribution.sh.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7915 from vanzin/SPARK-9583 and squashes the following commits:

6469e60 [Marcelo Vanzin] [SPARK-9583] [build] Do not print mvn debug messages to stdout.
---
 build/mvn | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/build/mvn b/build/mvn
index f62f61ee1c416..4a1664b04053d 100755
--- a/build/mvn
+++ b/build/mvn
@@ -51,11 +51,11 @@ install_app() {
     # check if we have curl installed
     # download application
     [ ! -f "${local_tarball}" ] && [ $(command -v curl) ] && \
-      echo "exec: curl ${curl_opts} ${remote_tarball}" && \
+      echo "exec: curl ${curl_opts} ${remote_tarball}" 1>&2 && \
       curl ${curl_opts} "${remote_tarball}" > "${local_tarball}"
     # if the file still doesn't exist, lets try `wget` and cross our fingers
     [ ! -f "${local_tarball}" ] && [ $(command -v wget) ] && \
-      echo "exec: wget ${wget_opts} ${remote_tarball}" && \
+      echo "exec: wget ${wget_opts} ${remote_tarball}" 1>&2 && \
       wget ${wget_opts} -O "${local_tarball}" "${remote_tarball}"
     # if both were unsuccessful, exit
     [ ! -f "${local_tarball}" ] && \
@@ -146,7 +146,7 @@ fi
 # Set any `mvn` options if not already present
 export MAVEN_OPTS=${MAVEN_OPTS:-"$_COMPILE_JVM_OPTS"}
 
-echo "Using \`mvn\` from path: $MVN_BIN"
+echo "Using \`mvn\` from path: $MVN_BIN" 1>&2
 
 # Last, call the `mvn` command as usual
 ${MVN_BIN} "$@"

From b1f88a38d53aebe7cabb762cdd2f1cc64726b0b4 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Tue, 4 Aug 2015 08:59:42 -0700
Subject: [PATCH 0825/1454] [SPARK-8244] [SQL] string function: find in set

This PR is based on #7186 (just fix the conflict), thanks to tarekauel .

find_in_set(string str, string strList): int

Returns the first occurance of str in strList where strList is a comma-delimited string. Returns null if either argument is null. Returns 0 if the first argument contains any commas. For example, find_in_set('ab', 'abc,b,ab,c,def') returns 3.

Only add this to SQL, not DataFrame.

Closes #7186

Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Davies Liu <davies@databricks.com>

Closes #7900 from davies/find_in_set and squashes the following commits:

4334209 [Davies Liu] Merge branch 'master' of github.com:apache/spark into find_in_set
8f00572 [Davies Liu] Merge branch 'master' of github.com:apache/spark into find_in_set
243ede4 [Tarek Auel] [SPARK-8244][SQL] hive compatibility
1aaf64e [Tarek Auel] [SPARK-8244][SQL] unit test fix
e4093a4 [Tarek Auel] [SPARK-8244][SQL] final modifier for COMMA_UTF8
0d05df5 [Tarek Auel] Merge branch 'master' into SPARK-8244
208d710 [Tarek Auel] [SPARK-8244] address comments & bug fix
71b2e69 [Tarek Auel] [SPARK-8244] find_in_set
66c7fda [Tarek Auel] Merge branch 'master' into SPARK-8244
61b8ca2 [Tarek Auel] [SPARK-8224] removed loop and split; use unsafe String comparison
4f75a65 [Tarek Auel] Merge branch 'master' into SPARK-8244
e3b20c8 [Tarek Auel] [SPARK-8244] added type check
1c2bbb7 [Tarek Auel] [SPARK-8244] findInSet
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/stringOperations.scala        | 25 +++++++++++--
 .../expressions/StringExpressionsSuite.scala  | 10 ++++++
 .../spark/sql/DataFrameFunctionsSuite.scala   |  8 +++++
 .../apache/spark/unsafe/types/UTF8String.java | 35 +++++++++++++++++--
 .../spark/unsafe/types/UTF8StringSuite.java   | 12 +++++++
 6 files changed, 87 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index bc0846646174a..6140d1b129c64 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -177,6 +177,7 @@ object FunctionRegistry {
     expression[ConcatWs]("concat_ws"),
     expression[Encode]("encode"),
     expression[Decode]("decode"),
+    expression[FindInSet]("find_in_set"),
     expression[FormatNumber]("format_number"),
     expression[InitCap]("initcap"),
     expression[Lower]("lcase"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 56225290cd6b1..0cc785d9f3a49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.text.DecimalFormat
-import java.util.Arrays
-import java.util.Locale
+import java.util.{Arrays, Locale}
 import java.util.regex.{MatchResult, Pattern}
 
 import org.apache.commons.lang3.StringEscapeUtils
@@ -350,6 +349,28 @@ case class EndsWith(left: Expression, right: Expression)
   }
 }
 
+/**
+ * A function that returns the index (1-based) of the given string (left) in the comma-
+ * delimited list (right). Returns 0, if the string wasn't found or if the given
+ * string (left) contains a comma.
+ */
+case class FindInSet(left: Expression, right: Expression) extends BinaryExpression
+    with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType)
+
+  override protected def nullSafeEval(word: Any, set: Any): Any =
+    set.asInstanceOf[UTF8String].findInSet(word.asInstanceOf[UTF8String])
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (word, set) =>
+      s"${ev.primitive} = $set.findInSet($word);"
+    )
+  }
+
+  override def dataType: DataType = IntegerType
+}
+
 /**
  * A function that trim the spaces from both ends for the specified string.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 906be701beed7..23f36ca43d663 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -675,4 +675,14 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(FormatNumber(Literal.create(null, IntegerType), Literal(3)), null)
     checkEvaluation(FormatNumber(Literal.create(null, NullType), Literal(3)), null)
   }
+
+  test("find in set") {
+    checkEvaluation(
+      FindInSet(Literal.create(null, StringType), Literal.create(null, StringType)), null)
+    checkEvaluation(FindInSet(Literal("ab"), Literal.create(null, StringType)), null)
+    checkEvaluation(FindInSet(Literal.create(null, StringType), Literal("abc,b,ab,c,def")), null)
+    checkEvaluation(FindInSet(Literal("ab"), Literal("abc,b,ab,c,def")), 3)
+    checkEvaluation(FindInSet(Literal("abf"), Literal("abc,b,ab,c,def")), 0)
+    checkEvaluation(FindInSet(Literal("ab,"), Literal("abc,b,ab,c,def")), 0)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 431dcf7382f16..6137527757f85 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -208,6 +208,14 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(2743272264L, 2180413220L))
   }
 
+  test("string function find_in_set") {
+    val df = Seq(("abc,b,ab,c,def", "abc,b,ab,c,def")).toDF("a", "b")
+
+    checkAnswer(
+      df.selectExpr("find_in_set('ab', a)", "find_in_set('x', b)"),
+      Row(3, 0))
+  }
+
   test("conditional function: least") {
     checkAnswer(
       testData2.select(least(lit(-1), lit(0), col("a"), col("b"))).limit(1),
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index d80bd57bd2048..febbe3d4e54d1 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -54,8 +54,9 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
     5, 5, 5, 5,
     6, 6};
 
-  private static ByteOrder byteOrder = ByteOrder.nativeOrder();
+  private static boolean isLittleEndian = ByteOrder.nativeOrder() == ByteOrder.LITTLE_ENDIAN;
 
+  private static final UTF8String COMMA_UTF8 = UTF8String.fromString(",");
   public static final UTF8String EMPTY_UTF8 = UTF8String.fromString("");
 
   /**
@@ -179,7 +180,7 @@ public long getPrefix() {
     // After getting the data, we use a mask to mask out data that is not part of the string.
     long p;
     long mask = 0;
-    if (byteOrder == ByteOrder.LITTLE_ENDIAN) {
+    if (isLittleEndian) {
       if (numBytes >= 8) {
         p = PlatformDependent.UNSAFE.getLong(base, offset);
       } else if (numBytes > 4) {
@@ -411,6 +412,36 @@ private UTF8String toTitleCaseSlow() {
     return fromString(sb.toString());
   }
 
+  /*
+   * Returns the index of the string `match` in this String. This string has to be a comma separated
+   * list. If `match` contains a comma 0 will be returned. If the `match` isn't part of this String,
+   * 0 will be returned, else the index of match (1-based index)
+   */
+  public int findInSet(UTF8String match) {
+    if (match.contains(COMMA_UTF8)) {
+      return 0;
+    }
+
+    int n = 1, lastComma = -1;
+    for (int i = 0; i < numBytes; i++) {
+      if (getByte(i) == (byte) ',') {
+        if (i - (lastComma + 1) == match.numBytes &&
+          ByteArrayMethods.arrayEquals(base, offset + (lastComma + 1), match.base, match.offset,
+            match.numBytes)) {
+          return n;
+        }
+        lastComma = i;
+        n++;
+      }
+    }
+    if (numBytes - (lastComma + 1) == match.numBytes &&
+      ByteArrayMethods.arrayEquals(base, offset + (lastComma + 1), match.base, match.offset,
+        match.numBytes)) {
+      return n;
+    }
+    return 0;
+  }
+
   /**
    * Copy the bytes from the current UTF8String, and make a new UTF8String.
    * @param start the start position of the current UTF8String in bytes.
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 9b3190f8f0c3f..b30c94c1c1f80 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -399,6 +399,18 @@ public void createBlankString() {
     assertEquals(fromString(""), blankString(0));
   }
 
+  @Test
+  public void findInSet() {
+    assertEquals(fromString("ab").findInSet(fromString("ab")), 1);
+    assertEquals(fromString("a,b").findInSet(fromString("b")), 2);
+    assertEquals(fromString("abc,b,ab,c,def").findInSet(fromString("ab")), 3);
+    assertEquals(fromString("ab,abc,b,ab,c,def").findInSet(fromString("ab")), 1);
+    assertEquals(fromString(",,,ab,abc,b,ab,c,def").findInSet(fromString("ab")), 4);
+    assertEquals(fromString(",ab,abc,b,ab,c,def").findInSet(fromString("")), 1);
+    assertEquals(fromString("数据砖头,abc,b,ab,c,def").findInSet(fromString("ab")), 4);
+    assertEquals(fromString("数据砖头,abc,b,ab,c,def").findInSet(fromString("def")), 6);
+  }
+
   @Test
   public void soundex() {
     assertEquals(fromString("Robert").soundex(), fromString("R163"));

From 73dedb589d06f7c7a525cc4f07721a77f480c434 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 4 Aug 2015 09:07:09 -0700
Subject: [PATCH 0826/1454] [SPARK-8246] [SQL] Implement get_json_object
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This is based on #7485 , thanks to NathanHowell

Tests were copied from Hive, but do not seem to be super comprehensive. I've generally replicated Hive's unusual behavior rather than following a JSONPath reference, except for one case (as noted in the comments). I don't know if there is a way of fully replicating Hive's behavior without a slower TreeNode implementation, so I've erred on the side of performance instead.

Author: Davies Liu <davies@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Nathan Howell <nhowell@godaddy.com>

Closes #7901 from davies/get_json_object and squashes the following commits:

3ace9b9 [Davies Liu] Merge branch 'get_json_object' of github.com:davies/spark into get_json_object
98766fc [Davies Liu] Merge branch 'master' of github.com:apache/spark into get_json_object
a7dc6d0 [Davies Liu] Update JsonExpressionsSuite.scala
c818519 [Yin Huai] new results.
18ce26b [Davies Liu] fix tests
6ac29fb [Yin Huai] Golden files.
25eebef [Davies Liu] use HiveQuerySuite
e0ac6ec [Yin Huai] Golden answer files.
940c060 [Davies Liu] tweat code style
44084c5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into get_json_object
9192d09 [Nathan Howell] Match Hive’s behavior for unwrapping arrays of one element
8dab647 [Nathan Howell] [SPARK-8246] [SQL] Implement get_json_object
---
 .../catalyst/analysis/FunctionRegistry.scala  |   1 +
 .../catalyst/expressions/jsonFunctions.scala  | 309 ++++++++++++++++++
 .../expressions/JsonExpressionsSuite.scala    | 202 ++++++++++++
 .../apache/spark/sql/JsonFunctionsSuite.scala |  32 ++
 .../execution/HiveCompatibilitySuite.scala    |   3 +
 .../apache/spark/sql/hive/test/TestHive.scala |   6 +-
 ...ject #1-0-f01b340b5662c45bb5f1e3b7c6900e1f |   1 +
 ...ect #10-0-f3f47d06d7c51d493d68112b0bd6c1fc |   1 +
 ...bject #2-0-e84c2f8136919830fd665a278e4158a |   1 +
 ...ject #3-0-bf140c65c31f8d892ec23e41e16e58bb |   1 +
 ...ject #4-0-f0bd902edc1990c9a6c65a6bb672c4d5 |   1 +
 ...bject #5-0-3c09f4316a1533049aee8af749cdcab |   1 +
 ...ject #6-0-8334d1ddbe0f41fc7b80d4e6b45409da |   1 +
 ...ject #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0 |   1 +
 ...ject #8-0-180b4b6fdb26011fec05a7ca99fd9844 |   1 +
 ...ject #9-0-47c451a969d856f008f4d6b3d378d94b |   1 +
 .../sql/hive/execution/HiveQuerySuite.scala   |  51 +++
 17 files changed, 613 insertions(+), 1 deletion(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 6140d1b129c64..43e3e9b910e7d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -179,6 +179,7 @@ object FunctionRegistry {
     expression[Decode]("decode"),
     expression[FindInSet]("find_in_set"),
     expression[FormatNumber]("format_number"),
+    expression[GetJsonObject]("get_json_object"),
     expression[InitCap]("initcap"),
     expression[Lower]("lcase"),
     expression[Lower]("lower"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
new file mode 100644
index 0000000000000..23bfa18c94286
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
@@ -0,0 +1,309 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.io.{StringWriter, ByteArrayOutputStream}
+
+import com.fasterxml.jackson.core._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.types.{StringType, DataType}
+import org.apache.spark.unsafe.types.UTF8String
+
+import scala.util.parsing.combinator.RegexParsers
+
+private[this] sealed trait PathInstruction
+private[this] object PathInstruction {
+  private[expressions] case object Subscript extends PathInstruction
+  private[expressions] case object Wildcard extends PathInstruction
+  private[expressions] case object Key extends PathInstruction
+  private[expressions] case class Index(index: Long) extends PathInstruction
+  private[expressions] case class Named(name: String) extends PathInstruction
+}
+
+private[this] sealed trait WriteStyle
+private[this] object WriteStyle {
+  private[expressions] case object RawStyle extends WriteStyle
+  private[expressions] case object QuotedStyle extends WriteStyle
+  private[expressions] case object FlattenStyle extends WriteStyle
+}
+
+private[this] object JsonPathParser extends RegexParsers {
+  import PathInstruction._
+
+  def root: Parser[Char] = '$'
+
+  def long: Parser[Long] = "\\d+".r ^? {
+    case x => x.toLong
+  }
+
+  // parse `[*]` and `[123]` subscripts
+  def subscript: Parser[List[PathInstruction]] =
+    for {
+      operand <- '[' ~> ('*' ^^^ Wildcard | long ^^ Index) <~ ']'
+    } yield {
+      Subscript :: operand :: Nil
+    }
+
+  // parse `.name` or `['name']` child expressions
+  def named: Parser[List[PathInstruction]] =
+    for {
+      name <- '.' ~> "[^\\.\\[]+".r | "[\\'" ~> "[^\\'\\?]+" <~ "\\']"
+    } yield {
+      Key :: Named(name) :: Nil
+    }
+
+  // child wildcards: `..`, `.*` or `['*']`
+  def wildcard: Parser[List[PathInstruction]] =
+    (".*" | "['*']") ^^^ List(Wildcard)
+
+  def node: Parser[List[PathInstruction]] =
+    wildcard |
+      named |
+      subscript
+
+  val expression: Parser[List[PathInstruction]] = {
+    phrase(root ~> rep(node) ^^ (x => x.flatten))
+  }
+
+  def parse(str: String): Option[List[PathInstruction]] = {
+    this.parseAll(expression, str) match {
+      case Success(result, _) =>
+        Some(result)
+
+      case NoSuccess(msg, next) =>
+        None
+    }
+  }
+}
+
+private[this] object GetJsonObject {
+  private val jsonFactory = new JsonFactory()
+
+  // Enabled for Hive compatibility
+  jsonFactory.enable(JsonParser.Feature.ALLOW_UNQUOTED_CONTROL_CHARS)
+}
+
+/**
+ * Extracts json object from a json string based on json path specified, and returns json string
+ * of the extracted json object. It will return null if the input json string is invalid.
+ */
+case class GetJsonObject(json: Expression, path: Expression)
+  extends BinaryExpression with ExpectsInputTypes with CodegenFallback {
+
+  import GetJsonObject._
+  import PathInstruction._
+  import WriteStyle._
+  import com.fasterxml.jackson.core.JsonToken._
+
+  override def left: Expression = json
+  override def right: Expression = path
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+  override def dataType: DataType = StringType
+  override def prettyName: String = "get_json_object"
+
+  @transient private lazy val parsedPath = parsePath(path.eval().asInstanceOf[UTF8String])
+
+  override def eval(input: InternalRow): Any = {
+    val jsonStr = json.eval(input).asInstanceOf[UTF8String]
+    if (jsonStr == null) {
+      return null
+    }
+
+    val parsed = if (path.foldable) {
+      parsedPath
+    } else {
+      parsePath(path.eval(input).asInstanceOf[UTF8String])
+    }
+
+    if (parsed.isDefined) {
+      try {
+        val parser = jsonFactory.createParser(jsonStr.getBytes)
+        val output = new ByteArrayOutputStream()
+        val generator = jsonFactory.createGenerator(output, JsonEncoding.UTF8)
+        parser.nextToken()
+        val matched = evaluatePath(parser, generator, RawStyle, parsed.get)
+        generator.close()
+        if (matched) {
+          UTF8String.fromBytes(output.toByteArray)
+        } else {
+          null
+        }
+      } catch {
+        case _: JsonProcessingException => null
+      }
+    } else {
+      null
+    }
+  }
+
+  private def parsePath(path: UTF8String): Option[List[PathInstruction]] = {
+    if (path != null) {
+      JsonPathParser.parse(path.toString)
+    } else {
+      None
+    }
+  }
+
+  // advance to the desired array index, assumes to start at the START_ARRAY token
+  private def arrayIndex(p: JsonParser, f: () => Boolean): Long => Boolean = {
+    case _ if p.getCurrentToken == END_ARRAY =>
+      // terminate, nothing has been written
+      false
+
+    case 0 =>
+      // we've reached the desired index
+      val dirty = f()
+
+      while (p.nextToken() != END_ARRAY) {
+        // advance the token stream to the end of the array
+        p.skipChildren()
+      }
+
+      dirty
+
+    case i if i > 0 =>
+      // skip this token and evaluate the next
+      p.skipChildren()
+      p.nextToken()
+      arrayIndex(p, f)(i - 1)
+  }
+
+  /**
+   * Evaluate a list of JsonPath instructions, returning a bool that indicates if any leaf nodes
+   * have been written to the generator
+   */
+  private def evaluatePath(
+      p: JsonParser,
+      g: JsonGenerator,
+      style: WriteStyle,
+      path: List[PathInstruction]): Boolean = {
+    (p.getCurrentToken, path) match {
+      case (VALUE_STRING, Nil) if style == RawStyle =>
+        // there is no array wildcard or slice parent, emit this string without quotes
+        if (p.hasTextCharacters) {
+          g.writeRaw(p.getTextCharacters, p.getTextOffset, p.getTextLength)
+        } else {
+          g.writeRaw(p.getText)
+        }
+        true
+
+      case (START_ARRAY, Nil) if style == FlattenStyle =>
+        // flatten this array into the parent
+        var dirty = false
+        while (p.nextToken() != END_ARRAY) {
+          dirty |= evaluatePath(p, g, style, Nil)
+        }
+        dirty
+
+      case (_, Nil) =>
+        // general case: just copy the child tree verbatim
+        g.copyCurrentStructure(p)
+        true
+
+      case (START_OBJECT, Key :: xs) =>
+        var dirty = false
+        while (p.nextToken() != END_OBJECT) {
+          if (dirty) {
+            // once a match has been found we can skip other fields
+            p.skipChildren()
+          } else {
+            dirty = evaluatePath(p, g, style, xs)
+          }
+        }
+        dirty
+
+      case (START_ARRAY, Subscript :: Wildcard :: Subscript :: Wildcard :: xs) =>
+        // special handling for the non-structure preserving double wildcard behavior in Hive
+        var dirty = false
+        g.writeStartArray()
+        while (p.nextToken() != END_ARRAY) {
+          dirty |= evaluatePath(p, g, FlattenStyle, xs)
+        }
+        g.writeEndArray()
+        dirty
+
+      case (START_ARRAY, Subscript :: Wildcard :: xs) if style != QuotedStyle =>
+        // retain Flatten, otherwise use Quoted... cannot use Raw within an array
+        val nextStyle = style match {
+          case RawStyle => QuotedStyle
+          case FlattenStyle => FlattenStyle
+          case QuotedStyle => throw new IllegalStateException()
+        }
+
+        // temporarily buffer child matches, the emitted json will need to be
+        // modified slightly if there is only a single element written
+        val buffer = new StringWriter()
+        val flattenGenerator = jsonFactory.createGenerator(buffer)
+        flattenGenerator.writeStartArray()
+
+        var dirty = 0
+        while (p.nextToken() != END_ARRAY) {
+          // track the number of array elements and only emit an outer array if
+          // we've written more than one element, this matches Hive's behavior
+          dirty += (if (evaluatePath(p, flattenGenerator, nextStyle, xs)) 1 else 0)
+        }
+        flattenGenerator.writeEndArray()
+        flattenGenerator.close()
+
+        val buf = buffer.getBuffer
+        if (dirty > 1) {
+          g.writeRawValue(buf.toString)
+        } else if (dirty == 1) {
+          // remove outer array tokens
+          g.writeRawValue(buf.substring(1, buf.length()-1))
+        } // else do not write anything
+
+        dirty > 0
+
+      case (START_ARRAY, Subscript :: Wildcard :: xs) =>
+        var dirty = false
+        g.writeStartArray()
+        while (p.nextToken() != END_ARRAY) {
+          // wildcards can have multiple matches, continually update the dirty count
+          dirty |= evaluatePath(p, g, QuotedStyle, xs)
+        }
+        g.writeEndArray()
+
+        dirty
+
+      case (START_ARRAY, Subscript :: Index(idx) :: (xs@Subscript :: Wildcard :: _)) =>
+        p.nextToken()
+        // we're going to have 1 or more results, switch to QuotedStyle
+        arrayIndex(p, () => evaluatePath(p, g, QuotedStyle, xs))(idx)
+
+      case (START_ARRAY, Subscript :: Index(idx) :: xs) =>
+        p.nextToken()
+        arrayIndex(p, () => evaluatePath(p, g, style, xs))(idx)
+
+      case (FIELD_NAME, Named(name) :: xs) if p.getCurrentName == name =>
+        // exact field match
+        p.nextToken()
+        evaluatePath(p, g, style, xs)
+
+      case (FIELD_NAME, Wildcard :: xs) =>
+        // wildcard field match
+        p.nextToken()
+        evaluatePath(p, g, style, xs)
+
+      case _ =>
+        p.skipChildren()
+        false
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
new file mode 100644
index 0000000000000..4addbaf0cbce7
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
@@ -0,0 +1,202 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.SparkFunSuite
+
+class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+  val json =
+    """
+      |{"store":{"fruit":[{"weight":8,"type":"apple"},{"weight":9,"type":"pear"}],
+      |"basket":[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]],"book":[{"author":"Nigel Rees",
+      |"title":"Sayings of the Century","category":"reference","price":8.95},
+      |{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,
+      |"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings",
+      |"category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],
+      |"price":22.99,"isbn":"0-395-19395-8"}],"bicycle":{"price":19.95,"color":"red"}},
+      |"email":"amy@only_for_json_udf_test.net","owner":"amy","zip code":"94025",
+      |"fb:testid":"1234"}
+      |""".stripMargin
+
+  test("$.store.bicycle") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.bicycle")),
+      """{"price":19.95,"color":"red"}""")
+  }
+
+  test("$.store.book") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book")),
+      """[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference",
+        |"price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction",
+        |"price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":
+        |"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},
+        |{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
+        |""".stripMargin.replace("\n", ""))
+  }
+
+  test("$.store.book[0]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[0]")),
+      """{"author":"Nigel Rees","title":"Sayings of the Century",
+        |"category":"reference","price":8.95}""".stripMargin.replace("\n", ""))
+  }
+
+  test("$.store.book[*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*]")),
+      """[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference",
+        |"price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction",
+        |"price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":
+        |"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},
+        |{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
+        |""".stripMargin.replace("\n", ""))
+  }
+
+  test("$") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$")),
+      json.replace("\n", ""))
+  }
+
+  test("$.store.book[0].category") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[0].category")),
+      "reference")
+  }
+
+  test("$.store.book[*].category") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*].category")),
+      """["reference","fiction","fiction"]""")
+  }
+
+  test("$.store.book[*].isbn") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*].isbn")),
+      """["0-553-21311-3","0-395-19395-8"]""")
+  }
+
+  test("$.store.book[*].reader") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*].reader")),
+      """[{"age":25,"name":"bob"},{"age":26,"name":"jack"}]""")
+  }
+
+  test("$.store.basket[0][1]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][1]")),
+      "2")
+  }
+
+  test("$.store.basket[*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*]")),
+      """[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]]""")
+  }
+
+  test("$.store.basket[*][0]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*][0]")),
+      "[1,3,5]")
+  }
+
+  test("$.store.basket[0][*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][*]")),
+      """[1,2,{"b":"y","a":"x"}]""")
+  }
+
+  test("$.store.basket[*][*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*][*]")),
+      """[1,2,{"b":"y","a":"x"},3,4,5,6]""")
+  }
+
+  test("$.store.basket[0][2].b") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][2].b")),
+      "y")
+  }
+
+  test("$.store.basket[0][*].b") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][*].b")),
+      """["y"]""")
+  }
+
+  test("$.zip code") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.zip code")),
+      "94025")
+  }
+
+  test("$.fb:testid") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.fb:testid")),
+      "1234")
+  }
+
+  test("preserve newlines") {
+    checkEvaluation(
+      GetJsonObject(Literal("""{"a":"b\nc"}"""), Literal("$.a")),
+      "b\nc")
+  }
+
+  test("escape") {
+    checkEvaluation(
+      GetJsonObject(Literal("""{"a":"b\"c"}"""), Literal("$.a")),
+      "b\"c")
+  }
+
+  test("$.non_exist_key") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.non_exist_key")),
+      null)
+  }
+
+  test("$..no_recursive") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$..no_recursive")),
+      null)
+  }
+
+  test("$.store.book[10]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[10]")),
+      null)
+  }
+
+  test("$.store.book[0].non_exist_key") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[0].non_exist_key")),
+      null)
+  }
+
+  test("$.store.basket[*].non_exist_key") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*].non_exist_key")),
+      null)
+  }
+
+  test("non foldable literal") {
+    checkEvaluation(
+      GetJsonObject(NonFoldableLiteral(json), NonFoldableLiteral("$.fb:testid")),
+      "1234")
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
new file mode 100644
index 0000000000000..71c26a6f8d367
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+class JsonFunctionsSuite extends QueryTest {
+
+  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+  import ctx.implicits._
+
+  test("function get_json_object") {
+    val df: DataFrame = Seq(("""{"name": "alice", "age": 5}""", "")).toDF("a", "b")
+    checkAnswer(
+      df.selectExpr("get_json_object(a, '$.name')", "get_json_object(a, '$.age')"),
+      Row("alice", "5"))
+  }
+
+}
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index c46a4a4b0be54..d4fc6c2b6ebc0 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -266,6 +266,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // Hive returns string from UTC formatted timestamp, spark returns timestamp type
     "date_udf",
 
+    // Can't compare the result that have newline in it
+    "udf_get_json_object",
+
     // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
     "udf7",
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 8d0bf46e8fad7..167086db5bfe2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -374,7 +374,11 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         INSERT OVERWRITE TABLE episodes_part PARTITION (doctor_pt=1)
         SELECT title, air_date, doctor FROM episodes
       """.cmd
-      )
+      ),
+    TestTable("src_json",
+      s"""CREATE TABLE src_json (json STRING) STORED AS TEXTFILE
+       """.stripMargin.cmd,
+      s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/json.txt")}' INTO TABLE src_json".cmd)
   )
 
   hiveQTestUtilTables.foreach(registerTestTable)
diff --git a/sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f b/sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f
new file mode 100644
index 0000000000000..1dcda4315a14d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f	
@@ -0,0 +1 @@
+{"store":{"fruit":[{"weight":8,"type":"apple"},{"weight":9,"type":"pear"}],"basket":[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]],"book":[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}],"bicycle":{"price":19.95,"color":"red"}},"email":"amy@only_for_json_udf_test.net","owner":"amy","zip code":"94025","fb:testid":"1234"}
diff --git a/sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc b/sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc
new file mode 100644
index 0000000000000..81c545efebe5f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc	
@@ -0,0 +1 @@
+1234
diff --git a/sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a b/sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a
new file mode 100644
index 0000000000000..99127db9e311c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a	
@@ -0,0 +1 @@
+amy	{"fruit":[{"weight":8,"type":"apple"},{"weight":9,"type":"pear"}],"basket":[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]],"book":[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}],"bicycle":{"price":19.95,"color":"red"}}
diff --git a/sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb b/sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb
new file mode 100644
index 0000000000000..0bc03998296af
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb	
@@ -0,0 +1 @@
+{"price":19.95,"color":"red"}	[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5 b/sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5
new file mode 100644
index 0000000000000..4f7e09bd3fa73
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5	
@@ -0,0 +1 @@
+{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95}	[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab b/sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab
new file mode 100644
index 0000000000000..b2d212a597d99
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab	
@@ -0,0 +1 @@
+reference	["reference","fiction","fiction"]	["0-553-21311-3","0-395-19395-8"]	[{"age":25,"name":"bob"},{"age":26,"name":"jack"}]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da b/sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da
new file mode 100644
index 0000000000000..21d88629fcdb6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da	
@@ -0,0 +1 @@
+25	[25,26]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0 b/sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0
new file mode 100644
index 0000000000000..e60721e1dd240
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0	
@@ -0,0 +1 @@
+2	[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]]	1	[1,2,{"b":"y","a":"x"}]	[1,2,{"b":"y","a":"x"},3,4,5,6]	y	["y"]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844 b/sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844
new file mode 100644
index 0000000000000..356fcdf7139bd
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844	
@@ -0,0 +1 @@
+NULL	NULL	NULL	NULL	NULL	NULL
diff --git a/sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b b/sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b
new file mode 100644
index 0000000000000..ef4a39675ed6d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b	
@@ -0,0 +1 @@
+94025
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index a7cfac51cc097..edb27553671d1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -587,6 +587,57 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |select * where key = 4
     """.stripMargin)
 
+  // test get_json_object again Hive, because the HiveCompatabilitySuite cannot handle result
+  // with newline in it.
+  createQueryTest("get_json_object #1",
+    "SELECT get_json_object(src_json.json, '$') FROM src_json")
+
+  createQueryTest("get_json_object #2",
+    "SELECT get_json_object(src_json.json, '$.owner'), get_json_object(src_json.json, '$.store')" +
+      " FROM src_json")
+
+  createQueryTest("get_json_object #3",
+    "SELECT get_json_object(src_json.json, '$.store.bicycle'), " +
+      "get_json_object(src_json.json, '$.store.book') FROM src_json")
+
+  createQueryTest("get_json_object #4",
+    "SELECT get_json_object(src_json.json, '$.store.book[0]'), " +
+      "get_json_object(src_json.json, '$.store.book[*]') FROM src_json")
+
+  createQueryTest("get_json_object #5",
+    "SELECT get_json_object(src_json.json, '$.store.book[0].category'), " +
+      "get_json_object(src_json.json, '$.store.book[*].category'), " +
+      "get_json_object(src_json.json, '$.store.book[*].isbn'), " +
+      "get_json_object(src_json.json, '$.store.book[*].reader') FROM src_json")
+
+  createQueryTest("get_json_object #6",
+    "SELECT get_json_object(src_json.json, '$.store.book[*].reader[0].age'), " +
+      "get_json_object(src_json.json, '$.store.book[*].reader[*].age') FROM src_json")
+
+  createQueryTest("get_json_object #7",
+    "SELECT get_json_object(src_json.json, '$.store.basket[0][1]'), " +
+      "get_json_object(src_json.json, '$.store.basket[*]'), " +
+      // Hive returns wrong result with [*][0], so this expression is change to make test pass
+      "get_json_object(src_json.json, '$.store.basket[0][0]'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][*]'), " +
+      "get_json_object(src_json.json, '$.store.basket[*][*]'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][2].b'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][*].b') FROM src_json")
+
+  createQueryTest("get_json_object #8",
+    "SELECT get_json_object(src_json.json, '$.non_exist_key'), " +
+      "get_json_object(src_json.json, '$..no_recursive'), " +
+      "get_json_object(src_json.json, '$.store.book[10]'), " +
+      "get_json_object(src_json.json, '$.store.book[0].non_exist_key'), " +
+      "get_json_object(src_json.json, '$.store.basket[*].non_exist_key'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][*].non_exist_key') FROM src_json")
+
+  createQueryTest("get_json_object #9",
+    "SELECT get_json_object(src_json.json, '$.zip code') FROM src_json")
+
+  createQueryTest("get_json_object #10",
+    "SELECT get_json_object(src_json.json, '$.fb:testid') FROM src_json")
+
   test("predicates contains an empty AttributeSet() references") {
     sql(
       """

From b5034c9c59947f20423faa46bc6606aad56836b0 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 4 Aug 2015 09:09:52 -0700
Subject: [PATCH 0827/1454] [SPARK-9541] [SQL] DataTimeUtils cleanup

JIRA: https://issues.apache.org/jira/browse/SPARK-9541

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7870 from yjshen/datetime_cleanup and squashes the following commits:

9203e33 [Yijie Shen] revert getMonth & getDayOfMonth
5cad119 [Yijie Shen] rebase code
7d62a74 [Yijie Shen] remove tmp tuple inside split date
e98aaac [Yijie Shen] DataTimeUtils cleanup
---
 .../sql/catalyst/util/DateTimeUtils.scala     | 142 +++++++-----------
 1 file changed, 57 insertions(+), 85 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index f645eb5f7bb01..063940cb9e2c3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -493,6 +493,50 @@ object DateTimeUtils {
     }
   }
 
+  /**
+   * Split date (expressed in days since 1.1.1970) into four fields:
+   * year, month (Jan is Month 1), dayInMonth, daysToMonthEnd (0 if it's last day of month).
+   */
+  def splitDate(date: Int): (Int, Int, Int, Int) = {
+    var (year, dayInYear) = getYearAndDayInYear(date)
+    val isLeap = isLeapYear(year)
+    if (isLeap && dayInYear == 60) {
+      (year, 2, 29, 0)
+    } else {
+      if (isLeap && dayInYear > 60) dayInYear -= 1
+
+      if (dayInYear <= 181) {
+        if (dayInYear <= 31) {
+          (year, 1, dayInYear, 31 - dayInYear)
+        } else if (dayInYear <= 59) {
+          (year, 2, dayInYear - 31, if (isLeap) 60 - dayInYear else 59 - dayInYear)
+        } else if (dayInYear <= 90) {
+          (year, 3, dayInYear - 59, 90 - dayInYear)
+        } else if (dayInYear <= 120) {
+          (year, 4, dayInYear - 90, 120 - dayInYear)
+        } else if (dayInYear <= 151) {
+          (year, 5, dayInYear - 120, 151 - dayInYear)
+        } else {
+          (year, 6, dayInYear - 151, 181 - dayInYear)
+        }
+      } else {
+        if (dayInYear <= 212) {
+          (year, 7, dayInYear - 181, 212 - dayInYear)
+        } else if (dayInYear <= 243) {
+          (year, 8, dayInYear - 212, 243 - dayInYear)
+        } else if (dayInYear <= 273) {
+          (year, 9, dayInYear - 243, 273 - dayInYear)
+        } else if (dayInYear <= 304) {
+          (year, 10, dayInYear - 273, 304 - dayInYear)
+        } else if (dayInYear <= 334) {
+          (year, 11, dayInYear - 304, 334 - dayInYear)
+        } else {
+          (year, 12, dayInYear - 334, 365 - dayInYear)
+        }
+      }
+    }
+  }
+
   /**
    * Returns the month value for the given date. The date is expressed in days
    * since 1.1.1970. January is month 1.
@@ -613,15 +657,16 @@ object DateTimeUtils {
    * Returns a date value, expressed in days since 1.1.1970.
    */
   def dateAddMonths(days: Int, months: Int): Int = {
-    val absoluteMonth = (getYear(days) - YearZero) * 12 + getMonth(days) - 1 + months
+    val (year, monthInYear, dayOfMonth, daysToMonthEnd) = splitDate(days)
+    val absoluteMonth = (year - YearZero) * 12 + monthInYear - 1 + months
     val nonNegativeMonth = if (absoluteMonth >= 0) absoluteMonth else 0
     val currentMonthInYear = nonNegativeMonth % 12
     val currentYear = nonNegativeMonth / 12
+
     val leapDay = if (currentMonthInYear == 1 && isLeapYear(currentYear + YearZero)) 1 else 0
     val lastDayOfMonth = monthDays(currentMonthInYear) + leapDay
 
-    val dayOfMonth = getDayOfMonth(days)
-    val currentDayInMonth = if (getDayOfMonth(days + 1) == 1 || dayOfMonth >= lastDayOfMonth) {
+    val currentDayInMonth = if (daysToMonthEnd == 0 || dayOfMonth >= lastDayOfMonth) {
       // last day of the month
       lastDayOfMonth
     } else {
@@ -640,46 +685,6 @@ object DateTimeUtils {
     daysToMillis(newDays) * 1000L + start - daysToMillis(days) * 1000L + microseconds
   }
 
-  /**
-   * Returns the last dayInMonth in the month it belongs to. The date is expressed
-   * in days since 1.1.1970. the return value starts from 1.
-   */
-  private def getLastDayInMonthOfMonth(date: Int): Int = {
-    var (year, dayInYear) = getYearAndDayInYear(date)
-    if (isLeapYear(year)) {
-      if (dayInYear > 31 && dayInYear <= 60) {
-        return 29
-      } else if (dayInYear > 60) {
-        dayInYear = dayInYear - 1
-      }
-    }
-    if (dayInYear <= 31) {
-      31
-    } else if (dayInYear <= 59) {
-      28
-    } else if (dayInYear <= 90) {
-      31
-    } else if (dayInYear <= 120) {
-      30
-    } else if (dayInYear <= 151) {
-      31
-    } else if (dayInYear <= 181) {
-      30
-    } else if (dayInYear <= 212) {
-      31
-    } else if (dayInYear <= 243) {
-      31
-    } else if (dayInYear <= 273) {
-      30
-    } else if (dayInYear <= 304) {
-      31
-    } else if (dayInYear <= 334) {
-      30
-    } else {
-      31
-    }
-  }
-
   /**
    * Returns number of months between time1 and time2. time1 and time2 are expressed in
    * microseconds since 1.1.1970.
@@ -695,14 +700,13 @@ object DateTimeUtils {
     val millis2 = time2 / 1000L
     val date1 = millisToDays(millis1)
     val date2 = millisToDays(millis2)
-    // TODO(davies): get year, month, dayOfMonth from single function
-    val dayInMonth1 = getDayOfMonth(date1)
-    val dayInMonth2 = getDayOfMonth(date2)
-    val months1 = getYear(date1) * 12 + getMonth(date1)
-    val months2 = getYear(date2) * 12 + getMonth(date2)
-
-    if (dayInMonth1 == dayInMonth2 || (dayInMonth1 == getLastDayInMonthOfMonth(date1)
-      && dayInMonth2 == getLastDayInMonthOfMonth(date2))) {
+    val (year1, monthInYear1, dayInMonth1, daysToMonthEnd1) = splitDate(date1)
+    val (year2, monthInYear2, dayInMonth2, daysToMonthEnd2) = splitDate(date2)
+
+    val months1 = year1 * 12 + monthInYear1
+    val months2 = year2 * 12 + monthInYear2
+
+    if (dayInMonth1 == dayInMonth2 || ((daysToMonthEnd1 == 0) && (daysToMonthEnd2 == 0))) {
       return (months1 - months2).toDouble
     }
     // milliseconds is enough for 8 digits precision on the right side
@@ -745,40 +749,8 @@ object DateTimeUtils {
    * since 1.1.1970.
    */
   def getLastDayOfMonth(date: Int): Int = {
-    var (year, dayInYear) = getYearAndDayInYear(date)
-    if (isLeapYear(year)) {
-      if (dayInYear > 31 && dayInYear <= 60) {
-        return date + (60 - dayInYear)
-      } else if (dayInYear > 60) {
-        dayInYear = dayInYear - 1
-      }
-    }
-    val lastDayOfMonthInYear = if (dayInYear <= 31) {
-      31
-    } else if (dayInYear <= 59) {
-      59
-    } else if (dayInYear <= 90) {
-      90
-    } else if (dayInYear <= 120) {
-      120
-    } else if (dayInYear <= 151) {
-      151
-    } else if (dayInYear <= 181) {
-      181
-    } else if (dayInYear <= 212) {
-      212
-    } else if (dayInYear <= 243) {
-      243
-    } else if (dayInYear <= 273) {
-      273
-    } else if (dayInYear <= 304) {
-      304
-    } else if (dayInYear <= 334) {
-      334
-    } else {
-      365
-    }
-    date + (lastDayOfMonthInYear - dayInYear)
+    val (_, _, _, daysToMonthEnd) = splitDate(date)
+    date + daysToMonthEnd
   }
 
   private val TRUNC_TO_YEAR = 1

From 6a0f8b994de36b7a7bdfb9958d39dbd011776107 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Tue, 4 Aug 2015 09:40:07 -0700
Subject: [PATCH 0828/1454] [SPARK-9562] Change reference to amplab/spark-ec2
 from mesos/

cc srowen pwendell nchammas

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7899 from shivaram/spark-ec2-move and squashes the following commits:

7cc22c9 [Shivaram Venkataraman] Change reference to amplab/spark-ec2 from mesos/
---
 ec2/spark_ec2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index ccf922d9371fb..11fd7ee0ec8df 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -90,7 +90,7 @@
 DEFAULT_SPARK_GITHUB_REPO = "https://github.com/apache/spark"
 
 # Default location to get the spark-ec2 scripts (and ami-list) from
-DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/mesos/spark-ec2"
+DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/amplab/spark-ec2"
 DEFAULT_SPARK_EC2_BRANCH = "branch-1.4"
 
 

From 34a0eb2e89d59b0823efc035ddf2dc93f19540c1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 4 Aug 2015 10:07:53 -0700
Subject: [PATCH 0829/1454] [SPARK-9512][SQL] Revert SPARK-9251, Allow
 evaluation while sorting

The analysis rule has a bug and we ended up making the sorter still capable of doing evaluation, so lets revert this for now.

Author: Michael Armbrust <michael@databricks.com>

Closes #7906 from marmbrus/revertSortProjection and squashes the following commits:

2da6972 [Michael Armbrust] unrevert unrelated changes
4f2b00c [Michael Armbrust] Revert "[SPARK-9251][SQL] do not order by expressions which still need evaluation"
---
 .../sql/catalyst/analysis/Analyzer.scala      | 58 -------------------
 .../plans/logical/basicOperators.scala        | 13 ++---
 .../sql/catalyst/analysis/AnalysisSuite.scala | 36 ++----------
 3 files changed, 8 insertions(+), 99 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index f5daba1543da9..ca17f3e3d06ff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -79,7 +79,6 @@ class Analyzer(
       ExtractWindowExpressions ::
       GlobalAggregates ::
       UnresolvedHavingClauseAttributes ::
-      RemoveEvaluationFromSort ::
       HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
@@ -955,63 +954,6 @@ class Analyzer(
         Project(p.output, newPlan.withNewChildren(newChild :: Nil))
     }
   }
-
-  /**
-   * Removes all still-need-evaluate ordering expressions from sort and use an inner project to
-   * materialize them, finally use a outer project to project them away to keep the result same.
-   * Then we can make sure we only sort by [[AttributeReference]]s.
-   *
-   * As an example,
-   * {{{
-   *   Sort('a, 'b + 1,
-   *     Relation('a, 'b))
-   * }}}
-   * will be turned into:
-   * {{{
-   *   Project('a, 'b,
-   *     Sort('a, '_sortCondition,
-   *       Project('a, 'b, ('b + 1).as("_sortCondition"),
-   *         Relation('a, 'b))))
-   * }}}
-   */
-  object RemoveEvaluationFromSort extends Rule[LogicalPlan] {
-    private def hasAlias(expr: Expression) = {
-      expr.find {
-        case a: Alias => true
-        case _ => false
-      }.isDefined
-    }
-
-    override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      // The ordering expressions have no effect to the output schema of `Sort`,
-      // so `Alias`s in ordering expressions are unnecessary and we should remove them.
-      case s @ Sort(ordering, _, _) if ordering.exists(hasAlias) =>
-        val newOrdering = ordering.map(_.transformUp {
-          case Alias(child, _) => child
-        }.asInstanceOf[SortOrder])
-        s.copy(order = newOrdering)
-
-      case s @ Sort(ordering, global, child)
-        if s.expressions.forall(_.resolved) && s.childrenResolved && !s.hasNoEvaluation =>
-
-        val (ref, needEval) = ordering.partition(_.child.isInstanceOf[AttributeReference])
-
-        val namedExpr = needEval.map(_.child match {
-          case n: NamedExpression => n
-          case e => Alias(e, "_sortCondition")()
-        })
-
-        val newOrdering = ref ++ needEval.zip(namedExpr).map { case (order, ne) =>
-          order.copy(child = ne.toAttribute)
-        }
-
-        // Add still-need-evaluate ordering expressions into inner project and then project
-        // them away after the sort.
-        Project(child.output,
-          Sort(newOrdering, global,
-            Project(child.output ++ namedExpr, child)))
-    }
-  }
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index aacfc86ab0e49..7c404722d811c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -34,7 +34,7 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend
       }.nonEmpty
     )
 
-    expressions.forall(_.resolved) && childrenResolved && !hasSpecialExpressions
+    !expressions.exists(!_.resolved) && childrenResolved && !hasSpecialExpressions
   }
 }
 
@@ -68,7 +68,7 @@ case class Generate(
     generator.resolved &&
       childrenResolved &&
       generator.elementTypes.length == generatorOutput.length &&
-      generatorOutput.forall(_.resolved)
+      !generatorOutput.exists(!_.resolved)
   }
 
   // we don't want the gOutput to be taken as part of the expressions
@@ -188,7 +188,7 @@ case class WithWindowDefinition(
 }
 
 /**
- * @param order  The ordering expressions, should all be [[AttributeReference]]
+ * @param order  The ordering expressions
  * @param global True means global sorting apply for entire data set,
  *               False means sorting only apply within the partition.
  * @param child  Child logical plan
@@ -198,11 +198,6 @@ case class Sort(
     global: Boolean,
     child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
-
-  def hasNoEvaluation: Boolean = order.forall(_.child.isInstanceOf[AttributeReference])
-
-  override lazy val resolved: Boolean =
-    expressions.forall(_.resolved) && childrenResolved && hasNoEvaluation
 }
 
 case class Aggregate(
@@ -217,7 +212,7 @@ case class Aggregate(
       }.nonEmpty
     )
 
-    expressions.forall(_.resolved) && childrenResolved && !hasWindowExpressions
+    !expressions.exists(!_.resolved) && childrenResolved && !hasWindowExpressions
   }
 
   lazy val newAggregation: Option[Aggregate] = Utils.tryConvert(this)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index a86cefe941e8e..221b4e92f086c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -165,39 +165,11 @@ class AnalysisSuite extends AnalysisTest {
 
   test("pull out nondeterministic expressions from Sort") {
     val plan = Sort(Seq(SortOrder(Rand(33), Ascending)), false, testRelation)
-    val analyzed = caseSensitiveAnalyzer.execute(plan)
-    analyzed.transform {
-      case s: Sort if s.expressions.exists(!_.deterministic) =>
-        fail("nondeterministic expressions are not allowed in Sort")
-    }
-  }
-
-  test("remove still-need-evaluate ordering expressions from sort") {
-    val a = testRelation2.output(0)
-    val b = testRelation2.output(1)
-
-    def makeOrder(e: Expression): SortOrder = SortOrder(e, Ascending)
-
-    val noEvalOrdering = makeOrder(a)
-    val noEvalOrderingWithAlias = makeOrder(Alias(Alias(b, "name1")(), "name2")())
-
-    val needEvalExpr = Coalesce(Seq(a, Literal("1")))
-    val needEvalExpr2 = Coalesce(Seq(a, b))
-    val needEvalOrdering = makeOrder(needEvalExpr)
-    val needEvalOrdering2 = makeOrder(needEvalExpr2)
-
-    val plan = Sort(
-      Seq(noEvalOrdering, noEvalOrderingWithAlias, needEvalOrdering, needEvalOrdering2),
-      false, testRelation2)
-
-    val evaluatedOrdering = makeOrder(AttributeReference("_sortCondition", StringType)())
-    val materializedExprs = Seq(needEvalExpr, needEvalExpr2).map(e => Alias(e, "_sortCondition")())
-
+    val projected = Alias(Rand(33), "_nondeterministic")()
     val expected =
-      Project(testRelation2.output,
-        Sort(Seq(makeOrder(a), makeOrder(b), evaluatedOrdering, evaluatedOrdering), false,
-          Project(testRelation2.output ++ materializedExprs, testRelation2)))
-
+      Project(testRelation.output,
+        Sort(Seq(SortOrder(projected.toAttribute, Ascending)), false,
+          Project(testRelation.output :+ projected, testRelation)))
     checkAnalysis(plan, expected)
   }
 }

From 5a23213c148bfe362514f9c71f5273ebda0a848a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 4 Aug 2015 10:12:22 -0700
Subject: [PATCH 0830/1454] [SPARK-8069] [ML] Add multiclass thresholds for
 ProbabilisticClassifier

This PR replaces the old "threshold" with a generalized "thresholds" Param.  We keep getThreshold,setThreshold for backwards compatibility for binary classification.

Note that the primary author of this PR is holdenk

Author: Holden Karau <holden@pigscanfly.ca>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7909 from jkbradley/holdenk-SPARK-8069-add-cutoff-aka-threshold-to-random-forest and squashes the following commits:

3952977 [Joseph K. Bradley] fixed pyspark doc test
85febc8 [Joseph K. Bradley] made python unit tests a little more robust
7eb1d86 [Joseph K. Bradley] small cleanups
6cc2ed8 [Joseph K. Bradley] Fixed remaining merge issues.
0255e44 [Joseph K. Bradley] Many cleanups for thresholds, some more tests
7565a60 [Holden Karau] fix pep8 style checks, add a getThreshold method similar to our LogisticRegression.scala one for API compat
be87f26 [Holden Karau] Convert threshold to thresholds in the python code, add specialized support for Array[Double] to shared parems codegen, etc.
6747dad [Holden Karau] Override raw2prediction for ProbabilisticClassifier, fix some tests
25df168 [Holden Karau] Fix handling of thresholds in LogisticRegression
c02d6c0 [Holden Karau] No default for thresholds
5e43628 [Holden Karau] CR feedback and fixed the renamed test
f3fbbd1 [Holden Karau] revert the changes to random forest :(
51f581c [Holden Karau] Add explicit types to public methods, fix long line
f7032eb [Holden Karau] Fix a java test bug, remove some unecessary changes
adf15b4 [Holden Karau] rename the classifier suite test to ProbabilisticClassifierSuite now that we only have it in Probabilistic
398078a [Holden Karau] move the thresholding around a bunch based on the design doc
4893bdc [Holden Karau] Use numtrees of 3 since previous result was tied (one tree for each) and the switch from different max methods picked a different element (since they were equal I think this is ok)
638854c [Holden Karau] Add a scala RandomForestClassifierSuite test based on corresponding python test
e09919c [Holden Karau] Fix return type, I need more coffee....
8d92cac [Holden Karau] Use ClassifierParams as the head
3456ed3 [Holden Karau] Add explicit return types even though just test
a0f3b0c [Holden Karau] scala style fixes
6f14314 [Holden Karau] Since hasthreshold/hasthresholds is in root classifier now
ffc8dab [Holden Karau] Update the sharedParams
0420290 [Holden Karau] Allow us to override the get methods selectively
978e77a [Holden Karau] Move HasThreshold into classifier params and start defining the overloaded getThreshold/getThresholds functions
1433e52 [Holden Karau] Revert "try and hide threshold but chainges the API so no dice there"
1f09a2e [Holden Karau] try and hide threshold but chainges the API so no dice there
efb9084 [Holden Karau] move setThresholds only to where its used
6b34809 [Holden Karau] Add a test with thresholding for the RFCS
74f54c3 [Holden Karau] Fix creation of vote array
1986fa8 [Holden Karau] Setting the thresholds only makes sense if the underlying class hasn't overridden predict, so lets push it down.
2f44b18 [Holden Karau] Add a global default of null for thresholds param
f338cfc [Holden Karau] Wait that wasn't a good idea, Revert "Some progress towards unifying threshold and thresholds"
634b06f [Holden Karau] Some progress towards unifying threshold and thresholds
85c9e01 [Holden Karau] Test passes again... little fnur
099c0f3 [Holden Karau] Move thresholds around some more (set on model not trainer)
0f46836 [Holden Karau] Start adding a classifiersuite
f70eb5e [Holden Karau] Fix test compile issues
a7d59c8 [Holden Karau] Move thresholding into Classifier trait
5d999d2 [Holden Karau] Some more progress, start adding a test (maybe try and see if we can find a better thing to use for the base of the test)
1fed644 [Holden Karau] Use thresholds to scale scores in random forest classifcation
31d6bf2 [Holden Karau] Start threading the threshold info through
0ef228c [Holden Karau] Add hasthresholds
---
 .../examples/ml/JavaSimpleParamsExample.java  |  3 +-
 .../main/python/ml/simple_params_example.py   |  2 +-
 .../examples/ml/SimpleParamsExample.scala     |  2 +-
 .../spark/ml/classification/Classifier.scala  |  3 +-
 .../classification/LogisticRegression.scala   | 47 ++++++++++--
 .../ProbabilisticClassifier.scala             | 41 +++++++++--
 .../ml/param/shared/SharedParamsCodeGen.scala | 19 ++++-
 .../spark/ml/param/shared/sharedParams.scala  | 17 ++++-
 .../org/apache/spark/ml/tree/treeParams.scala |  3 +-
 .../JavaLogisticRegressionSuite.java          |  9 ++-
 .../LogisticRegressionSuite.scala             | 28 +++++++-
 .../ml/classification/OneVsRestSuite.scala    |  2 +-
 .../ProbabilisticClassifierSuite.scala        | 57 +++++++++++++++
 python/pyspark/ml/classification.py           | 72 +++++++++++++++----
 14 files changed, 265 insertions(+), 40 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
index dac649d1d5ae6..94beeced3d479 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
@@ -77,7 +77,8 @@ public static void main(String[] args) {
     ParamMap paramMap = new ParamMap();
     paramMap.put(lr.maxIter().w(20)); // Specify 1 Param.
     paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
-    paramMap.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
+    double thresholds[] = {0.45, 0.55};
+    paramMap.put(lr.regParam().w(0.1), lr.thresholds().w(thresholds)); // Specify multiple Params.
 
     // One can also combine ParamMaps.
     ParamMap paramMap2 = new ParamMap();
diff --git a/examples/src/main/python/ml/simple_params_example.py b/examples/src/main/python/ml/simple_params_example.py
index a9f29dab2d602..2d6d115d54d02 100644
--- a/examples/src/main/python/ml/simple_params_example.py
+++ b/examples/src/main/python/ml/simple_params_example.py
@@ -70,7 +70,7 @@
 
     # We may alternatively specify parameters using a parameter map.
     # paramMap overrides all lr parameters set earlier.
-    paramMap = {lr.maxIter: 20, lr.threshold: 0.55, lr.probabilityCol: "myProbability"}
+    paramMap = {lr.maxIter: 20, lr.thresholds: [0.45, 0.55], lr.probabilityCol: "myProbability"}
 
     # Now learn a new model using the new parameters.
     model2 = lr.fit(training, paramMap)
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
index 58d7b67674ff7..f4d1fe57856a1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
@@ -70,7 +70,7 @@ object SimpleParamsExample {
     // which supports several methods for specifying parameters.
     val paramMap = ParamMap(lr.maxIter -> 20)
     paramMap.put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
-    paramMap.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
+    paramMap.put(lr.regParam -> 0.1, lr.thresholds -> Array(0.45, 0.55)) // Specify multiple Params.
 
     // One can also combine ParamMaps.
     val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index 581d8fa7749be..45df557a89908 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -18,14 +18,13 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor}
 import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 8fc9199fb4602..c937b9602b8c1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -41,7 +41,39 @@ import org.apache.spark.storage.StorageLevel
  */
 private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol
-  with HasThreshold with HasStandardization
+  with HasStandardization {
+
+  /**
+   * Version of setThresholds() for binary classification, available for backwards
+   * compatibility.
+   *
+   * Calling this with threshold p will effectively call `setThresholds(Array(1-p, p))`.
+   *
+   * Default is effectively 0.5.
+   * @group setParam
+   */
+  def setThreshold(value: Double): this.type = set(thresholds, Array(1.0 - value, value))
+
+  /**
+   * Version of [[getThresholds()]] for binary classification, available for backwards
+   * compatibility.
+   *
+   * Param thresholds must have length 2 (or not be specified).
+   * This returns {{{1 / (1 + thresholds(0) / thresholds(1))}}}.
+   * @group getParam
+   */
+  def getThreshold: Double = {
+    if (isDefined(thresholds)) {
+      val thresholdValues = $(thresholds)
+      assert(thresholdValues.length == 2, "Logistic Regression getThreshold only applies to" +
+        " binary classification, but thresholds has length != 2." +
+        s"  thresholds: ${thresholdValues.mkString(",")}")
+      1.0 / (1.0 + thresholdValues(0) / thresholdValues(1))
+    } else {
+      0.5
+    }
+  }
+}
 
 /**
  * :: Experimental ::
@@ -110,9 +142,9 @@ class LogisticRegression(override val uid: String)
   def setStandardization(value: Boolean): this.type = set(standardization, value)
   setDefault(standardization -> true)
 
-  /** @group setParam */
-  def setThreshold(value: Double): this.type = set(threshold, value)
-  setDefault(threshold -> 0.5)
+  override def setThreshold(value: Double): this.type = super.setThreshold(value)
+
+  override def getThreshold: Double = super.getThreshold
 
   override protected def train(dataset: DataFrame): LogisticRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
@@ -270,8 +302,9 @@ class LogisticRegressionModel private[ml] (
   extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel]
   with LogisticRegressionParams {
 
-  /** @group setParam */
-  def setThreshold(value: Double): this.type = set(threshold, value)
+  override def setThreshold(value: Double): this.type = super.setThreshold(value)
+
+  override def getThreshold: Double = super.getThreshold
 
   /** Margin (rawPrediction) for class label 1.  For binary classification only. */
   private val margin: Vector => Double = (features) => {
@@ -288,7 +321,7 @@ class LogisticRegressionModel private[ml] (
 
   /**
    * Predict label for the given feature vector.
-   * The behavior of this can be adjusted using [[threshold]].
+   * The behavior of this can be adjusted using [[thresholds]].
    */
   override protected def predict(features: Vector): Double = {
     if (score(features) > getThreshold) 1 else 0
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index f9c9c2371f5cd..1e50a895a9a05 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -20,17 +20,16 @@ package org.apache.spark.ml.classification
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
-import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{DenseVector, Vector, VectorUDT, Vectors}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{DoubleType, DataType, StructType}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 /**
  * (private[classification])  Params for probabilistic classification.
  */
 private[classification] trait ProbabilisticClassifierParams
-  extends ClassifierParams with HasProbabilityCol {
-
+  extends ClassifierParams with HasProbabilityCol with HasThresholds {
   override protected def validateAndTransformSchema(
       schema: StructType,
       fitting: Boolean,
@@ -59,6 +58,9 @@ private[spark] abstract class ProbabilisticClassifier[
 
   /** @group setParam */
   def setProbabilityCol(value: String): E = set(probabilityCol, value).asInstanceOf[E]
+
+  /** @group setParam */
+  def setThresholds(value: Array[Double]): E = set(thresholds, value).asInstanceOf[E]
 }
 
 
@@ -80,6 +82,9 @@ private[spark] abstract class ProbabilisticClassificationModel[
   /** @group setParam */
   def setProbabilityCol(value: String): M = set(probabilityCol, value).asInstanceOf[M]
 
+  /** @group setParam */
+  def setThresholds(value: Array[Double]): M = set(thresholds, value).asInstanceOf[M]
+
   /**
    * Transforms dataset by reading from [[featuresCol]], and appending new columns as specified by
    * parameters:
@@ -92,6 +97,11 @@ private[spark] abstract class ProbabilisticClassificationModel[
    */
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema, logging = true)
+    if (isDefined(thresholds)) {
+      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
+        ".transform() called with non-matching numClasses and thresholds.length." +
+        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
+    }
 
     // Output selected columns only.
     // This is a bit complicated since it tries to avoid repeated computation.
@@ -155,6 +165,14 @@ private[spark] abstract class ProbabilisticClassificationModel[
     raw2probabilityInPlace(probs)
   }
 
+  override protected def raw2prediction(rawPrediction: Vector): Double = {
+    if (!isDefined(thresholds)) {
+      rawPrediction.argmax
+    } else {
+      probability2prediction(raw2probability(rawPrediction))
+    }
+  }
+
   /**
    * Predict the probability of each class given the features.
    * These predictions are also called class conditional probabilities.
@@ -170,10 +188,21 @@ private[spark] abstract class ProbabilisticClassificationModel[
 
   /**
    * Given a vector of class conditional probabilities, select the predicted label.
-   * This may be overridden to support thresholds which favor particular labels.
+   * This supports thresholds which favor particular labels.
    * @return  predicted label
    */
-  protected def probability2prediction(probability: Vector): Double = probability.argmax
+  protected def probability2prediction(probability: Vector): Double = {
+    if (!isDefined(thresholds)) {
+      probability.argmax
+    } else {
+      val thresholds: Array[Double] = getThresholds
+      val scaledProbability: Array[Double] =
+        probability.toArray.zip(thresholds).map { case (p, t) =>
+          if (t == 0.0) Double.PositiveInfinity else p / t
+        }
+      Vectors.dense(scaledProbability).argmax
+    }
+  }
 }
 
 private[ml] object ProbabilisticClassificationModel {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index f7ae1de522e01..a97c8059b8d45 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -46,7 +46,13 @@ private[shared] object SharedParamsCodeGen {
         Some("\"probability\"")),
       ParamDesc[Double]("threshold",
         "threshold in binary classification prediction, in range [0, 1]",
-        isValid = "ParamValidators.inRange(0, 1)"),
+        isValid = "ParamValidators.inRange(0, 1)", finalMethods = false),
+      ParamDesc[Array[Double]]("thresholds", "Thresholds in multi-class classification" +
+        " to adjust the probability of predicting each class." +
+        " Array must have length equal to the number of classes, with values >= 0." +
+        " The class with largest value p/t is predicted, where p is the original probability" +
+        " of that class and t is the class' threshold.",
+        isValid = "(t: Array[Double]) => t.forall(_ >= 0)"),
       ParamDesc[String]("inputCol", "input column name"),
       ParamDesc[Array[String]]("inputCols", "input column names"),
       ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")),
@@ -74,7 +80,8 @@ private[shared] object SharedParamsCodeGen {
       name: String,
       doc: String,
       defaultValueStr: Option[String] = None,
-      isValid: String = "") {
+      isValid: String = "",
+      finalMethods: Boolean = true) {
 
     require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.")
     require(doc.nonEmpty) // TODO: more rigorous on doc
@@ -88,6 +95,7 @@ private[shared] object SharedParamsCodeGen {
         case _ if c == classOf[Double] => "DoubleParam"
         case _ if c == classOf[Boolean] => "BooleanParam"
         case _ if c.isArray && c.getComponentType == classOf[String] => s"StringArrayParam"
+        case _ if c.isArray && c.getComponentType == classOf[Double] => s"DoubleArrayParam"
         case _ => s"Param[${getTypeString(c)}]"
       }
     }
@@ -131,6 +139,11 @@ private[shared] object SharedParamsCodeGen {
     } else {
       ""
     }
+    val methodStr = if (param.finalMethods) {
+      "final def"
+    } else {
+      "def"
+    }
 
     s"""
       |/**
@@ -145,7 +158,7 @@ private[shared] object SharedParamsCodeGen {
       |  final val $name: $Param = new $Param(this, "$name", "$doc"$isValid)
       |$setDefault
       |  /** @group getParam */
-      |  final def get$Name: $T = $$($name)
+      |  $methodStr get$Name: $T = $$($name)
       |}
       |""".stripMargin
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index 65e48e4ee5083..f332630c32f1b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -150,7 +150,22 @@ private[ml] trait HasThreshold extends Params {
   final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1))
 
   /** @group getParam */
-  final def getThreshold: Double = $(threshold)
+  def getThreshold: Double = $(threshold)
+}
+
+/**
+ * Trait for shared param thresholds.
+ */
+private[ml] trait HasThresholds extends Params {
+
+  /**
+   * Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold..
+   * @group param
+   */
+  final val thresholds: DoubleArrayParam = new DoubleArrayParam(this, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.", (t: Array[Double]) => t.forall(_ >= 0))
+
+  /** @group getParam */
+  final def getThresholds: Array[Double] = $(thresholds)
 }
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index a0c5238d966bf..e817090f8a16b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.ml.tree
 
+import org.apache.spark.ml.classification.ClassifierParams
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed}
+import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed, HasThresholds}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance}
 import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index f75e024a713ee..fb1de51163f2e 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -87,6 +87,8 @@ public void logisticRegressionWithSetters() {
     LogisticRegression parent = (LogisticRegression) model.parent();
     assert(parent.getMaxIter() == 10);
     assert(parent.getRegParam() == 1.0);
+    assert(parent.getThresholds()[0] == 0.4);
+    assert(parent.getThresholds()[1] == 0.6);
     assert(parent.getThreshold() == 0.6);
     assert(model.getThreshold() == 0.6);
 
@@ -98,7 +100,9 @@ public void logisticRegressionWithSetters() {
       assert(r.getDouble(0) == 0.0);
     }
     // Call transform with params, and check that the params worked.
-    model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
+    double[] thresholds = {1.0, 0.0};
+    model.transform(
+      dataset, model.thresholds().w(thresholds), model.probabilityCol().w("myProb"))
       .registerTempTable("predNotAllZero");
     DataFrame predNotAllZero = jsql.sql("SELECT prediction, myProb FROM predNotAllZero");
     boolean foundNonZero = false;
@@ -108,8 +112,9 @@ public void logisticRegressionWithSetters() {
     assert(foundNonZero);
 
     // Call fit() with new params, and check as many params as we can.
+    double[] thresholds2 = {0.6, 0.4};
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
-        lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
+        lr.thresholds().w(thresholds2), lr.probabilityCol().w("theProb"));
     LogisticRegression parent2 = (LogisticRegression) model2.parent();
     assert(parent2.getMaxIter() == 5);
     assert(parent2.getRegParam() == 0.1);
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index b7dd44753896a..da13dcb42d1ca 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -91,6 +91,28 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(model.hasParent)
   }
 
+  test("setThreshold, getThreshold") {
+    val lr = new LogisticRegression
+    // default
+    withClue("LogisticRegression should not have thresholds set by default") {
+      intercept[java.util.NoSuchElementException] {
+        lr.getThresholds
+      }
+    }
+    // Set via thresholds.
+    // Intuition: Large threshold or large thresholds(1) makes class 0 more likely.
+    lr.setThreshold(1.0)
+    assert(lr.getThresholds === Array(0.0, 1.0))
+    lr.setThreshold(0.0)
+    assert(lr.getThresholds === Array(1.0, 0.0))
+    lr.setThreshold(0.5)
+    assert(lr.getThresholds === Array(0.5, 0.5))
+    // Test getThreshold
+    lr.setThresholds(Array(0.3, 0.7))
+    val expectedThreshold = 1.0 / (1.0 + 0.3 / 0.7)
+    assert(lr.getThreshold ~== expectedThreshold relTol 1E-7)
+  }
+
   test("logistic regression doesn't fit intercept when fitIntercept is off") {
     val lr = new LogisticRegression
     lr.setFitIntercept(false)
@@ -123,14 +145,16 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       s" ${predAllZero.count(_ === 0)} of ${dataset.count()} were 0.")
     // Call transform with params, and check that the params worked.
     val predNotAllZero =
-      model.transform(dataset, model.threshold -> 0.0, model.probabilityCol -> "myProb")
+      model.transform(dataset, model.thresholds -> Array(1.0, 0.0),
+        model.probabilityCol -> "myProb")
         .select("prediction", "myProb")
         .collect()
         .map { case Row(pred: Double, prob: Vector) => pred }
     assert(predNotAllZero.exists(_ !== 0.0))
 
     // Call fit() with new params, and check as many params as we can.
-    val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4,
+    val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1,
+      lr.thresholds -> Array(0.6, 0.4),
       lr.probabilityCol -> "theProb")
     val parent2 = model2.parent.asInstanceOf[LogisticRegression]
     assert(parent2.getMaxIter === 5)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index 3775292f6dca7..bd8e819f6926c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -151,7 +151,7 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext {
     require(ovr1.getClassifier.getOrDefault(lr.maxIter) === 10,
       "copy should handle extra classifier params")
 
-    val ovrModel = ovr1.fit(dataset).copy(ParamMap(lr.threshold -> 0.1))
+    val ovrModel = ovr1.fit(dataset).copy(ParamMap(lr.thresholds -> Array(0.9, 0.1)))
     ovrModel.models.foreach { case m: LogisticRegressionModel =>
       require(m.getThreshold === 0.1, "copy should handle extra model params")
     }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
new file mode 100644
index 0000000000000..8f50cb924e64d
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+
+final class TestProbabilisticClassificationModel(
+    override val uid: String,
+    override val numClasses: Int)
+  extends ProbabilisticClassificationModel[Vector, TestProbabilisticClassificationModel] {
+
+  override def copy(extra: org.apache.spark.ml.param.ParamMap): this.type = defaultCopy(extra)
+
+  override protected def predictRaw(input: Vector): Vector = {
+    input
+  }
+
+  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
+    rawPrediction
+  }
+
+  def friendlyPredict(input: Vector): Double = {
+    predict(input)
+  }
+}
+
+
+class ProbabilisticClassifierSuite extends SparkFunSuite {
+
+  test("test thresholding") {
+    val thresholds = Array(0.5, 0.2)
+    val testModel = new TestProbabilisticClassificationModel("myuid", 2).setThresholds(thresholds)
+    assert(testModel.friendlyPredict(Vectors.dense(Array(1.0, 1.0))) === 1.0)
+    assert(testModel.friendlyPredict(Vectors.dense(Array(1.0, 0.2))) === 0.0)
+  }
+
+  test("test thresholding not required") {
+    val testModel = new TestProbabilisticClassificationModel("myuid", 2)
+    assert(testModel.friendlyPredict(Vectors.dense(Array(1.0, 2.0))) === 1.0)
+  }
+}
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index b5814f76de000..291320f881e7c 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -69,17 +69,25 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
               "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
               "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
     fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
-    threshold = Param(Params._dummy(), "threshold",
-                      "threshold in binary classification prediction, in range [0, 1].")
+    thresholds = Param(Params._dummy(), "thresholds",
+                       "Thresholds in multi-class classification" +
+                       " to adjust the probability of predicting each class." +
+                       " Array must have length equal to the number of classes, with values >= 0." +
+                       " The class with largest value p/t is predicted, where p is the original" +
+                       " probability of that class and t is the class' threshold.")
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                 threshold=None, thresholds=None,
+                 probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
+                 threshold=None, thresholds=None, \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction")
+        Param thresholds overrides Param threshold; threshold is provided
+        for backwards compatibility and only applies to binary classification.
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -93,23 +101,35 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
         #: param for whether to fit an intercept term.
         self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
         #: param for threshold in binary classification prediction, in range [0, 1].
-        self.threshold = Param(self, "threshold",
-                               "threshold in binary classification prediction, in range [0, 1].")
+        self.thresholds = \
+            Param(self, "thresholds",
+                  "Thresholds in multi-class classification" +
+                  " to adjust the probability of predicting each class." +
+                  " Array must have length equal to the number of classes, with values >= 0." +
+                  " The class with largest value p/t is predicted, where p is the original" +
+                  " probability of that class and t is the class' threshold.")
         self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
-                         fitIntercept=True, threshold=0.5)
+                         fitIntercept=True)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                  threshold=None, thresholds=None,
+                  probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
+                  threshold=None, thresholds=None, \
+                  probabilityCol="probability", rawPredictionCol="rawPrediction")
         Sets params for logistic regression.
+        Param thresholds overrides Param threshold; threshold is provided
+        for backwards compatibility and only applies to binary classification.
         """
+        # Under the hood we use thresholds so translate threshold to thresholds if applicable
+        if thresholds is None and threshold is not None:
+            kwargs[thresholds] = [1-threshold, threshold]
         kwargs = self.setParams._input_kwargs
         return self._set(**kwargs)
 
@@ -144,16 +164,44 @@ def getFitIntercept(self):
 
     def setThreshold(self, value):
         """
-        Sets the value of :py:attr:`threshold`.
+        Sets the value of :py:attr:`thresholds` using [1-value, value].
+
+        >>> lr = LogisticRegression()
+        >>> lr.getThreshold()
+        0.5
+        >>> lr.setThreshold(0.6)
+        LogisticRegression_...
+        >>> abs(lr.getThreshold() - 0.6) < 1e-5
+        True
+        """
+        return self.setThresholds([1-value, value])
+
+    def setThresholds(self, value):
+        """
+        Sets the value of :py:attr:`thresholds`.
         """
-        self._paramMap[self.threshold] = value
+        self._paramMap[self.thresholds] = value
         return self
 
+    def getThresholds(self):
+        """
+        Gets the value of thresholds or its default value.
+        """
+        return self.getOrDefault(self.thresholds)
+
     def getThreshold(self):
         """
         Gets the value of threshold or its default value.
         """
-        return self.getOrDefault(self.threshold)
+        if self.isDefined(self.thresholds):
+            thresholds = self.getOrDefault(self.thresholds)
+            if len(thresholds) != 2:
+                raise ValueError("Logistic Regression getThreshold only applies to" +
+                                 " binary classification, but thresholds has length != 2." +
+                                 "  thresholds: " + ",".join(thresholds))
+            return 1.0/(1.0+thresholds[0]/thresholds[1])
+        else:
+            return 0.5
 
 
 class LogisticRegressionModel(JavaModel):

From a0cc01759b0c2cecf340c885d391976eb4e3fad6 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 4 Aug 2015 12:19:52 -0700
Subject: [PATCH 0831/1454] [SPARK-9606] [SQL] Ignore flaky thrift server tests

Author: Michael Armbrust <michael@databricks.com>

Closes #7939 from marmbrus/turnOffThriftTests and squashes the following commits:

80d618e [Michael Armbrust] [SPARK-9606][SQL] Ignore flaky thrift server tests
---
 .../spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 8374629b5d45a..17e7044c46ecb 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -38,7 +38,7 @@ import org.apache.hive.service.cli.thrift.TCLIService.Client
 import org.apache.hive.service.cli.thrift.ThriftCLIServiceClient
 import org.apache.thrift.protocol.TBinaryProtocol
 import org.apache.thrift.transport.TSocket
-import org.scalatest.BeforeAndAfterAll
+import org.scalatest.{Ignore, BeforeAndAfterAll}
 
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.hive.HiveContext
@@ -53,6 +53,7 @@ object TestData {
   val smallKvWithNull = getTestDataFilePath("small_kv_with_null.txt")
 }
 
+@Ignore // SPARK-9606
 class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.binary
 
@@ -379,6 +380,7 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   }
 }
 
+@Ignore // SPARK-9606
 class HiveThriftHttpServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.http
 

From f4b1ac08a1327e6d0ddc317cdf3997a0f68dec72 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 4 Aug 2015 14:40:46 -0700
Subject: [PATCH 0832/1454] [SPARK-9553][SQL] remove the no-longer-necessary
 createCode and createStructCode, and replace the usage

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7890 from cloud-fan/minor and squashes the following commits:

c3b1be3 [Wenchen Fan] fix style
b0cbe2e [Wenchen Fan] remove the createCode and createStructCode, and replace the usage of them by createStructCode
---
 .../codegen/GenerateUnsafeProjection.scala    | 161 +-----------------
 .../expressions/complexTypeCreator.scala      |  10 +-
 2 files changed, 17 insertions(+), 154 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index fc3ecf5451426..71f8ea09f0770 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -116,154 +116,6 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       throw new UnsupportedOperationException(s"Not supported DataType: $fieldType")
   }
 
-  /**
-   * Generates the code to create an [[UnsafeRow]] object based on the input expressions.
-   * @param ctx context for code generation
-   * @param ev specifies the name of the variable for the output [[UnsafeRow]] object
-   * @param expressions input expressions
-   * @return generated code to put the expression output into an [[UnsafeRow]]
-   */
-  def createCode(ctx: CodeGenContext, ev: GeneratedExpressionCode, expressions: Seq[Expression])
-    : String = {
-
-    val ret = ev.primitive
-    ctx.addMutableState("UnsafeRow", ret, s"$ret = new UnsafeRow();")
-    val buffer = ctx.freshName("buffer")
-    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
-    val cursor = ctx.freshName("cursor")
-    val numBytes = ctx.freshName("numBytes")
-
-    val exprs = expressions.map { e => e.dataType match {
-      case st: StructType => createCodeForStruct(ctx, e.gen(ctx), st)
-      case _ => e.gen(ctx)
-    }}
-    val allExprs = exprs.map(_.code).mkString("\n")
-
-    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
-    val additionalSize = expressions.zipWithIndex.map {
-      case (e, i) => genAdditionalSize(e.dataType, exprs(i))
-    }.mkString("")
-
-    val writers = expressions.zipWithIndex.map { case (e, i) =>
-      val update = genFieldWriter(ctx, e.dataType, exprs(i), ret, i, cursor)
-      s"""if (${exprs(i).isNull}) {
-            $ret.setNullAt($i);
-          } else {
-            $update;
-          }"""
-    }.mkString("\n          ")
-
-    s"""
-      $allExprs
-      int $numBytes = $fixedSize $additionalSize;
-      if ($numBytes > $buffer.length) {
-        $buffer = new byte[$numBytes];
-      }
-
-      $ret.pointTo(
-        $buffer,
-        $PlatformDependent.BYTE_ARRAY_OFFSET,
-        ${expressions.size},
-        $numBytes);
-      int $cursor = $fixedSize;
-
-      $writers
-      boolean ${ev.isNull} = false;
-     """
-  }
-
-  /**
-   * Generates the Java code to convert a struct (backed by InternalRow) to UnsafeRow.
-   *
-   * This function also handles nested structs by recursively generating the code to do conversion.
-   *
-   * @param ctx code generation context
-   * @param input the input struct, identified by a [[GeneratedExpressionCode]]
-   * @param schema schema of the struct field
-   */
-  // TODO: refactor createCode and this function to reduce code duplication.
-  private def createCodeForStruct(
-      ctx: CodeGenContext,
-      input: GeneratedExpressionCode,
-      schema: StructType): GeneratedExpressionCode = {
-
-    val isNull = input.isNull
-    val primitive = ctx.freshName("structConvert")
-    ctx.addMutableState("UnsafeRow", primitive, s"$primitive = new UnsafeRow();")
-    val buffer = ctx.freshName("buffer")
-    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
-    val cursor = ctx.freshName("cursor")
-
-    val exprs: Seq[GeneratedExpressionCode] = schema.map(_.dataType).zipWithIndex.map {
-      case (dt, i) => dt match {
-        case st: StructType =>
-          val nestedStructEv = GeneratedExpressionCode(
-            code = "",
-            isNull = s"${input.primitive}.isNullAt($i)",
-            primitive = s"${ctx.getValue(input.primitive, dt, i.toString)}"
-          )
-          createCodeForStruct(ctx, nestedStructEv, st)
-        case _ =>
-          GeneratedExpressionCode(
-            code = "",
-            isNull = s"${input.primitive}.isNullAt($i)",
-            primitive = s"${ctx.getValue(input.primitive, dt, i.toString)}"
-          )
-        }
-    }
-    val allExprs = exprs.map(_.code).mkString("\n")
-
-    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
-    val additionalSize = schema.toSeq.map(_.dataType).zip(exprs).map { case (dt, ev) =>
-      genAdditionalSize(dt, ev)
-    }.mkString("")
-
-    val writers = schema.toSeq.map(_.dataType).zip(exprs).zipWithIndex.map { case ((dt, ev), i) =>
-      val update = genFieldWriter(ctx, dt, ev, primitive, i, cursor)
-      s"""
-          if (${exprs(i).isNull}) {
-            $primitive.setNullAt($i);
-          } else {
-            $update;
-          }
-        """
-    }.mkString("\n          ")
-
-    // Note that we add a shortcut here for performance: if the input is already an UnsafeRow,
-    // just copy the bytes directly into our buffer space without running any conversion.
-    // We also had to use a hack to introduce a "tmp" variable, to avoid the Java compiler from
-    // complaining that a GenericMutableRow (generated by expressions) cannot be cast to UnsafeRow.
-    val tmp = ctx.freshName("tmp")
-    val numBytes = ctx.freshName("numBytes")
-    val code = s"""
-       |${input.code}
-       |if (!${input.isNull}) {
-       |  Object $tmp = (Object) ${input.primitive};
-       |  if ($tmp instanceof UnsafeRow) {
-       |    $primitive = (UnsafeRow) $tmp;
-       |  } else {
-       |    $allExprs
-       |
-       |    int $numBytes = $fixedSize $additionalSize;
-       |    if ($numBytes > $buffer.length) {
-       |      $buffer = new byte[$numBytes];
-       |    }
-       |
-       |    $primitive.pointTo(
-       |      $buffer,
-       |      $PlatformDependent.BYTE_ARRAY_OFFSET,
-       |      ${exprs.size},
-       |      $numBytes);
-       |    int $cursor = $fixedSize;
-       |
-       |    $writers
-       |  }
-       |}
-     """.stripMargin
-
-    GeneratedExpressionCode(code, isNull, primitive)
-  }
-
   /**
    * Generates the Java code to convert a struct (backed by InternalRow) to UnsafeRow.
    *
@@ -271,7 +123,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
    * @param inputs could be the codes for expressions or input struct fields.
    * @param inputTypes types of the inputs
    */
-  private def createCodeForStruct2(
+  private def createCodeForStruct(
       ctx: CodeGenContext,
       inputs: Seq[GeneratedExpressionCode],
       inputTypes: Seq[DataType]): GeneratedExpressionCode = {
@@ -537,7 +389,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         val fieldIsNull = s"$tmp.isNullAt($i)"
         GeneratedExpressionCode("", fieldIsNull, getFieldCode)
       }
-      val converter = createCodeForStruct2(ctx, fieldEvals, fieldTypes)
+      val converter = createCodeForStruct(ctx, fieldEvals, fieldTypes)
       val code = s"""
         ${input.code}
          UnsafeRow $output = null;
@@ -561,6 +413,12 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     case _ => input
   }
 
+  def createCode(ctx: CodeGenContext, expressions: Seq[Expression]): GeneratedExpressionCode = {
+    val exprEvals = expressions.map(e => e.gen(ctx))
+    val exprTypes = expressions.map(_.dataType)
+    createCodeForStruct(ctx, exprEvals, exprTypes)
+  }
+
   protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
     in.map(ExpressionCanonicalizer.execute)
 
@@ -570,8 +428,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   protected def create(expressions: Seq[Expression]): UnsafeProjection = {
     val ctx = newCodeGenContext()
 
-    val exprEvals = expressions.map(e => e.gen(ctx))
-    val eval = createCodeForStruct2(ctx, exprEvals, expressions.map(_.dataType))
+    val eval = createCode(ctx, expressions)
 
     val code = s"""
       public Object generate($exprType[] exprs) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index a145dfb4bbf08..4a071e663e0d1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -211,7 +211,10 @@ case class CreateStructUnsafe(children: Seq[Expression]) extends Expression {
   override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    GenerateUnsafeProjection.createCode(ctx, ev, children)
+    val eval = GenerateUnsafeProjection.createCode(ctx, children)
+    ev.isNull = eval.isNull
+    ev.primitive = eval.primitive
+    eval.code
   }
 
   override def prettyName: String = "struct_unsafe"
@@ -246,7 +249,10 @@ case class CreateNamedStructUnsafe(children: Seq[Expression]) extends Expression
   override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    GenerateUnsafeProjection.createCode(ctx, ev, valExprs)
+    val eval = GenerateUnsafeProjection.createCode(ctx, valExprs)
+    ev.isNull = eval.isNull
+    ev.primitive = eval.primitive
+    eval.code
   }
 
   override def prettyName: String = "named_struct_unsafe"

From ab8ee1a3b93286a62949569615086ef5030e9fae Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 4 Aug 2015 14:42:11 -0700
Subject: [PATCH 0833/1454] [SPARK-9452] [SQL] Support records larger than page
 size in UnsafeExternalSorter

This patch extends UnsafeExternalSorter to support records larger than the page size. The basic strategy is the same as in #7762: store large records in their own overflow pages.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7891 from JoshRosen/large-records-in-sql-sorter and squashes the following commits:

967580b [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-records-in-sql-sorter
948c344 [Josh Rosen] Add large records tests for KV sorter.
3c17288 [Josh Rosen] Combine memory and disk cleanup into general cleanupResources() method
380f217 [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-records-in-sql-sorter
27eafa0 [Josh Rosen] Fix page size in PackedRecordPointerSuite
a49baef [Josh Rosen] Address initial round of review comments
3edb931 [Josh Rosen] Remove accidentally-committed debug statements.
2b164e2 [Josh Rosen] Support large records in UnsafeExternalSorter.
---
 .../unsafe/sort/UnsafeExternalSorter.java     | 173 ++++++++++-----
 .../unsafe/PackedRecordPointerSuite.java      |   8 +-
 .../sort/UnsafeExternalSorterSuite.java       | 129 ++++++++---
 .../execution/UnsafeExternalRowSorter.java    |   2 +-
 .../sql/execution/UnsafeKVExternalSorter.java |  11 +-
 .../UnsafeKVExternalSorterSuite.scala         | 210 +++++++++++-------
 .../unsafe/memory/HeapMemoryAllocator.java    |   3 +
 .../unsafe/memory/UnsafeMemoryAllocator.java  |   3 +
 8 files changed, 372 insertions(+), 167 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index dec7fcfa0ddc1..e6ddd08e5fa99 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -34,6 +34,7 @@
 import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.storage.BlockManager;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -143,8 +144,7 @@ private UnsafeExternalSorter(
     taskContext.addOnCompleteCallback(new AbstractFunction0<BoxedUnit>() {
       @Override
       public BoxedUnit apply() {
-        deleteSpillFiles();
-        freeMemory();
+        cleanupResources();
         return null;
       }
     });
@@ -249,7 +249,7 @@ public int getNumberOfAllocatedPages() {
    *
    * @return the number of bytes freed.
    */
-  public long freeMemory() {
+  private long freeMemory() {
     updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
@@ -275,44 +275,32 @@ public long freeMemory() {
   /**
    * Deletes any spill files created by this sorter.
    */
-  public void deleteSpillFiles() {
+  private void deleteSpillFiles() {
     for (UnsafeSorterSpillWriter spill : spillWriters) {
       File file = spill.getFile();
       if (file != null && file.exists()) {
         if (!file.delete()) {
           logger.error("Was unable to delete spill file {}", file.getAbsolutePath());
-        };
+        }
       }
     }
   }
 
   /**
-   * Checks whether there is enough space to insert a new record into the sorter.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
-
-   * @return true if the record can be inserted without requiring more allocations, false otherwise.
+   * Frees this sorter's in-memory data structures and cleans up its spill files.
    */
-  private boolean haveSpaceForRecord(int requiredSpace) {
-    assert(requiredSpace > 0);
-    assert(inMemSorter != null);
-    return (inMemSorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
+  public void cleanupResources() {
+    deleteSpillFiles();
+    freeMemory();
   }
 
   /**
-   * Allocates more memory in order to insert an additional record. This will request additional
-   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
-   * obtained.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
+   * Checks whether there is enough space to insert an additional record in to the sort pointer
+   * array and grows the array if additional space is required. If the required space cannot be
+   * obtained, then the in-memory data will be spilled to disk.
    */
-  private void allocateSpaceForRecord(int requiredSpace) throws IOException {
+  private void growPointerArrayIfNecessary() throws IOException {
     assert(inMemSorter != null);
-    // TODO: merge these steps to first calculate total memory requirements for this insert,
-    // then try to acquire; no point in acquiring sort buffer only to spill due to no space in the
-    // data page.
     if (!inMemSorter.hasSpaceForAnotherRecord()) {
       logger.debug("Attempting to expand sort pointer array");
       final long oldPointerArrayMemoryUsage = inMemSorter.getMemoryUsage();
@@ -326,7 +314,20 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
         shuffleMemoryManager.release(oldPointerArrayMemoryUsage);
       }
     }
+  }
 
+  /**
+   * Allocates more memory in order to insert an additional record. This will request additional
+   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
+   * obtained.
+   *
+   * @param requiredSpace the required space in the data page, in bytes, including space for storing
+   *                      the record size. This must be less than or equal to the page size (records
+   *                      that exceed the page size are handled via a different code path which uses
+   *                      special overflow pages).
+   */
+  private void acquireNewPageIfNecessary(int requiredSpace) throws IOException {
+    assert (requiredSpace <= pageSizeBytes);
     if (requiredSpace > freeSpaceInCurrentPage) {
       logger.trace("Required space {} is less than free space in current page ({})", requiredSpace,
         freeSpaceInCurrentPage);
@@ -339,9 +340,7 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
       } else {
         final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
         if (memoryAcquired < pageSizeBytes) {
-          if (memoryAcquired > 0) {
-            shuffleMemoryManager.release(memoryAcquired);
-          }
+          shuffleMemoryManager.release(memoryAcquired);
           spill();
           final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
           if (memoryAcquiredAfterSpilling != pageSizeBytes) {
@@ -365,26 +364,59 @@ public void insertRecord(
       long recordBaseOffset,
       int lengthInBytes,
       long prefix) throws IOException {
+
+    growPointerArrayIfNecessary();
     // Need 4 bytes to store the record length.
     final int totalSpaceRequired = lengthInBytes + 4;
-    if (!haveSpaceForRecord(totalSpaceRequired)) {
-      allocateSpaceForRecord(totalSpaceRequired);
+
+    // --- Figure out where to insert the new record ----------------------------------------------
+
+    final MemoryBlock dataPage;
+    long dataPagePosition;
+    boolean useOverflowPage = totalSpaceRequired > pageSizeBytes;
+    if (useOverflowPage) {
+      long overflowPageSize = ByteArrayMethods.roundNumberOfBytesToNearestWord(totalSpaceRequired);
+      // The record is larger than the page size, so allocate a special overflow page just to hold
+      // that record.
+      final long memoryGranted = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+      if (memoryGranted != overflowPageSize) {
+        shuffleMemoryManager.release(memoryGranted);
+        spill();
+        final long memoryGrantedAfterSpill = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+        if (memoryGrantedAfterSpill != overflowPageSize) {
+          shuffleMemoryManager.release(memoryGrantedAfterSpill);
+          throw new IOException("Unable to acquire " + overflowPageSize + " bytes of memory");
+        }
+      }
+      MemoryBlock overflowPage = taskMemoryManager.allocatePage(overflowPageSize);
+      allocatedPages.add(overflowPage);
+      dataPage = overflowPage;
+      dataPagePosition = overflowPage.getBaseOffset();
+    } else {
+      // The record is small enough to fit in a regular data page, but the current page might not
+      // have enough space to hold it (or no pages have been allocated yet).
+      acquireNewPageIfNecessary(totalSpaceRequired);
+      dataPage = currentPage;
+      dataPagePosition = currentPagePosition;
+      // Update bookkeeping information
+      freeSpaceInCurrentPage -= totalSpaceRequired;
+      currentPagePosition += totalSpaceRequired;
     }
-    assert(inMemSorter != null);
+    final Object dataPageBaseObject = dataPage.getBaseObject();
+
+    // --- Insert the record ----------------------------------------------------------------------
 
     final long recordAddress =
-      taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
-    final Object dataPageBaseObject = currentPage.getBaseObject();
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, lengthInBytes);
-    currentPagePosition += 4;
+      taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    dataPagePosition += 4;
     PlatformDependent.copyMemory(
       recordBaseObject,
       recordBaseOffset,
       dataPageBaseObject,
-      currentPagePosition,
+      dataPagePosition,
       lengthInBytes);
-    currentPagePosition += lengthInBytes;
-    freeSpaceInCurrentPage -= totalSpaceRequired;
+    assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
   }
 
@@ -399,33 +431,70 @@ public void insertRecord(
   public void insertKVRecord(
       Object keyBaseObj, long keyOffset, int keyLen,
       Object valueBaseObj, long valueOffset, int valueLen, long prefix) throws IOException {
+
+    growPointerArrayIfNecessary();
     final int totalSpaceRequired = keyLen + valueLen + 4 + 4;
-    if (!haveSpaceForRecord(totalSpaceRequired)) {
-      allocateSpaceForRecord(totalSpaceRequired);
+
+    // --- Figure out where to insert the new record ----------------------------------------------
+
+    final MemoryBlock dataPage;
+    long dataPagePosition;
+    boolean useOverflowPage = totalSpaceRequired > pageSizeBytes;
+    if (useOverflowPage) {
+      long overflowPageSize = ByteArrayMethods.roundNumberOfBytesToNearestWord(totalSpaceRequired);
+      // The record is larger than the page size, so allocate a special overflow page just to hold
+      // that record.
+      final long memoryGranted = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+      if (memoryGranted != overflowPageSize) {
+        shuffleMemoryManager.release(memoryGranted);
+        spill();
+        final long memoryGrantedAfterSpill = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+        if (memoryGrantedAfterSpill != overflowPageSize) {
+          shuffleMemoryManager.release(memoryGrantedAfterSpill);
+          throw new IOException("Unable to acquire " + overflowPageSize + " bytes of memory");
+        }
+      }
+      MemoryBlock overflowPage = taskMemoryManager.allocatePage(overflowPageSize);
+      allocatedPages.add(overflowPage);
+      dataPage = overflowPage;
+      dataPagePosition = overflowPage.getBaseOffset();
+    } else {
+      // The record is small enough to fit in a regular data page, but the current page might not
+      // have enough space to hold it (or no pages have been allocated yet).
+      acquireNewPageIfNecessary(totalSpaceRequired);
+      dataPage = currentPage;
+      dataPagePosition = currentPagePosition;
+      // Update bookkeeping information
+      freeSpaceInCurrentPage -= totalSpaceRequired;
+      currentPagePosition += totalSpaceRequired;
     }
-    assert(inMemSorter != null);
+    final Object dataPageBaseObject = dataPage.getBaseObject();
+
+    // --- Insert the record ----------------------------------------------------------------------
 
     final long recordAddress =
-      taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
-    final Object dataPageBaseObject = currentPage.getBaseObject();
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, keyLen + valueLen + 4);
-    currentPagePosition += 4;
+      taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen + valueLen + 4);
+    dataPagePosition += 4;
 
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, keyLen);
-    currentPagePosition += 4;
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen);
+    dataPagePosition += 4;
 
     PlatformDependent.copyMemory(
-      keyBaseObj, keyOffset, dataPageBaseObject, currentPagePosition, keyLen);
-    currentPagePosition += keyLen;
+      keyBaseObj, keyOffset, dataPageBaseObject, dataPagePosition, keyLen);
+    dataPagePosition += keyLen;
 
     PlatformDependent.copyMemory(
-      valueBaseObj, valueOffset, dataPageBaseObject, currentPagePosition, valueLen);
-    currentPagePosition += valueLen;
+      valueBaseObj, valueOffset, dataPageBaseObject, dataPagePosition, valueLen);
 
-    freeSpaceInCurrentPage -= totalSpaceRequired;
+    assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
   }
 
+  /**
+   * Returns a sorted iterator. It is the caller's responsibility to call `cleanupResources()`
+   * after consuming this iterator.
+   */
   public UnsafeSorterIterator getSortedIterator() throws IOException {
     assert(inMemSorter != null);
     final UnsafeInMemorySorter.SortedIterator inMemoryIterator = inMemSorter.getSortedIterator();
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java
index db9e82759090a..934b7e03050b6 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java
@@ -32,8 +32,8 @@ public class PackedRecordPointerSuite {
   public void heap() {
     final TaskMemoryManager memoryManager =
       new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP));
-    final MemoryBlock page0 = memoryManager.allocatePage(100);
-    final MemoryBlock page1 = memoryManager.allocatePage(100);
+    final MemoryBlock page0 = memoryManager.allocatePage(128);
+    final MemoryBlock page1 = memoryManager.allocatePage(128);
     final long addressInPage1 = memoryManager.encodePageNumberAndOffset(page1,
       page1.getBaseOffset() + 42);
     PackedRecordPointer packedPointer = new PackedRecordPointer();
@@ -50,8 +50,8 @@ public void heap() {
   public void offHeap() {
     final TaskMemoryManager memoryManager =
       new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.UNSAFE));
-    final MemoryBlock page0 = memoryManager.allocatePage(100);
-    final MemoryBlock page1 = memoryManager.allocatePage(100);
+    final MemoryBlock page0 = memoryManager.allocatePage(128);
+    final MemoryBlock page1 = memoryManager.allocatePage(128);
     final long addressInPage1 = memoryManager.encodePageNumberAndOffset(page1,
       page1.getBaseOffset() + 42);
     PackedRecordPointer packedPointer = new PackedRecordPointer();
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index c11949d57a0ea..968185bde78ab 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -18,8 +18,10 @@
 package org.apache.spark.util.collection.unsafe.sort;
 
 import java.io.File;
+import java.io.IOException;
 import java.io.InputStream;
 import java.io.OutputStream;
+import java.util.Arrays;
 import java.util.LinkedList;
 import java.util.UUID;
 
@@ -34,6 +36,7 @@
 import org.mockito.MockitoAnnotations;
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
+import static org.hamcrest.Matchers.greaterThanOrEqualTo;
 import static org.junit.Assert.*;
 import static org.mockito.AdditionalAnswers.returnsSecondArg;
 import static org.mockito.Answers.RETURNS_SMART_NULLS;
@@ -77,12 +80,13 @@ public int compare(
     }
   };
 
+  SparkConf sparkConf;
+  File tempDir;
   ShuffleMemoryManager shuffleMemoryManager;
   @Mock(answer = RETURNS_SMART_NULLS) BlockManager blockManager;
   @Mock(answer = RETURNS_SMART_NULLS) DiskBlockManager diskBlockManager;
   @Mock(answer = RETURNS_SMART_NULLS) TaskContext taskContext;
 
-  File tempDir;
 
   private final long pageSizeBytes = new SparkConf().getSizeAsBytes("spark.buffer.pageSize", "64m");
 
@@ -96,6 +100,7 @@ public OutputStream apply(OutputStream stream) {
   @Before
   public void setUp() {
     MockitoAnnotations.initMocks(this);
+    sparkConf = new SparkConf();
     tempDir = new File(Utils.createTempDir$default$1());
     shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
     spillFilesCreated.clear();
@@ -155,14 +160,19 @@ private void assertSpillFilesWereCleanedUp() {
   }
 
   private static void insertNumber(UnsafeExternalSorter sorter, int value) throws Exception {
-    final int[] arr = new int[] { value };
+    final int[] arr = new int[]{ value };
     sorter.insertRecord(arr, PlatformDependent.INT_ARRAY_OFFSET, 4, value);
   }
 
-  @Test
-  public void testSortingOnlyByPrefix() throws Exception {
+  private static void insertRecord(
+      UnsafeExternalSorter sorter,
+      int[] record,
+      long prefix) throws IOException {
+    sorter.insertRecord(record, PlatformDependent.INT_ARRAY_OFFSET, record.length * 4, prefix);
+  }
 
-    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
+  private UnsafeExternalSorter newSorter() throws IOException {
+    return UnsafeExternalSorter.create(
       taskMemoryManager,
       shuffleMemoryManager,
       blockManager,
@@ -171,7 +181,11 @@ public void testSortingOnlyByPrefix() throws Exception {
       prefixComparator,
       /* initialSize */ 1024,
       pageSizeBytes);
+  }
 
+  @Test
+  public void testSortingOnlyByPrefix() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
     insertNumber(sorter, 5);
     insertNumber(sorter, 1);
     insertNumber(sorter, 3);
@@ -186,26 +200,16 @@ public void testSortingOnlyByPrefix() throws Exception {
       iter.loadNext();
       assertEquals(i, iter.getKeyPrefix());
       assertEquals(4, iter.getRecordLength());
-      // TODO: read rest of value.
+      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     }
 
-    sorter.freeMemory();
+    sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
   }
 
   @Test
   public void testSortingEmptyArrays() throws Exception {
-
-    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
-      taskMemoryManager,
-      shuffleMemoryManager,
-      blockManager,
-      taskContext,
-      recordComparator,
-      prefixComparator,
-      /* initialSize */ 1024,
-      pageSizeBytes);
-
+    final UnsafeExternalSorter sorter = newSorter();
     sorter.insertRecord(null, 0, 0, 0);
     sorter.insertRecord(null, 0, 0, 0);
     sorter.spill();
@@ -222,28 +226,89 @@ public void testSortingEmptyArrays() throws Exception {
       assertEquals(0, iter.getRecordLength());
     }
 
-    sorter.freeMemory();
+    sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
   }
 
   @Test
-  public void testFillingPage() throws Exception {
+  public void spillingOccursInResponseToMemoryPressure() throws Exception {
+    shuffleMemoryManager = new ShuffleMemoryManager(pageSizeBytes * 2);
+    final UnsafeExternalSorter sorter = newSorter();
+    final int numRecords = 100000;
+    for (int i = 0; i <= numRecords; i++) {
+      insertNumber(sorter, numRecords - i);
+    }
+    // Ensure that spill files were created
+    assertThat(tempDir.listFiles().length, greaterThanOrEqualTo(1));
+    // Read back the sorted data:
+    UnsafeSorterIterator iter = sorter.getSortedIterator();
 
-    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
-      taskMemoryManager,
-      shuffleMemoryManager,
-      blockManager,
-      taskContext,
-      recordComparator,
-      prefixComparator,
-      /* initialSize */ 1024,
-      pageSizeBytes);
+    int i = 0;
+    while (iter.hasNext()) {
+      iter.loadNext();
+      assertEquals(i, iter.getKeyPrefix());
+      assertEquals(4, iter.getRecordLength());
+      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+      i++;
+    }
+    sorter.cleanupResources();
+    assertSpillFilesWereCleanedUp();
+  }
 
+  @Test
+  public void testFillingPage() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
     byte[] record = new byte[16];
     while (sorter.getNumberOfAllocatedPages() < 2) {
       sorter.insertRecord(record, PlatformDependent.BYTE_ARRAY_OFFSET, record.length, 0);
     }
-    sorter.freeMemory();
+    sorter.cleanupResources();
+    assertSpillFilesWereCleanedUp();
+  }
+
+  @Test
+  public void sortingRecordsThatExceedPageSize() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
+    final int[] largeRecord = new int[(int) pageSizeBytes + 16];
+    Arrays.fill(largeRecord, 456);
+    final int[] smallRecord = new int[100];
+    Arrays.fill(smallRecord, 123);
+
+    insertRecord(sorter, largeRecord, 456);
+    sorter.spill();
+    insertRecord(sorter, smallRecord, 123);
+    sorter.spill();
+    insertRecord(sorter, smallRecord, 123);
+    insertRecord(sorter, largeRecord, 456);
+
+    UnsafeSorterIterator iter = sorter.getSortedIterator();
+    // Small record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(123, iter.getKeyPrefix());
+    assertEquals(smallRecord.length * 4, iter.getRecordLength());
+    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    // Small record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(123, iter.getKeyPrefix());
+    assertEquals(smallRecord.length * 4, iter.getRecordLength());
+    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    // Large record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(456, iter.getKeyPrefix());
+    assertEquals(largeRecord.length * 4, iter.getRecordLength());
+    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    // Large record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(456, iter.getKeyPrefix());
+    assertEquals(largeRecord.length * 4, iter.getRecordLength());
+    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+
+    assertFalse(iter.hasNext());
+    sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
   }
 
@@ -289,8 +354,10 @@ public void testPeakMemoryUsed() throws Exception {
       newPeakMemory = sorter.getPeakMemoryUsedBytes();
       assertEquals(previousPeakMemory, newPeakMemory);
     } finally {
-      sorter.freeMemory();
+      sorter.cleanupResources();
+      assertSpillFilesWereCleanedUp();
     }
   }
 
 }
+
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 193906d24790e..a5ae2b9736527 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -114,7 +114,7 @@ public long getPeakMemoryUsage() {
   }
 
   private void cleanupResources() {
-    sorter.freeMemory();
+    sorter.cleanupResources();
   }
 
   @VisibleForTesting
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 312ec8ea0dd9d..86a563df992d0 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -134,6 +134,10 @@ public void insertKV(UnsafeRow key, UnsafeRow value) throws IOException {
       value.getBaseObject(), value.getBaseOffset(), value.getSizeInBytes(), prefix);
   }
 
+  /**
+   * Returns a sorted iterator. It is the caller's responsibility to call `cleanupResources()`
+   * after consuming this iterator.
+   */
   public KVSorterIterator sortedIterator() throws IOException {
     try {
       final UnsafeSorterIterator underlying = sorter.getSortedIterator();
@@ -158,8 +162,11 @@ void closeCurrentPage() {
     sorter.closeCurrentPage();
   }
 
-  private void cleanupResources() {
-    sorter.freeMemory();
+  /**
+   * Frees this sorter's in-memory data structures and cleans up its spill files.
+   */
+  public void cleanupResources() {
+    sorter.cleanupResources();
   }
 
   private static final class KVComparator extends RecordComparator {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index 601a5a07ad002..08156f0e39ce8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution
 import scala.util.Random
 
 import org.apache.spark._
-import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.{RandomDataGenerator, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, RowOrdering, UnsafeProjection}
 import org.apache.spark.sql.test.TestSQLContext
@@ -46,6 +46,7 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
     testKVSorter(keySchema, valueSchema, spill = i > 3)
   }
 
+
   /**
    * Create a test case using randomly generated data for the given key and value schema.
    *
@@ -60,96 +61,151 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
    * If spill is set to true, the sorter will spill probabilistically roughly every 100 records.
    */
   private def testKVSorter(keySchema: StructType, valueSchema: StructType, spill: Boolean): Unit = {
+    // Create the data converters
+    val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
+    val vExternalConverter = CatalystTypeConverters.createToCatalystConverter(valueSchema)
+    val kConverter = UnsafeProjection.create(keySchema)
+    val vConverter = UnsafeProjection.create(valueSchema)
+
+    val keyDataGen = RandomDataGenerator.forType(keySchema, nullable = false).get
+    val valueDataGen = RandomDataGenerator.forType(valueSchema, nullable = false).get
+
+    val inputData = Seq.fill(1024) {
+      val k = kConverter(kExternalConverter.apply(keyDataGen.apply()).asInstanceOf[InternalRow])
+      val v = vConverter(vExternalConverter.apply(valueDataGen.apply()).asInstanceOf[InternalRow])
+      (k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
+    }
 
     val keySchemaStr = keySchema.map(_.dataType.simpleString).mkString("[", ",", "]")
     val valueSchemaStr = valueSchema.map(_.dataType.simpleString).mkString("[", ",", "]")
 
     test(s"kv sorting key schema $keySchemaStr and value schema $valueSchemaStr") {
-      // Calling this make sure we have block manager and everything else setup.
-      TestSQLContext
-
-      val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
-      val shuffleMemMgr = new TestShuffleMemoryManager
-      TaskContext.setTaskContext(new TaskContextImpl(
-        stageId = 0,
-        partitionId = 0,
-        taskAttemptId = 98456,
-        attemptNumber = 0,
-        taskMemoryManager = taskMemMgr,
-        metricsSystem = null,
-        internalAccumulators = Seq.empty))
-
-      // Create the data converters
-      val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
-      val vExternalConverter = CatalystTypeConverters.createToCatalystConverter(valueSchema)
-      val kConverter = UnsafeProjection.create(keySchema)
-      val vConverter = UnsafeProjection.create(valueSchema)
-
-      val keyDataGen = RandomDataGenerator.forType(keySchema, nullable = false).get
-      val valueDataGen = RandomDataGenerator.forType(valueSchema, nullable = false).get
-
-      val input = Seq.fill(1024) {
-        val k = kConverter(kExternalConverter.apply(keyDataGen.apply()).asInstanceOf[InternalRow])
-        val v = vConverter(vExternalConverter.apply(valueDataGen.apply()).asInstanceOf[InternalRow])
-        (k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
-      }
-
-      val sorter = new UnsafeKVExternalSorter(
-        keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr, 16 * 1024 * 1024)
-
-      // Insert generated keys and values into the sorter
-      input.foreach { case (k, v) =>
-        sorter.insertKV(k.asInstanceOf[UnsafeRow], v.asInstanceOf[UnsafeRow])
-        // 1% chance we will spill
-        if (rand.nextDouble() < 0.01 && spill) {
-          shuffleMemMgr.markAsOutOfMemory()
-          sorter.closeCurrentPage()
-        }
-      }
+      testKVSorter(
+        keySchema,
+        valueSchema,
+        inputData,
+        pageSize = 16 * 1024 * 1024,
+        spill
+      )
+    }
+  }
 
-      // Collect the sorted output
-      val out = new scala.collection.mutable.ArrayBuffer[(InternalRow, InternalRow)]
-      val iter = sorter.sortedIterator()
-      while (iter.next()) {
-        out += Tuple2(iter.getKey.copy(), iter.getValue.copy())
+  /**
+   * Create a test case using the given input data for the given key and value schema.
+   *
+   * The approach works as follows:
+   *
+   * - Create input by randomly generating data based on the given schema
+   * - Run [[UnsafeKVExternalSorter]] on the input data
+   * - Collect the output from the sorter, and make sure the keys are sorted in ascending order
+   * - Sort the input by both key and value, and sort the sorter output also by both key and value.
+   *   Compare the sorted input and sorted output together to make sure all the key/values match.
+   *
+   * If spill is set to true, the sorter will spill probabilistically roughly every 100 records.
+   */
+  private def testKVSorter(
+      keySchema: StructType,
+      valueSchema: StructType,
+      inputData: Seq[(InternalRow, InternalRow)],
+      pageSize: Long,
+      spill: Boolean): Unit = {
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    val shuffleMemMgr = new TestShuffleMemoryManager
+    TaskContext.setTaskContext(new TaskContextImpl(
+      stageId = 0,
+      partitionId = 0,
+      taskAttemptId = 98456,
+      attemptNumber = 0,
+      taskMemoryManager = taskMemMgr,
+      metricsSystem = null,
+      internalAccumulators = Seq.empty))
+
+    val sorter = new UnsafeKVExternalSorter(
+      keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr, pageSize)
+
+    // Insert the keys and values into the sorter
+    inputData.foreach { case (k, v) =>
+      sorter.insertKV(k.asInstanceOf[UnsafeRow], v.asInstanceOf[UnsafeRow])
+      // 1% chance we will spill
+      if (rand.nextDouble() < 0.01 && spill) {
+        shuffleMemMgr.markAsOutOfMemory()
+        sorter.closeCurrentPage()
       }
+    }
 
-      val keyOrdering = RowOrdering.forSchema(keySchema.map(_.dataType))
-      val valueOrdering = RowOrdering.forSchema(valueSchema.map(_.dataType))
-      val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
-        override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
-          keyOrdering.compare(x._1, y._1) match {
-            case 0 => valueOrdering.compare(x._2, y._2)
-            case cmp => cmp
-          }
+    // Collect the sorted output
+    val out = new scala.collection.mutable.ArrayBuffer[(InternalRow, InternalRow)]
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      out += Tuple2(iter.getKey.copy(), iter.getValue.copy())
+    }
+    sorter.cleanupResources()
+
+    val keyOrdering = RowOrdering.forSchema(keySchema.map(_.dataType))
+    val valueOrdering = RowOrdering.forSchema(valueSchema.map(_.dataType))
+    val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
+      override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
+        keyOrdering.compare(x._1, y._1) match {
+          case 0 => valueOrdering.compare(x._2, y._2)
+          case cmp => cmp
         }
       }
+    }
 
-      // Testing to make sure output from the sorter is sorted by key
-      var prevK: InternalRow = null
-      out.zipWithIndex.foreach { case ((k, v), i) =>
-        if (prevK != null) {
-          assert(keyOrdering.compare(prevK, k) <= 0,
-            s"""
-               |key is not in sorted order:
-               |previous key: $prevK
-               |current key : $k
-               """.stripMargin)
-        }
-        prevK = k
+    // Testing to make sure output from the sorter is sorted by key
+    var prevK: InternalRow = null
+    out.zipWithIndex.foreach { case ((k, v), i) =>
+      if (prevK != null) {
+        assert(keyOrdering.compare(prevK, k) <= 0,
+          s"""
+             |key is not in sorted order:
+             |previous key: $prevK
+             |current key : $k
+             """.stripMargin)
       }
+      prevK = k
+    }
 
-      // Testing to make sure the key/value in output matches input
-      assert(out.sorted(kvOrdering) === input.sorted(kvOrdering))
+    // Testing to make sure the key/value in output matches input
+    assert(out.sorted(kvOrdering) === inputData.sorted(kvOrdering))
 
-      // Make sure there is no memory leak
-      val leakedUnsafeMemory: Long = taskMemMgr.cleanUpAllAllocatedMemory
-      if (shuffleMemMgr != null) {
-        val leakedShuffleMemory: Long = shuffleMemMgr.getMemoryConsumptionForThisTask()
-        assert(0L === leakedShuffleMemory)
-      }
-      assert(0 === leakedUnsafeMemory)
-      TaskContext.unset()
+    // Make sure there is no memory leak
+    val leakedUnsafeMemory: Long = taskMemMgr.cleanUpAllAllocatedMemory
+    if (shuffleMemMgr != null) {
+      val leakedShuffleMemory: Long = shuffleMemMgr.getMemoryConsumptionForThisTask()
+      assert(0L === leakedShuffleMemory)
     }
+    assert(0 === leakedUnsafeMemory)
+    TaskContext.unset()
+  }
+
+  test("kv sorting with records that exceed page size") {
+    val pageSize = 128
+
+    val schema = StructType(StructField("b", BinaryType) :: Nil)
+    val externalConverter = CatalystTypeConverters.createToCatalystConverter(schema)
+    val converter = UnsafeProjection.create(schema)
+
+    val rand = new Random()
+    val inputData = Seq.fill(1024) {
+      val kBytes = new Array[Byte](rand.nextInt(pageSize))
+      val vBytes = new Array[Byte](rand.nextInt(pageSize))
+      rand.nextBytes(kBytes)
+      rand.nextBytes(vBytes)
+      val k = converter(externalConverter.apply(Row(kBytes)).asInstanceOf[InternalRow])
+      val v = converter(externalConverter.apply(Row(vBytes)).asInstanceOf[InternalRow])
+      (k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
+    }
+
+    testKVSorter(
+      schema,
+      schema,
+      inputData,
+      pageSize,
+      spill = true
+    )
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java
index bbe83d36cf36b..6722301df19d1 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java
@@ -24,6 +24,9 @@ public class HeapMemoryAllocator implements MemoryAllocator {
 
   @Override
   public MemoryBlock allocate(long size) throws OutOfMemoryError {
+    if (size % 8 != 0) {
+      throw new IllegalArgumentException("Size " + size + " was not a multiple of 8");
+    }
     long[] array = new long[(int) (size / 8)];
     return MemoryBlock.fromLongArray(array);
   }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
index 15898771fef25..62f4459696c28 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
@@ -26,6 +26,9 @@ public class UnsafeMemoryAllocator implements MemoryAllocator {
 
   @Override
   public MemoryBlock allocate(long size) throws OutOfMemoryError {
+    if (size % 8 != 0) {
+      throw new IllegalArgumentException("Size " + size + " was not a multiple of 8");
+    }
     long address = PlatformDependent.UNSAFE.allocateMemory(size);
     return new MemoryBlock(null, address, size);
   }

From 9d668b73687e697cad2ef7fd3c3ba405e9795593 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Tue, 4 Aug 2015 14:54:11 -0700
Subject: [PATCH 0834/1454] [SPARK-9602] remove "Akka/Actor" words from
 comments

https://issues.apache.org/jira/browse/SPARK-9602

Although we have hidden Akka behind RPC interface, I found that the Akka/Actor-related comments are still spreading everywhere. To make it consistent, we shall remove "actor"/"akka" words from the comments...

Author: CodingCat <zhunansjtu@gmail.com>

Closes #7936 from CodingCat/SPARK-9602 and squashes the following commits:

e8296a3 [CodingCat] remove actor words from comments
---
 .../main/scala/org/apache/spark/api/python/PythonRDD.scala | 2 +-
 .../scala/org/apache/spark/deploy/LocalSparkCluster.scala  | 4 ----
 .../scala/org/apache/spark/deploy/client/AppClient.scala   | 2 +-
 .../apache/spark/deploy/master/LeaderElectionAgent.scala   | 6 +++---
 .../org/apache/spark/deploy/master/MasterMessages.scala    | 2 +-
 .../spark/deploy/master/ZooKeeperLeaderElectionAgent.scala | 6 +++---
 .../main/scala/org/apache/spark/deploy/worker/Worker.scala | 7 ++++---
 .../org/apache/spark/deploy/worker/WorkerWatcher.scala     | 4 ++--
 .../main/scala/org/apache/spark/rpc/RpcEndpointRef.scala   | 2 +-
 .../apache/spark/scheduler/OutputCommitCoordinator.scala   | 2 +-
 .../org/apache/spark/scheduler/cluster/ExecutorData.scala  | 2 +-
 .../src/main/scala/org/apache/spark/util/IdGenerator.scala | 6 +++---
 .../spark/deploy/master/CustomRecoveryModeFactory.scala    | 4 ++--
 .../apache/spark/deploy/worker/WorkerWatcherSuite.scala    | 5 ++---
 project/MimaExcludes.scala                                 | 2 +-
 .../src/main/scala/org/apache/spark/repl/SparkILoop.scala  | 2 +-
 16 files changed, 27 insertions(+), 31 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 55e563ee968be..2a56bf28d7027 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -794,7 +794,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort:
 
   /**
    * We try to reuse a single Socket to transfer accumulator updates, as they are all added
-   * by the DAGScheduler's single-threaded actor anyway.
+   * by the DAGScheduler's single-threaded RpcEndpoint anyway.
    */
   @transient var socket: Socket = _
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
index 53356addf6edb..83ccaadfe7447 100644
--- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
@@ -73,12 +73,8 @@ class LocalSparkCluster(
   def stop() {
     logInfo("Shutting down local Spark cluster.")
     // Stop the workers before the master so they don't get upset that it disconnected
-    // TODO: In Akka 2.1.x, ActorSystem.awaitTermination hangs when you have remote actors!
-    //       This is unfortunate, but for now we just comment it out.
     workerRpcEnvs.foreach(_.shutdown())
-    // workerActorSystems.foreach(_.awaitTermination())
     masterRpcEnvs.foreach(_.shutdown())
-    // masterActorSystems.foreach(_.awaitTermination())
     masterRpcEnvs.clear()
     workerRpcEnvs.clear()
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index 7576a2985ee7b..25ea6925434ab 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -257,7 +257,7 @@ private[spark] class AppClient(
   }
 
   def start() {
-    // Just launch an actor; it will call back into the listener.
+    // Just launch an rpcEndpoint; it will call back into the listener.
     endpoint = rpcEnv.setupEndpoint("AppClient", new ClientEndpoint(rpcEnv))
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala
index cf77c86d760cf..70f21fbe0de85 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala
@@ -26,7 +26,7 @@ import org.apache.spark.annotation.DeveloperApi
  */
 @DeveloperApi
 trait LeaderElectionAgent {
-  val masterActor: LeaderElectable
+  val masterInstance: LeaderElectable
   def stop() {} // to avoid noops in implementations.
 }
 
@@ -37,7 +37,7 @@ trait LeaderElectable {
 }
 
 /** Single-node implementation of LeaderElectionAgent -- we're initially and always the leader. */
-private[spark] class MonarchyLeaderAgent(val masterActor: LeaderElectable)
+private[spark] class MonarchyLeaderAgent(val masterInstance: LeaderElectable)
   extends LeaderElectionAgent {
-  masterActor.electedLeader()
+  masterInstance.electedLeader()
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
index 68c937188b333..a952cee36eb44 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
@@ -38,5 +38,5 @@ private[master] object MasterMessages {
 
   case object BoundPortsRequest
 
-  case class BoundPortsResponse(actorPort: Int, webUIPort: Int, restPort: Option[Int])
+  case class BoundPortsResponse(rpcEndpointPort: Int, webUIPort: Int, restPort: Option[Int])
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
index 6fdff86f66e01..d317206a614fb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
@@ -22,7 +22,7 @@ import org.apache.curator.framework.CuratorFramework
 import org.apache.curator.framework.recipes.leader.{LeaderLatchListener, LeaderLatch}
 import org.apache.spark.deploy.SparkCuratorUtil
 
-private[master] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElectable,
+private[master] class ZooKeeperLeaderElectionAgent(val masterInstance: LeaderElectable,
     conf: SparkConf) extends LeaderLatchListener with LeaderElectionAgent with Logging  {
 
   val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/leader_election"
@@ -73,10 +73,10 @@ private[master] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElecta
   private def updateLeadershipStatus(isLeader: Boolean) {
     if (isLeader && status == LeadershipStatus.NOT_LEADER) {
       status = LeadershipStatus.LEADER
-      masterActor.electedLeader()
+      masterInstance.electedLeader()
     } else if (!isLeader && status == LeadershipStatus.LEADER) {
       status = LeadershipStatus.NOT_LEADER
-      masterActor.revokedLeadership()
+      masterInstance.revokedLeadership()
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index c82a7ccab54dc..6792d3310b06c 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -228,7 +228,7 @@ private[deploy] class Worker(
   /**
    * Re-register with the master because a network failure or a master failure has occurred.
    * If the re-registration attempt threshold is exceeded, the worker exits with error.
-   * Note that for thread-safety this should only be called from the actor.
+   * Note that for thread-safety this should only be called from the rpcEndpoint.
    */
   private def reregisterWithMaster(): Unit = {
     Utils.tryOrExit {
@@ -365,7 +365,8 @@ private[deploy] class Worker(
       if (connected) { sendToMaster(Heartbeat(workerId, self)) }
 
     case WorkDirCleanup =>
-      // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker actor
+      // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker
+      // rpcEndpoint.
       // Copy ids so that it can be used in the cleanup thread.
       val appIds = executors.values.map(_.appId).toSet
       val cleanupFuture = concurrent.future {
@@ -684,7 +685,7 @@ private[deploy] object Worker extends Logging {
       workerNumber: Option[Int] = None,
       conf: SparkConf = new SparkConf): RpcEnv = {
 
-    // The LocalSparkCluster runs multiple local sparkWorkerX actor systems
+    // The LocalSparkCluster runs multiple local sparkWorkerX RPC Environments
     val systemName = SYSTEM_NAME + workerNumber.map(_.toString).getOrElse("")
     val securityMgr = new SecurityManager(conf)
     val rpcEnv = RpcEnv.create(systemName, host, port, conf, securityMgr)
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
index fae5640b9a213..735c4f0927150 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
@@ -43,7 +43,7 @@ private[spark] class WorkerWatcher(override val rpcEnv: RpcEnv, workerUrl: Strin
   private[deploy] def setTesting(testing: Boolean) = isTesting = testing
   private var isTesting = false
 
-  // Lets us filter events only from the worker's actor system
+  // Lets filter events only from the worker's rpc system
   private val expectedAddress = RpcAddress.fromURIString(workerUrl)
   private def isWorker(address: RpcAddress) = expectedAddress == address
 
@@ -62,7 +62,7 @@ private[spark] class WorkerWatcher(override val rpcEnv: RpcEnv, workerUrl: Strin
   override def onDisconnected(remoteAddress: RpcAddress): Unit = {
     if (isWorker(remoteAddress)) {
       // This log message will never be seen
-      logError(s"Lost connection to worker actor $workerUrl. Exiting.")
+      logError(s"Lost connection to worker rpc endpoint $workerUrl. Exiting.")
       exitNonZero()
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
index 6ae47894598be..7409ac8859991 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
@@ -100,7 +100,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
         val future = ask[T](message, timeout)
         val result = timeout.awaitResult(future)
         if (result == null) {
-          throw new SparkException("Actor returned null")
+          throw new SparkException("RpcEndpoint returned null")
         }
         return result
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
index 8321037cdc026..5d926377ce86b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
@@ -162,7 +162,7 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
 
 private[spark] object OutputCommitCoordinator {
 
-  // This actor is used only for RPC
+  // This endpoint is used only for RPC
   private[spark] class OutputCommitCoordinatorEndpoint(
       override val rpcEnv: RpcEnv, outputCommitCoordinator: OutputCommitCoordinator)
     extends RpcEndpoint with Logging {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala
index 26e72c0bff38d..626a2b7d69abe 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala
@@ -22,7 +22,7 @@ import org.apache.spark.rpc.{RpcEndpointRef, RpcAddress}
 /**
  * Grouping of data for an executor used by CoarseGrainedSchedulerBackend.
  *
- * @param executorEndpoint The ActorRef representing this executor
+ * @param executorEndpoint The RpcEndpointRef representing this executor
  * @param executorAddress The network address of this executor
  * @param executorHost The hostname that this executor is running on
  * @param freeCores  The current number of cores available for work on the executor
diff --git a/core/src/main/scala/org/apache/spark/util/IdGenerator.scala b/core/src/main/scala/org/apache/spark/util/IdGenerator.scala
index 17e55f7996bf7..53934ad4ce477 100644
--- a/core/src/main/scala/org/apache/spark/util/IdGenerator.scala
+++ b/core/src/main/scala/org/apache/spark/util/IdGenerator.scala
@@ -22,10 +22,10 @@ import java.util.concurrent.atomic.AtomicInteger
 /**
  * A util used to get a unique generation ID. This is a wrapper around Java's
  * AtomicInteger. An example usage is in BlockManager, where each BlockManager
- * instance would start an Akka actor and we use this utility to assign the Akka
- * actors unique names.
+ * instance would start an RpcEndpoint and we use this utility to assign the RpcEndpoints'
+ * unique names.
  */
 private[spark] class IdGenerator {
-  private var id = new AtomicInteger
+  private val id = new AtomicInteger
   def next: Int = id.incrementAndGet
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
index 8c96b0e71dfdd..4b86da536768c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
@@ -99,7 +99,7 @@ object CustomPersistenceEngine {
   @volatile var lastInstance: Option[CustomPersistenceEngine] = None
 }
 
-class CustomLeaderElectionAgent(val masterActor: LeaderElectable) extends LeaderElectionAgent {
-  masterActor.electedLeader()
+class CustomLeaderElectionAgent(val masterInstance: LeaderElectable) extends LeaderElectionAgent {
+  masterInstance.electedLeader()
 }
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
index cd24d79423316..e9034e39a715c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
@@ -38,12 +38,11 @@ class WorkerWatcherSuite extends SparkFunSuite {
     val conf = new SparkConf()
     val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
     val targetWorkerUrl = rpcEnv.uriOf("test", RpcAddress("1.2.3.4", 1234), "Worker")
-    val otherAddress = "akka://test@4.3.2.1:1234/user/OtherActor"
-    val otherAkkaAddress = RpcAddress("4.3.2.1", 1234)
+    val otherRpcAddress = RpcAddress("4.3.2.1", 1234)
     val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl)
     workerWatcher.setTesting(testing = true)
     rpcEnv.setupEndpoint("worker-watcher", workerWatcher)
-    workerWatcher.onDisconnected(otherAkkaAddress)
+    workerWatcher.onDisconnected(otherRpcAddress)
     assert(!workerWatcher.isShutDown)
     rpcEnv.shutdown()
   }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 280aac931915d..b60ae784c3798 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -182,7 +182,7 @@ object MimaExcludes {
             ProblemFilters.exclude[IncompatibleResultTypeProblem](
               "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"),
             ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorActor")
+              "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint")
           ) ++ Seq(
             // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though
             // the stage class is defined as private[spark]
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 8130868fe1487..304b1e8cdbed5 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -981,7 +981,7 @@ class SparkILoop(
     // which spins off a separate thread, then print the prompt and try
     // our best to look ready.  The interlocking lazy vals tend to
     // inter-deadlock, so we break the cycle with a single asynchronous
-    // message to an actor.
+    // message to an rpcEndpoint.
     if (isAsync) {
       intp initialize initializedCallback()
       createAsyncListener() // listens for signal to run postInitialization

From e375456063617cd7000d796024f41e5927f21edd Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 4 Aug 2015 14:54:26 -0700
Subject: [PATCH 0835/1454] [SPARK-9447] [ML] [PYTHON] Added
 HasRawPredictionCol, HasProbabilityCol to RandomForestClassifier

Added HasRawPredictionCol, HasProbabilityCol to RandomForestClassifier, plus doc tests for those columns.

CC: holdenk yanboliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7903 from jkbradley/rf-prob-python and squashes the following commits:

c62a83f [Joseph K. Bradley] made unit test more robust
14eeba2 [Joseph K. Bradley] added HasRawPredictionCol, HasProbabilityCol to RandomForestClassifier in PySpark
---
 python/pyspark/ml/classification.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 291320f881e7c..5978d8f4d3a01 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -347,6 +347,7 @@ class DecisionTreeClassificationModel(DecisionTreeModel):
 
 @inherit_doc
 class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
+                             HasRawPredictionCol, HasProbabilityCol,
                              DecisionTreeParams, HasCheckpointInterval):
     """
     `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
@@ -354,6 +355,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     It supports both binary and multiclass labels, as well as both continuous and categorical
     features.
 
+    >>> import numpy
     >>> from numpy import allclose
     >>> from pyspark.mllib.linalg import Vectors
     >>> from pyspark.ml.feature import StringIndexer
@@ -368,8 +370,13 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> allclose(model.treeWeights, [1.0, 1.0, 1.0])
     True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
-    >>> model.transform(test0).head().prediction
+    >>> result = model.transform(test0).head()
+    >>> result.prediction
     0.0
+    >>> numpy.argmax(result.probability)
+    0
+    >>> numpy.argmax(result.rawPrediction)
+    0
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     1.0
@@ -390,11 +397,13 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 probabilityCol="probability", rawPredictionCol="rawPrediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini",
                  numTrees=20, featureSubsetStrategy="auto", seed=None):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini", \
                  numTrees=20, featureSubsetStrategy="auto", seed=None)
@@ -427,11 +436,13 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  probabilityCol="probability", rawPredictionCol="rawPrediction",
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
                   impurity="gini", numTrees=20, featureSubsetStrategy="auto"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction", \
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \
                   impurity="gini", numTrees=20, featureSubsetStrategy="auto")

From 1833d9c08f021d991334424d0a6d5ec21d1fccb2 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 4 Aug 2015 15:43:13 -0700
Subject: [PATCH 0836/1454] [SPARK-9582] [ML] LDA cleanups

Small cleanups to recent LDA additions and docs.

CC: feynmanliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7916 from jkbradley/lda-cleanups and squashes the following commits:

f7021d9 [Joseph K. Bradley] broadcasting large matrices for LDA in local model and online learning
97947aa [Joseph K. Bradley] a few more cleanups
5b03f88 [Joseph K. Bradley] reverted split of lda log likelihood
c566915 [Joseph K. Bradley] small edit to make review easier
63f6c7d [Joseph K. Bradley] clarified log likelihood for lda models
---
 .../spark/mllib/clustering/LDAModel.scala     | 82 +++++++++++--------
 .../spark/mllib/clustering/LDAOptimizer.scala | 19 +++--
 2 files changed, 58 insertions(+), 43 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 6af90d7287ff8..33babda69bbb9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -27,6 +27,7 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaPairRDD
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -217,26 +218,28 @@ class LocalLDAModel private[clustering] (
   // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
   /**
    * Calculates a lower bound on the log likelihood of the entire corpus.
+   *
+   * See Equation (16) in original Online LDA paper.
+   *
    * @param documents test corpus to use for calculating log likelihood
    * @return variational lower bound on the log likelihood of the entire corpus
    */
-  def logLikelihood(documents: RDD[(Long, Vector)]): Double = bound(documents,
+  def logLikelihood(documents: RDD[(Long, Vector)]): Double = logLikelihoodBound(documents,
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
   /**
-   * Calculate an upper bound bound on perplexity. See Equation (16) in original Online
-   * LDA paper.
+   * Calculate an upper bound bound on perplexity.  (Lower is better.)
+   * See Equation (16) in original Online LDA paper.
+   *
    * @param documents test corpus to use for calculating perplexity
-   * @return variational upper bound on log perplexity per word
+   * @return Variational upper bound on log perplexity per token.
    */
   def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
-    val corpusWords = documents
+    val corpusTokenCount = documents
       .map { case (_, termCounts) => termCounts.toArray.sum }
       .sum()
-    val perWordBound = -logLikelihood(documents) / corpusWords
-
-    perWordBound
+    -logLikelihood(documents) / corpusTokenCount
   }
 
   /**
@@ -244,17 +247,20 @@ class LocalLDAModel private[clustering] (
    *    log p(documents) >= E_q[log p(documents)] - E_q[log q(documents)]
    * This bound is derived by decomposing the LDA model to:
    *    log p(documents) = E_q[log p(documents)] - E_q[log q(documents)] + D(q|p)
-   * and noting that the KL-divergence D(q|p) >= 0. See Equation (16) in original Online LDA paper.
+   * and noting that the KL-divergence D(q|p) >= 0.
+   *
+   * See Equation (16) in original Online LDA paper, as well as Appendix A.3 in the JMLR version of
+   * the original LDA paper.
    * @param documents a subset of the test corpus
    * @param alpha document-topic Dirichlet prior parameters
-   * @param eta topic-word Dirichlet prior parameters
+   * @param eta topic-word Dirichlet prior parameter
    * @param lambda parameters for variational q(beta | lambda) topic-word distributions
    * @param gammaShape shape parameter for random initialization of variational q(theta | gamma)
    *                   topic mixture distributions
    * @param k number of topics
    * @param vocabSize number of unique terms in the entire test corpus
    */
-  private def bound(
+  private def logLikelihoodBound(
       documents: RDD[(Long, Vector)],
       alpha: Vector,
       eta: Double,
@@ -266,33 +272,38 @@ class LocalLDAModel private[clustering] (
     // transpose because dirichletExpectation normalizes by row and we need to normalize
     // by topic (columns of lambda)
     val Elogbeta = LDAUtils.dirichletExpectation(lambda.t).t
+    val ElogbetaBc = documents.sparkContext.broadcast(Elogbeta)
+
+    // Sum bound components for each document:
+    //  component for prob(tokens) + component for prob(document-topic distribution)
+    val corpusPart =
+      documents.filter(_._2.numNonzeros > 0).map { case (id: Long, termCounts: Vector) =>
+        val localElogbeta = ElogbetaBc.value
+        var docBound = 0.0D
+        val (gammad: BDV[Double], _) = OnlineLDAOptimizer.variationalTopicInference(
+          termCounts, exp(localElogbeta), brzAlpha, gammaShape, k)
+        val Elogthetad: BDV[Double] = LDAUtils.dirichletExpectation(gammad)
+
+        // E[log p(doc | theta, beta)]
+        termCounts.foreachActive { case (idx, count) =>
+          docBound += count * LDAUtils.logSumExp(Elogthetad + localElogbeta(idx, ::).t)
+        }
+        // E[log p(theta | alpha) - log q(theta | gamma)]
+        docBound += sum((brzAlpha - gammad) :* Elogthetad)
+        docBound += sum(lgamma(gammad) - lgamma(brzAlpha))
+        docBound += lgamma(sum(brzAlpha)) - lgamma(sum(gammad))
 
-    var score = documents.filter(_._2.numNonzeros > 0).map { case (id: Long, termCounts: Vector) =>
-      var docScore = 0.0D
-      val (gammad: BDV[Double], _) = OnlineLDAOptimizer.variationalTopicInference(
-        termCounts, exp(Elogbeta), brzAlpha, gammaShape, k)
-      val Elogthetad: BDV[Double] = LDAUtils.dirichletExpectation(gammad)
-
-      // E[log p(doc | theta, beta)]
-      termCounts.foreachActive { case (idx, count) =>
-        docScore += count * LDAUtils.logSumExp(Elogthetad + Elogbeta(idx, ::).t)
-      }
-      // E[log p(theta | alpha) - log q(theta | gamma)]; assumes alpha is a vector
-      docScore += sum((brzAlpha - gammad) :* Elogthetad)
-      docScore += sum(lgamma(gammad) - lgamma(brzAlpha))
-      docScore += lgamma(sum(brzAlpha)) - lgamma(sum(gammad))
-
-      docScore
-    }.sum()
-
-    // E[log p(beta | eta) - log q (beta | lambda)]; assumes eta is a scalar
-    score += sum((eta - lambda) :* Elogbeta)
-    score += sum(lgamma(lambda) - lgamma(eta))
+        docBound
+      }.sum()
 
+    // Bound component for prob(topic-term distributions):
+    //   E[log p(beta | eta) - log q(beta | lambda)]
     val sumEta = eta * vocabSize
-    score += sum(lgamma(sumEta) - lgamma(sum(lambda(::, breeze.linalg.*))))
+    val topicsPart = sum((eta - lambda) :* Elogbeta) +
+      sum(lgamma(lambda) - lgamma(eta)) +
+      sum(lgamma(sumEta) - lgamma(sum(lambda(::, breeze.linalg.*))))
 
-    score
+    corpusPart + topicsPart
   }
 
   /**
@@ -310,6 +321,7 @@ class LocalLDAModel private[clustering] (
     // Double transpose because dirichletExpectation normalizes by row and we need to normalize
     // by topic (columns of lambda)
     val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.toBreeze.toDenseMatrix.t).t)
+    val expElogbetaBc = documents.sparkContext.broadcast(expElogbeta)
     val docConcentrationBrz = this.docConcentration.toBreeze
     val gammaShape = this.gammaShape
     val k = this.k
@@ -320,7 +332,7 @@ class LocalLDAModel private[clustering] (
       } else {
         val (gamma, _) = OnlineLDAOptimizer.variationalTopicInference(
           termCounts,
-          expElogbeta,
+          expElogbetaBc.value,
           docConcentrationBrz,
           gammaShape,
           k)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index b0e14cb8296a6..afba2866c7040 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -419,6 +419,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     val k = this.k
     val vocabSize = this.vocabSize
     val expElogbeta = exp(LDAUtils.dirichletExpectation(lambda)).t
+    val expElogbetaBc = batch.sparkContext.broadcast(expElogbeta)
     val alpha = this.alpha.toBreeze
     val gammaShape = this.gammaShape
 
@@ -433,13 +434,14 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
           case v: SparseVector => v.indices.toList
         }
         val (gammad, sstats) = OnlineLDAOptimizer.variationalTopicInference(
-          termCounts, expElogbeta, alpha, gammaShape, k)
+          termCounts, expElogbetaBc.value, alpha, gammaShape, k)
         stat(::, ids) := stat(::, ids).toDenseMatrix + sstats
         gammaPart = gammad :: gammaPart
       }
       Iterator((stat, gammaPart))
     }
     val statsSum: BDM[Double] = stats.map(_._1).reduce(_ += _)
+    expElogbetaBc.unpersist()
     val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(
       stats.map(_._2).reduce(_ ++ _).map(_.toDenseMatrix): _*)
     val batchResult = statsSum :* expElogbeta.t
@@ -540,21 +542,22 @@ private[clustering] object OnlineLDAOptimizer {
     val expElogthetad: BDV[Double] = exp(LDAUtils.dirichletExpectation(gammad))  // K
     val expElogbetad = expElogbeta(ids, ::).toDenseMatrix                        // ids * K
 
-    val phinorm: BDV[Double] = expElogbetad * expElogthetad :+ 1e-100            // ids
-    var meanchange = 1D
+    val phiNorm: BDV[Double] = expElogbetad * expElogthetad :+ 1e-100            // ids
+    var meanGammaChange = 1D
     val ctsVector = new BDV[Double](cts)                                         // ids
 
     // Iterate between gamma and phi until convergence
-    while (meanchange > 1e-3) {
+    while (meanGammaChange > 1e-3) {
       val lastgamma = gammad.copy
       //        K                  K * ids               ids
-      gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/ phinorm))) :+ alpha
+      gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/ phiNorm))) :+ alpha
       expElogthetad := exp(LDAUtils.dirichletExpectation(gammad))
-      phinorm := expElogbetad * expElogthetad :+ 1e-100
-      meanchange = sum(abs(gammad - lastgamma)) / k
+      // TODO: Keep more values in log space, and only exponentiate when needed.
+      phiNorm := expElogbetad * expElogthetad :+ 1e-100
+      meanGammaChange = sum(abs(gammad - lastgamma)) / k
     }
 
-    val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector :/ phinorm).asDenseMatrix
+    val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector :/ phiNorm).asDenseMatrix
     (gammad, sstatsd)
   }
 }

From 571d5b5363ff4dbbce1f7019ab8e86cbc3cba4d5 Mon Sep 17 00:00:00 2001
From: Mike Dusenberry <mwdusenb@us.ibm.com>
Date: Tue, 4 Aug 2015 16:30:03 -0700
Subject: [PATCH 0837/1454] [SPARK-6485] [MLLIB] [PYTHON] Add
 CoordinateMatrix/RowMatrix/IndexedRowMatrix to PySpark.

This PR adds the RowMatrix, IndexedRowMatrix, and CoordinateMatrix distributed matrices to PySpark.  Each distributed matrix class acts as a wrapper around the Scala/Java counterpart by maintaining a reference to the Java object.  New distributed matrices can be created using factory methods added to DistributedMatrices, which creates the Java distributed matrix and then wraps it with the corresponding PySpark class.  This design allows for simple conversion between the various distributed matrices, and lets us re-use the Scala code.  Serialization between Python and Java is implemented using DataFrames as needed for IndexedRowMatrix and CoordinateMatrix for simplicity.  Associated documentation and unit-tests have also been added.  To facilitate code review, this PR implements access to the rows/entries as RDDs, the number of rows & columns, and conversions between the various distributed matrices (not including BlockMatrix), and does not implement the other linear algebra functions of the matrices, although this will be very simple to add now.

Author: Mike Dusenberry <mwdusenb@us.ibm.com>

Closes #7554 from dusenberrymw/SPARK-6485_Add_CoordinateMatrix_RowMatrix_IndexedMatrix_to_PySpark and squashes the following commits:

bb039cb [Mike Dusenberry] Minor documentation update.
b887c18 [Mike Dusenberry] Updating the matrix conversion logic again to make it even cleaner.  Now, we allow the 'rows' parameter in the constructors to be either an RDD or the Java matrix object. If 'rows' is an RDD, we create a Java matrix object, wrap it, and then store that.  If 'rows' is a Java matrix object of the correct type, we just wrap and store that directly.  This is only for internal usage, and publicly, we still require 'rows' to be an RDD.  We no longer store the 'rows' RDD, and instead just compute it from the Java object when needed.  The point of this is that when we do matrix conversions, we do the conversion on the Scala/Java side, which returns a Java object, so we should use that directly, but exposing 'java_matrix' parameter in the public API is not ideal. This non-public feature of allowing 'rows' to be a Java matrix object is documented in the '__init__' constructor docstrings, which are not part of the generated public API, and doctests are also included.
7f0dcb6 [Mike Dusenberry] Updating module docstring.
cfc1be5 [Mike Dusenberry] Use 'new SQLContext(matrix.rows.sparkContext)' rather than 'SQLContext.getOrCreate', as the later doesn't guarantee that the SparkContext will be the same as for the matrix.rows data.
687e345 [Mike Dusenberry] Improving conversion performance.  This adds an optional 'java_matrix' parameter to the constructors, and pulls the conversion logic out into a '_create_from_java' function. Now, if the constructors are given a valid Java distributed matrix object as 'java_matrix', they will store those internally, rather than create a new one on the Scala/Java side.
3e50b6e [Mike Dusenberry] Moving the distributed matrices to pyspark.mllib.linalg.distributed.
308f197 [Mike Dusenberry] Using properties for better documentation.
1633f86 [Mike Dusenberry] Minor documentation cleanup.
f0c13a7 [Mike Dusenberry] CoordinateMatrix should inherit from DistributedMatrix.
ffdd724 [Mike Dusenberry] Updating doctests to make documentation cleaner.
3fd4016 [Mike Dusenberry] Updating docstrings.
27cd5f6 [Mike Dusenberry] Simplifying input conversions in the constructors for each distributed matrix.
a409cf5 [Mike Dusenberry] Updating doctests to be less verbose by using lists instead of DenseVectors explicitly.
d19b0ba [Mike Dusenberry] Updating code and documentation to note that a vector-like object (numpy array, list, etc.) can be used in place of explicit Vector object, and adding conversions when necessary to RowMatrix construction.
4bd756d [Mike Dusenberry] Adding param documentation to IndexedRow and MatrixEntry.
c6bded5 [Mike Dusenberry] Move conversion logic from tuples to IndexedRow or MatrixEntry types from within the IndexedRowMatrix and CoordinateMatrix constructors to separate _convert_to_indexed_row and _convert_to_matrix_entry functions.
329638b [Mike Dusenberry] Moving the Experimental tag to the top of each docstring.
0be6826 [Mike Dusenberry] Simplifying doctests by removing duplicated rows/entries RDDs within the various tests.
c0900df [Mike Dusenberry] Adding the colons that were accidentally not inserted.
4ad6819 [Mike Dusenberry] Documenting the  and  parameters.
3b854b9 [Mike Dusenberry] Minor updates to documentation.
10046e8 [Mike Dusenberry] Updating documentation to use class constructors instead of the removed DistributedMatrices factory methods.
119018d [Mike Dusenberry] Adding static  methods to each of the distributed matrix classes to consolidate conversion logic.
4d7af86 [Mike Dusenberry] Adding type checks to the constructors.  Although it is slightly verbose, it is better for the user to have a good error message than a cryptic stacktrace.
93b6a3d [Mike Dusenberry] Pulling the DistributedMatrices Python class out of this pull request.
f6f3c68 [Mike Dusenberry] Pulling the DistributedMatrices Scala class out of this pull request.
6a3ecb7 [Mike Dusenberry] Updating pattern matching.
08f287b [Mike Dusenberry] Slight reformatting of the documentation.
a245dc0 [Mike Dusenberry] Updating Python doctests for compatability between Python 2 & 3. Since Python 3 removed the idea of a separate 'long' type, all values that would have been outputted as a 'long' (ex: '4L') will now be treated as an 'int' and outputed as one (ex: '4').  The doctests now explicitly convert to ints so that both Python 2 and 3 will have the same output.  This is fine since the values are all small, and thus can be easily represented as ints.
4d3a37e [Mike Dusenberry] Reformatting a few long Python doctest lines.
7e3ca16 [Mike Dusenberry] Fixing long lines.
f721ead [Mike Dusenberry] Updating documentation for each of the distributed matrices.
ab0e8b6 [Mike Dusenberry] Updating unit test to be more useful.
dda2f89 [Mike Dusenberry] Added wrappers for the conversions between the various distributed matrices.  Added logic to be able to access the rows/entries of the distributed matrices, which requires serialization through DataFrames for IndexedRowMatrix and CoordinateMatrix types. Added unit tests.
0cd7166 [Mike Dusenberry] Implemented the CoordinateMatrix API in PySpark, following the idea of the IndexedRowMatrix API, including using DataFrames for serialization.
3c369cb [Mike Dusenberry] Updating the architecture a bit to make conversions between the various distributed matrix types easier.  The different distributed matrix classes are now only wrappers around the Java objects, and take the Java object as an argument during construction.  This way, we can call  for example on an , which returns a reference to a Java RowMatrix object, and then construct a PySpark RowMatrix object wrapped around the Java object.  This is analogous to the behavior of PySpark RDDs and DataFrames.  We now delegate creation of the various distributed matrices from scratch in PySpark to the factory methods on .
4bdd09b [Mike Dusenberry] Implemented the IndexedRowMatrix API in PySpark, following the idea of the RowMatrix API.  Note that for the IndexedRowMatrix, we use DataFrames to serialize the data between Python and Scala/Java, so we accept PySpark RDDs, then convert to a DataFrame, then convert back to RDDs on the Scala/Java side before constructing the IndexedRowMatrix.
23bf1ec [Mike Dusenberry] Updating documentation to add PySpark RowMatrix. Inserting newline above doctest so that it renders properly in API docs.
b194623 [Mike Dusenberry] Updating design to have a PySpark RowMatrix simply create and keep a reference to a wrapper over a Java RowMatrix.  Updating DistributedMatrices factory methods to accept numRows and numCols with default values.  Updating PySpark DistributedMatrices factory method to simply create a PySpark RowMatrix. Adding additional doctests for numRows and numCols parameters.
bc2d220 [Mike Dusenberry] Adding unit tests for RowMatrix methods.
d7e316f [Mike Dusenberry] Implemented the RowMatrix API in PySpark by doing the following: Added a DistributedMatrices class to contain factory methods for creating the various distributed matrices.  Added a factory method for creating a RowMatrix from an RDD of Vectors.  Added a createRowMatrix function to the PythonMLlibAPI to interface with the factory method.  Added DistributedMatrix, DistributedMatrices, and RowMatrix classes to the pyspark.mllib.linalg api.
---
 dev/sparktestsupport/modules.py               |   1 +
 docs/mllib-data-types.md                      | 106 +++-
 .../mllib/api/python/PythonMLLibAPI.scala     |  53 +-
 python/docs/pyspark.mllib.rst                 |   8 +
 python/pyspark/mllib/common.py                |   2 +
 python/pyspark/mllib/linalg/distributed.py    | 537 ++++++++++++++++++
 6 files changed, 704 insertions(+), 3 deletions(-)
 create mode 100644 python/pyspark/mllib/linalg/distributed.py

diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 956dc81b62e93..a9717ff9569c7 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -331,6 +331,7 @@ def contains_file(self, filename):
         "pyspark.mllib.feature",
         "pyspark.mllib.fpm",
         "pyspark.mllib.linalg.__init__",
+        "pyspark.mllib.linalg.distributed",
         "pyspark.mllib.random",
         "pyspark.mllib.recommendation",
         "pyspark.mllib.regression",
diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index 3aa040046fca5..11033bf4f9c37 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -372,12 +372,37 @@ long m = mat.numRows();
 long n = mat.numCols();
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+A [`RowMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.RowMatrix) can be 
+created from an `RDD` of vectors.
+
+{% highlight python %}
+from pyspark.mllib.linalg.distributed import RowMatrix
+
+# Create an RDD of vectors.
+rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
+
+# Create a RowMatrix from an RDD of vectors.
+mat = RowMatrix(rows)
+
+# Get its size.
+m = mat.numRows()  # 4
+n = mat.numCols()  # 3
+
+# Get the rows as an RDD of vectors again.
+rowsRDD = mat.rows
+{% endhighlight %}
+</div>
+
 </div>
 
 ### IndexedRowMatrix
 
 An `IndexedRowMatrix` is similar to a `RowMatrix` but with meaningful row indices.  It is backed by
-an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local vector.
+an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local 
+vector.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -431,7 +456,48 @@ long n = mat.numCols();
 // Drop its row indices.
 RowMatrix rowMat = mat.toRowMatrix();
 {% endhighlight %}
-</div></div>
+</div>
+
+<div data-lang="python" markdown="1">
+
+An [`IndexedRowMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.IndexedRowMatrix)
+can be created from an `RDD` of `IndexedRow`s, where 
+[`IndexedRow`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.IndexedRow) is a 
+wrapper over `(long, vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
+its row indices.
+
+{% highlight python %}
+from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
+
+# Create an RDD of indexed rows.
+#   - This can be done explicitly with the IndexedRow class:
+indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]), 
+                              IndexedRow(1, [4, 5, 6]), 
+                              IndexedRow(2, [7, 8, 9]), 
+                              IndexedRow(3, [10, 11, 12])])
+#   - or by using (long, vector) tuples:
+indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]), 
+                              (2, [7, 8, 9]), (3, [10, 11, 12])])
+
+# Create an IndexedRowMatrix from an RDD of IndexedRows.
+mat = IndexedRowMatrix(indexedRows)
+
+# Get its size.
+m = mat.numRows()  # 4
+n = mat.numCols()  # 3
+
+# Get the rows as an RDD of IndexedRows.
+rowsRDD = mat.rows
+
+# Convert to a RowMatrix by dropping the row indices.
+rowMat = mat.toRowMatrix()
+
+# Convert to a CoordinateMatrix.
+coordinateMat = mat.toCoordinateMatrix()
+{% endhighlight %}
+</div>
+
+</div>
 
 ### CoordinateMatrix
 
@@ -495,6 +561,42 @@ long n = mat.numCols();
 IndexedRowMatrix indexedRowMatrix = mat.toIndexedRowMatrix();
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+A [`CoordinateMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.CoordinateMatrix)
+can be created from an `RDD` of `MatrixEntry` entries, where 
+[`MatrixEntry`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.MatrixEntry) is a 
+wrapper over `(long, long, float)`.  A `CoordinateMatrix` can be converted to a `RowMatrix` by 
+calling `toRowMatrix`, or to an `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`.
+
+{% highlight python %}
+from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry
+
+# Create an RDD of coordinate entries.
+#   - This can be done explicitly with the MatrixEntry class:
+entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)])
+#   - or using (long, long, float) tuples:
+entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)])
+
+# Create an CoordinateMatrix from an RDD of MatrixEntries.
+mat = CoordinateMatrix(entries)
+
+# Get its size.
+m = mat.numRows()  # 3
+n = mat.numCols()  # 2
+
+# Get the entries as an RDD of MatrixEntries.
+entriesRDD = mat.entries
+
+# Convert to a RowMatrix.
+rowMat = mat.toRowMatrix()
+
+# Convert to an IndexedRowMatrix.
+indexedRowMat = mat.toIndexedRowMatrix()
+{% endhighlight %}
+</div>
+
 </div>
 
 ### BlockMatrix
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 6f080d32bbf4d..d2b3fae381acb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -37,6 +37,7 @@ import org.apache.spark.mllib.evaluation.RankingMetrics
 import org.apache.spark.mllib.feature._
 import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
 import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.linalg.distributed._
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.random.{RandomRDDs => RG}
 import org.apache.spark.mllib.recommendation._
@@ -54,7 +55,7 @@ import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomFo
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.util.LinearDataGenerator
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.Utils
 
@@ -1096,6 +1097,56 @@ private[python] class PythonMLLibAPI extends Serializable {
     Statistics.kolmogorovSmirnovTest(data, distName, paramsSeq: _*)
   }
 
+  /**
+   * Wrapper around RowMatrix constructor.
+   */
+  def createRowMatrix(rows: JavaRDD[Vector], numRows: Long, numCols: Int): RowMatrix = {
+    new RowMatrix(rows.rdd, numRows, numCols)
+  }
+
+  /**
+   * Wrapper around IndexedRowMatrix constructor.
+   */
+  def createIndexedRowMatrix(rows: DataFrame, numRows: Long, numCols: Int): IndexedRowMatrix = {
+    // We use DataFrames for serialization of IndexedRows from Python,
+    // so map each Row in the DataFrame back to an IndexedRow.
+    val indexedRows = rows.map {
+      case Row(index: Long, vector: Vector) => IndexedRow(index, vector)
+    }
+    new IndexedRowMatrix(indexedRows, numRows, numCols)
+  }
+
+  /**
+   * Wrapper around CoordinateMatrix constructor.
+   */
+  def createCoordinateMatrix(rows: DataFrame, numRows: Long, numCols: Long): CoordinateMatrix = {
+    // We use DataFrames for serialization of MatrixEntry entries from
+    // Python, so map each Row in the DataFrame back to a MatrixEntry.
+    val entries = rows.map {
+      case Row(i: Long, j: Long, value: Double) => MatrixEntry(i, j, value)
+    }
+    new CoordinateMatrix(entries, numRows, numCols)
+  }
+
+  /**
+   * Return the rows of an IndexedRowMatrix.
+   */
+  def getIndexedRows(indexedRowMatrix: IndexedRowMatrix): DataFrame = {
+    // We use DataFrames for serialization of IndexedRows to Python,
+    // so return a DataFrame.
+    val sqlContext = new SQLContext(indexedRowMatrix.rows.sparkContext)
+    sqlContext.createDataFrame(indexedRowMatrix.rows)
+  }
+
+  /**
+   * Return the entries of a CoordinateMatrix.
+   */
+  def getMatrixEntries(coordinateMatrix: CoordinateMatrix): DataFrame = {
+    // We use DataFrames for serialization of MatrixEntry entries to
+    // Python, so return a DataFrame.
+    val sqlContext = new SQLContext(coordinateMatrix.entries.sparkContext)
+    sqlContext.createDataFrame(coordinateMatrix.entries)
+  }
 }
 
 /**
diff --git a/python/docs/pyspark.mllib.rst b/python/docs/pyspark.mllib.rst
index 26ece4c2c389a..2d54ab118b94b 100644
--- a/python/docs/pyspark.mllib.rst
+++ b/python/docs/pyspark.mllib.rst
@@ -46,6 +46,14 @@ pyspark.mllib.linalg module
     :undoc-members:
     :show-inheritance:
 
+pyspark.mllib.linalg.distributed module
+---------------------------------------
+
+.. automodule:: pyspark.mllib.linalg.distributed
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 pyspark.mllib.random module
 ---------------------------
 
diff --git a/python/pyspark/mllib/common.py b/python/pyspark/mllib/common.py
index 855e85f57155e..a439a488de5cc 100644
--- a/python/pyspark/mllib/common.py
+++ b/python/pyspark/mllib/common.py
@@ -73,6 +73,8 @@ def _py2java(sc, obj):
     """ Convert Python object into Java """
     if isinstance(obj, RDD):
         obj = _to_java_object_rdd(obj)
+    elif isinstance(obj, DataFrame):
+        obj = obj._jdf
     elif isinstance(obj, SparkContext):
         obj = obj._jsc
     elif isinstance(obj, list):
diff --git a/python/pyspark/mllib/linalg/distributed.py b/python/pyspark/mllib/linalg/distributed.py
new file mode 100644
index 0000000000000..666d833019562
--- /dev/null
+++ b/python/pyspark/mllib/linalg/distributed.py
@@ -0,0 +1,537 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Package for distributed linear algebra.
+"""
+
+import sys
+
+if sys.version >= '3':
+    long = int
+
+from py4j.java_gateway import JavaObject
+
+from pyspark import RDD
+from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
+from pyspark.mllib.linalg import _convert_to_vector
+
+
+__all__ = ['DistributedMatrix', 'RowMatrix', 'IndexedRow',
+           'IndexedRowMatrix', 'MatrixEntry', 'CoordinateMatrix']
+
+
+class DistributedMatrix(object):
+    """
+    .. note:: Experimental
+
+    Represents a distributively stored matrix backed by one or
+    more RDDs.
+
+    """
+    def numRows(self):
+        """Get or compute the number of rows."""
+        raise NotImplementedError
+
+    def numCols(self):
+        """Get or compute the number of cols."""
+        raise NotImplementedError
+
+
+class RowMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a row-oriented distributed Matrix with no meaningful
+    row indices.
+
+    :param rows: An RDD of vectors.
+    :param numRows: Number of rows in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of rows will be determined by the number of
+                    records in the `rows` RDD.
+    :param numCols: Number of columns in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of columns will be determined by the size of
+                    the first row.
+    """
+    def __init__(self, rows, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java RowMatrix.
+
+        Publicly, we require that `rows` be an RDD.  However, for
+        internal usage, `rows` can also be a Java RowMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6]])
+        >>> mat = RowMatrix(rows)
+
+        >>> mat_diff = RowMatrix(rows)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = RowMatrix(mat._java_matrix_wrapper._java_model)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(rows, RDD):
+            rows = rows.map(_convert_to_vector)
+            java_matrix = callMLlibFunc("createRowMatrix", rows, long(numRows), int(numCols))
+        elif (isinstance(rows, JavaObject)
+              and rows.getClass().getSimpleName() == "RowMatrix"):
+            java_matrix = rows
+        else:
+            raise TypeError("rows should be an RDD of vectors, got %s" % type(rows))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def rows(self):
+        """
+        Rows of the RowMatrix stored as an RDD of vectors.
+
+        >>> mat = RowMatrix(sc.parallelize([[1, 2, 3], [4, 5, 6]]))
+        >>> rows = mat.rows
+        >>> rows.first()
+        DenseVector([1.0, 2.0, 3.0])
+        """
+        return self._java_matrix_wrapper.call("rows")
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
+        ...                        [7, 8, 9], [10, 11, 12]])
+
+        >>> mat = RowMatrix(rows)
+        >>> print(mat.numRows())
+        4
+
+        >>> mat = RowMatrix(rows, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
+        ...                        [7, 8, 9], [10, 11, 12]])
+
+        >>> mat = RowMatrix(rows)
+        >>> print(mat.numCols())
+        3
+
+        >>> mat = RowMatrix(rows, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+
+class IndexedRow(object):
+    """
+    .. note:: Experimental
+
+    Represents a row of an IndexedRowMatrix.
+
+    Just a wrapper over a (long, vector) tuple.
+
+    :param index: The index for the given row.
+    :param vector: The row in the matrix at the given index.
+    """
+    def __init__(self, index, vector):
+        self.index = long(index)
+        self.vector = _convert_to_vector(vector)
+
+    def __repr__(self):
+        return "IndexedRow(%s, %s)" % (self.index, self.vector)
+
+
+def _convert_to_indexed_row(row):
+    if isinstance(row, IndexedRow):
+        return row
+    elif isinstance(row, tuple) and len(row) == 2:
+        return IndexedRow(*row)
+    else:
+        raise TypeError("Cannot convert type %s into IndexedRow" % type(row))
+
+
+class IndexedRowMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a row-oriented distributed Matrix with indexed rows.
+
+    :param rows: An RDD of IndexedRows or (long, vector) tuples.
+    :param numRows: Number of rows in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of rows will be determined by the max row
+                    index plus one.
+    :param numCols: Number of columns in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of columns will be determined by the size of
+                    the first row.
+    """
+    def __init__(self, rows, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java IndexedRowMatrix.
+
+        Publicly, we require that `rows` be an RDD.  However, for
+        internal usage, `rows` can also be a Java IndexedRowMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(1, [4, 5, 6])])
+        >>> mat = IndexedRowMatrix(rows)
+
+        >>> mat_diff = IndexedRowMatrix(rows)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = IndexedRowMatrix(mat._java_matrix_wrapper._java_model)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(rows, RDD):
+            rows = rows.map(_convert_to_indexed_row)
+            # We use DataFrames for serialization of IndexedRows from
+            # Python, so first convert the RDD to a DataFrame on this
+            # side. This will convert each IndexedRow to a Row
+            # containing the 'index' and 'vector' values, which can
+            # both be easily serialized.  We will convert back to
+            # IndexedRows on the Scala side.
+            java_matrix = callMLlibFunc("createIndexedRowMatrix", rows.toDF(),
+                                        long(numRows), int(numCols))
+        elif (isinstance(rows, JavaObject)
+              and rows.getClass().getSimpleName() == "IndexedRowMatrix"):
+            java_matrix = rows
+        else:
+            raise TypeError("rows should be an RDD of IndexedRows or (long, vector) tuples, "
+                            "got %s" % type(rows))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def rows(self):
+        """
+        Rows of the IndexedRowMatrix stored as an RDD of IndexedRows.
+
+        >>> mat = IndexedRowMatrix(sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                                        IndexedRow(1, [4, 5, 6])]))
+        >>> rows = mat.rows
+        >>> rows.first()
+        IndexedRow(0, [1.0,2.0,3.0])
+        """
+        # We use DataFrames for serialization of IndexedRows from
+        # Java, so we first convert the RDD of rows to a DataFrame
+        # on the Scala/Java side. Then we map each Row in the
+        # DataFrame back to an IndexedRow on this side.
+        rows_df = callMLlibFunc("getIndexedRows", self._java_matrix_wrapper._java_model)
+        rows = rows_df.map(lambda row: IndexedRow(row[0], row[1]))
+        return rows
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(1, [4, 5, 6]),
+        ...                        IndexedRow(2, [7, 8, 9]),
+        ...                        IndexedRow(3, [10, 11, 12])])
+
+        >>> mat = IndexedRowMatrix(rows)
+        >>> print(mat.numRows())
+        4
+
+        >>> mat = IndexedRowMatrix(rows, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(1, [4, 5, 6]),
+        ...                        IndexedRow(2, [7, 8, 9]),
+        ...                        IndexedRow(3, [10, 11, 12])])
+
+        >>> mat = IndexedRowMatrix(rows)
+        >>> print(mat.numCols())
+        3
+
+        >>> mat = IndexedRowMatrix(rows, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+    def toRowMatrix(self):
+        """
+        Convert this matrix to a RowMatrix.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(6, [4, 5, 6])])
+        >>> mat = IndexedRowMatrix(rows).toRowMatrix()
+        >>> mat.rows.collect()
+        [DenseVector([1.0, 2.0, 3.0]), DenseVector([4.0, 5.0, 6.0])]
+        """
+        java_row_matrix = self._java_matrix_wrapper.call("toRowMatrix")
+        return RowMatrix(java_row_matrix)
+
+    def toCoordinateMatrix(self):
+        """
+        Convert this matrix to a CoordinateMatrix.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 0]),
+        ...                        IndexedRow(6, [0, 5])])
+        >>> mat = IndexedRowMatrix(rows).toCoordinateMatrix()
+        >>> mat.entries.take(3)
+        [MatrixEntry(0, 0, 1.0), MatrixEntry(0, 1, 0.0), MatrixEntry(6, 0, 0.0)]
+        """
+        java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix")
+        return CoordinateMatrix(java_coordinate_matrix)
+
+
+class MatrixEntry(object):
+    """
+    .. note:: Experimental
+
+    Represents an entry of a CoordinateMatrix.
+
+    Just a wrapper over a (long, long, float) tuple.
+
+    :param i: The row index of the matrix.
+    :param j: The column index of the matrix.
+    :param value: The (i, j)th entry of the matrix, as a float.
+    """
+    def __init__(self, i, j, value):
+        self.i = long(i)
+        self.j = long(j)
+        self.value = float(value)
+
+    def __repr__(self):
+        return "MatrixEntry(%s, %s, %s)" % (self.i, self.j, self.value)
+
+
+def _convert_to_matrix_entry(entry):
+    if isinstance(entry, MatrixEntry):
+        return entry
+    elif isinstance(entry, tuple) and len(entry) == 3:
+        return MatrixEntry(*entry)
+    else:
+        raise TypeError("Cannot convert type %s into MatrixEntry" % type(entry))
+
+
+class CoordinateMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a matrix in coordinate format.
+
+    :param entries: An RDD of MatrixEntry inputs or
+                    (long, long, float) tuples.
+    :param numRows: Number of rows in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of rows will be determined by the max row
+                    index plus one.
+    :param numCols: Number of columns in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of columns will be determined by the max row
+                    index plus one.
+    """
+    def __init__(self, entries, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java CoordinateMatrix.
+
+        Publicly, we require that `rows` be an RDD.  However, for
+        internal usage, `rows` can also be a Java CoordinateMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries)
+
+        >>> mat_diff = CoordinateMatrix(entries)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = CoordinateMatrix(mat._java_matrix_wrapper._java_model)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(entries, RDD):
+            entries = entries.map(_convert_to_matrix_entry)
+            # We use DataFrames for serialization of MatrixEntry entries
+            # from Python, so first convert the RDD to a DataFrame on
+            # this side. This will convert each MatrixEntry to a Row
+            # containing the 'i', 'j', and 'value' values, which can
+            # each be easily serialized. We will convert back to
+            # MatrixEntry inputs on the Scala side.
+            java_matrix = callMLlibFunc("createCoordinateMatrix", entries.toDF(),
+                                        long(numRows), long(numCols))
+        elif (isinstance(entries, JavaObject)
+              and entries.getClass().getSimpleName() == "CoordinateMatrix"):
+            java_matrix = entries
+        else:
+            raise TypeError("entries should be an RDD of MatrixEntry entries or "
+                            "(long, long, float) tuples, got %s" % type(entries))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def entries(self):
+        """
+        Entries of the CoordinateMatrix stored as an RDD of
+        MatrixEntries.
+
+        >>> mat = CoordinateMatrix(sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                                        MatrixEntry(6, 4, 2.1)]))
+        >>> entries = mat.entries
+        >>> entries.first()
+        MatrixEntry(0, 0, 1.2)
+        """
+        # We use DataFrames for serialization of MatrixEntry entries
+        # from Java, so we first convert the RDD of entries to a
+        # DataFrame on the Scala/Java side. Then we map each Row in
+        # the DataFrame back to a MatrixEntry on this side.
+        entries_df = callMLlibFunc("getMatrixEntries", self._java_matrix_wrapper._java_model)
+        entries = entries_df.map(lambda row: MatrixEntry(row[0], row[1], row[2]))
+        return entries
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(1, 0, 2),
+        ...                           MatrixEntry(2, 1, 3.7)])
+
+        >>> mat = CoordinateMatrix(entries)
+        >>> print(mat.numRows())
+        3
+
+        >>> mat = CoordinateMatrix(entries, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(1, 0, 2),
+        ...                           MatrixEntry(2, 1, 3.7)])
+
+        >>> mat = CoordinateMatrix(entries)
+        >>> print(mat.numCols())
+        2
+
+        >>> mat = CoordinateMatrix(entries, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+    def toRowMatrix(self):
+        """
+        Convert this matrix to a RowMatrix.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+
+        >>> # This CoordinateMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, but the ensuing RowMatrix
+        >>> # will only have 2 rows since there are only entries on 2
+        >>> # unique rows.
+        >>> mat = CoordinateMatrix(entries).toRowMatrix()
+        >>> print(mat.numRows())
+        2
+
+        >>> # This CoordinateMatrix will have 5 columns, due to the
+        >>> # highest column index being 4, and the ensuing RowMatrix
+        >>> # will have 5 columns as well.
+        >>> mat = CoordinateMatrix(entries).toRowMatrix()
+        >>> print(mat.numCols())
+        5
+        """
+        java_row_matrix = self._java_matrix_wrapper.call("toRowMatrix")
+        return RowMatrix(java_row_matrix)
+
+    def toIndexedRowMatrix(self):
+        """
+        Convert this matrix to an IndexedRowMatrix.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+
+        >>> # This CoordinateMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, and the ensuing
+        >>> # IndexedRowMatrix will have 7 rows as well.
+        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
+        >>> print(mat.numRows())
+        7
+
+        >>> # This CoordinateMatrix will have 5 columns, due to the
+        >>> # highest column index being 4, and the ensuing
+        >>> # IndexedRowMatrix will have 5 columns as well.
+        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
+        >>> print(mat.numCols())
+        5
+        """
+        java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix")
+        return IndexedRowMatrix(java_indexed_row_matrix)
+
+
+def _test():
+    import doctest
+    from pyspark import SparkContext
+    from pyspark.sql import SQLContext
+    import pyspark.mllib.linalg.distributed
+    globs = pyspark.mllib.linalg.distributed.__dict__.copy()
+    globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
+    globs['sqlContext'] = SQLContext(globs['sc'])
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
+    globs['sc'].stop()
+    if failure_count:
+        exit(-1)
+
+if __name__ == "__main__":
+    _test()

From b77d3b9688d56d33737909375d1d0db07da5827b Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 4 Aug 2015 16:52:43 -0700
Subject: [PATCH 0838/1454] [SPARK-9586] [ML] Update
 BinaryClassificationEvaluator to use setRawPredictionCol

Update BinaryClassificationEvaluator to use setRawPredictionCol, rather than setScoreCol. Deprecated setScoreCol.

I don't think setScoreCol was actually used anywhere (based on search).

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7921 from jkbradley/binary-eval-rawpred and squashes the following commits:

e5d7dfa [Joseph K. Bradley] Update BinaryClassificationEvaluator to use setRawPredictionCol
---
 .../ml/evaluation/BinaryClassificationEvaluator.scala    | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 4a82b77f0edcb..5d5cb7e94f45b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
- * Evaluator for binary classification, which expects two input columns: score and label.
+ * Evaluator for binary classification, which expects two input columns: rawPrediction and label.
  */
 @Experimental
 class BinaryClassificationEvaluator(override val uid: String)
@@ -50,6 +50,13 @@ class BinaryClassificationEvaluator(override val uid: String)
   def setMetricName(value: String): this.type = set(metricName, value)
 
   /** @group setParam */
+  def setRawPredictionCol(value: String): this.type = set(rawPredictionCol, value)
+
+  /**
+   * @group setParam
+   * @deprecated use [[setRawPredictionCol()]] instead
+   */
+  @deprecated("use setRawPredictionCol instead", "1.5.0")
   def setScoreCol(value: String): this.type = set(rawPredictionCol, value)
 
   /** @group setParam */

From 7c8fc1f7cb837ff5c32811fdeb3ee2b84de2dea4 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 4 Aug 2015 17:05:19 -0700
Subject: [PATCH 0839/1454] [SPARK-9598][SQL] do not expose generic getter in
 internal row

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7932 from cloud-fan/generic-getter and squashes the following commits:

c60de4c [Wenchen Fan] do not expose generic getter in internal row
---
 .../sql/catalyst/expressions/UnsafeRow.java   |  5 --
 .../spark/sql/catalyst/InternalRow.scala      | 37 +++++++++--
 .../GenericSpecializedGetters.scala           | 61 -------------------
 .../sql/catalyst/expressions/Projection.scala |  4 +-
 .../expressions/SpecificMutableRow.scala      |  2 +-
 .../sql/catalyst/expressions/aggregates.scala |  2 +-
 .../codegen/GenerateProjection.scala          |  2 +-
 .../spark/sql/catalyst/expressions/rows.scala | 12 ++--
 .../spark/sql/types/GenericArrayData.scala    | 37 +++++++----
 .../datasources/DataSourceStrategy.scala      |  6 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala | 20 +++---
 11 files changed, 80 insertions(+), 108 deletions(-)
 delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index e6750fce4fa80..e3e1622de08ba 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -253,11 +253,6 @@ public void setDecimal(int ordinal, Decimal value, int precision) {
     }
   }
 
-  @Override
-  public Object genericGet(int ordinal) {
-    throw new UnsupportedOperationException();
-  }
-
   @Override
   public Object get(int ordinal, DataType dataType) {
     if (isNullAt(ordinal) || dataType instanceof NullType) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 7656d054dc361..7d17cca808791 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.sql.catalyst
 
-import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
  * internal types.
  */
-// todo: make InternalRow just extends SpecializedGetters, remove generic getter
-abstract class InternalRow extends GenericSpecializedGetters with Serializable {
+abstract class InternalRow extends SpecializedGetters with Serializable {
 
   def numFields: Int
 
@@ -50,6 +50,31 @@ abstract class InternalRow extends GenericSpecializedGetters with Serializable {
     false
   }
 
+  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
+  // or implement this genericGet.
+  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
+    "Concrete internal rows should implement genericGet, " +
+      "or implement all special getters and equals/hashCode")
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
   override def equals(o: Any): Boolean = {
     if (!o.isInstanceOf[InternalRow]) {
       return false
@@ -159,15 +184,15 @@ abstract class InternalRow extends GenericSpecializedGetters with Serializable {
 
 object InternalRow {
   /**
-   * This method can be used to construct a [[Row]] with the given values.
+   * This method can be used to construct a [[InternalRow]] with the given values.
    */
   def apply(values: Any*): InternalRow = new GenericInternalRow(values.toArray)
 
   /**
-   * This method can be used to construct a [[Row]] from a [[Seq]] of values.
+   * This method can be used to construct a [[InternalRow]] from a [[Seq]] of values.
    */
   def fromSeq(values: Seq[Any]): InternalRow = new GenericInternalRow(values.toArray)
 
-  /** Returns an empty row. */
+  /** Returns an empty [[InternalRow]]. */
   val empty = apply()
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala
deleted file mode 100644
index 6e957928e02a8..0000000000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.expressions
-
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
-
-trait GenericSpecializedGetters extends SpecializedGetters {
-
-  def genericGet(ordinal: Int): Any
-
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-
-  override def get(ordinal: Int, elementType: DataType): AnyRef = getAs(ordinal)
-
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 79649741025a3..4296b4b123fc0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -207,8 +207,8 @@ class JoinedRow extends InternalRow {
 
   override def numFields: Int = row1.numFields + row2.numFields
 
-  override def genericGet(i: Int): Any =
-    if (i < row1.numFields) row1.genericGet(i) else row2.genericGet(i - row1.numFields)
+  override def get(i: Int, dt: DataType): AnyRef =
+    if (i < row1.numFields) row1.get(i, dt) else row2.get(i - row1.numFields, dt)
 
   override def isNullAt(i: Int): Boolean =
     if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index d149a5b179b63..b94df6bd66e04 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -232,7 +232,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
     new GenericInternalRow(newValues)
   }
 
-  override def genericGet(i: Int): Any = values(i).boxed
+  override protected def genericGet(i: Int): Any = values(i).boxed
 
   override def update(ordinal: Int, value: Any) {
     if (value == null) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 5d4b349b1597a..2cf8312ea59aa 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -623,7 +623,7 @@ case class CombineSetsAndSumFunction(
       null
     } else {
       Cast(Literal(
-        casted.iterator.map(f => f.genericGet(0)).reduceLeft(
+        casted.iterator.map(f => f.get(0, null)).reduceLeft(
           base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus)),
         base.dataType).eval(null)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 1572b2b99ab66..c04fe734d554e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -184,7 +184,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        public Object genericGet(int i) {
+        protected Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index d04434b953e41..5e5de1d1dc6a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -19,8 +19,8 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.{Decimal, DataType, StructType, AtomicType}
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
@@ -76,13 +76,13 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(protected[sql] val values: Array[Any]) extends InternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
-  override def genericGet(ordinal: Int): Any = values(ordinal)
+  override protected def genericGet(ordinal: Int) = values(ordinal)
 
   override def toSeq: Seq[Any] = values
 
@@ -103,13 +103,13 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(val values: Array[Any]) extends MutableRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
-  override def genericGet(ordinal: Int): Any = values(ordinal)
+  override protected def genericGet(ordinal: Int) = values(ordinal)
 
   override def toSeq: Seq[Any] = values
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
index b314acdfe3644..459fcb6fc0acc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
@@ -17,22 +17,33 @@
 
 package org.apache.spark.sql.types
 
-import scala.reflect.ClassTag
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
-import org.apache.spark.sql.catalyst.expressions.GenericSpecializedGetters
-
-class GenericArrayData(private[sql] val array: Array[Any])
-  extends ArrayData with GenericSpecializedGetters {
-
-  override def genericGet(ordinal: Int): Any = array(ordinal)
+class GenericArrayData(private[sql] val array: Array[Any]) extends ArrayData {
 
   override def copy(): ArrayData = new GenericArrayData(array.clone())
 
-  // todo: Array is invariant in scala, maybe use toSeq instead?
-  override def toArray[T: ClassTag](elementType: DataType): Array[T] = array.map(_.asInstanceOf[T])
-
   override def numElements(): Int = array.length
 
+  private def getAs[T](ordinal: Int) = array(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, elementType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+
   override def toString(): String = array.mkString("[", ",", "]")
 
   override def equals(o: Any): Boolean = {
@@ -56,8 +67,8 @@ class GenericArrayData(private[sql] val array: Array[Any])
         return false
       }
       if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
+        val o1 = array(i)
+        val o2 = other.array(i)
         o1 match {
           case b1: Array[Byte] =>
             if (!o2.isInstanceOf[Array[Byte]] ||
@@ -91,7 +102,7 @@ class GenericArrayData(private[sql] val array: Array[Any])
         if (isNullAt(i)) {
           0
         } else {
-          genericGet(i) match {
+          array(i) match {
             case b: Boolean => if (b) 0 else 1
             case b: Byte => b.toInt
             case s: Short => s.toInt
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 6b91e51ca52fb..d9d7bc19bd419 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -187,15 +187,17 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         // To see whether the `index`-th column is a partition column...
         val i = partitionColumns.indexOf(name)
         if (i != -1) {
+          val dt = schema(partitionColumns(i)).dataType
           // If yes, gets column value from partition values.
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = partitionValues.genericGet(i)
+            mutableRow(ordinal) = partitionValues.get(i, dt)
           }
         } else {
           // Otherwise, inherits the value from scanned data.
           val i = nonPartitionColumns.indexOf(name)
+          val dt = schema(nonPartitionColumns(i)).dataType
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = dataRow.genericGet(i)
+            mutableRow(ordinal) = dataRow.get(i, dt)
           }
         }
       }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 66014ddca0596..16e0187ed20a0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -61,11 +61,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.genericGet(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.genericGet(1))
-      assertResult(10, "Wrong null count")(stats.genericGet(2))
-      assertResult(20, "Wrong row count")(stats.genericGet(3))
-      assertResult(stats.genericGet(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -96,11 +96,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.genericGet(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.genericGet(1))
-      assertResult(10, "Wrong null count")(stats.genericGet(2))
-      assertResult(20, "Wrong row count")(stats.genericGet(3))
-      assertResult(stats.genericGet(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum

From 629e26f7ee916e70f59b017cb6083aa441b26b2c Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 4 Aug 2015 18:13:18 -0700
Subject: [PATCH 0840/1454] [SPARK-9609] [MLLIB] Fix spelling of
 Strategy.defaultStrategy

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #7941 from feynmanliang/SPARK-9609-stategy-spelling and squashes the following commits:

d2aafb1 [Feynman Liang] Add deprecated backwards compatibility
aa090a8 [Feynman Liang] Fix spelling
---
 .../main/scala/org/apache/spark/ml/tree/treeParams.scala  | 2 +-
 .../spark/mllib/tree/configuration/BoostingStrategy.scala | 2 +-
 .../apache/spark/mllib/tree/configuration/Strategy.scala  | 8 ++++++--
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index e817090f8a16b..dbd8d31571d2e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -163,7 +163,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams {
       oldAlgo: OldAlgo.Algo,
       oldImpurity: OldImpurity,
       subsamplingRate: Double): OldStrategy = {
-    val strategy = OldStrategy.defaultStategy(oldAlgo)
+    val strategy = OldStrategy.defaultStrategy(oldAlgo)
     strategy.impurity = oldImpurity
     strategy.checkpointInterval = getCheckpointInterval
     strategy.maxBins = getMaxBins
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 9fd30c9b56319..50fe2ac53da9d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -90,7 +90,7 @@ object BoostingStrategy {
    * @return Configuration for boosting algorithm
    */
   def defaultParams(algo: Algo): BoostingStrategy = {
-    val treeStrategy = Strategy.defaultStategy(algo)
+    val treeStrategy = Strategy.defaultStrategy(algo)
     treeStrategy.maxDepth = 3
     algo match {
       case Algo.Classification =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index ada227c200a79..de2c784809443 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -178,14 +178,14 @@ object Strategy {
    * @param algo  "Classification" or "Regression"
    */
   def defaultStrategy(algo: String): Strategy = {
-    defaultStategy(Algo.fromString(algo))
+    defaultStrategy(Algo.fromString(algo))
   }
 
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo Algo.Classification or Algo.Regression
    */
-  def defaultStategy(algo: Algo): Strategy = algo match {
+  def defaultStrategy(algo: Algo): Strategy = algo match {
     case Algo.Classification =>
       new Strategy(algo = Classification, impurity = Gini, maxDepth = 10,
         numClasses = 2)
@@ -193,4 +193,8 @@ object Strategy {
       new Strategy(algo = Regression, impurity = Variance, maxDepth = 10,
         numClasses = 0)
   }
+
+  @deprecated("Use Strategy.defaultStrategy instead.", "1.5.0")
+  def defaultStategy(algo: Algo): Strategy = defaultStrategy(algo)
+
 }

From d92fa14179287c996407d9c7d249103109f9cdef Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 4 Aug 2015 18:15:26 -0700
Subject: [PATCH 0841/1454] [SPARK-8601] [ML] Add an option to disable
 standardization for linear regression

All compressed sensing applications, and some of the regression use-cases will have better result by turning the feature scaling off. However, if we implement this naively by training the dataset without doing any standardization, the rate of convergency will not be good. This can be implemented by still standardizing the training dataset but we penalize each component differently to get effectively the same objective function but a better numerical problem. As a result, for those columns with high variances, they will be penalized less, and vice versa. Without this, since all the features are standardized, so they will be penalized the same.

In R, there is an option for this.
standardize

Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".

Note that the primary author for this PR is holdenk

Author: Holden Karau <holden@pigscanfly.ca>
Author: DB Tsai <dbt@netflix.com>

Closes #7875 from dbtsai/SPARK-8522 and squashes the following commits:

e856036 [DB Tsai] scala doc
596e96c [DB Tsai] minor
bbff347 [DB Tsai] naming
baa0805 [DB Tsai] touch up
d6234ba [DB Tsai] Merge branch 'master' into SPARK-8522-Disable-Linear_featureScaling-Spark-8601-in-Linear_regression
6b1dc09 [Holden Karau] Merge branch 'master' into SPARK-8522-Disable-Linear_featureScaling-Spark-8601-in-Linear_regression
332f140 [Holden Karau] Merge in master
eebe10a [Holden Karau] Use same comparision operator throughout the test
3f92935 [Holden Karau] merge
b83a41e [Holden Karau] Expand the tests and make them similar to the other PR also providing an option to disable standardization (but for LoR).
0c334a2 [Holden Karau] Remove extra line
99ce053 [Holden Karau] merge in master
e54a8a9 [Holden Karau] Fix long line
e47c574 [Holden Karau] Add support for L2 without standardization.
55d3a66 [Holden Karau] Add standardization param for linear regression
00a1dc5 [Holden Karau] Add the param to the linearregression impl
---
 .../classification/LogisticRegression.scala   |   6 +-
 .../ml/regression/LinearRegression.scala      |  70 ++++-
 .../ml/regression/LinearRegressionSuite.scala | 278 +++++++++++++-----
 3 files changed, 268 insertions(+), 86 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index c937b9602b8c1..0d073839259c6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -133,9 +133,9 @@ class LogisticRegression(override val uid: String)
   /**
    * Whether to standardize the training features before fitting the model.
    * The coefficients of models will be always returned on the original scale,
-   * so it will be transparent for users. Note that when no regularization,
-   * with or without standardization, the models should be always converged to
-   * the same solution.
+   * so it will be transparent for users. Note that with/without standardization,
+   * the models should be always converged to the same solution when no regularization
+   * is applied. In R's GLMNET package, the default behavior is true as well.
    * Default is true.
    * @group setParam
    * */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 3b85ba001b128..92d819bad8654 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -45,7 +45,7 @@ import org.apache.spark.util.StatCounter
  */
 private[regression] trait LinearRegressionParams extends PredictorParams
     with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
-    with HasFitIntercept
+    with HasFitIntercept with HasStandardization
 
 /**
  * :: Experimental ::
@@ -84,6 +84,18 @@ class LinearRegression(override val uid: String)
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
   setDefault(fitIntercept -> true)
 
+  /**
+   * Whether to standardize the training features before fitting the model.
+   * The coefficients of models will be always returned on the original scale,
+   * so it will be transparent for users. Note that with/without standardization,
+   * the models should be always converged to the same solution when no regularization
+   * is applied. In R's GLMNET package, the default behavior is true as well.
+   * Default is true.
+   * @group setParam
+   */
+  def setStandardization(value: Boolean): this.type = set(standardization, value)
+  setDefault(standardization -> true)
+
   /**
    * Set the ElasticNet mixing parameter.
    * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
@@ -165,12 +177,24 @@ class LinearRegression(override val uid: String)
     val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam
 
     val costFun = new LeastSquaresCostFun(instances, yStd, yMean, $(fitIntercept),
-      featuresStd, featuresMean, effectiveL2RegParam)
+      $(standardization), featuresStd, featuresMean, effectiveL2RegParam)
 
     val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
       new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
     } else {
-      new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegParam, $(tol))
+      def effectiveL1RegFun = (index: Int) => {
+        if ($(standardization)) {
+          effectiveL1RegParam
+        } else {
+          // If `standardization` is false, we still standardize the data
+          // to improve the rate of convergence; as a result, we have to
+          // perform this reverse standardization by penalizing each component
+          // differently to get effectively the same objective function when
+          // the training dataset is not standardized.
+          if (featuresStd(index) != 0.0) effectiveL1RegParam / featuresStd(index) else 0.0
+        }
+      }
+      new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegFun, $(tol))
     }
 
     val initialWeights = Vectors.zeros(numFeatures)
@@ -456,6 +480,7 @@ class LinearRegressionSummary private[regression] (
  * @param weights The weights/coefficients corresponding to the features.
  * @param labelStd The standard deviation value of the label.
  * @param labelMean The mean value of the label.
+ * @param fitIntercept Whether to fit an intercept term.
  * @param featuresStd The standard deviation values of the features.
  * @param featuresMean The mean values of the features.
  */
@@ -568,6 +593,7 @@ private class LeastSquaresCostFun(
     labelStd: Double,
     labelMean: Double,
     fitIntercept: Boolean,
+    standardization: Boolean,
     featuresStd: Array[Double],
     featuresMean: Array[Double],
     effectiveL2regParam: Double) extends DiffFunction[BDV[Double]] {
@@ -584,14 +610,38 @@ private class LeastSquaresCostFun(
           case (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
         })
 
-    // regVal is the sum of weight squares for L2 regularization
-    val norm = brzNorm(weights, 2.0)
-    val regVal = 0.5 * effectiveL2regParam * norm * norm
+    val totalGradientArray = leastSquaresAggregator.gradient.toArray
 
-    val loss = leastSquaresAggregator.loss + regVal
-    val gradient = leastSquaresAggregator.gradient
-    axpy(effectiveL2regParam, w, gradient)
+    val regVal = if (effectiveL2regParam == 0.0) {
+      0.0
+    } else {
+      var sum = 0.0
+      w.foreachActive { (index, value) =>
+        // The following code will compute the loss of the regularization; also
+        // the gradient of the regularization, and add back to totalGradientArray.
+        sum += {
+          if (standardization) {
+            totalGradientArray(index) += effectiveL2regParam * value
+            value * value
+          } else {
+            if (featuresStd(index) != 0.0) {
+              // If `standardization` is false, we still standardize the data
+              // to improve the rate of convergence; as a result, we have to
+              // perform this reverse standardization by penalizing each component
+              // differently to get effectively the same objective function when
+              // the training dataset is not standardized.
+              val temp = value / (featuresStd(index) * featuresStd(index))
+              totalGradientArray(index) += effectiveL2regParam * temp
+              value * temp
+            } else {
+              0.0
+            }
+          }
+        }
+      }
+      0.5 * effectiveL2regParam * sum
+    }
 
-    (loss, gradient.toBreeze.asInstanceOf[BDV[Double]])
+    (leastSquaresAggregator.loss + regVal, new BDV(totalGradientArray))
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 7cdda3db88ad1..21ad8225bd9f7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -70,6 +70,7 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lir.getRegParam === 0.0)
     assert(lir.getElasticNetParam === 0.0)
     assert(lir.getFitIntercept)
+    assert(lir.getStandardization)
     val model = lir.fit(dataset)
     model.transform(dataset)
       .select("label", "prediction")
@@ -81,8 +82,11 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("linear regression with intercept without regularization") {
-    val trainer = new LinearRegression
-    val model = trainer.fit(dataset)
+    val trainer1 = new LinearRegression
+    // The result should be the same regardless of standardization without regularization
+    val trainer2 = (new LinearRegression).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -95,28 +99,36 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        > weights
         3 x 1 sparse Matrix of class "dgCMatrix"
                                  s0
-       (Intercept)         6.300528
-       as.numeric.data.V2. 4.701024
-       as.numeric.data.V3. 7.198257
+       (Intercept)         6.298698
+       as.numeric.data.V2. 4.700706
+       as.numeric.data.V3. 7.199082
      */
     val interceptR = 6.298698
     val weightsR = Vectors.dense(4.700706, 7.199082)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR relTol 1E-3)
+    assert(model1.weights ~= weightsR relTol 1E-3)
+    assert(model2.intercept ~== interceptR relTol 1E-3)
+    assert(model2.weights ~= weightsR relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept without regularization") {
-    val trainer = (new LinearRegression).setFitIntercept(false)
-    val model = trainer.fit(dataset)
-    val modelWithoutIntercept = trainer.fit(datasetWithoutIntercept)
+    val trainer1 = (new LinearRegression).setFitIntercept(false)
+    // Without regularization the results should be the same
+    val trainer2 = (new LinearRegression).setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val modelWithoutIntercept1 = trainer1.fit(datasetWithoutIntercept)
+    val model2 = trainer2.fit(dataset)
+    val modelWithoutIntercept2 = trainer2.fit(datasetWithoutIntercept)
+
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0,
@@ -130,26 +142,34 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
      */
     val weightsR = Vectors.dense(6.995908, 5.275131)
 
-    assert(model.intercept ~== 0 absTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== 0 absTol 1E-3)
+    assert(model1.weights ~= weightsR relTol 1E-3)
+    assert(model2.intercept ~== 0 absTol 1E-3)
+    assert(model2.weights ~= weightsR relTol 1E-3)
+
     /*
        Then again with the data with no intercept:
        > weightsWithoutIntercept
        3 x 1 sparse Matrix of class "dgCMatrix"
-                                   s0
+                                 s0
        (Intercept)           .
        as.numeric.data3.V2. 4.70011
        as.numeric.data3.V3. 7.19943
      */
     val weightsWithoutInterceptR = Vectors.dense(4.70011, 7.19943)
 
-    assert(modelWithoutIntercept.intercept ~== 0 absTol 1E-3)
-    assert(modelWithoutIntercept.weights ~= weightsWithoutInterceptR relTol 1E-3)
+    assert(modelWithoutIntercept1.intercept ~== 0 absTol 1E-3)
+    assert(modelWithoutIntercept1.weights ~= weightsWithoutInterceptR relTol 1E-3)
+    assert(modelWithoutIntercept2.intercept ~== 0 absTol 1E-3)
+    assert(modelWithoutIntercept2.weights ~= weightsWithoutInterceptR relTol 1E-3)
   }
 
   test("linear regression with intercept with L1 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
-    val model = trainer.fit(dataset)
+    val trainer1 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+    val trainer2 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+      .setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57))
@@ -160,24 +180,44 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 4.024821
        as.numeric.data.V3. 6.679841
      */
-    val interceptR = 6.24300
-    val weightsR = Vectors.dense(4.024821, 6.679841)
+    val interceptR1 = 6.24300
+    val weightsR1 = Vectors.dense(4.024821, 6.679841)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
+        standardize=FALSE))
+      > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                s0
+      (Intercept)         6.416948
+      as.numeric.data.V2. 3.893869
+      as.numeric.data.V3. 6.724286
+     */
+    val interceptR2 = 6.416948
+    val weightsR2 = Vectors.dense(3.893869, 6.724286)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept with L1 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+    val trainer1 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
       .setFitIntercept(false)
-    val model = trainer.fit(dataset)
+    val trainer2 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+      .setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
@@ -189,51 +229,90 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 6.299752
        as.numeric.data.V3. 4.772913
      */
-    val interceptR = 0.0
-    val weightsR = Vectors.dense(6.299752, 4.772913)
+    val interceptR1 = 0.0
+    val weightsR1 = Vectors.dense(6.299752, 4.772913)
 
-    assert(model.intercept ~== interceptR absTol 1E-5)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
+         intercept=FALSE, standardize=FALSE))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 6.232193
+       as.numeric.data.V3. 4.764229
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Vectors.dense(6.232193, 4.764229)
+
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression with intercept with L2 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
-    val model = trainer.fit(dataset)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+      .setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
-       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
-       > weights
-        3 x 1 sparse Matrix of class "dgCMatrix"
-                                 s0
-       (Intercept)         6.328062
-       as.numeric.data.V2. 3.222034
-       as.numeric.data.V3. 4.926260
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
+      > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                s0
+      (Intercept)         5.269376
+      as.numeric.data.V2. 3.736216
+      as.numeric.data.V3. 5.712356)
      */
-    val interceptR = 5.269376
-    val weightsR = Vectors.dense(3.736216, 5.712356)
+    val interceptR1 = 5.269376
+    val weightsR1 = Vectors.dense(3.736216, 5.712356)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
+        standardize=FALSE))
+      > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                s0
+      (Intercept)         5.791109
+      as.numeric.data.V2. 3.435466
+      as.numeric.data.V3. 5.910406
+     */
+    val interceptR2 = 5.791109
+    val weightsR2 = Vectors.dense(3.435466, 5.910406)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept with L2 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
       .setFitIntercept(false)
-    val model = trainer.fit(dataset)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+      .setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
@@ -245,23 +324,42 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 5.522875
        as.numeric.data.V3. 4.214502
      */
-    val interceptR = 0.0
-    val weightsR = Vectors.dense(5.522875, 4.214502)
+    val interceptR1 = 0.0
+    val weightsR1 = Vectors.dense(5.522875, 4.214502)
 
-    assert(model.intercept ~== interceptR absTol 1E-3)
-    assert(model.weights ~== weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
+         intercept = FALSE, standardize=FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 5.263704
+       as.numeric.data.V3. 4.187419
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Vectors.dense(5.263704, 4.187419)
+
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression with intercept with ElasticNet regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
-    val model = trainer.fit(dataset)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+      .setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6))
@@ -272,24 +370,43 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 3.168435
        as.numeric.data.V3. 5.200403
      */
-    val interceptR = 5.696056
-    val weightsR = Vectors.dense(3.670489, 6.001122)
+    val interceptR1 = 5.696056
+    val weightsR1 = Vectors.dense(3.670489, 6.001122)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~== weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6
+       standardize=FALSE))
+      > weights
+      3 x 1 sparse Matrix of class "dgCMatrix"
+      s0
+      (Intercept)         6.114723
+      as.numeric.data.V2. 3.409937
+      as.numeric.data.V3. 6.146531
+     */
+    val interceptR2 = 6.114723
+    val weightsR2 = Vectors.dense(3.409937, 6.146531)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept with ElasticNet regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
       .setFitIntercept(false)
-    val model = trainer.fit(dataset)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+      .setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
@@ -301,16 +418,32 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.dataM.V2. 5.673348
        as.numeric.dataM.V3. 4.322251
      */
-    val interceptR = 0.0
-    val weightsR = Vectors.dense(5.673348, 4.322251)
+    val interceptR1 = 0.0
+    val weightsR1 = Vectors.dense(5.673348, 4.322251)
 
-    assert(model.intercept ~== interceptR absTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
+         intercept=FALSE, standardize=FALSE))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+       s0
+       (Intercept)         .
+       as.numeric.data.V2. 5.477988
+       as.numeric.data.V3. 4.297622
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Vectors.dense(5.477988, 4.297622)
+
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
@@ -372,5 +505,4 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .zip(testSummary.residuals.select("residuals").collect())
       .forall { case (Row(r1: Double), Row(r2: Double)) => r1 ~== r2 relTol 1E-5 }
   }
-
 }

From a7fe48f68727d5c0247698cff329fb12faff1d50 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 4 Aug 2015 18:19:26 -0700
Subject: [PATCH 0842/1454] [SPARK-9432][SQL] Audit expression unit tests to
 make sure we pass the proper numeric ranges

JIRA: https://issues.apache.org/jira/browse/SPARK-9432

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7933 from yjshen/numeric_ranges and squashes the following commits:

e719f78 [Yijie Shen] proper integral range check
---
 .../ArithmeticExpressionSuite.scala           | 51 +++++++++++++++++--
 .../expressions/BitwiseFunctionsSuite.scala   | 21 ++++++++
 .../expressions/DateExpressionsSuite.scala    | 14 +++++
 .../IntegralLiteralTestUtils.scala            | 42 +++++++++++++++
 .../expressions/MathFunctionsSuite.scala      | 40 +++++++++++++++
 5 files changed, 164 insertions(+), 4 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index 0bae8fe2fd8aa..a1f15e4f0f25a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -23,6 +23,8 @@ import org.apache.spark.sql.types._
 
 class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   /**
    * Runs through the testFunc for all numeric data types.
    *
@@ -47,6 +49,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Add(Literal.create(null, left.dataType), right), null)
       checkEvaluation(Add(left, Literal.create(null, right.dataType)), null)
     }
+    checkEvaluation(Add(positiveShortLit, negativeShortLit), -1.toShort)
+    checkEvaluation(Add(positiveIntLit, negativeIntLit), -1)
+    checkEvaluation(Add(positiveLongLit, negativeLongLit), -1L)
   }
 
   test("- (UnaryMinus)") {
@@ -60,6 +65,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(UnaryMinus(Literal(Int.MinValue)), Int.MinValue)
     checkEvaluation(UnaryMinus(Literal(Short.MinValue)), Short.MinValue)
     checkEvaluation(UnaryMinus(Literal(Byte.MinValue)), Byte.MinValue)
+    checkEvaluation(UnaryMinus(positiveShortLit), (- positiveShort).toShort)
+    checkEvaluation(UnaryMinus(negativeShortLit), (- negativeShort).toShort)
+    checkEvaluation(UnaryMinus(positiveIntLit), - positiveInt)
+    checkEvaluation(UnaryMinus(negativeIntLit), - negativeInt)
+    checkEvaluation(UnaryMinus(positiveLongLit), - positiveLong)
+    checkEvaluation(UnaryMinus(negativeLongLit), - negativeLong)
   }
 
   test("- (Minus)") {
@@ -70,6 +81,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Subtract(Literal.create(null, left.dataType), right), null)
       checkEvaluation(Subtract(left, Literal.create(null, right.dataType)), null)
     }
+    checkEvaluation(Subtract(positiveShortLit, negativeShortLit),
+      (positiveShort - negativeShort).toShort)
+    checkEvaluation(Subtract(positiveIntLit, negativeIntLit), positiveInt - negativeInt)
+    checkEvaluation(Subtract(positiveLongLit, negativeLongLit), positiveLong - negativeLong)
   }
 
   test("* (Multiply)") {
@@ -80,6 +95,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Multiply(Literal.create(null, left.dataType), right), null)
       checkEvaluation(Multiply(left, Literal.create(null, right.dataType)), null)
     }
+    checkEvaluation(Multiply(positiveShortLit, negativeShortLit),
+      (positiveShort * negativeShort).toShort)
+    checkEvaluation(Multiply(positiveIntLit, negativeIntLit), positiveInt * negativeInt)
+    checkEvaluation(Multiply(positiveLongLit, negativeLongLit), positiveLong * negativeLong)
   }
 
   test("/ (Divide) basic") {
@@ -99,6 +118,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Divide(Literal(1.toShort), Literal(2.toShort)), 0.toShort)
     checkEvaluation(Divide(Literal(1), Literal(2)), 0)
     checkEvaluation(Divide(Literal(1.toLong), Literal(2.toLong)), 0.toLong)
+    checkEvaluation(Divide(positiveShortLit, negativeShortLit), 0.toShort)
+    checkEvaluation(Divide(positiveIntLit, negativeIntLit), 0)
+    checkEvaluation(Divide(positiveLongLit, negativeLongLit), 0L)
   }
 
   test("/ (Divide) for floating point") {
@@ -116,6 +138,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Remainder(left, Literal.create(null, right.dataType)), null)
       checkEvaluation(Remainder(left, Literal(convert(0))), null)  // mod by 0
     }
+    checkEvaluation(Remainder(positiveShortLit, positiveShortLit), 0.toShort)
+    checkEvaluation(Remainder(negativeShortLit, negativeShortLit), 0.toShort)
+    checkEvaluation(Remainder(positiveIntLit, positiveIntLit), 0)
+    checkEvaluation(Remainder(negativeIntLit, negativeIntLit), 0)
+    checkEvaluation(Remainder(positiveLongLit, positiveLongLit), 0L)
+    checkEvaluation(Remainder(negativeLongLit, negativeLongLit), 0L)
   }
 
   test("Abs") {
@@ -127,6 +155,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Abs(Literal(convert(-1))), convert(1))
       checkEvaluation(Abs(Literal.create(null, dataType)), null)
     }
+    checkEvaluation(Abs(positiveShortLit), positiveShort)
+    checkEvaluation(Abs(negativeShortLit), (- negativeShort).toShort)
+    checkEvaluation(Abs(positiveIntLit), positiveInt)
+    checkEvaluation(Abs(negativeIntLit), - negativeInt)
+    checkEvaluation(Abs(positiveLongLit), positiveLong)
+    checkEvaluation(Abs(negativeLongLit), - negativeLong)
   }
 
   test("MaxOf basic") {
@@ -138,6 +172,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(MaxOf(Literal.create(null, small.dataType), large), convert(2))
       checkEvaluation(MaxOf(large, Literal.create(null, small.dataType)), convert(2))
     }
+    checkEvaluation(MaxOf(positiveShortLit, negativeShortLit), (positiveShort).toShort)
+    checkEvaluation(MaxOf(positiveIntLit, negativeIntLit), positiveInt)
+    checkEvaluation(MaxOf(positiveLongLit, negativeLongLit), positiveLong)
   }
 
   test("MaxOf for atomic type") {
@@ -156,6 +193,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(MinOf(Literal.create(null, small.dataType), large), convert(2))
       checkEvaluation(MinOf(small, Literal.create(null, small.dataType)), convert(1))
     }
+    checkEvaluation(MinOf(positiveShortLit, negativeShortLit), (negativeShort).toShort)
+    checkEvaluation(MinOf(positiveIntLit, negativeIntLit), negativeInt)
+    checkEvaluation(MinOf(positiveLongLit, negativeLongLit), negativeLong)
   }
 
   test("MinOf for atomic type") {
@@ -174,9 +214,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Pmod(left, Literal.create(null, right.dataType)), null)
       checkEvaluation(Remainder(left, Literal(convert(0))), null)  // mod by 0
     }
-    checkEvaluation(Pmod(-7, 3), 2)
-    checkEvaluation(Pmod(7.2D, 4.1D), 3.1000000000000005)
-    checkEvaluation(Pmod(Decimal(0.7), Decimal(0.2)), Decimal(0.1))
-    checkEvaluation(Pmod(2L, Long.MaxValue), 2L)
+    checkEvaluation(Pmod(Literal(-7), Literal(3)), 2)
+    checkEvaluation(Pmod(Literal(7.2D), Literal(4.1D)), 3.1000000000000005)
+    checkEvaluation(Pmod(Literal(Decimal(0.7)), Literal(Decimal(0.2))), Decimal(0.1))
+    checkEvaluation(Pmod(Literal(2L), Literal(Long.MaxValue)), 2L)
+    checkEvaluation(Pmod(positiveShort, negativeShort), positiveShort.toShort)
+    checkEvaluation(Pmod(positiveInt, negativeInt), positiveInt)
+    checkEvaluation(Pmod(positiveLong, negativeLong), positiveLong)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
index fa30fbe528479..4fc1c06153595 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
@@ -23,6 +23,8 @@ import org.apache.spark.sql.types._
 
 class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   test("BitwiseNOT") {
     def check(input: Any, expected: Any): Unit = {
       val expr = BitwiseNot(Literal(input))
@@ -37,6 +39,12 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     check(123456789123L, ~123456789123L)
 
     checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null)
+    checkEvaluation(BitwiseNot(positiveShortLit), (~positiveShort).toShort)
+    checkEvaluation(BitwiseNot(negativeShortLit), (~negativeShort).toShort)
+    checkEvaluation(BitwiseNot(positiveIntLit), ~positiveInt)
+    checkEvaluation(BitwiseNot(negativeIntLit), ~negativeInt)
+    checkEvaluation(BitwiseNot(positiveLongLit), ~positiveLong)
+    checkEvaluation(BitwiseNot(negativeLongLit), ~negativeLong)
   }
 
   test("BitwiseAnd") {
@@ -56,6 +64,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseAnd(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseAnd(Literal(1), nullLit), null)
     checkEvaluation(BitwiseAnd(nullLit, nullLit), null)
+    checkEvaluation(BitwiseAnd(positiveShortLit, negativeShortLit),
+      (positiveShort & negativeShort).toShort)
+    checkEvaluation(BitwiseAnd(positiveIntLit, negativeIntLit), positiveInt & negativeInt)
+    checkEvaluation(BitwiseAnd(positiveLongLit, negativeLongLit), positiveLong & negativeLong)
   }
 
   test("BitwiseOr") {
@@ -75,6 +87,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseOr(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseOr(Literal(1), nullLit), null)
     checkEvaluation(BitwiseOr(nullLit, nullLit), null)
+    checkEvaluation(BitwiseOr(positiveShortLit, negativeShortLit),
+      (positiveShort | negativeShort).toShort)
+    checkEvaluation(BitwiseOr(positiveIntLit, negativeIntLit), positiveInt | negativeInt)
+    checkEvaluation(BitwiseOr(positiveLongLit, negativeLongLit), positiveLong | negativeLong)
   }
 
   test("BitwiseXor") {
@@ -94,5 +110,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseXor(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseXor(Literal(1), nullLit), null)
     checkEvaluation(BitwiseXor(nullLit, nullLit), null)
+
+    checkEvaluation(BitwiseXor(positiveShortLit, negativeShortLit),
+      (positiveShort ^ negativeShort).toShort)
+    checkEvaluation(BitwiseXor(positiveIntLit, negativeIntLit), positiveInt ^ negativeInt)
+    checkEvaluation(BitwiseXor(positiveLongLit, negativeLongLit), positiveLong ^ negativeLong)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index e6e8790e90926..f9b73f1a75e73 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -28,6 +28,8 @@ import org.apache.spark.unsafe.types.CalendarInterval
 
 class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
   val sdfDate = new SimpleDateFormat("yyyy-MM-dd")
   val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
@@ -212,6 +214,10 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       null)
     checkEvaluation(DateAdd(Literal.create(null, DateType), Literal.create(null, IntegerType)),
       null)
+    checkEvaluation(
+      DateAdd(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 49627)
+    checkEvaluation(
+      DateAdd(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -15910)
   }
 
   test("date_sub") {
@@ -226,6 +232,10 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       null)
     checkEvaluation(DateSub(Literal.create(null, DateType), Literal.create(null, IntegerType)),
       null)
+    checkEvaluation(
+      DateSub(Literal(Date.valueOf("2016-02-28")), positiveIntLit), -15909)
+    checkEvaluation(
+      DateSub(Literal(Date.valueOf("2016-02-28")), negativeIntLit), 49628)
   }
 
   test("time_add") {
@@ -282,6 +292,10 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       null)
     checkEvaluation(
       AddMonths(Literal(Date.valueOf("2015-01-30")), Literal(Int.MinValue)), -7293498)
+    checkEvaluation(
+      AddMonths(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 1014213)
+    checkEvaluation(
+      AddMonths(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -980528)
   }
 
   test("months_between") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala
new file mode 100644
index 0000000000000..2e5a121f4ec56
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+/**
+ * Utilities to make sure we pass the proper numeric ranges
+ */
+object IntegralLiteralTestUtils {
+
+  val positiveShort: Short = (Byte.MaxValue + 1).toShort
+  val negativeShort: Short = (Byte.MinValue - 1).toShort
+
+  val positiveShortLit: Literal = Literal(positiveShort)
+  val negativeShortLit: Literal = Literal(negativeShort)
+
+  val positiveInt: Int = Short.MaxValue + 1
+  val negativeInt: Int = Short.MinValue - 1
+
+  val positiveIntLit: Literal = Literal(positiveInt)
+  val negativeIntLit: Literal = Literal(negativeInt)
+
+  val positiveLong: Long = Int.MaxValue + 1L
+  val negativeLong: Long = Int.MinValue - 1L
+
+  val positiveLongLit: Literal = Literal(positiveLong)
+  val negativeLongLit: Literal = Literal(negativeLong)
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 9fcb548af6bbb..033792eee6c0f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -30,6 +30,8 @@ import org.apache.spark.sql.types._
 
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   /**
    * Used for testing leaf math expressions.
    *
@@ -293,6 +295,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Bin(l3), java.lang.Long.toBinaryString(123), row)
     checkEvaluation(Bin(l4), java.lang.Long.toBinaryString(1234), row)
     checkEvaluation(Bin(l5), java.lang.Long.toBinaryString(-123), row)
+
+    checkEvaluation(Bin(positiveLongLit), java.lang.Long.toBinaryString(positiveLong))
+    checkEvaluation(Bin(negativeLongLit), java.lang.Long.toBinaryString(negativeLong))
   }
 
   test("log2") {
@@ -324,6 +329,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(ShiftLeft(Literal(21.toLong), Literal(1)), 42.toLong)
     checkEvaluation(ShiftLeft(Literal(-21.toLong), Literal(1)), -42.toLong)
+
+    checkEvaluation(ShiftLeft(positiveIntLit, positiveIntLit), positiveInt << positiveInt)
+    checkEvaluation(ShiftLeft(positiveIntLit, negativeIntLit), positiveInt << negativeInt)
+    checkEvaluation(ShiftLeft(negativeIntLit, positiveIntLit), negativeInt << positiveInt)
+    checkEvaluation(ShiftLeft(negativeIntLit, negativeIntLit), negativeInt << negativeInt)
+    checkEvaluation(ShiftLeft(positiveLongLit, positiveIntLit), positiveLong << positiveInt)
+    checkEvaluation(ShiftLeft(positiveLongLit, negativeIntLit), positiveLong << negativeInt)
+    checkEvaluation(ShiftLeft(negativeLongLit, positiveIntLit), negativeLong << positiveInt)
+    checkEvaluation(ShiftLeft(negativeLongLit, negativeIntLit), negativeLong << negativeInt)
   }
 
   test("shift right") {
@@ -335,6 +349,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(ShiftRight(Literal(42.toLong), Literal(1)), 21.toLong)
     checkEvaluation(ShiftRight(Literal(-42.toLong), Literal(1)), -21.toLong)
+
+    checkEvaluation(ShiftRight(positiveIntLit, positiveIntLit), positiveInt >> positiveInt)
+    checkEvaluation(ShiftRight(positiveIntLit, negativeIntLit), positiveInt >> negativeInt)
+    checkEvaluation(ShiftRight(negativeIntLit, positiveIntLit), negativeInt >> positiveInt)
+    checkEvaluation(ShiftRight(negativeIntLit, negativeIntLit), negativeInt >> negativeInt)
+    checkEvaluation(ShiftRight(positiveLongLit, positiveIntLit), positiveLong >> positiveInt)
+    checkEvaluation(ShiftRight(positiveLongLit, negativeIntLit), positiveLong >> negativeInt)
+    checkEvaluation(ShiftRight(negativeLongLit, positiveIntLit), negativeLong >> positiveInt)
+    checkEvaluation(ShiftRight(negativeLongLit, negativeIntLit), negativeLong >> negativeInt)
   }
 
   test("shift right unsigned") {
@@ -346,6 +369,23 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(ShiftRightUnsigned(Literal(42.toLong), Literal(1)), 21.toLong)
     checkEvaluation(ShiftRightUnsigned(Literal(-42.toLong), Literal(1)), 9223372036854775787L)
+
+    checkEvaluation(ShiftRightUnsigned(positiveIntLit, positiveIntLit),
+      positiveInt >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(positiveIntLit, negativeIntLit),
+      positiveInt >>> negativeInt)
+    checkEvaluation(ShiftRightUnsigned(negativeIntLit, positiveIntLit),
+      negativeInt >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(negativeIntLit, negativeIntLit),
+      negativeInt >>> negativeInt)
+    checkEvaluation(ShiftRightUnsigned(positiveLongLit, positiveIntLit),
+      positiveLong >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(positiveLongLit, negativeIntLit),
+      positiveLong >>> negativeInt)
+    checkEvaluation(ShiftRightUnsigned(negativeLongLit, positiveIntLit),
+      negativeLong >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(negativeLongLit, negativeIntLit),
+      negativeLong >>> negativeInt)
   }
 
   test("hex") {

From c9a4c36d052456c2dd1f7e0a871c6b764b5064d2 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Tue, 4 Aug 2015 18:20:12 -0700
Subject: [PATCH 0843/1454] [SPARK-8313] R Spark packages support

shivaram cafreeman Could you please help me in testing this out? Exposing and running `rPackageBuilder` from inside the shell works, but for some reason, I can't get it to work during Spark Submit. It just starts relaunching Spark Submit.

For testing, you may use the R branch with [sbt-spark-package](https://github.com/databricks/sbt-spark-package). You can call spPackage, and then pass the jar using `--jars`.

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7139 from brkyvz/r-submit and squashes the following commits:

0de384f [Burak Yavuz] remove unused imports 2
d253708 [Burak Yavuz] removed unused imports
6603d0d [Burak Yavuz] addressed comments
4258ffe [Burak Yavuz] merged master
ddfcc06 [Burak Yavuz] added zipping test
3a1be7d [Burak Yavuz] don't zip
77995df [Burak Yavuz] fix URI
ac45527 [Burak Yavuz] added zipping of all libs
e6bf7b0 [Burak Yavuz] add println ignores
1bc5554 [Burak Yavuz] add assumes for tests
9778e03 [Burak Yavuz] addressed comments
b42b300 [Burak Yavuz] merged master
ffd134e [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into r-submit
d867756 [Burak Yavuz] add apache header
eff5ba1 [Burak Yavuz] ready for review
8838edb [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into r-submit
e5b5a06 [Burak Yavuz] added doc
bb751ce [Burak Yavuz] fix null bug
0226768 [Burak Yavuz] fixed issues
8810beb [Burak Yavuz] R packages support
---
 R/install-dev.sh                              |   4 -
 R/pkg/inst/tests/packageInAJarTest.R          |  30 +++
 .../scala/org/apache/spark/api/r/RUtils.scala |  14 +-
 .../apache/spark/deploy/RPackageUtils.scala   | 232 ++++++++++++++++++
 .../org/apache/spark/deploy/SparkSubmit.scala |  11 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   1 -
 .../apache/spark/deploy/IvyTestUtils.scala    | 101 ++++++--
 .../spark/deploy/RPackageUtilsSuite.scala     | 156 ++++++++++++
 .../spark/deploy/SparkSubmitSuite.scala       |  24 ++
 9 files changed, 538 insertions(+), 35 deletions(-)
 create mode 100644 R/pkg/inst/tests/packageInAJarTest.R
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
 create mode 100644 core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala

diff --git a/R/install-dev.sh b/R/install-dev.sh
index 4972bb9217072..59d98c9c7a646 100755
--- a/R/install-dev.sh
+++ b/R/install-dev.sh
@@ -42,8 +42,4 @@ Rscript -e ' if("devtools" %in% rownames(installed.packages())) { library(devtoo
 # Install SparkR to $LIB_DIR
 R CMD INSTALL --library=$LIB_DIR $FWDIR/pkg/
 
-# Zip the SparkR package so that it can be distributed to worker nodes on YARN
-cd $LIB_DIR
-jar cfM "$LIB_DIR/sparkr.zip" SparkR
-
 popd > /dev/null
diff --git a/R/pkg/inst/tests/packageInAJarTest.R b/R/pkg/inst/tests/packageInAJarTest.R
new file mode 100644
index 0000000000000..207a37a0cb47f
--- /dev/null
+++ b/R/pkg/inst/tests/packageInAJarTest.R
@@ -0,0 +1,30 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+library(SparkR)
+library(sparkPackageTest)
+
+sc <- sparkR.init()
+
+run1 <- myfunc(5L)
+
+run2 <- myfunc(-4L)
+
+sparkR.stop()
+
+if(run1 != 6) quit(save = "no", status = 1)
+
+if(run2 != -3) quit(save = "no", status = 1)
diff --git a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
index d53abd3408c55..93b3bea578676 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
@@ -19,6 +19,8 @@ package org.apache.spark.api.r
 
 import java.io.File
 
+import scala.collection.JavaConversions._
+
 import org.apache.spark.{SparkEnv, SparkException}
 
 private[spark] object RUtils {
@@ -26,7 +28,7 @@ private[spark] object RUtils {
    * Get the SparkR package path in the local spark distribution.
    */
   def localSparkRPackagePath: Option[String] = {
-    val sparkHome = sys.env.get("SPARK_HOME")
+    val sparkHome = sys.env.get("SPARK_HOME").orElse(sys.props.get("spark.test.home"))
     sparkHome.map(
       Seq(_, "R", "lib").mkString(File.separator)
     )
@@ -46,8 +48,8 @@ private[spark] object RUtils {
         (sparkConf.get("spark.master"), sparkConf.get("spark.submit.deployMode"))
       }
 
-    val isYarnCluster = master.contains("yarn") && deployMode == "cluster"
-    val isYarnClient = master.contains("yarn") && deployMode == "client"
+    val isYarnCluster = master != null && master.contains("yarn") && deployMode == "cluster"
+    val isYarnClient = master != null && master.contains("yarn") && deployMode == "client"
 
     // In YARN mode, the SparkR package is distributed as an archive symbolically
     // linked to the "sparkr" file in the current directory. Note that this does not apply
@@ -62,4 +64,10 @@ private[spark] object RUtils {
       }
     }
   }
+
+  /** Check if R is installed before running tests that use R commands. */
+  def isRInstalled: Boolean = {
+    val builder = new ProcessBuilder(Seq("R", "--version"))
+    builder.start().waitFor() == 0
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
new file mode 100644
index 0000000000000..ed1e972955679
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
@@ -0,0 +1,232 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.io._
+import java.util.jar.JarFile
+import java.util.logging.Level
+import java.util.zip.{ZipEntry, ZipOutputStream}
+
+import scala.collection.JavaConversions._
+
+import com.google.common.io.{ByteStreams, Files}
+
+import org.apache.spark.{SparkException, Logging}
+import org.apache.spark.api.r.RUtils
+import org.apache.spark.util.{RedirectThread, Utils}
+
+private[deploy] object RPackageUtils extends Logging {
+
+  /** The key in the MANIFEST.mf that we look for, in case a jar contains R code. */
+  private final val hasRPackage = "Spark-HasRPackage"
+
+  /** Base of the shell command used in order to install R packages. */
+  private final val baseInstallCmd = Seq("R", "CMD", "INSTALL", "-l")
+
+  /** R source code should exist under R/pkg in a jar. */
+  private final val RJarEntries = "R/pkg"
+
+  /** Documentation on how the R source file layout should be in the jar. */
+  private[deploy] final val RJarDoc =
+    s"""In order for Spark to build R packages that are parts of Spark Packages, there are a few
+      |requirements. The R source code must be shipped in a jar, with additional Java/Scala
+      |classes. The jar must be in the following format:
+      |  1- The Manifest (META-INF/MANIFEST.mf) must contain the key-value: $hasRPackage: true
+      |  2- The standard R package layout must be preserved under R/pkg/ inside the jar. More
+      |  information on the standard R package layout can be found in:
+      |  http://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf
+      |  An example layout is given below. After running `jar tf $$JAR_FILE | sort`:
+      |
+      |META-INF/MANIFEST.MF
+      |R/
+      |R/pkg/
+      |R/pkg/DESCRIPTION
+      |R/pkg/NAMESPACE
+      |R/pkg/R/
+      |R/pkg/R/myRcode.R
+      |org/
+      |org/apache/
+      |...
+    """.stripMargin.trim
+
+  /** Internal method for logging. We log to a printStream in tests, for debugging purposes. */
+  private def print(
+      msg: String,
+      printStream: PrintStream,
+      level: Level = Level.FINE,
+      e: Throwable = null): Unit = {
+    if (printStream != null) {
+      // scalastyle:off println
+      printStream.println(msg)
+      // scalastyle:on println
+      if (e != null) {
+        e.printStackTrace(printStream)
+      }
+    } else {
+      level match {
+        case Level.INFO => logInfo(msg)
+        case Level.WARNING => logWarning(msg)
+        case Level.SEVERE => logError(msg, e)
+        case _ => logDebug(msg)
+      }
+    }
+  }
+
+  /**
+   * Checks the manifest of the Jar whether there is any R source code bundled with it.
+   * Exposed for testing.
+   */
+  private[deploy] def checkManifestForR(jar: JarFile): Boolean = {
+    val manifest = jar.getManifest.getMainAttributes
+    manifest.getValue(hasRPackage) != null && manifest.getValue(hasRPackage).trim == "true"
+  }
+
+  /**
+   * Runs the standard R package installation code to build the R package from source.
+   * Multiple runs don't cause problems.
+   */
+  private def rPackageBuilder(dir: File, printStream: PrintStream, verbose: Boolean): Boolean = {
+    // this code should be always running on the driver.
+    val pathToSparkR = RUtils.localSparkRPackagePath.getOrElse(
+      throw new SparkException("SPARK_HOME not set. Can't locate SparkR package."))
+    val pathToPkg = Seq(dir, "R", "pkg").mkString(File.separator)
+    val installCmd = baseInstallCmd ++ Seq(pathToSparkR, pathToPkg)
+    if (verbose) {
+      print(s"Building R package with the command: $installCmd", printStream)
+    }
+    try {
+      val builder = new ProcessBuilder(installCmd)
+      builder.redirectErrorStream(true)
+      val env = builder.environment()
+      env.clear()
+      val process = builder.start()
+      new RedirectThread(process.getInputStream, printStream, "redirect R packaging").start()
+      process.waitFor() == 0
+    } catch {
+      case e: Throwable =>
+        print("Failed to build R package.", printStream, Level.SEVERE, e)
+        false
+    }
+  }
+
+  /**
+   * Extracts the files under /R in the jar to a temporary directory for building.
+   */
+  private def extractRFolder(jar: JarFile, printStream: PrintStream, verbose: Boolean): File = {
+    val tempDir = Utils.createTempDir(null)
+    val jarEntries = jar.entries()
+    while (jarEntries.hasMoreElements) {
+      val entry = jarEntries.nextElement()
+      val entryRIndex = entry.getName.indexOf(RJarEntries)
+      if (entryRIndex > -1) {
+        val entryPath = entry.getName.substring(entryRIndex)
+        if (entry.isDirectory) {
+          val dir = new File(tempDir, entryPath)
+          if (verbose) {
+            print(s"Creating directory: $dir", printStream)
+          }
+          dir.mkdirs
+        } else {
+          val inStream = jar.getInputStream(entry)
+          val outPath = new File(tempDir, entryPath)
+          Files.createParentDirs(outPath)
+          val outStream = new FileOutputStream(outPath)
+          if (verbose) {
+            print(s"Extracting $entry to $outPath", printStream)
+          }
+          Utils.copyStream(inStream, outStream, closeStreams = true)
+        }
+      }
+    }
+    tempDir
+  }
+
+  /**
+   * Extracts the files under /R in the jar to a temporary directory for building.
+   */
+  private[deploy] def checkAndBuildRPackage(
+      jars: String,
+      printStream: PrintStream = null,
+      verbose: Boolean = false): Unit = {
+    jars.split(",").foreach { jarPath =>
+      val file = new File(Utils.resolveURI(jarPath))
+      if (file.exists()) {
+        val jar = new JarFile(file)
+        if (checkManifestForR(jar)) {
+          print(s"$file contains R source code. Now installing package.", printStream, Level.INFO)
+          val rSource = extractRFolder(jar, printStream, verbose)
+          try {
+            if (!rPackageBuilder(rSource, printStream, verbose)) {
+              print(s"ERROR: Failed to build R package in $file.", printStream)
+              print(RJarDoc, printStream)
+            }
+          } finally {
+            rSource.delete() // clean up
+          }
+        } else {
+          if (verbose) {
+            print(s"$file doesn't contain R source code, skipping...", printStream)
+          }
+        }
+      } else {
+        print(s"WARN: $file resolved as dependency, but not found.", printStream, Level.WARNING)
+      }
+    }
+  }
+
+  private def listFilesRecursively(dir: File, excludePatterns: Seq[String]): Set[File] = {
+    if (!dir.exists()) {
+      Set.empty[File]
+    } else {
+      if (dir.isDirectory) {
+        val subDir = dir.listFiles(new FilenameFilter {
+          override def accept(dir: File, name: String): Boolean = {
+            !excludePatterns.map(name.contains).reduce(_ || _) // exclude files with given pattern
+          }
+        })
+        subDir.flatMap(listFilesRecursively(_, excludePatterns)).toSet
+      } else {
+        Set(dir)
+      }
+    }
+  }
+
+  /** Zips all the libraries found with SparkR in the R/lib directory for distribution with Yarn. */
+  private[deploy] def zipRLibraries(dir: File, name: String): File = {
+    val filesToBundle = listFilesRecursively(dir, Seq(".zip"))
+    // create a zip file from scratch, do not append to existing file.
+    val zipFile = new File(dir, name)
+    zipFile.delete()
+    val zipOutputStream = new ZipOutputStream(new FileOutputStream(zipFile, false))
+    try {
+      filesToBundle.foreach { file =>
+        // get the relative paths for proper naming in the zip file
+        val relPath = file.getAbsolutePath.replaceFirst(dir.getAbsolutePath, "")
+        val fis = new FileInputStream(file)
+        val zipEntry = new ZipEntry(relPath)
+        zipOutputStream.putNextEntry(zipEntry)
+        ByteStreams.copy(fis, zipOutputStream)
+        zipOutputStream.closeEntry()
+        fis.close()
+      }
+    } finally {
+      zipOutputStream.close()
+    }
+    zipFile
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 31185c8e77def..1186bed485250 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -292,6 +292,12 @@ object SparkSubmit {
       }
     }
 
+    // install any R packages that may have been passed through --jars or --packages.
+    // Spark Packages may contain R source code inside the jar.
+    if (args.isR && !StringUtils.isBlank(args.jars)) {
+      RPackageUtils.checkAndBuildRPackage(args.jars, printStream, args.verbose)
+    }
+
     // Require all python files to be local, so we can add them to the PYTHONPATH
     // In YARN cluster mode, python files are distributed as regular files, which can be non-local
     if (args.isPython && !isYarnCluster) {
@@ -361,7 +367,8 @@ object SparkSubmit {
       if (rPackagePath.isEmpty) {
         printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
       }
-      val rPackageFile = new File(rPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
+      val rPackageFile =
+        RPackageUtils.zipRLibraries(new File(rPackagePath.get), SPARKR_PACKAGE_ARCHIVE)
       if (!rPackageFile.exists()) {
         printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
       }
@@ -987,11 +994,9 @@ private[spark] object SparkSubmitUtils {
         addExclusionRules(ivySettings, ivyConfName, md)
         // add all supplied maven artifacts as dependencies
         addDependenciesToIvy(md, artifacts, ivyConfName)
-
         exclusions.foreach { e =>
           md.addExcludeRule(createExclusion(e + ":*", ivySettings, ivyConfName))
         }
-
         // resolve dependencies
         val rr: ResolveReport = ivy.resolve(md, resolveOptions)
         if (rr.hasError) {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 44852ce4e84ac..3f3c6627c21fb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -611,5 +611,4 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
       System.setErr(currentErr)
     }
   }
-
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala b/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala
index 823050b0aabbe..d93febcfd23fd 100644
--- a/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala
@@ -19,6 +19,10 @@ package org.apache.spark.deploy
 
 import java.io.{File, FileInputStream, FileOutputStream}
 import java.util.jar.{JarEntry, JarOutputStream}
+import java.util.jar.Attributes.Name
+import java.util.jar.Manifest
+
+import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.io.{Files, ByteStreams}
 
@@ -35,7 +39,7 @@ private[deploy] object IvyTestUtils {
    * Create the path for the jar and pom from the maven coordinate. Extension should be `jar`
    * or `pom`.
    */
-  private def pathFromCoordinate(
+  private[deploy] def pathFromCoordinate(
       artifact: MavenCoordinate,
       prefix: File,
       ext: String,
@@ -52,7 +56,7 @@ private[deploy] object IvyTestUtils {
   }
 
   /** Returns the artifact naming based on standard ivy or maven format. */
-  private def artifactName(
+  private[deploy] def artifactName(
       artifact: MavenCoordinate,
       useIvyLayout: Boolean,
       ext: String = ".jar"): String = {
@@ -73,7 +77,7 @@ private[deploy] object IvyTestUtils {
   }
 
   /** Write the contents to a file to the supplied directory. */
-  private def writeFile(dir: File, fileName: String, contents: String): File = {
+  private[deploy] def writeFile(dir: File, fileName: String, contents: String): File = {
     val outputFile = new File(dir, fileName)
     val outputStream = new FileOutputStream(outputFile)
     outputStream.write(contents.toCharArray.map(_.toByte))
@@ -90,6 +94,42 @@ private[deploy] object IvyTestUtils {
     writeFile(dir, "mylib.py", contents)
   }
 
+  /** Create an example R package that calls the given Java class. */
+  private def createRFiles(
+      dir: File,
+      className: String,
+      packageName: String): Seq[(String, File)] = {
+    val rFilesDir = new File(dir, "R" + File.separator + "pkg")
+    Files.createParentDirs(new File(rFilesDir, "R" + File.separator + "mylib.R"))
+    val contents =
+      s"""myfunc <- function(x) {
+        |  SparkR:::callJStatic("$packageName.$className", "myFunc", x)
+        |}
+      """.stripMargin
+    val source = writeFile(new File(rFilesDir, "R"), "mylib.R", contents)
+    val description =
+      """Package: sparkPackageTest
+        |Type: Package
+        |Title: Test for building an R package
+        |Version: 0.1
+        |Date: 2015-07-08
+        |Author: Burak Yavuz
+        |Imports: methods, SparkR
+        |Depends: R (>= 3.1), methods, SparkR
+        |Suggests: testthat
+        |Description: Test for building an R package within a jar
+        |License: Apache License (== 2.0)
+        |Collate: 'mylib.R'
+      """.stripMargin
+    val descFile = writeFile(rFilesDir, "DESCRIPTION", description)
+    val namespace =
+      """import(SparkR)
+        |export("myfunc")
+      """.stripMargin
+    val nameFile = writeFile(rFilesDir, "NAMESPACE", namespace)
+    Seq(("R/pkg/R/mylib.R", source), ("R/pkg/DESCRIPTION", descFile), ("R/pkg/NAMESPACE", nameFile))
+  }
+
   /** Create a simple testable Class. */
   private def createJavaClass(dir: File, className: String, packageName: String): File = {
     val contents =
@@ -97,17 +137,14 @@ private[deploy] object IvyTestUtils {
         |
         |import java.lang.Integer;
         |
-        |class $className implements java.io.Serializable {
-        |
-        | public $className() {}
-        |
-        | public Integer myFunc(Integer x) {
+        |public class $className implements java.io.Serializable {
+        | public static Integer myFunc(Integer x) {
         |   return x + 1;
         | }
         |}
       """.stripMargin
     val sourceFile =
-      new JavaSourceFromString(new File(dir, className + ".java").getAbsolutePath, contents)
+      new JavaSourceFromString(new File(dir, className).getAbsolutePath, contents)
     createCompiledClass(className, dir, sourceFile, Seq.empty)
   }
 
@@ -199,14 +236,25 @@ private[deploy] object IvyTestUtils {
   }
 
   /** Create the jar for the given maven coordinate, using the supplied files. */
-  private def packJar(
+  private[deploy] def packJar(
       dir: File,
       artifact: MavenCoordinate,
       files: Seq[(String, File)],
-      useIvyLayout: Boolean): File = {
+      useIvyLayout: Boolean,
+      withR: Boolean,
+      withManifest: Option[Manifest] = None): File = {
     val jarFile = new File(dir, artifactName(artifact, useIvyLayout))
     val jarFileStream = new FileOutputStream(jarFile)
-    val jarStream = new JarOutputStream(jarFileStream, new java.util.jar.Manifest())
+    val manifest = withManifest.getOrElse {
+      val mani = new Manifest()
+      if (withR) {
+        val attr = mani.getMainAttributes
+        attr.put(Name.MANIFEST_VERSION, "1.0")
+        attr.put(new Name("Spark-HasRPackage"), "true")
+      }
+      mani
+    }
+    val jarStream = new JarOutputStream(jarFileStream, manifest)
 
     for (file <- files) {
       val jarEntry = new JarEntry(file._1)
@@ -239,7 +287,8 @@ private[deploy] object IvyTestUtils {
       dependencies: Option[Seq[MavenCoordinate]] = None,
       tempDir: Option[File] = None,
       useIvyLayout: Boolean = false,
-      withPython: Boolean = false): File = {
+      withPython: Boolean = false,
+      withR: Boolean = false): File = {
     // Where the root of the repository exists, and what Ivy will search in
     val tempPath = tempDir.getOrElse(Files.createTempDir())
     // Create directory if it doesn't exist
@@ -255,14 +304,16 @@ private[deploy] object IvyTestUtils {
       val javaClass = createJavaClass(root, className, artifact.groupId)
       // A tuple of files representation in the jar, and the file
       val javaFile = (artifact.groupId.replace(".", "/") + "/" + javaClass.getName, javaClass)
-      val allFiles =
-        if (withPython) {
-          val pythonFile = createPythonFile(root)
-          Seq(javaFile, (pythonFile.getName, pythonFile))
-        } else {
-          Seq(javaFile)
-        }
-      val jarFile = packJar(jarPath, artifact, allFiles, useIvyLayout)
+      val allFiles = ArrayBuffer[(String, File)](javaFile)
+      if (withPython) {
+        val pythonFile = createPythonFile(root)
+        allFiles.append((pythonFile.getName, pythonFile))
+      }
+      if (withR) {
+        val rFiles = createRFiles(root, className, artifact.groupId)
+        allFiles.append(rFiles: _*)
+      }
+      val jarFile = packJar(jarPath, artifact, allFiles, useIvyLayout, withR)
       assert(jarFile.exists(), "Problem creating Jar file")
       val descriptor = createDescriptor(tempPath, artifact, dependencies, useIvyLayout)
       assert(descriptor.exists(), "Problem creating Pom file")
@@ -286,9 +337,10 @@ private[deploy] object IvyTestUtils {
       dependencies: Option[String],
       rootDir: Option[File],
       useIvyLayout: Boolean = false,
-      withPython: Boolean = false): File = {
+      withPython: Boolean = false,
+      withR: Boolean = false): File = {
     val deps = dependencies.map(SparkSubmitUtils.extractMavenCoordinates)
-    val mainRepo = createLocalRepository(artifact, deps, rootDir, useIvyLayout, withPython)
+    val mainRepo = createLocalRepository(artifact, deps, rootDir, useIvyLayout, withPython, withR)
     deps.foreach { seq => seq.foreach { dep =>
       createLocalRepository(dep, None, Some(mainRepo), useIvyLayout, withPython = false)
     }}
@@ -311,11 +363,12 @@ private[deploy] object IvyTestUtils {
       rootDir: Option[File],
       useIvyLayout: Boolean = false,
       withPython: Boolean = false,
+      withR: Boolean = false,
       ivySettings: IvySettings = new IvySettings)(f: String => Unit): Unit = {
     val deps = dependencies.map(SparkSubmitUtils.extractMavenCoordinates)
     purgeLocalIvyCache(artifact, deps, ivySettings)
     val repo = createLocalRepositoryForTests(artifact, dependencies, rootDir, useIvyLayout,
-      withPython)
+      withPython, withR)
     try {
       f(repo.toURI.toString)
     } finally {
diff --git a/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
new file mode 100644
index 0000000000000..47a64081e297e
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.io.{PrintStream, OutputStream, File}
+import java.net.URI
+import java.util.jar.Attributes.Name
+import java.util.jar.{JarFile, Manifest}
+import java.util.zip.{ZipEntry, ZipFile}
+
+import org.scalatest.BeforeAndAfterEach
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+import com.google.common.io.Files
+import org.apache.commons.io.FileUtils
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.api.r.RUtils
+import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate
+
+class RPackageUtilsSuite extends SparkFunSuite with BeforeAndAfterEach {
+
+  private val main = MavenCoordinate("a", "b", "c")
+  private val dep1 = MavenCoordinate("a", "dep1", "c")
+  private val dep2 = MavenCoordinate("a", "dep2", "d")
+
+  private def getJarPath(coord: MavenCoordinate, repo: File): File = {
+    new File(IvyTestUtils.pathFromCoordinate(coord, repo, "jar", useIvyLayout = false),
+      IvyTestUtils.artifactName(coord, useIvyLayout = false, ".jar"))
+  }
+
+  private val lineBuffer = ArrayBuffer[String]()
+
+  private val noOpOutputStream = new OutputStream {
+    def write(b: Int) = {}
+  }
+
+  /** Simple PrintStream that reads data into a buffer */
+  private class BufferPrintStream extends PrintStream(noOpOutputStream) {
+    // scalastyle:off println
+    override def println(line: String) {
+    // scalastyle:on println
+      lineBuffer += line
+    }
+  }
+
+  def beforeAll() {
+    System.setProperty("spark.testing", "true")
+  }
+
+  override def beforeEach(): Unit = {
+    lineBuffer.clear()
+  }
+
+  test("pick which jars to unpack using the manifest") {
+    val deps = Seq(dep1, dep2).mkString(",")
+    IvyTestUtils.withRepository(main, Some(deps), None, withR = true) { repo =>
+      val jars = Seq(main, dep1, dep2).map(c => new JarFile(getJarPath(c, new File(new URI(repo)))))
+      assert(RPackageUtils.checkManifestForR(jars(0)), "should have R code")
+      assert(!RPackageUtils.checkManifestForR(jars(1)), "should not have R code")
+      assert(!RPackageUtils.checkManifestForR(jars(2)), "should not have R code")
+    }
+  }
+
+  test("build an R package from a jar end to end") {
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    val deps = Seq(dep1, dep2).mkString(",")
+    IvyTestUtils.withRepository(main, Some(deps), None, withR = true) { repo =>
+      val jars = Seq(main, dep1, dep2).map { c =>
+        getJarPath(c, new File(new URI(repo)))
+      }.mkString(",")
+      RPackageUtils.checkAndBuildRPackage(jars, new BufferPrintStream, verbose = true)
+      val firstJar = jars.substring(0, jars.indexOf(","))
+      val output = lineBuffer.mkString("\n")
+      assert(output.contains("Building R package"))
+      assert(output.contains("Extracting"))
+      assert(output.contains(s"$firstJar contains R source code. Now installing package."))
+      assert(output.contains("doesn't contain R source code, skipping..."))
+    }
+  }
+
+  test("jars that don't exist are skipped and print warning") {
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    val deps = Seq(dep1, dep2).mkString(",")
+    IvyTestUtils.withRepository(main, Some(deps), None, withR = true) { repo =>
+      val jars = Seq(main, dep1, dep2).map { c =>
+        getJarPath(c, new File(new URI(repo))) + "dummy"
+      }.mkString(",")
+      RPackageUtils.checkAndBuildRPackage(jars, new BufferPrintStream, verbose = true)
+      val individualJars = jars.split(",")
+      val output = lineBuffer.mkString("\n")
+      individualJars.foreach { jarFile =>
+        assert(output.contains(s"$jarFile"))
+      }
+    }
+  }
+
+  test("faulty R package shows documentation") {
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    IvyTestUtils.withRepository(main, None, None) { repo =>
+      val manifest = new Manifest
+      val attr = manifest.getMainAttributes
+      attr.put(Name.MANIFEST_VERSION, "1.0")
+      attr.put(new Name("Spark-HasRPackage"), "true")
+      val jar = IvyTestUtils.packJar(new File(new URI(repo)), dep1, Nil,
+        useIvyLayout = false, withR = false, Some(manifest))
+      RPackageUtils.checkAndBuildRPackage(jar.getAbsolutePath, new BufferPrintStream,
+        verbose = true)
+      val output = lineBuffer.mkString("\n")
+      assert(output.contains(RPackageUtils.RJarDoc))
+    }
+  }
+
+  test("SparkR zipping works properly") {
+    val tempDir = Files.createTempDir()
+    try {
+      IvyTestUtils.writeFile(tempDir, "test.R", "abc")
+      val fakeSparkRDir = new File(tempDir, "SparkR")
+      assert(fakeSparkRDir.mkdirs())
+      IvyTestUtils.writeFile(fakeSparkRDir, "abc.R", "abc")
+      IvyTestUtils.writeFile(fakeSparkRDir, "DESCRIPTION", "abc")
+      IvyTestUtils.writeFile(tempDir, "package.zip", "abc") // fake zip file :)
+      val fakePackageDir = new File(tempDir, "packageTest")
+      assert(fakePackageDir.mkdirs())
+      IvyTestUtils.writeFile(fakePackageDir, "def.R", "abc")
+      IvyTestUtils.writeFile(fakePackageDir, "DESCRIPTION", "abc")
+      val finalZip = RPackageUtils.zipRLibraries(tempDir, "sparkr.zip")
+      assert(finalZip.exists())
+      val entries = new ZipFile(finalZip).entries().toSeq.map(_.getName)
+      assert(entries.contains("/test.R"))
+      assert(entries.contains("/SparkR/abc.R"))
+      assert(entries.contains("/SparkR/DESCRIPTION"))
+      assert(!entries.contains("/package.zip"))
+      assert(entries.contains("/packageTest/def.R"))
+      assert(entries.contains("/packageTest/DESCRIPTION"))
+    } finally {
+      FileUtils.deleteDirectory(tempDir)
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index aa78bfe30974c..757e0ce3d278b 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -362,6 +362,30 @@ class SparkSubmitSuite
     }
   }
 
+  test("correctly builds R packages included in a jar with --packages") {
+    // TODO(SPARK-9603): Building a package to $SPARK_HOME/R/lib is unavailable on Jenkins.
+    // It's hard to write the test in SparkR (because we can't create the repository dynamically)
+    /*
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    val main = MavenCoordinate("my.great.lib", "mylib", "0.1")
+    val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
+    val rScriptDir =
+      Seq(sparkHome, "R", "pkg", "inst", "tests", "packageInAJarTest.R").mkString(File.separator)
+    assert(new File(rScriptDir).exists)
+    IvyTestUtils.withRepository(main, None, None, withR = true) { repo =>
+      val args = Seq(
+        "--name", "testApp",
+        "--master", "local-cluster[2,1,1024]",
+        "--packages", main.toString,
+        "--repositories", repo,
+        "--verbose",
+        "--conf", "spark.ui.enabled=false",
+        rScriptDir)
+      runSparkSubmit(args)
+    }
+    */
+  }
+
   test("resolves command line argument paths correctly") {
     val jars = "/jar1,/jar2"                 // --jars
     val files = "hdfs:/file1,file2"          // --files

From 6f8f0e265a29e89bd5192a8d5217cba19f0875da Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Tue, 4 Aug 2015 18:26:05 -0700
Subject: [PATCH 0844/1454] [SPARK-7119] [SQL] Give script a default serde with
 the user specific types

This is to address this issue that there would be not compatible type exception when running this:
`from (from src select transform(key, value) using 'cat' as (thing1 int, thing2 string)) t select thing1 + 2;`

15/04/24 00:58:55 ERROR CliDriver: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.ClassCastException: org.apache.spark.sql.types.UTF8String cannot be cast to java.lang.Integer
	at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
	at scala.math.Numeric$IntIsIntegral$.plus(Numeric.scala:57)
	at org.apache.spark.sql.catalyst.expressions.Add.eval(arithmetic.scala:127)
	at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:118)
	at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:68)
	at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:52)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
	at scala.collection.AbstractIterator.to(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
	at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
	at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
	at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1618)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1618)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
	at org.apache.spark.scheduler.Task.run(Task.scala:64)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:209)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1110)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:603)
	at java.lang.Thread.run(Thread.java:722)

chenghao-intel marmbrus

Author: zhichao.li <zhichao.li@intel.com>

Closes #6638 from zhichao-li/transDataType2 and squashes the following commits:

a36cc7c [zhichao.li] style
b9252a8 [zhichao.li] delete cacheRow
f6968a4 [zhichao.li] give script a default serde
---
 .../org/apache/spark/sql/hive/HiveQl.scala    |  3 +-
 .../hive/execution/ScriptTransformation.scala | 96 +++++++------------
 .../sql/hive/execution/SQLQuerySuite.scala    | 10 ++
 3 files changed, 49 insertions(+), 60 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index e2fdfc6163a00..f43e403ce9a9d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -21,6 +21,7 @@ import java.sql.Date
 import java.util.Locale
 
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.ql.{ErrorMsg, Context}
 import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo}
@@ -907,7 +908,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 }
                 (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), serdeProps)
 
-              case Nil => (Nil, None, Nil)
+              case Nil => (Nil, Option(hiveConf().getVar(ConfVars.HIVESCRIPTSERDE)), Nil)
             }
 
             val (inRowFormat, inSerdeClass, inSerdeProps) = matchSerDe(inputSerdeClause)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index fbb86406f40cb..97e4ea2081d56 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -27,11 +27,11 @@ import scala.util.control.NonFatal
 import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.serde2.AbstractSerDe
 import org.apache.hadoop.hive.serde2.objectinspector._
+import org.apache.hadoop.io.Writable
 
 import org.apache.spark.{TaskContext, Logging}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema
 import org.apache.spark.sql.execution._
@@ -106,9 +106,15 @@ case class ScriptTransformation(
 
       val reader = new BufferedReader(new InputStreamReader(inputStream))
       val outputIterator: Iterator[InternalRow] = new Iterator[InternalRow] with HiveInspectors {
-        var cacheRow: InternalRow = null
         var curLine: String = null
-        var eof: Boolean = false
+        val scriptOutputStream = new DataInputStream(inputStream)
+        var scriptOutputWritable: Writable = null
+        val reusedWritableObject: Writable = if (null != outputSerde) {
+          outputSerde.getSerializedClass().newInstance
+        } else {
+          null
+        }
+        val mutableRow = new SpecificMutableRow(output.map(_.dataType))
 
         override def hasNext: Boolean = {
           if (outputSerde == null) {
@@ -125,45 +131,20 @@ case class ScriptTransformation(
             } else {
               true
             }
-          } else {
-            if (eof) {
-              if (writerThread.exception.isDefined) {
-                throw writerThread.exception.get
-              }
-              false
-            } else {
+          } else if (scriptOutputWritable == null) {
+            scriptOutputWritable = reusedWritableObject
+            try {
+              scriptOutputWritable.readFields(scriptOutputStream)
               true
+            } catch {
+              case _: EOFException =>
+                if (writerThread.exception.isDefined) {
+                  throw writerThread.exception.get
+                }
+                false
             }
-          }
-        }
-
-        def deserialize(): InternalRow = {
-          if (cacheRow != null) return cacheRow
-
-          val mutableRow = new SpecificMutableRow(output.map(_.dataType))
-          try {
-            val dataInputStream = new DataInputStream(inputStream)
-            val writable = outputSerde.getSerializedClass().newInstance
-            writable.readFields(dataInputStream)
-
-            val raw = outputSerde.deserialize(writable)
-            val dataList = outputSoi.getStructFieldsDataAsList(raw)
-            val fieldList = outputSoi.getAllStructFieldRefs()
-
-            var i = 0
-            dataList.foreach( element => {
-              if (element == null) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow(i) = unwrap(element, fieldList(i).getFieldObjectInspector)
-              }
-              i += 1
-            })
-            mutableRow
-          } catch {
-            case e: EOFException =>
-              eof = true
-              null
+          } else {
+            true
           }
         }
 
@@ -171,7 +152,6 @@ case class ScriptTransformation(
           if (!hasNext) {
             throw new NoSuchElementException
           }
-
           if (outputSerde == null) {
             val prevLine = curLine
             curLine = reader.readLine()
@@ -185,12 +165,20 @@ case class ScriptTransformation(
                   .map(CatalystTypeConverters.convertToCatalyst))
             }
           } else {
-            val ret = deserialize()
-            if (!eof) {
-              cacheRow = null
-              cacheRow = deserialize()
+            val raw = outputSerde.deserialize(scriptOutputWritable)
+            scriptOutputWritable = null
+            val dataList = outputSoi.getStructFieldsDataAsList(raw)
+            val fieldList = outputSoi.getAllStructFieldRefs()
+            var i = 0
+            while (i < dataList.size()) {
+              if (dataList(i) == null) {
+                mutableRow.setNullAt(i)
+              } else {
+                mutableRow(i) = unwrap(dataList(i), fieldList(i).getFieldObjectInspector)
+              }
+              i += 1
             }
-            ret
+            mutableRow
           }
         }
       }
@@ -320,18 +308,8 @@ case class HiveScriptIOSchema (
   }
 
   private def parseAttrs(attrs: Seq[Expression]): (Seq[String], Seq[DataType]) = {
-    val columns = attrs.map {
-      case aref: AttributeReference => aref.name
-      case e: NamedExpression => e.name
-      case _ => null
-    }
-
-    val columnTypes = attrs.map {
-      case aref: AttributeReference => aref.dataType
-      case e: NamedExpression => e.dataType
-      case _ => null
-    }
-
+    val columns = attrs.zipWithIndex.map(e => s"${e._1.prettyName}_${e._2}")
+    val columnTypes = attrs.map(_.dataType)
     (columns, columnTypes)
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index fb41451803634..ff9a3694d612e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -751,6 +751,16 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
         .queryExecution.toRdd.count())
   }
 
+  test("test script transform data type") {
+    val data = (1 to 5).map { i => (i, i) }
+    data.toDF("key", "value").registerTempTable("test")
+    checkAnswer(
+      sql("""FROM
+          |(FROM test SELECT TRANSFORM(key, value) USING 'cat' AS (thing1 int, thing2 string)) t
+          |SELECT thing1 + 1
+        """.stripMargin), (2 to 6).map(i => Row(i)))
+  }
+
   test("window function: udaf with aggregate expressin") {
     val data = Seq(
       WindowData(1, "a", 5),

From 2b67fdb60be95778e016efae4f0a9cdf2fbfe779 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 4 Aug 2015 19:25:24 -0700
Subject: [PATCH 0845/1454] [SPARK-9513] [SQL] [PySpark] Add python API for
 DataFrame functions

This adds Python API for those DataFrame functions that is introduced in 1.5.

There is issue with serialize byte_array in Python 3, so some of functions (for BinaryType) does not have tests.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7922 from davies/python_functions and squashes the following commits:

8ad942f [Davies Liu] fix test
5fb6ec3 [Davies Liu] fix bugs
3495ed3 [Davies Liu] fix issues
ea5f7bb [Davies Liu] Add python API for DataFrame functions
---
 python/pyspark/sql/functions.py               | 885 ++++++++++++------
 .../org/apache/spark/sql/functions.scala      |  80 +-
 2 files changed, 628 insertions(+), 337 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index a73ecc7d93361..e65b14dc0e2b3 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -32,41 +32,6 @@
 from pyspark.sql.column import Column, _to_java_column, _to_seq
 
 
-__all__ = [
-    'array',
-    'approxCountDistinct',
-    'bin',
-    'coalesce',
-    'countDistinct',
-    'explode',
-    'format_number',
-    'length',
-    'log2',
-    'md5',
-    'monotonicallyIncreasingId',
-    'rand',
-    'randn',
-    'regexp_extract',
-    'regexp_replace',
-    'sha1',
-    'sha2',
-    'size',
-    'sort_array',
-    'sparkPartitionId',
-    'struct',
-    'udf',
-    'when']
-
-__all__ += ['lag', 'lead', 'ntile']
-
-__all__ += [
-    'date_format', 'date_add', 'date_sub', 'add_months', 'months_between',
-    'year', 'quarter', 'month', 'hour', 'minute', 'second',
-    'dayofmonth', 'dayofyear', 'weekofyear']
-
-__all__ += ['soundex', 'substring', 'substring_index']
-
-
 def _create_function(name, doc=""):
     """ Create a function for aggregator by name"""
     def _(col):
@@ -208,30 +173,6 @@ def _():
 for _name, _doc in _window_functions.items():
     globals()[_name] = since(1.4)(_create_window_function(_name, _doc))
 del _name, _doc
-__all__ += _functions.keys()
-__all__ += _functions_1_4.keys()
-__all__ += _binary_mathfunctions.keys()
-__all__ += _window_functions.keys()
-__all__.sort()
-
-
-@since(1.4)
-def array(*cols):
-    """Creates a new array column.
-
-    :param cols: list of column names (string) or list of :class:`Column` expressions that have
-        the same data type.
-
-    >>> df.select(array('age', 'age').alias("arr")).collect()
-    [Row(arr=[2, 2]), Row(arr=[5, 5])]
-    >>> df.select(array([df.age, df.age]).alias("arr")).collect()
-    [Row(arr=[2, 2]), Row(arr=[5, 5])]
-    """
-    sc = SparkContext._active_spark_context
-    if len(cols) == 1 and isinstance(cols[0], (list, set)):
-        cols = cols[0]
-    jc = sc._jvm.functions.array(_to_seq(sc, cols, _to_java_column))
-    return Column(jc)
 
 
 @since(1.3)
@@ -249,19 +190,6 @@ def approxCountDistinct(col, rsd=None):
     return Column(jc)
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def bin(col):
-    """Returns the string representation of the binary value of the given column.
-
-    >>> df.select(bin(df.age).alias('c')).collect()
-    [Row(c=u'10'), Row(c=u'101')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.bin(_to_java_column(col))
-    return Column(jc)
-
-
 @since(1.4)
 def coalesce(*cols):
     """Returns the first column that is not null.
@@ -314,82 +242,6 @@ def countDistinct(col, *cols):
     return Column(jc)
 
 
-@since(1.4)
-def explode(col):
-    """Returns a new row for each element in the given array or map.
-
-    >>> from pyspark.sql import Row
-    >>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
-    >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
-    [Row(anInt=1), Row(anInt=2), Row(anInt=3)]
-
-    >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
-    +---+-----+
-    |key|value|
-    +---+-----+
-    |  a|    b|
-    +---+-----+
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.explode(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def levenshtein(left, right):
-    """Computes the Levenshtein distance of the two given strings.
-
-    >>> df0 = sqlContext.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])
-    >>> df0.select(levenshtein('l', 'r').alias('d')).collect()
-    [Row(d=3)]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.levenshtein(_to_java_column(left), _to_java_column(right))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def regexp_extract(str, pattern, idx):
-    """Extract a specific(idx) group identified by a java regex, from the specified string column.
-
-    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
-    >>> df.select(regexp_extract('str', '(\d+)-(\d+)', 1).alias('d')).collect()
-    [Row(d=u'100')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.regexp_extract(_to_java_column(str), pattern, idx)
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def regexp_replace(str, pattern, replacement):
-    """Replace all substrings of the specified string value that match regexp with rep.
-
-    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
-    >>> df.select(regexp_replace('str', '(\\d+)', '##').alias('d')).collect()
-    [Row(d=u'##-##')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.regexp_replace(_to_java_column(str), pattern, replacement)
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def md5(col):
-    """Calculates the MD5 digest and returns the value as a 32 character hex string.
-
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(md5('a').alias('hash')).collect()
-    [Row(hash=u'902fbdd2b1df0c4f70b4a5d23525e932')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.md5(_to_java_column(col))
-    return Column(jc)
-
-
 @since(1.4)
 def monotonicallyIncreasingId():
     """A column that generates monotonically increasing 64-bit integers.
@@ -435,63 +287,17 @@ def randn(seed=None):
     return Column(jc)
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def hex(col):
-    """Computes hex value of the given column, which could be StringType,
-    BinaryType, IntegerType or LongType.
-
-    >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
-    [Row(hex(a)=u'414243', hex(b)=u'3')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.hex(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def unhex(col):
-    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
-    and converts to the byte representation of number.
-
-    >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
-    [Row(unhex(a)=bytearray(b'ABC'))]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.unhex(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
 @since(1.5)
-def sha1(col):
-    """Returns the hex string result of SHA-1.
-
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
-    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
+def round(col, scale=0):
     """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.sha1(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def sha2(col, numBits):
-    """Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,
-    and SHA-512). The numBits indicates the desired bit length of the result, which must have a
-    value of 224, 256, 384, 512, or 0 (which is equivalent to 256).
+    Round the value of `e` to `scale` decimal places if `scale` >= 0
+    or at integral part when `scale` < 0.
 
-    >>> digests = df.select(sha2(df.name, 256).alias('s')).collect()
-    >>> digests[0]
-    Row(s=u'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043')
-    >>> digests[1]
-    Row(s=u'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961')
+    >>> sqlContext.createDataFrame([(2.546,)], ['a']).select(round('a', 1).alias('r')).collect()
+    [Row(r=2.5)]
     """
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.sha2(_to_java_column(col), numBits)
-    return Column(jc)
+    return Column(sc._jvm.functions.round(_to_java_column(col), scale))
 
 
 @since(1.5)
@@ -502,8 +308,7 @@ def shiftLeft(col, numBits):
     [Row(r=42)]
     """
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.shiftLeft(_to_java_column(col), numBits)
-    return Column(jc)
+    return Column(sc._jvm.functions.shiftLeft(_to_java_column(col), numBits))
 
 
 @since(1.5)
@@ -522,8 +327,8 @@ def shiftRight(col, numBits):
 def shiftRightUnsigned(col, numBits):
     """Unsigned shift the the given value numBits right.
 
-    >>> sqlContext.createDataFrame([(-42,)], ['a']).select(shiftRightUnsigned('a', 1).alias('r'))\
-    .collect()
+    >>> df = sqlContext.createDataFrame([(-42,)], ['a'])
+    >>> df.select(shiftRightUnsigned('a', 1).alias('r')).collect()
     [Row(r=9223372036854775787)]
     """
     sc = SparkContext._active_spark_context
@@ -544,6 +349,7 @@ def sparkPartitionId():
     return Column(sc._jvm.functions.sparkPartitionId())
 
 
+@since(1.5)
 def expr(str):
     """Parses the expression string into the column that it represents
 
@@ -554,34 +360,6 @@ def expr(str):
     return Column(sc._jvm.functions.expr(str))
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def length(col):
-    """Calculates the length of a string or binary expression.
-
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(length('a').alias('length')).collect()
-    [Row(length=3)]
-    """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.length(_to_java_column(col)))
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def format_number(col, d):
-    """Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
-       and returns the result as a string.
-
-    :param col: the column name of the numeric value to be formatted
-    :param d: the N decimal places
-
-    >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
-    [Row(v=u'5.0000')]
-    """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.format_number(_to_java_column(col), d))
-
-
 @ignore_unicode_prefix
 @since(1.4)
 def struct(*cols):
@@ -601,6 +379,38 @@ def struct(*cols):
     return Column(jc)
 
 
+@since(1.5)
+def greatest(*cols):
+    """
+    Returns the greatest value of the list of column names, skipping null values.
+    This function takes at least 2 parameters. It will return null iff all parameters are null.
+
+    >>> df = sqlContext.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
+    >>> df.select(greatest(df.a, df.b, df.c).alias("greatest")).collect()
+    [Row(greatest=4)]
+    """
+    if len(cols) < 2:
+        raise ValueError("greatest should take at least two columns")
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.greatest(_to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+def least(*cols):
+    """
+    Returns the least value of the list of column names, skipping null values.
+    This function takes at least 2 parameters. It will return null iff all parameters are null.
+
+    >>> df = sqlContext.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
+    >>> df.select(least(df.a, df.b, df.c).alias("least")).collect()
+    [Row(least=1)]
+    """
+    if len(cols) < 2:
+        raise ValueError("least should take at least two columns")
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.least(_to_seq(sc, cols, _to_java_column)))
+
+
 @since(1.4)
 def when(condition, value):
     """Evaluates a list of conditions and returns one of multiple possible result expressions.
@@ -654,6 +464,35 @@ def log2(col):
     return Column(sc._jvm.functions.log2(_to_java_column(col)))
 
 
+@since(1.5)
+@ignore_unicode_prefix
+def conv(col, fromBase, toBase):
+    """
+    Convert a number in a string column from one base to another.
+
+    >>> df = sqlContext.createDataFrame([("010101",)], ['n'])
+    >>> df.select(conv(df.n, 2, 16).alias('hex')).collect()
+    [Row(hex=u'15')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.conv(_to_java_column(col), fromBase, toBase))
+
+
+@since(1.5)
+def factorial(col):
+    """
+    Computes the factorial of the given value.
+
+    >>> df = sqlContext.createDataFrame([(5,)], ['n'])
+    >>> df.select(factorial(df.n).alias('f')).collect()
+    [Row(f=120)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.factorial(_to_java_column(col)))
+
+
+# ---------------  Window functions ------------------------
+
 @since(1.4)
 def lag(col, count=1, default=None):
     """
@@ -703,9 +542,28 @@ def ntile(n):
     return Column(sc._jvm.functions.ntile(int(n)))
 
 
+# ---------------------- Date/Timestamp functions ------------------------------
+
+@since(1.5)
+def current_date():
+    """
+    Returns the current date as a date column.
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.current_date())
+
+
+def current_timestamp():
+    """
+    Returns the current timestamp as a timestamp column.
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.current_timestamp())
+
+
 @ignore_unicode_prefix
 @since(1.5)
-def date_format(dateCol, format):
+def date_format(date, format):
     """
     Converts a date/timestamp/string to a value of string in the format specified by the date
     format given by the second argument.
@@ -721,7 +579,7 @@ def date_format(dateCol, format):
     [Row(date=u'04/08/2015')]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.date_format(_to_java_column(dateCol), format))
+    return Column(sc._jvm.functions.date_format(_to_java_column(date), format))
 
 
 @since(1.5)
@@ -867,6 +725,19 @@ def date_sub(start, days):
     return Column(sc._jvm.functions.date_sub(_to_java_column(start), days))
 
 
+@since(1.5)
+def datediff(end, start):
+    """
+    Returns the number of days from `start` to `end`.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2'])
+    >>> df.select(datediff(df.d2, df.d1).alias('diff')).collect()
+    [Row(diff=32)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.datediff(_to_java_column(end), _to_java_column(start)))
+
+
 @since(1.5)
 def add_months(start, months):
     """
@@ -924,33 +795,296 @@ def trunc(date, format):
 
 
 @since(1.5)
-@ignore_unicode_prefix
-def substring(str, pos, len):
+def next_day(date, dayOfWeek):
     """
-    Substring starts at `pos` and is of length `len` when str is String type or
-    returns the slice of byte array that starts at `pos` in byte and is of length `len`
-    when str is Binary type
+    Returns the first date which is later than the value of the date column.
 
-    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
-    >>> df.select(substring(df.s, 1, 2).alias('s')).collect()
-    [Row(s=u'ab')]
+    Day of the week parameter is case insensitive, and accepts:
+        "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".
+
+    >>> df = sqlContext.createDataFrame([('2015-07-27',)], ['d'])
+    >>> df.select(next_day(df.d, 'Sun').alias('date')).collect()
+    [Row(date=datetime.date(2015, 8, 2))]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.substring(_to_java_column(str), pos, len))
+    return Column(sc._jvm.functions.next_day(_to_java_column(date), dayOfWeek))
 
 
 @since(1.5)
-@ignore_unicode_prefix
-def substring_index(str, delim, count):
+def last_day(date):
     """
-    Returns the substring from string str before count occurrences of the delimiter delim.
-    If count is positive, everything the left of the final delimiter (counting from left) is
-    returned. If count is negative, every to the right of the final delimiter (counting from the
-    right) is returned. substring_index performs a case-sensitive match when searching for delim.
+    Returns the last day of the month which the given date belongs to.
 
-    >>> df = sqlContext.createDataFrame([('a.b.c.d',)], ['s'])
-    >>> df.select(substring_index(df.s, '.', 2).alias('s')).collect()
-    [Row(s=u'a.b')]
+    >>> df = sqlContext.createDataFrame([('1997-02-10',)], ['d'])
+    >>> df.select(last_day(df.d).alias('date')).collect()
+    [Row(date=datetime.date(1997, 2, 28))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.last_day(_to_java_column(date)))
+
+
+@since(1.5)
+def from_unixtime(timestamp, format="yyyy-MM-dd HH:mm:ss"):
+    """
+    Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
+    representing the timestamp of that moment in the current system time zone in the given
+    format.
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.from_unixtime(_to_java_column(timestamp), format))
+
+
+@since(1.5)
+def unix_timestamp(timestamp=None, format='yyyy-MM-dd HH:mm:ss'):
+    """
+    Convert time string with given pattern ('yyyy-MM-dd HH:mm:ss', by default)
+    to Unix time stamp (in seconds), using the default timezone and the default
+    locale, return null if fail.
+
+    if `timestamp` is None, then it returns current timestamp.
+    """
+    sc = SparkContext._active_spark_context
+    if timestamp is None:
+        return Column(sc._jvm.functions.unix_timestamp())
+    return Column(sc._jvm.functions.unix_timestamp(_to_java_column(timestamp), format))
+
+
+@since(1.5)
+def from_utc_timestamp(timestamp, tz):
+    """
+    Assumes given timestamp is UTC and converts to given timezone.
+
+    >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
+    >>> df.select(from_utc_timestamp(df.t, "PST").alias('t')).collect()
+    [Row(t=datetime.datetime(1997, 2, 28, 2, 30))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.from_utc_timestamp(_to_java_column(timestamp), tz))
+
+
+@since(1.5)
+def to_utc_timestamp(timestamp, tz):
+    """
+    Assumes given timestamp is in given timezone and converts to UTC.
+
+    >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
+    >>> df.select(to_utc_timestamp(df.t, "PST").alias('t')).collect()
+    [Row(t=datetime.datetime(1997, 2, 28, 18, 30))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.to_utc_timestamp(_to_java_column(timestamp), tz))
+
+
+# ---------------------------- misc functions ----------------------------------
+
+@since(1.5)
+@ignore_unicode_prefix
+def crc32(col):
+    """
+    Calculates the cyclic redundancy check value  (CRC32) of a binary column and
+    returns the value as a bigint.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(crc32('a').alias('crc32')).collect()
+    [Row(crc32=u'902fbdd2b1df0c4f70b4a5d23525e932')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.md5(_to_java_column(col)))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def md5(col):
+    """Calculates the MD5 digest and returns the value as a 32 character hex string.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(md5('a').alias('hash')).collect()
+    [Row(hash=u'902fbdd2b1df0c4f70b4a5d23525e932')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.md5(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def sha1(col):
+    """Returns the hex string result of SHA-1.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
+    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.sha1(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def sha2(col, numBits):
+    """Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,
+    and SHA-512). The numBits indicates the desired bit length of the result, which must have a
+    value of 224, 256, 384, 512, or 0 (which is equivalent to 256).
+
+    >>> digests = df.select(sha2(df.name, 256).alias('s')).collect()
+    >>> digests[0]
+    Row(s=u'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043')
+    >>> digests[1]
+    Row(s=u'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961')
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.sha2(_to_java_column(col), numBits)
+    return Column(jc)
+
+
+# ---------------------- String/Binary functions ------------------------------
+
+_string_functions = {
+    'ascii': 'Computes the numeric value of the first character of the string column.',
+    'base64': 'Computes the BASE64 encoding of a binary column and returns it as a string column.',
+    'unbase64': 'Decodes a BASE64 encoded string column and returns it as a binary column.',
+    'initcap': 'Returns a new string column by converting the first letter of each word to ' +
+               'uppercase. Words are delimited by whitespace.',
+    'lower': 'Converts a string column to lower case.',
+    'upper': 'Converts a string column to upper case.',
+    'reverse': 'Reverses the string column and returns it as a new string column.',
+    'ltrim': 'Trim the spaces from right end for the specified string value.',
+    'rtrim': 'Trim the spaces from right end for the specified string value.',
+    'trim': 'Trim the spaces from both ends for the specified string column.',
+}
+
+
+for _name, _doc in _string_functions.items():
+    globals()[_name] = since(1.5)(_create_function(_name, _doc))
+del _name, _doc
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def concat(*cols):
+    """
+    Concatenates multiple input string columns together into a single string column.
+
+    >>> df = sqlContext.createDataFrame([('abcd','123')], ['s', 'd'])
+    >>> df.select(concat(df.s, df.d).alias('s')).collect()
+    [Row(s=u'abcd123')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.concat(_to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def concat_ws(sep, *cols):
+    """
+    Concatenates multiple input string columns together into a single string column,
+    using the given separator.
+
+    >>> df = sqlContext.createDataFrame([('abcd','123')], ['s', 'd'])
+    >>> df.select(concat_ws('-', df.s, df.d).alias('s')).collect()
+    [Row(s=u'abcd-123')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.concat_ws(sep, _to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+def decode(col, charset):
+    """
+    Computes the first argument into a string from a binary using the provided character set
+    (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.decode(_to_java_column(col), charset))
+
+
+@since(1.5)
+def encode(col, charset):
+    """
+    Computes the first argument into a binary from a string using the provided character set
+    (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.encode(_to_java_column(col), charset))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def format_number(col, d):
+    """
+    Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places,
+    and returns the result as a string.
+
+    :param col: the column name of the numeric value to be formatted
+    :param d: the N decimal places
+
+    >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
+    [Row(v=u'5.0000')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.format_number(_to_java_column(col), d))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def format_string(format, *cols):
+    """
+    Formats the arguments in printf-style and returns the result as a string column.
+
+    :param col: the column name of the numeric value to be formatted
+    :param d: the N decimal places
+
+    >>> df = sqlContext.createDataFrame([(5, "hello")], ['a', 'b'])
+    >>> df.select(format_string('%d %s', df.a, df.b).alias('v')).collect()
+    [Row(v=u'5 hello')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.format_string(format, _to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+def instr(str, substr):
+    """
+    Locate the position of the first occurrence of substr column in the given string.
+    Returns null if either of the arguments are null.
+
+    NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+    could not be found in str.
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(instr(df.s, 'b').alias('s')).collect()
+    [Row(s=2)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.instr(_to_java_column(str), substr))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def substring(str, pos, len):
+    """
+    Substring starts at `pos` and is of length `len` when str is String type or
+    returns the slice of byte array that starts at `pos` in byte and is of length `len`
+    when str is Binary type
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(substring(df.s, 1, 2).alias('s')).collect()
+    [Row(s=u'ab')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.substring(_to_java_column(str), pos, len))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def substring_index(str, delim, count):
+    """
+    Returns the substring from string str before count occurrences of the delimiter delim.
+    If count is positive, everything the left of the final delimiter (counting from left) is
+    returned. If count is negative, every to the right of the final delimiter (counting from the
+    right) is returned. substring_index performs a case-sensitive match when searching for delim.
+
+    >>> df = sqlContext.createDataFrame([('a.b.c.d',)], ['s'])
+    >>> df.select(substring_index(df.s, '.', 2).alias('s')).collect()
+    [Row(s=u'a.b')]
     >>> df.select(substring_index(df.s, '.', -3).alias('s')).collect()
     [Row(s=u'b.c.d')]
     """
@@ -958,6 +1092,126 @@ def substring_index(str, delim, count):
     return Column(sc._jvm.functions.substring_index(_to_java_column(str), delim, count))
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def levenshtein(left, right):
+    """Computes the Levenshtein distance of the two given strings.
+
+    >>> df0 = sqlContext.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])
+    >>> df0.select(levenshtein('l', 'r').alias('d')).collect()
+    [Row(d=3)]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.levenshtein(_to_java_column(left), _to_java_column(right))
+    return Column(jc)
+
+
+@since(1.5)
+def locate(substr, str, pos=0):
+    """
+    Locate the position of the first occurrence of substr in a string column, after position pos.
+
+    NOTE: The position is not zero based, but 1 based index. returns 0 if substr
+    could not be found in str.
+
+    :param substr: a string
+    :param str: a Column of StringType
+    :param pos: start position (zero based)
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(locate('b', df.s, 1).alias('s')).collect()
+    [Row(s=2)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.locate(substr, _to_java_column(str), pos))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def lpad(col, len, pad):
+    """
+    Left-pad the string column to width `len` with `pad`.
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
+    [Row(s=u'##abcd')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.lpad(_to_java_column(col), len, pad))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def rpad(col, len, pad):
+    """
+    Right-pad the string column to width `len` with `pad`.
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(rpad(df.s, 6, '#').alias('s')).collect()
+    [Row(s=u'abcd##')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.rpad(_to_java_column(col), len, pad))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def repeat(col, n):
+    """
+    Repeats a string column n times, and returns it as a new string column.
+
+    >>> df = sqlContext.createDataFrame([('ab',)], ['s',])
+    >>> df.select(repeat(df.s, 3).alias('s')).collect()
+    [Row(s=u'ababab')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.repeat(_to_java_column(col), n))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def split(str, pattern):
+    """
+    Splits str around pattern (pattern is a regular expression).
+
+    NOTE: pattern is a string represent the regular expression.
+
+    >>> df = sqlContext.createDataFrame([('ab12cd',)], ['s',])
+    >>> df.select(split(df.s, '[0-9]+').alias('s')).collect()
+    [Row(s=[u'ab', u'cd'])]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.split(_to_java_column(str), pattern))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def regexp_extract(str, pattern, idx):
+    """Extract a specific(idx) group identified by a java regex, from the specified string column.
+
+    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
+    >>> df.select(regexp_extract('str', '(\d+)-(\d+)', 1).alias('d')).collect()
+    [Row(d=u'100')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.regexp_extract(_to_java_column(str), pattern, idx)
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def regexp_replace(str, pattern, replacement):
+    """Replace all substrings of the specified string value that match regexp with rep.
+
+    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
+    >>> df.select(regexp_replace('str', '(\\d+)', '--').alias('d')).collect()
+    [Row(d=u'-----')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.regexp_replace(_to_java_column(str), pattern, replacement)
+    return Column(jc)
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def initcap(col):
@@ -970,6 +1224,114 @@ def initcap(col):
     return Column(sc._jvm.functions.initcap(_to_java_column(col)))
 
 
+@since(1.5)
+@ignore_unicode_prefix
+def soundex(col):
+    """
+    Returns the SoundEx encoding for a string
+
+    >>> df = sqlContext.createDataFrame([("Peters",),("Uhrbach",)], ['name'])
+    >>> df.select(soundex(df.name).alias("soundex")).collect()
+    [Row(soundex=u'P362'), Row(soundex=u'U612')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.soundex(_to_java_column(col)))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def bin(col):
+    """Returns the string representation of the binary value of the given column.
+
+    >>> df.select(bin(df.age).alias('c')).collect()
+    [Row(c=u'10'), Row(c=u'101')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.bin(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def hex(col):
+    """Computes hex value of the given column, which could be StringType,
+    BinaryType, IntegerType or LongType.
+
+    >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
+    [Row(hex(a)=u'414243', hex(b)=u'3')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.hex(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def unhex(col):
+    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
+    and converts to the byte representation of number.
+
+    >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
+    [Row(unhex(a)=bytearray(b'ABC'))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.unhex(_to_java_column(col)))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def length(col):
+    """Calculates the length of a string or binary expression.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(length('a').alias('length')).collect()
+    [Row(length=3)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.length(_to_java_column(col)))
+
+
+# ---------------------- Collection functions ------------------------------
+
+@since(1.4)
+def array(*cols):
+    """Creates a new array column.
+
+    :param cols: list of column names (string) or list of :class:`Column` expressions that have
+        the same data type.
+
+    >>> df.select(array('age', 'age').alias("arr")).collect()
+    [Row(arr=[2, 2]), Row(arr=[5, 5])]
+    >>> df.select(array([df.age, df.age]).alias("arr")).collect()
+    [Row(arr=[2, 2]), Row(arr=[5, 5])]
+    """
+    sc = SparkContext._active_spark_context
+    if len(cols) == 1 and isinstance(cols[0], (list, set)):
+        cols = cols[0]
+    jc = sc._jvm.functions.array(_to_seq(sc, cols, _to_java_column))
+    return Column(jc)
+
+
+@since(1.4)
+def explode(col):
+    """Returns a new row for each element in the given array or map.
+
+    >>> from pyspark.sql import Row
+    >>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
+    >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
+    [Row(anInt=1), Row(anInt=2), Row(anInt=3)]
+
+    >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
+    +---+-----+
+    |key|value|
+    +---+-----+
+    |  a|    b|
+    +---+-----+
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.explode(_to_java_column(col))
+    return Column(jc)
+
+
 @since(1.5)
 def size(col):
     """
@@ -1002,19 +1364,7 @@ def sort_array(col, asc=True):
     return Column(sc._jvm.functions.sort_array(_to_java_column(col), asc))
 
 
-@since
-@ignore_unicode_prefix
-def soundex(col):
-    """
-    Returns the SoundEx encoding for a string
-
-    >>> df = sqlContext.createDataFrame([("Peters",),("Uhrbach",)], ['name'])
-    >>> df.select(soundex(df.name).alias("soundex")).collect()
-    [Row(soundex=u'P362'), Row(soundex=u'U612')]
-    """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.size(_to_java_column(col)))
-
+# ---------------------------- User Defined Function ----------------------------------
 
 class UserDefinedFunction(object):
     """
@@ -1066,6 +1416,11 @@ def udf(f, returnType=StringType()):
     """
     return UserDefinedFunction(f, returnType)
 
+blacklist = ['map', 'since', 'ignore_unicode_prefix']
+__all__ = [k for k, v in globals().items()
+           if not k.startswith('_') and k[0].islower() and callable(v) and k not in blacklist]
+__all__.sort()
+
 
 def _test():
     import doctest
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 35958299076ce..bff70172546ed 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1058,14 +1058,6 @@ object functions {
    */
   def factorial(e: Column): Column = Factorial(e.expr)
 
-  /**
-   * Computes the factorial of the given column.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def factorial(columnName: String): Column = factorial(Column(columnName))
-
   /**
    * Computes the floor of the given value.
    *
@@ -1366,15 +1358,6 @@ object functions {
    */
   def pmod(dividend: Column, divisor: Column): Column = Pmod(dividend.expr, divisor.expr)
 
-  /**
-   * Returns the positive value of dividend mod divisor.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def pmod(dividendColName: String, divisorColName: String): Column =
-    pmod(Column(dividendColName), Column(divisorColName))
-
   /**
    * Returns the double value that is closest in value to the argument and
    * is equal to a mathematical integer.
@@ -1401,14 +1384,6 @@ object functions {
    */
   def round(e: Column): Column = round(e.expr, 0)
 
-  /**
-   * Returns the value of the given column rounded to 0 decimal places.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def round(columnName: String): Column = round(Column(columnName), 0)
-
   /**
    * Round the value of `e` to `scale` decimal places if `scale` >= 0
    * or at integral part when `scale` < 0.
@@ -1418,15 +1393,6 @@ object functions {
    */
   def round(e: Column, scale: Int): Column = Round(e.expr, Literal(scale))
 
-  /**
-   * Round the value of the given column to `scale` decimal places if `scale` >= 0
-   * or at integral part when `scale` < 0.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def round(columnName: String, scale: Int): Column = round(Column(columnName), scale)
-
   /**
    * Shift the the given value numBits left. If the given value is a long value, this function
    * will return a long value else it will return an integer value.
@@ -1436,16 +1402,6 @@ object functions {
    */
   def shiftLeft(e: Column, numBits: Int): Column = ShiftLeft(e.expr, lit(numBits).expr)
 
-  /**
-   * Shift the the given value numBits left. If the given value is a long value, this function
-   * will return a long value else it will return an integer value.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def shiftLeft(columnName: String, numBits: Int): Column =
-    shiftLeft(Column(columnName), numBits)
-
   /**
    * Shift the the given value numBits right. If the given value is a long value, it will return
    * a long value else it will return an integer value.
@@ -1455,16 +1411,6 @@ object functions {
    */
   def shiftRight(e: Column, numBits: Int): Column = ShiftRight(e.expr, lit(numBits).expr)
 
-  /**
-   * Unsigned shift the the given value numBits right. If the given value is a long value,
-   * it will return a long value else it will return an integer value.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def shiftRightUnsigned(columnName: String, numBits: Int): Column =
-    shiftRightUnsigned(Column(columnName), numBits)
-
   /**
    * Unsigned shift the the given value numBits right. If the given value is a long value,
    * it will return a long value else it will return an integer value.
@@ -1475,16 +1421,6 @@ object functions {
   def shiftRightUnsigned(e: Column, numBits: Int): Column =
     ShiftRightUnsigned(e.expr, lit(numBits).expr)
 
-  /**
-   * Shift the the given value numBits right. If the given value is a long value, it will return
-   * a long value else it will return an integer value.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def shiftRight(columnName: String, numBits: Int): Column =
-    shiftRight(Column(columnName), numBits)
-
   /**
    * Computes the signum of the given value.
    *
@@ -1788,14 +1724,6 @@ object functions {
     new StringLocate(lit(substr).expr, str.expr)
   }
 
-  /**
-   * Trim the spaces from left end for the specified string value.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def ltrim(e: Column): Column = StringTrimLeft(e.expr)
-
   /**
    * Locate the position of the first occurrence of substr in a string column, after position pos.
    *
@@ -1819,6 +1747,14 @@ object functions {
     StringLPad(str.expr, lit(len).expr, lit(pad).expr)
   }
 
+  /**
+   * Trim the spaces from left end for the specified string value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def ltrim(e: Column): Column = StringTrimLeft(e.expr)
+
   /**
    * Extract a specific(idx) group identified by a java regex, from the specified string column.
    *

From d34bac0e156432ca6a260db73dbe1318060e309c Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 4 Aug 2015 20:09:15 -0700
Subject: [PATCH 0846/1454] [SPARK-9504] [STREAMING] [TESTS] Fix
 o.a.s.streaming.StreamingContextSuite.stop gracefully again

The test failure is here: https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/3150/AMPLAB_JENKINS_BUILD_PROFILE=hadoop1.0,label=centos/testReport/junit/org.apache.spark.streaming/StreamingContextSuite/stop_gracefully/

There is a race condition in TestReceiver that it may add 1 record and increase `TestReceiver.counter` after stopping `BlockGenerator`. This PR just adds `join` to wait the pushing thread.

Author: zsxwing <zsxwing@gmail.com>

Closes #7934 from zsxwing/SPARK-9504-2 and squashes the following commits:

cfd7973 [zsxwing] Wait for the thread to make sure we won't change TestReceiver.counter after stopping BlockGenerator
---
 .../org/apache/spark/streaming/StreamingContextSuite.scala     | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index b7db280f63588..7423ef6bcb6ea 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -789,7 +789,8 @@ class TestReceiver extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging
   }
 
   def onStop() {
-    // no clean to be done, the receiving thread should stop on it own
+    // no clean to be done, the receiving thread should stop on it own, so just wait for it.
+    receivingThreadOption.foreach(_.join())
   }
 }
 

From f7abd6bec9d51ed4ab6359e50eac853e64ecae86 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 4 Aug 2015 22:17:14 -0700
Subject: [PATCH 0847/1454] Update docs/README.md to put all prereqs together.

This pull request groups all the prereq requirements into a single section.

cc srowen shivaram

Author: Reynold Xin <rxin@databricks.com>

Closes #7951 from rxin/readme-docs and squashes the following commits:

ab7ded0 [Reynold Xin] Updated docs/README.md to put all prereqs together.
---
 docs/README.md | 43 ++++++++-----------------------------------
 1 file changed, 8 insertions(+), 35 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 50209896f986c..1f4fd3e56ed5f 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -9,12 +9,13 @@ documentation yourself. Why build it yourself? So that you have the docs that co
 whichever version of Spark you currently have checked out of revision control.
 
 ## Prerequisites
-The Spark documenation build uses a number of tools to build HTML docs and API docs in Scala, Python
-and R. To get started you can run the following commands
+The Spark documentation build uses a number of tools to build HTML docs and API docs in Scala,
+Python and R. To get started you can run the following commands
 
     $ sudo gem install jekyll
     $ sudo gem install jekyll-redirect-from
     $ sudo pip install Pygments
+    $ sudo pip install sphinx
     $ Rscript -e 'install.packages(c("knitr", "devtools"), repos="http://cran.stat.ucla.edu/")'
 
 
@@ -29,17 +30,12 @@ you have checked out or downloaded.
 In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can
 read those text files directly if you want. Start with index.md.
 
-The markdown code can be compiled to HTML using the [Jekyll tool](http://jekyllrb.com).
-`Jekyll` and a few dependencies must be installed for this to work. We recommend
-installing via the Ruby Gem dependency manager. Since the exact HTML output
-varies between versions of Jekyll and its dependencies, we list specific versions here
-in some cases:
+Execute `jekyll build` from the `docs/` directory to compile the site. Compiling the site with
+Jekyll will create a directory called `_site` containing index.html as well as the rest of the
+compiled files.
 
-    $ sudo gem install jekyll
-    $ sudo gem install jekyll-redirect-from
-
-Execute `jekyll build` from the `docs/` directory to compile the site. Compiling the site with Jekyll will create a directory
-called `_site` containing index.html as well as the rest of the compiled files.
+    $ cd docs
+    $ jekyll build
 
 You can modify the default Jekyll build as follows:
 
@@ -50,29 +46,6 @@ You can modify the default Jekyll build as follows:
     # Build the site with extra features used on the live page
     $ PRODUCTION=1 jekyll build
 
-## Pygments
-
-We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages,
-so you will also need to install that (it requires Python) by running `sudo pip install Pygments`.
-
-To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile
-phase, use the following sytax:
-
-    {% highlight scala %}
-    // Your scala code goes here, you can replace scala with many other
-    // supported languages too.
-    {% endhighlight %}
-
-## Sphinx
-
-We use Sphinx to generate Python API docs, so you will need to install it by running
-`sudo pip install sphinx`.
-
-## knitr, devtools
-
-SparkR documentation is written using `roxygen2` and we use `knitr`, `devtools` to generate
-documentation. To install these packages you can run `install.packages(c("knitr", "devtools"))` from a
-R console.
 
 ## API Docs (Scaladoc, Sphinx, roxygen2)
 

From a02bcf20c4fc9e2e182630d197221729e996afc2 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 4 Aug 2015 22:28:49 -0700
Subject: [PATCH 0848/1454] [SPARK-9540] [MLLIB] optimize PrefixSpan
 implementation

This is a major refactoring of the PrefixSpan implementation. It contains the following changes:

1. Expand prefix with one item at a time. The existing implementation generates all subsets for each itemset, which might have scalability issue when the itemset is large.
2. Use a new internal format. `<(12)(31)>` is represented by `[0, 1, 2, 0, 1, 3, 0]` internally. We use `0` because negative numbers are used to indicates partial prefix items, e.g., `_2` is represented by `-2`.
3. Remember the start indices of all partial projections in the projected postfix to help next projection.
4. Reuse the original sequence array for projected postfixes.
5. Use `Prefix` IDs in aggregation rather than its content.
6. Use `ArrayBuilder` for building primitive arrays.
7. Expose `maxLocalProjDBSize`.
8. Tests are not changed except using `0` instead of `-1` as the delimiter.

`Postfix`'s API doc should be a good place to start.

Closes #7594

feynmanliang zhangjiajin

Author: Xiangrui Meng <meng@databricks.com>

Closes #7937 from mengxr/SPARK-9540 and squashes the following commits:

2d0ec31 [Xiangrui Meng] address more comments
48f450c [Xiangrui Meng] address comments from Feynman; fixed a bug in project and added a test
65f90e8 [Xiangrui Meng] naming and documentation
8afc86a [Xiangrui Meng] refactor impl
---
 .../spark/mllib/fpm/LocalPrefixSpan.scala     | 132 ++--
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 587 ++++++++++++------
 .../spark/mllib/fpm/PrefixSpanSuite.scala     | 271 ++++----
 3 files changed, 599 insertions(+), 391 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
index ccebf951c850d..3ea10779a1837 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
@@ -22,85 +22,89 @@ import scala.collection.mutable
 import org.apache.spark.Logging
 
 /**
- * Calculate all patterns of a projected database in local.
+ * Calculate all patterns of a projected database in local mode.
+ *
+ * @param minCount minimal count for a frequent pattern
+ * @param maxPatternLength max pattern length for a frequent pattern
  */
-private[fpm] object LocalPrefixSpan extends Logging with Serializable {
-  import PrefixSpan._
+private[fpm] class LocalPrefixSpan(
+    val minCount: Long,
+    val maxPatternLength: Int) extends Logging with Serializable {
+  import PrefixSpan.Postfix
+  import LocalPrefixSpan.ReversedPrefix
+
   /**
-   * Calculate all patterns of a projected database.
-   * @param minCount minimum count
-   * @param maxPatternLength maximum pattern length
-   * @param prefixes prefixes in reversed order
-   * @param database the projected database
-   * @return a set of sequential pattern pairs,
-   *         the key of pair is sequential pattern (a list of items in reversed order),
-   *         the value of pair is the pattern's count.
+   * Generates frequent patterns on the input array of postfixes.
+   * @param postfixes an array of postfixes
+   * @return an iterator of (frequent pattern, count)
    */
-  def run(
-      minCount: Long,
-      maxPatternLength: Int,
-      prefixes: List[Set[Int]],
-      database: Iterable[List[Set[Int]]]): Iterator[(List[Set[Int]], Long)] = {
-    if (prefixes.length == maxPatternLength || database.isEmpty) {
-      return Iterator.empty
-    }
-    val freqItemSetsAndCounts = getFreqItemAndCounts(minCount, database)
-    val freqItems = freqItemSetsAndCounts.keys.flatten.toSet
-    val filteredDatabase = database.map { suffix =>
-      suffix
-        .map(item => freqItems.intersect(item))
-        .filter(_.nonEmpty)
-    }
-    freqItemSetsAndCounts.iterator.flatMap { case (item, count) =>
-      val newPrefixes = item :: prefixes
-      val newProjected = project(filteredDatabase, item)
-      Iterator.single((newPrefixes, count)) ++
-        run(minCount, maxPatternLength, newPrefixes, newProjected)
+  def run(postfixes: Array[Postfix]): Iterator[(Array[Int], Long)] = {
+    genFreqPatterns(ReversedPrefix.empty, postfixes).map { case (prefix, count) =>
+      (prefix.toSequence, count)
     }
   }
 
   /**
-   * Calculate suffix sequence immediately after the first occurrence of an item.
-   * @param item itemset to get suffix after
-   * @param sequence sequence to extract suffix from
-   * @return suffix sequence
+   * Recursively generates frequent patterns.
+   * @param prefix current prefix
+   * @param postfixes projected postfixes w.r.t. the prefix
+   * @return an iterator of (prefix, count)
    */
-  def getSuffix(item: Set[Int], sequence: List[Set[Int]]): List[Set[Int]] = {
-    val itemsetSeq = sequence
-    val index = itemsetSeq.indexWhere(item.subsetOf(_))
-    if (index == -1) {
-      List()
-    } else {
-      itemsetSeq.drop(index + 1)
+  private def genFreqPatterns(
+      prefix: ReversedPrefix,
+      postfixes: Array[Postfix]): Iterator[(ReversedPrefix, Long)] = {
+    if (maxPatternLength == prefix.length || postfixes.length < minCount) {
+      return Iterator.empty
+    }
+    // find frequent items
+    val counts = mutable.Map.empty[Int, Long].withDefaultValue(0)
+    postfixes.foreach { postfix =>
+      postfix.genPrefixItems.foreach { case (x, _) =>
+        counts(x) += 1L
+      }
+    }
+    val freqItems = counts.toSeq.filter { case (_, count) =>
+      count >= minCount
+    }.sorted
+    // project and recursively call genFreqPatterns
+    freqItems.toIterator.flatMap { case (item, count) =>
+      val newPrefix = prefix :+ item
+      Iterator.single((newPrefix, count)) ++ {
+        val projected = postfixes.map(_.project(item)).filter(_.nonEmpty)
+        genFreqPatterns(newPrefix, projected)
+      }
     }
   }
+}
 
-  def project(
-      database: Iterable[List[Set[Int]]],
-      prefix: Set[Int]): Iterable[List[Set[Int]]] = {
-    database
-      .map(getSuffix(prefix, _))
-      .filter(_.nonEmpty)
-  }
+private object LocalPrefixSpan {
 
   /**
-   * Generates frequent items by filtering the input data using minimal count level.
-   * @param minCount the minimum count for an item to be frequent
-   * @param database database of sequences
-   * @return freq item to count map
+   * Represents a prefix stored as a list in reversed order.
+   * @param items items in the prefix in reversed order
+   * @param length length of the prefix, not counting delimiters
    */
-  private def getFreqItemAndCounts(
-      minCount: Long,
-      database: Iterable[List[Set[Int]]]): Map[Set[Int], Long] = {
-    // TODO: use PrimitiveKeyOpenHashMap
-    val counts = mutable.Map[Set[Int], Long]().withDefaultValue(0L)
-    database.foreach { sequence =>
-      sequence.flatMap(nonemptySubsets(_)).distinct.foreach { item =>
-        counts(item) += 1L
+  class ReversedPrefix private (val items: List[Int], val length: Int) extends Serializable {
+    /**
+     * Expands the prefix by one item.
+     */
+    def :+(item: Int): ReversedPrefix = {
+      require(item != 0)
+      if (item < 0) {
+        new ReversedPrefix(-item :: items, length + 1)
+      } else {
+        new ReversedPrefix(item :: 0 :: items, length + 1)
       }
     }
-    counts
-      .filter { case (_, count) => count >= minCount }
-      .toMap
+
+    /**
+     * Converts this prefix to a sequence.
+     */
+    def toSequence: Array[Int] = (0 :: items).toArray.reverse
+  }
+
+  object ReversedPrefix {
+    /** An empty prefix. */
+    val empty: ReversedPrefix = new ReversedPrefix(List.empty, 0)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index 9eaf733fada22..d5f0c926c69bb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -18,9 +18,10 @@
 package org.apache.spark.mllib.fpm
 
 import java.{lang => jl, util => ju}
+import java.util.concurrent.atomic.AtomicInteger
 
+import scala.collection.mutable
 import scala.collection.JavaConverters._
-import scala.collection.mutable.ArrayBuilder
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
@@ -31,17 +32,20 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
 /**
- *
  * :: Experimental ::
  *
- * A parallel PrefixSpan algorithm to mine sequential pattern.
- * The PrefixSpan algorithm is described in
- * [[http://doi.org/10.1109/ICDE.2001.914830]].
+ * A parallel PrefixSpan algorithm to mine frequent sequential patterns.
+ * The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns
+ * Efficiently by Prefix-Projected Pattern Growth ([[http://doi.org/10.1109/ICDE.2001.914830]]).
  *
  * @param minSupport the minimal support level of the sequential pattern, any pattern appears
  *                   more than  (minSupport * size-of-the-dataset) times will be output
  * @param maxPatternLength the maximal length of the sequential pattern, any pattern appears
- *                   less than maxPatternLength will be output
+ *                         less than maxPatternLength will be output
+ * @param maxLocalProjDBSize The maximum number of items (including delimiters used in the internal
+ *                           storage format) allowed in a projected database before local
+ *                           processing. If a projected database exceeds this size, another
+ *                           iteration of distributed prefix growth is run.
  *
  * @see [[https://en.wikipedia.org/wiki/Sequential_Pattern_Mining Sequential Pattern Mining
  *       (Wikipedia)]]
@@ -49,33 +53,28 @@ import org.apache.spark.storage.StorageLevel
 @Experimental
 class PrefixSpan private (
     private var minSupport: Double,
-    private var maxPatternLength: Int) extends Logging with Serializable {
+    private var maxPatternLength: Int,
+    private var maxLocalProjDBSize: Long) extends Logging with Serializable {
   import PrefixSpan._
 
-  /**
-   * The maximum number of items allowed in a projected database before local processing. If a
-   * projected database exceeds this size, another iteration of distributed PrefixSpan is run.
-   */
-  // TODO: make configurable with a better default value
-  private val maxLocalProjDBSize: Long = 32000000L
-
   /**
    * Constructs a default instance with default parameters
-   * {minSupport: `0.1`, maxPatternLength: `10`}.
+   * {minSupport: `0.1`, maxPatternLength: `10`, maxLocalProjDBSize: `32000000L`}.
    */
-  def this() = this(0.1, 10)
+  def this() = this(0.1, 10, 32000000L)
 
   /**
    * Get the minimal support (i.e. the frequency of occurrence before a pattern is considered
    * frequent).
    */
-  def getMinSupport: Double = this.minSupport
+  def getMinSupport: Double = minSupport
 
   /**
    * Sets the minimal support level (default: `0.1`).
    */
   def setMinSupport(minSupport: Double): this.type = {
-    require(minSupport >= 0 && minSupport <= 1, "The minimum support value must be in [0, 1].")
+    require(minSupport >= 0 && minSupport <= 1,
+      s"The minimum support value must be in [0, 1], but got $minSupport.")
     this.minSupport = minSupport
     this
   }
@@ -83,45 +82,115 @@ class PrefixSpan private (
   /**
    * Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
    */
-  def getMaxPatternLength: Double = this.maxPatternLength
+  def getMaxPatternLength: Double = maxPatternLength
 
   /**
    * Sets maximal pattern length (default: `10`).
    */
   def setMaxPatternLength(maxPatternLength: Int): this.type = {
     // TODO: support unbounded pattern length when maxPatternLength = 0
-    require(maxPatternLength >= 1, "The maximum pattern length value must be greater than 0.")
+    require(maxPatternLength >= 1,
+      s"The maximum pattern length value must be greater than 0, but got $maxPatternLength.")
     this.maxPatternLength = maxPatternLength
     this
   }
 
   /**
-   * Find the complete set of sequential patterns in the input sequences of itemsets.
-   * @param data ordered sequences of itemsets.
-   * @return a [[PrefixSpanModel]] that contains the frequent sequences
+   * Gets the maximum number of items allowed in a projected database before local processing.
+   */
+  def getMaxLocalProjDBSize: Long = maxLocalProjDBSize
+
+  /**
+   * Sets the maximum number of items (including delimiters used in the internal storage format)
+   * allowed in a projected database before local processing (default: `32000000L`).
+   */
+  def setMaxLocalProjDBSize(maxLocalProjDBSize: Long): this.type = {
+    require(maxLocalProjDBSize >= 0L,
+      s"The maximum local projected database size must be nonnegative, but got $maxLocalProjDBSize")
+    this.maxLocalProjDBSize = maxLocalProjDBSize
+    this
+  }
+
+  /**
+   * Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
+   * @param data sequences of itemsets.
+   * @return a [[PrefixSpanModel]] that contains the frequent patterns
    */
   def run[Item: ClassTag](data: RDD[Array[Array[Item]]]): PrefixSpanModel[Item] = {
-    val itemToInt = data.aggregate(Set[Item]())(
-      seqOp = { (uniqItems, item) => uniqItems ++ item.flatten.toSet },
-      combOp = { _ ++ _ }
-    ).zipWithIndex.toMap
-    val intToItem = Map() ++ (itemToInt.map { case (k, v) => (v, k) })
-
-    val dataInternalRepr = data.map { seq =>
-      seq.map(itemset => itemset.map(itemToInt)).reduce((a, b) => a ++ (DELIMITER +: b))
+    if (data.getStorageLevel == StorageLevel.NONE) {
+      logWarning("Input data is not cached.")
     }
-    val results = run(dataInternalRepr)
 
-    def toPublicRepr(pattern: Iterable[Int]): List[Array[Item]] = {
-      pattern.span(_ != DELIMITER) match {
-        case (x, xs) if xs.size > 1 => x.map(intToItem).toArray :: toPublicRepr(xs.tail)
-        case (x, xs) => List(x.map(intToItem).toArray)
+    val totalCount = data.count()
+    logInfo(s"number of sequences: $totalCount")
+    val minCount = math.ceil(minSupport * totalCount).toLong
+    logInfo(s"minimum count for a frequent pattern: $minCount")
+
+    // Find frequent items.
+    val freqItemAndCounts = data.flatMap { itemsets =>
+        val uniqItems = mutable.Set.empty[Item]
+        itemsets.foreach { _.foreach { item =>
+          uniqItems += item
+        }}
+        uniqItems.toIterator.map((_, 1L))
+      }.reduceByKey(_ + _)
+      .filter { case (_, count) =>
+        count >= minCount
+      }.collect()
+    val freqItems = freqItemAndCounts.sortBy(-_._2).map(_._1)
+    logInfo(s"number of frequent items: ${freqItems.length}")
+
+    // Keep only frequent items from input sequences and convert them to internal storage.
+    val itemToInt = freqItems.zipWithIndex.toMap
+    val dataInternalRepr = data.flatMap { itemsets =>
+      val allItems = mutable.ArrayBuilder.make[Int]
+      var containsFreqItems = false
+      allItems += 0
+      itemsets.foreach { itemsets =>
+        val items = mutable.ArrayBuilder.make[Int]
+        itemsets.foreach { item =>
+          if (itemToInt.contains(item)) {
+            items += itemToInt(item) + 1 // using 1-indexing in internal format
+          }
+        }
+        val result = items.result()
+        if (result.nonEmpty) {
+          containsFreqItems = true
+          allItems ++= result.sorted
+        }
+        allItems += 0
+      }
+      if (containsFreqItems) {
+        Iterator.single(allItems.result())
+      } else {
+        Iterator.empty
+      }
+    }.persist(StorageLevel.MEMORY_AND_DISK)
+
+    val results = genFreqPatterns(dataInternalRepr, minCount, maxPatternLength, maxLocalProjDBSize)
+
+    def toPublicRepr(pattern: Array[Int]): Array[Array[Item]] = {
+      val sequenceBuilder = mutable.ArrayBuilder.make[Array[Item]]
+      val itemsetBuilder = mutable.ArrayBuilder.make[Item]
+      val n = pattern.length
+      var i = 1
+      while (i < n) {
+        val x = pattern(i)
+        if (x == 0) {
+          sequenceBuilder += itemsetBuilder.result()
+          itemsetBuilder.clear()
+        } else {
+          itemsetBuilder += freqItems(x - 1) // using 1-indexing in internal format
+        }
+        i += 1
       }
+      sequenceBuilder.result()
     }
+
     val freqSequences = results.map { case (seq: Array[Int], count: Long) =>
-      new FreqSequence[Item](toPublicRepr(seq).toArray, count)
+      new FreqSequence(toPublicRepr(seq), count)
     }
-    new PrefixSpanModel[Item](freqSequences)
+    new PrefixSpanModel(freqSequences)
   }
 
   /**
@@ -131,7 +200,7 @@ class PrefixSpan private (
    * @tparam Item item type
    * @tparam Itemset itemset type, which is an Iterable of Items
    * @tparam Sequence sequence type, which is an Iterable of Itemsets
-   * @return a [[PrefixSpanModel]] that contains the frequent sequences
+   * @return a [[PrefixSpanModel]] that contains the frequent sequential patterns
    */
   def run[Item, Itemset <: jl.Iterable[Item], Sequence <: jl.Iterable[Itemset]](
       data: JavaRDD[Sequence]): PrefixSpanModel[Item] = {
@@ -139,200 +208,320 @@ class PrefixSpan private (
     run(data.rdd.map(_.asScala.map(_.asScala.toArray).toArray))
   }
 
+}
+
+@Experimental
+object PrefixSpan extends Logging {
+
   /**
-   * Find the complete set of sequential patterns in the input sequences. This method utilizes
-   * the internal representation of itemsets as Array[Int] where each itemset is represented by
-   * a contiguous sequence of non-negative integers and delimiters represented by [[DELIMITER]].
-   * @param data ordered sequences of itemsets. Items are represented by non-negative integers.
-   *             Each itemset has one or more items and is delimited by [[DELIMITER]].
-   * @return a set of sequential pattern pairs,
-   *         the key of pair is pattern (a list of elements),
-   *         the value of pair is the pattern's count.
+   * Find the complete set of frequent sequential patterns in the input sequences.
+   * @param data ordered sequences of itemsets. We represent a sequence internally as Array[Int],
+   *             where each itemset is represented by a contiguous sequence of distinct and ordered
+   *             positive integers. We use 0 as the delimiter at itemset boundaries, including the
+   *             first and the last position.
+   * @return an RDD of (frequent sequential pattern, count) pairs,
+   * @see [[Postfix]]
    */
-  private[fpm] def run(data: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
+  private[fpm] def genFreqPatterns(
+      data: RDD[Array[Int]],
+      minCount: Long,
+      maxPatternLength: Int,
+      maxLocalProjDBSize: Long): RDD[(Array[Int], Long)] = {
     val sc = data.sparkContext
 
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
     }
 
-    // Use List[Set[Item]] for internal computation
-    val sequences = data.map { seq => splitSequence(seq.toList) }
-
-    // Convert min support to a min number of transactions for this dataset
-    val minCount = if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
-
-    // (Frequent items -> number of occurrences, all items here satisfy the `minSupport` threshold
-    val freqItemCounts = sequences
-      .flatMap(seq => seq.flatMap(nonemptySubsets(_)).distinct.map(item => (item, 1L)))
-      .reduceByKey(_ + _)
-      .filter { case (item, count) => (count >= minCount) }
-      .collect()
-      .toMap
-
-    // Pairs of (length 1 prefix, suffix consisting of frequent items)
-    val itemSuffixPairs = {
-      val freqItemSets = freqItemCounts.keys.toSet
-      val freqItems = freqItemSets.flatten
-      sequences.flatMap { seq =>
-        val filteredSeq = seq.map(item => freqItems.intersect(item)).filter(_.nonEmpty)
-        freqItemSets.flatMap { item =>
-          val candidateSuffix = LocalPrefixSpan.getSuffix(item, filteredSeq)
-          candidateSuffix match {
-            case suffix if !suffix.isEmpty => Some((List(item), suffix))
-            case _ => None
+    val postfixes = data.map(items => new Postfix(items))
+
+    // Local frequent patterns (prefixes) and their counts.
+    val localFreqPatterns = mutable.ArrayBuffer.empty[(Array[Int], Long)]
+    // Prefixes whose projected databases are small.
+    val smallPrefixes = mutable.Map.empty[Int, Prefix]
+    val emptyPrefix = Prefix.empty
+    // Prefixes whose projected databases are large.
+    var largePrefixes = mutable.Map(emptyPrefix.id -> emptyPrefix)
+    while (largePrefixes.nonEmpty) {
+      val numLocalFreqPatterns = localFreqPatterns.length
+      logInfo(s"number of local frequent patterns: $numLocalFreqPatterns")
+      if (numLocalFreqPatterns > 1000000) {
+        logWarning(
+          s"""
+             | Collected $numLocalFreqPatterns local frequent patterns. You may want to consider:
+             |   1. increase minSupport,
+             |   2. decrease maxPatternLength,
+             |   3. increase maxLocalProjDBSize.
+           """.stripMargin)
+      }
+      logInfo(s"number of small prefixes: ${smallPrefixes.size}")
+      logInfo(s"number of large prefixes: ${largePrefixes.size}")
+      val largePrefixArray = largePrefixes.values.toArray
+      val freqPrefixes = postfixes.flatMap { postfix =>
+          largePrefixArray.flatMap { prefix =>
+            postfix.project(prefix).genPrefixItems.map { case (item, postfixSize) =>
+              ((prefix.id, item), (1L, postfixSize))
+            }
+          }
+        }.reduceByKey { case ((c0, s0), (c1, s1)) =>
+          (c0 + c1, s0 + s1)
+        }.filter { case (_, (c, _)) => c >= minCount }
+        .collect()
+      val newLargePrefixes = mutable.Map.empty[Int, Prefix]
+      freqPrefixes.foreach { case ((id, item), (count, projDBSize)) =>
+        val newPrefix = largePrefixes(id) :+ item
+        localFreqPatterns += ((newPrefix.items :+ 0, count))
+        if (newPrefix.length < maxPatternLength) {
+          if (projDBSize > maxLocalProjDBSize) {
+            newLargePrefixes += newPrefix.id -> newPrefix
+          } else {
+            smallPrefixes += newPrefix.id -> newPrefix
           }
         }
       }
+      largePrefixes = newLargePrefixes
     }
 
-    // Accumulator for the computed results to be returned, initialized to the frequent items (i.e.
-    // frequent length-one prefixes)
-    var resultsAccumulator = freqItemCounts.map { case (item, count) => (List(item), count) }.toList
-
-    // Remaining work to be locally and distributively processed respectfully
-    var (pairsForLocal, pairsForDistributed) = partitionByProjDBSize(itemSuffixPairs)
-
-    // Continue processing until no pairs for distributed processing remain (i.e. all prefixes have
-    // projected database sizes <= `maxLocalProjDBSize`) or `maxPatternLength` is reached
-    var patternLength = 1
-    while (pairsForDistributed.count() != 0 && patternLength < maxPatternLength) {
-      val (nextPatternAndCounts, nextPrefixSuffixPairs) =
-        extendPrefixes(minCount, pairsForDistributed)
-      pairsForDistributed.unpersist()
-      val (smallerPairsPart, largerPairsPart) = partitionByProjDBSize(nextPrefixSuffixPairs)
-      pairsForDistributed = largerPairsPart
-      pairsForDistributed.persist(StorageLevel.MEMORY_AND_DISK)
-      pairsForLocal ++= smallerPairsPart
-      resultsAccumulator ++= nextPatternAndCounts.collect()
-      patternLength += 1 // pattern length grows one per iteration
+    // Switch to local processing.
+    val bcSmallPrefixes = sc.broadcast(smallPrefixes)
+    val distributedFreqPattern = postfixes.flatMap { postfix =>
+      bcSmallPrefixes.value.values.map { prefix =>
+        (prefix.id, postfix.project(prefix).compressed)
+      }.filter(_._2.nonEmpty)
+    }.groupByKey().flatMap { case (id, projPostfixes) =>
+      val prefix = bcSmallPrefixes.value(id)
+      val localPrefixSpan = new LocalPrefixSpan(minCount, maxPatternLength - prefix.length)
+      // TODO: We collect projected postfixes into memory. We should also compare the performance
+      // TODO: of keeping them on shuffle files.
+      localPrefixSpan.run(projPostfixes.toArray).map { case (pattern, count) =>
+        (prefix.items ++ pattern, count)
+      }
     }
 
-    // Process the small projected databases locally
-    val remainingResults = getPatternsInLocal(
-      minCount, sc.parallelize(pairsForLocal, 1).groupByKey())
-
-    (sc.parallelize(resultsAccumulator, 1) ++ remainingResults)
-      .map { case (pattern, count) => (flattenSequence(pattern.reverse).toArray, count) }
+    // Union local frequent patterns and distributed ones.
+    val freqPatterns = (sc.parallelize(localFreqPatterns, 1) ++ distributedFreqPattern)
+      .persist(StorageLevel.MEMORY_AND_DISK)
+    freqPatterns
   }
 
-
   /**
-   * Partitions the prefix-suffix pairs by projected database size.
-   * @param prefixSuffixPairs prefix (length n) and suffix pairs,
-   * @return prefix-suffix pairs partitioned by whether their projected database size is <= or
-   *         greater than [[maxLocalProjDBSize]]
+   * Represents a prefix.
+   * @param items items in this prefix, using the internal format
+   * @param length length of this prefix, not counting 0
    */
-  private def partitionByProjDBSize(prefixSuffixPairs: RDD[(List[Set[Int]], List[Set[Int]])])
-    : (List[(List[Set[Int]], List[Set[Int]])], RDD[(List[Set[Int]], List[Set[Int]])]) = {
-    val prefixToSuffixSize = prefixSuffixPairs
-      .aggregateByKey(0)(
-        seqOp = { case (count, suffix) => count + suffix.length },
-        combOp = { _ + _ })
-    val smallPrefixes = prefixToSuffixSize
-      .filter(_._2 <= maxLocalProjDBSize)
-      .keys
-      .collect()
-      .toSet
-    val small = prefixSuffixPairs.filter { case (prefix, _) => smallPrefixes.contains(prefix) }
-    val large = prefixSuffixPairs.filter { case (prefix, _) => !smallPrefixes.contains(prefix) }
-    (small.collect().toList, large)
+  private[fpm] class Prefix private (val items: Array[Int], val length: Int) extends Serializable {
+
+    /** A unique id for this prefix. */
+    val id: Int = Prefix.nextId
+
+    /** Expands this prefix by the input item. */
+    def :+(item: Int): Prefix = {
+      require(item != 0)
+      if (item < 0) {
+        new Prefix(items :+ -item, length + 1)
+      } else {
+        new Prefix(items ++ Array(0, item), length + 1)
+      }
+    }
   }
 
-  /**
-   * Extends all prefixes by one itemset from their suffix and computes the resulting frequent
-   * prefixes and remaining work.
-   * @param minCount minimum count
-   * @param prefixSuffixPairs prefix (length N) and suffix pairs,
-   * @return (frequent length N+1 extended prefix, count) pairs and (frequent length N+1 extended
-   *         prefix, corresponding suffix) pairs.
-   */
-  private def extendPrefixes(
-      minCount: Long,
-      prefixSuffixPairs: RDD[(List[Set[Int]], List[Set[Int]])])
-    : (RDD[(List[Set[Int]], Long)], RDD[(List[Set[Int]], List[Set[Int]])]) = {
-
-    // (length N prefix, itemset from suffix) pairs and their corresponding number of occurrences
-    // Every (prefix :+ suffix) is guaranteed to have support exceeding `minSupport`
-    val prefixItemPairAndCounts = prefixSuffixPairs
-      .flatMap { case (prefix, suffix) =>
-      suffix.flatMap(nonemptySubsets(_)).distinct.map(y => ((prefix, y), 1L)) }
-      .reduceByKey(_ + _)
-      .filter { case (item, count) => (count >= minCount) }
-
-    // Map from prefix to set of possible next items from suffix
-    val prefixToNextItems = prefixItemPairAndCounts
-      .keys
-      .groupByKey()
-      .mapValues(_.toSet)
-      .collect()
-      .toMap
-
-    // Frequent patterns with length N+1 and their corresponding counts
-    val extendedPrefixAndCounts = prefixItemPairAndCounts
-      .map { case ((prefix, item), count) => (item :: prefix, count) }
-
-    // Remaining work, all prefixes will have length N+1
-    val extendedPrefixAndSuffix = prefixSuffixPairs
-      .filter(x => prefixToNextItems.contains(x._1))
-      .flatMap { case (prefix, suffix) =>
-        val frequentNextItemSets = prefixToNextItems(prefix)
-        val frequentNextItems = frequentNextItemSets.flatten
-        val filteredSuffix = suffix
-          .map(item => frequentNextItems.intersect(item))
-          .filter(_.nonEmpty)
-        frequentNextItemSets.flatMap { item =>
-          LocalPrefixSpan.getSuffix(item, filteredSuffix) match {
-            case suffix if !suffix.isEmpty => Some(item :: prefix, suffix)
-            case _ => None
-          }
-        }
-      }
+  private[fpm] object Prefix {
+    /** Internal counter to generate unique IDs. */
+    private val counter: AtomicInteger = new AtomicInteger(-1)
 
-    (extendedPrefixAndCounts, extendedPrefixAndSuffix)
+    /** Gets the next unique ID. */
+    private def nextId: Int = counter.incrementAndGet()
+
+    /** An empty [[Prefix]] instance. */
+    val empty: Prefix = new Prefix(Array.empty, 0)
   }
 
   /**
-   * Calculate the patterns in local.
-   * @param minCount the absolute minimum count
-   * @param data prefixes and projected sequences data data
-   * @return patterns
+   * An internal representation of a postfix from some projection.
+   * We use one int array to store the items, which might also contains other items from the
+   * original sequence.
+   * Items are represented by positive integers, and items in each itemset must be distinct and
+   * ordered.
+   * we use 0 as the delimiter between itemsets.
+   * For example, a sequence `<(12)(31)1>` is represented by `[0, 1, 2, 0, 1, 3, 0, 1, 0]`.
+   * The postfix of this sequence w.r.t. to prefix `<1>` is `<(_2)(13)1>`.
+   * We may reuse the original items array `[0, 1, 2, 0, 1, 3, 0, 1, 0]` to represent the postfix,
+   * and mark the start index of the postfix, which is `2` in this example.
+   * So the active items in this postfix are `[2, 0, 1, 3, 0, 1, 0]`.
+   * We also remember the start indices of partial projections, the ones that split an itemset.
+   * For example, another possible partial projection w.r.t. `<1>` is `<(_3)1>`.
+   * We remember the start indices of partial projections, which is `[2, 5]` in this example.
+   * This data structure makes it easier to do projections.
+   *
+   * @param items a sequence stored as `Array[Int]` containing this postfix
+   * @param start the start index of this postfix in items
+   * @param partialStarts start indices of possible partial projections, strictly increasing
    */
-  private def getPatternsInLocal(
-      minCount: Long,
-      data: RDD[(List[Set[Int]], Iterable[List[Set[Int]]])]): RDD[(List[Set[Int]], Long)] = {
-    data.flatMap {
-      case (prefix, projDB) => LocalPrefixSpan.run(minCount, maxPatternLength, prefix, projDB)
+  private[fpm] class Postfix(
+      val items: Array[Int],
+      val start: Int = 0,
+      val partialStarts: Array[Int] = Array.empty) extends Serializable {
+
+    require(items.last == 0, s"The last item in a postfix must be zero, but got ${items.last}.")
+    if (partialStarts.nonEmpty) {
+      require(partialStarts.head >= start,
+        "The first partial start cannot be smaller than the start index," +
+          s"but got partialStarts.head = ${partialStarts.head} < start = $start.")
     }
-  }
 
-}
+    /**
+     * Start index of the first full itemset contained in this postfix.
+     */
+    private[this] def fullStart: Int = {
+      var i = start
+      while (items(i) != 0) {
+        i += 1
+      }
+      i
+    }
+
+    /**
+     * Generates length-1 prefix items of this postfix with the corresponding postfix sizes.
+     * There are two types of prefix items:
+     *   a) The item can be assembled to the last itemset of the prefix. For example,
+     *      the postfix of `<(12)(123)>1` w.r.t. `<1>` is `<(_2)(123)1>`. The prefix items of this
+     *      postfix can be assembled to `<1>` is `_2` and `_3`, resulting new prefixes `<(12)>` and
+     *      `<(13)>`. We flip the sign in the output to indicate that this is a partial prefix item.
+     *   b) The item can be appended to the prefix. Taking the same example above, the prefix items
+     *      can be appended to `<1>` is `1`, `2`, and `3`, resulting new prefixes `<11>`, `<12>`,
+     *      and `<13>`.
+     * @return an iterator of (prefix item, corresponding postfix size). If the item is negative, it
+     *         indicates a partial prefix item, which should be assembled to the last itemset of the
+     *         current prefix. Otherwise, the item should be appended to the current prefix.
+     */
+    def genPrefixItems: Iterator[(Int, Long)] = {
+      val n1 = items.length - 1
+      // For each unique item (subject to sign) in this sequence, we output exact one split.
+      // TODO: use PrimitiveKeyOpenHashMap
+      val prefixes = mutable.Map.empty[Int, Long]
+      // a) items that can be assembled to the last itemset of the prefix
+      partialStarts.foreach { start =>
+        var i = start
+        var x = -items(i)
+        while (x != 0) {
+          if (!prefixes.contains(x)) {
+            prefixes(x) = n1 - i
+          }
+          i += 1
+          x = -items(i)
+        }
+      }
+      // b) items that can be appended to the prefix
+      var i = fullStart
+      while (i < n1) {
+        val x = items(i)
+        if (x != 0 && !prefixes.contains(x)) {
+          prefixes(x) = n1 - i
+        }
+        i += 1
+      }
+      prefixes.toIterator
+    }
 
-object PrefixSpan {
-  private[fpm] val DELIMITER = -1
+    /** Tests whether this postfix is non-empty. */
+    def nonEmpty: Boolean = items.length > start + 1
 
-  /** Splits an array of itemsets delimited by [[DELIMITER]]. */
-  private[fpm] def splitSequence(sequence: List[Int]): List[Set[Int]] = {
-    sequence.span(_ != DELIMITER) match {
-      case (x, xs) if xs.length > 1 => x.toSet :: splitSequence(xs.tail)
-      case (x, xs) => List(x.toSet)
+    /**
+     * Projects this postfix with respect to the input prefix item.
+     * @param prefix prefix item. If prefix is positive, we match items in any full itemset; if it
+     *               is negative, we do partial projections.
+     * @return the projected postfix
+     */
+    def project(prefix: Int): Postfix = {
+      require(prefix != 0)
+      val n1 = items.length - 1
+      var matched = false
+      var newStart = n1
+      val newPartialStarts = mutable.ArrayBuilder.make[Int]
+      if (prefix < 0) {
+        // Search for partial projections.
+        val target = -prefix
+        partialStarts.foreach { start =>
+          var i = start
+          var x = items(i)
+          while (x != target && x != 0) {
+            i += 1
+            x = items(i)
+          }
+          if (x == target) {
+            i += 1
+            if (!matched) {
+              newStart = i
+              matched = true
+            }
+            if (items(i) != 0) {
+              newPartialStarts += i
+            }
+          }
+        }
+      } else {
+        // Search for items in full itemsets.
+        // Though the items are ordered in each itemsets, they should be small in practice.
+        // So a sequential scan is sufficient here, compared to bisection search.
+        val target = prefix
+        var i = fullStart
+        while (i < n1) {
+          val x = items(i)
+          if (x == target) {
+            if (!matched) {
+              newStart = i
+              matched = true
+            }
+            if (items(i + 1) != 0) {
+              newPartialStarts += i + 1
+            }
+          }
+          i += 1
+        }
+      }
+      new Postfix(items, newStart, newPartialStarts.result())
     }
-  }
 
-  /** Flattens a sequence of itemsets into an Array, inserting[[DELIMITER]] between itemsets. */
-  private[fpm] def flattenSequence(sequence: List[Set[Int]]): List[Int] = {
-    val builder = ArrayBuilder.make[Int]()
-    for (itemSet <- sequence) {
-      builder += DELIMITER
-      builder ++= itemSet.toSeq.sorted
+    /**
+     * Projects this postfix with respect to the input prefix.
+     */
+    private def project(prefix: Array[Int]): Postfix = {
+      var partial = true
+      var cur = this
+      var i = 0
+      val np = prefix.length
+      while (i < np && cur.nonEmpty) {
+        val x = prefix(i)
+        if (x == 0) {
+          partial = false
+        } else {
+          if (partial) {
+            cur = cur.project(-x)
+          } else {
+            cur = cur.project(x)
+            partial = true
+          }
+        }
+        i += 1
+      }
+      cur
     }
-    builder.result().toList.drop(1) // drop trailing delimiter
-  }
 
-  /** Returns an iterator over all non-empty subsets of `itemSet` */
-  private[fpm] def nonemptySubsets(itemSet: Set[Int]): Iterator[Set[Int]] = {
-    // TODO: improve complexity by using partial prefixes, considering one item at a time
-    itemSet.subsets.filter(_ != Set.empty[Int])
+    /**
+     * Projects this postfix with respect to the input prefix.
+     */
+    def project(prefix: Prefix): Postfix = project(prefix.items)
+
+    /**
+     * Returns the same sequence with compressed storage if possible.
+     */
+    def compressed: Postfix = {
+      if (start > 0) {
+        new Postfix(items.slice(start, items.length), 0, partialStarts.map(_ - start))
+      } else {
+        this
+      }
+    }
   }
 
   /**
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
index 0ae48d62cc6b5..a83e543859b8a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -21,7 +21,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
 
-  test("PrefixSpan internal (integer seq, -1 delim) run, singleton itemsets") {
+  test("PrefixSpan internal (integer seq, 0 delim) run, singleton itemsets") {
 
     /*
       library("arulesSequences")
@@ -35,83 +35,81 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     */
 
     val sequences = Array(
-      Array(1, -1, 3, -1, 4, -1, 5),
-      Array(2, -1, 3, -1, 1),
-      Array(2, -1, 4, -1, 1),
-      Array(3, -1, 1, -1, 3, -1, 4, -1, 5),
-      Array(3, -1, 4, -1, 4, -1, 3),
-      Array(6, -1, 5, -1, 3))
+      Array(0, 1, 0, 3, 0, 4, 0, 5, 0),
+      Array(0, 2, 0, 3, 0, 1, 0),
+      Array(0, 2, 0, 4, 0, 1, 0),
+      Array(0, 3, 0, 1, 0, 3, 0, 4, 0, 5, 0),
+      Array(0, 3, 0, 4, 0, 4, 0, 3, 0),
+      Array(0, 6, 0, 5, 0, 3, 0))
 
     val rdd = sc.parallelize(sequences, 2).cache()
 
-    val prefixspan = new PrefixSpan()
-      .setMinSupport(0.33)
-      .setMaxPatternLength(50)
-    val result1 = prefixspan.run(rdd)
+    val result1 = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 2L, maxPatternLength = 50, maxLocalProjDBSize = 16L)
     val expectedValue1 = Array(
-      (Array(1), 4L),
-      (Array(1, -1, 3), 2L),
-      (Array(1, -1, 3, -1, 4), 2L),
-      (Array(1, -1, 3, -1, 4, -1, 5), 2L),
-      (Array(1, -1, 3, -1, 5), 2L),
-      (Array(1, -1, 4), 2L),
-      (Array(1, -1, 4, -1, 5), 2L),
-      (Array(1, -1, 5), 2L),
-      (Array(2), 2L),
-      (Array(2, -1, 1), 2L),
-      (Array(3), 5L),
-      (Array(3, -1, 1), 2L),
-      (Array(3, -1, 3), 2L),
-      (Array(3, -1, 4), 3L),
-      (Array(3, -1, 4, -1, 5), 2L),
-      (Array(3, -1, 5), 2L),
-      (Array(4), 4L),
-      (Array(4, -1, 5), 2L),
-      (Array(5), 3L)
+      (Array(0, 1, 0), 4L),
+      (Array(0, 1, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 4, 0, 5, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 5, 0), 2L),
+      (Array(0, 1, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 4, 0, 5, 0), 2L),
+      (Array(0, 1, 0, 5, 0), 2L),
+      (Array(0, 2, 0), 2L),
+      (Array(0, 2, 0, 1, 0), 2L),
+      (Array(0, 3, 0), 5L),
+      (Array(0, 3, 0, 1, 0), 2L),
+      (Array(0, 3, 0, 3, 0), 2L),
+      (Array(0, 3, 0, 4, 0), 3L),
+      (Array(0, 3, 0, 4, 0, 5, 0), 2L),
+      (Array(0, 3, 0, 5, 0), 2L),
+      (Array(0, 4, 0), 4L),
+      (Array(0, 4, 0, 5, 0), 2L),
+      (Array(0, 5, 0), 3L)
     )
     compareInternalResults(expectedValue1, result1.collect())
 
-    prefixspan.setMinSupport(0.5).setMaxPatternLength(50)
-    val result2 = prefixspan.run(rdd)
+    val result2 = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 3, maxPatternLength = 50, maxLocalProjDBSize = 32L)
     val expectedValue2 = Array(
-      (Array(1), 4L),
-      (Array(3), 5L),
-      (Array(3, -1, 4), 3L),
-      (Array(4), 4L),
-      (Array(5), 3L)
+      (Array(0, 1, 0), 4L),
+      (Array(0, 3, 0), 5L),
+      (Array(0, 3, 0, 4, 0), 3L),
+      (Array(0, 4, 0), 4L),
+      (Array(0, 5, 0), 3L)
     )
     compareInternalResults(expectedValue2, result2.collect())
 
-    prefixspan.setMinSupport(0.33).setMaxPatternLength(2)
-    val result3 = prefixspan.run(rdd)
+    val result3 = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 2, maxPatternLength = 2, maxLocalProjDBSize = 32L)
     val expectedValue3 = Array(
-      (Array(1), 4L),
-      (Array(1, -1, 3), 2L),
-      (Array(1, -1, 4), 2L),
-      (Array(1, -1, 5), 2L),
-      (Array(2, -1, 1), 2L),
-      (Array(2), 2L),
-      (Array(3), 5L),
-      (Array(3, -1, 1), 2L),
-      (Array(3, -1, 3), 2L),
-      (Array(3, -1, 4), 3L),
-      (Array(3, -1, 5), 2L),
-      (Array(4), 4L),
-      (Array(4, -1, 5), 2L),
-      (Array(5), 3L)
+      (Array(0, 1, 0), 4L),
+      (Array(0, 1, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 5, 0), 2L),
+      (Array(0, 2, 0, 1, 0), 2L),
+      (Array(0, 2, 0), 2L),
+      (Array(0, 3, 0), 5L),
+      (Array(0, 3, 0, 1, 0), 2L),
+      (Array(0, 3, 0, 3, 0), 2L),
+      (Array(0, 3, 0, 4, 0), 3L),
+      (Array(0, 3, 0, 5, 0), 2L),
+      (Array(0, 4, 0), 4L),
+      (Array(0, 4, 0, 5, 0), 2L),
+      (Array(0, 5, 0), 3L)
     )
     compareInternalResults(expectedValue3, result3.collect())
   }
 
   test("PrefixSpan internal (integer seq, -1 delim) run, variable-size itemsets") {
     val sequences = Array(
-      Array(1, -1, 1, 2, 3, -1, 1, 3, -1, 4, -1, 3, 6),
-      Array(1, 4, -1, 3, -1, 2, 3, -1, 1, 5),
-      Array(5, 6, -1, 1, 2, -1, 4, 6, -1, 3, -1, 2),
-      Array(5, -1, 7, -1, 1, 6, -1, 3, -1, 2, -1, 3))
+      Array(0, 1, 0, 1, 2, 3, 0, 1, 3, 0, 4, 0, 3, 6, 0),
+      Array(0, 1, 4, 0, 3, 0, 2, 3, 0, 1, 5, 0),
+      Array(0, 5, 6, 0, 1, 2, 0, 4, 6, 0, 3, 0, 2, 0),
+      Array(0, 5, 0, 7, 0, 1, 6, 0, 3, 0, 2, 0, 3, 0))
     val rdd = sc.parallelize(sequences, 2).cache()
-    val prefixspan = new PrefixSpan().setMinSupport(0.5).setMaxPatternLength(5)
-    val result = prefixspan.run(rdd)
+    val result = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 2, maxPatternLength = 5, maxLocalProjDBSize = 128L)
 
     /*
       To verify results, create file "prefixSpanSeqs" with content
@@ -200,63 +198,87 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
         53     <{1},{2},{1}>    0.50
      */
     val expectedValue = Array(
-      (Array(1), 4L),
-      (Array(2), 4L),
-      (Array(3), 4L),
-      (Array(4), 3L),
-      (Array(5), 3L),
-      (Array(6), 3L),
-      (Array(1, -1, 6), 2L),
-      (Array(2, -1, 6), 2L),
-      (Array(5, -1, 6), 2L),
-      (Array(1, 2, -1, 6), 2L),
-      (Array(1, -1, 4), 2L),
-      (Array(2, -1, 4), 2L),
-      (Array(1, 2, -1, 4), 2L),
-      (Array(1, -1, 3), 4L),
-      (Array(2, -1, 3), 3L),
-      (Array(2, 3), 2L),
-      (Array(3, -1, 3), 3L),
-      (Array(4, -1, 3), 3L),
-      (Array(5, -1, 3), 2L),
-      (Array(6, -1, 3), 2L),
-      (Array(5, -1, 6, -1, 3), 2L),
-      (Array(6, -1, 2, -1, 3), 2L),
-      (Array(5, -1, 2, -1, 3), 2L),
-      (Array(5, -1, 1, -1, 3), 2L),
-      (Array(2, -1, 4, -1, 3), 2L),
-      (Array(1, -1, 4, -1, 3), 2L),
-      (Array(1, 2, -1, 4, -1, 3), 2L),
-      (Array(1, -1, 3, -1, 3), 3L),
-      (Array(1, 2, -1, 3), 2L),
-      (Array(1, -1, 2, -1, 3), 2L),
-      (Array(1, -1, 2, 3), 2L),
-      (Array(1, -1, 2), 4L),
-      (Array(1, 2), 2L),
-      (Array(3, -1, 2), 3L),
-      (Array(4, -1, 2), 2L),
-      (Array(5, -1, 2), 2L),
-      (Array(6, -1, 2), 2L),
-      (Array(5, -1, 6, -1, 2), 2L),
-      (Array(6, -1, 3, -1, 2), 2L),
-      (Array(5, -1, 3, -1, 2), 2L),
-      (Array(5, -1, 1, -1, 2), 2L),
-      (Array(4, -1, 3, -1, 2), 2L),
-      (Array(1, -1, 3, -1, 2), 3L),
-      (Array(5, -1, 6, -1, 3, -1, 2), 2L),
-      (Array(5, -1, 1, -1, 3, -1, 2), 2L),
-      (Array(1, -1, 1), 2L),
-      (Array(2, -1, 1), 2L),
-      (Array(3, -1, 1), 2L),
-      (Array(5, -1, 1), 2L),
-      (Array(2, 3, -1, 1), 2L),
-      (Array(1, -1, 3, -1, 1), 2L),
-      (Array(1, -1, 2, 3, -1, 1), 2L),
-      (Array(1, -1, 2, -1, 1), 2L))
+      (Array(0, 1, 0), 4L),
+      (Array(0, 2, 0), 4L),
+      (Array(0, 3, 0), 4L),
+      (Array(0, 4, 0), 3L),
+      (Array(0, 5, 0), 3L),
+      (Array(0, 6, 0), 3L),
+      (Array(0, 1, 0, 6, 0), 2L),
+      (Array(0, 2, 0, 6, 0), 2L),
+      (Array(0, 5, 0, 6, 0), 2L),
+      (Array(0, 1, 2, 0, 6, 0), 2L),
+      (Array(0, 1, 0, 4, 0), 2L),
+      (Array(0, 2, 0, 4, 0), 2L),
+      (Array(0, 1, 2, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 3, 0), 4L),
+      (Array(0, 2, 0, 3, 0), 3L),
+      (Array(0, 2, 3, 0), 2L),
+      (Array(0, 3, 0, 3, 0), 3L),
+      (Array(0, 4, 0, 3, 0), 3L),
+      (Array(0, 5, 0, 3, 0), 2L),
+      (Array(0, 6, 0, 3, 0), 2L),
+      (Array(0, 5, 0, 6, 0, 3, 0), 2L),
+      (Array(0, 6, 0, 2, 0, 3, 0), 2L),
+      (Array(0, 5, 0, 2, 0, 3, 0), 2L),
+      (Array(0, 5, 0, 1, 0, 3, 0), 2L),
+      (Array(0, 2, 0, 4, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 4, 0, 3, 0), 2L),
+      (Array(0, 1, 2, 0, 4, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 3, 0), 3L),
+      (Array(0, 1, 2, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 2, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 2, 3, 0), 2L),
+      (Array(0, 1, 0, 2, 0), 4L),
+      (Array(0, 1, 2, 0), 2L),
+      (Array(0, 3, 0, 2, 0), 3L),
+      (Array(0, 4, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 2, 0), 2L),
+      (Array(0, 6, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 6, 0, 2, 0), 2L),
+      (Array(0, 6, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 1, 0, 2, 0), 2L),
+      (Array(0, 4, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 2, 0), 3L),
+      (Array(0, 5, 0, 6, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 1, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 1, 0, 1, 0), 2L),
+      (Array(0, 2, 0, 1, 0), 2L),
+      (Array(0, 3, 0, 1, 0), 2L),
+      (Array(0, 5, 0, 1, 0), 2L),
+      (Array(0, 2, 3, 0, 1, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 1, 0), 2L),
+      (Array(0, 1, 0, 2, 3, 0, 1, 0), 2L),
+      (Array(0, 1, 0, 2, 0, 1, 0), 2L))
 
     compareInternalResults(expectedValue, result.collect())
   }
 
+  test("PrefixSpan projections with multiple partial starts") {
+    val sequences = Seq(
+      Array(Array(1, 2), Array(1, 2, 3)))
+    val rdd = sc.parallelize(sequences, 2)
+    val prefixSpan = new PrefixSpan()
+      .setMinSupport(1.0)
+      .setMaxPatternLength(2)
+    val model = prefixSpan.run(rdd)
+    val expected = Array(
+      (Array(Array(1)), 1L),
+      (Array(Array(1, 2)), 1L),
+      (Array(Array(1), Array(1)), 1L),
+      (Array(Array(1), Array(2)), 1L),
+      (Array(Array(1), Array(3)), 1L),
+      (Array(Array(1, 3)), 1L),
+      (Array(Array(2)), 1L),
+      (Array(Array(2, 3)), 1L),
+      (Array(Array(2), Array(1)), 1L),
+      (Array(Array(2), Array(2)), 1L),
+      (Array(Array(2), Array(3)), 1L),
+      (Array(Array(3)), 1L))
+    compareResults(expected, model.freqSequences.collect())
+  }
+
   test("PrefixSpan Integer type, variable-size itemsets") {
     val sequences = Seq(
       Array(Array(1, 2), Array(3)),
@@ -265,7 +287,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       Array(Array(6)))
     val rdd = sc.parallelize(sequences, 2).cache()
 
-    val prefixspan = new PrefixSpan()
+    val prefixSpan = new PrefixSpan()
       .setMinSupport(0.5)
       .setMaxPatternLength(5)
 
@@ -296,7 +318,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
         5   <{1,2}>    0.75
      */
 
-    val model = prefixspan.run(rdd)
+    val model = prefixSpan.run(rdd)
     val expected = Array(
       (Array(Array(1)), 3L),
       (Array(Array(2)), 3L),
@@ -304,7 +326,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(Array(1), Array(3)), 2L),
       (Array(Array(1, 2)), 3L)
     )
-    compareResults(expected, model.freqSequences.collect().map(x => (x.sequence, x.freq)))
+    compareResults(expected, model.freqSequences.collect())
   }
 
   test("PrefixSpan String type, variable-size itemsets") {
@@ -318,11 +340,11 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       Array(Array(6))).map(seq => seq.map(itemSet => itemSet.map(intToString)))
     val rdd = sc.parallelize(sequences, 2).cache()
 
-    val prefixspan = new PrefixSpan()
+    val prefixSpan = new PrefixSpan()
       .setMinSupport(0.5)
       .setMaxPatternLength(5)
 
-    val model = prefixspan.run(rdd)
+    val model = prefixSpan.run(rdd)
     val expected = Array(
       (Array(Array(1)), 3L),
       (Array(Array(2)), 3L),
@@ -332,17 +354,17 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     ).map { case (pattern, count) =>
       (pattern.map(itemSet => itemSet.map(intToString)), count)
     }
-    compareResults(expected, model.freqSequences.collect().map(x => (x.sequence, x.freq)))
+    compareResults(expected, model.freqSequences.collect())
   }
 
   private def compareResults[Item](
       expectedValue: Array[(Array[Array[Item]], Long)],
-      actualValue: Array[(Array[Array[Item]], Long)]): Unit = {
+      actualValue: Array[PrefixSpan.FreqSequence[Item]]): Unit = {
     val expectedSet = expectedValue.map { case (pattern: Array[Array[Item]], count: Long) =>
       (pattern.map(itemSet => itemSet.toSet).toSeq, count)
     }.toSet
-    val actualSet = actualValue.map { case (pattern: Array[Array[Item]], count: Long) =>
-      (pattern.map(itemSet => itemSet.toSet).toSeq, count)
+    val actualSet = actualValue.map { x =>
+      (x.sequence.map(_.toSet).toSeq, x.freq)
     }.toSet
     assert(expectedSet === actualSet)
   }
@@ -354,11 +376,4 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     val actualSet = actualValue.map(x => (x._1.toSeq, x._2)).toSet
     assert(expectedSet === actualSet)
   }
-
-  private def insertDelimiter(sequence: Array[Int]): Array[Int] = {
-    sequence.zip(Seq.fill(sequence.length)(PrefixSpan.DELIMITER)).map { case (a, b) =>
-      List(a, b)
-    }.flatten
-  }
-
 }

From d34548587ab55bc2136c8f823b9e6ae96e1355a4 Mon Sep 17 00:00:00 2001
From: Pedro Rodriguez <prodriguez@trulia.com>
Date: Tue, 4 Aug 2015 22:32:21 -0700
Subject: [PATCH 0849/1454] [SPARK-8231] [SQL] Add array_contains

This PR is based on #7580 , thanks to EntilZha

PR for work on https://issues.apache.org/jira/browse/SPARK-8231

Currently, I have an initial implementation for contains. Based on discussion on JIRA, it should behave same as Hive: https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFArrayContains.java#L102-L128

Main points are:
1. If the array is empty, null, or the value is null, return false
2. If there is a type mismatch, throw error
3. If comparison is not supported, throw error

Closes #7580

Author: Pedro Rodriguez <prodriguez@trulia.com>
Author: Pedro Rodriguez <ski.rodriguez@gmail.com>
Author: Davies Liu <davies@databricks.com>

Closes #7949 from davies/array_contains and squashes the following commits:

d3c08bc [Davies Liu] use foreach() to avoid copy
bc3d1fe [Davies Liu] fix array_contains
719e37d [Davies Liu] Merge branch 'master' of github.com:apache/spark into array_contains
e352cf9 [Pedro Rodriguez] fixed diff from master
4d5b0ff [Pedro Rodriguez] added docs and another type check
ffc0591 [Pedro Rodriguez] fixed unit test
7a22deb [Pedro Rodriguez] Changed test to use strings instead of long/ints which are different between python 2 an 3
b5ffae8 [Pedro Rodriguez] fixed pyspark test
4e7dce3 [Pedro Rodriguez] added more docs
3082399 [Pedro Rodriguez] fixed unit test
46f9789 [Pedro Rodriguez] reverted change
d3ca013 [Pedro Rodriguez] Fixed type checking to match hive behavior, then added tests to insure this
8528027 [Pedro Rodriguez] added more tests
686e029 [Pedro Rodriguez] fix scala style
d262e9d [Pedro Rodriguez] reworked type checking code and added more tests
2517a58 [Pedro Rodriguez] removed unused import
28b4f71 [Pedro Rodriguez] fixed bug with type conversions and re-added tests
12f8795 [Pedro Rodriguez] fix scala style checks
e8a20a9 [Pedro Rodriguez] added python df (broken atm)
65b562c [Pedro Rodriguez] made array_contains nullable false
33b45aa [Pedro Rodriguez] reordered test
9623c64 [Pedro Rodriguez] fixed test
4b4425b [Pedro Rodriguez] changed Arrays in tests to Seqs
72cb4b1 [Pedro Rodriguez] added checkInputTypes and docs
69c46fb [Pedro Rodriguez] added tests and codegen
9e0bfc4 [Pedro Rodriguez] initial attempt at implementation
---
 python/pyspark/sql/functions.py               | 17 ++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/collectionOperations.scala    | 78 ++++++++++++++++++-
 .../CollectionFunctionsSuite.scala            | 15 ++++
 .../org/apache/spark/sql/functions.scala      |  8 ++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 49 +++++++++++-
 6 files changed, 163 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index e65b14dc0e2b3..9f0d71d7960cf 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -1311,6 +1311,23 @@ def array(*cols):
     return Column(jc)
 
 
+@since(1.5)
+def array_contains(col, value):
+    """
+    Collection function: returns True if the array contains the given value. The collection
+    elements and value must be of the same type.
+
+    :param col: name of column containing array
+    :param value: value to check for in array
+
+    >>> df = sqlContext.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
+    >>> df.select(array_contains(df.data, "a")).collect()
+    [Row(array_contains(data,a)=True), Row(array_contains(data,a)=False)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.array_contains(_to_java_column(col), value))
+
+
 @since(1.4)
 def explode(col):
     """Returns a new row for each element in the given array or map.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 43e3e9b910e7d..94c355f838fa0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -240,6 +240,7 @@ object FunctionRegistry {
     // collection functions
     expression[Size]("size"),
     expression[SortArray]("sort_array"),
+    expression[ArrayContains]("array_contains"),
 
     // misc functions
     expression[Crc32]("crc32"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 6ccb56578f790..646afa4047d84 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.catalyst.expressions
 import java.util.Comparator
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.util.TypeUtils
+import org.apache.spark.sql.catalyst.expressions.codegen.{
+  CodegenFallback, CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
 /**
@@ -115,3 +116,76 @@ case class SortArray(base: Expression, ascendingOrder: Expression)
 
   override def prettyName: String = "sort_array"
 }
+
+/**
+ * Checks if the array (left) has the element (right)
+ */
+case class ArrayContains(left: Expression, right: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def dataType: DataType = BooleanType
+
+  override def inputTypes: Seq[AbstractDataType] = right.dataType match {
+    case NullType => Seq()
+    case _ => left.dataType match {
+      case n @ ArrayType(element, _) => Seq(n, element)
+      case _ => Seq()
+    }
+  }
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (right.dataType == NullType) {
+      TypeCheckResult.TypeCheckFailure("Null typed values cannot be used as arguments")
+    } else if (!left.dataType.isInstanceOf[ArrayType]
+      || left.dataType.asInstanceOf[ArrayType].elementType != right.dataType) {
+      TypeCheckResult.TypeCheckFailure(
+        "Arguments must be an array followed by a value of same type as the array members")
+    } else {
+      TypeCheckResult.TypeCheckSuccess
+    }
+  }
+
+  override def nullable: Boolean = false
+
+  override def eval(input: InternalRow): Boolean = {
+    val arr = left.eval(input)
+    if (arr == null) {
+      false
+    } else {
+      val value = right.eval(input)
+      if (value == null) {
+        false
+      } else {
+        arr.asInstanceOf[ArrayData].foreach(right.dataType, (i, v) =>
+          if (v == value) return true
+        )
+        false
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val arrGen = left.gen(ctx)
+    val elementGen = right.gen(ctx)
+    val i = ctx.freshName("i")
+    val getValue = ctx.getValue(arrGen.primitive, right.dataType, i)
+    s"""
+      ${arrGen.code}
+      boolean ${ev.isNull} = false;
+      boolean ${ev.primitive} = false;
+      if (!${arrGen.isNull}) {
+        ${elementGen.code}
+        if (!${elementGen.isNull}) {
+          for (int $i = 0; $i < ${arrGen.primitive}.numElements(); $i ++) {
+            if (${ctx.genEqual(right.dataType, elementGen.primitive, getValue)}) {
+              ${ev.primitive} = true;
+              break;
+            }
+          }
+        }
+      }
+     """
+  }
+
+  override def prettyName: String = "array_contains"
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
index 2c7e85c446ec6..95f0e38212a1a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -65,4 +65,19 @@ class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
   }
+
+  test("Array contains") {
+    val a0 = Literal.create(Seq(1, 2, 3), ArrayType(IntegerType))
+    val a1 = Literal.create(Seq[String](null, ""), ArrayType(StringType))
+    val a2 = Literal.create(Seq(null), ArrayType(LongType))
+
+    checkEvaluation(ArrayContains(a0, Literal(1)), true)
+    checkEvaluation(ArrayContains(a0, Literal(0)), false)
+    checkEvaluation(ArrayContains(a0, Literal(null)), false)
+
+    checkEvaluation(ArrayContains(a1, Literal("")), true)
+    checkEvaluation(ArrayContains(a1, Literal(null)), false)
+
+    checkEvaluation(ArrayContains(a2, Literal(null)), false)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index bff70172546ed..5a10c3891ad6c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2120,6 +2120,14 @@ object functions {
   // Collection functions
   //////////////////////////////////////////////////////////////////////////////////////////////
 
+  /**
+   * Returns true if the array contain the value
+   * @group collection_funcs
+   * @since 1.5.0
+   */
+  def array_contains(column: Column, value: Any): Column =
+    ArrayContains(column.expr, Literal(value))
+
   /**
    * Creates a new row for each element in the given array or map column.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 6137527757f85..03116a374f3be 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -323,9 +323,9 @@ class DataFrameFunctionsSuite extends QueryTest {
 
   test("array size function") {
     val df = Seq(
-      (Array[Int](1, 2), "x"),
-      (Array[Int](), "y"),
-      (Array[Int](1, 2, 3), "z")
+      (Seq[Int](1, 2), "x"),
+      (Seq[Int](), "y"),
+      (Seq[Int](1, 2, 3), "z")
     ).toDF("a", "b")
     checkAnswer(
       df.select(size($"a")),
@@ -352,4 +352,47 @@ class DataFrameFunctionsSuite extends QueryTest {
       Seq(Row(2), Row(0), Row(3))
     )
   }
+
+  test("array contains function") {
+    val df = Seq(
+      (Seq[Int](1, 2), "x"),
+      (Seq[Int](), "x")
+    ).toDF("a", "b")
+
+    // Simple test cases
+    checkAnswer(
+      df.select(array_contains(df("a"), 1)),
+      Seq(Row(true), Row(false))
+    )
+    checkAnswer(
+      df.selectExpr("array_contains(a, 1)"),
+      Seq(Row(true), Row(false))
+    )
+    checkAnswer(
+      df.select(array_contains(array(lit(2), lit(null)), 1)),
+      Seq(Row(false), Row(false))
+    )
+
+    // In hive, this errors because null has no type information
+    intercept[AnalysisException] {
+      df.select(array_contains(df("a"), null))
+    }
+    intercept[AnalysisException] {
+      df.selectExpr("array_contains(a, null)")
+    }
+    intercept[AnalysisException] {
+      df.selectExpr("array_contains(null, 1)")
+    }
+
+    // In hive, if either argument has a matching type has a null value, return false, even if
+    // the first argument array contains a null and the second argument is null
+    checkAnswer(
+      df.selectExpr("array_contains(array(array(1), null)[1], 1)"),
+      Seq(Row(false), Row(false))
+    )
+    checkAnswer(
+      df.selectExpr("array_contains(array(0, null), array(1, null)[1])"),
+      Seq(Row(false), Row(false))
+    )
+  }
 }

From 781c8d71a0a6a86c84048a4f22cb3a7d035a5be2 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 4 Aug 2015 23:12:49 -0700
Subject: [PATCH 0850/1454] [SPARK-9119] [SPARK-8359] [SQL] match
 Decimal.precision/scale with DecimalType

Let Decimal carry the correct precision and scale with DecimalType.

cc rxin yhuai

Author: Davies Liu <davies@databricks.com>

Closes #7925 from davies/decimal_scale and squashes the following commits:

e19701a [Davies Liu] some tweaks
57d78d2 [Davies Liu] fix tests
5d5bc69 [Davies Liu] match precision and scale with DecimalType
---
 .../main/scala/org/apache/spark/sql/Row.scala |  4 ++
 .../sql/catalyst/CatalystTypeConverters.scala | 21 +++++++----
 .../catalyst/analysis/HiveTypeCoercion.scala  |  4 +-
 .../spark/sql/catalyst/expressions/Cast.scala |  6 +--
 .../sql/catalyst/expressions/arithmetic.scala |  2 +-
 .../org/apache/spark/sql/types/Decimal.scala  | 37 +++++++++++++++----
 .../sql/types/decimal/DecimalSuite.scala      | 21 +++++++++++
 .../sql/execution/SparkSqlSerializer2.scala   |  3 +-
 .../spark/sql/execution/pythonUDFs.scala      |  3 +-
 .../apache/spark/sql/json/InferSchema.scala   | 21 +++++++++--
 .../apache/spark/sql/json/JacksonParser.scala |  5 +--
 .../spark/sql/JavaApplySchemaSuite.java       |  2 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |  2 +-
 .../org/apache/spark/sql/json/JsonSuite.scala | 26 ++++++-------
 .../spark/sql/parquet/ParquetQuerySuite.scala | 13 +++++++
 .../hive/execution/ScriptTransformation.scala |  2 +-
 16 files changed, 122 insertions(+), 50 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 91449479fa539..40159aaf14d34 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -417,6 +417,10 @@ trait Row extends Serializable {
             if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
               return false
             }
+          case d1: java.math.BigDecimal if o2.isInstanceOf[java.math.BigDecimal] =>
+            if (d1.compareTo(o2.asInstanceOf[java.math.BigDecimal]) != 0) {
+              return false
+            }
           case _ => if (o1 != o2) {
             return false
           }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index c666864e43abd..8d0c64eae4774 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -317,18 +317,23 @@ object CatalystTypeConverters {
 
   private class DecimalConverter(dataType: DecimalType)
     extends CatalystTypeConverter[Any, JavaBigDecimal, Decimal] {
-    override def toCatalystImpl(scalaValue: Any): Decimal = scalaValue match {
-      case d: BigDecimal => Decimal(d)
-      case d: JavaBigDecimal => Decimal(d)
-      case d: Decimal => d
+    override def toCatalystImpl(scalaValue: Any): Decimal = {
+      val decimal = scalaValue match {
+        case d: BigDecimal => Decimal(d)
+        case d: JavaBigDecimal => Decimal(d)
+        case d: Decimal => d
+      }
+      if (decimal.changePrecision(dataType.precision, dataType.scale)) {
+        decimal
+      } else {
+        null
+      }
     }
     override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal
     override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal =
       row.getDecimal(column, dataType.precision, dataType.scale).toJavaBigDecimal
   }
 
-  private object BigDecimalConverter extends DecimalConverter(DecimalType.SYSTEM_DEFAULT)
-
   private abstract class PrimitiveConverter[T] extends CatalystTypeConverter[T, Any, Any] {
     final override def toScala(catalystValue: Any): Any = catalystValue
     final override def toCatalystImpl(scalaValue: T): Any = scalaValue
@@ -413,8 +418,8 @@ object CatalystTypeConverters {
     case s: String => StringConverter.toCatalyst(s)
     case d: Date => DateConverter.toCatalyst(d)
     case t: Timestamp => TimestampConverter.toCatalyst(t)
-    case d: BigDecimal => BigDecimalConverter.toCatalyst(d)
-    case d: JavaBigDecimal => BigDecimalConverter.toCatalyst(d)
+    case d: BigDecimal => new DecimalConverter(DecimalType(d.precision, d.scale)).toCatalyst(d)
+    case d: JavaBigDecimal => new DecimalConverter(DecimalType(d.precision, d.scale)).toCatalyst(d)
     case seq: Seq[Any] => new GenericArrayData(seq.map(convertToCatalyst).toArray)
     case r: Row => InternalRow(r.toSeq.map(convertToCatalyst): _*)
     case arr: Array[Any] => new GenericArrayData(arr.map(convertToCatalyst))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 422d423747026..490f3dc07b6ed 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -442,8 +442,8 @@ object HiveTypeCoercion {
    * Changes numeric values to booleans so that expressions like true = 1 can be evaluated.
    */
   object BooleanEquality extends Rule[LogicalPlan] {
-    private val trueValues = Seq(1.toByte, 1.toShort, 1, 1L, Decimal(1))
-    private val falseValues = Seq(0.toByte, 0.toShort, 0, 0L, Decimal(0))
+    private val trueValues = Seq(1.toByte, 1.toShort, 1, 1L, Decimal.ONE)
+    private val falseValues = Seq(0.toByte, 0.toShort, 0, 0L, Decimal.ZERO)
 
     private def buildCaseKeyWhen(booleanExpr: Expression, numericExpr: Expression) = {
       CaseKeyWhen(numericExpr, Seq(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 88429bb84b1e3..39f99700c8a26 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -26,8 +26,6 @@ import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
-import scala.collection.mutable
-
 
 object Cast {
 
@@ -157,7 +155,7 @@ case class Cast(child: Expression, dataType: DataType)
     case ByteType =>
       buildCast[Byte](_, _ != 0)
     case DecimalType() =>
-      buildCast[Decimal](_, _ != Decimal(0))
+      buildCast[Decimal](_, _ != Decimal.ZERO)
     case DoubleType =>
       buildCast[Double](_, _ != 0)
     case FloatType =>
@@ -311,7 +309,7 @@ case class Cast(child: Expression, dataType: DataType)
         case _: NumberFormatException => null
       })
     case BooleanType =>
-      buildCast[Boolean](_, b => changePrecision(if (b) Decimal(1) else Decimal(0), target))
+      buildCast[Boolean](_, b => changePrecision(if (b) Decimal.ONE else Decimal.ZERO, target))
     case DateType =>
       buildCast[Int](_, d => null) // date can't cast to decimal in Hive
     case TimestampType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 0891b55494710..5808e3f66de3c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -511,6 +511,6 @@ case class Pmod(left: Expression, right: Expression) extends BinaryArithmetic {
 
   private def pmod(a: Decimal, n: Decimal): Decimal = {
     val r = a % n
-    if (r.compare(Decimal(0)) < 0) {(r + n) % n} else r
+    if (r.compare(Decimal.ZERO) < 0) {(r + n) % n} else r
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index c0155eeb450a6..624c3f3d7fa97 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.types
 
+import java.math.{RoundingMode, MathContext}
+
 import org.apache.spark.annotation.DeveloperApi
 
 /**
@@ -28,7 +30,7 @@ import org.apache.spark.annotation.DeveloperApi
  * - Otherwise, the decimal value is longVal / (10 ** _scale)
  */
 final class Decimal extends Ordered[Decimal] with Serializable {
-  import org.apache.spark.sql.types.Decimal.{BIG_DEC_ZERO, MAX_LONG_DIGITS, POW_10, ROUNDING_MODE}
+  import org.apache.spark.sql.types.Decimal._
 
   private var decimalVal: BigDecimal = null
   private var longVal: Long = 0L
@@ -137,9 +139,9 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def toBigDecimal: BigDecimal = {
     if (decimalVal.ne(null)) {
-      decimalVal
+      decimalVal(MATH_CONTEXT)
     } else {
-      BigDecimal(longVal, _scale)
+      BigDecimal(longVal, _scale)(MATH_CONTEXT)
     }
   }
 
@@ -261,10 +263,23 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def isZero: Boolean = if (decimalVal.ne(null)) decimalVal == BIG_DEC_ZERO else longVal == 0
 
-  def + (that: Decimal): Decimal = Decimal(toBigDecimal + that.toBigDecimal)
+  def + (that: Decimal): Decimal = {
+    if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
+      Decimal(longVal + that.longVal, Math.max(precision, that.precision), scale)
+    } else {
+      Decimal(toBigDecimal + that.toBigDecimal, precision, scale)
+    }
+  }
 
-  def - (that: Decimal): Decimal = Decimal(toBigDecimal - that.toBigDecimal)
+  def - (that: Decimal): Decimal = {
+    if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
+      Decimal(longVal - that.longVal, Math.max(precision, that.precision), scale)
+    } else {
+      Decimal(toBigDecimal - that.toBigDecimal, precision, scale)
+    }
+  }
 
+  // HiveTypeCoercion will take care of the precision, scale of result
   def * (that: Decimal): Decimal = Decimal(toBigDecimal * that.toBigDecimal)
 
   def / (that: Decimal): Decimal =
@@ -277,13 +292,13 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def unary_- : Decimal = {
     if (decimalVal.ne(null)) {
-      Decimal(-decimalVal)
+      Decimal(-decimalVal, precision, scale)
     } else {
       Decimal(-longVal, precision, scale)
     }
   }
 
-  def abs: Decimal = if (this.compare(Decimal(0)) < 0) this.unary_- else this
+  def abs: Decimal = if (this.compare(Decimal.ZERO) < 0) this.unary_- else this
 }
 
 object Decimal {
@@ -296,6 +311,11 @@ object Decimal {
 
   private val BIG_DEC_ZERO = BigDecimal(0)
 
+  private val MATH_CONTEXT = new MathContext(DecimalType.MAX_PRECISION, RoundingMode.HALF_UP)
+
+  private[sql] val ZERO = Decimal(0)
+  private[sql] val ONE = Decimal(1)
+
   def apply(value: Double): Decimal = new Decimal().set(value)
 
   def apply(value: Long): Decimal = new Decimal().set(value)
@@ -309,6 +329,9 @@ object Decimal {
   def apply(value: BigDecimal, precision: Int, scale: Int): Decimal =
     new Decimal().set(value, precision, scale)
 
+  def apply(value: java.math.BigDecimal, precision: Int, scale: Int): Decimal =
+    new Decimal().set(value, precision, scale)
+
   def apply(unscaled: Long, precision: Int, scale: Int): Decimal =
     new Decimal().set(unscaled, precision, scale)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
index 1d297beb3868d..6921d15958a55 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
@@ -166,6 +166,27 @@ class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
     assert(Decimal(100) % Decimal(0) === null)
   }
 
+  // regression test for SPARK-8359
+  test("accurate precision after multiplication") {
+    val decimal = (Decimal(Long.MaxValue, 38, 0) * Decimal(Long.MaxValue, 38, 0)).toJavaBigDecimal
+    assert(decimal.unscaledValue.toString === "85070591730234615847396907784232501249")
+  }
+
+  // regression test for SPARK-8677
+  test("fix non-terminating decimal expansion problem") {
+    val decimal = Decimal(1.0, 10, 3) / Decimal(3.0, 10, 3)
+    // The difference between decimal should not be more than 0.001.
+    assert(decimal.toDouble - 0.333 < 0.001)
+  }
+
+  // regression test for SPARK-8800
+  test("fix loss of precision/scale when doing division operation") {
+    val a = Decimal(2) / Decimal(3)
+    assert(a.toDouble < 1.0 && a.toDouble > 0.6)
+    val b = Decimal(1) / Decimal(8)
+    assert(b.toDouble === 0.125)
+  }
+
   test("set/setOrNull") {
     assert(new Decimal().set(10L, 10, 0).toUnscaledLong === 10L)
     assert(new Decimal().set(100L, 10, 0).toUnscaledLong === 100L)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index e5bbd0aaed0a5..e811f1de3e6dd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -412,7 +412,8 @@ private[sql] object SparkSqlSerializer2 {
                 // Then, read the scale.
                 val scale = in.readInt()
                 // Finally, create the Decimal object and set it in the row.
-                mutableRow.update(i, Decimal(new BigDecimal(unscaledVal, scale)))
+                mutableRow.update(i,
+                  Decimal(new BigDecimal(unscaledVal, scale), decimal.precision, decimal.scale))
               }
           }
           i += 1
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index aade2e769ccd6..dedc7c4dfb4d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -21,7 +21,6 @@ import java.io.OutputStream
 import java.util.{List => JList, Map => JMap}
 
 import scala.collection.JavaConversions._
-import scala.collection.JavaConverters._
 
 import net.razorvine.pickle._
 
@@ -182,7 +181,7 @@ object EvaluatePython {
 
     case (c: Double, DoubleType) => c
 
-    case (c: java.math.BigDecimal, dt: DecimalType) => Decimal(c)
+    case (c: java.math.BigDecimal, dt: DecimalType) => Decimal(c, dt.precision, dt.scale)
 
     case (c: Int, DateType) => c
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
index 04ab5e2217882..ec5668c6b95a1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
@@ -113,8 +113,12 @@ private[sql] object InferSchema {
           case INT | LONG => LongType
           // Since we do not have a data type backed by BigInteger,
           // when we see a Java BigInteger, we use DecimalType.
-          case BIG_INTEGER | BIG_DECIMAL => DecimalType.SYSTEM_DEFAULT
-          case FLOAT | DOUBLE => DoubleType
+          case BIG_INTEGER | BIG_DECIMAL =>
+            val v = parser.getDecimalValue
+            DecimalType(v.precision(), v.scale())
+          case FLOAT | DOUBLE =>
+            // TODO(davies): Should we use decimal if possible?
+            DoubleType
         }
 
       case VALUE_TRUE | VALUE_FALSE => BooleanType
@@ -171,9 +175,18 @@ private[sql] object InferSchema {
         // Double support larger range than fixed decimal, DecimalType.Maximum should be enough
         // in most case, also have better precision.
         case (DoubleType, t: DecimalType) =>
-          if (t == DecimalType.SYSTEM_DEFAULT) t else DoubleType
+          DoubleType
         case (t: DecimalType, DoubleType) =>
-          if (t == DecimalType.SYSTEM_DEFAULT) t else DoubleType
+          DoubleType
+        case (t1: DecimalType, t2: DecimalType) =>
+          val scale = math.max(t1.scale, t2.scale)
+          val range = math.max(t1.precision - t1.scale, t2.precision - t2.scale)
+          if (range + scale > 38) {
+            // DecimalType can't support precision > 38
+            DoubleType
+          } else {
+            DecimalType(range + scale, scale)
+          }
 
         case (StructType(fields1), StructType(fields2)) =>
           val newFields = (fields1 ++ fields2).groupBy(field => field.name).map {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index bf0448ee9645d..f1a66c84fc96a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -84,9 +84,8 @@ private[sql] object JacksonParser {
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
         parser.getDoubleValue
 
-      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DecimalType()) =>
-        // TODO: add fixed precision and scale handling
-        Decimal(parser.getDecimalValue)
+      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, dt: DecimalType) =>
+        Decimal(parser.getDecimalValue, dt.precision, dt.scale)
 
       case (VALUE_NUMBER_INT, ByteType) =>
         parser.getByteValue
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index cb84e78d628ca..e912eb835d169 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -164,7 +164,7 @@ public void applySchemaToJSON() {
         "\"bigInteger\":92233720368547758069, \"double\":1.7976931348623157E305, " +
         "\"boolean\":false, \"null\":null}"));
     List<StructField> fields = new ArrayList<StructField>(7);
-    fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(38, 18),
+    fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(20, 0),
       true));
     fields.add(DataTypes.createStructField("boolean", DataTypes.BooleanType, true));
     fields.add(DataTypes.createStructField("double", DataTypes.DoubleType, true));
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 037e2048a8631..9bca4e7e660d6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -148,7 +148,7 @@ class InMemoryColumnarQuerySuite extends QueryTest {
     val dataTypes =
       Seq(StringType, BinaryType, NullType, BooleanType,
         ByteType, ShortType, IntegerType, LongType,
-        FloatType, DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType(6, 5),
+        FloatType, DoubleType, DecimalType(25, 5), DecimalType(6, 5),
         DateType, TimestampType,
         ArrayType(IntegerType), MapType(StringType, LongType), struct)
     val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index f19f22fca7d54..16a5c5706009a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -73,8 +73,6 @@ class JsonSuite extends QueryTest with TestJsonData {
 
     val doubleNumber: Double = 1.7976931348623157E308d
     checkTypePromotion(doubleNumber.toDouble, enforceCorrectType(doubleNumber, DoubleType))
-    checkTypePromotion(
-      Decimal(doubleNumber), enforceCorrectType(doubleNumber, DecimalType.SYSTEM_DEFAULT))
 
     checkTypePromotion(DateTimeUtils.fromJavaTimestamp(new Timestamp(intNumber)),
         enforceCorrectType(intNumber, TimestampType))
@@ -150,7 +148,7 @@ class JsonSuite extends QueryTest with TestJsonData {
 
     // DoubleType
     checkDataType(DoubleType, DoubleType, DoubleType)
-    checkDataType(DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType.SYSTEM_DEFAULT)
+    checkDataType(DoubleType, DecimalType.SYSTEM_DEFAULT, DoubleType)
     checkDataType(DoubleType, StringType, StringType)
     checkDataType(DoubleType, ArrayType(IntegerType), StringType)
     checkDataType(DoubleType, StructType(Nil), StringType)
@@ -241,7 +239,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val jsonDF = ctx.read.json(primitiveFieldAndType)
 
     val expectedSchema = StructType(
-      StructField("bigInteger", DecimalType.SYSTEM_DEFAULT, true) ::
+      StructField("bigInteger", DecimalType(20, 0), true) ::
       StructField("boolean", BooleanType, true) ::
       StructField("double", DoubleType, true) ::
       StructField("integer", LongType, true) ::
@@ -271,7 +269,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val expectedSchema = StructType(
       StructField("arrayOfArray1", ArrayType(ArrayType(StringType, true), true), true) ::
       StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType, true), true), true) ::
-      StructField("arrayOfBigInteger", ArrayType(DecimalType.SYSTEM_DEFAULT, true), true) ::
+      StructField("arrayOfBigInteger", ArrayType(DecimalType(21, 0), true), true) ::
       StructField("arrayOfBoolean", ArrayType(BooleanType, true), true) ::
       StructField("arrayOfDouble", ArrayType(DoubleType, true), true) ::
       StructField("arrayOfInteger", ArrayType(LongType, true), true) ::
@@ -285,7 +283,7 @@ class JsonSuite extends QueryTest with TestJsonData {
           StructField("field3", StringType, true) :: Nil), true), true) ::
       StructField("struct", StructType(
         StructField("field1", BooleanType, true) ::
-        StructField("field2", DecimalType.SYSTEM_DEFAULT, true) :: Nil), true) ::
+        StructField("field2", DecimalType(20, 0), true) :: Nil), true) ::
       StructField("structWithArrayFields", StructType(
         StructField("field1", ArrayType(LongType, true), true) ::
         StructField("field2", ArrayType(StringType, true), true) :: Nil), true) :: Nil)
@@ -386,7 +384,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val expectedSchema = StructType(
       StructField("num_bool", StringType, true) ::
       StructField("num_num_1", LongType, true) ::
-      StructField("num_num_2", DecimalType.SYSTEM_DEFAULT, true) ::
+      StructField("num_num_2", DoubleType, true) ::
       StructField("num_num_3", DoubleType, true) ::
       StructField("num_str", StringType, true) ::
       StructField("str_bool", StringType, true) :: Nil)
@@ -398,11 +396,9 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkAnswer(
       sql("select * from jsonTable"),
       Row("true", 11L, null, 1.1, "13.1", "str1") ::
-        Row("12", null, new java.math.BigDecimal("21474836470.9"), null, null, "true") ::
-        Row("false", 21474836470L,
-          new java.math.BigDecimal("92233720368547758070"), 100, "str1", "false") ::
-        Row(null, 21474836570L,
-          new java.math.BigDecimal("1.1"), 21474836470L, "92233720368547758070", null) :: Nil
+        Row("12", null, 21474836470.9, null, null, "true") ::
+        Row("false", 21474836470L, 92233720368547758070d, 100, "str1", "false") ::
+        Row(null, 21474836570L, 1.1, 21474836470L, "92233720368547758070", null) :: Nil
     )
 
     // Number and Boolean conflict: resolve the type as number in this query.
@@ -425,8 +421,8 @@ class JsonSuite extends QueryTest with TestJsonData {
     // Widening to DecimalType
     checkAnswer(
       sql("select num_num_2 + 1.3 from jsonTable where num_num_2 > 1.1"),
-      Row(BigDecimal("21474836472.2")) ::
-        Row(BigDecimal("92233720368547758071.3")) :: Nil
+      Row(21474836472.2) ::
+        Row(92233720368547758071.3) :: Nil
     )
 
     // Widening to Double
@@ -611,7 +607,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val jsonDF = ctx.read.json(path)
 
     val expectedSchema = StructType(
-      StructField("bigInteger", DecimalType.SYSTEM_DEFAULT, true) ::
+      StructField("bigInteger", DecimalType(20, 0), true) ::
       StructField("boolean", BooleanType, true) ::
       StructField("double", DoubleType, true) ::
       StructField("integer", LongType, true) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index a95f70f2bba69..5c65a8ec57f00 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -189,4 +189,17 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
       }
     }
   }
+
+  test("SPARK-9119 Decimal should be correctly written into parquet") {
+    withTempPath { dir =>
+      val basePath = dir.getCanonicalPath
+      val schema = StructType(Array(StructField("name", DecimalType(10, 5), false)))
+      val rowRDD = sqlContext.sparkContext.parallelize(Array(Row(Decimal("67123.45"))))
+      val df = sqlContext.createDataFrame(rowRDD, schema)
+      df.write.parquet(basePath)
+
+      val decimal = sqlContext.read.parquet(basePath).first().getDecimal(0)
+      assert(Decimal("67123.45") === Decimal(decimal))
+    }
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 97e4ea2081d56..a6a343d395995 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -29,7 +29,6 @@ import org.apache.hadoop.hive.serde2.AbstractSerDe
 import org.apache.hadoop.hive.serde2.objectinspector._
 import org.apache.hadoop.io.Writable
 
-import org.apache.spark.{TaskContext, Logging}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
@@ -39,6 +38,7 @@ import org.apache.spark.sql.hive.HiveShim._
 import org.apache.spark.sql.hive.{HiveContext, HiveInspectors}
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.util.{CircularBuffer, RedirectThread, Utils}
+import org.apache.spark.{Logging, TaskContext}
 
 /**
  * Transforms the input by forking and running the specified script.

From c2a71f0714b7a6ab30c1c4998f606f782428971c Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 5 Aug 2015 00:20:26 -0700
Subject: [PATCH 0851/1454] [SPARK-9217] [STREAMING] Make the kinesis receiver
 reliable by recording sequence numbers

This PR is the second one in the larger issue of making the Kinesis integration reliable and provide WAL-free at-least once guarantee. It is based on the design doc - https://docs.google.com/document/d/1k0dl270EnK7uExrsCE7jYw7PYx0YC935uBcxn3p0f58/edit

In this PR, I have updated the Kinesis Receiver to do the following.
- Control the block generation, by creating its own BlockGenerator with own callback methods and using it to keep track of the ranges of sequence numbers that go into each block.
- More specifically, as the KinesisRecordProcessor provides small batches of records, the records are atomically inserted into the block (that is, either the whole batch is in the block, or not). Accordingly the sequence number range of the batch is recorded. Since there may be many batches added to a block, the receiver tracks all the range of sequence numbers that is added to a block.
- When the block is ready to be pushed, the block is pushed and the ranges are reported as metadata of the block. In addition, the ranges are used to find out the latest sequence number for each shard that can be checkpointed through the DynamoDB.
- Periodically, each KinesisRecordProcessor checkpoints the latest successfully stored sequence number for it own shard.
- The array of ranges in the block metadata is used to create KinesisBackedBlockRDDs. The ReceiverInputDStream has been slightly refactored to allow the creation of KinesisBackedBlockRDDs instead of the WALBackedBlockRDDs.

Things to be done
- [x] Add new test to verify that the sequence numbers are recovered.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7825 from tdas/kinesis-receiver and squashes the following commits:

2159be9 [Tathagata Das] Fixed bug
569be83 [Tathagata Das] Fix scala style issue
bf31e22 [Tathagata Das] Added more documentation to make the kinesis test endpoint more configurable
3ad8361 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into kinesis-receiver
c693a63 [Tathagata Das] Removed unnecessary constructor params from KinesisTestUtils
e1f1d0a [Tathagata Das] Addressed PR comments
b9fa6bf [Tathagata Das] Fix serialization issues
f8b7680 [Tathagata Das] Updated doc
33fe43a [Tathagata Das] Added more tests
7997138 [Tathagata Das] Fix style errors
a806710 [Tathagata Das] Fixed unit test and use KinesisInputDStream
40a1709 [Tathagata Das] Fixed KinesisReceiverSuite tests
7e44df6 [Tathagata Das] Added documentation and fixed checkpointing
096383f [Tathagata Das] Added test, and addressed some of the comments.
84a7892 [Tathagata Das] fixed scala style issue
e19e37d [Tathagata Das] Added license
1cd7b66 [Tathagata Das] Updated kinesis receiver
---
 .../kinesis/KinesisBackedBlockRDD.scala       |  20 +-
 .../kinesis/KinesisInputDStream.scala         |  71 ++++++
 .../streaming/kinesis/KinesisReceiver.scala   | 195 +++++++++++++--
 .../kinesis/KinesisRecordProcessor.scala      |  76 +++---
 .../streaming/kinesis/KinesisTestUtils.scala  |  63 +++--
 .../streaming/kinesis/KinesisUtils.scala      |  21 +-
 .../kinesis/KinesisBackedBlockRDDSuite.scala  |  18 +-
 .../streaming/kinesis/KinesisFunSuite.scala   |   4 +-
 .../kinesis/KinesisReceiverSuite.scala        |  41 +++-
 .../kinesis/KinesisStreamSuite.scala          | 222 +++++++++++++++---
 .../dstream/ReceiverInputDStream.scala        |  71 +++---
 11 files changed, 605 insertions(+), 197 deletions(-)
 create mode 100644 extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala

diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
index 8f144a4d974a8..a003ddf325e6e 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
@@ -37,16 +37,18 @@ case class SequenceNumberRange(
 
 /** Class representing an array of Kinesis sequence number ranges */
 private[kinesis]
-case class SequenceNumberRanges(ranges: Array[SequenceNumberRange]) {
+case class SequenceNumberRanges(ranges: Seq[SequenceNumberRange]) {
   def isEmpty(): Boolean = ranges.isEmpty
+
   def nonEmpty(): Boolean = ranges.nonEmpty
+
   override def toString(): String = ranges.mkString("SequenceNumberRanges(", ", ", ")")
 }
 
 private[kinesis]
 object SequenceNumberRanges {
   def apply(range: SequenceNumberRange): SequenceNumberRanges = {
-    new SequenceNumberRanges(Array(range))
+    new SequenceNumberRanges(Seq(range))
   }
 }
 
@@ -66,14 +68,14 @@ class KinesisBackedBlockRDDPartition(
  */
 private[kinesis]
 class KinesisBackedBlockRDD(
-    sc: SparkContext,
-    regionId: String,
-    endpointUrl: String,
+    @transient sc: SparkContext,
+    val regionName: String,
+    val endpointUrl: String,
     @transient blockIds: Array[BlockId],
-    @transient arrayOfseqNumberRanges: Array[SequenceNumberRanges],
+    @transient val arrayOfseqNumberRanges: Array[SequenceNumberRanges],
     @transient isBlockIdValid: Array[Boolean] = Array.empty,
-    retryTimeoutMs: Int = 10000,
-    awsCredentialsOption: Option[SerializableAWSCredentials] = None
+    val retryTimeoutMs: Int = 10000,
+    val awsCredentialsOption: Option[SerializableAWSCredentials] = None
   ) extends BlockRDD[Array[Byte]](sc, blockIds) {
 
   require(blockIds.length == arrayOfseqNumberRanges.length,
@@ -104,7 +106,7 @@ class KinesisBackedBlockRDD(
       }
       partition.seqNumberRanges.ranges.iterator.flatMap { range =>
         new KinesisSequenceRangeIterator(
-          credenentials, endpointUrl, regionId, range, retryTimeoutMs)
+          credenentials, endpointUrl, regionName, range, retryTimeoutMs)
       }
     }
     if (partition.isBlockIdValid) {
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala
new file mode 100644
index 0000000000000..2e4204dcb6f1a
--- /dev/null
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kinesis
+
+import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.{BlockId, StorageLevel}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
+import org.apache.spark.streaming.receiver.Receiver
+import org.apache.spark.streaming.scheduler.ReceivedBlockInfo
+import org.apache.spark.streaming.{Duration, StreamingContext, Time}
+
+private[kinesis] class KinesisInputDStream(
+    @transient _ssc: StreamingContext,
+    streamName: String,
+    endpointUrl: String,
+    regionName: String,
+    initialPositionInStream: InitialPositionInStream,
+    checkpointAppName: String,
+    checkpointInterval: Duration,
+    storageLevel: StorageLevel,
+    awsCredentialsOption: Option[SerializableAWSCredentials]
+  ) extends ReceiverInputDStream[Array[Byte]](_ssc) {
+
+  private[streaming]
+  override def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[Array[Byte]] = {
+
+    // This returns true even for when blockInfos is empty
+    val allBlocksHaveRanges = blockInfos.map { _.metadataOption }.forall(_.nonEmpty)
+
+    if (allBlocksHaveRanges) {
+      // Create a KinesisBackedBlockRDD, even when there are no blocks
+      val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
+      val seqNumRanges = blockInfos.map {
+        _.metadataOption.get.asInstanceOf[SequenceNumberRanges] }.toArray
+      val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
+      logDebug(s"Creating KinesisBackedBlockRDD for $time with ${seqNumRanges.length} " +
+          s"seq number ranges: ${seqNumRanges.mkString(", ")} ")
+      new KinesisBackedBlockRDD(
+        context.sc, regionName, endpointUrl, blockIds, seqNumRanges,
+        isBlockIdValid = isBlockIdValid,
+        retryTimeoutMs = ssc.graph.batchDuration.milliseconds.toInt,
+        awsCredentialsOption = awsCredentialsOption)
+    } else {
+      logWarning("Kinesis sequence number information was not present with some block metadata," +
+        " it may not be possible to recover from failures")
+      super.createBlockRDD(time, blockInfos)
+    }
+  }
+
+  override def getReceiver(): Receiver[Array[Byte]] = {
+    new KinesisReceiver(streamName, endpointUrl, regionName, initialPositionInStream,
+      checkpointAppName, checkpointInterval, storageLevel, awsCredentialsOption)
+  }
+}
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
index 1a8a4cecc1141..a4baeec0846b4 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
@@ -18,17 +18,20 @@ package org.apache.spark.streaming.kinesis
 
 import java.util.UUID
 
+import scala.collection.JavaConversions.asScalaIterator
+import scala.collection.mutable
 import scala.util.control.NonFatal
 
-import com.amazonaws.auth.{AWSCredentials, AWSCredentialsProvider, BasicAWSCredentials, DefaultAWSCredentialsProviderChain}
+import com.amazonaws.auth.{AWSCredentials, AWSCredentialsProvider, DefaultAWSCredentialsProviderChain}
 import com.amazonaws.services.kinesis.clientlibrary.interfaces.{IRecordProcessor, IRecordProcessorFactory}
 import com.amazonaws.services.kinesis.clientlibrary.lib.worker.{InitialPositionInStream, KinesisClientLibConfiguration, Worker}
+import com.amazonaws.services.kinesis.model.Record
 
-import org.apache.spark.Logging
-import org.apache.spark.storage.StorageLevel
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
 import org.apache.spark.streaming.Duration
-import org.apache.spark.streaming.receiver.Receiver
+import org.apache.spark.streaming.receiver.{BlockGenerator, BlockGeneratorListener, Receiver}
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkEnv}
 
 
 private[kinesis]
@@ -42,38 +45,47 @@ case class SerializableAWSCredentials(accessKeyId: String, secretKey: String)
  * Custom AWS Kinesis-specific implementation of Spark Streaming's Receiver.
  * This implementation relies on the Kinesis Client Library (KCL) Worker as described here:
  * https://github.com/awslabs/amazon-kinesis-client
- * This is a custom receiver used with StreamingContext.receiverStream(Receiver) as described here:
- *   http://spark.apache.org/docs/latest/streaming-custom-receivers.html
- * Instances of this class will get shipped to the Spark Streaming Workers to run within a
- *   Spark Executor.
  *
- * @param appName  Kinesis application name. Kinesis Apps are mapped to Kinesis Streams
- *                 by the Kinesis Client Library.  If you change the App name or Stream name,
- *                 the KCL will throw errors.  This usually requires deleting the backing
- *                 DynamoDB table with the same name this Kinesis application.
+ * The way this Receiver works is as follows:
+ * - The receiver starts a KCL Worker, which is essentially runs a threadpool of multiple
+ *   KinesisRecordProcessor
+ * - Each KinesisRecordProcessor receives data from a Kinesis shard in batches. Each batch is
+ *   inserted into a Block Generator, and the corresponding range of sequence numbers is recorded.
+ * - When the block generator defines a block, then the recorded sequence number ranges that were
+ *   inserted into the block are recorded separately for being used later.
+ * - When the block is ready to be pushed, the block is pushed and the ranges are reported as
+ *   metadata of the block. In addition, the ranges are used to find out the latest sequence
+ *   number for each shard that can be checkpointed through the DynamoDB.
+ * - Periodically, each KinesisRecordProcessor checkpoints the latest successfully stored sequence
+ *   number for it own shard.
+ *
  * @param streamName   Kinesis stream name
  * @param endpointUrl  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
  * @param regionName  Region name used by the Kinesis Client Library for
  *                    DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
- * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
- *                            See the Kinesis Spark Streaming documentation for more
- *                            details on the different types of checkpoints.
  * @param initialPositionInStream  In the absence of Kinesis checkpoint info, this is the
  *                                 worker's initial starting position in the stream.
  *                                 The values are either the beginning of the stream
  *                                 per Kinesis' limit of 24 hours
  *                                 (InitialPositionInStream.TRIM_HORIZON) or
  *                                 the tip of the stream (InitialPositionInStream.LATEST).
+ * @param checkpointAppName  Kinesis application name. Kinesis Apps are mapped to Kinesis Streams
+ *                 by the Kinesis Client Library.  If you change the App name or Stream name,
+ *                 the KCL will throw errors.  This usually requires deleting the backing
+ *                 DynamoDB table with the same name this Kinesis application.
+ * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
+ *                            See the Kinesis Spark Streaming documentation for more
+ *                            details on the different types of checkpoints.
  * @param storageLevel Storage level to use for storing the received objects
  * @param awsCredentialsOption Optional AWS credentials, used when user directly specifies
  *                             the credentials
  */
 private[kinesis] class KinesisReceiver(
-    appName: String,
-    streamName: String,
+    val streamName: String,
     endpointUrl: String,
     regionName: String,
     initialPositionInStream: InitialPositionInStream,
+    checkpointAppName: String,
     checkpointInterval: Duration,
     storageLevel: StorageLevel,
     awsCredentialsOption: Option[SerializableAWSCredentials]
@@ -90,7 +102,7 @@ private[kinesis] class KinesisReceiver(
    * workerId is used by the KCL should be based on the ip address of the actual Spark Worker
    * where this code runs (not the driver's IP address.)
    */
-  private var workerId: String = null
+  @volatile private var workerId: String = null
 
   /**
    * Worker is the core client abstraction from the Kinesis Client Library (KCL).
@@ -98,22 +110,40 @@ private[kinesis] class KinesisReceiver(
    * Each shard is assigned its own IRecordProcessor and the worker run multiple such
    * processors.
    */
-  private var worker: Worker = null
+  @volatile private var worker: Worker = null
+  @volatile private var workerThread: Thread = null
 
-  /** Thread running the worker */
-  private var workerThread: Thread = null
+  /** BlockGenerator used to generates blocks out of Kinesis data */
+  @volatile private var blockGenerator: BlockGenerator = null
 
+  /**
+   * Sequence number ranges added to the current block being generated.
+   * Accessing and updating of this map is synchronized by locks in BlockGenerator.
+   */
+  private val seqNumRangesInCurrentBlock = new mutable.ArrayBuffer[SequenceNumberRange]
+
+  /** Sequence number ranges of data added to each generated block */
+  private val blockIdToSeqNumRanges = new mutable.HashMap[StreamBlockId, SequenceNumberRanges]
+    with mutable.SynchronizedMap[StreamBlockId, SequenceNumberRanges]
+
+  /**
+   * Latest sequence number ranges that have been stored successfully.
+   * This is used for checkpointing through KCL */
+  private val shardIdToLatestStoredSeqNum = new mutable.HashMap[String, String]
+    with mutable.SynchronizedMap[String, String]
   /**
    * This is called when the KinesisReceiver starts and must be non-blocking.
    * The KCL creates and manages the receiving/processing thread pool through Worker.run().
    */
   override def onStart() {
+    blockGenerator = new BlockGenerator(new GeneratedBlockHandler, streamId, SparkEnv.get.conf)
+
     workerId = Utils.localHostName() + ":" + UUID.randomUUID()
 
     // KCL config instance
     val awsCredProvider = resolveAWSCredentialsProvider()
     val kinesisClientLibConfiguration =
-      new KinesisClientLibConfiguration(appName, streamName, awsCredProvider, workerId)
+      new KinesisClientLibConfiguration(checkpointAppName, streamName, awsCredProvider, workerId)
       .withKinesisEndpoint(endpointUrl)
       .withInitialPositionInStream(initialPositionInStream)
       .withTaskBackoffTimeMillis(500)
@@ -141,6 +171,10 @@ private[kinesis] class KinesisReceiver(
         }
       }
     }
+
+    blockIdToSeqNumRanges.clear()
+    blockGenerator.start()
+
     workerThread.setName(s"Kinesis Receiver ${streamId}")
     workerThread.setDaemon(true)
     workerThread.start()
@@ -165,6 +199,81 @@ private[kinesis] class KinesisReceiver(
     workerId = null
   }
 
+  /** Add records of the given shard to the current block being generated */
+  private[kinesis] def addRecords(shardId: String, records: java.util.List[Record]): Unit = {
+    if (records.size > 0) {
+      val dataIterator = records.iterator().map { record =>
+        val byteBuffer = record.getData()
+        val byteArray = new Array[Byte](byteBuffer.remaining())
+        byteBuffer.get(byteArray)
+        byteArray
+      }
+      val metadata = SequenceNumberRange(streamName, shardId,
+        records.get(0).getSequenceNumber(), records.get(records.size() - 1).getSequenceNumber())
+      blockGenerator.addMultipleDataWithCallback(dataIterator, metadata)
+
+    }
+  }
+
+  /** Get the latest sequence number for the given shard that can be checkpointed through KCL */
+  private[kinesis] def getLatestSeqNumToCheckpoint(shardId: String): Option[String] = {
+    shardIdToLatestStoredSeqNum.get(shardId)
+  }
+
+  /**
+   * Remember the range of sequence numbers that was added to the currently active block.
+   * Internally, this is synchronized with `finalizeRangesForCurrentBlock()`.
+   */
+  private def rememberAddedRange(range: SequenceNumberRange): Unit = {
+    seqNumRangesInCurrentBlock += range
+  }
+
+  /**
+   * Finalize the ranges added to the block that was active and prepare the ranges buffer
+   * for next block. Internally, this is synchronized with `rememberAddedRange()`.
+   */
+  private def finalizeRangesForCurrentBlock(blockId: StreamBlockId): Unit = {
+    blockIdToSeqNumRanges(blockId) = SequenceNumberRanges(seqNumRangesInCurrentBlock.toArray)
+    seqNumRangesInCurrentBlock.clear()
+    logDebug(s"Generated block $blockId has $blockIdToSeqNumRanges")
+  }
+
+  /** Store the block along with its associated ranges */
+  private def storeBlockWithRanges(
+      blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[Array[Byte]]): Unit = {
+    val rangesToReportOption = blockIdToSeqNumRanges.remove(blockId)
+    if (rangesToReportOption.isEmpty) {
+      stop("Error while storing block into Spark, could not find sequence number ranges " +
+        s"for block $blockId")
+      return
+    }
+
+    val rangesToReport = rangesToReportOption.get
+    var attempt = 0
+    var stored = false
+    var throwable: Throwable = null
+    while (!stored && attempt <= 3) {
+      try {
+        store(arrayBuffer, rangesToReport)
+        stored = true
+      } catch {
+        case NonFatal(th) =>
+          attempt += 1
+          throwable = th
+      }
+    }
+    if (!stored) {
+      stop("Error while storing block into Spark", throwable)
+    }
+
+    // Update the latest sequence number that have been successfully stored for each shard
+    // Note that we are doing this sequentially because the array of sequence number ranges
+    // is assumed to be
+    rangesToReport.ranges.foreach { range =>
+      shardIdToLatestStoredSeqNum(range.shardId) = range.toSeqNumber
+    }
+  }
+
   /**
    * If AWS credential is provided, return a AWSCredentialProvider returning that credential.
    * Otherwise, return the DefaultAWSCredentialsProviderChain.
@@ -182,4 +291,46 @@ private[kinesis] class KinesisReceiver(
         new DefaultAWSCredentialsProviderChain()
     }
   }
+
+
+  /**
+   * Class to handle blocks generated by this receiver's block generator. Specifically, in
+   * the context of the Kinesis Receiver, this handler does the following.
+   *
+   * - When an array of records is added to the current active block in the block generator,
+   *   this handler keeps track of the corresponding sequence number range.
+   * - When the currently active block is ready to sealed (not more records), this handler
+   *   keep track of the list of ranges added into this block in another H
+   */
+  private class GeneratedBlockHandler extends BlockGeneratorListener {
+
+    /**
+     * Callback method called after a data item is added into the BlockGenerator.
+     * The data addition, block generation, and calls to onAddData and onGenerateBlock
+     * are all synchronized through the same lock.
+     */
+    def onAddData(data: Any, metadata: Any): Unit = {
+      rememberAddedRange(metadata.asInstanceOf[SequenceNumberRange])
+    }
+
+    /**
+     * Callback method called after a block has been generated.
+     * The data addition, block generation, and calls to onAddData and onGenerateBlock
+     * are all synchronized through the same lock.
+     */
+    def onGenerateBlock(blockId: StreamBlockId): Unit = {
+      finalizeRangesForCurrentBlock(blockId)
+    }
+
+    /** Callback method called when a block is ready to be pushed / stored. */
+    def onPushBlock(blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[_]): Unit = {
+      storeBlockWithRanges(blockId,
+        arrayBuffer.asInstanceOf[mutable.ArrayBuffer[Array[Byte]]])
+    }
+
+    /** Callback called in case of any error in internal of the BlockGenerator */
+    def onError(message: String, throwable: Throwable): Unit = {
+      reportError(message, throwable)
+    }
+  }
 }
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
index fe9e3a0c793e2..b2405123321e3 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
@@ -18,20 +18,16 @@ package org.apache.spark.streaming.kinesis
 
 import java.util.List
 
-import scala.collection.JavaConversions.asScalaBuffer
 import scala.util.Random
+import scala.util.control.NonFatal
 
-import org.apache.spark.Logging
-
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.InvalidStateException
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.KinesisClientLibDependencyException
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.ShutdownException
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.ThrottlingException
-import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessor
-import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorCheckpointer
+import com.amazonaws.services.kinesis.clientlibrary.exceptions.{InvalidStateException, KinesisClientLibDependencyException, ShutdownException, ThrottlingException}
+import com.amazonaws.services.kinesis.clientlibrary.interfaces.{IRecordProcessor, IRecordProcessorCheckpointer}
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
 
+import org.apache.spark.Logging
+
 /**
  * Kinesis-specific implementation of the Kinesis Client Library (KCL) IRecordProcessor.
  * This implementation operates on the Array[Byte] from the KinesisReceiver.
@@ -51,6 +47,7 @@ private[kinesis] class KinesisRecordProcessor(
     checkpointState: KinesisCheckpointState) extends IRecordProcessor with Logging {
 
   // shardId to be populated during initialize()
+  @volatile
   private var shardId: String = _
 
   /**
@@ -75,47 +72,38 @@ private[kinesis] class KinesisRecordProcessor(
   override def processRecords(batch: List[Record], checkpointer: IRecordProcessorCheckpointer) {
     if (!receiver.isStopped()) {
       try {
-        /*
-         * Notes:
-         * 1) If we try to store the raw ByteBuffer from record.getData(), the Spark Streaming
-         *    Receiver.store(ByteBuffer) attempts to deserialize the ByteBuffer using the
-         *    internally-configured Spark serializer (kryo, etc).
-         * 2) This is not desirable, so we instead store a raw Array[Byte] and decouple
-         *    ourselves from Spark's internal serialization strategy.
-         * 3) For performance, the BlockGenerator is asynchronously queuing elements within its
-         *    memory before creating blocks.  This prevents the small block scenario, but requires
-         *    that you register callbacks to know when a block has been generated and stored
-         *    (WAL is sufficient for storage) before can checkpoint back to the source.
-        */
-        batch.foreach(record => receiver.store(record.getData().array()))
-
-        logDebug(s"Stored:  Worker $workerId stored ${batch.size} records for shardId $shardId")
+        receiver.addRecords(shardId, batch)
+        logDebug(s"Stored: Worker $workerId stored ${batch.size} records for shardId $shardId")
 
         /*
-         * Checkpoint the sequence number of the last record successfully processed/stored
-         *   in the batch.
-         * In this implementation, we're checkpointing after the given checkpointIntervalMillis.
-         * Note that this logic requires that processRecords() be called AND that it's time to
-         *   checkpoint.  I point this out because there is no background thread running the
-         *   checkpointer.  Checkpointing is tested and trigger only when a new batch comes in.
-         * If the worker is shutdown cleanly, checkpoint will happen (see shutdown() below).
-         * However, if the worker dies unexpectedly, a checkpoint may not happen.
-         * This could lead to records being processed more than once.
+         *
+         * Checkpoint the sequence number of the last record successfully stored.
+         * Note that in this current implementation, the checkpointing occurs only when after
+         * checkpointIntervalMillis from the last checkpoint, AND when there is new record
+         * to process. This leads to the checkpointing lagging behind what records have been
+         * stored by the receiver. Ofcourse, this can lead records processed more than once,
+         * under failures and restarts.
+         *
+         * TODO: Instead of checkpointing here, run a separate timer task to perform
+         * checkpointing so that it checkpoints in a timely manner independent of whether
+         * new records are available or not.
          */
         if (checkpointState.shouldCheckpoint()) {
-          /* Perform the checkpoint */
-          KinesisRecordProcessor.retryRandom(checkpointer.checkpoint(), 4, 100)
+          receiver.getLatestSeqNumToCheckpoint(shardId).foreach { latestSeqNum =>
+            /* Perform the checkpoint */
+            KinesisRecordProcessor.retryRandom(checkpointer.checkpoint(latestSeqNum), 4, 100)
 
-          /* Update the next checkpoint time */
-          checkpointState.advanceCheckpoint()
+            /* Update the next checkpoint time */
+            checkpointState.advanceCheckpoint()
 
-          logDebug(s"Checkpoint:  WorkerId $workerId completed checkpoint of ${batch.size}" +
+            logDebug(s"Checkpoint:  WorkerId $workerId completed checkpoint of ${batch.size}" +
               s" records for shardId $shardId")
-          logDebug(s"Checkpoint:  Next checkpoint is at " +
+            logDebug(s"Checkpoint:  Next checkpoint is at " +
               s" ${checkpointState.checkpointClock.getTimeMillis()} for shardId $shardId")
+          }
         }
       } catch {
-        case e: Throwable => {
+        case NonFatal(e) => {
           /*
            *  If there is a failure within the batch, the batch will not be checkpointed.
            *  This will potentially cause records since the last checkpoint to be processed
@@ -130,7 +118,7 @@ private[kinesis] class KinesisRecordProcessor(
       }
     } else {
       /* RecordProcessor has been stopped. */
-      logInfo(s"Stopped:  The Spark KinesisReceiver has stopped for workerId $workerId" +
+      logInfo(s"Stopped:  KinesisReceiver has stopped for workerId $workerId" +
           s" and shardId $shardId.  No more records will be processed.")
     }
   }
@@ -154,7 +142,11 @@ private[kinesis] class KinesisRecordProcessor(
        * It's now OK to read from the new shards that resulted from a resharding event.
        */
       case ShutdownReason.TERMINATE =>
-        KinesisRecordProcessor.retryRandom(checkpointer.checkpoint(), 4, 100)
+        val latestSeqNumToCheckpointOption = receiver.getLatestSeqNumToCheckpoint(shardId)
+        if (latestSeqNumToCheckpointOption.nonEmpty) {
+          KinesisRecordProcessor.retryRandom(
+            checkpointer.checkpoint(latestSeqNumToCheckpointOption.get), 4, 100)
+        }
 
       /*
        * ZOMBIE Use Case.  NoOp.
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index 255ac27f793ba..711aade182945 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -36,22 +36,10 @@ import org.apache.spark.Logging
 /**
  * Shared utility methods for performing Kinesis tests that actually transfer data
  */
-private class KinesisTestUtils(val endpointUrl: String, _regionName: String) extends Logging {
-
-  def this() {
-    this("https://kinesis.us-west-2.amazonaws.com", "")
-  }
-
-  def this(endpointUrl: String) {
-    this(endpointUrl, "")
-  }
-
-  val regionName = if (_regionName.length == 0) {
-    RegionUtils.getRegionByEndpoint(endpointUrl).getName()
-  } else {
-    RegionUtils.getRegion(_regionName).getName()
-  }
+private class KinesisTestUtils extends Logging {
 
+  val endpointUrl = KinesisTestUtils.endpointUrl
+  val regionName = RegionUtils.getRegionByEndpoint(endpointUrl).getName()
   val streamShardCount = 2
 
   private val createStreamTimeoutSeconds = 300
@@ -81,11 +69,11 @@ private class KinesisTestUtils(val endpointUrl: String, _regionName: String) ext
   }
 
   def createStream(): Unit = {
-    logInfo("Creating stream")
     require(!streamCreated, "Stream already created")
     _streamName = findNonExistentStreamName()
 
     // Create a stream. The number of shards determines the provisioned throughput.
+    logInfo(s"Creating stream ${_streamName}")
     val createStreamRequest = new CreateStreamRequest()
     createStreamRequest.setStreamName(_streamName)
     createStreamRequest.setShardCount(2)
@@ -94,7 +82,7 @@ private class KinesisTestUtils(val endpointUrl: String, _regionName: String) ext
     // The stream is now being created. Wait for it to become active.
     waitForStreamToBeActive(_streamName)
     streamCreated = true
-    logInfo("Created stream")
+    logInfo(s"Created stream ${_streamName}")
   }
 
   /**
@@ -191,9 +179,38 @@ private class KinesisTestUtils(val endpointUrl: String, _regionName: String) ext
 
 private[kinesis] object KinesisTestUtils {
 
-  val envVarName = "ENABLE_KINESIS_TESTS"
+  val envVarNameForEnablingTests = "ENABLE_KINESIS_TESTS"
+  val endVarNameForEndpoint = "KINESIS_TEST_ENDPOINT_URL"
+  val defaultEndpointUrl = "https://kinesis.us-west-2.amazonaws.com"
+
+  lazy val shouldRunTests = {
+    val isEnvSet = sys.env.get(envVarNameForEnablingTests) == Some("1")
+    if (isEnvSet) {
+      // scalastyle:off println
+      // Print this so that they are easily visible on the console and not hidden in the log4j logs.
+      println(
+        s"""
+          |Kinesis tests that actually send data has been enabled by setting the environment
+          |variable $envVarNameForEnablingTests to 1. This will create Kinesis Streams and
+          |DynamoDB tables in AWS. Please be aware that this may incur some AWS costs.
+          |By default, the tests use the endpoint URL $defaultEndpointUrl to create Kinesis streams.
+          |To change this endpoint URL to a different region, you can set the environment variable
+          |$endVarNameForEndpoint to the desired endpoint URL
+          |(e.g. $endVarNameForEndpoint="https://kinesis.us-west-2.amazonaws.com").
+        """.stripMargin)
+      // scalastyle:on println
+    }
+    isEnvSet
+  }
 
-  val shouldRunTests = sys.env.get(envVarName) == Some("1")
+  lazy val endpointUrl = {
+    val url = sys.env.getOrElse(endVarNameForEndpoint, defaultEndpointUrl)
+    // scalastyle:off println
+    // Print this so that they are easily visible on the console and not hidden in the log4j logs.
+    println(s"Using endpoint URL $url for creating Kinesis streams for tests.")
+    // scalastyle:on println
+    url
+  }
 
   def isAWSCredentialsPresent: Boolean = {
     Try { new DefaultAWSCredentialsProviderChain().getCredentials() }.isSuccess
@@ -205,7 +222,13 @@ private[kinesis] object KinesisTestUtils {
     Try { new DefaultAWSCredentialsProviderChain().getCredentials() } match {
       case Success(cred) => cred
       case Failure(e) =>
-        throw new Exception("Kinesis tests enabled, but could get not AWS credentials")
+        throw new Exception(
+          s"""
+             |Kinesis tests enabled using environment variable $envVarNameForEnablingTests
+             |but could not find AWS credentials. Please follow instructions in AWS documentation
+             |to set the credentials in your system such that the DefaultAWSCredentialsProviderChain
+             |can find the credentials.
+           """.stripMargin)
     }
   }
 }
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
index 7dab17eba8483..c799fadf2d5ce 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
@@ -65,9 +65,8 @@ object KinesisUtils {
     ): ReceiverInputDStream[Array[Byte]] = {
     // Setting scope to override receiver stream's scope of "receiver stream"
     ssc.withNamedScope("kinesis stream") {
-      ssc.receiverStream(
-        new KinesisReceiver(kinesisAppName, streamName, endpointUrl, validateRegion(regionName),
-          initialPositionInStream, checkpointInterval, storageLevel, None))
+      new KinesisInputDStream(ssc, streamName, endpointUrl, validateRegion(regionName),
+        initialPositionInStream, kinesisAppName, checkpointInterval, storageLevel, None)
     }
   }
 
@@ -112,10 +111,11 @@ object KinesisUtils {
       awsAccessKeyId: String,
       awsSecretKey: String
     ): ReceiverInputDStream[Array[Byte]] = {
-    ssc.receiverStream(
-      new KinesisReceiver(kinesisAppName, streamName, endpointUrl, validateRegion(regionName),
-        initialPositionInStream, checkpointInterval, storageLevel,
-        Some(SerializableAWSCredentials(awsAccessKeyId, awsSecretKey))))
+    ssc.withNamedScope("kinesis stream") {
+      new KinesisInputDStream(ssc, streamName, endpointUrl, validateRegion(regionName),
+        initialPositionInStream, kinesisAppName, checkpointInterval, storageLevel,
+        Some(SerializableAWSCredentials(awsAccessKeyId, awsSecretKey)))
+    }
   }
 
   /**
@@ -155,9 +155,10 @@ object KinesisUtils {
       initialPositionInStream: InitialPositionInStream,
       storageLevel: StorageLevel
     ): ReceiverInputDStream[Array[Byte]] = {
-    ssc.receiverStream(
-      new KinesisReceiver(ssc.sc.appName, streamName, endpointUrl, getRegionByEndpoint(endpointUrl),
-        initialPositionInStream, checkpointInterval, storageLevel, None))
+    ssc.withNamedScope("kinesis stream") {
+      new KinesisInputDStream(ssc, streamName, endpointUrl, getRegionByEndpoint(endpointUrl),
+        initialPositionInStream, ssc.sc.appName, checkpointInterval, storageLevel, None)
+    }
   }
 
   /**
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
index e81fb11e5959f..a89e5627e014c 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
@@ -24,8 +24,6 @@ import org.apache.spark.{SparkConf, SparkContext, SparkException}
 
 class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll {
 
-  private val regionId = "us-east-1"
-  private val endpointUrl = "https://kinesis.us-east-1.amazonaws.com"
   private val testData = 1 to 8
 
   private var testUtils: KinesisTestUtils = null
@@ -42,7 +40,7 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
 
   override def beforeAll(): Unit = {
     runIfTestsEnabled("Prepare KinesisTestUtils") {
-      testUtils = new KinesisTestUtils(endpointUrl)
+      testUtils = new KinesisTestUtils()
       testUtils.createStream()
 
       shardIdToDataAndSeqNumbers = testUtils.pushData(testData)
@@ -75,21 +73,21 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
 
   testIfEnabled("Basic reading from Kinesis") {
     // Verify all data using multiple ranges in a single RDD partition
-    val receivedData1 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+    val receivedData1 = new KinesisBackedBlockRDD(sc, testUtils.regionName, testUtils.endpointUrl,
       fakeBlockIds(1),
       Array(SequenceNumberRanges(allRanges.toArray))
     ).map { bytes => new String(bytes).toInt }.collect()
     assert(receivedData1.toSet === testData.toSet)
 
     // Verify all data using one range in each of the multiple RDD partitions
-    val receivedData2 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+    val receivedData2 = new KinesisBackedBlockRDD(sc, testUtils.regionName, testUtils.endpointUrl,
       fakeBlockIds(allRanges.size),
       allRanges.map { range => SequenceNumberRanges(Array(range)) }.toArray
     ).map { bytes => new String(bytes).toInt }.collect()
     assert(receivedData2.toSet === testData.toSet)
 
     // Verify ordering within each partition
-    val receivedData3 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+    val receivedData3 = new KinesisBackedBlockRDD(sc, testUtils.regionName, testUtils.endpointUrl,
       fakeBlockIds(allRanges.size),
       allRanges.map { range => SequenceNumberRanges(Array(range)) }.toArray
     ).map { bytes => new String(bytes).toInt }.collectPartitions()
@@ -211,7 +209,8 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
       }, "Incorrect configuration of RDD, unexpected ranges set"
     )
 
-    val rdd = new KinesisBackedBlockRDD(sc, regionId, endpointUrl, blockIds, ranges)
+    val rdd = new KinesisBackedBlockRDD(
+      sc, testUtils.regionName, testUtils.endpointUrl, blockIds, ranges)
     val collectedData = rdd.map { bytes =>
       new String(bytes).toInt
     }.collect()
@@ -224,8 +223,9 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
     if (testIsBlockValid) {
       require(numPartitionsInBM === numPartitions, "All partitions must be in BlockManager")
       require(numPartitionsInKinesis === 0, "No partitions must be in Kinesis")
-      val rdd2 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl, blockIds.toArray,
-        ranges, isBlockIdValid = Array.fill(blockIds.length)(false))
+      val rdd2 = new KinesisBackedBlockRDD(
+        sc, testUtils.regionName, testUtils.endpointUrl, blockIds.toArray, ranges,
+        isBlockIdValid = Array.fill(blockIds.length)(false))
       intercept[SparkException] {
         rdd2.collect()
       }
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
index 8373138785a89..ee428f31d6ce3 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
@@ -31,7 +31,7 @@ trait KinesisFunSuite extends SparkFunSuite  {
     if (shouldRunTests) {
       test(testName)(testBody)
     } else {
-      ignore(s"$testName [enable by setting env var $envVarName=1]")(testBody)
+      ignore(s"$testName [enable by setting env var $envVarNameForEnablingTests=1]")(testBody)
     }
   }
 
@@ -40,7 +40,7 @@ trait KinesisFunSuite extends SparkFunSuite  {
     if (shouldRunTests) {
       body
     } else {
-      ignore(s"$message [enable by setting env var $envVarName=1]")()
+      ignore(s"$message [enable by setting env var $envVarNameForEnablingTests=1]")()
     }
   }
 }
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
index 98f2c7c4f1bfb..ceb135e0651aa 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
@@ -22,15 +22,14 @@ import scala.collection.JavaConversions.seqAsJavaList
 
 import com.amazonaws.services.kinesis.clientlibrary.exceptions.{InvalidStateException, KinesisClientLibDependencyException, ShutdownException, ThrottlingException}
 import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorCheckpointer
-import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
+import org.mockito.Matchers._
 import org.mockito.Mockito._
-import org.scalatest.{BeforeAndAfter, Matchers}
 import org.scalatest.mock.MockitoSugar
+import org.scalatest.{BeforeAndAfter, Matchers}
 
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext, TestSuiteBase}
+import org.apache.spark.streaming.{Milliseconds, TestSuiteBase}
 import org.apache.spark.util.{Clock, ManualClock, Utils}
 
 /**
@@ -44,6 +43,8 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   val endpoint = "endpoint-url"
   val workerId = "dummyWorkerId"
   val shardId = "dummyShardId"
+  val seqNum = "dummySeqNum"
+  val someSeqNum = Some(seqNum)
 
   val record1 = new Record()
   record1.setData(ByteBuffer.wrap("Spark In Action".getBytes()))
@@ -80,16 +81,18 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
 
   test("process records including store and checkpoint") {
     when(receiverMock.isStopped()).thenReturn(false)
+    when(receiverMock.getLatestSeqNumToCheckpoint(shardId)).thenReturn(someSeqNum)
     when(checkpointStateMock.shouldCheckpoint()).thenReturn(true)
 
     val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
+    recordProcessor.initialize(shardId)
     recordProcessor.processRecords(batch, checkpointerMock)
 
     verify(receiverMock, times(1)).isStopped()
-    verify(receiverMock, times(1)).store(record1.getData().array())
-    verify(receiverMock, times(1)).store(record2.getData().array())
+    verify(receiverMock, times(1)).addRecords(shardId, batch)
+    verify(receiverMock, times(1)).getLatestSeqNumToCheckpoint(shardId)
     verify(checkpointStateMock, times(1)).shouldCheckpoint()
-    verify(checkpointerMock, times(1)).checkpoint()
+    verify(checkpointerMock, times(1)).checkpoint(anyString)
     verify(checkpointStateMock, times(1)).advanceCheckpoint()
   }
 
@@ -100,19 +103,25 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
     recordProcessor.processRecords(batch, checkpointerMock)
 
     verify(receiverMock, times(1)).isStopped()
+    verify(receiverMock, never).addRecords(anyString, anyListOf(classOf[Record]))
+    verify(checkpointerMock, never).checkpoint(anyString)
   }
 
   test("shouldn't checkpoint when exception occurs during store") {
     when(receiverMock.isStopped()).thenReturn(false)
-    when(receiverMock.store(record1.getData().array())).thenThrow(new RuntimeException())
+    when(
+      receiverMock.addRecords(anyString, anyListOf(classOf[Record]))
+    ).thenThrow(new RuntimeException())
 
     intercept[RuntimeException] {
       val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
+      recordProcessor.initialize(shardId)
       recordProcessor.processRecords(batch, checkpointerMock)
     }
 
     verify(receiverMock, times(1)).isStopped()
-    verify(receiverMock, times(1)).store(record1.getData().array())
+    verify(receiverMock, times(1)).addRecords(shardId, batch)
+    verify(checkpointerMock, never).checkpoint(anyString)
   }
 
   test("should set checkpoint time to currentTime + checkpoint interval upon instantiation") {
@@ -158,19 +167,25 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   }
 
   test("shutdown should checkpoint if the reason is TERMINATE") {
+    when(receiverMock.getLatestSeqNumToCheckpoint(shardId)).thenReturn(someSeqNum)
+
     val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
-    val reason = ShutdownReason.TERMINATE
-    recordProcessor.shutdown(checkpointerMock, reason)
+    recordProcessor.initialize(shardId)
+    recordProcessor.shutdown(checkpointerMock, ShutdownReason.TERMINATE)
 
-    verify(checkpointerMock, times(1)).checkpoint()
+    verify(receiverMock, times(1)).getLatestSeqNumToCheckpoint(shardId)
+    verify(checkpointerMock, times(1)).checkpoint(anyString)
   }
 
   test("shutdown should not checkpoint if the reason is something other than TERMINATE") {
+    when(receiverMock.getLatestSeqNumToCheckpoint(shardId)).thenReturn(someSeqNum)
+
     val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
+    recordProcessor.initialize(shardId)
     recordProcessor.shutdown(checkpointerMock, ShutdownReason.ZOMBIE)
     recordProcessor.shutdown(checkpointerMock, null)
 
-    verify(checkpointerMock, never()).checkpoint()
+    verify(checkpointerMock, never).checkpoint(anyString)
   }
 
   test("retry success on first attempt") {
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
index b88c9c6478d56..1177dc758100d 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -22,34 +22,67 @@ import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.util.Random
 
+import com.amazonaws.regions.RegionUtils
 import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
+import org.scalatest.Matchers._
 import org.scalatest.concurrent.Eventually
 import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
 
-import org.apache.spark.storage.StorageLevel
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
 import org.apache.spark.streaming._
-import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark.streaming.kinesis.KinesisTestUtils._
+import org.apache.spark.streaming.receiver.BlockManagerBasedStoreResult
+import org.apache.spark.streaming.scheduler.ReceivedBlockInfo
+import org.apache.spark.util.Utils
+import org.apache.spark.{SparkConf, SparkContext}
 
 class KinesisStreamSuite extends KinesisFunSuite
   with Eventually with BeforeAndAfter with BeforeAndAfterAll {
 
-  // This is the name that KCL uses to save metadata to DynamoDB
-  private val kinesisAppName = s"KinesisStreamSuite-${math.abs(Random.nextLong())}"
+  // This is the name that KCL will use to save metadata to DynamoDB
+  private val appName = s"KinesisStreamSuite-${math.abs(Random.nextLong())}"
+  private val batchDuration = Seconds(1)
 
-  private var ssc: StreamingContext = _
-  private var sc: SparkContext = _
+  // Dummy parameters for API testing
+  private val dummyEndpointUrl = defaultEndpointUrl
+  private val dummyRegionName = RegionUtils.getRegionByEndpoint(dummyEndpointUrl).getName()
+  private val dummyAWSAccessKey = "dummyAccessKey"
+  private val dummyAWSSecretKey = "dummySecretKey"
+
+  private var testUtils: KinesisTestUtils = null
+  private var ssc: StreamingContext = null
+  private var sc: SparkContext = null
 
   override def beforeAll(): Unit = {
     val conf = new SparkConf()
       .setMaster("local[4]")
       .setAppName("KinesisStreamSuite") // Setting Spark app name to Kinesis app name
     sc = new SparkContext(conf)
+
+    runIfTestsEnabled("Prepare KinesisTestUtils") {
+      testUtils = new KinesisTestUtils()
+      testUtils.createStream()
+    }
   }
 
   override def afterAll(): Unit = {
-    sc.stop()
-    // Delete the Kinesis stream as well as the DynamoDB table generated by
-    // Kinesis Client Library when consuming the stream
+    if (ssc != null) {
+      ssc.stop()
+    }
+    if (sc != null) {
+      sc.stop()
+    }
+    if (testUtils != null) {
+      // Delete the Kinesis stream as well as the DynamoDB table generated by
+      // Kinesis Client Library when consuming the stream
+      testUtils.deleteStream()
+      testUtils.deleteDynamoDBTable(appName)
+    }
+  }
+
+  before {
+    ssc = new StreamingContext(sc, batchDuration)
   }
 
   after {
@@ -57,21 +90,75 @@ class KinesisStreamSuite extends KinesisFunSuite
       ssc.stop(stopSparkContext = false)
       ssc = null
     }
+    if (testUtils != null) {
+      testUtils.deleteDynamoDBTable(appName)
+    }
   }
 
   test("KinesisUtils API") {
-    ssc = new StreamingContext(sc, Seconds(1))
     // Tests the API, does not actually test data receiving
     val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", Seconds(2),
+      dummyEndpointUrl, Seconds(2),
       InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2)
     val kinesisStream2 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+      dummyEndpointUrl, dummyRegionName,
       InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2)
     val kinesisStream3 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+      dummyEndpointUrl, dummyRegionName,
       InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2,
-      "awsAccessKey", "awsSecretKey")
+      dummyAWSAccessKey, dummyAWSSecretKey)
+  }
+
+  test("RDD generation") {
+    val inputStream = KinesisUtils.createStream(ssc, appName, "dummyStream",
+      dummyEndpointUrl, dummyRegionName, InitialPositionInStream.LATEST, Seconds(2),
+      StorageLevel.MEMORY_AND_DISK_2, dummyAWSAccessKey, dummyAWSSecretKey)
+    assert(inputStream.isInstanceOf[KinesisInputDStream])
+
+    val kinesisStream = inputStream.asInstanceOf[KinesisInputDStream]
+    val time = Time(1000)
+
+    // Generate block info data for testing
+    val seqNumRanges1 = SequenceNumberRanges(
+      SequenceNumberRange("fakeStream", "fakeShardId", "xxx", "yyy"))
+    val blockId1 = StreamBlockId(kinesisStream.id, 123)
+    val blockInfo1 = ReceivedBlockInfo(
+      0, None, Some(seqNumRanges1), new BlockManagerBasedStoreResult(blockId1, None))
+
+    val seqNumRanges2 = SequenceNumberRanges(
+      SequenceNumberRange("fakeStream", "fakeShardId", "aaa", "bbb"))
+    val blockId2 = StreamBlockId(kinesisStream.id, 345)
+    val blockInfo2 = ReceivedBlockInfo(
+      0, None, Some(seqNumRanges2), new BlockManagerBasedStoreResult(blockId2, None))
+
+    // Verify that the generated KinesisBackedBlockRDD has the all the right information
+    val blockInfos = Seq(blockInfo1, blockInfo2)
+    val nonEmptyRDD = kinesisStream.createBlockRDD(time, blockInfos)
+    nonEmptyRDD shouldBe a [KinesisBackedBlockRDD]
+    val kinesisRDD = nonEmptyRDD.asInstanceOf[KinesisBackedBlockRDD]
+    assert(kinesisRDD.regionName === dummyRegionName)
+    assert(kinesisRDD.endpointUrl === dummyEndpointUrl)
+    assert(kinesisRDD.retryTimeoutMs === batchDuration.milliseconds)
+    assert(kinesisRDD.awsCredentialsOption ===
+      Some(SerializableAWSCredentials(dummyAWSAccessKey, dummyAWSSecretKey)))
+    assert(nonEmptyRDD.partitions.size === blockInfos.size)
+    nonEmptyRDD.partitions.foreach { _ shouldBe a [KinesisBackedBlockRDDPartition] }
+    val partitions = nonEmptyRDD.partitions.map {
+      _.asInstanceOf[KinesisBackedBlockRDDPartition] }.toSeq
+    assert(partitions.map { _.seqNumberRanges } === Seq(seqNumRanges1, seqNumRanges2))
+    assert(partitions.map { _.blockId } === Seq(blockId1, blockId2))
+    assert(partitions.forall { _.isBlockIdValid === true })
+
+    // Verify that KinesisBackedBlockRDD is generated even when there are no blocks
+    val emptyRDD = kinesisStream.createBlockRDD(time, Seq.empty)
+    emptyRDD shouldBe a [KinesisBackedBlockRDD]
+    emptyRDD.partitions shouldBe empty
+
+    // Verify that the KinesisBackedBlockRDD has isBlockValid = false when blocks are invalid
+    blockInfos.foreach { _.setBlockIdInvalid() }
+    kinesisStream.createBlockRDD(time, blockInfos).partitions.foreach { partition =>
+      assert(partition.asInstanceOf[KinesisBackedBlockRDDPartition].isBlockIdValid === false)
+    }
   }
 
 
@@ -84,32 +171,91 @@ class KinesisStreamSuite extends KinesisFunSuite
    * and you have to set the system environment variable RUN_KINESIS_TESTS=1 .
    */
   testIfEnabled("basic operation") {
-    val kinesisTestUtils = new KinesisTestUtils()
-    try {
-      kinesisTestUtils.createStream()
-      ssc = new StreamingContext(sc, Seconds(1))
-      val awsCredentials = KinesisTestUtils.getAWSCredentials()
-      val stream = KinesisUtils.createStream(ssc, kinesisAppName, kinesisTestUtils.streamName,
-        kinesisTestUtils.endpointUrl, kinesisTestUtils.regionName, InitialPositionInStream.LATEST,
-        Seconds(10), StorageLevel.MEMORY_ONLY,
-        awsCredentials.getAWSAccessKeyId, awsCredentials.getAWSSecretKey)
-
-      val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
-      stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>
-        collected ++= rdd.collect()
-        logInfo("Collected = " + rdd.collect().toSeq.mkString(", "))
-      }
-      ssc.start()
+    val awsCredentials = KinesisTestUtils.getAWSCredentials()
+    val stream = KinesisUtils.createStream(ssc, appName, testUtils.streamName,
+      testUtils.endpointUrl, testUtils.regionName, InitialPositionInStream.LATEST,
+      Seconds(10), StorageLevel.MEMORY_ONLY,
+      awsCredentials.getAWSAccessKeyId, awsCredentials.getAWSSecretKey)
 
-      val testData = 1 to 10
-      eventually(timeout(120 seconds), interval(10 second)) {
-        kinesisTestUtils.pushData(testData)
-        assert(collected === testData.toSet, "\nData received does not match data sent")
+    val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
+    stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>
+      collected ++= rdd.collect()
+      logInfo("Collected = " + rdd.collect().toSeq.mkString(", "))
+    }
+    ssc.start()
+
+    val testData = 1 to 10
+    eventually(timeout(120 seconds), interval(10 second)) {
+      testUtils.pushData(testData)
+      assert(collected === testData.toSet, "\nData received does not match data sent")
+    }
+    ssc.stop(stopSparkContext = false)
+  }
+
+  testIfEnabled("failure recovery") {
+    val sparkConf = new SparkConf().setMaster("local[4]").setAppName(this.getClass.getSimpleName)
+    val checkpointDir = Utils.createTempDir().getAbsolutePath
+
+    ssc = new StreamingContext(sc, Milliseconds(1000))
+    ssc.checkpoint(checkpointDir)
+
+    val awsCredentials = KinesisTestUtils.getAWSCredentials()
+    val collectedData = new mutable.HashMap[Time, (Array[SequenceNumberRanges], Seq[Int])]
+      with mutable.SynchronizedMap[Time, (Array[SequenceNumberRanges], Seq[Int])]
+
+    val kinesisStream = KinesisUtils.createStream(ssc, appName, testUtils.streamName,
+      testUtils.endpointUrl, testUtils.regionName, InitialPositionInStream.LATEST,
+      Seconds(10), StorageLevel.MEMORY_ONLY,
+      awsCredentials.getAWSAccessKeyId, awsCredentials.getAWSSecretKey)
+
+    // Verify that the generated RDDs are KinesisBackedBlockRDDs, and collect the data in each batch
+    kinesisStream.foreachRDD((rdd: RDD[Array[Byte]], time: Time) => {
+      val kRdd = rdd.asInstanceOf[KinesisBackedBlockRDD]
+      val data = rdd.map { bytes => new String(bytes).toInt }.collect().toSeq
+      collectedData(time) = (kRdd.arrayOfseqNumberRanges, data)
+    })
+
+    ssc.remember(Minutes(60)) // remember all the batches so that they are all saved in checkpoint
+    ssc.start()
+
+    def numBatchesWithData: Int = collectedData.count(_._2._2.nonEmpty)
+
+    def isCheckpointPresent: Boolean = Checkpoint.getCheckpointFiles(checkpointDir).nonEmpty
+
+    // Run until there are at least 10 batches with some data in them
+    // If this times out because numBatchesWithData is empty, then its likely that foreachRDD
+    // function failed with exceptions, and nothing got added to `collectedData`
+    eventually(timeout(2 minutes), interval(1 seconds)) {
+      testUtils.pushData(1 to 5)
+      assert(isCheckpointPresent && numBatchesWithData > 10)
+    }
+    ssc.stop(stopSparkContext = true)  // stop the SparkContext so that the blocks are not reused
+
+    // Restart the context from checkpoint and verify whether the
+    logInfo("Restarting from checkpoint")
+    ssc = new StreamingContext(checkpointDir)
+    ssc.start()
+    val recoveredKinesisStream = ssc.graph.getInputStreams().head
+
+    // Verify that the recomputed RDDs are KinesisBackedBlockRDDs with the same sequence ranges
+    // and return the same data
+    val times = collectedData.keySet
+    times.foreach { time =>
+      val (arrayOfSeqNumRanges, data) = collectedData(time)
+      val rdd = recoveredKinesisStream.getOrCompute(time).get.asInstanceOf[RDD[Array[Byte]]]
+      rdd shouldBe a [KinesisBackedBlockRDD]
+
+      // Verify the recovered sequence ranges
+      val kRdd = rdd.asInstanceOf[KinesisBackedBlockRDD]
+      assert(kRdd.arrayOfseqNumberRanges.size === arrayOfSeqNumRanges.size)
+      arrayOfSeqNumRanges.zip(kRdd.arrayOfseqNumberRanges).foreach { case (expected, found) =>
+        assert(expected.ranges.toSeq === found.ranges.toSeq)
       }
-      ssc.stop()
-    } finally {
-      kinesisTestUtils.deleteStream()
-      kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
+
+      // Verify the recovered data
+      assert(rdd.map { bytes => new String(bytes).toInt }.collect().toSeq === data)
     }
+    ssc.stop()
   }
+
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index 670ef8d296a0b..a15800917c6f4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -21,12 +21,12 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.storage.BlockId
-import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
 import org.apache.spark.streaming.receiver.Receiver
-import org.apache.spark.streaming.scheduler.{RateController, StreamInputInfo}
 import org.apache.spark.streaming.scheduler.rate.RateEstimator
+import org.apache.spark.streaming.scheduler.{ReceivedBlockInfo, RateController, StreamInputInfo}
 import org.apache.spark.streaming.util.WriteAheadLogUtils
+import org.apache.spark.streaming.{StreamingContext, Time}
 
 /**
  * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]]
@@ -79,48 +79,55 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
         // for this batch
         val receiverTracker = ssc.scheduler.receiverTracker
         val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)
-        val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
 
         // Register the input blocks information into InputInfoTracker
         val inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum)
         ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
 
-        if (blockInfos.nonEmpty) {
-          // Are WAL record handles present with all the blocks
-          val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }
+        // Create the BlockRDD
+        createBlockRDD(validTime, blockInfos)
+      }
+    }
+    Some(blockRDD)
+  }
 
-          if (areWALRecordHandlesPresent) {
-            // If all the blocks have WAL record handle, then create a WALBackedBlockRDD
-            val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
-            val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray
-            new WriteAheadLogBackedBlockRDD[T](
-              ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)
-          } else {
-            // Else, create a BlockRDD. However, if there are some blocks with WAL info but not
-            // others then that is unexpected and log a warning accordingly.
-            if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {
-              if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
-                logError("Some blocks do not have Write Ahead Log information; " +
-                  "this is unexpected and data may not be recoverable after driver failures")
-              } else {
-                logWarning("Some blocks have Write Ahead Log information; this is unexpected")
-              }
-            }
-            new BlockRDD[T](ssc.sc, blockIds)
-          }
-        } else {
-          // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD
-          // according to the configuration
+  private[streaming] def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[T] = {
+
+    if (blockInfos.nonEmpty) {
+      val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
+
+      // Are WAL record handles present with all the blocks
+      val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }
+
+      if (areWALRecordHandlesPresent) {
+        // If all the blocks have WAL record handle, then create a WALBackedBlockRDD
+        val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
+        val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray
+        new WriteAheadLogBackedBlockRDD[T](
+          ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)
+      } else {
+        // Else, create a BlockRDD. However, if there are some blocks with WAL info but not
+        // others then that is unexpected and log a warning accordingly.
+        if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {
           if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
-            new WriteAheadLogBackedBlockRDD[T](
-              ssc.sparkContext, Array.empty, Array.empty, Array.empty)
+            logError("Some blocks do not have Write Ahead Log information; " +
+              "this is unexpected and data may not be recoverable after driver failures")
           } else {
-            new BlockRDD[T](ssc.sc, Array.empty)
+            logWarning("Some blocks have Write Ahead Log information; this is unexpected")
           }
         }
+        new BlockRDD[T](ssc.sc, blockIds)
+      }
+    } else {
+      // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD
+      // according to the configuration
+      if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
+        new WriteAheadLogBackedBlockRDD[T](
+          ssc.sparkContext, Array.empty, Array.empty, Array.empty)
+      } else {
+        new BlockRDD[T](ssc.sc, Array.empty)
       }
     }
-    Some(blockRDD)
   }
 
   /**

From 1d1a76c8c56148beec70dd14e4fdeb86de65018a Mon Sep 17 00:00:00 2001
From: Emiliano Leporati <emiliano.leporati@gmail.com>
Date: Wed, 5 Aug 2015 00:42:08 -0700
Subject: [PATCH 0852/1454] [SPARK-9581][SQL] Add unit test for JSON UDT

This brings #7416 up-to-date by drubbo.

Author: Emiliano Leporati <emiliano.leporati@gmail.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #7917 from rxin/udt-json-test and squashes the following commits:

93e3954 [Reynold Xin] Fix test.
7035308 [Reynold Xin] Merge pull request #7416 from drubbo/master
b5bcd94 [Emiliano Leporati] removed unneded case in MyDenseVector::equals
508a399 [Emiliano Leporati] Merge remote branch 'upstream/master'
7569e42 [Emiliano Leporati] using checkAnswer
62daccd [Emiliano Leporati] added coverage for UDTs in JSON RDDs
---
 .../apache/spark/sql/json/JacksonParser.scala |  2 +-
 .../spark/sql/UserDefinedTypeSuite.scala      | 20 +++++++++++++++++++
 2 files changed, 21 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index f1a66c84fc96a..b8fd3b9cc150e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -125,7 +125,7 @@ private[sql] object JacksonParser {
         convertMap(factory, parser, kt)
 
       case (_, udt: UserDefinedType[_]) =>
-        udt.deserialize(convertField(factory, parser, udt.sqlType))
+        convertField(factory, parser, udt.sqlType)
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index f29935224e5b6..9181222f6922b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -138,4 +138,24 @@ class UserDefinedTypeSuite extends QueryTest {
     val actual = openHashSetUDT.deserialize(openHashSetUDT.serialize(set))
     assert(actual.iterator.toSet === set.iterator.toSet)
   }
+
+  test("UDTs with JSON") {
+    val data = Seq(
+      "{\"id\":1,\"vec\":[1.1,2.2,3.3,4.4]}",
+      "{\"id\":2,\"vec\":[2.25,4.5,8.75]}"
+    )
+    val schema = StructType(Seq(
+      StructField("id", IntegerType, false),
+      StructField("vec", new MyDenseVectorUDT, false)
+    ))
+
+    val stringRDD = ctx.sparkContext.parallelize(data)
+    val jsonRDD = ctx.read.schema(schema).json(stringRDD)
+    checkAnswer(
+      jsonRDD,
+      Row(1, new MyDenseVector(Array(1.1, 2.2, 3.3, 4.4))) ::
+        Row(2, new MyDenseVector(Array(2.25, 4.5, 8.75))) ::
+        Nil
+    )
+  }
 }

From d8ef538e56645ebda29a64f1456822aad92c2bde Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 00:44:31 -0700
Subject: [PATCH 0853/1454] Closes #7917


From 6d8a6e4161176e391514153d7535da14b52194be Mon Sep 17 00:00:00 2001
From: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Date: Wed, 5 Aug 2015 00:54:31 -0700
Subject: [PATCH 0854/1454] [SPARK-9360] [SQL] Support BinaryType in
 PrefixComparators for UnsafeExternalSort

The current implementation of UnsafeExternalSort uses NoOpPrefixComparator for binary-typed data.
So, we need to add BinaryPrefixComparator in PrefixComparators.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #7676 from maropu/BinaryTypePrefixComparator and squashes the following commits:

fe6f31b [Takeshi YAMAMURO] Apply comments
d943c04 [Takeshi YAMAMURO] Add a codegen'd entry for BinaryType in SortPrefix
ecf3ac5 [Takeshi YAMAMURO] Support BinaryType in PrefixComparator
---
 .../unsafe/sort/PrefixComparators.java        | 35 +++++++++++++++++
 .../unsafe/sort/PrefixComparatorsSuite.scala  | 38 +++++++++++++++++++
 .../sql/catalyst/expressions/SortOrder.scala  |  3 ++
 .../spark/sql/execution/SortPrefixUtils.scala |  2 +
 4 files changed, 78 insertions(+)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index 4d7e5b3dfba6e..b5f661c0d5481 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -20,6 +20,7 @@
 import com.google.common.primitives.UnsignedLongs;
 
 import org.apache.spark.annotation.Private;
+import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.types.UTF8String;
 import org.apache.spark.util.Utils;
 
@@ -29,6 +30,8 @@ private PrefixComparators() {}
 
   public static final StringPrefixComparator STRING = new StringPrefixComparator();
   public static final StringPrefixComparatorDesc STRING_DESC = new StringPrefixComparatorDesc();
+  public static final BinaryPrefixComparator BINARY = new BinaryPrefixComparator();
+  public static final BinaryPrefixComparatorDesc BINARY_DESC = new BinaryPrefixComparatorDesc();
   public static final LongPrefixComparator LONG = new LongPrefixComparator();
   public static final LongPrefixComparatorDesc LONG_DESC = new LongPrefixComparatorDesc();
   public static final DoublePrefixComparator DOUBLE = new DoublePrefixComparator();
@@ -52,6 +55,38 @@ public int compare(long bPrefix, long aPrefix) {
     }
   }
 
+  public static final class BinaryPrefixComparator extends PrefixComparator {
+    @Override
+    public int compare(long aPrefix, long bPrefix) {
+      return UnsignedLongs.compare(aPrefix, bPrefix);
+    }
+
+    public static long computePrefix(byte[] bytes) {
+      if (bytes == null) {
+        return 0L;
+      } else {
+        /**
+         * TODO: If a wrapper for BinaryType is created (SPARK-8786),
+         * these codes below will be in the wrapper class.
+         */
+        final int minLen = Math.min(bytes.length, 8);
+        long p = 0;
+        for (int i = 0; i < minLen; ++i) {
+          p |= (128L + PlatformDependent.UNSAFE.getByte(bytes, BYTE_ARRAY_OFFSET + i))
+              << (56 - 8 * i);
+        }
+        return p;
+      }
+    }
+  }
+
+  public static final class BinaryPrefixComparatorDesc extends PrefixComparator {
+    @Override
+    public int compare(long bPrefix, long aPrefix) {
+      return UnsignedLongs.compare(aPrefix, bPrefix);
+    }
+  }
+
   public static final class LongPrefixComparator extends PrefixComparator {
     @Override
     public int compare(long a, long b) {
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
index 26a2e96edaaa2..0326ed70b5edb 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -55,6 +55,44 @@ class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
     forAll { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
   }
 
+  test("Binary prefix comparator") {
+
+     def compareBinary(x: Array[Byte], y: Array[Byte]): Int = {
+      for (i <- 0 until x.length; if i < y.length) {
+        val res = x(i).compare(y(i))
+        if (res != 0) return res
+      }
+      x.length - y.length
+    }
+
+    def testPrefixComparison(x: Array[Byte], y: Array[Byte]): Unit = {
+      val s1Prefix = PrefixComparators.BinaryPrefixComparator.computePrefix(x)
+      val s2Prefix = PrefixComparators.BinaryPrefixComparator.computePrefix(y)
+      val prefixComparisonResult =
+        PrefixComparators.BINARY.compare(s1Prefix, s2Prefix)
+      assert(
+        (prefixComparisonResult == 0) ||
+        (prefixComparisonResult < 0 && compareBinary(x, y) < 0) ||
+        (prefixComparisonResult > 0 && compareBinary(x, y) > 0))
+    }
+
+    // scalastyle:off
+    val regressionTests = Table(
+      ("s1", "s2"),
+      ("abc", "世界"),
+      ("你好", "世界"),
+      ("你好123", "你好122")
+    )
+    // scalastyle:on
+
+    forAll (regressionTests) { (s1: String, s2: String) =>
+      testPrefixComparison(s1.getBytes("UTF-8"), s2.getBytes("UTF-8"))
+    }
+    forAll { (s1: String, s2: String) =>
+      testPrefixComparison(s1.getBytes("UTF-8"), s2.getBytes("UTF-8"))
+    }
+  }
+
   test("double prefix comparator handles NaNs properly") {
     val nan1: Double = java.lang.Double.longBitsToDouble(0x7ff0000000000001L)
     val nan2: Double = java.lang.Double.longBitsToDouble(0x7fffffffffffffffL)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index f6a872ba446eb..98e029035ab6f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -21,6 +21,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types._
+import org.apache.spark.util.collection.unsafe.sort.PrefixComparators.BinaryPrefixComparator
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparators.DoublePrefixComparator
 
 abstract sealed class SortDirection
@@ -63,6 +64,7 @@ case class SortPrefix(child: SortOrder) extends UnaryExpression {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val childCode = child.child.gen(ctx)
     val input = childCode.primitive
+    val BinaryPrefixCmp = classOf[BinaryPrefixComparator].getName
     val DoublePrefixCmp = classOf[DoublePrefixComparator].getName
 
     val (nullValue: Long, prefixCode: String) = child.child.dataType match {
@@ -76,6 +78,7 @@ case class SortPrefix(child: SortOrder) extends UnaryExpression {
         (DoublePrefixComparator.computePrefix(Double.NegativeInfinity),
           s"$DoublePrefixCmp.computePrefix((double)$input)")
       case StringType => (0L, s"$input.getPrefix()")
+      case BinaryType => (0L, s"$BinaryPrefixCmp.computePrefix($input)")
       case dt: DecimalType if dt.precision - dt.scale <= Decimal.MAX_LONG_DIGITS =>
         val prefix = if (dt.precision <= Decimal.MAX_LONG_DIGITS) {
           s"$input.toUnscaledLong()"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 49adf215379cf..e17b50edc62dd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -38,6 +38,8 @@ object SortPrefixUtils {
     sortOrder.dataType match {
       case StringType =>
         if (sortOrder.isAscending) PrefixComparators.STRING else PrefixComparators.STRING_DESC
+      case BinaryType =>
+        if (sortOrder.isAscending) PrefixComparators.BINARY else PrefixComparators.BINARY_DESC
       case BooleanType | ByteType | ShortType | IntegerType | LongType | DateType | TimestampType =>
         if (sortOrder.isAscending) PrefixComparators.LONG else PrefixComparators.LONG_DESC
       case dt: DecimalType if dt.precision - dt.scale <= Decimal.MAX_LONG_DIGITS =>

From 1bf608b5ef1e6e2ae4325e13c2bd5e34db62450f Mon Sep 17 00:00:00 2001
From: Namit Katariya <katariya.namit@gmail.com>
Date: Wed, 5 Aug 2015 01:07:33 -0700
Subject: [PATCH 0855/1454] [SPARK-9601] [DOCS] Fix JavaPairDStream signature
 for stream-stream and windowed join in streaming guide doc

Author: Namit Katariya <katariya.namit@gmail.com>

Closes #7935 from namitk/SPARK-9601 and squashes the following commits:

03b5784 [Namit Katariya] [SPARK-9601] Fix signature of JavaPairDStream for stream-stream and windowed join in streaming guide doc
---
 docs/streaming-programming-guide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 4663b3f14c527..dbfdb619f89e2 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1141,7 +1141,7 @@ val joinedStream = stream1.join(stream2)
 {% highlight java %}
 JavaPairDStream<String, String> stream1 = ...
 JavaPairDStream<String, String> stream2 = ...
-JavaPairDStream<String, String> joinedStream = stream1.join(stream2);
+JavaPairDStream<String, Tuple2<String, String>> joinedStream = stream1.join(stream2);
 {% endhighlight %}
 </div>
 <div data-lang="python" markdown="1">
@@ -1166,7 +1166,7 @@ val joinedStream = windowedStream1.join(windowedStream2)
 {% highlight java %}
 JavaPairDStream<String, String> windowedStream1 = stream1.window(Durations.seconds(20));
 JavaPairDStream<String, String> windowedStream2 = stream2.window(Durations.minutes(1));
-JavaPairDStream<String, String> joinedStream = windowedStream1.join(windowedStream2);
+JavaPairDStream<String, Tuple2<String, String>> joinedStream = windowedStream1.join(windowedStream2);
 {% endhighlight %}
 </div>
 <div data-lang="python" markdown="1">

From 1b0317f64cfe99ff70580eeb99753cd0d31f849a Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 5 Aug 2015 01:51:22 -0700
Subject: [PATCH 0856/1454] [SPARK-8861][SPARK-8862][SQL] Add basic
 instrumentation to each SparkPlan operator and add a new SQL tab

This PR includes the following changes:

### SPARK-8862: Add basic instrumentation to each SparkPlan operator

A SparkPlan can override `def accumulators: Map[String, Accumulator[_]]` to expose its metrics that can be displayed in UI. The UI will use them to track the updates and show them in the web page in real-time.

### SparkSQLExecution and SQLSparkListener

`SparkSQLExecution.withNewExecutionId` will set `spark.sql.execution.id` to the local properties so that we can use it to track all jobs that belong to the same query.

SQLSparkListener is a listener to track all accumulator updates of all tasks for a query. It receives them from heartbeats can the UI can query them in real-time.

When running a query, `SQLSparkListener.onExecutionStart` will be called. When a query is finished,  `SQLSparkListener.onExecutionEnd` will be called. And the Spark jobs with the same execution id will be tracked and stored with this query.

`SQLSparkListener` has to store all accumulator updates for tasks separately. When a task fails and starts to retry, we need to drop the old accumulator updates. Because we can not revert our changes to an accumulator, we have to maintain these accumulator updates by ourselves so as to drop accumulator updates for a failed task.

### SPARK-8862: A new SQL tab
Includes two pages:
#### A page for all DataFrame/SQL queries
It will show the running, completed and failed queries in 3 tables. It also displays the jobs and their links for a query in each row.
#### A detail page for a DataFrame/SQL query
In this page, it also shows the SparkPlan metrics in real-time. Run a long-running query, such as
```
val testData = sc.parallelize((1 to 1000000).map(i => (i, i.toString))).toDF()
testData.select($"_1").filter($"_1" < 1000).foreach(_ => Thread.sleep(60))
```
and you will see the metrics keep updating in real-time.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7774)
<!-- Reviewable:end -->

Author: zsxwing <zsxwing@gmail.com>

Closes #7774 from zsxwing/sql-ui and squashes the following commits:

5a2bc99 [zsxwing] Remove UISeleniumSuite and its dependency
57d4cd2 [zsxwing] Use VisibleForTesting annotation
cc1c736 [zsxwing] Add SparkPlan.trackNumOfRowsEnabled to make subclasses easy to track the number of rows; fix the issue that the "save" action cannot collect metrics
3771ab0 [zsxwing] Register SQL metrics accmulators
3a101c0 [zsxwing] Change prepareCalled's type to AtomicBoolean for thread-safety
b8d5605 [zsxwing] Make prepare idempotent; call children's prepare in SparkPlan.prepare; change doPrepare to def
4ed11a1 [zsxwing] var -> val
332639c [zsxwing] Ignore UISeleniumSuite and SQLListenerSuite."no memory leak" because of SPARK-9580
bb52359 [zsxwing] Address other commens in SQLListener
c4d0f5d [zsxwing] Move newPredicate out of the iterator loop
957473c [zsxwing] Move STATIC_RESOURCE_DIR to object SQLTab
7ab4816 [zsxwing] Make SparkPlan accumulator API private[sql]
dae195e [zsxwing] Fix the code style and comments
3a66207 [zsxwing] Ignore irrelevant accumulators
b8484a1 [zsxwing] Merge branch 'master' into sql-ui
9406592 [zsxwing] Implement the SparkPlan viz
4ebce68 [zsxwing] Add SparkPlan.prepare to support BroadcastHashJoin to run background work in parallel
ca1811f [zsxwing] Merge branch 'master' into sql-ui
fef6fc6 [zsxwing] Fix a corner case
25f335c [zsxwing] Fix the code style
6eae828 [zsxwing] SQLSparkListener -> SQLListener; SparkSQLExecutionUIData -> SQLExecutionUIData; SparkSQLExecution -> SQLExecution
822af75 [zsxwing] Add SQLSparkListenerSuite and fix the issue about onExecutionEnd and onJobEnd
6be626f [zsxwing] Add UISeleniumSuite to test UI
d02a24d [zsxwing] Make ExecutionPage private
23abf73 [zsxwing] [SPARK-8862][SPARK-8862][SQL] Add basic instrumentation to each SparkPlan operator and add a new SQL tab
---
 .../scala/org/apache/spark/Accumulators.scala |  15 +-
 .../scala/org/apache/spark/SparkContext.scala |  15 +
 .../org/apache/spark/executor/Executor.scala  |   2 +
 .../spark/sql/ui/static/spark-sql-viz.css     |  37 ++
 .../spark/sql/ui/static/spark-sql-viz.js      | 160 ++++++++
 .../org/apache/spark/sql/DataFrame.scala      |  29 +-
 .../org/apache/spark/sql/SQLContext.scala     |   6 +
 .../spark/sql/execution/ExistingRDD.scala     |   3 +
 .../spark/sql/execution/LocalTableScan.scala  |   2 +
 .../spark/sql/execution/SQLExecution.scala    | 105 ++++++
 .../spark/sql/execution/SparkPlan.scala       |  62 ++-
 .../spark/sql/execution/basicOperators.scala  |  18 +-
 .../sql/execution/datasources/commands.scala  |  39 +-
 .../execution/joins/BroadcastHashJoin.scala   |  31 +-
 .../joins/BroadcastHashOuterJoin.scala        |  29 +-
 .../execution/joins/ShuffledHashJoin.scala    |   2 +
 .../sql/execution/joins/SortMergeJoin.scala   |   2 +
 .../spark/sql/ui/AllExecutionsPage.scala      | 238 ++++++++++++
 .../apache/spark/sql/ui/ExecutionPage.scala   | 127 +++++++
 .../org/apache/spark/sql/ui/SQLListener.scala | 354 ++++++++++++++++++
 .../org/apache/spark/sql/ui/SQLTab.scala      |  41 ++
 .../apache/spark/sql/ui/SparkPlanGraph.scala  | 118 ++++++
 .../apache/spark/sql/CachedTableSuite.scala   |   8 +-
 .../spark/sql/ui/SQLListenerSuite.scala       | 347 +++++++++++++++++
 24 files changed, 1735 insertions(+), 55 deletions(-)
 create mode 100644 sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
 create mode 100644 sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index b6a0119c696fd..462d5c96d480b 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -153,11 +153,12 @@ class Accumulable[R, T] private[spark] (
     value_ = zero
     deserialized = true
     // Automatically register the accumulator when it is deserialized with the task closure.
-    // Note that internal accumulators are deserialized before the TaskContext is created and
-    // are registered in the TaskContext constructor.
-    if (!isInternal) {
-      val taskContext = TaskContext.get()
-      assume(taskContext != null, "Task context was null when deserializing user accumulators")
+    //
+    // Note internal accumulators sent with task are deserialized before the TaskContext is created
+    // and are registered in the TaskContext constructor. Other internal accumulators, such SQL
+    // metrics, still need to register here.
+    val taskContext = TaskContext.get()
+    if (taskContext != null) {
       taskContext.registerAccumulator(this)
     }
   }
@@ -255,8 +256,8 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa
  * @tparam T result type
  */
 class Accumulator[T] private[spark] (
-    @transient initialValue: T,
-    param: AccumulatorParam[T],
+    @transient private[spark] val initialValue: T,
+    private[spark] val param: AccumulatorParam[T],
     name: Option[String],
     internal: Boolean)
   extends Accumulable[T, T](initialValue, param, name, internal) {
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 4380cf45cc1b0..0c0705325b169 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1238,6 +1238,21 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     acc
   }
 
+  /**
+   * Create an [[org.apache.spark.Accumulator]] variable of a given type, with a name for display
+   * in the Spark UI. Tasks can "add" values to the accumulator using the `+=` method. Only the
+   * driver can access the accumulator's `value`. The latest local value of such accumulator will be
+   * sent back to the driver via heartbeats.
+   *
+   * @tparam T type that can be added to the accumulator, must be thread safe
+   */
+  private[spark] def internalAccumulator[T](initialValue: T, name: String)(
+    implicit param: AccumulatorParam[T]): Accumulator[T] = {
+    val acc = new Accumulator(initialValue, param, Some(name), internal = true)
+    cleaner.foreach(_.registerAccumulatorForCleanup(acc))
+    acc
+  }
+
   /**
    * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values
    * with `+=`. Only the driver can access the accumuable's `value`.
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 7bc7fce7ae8dd..5d78a9dc8885e 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -249,6 +249,7 @@ private[spark] class Executor(
           m.setExecutorRunTime((taskFinish - taskStart) - task.executorDeserializeTime)
           m.setJvmGCTime(computeTotalGcTime() - startGCTime)
           m.setResultSerializationTime(afterSerialization - beforeSerialization)
+          m.updateAccumulators()
         }
 
         val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.orNull)
@@ -300,6 +301,7 @@ private[spark] class Executor(
             task.metrics.map { m =>
               m.setExecutorRunTime(System.currentTimeMillis() - taskStart)
               m.setJvmGCTime(computeTotalGcTime() - startGCTime)
+              m.updateAccumulators()
               m
             }
           }
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
new file mode 100644
index 0000000000000..ddd3a91dd8ef8
--- /dev/null
+++ b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#plan-viz-graph .label {
+  font-weight: normal;
+  text-shadow: none;
+}
+
+#plan-viz-graph svg g.node rect {
+  fill: #C3EBFF;
+  stroke: #3EC0FF;
+  stroke-width: 1px;
+}
+
+/* Hightlight the SparkPlan node name */
+#plan-viz-graph svg text :first-child {
+  font-weight: bold;
+}
+
+#plan-viz-graph svg path {
+  stroke: #444;
+  stroke-width: 1.5px;
+}
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
new file mode 100644
index 0000000000000..5161fcde669e7
--- /dev/null
+++ b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+var PlanVizConstants = {
+  svgMarginX: 16,
+  svgMarginY: 16
+};
+
+function renderPlanViz() {
+  var svg = planVizContainer().append("svg");
+  var metadata = d3.select("#plan-viz-metadata");
+  var dot = metadata.select(".dot-file").text().trim();
+  var graph = svg.append("g");
+
+  var g = graphlibDot.read(dot);
+  preprocessGraphLayout(g);
+  var renderer = new dagreD3.render();
+  renderer(graph, g);
+
+  // Round corners on rectangles
+  svg
+    .selectAll("rect")
+    .attr("rx", "5")
+    .attr("ry", "5");
+
+  var nodeSize = parseInt($("#plan-viz-metadata-size").text());
+  for (var i = 0; i < nodeSize; i++) {
+    setupTooltipForSparkPlanNode(i);
+  }
+
+  resizeSvg(svg)
+}
+
+/* -------------------- *
+ * | Helper functions | *
+ * -------------------- */
+
+function planVizContainer() { return d3.select("#plan-viz-graph"); }
+
+/*
+ * Set up the tooltip for a SparkPlan node using metadata. When the user moves the mouse on the
+ * node, it will display the details of this SparkPlan node in the right.
+ */
+function setupTooltipForSparkPlanNode(nodeId) {
+  var nodeTooltip = d3.select("#plan-meta-data-" + nodeId).text()
+  d3.select("svg g .node_" + nodeId)
+    .on('mouseover', function(d) {
+      var domNode = d3.select(this).node();
+      $(domNode).tooltip({
+        title: nodeTooltip, trigger: "manual", container: "body", placement: "right"
+      });
+      $(domNode).tooltip("show");
+    })
+    .on('mouseout', function(d) {
+      var domNode = d3.select(this).node();
+      $(domNode).tooltip("destroy");
+    })
+}
+
+/*
+ * Helper function to pre-process the graph layout.
+ * This step is necessary for certain styles that affect the positioning
+ * and sizes of graph elements, e.g. padding, font style, shape.
+ */
+function preprocessGraphLayout(g) {
+  var nodes = g.nodes();
+  for (var i = 0; i < nodes.length; i++) {
+      var node = g.node(nodes[i]);
+      node.padding = "5";
+  }
+  // Curve the edges
+  var edges = g.edges();
+  for (var j = 0; j < edges.length; j++) {
+    var edge = g.edge(edges[j]);
+    edge.lineInterpolate = "basis";
+  }
+}
+
+/*
+ * Helper function to size the SVG appropriately such that all elements are displayed.
+ * This assumes that all outermost elements are clusters (rectangles).
+ */
+function resizeSvg(svg) {
+  var allClusters = svg.selectAll("g rect")[0];
+  console.log(allClusters);
+  var startX = -PlanVizConstants.svgMarginX +
+    toFloat(d3.min(allClusters, function(e) {
+      console.log(e);
+      return getAbsolutePosition(d3.select(e)).x;
+    }));
+  var startY = -PlanVizConstants.svgMarginY +
+    toFloat(d3.min(allClusters, function(e) {
+      return getAbsolutePosition(d3.select(e)).y;
+    }));
+  var endX = PlanVizConstants.svgMarginX +
+    toFloat(d3.max(allClusters, function(e) {
+      var t = d3.select(e);
+      return getAbsolutePosition(t).x + toFloat(t.attr("width"));
+    }));
+  var endY = PlanVizConstants.svgMarginY +
+    toFloat(d3.max(allClusters, function(e) {
+      var t = d3.select(e);
+      return getAbsolutePosition(t).y + toFloat(t.attr("height"));
+    }));
+  var width = endX - startX;
+  var height = endY - startY;
+  svg.attr("viewBox", startX + " " + startY + " " + width + " " + height)
+     .attr("width", width)
+     .attr("height", height);
+}
+
+/* Helper function to convert attributes to numeric values. */
+function toFloat(f) {
+  if (f) {
+    return parseFloat(f.toString().replace(/px$/, ""));
+  } else {
+    return f;
+  }
+}
+
+/*
+ * Helper function to compute the absolute position of the specified element in our graph.
+ */
+function getAbsolutePosition(d3selection) {
+  if (d3selection.empty()) {
+    throw "Attempted to get absolute position of an empty selection.";
+  }
+  var obj = d3selection;
+  var _x = toFloat(obj.attr("x")) || 0;
+  var _y = toFloat(obj.attr("y")) || 0;
+  while (!obj.empty()) {
+    var transformText = obj.attr("transform");
+    if (transformText) {
+      var translate = d3.transform(transformText).translate;
+      _x += toFloat(translate[0]);
+      _y += toFloat(translate[1]);
+    }
+    // Climb upwards to find how our parents are translated
+    obj = d3.select(obj.node().parentNode);
+    // Stop when we've reached the graph container itself
+    if (obj.node() == planVizContainer().node()) {
+      break;
+    }
+  }
+  return { x: _x, y: _y };
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 3ea0f9ed3bddd..1f83567df88cc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -39,7 +39,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
-import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD}
+import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
 import org.apache.spark.sql.json.{JacksonGenerator, JSONRelation}
 import org.apache.spark.sql.sources.HadoopFsRelation
@@ -119,6 +119,9 @@ class DataFrame private[sql](
     @transient val sqlContext: SQLContext,
     @DeveloperApi @transient val queryExecution: SQLContext#QueryExecution) extends Serializable {
 
+  // Note for Spark contributors: if adding or updating any action in `DataFrame`, please make sure
+  // you wrap it with `withNewExecutionId` if this actions doesn't call other action.
+
   /**
    * A constructor that automatically analyzes the logical plan.
    *
@@ -1356,14 +1359,18 @@ class DataFrame private[sql](
    * @group rdd
    * @since 1.3.0
    */
-  def foreach(f: Row => Unit): Unit = rdd.foreach(f)
+  def foreach(f: Row => Unit): Unit = withNewExecutionId {
+    rdd.foreach(f)
+  }
 
   /**
    * Applies a function f to each partition of this [[DataFrame]].
    * @group rdd
    * @since 1.3.0
    */
-  def foreachPartition(f: Iterator[Row] => Unit): Unit = rdd.foreachPartition(f)
+  def foreachPartition(f: Iterator[Row] => Unit): Unit = withNewExecutionId {
+    rdd.foreachPartition(f)
+  }
 
   /**
    * Returns the first `n` rows in the [[DataFrame]].
@@ -1377,14 +1384,18 @@ class DataFrame private[sql](
    * @group action
    * @since 1.3.0
    */
-  def collect(): Array[Row] = queryExecution.executedPlan.executeCollect()
+  def collect(): Array[Row] = withNewExecutionId {
+    queryExecution.executedPlan.executeCollect()
+  }
 
   /**
    * Returns a Java list that contains all of [[Row]]s in this [[DataFrame]].
    * @group action
    * @since 1.3.0
    */
-  def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() : _*)
+  def collectAsList(): java.util.List[Row] = withNewExecutionId {
+    java.util.Arrays.asList(rdd.collect() : _*)
+  }
 
   /**
    * Returns the number of rows in the [[DataFrame]].
@@ -1863,6 +1874,14 @@ class DataFrame private[sql](
     write.mode(SaveMode.Append).insertInto(tableName)
   }
 
+  /**
+   * Wrap a DataFrame action to track all Spark jobs in the body so that we can connect them with
+   * an execution.
+   */
+  private[sql] def withNewExecutionId[T](body: => T): T = {
+    SQLExecution.withNewExecutionId(sqlContext, queryExecution)(body)
+  }
+
   ////////////////////////////////////////////////////////////////////////////
   ////////////////////////////////////////////////////////////////////////////
   // End of deprecated methods
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index dbb2a09846548..ffc2baf7a8826 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -43,6 +43,7 @@ import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.ui.{SQLListener, SQLTab}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
 
@@ -74,6 +75,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   protected[sql] def conf = currentSession().conf
 
+  // `listener` should be only used in the driver
+  @transient private[sql] val listener = new SQLListener(this)
+  sparkContext.addSparkListener(listener)
+  sparkContext.ui.foreach(new SQLTab(this, _))
+
   /**
    * Set Spark SQL configuration properties.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
index da27a753a710f..fbaa8e276ddb7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
@@ -96,6 +96,9 @@ private[sql] case class LogicalRDD(
 private[sql] case class PhysicalRDD(
     output: Seq[Attribute],
     rdd: RDD[InternalRow]) extends LeafNode {
+
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   protected override def doExecute(): RDD[InternalRow] = rdd
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
index 34e926e4582be..858dd85fd1fa6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
@@ -30,6 +30,8 @@ private[sql] case class LocalTableScan(
     output: Seq[Attribute],
     rows: Seq[InternalRow]) extends LeafNode {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   private lazy val rdd = sqlContext.sparkContext.parallelize(rows)
 
   protected override def doExecute(): RDD[InternalRow] = rdd
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
new file mode 100644
index 0000000000000..97f1323e97835
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import java.util.concurrent.atomic.AtomicLong
+
+import org.apache.spark.SparkContext
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.ui.SparkPlanGraph
+import org.apache.spark.util.Utils
+
+private[sql] object SQLExecution {
+
+  val EXECUTION_ID_KEY = "spark.sql.execution.id"
+
+  private val _nextExecutionId = new AtomicLong(0)
+
+  private def nextExecutionId: Long = _nextExecutionId.getAndIncrement
+
+  /**
+   * Wrap an action that will execute "queryExecution" to track all Spark jobs in the body so that
+   * we can connect them with an execution.
+   */
+  def withNewExecutionId[T](
+      sqlContext: SQLContext, queryExecution: SQLContext#QueryExecution)(body: => T): T = {
+    val sc = sqlContext.sparkContext
+    val oldExecutionId = sc.getLocalProperty(EXECUTION_ID_KEY)
+    if (oldExecutionId == null) {
+      val executionId = SQLExecution.nextExecutionId
+      sc.setLocalProperty(EXECUTION_ID_KEY, executionId.toString)
+      val r = try {
+        val callSite = Utils.getCallSite()
+        sqlContext.listener.onExecutionStart(
+          executionId,
+          callSite.shortForm,
+          callSite.longForm,
+          queryExecution.toString,
+          SparkPlanGraph(queryExecution.executedPlan),
+          System.currentTimeMillis())
+        try {
+          body
+        } finally {
+          // Ideally, we need to make sure onExecutionEnd happens after onJobStart and onJobEnd.
+          // However, onJobStart and onJobEnd run in the listener thread. Because we cannot add new
+          // SQL event types to SparkListener since it's a public API, we cannot guarantee that.
+          //
+          // SQLListener should handle the case that onExecutionEnd happens before onJobEnd.
+          //
+          // The worst case is onExecutionEnd may happen before onJobStart when the listener thread
+          // is very busy. If so, we cannot track the jobs for the execution. It seems acceptable.
+          sqlContext.listener.onExecutionEnd(executionId, System.currentTimeMillis())
+        }
+      } finally {
+        sc.setLocalProperty(EXECUTION_ID_KEY, null)
+      }
+      r
+    } else {
+      // Don't support nested `withNewExecutionId`. This is an example of the nested
+      // `withNewExecutionId`:
+      //
+      // class DataFrame {
+      //   def foo: T = withNewExecutionId { something.createNewDataFrame().collect() }
+      // }
+      //
+      // Note: `collect` will call withNewExecutionId
+      // In this case, only the "executedPlan" for "collect" will be executed. The "executedPlan"
+      // for the outer DataFrame won't be executed. So it's meaningless to create a new Execution
+      // for the outer DataFrame. Even if we track it, since its "executedPlan" doesn't run,
+      // all accumulator metrics will be 0. It will confuse people if we show them in Web UI.
+      //
+      // A real case is the `DataFrame.count` method.
+      throw new IllegalArgumentException(s"$EXECUTION_ID_KEY is already set")
+    }
+  }
+
+  /**
+   * Wrap an action with a known executionId. When running a different action in a different
+   * thread from the original one, this method can be used to connect the Spark jobs in this action
+   * with the known executionId, e.g., `BroadcastHashJoin.broadcastFuture`.
+   */
+  def withExecutionId[T](sc: SparkContext, executionId: String)(body: => T): T = {
+    val oldExecutionId = sc.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+    try {
+      sc.setLocalProperty(SQLExecution.EXECUTION_ID_KEY, executionId)
+      body
+    } finally {
+      sc.setLocalProperty(SQLExecution.EXECUTION_ID_KEY, oldExecutionId)
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 50c27def8ea54..73b237fffece8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -17,9 +17,11 @@
 
 package org.apache.spark.sql.execution
 
+import java.util.concurrent.atomic.AtomicBoolean
+
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.Logging
+import org.apache.spark.{Accumulator, Logging}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.sql.SQLContext
@@ -59,12 +61,39 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     false
   }
 
+  /**
+   * Whether the "prepare" method is called.
+   */
+  private val prepareCalled = new AtomicBoolean(false)
+
   /** Overridden make copy also propogates sqlContext to copied plan. */
   override def makeCopy(newArgs: Array[AnyRef]): this.type = {
     SparkPlan.currentContext.set(sqlContext)
     super.makeCopy(newArgs)
   }
 
+  /**
+   * Whether track the number of rows output by this SparkPlan
+   */
+  protected[sql] def trackNumOfRowsEnabled: Boolean = false
+
+  private lazy val numOfRowsAccumulator = sparkContext.internalAccumulator(0L, "number of rows")
+
+  /**
+   * Return all accumulators containing metrics of this SparkPlan.
+   */
+  private[sql] def accumulators: Map[String, Accumulator[_]] = if (trackNumOfRowsEnabled) {
+      Map("numRows" -> numOfRowsAccumulator)
+    } else {
+      Map.empty
+    }
+
+  /**
+   * Return the accumulator according to the name.
+   */
+  private[sql] def accumulator[T](name: String): Accumulator[T] =
+    accumulators(name).asInstanceOf[Accumulator[T]]
+
   // TODO: Move to `DistributedPlan`
   /** Specifies how data is partitioned across different nodes in the cluster. */
   def outputPartitioning: Partitioning = UnknownPartitioning(0) // TODO: WRONG WIDTH!
@@ -110,10 +139,39 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
         "Operator will receive unsafe rows as input but cannot process unsafe rows")
     }
     RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
-      doExecute()
+      prepare()
+      if (trackNumOfRowsEnabled) {
+        val numRows = accumulator[Long]("numRows")
+        doExecute().map { row =>
+          numRows += 1
+          row
+        }
+      } else {
+        doExecute()
+      }
     }
   }
 
+  /**
+   * Prepare a SparkPlan for execution. It's idempotent.
+   */
+  final def prepare(): Unit = {
+    if (prepareCalled.compareAndSet(false, true)) {
+      doPrepare
+      children.foreach(_.prepare())
+    }
+  }
+
+  /**
+   * Overridden by concrete implementations of SparkPlan. It is guaranteed to run before any
+   * `execute` of SparkPlan. This is helpful if we want to set up some state before executing the
+   * query, e.g., `BroadcastHashJoin` uses it to broadcast asynchronously.
+   *
+   * Note: the prepare method has already walked down the tree, so the implementation doesn't need
+   * to call children's prepare methods.
+   */
+  protected def doPrepare(): Unit = {}
+
   /**
    * Overridden by concrete implementations of SparkPlan.
    * Produces the result of the query as an RDD[InternalRow]
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 5a1b000e89875..477170297c2ac 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -81,8 +81,22 @@ case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan)
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
-  protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    iter.filter(newPredicate(condition, child.output))
+  private[sql] override lazy val accumulators = Map(
+    "numInputRows" -> sparkContext.internalAccumulator(0L, "number of input rows"),
+    "numOutputRows" -> sparkContext.internalAccumulator(0L, "number of output rows"))
+
+  protected override def doExecute(): RDD[InternalRow] = {
+    val numInputRows = accumulator[Long]("numInputRows")
+    val numOutputRows = accumulator[Long]("numOutputRows")
+    child.execute().mapPartitions { iter =>
+      val predicate = newPredicate(condition, child.output)
+      iter.filter { row =>
+        numInputRows += 1
+        val r = predicate(row)
+        if (r) numOutputRows += 1
+        r
+      }
+    }
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
index d551f386eee6e..cf199118287dc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
@@ -33,7 +33,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.execution.RunnableCommand
+import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StringType
 import org.apache.spark.util.SerializableConfiguration
@@ -122,25 +122,26 @@ private[sql] case class InsertIntoHadoopFsRelation(
 
       // We create a DataFrame by applying the schema of relation to the data to make sure.
       // We are writing data based on the expected schema,
-      val df = {
-        // For partitioned relation r, r.schema's column ordering can be different from the column
-        // ordering of data.logicalPlan (partition columns are all moved after data column). We
-        // need a Project to adjust the ordering, so that inside InsertIntoHadoopFsRelation, we can
-        // safely apply the schema of r.schema to the data.
-        val project = Project(
-          relation.schema.map(field => new UnresolvedAttribute(Seq(field.name))), query)
-
-        sqlContext.internalCreateDataFrame(
-          DataFrame(sqlContext, project).queryExecution.toRdd, relation.schema)
-      }
 
-      val partitionColumns = relation.partitionColumns.fieldNames
-      if (partitionColumns.isEmpty) {
-        insert(new DefaultWriterContainer(relation, job, isAppend), df)
-      } else {
-        val writerContainer = new DynamicPartitionWriterContainer(
-          relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME, isAppend)
-        insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns)
+      // For partitioned relation r, r.schema's column ordering can be different from the column
+      // ordering of data.logicalPlan (partition columns are all moved after data column). We
+      // need a Project to adjust the ordering, so that inside InsertIntoHadoopFsRelation, we can
+      // safely apply the schema of r.schema to the data.
+      val project = Project(
+        relation.schema.map(field => new UnresolvedAttribute(Seq(field.name))), query)
+
+      val queryExecution = DataFrame(sqlContext, project).queryExecution
+      SQLExecution.withNewExecutionId(sqlContext, queryExecution) {
+        val df = sqlContext.internalCreateDataFrame(queryExecution.toRdd, relation.schema)
+
+        val partitionColumns = relation.partitionColumns.fieldNames
+        if (partitionColumns.isEmpty) {
+          insert(new DefaultWriterContainer(relation, job, isAppend), df)
+        } else {
+          val writerContainer = new DynamicPartitionWriterContainer(
+            relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME, isAppend)
+          insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns)
+        }
       }
     }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index e73e2523a777f..ec1a148342fc6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -26,7 +26,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
 import org.apache.spark.util.ThreadUtils
 
 /**
@@ -45,6 +45,8 @@ case class BroadcastHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   val timeout: Duration = {
     val timeoutValue = sqlContext.conf.broadcastTimeout
     if (timeoutValue < 0) {
@@ -59,13 +61,28 @@ case class BroadcastHashJoin(
   override def requiredChildDistribution: Seq[Distribution] =
     UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
 
+  // Use lazy so that we won't do broadcast when calling explain but still cache the broadcast value
+  // for the same query.
   @transient
-  private val broadcastFuture = future {
-    // Note that we use .execute().collect() because we don't want to convert data to Scala types
-    val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.size)
-    sparkContext.broadcast(hashed)
-  }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
+  private lazy val broadcastFuture = {
+    // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
+    val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+    future {
+      // This will run in another thread. Set the execution id so that we can connect these jobs
+      // with the correct execution.
+      SQLExecution.withExecutionId(sparkContext, executionId) {
+        // Note that we use .execute().collect() because we don't want to convert data to Scala
+        // types
+        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
+        val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.size)
+        sparkContext.broadcast(hashed)
+      }
+    }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
+  }
+
+  protected override def doPrepare(): Unit = {
+    broadcastFuture
+  }
 
   protected override def doExecute(): RDD[InternalRow] = {
     val broadcastRelation = Await.result(broadcastFuture, timeout)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index c35e439cc9deb..e342fd914d321 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
 import org.apache.spark.util.ThreadUtils
 
 /**
@@ -60,13 +60,28 @@ case class BroadcastHashOuterJoin(
 
   override def outputPartitioning: Partitioning = streamedPlan.outputPartitioning
 
+  // Use lazy so that we won't do broadcast when calling explain but still cache the broadcast value
+  // for the same query.
   @transient
-  private val broadcastFuture = future {
-    // Note that we use .execute().collect() because we don't want to convert data to Scala types
-    val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
-    sparkContext.broadcast(hashed)
-  }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
+  private lazy val broadcastFuture = {
+    // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
+    val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+    future {
+      // This will run in another thread. Set the execution id so that we can connect these jobs
+      // with the correct execution.
+      SQLExecution.withExecutionId(sparkContext, executionId) {
+        // Note that we use .execute().collect() because we don't want to convert data to Scala
+        // types
+        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
+        val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
+        sparkContext.broadcast(hashed)
+      }
+    }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
+  }
+
+  protected override def doPrepare(): Unit = {
+    broadcastFuture
+  }
 
   override def doExecute(): RDD[InternalRow] = {
     val broadcastRelation = Await.result(broadcastFuture, timeout)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
index fc6efe87bceb5..c923dc837c449 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
@@ -38,6 +38,8 @@ case class ShuffledHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   override def outputPartitioning: Partitioning =
     PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 41be78afd37e6..eb595490fbf28 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -38,6 +38,8 @@ case class SortMergeJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   override def output: Seq[Attribute] = left.output ++ right.output
 
   override def outputPartitioning: Partitioning =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
new file mode 100644
index 0000000000000..727fc4b37fa48
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
@@ -0,0 +1,238 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.collection.mutable
+import scala.xml.Node
+
+import org.apache.commons.lang3.StringEscapeUtils
+
+import org.apache.spark.Logging
+import org.apache.spark.ui.{UIUtils, WebUIPage}
+
+private[ui] class AllExecutionsPage(parent: SQLTab) extends WebUIPage("") with Logging {
+
+  private val listener = parent.listener
+
+  override def render(request: HttpServletRequest): Seq[Node] = {
+    val currentTime = System.currentTimeMillis()
+    val content = listener.synchronized {
+      val _content = mutable.ListBuffer[Node]()
+      if (listener.getRunningExecutions.nonEmpty) {
+        _content ++=
+          new RunningExecutionTable(
+            parent, "Running Queries", currentTime,
+            listener.getRunningExecutions.sortBy(_.submissionTime).reverse).toNodeSeq
+      }
+      if (listener.getCompletedExecutions.nonEmpty) {
+        _content ++=
+          new CompletedExecutionTable(
+            parent, "Completed Queries", currentTime,
+            listener.getCompletedExecutions.sortBy(_.submissionTime).reverse).toNodeSeq
+      }
+      if (listener.getFailedExecutions.nonEmpty) {
+        _content ++=
+          new FailedExecutionTable(
+            parent, "Failed Queries", currentTime,
+            listener.getFailedExecutions.sortBy(_.submissionTime).reverse).toNodeSeq
+      }
+      _content
+    }
+    UIUtils.headerSparkPage("SQL", content, parent, Some(5000))
+  }
+}
+
+private[ui] abstract class ExecutionTable(
+    parent: SQLTab,
+    tableId: String,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData],
+    showRunningJobs: Boolean,
+    showSucceededJobs: Boolean,
+    showFailedJobs: Boolean) {
+
+  protected def baseHeader: Seq[String] = Seq(
+    "ID",
+    "Description",
+    "Submitted",
+    "Duration")
+
+  protected def header: Seq[String]
+
+  protected def row(currentTime: Long, executionUIData: SQLExecutionUIData): Seq[Node] = {
+    val submissionTime = executionUIData.submissionTime
+    val duration = executionUIData.completionTime.getOrElse(currentTime) - submissionTime
+
+    val runningJobs = executionUIData.runningJobs.map { jobId =>
+      <a href={jobURL(jobId)}>{jobId.toString}</a><br/>
+    }
+    val succeededJobs = executionUIData.succeededJobs.sorted.map { jobId =>
+      <a href={jobURL(jobId)}>{jobId.toString}</a><br/>
+    }
+    val failedJobs = executionUIData.failedJobs.sorted.map { jobId =>
+      <a href={jobURL(jobId)}>{jobId.toString}</a><br/>
+    }
+    <tr>
+      <td>
+        {executionUIData.executionId.toString}
+      </td>
+      <td>
+        {descriptionCell(executionUIData)}
+      </td>
+      <td sorttable_customkey={submissionTime.toString}>
+        {UIUtils.formatDate(submissionTime)}
+      </td>
+      <td sorttable_customkey={duration.toString}>
+        {UIUtils.formatDuration(duration)}
+      </td>
+      {if (showRunningJobs) {
+        <td>
+          {runningJobs}
+        </td>
+      }}
+      {if (showSucceededJobs) {
+        <td>
+          {succeededJobs}
+        </td>
+      }}
+      {if (showFailedJobs) {
+        <td>
+          {failedJobs}
+        </td>
+      }}
+      {detailCell(executionUIData.physicalPlanDescription)}
+    </tr>
+  }
+
+  private def descriptionCell(execution: SQLExecutionUIData): Seq[Node] = {
+    val details = if (execution.details.nonEmpty) {
+      <span onclick="this.parentNode.querySelector('.stage-details').classList.toggle('collapsed')"
+            class="expand-details">
+        +details
+      </span> ++
+      <div class="stage-details collapsed">
+        <pre>{execution.details}</pre>
+      </div>
+    } else {
+      Nil
+    }
+
+    val desc = {
+      <a href={executionURL(execution.executionId)}>{execution.description}</a>
+    }
+
+    <div>{desc} {details}</div>
+  }
+
+  private def detailCell(physicalPlan: String): Seq[Node] = {
+    val isMultiline = physicalPlan.indexOf('\n') >= 0
+    val summary = StringEscapeUtils.escapeHtml4(
+      if (isMultiline) {
+        physicalPlan.substring(0, physicalPlan.indexOf('\n'))
+      } else {
+        physicalPlan
+      })
+    val details = if (isMultiline) {
+      // scalastyle:off
+      <span onclick="this.parentNode.querySelector('.stacktrace-details').classList.toggle('collapsed')"
+            class="expand-details">
+        +details
+      </span> ++
+        <div class="stacktrace-details collapsed">
+          <pre>{physicalPlan}</pre>
+        </div>
+      // scalastyle:on
+    } else {
+      ""
+    }
+    <td>{summary}{details}</td>
+  }
+
+  def toNodeSeq: Seq[Node] = {
+    <div>
+      <h4>{tableName}</h4>
+      {UIUtils.listingTable[SQLExecutionUIData](
+        header, row(currentTime, _), executionUIDatas, id = Some(tableId))}
+    </div>
+  }
+
+  private def jobURL(jobId: Long): String =
+    "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
+
+  private def executionURL(executionID: Long): String =
+    "%s/sql/execution?id=%s".format(UIUtils.prependBaseUri(parent.basePath), executionID)
+}
+
+private[ui] class RunningExecutionTable(
+    parent: SQLTab,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData])
+  extends ExecutionTable(
+    parent,
+    "running-execution-table",
+    tableName,
+    currentTime,
+    executionUIDatas,
+    showRunningJobs = true,
+    showSucceededJobs = true,
+    showFailedJobs = true) {
+
+  override protected def header: Seq[String] =
+    baseHeader ++ Seq("Running Jobs", "Succeeded Jobs", "Failed Jobs", "Detail")
+}
+
+private[ui] class CompletedExecutionTable(
+    parent: SQLTab,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData])
+  extends ExecutionTable(
+    parent,
+    "completed-execution-table",
+    tableName,
+    currentTime,
+    executionUIDatas,
+    showRunningJobs = false,
+    showSucceededJobs = true,
+    showFailedJobs = false) {
+
+  override protected def header: Seq[String] = baseHeader ++ Seq("Jobs", "Detail")
+}
+
+private[ui] class FailedExecutionTable(
+    parent: SQLTab,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData])
+  extends ExecutionTable(
+    parent,
+    "failed-execution-table",
+    tableName,
+    currentTime,
+    executionUIDatas,
+    showRunningJobs = false,
+    showSucceededJobs = true,
+    showFailedJobs = true) {
+
+  override protected def header: Seq[String] =
+    baseHeader ++ Seq("Succeeded Jobs", "Failed Jobs", "Detail")
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
new file mode 100644
index 0000000000000..52ddf99e9266a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.{Node, Unparsed}
+
+import org.apache.commons.lang3.StringEscapeUtils
+
+import org.apache.spark.Logging
+import org.apache.spark.ui.{UIUtils, WebUIPage}
+
+private[sql] class ExecutionPage(parent: SQLTab) extends WebUIPage("execution") with Logging {
+
+  private val listener = parent.listener
+
+  override def render(request: HttpServletRequest): Seq[Node] = listener.synchronized {
+    val parameterExecutionId = request.getParameter("id")
+    require(parameterExecutionId != null && parameterExecutionId.nonEmpty,
+      "Missing execution id parameter")
+
+    val executionId = parameterExecutionId.toLong
+    val content = listener.getExecution(executionId).map { executionUIData =>
+      val currentTime = System.currentTimeMillis()
+      val duration =
+        executionUIData.completionTime.getOrElse(currentTime) - executionUIData.submissionTime
+
+      val summary =
+        <div>
+          <ul class="unstyled">
+            <li>
+              <strong>Submitted Time: </strong>{UIUtils.formatDate(executionUIData.submissionTime)}
+            </li>
+            <li>
+              <strong>Duration: </strong>{UIUtils.formatDuration(duration)}
+            </li>
+            {if (executionUIData.runningJobs.nonEmpty) {
+              <li>
+                <strong>Running Jobs: </strong>
+                {executionUIData.runningJobs.sorted.map { jobId =>
+                <a href={jobURL(jobId)}>{jobId.toString}</a><span>&nbsp;</span>
+              }}
+              </li>
+            }}
+            {if (executionUIData.succeededJobs.nonEmpty) {
+              <li>
+                <strong>Succeeded Jobs: </strong>
+                {executionUIData.succeededJobs.sorted.map { jobId =>
+                  <a href={jobURL(jobId)}>{jobId.toString}</a><span>&nbsp;</span>
+                }}
+              </li>
+            }}
+            {if (executionUIData.failedJobs.nonEmpty) {
+              <li>
+                <strong>Failed Jobs: </strong>
+                {executionUIData.failedJobs.sorted.map { jobId =>
+                  <a href={jobURL(jobId)}>{jobId.toString}</a><span>&nbsp;</span>
+                }}
+              </li>
+            }}
+            <li>
+              <strong>Detail: </strong><br/>
+              <pre>{executionUIData.physicalPlanDescription}</pre>
+            </li>
+          </ul>
+        </div>
+
+      val metrics = listener.getExecutionMetrics(executionId)
+
+      summary ++ planVisualization(metrics, executionUIData.physicalPlanGraph)
+    }.getOrElse {
+      <div>No information to display for Plan {executionId}</div>
+    }
+
+    UIUtils.headerSparkPage(s"Details for Query $executionId", content, parent, Some(5000))
+  }
+
+
+  private def planVisualizationResources: Seq[Node] = {
+    // scalastyle:off
+    <link rel="stylesheet" href={UIUtils.prependBaseUri("/static/sql/spark-sql-viz.css")} type="text/css"/>
+    <script src={UIUtils.prependBaseUri("/static/d3.min.js")}></script>
+    <script src={UIUtils.prependBaseUri("/static/dagre-d3.min.js")}></script>
+    <script src={UIUtils.prependBaseUri("/static/graphlib-dot.min.js")}></script>
+    <script src={UIUtils.prependBaseUri("/static/sql/spark-sql-viz.js")}></script>
+    // scalastyle:on
+  }
+
+  private def planVisualization(metrics: Map[Long, Any], graph: SparkPlanGraph): Seq[Node] = {
+    val metadata = graph.nodes.flatMap { node =>
+      val nodeId = s"plan-meta-data-${node.id}"
+      <div id={nodeId}>{node.desc}</div>
+    }
+
+    <div>
+      <div id="plan-viz-graph"></div>
+      <div id="plan-viz-metadata" style="display:none">
+        <div class="dot-file">
+          {graph.makeDotFile(metrics)}
+        </div>
+        <div id="plan-viz-metadata-size">{graph.nodes.size.toString}</div>
+        {metadata}
+      </div>
+      {planVisualizationResources}
+      <script>$(function(){{ renderPlanViz(); }})</script>
+    </div>
+  }
+
+  private def jobURL(jobId: Long): String =
+    "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
new file mode 100644
index 0000000000000..e7b1dd1ffac68
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
@@ -0,0 +1,354 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import scala.collection.mutable
+
+import com.google.common.annotations.VisibleForTesting
+
+import org.apache.spark.{AccumulatorParam, JobExecutionStatus, Logging}
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler._
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.execution.SQLExecution
+
+private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener with Logging {
+
+  private val retainedExecutions =
+    sqlContext.sparkContext.conf.getInt("spark.sql.ui.retainedExecutions", 1000)
+
+  private val activeExecutions = mutable.HashMap[Long, SQLExecutionUIData]()
+
+  // Old data in the following fields must be removed in "trimExecutionsIfNecessary".
+  // If adding new fields, make sure "trimExecutionsIfNecessary" can clean up old data
+
+  // VisibleForTesting
+  private val _executionIdToData = mutable.HashMap[Long, SQLExecutionUIData]()
+
+  /**
+   * Maintain the relation between job id and execution id so that we can get the execution id in
+   * the "onJobEnd" method.
+   */
+  private val _jobIdToExecutionId = mutable.HashMap[Long, Long]()
+
+  private val _stageIdToStageMetrics = mutable.HashMap[Long, SQLStageMetrics]()
+
+  private val failedExecutions = mutable.ListBuffer[SQLExecutionUIData]()
+
+  private val completedExecutions = mutable.ListBuffer[SQLExecutionUIData]()
+
+  @VisibleForTesting
+  def executionIdToData: Map[Long, SQLExecutionUIData] = synchronized {
+    _executionIdToData.toMap
+  }
+
+  @VisibleForTesting
+  def jobIdToExecutionId: Map[Long, Long] = synchronized {
+    _jobIdToExecutionId.toMap
+  }
+
+  @VisibleForTesting
+  def stageIdToStageMetrics: Map[Long, SQLStageMetrics] = synchronized {
+    _stageIdToStageMetrics.toMap
+  }
+
+  private def trimExecutionsIfNecessary(
+      executions: mutable.ListBuffer[SQLExecutionUIData]): Unit = {
+    if (executions.size > retainedExecutions) {
+      val toRemove = math.max(retainedExecutions / 10, 1)
+      executions.take(toRemove).foreach { execution =>
+        for (executionUIData <- _executionIdToData.remove(execution.executionId)) {
+          for (jobId <- executionUIData.jobs.keys) {
+            _jobIdToExecutionId.remove(jobId)
+          }
+          for (stageId <- executionUIData.stages) {
+            _stageIdToStageMetrics.remove(stageId)
+          }
+        }
+      }
+      executions.trimStart(toRemove)
+    }
+  }
+
+  override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
+    val executionIdString = jobStart.properties.getProperty(SQLExecution.EXECUTION_ID_KEY)
+    if (executionIdString == null) {
+      // This is not a job created by SQL
+      return
+    }
+    val executionId = executionIdString.toLong
+    val jobId = jobStart.jobId
+    val stageIds = jobStart.stageIds
+
+    synchronized {
+      activeExecutions.get(executionId).foreach { executionUIData =>
+        executionUIData.jobs(jobId) = JobExecutionStatus.RUNNING
+        executionUIData.stages ++= stageIds
+        stageIds.foreach(stageId =>
+          _stageIdToStageMetrics(stageId) = new SQLStageMetrics(stageAttemptId = 0))
+        _jobIdToExecutionId(jobId) = executionId
+      }
+    }
+  }
+
+  override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = synchronized {
+    val jobId = jobEnd.jobId
+    for (executionId <- _jobIdToExecutionId.get(jobId);
+         executionUIData <- _executionIdToData.get(executionId)) {
+      jobEnd.jobResult match {
+        case JobSucceeded => executionUIData.jobs(jobId) = JobExecutionStatus.SUCCEEDED
+        case JobFailed(_) => executionUIData.jobs(jobId) = JobExecutionStatus.FAILED
+      }
+      if (executionUIData.completionTime.nonEmpty && !executionUIData.hasRunningJobs) {
+        // We are the last job of this execution, so mark the execution as finished. Note that
+        // `onExecutionEnd` also does this, but currently that can be called before `onJobEnd`
+        // since these are called on different threads.
+        markExecutionFinished(executionId)
+      }
+    }
+  }
+
+  override def onExecutorMetricsUpdate(
+      executorMetricsUpdate: SparkListenerExecutorMetricsUpdate): Unit = synchronized {
+    for ((taskId, stageId, stageAttemptID, metrics) <- executorMetricsUpdate.taskMetrics) {
+      updateTaskAccumulatorValues(taskId, stageId, stageAttemptID, metrics, finishTask = false)
+    }
+  }
+
+  override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = synchronized {
+    val stageId = stageSubmitted.stageInfo.stageId
+    val stageAttemptId = stageSubmitted.stageInfo.attemptId
+    // Always override metrics for old stage attempt
+    _stageIdToStageMetrics(stageId) = new SQLStageMetrics(stageAttemptId)
+  }
+
+  override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized {
+    updateTaskAccumulatorValues(
+      taskEnd.taskInfo.taskId,
+      taskEnd.stageId,
+      taskEnd.stageAttemptId,
+      taskEnd.taskMetrics,
+      finishTask = true)
+  }
+
+  /**
+   * Update the accumulator values of a task with the latest metrics for this task. This is called
+   * every time we receive an executor heartbeat or when a task finishes.
+   */
+  private def updateTaskAccumulatorValues(
+      taskId: Long,
+      stageId: Int,
+      stageAttemptID: Int,
+      metrics: TaskMetrics,
+      finishTask: Boolean): Unit = {
+    if (metrics == null) {
+      return
+    }
+
+    _stageIdToStageMetrics.get(stageId) match {
+      case Some(stageMetrics) =>
+        if (stageAttemptID < stageMetrics.stageAttemptId) {
+          // A task of an old stage attempt. Because a new stage is submitted, we can ignore it.
+        } else if (stageAttemptID > stageMetrics.stageAttemptId) {
+          logWarning(s"A task should not have a higher stageAttemptID ($stageAttemptID) then " +
+            s"what we have seen (${stageMetrics.stageAttemptId}})")
+        } else {
+          // TODO We don't know the attemptId. Currently, what we can do is overriding the
+          // accumulator updates. However, if there are two same task are running, such as
+          // speculation, the accumulator updates will be overriding by different task attempts,
+          // the results will be weird.
+          stageMetrics.taskIdToMetricUpdates.get(taskId) match {
+            case Some(taskMetrics) =>
+              if (finishTask) {
+                taskMetrics.finished = true
+                taskMetrics.accumulatorUpdates = metrics.accumulatorUpdates()
+              } else if (!taskMetrics.finished) {
+                taskMetrics.accumulatorUpdates = metrics.accumulatorUpdates()
+              } else {
+                // If a task is finished, we should not override with accumulator updates from
+                // heartbeat reports
+              }
+            case None =>
+              // TODO Now just set attemptId to 0. Should fix here when we can get the attempt
+              // id from SparkListenerExecutorMetricsUpdate
+              stageMetrics.taskIdToMetricUpdates(taskId) = new SQLTaskMetrics(
+                  attemptId = 0, finished = finishTask, metrics.accumulatorUpdates())
+          }
+        }
+      case None =>
+      // This execution and its stage have been dropped
+    }
+  }
+
+  def onExecutionStart(
+      executionId: Long,
+      description: String,
+      details: String,
+      physicalPlanDescription: String,
+      physicalPlanGraph: SparkPlanGraph,
+      time: Long): Unit = {
+    val sqlPlanMetrics = physicalPlanGraph.nodes.flatMap { node =>
+      node.metrics.map(metric => metric.accumulatorId -> metric)
+    }
+
+    val executionUIData = new SQLExecutionUIData(executionId, description, details,
+      physicalPlanDescription, physicalPlanGraph, sqlPlanMetrics.toMap, time)
+    synchronized {
+      activeExecutions(executionId) = executionUIData
+      _executionIdToData(executionId) = executionUIData
+    }
+  }
+
+  def onExecutionEnd(executionId: Long, time: Long): Unit = synchronized {
+    _executionIdToData.get(executionId).foreach { executionUIData =>
+      executionUIData.completionTime = Some(time)
+      if (!executionUIData.hasRunningJobs) {
+        // onExecutionEnd happens after all "onJobEnd"s
+        // So we should update the execution lists.
+        markExecutionFinished(executionId)
+      } else {
+        // There are some running jobs, onExecutionEnd happens before some "onJobEnd"s.
+        // Then we don't if the execution is successful, so let the last onJobEnd updates the
+        // execution lists.
+      }
+    }
+  }
+
+  private def markExecutionFinished(executionId: Long): Unit = {
+    activeExecutions.remove(executionId).foreach { executionUIData =>
+      if (executionUIData.isFailed) {
+        failedExecutions += executionUIData
+        trimExecutionsIfNecessary(failedExecutions)
+      } else {
+        completedExecutions += executionUIData
+        trimExecutionsIfNecessary(completedExecutions)
+      }
+    }
+  }
+
+  def getRunningExecutions: Seq[SQLExecutionUIData] = synchronized {
+    activeExecutions.values.toSeq
+  }
+
+  def getFailedExecutions: Seq[SQLExecutionUIData] = synchronized {
+    failedExecutions
+  }
+
+  def getCompletedExecutions: Seq[SQLExecutionUIData] = synchronized {
+    completedExecutions
+  }
+
+  def getExecution(executionId: Long): Option[SQLExecutionUIData] = synchronized {
+    _executionIdToData.get(executionId)
+  }
+
+  /**
+   * Get all accumulator updates from all tasks which belong to this execution and merge them.
+   */
+  def getExecutionMetrics(executionId: Long): Map[Long, Any] = synchronized {
+    _executionIdToData.get(executionId) match {
+      case Some(executionUIData) =>
+        val accumulatorUpdates = {
+          for (stageId <- executionUIData.stages;
+               stageMetrics <- _stageIdToStageMetrics.get(stageId).toIterable;
+               taskMetrics <- stageMetrics.taskIdToMetricUpdates.values;
+               accumulatorUpdate <- taskMetrics.accumulatorUpdates.toSeq) yield {
+            accumulatorUpdate
+          }
+        }.filter { case (id, _) => executionUIData.accumulatorMetrics.keySet(id) }
+        mergeAccumulatorUpdates(accumulatorUpdates, accumulatorId =>
+          executionUIData.accumulatorMetrics(accumulatorId).accumulatorParam)
+      case None =>
+        // This execution has been dropped
+        Map.empty
+    }
+  }
+
+  private def mergeAccumulatorUpdates(
+      accumulatorUpdates: Seq[(Long, Any)],
+      paramFunc: Long => AccumulatorParam[Any]): Map[Long, Any] = {
+    accumulatorUpdates.groupBy(_._1).map { case (accumulatorId, values) =>
+      val param = paramFunc(accumulatorId)
+      (accumulatorId, values.map(_._2).reduceLeft(param.addInPlace))
+    }
+  }
+
+}
+
+/**
+ * Represent all necessary data for an execution that will be used in Web UI.
+ */
+private[ui] class SQLExecutionUIData(
+    val executionId: Long,
+    val description: String,
+    val details: String,
+    val physicalPlanDescription: String,
+    val physicalPlanGraph: SparkPlanGraph,
+    val accumulatorMetrics: Map[Long, SQLPlanMetric],
+    val submissionTime: Long,
+    var completionTime: Option[Long] = None,
+    val jobs: mutable.HashMap[Long, JobExecutionStatus] = mutable.HashMap.empty,
+    val stages: mutable.ArrayBuffer[Int] = mutable.ArrayBuffer()) {
+
+  /**
+   * Return whether there are running jobs in this execution.
+   */
+  def hasRunningJobs: Boolean = jobs.values.exists(_ == JobExecutionStatus.RUNNING)
+
+  /**
+   * Return whether there are any failed jobs in this execution.
+   */
+  def isFailed: Boolean = jobs.values.exists(_ == JobExecutionStatus.FAILED)
+
+  def runningJobs: Seq[Long] =
+    jobs.filter { case (_, status) => status == JobExecutionStatus.RUNNING }.keys.toSeq
+
+  def succeededJobs: Seq[Long] =
+    jobs.filter { case (_, status) => status == JobExecutionStatus.SUCCEEDED }.keys.toSeq
+
+  def failedJobs: Seq[Long] =
+    jobs.filter { case (_, status) => status == JobExecutionStatus.FAILED }.keys.toSeq
+}
+
+/**
+ * Represent a metric in a SQLPlan.
+ *
+ * Because we cannot revert our changes for an "Accumulator", we need to maintain accumulator
+ * updates for each task. So that if a task is retried, we can simply override the old updates with
+ * the new updates of the new attempt task. Since we cannot add them to accumulator, we need to use
+ * "AccumulatorParam" to get the aggregation value.
+ */
+private[ui] case class SQLPlanMetric(
+    name: String,
+    accumulatorId: Long,
+    accumulatorParam: AccumulatorParam[Any])
+
+/**
+ * Store all accumulatorUpdates for all tasks in a Spark stage.
+ */
+private[ui] class SQLStageMetrics(
+    val stageAttemptId: Long,
+    val taskIdToMetricUpdates: mutable.HashMap[Long, SQLTaskMetrics] = mutable.HashMap.empty)
+
+/**
+ * Store all accumulatorUpdates for a Spark task.
+ */
+private[ui] class SQLTaskMetrics(
+    val attemptId: Long, // TODO not used yet
+    var finished: Boolean,
+    var accumulatorUpdates: Map[Long, Any])
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
new file mode 100644
index 0000000000000..a9e5226303978
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.ui.{SparkUI, SparkUITab}
+
+private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
+  extends SparkUITab(sparkUI, "sql") with Logging {
+
+
+  val parent = sparkUI
+  val listener = sqlContext.listener
+
+  attachPage(new AllExecutionsPage(this))
+  attachPage(new ExecutionPage(this))
+  parent.attachTab(this)
+
+  parent.addStaticHandler(SQLTab.STATIC_RESOURCE_DIR, "/static/sql")
+}
+
+private[sql] object SQLTab {
+
+  private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/ui/static"
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
new file mode 100644
index 0000000000000..7910c163ba453
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
@@ -0,0 +1,118 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import java.util.concurrent.atomic.AtomicLong
+
+import scala.collection.mutable
+
+import org.apache.spark.AccumulatorParam
+import org.apache.spark.sql.execution.SparkPlan
+
+/**
+ * A graph used for storing information of an executionPlan of DataFrame.
+ *
+ * Each graph is defined with a set of nodes and a set of edges. Each node represents a node in the
+ * SparkPlan tree, and each edge represents a parent-child relationship between two nodes.
+ */
+private[ui] case class SparkPlanGraph(
+    nodes: Seq[SparkPlanGraphNode], edges: Seq[SparkPlanGraphEdge]) {
+
+  def makeDotFile(metrics: Map[Long, Any]): String = {
+    val dotFile = new StringBuilder
+    dotFile.append("digraph G {\n")
+    nodes.foreach(node => dotFile.append(node.makeDotNode(metrics) + "\n"))
+    edges.foreach(edge => dotFile.append(edge.makeDotEdge + "\n"))
+    dotFile.append("}")
+    dotFile.toString()
+  }
+}
+
+private[sql] object SparkPlanGraph {
+
+  /**
+   * Build a SparkPlanGraph from the root of a SparkPlan tree.
+   */
+  def apply(plan: SparkPlan): SparkPlanGraph = {
+    val nodeIdGenerator = new AtomicLong(0)
+    val nodes = mutable.ArrayBuffer[SparkPlanGraphNode]()
+    val edges = mutable.ArrayBuffer[SparkPlanGraphEdge]()
+    buildSparkPlanGraphNode(plan, nodeIdGenerator, nodes, edges)
+    new SparkPlanGraph(nodes, edges)
+  }
+
+  private def buildSparkPlanGraphNode(
+      plan: SparkPlan,
+      nodeIdGenerator: AtomicLong,
+      nodes: mutable.ArrayBuffer[SparkPlanGraphNode],
+      edges: mutable.ArrayBuffer[SparkPlanGraphEdge]): SparkPlanGraphNode = {
+    val metrics = plan.accumulators.toSeq.map { case (key, accumulator) =>
+      SQLPlanMetric(accumulator.name.getOrElse(key), accumulator.id,
+        accumulator.param.asInstanceOf[AccumulatorParam[Any]])
+    }
+    val node = SparkPlanGraphNode(
+      nodeIdGenerator.getAndIncrement(), plan.nodeName, plan.simpleString, metrics)
+    nodes += node
+    val childrenNodes = plan.children.map(
+      child => buildSparkPlanGraphNode(child, nodeIdGenerator, nodes, edges))
+    for (child <- childrenNodes) {
+      edges += SparkPlanGraphEdge(child.id, node.id)
+    }
+    node
+  }
+}
+
+/**
+ * Represent a node in the SparkPlan tree, along with its metrics.
+ *
+ * @param id generated by "SparkPlanGraph". There is no duplicate id in a graph
+ * @param name the name of this SparkPlan node
+ * @param metrics metrics that this SparkPlan node will track
+ */
+private[ui] case class SparkPlanGraphNode(
+    id: Long, name: String, desc: String, metrics: Seq[SQLPlanMetric]) {
+
+  def makeDotNode(metricsValue: Map[Long, Any]): String = {
+    val values = {
+      for (metric <- metrics;
+           value <- metricsValue.get(metric.accumulatorId)) yield {
+        metric.name + ": " + value
+      }
+    }
+    val label = if (values.isEmpty) {
+        name
+      } else {
+        // If there are metrics, display all metrics in a separate line. We should use an escaped
+        // "\n" here to follow the dot syntax.
+        //
+        // Note: whitespace between two "\n"s is to create an empty line between the name of
+        // SparkPlan and metrics. If removing it, it won't display the empty line in UI.
+        name + "\\n \\n" + values.mkString("\\n")
+      }
+    s"""  $id [label="$label"];"""
+  }
+}
+
+/**
+ * Represent an edge in the SparkPlan tree. `fromId` is the parent node id, and `toId` is the child
+ * node id.
+ */
+private[ui] case class SparkPlanGraphEdge(fromId: Long, toId: Long) {
+
+  def makeDotEdge: String = s"""  $fromId->$toId;\n"""
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index eb3e913322062..e9dd7ef226e42 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -305,12 +305,8 @@ class CachedTableSuite extends QueryTest {
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
 
-    Accumulators.synchronized {
-      val accsSize = Accumulators.originals.size
-      ctx.cacheTable("t1")
-      ctx.cacheTable("t2")
-      assert((accsSize + 2) == Accumulators.originals.size)
-    }
+    ctx.cacheTable("t1")
+    ctx.cacheTable("t2")
 
     sql("SELECT * FROM t1").count()
     sql("SELECT * FROM t2").count()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
new file mode 100644
index 0000000000000..f1fcaf59532b8
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
@@ -0,0 +1,347 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import java.util.Properties
+
+import org.apache.spark.{SparkException, SparkContext, SparkConf, SparkFunSuite}
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler._
+import org.apache.spark.sql.{DataFrame, SQLContext}
+import org.apache.spark.sql.execution.SQLExecution
+import org.apache.spark.sql.test.TestSQLContext
+
+class SQLListenerSuite extends SparkFunSuite {
+
+  private def createTestDataFrame: DataFrame = {
+    import TestSQLContext.implicits._
+    Seq(
+      (1, 1),
+      (2, 2)
+    ).toDF().filter("_1 > 1")
+  }
+
+  private def createProperties(executionId: Long): Properties = {
+    val properties = new Properties()
+    properties.setProperty(SQLExecution.EXECUTION_ID_KEY, executionId.toString)
+    properties
+  }
+
+  private def createStageInfo(stageId: Int, attemptId: Int): StageInfo = new StageInfo(
+    stageId = stageId,
+    attemptId = attemptId,
+    // The following fields are not used in tests
+    name = "",
+    numTasks = 0,
+    rddInfos = Nil,
+    parentIds = Nil,
+    details = ""
+  )
+
+  private def createTaskInfo(taskId: Int, attempt: Int): TaskInfo = new TaskInfo(
+    taskId = taskId,
+    attempt = attempt,
+    // The following fields are not used in tests
+    index = 0,
+    launchTime = 0,
+    executorId = "",
+    host = "",
+    taskLocality = null,
+    speculative = false
+  )
+
+  private def createTaskMetrics(accumulatorUpdates: Map[Long, Any]): TaskMetrics = {
+    val metrics = new TaskMetrics
+    metrics.setAccumulatorsUpdater(() => accumulatorUpdates)
+    metrics.updateAccumulators()
+    metrics
+  }
+
+  test("basic") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    val accumulatorIds =
+      SparkPlanGraph(df.queryExecution.executedPlan).nodes.flatMap(_.metrics.map(_.accumulatorId))
+    // Assume all accumulators are long
+    var accumulatorValue = 0L
+    val accumulatorUpdates = accumulatorIds.map { id =>
+      accumulatorValue += 1L
+      (id, accumulatorValue)
+    }.toMap
+
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+
+    val executionUIData = listener.executionIdToData(0)
+
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Seq(
+        createStageInfo(0, 0),
+        createStageInfo(1, 0)
+      ),
+      createProperties(executionId)))
+    listener.onStageSubmitted(SparkListenerStageSubmitted(createStageInfo(0, 0)))
+
+    assert(listener.getExecutionMetrics(0).isEmpty)
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 0, 0, createTaskMetrics(accumulatorUpdates)),
+      (1L, 0, 0, createTaskMetrics(accumulatorUpdates))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 2))
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 0, 0, createTaskMetrics(accumulatorUpdates)),
+      (1L, 0, 0, createTaskMetrics(accumulatorUpdates.mapValues(_ * 2)))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 3))
+
+    // Retrying a stage should reset the metrics
+    listener.onStageSubmitted(SparkListenerStageSubmitted(createStageInfo(0, 1)))
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 0, 1, createTaskMetrics(accumulatorUpdates)),
+      (1L, 0, 1, createTaskMetrics(accumulatorUpdates))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 2))
+
+    // Ignore the task end for the first attempt
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 0,
+      stageAttemptId = 0,
+      taskType = "",
+      reason = null,
+      createTaskInfo(0, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 100))))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 2))
+
+    // Finish two tasks
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 0,
+      stageAttemptId = 1,
+      taskType = "",
+      reason = null,
+      createTaskInfo(0, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 2))))
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 0,
+      stageAttemptId = 1,
+      taskType = "",
+      reason = null,
+      createTaskInfo(1, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 3))))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 5))
+
+    // Summit a new stage
+    listener.onStageSubmitted(SparkListenerStageSubmitted(createStageInfo(1, 0)))
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 1, 0, createTaskMetrics(accumulatorUpdates)),
+      (1L, 1, 0, createTaskMetrics(accumulatorUpdates))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 7))
+
+    // Finish two tasks
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 1,
+      stageAttemptId = 0,
+      taskType = "",
+      reason = null,
+      createTaskInfo(0, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 3))))
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 1,
+      stageAttemptId = 0,
+      taskType = "",
+      reason = null,
+      createTaskInfo(1, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 3))))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 11))
+
+    assert(executionUIData.runningJobs === Seq(0))
+    assert(executionUIData.succeededJobs.isEmpty)
+    assert(executionUIData.failedJobs.isEmpty)
+
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      JobSucceeded
+    ))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs === Seq(0))
+    assert(executionUIData.failedJobs.isEmpty)
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 11))
+  }
+
+  test("onExecutionEnd happens before onJobEnd(JobSucceeded)") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Nil,
+      createProperties(executionId)))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      JobSucceeded
+    ))
+
+    val executionUIData = listener.executionIdToData(0)
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs === Seq(0))
+    assert(executionUIData.failedJobs.isEmpty)
+  }
+
+  test("onExecutionEnd happens before multiple onJobEnd(JobSucceeded)s") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Nil,
+      createProperties(executionId)))
+    listener.onJobEnd(SparkListenerJobEnd(
+        jobId = 0,
+        time = System.currentTimeMillis(),
+        JobSucceeded
+    ))
+
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 1,
+      time = System.currentTimeMillis(),
+      stageInfos = Nil,
+      createProperties(executionId)))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 1,
+      time = System.currentTimeMillis(),
+      JobSucceeded
+    ))
+
+    val executionUIData = listener.executionIdToData(0)
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs.sorted === Seq(0, 1))
+    assert(executionUIData.failedJobs.isEmpty)
+  }
+
+  test("onExecutionEnd happens before onJobEnd(JobFailed)") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Seq.empty,
+      createProperties(executionId)))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      JobFailed(new RuntimeException("Oops"))
+    ))
+
+    val executionUIData = listener.executionIdToData(0)
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs.isEmpty)
+    assert(executionUIData.failedJobs === Seq(0))
+  }
+
+  ignore("no memory leak") {
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName("test")
+      .set("spark.task.maxFailures", "1") // Don't retry the tasks to run this test quickly
+      .set("spark.sql.ui.retainedExecutions", "50") // Set it to 50 to run this test quickly
+    val sc = new SparkContext(conf)
+    try {
+      val sqlContext = new SQLContext(sc)
+      import sqlContext.implicits._
+      // Run 100 successful executions and 100 failed executions.
+      // Each execution only has one job and one stage.
+      for (i <- 0 until 100) {
+        val df = Seq(
+          (1, 1),
+          (2, 2)
+        ).toDF()
+        df.collect()
+        try {
+          df.foreach(_ => throw new RuntimeException("Oops"))
+        } catch {
+          case e: SparkException => // This is expected for a failed job
+        }
+      }
+      sc.listenerBus.waitUntilEmpty(10000)
+      assert(sqlContext.listener.getCompletedExecutions.size <= 50)
+      assert(sqlContext.listener.getFailedExecutions.size <= 50)
+      // 50 for successful executions and 50 for failed executions
+      assert(sqlContext.listener.executionIdToData.size <= 100)
+      assert(sqlContext.listener.jobIdToExecutionId.size <= 100)
+      assert(sqlContext.listener.stageIdToStageMetrics.size <= 100)
+    } finally {
+      sc.stop()
+    }
+  }
+
+}

From 84ca3183b6fdd07ce56c5eb6686ac38dc5e96f9e Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 5 Aug 2015 02:04:28 -0700
Subject: [PATCH 0857/1454] [SPARK-9628][SQL]Rename int to SQLDate, long to
 SQLTimestamp for better readability

JIRA: https://issues.apache.org/jira/browse/SPARK-9628

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7953 from yjshen/datetime_alias and squashes the following commits:

3cac3cc [Yijie Shen] rename int to SQLDate, long to SQLTimestamp for better readability
---
 .../sql/catalyst/util/DateTimeUtils.scala     | 69 ++++++++++---------
 1 file changed, 37 insertions(+), 32 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 063940cb9e2c3..672620460c3c5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -31,6 +31,11 @@ import org.apache.spark.unsafe.types.UTF8String
  * precision.
  */
 object DateTimeUtils {
+
+  // we use Int and Long internally to represent [[DateType]] and [[TimestampType]]
+  type SQLDate = Int
+  type SQLTimestamp = Long
+
   // see http://stackoverflow.com/questions/466321/convert-unix-timestamp-to-julian
   final val JULIAN_DAY_OF_EPOCH = 2440587  // and .5
   final val SECONDS_PER_DAY = 60 * 60 * 24L
@@ -72,7 +77,7 @@ object DateTimeUtils {
   }
 
   // we should use the exact day as Int, for example, (year, month, day) -> day
-  def millisToDays(millisUtc: Long): Int = {
+  def millisToDays(millisUtc: Long): SQLDate = {
     // SPARK-6785: use Math.floor so negative number of days (dates before 1970)
     // will correctly work as input for function toJavaDate(Int)
     val millisLocal = millisUtc + threadLocalLocalTimeZone.get().getOffset(millisUtc)
@@ -80,16 +85,16 @@ object DateTimeUtils {
   }
 
   // reverse of millisToDays
-  def daysToMillis(days: Int): Long = {
+  def daysToMillis(days: SQLDate): Long = {
     val millisUtc = days.toLong * MILLIS_PER_DAY
     millisUtc - threadLocalLocalTimeZone.get().getOffset(millisUtc)
   }
 
-  def dateToString(days: Int): String =
+  def dateToString(days: SQLDate): String =
     threadLocalDateFormat.get.format(toJavaDate(days))
 
   // Converts Timestamp to string according to Hive TimestampWritable convention.
-  def timestampToString(us: Long): String = {
+  def timestampToString(us: SQLTimestamp): String = {
     val ts = toJavaTimestamp(us)
     val timestampString = ts.toString
     val formatted = threadLocalTimestampFormat.get.format(ts)
@@ -132,21 +137,21 @@ object DateTimeUtils {
   /**
    * Returns the number of days since epoch from from java.sql.Date.
    */
-  def fromJavaDate(date: Date): Int = {
+  def fromJavaDate(date: Date): SQLDate = {
     millisToDays(date.getTime)
   }
 
   /**
    * Returns a java.sql.Date from number of days since epoch.
    */
-  def toJavaDate(daysSinceEpoch: Int): Date = {
+  def toJavaDate(daysSinceEpoch: SQLDate): Date = {
     new Date(daysToMillis(daysSinceEpoch))
   }
 
   /**
    * Returns a java.sql.Timestamp from number of micros since epoch.
    */
-  def toJavaTimestamp(us: Long): Timestamp = {
+  def toJavaTimestamp(us: SQLTimestamp): Timestamp = {
     // setNanos() will overwrite the millisecond part, so the milliseconds should be
     // cut off at seconds
     var seconds = us / MICROS_PER_SECOND
@@ -164,7 +169,7 @@ object DateTimeUtils {
   /**
    * Returns the number of micros since epoch from java.sql.Timestamp.
    */
-  def fromJavaTimestamp(t: Timestamp): Long = {
+  def fromJavaTimestamp(t: Timestamp): SQLTimestamp = {
     if (t != null) {
       t.getTime() * 1000L + (t.getNanos().toLong / 1000) % 1000L
     } else {
@@ -176,7 +181,7 @@ object DateTimeUtils {
    * Returns the number of microseconds since epoch from Julian day
    * and nanoseconds in a day
    */
-  def fromJulianDay(day: Int, nanoseconds: Long): Long = {
+  def fromJulianDay(day: Int, nanoseconds: Long): SQLTimestamp = {
     // use Long to avoid rounding errors
     val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY - SECONDS_PER_DAY / 2
     seconds * MICROS_PER_SECOND + nanoseconds / 1000L
@@ -185,7 +190,7 @@ object DateTimeUtils {
   /**
    * Returns Julian day and nanoseconds in a day from the number of microseconds
    */
-  def toJulianDay(us: Long): (Int, Long) = {
+  def toJulianDay(us: SQLTimestamp): (Int, Long) = {
     val seconds = us / MICROS_PER_SECOND + SECONDS_PER_DAY / 2
     val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
     val secondsInDay = seconds % SECONDS_PER_DAY
@@ -219,7 +224,7 @@ object DateTimeUtils {
    * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]-[h]h:[m]m`
    * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]+[h]h:[m]m`
    */
-  def stringToTimestamp(s: UTF8String): Option[Long] = {
+  def stringToTimestamp(s: UTF8String): Option[SQLTimestamp] = {
     if (s == null) {
       return None
     }
@@ -355,7 +360,7 @@ object DateTimeUtils {
    * `yyyy-[m]m-[d]d *`
    * `yyyy-[m]m-[d]dT*`
    */
-  def stringToDate(s: UTF8String): Option[Int] = {
+  def stringToDate(s: UTF8String): Option[SQLDate] = {
     if (s == null) {
       return None
     }
@@ -394,7 +399,7 @@ object DateTimeUtils {
   /**
    * Returns the hour value of a given timestamp value. The timestamp is expressed in microseconds.
    */
-  def getHours(timestamp: Long): Int = {
+  def getHours(timestamp: SQLTimestamp): Int = {
     val localTs = (timestamp / 1000) + defaultTimeZone.getOffset(timestamp / 1000)
     ((localTs / 1000 / 3600) % 24).toInt
   }
@@ -403,7 +408,7 @@ object DateTimeUtils {
    * Returns the minute value of a given timestamp value. The timestamp is expressed in
    * microseconds.
    */
-  def getMinutes(timestamp: Long): Int = {
+  def getMinutes(timestamp: SQLTimestamp): Int = {
     val localTs = (timestamp / 1000) + defaultTimeZone.getOffset(timestamp / 1000)
     ((localTs / 1000 / 60) % 60).toInt
   }
@@ -412,7 +417,7 @@ object DateTimeUtils {
    * Returns the second value of a given timestamp value. The timestamp is expressed in
    * microseconds.
    */
-  def getSeconds(timestamp: Long): Int = {
+  def getSeconds(timestamp: SQLTimestamp): Int = {
     ((timestamp / 1000 / 1000) % 60).toInt
   }
 
@@ -447,7 +452,7 @@ object DateTimeUtils {
    * The calculation uses the fact that the period 1.1.2001 until 31.12.2400 is
    * equals to the period 1.1.1601 until 31.12.2000.
    */
-  private[this] def getYearAndDayInYear(daysSince1970: Int): (Int, Int) = {
+  private[this] def getYearAndDayInYear(daysSince1970: SQLDate): (Int, Int) = {
     // add the difference (in days) between 1.1.1970 and the artificial year 0 (-17999)
     val daysNormalized = daysSince1970 + toYearZero
     val numOfQuarterCenturies = daysNormalized / daysIn400Years
@@ -461,7 +466,7 @@ object DateTimeUtils {
    * Returns the 'day in year' value for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getDayInYear(date: Int): Int = {
+  def getDayInYear(date: SQLDate): Int = {
     getYearAndDayInYear(date)._2
   }
 
@@ -469,7 +474,7 @@ object DateTimeUtils {
    * Returns the year value for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getYear(date: Int): Int = {
+  def getYear(date: SQLDate): Int = {
     getYearAndDayInYear(date)._1
   }
 
@@ -477,7 +482,7 @@ object DateTimeUtils {
    * Returns the quarter for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getQuarter(date: Int): Int = {
+  def getQuarter(date: SQLDate): Int = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     if (isLeapYear(year)) {
       dayInYear = dayInYear - 1
@@ -497,7 +502,7 @@ object DateTimeUtils {
    * Split date (expressed in days since 1.1.1970) into four fields:
    * year, month (Jan is Month 1), dayInMonth, daysToMonthEnd (0 if it's last day of month).
    */
-  def splitDate(date: Int): (Int, Int, Int, Int) = {
+  def splitDate(date: SQLDate): (Int, Int, Int, Int) = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     val isLeap = isLeapYear(year)
     if (isLeap && dayInYear == 60) {
@@ -541,7 +546,7 @@ object DateTimeUtils {
    * Returns the month value for the given date. The date is expressed in days
    * since 1.1.1970. January is month 1.
    */
-  def getMonth(date: Int): Int = {
+  def getMonth(date: SQLDate): Int = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     if (isLeapYear(year)) {
       if (dayInYear == 60) {
@@ -582,7 +587,7 @@ object DateTimeUtils {
    * Returns the 'day of month' value for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getDayOfMonth(date: Int): Int = {
+  def getDayOfMonth(date: SQLDate): Int = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     if (isLeapYear(year)) {
       if (dayInYear == 60) {
@@ -628,7 +633,7 @@ object DateTimeUtils {
    * Returns the date value for the first day of the given month.
    * The month is expressed in months since year zero (17999 BC), starting from 0.
    */
-  private def firstDayOfMonth(absoluteMonth: Int): Int = {
+  private def firstDayOfMonth(absoluteMonth: Int): SQLDate = {
     val absoluteYear = absoluteMonth / 12
     var monthInYear = absoluteMonth - absoluteYear * 12
     var date = getDateFromYear(absoluteYear)
@@ -646,7 +651,7 @@ object DateTimeUtils {
    * Returns the date value for January 1 of the given year.
    * The year is expressed in years since year zero (17999 BC), starting from 0.
    */
-  private def getDateFromYear(absoluteYear: Int): Int = {
+  private def getDateFromYear(absoluteYear: Int): SQLDate = {
     val absoluteDays = (absoluteYear * 365 + absoluteYear / 400 - absoluteYear / 100
       + absoluteYear / 4)
     absoluteDays - toYearZero
@@ -656,7 +661,7 @@ object DateTimeUtils {
    * Add date and year-month interval.
    * Returns a date value, expressed in days since 1.1.1970.
    */
-  def dateAddMonths(days: Int, months: Int): Int = {
+  def dateAddMonths(days: SQLDate, months: Int): SQLDate = {
     val (year, monthInYear, dayOfMonth, daysToMonthEnd) = splitDate(days)
     val absoluteMonth = (year - YearZero) * 12 + monthInYear - 1 + months
     val nonNegativeMonth = if (absoluteMonth >= 0) absoluteMonth else 0
@@ -679,7 +684,7 @@ object DateTimeUtils {
    * Add timestamp and full interval.
    * Returns a timestamp value, expressed in microseconds since 1.1.1970 00:00:00.
    */
-  def timestampAddInterval(start: Long, months: Int, microseconds: Long): Long = {
+  def timestampAddInterval(start: SQLTimestamp, months: Int, microseconds: Long): SQLTimestamp = {
     val days = millisToDays(start / 1000L)
     val newDays = dateAddMonths(days, months)
     daysToMillis(newDays) * 1000L + start - daysToMillis(days) * 1000L + microseconds
@@ -695,7 +700,7 @@ object DateTimeUtils {
    * Otherwise, the difference is calculated based on 31 days per month, and rounding to
    * 8 digits.
    */
-  def monthsBetween(time1: Long, time2: Long): Double = {
+  def monthsBetween(time1: SQLTimestamp, time2: SQLTimestamp): Double = {
     val millis1 = time1 / 1000L
     val millis2 = time2 / 1000L
     val date1 = millisToDays(millis1)
@@ -740,7 +745,7 @@ object DateTimeUtils {
    * Returns the first date which is later than startDate and is of the given dayOfWeek.
    * dayOfWeek is an integer ranges in [0, 6], and 0 is Thu, 1 is Fri, etc,.
    */
-  def getNextDateForDayOfWeek(startDate: Int, dayOfWeek: Int): Int = {
+  def getNextDateForDayOfWeek(startDate: SQLDate, dayOfWeek: Int): SQLDate = {
     startDate + 1 + ((dayOfWeek - 1 - startDate) % 7 + 7) % 7
   }
 
@@ -748,7 +753,7 @@ object DateTimeUtils {
    * Returns last day of the month for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getLastDayOfMonth(date: Int): Int = {
+  def getLastDayOfMonth(date: SQLDate): SQLDate = {
     val (_, _, _, daysToMonthEnd) = splitDate(date)
     date + daysToMonthEnd
   }
@@ -761,7 +766,7 @@ object DateTimeUtils {
    * Returns the trunc date from original date and trunc level.
    * Trunc level should be generated using `parseTruncLevel()`, should only be 1 or 2.
    */
-  def truncDate(d: Int, level: Int): Int = {
+  def truncDate(d: SQLDate, level: Int): SQLDate = {
     if (level == TRUNC_TO_YEAR) {
       d - DateTimeUtils.getDayInYear(d) + 1
     } else if (level == TRUNC_TO_MONTH) {
@@ -792,7 +797,7 @@ object DateTimeUtils {
    * Returns a timestamp of given timezone from utc timestamp, with the same string
    * representation in their timezone.
    */
-  def fromUTCTime(time: Long, timeZone: String): Long = {
+  def fromUTCTime(time: SQLTimestamp, timeZone: String): SQLTimestamp = {
     val tz = TimeZone.getTimeZone(timeZone)
     val offset = tz.getOffset(time / 1000L)
     time + offset * 1000L
@@ -802,7 +807,7 @@ object DateTimeUtils {
    * Returns a utc timestamp from a given timestamp from a given timezone, with the same
    * string representation in their timezone.
    */
-  def toUTCTime(time: Long, timeZone: String): Long = {
+  def toUTCTime(time: SQLTimestamp, timeZone: String): SQLTimestamp = {
     val tz = TimeZone.getTimeZone(timeZone)
     val offset = tz.getOffset(time / 1000L)
     time - offset * 1000L

From 26b06f1c4630ccc98b0eb1bf7294347899c4314f Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 5 Aug 2015 02:39:41 -0700
Subject: [PATCH 0858/1454] [HOTFIX] Add static import to fix build break from
 #7676.

---
 .../spark/util/collection/unsafe/sort/PrefixComparators.java     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index b5f661c0d5481..5e002ae1b7568 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -23,6 +23,7 @@
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.types.UTF8String;
 import org.apache.spark.util.Utils;
+import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET;
 
 @Private
 public class PrefixComparators {

From e27a8c4cb3564f1b2d1ee5445dff341c8e0087b0 Mon Sep 17 00:00:00 2001
From: Ryan Williams <ryan.blake.williams@gmail.com>
Date: Wed, 5 Aug 2015 11:10:47 +0100
Subject: [PATCH 0859/1454] [SPARK-9607] [SPARK-9608] fix zinc-port handling in
 build/mvn

- pass `$ZINC_PORT` to zinc status/shutdown commands
- fix path check that sets `$ZINC_INSTALL_FLAG`, which was incorrectly
  causing zinc to be shutdown and restarted every time (with mismatched
  ports on those commands to boot)
- pass `-DzincPort=${ZINC_PORT}` to maven, to use the correct zinc port
  when building

Author: Ryan Williams <ryan.blake.williams@gmail.com>

Closes #7944 from ryan-williams/zinc-status and squashes the following commits:

619c520 [Ryan Williams] fix zinc status/shutdown commands
---
 build/mvn | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/build/mvn b/build/mvn
index 4a1664b04053d..ec0380afad319 100755
--- a/build/mvn
+++ b/build/mvn
@@ -82,7 +82,7 @@ install_mvn() {
 # Install zinc under the build/ folder
 install_zinc() {
   local zinc_path="zinc-0.3.5.3/bin/zinc"
-  [ ! -f "${zinc_path}" ] && ZINC_INSTALL_FLAG=1
+  [ ! -f "${_DIR}/${zinc_path}" ] && ZINC_INSTALL_FLAG=1
   install_app \
     "http://downloads.typesafe.com/zinc/0.3.5.3" \
     "zinc-0.3.5.3.tgz" \
@@ -135,9 +135,9 @@ cd "${_CALLING_DIR}"
 
 # Now that zinc is ensured to be installed, check its status and, if its
 # not running or just installed, start it
-if [ -n "${ZINC_INSTALL_FLAG}" -o -z "`${ZINC_BIN} -status`" ]; then
+if [ -n "${ZINC_INSTALL_FLAG}" -o -z "`${ZINC_BIN} -status -port ${ZINC_PORT}`" ]; then
   export ZINC_OPTS=${ZINC_OPTS:-"$_COMPILE_JVM_OPTS"}
-  ${ZINC_BIN} -shutdown
+  ${ZINC_BIN} -shutdown -port ${ZINC_PORT}
   ${ZINC_BIN} -start -port ${ZINC_PORT} \
     -scala-compiler "${SCALA_COMPILER}" \
     -scala-library "${SCALA_LIBRARY}" &>/dev/null
@@ -149,4 +149,4 @@ export MAVEN_OPTS=${MAVEN_OPTS:-"$_COMPILE_JVM_OPTS"}
 echo "Using \`mvn\` from path: $MVN_BIN" 1>&2
 
 # Last, call the `mvn` command as usual
-${MVN_BIN} "$@"
+${MVN_BIN} -DzincPort=${ZINC_PORT} "$@"

From 70112ff22bd1aee7689c5d3af9b66c9b8ceb3ec3 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 5 Aug 2015 20:03:54 +0800
Subject: [PATCH 0860/1454] [SPARK-9593] [SQL] Fixes Hadoop shims loading

This PR is used to workaround CDH Hadoop versions like 2.0.0-mr1-cdh4.1.1.

Internally, Hive `ShimLoader` tries to load different versions of Hadoop shims by checking version information gathered from Hadoop jar files.  If the major version number is 1, `Hadoop20SShims` will be loaded.  Otherwise, if the major version number is 2, `Hadoop23Shims` will be chosen.  However, CDH Hadoop versions like 2.0.0-mr1-cdh4.1.1 have 2 as major version number, but contain Hadoop 1 code.  This confuses Hive `ShimLoader` and loads wrong version of shims.

In this PR we check for existence of the `Path.getPathWithoutSchemeAndAuthority` method, which doesn't exist in Hadoop 1 (it's also the method that reveals this shims loading issue), and load `Hadoop20SShims` when it doesn't exist.

Author: Cheng Lian <lian@databricks.com>

Closes #7929 from liancheng/spark-9593/fix-hadoop-shims-loading and squashes the following commits:

c99b497 [Cheng Lian] Narrows down the fix to handle "2.0.0-*cdh4*" Hadoop versions only
b17e955 [Cheng Lian] Updates comments
490d8f2 [Cheng Lian] Fixes Scala style issue
9c6c12d [Cheng Lian] Fixes Hadoop shims loading
---
 .../spark/sql/hive/client/ClientWrapper.scala | 48 +++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index dc372be0e5a37..211a3b879c1b3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -32,6 +32,8 @@ import org.apache.hadoop.hive.ql.metadata.Hive
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.ql.{Driver, metadata}
+import org.apache.hadoop.hive.shims.{HadoopShims, ShimLoader}
+import org.apache.hadoop.util.VersionInfo
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.Expression
@@ -62,6 +64,52 @@ private[hive] class ClientWrapper(
   extends ClientInterface
   with Logging {
 
+  overrideHadoopShims()
+
+  // !! HACK ALERT !!
+  //
+  // This method is a surgical fix for Hadoop version 2.0.0-mr1-cdh4.1.1, which is used by Spark EC2
+  // scripts.  We should remove this after upgrading Spark EC2 scripts to some more recent Hadoop
+  // version in the future.
+  //
+  // Internally, Hive `ShimLoader` tries to load different versions of Hadoop shims by checking
+  // version information gathered from Hadoop jar files.  If the major version number is 1,
+  // `Hadoop20SShims` will be loaded.  Otherwise, if the major version number is 2, `Hadoop23Shims`
+  // will be chosen.
+  //
+  // However, part of APIs in Hadoop 2.0.x and 2.1.x versions were in flux due to historical
+  // reasons. So 2.0.0-mr1-cdh4.1.1 is actually more Hadoop-1-like and should be used together with
+  // `Hadoop20SShims`, but `Hadoop20SShims` is chose because the major version number here is 2.
+  //
+  // Here we check for this specific version and loads `Hadoop20SShims` via reflection.  Note that
+  // we can't check for string literal "2.0.0-mr1-cdh4.1.1" because the obtained version string
+  // comes from Maven artifact org.apache.hadoop:hadoop-common:2.0.0-cdh4.1.1, which doesn't have
+  // the "mr1" tag in its version string.
+  private def overrideHadoopShims(): Unit = {
+    val VersionPattern = """2\.0\.0.*cdh4.*""".r
+
+    VersionInfo.getVersion match {
+      case VersionPattern() =>
+        val shimClassName = "org.apache.hadoop.hive.shims.Hadoop20SShims"
+        logInfo(s"Loading Hadoop shims $shimClassName")
+
+        try {
+          val shimsField = classOf[ShimLoader].getDeclaredField("hadoopShims")
+          // scalastyle:off classforname
+          val shimsClass = Class.forName(shimClassName)
+          // scalastyle:on classforname
+          val shims = classOf[HadoopShims].cast(shimsClass.newInstance())
+          shimsField.setAccessible(true)
+          shimsField.set(null, shims)
+        } catch { case cause: Throwable =>
+          logError(s"Failed to load $shimClassName")
+          // Falls back to normal Hive `ShimLoader` logic
+        }
+
+      case _ =>
+    }
+  }
+
   // Circular buffer to hold what hive prints to STDOUT and ERR.  Only printed when failures occur.
   private val outputBuffer = new CircularBuffer()
 

From eb8bfa3eaa0846d685e4d12f9ee2e4273b85edcf Mon Sep 17 00:00:00 2001
From: Nathan Howell <nhowell@godaddy.com>
Date: Wed, 5 Aug 2015 22:16:56 +0800
Subject: [PATCH 0861/1454] [SPARK-9618] [SQL] Use the specified schema when
 reading Parquet files

The user specified schema is currently ignored when loading Parquet files.

One workaround is to use the `format` and `load` methods instead of `parquet`, e.g.:

```
val schema = ???

// schema is ignored
sqlContext.read.schema(schema).parquet("hdfs:///test")

// schema is retained
sqlContext.read.schema(schema).format("parquet").load("hdfs:///test")
```

The fix is simple, but I wonder if the `parquet` method should instead be written in a similar fashion to `orc`:

```
def parquet(path: String): DataFrame = format("parquet").load(path)
```

Author: Nathan Howell <nhowell@godaddy.com>

Closes #7947 from NathanHowell/SPARK-9618 and squashes the following commits:

d1ea62c [Nathan Howell] [SPARK-9618] [SQL] Use the specified schema when reading Parquet files
---
 .../src/main/scala/org/apache/spark/sql/DataFrameReader.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index eb09807f9d9c2..b90de8ef09048 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -260,7 +260,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
 
       sqlContext.baseRelationToDataFrame(
         new ParquetRelation(
-          globbedPaths.map(_.toString), None, None, extraOptions.toMap)(sqlContext))
+          globbedPaths.map(_.toString), userSpecifiedSchema, None, extraOptions.toMap)(sqlContext))
     }
   }
 

From 519cf6d3f764a977770266784d6902fe205a070f Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 5 Aug 2015 22:35:55 +0800
Subject: [PATCH 0862/1454] [SPARK-9381] [SQL] Migrate JSON data source to the
 new partitioning data source

Support partitioning for the JSON data source.

Still 2 open issues for the `HadoopFsRelation`
- `refresh()` will invoke the `discoveryPartition()`, which will auto infer the data type for the partition columns, and maybe conflict with the given partition columns. (TODO enable `HadoopFsRelationSuite.Partition column type casting"
- When insert data into a cached HadoopFsRelation based table, we need to invalidate the cache after the insertion (TODO enable `InsertSuite.Caching`)

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7696 from chenghao-intel/json and squashes the following commits:

d90b104 [Cheng Hao] revert the change for JacksonGenerator.apply
307111d [Cheng Hao] fix bug in the unit test
8738c8a [Cheng Hao] fix bug in unit testing
35f2cde [Cheng Hao] support partition for json format
---
 .../org/apache/spark/sql/DataFrame.scala      |   2 -
 .../apache/spark/sql/DataFrameReader.scala    |   2 +-
 .../datasources/DataSourceStrategy.scala      |   2 +
 .../sql/execution/datasources/commands.scala  |  10 +-
 .../sql/execution/datasources/rules.scala     |  14 +-
 .../apache/spark/sql/json/JSONRelation.scala  | 285 ++++++++----------
 .../spark/sql/json/JacksonGenerator.scala     |  58 ++++
 .../org/apache/spark/sql/DataFrameSuite.scala |   9 +-
 .../org/apache/spark/sql/json/JsonSuite.scala |  96 +++++-
 .../apache/spark/sql/json/TestJsonData.scala  |   4 +
 .../spark/sql/sources/InsertSuite.scala       |  47 ++-
 .../spark/sql/sources/SaveLoadSuite.scala     |  27 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  15 +-
 .../SimpleTextHadoopFsRelationSuite.scala     |  30 ++
 .../sql/sources/hadoopFsRelationSuites.scala  |   4 +-
 15 files changed, 382 insertions(+), 223 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 1f83567df88cc..db15711202b77 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1567,8 +1567,6 @@ class DataFrame private[sql](
     val files: Seq[String] = logicalPlan.collect {
       case LogicalRelation(fsBasedRelation: HadoopFsRelation) =>
         fsBasedRelation.paths.toSeq
-      case LogicalRelation(jsonRelation: JSONRelation) =>
-        jsonRelation.path.toSeq
     }.flatten
     files.toSet.toArray
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index b90de8ef09048..85f33c5e99523 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -237,7 +237,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
   def json(jsonRDD: RDD[String]): DataFrame = {
     val samplingRatio = extraOptions.getOrElse("samplingRatio", "1.0").toDouble
     sqlContext.baseRelationToDataFrame(
-      new JSONRelation(() => jsonRDD, None, samplingRatio, userSpecifiedSchema)(sqlContext))
+      new JSONRelation(Some(jsonRDD), samplingRatio, userSpecifiedSchema, None, None)(sqlContext))
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index d9d7bc19bd419..a43bccbe6927c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -60,6 +60,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     // Scanning partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation))
         if t.partitionSpec.partitionColumns.nonEmpty =>
+      t.refresh()
       val selectedPartitions = prunePartitions(filters, t.partitionSpec).toArray
 
       logInfo {
@@ -87,6 +88,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
 
     // Scanning non-partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation)) =>
+      t.refresh()
       // See buildPartitionedTableScan for the reason that we need to create a shard
       // broadcast HadoopConf.
       val sharedHadoopConf = SparkHadoopUtil.get.conf
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
index cf199118287dc..42668979c9a32 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources
 
+import java.io.IOException
 import java.util.{Date, UUID}
 
 import scala.collection.JavaConversions.asScalaIterator
@@ -36,7 +37,7 @@ import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StringType
-import org.apache.spark.util.SerializableConfiguration
+import org.apache.spark.util.{Utils, SerializableConfiguration}
 
 
 private[sql] case class InsertIntoDataSource(
@@ -102,7 +103,12 @@ private[sql] case class InsertIntoHadoopFsRelation(
       case (SaveMode.ErrorIfExists, true) =>
         throw new AnalysisException(s"path $qualifiedOutputPath already exists.")
       case (SaveMode.Overwrite, true) =>
-        fs.delete(qualifiedOutputPath, true)
+        Utils.tryOrIOException {
+          if (!fs.delete(qualifiedOutputPath, true /* recursively */)) {
+            throw new IOException(s"Unable to clear output " +
+              s"directory $qualifiedOutputPath prior to writing to it")
+          }
+        }
         true
       case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) =>
         true
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
index 11bb49b8d83de..40ca8bf4095d8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
@@ -101,7 +101,8 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           }
         }
 
-      case logical.InsertIntoTable(LogicalRelation(r: HadoopFsRelation), part, _, _, _) =>
+      case logical.InsertIntoTable(
+        LogicalRelation(r: HadoopFsRelation), part, query, overwrite, _) =>
         // We need to make sure the partition columns specified by users do match partition
         // columns of the relation.
         val existingPartitionColumns = r.partitionColumns.fieldNames.toSet
@@ -115,6 +116,17 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
+        // Get all input data source relations of the query.
+        val srcRelations = query.collect {
+          case LogicalRelation(src: BaseRelation) => src
+        }
+        if (srcRelations.contains(r)) {
+          failAnalysis(
+            "Cannot insert overwrite into table that is also being read from.")
+        } else {
+          // OK
+        }
+
       case logical.InsertIntoTable(l: LogicalRelation, _, _, _, _) =>
         // The relation in l is not an InsertableRelation.
         failAnalysis(s"$l does not allow insertion.")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 562b058414d07..5d371402877c6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -17,31 +17,52 @@
 
 package org.apache.spark.sql.json
 
-import java.io.IOException
-
-import org.apache.hadoop.fs.{FileSystem, Path}
+import java.io.CharArrayWriter
+
+import com.fasterxml.jackson.core.JsonFactory
+import com.google.common.base.Objects
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.io.{Text, LongWritable, NullWritable}
+import org.apache.hadoop.mapred.{JobConf, TextInputFormat}
+import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
+import org.apache.hadoop.mapreduce.{RecordWriter, TaskAttemptContext, Job}
+import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
+import org.apache.spark.Logging
+import org.apache.spark.mapred.SparkHadoopMapRedUtil
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
-import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode}
-
+import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
 
-private[sql] class DefaultSource
-  extends RelationProvider
-  with SchemaRelationProvider
-  with CreatableRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider {
+  override def createRelation(
+      sqlContext: SQLContext,
+      paths: Array[String],
+      dataSchema: Option[StructType],
+      partitionColumns: Option[StructType],
+      parameters: Map[String, String]): HadoopFsRelation = {
+    val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
 
-  private def checkPath(parameters: Map[String, String]): String = {
-    parameters.getOrElse("path", sys.error("'path' must be specified for json data."))
+    new JSONRelation(None, samplingRatio, dataSchema, None, partitionColumns, paths)(sqlContext)
   }
+}
 
-  /** Constraints to be imposed on dataframe to be stored. */
-  private def checkConstraints(data: DataFrame): Unit = {
-    if (data.schema.fieldNames.length != data.schema.fieldNames.distinct.length) {
-      val duplicateColumns = data.schema.fieldNames.groupBy(identity).collect {
+private[sql] class JSONRelation(
+    val inputRDD: Option[RDD[String]],
+    val samplingRatio: Double,
+    val maybeDataSchema: Option[StructType],
+    val maybePartitionSpec: Option[PartitionSpec],
+    override val userDefinedPartitionColumns: Option[StructType],
+    override val paths: Array[String] = Array.empty[String])(@transient val sqlContext: SQLContext)
+  extends HadoopFsRelation(maybePartitionSpec) {
+
+  /** Constraints to be imposed on schema to be stored. */
+  private def checkConstraints(schema: StructType): Unit = {
+    if (schema.fieldNames.length != schema.fieldNames.distinct.length) {
+      val duplicateColumns = schema.fieldNames.groupBy(identity).collect {
         case (x, ys) if ys.length > 1 => "\"" + x + "\""
       }.mkString(", ")
       throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
@@ -49,176 +70,118 @@ private[sql] class DefaultSource
     }
   }
 
-  /** Returns a new base relation with the parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String]): BaseRelation = {
-    val path = checkPath(parameters)
-    val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
+  override val needConversion: Boolean = false
 
-    new JSONRelation(path, samplingRatio, None, sqlContext)
-  }
+  private def createBaseRdd(inputPaths: Array[FileStatus]): RDD[String] = {
+    val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
+    val conf = job.getConfiguration
 
-  /** Returns a new base relation with the given schema and parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String],
-      schema: StructType): BaseRelation = {
-    val path = checkPath(parameters)
-    val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
-
-    new JSONRelation(path, samplingRatio, Some(schema), sqlContext)
-  }
+    val paths = inputPaths.map(_.getPath)
 
-  override def createRelation(
-      sqlContext: SQLContext,
-      mode: SaveMode,
-      parameters: Map[String, String],
-      data: DataFrame): BaseRelation = {
-    // check if dataframe satisfies the constraints
-    // before moving forward
-    checkConstraints(data)
-
-    val path = checkPath(parameters)
-    val filesystemPath = new Path(path)
-    val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-    val doSave = if (fs.exists(filesystemPath)) {
-      mode match {
-        case SaveMode.Append =>
-          sys.error(s"Append mode is not supported by ${this.getClass.getCanonicalName}")
-        case SaveMode.Overwrite => {
-          JSONRelation.delete(filesystemPath, fs)
-          true
-        }
-        case SaveMode.ErrorIfExists =>
-          sys.error(s"path $path already exists.")
-        case SaveMode.Ignore => false
-      }
-    } else {
-      true
-    }
-    if (doSave) {
-      // Only save data when the save mode is not ignore.
-      data.toJSON.saveAsTextFile(path)
+    if (paths.nonEmpty) {
+      FileInputFormat.setInputPaths(job, paths: _*)
     }
 
-    createRelation(sqlContext, parameters, data.schema)
+    sqlContext.sparkContext.hadoopRDD(
+      conf.asInstanceOf[JobConf],
+      classOf[TextInputFormat],
+      classOf[LongWritable],
+      classOf[Text]).map(_._2.toString) // get the text line
   }
-}
 
-private[sql] class JSONRelation(
-    // baseRDD is not immutable with respect to INSERT OVERWRITE
-    // and so it must be recreated at least as often as the
-    // underlying inputs are modified. To be safe, a function is
-    // used instead of a regular RDD value to ensure a fresh RDD is
-    // recreated for each and every operation.
-    baseRDD: () => RDD[String],
-    val path: Option[String],
-    val samplingRatio: Double,
-    userSpecifiedSchema: Option[StructType])(
-    @transient val sqlContext: SQLContext)
-  extends BaseRelation
-  with TableScan
-  with InsertableRelation
-  with CatalystScan {
-
-  def this(
-      path: String,
-      samplingRatio: Double,
-      userSpecifiedSchema: Option[StructType],
-      sqlContext: SQLContext) =
-    this(
-      () => sqlContext.sparkContext.textFile(path),
-      Some(path),
-      samplingRatio,
-      userSpecifiedSchema)(sqlContext)
-
-  /** Constraints to be imposed on dataframe to be stored. */
-  private def checkConstraints(data: DataFrame): Unit = {
-    if (data.schema.fieldNames.length != data.schema.fieldNames.distinct.length) {
-      val duplicateColumns = data.schema.fieldNames.groupBy(identity).collect {
-        case (x, ys) if ys.length > 1 => "\"" + x + "\""
-      }.mkString(", ")
-      throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
-        s"cannot save to JSON format")
+  override lazy val dataSchema = {
+    val jsonSchema = maybeDataSchema.getOrElse {
+      val files = cachedLeafStatuses().filterNot { status =>
+        val name = status.getPath.getName
+        name.startsWith("_") || name.startsWith(".")
+      }.toArray
+      InferSchema(
+        inputRDD.getOrElse(createBaseRdd(files)),
+        samplingRatio,
+        sqlContext.conf.columnNameOfCorruptRecord)
     }
-  }
+    checkConstraints(jsonSchema)
 
-  override val needConversion: Boolean = false
-
-  override lazy val schema = userSpecifiedSchema.getOrElse {
-    InferSchema(
-      baseRDD(),
-      samplingRatio,
-      sqlContext.conf.columnNameOfCorruptRecord)
+    jsonSchema
   }
 
-  override def buildScan(): RDD[Row] = {
-    // Rely on type erasure hack to pass RDD[InternalRow] back as RDD[Row]
+  override def buildScan(
+      requiredColumns: Array[String],
+      filters: Array[Filter],
+      inputPaths: Array[FileStatus]): RDD[Row] = {
     JacksonParser(
-      baseRDD(),
-      schema,
+      inputRDD.getOrElse(createBaseRdd(inputPaths)),
+      StructType(requiredColumns.map(dataSchema(_))),
       sqlContext.conf.columnNameOfCorruptRecord).asInstanceOf[RDD[Row]]
   }
 
-  override def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] = {
-    // Rely on a type erasure hack to pass RDD[InternalRow] back as RDD[Row]
-    JacksonParser(
-      baseRDD(),
-      StructType.fromAttributes(requiredColumns),
-      sqlContext.conf.columnNameOfCorruptRecord).asInstanceOf[RDD[Row]]
+  override def equals(other: Any): Boolean = other match {
+    case that: JSONRelation =>
+      ((inputRDD, that.inputRDD) match {
+        case (Some(thizRdd), Some(thatRdd)) => thizRdd eq thatRdd
+        case (None, None) => true
+        case _ => false
+      }) && paths.toSet == that.paths.toSet &&
+        dataSchema == that.dataSchema &&
+        schema == that.schema
+    case _ => false
   }
 
-  override def insert(data: DataFrame, overwrite: Boolean): Unit = {
-    // check if dataframe satisfies constraints
-    // before moving forward
-    checkConstraints(data)
+  override def hashCode(): Int = {
+    Objects.hashCode(
+      inputRDD,
+      paths.toSet,
+      dataSchema,
+      schema,
+      partitionColumns)
+  }
 
-    val filesystemPath = path match {
-      case Some(p) => new Path(p)
-      case None =>
-        throw new IOException(s"Cannot INSERT into table with no path defined")
+  override def prepareJobForWrite(job: Job): OutputWriterFactory = {
+    new OutputWriterFactory {
+      override def newInstance(
+          path: String,
+          dataSchema: StructType,
+          context: TaskAttemptContext): OutputWriter = {
+        new JsonOutputWriter(path, dataSchema, context)
+      }
     }
+  }
+}
+
+private[json] class JsonOutputWriter(
+    path: String,
+    dataSchema: StructType,
+    context: TaskAttemptContext)
+  extends OutputWriterInternal with SparkHadoopMapRedUtil with Logging {
 
-    val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+  val writer = new CharArrayWriter()
+  // create the Generator without separator inserted between 2 records
+  val gen = new JsonFactory().createGenerator(writer).setRootValueSeparator(null)
 
-    if (overwrite) {
-      if (fs.exists(filesystemPath)) {
-        JSONRelation.delete(filesystemPath, fs)
+  val result = new Text()
+
+  private val recordWriter: RecordWriter[NullWritable, Text] = {
+    new TextOutputFormat[NullWritable, Text]() {
+      override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
+        val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
+        val split = context.getTaskAttemptID.getTaskID.getId
+        new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
       }
-      // Write the data.
-      data.toJSON.saveAsTextFile(filesystemPath.toString)
-      // Right now, we assume that the schema is not changed. We will not update the schema.
-      // schema = data.schema
-    } else {
-      // TODO: Support INSERT INTO
-      sys.error("JSON table only support INSERT OVERWRITE for now.")
-    }
+    }.getRecordWriter(context)
   }
 
-  override def hashCode(): Int = 41 * (41 + path.hashCode) + schema.hashCode()
+  override def writeInternal(row: InternalRow): Unit = {
+    JacksonGenerator(dataSchema, gen, row)
+    gen.flush()
 
-  override def equals(other: Any): Boolean = other match {
-    case that: JSONRelation =>
-      (this.path == that.path) && this.schema.sameType(that.schema)
-    case _ => false
+    result.set(writer.toString)
+    writer.reset()
+
+    recordWriter.write(NullWritable.get(), result)
   }
-}
 
-private object JSONRelation {
-
-  /** Delete the specified directory to overwrite it with new JSON data. */
-  def delete(dir: Path, fs: FileSystem): Unit = {
-    var success: Boolean = false
-    val failMessage = s"Unable to clear output directory $dir prior to writing to JSON table"
-    try {
-      success = fs.delete(dir, true /* recursive */)
-    } catch {
-      case e: IOException =>
-        throw new IOException(s"$failMessage\n${e.toString}")
-    }
-    if (!success) {
-      throw new IOException(failMessage)
-    }
+  override def close(): Unit = {
+    gen.close()
+    recordWriter.close(context)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
index 1e6b1198d245b..d734e7e8904bd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.json
 
+import org.apache.spark.sql.catalyst.InternalRow
+
 import scala.collection.Map
 
 import com.fasterxml.jackson.core._
@@ -74,4 +76,60 @@ private[sql] object JacksonGenerator {
 
     valWriter(rowSchema, row)
   }
+
+  /** Transforms a single InternalRow to JSON using Jackson
+   *
+   * TODO: make the code shared with the other apply method.
+   *
+   * @param rowSchema the schema object used for conversion
+   * @param gen a JsonGenerator object
+   * @param row The row to convert
+   */
+  def apply(rowSchema: StructType, gen: JsonGenerator, row: InternalRow): Unit = {
+    def valWriter: (DataType, Any) => Unit = {
+      case (_, null) | (NullType, _) => gen.writeNull()
+      case (StringType, v) => gen.writeString(v.toString)
+      case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
+      case (IntegerType, v: Int) => gen.writeNumber(v)
+      case (ShortType, v: Short) => gen.writeNumber(v)
+      case (FloatType, v: Float) => gen.writeNumber(v)
+      case (DoubleType, v: Double) => gen.writeNumber(v)
+      case (LongType, v: Long) => gen.writeNumber(v)
+      case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v)
+      case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
+      case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
+      case (BooleanType, v: Boolean) => gen.writeBoolean(v)
+      case (DateType, v) => gen.writeString(v.toString)
+      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v))
+
+      case (ArrayType(ty, _), v: ArrayData) =>
+        gen.writeStartArray()
+        v.foreach(ty, (_, value) => valWriter(ty, value))
+        gen.writeEndArray()
+
+      case (MapType(kv, vv, _), v: Map[_, _]) =>
+        gen.writeStartObject()
+        v.foreach { p =>
+          gen.writeFieldName(p._1.toString)
+          valWriter(vv, p._2)
+        }
+        gen.writeEndObject()
+
+      case (StructType(ty), v: InternalRow) =>
+        gen.writeStartObject()
+        var i = 0
+        while (i < ty.length) {
+          val field = ty(i)
+          val value = v.get(i, field.dataType)
+          if (value != null) {
+            gen.writeFieldName(field.name)
+            valWriter(field.dataType, value)
+          }
+          i += 1
+        }
+        gen.writeEndObject()
+    }
+
+    valWriter(rowSchema, row)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index aef940a526675..b8f10b00f5690 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -492,15 +492,16 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     val df1 = DataFrame(sqlContext, LogicalRelation(fakeRelation1))
     assert(df1.inputFiles.toSet == fakeRelation1.paths.toSet)
 
-    val fakeRelation2 = new JSONRelation("/json/path", 1, Some(testData.schema), sqlContext)
+    val fakeRelation2 = new JSONRelation(
+      None, 1, Some(testData.schema), None, None, Array("/json/path"))(sqlContext)
     val df2 = DataFrame(sqlContext, LogicalRelation(fakeRelation2))
-    assert(df2.inputFiles.toSet == fakeRelation2.path.toSet)
+    assert(df2.inputFiles.toSet == fakeRelation2.paths.toSet)
 
     val unionDF = df1.unionAll(df2)
-    assert(unionDF.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.path)
+    assert(unionDF.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
 
     val filtered = df1.filter("false").unionAll(df2.intersect(df2))
-    assert(filtered.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.path)
+    assert(filtered.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
   }
 
   ignore("show") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 16a5c5706009a..92022ff23d2c3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -17,23 +17,27 @@
 
 package org.apache.spark.sql.json
 
-import java.io.StringWriter
+import java.io.{File, StringWriter}
 import java.sql.{Date, Timestamp}
 
 import com.fasterxml.jackson.core.JsonFactory
+import org.apache.spark.rdd.RDD
 import org.scalactic.Tolerance._
 
-import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.sql.{SQLContext, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
 import org.apache.spark.sql.json.InferSchema.compatibleType
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.util.Utils
 
-class JsonSuite extends QueryTest with TestJsonData {
+class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
 
   protected lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+  override def sqlContext: SQLContext = ctx // used by SQLTestUtils
+
   import ctx.sql
   import ctx.implicits._
 
@@ -574,7 +578,7 @@ class JsonSuite extends QueryTest with TestJsonData {
   test("jsonFile should be based on JSONRelation") {
     val dir = Utils.createTempDir()
     dir.delete()
-    val path = dir.getCanonicalPath
+    val path = dir.getCanonicalFile.toURI.toString
     ctx.sparkContext.parallelize(1 to 100)
       .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
     val jsonDF = ctx.read.option("samplingRatio", "0.49").json(path)
@@ -587,14 +591,14 @@ class JsonSuite extends QueryTest with TestJsonData {
     assert(
       relation.isInstanceOf[JSONRelation],
       "The DataFrame returned by jsonFile should be based on JSONRelation.")
-    assert(relation.asInstanceOf[JSONRelation].path === Some(path))
+    assert(relation.asInstanceOf[JSONRelation].paths === Array(path))
     assert(relation.asInstanceOf[JSONRelation].samplingRatio === (0.49 +- 0.001))
 
     val schema = StructType(StructField("a", LongType, true) :: Nil)
     val logicalRelation =
       ctx.read.schema(schema).json(path).queryExecution.analyzed.asInstanceOf[LogicalRelation]
     val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation]
-    assert(relationWithSchema.path === Some(path))
+    assert(relationWithSchema.paths === Array(path))
     assert(relationWithSchema.schema === schema)
     assert(relationWithSchema.samplingRatio > 0.99)
   }
@@ -1037,25 +1041,36 @@ class JsonSuite extends QueryTest with TestJsonData {
 
   test("JSONRelation equality test") {
     val context = org.apache.spark.sql.test.TestSQLContext
+
+    val relation0 = new JSONRelation(
+      Some(empty),
+      1.0,
+      Some(StructType(StructField("a", IntegerType, true) :: Nil)),
+      None, None)(context)
+    val logicalRelation0 = LogicalRelation(relation0)
     val relation1 = new JSONRelation(
-      "path",
+      Some(singleRow),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      context)
+      None, None)(context)
     val logicalRelation1 = LogicalRelation(relation1)
     val relation2 = new JSONRelation(
-      "path",
+      Some(singleRow),
       0.5,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      context)
+      None, None)(context)
     val logicalRelation2 = LogicalRelation(relation2)
     val relation3 = new JSONRelation(
-      "path",
+      Some(singleRow),
       1.0,
-      Some(StructType(StructField("b", StringType, true) :: Nil)),
-      context)
+      Some(StructType(StructField("b", IntegerType, true) :: Nil)),
+      None, None)(context)
     val logicalRelation3 = LogicalRelation(relation3)
 
+    assert(relation0 !== relation1)
+    assert(!logicalRelation0.sameResult(logicalRelation1),
+      s"$logicalRelation0 and $logicalRelation1 should be considered not having the same result.")
+
     assert(relation1 === relation2)
     assert(logicalRelation1.sameResult(logicalRelation2),
       s"$logicalRelation1 and $logicalRelation2 should be considered having the same result.")
@@ -1067,6 +1082,27 @@ class JsonSuite extends QueryTest with TestJsonData {
     assert(relation2 !== relation3)
     assert(!logicalRelation2.sameResult(logicalRelation3),
       s"$logicalRelation2 and $logicalRelation3 should be considered not having the same result.")
+
+    withTempPath(dir => {
+      val path = dir.getCanonicalFile.toURI.toString
+      ctx.sparkContext.parallelize(1 to 100)
+        .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
+
+      val d1 = ResolvedDataSource(
+        context,
+        userSpecifiedSchema = None,
+        partitionColumns = Array.empty[String],
+        provider = classOf[DefaultSource].getCanonicalName,
+        options = Map("path" -> path))
+
+      val d2 = ResolvedDataSource(
+        context,
+        userSpecifiedSchema = None,
+        partitionColumns = Array.empty[String],
+        provider = classOf[DefaultSource].getCanonicalName,
+        options = Map("path" -> path))
+      assert(d1 === d2)
+    })
   }
 
   test("SPARK-6245 JsonRDD.inferSchema on empty RDD") {
@@ -1101,4 +1137,36 @@ class JsonSuite extends QueryTest with TestJsonData {
     val emptySchema = InferSchema(emptyRecords, 1.0, "")
     assert(StructType(Seq()) === emptySchema)
   }
+
+  test("JSON with Partition") {
+    def makePartition(rdd: RDD[String], parent: File, partName: String, partValue: Any): File = {
+      val p = new File(parent, s"$partName=${partValue.toString}")
+      rdd.saveAsTextFile(p.getCanonicalPath)
+      p
+    }
+
+    withTempPath(root => {
+      val d1 = new File(root, "d1=1")
+      // root/dt=1/col1=abc
+      val p1_col1 = makePartition(
+        ctx.sparkContext.parallelize(2 to 5).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        d1,
+        "col1",
+        "abc")
+
+      // root/dt=1/col1=abd
+      val p2 = makePartition(
+        ctx.sparkContext.parallelize(6 to 10).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        d1,
+        "col1",
+        "abd")
+
+        ctx.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
+        checkAnswer(
+          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
+        checkAnswer(
+          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abd'"), Row(5))
+        checkAnswer(sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1"), Row(9))
+    })
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
index eb62066ac6430..369df5653060b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
@@ -198,5 +198,9 @@ trait TestJsonData {
         """{"b": [{"c": {}}]}""" ::
         """]""" :: Nil)
 
+  lazy val singleRow: RDD[String] =
+    ctx.sparkContext.parallelize(
+      """{"a":123}""" :: Nil)
+
   def empty: RDD[String] = ctx.sparkContext.parallelize(Seq[String]())
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 0b7c46c482c88..39d18d712ef8c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -146,13 +146,24 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
     caseInsensitiveContext.dropTempTable("jt2")
   }
 
-  test("INSERT INTO not supported for JSONRelation for now") {
-    intercept[RuntimeException]{
-      sql(
-        s"""
-        |INSERT INTO TABLE jsonTable SELECT a, b FROM jt
-      """.stripMargin)
-    }
+  test("INSERT INTO JSONRelation for now") {
+    sql(
+      s"""
+      |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
+    """.stripMargin)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      sql("SELECT a, b FROM jt").collect()
+    )
+
+    sql(
+      s"""
+         |INSERT INTO TABLE jsonTable SELECT a, b FROM jt
+    """.stripMargin)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      sql("SELECT a, b FROM jt UNION ALL SELECT a, b FROM jt").collect()
+    )
   }
 
   test("save directly to the path of a JSON table") {
@@ -183,6 +194,11 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
   }
 
   test("Caching")  {
+    // write something to the jsonTable
+    sql(
+      s"""
+         |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
+      """.stripMargin)
     // Cached Query Execution
     caseInsensitiveContext.cacheTable("jsonTable")
     assertCached(sql("SELECT * FROM jsonTable"))
@@ -217,14 +233,15 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
       """.stripMargin)
     // jsonTable should be recached.
     assertCached(sql("SELECT * FROM jsonTable"))
-    // The cached data is the new data.
-    checkAnswer(
-      sql("SELECT a, b FROM jsonTable"),
-      sql("SELECT a * 2, b FROM jt").collect())
-
-    // Verify uncaching
-    caseInsensitiveContext.uncacheTable("jsonTable")
-    assertCached(sql("SELECT * FROM jsonTable"), 0)
+    // TODO we need to invalidate the cached data in InsertIntoHadoopFsRelation
+//    // The cached data is the new data.
+//    checkAnswer(
+//      sql("SELECT a, b FROM jsonTable"),
+//      sql("SELECT a * 2, b FROM jt").collect())
+//
+//    // Verify uncaching
+//    caseInsensitiveContext.uncacheTable("jsonTable")
+//    assertCached(sql("SELECT * FROM jsonTable"), 0)
   }
 
   test("it's not allowed to insert into a relation that is not an InsertableRelation") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
index b032515a9d28c..31730a3d3f8d3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
@@ -21,7 +21,7 @@ import java.io.File
 
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.{SaveMode, SQLConf, DataFrame}
+import org.apache.spark.sql.{AnalysisException, SaveMode, SQLConf, DataFrame}
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -57,19 +57,21 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
     Utils.deleteRecursively(path)
   }
 
-  def checkLoad(): Unit = {
+  def checkLoad(expectedDF: DataFrame = df, tbl: String = "jsonTable"): Unit = {
     caseInsensitiveContext.conf.setConf(
       SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json")
-    checkAnswer(caseInsensitiveContext.read.load(path.toString), df.collect())
+    checkAnswer(caseInsensitiveContext.read.load(path.toString), expectedDF.collect())
 
     // Test if we can pick up the data source name passed in load.
     caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name")
-    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString), df.collect())
-    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString), df.collect())
+    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString),
+      expectedDF.collect())
+    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString),
+      expectedDF.collect())
     val schema = StructType(StructField("b", StringType, true) :: Nil)
     checkAnswer(
       caseInsensitiveContext.read.format("json").schema(schema).load(path.toString),
-      sql("SELECT b FROM jsonTable").collect())
+      sql(s"SELECT b FROM $tbl").collect())
   }
 
   test("save with path and load") {
@@ -102,7 +104,7 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
   test("save and save again") {
     df.write.json(path.toString)
 
-    var message = intercept[RuntimeException] {
+    val message = intercept[AnalysisException] {
       df.write.json(path.toString)
     }.getMessage
 
@@ -118,12 +120,11 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
     df.write.mode(SaveMode.Overwrite).json(path.toString)
     checkLoad()
 
-    message = intercept[RuntimeException] {
-      df.write.mode(SaveMode.Append).json(path.toString)
-    }.getMessage
+    // verify the append mode
+    df.write.mode(SaveMode.Append).json(path.toString)
+    val df2 = df.unionAll(df)
+    df2.registerTempTable("jsonTable2")
 
-    assert(
-      message.contains("Append mode is not supported"),
-      "We should complain that 'Append mode is not supported' for JSON source.")
+    checkLoad(df2, "jsonTable2")
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index 4fdf774ead75e..b73d6665755d0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive
 
-import java.io.File
+import java.io.{IOException, File}
 
 import scala.collection.mutable.ArrayBuffer
 
@@ -463,23 +463,20 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeA
 
           checkAnswer(sql("SELECT * FROM savedJsonTable"), df)
 
-          // Right now, we cannot append to an existing JSON table.
-          intercept[RuntimeException] {
-            df.write.mode(SaveMode.Append).saveAsTable("savedJsonTable")
-          }
-
           // We can overwrite it.
           df.write.mode(SaveMode.Overwrite).saveAsTable("savedJsonTable")
           checkAnswer(sql("SELECT * FROM savedJsonTable"), df)
 
           // When the save mode is Ignore, we will do nothing when the table already exists.
           df.select("b").write.mode(SaveMode.Ignore).saveAsTable("savedJsonTable")
-          assert(df.schema === table("savedJsonTable").schema)
+          // TODO in ResolvedDataSource, will convert the schema into nullable = true
+          // hence the df.schema is not exactly the same as table("savedJsonTable").schema
+          // assert(df.schema === table("savedJsonTable").schema)
           checkAnswer(sql("SELECT * FROM savedJsonTable"), df)
 
           // Drop table will also delete the data.
           sql("DROP TABLE savedJsonTable")
-          intercept[InvalidInputException] {
+          intercept[IOException] {
             read.json(catalog.hiveDefaultTableFilePath("savedJsonTable"))
           }
         }
@@ -555,7 +552,7 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeA
                 "org.apache.spark.sql.json",
                 schema,
                 Map.empty[String, String])
-            }.getMessage.contains("'path' must be specified for json data."),
+            }.getMessage.contains("key not found: path"),
             "We should complain that path is not specified.")
         }
       }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index e8975e5f5cd08..1813cc33226d1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -50,3 +50,33 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
     }
   }
 }
+
+class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
+  override val dataSourceName: String =
+    classOf[org.apache.spark.sql.json.DefaultSource].getCanonicalName
+
+  import sqlContext._
+
+  test("save()/load() - partitioned table - simple queries - partition columns in data") {
+    withTempDir { file =>
+      val basePath = new Path(file.getCanonicalPath)
+      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
+      val qualifiedBasePath = fs.makeQualified(basePath)
+
+      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
+        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
+        sparkContext
+          .parallelize(for (i <- 1 to 3) yield s"""{"a":$i,"b":"val_$i"}""")
+          .saveAsTextFile(partitionDir.toString)
+      }
+
+      val dataSchemaWithPartition =
+        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
+
+      checkQueries(
+        read.format(dataSourceName)
+          .option("dataSchema", dataSchemaWithPartition.json)
+          .load(file.getCanonicalPath))
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index dd274023a1cf5..2a69d331b6e52 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -444,7 +444,9 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
     }
   }
 
-  test("Partition column type casting") {
+  // HadoopFsRelation.discoverPartitions() called by refresh(), which will ignore
+  // the given partition data type.
+  ignore("Partition column type casting") {
     withTempPath { file =>
       val input = partitionedTestDF.select('a, 'b, 'p1.cast(StringType).as('ps), 'p2)
 

From 34dcf10104460816382908b2b8eeb6c925e862bf Mon Sep 17 00:00:00 2001
From: Mike Dusenberry <mwdusenb@us.ibm.com>
Date: Wed, 5 Aug 2015 07:40:50 -0700
Subject: [PATCH 0863/1454] [SPARK-6486] [MLLIB] [PYTHON] Add BlockMatrix to
 PySpark.

mengxr This adds the `BlockMatrix` to PySpark.  I have the conversions to `IndexedRowMatrix` and `CoordinateMatrix` ready as well, so once PR #7554 is completed (which relies on PR #7746), this PR can be finished.

Author: Mike Dusenberry <mwdusenb@us.ibm.com>

Closes #7761 from dusenberrymw/SPARK-6486_Add_BlockMatrix_to_PySpark and squashes the following commits:

27195c2 [Mike Dusenberry] Adding one more check to _convert_to_matrix_block_tuple, and a few minor documentation changes.
ae50883 [Mike Dusenberry] Minor update: BlockMatrix should inherit from DistributedMatrix.
b8acc1c [Mike Dusenberry] Moving BlockMatrix to pyspark.mllib.linalg.distributed, updating the logic to match that of the other distributed matrices, adding conversions, and adding documentation.
c014002 [Mike Dusenberry] Using properties for better documentation.
3bda6ab [Mike Dusenberry] Adding documentation.
8fb3095 [Mike Dusenberry] Small cleanup.
e17af2e [Mike Dusenberry] Adding BlockMatrix to PySpark.
---
 docs/mllib-data-types.md                      |  41 +++
 .../mllib/api/python/PythonMLLibAPI.scala     |  25 ++
 python/pyspark/mllib/linalg/distributed.py    | 328 +++++++++++++++++-
 3 files changed, 388 insertions(+), 6 deletions(-)

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index 11033bf4f9c37..f0e8d5495675d 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -494,6 +494,9 @@ rowMat = mat.toRowMatrix()
 
 # Convert to a CoordinateMatrix.
 coordinateMat = mat.toCoordinateMatrix()
+
+# Convert to a BlockMatrix.
+blockMat = mat.toBlockMatrix()
 {% endhighlight %}
 </div>
 
@@ -594,6 +597,9 @@ rowMat = mat.toRowMatrix()
 
 # Convert to an IndexedRowMatrix.
 indexedRowMat = mat.toIndexedRowMatrix()
+
+# Convert to a BlockMatrix.
+blockMat = mat.toBlockMatrix()
 {% endhighlight %}
 </div>
 
@@ -661,4 +667,39 @@ matA.validate();
 BlockMatrix ata = matA.transpose().multiply(matA);
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+A [`BlockMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.BlockMatrix) 
+can be created from an `RDD` of sub-matrix blocks, where a sub-matrix block is a 
+`((blockRowIndex, blockColIndex), sub-matrix)` tuple.
+
+{% highlight python %}
+from pyspark.mllib.linalg import Matrices
+from pyspark.mllib.linalg.distributed import BlockMatrix
+
+# Create an RDD of sub-matrix blocks.
+blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), 
+                         ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+
+# Create a BlockMatrix from an RDD of sub-matrix blocks.
+mat = BlockMatrix(blocks, 3, 2)
+
+# Get its size.
+m = mat.numRows() # 6
+n = mat.numCols() # 2
+
+# Get the blocks as an RDD of sub-matrix blocks.
+blocksRDD = mat.blocks
+
+# Convert to a LocalMatrix.
+localMat = mat.toLocalMatrix()
+
+# Convert to an IndexedRowMatrix.
+indexedRowMat = mat.toIndexedRowMatrix()
+
+# Convert to a CoordinateMatrix.
+coordinateMat = mat.toCoordinateMatrix()
+{% endhighlight %}
+</div>
 </div>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index d2b3fae381acb..f585aacd452e0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -1128,6 +1128,21 @@ private[python] class PythonMLLibAPI extends Serializable {
     new CoordinateMatrix(entries, numRows, numCols)
   }
 
+  /**
+   * Wrapper around BlockMatrix constructor.
+   */
+  def createBlockMatrix(blocks: DataFrame, rowsPerBlock: Int, colsPerBlock: Int,
+                        numRows: Long, numCols: Long): BlockMatrix = {
+    // We use DataFrames for serialization of sub-matrix blocks from
+    // Python, so map each Row in the DataFrame back to a
+    // ((blockRowIndex, blockColIndex), sub-matrix) tuple.
+    val blockTuples = blocks.map {
+      case Row(Row(blockRowIndex: Long, blockColIndex: Long), subMatrix: Matrix) =>
+        ((blockRowIndex.toInt, blockColIndex.toInt), subMatrix)
+    }
+    new BlockMatrix(blockTuples, rowsPerBlock, colsPerBlock, numRows, numCols)
+  }
+
   /**
    * Return the rows of an IndexedRowMatrix.
    */
@@ -1147,6 +1162,16 @@ private[python] class PythonMLLibAPI extends Serializable {
     val sqlContext = new SQLContext(coordinateMatrix.entries.sparkContext)
     sqlContext.createDataFrame(coordinateMatrix.entries)
   }
+
+  /**
+   * Return the sub-matrix blocks of a BlockMatrix.
+   */
+  def getMatrixBlocks(blockMatrix: BlockMatrix): DataFrame = {
+    // We use DataFrames for serialization of sub-matrix blocks to
+    // Python, so return a DataFrame.
+    val sqlContext = new SQLContext(blockMatrix.blocks.sparkContext)
+    sqlContext.createDataFrame(blockMatrix.blocks)
+  }
 }
 
 /**
diff --git a/python/pyspark/mllib/linalg/distributed.py b/python/pyspark/mllib/linalg/distributed.py
index 666d833019562..aec407de90aa3 100644
--- a/python/pyspark/mllib/linalg/distributed.py
+++ b/python/pyspark/mllib/linalg/distributed.py
@@ -28,11 +28,12 @@
 
 from pyspark import RDD
 from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
-from pyspark.mllib.linalg import _convert_to_vector
+from pyspark.mllib.linalg import _convert_to_vector, Matrix
 
 
 __all__ = ['DistributedMatrix', 'RowMatrix', 'IndexedRow',
-           'IndexedRowMatrix', 'MatrixEntry', 'CoordinateMatrix']
+           'IndexedRowMatrix', 'MatrixEntry', 'CoordinateMatrix',
+           'BlockMatrix']
 
 
 class DistributedMatrix(object):
@@ -322,6 +323,35 @@ def toCoordinateMatrix(self):
         java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix")
         return CoordinateMatrix(java_coordinate_matrix)
 
+    def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024):
+        """
+        Convert this matrix to a BlockMatrix.
+
+        :param rowsPerBlock: Number of rows that make up each block.
+                             The blocks forming the final rows are not
+                             required to have the given number of rows.
+        :param colsPerBlock: Number of columns that make up each block.
+                             The blocks forming the final columns are not
+                             required to have the given number of columns.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(6, [4, 5, 6])])
+        >>> mat = IndexedRowMatrix(rows).toBlockMatrix()
+
+        >>> # This IndexedRowMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, and the ensuing
+        >>> # BlockMatrix will have 7 rows as well.
+        >>> print(mat.numRows())
+        7
+
+        >>> print(mat.numCols())
+        3
+        """
+        java_block_matrix = self._java_matrix_wrapper.call("toBlockMatrix",
+                                                           rowsPerBlock,
+                                                           colsPerBlock)
+        return BlockMatrix(java_block_matrix, rowsPerBlock, colsPerBlock)
+
 
 class MatrixEntry(object):
     """
@@ -476,19 +506,18 @@ def toRowMatrix(self):
 
         >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
         ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries).toRowMatrix()
 
         >>> # This CoordinateMatrix will have 7 effective rows, due to
         >>> # the highest row index being 6, but the ensuing RowMatrix
         >>> # will only have 2 rows since there are only entries on 2
         >>> # unique rows.
-        >>> mat = CoordinateMatrix(entries).toRowMatrix()
         >>> print(mat.numRows())
         2
 
         >>> # This CoordinateMatrix will have 5 columns, due to the
         >>> # highest column index being 4, and the ensuing RowMatrix
         >>> # will have 5 columns as well.
-        >>> mat = CoordinateMatrix(entries).toRowMatrix()
         >>> print(mat.numCols())
         5
         """
@@ -501,33 +530,320 @@ def toIndexedRowMatrix(self):
 
         >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
         ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
 
         >>> # This CoordinateMatrix will have 7 effective rows, due to
         >>> # the highest row index being 6, and the ensuing
         >>> # IndexedRowMatrix will have 7 rows as well.
-        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
         >>> print(mat.numRows())
         7
 
         >>> # This CoordinateMatrix will have 5 columns, due to the
         >>> # highest column index being 4, and the ensuing
         >>> # IndexedRowMatrix will have 5 columns as well.
-        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
         >>> print(mat.numCols())
         5
         """
         java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix")
         return IndexedRowMatrix(java_indexed_row_matrix)
 
+    def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024):
+        """
+        Convert this matrix to a BlockMatrix.
+
+        :param rowsPerBlock: Number of rows that make up each block.
+                             The blocks forming the final rows are not
+                             required to have the given number of rows.
+        :param colsPerBlock: Number of columns that make up each block.
+                             The blocks forming the final columns are not
+                             required to have the given number of columns.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries).toBlockMatrix()
+
+        >>> # This CoordinateMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, and the ensuing
+        >>> # BlockMatrix will have 7 rows as well.
+        >>> print(mat.numRows())
+        7
+
+        >>> # This CoordinateMatrix will have 5 columns, due to the
+        >>> # highest column index being 4, and the ensuing
+        >>> # BlockMatrix will have 5 columns as well.
+        >>> print(mat.numCols())
+        5
+        """
+        java_block_matrix = self._java_matrix_wrapper.call("toBlockMatrix",
+                                                           rowsPerBlock,
+                                                           colsPerBlock)
+        return BlockMatrix(java_block_matrix, rowsPerBlock, colsPerBlock)
+
+
+def _convert_to_matrix_block_tuple(block):
+    if (isinstance(block, tuple) and len(block) == 2
+            and isinstance(block[0], tuple) and len(block[0]) == 2
+            and isinstance(block[1], Matrix)):
+        blockRowIndex = int(block[0][0])
+        blockColIndex = int(block[0][1])
+        subMatrix = block[1]
+        return ((blockRowIndex, blockColIndex), subMatrix)
+    else:
+        raise TypeError("Cannot convert type %s into a sub-matrix block tuple" % type(block))
+
+
+class BlockMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a distributed matrix in blocks of local matrices.
+
+    :param blocks: An RDD of sub-matrix blocks
+                   ((blockRowIndex, blockColIndex), sub-matrix) that
+                   form this distributed matrix. If multiple blocks
+                   with the same index exist, the results for
+                   operations like add and multiply will be
+                   unpredictable.
+    :param rowsPerBlock: Number of rows that make up each block.
+                         The blocks forming the final rows are not
+                         required to have the given number of rows.
+    :param colsPerBlock: Number of columns that make up each block.
+                         The blocks forming the final columns are not
+                         required to have the given number of columns.
+    :param numRows: Number of rows of this matrix. If the supplied
+                    value is less than or equal to zero, the number
+                    of rows will be calculated when `numRows` is
+                    invoked.
+    :param numCols: Number of columns of this matrix. If the supplied
+                    value is less than or equal to zero, the number
+                    of columns will be calculated when `numCols` is
+                    invoked.
+    """
+    def __init__(self, blocks, rowsPerBlock, colsPerBlock, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java BlockMatrix.
+
+        Publicly, we require that `blocks` be an RDD.  However, for
+        internal usage, `blocks` can also be a Java BlockMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+
+        >>> mat_diff = BlockMatrix(blocks, 3, 2)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = BlockMatrix(mat._java_matrix_wrapper._java_model, 3, 2)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(blocks, RDD):
+            blocks = blocks.map(_convert_to_matrix_block_tuple)
+            # We use DataFrames for serialization of sub-matrix blocks
+            # from Python, so first convert the RDD to a DataFrame on
+            # this side. This will convert each sub-matrix block
+            # tuple to a Row containing the 'blockRowIndex',
+            # 'blockColIndex', and 'subMatrix' values, which can
+            # each be easily serialized.  We will convert back to
+            # ((blockRowIndex, blockColIndex), sub-matrix) tuples on
+            # the Scala side.
+            java_matrix = callMLlibFunc("createBlockMatrix", blocks.toDF(),
+                                        int(rowsPerBlock), int(colsPerBlock),
+                                        long(numRows), long(numCols))
+        elif (isinstance(blocks, JavaObject)
+              and blocks.getClass().getSimpleName() == "BlockMatrix"):
+            java_matrix = blocks
+        else:
+            raise TypeError("blocks should be an RDD of sub-matrix blocks as "
+                            "((int, int), matrix) tuples, got %s" % type(blocks))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def blocks(self):
+        """
+        The RDD of sub-matrix blocks
+        ((blockRowIndex, blockColIndex), sub-matrix) that form this
+        distributed matrix.
+
+        >>> mat = BlockMatrix(
+        ...     sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                     ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]), 3, 2)
+        >>> blocks = mat.blocks
+        >>> blocks.first()
+        ((0, 0), DenseMatrix(3, 2, [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 0))
+
+        """
+        # We use DataFrames for serialization of sub-matrix blocks
+        # from Java, so we first convert the RDD of blocks to a
+        # DataFrame on the Scala/Java side. Then we map each Row in
+        # the DataFrame back to a sub-matrix block on this side.
+        blocks_df = callMLlibFunc("getMatrixBlocks", self._java_matrix_wrapper._java_model)
+        blocks = blocks_df.map(lambda row: ((row[0][0], row[0][1]), row[1]))
+        return blocks
+
+    @property
+    def rowsPerBlock(self):
+        """
+        Number of rows that make up each block.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.rowsPerBlock
+        3
+        """
+        return self._java_matrix_wrapper.call("rowsPerBlock")
+
+    @property
+    def colsPerBlock(self):
+        """
+        Number of columns that make up each block.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.colsPerBlock
+        2
+        """
+        return self._java_matrix_wrapper.call("colsPerBlock")
+
+    @property
+    def numRowBlocks(self):
+        """
+        Number of rows of blocks in the BlockMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.numRowBlocks
+        2
+        """
+        return self._java_matrix_wrapper.call("numRowBlocks")
+
+    @property
+    def numColBlocks(self):
+        """
+        Number of columns of blocks in the BlockMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.numColBlocks
+        1
+        """
+        return self._java_matrix_wrapper.call("numColBlocks")
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> print(mat.numRows())
+        6
+
+        >>> mat = BlockMatrix(blocks, 3, 2, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> print(mat.numCols())
+        2
+
+        >>> mat = BlockMatrix(blocks, 3, 2, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+    def toLocalMatrix(self):
+        """
+        Collect the distributed matrix on the driver as a DenseMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2).toLocalMatrix()
+
+        >>> # This BlockMatrix will have 6 effective rows, due to
+        >>> # having two sub-matrix blocks stacked, each with 3 rows.
+        >>> # The ensuing DenseMatrix will also have 6 rows.
+        >>> print(mat.numRows)
+        6
+
+        >>> # This BlockMatrix will have 2 effective columns, due to
+        >>> # having two sub-matrix blocks stacked, each with 2
+        >>> # columns. The ensuing DenseMatrix will also have 2 columns.
+        >>> print(mat.numCols)
+        2
+        """
+        return self._java_matrix_wrapper.call("toLocalMatrix")
+
+    def toIndexedRowMatrix(self):
+        """
+        Convert this matrix to an IndexedRowMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2).toIndexedRowMatrix()
+
+        >>> # This BlockMatrix will have 6 effective rows, due to
+        >>> # having two sub-matrix blocks stacked, each with 3 rows.
+        >>> # The ensuing IndexedRowMatrix will also have 6 rows.
+        >>> print(mat.numRows())
+        6
+
+        >>> # This BlockMatrix will have 2 effective columns, due to
+        >>> # having two sub-matrix blocks stacked, each with 2 columns.
+        >>> # The ensuing IndexedRowMatrix will also have 2 columns.
+        >>> print(mat.numCols())
+        2
+        """
+        java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix")
+        return IndexedRowMatrix(java_indexed_row_matrix)
+
+    def toCoordinateMatrix(self):
+        """
+        Convert this matrix to a CoordinateMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(1, 2, [1, 2])),
+        ...                          ((1, 0), Matrices.dense(1, 2, [7, 8]))])
+        >>> mat = BlockMatrix(blocks, 1, 2).toCoordinateMatrix()
+        >>> mat.entries.take(3)
+        [MatrixEntry(0, 0, 1.0), MatrixEntry(0, 1, 2.0), MatrixEntry(1, 0, 7.0)]
+        """
+        java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix")
+        return CoordinateMatrix(java_coordinate_matrix)
+
 
 def _test():
     import doctest
     from pyspark import SparkContext
     from pyspark.sql import SQLContext
+    from pyspark.mllib.linalg import Matrices
     import pyspark.mllib.linalg.distributed
     globs = pyspark.mllib.linalg.distributed.__dict__.copy()
     globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
     globs['sqlContext'] = SQLContext(globs['sc'])
+    globs['Matrices'] = Matrices
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:

From 23d982204bb9ef74d3b788a32ce6608116968719 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 5 Aug 2015 09:01:45 -0700
Subject: [PATCH 0864/1454] [SPARK-9141] [SQL] Remove project collapsing from
 DataFrame API

Currently we collapse successive projections that are added by `withColumn`.  However, this optimization violates the constraint that adding nodes to a plan will never change its analyzed form and thus breaks caching.  Instead of doing early optimization, in this PR I just fix some low-hanging slowness in the analyzer.  In particular, I add a mechanism for skipping already analyzed subplans, `resolveOperators` and `resolveExpression`.  Since trees are generally immutable after construction, it's safe to annotate a plan as already analyzed as any transformation will create a new tree with this bit no longer set.

Together these result in a faster analyzer than before, even with added timing instrumentation.

```
Original Code
[info] 3430ms
[info] 2205ms
[info] 1973ms
[info] 1982ms
[info] 1916ms

Without Project Collapsing in DataFrame
[info] 44610ms
[info] 45977ms
[info] 46423ms
[info] 46306ms
[info] 54723ms

With analyzer optimizations
[info] 6394ms
[info] 4630ms
[info] 4388ms
[info] 4093ms
[info] 4113ms

With resolveOperators
[info] 2495ms
[info] 1380ms
[info] 1685ms
[info] 1414ms
[info] 1240ms
```

Author: Michael Armbrust <michael@databricks.com>

Closes #7920 from marmbrus/withColumnCache and squashes the following commits:

2145031 [Michael Armbrust] fix hive udfs tests
5a5a525 [Michael Armbrust] remove wrong comment
7a507d5 [Michael Armbrust] style
b59d710 [Michael Armbrust] revert small change
1fa5949 [Michael Armbrust] move logic into LogicalPlan, add tests
0e2cb43 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into withColumnCache
c926e24 [Michael Armbrust] naming
e593a2d [Michael Armbrust] style
f5a929e [Michael Armbrust] [SPARK-9141][SQL] Remove project collapsing from DataFrame API
38b1c83 [Michael Armbrust] WIP
---
 .../sql/catalyst/analysis/Analyzer.scala      | 28 ++++----
 .../sql/catalyst/analysis/CheckAnalysis.scala |  3 +
 .../catalyst/analysis/HiveTypeCoercion.scala  | 30 ++++----
 .../spark/sql/catalyst/plans/QueryPlan.scala  |  5 +-
 .../catalyst/plans/logical/LogicalPlan.scala  | 51 ++++++++++++-
 .../sql/catalyst/rules/RuleExecutor.scala     | 22 ++++++
 .../spark/sql/catalyst/trees/TreeNode.scala   | 64 ++++-------------
 .../sql/catalyst/plans/LogicalPlanSuite.scala | 72 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrame.scala      |  6 +-
 .../spark/sql/execution/SparkPlan.scala       |  2 +-
 .../spark/sql/execution/pythonUDFs.scala      |  2 +-
 .../apache/spark/sql/CachedTableSuite.scala   | 20 ++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 12 ----
 .../execution/HiveCompatibilitySuite.scala    |  5 ++
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  5 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |  3 +-
 .../sql/hive/execution/HiveUDFSuite.scala     |  8 +--
 .../sql/hive/execution/SQLQuerySuite.scala    |  2 +
 18 files changed, 234 insertions(+), 106 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index ca17f3e3d06ff..6de31f42dd30c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -90,7 +90,7 @@ class Analyzer(
    */
   object CTESubstitution extends Rule[LogicalPlan] {
     // TODO allow subquery to define CTE
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform  {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators  {
       case With(child, relations) => substituteCTE(child, relations)
       case other => other
     }
@@ -116,7 +116,7 @@ class Analyzer(
    * Substitute child plan with WindowSpecDefinitions.
    */
   object WindowsSubstitution extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       // Lookup WindowSpecDefinitions. This rule works with unresolved children.
       case WithWindowDefinition(windowDefinitions, child) =>
         child.transform {
@@ -140,7 +140,7 @@ class Analyzer(
   object ResolveAliases extends Rule[LogicalPlan] {
     private def assignAliases(exprs: Seq[NamedExpression]) = {
       // The `UnresolvedAlias`s will appear only at root of a expression tree, we don't need
-      // to transform down the whole tree.
+      // to resolveOperator down the whole tree.
       exprs.zipWithIndex.map {
         case (u @ UnresolvedAlias(child), i) =>
           child match {
@@ -156,7 +156,7 @@ class Analyzer(
       }
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case Aggregate(groups, aggs, child)
         if child.resolved && aggs.exists(_.isInstanceOf[UnresolvedAlias]) =>
         Aggregate(groups, assignAliases(aggs), child)
@@ -198,7 +198,7 @@ class Analyzer(
       Seq.tabulate(1 << c.groupByExprs.length)(i => i)
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case a if !a.childrenResolved => a // be sure all of the children are resolved.
       case a: Cube =>
         GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations)
@@ -261,7 +261,7 @@ class Analyzer(
       }
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case i @ InsertIntoTable(u: UnresolvedRelation, _, _, _, _) =>
         i.copy(table = EliminateSubQueries(getTable(u)))
       case u: UnresolvedRelation =>
@@ -274,7 +274,7 @@ class Analyzer(
    * a logical plan node's children.
    */
   object ResolveReferences extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p: LogicalPlan if !p.childrenResolved => p
 
       // If the projection list contains Stars, expand it.
@@ -444,7 +444,7 @@ class Analyzer(
    * remove these attributes after sorting.
    */
   object ResolveSortReferences extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case s @ Sort(ordering, global, p @ Project(projectList, child))
           if !s.resolved && p.resolved =>
         val (newOrdering, missing) = resolveAndFindMissing(ordering, p, child)
@@ -519,7 +519,7 @@ class Analyzer(
    * Replaces [[UnresolvedFunction]]s with concrete [[Expression]]s.
    */
   object ResolveFunctions extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case q: LogicalPlan =>
         q transformExpressions {
           case u @ UnresolvedFunction(name, children, isDistinct) =>
@@ -551,7 +551,7 @@ class Analyzer(
    * Turns projections that contain aggregate expressions into aggregations.
    */
   object GlobalAggregates extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case Project(projectList, child) if containsAggregates(projectList) =>
         Aggregate(Nil, projectList, child)
     }
@@ -571,7 +571,7 @@ class Analyzer(
    * aggregates and then projects them away above the filter.
    */
   object UnresolvedHavingClauseAttributes extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case filter @ Filter(havingCondition, aggregate @ Aggregate(_, originalAggExprs, _))
           if aggregate.resolved && containsAggregate(havingCondition) =>
 
@@ -601,7 +601,7 @@ class Analyzer(
    * [[AnalysisException]] is throw.
    */
   object ResolveGenerate extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p: Generate if !p.child.resolved || !p.generator.resolved => p
       case g: Generate if !g.resolved =>
         g.copy(generatorOutput = makeGeneratorOutput(g.generator, g.generatorOutput.map(_.name)))
@@ -872,6 +872,8 @@ class Analyzer(
     // We have to use transformDown at here to make sure the rule of
     // "Aggregate with Having clause" will be triggered.
     def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
+
+
       // Aggregate with Having clause. This rule works with an unresolved Aggregate because
       // a resolved Aggregate will not have Window Functions.
       case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))
@@ -927,7 +929,7 @@ class Analyzer(
    * put them into an inner Project and finally project them away at the outer Project.
    */
   object PullOutNondeterministic extends Rule[LogicalPlan] {
-    override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p: Project => p
       case f: Filter => f
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 187b238045f85..39f554c137c98 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -47,6 +47,7 @@ trait CheckAnalysis {
     // We transform up and order the rules so as to catch the first possible failure instead
     // of the result of cascading resolution failures.
     plan.foreachUp {
+      case p if p.analyzed => // Skip already analyzed sub-plans
 
       case operator: LogicalPlan =>
         operator transformExpressionsUp {
@@ -179,5 +180,7 @@ trait CheckAnalysis {
         }
     }
     extendedCheckRules.foreach(_(plan))
+
+    plan.foreach(_.setAnalyzed())
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 490f3dc07b6ed..970f3c8282c81 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -144,7 +144,8 @@ object HiveTypeCoercion {
    * instances higher in the query tree.
    */
   object PropagateTypes extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+
       // No propagation required for leaf nodes.
       case q: LogicalPlan if q.children.isEmpty => q
 
@@ -225,7 +226,9 @@ object HiveTypeCoercion {
       }
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+      case p if p.analyzed => p
+
       case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
         val (newLeft, newRight) = widenOutputTypes(u.nodeName, left, right)
         Union(newLeft, newRight)
@@ -242,7 +245,7 @@ object HiveTypeCoercion {
    * Promotes strings that appear in arithmetic expressions.
    */
   object PromoteStrings extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -305,7 +308,7 @@ object HiveTypeCoercion {
    * Convert all expressions in in() list to the left operator type
    */
   object InConversion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -372,7 +375,8 @@ object HiveTypeCoercion {
       ChangeDecimalPrecision(Cast(e, dataType))
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+
       // fix decimal precision for expressions
       case q => q.transformExpressions {
         // Skip nodes whose children have not been resolved yet
@@ -466,7 +470,7 @@ object HiveTypeCoercion {
       ))
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -508,7 +512,7 @@ object HiveTypeCoercion {
    * truncated version of this number.
    */
   object StringToIntegralCasts extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -521,7 +525,7 @@ object HiveTypeCoercion {
    * This ensure that the types for various functions are as expected.
    */
   object FunctionArgumentConversion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -575,7 +579,7 @@ object HiveTypeCoercion {
    * converted to fractional types.
    */
   object Division extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who has not been resolved yet,
       // as this is an extra rule which should be applied at last.
       case e if !e.resolved => e
@@ -592,7 +596,7 @@ object HiveTypeCoercion {
    * Coerces the type of different branches of a CASE WHEN statement to a common type.
    */
   object CaseWhenCoercion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       case c: CaseWhenLike if c.childrenResolved && !c.valueTypesEqual =>
         logDebug(s"Input values for null casting ${c.valueTypes.mkString(",")}")
         val maybeCommonType = findTightestCommonTypeAndPromoteToString(c.valueTypes)
@@ -628,7 +632,7 @@ object HiveTypeCoercion {
    * Coerces the type of different branches of If statement to a common type.
    */
   object IfCoercion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Find tightest common type for If, if the true value and false value have different types.
       case i @ If(pred, left, right) if left.dataType != right.dataType =>
         findTightestCommonTypeToString(left.dataType, right.dataType).map { widestType =>
@@ -652,7 +656,7 @@ object HiveTypeCoercion {
 
     private val acceptedTypes = Seq(DateType, TimestampType, StringType)
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -669,7 +673,7 @@ object HiveTypeCoercion {
    * Casts types according to the expected input types for [[Expression]]s.
    */
   object ImplicitTypeCasts extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index c610f70d38437..55286f9f2fc5c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.plans
 
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression, VirtualColumn}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types.{DataType, StructType}
 
@@ -92,7 +93,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
 
     val newArgs = productIterator.map(recursiveTransform).toArray
 
-    if (changed) makeCopy(newArgs) else this
+    if (changed) makeCopy(newArgs).asInstanceOf[this.type] else this
   }
 
   /**
@@ -124,7 +125,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
 
     val newArgs = productIterator.map(recursiveTransform).toArray
 
-    if (changed) makeCopy(newArgs) else this
+    if (changed) makeCopy(newArgs).asInstanceOf[this.type] else this
   }
 
   /** Returns the result of running [[transformExpressions]] on this node
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index bedeaf06adf12..9b52f020093f0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -22,11 +22,60 @@ import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
-import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.catalyst.trees.{CurrentOrigin, TreeNode}
 
 
 abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
 
+  private var _analyzed: Boolean = false
+
+  /**
+   * Marks this plan as already analyzed.  This should only be called by CheckAnalysis.
+   */
+  private[catalyst] def setAnalyzed(): Unit = { _analyzed = true }
+
+  /**
+   * Returns true if this node and its children have already been gone through analysis and
+   * verification.  Note that this is only an optimization used to avoid analyzing trees that
+   * have already been analyzed, and can be reset by transformations.
+   */
+  def analyzed: Boolean = _analyzed
+
+  /**
+   * Returns a copy of this node where `rule` has been recursively applied first to all of its
+   * children and then itself (post-order). When `rule` does not apply to a given node, it is left
+   * unchanged.  This function is similar to `transformUp`, but skips sub-trees that have already
+   * been marked as analyzed.
+   *
+   * @param rule the function use to transform this nodes children
+   */
+  def resolveOperators(rule: PartialFunction[LogicalPlan, LogicalPlan]): LogicalPlan = {
+    if (!analyzed) {
+      val afterRuleOnChildren = transformChildren(rule, (t, r) => t.resolveOperators(r))
+      if (this fastEquals afterRuleOnChildren) {
+        CurrentOrigin.withOrigin(origin) {
+          rule.applyOrElse(this, identity[LogicalPlan])
+        }
+      } else {
+        CurrentOrigin.withOrigin(origin) {
+          rule.applyOrElse(afterRuleOnChildren, identity[LogicalPlan])
+        }
+      }
+    } else {
+      this
+    }
+  }
+
+  /**
+   * Recursively transforms the expressions of a tree, skipping nodes that have already
+   * been analyzed.
+   */
+  def resolveExpressions(r: PartialFunction[Expression, Expression]): LogicalPlan = {
+    this resolveOperators  {
+      case p => p.transformExpressions(r)
+    }
+  }
+
   /**
    * Computes [[Statistics]] for this plan. The default implementation assumes the output
    * cardinality is the product of of all child plan's cardinality, i.e. applies in the case
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
index 3f9858b0c4a43..8b824511a79da 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
@@ -21,6 +21,23 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.catalyst.util.sideBySide
 
+import scala.collection.mutable
+
+object RuleExecutor {
+  protected val timeMap = new mutable.HashMap[String, Long].withDefault(_ => 0)
+
+  /** Resets statistics about time spent running specific rules */
+  def resetTime(): Unit = timeMap.clear()
+
+  /** Dump statistics about time spent running specific rules. */
+  def dumpTimeSpent(): String = {
+    val maxSize = timeMap.keys.map(_.toString.length).max
+    timeMap.toSeq.sortBy(_._2).reverseMap { case (k, v) =>
+      s"${k.padTo(maxSize, " ").mkString} $v"
+    }.mkString("\n")
+  }
+}
+
 abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
 
   /**
@@ -41,6 +58,7 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
   /** Defines a sequence of rule batches, to be overridden by the implementation. */
   protected val batches: Seq[Batch]
 
+
   /**
    * Executes the batches of rules defined by the subclass. The batches are executed serially
    * using the defined execution strategy. Within each batch, rules are also executed serially.
@@ -58,7 +76,11 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
       while (continue) {
         curPlan = batch.rules.foldLeft(curPlan) {
           case (plan, rule) =>
+            val startTime = System.nanoTime()
             val result = rule(plan)
+            val runTime = System.nanoTime() - startTime
+            RuleExecutor.timeMap(rule.ruleName) = RuleExecutor.timeMap(rule.ruleName) + runTime
+
             if (!result.fastEquals(plan)) {
               logTrace(
                 s"""
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 122e9fc5ed77f..7971e25188e8d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -149,7 +149,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
   /**
    * Returns a copy of this node where `f` has been applied to all the nodes children.
    */
-  def mapChildren(f: BaseType => BaseType): this.type = {
+  def mapChildren(f: BaseType => BaseType): BaseType = {
     var changed = false
     val newArgs = productIterator.map {
       case arg: TreeNode[_] if containsChild(arg) =>
@@ -170,7 +170,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * Returns a copy of this node with the children replaced.
    * TODO: Validate somewhere (in debug mode?) that children are ordered correctly.
    */
-  def withNewChildren(newChildren: Seq[BaseType]): this.type = {
+  def withNewChildren(newChildren: Seq[BaseType]): BaseType = {
     assert(newChildren.size == children.size, "Incorrect number of children")
     var changed = false
     val remainingNewChildren = newChildren.toBuffer
@@ -229,9 +229,9 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
 
     // Check if unchanged and then possibly return old copy to avoid gc churn.
     if (this fastEquals afterRule) {
-      transformChildrenDown(rule)
+      transformChildren(rule, (t, r) => t.transformDown(r))
     } else {
-      afterRule.transformChildrenDown(rule)
+      afterRule.transformChildren(rule, (t, r) => t.transformDown(r))
     }
   }
 
@@ -240,11 +240,13 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * this node.  When `rule` does not apply to a given node it is left unchanged.
    * @param rule the function used to transform this nodes children
    */
-  def transformChildrenDown(rule: PartialFunction[BaseType, BaseType]): this.type = {
+  protected def transformChildren(
+      rule: PartialFunction[BaseType, BaseType],
+      nextOperation: (BaseType, PartialFunction[BaseType, BaseType]) => BaseType): BaseType = {
     var changed = false
     val newArgs = productIterator.map {
       case arg: TreeNode[_] if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformDown(rule)
+        val newChild = nextOperation(arg.asInstanceOf[BaseType], rule)
         if (!(newChild fastEquals arg)) {
           changed = true
           newChild
@@ -252,7 +254,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
           arg
         }
       case Some(arg: TreeNode[_]) if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformDown(rule)
+        val newChild = nextOperation(arg.asInstanceOf[BaseType], rule)
         if (!(newChild fastEquals arg)) {
           changed = true
           Some(newChild)
@@ -263,7 +265,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
       case d: DataType => d // Avoid unpacking Structs
       case args: Traversable[_] => args.map {
         case arg: TreeNode[_] if containsChild(arg) =>
-          val newChild = arg.asInstanceOf[BaseType].transformDown(rule)
+          val newChild = nextOperation(arg.asInstanceOf[BaseType], rule)
           if (!(newChild fastEquals arg)) {
             changed = true
             newChild
@@ -285,7 +287,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * @param rule the function use to transform this nodes children
    */
   def transformUp(rule: PartialFunction[BaseType, BaseType]): BaseType = {
-    val afterRuleOnChildren = transformChildrenUp(rule)
+    val afterRuleOnChildren = transformChildren(rule, (t, r) => t.transformUp(r))
     if (this fastEquals afterRuleOnChildren) {
       CurrentOrigin.withOrigin(origin) {
         rule.applyOrElse(this, identity[BaseType])
@@ -297,44 +299,6 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
     }
   }
 
-  def transformChildrenUp(rule: PartialFunction[BaseType, BaseType]): this.type = {
-    var changed = false
-    val newArgs = productIterator.map {
-      case arg: TreeNode[_] if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformUp(rule)
-        if (!(newChild fastEquals arg)) {
-          changed = true
-          newChild
-        } else {
-          arg
-        }
-      case Some(arg: TreeNode[_]) if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformUp(rule)
-        if (!(newChild fastEquals arg)) {
-          changed = true
-          Some(newChild)
-        } else {
-          Some(arg)
-        }
-      case m: Map[_, _] => m
-      case d: DataType => d // Avoid unpacking Structs
-      case args: Traversable[_] => args.map {
-        case arg: TreeNode[_] if containsChild(arg) =>
-          val newChild = arg.asInstanceOf[BaseType].transformUp(rule)
-          if (!(newChild fastEquals arg)) {
-            changed = true
-            newChild
-          } else {
-            arg
-          }
-        case other => other
-      }
-      case nonChild: AnyRef => nonChild
-      case null => null
-    }.toArray
-    if (changed) makeCopy(newArgs) else this
-  }
-
   /**
    * Args to the constructor that should be copied, but not transformed.
    * These are appended to the transformed args automatically by makeCopy
@@ -348,7 +312,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * that are not present in the productIterator.
    * @param newArgs the new product arguments.
    */
-  def makeCopy(newArgs: Array[AnyRef]): this.type = attachTree(this, "makeCopy") {
+  def makeCopy(newArgs: Array[AnyRef]): BaseType = attachTree(this, "makeCopy") {
     val ctors = getClass.getConstructors.filter(_.getParameterTypes.size != 0)
     if (ctors.isEmpty) {
       sys.error(s"No valid constructor for $nodeName")
@@ -359,9 +323,9 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
       CurrentOrigin.withOrigin(origin) {
         // Skip no-arg constructors that are just there for kryo.
         if (otherCopyArgs.isEmpty) {
-          defaultCtor.newInstance(newArgs: _*).asInstanceOf[this.type]
+          defaultCtor.newInstance(newArgs: _*).asInstanceOf[BaseType]
         } else {
-          defaultCtor.newInstance((newArgs ++ otherCopyArgs).toArray: _*).asInstanceOf[this.type]
+          defaultCtor.newInstance((newArgs ++ otherCopyArgs).toArray: _*).asInstanceOf[BaseType]
         }
       }
     } catch {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
new file mode 100644
index 0000000000000..797b29f23cbb9
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.plans
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.util._
+
+/**
+ * Provides helper methods for comparing plans.
+ */
+class LogicalPlanSuite extends SparkFunSuite {
+  private var invocationCount = 0
+  private val function: PartialFunction[LogicalPlan, LogicalPlan] = {
+    case p: Project =>
+      invocationCount += 1
+      p
+  }
+
+  private val testRelation = LocalRelation()
+
+  test("resolveOperator runs on operators") {
+    invocationCount = 0
+    val plan = Project(Nil, testRelation)
+    plan resolveOperators function
+
+    assert(invocationCount === 1)
+  }
+
+  test("resolveOperator runs on operators recursively") {
+    invocationCount = 0
+    val plan = Project(Nil, Project(Nil, testRelation))
+    plan resolveOperators function
+
+    assert(invocationCount === 2)
+  }
+
+  test("resolveOperator skips all ready resolved plans") {
+    invocationCount = 0
+    val plan = Project(Nil, Project(Nil, testRelation))
+    plan.foreach(_.setAnalyzed())
+    plan resolveOperators function
+
+    assert(invocationCount === 0)
+  }
+
+  test("resolveOperator skips partially resolved plans") {
+    invocationCount = 0
+    val plan1 = Project(Nil, testRelation)
+    val plan2 = Project(Nil, plan1)
+    plan1.foreach(_.setAnalyzed())
+    plan2 resolveOperators function
+
+    assert(invocationCount === 1)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index db15711202b77..e57acec59d327 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.io.CharArrayWriter
 import java.util.Properties
 
+import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.unsafe.types.UTF8String
 
 import scala.language.implicitConversions
@@ -54,7 +55,6 @@ private[sql] object DataFrame {
   }
 }
 
-
 /**
  * :: Experimental ::
  * A distributed collection of data organized into named columns.
@@ -690,9 +690,7 @@ class DataFrame private[sql](
       case Column(explode: Explode) => MultiAlias(explode, Nil)
       case Column(expr: Expression) => Alias(expr, expr.prettyString)()
     }
-    // When user continuously call `select`, speed up analysis by collapsing `Project`
-    import org.apache.spark.sql.catalyst.optimizer.ProjectCollapsing
-    Project(namedExpressions.toSeq, ProjectCollapsing(logicalPlan))
+    Project(namedExpressions.toSeq, logicalPlan)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 73b237fffece8..dbc0cefbe2e10 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -67,7 +67,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   private val prepareCalled = new AtomicBoolean(false)
 
   /** Overridden make copy also propogates sqlContext to copied plan. */
-  override def makeCopy(newArgs: Array[AnyRef]): this.type = {
+  override def makeCopy(newArgs: Array[AnyRef]): SparkPlan = {
     SparkPlan.currentContext.set(sqlContext)
     super.makeCopy(newArgs)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index dedc7c4dfb4d1..59f8b079ab333 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -65,7 +65,7 @@ private[spark] case class PythonUDF(
  * multiple child operators.
  */
 private[spark] object ExtractPythonUDFs extends Rule[LogicalPlan] {
-  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+  def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
     // Skip EvaluatePython nodes.
     case plan: EvaluatePython => plan
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index e9dd7ef226e42..a88df91b1001c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -25,6 +25,7 @@ import org.scalatest.concurrent.Eventually._
 import org.apache.spark.Accumulators
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.columnar._
+import org.apache.spark.sql.functions._
 import org.apache.spark.storage.{StorageLevel, RDDBlockId}
 
 case class BigData(s: String)
@@ -50,6 +51,25 @@ class CachedTableSuite extends QueryTest {
     ctx.sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty
   }
 
+  test("withColumn doesn't invalidate cached dataframe") {
+    var evalCount = 0
+    val myUDF = udf((x: String) => { evalCount += 1; "result" })
+    val df = Seq(("test", 1)).toDF("s", "i").select(myUDF($"s"))
+    df.cache()
+
+    df.collect()
+    assert(evalCount === 1)
+
+    df.collect()
+    assert(evalCount === 1)
+
+    val df2 = df.withColumn("newColumn", lit(1))
+    df2.collect()
+
+    // We should not reevaluate the cached dataframe
+    assert(evalCount === 1)
+  }
+
   test("cache temp table") {
     testData.select('key).registerTempTable("tempTable")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"), 0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index b8f10b00f5690..f9cc6d1f3c250 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -686,18 +686,6 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
       Seq(Row(2, 1, 2), Row(1, 1, 1)))
   }
 
-  test("SPARK-7276: Project collapse for continuous select") {
-    var df = testData
-    for (i <- 1 to 5) {
-      df = df.select($"*")
-    }
-
-    import org.apache.spark.sql.catalyst.plans.logical.Project
-    // make sure df have at most two Projects
-    val p = df.logicalPlan.asInstanceOf[Project].child.asInstanceOf[Project]
-    assert(!p.child.isInstanceOf[Project])
-  }
-
   test("SPARK-7150 range api") {
     // numSlice is greater than length
     val res1 = sqlContext.range(0, 10, 1, 15).select("id")
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index d4fc6c2b6ebc0..ab309e0a1d36b 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.hive.execution
 import java.io.File
 import java.util.{Locale, TimeZone}
 
+import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.SQLConf
@@ -50,6 +51,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, 5)
     // Enable in-memory partition pruning for testing purposes
     TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
+    RuleExecutor.resetTime()
   }
 
   override def afterAll() {
@@ -58,6 +60,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     Locale.setDefault(originalLocale)
     TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
     TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
+
+    // For debugging dump some statistics about how much time was spent in various optimizer rules.
+    logWarning(RuleExecutor.dumpTimeSpent())
   }
 
   /** A list of tests deemed out of scope currently and thus completely disregarded. */
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 16c186627f6cc..6b37af99f4677 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -391,7 +391,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
    */
   object ParquetConversions extends Rule[LogicalPlan] {
     override def apply(plan: LogicalPlan): LogicalPlan = {
-      if (!plan.resolved) {
+      if (!plan.resolved || plan.analyzed) {
         return plan
       }
 
@@ -418,8 +418,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
           (relation, parquetRelation, attributedRewrites)
 
         // Read path
-        case p @ PhysicalOperation(_, _, relation: MetastoreRelation)
-            if hive.convertMetastoreParquet &&
+        case relation: MetastoreRelation if hive.convertMetastoreParquet &&
               relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") =>
           val parquetRelation = convertToParquetRelation(relation)
           val attributedRewrites = relation.output.zip(parquetRelation.output)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 8a86a87368f29..7182246e466a4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -133,8 +133,7 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
   @transient
   private lazy val conversionHelper = new ConversionHelper(method, arguments)
 
-  @transient
-  lazy val dataType = javaClassToDataType(method.getReturnType)
+  val dataType = javaClassToDataType(method.getReturnType)
 
   @transient
   lazy val returnInspector = ObjectInspectorFactory.getReflectionObjectInspector(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 7069afc9f7da2..10f2902e5eef0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -182,7 +182,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToListString(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "List type in java is unsupported because " +
+    assert(errMsg.getMessage contains "List type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListString")
@@ -197,7 +197,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToListInt(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "List type in java is unsupported because " +
+    assert(errMsg.getMessage contains "List type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListInt")
@@ -213,7 +213,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToStringIntMap(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "Map type in java is unsupported because " +
+    assert(errMsg.getMessage contains "Map type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToStringIntMap")
@@ -229,7 +229,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToIntIntMap(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "Map type in java is unsupported because " +
+    assert(errMsg.getMessage contains "Map type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToIntIntMap")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index ff9a3694d612e..1dff07a6de8ad 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -948,6 +948,8 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-7595: Window will cause resolve failed with self join") {
+    sql("SELECT * FROM src") // Force loading of src table.
+
     checkAnswer(sql(
       """
         |with

From 7a969a6967c4ecc0f004b73bff27a75257a94e86 Mon Sep 17 00:00:00 2001
From: linweizhong <linweizhong@huawei.com>
Date: Wed, 5 Aug 2015 10:16:12 -0700
Subject: [PATCH 0865/1454] [SPARK-9519] [YARN] Confirm stop sc successfully
 when application was killed

Currently, when we kill application on Yarn, then will call sc.stop() at Yarn application state monitor thread, then in YarnClientSchedulerBackend.stop() will call interrupt this will cause SparkContext not stop fully as we will wait executor to exit.

Author: linweizhong <linweizhong@huawei.com>

Closes #7846 from Sephiroth-Lin/SPARK-9519 and squashes the following commits:

1ae736d [linweizhong] Update comments
2e8e365 [linweizhong] Add comment explaining the code
ad0e23b [linweizhong] Update
243d2c7 [linweizhong] Confirm stop sc successfully when application was killed
---
 .../cluster/YarnClientSchedulerBackend.scala  | 47 +++++++++++++------
 1 file changed, 32 insertions(+), 15 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index d97fa2e2151bc..d225061fcd1b4 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -33,7 +33,7 @@ private[spark] class YarnClientSchedulerBackend(
 
   private var client: Client = null
   private var appId: ApplicationId = null
-  private var monitorThread: Thread = null
+  private var monitorThread: MonitorThread = null
 
   /**
    * Create a Yarn client to submit an application to the ResourceManager.
@@ -131,24 +131,42 @@ private[spark] class YarnClientSchedulerBackend(
     }
   }
 
+  /**
+   * We create this class for SPARK-9519. Basically when we interrupt the monitor thread it's
+   * because the SparkContext is being shut down(sc.stop() called by user code), but if
+   * monitorApplication return, it means the Yarn application finished before sc.stop() was called,
+   * which means we should call sc.stop() here, and we don't allow the monitor to be interrupted
+   * before SparkContext stops successfully.
+   */
+  private class MonitorThread extends Thread {
+    private var allowInterrupt = true
+
+    override def run() {
+      try {
+        val (state, _) = client.monitorApplication(appId, logApplicationReport = false)
+        logError(s"Yarn application has already exited with state $state!")
+        allowInterrupt = false
+        sc.stop()
+      } catch {
+        case e: InterruptedException => logInfo("Interrupting monitor thread")
+      }
+    }
+
+    def stopMonitor(): Unit = {
+      if (allowInterrupt) {
+        this.interrupt()
+      }
+    }
+  }
+
   /**
    * Monitor the application state in a separate thread.
    * If the application has exited for any reason, stop the SparkContext.
    * This assumes both `client` and `appId` have already been set.
    */
-  private def asyncMonitorApplication(): Thread = {
+  private def asyncMonitorApplication(): MonitorThread = {
     assert(client != null && appId != null, "Application has not been submitted yet!")
-    val t = new Thread {
-      override def run() {
-        try {
-          val (state, _) = client.monitorApplication(appId, logApplicationReport = false)
-          logError(s"Yarn application has already exited with state $state!")
-          sc.stop()
-        } catch {
-          case e: InterruptedException => logInfo("Interrupting monitor thread")
-        }
-      }
-    }
+    val t = new MonitorThread
     t.setName("Yarn application state monitor")
     t.setDaemon(true)
     t
@@ -160,7 +178,7 @@ private[spark] class YarnClientSchedulerBackend(
   override def stop() {
     assert(client != null, "Attempted to stop this scheduler before starting it!")
     if (monitorThread != null) {
-      monitorThread.interrupt()
+      monitorThread.stopMonitor()
     }
     super.stop()
     client.stop()
@@ -174,5 +192,4 @@ private[spark] class YarnClientSchedulerBackend(
       super.applicationId
     }
   }
-
 }

From 1f8c364b9c6636f06986f5f80d5a49b7a7772ac3 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 5 Aug 2015 11:03:02 -0700
Subject: [PATCH 0866/1454] [SPARK-9141] [SQL] [MINOR] Fix comments of PR #7920

This is a follow-up of https://github.com/apache/spark/pull/7920 to fix comments.

Author: Yin Huai <yhuai@databricks.com>

Closes #7964 from yhuai/SPARK-9141-follow-up and squashes the following commits:

4d0ee80 [Yin Huai] Fix comments.
---
 .../org/apache/spark/sql/catalyst/analysis/Analyzer.scala      | 3 +--
 .../org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala | 3 ++-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 6de31f42dd30c..82158e61e3fb5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -140,7 +140,7 @@ class Analyzer(
   object ResolveAliases extends Rule[LogicalPlan] {
     private def assignAliases(exprs: Seq[NamedExpression]) = {
       // The `UnresolvedAlias`s will appear only at root of a expression tree, we don't need
-      // to resolveOperator down the whole tree.
+      // to traverse the whole tree.
       exprs.zipWithIndex.map {
         case (u @ UnresolvedAlias(child), i) =>
           child match {
@@ -873,7 +873,6 @@ class Analyzer(
     // "Aggregate with Having clause" will be triggered.
     def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
 
-
       // Aggregate with Having clause. This rule works with an unresolved Aggregate because
       // a resolved Aggregate will not have Window Functions.
       case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
index 797b29f23cbb9..455a3810c719e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
@@ -23,7 +23,8 @@ import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.util._
 
 /**
- * Provides helper methods for comparing plans.
+ * This suite is used to test [[LogicalPlan]]'s `resolveOperators` and make sure it can correctly
+ * skips sub-trees that have already been marked as analyzed.
  */
 class LogicalPlanSuite extends SparkFunSuite {
   private var invocationCount = 0

From e1e05873fc75781b6dd3f7fadbfb57824f83054e Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 5 Aug 2015 11:38:56 -0700
Subject: [PATCH 0867/1454] [SPARK-9403] [SQL] Add codegen support in In and
 InSet

This continues tarekauel's work in #7778.

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7893 from viirya/codegen_in and squashes the following commits:

81ff97b [Liang-Chi Hsieh] For comments.
47761c6 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
cf4bf41 [Liang-Chi Hsieh] For comments.
f532b3c [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
446bbcd [Liang-Chi Hsieh] Fix bug.
b3d0ab4 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
4610eff [Liang-Chi Hsieh] Relax the types of references and update optimizer test.
224f18e [Liang-Chi Hsieh] Beef up the test cases for In and InSet to include all primitive data types.
86dc8aa [Liang-Chi Hsieh] Only convert In to InSet when the number of items in set is more than the threshold.
b7ded7e [Tarek Auel] [SPARK-9403][SQL] codeGen in / inSet
---
 .../sql/catalyst/expressions/predicates.scala | 63 +++++++++++++++++--
 .../sql/catalyst/optimizer/Optimizer.scala    |  2 +-
 .../catalyst/expressions/PredicateSuite.scala | 37 ++++++++++-
 .../catalyst/optimizer/OptimizeInSuite.scala  | 14 ++++-
 .../datasources/DataSourceStrategy.scala      |  7 +++
 .../spark/sql/ColumnExpressionSuite.scala     |  6 ++
 6 files changed, 119 insertions(+), 10 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index b69bbabee7e81..68c832d7194d4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import scala.collection.mutable
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
@@ -97,32 +100,80 @@ case class Not(child: Expression)
 /**
  * Evaluates to `true` if `list` contains `value`.
  */
-case class In(value: Expression, list: Seq[Expression]) extends Predicate with CodegenFallback {
+case class In(value: Expression, list: Seq[Expression]) extends Predicate
+    with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = value.dataType +: list.map(_.dataType)
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (list.exists(l => l.dataType != value.dataType)) {
+      TypeCheckResult.TypeCheckFailure(
+        "Arguments must be same type")
+    } else {
+      TypeCheckResult.TypeCheckSuccess
+    }
+  }
+
   override def children: Seq[Expression] = value +: list
 
-  override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN.
+  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
   override def toString: String = s"$value IN ${list.mkString("(", ",", ")")}"
 
   override def eval(input: InternalRow): Any = {
     val evaluatedValue = value.eval(input)
     list.exists(e => e.eval(input) == evaluatedValue)
   }
-}
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val valueGen = value.gen(ctx)
+    val listGen = list.map(_.gen(ctx))
+    val listCode = listGen.map(x =>
+      s"""
+        if (!${ev.primitive}) {
+          ${x.code}
+          if (${ctx.genEqual(value.dataType, valueGen.primitive, x.primitive)}) {
+            ${ev.primitive} = true;
+          }
+        }
+       """).mkString("\n")
+    s"""
+      ${valueGen.code}
+      boolean ${ev.primitive} = false;
+      boolean ${ev.isNull} = false;
+      $listCode
+    """
+  }
+}
 
 /**
  * Optimized version of In clause, when all filter values of In clause are
  * static.
  */
-case class InSet(child: Expression, hset: Set[Any])
-  extends UnaryExpression with Predicate with CodegenFallback {
+case class InSet(child: Expression, hset: Set[Any]) extends UnaryExpression with Predicate {
 
-  override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN.
+  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
   override def toString: String = s"$child INSET ${hset.mkString("(", ",", ")")}"
 
   override def eval(input: InternalRow): Any = {
     hset.contains(child.eval(input))
   }
+
+  def getHSet(): Set[Any] = hset
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val setName = classOf[Set[Any]].getName
+    val InSetName = classOf[InSet].getName
+    val childGen = child.gen(ctx)
+    ctx.references += this
+    val hsetTerm = ctx.freshName("hset")
+    ctx.addMutableState(setName, hsetTerm,
+      s"$hsetTerm = (($InSetName)expressions[${ctx.references.size - 1}]).getHSet();")
+    s"""
+      ${childGen.code}
+      boolean ${ev.isNull} = false;
+      boolean ${ev.primitive} = $hsetTerm.contains(${childGen.primitive});
+     """
+  }
 }
 
 case class And(left: Expression, right: Expression) extends BinaryOperator with Predicate {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 29d706dcb39a7..4ab5ac2c61e3c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -393,7 +393,7 @@ object ConstantFolding extends Rule[LogicalPlan] {
 object OptimizeIn extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     case q: LogicalPlan => q transformExpressionsDown {
-      case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) =>
+      case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) && list.size > 10 =>
         val hSet = list.map(e => e.eval(EmptyRow))
         InSet(v, HashSet() ++ hSet)
     }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index d7eb13c50b134..7beef71845e43 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -21,7 +21,8 @@ import scala.collection.immutable.HashSet
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{Decimal, DoubleType, IntegerType, BooleanType}
+import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.types._
 
 
 class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -118,6 +119,23 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("^Ba*n"))), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^Ba*n"))), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^n"))), false)
+
+    val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
+      LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
+    primitiveTypes.map { t =>
+      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val inputData = Seq.fill(10) {
+        val value = dataGen.apply()
+        value match {
+          case d: Double if d.isNaN => 0.0d
+          case f: Float if f.isNaN => 0.0f
+          case _ => value
+        }
+      }
+      val input = inputData.map(Literal(_))
+      checkEvaluation(In(input(0), input.slice(1, 10)),
+        inputData.slice(1, 10).contains(inputData(0)))
+    }
   }
 
   test("INSET") {
@@ -134,6 +152,23 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(InSet(three, hS), false)
     checkEvaluation(InSet(three, nS), false)
     checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true)
+
+    val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
+      LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
+    primitiveTypes.map { t =>
+      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val inputData = Seq.fill(10) {
+        val value = dataGen.apply()
+        value match {
+          case d: Double if d.isNaN => 0.0d
+          case f: Float if f.isNaN => 0.0f
+          case _ => value
+        }
+      }
+      val input = inputData.map(Literal(_))
+      checkEvaluation(InSet(input(0), inputData.slice(1, 10).toSet),
+        inputData.slice(1, 10).contains(inputData(0)))
+    }
   }
 
   private val smallValues = Seq(1, Decimal(1), Array(1.toByte), "a", 0f, 0d, false).map(Literal(_))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala
index 1d433275fed2e..6f7b5b9572e22 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala
@@ -43,16 +43,26 @@ class OptimizeInSuite extends PlanTest {
 
   val testRelation = LocalRelation('a.int, 'b.int, 'c.int)
 
-  test("OptimizedIn test: In clause optimized to InSet") {
+  test("OptimizedIn test: In clause not optimized to InSet when less than 10 items") {
     val originalQuery =
       testRelation
         .where(In(UnresolvedAttribute("a"), Seq(Literal(1), Literal(2))))
         .analyze
 
+    val optimized = Optimize.execute(originalQuery.analyze)
+    comparePlans(optimized, originalQuery)
+  }
+
+  test("OptimizedIn test: In clause optimized to InSet when more than 10 items") {
+    val originalQuery =
+      testRelation
+        .where(In(UnresolvedAttribute("a"), (1 to 11).map(Literal(_))))
+        .analyze
+
     val optimized = Optimize.execute(originalQuery.analyze)
     val correctAnswer =
       testRelation
-        .where(InSet(UnresolvedAttribute("a"), HashSet[Any]() + 1 + 2))
+        .where(InSet(UnresolvedAttribute("a"), (1 to 11).toSet))
         .analyze
 
     comparePlans(optimized, correctAnswer)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index a43bccbe6927c..e5dc676b87841 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -366,6 +366,13 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       case expressions.InSet(a: Attribute, set) =>
         Some(sources.In(a.name, set.toArray))
 
+      // Because we only convert In to InSet in Optimizer when there are more than certain
+      // items. So it is possible we still get an In expression here that needs to be pushed
+      // down.
+      case expressions.In(a: Attribute, list) if !list.exists(!_.isInstanceOf[Literal]) =>
+        val hSet = list.map(e => e.eval(EmptyRow))
+        Some(sources.In(a.name, hSet.toArray))
+
       case expressions.IsNull(a: Attribute) =>
         Some(sources.IsNull(a.name))
       case expressions.IsNotNull(a: Attribute) =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 35ca0b4c7cc21..b351380373259 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -357,6 +357,12 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "x"))
     checkAnswer(df.filter($"b".in("z", "y")),
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "y"))
+
+    val df2 = Seq((1, Seq(1)), (2, Seq(2)), (3, Seq(3))).toDF("a", "b")
+
+    intercept[AnalysisException] {
+      df2.filter($"a".in($"b"))
+    }
   }
 
   val booleanData = ctx.createDataFrame(ctx.sparkContext.parallelize(

From eb5b8f4a603e0f289bdaa0a2164cde2cfe4feecb Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 12:51:12 -0700
Subject: [PATCH 0868/1454] Closes #7778 since it is done as #7893.


From 5f0fb6466f5e3607f7fca9b2371a73b3deef3fdf Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 5 Aug 2015 14:12:22 -0700
Subject: [PATCH 0869/1454] [SPARK-9649] Fix flaky test MasterSuite - randomize
 ports

```
Error Message

Failed to bind to: /127.0.0.1:7093: Service 'sparkMaster' failed after 16 retries!
Stacktrace

      java.net.BindException: Failed to bind to: /127.0.0.1:7093: Service 'sparkMaster' failed after 16 retries!
      at org.jboss.netty.bootstrap.ServerBootstrap.bind(ServerBootstrap.java:272)
      at akka.remote.transport.netty.NettyTransport$$anonfun$listen$1.apply(NettyTransport.scala:393)
      at akka.remote.transport.netty.NettyTransport$$anonfun$listen$1.apply(NettyTransport.scala:389)
      at scala.util.Success$$anonfun$map$1.apply(Try.scala:206)
      at scala.util.Try$.apply(Try.scala:161)
```

Author: Andrew Or <andrew@databricks.com>

Closes #7968 from andrewor14/fix-master-flaky-test and squashes the following commits:

fcc42ef [Andrew Or] Randomize port
---
 .../org/apache/spark/deploy/master/MasterSuite.scala      | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 30780a0da7f8d..ae0e037d822ea 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -93,8 +93,8 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
       publicAddress = ""
     )
 
-    val (rpcEnv, uiPort, restPort) =
-      Master.startRpcEnvAndEndpoint("127.0.0.1", 7077, 8080, conf)
+    val (rpcEnv, _, _) =
+      Master.startRpcEnvAndEndpoint("127.0.0.1", 0, 0, conf)
 
     try {
       rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, rpcEnv.address, Master.ENDPOINT_NAME)
@@ -343,8 +343,8 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
 
   private def makeMaster(conf: SparkConf = new SparkConf): Master = {
     val securityMgr = new SecurityManager(conf)
-    val rpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 7077, conf, securityMgr)
-    val master = new Master(rpcEnv, rpcEnv.address, 8080, securityMgr, conf)
+    val rpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 0, conf, securityMgr)
+    val master = new Master(rpcEnv, rpcEnv.address, 0, securityMgr, conf)
     master
   }
 

From f9c2a2af1e883b36c5e51b87ef660a1b9ad0f586 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 14:15:57 -0700
Subject: [PATCH 0870/1454] Closes #7474 since it's marked as won't fix.


From dac090d1e9be7dec6c5ebdb2a81105b87e853193 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 5 Aug 2015 15:42:18 -0700
Subject: [PATCH 0871/1454] [SPARK-9657] Fix return type of getMaxPatternLength

mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #7974 from feynmanliang/SPARK-9657 and squashes the following commits:

7ca533f [Feynman Liang] Fix return type of getMaxPatternLength
---
 .../src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index d5f0c926c69bb..ad6715b52f337 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -82,7 +82,7 @@ class PrefixSpan private (
   /**
    * Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
    */
-  def getMaxPatternLength: Double = maxPatternLength
+  def getMaxPatternLength: Int = maxPatternLength
 
   /**
    * Sets maximal pattern length (default: `10`).

From 9c878923db6634effed98c99bf24dd263bb7c6ad Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 5 Aug 2015 16:33:42 -0700
Subject: [PATCH 0872/1454] [SPARK-9054] [SQL] Rename RowOrdering to
 InterpretedOrdering; use newOrdering in SMJ

This patches renames `RowOrdering` to `InterpretedOrdering` and updates SortMergeJoin to use the `SparkPlan` methods for constructing its ordering so that it may benefit from codegen.

This is an updated version of #7408.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7973 from JoshRosen/SPARK-9054 and squashes the following commits:

e610655 [Josh Rosen] Add comment RE: Ascending ordering
34b8e0c [Josh Rosen] Import ordering
be19a0f [Josh Rosen] [SPARK-9054] [SQL] Rename RowOrdering to InterpretedOrdering; use newOrdering in more places.
---
 .../sql/catalyst/expressions/arithmetic.scala |  4 +--
 .../catalyst/expressions/conditionals.scala   |  4 +--
 .../{RowOrdering.scala => ordering.scala}     | 27 ++++++++++---------
 .../sql/catalyst/expressions/predicates.scala |  8 +++---
 .../spark/sql/catalyst/util/TypeUtils.scala   |  4 +--
 .../apache/spark/sql/types/StructType.scala   |  4 +--
 .../expressions/CodeGenerationSuite.scala     |  2 +-
 .../apache/spark/sql/execution/Exchange.scala |  5 +++-
 .../spark/sql/execution/SparkPlan.scala       | 14 ++++++++--
 .../spark/sql/execution/basicOperators.scala  |  4 ++-
 .../sql/execution/joins/SortMergeJoin.scala   |  9 ++++---
 .../UnsafeKVExternalSorterSuite.scala         |  6 ++---
 12 files changed, 55 insertions(+), 36 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{RowOrdering.scala => ordering.scala} (85%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 5808e3f66de3c..98464edf4d390 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -320,7 +320,7 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
 
   override def nullable: Boolean = left.nullable && right.nullable
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def eval(input: InternalRow): Any = {
     val input1 = left.eval(input)
@@ -374,7 +374,7 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
 
   override def nullable: Boolean = left.nullable && right.nullable
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def eval(input: InternalRow): Any = {
     val input1 = left.eval(input)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index 961b1d8616801..d51f3d3cef588 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -319,7 +319,7 @@ case class Least(children: Seq[Expression]) extends Expression {
   override def nullable: Boolean = children.forall(_.nullable)
   override def foldable: Boolean = children.forall(_.foldable)
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (children.length <= 1) {
@@ -374,7 +374,7 @@ case class Greatest(children: Seq[Expression]) extends Expression {
   override def nullable: Boolean = children.forall(_.nullable)
   override def foldable: Boolean = children.forall(_.foldable)
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (children.length <= 1) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ordering.scala
similarity index 85%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ordering.scala
index 873f5324c573e..6407c73bc97d9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ordering.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.types._
 /**
  * An interpreted row ordering comparator.
  */
-class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
+class InterpretedOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
 
   def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
     this(ordering.map(BindReferences.bindReference(_, inputSchema)))
@@ -49,9 +49,9 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
           case dt: AtomicType if order.direction == Descending =>
             dt.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
           case s: StructType if order.direction == Ascending =>
-            s.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
+            s.interpretedOrdering.asInstanceOf[Ordering[Any]].compare(left, right)
           case s: StructType if order.direction == Descending =>
-            s.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
+            s.interpretedOrdering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
           case other =>
             throw new IllegalArgumentException(s"Type $other does not support ordered operations")
         }
@@ -65,6 +65,18 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
   }
 }
 
+object InterpretedOrdering {
+
+  /**
+   * Creates a [[InterpretedOrdering]] for the given schema, in natural ascending order.
+   */
+  def forSchema(dataTypes: Seq[DataType]): InterpretedOrdering = {
+    new InterpretedOrdering(dataTypes.zipWithIndex.map {
+      case (dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
+    })
+  }
+}
+
 object RowOrdering {
 
   /**
@@ -81,13 +93,4 @@ object RowOrdering {
    * Returns true iff outputs from the expressions can be ordered.
    */
   def isOrderable(exprs: Seq[Expression]): Boolean = exprs.forall(e => isOrderable(e.dataType))
-
-  /**
-   * Creates a [[RowOrdering]] for the given schema, in natural ascending order.
-   */
-  def forSchema(dataTypes: Seq[DataType]): RowOrdering = {
-    new RowOrdering(dataTypes.zipWithIndex.map {
-      case (dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
-    })
-  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 68c832d7194d4..fe7dffb815987 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -376,7 +376,7 @@ case class LessThan(left: Expression, right: Expression) extends BinaryCompariso
 
   override def symbol: String = "<"
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lt(input1, input2)
 }
@@ -388,7 +388,7 @@ case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryCo
 
   override def symbol: String = "<="
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lteq(input1, input2)
 }
@@ -400,7 +400,7 @@ case class GreaterThan(left: Expression, right: Expression) extends BinaryCompar
 
   override def symbol: String = ">"
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.gt(input1, input2)
 }
@@ -412,7 +412,7 @@ case class GreaterThanOrEqual(left: Expression, right: Expression) extends Binar
 
   override def symbol: String = ">="
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.gteq(input1, input2)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
index 0b41f92c6193c..bcf4d78fb9371 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
@@ -54,10 +54,10 @@ object TypeUtils {
   def getNumeric(t: DataType): Numeric[Any] =
     t.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]]
 
-  def getOrdering(t: DataType): Ordering[Any] = {
+  def getInterpretedOrdering(t: DataType): Ordering[Any] = {
     t match {
       case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]]
-      case s: StructType => s.ordering.asInstanceOf[Ordering[Any]]
+      case s: StructType => s.interpretedOrdering.asInstanceOf[Ordering[Any]]
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 6928707f7bf6e..9cbc207538d4f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute, RowOrdering}
+import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, AttributeReference, Attribute, InterpretedOrdering$}
 
 
 /**
@@ -301,7 +301,7 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
     StructType(newFields)
   }
 
-  private[sql] val ordering = RowOrdering.forSchema(this.fields.map(_.dataType))
+  private[sql] val interpretedOrdering = InterpretedOrdering.forSchema(this.fields.map(_.dataType))
 }
 
 object StructType extends AbstractDataType {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index cc82f7c3f5a73..e310aee221666 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -54,7 +54,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
   // GenerateOrdering agrees with RowOrdering.
   (DataTypeTestUtils.atomicTypes ++ Set(NullType)).foreach { dataType =>
     test(s"GenerateOrdering with $dataType") {
-      val rowOrdering = RowOrdering.forSchema(Seq(dataType, dataType))
+      val rowOrdering = InterpretedOrdering.forSchema(Seq(dataType, dataType))
       val genOrdering = GenerateOrdering.generate(
         BoundReference(0, dataType, nullable = true).asc ::
           BoundReference(1, dataType, nullable = true).asc :: Nil)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 05b009d1935bb..6ea5eeedf1bbe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -156,7 +156,10 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
           val mutablePair = new MutablePair[InternalRow, Null]()
           iter.map(row => mutablePair.update(row.copy(), null))
         }
-        implicit val ordering = new RowOrdering(sortingExpressions, child.output)
+        // We need to use an interpreted ordering here because generated orderings cannot be
+        // serialized and this ordering needs to be created on the driver in order to be passed into
+        // Spark core code.
+        implicit val ordering = new InterpretedOrdering(sortingExpressions, child.output)
         new RangePartitioner(numPartitions, rddForSampling, ascending = true)
       case SinglePartition =>
         new Partitioner {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index dbc0cefbe2e10..2f29067f5646a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -32,6 +32,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.types.DataType
 
 object SparkPlan {
   protected[sql] val currentContext = new ThreadLocal[SQLContext]()
@@ -309,13 +310,22 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
             throw e
           } else {
             log.error("Failed to generate ordering, fallback to interpreted", e)
-            new RowOrdering(order, inputSchema)
+            new InterpretedOrdering(order, inputSchema)
           }
       }
     } else {
-      new RowOrdering(order, inputSchema)
+      new InterpretedOrdering(order, inputSchema)
     }
   }
+  /**
+   * Creates a row ordering for the given schema, in natural ascending order.
+   */
+  protected def newNaturalAscendingOrdering(dataTypes: Seq[DataType]): Ordering[InternalRow] = {
+    val order: Seq[SortOrder] = dataTypes.zipWithIndex.map {
+      case (dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
+    }
+    newOrdering(order, Seq.empty)
+  }
 }
 
 private[sql] trait LeafNode extends SparkPlan {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 477170297c2ac..f4677b4ee86bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -212,7 +212,9 @@ case class TakeOrderedAndProject(
 
   override def outputPartitioning: Partitioning = SinglePartition
 
-  private val ord: RowOrdering = new RowOrdering(sortOrder, child.output)
+  // We need to use an interpreted ordering here because generated orderings cannot be serialized
+  // and this ordering needs to be created on the driver in order to be passed into Spark core code.
+  private val ord: InterpretedOrdering = new InterpretedOrdering(sortOrder, child.output)
 
   // TODO: remove @transient after figure out how to clean closure at InsertIntoHiveTable.
   @transient private val projection = projectList.map(new InterpretedProjection(_, child.output))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index eb595490fbf28..4ae23c186cf7b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -48,9 +48,6 @@ case class SortMergeJoin(
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
-  // this is to manually construct an ordering that can be used to compare keys from both sides
-  private val keyOrdering: RowOrdering = RowOrdering.forSchema(leftKeys.map(_.dataType))
-
   override def outputOrdering: Seq[SortOrder] = requiredOrders(leftKeys)
 
   override def requiredChildOrdering: Seq[Seq[SortOrder]] =
@@ -59,8 +56,10 @@ case class SortMergeJoin(
   @transient protected lazy val leftKeyGenerator = newProjection(leftKeys, left.output)
   @transient protected lazy val rightKeyGenerator = newProjection(rightKeys, right.output)
 
-  private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] =
+  private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = {
+    // This must be ascending in order to agree with the `keyOrdering` defined in `doExecute()`.
     keys.map(SortOrder(_, Ascending))
+  }
 
   protected override def doExecute(): RDD[InternalRow] = {
     val leftResults = left.execute().map(_.copy())
@@ -68,6 +67,8 @@ case class SortMergeJoin(
 
     leftResults.zipPartitions(rightResults) { (leftIter, rightIter) =>
       new Iterator[InternalRow] {
+        // An ordering that can be used to compare keys from both sides.
+        private[this] val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
         // Mutable per row objects.
         private[this] val joinRow = new JoinedRow
         private[this] var leftElement: InternalRow = _
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index 08156f0e39ce8..a9515a03acf2c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -22,7 +22,7 @@ import scala.util.Random
 import org.apache.spark._
 import org.apache.spark.sql.{RandomDataGenerator, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, RowOrdering, UnsafeProjection}
+import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, UnsafeRow, UnsafeProjection}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
@@ -144,8 +144,8 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
     }
     sorter.cleanupResources()
 
-    val keyOrdering = RowOrdering.forSchema(keySchema.map(_.dataType))
-    val valueOrdering = RowOrdering.forSchema(valueSchema.map(_.dataType))
+    val keyOrdering = InterpretedOrdering.forSchema(keySchema.map(_.dataType))
+    val valueOrdering = InterpretedOrdering.forSchema(valueSchema.map(_.dataType))
     val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
       override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
         keyOrdering.compare(x._1, y._1) match {

From a018b85716fd510ae95a3c66d676bbdb90f8d4e7 Mon Sep 17 00:00:00 2001
From: Xusen Yin <yinxusen@gmail.com>
Date: Wed, 5 Aug 2015 17:07:55 -0700
Subject: [PATCH 0873/1454] [SPARK-5895] [ML] Add VectorSlicer - updated

Add VectorSlicer transformer to spark.ml, with features specified as either indices or names.  Transfers feature attributes for selected features.

Updated version of [https://github.com/apache/spark/pull/5731]

CC: yinxusen This updates your PR.  You'll still be the primary author of this PR.

CC: mengxr

Author: Xusen Yin <yinxusen@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7972 from jkbradley/yinxusen-SPARK-5895 and squashes the following commits:

b16e86e [Joseph K. Bradley] fixed scala style
71c65d2 [Joseph K. Bradley] fix import order
86e9739 [Joseph K. Bradley] cleanups per code review
9d8d6f1 [Joseph K. Bradley] style fix
83bc2e9 [Joseph K. Bradley] Updated VectorSlicer
98c6939 [Xusen Yin] fix style error
ecbf2d3 [Xusen Yin] change interfaces and params
f6be302 [Xusen Yin] Merge branch 'master' into SPARK-5895
e4781f2 [Xusen Yin] fix commit error
fd154d7 [Xusen Yin] add test suite of vector slicer
17171f8 [Xusen Yin] fix slicer
9ab9747 [Xusen Yin] add vector slicer
aa5a0bf [Xusen Yin] add vector slicer
---
 .../spark/ml/feature/VectorSlicer.scala       | 170 ++++++++++++++++++
 .../apache/spark/ml/util/MetadataUtils.scala  |  17 ++
 .../apache/spark/mllib/linalg/Vectors.scala   |  24 +++
 .../spark/ml/feature/VectorSlicerSuite.scala  | 109 +++++++++++
 .../spark/mllib/linalg/VectorsSuite.scala     |   7 +
 5 files changed, 327 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
new file mode 100644
index 0000000000000..772bebeff214b
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
@@ -0,0 +1,170 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup}
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.param.{IntArrayParam, ParamMap, StringArrayParam}
+import org.apache.spark.ml.util.{Identifiable, MetadataUtils, SchemaUtils}
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.StructType
+
+/**
+ * :: Experimental ::
+ * This class takes a feature vector and outputs a new feature vector with a subarray of the
+ * original features.
+ *
+ * The subset of features can be specified with either indices ([[setIndices()]])
+ * or names ([[setNames()]]).  At least one feature must be selected. Duplicate features
+ * are not allowed, so there can be no overlap between selected indices and names.
+ *
+ * The output vector will order features with the selected indices first (in the order given),
+ * followed by the selected names (in the order given).
+ */
+@Experimental
+final class VectorSlicer(override val uid: String)
+  extends Transformer with HasInputCol with HasOutputCol {
+
+  def this() = this(Identifiable.randomUID("vectorSlicer"))
+
+  /**
+   * An array of indices to select features from a vector column.
+   * There can be no overlap with [[names]].
+   * @group param
+   */
+  val indices = new IntArrayParam(this, "indices",
+    "An array of indices to select features from a vector column." +
+      " There can be no overlap with names.", VectorSlicer.validIndices)
+
+  setDefault(indices -> Array.empty[Int])
+
+  /** @group getParam */
+  def getIndices: Array[Int] = $(indices)
+
+  /** @group setParam */
+  def setIndices(value: Array[Int]): this.type = set(indices, value)
+
+  /**
+   * An array of feature names to select features from a vector column.
+   * These names must be specified by ML [[org.apache.spark.ml.attribute.Attribute]]s.
+   * There can be no overlap with [[indices]].
+   * @group param
+   */
+  val names = new StringArrayParam(this, "names",
+    "An array of feature names to select features from a vector column." +
+      " There can be no overlap with indices.", VectorSlicer.validNames)
+
+  setDefault(names -> Array.empty[String])
+
+  /** @group getParam */
+  def getNames: Array[String] = $(names)
+
+  /** @group setParam */
+  def setNames(value: Array[String]): this.type = set(names, value)
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  override def validateParams(): Unit = {
+    require($(indices).length > 0 || $(names).length > 0,
+      s"VectorSlicer requires that at least one feature be selected.")
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    // Validity checks
+    transformSchema(dataset.schema)
+    val inputAttr = AttributeGroup.fromStructField(dataset.schema($(inputCol)))
+    inputAttr.numAttributes.foreach { numFeatures =>
+      val maxIndex = $(indices).max
+      require(maxIndex < numFeatures,
+        s"Selected feature index $maxIndex invalid for only $numFeatures input features.")
+    }
+
+    // Prepare output attributes
+    val inds = getSelectedFeatureIndices(dataset.schema)
+    val selectedAttrs: Option[Array[Attribute]] = inputAttr.attributes.map { attrs =>
+      inds.map(index => attrs(index))
+    }
+    val outputAttr = selectedAttrs match {
+      case Some(attrs) => new AttributeGroup($(outputCol), attrs)
+      case None => new AttributeGroup($(outputCol), inds.length)
+    }
+
+    // Select features
+    val slicer = udf { vec: Vector =>
+      vec match {
+        case features: DenseVector => Vectors.dense(inds.map(features.apply))
+        case features: SparseVector => features.slice(inds)
+      }
+    }
+    dataset.withColumn($(outputCol),
+      slicer(dataset($(inputCol))).as($(outputCol), outputAttr.toMetadata()))
+  }
+
+  /** Get the feature indices in order: indices, names */
+  private def getSelectedFeatureIndices(schema: StructType): Array[Int] = {
+    val nameFeatures = MetadataUtils.getFeatureIndicesFromNames(schema($(inputCol)), $(names))
+    val indFeatures = $(indices)
+    val numDistinctFeatures = (nameFeatures ++ indFeatures).distinct.length
+    lazy val errMsg = "VectorSlicer requires indices and names to be disjoint" +
+      s" sets of features, but they overlap." +
+      s" indices: ${indFeatures.mkString("[", ",", "]")}." +
+      s" names: " +
+      nameFeatures.zip($(names)).map { case (i, n) => s"$i:$n" }.mkString("[", ",", "]")
+    require(nameFeatures.length + indFeatures.length == numDistinctFeatures, errMsg)
+    indFeatures ++ nameFeatures
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
+
+    if (schema.fieldNames.contains($(outputCol))) {
+      throw new IllegalArgumentException(s"Output column ${$(outputCol)} already exists.")
+    }
+    val numFeaturesSelected = $(indices).length + $(names).length
+    val outputAttr = new AttributeGroup($(outputCol), numFeaturesSelected)
+    val outputFields = schema.fields :+ outputAttr.toStructField()
+    StructType(outputFields)
+  }
+
+  override def copy(extra: ParamMap): VectorSlicer = defaultCopy(extra)
+}
+
+private[feature] object VectorSlicer {
+
+  /** Return true if given feature indices are valid */
+  def validIndices(indices: Array[Int]): Boolean = {
+    if (indices.isEmpty) {
+      true
+    } else {
+      indices.length == indices.distinct.length && indices.forall(_ >= 0)
+    }
+  }
+
+  /** Return true if given feature names are valid */
+  def validNames(names: Array[String]): Boolean = {
+    names.forall(_.nonEmpty) && names.length == names.distinct.length
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
index 2a1db90f2ca2b..fcb517b5f735e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
@@ -20,6 +20,7 @@ package org.apache.spark.ml.util
 import scala.collection.immutable.HashMap
 
 import org.apache.spark.ml.attribute._
+import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.sql.types.StructField
 
 
@@ -74,4 +75,20 @@ private[spark] object MetadataUtils {
     }
   }
 
+  /**
+   * Takes a Vector column and a list of feature names, and returns the corresponding list of
+   * feature indices in the column, in order.
+   * @param col  Vector column which must have feature names specified via attributes
+   * @param names  List of feature names
+   */
+  def getFeatureIndicesFromNames(col: StructField, names: Array[String]): Array[Int] = {
+    require(col.dataType.isInstanceOf[VectorUDT], s"getFeatureIndicesFromNames expected column $col"
+      + s" to be Vector type, but it was type ${col.dataType} instead.")
+    val inputAttr = AttributeGroup.fromStructField(col)
+    names.map { name =>
+      require(inputAttr.hasAttr(name),
+        s"getFeatureIndicesFromNames found no feature with name $name in column $col.")
+      inputAttr.getAttr(name).index.get
+    }
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 96d1f48ba2ba3..86c461fa91633 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -766,6 +766,30 @@ class SparseVector(
       maxIdx
     }
   }
+
+  /**
+   * Create a slice of this vector based on the given indices.
+   * @param selectedIndices Unsorted list of indices into the vector.
+   *                        This does NOT do bound checking.
+   * @return  New SparseVector with values in the order specified by the given indices.
+   *
+   * NOTE: The API needs to be discussed before making this public.
+   *       Also, if we have a version assuming indices are sorted, we should optimize it.
+   */
+  private[spark] def slice(selectedIndices: Array[Int]): SparseVector = {
+    var currentIdx = 0
+    val (sliceInds, sliceVals) = selectedIndices.flatMap { origIdx =>
+      val iIdx = java.util.Arrays.binarySearch(this.indices, origIdx)
+      val i_v = if (iIdx >= 0) {
+        Iterator((currentIdx, this.values(iIdx)))
+      } else {
+        Iterator()
+      }
+      currentIdx += 1
+      i_v
+    }.unzip
+    new SparseVector(selectedIndices.length, sliceInds.toArray, sliceVals.toArray)
+  }
 }
 
 object SparseVector {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala
new file mode 100644
index 0000000000000..a6c2fba8360dd
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala
@@ -0,0 +1,109 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+class VectorSlicerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    val slicer = new VectorSlicer
+    ParamsSuite.checkParams(slicer)
+    assert(slicer.getIndices.length === 0)
+    assert(slicer.getNames.length === 0)
+    withClue("VectorSlicer should not have any features selected by default") {
+      intercept[IllegalArgumentException] {
+        slicer.validateParams()
+      }
+    }
+  }
+
+  test("feature validity checks") {
+    import VectorSlicer._
+    assert(validIndices(Array(0, 1, 8, 2)))
+    assert(validIndices(Array.empty[Int]))
+    assert(!validIndices(Array(-1)))
+    assert(!validIndices(Array(1, 2, 1)))
+
+    assert(validNames(Array("a", "b")))
+    assert(validNames(Array.empty[String]))
+    assert(!validNames(Array("", "b")))
+    assert(!validNames(Array("a", "b", "a")))
+  }
+
+  test("Test vector slicer") {
+    val sqlContext = new SQLContext(sc)
+
+    val data = Array(
+      Vectors.sparse(5, Seq((0, -2.0), (1, 2.3))),
+      Vectors.dense(-2.0, 2.3, 0.0, 0.0, 1.0),
+      Vectors.dense(0.0, 0.0, 0.0, 0.0, 0.0),
+      Vectors.dense(0.6, -1.1, -3.0, 4.5, 3.3),
+      Vectors.sparse(5, Seq())
+    )
+
+    // Expected after selecting indices 1, 4
+    val expected = Array(
+      Vectors.sparse(2, Seq((0, 2.3))),
+      Vectors.dense(2.3, 1.0),
+      Vectors.dense(0.0, 0.0),
+      Vectors.dense(-1.1, 3.3),
+      Vectors.sparse(2, Seq())
+    )
+
+    val defaultAttr = NumericAttribute.defaultAttr
+    val attrs = Array("f0", "f1", "f2", "f3", "f4").map(defaultAttr.withName)
+    val attrGroup = new AttributeGroup("features", attrs.asInstanceOf[Array[Attribute]])
+
+    val resultAttrs = Array("f1", "f4").map(defaultAttr.withName)
+    val resultAttrGroup = new AttributeGroup("expected", resultAttrs.asInstanceOf[Array[Attribute]])
+
+    val rdd = sc.parallelize(data.zip(expected)).map { case (a, b) => Row(a, b) }
+    val df = sqlContext.createDataFrame(rdd,
+      StructType(Array(attrGroup.toStructField(), resultAttrGroup.toStructField())))
+
+    val vectorSlicer = new VectorSlicer().setInputCol("features").setOutputCol("result")
+
+    def validateResults(df: DataFrame): Unit = {
+      df.select("result", "expected").collect().foreach { case Row(vec1: Vector, vec2: Vector) =>
+        assert(vec1 === vec2)
+      }
+      val resultMetadata = AttributeGroup.fromStructField(df.schema("result"))
+      val expectedMetadata = AttributeGroup.fromStructField(df.schema("expected"))
+      assert(resultMetadata.numAttributes === expectedMetadata.numAttributes)
+      resultMetadata.attributes.get.zip(expectedMetadata.attributes.get).foreach { case (a, b) =>
+        assert(a === b)
+      }
+    }
+
+    vectorSlicer.setIndices(Array(1, 4)).setNames(Array.empty)
+    validateResults(vectorSlicer.transform(df))
+
+    vectorSlicer.setIndices(Array(1)).setNames(Array("f4"))
+    validateResults(vectorSlicer.transform(df))
+
+    vectorSlicer.setIndices(Array.empty).setNames(Array("f1", "f4"))
+    validateResults(vectorSlicer.transform(df))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index 1c37ea5123e82..6508ddeba4206 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -367,4 +367,11 @@ class VectorsSuite extends SparkFunSuite with Logging {
     val sv1c = sv1.compressed.asInstanceOf[DenseVector]
     assert(sv1 === sv1c)
   }
+
+  test("SparseVector.slice") {
+    val v = new SparseVector(5, Array(1, 2, 4), Array(1.1, 2.2, 4.4))
+    assert(v.slice(Array(0, 2)) === new SparseVector(2, Array(1), Array(2.2)))
+    assert(v.slice(Array(2, 0)) === new SparseVector(2, Array(0), Array(2.2)))
+    assert(v.slice(Array(2, 0, 3, 4)) === new SparseVector(4, Array(0, 3), Array(2.2, 4.4)))
+  }
 }

From 8c320e45b5c9ffd7f0e35c1c7e6b5fc355377ea6 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 5 Aug 2015 17:28:23 -0700
Subject: [PATCH 0874/1454] [SPARK-6591] [SQL] Python data source load options
 should auto convert common types into strings

JIRA: https://issues.apache.org/jira/browse/SPARK-6591

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7926 from yjshen/py_dsload_opt and squashes the following commits:

b207832 [Yijie Shen] fix style
efdf834 [Yijie Shen] resolve comment
7a8f6a2 [Yijie Shen] lowercase
822e769 [Yijie Shen] convert load opts to string
---
 python/pyspark/sql/readwriter.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index dea8bad79e187..bf6ac084bbbf8 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -24,6 +24,16 @@
 __all__ = ["DataFrameReader", "DataFrameWriter"]
 
 
+def to_str(value):
+    """
+    A wrapper over str(), but convert bool values to lower case string
+    """
+    if isinstance(value, bool):
+        return str(value).lower()
+    else:
+        return str(value)
+
+
 class DataFrameReader(object):
     """
     Interface used to load a :class:`DataFrame` from external storage systems
@@ -77,7 +87,7 @@ def schema(self, schema):
     def option(self, key, value):
         """Adds an input option for the underlying data source.
         """
-        self._jreader = self._jreader.option(key, value)
+        self._jreader = self._jreader.option(key, to_str(value))
         return self
 
     @since(1.4)
@@ -85,7 +95,7 @@ def options(self, **options):
         """Adds input options for the underlying data source.
         """
         for k in options:
-            self._jreader = self._jreader.option(k, options[k])
+            self._jreader = self._jreader.option(k, to_str(options[k]))
         return self
 
     @since(1.4)
@@ -97,7 +107,8 @@ def load(self, path=None, format=None, schema=None, **options):
         :param schema: optional :class:`StructType` for the input schema.
         :param options: all other string options
 
-        >>> df = sqlContext.read.load('python/test_support/sql/parquet_partitioned')
+        >>> df = sqlContext.read.load('python/test_support/sql/parquet_partitioned', opt1=True,
+        ...     opt2=1, opt3='str')
         >>> df.dtypes
         [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
         """

From 4399b7b0903d830313ab7e69731c11d587ae567c Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 5 Aug 2015 17:58:36 -0700
Subject: [PATCH 0875/1454] [SPARK-9651] Fix UnsafeExternalSorterSuite.

First, it's probably a bad idea to call generated Scala methods
from Java. In this case, the method being called wasn't actually
"Utils.createTempDir()", but actually the method that returns the
first default argument to the actual createTempDir method, which
is just the location of java.io.tmpdir; meaning that all tests in
the class were using the same temp dir, and thus affecting each
other.

Second, spillingOccursInResponseToMemoryPressure was not writing
enough records to actually cause a spill.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7970 from vanzin/SPARK-9651 and squashes the following commits:

74d357f [Marcelo Vanzin] Clean up temp dir on test tear down.
a64f36a [Marcelo Vanzin] [SPARK-9651] Fix UnsafeExternalSorterSuite.
---
 .../sort/UnsafeExternalSorterSuite.java       | 21 ++++++++++++-------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 968185bde78ab..117745f9a9c00 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -101,7 +101,7 @@ public OutputStream apply(OutputStream stream) {
   public void setUp() {
     MockitoAnnotations.initMocks(this);
     sparkConf = new SparkConf();
-    tempDir = new File(Utils.createTempDir$default$1());
+    tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "unsafe-test");
     shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
     spillFilesCreated.clear();
     taskContext = mock(TaskContext.class);
@@ -143,13 +143,18 @@ public DiskBlockObjectWriter answer(InvocationOnMock invocationOnMock) throws Th
 
   @After
   public void tearDown() {
-    long leakedUnsafeMemory = taskMemoryManager.cleanUpAllAllocatedMemory();
-    if (shuffleMemoryManager != null) {
-      long leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask();
-      shuffleMemoryManager = null;
-      assertEquals(0L, leakedShuffleMemory);
+    try {
+      long leakedUnsafeMemory = taskMemoryManager.cleanUpAllAllocatedMemory();
+      if (shuffleMemoryManager != null) {
+        long leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask();
+        shuffleMemoryManager = null;
+        assertEquals(0L, leakedShuffleMemory);
+      }
+      assertEquals(0, leakedUnsafeMemory);
+    } finally {
+      Utils.deleteRecursively(tempDir);
+      tempDir = null;
     }
-    assertEquals(0, leakedUnsafeMemory);
   }
 
   private void assertSpillFilesWereCleanedUp() {
@@ -234,7 +239,7 @@ public void testSortingEmptyArrays() throws Exception {
   public void spillingOccursInResponseToMemoryPressure() throws Exception {
     shuffleMemoryManager = new ShuffleMemoryManager(pageSizeBytes * 2);
     final UnsafeExternalSorter sorter = newSorter();
-    final int numRecords = 100000;
+    final int numRecords = (int) pageSizeBytes / 4;
     for (int i = 0; i <= numRecords; i++) {
       insertNumber(sorter, numRecords - i);
     }

From 4581badbc8aa7e5a37ba7f7f83cc3860240f5dd3 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 5 Aug 2015 19:19:09 -0700
Subject: [PATCH 0876/1454] [SPARK-9611] [SQL] Fixes a few corner cases when we
 spill a UnsafeFixedWidthAggregationMap

This PR has the following three small fixes.

1. UnsafeKVExternalSorter does not use 0 as the initialSize to create an UnsafeInMemorySorter if its BytesToBytesMap is empty.
2. We will not not spill a InMemorySorter if it is empty.
3. We will not add a SpillReader to a SpillMerger if this SpillReader is empty.

JIRA: https://issues.apache.org/jira/browse/SPARK-9611

Author: Yin Huai <yhuai@databricks.com>

Closes #7948 from yhuai/unsafeEmptyMap and squashes the following commits:

9727abe [Yin Huai] Address Josh's comments.
34b6f76 [Yin Huai] 1. UnsafeKVExternalSorter does not use 0 as the initialSize to create an UnsafeInMemorySorter if its BytesToBytesMap is empty. 2. Do not spill a InMemorySorter if it is empty. 3. Do not add spill to SpillMerger if this spill is empty.
---
 .../unsafe/sort/UnsafeExternalSorter.java     |  36 +++---
 .../unsafe/sort/UnsafeSorterSpillMerger.java  |  12 +-
 .../sql/execution/UnsafeKVExternalSorter.java |   6 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala | 108 +++++++++++++++++-
 4 files changed, 141 insertions(+), 21 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index e6ddd08e5fa99..8f78fc5a41629 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -191,24 +191,29 @@ public void spill() throws IOException {
       spillWriters.size(),
       spillWriters.size() > 1 ? " times" : " time");
 
-    final UnsafeSorterSpillWriter spillWriter =
-      new UnsafeSorterSpillWriter(blockManager, fileBufferSizeBytes, writeMetrics,
-        inMemSorter.numRecords());
-    spillWriters.add(spillWriter);
-    final UnsafeSorterIterator sortedRecords = inMemSorter.getSortedIterator();
-    while (sortedRecords.hasNext()) {
-      sortedRecords.loadNext();
-      final Object baseObject = sortedRecords.getBaseObject();
-      final long baseOffset = sortedRecords.getBaseOffset();
-      final int recordLength = sortedRecords.getRecordLength();
-      spillWriter.write(baseObject, baseOffset, recordLength, sortedRecords.getKeyPrefix());
+    // We only write out contents of the inMemSorter if it is not empty.
+    if (inMemSorter.numRecords() > 0) {
+      final UnsafeSorterSpillWriter spillWriter =
+        new UnsafeSorterSpillWriter(blockManager, fileBufferSizeBytes, writeMetrics,
+          inMemSorter.numRecords());
+      spillWriters.add(spillWriter);
+      final UnsafeSorterIterator sortedRecords = inMemSorter.getSortedIterator();
+      while (sortedRecords.hasNext()) {
+        sortedRecords.loadNext();
+        final Object baseObject = sortedRecords.getBaseObject();
+        final long baseOffset = sortedRecords.getBaseOffset();
+        final int recordLength = sortedRecords.getRecordLength();
+        spillWriter.write(baseObject, baseOffset, recordLength, sortedRecords.getKeyPrefix());
+      }
+      spillWriter.close();
     }
-    spillWriter.close();
+
     final long spillSize = freeMemory();
     // Note that this is more-or-less going to be a multiple of the page size, so wasted space in
     // pages will currently be counted as memory spilled even though that space isn't actually
     // written to disk. This also counts the space needed to store the sorter's pointer array.
     taskContext.taskMetrics().incMemoryBytesSpilled(spillSize);
+
     initializeForWriting();
   }
 
@@ -505,12 +510,11 @@ public UnsafeSorterIterator getSortedIterator() throws IOException {
       final UnsafeSorterSpillMerger spillMerger =
         new UnsafeSorterSpillMerger(recordComparator, prefixComparator, numIteratorsToMerge);
       for (UnsafeSorterSpillWriter spillWriter : spillWriters) {
-        spillMerger.addSpill(spillWriter.getReader(blockManager));
+        spillMerger.addSpillIfNotEmpty(spillWriter.getReader(blockManager));
       }
       spillWriters.clear();
-      if (inMemoryIterator.hasNext()) {
-        spillMerger.addSpill(inMemoryIterator);
-      }
+      spillMerger.addSpillIfNotEmpty(inMemoryIterator);
+
       return spillMerger.getSortedIterator();
     }
   }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
index 8272c2a5be0d1..3874a9f9cbdb6 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
@@ -47,11 +47,19 @@ public int compare(UnsafeSorterIterator left, UnsafeSorterIterator right) {
     priorityQueue = new PriorityQueue<UnsafeSorterIterator>(numSpills, comparator);
   }
 
-  public void addSpill(UnsafeSorterIterator spillReader) throws IOException {
+  /**
+   * Add an UnsafeSorterIterator to this merger
+   */
+  public void addSpillIfNotEmpty(UnsafeSorterIterator spillReader) throws IOException {
     if (spillReader.hasNext()) {
+      // We only add the spillReader to the priorityQueue if it is not empty. We do this to
+      // make sure the hasNext method of UnsafeSorterIterator returned by getSortedIterator
+      // does not return wrong result because hasNext will returns true
+      // at least priorityQueue.size() times. If we allow n spillReaders in the
+      // priorityQueue, we will have n extra empty records in the result of the UnsafeSorterIterator.
       spillReader.loadNext();
+      priorityQueue.add(spillReader);
     }
-    priorityQueue.add(spillReader);
   }
 
   public UnsafeSorterIterator getSortedIterator() throws IOException {
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 86a563df992d0..6c1cf136d9b81 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -82,8 +82,11 @@ public UnsafeKVExternalSorter(StructType keySchema, StructType valueSchema,
         pageSizeBytes);
     } else {
       // Insert the records into the in-memory sorter.
+      // We will use the number of elements in the map as the initialSize of the
+      // UnsafeInMemorySorter. Because UnsafeInMemorySorter does not accept 0 as the initialSize,
+      // we will use 1 as its initial size if the map is empty.
       final UnsafeInMemorySorter inMemSorter = new UnsafeInMemorySorter(
-        taskMemoryManager, recordComparator, prefixComparator, map.numElements());
+        taskMemoryManager, recordComparator, prefixComparator, Math.max(1, map.numElements()));
 
       final int numKeyFields = keySchema.size();
       BytesToBytesMap.BytesToBytesMapIterator iter = map.iterator();
@@ -214,7 +217,6 @@ public boolean next() throws IOException {
           // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
           int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
           int valueLen = recordLen - keyLen - 4;
-
           key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
           value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index ef827b0fe9b5b..b513c970ccfe2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -23,7 +23,7 @@ import scala.util.{Try, Random}
 
 import org.scalatest.Matchers
 
-import org.apache.spark.sql.catalyst.expressions.UnsafeProjection
+import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
 import org.apache.spark.{TaskContextImpl, TaskContext, SparkFunSuite}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.test.TestSQLContext
@@ -231,4 +231,110 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
 
     map.free()
   }
+
+  testWithMemoryLeakDetection("test external sorting with an empty map") {
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    val map = new UnsafeFixedWidthAggregationMap(
+      emptyAggregationBuffer,
+      aggBufferSchema,
+      groupKeySchema,
+      taskMemoryManager,
+      shuffleMemoryManager,
+      128, // initial capacity
+      PAGE_SIZE_BYTES,
+      false // disable perf metrics
+    )
+
+    // Convert the map into a sorter
+    val sorter = map.destructAndCreateExternalSorter()
+
+    // Add more keys to the sorter and make sure the results come out sorted.
+    val additionalKeys = randomStrings(1024)
+    val keyConverter = UnsafeProjection.create(groupKeySchema)
+    val valueConverter = UnsafeProjection.create(aggBufferSchema)
+
+    additionalKeys.zipWithIndex.foreach { case (str, i) =>
+      val k = InternalRow(UTF8String.fromString(str))
+      val v = InternalRow(str.length)
+      sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+
+      if ((i % 100) == 0) {
+        shuffleMemoryManager.markAsOutOfMemory()
+        sorter.closeCurrentPage()
+      }
+    }
+
+    val out = new scala.collection.mutable.ArrayBuffer[String]
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      // At here, we also test if copy is correct.
+      val key = iter.getKey.copy()
+      val value = iter.getValue.copy()
+      assert(key.getString(0).length === value.getInt(0))
+      out += key.getString(0)
+    }
+
+    assert(out === (additionalKeys).sorted)
+
+    map.free()
+  }
+
+  testWithMemoryLeakDetection("test external sorting with empty records") {
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    // Memory consumption in the beginning of the task.
+    val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
+
+    val map = new UnsafeFixedWidthAggregationMap(
+      emptyAggregationBuffer,
+      StructType(Nil),
+      StructType(Nil),
+      taskMemoryManager,
+      shuffleMemoryManager,
+      128, // initial capacity
+      PAGE_SIZE_BYTES,
+      false // disable perf metrics
+    )
+
+    (1 to 10).foreach { i =>
+      val buf = map.getAggregationBuffer(UnsafeRow.createFromByteArray(0, 0))
+      assert(buf != null)
+    }
+
+    // Convert the map into a sorter. Right now, it contains one record.
+    val sorter = map.destructAndCreateExternalSorter()
+
+    withClue(s"destructAndCreateExternalSorter should release memory used by the map") {
+      // 4096 * 16 is the initial size allocated for the pointer/prefix array in the in-mem sorter.
+      assert(shuffleMemoryManager.getMemoryConsumptionForThisTask() ===
+        initialMemoryConsumption + 4096 * 16)
+    }
+
+    // Add more keys to the sorter and make sure the results come out sorted.
+    (1 to 4096).foreach { i =>
+      sorter.insertKV(UnsafeRow.createFromByteArray(0, 0), UnsafeRow.createFromByteArray(0, 0))
+
+      if ((i % 100) == 0) {
+        shuffleMemoryManager.markAsOutOfMemory()
+        sorter.closeCurrentPage()
+      }
+    }
+
+    var count = 0
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      // At here, we also test if copy is correct.
+      iter.getKey.copy()
+      iter.getValue.copy()
+      count += 1;
+    }
+
+    // 1 record was from the map and 4096 records were explicitly inserted.
+    assert(count === 4097)
+
+    map.free()
+  }
 }

From 119b59053870df7be899bf5c1c0d321406af96f9 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 6 Aug 2015 11:13:44 +0800
Subject: [PATCH 0877/1454] [SPARK-6923] [SPARK-7550] [SQL] Persists data
 source relations in Hive compatible format when possible

This PR is a fork of PR #5733 authored by chenghao-intel.  For committers who's going to merge this PR, please set the author to "Cheng Hao <hao.chengintel.com>".

----

When a data source relation meets the following requirements, we persist it in Hive compatible format, so that other systems like Hive can access it:

1. It's a `HadoopFsRelation`
2. It has only one input path
3. It's non-partitioned
4. It's data source provider can be naturally mapped to a Hive builtin SerDe (e.g. ORC and Parquet)

Author: Cheng Lian <lian@databricks.com>
Author: Cheng Hao <hao.cheng@intel.com>

Closes #7967 from liancheng/spark-6923/refactoring-pr-5733 and squashes the following commits:

5175ee6 [Cheng Lian] Fixes an oudated comment
3870166 [Cheng Lian] Fixes build error and comments
864acee [Cheng Lian] Refactors PR #5733
3490cdc [Cheng Hao] update the scaladoc
6f57669 [Cheng Hao] write schema info to hivemetastore for data source
---
 .../org/apache/spark/sql/DataFrame.scala      |  53 +++++--
 .../apache/spark/sql/DataFrameWriter.scala    |   7 +
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 146 +++++++++++++++++-
 .../org/apache/spark/sql/hive/HiveQl.scala    |  49 ++----
 .../spark/sql/hive/orc/OrcRelation.scala      |   6 +-
 .../sql/hive/HiveMetastoreCatalogSuite.scala  | 133 ++++++++++++++--
 6 files changed, 324 insertions(+), 70 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index e57acec59d327..405b5a4a9a7f9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -20,9 +20,6 @@ package org.apache.spark.sql
 import java.io.CharArrayWriter
 import java.util.Properties
 
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.unsafe.types.UTF8String
-
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
@@ -42,7 +39,7 @@ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
 import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
-import org.apache.spark.sql.json.{JacksonGenerator, JSONRelation}
+import org.apache.spark.sql.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.storage.StorageLevel
@@ -1650,8 +1647,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().saveAsTable(tableName)`.
    */
@@ -1669,8 +1670,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().mode(mode).saveAsTable(tableName)`.
    */
@@ -1689,8 +1694,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().format(source).saveAsTable(tableName)`.
    */
@@ -1709,8 +1718,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().mode(mode).saveAsTable(tableName)`.
    */
@@ -1728,8 +1741,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by
    *            `write().format(source).mode(mode).options(options).saveAsTable(tableName)`.
@@ -1754,8 +1771,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by
    *            `write().format(source).mode(mode).options(options).saveAsTable(tableName)`.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 7e3318cefe62c..2a4992db09bc2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.plans.logical.InsertIntoTable
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, ResolvedDataSource}
 import org.apache.spark.sql.jdbc.{JDBCWriteDetails, JdbcUtils}
+import org.apache.spark.sql.sources.HadoopFsRelation
 
 
 /**
@@ -185,6 +186,12 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    * When `mode` is `Append`, the schema of the [[DataFrame]] need to be
    * the same as that of the existing table, and format or options will be ignored.
    *
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @since 1.4.0
    */
   def saveAsTable(tableName: String): Unit = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 6b37af99f4677..1523ebe9d5493 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -18,11 +18,13 @@
 package org.apache.spark.sql.hive
 
 import scala.collection.JavaConversions._
+import scala.collection.mutable
 
 import com.google.common.base.Objects
 import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache}
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.common.StatsSetupConst
+import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.metastore.Warehouse
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 import org.apache.hadoop.hive.ql.metadata._
@@ -40,9 +42,59 @@ import org.apache.spark.sql.execution.datasources
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
 
+private[hive] case class HiveSerDe(
+    inputFormat: Option[String] = None,
+    outputFormat: Option[String] = None,
+    serde: Option[String] = None)
+
+private[hive] object HiveSerDe {
+  /**
+   * Get the Hive SerDe information from the data source abbreviation string or classname.
+   *
+   * @param source Currently the source abbreviation can be one of the following:
+   *               SequenceFile, RCFile, ORC, PARQUET, and case insensitive.
+   * @param hiveConf Hive Conf
+   * @return HiveSerDe associated with the specified source
+   */
+  def sourceToSerDe(source: String, hiveConf: HiveConf): Option[HiveSerDe] = {
+    val serdeMap = Map(
+      "sequencefile" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
+          outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat")),
+
+      "rcfile" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"),
+          serde = Option(hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTRCFILESERDE))),
+
+      "orc" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"),
+          serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde")),
+
+      "parquet" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"),
+          serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")))
+
+    val key = source.toLowerCase match {
+      case _ if source.startsWith("org.apache.spark.sql.parquet") => "parquet"
+      case _ if source.startsWith("org.apache.spark.sql.orc") => "orc"
+      case _ => source.toLowerCase
+    }
+
+    serdeMap.get(key)
+  }
+}
+
 private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: HiveContext)
   extends Catalog with Logging {
 
@@ -164,15 +216,15 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       processDatabaseAndTableName(database, tableIdent.table)
     }
 
-    val tableProperties = new scala.collection.mutable.HashMap[String, String]
+    val tableProperties = new mutable.HashMap[String, String]
     tableProperties.put("spark.sql.sources.provider", provider)
 
     // Saves optional user specified schema.  Serialized JSON schema string may be too long to be
     // stored into a single metastore SerDe property.  In this case, we split the JSON string and
     // store each part as a separate SerDe property.
-    if (userSpecifiedSchema.isDefined) {
+    userSpecifiedSchema.foreach { schema =>
       val threshold = conf.schemaStringLengthThreshold
-      val schemaJsonString = userSpecifiedSchema.get.json
+      val schemaJsonString = schema.json
       // Split the JSON string.
       val parts = schemaJsonString.grouped(threshold).toSeq
       tableProperties.put("spark.sql.sources.schema.numParts", parts.size.toString)
@@ -194,7 +246,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         // The table does not have a specified schema, which means that the schema will be inferred
         // when we load the table. So, we are not expecting partition columns and we will discover
         // partitions when we load the table. However, if there are specified partition columns,
-        // we simplily ignore them and provide a warning message..
+        // we simply ignore them and provide a warning message.
         logWarning(
           s"The schema and partitions of table $tableIdent will be inferred when it is loaded. " +
             s"Specified partition columns (${partitionColumns.mkString(",")}) will be ignored.")
@@ -210,7 +262,11 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       ManagedTable
     }
 
-    client.createTable(
+    val maybeSerDe = HiveSerDe.sourceToSerDe(provider, hive.hiveconf)
+    val dataSource = ResolvedDataSource(
+      hive, userSpecifiedSchema, partitionColumns, provider, options)
+
+    def newSparkSQLSpecificMetastoreTable(): HiveTable = {
       HiveTable(
         specifiedDatabase = Option(dbName),
         name = tblName,
@@ -218,7 +274,83 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         partitionColumns = metastorePartitionColumns,
         tableType = tableType,
         properties = tableProperties.toMap,
-        serdeProperties = options))
+        serdeProperties = options)
+    }
+
+    def newHiveCompatibleMetastoreTable(relation: HadoopFsRelation, serde: HiveSerDe): HiveTable = {
+      def schemaToHiveColumn(schema: StructType): Seq[HiveColumn] = {
+        schema.map { field =>
+          HiveColumn(
+            name = field.name,
+            hiveType = HiveMetastoreTypes.toMetastoreType(field.dataType),
+            comment = "")
+        }
+      }
+
+      val partitionColumns = schemaToHiveColumn(relation.partitionColumns)
+      val dataColumns = schemaToHiveColumn(relation.schema).filterNot(partitionColumns.contains)
+
+      HiveTable(
+        specifiedDatabase = Option(dbName),
+        name = tblName,
+        schema = dataColumns,
+        partitionColumns = partitionColumns,
+        tableType = tableType,
+        properties = tableProperties.toMap,
+        serdeProperties = options,
+        location = Some(relation.paths.head),
+        viewText = None, // TODO We need to place the SQL string here.
+        inputFormat = serde.inputFormat,
+        outputFormat = serde.outputFormat,
+        serde = serde.serde)
+    }
+
+    // TODO: Support persisting partitioned data source relations in Hive compatible format
+    val hiveTable = (maybeSerDe, dataSource.relation) match {
+      case (Some(serde), relation: HadoopFsRelation)
+          if relation.paths.length == 1 && relation.partitionColumns.isEmpty =>
+        logInfo {
+          "Persisting data source relation with a single input path into Hive metastore in Hive " +
+            s"compatible format.  Input path: ${relation.paths.head}"
+        }
+        newHiveCompatibleMetastoreTable(relation, serde)
+
+      case (Some(serde), relation: HadoopFsRelation) if relation.partitionColumns.nonEmpty =>
+        logWarning {
+          val paths = relation.paths.mkString(", ")
+          "Persisting partitioned data source relation into Hive metastore in " +
+            s"Spark SQL specific format, which is NOT compatible with Hive.  Input path(s): " +
+            paths.mkString("\n", "\n", "")
+        }
+        newSparkSQLSpecificMetastoreTable()
+
+      case (Some(serde), relation: HadoopFsRelation) =>
+        logWarning {
+          val paths = relation.paths.mkString(", ")
+          "Persisting data source relation with multiple input paths into Hive metastore in " +
+            s"Spark SQL specific format, which is NOT compatible with Hive.  Input paths: " +
+            paths.mkString("\n", "\n", "")
+        }
+        newSparkSQLSpecificMetastoreTable()
+
+      case (Some(serde), _) =>
+        logWarning {
+          s"Data source relation is not a ${classOf[HadoopFsRelation].getSimpleName}. " +
+            "Persisting it into Hive metastore in Spark SQL specific format, " +
+            "which is NOT compatible with Hive."
+        }
+        newSparkSQLSpecificMetastoreTable()
+
+      case _ =>
+        logWarning {
+          s"Couldn't find corresponding Hive SerDe for data source provider $provider. " +
+            "Persisting data source relation into Hive metastore in Spark SQL specific format, " +
+            "which is NOT compatible with Hive."
+        }
+        newSparkSQLSpecificMetastoreTable()
+    }
+
+    client.createTable(hiveTable)
   }
 
   def hiveDefaultTableFilePath(tableName: String): String = {
@@ -463,7 +595,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       case p: LogicalPlan if !p.childrenResolved => p
       case p: LogicalPlan if p.resolved => p
       case p @ CreateTableAsSelect(table, child, allowExisting) =>
-        val schema = if (table.schema.size > 0) {
+        val schema = if (table.schema.nonEmpty) {
           table.schema
         } else {
           child.output.map {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index f43e403ce9a9d..7d7b4b9167306 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -32,6 +32,7 @@ import org.apache.hadoop.hive.ql.session.SessionState
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
@@ -261,8 +262,8 @@ private[hive] object HiveQl extends Logging {
   /**
    * Returns the HiveConf
    */
-  private[this] def hiveConf(): HiveConf = {
-    val ss = SessionState.get() // SessionState is lazy initializaion, it can be null here
+  private[this] def hiveConf: HiveConf = {
+    val ss = SessionState.get() // SessionState is lazy initialization, it can be null here
     if (ss == null) {
       new HiveConf()
     } else {
@@ -604,38 +605,18 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         serde = None,
         viewText = None)
 
-      // default storage type abbriviation (e.g. RCFile, ORC, PARQUET etc.)
+      // default storage type abbreviation (e.g. RCFile, ORC, PARQUET etc.)
       val defaultStorageType = hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTFILEFORMAT)
-      // handle the default format for the storage type abbriviation
-      tableDesc = if ("SequenceFile".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
-            outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat"))
-        } else if ("RCFile".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"),
-            serde = Option(hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTRCFILESERDE)))
-        } else if ("ORC".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"),
-            serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde"))
-        } else if ("PARQUET".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
-            outputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"),
-            serde =
-              Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"))
-        } else {
-          tableDesc.copy(
-            inputFormat =
-              Option("org.apache.hadoop.mapred.TextInputFormat"),
-            outputFormat =
-              Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat"))
-        }
+      // handle the default format for the storage type abbreviation
+      val hiveSerDe = HiveSerDe.sourceToSerDe(defaultStorageType, hiveConf).getOrElse {
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.mapred.TextInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat"))
+      }
+
+      hiveSerDe.inputFormat.foreach(f => tableDesc = tableDesc.copy(inputFormat = Some(f)))
+      hiveSerDe.outputFormat.foreach(f => tableDesc = tableDesc.copy(outputFormat = Some(f)))
+      hiveSerDe.serde.foreach(f => tableDesc = tableDesc.copy(serde = Some(f)))
 
       children.collect {
         case list @ Token("TOK_TABCOLLIST", _) =>
@@ -908,7 +889,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 }
                 (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), serdeProps)
 
-              case Nil => (Nil, Option(hiveConf().getVar(ConfVars.HIVESCRIPTSERDE)), Nil)
+              case Nil => (Nil, Option(hiveConf.getVar(ConfVars.HIVESCRIPTSERDE)), Nil)
             }
 
             val (inRowFormat, inSerdeClass, inSerdeProps) = matchSerDe(inputSerdeClause)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 6fa599734892b..4a310ff4e9016 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -291,9 +291,11 @@ private[orc] case class OrcTableScan(
     // Sets requested columns
     addColumnIds(attributes, relation, conf)
 
-    if (inputPaths.nonEmpty) {
-      FileInputFormat.setInputPaths(job, inputPaths.map(_.getPath): _*)
+    if (inputPaths.isEmpty) {
+      // the input path probably be pruned, return an empty RDD.
+      return sqlContext.sparkContext.emptyRDD[InternalRow]
     }
+    FileInputFormat.setInputPaths(job, inputPaths.map(_.getPath): _*)
 
     val inputFormatClass =
       classOf[OrcInputFormat]
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 983c013bcf86a..332c3ec0c28b8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -17,31 +17,142 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.{Logging, SparkFunSuite}
-import org.apache.spark.sql.hive.test.TestHive
+import java.io.File
 
-import org.apache.spark.sql.test.ExamplePointUDT
+import org.apache.spark.sql.hive.client.{ExternalTable, HiveColumn, ManagedTable}
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.sources.DataSourceTest
+import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
 import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.{Row, SaveMode}
+import org.apache.spark.{Logging, SparkFunSuite}
+
 
 class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
 
   test("struct field should accept underscore in sub-column name") {
-    val metastr = "struct<a: int, b_1: string, c: string>"
-
-    val datatype = HiveMetastoreTypes.toDataType(metastr)
-    assert(datatype.isInstanceOf[StructType])
+    val hiveTypeStr = "struct<a: int, b_1: string, c: string>"
+    val dateType = HiveMetastoreTypes.toDataType(hiveTypeStr)
+    assert(dateType.isInstanceOf[StructType])
   }
 
   test("udt to metastore type conversion") {
     val udt = new ExamplePointUDT
-    assert(HiveMetastoreTypes.toMetastoreType(udt) ===
-      HiveMetastoreTypes.toMetastoreType(udt.sqlType))
+    assertResult(HiveMetastoreTypes.toMetastoreType(udt.sqlType)) {
+      HiveMetastoreTypes.toMetastoreType(udt)
+    }
   }
 
   test("duplicated metastore relations") {
-    import TestHive.implicits._
-    val df = TestHive.sql("SELECT * FROM src")
+    val df = sql("SELECT * FROM src")
     logInfo(df.queryExecution.toString)
     df.as('a).join(df.as('b), $"a.key" === $"b.key")
   }
 }
+
+class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
+  override val sqlContext = TestHive
+
+  private val testDF = (1 to 2).map(i => (i, s"val_$i")).toDF("d1", "d2").coalesce(1)
+
+  Seq(
+    "parquet" -> (
+      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat",
+      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat",
+      "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"
+    ),
+
+    "orc" -> (
+      "org.apache.hadoop.hive.ql.io.orc.OrcInputFormat",
+      "org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat",
+      "org.apache.hadoop.hive.ql.io.orc.OrcSerde"
+    )
+  ).foreach { case (provider, (inputFormat, outputFormat, serde)) =>
+    test(s"Persist non-partitioned $provider relation into metastore as managed table") {
+      withTable("t") {
+        testDF
+          .write
+          .mode(SaveMode.Overwrite)
+          .format(provider)
+          .saveAsTable("t")
+
+        val hiveTable = catalog.client.getTable("default", "t")
+        assert(hiveTable.inputFormat === Some(inputFormat))
+        assert(hiveTable.outputFormat === Some(outputFormat))
+        assert(hiveTable.serde === Some(serde))
+
+        assert(!hiveTable.isPartitioned)
+        assert(hiveTable.tableType === ManagedTable)
+
+        val columns = hiveTable.schema
+        assert(columns.map(_.name) === Seq("d1", "d2"))
+        assert(columns.map(_.hiveType) === Seq("int", "string"))
+
+        checkAnswer(table("t"), testDF)
+        assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+      }
+    }
+
+    test(s"Persist non-partitioned $provider relation into metastore as external table") {
+      withTempPath { dir =>
+        withTable("t") {
+          val path = dir.getCanonicalFile
+
+          testDF
+            .write
+            .mode(SaveMode.Overwrite)
+            .format(provider)
+            .option("path", path.toString)
+            .saveAsTable("t")
+
+          val hiveTable = catalog.client.getTable("default", "t")
+          assert(hiveTable.inputFormat === Some(inputFormat))
+          assert(hiveTable.outputFormat === Some(outputFormat))
+          assert(hiveTable.serde === Some(serde))
+
+          assert(hiveTable.tableType === ExternalTable)
+          assert(hiveTable.location.get === path.toURI.toString.stripSuffix(File.separator))
+
+          val columns = hiveTable.schema
+          assert(columns.map(_.name) === Seq("d1", "d2"))
+          assert(columns.map(_.hiveType) === Seq("int", "string"))
+
+          checkAnswer(table("t"), testDF)
+          assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+        }
+      }
+    }
+
+    test(s"Persist non-partitioned $provider relation into metastore as managed table using CTAS") {
+      withTempPath { dir =>
+        withTable("t") {
+          val path = dir.getCanonicalPath
+
+          sql(
+            s"""CREATE TABLE t USING $provider
+               |OPTIONS (path '$path')
+               |AS SELECT 1 AS d1, "val_1" AS d2
+             """.stripMargin)
+
+          val hiveTable = catalog.client.getTable("default", "t")
+          assert(hiveTable.inputFormat === Some(inputFormat))
+          assert(hiveTable.outputFormat === Some(outputFormat))
+          assert(hiveTable.serde === Some(serde))
+
+          assert(hiveTable.isPartitioned === false)
+          assert(hiveTable.tableType === ExternalTable)
+          assert(hiveTable.partitionColumns.length === 0)
+
+          val columns = hiveTable.schema
+          assert(columns.map(_.name) === Seq("d1", "d2"))
+          assert(columns.map(_.hiveType) === Seq("int", "string"))
+
+          checkAnswer(table("t"), Row(1, "val_1"))
+          assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1"))
+        }
+      }
+    }
+  }
+}

From 9270bd06fd0b16892e3f37213b5bc7813ea11fdd Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 21:50:14 -0700
Subject: [PATCH 0878/1454] [SPARK-9674][SQL] Remove GeneratedAggregate.

The new aggregate replaces the old GeneratedAggregate.

Author: Reynold Xin <rxin@databricks.com>

Closes #7983 from rxin/remove-generated-agg and squashes the following commits:

8334aae [Reynold Xin] [SPARK-9674][SQL] Remove GeneratedAggregate.
---
 .../sql/execution/GeneratedAggregate.scala    | 352 ------------------
 .../spark/sql/execution/SparkStrategies.scala |  34 --
 .../org/apache/spark/sql/SQLQuerySuite.scala  |   5 +-
 .../spark/sql/execution/AggregateSuite.scala  |  48 ---
 4 files changed, 2 insertions(+), 437 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
deleted file mode 100644
index bf4905dc1eef9..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ /dev/null
@@ -1,352 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import java.io.IOException
-
-import org.apache.spark.{InternalAccumulator, SparkEnv, TaskContext}
-import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.catalyst.trees._
-import org.apache.spark.sql.types._
-
-case class AggregateEvaluation(
-    schema: Seq[Attribute],
-    initialValues: Seq[Expression],
-    update: Seq[Expression],
-    result: Expression)
-
-/**
- * :: DeveloperApi ::
- * Alternate version of aggregation that leverages projection and thus code generation.
- * Aggregations are converted into a set of projections from a aggregation buffer tuple back onto
- * itself. Currently only used for simple aggregations like SUM, COUNT, or AVERAGE are supported.
- *
- * @param partial if true then aggregation is done partially on local data without shuffling to
- *                ensure all values where `groupingExpressions` are equal are present.
- * @param groupingExpressions expressions that are evaluated to determine grouping.
- * @param aggregateExpressions expressions that are computed for each group.
- * @param unsafeEnabled whether to allow Unsafe-based aggregation buffers to be used.
- * @param child the input data source.
- */
-@DeveloperApi
-case class GeneratedAggregate(
-    partial: Boolean,
-    groupingExpressions: Seq[Expression],
-    aggregateExpressions: Seq[NamedExpression],
-    unsafeEnabled: Boolean,
-    child: SparkPlan)
-  extends UnaryNode {
-
-  override def requiredChildDistribution: Seq[Distribution] =
-    if (partial) {
-      UnspecifiedDistribution :: Nil
-    } else {
-      if (groupingExpressions == Nil) {
-        AllTuples :: Nil
-      } else {
-        ClusteredDistribution(groupingExpressions) :: Nil
-      }
-    }
-
-  override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute)
-
-  protected override def doExecute(): RDD[InternalRow] = {
-    val aggregatesToCompute = aggregateExpressions.flatMap { a =>
-      a.collect { case agg: AggregateExpression1 => agg}
-    }
-
-    // If you add any new function support, please add tests in org.apache.spark.sql.SQLQuerySuite
-    // (in test "aggregation with codegen").
-    val computeFunctions = aggregatesToCompute.map {
-      case c @ Count(expr) =>
-        // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its
-        // UnscaledValue will be null if and only if x is null; helps with Average on decimals
-        val toCount = expr match {
-          case UnscaledValue(e) => e
-          case _ => expr
-        }
-        val currentCount = AttributeReference("currentCount", LongType, nullable = false)()
-        val initialValue = Literal(0L)
-        val updateFunction = If(IsNotNull(toCount), Add(currentCount, Literal(1L)), currentCount)
-        val result = currentCount
-
-        AggregateEvaluation(currentCount :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
-
-      case s @ Sum(expr) =>
-        val calcType =
-          expr.dataType match {
-            case DecimalType.Fixed(p, s) =>
-              DecimalType.bounded(p + 10, s)
-            case _ =>
-              expr.dataType
-          }
-
-        val currentSum = AttributeReference("currentSum", calcType, nullable = true)()
-        val initialValue = Literal.create(null, calcType)
-
-        // Coalesce avoids double calculation...
-        // but really, common sub expression elimination would be better....
-        val zero = Cast(Literal(0), calcType)
-        val updateFunction = Coalesce(
-          Add(
-            Coalesce(currentSum :: zero :: Nil),
-            Cast(expr, calcType)
-          ) :: currentSum :: Nil)
-        val result =
-          expr.dataType match {
-            case DecimalType.Fixed(_, _) =>
-              Cast(currentSum, s.dataType)
-            case _ => currentSum
-          }
-
-        AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
-
-      case m @ Max(expr) =>
-        val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)()
-        val initialValue = Literal.create(null, expr.dataType)
-        val updateMax = MaxOf(currentMax, expr)
-
-        AggregateEvaluation(
-          currentMax :: Nil,
-          initialValue :: Nil,
-          updateMax :: Nil,
-          currentMax)
-
-      case m @ Min(expr) =>
-        val currentMin = AttributeReference("currentMin", expr.dataType, nullable = true)()
-        val initialValue = Literal.create(null, expr.dataType)
-        val updateMin = MinOf(currentMin, expr)
-
-        AggregateEvaluation(
-          currentMin :: Nil,
-          initialValue :: Nil,
-          updateMin :: Nil,
-          currentMin)
-
-      case CollectHashSet(Seq(expr)) =>
-        val set =
-          AttributeReference("hashSet", new OpenHashSetUDT(expr.dataType), nullable = false)()
-        val initialValue = NewSet(expr.dataType)
-        val addToSet = AddItemToSet(expr, set)
-
-        AggregateEvaluation(
-          set :: Nil,
-          initialValue :: Nil,
-          addToSet :: Nil,
-          set)
-
-      case CombineSetsAndCount(inputSet) =>
-        val elementType = inputSet.dataType.asInstanceOf[OpenHashSetUDT].elementType
-        val set =
-          AttributeReference("hashSet", new OpenHashSetUDT(elementType), nullable = false)()
-        val initialValue = NewSet(elementType)
-        val collectSets = CombineSets(set, inputSet)
-
-        AggregateEvaluation(
-          set :: Nil,
-          initialValue :: Nil,
-          collectSets :: Nil,
-          CountSet(set))
-
-      case o => sys.error(s"$o can't be codegened.")
-    }
-
-    val computationSchema = computeFunctions.flatMap(_.schema)
-
-    val resultMap: Map[TreeNodeRef, Expression] =
-      aggregatesToCompute.zip(computeFunctions).map {
-        case (agg, func) => new TreeNodeRef(agg) -> func.result
-      }.toMap
-
-    val namedGroups = groupingExpressions.zipWithIndex.map {
-      case (ne: NamedExpression, _) => (ne, ne.toAttribute)
-      case (e, i) => (e, Alias(e, s"GroupingExpr$i")().toAttribute)
-    }
-
-    // The set of expressions that produce the final output given the aggregation buffer and the
-    // grouping expressions.
-    val resultExpressions = aggregateExpressions.map(_.transform {
-      case e: Expression if resultMap.contains(new TreeNodeRef(e)) => resultMap(new TreeNodeRef(e))
-      case e: Expression =>
-        namedGroups.collectFirst {
-          case (expr, attr) if expr semanticEquals e => attr
-        }.getOrElse(e)
-    })
-
-    val aggregationBufferSchema: StructType = StructType.fromAttributes(computationSchema)
-
-    val groupKeySchema: StructType = {
-      val fields = groupingExpressions.zipWithIndex.map { case (expr, idx) =>
-        // This is a dummy field name
-        StructField(idx.toString, expr.dataType, expr.nullable)
-      }
-      StructType(fields)
-    }
-
-    val schemaSupportsUnsafe: Boolean = {
-      UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
-        UnsafeProjection.canSupport(groupKeySchema)
-    }
-
-    child.execute().mapPartitions { iter =>
-      // Builds a new custom class for holding the results of aggregation for a group.
-      val initialValues = computeFunctions.flatMap(_.initialValues)
-      val newAggregationBuffer = newProjection(initialValues, child.output)
-      log.info(s"Initial values: ${initialValues.mkString(",")}")
-
-      // A projection that computes the group given an input tuple.
-      val groupProjection = newProjection(groupingExpressions, child.output)
-      log.info(s"Grouping Projection: ${groupingExpressions.mkString(",")}")
-
-      // A projection that is used to update the aggregate values for a group given a new tuple.
-      // This projection should be targeted at the current values for the group and then applied
-      // to a joined row of the current values with the new input row.
-      val updateExpressions = computeFunctions.flatMap(_.update)
-      val updateSchema = computeFunctions.flatMap(_.schema) ++ child.output
-      val updateProjection = newMutableProjection(updateExpressions, updateSchema)()
-      log.info(s"Update Expressions: ${updateExpressions.mkString(",")}")
-
-      // A projection that produces the final result, given a computation.
-      val resultProjectionBuilder =
-        newMutableProjection(
-          resultExpressions,
-          namedGroups.map(_._2) ++ computationSchema)
-      log.info(s"Result Projection: ${resultExpressions.mkString(",")}")
-
-      val joinedRow = new JoinedRow
-
-      if (!iter.hasNext) {
-        // This is an empty input, so return early so that we do not allocate data structures
-        // that won't be cleaned up (see SPARK-8357).
-        if (groupingExpressions.isEmpty) {
-          // This is a global aggregate, so return an empty aggregation buffer.
-          val resultProjection = resultProjectionBuilder()
-          Iterator(resultProjection(newAggregationBuffer(EmptyRow)))
-        } else {
-          // This is a grouped aggregate, so return an empty iterator.
-          Iterator[InternalRow]()
-        }
-      } else if (groupingExpressions.isEmpty) {
-        // TODO: Codegening anything other than the updateProjection is probably over kill.
-        val buffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
-        var currentRow: InternalRow = null
-        updateProjection.target(buffer)
-
-        while (iter.hasNext) {
-          currentRow = iter.next()
-          updateProjection(joinedRow(buffer, currentRow))
-        }
-
-        val resultProjection = resultProjectionBuilder()
-        Iterator(resultProjection(buffer))
-
-      } else if (unsafeEnabled && schemaSupportsUnsafe) {
-        assert(iter.hasNext, "There should be at least one row for this path")
-        log.info("Using Unsafe-based aggregator")
-        val pageSizeBytes = SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
-        val taskContext = TaskContext.get()
-        val aggregationMap = new UnsafeFixedWidthAggregationMap(
-          newAggregationBuffer(EmptyRow),
-          aggregationBufferSchema,
-          groupKeySchema,
-          taskContext.taskMemoryManager(),
-          SparkEnv.get.shuffleMemoryManager,
-          1024 * 16, // initial capacity
-          pageSizeBytes,
-          false // disable tracking of performance metrics
-        )
-
-        while (iter.hasNext) {
-          val currentRow: InternalRow = iter.next()
-          val groupKey: InternalRow = groupProjection(currentRow)
-          val aggregationBuffer = aggregationMap.getAggregationBuffer(groupKey)
-          if (aggregationBuffer == null) {
-            throw new IOException("Could not allocate memory to grow aggregation buffer")
-          }
-          updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow))
-        }
-
-        // Record memory used in the process
-        taskContext.internalMetricsToAccumulators(
-          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(aggregationMap.getMemoryUsage)
-
-        new Iterator[InternalRow] {
-          private[this] val mapIterator = aggregationMap.iterator()
-          private[this] val resultProjection = resultProjectionBuilder()
-          private[this] var _hasNext = mapIterator.next()
-
-          def hasNext: Boolean = _hasNext
-
-          def next(): InternalRow = {
-            if (_hasNext) {
-              val result = resultProjection(joinedRow(mapIterator.getKey, mapIterator.getValue))
-              _hasNext = mapIterator.next()
-              if (_hasNext) {
-                result
-              } else {
-                // This is the last element in the iterator, so let's free the buffer. Before we do,
-                // though, we need to make a defensive copy of the result so that we don't return an
-                // object that might contain dangling pointers to the freed memory.
-                val resultCopy = result.copy()
-                aggregationMap.free()
-                resultCopy
-              }
-            } else {
-              throw new java.util.NoSuchElementException
-            }
-          }
-        }
-      } else {
-        if (unsafeEnabled) {
-          log.info("Not using Unsafe-based aggregator because it is not supported for this schema")
-        }
-        val buffers = new java.util.HashMap[InternalRow, MutableRow]()
-
-        var currentRow: InternalRow = null
-        while (iter.hasNext) {
-          currentRow = iter.next()
-          val currentGroup = groupProjection(currentRow)
-          var currentBuffer = buffers.get(currentGroup)
-          if (currentBuffer == null) {
-            currentBuffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
-            buffers.put(currentGroup, currentBuffer)
-          }
-          // Target the projection at the current aggregation buffer and then project the updated
-          // values.
-          updateProjection.target(currentBuffer)(joinedRow(currentBuffer, currentRow))
-        }
-
-        new Iterator[InternalRow] {
-          private[this] val resultIterator = buffers.entrySet.iterator()
-          private[this] val resultProjection = resultProjectionBuilder()
-
-          def hasNext: Boolean = resultIterator.hasNext
-
-          def next(): InternalRow = {
-            val currentGroup = resultIterator.next()
-            resultProjection(joinedRow(currentGroup.getKey, currentGroup.getValue))
-          }
-        }
-      }
-    }
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 952ba7d45c13e..a730ffbb217c0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -136,32 +136,6 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   object HashAggregation extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       // Aggregations that can be performed in two phases, before and after the shuffle.
-
-      // Cases where all aggregates can be codegened.
-      case PartialAggregation(
-             namedGroupingAttributes,
-             rewrittenAggregateExpressions,
-             groupingExpressions,
-             partialComputation,
-             child)
-             if canBeCodeGened(
-                  allAggregates(partialComputation) ++
-                  allAggregates(rewrittenAggregateExpressions)) &&
-               codegenEnabled &&
-               !canBeConvertedToNewAggregation(plan) =>
-          execution.GeneratedAggregate(
-            partial = false,
-            namedGroupingAttributes,
-            rewrittenAggregateExpressions,
-            unsafeEnabled,
-            execution.GeneratedAggregate(
-              partial = true,
-              groupingExpressions,
-              partialComputation,
-              unsafeEnabled,
-              planLater(child))) :: Nil
-
-      // Cases where some aggregate can not be codegened
       case PartialAggregation(
              namedGroupingAttributes,
              rewrittenAggregateExpressions,
@@ -192,14 +166,6 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case _ => false
     }
 
-    def canBeCodeGened(aggs: Seq[AggregateExpression1]): Boolean = aggs.forall {
-      case _: Sum | _: Count | _: Max | _: Min |  _: CombineSetsAndCount => true
-      // The generated set implementation is pretty limited ATM.
-      case CollectHashSet(exprs) if exprs.size == 1  &&
-           Seq(IntegerType, LongType).contains(exprs.head.dataType) => true
-      case _ => false
-    }
-
     def allAggregates(exprs: Seq[Expression]): Seq[AggregateExpression1] =
       exprs.flatMap(_.collect { case a: AggregateExpression1 => a })
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 29dfcf2575227..cef40dd324d9e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -26,7 +26,6 @@ import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
-import org.apache.spark.sql.execution.GeneratedAggregate
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.test.SQLTestUtils
@@ -263,7 +262,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     val df = sql(sqlText)
     // First, check if we have GeneratedAggregate.
     val hasGeneratedAgg = df.queryExecution.executedPlan
-      .collect { case _: GeneratedAggregate | _: aggregate.Aggregate => true }
+      .collect { case _: aggregate.Aggregate => true }
       .nonEmpty
     if (!hasGeneratedAgg) {
       fail(
@@ -1603,7 +1602,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 
-  test("aggregation with codegen updates peak execution memory") {
+  ignore("aggregation with codegen updates peak execution memory") {
     withSQLConf(
         (SQLConf.CODEGEN_ENABLED.key, "true"),
         (SQLConf.USE_SQL_AGGREGATE2.key, "false")) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala
deleted file mode 100644
index 20def6bef0c17..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.test.TestSQLContext
-
-class AggregateSuite extends SparkPlanTest {
-
-  test("SPARK-8357 unsafe aggregation path should not leak memory with empty input") {
-    val codegenDefault = TestSQLContext.getConf(SQLConf.CODEGEN_ENABLED)
-    val unsafeDefault = TestSQLContext.getConf(SQLConf.UNSAFE_ENABLED)
-    try {
-      TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, true)
-      TestSQLContext.setConf(SQLConf.UNSAFE_ENABLED, true)
-      val df = Seq.empty[(Int, Int)].toDF("a", "b")
-      checkAnswer(
-        df,
-        GeneratedAggregate(
-          partial = true,
-          Seq(df.col("b").expr),
-          Seq(Alias(Count(df.col("a").expr), "cnt")()),
-          unsafeEnabled = true,
-          _: SparkPlan),
-        Seq.empty
-      )
-    } finally {
-      TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
-      TestSQLContext.setConf(SQLConf.UNSAFE_ENABLED, unsafeDefault)
-    }
-  }
-}

From d5a9af3230925c347d0904fe7f2402e468e80bc8 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 5 Aug 2015 21:50:35 -0700
Subject: [PATCH 0879/1454] [SPARK-9664] [SQL] Remove UDAFRegistration and add
 apply to UserDefinedAggregateFunction.

https://issues.apache.org/jira/browse/SPARK-9664

Author: Yin Huai <yhuai@databricks.com>

Closes #7982 from yhuai/udafRegister and squashes the following commits:

0cc2287 [Yin Huai] Remove UDAFRegistration and add apply to UserDefinedAggregateFunction.
---
 .../org/apache/spark/sql/SQLContext.scala     |  3 --
 .../apache/spark/sql/UDAFRegistration.scala   | 36 -------------------
 .../apache/spark/sql/UDFRegistration.scala    | 16 +++++++++
 .../spark/sql/execution/aggregate/udaf.scala  |  8 ++---
 .../apache/spark/sql/expressions/udaf.scala   | 32 ++++++++++++++++-
 .../org/apache/spark/sql/functions.scala      |  1 +
 .../spark/sql/hive/JavaDataFrameSuite.java    | 26 ++++++++++++++
 .../execution/AggregationQuerySuite.scala     |  4 +--
 8 files changed, 80 insertions(+), 46 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index ffc2baf7a8826..6f8ffb54402a7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -291,9 +291,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
   @transient
   val udf: UDFRegistration = new UDFRegistration(this)
 
-  @transient
-  val udaf: UDAFRegistration = new UDAFRegistration(this)
-
   /**
    * Returns true if the table is currently cached in-memory.
    * @group cachemgmt
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
deleted file mode 100644
index 0d4e30f29255e..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.expressions.{Expression}
-import org.apache.spark.sql.execution.aggregate.ScalaUDAF
-import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
-class UDAFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
-
-  private val functionRegistry = sqlContext.functionRegistry
-
-  def register(
-      name: String,
-      func: UserDefinedAggregateFunction): UserDefinedAggregateFunction = {
-    def builder(children: Seq[Expression]) = ScalaUDAF(children, func)
-    functionRegistry.registerFunction(name, builder)
-    func
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
index 7cd7421a518c9..1f270560d7bc1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
@@ -26,6 +26,8 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.api.java._
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
+import org.apache.spark.sql.execution.aggregate.ScalaUDAF
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -52,6 +54,20 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
     functionRegistry.registerFunction(name, udf.builder)
   }
 
+  /**
+   * Register a user-defined aggregate function (UDAF).
+   * @param name the name of the UDAF.
+   * @param udaf the UDAF needs to be registered.
+   * @return the registered UDAF.
+   */
+  def register(
+      name: String,
+      udaf: UserDefinedAggregateFunction): UserDefinedAggregateFunction = {
+    def builder(children: Seq[Expression]) = ScalaUDAF(children, udaf)
+    functionRegistry.registerFunction(name, builder)
+    udaf
+  }
+
   // scalastyle:off
 
   /* register 0-22 were generated by this script
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
index 5fafc916bfa0b..7619f3ec9f0a7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
@@ -316,7 +316,7 @@ private[sql] case class ScalaUDAF(
 
   override lazy val cloneBufferAttributes = bufferAttributes.map(_.newInstance())
 
-  private[this] val childrenSchema: StructType = {
+  private[this] lazy val childrenSchema: StructType = {
     val inputFields = children.zipWithIndex.map {
       case (child, index) =>
         StructField(s"input$index", child.dataType, child.nullable, Metadata.empty)
@@ -337,16 +337,16 @@ private[sql] case class ScalaUDAF(
     }
   }
 
-  private[this] val inputToScalaConverters: Any => Any =
+  private[this] lazy val inputToScalaConverters: Any => Any =
     CatalystTypeConverters.createToScalaConverter(childrenSchema)
 
-  private[this] val bufferValuesToCatalystConverters: Array[Any => Any] = {
+  private[this] lazy val bufferValuesToCatalystConverters: Array[Any => Any] = {
     bufferSchema.fields.map { field =>
       CatalystTypeConverters.createToCatalystConverter(field.dataType)
     }
   }
 
-  private[this] val bufferValuesToScalaConverters: Array[Any => Any] = {
+  private[this] lazy val bufferValuesToScalaConverters: Array[Any => Any] = {
     bufferSchema.fields.map { field =>
       CatalystTypeConverters.createToScalaConverter(field.dataType)
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
index 278dd438fab4a..5180871585f25 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
@@ -17,7 +17,10 @@
 
 package org.apache.spark.sql.expressions
 
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions.ScalaUDF
+import org.apache.spark.sql.catalyst.expressions.aggregate.{Complete, AggregateExpression2}
+import org.apache.spark.sql.execution.aggregate.ScalaUDAF
+import org.apache.spark.sql.{Column, Row}
 import org.apache.spark.sql.types._
 import org.apache.spark.annotation.Experimental
 
@@ -87,6 +90,33 @@ abstract class UserDefinedAggregateFunction extends Serializable {
    * aggregation buffer.
    */
   def evaluate(buffer: Row): Any
+
+  /**
+   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
+   */
+  @scala.annotation.varargs
+  def apply(exprs: Column*): Column = {
+    val aggregateExpression =
+      AggregateExpression2(
+        ScalaUDAF(exprs.map(_.expr), this),
+        Complete,
+        isDistinct = false)
+    Column(aggregateExpression)
+  }
+
+  /**
+   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
+   * If `isDistinct` is true, this UDAF is working on distinct input values.
+   */
+  @scala.annotation.varargs
+  def apply(isDistinct: Boolean, exprs: Column*): Column = {
+    val aggregateExpression =
+      AggregateExpression2(
+        ScalaUDAF(exprs.map(_.expr), this),
+        Complete,
+        isDistinct = isDistinct)
+    Column(aggregateExpression)
+  }
 }
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 5a10c3891ad6c..39aa905c8532a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2500,6 +2500,7 @@ object functions {
    * @group udf_funcs
    * @since 1.5.0
    */
+  @scala.annotation.varargs
   def callUDF(udfName: String, cols: Column*): Column = {
     UnresolvedFunction(udfName, cols.map(_.expr), isDistinct = false)
   }
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 613b2bcc80e37..21b053f07a3ba 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -29,8 +29,12 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.sql.*;
 import org.apache.spark.sql.expressions.Window;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import static org.apache.spark.sql.functions.*;
 import org.apache.spark.sql.hive.HiveContext;
 import org.apache.spark.sql.hive.test.TestHive$;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import test.org.apache.spark.sql.hive.aggregate.MyDoubleSum;
 
 public class JavaDataFrameSuite {
   private transient JavaSparkContext sc;
@@ -77,4 +81,26 @@ public void saveTableAndQueryIt() {
         "      ROWS BETWEEN 1 preceding and 1 following) " +
         "FROM window_table").collectAsList());
   }
+
+  @Test
+  public void testUDAF() {
+    DataFrame df = hc.range(0, 100).unionAll(hc.range(0, 100)).select(col("id").as("value"));
+    UserDefinedAggregateFunction udaf = new MyDoubleSum();
+    UserDefinedAggregateFunction registeredUDAF = hc.udf().register("mydoublesum", udaf);
+    // Create Columns for the UDAF. For now, callUDF does not take an argument to specific if
+    // we want to use distinct aggregation.
+    DataFrame aggregatedDF =
+      df.groupBy()
+        .agg(
+          udaf.apply(true, col("value")),
+          udaf.apply(col("value")),
+          registeredUDAF.apply(col("value")),
+          callUDF("mydoublesum", col("value")));
+
+    List<Row> expectedResult = new ArrayList<Row>();
+    expectedResult.add(RowFactory.create(4950.0, 9900.0, 9900.0, 9900.0));
+    checkAnswer(
+      aggregatedDF,
+      expectedResult);
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 6f0db27775e4d..4b35c8fd83533 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -73,8 +73,8 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
     emptyDF.registerTempTable("emptyTable")
 
     // Register UDAFs
-    sqlContext.udaf.register("mydoublesum", new MyDoubleSum)
-    sqlContext.udaf.register("mydoubleavg", new MyDoubleAvg)
+    sqlContext.udf.register("mydoublesum", new MyDoubleSum)
+    sqlContext.udf.register("mydoubleavg", new MyDoubleAvg)
   }
 
   override def afterAll(): Unit = {

From aead18ffca36830e854fba32a1cac11a0b2e31d5 Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Thu, 6 Aug 2015 09:02:30 -0700
Subject: [PATCH 0880/1454] [SPARK-8266] [SQL] add function translate

![translate](http://www.w3resource.com/PostgreSQL/postgresql-translate-function.png)

Author: zhichao.li <zhichao.li@intel.com>

Closes #7709 from zhichao-li/translate and squashes the following commits:

9418088 [zhichao.li] refine checking condition
f2ab77a [zhichao.li] clone string
9d88f2d [zhichao.li] fix indent
6aa2962 [zhichao.li] style
e575ead [zhichao.li] add python api
9d4bab0 [zhichao.li] add special case for fodable and refactor unittest
eda7ad6 [zhichao.li] update to use TernaryExpression
cdfd4be [zhichao.li] add function translate
---
 python/pyspark/sql/functions.py               | 16 ++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../sql/catalyst/expressions/Expression.scala |  4 +-
 .../expressions/stringOperations.scala        | 79 ++++++++++++++++++-
 .../expressions/StringExpressionsSuite.scala  | 14 ++++
 .../org/apache/spark/sql/functions.scala      | 21 +++--
 .../spark/sql/StringFunctionsSuite.scala      |  6 ++
 .../apache/spark/unsafe/types/UTF8String.java | 16 ++++
 .../spark/unsafe/types/UTF8StringSuite.java   | 31 ++++++++
 9 files changed, 180 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 9f0d71d7960cf..b5c6a01f18858 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -1290,6 +1290,22 @@ def length(col):
     return Column(sc._jvm.functions.length(_to_java_column(col)))
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def translate(srcCol, matching, replace):
+    """A function translate any character in the `srcCol` by a character in `matching`.
+    The characters in `replace` is corresponding to the characters in `matching`.
+    The translate will happen when any character in the string matching with the character
+    in the `matching`.
+
+    >>> sqlContext.createDataFrame([('translate',)], ['a']).select(translate('a', "rnlt", "123")\
+    .alias('r')).collect()
+    [Row(r=u'1a2s3ae')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.translate(_to_java_column(srcCol), matching, replace))
+
+
 # ---------------------- Collection functions ------------------------------
 
 @since(1.4)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 94c355f838fa0..cd5a90d788151 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -203,6 +203,7 @@ object FunctionRegistry {
     expression[Substring]("substr"),
     expression[Substring]("substring"),
     expression[SubstringIndex]("substring_index"),
+    expression[StringTranslate]("translate"),
     expression[StringTrim]("trim"),
     expression[UnBase64]("unbase64"),
     expression[Upper]("ucase"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index ef2fc2e8c29d4..0b98f555a1d60 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -444,7 +444,7 @@ abstract class TernaryExpression extends Expression {
   override def nullable: Boolean = children.exists(_.nullable)
 
   /**
-   * Default behavior of evaluation according to the default nullability of BinaryExpression.
+   * Default behavior of evaluation according to the default nullability of TernaryExpression.
    * If subclass of BinaryExpression override nullable, probably should also override this.
    */
   override def eval(input: InternalRow): Any = {
@@ -463,7 +463,7 @@ abstract class TernaryExpression extends Expression {
   }
 
   /**
-   * Called by default [[eval]] implementation.  If subclass of BinaryExpression keep the default
+   * Called by default [[eval]] implementation.  If subclass of TernaryExpression keep the default
    * nullability, they can override this method to save null-check code.  If we need full control
    * of evaluation process, we should override [[eval]].
    */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 0cc785d9f3a49..76666bd6b3d27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -18,7 +18,9 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.text.DecimalFormat
-import java.util.{Arrays, Locale}
+import java.util.Arrays
+import java.util.{Map => JMap, HashMap}
+import java.util.Locale
 import java.util.regex.{MatchResult, Pattern}
 
 import org.apache.commons.lang3.StringEscapeUtils
@@ -349,6 +351,81 @@ case class EndsWith(left: Expression, right: Expression)
   }
 }
 
+object StringTranslate {
+
+  def buildDict(matchingString: UTF8String, replaceString: UTF8String)
+    : JMap[Character, Character] = {
+    val matching = matchingString.toString()
+    val replace = replaceString.toString()
+    val dict = new HashMap[Character, Character]()
+    var i = 0
+    while (i < matching.length()) {
+      val rep = if (i < replace.length()) replace.charAt(i) else '\0'
+      if (null == dict.get(matching.charAt(i))) {
+        dict.put(matching.charAt(i), rep)
+      }
+      i += 1
+    }
+    dict
+  }
+}
+
+/**
+ * A function translate any character in the `srcExpr` by a character in `replaceExpr`.
+ * The characters in `replaceExpr` is corresponding to the characters in `matchingExpr`.
+ * The translate will happen when any character in the string matching with the character
+ * in the `matchingExpr`.
+ */
+case class StringTranslate(srcExpr: Expression, matchingExpr: Expression, replaceExpr: Expression)
+  extends TernaryExpression with ImplicitCastInputTypes {
+
+  @transient private var lastMatching: UTF8String = _
+  @transient private var lastReplace: UTF8String = _
+  @transient private var dict: JMap[Character, Character] = _
+
+  override def nullSafeEval(srcEval: Any, matchingEval: Any, replaceEval: Any): Any = {
+    if (matchingEval != lastMatching || replaceEval != lastReplace) {
+      lastMatching = matchingEval.asInstanceOf[UTF8String].clone()
+      lastReplace = replaceEval.asInstanceOf[UTF8String].clone()
+      dict = StringTranslate.buildDict(lastMatching, lastReplace)
+    }
+    srcEval.asInstanceOf[UTF8String].translate(dict)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastMatching = ctx.freshName("lastMatching")
+    val termLastReplace = ctx.freshName("lastReplace")
+    val termDict = ctx.freshName("dict")
+    val classNameDict = classOf[JMap[Character, Character]].getCanonicalName
+
+    ctx.addMutableState("UTF8String", termLastMatching, s"${termLastMatching} = null;")
+    ctx.addMutableState("UTF8String", termLastReplace, s"${termLastReplace} = null;")
+    ctx.addMutableState(classNameDict, termDict, s"${termDict} = null;")
+
+    nullSafeCodeGen(ctx, ev, (src, matching, replace) => {
+      val check = if (matchingExpr.foldable && replaceExpr.foldable) {
+        s"${termDict} == null"
+      } else {
+        s"!${matching}.equals(${termLastMatching}) || !${replace}.equals(${termLastReplace})"
+      }
+      s"""if ($check) {
+        // Not all of them is literal or matching or replace value changed
+        ${termLastMatching} = ${matching}.clone();
+        ${termLastReplace} = ${replace}.clone();
+        ${termDict} = org.apache.spark.sql.catalyst.expressions.StringTranslate
+          .buildDict(${termLastMatching}, ${termLastReplace});
+      }
+      ${ev.primitive} = ${src}.translate(${termDict});
+      """
+    })
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType, StringType)
+  override def children: Seq[Expression] = srcExpr :: matchingExpr :: replaceExpr :: Nil
+  override def prettyName: String = "translate"
+}
+
 /**
  * A function that returns the index (1-based) of the given string (left) in the comma-
  * delimited list (right). Returns 0, if the string wasn't found or if the given
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 23f36ca43d663..426dc272471ae 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -431,6 +431,20 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(SoundEx(Literal("!!")), "!!")
   }
 
+  test("translate") {
+    checkEvaluation(
+      StringTranslate(Literal("translate"), Literal("rnlt"), Literal("123")), "1a2s3ae")
+    checkEvaluation(StringTranslate(Literal("translate"), Literal(""), Literal("123")), "translate")
+    checkEvaluation(StringTranslate(Literal("translate"), Literal("rnlt"), Literal("")), "asae")
+    // test for multiple mapping
+    checkEvaluation(StringTranslate(Literal("abcd"), Literal("aba"), Literal("123")), "12cd")
+    checkEvaluation(StringTranslate(Literal("abcd"), Literal("aba"), Literal("12")), "12cd")
+    // scalastyle:off
+    // non ascii characters are not allowed in the source code, so we disable the scalastyle.
+    checkEvaluation(StringTranslate(Literal("花花世界"), Literal("花界"), Literal("ab")), "aa世b")
+    // scalastyle:on
+  }
+
   test("TRIM/LTRIM/RTRIM") {
     val s = 'a.string.at(0)
     checkEvaluation(StringTrim(Literal(" aa  ")), "aa", create_row(" abdef "))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 39aa905c8532a..79c5f596661d4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1100,11 +1100,11 @@ object functions {
   }
 
   /**
-    * Computes hex value of the given column.
-    *
-    * @group math_funcs
-    * @since 1.5.0
-    */
+   * Computes hex value of the given column.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
   def hex(column: Column): Column = Hex(column.expr)
 
   /**
@@ -1863,6 +1863,17 @@ object functions {
   def substring_index(str: Column, delim: String, count: Int): Column =
     SubstringIndex(str.expr, lit(delim).expr, lit(count).expr)
 
+  /* Translate any character in the src by a character in replaceString.
+  * The characters in replaceString is corresponding to the characters in matchingString.
+  * The translate will happen when any character in the string matching with the character
+  * in the matchingString.
+  *
+  * @group string_funcs
+  * @since 1.5.0
+  */
+  def translate(src: Column, matchingString: String, replaceString: String): Column =
+    StringTranslate(src.expr, lit(matchingString).expr, lit(replaceString).expr)
+
   /**
    * Trim the spaces from both ends for the specified string column.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index ab5da6ee79f1b..ca298b2434410 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -128,6 +128,12 @@ class StringFunctionsSuite extends QueryTest {
     // scalastyle:on
   }
 
+  test("string translate") {
+    val df = Seq(("translate", "")).toDF("a", "b")
+    checkAnswer(df.select(translate($"a", "rnlt", "123")), Row("1a2s3ae"))
+    checkAnswer(df.selectExpr("""translate(a, "rnlt", "")"""), Row("asae"))
+  }
+
   test("string trim functions") {
     val df = Seq(("  example  ", "")).toDF("a", "b")
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index febbe3d4e54d1..d1014426c0f49 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -22,6 +22,7 @@
 import java.io.UnsupportedEncodingException;
 import java.nio.ByteOrder;
 import java.util.Arrays;
+import java.util.Map;
 
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
@@ -795,6 +796,21 @@ public UTF8String[] split(UTF8String pattern, int limit) {
     return res;
   }
 
+  // TODO: Need to use `Code Point` here instead of Char in case the character longer than 2 bytes
+  public UTF8String translate(Map<Character, Character> dict) {
+    String srcStr = this.toString();
+
+    StringBuilder sb = new StringBuilder();
+    for(int k = 0; k< srcStr.length(); k++) {
+      if (null == dict.get(srcStr.charAt(k))) {
+        sb.append(srcStr.charAt(k));
+      } else if ('\0' != dict.get(srcStr.charAt(k))){
+        sb.append(dict.get(srcStr.charAt(k)));
+      }
+    }
+    return fromString(sb.toString());
+  }
+
   @Override
   public String toString() {
     try {
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index b30c94c1c1f80..98aa8a2469a75 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -19,7 +19,9 @@
 
 import java.io.UnsupportedEncodingException;
 import java.util.Arrays;
+import java.util.HashMap;
 
+import com.google.common.collect.ImmutableMap;
 import org.junit.Test;
 
 import static junit.framework.Assert.*;
@@ -391,6 +393,35 @@ public void levenshteinDistance() {
     assertEquals(fromString("世界千世").levenshteinDistance(fromString("千a世b")),4);
   }
 
+  @Test
+  public void translate() {
+    assertEquals(
+      fromString("1a2s3ae"),
+      fromString("translate").translate(ImmutableMap.of(
+        'r', '1',
+        'n', '2',
+        'l', '3',
+        't', '\0'
+      )));
+    assertEquals(
+      fromString("translate"),
+      fromString("translate").translate(new HashMap<Character, Character>()));
+    assertEquals(
+      fromString("asae"),
+      fromString("translate").translate(ImmutableMap.of(
+        'r', '\0',
+        'n', '\0',
+        'l', '\0',
+        't', '\0'
+      )));
+    assertEquals(
+      fromString("aa世b"),
+      fromString("花花世界").translate(ImmutableMap.of(
+        '花', 'a',
+        '界', 'b'
+      )));
+  }
+
   @Test
   public void createBlankString() {
     assertEquals(fromString(" "), blankString(1));

From 5b965d64ee1687145ba793da749659c8f67384e8 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 09:10:57 -0700
Subject: [PATCH 0881/1454] [SPARK-9644] [SQL] Support update DecimalType with
 precision > 18 in UnsafeRow

In order to support update a varlength (actually fixed length) object, the space should be preserved even  it's null. And, we can't call setNullAt(i) for it anymore, we because setNullAt(i) will remove the offset of the preserved space, should call setDecimal(i, null, precision) instead.

After this, we can do hash based aggregation on DecimalType with precision > 18. In a tests, this could decrease the end-to-end run time of aggregation query from 37 seconds (sort based) to 24 seconds (hash based).

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7978 from davies/update_decimal and squashes the following commits:

bed8100 [Davies Liu] isSettable -> isMutable
923c9eb [Davies Liu] address comments and fix bug
385891d [Davies Liu] Merge branch 'master' of github.com:apache/spark into update_decimal
36a1872 [Davies Liu] fix tests
cd6c524 [Davies Liu] support set decimal with precision > 18
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 74 +++++++++++++++----
 .../expressions/UnsafeRowWriters.java         | 41 ++++++----
 .../codegen/GenerateMutableProjection.scala   | 15 +++-
 .../codegen/GenerateUnsafeProjection.scala    | 53 +++++++------
 .../spark/sql/catalyst/expressions/rows.scala |  8 +-
 .../expressions/UnsafeRowConverterSuite.scala | 17 ++++-
 .../UnsafeFixedWidthAggregationMap.java       |  4 +-
 .../SortBasedAggregationIterator.scala        |  4 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala |  2 +-
 .../spark/unsafe/PlatformDependent.java       | 26 +++++++
 10 files changed, 183 insertions(+), 61 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index e3e1622de08ba..e829acb6285f1 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -65,11 +65,11 @@ public static int calculateBitSetWidthInBytes(int numFields) {
   /**
    * Field types that can be updated in place in UnsafeRows (e.g. we support set() for these types)
    */
-  public static final Set<DataType> settableFieldTypes;
+  public static final Set<DataType> mutableFieldTypes;
 
-  // DecimalType(precision <= 18) is settable
+  // DecimalType is also mutable
   static {
-    settableFieldTypes = Collections.unmodifiableSet(
+    mutableFieldTypes = Collections.unmodifiableSet(
       new HashSet<>(
         Arrays.asList(new DataType[] {
           NullType,
@@ -87,12 +87,16 @@ public static int calculateBitSetWidthInBytes(int numFields) {
 
   public static boolean isFixedLength(DataType dt) {
     if (dt instanceof DecimalType) {
-      return ((DecimalType) dt).precision() < Decimal.MAX_LONG_DIGITS();
+      return ((DecimalType) dt).precision() <= Decimal.MAX_LONG_DIGITS();
     } else {
-      return settableFieldTypes.contains(dt);
+      return mutableFieldTypes.contains(dt);
     }
   }
 
+  public static boolean isMutable(DataType dt) {
+    return mutableFieldTypes.contains(dt) || dt instanceof DecimalType;
+  }
+
   //////////////////////////////////////////////////////////////////////////////
   // Private fields and methods
   //////////////////////////////////////////////////////////////////////////////
@@ -238,17 +242,45 @@ public void setFloat(int ordinal, float value) {
     PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
+  /**
+   * Updates the decimal column.
+   *
+   * Note: In order to support update a decimal with precision > 18, CAN NOT call
+   * setNullAt() for this column.
+   */
   @Override
   public void setDecimal(int ordinal, Decimal value, int precision) {
     assertIndexIsValid(ordinal);
-    if (value == null) {
-      setNullAt(ordinal);
-    } else {
-      if (precision <= Decimal.MAX_LONG_DIGITS()) {
+    if (precision <= Decimal.MAX_LONG_DIGITS()) {
+      // compact format
+      if (value == null) {
+        setNullAt(ordinal);
+      } else {
         setLong(ordinal, value.toUnscaledLong());
+      }
+    } else {
+      // fixed length
+      long cursor = getLong(ordinal) >>> 32;
+      assert cursor > 0 : "invalid cursor " + cursor;
+      // zero-out the bytes
+      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor, 0L);
+      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor + 8, 0L);
+
+      if (value == null) {
+        setNullAt(ordinal);
+        // keep the offset for future update
+        PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), cursor << 32);
       } else {
-        // TODO(davies): support update decimal (hold a bounded space even it's null)
-        throw new UnsupportedOperationException();
+
+        final BigInteger integer = value.toJavaBigDecimal().unscaledValue();
+        final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
+          PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+        assert(mag.length <= 4);
+
+        // Write the bytes to the variable length portion.
+        PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
+          baseObject, baseOffset + cursor, mag.length * 4);
+        setLong(ordinal, (cursor << 32) | ((long) (((integer.signum() + 1) << 8) + mag.length)));
       }
     }
   }
@@ -343,6 +375,8 @@ public double getDouble(int ordinal) {
     return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
+  private static byte[] EMPTY = new byte[0];
+
   @Override
   public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (isNullAt(ordinal)) {
@@ -351,10 +385,20 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (precision <= Decimal.MAX_LONG_DIGITS()) {
       return Decimal.apply(getLong(ordinal), precision, scale);
     } else {
-      byte[] bytes = getBinary(ordinal);
-      BigInteger bigInteger = new BigInteger(bytes);
-      BigDecimal javaDecimal = new BigDecimal(bigInteger, scale);
-      return Decimal.apply(new scala.math.BigDecimal(javaDecimal), precision, scale);
+      long offsetAndSize = getLong(ordinal);
+      long offset = offsetAndSize >>> 32;
+      int signum = ((int) (offsetAndSize & 0xfff) >> 8);
+      assert signum >=0 && signum <= 2 : "invalid signum " + signum;
+      int size = (int) (offsetAndSize & 0xff);
+      int[] mag = new int[size];
+      PlatformDependent.copyMemory(baseObject, baseOffset + offset,
+        mag, PlatformDependent.INT_ARRAY_OFFSET, size * 4);
+
+      // create a BigInteger using signum and mag
+      BigInteger v = new BigInteger(0, EMPTY);  // create the initial object
+      PlatformDependent.UNSAFE.putInt(v, PlatformDependent.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
+      PlatformDependent.UNSAFE.putObjectVolatile(v, PlatformDependent.BIG_INTEGER_MAG_OFFSET, mag);
+      return Decimal.apply(new BigDecimal(v, scale), precision, scale);
     }
   }
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 31928731545da..28e7ec0a0f120 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -17,9 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
+import java.math.BigInteger;
+
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.sql.types.MapData;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
@@ -47,29 +48,41 @@ public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input
 
   /** Writer for Decimal with precision larger than 18. */
   public static class DecimalWriter {
-
+    private static final int SIZE = 16;
     public static int getSize(Decimal input) {
       // bounded size
-      return 16;
+      return SIZE;
     }
 
     public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input) {
+      final Object base = target.getBaseObject();
       final long offset = target.getBaseOffset() + cursor;
-      final byte[] bytes = input.toJavaBigDecimal().unscaledValue().toByteArray();
-      final int numBytes = bytes.length;
-      assert(numBytes <= 16);
-
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset, 0L);
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset + 8, 0L);
+      PlatformDependent.UNSAFE.putLong(base, offset, 0L);
+      PlatformDependent.UNSAFE.putLong(base, offset + 8, 0L);
+
+      if (input == null) {
+        target.setNullAt(ordinal);
+        // keep the offset and length for update
+        int fieldOffset = UnsafeRow.calculateBitSetWidthInBytes(target.numFields()) + ordinal * 8;
+        PlatformDependent.UNSAFE.putLong(base, target.getBaseOffset() + fieldOffset,
+          ((long) cursor) << 32);
+        return SIZE;
+      }
 
-      // Write the bytes to the variable length portion.
-      PlatformDependent.copyMemory(bytes, PlatformDependent.BYTE_ARRAY_OFFSET,
-        target.getBaseObject(), offset, numBytes);
+      final BigInteger integer = input.toJavaBigDecimal().unscaledValue();
+      int signum = integer.signum() + 1;
+      final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
+        PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+      assert(mag.length <= 4);
 
+      // Write the bytes to the variable length portion.
+      PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
+        base, target.getBaseOffset() + cursor, mag.length * 4);
       // Set the fixed length portion.
-      target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
-      return 16;
+      target.setLong(ordinal, (((long) cursor) << 32) | ((long) ((signum << 8) + mag.length)));
+
+      return SIZE;
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index e4a8fc24dac2f..ac58423cd884d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -21,6 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
+import org.apache.spark.sql.types.DecimalType
 
 // MutableProjection is not accessible in Java
 abstract class BaseMutableProjection extends MutableProjection
@@ -43,14 +44,26 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
-        evaluationCode.code +
+        if (e.dataType.isInstanceOf[DecimalType]) {
+          // Can't call setNullAt on DecimalType, because we need to keep the offset
           s"""
+            ${evaluationCode.code}
+            if (${evaluationCode.isNull}) {
+              ${ctx.setColumn("mutableRow", e.dataType, i, null)};
+            } else {
+              ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
+            }
+          """
+        } else {
+          s"""
+            ${evaluationCode.code}
             if (${evaluationCode.isNull}) {
               mutableRow.setNullAt($i);
             } else {
               ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
             }
           """
+        }
     }
     // collect projections into blocks as function has 64kb codesize limit in JVM
     val projectionBlocks = new ArrayBuffer[String]()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 71f8ea09f0770..d8912df694a10 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -45,10 +45,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
+    case NullType => true
     case t: AtomicType => true
     case _: CalendarIntervalType => true
     case t: StructType => t.toSeq.forall(field => canSupport(field.dataType))
-    case NullType => true
     case t: ArrayType if canSupport(t.elementType) => true
     case MapType(kt, vt, _) if canSupport(kt) && canSupport(vt) => true
     case _ => false
@@ -56,7 +56,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
   def genAdditionalSize(dt: DataType, ev: GeneratedExpressionCode): String = dt match {
     case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
-      s" + (${ev.isNull} ? 0 : $DecimalWriter.getSize(${ev.primitive}))"
+      s" + $DecimalWriter.getSize(${ev.primitive})"
     case StringType =>
       s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
     case BinaryType =>
@@ -76,41 +76,41 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       ctx: CodeGenContext,
       fieldType: DataType,
       ev: GeneratedExpressionCode,
-      primitive: String,
+      target: String,
       index: Int,
       cursor: String): String = fieldType match {
     case _ if ctx.isPrimitiveType(fieldType) =>
-      s"${ctx.setColumn(primitive, fieldType, index, ev.primitive)}"
+      s"${ctx.setColumn(target, fieldType, index, ev.primitive)}"
     case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
       s"""
        // make sure Decimal object has the same scale as DecimalType
        if (${ev.primitive}.changePrecision(${t.precision}, ${t.scale})) {
-         $CompactDecimalWriter.write($primitive, $index, $cursor, ${ev.primitive});
+         $CompactDecimalWriter.write($target, $index, $cursor, ${ev.primitive});
        } else {
-         $primitive.setNullAt($index);
+         $target.setNullAt($index);
        }
        """
     case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
       s"""
        // make sure Decimal object has the same scale as DecimalType
        if (${ev.primitive}.changePrecision(${t.precision}, ${t.scale})) {
-         $cursor += $DecimalWriter.write($primitive, $index, $cursor, ${ev.primitive});
+         $cursor += $DecimalWriter.write($target, $index, $cursor, ${ev.primitive});
        } else {
-         $primitive.setNullAt($index);
+         $cursor += $DecimalWriter.write($target, $index, $cursor, null);
        }
        """
     case StringType =>
-      s"$cursor += $StringWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $StringWriter.write($target, $index, $cursor, ${ev.primitive})"
     case BinaryType =>
-      s"$cursor += $BinaryWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $BinaryWriter.write($target, $index, $cursor, ${ev.primitive})"
     case CalendarIntervalType =>
-      s"$cursor += $IntervalWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $IntervalWriter.write($target, $index, $cursor, ${ev.primitive})"
     case _: StructType =>
-      s"$cursor += $StructWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $StructWriter.write($target, $index, $cursor, ${ev.primitive})"
     case _: ArrayType =>
-      s"$cursor += $ArrayWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $ArrayWriter.write($target, $index, $cursor, ${ev.primitive})"
     case _: MapType =>
-      s"$cursor += $MapWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $MapWriter.write($target, $index, $cursor, ${ev.primitive})"
     case NullType => ""
     case _ =>
       throw new UnsupportedOperationException(s"Not supported DataType: $fieldType")
@@ -146,13 +146,24 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val fieldWriters = inputTypes.zip(convertedFields).zipWithIndex.map { case ((dt, ev), i) =>
       val update = genFieldWriter(ctx, dt, ev, output, i, cursor)
-      s"""
-        if (${ev.isNull}) {
-          $output.setNullAt($i);
-        } else {
-          $update;
-        }
-      """
+      if (dt.isInstanceOf[DecimalType]) {
+        // Can't call setNullAt() for DecimalType
+        s"""
+          if (${ev.isNull}) {
+           $cursor += $DecimalWriter.write($output, $i, $cursor, null);
+          } else {
+           $update;
+          }
+        """
+      } else {
+        s"""
+          if (${ev.isNull}) {
+            $output.setNullAt($i);
+          } else {
+            $update;
+          }
+        """
+      }
     }.mkString("\n")
 
     val code = s"""
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 5e5de1d1dc6a7..7657fb535dcf4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
@@ -39,6 +38,13 @@ abstract class MutableRow extends InternalRow {
   def setLong(i: Int, value: Long): Unit = { update(i, value) }
   def setFloat(i: Int, value: Float): Unit = { update(i, value) }
   def setDouble(i: Int, value: Double): Unit = { update(i, value) }
+
+  /**
+   * Update the decimal column at `i`.
+   *
+   * Note: In order to support update decimal with precision > 18 in UnsafeRow,
+   * CAN NOT call setNullAt() for decimal column on UnsafeRow, call setDecimal(i, null, precision).
+   */
   def setDecimal(i: Int, value: Decimal, precision: Int) { update(i, value) }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 59491c5ba160e..8c72203193630 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -123,7 +123,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       DoubleType,
       StringType,
       BinaryType,
-      DecimalType.USER_DEFAULT
+      DecimalType.USER_DEFAULT,
+      DecimalType.SYSTEM_DEFAULT
       // ArrayType(IntegerType)
     )
     val converter = UnsafeProjection.create(fieldTypes)
@@ -151,6 +152,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(createdFromNull.getUTF8String(8) === null)
     assert(createdFromNull.getBinary(9) === null)
     assert(createdFromNull.getDecimal(10, 10, 0) === null)
+    assert(createdFromNull.getDecimal(11, 38, 18) === null)
     // assert(createdFromNull.get(11) === null)
 
     // If we have an UnsafeRow with columns that are initially non-null and we null out those
@@ -169,6 +171,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       r.update(8, UTF8String.fromString("hello"))
       r.update(9, "world".getBytes)
       r.setDecimal(10, Decimal(10), 10)
+      r.setDecimal(11, Decimal(10.00, 38, 18), 38)
       // r.update(11, Array(11))
       r
     }
@@ -187,10 +190,17 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.getBinary(9) === rowWithNoNullColumns.getBinary(9))
     assert(setToNullAfterCreation.getDecimal(10, 10, 0) ===
       rowWithNoNullColumns.getDecimal(10, 10, 0))
+    assert(setToNullAfterCreation.getDecimal(11, 38, 18) ===
+      rowWithNoNullColumns.getDecimal(11, 38, 18))
     // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
 
     for (i <- fieldTypes.indices) {
-      setToNullAfterCreation.setNullAt(i)
+      // Cann't call setNullAt() on DecimalType
+      if (i == 11) {
+        setToNullAfterCreation.setDecimal(11, null, 38)
+      } else {
+        setToNullAfterCreation.setNullAt(i)
+      }
     }
     // There are some garbage left in the var-length area
     assert(Arrays.equals(createdFromNull.getBytes, setToNullAfterCreation.getBytes()))
@@ -206,6 +216,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     // setToNullAfterCreation.update(8, UTF8String.fromString("hello"))
     // setToNullAfterCreation.update(9, "world".getBytes)
     setToNullAfterCreation.setDecimal(10, Decimal(10), 10)
+    setToNullAfterCreation.setDecimal(11, Decimal(10.00, 38, 18), 38)
     // setToNullAfterCreation.update(11, Array(11))
 
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
@@ -220,6 +231,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     // assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
     assert(setToNullAfterCreation.getDecimal(10, 10, 0) ===
       rowWithNoNullColumns.getDecimal(10, 10, 0))
+    assert(setToNullAfterCreation.getDecimal(11, 38, 18) ===
+      rowWithNoNullColumns.getDecimal(11, 38, 18))
     // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
   }
 
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 43d06ce9bdfa3..02458030b00e9 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -72,7 +72,7 @@ public final class UnsafeFixedWidthAggregationMap {
    */
   public static boolean supportsAggregationBufferSchema(StructType schema) {
     for (StructField field: schema.fields()) {
-      if (!UnsafeRow.isFixedLength(field.dataType())) {
+      if (!UnsafeRow.isMutable(field.dataType())) {
         return false;
       }
     }
@@ -111,8 +111,6 @@ public UnsafeFixedWidthAggregationMap(
     // Initialize the buffer for aggregation value
     final UnsafeProjection valueProjection = UnsafeProjection.create(aggregationBufferSchema);
     this.emptyAggregationBuffer = valueProjection.apply(emptyAggregationBuffer).getBytes();
-    assert(this.emptyAggregationBuffer.length == aggregationBufferSchema.length() * 8 +
-      UnsafeRow.calculateBitSetWidthInBytes(aggregationBufferSchema.length()));
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
index 78bcee16c9d00..40f6bff53d2b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
@@ -20,8 +20,6 @@ package org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2, AggregateFunction2}
-import org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap
-import org.apache.spark.sql.types.StructType
 import org.apache.spark.unsafe.KVIterator
 
 /**
@@ -57,7 +55,7 @@ class SortBasedAggregationIterator(
     val bufferRowSize: Int = bufferSchema.length
 
     val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
-    val useUnsafeBuffer = bufferSchema.map(_.dataType).forall(UnsafeRow.isFixedLength)
+    val useUnsafeBuffer = bufferSchema.map(_.dataType).forall(UnsafeRow.isMutable)
 
     val buffer = if (useUnsafeBuffer) {
       val unsafeProjection =
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index b513c970ccfe2..e03473041c3e9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -93,7 +93,7 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   testWithMemoryLeakDetection("supported schemas") {
     assert(supportsAggregationBufferSchema(
       StructType(StructField("x", DecimalType.USER_DEFAULT) :: Nil)))
-    assert(!supportsAggregationBufferSchema(
+    assert(supportsAggregationBufferSchema(
       StructType(StructField("x", DecimalType.SYSTEM_DEFAULT) :: Nil)))
     assert(!supportsAggregationBufferSchema(StructType(StructField("x", StringType) :: Nil)))
     assert(
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
index 192c6714b2406..b2de2a2590f05 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
@@ -18,6 +18,7 @@
 package org.apache.spark.unsafe;
 
 import java.lang.reflect.Field;
+import java.math.BigInteger;
 
 import sun.misc.Unsafe;
 
@@ -87,6 +88,14 @@ public static void putDouble(Object object, long offset, double value) {
       _UNSAFE.putDouble(object, offset, value);
     }
 
+    public static Object getObjectVolatile(Object object, long offset) {
+      return _UNSAFE.getObjectVolatile(object, offset);
+    }
+
+    public static void putObjectVolatile(Object object, long offset, Object value) {
+      _UNSAFE.putObjectVolatile(object, offset, value);
+    }
+
     public static long allocateMemory(long size) {
       return _UNSAFE.allocateMemory(size);
     }
@@ -107,6 +116,10 @@ public static void freeMemory(long address) {
 
   public static final int DOUBLE_ARRAY_OFFSET;
 
+  // Support for resetting final fields while deserializing
+  public static final long BIG_INTEGER_SIGNUM_OFFSET;
+  public static final long BIG_INTEGER_MAG_OFFSET;
+
   /**
    * Limits the number of bytes to copy per {@link Unsafe#copyMemory(long, long, long)} to
    * allow safepoint polling during a large copy.
@@ -129,11 +142,24 @@ public static void freeMemory(long address) {
       INT_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(int[].class);
       LONG_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(long[].class);
       DOUBLE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(double[].class);
+
+      long signumOffset = 0;
+      long magOffset = 0;
+      try {
+        signumOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("signum"));
+        magOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("mag"));
+      } catch (Exception ex) {
+        // should not happen
+      }
+      BIG_INTEGER_SIGNUM_OFFSET = signumOffset;
+      BIG_INTEGER_MAG_OFFSET = magOffset;
     } else {
       BYTE_ARRAY_OFFSET = 0;
       INT_ARRAY_OFFSET = 0;
       LONG_ARRAY_OFFSET = 0;
       DOUBLE_ARRAY_OFFSET = 0;
+      BIG_INTEGER_SIGNUM_OFFSET = 0;
+      BIG_INTEGER_MAG_OFFSET = 0;
     }
   }
 

From 93085c992e40dbc06714cb1a64c838e25e683a6f Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 09:12:41 -0700
Subject: [PATCH 0882/1454] [SPARK-9482] [SQL] Fix thread-safey issue of using
 UnsafeProjection in join

This PR also change to use `def` instead of `lazy val` for UnsafeProjection, because it's not thread safe.

TODO: cleanup the debug code once the flaky test passed 100 times.

Author: Davies Liu <davies@databricks.com>

Closes #7940 from davies/semijoin and squashes the following commits:

93baac7 [Davies Liu] fix outerjoin
5c40ded [Davies Liu] address comments
aa3de46 [Davies Liu] Merge branch 'master' of github.com:apache/spark into semijoin
7590a25 [Davies Liu] Merge branch 'master' of github.com:apache/spark into semijoin
2d4085b [Davies Liu] use def for resultProjection
0833407 [Davies Liu] Merge branch 'semijoin' of github.com:davies/spark into semijoin
e0d8c71 [Davies Liu] use lazy val
6a59e8f [Davies Liu] Update HashedRelation.scala
0fdacaf [Davies Liu] fix broadcast and thread-safety of UnsafeProjection
2fc3ef6 [Davies Liu] reproduce failure in semijoin
---
 .../execution/joins/BroadcastHashJoin.scala   |  6 ++---
 .../joins/BroadcastHashOuterJoin.scala        | 20 ++++++----------
 .../joins/BroadcastNestedLoopJoin.scala       | 17 ++++++++------
 .../spark/sql/execution/joins/HashJoin.scala  |  4 ++--
 .../sql/execution/joins/HashOuterJoin.scala   | 23 ++++++++++---------
 .../sql/execution/joins/HashSemiJoin.scala    |  8 +++----
 .../sql/execution/joins/HashedRelation.scala  |  4 ++--
 .../joins/ShuffledHashOuterJoin.scala         |  6 +++--
 8 files changed, 44 insertions(+), 44 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index ec1a148342fc6..f7a68e4f5d445 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -20,14 +20,14 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
-import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
+import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
 import org.apache.spark.util.ThreadUtils
+import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
  * :: DeveloperApi ::
@@ -102,6 +102,6 @@ case class BroadcastHashJoin(
 
 object BroadcastHashJoin {
 
-  private val broadcastHashJoinExecutionContext = ExecutionContext.fromExecutorService(
+  private[joins] val broadcastHashJoinExecutionContext = ExecutionContext.fromExecutorService(
     ThreadUtils.newDaemonCachedThreadPool("broadcast-hash-join", 128))
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index e342fd914d321..a3626de49aeab 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -20,15 +20,14 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
-import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, UnspecifiedDistribution}
+import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
-import org.apache.spark.util.ThreadUtils
+import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
+import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
  * :: DeveloperApi ::
@@ -76,7 +75,7 @@ case class BroadcastHashOuterJoin(
         val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
         sparkContext.broadcast(hashed)
       }
-    }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
+    }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
   }
 
   protected override def doPrepare(): Unit = {
@@ -98,19 +97,20 @@ case class BroadcastHashOuterJoin(
         case _ =>
       }
 
+      val resultProj = resultProjection
       joinType match {
         case LeftOuter =>
           streamedIter.flatMap(currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey))
+            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey), resultProj)
           })
 
         case RightOuter =>
           streamedIter.flatMap(currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow)
+            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow, resultProj)
           })
 
         case x =>
@@ -120,9 +120,3 @@ case class BroadcastHashOuterJoin(
     }
   }
 }
-
-object BroadcastHashOuterJoin {
-
-  private val broadcastHashOuterJoinExecutionContext = ExecutionContext.fromExecutorService(
-    ThreadUtils.newDaemonCachedThreadPool("broadcast-hash-outer-join", 128))
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 83b726a8e2897..23aebf4b068b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -47,7 +47,7 @@ case class BroadcastNestedLoopJoin(
   override def outputsUnsafeRows: Boolean = left.outputsUnsafeRows || right.outputsUnsafeRows
   override def canProcessUnsafeRows: Boolean = true
 
-  @transient private[this] lazy val resultProjection: InternalRow => InternalRow = {
+  private[this] def genResultProjection: InternalRow => InternalRow = {
     if (outputsUnsafeRows) {
       UnsafeProjection.create(schema)
     } else {
@@ -88,6 +88,7 @@ case class BroadcastNestedLoopJoin(
 
       val leftNulls = new GenericMutableRow(left.output.size)
       val rightNulls = new GenericMutableRow(right.output.size)
+      val resultProj = genResultProjection
 
       streamedIter.foreach { streamedRow =>
         var i = 0
@@ -97,11 +98,11 @@ case class BroadcastNestedLoopJoin(
           val broadcastedRow = broadcastedRelation.value(i)
           buildSide match {
             case BuildRight if boundCondition(joinedRow(streamedRow, broadcastedRow)) =>
-              matchedRows += resultProjection(joinedRow(streamedRow, broadcastedRow)).copy()
+              matchedRows += resultProj(joinedRow(streamedRow, broadcastedRow)).copy()
               streamRowMatched = true
               includedBroadcastTuples += i
             case BuildLeft if boundCondition(joinedRow(broadcastedRow, streamedRow)) =>
-              matchedRows += resultProjection(joinedRow(broadcastedRow, streamedRow)).copy()
+              matchedRows += resultProj(joinedRow(broadcastedRow, streamedRow)).copy()
               streamRowMatched = true
               includedBroadcastTuples += i
             case _ =>
@@ -111,9 +112,9 @@ case class BroadcastNestedLoopJoin(
 
         (streamRowMatched, joinType, buildSide) match {
           case (false, LeftOuter | FullOuter, BuildRight) =>
-            matchedRows += resultProjection(joinedRow(streamedRow, rightNulls)).copy()
+            matchedRows += resultProj(joinedRow(streamedRow, rightNulls)).copy()
           case (false, RightOuter | FullOuter, BuildLeft) =>
-            matchedRows += resultProjection(joinedRow(leftNulls, streamedRow)).copy()
+            matchedRows += resultProj(joinedRow(leftNulls, streamedRow)).copy()
           case _ =>
         }
       }
@@ -127,6 +128,8 @@ case class BroadcastNestedLoopJoin(
 
     val leftNulls = new GenericMutableRow(left.output.size)
     val rightNulls = new GenericMutableRow(right.output.size)
+    val resultProj = genResultProjection
+
     /** Rows from broadcasted joined with nulls. */
     val broadcastRowsWithNulls: Seq[InternalRow] = {
       val buf: CompactBuffer[InternalRow] = new CompactBuffer()
@@ -138,7 +141,7 @@ case class BroadcastNestedLoopJoin(
           joinedRow.withLeft(leftNulls)
           while (i < rel.length) {
             if (!allIncludedBroadcastTuples.contains(i)) {
-              buf += resultProjection(joinedRow.withRight(rel(i))).copy()
+              buf += resultProj(joinedRow.withRight(rel(i))).copy()
             }
             i += 1
           }
@@ -147,7 +150,7 @@ case class BroadcastNestedLoopJoin(
           joinedRow.withRight(rightNulls)
           while (i < rel.length) {
             if (!allIncludedBroadcastTuples.contains(i)) {
-              buf += resultProjection(joinedRow.withLeft(rel(i))).copy()
+              buf += resultProj(joinedRow.withLeft(rel(i))).copy()
             }
             i += 1
           }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 6b3d1652923fd..5e9cd9fd2345a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -52,14 +52,14 @@ trait HashJoin {
   override def canProcessUnsafeRows: Boolean = isUnsafeMode
   override def canProcessSafeRows: Boolean = !isUnsafeMode
 
-  @transient protected lazy val buildSideKeyGenerator: Projection =
+  protected def buildSideKeyGenerator: Projection =
     if (isUnsafeMode) {
       UnsafeProjection.create(buildKeys, buildPlan.output)
     } else {
       newMutableProjection(buildKeys, buildPlan.output)()
     }
 
-  @transient protected lazy val streamSideKeyGenerator: Projection =
+  protected def streamSideKeyGenerator: Projection =
     if (isUnsafeMode) {
       UnsafeProjection.create(streamedKeys, streamedPlan.output)
     } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index a323aea4ea2c4..346337e64245c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -76,14 +76,14 @@ trait HashOuterJoin {
   override def canProcessUnsafeRows: Boolean = isUnsafeMode
   override def canProcessSafeRows: Boolean = !isUnsafeMode
 
-  @transient protected lazy val buildKeyGenerator: Projection =
+  protected def buildKeyGenerator: Projection =
     if (isUnsafeMode) {
       UnsafeProjection.create(buildKeys, buildPlan.output)
     } else {
       newMutableProjection(buildKeys, buildPlan.output)()
     }
 
-  @transient protected[this] lazy val streamedKeyGenerator: Projection = {
+  protected[this] def streamedKeyGenerator: Projection = {
     if (isUnsafeMode) {
       UnsafeProjection.create(streamedKeys, streamedPlan.output)
     } else {
@@ -91,7 +91,7 @@ trait HashOuterJoin {
     }
   }
 
-  @transient private[this] lazy val resultProjection: InternalRow => InternalRow = {
+  protected[this] def resultProjection: InternalRow => InternalRow = {
     if (isUnsafeMode) {
       UnsafeProjection.create(self.schema)
     } else {
@@ -113,7 +113,8 @@ trait HashOuterJoin {
   protected[this] def leftOuterIterator(
       key: InternalRow,
       joinedRow: JoinedRow,
-      rightIter: Iterable[InternalRow]): Iterator[InternalRow] = {
+      rightIter: Iterable[InternalRow],
+      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (rightIter != null) {
@@ -124,12 +125,12 @@ trait HashOuterJoin {
           List.empty
         }
         if (temp.isEmpty) {
-          resultProjection(joinedRow.withRight(rightNullRow)).copy :: Nil
+          resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
         } else {
           temp
         }
       } else {
-        resultProjection(joinedRow.withRight(rightNullRow)).copy :: Nil
+        resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
       }
     }
     ret.iterator
@@ -138,24 +139,24 @@ trait HashOuterJoin {
   protected[this] def rightOuterIterator(
       key: InternalRow,
       leftIter: Iterable[InternalRow],
-      joinedRow: JoinedRow): Iterator[InternalRow] = {
+      joinedRow: JoinedRow,
+      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (leftIter != null) {
           leftIter.collect {
-            case l if boundCondition(joinedRow.withLeft(l)) =>
-              resultProjection(joinedRow).copy()
+            case l if boundCondition(joinedRow.withLeft(l)) => resultProjection(joinedRow).copy()
           }
         } else {
           List.empty
         }
         if (temp.isEmpty) {
-          resultProjection(joinedRow.withLeft(leftNullRow)).copy :: Nil
+          resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
         } else {
           temp
         }
       } else {
-        resultProjection(joinedRow.withLeft(leftNullRow)).copy :: Nil
+        resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
       }
     }
     ret.iterator
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 97fde8f975bfd..47a7d370f5415 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -43,14 +43,14 @@ trait HashSemiJoin {
   override def canProcessUnsafeRows: Boolean = supportUnsafe
   override def canProcessSafeRows: Boolean = !supportUnsafe
 
-  @transient protected lazy val leftKeyGenerator: Projection =
+  protected def leftKeyGenerator: Projection =
     if (supportUnsafe) {
       UnsafeProjection.create(leftKeys, left.output)
     } else {
       newMutableProjection(leftKeys, left.output)()
     }
 
-  @transient protected lazy val rightKeyGenerator: Projection =
+  protected def rightKeyGenerator: Projection =
     if (supportUnsafe) {
       UnsafeProjection.create(rightKeys, right.output)
     } else {
@@ -62,12 +62,11 @@ trait HashSemiJoin {
 
   protected def buildKeyHashSet(buildIter: Iterator[InternalRow]): java.util.Set[InternalRow] = {
     val hashSet = new java.util.HashSet[InternalRow]()
-    var currentRow: InternalRow = null
 
     // Create a Hash set of buildKeys
     val rightKey = rightKeyGenerator
     while (buildIter.hasNext) {
-      currentRow = buildIter.next()
+      val currentRow = buildIter.next()
       val rowKey = rightKey(currentRow)
       if (!rowKey.anyNull) {
         val keyExists = hashSet.contains(rowKey)
@@ -76,6 +75,7 @@ trait HashSemiJoin {
         }
       }
     }
+
     hashSet
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 58b4236f7b5b5..3f257ecdd156c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -17,12 +17,11 @@
 
 package org.apache.spark.sql.execution.joins
 
-import java.io.{IOException, Externalizable, ObjectInput, ObjectOutput}
+import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput}
 import java.nio.ByteOrder
 import java.util.{HashMap => JavaHashMap}
 
 import org.apache.spark.shuffle.ShuffleMemoryManager
-import org.apache.spark.{SparkConf, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
@@ -31,6 +30,7 @@ import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.CompactBuffer
+import org.apache.spark.{SparkConf, SparkEnv}
 
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index eee8ad800f98e..6a8c35efca8f4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -60,19 +60,21 @@ case class ShuffledHashOuterJoin(
         case LeftOuter =>
           val hashed = HashedRelation(rightIter, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
+          val resultProj = resultProjection
           leftIter.flatMap( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey))
+            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey), resultProj)
           })
 
         case RightOuter =>
           val hashed = HashedRelation(leftIter, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
+          val resultProj = resultProjection
           rightIter.flatMap ( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow)
+            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow, resultProj)
           })
 
         case FullOuter =>

From 9f94c85ff35df6289371f80edde51c2aa6c4bcdc Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 6 Aug 2015 09:53:53 -0700
Subject: [PATCH 0883/1454] [SPARK-9593] [SQL] [HOTFIX] Makes the Hadoop shims
 loading fix more robust

This is a follow-up of #7929.

We found that Jenkins SBT master build still fails because of the Hadoop shims loading issue. But the failure doesn't appear to be deterministic. My suspect is that Hadoop `VersionInfo` class may fail to inspect Hadoop version, and the shims loading branch is skipped.

This PR tries to make the fix more robust:

1. When Hadoop version is available, we load `Hadoop20SShims` for versions <= 2.0.x as srowen suggested in PR #7929.
2. Otherwise, we use `Path.getPathWithoutSchemeAndAuthority` as a probe method, which doesn't exist in Hadoop 1.x or 2.0.x. If this method is not found, `Hadoop20SShims` is also loaded.

Author: Cheng Lian <lian@databricks.com>

Closes #7994 from liancheng/spark-9593/fix-hadoop-shims and squashes the following commits:

e1d3d70 [Cheng Lian] Fixes typo in comments
8d971da [Cheng Lian] Makes the Hadoop shims loading fix more robust
---
 .../spark/sql/hive/client/ClientWrapper.scala | 88 ++++++++++++-------
 1 file changed, 55 insertions(+), 33 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 211a3b879c1b3..3d05b583cf9e0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -68,45 +68,67 @@ private[hive] class ClientWrapper(
 
   // !! HACK ALERT !!
   //
-  // This method is a surgical fix for Hadoop version 2.0.0-mr1-cdh4.1.1, which is used by Spark EC2
-  // scripts.  We should remove this after upgrading Spark EC2 scripts to some more recent Hadoop
-  // version in the future.
-  //
   // Internally, Hive `ShimLoader` tries to load different versions of Hadoop shims by checking
-  // version information gathered from Hadoop jar files.  If the major version number is 1,
-  // `Hadoop20SShims` will be loaded.  Otherwise, if the major version number is 2, `Hadoop23Shims`
-  // will be chosen.
+  // major version number gathered from Hadoop jar files:
+  //
+  // - For major version number 1, load `Hadoop20SShims`, where "20S" stands for Hadoop 0.20 with
+  //   security.
+  // - For major version number 2, load `Hadoop23Shims`, where "23" stands for Hadoop 0.23.
   //
-  // However, part of APIs in Hadoop 2.0.x and 2.1.x versions were in flux due to historical
-  // reasons. So 2.0.0-mr1-cdh4.1.1 is actually more Hadoop-1-like and should be used together with
-  // `Hadoop20SShims`, but `Hadoop20SShims` is chose because the major version number here is 2.
+  // However, APIs in Hadoop 2.0.x and 2.1.x versions were in flux due to historical reasons. It
+  // turns out that Hadoop 2.0.x versions should also be used together with `Hadoop20SShims`, but
+  // `Hadoop23Shims` is chosen because the major version number here is 2.
   //
-  // Here we check for this specific version and loads `Hadoop20SShims` via reflection.  Note that
-  // we can't check for string literal "2.0.0-mr1-cdh4.1.1" because the obtained version string
-  // comes from Maven artifact org.apache.hadoop:hadoop-common:2.0.0-cdh4.1.1, which doesn't have
-  // the "mr1" tag in its version string.
+  // To fix this issue, we try to inspect Hadoop version via `org.apache.hadoop.utils.VersionInfo`
+  // and load `Hadoop20SShims` for Hadoop 1.x and 2.0.x versions.  If Hadoop version information is
+  // not available, we decide whether to override the shims or not by checking for existence of a
+  // probe method which doesn't exist in Hadoop 1.x or 2.0.x versions.
   private def overrideHadoopShims(): Unit = {
-    val VersionPattern = """2\.0\.0.*cdh4.*""".r
-
-    VersionInfo.getVersion match {
-      case VersionPattern() =>
-        val shimClassName = "org.apache.hadoop.hive.shims.Hadoop20SShims"
-        logInfo(s"Loading Hadoop shims $shimClassName")
-
-        try {
-          val shimsField = classOf[ShimLoader].getDeclaredField("hadoopShims")
-          // scalastyle:off classforname
-          val shimsClass = Class.forName(shimClassName)
-          // scalastyle:on classforname
-          val shims = classOf[HadoopShims].cast(shimsClass.newInstance())
-          shimsField.setAccessible(true)
-          shimsField.set(null, shims)
-        } catch { case cause: Throwable =>
-          logError(s"Failed to load $shimClassName")
-          // Falls back to normal Hive `ShimLoader` logic
+    val hadoopVersion = VersionInfo.getVersion
+    val VersionPattern = """(\d+)\.(\d+).*""".r
+
+    hadoopVersion match {
+      case null =>
+        logError("Failed to inspect Hadoop version")
+
+        // Using "Path.getPathWithoutSchemeAndAuthority" as the probe method.
+        val probeMethod = "getPathWithoutSchemeAndAuthority"
+        if (!classOf[Path].getDeclaredMethods.exists(_.getName == probeMethod)) {
+          logInfo(
+            s"Method ${classOf[Path].getCanonicalName}.$probeMethod not found, " +
+              s"we are probably using Hadoop 1.x or 2.0.x")
+          loadHadoop20SShims()
+        }
+
+      case VersionPattern(majorVersion, minorVersion) =>
+        logInfo(s"Inspected Hadoop version: $hadoopVersion")
+
+        // Loads Hadoop20SShims for 1.x and 2.0.x versions
+        val (major, minor) = (majorVersion.toInt, minorVersion.toInt)
+        if (major < 2 || (major == 2 && minor == 0)) {
+          loadHadoop20SShims()
         }
+    }
+
+    // Logs the actual loaded Hadoop shims class
+    val loadedShimsClassName = ShimLoader.getHadoopShims.getClass.getCanonicalName
+    logInfo(s"Loaded $loadedShimsClassName for Hadoop version $hadoopVersion")
+  }
 
-      case _ =>
+  private def loadHadoop20SShims(): Unit = {
+    val hadoop20SShimsClassName = "org.apache.hadoop.hive.shims.Hadoop20SShims"
+    logInfo(s"Loading Hadoop shims $hadoop20SShimsClassName")
+
+    try {
+      val shimsField = classOf[ShimLoader].getDeclaredField("hadoopShims")
+      // scalastyle:off classforname
+      val shimsClass = Class.forName(hadoop20SShimsClassName)
+      // scalastyle:on classforname
+      val shims = classOf[HadoopShims].cast(shimsClass.newInstance())
+      shimsField.setAccessible(true)
+      shimsField.set(null, shims)
+    } catch { case cause: Throwable =>
+      throw new RuntimeException(s"Failed to load $hadoop20SShimsClassName", cause)
     }
   }
 

From c5c6aded641048a3e66ac79d9e84d34e4b1abae7 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 6 Aug 2015 10:08:33 -0700
Subject: [PATCH 0884/1454] [SPARK-9112] [ML] Implement Stats for
 LogisticRegression

I have added support for stats in LogisticRegression. The API is similar to that of LinearRegression with LogisticRegressionTrainingSummary and LogisticRegressionSummary

I have some queries and asked them inline.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7538 from MechCoder/log_reg_stats and squashes the following commits:

2e9f7c7 [MechCoder] Change defs into lazy vals
d775371 [MechCoder] Clean up class inheritance
9586125 [MechCoder] Add abstraction to handle Multiclass Metrics
40ad8ef [MechCoder] minor
640376a [MechCoder] remove unnecessary dataframe stuff and add docs
80d9954 [MechCoder] Added tests
fbed861 [MechCoder] DataFrame support for metrics
70a0fc4 [MechCoder] [SPARK-9112] [ML] Implement Stats for LogisticRegression
---
 .../classification/LogisticRegression.scala   | 166 +++++++++++++++++-
 .../JavaLogisticRegressionSuite.java          |   9 +
 .../LogisticRegressionSuite.scala             |  37 +++-
 3 files changed, 209 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 0d073839259c6..f55134d258857 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -30,10 +30,12 @@ import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.storage.StorageLevel
 
 /**
@@ -284,7 +286,13 @@ class LogisticRegression(override val uid: String)
 
     if (handlePersistence) instances.unpersist()
 
-    copyValues(new LogisticRegressionModel(uid, weights, intercept))
+    val model = copyValues(new LogisticRegressionModel(uid, weights, intercept))
+    val logRegSummary = new BinaryLogisticRegressionTrainingSummary(
+      model.transform(dataset),
+      $(probabilityCol),
+      $(labelCol),
+      objectiveHistory)
+    model.setSummary(logRegSummary)
   }
 
   override def copy(extra: ParamMap): LogisticRegression = defaultCopy(extra)
@@ -319,6 +327,38 @@ class LogisticRegressionModel private[ml] (
 
   override val numClasses: Int = 2
 
+  private var trainingSummary: Option[LogisticRegressionTrainingSummary] = None
+
+  /**
+   * Gets summary of model on training set. An exception is
+   * thrown if `trainingSummary == None`.
+   */
+  def summary: LogisticRegressionTrainingSummary = trainingSummary match {
+    case Some(summ) => summ
+    case None =>
+      throw new SparkException(
+        "No training summary available for this LogisticRegressionModel",
+        new NullPointerException())
+  }
+
+  private[classification] def setSummary(
+      summary: LogisticRegressionTrainingSummary): this.type = {
+    this.trainingSummary = Some(summary)
+    this
+  }
+
+  /** Indicates whether a training summary exists for this model instance. */
+  def hasSummary: Boolean = trainingSummary.isDefined
+
+  /**
+   * Evaluates the model on a testset.
+   * @param dataset Test dataset to evaluate model on.
+   */
+  // TODO: decide on a good name before exposing to public API
+  private[classification] def evaluate(dataset: DataFrame): LogisticRegressionSummary = {
+    new BinaryLogisticRegressionSummary(this.transform(dataset), $(probabilityCol), $(labelCol))
+  }
+
   /**
    * Predict label for the given feature vector.
    * The behavior of this can be adjusted using [[thresholds]].
@@ -440,6 +480,128 @@ private[classification] class MultiClassSummarizer extends Serializable {
   }
 }
 
+/**
+ * Abstraction for multinomial Logistic Regression Training results.
+ */
+sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary {
+
+  /** objective function (scaled loss + regularization) at each iteration. */
+  def objectiveHistory: Array[Double]
+
+  /** Number of training iterations until termination */
+  def totalIterations: Int = objectiveHistory.length
+
+}
+
+/**
+ * Abstraction for Logistic Regression Results for a given model.
+ */
+sealed trait LogisticRegressionSummary extends Serializable {
+
+  /** Dataframe outputted by the model's `transform` method. */
+  def predictions: DataFrame
+
+  /** Field in "predictions" which gives the calibrated probability of each sample as a vector. */
+  def probabilityCol: String
+
+  /** Field in "predictions" which gives the the true label of each sample. */
+  def labelCol: String
+
+}
+
+/**
+ * :: Experimental ::
+ * Logistic regression training results.
+ * @param predictions dataframe outputted by the model's `transform` method.
+ * @param probabilityCol field in "predictions" which gives the calibrated probability of
+ *                       each sample as a vector.
+ * @param labelCol field in "predictions" which gives the true label of each sample.
+ * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
+ */
+@Experimental
+class BinaryLogisticRegressionTrainingSummary private[classification] (
+    predictions: DataFrame,
+    probabilityCol: String,
+    labelCol: String,
+    val objectiveHistory: Array[Double])
+  extends BinaryLogisticRegressionSummary(predictions, probabilityCol, labelCol)
+  with LogisticRegressionTrainingSummary {
+
+}
+
+/**
+ * :: Experimental ::
+ * Binary Logistic regression results for a given model.
+ * @param predictions dataframe outputted by the model's `transform` method.
+ * @param probabilityCol field in "predictions" which gives the calibrated probability of
+ *                       each sample.
+ * @param labelCol field in "predictions" which gives the true label of each sample.
+ */
+@Experimental
+class BinaryLogisticRegressionSummary private[classification] (
+    @transient override val predictions: DataFrame,
+    override val probabilityCol: String,
+    override val labelCol: String) extends LogisticRegressionSummary {
+
+  private val sqlContext = predictions.sqlContext
+  import sqlContext.implicits._
+
+  /**
+   * Returns a BinaryClassificationMetrics object.
+   */
+  // TODO: Allow the user to vary the number of bins using a setBins method in
+  // BinaryClassificationMetrics. For now the default is set to 100.
+  @transient private val binaryMetrics = new BinaryClassificationMetrics(
+    predictions.select(probabilityCol, labelCol).map {
+      case Row(score: Vector, label: Double) => (score(1), label)
+    }, 100
+  )
+
+  /**
+   * Returns the receiver operating characteristic (ROC) curve,
+   * which is an Dataframe having two fields (FPR, TPR)
+   * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
+   * @see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
+   */
+  @transient lazy val roc: DataFrame = binaryMetrics.roc().toDF("FPR", "TPR")
+
+  /**
+   * Computes the area under the receiver operating characteristic (ROC) curve.
+   */
+  lazy val areaUnderROC: Double = binaryMetrics.areaUnderROC()
+
+  /**
+   * Returns the precision-recall curve, which is an Dataframe containing
+   * two fields recall, precision with (0.0, 1.0) prepended to it.
+   */
+  @transient lazy val pr: DataFrame = binaryMetrics.pr().toDF("recall", "precision")
+
+  /**
+   * Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
+   */
+  @transient lazy val fMeasureByThreshold: DataFrame = {
+    binaryMetrics.fMeasureByThreshold().toDF("threshold", "F-Measure")
+  }
+
+  /**
+   * Returns a dataframe with two fields (threshold, precision) curve.
+   * Every possible probability obtained in transforming the dataset are used
+   * as thresholds used in calculating the precision.
+   */
+  @transient lazy val precisionByThreshold: DataFrame = {
+    binaryMetrics.precisionByThreshold().toDF("threshold", "precision")
+  }
+
+  /**
+   * Returns a dataframe with two fields (threshold, recall) curve.
+   * Every possible probability obtained in transforming the dataset are used
+   * as thresholds used in calculating the recall.
+   */
+  @transient lazy val recallByThreshold: DataFrame = {
+    binaryMetrics.recallByThreshold().toDF("threshold", "recall")
+  }
+}
+
 /**
  * LogisticAggregator computes the gradient and loss for binary logistic loss function, as used
  * in binary classification for samples in sparse or dense vector in a online fashion.
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index fb1de51163f2e..7e9aa383728f0 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -152,4 +152,13 @@ public void logisticRegressionPredictorClassifierMethods() {
       }
     }
   }
+
+  @Test
+  public void logisticRegressionTrainingSummary() {
+    LogisticRegression lr = new LogisticRegression();
+    LogisticRegressionModel model = lr.fit(dataset);
+
+    LogisticRegressionTrainingSummary summary = model.summary();
+    assert(summary.totalIterations() == summary.objectiveHistory().length);
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index da13dcb42d1ca..8c3d4590f5ae9 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -723,6 +723,41 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val weightsR = Vectors.dense(0.0, 0.0, 0.0, 0.0)
 
     assert(model1.intercept ~== interceptR relTol 1E-5)
-    assert(model1.weights ~= weightsR absTol 1E-6)
+    assert(model1.weights ~== weightsR absTol 1E-6)
+  }
+
+  test("evaluate on test set") {
+    // Evaluate on test set should be same as that of the transformed training data.
+    val lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(1.0)
+      .setThreshold(0.6)
+    val model = lr.fit(dataset)
+    val summary = model.summary.asInstanceOf[BinaryLogisticRegressionSummary]
+
+    val sameSummary = model.evaluate(dataset).asInstanceOf[BinaryLogisticRegressionSummary]
+    assert(summary.areaUnderROC === sameSummary.areaUnderROC)
+    assert(summary.roc.collect() === sameSummary.roc.collect())
+    assert(summary.pr.collect === sameSummary.pr.collect())
+    assert(
+      summary.fMeasureByThreshold.collect() === sameSummary.fMeasureByThreshold.collect())
+    assert(summary.recallByThreshold.collect() === sameSummary.recallByThreshold.collect())
+    assert(
+      summary.precisionByThreshold.collect() === sameSummary.precisionByThreshold.collect())
+  }
+
+  test("statistics on training data") {
+    // Test that loss is monotonically decreasing.
+    val lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(1.0)
+      .setThreshold(0.6)
+    val model = lr.fit(dataset)
+    assert(
+      model.summary
+        .objectiveHistory
+        .sliding(2)
+        .forall(x => x(0) >= x(1)))
+
   }
 }

From 076ec056818a65216eaf51aa5b3bd8f697c34748 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 6 Aug 2015 10:09:58 -0700
Subject: [PATCH 0885/1454] [SPARK-9533] [PYSPARK] [ML] Add missing methods in
 Word2Vec ML

After https://github.com/apache/spark/pull/7263 it is pretty straightforward to Python wrappers.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7930 from MechCoder/spark-9533 and squashes the following commits:

1bea394 [MechCoder] make getVectors a lazy val
5522756 [MechCoder] [SPARK-9533] [PySpark] [ML] Add missing methods in Word2Vec ML
---
 .../apache/spark/ml/feature/Word2Vec.scala    |  2 +-
 python/pyspark/ml/feature.py                  | 40 +++++++++++++++++++
 2 files changed, 41 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index b4f46cef798dd..29acc3eb5865f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -153,7 +153,7 @@ class Word2VecModel private[ml] (
    * Returns a dataframe with two fields, "word" and "vector", with "word" being a String and
    * and the vector the DenseVector that it is mapped to.
    */
-  val getVectors: DataFrame = {
+  @transient lazy val getVectors: DataFrame = {
     val sc = SparkContext.getOrCreate()
     val sqlContext = SQLContext.getOrCreate(sc)
     import sqlContext.implicits._
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 3f04c41ac5ab6..cb4dfa21298ce 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -15,11 +15,16 @@
 # limitations under the License.
 #
 
+import sys
+if sys.version > '3':
+    basestring = str
+
 from pyspark.rdd import ignore_unicode_prefix
 from pyspark.ml.param.shared import *
 from pyspark.ml.util import keyword_only
 from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer
 from pyspark.mllib.common import inherit_doc
+from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
            'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
@@ -954,6 +959,23 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
     >>> sent = ("a b " * 100 + "a c " * 10).split(" ")
     >>> doc = sqlContext.createDataFrame([(sent,), (sent,)], ["sentence"])
     >>> model = Word2Vec(vectorSize=5, seed=42, inputCol="sentence", outputCol="model").fit(doc)
+    >>> model.getVectors().show()
+    +----+--------------------+
+    |word|              vector|
+    +----+--------------------+
+    |   a|[-0.3511952459812...|
+    |   b|[0.29077222943305...|
+    |   c|[0.02315592765808...|
+    +----+--------------------+
+    ...
+    >>> model.findSynonyms("a", 2).show()
+    +----+-------------------+
+    |word|         similarity|
+    +----+-------------------+
+    |   b|0.29255685145799626|
+    |   c|-0.5414068302988307|
+    +----+-------------------+
+    ...
     >>> model.transform(doc).head().model
     DenseVector([-0.0422, -0.5138, -0.2546, 0.6885, 0.276])
     """
@@ -1047,6 +1069,24 @@ class Word2VecModel(JavaModel):
     Model fitted by Word2Vec.
     """
 
+    def getVectors(self):
+        """
+        Returns the vector representation of the words as a dataframe
+        with two fields, word and vector.
+        """
+        return self._call_java("getVectors")
+
+    def findSynonyms(self, word, num):
+        """
+        Find "num" number of words closest in similarity to "word".
+        word can be a string or vector representation.
+        Returns a dataframe with two fields word and similarity (which
+        gives the cosine similarity).
+        """
+        if not isinstance(word, basestring):
+            word = _convert_to_vector(word)
+        return self._call_java("findSynonyms", word, num)
+
 
 @inherit_doc
 class PCA(JavaEstimator, HasInputCol, HasOutputCol):

From 98e69467d4fda2c26a951409b5b7c6f1e9345ce4 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Thu, 6 Aug 2015 10:29:40 -0700
Subject: [PATCH 0886/1454] [SPARK-9615] [SPARK-9616] [SQL] [MLLIB] Bugs
 related to FrequentItems when merging and with Tungsten

In short:
1- FrequentItems should not use the InternalRow representation, because the keys in the map get messed up. For example, every key in the Map correspond to the very last element observed in the partition, when the elements are strings.

2- Merging two partitions had a bug:

**Existing behavior with size 3**
Partition A -> Map(1 -> 3, 2 -> 3, 3 -> 4)
Partition B -> Map(4 -> 25)
Result -> Map()

**Correct Behavior:**
Partition A -> Map(1 -> 3, 2 -> 3, 3 -> 4)
Partition B -> Map(4 -> 25)
Result -> Map(3 -> 1, 4 -> 22)

cc mengxr rxin JoshRosen

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7945 from brkyvz/freq-fix and squashes the following commits:

07fa001 [Burak Yavuz] address 2
1dc61a8 [Burak Yavuz] address 1
506753e [Burak Yavuz] fixed and added reg test
47bfd50 [Burak Yavuz] pushing
---
 .../sql/execution/stat/FrequentItems.scala    | 26 +++++++++++--------
 .../apache/spark/sql/DataFrameStatSuite.scala | 24 ++++++++++++++---
 2 files changed, 36 insertions(+), 14 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
index 9329148aa233c..db463029aedf7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
@@ -20,17 +20,15 @@ package org.apache.spark.sql.execution.stat
 import scala.collection.mutable.{Map => MutableMap}
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Column, DataFrame}
+import org.apache.spark.sql.{Row, Column, DataFrame}
 
 private[sql] object FrequentItems extends Logging {
 
   /** A helper class wrapping `MutableMap[Any, Long]` for simplicity. */
   private class FreqItemCounter(size: Int) extends Serializable {
     val baseMap: MutableMap[Any, Long] = MutableMap.empty[Any, Long]
-
     /**
      * Add a new example to the counts if it exists, otherwise deduct the count
      * from existing items.
@@ -42,9 +40,15 @@ private[sql] object FrequentItems extends Logging {
         if (baseMap.size < size) {
           baseMap += key -> count
         } else {
-          // TODO: Make this more efficient... A flatMap?
-          baseMap.retain((k, v) => v > count)
-          baseMap.transform((k, v) => v - count)
+          val minCount = baseMap.values.min
+          val remainder = count - minCount
+          if (remainder >= 0) {
+            baseMap += key -> count // something will get kicked out, so we can add this
+            baseMap.retain((k, v) => v > minCount)
+            baseMap.transform((k, v) => v - minCount)
+          } else {
+            baseMap.transform((k, v) => v - count)
+          }
         }
       }
       this
@@ -90,12 +94,12 @@ private[sql] object FrequentItems extends Logging {
       (name, originalSchema.fields(index).dataType)
     }.toArray
 
-    val freqItems = df.select(cols.map(Column(_)) : _*).queryExecution.toRdd.aggregate(countMaps)(
+    val freqItems = df.select(cols.map(Column(_)) : _*).rdd.aggregate(countMaps)(
       seqOp = (counts, row) => {
         var i = 0
         while (i < numCols) {
           val thisMap = counts(i)
-          val key = row.get(i, colInfo(i)._2)
+          val key = row.get(i)
           thisMap.add(key, 1L)
           i += 1
         }
@@ -110,13 +114,13 @@ private[sql] object FrequentItems extends Logging {
         baseCounts
       }
     )
-    val justItems = freqItems.map(m => m.baseMap.keys.toArray).map(new GenericArrayData(_))
-    val resultRow = InternalRow(justItems : _*)
+    val justItems = freqItems.map(m => m.baseMap.keys.toArray)
+    val resultRow = Row(justItems : _*)
     // append frequent Items to the column name for easy debugging
     val outputCols = colInfo.map { v =>
       StructField(v._1 + "_freqItems", ArrayType(v._2, false))
     }
     val schema = StructType(outputCols).toAttributes
-    new DataFrame(df.sqlContext, LocalRelation(schema, Seq(resultRow)))
+    new DataFrame(df.sqlContext, LocalRelation.fromExternalRows(schema, Seq(resultRow)))
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 07a675e64f527..0e7659f443ecd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -123,12 +123,30 @@ class DataFrameStatSuite extends QueryTest {
 
     val results = df.stat.freqItems(Array("numbers", "letters"), 0.1)
     val items = results.collect().head
-    items.getSeq[Int](0) should contain (1)
-    items.getSeq[String](1) should contain (toLetter(1))
+    assert(items.getSeq[Int](0).contains(1))
+    assert(items.getSeq[String](1).contains(toLetter(1)))
 
     val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1)
     val items2 = singleColResults.collect().head
-    items2.getSeq[Double](0) should contain (-1.0)
+    assert(items2.getSeq[Double](0).contains(-1.0))
+  }
+
+  test("Frequent Items 2") {
+    val rows = sqlCtx.sparkContext.parallelize(Seq.empty[Int], 4)
+    // this is a regression test, where when merging partitions, we omitted values with higher
+    // counts than those that existed in the map when the map was full. This test should also fail
+    // if anything like SPARK-9614 is observed once again
+    val df = rows.mapPartitionsWithIndex { (idx, iter) =>
+      if (idx == 3) { // must come from one of the later merges, therefore higher partition index
+        Iterator("3", "3", "3", "3", "3")
+      } else {
+        Iterator("0", "1", "2", "3", "4")
+      }
+    }.toDF("a")
+    val results = df.stat.freqItems(Array("a"), 0.25)
+    val items = results.collect().head.getSeq[String](0)
+    assert(items.contains("3"))
+    assert(items.length === 1)
   }
 
   test("sampleBy") {

From 5e1b0ef07942a041195b3decd05d86c289bc8d2b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 6 Aug 2015 10:39:16 -0700
Subject: [PATCH 0887/1454] [SPARK-9659][SQL] Rename inSet to isin to match
 Pandas function.

Inspiration drawn from this blog post: https://lab.getbase.com/pandarize-spark-dataframes/

Author: Reynold Xin <rxin@databricks.com>

Closes #7977 from rxin/isin and squashes the following commits:

9b1d3d6 [Reynold Xin] Added return.
2197d37 [Reynold Xin] Fixed test case.
7c1b6cf [Reynold Xin] Import warnings.
4f4a35d [Reynold Xin] [SPARK-9659][SQL] Rename inSet to isin to match Pandas function.
---
 python/pyspark/sql/column.py                  | 20 ++++++++++++++++++-
 .../scala/org/apache/spark/sql/Column.scala   | 13 +++++++++++-
 .../spark/sql/ColumnExpressionSuite.scala     | 14 ++++++-------
 3 files changed, 38 insertions(+), 9 deletions(-)

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 0a85da7443d3d..8af8637cf948d 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -16,6 +16,7 @@
 #
 
 import sys
+import warnings
 
 if sys.version >= '3':
     basestring = str
@@ -254,12 +255,29 @@ def inSet(self, *cols):
         [Row(age=5, name=u'Bob')]
         >>> df[df.age.inSet([1, 2, 3])].collect()
         [Row(age=2, name=u'Alice')]
+
+        .. note:: Deprecated in 1.5, use :func:`Column.isin` instead.
+        """
+        warnings.warn("inSet is deprecated. Use isin() instead.")
+        return self.isin(*cols)
+
+    @ignore_unicode_prefix
+    @since(1.5)
+    def isin(self, *cols):
+        """
+        A boolean expression that is evaluated to true if the value of this
+        expression is contained by the evaluated values of the arguments.
+
+        >>> df[df.name.isin("Bob", "Mike")].collect()
+        [Row(age=5, name=u'Bob')]
+        >>> df[df.age.isin([1, 2, 3])].collect()
+        [Row(age=2, name=u'Alice')]
         """
         if len(cols) == 1 and isinstance(cols[0], (list, set)):
             cols = cols[0]
         cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols]
         sc = SparkContext._active_spark_context
-        jc = getattr(self._jc, "in")(_to_seq(sc, cols))
+        jc = getattr(self._jc, "isin")(_to_seq(sc, cols))
         return Column(jc)
 
     # order
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index b25dcbca82b9f..75365fbcec757 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -627,8 +627,19 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    * @group expr_ops
    * @since 1.3.0
    */
+  @deprecated("use isin", "1.5.0")
   @scala.annotation.varargs
-  def in(list: Any*): Column = In(expr, list.map(lit(_).expr))
+  def in(list: Any*): Column = isin(list : _*)
+
+  /**
+   * A boolean expression that is evaluated to true if the value of this expression is contained
+   * by the evaluated values of the arguments.
+   *
+   * @group expr_ops
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def isin(list: Any*): Column = In(expr, list.map(lit(_).expr))
 
   /**
    * SQL like expression.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index b351380373259..e1b3443d74993 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -345,23 +345,23 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
 
   test("in") {
     val df = Seq((1, "x"), (2, "y"), (3, "z")).toDF("a", "b")
-    checkAnswer(df.filter($"a".in(1, 2)),
+    checkAnswer(df.filter($"a".isin(1, 2)),
       df.collect().toSeq.filter(r => r.getInt(0) == 1 || r.getInt(0) == 2))
-    checkAnswer(df.filter($"a".in(3, 2)),
+    checkAnswer(df.filter($"a".isin(3, 2)),
       df.collect().toSeq.filter(r => r.getInt(0) == 3 || r.getInt(0) == 2))
-    checkAnswer(df.filter($"a".in(3, 1)),
+    checkAnswer(df.filter($"a".isin(3, 1)),
       df.collect().toSeq.filter(r => r.getInt(0) == 3 || r.getInt(0) == 1))
-    checkAnswer(df.filter($"b".in("y", "x")),
+    checkAnswer(df.filter($"b".isin("y", "x")),
       df.collect().toSeq.filter(r => r.getString(1) == "y" || r.getString(1) == "x"))
-    checkAnswer(df.filter($"b".in("z", "x")),
+    checkAnswer(df.filter($"b".isin("z", "x")),
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "x"))
-    checkAnswer(df.filter($"b".in("z", "y")),
+    checkAnswer(df.filter($"b".isin("z", "y")),
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "y"))
 
     val df2 = Seq((1, Seq(1)), (2, Seq(2)), (3, Seq(3))).toDF("a", "b")
 
     intercept[AnalysisException] {
-      df2.filter($"a".in($"b"))
+      df2.filter($"a".isin($"b"))
     }
   }
 

From 6e009cb9c4d7a395991e10dab427f37019283758 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 6 Aug 2015 10:40:54 -0700
Subject: [PATCH 0888/1454] [SPARK-9632][SQL] update InternalRow.toSeq to make
 it accept data type info

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7955 from cloud-fan/toSeq and squashes the following commits:

21665e2 [Wenchen Fan] fix hive again...
4addf29 [Wenchen Fan] fix hive
bc16c59 [Wenchen Fan] minor fix
33d802c [Wenchen Fan] pass data type info to InternalRow.toSeq
3dd033e [Wenchen Fan] move the default special getters implementation from InternalRow to BaseGenericInternalRow
---
 .../spark/sql/catalyst/InternalRow.scala      | 132 ++----------------
 .../sql/catalyst/expressions/Projection.scala |  12 +-
 .../expressions/SpecificMutableRow.scala      |   5 +-
 .../codegen/GenerateProjection.scala          |   8 +-
 .../spark/sql/catalyst/expressions/rows.scala | 132 +++++++++++++++++-
 .../expressions/CodeGenerationSuite.scala     |   2 +-
 .../spark/sql/columnar/ColumnStats.scala      |  51 +++----
 .../columnar/InMemoryColumnarTableScan.scala  |  11 +-
 .../spark/sql/execution/debug/package.scala   |   4 +-
 .../apache/spark/sql/sources/interfaces.scala |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  54 +++----
 .../spark/sql/hive/HiveInspectors.scala       |   6 +-
 .../hive/execution/ScriptTransformation.scala |  21 ++-
 .../spark/sql/hive/hiveWriterContainers.scala |  24 ++--
 .../spark/sql/hive/HiveInspectorSuite.scala   |  10 +-
 15 files changed, 259 insertions(+), 217 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 7d17cca808791..85b4bf3b6aef5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -32,8 +31,6 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
-  override def toString: String = mkString("[", ",", "]")
-
   /**
    * Make a copy of the current [[InternalRow]] object.
    */
@@ -50,136 +47,25 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
     false
   }
 
-  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
-  // or implement this genericGet.
-  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
-    "Concrete internal rows should implement genericGet, " +
-      "or implement all special getters and equals/hashCode")
-
-  // default implementation (slow)
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[InternalRow]) {
-      return false
-    }
-
-    val other = o.asInstanceOf[InternalRow]
-    if (other eq null) {
-      return false
-    }
-
-    val len = numFields
-    if (len != other.numFields) {
-      return false
-    }
-
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
-        o1 match {
-          case b1: Array[Byte] =>
-            if (!o2.isInstanceOf[Array[Byte]] ||
-              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-              return false
-            }
-          case f1: Float if java.lang.Float.isNaN(f1) =>
-            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
-              return false
-            }
-          case d1: Double if java.lang.Double.isNaN(d1) =>
-            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
-              return false
-            }
-          case _ => if (o1 != o2) {
-            return false
-          }
-        }
-      }
-      i += 1
-    }
-    true
-  }
-
-  // Custom hashCode function that matches the efficient code generated version.
-  override def hashCode: Int = {
-    var result: Int = 37
-    var i = 0
-    val len = numFields
-    while (i < len) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          genericGet(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case a: Array[Byte] => java.util.Arrays.hashCode(a)
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
-      i += 1
-    }
-    result
-  }
-
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
    * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  // todo: remove this as it needs the generic getter
-  def toSeq: Seq[Any] = {
-    val n = numFields
-    val values = new Array[Any](n)
+  def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    val len = numFields
+    assert(len == fieldTypes.length)
+
+    val values = new Array[Any](len)
     var i = 0
-    while (i < n) {
-      values.update(i, genericGet(i))
+    while (i < len) {
+      values(i) = get(i, fieldTypes(i))
       i += 1
     }
     values
   }
 
-  /** Displays all elements of this sequence in a string (without a separator). */
-  def mkString: String = toSeq.mkString
-
-  /** Displays all elements of this sequence in a string using a separator string. */
-  def mkString(sep: String): String = toSeq.mkString(sep)
-
-  /**
-   * Displays all elements of this traversable or iterator in a string using
-   * start, end, and separator strings.
-   */
-  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
+  def toSeq(schema: StructType): Seq[Any] = toSeq(schema.map(_.dataType))
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 4296b4b123fc0..59ce7fc4f2c63 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -203,7 +203,11 @@ class JoinedRow extends InternalRow {
     this
   }
 
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    assert(fieldTypes.length == row1.numFields + row2.numFields)
+    val (left, right) = fieldTypes.splitAt(row1.numFields)
+    row1.toSeq(left) ++ row2.toSeq(right)
+  }
 
   override def numFields: Int = row1.numFields + row2.numFields
 
@@ -276,11 +280,11 @@ class JoinedRow extends InternalRow {
     if ((row1 eq null) && (row2 eq null)) {
       "[ empty row ]"
     } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
+      row2.toString
     } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
+      row1.toString
     } else {
-      mkString("[", ",", "]")
+      s"{${row1.toString} + ${row2.toString}}"
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index b94df6bd66e04..4f56f94bd4ca4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -192,7 +192,8 @@ final class MutableAny extends MutableValue {
  * based on the dataTypes of each column.  The intent is to decrease garbage when modifying the
  * values of primitive columns.
  */
-final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableRow {
+final class SpecificMutableRow(val values: Array[MutableValue])
+  extends MutableRow with BaseGenericInternalRow {
 
   def this(dataTypes: Seq[DataType]) =
     this(
@@ -213,8 +214,6 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
 
   override def numFields: Int = values.length
 
-  override def toSeq: Seq[Any] = values.map(_.boxed)
-
   override def setNullAt(i: Int): Unit = {
     values(i).isNull = true
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c04fe734d554e..c744e84d822e8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 
@@ -25,6 +26,8 @@ import org.apache.spark.sql.types._
  */
 abstract class BaseProjection extends Projection {}
 
+abstract class CodeGenMutableRow extends MutableRow with BaseGenericInternalRow
+
 /**
  * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input
  * [[Expression Expressions]] and a given input [[InternalRow]].  The returned [[InternalRow]]
@@ -171,7 +174,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         return new SpecificRow((InternalRow) r);
       }
 
-      final class SpecificRow extends ${classOf[MutableRow].getName} {
+      final class SpecificRow extends ${classOf[CodeGenMutableRow].getName} {
 
         $columns
 
@@ -184,7 +187,8 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        protected Object genericGet(int i) {
+        @Override
+        public Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 7657fb535dcf4..207e667792660 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -21,6 +21,130 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
+/**
+ * An extended version of [[InternalRow]] that implements all special getters, toString
+ * and equals/hashCode by `genericGet`.
+ */
+trait BaseGenericInternalRow extends InternalRow {
+
+  protected def genericGet(ordinal: Int): Any
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def toString(): String = {
+    if (numFields == 0) {
+      "[empty row]"
+    } else {
+      val sb = new StringBuilder
+      sb.append("[")
+      sb.append(genericGet(0))
+      val len = numFields
+      var i = 1
+      while (i < len) {
+        sb.append(",")
+        sb.append(genericGet(i))
+        i += 1
+      }
+      sb.append("]")
+      sb.toString()
+    }
+  }
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[BaseGenericInternalRow]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[BaseGenericInternalRow]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numFields
+    if (len != other.numFields) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numFields
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          genericGet(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+}
+
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
  * Setting a value through a primitive function implicitly marks that column as not null.
@@ -82,7 +206,7 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -90,7 +214,7 @@ class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRo
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
@@ -109,7 +233,7 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(values: Array[Any]) extends MutableRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -117,7 +241,7 @@ class GenericMutableRow(values: Array[Any]) extends MutableRow {
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index e310aee221666..e323467af5f4a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -87,7 +87,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     val length = 5000
     val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
     val plan = GenerateMutableProjection.generate(expressions)()
-    val actual = plan(new GenericMutableRow(length)).toSeq
+    val actual = plan(new GenericMutableRow(length)).toSeq(expressions.map(_.dataType))
     val expected = Seq.fill(length)(true)
 
     if (!checkResult(actual, expected)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index af1a8ecca9b57..5cbd52bc0590e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, Attribute, AttributeMap, AttributeReference}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -66,7 +66,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
    * Column statistics represented as a single row, currently including closed lower bound, closed
    * upper bound and null count.
    */
-  def collectedStatistics: InternalRow
+  def collectedStatistics: GenericInternalRow
 }
 
 /**
@@ -75,7 +75,8 @@ private[sql] sealed trait ColumnStats extends Serializable {
 private[sql] class NoopColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = super.gatherStats(row, ordinal)
 
-  override def collectedStatistics: InternalRow = InternalRow(null, null, nullCount, count, 0L)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, 0L))
 }
 
 private[sql] class BooleanColumnStats extends ColumnStats {
@@ -92,8 +93,8 @@ private[sql] class BooleanColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ByteColumnStats extends ColumnStats {
@@ -110,8 +111,8 @@ private[sql] class ByteColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ShortColumnStats extends ColumnStats {
@@ -128,8 +129,8 @@ private[sql] class ShortColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class IntColumnStats extends ColumnStats {
@@ -146,8 +147,8 @@ private[sql] class IntColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class LongColumnStats extends ColumnStats {
@@ -164,8 +165,8 @@ private[sql] class LongColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FloatColumnStats extends ColumnStats {
@@ -182,8 +183,8 @@ private[sql] class FloatColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DoubleColumnStats extends ColumnStats {
@@ -200,8 +201,8 @@ private[sql] class DoubleColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class StringColumnStats extends ColumnStats {
@@ -218,8 +219,8 @@ private[sql] class StringColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class BinaryColumnStats extends ColumnStats {
@@ -230,8 +231,8 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends ColumnStats {
@@ -248,8 +249,8 @@ private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends C
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
@@ -262,8 +263,8 @@ private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DateColumnStats extends IntColumnStats
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 5d5b0697d7016..d553bb6169ecc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -148,7 +148,7 @@ private[sql] case class InMemoryRelation(
           }
 
           val stats = InternalRow.fromSeq(columnBuilders.map(_.columnStats.collectedStatistics)
-                        .flatMap(_.toSeq))
+                        .flatMap(_.values))
 
           batchStats += stats
           CachedBatch(columnBuilders.map(_.build().array()), stats)
@@ -330,10 +330,11 @@ private[sql] case class InMemoryColumnarTableScan(
         if (inMemoryPartitionPruningEnabled) {
           cachedBatchIterator.filter { cachedBatch =>
             if (!partitionFilter(cachedBatch.stats)) {
-              def statsString: String = relation.partitionStatistics.schema
-                .zip(cachedBatch.stats.toSeq)
-                .map { case (a, s) => s"${a.name}: $s" }
-                .mkString(", ")
+              def statsString: String = relation.partitionStatistics.schema.zipWithIndex.map {
+                case (a, i) =>
+                  val value = cachedBatch.stats.get(i, a.dataType)
+                  s"${a.name}: $value"
+              }.mkString(", ")
               logInfo(s"Skipping partition based on stats $statsString")
               false
             } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index c37007f1eece7..dd3858ea2b520 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -156,8 +156,8 @@ package object debug {
     def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
       case (null, _) =>
 
-      case (row: InternalRow, StructType(fields)) =>
-        row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
+      case (row: InternalRow, s: StructType) =>
+        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
       case (a: ArrayData, ArrayType(elemType, _)) =>
         a.foreach(elemType, (_, e) => {
           typeCheck(e, elemType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 7126145ddc010..c04557e5a0818 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -461,8 +461,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
             val spec = discoverPartitions()
             val partitionColumnTypes = spec.partitionColumns.map(_.dataType)
             val castedPartitions = spec.partitions.map { case p @ Partition(values, path) =>
-              val literals = values.toSeq.zip(partitionColumnTypes).map {
-                case (value, dataType) => Literal.create(value, dataType)
+              val literals = partitionColumnTypes.zipWithIndex.map { case (dt, i) =>
+                Literal.create(values.get(i, dt), dt)
               }
               val castedValues = partitionSchema.zip(literals).map { case (field, literal) =>
                 Cast(literal, field.dataType).eval()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 16e0187ed20a0..d0430d2a60e75 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -19,33 +19,36 @@ package org.apache.spark.sql.columnar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
-  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, InternalRow(true, false, 0))
-  testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0))
-  testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0))
-  testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, createRow(true, false, 0))
+  testColumnStats(classOf[ByteColumnStats], BYTE, createRow(Byte.MaxValue, Byte.MinValue, 0))
+  testColumnStats(classOf[ShortColumnStats], SHORT, createRow(Short.MaxValue, Short.MinValue, 0))
+  testColumnStats(classOf[IntColumnStats], INT, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[DateColumnStats], DATE, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[LongColumnStats], LONG, createRow(Long.MaxValue, Long.MinValue, 0))
   testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
-    InternalRow(Long.MaxValue, Long.MinValue, 0))
-  testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0))
+    createRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[FloatColumnStats], FLOAT, createRow(Float.MaxValue, Float.MinValue, 0))
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
-    InternalRow(Double.MaxValue, Double.MinValue, 0))
-  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
-  testDecimalColumnStats(InternalRow(null, null, 0))
+    createRow(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, createRow(null, null, 0))
+  testDecimalColumnStats(createRow(null, null, 0))
+
+  def createRow(values: Any*): GenericInternalRow = new GenericInternalRow(values.toArray)
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
       columnType: NativeColumnType[T],
-      initialStatistics: InternalRow): Unit = {
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -61,11 +64,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -73,14 +76,15 @@ class ColumnStatsSuite extends SparkFunSuite {
     }
   }
 
-  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](initialStatistics: InternalRow) {
+  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = classOf[FixedDecimalColumnStats].getSimpleName
     val columnType = FIXED_DECIMAL(15, 10)
 
     test(s"$columnStatsName: empty") {
       val columnStats = new FixedDecimalColumnStats(15, 10)
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -96,11 +100,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 39d798d072aeb..9824dad239596 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -390,8 +390,10 @@ private[hive] trait HiveInspectors {
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
-          (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[InternalRow].toSeq).zipped.foreach {
-            (field, wrapper, data) => soi.setStructFieldData(struct, field, wrapper(data))
+          val row = o.asInstanceOf[InternalRow]
+          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
+            case ((field, wrapper), i) =>
+              soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
           }
           struct
         } else {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index a6a343d395995..ade27454b9d29 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -88,6 +88,7 @@ case class ScriptTransformation(
       // external process. That process's output will be read by this current thread.
       val writerThread = new ScriptTransformationWriterThread(
         inputIterator,
+        input.map(_.dataType),
         outputProjection,
         inputSerde,
         inputSoi,
@@ -201,6 +202,7 @@ case class ScriptTransformation(
 
 private class ScriptTransformationWriterThread(
     iter: Iterator[InternalRow],
+    inputSchema: Seq[DataType],
     outputProjection: Projection,
     @Nullable inputSerde: AbstractSerDe,
     @Nullable inputSoi: ObjectInspector,
@@ -226,12 +228,25 @@ private class ScriptTransformationWriterThread(
     // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
     // let's use a variable to record whether the `finally` block was hit due to an exception
     var threwException: Boolean = true
+    val len = inputSchema.length
     try {
       iter.map(outputProjection).foreach { row =>
         if (inputSerde == null) {
-          val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
-            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
-          outputStream.write(data)
+          val data = if (len == 0) {
+            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+          } else {
+            val sb = new StringBuilder
+            sb.append(row.get(0, inputSchema(0)))
+            var i = 1
+            while (i < len) {
+              sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+              sb.append(row.get(i, inputSchema(i)))
+              i += 1
+            }
+            sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES"))
+            sb.toString()
+          }
+          outputStream.write(data.getBytes("utf-8"))
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 684ea1d137b49..8dc796b056a72 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -211,18 +211,18 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
       }
     }
 
-    val dynamicPartPath = dynamicPartColNames
-      .zip(row.toSeq.takeRight(dynamicPartColNames.length))
-      .map { case (col, rawVal) =>
-        val string = if (rawVal == null) null else convertToHiveRawString(col, rawVal)
-        val colString =
-          if (string == null || string.isEmpty) {
-            defaultPartName
-          } else {
-            FileUtils.escapePathName(string, defaultPartName)
-          }
-        s"/$col=$colString"
-      }.mkString
+    val nonDynamicPartLen = row.numFields - dynamicPartColNames.length
+    val dynamicPartPath = dynamicPartColNames.zipWithIndex.map { case (colName, i) =>
+      val rawVal = row.get(nonDynamicPartLen + i, schema(colName).dataType)
+      val string = if (rawVal == null) null else convertToHiveRawString(colName, rawVal)
+      val colString =
+        if (string == null || string.isEmpty) {
+          defaultPartName
+        } else {
+          FileUtils.escapePathName(string, defaultPartName)
+        }
+      s"/$colName=$colString"
+    }.mkString
 
     def newWriter(): FileSinkOperator.RecordWriter = {
       val newFileSinkDesc = new FileSinkDesc(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 99e95fb921301..81a70b8d42267 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -133,8 +133,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     }
   }
 
-  def checkValues(row1: Seq[Any], row2: InternalRow): Unit = {
-    row1.zip(row2.toSeq).foreach { case (r1, r2) =>
+  def checkValues(row1: Seq[Any], row2: InternalRow, row2Schema: StructType): Unit = {
+    row1.zip(row2.toSeq(row2Schema)).foreach { case (r1, r2) =>
       checkValue(r1, r2)
     }
   }
@@ -211,8 +211,10 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       case (t, idx) => StructField(s"c_$idx", t)
     })
     val inspector = toInspector(dt)
-    checkValues(row,
-      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow])
+    checkValues(
+      row,
+      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow],
+      dt)
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
   }
 

From 2eca46a17a3d46a605804ff89c010017da91e1bc Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 6 Aug 2015 11:15:37 -0700
Subject: [PATCH 0889/1454] Revert "[SPARK-9632][SQL] update InternalRow.toSeq
 to make it accept data type info"

This reverts commit 6e009cb9c4d7a395991e10dab427f37019283758.
---
 .../spark/sql/catalyst/InternalRow.scala      | 132 ++++++++++++++++--
 .../sql/catalyst/expressions/Projection.scala |  12 +-
 .../expressions/SpecificMutableRow.scala      |   5 +-
 .../codegen/GenerateProjection.scala          |   8 +-
 .../spark/sql/catalyst/expressions/rows.scala | 132 +-----------------
 .../expressions/CodeGenerationSuite.scala     |   2 +-
 .../spark/sql/columnar/ColumnStats.scala      |  51 ++++---
 .../columnar/InMemoryColumnarTableScan.scala  |  11 +-
 .../spark/sql/execution/debug/package.scala   |   4 +-
 .../apache/spark/sql/sources/interfaces.scala |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  54 ++++---
 .../spark/sql/hive/HiveInspectors.scala       |   6 +-
 .../hive/execution/ScriptTransformation.scala |  21 +--
 .../spark/sql/hive/hiveWriterContainers.scala |  24 ++--
 .../spark/sql/hive/HiveInspectorSuite.scala   |  10 +-
 15 files changed, 217 insertions(+), 259 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 85b4bf3b6aef5..7d17cca808791 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -18,7 +18,8 @@
 package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{DataType, StructType}
+import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -31,6 +32,8 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
+  override def toString: String = mkString("[", ",", "]")
+
   /**
    * Make a copy of the current [[InternalRow]] object.
    */
@@ -47,25 +50,136 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
     false
   }
 
+  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
+  // or implement this genericGet.
+  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
+    "Concrete internal rows should implement genericGet, " +
+      "or implement all special getters and equals/hashCode")
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[InternalRow]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[InternalRow]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numFields
+    if (len != other.numFields) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numFields
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          genericGet(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
    * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
-    val len = numFields
-    assert(len == fieldTypes.length)
-
-    val values = new Array[Any](len)
+  // todo: remove this as it needs the generic getter
+  def toSeq: Seq[Any] = {
+    val n = numFields
+    val values = new Array[Any](n)
     var i = 0
-    while (i < len) {
-      values(i) = get(i, fieldTypes(i))
+    while (i < n) {
+      values.update(i, genericGet(i))
       i += 1
     }
     values
   }
 
-  def toSeq(schema: StructType): Seq[Any] = toSeq(schema.map(_.dataType))
+  /** Displays all elements of this sequence in a string (without a separator). */
+  def mkString: String = toSeq.mkString
+
+  /** Displays all elements of this sequence in a string using a separator string. */
+  def mkString(sep: String): String = toSeq.mkString(sep)
+
+  /**
+   * Displays all elements of this traversable or iterator in a string using
+   * start, end, and separator strings.
+   */
+  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 59ce7fc4f2c63..4296b4b123fc0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -203,11 +203,7 @@ class JoinedRow extends InternalRow {
     this
   }
 
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
-    assert(fieldTypes.length == row1.numFields + row2.numFields)
-    val (left, right) = fieldTypes.splitAt(row1.numFields)
-    row1.toSeq(left) ++ row2.toSeq(right)
-  }
+  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
   override def numFields: Int = row1.numFields + row2.numFields
 
@@ -280,11 +276,11 @@ class JoinedRow extends InternalRow {
     if ((row1 eq null) && (row2 eq null)) {
       "[ empty row ]"
     } else if (row1 eq null) {
-      row2.toString
+      row2.mkString("[", ",", "]")
     } else if (row2 eq null) {
-      row1.toString
+      row1.mkString("[", ",", "]")
     } else {
-      s"{${row1.toString} + ${row2.toString}}"
+      mkString("[", ",", "]")
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 4f56f94bd4ca4..b94df6bd66e04 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -192,8 +192,7 @@ final class MutableAny extends MutableValue {
  * based on the dataTypes of each column.  The intent is to decrease garbage when modifying the
  * values of primitive columns.
  */
-final class SpecificMutableRow(val values: Array[MutableValue])
-  extends MutableRow with BaseGenericInternalRow {
+final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableRow {
 
   def this(dataTypes: Seq[DataType]) =
     this(
@@ -214,6 +213,8 @@ final class SpecificMutableRow(val values: Array[MutableValue])
 
   override def numFields: Int = values.length
 
+  override def toSeq: Seq[Any] = values.map(_.boxed)
+
   override def setNullAt(i: Int): Unit = {
     values(i).isNull = true
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c744e84d822e8..c04fe734d554e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 
@@ -26,8 +25,6 @@ import org.apache.spark.sql.types._
  */
 abstract class BaseProjection extends Projection {}
 
-abstract class CodeGenMutableRow extends MutableRow with BaseGenericInternalRow
-
 /**
  * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input
  * [[Expression Expressions]] and a given input [[InternalRow]].  The returned [[InternalRow]]
@@ -174,7 +171,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         return new SpecificRow((InternalRow) r);
       }
 
-      final class SpecificRow extends ${classOf[CodeGenMutableRow].getName} {
+      final class SpecificRow extends ${classOf[MutableRow].getName} {
 
         $columns
 
@@ -187,8 +184,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        @Override
-        public Object genericGet(int i) {
+        protected Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 207e667792660..7657fb535dcf4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -21,130 +21,6 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
-/**
- * An extended version of [[InternalRow]] that implements all special getters, toString
- * and equals/hashCode by `genericGet`.
- */
-trait BaseGenericInternalRow extends InternalRow {
-
-  protected def genericGet(ordinal: Int): Any
-
-  // default implementation (slow)
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def toString(): String = {
-    if (numFields == 0) {
-      "[empty row]"
-    } else {
-      val sb = new StringBuilder
-      sb.append("[")
-      sb.append(genericGet(0))
-      val len = numFields
-      var i = 1
-      while (i < len) {
-        sb.append(",")
-        sb.append(genericGet(i))
-        i += 1
-      }
-      sb.append("]")
-      sb.toString()
-    }
-  }
-
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[BaseGenericInternalRow]) {
-      return false
-    }
-
-    val other = o.asInstanceOf[BaseGenericInternalRow]
-    if (other eq null) {
-      return false
-    }
-
-    val len = numFields
-    if (len != other.numFields) {
-      return false
-    }
-
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
-        o1 match {
-          case b1: Array[Byte] =>
-            if (!o2.isInstanceOf[Array[Byte]] ||
-              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-              return false
-            }
-          case f1: Float if java.lang.Float.isNaN(f1) =>
-            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
-              return false
-            }
-          case d1: Double if java.lang.Double.isNaN(d1) =>
-            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
-              return false
-            }
-          case _ => if (o1 != o2) {
-            return false
-          }
-        }
-      }
-      i += 1
-    }
-    true
-  }
-
-  // Custom hashCode function that matches the efficient code generated version.
-  override def hashCode: Int = {
-    var result: Int = 37
-    var i = 0
-    val len = numFields
-    while (i < len) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          genericGet(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case a: Array[Byte] => java.util.Arrays.hashCode(a)
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
-      i += 1
-    }
-    result
-  }
-}
-
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
  * Setting a value through a primitive function implicitly marks that column as not null.
@@ -206,7 +82,7 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGenericInternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -214,7 +90,7 @@ class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGeneri
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
+  override def toSeq: Seq[Any] = values
 
   override def numFields: Int = values.length
 
@@ -233,7 +109,7 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericInternalRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -241,7 +117,7 @@ class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericI
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
+  override def toSeq: Seq[Any] = values
 
   override def numFields: Int = values.length
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index e323467af5f4a..e310aee221666 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -87,7 +87,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     val length = 5000
     val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
     val plan = GenerateMutableProjection.generate(expressions)()
-    val actual = plan(new GenericMutableRow(length)).toSeq(expressions.map(_.dataType))
+    val actual = plan(new GenericMutableRow(length)).toSeq
     val expected = Seq.fill(length)(true)
 
     if (!checkResult(actual, expected)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 5cbd52bc0590e..af1a8ecca9b57 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, Attribute, AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -66,7 +66,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
    * Column statistics represented as a single row, currently including closed lower bound, closed
    * upper bound and null count.
    */
-  def collectedStatistics: GenericInternalRow
+  def collectedStatistics: InternalRow
 }
 
 /**
@@ -75,8 +75,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
 private[sql] class NoopColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = super.gatherStats(row, ordinal)
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](null, null, nullCount, count, 0L))
+  override def collectedStatistics: InternalRow = InternalRow(null, null, nullCount, count, 0L)
 }
 
 private[sql] class BooleanColumnStats extends ColumnStats {
@@ -93,8 +92,8 @@ private[sql] class BooleanColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class ByteColumnStats extends ColumnStats {
@@ -111,8 +110,8 @@ private[sql] class ByteColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class ShortColumnStats extends ColumnStats {
@@ -129,8 +128,8 @@ private[sql] class ShortColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class IntColumnStats extends ColumnStats {
@@ -147,8 +146,8 @@ private[sql] class IntColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class LongColumnStats extends ColumnStats {
@@ -165,8 +164,8 @@ private[sql] class LongColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class FloatColumnStats extends ColumnStats {
@@ -183,8 +182,8 @@ private[sql] class FloatColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class DoubleColumnStats extends ColumnStats {
@@ -201,8 +200,8 @@ private[sql] class DoubleColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class StringColumnStats extends ColumnStats {
@@ -219,8 +218,8 @@ private[sql] class StringColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class BinaryColumnStats extends ColumnStats {
@@ -231,8 +230,8 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(null, null, nullCount, count, sizeInBytes)
 }
 
 private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends ColumnStats {
@@ -249,8 +248,8 @@ private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends C
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
@@ -263,8 +262,8 @@ private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(null, null, nullCount, count, sizeInBytes)
 }
 
 private[sql] class DateColumnStats extends IntColumnStats
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index d553bb6169ecc..5d5b0697d7016 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -148,7 +148,7 @@ private[sql] case class InMemoryRelation(
           }
 
           val stats = InternalRow.fromSeq(columnBuilders.map(_.columnStats.collectedStatistics)
-                        .flatMap(_.values))
+                        .flatMap(_.toSeq))
 
           batchStats += stats
           CachedBatch(columnBuilders.map(_.build().array()), stats)
@@ -330,11 +330,10 @@ private[sql] case class InMemoryColumnarTableScan(
         if (inMemoryPartitionPruningEnabled) {
           cachedBatchIterator.filter { cachedBatch =>
             if (!partitionFilter(cachedBatch.stats)) {
-              def statsString: String = relation.partitionStatistics.schema.zipWithIndex.map {
-                case (a, i) =>
-                  val value = cachedBatch.stats.get(i, a.dataType)
-                  s"${a.name}: $value"
-              }.mkString(", ")
+              def statsString: String = relation.partitionStatistics.schema
+                .zip(cachedBatch.stats.toSeq)
+                .map { case (a, s) => s"${a.name}: $s" }
+                .mkString(", ")
               logInfo(s"Skipping partition based on stats $statsString")
               false
             } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index dd3858ea2b520..c37007f1eece7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -156,8 +156,8 @@ package object debug {
     def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
       case (null, _) =>
 
-      case (row: InternalRow, s: StructType) =>
-        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
+      case (row: InternalRow, StructType(fields)) =>
+        row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
       case (a: ArrayData, ArrayType(elemType, _)) =>
         a.foreach(elemType, (_, e) => {
           typeCheck(e, elemType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index c04557e5a0818..7126145ddc010 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -461,8 +461,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
             val spec = discoverPartitions()
             val partitionColumnTypes = spec.partitionColumns.map(_.dataType)
             val castedPartitions = spec.partitions.map { case p @ Partition(values, path) =>
-              val literals = partitionColumnTypes.zipWithIndex.map { case (dt, i) =>
-                Literal.create(values.get(i, dt), dt)
+              val literals = values.toSeq.zip(partitionColumnTypes).map {
+                case (value, dataType) => Literal.create(value, dataType)
               }
               val castedValues = partitionSchema.zip(literals).map { case (field, literal) =>
                 Cast(literal, field.dataType).eval()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index d0430d2a60e75..16e0187ed20a0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -19,36 +19,33 @@ package org.apache.spark.sql.columnar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
-  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, createRow(true, false, 0))
-  testColumnStats(classOf[ByteColumnStats], BYTE, createRow(Byte.MaxValue, Byte.MinValue, 0))
-  testColumnStats(classOf[ShortColumnStats], SHORT, createRow(Short.MaxValue, Short.MinValue, 0))
-  testColumnStats(classOf[IntColumnStats], INT, createRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[DateColumnStats], DATE, createRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[LongColumnStats], LONG, createRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, InternalRow(true, false, 0))
+  testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0))
+  testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0))
+  testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0))
   testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
-    createRow(Long.MaxValue, Long.MinValue, 0))
-  testColumnStats(classOf[FloatColumnStats], FLOAT, createRow(Float.MaxValue, Float.MinValue, 0))
+    InternalRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0))
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
-    createRow(Double.MaxValue, Double.MinValue, 0))
-  testColumnStats(classOf[StringColumnStats], STRING, createRow(null, null, 0))
-  testDecimalColumnStats(createRow(null, null, 0))
-
-  def createRow(values: Any*): GenericInternalRow = new GenericInternalRow(values.toArray)
+    InternalRow(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
+  testDecimalColumnStats(InternalRow(null, null, 0))
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
       columnType: NativeColumnType[T],
-      initialStatistics: GenericInternalRow): Unit = {
+      initialStatistics: InternalRow): Unit = {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
+      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -64,11 +61,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
-      assertResult(10, "Wrong null count")(stats.values(2))
-      assertResult(20, "Wrong row count")(stats.values(3))
-      assertResult(stats.values(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -76,15 +73,14 @@ class ColumnStatsSuite extends SparkFunSuite {
     }
   }
 
-  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](
-      initialStatistics: GenericInternalRow): Unit = {
+  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](initialStatistics: InternalRow) {
 
     val columnStatsName = classOf[FixedDecimalColumnStats].getSimpleName
     val columnType = FIXED_DECIMAL(15, 10)
 
     test(s"$columnStatsName: empty") {
       val columnStats = new FixedDecimalColumnStats(15, 10)
-      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
+      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -100,11 +96,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
-      assertResult(10, "Wrong null count")(stats.values(2))
-      assertResult(20, "Wrong row count")(stats.values(3))
-      assertResult(stats.values(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 9824dad239596..39d798d072aeb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -390,10 +390,8 @@ private[hive] trait HiveInspectors {
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
-          val row = o.asInstanceOf[InternalRow]
-          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
-            case ((field, wrapper), i) =>
-              soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
+          (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[InternalRow].toSeq).zipped.foreach {
+            (field, wrapper, data) => soi.setStructFieldData(struct, field, wrapper(data))
           }
           struct
         } else {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index ade27454b9d29..a6a343d395995 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -88,7 +88,6 @@ case class ScriptTransformation(
       // external process. That process's output will be read by this current thread.
       val writerThread = new ScriptTransformationWriterThread(
         inputIterator,
-        input.map(_.dataType),
         outputProjection,
         inputSerde,
         inputSoi,
@@ -202,7 +201,6 @@ case class ScriptTransformation(
 
 private class ScriptTransformationWriterThread(
     iter: Iterator[InternalRow],
-    inputSchema: Seq[DataType],
     outputProjection: Projection,
     @Nullable inputSerde: AbstractSerDe,
     @Nullable inputSoi: ObjectInspector,
@@ -228,25 +226,12 @@ private class ScriptTransformationWriterThread(
     // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
     // let's use a variable to record whether the `finally` block was hit due to an exception
     var threwException: Boolean = true
-    val len = inputSchema.length
     try {
       iter.map(outputProjection).foreach { row =>
         if (inputSerde == null) {
-          val data = if (len == 0) {
-            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
-          } else {
-            val sb = new StringBuilder
-            sb.append(row.get(0, inputSchema(0)))
-            var i = 1
-            while (i < len) {
-              sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
-              sb.append(row.get(i, inputSchema(i)))
-              i += 1
-            }
-            sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES"))
-            sb.toString()
-          }
-          outputStream.write(data.getBytes("utf-8"))
+          val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
+            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
+          outputStream.write(data)
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 8dc796b056a72..684ea1d137b49 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -211,18 +211,18 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
       }
     }
 
-    val nonDynamicPartLen = row.numFields - dynamicPartColNames.length
-    val dynamicPartPath = dynamicPartColNames.zipWithIndex.map { case (colName, i) =>
-      val rawVal = row.get(nonDynamicPartLen + i, schema(colName).dataType)
-      val string = if (rawVal == null) null else convertToHiveRawString(colName, rawVal)
-      val colString =
-        if (string == null || string.isEmpty) {
-          defaultPartName
-        } else {
-          FileUtils.escapePathName(string, defaultPartName)
-        }
-      s"/$colName=$colString"
-    }.mkString
+    val dynamicPartPath = dynamicPartColNames
+      .zip(row.toSeq.takeRight(dynamicPartColNames.length))
+      .map { case (col, rawVal) =>
+        val string = if (rawVal == null) null else convertToHiveRawString(col, rawVal)
+        val colString =
+          if (string == null || string.isEmpty) {
+            defaultPartName
+          } else {
+            FileUtils.escapePathName(string, defaultPartName)
+          }
+        s"/$col=$colString"
+      }.mkString
 
     def newWriter(): FileSinkOperator.RecordWriter = {
       val newFileSinkDesc = new FileSinkDesc(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 81a70b8d42267..99e95fb921301 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -133,8 +133,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     }
   }
 
-  def checkValues(row1: Seq[Any], row2: InternalRow, row2Schema: StructType): Unit = {
-    row1.zip(row2.toSeq(row2Schema)).foreach { case (r1, r2) =>
+  def checkValues(row1: Seq[Any], row2: InternalRow): Unit = {
+    row1.zip(row2.toSeq).foreach { case (r1, r2) =>
       checkValue(r1, r2)
     }
   }
@@ -211,10 +211,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       case (t, idx) => StructField(s"c_$idx", t)
     })
     val inspector = toInspector(dt)
-    checkValues(
-      row,
-      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow],
-      dt)
+    checkValues(row,
+      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow])
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
   }
 

From cdd53b762bf358616b313e3334b5f6945caf9ab1 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 6 Aug 2015 11:15:54 -0700
Subject: [PATCH 0890/1454] [SPARK-9632] [SQL] [HOT-FIX] Fix build.

seems https://github.com/apache/spark/pull/7955 breaks the build.

Author: Yin Huai <yhuai@databricks.com>

Closes #8001 from yhuai/SPARK-9632-fixBuild and squashes the following commits:

6c257dd [Yin Huai] Fix build.
---
 .../scala/org/apache/spark/sql/catalyst/expressions/rows.scala   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 7657fb535dcf4..fd42fac3d2cd4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.

From 0d7aac99da660cc42eb5a9be8e262bd9bd8a770f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 6 Aug 2015 19:29:42 +0100
Subject: [PATCH 0891/1454] [SPARK-9641] [DOCS] spark.shuffle.service.port is
 not documented

Document spark.shuffle.service.{enabled,port}

CC sryza tgravescs
This is pretty minimal; is there more to say here about the service?

Author: Sean Owen <sowen@cloudera.com>

Closes #7991 from srowen/SPARK-9641 and squashes the following commits:

3bb946e [Sean Owen] Add link to docs for setup and config of external shuffle service
2302e01 [Sean Owen] Document spark.shuffle.service.{enabled,port}
---
 docs/configuration.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/docs/configuration.md b/docs/configuration.md
index 24b606356a149..c60dd16839c02 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -473,6 +473,25 @@ Apart from these, the following properties are also available, and may be useful
     <code>spark.storage.memoryFraction</code>.
   </td>
 </tr>
+<tr>
+  <td><code>spark.shuffle.service.enabled</code></td>
+  <td>false</td>
+  <td>
+    Enables the external shuffle service. This service preserves the shuffle files written by 
+    executors so the executors can be safely removed. This must be enabled if 
+    <code>spark.dynamicAllocation.enabled</code> is "true". The external shuffle service
+    must be set up in order to enable it. See
+    <a href="job-scheduling.html#configuration-and-setup">dynamic allocation 
+    configuration and setup documentation</a> for more information.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.shuffle.service.port</code></td>
+  <td>7337</td>
+  <td>
+    Port on which the external shuffle service will run.
+  </td>
+</tr>
 <tr>
   <td><code>spark.shuffle.sort.bypassMergeThreshold</code></td>
   <td>200</td>

From a1bbf1bc5c51cd796015ac159799cf024de6fa07 Mon Sep 17 00:00:00 2001
From: Nilanjan Raychaudhuri <nraychaudhuri@gmail.com>
Date: Thu, 6 Aug 2015 12:50:08 -0700
Subject: [PATCH 0892/1454] [SPARK-8978] [STREAMING] Implements the
 DirectKafkaRateController
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Author: Dean Wampler <dean@concurrentthought.com>
Author: Nilanjan Raychaudhuri <nraychaudhuri@gmail.com>
Author: François Garillot <francois@garillot.net>

Closes #7796 from dragos/topic/streaming-bp/kafka-direct and squashes the following commits:

50d1f21 [Nilanjan Raychaudhuri] Taking care of the remaining nits
648c8b1 [Dean Wampler] Refactored rate controller test to be more predictable and run faster.
e43f678 [Nilanjan Raychaudhuri] fixing doc and nits
ce19d2a [Dean Wampler] Removing an unreliable assertion.
9615320 [Dean Wampler] Give me a break...
6372478 [Dean Wampler] Found a few ways to make this test more robust...
9e69e37 [Dean Wampler] Attempt to fix flakey test that fails in CI, but not locally :(
d3db1ea [Dean Wampler] Fixing stylecheck errors.
d04a288 [Nilanjan Raychaudhuri] adding test to make sure rate controller is used to calculate maxMessagesPerPartition
b6ecb67 [Nilanjan Raychaudhuri] Fixed styling issue
3110267 [Nilanjan Raychaudhuri] [SPARK-8978][Streaming] Implements the DirectKafkaRateController
393c580 [François Garillot] [SPARK-8978][Streaming] Implements the DirectKafkaRateController
51e78c6 [Nilanjan Raychaudhuri] Rename and fix build failure
2795509 [Nilanjan Raychaudhuri] Added missing RateController
19200f5 [Dean Wampler] Removed usage of infix notation. Changed a private variable name to be more consistent with usage.
aa4a70b [François Garillot] [SPARK-8978][Streaming] Implements the DirectKafkaController
---
 .../kafka/DirectKafkaInputDStream.scala       | 47 ++++++++--
 .../kafka/DirectKafkaStreamSuite.scala        | 89 +++++++++++++++++++
 2 files changed, 127 insertions(+), 9 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 48a1933d92f85..8a177077775c6 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -29,7 +29,8 @@ import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
-import org.apache.spark.streaming.scheduler.StreamInputInfo
+import org.apache.spark.streaming.scheduler.{RateController, StreamInputInfo}
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
 
 /**
  *  A stream of {@link org.apache.spark.streaming.kafka.KafkaRDD} where
@@ -61,7 +62,7 @@ class DirectKafkaInputDStream[
     val kafkaParams: Map[String, String],
     val fromOffsets: Map[TopicAndPartition, Long],
     messageHandler: MessageAndMetadata[K, V] => R
-) extends InputDStream[R](ssc_) with Logging {
+  ) extends InputDStream[R](ssc_) with Logging {
   val maxRetries = context.sparkContext.getConf.getInt(
     "spark.streaming.kafka.maxRetries", 1)
 
@@ -71,14 +72,35 @@ class DirectKafkaInputDStream[
   protected[streaming] override val checkpointData =
     new DirectKafkaInputDStreamCheckpointData
 
+
+  /**
+   * Asynchronously maintains & sends new rate limits to the receiver through the receiver tracker.
+   */
+  override protected[streaming] val rateController: Option[RateController] = {
+    if (RateController.isBackPressureEnabled(ssc.conf)) {
+      Some(new DirectKafkaRateController(id,
+        RateEstimator.create(ssc.conf, ssc_.graph.batchDuration)))
+    } else {
+      None
+    }
+  }
+
   protected val kc = new KafkaCluster(kafkaParams)
 
-  protected val maxMessagesPerPartition: Option[Long] = {
-    val ratePerSec = context.sparkContext.getConf.getInt(
+  private val maxRateLimitPerPartition: Int = context.sparkContext.getConf.getInt(
       "spark.streaming.kafka.maxRatePerPartition", 0)
-    if (ratePerSec > 0) {
+  protected def maxMessagesPerPartition: Option[Long] = {
+    val estimatedRateLimit = rateController.map(_.getLatestRate().toInt)
+    val numPartitions = currentOffsets.keys.size
+
+    val effectiveRateLimitPerPartition = estimatedRateLimit
+      .filter(_ > 0)
+      .map(limit => Math.min(maxRateLimitPerPartition, (limit / numPartitions)))
+      .getOrElse(maxRateLimitPerPartition)
+
+    if (effectiveRateLimitPerPartition > 0) {
       val secsPerBatch = context.graph.batchDuration.milliseconds.toDouble / 1000
-      Some((secsPerBatch * ratePerSec).toLong)
+      Some((secsPerBatch * effectiveRateLimitPerPartition).toLong)
     } else {
       None
     }
@@ -170,11 +192,18 @@ class DirectKafkaInputDStream[
       val leaders = KafkaCluster.checkErrors(kc.findLeaders(topics))
 
       batchForTime.toSeq.sortBy(_._1)(Time.ordering).foreach { case (t, b) =>
-          logInfo(s"Restoring KafkaRDD for time $t ${b.mkString("[", ", ", "]")}")
-          generatedRDDs += t -> new KafkaRDD[K, V, U, T, R](
-            context.sparkContext, kafkaParams, b.map(OffsetRange(_)), leaders, messageHandler)
+         logInfo(s"Restoring KafkaRDD for time $t ${b.mkString("[", ", ", "]")}")
+         generatedRDDs += t -> new KafkaRDD[K, V, U, T, R](
+           context.sparkContext, kafkaParams, b.map(OffsetRange(_)), leaders, messageHandler)
       }
     }
   }
 
+  /**
+   * A RateController to retrieve the rate from RateEstimator.
+   */
+  private[streaming] class DirectKafkaRateController(id: Int, estimator: RateEstimator)
+    extends RateController(id, estimator) {
+    override def publish(rate: Long): Unit = ()
+  }
 }
diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
index 5b3c79444aa68..02225d5aa7cc5 100644
--- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
+++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
@@ -20,6 +20,9 @@ package org.apache.spark.streaming.kafka
 import java.io.File
 import java.util.concurrent.atomic.AtomicLong
 
+import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
+
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
@@ -350,6 +353,77 @@ class DirectKafkaStreamSuite
     ssc.stop()
   }
 
+  test("using rate controller") {
+    val topic = "backpressure"
+    val topicPartition = TopicAndPartition(topic, 0)
+    kafkaTestUtils.createTopic(topic)
+    val kafkaParams = Map(
+      "metadata.broker.list" -> kafkaTestUtils.brokerAddress,
+      "auto.offset.reset" -> "smallest"
+    )
+
+    val batchIntervalMilliseconds = 100
+    val estimator = new ConstantEstimator(100)
+    val messageKeys = (1 to 200).map(_.toString)
+    val messages = messageKeys.map((_, 1)).toMap
+
+    val sparkConf = new SparkConf()
+      // Safe, even with streaming, because we're using the direct API.
+      // Using 1 core is useful to make the test more predictable.
+      .setMaster("local[1]")
+      .setAppName(this.getClass.getSimpleName)
+      .set("spark.streaming.kafka.maxRatePerPartition", "100")
+
+    // Setup the streaming context
+    ssc = new StreamingContext(sparkConf, Milliseconds(batchIntervalMilliseconds))
+
+    val kafkaStream = withClue("Error creating direct stream") {
+      val kc = new KafkaCluster(kafkaParams)
+      val messageHandler = (mmd: MessageAndMetadata[String, String]) => (mmd.key, mmd.message)
+      val m = kc.getEarliestLeaderOffsets(Set(topicPartition))
+        .fold(e => Map.empty[TopicAndPartition, Long], m => m.mapValues(lo => lo.offset))
+
+      new DirectKafkaInputDStream[String, String, StringDecoder, StringDecoder, (String, String)](
+        ssc, kafkaParams, m, messageHandler) {
+        override protected[streaming] val rateController =
+          Some(new DirectKafkaRateController(id, estimator))
+      }
+    }
+
+    val collectedData =
+      new mutable.ArrayBuffer[Array[String]]() with mutable.SynchronizedBuffer[Array[String]]
+
+    // Used for assertion failure messages.
+    def dataToString: String =
+      collectedData.map(_.mkString("[", ",", "]")).mkString("{", ", ", "}")
+
+    // This is to collect the raw data received from Kafka
+    kafkaStream.foreachRDD { (rdd: RDD[(String, String)], time: Time) =>
+      val data = rdd.map { _._2 }.collect()
+      collectedData += data
+    }
+
+    ssc.start()
+
+    // Try different rate limits.
+    // Send data to Kafka and wait for arrays of data to appear matching the rate.
+    Seq(100, 50, 20).foreach { rate =>
+      collectedData.clear()       // Empty this buffer on each pass.
+      estimator.updateRate(rate)  // Set a new rate.
+      // Expect blocks of data equal to "rate", scaled by the interval length in secs.
+      val expectedSize = Math.round(rate * batchIntervalMilliseconds * 0.001)
+      kafkaTestUtils.sendMessages(topic, messages)
+      eventually(timeout(5.seconds), interval(batchIntervalMilliseconds.milliseconds)) {
+        // Assert that rate estimator values are used to determine maxMessagesPerPartition.
+        // Funky "-" in message makes the complete assertion message read better.
+        assert(collectedData.exists(_.size == expectedSize),
+          s" - No arrays of size $expectedSize for rate $rate found in $dataToString")
+      }
+    }
+
+    ssc.stop()
+  }
+
   /** Get the generated offset ranges from the DirectKafkaStream */
   private def getOffsetRanges[K, V](
       kafkaStream: DStream[(K, V)]): Seq[(Time, Array[OffsetRange])] = {
@@ -381,3 +455,18 @@ object DirectKafkaStreamSuite {
     }
   }
 }
+
+private[streaming] class ConstantEstimator(@volatile private var rate: Long)
+  extends RateEstimator {
+
+  def updateRate(newRate: Long): Unit = {
+    rate = newRate
+  }
+
+  def compute(
+      time: Long,
+      elements: Long,
+      processingDelay: Long,
+      schedulingDelay: Long): Option[Double] = Some(rate)
+}
+

From 1f62f104c7a2aeac625b17d9e5ac62f1f10a2b21 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 6 Aug 2015 13:11:59 -0700
Subject: [PATCH 0893/1454] [SPARK-9632][SQL] update InternalRow.toSeq to make
 it accept data type info

This re-applies #7955, which was reverted due to a race condition to fix build breaking.

Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #8002 from rxin/InternalRow-toSeq and squashes the following commits:

332416a [Reynold Xin] Merge pull request #7955 from cloud-fan/toSeq
21665e2 [Wenchen Fan] fix hive again...
4addf29 [Wenchen Fan] fix hive
bc16c59 [Wenchen Fan] minor fix
33d802c [Wenchen Fan] pass data type info to InternalRow.toSeq
3dd033e [Wenchen Fan] move the default special getters implementation from InternalRow to BaseGenericInternalRow
---
 .../spark/sql/catalyst/InternalRow.scala      | 132 ++----------------
 .../sql/catalyst/expressions/Projection.scala |  12 +-
 .../expressions/SpecificMutableRow.scala      |   5 +-
 .../codegen/GenerateProjection.scala          |   8 +-
 .../spark/sql/catalyst/expressions/rows.scala | 132 +++++++++++++++++-
 .../expressions/CodeGenerationSuite.scala     |   2 +-
 .../spark/sql/columnar/ColumnStats.scala      |  51 +++----
 .../columnar/InMemoryColumnarTableScan.scala  |  11 +-
 .../spark/sql/execution/debug/package.scala   |   4 +-
 .../apache/spark/sql/sources/interfaces.scala |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  54 +++----
 .../spark/sql/hive/HiveInspectors.scala       |   6 +-
 .../hive/execution/ScriptTransformation.scala |  21 ++-
 .../spark/sql/hive/hiveWriterContainers.scala |  24 ++--
 .../spark/sql/hive/HiveInspectorSuite.scala   |  10 +-
 15 files changed, 259 insertions(+), 217 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 7d17cca808791..85b4bf3b6aef5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -32,8 +31,6 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
-  override def toString: String = mkString("[", ",", "]")
-
   /**
    * Make a copy of the current [[InternalRow]] object.
    */
@@ -50,136 +47,25 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
     false
   }
 
-  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
-  // or implement this genericGet.
-  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
-    "Concrete internal rows should implement genericGet, " +
-      "or implement all special getters and equals/hashCode")
-
-  // default implementation (slow)
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[InternalRow]) {
-      return false
-    }
-
-    val other = o.asInstanceOf[InternalRow]
-    if (other eq null) {
-      return false
-    }
-
-    val len = numFields
-    if (len != other.numFields) {
-      return false
-    }
-
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
-        o1 match {
-          case b1: Array[Byte] =>
-            if (!o2.isInstanceOf[Array[Byte]] ||
-              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-              return false
-            }
-          case f1: Float if java.lang.Float.isNaN(f1) =>
-            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
-              return false
-            }
-          case d1: Double if java.lang.Double.isNaN(d1) =>
-            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
-              return false
-            }
-          case _ => if (o1 != o2) {
-            return false
-          }
-        }
-      }
-      i += 1
-    }
-    true
-  }
-
-  // Custom hashCode function that matches the efficient code generated version.
-  override def hashCode: Int = {
-    var result: Int = 37
-    var i = 0
-    val len = numFields
-    while (i < len) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          genericGet(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case a: Array[Byte] => java.util.Arrays.hashCode(a)
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
-      i += 1
-    }
-    result
-  }
-
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
    * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  // todo: remove this as it needs the generic getter
-  def toSeq: Seq[Any] = {
-    val n = numFields
-    val values = new Array[Any](n)
+  def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    val len = numFields
+    assert(len == fieldTypes.length)
+
+    val values = new Array[Any](len)
     var i = 0
-    while (i < n) {
-      values.update(i, genericGet(i))
+    while (i < len) {
+      values(i) = get(i, fieldTypes(i))
       i += 1
     }
     values
   }
 
-  /** Displays all elements of this sequence in a string (without a separator). */
-  def mkString: String = toSeq.mkString
-
-  /** Displays all elements of this sequence in a string using a separator string. */
-  def mkString(sep: String): String = toSeq.mkString(sep)
-
-  /**
-   * Displays all elements of this traversable or iterator in a string using
-   * start, end, and separator strings.
-   */
-  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
+  def toSeq(schema: StructType): Seq[Any] = toSeq(schema.map(_.dataType))
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 4296b4b123fc0..59ce7fc4f2c63 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -203,7 +203,11 @@ class JoinedRow extends InternalRow {
     this
   }
 
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    assert(fieldTypes.length == row1.numFields + row2.numFields)
+    val (left, right) = fieldTypes.splitAt(row1.numFields)
+    row1.toSeq(left) ++ row2.toSeq(right)
+  }
 
   override def numFields: Int = row1.numFields + row2.numFields
 
@@ -276,11 +280,11 @@ class JoinedRow extends InternalRow {
     if ((row1 eq null) && (row2 eq null)) {
       "[ empty row ]"
     } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
+      row2.toString
     } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
+      row1.toString
     } else {
-      mkString("[", ",", "]")
+      s"{${row1.toString} + ${row2.toString}}"
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index b94df6bd66e04..4f56f94bd4ca4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -192,7 +192,8 @@ final class MutableAny extends MutableValue {
  * based on the dataTypes of each column.  The intent is to decrease garbage when modifying the
  * values of primitive columns.
  */
-final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableRow {
+final class SpecificMutableRow(val values: Array[MutableValue])
+  extends MutableRow with BaseGenericInternalRow {
 
   def this(dataTypes: Seq[DataType]) =
     this(
@@ -213,8 +214,6 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
 
   override def numFields: Int = values.length
 
-  override def toSeq: Seq[Any] = values.map(_.boxed)
-
   override def setNullAt(i: Int): Unit = {
     values(i).isNull = true
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c04fe734d554e..c744e84d822e8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 
@@ -25,6 +26,8 @@ import org.apache.spark.sql.types._
  */
 abstract class BaseProjection extends Projection {}
 
+abstract class CodeGenMutableRow extends MutableRow with BaseGenericInternalRow
+
 /**
  * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input
  * [[Expression Expressions]] and a given input [[InternalRow]].  The returned [[InternalRow]]
@@ -171,7 +174,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         return new SpecificRow((InternalRow) r);
       }
 
-      final class SpecificRow extends ${classOf[MutableRow].getName} {
+      final class SpecificRow extends ${classOf[CodeGenMutableRow].getName} {
 
         $columns
 
@@ -184,7 +187,8 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        protected Object genericGet(int i) {
+        @Override
+        public Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index fd42fac3d2cd4..11d10b2d8a48b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -22,6 +22,130 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
+/**
+ * An extended version of [[InternalRow]] that implements all special getters, toString
+ * and equals/hashCode by `genericGet`.
+ */
+trait BaseGenericInternalRow extends InternalRow {
+
+  protected def genericGet(ordinal: Int): Any
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def toString(): String = {
+    if (numFields == 0) {
+      "[empty row]"
+    } else {
+      val sb = new StringBuilder
+      sb.append("[")
+      sb.append(genericGet(0))
+      val len = numFields
+      var i = 1
+      while (i < len) {
+        sb.append(",")
+        sb.append(genericGet(i))
+        i += 1
+      }
+      sb.append("]")
+      sb.toString()
+    }
+  }
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[BaseGenericInternalRow]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[BaseGenericInternalRow]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numFields
+    if (len != other.numFields) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numFields
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          genericGet(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+}
+
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
  * Setting a value through a primitive function implicitly marks that column as not null.
@@ -83,7 +207,7 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -91,7 +215,7 @@ class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRo
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
@@ -110,7 +234,7 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(values: Array[Any]) extends MutableRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -118,7 +242,7 @@ class GenericMutableRow(values: Array[Any]) extends MutableRow {
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index e310aee221666..e323467af5f4a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -87,7 +87,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     val length = 5000
     val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
     val plan = GenerateMutableProjection.generate(expressions)()
-    val actual = plan(new GenericMutableRow(length)).toSeq
+    val actual = plan(new GenericMutableRow(length)).toSeq(expressions.map(_.dataType))
     val expected = Seq.fill(length)(true)
 
     if (!checkResult(actual, expected)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index af1a8ecca9b57..5cbd52bc0590e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, Attribute, AttributeMap, AttributeReference}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -66,7 +66,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
    * Column statistics represented as a single row, currently including closed lower bound, closed
    * upper bound and null count.
    */
-  def collectedStatistics: InternalRow
+  def collectedStatistics: GenericInternalRow
 }
 
 /**
@@ -75,7 +75,8 @@ private[sql] sealed trait ColumnStats extends Serializable {
 private[sql] class NoopColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = super.gatherStats(row, ordinal)
 
-  override def collectedStatistics: InternalRow = InternalRow(null, null, nullCount, count, 0L)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, 0L))
 }
 
 private[sql] class BooleanColumnStats extends ColumnStats {
@@ -92,8 +93,8 @@ private[sql] class BooleanColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ByteColumnStats extends ColumnStats {
@@ -110,8 +111,8 @@ private[sql] class ByteColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ShortColumnStats extends ColumnStats {
@@ -128,8 +129,8 @@ private[sql] class ShortColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class IntColumnStats extends ColumnStats {
@@ -146,8 +147,8 @@ private[sql] class IntColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class LongColumnStats extends ColumnStats {
@@ -164,8 +165,8 @@ private[sql] class LongColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FloatColumnStats extends ColumnStats {
@@ -182,8 +183,8 @@ private[sql] class FloatColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DoubleColumnStats extends ColumnStats {
@@ -200,8 +201,8 @@ private[sql] class DoubleColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class StringColumnStats extends ColumnStats {
@@ -218,8 +219,8 @@ private[sql] class StringColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class BinaryColumnStats extends ColumnStats {
@@ -230,8 +231,8 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends ColumnStats {
@@ -248,8 +249,8 @@ private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends C
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
@@ -262,8 +263,8 @@ private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DateColumnStats extends IntColumnStats
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 5d5b0697d7016..d553bb6169ecc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -148,7 +148,7 @@ private[sql] case class InMemoryRelation(
           }
 
           val stats = InternalRow.fromSeq(columnBuilders.map(_.columnStats.collectedStatistics)
-                        .flatMap(_.toSeq))
+                        .flatMap(_.values))
 
           batchStats += stats
           CachedBatch(columnBuilders.map(_.build().array()), stats)
@@ -330,10 +330,11 @@ private[sql] case class InMemoryColumnarTableScan(
         if (inMemoryPartitionPruningEnabled) {
           cachedBatchIterator.filter { cachedBatch =>
             if (!partitionFilter(cachedBatch.stats)) {
-              def statsString: String = relation.partitionStatistics.schema
-                .zip(cachedBatch.stats.toSeq)
-                .map { case (a, s) => s"${a.name}: $s" }
-                .mkString(", ")
+              def statsString: String = relation.partitionStatistics.schema.zipWithIndex.map {
+                case (a, i) =>
+                  val value = cachedBatch.stats.get(i, a.dataType)
+                  s"${a.name}: $value"
+              }.mkString(", ")
               logInfo(s"Skipping partition based on stats $statsString")
               false
             } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index c37007f1eece7..dd3858ea2b520 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -156,8 +156,8 @@ package object debug {
     def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
       case (null, _) =>
 
-      case (row: InternalRow, StructType(fields)) =>
-        row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
+      case (row: InternalRow, s: StructType) =>
+        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
       case (a: ArrayData, ArrayType(elemType, _)) =>
         a.foreach(elemType, (_, e) => {
           typeCheck(e, elemType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 7126145ddc010..c04557e5a0818 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -461,8 +461,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
             val spec = discoverPartitions()
             val partitionColumnTypes = spec.partitionColumns.map(_.dataType)
             val castedPartitions = spec.partitions.map { case p @ Partition(values, path) =>
-              val literals = values.toSeq.zip(partitionColumnTypes).map {
-                case (value, dataType) => Literal.create(value, dataType)
+              val literals = partitionColumnTypes.zipWithIndex.map { case (dt, i) =>
+                Literal.create(values.get(i, dt), dt)
               }
               val castedValues = partitionSchema.zip(literals).map { case (field, literal) =>
                 Cast(literal, field.dataType).eval()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 16e0187ed20a0..d0430d2a60e75 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -19,33 +19,36 @@ package org.apache.spark.sql.columnar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
-  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, InternalRow(true, false, 0))
-  testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0))
-  testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0))
-  testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, createRow(true, false, 0))
+  testColumnStats(classOf[ByteColumnStats], BYTE, createRow(Byte.MaxValue, Byte.MinValue, 0))
+  testColumnStats(classOf[ShortColumnStats], SHORT, createRow(Short.MaxValue, Short.MinValue, 0))
+  testColumnStats(classOf[IntColumnStats], INT, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[DateColumnStats], DATE, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[LongColumnStats], LONG, createRow(Long.MaxValue, Long.MinValue, 0))
   testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
-    InternalRow(Long.MaxValue, Long.MinValue, 0))
-  testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0))
+    createRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[FloatColumnStats], FLOAT, createRow(Float.MaxValue, Float.MinValue, 0))
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
-    InternalRow(Double.MaxValue, Double.MinValue, 0))
-  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
-  testDecimalColumnStats(InternalRow(null, null, 0))
+    createRow(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, createRow(null, null, 0))
+  testDecimalColumnStats(createRow(null, null, 0))
+
+  def createRow(values: Any*): GenericInternalRow = new GenericInternalRow(values.toArray)
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
       columnType: NativeColumnType[T],
-      initialStatistics: InternalRow): Unit = {
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -61,11 +64,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -73,14 +76,15 @@ class ColumnStatsSuite extends SparkFunSuite {
     }
   }
 
-  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](initialStatistics: InternalRow) {
+  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = classOf[FixedDecimalColumnStats].getSimpleName
     val columnType = FIXED_DECIMAL(15, 10)
 
     test(s"$columnStatsName: empty") {
       val columnStats = new FixedDecimalColumnStats(15, 10)
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -96,11 +100,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 39d798d072aeb..9824dad239596 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -390,8 +390,10 @@ private[hive] trait HiveInspectors {
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
-          (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[InternalRow].toSeq).zipped.foreach {
-            (field, wrapper, data) => soi.setStructFieldData(struct, field, wrapper(data))
+          val row = o.asInstanceOf[InternalRow]
+          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
+            case ((field, wrapper), i) =>
+              soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
           }
           struct
         } else {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index a6a343d395995..ade27454b9d29 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -88,6 +88,7 @@ case class ScriptTransformation(
       // external process. That process's output will be read by this current thread.
       val writerThread = new ScriptTransformationWriterThread(
         inputIterator,
+        input.map(_.dataType),
         outputProjection,
         inputSerde,
         inputSoi,
@@ -201,6 +202,7 @@ case class ScriptTransformation(
 
 private class ScriptTransformationWriterThread(
     iter: Iterator[InternalRow],
+    inputSchema: Seq[DataType],
     outputProjection: Projection,
     @Nullable inputSerde: AbstractSerDe,
     @Nullable inputSoi: ObjectInspector,
@@ -226,12 +228,25 @@ private class ScriptTransformationWriterThread(
     // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
     // let's use a variable to record whether the `finally` block was hit due to an exception
     var threwException: Boolean = true
+    val len = inputSchema.length
     try {
       iter.map(outputProjection).foreach { row =>
         if (inputSerde == null) {
-          val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
-            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
-          outputStream.write(data)
+          val data = if (len == 0) {
+            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+          } else {
+            val sb = new StringBuilder
+            sb.append(row.get(0, inputSchema(0)))
+            var i = 1
+            while (i < len) {
+              sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+              sb.append(row.get(i, inputSchema(i)))
+              i += 1
+            }
+            sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES"))
+            sb.toString()
+          }
+          outputStream.write(data.getBytes("utf-8"))
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 684ea1d137b49..8dc796b056a72 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -211,18 +211,18 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
       }
     }
 
-    val dynamicPartPath = dynamicPartColNames
-      .zip(row.toSeq.takeRight(dynamicPartColNames.length))
-      .map { case (col, rawVal) =>
-        val string = if (rawVal == null) null else convertToHiveRawString(col, rawVal)
-        val colString =
-          if (string == null || string.isEmpty) {
-            defaultPartName
-          } else {
-            FileUtils.escapePathName(string, defaultPartName)
-          }
-        s"/$col=$colString"
-      }.mkString
+    val nonDynamicPartLen = row.numFields - dynamicPartColNames.length
+    val dynamicPartPath = dynamicPartColNames.zipWithIndex.map { case (colName, i) =>
+      val rawVal = row.get(nonDynamicPartLen + i, schema(colName).dataType)
+      val string = if (rawVal == null) null else convertToHiveRawString(colName, rawVal)
+      val colString =
+        if (string == null || string.isEmpty) {
+          defaultPartName
+        } else {
+          FileUtils.escapePathName(string, defaultPartName)
+        }
+      s"/$colName=$colString"
+    }.mkString
 
     def newWriter(): FileSinkOperator.RecordWriter = {
       val newFileSinkDesc = new FileSinkDesc(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 99e95fb921301..81a70b8d42267 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -133,8 +133,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     }
   }
 
-  def checkValues(row1: Seq[Any], row2: InternalRow): Unit = {
-    row1.zip(row2.toSeq).foreach { case (r1, r2) =>
+  def checkValues(row1: Seq[Any], row2: InternalRow, row2Schema: StructType): Unit = {
+    row1.zip(row2.toSeq(row2Schema)).foreach { case (r1, r2) =>
       checkValue(r1, r2)
     }
   }
@@ -211,8 +211,10 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       case (t, idx) => StructField(s"c_$idx", t)
     })
     val inspector = toInspector(dt)
-    checkValues(row,
-      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow])
+    checkValues(
+      row,
+      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow],
+      dt)
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
   }
 

From 54c0789a05a783ce90e0e9848079be442a82966b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 6 Aug 2015 13:29:31 -0700
Subject: [PATCH 0894/1454] [SPARK-9493] [ML] add featureIndex to handle vector
 features in IsotonicRegression

This PR contains the following changes:
* add `featureIndex` to handle vector features (in order to chain isotonic regression easily with output from logistic regression
* make getter/setter names consistent with params
* remove inheritance from Regressor because it is tricky to handle both `DoubleType` and `VectorType`
* simplify test data generation

jkbradley zapletal-martin

Author: Xiangrui Meng <meng@databricks.com>

Closes #7952 from mengxr/SPARK-9493 and squashes the following commits:

8818ac3 [Xiangrui Meng] address comments
05e2216 [Xiangrui Meng] address comments
8d08090 [Xiangrui Meng] add featureIndex to handle vector features make getter/setter names consistent with params remove inheritance from Regressor
---
 .../ml/regression/IsotonicRegression.scala    | 202 +++++++++++++-----
 .../regression/IsotonicRegressionSuite.scala  |  82 ++++---
 2 files changed, 194 insertions(+), 90 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index 4ece8cf8cf0b6..f570590960a62 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -17,44 +17,113 @@
 
 package org.apache.spark.ml.regression
 
+import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.PredictorParams
-import org.apache.spark.ml.param.{Param, ParamMap, BooleanParam}
-import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
-import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression}
-import org.apache.spark.mllib.regression.{IsotonicRegressionModel => MLlibIsotonicRegressionModel}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
+import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression, IsotonicRegressionModel => MLlibIsotonicRegressionModel}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{DoubleType, DataType}
-import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.{col, lit, udf}
+import org.apache.spark.sql.types.{DoubleType, StructType}
 import org.apache.spark.storage.StorageLevel
 
 /**
  * Params for isotonic regression.
  */
-private[regression] trait IsotonicRegressionParams extends PredictorParams {
+private[regression] trait IsotonicRegressionBase extends Params with HasFeaturesCol
+  with HasLabelCol with HasPredictionCol with Logging {
 
   /**
-   * Param for weight column name.
-   * TODO: Move weightCol to sharedParams.
-   *
+   * Param for weight column name (default: none).
    * @group param
    */
+  // TODO: Move weightCol to sharedParams.
   final val weightCol: Param[String] =
-    new Param[String](this, "weightCol", "weight column name")
+    new Param[String](this, "weightCol",
+      "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
 
   /** @group getParam */
   final def getWeightCol: String = $(weightCol)
 
   /**
-   * Param for isotonic parameter.
-   * Isotonic (increasing) or antitonic (decreasing) sequence.
+   * Param for whether the output sequence should be isotonic/increasing (true) or
+   * antitonic/decreasing (false).
    * @group param
    */
   final val isotonic: BooleanParam =
-    new BooleanParam(this, "isotonic", "isotonic (increasing) or antitonic (decreasing) sequence")
+    new BooleanParam(this, "isotonic",
+      "whether the output sequence should be isotonic/increasing (true) or" +
+        "antitonic/decreasing (false)")
 
   /** @group getParam */
-  final def getIsotonicParam: Boolean = $(isotonic)
+  final def getIsotonic: Boolean = $(isotonic)
+
+  /**
+   * Param for the index of the feature if [[featuresCol]] is a vector column (default: `0`), no
+   * effect otherwise.
+   * @group param
+   */
+  final val featureIndex: IntParam = new IntParam(this, "featureIndex",
+    "The index of the feature if featuresCol is a vector column, no effect otherwise.")
+
+  /** @group getParam */
+  final def getFeatureIndex: Int = $(featureIndex)
+
+  setDefault(isotonic -> true, featureIndex -> 0)
+
+  /** Checks whether the input has weight column. */
+  protected[ml] def hasWeightCol: Boolean = {
+    isDefined(weightCol) && $(weightCol) != ""
+  }
+
+  /**
+   * Extracts (label, feature, weight) from input dataset.
+   */
+  protected[ml] def extractWeightedLabeledPoints(
+      dataset: DataFrame): RDD[(Double, Double, Double)] = {
+    val f = if (dataset.schema($(featuresCol)).dataType.isInstanceOf[VectorUDT]) {
+      val idx = $(featureIndex)
+      val extract = udf { v: Vector => v(idx) }
+      extract(col($(featuresCol)))
+    } else {
+      col($(featuresCol))
+    }
+    val w = if (hasWeightCol) {
+      col($(weightCol))
+    } else {
+      lit(1.0)
+    }
+    dataset.select(col($(labelCol)), f, w)
+      .map { case Row(label: Double, feature: Double, weights: Double) =>
+      (label, feature, weights)
+    }
+  }
+
+  /**
+   * Validates and transforms input schema.
+   * @param schema input schema
+   * @param fitting whether this is in fitting or prediction
+   * @return output schema
+   */
+  protected[ml] def validateAndTransformSchema(
+      schema: StructType,
+      fitting: Boolean): StructType = {
+    if (fitting) {
+      SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
+      if (hasWeightCol) {
+        SchemaUtils.checkColumnType(schema, $(weightCol), DoubleType)
+      } else {
+        logInfo("The weight column is not defined. Treat all instance weights as 1.0.")
+      }
+    }
+    val featuresType = schema($(featuresCol)).dataType
+    require(featuresType == DoubleType || featuresType.isInstanceOf[VectorUDT])
+    SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType)
+  }
 }
 
 /**
@@ -67,52 +136,46 @@ private[regression] trait IsotonicRegressionParams extends PredictorParams {
  * Uses [[org.apache.spark.mllib.regression.IsotonicRegression]].
  */
 @Experimental
-class IsotonicRegression(override val uid: String)
-  extends Regressor[Double, IsotonicRegression, IsotonicRegressionModel]
-  with IsotonicRegressionParams {
+class IsotonicRegression(override val uid: String) extends Estimator[IsotonicRegressionModel]
+  with IsotonicRegressionBase {
 
   def this() = this(Identifiable.randomUID("isoReg"))
 
-  /**
-   * Set the isotonic parameter.
-   * Default is true.
-   * @group setParam
-   */
-  def setIsotonicParam(value: Boolean): this.type = set(isotonic, value)
-  setDefault(isotonic -> true)
+  /** @group setParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
 
-  /**
-   * Set weight column param.
-   * Default is weight.
-   * @group setParam
-   */
-  def setWeightParam(value: String): this.type = set(weightCol, value)
-  setDefault(weightCol -> "weight")
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
-  override private[ml] def featuresDataType: DataType = DoubleType
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
-  override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra)
+  /** @group setParam */
+  def setIsotonic(value: Boolean): this.type = set(isotonic, value)
 
-  private[this] def extractWeightedLabeledPoints(
-      dataset: DataFrame): RDD[(Double, Double, Double)] = {
+  /** @group setParam */
+  def setWeightCol(value: String): this.type = set(weightCol, value)
 
-    dataset.select($(labelCol), $(featuresCol), $(weightCol))
-      .map { case Row(label: Double, features: Double, weights: Double) =>
-        (label, features, weights)
-      }
-  }
+  /** @group setParam */
+  def setFeatureIndex(value: Int): this.type = set(featureIndex, value)
 
-  override protected def train(dataset: DataFrame): IsotonicRegressionModel = {
-    SchemaUtils.checkColumnType(dataset.schema, $(weightCol), DoubleType)
+  override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra)
+
+  override def fit(dataset: DataFrame): IsotonicRegressionModel = {
+    validateAndTransformSchema(dataset.schema, fitting = true)
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
     val instances = extractWeightedLabeledPoints(dataset)
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
     val isotonicRegression = new MLlibIsotonicRegression().setIsotonic($(isotonic))
-    val parentModel = isotonicRegression.run(instances)
+    val oldModel = isotonicRegression.run(instances)
 
-    new IsotonicRegressionModel(uid, parentModel)
+    copyValues(new IsotonicRegressionModel(uid, oldModel).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema, fitting = true)
   }
 }
 
@@ -123,22 +186,49 @@ class IsotonicRegression(override val uid: String)
  *
  * For detailed rules see [[org.apache.spark.mllib.regression.IsotonicRegressionModel.predict()]].
  *
- * @param parentModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]]
- *                    model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]].
+ * @param oldModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]]
+ *                 model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]].
  */
+@Experimental
 class IsotonicRegressionModel private[ml] (
     override val uid: String,
-    private[ml] val parentModel: MLlibIsotonicRegressionModel)
-  extends RegressionModel[Double, IsotonicRegressionModel]
-  with IsotonicRegressionParams {
+    private val oldModel: MLlibIsotonicRegressionModel)
+  extends Model[IsotonicRegressionModel] with IsotonicRegressionBase {
 
-  override def featuresDataType: DataType = DoubleType
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
-  override protected def predict(features: Double): Double = {
-    parentModel.predict(features)
-  }
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
+
+  /** @group setParam */
+  def setFeatureIndex(value: Int): this.type = set(featureIndex, value)
+
+  /** Boundaries in increasing order for which predictions are known. */
+  def boundaries: Vector = Vectors.dense(oldModel.boundaries)
+
+  /**
+   * Predictions associated with the boundaries at the same index, monotone because of isotonic
+   * regression.
+   */
+  def predictions: Vector = Vectors.dense(oldModel.predictions)
 
   override def copy(extra: ParamMap): IsotonicRegressionModel = {
-    copyValues(new IsotonicRegressionModel(uid, parentModel), extra)
+    copyValues(new IsotonicRegressionModel(uid, oldModel), extra)
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val predict = dataset.schema($(featuresCol)).dataType match {
+      case DoubleType =>
+        udf { feature: Double => oldModel.predict(feature) }
+      case _: VectorUDT =>
+        val idx = $(featureIndex)
+        udf { features: Vector => oldModel.predict(features(idx)) }
+    }
+    dataset.withColumn($(predictionCol), predict(col($(featuresCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema, fitting = false)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
index 66e4b170bae80..c0ab00b68a2f3 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
@@ -19,57 +19,46 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row}
 
 class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
-  private val schema = StructType(
-    Array(
-      StructField("label", DoubleType),
-      StructField("features", DoubleType),
-      StructField("weight", DoubleType)))
-
-  private val predictionSchema = StructType(Array(StructField("features", DoubleType)))
-
   private def generateIsotonicInput(labels: Seq[Double]): DataFrame = {
-    val data = Seq.tabulate(labels.size)(i => Row(labels(i), i.toDouble, 1d))
-    val parallelData = sc.parallelize(data)
-
-    sqlContext.createDataFrame(parallelData, schema)
+    sqlContext.createDataFrame(
+      labels.zipWithIndex.map { case (label, i) => (label, i.toDouble, 1.0) }
+    ).toDF("label", "features", "weight")
   }
 
   private def generatePredictionInput(features: Seq[Double]): DataFrame = {
-    val data = Seq.tabulate(features.size)(i => Row(features(i)))
-
-    val parallelData = sc.parallelize(data)
-    sqlContext.createDataFrame(parallelData, predictionSchema)
+    sqlContext.createDataFrame(features.map(Tuple1.apply))
+      .toDF("features")
   }
 
   test("isotonic regression predictions") {
     val dataset = generateIsotonicInput(Seq(1, 2, 3, 1, 6, 17, 16, 17, 18))
-    val trainer = new IsotonicRegression().setIsotonicParam(true)
+    val ir = new IsotonicRegression().setIsotonic(true)
 
-    val model = trainer.fit(dataset)
+    val model = ir.fit(dataset)
 
     val predictions = model
       .transform(dataset)
-      .select("prediction").map {
-        case Row(pred) => pred
+      .select("prediction").map { case Row(pred) =>
+        pred
       }.collect()
 
     assert(predictions === Array(1, 2, 2, 2, 6, 16.5, 16.5, 17, 18))
 
-    assert(model.parentModel.boundaries === Array(0, 1, 3, 4, 5, 6, 7, 8))
-    assert(model.parentModel.predictions === Array(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0))
-    assert(model.parentModel.isotonic)
+    assert(model.boundaries === Vectors.dense(0, 1, 3, 4, 5, 6, 7, 8))
+    assert(model.predictions === Vectors.dense(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0))
+    assert(model.getIsotonic)
   }
 
   test("antitonic regression predictions") {
     val dataset = generateIsotonicInput(Seq(7, 5, 3, 5, 1))
-    val trainer = new IsotonicRegression().setIsotonicParam(false)
+    val ir = new IsotonicRegression().setIsotonic(false)
 
-    val model = trainer.fit(dataset)
+    val model = ir.fit(dataset)
     val features = generatePredictionInput(Seq(-2.0, -1.0, 0.5, 0.75, 1.0, 2.0, 9.0))
 
     val predictions = model
@@ -94,9 +83,10 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val ir = new IsotonicRegression()
     assert(ir.getLabelCol === "label")
     assert(ir.getFeaturesCol === "features")
-    assert(ir.getWeightCol === "weight")
     assert(ir.getPredictionCol === "prediction")
-    assert(ir.getIsotonicParam === true)
+    assert(!ir.isDefined(ir.weightCol))
+    assert(ir.getIsotonic)
+    assert(ir.getFeatureIndex === 0)
 
     val model = ir.fit(dataset)
     model.transform(dataset)
@@ -105,21 +95,22 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     assert(model.getLabelCol === "label")
     assert(model.getFeaturesCol === "features")
-    assert(model.getWeightCol === "weight")
     assert(model.getPredictionCol === "prediction")
-    assert(model.getIsotonicParam === true)
+    assert(!model.isDefined(model.weightCol))
+    assert(model.getIsotonic)
+    assert(model.getFeatureIndex === 0)
     assert(model.hasParent)
   }
 
   test("set parameters") {
     val isotonicRegression = new IsotonicRegression()
-      .setIsotonicParam(false)
-      .setWeightParam("w")
+      .setIsotonic(false)
+      .setWeightCol("w")
       .setFeaturesCol("f")
       .setLabelCol("l")
       .setPredictionCol("p")
 
-    assert(isotonicRegression.getIsotonicParam === false)
+    assert(!isotonicRegression.getIsotonic)
     assert(isotonicRegression.getWeightCol === "w")
     assert(isotonicRegression.getFeaturesCol === "f")
     assert(isotonicRegression.getLabelCol === "l")
@@ -130,7 +121,7 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val dataset = generateIsotonicInput(Seq(1, 2, 3))
 
     intercept[IllegalArgumentException] {
-      new IsotonicRegression().setWeightParam("w").fit(dataset)
+      new IsotonicRegression().setWeightCol("w").fit(dataset)
     }
 
     intercept[IllegalArgumentException] {
@@ -145,4 +136,27 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       new IsotonicRegression().fit(dataset).setFeaturesCol("f").transform(dataset)
     }
   }
+
+  test("vector features column with feature index") {
+    val dataset = sqlContext.createDataFrame(Seq(
+      (4.0, Vectors.dense(0.0, 1.0)),
+      (3.0, Vectors.dense(0.0, 2.0)),
+      (5.0, Vectors.sparse(2, Array(1), Array(3.0))))
+    ).toDF("label", "features")
+
+    val ir = new IsotonicRegression()
+      .setFeatureIndex(1)
+
+    val model = ir.fit(dataset)
+
+    val features = generatePredictionInput(Seq(2.0, 3.0, 4.0, 5.0))
+
+    val predictions = model
+      .transform(features)
+      .select("prediction").map {
+      case Row(pred) => pred
+    }.collect()
+
+    assert(predictions === Array(3.5, 5.0, 5.0, 5.0))
+  }
 }

From abfedb9cd70af60c8290bd2f5a5cec1047845ba0 Mon Sep 17 00:00:00 2001
From: Christian Kadner <ckadner@us.ibm.com>
Date: Thu, 6 Aug 2015 14:15:42 -0700
Subject: [PATCH 0895/1454] [SPARK-9211] [SQL] [TEST] normalize line separators
 before generating MD5 hash

The golden answer file names for the existing Hive comparison tests were generated using a MD5 hash of the query text which uses Unix-style line separator characters `\n` (LF).
This PR ensures that all occurrences of the Windows-style line separator `\r\n` (CR) are replaced with `\n` (LF) before generating the MD5 hash to produce an identical MD5 hash for golden answer file names generated on Windows.

Author: Christian Kadner <ckadner@us.ibm.com>

Closes #7563 from ckadner/SPARK-9211_working and squashes the following commits:

d541db0 [Christian Kadner] [SPARK-9211][SQL] normalize line separators before MD5 hash
---
 .../spark/sql/hive/execution/HiveComparisonTest.scala     | 2 +-
 .../apache/spark/sql/hive/execution/HiveQuerySuite.scala  | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 638b9c810372a..2bdb0e11878e5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -124,7 +124,7 @@ abstract class HiveComparisonTest
   protected val cacheDigest = java.security.MessageDigest.getInstance("MD5")
   protected def getMd5(str: String): String = {
     val digest = java.security.MessageDigest.getInstance("MD5")
-    digest.update(str.getBytes("utf-8"))
+    digest.update(str.replaceAll(System.lineSeparator(), "\n").getBytes("utf-8"))
     new java.math.BigInteger(1, digest.digest).toString(16)
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index edb27553671d1..83f9f3eaa3a5e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -427,7 +427,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
       |USING 'cat' AS (tKey, tValue) ROW FORMAT SERDE
       |'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' FROM src;
-    """.stripMargin.replaceAll("\n", " "))
+    """.stripMargin.replaceAll(System.lineSeparator(), " "))
 
   test("transform with SerDe2") {
 
@@ -446,7 +446,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
         |('avro.schema.literal'='{"namespace": "testing.hive.avro.serde","name":
         |"src","type": "record","fields": [{"name":"key","type":"int"}]}')
         |FROM small_src
-      """.stripMargin.replaceAll("\n", " ")).collect().head
+      """.stripMargin.replaceAll(System.lineSeparator(), " ")).collect().head
 
     assert(expected(0) === res(0))
   }
@@ -458,7 +458,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |('serialization.last.column.takes.rest'='true') USING 'cat' AS (tKey, tValue)
       |ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
       |WITH SERDEPROPERTIES ('serialization.last.column.takes.rest'='true') FROM src;
-    """.stripMargin.replaceAll("\n", " "))
+    """.stripMargin.replaceAll(System.lineSeparator(), " "))
 
   createQueryTest("transform with SerDe4",
     """
@@ -467,7 +467,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |('serialization.last.column.takes.rest'='true') USING 'cat' ROW FORMAT SERDE
       |'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' WITH SERDEPROPERTIES
       |('serialization.last.column.takes.rest'='true') FROM src;
-    """.stripMargin.replaceAll("\n", " "))
+    """.stripMargin.replaceAll(System.lineSeparator(), " "))
 
   createQueryTest("LIKE",
     "SELECT * FROM src WHERE value LIKE '%1%'")

From 21fdfd7d6f89adbd37066c169e6ba9ccd337683e Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 6 Aug 2015 14:33:29 -0700
Subject: [PATCH 0896/1454] [SPARK-9548][SQL] Add a destructive iterator for
 BytesToBytesMap

This pull request adds a destructive iterator to BytesToBytesMap. When used, the iterator frees pages as it traverses them. This is part of the effort to avoid starving when we have more than one operators that can exhaust memory.

This is based on #7924, but fixes a bug there (Don't use destructive iterator in UnsafeKVExternalSorter).

Closes #7924.

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #8003 from rxin/map-destructive-iterator and squashes the following commits:

6b618c3 [Reynold Xin] Don't use destructive iterator in UnsafeKVExternalSorter.
a7bd8ec [Reynold Xin] Merge remote-tracking branch 'viirya/destructive_iter' into map-destructive-iterator
7652083 [Liang-Chi Hsieh] For comments: add destructiveIterator(), modify unit test, remove code block.
4a3e9de [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into destructive_iter
581e9e3 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into destructive_iter
f0ff783 [Liang-Chi Hsieh] No need to free last page.
9e9d2a3 [Liang-Chi Hsieh] Add a destructive iterator for BytesToBytesMap.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 33 +++++++++++++++--
 .../map/AbstractBytesToBytesMapSuite.java     | 37 ++++++++++++++++---
 .../UnsafeFixedWidthAggregationMap.java       |  7 +++-
 .../sql/execution/UnsafeKVExternalSorter.java |  5 ++-
 4 files changed, 71 insertions(+), 11 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 20347433e16b2..5ac3736ac62aa 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -227,22 +227,35 @@ public static final class BytesToBytesMapIterator implements Iterator<Location>
     private final Iterator<MemoryBlock> dataPagesIterator;
     private final Location loc;
 
-    private MemoryBlock currentPage;
+    private MemoryBlock currentPage = null;
     private int currentRecordNumber = 0;
     private Object pageBaseObject;
     private long offsetInPage;
 
+    // If this iterator destructive or not. When it is true, it frees each page as it moves onto
+    // next one.
+    private boolean destructive = false;
+    private BytesToBytesMap bmap;
+
     private BytesToBytesMapIterator(
-        int numRecords, Iterator<MemoryBlock> dataPagesIterator, Location loc) {
+        int numRecords, Iterator<MemoryBlock> dataPagesIterator, Location loc,
+        boolean destructive, BytesToBytesMap bmap) {
       this.numRecords = numRecords;
       this.dataPagesIterator = dataPagesIterator;
       this.loc = loc;
+      this.destructive = destructive;
+      this.bmap = bmap;
       if (dataPagesIterator.hasNext()) {
         advanceToNextPage();
       }
     }
 
     private void advanceToNextPage() {
+      if (destructive && currentPage != null) {
+        dataPagesIterator.remove();
+        this.bmap.taskMemoryManager.freePage(currentPage);
+        this.bmap.shuffleMemoryManager.release(currentPage.size());
+      }
       currentPage = dataPagesIterator.next();
       pageBaseObject = currentPage.getBaseObject();
       offsetInPage = currentPage.getBaseOffset();
@@ -281,7 +294,21 @@ public void remove() {
    * `lookup()`, the behavior of the returned iterator is undefined.
    */
   public BytesToBytesMapIterator iterator() {
-    return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc);
+    return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc, false, this);
+  }
+
+  /**
+   * Returns a destructive iterator for iterating over the entries of this map. It frees each page
+   * as it moves onto next one. Notice: it is illegal to call any method on the map after
+   * `destructiveIterator()` has been called.
+   *
+   * For efficiency, all calls to `next()` will return the same {@link Location} object.
+   *
+   * If any other lookups or operations are performed on this map while iterating over it, including
+   * `lookup()`, the behavior of the returned iterator is undefined.
+   */
+  public BytesToBytesMapIterator destructiveIterator() {
+    return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc, true, this);
   }
 
   /**
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 0e23a64fb74bb..3c5003380162f 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -183,8 +183,7 @@ public void setAndRetrieveAKey() {
     }
   }
 
-  @Test
-  public void iteratorTest() throws Exception {
+  private void iteratorTestBase(boolean destructive) throws Exception {
     final int size = 4096;
     BytesToBytesMap map = new BytesToBytesMap(
       taskMemoryManager, shuffleMemoryManager, size / 2, PAGE_SIZE_BYTES);
@@ -216,7 +215,14 @@ public void iteratorTest() throws Exception {
         }
       }
       final java.util.BitSet valuesSeen = new java.util.BitSet(size);
-      final Iterator<BytesToBytesMap.Location> iter = map.iterator();
+      final Iterator<BytesToBytesMap.Location> iter;
+      if (destructive) {
+        iter = map.destructiveIterator();
+      } else {
+        iter = map.iterator();
+      }
+      int numPages = map.getNumDataPages();
+      int countFreedPages = 0;
       while (iter.hasNext()) {
         final BytesToBytesMap.Location loc = iter.next();
         Assert.assertTrue(loc.isDefined());
@@ -228,11 +234,22 @@ public void iteratorTest() throws Exception {
         if (keyLength == 0) {
           Assert.assertTrue("value " + value + " was not divisible by 5", value % 5 == 0);
         } else {
-        final long key = PlatformDependent.UNSAFE.getLong(
-          keyAddress.getBaseObject(), keyAddress.getBaseOffset());
+          final long key = PlatformDependent.UNSAFE.getLong(
+            keyAddress.getBaseObject(), keyAddress.getBaseOffset());
           Assert.assertEquals(value, key);
         }
         valuesSeen.set((int) value);
+        if (destructive) {
+          // The iterator moves onto next page and frees previous page
+          if (map.getNumDataPages() < numPages) {
+            numPages = map.getNumDataPages();
+            countFreedPages++;
+          }
+        }
+      }
+      if (destructive) {
+        // Latest page is not freed by iterator but by map itself
+        Assert.assertEquals(countFreedPages, numPages - 1);
       }
       Assert.assertEquals(size, valuesSeen.cardinality());
     } finally {
@@ -240,6 +257,16 @@ public void iteratorTest() throws Exception {
     }
   }
 
+  @Test
+  public void iteratorTest() throws Exception {
+    iteratorTestBase(false);
+  }
+
+  @Test
+  public void destructiveIteratorTest() throws Exception {
+    iteratorTestBase(true);
+  }
+
   @Test
   public void iteratingOverDataPagesWithWastedSpace() throws Exception {
     final int NUM_ENTRIES = 1000 * 1000;
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 02458030b00e9..efb33530dac86 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -154,14 +154,17 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
   }
 
   /**
-   * Returns an iterator over the keys and values in this map.
+   * Returns an iterator over the keys and values in this map. This uses destructive iterator of
+   * BytesToBytesMap. So it is illegal to call any other method on this map after `iterator()` has
+   * been called.
    *
    * For efficiency, each call returns the same object.
    */
   public KVIterator<UnsafeRow, UnsafeRow> iterator() {
     return new KVIterator<UnsafeRow, UnsafeRow>() {
 
-      private final BytesToBytesMap.BytesToBytesMapIterator mapLocationIterator = map.iterator();
+      private final BytesToBytesMap.BytesToBytesMapIterator mapLocationIterator =
+        map.destructiveIterator();
       private final UnsafeRow key = new UnsafeRow();
       private final UnsafeRow value = new UnsafeRow();
 
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 6c1cf136d9b81..9a65c9d3a404a 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -88,8 +88,11 @@ public UnsafeKVExternalSorter(StructType keySchema, StructType valueSchema,
       final UnsafeInMemorySorter inMemSorter = new UnsafeInMemorySorter(
         taskMemoryManager, recordComparator, prefixComparator, Math.max(1, map.numElements()));
 
-      final int numKeyFields = keySchema.size();
+      // We cannot use the destructive iterator here because we are reusing the existing memory
+      // pages in BytesToBytesMap to hold records during sorting.
+      // The only new memory we are allocating is the pointer/prefix array.
       BytesToBytesMap.BytesToBytesMapIterator iter = map.iterator();
+      final int numKeyFields = keySchema.size();
       UnsafeRow row = new UnsafeRow();
       while (iter.hasNext()) {
         final BytesToBytesMap.Location loc = iter.next();

From 0a078303d08ad2bb92b9a8a6969563d75b512290 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 6 Aug 2015 14:35:30 -0700
Subject: [PATCH 0897/1454] [SPARK-9556] [SPARK-9619] [SPARK-9624] [STREAMING]
 Make BlockGenerator more robust and make all BlockGenerators subscribe to
 rate limit updates

In some receivers, instead of using the default `BlockGenerator` in `ReceiverSupervisorImpl`, custom generator with their custom listeners are used for reliability (see [`ReliableKafkaReceiver`](https://github.com/apache/spark/blob/master/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala#L99) and [updated `KinesisReceiver`](https://github.com/apache/spark/pull/7825/files)). These custom generators do not receive rate updates. This PR modifies the code to allow custom `BlockGenerator`s to be created through the `ReceiverSupervisorImpl` so that they can be kept track and rate updates can be applied.

In the process, I did some simplification, and de-flaki-fication of some rate controller related tests. In particular.
- Renamed `Receiver.executor` to `Receiver.supervisor` (to match `ReceiverSupervisor`)
- Made `RateControllerSuite` faster (by increasing batch interval) and less flaky
- Changed a few internal API to return the current rate of block generators as Long instead of Option\[Long\] (was inconsistent at places).
- Updated existing `ReceiverTrackerSuite` to test that custom block generators get rate updates as well.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7913 from tdas/SPARK-9556 and squashes the following commits:

41d4461 [Tathagata Das] fix scala style
eb9fd59 [Tathagata Das] Updated kinesis receiver
d24994d [Tathagata Das] Updated BlockGeneratorSuite to use manual clock in BlockGenerator
d70608b [Tathagata Das] Updated BlockGenerator with states and proper synchronization
f6bd47e [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9556
31da173 [Tathagata Das] Fix bug
12116df [Tathagata Das] Add BlockGeneratorSuite
74bd069 [Tathagata Das] Fix style
989bb5c [Tathagata Das] Made BlockGenerator fail is used after stop, and added better unit tests for it
3ff618c [Tathagata Das] Fix test
b40eff8 [Tathagata Das] slight refactoring
f0df0f1 [Tathagata Das] Scala style fixes
51759cb [Tathagata Das] Refactored rate controller tests and added the ability to update rate of any custom block generator
---
 .../org/apache/spark/util/ManualClock.scala   |   2 +-
 .../kafka/ReliableKafkaReceiver.scala         |   2 +-
 .../streaming/kinesis/KinesisReceiver.scala   |   2 +-
 .../streaming/receiver/ActorReceiver.scala    |   8 +-
 .../streaming/receiver/BlockGenerator.scala   | 131 ++++++---
 .../streaming/receiver/RateLimiter.scala      |   3 +-
 .../spark/streaming/receiver/Receiver.scala   |  52 ++--
 .../receiver/ReceiverSupervisor.scala         |  27 +-
 .../receiver/ReceiverSupervisorImpl.scala     |  33 ++-
 .../spark/streaming/CheckpointSuite.scala     |  16 +-
 .../spark/streaming/ReceiverSuite.scala       |  31 +--
 .../receiver/BlockGeneratorSuite.scala        | 253 ++++++++++++++++++
 .../scheduler/RateControllerSuite.scala       |  64 ++---
 .../scheduler/ReceiverTrackerSuite.scala      | 129 +++++----
 14 files changed, 534 insertions(+), 219 deletions(-)
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/util/ManualClock.scala b/core/src/main/scala/org/apache/spark/util/ManualClock.scala
index 1718554061985..e7a65d74a440e 100644
--- a/core/src/main/scala/org/apache/spark/util/ManualClock.scala
+++ b/core/src/main/scala/org/apache/spark/util/ManualClock.scala
@@ -58,7 +58,7 @@ private[spark] class ManualClock(private var time: Long) extends Clock {
    */
   def waitTillTime(targetTime: Long): Long = synchronized {
     while (time < targetTime) {
-      wait(100)
+      wait(10)
     }
     getTimeMillis()
   }
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala
index 75f0dfc22b9dc..764d170934aa6 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala
@@ -96,7 +96,7 @@ class ReliableKafkaReceiver[
     blockOffsetMap = new ConcurrentHashMap[StreamBlockId, Map[TopicAndPartition, Long]]()
 
     // Initialize the block generator for storing Kafka message.
-    blockGenerator = new BlockGenerator(new GeneratedBlockHandler, streamId, conf)
+    blockGenerator = supervisor.createBlockGenerator(new GeneratedBlockHandler)
 
     if (kafkaParams.contains(AUTO_OFFSET_COMMIT) && kafkaParams(AUTO_OFFSET_COMMIT) == "true") {
       logWarning(s"$AUTO_OFFSET_COMMIT should be set to false in ReliableKafkaReceiver, " +
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
index a4baeec0846b4..22324e821ce94 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
@@ -136,7 +136,7 @@ private[kinesis] class KinesisReceiver(
    * The KCL creates and manages the receiving/processing thread pool through Worker.run().
    */
   override def onStart() {
-    blockGenerator = new BlockGenerator(new GeneratedBlockHandler, streamId, SparkEnv.get.conf)
+    blockGenerator = supervisor.createBlockGenerator(new GeneratedBlockHandler)
 
     workerId = Utils.localHostName() + ":" + UUID.randomUUID()
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
index cd309788a7717..7ec74016a1c2c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
@@ -144,7 +144,7 @@ private[streaming] class ActorReceiver[T: ClassTag](
     receiverSupervisorStrategy: SupervisorStrategy
   ) extends Receiver[T](storageLevel) with Logging {
 
-  protected lazy val supervisor = SparkEnv.get.actorSystem.actorOf(Props(new Supervisor),
+  protected lazy val actorSupervisor = SparkEnv.get.actorSystem.actorOf(Props(new Supervisor),
     "Supervisor" + streamId)
 
   class Supervisor extends Actor {
@@ -191,11 +191,11 @@ private[streaming] class ActorReceiver[T: ClassTag](
   }
 
   def onStart(): Unit = {
-    supervisor
-    logInfo("Supervision tree for receivers initialized at:" + supervisor.path)
+    actorSupervisor
+    logInfo("Supervision tree for receivers initialized at:" + actorSupervisor.path)
   }
 
   def onStop(): Unit = {
-    supervisor ! PoisonPill
+    actorSupervisor ! PoisonPill
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
index 92b51ce39234c..794dece370b2c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
@@ -21,10 +21,10 @@ import java.util.concurrent.{ArrayBlockingQueue, TimeUnit}
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.{SparkException, Logging, SparkConf}
 import org.apache.spark.storage.StreamBlockId
 import org.apache.spark.streaming.util.RecurringTimer
-import org.apache.spark.util.SystemClock
+import org.apache.spark.util.{Clock, SystemClock}
 
 /** Listener object for BlockGenerator events */
 private[streaming] trait BlockGeneratorListener {
@@ -69,16 +69,35 @@ private[streaming] trait BlockGeneratorListener {
  * named blocks at regular intervals. This class starts two threads,
  * one to periodically start a new batch and prepare the previous batch of as a block,
  * the other to push the blocks into the block manager.
+ *
+ * Note: Do not create BlockGenerator instances directly inside receivers. Use
+ * `ReceiverSupervisor.createBlockGenerator` to create a BlockGenerator and use it.
  */
 private[streaming] class BlockGenerator(
     listener: BlockGeneratorListener,
     receiverId: Int,
-    conf: SparkConf
+    conf: SparkConf,
+    clock: Clock = new SystemClock()
   ) extends RateLimiter(conf) with Logging {
 
   private case class Block(id: StreamBlockId, buffer: ArrayBuffer[Any])
 
-  private val clock = new SystemClock()
+  /**
+   * The BlockGenerator can be in 5 possible states, in the order as follows.
+   * - Initialized: Nothing has been started
+   * - Active: start() has been called, and it is generating blocks on added data.
+   * - StoppedAddingData: stop() has been called, the adding of data has been stopped,
+   *                      but blocks are still being generated and pushed.
+   * - StoppedGeneratingBlocks: Generating of blocks has been stopped, but
+   *                            they are still being pushed.
+   * - StoppedAll: Everything has stopped, and the BlockGenerator object can be GCed.
+   */
+  private object GeneratorState extends Enumeration {
+    type GeneratorState = Value
+    val Initialized, Active, StoppedAddingData, StoppedGeneratingBlocks, StoppedAll = Value
+  }
+  import GeneratorState._
+
   private val blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms")
   require(blockIntervalMs > 0, s"'spark.streaming.blockInterval' should be a positive value")
 
@@ -89,59 +108,100 @@ private[streaming] class BlockGenerator(
   private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
 
   @volatile private var currentBuffer = new ArrayBuffer[Any]
-  @volatile private var stopped = false
+  @volatile private var state = Initialized
 
   /** Start block generating and pushing threads. */
-  def start() {
-    blockIntervalTimer.start()
-    blockPushingThread.start()
-    logInfo("Started BlockGenerator")
+  def start(): Unit = synchronized {
+    if (state == Initialized) {
+      state = Active
+      blockIntervalTimer.start()
+      blockPushingThread.start()
+      logInfo("Started BlockGenerator")
+    } else {
+      throw new SparkException(
+        s"Cannot start BlockGenerator as its not in the Initialized state [state = $state]")
+    }
   }
 
-  /** Stop all threads. */
-  def stop() {
+  /**
+   * Stop everything in the right order such that all the data added is pushed out correctly.
+   * - First, stop adding data to the current buffer.
+   * - Second, stop generating blocks.
+   * - Finally, wait for queue of to-be-pushed blocks to be drained.
+   */
+  def stop(): Unit = {
+    // Set the state to stop adding data
+    synchronized {
+      if (state == Active) {
+        state = StoppedAddingData
+      } else {
+        logWarning(s"Cannot stop BlockGenerator as its not in the Active state [state = $state]")
+        return
+      }
+    }
+
+    // Stop generating blocks and set the state for block pushing thread to start draining the queue
     logInfo("Stopping BlockGenerator")
     blockIntervalTimer.stop(interruptTimer = false)
-    stopped = true
-    logInfo("Waiting for block pushing thread")
+    synchronized { state = StoppedGeneratingBlocks }
+
+    // Wait for the queue to drain and mark generated as stopped
+    logInfo("Waiting for block pushing thread to terminate")
     blockPushingThread.join()
+    synchronized { state = StoppedAll }
     logInfo("Stopped BlockGenerator")
   }
 
   /**
-   * Push a single data item into the buffer. All received data items
-   * will be periodically pushed into BlockManager.
+   * Push a single data item into the buffer.
    */
-  def addData (data: Any): Unit = synchronized {
-    waitToPush()
-    currentBuffer += data
+  def addData(data: Any): Unit = synchronized {
+    if (state == Active) {
+      waitToPush()
+      currentBuffer += data
+    } else {
+      throw new SparkException(
+        "Cannot add data as BlockGenerator has not been started or has been stopped")
+    }
   }
 
   /**
    * Push a single data item into the buffer. After buffering the data, the
-   * `BlockGeneratorListener.onAddData` callback will be called. All received data items
-   * will be periodically pushed into BlockManager.
+   * `BlockGeneratorListener.onAddData` callback will be called.
    */
   def addDataWithCallback(data: Any, metadata: Any): Unit = synchronized {
-    waitToPush()
-    currentBuffer += data
-    listener.onAddData(data, metadata)
+    if (state == Active) {
+      waitToPush()
+      currentBuffer += data
+      listener.onAddData(data, metadata)
+    } else {
+      throw new SparkException(
+        "Cannot add data as BlockGenerator has not been started or has been stopped")
+    }
   }
 
   /**
    * Push multiple data items into the buffer. After buffering the data, the
-   * `BlockGeneratorListener.onAddData` callback will be called. All received data items
-   * will be periodically pushed into BlockManager. Note that all the data items is guaranteed
-   * to be present in a single block.
+   * `BlockGeneratorListener.onAddData` callback will be called. Note that all the data items
+   * are atomically added to the buffer, and are hence guaranteed to be present in a single block.
    */
   def addMultipleDataWithCallback(dataIterator: Iterator[Any], metadata: Any): Unit = synchronized {
-    dataIterator.foreach { data =>
-      waitToPush()
-      currentBuffer += data
+    if (state == Active) {
+      dataIterator.foreach { data =>
+        waitToPush()
+        currentBuffer += data
+      }
+      listener.onAddData(dataIterator, metadata)
+    } else {
+      throw new SparkException(
+        "Cannot add data as BlockGenerator has not been started or has been stopped")
     }
-    listener.onAddData(dataIterator, metadata)
   }
 
+  def isActive(): Boolean = state == Active
+
+  def isStopped(): Boolean = state == StoppedAll
+
   /** Change the buffer to which single records are added to. */
   private def updateCurrentBuffer(time: Long): Unit = synchronized {
     try {
@@ -165,18 +225,21 @@ private[streaming] class BlockGenerator(
   /** Keep pushing blocks to the BlockManager. */
   private def keepPushingBlocks() {
     logInfo("Started block pushing thread")
+
+    def isGeneratingBlocks = synchronized { state == Active || state == StoppedAddingData }
     try {
-      while (!stopped) {
-        Option(blocksForPushing.poll(100, TimeUnit.MILLISECONDS)) match {
+      while (isGeneratingBlocks) {
+        Option(blocksForPushing.poll(10, TimeUnit.MILLISECONDS)) match {
           case Some(block) => pushBlock(block)
           case None =>
         }
       }
-      // Push out the blocks that are still left
+
+      // At this point, state is StoppedGeneratingBlock. So drain the queue of to-be-pushed blocks.
       logInfo("Pushing out the last " + blocksForPushing.size() + " blocks")
       while (!blocksForPushing.isEmpty) {
-        logDebug("Getting block ")
         val block = blocksForPushing.take()
+        logDebug(s"Pushing block $block")
         pushBlock(block)
         logInfo("Blocks left to push " + blocksForPushing.size())
       }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
index f663def4c0511..bca1fbc8fda2f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
@@ -45,8 +45,7 @@ private[receiver] abstract class RateLimiter(conf: SparkConf) extends Logging {
   /**
    * Return the current rate limit. If no limit has been set so far, it returns {{{Long.MaxValue}}}.
    */
-  def getCurrentLimit: Long =
-    rateLimiter.getRate.toLong
+  def getCurrentLimit: Long = rateLimiter.getRate.toLong
 
   /**
    * Set the rate limit to `newRate`. The new rate will not exceed the maximum rate configured by
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index 7504fa44d9fae..554aae0117b24 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -116,12 +116,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * being pushed into Spark's memory.
    */
   def store(dataItem: T) {
-    executor.pushSingle(dataItem)
+    supervisor.pushSingle(dataItem)
   }
 
   /** Store an ArrayBuffer of received data as a data block into Spark's memory. */
   def store(dataBuffer: ArrayBuffer[T]) {
-    executor.pushArrayBuffer(dataBuffer, None, None)
+    supervisor.pushArrayBuffer(dataBuffer, None, None)
   }
 
   /**
@@ -130,12 +130,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataBuffer: ArrayBuffer[T], metadata: Any) {
-    executor.pushArrayBuffer(dataBuffer, Some(metadata), None)
+    supervisor.pushArrayBuffer(dataBuffer, Some(metadata), None)
   }
 
   /** Store an iterator of received data as a data block into Spark's memory. */
   def store(dataIterator: Iterator[T]) {
-    executor.pushIterator(dataIterator, None, None)
+    supervisor.pushIterator(dataIterator, None, None)
   }
 
   /**
@@ -144,12 +144,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataIterator: java.util.Iterator[T], metadata: Any) {
-    executor.pushIterator(dataIterator, Some(metadata), None)
+    supervisor.pushIterator(dataIterator, Some(metadata), None)
   }
 
   /** Store an iterator of received data as a data block into Spark's memory. */
   def store(dataIterator: java.util.Iterator[T]) {
-    executor.pushIterator(dataIterator, None, None)
+    supervisor.pushIterator(dataIterator, None, None)
   }
 
   /**
@@ -158,7 +158,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataIterator: Iterator[T], metadata: Any) {
-    executor.pushIterator(dataIterator, Some(metadata), None)
+    supervisor.pushIterator(dataIterator, Some(metadata), None)
   }
 
   /**
@@ -167,7 +167,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * that Spark is configured to use.
    */
   def store(bytes: ByteBuffer) {
-    executor.pushBytes(bytes, None, None)
+    supervisor.pushBytes(bytes, None, None)
   }
 
   /**
@@ -176,12 +176,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(bytes: ByteBuffer, metadata: Any) {
-    executor.pushBytes(bytes, Some(metadata), None)
+    supervisor.pushBytes(bytes, Some(metadata), None)
   }
 
   /** Report exceptions in receiving data. */
   def reportError(message: String, throwable: Throwable) {
-    executor.reportError(message, throwable)
+    supervisor.reportError(message, throwable)
   }
 
   /**
@@ -193,7 +193,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * The `message` will be reported to the driver.
    */
   def restart(message: String) {
-    executor.restartReceiver(message)
+    supervisor.restartReceiver(message)
   }
 
   /**
@@ -205,7 +205,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * The `message` and `exception` will be reported to the driver.
    */
   def restart(message: String, error: Throwable) {
-    executor.restartReceiver(message, Some(error))
+    supervisor.restartReceiver(message, Some(error))
   }
 
   /**
@@ -215,22 +215,22 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * in a background thread.
    */
   def restart(message: String, error: Throwable, millisecond: Int) {
-    executor.restartReceiver(message, Some(error), millisecond)
+    supervisor.restartReceiver(message, Some(error), millisecond)
   }
 
   /** Stop the receiver completely. */
   def stop(message: String) {
-    executor.stop(message, None)
+    supervisor.stop(message, None)
   }
 
   /** Stop the receiver completely due to an exception */
   def stop(message: String, error: Throwable) {
-    executor.stop(message, Some(error))
+    supervisor.stop(message, Some(error))
   }
 
   /** Check if the receiver has started or not. */
   def isStarted(): Boolean = {
-    executor.isReceiverStarted()
+    supervisor.isReceiverStarted()
   }
 
   /**
@@ -238,7 +238,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * the receiving of data should be stopped.
    */
   def isStopped(): Boolean = {
-    executor.isReceiverStopped()
+    supervisor.isReceiverStopped()
   }
 
   /**
@@ -257,7 +257,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
   private var id: Int = -1
 
   /** Handler object that runs the receiver. This is instantiated lazily in the worker. */
-  private[streaming] var executor_ : ReceiverSupervisor = null
+  @transient private var _supervisor : ReceiverSupervisor = null
 
   /** Set the ID of the DStream that this receiver is associated with. */
   private[streaming] def setReceiverId(id_ : Int) {
@@ -265,15 +265,17 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
   }
 
   /** Attach Network Receiver executor to this receiver. */
-  private[streaming] def attachExecutor(exec: ReceiverSupervisor) {
-    assert(executor_ == null)
-    executor_ = exec
+  private[streaming] def attachSupervisor(exec: ReceiverSupervisor) {
+    assert(_supervisor == null)
+    _supervisor = exec
   }
 
-  /** Get the attached executor. */
-  private def executor: ReceiverSupervisor = {
-    assert(executor_ != null, "Executor has not been attached to this receiver")
-    executor_
+  /** Get the attached supervisor. */
+  private[streaming] def supervisor: ReceiverSupervisor = {
+    assert(_supervisor != null,
+      "A ReceiverSupervisor have not been attached to the receiver yet. Maybe you are starting " +
+        "some computation in the receiver before the Receiver.onStart() has been called.")
+    _supervisor
   }
 }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
index e98017a63756e..158d1ba2f183a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
@@ -44,8 +44,8 @@ private[streaming] abstract class ReceiverSupervisor(
   }
   import ReceiverState._
 
-  // Attach the executor to the receiver
-  receiver.attachExecutor(this)
+  // Attach the supervisor to the receiver
+  receiver.attachSupervisor(this)
 
   private val futureExecutionContext = ExecutionContext.fromExecutorService(
     ThreadUtils.newDaemonCachedThreadPool("receiver-supervisor-future", 128))
@@ -60,7 +60,7 @@ private[streaming] abstract class ReceiverSupervisor(
   private val defaultRestartDelay = conf.getInt("spark.streaming.receiverRestartDelay", 2000)
 
   /** The current maximum rate limit for this receiver. */
-  private[streaming] def getCurrentRateLimit: Option[Long] = None
+  private[streaming] def getCurrentRateLimit: Long = Long.MaxValue
 
   /** Exception associated with the stopping of the receiver */
   @volatile protected var stoppingError: Throwable = null
@@ -92,13 +92,30 @@ private[streaming] abstract class ReceiverSupervisor(
       optionalBlockId: Option[StreamBlockId]
     )
 
+  /**
+   * Create a custom [[BlockGenerator]] that the receiver implementation can directly control
+   * using their provided [[BlockGeneratorListener]].
+   *
+   * Note: Do not explicitly start or stop the `BlockGenerator`, the `ReceiverSupervisorImpl`
+   * will take care of it.
+   */
+  def createBlockGenerator(blockGeneratorListener: BlockGeneratorListener): BlockGenerator
+
   /** Report errors. */
   def reportError(message: String, throwable: Throwable)
 
-  /** Called when supervisor is started */
+  /**
+   * Called when supervisor is started.
+   * Note that this must be called before the receiver.onStart() is called to ensure
+   * things like [[BlockGenerator]]s are started before the receiver starts sending data.
+   */
   protected def onStart() { }
 
-  /** Called when supervisor is stopped */
+  /**
+   * Called when supervisor is stopped.
+   * Note that this must be called after the receiver.onStop() is called to ensure
+   * things like [[BlockGenerator]]s are cleaned up after the receiver stops sending data.
+   */
   protected def onStop(message: String, error: Option[Throwable]) { }
 
   /** Called when receiver is started. Return true if the driver accepts us */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index 0d802f83549af..59ef58d232ee7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming.receiver
 import java.nio.ByteBuffer
 import java.util.concurrent.atomic.AtomicLong
 
+import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.base.Throwables
@@ -81,15 +82,20 @@ private[streaming] class ReceiverSupervisorImpl(
           cleanupOldBlocks(threshTime)
         case UpdateRateLimit(eps) =>
           logInfo(s"Received a new rate limit: $eps.")
-          blockGenerator.updateRate(eps)
+          registeredBlockGenerators.foreach { bg =>
+            bg.updateRate(eps)
+          }
       }
     })
 
   /** Unique block ids if one wants to add blocks directly */
   private val newBlockId = new AtomicLong(System.currentTimeMillis())
 
+  private val registeredBlockGenerators = new mutable.ArrayBuffer[BlockGenerator]
+    with mutable.SynchronizedBuffer[BlockGenerator]
+
   /** Divides received data records into data blocks for pushing in BlockManager. */
-  private val blockGenerator = new BlockGenerator(new BlockGeneratorListener {
+  private val defaultBlockGeneratorListener = new BlockGeneratorListener {
     def onAddData(data: Any, metadata: Any): Unit = { }
 
     def onGenerateBlock(blockId: StreamBlockId): Unit = { }
@@ -101,14 +107,15 @@ private[streaming] class ReceiverSupervisorImpl(
     def onPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]) {
       pushArrayBuffer(arrayBuffer, None, Some(blockId))
     }
-  }, streamId, env.conf)
+  }
+  private val defaultBlockGenerator = createBlockGenerator(defaultBlockGeneratorListener)
 
-  override private[streaming] def getCurrentRateLimit: Option[Long] =
-    Some(blockGenerator.getCurrentLimit)
+  /** Get the current rate limit of the default block generator */
+  override private[streaming] def getCurrentRateLimit: Long = defaultBlockGenerator.getCurrentLimit
 
   /** Push a single record of received data into block generator. */
   def pushSingle(data: Any) {
-    blockGenerator.addData(data)
+    defaultBlockGenerator.addData(data)
   }
 
   /** Store an ArrayBuffer of received data as a data block into Spark's memory. */
@@ -162,11 +169,11 @@ private[streaming] class ReceiverSupervisorImpl(
   }
 
   override protected def onStart() {
-    blockGenerator.start()
+    registeredBlockGenerators.foreach { _.start() }
   }
 
   override protected def onStop(message: String, error: Option[Throwable]) {
-    blockGenerator.stop()
+    registeredBlockGenerators.foreach { _.stop() }
     env.rpcEnv.stop(endpoint)
   }
 
@@ -183,6 +190,16 @@ private[streaming] class ReceiverSupervisorImpl(
     logInfo("Stopped receiver " + streamId)
   }
 
+  override def createBlockGenerator(
+      blockGeneratorListener: BlockGeneratorListener): BlockGenerator = {
+    // Cleanup BlockGenerators that have already been stopped
+    registeredBlockGenerators --= registeredBlockGenerators.filter{ _.isStopped() }
+
+    val newBlockGenerator = new BlockGenerator(blockGeneratorListener, streamId, env.conf)
+    registeredBlockGenerators += newBlockGenerator
+    newBlockGenerator
+  }
+
   /** Generate new block ID */
   private def nextBlockId = StreamBlockId(streamId, newBlockId.getAndIncrement)
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index 67c2d900940ab..1bba7a143edf2 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming
 
 import java.io.File
 
-import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
+import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
 import scala.reflect.ClassTag
 
 import com.google.common.base.Charsets
@@ -33,7 +33,7 @@ import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark.streaming.dstream.{DStream, FileInputDStream}
-import org.apache.spark.streaming.scheduler.{RateLimitInputDStream, ConstantEstimator, SingletonTestRateReceiver}
+import org.apache.spark.streaming.scheduler.{ConstantEstimator, RateTestInputDStream, RateTestReceiver}
 import org.apache.spark.util.{Clock, ManualClock, Utils}
 
 /**
@@ -397,26 +397,24 @@ class CheckpointSuite extends TestSuiteBase {
     ssc = new StreamingContext(conf, batchDuration)
     ssc.checkpoint(checkpointDir)
 
-    val dstream = new RateLimitInputDStream(ssc) {
+    val dstream = new RateTestInputDStream(ssc) {
       override val rateController =
-        Some(new ReceiverRateController(id, new ConstantEstimator(200.0)))
+        Some(new ReceiverRateController(id, new ConstantEstimator(200)))
     }
-    SingletonTestRateReceiver.reset()
 
     val output = new TestOutputStreamWithPartitions(dstream.checkpoint(batchDuration * 2))
     output.register()
     runStreams(ssc, 5, 5)
 
-    SingletonTestRateReceiver.reset()
     ssc = new StreamingContext(checkpointDir)
     ssc.start()
     val outputNew = advanceTimeWithRealDelay(ssc, 2)
 
-    eventually(timeout(5.seconds)) {
-      assert(dstream.getCurrentRateLimit === Some(200))
+    eventually(timeout(10.seconds)) {
+      assert(RateTestReceiver.getActive().nonEmpty)
+      assert(RateTestReceiver.getActive().get.getDefaultBlockGeneratorRateLimit() === 200)
     }
     ssc.stop()
-    ssc = null
   }
 
   // This tests whether file input stream remembers what files were seen before
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
index 13b4d17c86183..01279b34f73dc 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
@@ -129,32 +129,6 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable {
     }
   }
 
-  test("block generator") {
-    val blockGeneratorListener = new FakeBlockGeneratorListener
-    val blockIntervalMs = 200
-    val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms")
-    val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf)
-    val expectedBlocks = 5
-    val waitTime = expectedBlocks * blockIntervalMs + (blockIntervalMs / 2)
-    val generatedData = new ArrayBuffer[Int]
-
-    // Generate blocks
-    val startTime = System.currentTimeMillis()
-    blockGenerator.start()
-    var count = 0
-    while(System.currentTimeMillis - startTime < waitTime) {
-      blockGenerator.addData(count)
-      generatedData += count
-      count += 1
-      Thread.sleep(10)
-    }
-    blockGenerator.stop()
-
-    val recordedData = blockGeneratorListener.arrayBuffers.flatten
-    assert(blockGeneratorListener.arrayBuffers.size > 0)
-    assert(recordedData.toSet === generatedData.toSet)
-  }
-
   ignore("block generator throttling") {
     val blockGeneratorListener = new FakeBlockGeneratorListener
     val blockIntervalMs = 100
@@ -348,6 +322,11 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable {
     }
 
     override protected def onReceiverStart(): Boolean = true
+
+    override def createBlockGenerator(
+        blockGeneratorListener: BlockGeneratorListener): BlockGenerator = {
+      null
+    }
   }
 
   /**
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala
new file mode 100644
index 0000000000000..a38cc603f2190
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala
@@ -0,0 +1,253 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.receiver
+
+import scala.collection.mutable
+
+import org.scalatest.BeforeAndAfter
+import org.scalatest.Matchers._
+import org.scalatest.concurrent.Timeouts._
+import org.scalatest.concurrent.Eventually._
+import org.scalatest.time.SpanSugar._
+
+import org.apache.spark.storage.StreamBlockId
+import org.apache.spark.util.ManualClock
+import org.apache.spark.{SparkException, SparkConf, SparkFunSuite}
+
+class BlockGeneratorSuite extends SparkFunSuite with BeforeAndAfter {
+
+  private val blockIntervalMs = 10
+  private val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms")
+  @volatile private var blockGenerator: BlockGenerator = null
+
+  after {
+    if (blockGenerator != null) {
+      blockGenerator.stop()
+    }
+  }
+
+  test("block generation and data callbacks") {
+    val listener = new TestBlockGeneratorListener
+    val clock = new ManualClock()
+
+    require(blockIntervalMs > 5)
+    require(listener.onAddDataCalled === false)
+    require(listener.onGenerateBlockCalled === false)
+    require(listener.onPushBlockCalled === false)
+
+    // Verify that creating the generator does not start it
+    blockGenerator = new BlockGenerator(listener, 0, conf, clock)
+    assert(blockGenerator.isActive() === false, "block generator active before start()")
+    assert(blockGenerator.isStopped() === false, "block generator stopped before start()")
+    assert(listener.onAddDataCalled === false)
+    assert(listener.onGenerateBlockCalled === false)
+    assert(listener.onPushBlockCalled === false)
+
+    // Verify start marks the generator active, but does not call the callbacks
+    blockGenerator.start()
+    assert(blockGenerator.isActive() === true, "block generator active after start()")
+    assert(blockGenerator.isStopped() === false, "block generator stopped after start()")
+    withClue("callbacks called before adding data") {
+      assert(listener.onAddDataCalled === false)
+      assert(listener.onGenerateBlockCalled === false)
+      assert(listener.onPushBlockCalled === false)
+    }
+
+    // Verify whether addData() adds data that is present in generated blocks
+    val data1 = 1 to 10
+    data1.foreach { blockGenerator.addData _ }
+    withClue("callbacks called on adding data without metadata and without block generation") {
+      assert(listener.onAddDataCalled === false) // should be called only with addDataWithCallback()
+      assert(listener.onGenerateBlockCalled === false)
+      assert(listener.onPushBlockCalled === false)
+    }
+    clock.advance(blockIntervalMs)  // advance clock to generate blocks
+    withClue("blocks not generated or pushed") {
+      eventually(timeout(1 second)) {
+        assert(listener.onGenerateBlockCalled === true)
+        assert(listener.onPushBlockCalled === true)
+      }
+    }
+    listener.pushedData should contain theSameElementsInOrderAs (data1)
+    assert(listener.onAddDataCalled === false) // should be called only with addDataWithCallback()
+
+    // Verify addDataWithCallback() add data+metadata and and callbacks are called correctly
+    val data2 = 11 to 20
+    val metadata2 = data2.map { _.toString }
+    data2.zip(metadata2).foreach { case (d, m) => blockGenerator.addDataWithCallback(d, m) }
+    assert(listener.onAddDataCalled === true)
+    listener.addedData should contain theSameElementsInOrderAs (data2)
+    listener.addedMetadata should contain theSameElementsInOrderAs (metadata2)
+    clock.advance(blockIntervalMs)  // advance clock to generate blocks
+    eventually(timeout(1 second)) {
+      listener.pushedData should contain theSameElementsInOrderAs (data1 ++ data2)
+    }
+
+    // Verify addMultipleDataWithCallback() add data+metadata and and callbacks are called correctly
+    val data3 = 21 to 30
+    val metadata3 = "metadata"
+    blockGenerator.addMultipleDataWithCallback(data3.iterator, metadata3)
+    listener.addedMetadata should contain theSameElementsInOrderAs (metadata2 :+ metadata3)
+    clock.advance(blockIntervalMs)  // advance clock to generate blocks
+    eventually(timeout(1 second)) {
+      listener.pushedData should contain theSameElementsInOrderAs (data1 ++ data2 ++ data3)
+    }
+
+    // Stop the block generator by starting the stop on a different thread and
+    // then advancing the manual clock for the stopping to proceed.
+    val thread = stopBlockGenerator(blockGenerator)
+    eventually(timeout(1 second), interval(10 milliseconds)) {
+      clock.advance(blockIntervalMs)
+      assert(blockGenerator.isStopped() === true)
+    }
+    thread.join()
+
+    // Verify that the generator cannot be used any more
+    intercept[SparkException] {
+      blockGenerator.addData(1)
+    }
+    intercept[SparkException] {
+      blockGenerator.addDataWithCallback(1, 1)
+    }
+    intercept[SparkException] {
+      blockGenerator.addMultipleDataWithCallback(Iterator(1), 1)
+    }
+    intercept[SparkException] {
+      blockGenerator.start()
+    }
+    blockGenerator.stop()   // Calling stop again should be fine
+  }
+
+  test("stop ensures correct shutdown") {
+    val listener = new TestBlockGeneratorListener
+    val clock = new ManualClock()
+    blockGenerator = new BlockGenerator(listener, 0, conf, clock)
+    require(listener.onGenerateBlockCalled === false)
+    blockGenerator.start()
+    assert(blockGenerator.isActive() === true, "block generator")
+    assert(blockGenerator.isStopped() === false)
+
+    val data = 1 to 1000
+    data.foreach { blockGenerator.addData _ }
+
+    // Verify that stop() shutdowns everything in the right order
+    // - First, stop receiving new data
+    // - Second, wait for final block with all buffered data to be generated
+    // - Finally, wait for all blocks to be pushed
+    clock.advance(1) // to make sure that the timer for another interval to complete
+    val thread = stopBlockGenerator(blockGenerator)
+    eventually(timeout(1 second), interval(10 milliseconds)) {
+      assert(blockGenerator.isActive() === false)
+    }
+    assert(blockGenerator.isStopped() === false)
+
+    // Verify that data cannot be added
+    intercept[SparkException] {
+      blockGenerator.addData(1)
+    }
+    intercept[SparkException] {
+      blockGenerator.addDataWithCallback(1, null)
+    }
+    intercept[SparkException] {
+      blockGenerator.addMultipleDataWithCallback(Iterator(1), null)
+    }
+
+    // Verify that stop() stays blocked until another block containing all the data is generated
+    // This intercept always succeeds, as the body either will either throw a timeout exception
+    // (expected as stop() should never complete) or a SparkException (unexpected as stop()
+    // completed and thread terminated).
+    val exception = intercept[Exception] {
+      failAfter(200 milliseconds) {
+        thread.join()
+        throw new SparkException(
+          "BlockGenerator.stop() completed before generating timer was stopped")
+      }
+    }
+    exception should not be a [SparkException]
+
+
+    // Verify that the final data is present in the final generated block and
+    // pushed before complete stop
+    assert(blockGenerator.isStopped() === false) // generator has not stopped yet
+    clock.advance(blockIntervalMs)   // force block generation
+    failAfter(1 second) {
+      thread.join()
+    }
+    assert(blockGenerator.isStopped() === true) // generator has finally been completely stopped
+    assert(listener.pushedData === data, "All data not pushed by stop()")
+  }
+
+  test("block push errors are reported") {
+    val listener = new TestBlockGeneratorListener {
+      @volatile var errorReported = false
+      override def onPushBlock(
+          blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[_]): Unit = {
+        throw new SparkException("test")
+      }
+      override def onError(message: String, throwable: Throwable): Unit = {
+        errorReported = true
+      }
+    }
+    blockGenerator = new BlockGenerator(listener, 0, conf)
+    blockGenerator.start()
+    assert(listener.errorReported === false)
+    blockGenerator.addData(1)
+    eventually(timeout(1 second), interval(10 milliseconds)) {
+      assert(listener.errorReported === true)
+    }
+    blockGenerator.stop()
+  }
+
+  /**
+   * Helper method to stop the block generator with manual clock in a different thread,
+   * so that the main thread can advance the clock that allows the stopping to proceed.
+   */
+  private def stopBlockGenerator(blockGenerator: BlockGenerator): Thread = {
+    val thread = new Thread() {
+      override def run(): Unit = {
+        blockGenerator.stop()
+      }
+    }
+    thread.start()
+    thread
+  }
+
+  /** A listener for BlockGenerator that records the data in the callbacks */
+  private class TestBlockGeneratorListener extends BlockGeneratorListener {
+    val pushedData = new mutable.ArrayBuffer[Any] with mutable.SynchronizedBuffer[Any]
+    val addedData = new mutable.ArrayBuffer[Any] with mutable.SynchronizedBuffer[Any]
+    val addedMetadata = new mutable.ArrayBuffer[Any] with mutable.SynchronizedBuffer[Any]
+    @volatile var onGenerateBlockCalled = false
+    @volatile var onAddDataCalled = false
+    @volatile var onPushBlockCalled = false
+
+    override def onPushBlock(blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[_]): Unit = {
+      pushedData ++= arrayBuffer
+      onPushBlockCalled = true
+    }
+    override def onError(message: String, throwable: Throwable): Unit = {}
+    override def onGenerateBlock(blockId: StreamBlockId): Unit = {
+      onGenerateBlockCalled = true
+    }
+    override def onAddData(data: Any, metadata: Any): Unit = {
+      addedData += data
+      addedMetadata += metadata
+      onAddDataCalled = true
+    }
+  }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
index 921da773f6c11..1eb52b7029a21 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
@@ -18,10 +18,7 @@
 package org.apache.spark.streaming.scheduler
 
 import scala.collection.mutable
-import scala.reflect.ClassTag
-import scala.util.control.NonFatal
 
-import org.scalatest.Matchers._
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
@@ -32,72 +29,63 @@ class RateControllerSuite extends TestSuiteBase {
 
   override def useManualClock: Boolean = false
 
-  test("rate controller publishes updates") {
+  override def batchDuration: Duration = Milliseconds(50)
+
+  test("RateController - rate controller publishes updates after batches complete") {
     val ssc = new StreamingContext(conf, batchDuration)
     withStreamingContext(ssc) { ssc =>
-      val dstream = new RateLimitInputDStream(ssc)
+      val dstream = new RateTestInputDStream(ssc)
       dstream.register()
       ssc.start()
 
       eventually(timeout(10.seconds)) {
-        assert(dstream.publishCalls > 0)
+        assert(dstream.publishedRates > 0)
       }
     }
   }
 
-  test("publish rates reach receivers") {
+  test("ReceiverRateController - published rates reach receivers") {
     val ssc = new StreamingContext(conf, batchDuration)
     withStreamingContext(ssc) { ssc =>
-      val dstream = new RateLimitInputDStream(ssc) {
+      val estimator = new ConstantEstimator(100)
+      val dstream = new RateTestInputDStream(ssc) {
         override val rateController =
-          Some(new ReceiverRateController(id, new ConstantEstimator(200.0)))
+          Some(new ReceiverRateController(id, estimator))
       }
       dstream.register()
-      SingletonTestRateReceiver.reset()
       ssc.start()
 
-      eventually(timeout(10.seconds)) {
-        assert(dstream.getCurrentRateLimit === Some(200))
+      // Wait for receiver to start
+      eventually(timeout(5.seconds)) {
+        RateTestReceiver.getActive().nonEmpty
       }
-    }
-  }
 
-  test("multiple publish rates reach receivers") {
-    val ssc = new StreamingContext(conf, batchDuration)
-    withStreamingContext(ssc) { ssc =>
-      val rates = Seq(100L, 200L, 300L)
-
-      val dstream = new RateLimitInputDStream(ssc) {
-        override val rateController =
-          Some(new ReceiverRateController(id, new ConstantEstimator(rates.map(_.toDouble): _*)))
+      // Update rate in the estimator and verify whether the rate was published to the receiver
+      def updateRateAndVerify(rate: Long): Unit = {
+        estimator.updateRate(rate)
+        eventually(timeout(5.seconds)) {
+          assert(RateTestReceiver.getActive().get.getDefaultBlockGeneratorRateLimit() === rate)
+        }
       }
-      SingletonTestRateReceiver.reset()
-      dstream.register()
-
-      val observedRates = mutable.HashSet.empty[Long]
-      ssc.start()
 
-      eventually(timeout(20.seconds)) {
-        dstream.getCurrentRateLimit.foreach(observedRates += _)
-        // Long.MaxValue (essentially, no rate limit) is the initial rate limit for any Receiver
-        observedRates should contain theSameElementsAs (rates :+ Long.MaxValue)
+      // Verify multiple rate update
+      Seq(100, 200, 300).foreach { rate =>
+        updateRateAndVerify(rate)
       }
     }
   }
 }
 
-private[streaming] class ConstantEstimator(rates: Double*) extends RateEstimator {
-  private var idx: Int = 0
+private[streaming] class ConstantEstimator(@volatile private var rate: Long)
+  extends RateEstimator {
 
-  private def nextRate(): Double = {
-    val rate = rates(idx)
-    idx = (idx + 1) % rates.size
-    rate
+  def updateRate(newRate: Long): Unit = {
+    rate = newRate
   }
 
   def compute(
       time: Long,
       elements: Long,
       processingDelay: Long,
-      schedulingDelay: Long): Option[Double] = Some(nextRate())
+      schedulingDelay: Long): Option[Double] = Some(rate)
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index afad5f16dbc71..dd292ba4dd949 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -17,48 +17,43 @@
 
 package org.apache.spark.streaming.scheduler
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.SparkConf
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.receiver._
 import org.apache.spark.streaming.dstream.ReceiverInputDStream
-import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.receiver._
 
 /** Testsuite for receiver scheduling */
 class ReceiverTrackerSuite extends TestSuiteBase {
-  val sparkConf = new SparkConf().setMaster("local[8]").setAppName("test")
-
-  test("Receiver tracker - propagates rate limit") {
-    withStreamingContext(new StreamingContext(sparkConf, Milliseconds(100))) { ssc =>
-      object ReceiverStartedWaiter extends StreamingListener {
-        @volatile
-        var started = false
-
-        override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = {
-          started = true
-        }
-      }
 
-      ssc.addStreamingListener(ReceiverStartedWaiter)
+  test("send rate update to receivers") {
+    withStreamingContext(new StreamingContext(conf, Milliseconds(100))) { ssc =>
       ssc.scheduler.listenerBus.start(ssc.sc)
-      SingletonTestRateReceiver.reset()
 
       val newRateLimit = 100L
-      val inputDStream = new RateLimitInputDStream(ssc)
+      val inputDStream = new RateTestInputDStream(ssc)
       val tracker = new ReceiverTracker(ssc)
       tracker.start()
       try {
         // we wait until the Receiver has registered with the tracker,
         // otherwise our rate update is lost
         eventually(timeout(5 seconds)) {
-          assert(ReceiverStartedWaiter.started)
+          assert(RateTestReceiver.getActive().nonEmpty)
         }
+
+
+        // Verify that the rate of the block generator in the receiver get updated
+        val activeReceiver = RateTestReceiver.getActive().get
         tracker.sendRateUpdate(inputDStream.id, newRateLimit)
-        // this is an async message, we need to wait a bit for it to be processed
-        eventually(timeout(3 seconds)) {
-          assert(inputDStream.getCurrentRateLimit.get === newRateLimit)
+        eventually(timeout(5 seconds)) {
+          assert(activeReceiver.getDefaultBlockGeneratorRateLimit() === newRateLimit,
+            "default block generator did not receive rate update")
+          assert(activeReceiver.getCustomBlockGeneratorRateLimit() === newRateLimit,
+            "other block generator did not receive rate update")
         }
       } finally {
         tracker.stop(false)
@@ -67,69 +62,73 @@ class ReceiverTrackerSuite extends TestSuiteBase {
   }
 }
 
-/**
- * An input DStream with a hard-coded receiver that gives access to internals for testing.
- *
- * @note Make sure to call {{{SingletonDummyReceiver.reset()}}} before using this in a test,
- *       or otherwise you may get {{{NotSerializableException}}} when trying to serialize
- *       the receiver.
- * @see [[[SingletonDummyReceiver]]].
- */
-private[streaming] class RateLimitInputDStream(@transient ssc_ : StreamingContext)
+/** An input DStream with for testing rate controlling */
+private[streaming] class RateTestInputDStream(@transient ssc_ : StreamingContext)
   extends ReceiverInputDStream[Int](ssc_) {
 
-  override def getReceiver(): RateTestReceiver = SingletonTestRateReceiver
-
-  def getCurrentRateLimit: Option[Long] = {
-    invokeExecutorMethod.getCurrentRateLimit
-  }
+  override def getReceiver(): Receiver[Int] = new RateTestReceiver(id)
 
   @volatile
-  var publishCalls = 0
+  var publishedRates = 0
 
   override val rateController: Option[RateController] = {
-    Some(new RateController(id, new ConstantEstimator(100.0)) {
+    Some(new RateController(id, new ConstantEstimator(100)) {
       override def publish(rate: Long): Unit = {
-        publishCalls += 1
+        publishedRates += 1
       }
     })
   }
+}
 
-  private def invokeExecutorMethod: ReceiverSupervisor = {
-    val c = classOf[Receiver[_]]
-    val ex = c.getDeclaredMethod("executor")
-    ex.setAccessible(true)
-    ex.invoke(SingletonTestRateReceiver).asInstanceOf[ReceiverSupervisor]
+/** A receiver implementation for testing rate controlling */
+private[streaming] class RateTestReceiver(receiverId: Int, host: Option[String] = None)
+  extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
+
+  private lazy val customBlockGenerator = supervisor.createBlockGenerator(
+    new BlockGeneratorListener {
+      override def onPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]): Unit = {}
+      override def onError(message: String, throwable: Throwable): Unit = {}
+      override def onGenerateBlock(blockId: StreamBlockId): Unit = {}
+      override def onAddData(data: Any, metadata: Any): Unit = {}
+    }
+  )
+
+  setReceiverId(receiverId)
+
+  override def onStart(): Unit = {
+    customBlockGenerator
+    RateTestReceiver.registerReceiver(this)
   }
-}
 
-/**
- * A Receiver as an object so we can read its rate limit. Make sure to call `reset()` when
- * reusing this receiver, otherwise a non-null `executor_` field will prevent it from being
- * serialized when receivers are installed on executors.
- *
- * @note It's necessary to be a top-level object, or else serialization would create another
- *       one on the executor side and we won't be able to read its rate limit.
- */
-private[streaming] object SingletonTestRateReceiver extends RateTestReceiver(0) {
+  override def onStop(): Unit = {
+    RateTestReceiver.deregisterReceiver()
+  }
+
+  override def preferredLocation: Option[String] = host
 
-  /** Reset the object to be usable in another test. */
-  def reset(): Unit = {
-    executor_ = null
+  def getDefaultBlockGeneratorRateLimit(): Long = {
+    supervisor.getCurrentRateLimit
+  }
+
+  def getCustomBlockGeneratorRateLimit(): Long = {
+    customBlockGenerator.getCurrentLimit
   }
 }
 
 /**
- * Dummy receiver implementation
+ * A helper object to RateTestReceiver that give access to the currently active RateTestReceiver
+ * instance.
  */
-private[streaming] class RateTestReceiver(receiverId: Int, host: Option[String] = None)
-  extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
+private[streaming] object RateTestReceiver {
+  @volatile private var activeReceiver: RateTestReceiver = null
 
-  setReceiverId(receiverId)
-
-  override def onStart(): Unit = {}
+  def registerReceiver(receiver: RateTestReceiver): Unit = {
+    activeReceiver = receiver
+  }
 
-  override def onStop(): Unit = {}
+  def deregisterReceiver(): Unit = {
+    activeReceiver = null
+  }
 
-  override def preferredLocation: Option[String] = host
+  def getActive(): Option[RateTestReceiver] = Option(activeReceiver)
 }

From 1723e34893f9b087727ea0e5c8b335645f42c295 Mon Sep 17 00:00:00 2001
From: cody koeninger <cody@koeninger.org>
Date: Thu, 6 Aug 2015 14:37:25 -0700
Subject: [PATCH 0898/1454] =?UTF-8?q?[DOCS]=20[STREAMING]=20make=20the=20e?=
 =?UTF-8?q?xisting=20parameter=20docs=20for=20OffsetRange=20ac=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…tually visible

Author: cody koeninger <cody@koeninger.org>

Closes #7995 from koeninger/doc-fixes and squashes the following commits:

87af9ea [cody koeninger] [Docs][Streaming] make the existing parameter docs for OffsetRange actually visible
---
 .../org/apache/spark/streaming/kafka/OffsetRange.scala    | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
index f326e7f1f6f8d..2f8981d4898bd 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
@@ -42,16 +42,16 @@ trait HasOffsetRanges {
  * :: Experimental ::
  * Represents a range of offsets from a single Kafka TopicAndPartition. Instances of this class
  * can be created with `OffsetRange.create()`.
+ * @param topic Kafka topic name
+ * @param partition Kafka partition id
+ * @param fromOffset Inclusive starting offset
+ * @param untilOffset Exclusive ending offset
  */
 @Experimental
 final class OffsetRange private(
-    /** Kafka topic name */
     val topic: String,
-    /** Kafka partition id */
     val partition: Int,
-    /** inclusive starting offset */
     val fromOffset: Long,
-    /** exclusive ending offset */
     val untilOffset: Long) extends Serializable {
   import OffsetRange.OffsetRangeTuple
 

From 346209097e88fe79015359e40b49c32cc0bdc439 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 6 Aug 2015 14:39:36 -0700
Subject: [PATCH 0899/1454] [SPARK-9639] [STREAMING] Fix a potential NPE in
 Streaming JobScheduler

Because `JobScheduler.stop(false)` may set `eventLoop` to null when `JobHandler` is running, then it's possible that when `post` is called, `eventLoop` happens to null.

This PR fixed this bug and also set threads in `jobExecutor` to `daemon`.

Author: zsxwing <zsxwing@gmail.com>

Closes #7960 from zsxwing/fix-npe and squashes the following commits:

b0864c4 [zsxwing] Fix a potential NPE in Streaming JobScheduler
---
 .../streaming/scheduler/JobScheduler.scala    | 32 +++++++++++++------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
index 7e735562dca33..6d4cdc4aa6b10 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.scheduler
 
-import java.util.concurrent.{TimeUnit, ConcurrentHashMap, Executors}
+import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
 
 import scala.collection.JavaConversions._
 import scala.util.{Failure, Success}
@@ -25,7 +25,7 @@ import scala.util.{Failure, Success}
 import org.apache.spark.Logging
 import org.apache.spark.rdd.PairRDDFunctions
 import org.apache.spark.streaming._
-import org.apache.spark.util.EventLoop
+import org.apache.spark.util.{EventLoop, ThreadUtils}
 
 
 private[scheduler] sealed trait JobSchedulerEvent
@@ -44,7 +44,8 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
   // https://gist.github.com/AlainODea/1375759b8720a3f9f094
   private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]
   private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
-  private val jobExecutor = Executors.newFixedThreadPool(numConcurrentJobs)
+  private val jobExecutor =
+    ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")
   private val jobGenerator = new JobGenerator(this)
   val clock = jobGenerator.clock
   val listenerBus = new StreamingListenerBus()
@@ -193,14 +194,25 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
       ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString)
       ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString)
       try {
-        eventLoop.post(JobStarted(job))
-        // Disable checks for existing output directories in jobs launched by the streaming
-        // scheduler, since we may need to write output to an existing directory during checkpoint
-        // recovery; see SPARK-4835 for more details.
-        PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
-          job.run()
+        // We need to assign `eventLoop` to a temp variable. Otherwise, because
+        // `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then
+        // it's possible that when `post` is called, `eventLoop` happens to null.
+        var _eventLoop = eventLoop
+        if (_eventLoop != null) {
+          _eventLoop.post(JobStarted(job))
+          // Disable checks for existing output directories in jobs launched by the streaming
+          // scheduler, since we may need to write output to an existing directory during checkpoint
+          // recovery; see SPARK-4835 for more details.
+          PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
+            job.run()
+          }
+          _eventLoop = eventLoop
+          if (_eventLoop != null) {
+            _eventLoop.post(JobCompleted(job))
+          }
+        } else {
+          // JobScheduler has been stopped.
         }
-        eventLoop.post(JobCompleted(job))
       } finally {
         ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null)
         ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null)

From 3504bf3aa9f7b75c0985f04ce2944833d8c5b5bd Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 6 Aug 2015 15:04:44 -0700
Subject: [PATCH 0900/1454] [SPARK-9630] [SQL] Clean up new aggregate operators
 (SPARK-9240 follow up)

This is the followup of https://github.com/apache/spark/pull/7813. It renames `HybridUnsafeAggregationIterator` to `TungstenAggregationIterator` and makes it only work with `UnsafeRow`. Also, I add a `TungstenAggregate` that uses `TungstenAggregationIterator` and make `SortBasedAggregate` (renamed from `SortBasedAggregate`) only works with `SafeRow`.

Author: Yin Huai <yhuai@databricks.com>

Closes #7954 from yhuai/agg-followUp and squashes the following commits:

4d2f4fc [Yin Huai] Add comments and free map.
0d7ddb9 [Yin Huai] Add TungstenAggregationQueryWithControlledFallbackSuite to test fall back process.
91d69c2 [Yin Huai] Rename UnsafeHybridAggregationIterator to  TungstenAggregateIteraotr and make it only work with UnsafeRow.
---
 .../expressions/aggregate/functions.scala     |  14 +-
 .../spark/sql/execution/SparkStrategies.scala |   3 +-
 .../sql/execution/UnsafeRowSerializer.scala   |  20 +-
 .../sql/execution/aggregate/Aggregate.scala   | 182 -----
 .../aggregate/SortBasedAggregate.scala        | 103 +++
 .../SortBasedAggregationIterator.scala        |  26 -
 .../aggregate/TungstenAggregate.scala         | 102 +++
 .../TungstenAggregationIterator.scala         | 667 ++++++++++++++++++
 .../UnsafeHybridAggregationIterator.scala     | 372 ----------
 .../spark/sql/execution/aggregate/utils.scala | 260 +++++--
 .../org/apache/spark/sql/SQLQuerySuite.scala  |   2 +-
 .../execution/AggregationQuerySuite.scala     | 104 ++-
 12 files changed, 1192 insertions(+), 663 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
index 88fb516e64aaf..a73024d6adba1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
@@ -31,8 +31,11 @@ case class Average(child: Expression) extends AlgebraicAggregate {
   override def dataType: DataType = resultType
 
   // Expected input data type.
-  // TODO: Once we remove the old code path, we can use our analyzer to cast NullType
-  // to the default data type of the NumericType.
+  // TODO: Right now, we replace old aggregate functions (based on AggregateExpression1) to the
+  // new version at planning time (after analysis phase). For now, NullType is added at here
+  // to make it resolved when we have cases like `select avg(null)`.
+  // We can use our analyzer to cast NullType to the default data type of the NumericType once
+  // we remove the old aggregate functions. Then, we will not need NullType at here.
   override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(NumericType, NullType))
 
   private val resultType = child.dataType match {
@@ -256,12 +259,19 @@ case class Sum(child: Expression) extends AlgebraicAggregate {
   override def dataType: DataType = resultType
 
   // Expected input data type.
+  // TODO: Right now, we replace old aggregate functions (based on AggregateExpression1) to the
+  // new version at planning time (after analysis phase). For now, NullType is added at here
+  // to make it resolved when we have cases like `select sum(null)`.
+  // We can use our analyzer to cast NullType to the default data type of the NumericType once
+  // we remove the old aggregate functions. Then, we will not need NullType at here.
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(LongType, DoubleType, DecimalType, NullType))
 
   private val resultType = child.dataType match {
     case DecimalType.Fixed(precision, scale) =>
       DecimalType.bounded(precision + 10, scale)
+    // TODO: Remove this line once we remove the NullType from inputTypes.
+    case NullType => IntegerType
     case _ => child.dataType
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index a730ffbb217c0..c5aaebe673225 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -191,8 +191,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
             // aggregate function to the corresponding attribute of the function.
             val aggregateFunctionMap = aggregateExpressions.map { agg =>
               val aggregateFunction = agg.aggregateFunction
+              val attribtue = Alias(aggregateFunction, aggregateFunction.toString)().toAttribute
               (aggregateFunction, agg.isDistinct) ->
-                Alias(aggregateFunction, aggregateFunction.toString)().toAttribute
+                (aggregateFunction -> attribtue)
             }.toMap
 
             val (functionsWithDistinct, functionsWithoutDistinct) =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 16498da080c88..39f8f992a9f00 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import java.io.{DataInputStream, DataOutputStream, OutputStream, InputStream}
+import java.io._
 import java.nio.ByteBuffer
 
 import scala.reflect.ClassTag
@@ -58,11 +58,26 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
    */
   override def serializeStream(out: OutputStream): SerializationStream = new SerializationStream {
     private[this] var writeBuffer: Array[Byte] = new Array[Byte](4096)
+    // When `out` is backed by ChainedBufferOutputStream, we will get an
+    // UnsupportedOperationException when we call dOut.writeInt because it internally calls
+    // ChainedBufferOutputStream's write(b: Int), which is not supported.
+    // To workaround this issue, we create an array for sorting the int value.
+    // To reproduce the problem, use dOut.writeInt(row.getSizeInBytes) and
+    // run SparkSqlSerializer2SortMergeShuffleSuite.
+    private[this] var intBuffer: Array[Byte] = new Array[Byte](4)
     private[this] val dOut: DataOutputStream = new DataOutputStream(out)
 
     override def writeValue[T: ClassTag](value: T): SerializationStream = {
       val row = value.asInstanceOf[UnsafeRow]
-      dOut.writeInt(row.getSizeInBytes)
+      val size = row.getSizeInBytes
+      // This part is based on DataOutputStream's writeInt.
+      // It is for dOut.writeInt(row.getSizeInBytes).
+      intBuffer(0) = ((size >>> 24) & 0xFF).toByte
+      intBuffer(1) = ((size >>> 16) & 0xFF).toByte
+      intBuffer(2) = ((size >>> 8) & 0xFF).toByte
+      intBuffer(3) = ((size >>> 0) & 0xFF).toByte
+      dOut.write(intBuffer, 0, 4)
+
       row.writeToStream(out, writeBuffer)
       this
     }
@@ -90,6 +105,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
 
     override def close(): Unit = {
       writeBuffer = null
+      intBuffer = null
       dOut.writeInt(EOF)
       dOut.close()
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
deleted file mode 100644
index cf568dc048674..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
+++ /dev/null
@@ -1,182 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.aggregate
-
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.errors._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
-import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan, UnaryNode}
-import org.apache.spark.sql.types.StructType
-
-/**
- * An Aggregate Operator used to evaluate [[AggregateFunction2]]. Based on the data types
- * of the grouping expressions and aggregate functions, it determines if it uses
- * sort-based aggregation and hybrid (hash-based with sort-based as the fallback) to
- * process input rows.
- */
-case class Aggregate(
-    requiredChildDistributionExpressions: Option[Seq[Expression]],
-    groupingExpressions: Seq[NamedExpression],
-    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-    nonCompleteAggregateAttributes: Seq[Attribute],
-    completeAggregateExpressions: Seq[AggregateExpression2],
-    completeAggregateAttributes: Seq[Attribute],
-    initialInputBufferOffset: Int,
-    resultExpressions: Seq[NamedExpression],
-    child: SparkPlan)
-  extends UnaryNode {
-
-  private[this] val allAggregateExpressions =
-    nonCompleteAggregateExpressions ++ completeAggregateExpressions
-
-  private[this] val hasNonAlgebricAggregateFunctions =
-    !allAggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate])
-
-  // Use the hybrid iterator if (1) unsafe is enabled, (2) the schemata of
-  // grouping key and aggregation buffer is supported; and (3) all
-  // aggregate functions are algebraic.
-  private[this] val supportsHybridIterator: Boolean = {
-    val aggregationBufferSchema: StructType =
-      StructType.fromAttributes(
-        allAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes))
-    val groupKeySchema: StructType =
-      StructType.fromAttributes(groupingExpressions.map(_.toAttribute))
-
-    val schemaSupportsUnsafe: Boolean =
-      UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
-        UnsafeProjection.canSupport(groupKeySchema)
-
-    // TODO: Use the hybrid iterator for non-algebric aggregate functions.
-    sqlContext.conf.unsafeEnabled && schemaSupportsUnsafe && !hasNonAlgebricAggregateFunctions
-  }
-
-  // We need to use sorted input if we have grouping expressions, and
-  // we cannot use the hybrid iterator or the hybrid is disabled.
-  private[this] val requiresSortedInput: Boolean = {
-    groupingExpressions.nonEmpty && !supportsHybridIterator
-  }
-
-  override def canProcessUnsafeRows: Boolean = !hasNonAlgebricAggregateFunctions
-
-  // If result expressions' data types are all fixed length, we generate unsafe rows
-  // (We have this requirement instead of check the result of UnsafeProjection.canSupport
-  // is because we use a mutable projection to generate the result).
-  override def outputsUnsafeRows: Boolean = {
-    // resultExpressions.map(_.dataType).forall(UnsafeRow.isFixedLength)
-    // TODO: Supports generating UnsafeRows. We can just re-enable the line above and fix
-    // any issue we get.
-    false
-  }
-
-  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
-
-  override def requiredChildDistribution: List[Distribution] = {
-    requiredChildDistributionExpressions match {
-      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
-      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
-      case None => UnspecifiedDistribution :: Nil
-    }
-  }
-
-  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
-    if (requiresSortedInput) {
-      // TODO: We should not sort the input rows if they are just in reversed order.
-      groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
-    } else {
-      Seq.fill(children.size)(Nil)
-    }
-  }
-
-  override def outputOrdering: Seq[SortOrder] = {
-    if (requiresSortedInput) {
-      // It is possible that the child.outputOrdering starts with the required
-      // ordering expressions (e.g. we require [a] as the sort expression and the
-      // child's outputOrdering is [a, b]). We can only guarantee the output rows
-      // are sorted by values of groupingExpressions.
-      groupingExpressions.map(SortOrder(_, Ascending))
-    } else {
-      Nil
-    }
-  }
-
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
-    child.execute().mapPartitions { iter =>
-      // Because the constructor of an aggregation iterator will read at least the first row,
-      // we need to get the value of iter.hasNext first.
-      val hasInput = iter.hasNext
-      val useHybridIterator =
-        hasInput &&
-          supportsHybridIterator &&
-          groupingExpressions.nonEmpty
-      if (useHybridIterator) {
-        UnsafeHybridAggregationIterator.createFromInputIterator(
-          groupingExpressions,
-          nonCompleteAggregateExpressions,
-          nonCompleteAggregateAttributes,
-          completeAggregateExpressions,
-          completeAggregateAttributes,
-          initialInputBufferOffset,
-          resultExpressions,
-          newMutableProjection _,
-          child.output,
-          iter,
-          outputsUnsafeRows)
-      } else {
-        if (!hasInput && groupingExpressions.nonEmpty) {
-          // This is a grouped aggregate and the input iterator is empty,
-          // so return an empty iterator.
-          Iterator[InternalRow]()
-        } else {
-          val outputIter = SortBasedAggregationIterator.createFromInputIterator(
-            groupingExpressions,
-            nonCompleteAggregateExpressions,
-            nonCompleteAggregateAttributes,
-            completeAggregateExpressions,
-            completeAggregateAttributes,
-            initialInputBufferOffset,
-            resultExpressions,
-            newMutableProjection _ ,
-            newProjection _,
-            child.output,
-            iter,
-            outputsUnsafeRows)
-          if (!hasInput && groupingExpressions.isEmpty) {
-            // There is no input and there is no grouping expressions.
-            // We need to output a single row as the output.
-            Iterator[InternalRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
-          } else {
-            outputIter
-          }
-        }
-      }
-    }
-  }
-
-  override def simpleString: String = {
-    val iterator = if (supportsHybridIterator && groupingExpressions.nonEmpty) {
-      classOf[UnsafeHybridAggregationIterator].getSimpleName
-    } else {
-      classOf[SortBasedAggregationIterator].getSimpleName
-    }
-
-    s"""NewAggregate with $iterator ${groupingExpressions} ${allAggregateExpressions}"""
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
new file mode 100644
index 0000000000000..ad428ad663f30
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
@@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.errors._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
+import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan, UnaryNode}
+import org.apache.spark.sql.types.StructType
+
+case class SortBasedAggregate(
+    requiredChildDistributionExpressions: Option[Seq[Expression]],
+    groupingExpressions: Seq[NamedExpression],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    nonCompleteAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def outputsUnsafeRows: Boolean = false
+
+  override def canProcessUnsafeRows: Boolean = false
+
+  override def canProcessSafeRows: Boolean = true
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  override def requiredChildDistribution: List[Distribution] = {
+    requiredChildDistributionExpressions match {
+      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
+      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
+      case None => UnspecifiedDistribution :: Nil
+    }
+  }
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
+    groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
+  }
+
+  override def outputOrdering: Seq[SortOrder] = {
+    groupingExpressions.map(SortOrder(_, Ascending))
+  }
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    child.execute().mapPartitions { iter =>
+      // Because the constructor of an aggregation iterator will read at least the first row,
+      // we need to get the value of iter.hasNext first.
+      val hasInput = iter.hasNext
+      if (!hasInput && groupingExpressions.nonEmpty) {
+        // This is a grouped aggregate and the input iterator is empty,
+        // so return an empty iterator.
+        Iterator[InternalRow]()
+      } else {
+        val outputIter = SortBasedAggregationIterator.createFromInputIterator(
+          groupingExpressions,
+          nonCompleteAggregateExpressions,
+          nonCompleteAggregateAttributes,
+          completeAggregateExpressions,
+          completeAggregateAttributes,
+          initialInputBufferOffset,
+          resultExpressions,
+          newMutableProjection _,
+          newProjection _,
+          child.output,
+          iter,
+          outputsUnsafeRows)
+        if (!hasInput && groupingExpressions.isEmpty) {
+          // There is no input and there is no grouping expressions.
+          // We need to output a single row as the output.
+          Iterator[InternalRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
+        } else {
+          outputIter
+        }
+      }
+    }
+  }
+
+  override def simpleString: String = {
+    val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
+    s"""SortBasedAggregate ${groupingExpressions} ${allAggregateExpressions}"""
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
index 40f6bff53d2b7..67ebafde25ad3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
@@ -204,31 +204,5 @@ object SortBasedAggregationIterator {
       newMutableProjection,
       outputsUnsafeRows)
   }
-
-  def createFromKVIterator(
-      groupingKeyAttributes: Seq[Attribute],
-      valueAttributes: Seq[Attribute],
-      inputKVIterator: KVIterator[InternalRow, InternalRow],
-      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-      nonCompleteAggregateAttributes: Seq[Attribute],
-      completeAggregateExpressions: Seq[AggregateExpression2],
-      completeAggregateAttributes: Seq[Attribute],
-      initialInputBufferOffset: Int,
-      resultExpressions: Seq[NamedExpression],
-      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-      outputsUnsafeRows: Boolean): SortBasedAggregationIterator = {
-    new SortBasedAggregationIterator(
-      groupingKeyAttributes,
-      valueAttributes,
-      inputKVIterator,
-      nonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes,
-      completeAggregateExpressions,
-      completeAggregateAttributes,
-      initialInputBufferOffset,
-      resultExpressions,
-      newMutableProjection,
-      outputsUnsafeRows)
-  }
   // scalastyle:on
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
new file mode 100644
index 0000000000000..5a0b4d47d62f8
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.errors._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
+import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
+
+case class TungstenAggregate(
+    requiredChildDistributionExpressions: Option[Seq[Expression]],
+    groupingExpressions: Seq[NamedExpression],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def outputsUnsafeRows: Boolean = true
+
+  override def canProcessUnsafeRows: Boolean = true
+
+  override def canProcessSafeRows: Boolean = false
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  override def requiredChildDistribution: List[Distribution] = {
+    requiredChildDistributionExpressions match {
+      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
+      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
+      case None => UnspecifiedDistribution :: Nil
+    }
+  }
+
+  // This is for testing. We force TungstenAggregationIterator to fall back to sort-based
+  // aggregation once it has processed a given number of input rows.
+  private val testFallbackStartsAt: Option[Int] = {
+    sqlContext.getConf("spark.sql.TungstenAggregate.testFallbackStartsAt", null) match {
+      case null | "" => None
+      case fallbackStartsAt => Some(fallbackStartsAt.toInt)
+    }
+  }
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    child.execute().mapPartitions { iter =>
+      val hasInput = iter.hasNext
+      if (!hasInput && groupingExpressions.nonEmpty) {
+        // This is a grouped aggregate and the input iterator is empty,
+        // so return an empty iterator.
+        Iterator.empty.asInstanceOf[Iterator[UnsafeRow]]
+      } else {
+        val aggregationIterator =
+          new TungstenAggregationIterator(
+            groupingExpressions,
+            nonCompleteAggregateExpressions,
+            completeAggregateExpressions,
+            initialInputBufferOffset,
+            resultExpressions,
+            newMutableProjection,
+            child.output,
+            iter.asInstanceOf[Iterator[UnsafeRow]],
+            testFallbackStartsAt)
+
+        if (!hasInput && groupingExpressions.isEmpty) {
+          Iterator.single[UnsafeRow](aggregationIterator.outputForEmptyGroupingKeyWithoutInput())
+        } else {
+          aggregationIterator
+        }
+      }
+    }
+  }
+
+  override def simpleString: String = {
+    val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
+
+    testFallbackStartsAt match {
+      case None => s"TungstenAggregate ${groupingExpressions} ${allAggregateExpressions}"
+      case Some(fallbackStartsAt) =>
+        s"TungstenAggregateWithControlledFallback ${groupingExpressions} " +
+          s"${allAggregateExpressions} fallbackStartsAt=$fallbackStartsAt"
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
new file mode 100644
index 0000000000000..b9d44aace1009
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -0,0 +1,667 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.unsafe.KVIterator
+import org.apache.spark.{Logging, SparkEnv, TaskContext}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
+import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
+import org.apache.spark.sql.types.StructType
+
+/**
+ * An iterator used to evaluate aggregate functions. It operates on [[UnsafeRow]]s.
+ *
+ * This iterator first uses hash-based aggregation to process input rows. It uses
+ * a hash map to store groups and their corresponding aggregation buffers. If we
+ * this map cannot allocate memory from [[org.apache.spark.shuffle.ShuffleMemoryManager]],
+ * it switches to sort-based aggregation. The process of the switch has the following step:
+ *  - Step 1: Sort all entries of the hash map based on values of grouping expressions and
+ *            spill them to disk.
+ *  - Step 2: Create a external sorter based on the spilled sorted map entries.
+ *  - Step 3: Redirect all input rows to the external sorter.
+ *  - Step 4: Get a sorted [[KVIterator]] from the external sorter.
+ *  - Step 5: Initialize sort-based aggregation.
+ * Then, this iterator works in the way of sort-based aggregation.
+ *
+ * The code of this class is organized as follows:
+ *  - Part 1: Initializing aggregate functions.
+ *  - Part 2: Methods and fields used by setting aggregation buffer values,
+ *            processing input rows from inputIter, and generating output
+ *            rows.
+ *  - Part 3: Methods and fields used by hash-based aggregation.
+ *  - Part 4: The function used to switch this iterator from hash-based
+ *            aggregation to sort-based aggregation.
+ *  - Part 5: Methods and fields used by sort-based aggregation.
+ *  - Part 6: Loads input and process input rows.
+ *  - Part 7: Public methods of this iterator.
+ *  - Part 8: A utility function used to generate a result when there is no
+ *            input and there is no grouping expression.
+ *
+ * @param groupingExpressions
+ *   expressions for grouping keys
+ * @param nonCompleteAggregateExpressions
+ *   [[AggregateExpression2]] containing [[AggregateFunction2]]s with mode [[Partial]],
+ *   [[PartialMerge]], or [[Final]].
+ * @param completeAggregateExpressions
+ *   [[AggregateExpression2]] containing [[AggregateFunction2]]s with mode [[Complete]].
+ * @param initialInputBufferOffset
+ *   If this iterator is used to handle functions with mode [[PartialMerge]] or [[Final]].
+ *   The input rows have the format of `grouping keys + aggregation buffer`.
+ *   This offset indicates the starting position of aggregation buffer in a input row.
+ * @param resultExpressions
+ *   expressions for generating output rows.
+ * @param newMutableProjection
+ *   the function used to create mutable projections.
+ * @param originalInputAttributes
+ *   attributes of representing input rows from `inputIter`.
+ * @param inputIter
+ *   the iterator containing input [[UnsafeRow]]s.
+ */
+class TungstenAggregationIterator(
+    groupingExpressions: Seq[NamedExpression],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    originalInputAttributes: Seq[Attribute],
+    inputIter: Iterator[UnsafeRow],
+    testFallbackStartsAt: Option[Int])
+  extends Iterator[UnsafeRow] with Logging {
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 1: Initializing aggregate functions.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // A Seq containing all AggregateExpressions.
+  // It is important that all AggregateExpressions with the mode Partial, PartialMerge or Final
+  // are at the beginning of the allAggregateExpressions.
+  private[this] val allAggregateExpressions: Seq[AggregateExpression2] =
+    nonCompleteAggregateExpressions ++ completeAggregateExpressions
+
+  // Check to make sure we do not have more than three modes in our AggregateExpressions.
+  // If we have, users are hitting a bug and we throw an IllegalStateException.
+  if (allAggregateExpressions.map(_.mode).distinct.length > 2) {
+    throw new IllegalStateException(
+      s"$allAggregateExpressions should have no more than 2 kinds of modes.")
+  }
+
+  //
+  // The modes of AggregateExpressions. Right now, we can handle the following mode:
+  //  - Partial-only:
+  //      All AggregateExpressions have the mode of Partial.
+  //      For this case, aggregationMode is (Some(Partial), None).
+  //  - PartialMerge-only:
+  //      All AggregateExpressions have the mode of PartialMerge).
+  //      For this case, aggregationMode is (Some(PartialMerge), None).
+  //  - Final-only:
+  //      All AggregateExpressions have the mode of Final.
+  //      For this case, aggregationMode is (Some(Final), None).
+  //  - Final-Complete:
+  //      Some AggregateExpressions have the mode of Final and
+  //      others have the mode of Complete. For this case,
+  //      aggregationMode is (Some(Final), Some(Complete)).
+  //  - Complete-only:
+  //      nonCompleteAggregateExpressions is empty and we have AggregateExpressions
+  //      with mode Complete in completeAggregateExpressions. For this case,
+  //      aggregationMode is (None, Some(Complete)).
+  //  - Grouping-only:
+  //      There is no AggregateExpression. For this case, AggregationMode is (None,None).
+  //
+  private[this] var aggregationMode: (Option[AggregateMode], Option[AggregateMode]) = {
+    nonCompleteAggregateExpressions.map(_.mode).distinct.headOption ->
+      completeAggregateExpressions.map(_.mode).distinct.headOption
+  }
+
+  // All aggregate functions. TungstenAggregationIterator only handles AlgebraicAggregates.
+  // If there is any functions that is not an AlgebraicAggregate, we throw an
+  // IllegalStateException.
+  private[this] val allAggregateFunctions: Array[AlgebraicAggregate] = {
+    if (!allAggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate])) {
+      throw new IllegalStateException(
+        "Only AlgebraicAggregates should be passed in TungstenAggregationIterator.")
+    }
+
+    allAggregateExpressions
+      .map(_.aggregateFunction.asInstanceOf[AlgebraicAggregate])
+      .toArray
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 2: Methods and fields used by setting aggregation buffer values,
+  //         processing input rows from inputIter, and generating output
+  //         rows.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // The projection used to initialize buffer values.
+  private[this] val algebraicInitialProjection: MutableProjection = {
+    val initExpressions = allAggregateFunctions.flatMap(_.initialValues)
+    newMutableProjection(initExpressions, Nil)()
+  }
+
+  // Creates a new aggregation buffer and initializes buffer values.
+  // This functions should be only called at most three times (when we create the hash map,
+  // when we switch to sort-based aggregation, and when we create the re-used buffer for
+  // sort-based aggregation).
+  private def createNewAggregationBuffer(): UnsafeRow = {
+    val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val bufferRowSize: Int = bufferSchema.length
+
+    val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
+    val unsafeProjection =
+      UnsafeProjection.create(bufferSchema.map(_.dataType))
+    val buffer = unsafeProjection.apply(genericMutableBuffer)
+    algebraicInitialProjection.target(buffer)(EmptyRow)
+    buffer
+  }
+
+  // Creates a function used to process a row based on the given inputAttributes.
+  private def generateProcessRow(
+      inputAttributes: Seq[Attribute]): (UnsafeRow, UnsafeRow) => Unit = {
+
+    val aggregationBufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val aggregationBufferSchema = StructType.fromAttributes(aggregationBufferAttributes)
+    val inputSchema = StructType.fromAttributes(inputAttributes)
+    val unsafeRowJoiner =
+      GenerateUnsafeRowJoiner.create(aggregationBufferSchema, inputSchema)
+
+    aggregationMode match {
+      // Partial-only
+      case (Some(Partial), None) =>
+        val updateExpressions = allAggregateFunctions.flatMap(_.updateExpressions)
+        val algebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          algebraicUpdateProjection.target(currentBuffer)
+          algebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+        }
+
+      // PartialMerge-only or Final-only
+      case (Some(PartialMerge), None) | (Some(Final), None) =>
+        val mergeExpressions = allAggregateFunctions.flatMap(_.mergeExpressions)
+        // This projection is used to merge buffer values for all AlgebraicAggregates.
+        val algebraicMergeProjection =
+          newMutableProjection(
+            mergeExpressions,
+            aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          // Process all algebraic aggregate functions.
+          algebraicMergeProjection.target(currentBuffer)
+          algebraicMergeProjection(unsafeRowJoiner.join(currentBuffer, row))
+        }
+
+      // Final-Complete
+      case (Some(Final), Some(Complete)) =>
+        val nonCompleteAggregateFunctions: Array[AlgebraicAggregate] =
+          allAggregateFunctions.take(nonCompleteAggregateExpressions.length)
+        val completeAggregateFunctions: Array[AlgebraicAggregate] =
+          allAggregateFunctions.takeRight(completeAggregateExpressions.length)
+
+        val completeOffsetExpressions =
+          Seq.fill(completeAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
+        val mergeExpressions =
+          nonCompleteAggregateFunctions.flatMap(_.mergeExpressions) ++ completeOffsetExpressions
+        val finalAlgebraicMergeProjection =
+          newMutableProjection(
+            mergeExpressions,
+            aggregationBufferAttributes ++ inputAttributes)()
+
+        // We do not touch buffer values of aggregate functions with the Final mode.
+        val finalOffsetExpressions =
+          Seq.fill(nonCompleteAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
+        val updateExpressions =
+          finalOffsetExpressions ++ completeAggregateFunctions.flatMap(_.updateExpressions)
+        val completeAlgebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          val input = unsafeRowJoiner.join(currentBuffer, row)
+          // For all aggregate functions with mode Complete, update the given currentBuffer.
+          completeAlgebraicUpdateProjection.target(currentBuffer)(input)
+
+          // For all aggregate functions with mode Final, merge buffer values in row to
+          // currentBuffer.
+          finalAlgebraicMergeProjection.target(currentBuffer)(input)
+        }
+
+      // Complete-only
+      case (None, Some(Complete)) =>
+        val completeAggregateFunctions: Array[AlgebraicAggregate] =
+          allAggregateFunctions.takeRight(completeAggregateExpressions.length)
+
+        val updateExpressions =
+          completeAggregateFunctions.flatMap(_.updateExpressions)
+        val completeAlgebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          completeAlgebraicUpdateProjection.target(currentBuffer)
+          // For all aggregate functions with mode Complete, update the given currentBuffer.
+          completeAlgebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+        }
+
+      // Grouping only.
+      case (None, None) => (currentBuffer: UnsafeRow, row: UnsafeRow) => {}
+
+      case other =>
+        throw new IllegalStateException(
+          s"${aggregationMode} should not be passed into TungstenAggregationIterator.")
+    }
+  }
+
+  // Creates a function used to generate output rows.
+  private def generateResultProjection(): (UnsafeRow, UnsafeRow) => UnsafeRow = {
+
+    val groupingAttributes = groupingExpressions.map(_.toAttribute)
+    val groupingKeySchema = StructType.fromAttributes(groupingAttributes)
+    val bufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val bufferSchema = StructType.fromAttributes(bufferAttributes)
+    val unsafeRowJoiner = GenerateUnsafeRowJoiner.create(groupingKeySchema, bufferSchema)
+
+    aggregationMode match {
+      // Partial-only or PartialMerge-only: every output row is basically the values of
+      // the grouping expressions and the corresponding aggregation buffer.
+      case (Some(Partial), None) | (Some(PartialMerge), None) =>
+        (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
+          unsafeRowJoiner.join(currentGroupingKey, currentBuffer)
+        }
+
+      // Final-only, Complete-only and Final-Complete: a output row is generated based on
+      // resultExpressions.
+      case (Some(Final), None) | (Some(Final) | None, Some(Complete)) =>
+        val resultProjection =
+          UnsafeProjection.create(resultExpressions, groupingAttributes ++ bufferAttributes)
+
+        (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
+          resultProjection(unsafeRowJoiner.join(currentGroupingKey, currentBuffer))
+        }
+
+      // Grouping-only: a output row is generated from values of grouping expressions.
+      case (None, None) =>
+        val resultProjection =
+          UnsafeProjection.create(resultExpressions, groupingAttributes)
+
+        (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
+          resultProjection(currentGroupingKey)
+        }
+
+      case other =>
+        throw new IllegalStateException(
+          s"${aggregationMode} should not be passed into TungstenAggregationIterator.")
+    }
+  }
+
+  // An UnsafeProjection used to extract grouping keys from the input rows.
+  private[this] val groupProjection =
+    UnsafeProjection.create(groupingExpressions, originalInputAttributes)
+
+  // A function used to process a input row. Its first argument is the aggregation buffer
+  // and the second argument is the input row.
+  private[this] var processRow: (UnsafeRow, UnsafeRow) => Unit =
+    generateProcessRow(originalInputAttributes)
+
+  // A function used to generate output rows based on the grouping keys (first argument)
+  // and the corresponding aggregation buffer (second argument).
+  private[this] var generateOutput: (UnsafeRow, UnsafeRow) => UnsafeRow =
+    generateResultProjection()
+
+  // An aggregation buffer containing initial buffer values. It is used to
+  // initialize other aggregation buffers.
+  private[this] val initialAggregationBuffer: UnsafeRow = createNewAggregationBuffer()
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 3: Methods and fields used by hash-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // This is the hash map used for hash-based aggregation. It is backed by an
+  // UnsafeFixedWidthAggregationMap and it is used to store
+  // all groups and their corresponding aggregation buffers for hash-based aggregation.
+  private[this] val hashMap = new UnsafeFixedWidthAggregationMap(
+    initialAggregationBuffer,
+    StructType.fromAttributes(allAggregateFunctions.flatMap(_.bufferAttributes)),
+    StructType.fromAttributes(groupingExpressions.map(_.toAttribute)),
+    TaskContext.get.taskMemoryManager(),
+    SparkEnv.get.shuffleMemoryManager,
+    1024 * 16, // initial capacity
+    SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m"),
+    false // disable tracking of performance metrics
+  )
+
+  // The function used to read and process input rows. When processing input rows,
+  // it first uses hash-based aggregation by putting groups and their buffers in
+  // hashMap. If we could not allocate more memory for the map, we switch to
+  // sort-based aggregation (by calling switchToSortBasedAggregation).
+  private def processInputs(): Unit = {
+    while (!sortBased && inputIter.hasNext) {
+      val newInput = inputIter.next()
+      val groupingKey = groupProjection.apply(newInput)
+      val buffer: UnsafeRow = hashMap.getAggregationBuffer(groupingKey)
+      if (buffer == null) {
+        // buffer == null means that we could not allocate more memory.
+        // Now, we need to spill the map and switch to sort-based aggregation.
+        switchToSortBasedAggregation(groupingKey, newInput)
+      } else {
+        processRow(buffer, newInput)
+      }
+    }
+  }
+
+  // This function is only used for testing. It basically the same as processInputs except
+  // that it switch to sort-based aggregation after `fallbackStartsAt` input rows have
+  // been processed.
+  private def processInputsWithControlledFallback(fallbackStartsAt: Int): Unit = {
+    var i = 0
+    while (!sortBased && inputIter.hasNext) {
+      val newInput = inputIter.next()
+      val groupingKey = groupProjection.apply(newInput)
+      val buffer: UnsafeRow = if (i < fallbackStartsAt) {
+        hashMap.getAggregationBuffer(groupingKey)
+      } else {
+        null
+      }
+      if (buffer == null) {
+        // buffer == null means that we could not allocate more memory.
+        // Now, we need to spill the map and switch to sort-based aggregation.
+        switchToSortBasedAggregation(groupingKey, newInput)
+      } else {
+        processRow(buffer, newInput)
+      }
+      i += 1
+    }
+  }
+
+  // The iterator created from hashMap. It is used to generate output rows when we
+  // are using hash-based aggregation.
+  private[this] var aggregationBufferMapIterator: KVIterator[UnsafeRow, UnsafeRow] = null
+
+  // Indicates if aggregationBufferMapIterator still has key-value pairs.
+  private[this] var mapIteratorHasNext: Boolean = false
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 4: The function used to switch this iterator from hash-based
+  // aggregation to sort-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: UnsafeRow): Unit = {
+    logInfo("falling back to sort based aggregation.")
+    // Step 1: Get the ExternalSorter containing sorted entries of the map.
+    val externalSorter: UnsafeKVExternalSorter = hashMap.destructAndCreateExternalSorter()
+
+    // Step 2: Free the memory used by the map.
+    hashMap.free()
+
+    // Step 3: If we have aggregate function with mode Partial or Complete,
+    // we need to process input rows to get aggregation buffer.
+    // So, later in the sort-based aggregation iterator, we can do merge.
+    // If aggregate functions are with mode Final and PartialMerge,
+    // we just need to project the aggregation buffer from an input row.
+    val needsProcess = aggregationMode match {
+      case (Some(Partial), None) => true
+      case (None, Some(Complete)) => true
+      case (Some(Final), Some(Complete)) => true
+      case _ => false
+    }
+
+    if (needsProcess) {
+      // First, we create a buffer.
+      val buffer = createNewAggregationBuffer()
+
+      // Process firstKey and firstInput.
+      // Initialize buffer.
+      buffer.copyFrom(initialAggregationBuffer)
+      processRow(buffer, firstInput)
+      externalSorter.insertKV(firstKey, buffer)
+
+      // Process the rest of input rows.
+      while (inputIter.hasNext) {
+        val newInput = inputIter.next()
+        val groupingKey = groupProjection.apply(newInput)
+        buffer.copyFrom(initialAggregationBuffer)
+        processRow(buffer, newInput)
+        externalSorter.insertKV(groupingKey, buffer)
+      }
+    } else {
+      // When needsProcess is false, the format of input rows is groupingKey + aggregation buffer.
+      // We need to project the aggregation buffer part from an input row.
+      val buffer = createNewAggregationBuffer()
+      // The originalInputAttributes are using cloneBufferAttributes. So, we need to use
+      // allAggregateFunctions.flatMap(_.cloneBufferAttributes).
+      val bufferExtractor = newMutableProjection(
+        allAggregateFunctions.flatMap(_.cloneBufferAttributes),
+        originalInputAttributes)()
+      bufferExtractor.target(buffer)
+
+      // Insert firstKey and its buffer.
+      bufferExtractor(firstInput)
+      externalSorter.insertKV(firstKey, buffer)
+
+      // Insert the rest of input rows.
+      while (inputIter.hasNext) {
+        val newInput = inputIter.next()
+        val groupingKey = groupProjection.apply(newInput)
+        bufferExtractor(newInput)
+        externalSorter.insertKV(groupingKey, buffer)
+      }
+    }
+
+    // Set aggregationMode, processRow, and generateOutput for sort-based aggregation.
+    val newAggregationMode = aggregationMode match {
+      case (Some(Partial), None) => (Some(PartialMerge), None)
+      case (None, Some(Complete)) => (Some(Final), None)
+      case (Some(Final), Some(Complete)) => (Some(Final), None)
+      case other => other
+    }
+    aggregationMode = newAggregationMode
+
+    // Basically the value of the KVIterator returned by externalSorter
+    // will just aggregation buffer. At here, we use cloneBufferAttributes.
+    val newInputAttributes: Seq[Attribute] =
+      allAggregateFunctions.flatMap(_.cloneBufferAttributes)
+
+    // Set up new processRow and generateOutput.
+    processRow = generateProcessRow(newInputAttributes)
+    generateOutput = generateResultProjection()
+
+    // Step 5: Get the sorted iterator from the externalSorter.
+    sortedKVIterator = externalSorter.sortedIterator()
+
+    // Step 6: Pre-load the first key-value pair from the sorted iterator to make
+    // hasNext idempotent.
+    sortedInputHasNewGroup = sortedKVIterator.next()
+
+    // Copy the first key and value (aggregation buffer).
+    if (sortedInputHasNewGroup) {
+      val key = sortedKVIterator.getKey
+      val value = sortedKVIterator.getValue
+      nextGroupingKey = key.copy()
+      currentGroupingKey = key.copy()
+      firstRowInNextGroup = value.copy()
+    }
+
+    // Step 7: set sortBased to true.
+    sortBased = true
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 5: Methods and fields used by sort-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // Indicates if we are using sort-based aggregation. Because we first try to use
+  // hash-based aggregation, its initial value is false.
+  private[this] var sortBased: Boolean = false
+
+  // The KVIterator containing input rows for the sort-based aggregation. It will be
+  // set in switchToSortBasedAggregation when we switch to sort-based aggregation.
+  private[this] var sortedKVIterator: UnsafeKVExternalSorter#KVSorterIterator = null
+
+  // The grouping key of the current group.
+  private[this] var currentGroupingKey: UnsafeRow = null
+
+  // The grouping key of next group.
+  private[this] var nextGroupingKey: UnsafeRow = null
+
+  // The first row of next group.
+  private[this] var firstRowInNextGroup: UnsafeRow = null
+
+  // Indicates if we has new group of rows from the sorted input iterator.
+  private[this] var sortedInputHasNewGroup: Boolean = false
+
+  // The aggregation buffer used by the sort-based aggregation.
+  private[this] val sortBasedAggregationBuffer: UnsafeRow = createNewAggregationBuffer()
+
+  // Processes rows in the current group. It will stop when it find a new group.
+  private def processCurrentSortedGroup(): Unit = {
+    // First, we need to copy nextGroupingKey to currentGroupingKey.
+    currentGroupingKey.copyFrom(nextGroupingKey)
+    // Now, we will start to find all rows belonging to this group.
+    // We create a variable to track if we see the next group.
+    var findNextPartition = false
+    // firstRowInNextGroup is the first row of this group. We first process it.
+    processRow(sortBasedAggregationBuffer, firstRowInNextGroup)
+
+    // The search will stop when we see the next group or there is no
+    // input row left in the iter.
+    // Pre-load the first key-value pair to make the condition of the while loop
+    // has no action (we do not trigger loading a new key-value pair
+    // when we evaluate the condition).
+    var hasNext = sortedKVIterator.next()
+    while (!findNextPartition && hasNext) {
+      // Get the grouping key and value (aggregation buffer).
+      val groupingKey = sortedKVIterator.getKey
+      val inputAggregationBuffer = sortedKVIterator.getValue
+
+      // Check if the current row belongs the current input row.
+      if (currentGroupingKey.equals(groupingKey)) {
+        processRow(sortBasedAggregationBuffer, inputAggregationBuffer)
+
+        hasNext = sortedKVIterator.next()
+      } else {
+        // We find a new group.
+        findNextPartition = true
+        // copyFrom will fail when
+        nextGroupingKey.copyFrom(groupingKey) // = groupingKey.copy()
+        firstRowInNextGroup.copyFrom(inputAggregationBuffer) // = inputAggregationBuffer.copy()
+
+      }
+    }
+    // We have not seen a new group. It means that there is no new row in the input
+    // iter. The current group is the last group of the sortedKVIterator.
+    if (!findNextPartition) {
+      sortedInputHasNewGroup = false
+      sortedKVIterator.close()
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 6: Loads input rows and setup aggregationBufferMapIterator if we
+  //         have not switched to sort-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // Starts to process input rows.
+  testFallbackStartsAt match {
+    case None =>
+      processInputs()
+    case Some(fallbackStartsAt) =>
+      // This is the testing path. processInputsWithControlledFallback is same as processInputs
+      // except that it switches to sort-based aggregation after `fallbackStartsAt` input rows
+      // have been processed.
+      processInputsWithControlledFallback(fallbackStartsAt)
+  }
+
+  // If we did not switch to sort-based aggregation in processInputs,
+  // we pre-load the first key-value pair from the map (to make hasNext idempotent).
+  if (!sortBased) {
+    // First, set aggregationBufferMapIterator.
+    aggregationBufferMapIterator = hashMap.iterator()
+    // Pre-load the first key-value pair from the aggregationBufferMapIterator.
+    mapIteratorHasNext = aggregationBufferMapIterator.next()
+    // If the map is empty, we just free it.
+    if (!mapIteratorHasNext) {
+      hashMap.free()
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Par 7: Iterator's public methods.
+  ///////////////////////////////////////////////////////////////////////////
+
+  override final def hasNext: Boolean = {
+    (sortBased && sortedInputHasNewGroup) || (!sortBased && mapIteratorHasNext)
+  }
+
+  override final def next(): UnsafeRow = {
+    if (hasNext) {
+      if (sortBased) {
+        // Process the current group.
+        processCurrentSortedGroup()
+        // Generate output row for the current group.
+        val outputRow = generateOutput(currentGroupingKey, sortBasedAggregationBuffer)
+        // Initialize buffer values for the next group.
+        sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
+
+        outputRow
+      } else {
+        // We did not fall back to sort-based aggregation.
+        val result =
+          generateOutput(
+            aggregationBufferMapIterator.getKey,
+            aggregationBufferMapIterator.getValue)
+
+        // Pre-load next key-value pair form aggregationBufferMapIterator to make hasNext
+        // idempotent.
+        mapIteratorHasNext = aggregationBufferMapIterator.next()
+
+        if (!mapIteratorHasNext) {
+          // If there is no input from aggregationBufferMapIterator, we copy current result.
+          val resultCopy = result.copy()
+          // Then, we free the map.
+          hashMap.free()
+
+          resultCopy
+        } else {
+          result
+        }
+      }
+    } else {
+      // no more result
+      throw new NoSuchElementException
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 8: A utility function used to generate a output row when there is no
+  // input and there is no grouping expression.
+  ///////////////////////////////////////////////////////////////////////////
+  def outputForEmptyGroupingKeyWithoutInput(): UnsafeRow = {
+    if (groupingExpressions.isEmpty) {
+      sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
+      // We create a output row and copy it. So, we can free the map.
+      val resultCopy =
+        generateOutput(UnsafeRow.createFromByteArray(0, 0), sortBasedAggregationBuffer).copy()
+      hashMap.free()
+      resultCopy
+    } else {
+      throw new IllegalStateException(
+        "This method should not be called when groupingExpressions is not empty.")
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
deleted file mode 100644
index b465787fe8cbd..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
+++ /dev/null
@@ -1,372 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.aggregate
-
-import org.apache.spark.unsafe.KVIterator
-import org.apache.spark.{SparkEnv, TaskContext}
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
-import org.apache.spark.sql.types.StructType
-
-/**
- * An iterator used to evaluate [[AggregateFunction2]].
- * It first tries to use in-memory hash-based aggregation. If we cannot allocate more
- * space for the hash map, we spill the sorted map entries, free the map, and then
- * switch to sort-based aggregation.
- */
-class UnsafeHybridAggregationIterator(
-    groupingKeyAttributes: Seq[Attribute],
-    valueAttributes: Seq[Attribute],
-    inputKVIterator: KVIterator[UnsafeRow, InternalRow],
-    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-    nonCompleteAggregateAttributes: Seq[Attribute],
-    completeAggregateExpressions: Seq[AggregateExpression2],
-    completeAggregateAttributes: Seq[Attribute],
-    initialInputBufferOffset: Int,
-    resultExpressions: Seq[NamedExpression],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    outputsUnsafeRows: Boolean)
-  extends AggregationIterator(
-    groupingKeyAttributes,
-    valueAttributes,
-    nonCompleteAggregateExpressions,
-    nonCompleteAggregateAttributes,
-    completeAggregateExpressions,
-    completeAggregateAttributes,
-    initialInputBufferOffset,
-    resultExpressions,
-    newMutableProjection,
-    outputsUnsafeRows) {
-
-  require(groupingKeyAttributes.nonEmpty)
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Unsafe Aggregation buffers
-  ///////////////////////////////////////////////////////////////////////////
-
-  // This is the Unsafe Aggregation Map used to store all buffers.
-  private[this] val buffers = new UnsafeFixedWidthAggregationMap(
-    newBuffer,
-    StructType.fromAttributes(allAggregateFunctions.flatMap(_.bufferAttributes)),
-    StructType.fromAttributes(groupingKeyAttributes),
-    TaskContext.get.taskMemoryManager(),
-    SparkEnv.get.shuffleMemoryManager,
-    1024 * 16, // initial capacity
-    SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m"),
-    false // disable tracking of performance metrics
-  )
-
-  override protected def newBuffer: UnsafeRow = {
-    val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
-    val bufferRowSize: Int = bufferSchema.length
-
-    val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
-    val unsafeProjection =
-      UnsafeProjection.create(bufferSchema.map(_.dataType))
-    val buffer = unsafeProjection.apply(genericMutableBuffer)
-    initializeBuffer(buffer)
-    buffer
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Methods and variables related to switching to sort-based aggregation
-  ///////////////////////////////////////////////////////////////////////////
-  private[this] var sortBased = false
-
-  private[this] var sortBasedAggregationIterator: SortBasedAggregationIterator = _
-
-  // The value part of the input KV iterator is used to store original input values of
-  // aggregate functions, we need to convert them to aggregation buffers.
-  private def processOriginalInput(
-      firstKey: UnsafeRow,
-      firstValue: InternalRow): KVIterator[UnsafeRow, UnsafeRow] = {
-    new KVIterator[UnsafeRow, UnsafeRow] {
-      private[this] var isFirstRow = true
-
-      private[this] var groupingKey: UnsafeRow = _
-
-      private[this] val buffer: UnsafeRow = newBuffer
-
-      override def next(): Boolean = {
-        initializeBuffer(buffer)
-        if (isFirstRow) {
-          isFirstRow = false
-          groupingKey = firstKey
-          processRow(buffer, firstValue)
-
-          true
-        } else if (inputKVIterator.next()) {
-          groupingKey = inputKVIterator.getKey()
-          val value = inputKVIterator.getValue()
-          processRow(buffer, value)
-
-          true
-        } else {
-          false
-        }
-      }
-
-      override def getKey(): UnsafeRow = {
-        groupingKey
-      }
-
-      override def getValue(): UnsafeRow = {
-        buffer
-      }
-
-      override def close(): Unit = {
-        // Do nothing.
-      }
-    }
-  }
-
-  // The value of the input KV Iterator has the format of groupingExprs + aggregation buffer.
-  // We need to project the aggregation buffer out.
-  private def projectInputBufferToUnsafe(
-      firstKey: UnsafeRow,
-      firstValue: InternalRow): KVIterator[UnsafeRow, UnsafeRow] = {
-    new KVIterator[UnsafeRow, UnsafeRow] {
-      private[this] var isFirstRow = true
-
-      private[this] var groupingKey: UnsafeRow = _
-
-      private[this] val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
-
-      private[this] val value: UnsafeRow = {
-        val genericMutableRow = new GenericMutableRow(bufferSchema.length)
-        UnsafeProjection.create(bufferSchema.map(_.dataType)).apply(genericMutableRow)
-      }
-
-      private[this] val projectInputBuffer = {
-        newMutableProjection(bufferSchema, valueAttributes)().target(value)
-      }
-
-      override def next(): Boolean = {
-        if (isFirstRow) {
-          isFirstRow = false
-          groupingKey = firstKey
-          projectInputBuffer(firstValue)
-
-          true
-        } else if (inputKVIterator.next()) {
-          groupingKey = inputKVIterator.getKey()
-          projectInputBuffer(inputKVIterator.getValue())
-
-          true
-        } else {
-          false
-        }
-      }
-
-      override def getKey(): UnsafeRow = {
-        groupingKey
-      }
-
-      override def getValue(): UnsafeRow = {
-        value
-      }
-
-      override def close(): Unit = {
-        // Do nothing.
-      }
-    }
-  }
-
-  /**
-   * We need to fall back to sort based aggregation because we do not have enough memory
-   * for our in-memory hash map (i.e. `buffers`).
-   */
-  private def switchToSortBasedAggregation(
-      currentGroupingKey: UnsafeRow,
-      currentRow: InternalRow): Unit = {
-    logInfo("falling back to sort based aggregation.")
-
-    // Step 1: Get the ExternalSorter containing entries of the map.
-    val externalSorter = buffers.destructAndCreateExternalSorter()
-
-    // Step 2: Free the memory used by the map.
-    buffers.free()
-
-    // Step 3: If we have aggregate function with mode Partial or Complete,
-    // we need to process them to get aggregation buffer.
-    // So, later in the sort-based aggregation iterator, we can do merge.
-    // If aggregate functions are with mode Final and PartialMerge,
-    // we just need to project the aggregation buffer from the input.
-    val needsProcess = aggregationMode match {
-      case (Some(Partial), None) => true
-      case (None, Some(Complete)) => true
-      case (Some(Final), Some(Complete)) => true
-      case _ => false
-    }
-
-    val processedIterator = if (needsProcess) {
-      processOriginalInput(currentGroupingKey, currentRow)
-    } else {
-      // The input value's format is groupingExprs + buffer.
-      // We need to project the buffer part out.
-      projectInputBufferToUnsafe(currentGroupingKey, currentRow)
-    }
-
-    // Step 4: Redirect processedIterator to externalSorter.
-    while (processedIterator.next()) {
-      externalSorter.insertKV(processedIterator.getKey(), processedIterator.getValue())
-    }
-
-    // Step 5: Get the sorted iterator from the externalSorter.
-    val sortedKVIterator: UnsafeKVExternalSorter#KVSorterIterator = externalSorter.sortedIterator()
-
-    // Step 6: We now create a SortBasedAggregationIterator based on sortedKVIterator.
-    // For a aggregate function with mode Partial, its mode in the SortBasedAggregationIterator
-    // will be PartialMerge. For a aggregate function with mode Complete,
-    // its mode in the SortBasedAggregationIterator will be Final.
-    val newNonCompleteAggregateExpressions = allAggregateExpressions.map {
-        case AggregateExpression2(func, Partial, isDistinct) =>
-          AggregateExpression2(func, PartialMerge, isDistinct)
-        case AggregateExpression2(func, Complete, isDistinct) =>
-          AggregateExpression2(func, Final, isDistinct)
-        case other => other
-      }
-    val newNonCompleteAggregateAttributes =
-      nonCompleteAggregateAttributes ++ completeAggregateAttributes
-
-    val newValueAttributes =
-      allAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
-
-    sortBasedAggregationIterator = SortBasedAggregationIterator.createFromKVIterator(
-      groupingKeyAttributes = groupingKeyAttributes,
-      valueAttributes = newValueAttributes,
-      inputKVIterator = sortedKVIterator.asInstanceOf[KVIterator[InternalRow, InternalRow]],
-      nonCompleteAggregateExpressions = newNonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes = newNonCompleteAggregateAttributes,
-      completeAggregateExpressions = Nil,
-      completeAggregateAttributes = Nil,
-      initialInputBufferOffset = 0,
-      resultExpressions = resultExpressions,
-      newMutableProjection = newMutableProjection,
-      outputsUnsafeRows = outputsUnsafeRows)
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Methods used to initialize this iterator.
-  ///////////////////////////////////////////////////////////////////////////
-
-  /** Starts to read input rows and falls back to sort-based aggregation if necessary. */
-  protected def initialize(): Unit = {
-    var hasNext = inputKVIterator.next()
-    while (!sortBased && hasNext) {
-      val groupingKey = inputKVIterator.getKey()
-      val currentRow = inputKVIterator.getValue()
-      val buffer = buffers.getAggregationBuffer(groupingKey)
-      if (buffer == null) {
-        // buffer == null means that we could not allocate more memory.
-        // Now, we need to spill the map and switch to sort-based aggregation.
-        switchToSortBasedAggregation(groupingKey, currentRow)
-        sortBased = true
-      } else {
-        processRow(buffer, currentRow)
-        hasNext = inputKVIterator.next()
-      }
-    }
-  }
-
-  // This is the starting point of this iterator.
-  initialize()
-
-  // Creates the iterator for the Hash Aggregation Map after we have populated
-  // contents of that map.
-  private[this] val aggregationBufferMapIterator = buffers.iterator()
-
-  private[this] var _mapIteratorHasNext = false
-
-  // Pre-load the first key-value pair from the map to make hasNext idempotent.
-  if (!sortBased) {
-    _mapIteratorHasNext = aggregationBufferMapIterator.next()
-    // If the map is empty, we just free it.
-    if (!_mapIteratorHasNext) {
-      buffers.free()
-    }
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Iterator's public methods
-  ///////////////////////////////////////////////////////////////////////////
-
-  override final def hasNext: Boolean = {
-    (sortBased && sortBasedAggregationIterator.hasNext) || (!sortBased && _mapIteratorHasNext)
-  }
-
-
-  override final def next(): InternalRow = {
-    if (hasNext) {
-      if (sortBased) {
-        sortBasedAggregationIterator.next()
-      } else {
-        // We did not fall back to the sort-based aggregation.
-        val result =
-          generateOutput(
-            aggregationBufferMapIterator.getKey,
-            aggregationBufferMapIterator.getValue)
-        // Pre-load next key-value pair form aggregationBufferMapIterator.
-        _mapIteratorHasNext = aggregationBufferMapIterator.next()
-
-        if (!_mapIteratorHasNext) {
-          val resultCopy = result.copy()
-          buffers.free()
-          resultCopy
-        } else {
-          result
-        }
-      }
-    } else {
-      // no more result
-      throw new NoSuchElementException
-    }
-  }
-}
-
-object UnsafeHybridAggregationIterator {
-  // scalastyle:off
-  def createFromInputIterator(
-      groupingExprs: Seq[NamedExpression],
-      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-      nonCompleteAggregateAttributes: Seq[Attribute],
-      completeAggregateExpressions: Seq[AggregateExpression2],
-      completeAggregateAttributes: Seq[Attribute],
-      initialInputBufferOffset: Int,
-      resultExpressions: Seq[NamedExpression],
-      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-      inputAttributes: Seq[Attribute],
-      inputIter: Iterator[InternalRow],
-      outputsUnsafeRows: Boolean): UnsafeHybridAggregationIterator = {
-    new UnsafeHybridAggregationIterator(
-      groupingExprs.map(_.toAttribute),
-      inputAttributes,
-      AggregationIterator.unsafeKVIterator(groupingExprs, inputAttributes, inputIter),
-      nonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes,
-      completeAggregateExpressions,
-      completeAggregateAttributes,
-      initialInputBufferOffset,
-      resultExpressions,
-      newMutableProjection,
-      outputsUnsafeRows)
-  }
-  // scalastyle:on
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
index 960be08f84d94..80816a095ea8c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
@@ -17,20 +17,41 @@
 
 package org.apache.spark.sql.execution.aggregate
 
+import scala.collection.mutable
+
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan}
+import org.apache.spark.sql.types.StructType
 
 /**
  * Utility functions used by the query planner to convert our plan to new aggregation code path.
  */
 object Utils {
+  def supportsTungstenAggregate(
+      groupingExpressions: Seq[Expression],
+      aggregateBufferAttributes: Seq[Attribute]): Boolean = {
+    val aggregationBufferSchema = StructType.fromAttributes(aggregateBufferAttributes)
+
+    UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
+      UnsafeProjection.canSupport(groupingExpressions)
+  }
+
   def planAggregateWithoutDistinct(
       groupingExpressions: Seq[Expression],
       aggregateExpressions: Seq[AggregateExpression2],
-      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), Attribute],
+      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), (AggregateFunction2, Attribute)],
       resultExpressions: Seq[NamedExpression],
       child: SparkPlan): Seq[SparkPlan] = {
+    // Check if we can use TungstenAggregate.
+    val usesTungstenAggregate =
+      child.sqlContext.conf.unsafeEnabled &&
+      aggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate]) &&
+      supportsTungstenAggregate(
+        groupingExpressions,
+        aggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes))
+
+
     // 1. Create an Aggregate Operator for partial aggregations.
     val namedGroupingExpressions = groupingExpressions.map {
       case ne: NamedExpression => ne -> ne
@@ -44,11 +65,23 @@ object Utils {
     val groupExpressionMap = namedGroupingExpressions.toMap
     val namedGroupingAttributes = namedGroupingExpressions.map(_._2.toAttribute)
     val partialAggregateExpressions = aggregateExpressions.map(_.copy(mode = Partial))
-    val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
-      agg.aggregateFunction.bufferAttributes
-    }
-    val partialAggregate =
-      Aggregate(
+    val partialAggregateAttributes =
+      partialAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes)
+    val partialResultExpressions =
+      namedGroupingAttributes ++
+        partialAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
+
+    val partialAggregate = if (usesTungstenAggregate) {
+      TungstenAggregate(
+        requiredChildDistributionExpressions = None: Option[Seq[Expression]],
+        groupingExpressions = namedGroupingExpressions.map(_._2),
+        nonCompleteAggregateExpressions = partialAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = 0,
+        resultExpressions = partialResultExpressions,
+        child = child)
+    } else {
+      SortBasedAggregate(
         requiredChildDistributionExpressions = None: Option[Seq[Expression]],
         groupingExpressions = namedGroupingExpressions.map(_._2),
         nonCompleteAggregateExpressions = partialAggregateExpressions,
@@ -56,29 +89,57 @@ object Utils {
         completeAggregateExpressions = Nil,
         completeAggregateAttributes = Nil,
         initialInputBufferOffset = 0,
-        resultExpressions = namedGroupingAttributes ++ partialAggregateAttributes,
+        resultExpressions = partialResultExpressions,
         child = child)
+    }
 
     // 2. Create an Aggregate Operator for final aggregations.
     val finalAggregateExpressions = aggregateExpressions.map(_.copy(mode = Final))
     val finalAggregateAttributes =
       finalAggregateExpressions.map {
-        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)._2
       }
-    val rewrittenResultExpressions = resultExpressions.map { expr =>
-      expr.transformDown {
-        case agg: AggregateExpression2 =>
-          aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct).toAttribute
-        case expression =>
-          // We do not rely on the equality check at here since attributes may
-          // different cosmetically. Instead, we use semanticEquals.
-          groupExpressionMap.collectFirst {
-            case (expr, ne) if expr semanticEquals expression => ne.toAttribute
-          }.getOrElse(expression)
-      }.asInstanceOf[NamedExpression]
-    }
-    val finalAggregate =
-      Aggregate(
+
+    val finalAggregate = if (usesTungstenAggregate) {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transformDown {
+          case agg: AggregateExpression2 =>
+            // aggregateFunctionMap contains unique aggregate functions.
+            val aggregateFunction =
+              aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct)._1
+            aggregateFunction.asInstanceOf[AlgebraicAggregate].evaluateExpression
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+
+      TungstenAggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes,
+        nonCompleteAggregateExpressions = finalAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = namedGroupingAttributes.length,
+        resultExpressions = rewrittenResultExpressions,
+        child = partialAggregate)
+    } else {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transformDown {
+          case agg: AggregateExpression2 =>
+            aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct)._2
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+
+      SortBasedAggregate(
         requiredChildDistributionExpressions = Some(namedGroupingAttributes),
         groupingExpressions = namedGroupingAttributes,
         nonCompleteAggregateExpressions = finalAggregateExpressions,
@@ -88,6 +149,7 @@ object Utils {
         initialInputBufferOffset = namedGroupingAttributes.length,
         resultExpressions = rewrittenResultExpressions,
         child = partialAggregate)
+    }
 
     finalAggregate :: Nil
   }
@@ -96,10 +158,18 @@ object Utils {
       groupingExpressions: Seq[Expression],
       functionsWithDistinct: Seq[AggregateExpression2],
       functionsWithoutDistinct: Seq[AggregateExpression2],
-      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), Attribute],
+      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), (AggregateFunction2, Attribute)],
       resultExpressions: Seq[NamedExpression],
       child: SparkPlan): Seq[SparkPlan] = {
 
+    val aggregateExpressions = functionsWithDistinct ++ functionsWithoutDistinct
+    val usesTungstenAggregate =
+      child.sqlContext.conf.unsafeEnabled &&
+        aggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate]) &&
+        supportsTungstenAggregate(
+          groupingExpressions,
+          aggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes))
+
     // 1. Create an Aggregate Operator for partial aggregations.
     // The grouping expressions are original groupingExpressions and
     // distinct columns. For example, for avg(distinct value) ... group by key
@@ -129,19 +199,26 @@ object Utils {
     val distinctColumnExpressionMap = namedDistinctColumnExpressions.toMap
     val distinctColumnAttributes = namedDistinctColumnExpressions.map(_._2.toAttribute)
 
-    val partialAggregateExpressions = functionsWithoutDistinct.map {
-      case AggregateExpression2(aggregateFunction, mode, _) =>
-        AggregateExpression2(aggregateFunction, Partial, false)
-    }
-    val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
-      agg.aggregateFunction.bufferAttributes
-    }
+    val partialAggregateExpressions = functionsWithoutDistinct.map(_.copy(mode = Partial))
+    val partialAggregateAttributes =
+      partialAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes)
     val partialAggregateGroupingExpressions =
       (namedGroupingExpressions ++ namedDistinctColumnExpressions).map(_._2)
     val partialAggregateResult =
-      namedGroupingAttributes ++ distinctColumnAttributes ++ partialAggregateAttributes
-    val partialAggregate =
-      Aggregate(
+      namedGroupingAttributes ++
+        distinctColumnAttributes ++
+        partialAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
+    val partialAggregate = if (usesTungstenAggregate) {
+      TungstenAggregate(
+        requiredChildDistributionExpressions = None: Option[Seq[Expression]],
+        groupingExpressions = partialAggregateGroupingExpressions,
+        nonCompleteAggregateExpressions = partialAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = 0,
+        resultExpressions = partialAggregateResult,
+        child = child)
+    } else {
+      SortBasedAggregate(
         requiredChildDistributionExpressions = None: Option[Seq[Expression]],
         groupingExpressions = partialAggregateGroupingExpressions,
         nonCompleteAggregateExpressions = partialAggregateExpressions,
@@ -151,20 +228,27 @@ object Utils {
         initialInputBufferOffset = 0,
         resultExpressions = partialAggregateResult,
         child = child)
+    }
 
     // 2. Create an Aggregate Operator for partial merge aggregations.
-    val partialMergeAggregateExpressions = functionsWithoutDistinct.map {
-      case AggregateExpression2(aggregateFunction, mode, _) =>
-        AggregateExpression2(aggregateFunction, PartialMerge, false)
-    }
+    val partialMergeAggregateExpressions = functionsWithoutDistinct.map(_.copy(mode = PartialMerge))
     val partialMergeAggregateAttributes =
-      partialMergeAggregateExpressions.flatMap { agg =>
-        agg.aggregateFunction.bufferAttributes
-      }
+      partialMergeAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes)
     val partialMergeAggregateResult =
-      namedGroupingAttributes ++ distinctColumnAttributes ++ partialMergeAggregateAttributes
-    val partialMergeAggregate =
-      Aggregate(
+      namedGroupingAttributes ++
+        distinctColumnAttributes ++
+        partialMergeAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
+    val partialMergeAggregate = if (usesTungstenAggregate) {
+      TungstenAggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes ++ distinctColumnAttributes,
+        nonCompleteAggregateExpressions = partialMergeAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
+        resultExpressions = partialMergeAggregateResult,
+        child = partialAggregate)
+    } else {
+      SortBasedAggregate(
         requiredChildDistributionExpressions = Some(namedGroupingAttributes),
         groupingExpressions = namedGroupingAttributes ++ distinctColumnAttributes,
         nonCompleteAggregateExpressions = partialMergeAggregateExpressions,
@@ -174,48 +258,91 @@ object Utils {
         initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
         resultExpressions = partialMergeAggregateResult,
         child = partialAggregate)
+    }
 
     // 3. Create an Aggregate Operator for partial merge aggregations.
-    val finalAggregateExpressions = functionsWithoutDistinct.map {
-      case AggregateExpression2(aggregateFunction, mode, _) =>
-        AggregateExpression2(aggregateFunction, Final, false)
-    }
+    val finalAggregateExpressions = functionsWithoutDistinct.map(_.copy(mode = Final))
     val finalAggregateAttributes =
       finalAggregateExpressions.map {
-        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)._2
       }
+    // Create a map to store those rewritten aggregate functions. We always need to use
+    // both function and its corresponding isDistinct flag as the key because function itself
+    // does not knows if it is has distinct keyword or now.
+    val rewrittenAggregateFunctions =
+      mutable.Map.empty[(AggregateFunction2, Boolean), AggregateFunction2]
     val (completeAggregateExpressions, completeAggregateAttributes) = functionsWithDistinct.map {
       // Children of an AggregateFunction with DISTINCT keyword has already
       // been evaluated. At here, we need to replace original children
       // to AttributeReferences.
-      case agg @ AggregateExpression2(aggregateFunction, mode, isDistinct) =>
+      case agg @ AggregateExpression2(aggregateFunction, mode, true) =>
         val rewrittenAggregateFunction = aggregateFunction.transformDown {
           case expr if distinctColumnExpressionMap.contains(expr) =>
             distinctColumnExpressionMap(expr).toAttribute
         }.asInstanceOf[AggregateFunction2]
+        // Because we have rewritten the aggregate function, we use rewrittenAggregateFunctions
+        // to track the old version and the new version of this function.
+        rewrittenAggregateFunctions += (aggregateFunction, true) -> rewrittenAggregateFunction
         // We rewrite the aggregate function to a non-distinct aggregation because
         // its input will have distinct arguments.
+        // We just keep the isDistinct setting to true, so when users look at the query plan,
+        // they still can see distinct aggregations.
         val rewrittenAggregateExpression =
-          AggregateExpression2(rewrittenAggregateFunction, Complete, false)
+          AggregateExpression2(rewrittenAggregateFunction, Complete, true)
 
-        val aggregateFunctionAttribute = aggregateFunctionMap(agg.aggregateFunction, isDistinct)
+        val aggregateFunctionAttribute =
+          aggregateFunctionMap(agg.aggregateFunction, true)._2
         (rewrittenAggregateExpression -> aggregateFunctionAttribute)
     }.unzip
 
-    val rewrittenResultExpressions = resultExpressions.map { expr =>
-      expr.transform {
-        case agg: AggregateExpression2 =>
-          aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct).toAttribute
-        case expression =>
-          // We do not rely on the equality check at here since attributes may
-          // different cosmetically. Instead, we use semanticEquals.
-          groupExpressionMap.collectFirst {
-            case (expr, ne) if expr semanticEquals expression => ne.toAttribute
-          }.getOrElse(expression)
-      }.asInstanceOf[NamedExpression]
-    }
-    val finalAndCompleteAggregate =
-      Aggregate(
+    val finalAndCompleteAggregate = if (usesTungstenAggregate) {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transform {
+          case agg: AggregateExpression2 =>
+            val function = agg.aggregateFunction
+            val isDistinct = agg.isDistinct
+            val aggregateFunction =
+              if (rewrittenAggregateFunctions.contains(function, isDistinct)) {
+                // If this function has been rewritten, we get the rewritten version from
+                // rewrittenAggregateFunctions.
+                rewrittenAggregateFunctions(function, isDistinct)
+              } else {
+                // Oterwise, we get it from aggregateFunctionMap, which contains unique
+                // aggregate functions that have not been rewritten.
+                aggregateFunctionMap(function, isDistinct)._1
+              }
+            aggregateFunction.asInstanceOf[AlgebraicAggregate].evaluateExpression
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+
+      TungstenAggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes,
+        nonCompleteAggregateExpressions = finalAggregateExpressions,
+        completeAggregateExpressions = completeAggregateExpressions,
+        initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
+        resultExpressions = rewrittenResultExpressions,
+        child = partialMergeAggregate)
+    } else {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transform {
+          case agg: AggregateExpression2 =>
+            aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct)._2
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+      SortBasedAggregate(
         requiredChildDistributionExpressions = Some(namedGroupingAttributes),
         groupingExpressions = namedGroupingAttributes,
         nonCompleteAggregateExpressions = finalAggregateExpressions,
@@ -225,6 +352,7 @@ object Utils {
         initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
         resultExpressions = rewrittenResultExpressions,
         child = partialMergeAggregate)
+    }
 
     finalAndCompleteAggregate :: Nil
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index cef40dd324d9e..c64aa7a07dc2b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -262,7 +262,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     val df = sql(sqlText)
     // First, check if we have GeneratedAggregate.
     val hasGeneratedAgg = df.queryExecution.executedPlan
-      .collect { case _: aggregate.Aggregate => true }
+      .collect { case _: aggregate.TungstenAggregate => true }
       .nonEmpty
     if (!hasGeneratedAgg) {
       fail(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 4b35c8fd83533..7b5aa4763fd9e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -21,9 +21,9 @@ import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import org.apache.spark.sql.{SQLConf, AnalysisException, QueryTest, Row}
+import org.apache.spark.sql._
 import org.scalatest.BeforeAndAfterAll
-import test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
+import _root_.test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
 
 abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
 
@@ -141,6 +141,22 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
       Nil)
   }
 
+  test("null literal") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  AVG(null),
+          |  COUNT(null),
+          |  FIRST(null),
+          |  LAST(null),
+          |  MAX(null),
+          |  MIN(null),
+          |  SUM(null)
+        """.stripMargin),
+      Row(null, 0, null, null, null, null, null) :: Nil)
+  }
+
   test("only do grouping") {
     checkAnswer(
       sqlContext.sql(
@@ -266,13 +282,6 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
           |SELECT avg(value) FROM agg1
         """.stripMargin),
       Row(11.125) :: Nil)
-
-    checkAnswer(
-      sqlContext.sql(
-        """
-          |SELECT avg(null)
-        """.stripMargin),
-      Row(null) :: Nil)
   }
 
   test("udaf") {
@@ -364,7 +373,7 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
           |  max(distinct value1)
           |FROM agg2
         """.stripMargin),
-      Row(-60, 70.0, 101.0/9.0, 5.6, 100.0))
+      Row(-60, 70.0, 101.0/9.0, 5.6, 100))
 
     checkAnswer(
       sqlContext.sql(
@@ -402,6 +411,23 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
         Row(2, 100.0, 3.0, 0.0, 100.0, 1.0/3.0 + 100.0) ::
         Row(3, null, 3.0, null, null, null) ::
         Row(null, 110.0, 60.0, 30.0, 110.0, 110.0) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  count(value1),
+          |  count(*),
+          |  count(1),
+          |  count(DISTINCT value1),
+          |  key
+          |FROM agg2
+          |GROUP BY key
+        """.stripMargin),
+      Row(3, 3, 3, 2, 1) ::
+        Row(3, 4, 4, 2, 2) ::
+        Row(0, 2, 2, 0, 3) ::
+        Row(3, 4, 4, 3, null) :: Nil)
   }
 
   test("test count") {
@@ -496,7 +522,8 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
           |FROM agg1
           |GROUP BY key
         """.stripMargin).queryExecution.executedPlan.collect {
-        case agg: aggregate.Aggregate => agg
+        case agg: aggregate.SortBasedAggregate => agg
+        case agg: aggregate.TungstenAggregate => agg
       }
       val message =
         "We should fallback to the old aggregation code path if " +
@@ -537,3 +564,58 @@ class TungstenAggregationQuerySuite extends AggregationQuerySuite {
     sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, originalUnsafeEnabled.toString)
   }
 }
+
+class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQuerySuite {
+
+  var originalUnsafeEnabled: Boolean = _
+
+  override def beforeAll(): Unit = {
+    originalUnsafeEnabled = sqlContext.conf.unsafeEnabled
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, "true")
+    super.beforeAll()
+  }
+
+  override def afterAll(): Unit = {
+    super.afterAll()
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, originalUnsafeEnabled.toString)
+    sqlContext.conf.unsetConf("spark.sql.TungstenAggregate.testFallbackStartsAt")
+  }
+
+  override protected def checkAnswer(actual: DataFrame, expectedAnswer: Seq[Row]): Unit = {
+    (0 to 2).foreach { fallbackStartsAt =>
+      sqlContext.setConf(
+        "spark.sql.TungstenAggregate.testFallbackStartsAt",
+        fallbackStartsAt.toString)
+
+      // Create a new df to make sure its physical operator picks up
+      // spark.sql.TungstenAggregate.testFallbackStartsAt.
+      val newActual = DataFrame(sqlContext, actual.logicalPlan)
+
+      QueryTest.checkAnswer(newActual, expectedAnswer) match {
+        case Some(errorMessage) =>
+          val newErrorMessage =
+            s"""
+              |The following aggregation query failed when using TungstenAggregate with
+              |controlled fallback (it falls back to sort-based aggregation once it has processed
+              |$fallbackStartsAt input rows). The query is
+              |${actual.queryExecution}
+              |
+              |$errorMessage
+            """.stripMargin
+
+          fail(newErrorMessage)
+        case None =>
+      }
+    }
+  }
+
+  // Override it to make sure we call the actually overridden checkAnswer.
+  override protected def checkAnswer(df: DataFrame, expectedAnswer: Row): Unit = {
+    checkAnswer(df, Seq(expectedAnswer))
+  }
+
+  // Override it to make sure we call the actually overridden checkAnswer.
+  override protected def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = {
+    checkAnswer(df, expectedAnswer.collect())
+  }
+}

From e234ea1b49d30bb6c8b8c001bd98c43de290dcff Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 6 Aug 2015 15:30:27 -0700
Subject: [PATCH 0901/1454] [SPARK-9645] [YARN] [CORE] Allow shuffle service to
 read shuffle files.

Spark should not mess with the permissions of directories created
by the cluster manager. Here, by setting the block manager dir
permissions to 700, the shuffle service (running as the YARN user)
wouldn't be able to serve shuffle files created by applications.

Also, the code to protect the local app dir was missing in standalone's
Worker; that has been now added. Since all processes run as the same
user in standalone, `chmod 700` should not cause problems.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7966 from vanzin/SPARK-9645 and squashes the following commits:

6e07b31 [Marcelo Vanzin] Protect the app dir in standalone mode.
384ba6a [Marcelo Vanzin] [SPARK-9645] [yarn] [core] Allow shuffle service to read shuffle files.
---
 .../main/scala/org/apache/spark/deploy/worker/Worker.scala    | 4 +++-
 .../scala/org/apache/spark/storage/DiskBlockManager.scala     | 1 -
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 6792d3310b06c..79b1536d94016 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -428,7 +428,9 @@ private[deploy] class Worker(
           // application finishes.
           val appLocalDirs = appDirectories.get(appId).getOrElse {
             Utils.getOrCreateLocalRootDirs(conf).map { dir =>
-              Utils.createDirectory(dir, namePrefix = "executor").getAbsolutePath()
+              val appDir = Utils.createDirectory(dir, namePrefix = "executor")
+              Utils.chmod700(appDir)
+              appDir.getAbsolutePath()
             }.toSeq
           }
           appDirectories(appId) = appLocalDirs
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 5f537692a16c5..56a33d5ca7d60 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -133,7 +133,6 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
     Utils.getConfiguredLocalDirs(conf).flatMap { rootDir =>
       try {
         val localDir = Utils.createDirectory(rootDir, "blockmgr")
-        Utils.chmod700(localDir)
         logInfo(s"Created local directory at $localDir")
         Some(localDir)
       } catch {

From 681e3024b6c2fcb54b42180d94d3ba3eed52a2d4 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 6 Aug 2015 23:43:52 +0100
Subject: [PATCH 0902/1454] [SPARK-9633] [BUILD] SBT download locations
 outdated; need an update

Remove 2 defunct SBT download URLs and replace with the 1 known download URL. Also, use https.
Follow up on https://github.com/apache/spark/pull/7792

Author: Sean Owen <sowen@cloudera.com>

Closes #7956 from srowen/SPARK-9633 and squashes the following commits:

caa40bd [Sean Owen] Remove 2 defunct SBT download URLs and replace with the 1 known download URL. Also, use https.
---
 build/sbt-launch-lib.bash | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/build/sbt-launch-lib.bash b/build/sbt-launch-lib.bash
index 7930a38b9674a..615f848394650 100755
--- a/build/sbt-launch-lib.bash
+++ b/build/sbt-launch-lib.bash
@@ -38,8 +38,7 @@ dlog () {
 
 acquire_sbt_jar () {
   SBT_VERSION=`awk -F "=" '/sbt\.version/ {print $2}' ./project/build.properties`
-  URL1=http://typesafe.artifactoryonline.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
-  URL2=http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
+  URL1=https://dl.bintray.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
   JAR=build/sbt-launch-${SBT_VERSION}.jar
 
   sbt_jar=$JAR
@@ -51,12 +50,10 @@ acquire_sbt_jar () {
     printf "Attempting to fetch sbt\n"
     JAR_DL="${JAR}.part"
     if [ $(command -v curl) ]; then
-      (curl --fail --location --silent ${URL1} > "${JAR_DL}" ||\
-        (rm -f "${JAR_DL}" && curl --fail --location --silent ${URL2} > "${JAR_DL}")) &&\
+      curl --fail --location --silent ${URL1} > "${JAR_DL}" &&\
         mv "${JAR_DL}" "${JAR}"
     elif [ $(command -v wget) ]; then
-      (wget --quiet ${URL1} -O "${JAR_DL}" ||\
-        (rm -f "${JAR_DL}" && wget --quiet ${URL2} -O "${JAR_DL}")) &&\
+      wget --quiet ${URL1} -O "${JAR_DL}" &&\
         mv "${JAR_DL}" "${JAR}"
     else
       printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n"

From baf4587a569b49e39020c04c2785041bdd00789b Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 6 Aug 2015 17:03:14 -0700
Subject: [PATCH 0903/1454] [SPARK-9691] [SQL] PySpark SQL rand function treats
 seed 0 as no seed

https://issues.apache.org/jira/browse/SPARK-9691

jkbradley rxin

Author: Yin Huai <yhuai@databricks.com>

Closes #7999 from yhuai/pythonRand and squashes the following commits:

4187e0c [Yin Huai] Regression test.
a985ef9 [Yin Huai] Use "if seed is not None" instead "if seed" because "if seed" returns false when seed is 0.
---
 python/pyspark/sql/functions.py |  4 ++--
 python/pyspark/sql/tests.py     | 10 ++++++++++
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index b5c6a01f18858..95f46044d324a 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -268,7 +268,7 @@ def rand(seed=None):
     """Generates a random column with i.i.d. samples from U[0.0, 1.0].
     """
     sc = SparkContext._active_spark_context
-    if seed:
+    if seed is not None:
         jc = sc._jvm.functions.rand(seed)
     else:
         jc = sc._jvm.functions.rand()
@@ -280,7 +280,7 @@ def randn(seed=None):
     """Generates a column with i.i.d. samples from the standard normal distribution.
     """
     sc = SparkContext._active_spark_context
-    if seed:
+    if seed is not None:
         jc = sc._jvm.functions.randn(seed)
     else:
         jc = sc._jvm.functions.randn()
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index ebd3ea8db6a43..1e3444dd9e3b4 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -629,6 +629,16 @@ def test_rand_functions(self):
         for row in rndn:
             assert row[1] >= -4.0 and row[1] <= 4.0, "got: %s" % row[1]
 
+        # If the specified seed is 0, we should use it.
+        # https://issues.apache.org/jira/browse/SPARK-9691
+        rnd1 = df.select('key', functions.rand(0)).collect()
+        rnd2 = df.select('key', functions.rand(0)).collect()
+        self.assertEqual(sorted(rnd1), sorted(rnd2))
+
+        rndn1 = df.select('key', functions.randn(0)).collect()
+        rndn2 = df.select('key', functions.randn(0)).collect()
+        self.assertEqual(sorted(rndn1), sorted(rndn2))
+
     def test_between_function(self):
         df = self.sc.parallelize([
             Row(a=1, b=2, c=3),

From 4e70e8256ce2f45b438642372329eac7b1e9e8cf Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 17:30:31 -0700
Subject: [PATCH 0904/1454] [SPARK-9228] [SQL] use tungsten.enabled in public
 for both of codegen/unsafe

spark.sql.tungsten.enabled will be the default value for both codegen and unsafe, they are kept internally for debug/testing.

cc marmbrus rxin

Author: Davies Liu <davies@databricks.com>

Closes #7998 from davies/tungsten and squashes the following commits:

c1c16da [Davies Liu] update doc
1a47be1 [Davies Liu] use tungsten.enabled for both of codegen/unsafe
---
 docs/sql-programming-guide.md                 |  6 +++---
 .../scala/org/apache/spark/sql/SQLConf.scala  | 20 ++++++++++++-------
 .../spark/sql/execution/SparkPlan.scala       |  8 +++++++-
 .../spark/sql/execution/joins/HashJoin.scala  |  3 ++-
 .../sql/execution/joins/HashOuterJoin.scala   |  2 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  3 ++-
 6 files changed, 28 insertions(+), 14 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 3ea77e82422fb..6c317175d3278 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1884,11 +1884,11 @@ that these options will be deprecated in future release as more optimizations ar
     </td>
   </tr>
   <tr>
-    <td><code>spark.sql.codegen</code></td>
+    <td><code>spark.sql.tungsten.enabled</code></td>
     <td>true</td>
     <td>
-      When true, code will be dynamically generated at runtime for expression evaluation in a specific
-      query. For some queries with complicated expression this option can lead to significant speed-ups.
+      When true, use the optimized Tungsten physical execution backend which explicitly manages memory
+      and dynamically generates bytecode for expression evaluation.
     </td>
   </tr>
   <tr>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index f836122b3e0e4..ef35c133d9cc3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -223,14 +223,21 @@ private[spark] object SQLConf {
     defaultValue = Some(200),
     doc = "The default number of partitions to use when shuffling data for joins or aggregations.")
 
-  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+  val TUNGSTEN_ENABLED = booleanConf("spark.sql.tungsten.enabled",
     defaultValue = Some(true),
+    doc = "When true, use the optimized Tungsten physical execution backend which explicitly " +
+          "manages memory and dynamically generates bytecode for expression evaluation.")
+
+  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
     doc = "When true, code will be dynamically generated at runtime for expression evaluation in" +
-      " a specific query.")
+      " a specific query.",
+    isPublic = false)
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
-    defaultValue = Some(true),
-    doc = "When true, use the new optimized Tungsten physical execution backend.")
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
+    doc = "When true, use the new optimized Tungsten physical execution backend.",
+    isPublic = false)
 
   val DIALECT = stringConf(
     "spark.sql.dialect",
@@ -427,7 +434,6 @@ private[spark] object SQLConf {
  *
  * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
-
 private[sql] class SQLConf extends Serializable with CatalystConf {
   import SQLConf._
 
@@ -474,11 +480,11 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
 
-  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED)
+  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
 
-  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
+  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 2f29067f5646a..3fff79cd1b281 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -55,12 +55,18 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   protected def sparkContext = sqlContext.sparkContext
 
   // sqlContext will be null when we are being deserialized on the slaves.  In this instance
-  // the value of codegenEnabled will be set by the desserializer after the constructor has run.
+  // the value of codegenEnabled/unsafeEnabled will be set by the desserializer after the
+  // constructor has run.
   val codegenEnabled: Boolean = if (sqlContext != null) {
     sqlContext.conf.codegenEnabled
   } else {
     false
   }
+  val unsafeEnabled: Boolean = if (sqlContext != null) {
+    sqlContext.conf.unsafeEnabled
+  } else {
+    false
+  }
 
   /**
    * Whether the "prepare" method is called.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 5e9cd9fd2345a..22d46d1c3e3b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -44,7 +44,8 @@ trait HashJoin {
   override def output: Seq[Attribute] = left.output ++ right.output
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 346337e64245c..701bd3cd86372 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -67,7 +67,7 @@ trait HashOuterJoin {
   }
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && joinType != FullOuter
+    (self.codegenEnabled && self.unsafeEnabled && joinType != FullOuter
       && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 47a7d370f5415..82dd6eb7e7ed0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -33,7 +33,8 @@ trait HashSemiJoin {
   override def output: Seq[Attribute] = left.output
 
   protected[this] def supportUnsafe: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
       && UnsafeProjection.canSupport(rightKeys)
       && UnsafeProjection.canSupport(left.schema)
       && UnsafeProjection.canSupport(right.schema))

From 0867b23c74a3e6347d718b67ddabff17b468eded Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 6 Aug 2015 17:31:16 -0700
Subject: [PATCH 0905/1454] [SPARK-9650][SQL] Fix quoting behavior on
 interpolated column names

Make sure that `$"column"` is consistent with other methods with respect to backticks.  Adds a bunch of tests for various ways of constructing columns.

Author: Michael Armbrust <michael@databricks.com>

Closes #7969 from marmbrus/namesWithDots and squashes the following commits:

53ef3d7 [Michael Armbrust] [SPARK-9650][SQL] Fix quoting behavior on interpolated column names
2bf7a92 [Michael Armbrust] WIP
---
 .../sql/catalyst/analysis/unresolved.scala    | 57 ++++++++++++++++
 .../catalyst/plans/logical/LogicalPlan.scala  | 42 +-----------
 .../scala/org/apache/spark/sql/Column.scala   |  2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  2 +-
 .../spark/sql/ColumnExpressionSuite.scala     | 68 +++++++++++++++++++
 5 files changed, 128 insertions(+), 43 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index 03da45b09f928..43ee3191935eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.errors
 import org.apache.spark.sql.catalyst.expressions._
@@ -69,8 +70,64 @@ case class UnresolvedAttribute(nameParts: Seq[String]) extends Attribute with Un
 }
 
 object UnresolvedAttribute {
+  /**
+   * Creates an [[UnresolvedAttribute]], parsing segments separated by dots ('.').
+   */
   def apply(name: String): UnresolvedAttribute = new UnresolvedAttribute(name.split("\\."))
+
+  /**
+   * Creates an [[UnresolvedAttribute]], from a single quoted string (for example using backticks in
+   * HiveQL.  Since the string is consider quoted, no processing is done on the name.
+   */
   def quoted(name: String): UnresolvedAttribute = new UnresolvedAttribute(Seq(name))
+
+  /**
+   * Creates an [[UnresolvedAttribute]] from a string in an embedded language.  In this case
+   * we treat it as a quoted identifier, except for '.', which must be further quoted using
+   * backticks if it is part of a column name.
+   */
+  def quotedString(name: String): UnresolvedAttribute =
+    new UnresolvedAttribute(parseAttributeName(name))
+
+  /**
+   * Used to split attribute name by dot with backticks rule.
+   * Backticks must appear in pairs, and the quoted string must be a complete name part,
+   * which means `ab..c`e.f is not allowed.
+   * Escape character is not supported now, so we can't use backtick inside name part.
+   */
+  def parseAttributeName(name: String): Seq[String] = {
+    def e = new AnalysisException(s"syntax error in attribute name: $name")
+    val nameParts = scala.collection.mutable.ArrayBuffer.empty[String]
+    val tmp = scala.collection.mutable.ArrayBuffer.empty[Char]
+    var inBacktick = false
+    var i = 0
+    while (i < name.length) {
+      val char = name(i)
+      if (inBacktick) {
+        if (char == '`') {
+          inBacktick = false
+          if (i + 1 < name.length && name(i + 1) != '.') throw e
+        } else {
+          tmp += char
+        }
+      } else {
+        if (char == '`') {
+          if (tmp.nonEmpty) throw e
+          inBacktick = true
+        } else if (char == '.') {
+          if (name(i - 1) == '.' || i == name.length - 1) throw e
+          nameParts += tmp.mkString
+          tmp.clear()
+        } else {
+          tmp += char
+        }
+      }
+      i += 1
+    }
+    if (inBacktick) throw e
+    nameParts += tmp.mkString
+    nameParts.toSeq
+  }
 }
 
 case class UnresolvedFunction(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 9b52f020093f0..c290e6acb361c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -179,47 +179,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
   def resolveQuoted(
       name: String,
       resolver: Resolver): Option[NamedExpression] = {
-    resolve(parseAttributeName(name), output, resolver)
-  }
-
-  /**
-   * Internal method, used to split attribute name by dot with backticks rule.
-   * Backticks must appear in pairs, and the quoted string must be a complete name part,
-   * which means `ab..c`e.f is not allowed.
-   * Escape character is not supported now, so we can't use backtick inside name part.
-   */
-  private def parseAttributeName(name: String): Seq[String] = {
-    val e = new AnalysisException(s"syntax error in attribute name: $name")
-    val nameParts = scala.collection.mutable.ArrayBuffer.empty[String]
-    val tmp = scala.collection.mutable.ArrayBuffer.empty[Char]
-    var inBacktick = false
-    var i = 0
-    while (i < name.length) {
-      val char = name(i)
-      if (inBacktick) {
-        if (char == '`') {
-          inBacktick = false
-          if (i + 1 < name.length && name(i + 1) != '.') throw e
-        } else {
-          tmp += char
-        }
-      } else {
-        if (char == '`') {
-          if (tmp.nonEmpty) throw e
-          inBacktick = true
-        } else if (char == '.') {
-          if (name(i - 1) == '.' || i == name.length - 1) throw e
-          nameParts += tmp.mkString
-          tmp.clear()
-        } else {
-          tmp += char
-        }
-      }
-      i += 1
-    }
-    if (inBacktick) throw e
-    nameParts += tmp.mkString
-    nameParts.toSeq
+    resolve(UnresolvedAttribute.parseAttributeName(name), output, resolver)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 75365fbcec757..27bd084847346 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -54,7 +54,7 @@ class Column(protected[sql] val expr: Expression) extends Logging {
   def this(name: String) = this(name match {
     case "*" => UnresolvedStar(None)
     case _ if name.endsWith(".*") => UnresolvedStar(Some(name.substring(0, name.length - 2)))
-    case _ => UnresolvedAttribute(name)
+    case _ => UnresolvedAttribute.quotedString(name)
   })
 
   /** Creates a column based on the given expression. */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 6f8ffb54402a7..075c0ea2544b2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -343,7 +343,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
      */
     implicit class StringToColumn(val sc: StringContext) {
       def $(args: Any*): ColumnName = {
-        new ColumnName(sc.s(args : _*))
+        new ColumnName(sc.s(args: _*))
       }
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index e1b3443d74993..6a09a3b72c081 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -32,6 +32,74 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
 
   override def sqlContext(): SQLContext = ctx
 
+  test("column names with space") {
+    val df = Seq((1, "a")).toDF("name with space", "name.with.dot")
+
+    checkAnswer(
+      df.select(df("name with space")),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select($"name with space"),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select(col("name with space")),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select("name with space"),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select(expr("`name with space`")),
+      Row(1) :: Nil)
+  }
+
+  test("column names with dot") {
+    val df = Seq((1, "a")).toDF("name with space", "name.with.dot").as("a")
+
+    checkAnswer(
+      df.select(df("`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select($"`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(col("`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select("`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(expr("`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(df("a.`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select($"a.`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(col("a.`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select("a.`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(expr("a.`name.with.dot`")),
+      Row("a") :: Nil)
+  }
+
   test("alias") {
     val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList")
     assert(df.select(df("a").as("b")).columns.head === "b")

From 49b1504fe3733eb36a7fc6317ec19aeba5d46f97 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 6 Aug 2015 17:36:12 -0700
Subject: [PATCH 0906/1454] Revert "[SPARK-9228] [SQL] use tungsten.enabled in
 public for both of codegen/unsafe"

This reverts commit 4e70e8256ce2f45b438642372329eac7b1e9e8cf.
---
 docs/sql-programming-guide.md                 |  6 +++---
 .../scala/org/apache/spark/sql/SQLConf.scala  | 20 +++++++------------
 .../spark/sql/execution/SparkPlan.scala       |  8 +-------
 .../spark/sql/execution/joins/HashJoin.scala  |  3 +--
 .../sql/execution/joins/HashOuterJoin.scala   |  2 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  3 +--
 6 files changed, 14 insertions(+), 28 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 6c317175d3278..3ea77e82422fb 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1884,11 +1884,11 @@ that these options will be deprecated in future release as more optimizations ar
     </td>
   </tr>
   <tr>
-    <td><code>spark.sql.tungsten.enabled</code></td>
+    <td><code>spark.sql.codegen</code></td>
     <td>true</td>
     <td>
-      When true, use the optimized Tungsten physical execution backend which explicitly manages memory
-      and dynamically generates bytecode for expression evaluation.
+      When true, code will be dynamically generated at runtime for expression evaluation in a specific
+      query. For some queries with complicated expression this option can lead to significant speed-ups.
     </td>
   </tr>
   <tr>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index ef35c133d9cc3..f836122b3e0e4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -223,21 +223,14 @@ private[spark] object SQLConf {
     defaultValue = Some(200),
     doc = "The default number of partitions to use when shuffling data for joins or aggregations.")
 
-  val TUNGSTEN_ENABLED = booleanConf("spark.sql.tungsten.enabled",
-    defaultValue = Some(true),
-    doc = "When true, use the optimized Tungsten physical execution backend which explicitly " +
-          "manages memory and dynamically generates bytecode for expression evaluation.")
-
   val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
-    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
+    defaultValue = Some(true),
     doc = "When true, code will be dynamically generated at runtime for expression evaluation in" +
-      " a specific query.",
-    isPublic = false)
+      " a specific query.")
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
-    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
-    doc = "When true, use the new optimized Tungsten physical execution backend.",
-    isPublic = false)
+    defaultValue = Some(true),
+    doc = "When true, use the new optimized Tungsten physical execution backend.")
 
   val DIALECT = stringConf(
     "spark.sql.dialect",
@@ -434,6 +427,7 @@ private[spark] object SQLConf {
  *
  * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
+
 private[sql] class SQLConf extends Serializable with CatalystConf {
   import SQLConf._
 
@@ -480,11 +474,11 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
 
-  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, getConf(TUNGSTEN_ENABLED))
+  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED)
 
   def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
 
-  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, getConf(TUNGSTEN_ENABLED))
+  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 3fff79cd1b281..2f29067f5646a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -55,18 +55,12 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   protected def sparkContext = sqlContext.sparkContext
 
   // sqlContext will be null when we are being deserialized on the slaves.  In this instance
-  // the value of codegenEnabled/unsafeEnabled will be set by the desserializer after the
-  // constructor has run.
+  // the value of codegenEnabled will be set by the desserializer after the constructor has run.
   val codegenEnabled: Boolean = if (sqlContext != null) {
     sqlContext.conf.codegenEnabled
   } else {
     false
   }
-  val unsafeEnabled: Boolean = if (sqlContext != null) {
-    sqlContext.conf.unsafeEnabled
-  } else {
-    false
-  }
 
   /**
    * Whether the "prepare" method is called.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 22d46d1c3e3b7..5e9cd9fd2345a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -44,8 +44,7 @@ trait HashJoin {
   override def output: Seq[Attribute] = left.output ++ right.output
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && self.unsafeEnabled
-      && UnsafeProjection.canSupport(buildKeys)
+    (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 701bd3cd86372..346337e64245c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -67,7 +67,7 @@ trait HashOuterJoin {
   }
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && self.unsafeEnabled && joinType != FullOuter
+    (self.codegenEnabled && joinType != FullOuter
       && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 82dd6eb7e7ed0..47a7d370f5415 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -33,8 +33,7 @@ trait HashSemiJoin {
   override def output: Seq[Attribute] = left.output
 
   protected[this] def supportUnsafe: Boolean = {
-    (self.codegenEnabled && self.unsafeEnabled
-      && UnsafeProjection.canSupport(leftKeys)
+    (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
       && UnsafeProjection.canSupport(rightKeys)
       && UnsafeProjection.canSupport(left.schema)
       && UnsafeProjection.canSupport(right.schema))

From b87825310ac87485672868bf6a9ed01d154a3626 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 6 Aug 2015 18:25:38 -0700
Subject: [PATCH 0907/1454] [SPARK-9692] Remove SqlNewHadoopRDD's generated
 Tuple2 and InterruptibleIterator.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

A small performance optimization – we don't need to generate a Tuple2 and then immediately discard the key. We also don't need an extra wrapper from InterruptibleIterator.

Author: Reynold Xin <rxin@databricks.com>

Closes #8000 from rxin/SPARK-9692 and squashes the following commits:

1d4d0b3 [Reynold Xin] [SPARK-9692] Remove SqlNewHadoopRDD's generated Tuple2 and InterruptibleIterator.
---
 .../apache/spark/rdd/SqlNewHadoopRDD.scala    | 44 +++++++------------
 .../spark/sql/parquet/ParquetRelation.scala   |  3 +-
 2 files changed, 18 insertions(+), 29 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index 35e44cb59c1be..6a95e44c57fec 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -26,14 +26,12 @@ import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{CombineFileSplit, FileSplit}
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.{Partition => SparkPartition, _}
-import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
@@ -60,18 +58,16 @@ private[spark] class SqlNewHadoopPartition(
  *    and the executor side to the shared Hadoop Configuration.
  *
  * Note: This is RDD is basically a cloned version of [[org.apache.spark.rdd.NewHadoopRDD]] with
- * changes based on [[org.apache.spark.rdd.HadoopRDD]]. In future, this functionality will be
- * folded into core.
+ * changes based on [[org.apache.spark.rdd.HadoopRDD]].
  */
-private[spark] class SqlNewHadoopRDD[K, V](
+private[spark] class SqlNewHadoopRDD[V: ClassTag](
     @transient sc : SparkContext,
     broadcastedConf: Broadcast[SerializableConfiguration],
     @transient initDriverSideJobFuncOpt: Option[Job => Unit],
     initLocalJobFuncOpt: Option[Job => Unit],
-    inputFormatClass: Class[_ <: InputFormat[K, V]],
-    keyClass: Class[K],
+    inputFormatClass: Class[_ <: InputFormat[Void, V]],
     valueClass: Class[V])
-  extends RDD[(K, V)](sc, Nil)
+  extends RDD[V](sc, Nil)
   with SparkHadoopMapReduceUtil
   with Logging {
 
@@ -120,8 +116,8 @@ private[spark] class SqlNewHadoopRDD[K, V](
 
   override def compute(
       theSplit: SparkPartition,
-      context: TaskContext): InterruptibleIterator[(K, V)] = {
-    val iter = new Iterator[(K, V)] {
+      context: TaskContext): Iterator[V] = {
+    val iter = new Iterator[V] {
       val split = theSplit.asInstanceOf[SqlNewHadoopPartition]
       logInfo("Input split: " + split.serializableHadoopSplit)
       val conf = getConf(isDriverSide = false)
@@ -154,17 +150,20 @@ private[spark] class SqlNewHadoopRDD[K, V](
           configurable.setConf(conf)
         case _ =>
       }
-      private var reader = format.createRecordReader(
+      private[this] var reader = format.createRecordReader(
         split.serializableHadoopSplit.value, hadoopAttemptContext)
       reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
 
       // Register an on-task-completion callback to close the input stream.
       context.addTaskCompletionListener(context => close())
-      var havePair = false
-      var finished = false
-      var recordsSinceMetricsUpdate = 0
+
+      private[this] var havePair = false
+      private[this] var finished = false
 
       override def hasNext: Boolean = {
+        if (context.isInterrupted) {
+          throw new TaskKilledException
+        }
         if (!finished && !havePair) {
           finished = !reader.nextKeyValue
           if (finished) {
@@ -178,7 +177,7 @@ private[spark] class SqlNewHadoopRDD[K, V](
         !finished
       }
 
-      override def next(): (K, V) = {
+      override def next(): V = {
         if (!hasNext) {
           throw new java.util.NoSuchElementException("End of stream")
         }
@@ -186,7 +185,7 @@ private[spark] class SqlNewHadoopRDD[K, V](
         if (!finished) {
           inputMetrics.incRecordsRead(1)
         }
-        (reader.getCurrentKey, reader.getCurrentValue)
+        reader.getCurrentValue
       }
 
       private def close() {
@@ -212,23 +211,14 @@ private[spark] class SqlNewHadoopRDD[K, V](
             }
           }
         } catch {
-          case e: Exception => {
+          case e: Exception =>
             if (!Utils.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
-          }
         }
       }
     }
-    new InterruptibleIterator(context, iter)
-  }
-
-  /** Maps over a partition, providing the InputSplit that was used as the base of the partition. */
-  @DeveloperApi
-  def mapPartitionsWithInputSplit[U: ClassTag](
-      f: (InputSplit, Iterator[(K, V)]) => Iterator[U],
-      preservesPartitioning: Boolean = false): RDD[U] = {
-    new NewHadoopMapPartitionsWithSplitRDD(this, f, preservesPartitioning)
+    iter
   }
 
   override def getPreferredLocations(hsplit: SparkPartition): Seq[String] = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index b4337a48dbd80..29c388c22ef93 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -291,7 +291,6 @@ private[sql] class ParquetRelation(
         initDriverSideJobFuncOpt = Some(setInputPaths),
         initLocalJobFuncOpt = Some(initLocalJobFuncOpt),
         inputFormatClass = classOf[ParquetInputFormat[InternalRow]],
-        keyClass = classOf[Void],
         valueClass = classOf[InternalRow]) {
 
         val cacheMetadata = useMetadataCache
@@ -328,7 +327,7 @@ private[sql] class ParquetRelation(
             new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
           }
         }
-      }.values.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
+      }.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
     }
   }
 

From 014a9f9d8c9521180f7a448cc7cc96cc00537d5c Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 6 Aug 2015 19:04:57 -0700
Subject: [PATCH 0908/1454] [SPARK-9709] [SQL] Avoid starving unsafe operators
 that use sort

The issue is that a task may run multiple sorts, and the sorts run by the child operator (i.e. parent RDD) may acquire all available memory such that other sorts in the same task do not have enough to proceed. This manifests itself in an `IOException("Unable to acquire X bytes of memory")` thrown by `UnsafeExternalSorter`.

The solution is to reserve a page in each sorter in the chain before computing the child operator's (parent RDD's) partitions. This requires us to use a new special RDD that does some preparation before computing the parent's partitions.

Author: Andrew Or <andrew@databricks.com>

Closes #8011 from andrewor14/unsafe-starve-memory and squashes the following commits:

35b69a4 [Andrew Or] Simplify test
0b07782 [Andrew Or] Minor: update comments
5d5afdf [Andrew Or] Merge branch 'master' of github.com:apache/spark into unsafe-starve-memory
254032e [Andrew Or] Add tests
234acbd [Andrew Or] Reserve a page in sorter when preparing each partition
b889e08 [Andrew Or] MapPartitionsWithPreparationRDD
---
 .../unsafe/sort/UnsafeExternalSorter.java     | 43 ++++++++-----
 .../apache/spark/rdd/MapPartitionsRDD.scala   |  3 +
 .../rdd/MapPartitionsWithPreparationRDD.scala | 49 +++++++++++++++
 .../spark/shuffle/ShuffleMemoryManager.scala  |  2 +-
 .../sort/UnsafeExternalSorterSuite.java       | 19 +++++-
 ...MapPartitionsWithPreparationRDDSuite.scala | 60 +++++++++++++++++++
 .../spark/sql/execution/SparkPlan.scala       |  2 +-
 .../org/apache/spark/sql/execution/sort.scala | 28 +++++++--
 8 files changed, 184 insertions(+), 22 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
 create mode 100644 core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 8f78fc5a41629..4c54ba4bce408 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -138,6 +138,11 @@ private UnsafeExternalSorter(
       this.inMemSorter = existingInMemorySorter;
     }
 
+    // Acquire a new page as soon as we construct the sorter to ensure that we have at
+    // least one page to work with. Otherwise, other operators in the same task may starve
+    // this sorter (SPARK-9709).
+    acquireNewPage();
+
     // Register a cleanup task with TaskContext to ensure that memory is guaranteed to be freed at
     // the end of the task. This is necessary to avoid memory leaks in when the downstream operator
     // does not fully consume the sorter's output (e.g. sort followed by limit).
@@ -343,22 +348,32 @@ private void acquireNewPageIfNecessary(int requiredSpace) throws IOException {
         throw new IOException("Required space " + requiredSpace + " is greater than page size (" +
           pageSizeBytes + ")");
       } else {
-        final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
-        if (memoryAcquired < pageSizeBytes) {
-          shuffleMemoryManager.release(memoryAcquired);
-          spill();
-          final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
-          if (memoryAcquiredAfterSpilling != pageSizeBytes) {
-            shuffleMemoryManager.release(memoryAcquiredAfterSpilling);
-            throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
-          }
-        }
-        currentPage = taskMemoryManager.allocatePage(pageSizeBytes);
-        currentPagePosition = currentPage.getBaseOffset();
-        freeSpaceInCurrentPage = pageSizeBytes;
-        allocatedPages.add(currentPage);
+        acquireNewPage();
+      }
+    }
+  }
+
+  /**
+   * Acquire a new page from the {@link ShuffleMemoryManager}.
+   *
+   * If there is not enough space to allocate the new page, spill all existing ones
+   * and try again. If there is still not enough space, report error to the caller.
+   */
+  private void acquireNewPage() throws IOException {
+    final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+    if (memoryAcquired < pageSizeBytes) {
+      shuffleMemoryManager.release(memoryAcquired);
+      spill();
+      final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+      if (memoryAcquiredAfterSpilling != pageSizeBytes) {
+        shuffleMemoryManager.release(memoryAcquiredAfterSpilling);
+        throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
       }
     }
+    currentPage = taskMemoryManager.allocatePage(pageSizeBytes);
+    currentPagePosition = currentPage.getBaseOffset();
+    freeSpaceInCurrentPage = pageSizeBytes;
+    allocatedPages.add(currentPage);
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
index a838aac6e8d1a..4312d3a417759 100644
--- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
@@ -21,6 +21,9 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.{Partition, TaskContext}
 
+/**
+ * An RDD that applies the provided function to every partition of the parent RDD.
+ */
 private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag](
     prev: RDD[T],
     f: (TaskContext, Int, Iterator[T]) => Iterator[U],  // (TaskContext, partition index, iterator)
diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
new file mode 100644
index 0000000000000..b475bd8d79f85
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.{Partition, Partitioner, TaskContext}
+
+/**
+ * An RDD that applies a user provided function to every partition of the parent RDD, and
+ * additionally allows the user to prepare each partition before computing the parent partition.
+ */
+private[spark] class MapPartitionsWithPreparationRDD[U: ClassTag, T: ClassTag, M: ClassTag](
+    prev: RDD[T],
+    preparePartition: () => M,
+    executePartition: (TaskContext, Int, M, Iterator[T]) => Iterator[U],
+    preservesPartitioning: Boolean = false)
+  extends RDD[U](prev) {
+
+  override val partitioner: Option[Partitioner] = {
+    if (preservesPartitioning) firstParent[T].partitioner else None
+  }
+
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
+
+  /**
+   * Prepare a partition before computing it from its parent.
+   */
+  override def compute(partition: Partition, context: TaskContext): Iterator[U] = {
+    val preparedArgument = preparePartition()
+    val parentIterator = firstParent[T].iterator(partition, context)
+    executePartition(context, partition.index, preparedArgument, parentIterator)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index 00c1e078a441c..e3d229cc99821 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -124,7 +124,7 @@ private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
   }
 }
 
-private object ShuffleMemoryManager {
+private[spark] object ShuffleMemoryManager {
   /**
    * Figure out the shuffle memory limit from a SparkConf. We currently have both a fraction
    * of the memory pool and a safety factor since collections can sometimes grow bigger than
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 117745f9a9c00..f5300373d87ea 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -340,7 +340,8 @@ public void testPeakMemoryUsed() throws Exception {
       for (int i = 0; i < numRecordsPerPage * 10; i++) {
         insertNumber(sorter, i);
         newPeakMemory = sorter.getPeakMemoryUsedBytes();
-        if (i % numRecordsPerPage == 0) {
+        // The first page is pre-allocated on instantiation
+        if (i % numRecordsPerPage == 0 && i > 0) {
           // We allocated a new page for this record, so peak memory should change
           assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
@@ -364,5 +365,21 @@ public void testPeakMemoryUsed() throws Exception {
     }
   }
 
+  @Test
+  public void testReservePageOnInstantiation() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
+    try {
+      assertEquals(1, sorter.getNumberOfAllocatedPages());
+      // Inserting a new record doesn't allocate more memory since we already have a page
+      long peakMemory = sorter.getPeakMemoryUsedBytes();
+      insertNumber(sorter, 100);
+      assertEquals(peakMemory, sorter.getPeakMemoryUsedBytes());
+      assertEquals(1, sorter.getNumberOfAllocatedPages());
+    } finally {
+      sorter.cleanupResources();
+      assertSpillFilesWereCleanedUp();
+    }
+  }
+
 }
 
diff --git a/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
new file mode 100644
index 0000000000000..c16930e7d6491
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.collection.mutable
+
+import org.apache.spark.{LocalSparkContext, SparkContext, SparkFunSuite, TaskContext}
+
+class MapPartitionsWithPreparationRDDSuite extends SparkFunSuite with LocalSparkContext {
+
+  test("prepare called before parent partition is computed") {
+    sc = new SparkContext("local", "test")
+
+    // Have the parent partition push a number to the list
+    val parent = sc.parallelize(1 to 100, 1).mapPartitions { iter =>
+      TestObject.things.append(20)
+      iter
+    }
+
+    // Push a different number during the prepare phase
+    val preparePartition = () => { TestObject.things.append(10) }
+
+    // Push yet another number during the execution phase
+    val executePartition = (
+        taskContext: TaskContext,
+        partitionIndex: Int,
+        notUsed: Unit,
+        parentIterator: Iterator[Int]) => {
+      TestObject.things.append(30)
+      TestObject.things.iterator
+    }
+
+    // Verify that the numbers are pushed in the order expected
+    val result = {
+      new MapPartitionsWithPreparationRDD[Int, Int, Unit](
+        parent, preparePartition, executePartition).collect()
+    }
+    assert(result === Array(10, 20, 30))
+  }
+
+}
+
+private object TestObject {
+  val things = new mutable.ListBuffer[Int]
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 2f29067f5646a..490428965a61d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -158,7 +158,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
    */
   final def prepare(): Unit = {
     if (prepareCalled.compareAndSet(false, true)) {
-      doPrepare
+      doPrepare()
       children.foreach(_.prepare())
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index 3192b6ebe9075..7f69cdb08aa78 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.{InternalAccumulator, TaskContext}
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
@@ -123,7 +123,12 @@ case class TungstenSort(
     val schema = child.schema
     val childOutput = child.output
     val pageSize = sparkContext.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
-    child.execute().mapPartitions({ iter =>
+
+    /**
+     * Set up the sorter in each partition before computing the parent partition.
+     * This makes sure our sorter is not starved by other sorters used in the same task.
+     */
+    def preparePartition(): UnsafeExternalRowSorter = {
       val ordering = newOrdering(sortOrder, childOutput)
 
       // The comparator for comparing prefix
@@ -143,12 +148,25 @@ case class TungstenSort(
       if (testSpillFrequency > 0) {
         sorter.setTestSpillFrequency(testSpillFrequency)
       }
-      val sortedIterator = sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]])
-      val taskContext = TaskContext.get()
+      sorter
+    }
+
+    /** Compute a partition using the sorter already set up previously. */
+    def executePartition(
+        taskContext: TaskContext,
+        partitionIndex: Int,
+        sorter: UnsafeExternalRowSorter,
+        parentIterator: Iterator[InternalRow]): Iterator[InternalRow] = {
+      val sortedIterator = sorter.sort(parentIterator.asInstanceOf[Iterator[UnsafeRow]])
       taskContext.internalMetricsToAccumulators(
         InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.getPeakMemoryUsage)
       sortedIterator
-    }, preservesPartitioning = true)
+    }
+
+    // Note: we need to set up the external sorter in each partition before computing
+    // the parent partition, so we cannot simply use `mapPartitions` here (SPARK-9709).
+    new MapPartitionsWithPreparationRDD[InternalRow, InternalRow, UnsafeExternalRowSorter](
+      child.execute(), preparePartition, executePartition, preservesPartitioning = true)
   }
 
 }

From 17284db314f52bdb2065482b8a49656f7683d30a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 17:30:31 -0700
Subject: [PATCH 0909/1454] [SPARK-9228] [SQL] use tungsten.enabled in public
 for both of codegen/unsafe

spark.sql.tungsten.enabled will be the default value for both codegen and unsafe, they are kept internally for debug/testing.

cc marmbrus rxin

Author: Davies Liu <davies@databricks.com>

Closes #7998 from davies/tungsten and squashes the following commits:

c1c16da [Davies Liu] update doc
1a47be1 [Davies Liu] use tungsten.enabled for both of codegen/unsafe

(cherry picked from commit 4e70e8256ce2f45b438642372329eac7b1e9e8cf)
Signed-off-by: Reynold Xin <rxin@databricks.com>
---
 docs/sql-programming-guide.md                 |  6 +++---
 .../scala/org/apache/spark/sql/SQLConf.scala  | 20 ++++++++++++-------
 .../spark/sql/execution/SparkPlan.scala       |  8 +++++++-
 .../spark/sql/execution/joins/HashJoin.scala  |  3 ++-
 .../sql/execution/joins/HashOuterJoin.scala   |  2 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  3 ++-
 6 files changed, 28 insertions(+), 14 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 3ea77e82422fb..6c317175d3278 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1884,11 +1884,11 @@ that these options will be deprecated in future release as more optimizations ar
     </td>
   </tr>
   <tr>
-    <td><code>spark.sql.codegen</code></td>
+    <td><code>spark.sql.tungsten.enabled</code></td>
     <td>true</td>
     <td>
-      When true, code will be dynamically generated at runtime for expression evaluation in a specific
-      query. For some queries with complicated expression this option can lead to significant speed-ups.
+      When true, use the optimized Tungsten physical execution backend which explicitly manages memory
+      and dynamically generates bytecode for expression evaluation.
     </td>
   </tr>
   <tr>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index f836122b3e0e4..ef35c133d9cc3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -223,14 +223,21 @@ private[spark] object SQLConf {
     defaultValue = Some(200),
     doc = "The default number of partitions to use when shuffling data for joins or aggregations.")
 
-  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+  val TUNGSTEN_ENABLED = booleanConf("spark.sql.tungsten.enabled",
     defaultValue = Some(true),
+    doc = "When true, use the optimized Tungsten physical execution backend which explicitly " +
+          "manages memory and dynamically generates bytecode for expression evaluation.")
+
+  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
     doc = "When true, code will be dynamically generated at runtime for expression evaluation in" +
-      " a specific query.")
+      " a specific query.",
+    isPublic = false)
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
-    defaultValue = Some(true),
-    doc = "When true, use the new optimized Tungsten physical execution backend.")
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
+    doc = "When true, use the new optimized Tungsten physical execution backend.",
+    isPublic = false)
 
   val DIALECT = stringConf(
     "spark.sql.dialect",
@@ -427,7 +434,6 @@ private[spark] object SQLConf {
  *
  * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
-
 private[sql] class SQLConf extends Serializable with CatalystConf {
   import SQLConf._
 
@@ -474,11 +480,11 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
 
-  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED)
+  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
 
-  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
+  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 490428965a61d..719ad432e2fe0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -55,12 +55,18 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   protected def sparkContext = sqlContext.sparkContext
 
   // sqlContext will be null when we are being deserialized on the slaves.  In this instance
-  // the value of codegenEnabled will be set by the desserializer after the constructor has run.
+  // the value of codegenEnabled/unsafeEnabled will be set by the desserializer after the
+  // constructor has run.
   val codegenEnabled: Boolean = if (sqlContext != null) {
     sqlContext.conf.codegenEnabled
   } else {
     false
   }
+  val unsafeEnabled: Boolean = if (sqlContext != null) {
+    sqlContext.conf.unsafeEnabled
+  } else {
+    false
+  }
 
   /**
    * Whether the "prepare" method is called.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 5e9cd9fd2345a..22d46d1c3e3b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -44,7 +44,8 @@ trait HashJoin {
   override def output: Seq[Attribute] = left.output ++ right.output
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 346337e64245c..701bd3cd86372 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -67,7 +67,7 @@ trait HashOuterJoin {
   }
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && joinType != FullOuter
+    (self.codegenEnabled && self.unsafeEnabled && joinType != FullOuter
       && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 47a7d370f5415..82dd6eb7e7ed0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -33,7 +33,8 @@ trait HashSemiJoin {
   override def output: Seq[Attribute] = left.output
 
   protected[this] def supportUnsafe: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
       && UnsafeProjection.canSupport(rightKeys)
       && UnsafeProjection.canSupport(left.schema)
       && UnsafeProjection.canSupport(right.schema))

From fe12277b40082585e40e1bdf6aa2ebcfe80ed83f Mon Sep 17 00:00:00 2001
From: Jeff Zhang <zjffdu@apache.org>
Date: Thu, 6 Aug 2015 21:03:47 -0700
Subject: [PATCH 0910/1454] Fix doc typo

Straightforward fix on doc typo

Author: Jeff Zhang <zjffdu@apache.org>

Closes #8019 from zjffdu/master and squashes the following commits:

aed6e64 [Jeff Zhang] Fix doc typo
---
 docs/tuning.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tuning.md b/docs/tuning.md
index 572c7270e4999..6936912a6be54 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -240,7 +240,7 @@ worth optimizing.
 ## Data Locality
 
 Data locality can have a major impact on the performance of Spark jobs.  If data and the code that
-operates on it are together than computation tends to be fast.  But if code and data are separated,
+operates on it are together then computation tends to be fast.  But if code and data are separated,
 one must move to the other.  Typically it is faster to ship serialized code from place to place than
 a chunk of data because code size is much smaller than data.  Spark builds its scheduling around
 this general principle of data locality.

From 672f467668da1cf20895ee57652489c306120288 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 6 Aug 2015 21:42:42 -0700
Subject: [PATCH 0911/1454] [SPARK-8057][Core]Call
 TaskAttemptContext.getTaskAttemptID using Reflection

Someone may use the Spark core jar in the maven repo with hadoop 1. SPARK-2075 has already resolved the compatibility issue to support it. But `SparkHadoopMapRedUtil.commitTask` broke it recently.

This PR uses Reflection to call `TaskAttemptContext.getTaskAttemptID` to fix the compatibility issue.

Author: zsxwing <zsxwing@gmail.com>

Closes #6599 from zsxwing/SPARK-8057 and squashes the following commits:

f7a343c [zsxwing] Remove the redundant import
6b7f1af [zsxwing] Call TaskAttemptContext.getTaskAttemptID using Reflection
---
 .../org/apache/spark/deploy/SparkHadoopUtil.scala  | 14 ++++++++++++++
 .../spark/mapred/SparkHadoopMapRedUtil.scala       |  3 ++-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index e06b06e06fb4a..7e9dba42bebd8 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -34,6 +34,8 @@ import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter}
 import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier
 import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.mapreduce.JobContext
+import org.apache.hadoop.mapreduce.{TaskAttemptContext => MapReduceTaskAttemptContext}
+import org.apache.hadoop.mapreduce.{TaskAttemptID => MapReduceTaskAttemptID}
 import org.apache.hadoop.security.{Credentials, UserGroupInformation}
 
 import org.apache.spark.annotation.DeveloperApi
@@ -194,6 +196,18 @@ class SparkHadoopUtil extends Logging {
     method.invoke(context).asInstanceOf[Configuration]
   }
 
+  /**
+   * Using reflection to call `getTaskAttemptID` from TaskAttemptContext. If we directly
+   * call `TaskAttemptContext.getTaskAttemptID`, it will generate different byte codes
+   * for Hadoop 1.+ and Hadoop 2.+ because TaskAttemptContext is class in Hadoop 1.+
+   * while it's interface in Hadoop 2.+.
+   */
+  def getTaskAttemptIDFromTaskAttemptContext(
+      context: MapReduceTaskAttemptContext): MapReduceTaskAttemptID = {
+    val method = context.getClass.getMethod("getTaskAttemptID")
+    method.invoke(context).asInstanceOf[MapReduceTaskAttemptID]
+  }
+
   /**
    * Get [[FileStatus]] objects for all leaf children (files) under the given base path. If the
    * given path points to a file, return a single-element collection containing [[FileStatus]] of
diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
index 87df42748be44..f405b732e4725 100644
--- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
+++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
@@ -24,6 +24,7 @@ import org.apache.hadoop.mapred._
 import org.apache.hadoop.mapreduce.{TaskAttemptContext => MapReduceTaskAttemptContext}
 import org.apache.hadoop.mapreduce.{OutputCommitter => MapReduceOutputCommitter}
 
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.CommitDeniedException
 import org.apache.spark.{Logging, SparkEnv, TaskContext}
 import org.apache.spark.util.{Utils => SparkUtils}
@@ -93,7 +94,7 @@ object SparkHadoopMapRedUtil extends Logging {
       splitId: Int,
       attemptId: Int): Unit = {
 
-    val mrTaskAttemptID = mrTaskContext.getTaskAttemptID
+    val mrTaskAttemptID = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(mrTaskContext)
 
     // Called after we have decided to commit
     def performCommit(): Unit = {

From f0cda587fb80bf2f1ba53d35dc9dc87bf72ee338 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 6 Aug 2015 22:49:01 -0700
Subject: [PATCH 0912/1454] [SPARK-7550] [SQL] [MINOR] Fixes logs when
 persisting DataFrames

Author: Cheng Lian <lian@databricks.com>

Closes #8021 from liancheng/spark-7550/fix-logs and squashes the following commits:

b7bd0ed [Cheng Lian] Fixes logs
---
 .../org/apache/spark/sql/hive/HiveMetastoreCatalog.scala    | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 1523ebe9d5493..7198a32df4a02 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -317,19 +317,17 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
 
       case (Some(serde), relation: HadoopFsRelation) if relation.partitionColumns.nonEmpty =>
         logWarning {
-          val paths = relation.paths.mkString(", ")
           "Persisting partitioned data source relation into Hive metastore in " +
             s"Spark SQL specific format, which is NOT compatible with Hive.  Input path(s): " +
-            paths.mkString("\n", "\n", "")
+            relation.paths.mkString("\n", "\n", "")
         }
         newSparkSQLSpecificMetastoreTable()
 
       case (Some(serde), relation: HadoopFsRelation) =>
         logWarning {
-          val paths = relation.paths.mkString(", ")
           "Persisting data source relation with multiple input paths into Hive metastore in " +
             s"Spark SQL specific format, which is NOT compatible with Hive.  Input paths: " +
-            paths.mkString("\n", "\n", "")
+            relation.paths.mkString("\n", "\n", "")
         }
         newSparkSQLSpecificMetastoreTable()
 

From 7aaed1b114751a24835204b8c588533d5c5ffaf0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 6 Aug 2015 22:52:23 -0700
Subject: [PATCH 0913/1454] [SPARK-8862][SQL]Support multiple SQLContexts in
 Web UI

This is a follow-up PR to solve the UI issue when there are multiple SQLContexts. Each SQLContext has a separate tab and contains queries which are executed by this SQLContext.

<img width="1366" alt="multiple sqlcontexts" src="https://cloud.githubusercontent.com/assets/1000778/9088391/54584434-3bc2-11e5-9caf-94c2b0da528e.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #7962 from zsxwing/multi-sqlcontext-ui and squashes the following commits:

cf661e1 [zsxwing] sql -> SQL
39b0c97 [zsxwing] Support multiple SQLContexts in Web UI
---
 .../org/apache/spark/sql/ui/AllExecutionsPage.scala  |  2 +-
 .../main/scala/org/apache/spark/sql/ui/SQLTab.scala  | 12 ++++++++++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
index 727fc4b37fa48..cb7ca60b2fe48 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
@@ -178,7 +178,7 @@ private[ui] abstract class ExecutionTable(
     "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
 
   private def executionURL(executionID: Long): String =
-    "%s/sql/execution?id=%s".format(UIUtils.prependBaseUri(parent.basePath), executionID)
+    s"${UIUtils.prependBaseUri(parent.basePath)}/${parent.prefix}/execution?id=$executionID"
 }
 
 private[ui] class RunningExecutionTable(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
index a9e5226303978..3bba0afaf14eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
@@ -17,13 +17,14 @@
 
 package org.apache.spark.sql.ui
 
+import java.util.concurrent.atomic.AtomicInteger
+
 import org.apache.spark.Logging
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.ui.{SparkUI, SparkUITab}
 
 private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
-  extends SparkUITab(sparkUI, "sql") with Logging {
-
+  extends SparkUITab(sparkUI, SQLTab.nextTabName) with Logging {
 
   val parent = sparkUI
   val listener = sqlContext.listener
@@ -38,4 +39,11 @@ private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
 private[sql] object SQLTab {
 
   private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/ui/static"
+
+  private val nextTabId = new AtomicInteger(0)
+
+  private def nextTabName: String = {
+    val nextId = nextTabId.getAndIncrement()
+    if (nextId == 0) "SQL" else s"SQL${nextId}"
+  }
 }

From 4309262ec9146d7158ee9957a128bb152289d557 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 6 Aug 2015 23:18:29 -0700
Subject: [PATCH 0914/1454] [SPARK-9700] Pick default page size more
 intelligently.

Previously, we use 64MB as the default page size, which was way too big for a lot of Spark applications (especially for single node).

This patch changes it so that the default page size, if unset by the user, is determined by the number of cores available and the total execution memory available.

Author: Reynold Xin <rxin@databricks.com>

Closes #8012 from rxin/pagesize and squashes the following commits:

16f4756 [Reynold Xin] Fixed failing test.
5afd570 [Reynold Xin] private...
0d5fb98 [Reynold Xin] Update default value.
674a6cd [Reynold Xin] Address review feedback.
dc00e05 [Reynold Xin] Merge with master.
73ebdb6 [Reynold Xin] [SPARK-9700] Pick default page size more intelligently.
---
 R/run-tests.sh                                |  2 +-
 .../unsafe/UnsafeShuffleExternalSorter.java   |  3 +-
 .../spark/unsafe/map/BytesToBytesMap.java     |  8 +--
 .../unsafe/sort/UnsafeExternalSorter.java     |  1 -
 .../scala/org/apache/spark/SparkConf.scala    |  7 +++
 .../scala/org/apache/spark/SparkContext.scala |  2 +-
 .../scala/org/apache/spark/SparkEnv.scala     |  2 +-
 .../spark/shuffle/ShuffleMemoryManager.scala  | 53 +++++++++++++++++--
 .../unsafe/UnsafeShuffleWriterSuite.java      |  5 +-
 .../map/AbstractBytesToBytesMapSuite.java     |  6 +--
 .../sort/UnsafeExternalSorterSuite.java       |  4 +-
 .../shuffle/ShuffleMemoryManagerSuite.scala   | 14 ++---
 python/pyspark/java_gateway.py                |  1 -
 .../TungstenAggregationIterator.scala         |  2 +-
 .../sql/execution/joins/HashedRelation.scala  | 16 +++---
 .../org/apache/spark/sql/execution/sort.scala |  4 +-
 ...ypes.scala => ParquetTypesConverter.scala} |  0
 .../execution/TestShuffleMemoryManager.scala  |  2 +-
 .../apache/spark/sql/hive/test/TestHive.scala |  1 -
 .../spark/unsafe/array/ByteArrayMethods.java  |  6 +++
 20 files changed, 93 insertions(+), 46 deletions(-)
 rename sql/core/src/main/scala/org/apache/spark/sql/parquet/{ParquetTypes.scala => ParquetTypesConverter.scala} (100%)

diff --git a/R/run-tests.sh b/R/run-tests.sh
index 18a1e13bdc655..e82ad0ba2cd06 100755
--- a/R/run-tests.sh
+++ b/R/run-tests.sh
@@ -23,7 +23,7 @@ FAILED=0
 LOGFILE=$FWDIR/unit-tests.out
 rm -f $LOGFILE
 
-SPARK_TESTING=1 $FWDIR/../bin/sparkR --conf spark.buffer.pageSize=4m --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE
+SPARK_TESTING=1 $FWDIR/../bin/sparkR --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE
 FAILED=$((PIPESTATUS[0]||$FAILED))
 
 if [[ $FAILED != 0 ]]; then
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index bf4eaa59ff589..f6e0913a7a0b3 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -115,8 +115,7 @@ public UnsafeShuffleExternalSorter(
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
     this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
     this.pageSizeBytes = (int) Math.min(
-      PackedRecordPointer.MAXIMUM_PAGE_SIZE_BYTES,
-      conf.getSizeAsBytes("spark.buffer.pageSize", "64m"));
+      PackedRecordPointer.MAXIMUM_PAGE_SIZE_BYTES, shuffleMemoryManager.pageSizeBytes());
     this.maxRecordSizeBytes = pageSizeBytes - 4;
     this.writeMetrics = writeMetrics;
     initializeForWriting();
diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 5ac3736ac62aa..0636ae7c8df1a 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -642,7 +642,7 @@ public boolean putNewKey(
   private void allocate(int capacity) {
     assert (capacity >= 0);
     // The capacity needs to be divisible by 64 so that our bit set can be sized properly
-    capacity = Math.max((int) Math.min(MAX_CAPACITY, nextPowerOf2(capacity)), 64);
+    capacity = Math.max((int) Math.min(MAX_CAPACITY, ByteArrayMethods.nextPowerOf2(capacity)), 64);
     assert (capacity <= MAX_CAPACITY);
     longArray = new LongArray(MemoryBlock.fromLongArray(new long[capacity * 2]));
     bitset = new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64]));
@@ -770,10 +770,4 @@ void growAndRehash() {
       timeSpentResizingNs += System.nanoTime() - resizeStartTime;
     }
   }
-
-  /** Returns the next number greater or equal num that is power of 2. */
-  private static long nextPowerOf2(long num) {
-    final long highBit = Long.highestOneBit(num);
-    return (highBit == num) ? num : highBit << 1;
-  }
 }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 4c54ba4bce408..5ebbf9b068fd6 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -127,7 +127,6 @@ private UnsafeExternalSorter(
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility for units
     // this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
     this.fileBufferSizeBytes = 32 * 1024;
-    // this.pageSizeBytes = conf.getSizeAsBytes("spark.buffer.pageSize", "64m");
     this.pageSizeBytes = pageSizeBytes;
     this.writeMetrics = new ShuffleWriteMetrics();
 
diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 08bab4bf2739f..8ff154fb5e334 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -249,6 +249,13 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     Utils.byteStringAsBytes(get(key, defaultValue))
   }
 
+  /**
+   * Get a size parameter as bytes, falling back to a default if not set.
+   */
+  def getSizeAsBytes(key: String, defaultValue: Long): Long = {
+    Utils.byteStringAsBytes(get(key, defaultValue + "B"))
+  }
+
   /**
    * Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then Kibibytes are assumed.
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 0c0705325b169..5662686436900 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -629,7 +629,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * [[org.apache.spark.SparkContext.setLocalProperty]].
    */
   def getLocalProperty(key: String): String =
-    Option(localProperties.get).map(_.getProperty(key)).getOrElse(null)
+    Option(localProperties.get).map(_.getProperty(key)).orNull
 
   /** Set a human readable description of the current job. */
   def setJobDescription(value: String) {
diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index adfece4d6e7c0..a796e72850191 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -324,7 +324,7 @@ object SparkEnv extends Logging {
     val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
     val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)
 
-    val shuffleMemoryManager = new ShuffleMemoryManager(conf)
+    val shuffleMemoryManager = ShuffleMemoryManager.create(conf, numUsableCores)
 
     val blockTransferService =
       conf.get("spark.shuffle.blockTransferService", "netty").toLowerCase match {
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index e3d229cc99821..8c3a72644c38a 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -19,6 +19,9 @@ package org.apache.spark.shuffle
 
 import scala.collection.mutable
 
+import com.google.common.annotations.VisibleForTesting
+
+import org.apache.spark.unsafe.array.ByteArrayMethods
 import org.apache.spark.{Logging, SparkException, SparkConf, TaskContext}
 
 /**
@@ -34,11 +37,19 @@ import org.apache.spark.{Logging, SparkException, SparkConf, TaskContext}
  * set of active tasks and redo the calculations of 1 / 2N and 1 / N in waiting tasks whenever
  * this set changes. This is all done by synchronizing access on "this" to mutate state and using
  * wait() and notifyAll() to signal changes.
+ *
+ * Use `ShuffleMemoryManager.create()` factory method to create a new instance.
+ *
+ * @param maxMemory total amount of memory available for execution, in bytes.
+ * @param pageSizeBytes number of bytes for each page, by default.
  */
-private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
-  private val taskMemory = new mutable.HashMap[Long, Long]()  // taskAttemptId -> memory bytes
+private[spark]
+class ShuffleMemoryManager protected (
+    val maxMemory: Long,
+    val pageSizeBytes: Long)
+  extends Logging {
 
-  def this(conf: SparkConf) = this(ShuffleMemoryManager.getMaxMemory(conf))
+  private val taskMemory = new mutable.HashMap[Long, Long]()  // taskAttemptId -> memory bytes
 
   private def currentTaskAttemptId(): Long = {
     // In case this is called on the driver, return an invalid task attempt id.
@@ -124,15 +135,49 @@ private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
   }
 }
 
+
 private[spark] object ShuffleMemoryManager {
+
+  def create(conf: SparkConf, numCores: Int): ShuffleMemoryManager = {
+    val maxMemory = ShuffleMemoryManager.getMaxMemory(conf)
+    val pageSize = ShuffleMemoryManager.getPageSize(conf, maxMemory, numCores)
+    new ShuffleMemoryManager(maxMemory, pageSize)
+  }
+
+  def create(maxMemory: Long, pageSizeBytes: Long): ShuffleMemoryManager = {
+    new ShuffleMemoryManager(maxMemory, pageSizeBytes)
+  }
+
+  @VisibleForTesting
+  def createForTesting(maxMemory: Long): ShuffleMemoryManager = {
+    new ShuffleMemoryManager(maxMemory, 4 * 1024 * 1024)
+  }
+
   /**
    * Figure out the shuffle memory limit from a SparkConf. We currently have both a fraction
    * of the memory pool and a safety factor since collections can sometimes grow bigger than
    * the size we target before we estimate their sizes again.
    */
-  def getMaxMemory(conf: SparkConf): Long = {
+  private def getMaxMemory(conf: SparkConf): Long = {
     val memoryFraction = conf.getDouble("spark.shuffle.memoryFraction", 0.2)
     val safetyFraction = conf.getDouble("spark.shuffle.safetyFraction", 0.8)
     (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong
   }
+
+  /**
+   * Sets the page size, in bytes.
+   *
+   * If user didn't explicitly set "spark.buffer.pageSize", we figure out the default value
+   * by looking at the number of cores available to the process, and the total amount of memory,
+   * and then divide it by a factor of safety.
+   */
+  private def getPageSize(conf: SparkConf, maxMemory: Long, numCores: Int): Long = {
+    val minPageSize = 1L * 1024 * 1024   // 1MB
+    val maxPageSize = 64L * minPageSize  // 64MB
+    val cores = if (numCores > 0) numCores else Runtime.getRuntime.availableProcessors()
+    val safetyFactor = 8
+    val size = ByteArrayMethods.nextPowerOf2(maxMemory / cores / safetyFactor)
+    val default = math.min(maxPageSize, math.max(minPageSize, size))
+    conf.getSizeAsBytes("spark.buffer.pageSize", default)
+  }
 }
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 98c32bbc298d7..c68354ba49a46 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -115,6 +115,7 @@ public void setUp() throws IOException {
     taskMetrics = new TaskMetrics();
 
     when(shuffleMemoryManager.tryToAcquire(anyLong())).then(returnsFirstArg());
+    when(shuffleMemoryManager.pageSizeBytes()).thenReturn(128L * 1024 * 1024);
 
     when(blockManager.diskBlockManager()).thenReturn(diskBlockManager);
     when(blockManager.getDiskWriter(
@@ -549,14 +550,14 @@ public void testPeakMemoryUsed() throws Exception {
     final long recordLengthBytes = 8;
     final long pageSizeBytes = 256;
     final long numRecordsPerPage = pageSizeBytes / recordLengthBytes;
-    final SparkConf conf = new SparkConf().set("spark.buffer.pageSize", pageSizeBytes + "b");
+    when(shuffleMemoryManager.pageSizeBytes()).thenReturn(pageSizeBytes);
     final UnsafeShuffleWriter<Object, Object> writer =
       new UnsafeShuffleWriter<Object, Object>(
         blockManager,
         shuffleBlockResolver,
         taskMemoryManager,
         shuffleMemoryManager,
-        new UnsafeShuffleHandle<Object, Object>(0, 1, shuffleDep),
+        new UnsafeShuffleHandle<>(0, 1, shuffleDep),
         0, // map id
         taskContext,
         conf);
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 3c5003380162f..0b11562980b8e 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -48,7 +48,7 @@ public abstract class AbstractBytesToBytesMapSuite {
 
   @Before
   public void setup() {
-    shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
+    shuffleMemoryManager = ShuffleMemoryManager.create(Long.MAX_VALUE, PAGE_SIZE_BYTES);
     taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(getMemoryAllocator()));
     // Mocked memory manager for tests that check the maximum array size, since actually allocating
     // such large arrays will cause us to run out of memory in our tests.
@@ -441,7 +441,7 @@ public void randomizedTestWithRecordsLargerThanPageSize() {
 
   @Test
   public void failureToAllocateFirstPage() {
-    shuffleMemoryManager = new ShuffleMemoryManager(1024);
+    shuffleMemoryManager = ShuffleMemoryManager.createForTesting(1024);
     BytesToBytesMap map =
       new BytesToBytesMap(taskMemoryManager, shuffleMemoryManager, 1, PAGE_SIZE_BYTES);
     try {
@@ -461,7 +461,7 @@ public void failureToAllocateFirstPage() {
 
   @Test
   public void failureToGrow() {
-    shuffleMemoryManager = new ShuffleMemoryManager(1024 * 10);
+    shuffleMemoryManager = ShuffleMemoryManager.createForTesting(1024 * 10);
     BytesToBytesMap map = new BytesToBytesMap(taskMemoryManager, shuffleMemoryManager, 1, 1024);
     try {
       boolean success = true;
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index f5300373d87ea..83049b8a21fcf 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -102,7 +102,7 @@ public void setUp() {
     MockitoAnnotations.initMocks(this);
     sparkConf = new SparkConf();
     tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "unsafe-test");
-    shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
+    shuffleMemoryManager = ShuffleMemoryManager.create(Long.MAX_VALUE, pageSizeBytes);
     spillFilesCreated.clear();
     taskContext = mock(TaskContext.class);
     when(taskContext.taskMetrics()).thenReturn(new TaskMetrics());
@@ -237,7 +237,7 @@ public void testSortingEmptyArrays() throws Exception {
 
   @Test
   public void spillingOccursInResponseToMemoryPressure() throws Exception {
-    shuffleMemoryManager = new ShuffleMemoryManager(pageSizeBytes * 2);
+    shuffleMemoryManager = ShuffleMemoryManager.create(pageSizeBytes * 2, pageSizeBytes);
     final UnsafeExternalSorter sorter = newSorter();
     final int numRecords = (int) pageSizeBytes / 4;
     for (int i = 0; i <= numRecords; i++) {
diff --git a/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
index f495b6a037958..6d45b1a101be6 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
@@ -24,7 +24,7 @@ import org.mockito.Mockito._
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.{SparkFunSuite, TaskContext}
+import org.apache.spark.{SparkConf, SparkFunSuite, TaskContext}
 
 class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
 
@@ -50,7 +50,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
   }
 
   test("single task requesting memory") {
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     assert(manager.tryToAcquire(100L) === 100L)
     assert(manager.tryToAcquire(400L) === 400L)
@@ -72,7 +72,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // Two threads request 500 bytes first, wait for each other to get it, and then request
     // 500 more; we should immediately return 0 as both are now at 1 / N
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Result1 = -1L
@@ -124,7 +124,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // Two tasks request 250 bytes first, wait for each other to get it, and then request
     // 500 more; we should only grant 250 bytes to each of them on this second request
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Result1 = -1L
@@ -176,7 +176,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // for a bit and releases 250 bytes, which should then be granted to t2. Further requests
     // by t2 will return false right away because it now has 1 / 2N of the memory.
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Requested = false
@@ -241,7 +241,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // t1 grabs 1000 bytes and then waits until t2 is ready to make a request. It sleeps
     // for a bit and releases all its memory. t2 should now be able to grab all the memory.
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Requested = false
@@ -307,7 +307,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
   }
 
   test("tasks should not be granted a negative size") {
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
     manager.tryToAcquire(700L)
 
     val latch = new CountDownLatch(1)
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 60be85e53e2aa..cd4c55f79f18c 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -54,7 +54,6 @@ def launch_gateway():
         if os.environ.get("SPARK_TESTING"):
             submit_args = ' '.join([
                 "--conf spark.ui.enabled=false",
-                "--conf spark.buffer.pageSize=4mb",
                 submit_args
             ])
         command = [os.path.join(SPARK_HOME, script)] + shlex.split(submit_args)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index b9d44aace1009..4d5e98a3e90c8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -342,7 +342,7 @@ class TungstenAggregationIterator(
     TaskContext.get.taskMemoryManager(),
     SparkEnv.get.shuffleMemoryManager,
     1024 * 16, // initial capacity
-    SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m"),
+    SparkEnv.get.shuffleMemoryManager.pageSizeBytes,
     false // disable tracking of performance metrics
   )
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 3f257ecdd156c..953abf409f220 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -282,17 +282,15 @@ private[joins] final class UnsafeHashedRelation(
     // This is used in Broadcast, shared by multiple tasks, so we use on-heap memory
     val taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
 
+    val pageSizeBytes = Option(SparkEnv.get).map(_.shuffleMemoryManager.pageSizeBytes)
+      .getOrElse(new SparkConf().getSizeAsBytes("spark.buffer.pageSize", "16m"))
+
     // Dummy shuffle memory manager which always grants all memory allocation requests.
     // We use this because it doesn't make sense count shared broadcast variables' memory usage
     // towards individual tasks' quotas. In the future, we should devise a better way of handling
     // this.
-    val shuffleMemoryManager = new ShuffleMemoryManager(new SparkConf()) {
-      override def tryToAcquire(numBytes: Long): Long = numBytes
-      override def release(numBytes: Long): Unit = {}
-    }
-
-    val pageSizeBytes = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf())
-      .getSizeAsBytes("spark.buffer.pageSize", "64m")
+    val shuffleMemoryManager =
+      ShuffleMemoryManager.create(maxMemory = Long.MaxValue, pageSizeBytes = pageSizeBytes)
 
     binaryMap = new BytesToBytesMap(
       taskMemoryManager,
@@ -306,11 +304,11 @@ private[joins] final class UnsafeHashedRelation(
     while (i < nKeys) {
       val keySize = in.readInt()
       val valuesSize = in.readInt()
-      if (keySize > keyBuffer.size) {
+      if (keySize > keyBuffer.length) {
         keyBuffer = new Array[Byte](keySize)
       }
       in.readFully(keyBuffer, 0, keySize)
-      if (valuesSize > valuesBuffer.size) {
+      if (valuesSize > valuesBuffer.length) {
         valuesBuffer = new Array[Byte](valuesSize)
       }
       in.readFully(valuesBuffer, 0, valuesSize)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index 7f69cdb08aa78..e316930470127 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.{InternalAccumulator, TaskContext}
+import org.apache.spark.{SparkEnv, InternalAccumulator, TaskContext}
 import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
@@ -122,7 +122,7 @@ case class TungstenSort(
   protected override def doExecute(): RDD[InternalRow] = {
     val schema = child.schema
     val childOutput = child.output
-    val pageSize = sparkContext.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
+    val pageSize = SparkEnv.get.shuffleMemoryManager.pageSizeBytes
 
     /**
      * Set up the sorter in each partition before computing the parent partition.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
similarity index 100%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
index 53de2d0f0771f..48c3938ff87ba 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
@@ -22,7 +22,7 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 /**
  * A [[ShuffleMemoryManager]] that can be controlled to run out of memory.
  */
-class TestShuffleMemoryManager extends ShuffleMemoryManager(Long.MaxValue) {
+class TestShuffleMemoryManager extends ShuffleMemoryManager(Long.MaxValue, 4 * 1024 * 1024) {
   private var oom = false
 
   override def tryToAcquire(numBytes: Long): Long = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 167086db5bfe2..296cc5c5e0b04 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -52,7 +52,6 @@ object TestHive
         .set("spark.sql.test", "")
         .set("spark.sql.hive.metastore.barrierPrefixes",
           "org.apache.spark.sql.hive.execution.PairSerDe")
-        .set("spark.buffer.pageSize", "4m")
         // SPARK-8910
         .set("spark.ui.enabled", "false")))
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
index cf693d01a4f5b..70b81ce015ddc 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
@@ -25,6 +25,12 @@ private ByteArrayMethods() {
     // Private constructor, since this class only contains static methods.
   }
 
+  /** Returns the next number greater or equal num that is power of 2. */
+  public static long nextPowerOf2(long num) {
+    final long highBit = Long.highestOneBit(num);
+    return (highBit == num) ? num : highBit << 1;
+  }
+
   public static int roundNumberOfBytesToNearestWord(int numBytes) {
     int remainder = numBytes & 0x07;  // This is equivalent to `numBytes % 8`
     if (remainder == 0) {

From 15bd6f338dff4bcab4a1a3a2c568655022e49c32 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 23:40:38 -0700
Subject: [PATCH 0915/1454] [SPARK-9453] [SQL] support records larger than page
 size in UnsafeShuffleExternalSorter

This patch follows exactly #7891 (except testing)

Author: Davies Liu <davies@databricks.com>

Closes #8005 from davies/larger_record and squashes the following commits:

f9c4aff [Davies Liu] address comments
9de5c72 [Davies Liu] support records larger than page size in UnsafeShuffleExternalSorter
---
 .../unsafe/UnsafeShuffleExternalSorter.java   | 143 +++++++++++-------
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |  10 +-
 .../unsafe/UnsafeShuffleWriterSuite.java      |  60 ++------
 3 files changed, 103 insertions(+), 110 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index f6e0913a7a0b3..925b60a145886 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -17,10 +17,10 @@
 
 package org.apache.spark.shuffle.unsafe;
 
+import javax.annotation.Nullable;
 import java.io.File;
 import java.io.IOException;
 import java.util.LinkedList;
-import javax.annotation.Nullable;
 
 import scala.Tuple2;
 
@@ -34,8 +34,11 @@
 import org.apache.spark.serializer.DummySerializerInstance;
 import org.apache.spark.serializer.SerializerInstance;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
-import org.apache.spark.storage.*;
+import org.apache.spark.storage.BlockManager;
+import org.apache.spark.storage.DiskBlockObjectWriter;
+import org.apache.spark.storage.TempShuffleBlockId;
 import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 import org.apache.spark.util.Utils;
@@ -68,7 +71,7 @@ final class UnsafeShuffleExternalSorter {
   private final int pageSizeBytes;
   @VisibleForTesting
   final int maxRecordSizeBytes;
-  private final TaskMemoryManager memoryManager;
+  private final TaskMemoryManager taskMemoryManager;
   private final ShuffleMemoryManager shuffleMemoryManager;
   private final BlockManager blockManager;
   private final TaskContext taskContext;
@@ -91,7 +94,7 @@ final class UnsafeShuffleExternalSorter {
   private long peakMemoryUsedBytes;
 
   // These variables are reset after spilling:
-  @Nullable private UnsafeShuffleInMemorySorter sorter;
+  @Nullable private UnsafeShuffleInMemorySorter inMemSorter;
   @Nullable private MemoryBlock currentPage = null;
   private long currentPagePosition = -1;
   private long freeSpaceInCurrentPage = 0;
@@ -105,7 +108,7 @@ public UnsafeShuffleExternalSorter(
       int numPartitions,
       SparkConf conf,
       ShuffleWriteMetrics writeMetrics) throws IOException {
-    this.memoryManager = memoryManager;
+    this.taskMemoryManager = memoryManager;
     this.shuffleMemoryManager = shuffleMemoryManager;
     this.blockManager = blockManager;
     this.taskContext = taskContext;
@@ -133,7 +136,7 @@ private void initializeForWriting() throws IOException {
       throw new IOException("Could not acquire " + memoryRequested + " bytes of memory");
     }
 
-    this.sorter = new UnsafeShuffleInMemorySorter(initialSize);
+    this.inMemSorter = new UnsafeShuffleInMemorySorter(initialSize);
   }
 
   /**
@@ -160,7 +163,7 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
 
     // This call performs the actual sort.
     final UnsafeShuffleInMemorySorter.UnsafeShuffleSorterIterator sortedRecords =
-      sorter.getSortedIterator();
+      inMemSorter.getSortedIterator();
 
     // Currently, we need to open a new DiskBlockObjectWriter for each partition; we can avoid this
     // after SPARK-5581 is fixed.
@@ -206,8 +209,8 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
       }
 
       final long recordPointer = sortedRecords.packedRecordPointer.getRecordPointer();
-      final Object recordPage = memoryManager.getPage(recordPointer);
-      final long recordOffsetInPage = memoryManager.getOffsetInPage(recordPointer);
+      final Object recordPage = taskMemoryManager.getPage(recordPointer);
+      final long recordOffsetInPage = taskMemoryManager.getOffsetInPage(recordPointer);
       int dataRemaining = PlatformDependent.UNSAFE.getInt(recordPage, recordOffsetInPage);
       long recordReadPosition = recordOffsetInPage + 4; // skip over record length
       while (dataRemaining > 0) {
@@ -269,9 +272,9 @@ void spill() throws IOException {
       spills.size() > 1 ? " times" : " time");
 
     writeSortedFile(false);
-    final long sorterMemoryUsage = sorter.getMemoryUsage();
-    sorter = null;
-    shuffleMemoryManager.release(sorterMemoryUsage);
+    final long inMemSorterMemoryUsage = inMemSorter.getMemoryUsage();
+    inMemSorter = null;
+    shuffleMemoryManager.release(inMemSorterMemoryUsage);
     final long spillSize = freeMemory();
     taskContext.taskMetrics().incMemoryBytesSpilled(spillSize);
 
@@ -283,7 +286,7 @@ private long getMemoryUsage() {
     for (MemoryBlock page : allocatedPages) {
       totalPageSize += page.size();
     }
-    return ((sorter == null) ? 0 : sorter.getMemoryUsage()) + totalPageSize;
+    return ((inMemSorter == null) ? 0 : inMemSorter.getMemoryUsage()) + totalPageSize;
   }
 
   private void updatePeakMemoryUsed() {
@@ -305,7 +308,7 @@ private long freeMemory() {
     updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
-      memoryManager.freePage(block);
+      taskMemoryManager.freePage(block);
       shuffleMemoryManager.release(block.size());
       memoryFreed += block.size();
     }
@@ -319,54 +322,53 @@ private long freeMemory() {
   /**
    * Force all memory and spill files to be deleted; called by shuffle error-handling code.
    */
-  public void cleanupAfterError() {
+  public void cleanupResources() {
     freeMemory();
     for (SpillInfo spill : spills) {
       if (spill.file.exists() && !spill.file.delete()) {
         logger.error("Unable to delete spill file {}", spill.file.getPath());
       }
     }
-    if (sorter != null) {
-      shuffleMemoryManager.release(sorter.getMemoryUsage());
-      sorter = null;
+    if (inMemSorter != null) {
+      shuffleMemoryManager.release(inMemSorter.getMemoryUsage());
+      inMemSorter = null;
     }
   }
 
   /**
-   * Checks whether there is enough space to insert a new record into the sorter.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
-
-   * @return true if the record can be inserted without requiring more allocations, false otherwise.
-   */
-  private boolean haveSpaceForRecord(int requiredSpace) {
-    assert (requiredSpace > 0);
-    return (sorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
-  }
-
-  /**
-   * Allocates more memory in order to insert an additional record. This will request additional
-   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
-   * obtained.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
+   * Checks whether there is enough space to insert an additional record in to the sort pointer
+   * array and grows the array if additional space is required. If the required space cannot be
+   * obtained, then the in-memory data will be spilled to disk.
    */
-  private void allocateSpaceForRecord(int requiredSpace) throws IOException {
-    if (!sorter.hasSpaceForAnotherRecord()) {
+  private void growPointerArrayIfNecessary() throws IOException {
+    assert(inMemSorter != null);
+    if (!inMemSorter.hasSpaceForAnotherRecord()) {
       logger.debug("Attempting to expand sort pointer array");
-      final long oldPointerArrayMemoryUsage = sorter.getMemoryUsage();
+      final long oldPointerArrayMemoryUsage = inMemSorter.getMemoryUsage();
       final long memoryToGrowPointerArray = oldPointerArrayMemoryUsage * 2;
       final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryToGrowPointerArray);
       if (memoryAcquired < memoryToGrowPointerArray) {
         shuffleMemoryManager.release(memoryAcquired);
         spill();
       } else {
-        sorter.expandPointerArray();
+        inMemSorter.expandPointerArray();
         shuffleMemoryManager.release(oldPointerArrayMemoryUsage);
       }
     }
+  }
+  
+  /**
+   * Allocates more memory in order to insert an additional record. This will request additional
+   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
+   * obtained.
+   *
+   * @param requiredSpace the required space in the data page, in bytes, including space for storing
+   *                      the record size. This must be less than or equal to the page size (records
+   *                      that exceed the page size are handled via a different code path which uses
+   *                      special overflow pages).
+   */
+  private void acquireNewPageIfNecessary(int requiredSpace) throws IOException {
+    growPointerArrayIfNecessary();
     if (requiredSpace > freeSpaceInCurrentPage) {
       logger.trace("Required space {} is less than free space in current page ({})", requiredSpace,
         freeSpaceInCurrentPage);
@@ -387,7 +389,7 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
             throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
           }
         }
-        currentPage = memoryManager.allocatePage(pageSizeBytes);
+        currentPage = taskMemoryManager.allocatePage(pageSizeBytes);
         currentPagePosition = currentPage.getBaseOffset();
         freeSpaceInCurrentPage = pageSizeBytes;
         allocatedPages.add(currentPage);
@@ -403,27 +405,58 @@ public void insertRecord(
       long recordBaseOffset,
       int lengthInBytes,
       int partitionId) throws IOException {
+
+    growPointerArrayIfNecessary();
     // Need 4 bytes to store the record length.
     final int totalSpaceRequired = lengthInBytes + 4;
-    if (!haveSpaceForRecord(totalSpaceRequired)) {
-      allocateSpaceForRecord(totalSpaceRequired);
+
+    // --- Figure out where to insert the new record ----------------------------------------------
+
+    final MemoryBlock dataPage;
+    long dataPagePosition;
+    boolean useOverflowPage = totalSpaceRequired > pageSizeBytes;
+    if (useOverflowPage) {
+      long overflowPageSize = ByteArrayMethods.roundNumberOfBytesToNearestWord(totalSpaceRequired);
+      // The record is larger than the page size, so allocate a special overflow page just to hold
+      // that record.
+      final long memoryGranted = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+      if (memoryGranted != overflowPageSize) {
+        shuffleMemoryManager.release(memoryGranted);
+        spill();
+        final long memoryGrantedAfterSpill = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+        if (memoryGrantedAfterSpill != overflowPageSize) {
+          shuffleMemoryManager.release(memoryGrantedAfterSpill);
+          throw new IOException("Unable to acquire " + overflowPageSize + " bytes of memory");
+        }
+      }
+      MemoryBlock overflowPage = taskMemoryManager.allocatePage(overflowPageSize);
+      allocatedPages.add(overflowPage);
+      dataPage = overflowPage;
+      dataPagePosition = overflowPage.getBaseOffset();
+    } else {
+      // The record is small enough to fit in a regular data page, but the current page might not
+      // have enough space to hold it (or no pages have been allocated yet).
+      acquireNewPageIfNecessary(totalSpaceRequired);
+      dataPage = currentPage;
+      dataPagePosition = currentPagePosition;
+      // Update bookkeeping information
+      freeSpaceInCurrentPage -= totalSpaceRequired;
+      currentPagePosition += totalSpaceRequired;
     }
+    final Object dataPageBaseObject = dataPage.getBaseObject();
 
     final long recordAddress =
-      memoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
-    final Object dataPageBaseObject = currentPage.getBaseObject();
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, lengthInBytes);
-    currentPagePosition += 4;
-    freeSpaceInCurrentPage -= 4;
+      taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    dataPagePosition += 4;
     PlatformDependent.copyMemory(
       recordBaseObject,
       recordBaseOffset,
       dataPageBaseObject,
-      currentPagePosition,
+      dataPagePosition,
       lengthInBytes);
-    currentPagePosition += lengthInBytes;
-    freeSpaceInCurrentPage -= lengthInBytes;
-    sorter.insertRecord(recordAddress, partitionId);
+    assert(inMemSorter != null);
+    inMemSorter.insertRecord(recordAddress, partitionId);
   }
 
   /**
@@ -435,14 +468,14 @@ public void insertRecord(
    */
   public SpillInfo[] closeAndGetSpills() throws IOException {
     try {
-      if (sorter != null) {
+      if (inMemSorter != null) {
         // Do not count the final file towards the spill count.
         writeSortedFile(true);
         freeMemory();
       }
       return spills.toArray(new SpillInfo[spills.size()]);
     } catch (IOException e) {
-      cleanupAfterError();
+      cleanupResources();
       throw e;
     }
   }
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index 6e2eeb37c86f1..02084f9122e00 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -17,17 +17,17 @@
 
 package org.apache.spark.shuffle.unsafe;
 
+import javax.annotation.Nullable;
 import java.io.*;
 import java.nio.channels.FileChannel;
 import java.util.Iterator;
-import javax.annotation.Nullable;
 
 import scala.Option;
 import scala.Product2;
 import scala.collection.JavaConversions;
+import scala.collection.immutable.Map;
 import scala.reflect.ClassTag;
 import scala.reflect.ClassTag$;
-import scala.collection.immutable.Map;
 
 import com.google.common.annotations.VisibleForTesting;
 import com.google.common.io.ByteStreams;
@@ -38,10 +38,10 @@
 
 import org.apache.spark.*;
 import org.apache.spark.annotation.Private;
+import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.io.CompressionCodec;
 import org.apache.spark.io.CompressionCodec$;
 import org.apache.spark.io.LZFCompressionCodec;
-import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.network.util.LimitedInputStream;
 import org.apache.spark.scheduler.MapStatus;
 import org.apache.spark.scheduler.MapStatus$;
@@ -178,7 +178,7 @@ public void write(scala.collection.Iterator<Product2<K, V>> records) throws IOEx
     } finally {
       if (sorter != null) {
         try {
-          sorter.cleanupAfterError();
+          sorter.cleanupResources();
         } catch (Exception e) {
           // Only throw this error if we won't be masking another
           // error.
@@ -482,7 +482,7 @@ public Option<MapStatus> stop(boolean success) {
       if (sorter != null) {
         // If sorter is non-null, then this implies that we called stop() in response to an error,
         // so we need to clean up memory and spill files created by the sorter
-        sorter.cleanupAfterError();
+        sorter.cleanupResources();
       }
     }
   }
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index c68354ba49a46..94650be536b5f 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -475,62 +475,22 @@ public void writeRecordsThatAreBiggerThanDiskWriteBufferSize() throws Exception
 
   @Test
   public void writeRecordsThatAreBiggerThanMaxRecordSize() throws Exception {
-    // Use a custom serializer so that we have exact control over the size of serialized data.
-    final Serializer byteArraySerializer = new Serializer() {
-      @Override
-      public SerializerInstance newInstance() {
-        return new SerializerInstance() {
-          @Override
-          public SerializationStream serializeStream(final OutputStream s) {
-            return new SerializationStream() {
-              @Override
-              public void flush() { }
-
-              @Override
-              public <T> SerializationStream writeObject(T t, ClassTag<T> ev1) {
-                byte[] bytes = (byte[]) t;
-                try {
-                  s.write(bytes);
-                } catch (IOException e) {
-                  throw new RuntimeException(e);
-                }
-                return this;
-              }
-
-              @Override
-              public void close() { }
-            };
-          }
-          public <T> ByteBuffer serialize(T t, ClassTag<T> ev1) { return null; }
-          public DeserializationStream deserializeStream(InputStream s) { return null; }
-          public <T> T deserialize(ByteBuffer b, ClassLoader l, ClassTag<T> ev1) { return null; }
-          public <T> T deserialize(ByteBuffer bytes, ClassTag<T> ev1) { return null; }
-        };
-      }
-    };
-    when(shuffleDep.serializer()).thenReturn(Option.<Serializer>apply(byteArraySerializer));
     final UnsafeShuffleWriter<Object, Object> writer = createWriter(false);
-    // Insert a record and force a spill so that there's something to clean up:
-    writer.insertRecordIntoSorter(new Tuple2<Object, Object>(new byte[1], new byte[1]));
-    writer.forceSorterToSpill();
+    final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<Product2<Object, Object>>();
+    dataToWrite.add(new Tuple2<Object, Object>(1, ByteBuffer.wrap(new byte[1])));
     // We should be able to write a record that's right _at_ the max record size
     final byte[] atMaxRecordSize = new byte[writer.maxRecordSizeBytes()];
     new Random(42).nextBytes(atMaxRecordSize);
-    writer.insertRecordIntoSorter(new Tuple2<Object, Object>(new byte[0], atMaxRecordSize));
-    writer.forceSorterToSpill();
-    // Inserting a record that's larger than the max record size should fail:
+    dataToWrite.add(new Tuple2<Object, Object>(2, ByteBuffer.wrap(atMaxRecordSize)));
+    // Inserting a record that's larger than the max record size
     final byte[] exceedsMaxRecordSize = new byte[writer.maxRecordSizeBytes() + 1];
     new Random(42).nextBytes(exceedsMaxRecordSize);
-    Product2<Object, Object> hugeRecord =
-      new Tuple2<Object, Object>(new byte[0], exceedsMaxRecordSize);
-    try {
-      // Here, we write through the public `write()` interface instead of the test-only
-      // `insertRecordIntoSorter` interface:
-      writer.write(Collections.singletonList(hugeRecord).iterator());
-      fail("Expected exception to be thrown");
-    } catch (IOException e) {
-      // Pass
-    }
+    dataToWrite.add(new Tuple2<Object, Object>(3, ByteBuffer.wrap(exceedsMaxRecordSize)));
+    writer.write(dataToWrite.iterator());
+    writer.stop(true);
+    assertEquals(
+      HashMultiset.create(dataToWrite),
+      HashMultiset.create(readRecordsFromFile()));
     assertSpillFilesWereCleanedUp();
   }
 

From e57d6b56137bf3557efe5acea3ad390c1987b257 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 7 Aug 2015 00:00:43 -0700
Subject: [PATCH 0916/1454] [SPARK-9683] [SQL] copy UTF8String when convert
 unsafe array/map to safe

When we convert unsafe row to safe row, we will do copy if the column is struct or string type. However, the string inside unsafe array/map are not copied, which may cause problems.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7990 from cloud-fan/copy and squashes the following commits:

c13d1e3 [Wenchen Fan] change test name
fe36294 [Wenchen Fan] we should deep copy UTF8String when convert unsafe row to safe row
---
 .../sql/catalyst/expressions/FromUnsafe.scala |  3 ++
 .../execution/RowFormatConvertersSuite.scala  | 38 ++++++++++++++++++-
 2 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
index 3caf0fb3410c4..9b960b136f984 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 case class FromUnsafe(child: Expression) extends UnaryExpression
   with ExpectsInputTypes with CodegenFallback {
@@ -52,6 +53,8 @@ case class FromUnsafe(child: Expression) extends UnaryExpression
       }
       new GenericArrayData(result)
 
+    case StringType => value.asInstanceOf[UTF8String].clone()
+
     case MapType(kt, vt, _) =>
       val map = value.asInstanceOf[UnsafeMapData]
       val safeKeyArray = convert(map.keys, ArrayType(kt)).asInstanceOf[GenericArrayData]
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 707cd9c6d939b..8208b25b5708c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -17,9 +17,13 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.expressions.{Literal, IsNull}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute, Literal, IsNull}
 import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.types.{GenericArrayData, ArrayType, StructType, StringType}
+import org.apache.spark.unsafe.types.UTF8String
 
 class RowFormatConvertersSuite extends SparkPlanTest {
 
@@ -87,4 +91,36 @@ class RowFormatConvertersSuite extends SparkPlanTest {
       input.map(Row.fromTuple)
     )
   }
+
+  test("SPARK-9683: copy UTF8String when convert unsafe array/map to safe") {
+    SparkPlan.currentContext.set(TestSQLContext)
+    val schema = ArrayType(StringType)
+    val rows = (1 to 100).map { i =>
+      InternalRow(new GenericArrayData(Array[Any](UTF8String.fromString(i.toString))))
+    }
+    val relation = LocalTableScan(Seq(AttributeReference("t", schema)()), rows)
+
+    val plan =
+      DummyPlan(
+        ConvertToSafe(
+          ConvertToUnsafe(relation)))
+    assert(plan.execute().collect().map(_.getUTF8String(0).toString) === (1 to 100).map(_.toString))
+  }
+}
+
+case class DummyPlan(child: SparkPlan) extends UnaryNode {
+
+  override protected def doExecute(): RDD[InternalRow] = {
+    child.execute().mapPartitions { iter =>
+      // cache all strings to make sure we have deep copied UTF8String inside incoming
+      // safe InternalRow.
+      val strings = new scala.collection.mutable.ArrayBuffer[UTF8String]
+      iter.foreach { row =>
+        strings += row.getArray(0).getUTF8String(0)
+      }
+      strings.map(InternalRow(_)).iterator
+    }
+  }
+
+  override def output: Seq[Attribute] = Seq(AttributeReference("a", StringType)())
 }

From ebfd91c542aaead343cb154277fcf9114382fee7 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 7 Aug 2015 00:09:58 -0700
Subject: [PATCH 0917/1454] [SPARK-9467][SQL]Add SQLMetric to specialize
 accumulators to avoid boxing

This PR adds SQLMetric/SQLMetricParam/SQLMetricValue to specialize accumulators to avoid boxing. All SQL metrics should use these classes rather than `Accumulator`.

Author: zsxwing <zsxwing@gmail.com>

Closes #7996 from zsxwing/sql-accu and squashes the following commits:

14a5f0a [zsxwing] Address comments
367ca23 [zsxwing] Use localValue directly to avoid changing Accumulable
42f50c3 [zsxwing] Add SQLMetric to specialize accumulators to avoid boxing
---
 .../scala/org/apache/spark/Accumulators.scala |   2 +-
 .../scala/org/apache/spark/SparkContext.scala |  15 --
 .../spark/sql/execution/SparkPlan.scala       |  33 ++--
 .../spark/sql/execution/basicOperators.scala  |  11 +-
 .../apache/spark/sql/metric/SQLMetrics.scala  | 149 ++++++++++++++++++
 .../org/apache/spark/sql/ui/SQLListener.scala |  17 +-
 .../apache/spark/sql/ui/SparkPlanGraph.scala  |   8 +-
 .../spark/sql/metric/SQLMetricsSuite.scala    | 145 +++++++++++++++++
 .../spark/sql/ui/SQLListenerSuite.scala       |   5 +-
 9 files changed, 338 insertions(+), 47 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 462d5c96d480b..064246dfa7fc3 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -257,7 +257,7 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa
  */
 class Accumulator[T] private[spark] (
     @transient private[spark] val initialValue: T,
-    private[spark] val param: AccumulatorParam[T],
+    param: AccumulatorParam[T],
     name: Option[String],
     internal: Boolean)
   extends Accumulable[T, T](initialValue, param, name, internal) {
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 5662686436900..9ced44131b0d9 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1238,21 +1238,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     acc
   }
 
-  /**
-   * Create an [[org.apache.spark.Accumulator]] variable of a given type, with a name for display
-   * in the Spark UI. Tasks can "add" values to the accumulator using the `+=` method. Only the
-   * driver can access the accumulator's `value`. The latest local value of such accumulator will be
-   * sent back to the driver via heartbeats.
-   *
-   * @tparam T type that can be added to the accumulator, must be thread safe
-   */
-  private[spark] def internalAccumulator[T](initialValue: T, name: String)(
-    implicit param: AccumulatorParam[T]): Accumulator[T] = {
-    val acc = new Accumulator(initialValue, param, Some(name), internal = true)
-    cleaner.foreach(_.registerAccumulatorForCleanup(acc))
-    acc
-  }
-
   /**
    * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values
    * with `+=`. Only the driver can access the accumuable's `value`.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 719ad432e2fe0..1915496d16205 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -21,7 +21,7 @@ import java.util.concurrent.atomic.AtomicBoolean
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.{Accumulator, Logging}
+import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.sql.SQLContext
@@ -32,6 +32,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.metric.{IntSQLMetric, LongSQLMetric, SQLMetric, SQLMetrics}
 import org.apache.spark.sql.types.DataType
 
 object SparkPlan {
@@ -84,22 +85,30 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
    */
   protected[sql] def trackNumOfRowsEnabled: Boolean = false
 
-  private lazy val numOfRowsAccumulator = sparkContext.internalAccumulator(0L, "number of rows")
+  private lazy val defaultMetrics: Map[String, SQLMetric[_, _]] =
+    if (trackNumOfRowsEnabled) {
+      Map("numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
+    }
+    else {
+      Map.empty
+    }
 
   /**
-   * Return all accumulators containing metrics of this SparkPlan.
+   * Return all metrics containing metrics of this SparkPlan.
    */
-  private[sql] def accumulators: Map[String, Accumulator[_]] = if (trackNumOfRowsEnabled) {
-      Map("numRows" -> numOfRowsAccumulator)
-    } else {
-      Map.empty
-    }
+  private[sql] def metrics: Map[String, SQLMetric[_, _]] = defaultMetrics
+
+  /**
+   * Return a IntSQLMetric according to the name.
+   */
+  private[sql] def intMetric(name: String): IntSQLMetric =
+    metrics(name).asInstanceOf[IntSQLMetric]
 
   /**
-   * Return the accumulator according to the name.
+   * Return a LongSQLMetric according to the name.
    */
-  private[sql] def accumulator[T](name: String): Accumulator[T] =
-    accumulators(name).asInstanceOf[Accumulator[T]]
+  private[sql] def longMetric(name: String): LongSQLMetric =
+    metrics(name).asInstanceOf[LongSQLMetric]
 
   // TODO: Move to `DistributedPlan`
   /** Specifies how data is partitioned across different nodes in the cluster. */
@@ -148,7 +157,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
       prepare()
       if (trackNumOfRowsEnabled) {
-        val numRows = accumulator[Long]("numRows")
+        val numRows = longMetric("numRows")
         doExecute().map { row =>
           numRows += 1
           row
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index f4677b4ee86bb..0680f31d40f6d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.ExternalSorter
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
@@ -81,13 +82,13 @@ case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan)
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
-  private[sql] override lazy val accumulators = Map(
-    "numInputRows" -> sparkContext.internalAccumulator(0L, "number of input rows"),
-    "numOutputRows" -> sparkContext.internalAccumulator(0L, "number of output rows"))
+  private[sql] override lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
 
   protected override def doExecute(): RDD[InternalRow] = {
-    val numInputRows = accumulator[Long]("numInputRows")
-    val numOutputRows = accumulator[Long]("numOutputRows")
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     child.execute().mapPartitions { iter =>
       val predicate = newPredicate(condition, child.output)
       iter.filter { row =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala b/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
new file mode 100644
index 0000000000000..3b907e5da7897
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
@@ -0,0 +1,149 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.metric
+
+import org.apache.spark.{Accumulable, AccumulableParam, SparkContext}
+
+/**
+ * Create a layer for specialized metric. We cannot add `@specialized` to
+ * `Accumulable/AccumulableParam` because it will break Java source compatibility.
+ *
+ * An implementation of SQLMetric should override `+=` and `add` to avoid boxing.
+ */
+private[sql] abstract class SQLMetric[R <: SQLMetricValue[T], T](
+    name: String, val param: SQLMetricParam[R, T])
+  extends Accumulable[R, T](param.zero, param, Some(name), true)
+
+/**
+ * Create a layer for specialized metric. We cannot add `@specialized` to
+ * `Accumulable/AccumulableParam` because it will break Java source compatibility.
+ */
+private[sql] trait SQLMetricParam[R <: SQLMetricValue[T], T] extends AccumulableParam[R, T] {
+
+  def zero: R
+}
+
+/**
+ * Create a layer for specialized metric. We cannot add `@specialized` to
+ * `Accumulable/AccumulableParam` because it will break Java source compatibility.
+ */
+private[sql] trait SQLMetricValue[T] extends Serializable {
+
+  def value: T
+
+  override def toString: String = value.toString
+}
+
+/**
+ * A wrapper of Long to avoid boxing and unboxing when using Accumulator
+ */
+private[sql] class LongSQLMetricValue(private var _value : Long) extends SQLMetricValue[Long] {
+
+  def add(incr: Long): LongSQLMetricValue = {
+    _value += incr
+    this
+  }
+
+  // Although there is a boxing here, it's fine because it's only called in SQLListener
+  override def value: Long = _value
+}
+
+/**
+ * A wrapper of Int to avoid boxing and unboxing when using Accumulator
+ */
+private[sql] class IntSQLMetricValue(private var _value: Int) extends SQLMetricValue[Int] {
+
+  def add(term: Int): IntSQLMetricValue = {
+    _value += term
+    this
+  }
+
+  // Although there is a boxing here, it's fine because it's only called in SQLListener
+  override def value: Int = _value
+}
+
+/**
+ * A specialized long Accumulable to avoid boxing and unboxing when using Accumulator's
+ * `+=` and `add`.
+ */
+private[sql] class LongSQLMetric private[metric](name: String)
+  extends SQLMetric[LongSQLMetricValue, Long](name, LongSQLMetricParam) {
+
+  override def +=(term: Long): Unit = {
+    localValue.add(term)
+  }
+
+  override def add(term: Long): Unit = {
+    localValue.add(term)
+  }
+}
+
+/**
+ * A specialized int Accumulable to avoid boxing and unboxing when using Accumulator's
+ * `+=` and `add`.
+ */
+private[sql] class IntSQLMetric private[metric](name: String)
+  extends SQLMetric[IntSQLMetricValue, Int](name, IntSQLMetricParam) {
+
+  override def +=(term: Int): Unit = {
+    localValue.add(term)
+  }
+
+  override def add(term: Int): Unit = {
+    localValue.add(term)
+  }
+}
+
+private object LongSQLMetricParam extends SQLMetricParam[LongSQLMetricValue, Long] {
+
+  override def addAccumulator(r: LongSQLMetricValue, t: Long): LongSQLMetricValue = r.add(t)
+
+  override def addInPlace(r1: LongSQLMetricValue, r2: LongSQLMetricValue): LongSQLMetricValue =
+    r1.add(r2.value)
+
+  override def zero(initialValue: LongSQLMetricValue): LongSQLMetricValue = zero
+
+  override def zero: LongSQLMetricValue = new LongSQLMetricValue(0L)
+}
+
+private object IntSQLMetricParam extends SQLMetricParam[IntSQLMetricValue, Int] {
+
+  override def addAccumulator(r: IntSQLMetricValue, t: Int): IntSQLMetricValue = r.add(t)
+
+  override def addInPlace(r1: IntSQLMetricValue, r2: IntSQLMetricValue): IntSQLMetricValue =
+    r1.add(r2.value)
+
+  override def zero(initialValue: IntSQLMetricValue): IntSQLMetricValue = zero
+
+  override def zero: IntSQLMetricValue = new IntSQLMetricValue(0)
+}
+
+private[sql] object SQLMetrics {
+
+  def createIntMetric(sc: SparkContext, name: String): IntSQLMetric = {
+    val acc = new IntSQLMetric(name)
+    sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
+    acc
+  }
+
+  def createLongMetric(sc: SparkContext, name: String): LongSQLMetric = {
+    val acc = new LongSQLMetric(name)
+    sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
+    acc
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
index e7b1dd1ffac68..2fd4fc658d068 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
@@ -21,11 +21,12 @@ import scala.collection.mutable
 
 import com.google.common.annotations.VisibleForTesting
 
-import org.apache.spark.{AccumulatorParam, JobExecutionStatus, Logging}
+import org.apache.spark.{JobExecutionStatus, Logging}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.execution.SQLExecution
+import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
 
 private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener with Logging {
 
@@ -36,8 +37,6 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
 
   // Old data in the following fields must be removed in "trimExecutionsIfNecessary".
   // If adding new fields, make sure "trimExecutionsIfNecessary" can clean up old data
-
-  // VisibleForTesting
   private val _executionIdToData = mutable.HashMap[Long, SQLExecutionUIData]()
 
   /**
@@ -270,9 +269,10 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
                accumulatorUpdate <- taskMetrics.accumulatorUpdates.toSeq) yield {
             accumulatorUpdate
           }
-        }.filter { case (id, _) => executionUIData.accumulatorMetrics.keySet(id) }
+        }.filter { case (id, _) => executionUIData.accumulatorMetrics.contains(id) }
         mergeAccumulatorUpdates(accumulatorUpdates, accumulatorId =>
-          executionUIData.accumulatorMetrics(accumulatorId).accumulatorParam)
+          executionUIData.accumulatorMetrics(accumulatorId).metricParam).
+          mapValues(_.asInstanceOf[SQLMetricValue[_]].value)
       case None =>
         // This execution has been dropped
         Map.empty
@@ -281,10 +281,11 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
 
   private def mergeAccumulatorUpdates(
       accumulatorUpdates: Seq[(Long, Any)],
-      paramFunc: Long => AccumulatorParam[Any]): Map[Long, Any] = {
+      paramFunc: Long => SQLMetricParam[SQLMetricValue[Any], Any]): Map[Long, Any] = {
     accumulatorUpdates.groupBy(_._1).map { case (accumulatorId, values) =>
       val param = paramFunc(accumulatorId)
-      (accumulatorId, values.map(_._2).reduceLeft(param.addInPlace))
+      (accumulatorId,
+        values.map(_._2.asInstanceOf[SQLMetricValue[Any]]).foldLeft(param.zero)(param.addInPlace))
     }
   }
 
@@ -336,7 +337,7 @@ private[ui] class SQLExecutionUIData(
 private[ui] case class SQLPlanMetric(
     name: String,
     accumulatorId: Long,
-    accumulatorParam: AccumulatorParam[Any])
+    metricParam: SQLMetricParam[SQLMetricValue[Any], Any])
 
 /**
  * Store all accumulatorUpdates for all tasks in a Spark stage.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
index 7910c163ba453..1ba50b95becc1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
@@ -21,8 +21,8 @@ import java.util.concurrent.atomic.AtomicLong
 
 import scala.collection.mutable
 
-import org.apache.spark.AccumulatorParam
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
 
 /**
  * A graph used for storing information of an executionPlan of DataFrame.
@@ -61,9 +61,9 @@ private[sql] object SparkPlanGraph {
       nodeIdGenerator: AtomicLong,
       nodes: mutable.ArrayBuffer[SparkPlanGraphNode],
       edges: mutable.ArrayBuffer[SparkPlanGraphEdge]): SparkPlanGraphNode = {
-    val metrics = plan.accumulators.toSeq.map { case (key, accumulator) =>
-      SQLPlanMetric(accumulator.name.getOrElse(key), accumulator.id,
-        accumulator.param.asInstanceOf[AccumulatorParam[Any]])
+    val metrics = plan.metrics.toSeq.map { case (key, metric) =>
+      SQLPlanMetric(metric.name.getOrElse(key), metric.id,
+        metric.param.asInstanceOf[SQLMetricParam[SQLMetricValue[Any], Any]])
     }
     val node = SparkPlanGraphNode(
       nodeIdGenerator.getAndIncrement(), plan.nodeName, plan.simpleString, metrics)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
new file mode 100644
index 0000000000000..d22160f5384f4
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
@@ -0,0 +1,145 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.metric
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+
+import scala.collection.mutable
+
+import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm._
+import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.util.Utils
+
+
+class SQLMetricsSuite extends SparkFunSuite {
+
+  test("LongSQLMetric should not box Long") {
+    val l = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "long")
+    val f = () => { l += 1L }
+    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
+      val boxingFinder = new BoxingFinder()
+      cl.accept(boxingFinder, 0)
+      assert(boxingFinder.boxingInvokes.isEmpty, s"Found boxing: ${boxingFinder.boxingInvokes}")
+    }
+  }
+
+  test("IntSQLMetric should not box Int") {
+    val l = SQLMetrics.createIntMetric(TestSQLContext.sparkContext, "Int")
+    val f = () => { l += 1 }
+    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
+      val boxingFinder = new BoxingFinder()
+      cl.accept(boxingFinder, 0)
+      assert(boxingFinder.boxingInvokes.isEmpty, s"Found boxing: ${boxingFinder.boxingInvokes}")
+    }
+  }
+
+  test("Normal accumulator should do boxing") {
+    // We need this test to make sure BoxingFinder works.
+    val l = TestSQLContext.sparkContext.accumulator(0L)
+    val f = () => { l += 1L }
+    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
+      val boxingFinder = new BoxingFinder()
+      cl.accept(boxingFinder, 0)
+      assert(boxingFinder.boxingInvokes.nonEmpty, "Found find boxing in this test")
+    }
+  }
+}
+
+private case class MethodIdentifier[T](cls: Class[T], name: String, desc: String)
+
+/**
+ * If `method` is null, search all methods of this class recursively to find if they do some boxing.
+ * If `method` is specified, only search this method of the class to speed up the searching.
+ *
+ * This method will skip the methods in `visitedMethods` to avoid potential infinite cycles.
+ */
+private class BoxingFinder(
+    method: MethodIdentifier[_] = null,
+    val boxingInvokes: mutable.Set[String] = mutable.Set.empty,
+    visitedMethods: mutable.Set[MethodIdentifier[_]] = mutable.Set.empty)
+  extends ClassVisitor(ASM4) {
+
+  private val primitiveBoxingClassName =
+    Set("java/lang/Long",
+      "java/lang/Double",
+      "java/lang/Integer",
+      "java/lang/Float",
+      "java/lang/Short",
+      "java/lang/Character",
+      "java/lang/Byte",
+      "java/lang/Boolean")
+
+  override def visitMethod(
+      access: Int, name: String, desc: String, sig: String, exceptions: Array[String]):
+    MethodVisitor = {
+    if (method != null && (method.name != name || method.desc != desc)) {
+      // If method is specified, skip other methods.
+      return new MethodVisitor(ASM4) {}
+    }
+
+    new MethodVisitor(ASM4) {
+      override def visitMethodInsn(op: Int, owner: String, name: String, desc: String) {
+        if (op == INVOKESPECIAL && name == "<init>" || op == INVOKESTATIC && name == "valueOf") {
+          if (primitiveBoxingClassName.contains(owner)) {
+            // Find boxing methods, e.g, new java.lang.Long(l) or java.lang.Long.valueOf(l)
+            boxingInvokes.add(s"$owner.$name")
+          }
+        } else {
+          // scalastyle:off classforname
+          val classOfMethodOwner = Class.forName(owner.replace('/', '.'), false,
+            Thread.currentThread.getContextClassLoader)
+          // scalastyle:on classforname
+          val m = MethodIdentifier(classOfMethodOwner, name, desc)
+          if (!visitedMethods.contains(m)) {
+            // Keep track of visited methods to avoid potential infinite cycles
+            visitedMethods += m
+            BoxingFinder.getClassReader(classOfMethodOwner).foreach { cl =>
+              visitedMethods += m
+              cl.accept(new BoxingFinder(m, boxingInvokes, visitedMethods), 0)
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+private object BoxingFinder {
+
+  def getClassReader(cls: Class[_]): Option[ClassReader] = {
+    val className = cls.getName.replaceFirst("^.*\\.", "") + ".class"
+    val resourceStream = cls.getResourceAsStream(className)
+    val baos = new ByteArrayOutputStream(128)
+    // Copy data over, before delegating to ClassReader -
+    // else we can run out of open file handles.
+    Utils.copyStream(resourceStream, baos, true)
+    // ASM4 doesn't support Java 8 classes, which requires ASM5.
+    // So if the class is ASM5 (E.g., java.lang.Long when using JDK8 runtime to run these codes),
+    // then ClassReader will throw IllegalArgumentException,
+    // However, since this is only for testing, it's safe to skip these classes.
+    try {
+      Some(new ClassReader(new ByteArrayInputStream(baos.toByteArray)))
+    } catch {
+      case _: IllegalArgumentException => None
+    }
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
index f1fcaf59532b8..69a561e16aa17 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
@@ -21,6 +21,7 @@ import java.util.Properties
 
 import org.apache.spark.{SparkException, SparkContext, SparkConf, SparkFunSuite}
 import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.sql.metric.LongSQLMetricValue
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.{DataFrame, SQLContext}
 import org.apache.spark.sql.execution.SQLExecution
@@ -65,9 +66,9 @@ class SQLListenerSuite extends SparkFunSuite {
     speculative = false
   )
 
-  private def createTaskMetrics(accumulatorUpdates: Map[Long, Any]): TaskMetrics = {
+  private def createTaskMetrics(accumulatorUpdates: Map[Long, Long]): TaskMetrics = {
     val metrics = new TaskMetrics
-    metrics.setAccumulatorsUpdater(() => accumulatorUpdates)
+    metrics.setAccumulatorsUpdater(() => accumulatorUpdates.mapValues(new LongSQLMetricValue(_)))
     metrics.updateAccumulators()
     metrics
   }

From 76eaa701833a2ff23b50147d70ced41e85719572 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 11:02:53 -0700
Subject: [PATCH 0918/1454] [SPARK-9674][SPARK-9667] Remove SparkSqlSerializer2

It is now subsumed by various Tungsten operators.

Author: Reynold Xin <rxin@databricks.com>

Closes #7981 from rxin/SPARK-9674 and squashes the following commits:

144f96e [Reynold Xin] Re-enable test
58b7332 [Reynold Xin] Disable failing list.
fb797e3 [Reynold Xin] Match all UDTs.
be9f243 [Reynold Xin] Updated if.
71fc99c [Reynold Xin] [SPARK-9674][SPARK-9667] Remove GeneratedAggregate & SparkSqlSerializer2.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |   6 -
 .../apache/spark/sql/execution/Exchange.scala |  48 +-
 .../sql/execution/SparkSqlSerializer2.scala   | 426 ------------------
 .../execution/SparkSqlSerializer2Suite.scala  | 221 ---------
 4 files changed, 24 insertions(+), 677 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index ef35c133d9cc3..45d3d8c863512 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -416,10 +416,6 @@ private[spark] object SQLConf {
   val USE_SQL_AGGREGATE2 = booleanConf("spark.sql.useAggregate2",
     defaultValue = Some(true), doc = "<TODO>")
 
-  val USE_SQL_SERIALIZER2 = booleanConf(
-    "spark.sql.useSerializer2",
-    defaultValue = Some(true), isPublic = false)
-
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
@@ -488,8 +484,6 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
-  private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
-
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
 
   private[spark] def defaultSizeInBytes: Long =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 6ea5eeedf1bbe..60087f2ca4a3e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.types.UserDefinedType
 import org.apache.spark.util.MutablePair
 import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEnv}
 
@@ -39,21 +40,34 @@ import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEn
 @DeveloperApi
 case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode {
 
-  override def outputPartitioning: Partitioning = newPartitioning
-
-  override def output: Seq[Attribute] = child.output
-
-  override def outputsUnsafeRows: Boolean = child.outputsUnsafeRows
+  override def nodeName: String = if (tungstenMode) "TungstenExchange" else "Exchange"
 
-  override def canProcessSafeRows: Boolean = true
-
-  override def canProcessUnsafeRows: Boolean = {
+  /**
+   * Returns true iff the children outputs aggregate UDTs that are not part of the SQL type.
+   * This only happens with the old aggregate implementation and should be removed in 1.6.
+   */
+  private lazy val tungstenMode: Boolean = {
+    val unserializableUDT = child.schema.exists(_.dataType match {
+      case _: UserDefinedType[_] => true
+      case _ => false
+    })
     // Do not use the Unsafe path if we are using a RangePartitioning, since this may lead to
     // an interpreted RowOrdering being applied to an UnsafeRow, which will lead to
     // ClassCastExceptions at runtime. This check can be removed after SPARK-9054 is fixed.
-    !newPartitioning.isInstanceOf[RangePartitioning]
+    !unserializableUDT && !newPartitioning.isInstanceOf[RangePartitioning]
   }
 
+  override def outputPartitioning: Partitioning = newPartitioning
+
+  override def output: Seq[Attribute] = child.output
+
+  // This setting is somewhat counterintuitive:
+  // If the schema works with UnsafeRow, then we tell the planner that we don't support safe row,
+  // so the planner inserts a converter to convert data into UnsafeRow if needed.
+  override def outputsUnsafeRows: Boolean = tungstenMode
+  override def canProcessSafeRows: Boolean = !tungstenMode
+  override def canProcessUnsafeRows: Boolean = tungstenMode
+
   /**
    * Determines whether records must be defensively copied before being sent to the shuffle.
    * Several of Spark's shuffle components will buffer deserialized Java objects in memory. The
@@ -124,23 +138,9 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
   private val serializer: Serializer = {
     val rowDataTypes = child.output.map(_.dataType).toArray
-    // It is true when there is no field that needs to be write out.
-    // For now, we will not use SparkSqlSerializer2 when noField is true.
-    val noField = rowDataTypes == null || rowDataTypes.length == 0
-
-    val useSqlSerializer2 =
-        child.sqlContext.conf.useSqlSerializer2 &&   // SparkSqlSerializer2 is enabled.
-        SparkSqlSerializer2.support(rowDataTypes) &&  // The schema of row is supported.
-        !noField
-
-    if (child.outputsUnsafeRows) {
-      logInfo("Using UnsafeRowSerializer.")
+    if (tungstenMode) {
       new UnsafeRowSerializer(child.output.size)
-    } else if (useSqlSerializer2) {
-      logInfo("Using SparkSqlSerializer2.")
-      new SparkSqlSerializer2(rowDataTypes)
     } else {
-      logInfo("Using SparkSqlSerializer.")
       new SparkSqlSerializer(sparkConf)
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
deleted file mode 100644
index e811f1de3e6dd..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ /dev/null
@@ -1,426 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import java.io._
-import java.math.{BigDecimal, BigInteger}
-import java.nio.ByteBuffer
-
-import scala.reflect.ClassTag
-
-import org.apache.spark.Logging
-import org.apache.spark.serializer._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{MutableRow, SpecificMutableRow}
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
-
-/**
- * The serialization stream for [[SparkSqlSerializer2]]. It assumes that the object passed in
- * its `writeObject` are [[Product2]]. The serialization functions for the key and value of the
- * [[Product2]] are constructed based on their schemata.
- * The benefit of this serialization stream is that compared with general-purpose serializers like
- * Kryo and Java serializer, it can significantly reduce the size of serialized and has a lower
- * allocation cost, which can benefit the shuffle operation. Right now, its main limitations are:
- *  1. It does not support complex types, i.e. Map, Array, and Struct.
- *  2. It assumes that the objects passed in are [[Product2]]. So, it cannot be used when
- *     [[org.apache.spark.util.collection.ExternalSorter]]'s merge sort operation is used because
- *     the objects passed in the serializer are not in the type of [[Product2]]. Also also see
- *     the comment of the `serializer` method in [[Exchange]] for more information on it.
- */
-private[sql] class Serializer2SerializationStream(
-    rowSchema: Array[DataType],
-    out: OutputStream)
-  extends SerializationStream with Logging {
-
-  private val rowOut = new DataOutputStream(new BufferedOutputStream(out))
-  private val writeRowFunc = SparkSqlSerializer2.createSerializationFunction(rowSchema, rowOut)
-
-  override def writeObject[T: ClassTag](t: T): SerializationStream = {
-    val kv = t.asInstanceOf[Product2[InternalRow, InternalRow]]
-    writeKey(kv._1)
-    writeValue(kv._2)
-
-    this
-  }
-
-  override def writeKey[T: ClassTag](t: T): SerializationStream = {
-    // No-op.
-    this
-  }
-
-  override def writeValue[T: ClassTag](t: T): SerializationStream = {
-    writeRowFunc(t.asInstanceOf[InternalRow])
-    this
-  }
-
-  def flush(): Unit = {
-    rowOut.flush()
-  }
-
-  def close(): Unit = {
-    rowOut.close()
-  }
-}
-
-/**
- * The corresponding deserialization stream for [[Serializer2SerializationStream]].
- */
-private[sql] class Serializer2DeserializationStream(
-    rowSchema: Array[DataType],
-    in: InputStream)
-  extends DeserializationStream with Logging  {
-
-  private val rowIn = new DataInputStream(new BufferedInputStream(in))
-
-  private def rowGenerator(schema: Array[DataType]): () => (MutableRow) = {
-    if (schema == null) {
-      () => null
-    } else {
-      // It is safe to reuse the mutable row.
-      val mutableRow = new SpecificMutableRow(schema)
-      () => mutableRow
-    }
-  }
-
-  // Functions used to return rows for key and value.
-  private val getRow = rowGenerator(rowSchema)
-  // Functions used to read a serialized row from the InputStream and deserialize it.
-  private val readRowFunc = SparkSqlSerializer2.createDeserializationFunction(rowSchema, rowIn)
-
-  override def readObject[T: ClassTag](): T = {
-    readValue()
-  }
-
-  override def readKey[T: ClassTag](): T = {
-    null.asInstanceOf[T] // intentionally left blank.
-  }
-
-  override def readValue[T: ClassTag](): T = {
-    readRowFunc(getRow()).asInstanceOf[T]
-  }
-
-  override def close(): Unit = {
-    rowIn.close()
-  }
-}
-
-private[sql] class SparkSqlSerializer2Instance(
-    rowSchema: Array[DataType])
-  extends SerializerInstance {
-
-  def serialize[T: ClassTag](t: T): ByteBuffer =
-    throw new UnsupportedOperationException("Not supported.")
-
-  def deserialize[T: ClassTag](bytes: ByteBuffer): T =
-    throw new UnsupportedOperationException("Not supported.")
-
-  def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T =
-    throw new UnsupportedOperationException("Not supported.")
-
-  def serializeStream(s: OutputStream): SerializationStream = {
-    new Serializer2SerializationStream(rowSchema, s)
-  }
-
-  def deserializeStream(s: InputStream): DeserializationStream = {
-    new Serializer2DeserializationStream(rowSchema, s)
-  }
-}
-
-/**
- * SparkSqlSerializer2 is a special serializer that creates serialization function and
- * deserialization function based on the schema of data. It assumes that values passed in
- * are Rows.
- */
-private[sql] class SparkSqlSerializer2(rowSchema: Array[DataType])
-  extends Serializer
-  with Logging
-  with Serializable{
-
-  def newInstance(): SerializerInstance = new SparkSqlSerializer2Instance(rowSchema)
-
-  override def supportsRelocationOfSerializedObjects: Boolean = {
-    // SparkSqlSerializer2 is stateless and writes no stream headers
-    true
-  }
-}
-
-private[sql] object SparkSqlSerializer2 {
-
-  final val NULL = 0
-  final val NOT_NULL = 1
-
-  /**
-   * Check if rows with the given schema can be serialized with ShuffleSerializer.
-   * Right now, we do not support a schema having complex types or UDTs, or all data types
-   * of fields are NullTypes.
-   */
-  def support(schema: Array[DataType]): Boolean = {
-    if (schema == null) return true
-
-    var allNullTypes = true
-    var i = 0
-    while (i < schema.length) {
-      schema(i) match {
-        case NullType => // Do nothing
-        case udt: UserDefinedType[_] =>
-          allNullTypes = false
-          return false
-        case array: ArrayType =>
-          allNullTypes = false
-          return false
-        case map: MapType =>
-          allNullTypes = false
-          return false
-        case struct: StructType =>
-          allNullTypes = false
-          return false
-        case _ =>
-          allNullTypes = false
-      }
-      i += 1
-    }
-
-    // If types of fields are all NullTypes, we return false.
-    // Otherwise, we return true.
-    return !allNullTypes
-  }
-
-  /**
-   * The util function to create the serialization function based on the given schema.
-   */
-  def createSerializationFunction(schema: Array[DataType], out: DataOutputStream)
-    : InternalRow => Unit = {
-    (row: InternalRow) =>
-      // If the schema is null, the returned function does nothing when it get called.
-      if (schema != null) {
-        var i = 0
-        while (i < schema.length) {
-          schema(i) match {
-            // When we write values to the underlying stream, we also first write the null byte
-            // first. Then, if the value is not null, we write the contents out.
-
-            case NullType => // Write nothing.
-
-            case BooleanType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeBoolean(row.getBoolean(i))
-              }
-
-            case ByteType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeByte(row.getByte(i))
-              }
-
-            case ShortType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeShort(row.getShort(i))
-              }
-
-            case IntegerType | DateType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeInt(row.getInt(i))
-              }
-
-            case LongType | TimestampType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeLong(row.getLong(i))
-              }
-
-            case FloatType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeFloat(row.getFloat(i))
-              }
-
-            case DoubleType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeDouble(row.getDouble(i))
-              }
-
-            case StringType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                val bytes = row.getUTF8String(i).getBytes
-                out.writeInt(bytes.length)
-                out.write(bytes)
-              }
-
-            case BinaryType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                val bytes = row.getBinary(i)
-                out.writeInt(bytes.length)
-                out.write(bytes)
-              }
-
-            case decimal: DecimalType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                val value = row.getDecimal(i, decimal.precision, decimal.scale)
-                val javaBigDecimal = value.toJavaBigDecimal
-                // First, write out the unscaled value.
-                val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray
-                out.writeInt(bytes.length)
-                out.write(bytes)
-                // Then, write out the scale.
-                out.writeInt(javaBigDecimal.scale())
-              }
-          }
-          i += 1
-        }
-      }
-  }
-
-  /**
-   * The util function to create the deserialization function based on the given schema.
-   */
-  def createDeserializationFunction(
-      schema: Array[DataType],
-      in: DataInputStream): (MutableRow) => InternalRow = {
-    if (schema == null) {
-      (mutableRow: MutableRow) => null
-    } else {
-      (mutableRow: MutableRow) => {
-        var i = 0
-        while (i < schema.length) {
-          schema(i) match {
-            // When we read values from the underlying stream, we also first read the null byte
-            // first. Then, if the value is not null, we update the field of the mutable row.
-
-            case NullType => mutableRow.setNullAt(i) // Read nothing.
-
-            case BooleanType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setBoolean(i, in.readBoolean())
-              }
-
-            case ByteType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setByte(i, in.readByte())
-              }
-
-            case ShortType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setShort(i, in.readShort())
-              }
-
-            case IntegerType | DateType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setInt(i, in.readInt())
-              }
-
-            case LongType | TimestampType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setLong(i, in.readLong())
-              }
-
-            case FloatType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setFloat(i, in.readFloat())
-              }
-
-            case DoubleType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setDouble(i, in.readDouble())
-              }
-
-            case StringType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                val length = in.readInt()
-                val bytes = new Array[Byte](length)
-                in.readFully(bytes)
-                mutableRow.update(i, UTF8String.fromBytes(bytes))
-              }
-
-            case BinaryType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                val length = in.readInt()
-                val bytes = new Array[Byte](length)
-                in.readFully(bytes)
-                mutableRow.update(i, bytes)
-              }
-
-            case decimal: DecimalType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                // First, read in the unscaled value.
-                val length = in.readInt()
-                val bytes = new Array[Byte](length)
-                in.readFully(bytes)
-                val unscaledVal = new BigInteger(bytes)
-                // Then, read the scale.
-                val scale = in.readInt()
-                // Finally, create the Decimal object and set it in the row.
-                mutableRow.update(i,
-                  Decimal(new BigDecimal(unscaledVal, scale), decimal.precision, decimal.scale))
-              }
-          }
-          i += 1
-        }
-
-        mutableRow
-      }
-    }
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
deleted file mode 100644
index 7978ed57a937e..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
+++ /dev/null
@@ -1,221 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import java.sql.{Timestamp, Date}
-
-import org.apache.spark.sql.test.TestSQLContext
-import org.scalatest.BeforeAndAfterAll
-
-import org.apache.spark.rdd.ShuffledRDD
-import org.apache.spark.serializer.Serializer
-import org.apache.spark.{ShuffleDependency, SparkFunSuite}
-import org.apache.spark.sql.types._
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.{MyDenseVectorUDT, QueryTest}
-
-class SparkSqlSerializer2DataTypeSuite extends SparkFunSuite {
-  // Make sure that we will not use serializer2 for unsupported data types.
-  def checkSupported(dataType: DataType, isSupported: Boolean): Unit = {
-    val testName =
-      s"${if (dataType == null) null else dataType.toString} is " +
-        s"${if (isSupported) "supported" else "unsupported"}"
-
-    test(testName) {
-      assert(SparkSqlSerializer2.support(Array(dataType)) === isSupported)
-    }
-  }
-
-  checkSupported(null, isSupported = true)
-  checkSupported(BooleanType, isSupported = true)
-  checkSupported(ByteType, isSupported = true)
-  checkSupported(ShortType, isSupported = true)
-  checkSupported(IntegerType, isSupported = true)
-  checkSupported(LongType, isSupported = true)
-  checkSupported(FloatType, isSupported = true)
-  checkSupported(DoubleType, isSupported = true)
-  checkSupported(DateType, isSupported = true)
-  checkSupported(TimestampType, isSupported = true)
-  checkSupported(StringType, isSupported = true)
-  checkSupported(BinaryType, isSupported = true)
-  checkSupported(DecimalType(10, 5), isSupported = true)
-  checkSupported(DecimalType.SYSTEM_DEFAULT, isSupported = true)
-
-  // If NullType is the only data type in the schema, we do not support it.
-  checkSupported(NullType, isSupported = false)
-  // For now, ArrayType, MapType, and StructType are not supported.
-  checkSupported(ArrayType(DoubleType, true), isSupported = false)
-  checkSupported(ArrayType(StringType, false), isSupported = false)
-  checkSupported(MapType(IntegerType, StringType, true), isSupported = false)
-  checkSupported(MapType(IntegerType, ArrayType(DoubleType), false), isSupported = false)
-  checkSupported(StructType(StructField("a", IntegerType, true) :: Nil), isSupported = false)
-  // UDTs are not supported right now.
-  checkSupported(new MyDenseVectorUDT, isSupported = false)
-}
-
-abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll {
-  var allColumns: String = _
-  val serializerClass: Class[Serializer] =
-    classOf[SparkSqlSerializer2].asInstanceOf[Class[Serializer]]
-  var numShufflePartitions: Int = _
-  var useSerializer2: Boolean = _
-
-  protected lazy val ctx = TestSQLContext
-
-  override def beforeAll(): Unit = {
-    numShufflePartitions = ctx.conf.numShufflePartitions
-    useSerializer2 = ctx.conf.useSqlSerializer2
-
-    ctx.sql("set spark.sql.useSerializer2=true")
-
-    val supportedTypes =
-      Seq(StringType, BinaryType, NullType, BooleanType,
-        ByteType, ShortType, IntegerType, LongType,
-        FloatType, DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType(6, 5),
-        DateType, TimestampType)
-
-    val fields = supportedTypes.zipWithIndex.map { case (dataType, index) =>
-      StructField(s"col$index", dataType, true)
-    }
-    allColumns = fields.map(_.name).mkString(",")
-    val schema = StructType(fields)
-
-    // Create a RDD with all data types supported by SparkSqlSerializer2.
-    val rdd =
-      ctx.sparkContext.parallelize((1 to 1000), 10).map { i =>
-        Row(
-          s"str${i}: test serializer2.",
-          s"binary${i}: test serializer2.".getBytes("UTF-8"),
-          null,
-          i % 2 == 0,
-          i.toByte,
-          i.toShort,
-          i,
-          Long.MaxValue - i.toLong,
-          (i + 0.25).toFloat,
-          (i + 0.75),
-          BigDecimal(Long.MaxValue.toString + ".12345"),
-          new java.math.BigDecimal(s"${i % 9 + 1}" + ".23456"),
-          new Date(i),
-          new Timestamp(i))
-      }
-
-    ctx.createDataFrame(rdd, schema).registerTempTable("shuffle")
-
-    super.beforeAll()
-  }
-
-  override def afterAll(): Unit = {
-    ctx.dropTempTable("shuffle")
-    ctx.sql(s"set spark.sql.shuffle.partitions=$numShufflePartitions")
-    ctx.sql(s"set spark.sql.useSerializer2=$useSerializer2")
-    super.afterAll()
-  }
-
-  def checkSerializer[T <: Serializer](
-      executedPlan: SparkPlan,
-      expectedSerializerClass: Class[T]): Unit = {
-    executedPlan.foreach {
-      case exchange: Exchange =>
-        val shuffledRDD = exchange.execute()
-        val dependency = shuffledRDD.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
-        val serializerNotSetMessage =
-          s"Expected $expectedSerializerClass as the serializer of Exchange. " +
-          s"However, the serializer was not set."
-        val serializer = dependency.serializer.getOrElse(fail(serializerNotSetMessage))
-        val isExpectedSerializer =
-          serializer.getClass == expectedSerializerClass ||
-            serializer.getClass == classOf[UnsafeRowSerializer]
-        val wrongSerializerErrorMessage =
-          s"Expected ${expectedSerializerClass.getCanonicalName} or " +
-            s"${classOf[UnsafeRowSerializer].getCanonicalName}. But " +
-            s"${serializer.getClass.getCanonicalName} is used."
-        assert(isExpectedSerializer, wrongSerializerErrorMessage)
-      case _ => // Ignore other nodes.
-    }
-  }
-
-  test("key schema and value schema are not nulls") {
-    val df = ctx.sql(s"SELECT DISTINCT ${allColumns} FROM shuffle")
-    checkSerializer(df.queryExecution.executedPlan, serializerClass)
-    checkAnswer(
-      df,
-      ctx.table("shuffle").collect())
-  }
-
-  test("key schema is null") {
-    val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",")
-    val df = ctx.sql(s"SELECT $aggregations FROM shuffle")
-    checkSerializer(df.queryExecution.executedPlan, serializerClass)
-    checkAnswer(
-      df,
-      Row(1000, 1000, 0, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000))
-  }
-
-  test("value schema is null") {
-    val df = ctx.sql(s"SELECT col0 FROM shuffle ORDER BY col0")
-    checkSerializer(df.queryExecution.executedPlan, serializerClass)
-    assert(df.map(r => r.getString(0)).collect().toSeq ===
-      ctx.table("shuffle").select("col0").map(r => r.getString(0)).collect().sorted.toSeq)
-  }
-
-  test("no map output field") {
-    val df = ctx.sql(s"SELECT 1 + 1 FROM shuffle")
-    checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer])
-  }
-
-  test("types of fields are all NullTypes") {
-    // Test range partitioning code path.
-    val nulls = ctx.sql(s"SELECT null as a, null as b, null as c")
-    val df = nulls.unionAll(nulls).sort("a")
-    checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer])
-    checkAnswer(
-      df,
-      Row(null, null, null) :: Row(null, null, null) :: Nil)
-
-    // Test hash partitioning code path.
-    val oneRow = ctx.sql(s"SELECT DISTINCT null, null, null FROM shuffle")
-    checkSerializer(oneRow.queryExecution.executedPlan, classOf[SparkSqlSerializer])
-    checkAnswer(
-      oneRow,
-      Row(null, null, null))
-  }
-}
-
-/** Tests SparkSqlSerializer2 with sort based shuffle without sort merge. */
-class SparkSqlSerializer2SortShuffleSuite extends SparkSqlSerializer2Suite {
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    // Sort merge will not be triggered.
-    val bypassMergeThreshold =
-      ctx.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
-    ctx.sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold-1}")
-  }
-}
-
-/** For now, we will use SparkSqlSerializer for sort based shuffle with sort merge. */
-class SparkSqlSerializer2SortMergeShuffleSuite extends SparkSqlSerializer2Suite {
-
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    // To trigger the sort merge.
-    val bypassMergeThreshold =
-      ctx.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
-    ctx.sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold + 1}")
-  }
-}

From 2432c2e239f66049a7a7d7e0591204abcc993f1a Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 7 Aug 2015 11:28:43 -0700
Subject: [PATCH 0919/1454] [SPARK-8382] [SQL] Improve Analysis Unit test
 framework

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8025 from cloud-fan/analysis and squashes the following commits:

51461b1 [Wenchen Fan] move test file to test folder
ec88ace [Wenchen Fan] Improve Analysis Unit test framework
---
 .../analysis/AnalysisErrorSuite.scala         | 48 +++++-----------
 .../sql/catalyst/analysis/AnalysisSuite.scala | 55 +------------------
 .../sql/catalyst/analysis/AnalysisTest.scala  | 33 +----------
 .../sql/catalyst/analysis/TestRelations.scala | 51 +++++++++++++++++
 .../BooleanSimplificationSuite.scala          | 19 ++++---
 5 files changed, 79 insertions(+), 127 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 26935c6e3b24f..63b475b6366c2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -42,8 +42,8 @@ case class UnresolvedTestPlan() extends LeafNode {
   override def output: Seq[Attribute] = Nil
 }
 
-class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
-  import AnalysisSuite._
+class AnalysisErrorSuite extends AnalysisTest with BeforeAndAfter {
+  import TestRelations._
 
   def errorTest(
       name: String,
@@ -51,15 +51,7 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
       errorMessages: Seq[String],
       caseSensitive: Boolean = true): Unit = {
     test(name) {
-      val error = intercept[AnalysisException] {
-        if (caseSensitive) {
-          caseSensitiveAnalyze(plan)
-        } else {
-          caseInsensitiveAnalyze(plan)
-        }
-      }
-
-      errorMessages.foreach(m => assert(error.getMessage.toLowerCase.contains(m.toLowerCase)))
+      assertAnalysisError(plan, errorMessages, caseSensitive)
     }
   }
 
@@ -69,21 +61,21 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
     "single invalid type, single arg",
     testRelation.select(TestFunction(dateLit :: Nil, IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 1" :: "requires int type" ::
-    "'null' is of date type" ::Nil)
+    "'null' is of date type" :: Nil)
 
   errorTest(
     "single invalid type, second arg",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, DateType :: IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 2" :: "requires int type" ::
-    "'null' is of date type" ::Nil)
+    "'null' is of date type" :: Nil)
 
   errorTest(
     "multiple invalid type",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, IntegerType :: IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 1" :: "argument 2" ::
-    "requires int type" :: "'null' is of date type" ::Nil)
+    "requires int type" :: "'null' is of date type" :: Nil)
 
   errorTest(
     "unresolved window function",
@@ -169,11 +161,7 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
 
     assert(plan.resolved)
 
-    val message = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan)
-    }.getMessage
-
-    assert(message.contains("resolved attribute(s) a#1 missing from a#2"))
+    assertAnalysisError(plan, "resolved attribute(s) a#1 missing from a#2" :: Nil)
   }
 
   test("error test for self-join") {
@@ -194,10 +182,8 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
           AttributeReference("a", BinaryType)(exprId = ExprId(2)),
           AttributeReference("b", IntegerType)(exprId = ExprId(1))))
 
-    val error = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan)
-    }
-    assert(error.message.contains("binary type expression a cannot be used in grouping expression"))
+    assertAnalysisError(plan,
+      "binary type expression a cannot be used in grouping expression" :: Nil)
 
     val plan2 =
       Aggregate(
@@ -207,10 +193,8 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
           AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)),
           AttributeReference("b", IntegerType)(exprId = ExprId(1))))
 
-    val error2 = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan2)
-    }
-    assert(error2.message.contains("map type expression a cannot be used in grouping expression"))
+    assertAnalysisError(plan2,
+      "map type expression a cannot be used in grouping expression" :: Nil)
   }
 
   test("Join can't work on binary and map types") {
@@ -226,10 +210,7 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
         Some(EqualTo(AttributeReference("a", BinaryType)(exprId = ExprId(2)),
           AttributeReference("c", BinaryType)(exprId = ExprId(4)))))
 
-    val error = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan)
-    }
-    assert(error.message.contains("binary type expression a cannot be used in join conditions"))
+    assertAnalysisError(plan, "binary type expression a cannot be used in join conditions" :: Nil)
 
     val plan2 =
       Join(
@@ -243,9 +224,6 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
         Some(EqualTo(AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)),
           AttributeReference("c", MapType(IntegerType, StringType))(exprId = ExprId(4)))))
 
-    val error2 = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan2)
-    }
-    assert(error2.message.contains("map type expression a cannot be used in join conditions"))
+    assertAnalysisError(plan2, "map type expression a cannot be used in join conditions" :: Nil)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 221b4e92f086c..c944bc69e25b0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -24,61 +24,8 @@ import org.apache.spark.sql.catalyst.SimpleCatalystConf
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 
-// todo: remove this and use AnalysisTest instead.
-object AnalysisSuite {
-  val caseSensitiveConf = new SimpleCatalystConf(true)
-  val caseInsensitiveConf = new SimpleCatalystConf(false)
-
-  val caseSensitiveCatalog = new SimpleCatalog(caseSensitiveConf)
-  val caseInsensitiveCatalog = new SimpleCatalog(caseInsensitiveConf)
-
-  val caseSensitiveAnalyzer =
-    new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitiveConf) {
-      override val extendedResolutionRules = EliminateSubQueries :: Nil
-    }
-  val caseInsensitiveAnalyzer =
-    new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseInsensitiveConf) {
-      override val extendedResolutionRules = EliminateSubQueries :: Nil
-    }
-
-  def caseSensitiveAnalyze(plan: LogicalPlan): Unit =
-    caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer.execute(plan))
-
-  def caseInsensitiveAnalyze(plan: LogicalPlan): Unit =
-    caseInsensitiveAnalyzer.checkAnalysis(caseInsensitiveAnalyzer.execute(plan))
-
-  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
-  val testRelation2 = LocalRelation(
-    AttributeReference("a", StringType)(),
-    AttributeReference("b", StringType)(),
-    AttributeReference("c", DoubleType)(),
-    AttributeReference("d", DecimalType(10, 2))(),
-    AttributeReference("e", ShortType)())
-
-  val nestedRelation = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("duplicateField", StringType) ::
-        StructField("duplicateField", StringType) ::
-        StructField("differentCase", StringType) ::
-        StructField("differentcase", StringType) :: Nil
-    ))())
-
-  val nestedRelation2 = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("aField", StringType) ::
-        StructField("bField", StringType) ::
-        StructField("cField", StringType) :: Nil
-    ))())
-
-  val listRelation = LocalRelation(
-    AttributeReference("list", ArrayType(IntegerType))())
-
-  caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-  caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-}
-
-
 class AnalysisSuite extends AnalysisTest {
+  import TestRelations._
 
   test("union project *") {
     val plan = (1 to 100)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
index fdb4f28950daf..ee1f8f54251e0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
@@ -17,40 +17,11 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.SimpleCatalystConf
-import org.apache.spark.sql.types._
 
 trait AnalysisTest extends PlanTest {
-  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
-
-  val testRelation2 = LocalRelation(
-    AttributeReference("a", StringType)(),
-    AttributeReference("b", StringType)(),
-    AttributeReference("c", DoubleType)(),
-    AttributeReference("d", DecimalType(10, 2))(),
-    AttributeReference("e", ShortType)())
-
-  val nestedRelation = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("duplicateField", StringType) ::
-        StructField("duplicateField", StringType) ::
-        StructField("differentCase", StringType) ::
-        StructField("differentcase", StringType) :: Nil
-    ))())
-
-  val nestedRelation2 = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("aField", StringType) ::
-        StructField("bField", StringType) ::
-        StructField("cField", StringType) :: Nil
-    ))())
-
-  val listRelation = LocalRelation(
-    AttributeReference("list", ArrayType(IntegerType))())
 
   val (caseSensitiveAnalyzer, caseInsensitiveAnalyzer) = {
     val caseSensitiveConf = new SimpleCatalystConf(true)
@@ -59,8 +30,8 @@ trait AnalysisTest extends PlanTest {
     val caseSensitiveCatalog = new SimpleCatalog(caseSensitiveConf)
     val caseInsensitiveCatalog = new SimpleCatalog(caseInsensitiveConf)
 
-    caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-    caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
+    caseSensitiveCatalog.registerTable(Seq("TaBlE"), TestRelations.testRelation)
+    caseInsensitiveCatalog.registerTable(Seq("TaBlE"), TestRelations.testRelation)
 
     new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitiveConf) {
       override val extendedResolutionRules = EliminateSubQueries :: Nil
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala
new file mode 100644
index 0000000000000..05b870705e7ea
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala
@@ -0,0 +1,51 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.analysis
+
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.types._
+
+object TestRelations {
+  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
+
+  val testRelation2 = LocalRelation(
+    AttributeReference("a", StringType)(),
+    AttributeReference("b", StringType)(),
+    AttributeReference("c", DoubleType)(),
+    AttributeReference("d", DecimalType(10, 2))(),
+    AttributeReference("e", ShortType)())
+
+  val nestedRelation = LocalRelation(
+    AttributeReference("top", StructType(
+      StructField("duplicateField", StringType) ::
+        StructField("duplicateField", StringType) ::
+        StructField("differentCase", StringType) ::
+        StructField("differentcase", StringType) :: Nil
+    ))())
+
+  val nestedRelation2 = LocalRelation(
+    AttributeReference("top", StructType(
+      StructField("aField", StringType) ::
+        StructField("bField", StringType) ::
+        StructField("cField", StringType) :: Nil
+    ))())
+
+  val listRelation = LocalRelation(
+    AttributeReference("list", ArrayType(IntegerType))())
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
index d4916ea8d273a..1877cff1334bd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
@@ -17,7 +17,8 @@
 
 package org.apache.spark.sql.catalyst.optimizer
 
-import org.apache.spark.sql.catalyst.analysis.{AnalysisSuite, EliminateSubQueries}
+import org.apache.spark.sql.catalyst.SimpleCatalystConf
+import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.PlanTest
@@ -88,20 +89,24 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper {
       ('a === 'b || 'b > 3 && 'a > 3 && 'a < 5))
   }
 
-  private def caseInsensitiveAnalyse(plan: LogicalPlan) =
-    AnalysisSuite.caseInsensitiveAnalyzer.execute(plan)
+  private val caseInsensitiveAnalyzer =
+    new Analyzer(EmptyCatalog, EmptyFunctionRegistry, new SimpleCatalystConf(false))
 
   test("(a && b) || (a && c) => a && (b || c) when case insensitive") {
-    val plan = caseInsensitiveAnalyse(testRelation.where(('a > 2 && 'b > 3) || ('A > 2 && 'b < 5)))
+    val plan = caseInsensitiveAnalyzer.execute(
+      testRelation.where(('a > 2 && 'b > 3) || ('A > 2 && 'b < 5)))
     val actual = Optimize.execute(plan)
-    val expected = caseInsensitiveAnalyse(testRelation.where('a > 2 && ('b > 3 || 'b < 5)))
+    val expected = caseInsensitiveAnalyzer.execute(
+      testRelation.where('a > 2 && ('b > 3 || 'b < 5)))
     comparePlans(actual, expected)
   }
 
   test("(a || b) && (a || c) => a || (b && c) when case insensitive") {
-    val plan = caseInsensitiveAnalyse(testRelation.where(('a > 2 || 'b > 3) && ('A > 2 || 'b < 5)))
+    val plan = caseInsensitiveAnalyzer.execute(
+      testRelation.where(('a > 2 || 'b > 3) && ('A > 2 || 'b < 5)))
     val actual = Optimize.execute(plan)
-    val expected = caseInsensitiveAnalyse(testRelation.where('a > 2 || ('b > 3 && 'b < 5)))
+    val expected = caseInsensitiveAnalyzer.execute(
+      testRelation.where('a > 2 || ('b > 3 && 'b < 5)))
     comparePlans(actual, expected)
   }
 }

From 9897cc5e3d6c70f7e45e887e2c6fc24dfa1adada Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 11:29:13 -0700
Subject: [PATCH 0920/1454] [SPARK-9736] [SQL] JoinedRow.anyNull should
 delegate to the underlying rows.

JoinedRow.anyNull currently loops through every field to check for null, which is inefficient if the underlying rows are UnsafeRows. It should just delegate to the underlying implementation.

Author: Reynold Xin <rxin@databricks.com>

Closes #8027 from rxin/SPARK-9736 and squashes the following commits:

03a2e92 [Reynold Xin] Include all files.
90f1add [Reynold Xin] [SPARK-9736][SQL] JoinedRow.anyNull should delegate to the underlying rows.
---
 .../spark/sql/catalyst/InternalRow.scala      |  10 +-
 .../sql/catalyst/expressions/JoinedRow.scala  | 144 ++++++++++++++++++
 .../sql/catalyst/expressions/Projection.scala | 119 ---------------
 .../spark/sql/catalyst/expressions/rows.scala |  12 +-
 4 files changed, 156 insertions(+), 129 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 85b4bf3b6aef5..eba95c5c8b908 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -37,15 +37,7 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   def copy(): InternalRow
 
   /** Returns true if there are any NULL values in this row. */
-  def anyNull: Boolean = {
-    val len = numFields
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i)) { return true }
-      i += 1
-    }
-    false
-  }
+  def anyNull: Boolean
 
   /* ---------------------- utility methods for Scala ---------------------- */
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
new file mode 100644
index 0000000000000..b76757c93523d
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
@@ -0,0 +1,144 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
+
+
+/**
+ * A mutable wrapper that makes two rows appear as a single concatenated row.  Designed to
+ * be instantiated once per thread and reused.
+ */
+class JoinedRow extends InternalRow {
+  private[this] var row1: InternalRow = _
+  private[this] var row2: InternalRow = _
+
+  def this(left: InternalRow, right: InternalRow) = {
+    this()
+    row1 = left
+    row2 = right
+  }
+
+  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
+  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
+    row1 = r1
+    row2 = r2
+    this
+  }
+
+  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
+  def withLeft(newLeft: InternalRow): InternalRow = {
+    row1 = newLeft
+    this
+  }
+
+  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
+  def withRight(newRight: InternalRow): InternalRow = {
+    row2 = newRight
+    this
+  }
+
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    assert(fieldTypes.length == row1.numFields + row2.numFields)
+    val (left, right) = fieldTypes.splitAt(row1.numFields)
+    row1.toSeq(left) ++ row2.toSeq(right)
+  }
+
+  override def numFields: Int = row1.numFields + row2.numFields
+
+  override def get(i: Int, dt: DataType): AnyRef =
+    if (i < row1.numFields) row1.get(i, dt) else row2.get(i - row1.numFields, dt)
+
+  override def isNullAt(i: Int): Boolean =
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
+
+  override def getBoolean(i: Int): Boolean =
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
+
+  override def getByte(i: Int): Byte =
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
+
+  override def getShort(i: Int): Short =
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
+
+  override def getInt(i: Int): Int =
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
+
+  override def getLong(i: Int): Long =
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
+
+  override def getFloat(i: Int): Float =
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
+
+  override def getDouble(i: Int): Double =
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
+
+  override def getDecimal(i: Int, precision: Int, scale: Int): Decimal = {
+    if (i < row1.numFields) {
+      row1.getDecimal(i, precision, scale)
+    } else {
+      row2.getDecimal(i - row1.numFields, precision, scale)
+    }
+  }
+
+  override def getUTF8String(i: Int): UTF8String =
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
+
+  override def getBinary(i: Int): Array[Byte] =
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
+
+  override def getArray(i: Int): ArrayData =
+    if (i < row1.numFields) row1.getArray(i) else row2.getArray(i - row1.numFields)
+
+  override def getInterval(i: Int): CalendarInterval =
+    if (i < row1.numFields) row1.getInterval(i) else row2.getInterval(i - row1.numFields)
+
+  override def getMap(i: Int): MapData =
+    if (i < row1.numFields) row1.getMap(i) else row2.getMap(i - row1.numFields)
+
+  override def getStruct(i: Int, numFields: Int): InternalRow = {
+    if (i < row1.numFields) {
+      row1.getStruct(i, numFields)
+    } else {
+      row2.getStruct(i - row1.numFields, numFields)
+    }
+  }
+
+  override def anyNull: Boolean = row1.anyNull || row2.anyNull
+
+  override def copy(): InternalRow = {
+    val copy1 = row1.copy()
+    val copy2 = row2.copy()
+    new JoinedRow(copy1, copy2)
+  }
+
+  override def toString: String = {
+    // Make sure toString never throws NullPointerException.
+    if ((row1 eq null) && (row2 eq null)) {
+      "[ empty row ]"
+    } else if (row1 eq null) {
+      row2.toString
+    } else if (row2 eq null) {
+      row1.toString
+    } else {
+      s"{${row1.toString} + ${row2.toString}}"
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 59ce7fc4f2c63..796bc327a3db1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -169,122 +169,3 @@ object FromUnsafeProjection {
     GenerateSafeProjection.generate(exprs)
   }
 }
-
-/**
- * A mutable wrapper that makes two rows appear as a single concatenated row.  Designed to
- * be instantiated once per thread and reused.
- */
-class JoinedRow extends InternalRow {
-  private[this] var row1: InternalRow = _
-  private[this] var row2: InternalRow = _
-
-  def this(left: InternalRow, right: InternalRow) = {
-    this()
-    row1 = left
-    row2 = right
-  }
-
-  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
-    row1 = r1
-    row2 = r2
-    this
-  }
-
-  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
-    row1 = newLeft
-    this
-  }
-
-  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
-    row2 = newRight
-    this
-  }
-
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
-    assert(fieldTypes.length == row1.numFields + row2.numFields)
-    val (left, right) = fieldTypes.splitAt(row1.numFields)
-    row1.toSeq(left) ++ row2.toSeq(right)
-  }
-
-  override def numFields: Int = row1.numFields + row2.numFields
-
-  override def get(i: Int, dt: DataType): AnyRef =
-    if (i < row1.numFields) row1.get(i, dt) else row2.get(i - row1.numFields, dt)
-
-  override def isNullAt(i: Int): Boolean =
-    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
-
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
-
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
-
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
-
-  override def getInt(i: Int): Int =
-    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
-
-  override def getLong(i: Int): Long =
-    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
-
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
-
-  override def getDouble(i: Int): Double =
-    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
-
-  override def getDecimal(i: Int, precision: Int, scale: Int): Decimal = {
-    if (i < row1.numFields) {
-      row1.getDecimal(i, precision, scale)
-    } else {
-      row2.getDecimal(i - row1.numFields, precision, scale)
-    }
-  }
-
-  override def getUTF8String(i: Int): UTF8String =
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-
-  override def getBinary(i: Int): Array[Byte] =
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-
-  override def getArray(i: Int): ArrayData =
-    if (i < row1.numFields) row1.getArray(i) else row2.getArray(i - row1.numFields)
-
-  override def getInterval(i: Int): CalendarInterval =
-    if (i < row1.numFields) row1.getInterval(i) else row2.getInterval(i - row1.numFields)
-
-  override def getMap(i: Int): MapData =
-    if (i < row1.numFields) row1.getMap(i) else row2.getMap(i - row1.numFields)
-
-  override def getStruct(i: Int, numFields: Int): InternalRow = {
-    if (i < row1.numFields) {
-      row1.getStruct(i, numFields)
-    } else {
-      row2.getStruct(i - row1.numFields, numFields)
-    }
-  }
-
-  override def copy(): InternalRow = {
-    val copy1 = row1.copy()
-    val copy2 = row2.copy()
-    new JoinedRow(copy1, copy2)
-  }
-
-  override def toString: String = {
-    // Make sure toString never throws NullPointerException.
-    if ((row1 eq null) && (row2 eq null)) {
-      "[ empty row ]"
-    } else if (row1 eq null) {
-      row2.toString
-    } else if (row2 eq null) {
-      row1.toString
-    } else {
-      s"{${row1.toString} + ${row2.toString}}"
-    }
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 11d10b2d8a48b..017efd2a166a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -49,7 +49,17 @@ trait BaseGenericInternalRow extends InternalRow {
   override def getMap(ordinal: Int): MapData = getAs(ordinal)
   override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
 
-  override def toString(): String = {
+  override def anyNull: Boolean = {
+    val len = numFields
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i)) { return true }
+      i += 1
+    }
+    false
+  }
+
+  override def toString: String = {
     if (numFields == 0) {
       "[empty row]"
     } else {

From aeddeafc03d77a5149d2c8f9489b0ca83e6b3e03 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 13:26:03 -0700
Subject: [PATCH 0921/1454] [SPARK-9667][SQL] followup: Use
 GenerateUnsafeProjection.canSupport to test Exchange supported data types.

This way we recursively test the data types.

cc chenghao-intel

Author: Reynold Xin <rxin@databricks.com>

Closes #8036 from rxin/cansupport and squashes the following commits:

f7302ff [Reynold Xin] Can GenerateUnsafeProjection.canSupport to test Exchange supported data types.
---
 .../org/apache/spark/sql/execution/Exchange.scala | 15 ++++-----------
 1 file changed, 4 insertions(+), 11 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 60087f2ca4a3e..49bb729800863 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -27,9 +27,9 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.types.UserDefinedType
 import org.apache.spark.util.MutablePair
 import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEnv}
 
@@ -43,18 +43,11 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
   override def nodeName: String = if (tungstenMode) "TungstenExchange" else "Exchange"
 
   /**
-   * Returns true iff the children outputs aggregate UDTs that are not part of the SQL type.
-   * This only happens with the old aggregate implementation and should be removed in 1.6.
+   * Returns true iff we can support the data type, and we are not doing range partitioning.
    */
   private lazy val tungstenMode: Boolean = {
-    val unserializableUDT = child.schema.exists(_.dataType match {
-      case _: UserDefinedType[_] => true
-      case _ => false
-    })
-    // Do not use the Unsafe path if we are using a RangePartitioning, since this may lead to
-    // an interpreted RowOrdering being applied to an UnsafeRow, which will lead to
-    // ClassCastExceptions at runtime. This check can be removed after SPARK-9054 is fixed.
-    !unserializableUDT && !newPartitioning.isInstanceOf[RangePartitioning]
+    GenerateUnsafeProjection.canSupport(child.schema) &&
+      !newPartitioning.isInstanceOf[RangePartitioning]
   }
 
   override def outputPartitioning: Partitioning = newPartitioning

From 05d04e10a8ea030bea840c3c5ba93ecac479a039 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 13:41:45 -0700
Subject: [PATCH 0922/1454] [SPARK-9733][SQL] Improve physical plan explain for
 data sources

All data sources show up as "PhysicalRDD" in physical plan explain. It'd be better if we can show the name of the data source.

Without this patch:
```
== Physical Plan ==
NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Final,isDistinct=false))
 Exchange hashpartitioning(date#0,cat#1)
  NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Partial,isDistinct=false))
   PhysicalRDD [date#0,cat#1,count#2], MapPartitionsRDD[3] at
```

With this patch:
```
== Physical Plan ==
TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Final,isDistinct=false)]
 Exchange hashpartitioning(date#0,cat#1)
  TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Partial,isDistinct=false)]
   ConvertToUnsafe
    Scan ParquetRelation[file:/scratch/rxin/spark/sales4][date#0,cat#1,count#2]
```

Author: Reynold Xin <rxin@databricks.com>

Closes #8024 from rxin/SPARK-9733 and squashes the following commits:

811b90e [Reynold Xin] Fixed Python test case.
52cab77 [Reynold Xin] Cast.
eea9ccc [Reynold Xin] Fix test case.
fcecb22 [Reynold Xin] [SPARK-9733][SQL] Improve explain message for data source scan node.
---
 python/pyspark/sql/dataframe.py               |  4 +---
 .../spark/sql/catalyst/expressions/Cast.scala |  4 ++--
 .../expressions/aggregate/interfaces.scala    |  2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  4 ----
 .../spark/sql/execution/ExistingRDD.scala     | 15 ++++++++++++-
 .../spark/sql/execution/SparkStrategies.scala |  4 ++--
 .../aggregate/TungstenAggregate.scala         |  9 +++++---
 .../datasources/DataSourceStrategy.scala      | 22 +++++++++++++------
 .../apache/spark/sql/sources/interfaces.scala |  2 +-
 .../execution/RowFormatConvertersSuite.scala  |  4 ++--
 .../sql/hive/execution/HiveExplainSuite.scala |  2 +-
 11 files changed, 45 insertions(+), 27 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 0f3480c239187..47d5a6a43a84d 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -212,8 +212,7 @@ def explain(self, extended=False):
         :param extended: boolean, default ``False``. If ``False``, prints only the physical plan.
 
         >>> df.explain()
-        PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at applySchemaToPythonRDD at\
-          NativeMethodAccessorImpl.java:...
+        Scan PhysicalRDD[age#0,name#1]
 
         >>> df.explain(True)
         == Parsed Logical Plan ==
@@ -224,7 +223,6 @@ def explain(self, extended=False):
         ...
         == Physical Plan ==
         ...
-        == RDD ==
         """
         if extended:
             print(self._jdf.queryExecution().toString())
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 39f99700c8a26..946c5a9c04f14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -107,6 +107,8 @@ object Cast {
 case class Cast(child: Expression, dataType: DataType)
   extends UnaryExpression with CodegenFallback {
 
+  override def toString: String = s"cast($child as ${dataType.simpleString})"
+
   override def checkInputDataTypes(): TypeCheckResult = {
     if (Cast.canCast(child.dataType, dataType)) {
       TypeCheckResult.TypeCheckSuccess
@@ -118,8 +120,6 @@ case class Cast(child: Expression, dataType: DataType)
 
   override def nullable: Boolean = Cast.forceNullable(child.dataType, dataType) || child.nullable
 
-  override def toString: String = s"CAST($child, $dataType)"
-
   // [[func]] assumes the input is no longer null because eval already does the null check.
   @inline private[this] def buildCast[T](a: Any, func: T => Any): Any = func(a.asInstanceOf[T])
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
index 4abfdfe87d5e9..576d8c7a3a68a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -93,7 +93,7 @@ private[sql] case class AggregateExpression2(
     AttributeSet(childReferences)
   }
 
-  override def toString: String = s"(${aggregateFunction}2,mode=$mode,isDistinct=$isDistinct)"
+  override def toString: String = s"(${aggregateFunction},mode=$mode,isDistinct=$isDistinct)"
 }
 
 abstract class AggregateFunction2
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 075c0ea2544b2..832572571cabd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -1011,9 +1011,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
       def output =
         analyzed.output.map(o => s"${o.name}: ${o.dataType.simpleString}").mkString(", ")
 
-      // TODO previously will output RDD details by run (${stringOrError(toRdd.toDebugString)})
-      // however, the `toRdd` will cause the real execution, which is not what we want.
-      // We need to think about how to avoid the side effect.
       s"""== Parsed Logical Plan ==
          |${stringOrError(logical)}
          |== Analyzed Logical Plan ==
@@ -1024,7 +1021,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
          |== Physical Plan ==
          |${stringOrError(executedPlan)}
          |Code Generation: ${stringOrError(executedPlan.codegenEnabled)}
-         |== RDD ==
       """.stripMargin.trim
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
index fbaa8e276ddb7..cae7ca5cbdc88 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow}
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics}
+import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Row, SQLContext}
 
@@ -95,11 +96,23 @@ private[sql] case class LogicalRDD(
 /** Physical plan node for scanning data from an RDD. */
 private[sql] case class PhysicalRDD(
     output: Seq[Attribute],
-    rdd: RDD[InternalRow]) extends LeafNode {
+    rdd: RDD[InternalRow],
+    extraInformation: String) extends LeafNode {
 
   override protected[sql] val trackNumOfRowsEnabled = true
 
   protected override def doExecute(): RDD[InternalRow] = rdd
+
+  override def simpleString: String = "Scan " + extraInformation + output.mkString("[", ",", "]")
+}
+
+private[sql] object PhysicalRDD {
+  def createFromDataSource(
+      output: Seq[Attribute],
+      rdd: RDD[InternalRow],
+      relation: BaseRelation): PhysicalRDD = {
+    PhysicalRDD(output, rdd, relation.toString)
+  }
 }
 
 /** Logical plan node for scanning data from a local collection. */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index c5aaebe673225..c4b9b5acea4de 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -363,12 +363,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Generate(
           generator, join = join, outer = outer, g.output, planLater(child)) :: Nil
       case logical.OneRowRelation =>
-        execution.PhysicalRDD(Nil, singleRowRdd) :: Nil
+        execution.PhysicalRDD(Nil, singleRowRdd, "OneRowRelation") :: Nil
       case logical.RepartitionByExpression(expressions, child) =>
         execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil
       case e @ EvaluatePython(udf, child, _) =>
         BatchPythonEvaluation(udf, e.output, planLater(child)) :: Nil
-      case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd) :: Nil
+      case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd, "PhysicalRDD") :: Nil
       case BroadcastHint(child) => apply(child)
       case _ => Nil
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 5a0b4d47d62f8..c3dcbd2b71ee8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -93,10 +93,13 @@ case class TungstenAggregate(
     val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
 
     testFallbackStartsAt match {
-      case None => s"TungstenAggregate ${groupingExpressions} ${allAggregateExpressions}"
+      case None =>
+        val keyString = groupingExpressions.mkString("[", ",", "]")
+        val valueString = allAggregateExpressions.mkString("[", ",", "]")
+        s"TungstenAggregate(key=$keyString, value=$valueString"
       case Some(fallbackStartsAt) =>
-        s"TungstenAggregateWithControlledFallback ${groupingExpressions} " +
-          s"${allAggregateExpressions} fallbackStartsAt=$fallbackStartsAt"
+        s"TungstenAggregateWithControlledFallback $groupingExpressions " +
+          s"$allAggregateExpressions fallbackStartsAt=$fallbackStartsAt"
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index e5dc676b87841..5b5fa8c93ec52 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -101,8 +101,9 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         (a, f) =>
           toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray, f, t.paths, confBroadcast))) :: Nil
 
-    case l @ LogicalRelation(t: TableScan) =>
-      execution.PhysicalRDD(l.output, toCatalystRDD(l, t.buildScan())) :: Nil
+    case l @ LogicalRelation(baseRelation: TableScan) =>
+      execution.PhysicalRDD.createFromDataSource(
+        l.output, toCatalystRDD(l, baseRelation.buildScan()), baseRelation) :: Nil
 
     case i @ logical.InsertIntoTable(
       l @ LogicalRelation(t: InsertableRelation), part, query, overwrite, false) if part.isEmpty =>
@@ -169,7 +170,10 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
       }
 
-    execution.PhysicalRDD(projections.map(_.toAttribute), unionedRows)
+    execution.PhysicalRDD.createFromDataSource(
+      projections.map(_.toAttribute),
+      unionedRows,
+      logicalRelation.relation)
   }
 
   // TODO: refactor this thing. It is very complicated because it does projection internally.
@@ -299,14 +303,18 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         projects.asInstanceOf[Seq[Attribute]] // Safe due to if above.
           .map(relation.attributeMap)            // Match original case of attributes.
 
-      val scan = execution.PhysicalRDD(projects.map(_.toAttribute),
-        scanBuilder(requestedColumns, pushedFilters))
+      val scan = execution.PhysicalRDD.createFromDataSource(
+        projects.map(_.toAttribute),
+        scanBuilder(requestedColumns, pushedFilters),
+        relation.relation)
       filterCondition.map(execution.Filter(_, scan)).getOrElse(scan)
     } else {
       val requestedColumns = (projectSet ++ filterSet).map(relation.attributeMap).toSeq
 
-      val scan = execution.PhysicalRDD(requestedColumns,
-        scanBuilder(requestedColumns, pushedFilters))
+      val scan = execution.PhysicalRDD.createFromDataSource(
+        requestedColumns,
+        scanBuilder(requestedColumns, pushedFilters),
+        relation.relation)
       execution.Project(projects, filterCondition.map(execution.Filter(_, scan)).getOrElse(scan))
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index c04557e5a0818..0b2929661b657 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -383,7 +383,7 @@ private[sql] abstract class OutputWriterInternal extends OutputWriter {
 abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[PartitionSpec])
   extends BaseRelation with Logging {
 
-  logInfo("Constructing HadoopFsRelation")
+  override def toString: String = getClass.getSimpleName + paths.mkString("[", ",", "]")
 
   def this() = this(None)
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 8208b25b5708c..322966f423784 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -32,9 +32,9 @@ class RowFormatConvertersSuite extends SparkPlanTest {
     case c: ConvertToSafe => c
   }
 
-  private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null, "name"))
   assert(!outputsSafe.outputsUnsafeRows)
-  private val outputsUnsafe = TungstenSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  private val outputsUnsafe = TungstenSort(Nil, false, PhysicalRDD(Seq.empty, null, "name"))
   assert(outputsUnsafe.outputsUnsafeRows)
 
   test("planner should insert unsafe->safe conversions when required") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 697211222b90c..8215dd6c2e711 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -36,7 +36,7 @@ class HiveExplainSuite extends QueryTest {
                    "== Analyzed Logical Plan ==",
                    "== Optimized Logical Plan ==",
                    "== Physical Plan ==",
-                   "Code Generation", "== RDD ==")
+                   "Code Generation")
   }
 
   test("explain create table command") {

From 881548ab20fa4c4b635c51d956b14bd13981e2f4 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 7 Aug 2015 14:20:13 -0700
Subject: [PATCH 0923/1454] [SPARK-9674] Re-enable ignored test in
 SQLQuerySuite

The original code that this test tests is removed in https://github.com/apache/spark/commit/9270bd06fd0b16892e3f37213b5bc7813ea11fdd. It was ignored shortly before that so we never caught it. This patch re-enables the test and adds the code necessary to make it pass.

JoshRosen yhuai

Author: Andrew Or <andrew@databricks.com>

Closes #8015 from andrewor14/SPARK-9674 and squashes the following commits:

225eac2 [Andrew Or] Merge branch 'master' of github.com:apache/spark into SPARK-9674
8c24209 [Andrew Or] Fix NPE
e541d64 [Andrew Or] Track aggregation memory for both sort and hash
0be3a42 [Andrew Or] Fix test
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 37 +++++++++++++++++--
 .../map/AbstractBytesToBytesMapSuite.java     | 20 ++++++----
 .../UnsafeFixedWidthAggregationMap.java       |  7 ++--
 .../sql/execution/UnsafeKVExternalSorter.java |  7 ++++
 .../TungstenAggregationIterator.scala         | 32 +++++++++++++---
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  8 ++--
 6 files changed, 85 insertions(+), 26 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 0636ae7c8df1a..7f79cd13aab43 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -109,7 +109,7 @@ public final class BytesToBytesMap {
    * Position {@code 2 * i} in the array is used to track a pointer to the key at index {@code i},
    * while position {@code 2 * i + 1} in the array holds key's full 32-bit hashcode.
    */
-  private LongArray longArray;
+  @Nullable private LongArray longArray;
   // TODO: we're wasting 32 bits of space here; we can probably store fewer bits of the hashcode
   // and exploit word-alignment to use fewer bits to hold the address.  This might let us store
   // only one long per map entry, increasing the chance that this array will fit in cache at the
@@ -124,7 +124,7 @@ public final class BytesToBytesMap {
    * A {@link BitSet} used to track location of the map where the key is set.
    * Size of the bitset should be half of the size of the long array.
    */
-  private BitSet bitset;
+  @Nullable private BitSet bitset;
 
   private final double loadFactor;
 
@@ -166,6 +166,8 @@ public final class BytesToBytesMap {
 
   private long numHashCollisions = 0;
 
+  private long peakMemoryUsedBytes = 0L;
+
   public BytesToBytesMap(
       TaskMemoryManager taskMemoryManager,
       ShuffleMemoryManager shuffleMemoryManager,
@@ -321,6 +323,9 @@ public Location lookup(
       Object keyBaseObject,
       long keyBaseOffset,
       int keyRowLengthBytes) {
+    assert(bitset != null);
+    assert(longArray != null);
+
     if (enablePerfMetrics) {
       numKeyLookups++;
     }
@@ -410,6 +415,7 @@ private void updateAddressesAndSizes(final Object page, final long offsetInPage)
     }
 
     private Location with(int pos, int keyHashcode, boolean isDefined) {
+      assert(longArray != null);
       this.pos = pos;
       this.isDefined = isDefined;
       this.keyHashcode = keyHashcode;
@@ -525,6 +531,9 @@ public boolean putNewKey(
       assert (!isDefined) : "Can only set value once for a key";
       assert (keyLengthBytes % 8 == 0);
       assert (valueLengthBytes % 8 == 0);
+      assert(bitset != null);
+      assert(longArray != null);
+
       if (numElements == MAX_CAPACITY) {
         throw new IllegalStateException("BytesToBytesMap has reached maximum capacity");
       }
@@ -658,6 +667,7 @@ private void allocate(int capacity) {
    * This method is idempotent and can be called multiple times.
    */
   public void free() {
+    updatePeakMemoryUsed();
     longArray = null;
     bitset = null;
     Iterator<MemoryBlock> dataPagesIterator = dataPages.iterator();
@@ -684,14 +694,30 @@ public long getPageSizeBytes() {
 
   /**
    * Returns the total amount of memory, in bytes, consumed by this map's managed structures.
-   * Note that this is also the peak memory used by this map, since the map is append-only.
    */
   public long getTotalMemoryConsumption() {
     long totalDataPagesSize = 0L;
     for (MemoryBlock dataPage : dataPages) {
       totalDataPagesSize += dataPage.size();
     }
-    return totalDataPagesSize + bitset.memoryBlock().size() + longArray.memoryBlock().size();
+    return totalDataPagesSize +
+      ((bitset != null) ? bitset.memoryBlock().size() : 0L) +
+      ((longArray != null) ? longArray.memoryBlock().size() : 0L);
+  }
+
+  private void updatePeakMemoryUsed() {
+    long mem = getTotalMemoryConsumption();
+    if (mem > peakMemoryUsedBytes) {
+      peakMemoryUsedBytes = mem;
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
   }
 
   /**
@@ -731,6 +757,9 @@ int getNumDataPages() {
    */
   @VisibleForTesting
   void growAndRehash() {
+    assert(bitset != null);
+    assert(longArray != null);
+
     long resizeStartTime = -1;
     if (enablePerfMetrics) {
       resizeStartTime = System.nanoTime();
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 0b11562980b8e..e56a3f0b6d12c 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -525,7 +525,7 @@ public void resizingLargeMap() {
   }
 
   @Test
-  public void testTotalMemoryConsumption() {
+  public void testPeakMemoryUsed() {
     final long recordLengthBytes = 24;
     final long pageSizeBytes = 256 + 8; // 8 bytes for end-of-page marker
     final long numRecordsPerPage = (pageSizeBytes - 8) / recordLengthBytes;
@@ -536,8 +536,8 @@ public void testTotalMemoryConsumption() {
     // monotonically increasing. More specifically, every time we allocate a new page it
     // should increase by exactly the size of the page. In this regard, the memory usage
     // at any given time is also the peak memory used.
-    long previousMemory = map.getTotalMemoryConsumption();
-    long newMemory;
+    long previousPeakMemory = map.getPeakMemoryUsedBytes();
+    long newPeakMemory;
     try {
       for (long i = 0; i < numRecordsPerPage * 10; i++) {
         final long[] value = new long[]{i};
@@ -548,15 +548,21 @@ public void testTotalMemoryConsumption() {
           value,
           PlatformDependent.LONG_ARRAY_OFFSET,
           8);
-        newMemory = map.getTotalMemoryConsumption();
+        newPeakMemory = map.getPeakMemoryUsedBytes();
         if (i % numRecordsPerPage == 0) {
           // We allocated a new page for this record, so peak memory should change
-          assertEquals(previousMemory + pageSizeBytes, newMemory);
+          assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
-          assertEquals(previousMemory, newMemory);
+          assertEquals(previousPeakMemory, newPeakMemory);
         }
-        previousMemory = newMemory;
+        previousPeakMemory = newPeakMemory;
       }
+
+      // Freeing the map should not change the peak memory
+      map.free();
+      newPeakMemory = map.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+
     } finally {
       map.free();
     }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index efb33530dac86..b08a4a13a28be 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -210,11 +210,10 @@ public void close() {
   }
 
   /**
-   * The memory used by this map's managed structures, in bytes.
-   * Note that this is also the peak memory used by this map, since the map is append-only.
+   * Return the peak memory used so far, in bytes.
    */
-  public long getMemoryUsage() {
-    return map.getTotalMemoryConsumption();
+  public long getPeakMemoryUsedBytes() {
+    return map.getPeakMemoryUsedBytes();
   }
 
   /**
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 9a65c9d3a404a..69d6784713a24 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -159,6 +159,13 @@ public KVSorterIterator sortedIterator() throws IOException {
     }
   }
 
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    return sorter.getPeakMemoryUsedBytes();
+  }
+
   /**
    * Marks the current page as no-more-space-available, and as a result, either allocate a
    * new page or spill when we see the next record.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 4d5e98a3e90c8..440bef32f4e9b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution.aggregate
 
 import org.apache.spark.unsafe.KVIterator
-import org.apache.spark.{Logging, SparkEnv, TaskContext}
+import org.apache.spark.{InternalAccumulator, Logging, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
@@ -397,14 +397,20 @@ class TungstenAggregationIterator(
   private[this] var mapIteratorHasNext: Boolean = false
 
   ///////////////////////////////////////////////////////////////////////////
-  // Part 4: The function used to switch this iterator from hash-based
-  // aggregation to sort-based aggregation.
+  // Part 3: Methods and fields used by sort-based aggregation.
   ///////////////////////////////////////////////////////////////////////////
 
+  // This sorter is used for sort-based aggregation. It is initialized as soon as
+  // we switch from hash-based to sort-based aggregation. Otherwise, it is not used.
+  private[this] var externalSorter: UnsafeKVExternalSorter = null
+
+  /**
+   * Switch to sort-based aggregation when the hash-based approach is unable to acquire memory.
+   */
   private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: UnsafeRow): Unit = {
     logInfo("falling back to sort based aggregation.")
     // Step 1: Get the ExternalSorter containing sorted entries of the map.
-    val externalSorter: UnsafeKVExternalSorter = hashMap.destructAndCreateExternalSorter()
+    externalSorter = hashMap.destructAndCreateExternalSorter()
 
     // Step 2: Free the memory used by the map.
     hashMap.free()
@@ -601,7 +607,7 @@ class TungstenAggregationIterator(
   }
 
   ///////////////////////////////////////////////////////////////////////////
-  // Par 7: Iterator's public methods.
+  // Part 7: Iterator's public methods.
   ///////////////////////////////////////////////////////////////////////////
 
   override final def hasNext: Boolean = {
@@ -610,7 +616,7 @@ class TungstenAggregationIterator(
 
   override final def next(): UnsafeRow = {
     if (hasNext) {
-      if (sortBased) {
+      val res = if (sortBased) {
         // Process the current group.
         processCurrentSortedGroup()
         // Generate output row for the current group.
@@ -641,6 +647,19 @@ class TungstenAggregationIterator(
           result
         }
       }
+
+      // If this is the last record, update the task's peak memory usage. Since we destroy
+      // the map to create the sorter, their memory usages should not overlap, so it is safe
+      // to just use the max of the two.
+      if (!hasNext) {
+        val mapMemory = hashMap.getPeakMemoryUsedBytes
+        val sorterMemory = Option(externalSorter).map(_.getPeakMemoryUsedBytes).getOrElse(0L)
+        val peakMemory = Math.max(mapMemory, sorterMemory)
+        TaskContext.get().internalMetricsToAccumulators(
+          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemory)
+      }
+
+      res
     } else {
       // no more result
       throw new NoSuchElementException
@@ -651,6 +670,7 @@ class TungstenAggregationIterator(
   // Part 8: A utility function used to generate a output row when there is no
   // input and there is no grouping expression.
   ///////////////////////////////////////////////////////////////////////////
+
   def outputForEmptyGroupingKeyWithoutInput(): UnsafeRow = {
     if (groupingExpressions.isEmpty) {
       sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index c64aa7a07dc2b..b14ef9bab90cb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -267,7 +267,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     if (!hasGeneratedAgg) {
       fail(
         s"""
-           |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan.
+           |Codegen is enabled, but query $sqlText does not have TungstenAggregate in the plan.
            |${df.queryExecution.simpleString}
          """.stripMargin)
     }
@@ -1602,10 +1602,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 
-  ignore("aggregation with codegen updates peak execution memory") {
-    withSQLConf(
-        (SQLConf.CODEGEN_ENABLED.key, "true"),
-        (SQLConf.USE_SQL_AGGREGATE2.key, "false")) {
+  test("aggregation with codegen updates peak execution memory") {
+    withSQLConf((SQLConf.CODEGEN_ENABLED.key, "true")) {
       val sc = sqlContext.sparkContext
       AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "aggregation with codegen") {
         testCodeGen(

From e2fbbe73111d4624390f596a19a1799c86a05f6c Mon Sep 17 00:00:00 2001
From: Dariusz Kobylarz <darek.kobylarz@gmail.com>
Date: Fri, 7 Aug 2015 14:51:03 -0700
Subject: [PATCH 0924/1454] [SPARK-8481] [MLLIB] GaussianMixtureModel predict
 accepting single vector

Resubmit of [https://github.com/apache/spark/pull/6906] for adding single-vec predict to GMMs

CC: dkobylarz  mengxr

To be merged with master and branch-1.5
Primary author: dkobylarz

Author: Dariusz Kobylarz <darek.kobylarz@gmail.com>

Closes #8039 from jkbradley/gmm-predict-vec and squashes the following commits:

bfbedc4 [Dariusz Kobylarz] [SPARK-8481] [MLlib] GaussianMixtureModel predict accepting single vector
---
 .../mllib/clustering/GaussianMixtureModel.scala     | 13 +++++++++++++
 .../mllib/clustering/GaussianMixtureSuite.scala     | 10 ++++++++++
 2 files changed, 23 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index cb807c8038101..76aeebd703d4e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -66,6 +66,12 @@ class GaussianMixtureModel(
     responsibilityMatrix.map(r => r.indexOf(r.max))
   }
 
+  /** Maps given point to its cluster index. */
+  def predict(point: Vector): Int = {
+    val r = computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
+    r.indexOf(r.max)
+  }
+
   /** Java-friendly version of [[predict()]] */
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
@@ -83,6 +89,13 @@ class GaussianMixtureModel(
     }
   }
 
+  /**
+   * Given the input vector, return the membership values to all mixture components.
+   */
+  def predictSoft(point: Vector): Array[Double] = {
+    computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
+  }
+
   /**
    * Compute the partial assignments for each vector
    */
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
index b218d72f1268a..b636d02f786e6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
@@ -148,6 +148,16 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("model prediction, parallel and local") {
+    val data = sc.parallelize(GaussianTestData.data)
+    val gmm = new GaussianMixture().setK(2).setSeed(0).run(data)
+
+    val batchPredictions = gmm.predict(data)
+    batchPredictions.zip(data).collect().foreach { case (batchPred, datum) =>
+      assert(batchPred === gmm.predict(datum))
+    }
+  }
+
   object GaussianTestData {
 
     val data = Array(

From 902334fd55bbe40a57c1de2a9bdb25eddf1c8cf6 Mon Sep 17 00:00:00 2001
From: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>
Date: Fri, 7 Aug 2015 16:07:24 -0700
Subject: [PATCH 0925/1454] [SPARK-9748] [MLLIB] Centriod typo in KMeansModel

A minor typo (centriod -> centroid). Readable variable names help every users.

Author: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>

Closes #8037 from BertrandDechoux/kmeans-typo and squashes the following commits:

47632fe [Bertrand Dechoux] centriod typo
---
 .../apache/spark/mllib/clustering/KMeansModel.scala    | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 8ecb3df11d95e..96359024fa228 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -120,11 +120,11 @@ object KMeansModel extends Loader[KMeansModel] {
       assert(className == thisClassName)
       assert(formatVersion == thisFormatVersion)
       val k = (metadata \ "k").extract[Int]
-      val centriods = sqlContext.read.parquet(Loader.dataPath(path))
-      Loader.checkSchema[Cluster](centriods.schema)
-      val localCentriods = centriods.map(Cluster.apply).collect()
-      assert(k == localCentriods.size)
-      new KMeansModel(localCentriods.sortBy(_.id).map(_.point))
+      val centroids = sqlContext.read.parquet(Loader.dataPath(path))
+      Loader.checkSchema[Cluster](centroids.schema)
+      val localCentroids = centroids.map(Cluster.apply).collect()
+      assert(k == localCentroids.size)
+      new KMeansModel(localCentroids.sortBy(_.id).map(_.point))
     }
   }
 }

From 49702bd738de681255a7177339510e0e1b25a8db Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 7 Aug 2015 16:24:50 -0700
Subject: [PATCH 0926/1454] [SPARK-8890] [SQL] Fallback on sorting when writing
 many dynamic partitions

Previously, we would open a new file for each new dynamic written out using `HadoopFsRelation`.  For formats like parquet this is very costly due to the buffers required to get good compression.  In this PR I refactor the code allowing us to fall back on an external sort when many partitions are seen.  As such each task will open no more than `spark.sql.sources.maxFiles` files.  I also did the following cleanup:

 - Instead of keying the file HashMap on an expensive to compute string representation of the partition, we now use a fairly cheap UnsafeProjection that avoids heap allocations.
 - The control flow for instantiating and invoking a writer container has been simplified.  Now instead of switching in two places based on the use of partitioning, the specific writer container must implement a single method `writeRows` that is invoked using `runJob`.
 - `InternalOutputWriter` has been removed.  Instead we have a `private[sql]` method `writeInternal` that converts and calls the public method.  This method can be overridden by internal datasources to avoid the conversion.  This change remove a lot of code duplication and per-row `asInstanceOf` checks.
 - `commands.scala` has been split up.

Author: Michael Armbrust <michael@databricks.com>

Closes #8010 from marmbrus/fsWriting and squashes the following commits:

00804fe [Michael Armbrust] use shuffleMemoryManager.pageSizeBytes
775cc49 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into fsWriting
17b690e [Michael Armbrust] remove comment
40f0372 [Michael Armbrust] address comments
f5675bd [Michael Armbrust] char -> string
7e2d0a4 [Michael Armbrust] make sure we close current writer
8100100 [Michael Armbrust] delete empty commands.scala
71cc717 [Michael Armbrust] update comment
8ec75ac [Michael Armbrust] [SPARK-8890][SQL] Fallback on sorting when writing many dynamic partitions
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |   8 +-
 .../datasources/InsertIntoDataSource.scala    |  64 ++
 .../InsertIntoHadoopFsRelation.scala          | 165 +++++
 .../datasources/WriterContainer.scala         | 404 ++++++++++++
 .../sql/execution/datasources/commands.scala  | 606 ------------------
 .../apache/spark/sql/json/JSONRelation.scala  |   6 +-
 .../spark/sql/parquet/ParquetRelation.scala   |   6 +-
 .../apache/spark/sql/sources/interfaces.scala |  17 +-
 .../sql/sources/PartitionedWriteSuite.scala   |  56 ++
 .../spark/sql/hive/orc/OrcRelation.scala      |   6 +-
 10 files changed, 715 insertions(+), 623 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 45d3d8c863512..e9de14f025502 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -366,17 +366,21 @@ private[spark] object SQLConf {
       "storing additional schema information in Hive's metastore.",
     isPublic = false)
 
-  // Whether to perform partition discovery when loading external data sources.  Default to true.
   val PARTITION_DISCOVERY_ENABLED = booleanConf("spark.sql.sources.partitionDiscovery.enabled",
     defaultValue = Some(true),
     doc = "When true, automtically discover data partitions.")
 
-  // Whether to perform partition column type inference. Default to true.
   val PARTITION_COLUMN_TYPE_INFERENCE =
     booleanConf("spark.sql.sources.partitionColumnTypeInference.enabled",
       defaultValue = Some(true),
       doc = "When true, automatically infer the data types for partitioned columns.")
 
+  val PARTITION_MAX_FILES =
+    intConf("spark.sql.sources.maxConcurrentWrites",
+      defaultValue = Some(5),
+      doc = "The maximum number of concurent files to open before falling back on sorting when " +
+            "writing out files using dynamic partitioning.")
+
   // The output committer class used by HadoopFsRelation. The specified class needs to be a
   // subclass of org.apache.hadoop.mapreduce.OutputCommitter.
   //
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
new file mode 100644
index 0000000000000..6ccde7693bd34
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources
+
+import java.io.IOException
+import java.util.{Date, UUID}
+
+import scala.collection.JavaConversions.asScalaIterator
+
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
+import org.apache.spark._
+import org.apache.spark.mapred.SparkHadoopMapRedUtil
+import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
+import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types.StringType
+import org.apache.spark.util.{Utils, SerializableConfiguration}
+
+
+/**
+ * Inserts the results of `query` in to a relation that extends [[InsertableRelation]].
+ */
+private[sql] case class InsertIntoDataSource(
+    logicalRelation: LogicalRelation,
+    query: LogicalPlan,
+    overwrite: Boolean)
+  extends RunnableCommand {
+
+  override def run(sqlContext: SQLContext): Seq[Row] = {
+    val relation = logicalRelation.relation.asInstanceOf[InsertableRelation]
+    val data = DataFrame(sqlContext, query)
+    // Apply the schema of the existing table to the new data.
+    val df = sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, logicalRelation.schema)
+    relation.insert(df, overwrite)
+
+    // Invalidate the cache.
+    sqlContext.cacheManager.invalidateCache(logicalRelation)
+
+    Seq.empty[Row]
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala
new file mode 100644
index 0000000000000..735d52f808868
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala
@@ -0,0 +1,165 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources
+
+import java.io.IOException
+
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat
+import org.apache.spark._
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
+import org.apache.spark.sql.sources._
+import org.apache.spark.util.Utils
+
+
+/**
+ * A command for writing data to a [[HadoopFsRelation]].  Supports both overwriting and appending.
+ * Writing to dynamic partitions is also supported.  Each [[InsertIntoHadoopFsRelation]] issues a
+ * single write job, and owns a UUID that identifies this job.  Each concrete implementation of
+ * [[HadoopFsRelation]] should use this UUID together with task id to generate unique file path for
+ * each task output file.  This UUID is passed to executor side via a property named
+ * `spark.sql.sources.writeJobUUID`.
+ *
+ * Different writer containers, [[DefaultWriterContainer]] and [[DynamicPartitionWriterContainer]]
+ * are used to write to normal tables and tables with dynamic partitions.
+ *
+ * Basic work flow of this command is:
+ *
+ *   1. Driver side setup, including output committer initialization and data source specific
+ *      preparation work for the write job to be issued.
+ *   2. Issues a write job consists of one or more executor side tasks, each of which writes all
+ *      rows within an RDD partition.
+ *   3. If no exception is thrown in a task, commits that task, otherwise aborts that task;  If any
+ *      exception is thrown during task commitment, also aborts that task.
+ *   4. If all tasks are committed, commit the job, otherwise aborts the job;  If any exception is
+ *      thrown during job commitment, also aborts the job.
+ */
+private[sql] case class InsertIntoHadoopFsRelation(
+    @transient relation: HadoopFsRelation,
+    @transient query: LogicalPlan,
+    mode: SaveMode)
+  extends RunnableCommand {
+
+  override def run(sqlContext: SQLContext): Seq[Row] = {
+    require(
+      relation.paths.length == 1,
+      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
+
+    val hadoopConf = sqlContext.sparkContext.hadoopConfiguration
+    val outputPath = new Path(relation.paths.head)
+    val fs = outputPath.getFileSystem(hadoopConf)
+    val qualifiedOutputPath = outputPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+
+    val pathExists = fs.exists(qualifiedOutputPath)
+    val doInsertion = (mode, pathExists) match {
+      case (SaveMode.ErrorIfExists, true) =>
+        throw new AnalysisException(s"path $qualifiedOutputPath already exists.")
+      case (SaveMode.Overwrite, true) =>
+        Utils.tryOrIOException {
+          if (!fs.delete(qualifiedOutputPath, true /* recursively */)) {
+            throw new IOException(s"Unable to clear output " +
+              s"directory $qualifiedOutputPath prior to writing to it")
+          }
+        }
+        true
+      case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) =>
+        true
+      case (SaveMode.Ignore, exists) =>
+        !exists
+      case (s, exists) =>
+        throw new IllegalStateException(s"unsupported save mode $s ($exists)")
+    }
+    // If we are appending data to an existing dir.
+    val isAppend = pathExists && (mode == SaveMode.Append)
+
+    if (doInsertion) {
+      val job = new Job(hadoopConf)
+      job.setOutputKeyClass(classOf[Void])
+      job.setOutputValueClass(classOf[InternalRow])
+      FileOutputFormat.setOutputPath(job, qualifiedOutputPath)
+
+      // A partitioned relation schema's can be different from the input logicalPlan, since
+      // partition columns are all moved after data column. We Project to adjust the ordering.
+      // TODO: this belongs in the analyzer.
+      val project = Project(
+        relation.schema.map(field => UnresolvedAttribute.quoted(field.name)), query)
+      val queryExecution = DataFrame(sqlContext, project).queryExecution
+
+      SQLExecution.withNewExecutionId(sqlContext, queryExecution) {
+        val df = sqlContext.internalCreateDataFrame(queryExecution.toRdd, relation.schema)
+        val partitionColumns = relation.partitionColumns.fieldNames
+
+        // Some pre-flight checks.
+        require(
+          df.schema == relation.schema,
+          s"""DataFrame must have the same schema as the relation to which is inserted.
+             |DataFrame schema: ${df.schema}
+             |Relation schema: ${relation.schema}
+          """.stripMargin)
+        val partitionColumnsInSpec = relation.partitionColumns.fieldNames
+        require(
+          partitionColumnsInSpec.sameElements(partitionColumns),
+          s"""Partition columns mismatch.
+             |Expected: ${partitionColumnsInSpec.mkString(", ")}
+             |Actual: ${partitionColumns.mkString(", ")}
+          """.stripMargin)
+
+        val writerContainer = if (partitionColumns.isEmpty) {
+          new DefaultWriterContainer(relation, job, isAppend)
+        } else {
+          val output = df.queryExecution.executedPlan.output
+          val (partitionOutput, dataOutput) =
+            output.partition(a => partitionColumns.contains(a.name))
+
+          new DynamicPartitionWriterContainer(
+            relation,
+            job,
+            partitionOutput,
+            dataOutput,
+            output,
+            PartitioningUtils.DEFAULT_PARTITION_NAME,
+            sqlContext.conf.getConf(SQLConf.PARTITION_MAX_FILES),
+            isAppend)
+        }
+
+        // This call shouldn't be put into the `try` block below because it only initializes and
+        // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called.
+        writerContainer.driverSideSetup()
+
+        try {
+          sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writerContainer.writeRows _)
+          writerContainer.commitJob()
+          relation.refresh()
+        } catch { case cause: Throwable =>
+          logError("Aborting job.", cause)
+          writerContainer.abortJob()
+          throw new SparkException("Job aborted.", cause)
+        }
+      }
+    } else {
+      logInfo("Skipping insertion into a relation that already exists.")
+    }
+
+    Seq.empty[Row]
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
new file mode 100644
index 0000000000000..2f11f40422402
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -0,0 +1,404 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources
+
+import java.util.{Date, UUID}
+
+import scala.collection.JavaConverters._
+
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter}
+import org.apache.spark._
+import org.apache.spark.mapred.SparkHadoopMapRedUtil
+import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.UnsafeKVExternalSorter
+import org.apache.spark.sql.sources.{HadoopFsRelation, OutputWriter, OutputWriterFactory}
+import org.apache.spark.sql.types.{StructType, StringType}
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[sql] abstract class BaseWriterContainer(
+    @transient val relation: HadoopFsRelation,
+    @transient job: Job,
+    isAppend: Boolean)
+  extends SparkHadoopMapReduceUtil
+  with Logging
+  with Serializable {
+
+  protected val dataSchema = relation.dataSchema
+
+  protected val serializableConf = new SerializableConfiguration(job.getConfiguration)
+
+  // This UUID is used to avoid output file name collision between different appending write jobs.
+  // These jobs may belong to different SparkContext instances. Concrete data source implementations
+  // may use this UUID to generate unique file names (e.g., `part-r-<task-id>-<job-uuid>.parquet`).
+  //  The reason why this ID is used to identify a job rather than a single task output file is
+  // that, speculative tasks must generate the same output file name as the original task.
+  private val uniqueWriteJobId = UUID.randomUUID()
+
+  // This is only used on driver side.
+  @transient private val jobContext: JobContext = job
+
+  // The following fields are initialized and used on both driver and executor side.
+  @transient protected var outputCommitter: OutputCommitter = _
+  @transient private var jobId: JobID = _
+  @transient private var taskId: TaskID = _
+  @transient private var taskAttemptId: TaskAttemptID = _
+  @transient protected var taskAttemptContext: TaskAttemptContext = _
+
+  protected val outputPath: String = {
+    assert(
+      relation.paths.length == 1,
+      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
+    relation.paths.head
+  }
+
+  protected var outputWriterFactory: OutputWriterFactory = _
+
+  private var outputFormatClass: Class[_ <: OutputFormat[_, _]] = _
+
+  def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit
+
+  def driverSideSetup(): Unit = {
+    setupIDs(0, 0, 0)
+    setupConf()
+
+    // This UUID is sent to executor side together with the serialized `Configuration` object within
+    // the `Job` instance.  `OutputWriters` on the executor side should use this UUID to generate
+    // unique task output files.
+    job.getConfiguration.set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
+
+    // Order of the following two lines is important.  For Hadoop 1, TaskAttemptContext constructor
+    // clones the Configuration object passed in.  If we initialize the TaskAttemptContext first,
+    // configurations made in prepareJobForWrite(job) are not populated into the TaskAttemptContext.
+    //
+    // Also, the `prepareJobForWrite` call must happen before initializing output format and output
+    // committer, since their initialization involve the job configuration, which can be potentially
+    // decorated in `prepareJobForWrite`.
+    outputWriterFactory = relation.prepareJobForWrite(job)
+    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
+
+    outputFormatClass = job.getOutputFormatClass
+    outputCommitter = newOutputCommitter(taskAttemptContext)
+    outputCommitter.setupJob(jobContext)
+  }
+
+  def executorSideSetup(taskContext: TaskContext): Unit = {
+    setupIDs(taskContext.stageId(), taskContext.partitionId(), taskContext.attemptNumber())
+    setupConf()
+    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
+    outputCommitter = newOutputCommitter(taskAttemptContext)
+    outputCommitter.setupTask(taskAttemptContext)
+  }
+
+  protected def getWorkPath: String = {
+    outputCommitter match {
+      // FileOutputCommitter writes to a temporary location returned by `getWorkPath`.
+      case f: MapReduceFileOutputCommitter => f.getWorkPath.toString
+      case _ => outputPath
+    }
+  }
+
+  private def newOutputCommitter(context: TaskAttemptContext): OutputCommitter = {
+    val defaultOutputCommitter = outputFormatClass.newInstance().getOutputCommitter(context)
+
+    if (isAppend) {
+      // If we are appending data to an existing dir, we will only use the output committer
+      // associated with the file output format since it is not safe to use a custom
+      // committer for appending. For example, in S3, direct parquet output committer may
+      // leave partial data in the destination dir when the the appending job fails.
+      logInfo(
+        s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName} " +
+          "for appending.")
+      defaultOutputCommitter
+    } else {
+      val committerClass = context.getConfiguration.getClass(
+        SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
+
+      Option(committerClass).map { clazz =>
+        logInfo(s"Using user defined output committer class ${clazz.getCanonicalName}")
+
+        // Every output format based on org.apache.hadoop.mapreduce.lib.output.OutputFormat
+        // has an associated output committer. To override this output committer,
+        // we will first try to use the output committer set in SQLConf.OUTPUT_COMMITTER_CLASS.
+        // If a data source needs to override the output committer, it needs to set the
+        // output committer in prepareForWrite method.
+        if (classOf[MapReduceFileOutputCommitter].isAssignableFrom(clazz)) {
+          // The specified output committer is a FileOutputCommitter.
+          // So, we will use the FileOutputCommitter-specified constructor.
+          val ctor = clazz.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext])
+          ctor.newInstance(new Path(outputPath), context)
+        } else {
+          // The specified output committer is just a OutputCommitter.
+          // So, we will use the no-argument constructor.
+          val ctor = clazz.getDeclaredConstructor()
+          ctor.newInstance()
+        }
+      }.getOrElse {
+        // If output committer class is not set, we will use the one associated with the
+        // file output format.
+        logInfo(
+          s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName}")
+        defaultOutputCommitter
+      }
+    }
+  }
+
+  private def setupIDs(jobId: Int, splitId: Int, attemptId: Int): Unit = {
+    this.jobId = SparkHadoopWriter.createJobID(new Date, jobId)
+    this.taskId = new TaskID(this.jobId, true, splitId)
+    this.taskAttemptId = new TaskAttemptID(taskId, attemptId)
+  }
+
+  private def setupConf(): Unit = {
+    serializableConf.value.set("mapred.job.id", jobId.toString)
+    serializableConf.value.set("mapred.tip.id", taskAttemptId.getTaskID.toString)
+    serializableConf.value.set("mapred.task.id", taskAttemptId.toString)
+    serializableConf.value.setBoolean("mapred.task.is.map", true)
+    serializableConf.value.setInt("mapred.task.partition", 0)
+  }
+
+  def commitTask(): Unit = {
+    SparkHadoopMapRedUtil.commitTask(
+      outputCommitter, taskAttemptContext, jobId.getId, taskId.getId, taskAttemptId.getId)
+  }
+
+  def abortTask(): Unit = {
+    if (outputCommitter != null) {
+      outputCommitter.abortTask(taskAttemptContext)
+    }
+    logError(s"Task attempt $taskAttemptId aborted.")
+  }
+
+  def commitJob(): Unit = {
+    outputCommitter.commitJob(jobContext)
+    logInfo(s"Job $jobId committed.")
+  }
+
+  def abortJob(): Unit = {
+    if (outputCommitter != null) {
+      outputCommitter.abortJob(jobContext, JobStatus.State.FAILED)
+    }
+    logError(s"Job $jobId aborted.")
+  }
+}
+
+/**
+ * A writer that writes all of the rows in a partition to a single file.
+ */
+private[sql] class DefaultWriterContainer(
+    @transient relation: HadoopFsRelation,
+    @transient job: Job,
+    isAppend: Boolean)
+  extends BaseWriterContainer(relation, job, isAppend) {
+
+  def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
+    executorSideSetup(taskContext)
+    taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path", outputPath)
+    val writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
+    writer.initConverter(dataSchema)
+
+    // If anything below fails, we should abort the task.
+    try {
+      while (iterator.hasNext) {
+        val internalRow = iterator.next()
+        writer.writeInternal(internalRow)
+      }
+
+      commitTask()
+    } catch {
+      case cause: Throwable =>
+        logError("Aborting task.", cause)
+        abortTask()
+        throw new SparkException("Task failed while writing rows.", cause)
+    }
+
+    def commitTask(): Unit = {
+      try {
+        assert(writer != null, "OutputWriter instance should have been initialized")
+        writer.close()
+        super.commitTask()
+      } catch {
+        case cause: Throwable =>
+          // This exception will be handled in `InsertIntoHadoopFsRelation.insert$writeRows`, and
+          // will cause `abortTask()` to be invoked.
+          throw new RuntimeException("Failed to commit task", cause)
+      }
+    }
+
+    def abortTask(): Unit = {
+      try {
+        writer.close()
+      } finally {
+        super.abortTask()
+      }
+    }
+  }
+}
+
+/**
+ * A writer that dynamically opens files based on the given partition columns.  Internally this is
+ * done by maintaining a HashMap of open files until `maxFiles` is reached.  If this occurs, the
+ * writer externally sorts the remaining rows and then writes out them out one file at a time.
+ */
+private[sql] class DynamicPartitionWriterContainer(
+    @transient relation: HadoopFsRelation,
+    @transient job: Job,
+    partitionColumns: Seq[Attribute],
+    dataColumns: Seq[Attribute],
+    inputSchema: Seq[Attribute],
+    defaultPartitionName: String,
+    maxOpenFiles: Int,
+    isAppend: Boolean)
+  extends BaseWriterContainer(relation, job, isAppend) {
+
+  def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
+    val outputWriters = new java.util.HashMap[InternalRow, OutputWriter]
+    executorSideSetup(taskContext)
+
+    // Returns the partition key given an input row
+    val getPartitionKey = UnsafeProjection.create(partitionColumns, inputSchema)
+    // Returns the data columns to be written given an input row
+    val getOutputRow = UnsafeProjection.create(dataColumns, inputSchema)
+
+    // Expressions that given a partition key build a string like: col1=val/col2=val/...
+    val partitionStringExpression = partitionColumns.zipWithIndex.flatMap { case (c, i) =>
+      val escaped =
+        ScalaUDF(
+          PartitioningUtils.escapePathName _, StringType, Seq(Cast(c, StringType)), Seq(StringType))
+      val str = If(IsNull(c), Literal(defaultPartitionName), escaped)
+      val partitionName = Literal(c.name + "=") :: str :: Nil
+      if (i == 0) partitionName else Literal(Path.SEPARATOR_CHAR.toString) :: partitionName
+    }
+
+    // Returns the partition path given a partition key.
+    val getPartitionString =
+      UnsafeProjection.create(Concat(partitionStringExpression) :: Nil, partitionColumns)
+
+    // If anything below fails, we should abort the task.
+    try {
+      // This will be filled in if we have to fall back on sorting.
+      var sorter: UnsafeKVExternalSorter = null
+      while (iterator.hasNext && sorter == null) {
+        val inputRow = iterator.next()
+        val currentKey = getPartitionKey(inputRow)
+        var currentWriter = outputWriters.get(currentKey)
+
+        if (currentWriter == null) {
+          if (outputWriters.size < maxOpenFiles) {
+            currentWriter = newOutputWriter(currentKey)
+            outputWriters.put(currentKey.copy(), currentWriter)
+            currentWriter.writeInternal(getOutputRow(inputRow))
+          } else {
+            logInfo(s"Maximum partitions reached, falling back on sorting.")
+            sorter = new UnsafeKVExternalSorter(
+              StructType.fromAttributes(partitionColumns),
+              StructType.fromAttributes(dataColumns),
+              SparkEnv.get.blockManager,
+              SparkEnv.get.shuffleMemoryManager,
+              SparkEnv.get.shuffleMemoryManager.pageSizeBytes)
+            sorter.insertKV(currentKey, getOutputRow(inputRow))
+          }
+        } else {
+          currentWriter.writeInternal(getOutputRow(inputRow))
+        }
+      }
+
+      // If the sorter is not null that means that we reached the maxFiles above and need to finish
+      // using external sort.
+      if (sorter != null) {
+        while (iterator.hasNext) {
+          val currentRow = iterator.next()
+          sorter.insertKV(getPartitionKey(currentRow), getOutputRow(currentRow))
+        }
+
+        logInfo(s"Sorting complete. Writing out partition files one at a time.")
+
+        val sortedIterator = sorter.sortedIterator()
+        var currentKey: InternalRow = null
+        var currentWriter: OutputWriter = null
+        try {
+          while (sortedIterator.next()) {
+            if (currentKey != sortedIterator.getKey) {
+              if (currentWriter != null) {
+                currentWriter.close()
+              }
+              currentKey = sortedIterator.getKey.copy()
+              logDebug(s"Writing partition: $currentKey")
+
+              // Either use an existing file from before, or open a new one.
+              currentWriter = outputWriters.remove(currentKey)
+              if (currentWriter == null) {
+                currentWriter = newOutputWriter(currentKey)
+              }
+            }
+
+            currentWriter.writeInternal(sortedIterator.getValue)
+          }
+        } finally {
+          if (currentWriter != null) { currentWriter.close() }
+        }
+      }
+
+      commitTask()
+    } catch {
+      case cause: Throwable =>
+        logError("Aborting task.", cause)
+        abortTask()
+        throw new SparkException("Task failed while writing rows.", cause)
+    }
+
+    /** Open and returns a new OutputWriter given a partition key. */
+    def newOutputWriter(key: InternalRow): OutputWriter = {
+      val partitionPath = getPartitionString(key).getString(0)
+      val path = new Path(getWorkPath, partitionPath)
+      taskAttemptContext.getConfiguration.set(
+        "spark.sql.sources.output.path", new Path(outputPath, partitionPath).toString)
+      val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
+      newWriter.initConverter(dataSchema)
+      newWriter
+    }
+
+    def clearOutputWriters(): Unit = {
+      outputWriters.asScala.values.foreach(_.close())
+      outputWriters.clear()
+    }
+
+    def commitTask(): Unit = {
+      try {
+        clearOutputWriters()
+        super.commitTask()
+      } catch {
+        case cause: Throwable =>
+          throw new RuntimeException("Failed to commit task", cause)
+      }
+    }
+
+    def abortTask(): Unit = {
+      try {
+        clearOutputWriters()
+      } finally {
+        super.abortTask()
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
deleted file mode 100644
index 42668979c9a32..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ /dev/null
@@ -1,606 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.datasources
-
-import java.io.IOException
-import java.util.{Date, UUID}
-
-import scala.collection.JavaConversions.asScalaIterator
-
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
-import org.apache.spark._
-import org.apache.spark.mapred.SparkHadoopMapRedUtil
-import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
-import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
-import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.StringType
-import org.apache.spark.util.{Utils, SerializableConfiguration}
-
-
-private[sql] case class InsertIntoDataSource(
-    logicalRelation: LogicalRelation,
-    query: LogicalPlan,
-    overwrite: Boolean)
-  extends RunnableCommand {
-
-  override def run(sqlContext: SQLContext): Seq[Row] = {
-    val relation = logicalRelation.relation.asInstanceOf[InsertableRelation]
-    val data = DataFrame(sqlContext, query)
-    // Apply the schema of the existing table to the new data.
-    val df = sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, logicalRelation.schema)
-    relation.insert(df, overwrite)
-
-    // Invalidate the cache.
-    sqlContext.cacheManager.invalidateCache(logicalRelation)
-
-    Seq.empty[Row]
-  }
-}
-
-/**
- * A command for writing data to a [[HadoopFsRelation]].  Supports both overwriting and appending.
- * Writing to dynamic partitions is also supported.  Each [[InsertIntoHadoopFsRelation]] issues a
- * single write job, and owns a UUID that identifies this job.  Each concrete implementation of
- * [[HadoopFsRelation]] should use this UUID together with task id to generate unique file path for
- * each task output file.  This UUID is passed to executor side via a property named
- * `spark.sql.sources.writeJobUUID`.
- *
- * Different writer containers, [[DefaultWriterContainer]] and [[DynamicPartitionWriterContainer]]
- * are used to write to normal tables and tables with dynamic partitions.
- *
- * Basic work flow of this command is:
- *
- *   1. Driver side setup, including output committer initialization and data source specific
- *      preparation work for the write job to be issued.
- *   2. Issues a write job consists of one or more executor side tasks, each of which writes all
- *      rows within an RDD partition.
- *   3. If no exception is thrown in a task, commits that task, otherwise aborts that task;  If any
- *      exception is thrown during task commitment, also aborts that task.
- *   4. If all tasks are committed, commit the job, otherwise aborts the job;  If any exception is
- *      thrown during job commitment, also aborts the job.
- */
-private[sql] case class InsertIntoHadoopFsRelation(
-    @transient relation: HadoopFsRelation,
-    @transient query: LogicalPlan,
-    mode: SaveMode)
-  extends RunnableCommand {
-
-  override def run(sqlContext: SQLContext): Seq[Row] = {
-    require(
-      relation.paths.length == 1,
-      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
-
-    val hadoopConf = sqlContext.sparkContext.hadoopConfiguration
-    val outputPath = new Path(relation.paths.head)
-    val fs = outputPath.getFileSystem(hadoopConf)
-    val qualifiedOutputPath = outputPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
-
-    val pathExists = fs.exists(qualifiedOutputPath)
-    val doInsertion = (mode, pathExists) match {
-      case (SaveMode.ErrorIfExists, true) =>
-        throw new AnalysisException(s"path $qualifiedOutputPath already exists.")
-      case (SaveMode.Overwrite, true) =>
-        Utils.tryOrIOException {
-          if (!fs.delete(qualifiedOutputPath, true /* recursively */)) {
-            throw new IOException(s"Unable to clear output " +
-              s"directory $qualifiedOutputPath prior to writing to it")
-          }
-        }
-        true
-      case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) =>
-        true
-      case (SaveMode.Ignore, exists) =>
-        !exists
-      case (s, exists) =>
-        throw new IllegalStateException(s"unsupported save mode $s ($exists)")
-    }
-    // If we are appending data to an existing dir.
-    val isAppend = pathExists && (mode == SaveMode.Append)
-
-    if (doInsertion) {
-      val job = new Job(hadoopConf)
-      job.setOutputKeyClass(classOf[Void])
-      job.setOutputValueClass(classOf[InternalRow])
-      FileOutputFormat.setOutputPath(job, qualifiedOutputPath)
-
-      // We create a DataFrame by applying the schema of relation to the data to make sure.
-      // We are writing data based on the expected schema,
-
-      // For partitioned relation r, r.schema's column ordering can be different from the column
-      // ordering of data.logicalPlan (partition columns are all moved after data column). We
-      // need a Project to adjust the ordering, so that inside InsertIntoHadoopFsRelation, we can
-      // safely apply the schema of r.schema to the data.
-      val project = Project(
-        relation.schema.map(field => new UnresolvedAttribute(Seq(field.name))), query)
-
-      val queryExecution = DataFrame(sqlContext, project).queryExecution
-      SQLExecution.withNewExecutionId(sqlContext, queryExecution) {
-        val df = sqlContext.internalCreateDataFrame(queryExecution.toRdd, relation.schema)
-
-        val partitionColumns = relation.partitionColumns.fieldNames
-        if (partitionColumns.isEmpty) {
-          insert(new DefaultWriterContainer(relation, job, isAppend), df)
-        } else {
-          val writerContainer = new DynamicPartitionWriterContainer(
-            relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME, isAppend)
-          insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns)
-        }
-      }
-    }
-
-    Seq.empty[Row]
-  }
-
-  /**
-   * Inserts the content of the [[DataFrame]] into a table without any partitioning columns.
-   */
-  private def insert(writerContainer: BaseWriterContainer, df: DataFrame): Unit = {
-    // Uses local vals for serialization
-    val needsConversion = relation.needConversion
-    val dataSchema = relation.dataSchema
-
-    // This call shouldn't be put into the `try` block below because it only initializes and
-    // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called.
-    writerContainer.driverSideSetup()
-
-    try {
-      df.sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writeRows _)
-      writerContainer.commitJob()
-      relation.refresh()
-    } catch { case cause: Throwable =>
-      logError("Aborting job.", cause)
-      writerContainer.abortJob()
-      throw new SparkException("Job aborted.", cause)
-    }
-
-    def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
-      // If anything below fails, we should abort the task.
-      try {
-        writerContainer.executorSideSetup(taskContext)
-
-        if (needsConversion) {
-          val converter = CatalystTypeConverters.createToScalaConverter(dataSchema)
-            .asInstanceOf[InternalRow => Row]
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            writerContainer.outputWriterForRow(internalRow).write(converter(internalRow))
-          }
-        } else {
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            writerContainer.outputWriterForRow(internalRow)
-              .asInstanceOf[OutputWriterInternal].writeInternal(internalRow)
-          }
-        }
-
-        writerContainer.commitTask()
-      } catch { case cause: Throwable =>
-        logError("Aborting task.", cause)
-        writerContainer.abortTask()
-        throw new SparkException("Task failed while writing rows.", cause)
-      }
-    }
-  }
-
-  /**
-   * Inserts the content of the [[DataFrame]] into a table with partitioning columns.
-   */
-  private def insertWithDynamicPartitions(
-      sqlContext: SQLContext,
-      writerContainer: BaseWriterContainer,
-      df: DataFrame,
-      partitionColumns: Array[String]): Unit = {
-    // Uses a local val for serialization
-    val needsConversion = relation.needConversion
-    val dataSchema = relation.dataSchema
-
-    require(
-      df.schema == relation.schema,
-      s"""DataFrame must have the same schema as the relation to which is inserted.
-         |DataFrame schema: ${df.schema}
-         |Relation schema: ${relation.schema}
-       """.stripMargin)
-
-    val partitionColumnsInSpec = relation.partitionColumns.fieldNames
-    require(
-      partitionColumnsInSpec.sameElements(partitionColumns),
-      s"""Partition columns mismatch.
-         |Expected: ${partitionColumnsInSpec.mkString(", ")}
-         |Actual: ${partitionColumns.mkString(", ")}
-       """.stripMargin)
-
-    val output = df.queryExecution.executedPlan.output
-    val (partitionOutput, dataOutput) = output.partition(a => partitionColumns.contains(a.name))
-    val codegenEnabled = df.sqlContext.conf.codegenEnabled
-
-    // This call shouldn't be put into the `try` block below because it only initializes and
-    // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called.
-    writerContainer.driverSideSetup()
-
-    try {
-      df.sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writeRows _)
-      writerContainer.commitJob()
-      relation.refresh()
-    } catch { case cause: Throwable =>
-      logError("Aborting job.", cause)
-      writerContainer.abortJob()
-      throw new SparkException("Job aborted.", cause)
-    }
-
-    def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
-      // If anything below fails, we should abort the task.
-      try {
-        writerContainer.executorSideSetup(taskContext)
-
-        // Projects all partition columns and casts them to strings to build partition directories.
-        val partitionCasts = partitionOutput.map(Cast(_, StringType))
-        val partitionProj = newProjection(codegenEnabled, partitionCasts, output)
-        val dataProj = newProjection(codegenEnabled, dataOutput, output)
-
-        if (needsConversion) {
-          val converter = CatalystTypeConverters.createToScalaConverter(dataSchema)
-            .asInstanceOf[InternalRow => Row]
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            val partitionPart = partitionProj(internalRow)
-            val dataPart = converter(dataProj(internalRow))
-            writerContainer.outputWriterForRow(partitionPart).write(dataPart)
-          }
-        } else {
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            val partitionPart = partitionProj(internalRow)
-            val dataPart = dataProj(internalRow)
-            writerContainer.outputWriterForRow(partitionPart)
-              .asInstanceOf[OutputWriterInternal].writeInternal(dataPart)
-          }
-        }
-
-        writerContainer.commitTask()
-      } catch { case cause: Throwable =>
-        logError("Aborting task.", cause)
-        writerContainer.abortTask()
-        throw new SparkException("Task failed while writing rows.", cause)
-      }
-    }
-  }
-
-  // This is copied from SparkPlan, probably should move this to a more general place.
-  private def newProjection(
-      codegenEnabled: Boolean,
-      expressions: Seq[Expression],
-      inputSchema: Seq[Attribute]): Projection = {
-    log.debug(
-      s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
-    if (codegenEnabled) {
-
-      try {
-        GenerateProjection.generate(expressions, inputSchema)
-      } catch {
-        case e: Exception =>
-          if (sys.props.contains("spark.testing")) {
-            throw e
-          } else {
-            log.error("failed to generate projection, fallback to interpreted", e)
-            new InterpretedProjection(expressions, inputSchema)
-          }
-      }
-    } else {
-      new InterpretedProjection(expressions, inputSchema)
-    }
-  }
-}
-
-private[sql] abstract class BaseWriterContainer(
-    @transient val relation: HadoopFsRelation,
-    @transient job: Job,
-    isAppend: Boolean)
-  extends SparkHadoopMapReduceUtil
-  with Logging
-  with Serializable {
-
-  protected val serializableConf = new SerializableConfiguration(job.getConfiguration)
-
-  // This UUID is used to avoid output file name collision between different appending write jobs.
-  // These jobs may belong to different SparkContext instances. Concrete data source implementations
-  // may use this UUID to generate unique file names (e.g., `part-r-<task-id>-<job-uuid>.parquet`).
-  //  The reason why this ID is used to identify a job rather than a single task output file is
-  // that, speculative tasks must generate the same output file name as the original task.
-  private val uniqueWriteJobId = UUID.randomUUID()
-
-  // This is only used on driver side.
-  @transient private val jobContext: JobContext = job
-
-  // The following fields are initialized and used on both driver and executor side.
-  @transient protected var outputCommitter: OutputCommitter = _
-  @transient private var jobId: JobID = _
-  @transient private var taskId: TaskID = _
-  @transient private var taskAttemptId: TaskAttemptID = _
-  @transient protected var taskAttemptContext: TaskAttemptContext = _
-
-  protected val outputPath: String = {
-    assert(
-      relation.paths.length == 1,
-      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
-    relation.paths.head
-  }
-
-  protected val dataSchema = relation.dataSchema
-
-  protected var outputWriterFactory: OutputWriterFactory = _
-
-  private var outputFormatClass: Class[_ <: OutputFormat[_, _]] = _
-
-  def driverSideSetup(): Unit = {
-    setupIDs(0, 0, 0)
-    setupConf()
-
-    // This UUID is sent to executor side together with the serialized `Configuration` object within
-    // the `Job` instance.  `OutputWriters` on the executor side should use this UUID to generate
-    // unique task output files.
-    job.getConfiguration.set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
-
-    // Order of the following two lines is important.  For Hadoop 1, TaskAttemptContext constructor
-    // clones the Configuration object passed in.  If we initialize the TaskAttemptContext first,
-    // configurations made in prepareJobForWrite(job) are not populated into the TaskAttemptContext.
-    //
-    // Also, the `prepareJobForWrite` call must happen before initializing output format and output
-    // committer, since their initialization involve the job configuration, which can be potentially
-    // decorated in `prepareJobForWrite`.
-    outputWriterFactory = relation.prepareJobForWrite(job)
-    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
-
-    outputFormatClass = job.getOutputFormatClass
-    outputCommitter = newOutputCommitter(taskAttemptContext)
-    outputCommitter.setupJob(jobContext)
-  }
-
-  def executorSideSetup(taskContext: TaskContext): Unit = {
-    setupIDs(taskContext.stageId(), taskContext.partitionId(), taskContext.attemptNumber())
-    setupConf()
-    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
-    outputCommitter = newOutputCommitter(taskAttemptContext)
-    outputCommitter.setupTask(taskAttemptContext)
-    initWriters()
-  }
-
-  protected def getWorkPath: String = {
-    outputCommitter match {
-      // FileOutputCommitter writes to a temporary location returned by `getWorkPath`.
-      case f: MapReduceFileOutputCommitter => f.getWorkPath.toString
-      case _ => outputPath
-    }
-  }
-
-  private def newOutputCommitter(context: TaskAttemptContext): OutputCommitter = {
-    val defaultOutputCommitter = outputFormatClass.newInstance().getOutputCommitter(context)
-
-    if (isAppend) {
-      // If we are appending data to an existing dir, we will only use the output committer
-      // associated with the file output format since it is not safe to use a custom
-      // committer for appending. For example, in S3, direct parquet output committer may
-      // leave partial data in the destination dir when the the appending job fails.
-      logInfo(
-        s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName} " +
-        "for appending.")
-      defaultOutputCommitter
-    } else {
-      val committerClass = context.getConfiguration.getClass(
-        SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
-
-      Option(committerClass).map { clazz =>
-        logInfo(s"Using user defined output committer class ${clazz.getCanonicalName}")
-
-        // Every output format based on org.apache.hadoop.mapreduce.lib.output.OutputFormat
-        // has an associated output committer. To override this output committer,
-        // we will first try to use the output committer set in SQLConf.OUTPUT_COMMITTER_CLASS.
-        // If a data source needs to override the output committer, it needs to set the
-        // output committer in prepareForWrite method.
-        if (classOf[MapReduceFileOutputCommitter].isAssignableFrom(clazz)) {
-          // The specified output committer is a FileOutputCommitter.
-          // So, we will use the FileOutputCommitter-specified constructor.
-          val ctor = clazz.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext])
-          ctor.newInstance(new Path(outputPath), context)
-        } else {
-          // The specified output committer is just a OutputCommitter.
-          // So, we will use the no-argument constructor.
-          val ctor = clazz.getDeclaredConstructor()
-          ctor.newInstance()
-        }
-      }.getOrElse {
-        // If output committer class is not set, we will use the one associated with the
-        // file output format.
-        logInfo(
-          s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName}")
-        defaultOutputCommitter
-      }
-    }
-  }
-
-  private def setupIDs(jobId: Int, splitId: Int, attemptId: Int): Unit = {
-    this.jobId = SparkHadoopWriter.createJobID(new Date, jobId)
-    this.taskId = new TaskID(this.jobId, true, splitId)
-    this.taskAttemptId = new TaskAttemptID(taskId, attemptId)
-  }
-
-  private def setupConf(): Unit = {
-    serializableConf.value.set("mapred.job.id", jobId.toString)
-    serializableConf.value.set("mapred.tip.id", taskAttemptId.getTaskID.toString)
-    serializableConf.value.set("mapred.task.id", taskAttemptId.toString)
-    serializableConf.value.setBoolean("mapred.task.is.map", true)
-    serializableConf.value.setInt("mapred.task.partition", 0)
-  }
-
-  // Called on executor side when writing rows
-  def outputWriterForRow(row: InternalRow): OutputWriter
-
-  protected def initWriters(): Unit
-
-  def commitTask(): Unit = {
-    SparkHadoopMapRedUtil.commitTask(
-      outputCommitter, taskAttemptContext, jobId.getId, taskId.getId, taskAttemptId.getId)
-  }
-
-  def abortTask(): Unit = {
-    if (outputCommitter != null) {
-      outputCommitter.abortTask(taskAttemptContext)
-    }
-    logError(s"Task attempt $taskAttemptId aborted.")
-  }
-
-  def commitJob(): Unit = {
-    outputCommitter.commitJob(jobContext)
-    logInfo(s"Job $jobId committed.")
-  }
-
-  def abortJob(): Unit = {
-    if (outputCommitter != null) {
-      outputCommitter.abortJob(jobContext, JobStatus.State.FAILED)
-    }
-    logError(s"Job $jobId aborted.")
-  }
-}
-
-private[sql] class DefaultWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
-    isAppend: Boolean)
-  extends BaseWriterContainer(relation, job, isAppend) {
-
-  @transient private var writer: OutputWriter = _
-
-  override protected def initWriters(): Unit = {
-    taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path", outputPath)
-    writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
-  }
-
-  override def outputWriterForRow(row: InternalRow): OutputWriter = writer
-
-  override def commitTask(): Unit = {
-    try {
-      assert(writer != null, "OutputWriter instance should have been initialized")
-      writer.close()
-      super.commitTask()
-    } catch { case cause: Throwable =>
-      // This exception will be handled in `InsertIntoHadoopFsRelation.insert$writeRows`, and will
-      // cause `abortTask()` to be invoked.
-      throw new RuntimeException("Failed to commit task", cause)
-    }
-  }
-
-  override def abortTask(): Unit = {
-    try {
-      // It's possible that the task fails before `writer` gets initialized
-      if (writer != null) {
-        writer.close()
-      }
-    } finally {
-      super.abortTask()
-    }
-  }
-}
-
-private[sql] class DynamicPartitionWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
-    partitionColumns: Array[String],
-    defaultPartitionName: String,
-    isAppend: Boolean)
-  extends BaseWriterContainer(relation, job, isAppend) {
-
-  // All output writers are created on executor side.
-  @transient protected var outputWriters: java.util.HashMap[String, OutputWriter] = _
-
-  override protected def initWriters(): Unit = {
-    outputWriters = new java.util.HashMap[String, OutputWriter]
-  }
-
-  // The `row` argument is supposed to only contain partition column values which have been casted
-  // to strings.
-  override def outputWriterForRow(row: InternalRow): OutputWriter = {
-    val partitionPath = {
-      val partitionPathBuilder = new StringBuilder
-      var i = 0
-
-      while (i < partitionColumns.length) {
-        val col = partitionColumns(i)
-        val partitionValueString = {
-          val string = row.getUTF8String(i)
-          if (string.eq(null)) {
-            defaultPartitionName
-          } else {
-            PartitioningUtils.escapePathName(string.toString)
-          }
-        }
-
-        if (i > 0) {
-          partitionPathBuilder.append(Path.SEPARATOR_CHAR)
-        }
-
-        partitionPathBuilder.append(s"$col=$partitionValueString")
-        i += 1
-      }
-
-      partitionPathBuilder.toString()
-    }
-
-    val writer = outputWriters.get(partitionPath)
-    if (writer.eq(null)) {
-      val path = new Path(getWorkPath, partitionPath)
-      taskAttemptContext.getConfiguration.set(
-        "spark.sql.sources.output.path", new Path(outputPath, partitionPath).toString)
-      val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
-      outputWriters.put(partitionPath, newWriter)
-      newWriter
-    } else {
-      writer
-    }
-  }
-
-  private def clearOutputWriters(): Unit = {
-    if (!outputWriters.isEmpty) {
-      asScalaIterator(outputWriters.values().iterator()).foreach(_.close())
-      outputWriters.clear()
-    }
-  }
-
-  override def commitTask(): Unit = {
-    try {
-      clearOutputWriters()
-      super.commitTask()
-    } catch { case cause: Throwable =>
-      throw new RuntimeException("Failed to commit task", cause)
-    }
-  }
-
-  override def abortTask(): Unit = {
-    try {
-      clearOutputWriters()
-    } finally {
-      super.abortTask()
-    }
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 5d371402877c6..10f1367e6984c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -152,7 +152,7 @@ private[json] class JsonOutputWriter(
     path: String,
     dataSchema: StructType,
     context: TaskAttemptContext)
-  extends OutputWriterInternal with SparkHadoopMapRedUtil with Logging {
+  extends OutputWriter with SparkHadoopMapRedUtil with Logging {
 
   val writer = new CharArrayWriter()
   // create the Generator without separator inserted between 2 records
@@ -170,7 +170,9 @@ private[json] class JsonOutputWriter(
     }.getRecordWriter(context)
   }
 
-  override def writeInternal(row: InternalRow): Unit = {
+  override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = {
     JacksonGenerator(dataSchema, gen, row)
     gen.flush()
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 29c388c22ef93..48009b2fd007d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -62,7 +62,7 @@ private[sql] class DefaultSource extends HadoopFsRelationProvider {
 
 // NOTE: This class is instantiated and used on executor side only, no need to be serializable.
 private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext)
-  extends OutputWriterInternal {
+  extends OutputWriter {
 
   private val recordWriter: RecordWriter[Void, InternalRow] = {
     val outputFormat = {
@@ -87,7 +87,9 @@ private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext
     outputFormat.getRecordWriter(context)
   }
 
-  override def writeInternal(row: InternalRow): Unit = recordWriter.write(null, row)
+  override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = recordWriter.write(null, row)
 
   override def close(): Unit = recordWriter.close(context)
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 0b2929661b657..c5b7ee73eb784 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -342,18 +342,17 @@ abstract class OutputWriter {
    * @since 1.4.0
    */
   def close(): Unit
-}
 
-/**
- * This is an internal, private version of [[OutputWriter]] with an writeInternal method that
- * accepts an [[InternalRow]] rather than an [[Row]]. Data sources that return this must have
- * the conversion flag set to false.
- */
-private[sql] abstract class OutputWriterInternal extends OutputWriter {
+  private var converter: InternalRow => Row = _
 
-  override def write(row: Row): Unit = throw new UnsupportedOperationException
+  protected[sql] def initConverter(dataSchema: StructType) = {
+    converter =
+      CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow => Row]
+  }
 
-  def writeInternal(row: InternalRow): Unit
+  protected[sql] def writeInternal(row: InternalRow): Unit = {
+    write(converter(row))
+  }
 }
 
 /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
new file mode 100644
index 0000000000000..c86ddd7c83e53
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.sources
+
+import org.apache.spark.sql.{Row, QueryTest}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.util.Utils
+
+class PartitionedWriteSuite extends QueryTest {
+  import TestSQLContext.implicits._
+
+  test("write many partitions") {
+    val path = Utils.createTempDir()
+    path.delete()
+
+    val df = TestSQLContext.range(100).select($"id", lit(1).as("data"))
+    df.write.partitionBy("id").save(path.getCanonicalPath)
+
+    checkAnswer(
+      TestSQLContext.read.load(path.getCanonicalPath),
+      (0 to 99).map(Row(1, _)).toSeq)
+
+    Utils.deleteRecursively(path)
+  }
+
+  test("write many partitions with repeats") {
+    val path = Utils.createTempDir()
+    path.delete()
+
+    val base = TestSQLContext.range(100)
+    val df = base.unionAll(base).select($"id", lit(1).as("data"))
+    df.write.partitionBy("id").save(path.getCanonicalPath)
+
+    checkAnswer(
+      TestSQLContext.read.load(path.getCanonicalPath),
+      (0 to 99).map(Row(1, _)).toSeq ++ (0 to 99).map(Row(1, _)).toSeq)
+
+    Utils.deleteRecursively(path)
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 4a310ff4e9016..7c8704b47f286 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -66,7 +66,7 @@ private[orc] class OrcOutputWriter(
     path: String,
     dataSchema: StructType,
     context: TaskAttemptContext)
-  extends OutputWriterInternal with SparkHadoopMapRedUtil with HiveInspectors {
+  extends OutputWriter with SparkHadoopMapRedUtil with HiveInspectors {
 
   private val serializer = {
     val table = new Properties()
@@ -120,7 +120,9 @@ private[orc] class OrcOutputWriter(
     ).asInstanceOf[RecordWriter[NullWritable, Writable]]
   }
 
-  override def writeInternal(row: InternalRow): Unit = {
+  override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = {
     var i = 0
     while (i < row.numFields) {
       reusableOutputBuffer(i) = wrappers(i)(row.get(i, dataSchema(i).dataType))

From cd540c1e59561ad1fdac59af6170944c60e685d8 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Fri, 7 Aug 2015 17:19:48 -0700
Subject: [PATCH 0927/1454] [SPARK-9756] [ML] Make constructors in ML decision
 trees private

These should be made private until there is a public constructor for providing `rootNode: Node` to use these constructors.

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8046 from feynmanliang/SPARK-9756 and squashes the following commits:

2cbdf08 [Feynman Liang] Make RFRegressionModel aux constructor private
a06f596 [Feynman Liang] Make constructors in ML decision trees private
---
 .../spark/ml/classification/DecisionTreeClassifier.scala     | 2 +-
 .../spark/ml/classification/RandomForestClassifier.scala     | 5 ++++-
 .../apache/spark/ml/regression/DecisionTreeRegressor.scala   | 2 +-
 .../apache/spark/ml/regression/RandomForestRegressor.scala   | 2 +-
 4 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index f2b992f8ba249..29598f3f05c2d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -117,7 +117,7 @@ final class DecisionTreeClassificationModel private[ml] (
    * Construct a decision tree classification model.
    * @param rootNode  Root node of tree, with other nodes attached.
    */
-  def this(rootNode: Node, numClasses: Int) =
+  private[ml] def this(rootNode: Node, numClasses: Int) =
     this(Identifiable.randomUID("dtc"), rootNode, numClasses)
 
   override protected def predict(features: Vector): Double = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index b59826a59499a..156050aaf7a45 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -136,7 +136,10 @@ final class RandomForestClassificationModel private[ml] (
    * Construct a random forest classification model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeClassificationModel], numFeatures: Int, numClasses: Int) =
+  private[ml] def this(
+      trees: Array[DecisionTreeClassificationModel],
+      numFeatures: Int,
+      numClasses: Int) =
     this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 4d30e4b5548aa..dc94a14014542 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -107,7 +107,7 @@ final class DecisionTreeRegressionModel private[ml] (
    * Construct a decision tree regression model.
    * @param rootNode  Root node of tree, with other nodes attached.
    */
-  def this(rootNode: Node) = this(Identifiable.randomUID("dtr"), rootNode)
+  private[ml] def this(rootNode: Node) = this(Identifiable.randomUID("dtr"), rootNode)
 
   override protected def predict(features: Vector): Double = {
     rootNode.predictImpl(features).prediction
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 1ee43c8725732..db75c0d26392f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -125,7 +125,7 @@ final class RandomForestRegressionModel private[ml] (
    * Construct a random forest regression model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
+  private[ml] def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
     this(Identifiable.randomUID("rfr"), trees, numFeatures)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]

From 85be65b39ce669f937a898195a844844d757666b Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Fri, 7 Aug 2015 17:21:12 -0700
Subject: [PATCH 0928/1454] [SPARK-9719] [ML] Clean up Naive Bayes doc

Small documentation cleanups, including:
 * Adds documentation for `pi` and `theta`
 * setParam to `setModelType`

Author: Feynman Liang <fliang@databricks.com>

Closes #8047 from feynmanliang/SPARK-9719 and squashes the following commits:

b372438 [Feynman Liang] Clean up naive bayes doc
---
 .../scala/org/apache/spark/ml/classification/NaiveBayes.scala | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index b46b676204e0e..97cbaf1fa8761 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -86,6 +86,7 @@ class NaiveBayes(override val uid: String)
    * Set the model type using a string (case-sensitive).
    * Supported options: "multinomial" and "bernoulli".
    * Default is "multinomial"
+   * @group setParam
    */
   def setModelType(value: String): this.type = set(modelType, value)
   setDefault(modelType -> OldNaiveBayes.Multinomial)
@@ -101,6 +102,9 @@ class NaiveBayes(override val uid: String)
 
 /**
  * Model produced by [[NaiveBayes]]
+ * @param pi log of class priors, whose dimension is C (number of classes)
+ * @param theta log of class conditional probabilities, whose dimension is C (number of classes)
+ *              by D (number of features)
  */
 class NaiveBayesModel private[ml] (
     override val uid: String,

From 998f4ff94df1d9db1c9e32c04091017c25cd4e81 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 19:09:28 -0700
Subject: [PATCH 0929/1454] [SPARK-9754][SQL] Remove TypeCheck in debug
 package.

TypeCheck no longer applies in the new "Tungsten" world.

Author: Reynold Xin <rxin@databricks.com>

Closes #8043 from rxin/SPARK-9754 and squashes the following commits:

4ec471e [Reynold Xin] [SPARK-9754][SQL] Remove TypeCheck in debug package.
---
 .../spark/sql/execution/debug/package.scala   | 104 +-----------------
 .../sql/execution/debug/DebuggingSuite.scala  |   4 -
 2 files changed, 4 insertions(+), 104 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index dd3858ea2b520..74892e4e13fa4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -17,21 +17,16 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.Attribute
-import org.apache.spark.unsafe.types.UTF8String
-
 import scala.collection.mutable.HashSet
 
-import org.apache.spark.{AccumulatorParam, Accumulator, Logging}
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.trees.TreeNodeRef
-import org.apache.spark.sql.types._
+import org.apache.spark.{Accumulator, AccumulatorParam, Logging}
 
 /**
- * :: DeveloperApi ::
  * Contains methods for debugging query execution.
  *
  * Usage:
@@ -53,10 +48,8 @@ package object debug {
   }
 
   /**
-   * :: DeveloperApi ::
    * Augments [[DataFrame]]s with debug methods.
    */
-  @DeveloperApi
   implicit class DebugQuery(query: DataFrame) extends Logging {
     def debug(): Unit = {
       val plan = query.queryExecution.executedPlan
@@ -72,23 +65,6 @@ package object debug {
         case _ =>
       }
     }
-
-    def typeCheck(): Unit = {
-      val plan = query.queryExecution.executedPlan
-      val visited = new collection.mutable.HashSet[TreeNodeRef]()
-      val debugPlan = plan transform {
-        case s: SparkPlan if !visited.contains(new TreeNodeRef(s)) =>
-          visited += new TreeNodeRef(s)
-          TypeCheck(s)
-      }
-      try {
-        logDebug(s"Results returned: ${debugPlan.execute().count()}")
-      } catch {
-        case e: Exception =>
-          def unwrap(e: Throwable): Throwable = if (e.getCause == null) e else unwrap(e.getCause)
-          logDebug(s"Deepest Error: ${unwrap(e)}")
-      }
-    }
   }
 
   private[sql] case class DebugNode(child: SparkPlan) extends UnaryNode {
@@ -148,76 +124,4 @@ package object debug {
       }
     }
   }
-
-  /**
-   * Helper functions for checking that runtime types match a given schema.
-   */
-  private[sql] object TypeCheck {
-    def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
-      case (null, _) =>
-
-      case (row: InternalRow, s: StructType) =>
-        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
-      case (a: ArrayData, ArrayType(elemType, _)) =>
-        a.foreach(elemType, (_, e) => {
-          typeCheck(e, elemType)
-        })
-      case (m: MapData, MapType(keyType, valueType, _)) =>
-        m.keyArray().foreach(keyType, (_, e) => {
-          typeCheck(e, keyType)
-        })
-        m.valueArray().foreach(valueType, (_, e) => {
-          typeCheck(e, valueType)
-        })
-
-      case (_: Long, LongType) =>
-      case (_: Int, IntegerType) =>
-      case (_: UTF8String, StringType) =>
-      case (_: Float, FloatType) =>
-      case (_: Byte, ByteType) =>
-      case (_: Short, ShortType) =>
-      case (_: Boolean, BooleanType) =>
-      case (_: Double, DoubleType) =>
-      case (_: Int, DateType) =>
-      case (_: Long, TimestampType) =>
-      case (v, udt: UserDefinedType[_]) => typeCheck(v, udt.sqlType)
-
-      case (d, t) => sys.error(s"Invalid data found: got $d (${d.getClass}) expected $t")
-    }
-  }
-
-  /**
-   * Augments [[DataFrame]]s with debug methods.
-   */
-  private[sql] case class TypeCheck(child: SparkPlan) extends SparkPlan {
-    import TypeCheck._
-
-    override def nodeName: String = ""
-
-    /* Only required when defining this class in a REPL.
-    override def makeCopy(args: Array[Object]): this.type =
-      TypeCheck(args(0).asInstanceOf[SparkPlan]).asInstanceOf[this.type]
-    */
-
-    def output: Seq[Attribute] = child.output
-
-    def children: List[SparkPlan] = child :: Nil
-
-    protected override def doExecute(): RDD[InternalRow] = {
-      child.execute().map { row =>
-        try typeCheck(row, child.schema) catch {
-          case e: Exception =>
-            sys.error(
-              s"""
-                  |ERROR WHEN TYPE CHECKING QUERY
-                  |==============================
-                  |$e
-                  |======== BAD TREE ============
-                  |$child
-             """.stripMargin)
-        }
-        row
-      }
-    }
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
index 8ec3985e00360..239deb7973845 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
@@ -25,8 +25,4 @@ class DebuggingSuite extends SparkFunSuite {
   test("DataFrame.debug()") {
     testData.debug()
   }
-
-  test("DataFrame.typeCheck()") {
-    testData.typeCheck()
-  }
 }

From c564b27447ed99e55b359b3df1d586d5766b85ea Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 7 Aug 2015 20:04:17 -0700
Subject: [PATCH 0930/1454] [SPARK-9753] [SQL] TungstenAggregate should also
 accept InternalRow instead of just UnsafeRow

https://issues.apache.org/jira/browse/SPARK-9753

This PR makes TungstenAggregate to accept `InternalRow` instead of just `UnsafeRow`. Also, it adds an `getAggregationBufferFromUnsafeRow` method to `UnsafeFixedWidthAggregationMap`. It is useful when we already have grouping keys stored in `UnsafeRow`s. Finally, it wraps `InputStream` and `OutputStream` in `UnsafeRowSerializer` with `BufferedInputStream` and `BufferedOutputStream`, respectively.

Author: Yin Huai <yhuai@databricks.com>

Closes #8041 from yhuai/joinedRowForProjection and squashes the following commits:

7753e34 [Yin Huai] Use BufferedInputStream and BufferedOutputStream.
d68b74e [Yin Huai] Use joinedRow instead of UnsafeRowJoiner.
e93c009 [Yin Huai] Add getAggregationBufferFromUnsafeRow for cases that the given groupingKeyRow is already an UnsafeRow.
---
 .../UnsafeFixedWidthAggregationMap.java       |  4 ++
 .../sql/execution/UnsafeRowSerializer.scala   | 30 +++--------
 .../aggregate/TungstenAggregate.scala         |  4 +-
 .../TungstenAggregationIterator.scala         | 51 +++++++++----------
 4 files changed, 39 insertions(+), 50 deletions(-)

diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index b08a4a13a28be..00218f213054b 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -121,6 +121,10 @@ public UnsafeFixedWidthAggregationMap(
   public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     final UnsafeRow unsafeGroupingKeyRow = this.groupingKeyProjection.apply(groupingKey);
 
+    return getAggregationBufferFromUnsafeRow(unsafeGroupingKeyRow);
+  }
+
+  public UnsafeRow getAggregationBufferFromUnsafeRow(UnsafeRow unsafeGroupingKeyRow) {
     // Probe our map using the serialized key
     final BytesToBytesMap.Location loc = map.lookup(
       unsafeGroupingKeyRow.getBaseObject(),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 39f8f992a9f00..6c7e5cacc99e7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -58,27 +58,14 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
    */
   override def serializeStream(out: OutputStream): SerializationStream = new SerializationStream {
     private[this] var writeBuffer: Array[Byte] = new Array[Byte](4096)
-    // When `out` is backed by ChainedBufferOutputStream, we will get an
-    // UnsupportedOperationException when we call dOut.writeInt because it internally calls
-    // ChainedBufferOutputStream's write(b: Int), which is not supported.
-    // To workaround this issue, we create an array for sorting the int value.
-    // To reproduce the problem, use dOut.writeInt(row.getSizeInBytes) and
-    // run SparkSqlSerializer2SortMergeShuffleSuite.
-    private[this] var intBuffer: Array[Byte] = new Array[Byte](4)
-    private[this] val dOut: DataOutputStream = new DataOutputStream(out)
+    private[this] val dOut: DataOutputStream =
+      new DataOutputStream(new BufferedOutputStream(out))
 
     override def writeValue[T: ClassTag](value: T): SerializationStream = {
       val row = value.asInstanceOf[UnsafeRow]
-      val size = row.getSizeInBytes
-      // This part is based on DataOutputStream's writeInt.
-      // It is for dOut.writeInt(row.getSizeInBytes).
-      intBuffer(0) = ((size >>> 24) & 0xFF).toByte
-      intBuffer(1) = ((size >>> 16) & 0xFF).toByte
-      intBuffer(2) = ((size >>> 8) & 0xFF).toByte
-      intBuffer(3) = ((size >>> 0) & 0xFF).toByte
-      dOut.write(intBuffer, 0, 4)
-
-      row.writeToStream(out, writeBuffer)
+
+      dOut.writeInt(row.getSizeInBytes)
+      row.writeToStream(dOut, writeBuffer)
       this
     }
 
@@ -105,7 +92,6 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
 
     override def close(): Unit = {
       writeBuffer = null
-      intBuffer = null
       dOut.writeInt(EOF)
       dOut.close()
     }
@@ -113,7 +99,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
 
   override def deserializeStream(in: InputStream): DeserializationStream = {
     new DeserializationStream {
-      private[this] val dIn: DataInputStream = new DataInputStream(in)
+      private[this] val dIn: DataInputStream = new DataInputStream(new BufferedInputStream(in))
       // 1024 is a default buffer size; this buffer will grow to accommodate larger rows
       private[this] var rowBuffer: Array[Byte] = new Array[Byte](1024)
       private[this] var row: UnsafeRow = new UnsafeRow()
@@ -129,7 +115,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
             if (rowBuffer.length < rowSize) {
               rowBuffer = new Array[Byte](rowSize)
             }
-            ByteStreams.readFully(in, rowBuffer, 0, rowSize)
+            ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
             row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
             rowSize = dIn.readInt() // read the next row's size
             if (rowSize == EOF) { // We are returning the last row in this stream
@@ -163,7 +149,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
         if (rowBuffer.length < rowSize) {
           rowBuffer = new Array[Byte](rowSize)
         }
-        ByteStreams.readFully(in, rowBuffer, 0, rowSize)
+        ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
         row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
         row.asInstanceOf[T]
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index c3dcbd2b71ee8..1694794a53d9f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -39,7 +39,7 @@ case class TungstenAggregate(
 
   override def canProcessUnsafeRows: Boolean = true
 
-  override def canProcessSafeRows: Boolean = false
+  override def canProcessSafeRows: Boolean = true
 
   override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
 
@@ -77,7 +77,7 @@ case class TungstenAggregate(
             resultExpressions,
             newMutableProjection,
             child.output,
-            iter.asInstanceOf[Iterator[UnsafeRow]],
+            iter,
             testFallbackStartsAt)
 
         if (!hasInput && groupingExpressions.isEmpty) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 440bef32f4e9b..32160906c3bc8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -22,6 +22,7 @@ import org.apache.spark.{InternalAccumulator, Logging, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
 import org.apache.spark.sql.types.StructType
 
@@ -46,8 +47,7 @@ import org.apache.spark.sql.types.StructType
  *            processing input rows from inputIter, and generating output
  *            rows.
  *  - Part 3: Methods and fields used by hash-based aggregation.
- *  - Part 4: The function used to switch this iterator from hash-based
- *            aggregation to sort-based aggregation.
+ *  - Part 4: Methods and fields used when we switch to sort-based aggregation.
  *  - Part 5: Methods and fields used by sort-based aggregation.
  *  - Part 6: Loads input and process input rows.
  *  - Part 7: Public methods of this iterator.
@@ -82,7 +82,7 @@ class TungstenAggregationIterator(
     resultExpressions: Seq[NamedExpression],
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
     originalInputAttributes: Seq[Attribute],
-    inputIter: Iterator[UnsafeRow],
+    inputIter: Iterator[InternalRow],
     testFallbackStartsAt: Option[Int])
   extends Iterator[UnsafeRow] with Logging {
 
@@ -174,13 +174,10 @@ class TungstenAggregationIterator(
 
   // Creates a function used to process a row based on the given inputAttributes.
   private def generateProcessRow(
-      inputAttributes: Seq[Attribute]): (UnsafeRow, UnsafeRow) => Unit = {
+      inputAttributes: Seq[Attribute]): (UnsafeRow, InternalRow) => Unit = {
 
     val aggregationBufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
-    val aggregationBufferSchema = StructType.fromAttributes(aggregationBufferAttributes)
-    val inputSchema = StructType.fromAttributes(inputAttributes)
-    val unsafeRowJoiner =
-      GenerateUnsafeRowJoiner.create(aggregationBufferSchema, inputSchema)
+    val joinedRow = new JoinedRow()
 
     aggregationMode match {
       // Partial-only
@@ -189,9 +186,9 @@ class TungstenAggregationIterator(
         val algebraicUpdateProjection =
           newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
           algebraicUpdateProjection.target(currentBuffer)
-          algebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+          algebraicUpdateProjection(joinedRow(currentBuffer, row))
         }
 
       // PartialMerge-only or Final-only
@@ -203,10 +200,10 @@ class TungstenAggregationIterator(
             mergeExpressions,
             aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
           // Process all algebraic aggregate functions.
           algebraicMergeProjection.target(currentBuffer)
-          algebraicMergeProjection(unsafeRowJoiner.join(currentBuffer, row))
+          algebraicMergeProjection(joinedRow(currentBuffer, row))
         }
 
       // Final-Complete
@@ -233,8 +230,8 @@ class TungstenAggregationIterator(
         val completeAlgebraicUpdateProjection =
           newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
-          val input = unsafeRowJoiner.join(currentBuffer, row)
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
+          val input = joinedRow(currentBuffer, row)
           // For all aggregate functions with mode Complete, update the given currentBuffer.
           completeAlgebraicUpdateProjection.target(currentBuffer)(input)
 
@@ -253,14 +250,14 @@ class TungstenAggregationIterator(
         val completeAlgebraicUpdateProjection =
           newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
           completeAlgebraicUpdateProjection.target(currentBuffer)
           // For all aggregate functions with mode Complete, update the given currentBuffer.
-          completeAlgebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+          completeAlgebraicUpdateProjection(joinedRow(currentBuffer, row))
         }
 
       // Grouping only.
-      case (None, None) => (currentBuffer: UnsafeRow, row: UnsafeRow) => {}
+      case (None, None) => (currentBuffer: UnsafeRow, row: InternalRow) => {}
 
       case other =>
         throw new IllegalStateException(
@@ -272,15 +269,16 @@ class TungstenAggregationIterator(
   private def generateResultProjection(): (UnsafeRow, UnsafeRow) => UnsafeRow = {
 
     val groupingAttributes = groupingExpressions.map(_.toAttribute)
-    val groupingKeySchema = StructType.fromAttributes(groupingAttributes)
     val bufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
-    val bufferSchema = StructType.fromAttributes(bufferAttributes)
-    val unsafeRowJoiner = GenerateUnsafeRowJoiner.create(groupingKeySchema, bufferSchema)
 
     aggregationMode match {
       // Partial-only or PartialMerge-only: every output row is basically the values of
       // the grouping expressions and the corresponding aggregation buffer.
       case (Some(Partial), None) | (Some(PartialMerge), None) =>
+        val groupingKeySchema = StructType.fromAttributes(groupingAttributes)
+        val bufferSchema = StructType.fromAttributes(bufferAttributes)
+        val unsafeRowJoiner = GenerateUnsafeRowJoiner.create(groupingKeySchema, bufferSchema)
+
         (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
           unsafeRowJoiner.join(currentGroupingKey, currentBuffer)
         }
@@ -288,11 +286,12 @@ class TungstenAggregationIterator(
       // Final-only, Complete-only and Final-Complete: a output row is generated based on
       // resultExpressions.
       case (Some(Final), None) | (Some(Final) | None, Some(Complete)) =>
+        val joinedRow = new JoinedRow()
         val resultProjection =
           UnsafeProjection.create(resultExpressions, groupingAttributes ++ bufferAttributes)
 
         (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
-          resultProjection(unsafeRowJoiner.join(currentGroupingKey, currentBuffer))
+          resultProjection(joinedRow(currentGroupingKey, currentBuffer))
         }
 
       // Grouping-only: a output row is generated from values of grouping expressions.
@@ -316,7 +315,7 @@ class TungstenAggregationIterator(
 
   // A function used to process a input row. Its first argument is the aggregation buffer
   // and the second argument is the input row.
-  private[this] var processRow: (UnsafeRow, UnsafeRow) => Unit =
+  private[this] var processRow: (UnsafeRow, InternalRow) => Unit =
     generateProcessRow(originalInputAttributes)
 
   // A function used to generate output rows based on the grouping keys (first argument)
@@ -354,7 +353,7 @@ class TungstenAggregationIterator(
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
       val groupingKey = groupProjection.apply(newInput)
-      val buffer: UnsafeRow = hashMap.getAggregationBuffer(groupingKey)
+      val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
       if (buffer == null) {
         // buffer == null means that we could not allocate more memory.
         // Now, we need to spill the map and switch to sort-based aggregation.
@@ -374,7 +373,7 @@ class TungstenAggregationIterator(
       val newInput = inputIter.next()
       val groupingKey = groupProjection.apply(newInput)
       val buffer: UnsafeRow = if (i < fallbackStartsAt) {
-        hashMap.getAggregationBuffer(groupingKey)
+        hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
       } else {
         null
       }
@@ -397,7 +396,7 @@ class TungstenAggregationIterator(
   private[this] var mapIteratorHasNext: Boolean = false
 
   ///////////////////////////////////////////////////////////////////////////
-  // Part 3: Methods and fields used by sort-based aggregation.
+  // Part 4: Methods and fields used when we switch to sort-based aggregation.
   ///////////////////////////////////////////////////////////////////////////
 
   // This sorter is used for sort-based aggregation. It is initialized as soon as
@@ -407,7 +406,7 @@ class TungstenAggregationIterator(
   /**
    * Switch to sort-based aggregation when the hash-based approach is unable to acquire memory.
    */
-  private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: UnsafeRow): Unit = {
+  private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: InternalRow): Unit = {
     logInfo("falling back to sort based aggregation.")
     // Step 1: Get the ExternalSorter containing sorted entries of the map.
     externalSorter = hashMap.destructAndCreateExternalSorter()

From ef062c15992b0d08554495b8ea837bef3fabf6e9 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Fri, 7 Aug 2015 23:36:26 -0700
Subject: [PATCH 0931/1454] [SPARK-9731] Standalone scheduling incorrect cores
 if spark.executor.cores is not set

The issue only happens if `spark.executor.cores` is not set and executor memory is set to a high value.
For example, if we have a worker with 4G and 10 cores and we set `spark.executor.memory` to 3G, then only 1 core is assigned to the executor. The correct number should be 10 cores.
I've added a unit test to illustrate the issue.

Author: Carson Wang <carson.wang@intel.com>

Closes #8017 from carsonwang/SPARK-9731 and squashes the following commits:

d09ec48 [Carson Wang] Fix code style
86b651f [Carson Wang] Simplify the code
943cc4c [Carson Wang] fix scheduling correct cores to executors
---
 .../apache/spark/deploy/master/Master.scala   | 26 ++++++++++---------
 .../spark/deploy/master/MasterSuite.scala     | 15 +++++++++++
 2 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index e38e437fe1c5a..9217202b69a66 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -581,20 +581,22 @@ private[deploy] class Master(
 
     /** Return whether the specified worker can launch an executor for this app. */
     def canLaunchExecutor(pos: Int): Boolean = {
+      val keepScheduling = coresToAssign >= minCoresPerExecutor
+      val enoughCores = usableWorkers(pos).coresFree - assignedCores(pos) >= minCoresPerExecutor
+
       // If we allow multiple executors per worker, then we can always launch new executors.
-      // Otherwise, we may have already started assigning cores to the executor on this worker.
+      // Otherwise, if there is already an executor on this worker, just give it more cores.
       val launchingNewExecutor = !oneExecutorPerWorker || assignedExecutors(pos) == 0
-      val underLimit =
-        if (launchingNewExecutor) {
-          assignedExecutors.sum + app.executors.size < app.executorLimit
-        } else {
-          true
-        }
-      val assignedMemory = assignedExecutors(pos) * memoryPerExecutor
-      usableWorkers(pos).memoryFree - assignedMemory >= memoryPerExecutor &&
-      usableWorkers(pos).coresFree - assignedCores(pos) >= minCoresPerExecutor &&
-      coresToAssign >= minCoresPerExecutor &&
-      underLimit
+      if (launchingNewExecutor) {
+        val assignedMemory = assignedExecutors(pos) * memoryPerExecutor
+        val enoughMemory = usableWorkers(pos).memoryFree - assignedMemory >= memoryPerExecutor
+        val underLimit = assignedExecutors.sum + app.executors.size < app.executorLimit
+        keepScheduling && enoughCores && enoughMemory && underLimit
+      } else {
+        // We're adding cores to an existing executor, so no need
+        // to check memory and executor limits
+        keepScheduling && enoughCores
+      }
     }
 
     // Keep launching executors until no more workers can accommodate any
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index ae0e037d822ea..20d0201a364ab 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -151,6 +151,14 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     basicScheduling(spreadOut = false)
   }
 
+  test("basic scheduling with more memory - spread out") {
+    basicSchedulingWithMoreMemory(spreadOut = true)
+  }
+
+  test("basic scheduling with more memory - no spread out") {
+    basicSchedulingWithMoreMemory(spreadOut = false)
+  }
+
   test("scheduling with max cores - spread out") {
     schedulingWithMaxCores(spreadOut = true)
   }
@@ -214,6 +222,13 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     assert(scheduledCores === Array(10, 10, 10))
   }
 
+  private def basicSchedulingWithMoreMemory(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo = makeAppInfo(3072)
+    val scheduledCores = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    assert(scheduledCores === Array(10, 10, 10))
+  }
+
   private def schedulingWithMaxCores(spreadOut: Boolean): Unit = {
     val master = makeMaster()
     val appInfo1 = makeAppInfo(1024, maxCores = Some(8))

From 11caf1ce290b6931647c2f71268f847d1d48930e Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sat, 8 Aug 2015 18:09:48 +0800
Subject: [PATCH 0932/1454] [SPARK-4176] [SQL] [MINOR] Should use unscaled Long
 to write decimals for precision <= 18 rather than 8

This PR fixes a minor bug introduced in #7455: when writing decimals, we should use the unscaled Long for better performance when the precision <= 18 rather than 8 (should be a typo). This bug doesn't affect correctness, but hurts Parquet decimal writing performance.

This PR also replaced similar magic numbers with newly defined constants.

Author: Cheng Lian <lian@databricks.com>

Closes #8031 from liancheng/spark-4176/minor-fix-for-writing-decimals and squashes the following commits:

10d4ea3 [Cheng Lian] Should use unscaled Long to write decimals for precision <= 18 rather than 8
---
 .../sql/parquet/CatalystRowConverter.scala    |  2 +-
 .../sql/parquet/CatalystSchemaConverter.scala | 29 +++++++++++--------
 2 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
index 6938b071065cd..4fe8a39f20abd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
@@ -264,7 +264,7 @@ private[parquet] class CatalystRowConverter(
       val scale = decimalType.scale
       val bytes = value.getBytes
 
-      if (precision <= 8) {
+      if (precision <= CatalystSchemaConverter.MAX_PRECISION_FOR_INT64) {
         // Constructs a `Decimal` with an unscaled `Long` value if possible.
         var unscaled = 0L
         var i = 0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index d43ca95b4eea0..b12149dcf1c92 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -25,6 +25,7 @@ import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName._
 import org.apache.parquet.schema.Type.Repetition._
 import org.apache.parquet.schema._
 
+import org.apache.spark.sql.parquet.CatalystSchemaConverter.{MAX_PRECISION_FOR_INT32, MAX_PRECISION_FOR_INT64, maxPrecisionForBytes}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLConf}
 
@@ -155,7 +156,7 @@ private[parquet] class CatalystSchemaConverter(
           case INT_16 => ShortType
           case INT_32 | null => IntegerType
           case DATE => DateType
-          case DECIMAL => makeDecimalType(maxPrecisionForBytes(4))
+          case DECIMAL => makeDecimalType(MAX_PRECISION_FOR_INT32)
           case TIME_MILLIS => typeNotImplemented()
           case _ => illegalType()
         }
@@ -163,7 +164,7 @@ private[parquet] class CatalystSchemaConverter(
       case INT64 =>
         originalType match {
           case INT_64 | null => LongType
-          case DECIMAL => makeDecimalType(maxPrecisionForBytes(8))
+          case DECIMAL => makeDecimalType(MAX_PRECISION_FOR_INT64)
           case TIMESTAMP_MILLIS => typeNotImplemented()
           case _ => illegalType()
         }
@@ -405,7 +406,7 @@ private[parquet] class CatalystSchemaConverter(
 
       // Uses INT32 for 1 <= precision <= 9
       case DecimalType.Fixed(precision, scale)
-        if precision <= maxPrecisionForBytes(4) && followParquetFormatSpec =>
+          if precision <= MAX_PRECISION_FOR_INT32 && followParquetFormatSpec =>
         Types
           .primitive(INT32, repetition)
           .as(DECIMAL)
@@ -415,7 +416,7 @@ private[parquet] class CatalystSchemaConverter(
 
       // Uses INT64 for 1 <= precision <= 18
       case DecimalType.Fixed(precision, scale)
-        if precision <= maxPrecisionForBytes(8) && followParquetFormatSpec =>
+          if precision <= MAX_PRECISION_FOR_INT64 && followParquetFormatSpec =>
         Types
           .primitive(INT64, repetition)
           .as(DECIMAL)
@@ -534,14 +535,6 @@ private[parquet] class CatalystSchemaConverter(
         throw new AnalysisException(s"Unsupported data type $field.dataType")
     }
   }
-
-  // Max precision of a decimal value stored in `numBytes` bytes
-  private def maxPrecisionForBytes(numBytes: Int): Int = {
-    Math.round(                               // convert double to long
-      Math.floor(Math.log10(                  // number of base-10 digits
-        Math.pow(2, 8 * numBytes - 1) - 1)))  // max value stored in numBytes
-      .asInstanceOf[Int]
-  }
 }
 
 
@@ -584,4 +577,16 @@ private[parquet] object CatalystSchemaConverter {
       computeMinBytesForPrecision(precision)
     }
   }
+
+  val MAX_PRECISION_FOR_INT32 = maxPrecisionForBytes(4)
+
+  val MAX_PRECISION_FOR_INT64 = maxPrecisionForBytes(8)
+
+  // Max precision of a decimal value stored in `numBytes` bytes
+  def maxPrecisionForBytes(numBytes: Int): Int = {
+    Math.round(                               // convert double to long
+      Math.floor(Math.log10(                  // number of base-10 digits
+        Math.pow(2, 8 * numBytes - 1) - 1)))  // max value stored in numBytes
+      .asInstanceOf[Int]
+  }
 }

From 106c0789d8c83c7081bc9a335df78ba728e95872 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 8 Aug 2015 08:33:14 -0700
Subject: [PATCH 0933/1454] [SPARK-9738] [SQL] remove FromUnsafe and add its
 codegen version to GenerateSafe

In https://github.com/apache/spark/pull/7752 we added `FromUnsafe` to convert nexted unsafe data like array/map/struct to safe versions. It's a quick solution and we already have `GenerateSafe` to do the conversion which is codegened. So we should remove `FromUnsafe` and implement its codegen version in `GenerateSafe`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8029 from cloud-fan/from-unsafe and squashes the following commits:

ed40d8f [Wenchen Fan] add the copy back
a93fd4b [Wenchen Fan] cogengen FromUnsafe
---
 .../sql/catalyst/expressions/FromUnsafe.scala |  70 ----------
 .../sql/catalyst/expressions/Projection.scala |   8 +-
 .../codegen/GenerateSafeProjection.scala      | 120 +++++++++++++-----
 .../execution/RowFormatConvertersSuite.scala  |   4 +-
 4 files changed, 95 insertions(+), 107 deletions(-)
 delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
deleted file mode 100644
index 9b960b136f984..0000000000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.expressions
-
-import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
-
-case class FromUnsafe(child: Expression) extends UnaryExpression
-  with ExpectsInputTypes with CodegenFallback {
-
-  override def inputTypes: Seq[AbstractDataType] =
-    Seq(TypeCollection(ArrayType, StructType, MapType))
-
-  override def dataType: DataType = child.dataType
-
-  private def convert(value: Any, dt: DataType): Any = dt match {
-    case StructType(fields) =>
-      val row = value.asInstanceOf[UnsafeRow]
-      val result = new Array[Any](fields.length)
-      fields.map(_.dataType).zipWithIndex.foreach { case (dt, i) =>
-        if (!row.isNullAt(i)) {
-          result(i) = convert(row.get(i, dt), dt)
-        }
-      }
-      new GenericInternalRow(result)
-
-    case ArrayType(elementType, _) =>
-      val array = value.asInstanceOf[UnsafeArrayData]
-      val length = array.numElements()
-      val result = new Array[Any](length)
-      var i = 0
-      while (i < length) {
-        if (!array.isNullAt(i)) {
-          result(i) = convert(array.get(i, elementType), elementType)
-        }
-        i += 1
-      }
-      new GenericArrayData(result)
-
-    case StringType => value.asInstanceOf[UTF8String].clone()
-
-    case MapType(kt, vt, _) =>
-      val map = value.asInstanceOf[UnsafeMapData]
-      val safeKeyArray = convert(map.keys, ArrayType(kt)).asInstanceOf[GenericArrayData]
-      val safeValueArray = convert(map.values, ArrayType(vt)).asInstanceOf[GenericArrayData]
-      new ArrayBasedMapData(safeKeyArray, safeValueArray)
-
-    case _ => value
-  }
-
-  override def nullSafeEval(input: Any): Any = {
-    convert(input, dataType)
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 796bc327a3db1..afe52e6a667eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -152,13 +152,7 @@ object FromUnsafeProjection {
    */
   def apply(fields: Seq[DataType]): Projection = {
     create(fields.zipWithIndex.map(x => {
-      val b = new BoundReference(x._2, x._1, true)
-      // todo: this is quite slow, maybe remove this whole projection after remove generic getter of
-      // InternalRow?
-      b.dataType match {
-        case _: StructType | _: ArrayType | _: MapType => FromUnsafe(b)
-        case _ => b
-      }
+      new BoundReference(x._2, x._1, true)
     }))
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
index f06ffc5449e76..ef08ddf041afc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
-import org.apache.spark.sql.types.{StringType, StructType, DataType}
+import org.apache.spark.sql.types._
 
 
 /**
@@ -36,34 +36,94 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
   protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
     in.map(BindReferences.bindReference(_, inputSchema))
 
-  private def genUpdater(
+  private def createCodeForStruct(
       ctx: CodeGenContext,
-      setter: String,
-      dataType: DataType,
-      ordinal: Int,
-      value: String): String = {
-    dataType match {
-      case struct: StructType =>
-        val rowTerm = ctx.freshName("row")
-        val updates = struct.map(_.dataType).zipWithIndex.map { case (dt, i) =>
-          val colTerm = ctx.freshName("col")
-          s"""
-            if ($value.isNullAt($i)) {
-              $rowTerm.setNullAt($i);
-            } else {
-              ${ctx.javaType(dt)} $colTerm = ${ctx.getValue(value, dt, s"$i")};
-              ${genUpdater(ctx, rowTerm, dt, i, colTerm)};
-            }
-           """
-        }.mkString("\n")
-        s"""
-          $genericMutableRowType $rowTerm = new $genericMutableRowType(${struct.fields.length});
-          $updates
-          $setter.update($ordinal, $rowTerm.copy());
-        """
-      case _ =>
-        ctx.setColumn(setter, dataType, ordinal, value)
-    }
+      input: String,
+      schema: StructType): GeneratedExpressionCode = {
+    val tmp = ctx.freshName("tmp")
+    val output = ctx.freshName("safeRow")
+    val values = ctx.freshName("values")
+    val rowClass = classOf[GenericInternalRow].getName
+
+    val fieldWriters = schema.map(_.dataType).zipWithIndex.map { case (dt, i) =>
+      val converter = convertToSafe(ctx, ctx.getValue(tmp, dt, i.toString), dt)
+      s"""
+        if (!$tmp.isNullAt($i)) {
+          ${converter.code}
+          $values[$i] = ${converter.primitive};
+        }
+      """
+    }.mkString("\n")
+
+    val code = s"""
+      final InternalRow $tmp = $input;
+      final Object[] $values = new Object[${schema.length}];
+      $fieldWriters
+      final InternalRow $output = new $rowClass($values);
+    """
+
+    GeneratedExpressionCode(code, "false", output)
+  }
+
+  private def createCodeForArray(
+      ctx: CodeGenContext,
+      input: String,
+      elementType: DataType): GeneratedExpressionCode = {
+    val tmp = ctx.freshName("tmp")
+    val output = ctx.freshName("safeArray")
+    val values = ctx.freshName("values")
+    val numElements = ctx.freshName("numElements")
+    val index = ctx.freshName("index")
+    val arrayClass = classOf[GenericArrayData].getName
+
+    val elementConverter = convertToSafe(ctx, ctx.getValue(tmp, elementType, index), elementType)
+    val code = s"""
+      final ArrayData $tmp = $input;
+      final int $numElements = $tmp.numElements();
+      final Object[] $values = new Object[$numElements];
+      for (int $index = 0; $index < $numElements; $index++) {
+        if (!$tmp.isNullAt($index)) {
+          ${elementConverter.code}
+          $values[$index] = ${elementConverter.primitive};
+        }
+      }
+      final ArrayData $output = new $arrayClass($values);
+    """
+
+    GeneratedExpressionCode(code, "false", output)
+  }
+
+  private def createCodeForMap(
+      ctx: CodeGenContext,
+      input: String,
+      keyType: DataType,
+      valueType: DataType): GeneratedExpressionCode = {
+    val tmp = ctx.freshName("tmp")
+    val output = ctx.freshName("safeMap")
+    val mapClass = classOf[ArrayBasedMapData].getName
+
+    val keyConverter = createCodeForArray(ctx, s"$tmp.keyArray()", keyType)
+    val valueConverter = createCodeForArray(ctx, s"$tmp.valueArray()", valueType)
+    val code = s"""
+      final MapData $tmp = $input;
+      ${keyConverter.code}
+      ${valueConverter.code}
+      final MapData $output = new $mapClass(${keyConverter.primitive}, ${valueConverter.primitive});
+    """
+
+    GeneratedExpressionCode(code, "false", output)
+  }
+
+  private def convertToSafe(
+      ctx: CodeGenContext,
+      input: String,
+      dataType: DataType): GeneratedExpressionCode = dataType match {
+    case s: StructType => createCodeForStruct(ctx, input, s)
+    case ArrayType(elementType, _) => createCodeForArray(ctx, input, elementType)
+    case MapType(keyType, valueType, _) => createCodeForMap(ctx, input, keyType, valueType)
+    // UTF8String act as a pointer if it's inside UnsafeRow, so copy it to make it safe.
+    case StringType => GeneratedExpressionCode("", "false", s"$input.clone()")
+    case _ => GeneratedExpressionCode("", "false", input)
   }
 
   protected def create(expressions: Seq[Expression]): Projection = {
@@ -72,12 +132,14 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
+        val converter = convertToSafe(ctx, evaluationCode.primitive, e.dataType)
         evaluationCode.code +
           s"""
             if (${evaluationCode.isNull}) {
               mutableRow.setNullAt($i);
             } else {
-              ${genUpdater(ctx, "mutableRow", e.dataType, i, evaluationCode.primitive)};
+              ${converter.code}
+              ${ctx.setColumn("mutableRow", e.dataType, i, converter.primitive)};
             }
           """
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 322966f423784..dd08e9025a927 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -112,7 +112,9 @@ case class DummyPlan(child: SparkPlan) extends UnaryNode {
 
   override protected def doExecute(): RDD[InternalRow] = {
     child.execute().mapPartitions { iter =>
-      // cache all strings to make sure we have deep copied UTF8String inside incoming
+      // This `DummyPlan` is in safe mode, so we don't need to do copy even we hold some
+      // values gotten from the incoming rows.
+      // we cache all strings here to make sure we have deep copied UTF8String inside incoming
       // safe InternalRow.
       val strings = new scala.collection.mutable.ArrayBuffer[UTF8String]
       iter.foreach { row =>

From 74a6541aa82bcd7a052b2e57b5ca55b7c316495b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 8 Aug 2015 08:36:14 -0700
Subject: [PATCH 0934/1454] [SPARK-4561] [PYSPARK] [SQL] turn Row into dict
 recursively

Add an option `recursive` to `Row.asDict()`, when True (default is False), it will convert the nested Row into dict.

Author: Davies Liu <davies@databricks.com>

Closes #8006 from davies/as_dict and squashes the following commits:

922cc5a [Davies Liu] turn Row into dict recursively
---
 python/pyspark/sql/types.py | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 6f74b7162f7cc..e2e6f03ae9fd7 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1197,13 +1197,36 @@ def __new__(self, *args, **kwargs):
         else:
             raise ValueError("No args or kwargs")
 
-    def asDict(self):
+    def asDict(self, recursive=False):
         """
         Return as an dict
+
+        :param recursive: turns the nested Row as dict (default: False).
+
+        >>> Row(name="Alice", age=11).asDict() == {'name': 'Alice', 'age': 11}
+        True
+        >>> row = Row(key=1, value=Row(name='a', age=2))
+        >>> row.asDict() == {'key': 1, 'value': Row(age=2, name='a')}
+        True
+        >>> row.asDict(True) == {'key': 1, 'value': {'name': 'a', 'age': 2}}
+        True
         """
         if not hasattr(self, "__fields__"):
             raise TypeError("Cannot convert a Row class into dict")
-        return dict(zip(self.__fields__, self))
+
+        if recursive:
+            def conv(obj):
+                if isinstance(obj, Row):
+                    return obj.asDict(True)
+                elif isinstance(obj, list):
+                    return [conv(o) for o in obj]
+                elif isinstance(obj, dict):
+                    return dict((k, conv(v)) for k, v in obj.items())
+                else:
+                    return obj
+            return dict(zip(self.__fields__, (conv(o) for o in self)))
+        else:
+            return dict(zip(self.__fields__, self))
 
     # let object acts like class
     def __call__(self, *args):

From ac507a03c3371cd5404ca195ee0ba0306badfc23 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 8 Aug 2015 08:38:18 -0700
Subject: [PATCH 0935/1454] [SPARK-6902] [SQL] [PYSPARK] Row should be
 read-only

Raise an read-only exception when user try to mutable a Row.

Author: Davies Liu <davies@databricks.com>

Closes #8009 from davies/readonly_row and squashes the following commits:

8722f3f [Davies Liu] add tests
05a3d36 [Davies Liu] Row should be read-only
---
 python/pyspark/sql/tests.py | 15 +++++++++++++++
 python/pyspark/sql/types.py |  5 +++++
 2 files changed, 20 insertions(+)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 1e3444dd9e3b4..38c83c427a747 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -179,6 +179,21 @@ def tearDownClass(cls):
         ReusedPySparkTestCase.tearDownClass()
         shutil.rmtree(cls.tempdir.name, ignore_errors=True)
 
+    def test_row_should_be_read_only(self):
+        row = Row(a=1, b=2)
+        self.assertEqual(1, row.a)
+
+        def foo():
+            row.a = 3
+        self.assertRaises(Exception, foo)
+
+        row2 = self.sqlCtx.range(10).first()
+        self.assertEqual(0, row2.id)
+
+        def foo2():
+            row2.id = 2
+        self.assertRaises(Exception, foo2)
+
     def test_range(self):
         self.assertEqual(self.sqlCtx.range(1, 1).count(), 0)
         self.assertEqual(self.sqlCtx.range(1, 0, -1).count(), 1)
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index e2e6f03ae9fd7..c083bf89905bf 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1246,6 +1246,11 @@ def __getattr__(self, item):
         except ValueError:
             raise AttributeError(item)
 
+    def __setattr__(self, key, value):
+        if key != '__fields__':
+            raise Exception("Row is read-only")
+        self.__dict__[key] = value
+
     def __reduce__(self):
         """Returns a tuple so Python knows how to pickle Row."""
         if hasattr(self, "__fields__"):

From 23695f1d2d7ef9f3ea92cebcd96b1cf0e8904eb4 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sat, 8 Aug 2015 11:01:25 -0700
Subject: [PATCH 0936/1454] [SPARK-9728][SQL]Support CalendarIntervalType in
 HiveQL

This PR enables converting interval term in HiveQL to CalendarInterval Literal.

JIRA: https://issues.apache.org/jira/browse/SPARK-9728

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8034 from yjshen/interval_hiveql and squashes the following commits:

7fe9a5e [Yijie Shen] declare throw exception and add unit test
fce7795 [Yijie Shen] convert hiveql interval term into CalendarInterval literal
---
 .../org/apache/spark/sql/hive/HiveQl.scala    |  25 +++
 .../apache/spark/sql/hive/HiveQlSuite.scala   |  15 ++
 .../sql/hive/execution/SQLQuerySuite.scala    |  22 +++
 .../spark/unsafe/types/CalendarInterval.java  | 156 ++++++++++++++++++
 .../unsafe/types/CalendarIntervalSuite.java   |  91 ++++++++++
 5 files changed, 309 insertions(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 7d7b4b9167306..c3f29350101d3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -45,6 +45,7 @@ import org.apache.spark.sql.hive.HiveShim._
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.hive.execution.{HiveNativeCommand, DropTable, AnalyzeTable, HiveScriptIOSchema}
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
 import org.apache.spark.util.random.RandomSampler
 
 /* Implicit conversions */
@@ -1519,6 +1520,30 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     case ast: ASTNode if ast.getType == HiveParser.TOK_CHARSETLITERAL =>
       Literal(BaseSemanticAnalyzer.charSetString(ast.getChild(0).getText, ast.getChild(1).getText))
 
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_YEAR_MONTH_LITERAL =>
+      Literal(CalendarInterval.fromYearMonthString(ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_DAY_TIME_LITERAL =>
+      Literal(CalendarInterval.fromDayTimeString(ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_YEAR_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("year", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_MONTH_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("month", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_DAY_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("day", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_HOUR_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("hour", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_MINUTE_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("minute", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_SECOND_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("second", ast.getText))
+
     case a: ASTNode =>
       throw new NotImplementedError(
         s"""No parse rules for ASTNode type: ${a.getType}, text: ${a.getText} :
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala
index f765395e148af..79cf40aba4bf2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala
@@ -175,4 +175,19 @@ class HiveQlSuite extends SparkFunSuite with BeforeAndAfterAll {
     assert(desc.serde == Option("org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe"))
     assert(desc.properties == Map(("tbl_p1" -> "p11"), ("tbl_p2" -> "p22")))
   }
+
+  test("Invalid interval term should throw AnalysisException") {
+    def assertError(sql: String, errorMessage: String): Unit = {
+      val e = intercept[AnalysisException] {
+        HiveQl.parseSql(sql)
+      }
+      assert(e.getMessage.contains(errorMessage))
+    }
+    assertError("select interval '42-32' year to month",
+      "month 32 outside range [0, 11]")
+    assertError("select interval '5 49:12:15' day to second",
+      "hour 49 outside range [0, 23]")
+    assertError("select interval '.1111111111' second",
+      "nanosecond 1111111111 outside range")
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 1dff07a6de8ad..2fa7ae3fa2e12 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -33,6 +33,7 @@ import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
 import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
 
 case class Nested1(f1: Nested2)
 case class Nested2(f2: Nested3)
@@ -1115,4 +1116,25 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
     checkAnswer(sql("SELECT a.`c.b`, `b.$q`[0].`a@!.q`, `q.w`.`w.i&`[0] FROM t"), Row(1, 1, 1))
   }
+
+  test("Convert hive interval term into Literal of CalendarIntervalType") {
+    checkAnswer(sql("select interval '10-9' year to month"),
+      Row(CalendarInterval.fromString("interval 10 years 9 months")))
+    checkAnswer(sql("select interval '20 15:40:32.99899999' day to second"),
+      Row(CalendarInterval.fromString("interval 2 weeks 6 days 15 hours 40 minutes " +
+        "32 seconds 99 milliseconds 899 microseconds")))
+    checkAnswer(sql("select interval '30' year"),
+      Row(CalendarInterval.fromString("interval 30 years")))
+    checkAnswer(sql("select interval '25' month"),
+      Row(CalendarInterval.fromString("interval 25 months")))
+    checkAnswer(sql("select interval '-100' day"),
+      Row(CalendarInterval.fromString("interval -14 weeks -2 days")))
+    checkAnswer(sql("select interval '40' hour"),
+      Row(CalendarInterval.fromString("interval 1 days 16 hours")))
+    checkAnswer(sql("select interval '80' minute"),
+      Row(CalendarInterval.fromString("interval 1 hour 20 minutes")))
+    checkAnswer(sql("select interval '299.889987299' second"),
+      Row(CalendarInterval.fromString(
+        "interval 4 minutes 59 seconds 889 milliseconds 987 microseconds")))
+  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
index 92a5e4f86f234..30e1758076361 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
@@ -50,6 +50,14 @@ private static String unitRegex(String unit) {
     unitRegex("week") + unitRegex("day") + unitRegex("hour") + unitRegex("minute") +
     unitRegex("second") + unitRegex("millisecond") + unitRegex("microsecond"));
 
+  private static Pattern yearMonthPattern =
+    Pattern.compile("^(?:['|\"])?([+|-])?(\\d+)-(\\d+)(?:['|\"])?$");
+
+  private static Pattern dayTimePattern =
+    Pattern.compile("^(?:['|\"])?([+|-])?(\\d+) (\\d+):(\\d+):(\\d+)(\\.(\\d+))?(?:['|\"])?$");
+
+  private static Pattern quoteTrimPattern = Pattern.compile("^(?:['|\"])?(.*?)(?:['|\"])?$");
+
   private static long toLong(String s) {
     if (s == null) {
       return 0;
@@ -79,6 +87,154 @@ public static CalendarInterval fromString(String s) {
     }
   }
 
+  public static long toLongWithRange(String fieldName,
+      String s, long minValue, long maxValue) throws IllegalArgumentException {
+    long result = 0;
+    if (s != null) {
+      result = Long.valueOf(s);
+      if (result < minValue || result > maxValue) {
+        throw new IllegalArgumentException(String.format("%s %d outside range [%d, %d]",
+          fieldName, result, minValue, maxValue));
+      }
+    }
+    return result;
+  }
+
+  /**
+   * Parse YearMonth string in form: [-]YYYY-MM
+   *
+   * adapted from HiveIntervalYearMonth.valueOf
+   */
+  public static CalendarInterval fromYearMonthString(String s) throws IllegalArgumentException {
+    CalendarInterval result = null;
+    if (s == null) {
+      throw new IllegalArgumentException("Interval year-month string was null");
+    }
+    s = s.trim();
+    Matcher m = yearMonthPattern.matcher(s);
+    if (!m.matches()) {
+      throw new IllegalArgumentException(
+        "Interval string does not match year-month format of 'y-m': " + s);
+    } else {
+      try {
+        int sign = m.group(1) != null && m.group(1).equals("-") ? -1 : 1;
+        int years = (int) toLongWithRange("year", m.group(2), 0, Integer.MAX_VALUE);
+        int months = (int) toLongWithRange("month", m.group(3), 0, 11);
+        result = new CalendarInterval(sign * (years * 12 + months), 0);
+      } catch (Exception e) {
+        throw new IllegalArgumentException(
+          "Error parsing interval year-month string: " + e.getMessage(), e);
+      }
+    }
+    return result;
+  }
+
+  /**
+   * Parse dayTime string in form: [-]d HH:mm:ss.nnnnnnnnn
+   *
+   * adapted from HiveIntervalDayTime.valueOf
+   */
+  public static CalendarInterval fromDayTimeString(String s) throws IllegalArgumentException {
+    CalendarInterval result = null;
+    if (s == null) {
+      throw new IllegalArgumentException("Interval day-time string was null");
+    }
+    s = s.trim();
+    Matcher m = dayTimePattern.matcher(s);
+    if (!m.matches()) {
+      throw new IllegalArgumentException(
+        "Interval string does not match day-time format of 'd h:m:s.n': " + s);
+    } else {
+      try {
+        int sign = m.group(1) != null && m.group(1).equals("-") ? -1 : 1;
+        long days = toLongWithRange("day", m.group(2), 0, Integer.MAX_VALUE);
+        long hours = toLongWithRange("hour", m.group(3), 0, 23);
+        long minutes = toLongWithRange("minute", m.group(4), 0, 59);
+        long seconds = toLongWithRange("second", m.group(5), 0, 59);
+        // Hive allow nanosecond precision interval
+        long nanos = toLongWithRange("nanosecond", m.group(7), 0L, 999999999L);
+        result = new CalendarInterval(0, sign * (
+          days * MICROS_PER_DAY + hours * MICROS_PER_HOUR + minutes * MICROS_PER_MINUTE +
+          seconds * MICROS_PER_SECOND + nanos / 1000L));
+      } catch (Exception e) {
+        throw new IllegalArgumentException(
+          "Error parsing interval day-time string: " + e.getMessage(), e);
+      }
+    }
+    return result;
+  }
+
+  public static CalendarInterval fromSingleUnitString(String unit, String s)
+      throws IllegalArgumentException {
+
+    CalendarInterval result = null;
+    if (s == null) {
+      throw new IllegalArgumentException(String.format("Interval %s string was null", unit));
+    }
+    s = s.trim();
+    Matcher m = quoteTrimPattern.matcher(s);
+    if (!m.matches()) {
+      throw new IllegalArgumentException(
+        "Interval string does not match day-time format of 'd h:m:s.n': " + s);
+    } else {
+      try {
+        if (unit.equals("year")) {
+          int year = (int) toLongWithRange("year", m.group(1),
+            Integer.MIN_VALUE / 12, Integer.MAX_VALUE / 12);
+          result = new CalendarInterval(year * 12, 0L);
+
+        } else if (unit.equals("month")) {
+          int month = (int) toLongWithRange("month", m.group(1),
+            Integer.MIN_VALUE, Integer.MAX_VALUE);
+          result = new CalendarInterval(month, 0L);
+
+        } else if (unit.equals("day")) {
+          long day = toLongWithRange("day", m.group(1),
+            Long.MIN_VALUE / MICROS_PER_DAY, Long.MAX_VALUE / MICROS_PER_DAY);
+          result = new CalendarInterval(0, day * MICROS_PER_DAY);
+
+        } else if (unit.equals("hour")) {
+          long hour = toLongWithRange("hour", m.group(1),
+            Long.MIN_VALUE / MICROS_PER_HOUR, Long.MAX_VALUE / MICROS_PER_HOUR);
+          result = new CalendarInterval(0, hour * MICROS_PER_HOUR);
+
+        } else if (unit.equals("minute")) {
+          long minute = toLongWithRange("minute", m.group(1),
+            Long.MIN_VALUE / MICROS_PER_MINUTE, Long.MAX_VALUE / MICROS_PER_MINUTE);
+          result = new CalendarInterval(0, minute * MICROS_PER_MINUTE);
+
+        } else if (unit.equals("second")) {
+          long micros = parseSecondNano(m.group(1));
+          result = new CalendarInterval(0, micros);
+        }
+      } catch (Exception e) {
+        throw new IllegalArgumentException("Error parsing interval string: " + e.getMessage(), e);
+      }
+    }
+    return result;
+  }
+
+  /**
+   * Parse second_nano string in ss.nnnnnnnnn format to microseconds
+   */
+  public static long parseSecondNano(String secondNano) throws IllegalArgumentException {
+    String[] parts = secondNano.split("\\.");
+    if (parts.length == 1) {
+      return toLongWithRange("second", parts[0], Long.MIN_VALUE / MICROS_PER_SECOND,
+        Long.MAX_VALUE / MICROS_PER_SECOND) * MICROS_PER_SECOND;
+
+    } else if (parts.length == 2) {
+      long seconds = parts[0].equals("") ? 0L : toLongWithRange("second", parts[0],
+        Long.MIN_VALUE / MICROS_PER_SECOND, Long.MAX_VALUE / MICROS_PER_SECOND);
+      long nanos = toLongWithRange("nanosecond", parts[1], 0L, 999999999L);
+      return seconds * MICROS_PER_SECOND + nanos / 1000L;
+
+    } else {
+      throw new IllegalArgumentException(
+        "Interval string does not match second-nano format of ss.nnnnnnnnn");
+    }
+  }
+
   public final int months;
   public final long microseconds;
 
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
index 6274b92b47dd4..80d4982c4b576 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
@@ -101,6 +101,97 @@ public void fromStringTest() {
     assertEquals(CalendarInterval.fromString(input), null);
   }
 
+  @Test
+  public void fromYearMonthStringTest() {
+    String input;
+    CalendarInterval i;
+
+    input = "99-10";
+    i = new CalendarInterval(99 * 12 + 10, 0L);
+    assertEquals(CalendarInterval.fromYearMonthString(input), i);
+
+    input = "-8-10";
+    i = new CalendarInterval(-8 * 12 - 10, 0L);
+    assertEquals(CalendarInterval.fromYearMonthString(input), i);
+
+    try {
+      input = "99-15";
+      CalendarInterval.fromYearMonthString(input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("month 15 outside range"));
+    }
+  }
+
+  @Test
+  public void fromDayTimeStringTest() {
+    String input;
+    CalendarInterval i;
+
+    input = "5 12:40:30.999999999";
+    i = new CalendarInterval(0, 5 * MICROS_PER_DAY + 12 * MICROS_PER_HOUR +
+      40 * MICROS_PER_MINUTE + 30 * MICROS_PER_SECOND + 999999L);
+    assertEquals(CalendarInterval.fromDayTimeString(input), i);
+
+    input = "10 0:12:0.888";
+    i = new CalendarInterval(0, 10 * MICROS_PER_DAY + 12 * MICROS_PER_MINUTE);
+    assertEquals(CalendarInterval.fromDayTimeString(input), i);
+
+    input = "-3 0:0:0";
+    i = new CalendarInterval(0, -3 * MICROS_PER_DAY);
+    assertEquals(CalendarInterval.fromDayTimeString(input), i);
+
+    try {
+      input = "5 30:12:20";
+      CalendarInterval.fromDayTimeString(input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("hour 30 outside range"));
+    }
+
+    try {
+      input = "5 30-12";
+      CalendarInterval.fromDayTimeString(input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("not match day-time format"));
+    }
+  }
+
+  @Test
+  public void fromSingleUnitStringTest() {
+    String input;
+    CalendarInterval i;
+
+    input = "12";
+    i = new CalendarInterval(12 * 12, 0L);
+    assertEquals(CalendarInterval.fromSingleUnitString("year", input), i);
+
+    input = "100";
+    i = new CalendarInterval(0, 100 * MICROS_PER_DAY);
+    assertEquals(CalendarInterval.fromSingleUnitString("day", input), i);
+
+    input = "1999.38888";
+    i = new CalendarInterval(0, 1999 * MICROS_PER_SECOND + 38);
+    assertEquals(CalendarInterval.fromSingleUnitString("second", input), i);
+
+    try {
+      input = String.valueOf(Integer.MAX_VALUE);
+      CalendarInterval.fromSingleUnitString("year", input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("outside range"));
+    }
+
+    try {
+      input = String.valueOf(Long.MAX_VALUE / MICROS_PER_HOUR + 1);
+      CalendarInterval.fromSingleUnitString("hour", input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("outside range"));
+    }
+  }
+
   @Test
   public void addTest() {
     String input = "interval 3 month 1 hour";

From a3aec918bed22f8e33cf91dc0d6e712e6653c7d2 Mon Sep 17 00:00:00 2001
From: Joseph Batchik <joseph.batchik@cloudera.com>
Date: Sat, 8 Aug 2015 11:03:01 -0700
Subject: [PATCH 0937/1454] [SPARK-9486][SQL] Add data source aliasing for
 external packages

Users currently have to provide the full class name for external data sources, like:

`sqlContext.read.format("com.databricks.spark.avro").load(path)`

This allows external data source packages to register themselves using a Service Loader so that they can add custom alias like:

`sqlContext.read.format("avro").load(path)`

This makes it so that using external data source packages uses the same format as the internal data sources like parquet, json, etc.

Author: Joseph Batchik <joseph.batchik@cloudera.com>
Author: Joseph Batchik <josephbatchik@gmail.com>

Closes #7802 from JDrit/service_loader and squashes the following commits:

49a01ec [Joseph Batchik] fixed a couple of format / error bugs
e5e93b2 [Joseph Batchik] modified rat file to only excluded added services
72b349a [Joseph Batchik] fixed error with orc data source actually
9f93ea7 [Joseph Batchik] fixed error with orc data source
87b7f1c [Joseph Batchik] fixed typo
101cd22 [Joseph Batchik] removing unneeded changes
8f3cf43 [Joseph Batchik] merged in changes
b63d337 [Joseph Batchik] merged in master
95ae030 [Joseph Batchik] changed the new trait to be used as a mixin for data source to register themselves
74db85e [Joseph Batchik] reformatted class loader
ac2270d [Joseph Batchik] removing some added test
a6926db [Joseph Batchik] added test cases for data source loader
208a2a8 [Joseph Batchik] changes to do error catching if there are multiple data sources
946186e [Joseph Batchik] started working on service loader
---
 .rat-excludes                                 |  1 +
 ...pache.spark.sql.sources.DataSourceRegister |  3 +
 .../spark/sql/execution/datasources/ddl.scala | 52 ++++++------
 .../apache/spark/sql/jdbc/JDBCRelation.scala  |  5 +-
 .../apache/spark/sql/json/JSONRelation.scala  |  5 +-
 .../spark/sql/parquet/ParquetRelation.scala   |  5 +-
 .../apache/spark/sql/sources/interfaces.scala | 21 +++++
 ...pache.spark.sql.sources.DataSourceRegister |  3 +
 .../sql/sources/DDLSourceLoadSuite.scala      | 85 +++++++++++++++++++
 ...pache.spark.sql.sources.DataSourceRegister |  1 +
 .../spark/sql/hive/orc/OrcRelation.scala      |  5 +-
 11 files changed, 156 insertions(+), 30 deletions(-)
 create mode 100644 sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
 create mode 100644 sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
 create mode 100644 sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister

diff --git a/.rat-excludes b/.rat-excludes
index 236c2db05367c..72771465846b8 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -93,3 +93,4 @@ INDEX
 .lintr
 gen-java.*
 .*avpr
+org.apache.spark.sql.sources.DataSourceRegister
diff --git a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..cc32d4b72748e
--- /dev/null
+++ b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1,3 @@
+org.apache.spark.sql.jdbc.DefaultSource
+org.apache.spark.sql.json.DefaultSource
+org.apache.spark.sql.parquet.DefaultSource
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index 0cdb407ad57b9..8c2f297e42458 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -17,7 +17,12 @@
 
 package org.apache.spark.sql.execution.datasources
 
+import java.util.ServiceLoader
+
+import scala.collection.Iterator
+import scala.collection.JavaConversions._
 import scala.language.{existentials, implicitConversions}
+import scala.util.{Failure, Success, Try}
 import scala.util.matching.Regex
 
 import org.apache.hadoop.fs.Path
@@ -190,37 +195,32 @@ private[sql] class DDLParser(
     }
 }
 
-private[sql] object ResolvedDataSource {
-
-  private val builtinSources = Map(
-    "jdbc" -> "org.apache.spark.sql.jdbc.DefaultSource",
-    "json" -> "org.apache.spark.sql.json.DefaultSource",
-    "parquet" -> "org.apache.spark.sql.parquet.DefaultSource",
-    "orc" -> "org.apache.spark.sql.hive.orc.DefaultSource"
-  )
+private[sql] object ResolvedDataSource extends Logging {
 
   /** Given a provider name, look up the data source class definition. */
   def lookupDataSource(provider: String): Class[_] = {
+    val provider2 = s"$provider.DefaultSource"
     val loader = Utils.getContextOrSparkClassLoader
-
-    if (builtinSources.contains(provider)) {
-      return loader.loadClass(builtinSources(provider))
-    }
-
-    try {
-      loader.loadClass(provider)
-    } catch {
-      case cnf: java.lang.ClassNotFoundException =>
-        try {
-          loader.loadClass(provider + ".DefaultSource")
-        } catch {
-          case cnf: java.lang.ClassNotFoundException =>
-            if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
-              sys.error("The ORC data source must be used with Hive support enabled.")
-            } else {
-              sys.error(s"Failed to load class for data source: $provider")
-            }
+    val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
+
+    serviceLoader.iterator().filter(_.format().equalsIgnoreCase(provider)).toList match {
+      /** the provider format did not match any given registered aliases */
+      case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
+        case Success(dataSource) => dataSource
+        case Failure(error) => if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
+          throw new ClassNotFoundException(
+            "The ORC data source must be used with Hive support enabled.", error)
+        } else {
+          throw new ClassNotFoundException(
+            s"Failed to load class for data source: $provider", error)
         }
+      }
+      /** there is exactly one registered alias */
+      case head :: Nil => head.getClass
+      /** There are multiple registered aliases for the input */
+      case sources => sys.error(s"Multiple sources found for $provider, " +
+        s"(${sources.map(_.getClass.getName).mkString(", ")}), " +
+        "please specify the fully qualified class name")
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
index 41d0ecb4bbfbf..48d97ced9ca0a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
@@ -77,7 +77,10 @@ private[sql] object JDBCRelation {
   }
 }
 
-private[sql] class DefaultSource extends RelationProvider {
+private[sql] class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  def format(): String = "jdbc"
+
   /** Returns a new base relation with the given parameters. */
   override def createRelation(
       sqlContext: SQLContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 10f1367e6984c..b34a272ec547f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -37,7 +37,10 @@ import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  def format(): String = "json"
+
   override def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 48009b2fd007d..b6db71b5b8a62 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -49,7 +49,10 @@ import org.apache.spark.sql.types.{DataType, StructType}
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  def format(): String = "parquet"
+
   override def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index c5b7ee73eb784..4aafec0e2df27 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -37,6 +37,27 @@ import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql._
 import org.apache.spark.util.SerializableConfiguration
 
+/**
+ * ::DeveloperApi::
+ * Data sources should implement this trait so that they can register an alias to their data source.
+ * This allows users to give the data source alias as the format type over the fully qualified
+ * class name.
+ *
+ * ex: parquet.DefaultSource.format = "parquet".
+ *
+ * A new instance of this class with be instantiated each time a DDL call is made.
+ */
+@DeveloperApi
+trait DataSourceRegister {
+
+  /**
+   * The string that represents the format that this data source provider uses. This is
+   * overridden by children to provide a nice alias for the data source,
+   * ex: override def format(): String = "parquet"
+   */
+  def format(): String
+}
+
 /**
  * ::DeveloperApi::
  * Implemented by objects that produce relations for a specific kind of data source.  When
diff --git a/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..cfd7889b4ac2c
--- /dev/null
+++ b/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1,3 @@
+org.apache.spark.sql.sources.FakeSourceOne
+org.apache.spark.sql.sources.FakeSourceTwo
+org.apache.spark.sql.sources.FakeSourceThree
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
new file mode 100644
index 0000000000000..1a4d41b02ca68
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
@@ -0,0 +1,85 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.sources
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.types.{StringType, StructField, StructType}
+
+class FakeSourceOne extends RelationProvider with DataSourceRegister {
+
+  def format(): String = "Fluet da Bomb"
+
+  override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
+    new BaseRelation {
+      override def sqlContext: SQLContext = cont
+
+      override def schema: StructType =
+        StructType(Seq(StructField("stringType", StringType, nullable = false)))
+    }
+}
+
+class FakeSourceTwo extends RelationProvider  with DataSourceRegister {
+
+  def format(): String = "Fluet da Bomb"
+
+  override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
+    new BaseRelation {
+      override def sqlContext: SQLContext = cont
+
+      override def schema: StructType =
+        StructType(Seq(StructField("stringType", StringType, nullable = false)))
+    }
+}
+
+class FakeSourceThree extends RelationProvider with DataSourceRegister {
+
+  def format(): String = "gathering quorum"
+
+  override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
+    new BaseRelation {
+      override def sqlContext: SQLContext = cont
+
+      override def schema: StructType =
+        StructType(Seq(StructField("stringType", StringType, nullable = false)))
+    }
+}
+// please note that the META-INF/services had to be modified for the test directory for this to work
+class DDLSourceLoadSuite extends DataSourceTest {
+
+  test("data sources with the same name") {
+    intercept[RuntimeException] {
+      caseInsensitiveContext.read.format("Fluet da Bomb").load()
+    }
+  }
+
+  test("load data source from format alias") {
+    caseInsensitiveContext.read.format("gathering quorum").load().schema ==
+      StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("specify full classname with duplicate formats") {
+    caseInsensitiveContext.read.format("org.apache.spark.sql.sources.FakeSourceOne")
+      .load().schema == StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("Loading Orc") {
+    intercept[ClassNotFoundException] {
+      caseInsensitiveContext.read.format("orc").load()
+    }
+  }
+}
diff --git a/sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..4a774fbf1fdf8
--- /dev/null
+++ b/sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1 @@
+org.apache.spark.sql.hive.orc.DefaultSource
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 7c8704b47f286..0c344c63fde3f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -47,7 +47,10 @@ import org.apache.spark.util.SerializableConfiguration
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  def format(): String = "orc"
+
   def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],

From 25c363e93bc79119c5ba5c228fcad620061cff62 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Sat, 8 Aug 2015 18:22:46 -0700
Subject: [PATCH 0938/1454] [MINOR] inaccurate comments for showString()

Author: CodingCat <zhunansjtu@gmail.com>

Closes #8050 from CodingCat/minor and squashes the following commits:

5bc4b89 [CodingCat] inaccurate comments
---
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 405b5a4a9a7f9..570b8b2d5928d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -168,7 +168,7 @@ class DataFrame private[sql](
   }
 
   /**
-   * Internal API for Python
+   * Compose the string representing rows for output
    * @param _numRows Number of rows to show
    * @param truncate Whether truncate long strings and align cells right
    */

From 3ca995b78f373251081f6877623649bfba3040b2 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sat, 8 Aug 2015 21:05:50 -0700
Subject: [PATCH 0939/1454] [SPARK-6212] [SQL] The EXPLAIN output of CTAS only
 shows the analyzed plan

JIRA: https://issues.apache.org/jira/browse/SPARK-6212

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7986 from yjshen/ctas_explain and squashes the following commits:

bb6fee5 [Yijie Shen] refine test
f731041 [Yijie Shen] address comment
b2cf8ab [Yijie Shen] bug fix
bd7eb20 [Yijie Shen] ctas explain
---
 .../apache/spark/sql/execution/commands.scala |  2 ++
 .../hive/execution/CreateTableAsSelect.scala  |  4 ++-
 .../sql/hive/execution/HiveExplainSuite.scala | 35 +++++++++++++++++--
 3 files changed, 38 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 6b83025d5a153..95209e6634519 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -69,6 +69,8 @@ private[sql] case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan
     val converted = sideEffectResult.map(convert(_).asInstanceOf[InternalRow])
     sqlContext.sparkContext.parallelize(converted, 1)
   }
+
+  override def argString: String = cmd.toString
 }
 
 /**
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
index 84358cb73c9e3..8422287e177e5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
@@ -40,6 +40,8 @@ case class CreateTableAsSelect(
   def database: String = tableDesc.database
   def tableName: String = tableDesc.name
 
+  override def children: Seq[LogicalPlan] = Seq(query)
+
   override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     lazy val metastoreRelation: MetastoreRelation = {
@@ -91,6 +93,6 @@ case class CreateTableAsSelect(
   }
 
   override def argString: String = {
-    s"[Database:$database, TableName: $tableName, InsertIntoHiveTable]\n" + query.toString
+    s"[Database:$database, TableName: $tableName, InsertIntoHiveTable]"
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 8215dd6c2e711..44c5b80392fa5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -17,13 +17,18 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.{SQLContext, QueryTest}
+import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.test.SQLTestUtils
 
 /**
  * A set of tests that validates support for Hive Explain command.
  */
-class HiveExplainSuite extends QueryTest {
+class HiveExplainSuite extends QueryTest with SQLTestUtils {
+
+  def sqlContext: SQLContext = TestHive
+
   test("explain extended command") {
     checkExistence(sql(" explain   select * from src where key=123 "), true,
                    "== Physical Plan ==")
@@ -74,4 +79,30 @@ class HiveExplainSuite extends QueryTest {
       "Limit",
       "src")
   }
+
+  test("SPARK-6212: The EXPLAIN output of CTAS only shows the analyzed plan") {
+    withTempTable("jt") {
+      val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
+      read.json(rdd).registerTempTable("jt")
+      val outputs = sql(
+        s"""
+           |EXPLAIN EXTENDED
+           |CREATE TABLE t1
+           |AS
+           |SELECT * FROM jt
+      """.stripMargin).collect().map(_.mkString).mkString
+
+      val shouldContain =
+        "== Parsed Logical Plan ==" :: "== Analyzed Logical Plan ==" :: "Subquery" ::
+        "== Optimized Logical Plan ==" :: "== Physical Plan ==" ::
+        "CreateTableAsSelect" :: "InsertIntoHiveTable" :: "jt" :: Nil
+      for (key <- shouldContain) {
+        assert(outputs.contains(key), s"$key doesn't exist in result")
+      }
+
+      val physicalIndex = outputs.indexOf("== Physical Plan ==")
+      assert(!outputs.substring(physicalIndex).contains("Subquery"),
+        "Physical Plan should not contain Subquery since it's eliminated by optimizer")
+    }
+  }
 }

From e9c36938ba972b6fe3c9f6228508e3c9f1c876b2 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 9 Aug 2015 10:58:36 -0700
Subject: [PATCH 0940/1454] [SPARK-9752][SQL] Support UnsafeRow in Sample
 operator.

In order for this to work, I had to disable gap sampling.

Author: Reynold Xin <rxin@databricks.com>

Closes #8040 from rxin/SPARK-9752 and squashes the following commits:

f9e248c [Reynold Xin] Fix the test case for real this time.
adbccb3 [Reynold Xin] Fixed test case.
589fb23 [Reynold Xin] Merge branch 'SPARK-9752' of github.com:rxin/spark into SPARK-9752
55ccddc [Reynold Xin] Fixed core test.
78fa895 [Reynold Xin] [SPARK-9752][SQL] Support UnsafeRow in Sample operator.
c9e7112 [Reynold Xin] [SPARK-9752][SQL] Support UnsafeRow in Sample operator.
---
 .../spark/util/random/RandomSampler.scala     | 18 ++++++----
 .../spark/sql/execution/basicOperators.scala  | 18 +++++++---
 .../apache/spark/sql/DataFrameStatSuite.scala | 35 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 17 ---------
 4 files changed, 61 insertions(+), 27 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
index 786b97ad7b9ec..c156b03cdb7c4 100644
--- a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
@@ -176,10 +176,15 @@ class BernoulliSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T
  * A sampler for sampling with replacement, based on values drawn from Poisson distribution.
  *
  * @param fraction the sampling fraction (with replacement)
+ * @param useGapSamplingIfPossible if true, use gap sampling when sampling ratio is low.
  * @tparam T item type
  */
 @DeveloperApi
-class PoissonSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T] {
+class PoissonSampler[T: ClassTag](
+    fraction: Double,
+    useGapSamplingIfPossible: Boolean) extends RandomSampler[T, T] {
+
+  def this(fraction: Double) = this(fraction, useGapSamplingIfPossible = true)
 
   /** Epsilon slop to avoid failure from floating point jitter. */
   require(
@@ -199,17 +204,18 @@ class PoissonSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T]
   override def sample(items: Iterator[T]): Iterator[T] = {
     if (fraction <= 0.0) {
       Iterator.empty
-    } else if (fraction <= RandomSampler.defaultMaxGapSamplingFraction) {
-        new GapSamplingReplacementIterator(items, fraction, rngGap, RandomSampler.rngEpsilon)
+    } else if (useGapSamplingIfPossible &&
+               fraction <= RandomSampler.defaultMaxGapSamplingFraction) {
+      new GapSamplingReplacementIterator(items, fraction, rngGap, RandomSampler.rngEpsilon)
     } else {
-      items.flatMap { item => {
+      items.flatMap { item =>
         val count = rng.sample()
         if (count == 0) Iterator.empty else Iterator.fill(count)(item)
-      }}
+      }
     }
   }
 
-  override def clone: PoissonSampler[T] = new PoissonSampler[T](fraction)
+  override def clone: PoissonSampler[T] = new PoissonSampler[T](fraction, useGapSamplingIfPossible)
 }
 
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 0680f31d40f6d..c5d1ed0937b19 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.{RDD, ShuffledRDD}
+import org.apache.spark.rdd.{PartitionwiseSampledRDD, RDD, ShuffledRDD}
 import org.apache.spark.shuffle.sort.SortShuffleManager
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
@@ -30,6 +30,7 @@ import org.apache.spark.sql.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.ExternalSorter
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
+import org.apache.spark.util.random.PoissonSampler
 import org.apache.spark.util.{CompletionIterator, MutablePair}
 import org.apache.spark.{HashPartitioner, SparkEnv}
 
@@ -130,12 +131,21 @@ case class Sample(
 {
   override def output: Seq[Attribute] = child.output
 
-  // TODO: How to pick seed?
+  override def outputsUnsafeRows: Boolean = child.outputsUnsafeRows
+  override def canProcessUnsafeRows: Boolean = true
+  override def canProcessSafeRows: Boolean = true
+
   protected override def doExecute(): RDD[InternalRow] = {
     if (withReplacement) {
-      child.execute().map(_.copy()).sample(withReplacement, upperBound - lowerBound, seed)
+      // Disable gap sampling since the gap sampling method buffers two rows internally,
+      // requiring us to copy the row, which is more expensive than the random number generator.
+      new PartitionwiseSampledRDD[InternalRow, InternalRow](
+        child.execute(),
+        new PoissonSampler[InternalRow](upperBound - lowerBound, useGapSamplingIfPossible = false),
+        preservesPartitioning = true,
+        seed)
     } else {
-      child.execute().map(_.copy()).randomSampleWithRange(lowerBound, upperBound, seed)
+      child.execute().randomSampleWithRange(lowerBound, upperBound, seed)
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 0e7659f443ecd..8f5984e4a8ce2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -30,6 +30,41 @@ class DataFrameStatSuite extends QueryTest {
 
   private def toLetter(i: Int): String = (i + 97).toChar.toString
 
+  test("sample with replacement") {
+    val n = 100
+    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    checkAnswer(
+      data.sample(withReplacement = true, 0.05, seed = 13),
+      Seq(5, 10, 52, 73).map(Row(_))
+    )
+  }
+
+  test("sample without replacement") {
+    val n = 100
+    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    checkAnswer(
+      data.sample(withReplacement = false, 0.05, seed = 13),
+      Seq(16, 23, 88, 100).map(Row(_))
+    )
+  }
+
+  test("randomSplit") {
+    val n = 600
+    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    for (seed <- 1 to 5) {
+      val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
+      assert(splits.length == 3, "wrong number of splits")
+
+      assert(splits.reduce((a, b) => a.unionAll(b)).sort("id").collect().toList ==
+        data.collect().toList, "incomplete or wrong split")
+
+      val s = splits.map(_.count())
+      assert(math.abs(s(0) - 100) < 50) // std =  9.13
+      assert(math.abs(s(1) - 200) < 50) // std = 11.55
+      assert(math.abs(s(2) - 300) < 50) // std = 12.25
+    }
+  }
+
   test("pearson correlation") {
     val df = Seq.tabulate(10)(i => (i, 2 * i, i * -1.0)).toDF("a", "b", "c")
     val corr1 = df.stat.corr("a", "b", "pearson")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index f9cc6d1f3c250..0212637a829e5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -415,23 +415,6 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     assert(df.schema.map(_.name) === Seq("key", "valueRenamed", "newCol"))
   }
 
-  test("randomSplit") {
-    val n = 600
-    val data = sqlContext.sparkContext.parallelize(1 to n, 2).toDF("id")
-    for (seed <- 1 to 5) {
-      val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
-      assert(splits.length == 3, "wrong number of splits")
-
-      assert(splits.reduce((a, b) => a.unionAll(b)).sort("id").collect().toList ==
-        data.collect().toList, "incomplete or wrong split")
-
-      val s = splits.map(_.count())
-      assert(math.abs(s(0) - 100) < 50) // std =  9.13
-      assert(math.abs(s(1) - 200) < 50) // std = 11.55
-      assert(math.abs(s(2) - 300) < 50) // std = 12.25
-    }
-  }
-
   test("describe") {
     val describeTestData = Seq(
       ("Bob", 16, 176),

From 68ccc6e184598822b19a880fdd4597b66a1c2d92 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sun, 9 Aug 2015 11:44:51 -0700
Subject: [PATCH 0941/1454] [SPARK-8930] [SQL] Throw a AnalysisException with
 meaningful messages if DataFrame#explode takes a star in expressions

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8057 from yjshen/explode_star and squashes the following commits:

eae181d [Yijie Shen] change explaination message
54c9d11 [Yijie Shen] meaning message for * in explode
---
 .../spark/sql/catalyst/analysis/Analyzer.scala    |  4 +++-
 .../sql/catalyst/analysis/AnalysisTest.scala      |  4 +++-
 .../org/apache/spark/sql/DataFrameSuite.scala     | 15 +++++++++++++++
 3 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 82158e61e3fb5..a684dbc3afa42 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -408,7 +408,7 @@ class Analyzer(
     /**
      * Returns true if `exprs` contains a [[Star]].
      */
-    protected def containsStar(exprs: Seq[Expression]): Boolean =
+    def containsStar(exprs: Seq[Expression]): Boolean =
       exprs.exists(_.collect { case _: Star => true }.nonEmpty)
   }
 
@@ -602,6 +602,8 @@ class Analyzer(
    */
   object ResolveGenerate extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+      case g: Generate if ResolveReferences.containsStar(g.generator.children) =>
+        failAnalysis("Cannot explode *, explode can only be applied on a specific column.")
       case p: Generate if !p.child.resolved || !p.generator.resolved => p
       case g: Generate if !g.resolved =>
         g.copy(generatorOutput = makeGeneratorOutput(g.generator, g.generatorOutput.map(_.name)))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
index ee1f8f54251e0..53b3695a86be5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
@@ -71,6 +71,8 @@ trait AnalysisTest extends PlanTest {
     val e = intercept[Exception] {
       analyzer.checkAnalysis(analyzer.execute(inputPlan))
     }
-    expectedErrors.forall(e.getMessage.contains)
+    assert(expectedErrors.map(_.toLowerCase).forall(e.getMessage.toLowerCase.contains),
+      s"Expected to throw Exception contains: ${expectedErrors.mkString(", ")}, " +
+        s"actually we get ${e.getMessage}")
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 0212637a829e5..c49f256be5501 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -134,6 +134,21 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     )
   }
 
+  test("SPARK-8930: explode should fail with a meaningful message if it takes a star") {
+    val df = Seq(("1", "1,2"), ("2", "4"), ("3", "7,8,9")).toDF("prefix", "csv")
+    val e = intercept[AnalysisException] {
+      df.explode($"*") { case Row(prefix: String, csv: String) =>
+        csv.split(",").map(v => Tuple1(prefix + ":" + v)).toSeq
+      }.queryExecution.assertAnalyzed()
+    }
+    assert(e.getMessage.contains(
+      "Cannot explode *, explode can only be applied on a specific column."))
+
+    df.explode('prefix, 'csv) { case Row(prefix: String, csv: String) =>
+      csv.split(",").map(v => Tuple1(prefix + ":" + v)).toSeq
+    }.queryExecution.assertAnalyzed()
+  }
+
   test("explode alias and star") {
     val df = Seq((Array("a"), 1)).toDF("a", "b")
 

From 86fa4ba6d13f909cb508b7cb3b153d586fe59bc3 Mon Sep 17 00:00:00 2001
From: Yadong Qi <qiyadong2010@gmail.com>
Date: Sun, 9 Aug 2015 19:54:05 +0100
Subject: [PATCH 0942/1454] [SPARK-9737] [YARN] Add the suggested configuration
 when required executor memory is above the max threshold of this cluster on
 YARN mode

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #8028 from watermen/SPARK-9737 and squashes the following commits:

48bdf3d [Yadong Qi] Add suggested configuration.
---
 .../main/scala/org/apache/spark/deploy/yarn/Client.scala    | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index fc11bbf97e2ec..b4ba3f0221600 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -203,12 +203,14 @@ private[spark] class Client(
     val executorMem = args.executorMemory + executorMemoryOverhead
     if (executorMem > maxMem) {
       throw new IllegalArgumentException(s"Required executor memory (${args.executorMemory}" +
-        s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster!")
+        s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " +
+        "Please increase the value of 'yarn.scheduler.maximum-allocation-mb'.")
     }
     val amMem = args.amMemory + amMemoryOverhead
     if (amMem > maxMem) {
       throw new IllegalArgumentException(s"Required AM memory (${args.amMemory}" +
-        s"+$amMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster!")
+        s"+$amMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " +
+        "Please increase the value of 'yarn.scheduler.maximum-allocation-mb'.")
     }
     logInfo("Will allocate AM container, with %d MB memory including %d MB overhead".format(
       amMem,

From a863348fd85848e0d4325c4de359da12e5f548d2 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 9 Aug 2015 13:43:31 -0700
Subject: [PATCH 0943/1454] Disable JobGeneratorSuite "Do not clear received
 block data too soon".

---
 .../apache/spark/streaming/scheduler/JobGeneratorSuite.scala   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
index a2dbae149f311..9b6cd4bc4e315 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
@@ -56,7 +56,8 @@ class JobGeneratorSuite extends TestSuiteBase {
   // 4. allow subsequent batches to be generated (to allow premature deletion of 3rd batch metadata)
   // 5. verify whether 3rd batch's block metadata still exists
   //
-  test("SPARK-6222: Do not clear received block data too soon") {
+  // TODO: SPARK-7420 enable this test
+  ignore("SPARK-6222: Do not clear received block data too soon") {
     import JobGeneratorSuite._
     val checkpointDir = Utils.createTempDir()
     val testConf = conf

From 23cf5af08d98da771c41571c00a2f5cafedfebdd Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sun, 9 Aug 2015 14:26:01 -0700
Subject: [PATCH 0944/1454] [SPARK-9703] [SQL] Refactor EnsureRequirements to
 avoid certain unnecessary shuffles

This pull request refactors the `EnsureRequirements` planning rule in order to avoid the addition of certain unnecessary shuffles.

As an example of how unnecessary shuffles can occur, consider SortMergeJoin, which requires clustered distribution and sorted ordering of its children's input rows. Say that both of SMJ's children produce unsorted output but are both SinglePartition. In this case, we will need to inject sort operators but should not need to inject Exchanges. Unfortunately, it looks like the EnsureRequirements unnecessarily repartitions using a hash partitioning.

This patch solves this problem by refactoring `EnsureRequirements` to properly implement the `compatibleWith` checks that were broken in earlier implementations. See the significant inline comments for a better description of how this works. The majority of this PR is new comments and test cases, with few actual changes to the code.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7988 from JoshRosen/exchange-fixes and squashes the following commits:

38006e7 [Josh Rosen] Rewrite EnsureRequirements _yet again_ to make things even simpler
0983f75 [Josh Rosen] More guarantees vs. compatibleWith cleanup; delete BroadcastPartitioning.
8784bd9 [Josh Rosen] Giant comment explaining compatibleWith vs. guarantees
1307c50 [Josh Rosen] Update conditions for requiring child compatibility.
18cddeb [Josh Rosen] Rename DummyPlan to DummySparkPlan.
2c7e126 [Josh Rosen] Merge remote-tracking branch 'origin/master' into exchange-fixes
fee65c4 [Josh Rosen] Further refinement to comments / reasoning
642b0bb [Josh Rosen] Further expand comment / reasoning
06aba0c [Josh Rosen] Add more comments
8dbc845 [Josh Rosen] Add even more tests.
4f08278 [Josh Rosen] Fix the test by adding the compatibility check to EnsureRequirements
a1c12b9 [Josh Rosen] Add failing test to demonstrate allCompatible bug
0725a34 [Josh Rosen] Small assertion cleanup.
5172ac5 [Josh Rosen] Add test for requiresChildrenToProduceSameNumberOfPartitions.
2e0f33a [Josh Rosen] Write a more generic test for EnsureRequirements.
752b8de [Josh Rosen] style fix
c628daf [Josh Rosen] Revert accidental ExchangeSuite change.
c9fb231 [Josh Rosen] Rewrite exchange to fix better handle this case.
adcc742 [Josh Rosen] Move test to PlannerSuite.
0675956 [Josh Rosen] Preserving ordering and partitioning in row format converters also does not help.
cc5669c [Josh Rosen] Adding outputPartitioning to Repartition does not fix the test.
2dfc648 [Josh Rosen] Add failing test illustrating bad exchange planning.
---
 .../plans/physical/partitioning.scala         | 128 +++++++++++++--
 .../apache/spark/sql/execution/Exchange.scala | 104 ++++++------
 .../spark/sql/execution/basicOperators.scala  |   5 +
 .../sql/execution/rowFormatConverters.scala   |   5 +
 .../spark/sql/execution/PlannerSuite.scala    | 151 ++++++++++++++++++
 5 files changed, 328 insertions(+), 65 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index ec659ce789c27..5a89a90b735a6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -75,6 +75,37 @@ case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution {
   def clustering: Set[Expression] = ordering.map(_.child).toSet
 }
 
+/**
+ * Describes how an operator's output is split across partitions. The `compatibleWith`,
+ * `guarantees`, and `satisfies` methods describe relationships between child partitionings,
+ * target partitionings, and [[Distribution]]s. These relations are described more precisely in
+ * their individual method docs, but at a high level:
+ *
+ *  - `satisfies` is a relationship between partitionings and distributions.
+ *  - `compatibleWith` is relationships between an operator's child output partitionings.
+ *  - `guarantees` is a relationship between a child's existing output partitioning and a target
+ *     output partitioning.
+ *
+ *  Diagrammatically:
+ *
+ *            +--------------+
+ *            | Distribution |
+ *            +--------------+
+ *                    ^
+ *                    |
+ *               satisfies
+ *                    |
+ *            +--------------+                  +--------------+
+ *            |    Child     |                  |    Target    |
+ *       +----| Partitioning |----guarantees--->| Partitioning |
+ *       |    +--------------+                  +--------------+
+ *       |            ^
+ *       |            |
+ *       |     compatibleWith
+ *       |            |
+ *       +------------+
+ *
+ */
 sealed trait Partitioning {
   /** Returns the number of partitions that the data is split across */
   val numPartitions: Int
@@ -90,9 +121,66 @@ sealed trait Partitioning {
   /**
    * Returns true iff we can say that the partitioning scheme of this [[Partitioning]]
    * guarantees the same partitioning scheme described by `other`.
+   *
+   * Compatibility of partitionings is only checked for operators that have multiple children
+   * and that require a specific child output [[Distribution]], such as joins.
+   *
+   * Intuitively, partitionings are compatible if they route the same partitioning key to the same
+   * partition. For instance, two hash partitionings are only compatible if they produce the same
+   * number of output partitionings and hash records according to the same hash function and
+   * same partitioning key schema.
+   *
+   * Put another way, two partitionings are compatible with each other if they satisfy all of the
+   * same distribution guarantees.
    */
-  // TODO: Add an example once we have the `nullSafe` concept.
-  def guarantees(other: Partitioning): Boolean
+  def compatibleWith(other: Partitioning): Boolean
+
+  /**
+   * Returns true iff we can say that the partitioning scheme of this [[Partitioning]] guarantees
+   * the same partitioning scheme described by `other`. If a `A.guarantees(B)`, then repartitioning
+   * the child's output according to `B` will be unnecessary. `guarantees` is used as a performance
+   * optimization to allow the exchange planner to avoid redundant repartitionings. By default,
+   * a partitioning only guarantees partitionings that are equal to itself (i.e. the same number
+   * of partitions, same strategy (range or hash), etc).
+   *
+   * In order to enable more aggressive optimization, this strict equality check can be relaxed.
+   * For example, say that the planner needs to repartition all of an operator's children so that
+   * they satisfy the [[AllTuples]] distribution. One way to do this is to repartition all children
+   * to have the [[SinglePartition]] partitioning. If one of the operator's children already happens
+   * to be hash-partitioned with a single partition then we do not need to re-shuffle this child;
+   * this repartitioning can be avoided if a single-partition [[HashPartitioning]] `guarantees`
+   * [[SinglePartition]].
+   *
+   * The SinglePartition example given above is not particularly interesting; guarantees' real
+   * value occurs for more advanced partitioning strategies. SPARK-7871 will introduce a notion
+   * of null-safe partitionings, under which partitionings can specify whether rows whose
+   * partitioning keys contain null values will be grouped into the same partition or whether they
+   * will have an unknown / random distribution. If a partitioning does not require nulls to be
+   * clustered then a partitioning which _does_ cluster nulls will guarantee the null clustered
+   * partitioning. The converse is not true, however: a partitioning which clusters nulls cannot
+   * be guaranteed by one which does not cluster them. Thus, in general `guarantees` is not a
+   * symmetric relation.
+   *
+   * Another way to think about `guarantees`: if `A.guarantees(B)`, then any partitioning of rows
+   * produced by `A` could have also been produced by `B`.
+   */
+  def guarantees(other: Partitioning): Boolean = this == other
+}
+
+object Partitioning {
+  def allCompatible(partitionings: Seq[Partitioning]): Boolean = {
+    // Note: this assumes transitivity
+    partitionings.sliding(2).map {
+      case Seq(a) => true
+      case Seq(a, b) =>
+        if (a.numPartitions != b.numPartitions) {
+          assert(!a.compatibleWith(b) && !b.compatibleWith(a))
+          false
+        } else {
+          a.compatibleWith(b) && b.compatibleWith(a)
+        }
+    }.forall(_ == true)
+  }
 }
 
 case class UnknownPartitioning(numPartitions: Int) extends Partitioning {
@@ -101,6 +189,8 @@ case class UnknownPartitioning(numPartitions: Int) extends Partitioning {
     case _ => false
   }
 
+  override def compatibleWith(other: Partitioning): Boolean = false
+
   override def guarantees(other: Partitioning): Boolean = false
 }
 
@@ -109,21 +199,9 @@ case object SinglePartition extends Partitioning {
 
   override def satisfies(required: Distribution): Boolean = true
 
-  override def guarantees(other: Partitioning): Boolean = other match {
-    case SinglePartition => true
-    case _ => false
-  }
-}
-
-case object BroadcastPartitioning extends Partitioning {
-  val numPartitions = 1
+  override def compatibleWith(other: Partitioning): Boolean = other.numPartitions == 1
 
-  override def satisfies(required: Distribution): Boolean = true
-
-  override def guarantees(other: Partitioning): Boolean = other match {
-    case BroadcastPartitioning => true
-    case _ => false
-  }
+  override def guarantees(other: Partitioning): Boolean = other.numPartitions == 1
 }
 
 /**
@@ -147,6 +225,12 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
     case _ => false
   }
 
+  override def compatibleWith(other: Partitioning): Boolean = other match {
+    case o: HashPartitioning =>
+      this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
+    case _ => false
+  }
+
   override def guarantees(other: Partitioning): Boolean = other match {
     case o: HashPartitioning =>
       this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
@@ -185,6 +269,11 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
     case _ => false
   }
 
+  override def compatibleWith(other: Partitioning): Boolean = other match {
+    case o: RangePartitioning => this == o
+    case _ => false
+  }
+
   override def guarantees(other: Partitioning): Boolean = other match {
     case o: RangePartitioning => this == o
     case _ => false
@@ -228,6 +317,13 @@ case class PartitioningCollection(partitionings: Seq[Partitioning])
   override def satisfies(required: Distribution): Boolean =
     partitionings.exists(_.satisfies(required))
 
+  /**
+   * Returns true if any `partitioning` of this collection is compatible with
+   * the given [[Partitioning]].
+   */
+  override def compatibleWith(other: Partitioning): Boolean =
+    partitionings.exists(_.compatibleWith(other))
+
   /**
    * Returns true if any `partitioning` of this collection guarantees
    * the given [[Partitioning]].
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 49bb729800863..b89e634761eb1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -190,66 +190,72 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
  * of input data meets the
  * [[org.apache.spark.sql.catalyst.plans.physical.Distribution Distribution]] requirements for
  * each operator by inserting [[Exchange]] Operators where required.  Also ensure that the
- * required input partition ordering requirements are met.
+ * input partition ordering requirements are met.
  */
 private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[SparkPlan] {
   // TODO: Determine the number of partitions.
-  def numPartitions: Int = sqlContext.conf.numShufflePartitions
+  private def numPartitions: Int = sqlContext.conf.numShufflePartitions
 
-  def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
-    case operator: SparkPlan =>
-      // Adds Exchange or Sort operators as required
-      def addOperatorsIfNecessary(
-          partitioning: Partitioning,
-          rowOrdering: Seq[SortOrder],
-          child: SparkPlan): SparkPlan = {
-
-        def addShuffleIfNecessary(child: SparkPlan): SparkPlan = {
-          if (!child.outputPartitioning.guarantees(partitioning)) {
-            Exchange(partitioning, child)
-          } else {
-            child
-          }
-        }
+  /**
+   * Given a required distribution, returns a partitioning that satisfies that distribution.
+   */
+  private def canonicalPartitioning(requiredDistribution: Distribution): Partitioning = {
+    requiredDistribution match {
+      case AllTuples => SinglePartition
+      case ClusteredDistribution(clustering) => HashPartitioning(clustering, numPartitions)
+      case OrderedDistribution(ordering) => RangePartitioning(ordering, numPartitions)
+      case dist => sys.error(s"Do not know how to satisfy distribution $dist")
+    }
+  }
 
-        def addSortIfNecessary(child: SparkPlan): SparkPlan = {
-
-          if (rowOrdering.nonEmpty) {
-            // If child.outputOrdering is [a, b] and rowOrdering is [a], we do not need to sort.
-            val minSize = Seq(rowOrdering.size, child.outputOrdering.size).min
-            if (minSize == 0 || rowOrdering.take(minSize) != child.outputOrdering.take(minSize)) {
-              sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
-            } else {
-              child
-            }
-          } else {
-            child
-          }
-        }
+  private def ensureDistributionAndOrdering(operator: SparkPlan): SparkPlan = {
+    val requiredChildDistributions: Seq[Distribution] = operator.requiredChildDistribution
+    val requiredChildOrderings: Seq[Seq[SortOrder]] = operator.requiredChildOrdering
+    var children: Seq[SparkPlan] = operator.children
 
-        addSortIfNecessary(addShuffleIfNecessary(child))
+    // Ensure that the operator's children satisfy their output distribution requirements:
+    children = children.zip(requiredChildDistributions).map { case (child, distribution) =>
+      if (child.outputPartitioning.satisfies(distribution)) {
+        child
+      } else {
+        Exchange(canonicalPartitioning(distribution), child)
       }
+    }
 
-      val requirements =
-        (operator.requiredChildDistribution, operator.requiredChildOrdering, operator.children)
-
-      val fixedChildren = requirements.zipped.map {
-        case (AllTuples, rowOrdering, child) =>
-          addOperatorsIfNecessary(SinglePartition, rowOrdering, child)
-        case (ClusteredDistribution(clustering), rowOrdering, child) =>
-          addOperatorsIfNecessary(HashPartitioning(clustering, numPartitions), rowOrdering, child)
-        case (OrderedDistribution(ordering), rowOrdering, child) =>
-          addOperatorsIfNecessary(RangePartitioning(ordering, numPartitions), rowOrdering, child)
-
-        case (UnspecifiedDistribution, Seq(), child) =>
+    // If the operator has multiple children and specifies child output distributions (e.g. join),
+    // then the children's output partitionings must be compatible:
+    if (children.length > 1
+        && requiredChildDistributions.toSet != Set(UnspecifiedDistribution)
+        && !Partitioning.allCompatible(children.map(_.outputPartitioning))) {
+      children = children.zip(requiredChildDistributions).map { case (child, distribution) =>
+        val targetPartitioning = canonicalPartitioning(distribution)
+        if (child.outputPartitioning.guarantees(targetPartitioning)) {
           child
-        case (UnspecifiedDistribution, rowOrdering, child) =>
-          sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
+        } else {
+          Exchange(targetPartitioning, child)
+        }
+      }
+    }
 
-        case (dist, ordering, _) =>
-          sys.error(s"Don't know how to ensure $dist with ordering $ordering")
+    // Now that we've performed any necessary shuffles, add sorts to guarantee output orderings:
+    children = children.zip(requiredChildOrderings).map { case (child, requiredOrdering) =>
+      if (requiredOrdering.nonEmpty) {
+        // If child.outputOrdering is [a, b] and requiredOrdering is [a], we do not need to sort.
+        val minSize = Seq(requiredOrdering.size, child.outputOrdering.size).min
+        if (minSize == 0 || requiredOrdering.take(minSize) != child.outputOrdering.take(minSize)) {
+          sqlContext.planner.BasicOperators.getSortOperator(requiredOrdering, global = false, child)
+        } else {
+          child
+        }
+      } else {
+        child
       }
+    }
 
-      operator.withNewChildren(fixedChildren)
+    operator.withNewChildren(children)
+  }
+
+  def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
+    case operator: SparkPlan => ensureDistributionAndOrdering(operator)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index c5d1ed0937b19..24950f26061f7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -256,6 +256,11 @@ case class Repartition(numPartitions: Int, shuffle: Boolean, child: SparkPlan)
   extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
+  override def outputPartitioning: Partitioning = {
+    if (numPartitions == 1) SinglePartition
+    else UnknownPartitioning(numPartitions)
+  }
+
   protected override def doExecute(): RDD[InternalRow] = {
     child.execute().map(_.copy()).coalesce(numPartitions, shuffle)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
index 29f3beb3cb3c8..855555dd1d4c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
@@ -21,6 +21,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.catalyst.rules.Rule
 
 /**
@@ -33,6 +34,8 @@ case class ConvertToUnsafe(child: SparkPlan) extends UnaryNode {
   require(UnsafeProjection.canSupport(child.schema), s"Cannot convert ${child.schema} to Unsafe")
 
   override def output: Seq[Attribute] = child.output
+  override def outputPartitioning: Partitioning = child.outputPartitioning
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
   override def outputsUnsafeRows: Boolean = true
   override def canProcessUnsafeRows: Boolean = false
   override def canProcessSafeRows: Boolean = true
@@ -51,6 +54,8 @@ case class ConvertToUnsafe(child: SparkPlan) extends UnaryNode {
 @DeveloperApi
 case class ConvertToSafe(child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
+  override def outputPartitioning: Partitioning = child.outputPartitioning
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
   override def outputsUnsafeRows: Boolean = false
   override def canProcessUnsafeRows: Boolean = true
   override def canProcessSafeRows: Boolean = false
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 18b0e54dc7c53..5582caa0d366e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -18,9 +18,13 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, Literal, SortOrder}
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
@@ -202,4 +206,151 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
       }
     }
   }
+
+  // --- Unit tests of EnsureRequirements ---------------------------------------------------------
+
+  // When it comes to testing whether EnsureRequirements properly ensures distribution requirements,
+  // there two dimensions that need to be considered: are the child partitionings compatible and
+  // do they satisfy the distribution requirements? As a result, we need at least four test cases.
+
+  private def assertDistributionRequirementsAreSatisfied(outputPlan: SparkPlan): Unit = {
+    if (outputPlan.children.length > 1
+        && outputPlan.requiredChildDistribution.toSet != Set(UnspecifiedDistribution)) {
+      val childPartitionings = outputPlan.children.map(_.outputPartitioning)
+      if (!Partitioning.allCompatible(childPartitionings)) {
+        fail(s"Partitionings are not compatible: $childPartitionings")
+      }
+    }
+    outputPlan.children.zip(outputPlan.requiredChildDistribution).foreach {
+      case (child, requiredDist) =>
+        assert(child.outputPartitioning.satisfies(requiredDist),
+          s"$child output partitioning does not satisfy $requiredDist:\n$outputPlan")
+    }
+  }
+
+  test("EnsureRequirements with incompatible child partitionings which satisfy distribution") {
+    // Consider an operator that requires inputs that are clustered by two expressions (e.g.
+    // sort merge join where there are multiple columns in the equi-join condition)
+    val clusteringA = Literal(1) :: Nil
+    val clusteringB = Literal(2) :: Nil
+    val distribution = ClusteredDistribution(clusteringA ++ clusteringB)
+    // Say that the left and right inputs are each partitioned by _one_ of the two join columns:
+    val leftPartitioning = HashPartitioning(clusteringA, 1)
+    val rightPartitioning = HashPartitioning(clusteringB, 1)
+    // Individually, each input's partitioning satisfies the clustering distribution:
+    assert(leftPartitioning.satisfies(distribution))
+    assert(rightPartitioning.satisfies(distribution))
+    // However, these partitionings are not compatible with each other, so we still need to
+    // repartition both inputs prior to performing the join:
+    assert(!leftPartitioning.compatibleWith(rightPartitioning))
+    assert(!rightPartitioning.compatibleWith(leftPartitioning))
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = leftPartitioning),
+        DummySparkPlan(outputPartitioning = rightPartitioning)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.isEmpty) {
+      fail(s"Exchange should have been added:\n$outputPlan")
+    }
+  }
+
+  test("EnsureRequirements with child partitionings with different numbers of output partitions") {
+    // This is similar to the previous test, except it checks that partitionings are not compatible
+    // unless they produce the same number of partitions.
+    val clustering = Literal(1) :: Nil
+    val distribution = ClusteredDistribution(clustering)
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = HashPartitioning(clustering, 1)),
+        DummySparkPlan(outputPartitioning = HashPartitioning(clustering, 2))
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+  }
+
+  test("EnsureRequirements with compatible child partitionings that do not satisfy distribution") {
+    val distribution = ClusteredDistribution(Literal(1) :: Nil)
+    // The left and right inputs have compatible partitionings but they do not satisfy the
+    // distribution because they are clustered on different columns. Thus, we need to shuffle.
+    val childPartitioning = HashPartitioning(Literal(2) :: Nil, 1)
+    assert(!childPartitioning.satisfies(distribution))
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = childPartitioning),
+        DummySparkPlan(outputPartitioning = childPartitioning)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.isEmpty) {
+      fail(s"Exchange should have been added:\n$outputPlan")
+    }
+  }
+
+  test("EnsureRequirements with compatible child partitionings that satisfy distribution") {
+    // In this case, all requirements are satisfied and no exchange should be added.
+    val distribution = ClusteredDistribution(Literal(1) :: Nil)
+    val childPartitioning = HashPartitioning(Literal(1) :: Nil, 5)
+    assert(childPartitioning.satisfies(distribution))
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = childPartitioning),
+        DummySparkPlan(outputPartitioning = childPartitioning)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.nonEmpty) {
+      fail(s"Exchange should not have been added:\n$outputPlan")
+    }
+  }
+
+  // This is a regression test for SPARK-9703
+  test("EnsureRequirements should not repartition if only ordering requirement is unsatisfied") {
+    // Consider an operator that imposes both output distribution and  ordering requirements on its
+    // children, such as sort sort merge join. If the distribution requirements are satisfied but
+    // the output ordering requirements are unsatisfied, then the planner should only add sorts and
+    // should not need to add additional shuffles / exchanges.
+    val outputOrdering = Seq(SortOrder(Literal(1), Ascending))
+    val distribution = ClusteredDistribution(Literal(1) :: Nil)
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = SinglePartition),
+        DummySparkPlan(outputPartitioning = SinglePartition)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(outputOrdering, outputOrdering)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.nonEmpty) {
+      fail(s"No Exchanges should have been added:\n$outputPlan")
+    }
+  }
+
+  // ---------------------------------------------------------------------------------------------
+}
+
+// Used for unit-testing EnsureRequirements
+private case class DummySparkPlan(
+    override val children: Seq[SparkPlan] = Nil,
+    override val outputOrdering: Seq[SortOrder] = Nil,
+    override val outputPartitioning: Partitioning = UnknownPartitioning(0),
+    override val requiredChildDistribution: Seq[Distribution] = Nil,
+    override val requiredChildOrdering: Seq[Seq[SortOrder]] = Nil
+  ) extends SparkPlan {
+  override protected def doExecute(): RDD[InternalRow] = throw new NotImplementedError
+  override def output: Seq[Attribute] = Seq.empty
 }

From 46025616b414eaf1da01fcc1255d8041ea1554bc Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Sun, 9 Aug 2015 14:30:30 -0700
Subject: [PATCH 0945/1454] [CORE] [SPARK-9760] Use Option instead of Some for
 Ivy repos

This was introduced in #7599

cc rxin brkyvz

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8055 from shivaram/spark-packages-repo-fix and squashes the following commits:

890f306 [Shivaram Venkataraman] Remove test case
51d69ee [Shivaram Venkataraman] Add test case for --packages without --repository
c02e0b4 [Shivaram Venkataraman] Use Option instead of Some for Ivy repos
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 1186bed485250..7ac6cbce4cd1d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -284,7 +284,7 @@ object SparkSubmit {
         Nil
       }
     val resolvedMavenCoordinates = SparkSubmitUtils.resolveMavenCoordinates(args.packages,
-      Some(args.repositories), Some(args.ivyRepoPath), exclusions = exclusions)
+      Option(args.repositories), Option(args.ivyRepoPath), exclusions = exclusions)
     if (!StringUtils.isBlank(resolvedMavenCoordinates)) {
       args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates)
       if (args.isPython) {

From be80def0d07ed0f45d60453f4f82500d8c4c9106 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Sun, 9 Aug 2015 22:33:53 -0700
Subject: [PATCH 0946/1454] [SPARK-9777] [SQL] Window operator can accept
 UnsafeRows

https://issues.apache.org/jira/browse/SPARK-9777

Author: Yin Huai <yhuai@databricks.com>

Closes #8064 from yhuai/windowUnsafe and squashes the following commits:

8fb3537 [Yin Huai] Set canProcessUnsafeRows to true.
---
 .../src/main/scala/org/apache/spark/sql/execution/Window.scala  | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
index fe9f2c7028171..0269d6d4b7a1c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -101,6 +101,8 @@ case class Window(
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
 
+  override def canProcessUnsafeRows: Boolean = true
+
   /**
    * Create a bound ordering object for a given frame type and offset. A bound ordering object is
    * used to determine which input row lies within the frame boundaries of an output row.

From e3fef0f9e17b1766a3869cb80ce7e4cd521cb7b6 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 10 Aug 2015 09:07:08 -0700
Subject: [PATCH 0947/1454] [SPARK-9743] [SQL] Fixes JSONRelation refreshing

PR #7696 added two `HadoopFsRelation.refresh()` calls ([this] [1], and [this] [2]) in `DataSourceStrategy` to make test case `InsertSuite.save directly to the path of a JSON table` pass. However, this forces every `HadoopFsRelation` table scan to do a refresh, which can be super expensive for tables with large number of partitions.

The reason why the original test case fails without the `refresh()` calls is that, the old JSON relation builds the base RDD with the input paths, while `HadoopFsRelation` provides `FileStatus`es of leaf files. With the old JSON relation, we can create a temporary table based on a path, writing data to that, and then read newly written data without refreshing the table. This is no long true for `HadoopFsRelation`.

This PR removes those two expensive refresh calls, and moves the refresh into `JSONRelation` to fix this issue. We might want to update `HadoopFsRelation` interface to provide better support for this use case.

[1]: https://github.com/apache/spark/blob/ebfd91c542aaead343cb154277fcf9114382fee7/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala#L63
[2]: https://github.com/apache/spark/blob/ebfd91c542aaead343cb154277fcf9114382fee7/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala#L91

Author: Cheng Lian <lian@databricks.com>

Closes #8035 from liancheng/spark-9743/fix-json-relation-refreshing and squashes the following commits:

ec1957d [Cheng Lian] Fixes JSONRelation refreshing
---
 .../datasources/DataSourceStrategy.scala      |  2 --
 .../apache/spark/sql/json/JSONRelation.scala  | 19 +++++++++++++++----
 .../apache/spark/sql/sources/interfaces.scala |  2 +-
 .../spark/sql/sources/InsertSuite.scala       | 10 +++++-----
 4 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 5b5fa8c93ec52..78a4acdf4b1bf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -60,7 +60,6 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     // Scanning partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation))
         if t.partitionSpec.partitionColumns.nonEmpty =>
-      t.refresh()
       val selectedPartitions = prunePartitions(filters, t.partitionSpec).toArray
 
       logInfo {
@@ -88,7 +87,6 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
 
     // Scanning non-partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation)) =>
-      t.refresh()
       // See buildPartitionedTableScan for the reason that we need to create a shard
       // broadcast HadoopConf.
       val sharedHadoopConf = SparkHadoopUtil.get.conf
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index b34a272ec547f..5bb9e62310a50 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -22,20 +22,22 @@ import java.io.CharArrayWriter
 import com.fasterxml.jackson.core.JsonFactory
 import com.google.common.base.Objects
 import org.apache.hadoop.fs.{FileStatus, Path}
-import org.apache.hadoop.io.{Text, LongWritable, NullWritable}
+import org.apache.hadoop.io.{LongWritable, NullWritable, Text}
 import org.apache.hadoop.mapred.{JobConf, TextInputFormat}
-import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
-import org.apache.hadoop.mapreduce.{RecordWriter, TaskAttemptContext, Job}
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
+import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
+import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
+
 import org.apache.spark.Logging
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
-
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
+import org.apache.spark.util.SerializableConfiguration
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
@@ -108,6 +110,15 @@ private[sql] class JSONRelation(
     jsonSchema
   }
 
+  override private[sql] def buildScan(
+      requiredColumns: Array[String],
+      filters: Array[Filter],
+      inputPaths: Array[String],
+      broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = {
+    refresh()
+    super.buildScan(requiredColumns, filters, inputPaths, broadcastedConf)
+  }
+
   override def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 4aafec0e2df27..6bcabbab4f77b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -555,7 +555,7 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
     })
   }
 
-  private[sql] final def buildScan(
+  private[sql] def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
       inputPaths: Array[String],
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 39d18d712ef8c..cdbfaf6455fe4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -32,9 +32,9 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
 
   var path: File = null
 
-  override def beforeAll: Unit = {
+  override def beforeAll(): Unit = {
     path = Utils.createTempDir()
-    val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""))
+    val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
     caseInsensitiveContext.read.json(rdd).registerTempTable("jt")
     sql(
       s"""
@@ -46,7 +46,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
       """.stripMargin)
   }
 
-  override def afterAll: Unit = {
+  override def afterAll(): Unit = {
     caseInsensitiveContext.dropTempTable("jsonTable")
     caseInsensitiveContext.dropTempTable("jt")
     Utils.deleteRecursively(path)
@@ -110,7 +110,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
     )
 
     // Writing the table to less part files.
-    val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 5)
+    val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""), 5)
     caseInsensitiveContext.read.json(rdd1).registerTempTable("jt1")
     sql(
       s"""
@@ -122,7 +122,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
     )
 
     // Writing the table to more part files.
-    val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 10)
+    val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""), 10)
     caseInsensitiveContext.read.json(rdd2).registerTempTable("jt2")
     sql(
       s"""

From 0f3366a4c740147a7a7519922642912e2dd238f8 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 10 Aug 2015 10:10:40 -0700
Subject: [PATCH 0948/1454] [SPARK-9710] [TEST] Fix RPackageUtilsSuite when R
 is not available.

RUtils.isRInstalled throws an exception if R is not installed,
instead of returning false. Fix that.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8008 from vanzin/SPARK-9710 and squashes the following commits:

df72d8c [Marcelo Vanzin] [SPARK-9710] [test] Fix RPackageUtilsSuite when R is not available.
---
 core/src/main/scala/org/apache/spark/api/r/RUtils.scala | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
index 93b3bea578676..427b2bc7cbcbb 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
@@ -67,7 +67,11 @@ private[spark] object RUtils {
 
   /** Check if R is installed before running tests that use R commands. */
   def isRInstalled: Boolean = {
-    val builder = new ProcessBuilder(Seq("R", "--version"))
-    builder.start().waitFor() == 0
+    try {
+      val builder = new ProcessBuilder(Seq("R", "--version"))
+      builder.start().waitFor() == 0
+    } catch {
+      case e: Exception => false
+    }
   }
 }

From 00b655cced637e1c3b750c19266086b9dcd7c158 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 10 Aug 2015 11:01:45 -0700
Subject: [PATCH 0949/1454] [SPARK-9755] [MLLIB] Add docs to
 MultivariateOnlineSummarizer methods

Adds method documentations back to `MultivariateOnlineSummarizer`, which were present in 1.4 but disappeared somewhere along the way to 1.5.

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8045 from feynmanliang/SPARK-9755 and squashes the following commits:

af67fde [Feynman Liang] Add MultivariateOnlineSummarizer docs
---
 .../stat/MultivariateOnlineSummarizer.scala      | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index 62da9f2ef22a3..64e4be0ebb97e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -153,6 +153,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Sample mean of each dimension.
+   *
    * @since 1.1.0
    */
   override def mean: Vector = {
@@ -168,6 +170,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Sample variance of each dimension.
+   *
    * @since 1.1.0
    */
   override def variance: Vector = {
@@ -193,11 +197,15 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Sample size.
+   *
    * @since 1.1.0
    */
   override def count: Long = totalCnt
 
   /**
+   * Number of nonzero elements in each dimension.
+   *
    * @since 1.1.0
    */
   override def numNonzeros: Vector = {
@@ -207,6 +215,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Maximum value of each dimension.
+   *
    * @since 1.1.0
    */
   override def max: Vector = {
@@ -221,6 +231,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Minimum value of each dimension.
+   *
    * @since 1.1.0
    */
   override def min: Vector = {
@@ -235,6 +247,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * L2 (Euclidian) norm of each dimension.
+   *
    * @since 1.2.0
    */
   override def normL2: Vector = {
@@ -252,6 +266,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * L1 norm of each dimension.
+   *
    * @since 1.2.0
    */
   override def normL1: Vector = {

From d285212756168200383bf4df2c951bd80a492a7c Mon Sep 17 00:00:00 2001
From: Mahmoud Lababidi <lababidi@gmail.com>
Date: Mon, 10 Aug 2015 13:02:01 -0700
Subject: [PATCH 0950/1454] Fixed AtmoicReference<> Example

Author: Mahmoud Lababidi <lababidi@gmail.com>

Closes #8076 from lababidi/master and squashes the following commits:

af4553b [Mahmoud Lababidi] Fixed AtmoicReference<> Example
---
 docs/streaming-kafka-integration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md
index 775d508d4879b..7571e22575efd 100644
--- a/docs/streaming-kafka-integration.md
+++ b/docs/streaming-kafka-integration.md
@@ -152,7 +152,7 @@ Next, we discuss how to use this approach in your streaming application.
 	</div>
 	<div data-lang="java" markdown="1">
 		// Hold a reference to the current offset ranges, so it can be used downstream
-		final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference();
+		final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference<>();
 		
 		directKafkaStream.transformToPair(
 		  new Function<JavaPairRDD<String, String>, JavaPairRDD<String, String>>() {

From 0fe66744f16854fc8cd8a72174de93a788e3cf6c Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 10 Aug 2015 13:05:03 -0700
Subject: [PATCH 0951/1454] [SPARK-9784] [SQL] Exchange.isUnsafe should check
 whether codegen and unsafe are enabled

Exchange.isUnsafe should check whether codegen and unsafe are enabled.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8073 from JoshRosen/SPARK-9784 and squashes the following commits:

7a1019f [Josh Rosen] [SPARK-9784] Exchange.isUnsafe should check whether codegen and unsafe are enabled
---
 .../main/scala/org/apache/spark/sql/execution/Exchange.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index b89e634761eb1..029f2264a6a27 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -46,7 +46,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
    * Returns true iff we can support the data type, and we are not doing range partitioning.
    */
   private lazy val tungstenMode: Boolean = {
-    GenerateUnsafeProjection.canSupport(child.schema) &&
+    unsafeEnabled && codegenEnabled && GenerateUnsafeProjection.canSupport(child.schema) &&
       !newPartitioning.isInstanceOf[RangePartitioning]
   }
 

From 40ed2af587cedadc6e5249031857a922b3b234ca Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 10 Aug 2015 13:49:23 -0700
Subject: [PATCH 0952/1454] [SPARK-9763][SQL] Minimize exposure of internal SQL
 classes.

There are a few changes in this pull request:

1. Moved all data sources to execution.datasources, except the public JDBC APIs.
2. In order to maintain backward compatibility from 1, added a backward compatibility translation map in data source resolution.
3. Moved ui and metric package into execution.
4. Added more documentation on some internal classes.
5. Renamed DataSourceRegister.format -> shortName.
6. Added "override" modifier on shortName.
7. Removed IntSQLMetric.

Author: Reynold Xin <rxin@databricks.com>

Closes #8056 from rxin/SPARK-9763 and squashes the following commits:

9df4801 [Reynold Xin] Removed hardcoded name in test cases.
d9babc6 [Reynold Xin] Shorten.
e484419 [Reynold Xin] Removed VisibleForTesting.
171b812 [Reynold Xin] MimaExcludes.
2041389 [Reynold Xin] Compile ...
79dda42 [Reynold Xin] Compile.
0818ba3 [Reynold Xin] Removed IntSQLMetric.
c46884f [Reynold Xin] Two more fixes.
f9aa88d [Reynold Xin] [SPARK-9763][SQL] Minimize exposure of internal SQL classes.
---
 project/MimaExcludes.scala                    |  24 +-
 ...pache.spark.sql.sources.DataSourceRegister |   6 +-
 .../ui/static/spark-sql-viz.css               |   0
 .../ui/static/spark-sql-viz.js                |   0
 .../org/apache/spark/sql/DataFrame.scala      |   2 +-
 .../apache/spark/sql/DataFrameReader.scala    |   6 +-
 .../apache/spark/sql/DataFrameWriter.scala    |   6 +-
 .../org/apache/spark/sql/SQLContext.scala     |   2 +-
 .../spark/sql/execution/SQLExecution.scala    |   2 +-
 .../spark/sql/execution/SparkPlan.scala       |   8 +-
 .../spark/sql/execution/basicOperators.scala  |   2 +-
 .../sql/execution/datasources/DDLParser.scala | 185 +++++++++
 .../execution/datasources/DefaultSource.scala |  64 ++++
 .../datasources/InsertIntoDataSource.scala    |  23 +-
 .../datasources/ResolvedDataSource.scala      | 204 ++++++++++
 .../spark/sql/execution/datasources/ddl.scala | 352 +-----------------
 .../datasources/jdbc/DefaultSource.scala      |  62 +++
 .../datasources/jdbc/DriverRegistry.scala     |  60 +++
 .../datasources/jdbc/DriverWrapper.scala      |  48 +++
 .../datasources}/jdbc/JDBCRDD.scala           |   9 +-
 .../datasources}/jdbc/JDBCRelation.scala      |  41 +-
 .../datasources/jdbc/JdbcUtils.scala          | 219 +++++++++++
 .../datasources}/json/InferSchema.scala       |   4 +-
 .../datasources}/json/JSONRelation.scala      |   7 +-
 .../datasources}/json/JacksonGenerator.scala  |   2 +-
 .../datasources}/json/JacksonParser.scala     |   4 +-
 .../datasources}/json/JacksonUtils.scala      |   2 +-
 .../parquet/CatalystReadSupport.scala         |   2 +-
 .../parquet/CatalystRecordMaterializer.scala  |   2 +-
 .../parquet/CatalystRowConverter.scala        |   2 +-
 .../parquet/CatalystSchemaConverter.scala     |   4 +-
 .../DirectParquetOutputCommitter.scala        |   2 +-
 .../parquet/ParquetConverter.scala            |   2 +-
 .../datasources}/parquet/ParquetFilters.scala |   2 +-
 .../parquet/ParquetRelation.scala             |   4 +-
 .../parquet/ParquetTableSupport.scala         |   2 +-
 .../parquet/ParquetTypesConverter.scala       |   2 +-
 .../{ => execution}/metric/SQLMetrics.scala   |  36 +-
 .../apache/spark/sql/execution/package.scala  |   8 +-
 .../ui/AllExecutionsPage.scala                |   2 +-
 .../{ => execution}/ui/ExecutionPage.scala    |   2 +-
 .../sql/{ => execution}/ui/SQLListener.scala  |   7 +-
 .../spark/sql/{ => execution}/ui/SQLTab.scala |   6 +-
 .../{ => execution}/ui/SparkPlanGraph.scala   |   4 +-
 .../org/apache/spark/sql/jdbc/JdbcUtils.scala |  52 ---
 .../org/apache/spark/sql/jdbc/jdbc.scala      | 250 -------------
 .../apache/spark/sql/sources/interfaces.scala |  15 +-
 .../parquet/test/avro/CompatibilityTest.java  |   4 +-
 .../parquet/test/avro/Nested.java             |  30 +-
 .../parquet/test/avro/ParquetAvroCompat.java  | 106 +++---
 .../org/apache/spark/sql/DataFrameSuite.scala |   4 +-
 .../datasources}/json/JsonSuite.scala         |   4 +-
 .../datasources}/json/TestJsonData.scala      |   2 +-
 .../ParquetAvroCompatibilitySuite.scala       |   4 +-
 .../parquet/ParquetCompatibilityTest.scala    |   2 +-
 .../parquet/ParquetFilterSuite.scala          |   2 +-
 .../datasources}/parquet/ParquetIOSuite.scala |   4 +-
 .../ParquetPartitionDiscoverySuite.scala      |   2 +-
 .../parquet/ParquetQuerySuite.scala           |   2 +-
 .../parquet/ParquetSchemaSuite.scala          |   2 +-
 .../datasources}/parquet/ParquetTest.scala    |   2 +-
 .../ParquetThriftCompatibilitySuite.scala     |   2 +-
 .../metric/SQLMetricsSuite.scala              |  12 +-
 .../{ => execution}/ui/SQLListenerSuite.scala |   4 +-
 .../sources/CreateTableAsSelectSuite.scala    |  20 +-
 .../sql/sources/DDLSourceLoadSuite.scala      |  59 +--
 .../sql/sources/ResolvedDataSourceSuite.scala |  39 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |   2 +-
 .../spark/sql/hive/orc/OrcRelation.scala      |   6 +-
 .../spark/sql/hive/HiveParquetSuite.scala     |   2 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |   2 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |   2 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |   2 +-
 .../apache/spark/sql/hive/parquetSuites.scala |   2 +-
 .../ParquetHadoopFsRelationSuite.scala        |   4 +-
 .../SimpleTextHadoopFsRelationSuite.scala     |   2 +-
 76 files changed, 1114 insertions(+), 966 deletions(-)
 rename sql/core/src/main/resources/org/apache/spark/sql/{ => execution}/ui/static/spark-sql-viz.css (100%)
 rename sql/core/src/main/resources/org/apache/spark/sql/{ => execution}/ui/static/spark-sql-viz.js (100%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/jdbc/JDBCRDD.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/jdbc/JDBCRelation.scala (71%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/InferSchema.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JSONRelation.scala (97%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JacksonGenerator.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JacksonParser.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JacksonUtils.scala (95%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystReadSupport.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystRecordMaterializer.scala (96%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystRowConverter.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystSchemaConverter.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/DirectParquetOutputCommitter.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetConverter.scala (96%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetFilters.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetRelation.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetTableSupport.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetTypesConverter.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/metric/SQLMetrics.scala (77%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/AllExecutionsPage.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/ExecutionPage.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/SQLListener.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/SQLTab.scala (90%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/SparkPlanGraph.scala (97%)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
 rename sql/core/src/test/gen-java/org/apache/spark/sql/{ => execution/datasources}/parquet/test/avro/CompatibilityTest.java (93%)
 rename sql/core/src/test/gen-java/org/apache/spark/sql/{ => execution/datasources}/parquet/test/avro/Nested.java (78%)
 rename sql/core/src/test/gen-java/org/apache/spark/sql/{ => execution/datasources}/parquet/test/avro/ParquetAvroCompat.java (83%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/json/JsonSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/json/TestJsonData.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetAvroCompatibilitySuite.scala (96%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetCompatibilityTest.scala (97%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetFilterSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetIOSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetPartitionDiscoverySuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetQuerySuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetSchemaSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetTest.scala (98%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetThriftCompatibilitySuite.scala (98%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution}/metric/SQLMetricsSuite.scala (92%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution}/ui/SQLListenerSuite.scala (99%)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index b60ae784c3798..90261ca3d61aa 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -62,8 +62,6 @@ object MimaExcludes {
               "org.apache.spark.ml.classification.LogisticCostFun.this"),
             // SQL execution is considered private.
             excludePackage("org.apache.spark.sql.execution"),
-            // Parquet support is considered private.
-            excludePackage("org.apache.spark.sql.parquet"),
             // The old JSON RDD is removed in favor of streaming Jackson
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
@@ -155,7 +153,27 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException")
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException"),
+            // SPARK-9763 Minimize exposure of internal SQL classes
+            excludePackage("org.apache.spark.sql.parquet"),
+            excludePackage("org.apache.spark.sql.json"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$JDBCConversion"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$DriverWrapper"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DefaultSource"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation")
           ) ++ Seq(
             // SPARK-4751 Dynamic allocation for standalone mode
             ProblemFilters.exclude[MissingMethodProblem](
diff --git a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index cc32d4b72748e..ca50000b4756e 100644
--- a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,3 +1,3 @@
-org.apache.spark.sql.jdbc.DefaultSource
-org.apache.spark.sql.json.DefaultSource
-org.apache.spark.sql.parquet.DefaultSource
+org.apache.spark.sql.execution.datasources.jdbc.DefaultSource
+org.apache.spark.sql.execution.datasources.json.DefaultSource
+org.apache.spark.sql.execution.datasources.parquet.DefaultSource
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css b/sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.css
similarity index 100%
rename from sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
rename to sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.css
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js b/sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.js
similarity index 100%
rename from sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
rename to sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.js
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 570b8b2d5928d..27b994f1f0caf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -39,7 +39,7 @@ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
 import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
-import org.apache.spark.sql.json.JacksonGenerator
+import org.apache.spark.sql.execution.datasources.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.storage.StorageLevel
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 85f33c5e99523..9ea955b010017 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -25,10 +25,10 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.execution.datasources.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation}
+import org.apache.spark.sql.execution.datasources.json.JSONRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.execution.datasources.{LogicalRelation, ResolvedDataSource}
-import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation}
-import org.apache.spark.sql.json.JSONRelation
-import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.{Logging, Partition}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 2a4992db09bc2..5fa11da4c38cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -23,8 +23,8 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.{SqlParser, TableIdentifier}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.plans.logical.InsertIntoTable
+import org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, ResolvedDataSource}
-import org.apache.spark.sql.jdbc.{JDBCWriteDetails, JdbcUtils}
 import org.apache.spark.sql.sources.HadoopFsRelation
 
 
@@ -264,7 +264,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
 
       // Create the table if the table didn't exist.
       if (!tableExists) {
-        val schema = JDBCWriteDetails.schemaString(df, url)
+        val schema = JdbcUtils.schemaString(df, url)
         val sql = s"CREATE TABLE $table ($schema)"
         conn.prepareStatement(sql).executeUpdate()
       }
@@ -272,7 +272,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
       conn.close()
     }
 
-    JDBCWriteDetails.saveTable(df, url, table, connectionProperties)
+    JdbcUtils.saveTable(df, url, table, connectionProperties)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 832572571cabd..f73bb0488c984 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -43,7 +43,7 @@ import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.ui.{SQLListener, SQLTab}
+import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
index 97f1323e97835..cee58218a885b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
@@ -21,7 +21,7 @@ import java.util.concurrent.atomic.AtomicLong
 
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.ui.SparkPlanGraph
+import org.apache.spark.sql.execution.ui.SparkPlanGraph
 import org.apache.spark.util.Utils
 
 private[sql] object SQLExecution {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 1915496d16205..9ba5cf2d2b39e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.metric.{IntSQLMetric, LongSQLMetric, SQLMetric, SQLMetrics}
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetric, SQLMetrics}
 import org.apache.spark.sql.types.DataType
 
 object SparkPlan {
@@ -98,12 +98,6 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
    */
   private[sql] def metrics: Map[String, SQLMetric[_, _]] = defaultMetrics
 
-  /**
-   * Return a IntSQLMetric according to the name.
-   */
-  private[sql] def intMetric(name: String): IntSQLMetric =
-    metrics(name).asInstanceOf[IntSQLMetric]
-
   /**
    * Return a LongSQLMetric according to the name.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 24950f26061f7..bf2de244c8e4a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.metric.SQLMetrics
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.ExternalSorter
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
new file mode 100644
index 0000000000000..6c462fa30461b
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
@@ -0,0 +1,185 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources
+
+import scala.language.implicitConversions
+import scala.util.matching.Regex
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.SaveMode
+import org.apache.spark.sql.catalyst.{TableIdentifier, AbstractSparkSQLParser}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.types._
+
+
+/**
+ * A parser for foreign DDL commands.
+ */
+class DDLParser(parseQuery: String => LogicalPlan)
+  extends AbstractSparkSQLParser with DataTypeParser with Logging {
+
+  def parse(input: String, exceptionOnError: Boolean): LogicalPlan = {
+    try {
+      parse(input)
+    } catch {
+      case ddlException: DDLException => throw ddlException
+      case _ if !exceptionOnError => parseQuery(input)
+      case x: Throwable => throw x
+    }
+  }
+
+  // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword`
+  // properties via reflection the class in runtime for constructing the SqlLexical object
+  protected val CREATE = Keyword("CREATE")
+  protected val TEMPORARY = Keyword("TEMPORARY")
+  protected val TABLE = Keyword("TABLE")
+  protected val IF = Keyword("IF")
+  protected val NOT = Keyword("NOT")
+  protected val EXISTS = Keyword("EXISTS")
+  protected val USING = Keyword("USING")
+  protected val OPTIONS = Keyword("OPTIONS")
+  protected val DESCRIBE = Keyword("DESCRIBE")
+  protected val EXTENDED = Keyword("EXTENDED")
+  protected val AS = Keyword("AS")
+  protected val COMMENT = Keyword("COMMENT")
+  protected val REFRESH = Keyword("REFRESH")
+
+  protected lazy val ddl: Parser[LogicalPlan] = createTable | describeTable | refreshTable
+
+  protected def start: Parser[LogicalPlan] = ddl
+
+  /**
+   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
+   * USING org.apache.spark.sql.avro
+   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
+   * or
+   * `CREATE [TEMPORARY] TABLE avroTable(intField int, stringField string...) [IF NOT EXISTS]
+   * USING org.apache.spark.sql.avro
+   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
+   * or
+   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
+   * USING org.apache.spark.sql.avro
+   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
+   * AS SELECT ...
+   */
+  protected lazy val createTable: Parser[LogicalPlan] = {
+    // TODO: Support database.table.
+    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident ~
+      tableCols.? ~ (USING ~> className) ~ (OPTIONS ~> options).? ~ (AS ~> restInput).? ^^ {
+      case temp ~ allowExisting ~ tableName ~ columns ~ provider ~ opts ~ query =>
+        if (temp.isDefined && allowExisting.isDefined) {
+          throw new DDLException(
+            "a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause.")
+        }
+
+        val options = opts.getOrElse(Map.empty[String, String])
+        if (query.isDefined) {
+          if (columns.isDefined) {
+            throw new DDLException(
+              "a CREATE TABLE AS SELECT statement does not allow column definitions.")
+          }
+          // When IF NOT EXISTS clause appears in the query, the save mode will be ignore.
+          val mode = if (allowExisting.isDefined) {
+            SaveMode.Ignore
+          } else if (temp.isDefined) {
+            SaveMode.Overwrite
+          } else {
+            SaveMode.ErrorIfExists
+          }
+
+          val queryPlan = parseQuery(query.get)
+          CreateTableUsingAsSelect(tableName,
+            provider,
+            temp.isDefined,
+            Array.empty[String],
+            mode,
+            options,
+            queryPlan)
+        } else {
+          val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields)))
+          CreateTableUsing(
+            tableName,
+            userSpecifiedSchema,
+            provider,
+            temp.isDefined,
+            options,
+            allowExisting.isDefined,
+            managedIfNoPath = false)
+        }
+    }
+  }
+
+  protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")"
+
+  /*
+   * describe [extended] table avroTable
+   * This will display all columns of table `avroTable` includes column_name,column_type,comment
+   */
+  protected lazy val describeTable: Parser[LogicalPlan] =
+    (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident  ^^ {
+      case e ~ db ~ tbl =>
+        val tblIdentifier = db match {
+          case Some(dbName) =>
+            Seq(dbName, tbl)
+          case None =>
+            Seq(tbl)
+        }
+        DescribeCommand(UnresolvedRelation(tblIdentifier, None), e.isDefined)
+    }
+
+  protected lazy val refreshTable: Parser[LogicalPlan] =
+    REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ {
+      case maybeDatabaseName ~ tableName =>
+        RefreshTable(TableIdentifier(tableName, maybeDatabaseName))
+    }
+
+  protected lazy val options: Parser[Map[String, String]] =
+    "(" ~> repsep(pair, ",") <~ ")" ^^ { case s: Seq[(String, String)] => s.toMap }
+
+  protected lazy val className: Parser[String] = repsep(ident, ".") ^^ { case s => s.mkString(".")}
+
+  override implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch(
+    s"identifier matching regex $regex", {
+      case lexical.Identifier(str) if regex.unapplySeq(str).isDefined => str
+      case lexical.Keyword(str) if regex.unapplySeq(str).isDefined => str
+    }
+  )
+
+  protected lazy val optionPart: Parser[String] = "[_a-zA-Z][_a-zA-Z0-9]*".r ^^ {
+    case name => name
+  }
+
+  protected lazy val optionName: Parser[String] = repsep(optionPart, ".") ^^ {
+    case parts => parts.mkString(".")
+  }
+
+  protected lazy val pair: Parser[(String, String)] =
+    optionName ~ stringLit ^^ { case k ~ v => (k, v) }
+
+  protected lazy val column: Parser[StructField] =
+    ident ~ dataType ~ (COMMENT ~> stringLit).?  ^^ { case columnName ~ typ ~ cm =>
+      val meta = cm match {
+        case Some(comment) =>
+          new MetadataBuilder().putString(COMMENT.str.toLowerCase, comment).build()
+        case None => Metadata.empty
+      }
+
+      StructField(columnName, typ, nullable = true, meta)
+    }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
new file mode 100644
index 0000000000000..6e4cc4de7f651
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
@@ -0,0 +1,64 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources
+
+import java.util.Properties
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.execution.datasources.jdbc.{JDBCRelation, JDBCPartitioningInfo, DriverRegistry}
+import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
+
+
+class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  override def shortName(): String = "jdbc"
+
+  /** Returns a new base relation with the given parameters. */
+  override def createRelation(
+      sqlContext: SQLContext,
+      parameters: Map[String, String]): BaseRelation = {
+    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
+    val driver = parameters.getOrElse("driver", null)
+    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
+    val partitionColumn = parameters.getOrElse("partitionColumn", null)
+    val lowerBound = parameters.getOrElse("lowerBound", null)
+    val upperBound = parameters.getOrElse("upperBound", null)
+    val numPartitions = parameters.getOrElse("numPartitions", null)
+
+    if (driver != null) DriverRegistry.register(driver)
+
+    if (partitionColumn != null
+      && (lowerBound == null || upperBound == null || numPartitions == null)) {
+      sys.error("Partitioning incompletely specified")
+    }
+
+    val partitionInfo = if (partitionColumn == null) {
+      null
+    } else {
+      JDBCPartitioningInfo(
+        partitionColumn,
+        lowerBound.toLong,
+        upperBound.toLong,
+        numPartitions.toInt)
+    }
+    val parts = JDBCRelation.columnPartition(partitionInfo)
+    val properties = new Properties() // Additional properties that we will pass to getConnection
+    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
+    JDBCRelation(url, table, parts, properties)(sqlContext)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
index 6ccde7693bd34..3b7dc2e8d0210 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
@@ -17,27 +17,10 @@
 
 package org.apache.spark.sql.execution.datasources
 
-import java.io.IOException
-import java.util.{Date, UUID}
-
-import scala.collection.JavaConversions.asScalaIterator
-
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
-import org.apache.spark._
-import org.apache.spark.mapred.SparkHadoopMapRedUtil
-import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
-import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.StringType
-import org.apache.spark.util.{Utils, SerializableConfiguration}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.RunnableCommand
+import org.apache.spark.sql.sources.InsertableRelation
 
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
new file mode 100644
index 0000000000000..7770bbd712f04
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
@@ -0,0 +1,204 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources
+
+import java.util.ServiceLoader
+
+import scala.collection.JavaConversions._
+import scala.language.{existentials, implicitConversions}
+import scala.util.{Success, Failure, Try}
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.Logging
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.{DataFrame, SaveMode, AnalysisException, SQLContext}
+import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types.{CalendarIntervalType, StructType}
+import org.apache.spark.util.Utils
+
+
+case class ResolvedDataSource(provider: Class[_], relation: BaseRelation)
+
+
+object ResolvedDataSource extends Logging {
+
+  /** A map to maintain backward compatibility in case we move data sources around. */
+  private val backwardCompatibilityMap = Map(
+    "org.apache.spark.sql.jdbc" -> classOf[jdbc.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.jdbc.DefaultSource" -> classOf[jdbc.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.json" -> classOf[json.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.json.DefaultSource" -> classOf[json.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.parquet" -> classOf[parquet.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.parquet.DefaultSource" -> classOf[parquet.DefaultSource].getCanonicalName
+  )
+
+  /** Given a provider name, look up the data source class definition. */
+  def lookupDataSource(provider0: String): Class[_] = {
+    val provider = backwardCompatibilityMap.getOrElse(provider0, provider0)
+    val provider2 = s"$provider.DefaultSource"
+    val loader = Utils.getContextOrSparkClassLoader
+    val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
+
+    serviceLoader.iterator().filter(_.shortName().equalsIgnoreCase(provider)).toList match {
+      /** the provider format did not match any given registered aliases */
+      case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
+        case Success(dataSource) => dataSource
+        case Failure(error) =>
+          if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
+            throw new ClassNotFoundException(
+              "The ORC data source must be used with Hive support enabled.", error)
+          } else {
+            throw new ClassNotFoundException(
+              s"Failed to load class for data source: $provider.", error)
+          }
+      }
+      /** there is exactly one registered alias */
+      case head :: Nil => head.getClass
+      /** There are multiple registered aliases for the input */
+      case sources => sys.error(s"Multiple sources found for $provider, " +
+        s"(${sources.map(_.getClass.getName).mkString(", ")}), " +
+        "please specify the fully qualified class name.")
+    }
+  }
+
+  /** Create a [[ResolvedDataSource]] for reading data in. */
+  def apply(
+      sqlContext: SQLContext,
+      userSpecifiedSchema: Option[StructType],
+      partitionColumns: Array[String],
+      provider: String,
+      options: Map[String, String]): ResolvedDataSource = {
+    val clazz: Class[_] = lookupDataSource(provider)
+    def className: String = clazz.getCanonicalName
+    val relation = userSpecifiedSchema match {
+      case Some(schema: StructType) => clazz.newInstance() match {
+        case dataSource: SchemaRelationProvider =>
+          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema)
+        case dataSource: HadoopFsRelationProvider =>
+          val maybePartitionsSchema = if (partitionColumns.isEmpty) {
+            None
+          } else {
+            Some(partitionColumnsSchema(schema, partitionColumns))
+          }
+
+          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
+          val paths = {
+            val patternPath = new Path(caseInsensitiveOptions("path"))
+            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
+          }
+
+          val dataSchema =
+            StructType(schema.filterNot(f => partitionColumns.contains(f.name))).asNullable
+
+          dataSource.createRelation(
+            sqlContext,
+            paths,
+            Some(dataSchema),
+            maybePartitionsSchema,
+            caseInsensitiveOptions)
+        case dataSource: org.apache.spark.sql.sources.RelationProvider =>
+          throw new AnalysisException(s"$className does not allow user-specified schemas.")
+        case _ =>
+          throw new AnalysisException(s"$className is not a RelationProvider.")
+      }
+
+      case None => clazz.newInstance() match {
+        case dataSource: RelationProvider =>
+          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options))
+        case dataSource: HadoopFsRelationProvider =>
+          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
+          val paths = {
+            val patternPath = new Path(caseInsensitiveOptions("path"))
+            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
+          }
+          dataSource.createRelation(sqlContext, paths, None, None, caseInsensitiveOptions)
+        case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider =>
+          throw new AnalysisException(
+            s"A schema needs to be specified when using $className.")
+        case _ =>
+          throw new AnalysisException(
+            s"$className is neither a RelationProvider nor a FSBasedRelationProvider.")
+      }
+    }
+    new ResolvedDataSource(clazz, relation)
+  }
+
+  private def partitionColumnsSchema(
+      schema: StructType,
+      partitionColumns: Array[String]): StructType = {
+    StructType(partitionColumns.map { col =>
+      schema.find(_.name == col).getOrElse {
+        throw new RuntimeException(s"Partition column $col not found in schema $schema")
+      }
+    }).asNullable
+  }
+
+  /** Create a [[ResolvedDataSource]] for saving the content of the given DataFrame. */
+  def apply(
+      sqlContext: SQLContext,
+      provider: String,
+      partitionColumns: Array[String],
+      mode: SaveMode,
+      options: Map[String, String],
+      data: DataFrame): ResolvedDataSource = {
+    if (data.schema.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
+      throw new AnalysisException("Cannot save interval data type into external storage.")
+    }
+    val clazz: Class[_] = lookupDataSource(provider)
+    val relation = clazz.newInstance() match {
+      case dataSource: CreatableRelationProvider =>
+        dataSource.createRelation(sqlContext, mode, options, data)
+      case dataSource: HadoopFsRelationProvider =>
+        // Don't glob path for the write path.  The contracts here are:
+        //  1. Only one output path can be specified on the write path;
+        //  2. Output path must be a legal HDFS style file system path;
+        //  3. It's OK that the output path doesn't exist yet;
+        val caseInsensitiveOptions = new CaseInsensitiveMap(options)
+        val outputPath = {
+          val path = new Path(caseInsensitiveOptions("path"))
+          val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+          path.makeQualified(fs.getUri, fs.getWorkingDirectory)
+        }
+        val dataSchema = StructType(data.schema.filterNot(f => partitionColumns.contains(f.name)))
+        val r = dataSource.createRelation(
+          sqlContext,
+          Array(outputPath.toString),
+          Some(dataSchema.asNullable),
+          Some(partitionColumnsSchema(data.schema, partitionColumns)),
+          caseInsensitiveOptions)
+
+        // For partitioned relation r, r.schema's column ordering can be different from the column
+        // ordering of data.logicalPlan (partition columns are all moved after data column).  This
+        // will be adjusted within InsertIntoHadoopFsRelation.
+        sqlContext.executePlan(
+          InsertIntoHadoopFsRelation(
+            r,
+            data.logicalPlan,
+            mode)).toRdd
+        r
+      case _ =>
+        sys.error(s"${clazz.getCanonicalName} does not allow create table as select.")
+    }
+    ResolvedDataSource(clazz, relation)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index 8c2f297e42458..ecd304c30cdee 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -17,340 +17,12 @@
 
 package org.apache.spark.sql.execution.datasources
 
-import java.util.ServiceLoader
-
-import scala.collection.Iterator
-import scala.collection.JavaConversions._
-import scala.language.{existentials, implicitConversions}
-import scala.util.{Failure, Success, Try}
-import scala.util.matching.Regex
-
-import org.apache.hadoop.fs.Path
-
-import org.apache.spark.Logging
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
+import org.apache.spark.sql.catalyst.TableIdentifier
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.{AbstractSparkSQLParser, TableIdentifier}
 import org.apache.spark.sql.execution.RunnableCommand
-import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SQLContext, SaveMode}
-import org.apache.spark.util.Utils
-
-/**
- * A parser for foreign DDL commands.
- */
-private[sql] class DDLParser(
-    parseQuery: String => LogicalPlan)
-  extends AbstractSparkSQLParser with DataTypeParser with Logging {
-
-  def parse(input: String, exceptionOnError: Boolean): LogicalPlan = {
-    try {
-      parse(input)
-    } catch {
-      case ddlException: DDLException => throw ddlException
-      case _ if !exceptionOnError => parseQuery(input)
-      case x: Throwable => throw x
-    }
-  }
-
-  // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword`
-  // properties via reflection the class in runtime for constructing the SqlLexical object
-  protected val CREATE = Keyword("CREATE")
-  protected val TEMPORARY = Keyword("TEMPORARY")
-  protected val TABLE = Keyword("TABLE")
-  protected val IF = Keyword("IF")
-  protected val NOT = Keyword("NOT")
-  protected val EXISTS = Keyword("EXISTS")
-  protected val USING = Keyword("USING")
-  protected val OPTIONS = Keyword("OPTIONS")
-  protected val DESCRIBE = Keyword("DESCRIBE")
-  protected val EXTENDED = Keyword("EXTENDED")
-  protected val AS = Keyword("AS")
-  protected val COMMENT = Keyword("COMMENT")
-  protected val REFRESH = Keyword("REFRESH")
-
-  protected lazy val ddl: Parser[LogicalPlan] = createTable | describeTable | refreshTable
-
-  protected def start: Parser[LogicalPlan] = ddl
-
-  /**
-   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
-   * USING org.apache.spark.sql.avro
-   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
-   * or
-   * `CREATE [TEMPORARY] TABLE avroTable(intField int, stringField string...) [IF NOT EXISTS]
-   * USING org.apache.spark.sql.avro
-   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
-   * or
-   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
-   * USING org.apache.spark.sql.avro
-   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
-   * AS SELECT ...
-   */
-  protected lazy val createTable: Parser[LogicalPlan] =
-    // TODO: Support database.table.
-    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident ~
-      tableCols.? ~ (USING ~> className) ~ (OPTIONS ~> options).? ~ (AS ~> restInput).? ^^ {
-      case temp ~ allowExisting ~ tableName ~ columns ~ provider ~ opts ~ query =>
-        if (temp.isDefined && allowExisting.isDefined) {
-          throw new DDLException(
-            "a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause.")
-        }
-
-        val options = opts.getOrElse(Map.empty[String, String])
-        if (query.isDefined) {
-          if (columns.isDefined) {
-            throw new DDLException(
-              "a CREATE TABLE AS SELECT statement does not allow column definitions.")
-          }
-          // When IF NOT EXISTS clause appears in the query, the save mode will be ignore.
-          val mode = if (allowExisting.isDefined) {
-            SaveMode.Ignore
-          } else if (temp.isDefined) {
-            SaveMode.Overwrite
-          } else {
-            SaveMode.ErrorIfExists
-          }
-
-          val queryPlan = parseQuery(query.get)
-          CreateTableUsingAsSelect(tableName,
-            provider,
-            temp.isDefined,
-            Array.empty[String],
-            mode,
-            options,
-            queryPlan)
-        } else {
-          val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields)))
-          CreateTableUsing(
-            tableName,
-            userSpecifiedSchema,
-            provider,
-            temp.isDefined,
-            options,
-            allowExisting.isDefined,
-            managedIfNoPath = false)
-        }
-    }
-
-  protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")"
-
-  /*
-   * describe [extended] table avroTable
-   * This will display all columns of table `avroTable` includes column_name,column_type,comment
-   */
-  protected lazy val describeTable: Parser[LogicalPlan] =
-    (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident  ^^ {
-      case e ~ db ~ tbl =>
-        val tblIdentifier = db match {
-          case Some(dbName) =>
-            Seq(dbName, tbl)
-          case None =>
-            Seq(tbl)
-        }
-        DescribeCommand(UnresolvedRelation(tblIdentifier, None), e.isDefined)
-   }
-
-  protected lazy val refreshTable: Parser[LogicalPlan] =
-    REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ {
-      case maybeDatabaseName ~ tableName =>
-        RefreshTable(TableIdentifier(tableName, maybeDatabaseName))
-    }
-
-  protected lazy val options: Parser[Map[String, String]] =
-    "(" ~> repsep(pair, ",") <~ ")" ^^ { case s: Seq[(String, String)] => s.toMap }
-
-  protected lazy val className: Parser[String] = repsep(ident, ".") ^^ { case s => s.mkString(".")}
-
-  override implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch(
-    s"identifier matching regex $regex", {
-      case lexical.Identifier(str) if regex.unapplySeq(str).isDefined => str
-      case lexical.Keyword(str) if regex.unapplySeq(str).isDefined => str
-    }
-  )
-
-  protected lazy val optionPart: Parser[String] = "[_a-zA-Z][_a-zA-Z0-9]*".r ^^ {
-    case name => name
-  }
-
-  protected lazy val optionName: Parser[String] = repsep(optionPart, ".") ^^ {
-    case parts => parts.mkString(".")
-  }
-
-  protected lazy val pair: Parser[(String, String)] =
-    optionName ~ stringLit ^^ { case k ~ v => (k, v) }
-
-  protected lazy val column: Parser[StructField] =
-    ident ~ dataType ~ (COMMENT ~> stringLit).?  ^^ { case columnName ~ typ ~ cm =>
-      val meta = cm match {
-        case Some(comment) =>
-          new MetadataBuilder().putString(COMMENT.str.toLowerCase, comment).build()
-        case None => Metadata.empty
-      }
-
-      StructField(columnName, typ, nullable = true, meta)
-    }
-}
-
-private[sql] object ResolvedDataSource extends Logging {
-
-  /** Given a provider name, look up the data source class definition. */
-  def lookupDataSource(provider: String): Class[_] = {
-    val provider2 = s"$provider.DefaultSource"
-    val loader = Utils.getContextOrSparkClassLoader
-    val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
-
-    serviceLoader.iterator().filter(_.format().equalsIgnoreCase(provider)).toList match {
-      /** the provider format did not match any given registered aliases */
-      case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
-        case Success(dataSource) => dataSource
-        case Failure(error) => if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
-          throw new ClassNotFoundException(
-            "The ORC data source must be used with Hive support enabled.", error)
-        } else {
-          throw new ClassNotFoundException(
-            s"Failed to load class for data source: $provider", error)
-        }
-      }
-      /** there is exactly one registered alias */
-      case head :: Nil => head.getClass
-      /** There are multiple registered aliases for the input */
-      case sources => sys.error(s"Multiple sources found for $provider, " +
-        s"(${sources.map(_.getClass.getName).mkString(", ")}), " +
-        "please specify the fully qualified class name")
-    }
-  }
-
-  /** Create a [[ResolvedDataSource]] for reading data in. */
-  def apply(
-      sqlContext: SQLContext,
-      userSpecifiedSchema: Option[StructType],
-      partitionColumns: Array[String],
-      provider: String,
-      options: Map[String, String]): ResolvedDataSource = {
-    val clazz: Class[_] = lookupDataSource(provider)
-    def className: String = clazz.getCanonicalName
-    val relation = userSpecifiedSchema match {
-      case Some(schema: StructType) => clazz.newInstance() match {
-        case dataSource: SchemaRelationProvider =>
-          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema)
-        case dataSource: HadoopFsRelationProvider =>
-          val maybePartitionsSchema = if (partitionColumns.isEmpty) {
-            None
-          } else {
-            Some(partitionColumnsSchema(schema, partitionColumns))
-          }
-
-          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
-          val paths = {
-            val patternPath = new Path(caseInsensitiveOptions("path"))
-            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
-            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
-          }
-
-          val dataSchema =
-            StructType(schema.filterNot(f => partitionColumns.contains(f.name))).asNullable
-
-          dataSource.createRelation(
-            sqlContext,
-            paths,
-            Some(dataSchema),
-            maybePartitionsSchema,
-            caseInsensitiveOptions)
-        case dataSource: org.apache.spark.sql.sources.RelationProvider =>
-          throw new AnalysisException(s"$className does not allow user-specified schemas.")
-        case _ =>
-          throw new AnalysisException(s"$className is not a RelationProvider.")
-      }
-
-      case None => clazz.newInstance() match {
-        case dataSource: RelationProvider =>
-          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options))
-        case dataSource: HadoopFsRelationProvider =>
-          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
-          val paths = {
-            val patternPath = new Path(caseInsensitiveOptions("path"))
-            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
-            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
-          }
-          dataSource.createRelation(sqlContext, paths, None, None, caseInsensitiveOptions)
-        case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider =>
-          throw new AnalysisException(
-            s"A schema needs to be specified when using $className.")
-        case _ =>
-          throw new AnalysisException(
-            s"$className is neither a RelationProvider nor a FSBasedRelationProvider.")
-      }
-    }
-    new ResolvedDataSource(clazz, relation)
-  }
-
-  private def partitionColumnsSchema(
-      schema: StructType,
-      partitionColumns: Array[String]): StructType = {
-    StructType(partitionColumns.map { col =>
-      schema.find(_.name == col).getOrElse {
-        throw new RuntimeException(s"Partition column $col not found in schema $schema")
-      }
-    }).asNullable
-  }
-
-  /** Create a [[ResolvedDataSource]] for saving the content of the given [[DataFrame]]. */
-  def apply(
-      sqlContext: SQLContext,
-      provider: String,
-      partitionColumns: Array[String],
-      mode: SaveMode,
-      options: Map[String, String],
-      data: DataFrame): ResolvedDataSource = {
-    if (data.schema.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
-      throw new AnalysisException("Cannot save interval data type into external storage.")
-    }
-    val clazz: Class[_] = lookupDataSource(provider)
-    val relation = clazz.newInstance() match {
-      case dataSource: CreatableRelationProvider =>
-        dataSource.createRelation(sqlContext, mode, options, data)
-      case dataSource: HadoopFsRelationProvider =>
-        // Don't glob path for the write path.  The contracts here are:
-        //  1. Only one output path can be specified on the write path;
-        //  2. Output path must be a legal HDFS style file system path;
-        //  3. It's OK that the output path doesn't exist yet;
-        val caseInsensitiveOptions = new CaseInsensitiveMap(options)
-        val outputPath = {
-          val path = new Path(caseInsensitiveOptions("path"))
-          val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-          path.makeQualified(fs.getUri, fs.getWorkingDirectory)
-        }
-        val dataSchema = StructType(data.schema.filterNot(f => partitionColumns.contains(f.name)))
-        val r = dataSource.createRelation(
-          sqlContext,
-          Array(outputPath.toString),
-          Some(dataSchema.asNullable),
-          Some(partitionColumnsSchema(data.schema, partitionColumns)),
-          caseInsensitiveOptions)
-
-        // For partitioned relation r, r.schema's column ordering can be different from the column
-        // ordering of data.logicalPlan (partition columns are all moved after data column).  This
-        // will be adjusted within InsertIntoHadoopFsRelation.
-        sqlContext.executePlan(
-          InsertIntoHadoopFsRelation(
-            r,
-            data.logicalPlan,
-            mode)).toRdd
-        r
-      case _ =>
-        sys.error(s"${clazz.getCanonicalName} does not allow create table as select.")
-    }
-    new ResolvedDataSource(clazz, relation)
-  }
-}
-
-private[sql] case class ResolvedDataSource(provider: Class[_], relation: BaseRelation)
+import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode}
 
 /**
  * Returned for the "DESCRIBE [EXTENDED] [dbName.]tableName" command.
@@ -358,11 +30,12 @@ private[sql] case class ResolvedDataSource(provider: Class[_], relation: BaseRel
  * @param isExtended True if "DESCRIBE EXTENDED" is used. Otherwise, false.
  *                   It is effective only when the table is a Hive table.
  */
-private[sql] case class DescribeCommand(
+case class DescribeCommand(
     table: LogicalPlan,
     isExtended: Boolean) extends LogicalPlan with Command {
 
   override def children: Seq[LogicalPlan] = Seq.empty
+
   override val output: Seq[Attribute] = Seq(
     // Column names are based on Hive.
     AttributeReference("col_name", StringType, nullable = false,
@@ -370,7 +43,8 @@ private[sql] case class DescribeCommand(
     AttributeReference("data_type", StringType, nullable = false,
       new MetadataBuilder().putString("comment", "data type of the column").build())(),
     AttributeReference("comment", StringType, nullable = false,
-      new MetadataBuilder().putString("comment", "comment of the column").build())())
+      new MetadataBuilder().putString("comment", "comment of the column").build())()
+  )
 }
 
 /**
@@ -378,7 +52,7 @@ private[sql] case class DescribeCommand(
   * @param allowExisting If it is true, we will do nothing when the table already exists.
   *                      If it is false, an exception will be thrown
   */
-private[sql] case class CreateTableUsing(
+case class CreateTableUsing(
     tableName: String,
     userSpecifiedSchema: Option[StructType],
     provider: String,
@@ -397,7 +71,7 @@ private[sql] case class CreateTableUsing(
  * can analyze the logical plan that will be used to populate the table.
  * So, [[PreWriteCheck]] can detect cases that are not allowed.
  */
-private[sql] case class CreateTableUsingAsSelect(
+case class CreateTableUsingAsSelect(
     tableName: String,
     provider: String,
     temporary: Boolean,
@@ -410,7 +84,7 @@ private[sql] case class CreateTableUsingAsSelect(
   // override lazy val resolved = databaseName != None && childrenResolved
 }
 
-private[sql] case class CreateTempTableUsing(
+case class CreateTempTableUsing(
     tableName: String,
     userSpecifiedSchema: Option[StructType],
     provider: String,
@@ -425,7 +99,7 @@ private[sql] case class CreateTempTableUsing(
   }
 }
 
-private[sql] case class CreateTempTableUsingAsSelect(
+case class CreateTempTableUsingAsSelect(
     tableName: String,
     provider: String,
     partitionColumns: Array[String],
@@ -443,7 +117,7 @@ private[sql] case class CreateTempTableUsingAsSelect(
   }
 }
 
-private[sql] case class RefreshTable(tableIdent: TableIdentifier)
+case class RefreshTable(tableIdent: TableIdentifier)
   extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
@@ -472,7 +146,7 @@ private[sql] case class RefreshTable(tableIdent: TableIdentifier)
 /**
  * Builds a map in which keys are case insensitive
  */
-protected[sql] class CaseInsensitiveMap(map: Map[String, String]) extends Map[String, String]
+class CaseInsensitiveMap(map: Map[String, String]) extends Map[String, String]
   with Serializable {
 
   val baseMap = map.map(kv => kv.copy(_1 = kv._1.toLowerCase))
@@ -490,4 +164,4 @@ protected[sql] class CaseInsensitiveMap(map: Map[String, String]) extends Map[St
 /**
  * The exception thrown from the DDL parser.
  */
-protected[sql] class DDLException(message: String) extends Exception(message)
+class DDLException(message: String) extends RuntimeException(message)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala
new file mode 100644
index 0000000000000..6773afc794f9c
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala
@@ -0,0 +1,62 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.util.Properties
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.sources.{BaseRelation, RelationProvider, DataSourceRegister}
+
+class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  override def shortName(): String = "jdbc"
+
+  /** Returns a new base relation with the given parameters. */
+  override def createRelation(
+      sqlContext: SQLContext,
+      parameters: Map[String, String]): BaseRelation = {
+    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
+    val driver = parameters.getOrElse("driver", null)
+    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
+    val partitionColumn = parameters.getOrElse("partitionColumn", null)
+    val lowerBound = parameters.getOrElse("lowerBound", null)
+    val upperBound = parameters.getOrElse("upperBound", null)
+    val numPartitions = parameters.getOrElse("numPartitions", null)
+
+    if (driver != null) DriverRegistry.register(driver)
+
+    if (partitionColumn != null
+      && (lowerBound == null || upperBound == null || numPartitions == null)) {
+      sys.error("Partitioning incompletely specified")
+    }
+
+    val partitionInfo = if (partitionColumn == null) {
+      null
+    } else {
+      JDBCPartitioningInfo(
+        partitionColumn,
+        lowerBound.toLong,
+        upperBound.toLong,
+        numPartitions.toInt)
+    }
+    val parts = JDBCRelation.columnPartition(partitionInfo)
+    val properties = new Properties() // Additional properties that we will pass to getConnection
+    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
+    JDBCRelation(url, table, parts, properties)(sqlContext)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala
new file mode 100644
index 0000000000000..7ccd61ed469e9
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.sql.{Driver, DriverManager}
+
+import scala.collection.mutable
+
+import org.apache.spark.Logging
+import org.apache.spark.util.Utils
+
+/**
+ * java.sql.DriverManager is always loaded by bootstrap classloader,
+ * so it can't load JDBC drivers accessible by Spark ClassLoader.
+ *
+ * To solve the problem, drivers from user-supplied jars are wrapped into thin wrapper.
+ */
+object DriverRegistry extends Logging {
+
+  private val wrapperMap: mutable.Map[String, DriverWrapper] = mutable.Map.empty
+
+  def register(className: String): Unit = {
+    val cls = Utils.getContextOrSparkClassLoader.loadClass(className)
+    if (cls.getClassLoader == null) {
+      logTrace(s"$className has been loaded with bootstrap ClassLoader, wrapper is not required")
+    } else if (wrapperMap.get(className).isDefined) {
+      logTrace(s"Wrapper for $className already exists")
+    } else {
+      synchronized {
+        if (wrapperMap.get(className).isEmpty) {
+          val wrapper = new DriverWrapper(cls.newInstance().asInstanceOf[Driver])
+          DriverManager.registerDriver(wrapper)
+          wrapperMap(className) = wrapper
+          logTrace(s"Wrapper for $className registered")
+        }
+      }
+    }
+  }
+
+  def getDriverClassName(url: String): String = DriverManager.getDriver(url) match {
+    case wrapper: DriverWrapper => wrapper.wrapped.getClass.getCanonicalName
+    case driver => driver.getClass.getCanonicalName
+  }
+}
+
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala
new file mode 100644
index 0000000000000..18263fe227d04
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.sql.{Connection, Driver, DriverPropertyInfo, SQLFeatureNotSupportedException}
+import java.util.Properties
+
+/**
+ * A wrapper for a JDBC Driver to work around SPARK-6913.
+ *
+ * The problem is in `java.sql.DriverManager` class that can't access drivers loaded by
+ * Spark ClassLoader.
+ */
+class DriverWrapper(val wrapped: Driver) extends Driver {
+  override def acceptsURL(url: String): Boolean = wrapped.acceptsURL(url)
+
+  override def jdbcCompliant(): Boolean = wrapped.jdbcCompliant()
+
+  override def getPropertyInfo(url: String, info: Properties): Array[DriverPropertyInfo] = {
+    wrapped.getPropertyInfo(url, info)
+  }
+
+  override def getMinorVersion: Int = wrapped.getMinorVersion
+
+  def getParentLogger: java.util.logging.Logger = {
+    throw new SQLFeatureNotSupportedException(
+      s"${this.getClass.getName}.getParentLogger is not yet implemented.")
+  }
+
+  override def connect(url: String, info: Properties): Connection = wrapped.connect(url, info)
+
+  override def getMajorVersion: Int = wrapped.getMajorVersion
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 3cf70db6b7b09..8eab6a0adccc4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.jdbc
+package org.apache.spark.sql.execution.datasources.jdbc
 
 import java.sql.{Connection, DriverManager, ResultSet, ResultSetMetaData, SQLException}
 import java.util.Properties
@@ -26,6 +26,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.jdbc.JdbcDialects
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -180,9 +181,8 @@ private[sql] object JDBCRDD extends Logging {
       try {
         if (driver != null) DriverRegistry.register(driver)
       } catch {
-        case e: ClassNotFoundException => {
-          logWarning(s"Couldn't find class $driver", e);
-        }
+        case e: ClassNotFoundException =>
+          logWarning(s"Couldn't find class $driver", e)
       }
       DriverManager.getConnection(url, properties)
     }
@@ -344,7 +344,6 @@ private[sql] class JDBCRDD(
     }).toArray
   }
 
-
   /**
    * Runs the SQL query against the JDBC driver.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
similarity index 71%
rename from sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
index 48d97ced9ca0a..f9300dc2cb529 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.jdbc
+package org.apache.spark.sql.execution.datasources.jdbc
 
 import java.util.Properties
 
@@ -77,45 +77,6 @@ private[sql] object JDBCRelation {
   }
 }
 
-private[sql] class DefaultSource extends RelationProvider with DataSourceRegister {
-
-  def format(): String = "jdbc"
-
-  /** Returns a new base relation with the given parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String]): BaseRelation = {
-    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
-    val driver = parameters.getOrElse("driver", null)
-    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
-    val partitionColumn = parameters.getOrElse("partitionColumn", null)
-    val lowerBound = parameters.getOrElse("lowerBound", null)
-    val upperBound = parameters.getOrElse("upperBound", null)
-    val numPartitions = parameters.getOrElse("numPartitions", null)
-
-    if (driver != null) DriverRegistry.register(driver)
-
-    if (partitionColumn != null
-        && (lowerBound == null || upperBound == null || numPartitions == null)) {
-      sys.error("Partitioning incompletely specified")
-    }
-
-    val partitionInfo = if (partitionColumn == null) {
-      null
-    } else {
-      JDBCPartitioningInfo(
-        partitionColumn,
-        lowerBound.toLong,
-        upperBound.toLong,
-        numPartitions.toInt)
-    }
-    val parts = JDBCRelation.columnPartition(partitionInfo)
-    val properties = new Properties() // Additional properties that we will pass to getConnection
-    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
-    JDBCRelation(url, table, parts, properties)(sqlContext)
-  }
-}
-
 private[sql] case class JDBCRelation(
     url: String,
     table: String,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
new file mode 100644
index 0000000000000..039c13bf163ca
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -0,0 +1,219 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.sql.{Connection, DriverManager, PreparedStatement}
+import java.util.Properties
+
+import scala.util.Try
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.jdbc.JdbcDialects
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{DataFrame, Row}
+
+/**
+ * Util functions for JDBC tables.
+ */
+object JdbcUtils extends Logging {
+
+  /**
+   * Establishes a JDBC connection.
+   */
+  def createConnection(url: String, connectionProperties: Properties): Connection = {
+    DriverManager.getConnection(url, connectionProperties)
+  }
+
+  /**
+   * Returns true if the table already exists in the JDBC database.
+   */
+  def tableExists(conn: Connection, table: String): Boolean = {
+    // Somewhat hacky, but there isn't a good way to identify whether a table exists for all
+    // SQL database systems, considering "table" could also include the database name.
+    Try(conn.prepareStatement(s"SELECT 1 FROM $table LIMIT 1").executeQuery().next()).isSuccess
+  }
+
+  /**
+   * Drops a table from the JDBC database.
+   */
+  def dropTable(conn: Connection, table: String): Unit = {
+    conn.prepareStatement(s"DROP TABLE $table").executeUpdate()
+  }
+
+  /**
+   * Returns a PreparedStatement that inserts a row into table via conn.
+   */
+  def insertStatement(conn: Connection, table: String, rddSchema: StructType): PreparedStatement = {
+    val sql = new StringBuilder(s"INSERT INTO $table VALUES (")
+    var fieldsLeft = rddSchema.fields.length
+    while (fieldsLeft > 0) {
+      sql.append("?")
+      if (fieldsLeft > 1) sql.append(", ") else sql.append(")")
+      fieldsLeft = fieldsLeft - 1
+    }
+    conn.prepareStatement(sql.toString())
+  }
+
+  /**
+   * Saves a partition of a DataFrame to the JDBC database.  This is done in
+   * a single database transaction in order to avoid repeatedly inserting
+   * data as much as possible.
+   *
+   * It is still theoretically possible for rows in a DataFrame to be
+   * inserted into the database more than once if a stage somehow fails after
+   * the commit occurs but before the stage can return successfully.
+   *
+   * This is not a closure inside saveTable() because apparently cosmetic
+   * implementation changes elsewhere might easily render such a closure
+   * non-Serializable.  Instead, we explicitly close over all variables that
+   * are used.
+   */
+  def savePartition(
+      getConnection: () => Connection,
+      table: String,
+      iterator: Iterator[Row],
+      rddSchema: StructType,
+      nullTypes: Array[Int]): Iterator[Byte] = {
+    val conn = getConnection()
+    var committed = false
+    try {
+      conn.setAutoCommit(false) // Everything in the same db transaction.
+      val stmt = insertStatement(conn, table, rddSchema)
+      try {
+        while (iterator.hasNext) {
+          val row = iterator.next()
+          val numFields = rddSchema.fields.length
+          var i = 0
+          while (i < numFields) {
+            if (row.isNullAt(i)) {
+              stmt.setNull(i + 1, nullTypes(i))
+            } else {
+              rddSchema.fields(i).dataType match {
+                case IntegerType => stmt.setInt(i + 1, row.getInt(i))
+                case LongType => stmt.setLong(i + 1, row.getLong(i))
+                case DoubleType => stmt.setDouble(i + 1, row.getDouble(i))
+                case FloatType => stmt.setFloat(i + 1, row.getFloat(i))
+                case ShortType => stmt.setInt(i + 1, row.getShort(i))
+                case ByteType => stmt.setInt(i + 1, row.getByte(i))
+                case BooleanType => stmt.setBoolean(i + 1, row.getBoolean(i))
+                case StringType => stmt.setString(i + 1, row.getString(i))
+                case BinaryType => stmt.setBytes(i + 1, row.getAs[Array[Byte]](i))
+                case TimestampType => stmt.setTimestamp(i + 1, row.getAs[java.sql.Timestamp](i))
+                case DateType => stmt.setDate(i + 1, row.getAs[java.sql.Date](i))
+                case t: DecimalType => stmt.setBigDecimal(i + 1, row.getDecimal(i))
+                case _ => throw new IllegalArgumentException(
+                  s"Can't translate non-null value for field $i")
+              }
+            }
+            i = i + 1
+          }
+          stmt.executeUpdate()
+        }
+      } finally {
+        stmt.close()
+      }
+      conn.commit()
+      committed = true
+    } finally {
+      if (!committed) {
+        // The stage must fail.  We got here through an exception path, so
+        // let the exception through unless rollback() or close() want to
+        // tell the user about another problem.
+        conn.rollback()
+        conn.close()
+      } else {
+        // The stage must succeed.  We cannot propagate any exception close() might throw.
+        try {
+          conn.close()
+        } catch {
+          case e: Exception => logWarning("Transaction succeeded, but closing failed", e)
+        }
+      }
+    }
+    Array[Byte]().iterator
+  }
+
+  /**
+   * Compute the schema string for this RDD.
+   */
+  def schemaString(df: DataFrame, url: String): String = {
+    val sb = new StringBuilder()
+    val dialect = JdbcDialects.get(url)
+    df.schema.fields foreach { field => {
+      val name = field.name
+      val typ: String =
+        dialect.getJDBCType(field.dataType).map(_.databaseTypeDefinition).getOrElse(
+          field.dataType match {
+            case IntegerType => "INTEGER"
+            case LongType => "BIGINT"
+            case DoubleType => "DOUBLE PRECISION"
+            case FloatType => "REAL"
+            case ShortType => "INTEGER"
+            case ByteType => "BYTE"
+            case BooleanType => "BIT(1)"
+            case StringType => "TEXT"
+            case BinaryType => "BLOB"
+            case TimestampType => "TIMESTAMP"
+            case DateType => "DATE"
+            case t: DecimalType => s"DECIMAL(${t.precision}},${t.scale}})"
+            case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
+          })
+      val nullable = if (field.nullable) "" else "NOT NULL"
+      sb.append(s", $name $typ $nullable")
+    }}
+    if (sb.length < 2) "" else sb.substring(2)
+  }
+
+  /**
+   * Saves the RDD to the database in a single transaction.
+   */
+  def saveTable(
+      df: DataFrame,
+      url: String,
+      table: String,
+      properties: Properties = new Properties()) {
+    val dialect = JdbcDialects.get(url)
+    val nullTypes: Array[Int] = df.schema.fields.map { field =>
+      dialect.getJDBCType(field.dataType).map(_.jdbcNullType).getOrElse(
+        field.dataType match {
+          case IntegerType => java.sql.Types.INTEGER
+          case LongType => java.sql.Types.BIGINT
+          case DoubleType => java.sql.Types.DOUBLE
+          case FloatType => java.sql.Types.REAL
+          case ShortType => java.sql.Types.INTEGER
+          case ByteType => java.sql.Types.INTEGER
+          case BooleanType => java.sql.Types.BIT
+          case StringType => java.sql.Types.CLOB
+          case BinaryType => java.sql.Types.BLOB
+          case TimestampType => java.sql.Types.TIMESTAMP
+          case DateType => java.sql.Types.DATE
+          case t: DecimalType => java.sql.Types.DECIMAL
+          case _ => throw new IllegalArgumentException(
+            s"Can't translate null value for field $field")
+        })
+    }
+
+    val rddSchema = df.schema
+    val driver: String = DriverRegistry.getDriverClassName(url)
+    val getConnection: () => Connection = JDBCRDD.getConnector(driver, url, properties)
+    df.foreachPartition { iterator =>
+      savePartition(getConnection, table, iterator, rddSchema, nullTypes)
+    }
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/InferSchema.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/InferSchema.scala
index ec5668c6b95a1..b6f3410bad690 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/InferSchema.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import com.fasterxml.jackson.core._
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
-import org.apache.spark.sql.json.JacksonUtils.nextUntil
+import org.apache.spark.sql.execution.datasources.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.types._
 
 private[sql] object InferSchema {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
similarity index 97%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index 5bb9e62310a50..114c8b211891e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import java.io.CharArrayWriter
 
@@ -39,9 +39,10 @@ import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
 import org.apache.spark.util.SerializableConfiguration
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
-  def format(): String = "json"
+class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  override def shortName(): String = "json"
 
   override def createRelation(
       sqlContext: SQLContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index d734e7e8904bd..37c2b5a296c15 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import org.apache.spark.sql.catalyst.InternalRow
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
index b8fd3b9cc150e..cd68bd667c5c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import java.io.ByteArrayOutputStream
 
@@ -27,7 +27,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.json.JacksonUtils.nextUntil
+import org.apache.spark.sql.execution.datasources.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonUtils.scala
similarity index 95%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonUtils.scala
index fde96852ce68e..005546f37dda0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonUtils.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import com.fasterxml.jackson.core.{JsonParser, JsonToken}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 975fec101d9c2..4049795ed3bad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.util.{Map => JMap}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRecordMaterializer.scala
similarity index 96%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRecordMaterializer.scala
index 84f1dccfeb788..ed9e0aa65977b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRecordMaterializer.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.parquet.io.api.{GroupConverter, RecordMaterializer}
 import org.apache.parquet.schema.MessageType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index 4fe8a39f20abd..3542dfbae1292 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.math.{BigDecimal, BigInteger}
 import java.nio.ByteOrder
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index b12149dcf1c92..a3fc74cf7929b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import scala.collection.JavaConversions._
 
@@ -25,7 +25,7 @@ import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName._
 import org.apache.parquet.schema.Type.Repetition._
 import org.apache.parquet.schema._
 
-import org.apache.spark.sql.parquet.CatalystSchemaConverter.{MAX_PRECISION_FOR_INT32, MAX_PRECISION_FOR_INT64, maxPrecisionForBytes}
+import org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.{MAX_PRECISION_FOR_INT32, MAX_PRECISION_FOR_INT64, maxPrecisionForBytes}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLConf}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
index 1551afd7b7bf2..2c6b914328b60 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetConverter.scala
similarity index 96%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetConverter.scala
index 6ed3580af0729..ccd7ebf319af9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.{MapData, ArrayData}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index d57b789f5c1c7..9e2e232f50167 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.Serializable
 import java.nio.ByteBuffer
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index b6db71b5b8a62..4086a139bed72 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.net.URI
 import java.util.logging.{Level, Logger => JLogger}
@@ -51,7 +51,7 @@ import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
-  def format(): String = "parquet"
+  override def shortName(): String = "parquet"
 
   override def createRelation(
       sqlContext: SQLContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
index 9cd0250f9c510..3191cf3d121bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.math.BigInteger
 import java.nio.{ByteBuffer, ByteOrder}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 3854f5bd39fb1..019db34fc666d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.IOException
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
similarity index 77%
rename from sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
index 3b907e5da7897..1b51a5e5c8a8e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
@@ -15,7 +15,7 @@
 * limitations under the License.
 */
 
-package org.apache.spark.sql.metric
+package org.apache.spark.sql.execution.metric
 
 import org.apache.spark.{Accumulable, AccumulableParam, SparkContext}
 
@@ -93,22 +93,6 @@ private[sql] class LongSQLMetric private[metric](name: String)
   }
 }
 
-/**
- * A specialized int Accumulable to avoid boxing and unboxing when using Accumulator's
- * `+=` and `add`.
- */
-private[sql] class IntSQLMetric private[metric](name: String)
-  extends SQLMetric[IntSQLMetricValue, Int](name, IntSQLMetricParam) {
-
-  override def +=(term: Int): Unit = {
-    localValue.add(term)
-  }
-
-  override def add(term: Int): Unit = {
-    localValue.add(term)
-  }
-}
-
 private object LongSQLMetricParam extends SQLMetricParam[LongSQLMetricValue, Long] {
 
   override def addAccumulator(r: LongSQLMetricValue, t: Long): LongSQLMetricValue = r.add(t)
@@ -121,26 +105,8 @@ private object LongSQLMetricParam extends SQLMetricParam[LongSQLMetricValue, Lon
   override def zero: LongSQLMetricValue = new LongSQLMetricValue(0L)
 }
 
-private object IntSQLMetricParam extends SQLMetricParam[IntSQLMetricValue, Int] {
-
-  override def addAccumulator(r: IntSQLMetricValue, t: Int): IntSQLMetricValue = r.add(t)
-
-  override def addInPlace(r1: IntSQLMetricValue, r2: IntSQLMetricValue): IntSQLMetricValue =
-    r1.add(r2.value)
-
-  override def zero(initialValue: IntSQLMetricValue): IntSQLMetricValue = zero
-
-  override def zero: IntSQLMetricValue = new IntSQLMetricValue(0)
-}
-
 private[sql] object SQLMetrics {
 
-  def createIntMetric(sc: SparkContext, name: String): IntSQLMetric = {
-    val acc = new IntSQLMetric(name)
-    sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
-    acc
-  }
-
   def createLongMetric(sc: SparkContext, name: String): LongSQLMetric = {
     val acc = new LongSQLMetric(name)
     sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala
index 66237f8f1314b..28fa231e722d0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala
@@ -18,12 +18,6 @@
 package org.apache.spark.sql
 
 /**
- * :: DeveloperApi ::
- * An execution engine for relational query plans that runs on top Spark and returns RDDs.
- *
- * Note that the operators in this package are created automatically by a query planner using a
- * [[SQLContext]] and are not intended to be used directly by end users of Spark SQL.  They are
- * documented here in order to make it easier for others to understand the performance
- * characteristics of query plans that are generated by Spark SQL.
+ * The physical execution component of Spark SQL. Note that this is a private package.
  */
 package object execution
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala
index cb7ca60b2fe48..49646a99d68c8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import javax.servlet.http.HttpServletRequest
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
index 52ddf99e9266a..f0b56c2eb7a53 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import javax.servlet.http.HttpServletRequest
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
index 2fd4fc658d068..0b9bad987c488 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import scala.collection.mutable
 
@@ -26,7 +26,7 @@ import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.execution.SQLExecution
-import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
+import org.apache.spark.sql.execution.metric.{SQLMetricParam, SQLMetricValue}
 
 private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener with Logging {
 
@@ -51,17 +51,14 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
 
   private val completedExecutions = mutable.ListBuffer[SQLExecutionUIData]()
 
-  @VisibleForTesting
   def executionIdToData: Map[Long, SQLExecutionUIData] = synchronized {
     _executionIdToData.toMap
   }
 
-  @VisibleForTesting
   def jobIdToExecutionId: Map[Long, Long] = synchronized {
     _jobIdToExecutionId.toMap
   }
 
-  @VisibleForTesting
   def stageIdToStageMetrics: Map[Long, SQLStageMetrics] = synchronized {
     _stageIdToStageMetrics.toMap
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLTab.scala
similarity index 90%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLTab.scala
index 3bba0afaf14eb..0b0867f67eb6e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLTab.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import java.util.concurrent.atomic.AtomicInteger
 
@@ -38,12 +38,12 @@ private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
 
 private[sql] object SQLTab {
 
-  private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/ui/static"
+  private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/execution/ui/static"
 
   private val nextTabId = new AtomicInteger(0)
 
   private def nextTabName: String = {
     val nextId = nextTabId.getAndIncrement()
-    if (nextId == 0) "SQL" else s"SQL${nextId}"
+    if (nextId == 0) "SQL" else s"SQL$nextId"
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SparkPlanGraph.scala
similarity index 97%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SparkPlanGraph.scala
index 1ba50b95becc1..ae3d752dde348 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SparkPlanGraph.scala
@@ -15,14 +15,14 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import java.util.concurrent.atomic.AtomicLong
 
 import scala.collection.mutable
 
 import org.apache.spark.sql.execution.SparkPlan
-import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
+import org.apache.spark.sql.execution.metric.{SQLMetricParam, SQLMetricValue}
 
 /**
  * A graph used for storing information of an executionPlan of DataFrame.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala
deleted file mode 100644
index cc918c237192b..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.jdbc
-
-import java.sql.{Connection, DriverManager}
-import java.util.Properties
-
-import scala.util.Try
-
-/**
- * Util functions for JDBC tables.
- */
-private[sql] object JdbcUtils {
-
-  /**
-   * Establishes a JDBC connection.
-   */
-  def createConnection(url: String, connectionProperties: Properties): Connection = {
-    DriverManager.getConnection(url, connectionProperties)
-  }
-
-  /**
-   * Returns true if the table already exists in the JDBC database.
-   */
-  def tableExists(conn: Connection, table: String): Boolean = {
-    // Somewhat hacky, but there isn't a good way to identify whether a table exists for all
-    // SQL database systems, considering "table" could also include the database name.
-    Try(conn.prepareStatement(s"SELECT 1 FROM $table LIMIT 1").executeQuery().next()).isSuccess
-  }
-
-  /**
-   * Drops a table from the JDBC database.
-   */
-  def dropTable(conn: Connection, table: String): Unit = {
-    conn.prepareStatement(s"DROP TABLE $table").executeUpdate()
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
deleted file mode 100644
index 035e0510080ff..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
+++ /dev/null
@@ -1,250 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import java.sql.{Connection, Driver, DriverManager, DriverPropertyInfo, PreparedStatement, SQLFeatureNotSupportedException}
-import java.util.Properties
-
-import scala.collection.mutable
-
-import org.apache.spark.Logging
-import org.apache.spark.sql.types._
-import org.apache.spark.util.Utils
-
-package object jdbc {
-  private[sql] object JDBCWriteDetails extends Logging {
-    /**
-     * Returns a PreparedStatement that inserts a row into table via conn.
-     */
-    def insertStatement(conn: Connection, table: String, rddSchema: StructType):
-        PreparedStatement = {
-      val sql = new StringBuilder(s"INSERT INTO $table VALUES (")
-      var fieldsLeft = rddSchema.fields.length
-      while (fieldsLeft > 0) {
-        sql.append("?")
-        if (fieldsLeft > 1) sql.append(", ") else sql.append(")")
-        fieldsLeft = fieldsLeft - 1
-      }
-      conn.prepareStatement(sql.toString)
-    }
-
-    /**
-     * Saves a partition of a DataFrame to the JDBC database.  This is done in
-     * a single database transaction in order to avoid repeatedly inserting
-     * data as much as possible.
-     *
-     * It is still theoretically possible for rows in a DataFrame to be
-     * inserted into the database more than once if a stage somehow fails after
-     * the commit occurs but before the stage can return successfully.
-     *
-     * This is not a closure inside saveTable() because apparently cosmetic
-     * implementation changes elsewhere might easily render such a closure
-     * non-Serializable.  Instead, we explicitly close over all variables that
-     * are used.
-     */
-    def savePartition(
-        getConnection: () => Connection,
-        table: String,
-        iterator: Iterator[Row],
-        rddSchema: StructType,
-        nullTypes: Array[Int]): Iterator[Byte] = {
-      val conn = getConnection()
-      var committed = false
-      try {
-        conn.setAutoCommit(false) // Everything in the same db transaction.
-        val stmt = insertStatement(conn, table, rddSchema)
-        try {
-          while (iterator.hasNext) {
-            val row = iterator.next()
-            val numFields = rddSchema.fields.length
-            var i = 0
-            while (i < numFields) {
-              if (row.isNullAt(i)) {
-                stmt.setNull(i + 1, nullTypes(i))
-              } else {
-                rddSchema.fields(i).dataType match {
-                  case IntegerType => stmt.setInt(i + 1, row.getInt(i))
-                  case LongType => stmt.setLong(i + 1, row.getLong(i))
-                  case DoubleType => stmt.setDouble(i + 1, row.getDouble(i))
-                  case FloatType => stmt.setFloat(i + 1, row.getFloat(i))
-                  case ShortType => stmt.setInt(i + 1, row.getShort(i))
-                  case ByteType => stmt.setInt(i + 1, row.getByte(i))
-                  case BooleanType => stmt.setBoolean(i + 1, row.getBoolean(i))
-                  case StringType => stmt.setString(i + 1, row.getString(i))
-                  case BinaryType => stmt.setBytes(i + 1, row.getAs[Array[Byte]](i))
-                  case TimestampType => stmt.setTimestamp(i + 1, row.getAs[java.sql.Timestamp](i))
-                  case DateType => stmt.setDate(i + 1, row.getAs[java.sql.Date](i))
-                  case t: DecimalType => stmt.setBigDecimal(i + 1, row.getDecimal(i))
-                  case _ => throw new IllegalArgumentException(
-                      s"Can't translate non-null value for field $i")
-                }
-              }
-              i = i + 1
-            }
-            stmt.executeUpdate()
-          }
-        } finally {
-          stmt.close()
-        }
-        conn.commit()
-        committed = true
-      } finally {
-        if (!committed) {
-          // The stage must fail.  We got here through an exception path, so
-          // let the exception through unless rollback() or close() want to
-          // tell the user about another problem.
-          conn.rollback()
-          conn.close()
-        } else {
-          // The stage must succeed.  We cannot propagate any exception close() might throw.
-          try {
-            conn.close()
-          } catch {
-            case e: Exception => logWarning("Transaction succeeded, but closing failed", e)
-          }
-        }
-      }
-      Array[Byte]().iterator
-    }
-
-    /**
-     * Compute the schema string for this RDD.
-     */
-    def schemaString(df: DataFrame, url: String): String = {
-      val sb = new StringBuilder()
-      val dialect = JdbcDialects.get(url)
-      df.schema.fields foreach { field => {
-        val name = field.name
-        val typ: String =
-          dialect.getJDBCType(field.dataType).map(_.databaseTypeDefinition).getOrElse(
-          field.dataType match {
-            case IntegerType => "INTEGER"
-            case LongType => "BIGINT"
-            case DoubleType => "DOUBLE PRECISION"
-            case FloatType => "REAL"
-            case ShortType => "INTEGER"
-            case ByteType => "BYTE"
-            case BooleanType => "BIT(1)"
-            case StringType => "TEXT"
-            case BinaryType => "BLOB"
-            case TimestampType => "TIMESTAMP"
-            case DateType => "DATE"
-            case t: DecimalType => s"DECIMAL(${t.precision}},${t.scale}})"
-            case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
-          })
-        val nullable = if (field.nullable) "" else "NOT NULL"
-        sb.append(s", $name $typ $nullable")
-      }}
-      if (sb.length < 2) "" else sb.substring(2)
-    }
-
-    /**
-     * Saves the RDD to the database in a single transaction.
-     */
-    def saveTable(
-        df: DataFrame,
-        url: String,
-        table: String,
-        properties: Properties = new Properties()) {
-      val dialect = JdbcDialects.get(url)
-      val nullTypes: Array[Int] = df.schema.fields.map { field =>
-        dialect.getJDBCType(field.dataType).map(_.jdbcNullType).getOrElse(
-          field.dataType match {
-            case IntegerType => java.sql.Types.INTEGER
-            case LongType => java.sql.Types.BIGINT
-            case DoubleType => java.sql.Types.DOUBLE
-            case FloatType => java.sql.Types.REAL
-            case ShortType => java.sql.Types.INTEGER
-            case ByteType => java.sql.Types.INTEGER
-            case BooleanType => java.sql.Types.BIT
-            case StringType => java.sql.Types.CLOB
-            case BinaryType => java.sql.Types.BLOB
-            case TimestampType => java.sql.Types.TIMESTAMP
-            case DateType => java.sql.Types.DATE
-            case t: DecimalType => java.sql.Types.DECIMAL
-            case _ => throw new IllegalArgumentException(
-              s"Can't translate null value for field $field")
-          })
-      }
-
-      val rddSchema = df.schema
-      val driver: String = DriverRegistry.getDriverClassName(url)
-      val getConnection: () => Connection = JDBCRDD.getConnector(driver, url, properties)
-      df.foreachPartition { iterator =>
-        JDBCWriteDetails.savePartition(getConnection, table, iterator, rddSchema, nullTypes)
-      }
-    }
-
-  }
-
-  private [sql] class DriverWrapper(val wrapped: Driver) extends Driver {
-    override def acceptsURL(url: String): Boolean = wrapped.acceptsURL(url)
-
-    override def jdbcCompliant(): Boolean = wrapped.jdbcCompliant()
-
-    override def getPropertyInfo(url: String, info: Properties): Array[DriverPropertyInfo] = {
-      wrapped.getPropertyInfo(url, info)
-    }
-
-    override def getMinorVersion: Int = wrapped.getMinorVersion
-
-    def getParentLogger: java.util.logging.Logger =
-      throw new SQLFeatureNotSupportedException(
-        s"${this.getClass().getName}.getParentLogger is not yet implemented.")
-
-    override def connect(url: String, info: Properties): Connection = wrapped.connect(url, info)
-
-    override def getMajorVersion: Int = wrapped.getMajorVersion
-  }
-
-  /**
-   * java.sql.DriverManager is always loaded by bootstrap classloader,
-   * so it can't load JDBC drivers accessible by Spark ClassLoader.
-   *
-   * To solve the problem, drivers from user-supplied jars are wrapped
-   * into thin wrapper.
-   */
-  private [sql] object DriverRegistry extends Logging {
-
-    private val wrapperMap: mutable.Map[String, DriverWrapper] = mutable.Map.empty
-
-    def register(className: String): Unit = {
-      val cls = Utils.getContextOrSparkClassLoader.loadClass(className)
-      if (cls.getClassLoader == null) {
-        logTrace(s"$className has been loaded with bootstrap ClassLoader, wrapper is not required")
-      } else if (wrapperMap.get(className).isDefined) {
-        logTrace(s"Wrapper for $className already exists")
-      } else {
-        synchronized {
-          if (wrapperMap.get(className).isEmpty) {
-            val wrapper = new DriverWrapper(cls.newInstance().asInstanceOf[Driver])
-            DriverManager.registerDriver(wrapper)
-            wrapperMap(className) = wrapper
-            logTrace(s"Wrapper for $className registered")
-          }
-        }
-      }
-    }
-
-    def getDriverClassName(url: String): String = DriverManager.getDriver(url) match {
-      case wrapper: DriverWrapper => wrapper.wrapped.getClass.getCanonicalName
-      case driver => driver.getClass.getCanonicalName
-    }
-  }
-
-} // package object jdbc
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 6bcabbab4f77b..2f8417a48d32e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -43,19 +43,24 @@ import org.apache.spark.util.SerializableConfiguration
  * This allows users to give the data source alias as the format type over the fully qualified
  * class name.
  *
- * ex: parquet.DefaultSource.format = "parquet".
- *
  * A new instance of this class with be instantiated each time a DDL call is made.
+ *
+ * @since 1.5.0
  */
 @DeveloperApi
 trait DataSourceRegister {
 
   /**
    * The string that represents the format that this data source provider uses. This is
-   * overridden by children to provide a nice alias for the data source,
-   * ex: override def format(): String = "parquet"
+   * overridden by children to provide a nice alias for the data source. For example:
+   *
+   * {{{
+   *   override def format(): String = "parquet"
+   * }}}
+   *
+   * @since 1.5.0
    */
-  def format(): String
+  def shortName(): String
 }
 
 /**
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
similarity index 93%
rename from sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java
rename to sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
index daec65a5bbe57..70dec1a9d3c92 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
@@ -3,7 +3,7 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.parquet.test.avro;
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;
 
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
@@ -12,6 +12,6 @@ public interface CompatibilityTest {
 
   @SuppressWarnings("all")
   public interface Callback extends CompatibilityTest {
-    public static final org.apache.avro.Protocol PROTOCOL = org.apache.spark.sql.parquet.test.avro.CompatibilityTest.PROTOCOL;
+    public static final org.apache.avro.Protocol PROTOCOL = org.apache.spark.sql.execution.datasources.parquet.test.avro.CompatibilityTest.PROTOCOL;
   }
 }
\ No newline at end of file
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
similarity index 78%
rename from sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java
rename to sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
index 051f1ee903863..a0a406bcd10c1 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
@@ -3,7 +3,7 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.parquet.test.avro;  
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class Nested extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
@@ -77,18 +77,18 @@ public void setNestedStringColumn(java.lang.String value) {
   }
 
   /** Creates a new Nested RecordBuilder */
-  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder() {
-    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder();
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder();
   }
   
   /** Creates a new Nested RecordBuilder by copying an existing Builder */
-  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
-    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder(other);
   }
   
   /** Creates a new Nested RecordBuilder by copying an existing Nested instance */
-  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.Nested other) {
-    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder(other);
   }
   
   /**
@@ -102,11 +102,11 @@ public static class Builder extends org.apache.avro.specific.SpecificRecordBuild
 
     /** Creates a new Builder */
     private Builder() {
-      super(org.apache.spark.sql.parquet.test.avro.Nested.SCHEMA$);
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.SCHEMA$);
     }
     
     /** Creates a Builder by copying an existing Builder */
-    private Builder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder other) {
       super(other);
       if (isValidValue(fields()[0], other.nested_ints_column)) {
         this.nested_ints_column = data().deepCopy(fields()[0].schema(), other.nested_ints_column);
@@ -119,8 +119,8 @@ private Builder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
     }
     
     /** Creates a Builder by copying an existing Nested instance */
-    private Builder(org.apache.spark.sql.parquet.test.avro.Nested other) {
-            super(org.apache.spark.sql.parquet.test.avro.Nested.SCHEMA$);
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.SCHEMA$);
       if (isValidValue(fields()[0], other.nested_ints_column)) {
         this.nested_ints_column = data().deepCopy(fields()[0].schema(), other.nested_ints_column);
         fieldSetFlags()[0] = true;
@@ -137,7 +137,7 @@ public java.util.List<java.lang.Integer> getNestedIntsColumn() {
     }
     
     /** Sets the value of the 'nested_ints_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder setNestedIntsColumn(java.util.List<java.lang.Integer> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder setNestedIntsColumn(java.util.List<java.lang.Integer> value) {
       validate(fields()[0], value);
       this.nested_ints_column = value;
       fieldSetFlags()[0] = true;
@@ -150,7 +150,7 @@ public boolean hasNestedIntsColumn() {
     }
     
     /** Clears the value of the 'nested_ints_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder clearNestedIntsColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder clearNestedIntsColumn() {
       nested_ints_column = null;
       fieldSetFlags()[0] = false;
       return this;
@@ -162,7 +162,7 @@ public java.lang.String getNestedStringColumn() {
     }
     
     /** Sets the value of the 'nested_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder setNestedStringColumn(java.lang.String value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder setNestedStringColumn(java.lang.String value) {
       validate(fields()[1], value);
       this.nested_string_column = value;
       fieldSetFlags()[1] = true;
@@ -175,7 +175,7 @@ public boolean hasNestedStringColumn() {
     }
     
     /** Clears the value of the 'nested_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder clearNestedStringColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder clearNestedStringColumn() {
       nested_string_column = null;
       fieldSetFlags()[1] = false;
       return this;
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
similarity index 83%
rename from sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java
rename to sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
index 354c9d73cca31..6198b00b1e3ca 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
@@ -3,7 +3,7 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.parquet.test.avro;  
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
@@ -25,7 +25,7 @@ public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBa
   @Deprecated public java.lang.String maybe_string_column;
   @Deprecated public java.util.List<java.lang.String> strings_column;
   @Deprecated public java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
-  @Deprecated public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column;
+  @Deprecated public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
 
   /**
    * Default constructor.  Note that this does not initialize fields
@@ -37,7 +37,7 @@ public ParquetAvroCompat() {}
   /**
    * All-args constructor.
    */
-  public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column, java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column, java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column) {
+  public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column, java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column, java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column) {
     this.bool_column = bool_column;
     this.int_column = int_column;
     this.long_column = long_column;
@@ -101,7 +101,7 @@ public void put(int field$, java.lang.Object value$) {
     case 13: maybe_string_column = (java.lang.String)value$; break;
     case 14: strings_column = (java.util.List<java.lang.String>)value$; break;
     case 15: string_to_int_column = (java.util.Map<java.lang.String,java.lang.Integer>)value$; break;
-    case 16: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>>)value$; break;
+    case 16: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>)value$; break;
     default: throw new org.apache.avro.AvroRuntimeException("Bad index");
     }
   }
@@ -349,7 +349,7 @@ public void setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Intege
   /**
    * Gets the value of the 'complex_column' field.
    */
-  public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> getComplexColumn() {
+  public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> getComplexColumn() {
     return complex_column;
   }
 
@@ -357,23 +357,23 @@ public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parque
    * Sets the value of the 'complex_column' field.
    * @param value the value to set.
    */
-  public void setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> value) {
+  public void setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> value) {
     this.complex_column = value;
   }
 
   /** Creates a new ParquetAvroCompat RecordBuilder */
-  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder() {
-    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder();
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder();
   }
   
   /** Creates a new ParquetAvroCompat RecordBuilder by copying an existing Builder */
-  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder other) {
-    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder(other);
   }
   
   /** Creates a new ParquetAvroCompat RecordBuilder by copying an existing ParquetAvroCompat instance */
-  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat other) {
-    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder(other);
   }
   
   /**
@@ -398,15 +398,15 @@ public static class Builder extends org.apache.avro.specific.SpecificRecordBuild
     private java.lang.String maybe_string_column;
     private java.util.List<java.lang.String> strings_column;
     private java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
-    private java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column;
+    private java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
 
     /** Creates a new Builder */
     private Builder() {
-      super(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
     }
     
     /** Creates a Builder by copying an existing Builder */
-    private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder other) {
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder other) {
       super(other);
       if (isValidValue(fields()[0], other.bool_column)) {
         this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
@@ -479,8 +479,8 @@ private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder
     }
     
     /** Creates a Builder by copying an existing ParquetAvroCompat instance */
-    private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat other) {
-            super(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
       if (isValidValue(fields()[0], other.bool_column)) {
         this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
         fieldSetFlags()[0] = true;
@@ -557,7 +557,7 @@ public java.lang.Boolean getBoolColumn() {
     }
     
     /** Sets the value of the 'bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setBoolColumn(boolean value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBoolColumn(boolean value) {
       validate(fields()[0], value);
       this.bool_column = value;
       fieldSetFlags()[0] = true;
@@ -570,7 +570,7 @@ public boolean hasBoolColumn() {
     }
     
     /** Clears the value of the 'bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearBoolColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBoolColumn() {
       fieldSetFlags()[0] = false;
       return this;
     }
@@ -581,7 +581,7 @@ public java.lang.Integer getIntColumn() {
     }
     
     /** Sets the value of the 'int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setIntColumn(int value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setIntColumn(int value) {
       validate(fields()[1], value);
       this.int_column = value;
       fieldSetFlags()[1] = true;
@@ -594,7 +594,7 @@ public boolean hasIntColumn() {
     }
     
     /** Clears the value of the 'int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearIntColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearIntColumn() {
       fieldSetFlags()[1] = false;
       return this;
     }
@@ -605,7 +605,7 @@ public java.lang.Long getLongColumn() {
     }
     
     /** Sets the value of the 'long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setLongColumn(long value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setLongColumn(long value) {
       validate(fields()[2], value);
       this.long_column = value;
       fieldSetFlags()[2] = true;
@@ -618,7 +618,7 @@ public boolean hasLongColumn() {
     }
     
     /** Clears the value of the 'long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearLongColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearLongColumn() {
       fieldSetFlags()[2] = false;
       return this;
     }
@@ -629,7 +629,7 @@ public java.lang.Float getFloatColumn() {
     }
     
     /** Sets the value of the 'float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setFloatColumn(float value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setFloatColumn(float value) {
       validate(fields()[3], value);
       this.float_column = value;
       fieldSetFlags()[3] = true;
@@ -642,7 +642,7 @@ public boolean hasFloatColumn() {
     }
     
     /** Clears the value of the 'float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearFloatColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearFloatColumn() {
       fieldSetFlags()[3] = false;
       return this;
     }
@@ -653,7 +653,7 @@ public java.lang.Double getDoubleColumn() {
     }
     
     /** Sets the value of the 'double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setDoubleColumn(double value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setDoubleColumn(double value) {
       validate(fields()[4], value);
       this.double_column = value;
       fieldSetFlags()[4] = true;
@@ -666,7 +666,7 @@ public boolean hasDoubleColumn() {
     }
     
     /** Clears the value of the 'double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearDoubleColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearDoubleColumn() {
       fieldSetFlags()[4] = false;
       return this;
     }
@@ -677,7 +677,7 @@ public java.nio.ByteBuffer getBinaryColumn() {
     }
     
     /** Sets the value of the 'binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setBinaryColumn(java.nio.ByteBuffer value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBinaryColumn(java.nio.ByteBuffer value) {
       validate(fields()[5], value);
       this.binary_column = value;
       fieldSetFlags()[5] = true;
@@ -690,7 +690,7 @@ public boolean hasBinaryColumn() {
     }
     
     /** Clears the value of the 'binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearBinaryColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBinaryColumn() {
       binary_column = null;
       fieldSetFlags()[5] = false;
       return this;
@@ -702,7 +702,7 @@ public java.lang.String getStringColumn() {
     }
     
     /** Sets the value of the 'string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringColumn(java.lang.String value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringColumn(java.lang.String value) {
       validate(fields()[6], value);
       this.string_column = value;
       fieldSetFlags()[6] = true;
@@ -715,7 +715,7 @@ public boolean hasStringColumn() {
     }
     
     /** Clears the value of the 'string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringColumn() {
       string_column = null;
       fieldSetFlags()[6] = false;
       return this;
@@ -727,7 +727,7 @@ public java.lang.Boolean getMaybeBoolColumn() {
     }
     
     /** Sets the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBoolColumn(java.lang.Boolean value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBoolColumn(java.lang.Boolean value) {
       validate(fields()[7], value);
       this.maybe_bool_column = value;
       fieldSetFlags()[7] = true;
@@ -740,7 +740,7 @@ public boolean hasMaybeBoolColumn() {
     }
     
     /** Clears the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBoolColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBoolColumn() {
       maybe_bool_column = null;
       fieldSetFlags()[7] = false;
       return this;
@@ -752,7 +752,7 @@ public java.lang.Integer getMaybeIntColumn() {
     }
     
     /** Sets the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeIntColumn(java.lang.Integer value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeIntColumn(java.lang.Integer value) {
       validate(fields()[8], value);
       this.maybe_int_column = value;
       fieldSetFlags()[8] = true;
@@ -765,7 +765,7 @@ public boolean hasMaybeIntColumn() {
     }
     
     /** Clears the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeIntColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeIntColumn() {
       maybe_int_column = null;
       fieldSetFlags()[8] = false;
       return this;
@@ -777,7 +777,7 @@ public java.lang.Long getMaybeLongColumn() {
     }
     
     /** Sets the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeLongColumn(java.lang.Long value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeLongColumn(java.lang.Long value) {
       validate(fields()[9], value);
       this.maybe_long_column = value;
       fieldSetFlags()[9] = true;
@@ -790,7 +790,7 @@ public boolean hasMaybeLongColumn() {
     }
     
     /** Clears the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeLongColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeLongColumn() {
       maybe_long_column = null;
       fieldSetFlags()[9] = false;
       return this;
@@ -802,7 +802,7 @@ public java.lang.Float getMaybeFloatColumn() {
     }
     
     /** Sets the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeFloatColumn(java.lang.Float value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeFloatColumn(java.lang.Float value) {
       validate(fields()[10], value);
       this.maybe_float_column = value;
       fieldSetFlags()[10] = true;
@@ -815,7 +815,7 @@ public boolean hasMaybeFloatColumn() {
     }
     
     /** Clears the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeFloatColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeFloatColumn() {
       maybe_float_column = null;
       fieldSetFlags()[10] = false;
       return this;
@@ -827,7 +827,7 @@ public java.lang.Double getMaybeDoubleColumn() {
     }
     
     /** Sets the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeDoubleColumn(java.lang.Double value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeDoubleColumn(java.lang.Double value) {
       validate(fields()[11], value);
       this.maybe_double_column = value;
       fieldSetFlags()[11] = true;
@@ -840,7 +840,7 @@ public boolean hasMaybeDoubleColumn() {
     }
     
     /** Clears the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeDoubleColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeDoubleColumn() {
       maybe_double_column = null;
       fieldSetFlags()[11] = false;
       return this;
@@ -852,7 +852,7 @@ public java.nio.ByteBuffer getMaybeBinaryColumn() {
     }
     
     /** Sets the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
       validate(fields()[12], value);
       this.maybe_binary_column = value;
       fieldSetFlags()[12] = true;
@@ -865,7 +865,7 @@ public boolean hasMaybeBinaryColumn() {
     }
     
     /** Clears the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBinaryColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBinaryColumn() {
       maybe_binary_column = null;
       fieldSetFlags()[12] = false;
       return this;
@@ -877,7 +877,7 @@ public java.lang.String getMaybeStringColumn() {
     }
     
     /** Sets the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeStringColumn(java.lang.String value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeStringColumn(java.lang.String value) {
       validate(fields()[13], value);
       this.maybe_string_column = value;
       fieldSetFlags()[13] = true;
@@ -890,7 +890,7 @@ public boolean hasMaybeStringColumn() {
     }
     
     /** Clears the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeStringColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeStringColumn() {
       maybe_string_column = null;
       fieldSetFlags()[13] = false;
       return this;
@@ -902,7 +902,7 @@ public java.util.List<java.lang.String> getStringsColumn() {
     }
     
     /** Sets the value of the 'strings_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringsColumn(java.util.List<java.lang.String> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringsColumn(java.util.List<java.lang.String> value) {
       validate(fields()[14], value);
       this.strings_column = value;
       fieldSetFlags()[14] = true;
@@ -915,7 +915,7 @@ public boolean hasStringsColumn() {
     }
     
     /** Clears the value of the 'strings_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringsColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringsColumn() {
       strings_column = null;
       fieldSetFlags()[14] = false;
       return this;
@@ -927,7 +927,7 @@ public java.util.Map<java.lang.String,java.lang.Integer> getStringToIntColumn()
     }
     
     /** Sets the value of the 'string_to_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
       validate(fields()[15], value);
       this.string_to_int_column = value;
       fieldSetFlags()[15] = true;
@@ -940,19 +940,19 @@ public boolean hasStringToIntColumn() {
     }
     
     /** Clears the value of the 'string_to_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringToIntColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringToIntColumn() {
       string_to_int_column = null;
       fieldSetFlags()[15] = false;
       return this;
     }
 
     /** Gets the value of the 'complex_column' field */
-    public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> getComplexColumn() {
+    public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> getComplexColumn() {
       return complex_column;
     }
     
     /** Sets the value of the 'complex_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> value) {
       validate(fields()[16], value);
       this.complex_column = value;
       fieldSetFlags()[16] = true;
@@ -965,7 +965,7 @@ public boolean hasComplexColumn() {
     }
     
     /** Clears the value of the 'complex_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearComplexColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearComplexColumn() {
       complex_column = null;
       fieldSetFlags()[16] = false;
       return this;
@@ -991,7 +991,7 @@ public ParquetAvroCompat build() {
         record.maybe_string_column = fieldSetFlags()[13] ? this.maybe_string_column : (java.lang.String) defaultValue(fields()[13]);
         record.strings_column = fieldSetFlags()[14] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[14]);
         record.string_to_int_column = fieldSetFlags()[15] ? this.string_to_int_column : (java.util.Map<java.lang.String,java.lang.Integer>) defaultValue(fields()[15]);
-        record.complex_column = fieldSetFlags()[16] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>>) defaultValue(fields()[16]);
+        record.complex_column = fieldSetFlags()[16] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>) defaultValue(fields()[16]);
         return record;
       } catch (Exception e) {
         throw new org.apache.avro.AvroRuntimeException(e);
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index c49f256be5501..adbd95197d7ca 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -25,8 +25,8 @@ import scala.util.Random
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.json.JSONRelation
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.json.JSONRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SQLTestUtils}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 92022ff23d2c3..73d5621897819 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import java.io.{File, StringWriter}
 import java.sql.{Date, Timestamp}
@@ -28,7 +28,7 @@ import org.apache.spark.sql.{SQLContext, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
-import org.apache.spark.sql.json.InferSchema.compatibleType
+import org.apache.spark.sql.execution.datasources.json.InferSchema.compatibleType
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.util.Utils
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
index 369df5653060b..6b62c9a003df6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
similarity index 96%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index bfa427349ff6a..4d9c07bb7a570 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
@@ -25,7 +25,7 @@ import scala.collection.JavaConversions._
 import org.apache.hadoop.fs.Path
 import org.apache.parquet.avro.AvroParquetWriter
 
-import org.apache.spark.sql.parquet.test.avro.{Nested, ParquetAvroCompat}
+import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.{Row, SQLContext}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
similarity index 97%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 57478931cd509..68f35b1f3aa83 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 import java.io.File
 
 import scala.collection.JavaConversions._
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index b6a7c4fbddbdc..7dd9680d8cd65 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.parquet.filter2.predicate.Operators._
 import org.apache.parquet.filter2.predicate.{FilterPredicate, Operators}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index b415da5b8c136..ee925afe08508 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
@@ -373,7 +373,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest {
     // _temporary should be missing if direct output committer works.
     try {
       configuration.set("spark.sql.parquet.output.committer.class",
-        "org.apache.spark.sql.parquet.DirectParquetOutputCommitter")
+        classOf[DirectParquetOutputCommitter].getCanonicalName)
       sqlContext.udf.register("div0", (x: Int) => x / 0)
       withTempPath { dir =>
         intercept[org.apache.spark.SparkException] {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
index 2eef10189f11c..73152de244759 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.File
 import java.math.BigInteger
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index 5c65a8ec57f00..5e6d9c1cd44a8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.File
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 4a0b3b60f419d..8f06de7ce7c4f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
similarity index 98%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
index 64e94056f209a..3c6e54db4bca7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.File
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
similarity index 98%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
index 1c532d78790d2..92b1d822172d5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.{Row, SQLContext}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
similarity index 92%
rename from sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index d22160f5384f4..953284c98b208 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -15,7 +15,7 @@
 * limitations under the License.
 */
 
-package org.apache.spark.sql.metric
+package org.apache.spark.sql.execution.metric
 
 import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
 
@@ -41,16 +41,6 @@ class SQLMetricsSuite extends SparkFunSuite {
     }
   }
 
-  test("IntSQLMetric should not box Int") {
-    val l = SQLMetrics.createIntMetric(TestSQLContext.sparkContext, "Int")
-    val f = () => { l += 1 }
-    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
-      val boxingFinder = new BoxingFinder()
-      cl.accept(boxingFinder, 0)
-      assert(boxingFinder.boxingInvokes.isEmpty, s"Found boxing: ${boxingFinder.boxingInvokes}")
-    }
-  }
-
   test("Normal accumulator should do boxing") {
     // We need this test to make sure BoxingFinder works.
     val l = TestSQLContext.sparkContext.accumulator(0L)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
index 69a561e16aa17..41dd1896c15df 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import java.util.Properties
 
 import org.apache.spark.{SparkException, SparkContext, SparkConf, SparkFunSuite}
 import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.sql.metric.LongSQLMetricValue
+import org.apache.spark.sql.execution.metric.LongSQLMetricValue
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.{DataFrame, SQLContext}
 import org.apache.spark.sql.execution.SQLExecution
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
index 1907e643c85dd..562c279067048 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
@@ -51,7 +51,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -75,7 +75,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
            |CREATE TEMPORARY TABLE jsonTable
-           |USING org.apache.spark.sql.json.DefaultSource
+           |USING json
            |OPTIONS (
            |  path '${path.toString}'
            |) AS
@@ -92,7 +92,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -107,7 +107,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE IF NOT EXISTS jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -122,7 +122,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -139,7 +139,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -158,7 +158,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE IF NOT EXISTS jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -175,7 +175,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE jsonTable (a int, b string)
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -188,7 +188,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -199,7 +199,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
index 1a4d41b02ca68..392da0b0826b5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
@@ -20,9 +20,37 @@ package org.apache.spark.sql.sources
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 
+
+// please note that the META-INF/services had to be modified for the test directory for this to work
+class DDLSourceLoadSuite extends DataSourceTest {
+
+  test("data sources with the same name") {
+    intercept[RuntimeException] {
+      caseInsensitiveContext.read.format("Fluet da Bomb").load()
+    }
+  }
+
+  test("load data source from format alias") {
+    caseInsensitiveContext.read.format("gathering quorum").load().schema ==
+      StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("specify full classname with duplicate formats") {
+    caseInsensitiveContext.read.format("org.apache.spark.sql.sources.FakeSourceOne")
+      .load().schema == StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("should fail to load ORC without HiveContext") {
+    intercept[ClassNotFoundException] {
+      caseInsensitiveContext.read.format("orc").load()
+    }
+  }
+}
+
+
 class FakeSourceOne extends RelationProvider with DataSourceRegister {
 
-  def format(): String = "Fluet da Bomb"
+  def shortName(): String = "Fluet da Bomb"
 
   override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
     new BaseRelation {
@@ -35,7 +63,7 @@ class FakeSourceOne extends RelationProvider with DataSourceRegister {
 
 class FakeSourceTwo extends RelationProvider  with DataSourceRegister {
 
-  def format(): String = "Fluet da Bomb"
+  def shortName(): String = "Fluet da Bomb"
 
   override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
     new BaseRelation {
@@ -48,7 +76,7 @@ class FakeSourceTwo extends RelationProvider  with DataSourceRegister {
 
 class FakeSourceThree extends RelationProvider with DataSourceRegister {
 
-  def format(): String = "gathering quorum"
+  def shortName(): String = "gathering quorum"
 
   override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
     new BaseRelation {
@@ -58,28 +86,3 @@ class FakeSourceThree extends RelationProvider with DataSourceRegister {
         StructType(Seq(StructField("stringType", StringType, nullable = false)))
     }
 }
-// please note that the META-INF/services had to be modified for the test directory for this to work
-class DDLSourceLoadSuite extends DataSourceTest {
-
-  test("data sources with the same name") {
-    intercept[RuntimeException] {
-      caseInsensitiveContext.read.format("Fluet da Bomb").load()
-    }
-  }
-
-  test("load data source from format alias") {
-    caseInsensitiveContext.read.format("gathering quorum").load().schema ==
-      StructType(Seq(StructField("stringType", StringType, nullable = false)))
-  }
-
-  test("specify full classname with duplicate formats") {
-    caseInsensitiveContext.read.format("org.apache.spark.sql.sources.FakeSourceOne")
-      .load().schema == StructType(Seq(StructField("stringType", StringType, nullable = false)))
-  }
-
-  test("Loading Orc") {
-    intercept[ClassNotFoundException] {
-      caseInsensitiveContext.read.format("orc").load()
-    }
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
index 3cbf5467b253a..27d1cd92fca1a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
@@ -22,14 +22,39 @@ import org.apache.spark.sql.execution.datasources.ResolvedDataSource
 
 class ResolvedDataSourceSuite extends SparkFunSuite {
 
-  test("builtin sources") {
-    assert(ResolvedDataSource.lookupDataSource("jdbc") ===
-      classOf[org.apache.spark.sql.jdbc.DefaultSource])
+  test("jdbc") {
+    assert(
+      ResolvedDataSource.lookupDataSource("jdbc") ===
+      classOf[org.apache.spark.sql.execution.datasources.jdbc.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.execution.datasources.jdbc") ===
+      classOf[org.apache.spark.sql.execution.datasources.jdbc.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.jdbc") ===
+        classOf[org.apache.spark.sql.execution.datasources.jdbc.DefaultSource])
+  }
 
-    assert(ResolvedDataSource.lookupDataSource("json") ===
-      classOf[org.apache.spark.sql.json.DefaultSource])
+  test("json") {
+    assert(
+      ResolvedDataSource.lookupDataSource("json") ===
+      classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.execution.datasources.json") ===
+        classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.json") ===
+        classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource])
+  }
 
-    assert(ResolvedDataSource.lookupDataSource("parquet") ===
-      classOf[org.apache.spark.sql.parquet.DefaultSource])
+  test("parquet") {
+    assert(
+      ResolvedDataSource.lookupDataSource("parquet") ===
+      classOf[org.apache.spark.sql.execution.datasources.parquet.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.execution.datasources.parquet") ===
+        classOf[org.apache.spark.sql.execution.datasources.parquet.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.parquet") ===
+        classOf[org.apache.spark.sql.execution.datasources.parquet.DefaultSource])
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 7198a32df4a02..ac9aaed19d566 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.catalyst.{InternalRow, SqlParser, TableIdentifier}
 import org.apache.spark.sql.execution.datasources
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 0c344c63fde3f..9f4f8b5789afe 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -32,7 +32,6 @@ import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.{HadoopRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
@@ -49,9 +48,9 @@ import scala.collection.JavaConversions._
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
-  def format(): String = "orc"
+  override def shortName(): String = "orc"
 
-  def createRelation(
+  override def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],
       dataSchema: Option[StructType],
@@ -144,7 +143,6 @@ private[orc] class OrcOutputWriter(
   }
 }
 
-@DeveloperApi
 private[sql] class OrcRelation(
     override val paths: Array[String],
     maybeDataSchema: Option[StructType],
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
index a45c2d957278f..1fa005d5f9a15 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.parquet.ParquetTest
+import org.apache.spark.sql.execution.datasources.parquet.ParquetTest
 import org.apache.spark.sql.{QueryTest, Row}
 
 case class Cases(lower: String, UPPER: String)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index b73d6665755d0..7f36a483a3965 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index f00d3754c364a..80eb9f122ad90 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.hive
 import org.apache.hadoop.hive.conf.HiveConf
 
 import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.parquet.ParquetCompatibilityTest
+import org.apache.spark.sql.execution.datasources.parquet.ParquetCompatibilityTest
 import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
 class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 2fa7ae3fa2e12..79a136ae6f619 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index c4bc60086f6e1..50f02432dacce 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.hive.execution.HiveTableScan
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index d280543a071d9..cb4cedddbfddd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -23,12 +23,12 @@ import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.{AnalysisException, SaveMode, parquet}
+import org.apache.spark.sql.{AnalysisException, SaveMode}
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 
 class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = classOf[parquet.DefaultSource].getCanonicalName
+  override val dataSourceName: String = "parquet"
 
   import sqlContext._
   import sqlContext.implicits._
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index 1813cc33226d1..48c37a1fa1022 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -53,7 +53,7 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
 
 class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String =
-    classOf[org.apache.spark.sql.json.DefaultSource].getCanonicalName
+    classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource].getCanonicalName
 
   import sqlContext._
 

From fe2fb7fb7189d183a4273ad27514af4b6b461f26 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 10 Aug 2015 13:52:18 -0700
Subject: [PATCH 0953/1454] [SPARK-9620] [SQL] generated UnsafeProjection
 should support many columns or large exressions

Currently, generated UnsafeProjection can reach 64k byte code limit of Java. This patch will split the generated expressions into multiple functions, to avoid the limitation.

After this patch, we can work well with table that have up to 64k columns (hit max number of constants limit in Java), it should be enough in practice.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8044 from davies/wider_table and squashes the following commits:

9192e6c [Davies Liu] fix generated safe projection
d1ef81a [Davies Liu] fix failed tests
737b3d3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
ffcd132 [Davies Liu] address comments
1b95be4 [Davies Liu] put the generated class into sql package
77ed72d [Davies Liu] address comments
4518e17 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
75ccd01 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
495e932 [Davies Liu] support wider table with more than 1k columns for generated projections
---
 .../expressions/codegen/CodeGenerator.scala   |  48 ++++++-
 .../codegen/GenerateMutableProjection.scala   |  43 +-----
 .../codegen/GenerateSafeProjection.scala      |  52 ++------
 .../codegen/GenerateUnsafeProjection.scala    | 122 +++++++++---------
 .../codegen/GenerateUnsafeRowJoiner.scala     |   2 +-
 .../codegen/GeneratedProjectionSuite.scala    |  82 ++++++++++++
 6 files changed, 207 insertions(+), 142 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 7b41c9a3f3b8e..c21f4d626a74e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions.codegen
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 import scala.language.existentials
 
 import com.google.common.cache.{CacheBuilder, CacheLoader}
@@ -265,6 +266,45 @@ class CodeGenContext {
   def isPrimitiveType(jt: String): Boolean = primitiveTypes.contains(jt)
 
   def isPrimitiveType(dt: DataType): Boolean = isPrimitiveType(javaType(dt))
+
+  /**
+   * Splits the generated code of expressions into multiple functions, because function has
+   * 64kb code size limit in JVM
+   *
+   * @param row the variable name of row that is used by expressions
+   */
+  def splitExpressions(row: String, expressions: Seq[String]): String = {
+    val blocks = new ArrayBuffer[String]()
+    val blockBuilder = new StringBuilder()
+    for (code <- expressions) {
+      // We can't know how many byte code will be generated, so use the number of bytes as limit
+      if (blockBuilder.length > 64 * 1000) {
+        blocks.append(blockBuilder.toString())
+        blockBuilder.clear()
+      }
+      blockBuilder.append(code)
+    }
+    blocks.append(blockBuilder.toString())
+
+    if (blocks.length == 1) {
+      // inline execution if only one block
+      blocks.head
+    } else {
+      val apply = freshName("apply")
+      val functions = blocks.zipWithIndex.map { case (body, i) =>
+        val name = s"${apply}_$i"
+        val code = s"""
+           |private void $name(InternalRow $row) {
+           |  $body
+           |}
+         """.stripMargin
+         addNewFunction(name, code)
+         name
+      }
+
+      functions.map(name => s"$name($row);").mkString("\n")
+    }
+  }
 }
 
 /**
@@ -289,15 +329,15 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   protected def declareMutableStates(ctx: CodeGenContext): String = {
     ctx.mutableStates.map { case (javaType, variableName, _) =>
       s"private $javaType $variableName;"
-    }.mkString
+    }.mkString("\n")
   }
 
   protected def initMutableStates(ctx: CodeGenContext): String = {
-    ctx.mutableStates.map(_._3).mkString
+    ctx.mutableStates.map(_._3).mkString("\n")
   }
 
   protected def declareAddedFunctions(ctx: CodeGenContext): String = {
-    ctx.addedFuntions.map { case (funcName, funcCode) => funcCode }.mkString
+    ctx.addedFuntions.map { case (funcName, funcCode) => funcCode }.mkString("\n")
   }
 
   /**
@@ -328,6 +368,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   private[this] def doCompile(code: String): GeneratedClass = {
     val evaluator = new ClassBodyEvaluator()
     evaluator.setParentClassLoader(getClass.getClassLoader)
+    // Cannot be under package codegen, or fail with java.lang.InstantiationException
+    evaluator.setClassName("org.apache.spark.sql.catalyst.expressions.GeneratedClass")
     evaluator.setDefaultImports(Array(
       classOf[PlatformDependent].getName,
       classOf[InternalRow].getName,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index ac58423cd884d..b4d4df8934bd4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -40,7 +40,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
 
   protected def create(expressions: Seq[Expression]): (() => MutableProjection) = {
     val ctx = newCodeGenContext()
-    val projectionCode = expressions.zipWithIndex.map {
+    val projectionCodes = expressions.zipWithIndex.map {
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
@@ -65,49 +65,21 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
           """
         }
     }
-    // collect projections into blocks as function has 64kb codesize limit in JVM
-    val projectionBlocks = new ArrayBuffer[String]()
-    val blockBuilder = new StringBuilder()
-    for (projection <- projectionCode) {
-      if (blockBuilder.length > 16 * 1000) {
-        projectionBlocks.append(blockBuilder.toString())
-        blockBuilder.clear()
-      }
-      blockBuilder.append(projection)
-    }
-    projectionBlocks.append(blockBuilder.toString())
-
-    val (projectionFuns, projectionCalls) = {
-      // inline execution if codesize limit was not broken
-      if (projectionBlocks.length == 1) {
-        ("", projectionBlocks.head)
-      } else {
-        (
-          projectionBlocks.zipWithIndex.map { case (body, i) =>
-            s"""
-               |private void apply$i(InternalRow i) {
-               |  $body
-               |}
-             """.stripMargin
-          }.mkString,
-          projectionBlocks.indices.map(i => s"apply$i(i);").mkString("\n")
-        )
-      }
-    }
+    val allProjections = ctx.splitExpressions("i", projectionCodes)
 
     val code = s"""
       public Object generate($exprType[] expr) {
-        return new SpecificProjection(expr);
+        return new SpecificMutableProjection(expr);
       }
 
-      class SpecificProjection extends ${classOf[BaseMutableProjection].getName} {
+      class SpecificMutableProjection extends ${classOf[BaseMutableProjection].getName} {
 
         private $exprType[] expressions;
         private $mutableRowType mutableRow;
         ${declareMutableStates(ctx)}
         ${declareAddedFunctions(ctx)}
 
-        public SpecificProjection($exprType[] expr) {
+        public SpecificMutableProjection($exprType[] expr) {
           expressions = expr;
           mutableRow = new $genericMutableRowType(${expressions.size});
           ${initMutableStates(ctx)}
@@ -123,12 +95,9 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
           return (InternalRow) mutableRow;
         }
 
-        $projectionFuns
-
         public Object apply(Object _i) {
           InternalRow i = (InternalRow) _i;
-          $projectionCalls
-
+          $allProjections
           return mutableRow;
         }
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
index ef08ddf041afc..7ad352d7ce3e9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
 import org.apache.spark.sql.types._
@@ -43,6 +41,9 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
     val tmp = ctx.freshName("tmp")
     val output = ctx.freshName("safeRow")
     val values = ctx.freshName("values")
+    // These expressions could be splitted into multiple functions
+    ctx.addMutableState("Object[]", values, s"this.$values = null;")
+
     val rowClass = classOf[GenericInternalRow].getName
 
     val fieldWriters = schema.map(_.dataType).zipWithIndex.map { case (dt, i) =>
@@ -53,12 +54,12 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
           $values[$i] = ${converter.primitive};
         }
       """
-    }.mkString("\n")
-
+    }
+    val allFields = ctx.splitExpressions(tmp, fieldWriters)
     val code = s"""
       final InternalRow $tmp = $input;
-      final Object[] $values = new Object[${schema.length}];
-      $fieldWriters
+      this.$values = new Object[${schema.length}];
+      $allFields
       final InternalRow $output = new $rowClass($values);
     """
 
@@ -128,7 +129,7 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
 
   protected def create(expressions: Seq[Expression]): Projection = {
     val ctx = newCodeGenContext()
-    val projectionCode = expressions.zipWithIndex.map {
+    val expressionCodes = expressions.zipWithIndex.map {
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
@@ -143,36 +144,7 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
             }
           """
     }
-    // collect projections into blocks as function has 64kb codesize limit in JVM
-    val projectionBlocks = new ArrayBuffer[String]()
-    val blockBuilder = new StringBuilder()
-    for (projection <- projectionCode) {
-      if (blockBuilder.length > 16 * 1000) {
-        projectionBlocks.append(blockBuilder.toString())
-        blockBuilder.clear()
-      }
-      blockBuilder.append(projection)
-    }
-    projectionBlocks.append(blockBuilder.toString())
-
-    val (projectionFuns, projectionCalls) = {
-      // inline it if we have only one block
-      if (projectionBlocks.length == 1) {
-        ("", projectionBlocks.head)
-      } else {
-        (
-          projectionBlocks.zipWithIndex.map { case (body, i) =>
-            s"""
-               |private void apply$i(InternalRow i) {
-               |  $body
-               |}
-             """.stripMargin
-          }.mkString,
-          projectionBlocks.indices.map(i => s"apply$i(i);").mkString("\n")
-          )
-      }
-    }
-
+    val allExpressions = ctx.splitExpressions("i", expressionCodes)
     val code = s"""
       public Object generate($exprType[] expr) {
         return new SpecificSafeProjection(expr);
@@ -183,6 +155,7 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
         private $exprType[] expressions;
         private $mutableRowType mutableRow;
         ${declareMutableStates(ctx)}
+        ${declareAddedFunctions(ctx)}
 
         public SpecificSafeProjection($exprType[] expr) {
           expressions = expr;
@@ -190,12 +163,9 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
           ${initMutableStates(ctx)}
         }
 
-        $projectionFuns
-
         public Object apply(Object _i) {
           InternalRow i = (InternalRow) _i;
-          $projectionCalls
-
+          $allExpressions
           return mutableRow;
         }
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index d8912df694a10..29f6a7b981752 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
 
 /**
  * Generates a [[Projection]] that returns an [[UnsafeRow]].
@@ -41,8 +40,6 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   private val ArrayWriter = classOf[UnsafeRowWriters.ArrayWriter].getName
   private val MapWriter = classOf[UnsafeRowWriters.MapWriter].getName
 
-  private val PlatformDependent = classOf[PlatformDependent].getName
-
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
     case NullType => true
@@ -56,19 +53,19 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
   def genAdditionalSize(dt: DataType, ev: GeneratedExpressionCode): String = dt match {
     case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
-      s" + $DecimalWriter.getSize(${ev.primitive})"
+      s"$DecimalWriter.getSize(${ev.primitive})"
     case StringType =>
-      s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive})"
     case BinaryType =>
-      s" + (${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive})"
     case CalendarIntervalType =>
-      s" + (${ev.isNull} ? 0 : 16)"
+      s"${ev.isNull} ? 0 : 16"
     case _: StructType =>
-      s" + (${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive})"
     case _: ArrayType =>
-      s" + (${ev.isNull} ? 0 : $ArrayWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $ArrayWriter.getSize(${ev.primitive})"
     case _: MapType =>
-      s" + (${ev.isNull} ? 0 : $MapWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $MapWriter.getSize(${ev.primitive})"
     case _ => ""
   }
 
@@ -125,64 +122,69 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
    */
   private def createCodeForStruct(
       ctx: CodeGenContext,
+      row: String,
       inputs: Seq[GeneratedExpressionCode],
       inputTypes: Seq[DataType]): GeneratedExpressionCode = {
 
+    val fixedSize = 8 * inputTypes.length + UnsafeRow.calculateBitSetWidthInBytes(inputTypes.length)
+
     val output = ctx.freshName("convertedStruct")
-    ctx.addMutableState("UnsafeRow", output, s"$output = new UnsafeRow();")
+    ctx.addMutableState("UnsafeRow", output, s"this.$output = new UnsafeRow();")
     val buffer = ctx.freshName("buffer")
-    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
-    val numBytes = ctx.freshName("numBytes")
+    ctx.addMutableState("byte[]", buffer, s"this.$buffer = new byte[$fixedSize];")
     val cursor = ctx.freshName("cursor")
+    ctx.addMutableState("int", cursor, s"this.$cursor = 0;")
+    val tmp = ctx.freshName("tmpBuffer")
 
-    val convertedFields = inputTypes.zip(inputs).map { case (dt, input) =>
-      createConvertCode(ctx, input, dt)
-    }
-
-    val fixedSize = 8 * inputTypes.length + UnsafeRow.calculateBitSetWidthInBytes(inputTypes.length)
-    val additionalSize = inputTypes.zip(convertedFields).map { case (dt, ev) =>
-      genAdditionalSize(dt, ev)
-    }.mkString("")
-
-    val fieldWriters = inputTypes.zip(convertedFields).zipWithIndex.map { case ((dt, ev), i) =>
-      val update = genFieldWriter(ctx, dt, ev, output, i, cursor)
-      if (dt.isInstanceOf[DecimalType]) {
-        // Can't call setNullAt() for DecimalType
+    val convertedFields = inputTypes.zip(inputs).zipWithIndex.map { case ((dt, input), i) =>
+      val ev = createConvertCode(ctx, input, dt)
+      val growBuffer = if (!UnsafeRow.isFixedLength(dt)) {
+        val numBytes = ctx.freshName("numBytes")
         s"""
+          int $numBytes = $cursor + (${genAdditionalSize(dt, ev)});
+          if ($buffer.length < $numBytes) {
+            // This will not happen frequently, because the buffer is re-used.
+            byte[] $tmp = new byte[$numBytes * 2];
+            PlatformDependent.copyMemory($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
+              $tmp, PlatformDependent.BYTE_ARRAY_OFFSET, $buffer.length);
+            $buffer = $tmp;
+          }
+          $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
+            ${inputTypes.length}, $numBytes);
+         """
+      } else {
+        ""
+      }
+      val update = dt match {
+        case dt: DecimalType if dt.precision > Decimal.MAX_LONG_DIGITS =>
+          // Can't call setNullAt() for DecimalType
+          s"""
           if (${ev.isNull}) {
-           $cursor += $DecimalWriter.write($output, $i, $cursor, null);
+            $cursor += $DecimalWriter.write($output, $i, $cursor, null);
           } else {
-           $update;
+            ${genFieldWriter(ctx, dt, ev, output, i, cursor)};
           }
         """
-      } else {
-        s"""
+        case _ =>
+          s"""
           if (${ev.isNull}) {
             $output.setNullAt($i);
           } else {
-            $update;
+            ${genFieldWriter(ctx, dt, ev, output, i, cursor)};
           }
         """
       }
-    }.mkString("\n")
+      s"""
+        ${ev.code}
+        $growBuffer
+        $update
+      """
+    }
 
     val code = s"""
-      ${convertedFields.map(_.code).mkString("\n")}
-
-      final int $numBytes = $fixedSize $additionalSize;
-      if ($numBytes > $buffer.length) {
-        $buffer = new byte[$numBytes];
-      }
-
-      $output.pointTo(
-        $buffer,
-        $PlatformDependent.BYTE_ARRAY_OFFSET,
-        ${inputTypes.length},
-        $numBytes);
-
-      int $cursor = $fixedSize;
-
-      $fieldWriters
+      $cursor = $fixedSize;
+      $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $cursor);
+      ${ctx.splitExpressions(row, convertedFields)}
       """
     GeneratedExpressionCode(code, "false", output)
   }
@@ -265,17 +267,17 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
-          $PlatformDependent.UNSAFE.put${ctx.primitiveTypeName(elementType)}(
+          PlatformDependent.UNSAFE.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive});
           $cursor += $elementSize;
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
-          $PlatformDependent.UNSAFE.putLong(
+          PlatformDependent.UNSAFE.putLong(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive}.toUnscaledLong());
           $cursor += 8;
         """
@@ -284,7 +286,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         s"""
           $cursor += $writer.write(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
             $elements[$index]);
         """
     }
@@ -318,14 +320,14 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           for (int $index = 0; $index < $numElements; $index++) {
             if ($checkNull) {
               // If element is null, write the negative value address into offset region.
-              $PlatformDependent.UNSAFE.putInt(
+              PlatformDependent.UNSAFE.putInt(
                 $buffer,
-                $PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
+                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
                 -$cursor);
             } else {
-              $PlatformDependent.UNSAFE.putInt(
+              PlatformDependent.UNSAFE.putInt(
                 $buffer,
-                $PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
+                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
                 $cursor);
 
               $writeElement
@@ -334,7 +336,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
           $output.pointTo(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET,
+            PlatformDependent.BYTE_ARRAY_OFFSET,
             $numElements,
             $numBytes);
         }
@@ -400,7 +402,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         val fieldIsNull = s"$tmp.isNullAt($i)"
         GeneratedExpressionCode("", fieldIsNull, getFieldCode)
       }
-      val converter = createCodeForStruct(ctx, fieldEvals, fieldTypes)
+      val converter = createCodeForStruct(ctx, tmp, fieldEvals, fieldTypes)
       val code = s"""
         ${input.code}
          UnsafeRow $output = null;
@@ -427,7 +429,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   def createCode(ctx: CodeGenContext, expressions: Seq[Expression]): GeneratedExpressionCode = {
     val exprEvals = expressions.map(e => e.gen(ctx))
     val exprTypes = expressions.map(_.dataType)
-    createCodeForStruct(ctx, exprEvals, exprTypes)
+    createCodeForStruct(ctx, "i", exprEvals, exprTypes)
   }
 
   protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
index 30b51dd83fa9a..8aaa5b4300044 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
@@ -155,7 +155,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
            |$putLong(buf, $cursor, $getLong(buf, $cursor) + ($shift << 32));
          """.stripMargin
       }
-    }.mkString
+    }.mkString("\n")
 
     // ------------------------ Finally, put everything together  --------------------------- //
     val code = s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
new file mode 100644
index 0000000000000..8c7ee8720f7bb
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{StringType, IntegerType, StructField, StructType}
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * A test suite for generated projections
+ */
+class GeneratedProjectionSuite extends SparkFunSuite {
+
+  test("generated projections on wider table") {
+    val N = 1000
+    val wideRow1 = new GenericInternalRow((1 to N).toArray[Any])
+    val schema1 = StructType((1 to N).map(i => StructField("", IntegerType)))
+    val wideRow2 = new GenericInternalRow(
+      (1 to N).map(i => UTF8String.fromString(i.toString)).toArray[Any])
+    val schema2 = StructType((1 to N).map(i => StructField("", StringType)))
+    val joined = new JoinedRow(wideRow1, wideRow2)
+    val joinedSchema = StructType(schema1 ++ schema2)
+    val nested = new JoinedRow(InternalRow(joined, joined), joined)
+    val nestedSchema = StructType(
+      Seq(StructField("", joinedSchema), StructField("", joinedSchema)) ++ joinedSchema)
+
+    // test generated UnsafeProjection
+    val unsafeProj = UnsafeProjection.create(nestedSchema)
+    val unsafe: UnsafeRow = unsafeProj(nested)
+    (0 until N).foreach { i =>
+      val s = UTF8String.fromString((i + 1).toString)
+      assert(i + 1 === unsafe.getInt(i + 2))
+      assert(s === unsafe.getUTF8String(i + 2 + N))
+      assert(i + 1 === unsafe.getStruct(0, N * 2).getInt(i))
+      assert(s === unsafe.getStruct(0, N * 2).getUTF8String(i + N))
+      assert(i + 1 === unsafe.getStruct(1, N * 2).getInt(i))
+      assert(s === unsafe.getStruct(1, N * 2).getUTF8String(i + N))
+    }
+
+    // test generated SafeProjection
+    val safeProj = FromUnsafeProjection(nestedSchema)
+    val result = safeProj(unsafe)
+    // Can't compare GenericInternalRow with JoinedRow directly
+    (0 until N).foreach { i =>
+      val r = i + 1
+      val s = UTF8String.fromString((i + 1).toString)
+      assert(r === result.getInt(i + 2))
+      assert(s === result.getUTF8String(i + 2 + N))
+      assert(r === result.getStruct(0, N * 2).getInt(i))
+      assert(s === result.getStruct(0, N * 2).getUTF8String(i + N))
+      assert(r === result.getStruct(1, N * 2).getInt(i))
+      assert(s === result.getStruct(1, N * 2).getUTF8String(i + N))
+    }
+
+    // test generated MutableProjection
+    val exprs = nestedSchema.fields.zipWithIndex.map { case (f, i) =>
+      BoundReference(i, f.dataType, true)
+    }
+    val mutableProj = GenerateMutableProjection.generate(exprs)()
+    val row1 = mutableProj(result)
+    assert(result === row1)
+    val row2 = mutableProj(result)
+    assert(result === row2)
+  }
+}

From c4fd2a242228ee101904770446e3f37d49e39b76 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 10 Aug 2015 13:55:11 -0700
Subject: [PATCH 0954/1454] [SPARK-9759] [SQL] improve decimal.times() and
 cast(int, decimalType)

This patch optimize two things:

1. passing MathContext to JavaBigDecimal.multiply/divide/reminder to do right rounding, because java.math.BigDecimal.apply(MathContext) is expensive

2. Cast integer/short/byte to decimal directly (without double)

This two optimizations could speed up the end-to-end time of a aggregation (SUM(short * decimal(5, 2)) 75% (from 19s -> 10.8s)

Author: Davies Liu <davies@databricks.com>

Closes #8052 from davies/optimize_decimal and squashes the following commits:

225efad [Davies Liu] improve decimal.times() and cast(int, decimalType)
---
 .../spark/sql/catalyst/expressions/Cast.scala | 42 +++++++------------
 .../org/apache/spark/sql/types/Decimal.scala  | 12 +++---
 2 files changed, 22 insertions(+), 32 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 946c5a9c04f14..616b9e0e65b78 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -155,7 +155,7 @@ case class Cast(child: Expression, dataType: DataType)
     case ByteType =>
       buildCast[Byte](_, _ != 0)
     case DecimalType() =>
-      buildCast[Decimal](_, _ != Decimal.ZERO)
+      buildCast[Decimal](_, !_.isZero)
     case DoubleType =>
       buildCast[Double](_, _ != 0)
     case FloatType =>
@@ -315,13 +315,13 @@ case class Cast(child: Expression, dataType: DataType)
     case TimestampType =>
       // Note that we lose precision here.
       buildCast[Long](_, t => changePrecision(Decimal(timestampToDouble(t)), target))
-    case DecimalType() =>
+    case dt: DecimalType =>
       b => changePrecision(b.asInstanceOf[Decimal].clone(), target)
-    case LongType =>
-      b => changePrecision(Decimal(b.asInstanceOf[Long]), target)
-    case x: NumericType => // All other numeric types can be represented precisely as Doubles
+    case t: IntegralType =>
+      b => changePrecision(Decimal(t.integral.asInstanceOf[Integral[Any]].toLong(b)), target)
+    case x: FractionalType =>
       b => try {
-        changePrecision(Decimal(x.numeric.asInstanceOf[Numeric[Any]].toDouble(b)), target)
+        changePrecision(Decimal(x.fractional.asInstanceOf[Fractional[Any]].toDouble(b)), target)
       } catch {
         case _: NumberFormatException => null
       }
@@ -534,10 +534,7 @@ case class Cast(child: Expression, dataType: DataType)
         (c, evPrim, evNull) =>
           s"""
             try {
-              org.apache.spark.sql.types.Decimal tmpDecimal =
-                new org.apache.spark.sql.types.Decimal().set(
-                  new scala.math.BigDecimal(
-                    new java.math.BigDecimal($c.toString())));
+              Decimal tmpDecimal = Decimal.apply(new java.math.BigDecimal($c.toString()));
               ${changePrecision("tmpDecimal", target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
@@ -546,12 +543,7 @@ case class Cast(child: Expression, dataType: DataType)
       case BooleanType =>
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal = null;
-            if ($c) {
-              tmpDecimal = new org.apache.spark.sql.types.Decimal().set(1);
-            } else {
-              tmpDecimal = new org.apache.spark.sql.types.Decimal().set(0);
-            }
+            Decimal tmpDecimal = $c ? Decimal.apply(1) : Decimal.apply(0);
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
       case DateType =>
@@ -561,32 +553,28 @@ case class Cast(child: Expression, dataType: DataType)
         // Note that we lose precision here.
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal =
-              new org.apache.spark.sql.types.Decimal().set(
-                scala.math.BigDecimal.valueOf(${timestampToDoubleCode(c)}));
+            Decimal tmpDecimal = Decimal.apply(
+              scala.math.BigDecimal.valueOf(${timestampToDoubleCode(c)}));
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
       case DecimalType() =>
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal = $c.clone();
+            Decimal tmpDecimal = $c.clone();
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
-      case LongType =>
+      case x: IntegralType =>
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal =
-              new org.apache.spark.sql.types.Decimal().set($c);
+            Decimal tmpDecimal = Decimal.apply((long) $c);
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
-      case x: NumericType =>
+      case x: FractionalType =>
         // All other numeric types can be represented precisely as Doubles
         (c, evPrim, evNull) =>
           s"""
             try {
-              org.apache.spark.sql.types.Decimal tmpDecimal =
-                new org.apache.spark.sql.types.Decimal().set(
-                  scala.math.BigDecimal.valueOf((double) $c));
+              Decimal tmpDecimal = Decimal.apply(scala.math.BigDecimal.valueOf((double) $c));
               ${changePrecision("tmpDecimal", target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index 624c3f3d7fa97..d95805c24521c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -139,9 +139,9 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def toBigDecimal: BigDecimal = {
     if (decimalVal.ne(null)) {
-      decimalVal(MATH_CONTEXT)
+      decimalVal
     } else {
-      BigDecimal(longVal, _scale)(MATH_CONTEXT)
+      BigDecimal(longVal, _scale)
     }
   }
 
@@ -280,13 +280,15 @@ final class Decimal extends Ordered[Decimal] with Serializable {
   }
 
   // HiveTypeCoercion will take care of the precision, scale of result
-  def * (that: Decimal): Decimal = Decimal(toBigDecimal * that.toBigDecimal)
+  def * (that: Decimal): Decimal =
+    Decimal(toJavaBigDecimal.multiply(that.toJavaBigDecimal, MATH_CONTEXT))
 
   def / (that: Decimal): Decimal =
-    if (that.isZero) null else Decimal(toBigDecimal / that.toBigDecimal)
+    if (that.isZero) null else Decimal(toJavaBigDecimal.divide(that.toJavaBigDecimal, MATH_CONTEXT))
 
   def % (that: Decimal): Decimal =
-    if (that.isZero) null else Decimal(toBigDecimal % that.toBigDecimal)
+    if (that.isZero) null
+    else Decimal(toJavaBigDecimal.remainder(that.toJavaBigDecimal, MATH_CONTEXT))
 
   def remainder(that: Decimal): Decimal = this % that
 

From 853809e948e7c5092643587a30738115b6591a59 Mon Sep 17 00:00:00 2001
From: Prabeesh K <prabsmails@gmail.com>
Date: Mon, 10 Aug 2015 16:33:23 -0700
Subject: [PATCH 0955/1454] [SPARK-5155] [PYSPARK] [STREAMING] Mqtt streaming
 support in Python

This PR is based on #4229, thanks prabeesh.

Closes #4229

Author: Prabeesh K <prabsmails@gmail.com>
Author: zsxwing <zsxwing@gmail.com>
Author: prabs <prabsmails@gmail.com>
Author: Prabeesh K <prabeesh.k@namshi.com>

Closes #7833 from zsxwing/pr4229 and squashes the following commits:

9570bec [zsxwing] Fix the variable name and check null in finally
4a9c79e [zsxwing] Fix pom.xml indentation
abf5f18 [zsxwing] Merge branch 'master' into pr4229
935615c [zsxwing] Fix the flaky MQTT tests
47278c5 [zsxwing] Include the project class files
478f844 [zsxwing] Add unpack
5f8a1d4 [zsxwing] Make the maven build generate the test jar for Python MQTT tests
734db99 [zsxwing] Merge branch 'master' into pr4229
126608a [Prabeesh K] address the comments
b90b709 [Prabeesh K] Merge pull request #1 from zsxwing/pr4229
d07f454 [zsxwing] Register StreamingListerner before starting StreamingContext; Revert unncessary changes; fix the python unit test
a6747cb [Prabeesh K] wait for starting the receiver before publishing data
87fc677 [Prabeesh K] address the comments:
97244ec [zsxwing] Make sbt build the assembly test jar for streaming mqtt
80474d1 [Prabeesh K] fix
1f0cfe9 [Prabeesh K] python style fix
e1ee016 [Prabeesh K] scala style fix
a5a8f9f [Prabeesh K] added Python test
9767d82 [Prabeesh K] implemented Python-friendly class
a11968b [Prabeesh K] fixed python style
795ec27 [Prabeesh K] address comments
ee387ae [Prabeesh K] Fix assembly jar location of mqtt-assembly
3f4df12 [Prabeesh K] updated version
b34c3c1 [prabs] adress comments
3aa7fff [prabs] Added Python streaming mqtt word count example
b7d42ff [prabs] Mqtt streaming support in Python
---
 dev/run-tests.py                              |   2 +
 dev/sparktestsupport/modules.py               |   2 +
 docs/streaming-programming-guide.md           |   2 +-
 .../main/python/streaming/mqtt_wordcount.py   |  58 +++++++++
 external/mqtt-assembly/pom.xml                | 102 +++++++++++++++
 external/mqtt/pom.xml                         |  28 +++++
 external/mqtt/src/main/assembly/assembly.xml  |  44 +++++++
 .../spark/streaming/mqtt/MQTTUtils.scala      |  16 +++
 .../streaming/mqtt/MQTTStreamSuite.scala      | 118 +++---------------
 .../spark/streaming/mqtt/MQTTTestUtils.scala  | 111 ++++++++++++++++
 pom.xml                                       |   1 +
 project/SparkBuild.scala                      |  12 +-
 python/pyspark/streaming/mqtt.py              |  72 +++++++++++
 python/pyspark/streaming/tests.py             | 106 +++++++++++++++-
 14 files changed, 565 insertions(+), 109 deletions(-)
 create mode 100644 examples/src/main/python/streaming/mqtt_wordcount.py
 create mode 100644 external/mqtt-assembly/pom.xml
 create mode 100644 external/mqtt/src/main/assembly/assembly.xml
 create mode 100644 external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
 create mode 100644 python/pyspark/streaming/mqtt.py

diff --git a/dev/run-tests.py b/dev/run-tests.py
index d1852b95bb292..f689425ee40b6 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -303,6 +303,8 @@ def build_spark_sbt(hadoop_version):
                  "assembly/assembly",
                  "streaming-kafka-assembly/assembly",
                  "streaming-flume-assembly/assembly",
+                 "streaming-mqtt-assembly/assembly",
+                 "streaming-mqtt/test:assembly",
                  "streaming-kinesis-asl-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index a9717ff9569c7..d82c0cca37bc6 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -181,6 +181,7 @@ def contains_file(self, filename):
     dependencies=[streaming],
     source_file_regexes=[
         "external/mqtt",
+        "external/mqtt-assembly",
     ],
     sbt_test_goals=[
         "streaming-mqtt/test",
@@ -306,6 +307,7 @@ def contains_file(self, filename):
         streaming,
         streaming_kafka,
         streaming_flume_assembly,
+        streaming_mqtt,
         streaming_kinesis_asl
     ],
     source_file_regexes=[
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index dbfdb619f89e2..c59d936b43c88 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -683,7 +683,7 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea
 {:.no_toc}
 
 <span class="badge" style="background-color: grey">Python API</span> As of Spark {{site.SPARK_VERSION_SHORT}},
-out of these sources, *only* Kafka and Flume are available in the Python API. We will add more advanced sources in the Python API in future.
+out of these sources, *only* Kafka, Flume and MQTT are available in the Python API. We will add more advanced sources in the Python API in future.
 
 This category of sources require interfacing with external non-Spark libraries, some of them with
 complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts
diff --git a/examples/src/main/python/streaming/mqtt_wordcount.py b/examples/src/main/python/streaming/mqtt_wordcount.py
new file mode 100644
index 0000000000000..617ce5ea6775e
--- /dev/null
+++ b/examples/src/main/python/streaming/mqtt_wordcount.py
@@ -0,0 +1,58 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+ A sample wordcount with MqttStream stream
+ Usage: mqtt_wordcount.py <broker url> <topic>
+
+ To run this in your local machine, you need to setup a MQTT broker and publisher first,
+ Mosquitto is one of the open source MQTT Brokers, see
+ http://mosquitto.org/
+ Eclipse paho project provides number of clients and utilities for working with MQTT, see
+ http://www.eclipse.org/paho/#getting-started
+
+ and then run the example
+    `$ bin/spark-submit --jars external/mqtt-assembly/target/scala-*/\
+      spark-streaming-mqtt-assembly-*.jar examples/src/main/python/streaming/mqtt_wordcount.py \
+      tcp://localhost:1883 foo`
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+from pyspark.streaming.mqtt import MQTTUtils
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: mqtt_wordcount.py <broker url> <topic>"
+        exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingMQTTWordCount")
+    ssc = StreamingContext(sc, 1)
+
+    brokerUrl = sys.argv[1]
+    topic = sys.argv[2]
+
+    lines = MQTTUtils.createStream(ssc, brokerUrl, topic)
+    counts = lines.flatMap(lambda line: line.split(" ")) \
+        .map(lambda word: (word, 1)) \
+        .reduceByKey(lambda a, b: a+b)
+    counts.pprint()
+
+    ssc.start()
+    ssc.awaitTermination()
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
new file mode 100644
index 0000000000000..9c94473053d96
--- /dev/null
+++ b/external/mqtt-assembly/pom.xml
@@ -0,0 +1,102 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent_2.10</artifactId>
+    <version>1.5.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-mqtt-assembly_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External MQTT Assembly</name>
+  <url>http://spark.apache.org/</url>
+
+  <properties>
+    <sbt.project.name>streaming-mqtt-assembly</sbt.project.name>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-mqtt_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <scope>provided</scope>
+    </dependency>
+  </dependencies>
+
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-shade-plugin</artifactId>
+        <configuration>
+          <shadedArtifactAttached>false</shadedArtifactAttached>
+          <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-mqtt-assembly-${project.version}.jar</outputFile>
+          <artifactSet>
+            <includes>
+              <include>*:*</include>
+            </includes>
+          </artifactSet>
+          <filters>
+            <filter>
+              <artifact>*:*</artifact>
+              <excludes>
+                <exclude>META-INF/*.SF</exclude>
+                <exclude>META-INF/*.DSA</exclude>
+                <exclude>META-INF/*.RSA</exclude>
+              </excludes>
+            </filter>
+          </filters>
+        </configuration>
+        <executions>
+          <execution>
+            <phase>package</phase>
+            <goals>
+              <goal>shade</goal>
+            </goals>
+            <configuration>
+              <transformers>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                  <resource>reference.conf</resource>
+                </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
+                  <resource>log4j.properties</resource>
+                </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/>
+              </transformers>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 0e41e5781784b..69b309876a0db 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -78,5 +78,33 @@
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+
+    <plugins>
+      <!-- Assemble a jar with test dependencies for Python tests -->
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-assembly-plugin</artifactId>
+        <executions>
+          <execution>
+            <id>test-jar-with-dependencies</id>
+            <phase>package</phase>
+            <goals>
+              <goal>single</goal>
+            </goals>
+            <configuration>
+              <!-- Make sure the file path is same as the sbt build -->
+              <finalName>spark-streaming-mqtt-test-${project.version}</finalName>
+              <outputDirectory>${project.build.directory}/scala-${scala.binary.version}/</outputDirectory>
+              <appendAssemblyId>false</appendAssemblyId>
+              <!-- Don't publish it since it's only for Python tests -->
+              <attach>false</attach>
+              <descriptors>
+                <descriptor>src/main/assembly/assembly.xml</descriptor>
+              </descriptors>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
   </build>
 </project>
diff --git a/external/mqtt/src/main/assembly/assembly.xml b/external/mqtt/src/main/assembly/assembly.xml
new file mode 100644
index 0000000000000..ecab5b360eb3e
--- /dev/null
+++ b/external/mqtt/src/main/assembly/assembly.xml
@@ -0,0 +1,44 @@
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+<assembly>
+  <id>test-jar-with-dependencies</id>
+  <formats>
+    <format>jar</format>
+  </formats>
+  <includeBaseDirectory>false</includeBaseDirectory>
+
+  <fileSets>
+    <fileSet>
+      <directory>${project.build.directory}/scala-${scala.binary.version}/test-classes</directory>
+      <outputDirectory>/</outputDirectory>
+    </fileSet>
+  </fileSets>
+
+  <dependencySets>
+    <dependencySet>
+      <useTransitiveDependencies>true</useTransitiveDependencies>
+      <scope>test</scope>
+      <unpack>true</unpack>
+      <excludes>
+        <exclude>org.apache.hadoop:*:jar</exclude>
+        <exclude>org.apache.zookeeper:*:jar</exclude>
+        <exclude>org.apache.avro:*:jar</exclude>
+      </excludes>
+    </dependencySet>
+  </dependencySets>
+
+</assembly>
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
index 1142d0f56ba34..38a1114863d15 100644
--- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
@@ -74,3 +74,19 @@ object MQTTUtils {
     createStream(jssc.ssc, brokerUrl, topic, storageLevel)
   }
 }
+
+/**
+ * This is a helper class that wraps the methods in MQTTUtils into more Python-friendly class and
+ * function so that it can be easily instantiated and called from Python's MQTTUtils.
+ */
+private class MQTTUtilsPythonHelper {
+
+  def createStream(
+      jssc: JavaStreamingContext,
+      brokerUrl: String,
+      topic: String,
+      storageLevel: StorageLevel
+    ): JavaDStream[String] = {
+    MQTTUtils.createStream(jssc, brokerUrl, topic, storageLevel)
+  }
+}
diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
index c4bf5aa7869bb..a6a9249db8ed7 100644
--- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
+++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
@@ -17,46 +17,30 @@
 
 package org.apache.spark.streaming.mqtt
 
-import java.net.{URI, ServerSocket}
-import java.util.concurrent.CountDownLatch
-import java.util.concurrent.TimeUnit
-
 import scala.concurrent.duration._
 import scala.language.postfixOps
 
-import org.apache.activemq.broker.{TransportConnector, BrokerService}
-import org.apache.commons.lang3.RandomUtils
-import org.eclipse.paho.client.mqttv3._
-import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence
-
 import org.scalatest.BeforeAndAfter
 import org.scalatest.concurrent.Eventually
 
-import org.apache.spark.streaming.{Milliseconds, StreamingContext}
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.dstream.ReceiverInputDStream
-import org.apache.spark.streaming.scheduler.StreamingListener
-import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
 import org.apache.spark.{SparkConf, SparkFunSuite}
-import org.apache.spark.util.Utils
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{Milliseconds, StreamingContext}
 
 class MQTTStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfter {
 
   private val batchDuration = Milliseconds(500)
   private val master = "local[2]"
   private val framework = this.getClass.getSimpleName
-  private val freePort = findFreePort()
-  private val brokerUri = "//localhost:" + freePort
   private val topic = "def"
-  private val persistenceDir = Utils.createTempDir()
 
   private var ssc: StreamingContext = _
-  private var broker: BrokerService = _
-  private var connector: TransportConnector = _
+  private var mqttTestUtils: MQTTTestUtils = _
 
   before {
     ssc = new StreamingContext(master, framework, batchDuration)
-    setupMQTT()
+    mqttTestUtils = new MQTTTestUtils
+    mqttTestUtils.setup()
   }
 
   after {
@@ -64,14 +48,17 @@ class MQTTStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfter
       ssc.stop()
       ssc = null
     }
-    Utils.deleteRecursively(persistenceDir)
-    tearDownMQTT()
+    if (mqttTestUtils != null) {
+      mqttTestUtils.teardown()
+      mqttTestUtils = null
+    }
   }
 
   test("mqtt input stream") {
     val sendMessage = "MQTT demo for spark streaming"
-    val receiveStream =
-      MQTTUtils.createStream(ssc, "tcp:" + brokerUri, topic, StorageLevel.MEMORY_ONLY)
+    val receiveStream = MQTTUtils.createStream(ssc, "tcp://" + mqttTestUtils.brokerUri, topic,
+      StorageLevel.MEMORY_ONLY)
+
     @volatile var receiveMessage: List[String] = List()
     receiveStream.foreachRDD { rdd =>
       if (rdd.collect.length > 0) {
@@ -79,89 +66,14 @@ class MQTTStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfter
         receiveMessage
       }
     }
-    ssc.start()
 
-    // wait for the receiver to start before publishing data, or we risk failing
-    // the test nondeterministically. See SPARK-4631
-    waitForReceiverToStart()
+    ssc.start()
 
-    publishData(sendMessage)
+    // Retry it because we don't know when the receiver will start.
     eventually(timeout(10000 milliseconds), interval(100 milliseconds)) {
+      mqttTestUtils.publishData(topic, sendMessage)
       assert(sendMessage.equals(receiveMessage(0)))
     }
     ssc.stop()
   }
-
-  private def setupMQTT() {
-    broker = new BrokerService()
-    broker.setDataDirectoryFile(Utils.createTempDir())
-    connector = new TransportConnector()
-    connector.setName("mqtt")
-    connector.setUri(new URI("mqtt:" + brokerUri))
-    broker.addConnector(connector)
-    broker.start()
-  }
-
-  private def tearDownMQTT() {
-    if (broker != null) {
-      broker.stop()
-      broker = null
-    }
-    if (connector != null) {
-      connector.stop()
-      connector = null
-    }
-  }
-
-  private def findFreePort(): Int = {
-    val candidatePort = RandomUtils.nextInt(1024, 65536)
-    Utils.startServiceOnPort(candidatePort, (trialPort: Int) => {
-      val socket = new ServerSocket(trialPort)
-      socket.close()
-      (null, trialPort)
-    }, new SparkConf())._2
-  }
-
-  def publishData(data: String): Unit = {
-    var client: MqttClient = null
-    try {
-      val persistence = new MqttDefaultFilePersistence(persistenceDir.getAbsolutePath)
-      client = new MqttClient("tcp:" + brokerUri, MqttClient.generateClientId(), persistence)
-      client.connect()
-      if (client.isConnected) {
-        val msgTopic = client.getTopic(topic)
-        val message = new MqttMessage(data.getBytes("utf-8"))
-        message.setQos(1)
-        message.setRetained(true)
-
-        for (i <- 0 to 10) {
-          try {
-            msgTopic.publish(message)
-          } catch {
-            case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT =>
-              // wait for Spark streaming to consume something from the message queue
-              Thread.sleep(50)
-          }
-        }
-      }
-    } finally {
-      client.disconnect()
-      client.close()
-      client = null
-    }
-  }
-
-  /**
-   * Block until at least one receiver has started or timeout occurs.
-   */
-  private def waitForReceiverToStart() = {
-    val latch = new CountDownLatch(1)
-    ssc.addStreamingListener(new StreamingListener {
-      override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) {
-        latch.countDown()
-      }
-    })
-
-    assert(latch.await(10, TimeUnit.SECONDS), "Timeout waiting for receiver to start.")
-  }
 }
diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
new file mode 100644
index 0000000000000..1a371b7008824
--- /dev/null
+++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
@@ -0,0 +1,111 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.mqtt
+
+import java.net.{ServerSocket, URI}
+
+import scala.language.postfixOps
+
+import com.google.common.base.Charsets.UTF_8
+import org.apache.activemq.broker.{BrokerService, TransportConnector}
+import org.apache.commons.lang3.RandomUtils
+import org.eclipse.paho.client.mqttv3._
+import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence
+
+import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkConf}
+
+/**
+ * Share codes for Scala and Python unit tests
+ */
+private class MQTTTestUtils extends Logging {
+
+  private val persistenceDir = Utils.createTempDir()
+  private val brokerHost = "localhost"
+  private val brokerPort = findFreePort()
+
+  private var broker: BrokerService = _
+  private var connector: TransportConnector = _
+
+  def brokerUri: String = {
+    s"$brokerHost:$brokerPort"
+  }
+
+  def setup(): Unit = {
+    broker = new BrokerService()
+    broker.setDataDirectoryFile(Utils.createTempDir())
+    connector = new TransportConnector()
+    connector.setName("mqtt")
+    connector.setUri(new URI("mqtt://" + brokerUri))
+    broker.addConnector(connector)
+    broker.start()
+  }
+
+  def teardown(): Unit = {
+    if (broker != null) {
+      broker.stop()
+      broker = null
+    }
+    if (connector != null) {
+      connector.stop()
+      connector = null
+    }
+    Utils.deleteRecursively(persistenceDir)
+  }
+
+  private def findFreePort(): Int = {
+    val candidatePort = RandomUtils.nextInt(1024, 65536)
+    Utils.startServiceOnPort(candidatePort, (trialPort: Int) => {
+      val socket = new ServerSocket(trialPort)
+      socket.close()
+      (null, trialPort)
+    }, new SparkConf())._2
+  }
+
+  def publishData(topic: String, data: String): Unit = {
+    var client: MqttClient = null
+    try {
+      val persistence = new MqttDefaultFilePersistence(persistenceDir.getAbsolutePath)
+      client = new MqttClient("tcp://" + brokerUri, MqttClient.generateClientId(), persistence)
+      client.connect()
+      if (client.isConnected) {
+        val msgTopic = client.getTopic(topic)
+        val message = new MqttMessage(data.getBytes(UTF_8))
+        message.setQos(1)
+        message.setRetained(true)
+
+        for (i <- 0 to 10) {
+          try {
+            msgTopic.publish(message)
+          } catch {
+            case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT =>
+              // wait for Spark streaming to consume something from the message queue
+              Thread.sleep(50)
+          }
+        }
+      }
+    } finally {
+      if (client != null) {
+        client.disconnect()
+        client.close()
+        client = null
+      }
+    }
+  }
+
+}
diff --git a/pom.xml b/pom.xml
index 2bcc55b040a26..8942836a7da16 100644
--- a/pom.xml
+++ b/pom.xml
@@ -104,6 +104,7 @@
     <module>external/flume-sink</module>
     <module>external/flume-assembly</module>
     <module>external/mqtt</module>
+    <module>external/mqtt-assembly</module>
     <module>external/zeromq</module>
     <module>examples</module>
     <module>repl</module>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 9a33baa7c6ce1..41a85fa9de778 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -45,8 +45,8 @@ object BuildCommons {
     sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
     "kinesis-asl").map(ProjectRef(buildLocation, _))
 
-  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly, streamingKinesisAslAssembly) =
-    Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly", "streaming-kinesis-asl-assembly")
+  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly, streamingMqttAssembly, streamingKinesisAslAssembly) =
+    Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly", "streaming-mqtt-assembly", "streaming-kinesis-asl-assembly")
       .map(ProjectRef(buildLocation, _))
 
   val tools = ProjectRef(buildLocation, "tools")
@@ -212,6 +212,9 @@ object SparkBuild extends PomBuild {
   /* Enable Assembly for all assembly projects */
   assemblyProjects.foreach(enable(Assembly.settings))
 
+  /* Enable Assembly for streamingMqtt test */
+  enable(inConfig(Test)(Assembly.settings))(streamingMqtt)
+
   /* Package pyspark artifacts in a separate zip file for YARN. */
   enable(PySparkAssembly.settings)(assembly)
 
@@ -382,13 +385,16 @@ object Assembly {
         .getOrElse(SbtPomKeys.effectivePom.value.getProperties.get("hadoop.version").asInstanceOf[String])
     },
     jarName in assembly <<= (version, moduleName, hadoopVersion) map { (v, mName, hv) =>
-      if (mName.contains("streaming-flume-assembly") || mName.contains("streaming-kafka-assembly") || mName.contains("streaming-kinesis-asl-assembly")) {
+      if (mName.contains("streaming-flume-assembly") || mName.contains("streaming-kafka-assembly") || mName.contains("streaming-mqtt-assembly") || mName.contains("streaming-kinesis-asl-assembly")) {
         // This must match the same name used in maven (see external/kafka-assembly/pom.xml)
         s"${mName}-${v}.jar"
       } else {
         s"${mName}-${v}-hadoop${hv}.jar"
       }
     },
+    jarName in (Test, assembly) <<= (version, moduleName, hadoopVersion) map { (v, mName, hv) =>
+      s"${mName}-test-${v}.jar"
+    },
     mergeStrategy in assembly := {
       case PathList("org", "datanucleus", xs @ _*)             => MergeStrategy.discard
       case m if m.toLowerCase.endsWith("manifest.mf")          => MergeStrategy.discard
diff --git a/python/pyspark/streaming/mqtt.py b/python/pyspark/streaming/mqtt.py
new file mode 100644
index 0000000000000..f06598971c548
--- /dev/null
+++ b/python/pyspark/streaming/mqtt.py
@@ -0,0 +1,72 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from py4j.java_gateway import Py4JJavaError
+
+from pyspark.storagelevel import StorageLevel
+from pyspark.serializers import UTF8Deserializer
+from pyspark.streaming import DStream
+
+__all__ = ['MQTTUtils']
+
+
+class MQTTUtils(object):
+
+    @staticmethod
+    def createStream(ssc, brokerUrl, topic,
+                     storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2):
+        """
+        Create an input stream that pulls messages from a Mqtt Broker.
+        :param ssc:  StreamingContext object
+        :param brokerUrl:  Url of remote mqtt publisher
+        :param topic:  topic name to subscribe to
+        :param storageLevel:  RDD storage level.
+        :return: A DStream object
+        """
+        jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
+
+        try:
+            helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+                .loadClass("org.apache.spark.streaming.mqtt.MQTTUtilsPythonHelper")
+            helper = helperClass.newInstance()
+            jstream = helper.createStream(ssc._jssc, brokerUrl, topic, jlevel)
+        except Py4JJavaError as e:
+            if 'ClassNotFoundException' in str(e.java_exception):
+                MQTTUtils._printErrorMsg(ssc.sparkContext)
+            raise e
+
+        return DStream(jstream, ssc, UTF8Deserializer())
+
+    @staticmethod
+    def _printErrorMsg(sc):
+        print("""
+________________________________________________________________________________________________
+
+  Spark Streaming's MQTT libraries not found in class path. Try one of the following.
+
+  1. Include the MQTT library and its dependencies with in the
+     spark-submit command as
+
+     $ bin/spark-submit --packages org.apache.spark:spark-streaming-mqtt:%s ...
+
+  2. Download the JAR of the artifact from Maven Central http://search.maven.org/,
+     Group Id = org.apache.spark, Artifact Id = spark-streaming-mqtt-assembly, Version = %s.
+     Then, include the jar in the spark-submit command as
+
+     $ bin/spark-submit --jars <spark-streaming-mqtt-assembly.jar> ...
+________________________________________________________________________________________________
+""" % (sc.version, sc.version))
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 5cd544b2144ef..66ae3345f468f 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -40,6 +40,7 @@
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.kafka import Broker, KafkaUtils, OffsetRange, TopicAndPartition
 from pyspark.streaming.flume import FlumeUtils
+from pyspark.streaming.mqtt import MQTTUtils
 from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream
 
 
@@ -893,6 +894,68 @@ def test_flume_polling_multiple_hosts(self):
         self._testMultipleTimes(self._testFlumePollingMultipleHosts)
 
 
+class MQTTStreamTests(PySparkStreamingTestCase):
+    timeout = 20  # seconds
+    duration = 1
+
+    def setUp(self):
+        super(MQTTStreamTests, self).setUp()
+
+        MQTTTestUtilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+            .loadClass("org.apache.spark.streaming.mqtt.MQTTTestUtils")
+        self._MQTTTestUtils = MQTTTestUtilsClz.newInstance()
+        self._MQTTTestUtils.setup()
+
+    def tearDown(self):
+        if self._MQTTTestUtils is not None:
+            self._MQTTTestUtils.teardown()
+            self._MQTTTestUtils = None
+
+        super(MQTTStreamTests, self).tearDown()
+
+    def _randomTopic(self):
+        return "topic-%d" % random.randint(0, 10000)
+
+    def _startContext(self, topic):
+        # Start the StreamingContext and also collect the result
+        stream = MQTTUtils.createStream(self.ssc, "tcp://" + self._MQTTTestUtils.brokerUri(), topic)
+        result = []
+
+        def getOutput(_, rdd):
+            for data in rdd.collect():
+                result.append(data)
+
+        stream.foreachRDD(getOutput)
+        self.ssc.start()
+        return result
+
+    def test_mqtt_stream(self):
+        """Test the Python MQTT stream API."""
+        sendData = "MQTT demo for spark streaming"
+        topic = self._randomTopic()
+        result = self._startContext(topic)
+
+        def retry():
+            self._MQTTTestUtils.publishData(topic, sendData)
+            # Because "publishData" sends duplicate messages, here we should use > 0
+            self.assertTrue(len(result) > 0)
+            self.assertEqual(sendData, result[0])
+
+        # Retry it because we don't know when the receiver will start.
+        self._retry_or_timeout(retry)
+
+    def _retry_or_timeout(self, test_func):
+        start_time = time.time()
+        while True:
+            try:
+                test_func()
+                break
+            except:
+                if time.time() - start_time > self.timeout:
+                    raise
+                time.sleep(0.01)
+
+
 class KinesisStreamTests(PySparkStreamingTestCase):
 
     def test_kinesis_stream_api(self):
@@ -985,7 +1048,42 @@ def search_flume_assembly_jar():
             "'build/mvn package' before running this test")
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Flume assembly JARs in %s; please "
-                         "remove all but one") % flume_assembly_dir)
+                        "remove all but one") % flume_assembly_dir)
+    else:
+        return jars[0]
+
+
+def search_mqtt_assembly_jar():
+    SPARK_HOME = os.environ["SPARK_HOME"]
+    mqtt_assembly_dir = os.path.join(SPARK_HOME, "external/mqtt-assembly")
+    jars = glob.glob(
+        os.path.join(mqtt_assembly_dir, "target/scala-*/spark-streaming-mqtt-assembly-*.jar"))
+    if not jars:
+        raise Exception(
+            ("Failed to find Spark Streaming MQTT assembly jar in %s. " % mqtt_assembly_dir) +
+            "You need to build Spark with "
+            "'build/sbt assembly/assembly streaming-mqtt-assembly/assembly' or "
+            "'build/mvn package' before running this test")
+    elif len(jars) > 1:
+        raise Exception(("Found multiple Spark Streaming MQTT assembly JARs in %s; please "
+                         "remove all but one") % mqtt_assembly_dir)
+    else:
+        return jars[0]
+
+
+def search_mqtt_test_jar():
+    SPARK_HOME = os.environ["SPARK_HOME"]
+    mqtt_test_dir = os.path.join(SPARK_HOME, "external/mqtt")
+    jars = glob.glob(
+        os.path.join(mqtt_test_dir, "target/scala-*/spark-streaming-mqtt-test-*.jar"))
+    if not jars:
+        raise Exception(
+            ("Failed to find Spark Streaming MQTT test jar in %s. " % mqtt_test_dir) +
+            "You need to build Spark with "
+            "'build/sbt assembly/assembly streaming-mqtt/test:assembly'")
+    elif len(jars) > 1:
+        raise Exception(("Found multiple Spark Streaming MQTT test JARs in %s; please "
+                         "remove all but one") % mqtt_test_dir)
     else:
         return jars[0]
 
@@ -1012,8 +1110,12 @@ def search_kinesis_asl_assembly_jar():
 if __name__ == "__main__":
     kafka_assembly_jar = search_kafka_assembly_jar()
     flume_assembly_jar = search_flume_assembly_jar()
+    mqtt_assembly_jar = search_mqtt_assembly_jar()
+    mqtt_test_jar = search_mqtt_test_jar()
     kinesis_asl_assembly_jar = search_kinesis_asl_assembly_jar()
-    jars = "%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar)
+
+    jars = "%s,%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar,
+                               mqtt_assembly_jar, mqtt_test_jar)
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
     unittest.main()

From 3c9802d9400bea802984456683b2736a450ee17e Mon Sep 17 00:00:00 2001
From: Hao Zhu <viadeazhu@gmail.com>
Date: Mon, 10 Aug 2015 17:17:22 -0700
Subject: [PATCH 0956/1454] [SPARK-9801] [STREAMING] Check if file exists
 before deleting temporary files.

Spark streaming deletes the temp file and backup files without checking if they exist or not

Author: Hao Zhu <viadeazhu@gmail.com>

Closes #8082 from viadea/master and squashes the following commits:

242d05f [Hao Zhu] [SPARK-9801][Streaming]No need to check the existence of those files
fd143f2 [Hao Zhu] [SPARK-9801][Streaming]Check if backupFile exists before deleting backupFile files.
087daf0 [Hao Zhu] SPARK-9801
---
 .../scala/org/apache/spark/streaming/Checkpoint.scala     | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 2780d5b6adbcf..6f6b449accc3c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -192,7 +192,9 @@ class CheckpointWriter(
             + "'")
 
           // Write checkpoint to temp file
-          fs.delete(tempFile, true)   // just in case it exists
+          if (fs.exists(tempFile)) {
+            fs.delete(tempFile, true)   // just in case it exists
+          }
           val fos = fs.create(tempFile)
           Utils.tryWithSafeFinally {
             fos.write(bytes)
@@ -203,7 +205,9 @@ class CheckpointWriter(
           // If the checkpoint file exists, back it up
           // If the backup exists as well, just delete it, otherwise rename will fail
           if (fs.exists(checkpointFile)) {
-            fs.delete(backupFile, true) // just in case it exists
+            if (fs.exists(backupFile)){
+              fs.delete(backupFile, true) // just in case it exists
+            }
             if (!fs.rename(checkpointFile, backupFile)) {
               logWarning("Could not rename " + checkpointFile + " to " + backupFile)
             }

From 071bbad5db1096a548c886762b611a8484a52753 Mon Sep 17 00:00:00 2001
From: Damian Guy <damian.guy@gmail.com>
Date: Tue, 11 Aug 2015 12:46:33 +0800
Subject: [PATCH 0957/1454] [SPARK-9340] [SQL] Fixes converting unannotated
 Parquet lists

This PR is inspired by #8063 authored by dguy. Especially, testing Parquet files added here are all taken from that PR.

**Committer who merges this PR should attribute it to "Damian Guy <damian.guygmail.com>".**

----

SPARK-6776 and SPARK-6777 followed `parquet-avro` to implement backwards-compatibility rules defined in `parquet-format` spec. However, both Spark SQL and `parquet-avro` neglected the following statement in `parquet-format`:

> This does not affect repeated fields that are not annotated: A repeated field that is neither contained by a `LIST`- or `MAP`-annotated group nor annotated by `LIST` or `MAP` should be interpreted as a required list of required elements where the element type is the type of the field.

One of the consequences is that, Parquet files generated by `parquet-protobuf` containing unannotated repeated fields are not correctly converted to Catalyst arrays.

This PR fixes this issue by

1. Handling unannotated repeated fields in `CatalystSchemaConverter`.
2. Converting this kind of special repeated fields to Catalyst arrays in `CatalystRowConverter`.

   Two special converters, `RepeatedPrimitiveConverter` and `RepeatedGroupConverter`, are added. They delegate actual conversion work to a child `elementConverter` and accumulates elements in an `ArrayBuffer`.

   Two extra methods, `start()` and `end()`, are added to `ParentContainerUpdater`. So that they can be used to initialize new `ArrayBuffer`s for unannotated repeated fields, and propagate converted array values to upstream.

Author: Cheng Lian <lian@databricks.com>

Closes #8070 from liancheng/spark-9340/unannotated-parquet-list and squashes the following commits:

ace6df7 [Cheng Lian] Moves ParquetProtobufCompatibilitySuite
f1c7bfd [Cheng Lian] Updates .rat-excludes
420ad2b [Cheng Lian] Fixes converting unannotated Parquet lists
---
 .rat-excludes                                 |   1 +
 .../parquet/CatalystRowConverter.scala        | 151 ++++++++++++++----
 .../parquet/CatalystSchemaConverter.scala     |   7 +-
 .../resources/nested-array-struct.parquet     | Bin 0 -> 775 bytes
 .../test/resources/old-repeated-int.parquet   | Bin 0 -> 389 bytes
 .../resources/old-repeated-message.parquet    | Bin 0 -> 600 bytes
 .../src/test/resources/old-repeated.parquet   | Bin 0 -> 432 bytes
 .../parquet-thrift-compat.snappy.parquet      | Bin
 .../resources/proto-repeated-string.parquet   | Bin 0 -> 411 bytes
 .../resources/proto-repeated-struct.parquet   | Bin 0 -> 608 bytes
 .../proto-struct-with-array-many.parquet      | Bin 0 -> 802 bytes
 .../resources/proto-struct-with-array.parquet | Bin 0 -> 1576 bytes
 .../ParquetProtobufCompatibilitySuite.scala   |  91 +++++++++++
 .../parquet/ParquetSchemaSuite.scala          |  30 ++++
 14 files changed, 247 insertions(+), 33 deletions(-)
 create mode 100644 sql/core/src/test/resources/nested-array-struct.parquet
 create mode 100644 sql/core/src/test/resources/old-repeated-int.parquet
 create mode 100644 sql/core/src/test/resources/old-repeated-message.parquet
 create mode 100644 sql/core/src/test/resources/old-repeated.parquet
 mode change 100755 => 100644 sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet
 create mode 100644 sql/core/src/test/resources/proto-repeated-string.parquet
 create mode 100644 sql/core/src/test/resources/proto-repeated-struct.parquet
 create mode 100644 sql/core/src/test/resources/proto-struct-with-array-many.parquet
 create mode 100644 sql/core/src/test/resources/proto-struct-with-array.parquet
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala

diff --git a/.rat-excludes b/.rat-excludes
index 72771465846b8..9165872b9fb27 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -94,3 +94,4 @@ INDEX
 gen-java.*
 .*avpr
 org.apache.spark.sql.sources.DataSourceRegister
+.*parquet
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index 3542dfbae1292..ab5a6ddd41cfc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -21,11 +21,11 @@ import java.math.{BigDecimal, BigInteger}
 import java.nio.ByteOrder
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
 import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
+import org.apache.parquet.schema.OriginalType.LIST
 import org.apache.parquet.schema.Type.Repetition
 import org.apache.parquet.schema.{GroupType, PrimitiveType, Type}
 
@@ -42,6 +42,12 @@ import org.apache.spark.unsafe.types.UTF8String
  * values to an [[ArrayBuffer]].
  */
 private[parquet] trait ParentContainerUpdater {
+  /** Called before a record field is being converted */
+  def start(): Unit = ()
+
+  /** Called after a record field is being converted */
+  def end(): Unit = ()
+
   def set(value: Any): Unit = ()
   def setBoolean(value: Boolean): Unit = set(value)
   def setByte(value: Byte): Unit = set(value)
@@ -55,6 +61,32 @@ private[parquet] trait ParentContainerUpdater {
 /** A no-op updater used for root converter (who doesn't have a parent). */
 private[parquet] object NoopUpdater extends ParentContainerUpdater
 
+private[parquet] trait HasParentContainerUpdater {
+  def updater: ParentContainerUpdater
+}
+
+/**
+ * A convenient converter class for Parquet group types with an [[HasParentContainerUpdater]].
+ */
+private[parquet] abstract class CatalystGroupConverter(val updater: ParentContainerUpdater)
+  extends GroupConverter with HasParentContainerUpdater
+
+/**
+ * Parquet converter for Parquet primitive types.  Note that not all Spark SQL atomic types
+ * are handled by this converter.  Parquet primitive types are only a subset of those of Spark
+ * SQL.  For example, BYTE, SHORT, and INT in Spark SQL are all covered by INT32 in Parquet.
+ */
+private[parquet] class CatalystPrimitiveConverter(val updater: ParentContainerUpdater)
+  extends PrimitiveConverter with HasParentContainerUpdater {
+
+  override def addBoolean(value: Boolean): Unit = updater.setBoolean(value)
+  override def addInt(value: Int): Unit = updater.setInt(value)
+  override def addLong(value: Long): Unit = updater.setLong(value)
+  override def addFloat(value: Float): Unit = updater.setFloat(value)
+  override def addDouble(value: Double): Unit = updater.setDouble(value)
+  override def addBinary(value: Binary): Unit = updater.set(value.getBytes)
+}
+
 /**
  * A [[CatalystRowConverter]] is used to convert Parquet "structs" into Spark SQL [[InternalRow]]s.
  * Since any Parquet record is also a struct, this converter can also be used as root converter.
@@ -70,7 +102,7 @@ private[parquet] class CatalystRowConverter(
     parquetType: GroupType,
     catalystType: StructType,
     updater: ParentContainerUpdater)
-  extends GroupConverter {
+  extends CatalystGroupConverter(updater) {
 
   /**
    * Updater used together with field converters within a [[CatalystRowConverter]].  It propagates
@@ -89,13 +121,11 @@ private[parquet] class CatalystRowConverter(
 
   /**
    * Represents the converted row object once an entire Parquet record is converted.
-   *
-   * @todo Uses [[UnsafeRow]] for better performance.
    */
   val currentRow = new SpecificMutableRow(catalystType.map(_.dataType))
 
   // Converters for each field.
-  private val fieldConverters: Array[Converter] = {
+  private val fieldConverters: Array[Converter with HasParentContainerUpdater] = {
     parquetType.getFields.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
@@ -105,11 +135,19 @@ private[parquet] class CatalystRowConverter(
 
   override def getConverter(fieldIndex: Int): Converter = fieldConverters(fieldIndex)
 
-  override def end(): Unit = updater.set(currentRow)
+  override def end(): Unit = {
+    var i = 0
+    while (i < currentRow.numFields) {
+      fieldConverters(i).updater.end()
+      i += 1
+    }
+    updater.set(currentRow)
+  }
 
   override def start(): Unit = {
     var i = 0
     while (i < currentRow.numFields) {
+      fieldConverters(i).updater.start()
       currentRow.setNullAt(i)
       i += 1
     }
@@ -122,20 +160,20 @@ private[parquet] class CatalystRowConverter(
   private def newConverter(
       parquetType: Type,
       catalystType: DataType,
-      updater: ParentContainerUpdater): Converter = {
+      updater: ParentContainerUpdater): Converter with HasParentContainerUpdater = {
 
     catalystType match {
       case BooleanType | IntegerType | LongType | FloatType | DoubleType | BinaryType =>
         new CatalystPrimitiveConverter(updater)
 
       case ByteType =>
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           override def addInt(value: Int): Unit =
             updater.setByte(value.asInstanceOf[ByteType#InternalType])
         }
 
       case ShortType =>
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           override def addInt(value: Int): Unit =
             updater.setShort(value.asInstanceOf[ShortType#InternalType])
         }
@@ -148,7 +186,7 @@ private[parquet] class CatalystRowConverter(
 
       case TimestampType =>
         // TODO Implements `TIMESTAMP_MICROS` once parquet-mr has that.
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           // Converts nanosecond timestamps stored as INT96
           override def addBinary(value: Binary): Unit = {
             assert(
@@ -164,13 +202,23 @@ private[parquet] class CatalystRowConverter(
         }
 
       case DateType =>
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           override def addInt(value: Int): Unit = {
             // DateType is not specialized in `SpecificMutableRow`, have to box it here.
             updater.set(value.asInstanceOf[DateType#InternalType])
           }
         }
 
+      // A repeated field that is neither contained by a `LIST`- or `MAP`-annotated group nor
+      // annotated by `LIST` or `MAP` should be interpreted as a required list of required
+      // elements where the element type is the type of the field.
+      case t: ArrayType if parquetType.getOriginalType != LIST =>
+        if (parquetType.isPrimitive) {
+          new RepeatedPrimitiveConverter(parquetType, t.elementType, updater)
+        } else {
+          new RepeatedGroupConverter(parquetType, t.elementType, updater)
+        }
+
       case t: ArrayType =>
         new CatalystArrayConverter(parquetType.asGroupType(), t, updater)
 
@@ -195,27 +243,11 @@ private[parquet] class CatalystRowConverter(
     }
   }
 
-  /**
-   * Parquet converter for Parquet primitive types.  Note that not all Spark SQL atomic types
-   * are handled by this converter.  Parquet primitive types are only a subset of those of Spark
-   * SQL.  For example, BYTE, SHORT, and INT in Spark SQL are all covered by INT32 in Parquet.
-   */
-  private final class CatalystPrimitiveConverter(updater: ParentContainerUpdater)
-    extends PrimitiveConverter {
-
-    override def addBoolean(value: Boolean): Unit = updater.setBoolean(value)
-    override def addInt(value: Int): Unit = updater.setInt(value)
-    override def addLong(value: Long): Unit = updater.setLong(value)
-    override def addFloat(value: Float): Unit = updater.setFloat(value)
-    override def addDouble(value: Double): Unit = updater.setDouble(value)
-    override def addBinary(value: Binary): Unit = updater.set(value.getBytes)
-  }
-
   /**
    * Parquet converter for strings. A dictionary is used to minimize string decoding cost.
    */
   private final class CatalystStringConverter(updater: ParentContainerUpdater)
-    extends PrimitiveConverter {
+    extends CatalystPrimitiveConverter(updater) {
 
     private var expandedDictionary: Array[UTF8String] = null
 
@@ -242,7 +274,7 @@ private[parquet] class CatalystRowConverter(
   private final class CatalystDecimalConverter(
       decimalType: DecimalType,
       updater: ParentContainerUpdater)
-    extends PrimitiveConverter {
+    extends CatalystPrimitiveConverter(updater) {
 
     // Converts decimals stored as INT32
     override def addInt(value: Int): Unit = {
@@ -306,7 +338,7 @@ private[parquet] class CatalystRowConverter(
       parquetSchema: GroupType,
       catalystSchema: ArrayType,
       updater: ParentContainerUpdater)
-    extends GroupConverter {
+    extends CatalystGroupConverter(updater) {
 
     private var currentArray: ArrayBuffer[Any] = _
 
@@ -383,7 +415,7 @@ private[parquet] class CatalystRowConverter(
       parquetType: GroupType,
       catalystType: MapType,
       updater: ParentContainerUpdater)
-    extends GroupConverter {
+    extends CatalystGroupConverter(updater) {
 
     private var currentKeys: ArrayBuffer[Any] = _
     private var currentValues: ArrayBuffer[Any] = _
@@ -446,4 +478,61 @@ private[parquet] class CatalystRowConverter(
       }
     }
   }
+
+  private trait RepeatedConverter {
+    private var currentArray: ArrayBuffer[Any] = _
+
+    protected def newArrayUpdater(updater: ParentContainerUpdater) = new ParentContainerUpdater {
+      override def start(): Unit = currentArray = ArrayBuffer.empty[Any]
+      override def end(): Unit = updater.set(new GenericArrayData(currentArray.toArray))
+      override def set(value: Any): Unit = currentArray += value
+    }
+  }
+
+  /**
+   * A primitive converter for converting unannotated repeated primitive values to required arrays
+   * of required primitives values.
+   */
+  private final class RepeatedPrimitiveConverter(
+      parquetType: Type,
+      catalystType: DataType,
+      parentUpdater: ParentContainerUpdater)
+    extends PrimitiveConverter with RepeatedConverter with HasParentContainerUpdater {
+
+    val updater: ParentContainerUpdater = newArrayUpdater(parentUpdater)
+
+    private val elementConverter: PrimitiveConverter =
+      newConverter(parquetType, catalystType, updater).asPrimitiveConverter()
+
+    override def addBoolean(value: Boolean): Unit = elementConverter.addBoolean(value)
+    override def addInt(value: Int): Unit = elementConverter.addInt(value)
+    override def addLong(value: Long): Unit = elementConverter.addLong(value)
+    override def addFloat(value: Float): Unit = elementConverter.addFloat(value)
+    override def addDouble(value: Double): Unit = elementConverter.addDouble(value)
+    override def addBinary(value: Binary): Unit = elementConverter.addBinary(value)
+
+    override def setDictionary(dict: Dictionary): Unit = elementConverter.setDictionary(dict)
+    override def hasDictionarySupport: Boolean = elementConverter.hasDictionarySupport
+    override def addValueFromDictionary(id: Int): Unit = elementConverter.addValueFromDictionary(id)
+  }
+
+  /**
+   * A group converter for converting unannotated repeated group values to required arrays of
+   * required struct values.
+   */
+  private final class RepeatedGroupConverter(
+      parquetType: Type,
+      catalystType: DataType,
+      parentUpdater: ParentContainerUpdater)
+    extends GroupConverter with HasParentContainerUpdater with RepeatedConverter {
+
+    val updater: ParentContainerUpdater = newArrayUpdater(parentUpdater)
+
+    private val elementConverter: GroupConverter =
+      newConverter(parquetType, catalystType, updater).asGroupConverter()
+
+    override def getConverter(field: Int): Converter = elementConverter.getConverter(field)
+    override def end(): Unit = elementConverter.end()
+    override def start(): Unit = elementConverter.start()
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index a3fc74cf7929b..275646e8181ad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -100,8 +100,11 @@ private[parquet] class CatalystSchemaConverter(
           StructField(field.getName, convertField(field), nullable = false)
 
         case REPEATED =>
-          throw new AnalysisException(
-            s"REPEATED not supported outside LIST or MAP. Type: $field")
+          // A repeated field that is neither contained by a `LIST`- or `MAP`-annotated group nor
+          // annotated by `LIST` or `MAP` should be interpreted as a required list of required
+          // elements where the element type is the type of the field.
+          val arrayType = ArrayType(convertField(field), containsNull = false)
+          StructField(field.getName, arrayType, nullable = false)
       }
     }
 
diff --git a/sql/core/src/test/resources/nested-array-struct.parquet b/sql/core/src/test/resources/nested-array-struct.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..41a43fa35d39685e56ba4849a16cba4bb1aa86ae
GIT binary patch
literal 775
zcmaKr-%G+!6vvO#=8u;i;*JSE3{j~lAyWtuV%0Q3*U(Y)B(q&>u(@?NBZ;2+Px=dc
z?6I>s%N6u+cQ4=bIp1@3cBjdsBLbvCDhGte15a`Q8~~)V;d2WY3V@LYX{-@GMj#!6
z`;fvdgDblto20oxMhs#hdKzt*4*3w}iuUE6PW?b*Zs1NAv%1WfvAnT@2NhLn_L#fy
z<hdEiB!d}MPRx7Ea;(65qR`5Sd1ra<I-X6R*Ky7=!@xG99yQQpNPXY9r-WOj^S<lQ
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/old-repeated-int.parquet b/sql/core/src/test/resources/old-repeated-int.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..520922f73ebb75950c4e65dec6689af817cb7a33
GIT binary patch
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diff --git a/sql/core/src/test/resources/old-repeated-message.parquet b/sql/core/src/test/resources/old-repeated-message.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..548db991627774512b856b33b045327678b5a7ab
GIT binary patch
literal 600
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HcmV?d00001

diff --git a/sql/core/src/test/resources/old-repeated.parquet b/sql/core/src/test/resources/old-repeated.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..213f1a90291b30a8a3161b51c38f008f3ae9f6e5
GIT binary patch
literal 432
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet b/sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet
old mode 100755
new mode 100644
diff --git a/sql/core/src/test/resources/proto-repeated-string.parquet b/sql/core/src/test/resources/proto-repeated-string.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..8a7eea601d0164b1177ab90efb7116c6e4c900da
GIT binary patch
literal 411
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/proto-repeated-struct.parquet b/sql/core/src/test/resources/proto-repeated-struct.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..c29eee35c350e84efa2818febb2528309a8ac3ea
GIT binary patch
literal 608
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/proto-struct-with-array-many.parquet b/sql/core/src/test/resources/proto-struct-with-array-many.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..ff9809675fc04edbbfc43f4033593a5aa6141dfa
GIT binary patch
literal 802
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/proto-struct-with-array.parquet b/sql/core/src/test/resources/proto-struct-with-array.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..325a8370ad20ec31010fde0c816895da9f5dbd27
GIT binary patch
literal 1576
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
new file mode 100644
index 0000000000000..981334cf771cf
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+class ParquetProtobufCompatibilitySuite extends ParquetCompatibilityTest {
+  override def sqlContext: SQLContext = TestSQLContext
+
+  private def readParquetProtobufFile(name: String): DataFrame = {
+    val url = Thread.currentThread().getContextClassLoader.getResource(name)
+    sqlContext.read.parquet(url.toString)
+  }
+
+  test("unannotated array of primitive type") {
+    checkAnswer(readParquetProtobufFile("old-repeated-int.parquet"), Row(Seq(1, 2, 3)))
+  }
+
+  test("unannotated array of struct") {
+    checkAnswer(
+      readParquetProtobufFile("old-repeated-message.parquet"),
+      Row(
+        Seq(
+          Row("First inner", null, null),
+          Row(null, "Second inner", null),
+          Row(null, null, "Third inner"))))
+
+    checkAnswer(
+      readParquetProtobufFile("proto-repeated-struct.parquet"),
+      Row(
+        Seq(
+          Row("0 - 1", "0 - 2", "0 - 3"),
+          Row("1 - 1", "1 - 2", "1 - 3"))))
+
+    checkAnswer(
+      readParquetProtobufFile("proto-struct-with-array-many.parquet"),
+      Seq(
+        Row(
+          Seq(
+            Row("0 - 0 - 1", "0 - 0 - 2", "0 - 0 - 3"),
+            Row("0 - 1 - 1", "0 - 1 - 2", "0 - 1 - 3"))),
+        Row(
+          Seq(
+            Row("1 - 0 - 1", "1 - 0 - 2", "1 - 0 - 3"),
+            Row("1 - 1 - 1", "1 - 1 - 2", "1 - 1 - 3"))),
+        Row(
+          Seq(
+            Row("2 - 0 - 1", "2 - 0 - 2", "2 - 0 - 3"),
+            Row("2 - 1 - 1", "2 - 1 - 2", "2 - 1 - 3")))))
+  }
+
+  test("struct with unannotated array") {
+    checkAnswer(
+      readParquetProtobufFile("proto-struct-with-array.parquet"),
+      Row(10, 9, Seq.empty, null, Row(9), Seq(Row(9), Row(10))))
+  }
+
+  test("unannotated array of struct with unannotated array") {
+    checkAnswer(
+      readParquetProtobufFile("nested-array-struct.parquet"),
+      Seq(
+        Row(2, Seq(Row(1, Seq(Row(3))))),
+        Row(5, Seq(Row(4, Seq(Row(6))))),
+        Row(8, Seq(Row(7, Seq(Row(9)))))))
+  }
+
+  test("unannotated array of string") {
+    checkAnswer(
+      readParquetProtobufFile("proto-repeated-string.parquet"),
+      Seq(
+        Row(Seq("hello", "world")),
+        Row(Seq("good", "bye")),
+        Row(Seq("one", "two", "three"))))
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 8f06de7ce7c4f..971f71e27bfc6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -585,6 +585,36 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       |}
     """.stripMargin)
 
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type 7 - " +
+      "parquet-protobuf primitive lists",
+    new StructType()
+      .add("f1", ArrayType(IntegerType, containsNull = false), nullable = false),
+    """message root {
+      |  repeated int32 f1;
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type 8 - " +
+      "parquet-protobuf non-primitive lists",
+    {
+      val elementType =
+        new StructType()
+          .add("c1", StringType, nullable = true)
+          .add("c2", IntegerType, nullable = false)
+
+      new StructType()
+        .add("f1", ArrayType(elementType, containsNull = false), nullable = false)
+    },
+    """message root {
+      |  repeated group f1 {
+      |    optional binary c1 (UTF8);
+      |    required int32 c2;
+      |  }
+      |}
+    """.stripMargin)
+
   // =======================================================
   // Tests for converting Catalyst ArrayType to Parquet LIST
   // =======================================================

From 91e9389f39509e63654bd4bcb7bd919eaedda910 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 10 Aug 2015 22:04:41 -0700
Subject: [PATCH 0958/1454] [SPARK-9729] [SPARK-9363] [SQL] Use sort merge join
 for left and right outer join

This patch adds a new `SortMergeOuterJoin` operator that performs left and right outer joins using sort merge join.  It also refactors `SortMergeJoin` in order to improve performance and code clarity.

Along the way, I also performed a couple pieces of minor cleanup and optimization:

- Rename the `HashJoin` physical planner rule to `EquiJoinSelection`, since it's also used for non-hash joins.
- Rewrite the comment at the top of `HashJoin` to better explain the precedence for choosing join operators.
- Update `JoinSuite` to use `SqlTestUtils.withConf` for changing SQLConf settings.

This patch incorporates several ideas from adrian-wang's patch, #5717.

Closes #5717.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7904)
<!-- Reviewable:end -->

Author: Josh Rosen <joshrosen@databricks.com>
Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #7904 from JoshRosen/outer-join-smj and squashes 1 commits.
---
 .../sql/catalyst/expressions/JoinedRow.scala  |   6 +-
 .../org/apache/spark/sql/SQLContext.scala     |   2 +-
 .../spark/sql/execution/RowIterator.scala     |  93 +++++
 .../spark/sql/execution/SparkStrategies.scala |  45 ++-
 .../joins/BroadcastNestedLoopJoin.scala       |   5 +-
 .../sql/execution/joins/SortMergeJoin.scala   | 331 +++++++++++++-----
 .../execution/joins/SortMergeOuterJoin.scala  | 251 +++++++++++++
 .../org/apache/spark/sql/JoinSuite.scala      | 132 ++++---
 .../sql/execution/joins/InnerJoinSuite.scala  | 180 ++++++++++
 .../sql/execution/joins/OuterJoinSuite.scala  | 310 +++++++++++-----
 .../sql/execution/joins/SemiJoinSuite.scala   | 125 ++++---
 .../apache/spark/sql/test/SQLTestUtils.scala  |   2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 13 files changed, 1165 insertions(+), 319 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
index b76757c93523d..d3560df0792eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
@@ -37,20 +37,20 @@ class JoinedRow extends InternalRow {
   }
 
   /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
+  def apply(r1: InternalRow, r2: InternalRow): JoinedRow = {
     row1 = r1
     row2 = r2
     this
   }
 
   /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
+  def withLeft(newLeft: InternalRow): JoinedRow = {
     row1 = newLeft
     this
   }
 
   /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
+  def withRight(newRight: InternalRow): JoinedRow = {
     row2 = newRight
     this
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index f73bb0488c984..4bf00b3399e7a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -873,7 +873,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       HashAggregation ::
       Aggregation ::
       LeftSemiJoin ::
-      HashJoin ::
+      EquiJoinSelection ::
       InMemoryScans ::
       BasicOperators ::
       CartesianProduct ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala
new file mode 100644
index 0000000000000..7462dbc4eba3a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import java.util.NoSuchElementException
+
+import org.apache.spark.sql.catalyst.InternalRow
+
+/**
+ * An internal iterator interface which presents a more restrictive API than
+ * [[scala.collection.Iterator]].
+ *
+ * One major departure from the Scala iterator API is the fusing of the `hasNext()` and `next()`
+ * calls: Scala's iterator allows users to call `hasNext()` without immediately advancing the
+ * iterator to consume the next row, whereas RowIterator combines these calls into a single
+ * [[advanceNext()]] method.
+ */
+private[sql] abstract class RowIterator {
+  /**
+   * Advance this iterator by a single row. Returns `false` if this iterator has no more rows
+   * and `true` otherwise. If this returns `true`, then the new row can be retrieved by calling
+   * [[getRow]].
+   */
+  def advanceNext(): Boolean
+
+  /**
+   * Retrieve the row from this iterator. This method is idempotent. It is illegal to call this
+   * method after [[advanceNext()]] has returned `false`.
+   */
+  def getRow: InternalRow
+
+  /**
+   * Convert this RowIterator into a [[scala.collection.Iterator]].
+   */
+  def toScala: Iterator[InternalRow] = new RowIteratorToScala(this)
+}
+
+object RowIterator {
+  def fromScala(scalaIter: Iterator[InternalRow]): RowIterator = {
+    scalaIter match {
+      case wrappedRowIter: RowIteratorToScala => wrappedRowIter.rowIter
+      case _ => new RowIteratorFromScala(scalaIter)
+    }
+  }
+}
+
+private final class RowIteratorToScala(val rowIter: RowIterator) extends Iterator[InternalRow] {
+  private [this] var hasNextWasCalled: Boolean = false
+  private [this] var _hasNext: Boolean = false
+  override def hasNext: Boolean = {
+    // Idempotency:
+    if (!hasNextWasCalled) {
+      _hasNext = rowIter.advanceNext()
+      hasNextWasCalled = true
+    }
+    _hasNext
+  }
+  override def next(): InternalRow = {
+    if (!hasNext) throw new NoSuchElementException
+    hasNextWasCalled = false
+    rowIter.getRow
+  }
+}
+
+private final class RowIteratorFromScala(scalaIter: Iterator[InternalRow]) extends RowIterator {
+  private[this] var _next: InternalRow = null
+  override def advanceNext(): Boolean = {
+    if (scalaIter.hasNext) {
+      _next = scalaIter.next()
+      true
+    } else {
+      _next = null
+      false
+    }
+  }
+  override def getRow: InternalRow = _next
+  override def toScala: Iterator[InternalRow] = scalaIter
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index c4b9b5acea4de..1fc870d44b578 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -63,19 +63,23 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   }
 
   /**
-   * Uses the ExtractEquiJoinKeys pattern to find joins where at least some of the predicates can be
-   * evaluated by matching hash keys.
+   * Uses the [[ExtractEquiJoinKeys]] pattern to find joins where at least some of the predicates
+   * can be evaluated by matching join keys.
    *
-   * This strategy applies a simple optimization based on the estimates of the physical sizes of
-   * the two join sides.  When planning a [[joins.BroadcastHashJoin]], if one side has an
-   * estimated physical size smaller than the user-settable threshold
-   * [[org.apache.spark.sql.SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]], the planner would mark it as the
-   * ''build'' relation and mark the other relation as the ''stream'' side.  The build table will be
-   * ''broadcasted'' to all of the executors involved in the join, as a
-   * [[org.apache.spark.broadcast.Broadcast]] object.  If both estimates exceed the threshold, they
-   * will instead be used to decide the build side in a [[joins.ShuffledHashJoin]].
+   * Join implementations are chosen with the following precedence:
+   *
+   * - Broadcast: if one side of the join has an estimated physical size that is smaller than the
+   *     user-configurable [[org.apache.spark.sql.SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] threshold
+   *     or if that side has an explicit broadcast hint (e.g. the user applied the
+   *     [[org.apache.spark.sql.functions.broadcast()]] function to a DataFrame), then that side
+   *     of the join will be broadcasted and the other side will be streamed, with no shuffling
+   *     performed. If both sides of the join are eligible to be broadcasted then the
+   * - Sort merge: if the matching join keys are sortable and
+   *     [[org.apache.spark.sql.SQLConf.SORTMERGE_JOIN]] is enabled (default), then sort merge join
+   *     will be used.
+   * - Hash: will be chosen if neither of the above optimizations apply to this join.
    */
-  object HashJoin extends Strategy with PredicateHelper {
+  object EquiJoinSelection extends Strategy with PredicateHelper {
 
     private[this] def makeBroadcastHashJoin(
         leftKeys: Seq[Expression],
@@ -90,14 +94,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
 
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+
+      // --- Inner joins --------------------------------------------------------------------------
+
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildRight)
 
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildLeft)
 
-      // If the sort merge join option is set, we want to use sort merge join prior to hashjoin
-      // for now let's support inner join first, then add outer join
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
         if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
         val mergeJoin =
@@ -115,6 +120,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           leftKeys, rightKeys, buildSide, planLater(left), planLater(right))
         condition.map(Filter(_, hashJoin)).getOrElse(hashJoin) :: Nil
 
+      // --- Outer joins --------------------------------------------------------------------------
+
       case ExtractEquiJoinKeys(
              LeftOuter, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         joins.BroadcastHashOuterJoin(
@@ -125,10 +132,22 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
 
+      case ExtractEquiJoinKeys(LeftOuter, leftKeys, rightKeys, condition, left, right)
+        if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
+        joins.SortMergeOuterJoin(
+          leftKeys, rightKeys, LeftOuter, condition, planLater(left), planLater(right)) :: Nil
+
+      case ExtractEquiJoinKeys(RightOuter, leftKeys, rightKeys, condition, left, right)
+        if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
+        joins.SortMergeOuterJoin(
+          leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
+
       case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right) =>
         joins.ShuffledHashOuterJoin(
           leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil
 
+      // --- Cases where this strategy does not apply ---------------------------------------------
+
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 23aebf4b068b4..017a44b9ca863 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -65,8 +65,9 @@ case class BroadcastNestedLoopJoin(
         left.output.map(_.withNullability(true)) ++ right.output
       case FullOuter =>
         left.output.map(_.withNullability(true)) ++ right.output.map(_.withNullability(true))
-      case _ =>
-        left.output ++ right.output
+      case x =>
+        throw new IllegalArgumentException(
+          s"BroadcastNestedLoopJoin should not take $x as the JoinType")
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 4ae23c186cf7b..6d656ea2849a9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -17,15 +17,14 @@
 
 package org.apache.spark.sql.execution.joins
 
-import java.util.NoSuchElementException
+import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
-import org.apache.spark.util.collection.CompactBuffer
+import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
 
 /**
  * :: DeveloperApi ::
@@ -38,8 +37,6 @@ case class SortMergeJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
-
   override def output: Seq[Attribute] = left.output ++ right.output
 
   override def outputPartitioning: Partitioning =
@@ -56,117 +53,265 @@ case class SortMergeJoin(
   @transient protected lazy val leftKeyGenerator = newProjection(leftKeys, left.output)
   @transient protected lazy val rightKeyGenerator = newProjection(rightKeys, right.output)
 
+  protected[this] def isUnsafeMode: Boolean = {
+    (codegenEnabled && unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
+      && UnsafeProjection.canSupport(rightKeys)
+      && UnsafeProjection.canSupport(schema))
+  }
+
+  override def outputsUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessSafeRows: Boolean = !isUnsafeMode
+
   private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = {
     // This must be ascending in order to agree with the `keyOrdering` defined in `doExecute()`.
     keys.map(SortOrder(_, Ascending))
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
-    val leftResults = left.execute().map(_.copy())
-    val rightResults = right.execute().map(_.copy())
-
-    leftResults.zipPartitions(rightResults) { (leftIter, rightIter) =>
-      new Iterator[InternalRow] {
+    left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
+      new RowIterator {
         // An ordering that can be used to compare keys from both sides.
         private[this] val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
-        // Mutable per row objects.
+        private[this] var currentLeftRow: InternalRow = _
+        private[this] var currentRightMatches: ArrayBuffer[InternalRow] = _
+        private[this] var currentMatchIdx: Int = -1
+        private[this] val smjScanner = new SortMergeJoinScanner(
+          leftKeyGenerator,
+          rightKeyGenerator,
+          keyOrdering,
+          RowIterator.fromScala(leftIter),
+          RowIterator.fromScala(rightIter)
+        )
         private[this] val joinRow = new JoinedRow
-        private[this] var leftElement: InternalRow = _
-        private[this] var rightElement: InternalRow = _
-        private[this] var leftKey: InternalRow = _
-        private[this] var rightKey: InternalRow = _
-        private[this] var rightMatches: CompactBuffer[InternalRow] = _
-        private[this] var rightPosition: Int = -1
-        private[this] var stop: Boolean = false
-        private[this] var matchKey: InternalRow = _
-
-        // initialize iterator
-        initialize()
-
-        override final def hasNext: Boolean = nextMatchingPair()
-
-        override final def next(): InternalRow = {
-          if (hasNext) {
-            // we are using the buffered right rows and run down left iterator
-            val joinedRow = joinRow(leftElement, rightMatches(rightPosition))
-            rightPosition += 1
-            if (rightPosition >= rightMatches.size) {
-              rightPosition = 0
-              fetchLeft()
-              if (leftElement == null || keyOrdering.compare(leftKey, matchKey) != 0) {
-                stop = false
-                rightMatches = null
-              }
-            }
-            joinedRow
+        private[this] val resultProjection: (InternalRow) => InternalRow = {
+          if (isUnsafeMode) {
+            UnsafeProjection.create(schema)
           } else {
-            // no more result
-            throw new NoSuchElementException
+            identity[InternalRow]
           }
         }
 
-        private def fetchLeft() = {
-          if (leftIter.hasNext) {
-            leftElement = leftIter.next()
-            leftKey = leftKeyGenerator(leftElement)
-          } else {
-            leftElement = null
+        override def advanceNext(): Boolean = {
+          if (currentMatchIdx == -1 || currentMatchIdx == currentRightMatches.length) {
+            if (smjScanner.findNextInnerJoinRows()) {
+              currentRightMatches = smjScanner.getBufferedMatches
+              currentLeftRow = smjScanner.getStreamedRow
+              currentMatchIdx = 0
+            } else {
+              currentRightMatches = null
+              currentLeftRow = null
+              currentMatchIdx = -1
+            }
           }
-        }
-
-        private def fetchRight() = {
-          if (rightIter.hasNext) {
-            rightElement = rightIter.next()
-            rightKey = rightKeyGenerator(rightElement)
+          if (currentLeftRow != null) {
+            joinRow(currentLeftRow, currentRightMatches(currentMatchIdx))
+            currentMatchIdx += 1
+            true
           } else {
-            rightElement = null
+            false
           }
         }
 
-        private def initialize() = {
-          fetchLeft()
-          fetchRight()
+        override def getRow: InternalRow = resultProjection(joinRow)
+      }.toScala
+    }
+  }
+}
+
+/**
+ * Helper class that is used to implement [[SortMergeJoin]] and [[SortMergeOuterJoin]].
+ *
+ * To perform an inner (outer) join, users of this class call [[findNextInnerJoinRows()]]
+ * ([[findNextOuterJoinRows()]]), which returns `true` if a result has been produced and `false`
+ * otherwise. If a result has been produced, then the caller may call [[getStreamedRow]] to return
+ * the matching row from the streamed input and may call [[getBufferedMatches]] to return the
+ * sequence of matching rows from the buffered input (in the case of an outer join, this will return
+ * an empty sequence if there are no matches from the buffered input). For efficiency, both of these
+ * methods return mutable objects which are re-used across calls to the `findNext*JoinRows()`
+ * methods.
+ *
+ * @param streamedKeyGenerator a projection that produces join keys from the streamed input.
+ * @param bufferedKeyGenerator a projection that produces join keys from the buffered input.
+ * @param keyOrdering an ordering which can be used to compare join keys.
+ * @param streamedIter an input whose rows will be streamed.
+ * @param bufferedIter an input whose rows will be buffered to construct sequences of rows that
+ *                     have the same join key.
+ */
+private[joins] class SortMergeJoinScanner(
+    streamedKeyGenerator: Projection,
+    bufferedKeyGenerator: Projection,
+    keyOrdering: Ordering[InternalRow],
+    streamedIter: RowIterator,
+    bufferedIter: RowIterator) {
+  private[this] var streamedRow: InternalRow = _
+  private[this] var streamedRowKey: InternalRow = _
+  private[this] var bufferedRow: InternalRow = _
+  // Note: this is guaranteed to never have any null columns:
+  private[this] var bufferedRowKey: InternalRow = _
+  /**
+   * The join key for the rows buffered in `bufferedMatches`, or null if `bufferedMatches` is empty
+   */
+  private[this] var matchJoinKey: InternalRow = _
+  /** Buffered rows from the buffered side of the join. This is empty if there are no matches. */
+  private[this] val bufferedMatches: ArrayBuffer[InternalRow] = new ArrayBuffer[InternalRow]
+
+  // Initialization (note: do _not_ want to advance streamed here).
+  advancedBufferedToRowWithNullFreeJoinKey()
+
+  // --- Public methods ---------------------------------------------------------------------------
+
+  def getStreamedRow: InternalRow = streamedRow
+
+  def getBufferedMatches: ArrayBuffer[InternalRow] = bufferedMatches
+
+  /**
+   * Advances both input iterators, stopping when we have found rows with matching join keys.
+   * @return true if matching rows have been found and false otherwise. If this returns true, then
+   *         [[getStreamedRow]] and [[getBufferedMatches]] can be called to construct the join
+   *         results.
+   */
+  final def findNextInnerJoinRows(): Boolean = {
+    while (advancedStreamed() && streamedRowKey.anyNull) {
+      // Advance the streamed side of the join until we find the next row whose join key contains
+      // no nulls or we hit the end of the streamed iterator.
+    }
+    if (streamedRow == null) {
+      // We have consumed the entire streamed iterator, so there can be no more matches.
+      matchJoinKey = null
+      bufferedMatches.clear()
+      false
+    } else if (matchJoinKey != null && keyOrdering.compare(streamedRowKey, matchJoinKey) == 0) {
+      // The new streamed row has the same join key as the previous row, so return the same matches.
+      true
+    } else if (bufferedRow == null) {
+      // The streamed row's join key does not match the current batch of buffered rows and there are
+      // no more rows to read from the buffered iterator, so there can be no more matches.
+      matchJoinKey = null
+      bufferedMatches.clear()
+      false
+    } else {
+      // Advance both the streamed and buffered iterators to find the next pair of matching rows.
+      var comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
+      do {
+        if (streamedRowKey.anyNull) {
+          advancedStreamed()
+        } else {
+          assert(!bufferedRowKey.anyNull)
+          comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
+          if (comp > 0) advancedBufferedToRowWithNullFreeJoinKey()
+          else if (comp < 0) advancedStreamed()
         }
+      } while (streamedRow != null && bufferedRow != null && comp != 0)
+      if (streamedRow == null || bufferedRow == null) {
+        // We have either hit the end of one of the iterators, so there can be no more matches.
+        matchJoinKey = null
+        bufferedMatches.clear()
+        false
+      } else {
+        // The streamed row's join key matches the current buffered row's join, so walk through the
+        // buffered iterator to buffer the rest of the matching rows.
+        assert(comp == 0)
+        bufferMatchingRows()
+        true
+      }
+    }
+  }
 
-        /**
-         * Searches the right iterator for the next rows that have matches in left side, and store
-         * them in a buffer.
-         *
-         * @return true if the search is successful, and false if the right iterator runs out of
-         *         tuples.
-         */
-        private def nextMatchingPair(): Boolean = {
-          if (!stop && rightElement != null) {
-            // run both side to get the first match pair
-            while (!stop && leftElement != null && rightElement != null) {
-              val comparing = keyOrdering.compare(leftKey, rightKey)
-              // for inner join, we need to filter those null keys
-              stop = comparing == 0 && !leftKey.anyNull
-              if (comparing > 0 || rightKey.anyNull) {
-                fetchRight()
-              } else if (comparing < 0 || leftKey.anyNull) {
-                fetchLeft()
-              }
-            }
-            rightMatches = new CompactBuffer[InternalRow]()
-            if (stop) {
-              stop = false
-              // iterate the right side to buffer all rows that matches
-              // as the records should be ordered, exit when we meet the first that not match
-              while (!stop && rightElement != null) {
-                rightMatches += rightElement
-                fetchRight()
-                stop = keyOrdering.compare(leftKey, rightKey) != 0
-              }
-              if (rightMatches.size > 0) {
-                rightPosition = 0
-                matchKey = leftKey
-              }
-            }
+  /**
+   * Advances the streamed input iterator and buffers all rows from the buffered input that
+   * have matching keys.
+   * @return true if the streamed iterator returned a row, false otherwise. If this returns true,
+   *         then [getStreamedRow and [[getBufferedMatches]] can be called to produce the outer
+   *         join results.
+   */
+  final def findNextOuterJoinRows(): Boolean = {
+    if (!advancedStreamed()) {
+      // We have consumed the entire streamed iterator, so there can be no more matches.
+      matchJoinKey = null
+      bufferedMatches.clear()
+      false
+    } else {
+      if (matchJoinKey != null && keyOrdering.compare(streamedRowKey, matchJoinKey) == 0) {
+        // Matches the current group, so do nothing.
+      } else {
+        // The streamed row does not match the current group.
+        matchJoinKey = null
+        bufferedMatches.clear()
+        if (bufferedRow != null && !streamedRowKey.anyNull) {
+          // The buffered iterator could still contain matching rows, so we'll need to walk through
+          // it until we either find matches or pass where they would be found.
+          var comp = 1
+          do {
+            comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
+          } while (comp > 0 && advancedBufferedToRowWithNullFreeJoinKey())
+          if (comp == 0) {
+            // We have found matches, so buffer them (this updates matchJoinKey)
+            bufferMatchingRows()
+          } else {
+            // We have overshot the position where the row would be found, hence no matches.
           }
-          rightMatches != null && rightMatches.size > 0
         }
       }
+      // If there is a streamed input then we always return true
+      true
     }
   }
+
+  // --- Private methods --------------------------------------------------------------------------
+
+  /**
+   * Advance the streamed iterator and compute the new row's join key.
+   * @return true if the streamed iterator returned a row and false otherwise.
+   */
+  private def advancedStreamed(): Boolean = {
+    if (streamedIter.advanceNext()) {
+      streamedRow = streamedIter.getRow
+      streamedRowKey = streamedKeyGenerator(streamedRow)
+      true
+    } else {
+      streamedRow = null
+      streamedRowKey = null
+      false
+    }
+  }
+
+  /**
+   * Advance the buffered iterator until we find a row with join key that does not contain nulls.
+   * @return true if the buffered iterator returned a row and false otherwise.
+   */
+  private def advancedBufferedToRowWithNullFreeJoinKey(): Boolean = {
+    var foundRow: Boolean = false
+    while (!foundRow && bufferedIter.advanceNext()) {
+      bufferedRow = bufferedIter.getRow
+      bufferedRowKey = bufferedKeyGenerator(bufferedRow)
+      foundRow = !bufferedRowKey.anyNull
+    }
+    if (!foundRow) {
+      bufferedRow = null
+      bufferedRowKey = null
+      false
+    } else {
+      true
+    }
+  }
+
+  /**
+   * Called when the streamed and buffered join keys match in order to buffer the matching rows.
+   */
+  private def bufferMatchingRows(): Unit = {
+    assert(streamedRowKey != null)
+    assert(!streamedRowKey.anyNull)
+    assert(bufferedRowKey != null)
+    assert(!bufferedRowKey.anyNull)
+    assert(keyOrdering.compare(streamedRowKey, bufferedRowKey) == 0)
+    // This join key may have been produced by a mutable projection, so we need to make a copy:
+    matchJoinKey = streamedRowKey.copy()
+    bufferedMatches.clear()
+    do {
+      bufferedMatches += bufferedRow.copy() // need to copy mutable rows before buffering them
+      advancedBufferedToRowWithNullFreeJoinKey()
+    } while (bufferedRow != null && keyOrdering.compare(streamedRowKey, bufferedRowKey) == 0)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
new file mode 100644
index 0000000000000..5326966b07a66
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -0,0 +1,251 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+
+/**
+ * :: DeveloperApi ::
+ * Performs an sort merge outer join of two child relations.
+ *
+ * Note: this does not support full outer join yet; see SPARK-9730 for progress on this.
+ */
+@DeveloperApi
+case class SortMergeOuterJoin(
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    joinType: JoinType,
+    condition: Option[Expression],
+    left: SparkPlan,
+    right: SparkPlan) extends BinaryNode {
+
+  override def output: Seq[Attribute] = {
+    joinType match {
+      case LeftOuter =>
+        left.output ++ right.output.map(_.withNullability(true))
+      case RightOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output
+      case x =>
+        throw new IllegalArgumentException(
+          s"${getClass.getSimpleName} should not take $x as the JoinType")
+    }
+  }
+
+  override def outputPartitioning: Partitioning = joinType match {
+    // For left and right outer joins, the output is partitioned by the streamed input's join keys.
+    case LeftOuter => left.outputPartitioning
+    case RightOuter => right.outputPartitioning
+    case x =>
+      throw new IllegalArgumentException(
+        s"${getClass.getSimpleName} should not take $x as the JoinType")
+  }
+
+  override def outputOrdering: Seq[SortOrder] = joinType match {
+    // For left and right outer joins, the output is ordered by the streamed input's join keys.
+    case LeftOuter => requiredOrders(leftKeys)
+    case RightOuter => requiredOrders(rightKeys)
+    case x => throw new IllegalArgumentException(
+      s"SortMergeOuterJoin should not take $x as the JoinType")
+  }
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+    requiredOrders(leftKeys) :: requiredOrders(rightKeys) :: Nil
+
+  private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = {
+    // This must be ascending in order to agree with the `keyOrdering` defined in `doExecute()`.
+    keys.map(SortOrder(_, Ascending))
+  }
+
+  private def isUnsafeMode: Boolean = {
+    (codegenEnabled && unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
+      && UnsafeProjection.canSupport(rightKeys)
+      && UnsafeProjection.canSupport(schema))
+  }
+
+  override def outputsUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessSafeRows: Boolean = !isUnsafeMode
+
+  private def createLeftKeyGenerator(): Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(leftKeys, left.output)
+    } else {
+      newProjection(leftKeys, left.output)
+    }
+  }
+
+  private def createRightKeyGenerator(): Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(rightKeys, right.output)
+    } else {
+      newProjection(rightKeys, right.output)
+    }
+  }
+
+  override def doExecute(): RDD[InternalRow] = {
+    left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
+      // An ordering that can be used to compare keys from both sides.
+      val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
+      val boundCondition: (InternalRow) => Boolean = {
+        condition.map { cond =>
+          newPredicate(cond, left.output ++ right.output)
+        }.getOrElse {
+          (r: InternalRow) => true
+        }
+      }
+      val resultProj: InternalRow => InternalRow = {
+        if (isUnsafeMode) {
+          UnsafeProjection.create(schema)
+        } else {
+          identity[InternalRow]
+        }
+      }
+
+      joinType match {
+        case LeftOuter =>
+          val smjScanner = new SortMergeJoinScanner(
+            streamedKeyGenerator = createLeftKeyGenerator(),
+            bufferedKeyGenerator = createRightKeyGenerator(),
+            keyOrdering,
+            streamedIter = RowIterator.fromScala(leftIter),
+            bufferedIter = RowIterator.fromScala(rightIter)
+          )
+          val rightNullRow = new GenericInternalRow(right.output.length)
+          new LeftOuterIterator(smjScanner, rightNullRow, boundCondition, resultProj).toScala
+
+        case RightOuter =>
+          val smjScanner = new SortMergeJoinScanner(
+            streamedKeyGenerator = createRightKeyGenerator(),
+            bufferedKeyGenerator = createLeftKeyGenerator(),
+            keyOrdering,
+            streamedIter = RowIterator.fromScala(rightIter),
+            bufferedIter = RowIterator.fromScala(leftIter)
+          )
+          val leftNullRow = new GenericInternalRow(left.output.length)
+          new RightOuterIterator(smjScanner, leftNullRow, boundCondition, resultProj).toScala
+
+        case x =>
+          throw new IllegalArgumentException(
+            s"SortMergeOuterJoin should not take $x as the JoinType")
+      }
+    }
+  }
+}
+
+
+private class LeftOuterIterator(
+    smjScanner: SortMergeJoinScanner,
+    rightNullRow: InternalRow,
+    boundCondition: InternalRow => Boolean,
+    resultProj: InternalRow => InternalRow
+  ) extends RowIterator {
+  private[this] val joinedRow: JoinedRow = new JoinedRow()
+  private[this] var rightIdx: Int = 0
+  assert(smjScanner.getBufferedMatches.length == 0)
+
+  private def advanceLeft(): Boolean = {
+    rightIdx = 0
+    if (smjScanner.findNextOuterJoinRows()) {
+      joinedRow.withLeft(smjScanner.getStreamedRow)
+      if (smjScanner.getBufferedMatches.isEmpty) {
+        // There are no matching right rows, so return nulls for the right row
+        joinedRow.withRight(rightNullRow)
+      } else {
+        // Find the next row from the right input that satisfied the bound condition
+        if (!advanceRightUntilBoundConditionSatisfied()) {
+          joinedRow.withRight(rightNullRow)
+        }
+      }
+      true
+    } else {
+      // Left input has been exhausted
+      false
+    }
+  }
+
+  private def advanceRightUntilBoundConditionSatisfied(): Boolean = {
+    var foundMatch: Boolean = false
+    while (!foundMatch && rightIdx < smjScanner.getBufferedMatches.length) {
+      foundMatch = boundCondition(joinedRow.withRight(smjScanner.getBufferedMatches(rightIdx)))
+      rightIdx += 1
+    }
+    foundMatch
+  }
+
+  override def advanceNext(): Boolean = {
+    advanceRightUntilBoundConditionSatisfied() || advanceLeft()
+  }
+
+  override def getRow: InternalRow = resultProj(joinedRow)
+}
+
+private class RightOuterIterator(
+    smjScanner: SortMergeJoinScanner,
+    leftNullRow: InternalRow,
+    boundCondition: InternalRow => Boolean,
+    resultProj: InternalRow => InternalRow
+  ) extends RowIterator {
+  private[this] val joinedRow: JoinedRow = new JoinedRow()
+  private[this] var leftIdx: Int = 0
+  assert(smjScanner.getBufferedMatches.length == 0)
+
+  private def advanceRight(): Boolean = {
+    leftIdx = 0
+    if (smjScanner.findNextOuterJoinRows()) {
+      joinedRow.withRight(smjScanner.getStreamedRow)
+      if (smjScanner.getBufferedMatches.isEmpty) {
+        // There are no matching left rows, so return nulls for the left row
+        joinedRow.withLeft(leftNullRow)
+      } else {
+        // Find the next row from the left input that satisfied the bound condition
+        if (!advanceLeftUntilBoundConditionSatisfied()) {
+          joinedRow.withLeft(leftNullRow)
+        }
+      }
+      true
+    } else {
+      // Right input has been exhausted
+      false
+    }
+  }
+
+  private def advanceLeftUntilBoundConditionSatisfied(): Boolean = {
+    var foundMatch: Boolean = false
+    while (!foundMatch && leftIdx < smjScanner.getBufferedMatches.length) {
+      foundMatch = boundCondition(joinedRow.withLeft(smjScanner.getBufferedMatches(leftIdx)))
+      leftIdx += 1
+    }
+    foundMatch
+  }
+
+  override def advanceNext(): Boolean = {
+    advanceLeftUntilBoundConditionSatisfied() || advanceRight()
+  }
+
+  override def getRow: InternalRow = resultProj(joinedRow)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 5bef1d8966031..ae07eaf91c872 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -22,13 +22,14 @@ import org.scalatest.BeforeAndAfterEach
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.execution.joins._
-import org.apache.spark.sql.types.BinaryType
+import org.apache.spark.sql.test.SQLTestUtils
 
 
-class JoinSuite extends QueryTest with BeforeAndAfterEach {
+class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
   // Ensures tables are loaded.
   TestData
 
+  override def sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
   lazy val ctx = org.apache.spark.sql.test.TestSQLContext
   import ctx.implicits._
   import ctx.logicalPlanToSparkQuery
@@ -37,7 +38,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.optimizedPlan
-    val planned = ctx.planner.HashJoin(join)
+    val planned = ctx.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -55,6 +56,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       case j: BroadcastNestedLoopJoin => j
       case j: BroadcastLeftSemiJoinHash => j
       case j: SortMergeJoin => j
+      case j: SortMergeOuterJoin => j
     }
 
     assert(operators.size === 1)
@@ -66,7 +68,6 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
   test("join operator selection") {
     ctx.cacheManager.clearCache()
 
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
     Seq(
       ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]),
       ("SELECT * FROM testData LEFT SEMI JOIN testData2", classOf[LeftSemiJoinBNL]),
@@ -83,11 +84,11 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]),
       ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]),
       ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin]),
-      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
-        classOf[ShuffledHashOuterJoin]),
+        classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
-        classOf[ShuffledHashOuterJoin]),
+        classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData full outer join testData2 ON key = a",
         classOf[ShuffledHashOuterJoin]),
       ("SELECT * FROM testData left JOIN testData2 ON (key * a != key + a)",
@@ -97,82 +98,75 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       ("SELECT * FROM testData full JOIN testData2 ON (key * a != key + a)",
         classOf[BroadcastNestedLoopJoin])
     ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
       Seq(
-        ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]),
-        ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]),
-        ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin])
+        ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[ShuffledHashJoin]),
+        ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2",
+          classOf[ShuffledHashJoin]),
+        ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2",
+          classOf[ShuffledHashJoin]),
+        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
+          classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
+          classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData full outer join testData2 ON key = a",
+          classOf[ShuffledHashOuterJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
     }
   }
 
   test("SortMergeJoin shouldn't work on unsortable columns") {
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
         ("SELECT * FROM arrayData JOIN complexData ON data = a", classOf[ShuffledHashJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
     }
   }
 
   test("broadcasted hash join operator selection") {
     ctx.cacheManager.clearCache()
     ctx.sql("CACHE TABLE testData")
-
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
-    Seq(
-      ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]),
-      ("SELECT * FROM testData join testData2 ON key = a and key = 2", classOf[BroadcastHashJoin]),
-      ("SELECT * FROM testData join testData2 ON key = a where key = 2",
-        classOf[BroadcastHashJoin])
-    ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
-      Seq(
-        ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]),
-        ("SELECT * FROM testData join testData2 ON key = a and key = 2",
-          classOf[BroadcastHashJoin]),
-        ("SELECT * FROM testData join testData2 ON key = a where key = 2",
-          classOf[BroadcastHashJoin])
-      ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
+    for (sortMergeJoinEnabled <- Seq(true, false)) {
+      withClue(s"sortMergeJoinEnabled=$sortMergeJoinEnabled") {
+        withSQLConf(SQLConf.SORTMERGE_JOIN.key -> s"$sortMergeJoinEnabled") {
+          Seq(
+            ("SELECT * FROM testData join testData2 ON key = a",
+              classOf[BroadcastHashJoin]),
+            ("SELECT * FROM testData join testData2 ON key = a and key = 2",
+              classOf[BroadcastHashJoin]),
+            ("SELECT * FROM testData join testData2 ON key = a where key = 2",
+              classOf[BroadcastHashJoin])
+          ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
+        }
+      }
     }
-
     ctx.sql("UNCACHE TABLE testData")
   }
 
   test("broadcasted hash outer join operator selection") {
     ctx.cacheManager.clearCache()
     ctx.sql("CACHE TABLE testData")
-
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
-    Seq(
-      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
-      ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
-        classOf[BroadcastHashOuterJoin]),
-      ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
-        classOf[BroadcastHashOuterJoin])
-    ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
-        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a",
+          classOf[SortMergeOuterJoin]),
+        ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
+          classOf[BroadcastHashOuterJoin]),
+        ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
+          classOf[BroadcastHashOuterJoin])
+      ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
+    }
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      Seq(
+        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a",
+          classOf[ShuffledHashOuterJoin]),
         ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
           classOf[BroadcastHashOuterJoin]),
         ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
           classOf[BroadcastHashOuterJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
     }
-
     ctx.sql("UNCACHE TABLE testData")
   }
 
@@ -180,7 +174,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.optimizedPlan
-    val planned = ctx.planner.HashJoin(join)
+    val planned = ctx.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -457,25 +451,24 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
   test("broadcasted left semi join operator selection") {
     ctx.cacheManager.clearCache()
     ctx.sql("CACHE TABLE testData")
-    val tmp = ctx.conf.autoBroadcastJoinThreshold
 
-    ctx.sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=1000000000")
-    Seq(
-      ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a",
-        classOf[BroadcastLeftSemiJoinHash])
-    ).foreach {
-      case (query, joinClass) => assertJoin(query, joinClass)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "1000000000") {
+      Seq(
+        ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a",
+          classOf[BroadcastLeftSemiJoinHash])
+      ).foreach {
+        case (query, joinClass) => assertJoin(query, joinClass)
+      }
     }
 
-    ctx.sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1")
-
-    Seq(
-      ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash])
-    ).foreach {
-      case (query, joinClass) => assertJoin(query, joinClass)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") {
+      Seq(
+        ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash])
+      ).foreach {
+        case (query, joinClass) => assertJoin(query, joinClass)
+      }
     }
 
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, tmp)
     ctx.sql("UNCACHE TABLE testData")
   }
 
@@ -488,6 +481,5 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
         Row(2, 2) ::
         Row(3, 1) ::
         Row(3, 2) :: Nil)
-
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
new file mode 100644
index 0000000000000..ddff7cebcc17d
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
@@ -0,0 +1,180 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.Inner
+import org.apache.spark.sql.catalyst.plans.logical.Join
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
+import org.apache.spark.sql.{SQLConf, execution, Row, DataFrame}
+import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.execution._
+
+class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
+
+  private def testInnerJoin(
+      testName: String,
+      leftRows: DataFrame,
+      rightRows: DataFrame,
+      condition: Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+    ExtractEquiJoinKeys.unapply(join).foreach {
+      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
+
+        def makeBroadcastHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
+          val broadcastHashJoin =
+            execution.joins.BroadcastHashJoin(leftKeys, rightKeys, side, left, right)
+          boundCondition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin)
+        }
+
+        def makeShuffledHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
+          val shuffledHashJoin =
+            execution.joins.ShuffledHashJoin(leftKeys, rightKeys, side, left, right)
+          val filteredJoin =
+            boundCondition.map(Filter(_, shuffledHashJoin)).getOrElse(shuffledHashJoin)
+          EnsureRequirements(sqlContext).apply(filteredJoin)
+        }
+
+        def makeSortMergeJoin(left: SparkPlan, right: SparkPlan) = {
+          val sortMergeJoin =
+            execution.joins.SortMergeJoin(leftKeys, rightKeys, left, right)
+          val filteredJoin = boundCondition.map(Filter(_, sortMergeJoin)).getOrElse(sortMergeJoin)
+          EnsureRequirements(sqlContext).apply(filteredJoin)
+        }
+
+        test(s"$testName using BroadcastHashJoin (build=left)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeBroadcastHashJoin(left, right, joins.BuildLeft),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using BroadcastHashJoin (build=right)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeBroadcastHashJoin(left, right, joins.BuildRight),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using ShuffledHashJoin (build=left)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeShuffledHashJoin(left, right, joins.BuildLeft),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using ShuffledHashJoin (build=right)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeShuffledHashJoin(left, right, joins.BuildRight),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using SortMergeJoin") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeSortMergeJoin(left, right),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+    }
+  }
+
+  {
+    val upperCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+      Row(1, "A"),
+      Row(2, "B"),
+      Row(3, "C"),
+      Row(4, "D"),
+      Row(5, "E"),
+      Row(6, "F"),
+      Row(null, "G")
+    )), new StructType().add("N", IntegerType).add("L", StringType))
+
+    val lowerCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+      Row(1, "a"),
+      Row(2, "b"),
+      Row(3, "c"),
+      Row(4, "d"),
+      Row(null, "e")
+    )), new StructType().add("n", IntegerType).add("l", StringType))
+
+    testInnerJoin(
+      "inner join, one match per row",
+      upperCaseData,
+      lowerCaseData,
+      (upperCaseData.col("N") === lowerCaseData.col("n")).expr,
+      Seq(
+        (1, "A", 1, "a"),
+        (2, "B", 2, "b"),
+        (3, "C", 3, "c"),
+        (4, "D", 4, "d")
+      )
+    )
+  }
+
+  private val testData2 = Seq(
+    (1, 1),
+    (1, 2),
+    (2, 1),
+    (2, 2),
+    (3, 1),
+    (3, 2)
+  ).toDF("a", "b")
+
+  {
+    val left = testData2.where("a = 1")
+    val right = testData2.where("a = 1")
+    testInnerJoin(
+      "inner join, multiple matches",
+      left,
+      right,
+      (left.col("a") === right.col("a")).expr,
+      Seq(
+        (1, 1, 1, 1),
+        (1, 1, 1, 2),
+        (1, 2, 1, 1),
+        (1, 2, 1, 2)
+      )
+    )
+  }
+
+  {
+    val left = testData2.where("a = 1")
+    val right = testData2.where("a = 2")
+    testInnerJoin(
+      "inner join, no matches",
+      left,
+      right,
+      (left.col("a") === right.col("a")).expr,
+      Seq.empty
+    )
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index 2c27da596bc4f..e16f5e39aa2f4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -1,89 +1,221 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.joins
-
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.expressions.{Expression, LessThan}
-import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, RightOuter}
-import org.apache.spark.sql.execution.{SparkPlan, SparkPlanTest}
-
-class OuterJoinSuite extends SparkPlanTest {
-
-  val left = Seq(
-    (1, 2.0),
-    (2, 1.0),
-    (3, 3.0)
-  ).toDF("a", "b")
-
-  val right = Seq(
-    (2, 3.0),
-    (3, 2.0),
-    (4, 1.0)
-  ).toDF("c", "d")
-
-  val leftKeys: List[Expression] = 'a :: Nil
-  val rightKeys: List[Expression] = 'c :: Nil
-  val condition = Some(LessThan('b, 'd))
-
-  test("shuffled hash outer join") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      ShuffledHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
-      Seq(
-        (1, 2.0, null, null),
-        (2, 1.0, 2, 3.0),
-        (3, 3.0, null, null)
-      ).map(Row.fromTuple))
-
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      ShuffledHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
-      Seq(
-        (2, 1.0, 2, 3.0),
-        (null, null, 3, 2.0),
-        (null, null, 4, 1.0)
-      ).map(Row.fromTuple))
-
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      ShuffledHashOuterJoin(leftKeys, rightKeys, FullOuter, condition, left, right),
-      Seq(
-        (1, 2.0, null, null),
-        (2, 1.0, 2, 3.0),
-        (3, 3.0, null, null),
-        (null, null, 3, 2.0),
-        (null, null, 4, 1.0)
-      ).map(Row.fromTuple))
-  }
-
-  test("broadcast hash outer join") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      BroadcastHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
-      Seq(
-        (1, 2.0, null, null),
-        (2, 1.0, 2, 3.0),
-        (3, 3.0, null, null)
-      ).map(Row.fromTuple))
-
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      BroadcastHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
-      Seq(
-        (2, 1.0, 2, 3.0),
-        (null, null, 3, 2.0),
-        (null, null, 4, 1.0)
-      ).map(Row.fromTuple))
-  }
-}
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.logical.Join
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types.{IntegerType, DoubleType, StructType}
+import org.apache.spark.sql.{SQLConf, DataFrame, Row}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.execution.{EnsureRequirements, joins, SparkPlan, SparkPlanTest}
+
+class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
+
+  private def testOuterJoin(
+      testName: String,
+      leftRows: DataFrame,
+      rightRows: DataFrame,
+      joinType: JoinType,
+      condition: Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+    ExtractEquiJoinKeys.unapply(join).foreach {
+      case (_, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
+        test(s"$testName using ShuffledHashOuterJoin") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              EnsureRequirements(sqlContext).apply(
+                ShuffledHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        if (joinType != FullOuter) {
+          test(s"$testName using BroadcastHashOuterJoin") {
+            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+                BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
+                expectedAnswer.map(Row.fromTuple),
+                sortAnswers = true)
+            }
+          }
+
+          test(s"$testName using SortMergeOuterJoin") {
+            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+                EnsureRequirements(sqlContext).apply(
+                  SortMergeOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+                expectedAnswer.map(Row.fromTuple),
+                sortAnswers = false)
+            }
+          }
+        }
+    }
+
+    test(s"$testName using BroadcastNestedLoopJoin (build=left)") {
+      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+          joins.BroadcastNestedLoopJoin(left, right, joins.BuildLeft, joinType, Some(condition)),
+          expectedAnswer.map(Row.fromTuple),
+          sortAnswers = true)
+      }
+    }
+
+    test(s"$testName using BroadcastNestedLoopJoin (build=right)") {
+      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+          joins.BroadcastNestedLoopJoin(left, right, joins.BuildRight, joinType, Some(condition)),
+          expectedAnswer.map(Row.fromTuple),
+          sortAnswers = true)
+      }
+    }
+  }
+
+  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(1, 2.0),
+    Row(2, 100.0),
+    Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
+    Row(2, 1.0),
+    Row(3, 3.0),
+    Row(5, 1.0),
+    Row(6, 6.0),
+    Row(null, null)
+  )), new StructType().add("a", IntegerType).add("b", DoubleType))
+
+  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(0, 0.0),
+    Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
+    Row(2, -1.0),
+    Row(2, -1.0),
+    Row(2, 3.0),
+    Row(3, 2.0),
+    Row(4, 1.0),
+    Row(5, 3.0),
+    Row(7, 7.0),
+    Row(null, null)
+  )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  val condition = {
+    And(
+      (left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
+  }
+
+  // --- Basic outer joins ------------------------------------------------------------------------
+
+  testOuterJoin(
+    "basic left outer join",
+    left,
+    right,
+    LeftOuter,
+    condition,
+    Seq(
+      (null, null, null, null),
+      (1, 2.0, null, null),
+      (2, 100.0, null, null),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (3, 3.0, null, null),
+      (5, 1.0, 5, 3.0),
+      (6, 6.0, null, null)
+    )
+  )
+
+  testOuterJoin(
+    "basic right outer join",
+    left,
+    right,
+    RightOuter,
+    condition,
+    Seq(
+      (null, null, null, null),
+      (null, null, 0, 0.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (null, null, 2, -1.0),
+      (null, null, 2, -1.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (null, null, 3, 2.0),
+      (null, null, 4, 1.0),
+      (5, 1.0, 5, 3.0),
+      (null, null, 7, 7.0)
+    )
+  )
+
+  testOuterJoin(
+    "basic full outer join",
+    left,
+    right,
+    FullOuter,
+    condition,
+    Seq(
+      (1, 2.0, null, null),
+      (null, null, 2, -1.0),
+      (null, null, 2, -1.0),
+      (2, 100.0, null, null),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (3, 3.0, null, null),
+      (5, 1.0, 5, 3.0),
+      (6, 6.0, null, null),
+      (null, null, 0, 0.0),
+      (null, null, 3, 2.0),
+      (null, null, 4, 1.0),
+      (null, null, 7, 7.0),
+      (null, null, null, null),
+      (null, null, null, null)
+    )
+  )
+
+  // --- Both inputs empty ------------------------------------------------------------------------
+
+  testOuterJoin(
+    "left outer join with both inputs empty",
+    left.filter("false"),
+    right.filter("false"),
+    LeftOuter,
+    condition,
+    Seq.empty
+  )
+
+  testOuterJoin(
+    "right outer join with both inputs empty",
+    left.filter("false"),
+    right.filter("false"),
+    RightOuter,
+    condition,
+    Seq.empty
+  )
+
+  testOuterJoin(
+    "full outer join with both inputs empty",
+    left.filter("false"),
+    right.filter("false"),
+    FullOuter,
+    condition,
+    Seq.empty
+  )
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
index 927e85a7db3dc..4503ed251fcb1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
@@ -17,58 +17,91 @@
 
 package org.apache.spark.sql.execution.joins
 
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.expressions.{LessThan, Expression}
-import org.apache.spark.sql.execution.{SparkPlan, SparkPlanTest}
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.Inner
+import org.apache.spark.sql.catalyst.plans.logical.Join
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
+import org.apache.spark.sql.{SQLConf, DataFrame, Row}
+import org.apache.spark.sql.catalyst.expressions.{And, LessThan, Expression}
+import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
 
+class SemiJoinSuite extends SparkPlanTest with SQLTestUtils {
 
-class SemiJoinSuite extends SparkPlanTest{
-  val left = Seq(
-    (1, 2.0),
-    (1, 2.0),
-    (2, 1.0),
-    (2, 1.0),
-    (3, 3.0)
-  ).toDF("a", "b")
+  private def testLeftSemiJoin(
+      testName: String,
+      leftRows: DataFrame,
+      rightRows: DataFrame,
+      condition: Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+    ExtractEquiJoinKeys.unapply(join).foreach {
+      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
+        test(s"$testName using LeftSemiJoinHash") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              EnsureRequirements(left.sqlContext).apply(
+                LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
 
-  val right = Seq(
-    (2, 3.0),
-    (2, 3.0),
-    (3, 2.0),
-    (4, 1.0)
-  ).toDF("c", "d")
+        test(s"$testName using BroadcastLeftSemiJoinHash") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+    }
 
-  val leftKeys: List[Expression] = 'a :: Nil
-  val rightKeys: List[Expression] = 'c :: Nil
-  val condition = Some(LessThan('b, 'd))
-
-  test("left semi join hash") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      LeftSemiJoinHash(leftKeys, rightKeys, left, right, condition),
-      Seq(
-        (2, 1.0),
-        (2, 1.0)
-      ).map(Row.fromTuple))
+    test(s"$testName using LeftSemiJoinBNL") {
+      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+          LeftSemiJoinBNL(left, right, Some(condition)),
+          expectedAnswer.map(Row.fromTuple),
+          sortAnswers = true)
+      }
+    }
   }
 
-  test("left semi join BNL") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      LeftSemiJoinBNL(left, right, condition),
-      Seq(
-        (1, 2.0),
-        (1, 2.0),
-        (2, 1.0),
-        (2, 1.0)
-      ).map(Row.fromTuple))
-  }
+  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(1, 2.0),
+    Row(1, 2.0),
+    Row(2, 1.0),
+    Row(2, 1.0),
+    Row(3, 3.0),
+    Row(null, null),
+    Row(null, 5.0),
+    Row(6, null)
+  )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
-  test("broadcast left semi join hash") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, condition),
-      Seq(
-        (2, 1.0),
-        (2, 1.0)
-      ).map(Row.fromTuple))
+  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(2, 3.0),
+    Row(2, 3.0),
+    Row(3, 2.0),
+    Row(4, 1.0),
+    Row(null, null),
+    Row(null, 5.0),
+    Row(6, null)
+  )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  val condition = {
+    And(
+      (left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
   }
+
+  testLeftSemiJoin(
+    "basic test",
+    left,
+    right,
+    condition,
+    Seq(
+      (2, 1.0),
+      (2, 1.0)
+    )
+  )
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index 4c11acdab9ec0..1066695589778 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.util.Utils
 
 trait SQLTestUtils { this: SparkFunSuite =>
-  def sqlContext: SQLContext
+  protected def sqlContext: SQLContext
 
   protected def configuration = sqlContext.sparkContext.hadoopConfiguration
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 567d7fa12ff14..f17177a771c3b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -531,7 +531,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
       HashAggregation,
       Aggregation,
       LeftSemiJoin,
-      HashJoin,
+      EquiJoinSelection,
       BasicOperators,
       CartesianProduct,
       BroadcastNestedLoopJoin

From 0f90d6055e5bea9ceb1d454db84f4aa1d59b284d Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon, 10 Aug 2015 23:41:53 -0700
Subject: [PATCH 0959/1454] [SPARK-9640] [STREAMING] [TEST] Do not run Python
 Kinesis tests when the Kinesis assembly JAR has not been generated

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7961 from tdas/SPARK-9640 and squashes the following commits:

974ce19 [Tathagata Das] Undo changes related to SPARK-9727
004ae26 [Tathagata Das] style fixes
9bbb97d [Tathagata Das] Minor style fies
e6a677e [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9640
ca90719 [Tathagata Das] Removed extra line
ba9cfc7 [Tathagata Das] Improved kinesis test selection logic
88d59bd [Tathagata Das] updated test modules
871fcc8 [Tathagata Das] Fixed SparkBuild
94be631 [Tathagata Das] Fixed style
b858196 [Tathagata Das] Fixed conditions and few other things based on PR comments.
e292e64 [Tathagata Das] Added filters for Kinesis python tests
---
 python/pyspark/streaming/tests.py | 56 ++++++++++++++++++++++++-------
 1 file changed, 44 insertions(+), 12 deletions(-)

diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 66ae3345f468f..f0ed415f97120 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -971,8 +971,10 @@ def test_kinesis_stream_api(self):
             "awsAccessKey", "awsSecretKey")
 
     def test_kinesis_stream(self):
-        if os.environ.get('ENABLE_KINESIS_TESTS') != '1':
-            print("Skip test_kinesis_stream")
+        if not are_kinesis_tests_enabled:
+            sys.stderr.write(
+                "Skipped test_kinesis_stream (enable by setting environment variable %s=1"
+                % kinesis_test_environ_var)
             return
 
         import random
@@ -1013,6 +1015,7 @@ def get_output(_, rdd):
             traceback.print_exc()
             raise
         finally:
+            self.ssc.stop(False)
             kinesisTestUtils.deleteStream()
             kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
 
@@ -1027,7 +1030,7 @@ def search_kafka_assembly_jar():
             ("Failed to find Spark Streaming kafka assembly jar in %s. " % kafka_assembly_dir) +
             "You need to build Spark with "
             "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or "
-            "'build/mvn package' before running this test")
+            "'build/mvn package' before running this test.")
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Kafka assembly JARs in %s; please "
                          "remove all but one") % kafka_assembly_dir)
@@ -1045,7 +1048,7 @@ def search_flume_assembly_jar():
             ("Failed to find Spark Streaming Flume assembly jar in %s. " % flume_assembly_dir) +
             "You need to build Spark with "
             "'build/sbt assembly/assembly streaming-flume-assembly/assembly' or "
-            "'build/mvn package' before running this test")
+            "'build/mvn package' before running this test.")
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Flume assembly JARs in %s; please "
                         "remove all but one") % flume_assembly_dir)
@@ -1095,11 +1098,7 @@ def search_kinesis_asl_assembly_jar():
         os.path.join(kinesis_asl_assembly_dir,
                      "target/scala-*/spark-streaming-kinesis-asl-assembly-*.jar"))
     if not jars:
-        raise Exception(
-            ("Failed to find Spark Streaming Kinesis ASL assembly jar in %s. " %
-             kinesis_asl_assembly_dir) + "You need to build Spark with "
-            "'build/sbt -Pkinesis-asl assembly/assembly streaming-kinesis-asl-assembly/assembly' "
-            "or 'build/mvn -Pkinesis-asl package' before running this test")
+        return None
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs in %s; please "
                          "remove all but one") % kinesis_asl_assembly_dir)
@@ -1107,6 +1106,10 @@ def search_kinesis_asl_assembly_jar():
         return jars[0]
 
 
+# Must be same as the variable and condition defined in KinesisTestUtils.scala
+kinesis_test_environ_var = "ENABLE_KINESIS_TESTS"
+are_kinesis_tests_enabled = os.environ.get(kinesis_test_environ_var) == '1'
+
 if __name__ == "__main__":
     kafka_assembly_jar = search_kafka_assembly_jar()
     flume_assembly_jar = search_flume_assembly_jar()
@@ -1114,8 +1117,37 @@ def search_kinesis_asl_assembly_jar():
     mqtt_test_jar = search_mqtt_test_jar()
     kinesis_asl_assembly_jar = search_kinesis_asl_assembly_jar()
 
-    jars = "%s,%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar,
-                               mqtt_assembly_jar, mqtt_test_jar)
+    if kinesis_asl_assembly_jar is None:
+        kinesis_jar_present = False
+        jars = "%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, mqtt_assembly_jar,
+                                mqtt_test_jar)
+    else:
+        kinesis_jar_present = True
+        jars = "%s,%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, mqtt_assembly_jar,
+                                   mqtt_test_jar, kinesis_asl_assembly_jar)
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
-    unittest.main()
+    testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests,
+                 CheckpointTests, KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests]
+
+    if kinesis_jar_present is True:
+        testcases.append(KinesisStreamTests)
+    elif are_kinesis_tests_enabled is False:
+        sys.stderr.write("Skipping all Kinesis Python tests as the optional Kinesis project was "
+                         "not compiled with -Pkinesis-asl profile. To run these tests, "
+                         "you need to build Spark with 'build/sbt -Pkinesis-asl assembly/assembly "
+                         "streaming-kinesis-asl-assembly/assembly' or "
+                         "'build/mvn -Pkinesis-asl package' before running this test.")
+    else:
+        raise Exception(
+            ("Failed to find Spark Streaming Kinesis assembly jar in %s. "
+             % kinesis_asl_assembly_dir) +
+            "You need to build Spark with 'build/sbt -Pkinesis-asl "
+            "assembly/assembly streaming-kinesis-asl-assembly/assembly'"
+            "or 'build/mvn -Pkinesis-asl package' before running this test.")
+
+    sys.stderr.write("Running tests: %s \n" % (str(testcases)))
+    for testcase in testcases:
+        sys.stderr.write("[Running %s]\n" % (testcase))
+        tests = unittest.TestLoader().loadTestsFromTestCase(testcase)
+        unittest.TextTestRunner(verbosity=2).run(tests)

From 55752d88321925da815823f968128832de6fdbbb Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 11 Aug 2015 01:08:30 -0700
Subject: [PATCH 0960/1454] [SPARK-9810] [BUILD] Remove individual commit
 messages from the squash commit message

For more information, please see the JIRA ticket and the associated dev list discussion.

https://issues.apache.org/jira/browse/SPARK-9810

http://apache-spark-developers-list.1001551.n3.nabble.com/discuss-Removing-individual-commit-messages-from-the-squash-commit-message-td13295.html

Author: Reynold Xin <rxin@databricks.com>

Closes #8091 from rxin/SPARK-9810.
---
 dev/merge_spark_pr.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index ad4b76695c9ff..b9bdec3d70864 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -159,11 +159,7 @@ def merge_pr(pr_num, target_ref, title, body, pr_repo_desc):
         merge_message_flags += ["-m", message]
 
     # The string "Closes #%s" string is required for GitHub to correctly close the PR
-    merge_message_flags += [
-        "-m",
-        "Closes #%s from %s and squashes the following commits:" % (pr_num, pr_repo_desc)]
-    for c in commits:
-        merge_message_flags += ["-m", c]
+    merge_message_flags += ["-m", "Closes #%s from %s." % (pr_num, pr_repo_desc)]
 
     run_cmd(['git', 'commit', '--author="%s"' % primary_author] + merge_message_flags)
 

From 600031ebe27473d8fffe6ea436c2149223b82896 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 11 Aug 2015 02:41:03 -0700
Subject: [PATCH 0961/1454] [SPARK-9727] [STREAMING] [BUILD] Updated streaming
 kinesis SBT project name to be more consistent

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8092 from tdas/SPARK-9727 and squashes the following commits:

b1b01fd [Tathagata Das] Updated streaming kinesis project name
---
 dev/sparktestsupport/modules.py | 4 ++--
 extras/kinesis-asl/pom.xml      | 2 +-
 project/SparkBuild.scala        | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index d82c0cca37bc6..346452f3174e4 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -134,7 +134,7 @@ def contains_file(self, filename):
 # files in streaming_kinesis_asl are changed, so that if Kinesis experiences an outage, we don't
 # fail other PRs.
 streaming_kinesis_asl = Module(
-    name="kinesis-asl",
+    name="streaming-kinesis-asl",
     dependencies=[],
     source_file_regexes=[
         "extras/kinesis-asl/",
@@ -147,7 +147,7 @@ def contains_file(self, filename):
         "ENABLE_KINESIS_TESTS": "1"
     },
     sbt_test_goals=[
-        "kinesis-asl/test",
+        "streaming-kinesis-asl/test",
     ]
 )
 
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index c242e7a57b9ab..521b53e230c4a 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -31,7 +31,7 @@
   <name>Spark Kinesis Integration</name>
 
   <properties>
-    <sbt.project.name>kinesis-asl</sbt.project.name>
+    <sbt.project.name>streaming-kinesis-asl</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 41a85fa9de778..cad7067ade8c1 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -42,8 +42,8 @@ object BuildCommons {
       "streaming-zeromq", "launcher", "unsafe").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, java8Tests, sparkGangliaLgpl,
-    sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
-    "kinesis-asl").map(ProjectRef(buildLocation, _))
+    streamingKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
+    "streaming-kinesis-asl").map(ProjectRef(buildLocation, _))
 
   val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly, streamingMqttAssembly, streamingKinesisAslAssembly) =
     Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly", "streaming-mqtt-assembly", "streaming-kinesis-asl-assembly")

From d378396f86f625f006738d87fe5dbc2ff8fd913d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 11 Aug 2015 08:41:06 -0700
Subject: [PATCH 0962/1454] [SPARK-9815] Rename PlatformDependent.UNSAFE ->
 Platform.

PlatformDependent.UNSAFE is way too verbose.

Author: Reynold Xin <rxin@databricks.com>

Closes #8094 from rxin/SPARK-9815 and squashes the following commits:

229b603 [Reynold Xin] [SPARK-9815] Rename PlatformDependent.UNSAFE -> Platform.
---
 .../serializer/DummySerializerInstance.java   |   6 +-
 .../unsafe/UnsafeShuffleExternalSorter.java   |  22 +--
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |   4 +-
 .../spark/unsafe/map/BytesToBytesMap.java     |  20 +--
 .../unsafe/sort/PrefixComparators.java        |   5 +-
 .../unsafe/sort/UnsafeExternalSorter.java     |  22 +--
 .../unsafe/sort/UnsafeInMemorySorter.java     |   4 +-
 .../unsafe/sort/UnsafeSorterSpillReader.java  |   4 +-
 .../unsafe/sort/UnsafeSorterSpillWriter.java  |   6 +-
 .../UnsafeShuffleInMemorySorterSuite.java     |  20 +--
 .../map/AbstractBytesToBytesMapSuite.java     |  94 +++++-----
 .../sort/UnsafeExternalSorterSuite.java       |  20 +--
 .../sort/UnsafeInMemorySorterSuite.java       |  20 +--
 .../catalyst/expressions/UnsafeArrayData.java |  51 ++----
 .../catalyst/expressions/UnsafeReaders.java   |   8 +-
 .../sql/catalyst/expressions/UnsafeRow.java   | 108 +++++------
 .../expressions/UnsafeRowWriters.java         |  41 ++---
 .../catalyst/expressions/UnsafeWriters.java   |  43 ++---
 .../execution/UnsafeExternalRowSorter.java    |   4 +-
 .../expressions/codegen/CodeGenerator.scala   |   4 +-
 .../codegen/GenerateUnsafeProjection.scala    |  32 ++--
 .../codegen/GenerateUnsafeRowJoiner.scala     |  16 +-
 .../expressions/stringOperations.scala        |   4 +-
 .../GenerateUnsafeRowJoinerBitsetSuite.scala  |   4 +-
 .../UnsafeFixedWidthAggregationMap.java       |   4 +-
 .../sql/execution/UnsafeKVExternalSorter.java |   4 +-
 .../sql/execution/UnsafeRowSerializer.scala   |   6 +-
 .../sql/execution/joins/HashedRelation.scala  |  13 +-
 .../org/apache/spark/sql/UnsafeRowSuite.scala |   4 +-
 .../{PlatformDependent.java => Platform.java} | 170 ++++++++----------
 .../spark/unsafe/array/ByteArrayMethods.java  |  14 +-
 .../apache/spark/unsafe/array/LongArray.java  |   6 +-
 .../spark/unsafe/bitset/BitSetMethods.java    |  19 +-
 .../spark/unsafe/hash/Murmur3_x86_32.java     |   4 +-
 .../spark/unsafe/memory/MemoryBlock.java      |   4 +-
 .../unsafe/memory/UnsafeMemoryAllocator.java  |   6 +-
 .../apache/spark/unsafe/types/ByteArray.java  |  10 +-
 .../apache/spark/unsafe/types/UTF8String.java |  30 ++--
 .../unsafe/hash/Murmur3_x86_32Suite.java      |  14 +-
 39 files changed, 371 insertions(+), 499 deletions(-)
 rename unsafe/src/main/java/org/apache/spark/unsafe/{PlatformDependent.java => Platform.java} (55%)

diff --git a/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java b/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
index 0399abc63c235..0e58bb4f7101c 100644
--- a/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
+++ b/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
@@ -25,7 +25,7 @@
 import scala.reflect.ClassTag;
 
 import org.apache.spark.annotation.Private;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Unfortunately, we need a serializer instance in order to construct a DiskBlockObjectWriter.
@@ -49,7 +49,7 @@ public void flush() {
         try {
           s.flush();
         } catch (IOException e) {
-          PlatformDependent.throwException(e);
+          Platform.throwException(e);
         }
       }
 
@@ -64,7 +64,7 @@ public void close() {
         try {
           s.close();
         } catch (IOException e) {
-          PlatformDependent.throwException(e);
+          Platform.throwException(e);
         }
       }
     };
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 925b60a145886..3d1ef0c48adc5 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -37,7 +37,7 @@
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.storage.DiskBlockObjectWriter;
 import org.apache.spark.storage.TempShuffleBlockId;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -211,16 +211,12 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
       final long recordPointer = sortedRecords.packedRecordPointer.getRecordPointer();
       final Object recordPage = taskMemoryManager.getPage(recordPointer);
       final long recordOffsetInPage = taskMemoryManager.getOffsetInPage(recordPointer);
-      int dataRemaining = PlatformDependent.UNSAFE.getInt(recordPage, recordOffsetInPage);
+      int dataRemaining = Platform.getInt(recordPage, recordOffsetInPage);
       long recordReadPosition = recordOffsetInPage + 4; // skip over record length
       while (dataRemaining > 0) {
         final int toTransfer = Math.min(DISK_WRITE_BUFFER_SIZE, dataRemaining);
-        PlatformDependent.copyMemory(
-          recordPage,
-          recordReadPosition,
-          writeBuffer,
-          PlatformDependent.BYTE_ARRAY_OFFSET,
-          toTransfer);
+        Platform.copyMemory(
+          recordPage, recordReadPosition, writeBuffer, Platform.BYTE_ARRAY_OFFSET, toTransfer);
         writer.write(writeBuffer, 0, toTransfer);
         recordReadPosition += toTransfer;
         dataRemaining -= toTransfer;
@@ -447,14 +443,10 @@ public void insertRecord(
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
     dataPagePosition += 4;
-    PlatformDependent.copyMemory(
-      recordBaseObject,
-      recordBaseOffset,
-      dataPageBaseObject,
-      dataPagePosition,
-      lengthInBytes);
+    Platform.copyMemory(
+      recordBaseObject, recordBaseOffset, dataPageBaseObject, dataPagePosition, lengthInBytes);
     assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, partitionId);
   }
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index 02084f9122e00..2389c28b28395 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -53,7 +53,7 @@
 import org.apache.spark.shuffle.ShuffleWriter;
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.storage.TimeTrackingOutputStream;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 
 @Private
@@ -244,7 +244,7 @@ void insertRecordIntoSorter(Product2<K, V> record) throws IOException {
     assert (serializedRecordSize > 0);
 
     sorter.insertRecord(
-      serBuffer.getBuf(), PlatformDependent.BYTE_ARRAY_OFFSET, serializedRecordSize, partitionId);
+      serBuffer.getBuf(), Platform.BYTE_ARRAY_OFFSET, serializedRecordSize, partitionId);
   }
 
   @VisibleForTesting
diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 7f79cd13aab43..85b46ec8bfae3 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -270,10 +270,10 @@ public boolean hasNext() {
 
     @Override
     public Location next() {
-      int totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
+      int totalLength = Platform.getInt(pageBaseObject, offsetInPage);
       if (totalLength == END_OF_PAGE_MARKER) {
         advanceToNextPage();
-        totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
+        totalLength = Platform.getInt(pageBaseObject, offsetInPage);
       }
       loc.with(currentPage, offsetInPage);
       offsetInPage += 4 + totalLength;
@@ -402,9 +402,9 @@ private void updateAddressesAndSizes(long fullKeyAddress) {
 
     private void updateAddressesAndSizes(final Object page, final long offsetInPage) {
       long position = offsetInPage;
-      final int totalLength = PlatformDependent.UNSAFE.getInt(page, position);
+      final int totalLength = Platform.getInt(page, position);
       position += 4;
-      keyLength = PlatformDependent.UNSAFE.getInt(page, position);
+      keyLength = Platform.getInt(page, position);
       position += 4;
       valueLength = totalLength - keyLength - 4;
 
@@ -572,7 +572,7 @@ public boolean putNewKey(
           // There wasn't enough space in the current page, so write an end-of-page marker:
           final Object pageBaseObject = currentDataPage.getBaseObject();
           final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor;
-          PlatformDependent.UNSAFE.putInt(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
+          Platform.putInt(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
         }
         final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
         if (memoryGranted != pageSizeBytes) {
@@ -608,21 +608,21 @@ public boolean putNewKey(
       final long valueDataOffsetInPage = insertCursor;
       insertCursor += valueLengthBytes; // word used to store the value size
 
-      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, recordOffset,
+      Platform.putInt(dataPageBaseObject, recordOffset,
         keyLengthBytes + valueLengthBytes + 4);
-      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, keyLengthOffset, keyLengthBytes);
+      Platform.putInt(dataPageBaseObject, keyLengthOffset, keyLengthBytes);
       // Copy the key
-      PlatformDependent.copyMemory(
+      Platform.copyMemory(
         keyBaseObject, keyBaseOffset, dataPageBaseObject, keyDataOffsetInPage, keyLengthBytes);
       // Copy the value
-      PlatformDependent.copyMemory(valueBaseObject, valueBaseOffset, dataPageBaseObject,
+      Platform.copyMemory(valueBaseObject, valueBaseOffset, dataPageBaseObject,
         valueDataOffsetInPage, valueLengthBytes);
 
       // --- Update bookeeping data structures -----------------------------------------------------
 
       if (useOverflowPage) {
         // Store the end-of-page marker at the end of the data page
-        PlatformDependent.UNSAFE.putInt(dataPageBaseObject, insertCursor, END_OF_PAGE_MARKER);
+        Platform.putInt(dataPageBaseObject, insertCursor, END_OF_PAGE_MARKER);
       } else {
         pageCursor += requiredSize;
       }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index 5e002ae1b7568..71b76d5ddfaa7 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -20,10 +20,9 @@
 import com.google.common.primitives.UnsignedLongs;
 
 import org.apache.spark.annotation.Private;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.types.UTF8String;
 import org.apache.spark.util.Utils;
-import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET;
 
 @Private
 public class PrefixComparators {
@@ -73,7 +72,7 @@ public static long computePrefix(byte[] bytes) {
         final int minLen = Math.min(bytes.length, 8);
         long p = 0;
         for (int i = 0; i < minLen; ++i) {
-          p |= (128L + PlatformDependent.UNSAFE.getByte(bytes, BYTE_ARRAY_OFFSET + i))
+          p |= (128L + Platform.getByte(bytes, Platform.BYTE_ARRAY_OFFSET + i))
               << (56 - 8 * i);
         }
         return p;
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 5ebbf9b068fd6..9601aafe55464 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -35,7 +35,7 @@
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 import org.apache.spark.util.Utils;
@@ -427,14 +427,10 @@ public void insertRecord(
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
     dataPagePosition += 4;
-    PlatformDependent.copyMemory(
-      recordBaseObject,
-      recordBaseOffset,
-      dataPageBaseObject,
-      dataPagePosition,
-      lengthInBytes);
+    Platform.copyMemory(
+      recordBaseObject, recordBaseOffset, dataPageBaseObject, dataPagePosition, lengthInBytes);
     assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
   }
@@ -493,18 +489,16 @@ public void insertKVRecord(
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen + valueLen + 4);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, keyLen + valueLen + 4);
     dataPagePosition += 4;
 
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, keyLen);
     dataPagePosition += 4;
 
-    PlatformDependent.copyMemory(
-      keyBaseObj, keyOffset, dataPageBaseObject, dataPagePosition, keyLen);
+    Platform.copyMemory(keyBaseObj, keyOffset, dataPageBaseObject, dataPagePosition, keyLen);
     dataPagePosition += keyLen;
 
-    PlatformDependent.copyMemory(
-      valueBaseObj, valueOffset, dataPageBaseObject, dataPagePosition, valueLen);
+    Platform.copyMemory(valueBaseObj, valueOffset, dataPageBaseObject, dataPagePosition, valueLen);
 
     assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
index 1e4b8a116e11a..f7787e1019c2b 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
@@ -19,7 +19,7 @@
 
 import java.util.Comparator;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.util.collection.Sorter;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 
@@ -164,7 +164,7 @@ public void loadNext() {
       final long recordPointer = sortBuffer[position];
       baseObject = memoryManager.getPage(recordPointer);
       baseOffset = memoryManager.getOffsetInPage(recordPointer) + 4;  // Skip over record length
-      recordLength = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset - 4);
+      recordLength = Platform.getInt(baseObject, baseOffset - 4);
       keyPrefix = sortBuffer[position + 1];
       position += 2;
     }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
index ca1ccedc93c8e..4989b05d63e23 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
@@ -23,7 +23,7 @@
 
 import org.apache.spark.storage.BlockId;
 import org.apache.spark.storage.BlockManager;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Reads spill files written by {@link UnsafeSorterSpillWriter} (see that class for a description
@@ -42,7 +42,7 @@ final class UnsafeSorterSpillReader extends UnsafeSorterIterator {
 
   private byte[] arr = new byte[1024 * 1024];
   private Object baseObject = arr;
-  private final long baseOffset = PlatformDependent.BYTE_ARRAY_OFFSET;
+  private final long baseOffset = Platform.BYTE_ARRAY_OFFSET;
 
   public UnsafeSorterSpillReader(
       BlockManager blockManager,
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
index 44cf6c756d7c3..e59a84ff8d118 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
@@ -28,7 +28,7 @@
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.storage.DiskBlockObjectWriter;
 import org.apache.spark.storage.TempLocalBlockId;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Spills a list of sorted records to disk. Spill files have the following format:
@@ -117,11 +117,11 @@ public void write(
     long recordReadPosition = baseOffset;
     while (dataRemaining > 0) {
       final int toTransfer = Math.min(freeSpaceInWriteBuffer, dataRemaining);
-      PlatformDependent.copyMemory(
+      Platform.copyMemory(
         baseObject,
         recordReadPosition,
         writeBuffer,
-        PlatformDependent.BYTE_ARRAY_OFFSET + (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer),
+        Platform.BYTE_ARRAY_OFFSET + (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer),
         toTransfer);
       writer.write(writeBuffer, 0, (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer) + toTransfer);
       recordReadPosition += toTransfer;
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java
index 8fa72597db24d..40fefe2c9d140 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java
@@ -24,7 +24,7 @@
 import org.junit.Test;
 
 import org.apache.spark.HashPartitioner;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
 import org.apache.spark.unsafe.memory.MemoryAllocator;
 import org.apache.spark.unsafe.memory.MemoryBlock;
@@ -34,11 +34,7 @@ public class UnsafeShuffleInMemorySorterSuite {
 
   private static String getStringFromDataPage(Object baseObject, long baseOffset, int strLength) {
     final byte[] strBytes = new byte[strLength];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      strBytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET, strLength);
+    Platform.copyMemory(baseObject, baseOffset, strBytes, Platform.BYTE_ARRAY_OFFSET, strLength);
     return new String(strBytes);
   }
 
@@ -74,14 +70,10 @@ public void testBasicSorting() throws Exception {
     for (String str : dataToSort) {
       final long recordAddress = memoryManager.encodePageNumberAndOffset(dataPage, position);
       final byte[] strBytes = str.getBytes("utf-8");
-      PlatformDependent.UNSAFE.putInt(baseObject, position, strBytes.length);
+      Platform.putInt(baseObject, position, strBytes.length);
       position += 4;
-      PlatformDependent.copyMemory(
-        strBytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        baseObject,
-        position,
-        strBytes.length);
+      Platform.copyMemory(
+        strBytes, Platform.BYTE_ARRAY_OFFSET, baseObject, position, strBytes.length);
       position += strBytes.length;
       sorter.insertRecord(recordAddress, hashPartitioner.getPartition(str));
     }
@@ -98,7 +90,7 @@ public void testBasicSorting() throws Exception {
       Assert.assertTrue("Partition id " + partitionId + " should be >= prev id " + prevPartitionId,
         partitionId >= prevPartitionId);
       final long recordAddress = iter.packedRecordPointer.getRecordPointer();
-      final int recordLength = PlatformDependent.UNSAFE.getInt(
+      final int recordLength = Platform.getInt(
         memoryManager.getPage(recordAddress), memoryManager.getOffsetInPage(recordAddress));
       final String str = getStringFromDataPage(
         memoryManager.getPage(recordAddress),
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index e56a3f0b6d12c..1a79c20c35246 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -32,9 +32,7 @@
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.memory.*;
-import org.apache.spark.unsafe.PlatformDependent;
-import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET;
-import static org.apache.spark.unsafe.PlatformDependent.LONG_ARRAY_OFFSET;
+import org.apache.spark.unsafe.Platform;
 
 
 public abstract class AbstractBytesToBytesMapSuite {
@@ -80,13 +78,8 @@ public void tearDown() {
 
   private static byte[] getByteArray(MemoryLocation loc, int size) {
     final byte[] arr = new byte[size];
-    PlatformDependent.copyMemory(
-      loc.getBaseObject(),
-      loc.getBaseOffset(),
-      arr,
-      BYTE_ARRAY_OFFSET,
-      size
-    );
+    Platform.copyMemory(
+      loc.getBaseObject(), loc.getBaseOffset(), arr, Platform.BYTE_ARRAY_OFFSET, size);
     return arr;
   }
 
@@ -108,7 +101,7 @@ private static boolean arrayEquals(
       long actualLengthBytes) {
     return (actualLengthBytes == expected.length) && ByteArrayMethods.arrayEquals(
       expected,
-      BYTE_ARRAY_OFFSET,
+      Platform.BYTE_ARRAY_OFFSET,
       actualAddr.getBaseObject(),
       actualAddr.getBaseOffset(),
       expected.length
@@ -124,7 +117,7 @@ public void emptyMap() {
       final int keyLengthInWords = 10;
       final int keyLengthInBytes = keyLengthInWords * 8;
       final byte[] key = getRandomByteArray(keyLengthInWords);
-      Assert.assertFalse(map.lookup(key, BYTE_ARRAY_OFFSET, keyLengthInBytes).isDefined());
+      Assert.assertFalse(map.lookup(key, Platform.BYTE_ARRAY_OFFSET, keyLengthInBytes).isDefined());
       Assert.assertFalse(map.iterator().hasNext());
     } finally {
       map.free();
@@ -141,14 +134,14 @@ public void setAndRetrieveAKey() {
     final byte[] valueData = getRandomByteArray(recordLengthWords);
     try {
       final BytesToBytesMap.Location loc =
-        map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes);
+        map.lookup(keyData, Platform.BYTE_ARRAY_OFFSET, recordLengthBytes);
       Assert.assertFalse(loc.isDefined());
       Assert.assertTrue(loc.putNewKey(
         keyData,
-        BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         recordLengthBytes,
         valueData,
-        BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         recordLengthBytes
       ));
       // After storing the key and value, the other location methods should return results that
@@ -159,7 +152,8 @@ public void setAndRetrieveAKey() {
       Assert.assertArrayEquals(valueData, getByteArray(loc.getValueAddress(), recordLengthBytes));
 
       // After calling lookup() the location should still point to the correct data.
-      Assert.assertTrue(map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes).isDefined());
+      Assert.assertTrue(
+        map.lookup(keyData, Platform.BYTE_ARRAY_OFFSET, recordLengthBytes).isDefined());
       Assert.assertEquals(recordLengthBytes, loc.getKeyLength());
       Assert.assertEquals(recordLengthBytes, loc.getValueLength());
       Assert.assertArrayEquals(keyData, getByteArray(loc.getKeyAddress(), recordLengthBytes));
@@ -168,10 +162,10 @@ public void setAndRetrieveAKey() {
       try {
         Assert.assertTrue(loc.putNewKey(
           keyData,
-          BYTE_ARRAY_OFFSET,
+          Platform.BYTE_ARRAY_OFFSET,
           recordLengthBytes,
           valueData,
-          BYTE_ARRAY_OFFSET,
+          Platform.BYTE_ARRAY_OFFSET,
           recordLengthBytes
         ));
         Assert.fail("Should not be able to set a new value for a key");
@@ -191,25 +185,25 @@ private void iteratorTestBase(boolean destructive) throws Exception {
       for (long i = 0; i < size; i++) {
         final long[] value = new long[] { i };
         final BytesToBytesMap.Location loc =
-          map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8);
+          map.lookup(value, Platform.LONG_ARRAY_OFFSET, 8);
         Assert.assertFalse(loc.isDefined());
         // Ensure that we store some zero-length keys
         if (i % 5 == 0) {
           Assert.assertTrue(loc.putNewKey(
             null,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             0,
             value,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             8
           ));
         } else {
           Assert.assertTrue(loc.putNewKey(
             value,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             8,
             value,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             8
           ));
         }
@@ -228,14 +222,13 @@ private void iteratorTestBase(boolean destructive) throws Exception {
         Assert.assertTrue(loc.isDefined());
         final MemoryLocation keyAddress = loc.getKeyAddress();
         final MemoryLocation valueAddress = loc.getValueAddress();
-        final long value = PlatformDependent.UNSAFE.getLong(
+        final long value = Platform.getLong(
           valueAddress.getBaseObject(), valueAddress.getBaseOffset());
         final long keyLength = loc.getKeyLength();
         if (keyLength == 0) {
           Assert.assertTrue("value " + value + " was not divisible by 5", value % 5 == 0);
         } else {
-          final long key = PlatformDependent.UNSAFE.getLong(
-            keyAddress.getBaseObject(), keyAddress.getBaseOffset());
+          final long key = Platform.getLong(keyAddress.getBaseObject(), keyAddress.getBaseOffset());
           Assert.assertEquals(value, key);
         }
         valuesSeen.set((int) value);
@@ -284,16 +277,16 @@ public void iteratingOverDataPagesWithWastedSpace() throws Exception {
         final long[] value = new long[] { i, i, i, i, i }; // 5 * 8 = 40 bytes
         final BytesToBytesMap.Location loc = map.lookup(
           key,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           KEY_LENGTH
         );
         Assert.assertFalse(loc.isDefined());
         Assert.assertTrue(loc.putNewKey(
           key,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           KEY_LENGTH,
           value,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           VALUE_LENGTH
         ));
       }
@@ -308,18 +301,18 @@ public void iteratingOverDataPagesWithWastedSpace() throws Exception {
         Assert.assertTrue(loc.isDefined());
         Assert.assertEquals(KEY_LENGTH, loc.getKeyLength());
         Assert.assertEquals(VALUE_LENGTH, loc.getValueLength());
-        PlatformDependent.copyMemory(
+        Platform.copyMemory(
           loc.getKeyAddress().getBaseObject(),
           loc.getKeyAddress().getBaseOffset(),
           key,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           KEY_LENGTH
         );
-        PlatformDependent.copyMemory(
+        Platform.copyMemory(
           loc.getValueAddress().getBaseObject(),
           loc.getValueAddress().getBaseOffset(),
           value,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           VALUE_LENGTH
         );
         for (long j : key) {
@@ -354,16 +347,16 @@ public void randomizedStressTest() {
           expected.put(ByteBuffer.wrap(key), value);
           final BytesToBytesMap.Location loc = map.lookup(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length
           );
           Assert.assertFalse(loc.isDefined());
           Assert.assertTrue(loc.putNewKey(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length,
             value,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             value.length
           ));
           // After calling putNewKey, the following should be true, even before calling
@@ -379,7 +372,8 @@ public void randomizedStressTest() {
       for (Map.Entry<ByteBuffer, byte[]> entry : expected.entrySet()) {
         final byte[] key = entry.getKey().array();
         final byte[] value = entry.getValue();
-        final BytesToBytesMap.Location loc = map.lookup(key, BYTE_ARRAY_OFFSET, key.length);
+        final BytesToBytesMap.Location loc =
+          map.lookup(key, Platform.BYTE_ARRAY_OFFSET, key.length);
         Assert.assertTrue(loc.isDefined());
         Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), loc.getKeyLength()));
         Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), loc.getValueLength()));
@@ -405,16 +399,16 @@ public void randomizedTestWithRecordsLargerThanPageSize() {
           expected.put(ByteBuffer.wrap(key), value);
           final BytesToBytesMap.Location loc = map.lookup(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length
           );
           Assert.assertFalse(loc.isDefined());
           Assert.assertTrue(loc.putNewKey(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length,
             value,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             value.length
           ));
           // After calling putNewKey, the following should be true, even before calling
@@ -429,7 +423,8 @@ public void randomizedTestWithRecordsLargerThanPageSize() {
       for (Map.Entry<ByteBuffer, byte[]> entry : expected.entrySet()) {
         final byte[] key = entry.getKey().array();
         final byte[] value = entry.getValue();
-        final BytesToBytesMap.Location loc = map.lookup(key, BYTE_ARRAY_OFFSET, key.length);
+        final BytesToBytesMap.Location loc =
+          map.lookup(key, Platform.BYTE_ARRAY_OFFSET, key.length);
         Assert.assertTrue(loc.isDefined());
         Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), loc.getKeyLength()));
         Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), loc.getValueLength()));
@@ -447,12 +442,10 @@ public void failureToAllocateFirstPage() {
     try {
       final long[] emptyArray = new long[0];
       final BytesToBytesMap.Location loc =
-        map.lookup(emptyArray, PlatformDependent.LONG_ARRAY_OFFSET, 0);
+        map.lookup(emptyArray, Platform.LONG_ARRAY_OFFSET, 0);
       Assert.assertFalse(loc.isDefined());
       Assert.assertFalse(loc.putNewKey(
-        emptyArray, LONG_ARRAY_OFFSET, 0,
-        emptyArray, LONG_ARRAY_OFFSET, 0
-      ));
+        emptyArray, Platform.LONG_ARRAY_OFFSET, 0, emptyArray, Platform.LONG_ARRAY_OFFSET, 0));
     } finally {
       map.free();
     }
@@ -468,8 +461,9 @@ public void failureToGrow() {
       int i;
       for (i = 0; i < 1024; i++) {
         final long[] arr = new long[]{i};
-        final BytesToBytesMap.Location loc = map.lookup(arr, PlatformDependent.LONG_ARRAY_OFFSET, 8);
-        success = loc.putNewKey(arr, LONG_ARRAY_OFFSET, 8, arr, LONG_ARRAY_OFFSET, 8);
+        final BytesToBytesMap.Location loc = map.lookup(arr, Platform.LONG_ARRAY_OFFSET, 8);
+        success =
+          loc.putNewKey(arr, Platform.LONG_ARRAY_OFFSET, 8, arr, Platform.LONG_ARRAY_OFFSET, 8);
         if (!success) {
           break;
         }
@@ -541,12 +535,12 @@ public void testPeakMemoryUsed() {
     try {
       for (long i = 0; i < numRecordsPerPage * 10; i++) {
         final long[] value = new long[]{i};
-        map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8).putNewKey(
+        map.lookup(value, Platform.LONG_ARRAY_OFFSET, 8).putNewKey(
           value,
-          PlatformDependent.LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           8,
           value,
-          PlatformDependent.LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           8);
         newPeakMemory = map.getPeakMemoryUsedBytes();
         if (i % numRecordsPerPage == 0) {
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 83049b8a21fcf..445a37b83e98a 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -49,7 +49,7 @@
 import org.apache.spark.serializer.SerializerInstance;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.storage.*;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
 import org.apache.spark.unsafe.memory.MemoryAllocator;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -166,14 +166,14 @@ private void assertSpillFilesWereCleanedUp() {
 
   private static void insertNumber(UnsafeExternalSorter sorter, int value) throws Exception {
     final int[] arr = new int[]{ value };
-    sorter.insertRecord(arr, PlatformDependent.INT_ARRAY_OFFSET, 4, value);
+    sorter.insertRecord(arr, Platform.INT_ARRAY_OFFSET, 4, value);
   }
 
   private static void insertRecord(
       UnsafeExternalSorter sorter,
       int[] record,
       long prefix) throws IOException {
-    sorter.insertRecord(record, PlatformDependent.INT_ARRAY_OFFSET, record.length * 4, prefix);
+    sorter.insertRecord(record, Platform.INT_ARRAY_OFFSET, record.length * 4, prefix);
   }
 
   private UnsafeExternalSorter newSorter() throws IOException {
@@ -205,7 +205,7 @@ public void testSortingOnlyByPrefix() throws Exception {
       iter.loadNext();
       assertEquals(i, iter.getKeyPrefix());
       assertEquals(4, iter.getRecordLength());
-      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+      assertEquals(i, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     }
 
     sorter.cleanupResources();
@@ -253,7 +253,7 @@ public void spillingOccursInResponseToMemoryPressure() throws Exception {
       iter.loadNext();
       assertEquals(i, iter.getKeyPrefix());
       assertEquals(4, iter.getRecordLength());
-      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+      assertEquals(i, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
       i++;
     }
     sorter.cleanupResources();
@@ -265,7 +265,7 @@ public void testFillingPage() throws Exception {
     final UnsafeExternalSorter sorter = newSorter();
     byte[] record = new byte[16];
     while (sorter.getNumberOfAllocatedPages() < 2) {
-      sorter.insertRecord(record, PlatformDependent.BYTE_ARRAY_OFFSET, record.length, 0);
+      sorter.insertRecord(record, Platform.BYTE_ARRAY_OFFSET, record.length, 0);
     }
     sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
@@ -292,25 +292,25 @@ public void sortingRecordsThatExceedPageSize() throws Exception {
     iter.loadNext();
     assertEquals(123, iter.getKeyPrefix());
     assertEquals(smallRecord.length * 4, iter.getRecordLength());
-    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(123, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     // Small record
     assertTrue(iter.hasNext());
     iter.loadNext();
     assertEquals(123, iter.getKeyPrefix());
     assertEquals(smallRecord.length * 4, iter.getRecordLength());
-    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(123, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     // Large record
     assertTrue(iter.hasNext());
     iter.loadNext();
     assertEquals(456, iter.getKeyPrefix());
     assertEquals(largeRecord.length * 4, iter.getRecordLength());
-    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(456, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     // Large record
     assertTrue(iter.hasNext());
     iter.loadNext();
     assertEquals(456, iter.getKeyPrefix());
     assertEquals(largeRecord.length * 4, iter.getRecordLength());
-    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(456, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
 
     assertFalse(iter.hasNext());
     sorter.cleanupResources();
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
index 909500930539c..778e813df6b54 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
@@ -26,7 +26,7 @@
 import static org.mockito.Mockito.mock;
 
 import org.apache.spark.HashPartitioner;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
 import org.apache.spark.unsafe.memory.MemoryAllocator;
 import org.apache.spark.unsafe.memory.MemoryBlock;
@@ -36,11 +36,7 @@ public class UnsafeInMemorySorterSuite {
 
   private static String getStringFromDataPage(Object baseObject, long baseOffset, int length) {
     final byte[] strBytes = new byte[length];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      strBytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET, length);
+    Platform.copyMemory(baseObject, baseOffset, strBytes, Platform.BYTE_ARRAY_OFFSET, length);
     return new String(strBytes);
   }
 
@@ -76,14 +72,10 @@ public void testSortingOnlyByIntegerPrefix() throws Exception {
     long position = dataPage.getBaseOffset();
     for (String str : dataToSort) {
       final byte[] strBytes = str.getBytes("utf-8");
-      PlatformDependent.UNSAFE.putInt(baseObject, position, strBytes.length);
+      Platform.putInt(baseObject, position, strBytes.length);
       position += 4;
-      PlatformDependent.copyMemory(
-        strBytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        baseObject,
-        position,
-        strBytes.length);
+      Platform.copyMemory(
+        strBytes, Platform.BYTE_ARRAY_OFFSET, baseObject, position, strBytes.length);
       position += strBytes.length;
     }
     // Since the key fits within the 8-byte prefix, we don't need to do any record comparison, so
@@ -113,7 +105,7 @@ public int compare(long prefix1, long prefix2) {
     position = dataPage.getBaseOffset();
     for (int i = 0; i < dataToSort.length; i++) {
       // position now points to the start of a record (which holds its length).
-      final int recordLength = PlatformDependent.UNSAFE.getInt(baseObject, position);
+      final int recordLength = Platform.getInt(baseObject, position);
       final long address = memoryManager.encodePageNumberAndOffset(dataPage, position);
       final String str = getStringFromDataPage(baseObject, position + 4, recordLength);
       final int partitionId = hashPartitioner.getPartition(str);
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
index 0374846d71674..501dff090313c 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
@@ -22,7 +22,7 @@
 
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.*;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
 import org.apache.spark.unsafe.types.CalendarInterval;
@@ -59,7 +59,7 @@ public class UnsafeArrayData extends ArrayData {
   private int sizeInBytes;
 
   private int getElementOffset(int ordinal) {
-    return PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + ordinal * 4L);
+    return Platform.getInt(baseObject, baseOffset + ordinal * 4L);
   }
 
   private int getElementSize(int offset, int ordinal) {
@@ -157,7 +157,7 @@ public boolean getBoolean(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return false;
-    return PlatformDependent.UNSAFE.getBoolean(baseObject, baseOffset + offset);
+    return Platform.getBoolean(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -165,7 +165,7 @@ public byte getByte(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getByte(baseObject, baseOffset + offset);
+    return Platform.getByte(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -173,7 +173,7 @@ public short getShort(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getShort(baseObject, baseOffset + offset);
+    return Platform.getShort(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -181,7 +181,7 @@ public int getInt(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + offset);
+    return Platform.getInt(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -189,7 +189,7 @@ public long getLong(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+    return Platform.getLong(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -197,7 +197,7 @@ public float getFloat(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getFloat(baseObject, baseOffset + offset);
+    return Platform.getFloat(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -205,7 +205,7 @@ public double getDouble(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getDouble(baseObject, baseOffset + offset);
+    return Platform.getDouble(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -215,7 +215,7 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (offset < 0) return null;
 
     if (precision <= Decimal.MAX_LONG_DIGITS()) {
-      final long value = PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+      final long value = Platform.getLong(baseObject, baseOffset + offset);
       return Decimal.apply(value, precision, scale);
     } else {
       final byte[] bytes = getBinary(ordinal);
@@ -241,12 +241,7 @@ public byte[] getBinary(int ordinal) {
     if (offset < 0) return null;
     final int size = getElementSize(offset, ordinal);
     final byte[] bytes = new byte[size];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset + offset,
-      bytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      size);
+    Platform.copyMemory(baseObject, baseOffset + offset, bytes, Platform.BYTE_ARRAY_OFFSET, size);
     return bytes;
   }
 
@@ -255,9 +250,8 @@ public CalendarInterval getInterval(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return null;
-    final int months = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
-    final long microseconds =
-      PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset + 8);
+    final int months = (int) Platform.getLong(baseObject, baseOffset + offset);
+    final long microseconds = Platform.getLong(baseObject, baseOffset + offset + 8);
     return new CalendarInterval(months, microseconds);
   }
 
@@ -307,27 +301,16 @@ public boolean equals(Object other) {
   }
 
   public void writeToMemory(Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      target,
-      targetOffset,
-      sizeInBytes
-    );
+    Platform.copyMemory(baseObject, baseOffset, target, targetOffset, sizeInBytes);
   }
 
   @Override
   public UnsafeArrayData copy() {
     UnsafeArrayData arrayCopy = new UnsafeArrayData();
     final byte[] arrayDataCopy = new byte[sizeInBytes];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      arrayDataCopy,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      sizeInBytes
-    );
-    arrayCopy.pointTo(arrayDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numElements, sizeInBytes);
+    Platform.copyMemory(
+      baseObject, baseOffset, arrayDataCopy, Platform.BYTE_ARRAY_OFFSET, sizeInBytes);
+    arrayCopy.pointTo(arrayDataCopy, Platform.BYTE_ARRAY_OFFSET, numElements, sizeInBytes);
     return arrayCopy;
   }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
index b521b703389d3..7b03185a30e3c 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
@@ -17,13 +17,13 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 public class UnsafeReaders {
 
   public static UnsafeArrayData readArray(Object baseObject, long baseOffset, int numBytes) {
     // Read the number of elements from first 4 bytes.
-    final int numElements = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset);
+    final int numElements = Platform.getInt(baseObject, baseOffset);
     final UnsafeArrayData array = new UnsafeArrayData();
     // Skip the first 4 bytes.
     array.pointTo(baseObject, baseOffset + 4, numElements, numBytes - 4);
@@ -32,9 +32,9 @@ public static UnsafeArrayData readArray(Object baseObject, long baseOffset, int
 
   public static UnsafeMapData readMap(Object baseObject, long baseOffset, int numBytes) {
     // Read the number of elements from first 4 bytes.
-    final int numElements = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset);
+    final int numElements = Platform.getInt(baseObject, baseOffset);
     // Read the numBytes of key array in second 4 bytes.
-    final int keyArraySize = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + 4);
+    final int keyArraySize = Platform.getInt(baseObject, baseOffset + 4);
     final int valueArraySize = numBytes - 8 - keyArraySize;
 
     final UnsafeArrayData keyArray = new UnsafeArrayData();
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index e829acb6285f1..7fd94772090df 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -27,7 +27,7 @@
 import java.util.Set;
 
 import org.apache.spark.sql.types.*;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
@@ -169,7 +169,7 @@ public void pointTo(Object baseObject, long baseOffset, int numFields, int sizeI
    * @param sizeInBytes the number of bytes valid in the byte array
    */
   public void pointTo(byte[] buf, int numFields, int sizeInBytes) {
-    pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
+    pointTo(buf, Platform.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
   }
 
   @Override
@@ -179,7 +179,7 @@ public void setNullAt(int i) {
     // To preserve row equality, zero out the value when setting the column to null.
     // Since this row does does not currently support updates to variable-length values, we don't
     // have to worry about zeroing out that data.
-    PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(i), 0);
+    Platform.putLong(baseObject, getFieldOffset(i), 0);
   }
 
   @Override
@@ -191,14 +191,14 @@ public void update(int ordinal, Object value) {
   public void setInt(int ordinal, int value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putInt(baseObject, getFieldOffset(ordinal), value);
+    Platform.putInt(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setLong(int ordinal, long value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), value);
+    Platform.putLong(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
@@ -208,28 +208,28 @@ public void setDouble(int ordinal, double value) {
     if (Double.isNaN(value)) {
       value = Double.NaN;
     }
-    PlatformDependent.UNSAFE.putDouble(baseObject, getFieldOffset(ordinal), value);
+    Platform.putDouble(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setBoolean(int ordinal, boolean value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putBoolean(baseObject, getFieldOffset(ordinal), value);
+    Platform.putBoolean(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setShort(int ordinal, short value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putShort(baseObject, getFieldOffset(ordinal), value);
+    Platform.putShort(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setByte(int ordinal, byte value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putByte(baseObject, getFieldOffset(ordinal), value);
+    Platform.putByte(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
@@ -239,7 +239,7 @@ public void setFloat(int ordinal, float value) {
     if (Float.isNaN(value)) {
       value = Float.NaN;
     }
-    PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
+    Platform.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
   /**
@@ -263,23 +263,23 @@ public void setDecimal(int ordinal, Decimal value, int precision) {
       long cursor = getLong(ordinal) >>> 32;
       assert cursor > 0 : "invalid cursor " + cursor;
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor, 0L);
-      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor + 8, 0L);
+      Platform.putLong(baseObject, baseOffset + cursor, 0L);
+      Platform.putLong(baseObject, baseOffset + cursor + 8, 0L);
 
       if (value == null) {
         setNullAt(ordinal);
         // keep the offset for future update
-        PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), cursor << 32);
+        Platform.putLong(baseObject, getFieldOffset(ordinal), cursor << 32);
       } else {
 
         final BigInteger integer = value.toJavaBigDecimal().unscaledValue();
-        final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
-          PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+        final int[] mag = (int[]) Platform.getObjectVolatile(integer,
+          Platform.BIG_INTEGER_MAG_OFFSET);
         assert(mag.length <= 4);
 
         // Write the bytes to the variable length portion.
-        PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
-          baseObject, baseOffset + cursor, mag.length * 4);
+        Platform.copyMemory(
+          mag, Platform.INT_ARRAY_OFFSET, baseObject, baseOffset + cursor, mag.length * 4);
         setLong(ordinal, (cursor << 32) | ((long) (((integer.signum() + 1) << 8) + mag.length)));
       }
     }
@@ -336,43 +336,43 @@ public boolean isNullAt(int ordinal) {
   @Override
   public boolean getBoolean(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getBoolean(baseObject, getFieldOffset(ordinal));
+    return Platform.getBoolean(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public byte getByte(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getByte(baseObject, getFieldOffset(ordinal));
+    return Platform.getByte(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public short getShort(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getShort(baseObject, getFieldOffset(ordinal));
+    return Platform.getShort(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public int getInt(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getInt(baseObject, getFieldOffset(ordinal));
+    return Platform.getInt(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public long getLong(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(ordinal));
+    return Platform.getLong(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public float getFloat(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(ordinal));
+    return Platform.getFloat(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public double getDouble(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
+    return Platform.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
   private static byte[] EMPTY = new byte[0];
@@ -391,13 +391,13 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
       assert signum >=0 && signum <= 2 : "invalid signum " + signum;
       int size = (int) (offsetAndSize & 0xff);
       int[] mag = new int[size];
-      PlatformDependent.copyMemory(baseObject, baseOffset + offset,
-        mag, PlatformDependent.INT_ARRAY_OFFSET, size * 4);
+      Platform.copyMemory(
+        baseObject, baseOffset + offset, mag, Platform.INT_ARRAY_OFFSET, size * 4);
 
       // create a BigInteger using signum and mag
       BigInteger v = new BigInteger(0, EMPTY);  // create the initial object
-      PlatformDependent.UNSAFE.putInt(v, PlatformDependent.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
-      PlatformDependent.UNSAFE.putObjectVolatile(v, PlatformDependent.BIG_INTEGER_MAG_OFFSET, mag);
+      Platform.putInt(v, Platform.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
+      Platform.putObjectVolatile(v, Platform.BIG_INTEGER_MAG_OFFSET, mag);
       return Decimal.apply(new BigDecimal(v, scale), precision, scale);
     }
   }
@@ -420,11 +420,11 @@ public byte[] getBinary(int ordinal) {
       final int offset = (int) (offsetAndSize >> 32);
       final int size = (int) (offsetAndSize & ((1L << 32) - 1));
       final byte[] bytes = new byte[size];
-      PlatformDependent.copyMemory(
+      Platform.copyMemory(
         baseObject,
         baseOffset + offset,
         bytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         size
       );
       return bytes;
@@ -438,9 +438,8 @@ public CalendarInterval getInterval(int ordinal) {
     } else {
       final long offsetAndSize = getLong(ordinal);
       final int offset = (int) (offsetAndSize >> 32);
-      final int months = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
-      final long microseconds =
-        PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset + 8);
+      final int months = (int) Platform.getLong(baseObject, baseOffset + offset);
+      final long microseconds = Platform.getLong(baseObject, baseOffset + offset + 8);
       return new CalendarInterval(months, microseconds);
     }
   }
@@ -491,14 +490,14 @@ public MapData getMap(int ordinal) {
   public UnsafeRow copy() {
     UnsafeRow rowCopy = new UnsafeRow();
     final byte[] rowDataCopy = new byte[sizeInBytes];
-    PlatformDependent.copyMemory(
+    Platform.copyMemory(
       baseObject,
       baseOffset,
       rowDataCopy,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
+      Platform.BYTE_ARRAY_OFFSET,
       sizeInBytes
     );
-    rowCopy.pointTo(rowDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
+    rowCopy.pointTo(rowDataCopy, Platform.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
     return rowCopy;
   }
 
@@ -518,18 +517,13 @@ public static UnsafeRow createFromByteArray(int numBytes, int numFields) {
    */
   public void copyFrom(UnsafeRow row) {
     // copyFrom is only available for UnsafeRow created from byte array.
-    assert (baseObject instanceof byte[]) && baseOffset == PlatformDependent.BYTE_ARRAY_OFFSET;
+    assert (baseObject instanceof byte[]) && baseOffset == Platform.BYTE_ARRAY_OFFSET;
     if (row.sizeInBytes > this.sizeInBytes) {
       // resize the underlying byte[] if it's not large enough.
       this.baseObject = new byte[row.sizeInBytes];
     }
-    PlatformDependent.copyMemory(
-      row.baseObject,
-      row.baseOffset,
-      this.baseObject,
-      this.baseOffset,
-      row.sizeInBytes
-    );
+    Platform.copyMemory(
+      row.baseObject, row.baseOffset, this.baseObject, this.baseOffset, row.sizeInBytes);
     // update the sizeInBytes.
     this.sizeInBytes = row.sizeInBytes;
   }
@@ -544,19 +538,15 @@ public void copyFrom(UnsafeRow row) {
    */
   public void writeToStream(OutputStream out, byte[] writeBuffer) throws IOException {
     if (baseObject instanceof byte[]) {
-      int offsetInByteArray = (int) (PlatformDependent.BYTE_ARRAY_OFFSET - baseOffset);
+      int offsetInByteArray = (int) (Platform.BYTE_ARRAY_OFFSET - baseOffset);
       out.write((byte[]) baseObject, offsetInByteArray, sizeInBytes);
     } else {
       int dataRemaining = sizeInBytes;
       long rowReadPosition = baseOffset;
       while (dataRemaining > 0) {
         int toTransfer = Math.min(writeBuffer.length, dataRemaining);
-        PlatformDependent.copyMemory(
-          baseObject,
-          rowReadPosition,
-          writeBuffer,
-          PlatformDependent.BYTE_ARRAY_OFFSET,
-          toTransfer);
+        Platform.copyMemory(
+          baseObject, rowReadPosition, writeBuffer, Platform.BYTE_ARRAY_OFFSET, toTransfer);
         out.write(writeBuffer, 0, toTransfer);
         rowReadPosition += toTransfer;
         dataRemaining -= toTransfer;
@@ -584,13 +574,12 @@ public boolean equals(Object other) {
    * Returns the underlying bytes for this UnsafeRow.
    */
   public byte[] getBytes() {
-    if (baseObject instanceof byte[] && baseOffset == PlatformDependent.BYTE_ARRAY_OFFSET
+    if (baseObject instanceof byte[] && baseOffset == Platform.BYTE_ARRAY_OFFSET
       && (((byte[]) baseObject).length == sizeInBytes)) {
       return (byte[]) baseObject;
     } else {
       byte[] bytes = new byte[sizeInBytes];
-      PlatformDependent.copyMemory(baseObject, baseOffset, bytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET, sizeInBytes);
+      Platform.copyMemory(baseObject, baseOffset, bytes, Platform.BYTE_ARRAY_OFFSET, sizeInBytes);
       return bytes;
     }
   }
@@ -600,8 +589,7 @@ public byte[] getBytes() {
   public String toString() {
     StringBuilder build = new StringBuilder("[");
     for (int i = 0; i < sizeInBytes; i += 8) {
-      build.append(java.lang.Long.toHexString(
-        PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + i)));
+      build.append(java.lang.Long.toHexString(Platform.getLong(baseObject, baseOffset + i)));
       build.append(',');
     }
     build.append(']');
@@ -619,12 +607,6 @@ public boolean anyNull() {
    * bytes in this string.
    */
   public void writeToMemory(Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      target,
-      targetOffset,
-      sizeInBytes
-    );
+    Platform.copyMemory(baseObject, baseOffset, target, targetOffset, sizeInBytes);
   }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 28e7ec0a0f120..005351f0883e5 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -21,7 +21,7 @@
 
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
 import org.apache.spark.unsafe.types.CalendarInterval;
@@ -58,27 +58,27 @@ public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input
       final Object base = target.getBaseObject();
       final long offset = target.getBaseOffset() + cursor;
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(base, offset, 0L);
-      PlatformDependent.UNSAFE.putLong(base, offset + 8, 0L);
+      Platform.putLong(base, offset, 0L);
+      Platform.putLong(base, offset + 8, 0L);
 
       if (input == null) {
         target.setNullAt(ordinal);
         // keep the offset and length for update
         int fieldOffset = UnsafeRow.calculateBitSetWidthInBytes(target.numFields()) + ordinal * 8;
-        PlatformDependent.UNSAFE.putLong(base, target.getBaseOffset() + fieldOffset,
+        Platform.putLong(base, target.getBaseOffset() + fieldOffset,
           ((long) cursor) << 32);
         return SIZE;
       }
 
       final BigInteger integer = input.toJavaBigDecimal().unscaledValue();
       int signum = integer.signum() + 1;
-      final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
-        PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+      final int[] mag = (int[]) Platform.getObjectVolatile(
+        integer, Platform.BIG_INTEGER_MAG_OFFSET);
       assert(mag.length <= 4);
 
       // Write the bytes to the variable length portion.
-      PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
-        base, target.getBaseOffset() + cursor, mag.length * 4);
+      Platform.copyMemory(
+        mag, Platform.INT_ARRAY_OFFSET, base, target.getBaseOffset() + cursor, mag.length * 4);
       // Set the fixed length portion.
       target.setLong(ordinal, (((long) cursor) << 32) | ((long) ((signum << 8) + mag.length)));
 
@@ -99,8 +99,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UTF8String in
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes to the variable length portion.
@@ -125,8 +124,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, byte[] input)
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes to the variable length portion.
@@ -167,8 +165,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, InternalRow i
 
         // zero-out the padding bytes
         if ((numBytes & 0x07) > 0) {
-          PlatformDependent.UNSAFE.putLong(
-            target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+          Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
         }
 
         // Write the bytes to the variable length portion.
@@ -191,8 +188,8 @@ public static int write(UnsafeRow target, int ordinal, int cursor, CalendarInter
       final long offset = target.getBaseOffset() + cursor;
 
       // Write the months and microseconds fields of Interval to the variable length portion.
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset, input.months);
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset + 8, input.microseconds);
+      Platform.putLong(target.getBaseObject(), offset, input.months);
+      Platform.putLong(target.getBaseObject(), offset + 8, input.microseconds);
 
       // Set the fixed length portion.
       target.setLong(ordinal, ((long) cursor) << 32);
@@ -212,12 +209,11 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UnsafeArrayDa
       final long offset = target.getBaseOffset() + cursor;
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset, input.numElements());
+      Platform.putInt(target.getBaseObject(), offset, input.numElements());
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes to the variable length portion.
@@ -247,14 +243,13 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UnsafeMapData
       final int numBytes = 4 + 4 + keysNumBytes + valuesNumBytes;
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset, input.numElements());
+      Platform.putInt(target.getBaseObject(), offset, input.numElements());
       // write the numBytes of key array into second 4 bytes.
-      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset + 4, keysNumBytes);
+      Platform.putInt(target.getBaseObject(), offset + 4, keysNumBytes);
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes of key array to the variable length portion.
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
index 0e8e405d055de..cd83695fca033 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions;
 
 import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.unsafe.PlatformDependent;
-import org.apache.spark.unsafe.array.ByteArrayMethods;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 
@@ -36,17 +35,11 @@ public static void writeToMemory(
 
     // zero-out the padding bytes
 //    if ((numBytes & 0x07) > 0) {
-//      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + ((numBytes >> 3) << 3), 0L);
+//      Platform.putLong(targetObject, targetOffset + ((numBytes >> 3) << 3), 0L);
 //    }
 
     // Write the UnsafeData to the target memory.
-    PlatformDependent.copyMemory(
-      inputObject,
-      inputOffset,
-      targetObject,
-      targetOffset,
-      numBytes
-    );
+    Platform.copyMemory(inputObject, inputOffset, targetObject, targetOffset, numBytes);
   }
 
   public static int getRoundedSize(int size) {
@@ -68,16 +61,11 @@ public static int write(Object targetObject, long targetOffset, Decimal input) {
       assert(numBytes <= 16);
 
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset, 0L);
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + 8, 0L);
+      Platform.putLong(targetObject, targetOffset, 0L);
+      Platform.putLong(targetObject, targetOffset + 8, 0L);
 
       // Write the bytes to the variable length portion.
-      PlatformDependent.copyMemory(bytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        targetObject,
-        targetOffset,
-        numBytes);
-
+      Platform.copyMemory(bytes, Platform.BYTE_ARRAY_OFFSET, targetObject, targetOffset, numBytes);
       return 16;
     }
   }
@@ -111,8 +99,7 @@ public static int write(Object targetObject, long targetOffset, byte[] input) {
       final int numBytes = input.length;
 
       // Write the bytes to the variable length portion.
-      writeToMemory(input, PlatformDependent.BYTE_ARRAY_OFFSET,
-        targetObject, targetOffset, numBytes);
+      writeToMemory(input, Platform.BYTE_ARRAY_OFFSET, targetObject, targetOffset, numBytes);
 
       return getRoundedSize(numBytes);
     }
@@ -144,11 +131,9 @@ public static int getSize(UnsafeRow input) {
     }
 
     public static int write(Object targetObject, long targetOffset, CalendarInterval input) {
-
       // Write the months and microseconds fields of Interval to the variable length portion.
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset, input.months);
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + 8, input.microseconds);
-
+      Platform.putLong(targetObject, targetOffset, input.months);
+      Platform.putLong(targetObject, targetOffset + 8, input.microseconds);
       return 16;
     }
   }
@@ -165,11 +150,11 @@ public static int write(Object targetObject, long targetOffset, UnsafeArrayData
       final int numBytes = input.getSizeInBytes();
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset, input.numElements());
+      Platform.putInt(targetObject, targetOffset, input.numElements());
 
       // Write the bytes to the variable length portion.
-      writeToMemory(input.getBaseObject(), input.getBaseOffset(),
-        targetObject, targetOffset + 4, numBytes);
+      writeToMemory(
+        input.getBaseObject(), input.getBaseOffset(), targetObject, targetOffset + 4, numBytes);
 
       return getRoundedSize(numBytes + 4);
     }
@@ -190,9 +175,9 @@ public static int write(Object targetObject, long targetOffset, UnsafeMapData in
       final int numBytes = 4 + 4 + keysNumBytes + valuesNumBytes;
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset, input.numElements());
+      Platform.putInt(targetObject, targetOffset, input.numElements());
       // write the numBytes of key array into second 4 bytes.
-      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset + 4, keysNumBytes);
+      Platform.putInt(targetObject, targetOffset + 4, keysNumBytes);
 
       // Write the bytes of key array to the variable length portion.
       writeToMemory(keyArray.getBaseObject(), keyArray.getBaseOffset(),
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index a5ae2b9736527..1d27182912c8a 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -31,7 +31,7 @@
 import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
 import org.apache.spark.sql.types.StructType;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
 import org.apache.spark.util.collection.unsafe.sort.RecordComparator;
 import org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter;
@@ -157,7 +157,7 @@ public UnsafeRow next() {
             cleanupResources();
             // Scala iterators don't declare any checked exceptions, so we need to use this hack
             // to re-throw the exception:
-            PlatformDependent.throwException(e);
+            Platform.throwException(e);
           }
           throw new RuntimeException("Exception should have been re-thrown in next()");
         };
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index c21f4d626a74e..bf96248feaef7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -28,7 +28,7 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.types._
 
 
@@ -371,7 +371,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
     // Cannot be under package codegen, or fail with java.lang.InstantiationException
     evaluator.setClassName("org.apache.spark.sql.catalyst.expressions.GeneratedClass")
     evaluator.setDefaultImports(Array(
-      classOf[PlatformDependent].getName,
+      classOf[Platform].getName,
       classOf[InternalRow].getName,
       classOf[UnsafeRow].getName,
       classOf[UTF8String].getName,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 29f6a7b981752..b2fb913850794 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -145,12 +145,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           if ($buffer.length < $numBytes) {
             // This will not happen frequently, because the buffer is re-used.
             byte[] $tmp = new byte[$numBytes * 2];
-            PlatformDependent.copyMemory($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
-              $tmp, PlatformDependent.BYTE_ARRAY_OFFSET, $buffer.length);
+            Platform.copyMemory($buffer, Platform.BYTE_ARRAY_OFFSET,
+              $tmp, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
             $buffer = $tmp;
           }
-          $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
-            ${inputTypes.length}, $numBytes);
+          $output.pointTo($buffer, Platform.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $numBytes);
          """
       } else {
         ""
@@ -183,7 +182,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val code = s"""
       $cursor = $fixedSize;
-      $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $cursor);
+      $output.pointTo($buffer, Platform.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $cursor);
       ${ctx.splitExpressions(row, convertedFields)}
       """
     GeneratedExpressionCode(code, "false", output)
@@ -267,17 +266,17 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
-          PlatformDependent.UNSAFE.put${ctx.primitiveTypeName(elementType)}(
+          Platform.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            Platform.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive});
           $cursor += $elementSize;
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
-          PlatformDependent.UNSAFE.putLong(
+          Platform.putLong(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            Platform.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive}.toUnscaledLong());
           $cursor += 8;
         """
@@ -286,7 +285,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         s"""
           $cursor += $writer.write(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            Platform.BYTE_ARRAY_OFFSET + $cursor,
             $elements[$index]);
         """
     }
@@ -320,23 +319,16 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           for (int $index = 0; $index < $numElements; $index++) {
             if ($checkNull) {
               // If element is null, write the negative value address into offset region.
-              PlatformDependent.UNSAFE.putInt(
-                $buffer,
-                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
-                -$cursor);
+              Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, -$cursor);
             } else {
-              PlatformDependent.UNSAFE.putInt(
-                $buffer,
-                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
-                $cursor);
-
+              Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, $cursor);
               $writeElement
             }
           }
 
           $output.pointTo(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             $numElements,
             $numBytes);
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
index 8aaa5b4300044..da91ff29537b3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, Attribute}
 import org.apache.spark.sql.types.StructType
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 
 
 abstract class UnsafeRowJoiner {
@@ -52,9 +52,9 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
   }
 
   def create(schema1: StructType, schema2: StructType): UnsafeRowJoiner = {
-    val offset = PlatformDependent.BYTE_ARRAY_OFFSET
-    val getLong = "PlatformDependent.UNSAFE.getLong"
-    val putLong = "PlatformDependent.UNSAFE.putLong"
+    val offset = Platform.BYTE_ARRAY_OFFSET
+    val getLong = "Platform.getLong"
+    val putLong = "Platform.putLong"
 
     val bitset1Words = (schema1.size + 63) / 64
     val bitset2Words = (schema2.size + 63) / 64
@@ -96,7 +96,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     var cursor = offset + outputBitsetWords * 8
     val copyFixedLengthRow1 = s"""
        |// Copy fixed length data for row1
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj1, offset1 + ${bitset1Words * 8},
        |  buf, $cursor,
        |  ${schema1.size * 8});
@@ -106,7 +106,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     // --------------------- copy fixed length portion from row 2 ----------------------- //
     val copyFixedLengthRow2 = s"""
        |// Copy fixed length data for row2
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj2, offset2 + ${bitset2Words * 8},
        |  buf, $cursor,
        |  ${schema2.size * 8});
@@ -118,7 +118,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     val copyVariableLengthRow1 = s"""
        |// Copy variable length data for row1
        |long numBytesVariableRow1 = row1.getSizeInBytes() - $numBytesBitsetAndFixedRow1;
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj1, offset1 + ${(bitset1Words + schema1.size) * 8},
        |  buf, $cursor,
        |  numBytesVariableRow1);
@@ -129,7 +129,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     val copyVariableLengthRow2 = s"""
        |// Copy variable length data for row2
        |long numBytesVariableRow2 = row2.getSizeInBytes() - $numBytesBitsetAndFixedRow2;
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj2, offset2 + ${(bitset2Words + schema2.size) * 8},
        |  buf, $cursor + numBytesVariableRow1,
        |  numBytesVariableRow2);
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 76666bd6b3d27..134f1aa2af9a8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -1013,7 +1013,7 @@ case class Decode(bin: Expression, charset: Expression)
         try {
           ${ev.primitive} = UTF8String.fromString(new String($bytes, $charset.toString()));
         } catch (java.io.UnsupportedEncodingException e) {
-          org.apache.spark.unsafe.PlatformDependent.throwException(e);
+          org.apache.spark.unsafe.Platform.throwException(e);
         }
       """)
   }
@@ -1043,7 +1043,7 @@ case class Encode(value: Expression, charset: Expression)
         try {
           ${ev.primitive} = $string.toString().getBytes($charset.toString());
         } catch (java.io.UnsupportedEncodingException e) {
-          org.apache.spark.unsafe.PlatformDependent.throwException(e);
+          org.apache.spark.unsafe.Platform.throwException(e);
         }""")
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
index aff1bee99faad..796d60032e1a6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
@@ -22,7 +22,7 @@ import scala.util.Random
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 
 /**
  * A test suite for the bitset portion of the row concatenation.
@@ -96,7 +96,7 @@ class GenerateUnsafeRowJoinerBitsetSuite extends SparkFunSuite {
     // This way we can test the joiner when the input UnsafeRows are not the entire arrays.
     val offset = numFields * 8
     val buf = new Array[Byte](sizeInBytes + offset)
-    row.pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET + offset, numFields, sizeInBytes)
+    row.pointTo(buf, Platform.BYTE_ARRAY_OFFSET + offset, numFields, sizeInBytes)
     row
   }
 
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 00218f213054b..5cce41d5a7569 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -27,7 +27,7 @@
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.KVIterator;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
 import org.apache.spark.unsafe.memory.MemoryLocation;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -138,7 +138,7 @@ public UnsafeRow getAggregationBufferFromUnsafeRow(UnsafeRow unsafeGroupingKeyRo
         unsafeGroupingKeyRow.getBaseOffset(),
         unsafeGroupingKeyRow.getSizeInBytes(),
         emptyAggregationBuffer,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         emptyAggregationBuffer.length
       );
       if (!putSucceeded) {
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 69d6784713a24..7db6b7ff50f22 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -31,7 +31,7 @@
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.unsafe.KVIterator;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -225,7 +225,7 @@ public boolean next() throws IOException {
           int recordLen = underlying.getRecordLength();
 
           // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
-          int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
+          int keyLen = Platform.getInt(baseObj, recordOffset);
           int valueLen = recordLen - keyLen - 4;
           key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
           value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 6c7e5cacc99e7..3860c4bba9a99 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -26,7 +26,7 @@ import com.google.common.io.ByteStreams
 
 import org.apache.spark.serializer.{SerializationStream, DeserializationStream, SerializerInstance, Serializer}
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 
 /**
  * Serializer for serializing [[UnsafeRow]]s during shuffle. Since UnsafeRows are already stored as
@@ -116,7 +116,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
               rowBuffer = new Array[Byte](rowSize)
             }
             ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
-            row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
+            row.pointTo(rowBuffer, Platform.BYTE_ARRAY_OFFSET, numFields, rowSize)
             rowSize = dIn.readInt() // read the next row's size
             if (rowSize == EOF) { // We are returning the last row in this stream
               val _rowTuple = rowTuple
@@ -150,7 +150,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
           rowBuffer = new Array[Byte](rowSize)
         }
         ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
-        row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
+        row.pointTo(rowBuffer, Platform.BYTE_ARRAY_OFFSET, numFields, rowSize)
         row.asInstanceOf[T]
       }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 953abf409f220..63d35d0f02622 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -25,7 +25,7 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.util.Utils
@@ -218,8 +218,8 @@ private[joins] final class UnsafeHashedRelation(
         var offset = loc.getValueAddress.getBaseOffset
         val last = loc.getValueAddress.getBaseOffset + loc.getValueLength
         while (offset < last) {
-          val numFields = PlatformDependent.UNSAFE.getInt(base, offset)
-          val sizeInBytes = PlatformDependent.UNSAFE.getInt(base, offset + 4)
+          val numFields = Platform.getInt(base, offset)
+          val sizeInBytes = Platform.getInt(base, offset + 4)
           offset += 8
 
           val row = new UnsafeRow
@@ -314,10 +314,11 @@ private[joins] final class UnsafeHashedRelation(
       in.readFully(valuesBuffer, 0, valuesSize)
 
       // put it into binary map
-      val loc = binaryMap.lookup(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize)
+      val loc = binaryMap.lookup(keyBuffer, Platform.BYTE_ARRAY_OFFSET, keySize)
       assert(!loc.isDefined, "Duplicated key found!")
-      val putSuceeded = loc.putNewKey(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize,
-        valuesBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, valuesSize)
+      val putSuceeded = loc.putNewKey(
+        keyBuffer, Platform.BYTE_ARRAY_OFFSET, keySize,
+        valuesBuffer, Platform.BYTE_ARRAY_OFFSET, valuesSize)
       if (!putSuceeded) {
         throw new IOException("Could not allocate memory to grow BytesToBytesMap")
       }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index 89bad1bfdab0a..219435dff5bc8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -23,7 +23,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.memory.MemoryAllocator
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -51,7 +51,7 @@ class UnsafeRowSuite extends SparkFunSuite {
     val bytesFromOffheapRow: Array[Byte] = {
       val offheapRowPage = MemoryAllocator.UNSAFE.allocate(arrayBackedUnsafeRow.getSizeInBytes)
       try {
-        PlatformDependent.copyMemory(
+        Platform.copyMemory(
           arrayBackedUnsafeRow.getBaseObject,
           arrayBackedUnsafeRow.getBaseOffset,
           offheapRowPage.getBaseObject,
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
similarity index 55%
rename from unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
rename to unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
index b2de2a2590f05..18343efdc3437 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
@@ -22,103 +22,111 @@
 
 import sun.misc.Unsafe;
 
-public final class PlatformDependent {
+public final class Platform {
 
-  /**
-   * Facade in front of {@link sun.misc.Unsafe}, used to avoid directly exposing Unsafe outside of
-   * this package. This also lets us avoid accidental use of deprecated methods.
-   */
-  public static final class UNSAFE {
-
-    private UNSAFE() { }
+  private static final Unsafe _UNSAFE;
 
-    public static int getInt(Object object, long offset) {
-      return _UNSAFE.getInt(object, offset);
-    }
+  public static final int BYTE_ARRAY_OFFSET;
 
-    public static void putInt(Object object, long offset, int value) {
-      _UNSAFE.putInt(object, offset, value);
-    }
+  public static final int INT_ARRAY_OFFSET;
 
-    public static boolean getBoolean(Object object, long offset) {
-      return _UNSAFE.getBoolean(object, offset);
-    }
+  public static final int LONG_ARRAY_OFFSET;
 
-    public static void putBoolean(Object object, long offset, boolean value) {
-      _UNSAFE.putBoolean(object, offset, value);
-    }
+  public static final int DOUBLE_ARRAY_OFFSET;
 
-    public static byte getByte(Object object, long offset) {
-      return _UNSAFE.getByte(object, offset);
-    }
+  // Support for resetting final fields while deserializing
+  public static final long BIG_INTEGER_SIGNUM_OFFSET;
+  public static final long BIG_INTEGER_MAG_OFFSET;
 
-    public static void putByte(Object object, long offset, byte value) {
-      _UNSAFE.putByte(object, offset, value);
-    }
+  public static int getInt(Object object, long offset) {
+    return _UNSAFE.getInt(object, offset);
+  }
 
-    public static short getShort(Object object, long offset) {
-      return _UNSAFE.getShort(object, offset);
-    }
+  public static void putInt(Object object, long offset, int value) {
+    _UNSAFE.putInt(object, offset, value);
+  }
 
-    public static void putShort(Object object, long offset, short value) {
-      _UNSAFE.putShort(object, offset, value);
-    }
+  public static boolean getBoolean(Object object, long offset) {
+    return _UNSAFE.getBoolean(object, offset);
+  }
 
-    public static long getLong(Object object, long offset) {
-      return _UNSAFE.getLong(object, offset);
-    }
+  public static void putBoolean(Object object, long offset, boolean value) {
+    _UNSAFE.putBoolean(object, offset, value);
+  }
 
-    public static void putLong(Object object, long offset, long value) {
-      _UNSAFE.putLong(object, offset, value);
-    }
+  public static byte getByte(Object object, long offset) {
+    return _UNSAFE.getByte(object, offset);
+  }
 
-    public static float getFloat(Object object, long offset) {
-      return _UNSAFE.getFloat(object, offset);
-    }
+  public static void putByte(Object object, long offset, byte value) {
+    _UNSAFE.putByte(object, offset, value);
+  }
 
-    public static void putFloat(Object object, long offset, float value) {
-      _UNSAFE.putFloat(object, offset, value);
-    }
+  public static short getShort(Object object, long offset) {
+    return _UNSAFE.getShort(object, offset);
+  }
 
-    public static double getDouble(Object object, long offset) {
-      return _UNSAFE.getDouble(object, offset);
-    }
+  public static void putShort(Object object, long offset, short value) {
+    _UNSAFE.putShort(object, offset, value);
+  }
 
-    public static void putDouble(Object object, long offset, double value) {
-      _UNSAFE.putDouble(object, offset, value);
-    }
+  public static long getLong(Object object, long offset) {
+    return _UNSAFE.getLong(object, offset);
+  }
 
-    public static Object getObjectVolatile(Object object, long offset) {
-      return _UNSAFE.getObjectVolatile(object, offset);
-    }
+  public static void putLong(Object object, long offset, long value) {
+    _UNSAFE.putLong(object, offset, value);
+  }
 
-    public static void putObjectVolatile(Object object, long offset, Object value) {
-      _UNSAFE.putObjectVolatile(object, offset, value);
-    }
+  public static float getFloat(Object object, long offset) {
+    return _UNSAFE.getFloat(object, offset);
+  }
 
-    public static long allocateMemory(long size) {
-      return _UNSAFE.allocateMemory(size);
-    }
+  public static void putFloat(Object object, long offset, float value) {
+    _UNSAFE.putFloat(object, offset, value);
+  }
 
-    public static void freeMemory(long address) {
-      _UNSAFE.freeMemory(address);
-    }
+  public static double getDouble(Object object, long offset) {
+    return _UNSAFE.getDouble(object, offset);
+  }
 
+  public static void putDouble(Object object, long offset, double value) {
+    _UNSAFE.putDouble(object, offset, value);
   }
 
-  private static final Unsafe _UNSAFE;
+  public static Object getObjectVolatile(Object object, long offset) {
+    return _UNSAFE.getObjectVolatile(object, offset);
+  }
 
-  public static final int BYTE_ARRAY_OFFSET;
+  public static void putObjectVolatile(Object object, long offset, Object value) {
+    _UNSAFE.putObjectVolatile(object, offset, value);
+  }
 
-  public static final int INT_ARRAY_OFFSET;
+  public static long allocateMemory(long size) {
+    return _UNSAFE.allocateMemory(size);
+  }
 
-  public static final int LONG_ARRAY_OFFSET;
+  public static void freeMemory(long address) {
+    _UNSAFE.freeMemory(address);
+  }
 
-  public static final int DOUBLE_ARRAY_OFFSET;
+  public static void copyMemory(
+    Object src, long srcOffset, Object dst, long dstOffset, long length) {
+    while (length > 0) {
+      long size = Math.min(length, UNSAFE_COPY_THRESHOLD);
+      _UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size);
+      length -= size;
+      srcOffset += size;
+      dstOffset += size;
+    }
+  }
 
-  // Support for resetting final fields while deserializing
-  public static final long BIG_INTEGER_SIGNUM_OFFSET;
-  public static final long BIG_INTEGER_MAG_OFFSET;
+  /**
+   * Raises an exception bypassing compiler checks for checked exceptions.
+   */
+  public static void throwException(Throwable t) {
+    _UNSAFE.throwException(t);
+  }
 
   /**
    * Limits the number of bytes to copy per {@link Unsafe#copyMemory(long, long, long)} to
@@ -162,26 +170,4 @@ public static void freeMemory(long address) {
       BIG_INTEGER_MAG_OFFSET = 0;
     }
   }
-
-  static public void copyMemory(
-      Object src,
-      long srcOffset,
-      Object dst,
-      long dstOffset,
-      long length) {
-    while (length > 0) {
-      long size = Math.min(length, UNSAFE_COPY_THRESHOLD);
-      _UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size);
-      length -= size;
-      srcOffset += size;
-      dstOffset += size;
-    }
-  }
-
-  /**
-   * Raises an exception bypassing compiler checks for checked exceptions.
-   */
-  public static void throwException(Throwable t) {
-    _UNSAFE.throwException(t);
-  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
index 70b81ce015ddc..cf42877bf9fd4 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.array;
 
-import static org.apache.spark.unsafe.PlatformDependent.*;
+import org.apache.spark.unsafe.Platform;
 
 public class ByteArrayMethods {
 
@@ -45,20 +45,18 @@ public static int roundNumberOfBytesToNearestWord(int numBytes) {
    * @return true if the arrays are equal, false otherwise
    */
   public static boolean arrayEquals(
-      Object leftBase,
-      long leftOffset,
-      Object rightBase,
-      long rightOffset,
-      final long length) {
+      Object leftBase, long leftOffset, Object rightBase, long rightOffset, final long length) {
     int i = 0;
     while (i <= length - 8) {
-      if (UNSAFE.getLong(leftBase, leftOffset + i) != UNSAFE.getLong(rightBase, rightOffset + i)) {
+      if (Platform.getLong(leftBase, leftOffset + i) !=
+        Platform.getLong(rightBase, rightOffset + i)) {
         return false;
       }
       i += 8;
     }
     while (i < length) {
-      if (UNSAFE.getByte(leftBase, leftOffset + i) != UNSAFE.getByte(rightBase, rightOffset + i)) {
+      if (Platform.getByte(leftBase, leftOffset + i) !=
+        Platform.getByte(rightBase, rightOffset + i)) {
         return false;
       }
       i += 1;
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java
index 18d1f0d2d7eb2..74105050e4191 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.array;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 
 /**
@@ -64,7 +64,7 @@ public long size() {
   public void set(int index, long value) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     assert index < length : "index (" + index + ") should < length (" + length + ")";
-    PlatformDependent.UNSAFE.putLong(baseObj, baseOffset + index * WIDTH, value);
+    Platform.putLong(baseObj, baseOffset + index * WIDTH, value);
   }
 
   /**
@@ -73,6 +73,6 @@ public void set(int index, long value) {
   public long get(int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     assert index < length : "index (" + index + ") should < length (" + length + ")";
-    return PlatformDependent.UNSAFE.getLong(baseObj, baseOffset + index * WIDTH);
+    return Platform.getLong(baseObj, baseOffset + index * WIDTH);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
index 27462c7fa5e62..7857bf66a72ad 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.bitset;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Methods for working with fixed-size uncompressed bitsets.
@@ -41,8 +41,8 @@ public static void set(Object baseObject, long baseOffset, int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     final long mask = 1L << (index & 0x3f);  // mod 64 and shift
     final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE;
-    final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset);
-    PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word | mask);
+    final long word = Platform.getLong(baseObject, wordOffset);
+    Platform.putLong(baseObject, wordOffset, word | mask);
   }
 
   /**
@@ -52,8 +52,8 @@ public static void unset(Object baseObject, long baseOffset, int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     final long mask = 1L << (index & 0x3f);  // mod 64 and shift
     final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE;
-    final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset);
-    PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word & ~mask);
+    final long word = Platform.getLong(baseObject, wordOffset);
+    Platform.putLong(baseObject, wordOffset, word & ~mask);
   }
 
   /**
@@ -63,7 +63,7 @@ public static boolean isSet(Object baseObject, long baseOffset, int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     final long mask = 1L << (index & 0x3f);  // mod 64 and shift
     final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE;
-    final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset);
+    final long word = Platform.getLong(baseObject, wordOffset);
     return (word & mask) != 0;
   }
 
@@ -73,7 +73,7 @@ public static boolean isSet(Object baseObject, long baseOffset, int index) {
   public static boolean anySet(Object baseObject, long baseOffset, long bitSetWidthInWords) {
     long addr = baseOffset;
     for (int i = 0; i < bitSetWidthInWords; i++, addr += WORD_SIZE) {
-      if (PlatformDependent.UNSAFE.getLong(baseObject, addr) != 0) {
+      if (Platform.getLong(baseObject, addr) != 0) {
         return true;
       }
     }
@@ -109,8 +109,7 @@ public static int nextSetBit(
 
     // Try to find the next set bit in the current word
     final int subIndex = fromIndex & 0x3f;
-    long word =
-      PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE) >> subIndex;
+    long word = Platform.getLong(baseObject, baseOffset + wi * WORD_SIZE) >> subIndex;
     if (word != 0) {
       return (wi << 6) + subIndex + java.lang.Long.numberOfTrailingZeros(word);
     }
@@ -118,7 +117,7 @@ public static int nextSetBit(
     // Find the next set bit in the rest of the words
     wi += 1;
     while (wi < bitsetSizeInWords) {
-      word = PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE);
+      word = Platform.getLong(baseObject, baseOffset + wi * WORD_SIZE);
       if (word != 0) {
         return (wi << 6) + java.lang.Long.numberOfTrailingZeros(word);
       }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
index 61f483ced3217..4276f25c2165b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.hash;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * 32-bit Murmur3 hasher.  This is based on Guava's Murmur3_32HashFunction.
@@ -53,7 +53,7 @@ public static int hashUnsafeWords(Object base, long offset, int lengthInBytes, i
     assert (lengthInBytes % 8 == 0): "lengthInBytes must be a multiple of 8 (word-aligned)";
     int h1 = seed;
     for (int i = 0; i < lengthInBytes; i += 4) {
-      int halfWord = PlatformDependent.UNSAFE.getInt(base, offset + i);
+      int halfWord = Platform.getInt(base, offset + i);
       int k1 = mixK1(halfWord);
       h1 = mixH1(h1, k1);
     }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
index 91be46ba21ff8..dd75820834370 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
@@ -19,7 +19,7 @@
 
 import javax.annotation.Nullable;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * A consecutive block of memory, starting at a {@link MemoryLocation} with a fixed size.
@@ -50,6 +50,6 @@ public long size() {
    * Creates a memory block pointing to the memory used by the long array.
    */
   public static MemoryBlock fromLongArray(final long[] array) {
-    return new MemoryBlock(array, PlatformDependent.LONG_ARRAY_OFFSET, array.length * 8);
+    return new MemoryBlock(array, Platform.LONG_ARRAY_OFFSET, array.length * 8);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
index 62f4459696c28..cda7826c8c99b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.memory;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * A simple {@link MemoryAllocator} that uses {@code Unsafe} to allocate off-heap memory.
@@ -29,7 +29,7 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError {
     if (size % 8 != 0) {
       throw new IllegalArgumentException("Size " + size + " was not a multiple of 8");
     }
-    long address = PlatformDependent.UNSAFE.allocateMemory(size);
+    long address = Platform.allocateMemory(size);
     return new MemoryBlock(null, address, size);
   }
 
@@ -37,6 +37,6 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError {
   public void free(MemoryBlock memory) {
     assert (memory.obj == null) :
       "baseObject not null; are you trying to use the off-heap allocator to free on-heap memory?";
-    PlatformDependent.UNSAFE.freeMemory(memory.offset);
+    Platform.freeMemory(memory.offset);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
index 69b0e206cef18..c08c9c73d2396 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.types;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 public class ByteArray {
 
@@ -27,12 +27,6 @@ public class ByteArray {
    * hold all the bytes in this string.
    */
   public static void writeToMemory(byte[] src, Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      src,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      target,
-      targetOffset,
-      src.length
-    );
+    Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET, target, targetOffset, src.length);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index d1014426c0f49..667c00900f2c5 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -24,10 +24,10 @@
 import java.util.Arrays;
 import java.util.Map;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 
-import static org.apache.spark.unsafe.PlatformDependent.*;
+import static org.apache.spark.unsafe.Platform.*;
 
 
 /**
@@ -133,13 +133,7 @@ protected UTF8String(Object base, long offset, int numBytes) {
    * bytes in this string.
    */
   public void writeToMemory(Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      base,
-      offset,
-      target,
-      targetOffset,
-      numBytes
-    );
+    Platform.copyMemory(base, offset, target, targetOffset, numBytes);
   }
 
   /**
@@ -183,12 +177,12 @@ public long getPrefix() {
     long mask = 0;
     if (isLittleEndian) {
       if (numBytes >= 8) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
       } else if (numBytes > 4) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else if (numBytes > 0) {
-        p = (long) PlatformDependent.UNSAFE.getInt(base, offset);
+        p = (long) Platform.getInt(base, offset);
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else {
         p = 0;
@@ -197,12 +191,12 @@ public long getPrefix() {
     } else {
       // byteOrder == ByteOrder.BIG_ENDIAN
       if (numBytes >= 8) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
       } else if (numBytes > 4) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else if (numBytes > 0) {
-        p = ((long) PlatformDependent.UNSAFE.getInt(base, offset)) << 32;
+        p = ((long) Platform.getInt(base, offset)) << 32;
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else {
         p = 0;
@@ -293,7 +287,7 @@ public boolean contains(final UTF8String substring) {
    * Returns the byte at position `i`.
    */
   private byte getByte(int i) {
-    return UNSAFE.getByte(base, offset + i);
+    return Platform.getByte(base, offset + i);
   }
 
   private boolean matchAt(final UTF8String s, int pos) {
@@ -769,7 +763,7 @@ public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
         int len = inputs[i].numBytes;
         copyMemory(
           inputs[i].base, inputs[i].offset,
-          result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+          result, BYTE_ARRAY_OFFSET + offset,
           len);
         offset += len;
 
@@ -778,7 +772,7 @@ public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
         if (j < numInputs) {
           copyMemory(
             separator.base, separator.offset,
-            result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+            result, BYTE_ARRAY_OFFSET + offset,
             separator.numBytes);
           offset += separator.numBytes;
         }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java
index 3b9175835229c..2f8cb132ac8b4 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java
@@ -22,7 +22,7 @@
 import java.util.Set;
 
 import junit.framework.Assert;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.junit.Test;
 
 /**
@@ -83,11 +83,11 @@ public void randomizedStressTestBytes() {
       rand.nextBytes(bytes);
 
       Assert.assertEquals(
-        hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize),
-        hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        hasher.hashUnsafeWords(bytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize),
+        hasher.hashUnsafeWords(bytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
 
       hashcodes.add(hasher.hashUnsafeWords(
-        bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        bytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
     }
 
     // A very loose bound.
@@ -106,11 +106,11 @@ public void randomizedStressTestPaddedStrings() {
       System.arraycopy(strBytes, 0, paddedBytes, 0, strBytes.length);
 
       Assert.assertEquals(
-        hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize),
-        hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        hasher.hashUnsafeWords(paddedBytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize),
+        hasher.hashUnsafeWords(paddedBytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
 
       hashcodes.add(hasher.hashUnsafeWords(
-        paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        paddedBytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
     }
 
     // A very loose bound.

From dfe347d2cae3eb05d7539aaf72db3d309e711213 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 11 Aug 2015 08:52:15 -0700
Subject: [PATCH 0963/1454] [SPARK-9785] [SQL] HashPartitioning compatibility
 should consider expression ordering

HashPartitioning compatibility is currently defined w.r.t the _set_ of expressions, but the ordering of those expressions matters when computing hash codes; this could lead to incorrect answers if we mistakenly avoided a shuffle based on the assumption that HashPartitionings with the same expressions in different orders will produce equivalent row hashcodes. The first commit adds a regression test which illustrates this problem.

The fix for this is simple: make `HashPartitioning.compatibleWith` and `HashPartitioning.guarantees` sensitive to the expression ordering (i.e. do not perform set comparison).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8074 from JoshRosen/hashpartitioning-compatiblewith-fixes and squashes the following commits:

b61412f [Josh Rosen] Demonstrate that I haven't cheated in my fix
0b4d7d9 [Josh Rosen] Update so that clusteringSet is only used in satisfies().
dc9c9d7 [Josh Rosen] Add failing regression test for SPARK-9785
---
 .../plans/physical/partitioning.scala         | 15 ++---
 .../sql/catalyst/PartitioningSuite.scala      | 55 +++++++++++++++++++
 2 files changed, 60 insertions(+), 10 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index 5a89a90b735a6..5ac3f1f5b0cac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -216,26 +216,23 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
   override def nullable: Boolean = false
   override def dataType: DataType = IntegerType
 
-  lazy val clusteringSet = expressions.toSet
-
   override def satisfies(required: Distribution): Boolean = required match {
     case UnspecifiedDistribution => true
     case ClusteredDistribution(requiredClustering) =>
-      clusteringSet.subsetOf(requiredClustering.toSet)
+      expressions.toSet.subsetOf(requiredClustering.toSet)
     case _ => false
   }
 
   override def compatibleWith(other: Partitioning): Boolean = other match {
-    case o: HashPartitioning =>
-      this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
+    case o: HashPartitioning => this == o
     case _ => false
   }
 
   override def guarantees(other: Partitioning): Boolean = other match {
-    case o: HashPartitioning =>
-      this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
+    case o: HashPartitioning => this == o
     case _ => false
   }
+
 }
 
 /**
@@ -257,15 +254,13 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
   override def nullable: Boolean = false
   override def dataType: DataType = IntegerType
 
-  private[this] lazy val clusteringSet = ordering.map(_.child).toSet
-
   override def satisfies(required: Distribution): Boolean = required match {
     case UnspecifiedDistribution => true
     case OrderedDistribution(requiredOrdering) =>
       val minSize = Seq(requiredOrdering.size, ordering.size).min
       requiredOrdering.take(minSize) == ordering.take(minSize)
     case ClusteredDistribution(requiredClustering) =>
-      clusteringSet.subsetOf(requiredClustering.toSet)
+      ordering.map(_.child).toSet.subsetOf(requiredClustering.toSet)
     case _ => false
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala
new file mode 100644
index 0000000000000..5b802ccc637dd
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions.{InterpretedMutableProjection, Literal}
+import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, HashPartitioning}
+
+class PartitioningSuite extends SparkFunSuite {
+  test("HashPartitioning compatibility should be sensitive to expression ordering (SPARK-9785)") {
+    val expressions = Seq(Literal(2), Literal(3))
+    // Consider two HashPartitionings that have the same _set_ of hash expressions but which are
+    // created with different orderings of those expressions:
+    val partitioningA = HashPartitioning(expressions, 100)
+    val partitioningB = HashPartitioning(expressions.reverse, 100)
+    // These partitionings are not considered equal:
+    assert(partitioningA != partitioningB)
+    // However, they both satisfy the same clustered distribution:
+    val distribution = ClusteredDistribution(expressions)
+    assert(partitioningA.satisfies(distribution))
+    assert(partitioningB.satisfies(distribution))
+    // These partitionings compute different hashcodes for the same input row:
+    def computeHashCode(partitioning: HashPartitioning): Int = {
+      val hashExprProj = new InterpretedMutableProjection(partitioning.expressions, Seq.empty)
+      hashExprProj.apply(InternalRow.empty).hashCode()
+    }
+    assert(computeHashCode(partitioningA) != computeHashCode(partitioningB))
+    // Thus, these partitionings are incompatible:
+    assert(!partitioningA.compatibleWith(partitioningB))
+    assert(!partitioningB.compatibleWith(partitioningA))
+    assert(!partitioningA.guarantees(partitioningB))
+    assert(!partitioningB.guarantees(partitioningA))
+
+    // Just to be sure that we haven't cheated by having these methods always return false,
+    // check that identical partitionings are still compatible with and guarantee each other:
+    assert(partitioningA === partitioningA)
+    assert(partitioningA.guarantees(partitioningA))
+    assert(partitioningA.compatibleWith(partitioningA))
+  }
+}

From bce72797f3499f14455722600b0d0898d4fd87c9 Mon Sep 17 00:00:00 2001
From: Jeff Zhang <zjffdu@apache.org>
Date: Tue, 11 Aug 2015 10:42:17 -0700
Subject: [PATCH 0964/1454] Fix comment error

API is updated but its doc comment is not updated.

Author: Jeff Zhang <zjffdu@apache.org>

Closes #8097 from zjffdu/dev.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 9ced44131b0d9..6aafb4c5644d7 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -866,7 +866,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * }}}
    *
    * Do
-   * `val rdd = sparkContext.dataStreamFiles("hdfs://a-hdfs-path")`,
+   * `val rdd = sparkContext.binaryFiles("hdfs://a-hdfs-path")`,
    *
    * then `rdd` contains
    * {{{

From 8cad854ef6a2066de5adffcca6b79a205ccfd5f3 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 11 Aug 2015 11:01:59 -0700
Subject: [PATCH 0965/1454] [SPARK-8345] [ML] Add an SQL node as a feature
 transformer

Implements the transforms which are defined by SQL statement.
Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
where '__THIS__' represents the underlying table of the input dataset.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7465 from yanboliang/spark-8345 and squashes the following commits:

b403fcb [Yanbo Liang] address comments
0d4bb15 [Yanbo Liang] a better transformSchema() implementation
51eb9e7 [Yanbo Liang] Add an SQL node as a feature transformer
---
 .../spark/ml/feature/SQLTransformer.scala     | 72 +++++++++++++++++++
 .../ml/feature/SQLTransformerSuite.scala      | 44 ++++++++++++
 2 files changed, 116 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala
new file mode 100644
index 0000000000000..95e4305638730
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.{ParamMap, Param}
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.{SQLContext, DataFrame, Row}
+import org.apache.spark.sql.types.StructType
+
+/**
+ * :: Experimental ::
+ * Implements the transforms which are defined by SQL statement.
+ * Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
+ * where '__THIS__' represents the underlying table of the input dataset.
+ */
+@Experimental
+class SQLTransformer (override val uid: String) extends Transformer {
+
+  def this() = this(Identifiable.randomUID("sql"))
+
+  /**
+   * SQL statement parameter. The statement is provided in string form.
+   * @group param
+   */
+  final val statement: Param[String] = new Param[String](this, "statement", "SQL statement")
+
+  /** @group setParam */
+  def setStatement(value: String): this.type = set(statement, value)
+
+  /** @group getParam */
+  def getStatement: String = $(statement)
+
+  private val tableIdentifier: String = "__THIS__"
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val tableName = Identifiable.randomUID(uid)
+    dataset.registerTempTable(tableName)
+    val realStatement = $(statement).replace(tableIdentifier, tableName)
+    val outputDF = dataset.sqlContext.sql(realStatement)
+    outputDF
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    val sc = SparkContext.getOrCreate()
+    val sqlContext = SQLContext.getOrCreate(sc)
+    val dummyRDD = sc.parallelize(Seq(Row.empty))
+    val dummyDF = sqlContext.createDataFrame(dummyRDD, schema)
+    dummyDF.registerTempTable(tableIdentifier)
+    val outputSchema = sqlContext.sql($(statement)).schema
+    outputSchema
+  }
+
+  override def copy(extra: ParamMap): SQLTransformer = defaultCopy(extra)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala
new file mode 100644
index 0000000000000..d19052881ae45
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class SQLTransformerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    ParamsSuite.checkParams(new SQLTransformer())
+  }
+
+  test("transform numeric data") {
+    val original = sqlContext.createDataFrame(
+      Seq((0, 1.0, 3.0), (2, 2.0, 5.0))).toDF("id", "v1", "v2")
+    val sqlTrans = new SQLTransformer().setStatement(
+      "SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
+    val result = sqlTrans.transform(original)
+    val resultSchema = sqlTrans.transformSchema(original.schema)
+    val expected = sqlContext.createDataFrame(
+      Seq((0, 1.0, 3.0, 4.0, 3.0), (2, 2.0, 5.0, 7.0, 10.0)))
+      .toDF("id", "v1", "v2", "v3", "v4")
+    assert(result.schema.toString == resultSchema.toString)
+    assert(resultSchema == expected.schema)
+    assert(result.collect().toSeq == expected.collect().toSeq)
+  }
+}

From dbd778d84d094ca142bc08c351478595b280bc2a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 11 Aug 2015 11:33:36 -0700
Subject: [PATCH 0966/1454] [SPARK-8764] [ML] string indexer should take option
 to handle unseen values

As a precursor to adding a public constructor add an option to handle unseen values by skipping rather than throwing an exception (default remains throwing an exception),

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7266 from holdenk/SPARK-8764-string-indexer-should-take-option-to-handle-unseen-values and squashes the following commits:

38a4de9 [Holden Karau] fix long line
045bf22 [Holden Karau] Add a second b entry so b gets 0 for sure
81dd312 [Holden Karau] Update the docs for handleInvalid param to be more descriptive
7f37f6e [Holden Karau] remove extra space (scala style)
414e249 [Holden Karau] And switch to using handleInvalid instead of skipInvalid
1e53f9b [Holden Karau] update the param (codegen side)
7a22215 [Holden Karau] fix typo
100a39b [Holden Karau] Merge in master
aa5b093 [Holden Karau] Since we filter we should never go down this code path if getSkipInvalid is true
75ffa69 [Holden Karau] Remove extra newline
d69ef5e [Holden Karau] Add a test
b5734be [Holden Karau] Add support for unseen labels
afecd4e [Holden Karau] Add a param to skip invalid entries.
---
 .../spark/ml/feature/StringIndexer.scala      | 26 ++++++++++++---
 .../ml/param/shared/SharedParamsCodeGen.scala |  4 +++
 .../spark/ml/param/shared/sharedParams.scala  | 15 +++++++++
 .../spark/ml/feature/StringIndexerSuite.scala | 32 +++++++++++++++++++
 4 files changed, 73 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index ebfa972532358..e4485eb038409 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -33,7 +33,8 @@ import org.apache.spark.util.collection.OpenHashMap
 /**
  * Base trait for [[StringIndexer]] and [[StringIndexerModel]].
  */
-private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol {
+private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol
+    with HasHandleInvalid {
 
   /** Validates and transforms the input schema. */
   protected def validateAndTransformSchema(schema: StructType): StructType = {
@@ -65,13 +66,16 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
 
   def this() = this(Identifiable.randomUID("strIdx"))
 
+  /** @group setParam */
+  def setHandleInvalid(value: String): this.type = set(handleInvalid, value)
+  setDefault(handleInvalid, "error")
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  // TODO: handle unseen labels
 
   override def fit(dataset: DataFrame): StringIndexerModel = {
     val counts = dataset.select(col($(inputCol)).cast(StringType))
@@ -111,6 +115,10 @@ class StringIndexerModel private[ml] (
     map
   }
 
+  /** @group setParam */
+  def setHandleInvalid(value: String): this.type = set(handleInvalid, value)
+  setDefault(handleInvalid, "error")
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 
@@ -128,14 +136,24 @@ class StringIndexerModel private[ml] (
       if (labelToIndex.contains(label)) {
         labelToIndex(label)
       } else {
-        // TODO: handle unseen labels
         throw new SparkException(s"Unseen label: $label.")
       }
     }
+
     val outputColName = $(outputCol)
     val metadata = NominalAttribute.defaultAttr
       .withName(outputColName).withValues(labels).toMetadata()
-    dataset.select(col("*"),
+    // If we are skipping invalid records, filter them out.
+    val filteredDataset = (getHandleInvalid) match {
+      case "skip" => {
+        val filterer = udf { label: String =>
+          labelToIndex.contains(label)
+        }
+        dataset.where(filterer(dataset($(inputCol))))
+      }
+      case _ => dataset
+    }
+    filteredDataset.select(col("*"),
       indexer(dataset($(inputCol)).cast(StringType)).as(outputColName, metadata))
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index a97c8059b8d45..da4c076830391 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -59,6 +59,10 @@ private[shared] object SharedParamsCodeGen {
       ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)",
         isValid = "ParamValidators.gtEq(1)"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
+      ParamDesc[String]("handleInvalid", "how to handle invalid entries. Options are skip (which " +
+        "will filter out rows with bad values), or error (which will throw an errror). More " +
+        "options may be added later.",
+        isValid = "ParamValidators.inArray(Array(\"skip\", \"error\"))"),
       ParamDesc[Boolean]("standardization", "whether to standardize the training features" +
         " before fitting the model.", Some("true")),
       ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")),
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index f332630c32f1b..23e2b6cc43996 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -247,6 +247,21 @@ private[ml] trait HasFitIntercept extends Params {
   final def getFitIntercept: Boolean = $(fitIntercept)
 }
 
+/**
+ * Trait for shared param handleInvalid.
+ */
+private[ml] trait HasHandleInvalid extends Params {
+
+  /**
+   * Param for how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later..
+   * @group param
+   */
+  final val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.", ParamValidators.inArray(Array("skip", "error")))
+
+  /** @group getParam */
+  final def getHandleInvalid: String = $(handleInvalid)
+}
+
 /**
  * Trait for shared param standardization (default: true).
  */
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index d0295a0fe2fc1..b111036087e6a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.feature
 
+import org.apache.spark.SparkException
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
@@ -62,6 +63,37 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
       reversed2.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
   }
 
+  test("StringIndexerUnseen") {
+    val data = sc.parallelize(Seq((0, "a"), (1, "b"), (4, "b")), 2)
+    val data2 = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c")), 2)
+    val df = sqlContext.createDataFrame(data).toDF("id", "label")
+    val df2 = sqlContext.createDataFrame(data2).toDF("id", "label")
+    val indexer = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("labelIndex")
+      .fit(df)
+    // Verify we throw by default with unseen values
+    intercept[SparkException] {
+      indexer.transform(df2).collect()
+    }
+    val indexerSkipInvalid = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("labelIndex")
+      .setHandleInvalid("skip")
+      .fit(df)
+    // Verify that we skip the c record
+    val transformed = indexerSkipInvalid.transform(df2)
+    val attr = Attribute.fromStructField(transformed.schema("labelIndex"))
+      .asInstanceOf[NominalAttribute]
+    assert(attr.values.get === Array("b", "a"))
+    val output = transformed.select("id", "labelIndex").map { r =>
+      (r.getInt(0), r.getDouble(1))
+    }.collect().toSet
+    // a -> 1, b -> 0
+    val expected = Set((0, 1.0), (1, 0.0))
+    assert(output === expected)
+  }
+
   test("StringIndexer with a numeric input column") {
     val data = sc.parallelize(Seq((0, 100), (1, 200), (2, 300), (3, 100), (4, 100), (5, 300)), 2)
     val df = sqlContext.createDataFrame(data).toDF("id", "label")

From 5b8bb1b213b8738f563fcd00747604410fbb3087 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 11 Aug 2015 12:02:28 -0700
Subject: [PATCH 0967/1454] [SPARK-9572] [STREAMING] [PYSPARK] Added
 StreamingContext.getActiveOrCreate() in Python

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8080 from tdas/SPARK-9572 and squashes the following commits:

64a231d [Tathagata Das] Fix based on comments
741a0d0 [Tathagata Das] Fixed style
f4f094c [Tathagata Das] Tweaked test
9afcdbe [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9572
e21488d [Tathagata Das] Minor update
1a371d9 [Tathagata Das] Addressed comments.
60479da [Tathagata Das] Fixed indent
9c2da9c [Tathagata Das] Fixed bugs
b5bd32c [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9572
b55b348 [Tathagata Das] Removed prints
5781728 [Tathagata Das] Fix style issues
b711214 [Tathagata Das] Reverted run-tests.py
643b59d [Tathagata Das] Revert unnecessary change
150e58c [Tathagata Das] Added StreamingContext.getActiveOrCreate() in Python
---
 python/pyspark/streaming/context.py |  57 +++++++++++-
 python/pyspark/streaming/tests.py   | 133 +++++++++++++++++++++++++---
 python/run-tests.py                 |   2 +-
 3 files changed, 177 insertions(+), 15 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index ac5ba69e8dbbb..e3ba70e4e5e88 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -86,6 +86,9 @@ class StreamingContext(object):
     """
     _transformerSerializer = None
 
+    # Reference to a currently active StreamingContext
+    _activeContext = None
+
     def __init__(self, sparkContext, batchDuration=None, jssc=None):
         """
         Create a new StreamingContext.
@@ -142,10 +145,10 @@ def getOrCreate(cls, checkpointPath, setupFunc):
         Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
         If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be
         recreated from the checkpoint data. If the data does not exist, then the provided setupFunc
-        will be used to create a JavaStreamingContext.
+        will be used to create a new context.
 
-        @param checkpointPath: Checkpoint directory used in an earlier JavaStreamingContext program
-        @param setupFunc:      Function to create a new JavaStreamingContext and setup DStreams
+        @param checkpointPath: Checkpoint directory used in an earlier streaming program
+        @param setupFunc:      Function to create a new context and setup DStreams
         """
         # TODO: support checkpoint in HDFS
         if not os.path.exists(checkpointPath) or not os.listdir(checkpointPath):
@@ -170,6 +173,52 @@ def getOrCreate(cls, checkpointPath, setupFunc):
         cls._transformerSerializer.ctx = sc
         return StreamingContext(sc, None, jssc)
 
+    @classmethod
+    def getActive(cls):
+        """
+        Return either the currently active StreamingContext (i.e., if there is a context started
+        but not stopped) or None.
+        """
+        activePythonContext = cls._activeContext
+        if activePythonContext is not None:
+            # Verify that the current running Java StreamingContext is active and is the same one
+            # backing the supposedly active Python context
+            activePythonContextJavaId = activePythonContext._jssc.ssc().hashCode()
+            activeJvmContextOption = activePythonContext._jvm.StreamingContext.getActive()
+
+            if activeJvmContextOption.isEmpty():
+                cls._activeContext = None
+            elif activeJvmContextOption.get().hashCode() != activePythonContextJavaId:
+                cls._activeContext = None
+                raise Exception("JVM's active JavaStreamingContext is not the JavaStreamingContext "
+                                "backing the action Python StreamingContext. This is unexpected.")
+        return cls._activeContext
+
+    @classmethod
+    def getActiveOrCreate(cls, checkpointPath, setupFunc):
+        """
+        Either return the active StreamingContext (i.e. currently started but not stopped),
+        or recreate a StreamingContext from checkpoint data or create a new StreamingContext
+        using the provided setupFunc function. If the checkpointPath is None or does not contain
+        valid checkpoint data, then setupFunc will be called to create a new context and setup
+        DStreams.
+
+        @param checkpointPath: Checkpoint directory used in an earlier streaming program. Can be
+                               None if the intention is to always create a new context when there
+                               is no active context.
+        @param setupFunc:      Function to create a new JavaStreamingContext and setup DStreams
+        """
+
+        if setupFunc is None:
+            raise Exception("setupFunc cannot be None")
+        activeContext = cls.getActive()
+        if activeContext is not None:
+            return activeContext
+        elif checkpointPath is not None:
+            return cls.getOrCreate(checkpointPath, setupFunc)
+        else:
+            return setupFunc()
+
     @property
     def sparkContext(self):
         """
@@ -182,6 +231,7 @@ def start(self):
         Start the execution of the streams.
         """
         self._jssc.start()
+        StreamingContext._activeContext = self
 
     def awaitTermination(self, timeout=None):
         """
@@ -212,6 +262,7 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
                               of all received data to be completed
         """
         self._jssc.stop(stopSparkContext, stopGraceFully)
+        StreamingContext._activeContext = None
         if stopSparkContext:
             self._sc.stop()
 
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index f0ed415f97120..6108c845c1efe 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -24,6 +24,7 @@
 import tempfile
 import random
 import struct
+import shutil
 from functools import reduce
 
 if sys.version_info[:2] <= (2, 6):
@@ -59,12 +60,21 @@ def setUpClass(cls):
     @classmethod
     def tearDownClass(cls):
         cls.sc.stop()
+        # Clean up in the JVM just in case there has been some issues in Python API
+        jSparkContextOption = SparkContext._jvm.SparkContext.get()
+        if jSparkContextOption.nonEmpty():
+            jSparkContextOption.get().stop()
 
     def setUp(self):
         self.ssc = StreamingContext(self.sc, self.duration)
 
     def tearDown(self):
-        self.ssc.stop(False)
+        if self.ssc is not None:
+            self.ssc.stop(False)
+        # Clean up in the JVM just in case there has been some issues in Python API
+        jStreamingContextOption = StreamingContext._jvm.SparkContext.getActive()
+        if jStreamingContextOption.nonEmpty():
+            jStreamingContextOption.get().stop(False)
 
     def wait_for(self, result, n):
         start_time = time.time()
@@ -442,6 +452,7 @@ def test_reduce_by_invalid_window(self):
 class StreamingContextTests(PySparkStreamingTestCase):
 
     duration = 0.1
+    setupCalled = False
 
     def _add_input_stream(self):
         inputs = [range(1, x) for x in range(101)]
@@ -515,10 +526,85 @@ def func(rdds):
 
         self.assertEqual([2, 3, 1], self._take(dstream, 3))
 
+    def test_get_active(self):
+        self.assertEqual(StreamingContext.getActive(), None)
+
+        # Verify that getActive() returns the active context
+        self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+        self.ssc.start()
+        self.assertEqual(StreamingContext.getActive(), self.ssc)
+
+        # Verify that getActive() returns None
+        self.ssc.stop(False)
+        self.assertEqual(StreamingContext.getActive(), None)
+
+        # Verify that if the Java context is stopped, then getActive() returns None
+        self.ssc = StreamingContext(self.sc, self.duration)
+        self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+        self.ssc.start()
+        self.assertEqual(StreamingContext.getActive(), self.ssc)
+        self.ssc._jssc.stop(False)
+        self.assertEqual(StreamingContext.getActive(), None)
+
+    def test_get_active_or_create(self):
+        # Test StreamingContext.getActiveOrCreate() without checkpoint data
+        # See CheckpointTests for tests with checkpoint data
+        self.ssc = None
+        self.assertEqual(StreamingContext.getActive(), None)
+
+        def setupFunc():
+            ssc = StreamingContext(self.sc, self.duration)
+            ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+            self.setupCalled = True
+            return ssc
+
+        # Verify that getActiveOrCreate() (w/o checkpoint) calls setupFunc when no context is active
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc)
+        self.assertTrue(self.setupCalled)
+
+        # Verify that getActiveOrCreate() retuns active context and does not call the setupFunc
+        self.ssc.start()
+        self.setupCalled = False
+        self.assertEqual(StreamingContext.getActiveOrCreate(None, setupFunc), self.ssc)
+        self.assertFalse(self.setupCalled)
+
+        # Verify that getActiveOrCreate() calls setupFunc after active context is stopped
+        self.ssc.stop(False)
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc)
+        self.assertTrue(self.setupCalled)
+
+        # Verify that if the Java context is stopped, then getActive() returns None
+        self.ssc = StreamingContext(self.sc, self.duration)
+        self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+        self.ssc.start()
+        self.assertEqual(StreamingContext.getActive(), self.ssc)
+        self.ssc._jssc.stop(False)
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc)
+        self.assertTrue(self.setupCalled)
+
 
 class CheckpointTests(unittest.TestCase):
 
-    def test_get_or_create(self):
+    setupCalled = False
+
+    @staticmethod
+    def tearDownClass():
+        # Clean up in the JVM just in case there has been some issues in Python API
+        jStreamingContextOption = StreamingContext._jvm.SparkContext.getActive()
+        if jStreamingContextOption.nonEmpty():
+            jStreamingContextOption.get().stop()
+        jSparkContextOption = SparkContext._jvm.SparkContext.get()
+        if jSparkContextOption.nonEmpty():
+            jSparkContextOption.get().stop()
+
+    def tearDown(self):
+        if self.ssc is not None:
+            self.ssc.stop(True)
+
+    def test_get_or_create_and_get_active_or_create(self):
         inputd = tempfile.mkdtemp()
         outputd = tempfile.mkdtemp() + "/"
 
@@ -533,11 +619,12 @@ def setup():
             wc = dstream.updateStateByKey(updater)
             wc.map(lambda x: "%s,%d" % x).saveAsTextFiles(outputd + "test")
             wc.checkpoint(.5)
+            self.setupCalled = True
             return ssc
 
         cpd = tempfile.mkdtemp("test_streaming_cps")
-        ssc = StreamingContext.getOrCreate(cpd, setup)
-        ssc.start()
+        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        self.ssc.start()
 
         def check_output(n):
             while not os.listdir(outputd):
@@ -552,7 +639,7 @@ def check_output(n):
                     # not finished
                     time.sleep(0.01)
                     continue
-                ordd = ssc.sparkContext.textFile(p).map(lambda line: line.split(","))
+                ordd = self.ssc.sparkContext.textFile(p).map(lambda line: line.split(","))
                 d = ordd.values().map(int).collect()
                 if not d:
                     time.sleep(0.01)
@@ -568,13 +655,37 @@ def check_output(n):
 
         check_output(1)
         check_output(2)
-        ssc.stop(True, True)
 
+        # Verify the getOrCreate() recovers from checkpoint files
+        self.ssc.stop(True, True)
         time.sleep(1)
-        ssc = StreamingContext.getOrCreate(cpd, setup)
-        ssc.start()
+        self.setupCalled = False
+        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.ssc.start()
         check_output(3)
-        ssc.stop(True, True)
+
+        # Verify the getActiveOrCreate() recovers from checkpoint files
+        self.ssc.stop(True, True)
+        time.sleep(1)
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.ssc.start()
+        check_output(4)
+
+        # Verify that getActiveOrCreate() returns active context
+        self.setupCalled = False
+        self.assertEquals(StreamingContext.getActiveOrCreate(cpd, setup), self.ssc)
+        self.assertFalse(self.setupCalled)
+
+        # Verify that getActiveOrCreate() calls setup() in absence of checkpoint files
+        self.ssc.stop(True, True)
+        shutil.rmtree(cpd)  # delete checkpoint directory
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.assertTrue(self.setupCalled)
+        self.ssc.stop(True, True)
 
 
 class KafkaStreamTests(PySparkStreamingTestCase):
@@ -1134,7 +1245,7 @@ def search_kinesis_asl_assembly_jar():
         testcases.append(KinesisStreamTests)
     elif are_kinesis_tests_enabled is False:
         sys.stderr.write("Skipping all Kinesis Python tests as the optional Kinesis project was "
-                         "not compiled with -Pkinesis-asl profile. To run these tests, "
+                         "not compiled into a JAR. To run these tests, "
                          "you need to build Spark with 'build/sbt -Pkinesis-asl assembly/assembly "
                          "streaming-kinesis-asl-assembly/assembly' or "
                          "'build/mvn -Pkinesis-asl package' before running this test.")
@@ -1150,4 +1261,4 @@ def search_kinesis_asl_assembly_jar():
     for testcase in testcases:
         sys.stderr.write("[Running %s]\n" % (testcase))
         tests = unittest.TestLoader().loadTestsFromTestCase(testcase)
-        unittest.TextTestRunner(verbosity=2).run(tests)
+        unittest.TextTestRunner(verbosity=3).run(tests)
diff --git a/python/run-tests.py b/python/run-tests.py
index cc560779373b3..fd56c7ab6e0e2 100755
--- a/python/run-tests.py
+++ b/python/run-tests.py
@@ -158,7 +158,7 @@ def main():
     else:
         log_level = logging.INFO
     logging.basicConfig(stream=sys.stdout, level=log_level, format="%(message)s")
-    LOGGER.info("Running PySpark tests. Output is in python/%s", LOG_FILE)
+    LOGGER.info("Running PySpark tests. Output is in %s", LOG_FILE)
     if os.path.exists(LOG_FILE):
         os.remove(LOG_FILE)
     python_execs = opts.python_executables.split(',')

From 5831294a7a8fa2524133c5d718cbc8187d2b0620 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 11 Aug 2015 12:39:13 -0700
Subject: [PATCH 0968/1454] [SPARK-9646] [SQL] Add metrics for all join and
 aggregate operators

This PR added metrics for all join and aggregate operators. However, I found the metrics may be confusing in the following two case:
1. The iterator is not totally consumed and the metric values will be less.
2. Recreating the iterators will make metric values look bigger than the size of the input source, such as `CartesianProduct`.

Author: zsxwing <zsxwing@gmail.com>

Closes #8060 from zsxwing/sql-metrics and squashes the following commits:

40f3fc1 [zsxwing] Mark LongSQLMetric private[metric] to avoid using incorrectly and leak memory
b1b9071 [zsxwing] Merge branch 'master' into sql-metrics
4bef25a [zsxwing] Add metrics for SortMergeOuterJoin
95ccfc6 [zsxwing] Merge branch 'master' into sql-metrics
67cb4dd [zsxwing] Add metrics for Project and TungstenProject; remove metrics from PhysicalRDD and LocalTableScan
0eb47d4 [zsxwing] Merge branch 'master' into sql-metrics
dd9d932 [zsxwing] Avoid creating new Iterators
589ea26 [zsxwing] Add metrics for all join and aggregate operators
---
 .../spark/sql/execution/Aggregate.scala       |  11 +
 .../spark/sql/execution/ExistingRDD.scala     |   2 -
 .../spark/sql/execution/LocalTableScan.scala  |   2 -
 .../spark/sql/execution/SparkPlan.scala       |  25 +-
 .../aggregate/SortBasedAggregate.scala        |  12 +-
 .../SortBasedAggregationIterator.scala        |  18 +-
 .../aggregate/TungstenAggregate.scala         |  12 +-
 .../TungstenAggregationIterator.scala         |  11 +-
 .../spark/sql/execution/basicOperators.scala  |  36 +-
 .../execution/joins/BroadcastHashJoin.scala   |  30 +-
 .../joins/BroadcastHashOuterJoin.scala        |  40 +-
 .../joins/BroadcastLeftSemiJoinHash.scala     |  24 +-
 .../joins/BroadcastNestedLoopJoin.scala       |  27 +-
 .../execution/joins/CartesianProduct.scala    |  25 +-
 .../spark/sql/execution/joins/HashJoin.scala  |   7 +-
 .../sql/execution/joins/HashOuterJoin.scala   |  30 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  23 +-
 .../sql/execution/joins/HashedRelation.scala  |   8 +-
 .../sql/execution/joins/LeftSemiJoinBNL.scala |  19 +-
 .../execution/joins/LeftSemiJoinHash.scala    |  18 +-
 .../execution/joins/ShuffledHashJoin.scala    |  16 +-
 .../joins/ShuffledHashOuterJoin.scala         |  29 +-
 .../sql/execution/joins/SortMergeJoin.scala   |  21 +-
 .../execution/joins/SortMergeOuterJoin.scala  |  38 +-
 .../sql/execution/metric/SQLMetrics.scala     |   6 +
 .../execution/joins/HashedRelationSuite.scala |  14 +-
 .../execution/metric/SQLMetricsSuite.scala    | 450 +++++++++++++++++-
 27 files changed, 847 insertions(+), 107 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index e8c6a0f8f801d..f3b6a3a5f4a33 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -45,6 +46,10 @@ case class Aggregate(
     child: SparkPlan)
   extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def requiredChildDistribution: List[Distribution] = {
     if (partial) {
       UnspecifiedDistribution :: Nil
@@ -121,12 +126,15 @@ case class Aggregate(
   }
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     if (groupingExpressions.isEmpty) {
       child.execute().mapPartitions { iter =>
         val buffer = newAggregateBuffer()
         var currentRow: InternalRow = null
         while (iter.hasNext) {
           currentRow = iter.next()
+          numInputRows += 1
           var i = 0
           while (i < buffer.length) {
             buffer(i).update(currentRow)
@@ -142,6 +150,7 @@ case class Aggregate(
           i += 1
         }
 
+        numOutputRows += 1
         Iterator(resultProjection(aggregateResults))
       }
     } else {
@@ -152,6 +161,7 @@ case class Aggregate(
         var currentRow: InternalRow = null
         while (iter.hasNext) {
           currentRow = iter.next()
+          numInputRows += 1
           val currentGroup = groupingProjection(currentRow)
           var currentBuffer = hashTable.get(currentGroup)
           if (currentBuffer == null) {
@@ -180,6 +190,7 @@ case class Aggregate(
             val currentEntry = hashTableIter.next()
             val currentGroup = currentEntry.getKey
             val currentBuffer = currentEntry.getValue
+            numOutputRows += 1
 
             var i = 0
             while (i < currentBuffer.length) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
index cae7ca5cbdc88..abb60cf12e3a5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
@@ -99,8 +99,6 @@ private[sql] case class PhysicalRDD(
     rdd: RDD[InternalRow],
     extraInformation: String) extends LeafNode {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
-
   protected override def doExecute(): RDD[InternalRow] = rdd
 
   override def simpleString: String = "Scan " + extraInformation + output.mkString("[", ",", "]")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
index 858dd85fd1fa6..34e926e4582be 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
@@ -30,8 +30,6 @@ private[sql] case class LocalTableScan(
     output: Seq[Attribute],
     rows: Seq[InternalRow]) extends LeafNode {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
-
   private lazy val rdd = sqlContext.sparkContext.parallelize(rows)
 
   protected override def doExecute(): RDD[InternalRow] = rdd
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 9ba5cf2d2b39e..72f5450510a10 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -80,23 +80,10 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     super.makeCopy(newArgs)
   }
 
-  /**
-   * Whether track the number of rows output by this SparkPlan
-   */
-  protected[sql] def trackNumOfRowsEnabled: Boolean = false
-
-  private lazy val defaultMetrics: Map[String, SQLMetric[_, _]] =
-    if (trackNumOfRowsEnabled) {
-      Map("numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
-    }
-    else {
-      Map.empty
-    }
-
   /**
    * Return all metrics containing metrics of this SparkPlan.
    */
-  private[sql] def metrics: Map[String, SQLMetric[_, _]] = defaultMetrics
+  private[sql] def metrics: Map[String, SQLMetric[_, _]] = Map.empty
 
   /**
    * Return a LongSQLMetric according to the name.
@@ -150,15 +137,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     }
     RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
       prepare()
-      if (trackNumOfRowsEnabled) {
-        val numRows = longMetric("numRows")
-        doExecute().map { row =>
-          numRows += 1
-          row
-        }
-      } else {
-        doExecute()
-      }
+      doExecute()
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
index ad428ad663f30..ab26f9c58aa2e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
 import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan, UnaryNode}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 
 case class SortBasedAggregate(
@@ -38,6 +39,10 @@ case class SortBasedAggregate(
     child: SparkPlan)
   extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputsUnsafeRows: Boolean = false
 
   override def canProcessUnsafeRows: Boolean = false
@@ -63,6 +68,8 @@ case class SortBasedAggregate(
   }
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     child.execute().mapPartitions { iter =>
       // Because the constructor of an aggregation iterator will read at least the first row,
       // we need to get the value of iter.hasNext first.
@@ -84,10 +91,13 @@ case class SortBasedAggregate(
           newProjection _,
           child.output,
           iter,
-          outputsUnsafeRows)
+          outputsUnsafeRows,
+          numInputRows,
+          numOutputRows)
         if (!hasInput && groupingExpressions.isEmpty) {
           // There is no input and there is no grouping expressions.
           // We need to output a single row as the output.
+          numOutputRows += 1
           Iterator[InternalRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
         } else {
           outputIter
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
index 67ebafde25ad3..73d50e07cf0b5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2, AggregateFunction2}
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.unsafe.KVIterator
 
 /**
@@ -37,7 +38,9 @@ class SortBasedAggregationIterator(
     initialInputBufferOffset: Int,
     resultExpressions: Seq[NamedExpression],
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    outputsUnsafeRows: Boolean)
+    outputsUnsafeRows: Boolean,
+    numInputRows: LongSQLMetric,
+    numOutputRows: LongSQLMetric)
   extends AggregationIterator(
     groupingKeyAttributes,
     valueAttributes,
@@ -103,6 +106,7 @@ class SortBasedAggregationIterator(
       // Get the grouping key.
       val groupingKey = inputKVIterator.getKey
       val currentRow = inputKVIterator.getValue
+      numInputRows += 1
 
       // Check if the current row belongs the current input row.
       if (currentGroupingKey == groupingKey) {
@@ -137,7 +141,7 @@ class SortBasedAggregationIterator(
       val outputRow = generateOutput(currentGroupingKey, sortBasedAggregationBuffer)
       // Initialize buffer values for the next group.
       initializeBuffer(sortBasedAggregationBuffer)
-
+      numOutputRows += 1
       outputRow
     } else {
       // no more result
@@ -151,7 +155,7 @@ class SortBasedAggregationIterator(
 
       nextGroupingKey = inputKVIterator.getKey().copy()
       firstRowInNextGroup = inputKVIterator.getValue().copy()
-
+      numInputRows += 1
       sortedInputHasNewGroup = true
     } else {
       // This inputIter is empty.
@@ -181,7 +185,9 @@ object SortBasedAggregationIterator {
       newProjection: (Seq[Expression], Seq[Attribute]) => Projection,
       inputAttributes: Seq[Attribute],
       inputIter: Iterator[InternalRow],
-      outputsUnsafeRows: Boolean): SortBasedAggregationIterator = {
+      outputsUnsafeRows: Boolean,
+      numInputRows: LongSQLMetric,
+      numOutputRows: LongSQLMetric): SortBasedAggregationIterator = {
     val kvIterator = if (UnsafeProjection.canSupport(groupingExprs)) {
       AggregationIterator.unsafeKVIterator(
         groupingExprs,
@@ -202,7 +208,9 @@ object SortBasedAggregationIterator {
       initialInputBufferOffset,
       resultExpressions,
       newMutableProjection,
-      outputsUnsafeRows)
+      outputsUnsafeRows,
+      numInputRows,
+      numOutputRows)
   }
   // scalastyle:on
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 1694794a53d9f..6b5935a7ce296 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 case class TungstenAggregate(
     requiredChildDistributionExpressions: Option[Seq[Expression]],
@@ -35,6 +36,10 @@ case class TungstenAggregate(
     child: SparkPlan)
   extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputsUnsafeRows: Boolean = true
 
   override def canProcessUnsafeRows: Boolean = true
@@ -61,6 +66,8 @@ case class TungstenAggregate(
   }
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     child.execute().mapPartitions { iter =>
       val hasInput = iter.hasNext
       if (!hasInput && groupingExpressions.nonEmpty) {
@@ -78,9 +85,12 @@ case class TungstenAggregate(
             newMutableProjection,
             child.output,
             iter,
-            testFallbackStartsAt)
+            testFallbackStartsAt,
+            numInputRows,
+            numOutputRows)
 
         if (!hasInput && groupingExpressions.isEmpty) {
+          numOutputRows += 1
           Iterator.single[UnsafeRow](aggregationIterator.outputForEmptyGroupingKeyWithoutInput())
         } else {
           aggregationIterator
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 32160906c3bc8..1f383dd04482f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.sql.types.StructType
 
 /**
@@ -83,7 +84,9 @@ class TungstenAggregationIterator(
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
     originalInputAttributes: Seq[Attribute],
     inputIter: Iterator[InternalRow],
-    testFallbackStartsAt: Option[Int])
+    testFallbackStartsAt: Option[Int],
+    numInputRows: LongSQLMetric,
+    numOutputRows: LongSQLMetric)
   extends Iterator[UnsafeRow] with Logging {
 
   ///////////////////////////////////////////////////////////////////////////
@@ -352,6 +355,7 @@ class TungstenAggregationIterator(
   private def processInputs(): Unit = {
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
+      numInputRows += 1
       val groupingKey = groupProjection.apply(newInput)
       val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
       if (buffer == null) {
@@ -371,6 +375,7 @@ class TungstenAggregationIterator(
     var i = 0
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
+      numInputRows += 1
       val groupingKey = groupProjection.apply(newInput)
       val buffer: UnsafeRow = if (i < fallbackStartsAt) {
         hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
@@ -439,6 +444,7 @@ class TungstenAggregationIterator(
       // Process the rest of input rows.
       while (inputIter.hasNext) {
         val newInput = inputIter.next()
+        numInputRows += 1
         val groupingKey = groupProjection.apply(newInput)
         buffer.copyFrom(initialAggregationBuffer)
         processRow(buffer, newInput)
@@ -462,6 +468,7 @@ class TungstenAggregationIterator(
       // Insert the rest of input rows.
       while (inputIter.hasNext) {
         val newInput = inputIter.next()
+        numInputRows += 1
         val groupingKey = groupProjection.apply(newInput)
         bufferExtractor(newInput)
         externalSorter.insertKV(groupingKey, buffer)
@@ -657,7 +664,7 @@ class TungstenAggregationIterator(
         TaskContext.get().internalMetricsToAccumulators(
           InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemory)
       }
-
+      numOutputRows += 1
       res
     } else {
       // no more result
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index bf2de244c8e4a..247c900baae9e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -41,11 +41,20 @@ import org.apache.spark.{HashPartitioner, SparkEnv}
 case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
+  override private[sql] lazy val metrics = Map(
+    "numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
+
   @transient lazy val buildProjection = newMutableProjection(projectList, child.output)
 
-  protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    val reusableProjection = buildProjection()
-    iter.map(reusableProjection)
+  protected override def doExecute(): RDD[InternalRow] = {
+    val numRows = longMetric("numRows")
+    child.execute().mapPartitions { iter =>
+      val reusableProjection = buildProjection()
+      iter.map { row =>
+        numRows += 1
+        reusableProjection(row)
+      }
+    }
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
@@ -57,19 +66,28 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends
  */
 case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
+
   override def outputsUnsafeRows: Boolean = true
   override def canProcessUnsafeRows: Boolean = true
   override def canProcessSafeRows: Boolean = true
 
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
-  protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    this.transformAllExpressions {
-      case CreateStruct(children) => CreateStructUnsafe(children)
-      case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
+  protected override def doExecute(): RDD[InternalRow] = {
+    val numRows = longMetric("numRows")
+    child.execute().mapPartitions { iter =>
+      this.transformAllExpressions {
+        case CreateStruct(children) => CreateStructUnsafe(children)
+        case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
+      }
+      val project = UnsafeProjection.create(projectList, child.output)
+      iter.map { row =>
+        numRows += 1
+        project(row)
+      }
     }
-    val project = UnsafeProjection.create(projectList, child.output)
-    iter.map(project)
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index f7a68e4f5d445..2e108cb814516 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
 import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.util.ThreadUtils
 import org.apache.spark.{InternalAccumulator, TaskContext}
 
@@ -45,7 +46,10 @@ case class BroadcastHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
 
   val timeout: Duration = {
     val timeoutValue = sqlContext.conf.broadcastTimeout
@@ -65,6 +69,11 @@ case class BroadcastHashJoin(
   // for the same query.
   @transient
   private lazy val broadcastFuture = {
+    val numBuildRows = buildSide match {
+      case BuildLeft => longMetric("numLeftRows")
+      case BuildRight => longMetric("numRightRows")
+    }
+
     // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
     val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
     future {
@@ -73,8 +82,15 @@ case class BroadcastHashJoin(
       SQLExecution.withExecutionId(sparkContext, executionId) {
         // Note that we use .execute().collect() because we don't want to convert data to Scala
         // types
-        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-        val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.size)
+        val input: Array[InternalRow] = buildPlan.execute().map { row =>
+          numBuildRows += 1
+          row.copy()
+        }.collect()
+        // The following line doesn't run in a job so we cannot track the metric value. However, we
+        // have already tracked it in the above lines. So here we can use
+        // `SQLMetrics.nullLongMetric` to ignore it.
+        val hashed = HashedRelation(
+          input.iterator, SQLMetrics.nullLongMetric, buildSideKeyGenerator, input.size)
         sparkContext.broadcast(hashed)
       }
     }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
@@ -85,6 +101,12 @@ case class BroadcastHashJoin(
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numStreamedRows = buildSide match {
+      case BuildLeft => longMetric("numRightRows")
+      case BuildRight => longMetric("numLeftRows")
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastRelation = Await.result(broadcastFuture, timeout)
 
     streamedPlan.execute().mapPartitions { streamedIter =>
@@ -95,7 +117,7 @@ case class BroadcastHashJoin(
             InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
         case _ =>
       }
-      hashJoin(streamedIter, hashedRelation)
+      hashJoin(streamedIter, numStreamedRows, hashedRelation, numOutputRows)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index a3626de49aeab..69a8b95eaa7ec 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -27,6 +27,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
@@ -45,6 +46,11 @@ case class BroadcastHashOuterJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode with HashOuterJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   val timeout = {
     val timeoutValue = sqlContext.conf.broadcastTimeout
     if (timeoutValue < 0) {
@@ -63,6 +69,14 @@ case class BroadcastHashOuterJoin(
   // for the same query.
   @transient
   private lazy val broadcastFuture = {
+    val numBuildRows = joinType match {
+      case RightOuter => longMetric("numLeftRows")
+      case LeftOuter => longMetric("numRightRows")
+      case x =>
+        throw new IllegalArgumentException(
+          s"HashOuterJoin should not take $x as the JoinType")
+    }
+
     // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
     val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
     future {
@@ -71,8 +85,15 @@ case class BroadcastHashOuterJoin(
       SQLExecution.withExecutionId(sparkContext, executionId) {
         // Note that we use .execute().collect() because we don't want to convert data to Scala
         // types
-        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-        val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
+        val input: Array[InternalRow] = buildPlan.execute().map { row =>
+          numBuildRows += 1
+          row.copy()
+        }.collect()
+        // The following line doesn't run in a job so we cannot track the metric value. However, we
+        // have already tracked it in the above lines. So here we can use
+        // `SQLMetrics.nullLongMetric` to ignore it.
+        val hashed = HashedRelation(
+          input.iterator, SQLMetrics.nullLongMetric, buildKeyGenerator, input.size)
         sparkContext.broadcast(hashed)
       }
     }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
@@ -83,6 +104,15 @@ case class BroadcastHashOuterJoin(
   }
 
   override def doExecute(): RDD[InternalRow] = {
+    val numStreamedRows = joinType match {
+      case RightOuter => longMetric("numRightRows")
+      case LeftOuter => longMetric("numLeftRows")
+      case x =>
+        throw new IllegalArgumentException(
+          s"HashOuterJoin should not take $x as the JoinType")
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastRelation = Await.result(broadcastFuture, timeout)
 
     streamedPlan.execute().mapPartitions { streamedIter =>
@@ -101,16 +131,18 @@ case class BroadcastHashOuterJoin(
       joinType match {
         case LeftOuter =>
           streamedIter.flatMap(currentRow => {
+            numStreamedRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey), resultProj)
+            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey), resultProj, numOutputRows)
           })
 
         case RightOuter =>
           streamedIter.flatMap(currentRow => {
+            numStreamedRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow, resultProj)
+            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow, resultProj, numOutputRows)
           })
 
         case x =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index 5bd06fbdca605..78a8c16c62bca 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -23,6 +23,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -37,18 +38,31 @@ case class BroadcastLeftSemiJoinHash(
     right: SparkPlan,
     condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   protected override def doExecute(): RDD[InternalRow] = {
-    val input = right.execute().map(_.copy()).collect()
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
+    val input = right.execute().map { row =>
+      numRightRows += 1
+      row.copy()
+    }.collect()
 
     if (condition.isEmpty) {
-      val hashSet = buildKeyHashSet(input.toIterator)
+      val hashSet = buildKeyHashSet(input.toIterator, SQLMetrics.nullLongMetric)
       val broadcastedRelation = sparkContext.broadcast(hashSet)
 
       left.execute().mapPartitions { streamIter =>
-        hashSemiJoin(streamIter, broadcastedRelation.value)
+        hashSemiJoin(streamIter, numLeftRows, broadcastedRelation.value, numOutputRows)
       }
     } else {
-      val hashRelation = HashedRelation(input.toIterator, rightKeyGenerator, input.size)
+      val hashRelation =
+        HashedRelation(input.toIterator, SQLMetrics.nullLongMetric, rightKeyGenerator, input.size)
       val broadcastedRelation = sparkContext.broadcast(hashRelation)
 
       left.execute().mapPartitions { streamIter =>
@@ -59,7 +73,7 @@ case class BroadcastLeftSemiJoinHash(
               InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
           case _ =>
         }
-        hashSemiJoin(streamIter, hashedRelation)
+        hashSemiJoin(streamIter, numLeftRows, hashedRelation, numOutputRows)
       }
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 017a44b9ca863..28c88b1b03d02 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.util.collection.CompactBuffer
 
 /**
@@ -38,6 +39,11 @@ case class BroadcastNestedLoopJoin(
     condition: Option[Expression]) extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   /** BuildRight means the right relation <=> the broadcast relation. */
   private val (streamed, broadcast) = buildSide match {
     case BuildRight => (left, right)
@@ -75,9 +81,17 @@ case class BroadcastNestedLoopJoin(
     newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val (numStreamedRows, numBuildRows) = buildSide match {
+      case BuildRight => (longMetric("numLeftRows"), longMetric("numRightRows"))
+      case BuildLeft => (longMetric("numRightRows"), longMetric("numLeftRows"))
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastedRelation =
-      sparkContext.broadcast(broadcast.execute().map(_.copy())
-        .collect().toIndexedSeq)
+      sparkContext.broadcast(broadcast.execute().map { row =>
+        numBuildRows += 1
+        row.copy()
+      }.collect().toIndexedSeq)
 
     /** All rows that either match both-way, or rows from streamed joined with nulls. */
     val matchesOrStreamedRowsWithNulls = streamed.execute().mapPartitions { streamedIter =>
@@ -94,6 +108,7 @@ case class BroadcastNestedLoopJoin(
       streamedIter.foreach { streamedRow =>
         var i = 0
         var streamRowMatched = false
+        numStreamedRows += 1
 
         while (i < broadcastedRelation.value.size) {
           val broadcastedRow = broadcastedRelation.value(i)
@@ -162,6 +177,12 @@ case class BroadcastNestedLoopJoin(
 
     // TODO: Breaks lineage.
     sparkContext.union(
-      matchesOrStreamedRowsWithNulls.flatMap(_._1), sparkContext.makeRDD(broadcastRowsWithNulls))
+      matchesOrStreamedRowsWithNulls.flatMap(_._1),
+      sparkContext.makeRDD(broadcastRowsWithNulls)
+    ).map { row =>
+      // `broadcastRowsWithNulls` doesn't run in a job so that we have to track numOutputRows here.
+      numOutputRows += 1
+      row
+    }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala
index 261b4724159fb..2115f40702286 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala
@@ -22,6 +22,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, JoinedRow}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -30,13 +31,31 @@ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   override def output: Seq[Attribute] = left.output ++ right.output
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   protected override def doExecute(): RDD[InternalRow] = {
-    val leftResults = left.execute().map(_.copy())
-    val rightResults = right.execute().map(_.copy())
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
+    val leftResults = left.execute().map { row =>
+      numLeftRows += 1
+      row.copy()
+    }
+    val rightResults = right.execute().map { row =>
+      numRightRows += 1
+      row.copy()
+    }
 
     leftResults.cartesian(rightResults).mapPartitions { iter =>
       val joinedRow = new JoinedRow
-      iter.map(r => joinedRow(r._1, r._2))
+      iter.map { r =>
+        numOutputRows += 1
+        joinedRow(r._1, r._2)
+      }
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 22d46d1c3e3b7..7ce4a517838cb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 
 
 trait HashJoin {
@@ -69,7 +70,9 @@ trait HashJoin {
 
   protected def hashJoin(
       streamIter: Iterator[InternalRow],
-      hashedRelation: HashedRelation): Iterator[InternalRow] =
+      numStreamRows: LongSQLMetric,
+      hashedRelation: HashedRelation,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] =
   {
     new Iterator[InternalRow] {
       private[this] var currentStreamedRow: InternalRow = _
@@ -98,6 +101,7 @@ trait HashJoin {
           case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow)
         }
         currentMatchPosition += 1
+        numOutputRows += 1
         resultProjection(ret)
       }
 
@@ -113,6 +117,7 @@ trait HashJoin {
 
         while (currentHashMatches == null && streamIter.hasNext) {
           currentStreamedRow = streamIter.next()
+          numStreamRows += 1
           val key = joinKeys(currentStreamedRow)
           if (!key.anyNull) {
             currentHashMatches = hashedRelation.get(key)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 701bd3cd86372..66903347c88c1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.util.collection.CompactBuffer
 
 @DeveloperApi
@@ -114,22 +115,28 @@ trait HashOuterJoin {
       key: InternalRow,
       joinedRow: JoinedRow,
       rightIter: Iterable[InternalRow],
-      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
+      resultProjection: InternalRow => InternalRow,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (rightIter != null) {
           rightIter.collect {
-            case r if boundCondition(joinedRow.withRight(r)) => resultProjection(joinedRow).copy()
+            case r if boundCondition(joinedRow.withRight(r)) => {
+              numOutputRows += 1
+              resultProjection(joinedRow).copy()
+            }
           }
         } else {
           List.empty
         }
         if (temp.isEmpty) {
+          numOutputRows += 1
           resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
         } else {
           temp
         }
       } else {
+        numOutputRows += 1
         resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
       }
     }
@@ -140,22 +147,28 @@ trait HashOuterJoin {
       key: InternalRow,
       leftIter: Iterable[InternalRow],
       joinedRow: JoinedRow,
-      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
+      resultProjection: InternalRow => InternalRow,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (leftIter != null) {
           leftIter.collect {
-            case l if boundCondition(joinedRow.withLeft(l)) => resultProjection(joinedRow).copy()
+            case l if boundCondition(joinedRow.withLeft(l)) => {
+              numOutputRows += 1
+              resultProjection(joinedRow).copy()
+            }
           }
         } else {
           List.empty
         }
         if (temp.isEmpty) {
+          numOutputRows += 1
           resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
         } else {
           temp
         }
       } else {
+        numOutputRows += 1
         resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
       }
     }
@@ -164,7 +177,7 @@ trait HashOuterJoin {
 
   protected[this] def fullOuterIterator(
       key: InternalRow, leftIter: Iterable[InternalRow], rightIter: Iterable[InternalRow],
-      joinedRow: JoinedRow): Iterator[InternalRow] = {
+      joinedRow: JoinedRow, numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     if (!key.anyNull) {
       // Store the positions of records in right, if one of its associated row satisfy
       // the join condition.
@@ -177,6 +190,7 @@ trait HashOuterJoin {
           //    append them directly
 
           case (r, idx) if boundCondition(joinedRow.withRight(r)) =>
+            numOutputRows += 1
             matched = true
             // if the row satisfy the join condition, add its index into the matched set
             rightMatchedSet.add(idx)
@@ -189,6 +203,7 @@ trait HashOuterJoin {
           // as we don't know whether we need to append it until finish iterating all
           // of the records in right side.
           // If we didn't get any proper row, then append a single row with empty right.
+          numOutputRows += 1
           joinedRow.withRight(rightNullRow).copy()
         })
       } ++ rightIter.zipWithIndex.collect {
@@ -197,12 +212,15 @@ trait HashOuterJoin {
         // Re-visiting the records in right, and append additional row with empty left, if its not
         // in the matched set.
         case (r, idx) if !rightMatchedSet.contains(idx) =>
+          numOutputRows += 1
           joinedRow(leftNullRow, r).copy()
       }
     } else {
       leftIter.iterator.map[InternalRow] { l =>
+        numOutputRows += 1
         joinedRow(l, rightNullRow).copy()
       } ++ rightIter.iterator.map[InternalRow] { r =>
+        numOutputRows += 1
         joinedRow(leftNullRow, r).copy()
       }
     }
@@ -211,10 +229,12 @@ trait HashOuterJoin {
   // This is only used by FullOuter
   protected[this] def buildHashTable(
       iter: Iterator[InternalRow],
+      numIterRows: LongSQLMetric,
       keyGenerator: Projection): JavaHashMap[InternalRow, CompactBuffer[InternalRow]] = {
     val hashTable = new JavaHashMap[InternalRow, CompactBuffer[InternalRow]]()
     while (iter.hasNext) {
       val currentRow = iter.next()
+      numIterRows += 1
       val rowKey = keyGenerator(currentRow)
 
       var existingMatchList = hashTable.get(rowKey)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 82dd6eb7e7ed0..beb141ade616d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 
 
 trait HashSemiJoin {
@@ -61,13 +62,15 @@ trait HashSemiJoin {
   @transient private lazy val boundCondition =
     newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
-  protected def buildKeyHashSet(buildIter: Iterator[InternalRow]): java.util.Set[InternalRow] = {
+  protected def buildKeyHashSet(
+      buildIter: Iterator[InternalRow], numBuildRows: LongSQLMetric): java.util.Set[InternalRow] = {
     val hashSet = new java.util.HashSet[InternalRow]()
 
     // Create a Hash set of buildKeys
     val rightKey = rightKeyGenerator
     while (buildIter.hasNext) {
       val currentRow = buildIter.next()
+      numBuildRows += 1
       val rowKey = rightKey(currentRow)
       if (!rowKey.anyNull) {
         val keyExists = hashSet.contains(rowKey)
@@ -82,25 +85,35 @@ trait HashSemiJoin {
 
   protected def hashSemiJoin(
     streamIter: Iterator[InternalRow],
-    hashSet: java.util.Set[InternalRow]): Iterator[InternalRow] = {
+    numStreamRows: LongSQLMetric,
+    hashSet: java.util.Set[InternalRow],
+    numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val joinKeys = leftKeyGenerator
     streamIter.filter(current => {
+      numStreamRows += 1
       val key = joinKeys(current)
-      !key.anyNull && hashSet.contains(key)
+      val r = !key.anyNull && hashSet.contains(key)
+      if (r) numOutputRows += 1
+      r
     })
   }
 
   protected def hashSemiJoin(
       streamIter: Iterator[InternalRow],
-      hashedRelation: HashedRelation): Iterator[InternalRow] = {
+      numStreamRows: LongSQLMetric,
+      hashedRelation: HashedRelation,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val joinKeys = leftKeyGenerator
     val joinedRow = new JoinedRow
     streamIter.filter { current =>
+      numStreamRows += 1
       val key = joinKeys(current)
       lazy val rowBuffer = hashedRelation.get(key)
-      !key.anyNull && rowBuffer != null && rowBuffer.exists {
+      val r = !key.anyNull && rowBuffer != null && rowBuffer.exists {
         (row: InternalRow) => boundCondition(joinedRow(current, row))
       }
+      if (r) numOutputRows += 1
+      r
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 63d35d0f02622..c1bc7947aa39c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -25,6 +25,7 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
@@ -112,11 +113,13 @@ private[joins] object HashedRelation {
 
   def apply(
       input: Iterator[InternalRow],
+      numInputRows: LongSQLMetric,
       keyGenerator: Projection,
       sizeEstimate: Int = 64): HashedRelation = {
 
     if (keyGenerator.isInstanceOf[UnsafeProjection]) {
-      return UnsafeHashedRelation(input, keyGenerator.asInstanceOf[UnsafeProjection], sizeEstimate)
+      return UnsafeHashedRelation(
+        input, numInputRows, keyGenerator.asInstanceOf[UnsafeProjection], sizeEstimate)
     }
 
     // TODO: Use Spark's HashMap implementation.
@@ -130,6 +133,7 @@ private[joins] object HashedRelation {
     // Create a mapping of buildKeys -> rows
     while (input.hasNext) {
       currentRow = input.next()
+      numInputRows += 1
       val rowKey = keyGenerator(currentRow)
       if (!rowKey.anyNull) {
         val existingMatchList = hashTable.get(rowKey)
@@ -331,6 +335,7 @@ private[joins] object UnsafeHashedRelation {
 
   def apply(
       input: Iterator[InternalRow],
+      numInputRows: LongSQLMetric,
       keyGenerator: UnsafeProjection,
       sizeEstimate: Int): HashedRelation = {
 
@@ -340,6 +345,7 @@ private[joins] object UnsafeHashedRelation {
     // Create a mapping of buildKeys -> rows
     while (input.hasNext) {
       val unsafeRow = input.next().asInstanceOf[UnsafeRow]
+      numInputRows += 1
       val rowKey = keyGenerator(unsafeRow)
       if (!rowKey.anyNull) {
         val existingMatchList = hashTable.get(rowKey)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
index 4443455ef11fe..ad6362542f2ff 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -35,6 +36,11 @@ case class LeftSemiJoinBNL(
   extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputPartitioning: Partitioning = streamed.outputPartitioning
 
   override def output: Seq[Attribute] = left.output
@@ -52,13 +58,21 @@ case class LeftSemiJoinBNL(
     newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastedRelation =
-      sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
+      sparkContext.broadcast(broadcast.execute().map { row =>
+        numRightRows += 1
+        row.copy()
+      }.collect().toIndexedSeq)
 
     streamed.execute().mapPartitions { streamedIter =>
       val joinedRow = new JoinedRow
 
       streamedIter.filter(streamedRow => {
+        numLeftRows += 1
         var i = 0
         var matched = false
 
@@ -69,6 +83,9 @@ case class LeftSemiJoinBNL(
           }
           i += 1
         }
+        if (matched) {
+          numOutputRows += 1
+        }
         matched
       })
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
index 68ccd34d8ed9b..18808adaac63f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, ClusteredDistribution}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -37,19 +38,28 @@ case class LeftSemiJoinHash(
     right: SparkPlan,
     condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputPartitioning: Partitioning = left.outputPartitioning
 
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     right.execute().zipPartitions(left.execute()) { (buildIter, streamIter) =>
       if (condition.isEmpty) {
-        val hashSet = buildKeyHashSet(buildIter)
-        hashSemiJoin(streamIter, hashSet)
+        val hashSet = buildKeyHashSet(buildIter, numRightRows)
+        hashSemiJoin(streamIter, numLeftRows, hashSet, numOutputRows)
       } else {
-        val hashRelation = HashedRelation(buildIter, rightKeyGenerator)
-        hashSemiJoin(streamIter, hashRelation)
+        val hashRelation = HashedRelation(buildIter, numRightRows, rightKeyGenerator)
+        hashSemiJoin(streamIter, numLeftRows, hashRelation, numOutputRows)
       }
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
index c923dc837c449..fc8c9439a6f07 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -38,7 +39,10 @@ case class ShuffledHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
 
   override def outputPartitioning: Partitioning =
     PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
@@ -47,9 +51,15 @@ case class ShuffledHashJoin(
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val (numBuildRows, numStreamedRows) = buildSide match {
+      case BuildLeft => (longMetric("numLeftRows"), longMetric("numRightRows"))
+      case BuildRight => (longMetric("numRightRows"), longMetric("numLeftRows"))
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
-      val hashed = HashedRelation(buildIter, buildSideKeyGenerator)
-      hashJoin(streamIter, hashed)
+      val hashed = HashedRelation(buildIter, numBuildRows, buildSideKeyGenerator)
+      hashJoin(streamIter, numStreamedRows, hashed, numOutputRows)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index 6a8c35efca8f4..ed282f98b7d71 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -41,6 +42,11 @@ case class ShuffledHashOuterJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode with HashOuterJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
@@ -53,39 +59,48 @@ case class ShuffledHashOuterJoin(
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     val joinedRow = new JoinedRow()
     left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
       // TODO this probably can be replaced by external sort (sort merged join?)
       joinType match {
         case LeftOuter =>
-          val hashed = HashedRelation(rightIter, buildKeyGenerator)
+          val hashed = HashedRelation(rightIter, numRightRows, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
           val resultProj = resultProjection
           leftIter.flatMap( currentRow => {
+            numLeftRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey), resultProj)
+            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey), resultProj, numOutputRows)
           })
 
         case RightOuter =>
-          val hashed = HashedRelation(leftIter, buildKeyGenerator)
+          val hashed = HashedRelation(leftIter, numLeftRows, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
           val resultProj = resultProjection
           rightIter.flatMap ( currentRow => {
+            numRightRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow, resultProj)
+            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow, resultProj, numOutputRows)
           })
 
         case FullOuter =>
           // TODO(davies): use UnsafeRow
-          val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
-          val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
+          val leftHashTable =
+            buildHashTable(leftIter, numLeftRows, newProjection(leftKeys, left.output))
+          val rightHashTable =
+            buildHashTable(rightIter, numRightRows, newProjection(rightKeys, right.output))
           (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
             fullOuterIterator(key,
               leftHashTable.getOrElse(key, EMPTY_LIST),
               rightHashTable.getOrElse(key, EMPTY_LIST),
-              joinedRow)
+              joinedRow,
+              numOutputRows)
           }
 
         case x =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 6d656ea2849a9..6b7322671d6b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
 
 /**
  * :: DeveloperApi ::
@@ -37,6 +38,11 @@ case class SortMergeJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def output: Seq[Attribute] = left.output ++ right.output
 
   override def outputPartitioning: Partitioning =
@@ -70,6 +76,10 @@ case class SortMergeJoin(
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
       new RowIterator {
         // An ordering that can be used to compare keys from both sides.
@@ -82,7 +92,9 @@ case class SortMergeJoin(
           rightKeyGenerator,
           keyOrdering,
           RowIterator.fromScala(leftIter),
-          RowIterator.fromScala(rightIter)
+          numLeftRows,
+          RowIterator.fromScala(rightIter),
+          numRightRows
         )
         private[this] val joinRow = new JoinedRow
         private[this] val resultProjection: (InternalRow) => InternalRow = {
@@ -108,6 +120,7 @@ case class SortMergeJoin(
           if (currentLeftRow != null) {
             joinRow(currentLeftRow, currentRightMatches(currentMatchIdx))
             currentMatchIdx += 1
+            numOutputRows += 1
             true
           } else {
             false
@@ -144,7 +157,9 @@ private[joins] class SortMergeJoinScanner(
     bufferedKeyGenerator: Projection,
     keyOrdering: Ordering[InternalRow],
     streamedIter: RowIterator,
-    bufferedIter: RowIterator) {
+    numStreamedRows: LongSQLMetric,
+    bufferedIter: RowIterator,
+    numBufferedRows: LongSQLMetric) {
   private[this] var streamedRow: InternalRow = _
   private[this] var streamedRowKey: InternalRow = _
   private[this] var bufferedRow: InternalRow = _
@@ -269,6 +284,7 @@ private[joins] class SortMergeJoinScanner(
     if (streamedIter.advanceNext()) {
       streamedRow = streamedIter.getRow
       streamedRowKey = streamedKeyGenerator(streamedRow)
+      numStreamedRows += 1
       true
     } else {
       streamedRow = null
@@ -286,6 +302,7 @@ private[joins] class SortMergeJoinScanner(
     while (!foundRow && bufferedIter.advanceNext()) {
       bufferedRow = bufferedIter.getRow
       bufferedRowKey = bufferedKeyGenerator(bufferedRow)
+      numBufferedRows += 1
       foundRow = !bufferedRowKey.anyNull
     }
     if (!foundRow) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
index 5326966b07a66..dea9e5e580a1e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
 
 /**
  * :: DeveloperApi ::
@@ -40,6 +41,11 @@ case class SortMergeOuterJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def output: Seq[Attribute] = {
     joinType match {
       case LeftOuter =>
@@ -108,6 +114,10 @@ case class SortMergeOuterJoin(
   }
 
   override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
       // An ordering that can be used to compare keys from both sides.
       val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
@@ -133,10 +143,13 @@ case class SortMergeOuterJoin(
             bufferedKeyGenerator = createRightKeyGenerator(),
             keyOrdering,
             streamedIter = RowIterator.fromScala(leftIter),
-            bufferedIter = RowIterator.fromScala(rightIter)
+            numLeftRows,
+            bufferedIter = RowIterator.fromScala(rightIter),
+            numRightRows
           )
           val rightNullRow = new GenericInternalRow(right.output.length)
-          new LeftOuterIterator(smjScanner, rightNullRow, boundCondition, resultProj).toScala
+          new LeftOuterIterator(
+            smjScanner, rightNullRow, boundCondition, resultProj, numOutputRows).toScala
 
         case RightOuter =>
           val smjScanner = new SortMergeJoinScanner(
@@ -144,10 +157,13 @@ case class SortMergeOuterJoin(
             bufferedKeyGenerator = createLeftKeyGenerator(),
             keyOrdering,
             streamedIter = RowIterator.fromScala(rightIter),
-            bufferedIter = RowIterator.fromScala(leftIter)
+            numRightRows,
+            bufferedIter = RowIterator.fromScala(leftIter),
+            numLeftRows
           )
           val leftNullRow = new GenericInternalRow(left.output.length)
-          new RightOuterIterator(smjScanner, leftNullRow, boundCondition, resultProj).toScala
+          new RightOuterIterator(
+            smjScanner, leftNullRow, boundCondition, resultProj, numOutputRows).toScala
 
         case x =>
           throw new IllegalArgumentException(
@@ -162,7 +178,8 @@ private class LeftOuterIterator(
     smjScanner: SortMergeJoinScanner,
     rightNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
-    resultProj: InternalRow => InternalRow
+    resultProj: InternalRow => InternalRow,
+    numRows: LongSQLMetric
   ) extends RowIterator {
   private[this] val joinedRow: JoinedRow = new JoinedRow()
   private[this] var rightIdx: Int = 0
@@ -198,7 +215,9 @@ private class LeftOuterIterator(
   }
 
   override def advanceNext(): Boolean = {
-    advanceRightUntilBoundConditionSatisfied() || advanceLeft()
+    val r = advanceRightUntilBoundConditionSatisfied() || advanceLeft()
+    if (r) numRows += 1
+    r
   }
 
   override def getRow: InternalRow = resultProj(joinedRow)
@@ -208,7 +227,8 @@ private class RightOuterIterator(
     smjScanner: SortMergeJoinScanner,
     leftNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
-    resultProj: InternalRow => InternalRow
+    resultProj: InternalRow => InternalRow,
+    numRows: LongSQLMetric
   ) extends RowIterator {
   private[this] val joinedRow: JoinedRow = new JoinedRow()
   private[this] var leftIdx: Int = 0
@@ -244,7 +264,9 @@ private class RightOuterIterator(
   }
 
   override def advanceNext(): Boolean = {
-    advanceLeftUntilBoundConditionSatisfied() || advanceRight()
+    val r = advanceLeftUntilBoundConditionSatisfied() || advanceRight()
+    if (r) numRows += 1
+    r
   }
 
   override def getRow: InternalRow = resultProj(joinedRow)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
index 1b51a5e5c8a8e..7a2a98ec18cb8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
@@ -112,4 +112,10 @@ private[sql] object SQLMetrics {
     sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
     acc
   }
+
+  /**
+   * A metric that its value will be ignored. Use this one when we need a metric parameter but don't
+   * care about the value.
+   */
+  val nullLongMetric = new LongSQLMetric("null")
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 8b1a9b21a96b9..a1fa2c3864bdb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -22,6 +22,8 @@ import java.io.{ByteArrayInputStream, ByteArrayOutputStream, ObjectInputStream,
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.metric.SQLMetrics
+import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 import org.apache.spark.util.collection.CompactBuffer
 
@@ -35,7 +37,8 @@ class HashedRelationSuite extends SparkFunSuite {
 
   test("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val hashed = HashedRelation(data.iterator, keyProjection)
+    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
 
     assert(hashed.get(data(0)) === CompactBuffer[InternalRow](data(0)))
@@ -45,11 +48,13 @@ class HashedRelationSuite extends SparkFunSuite {
     val data2 = CompactBuffer[InternalRow](data(2))
     data2 += data(2)
     assert(hashed.get(data(2)) === data2)
+    assert(numDataRows.value.value === data.length)
   }
 
   test("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
-    val hashed = HashedRelation(data.iterator, keyProjection)
+    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
 
     assert(hashed.get(data(0)) === CompactBuffer[InternalRow](data(0)))
@@ -62,17 +67,19 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(uniqHashed.getValue(data(1)) === data(1))
     assert(uniqHashed.getValue(data(2)) === data(2))
     assert(uniqHashed.getValue(InternalRow(10)) === null)
+    assert(numDataRows.value.value === data.length)
   }
 
   test("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
+    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
     val toUnsafe = UnsafeProjection.create(schema)
     val unsafeData = data.map(toUnsafe(_).copy()).toArray
 
     val buildKey = Seq(BoundReference(0, IntegerType, false))
     val keyGenerator = UnsafeProjection.create(buildKey)
-    val hashed = UnsafeHashedRelation(unsafeData.iterator, keyGenerator, 1)
+    val hashed = UnsafeHashedRelation(unsafeData.iterator, numDataRows, keyGenerator, 1)
     assert(hashed.isInstanceOf[UnsafeHashedRelation])
 
     assert(hashed.get(unsafeData(0)) === CompactBuffer[InternalRow](unsafeData(0)))
@@ -94,5 +101,6 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed2.get(unsafeData(1)) === CompactBuffer[InternalRow](unsafeData(1)))
     assert(hashed2.get(toUnsafe(InternalRow(10))) === null)
     assert(hashed2.get(unsafeData(2)) === data2)
+    assert(numDataRows.value.value === data.length)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index 953284c98b208..7383d3f8fe024 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -25,15 +25,24 @@ import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm._
 import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql._
+import org.apache.spark.sql.execution.ui.SparkPlanGraph
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
 import org.apache.spark.util.Utils
 
+class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
-class SQLMetricsSuite extends SparkFunSuite {
+  override val sqlContext = TestSQLContext
+
+  import sqlContext.implicits._
 
   test("LongSQLMetric should not box Long") {
     val l = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "long")
-    val f = () => { l += 1L }
+    val f = () => {
+      l += 1L
+      l.add(1L)
+    }
     BoxingFinder.getClassReader(f.getClass).foreach { cl =>
       val boxingFinder = new BoxingFinder()
       cl.accept(boxingFinder, 0)
@@ -51,6 +60,441 @@ class SQLMetricsSuite extends SparkFunSuite {
       assert(boxingFinder.boxingInvokes.nonEmpty, "Found find boxing in this test")
     }
   }
+
+  /**
+   * Call `df.collect()` and verify if the collected metrics are same as "expectedMetrics".
+   *
+   * @param df `DataFrame` to run
+   * @param expectedNumOfJobs number of jobs that will run
+   * @param expectedMetrics the expected metrics. The format is
+   *                        `nodeId -> (operatorName, metric name -> metric value)`.
+   */
+  private def testSparkPlanMetrics(
+      df: DataFrame,
+      expectedNumOfJobs: Int,
+      expectedMetrics: Map[Long, (String, Map[String, Any])]): Unit = {
+    val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+    df.collect()
+    TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
+    val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+    assert(executionIds.size === 1)
+    val executionId = executionIds.head
+    val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+    // Use "<=" because there is a race condition that we may miss some jobs
+    // TODO Change it to "=" once we fix the race condition that missing the JobStarted event.
+    assert(jobs.size <= expectedNumOfJobs)
+    if (jobs.size == expectedNumOfJobs) {
+      // If we can track all jobs, check the metric values
+      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      val actualMetrics = SparkPlanGraph(df.queryExecution.executedPlan).nodes.filter { node =>
+        expectedMetrics.contains(node.id)
+      }.map { node =>
+        val nodeMetrics = node.metrics.map { metric =>
+          val metricValue = metricValues(metric.accumulatorId)
+          (metric.name, metricValue)
+        }.toMap
+        (node.id, node.name -> nodeMetrics)
+      }.toMap
+      assert(expectedMetrics === actualMetrics)
+    } else {
+      // TODO Remove this "else" once we fix the race condition that missing the JobStarted event.
+      // Since we cannot track all jobs, the metric values could be wrong and we should not check
+      // them.
+      logWarning("Due to a race condition, we miss some jobs and cannot verify the metric values")
+    }
+  }
+
+  test("Project metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "false",
+      SQLConf.CODEGEN_ENABLED.key -> "false",
+      SQLConf.TUNGSTEN_ENABLED.key -> "false") {
+      // Assume the execution plan is
+      // PhysicalRDD(nodeId = 1) -> Project(nodeId = 0)
+      val df = TestData.person.select('name)
+      testSparkPlanMetrics(df, 1, Map(
+        0L ->("Project", Map(
+          "number of rows" -> 2L)))
+      )
+    }
+  }
+
+  test("TungstenProject metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "true",
+      SQLConf.CODEGEN_ENABLED.key -> "true",
+      SQLConf.TUNGSTEN_ENABLED.key -> "true") {
+      // Assume the execution plan is
+      // PhysicalRDD(nodeId = 1) -> TungstenProject(nodeId = 0)
+      val df = TestData.person.select('name)
+      testSparkPlanMetrics(df, 1, Map(
+        0L ->("TungstenProject", Map(
+          "number of rows" -> 2L)))
+      )
+    }
+  }
+
+  test("Filter metrics") {
+    // Assume the execution plan is
+    // PhysicalRDD(nodeId = 1) -> Filter(nodeId = 0)
+    val df = TestData.person.filter('age < 25)
+    testSparkPlanMetrics(df, 1, Map(
+      0L -> ("Filter", Map(
+        "number of input rows" -> 2L,
+        "number of output rows" -> 1L)))
+    )
+  }
+
+  test("Aggregate metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "false",
+      SQLConf.CODEGEN_ENABLED.key -> "false",
+      SQLConf.TUNGSTEN_ENABLED.key -> "false") {
+      // Assume the execution plan is
+      // ... -> Aggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) -> Aggregate(nodeId = 0)
+      val df = TestData.testData2.groupBy().count() // 2 partitions
+      testSparkPlanMetrics(df, 1, Map(
+        2L -> ("Aggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 2L)),
+        0L -> ("Aggregate", Map(
+          "number of input rows" -> 2L,
+          "number of output rows" -> 1L)))
+      )
+
+      // 2 partitions and each partition contains 2 keys
+      val df2 = TestData.testData2.groupBy('a).count()
+      testSparkPlanMetrics(df2, 1, Map(
+        2L -> ("Aggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 4L)),
+        0L -> ("Aggregate", Map(
+          "number of input rows" -> 4L,
+          "number of output rows" -> 3L)))
+      )
+    }
+  }
+
+  test("SortBasedAggregate metrics") {
+    // Because SortBasedAggregate may skip different rows if the number of partitions is different,
+    // this test should use the deterministic number of partitions.
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "false",
+      SQLConf.CODEGEN_ENABLED.key -> "true",
+      SQLConf.TUNGSTEN_ENABLED.key -> "true") {
+      // Assume the execution plan is
+      // ... -> SortBasedAggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) ->
+      // SortBasedAggregate(nodeId = 0)
+      val df = TestData.testData2.groupBy().count() // 2 partitions
+      testSparkPlanMetrics(df, 1, Map(
+        2L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 2L)),
+        0L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 2L,
+          "number of output rows" -> 1L)))
+      )
+
+      // Assume the execution plan is
+      // ... -> SortBasedAggregate(nodeId = 3) -> TungstenExchange(nodeId = 2)
+      // -> ExternalSort(nodeId = 1)-> SortBasedAggregate(nodeId = 0)
+      // 2 partitions and each partition contains 2 keys
+      val df2 = TestData.testData2.groupBy('a).count()
+      testSparkPlanMetrics(df2, 1, Map(
+        3L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 4L)),
+        0L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 4L,
+          "number of output rows" -> 3L)))
+      )
+    }
+  }
+
+  test("TungstenAggregate metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "true",
+      SQLConf.CODEGEN_ENABLED.key -> "true",
+      SQLConf.TUNGSTEN_ENABLED.key -> "true") {
+      // Assume the execution plan is
+      // ... -> TungstenAggregate(nodeId = 2) -> Exchange(nodeId = 1)
+      // -> TungstenAggregate(nodeId = 0)
+      val df = TestData.testData2.groupBy().count() // 2 partitions
+      testSparkPlanMetrics(df, 1, Map(
+        2L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 2L)),
+        0L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 2L,
+          "number of output rows" -> 1L)))
+      )
+
+      // 2 partitions and each partition contains 2 keys
+      val df2 = TestData.testData2.groupBy('a).count()
+      testSparkPlanMetrics(df2, 1, Map(
+        2L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 4L)),
+        0L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 4L,
+          "number of output rows" -> 3L)))
+      )
+    }
+  }
+
+  test("SortMergeJoin metrics") {
+    // Because SortMergeJoin may skip different rows if the number of partitions is different, this
+    // test should use the deterministic number of partitions.
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> SortMergeJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 JOIN testDataForJoin ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df, 1, Map(
+          1L -> ("SortMergeJoin", Map(
+            // It's 4 because we only read 3 rows in the first partition and 1 row in the second one
+            "number of left rows" -> 4L,
+            "number of right rows" -> 2L,
+            "number of output rows" -> 4L)))
+        )
+      }
+    }
+  }
+
+  test("SortMergeOuterJoin metrics") {
+    // Because SortMergeOuterJoin may skip different rows if the number of partitions is different,
+    // this test should use the deterministic number of partitions.
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> SortMergeOuterJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 left JOIN testDataForJoin ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df, 1, Map(
+          1L -> ("SortMergeOuterJoin", Map(
+            // It's 4 because we only read 3 rows in the first partition and 1 row in the second one
+            "number of left rows" -> 6L,
+            "number of right rows" -> 2L,
+            "number of output rows" -> 8L)))
+        )
+
+        val df2 = sqlContext.sql(
+          "SELECT * FROM testDataForJoin right JOIN testData2 ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df2, 1, Map(
+          1L -> ("SortMergeOuterJoin", Map(
+            // It's 4 because we only read 3 rows in the first partition and 1 row in the second one
+            "number of left rows" -> 2L,
+            "number of right rows" -> 6L,
+            "number of output rows" -> 8L)))
+        )
+      }
+    }
+  }
+
+  test("BroadcastHashJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key", "value")
+      // Assume the execution plan is
+      // ... -> BroadcastHashJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+      val df = df1.join(broadcast(df2), "key")
+      testSparkPlanMetrics(df, 2, Map(
+        1L -> ("BroadcastHashJoin", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("ShuffledHashJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> ShuffledHashJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 JOIN testDataForJoin ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df, 1, Map(
+          1L -> ("ShuffledHashJoin", Map(
+            "number of left rows" -> 6L,
+            "number of right rows" -> 2L,
+            "number of output rows" -> 4L)))
+        )
+      }
+    }
+  }
+
+  test("ShuffledHashOuterJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false",
+      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "0") {
+      val df1 = Seq((1, "a"), (1, "b"), (4, "c")).toDF("key", "value")
+      val df2 = Seq((1, "a"), (1, "b"), (2, "c"), (3, "d")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> ShuffledHashOuterJoin(nodeId = 0)
+      val df = df1.join(df2, $"key" === $"key2", "left_outer")
+      testSparkPlanMetrics(df, 1, Map(
+        0L -> ("ShuffledHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 5L)))
+      )
+
+      val df3 = df1.join(df2, $"key" === $"key2", "right_outer")
+      testSparkPlanMetrics(df3, 1, Map(
+        0L -> ("ShuffledHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 6L)))
+      )
+
+      val df4 = df1.join(df2, $"key" === $"key2", "outer")
+      testSparkPlanMetrics(df4, 1, Map(
+        0L -> ("ShuffledHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 7L)))
+      )
+    }
+  }
+
+  test("BroadcastHashOuterJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "a"), (1, "b"), (4, "c")).toDF("key", "value")
+      val df2 = Seq((1, "a"), (1, "b"), (2, "c"), (3, "d")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> BroadcastHashOuterJoin(nodeId = 0)
+      val df = df1.join(broadcast(df2), $"key" === $"key2", "left_outer")
+      testSparkPlanMetrics(df, 2, Map(
+        0L -> ("BroadcastHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 5L)))
+      )
+
+      val df3 = df1.join(broadcast(df2), $"key" === $"key2", "right_outer")
+      testSparkPlanMetrics(df3, 2, Map(
+        0L -> ("BroadcastHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 6L)))
+      )
+    }
+  }
+
+  test("BroadcastNestedLoopJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> BroadcastNestedLoopJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 left JOIN testDataForJoin ON " +
+            "testData2.a * testDataForJoin.a != testData2.a + testDataForJoin.a")
+        testSparkPlanMetrics(df, 3, Map(
+          1L -> ("BroadcastNestedLoopJoin", Map(
+            "number of left rows" -> 12L, // left needs to be scanned twice
+            "number of right rows" -> 2L,
+            "number of output rows" -> 12L)))
+        )
+      }
+    }
+  }
+
+  test("BroadcastLeftSemiJoinHash metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> BroadcastLeftSemiJoinHash(nodeId = 0)
+      val df = df1.join(broadcast(df2), $"key" === $"key2", "leftsemi")
+      testSparkPlanMetrics(df, 2, Map(
+        0L -> ("BroadcastLeftSemiJoinHash", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("LeftSemiJoinHash metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true",
+      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "0") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> LeftSemiJoinHash(nodeId = 0)
+      val df = df1.join(df2, $"key" === $"key2", "leftsemi")
+      testSparkPlanMetrics(df, 1, Map(
+        0L -> ("LeftSemiJoinHash", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("LeftSemiJoinBNL metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> LeftSemiJoinBNL(nodeId = 0)
+      val df = df1.join(df2, $"key" < $"key2", "leftsemi")
+      testSparkPlanMetrics(df, 2, Map(
+        0L -> ("LeftSemiJoinBNL", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("CartesianProduct metrics") {
+    val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+    testDataForJoin.registerTempTable("testDataForJoin")
+    withTempTable("testDataForJoin") {
+      // Assume the execution plan is
+      // ... -> CartesianProduct(nodeId = 1) -> TungstenProject(nodeId = 0)
+      val df = sqlContext.sql(
+        "SELECT * FROM testData2 JOIN testDataForJoin")
+      testSparkPlanMetrics(df, 1, Map(
+        1L -> ("CartesianProduct", Map(
+          "number of left rows" -> 12L, // left needs to be scanned twice
+          "number of right rows" -> 12L, // right is read 6 times
+          "number of output rows" -> 12L)))
+      )
+    }
+  }
+
+  test("save metrics") {
+    withTempPath { file =>
+      val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+      // Assume the execution plan is
+      // PhysicalRDD(nodeId = 0)
+      TestData.person.select('name).write.format("json").save(file.getAbsolutePath)
+      TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
+      val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+      assert(executionIds.size === 1)
+      val executionId = executionIds.head
+      val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+      // Use "<=" because there is a race condition that we may miss some jobs
+      // TODO Change "<=" to "=" once we fix the race condition that missing the JobStarted event.
+      assert(jobs.size <= 1)
+      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      // Because "save" will create a new DataFrame internally, we cannot get the real metric id.
+      // However, we still can check the value.
+      assert(metricValues.values.toSeq === Seq(2L))
+    }
+  }
+
 }
 
 private case class MethodIdentifier[T](cls: Class[T], name: String, desc: String)

From 520ad44b17f72e6465bf990f64b4e289f8a83447 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 11 Aug 2015 12:49:47 -0700
Subject: [PATCH 0969/1454] [SPARK-9750] [MLLIB] Improve equals on SparseMatrix
 and DenseMatrix

Adds unit test for `equals` on `mllib.linalg.Matrix` class and `equals` to both `SparseMatrix` and `DenseMatrix`. Supports equality testing between `SparseMatrix` and `DenseMatrix`.

mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #8042 from feynmanliang/SPARK-9750 and squashes the following commits:

bb70d5e [Feynman Liang] Breeze compare for dense matrices as well, in case other is sparse
ab6f3c8 [Feynman Liang] Sparse matrix compare for equals
22782df [Feynman Liang] Add equality based on matrix semantics, not representation
78f9426 [Feynman Liang] Add casts
43d28fa [Feynman Liang] Fix failing test
6416fa0 [Feynman Liang] Add failing sparse matrix equals tests
---
 .../apache/spark/mllib/linalg/Matrices.scala   |  8 ++++++--
 .../spark/mllib/linalg/MatricesSuite.scala     | 18 ++++++++++++++++++
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 1c858348bf20e..1139ce36d50b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -257,8 +257,7 @@ class DenseMatrix(
     this(numRows, numCols, values, false)
 
   override def equals(o: Any): Boolean = o match {
-    case m: DenseMatrix =>
-      m.numRows == numRows && m.numCols == numCols && Arrays.equals(toArray, m.toArray)
+    case m: Matrix => toBreeze == m.toBreeze
     case _ => false
   }
 
@@ -519,6 +518,11 @@ class SparseMatrix(
       rowIndices: Array[Int],
       values: Array[Double]) = this(numRows, numCols, colPtrs, rowIndices, values, false)
 
+  override def equals(o: Any): Boolean = o match {
+    case m: Matrix => toBreeze == m.toBreeze
+    case _ => false
+  }
+
   private[mllib] def toBreeze: BM[Double] = {
      if (!isTransposed) {
        new BSM[Double](values, numRows, numCols, colPtrs, rowIndices)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
index a270ba2562db9..bfd6d5495f5e0 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
@@ -74,6 +74,24 @@ class MatricesSuite extends SparkFunSuite {
     }
   }
 
+  test("equals") {
+    val dm1 = Matrices.dense(2, 2, Array(0.0, 1.0, 2.0, 3.0))
+    assert(dm1 === dm1)
+    assert(dm1 !== dm1.transpose)
+
+    val dm2 = Matrices.dense(2, 2, Array(0.0, 2.0, 1.0, 3.0))
+    assert(dm1 === dm2.transpose)
+
+    val sm1 = dm1.asInstanceOf[DenseMatrix].toSparse
+    assert(sm1 === sm1)
+    assert(sm1 === dm1)
+    assert(sm1 !== sm1.transpose)
+
+    val sm2 = dm2.asInstanceOf[DenseMatrix].toSparse
+    assert(sm1 === sm2.transpose)
+    assert(sm1 === dm2.transpose)
+  }
+
   test("matrix copies are deep copies") {
     val m = 3
     val n = 2

From 2a3be4ddf9d9527353f07ea0ab204ce17dbcba9a Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Tue, 11 Aug 2015 14:02:23 -0700
Subject: [PATCH 0970/1454] [SPARK-7726] Add import so Scaladoc doesn't fail.

This is another import needed so Scala 2.11 doc generation doesn't fail.
See SPARK-7726 for more detail. I tested this locally and the 2.11
install goes from failing to succeeding with this patch.

Author: Patrick Wendell <patrick@databricks.com>

Closes #8095 from pwendell/scaladoc.
---
 .../spark/network/shuffle/protocol/mesos/RegisterDriver.java   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
index 1c28fc1dff246..94a61d6caadc4 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
@@ -23,6 +23,9 @@
 import org.apache.spark.network.protocol.Encoders;
 import org.apache.spark.network.shuffle.protocol.BlockTransferMessage;
 
+// Needed by ScalaDoc. See SPARK-7726
+import static org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type;
+
 /**
  * A message sent from the driver to register with the MesosExternalShuffleService.
  */

From 00c02728a6c6c4282c389ca90641dd78dd5e3d32 Mon Sep 17 00:00:00 2001
From: hyukjinkwon <gurwls223@gmail.com>
Date: Tue, 11 Aug 2015 14:04:09 -0700
Subject: [PATCH 0971/1454] [SPARK-9814] [SQL] EqualNotNull not passing to data
 sources
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Author: hyukjinkwon <gurwls223@gmail.com>
Author: 권혁진 <gurwls223@gmail.com>

Closes #8096 from HyukjinKwon/master.
---
 .../sql/execution/datasources/DataSourceStrategy.scala   | 5 +++++
 .../scala/org/apache/spark/sql/sources/filters.scala     | 9 +++++++++
 .../org/apache/spark/sql/sources/FilteredScanSuite.scala | 1 +
 3 files changed, 15 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 78a4acdf4b1bf..2a4c40db8bb66 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -349,6 +349,11 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       case expressions.EqualTo(Literal(v, _), a: Attribute) =>
         Some(sources.EqualTo(a.name, v))
 
+      case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
+        Some(sources.EqualNullSafe(a.name, v))
+      case expressions.EqualNullSafe(Literal(v, _), a: Attribute) =>
+        Some(sources.EqualNullSafe(a.name, v))
+
       case expressions.GreaterThan(a: Attribute, Literal(v, _)) =>
         Some(sources.GreaterThan(a.name, v))
       case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
index 4d942e4f9287a..3780cbbcc9631 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
@@ -36,6 +36,15 @@ abstract class Filter
  */
 case class EqualTo(attribute: String, value: Any) extends Filter
 
+/**
+ * Performs equality comparison, similar to [[EqualTo]]. However, this differs from [[EqualTo]]
+ * in that it returns `true` (rather than NULL) if both inputs are NULL, and `false`
+ * (rather than NULL) if one of the input is NULL and the other is not NULL.
+ *
+ * @since 1.5.0
+ */
+case class EqualNullSafe(attribute: String, value: Any) extends Filter
+
 /**
  * A filter that evaluates to `true` iff the attribute evaluates to a value
  * greater than `value`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
index 81b3a0f0c5b3a..5ef365797eace 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
@@ -56,6 +56,7 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL
     // Predicate test on integer column
     def translateFilterOnA(filter: Filter): Int => Boolean = filter match {
       case EqualTo("a", v) => (a: Int) => a == v
+      case EqualNullSafe("a", v) => (a: Int) => a == v
       case LessThan("a", v: Int) => (a: Int) => a < v
       case LessThanOrEqual("a", v: Int) => (a: Int) => a <= v
       case GreaterThan("a", v: Int) => (a: Int) => a > v

From f16bc68dfb25c7b746ae031a57840ace9bafa87f Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 11 Aug 2015 14:06:23 -0700
Subject: [PATCH 0972/1454] [SPARK-9824] [CORE] Fix the issue that
 InternalAccumulator leaks WeakReference

`InternalAccumulator.create` doesn't call `registerAccumulatorForCleanup` to register itself with ContextCleaner, so `WeakReference`s for these accumulators in `Accumulators.originals` won't be removed.

This PR added `registerAccumulatorForCleanup` for internal accumulators to avoid the memory leak.

Author: zsxwing <zsxwing@gmail.com>

Closes #8108 from zsxwing/internal-accumulators-leak.
---
 .../scala/org/apache/spark/Accumulators.scala | 22 +++++++++++--------
 .../org/apache/spark/scheduler/Stage.scala    |  2 +-
 .../org/apache/spark/AccumulatorSuite.scala   |  3 ++-
 3 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 064246dfa7fc3..c39c8667d013e 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -382,14 +382,18 @@ private[spark] object InternalAccumulator {
    * add to the same set of accumulators. We do this to report the distribution of accumulator
    * values across all tasks within each stage.
    */
-  def create(): Seq[Accumulator[Long]] = {
-    Seq(
-      // Execution memory refers to the memory used by internal data structures created
-      // during shuffles, aggregations and joins. The value of this accumulator should be
-      // approximately the sum of the peak sizes across all such data structures created
-      // in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
-      new Accumulator(
-        0L, AccumulatorParam.LongAccumulatorParam, Some(PEAK_EXECUTION_MEMORY), internal = true)
-    ) ++ maybeTestAccumulator.toSeq
+  def create(sc: SparkContext): Seq[Accumulator[Long]] = {
+    val internalAccumulators = Seq(
+        // Execution memory refers to the memory used by internal data structures created
+        // during shuffles, aggregations and joins. The value of this accumulator should be
+        // approximately the sum of the peak sizes across all such data structures created
+        // in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
+        new Accumulator(
+          0L, AccumulatorParam.LongAccumulatorParam, Some(PEAK_EXECUTION_MEMORY), internal = true)
+      ) ++ maybeTestAccumulator.toSeq
+    internalAccumulators.foreach { accumulator =>
+      sc.cleaner.foreach(_.registerAccumulatorForCleanup(accumulator))
+    }
+    internalAccumulators
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index de05ee256dbfc..1cf06856ffbc2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -81,7 +81,7 @@ private[spark] abstract class Stage(
    * accumulators here again will override partial values from the finished tasks.
    */
   def resetInternalAccumulators(): Unit = {
-    _internalAccumulators = InternalAccumulator.create()
+    _internalAccumulators = InternalAccumulator.create(rdd.sparkContext)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index 48f549575f4d1..0eb2293a9d063 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -160,7 +160,8 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
   }
 
   test("internal accumulators in TaskContext") {
-    val accums = InternalAccumulator.create()
+    sc = new SparkContext("local", "test")
+    val accums = InternalAccumulator.create(sc)
     val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null, accums)
     val internalMetricsToAccums = taskContext.internalMetricsToAccumulators
     val collectedInternalAccums = taskContext.collectInternalAccumulators()

From 423cdfd83d7fd02a4f8cf3e714db913fd3f9ca09 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 11 Aug 2015 14:08:09 -0700
Subject: [PATCH 0973/1454] Closes #1290 Closes #4934


From be3e27164133025db860781bd5cdd3ca233edd21 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 11 Aug 2015 14:21:53 -0700
Subject: [PATCH 0974/1454] [SPARK-9788] [MLLIB] Fix LDA Binary Compatibility
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

1. Add “asymmetricDocConcentration” and revert docConcentration changes. If the (internal) doc concentration vector is a single value, “getDocConcentration" returns it. If it is a constant vector, getDocConcentration returns the first item, and fails otherwise.
2. Give `LDAModel.gammaShape` a default value in `LDAModel` concrete class constructors.

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8077 from feynmanliang/SPARK-9788 and squashes the following commits:

6b07bc8 [Feynman Liang] Code review changes
9d6a71e [Feynman Liang] Add asymmetricAlpha alias
bf4e685 [Feynman Liang] Asymmetric docConcentration
4cab972 [Feynman Liang] Default gammaShape
---
 .../apache/spark/mllib/clustering/LDA.scala   | 27 ++++++++++++++++--
 .../spark/mllib/clustering/LDAModel.scala     | 11 ++++----
 .../spark/mllib/clustering/LDAOptimizer.scala | 28 +++++++++----------
 .../spark/mllib/clustering/LDASuite.scala     |  4 +--
 4 files changed, 46 insertions(+), 24 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index ab124e6d77c5e..0fc9b1ac4d716 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -79,7 +79,24 @@ class LDA private (
    *
    * This is the parameter to a Dirichlet distribution.
    */
-  def getDocConcentration: Vector = this.docConcentration
+  def getAsymmetricDocConcentration: Vector = this.docConcentration
+
+  /**
+   * Concentration parameter (commonly named "alpha") for the prior placed on documents'
+   * distributions over topics ("theta").
+   *
+   * This method assumes the Dirichlet distribution is symmetric and can be described by a single
+   * [[Double]] parameter. It should fail if docConcentration is asymmetric.
+   */
+  def getDocConcentration: Double = {
+    val parameter = docConcentration(0)
+    if (docConcentration.size == 1) {
+      parameter
+    } else {
+      require(docConcentration.toArray.forall(_ == parameter))
+      parameter
+    }
+  }
 
   /**
    * Concentration parameter (commonly named "alpha") for the prior placed on documents'
@@ -106,18 +123,22 @@ class LDA private (
    *       [[https://github.com/Blei-Lab/onlineldavb]].
    */
   def setDocConcentration(docConcentration: Vector): this.type = {
+    require(docConcentration.size > 0, "docConcentration must have > 0 elements")
     this.docConcentration = docConcentration
     this
   }
 
-  /** Replicates Double to create a symmetric prior */
+  /** Replicates a [[Double]] docConcentration to create a symmetric prior. */
   def setDocConcentration(docConcentration: Double): this.type = {
     this.docConcentration = Vectors.dense(docConcentration)
     this
   }
 
+  /** Alias for [[getAsymmetricDocConcentration]] */
+  def getAsymmetricAlpha: Vector = getAsymmetricDocConcentration
+
   /** Alias for [[getDocConcentration]] */
-  def getAlpha: Vector = getDocConcentration
+  def getAlpha: Double = getDocConcentration
 
   /** Alias for [[setDocConcentration()]] */
   def setAlpha(alpha: Vector): this.type = setDocConcentration(alpha)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 33babda69bbb9..5dc637ebdc133 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -27,7 +27,6 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaPairRDD
-import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -190,7 +189,8 @@ class LocalLDAModel private[clustering] (
     val topics: Matrix,
     override val docConcentration: Vector,
     override val topicConcentration: Double,
-    override protected[clustering] val gammaShape: Double) extends LDAModel with Serializable {
+    override protected[clustering] val gammaShape: Double = 100)
+  extends LDAModel with Serializable {
 
   override def k: Int = topics.numCols
 
@@ -455,8 +455,9 @@ class DistributedLDAModel private[clustering] (
     val vocabSize: Int,
     override val docConcentration: Vector,
     override val topicConcentration: Double,
-    override protected[clustering] val gammaShape: Double,
-    private[spark] val iterationTimes: Array[Double]) extends LDAModel {
+    private[spark] val iterationTimes: Array[Double],
+    override protected[clustering] val gammaShape: Double = 100)
+  extends LDAModel {
 
   import LDA._
 
@@ -756,7 +757,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
       val graph: Graph[LDA.TopicCounts, LDA.TokenCount] = Graph(vertices, edges)
 
       new DistributedLDAModel(graph, globalTopicTotals, globalTopicTotals.length, vocabSize,
-        docConcentration, topicConcentration, gammaShape, iterationTimes)
+        docConcentration, topicConcentration, iterationTimes, gammaShape)
     }
 
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index afba2866c7040..a0008f9c99ad7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -95,10 +95,8 @@ final class EMLDAOptimizer extends LDAOptimizer {
    * Compute bipartite term/doc graph.
    */
   override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer = {
-    val docConcentration = lda.getDocConcentration(0)
-    require({
-      lda.getDocConcentration.toArray.forall(_ == docConcentration)
-    }, "EMLDAOptimizer currently only supports symmetric document-topic priors")
+    // EMLDAOptimizer currently only supports symmetric document-topic priors
+    val docConcentration = lda.getDocConcentration
 
     val topicConcentration = lda.getTopicConcentration
     val k = lda.getK
@@ -209,11 +207,11 @@ final class EMLDAOptimizer extends LDAOptimizer {
   override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
     require(graph != null, "graph is null, EMLDAOptimizer not initialized.")
     this.graphCheckpointer.deleteAllCheckpoints()
-    // This assumes gammaShape = 100 in OnlineLDAOptimizer to ensure equivalence in LDAModel.toLocal
-    // conversion
+    // The constructor's default arguments assume gammaShape = 100 to ensure equivalence in
+    // LDAModel.toLocal conversion
     new DistributedLDAModel(this.graph, this.globalTopicTotals, this.k, this.vocabSize,
       Vectors.dense(Array.fill(this.k)(this.docConcentration)), this.topicConcentration,
-      100, iterationTimes)
+      iterationTimes)
   }
 }
 
@@ -378,18 +376,20 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     this.k = lda.getK
     this.corpusSize = docs.count()
     this.vocabSize = docs.first()._2.size
-    this.alpha = if (lda.getDocConcentration.size == 1) {
-      if (lda.getDocConcentration(0) == -1) Vectors.dense(Array.fill(k)(1.0 / k))
+    this.alpha = if (lda.getAsymmetricDocConcentration.size == 1) {
+      if (lda.getAsymmetricDocConcentration(0) == -1) Vectors.dense(Array.fill(k)(1.0 / k))
       else {
-        require(lda.getDocConcentration(0) >= 0, s"all entries in alpha must be >=0, got: $alpha")
-        Vectors.dense(Array.fill(k)(lda.getDocConcentration(0)))
+        require(lda.getAsymmetricDocConcentration(0) >= 0,
+          s"all entries in alpha must be >=0, got: $alpha")
+        Vectors.dense(Array.fill(k)(lda.getAsymmetricDocConcentration(0)))
       }
     } else {
-      require(lda.getDocConcentration.size == k, s"alpha must have length k, got: $alpha")
-      lda.getDocConcentration.foreachActive { case (_, x) =>
+      require(lda.getAsymmetricDocConcentration.size == k,
+        s"alpha must have length k, got: $alpha")
+      lda.getAsymmetricDocConcentration.foreachActive { case (_, x) =>
         require(x >= 0, s"all entries in alpha must be >= 0, got: $alpha")
       }
-      lda.getDocConcentration
+      lda.getAsymmetricDocConcentration
     }
     this.eta = if (lda.getTopicConcentration == -1) 1.0 / k else lda.getTopicConcentration
     this.randomGenerator = new Random(lda.getSeed)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index fdc2554ab853e..ce6a8eb8e8c46 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -160,8 +160,8 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("setter alias") {
     val lda = new LDA().setAlpha(2.0).setBeta(3.0)
-    assert(lda.getAlpha.toArray.forall(_ === 2.0))
-    assert(lda.getDocConcentration.toArray.forall(_ === 2.0))
+    assert(lda.getAsymmetricAlpha.toArray.forall(_ === 2.0))
+    assert(lda.getAsymmetricDocConcentration.toArray.forall(_ === 2.0))
     assert(lda.getBeta === 3.0)
     assert(lda.getTopicConcentration === 3.0)
   }

From 017b5de07ef6cff249e984a2ab781c520249ac76 Mon Sep 17 00:00:00 2001
From: Sudhakar Thota <sudhakarthota@yahoo.com>
Date: Tue, 11 Aug 2015 14:31:51 -0700
Subject: [PATCH 0975/1454] [SPARK-8925] [MLLIB] Add @since tags to mllib.util

Went thru the history of changes the file MLUtils.scala and picked up the version that the change went in.

Author: Sudhakar Thota <sudhakarthota@yahoo.com>
Author: Sudhakar Thota <sudhakarthota@sudhakars-mbp-2.usca.ibm.com>

Closes #7436 from sthota2014/SPARK-8925_thotas.
---
 .../org/apache/spark/mllib/util/MLUtils.scala | 22 ++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 7c5cfa7bd84ce..26eb84a8dc0b0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -64,6 +64,7 @@ object MLUtils {
    *                    feature dimensions.
    * @param minPartitions min number of partitions
    * @return labeled data stored as an RDD[LabeledPoint]
+   * @since 1.0.0
    */
   def loadLibSVMFile(
       sc: SparkContext,
@@ -113,7 +114,10 @@ object MLUtils {
   }
 
   // Convenient methods for `loadLibSVMFile`.
-
+  
+  /**
+   * @since 1.0.0
+   */
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -126,6 +130,7 @@ object MLUtils {
   /**
    * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
    * partitions.
+   * @since 1.0.0
    */
   def loadLibSVMFile(
       sc: SparkContext,
@@ -133,6 +138,9 @@ object MLUtils {
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures, sc.defaultMinPartitions)
 
+  /**
+   * @since 1.0.0
+   */
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -141,6 +149,9 @@ object MLUtils {
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures)
 
+  /**
+   * @since 1.0.0
+   */
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -151,6 +162,7 @@ object MLUtils {
   /**
    * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
    * features determined automatically and the default number of partitions.
+   * @since 1.0.0
    */
   def loadLibSVMFile(sc: SparkContext, path: String): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, -1)
@@ -181,12 +193,14 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return vectors stored as an RDD[Vector]
+   * @since 1.1.0
    */
   def loadVectors(sc: SparkContext, path: String, minPartitions: Int): RDD[Vector] =
     sc.textFile(path, minPartitions).map(Vectors.parse)
 
   /**
    * Loads vectors saved using `RDD[Vector].saveAsTextFile` with the default number of partitions.
+   * @since 1.1.0
    */
   def loadVectors(sc: SparkContext, path: String): RDD[Vector] =
     sc.textFile(path, sc.defaultMinPartitions).map(Vectors.parse)
@@ -197,6 +211,7 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return labeled points stored as an RDD[LabeledPoint]
+   * @since 1.1.0
    */
   def loadLabeledPoints(sc: SparkContext, path: String, minPartitions: Int): RDD[LabeledPoint] =
     sc.textFile(path, minPartitions).map(LabeledPoint.parse)
@@ -204,6 +219,7 @@ object MLUtils {
   /**
    * Loads labeled points saved using `RDD[LabeledPoint].saveAsTextFile` with the default number of
    * partitions.
+   * @since 1.1.0
    */
   def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint] =
     loadLabeledPoints(sc, dir, sc.defaultMinPartitions)
@@ -220,6 +236,7 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
+   * @since 1.0.0
    */
   @deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1")
   def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
@@ -241,6 +258,7 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
+   * @since 1.0.0
    */
   @deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1")
   def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
@@ -253,6 +271,7 @@ object MLUtils {
    * Return a k element array of pairs of RDDs with the first element of each pair
    * containing the training data, a complement of the validation data and the second
    * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.
+   * @since 1.0.0
    */
   @Experimental
   def kFold[T: ClassTag](rdd: RDD[T], numFolds: Int, seed: Int): Array[(RDD[T], RDD[T])] = {
@@ -268,6 +287,7 @@ object MLUtils {
 
   /**
    * Returns a new vector with `1.0` (bias) appended to the input vector.
+   * @since 1.0.0
    */
   def appendBias(vector: Vector): Vector = {
     vector match {

From 736af95bd0c41723d455246b634a0fb68b38a7c7 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 11 Aug 2015 14:52:52 -0700
Subject: [PATCH 0976/1454] [HOTFIX] Fix style error caused by 017b5de

---
 mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 26eb84a8dc0b0..11ed23176fc12 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -114,7 +114,7 @@ object MLUtils {
   }
 
   // Convenient methods for `loadLibSVMFile`.
-  
+
   /**
    * @since 1.0.0
    */

From 5a5bbc29961630d649d4bd4acd5d19eb537b5fd0 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 11 Aug 2015 16:33:08 -0700
Subject: [PATCH 0977/1454] [SPARK-9074] [LAUNCHER] Allow arbitrary Spark args
 to be set.

This change allows any Spark argument to be added to the app to
be started using SparkLauncher. Known arguments are properly
validated, while unknown arguments are allowed so that the
library can launch newer Spark versions (in case SPARK_HOME points
at one).

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7975 from vanzin/SPARK-9074 and squashes the following commits:

b5e451a [Marcelo Vanzin] [SPARK-9074] [launcher] Allow arbitrary Spark args to be set.
---
 .../apache/spark/launcher/SparkLauncher.java  | 101 +++++++++++++++++-
 .../launcher/SparkSubmitCommandBuilder.java   |   2 +-
 .../spark/launcher/SparkLauncherSuite.java    |  50 +++++++++
 3 files changed, 150 insertions(+), 3 deletions(-)

diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
index c0f89c9230692..03c9358bc865d 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
@@ -20,12 +20,13 @@
 import java.io.File;
 import java.io.IOException;
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 
 import static org.apache.spark.launcher.CommandBuilderUtils.*;
 
-/** 
+/**
  * Launcher for Spark applications.
  * <p>
  * Use this class to start Spark applications programmatically. The class uses a builder pattern
@@ -57,7 +58,8 @@ public class SparkLauncher {
   /** Configuration key for the number of executor CPU cores. */
   public static final String EXECUTOR_CORES = "spark.executor.cores";
 
-  private final SparkSubmitCommandBuilder builder;
+  // Visible for testing.
+  final SparkSubmitCommandBuilder builder;
 
   public SparkLauncher() {
     this(null);
@@ -187,6 +189,73 @@ public SparkLauncher setMainClass(String mainClass) {
     return this;
   }
 
+  /**
+   * Adds a no-value argument to the Spark invocation. If the argument is known, this method
+   * validates whether the argument is indeed a no-value argument, and throws an exception
+   * otherwise.
+   * <p/>
+   * Use this method with caution. It is possible to create an invalid Spark command by passing
+   * unknown arguments to this method, since those are allowed for forward compatibility.
+   *
+   * @param arg Argument to add.
+   * @return This launcher.
+   */
+  public SparkLauncher addSparkArg(String arg) {
+    SparkSubmitOptionParser validator = new ArgumentValidator(false);
+    validator.parse(Arrays.asList(arg));
+    builder.sparkArgs.add(arg);
+    return this;
+  }
+
+  /**
+   * Adds an argument with a value to the Spark invocation. If the argument name corresponds to
+   * a known argument, the code validates that the argument actually expects a value, and throws
+   * an exception otherwise.
+   * <p/>
+   * It is safe to add arguments modified by other methods in this class (such as
+   * {@link #setMaster(String)} - the last invocation will be the one to take effect.
+   * <p/>
+   * Use this method with caution. It is possible to create an invalid Spark command by passing
+   * unknown arguments to this method, since those are allowed for forward compatibility.
+   *
+   * @param name Name of argument to add.
+   * @param value Value of the argument.
+   * @return This launcher.
+   */
+  public SparkLauncher addSparkArg(String name, String value) {
+    SparkSubmitOptionParser validator = new ArgumentValidator(true);
+    if (validator.MASTER.equals(name)) {
+      setMaster(value);
+    } else if (validator.PROPERTIES_FILE.equals(name)) {
+      setPropertiesFile(value);
+    } else if (validator.CONF.equals(name)) {
+      String[] vals = value.split("=", 2);
+      setConf(vals[0], vals[1]);
+    } else if (validator.CLASS.equals(name)) {
+      setMainClass(value);
+    } else if (validator.JARS.equals(name)) {
+      builder.jars.clear();
+      for (String jar : value.split(",")) {
+        addJar(jar);
+      }
+    } else if (validator.FILES.equals(name)) {
+      builder.files.clear();
+      for (String file : value.split(",")) {
+        addFile(file);
+      }
+    } else if (validator.PY_FILES.equals(name)) {
+      builder.pyFiles.clear();
+      for (String file : value.split(",")) {
+        addPyFile(file);
+      }
+    } else {
+      validator.parse(Arrays.asList(name, value));
+      builder.sparkArgs.add(name);
+      builder.sparkArgs.add(value);
+    }
+    return this;
+  }
+
   /**
    * Adds command line arguments for the application.
    *
@@ -277,4 +346,32 @@ public Process launch() throws IOException {
     return pb.start();
   }
 
+  private static class ArgumentValidator extends SparkSubmitOptionParser {
+
+    private final boolean hasValue;
+
+    ArgumentValidator(boolean hasValue) {
+      this.hasValue = hasValue;
+    }
+
+    @Override
+    protected boolean handle(String opt, String value) {
+      if (value == null && hasValue) {
+        throw new IllegalArgumentException(String.format("'%s' does not expect a value.", opt));
+      }
+      return true;
+    }
+
+    @Override
+    protected boolean handleUnknown(String opt) {
+      // Do not fail on unknown arguments, to support future arguments added to SparkSubmit.
+      return true;
+    }
+
+    protected void handleExtraArgs(List<String> extra) {
+      // No op.
+    }
+
+  };
+
 }
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
index 87c43aa9980e1..4f354cedee66f 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
@@ -76,7 +76,7 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder {
       "spark-internal");
   }
 
-  private final List<String> sparkArgs;
+  final List<String> sparkArgs;
   private final boolean printHelp;
 
   /**
diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java b/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
index 252d5abae1ca3..d0c26dd05679b 100644
--- a/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
+++ b/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
@@ -20,6 +20,7 @@
 import java.io.BufferedReader;
 import java.io.InputStream;
 import java.io.InputStreamReader;
+import java.util.Arrays;
 import java.util.HashMap;
 import java.util.Map;
 
@@ -35,8 +36,54 @@ public class SparkLauncherSuite {
 
   private static final Logger LOG = LoggerFactory.getLogger(SparkLauncherSuite.class);
 
+  @Test
+  public void testSparkArgumentHandling() throws Exception {
+    SparkLauncher launcher = new SparkLauncher()
+      .setSparkHome(System.getProperty("spark.test.home"));
+    SparkSubmitOptionParser opts = new SparkSubmitOptionParser();
+
+    launcher.addSparkArg(opts.HELP);
+    try {
+      launcher.addSparkArg(opts.PROXY_USER);
+      fail("Expected IllegalArgumentException.");
+    } catch (IllegalArgumentException e) {
+      // Expected.
+    }
+
+    launcher.addSparkArg(opts.PROXY_USER, "someUser");
+    try {
+      launcher.addSparkArg(opts.HELP, "someValue");
+      fail("Expected IllegalArgumentException.");
+    } catch (IllegalArgumentException e) {
+      // Expected.
+    }
+
+    launcher.addSparkArg("--future-argument");
+    launcher.addSparkArg("--future-argument", "someValue");
+
+    launcher.addSparkArg(opts.MASTER, "myMaster");
+    assertEquals("myMaster", launcher.builder.master);
+
+    launcher.addJar("foo");
+    launcher.addSparkArg(opts.JARS, "bar");
+    assertEquals(Arrays.asList("bar"), launcher.builder.jars);
+
+    launcher.addFile("foo");
+    launcher.addSparkArg(opts.FILES, "bar");
+    assertEquals(Arrays.asList("bar"), launcher.builder.files);
+
+    launcher.addPyFile("foo");
+    launcher.addSparkArg(opts.PY_FILES, "bar");
+    assertEquals(Arrays.asList("bar"), launcher.builder.pyFiles);
+
+    launcher.setConf("spark.foo", "foo");
+    launcher.addSparkArg(opts.CONF, "spark.foo=bar");
+    assertEquals("bar", launcher.builder.conf.get("spark.foo"));
+  }
+
   @Test
   public void testChildProcLauncher() throws Exception {
+    SparkSubmitOptionParser opts = new SparkSubmitOptionParser();
     Map<String, String> env = new HashMap<String, String>();
     env.put("SPARK_PRINT_LAUNCH_COMMAND", "1");
 
@@ -44,9 +91,12 @@ public void testChildProcLauncher() throws Exception {
       .setSparkHome(System.getProperty("spark.test.home"))
       .setMaster("local")
       .setAppResource("spark-internal")
+      .addSparkArg(opts.CONF,
+        String.format("%s=-Dfoo=ShouldBeOverriddenBelow", SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS))
       .setConf(SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS,
         "-Dfoo=bar -Dtest.name=-testChildProcLauncher")
       .setConf(SparkLauncher.DRIVER_EXTRA_CLASSPATH, System.getProperty("java.class.path"))
+      .addSparkArg(opts.CLASS, "ShouldBeOverriddenBelow")
       .setMainClass(SparkLauncherTestApp.class.getName())
       .addAppArgs("proc");
     final Process app = launcher.launch();

From afa757c98c537965007cad4c61c436887f3ac6a6 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 11 Aug 2015 18:08:49 -0700
Subject: [PATCH 0978/1454] [SPARK-9849] [SQL] DirectParquetOutputCommitter
 qualified name should be backward compatible

DirectParquetOutputCommitter was moved in SPARK-9763. However, users can explicitly set the class as a config option, so we must be able to resolve the old committer qualified name.

Author: Reynold Xin <rxin@databricks.com>

Closes #8114 from rxin/SPARK-9849.
---
 .../datasources/parquet/ParquetRelation.scala |  7 +++++
 .../datasources/parquet/ParquetIOSuite.scala  | 27 ++++++++++++++++++-
 2 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 4086a139bed72..c71c69b6e80b1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -209,6 +209,13 @@ private[sql] class ParquetRelation(
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
     val conf = ContextUtil.getConfiguration(job)
 
+    // SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible
+    val committerClassname = conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key)
+    if (committerClassname == "org.apache.spark.sql.parquet.DirectParquetOutputCommitter") {
+      conf.set(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key,
+        classOf[DirectParquetOutputCommitter].getCanonicalName)
+    }
+
     val committerClass =
       conf.getClass(
         SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index ee925afe08508..cb166349fdb26 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -390,7 +390,32 @@ class ParquetIOSuite extends QueryTest with ParquetTest {
     }
   }
 
-  test("SPARK-8121: spark.sql.parquet.output.committer.class shouldn't be overriden") {
+  test("SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible") {
+    val clonedConf = new Configuration(configuration)
+
+    // Write to a parquet file and let it fail.
+    // _temporary should be missing if direct output committer works.
+    try {
+      configuration.set("spark.sql.parquet.output.committer.class",
+        "org.apache.spark.sql.parquet.DirectParquetOutputCommitter")
+      sqlContext.udf.register("div0", (x: Int) => x / 0)
+      withTempPath { dir =>
+        intercept[org.apache.spark.SparkException] {
+          sqlContext.sql("select div0(1)").write.parquet(dir.getCanonicalPath)
+        }
+        val path = new Path(dir.getCanonicalPath, "_temporary")
+        val fs = path.getFileSystem(configuration)
+        assert(!fs.exists(path))
+      }
+    } finally {
+      // Hadoop 1 doesn't have `Configuration.unset`
+      configuration.clear()
+      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+    }
+  }
+
+
+  test("SPARK-8121: spark.sql.parquet.output.committer.class shouldn't be overridden") {
     withTempPath { dir =>
       val clonedConf = new Configuration(configuration)
 

From ca8f70e9d473d2c81866f3c330cc6545c33bdac7 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 11 Aug 2015 20:46:58 -0700
Subject: [PATCH 0979/1454] [SPARK-9649] Fix flaky test MasterSuite again -
 disable REST

The REST server is not actually used in most tests and so we can disable it. It is a source of flakiness because it tries to bind to a specific port in vain. There was also some code that avoided the shuffle service in tests. This is actually not necessary because the shuffle service is already off by default.

Author: Andrew Or <andrew@databricks.com>

Closes #8084 from andrewor14/fix-master-suite-again.
---
 pom.xml                  | 1 +
 project/SparkBuild.scala | 1 +
 2 files changed, 2 insertions(+)

diff --git a/pom.xml b/pom.xml
index 8942836a7da16..cfd7d32563f2a 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1895,6 +1895,7 @@
               <java.io.tmpdir>${project.build.directory}/tmp</java.io.tmpdir>
               <spark.test.home>${spark.test.home}</spark.test.home>
               <spark.testing>1</spark.testing>
+              <spark.master.rest.enabled>false</spark.master.rest.enabled>
               <spark.ui.enabled>false</spark.ui.enabled>
               <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
               <spark.driver.allowMultipleContexts>true</spark.driver.allowMultipleContexts>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index cad7067ade8c1..74f815f941d5b 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -546,6 +546,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.test.home=" + sparkHome,
     javaOptions in Test += "-Dspark.testing=1",
     javaOptions in Test += "-Dspark.port.maxRetries=100",
+    javaOptions in Test += "-Dspark.master.rest.enabled=false",
     javaOptions in Test += "-Dspark.ui.enabled=false",
     javaOptions in Test += "-Dspark.ui.showConsoleProgress=false",
     javaOptions in Test += "-Dspark.driver.allowMultipleContexts=true",

From 3ef0f32928fc383ad3edd5ad167212aeb9eba6e1 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Tue, 11 Aug 2015 21:16:48 -0700
Subject: [PATCH 0980/1454] [SPARK-1517] Refactor release scripts to facilitate
 nightly publishing

This update contains some code changes to the release scripts that allow easier nightly publishing. I've been using these new scripts on Jenkins for cutting and publishing nightly snapshots for the last month or so, and it has been going well. I'd like to get them merged back upstream so this can be maintained by the community.

The main changes are:
1. Separates the release tagging from various build possibilities for an already tagged release (`release-tag.sh` and `release-build.sh`).
2. Allow for injecting credentials through the environment, including GPG keys. This is then paired with secure key injection in Jenkins.
3. Support for copying build results to a remote directory, and also "rotating" results, e.g. the ability to keep the last N copies of binary or doc builds.

I'm happy if anyone wants to take a look at this - it's not user facing but an internal utility used for generating releases.

Author: Patrick Wendell <patrick@databricks.com>

Closes #7411 from pwendell/release-script-updates and squashes the following commits:

74f9beb [Patrick Wendell] Moving maven build command to a variable
233ce85 [Patrick Wendell] [SPARK-1517] Refactor release scripts to facilitate nightly publishing
---
 dev/create-release/create-release.sh | 267 ----------------------
 dev/create-release/release-build.sh  | 321 +++++++++++++++++++++++++++
 dev/create-release/release-tag.sh    |  79 +++++++
 3 files changed, 400 insertions(+), 267 deletions(-)
 delete mode 100755 dev/create-release/create-release.sh
 create mode 100755 dev/create-release/release-build.sh
 create mode 100755 dev/create-release/release-tag.sh

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
deleted file mode 100755
index 4311c8c9e4ca6..0000000000000
--- a/dev/create-release/create-release.sh
+++ /dev/null
@@ -1,267 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Quick-and-dirty automation of making maven and binary releases. Not robust at all.
-# Publishes releases to Maven and packages/copies binary release artifacts.
-# Expects to be run in a totally empty directory.
-#
-# Options:
-#  --skip-create-release	Assume the desired release tag already exists
-#  --skip-publish 		Do not publish to Maven central
-#  --skip-package		Do not package and upload binary artifacts
-# Would be nice to add:
-#  - Send output to stderr and have useful logging in stdout
-
-# Note: The following variables must be set before use!
-ASF_USERNAME=${ASF_USERNAME:-pwendell}
-ASF_PASSWORD=${ASF_PASSWORD:-XXX}
-GPG_PASSPHRASE=${GPG_PASSPHRASE:-XXX}
-GIT_BRANCH=${GIT_BRANCH:-branch-1.0}
-RELEASE_VERSION=${RELEASE_VERSION:-1.2.0}
-# Allows publishing under a different version identifier than
-# was present in the actual release sources (e.g. rc-X)
-PUBLISH_VERSION=${PUBLISH_VERSION:-$RELEASE_VERSION} 
-NEXT_VERSION=${NEXT_VERSION:-1.2.1}
-RC_NAME=${RC_NAME:-rc2}
-
-M2_REPO=~/.m2/repository
-SPARK_REPO=$M2_REPO/org/apache/spark
-NEXUS_ROOT=https://repository.apache.org/service/local/staging
-NEXUS_PROFILE=d63f592e7eac0 # Profile for Spark staging uploads
-
-if [ -z "$JAVA_HOME" ]; then
-  echo "Error: JAVA_HOME is not set, cannot proceed."
-  exit -1
-fi
-JAVA_7_HOME=${JAVA_7_HOME:-$JAVA_HOME}
-
-set -e
-
-GIT_TAG=v$RELEASE_VERSION-$RC_NAME
-
-if [[ ! "$@" =~ --skip-create-release ]]; then
-  echo "Creating release commit and publishing to Apache repository"
-  # Artifact publishing
-  git clone https://$ASF_USERNAME:$ASF_PASSWORD@git-wip-us.apache.org/repos/asf/spark.git \
-    -b $GIT_BRANCH
-  pushd spark
-  export MAVEN_OPTS="-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g"
-
-  # Create release commits and push them to github
-  # NOTE: This is done "eagerly" i.e. we don't check if we can succesfully build
-  # or before we coin the release commit. This helps avoid races where
-  # other people add commits to this branch while we are in the middle of building.
-  cur_ver="${RELEASE_VERSION}-SNAPSHOT"
-  rel_ver="${RELEASE_VERSION}"
-  next_ver="${NEXT_VERSION}-SNAPSHOT"
-
-  old="^\( \{2,4\}\)<version>${cur_ver}<\/version>$"
-  new="\1<version>${rel_ver}<\/version>"
-  find . -name pom.xml | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-  find . -name package.scala | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-
-  git commit -a -m "Preparing Spark release $GIT_TAG"
-  echo "Creating tag $GIT_TAG at the head of $GIT_BRANCH"
-  git tag $GIT_TAG
-
-  old="^\( \{2,4\}\)<version>${rel_ver}<\/version>$"
-  new="\1<version>${next_ver}<\/version>"
-  find . -name pom.xml | grep -v dev | xargs -I {} sed -i \
-    -e "s/$old/$new/" {}
-  find . -name package.scala | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-  git commit -a -m "Preparing development version $next_ver"
-  git push origin $GIT_TAG
-  git push origin HEAD:$GIT_BRANCH
-  popd
-  rm -rf spark
-fi
-
-if [[ ! "$@" =~ --skip-publish ]]; then
-  git clone https://$ASF_USERNAME:$ASF_PASSWORD@git-wip-us.apache.org/repos/asf/spark.git
-  pushd spark
-  git checkout --force $GIT_TAG 
-  
-  # Substitute in case published version is different than released
-  old="^\( \{2,4\}\)<version>${RELEASE_VERSION}<\/version>$"
-  new="\1<version>${PUBLISH_VERSION}<\/version>"
-  find . -name pom.xml | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-
-  # Using Nexus API documented here:
-  # https://support.sonatype.com/entries/39720203-Uploading-to-a-Staging-Repository-via-REST-API
-  echo "Creating Nexus staging repository"
-  repo_request="<promoteRequest><data><description>Apache Spark $GIT_TAG (published as $PUBLISH_VERSION)</description></data></promoteRequest>"
-  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
-    -H "Content-Type:application/xml" -v \
-    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/start)
-  staged_repo_id=$(echo $out | sed -e "s/.*\(orgapachespark-[0-9]\{4\}\).*/\1/")
-  echo "Created Nexus staging repository: $staged_repo_id"
-
-  rm -rf $SPARK_REPO
-
-  build/mvn -DskipTests -Pyarn -Phive \
-    -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
-    clean install
-
-  ./dev/change-scala-version.sh 2.11
-  
-  build/mvn -DskipTests -Pyarn -Phive \
-    -Dscala-2.11 -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
-    clean install
-
-  ./dev/change-scala-version.sh 2.10
-
-  pushd $SPARK_REPO
-
-  # Remove any extra files generated during install
-  find . -type f |grep -v \.jar |grep -v \.pom | xargs rm
-
-  echo "Creating hash and signature files"
-  for file in $(find . -type f)
-  do
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --output $file.asc --detach-sig --armour $file;
-    if [ $(command -v md5) ]; then
-      # Available on OS X; -q to keep only hash
-      md5 -q $file > $file.md5
-    else
-      # Available on Linux; cut to keep only hash
-      md5sum $file | cut -f1 -d' ' > $file.md5
-    fi
-    shasum -a 1 $file | cut -f1 -d' ' > $file.sha1
-  done
-
-  nexus_upload=$NEXUS_ROOT/deployByRepositoryId/$staged_repo_id
-  echo "Uplading files to $nexus_upload"
-  for file in $(find . -type f)
-  do
-    # strip leading ./
-    file_short=$(echo $file | sed -e "s/\.\///")
-    dest_url="$nexus_upload/org/apache/spark/$file_short"
-    echo "  Uploading $file_short"
-    curl -u $ASF_USERNAME:$ASF_PASSWORD --upload-file $file_short $dest_url
-  done
-
-  echo "Closing nexus staging repository"
-  repo_request="<promoteRequest><data><stagedRepositoryId>$staged_repo_id</stagedRepositoryId><description>Apache Spark $GIT_TAG (published as $PUBLISH_VERSION)</description></data></promoteRequest>"
-  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
-    -H "Content-Type:application/xml" -v \
-    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/finish)
-  echo "Closed Nexus staging repository: $staged_repo_id"
-
-  popd
-  popd
-  rm -rf spark
-fi
-
-if [[ ! "$@" =~ --skip-package ]]; then
-  # Source and binary tarballs
-  echo "Packaging release tarballs"
-  git clone https://git-wip-us.apache.org/repos/asf/spark.git
-  cd spark
-  git checkout --force $GIT_TAG
-  release_hash=`git rev-parse HEAD`
-
-  rm .gitignore
-  rm -rf .git
-  cd ..
-
-  cp -r spark spark-$RELEASE_VERSION
-  tar cvzf spark-$RELEASE_VERSION.tgz spark-$RELEASE_VERSION
-  echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --armour --output spark-$RELEASE_VERSION.tgz.asc \
-    --detach-sig spark-$RELEASE_VERSION.tgz
-  echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md MD5 spark-$RELEASE_VERSION.tgz > \
-    spark-$RELEASE_VERSION.tgz.md5
-  echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md SHA512 spark-$RELEASE_VERSION.tgz > \
-    spark-$RELEASE_VERSION.tgz.sha
-  rm -rf spark-$RELEASE_VERSION
-  
-  # Updated for each binary build
-  make_binary_release() {
-    NAME=$1
-    FLAGS=$2
-    ZINC_PORT=$3
-    cp -r spark spark-$RELEASE_VERSION-bin-$NAME
-    
-    cd spark-$RELEASE_VERSION-bin-$NAME
-
-    # TODO There should probably be a flag to make-distribution to allow 2.11 support
-    if [[ $FLAGS == *scala-2.11* ]]; then
-      ./dev/change-scala-version.sh 2.11
-    fi
-
-    export ZINC_PORT=$ZINC_PORT
-    echo "Creating distribution: $NAME ($FLAGS)"
-    ./make-distribution.sh --name $NAME --tgz $FLAGS -DzincPort=$ZINC_PORT 2>&1 > \
-      ../binary-release-$NAME.log
-    cd ..
-    cp spark-$RELEASE_VERSION-bin-$NAME/spark-$RELEASE_VERSION-bin-$NAME.tgz .
-
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --armour \
-      --output spark-$RELEASE_VERSION-bin-$NAME.tgz.asc \
-      --detach-sig spark-$RELEASE_VERSION-bin-$NAME.tgz
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md \
-      MD5 spark-$RELEASE_VERSION-bin-$NAME.tgz > \
-      spark-$RELEASE_VERSION-bin-$NAME.tgz.md5
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md \
-      SHA512 spark-$RELEASE_VERSION-bin-$NAME.tgz > \
-      spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
-  }
-
-  # We increment the Zinc port each time to avoid OOM's and other craziness if multiple builds
-  # share the same Zinc server.
-  make_binary_release "hadoop1" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver" "3030" &
-  make_binary_release "hadoop1-scala2.11" "-Psparkr -Phadoop-1 -Phive -Dscala-2.11" "3031" &
-  make_binary_release "cdh4" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" "3032" &
-  make_binary_release "hadoop2.3" "-Psparkr -Phadoop-2.3 -Phive -Phive-thriftserver -Pyarn" "3033" &
-  make_binary_release "hadoop2.4" "-Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn" "3034" &
-  make_binary_release "mapr3" "-Pmapr3 -Psparkr -Phive -Phive-thriftserver" "3035" &
-  make_binary_release "mapr4" "-Pmapr4 -Psparkr -Pyarn -Phive -Phive-thriftserver" "3036" &
-  make_binary_release "hadoop2.4-without-hive" "-Psparkr -Phadoop-2.4 -Pyarn" "3037" &
-  wait
-  rm -rf spark-$RELEASE_VERSION-bin-*/
-
-  # Copy data
-  echo "Copying release tarballs"
-  rc_folder=spark-$RELEASE_VERSION-$RC_NAME
-  ssh $ASF_USERNAME@people.apache.org \
-    mkdir /home/$ASF_USERNAME/public_html/$rc_folder
-  scp spark-* \
-    $ASF_USERNAME@people.apache.org:/home/$ASF_USERNAME/public_html/$rc_folder/
-
-  # Docs
-  cd spark
-  sbt/sbt clean
-  cd docs
-  # Compile docs with Java 7 to use nicer format
-  JAVA_HOME="$JAVA_7_HOME" PRODUCTION=1 RELEASE_VERSION="$RELEASE_VERSION" jekyll build
-  echo "Copying release documentation"
-  rc_docs_folder=${rc_folder}-docs
-  ssh $ASF_USERNAME@people.apache.org \
-    mkdir /home/$ASF_USERNAME/public_html/$rc_docs_folder
-  rsync -r _site/* $ASF_USERNAME@people.apache.org:/home/$ASF_USERNAME/public_html/$rc_docs_folder
-
-  echo "Release $RELEASE_VERSION completed:"
-  echo "Git tag:\t $GIT_TAG"
-  echo "Release commit:\t $release_hash"
-  echo "Binary location:\t http://people.apache.org/~$ASF_USERNAME/$rc_folder"
-  echo "Doc location:\t http://people.apache.org/~$ASF_USERNAME/$rc_docs_folder"
-fi
diff --git a/dev/create-release/release-build.sh b/dev/create-release/release-build.sh
new file mode 100755
index 0000000000000..399c73e7bf6bc
--- /dev/null
+++ b/dev/create-release/release-build.sh
@@ -0,0 +1,321 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+function exit_with_usage {
+  cat << EOF
+usage: release-build.sh <package|docs|publish-snapshot|publish-release>
+Creates build deliverables from a Spark commit.
+
+Top level targets are
+  package: Create binary packages and copy them to people.apache
+  docs: Build docs and copy them to people.apache
+  publish-snapshot: Publish snapshot release to Apache snapshots
+  publish-release: Publish a release to Apache release repo
+
+All other inputs are environment variables
+
+GIT_REF - Release tag or commit to build from
+SPARK_VERSION - Release identifier used when publishing
+SPARK_PACKAGE_VERSION - Release identifier in top level package directory
+REMOTE_PARENT_DIR - Parent in which to create doc or release builds.
+REMOTE_PARENT_MAX_LENGTH - If set, parent directory will be cleaned to only
+ have this number of subdirectories (by deleting old ones). WARNING: This deletes data.
+
+ASF_USERNAME - Username of ASF committer account
+ASF_PASSWORD - Password of ASF committer account
+ASF_RSA_KEY - RSA private key file for ASF committer account
+
+GPG_KEY - GPG key used to sign release artifacts
+GPG_PASSPHRASE - Passphrase for GPG key
+EOF
+  exit 1
+}
+
+set -e
+
+if [ $# -eq 0 ]; then
+  exit_with_usage
+fi
+
+if [[ $@ == *"help"* ]]; then
+  exit_with_usage
+fi
+
+for env in ASF_USERNAME ASF_RSA_KEY GPG_PASSPHRASE GPG_KEY; do
+  if [ -z "${!env}" ]; then
+    echo "ERROR: $env must be set to run this script"
+    exit_with_usage
+  fi
+done
+
+# Commit ref to checkout when building
+GIT_REF=${GIT_REF:-master}
+
+# Destination directory parent on remote server
+REMOTE_PARENT_DIR=${REMOTE_PARENT_DIR:-/home/$ASF_USERNAME/public_html}
+
+SSH="ssh -o StrictHostKeyChecking=no -i $ASF_RSA_KEY"
+GPG="gpg --no-tty --batch"
+NEXUS_ROOT=https://repository.apache.org/service/local/staging
+NEXUS_PROFILE=d63f592e7eac0 # Profile for Spark staging uploads
+BASE_DIR=$(pwd)
+
+MVN="build/mvn --force"
+PUBLISH_PROFILES="-Pyarn -Phive -Phadoop-2.2"
+PUBLISH_PROFILES="$PUBLISH_PROFILES -Pspark-ganglia-lgpl -Pkinesis-asl"
+
+rm -rf spark
+git clone https://git-wip-us.apache.org/repos/asf/spark.git
+cd spark
+git checkout $GIT_REF
+git_hash=`git rev-parse --short HEAD`
+echo "Checked out Spark git hash $git_hash"
+
+if [ -z "$SPARK_VERSION" ]; then
+  SPARK_VERSION=$($MVN help:evaluate -Dexpression=project.version \
+    | grep -v INFO | grep -v WARNING | grep -v Download)
+fi
+
+if [ -z "$SPARK_PACKAGE_VERSION" ]; then
+  SPARK_PACKAGE_VERSION="${SPARK_VERSION}-$(date +%Y_%m_%d_%H_%M)-${git_hash}"
+fi
+
+DEST_DIR_NAME="spark-$SPARK_PACKAGE_VERSION"
+USER_HOST="$ASF_USERNAME@people.apache.org"
+
+rm .gitignore
+rm -rf .git
+cd ..
+
+if [ -n "$REMOTE_PARENT_MAX_LENGTH" ]; then
+  old_dirs=$($SSH $USER_HOST ls -t $REMOTE_PARENT_DIR | tail -n +$REMOTE_PARENT_MAX_LENGTH)
+  for old_dir in $old_dirs; do
+    echo "Removing directory: $old_dir"
+    $SSH $USER_HOST rm -r $REMOTE_PARENT_DIR/$old_dir
+  done
+fi
+
+if [[ "$1" == "package" ]]; then
+  # Source and binary tarballs
+  echo "Packaging release tarballs"
+  cp -r spark spark-$SPARK_VERSION
+  tar cvzf spark-$SPARK_VERSION.tgz spark-$SPARK_VERSION
+  echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour --output spark-$SPARK_VERSION.tgz.asc \
+    --detach-sig spark-$SPARK_VERSION.tgz
+  echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md MD5 spark-$SPARK_VERSION.tgz > \
+    spark-$SPARK_VERSION.tgz.md5
+  echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \
+    SHA512 spark-$SPARK_VERSION.tgz > spark-$SPARK_VERSION.tgz.sha
+  rm -rf spark-$SPARK_VERSION
+
+  # Updated for each binary build
+  make_binary_release() {
+    NAME=$1
+    FLAGS=$2
+    ZINC_PORT=$3
+    cp -r spark spark-$SPARK_VERSION-bin-$NAME
+
+    cd spark-$SPARK_VERSION-bin-$NAME
+
+    # TODO There should probably be a flag to make-distribution to allow 2.11 support
+    if [[ $FLAGS == *scala-2.11* ]]; then
+      ./dev/change-scala-version.sh 2.11
+    fi
+
+    export ZINC_PORT=$ZINC_PORT
+    echo "Creating distribution: $NAME ($FLAGS)"
+    ./make-distribution.sh --name $NAME --tgz $FLAGS -DzincPort=$ZINC_PORT 2>&1 > \
+      ../binary-release-$NAME.log
+    cd ..
+    cp spark-$SPARK_VERSION-bin-$NAME/spark-$SPARK_VERSION-bin-$NAME.tgz .
+
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour \
+      --output spark-$SPARK_VERSION-bin-$NAME.tgz.asc \
+      --detach-sig spark-$SPARK_VERSION-bin-$NAME.tgz
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \
+      MD5 spark-$SPARK_VERSION-bin-$NAME.tgz > \
+      spark-$SPARK_VERSION-bin-$NAME.tgz.md5
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \
+      SHA512 spark-$SPARK_VERSION-bin-$NAME.tgz > \
+      spark-$SPARK_VERSION-bin-$NAME.tgz.sha
+  }
+
+  # TODO: Check exit codes of children here:
+  # http://stackoverflow.com/questions/1570262/shell-get-exit-code-of-background-process
+
+  # We increment the Zinc port each time to avoid OOM's and other craziness if multiple builds
+  # share the same Zinc server.
+  make_binary_release "hadoop1" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver" "3030" &
+  make_binary_release "hadoop1-scala2.11" "-Psparkr -Phadoop-1 -Phive -Dscala-2.11" "3031" &
+  make_binary_release "cdh4" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" "3032" &
+  make_binary_release "hadoop2.3" "-Psparkr -Phadoop-2.3 -Phive -Phive-thriftserver -Pyarn" "3033" &
+  make_binary_release "hadoop2.4" "-Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn" "3034" &
+  make_binary_release "hadoop2.6" "-Psparkr -Phadoop-2.6 -Phive -Phive-thriftserver -Pyarn" "3034" &
+  make_binary_release "hadoop2.4-without-hive" "-Psparkr -Phadoop-2.4 -Pyarn" "3037" &
+  make_binary_release "without-hadoop" "-Psparkr -Phadoop-provided -Pyarn" "3038" &
+  wait
+  rm -rf spark-$SPARK_VERSION-bin-*/
+
+  # Copy data
+  dest_dir="$REMOTE_PARENT_DIR/${DEST_DIR_NAME}-bin"
+  echo "Copying release tarballs to $dest_dir"
+  $SSH $USER_HOST mkdir $dest_dir
+  rsync -e "$SSH" spark-* $USER_HOST:$dest_dir
+  echo "Linking /latest to $dest_dir"
+  $SSH $USER_HOST rm -f "$REMOTE_PARENT_DIR/latest"
+  $SSH $USER_HOST ln -s $dest_dir "$REMOTE_PARENT_DIR/latest"
+  exit 0
+fi
+
+if [[ "$1" == "docs" ]]; then
+  # Documentation
+  cd spark
+  echo "Building Spark docs"
+  dest_dir="$REMOTE_PARENT_DIR/${DEST_DIR_NAME}-docs"
+  cd docs
+  # Compile docs with Java 7 to use nicer format
+  # TODO: Make configurable to add this: PRODUCTION=1
+  PRODUCTION=1 RELEASE_VERSION="$SPARK_VERSION" jekyll build
+  echo "Copying release documentation to $dest_dir"
+  $SSH $USER_HOST mkdir $dest_dir
+  echo "Linking /latest to $dest_dir"
+  $SSH $USER_HOST rm -f "$REMOTE_PARENT_DIR/latest"
+  $SSH $USER_HOST ln -s $dest_dir "$REMOTE_PARENT_DIR/latest"
+  rsync -e "$SSH" -r _site/* $USER_HOST:$dest_dir
+  cd ..
+  exit 0
+fi
+
+if [[ "$1" == "publish-snapshot" ]]; then
+  cd spark
+  # Publish Spark to Maven release repo
+  echo "Deploying Spark SNAPSHOT at '$GIT_REF' ($git_hash)"
+  echo "Publish version is $SPARK_VERSION"
+  if [[ ! $SPARK_VERSION == *"SNAPSHOT"* ]]; then
+    echo "ERROR: Snapshots must have a version containing SNAPSHOT"
+    echo "ERROR: You gave version '$SPARK_VERSION'"
+    exit 1
+  fi
+  # Coerce the requested version
+  $MVN versions:set -DnewVersion=$SPARK_VERSION
+  tmp_settings="tmp-settings.xml"
+  echo "<settings><servers><server>" > $tmp_settings
+  echo "<id>apache.snapshots.https</id><username>$ASF_USERNAME</username>" >> $tmp_settings
+  echo "<password>$ASF_PASSWORD</password>" >> $tmp_settings
+  echo "</server></servers></settings>" >> $tmp_settings
+
+  # Generate random point for Zinc
+  export ZINC_PORT=$(python -S -c "import random; print random.randrange(3030,4030)")
+
+  $MVN -DzincPort=$ZINC_PORT --settings $tmp_settings -DskipTests $PUBLISH_PROFILES \
+    -Phive-thriftserver deploy
+  ./dev/change-scala-version.sh 2.10
+  $MVN -DzincPort=$ZINC_PORT -Dscala-2.11 --settings $tmp_settings \
+    -DskipTests $PUBLISH_PROFILES deploy
+
+  # Clean-up Zinc nailgun process
+  /usr/sbin/lsof -P |grep $ZINC_PORT | grep LISTEN | awk '{ print $2; }' | xargs kill
+
+  rm $tmp_settings
+  cd ..
+  exit 0
+fi
+
+if [[ "$1" == "publish-release" ]]; then
+  cd spark
+  # Publish Spark to Maven release repo
+  echo "Publishing Spark checkout at '$GIT_REF' ($git_hash)"
+  echo "Publish version is $SPARK_VERSION"
+  # Coerce the requested version
+  $MVN versions:set -DnewVersion=$SPARK_VERSION
+
+  # Using Nexus API documented here:
+  # https://support.sonatype.com/entries/39720203-Uploading-to-a-Staging-Repository-via-REST-API
+  echo "Creating Nexus staging repository"
+  repo_request="<promoteRequest><data><description>Apache Spark $SPARK_VERSION (commit $git_hash)</description></data></promoteRequest>"
+  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
+    -H "Content-Type:application/xml" -v \
+    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/start)
+  staged_repo_id=$(echo $out | sed -e "s/.*\(orgapachespark-[0-9]\{4\}\).*/\1/")
+  echo "Created Nexus staging repository: $staged_repo_id"
+
+  tmp_repo=$(mktemp -d spark-repo-XXXXX)
+
+  # Generate random point for Zinc
+  export ZINC_PORT=$(python -S -c "import random; print random.randrange(3030,4030)")
+
+  $MVN -DzincPort=$ZINC_PORT -Dmaven.repo.local=$tmp_repo -DskipTests $PUBLISH_PROFILES \
+    -Phive-thriftserver clean install
+
+  ./dev/change-scala-version.sh 2.11
+
+  $MVN -DzincPort=$ZINC_PORT -Dmaven.repo.local=$tmp_repo -Dscala-2.11 \
+    -DskipTests $PUBLISH_PROFILES clean install
+
+  # Clean-up Zinc nailgun process
+  /usr/sbin/lsof -P |grep $ZINC_PORT | grep LISTEN | awk '{ print $2; }' | xargs kill
+
+  ./dev/change-version-to-2.10.sh
+
+  pushd $tmp_repo/org/apache/spark
+
+  # Remove any extra files generated during install
+  find . -type f |grep -v \.jar |grep -v \.pom | xargs rm
+
+  echo "Creating hash and signature files"
+  for file in $(find . -type f)
+  do
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --output $file.asc \
+      --detach-sig --armour $file;
+    if [ $(command -v md5) ]; then
+      # Available on OS X; -q to keep only hash
+      md5 -q $file > $file.md5
+    else
+      # Available on Linux; cut to keep only hash
+      md5sum $file | cut -f1 -d' ' > $file.md5
+    fi
+    sha1sum $file | cut -f1 -d' ' > $file.sha1
+  done
+
+  nexus_upload=$NEXUS_ROOT/deployByRepositoryId/$staged_repo_id
+  echo "Uplading files to $nexus_upload"
+  for file in $(find . -type f)
+  do
+    # strip leading ./
+    file_short=$(echo $file | sed -e "s/\.\///")
+    dest_url="$nexus_upload/org/apache/spark/$file_short"
+    echo "  Uploading $file_short"
+    curl -u $ASF_USERNAME:$ASF_PASSWORD --upload-file $file_short $dest_url
+  done
+
+  echo "Closing nexus staging repository"
+  repo_request="<promoteRequest><data><stagedRepositoryId>$staged_repo_id</stagedRepositoryId><description>Apache Spark $SPARK_VERSION (commit $git_hash)</description></data></promoteRequest>"
+  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
+    -H "Content-Type:application/xml" -v \
+    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/finish)
+  echo "Closed Nexus staging repository: $staged_repo_id"
+  popd
+  rm -rf $tmp_repo
+  cd ..
+  exit 0
+fi
+
+cd ..
+rm -rf spark
+echo "ERROR: expects to be called with 'package', 'docs', 'publish-release' or 'publish-snapshot'"
diff --git a/dev/create-release/release-tag.sh b/dev/create-release/release-tag.sh
new file mode 100755
index 0000000000000..b0a3374becc6a
--- /dev/null
+++ b/dev/create-release/release-tag.sh
@@ -0,0 +1,79 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+function exit_with_usage {
+  cat << EOF
+usage: tag-release.sh
+Tags a Spark release on a particular branch.
+
+Inputs are specified with the following environment variables:
+ASF_USERNAME - Apache Username
+ASF_PASSWORD - Apache Password
+GIT_NAME - Name to use with git
+GIT_EMAIL - E-mail address to use with git
+GIT_BRANCH - Git branch on which to make release
+RELEASE_VERSION - Version used in pom files for release
+RELEASE_TAG - Name of release tag
+NEXT_VERSION - Development version after release
+EOF
+  exit 1
+}
+
+set -e
+
+if [[ $@ == *"help"* ]]; then
+  exit_with_usage
+fi
+
+for env in ASF_USERNAME ASF_PASSWORD RELEASE_VERSION RELEASE_TAG NEXT_VERSION GIT_EMAIL GIT_NAME GIT_BRANCH; do
+  if [ -z "${!env}" ]; then
+    echo "$env must be set to run this script"
+    exit 1
+  fi
+done
+
+ASF_SPARK_REPO="git-wip-us.apache.org/repos/asf/spark.git"
+MVN="build/mvn --force"
+
+rm -rf spark
+git clone https://$ASF_USERNAME:$ASF_PASSWORD@$ASF_SPARK_REPO -b $GIT_BRANCH
+cd spark
+
+git config user.name "$GIT_NAME"
+git config user.email $GIT_EMAIL
+
+# Create release version
+$MVN versions:set -DnewVersion=$RELEASE_VERSION | grep -v "no value" # silence logs
+git commit -a -m "Preparing Spark release $RELEASE_TAG"
+echo "Creating tag $RELEASE_TAG at the head of $GIT_BRANCH"
+git tag $RELEASE_TAG
+
+# TODO: It would be nice to do some verifications here
+#       i.e. check whether ec2 scripts have the new version
+
+# Create next version
+$MVN versions:set -DnewVersion=$NEXT_VERSION | grep -v "no value" # silence logs
+git commit -a -m "Preparing development version $NEXT_VERSION"
+
+# Push changes
+git push origin $RELEASE_TAG
+git push origin HEAD:$GIT_BRANCH
+
+cd ..
+rm -rf spark

From 74a293f4537c6982345166f8883538f81d850872 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Tue, 11 Aug 2015 21:26:03 -0700
Subject: [PATCH 0981/1454] [SPARK-9713] [ML] Document SparkR MLlib glm()
 integration in Spark 1.5

This documents the use of R model formulae in the SparkR guide. Also fixes some bugs in the R api doc.

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #8085 from ericl/docs.
---
 R/pkg/R/generics.R |  4 ++--
 R/pkg/R/mllib.R    |  8 ++++----
 docs/sparkr.md     | 37 ++++++++++++++++++++++++++++++++++++-
 3 files changed, 42 insertions(+), 7 deletions(-)

diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index c43b947129e87..379a78b1d833e 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -535,8 +535,8 @@ setGeneric("showDF", function(x,...) { standardGeneric("showDF") })
 #' @export
 setGeneric("summarize", function(x,...) { standardGeneric("summarize") })
 
-##' rdname summary
-##' @export
+#' @rdname summary
+#' @export
 setGeneric("summary", function(x, ...) { standardGeneric("summary") })
 
 # @rdname tojson
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index b524d1fd87496..cea3d760d05fe 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -56,10 +56,10 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "DataFram
 #'
 #' Makes predictions from a model produced by glm(), similarly to R's predict().
 #'
-#' @param model A fitted MLlib model
+#' @param object A fitted MLlib model
 #' @param newData DataFrame for testing
 #' @return DataFrame containing predicted values
-#' @rdname glm
+#' @rdname predict
 #' @export
 #' @examples
 #'\dontrun{
@@ -76,10 +76,10 @@ setMethod("predict", signature(object = "PipelineModel"),
 #'
 #' Returns the summary of a model produced by glm(), similarly to R's summary().
 #'
-#' @param model A fitted MLlib model
+#' @param x A fitted MLlib model
 #' @return a list with a 'coefficient' component, which is the matrix of coefficients. See
 #'         summary.glm for more information.
-#' @rdname glm
+#' @rdname summary
 #' @export
 #' @examples
 #'\dontrun{
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 4385a4eeacd5c..7139d16b4a068 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -11,7 +11,8 @@ title: SparkR (R on Spark)
 SparkR is an R package that provides a light-weight frontend to use Apache Spark from R.
 In Spark {{site.SPARK_VERSION}}, SparkR provides a distributed data frame implementation that
 supports operations like selection, filtering, aggregation etc. (similar to R data frames,
-[dplyr](https://github.com/hadley/dplyr)) but on large datasets.
+[dplyr](https://github.com/hadley/dplyr)) but on large datasets. SparkR also supports distributed
+machine learning using MLlib.
 
 # SparkR DataFrames
 
@@ -230,3 +231,37 @@ head(teenagers)
 
 {% endhighlight %}
 </div>
+
+# Machine Learning
+
+SparkR allows the fitting of generalized linear models over DataFrames using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', '+', and '-'. The example below shows the use of building a gaussian GLM model using SparkR.
+
+<div data-lang="r"  markdown="1">
+{% highlight r %}
+# Create the DataFrame
+df <- createDataFrame(sqlContext, iris)
+
+# Fit a linear model over the dataset.
+model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian")
+
+# Model coefficients are returned in a similar format to R's native glm().
+summary(model)
+##$coefficients
+##                    Estimate
+##(Intercept)        2.2513930
+##Sepal_Width        0.8035609
+##Species_versicolor 1.4587432
+##Species_virginica  1.9468169
+
+# Make predictions based on the model.
+predictions <- predict(model, newData = df)
+head(select(predictions, "Sepal_Length", "prediction"))
+##  Sepal_Length prediction
+##1          5.1   5.063856
+##2          4.9   4.662076
+##3          4.7   4.822788
+##4          4.6   4.742432
+##5          5.0   5.144212
+##6          5.4   5.385281
+{% endhighlight %}
+</div>

From c3e9a120e33159fb45cd99f3a55fc5cf16cd7c6c Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 11 Aug 2015 22:45:18 -0700
Subject: [PATCH 0982/1454] [SPARK-9831] [SQL] fix serialization with empty
 broadcast

Author: Davies Liu <davies@databricks.com>

Closes #8117 from davies/fix_serialization and squashes the following commits:

d21ac71 [Davies Liu] fix serialization with empty broadcast
---
 .../sql/execution/joins/HashedRelation.scala    |  2 +-
 .../execution/joins/HashedRelationSuite.scala   | 17 +++++++++++++++++
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index c1bc7947aa39c..076afe6e4e960 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -299,7 +299,7 @@ private[joins] final class UnsafeHashedRelation(
     binaryMap = new BytesToBytesMap(
       taskMemoryManager,
       shuffleMemoryManager,
-      nKeys * 2, // reduce hash collision
+      (nKeys * 1.5 + 1).toInt, // reduce hash collision
       pageSizeBytes)
 
     var i = 0
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index a1fa2c3864bdb..c635b2d51f464 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -103,4 +103,21 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed2.get(unsafeData(2)) === data2)
     assert(numDataRows.value.value === data.length)
   }
+
+  test("test serialization empty hash map") {
+    val os = new ByteArrayOutputStream()
+    val out = new ObjectOutputStream(os)
+    val hashed = new UnsafeHashedRelation(
+      new java.util.HashMap[UnsafeRow, CompactBuffer[UnsafeRow]])
+    hashed.writeExternal(out)
+    out.flush()
+    val in = new ObjectInputStream(new ByteArrayInputStream(os.toByteArray))
+    val hashed2 = new UnsafeHashedRelation()
+    hashed2.readExternal(in)
+
+    val schema = StructType(StructField("a", IntegerType, true) :: Nil)
+    val toUnsafe = UnsafeProjection.create(schema)
+    val row = toUnsafe(InternalRow(0))
+    assert(hashed2.get(row) === null)
+  }
 }

From b1581ac28840a4d2209ef8bb5c9f8700b4c1b286 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 11 Aug 2015 22:46:59 -0700
Subject: [PATCH 0983/1454] [SPARK-9854] [SQL] RuleExecutor.timeMap should be
 thread-safe

`RuleExecutor.timeMap` is currently a non-thread-safe mutable HashMap; this can lead to infinite loops if multiple threads are concurrently modifying the map.  I believe that this is responsible for some hangs that I've observed in HiveQuerySuite.

This patch addresses this by using a Guava `AtomicLongMap`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8120 from JoshRosen/rule-executor-time-map-fix.
---
 .../spark/sql/catalyst/rules/RuleExecutor.scala   | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
index 8b824511a79da..f80d2a93241d1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
@@ -17,22 +17,25 @@
 
 package org.apache.spark.sql.catalyst.rules
 
+import scala.collection.JavaConverters._
+
+import com.google.common.util.concurrent.AtomicLongMap
+
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.catalyst.util.sideBySide
 
-import scala.collection.mutable
-
 object RuleExecutor {
-  protected val timeMap = new mutable.HashMap[String, Long].withDefault(_ => 0)
+  protected val timeMap = AtomicLongMap.create[String]()
 
   /** Resets statistics about time spent running specific rules */
   def resetTime(): Unit = timeMap.clear()
 
   /** Dump statistics about time spent running specific rules. */
   def dumpTimeSpent(): String = {
-    val maxSize = timeMap.keys.map(_.toString.length).max
-    timeMap.toSeq.sortBy(_._2).reverseMap { case (k, v) =>
+    val map = timeMap.asMap().asScala
+    val maxSize = map.keys.map(_.toString.length).max
+    map.toSeq.sortBy(_._2).reverseMap { case (k, v) =>
       s"${k.padTo(maxSize, " ").mkString} $v"
     }.mkString("\n")
   }
@@ -79,7 +82,7 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
             val startTime = System.nanoTime()
             val result = rule(plan)
             val runTime = System.nanoTime() - startTime
-            RuleExecutor.timeMap(rule.ruleName) = RuleExecutor.timeMap(rule.ruleName) + runTime
+            RuleExecutor.timeMap.addAndGet(rule.ruleName, runTime)
 
             if (!result.fastEquals(plan)) {
               logTrace(

From b85f9a242a12e8096e331fa77d5ebd16e93c844d Mon Sep 17 00:00:00 2001
From: xutingjun <xutingjun@huawei.com>
Date: Tue, 11 Aug 2015 23:19:35 -0700
Subject: [PATCH 0984/1454] [SPARK-8366] maxNumExecutorsNeeded should properly
 handle failed tasks

Author: xutingjun <xutingjun@huawei.com>
Author: meiyoula <1039320815@qq.com>

Closes #6817 from XuTingjun/SPARK-8366.
---
 .../spark/ExecutorAllocationManager.scala     | 22 ++++++++++++-------
 .../ExecutorAllocationManagerSuite.scala      | 22 +++++++++++++++++--
 2 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
index 1877aaf2cac55..b93536e6536e2 100644
--- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
+++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
@@ -599,14 +599,8 @@ private[spark] class ExecutorAllocationManager(
 
         // If this is the last pending task, mark the scheduler queue as empty
         stageIdToTaskIndices.getOrElseUpdate(stageId, new mutable.HashSet[Int]) += taskIndex
-        val numTasksScheduled = stageIdToTaskIndices(stageId).size
-        val numTasksTotal = stageIdToNumTasks.getOrElse(stageId, -1)
-        if (numTasksScheduled == numTasksTotal) {
-          // No more pending tasks for this stage
-          stageIdToNumTasks -= stageId
-          if (stageIdToNumTasks.isEmpty) {
-            allocationManager.onSchedulerQueueEmpty()
-          }
+        if (totalPendingTasks() == 0) {
+          allocationManager.onSchedulerQueueEmpty()
         }
 
         // Mark the executor on which this task is scheduled as busy
@@ -618,6 +612,8 @@ private[spark] class ExecutorAllocationManager(
     override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
       val executorId = taskEnd.taskInfo.executorId
       val taskId = taskEnd.taskInfo.taskId
+      val taskIndex = taskEnd.taskInfo.index
+      val stageId = taskEnd.stageId
       allocationManager.synchronized {
         numRunningTasks -= 1
         // If the executor is no longer running any scheduled tasks, mark it as idle
@@ -628,6 +624,16 @@ private[spark] class ExecutorAllocationManager(
             allocationManager.onExecutorIdle(executorId)
           }
         }
+
+        // If the task failed, we expect it to be resubmitted later. To ensure we have
+        // enough resources to run the resubmitted task, we need to mark the scheduler
+        // as backlogged again if it's not already marked as such (SPARK-8366)
+        if (taskEnd.reason != Success) {
+          if (totalPendingTasks() == 0) {
+            allocationManager.onSchedulerBacklogged()
+          }
+          stageIdToTaskIndices.get(stageId).foreach { _.remove(taskIndex) }
+        }
       }
     }
 
diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
index 34caca892891c..f374f97f87448 100644
--- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
@@ -206,8 +206,8 @@ class ExecutorAllocationManagerSuite
 
     val task2Info = createTaskInfo(1, 0, "executor-1")
     sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, task2Info))
-    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, null, task1Info, null))
-    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, null, task2Info, null))
+    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, Success, task1Info, null))
+    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, Success, task2Info, null))
 
     assert(adjustRequestedExecutors(manager) === -1)
   }
@@ -787,6 +787,24 @@ class ExecutorAllocationManagerSuite
       Map("host2" -> 1, "host3" -> 2, "host4" -> 1, "host5" -> 2))
   }
 
+  test("SPARK-8366: maxNumExecutorsNeeded should properly handle failed tasks") {
+    sc = createSparkContext()
+    val manager = sc.executorAllocationManager.get
+    assert(maxNumExecutorsNeeded(manager) === 0)
+
+    sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(0, 1)))
+    assert(maxNumExecutorsNeeded(manager) === 1)
+
+    val taskInfo = createTaskInfo(1, 1, "executor-1")
+    sc.listenerBus.postToAll(SparkListenerTaskStart(0, 0, taskInfo))
+    assert(maxNumExecutorsNeeded(manager) === 1)
+
+    // If the task is failed, we expect it to be resubmitted later.
+    val taskEndReason = ExceptionFailure(null, null, null, null, null)
+    sc.listenerBus.postToAll(SparkListenerTaskEnd(0, 0, null, taskEndReason, taskInfo, null))
+    assert(maxNumExecutorsNeeded(manager) === 1)
+  }
+
   private def createSparkContext(
       minExecutors: Int = 1,
       maxExecutors: Int = 5,

From a807fcbe50b2ce18751d80d39e9d21842f7da32a Mon Sep 17 00:00:00 2001
From: Rohit Agarwal <rohita@qubole.com>
Date: Tue, 11 Aug 2015 23:20:39 -0700
Subject: [PATCH 0985/1454] [SPARK-9806] [WEB UI] Don't share ReplayListenerBus
 between multiple applications

Author: Rohit Agarwal <rohita@qubole.com>

Closes #8088 from mindprince/SPARK-9806.
---
 .../org/apache/spark/deploy/history/FsHistoryProvider.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index e3060ac3fa1a9..53c18ca3ff50c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -272,9 +272,9 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
    * Replay the log files in the list and merge the list of old applications with new ones
    */
   private def mergeApplicationListing(logs: Seq[FileStatus]): Unit = {
-    val bus = new ReplayListenerBus()
     val newAttempts = logs.flatMap { fileStatus =>
       try {
+        val bus = new ReplayListenerBus()
         val res = replay(fileStatus, bus)
         res match {
           case Some(r) => logDebug(s"Application log ${r.logPath} loaded successfully.")

From 4e3f4b934f74e8c7c06f4940d6381343f9fd4918 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 11 Aug 2015 23:23:17 -0700
Subject: [PATCH 0986/1454] [SPARK-9829] [WEBUI] Display the update value for
 peak execution memory

The peak execution memory is not correct because it shows the sum of finished tasks' values when a task finishes.

This PR fixes it by using the update value rather than the accumulator value.

Author: zsxwing <zsxwing@gmail.com>

Closes #8121 from zsxwing/SPARK-9829.
---
 core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 0c94204df6530..fb4556b836859 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -860,7 +860,7 @@ private[ui] class TaskDataSource(
     }
     val peakExecutionMemoryUsed = taskInternalAccumulables
       .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
-      .map { acc => acc.value.toLong }
+      .map { acc => acc.update.getOrElse("0").toLong }
       .getOrElse(0L)
 
     val maybeInput = metrics.flatMap(_.inputMetrics)

From bab89232854de7554e88f29cab76f1a1c349edc1 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Tue, 11 Aug 2015 23:25:02 -0700
Subject: [PATCH 0987/1454] [SPARK-9426] [WEBUI] Job page DAG visualization is
 not shown

To reproduce the issue, go to the stage page and click DAG Visualization once, then go to the job page to show the job DAG visualization. You will only see the first stage of the job.
Root cause: the java script use local storage to remember your selection. Once you click the stage DAG visualization, the local storage set `expand-dag-viz-arrow-stage` to true. When you go to the job page, the js checks `expand-dag-viz-arrow-stage` in the local storage first and will try to show stage DAG visualization on the job page.
To fix this, I set an id to the DAG span to differ job page and stage page. In the js code, we check the id and local storage together to make sure we show the correct DAG visualization.

Author: Carson Wang <carson.wang@intel.com>

Closes #8104 from carsonwang/SPARK-9426.
---
 .../resources/org/apache/spark/ui/static/spark-dag-viz.js | 8 ++++----
 core/src/main/scala/org/apache/spark/ui/UIUtils.scala     | 3 ++-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index 4a893bc0189aa..83dbea40b63f3 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -109,13 +109,13 @@ function toggleDagViz(forJob) {
 }
 
 $(function (){
-  if (window.localStorage.getItem(expandDagVizArrowKey(false)) == "true") {
+  if ($("#stage-dag-viz").length &&
+      window.localStorage.getItem(expandDagVizArrowKey(false)) == "true") {
     // Set it to false so that the click function can revert it
     window.localStorage.setItem(expandDagVizArrowKey(false), "false");
     toggleDagViz(false);
-  }
-
-  if (window.localStorage.getItem(expandDagVizArrowKey(true)) == "true") {
+  } else if ($("#job-dag-viz").length &&
+      window.localStorage.getItem(expandDagVizArrowKey(true)) == "true") {
     // Set it to false so that the click function can revert it
     window.localStorage.setItem(expandDagVizArrowKey(true), "false");
     toggleDagViz(true);
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index 718aea7e1dc22..f2da417724104 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -352,7 +352,8 @@ private[spark] object UIUtils extends Logging {
    */
   private def showDagViz(graphs: Seq[RDDOperationGraph], forJob: Boolean): Seq[Node] = {
     <div>
-      <span class="expand-dag-viz" onclick={s"toggleDagViz($forJob);"}>
+      <span id={if (forJob) "job-dag-viz" else "stage-dag-viz"}
+            class="expand-dag-viz" onclick={s"toggleDagViz($forJob);"}>
         <span class="expand-dag-viz-arrow arrow-closed"></span>
         <a data-toggle="tooltip" title={if (forJob) ToolTips.JOB_DAG else ToolTips.STAGE_DAG}
            data-placement="right">

From 5c99d8bf98cbf7f568345d02a814fc318cbfca75 Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Tue, 11 Aug 2015 23:26:33 -0700
Subject: [PATCH 0988/1454] [SPARK-8798] [MESOS] Allow additional uris to be
 fetched with mesos

Some users like to download additional files in their sandbox that they can refer to from their spark program, or even later mount these files to another directory.

Author: Timothy Chen <tnachen@gmail.com>

Closes #7195 from tnachen/mesos_files.
---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala       | 5 +++++
 .../scheduler/cluster/mesos/MesosClusterScheduler.scala   | 3 +++
 .../scheduler/cluster/mesos/MesosSchedulerBackend.scala   | 5 +++++
 .../scheduler/cluster/mesos/MesosSchedulerUtils.scala     | 6 ++++++
 docs/running-on-mesos.md                                  | 8 ++++++++
 5 files changed, 27 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 15a0915708c7c..d6e1e9e5bebc2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -194,6 +194,11 @@ private[spark] class CoarseMesosSchedulerBackend(
         s" --app-id $appId")
       command.addUris(CommandInfo.URI.newBuilder().setValue(uri.get))
     }
+
+    conf.getOption("spark.mesos.uris").map { uris =>
+      setupUris(uris, command)
+    }
+
     command.build()
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index f078547e71352..64ec2b8e3db15 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -403,6 +403,9 @@ private[spark] class MesosClusterScheduler(
     }
     builder.setValue(s"$executable $cmdOptions $jar $appArguments")
     builder.setEnvironment(envBuilder.build())
+    conf.getOption("spark.mesos.uris").map { uris =>
+      setupUris(uris, builder)
+    }
     builder.build()
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 3f63ec1c5832f..5c20606d58715 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -133,6 +133,11 @@ private[spark] class MesosSchedulerBackend(
 
     builder.addAllResources(usedCpuResources)
     builder.addAllResources(usedMemResources)
+
+    sc.conf.getOption("spark.mesos.uris").map { uris =>
+      setupUris(uris, command)
+    }
+
     val executorInfo = builder
       .setExecutorId(ExecutorID.newBuilder().setValue(execId).build())
       .setCommand(command)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
index c04920e4f5873..5b854aa5c2754 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
@@ -331,4 +331,10 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
       sc.executorMemory
   }
 
+  def setupUris(uris: String, builder: CommandInfo.Builder): Unit = {
+    uris.split(",").foreach { uri =>
+      builder.addUris(CommandInfo.URI.newBuilder().setValue(uri.trim()))
+    }
+  }
+
 }
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index debdd2adf22d6..55e6d4e83a725 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -306,6 +306,14 @@ See the [configuration page](configuration.html) for information on Spark config
     the final overhead will be this value.
   </td>
 </tr>
+<tr>
+  <td><code>spark.mesos.uris</code></td>
+  <td>(none)</td>
+  <td>
+    A list of URIs to be downloaded to the sandbox when driver or executor is launched by Mesos.
+    This applies to both coarse-grain and fine-grain mode.
+  </td>
+</tr>
 <tr>
   <td><code>spark.mesos.principal</code></td>
   <td>Framework principal to authenticate to Mesos</td>

From 741a29f98945538a475579ccc974cd42c1613be4 Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Tue, 11 Aug 2015 23:33:22 -0700
Subject: [PATCH 0989/1454] [SPARK-9575] [MESOS] Add docuemntation around Mesos
 shuffle service.

andrewor14

Author: Timothy Chen <tnachen@gmail.com>

Closes #7907 from tnachen/mesos_shuffle.
---
 docs/running-on-mesos.md | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 55e6d4e83a725..cfd219ab02e26 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -216,6 +216,20 @@ node. Please refer to [Hadoop on Mesos](https://github.com/mesos/hadoop).
 
 In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos.
 
+# Dynamic Resource Allocation with Mesos
+
+Mesos supports dynamic allocation only with coarse grain mode, which can resize the number of executors based on statistics
+of the application. While dynamic allocation supports both scaling up and scaling down the number of executors, the coarse grain scheduler only supports scaling down
+since it is already designed to run one executor per slave with the configured amount of resources. However, after scaling down the number of executors the coarse grain scheduler
+can scale back up to the same amount of executors when Spark signals more executors are needed.
+
+Users that like to utilize this feature should launch the Mesos Shuffle Service that
+provides shuffle data cleanup functionality on top of the Shuffle Service since Mesos doesn't yet support notifying another framework's
+termination. To launch/stop the Mesos Shuffle Service please use the provided sbin/start-mesos-shuffle-service.sh and sbin/stop-mesos-shuffle-service.sh
+scripts accordingly.
+
+The Shuffle Service is expected to be running on each slave node that will run Spark executors. One way to easily achieve this with Mesos
+is to launch the Shuffle Service with Marathon with a unique host constraint.
 
 # Configuration
 

From 9d0822455ddc8d765440d58c463367a4d67ef456 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 12 Aug 2015 19:54:00 +0800
Subject: [PATCH 0990/1454] [SPARK-9182] [SQL] Filters are not passed through
 to jdbc source

This PR fixes unable to push filter down to JDBC source caused by `Cast` during pattern matching.

While we are comparing columns of different type, there's a big chance we need a cast on the column, therefore not match the pattern directly on Attribute and would fail to push down.

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8049 from yjshen/jdbc_pushdown.
---
 .../datasources/DataSourceStrategy.scala      | 30 ++++++++++++++--
 .../execution/datasources/jdbc/JDBCRDD.scala  |  2 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 34 +++++++++++++++++++
 3 files changed, 63 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 2a4c40db8bb66..9eea2b0382535 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -20,13 +20,13 @@ package org.apache.spark.sql.execution.datasources
 import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
-import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, expressions}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.{StringType, StructType}
+import org.apache.spark.sql.types.{TimestampType, DateType, StringType, StructType}
 import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.{SerializableConfiguration, Utils}
@@ -343,11 +343,17 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
    * and convert them.
    */
   protected[sql] def selectFilters(filters: Seq[Expression]) = {
+    import CatalystTypeConverters._
+
     def translate(predicate: Expression): Option[Filter] = predicate match {
       case expressions.EqualTo(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualTo(a.name, v))
       case expressions.EqualTo(Literal(v, _), a: Attribute) =>
         Some(sources.EqualTo(a.name, v))
+      case expressions.EqualTo(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.EqualTo(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualNullSafe(a.name, v))
@@ -358,21 +364,41 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         Some(sources.GreaterThan(a.name, v))
       case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
         Some(sources.LessThan(a.name, v))
+      case expressions.GreaterThan(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.GreaterThan(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThan(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThan(a.name, v))
       case expressions.LessThan(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThan(a.name, v))
+      case expressions.LessThan(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.LessThan(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
       case expressions.GreaterThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.LessThanOrEqual(a.name, v))
+      case expressions.GreaterThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.GreaterThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.GreaterThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.LessThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThanOrEqual(a.name, v))
       case expressions.LessThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
+      case expressions.LessThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.LessThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.LessThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.GreaterThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.InSet(a: Attribute, set) =>
         Some(sources.In(a.name, set.toArray))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 8eab6a0adccc4..281943e23fcff 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -284,7 +284,7 @@ private[sql] class JDBCRDD(
   /**
    * `filters`, but as a WHERE clause suitable for injection into a SQL query.
    */
-  private val filterWhereClause: String = {
+  val filterWhereClause: String = {
     val filterStrings = filters map compileFilter filter (_ != null)
     if (filterStrings.size > 0) {
       val sb = new StringBuilder("WHERE ")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 42f2449afb0f9..b9cfae51e809c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -25,6 +25,8 @@ import org.h2.jdbc.JdbcSQLException
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD
+import org.apache.spark.sql.execution.PhysicalRDD
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -148,6 +150,18 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
         |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES', user 'testUser', password 'testPass')
       """.stripMargin.replaceAll("\n", " "))
 
+    conn.prepareStatement("create table test.decimals (a DECIMAL(7, 2), b DECIMAL(4, 0))").
+      executeUpdate()
+    conn.prepareStatement("insert into test.decimals values (12345.67, 1234)").executeUpdate()
+    conn.prepareStatement("insert into test.decimals values (34567.89, 1428)").executeUpdate()
+    conn.commit()
+    sql(
+      s"""
+         |CREATE TEMPORARY TABLE decimals
+         |USING org.apache.spark.sql.jdbc
+         |OPTIONS (url '$url', dbtable 'TEST.DECIMALS', user 'testUser', password 'testPass')
+      """.stripMargin.replaceAll("\n", " "))
+
     conn.prepareStatement(
       s"""
         |create table test.nulltypes (a INT, b BOOLEAN, c TINYINT, d BINARY(20), e VARCHAR(20),
@@ -445,4 +459,24 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
     assert(agg.getCatalystType(1, "", 1, null) === Some(StringType))
   }
 
+  test("SPARK-9182: filters are not passed through to jdbc source") {
+    def checkPushedFilter(query: String, filterStr: String): Unit = {
+      val rddOpt = sql(query).queryExecution.executedPlan.collectFirst {
+        case PhysicalRDD(_, rdd: JDBCRDD, _) => rdd
+      }
+      assert(rddOpt.isDefined)
+      val pushedFilterStr = rddOpt.get.filterWhereClause
+      assert(pushedFilterStr.contains(filterStr),
+        s"Expected to push [$filterStr], actually we pushed [$pushedFilterStr]")
+    }
+
+    checkPushedFilter("select * from foobar where NAME = 'fred'", "NAME = 'fred'")
+    checkPushedFilter("select * from inttypes where A > '15'", "A > 15")
+    checkPushedFilter("select * from inttypes where C <= 20", "C <= 20")
+    checkPushedFilter("select * from decimals where A > 1000", "A > 1000.00")
+    checkPushedFilter("select * from decimals where A > 1000 AND A < 2000",
+      "A > 1000.00 AND A < 2000.00")
+    checkPushedFilter("select * from decimals where A = 2000 AND B > 20", "A = 2000.00 AND B > 20")
+    checkPushedFilter("select * from timetypes where B > '1998-09-10'", "B > 1998-09-10")
+  }
 }

From 3ecb3794302dc12d0989f8d725483b2cc37762cf Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 12 Aug 2015 20:01:34 +0800
Subject: [PATCH 0991/1454] [SPARK-9407] [SQL] Relaxes Parquet ValidTypeMap to
 allow ENUM predicates to be pushed down

This PR adds a hacky workaround for PARQUET-201, and should be removed once we upgrade to parquet-mr 1.8.1 or higher versions.

In Parquet, not all types of columns can be used for filter push-down optimization.  The set of valid column types is controlled by `ValidTypeMap`.  Unfortunately, in parquet-mr 1.7.0 and prior versions, this limitation is too strict, and doesn't allow `BINARY (ENUM)` columns to be pushed down.  On the other hand, `BINARY (ENUM)` is commonly seen in Parquet files written by libraries like `parquet-avro`.

This restriction is problematic for Spark SQL, because Spark SQL doesn't have a type that maps to Parquet `BINARY (ENUM)` directly, and always converts `BINARY (ENUM)` to Catalyst `StringType`.  Thus, a predicate involving a `BINARY (ENUM)` is recognized as one involving a string field instead and can be pushed down by the query optimizer.  Such predicates are actually perfectly legal except that it fails the `ValidTypeMap` check.

The workaround added here is relaxing `ValidTypeMap` to include `BINARY (ENUM)`.  I also took the chance to simplify `ParquetCompatibilityTest` a little bit when adding regression test.

Author: Cheng Lian <lian@databricks.com>

Closes #8107 from liancheng/spark-9407/parquet-enum-filter-push-down.
---
 .../datasources/parquet/ParquetFilters.scala  |  38 ++++-
 .../datasources/parquet/ParquetRelation.scala |   2 +-
 sql/core/src/test/README.md                   |  16 +-
 sql/core/src/test/avro/parquet-compat.avdl    |  13 +-
 sql/core/src/test/avro/parquet-compat.avpr    |  13 +-
 .../parquet/test/avro/CompatibilityTest.java  |   2 +-
 .../datasources/parquet/test/avro/Nested.java |   4 +-
 .../parquet/test/avro/ParquetAvroCompat.java  |   4 +-
 .../parquet/test/avro/ParquetEnum.java        | 142 ++++++++++++++++++
 .../datasources/parquet/test/avro/Suit.java   |  13 ++
 .../ParquetAvroCompatibilitySuite.scala       | 105 +++++++------
 .../parquet/ParquetCompatibilityTest.scala    |  33 +---
 .../test/scripts/{gen-code.sh => gen-avro.sh} |  13 +-
 sql/core/src/test/scripts/gen-thrift.sh       |  27 ++++
 .../src/test/thrift/parquet-compat.thrift     |   2 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |  83 +++++-----
 16 files changed, 374 insertions(+), 136 deletions(-)
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java
 rename sql/core/src/test/scripts/{gen-code.sh => gen-avro.sh} (76%)
 create mode 100755 sql/core/src/test/scripts/gen-thrift.sh

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index 9e2e232f50167..63915e0a28655 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -25,9 +25,10 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.filter2.compat.FilterCompat
 import org.apache.parquet.filter2.compat.FilterCompat._
 import org.apache.parquet.filter2.predicate.FilterApi._
-import org.apache.parquet.filter2.predicate.{FilterApi, FilterPredicate, Statistics}
-import org.apache.parquet.filter2.predicate.UserDefinedPredicate
+import org.apache.parquet.filter2.predicate._
 import org.apache.parquet.io.api.Binary
+import org.apache.parquet.schema.OriginalType
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName
 
 import org.apache.spark.SparkEnv
 import org.apache.spark.sql.catalyst.expressions._
@@ -197,6 +198,8 @@ private[sql] object ParquetFilters {
   def createFilter(schema: StructType, predicate: sources.Filter): Option[FilterPredicate] = {
     val dataTypeOf = schema.map(f => f.name -> f.dataType).toMap
 
+    relaxParquetValidTypeMap
+
     // NOTE:
     //
     // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`,
@@ -239,6 +242,37 @@ private[sql] object ParquetFilters {
     }
   }
 
+  // !! HACK ALERT !!
+  //
+  // This lazy val is a workaround for PARQUET-201, and should be removed once we upgrade to
+  // parquet-mr 1.8.1 or higher versions.
+  //
+  // In Parquet, not all types of columns can be used for filter push-down optimization.  The set
+  // of valid column types is controlled by `ValidTypeMap`.  Unfortunately, in parquet-mr 1.7.0 and
+  // prior versions, the limitation is too strict, and doesn't allow `BINARY (ENUM)` columns to be
+  // pushed down.
+  //
+  // This restriction is problematic for Spark SQL, because Spark SQL doesn't have a type that maps
+  // to Parquet original type `ENUM` directly, and always converts `ENUM` to `StringType`.  Thus,
+  // a predicate involving a `ENUM` field can be pushed-down as a string column, which is perfectly
+  // legal except that it fails the `ValidTypeMap` check.
+  //
+  // Here we add `BINARY (ENUM)` into `ValidTypeMap` lazily via reflection to workaround this issue.
+  private lazy val relaxParquetValidTypeMap: Unit = {
+    val constructor = Class
+      .forName(classOf[ValidTypeMap].getCanonicalName + "$FullTypeDescriptor")
+      .getDeclaredConstructor(classOf[PrimitiveTypeName], classOf[OriginalType])
+
+    constructor.setAccessible(true)
+    val enumTypeDescriptor = constructor
+      .newInstance(PrimitiveTypeName.BINARY, OriginalType.ENUM)
+      .asInstanceOf[AnyRef]
+
+    val addMethod = classOf[ValidTypeMap].getDeclaredMethods.find(_.getName == "add").get
+    addMethod.setAccessible(true)
+    addMethod.invoke(null, classOf[Binary], enumTypeDescriptor)
+  }
+
   /**
    * Converts Catalyst predicate expressions to Parquet filter predicates.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index c71c69b6e80b1..52fac18ba187a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -678,7 +678,7 @@ private[sql] object ParquetRelation extends Logging {
     val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
     val serializedConf = new SerializableConfiguration(sqlContext.sparkContext.hadoopConfiguration)
 
-    // HACK ALERT:
+    // !! HACK ALERT !!
     //
     // Parquet requires `FileStatus`es to read footers.  Here we try to send cached `FileStatus`es
     // to executor side to avoid fetching them again.  However, `FileStatus` is not `Serializable`
diff --git a/sql/core/src/test/README.md b/sql/core/src/test/README.md
index 3dd9861b4896d..421c2ea4f7aed 100644
--- a/sql/core/src/test/README.md
+++ b/sql/core/src/test/README.md
@@ -6,23 +6,19 @@ The following directories and files are used for Parquet compatibility tests:
 .
 ├── README.md                   # This file
 ├── avro
-│   ├── parquet-compat.avdl     # Testing Avro IDL
-│   └── parquet-compat.avpr     # !! NO TOUCH !! Protocol file generated from parquet-compat.avdl
+│   ├── *.avdl                  # Testing Avro IDL(s)
+│   └── *.avpr                  # !! NO TOUCH !! Protocol files generated from Avro IDL(s)
 ├── gen-java                    # !! NO TOUCH !! Generated Java code
 ├── scripts
-│   └── gen-code.sh             # Script used to generate Java code for Thrift and Avro
+│   ├── gen-avro.sh             # Script used to generate Java code for Avro
+│   └── gen-thrift.sh           # Script used to generate Java code for Thrift
 └── thrift
-    └── parquet-compat.thrift   # Testing Thrift schema
+    └── *.thrift                # Testing Thrift schema(s)
 ```
 
-Generated Java code are used in the following test suites:
-
-- `org.apache.spark.sql.parquet.ParquetAvroCompatibilitySuite`
-- `org.apache.spark.sql.parquet.ParquetThriftCompatibilitySuite`
-
 To avoid code generation during build time, Java code generated from testing Thrift schema and Avro IDL are also checked in.
 
-When updating the testing Thrift schema and Avro IDL, please run `gen-code.sh` to update all the generated Java code.
+When updating the testing Thrift schema and Avro IDL, please run `gen-avro.sh` and `gen-thrift.sh` accordingly to update generated Java code.
 
 ## Prerequisites
 
diff --git a/sql/core/src/test/avro/parquet-compat.avdl b/sql/core/src/test/avro/parquet-compat.avdl
index 24729f6143e6c..8070d0a9170a3 100644
--- a/sql/core/src/test/avro/parquet-compat.avdl
+++ b/sql/core/src/test/avro/parquet-compat.avdl
@@ -16,8 +16,19 @@
  */
 
 // This is a test protocol for testing parquet-avro compatibility.
-@namespace("org.apache.spark.sql.parquet.test.avro")
+@namespace("org.apache.spark.sql.execution.datasources.parquet.test.avro")
 protocol CompatibilityTest {
+    enum Suit {
+        SPADES,
+        HEARTS,
+        DIAMONDS,
+        CLUBS
+    }
+
+    record ParquetEnum {
+        Suit suit;
+    }
+
     record Nested {
         array<int> nested_ints_column;
         string nested_string_column;
diff --git a/sql/core/src/test/avro/parquet-compat.avpr b/sql/core/src/test/avro/parquet-compat.avpr
index a83b7c990dd2e..060391765034b 100644
--- a/sql/core/src/test/avro/parquet-compat.avpr
+++ b/sql/core/src/test/avro/parquet-compat.avpr
@@ -1,7 +1,18 @@
 {
   "protocol" : "CompatibilityTest",
-  "namespace" : "org.apache.spark.sql.parquet.test.avro",
+  "namespace" : "org.apache.spark.sql.execution.datasources.parquet.test.avro",
   "types" : [ {
+    "type" : "enum",
+    "name" : "Suit",
+    "symbols" : [ "SPADES", "HEARTS", "DIAMONDS", "CLUBS" ]
+  }, {
+    "type" : "record",
+    "name" : "ParquetEnum",
+    "fields" : [ {
+      "name" : "suit",
+      "type" : "Suit"
+    } ]
+  }, {
     "type" : "record",
     "name" : "Nested",
     "fields" : [ {
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
index 70dec1a9d3c92..2368323cb36b9 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
@@ -8,7 +8,7 @@
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public interface CompatibilityTest {
-  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"types\":[{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
+  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"types\":[{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]},{\"type\":\"record\",\"name\":\"ParquetEnum\",\"fields\":[{\"name\":\"suit\",\"type\":\"Suit\"}]},{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
 
   @SuppressWarnings("all")
   public interface Callback extends CompatibilityTest {
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
index a0a406bcd10c1..a7bf4841919c5 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
@@ -3,11 +3,11 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.execution.datasources.parquet.test.avro;
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class Nested extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
-  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"Nested\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}");
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"Nested\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}");
   public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
   @Deprecated public java.util.List<java.lang.Integer> nested_ints_column;
   @Deprecated public java.lang.String nested_string_column;
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
index 6198b00b1e3ca..681cacbd12c7c 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
@@ -3,11 +3,11 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.execution.datasources.parquet.test.avro;
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
-  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
   public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
   @Deprecated public boolean bool_column;
   @Deprecated public int int_column;
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java
new file mode 100644
index 0000000000000..05fefe4cee754
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java
@@ -0,0 +1,142 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class ParquetEnum extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetEnum\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"suit\",\"type\":{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit suit;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public ParquetEnum() {}
+
+  /**
+   * All-args constructor.
+   */
+  public ParquetEnum(org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit suit) {
+    this.suit = suit;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return suit;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: suit = (org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'suit' field.
+   */
+  public org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit getSuit() {
+    return suit;
+  }
+
+  /**
+   * Sets the value of the 'suit' field.
+   * @param value the value to set.
+   */
+  public void setSuit(org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit value) {
+    this.suit = value;
+  }
+
+  /** Creates a new ParquetEnum RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder();
+  }
+  
+  /** Creates a new ParquetEnum RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder(other);
+  }
+  
+  /** Creates a new ParquetEnum RecordBuilder by copying an existing ParquetEnum instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for ParquetEnum instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<ParquetEnum>
+    implements org.apache.avro.data.RecordBuilder<ParquetEnum> {
+
+    private org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit suit;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.suit)) {
+        this.suit = data().deepCopy(fields()[0].schema(), other.suit);
+        fieldSetFlags()[0] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing ParquetEnum instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.SCHEMA$);
+      if (isValidValue(fields()[0], other.suit)) {
+        this.suit = data().deepCopy(fields()[0].schema(), other.suit);
+        fieldSetFlags()[0] = true;
+      }
+    }
+
+    /** Gets the value of the 'suit' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit getSuit() {
+      return suit;
+    }
+    
+    /** Sets the value of the 'suit' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder setSuit(org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit value) {
+      validate(fields()[0], value);
+      this.suit = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'suit' field has been set */
+    public boolean hasSuit() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'suit' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder clearSuit() {
+      suit = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    @Override
+    public ParquetEnum build() {
+      try {
+        ParquetEnum record = new ParquetEnum();
+        record.suit = fieldSetFlags()[0] ? this.suit : (org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit) defaultValue(fields()[0]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java
new file mode 100644
index 0000000000000..00711a0c2a267
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java
@@ -0,0 +1,13 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public enum Suit { 
+  SPADES, HEARTS, DIAMONDS, CLUBS  ;
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"enum\",\"name\":\"Suit\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 4d9c07bb7a570..866a975ad5404 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -22,10 +22,12 @@ import java.util.{List => JList, Map => JMap}
 
 import scala.collection.JavaConversions._
 
+import org.apache.avro.Schema
+import org.apache.avro.generic.IndexedRecord
 import org.apache.hadoop.fs.Path
 import org.apache.parquet.avro.AvroParquetWriter
 
-import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat}
+import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat, ParquetEnum, Suit}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.{Row, SQLContext}
 
@@ -34,52 +36,55 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
 
   override val sqlContext: SQLContext = TestSQLContext
 
-  override protected def beforeAll(): Unit = {
-    super.beforeAll()
-
-    val writer =
-      new AvroParquetWriter[ParquetAvroCompat](
-        new Path(parquetStore.getCanonicalPath),
-        ParquetAvroCompat.getClassSchema)
-
-    (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
-    writer.close()
+  private def withWriter[T <: IndexedRecord]
+      (path: String, schema: Schema)
+      (f: AvroParquetWriter[T] => Unit) = {
+    val writer = new AvroParquetWriter[T](new Path(path), schema)
+    try f(writer) finally writer.close()
   }
 
   test("Read Parquet file generated by parquet-avro") {
-    logInfo(
-      s"""Schema of the Parquet file written by parquet-avro:
-         |${readParquetSchema(parquetStore.getCanonicalPath)}
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[ParquetAvroCompat](path, ParquetAvroCompat.getClassSchema) { writer =>
+        (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
+      }
+
+      logInfo(
+        s"""Schema of the Parquet file written by parquet-avro:
+           |${readParquetSchema(path)}
        """.stripMargin)
 
-    checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), (0 until 10).map { i =>
-      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
-
-      Row(
-        i % 2 == 0,
-        i,
-        i.toLong * 10,
-        i.toFloat + 0.1f,
-        i.toDouble + 0.2d,
-        s"val_$i".getBytes,
-        s"val_$i",
-
-        nullable(i % 2 == 0: java.lang.Boolean),
-        nullable(i: Integer),
-        nullable(i.toLong: java.lang.Long),
-        nullable(i.toFloat + 0.1f: java.lang.Float),
-        nullable(i.toDouble + 0.2d: java.lang.Double),
-        nullable(s"val_$i".getBytes),
-        nullable(s"val_$i"),
-
-        Seq.tabulate(3)(n => s"arr_${i + n}"),
-        Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap,
-        Seq.tabulate(3) { n =>
-          (i + n).toString -> Seq.tabulate(3) { m =>
-            Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
-          }
-        }.toMap)
-    })
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
+
+        Row(
+          i % 2 == 0,
+          i,
+          i.toLong * 10,
+          i.toFloat + 0.1f,
+          i.toDouble + 0.2d,
+          s"val_$i".getBytes,
+          s"val_$i",
+
+          nullable(i % 2 == 0: java.lang.Boolean),
+          nullable(i: Integer),
+          nullable(i.toLong: java.lang.Long),
+          nullable(i.toFloat + 0.1f: java.lang.Float),
+          nullable(i.toDouble + 0.2d: java.lang.Double),
+          nullable(s"val_$i".getBytes),
+          nullable(s"val_$i"),
+
+          Seq.tabulate(3)(n => s"arr_${i + n}"),
+          Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap,
+          Seq.tabulate(3) { n =>
+            (i + n).toString -> Seq.tabulate(3) { m =>
+              Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
+            }
+          }.toMap)
+      })
+    }
   }
 
   def makeParquetAvroCompat(i: Int): ParquetAvroCompat = {
@@ -122,4 +127,20 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
 
       .build()
   }
+
+  test("SPARK-9407 Don't push down predicates involving Parquet ENUM columns") {
+    import sqlContext.implicits._
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[ParquetEnum](path, ParquetEnum.getClassSchema) { writer =>
+        (0 until 4).foreach { i =>
+          writer.write(ParquetEnum.newBuilder().setSuit(Suit.values.apply(i)).build())
+        }
+      }
+
+      checkAnswer(sqlContext.read.parquet(path).filter('suit === "SPADES"), Row("SPADES"))
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 68f35b1f3aa83..0ea64aa2a509b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -16,45 +16,28 @@
  */
 
 package org.apache.spark.sql.execution.datasources.parquet
-import java.io.File
 
 import scala.collection.JavaConversions._
 
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.parquet.hadoop.ParquetFileReader
 import org.apache.parquet.schema.MessageType
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.QueryTest
-import org.apache.spark.util.Utils
 
 abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with BeforeAndAfterAll {
-  protected var parquetStore: File = _
-
-  /**
-   * Optional path to a staging subdirectory which may be created during query processing
-   * (Hive does this).
-   * Parquet files under this directory will be ignored in [[readParquetSchema()]]
-   * @return an optional staging directory to ignore when scanning for parquet files.
-   */
-  protected def stagingDir: Option[String] = None
-
-  override protected def beforeAll(): Unit = {
-    parquetStore = Utils.createTempDir(namePrefix = "parquet-compat_")
-    parquetStore.delete()
-  }
-
-  override protected def afterAll(): Unit = {
-    Utils.deleteRecursively(parquetStore)
+  def readParquetSchema(path: String): MessageType = {
+    readParquetSchema(path, { path => !path.getName.startsWith("_") })
   }
 
-  def readParquetSchema(path: String): MessageType = {
+  def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
     val fsPath = new Path(path)
     val fs = fsPath.getFileSystem(configuration)
-    val parquetFiles = fs.listStatus(fsPath).toSeq.filterNot { status =>
-      status.getPath.getName.startsWith("_") ||
-        stagingDir.map(status.getPath.getName.startsWith).getOrElse(false)
-    }
+    val parquetFiles = fs.listStatus(fsPath, new PathFilter {
+      override def accept(path: Path): Boolean = pathFilter(path)
+    }).toSeq
+
     val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
     footers.head.getParquetMetadata.getFileMetaData.getSchema
   }
diff --git a/sql/core/src/test/scripts/gen-code.sh b/sql/core/src/test/scripts/gen-avro.sh
similarity index 76%
rename from sql/core/src/test/scripts/gen-code.sh
rename to sql/core/src/test/scripts/gen-avro.sh
index 5d8d8ad08555c..48174b287fd7c 100755
--- a/sql/core/src/test/scripts/gen-code.sh
+++ b/sql/core/src/test/scripts/gen-avro.sh
@@ -22,10 +22,9 @@ cd -
 rm -rf $BASEDIR/gen-java
 mkdir -p $BASEDIR/gen-java
 
-thrift\
-    --gen java\
-    -out $BASEDIR/gen-java\
-    $BASEDIR/thrift/parquet-compat.thrift
-
-avro-tools idl $BASEDIR/avro/parquet-compat.avdl > $BASEDIR/avro/parquet-compat.avpr
-avro-tools compile -string protocol $BASEDIR/avro/parquet-compat.avpr $BASEDIR/gen-java
+for input in `ls $BASEDIR/avro/*.avdl`; do
+    filename=$(basename "$input")
+    filename="${filename%.*}"
+    avro-tools idl $input> $BASEDIR/avro/${filename}.avpr
+    avro-tools compile -string protocol $BASEDIR/avro/${filename}.avpr $BASEDIR/gen-java
+done
diff --git a/sql/core/src/test/scripts/gen-thrift.sh b/sql/core/src/test/scripts/gen-thrift.sh
new file mode 100755
index 0000000000000..ada432c68ab95
--- /dev/null
+++ b/sql/core/src/test/scripts/gen-thrift.sh
@@ -0,0 +1,27 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+cd $(dirname $0)/..
+BASEDIR=`pwd`
+cd -
+
+rm -rf $BASEDIR/gen-java
+mkdir -p $BASEDIR/gen-java
+
+for input in `ls $BASEDIR/thrift/*.thrift`; do
+    thrift --gen java -out $BASEDIR/gen-java $input
+done
diff --git a/sql/core/src/test/thrift/parquet-compat.thrift b/sql/core/src/test/thrift/parquet-compat.thrift
index fa5ed8c62306a..98bf778aec5d6 100644
--- a/sql/core/src/test/thrift/parquet-compat.thrift
+++ b/sql/core/src/test/thrift/parquet-compat.thrift
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-namespace java org.apache.spark.sql.parquet.test.thrift
+namespace java org.apache.spark.sql.execution.datasources.parquet.test.thrift
 
 enum Suit {
     SPADES,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 80eb9f122ad90..251e0324bfa5f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -32,53 +32,54 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
    * Set the staging directory (and hence path to ignore Parquet files under)
    * to that set by [[HiveConf.ConfVars.STAGINGDIR]].
    */
-  override val stagingDir: Option[String] =
-    Some(new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR))
+  private val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
 
-  override protected def beforeAll(): Unit = {
-    super.beforeAll()
+  test("Read Parquet file generated by parquet-hive") {
+    withTable("parquet_compat") {
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
 
-    withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
-      withTempTable("data") {
-        sqlContext.sql(
-          s"""CREATE TABLE parquet_compat(
-             |  bool_column BOOLEAN,
-             |  byte_column TINYINT,
-             |  short_column SMALLINT,
-             |  int_column INT,
-             |  long_column BIGINT,
-             |  float_column FLOAT,
-             |  double_column DOUBLE,
-             |
-             |  strings_column ARRAY<STRING>,
-             |  int_to_string_column MAP<INT, STRING>
-             |)
-             |STORED AS PARQUET
-             |LOCATION '${parquetStore.getCanonicalPath}'
-           """.stripMargin)
+        withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
+          withTempTable("data") {
+            sqlContext.sql(
+              s"""CREATE TABLE parquet_compat(
+                 |  bool_column BOOLEAN,
+                 |  byte_column TINYINT,
+                 |  short_column SMALLINT,
+                 |  int_column INT,
+                 |  long_column BIGINT,
+                 |  float_column FLOAT,
+                 |  double_column DOUBLE,
+                 |
+                 |  strings_column ARRAY<STRING>,
+                 |  int_to_string_column MAP<INT, STRING>
+                 |)
+                 |STORED AS PARQUET
+                 |LOCATION '$path'
+               """.stripMargin)
 
-        val schema = sqlContext.table("parquet_compat").schema
-        val rowRDD = sqlContext.sparkContext.parallelize(makeRows).coalesce(1)
-        sqlContext.createDataFrame(rowRDD, schema).registerTempTable("data")
-        sqlContext.sql("INSERT INTO TABLE parquet_compat SELECT * FROM data")
-      }
-    }
-  }
+            val schema = sqlContext.table("parquet_compat").schema
+            val rowRDD = sqlContext.sparkContext.parallelize(makeRows).coalesce(1)
+            sqlContext.createDataFrame(rowRDD, schema).registerTempTable("data")
+            sqlContext.sql("INSERT INTO TABLE parquet_compat SELECT * FROM data")
+          }
+        }
 
-  override protected def afterAll(): Unit = {
-    sqlContext.sql("DROP TABLE parquet_compat")
-  }
+        val schema = readParquetSchema(path, { path =>
+          !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
+        })
 
-  test("Read Parquet file generated by parquet-hive") {
-    logInfo(
-      s"""Schema of the Parquet file written by parquet-hive:
-         |${readParquetSchema(parquetStore.getCanonicalPath)}
-       """.stripMargin)
+        logInfo(
+          s"""Schema of the Parquet file written by parquet-hive:
+             |$schema
+           """.stripMargin)
 
-    // Unfortunately parquet-hive doesn't add `UTF8` annotation to BINARY when writing strings.
-    // Have to assume all BINARY values are strings here.
-    withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
-      checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), makeRows)
+        // Unfortunately parquet-hive doesn't add `UTF8` annotation to BINARY when writing strings.
+        // Have to assume all BINARY values are strings here.
+        withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
+          checkAnswer(sqlContext.read.parquet(path), makeRows)
+        }
+      }
     }
   }
 

From 2e680668f7b6fc158aa068aedd19c1878ecf759e Mon Sep 17 00:00:00 2001
From: Tom White <tom@cloudera.com>
Date: Wed, 12 Aug 2015 10:06:27 -0500
Subject: [PATCH 0992/1454] [SPARK-8625] [CORE] Propagate user exceptions in
 tasks back to driver

This allows clients to retrieve the original exception from the
cause field of the SparkException that is thrown by the driver.
If the original exception is not in fact Serializable then it will
not be returned, but the message and stacktrace will be. (All Java
Throwables implement the Serializable interface, but this is no
guarantee that a particular implementation can actually be
serialized.)

Author: Tom White <tom@cloudera.com>

Closes #7014 from tomwhite/propagate-user-exceptions.
---
 .../org/apache/spark/TaskEndReason.scala      | 44 ++++++++++++-
 .../org/apache/spark/executor/Executor.scala  | 14 +++-
 .../apache/spark/scheduler/DAGScheduler.scala | 44 ++++++++-----
 .../spark/scheduler/DAGSchedulerEvent.scala   |  3 +-
 .../spark/scheduler/TaskSetManager.scala      | 12 ++--
 .../org/apache/spark/util/JsonProtocol.scala  |  2 +-
 .../ExecutorAllocationManagerSuite.scala      |  2 +-
 .../scala/org/apache/spark/FailureSuite.scala | 66 ++++++++++++++++++-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  2 +-
 .../spark/scheduler/TaskSetManagerSuite.scala |  5 +-
 .../ui/jobs/JobProgressListenerSuite.scala    |  2 +-
 .../apache/spark/util/JsonProtocolSuite.scala |  3 +-
 12 files changed, 165 insertions(+), 34 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index 48fd3e7e23d52..934d00dc708b9 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark
 
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.storage.BlockManagerId
@@ -90,6 +92,10 @@ case class FetchFailed(
  *
  * `fullStackTrace` is a better representation of the stack trace because it contains the whole
  * stack trace including the exception and its causes
+ *
+ * `exception` is the actual exception that caused the task to fail. It may be `None` in
+ * the case that the exception is not in fact serializable. If a task fails more than
+ * once (due to retries), `exception` is that one that caused the last failure.
  */
 @DeveloperApi
 case class ExceptionFailure(
@@ -97,11 +103,26 @@ case class ExceptionFailure(
     description: String,
     stackTrace: Array[StackTraceElement],
     fullStackTrace: String,
-    metrics: Option[TaskMetrics])
+    metrics: Option[TaskMetrics],
+    private val exceptionWrapper: Option[ThrowableSerializationWrapper])
   extends TaskFailedReason {
 
+  /**
+   * `preserveCause` is used to keep the exception itself so it is available to the
+   * driver. This may be set to `false` in the event that the exception is not in fact
+   * serializable.
+   */
+  private[spark] def this(e: Throwable, metrics: Option[TaskMetrics], preserveCause: Boolean) {
+    this(e.getClass.getName, e.getMessage, e.getStackTrace, Utils.exceptionString(e), metrics,
+      if (preserveCause) Some(new ThrowableSerializationWrapper(e)) else None)
+  }
+
   private[spark] def this(e: Throwable, metrics: Option[TaskMetrics]) {
-    this(e.getClass.getName, e.getMessage, e.getStackTrace, Utils.exceptionString(e), metrics)
+    this(e, metrics, preserveCause = true)
+  }
+
+  def exception: Option[Throwable] = exceptionWrapper.flatMap {
+    (w: ThrowableSerializationWrapper) => Option(w.exception)
   }
 
   override def toErrorString: String =
@@ -127,6 +148,25 @@ case class ExceptionFailure(
   }
 }
 
+/**
+ * A class for recovering from exceptions when deserializing a Throwable that was
+ * thrown in user task code. If the Throwable cannot be deserialized it will be null,
+ * but the stacktrace and message will be preserved correctly in SparkException.
+ */
+private[spark] class ThrowableSerializationWrapper(var exception: Throwable) extends
+    Serializable with Logging {
+  private def writeObject(out: ObjectOutputStream): Unit = {
+    out.writeObject(exception)
+  }
+  private def readObject(in: ObjectInputStream): Unit = {
+    try {
+      exception = in.readObject().asInstanceOf[Throwable]
+    } catch {
+      case e : Exception => log.warn("Task exception could not be deserialized", e)
+    }
+  }
+}
+
 /**
  * :: DeveloperApi ::
  * The task finished successfully, but the result was lost from the executor's block manager before
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 5d78a9dc8885e..42a85e42ea2b6 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.executor
 
-import java.io.File
+import java.io.{File, NotSerializableException}
 import java.lang.management.ManagementFactory
 import java.net.URL
 import java.nio.ByteBuffer
@@ -305,8 +305,16 @@ private[spark] class Executor(
               m
             }
           }
-          val taskEndReason = new ExceptionFailure(t, metrics)
-          execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(taskEndReason))
+          val serializedTaskEndReason = {
+            try {
+              ser.serialize(new ExceptionFailure(t, metrics))
+            } catch {
+              case _: NotSerializableException =>
+                // t is not serializable so just send the stacktrace
+                ser.serialize(new ExceptionFailure(t, metrics, false))
+            }
+          }
+          execBackend.statusUpdate(taskId, TaskState.FAILED, serializedTaskEndReason)
 
           // Don't forcibly exit unless the exception was inherently fatal, to avoid
           // stopping other tasks unnecessarily.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index bb489c6b6e98f..7ab5ccf50adb7 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -200,8 +200,8 @@ class DAGScheduler(
 
   // Called by TaskScheduler to cancel an entire TaskSet due to either repeated failures or
   // cancellation of the job itself.
-  def taskSetFailed(taskSet: TaskSet, reason: String): Unit = {
-    eventProcessLoop.post(TaskSetFailed(taskSet, reason))
+  def taskSetFailed(taskSet: TaskSet, reason: String, exception: Option[Throwable]): Unit = {
+    eventProcessLoop.post(TaskSetFailed(taskSet, reason, exception))
   }
 
   private[scheduler]
@@ -677,8 +677,11 @@ class DAGScheduler(
     submitWaitingStages()
   }
 
-  private[scheduler] def handleTaskSetFailed(taskSet: TaskSet, reason: String) {
-    stageIdToStage.get(taskSet.stageId).foreach {abortStage(_, reason) }
+  private[scheduler] def handleTaskSetFailed(
+      taskSet: TaskSet,
+      reason: String,
+      exception: Option[Throwable]): Unit = {
+    stageIdToStage.get(taskSet.stageId).foreach { abortStage(_, reason, exception) }
     submitWaitingStages()
   }
 
@@ -762,7 +765,7 @@ class DAGScheduler(
         }
       }
     } else {
-      abortStage(stage, "No active job for stage " + stage.id)
+      abortStage(stage, "No active job for stage " + stage.id, None)
     }
   }
 
@@ -816,7 +819,7 @@ class DAGScheduler(
       case NonFatal(e) =>
         stage.makeNewStageAttempt(partitionsToCompute.size)
         listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
-        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}")
+        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}", Some(e))
         runningStages -= stage
         return
     }
@@ -845,13 +848,13 @@ class DAGScheduler(
     } catch {
       // In the case of a failure during serialization, abort the stage.
       case e: NotSerializableException =>
-        abortStage(stage, "Task not serializable: " + e.toString)
+        abortStage(stage, "Task not serializable: " + e.toString, Some(e))
         runningStages -= stage
 
         // Abort execution
         return
       case NonFatal(e) =>
-        abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")
+        abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}", Some(e))
         runningStages -= stage
         return
     }
@@ -878,7 +881,7 @@ class DAGScheduler(
       }
     } catch {
       case NonFatal(e) =>
-        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}")
+        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}", Some(e))
         runningStages -= stage
         return
     }
@@ -1098,7 +1101,8 @@ class DAGScheduler(
           }
 
           if (disallowStageRetryForTest) {
-            abortStage(failedStage, "Fetch failure will not retry stage due to testing config")
+            abortStage(failedStage, "Fetch failure will not retry stage due to testing config",
+              None)
           } else if (failedStages.isEmpty) {
             // Don't schedule an event to resubmit failed stages if failed isn't empty, because
             // in that case the event will already have been scheduled.
@@ -1126,7 +1130,7 @@ class DAGScheduler(
       case commitDenied: TaskCommitDenied =>
         // Do nothing here, left up to the TaskScheduler to decide how to handle denied commits
 
-      case ExceptionFailure(className, description, stackTrace, fullStackTrace, metrics) =>
+      case exceptionFailure: ExceptionFailure =>
         // Do nothing here, left up to the TaskScheduler to decide how to handle user failures
 
       case TaskResultLost =>
@@ -1235,7 +1239,10 @@ class DAGScheduler(
    * Aborts all jobs depending on a particular Stage. This is called in response to a task set
    * being canceled by the TaskScheduler. Use taskSetFailed() to inject this event from outside.
    */
-  private[scheduler] def abortStage(failedStage: Stage, reason: String) {
+  private[scheduler] def abortStage(
+      failedStage: Stage,
+      reason: String,
+      exception: Option[Throwable]): Unit = {
     if (!stageIdToStage.contains(failedStage.id)) {
       // Skip all the actions if the stage has been removed.
       return
@@ -1244,7 +1251,7 @@ class DAGScheduler(
       activeJobs.filter(job => stageDependsOn(job.finalStage, failedStage)).toSeq
     failedStage.latestInfo.completionTime = Some(clock.getTimeMillis())
     for (job <- dependentJobs) {
-      failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason")
+      failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason", exception)
     }
     if (dependentJobs.isEmpty) {
       logInfo("Ignoring failure of " + failedStage + " because all jobs depending on it are done")
@@ -1252,8 +1259,11 @@ class DAGScheduler(
   }
 
   /** Fails a job and all stages that are only used by that job, and cleans up relevant state. */
-  private def failJobAndIndependentStages(job: ActiveJob, failureReason: String) {
-    val error = new SparkException(failureReason)
+  private def failJobAndIndependentStages(
+      job: ActiveJob,
+      failureReason: String,
+      exception: Option[Throwable] = None): Unit = {
+    val error = new SparkException(failureReason, exception.getOrElse(null))
     var ableToCancelStages = true
 
     val shouldInterruptThread =
@@ -1462,8 +1472,8 @@ private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler
     case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
       dagScheduler.handleTaskCompletion(completion)
 
-    case TaskSetFailed(taskSet, reason) =>
-      dagScheduler.handleTaskSetFailed(taskSet, reason)
+    case TaskSetFailed(taskSet, reason, exception) =>
+      dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
 
     case ResubmitFailedStages =>
       dagScheduler.resubmitFailedStages()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index a213d419cf033..f72a52e85dc15 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -73,6 +73,7 @@ private[scheduler] case class ExecutorAdded(execId: String, host: String) extend
 private[scheduler] case class ExecutorLost(execId: String) extends DAGSchedulerEvent
 
 private[scheduler]
-case class TaskSetFailed(taskSet: TaskSet, reason: String) extends DAGSchedulerEvent
+case class TaskSetFailed(taskSet: TaskSet, reason: String, exception: Option[Throwable])
+  extends DAGSchedulerEvent
 
 private[scheduler] case object ResubmitFailedStages extends DAGSchedulerEvent
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index 82455b0426a5d..818b95d67f6be 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -662,7 +662,7 @@ private[spark] class TaskSetManager(
 
     val failureReason = s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid, ${info.host}): " +
       reason.asInstanceOf[TaskFailedReason].toErrorString
-    reason match {
+    val failureException: Option[Throwable] = reason match {
       case fetchFailed: FetchFailed =>
         logWarning(failureReason)
         if (!successful(index)) {
@@ -671,6 +671,7 @@ private[spark] class TaskSetManager(
         }
         // Not adding to failed executors for FetchFailed.
         isZombie = true
+        None
 
       case ef: ExceptionFailure =>
         taskMetrics = ef.metrics.orNull
@@ -706,12 +707,15 @@ private[spark] class TaskSetManager(
             s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid) on executor ${info.host}: " +
             s"${ef.className} (${ef.description}) [duplicate $dupCount]")
         }
+        ef.exception
 
       case e: TaskFailedReason =>  // TaskResultLost, TaskKilled, and others
         logWarning(failureReason)
+        None
 
       case e: TaskEndReason =>
         logError("Unknown TaskEndReason: " + e)
+        None
     }
     // always add to failed executors
     failedExecutors.getOrElseUpdate(index, new HashMap[String, Long]()).
@@ -728,16 +732,16 @@ private[spark] class TaskSetManager(
         logError("Task %d in stage %s failed %d times; aborting job".format(
           index, taskSet.id, maxTaskFailures))
         abort("Task %d in stage %s failed %d times, most recent failure: %s\nDriver stacktrace:"
-          .format(index, taskSet.id, maxTaskFailures, failureReason))
+          .format(index, taskSet.id, maxTaskFailures, failureReason), failureException)
         return
       }
     }
     maybeFinishTaskSet()
   }
 
-  def abort(message: String): Unit = sched.synchronized {
+  def abort(message: String, exception: Option[Throwable] = None): Unit = sched.synchronized {
     // TODO: Kill running tasks if we were not terminated due to a Mesos error
-    sched.dagScheduler.taskSetFailed(taskSet, message)
+    sched.dagScheduler.taskSetFailed(taskSet, message, exception)
     isZombie = true
     maybeFinishTaskSet()
   }
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index c600319d9ddb4..cbc94fd6d54d9 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -790,7 +790,7 @@ private[spark] object JsonProtocol {
         val fullStackTrace = Utils.jsonOption(json \ "Full Stack Trace").
           map(_.extract[String]).orNull
         val metrics = Utils.jsonOption(json \ "Metrics").map(taskMetricsFromJson)
-        ExceptionFailure(className, description, stackTrace, fullStackTrace, metrics)
+        ExceptionFailure(className, description, stackTrace, fullStackTrace, metrics, None)
       case `taskResultLost` => TaskResultLost
       case `taskKilled` => TaskKilled
       case `executorLostFailure` =>
diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
index f374f97f87448..116f027a0f987 100644
--- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
@@ -800,7 +800,7 @@ class ExecutorAllocationManagerSuite
     assert(maxNumExecutorsNeeded(manager) === 1)
 
     // If the task is failed, we expect it to be resubmitted later.
-    val taskEndReason = ExceptionFailure(null, null, null, null, null)
+    val taskEndReason = ExceptionFailure(null, null, null, null, null, None)
     sc.listenerBus.postToAll(SparkListenerTaskEnd(0, 0, null, taskEndReason, taskInfo, null))
     assert(maxNumExecutorsNeeded(manager) === 1)
   }
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index 69cb4b44cf7ef..aa50a49c50232 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark
 
 import org.apache.spark.util.NonSerializable
 
-import java.io.NotSerializableException
+import java.io.{IOException, NotSerializableException, ObjectInputStream}
 
 // Common state shared by FailureSuite-launched tasks. We use a global object
 // for this because any local variables used in the task closures will rightfully
@@ -166,5 +166,69 @@ class FailureSuite extends SparkFunSuite with LocalSparkContext {
     assert(thrownDueToMemoryLeak.getMessage.contains("memory leak"))
   }
 
+  // Run a 3-task map job in which task 1 always fails with a exception message that
+  // depends on the failure number, and check that we get the last failure.
+  test("last failure cause is sent back to driver") {
+    sc = new SparkContext("local[1,2]", "test")
+    val data = sc.makeRDD(1 to 3, 3).map { x =>
+      FailureSuiteState.synchronized {
+        FailureSuiteState.tasksRun += 1
+        if (x == 3) {
+          FailureSuiteState.tasksFailed += 1
+          throw new UserException("oops",
+            new IllegalArgumentException("failed=" + FailureSuiteState.tasksFailed))
+        }
+      }
+      x * x
+    }
+    val thrown = intercept[SparkException] {
+      data.collect()
+    }
+    FailureSuiteState.synchronized {
+      assert(FailureSuiteState.tasksRun === 4)
+    }
+    assert(thrown.getClass === classOf[SparkException])
+    assert(thrown.getCause.getClass === classOf[UserException])
+    assert(thrown.getCause.getMessage === "oops")
+    assert(thrown.getCause.getCause.getClass === classOf[IllegalArgumentException])
+    assert(thrown.getCause.getCause.getMessage === "failed=2")
+    FailureSuiteState.clear()
+  }
+
+  test("failure cause stacktrace is sent back to driver if exception is not serializable") {
+    sc = new SparkContext("local", "test")
+    val thrown = intercept[SparkException] {
+      sc.makeRDD(1 to 3).foreach { _ => throw new NonSerializableUserException }
+    }
+    assert(thrown.getClass === classOf[SparkException])
+    assert(thrown.getCause === null)
+    assert(thrown.getMessage.contains("NonSerializableUserException"))
+    FailureSuiteState.clear()
+  }
+
+  test("failure cause stacktrace is sent back to driver if exception is not deserializable") {
+    sc = new SparkContext("local", "test")
+    val thrown = intercept[SparkException] {
+      sc.makeRDD(1 to 3).foreach { _ => throw new NonDeserializableUserException }
+    }
+    assert(thrown.getClass === classOf[SparkException])
+    assert(thrown.getCause === null)
+    assert(thrown.getMessage.contains("NonDeserializableUserException"))
+    FailureSuiteState.clear()
+  }
+
   // TODO: Need to add tests with shuffle fetch failures.
 }
+
+class UserException(message: String, cause: Throwable)
+  extends RuntimeException(message, cause)
+
+class NonSerializableUserException extends RuntimeException {
+  val nonSerializableInstanceVariable = new NonSerializable
+}
+
+class NonDeserializableUserException extends RuntimeException {
+  private def readObject(in: ObjectInputStream): Unit = {
+    throw new IOException("Intentional exception during deserialization.")
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 86dff8fb577d5..b0ca49cbea4f7 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -242,7 +242,7 @@ class DAGSchedulerSuite
 
   /** Sends TaskSetFailed to the scheduler. */
   private def failed(taskSet: TaskSet, message: String) {
-    runEvent(TaskSetFailed(taskSet, message))
+    runEvent(TaskSetFailed(taskSet, message, None))
   }
 
   /** Sends JobCancelled to the DAG scheduler. */
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index f7cc4bb61d574..edbdb485c5ea4 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -48,7 +48,10 @@ class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler)
 
   override def executorLost(execId: String) {}
 
-  override def taskSetFailed(taskSet: TaskSet, reason: String) {
+  override def taskSetFailed(
+      taskSet: TaskSet,
+      reason: String,
+      exception: Option[Throwable]): Unit = {
     taskScheduler.taskSetsFailed += taskSet.id
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index 56f7b9cf1f358..b140387d309f3 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -240,7 +240,7 @@ class JobProgressListenerSuite extends SparkFunSuite with LocalSparkContext with
     val taskFailedReasons = Seq(
       Resubmitted,
       new FetchFailed(null, 0, 0, 0, "ignored"),
-      ExceptionFailure("Exception", "description", null, null, None),
+      ExceptionFailure("Exception", "description", null, null, None, None),
       TaskResultLost,
       TaskKilled,
       ExecutorLostFailure("0"),
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index dde95f3778434..343a4139b0ca8 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -163,7 +163,8 @@ class JsonProtocolSuite extends SparkFunSuite {
   }
 
   test("ExceptionFailure backward compatibility") {
-    val exceptionFailure = ExceptionFailure("To be", "or not to be", stackTrace, null, None)
+    val exceptionFailure = ExceptionFailure("To be", "or not to be", stackTrace, null,
+      None, None)
     val oldEvent = JsonProtocol.taskEndReasonToJson(exceptionFailure)
       .removeField({ _._1 == "Full Stack Trace" })
     assertEquals(exceptionFailure, JsonProtocol.taskEndReasonFromJson(oldEvent))

From be5d1912076c2ffd21ec88611e53d3b3c59b7ecc Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 12 Aug 2015 09:24:50 -0700
Subject: [PATCH 0993/1454] [SPARK-9795] Dynamic allocation: avoid double
 counting when killing same executor twice

This is based on KaiXinXiaoLei's changes in #7716.

The issue is that when someone calls `sc.killExecutor("1")` on the same executor twice quickly, then the executor target will be adjusted downwards by 2 instead of 1 even though we're only actually killing one executor. In certain cases where we don't adjust the target back upwards quickly, we'll end up with jobs hanging.

This is a common danger because there are many places where this is called:
- `HeartbeatReceiver` kills an executor that has not been sending heartbeats
- `ExecutorAllocationManager` kills an executor that has been idle
- The user code might call this, which may interfere with the previous callers

While it's not clear whether this fixes SPARK-9745, fixing this potential race condition seems like a strict improvement. I've added a regression test to illustrate the issue.

Author: Andrew Or <andrew@databricks.com>

Closes #8078 from andrewor14/da-double-kill.
---
 .../CoarseGrainedSchedulerBackend.scala       | 11 ++++++----
 .../StandaloneDynamicAllocationSuite.scala    | 20 +++++++++++++++++++
 2 files changed, 27 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 6acf8a9a5e9b4..5730a87f960a0 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -422,16 +422,19 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
       logWarning(s"Executor to kill $id does not exist!")
     }
 
+    // If an executor is already pending to be removed, do not kill it again (SPARK-9795)
+    val executorsToKill = knownExecutors.filter { id => !executorsPendingToRemove.contains(id) }
+    executorsPendingToRemove ++= executorsToKill
+
     // If we do not wish to replace the executors we kill, sync the target number of executors
     // with the cluster manager to avoid allocating new ones. When computing the new target,
     // take into account executors that are pending to be added or removed.
     if (!replace) {
-      doRequestTotalExecutors(numExistingExecutors + numPendingExecutors
-        - executorsPendingToRemove.size - knownExecutors.size)
+      doRequestTotalExecutors(
+        numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size)
     }
 
-    executorsPendingToRemove ++= knownExecutors
-    doKillExecutors(knownExecutors)
+    doKillExecutors(executorsToKill)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
index 08c41a897a861..1f2a0f0d309ce 100644
--- a/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
@@ -283,6 +283,26 @@ class StandaloneDynamicAllocationSuite
     assert(master.apps.head.getExecutorLimit === 1000)
   }
 
+  test("kill the same executor twice (SPARK-9795)") {
+    sc = new SparkContext(appConf)
+    val appId = sc.applicationId
+    assert(master.apps.size === 1)
+    assert(master.apps.head.id === appId)
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === Int.MaxValue)
+    // sync executors between the Master and the driver, needed because
+    // the driver refuses to kill executors it does not know about
+    syncExecutors(sc)
+    // kill the same executor twice
+    val executors = getExecutorIds(sc)
+    assert(executors.size === 2)
+    assert(sc.killExecutor(executors.head))
+    assert(sc.killExecutor(executors.head))
+    assert(master.apps.head.executors.size === 1)
+    // The limit should not be lowered twice
+    assert(master.apps.head.getExecutorLimit === 1)
+  }
+
   // ===============================
   // | Utility methods for testing |
   // ===============================

From 66d87c1d76bea2b81993156ac1fa7dad6c312ebf Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Wed, 12 Aug 2015 09:35:32 -0700
Subject: [PATCH 0994/1454] [SPARK-7583] [MLLIB] User guide update for
 RegexTokenizer

jira: https://issues.apache.org/jira/browse/SPARK-7583

User guide update for RegexTokenizer

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7828 from hhbyyh/regexTokenizerDoc.
---
 docs/ml-features.md | 41 ++++++++++++++++++++++++++++++-----------
 1 file changed, 30 insertions(+), 11 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index fa0ad1f00ab12..cec2cbe673407 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -217,21 +217,32 @@ for feature in result.select("result").take(3):
 
 [Tokenization](http://en.wikipedia.org/wiki/Lexical_analysis#Tokenization) is the process of taking text (such as a sentence) and breaking it into individual terms (usually words).  A simple [Tokenizer](api/scala/index.html#org.apache.spark.ml.feature.Tokenizer) class provides this functionality.  The example below shows how to split sentences into sequences of words.
 
-Note: A more advanced tokenizer is provided via [RegexTokenizer](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer).
+[RegexTokenizer](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer) allows more
+ advanced tokenization based on regular expression (regex) matching.
+ By default, the parameter "pattern" (regex, default: \\s+) is used as delimiters to split the input text.
+ Alternatively, users can set parameter "gaps" to false indicating the regex "pattern" denotes
+ "tokens" rather than splitting gaps, and find all matching occurrences as the tokenization result.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
-import org.apache.spark.ml.feature.Tokenizer
+import org.apache.spark.ml.feature.{Tokenizer, RegexTokenizer}
 
 val sentenceDataFrame = sqlContext.createDataFrame(Seq(
   (0, "Hi I heard about Spark"),
-  (0, "I wish Java could use case classes"),
-  (1, "Logistic regression models are neat")
+  (1, "I wish Java could use case classes"),
+  (2, "Logistic,regression,models,are,neat")
 )).toDF("label", "sentence")
 val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
-val wordsDataFrame = tokenizer.transform(sentenceDataFrame)
-wordsDataFrame.select("words", "label").take(3).foreach(println)
+val regexTokenizer = new RegexTokenizer()
+  .setInputCol("sentence")
+  .setOutputCol("words")
+  .setPattern("\\W")  // alternatively .setPattern("\\w+").setGaps(false)
+
+val tokenized = tokenizer.transform(sentenceDataFrame)
+tokenized.select("words", "label").take(3).foreach(println)
+val regexTokenized = regexTokenizer.transform(sentenceDataFrame)
+regexTokenized.select("words", "label").take(3).foreach(println)
 {% endhighlight %}
 </div>
 
@@ -240,6 +251,7 @@ wordsDataFrame.select("words", "label").take(3).foreach(println)
 import com.google.common.collect.Lists;
 
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.RegexTokenizer;
 import org.apache.spark.ml.feature.Tokenizer;
 import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.sql.DataFrame;
@@ -252,8 +264,8 @@ import org.apache.spark.sql.types.StructType;
 
 JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
   RowFactory.create(0, "Hi I heard about Spark"),
-  RowFactory.create(0, "I wish Java could use case classes"),
-  RowFactory.create(1, "Logistic regression models are neat")
+  RowFactory.create(1, "I wish Java could use case classes"),
+  RowFactory.create(2, "Logistic,regression,models,are,neat")
 ));
 StructType schema = new StructType(new StructField[]{
   new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
@@ -267,22 +279,29 @@ for (Row r : wordsDataFrame.select("words", "label").take(3)) {
   for (String word : words) System.out.print(word + " ");
   System.out.println();
 }
+
+RegexTokenizer regexTokenizer = new RegexTokenizer()
+  .setInputCol("sentence")
+  .setOutputCol("words")
+  .setPattern("\\W");  // alternatively .setPattern("\\w+").setGaps(false);
 {% endhighlight %}
 </div>
 
 <div data-lang="python" markdown="1">
 {% highlight python %}
-from pyspark.ml.feature import Tokenizer
+from pyspark.ml.feature import Tokenizer, RegexTokenizer
 
 sentenceDataFrame = sqlContext.createDataFrame([
   (0, "Hi I heard about Spark"),
-  (0, "I wish Java could use case classes"),
-  (1, "Logistic regression models are neat")
+  (1, "I wish Java could use case classes"),
+  (2, "Logistic,regression,models,are,neat")
 ], ["label", "sentence"])
 tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
 wordsDataFrame = tokenizer.transform(sentenceDataFrame)
 for words_label in wordsDataFrame.select("words", "label").take(3):
   print(words_label)
+regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W")
+# alternatively, pattern="\\w+", gaps(False)
 {% endhighlight %}
 </div>
 </div>

From e0110792ef71ebfd3727b970346a2e13695990a4 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 12 Aug 2015 10:08:35 -0700
Subject: [PATCH 0995/1454] [SPARK-9747] [SQL] Avoid starving an unsafe
 operator in aggregation

This is the sister patch to #8011, but for aggregation.

In a nutshell: create the `TungstenAggregationIterator` before computing the parent partition. Internally this creates a `BytesToBytesMap` which acquires a page in the constructor as of this patch. This ensures that the aggregation operator is not starved since we reserve at least 1 page in advance.

rxin yhuai

Author: Andrew Or <andrew@databricks.com>

Closes #8038 from andrewor14/unsafe-starve-memory-agg.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 34 +++++--
 .../unsafe/sort/UnsafeExternalSorter.java     |  9 +-
 .../map/AbstractBytesToBytesMapSuite.java     | 11 ++-
 .../UnsafeFixedWidthAggregationMap.java       |  7 ++
 .../aggregate/TungstenAggregate.scala         | 72 +++++++++------
 .../TungstenAggregationIterator.scala         | 88 +++++++++++--------
 .../TungstenAggregationIteratorSuite.scala    | 56 ++++++++++++
 7 files changed, 201 insertions(+), 76 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 85b46ec8bfae3..87ed47e88c4ef 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -193,6 +193,11 @@ public BytesToBytesMap(
         TaskMemoryManager.MAXIMUM_PAGE_SIZE_BYTES);
     }
     allocate(initialCapacity);
+
+    // Acquire a new page as soon as we construct the map to ensure that we have at least
+    // one page to work with. Otherwise, other operators in the same task may starve this
+    // map (SPARK-9747).
+    acquireNewPage();
   }
 
   public BytesToBytesMap(
@@ -574,16 +579,9 @@ public boolean putNewKey(
           final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor;
           Platform.putInt(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
         }
-        final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
-        if (memoryGranted != pageSizeBytes) {
-          shuffleMemoryManager.release(memoryGranted);
-          logger.debug("Failed to acquire {} bytes of memory", pageSizeBytes);
+        if (!acquireNewPage()) {
           return false;
         }
-        MemoryBlock newPage = taskMemoryManager.allocatePage(pageSizeBytes);
-        dataPages.add(newPage);
-        pageCursor = 0;
-        currentDataPage = newPage;
         dataPage = currentDataPage;
         dataPageBaseObject = currentDataPage.getBaseObject();
         dataPageInsertOffset = currentDataPage.getBaseOffset();
@@ -642,6 +640,24 @@ public boolean putNewKey(
     }
   }
 
+  /**
+   * Acquire a new page from the {@link ShuffleMemoryManager}.
+   * @return whether there is enough space to allocate the new page.
+   */
+  private boolean acquireNewPage() {
+    final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+    if (memoryGranted != pageSizeBytes) {
+      shuffleMemoryManager.release(memoryGranted);
+      logger.debug("Failed to acquire {} bytes of memory", pageSizeBytes);
+      return false;
+    }
+    MemoryBlock newPage = taskMemoryManager.allocatePage(pageSizeBytes);
+    dataPages.add(newPage);
+    pageCursor = 0;
+    currentDataPage = newPage;
+    return true;
+  }
+
   /**
    * Allocate new data structures for this map. When calling this outside of the constructor,
    * make sure to keep references to the old data structures so that you can free them.
@@ -748,7 +764,7 @@ public long getNumHashCollisions() {
   }
 
   @VisibleForTesting
-  int getNumDataPages() {
+  public int getNumDataPages() {
     return dataPages.size();
   }
 
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 9601aafe55464..fc364e0a895b1 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -132,16 +132,15 @@ private UnsafeExternalSorter(
 
     if (existingInMemorySorter == null) {
       initializeForWriting();
+      // Acquire a new page as soon as we construct the sorter to ensure that we have at
+      // least one page to work with. Otherwise, other operators in the same task may starve
+      // this sorter (SPARK-9709). We don't need to do this if we already have an existing sorter.
+      acquireNewPage();
     } else {
       this.isInMemSorterExternal = true;
       this.inMemSorter = existingInMemorySorter;
     }
 
-    // Acquire a new page as soon as we construct the sorter to ensure that we have at
-    // least one page to work with. Otherwise, other operators in the same task may starve
-    // this sorter (SPARK-9709).
-    acquireNewPage();
-
     // Register a cleanup task with TaskContext to ensure that memory is guaranteed to be freed at
     // the end of the task. This is necessary to avoid memory leaks in when the downstream operator
     // does not fully consume the sorter's output (e.g. sort followed by limit).
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 1a79c20c35246..ab480b60adaed 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -543,7 +543,7 @@ public void testPeakMemoryUsed() {
           Platform.LONG_ARRAY_OFFSET,
           8);
         newPeakMemory = map.getPeakMemoryUsedBytes();
-        if (i % numRecordsPerPage == 0) {
+        if (i % numRecordsPerPage == 0 && i > 0) {
           // We allocated a new page for this record, so peak memory should change
           assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
@@ -561,4 +561,13 @@ public void testPeakMemoryUsed() {
       map.free();
     }
   }
+
+  @Test
+  public void testAcquirePageInConstructor() {
+    final BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, 1, PAGE_SIZE_BYTES);
+    assertEquals(1, map.getNumDataPages());
+    map.free();
+  }
+
 }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 5cce41d5a7569..09511ff35f785 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -19,6 +19,8 @@
 
 import java.io.IOException;
 
+import com.google.common.annotations.VisibleForTesting;
+
 import org.apache.spark.SparkEnv;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.sql.catalyst.InternalRow;
@@ -220,6 +222,11 @@ public long getPeakMemoryUsedBytes() {
     return map.getPeakMemoryUsedBytes();
   }
 
+  @VisibleForTesting
+  public int getNumDataPages() {
+    return map.getNumDataPages();
+  }
+
   /**
    * Free the memory associated with this map. This is idempotent and can be called multiple times.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 6b5935a7ce296..c40ca973796a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.sql.execution.aggregate
 
-import org.apache.spark.rdd.RDD
+import org.apache.spark.TaskContext
+import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
+import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
 import org.apache.spark.sql.execution.metric.SQLMetrics
 
@@ -68,35 +69,56 @@ case class TungstenAggregate(
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
     val numInputRows = longMetric("numInputRows")
     val numOutputRows = longMetric("numOutputRows")
-    child.execute().mapPartitions { iter =>
-      val hasInput = iter.hasNext
-      if (!hasInput && groupingExpressions.nonEmpty) {
-        // This is a grouped aggregate and the input iterator is empty,
-        // so return an empty iterator.
-        Iterator.empty.asInstanceOf[Iterator[UnsafeRow]]
-      } else {
-        val aggregationIterator =
-          new TungstenAggregationIterator(
-            groupingExpressions,
-            nonCompleteAggregateExpressions,
-            completeAggregateExpressions,
-            initialInputBufferOffset,
-            resultExpressions,
-            newMutableProjection,
-            child.output,
-            iter,
-            testFallbackStartsAt,
-            numInputRows,
-            numOutputRows)
-
-        if (!hasInput && groupingExpressions.isEmpty) {
+
+    /**
+     * Set up the underlying unsafe data structures used before computing the parent partition.
+     * This makes sure our iterator is not starved by other operators in the same task.
+     */
+    def preparePartition(): TungstenAggregationIterator = {
+      new TungstenAggregationIterator(
+        groupingExpressions,
+        nonCompleteAggregateExpressions,
+        completeAggregateExpressions,
+        initialInputBufferOffset,
+        resultExpressions,
+        newMutableProjection,
+        child.output,
+        testFallbackStartsAt,
+        numInputRows,
+        numOutputRows)
+    }
+
+    /** Compute a partition using the iterator already set up previously. */
+    def executePartition(
+        context: TaskContext,
+        partitionIndex: Int,
+        aggregationIterator: TungstenAggregationIterator,
+        parentIterator: Iterator[InternalRow]): Iterator[UnsafeRow] = {
+      val hasInput = parentIterator.hasNext
+      if (!hasInput) {
+        // We're not using the underlying map, so we just can free it here
+        aggregationIterator.free()
+        if (groupingExpressions.isEmpty) {
           numOutputRows += 1
           Iterator.single[UnsafeRow](aggregationIterator.outputForEmptyGroupingKeyWithoutInput())
         } else {
-          aggregationIterator
+          // This is a grouped aggregate and the input iterator is empty,
+          // so return an empty iterator.
+          Iterator[UnsafeRow]()
         }
+      } else {
+        aggregationIterator.start(parentIterator)
+        aggregationIterator
       }
     }
+
+    // Note: we need to set up the iterator in each partition before computing the
+    // parent partition, so we cannot simply use `mapPartitions` here (SPARK-9747).
+    val resultRdd = {
+      new MapPartitionsWithPreparationRDD[UnsafeRow, InternalRow, TungstenAggregationIterator](
+        child.execute(), preparePartition, executePartition, preservesPartitioning = true)
+    }
+    resultRdd.asInstanceOf[RDD[InternalRow]]
   }
 
   override def simpleString: String = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 1f383dd04482f..af7e0fcedbe4e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -72,8 +72,6 @@ import org.apache.spark.sql.types.StructType
  *   the function used to create mutable projections.
  * @param originalInputAttributes
  *   attributes of representing input rows from `inputIter`.
- * @param inputIter
- *   the iterator containing input [[UnsafeRow]]s.
  */
 class TungstenAggregationIterator(
     groupingExpressions: Seq[NamedExpression],
@@ -83,12 +81,14 @@ class TungstenAggregationIterator(
     resultExpressions: Seq[NamedExpression],
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
     originalInputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow],
     testFallbackStartsAt: Option[Int],
     numInputRows: LongSQLMetric,
     numOutputRows: LongSQLMetric)
   extends Iterator[UnsafeRow] with Logging {
 
+  // The parent partition iterator, to be initialized later in `start`
+  private[this] var inputIter: Iterator[InternalRow] = null
+
   ///////////////////////////////////////////////////////////////////////////
   // Part 1: Initializing aggregate functions.
   ///////////////////////////////////////////////////////////////////////////
@@ -348,11 +348,15 @@ class TungstenAggregationIterator(
     false // disable tracking of performance metrics
   )
 
+  // Exposed for testing
+  private[aggregate] def getHashMap: UnsafeFixedWidthAggregationMap = hashMap
+
   // The function used to read and process input rows. When processing input rows,
   // it first uses hash-based aggregation by putting groups and their buffers in
   // hashMap. If we could not allocate more memory for the map, we switch to
   // sort-based aggregation (by calling switchToSortBasedAggregation).
   private def processInputs(): Unit = {
+    assert(inputIter != null, "attempted to process input when iterator was null")
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
       numInputRows += 1
@@ -372,6 +376,7 @@ class TungstenAggregationIterator(
   // that it switch to sort-based aggregation after `fallbackStartsAt` input rows have
   // been processed.
   private def processInputsWithControlledFallback(fallbackStartsAt: Int): Unit = {
+    assert(inputIter != null, "attempted to process input when iterator was null")
     var i = 0
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
@@ -412,6 +417,7 @@ class TungstenAggregationIterator(
    * Switch to sort-based aggregation when the hash-based approach is unable to acquire memory.
    */
   private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: InternalRow): Unit = {
+    assert(inputIter != null, "attempted to process input when iterator was null")
     logInfo("falling back to sort based aggregation.")
     // Step 1: Get the ExternalSorter containing sorted entries of the map.
     externalSorter = hashMap.destructAndCreateExternalSorter()
@@ -431,6 +437,11 @@ class TungstenAggregationIterator(
       case _ => false
     }
 
+    // Note: Since we spill the sorter's contents immediately after creating it, we must insert
+    // something into the sorter here to ensure that we acquire at least a page of memory.
+    // This is done through `externalSorter.insertKV`, which will trigger the page allocation.
+    // Otherwise, children operators may steal the window of opportunity and starve our sorter.
+
     if (needsProcess) {
       // First, we create a buffer.
       val buffer = createNewAggregationBuffer()
@@ -588,27 +599,33 @@ class TungstenAggregationIterator(
   //         have not switched to sort-based aggregation.
   ///////////////////////////////////////////////////////////////////////////
 
-  // Starts to process input rows.
-  testFallbackStartsAt match {
-    case None =>
-      processInputs()
-    case Some(fallbackStartsAt) =>
-      // This is the testing path. processInputsWithControlledFallback is same as processInputs
-      // except that it switches to sort-based aggregation after `fallbackStartsAt` input rows
-      // have been processed.
-      processInputsWithControlledFallback(fallbackStartsAt)
-  }
+  /**
+   * Start processing input rows.
+   * Only after this method is called will this iterator be non-empty.
+   */
+  def start(parentIter: Iterator[InternalRow]): Unit = {
+    inputIter = parentIter
+    testFallbackStartsAt match {
+      case None =>
+        processInputs()
+      case Some(fallbackStartsAt) =>
+        // This is the testing path. processInputsWithControlledFallback is same as processInputs
+        // except that it switches to sort-based aggregation after `fallbackStartsAt` input rows
+        // have been processed.
+        processInputsWithControlledFallback(fallbackStartsAt)
+    }
 
-  // If we did not switch to sort-based aggregation in processInputs,
-  // we pre-load the first key-value pair from the map (to make hasNext idempotent).
-  if (!sortBased) {
-    // First, set aggregationBufferMapIterator.
-    aggregationBufferMapIterator = hashMap.iterator()
-    // Pre-load the first key-value pair from the aggregationBufferMapIterator.
-    mapIteratorHasNext = aggregationBufferMapIterator.next()
-    // If the map is empty, we just free it.
-    if (!mapIteratorHasNext) {
-      hashMap.free()
+    // If we did not switch to sort-based aggregation in processInputs,
+    // we pre-load the first key-value pair from the map (to make hasNext idempotent).
+    if (!sortBased) {
+      // First, set aggregationBufferMapIterator.
+      aggregationBufferMapIterator = hashMap.iterator()
+      // Pre-load the first key-value pair from the aggregationBufferMapIterator.
+      mapIteratorHasNext = aggregationBufferMapIterator.next()
+      // If the map is empty, we just free it.
+      if (!mapIteratorHasNext) {
+        hashMap.free()
+      }
     }
   }
 
@@ -673,21 +690,20 @@ class TungstenAggregationIterator(
   }
 
   ///////////////////////////////////////////////////////////////////////////
-  // Part 8: A utility function used to generate a output row when there is no
-  // input and there is no grouping expression.
+  // Part 8: Utility functions
   ///////////////////////////////////////////////////////////////////////////
 
+  /**
+   * Generate a output row when there is no input and there is no grouping expression.
+   */
   def outputForEmptyGroupingKeyWithoutInput(): UnsafeRow = {
-    if (groupingExpressions.isEmpty) {
-      sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
-      // We create a output row and copy it. So, we can free the map.
-      val resultCopy =
-        generateOutput(UnsafeRow.createFromByteArray(0, 0), sortBasedAggregationBuffer).copy()
-      hashMap.free()
-      resultCopy
-    } else {
-      throw new IllegalStateException(
-        "This method should not be called when groupingExpressions is not empty.")
-    }
+    assert(groupingExpressions.isEmpty)
+    assert(inputIter == null)
+    generateOutput(UnsafeRow.createFromByteArray(0, 0), initialAggregationBuffer)
+  }
+
+  /** Free memory used in the underlying map. */
+  def free(): Unit = {
+    hashMap.free()
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
new file mode 100644
index 0000000000000..ac22c2f3c0a58
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection
+import org.apache.spark.sql.execution.metric.SQLMetrics
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.unsafe.memory.TaskMemoryManager
+
+class TungstenAggregationIteratorSuite extends SparkFunSuite {
+
+  test("memory acquired on construction") {
+    // set up environment
+    val ctx = TestSQLContext
+
+    val taskMemoryManager = new TaskMemoryManager(SparkEnv.get.executorMemoryManager)
+    val taskContext = new TaskContextImpl(0, 0, 0, 0, taskMemoryManager, null, Seq.empty)
+    TaskContext.setTaskContext(taskContext)
+
+    // Assert that a page is allocated before processing starts
+    var iter: TungstenAggregationIterator = null
+    try {
+      val newMutableProjection = (expr: Seq[Expression], schema: Seq[Attribute]) => {
+        () => new InterpretedMutableProjection(expr, schema)
+      }
+      val dummyAccum = SQLMetrics.createLongMetric(ctx.sparkContext, "dummy")
+      iter = new TungstenAggregationIterator(Seq.empty, Seq.empty, Seq.empty, 0,
+        Seq.empty, newMutableProjection, Seq.empty, None, dummyAccum, dummyAccum)
+      val numPages = iter.getHashMap.getNumDataPages
+      assert(numPages === 1)
+    } finally {
+      // Clean up
+      if (iter != null) {
+        iter.free()
+      }
+      TaskContext.unset()
+    }
+  }
+}

From 57ec27dd7784ce15a2ece8a6c8ac7bd5fd25aea2 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 12 Aug 2015 10:38:30 -0700
Subject: [PATCH 0996/1454] [SPARK-9804] [HIVE] Use correct value for
 isSrcLocal parameter.

If the correct parameter is not provided, Hive will run into an error
because it calls methods that are specific to the local filesystem to
copy the data.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8086 from vanzin/SPARK-9804.
---
 .../org/apache/spark/sql/hive/client/HiveShim.scala | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 6e826ce552204..8fc8935b1dc3c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -25,7 +25,7 @@ import java.util.concurrent.TimeUnit
 
 import scala.collection.JavaConversions._
 
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.ql.Driver
 import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table}
@@ -429,7 +429,7 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       isSkewedStoreAsSubdir: Boolean): Unit = {
     loadPartitionMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean,
       holdDDLTime: JBoolean, inheritTableSpecs: JBoolean, isSkewedStoreAsSubdir: JBoolean,
-      JBoolean.TRUE, JBoolean.FALSE)
+      isSrcLocal(loadPath, hive.getConf()): JBoolean, JBoolean.FALSE)
   }
 
   override def loadTable(
@@ -439,7 +439,7 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       replace: Boolean,
       holdDDLTime: Boolean): Unit = {
     loadTableMethod.invoke(hive, loadPath, tableName, replace: JBoolean, holdDDLTime: JBoolean,
-      JBoolean.TRUE, JBoolean.FALSE, JBoolean.FALSE)
+      isSrcLocal(loadPath, hive.getConf()): JBoolean, JBoolean.FALSE, JBoolean.FALSE)
   }
 
   override def loadDynamicPartitions(
@@ -461,6 +461,13 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY,
       TimeUnit.MILLISECONDS).asInstanceOf[Long]
   }
+
+  protected def isSrcLocal(path: Path, conf: HiveConf): Boolean = {
+    val localFs = FileSystem.getLocal(conf)
+    val pathFs = FileSystem.get(path.toUri(), conf)
+    localFs.getUri() == pathFs.getUri()
+  }
+
 }
 
 private[client] class Shim_v1_0 extends Shim_v0_14 {

From 70fe558867ccb4bcff6ec673438b03608bb02252 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 12 Aug 2015 10:48:52 -0700
Subject: [PATCH 0997/1454] [SPARK-9847] [ML] Modified copyValues to
 distinguish between default, explicit param values

From JIRA: Currently, Params.copyValues copies default parameter values to the paramMap of the target instance, rather than the defaultParamMap. It should copy to the defaultParamMap because explicitly setting a parameter can change the semantics.
This issue arose in SPARK-9789, where 2 params "threshold" and "thresholds" for LogisticRegression can have mutually exclusive values. If thresholds is set, then fit() will copy the default value of threshold as well, easily resulting in inconsistent settings for the 2 params.

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8115 from jkbradley/copyvalues-fix.
---
 .../org/apache/spark/ml/param/params.scala    | 19 ++++++++++++++++---
 .../apache/spark/ml/param/ParamsSuite.scala   |  8 ++++++++
 2 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index d68f5ff0053c9..91c0a5631319d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -559,13 +559,26 @@ trait Params extends Identifiable with Serializable {
 
   /**
    * Copies param values from this instance to another instance for params shared by them.
-   * @param to the target instance
-   * @param extra extra params to be copied
+   *
+   * This handles default Params and explicitly set Params separately.
+   * Default Params are copied from and to [[defaultParamMap]], and explicitly set Params are
+   * copied from and to [[paramMap]].
+   * Warning: This implicitly assumes that this [[Params]] instance and the target instance
+   *          share the same set of default Params.
+   *
+   * @param to the target instance, which should work with the same set of default Params as this
+   *           source instance
+   * @param extra extra params to be copied to the target's [[paramMap]]
    * @return the target instance with param values copied
    */
   protected def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T = {
-    val map = extractParamMap(extra)
+    val map = paramMap ++ extra
     params.foreach { param =>
+      // copy default Params
+      if (defaultParamMap.contains(param) && to.hasParam(param.name)) {
+        to.defaultParamMap.put(to.getParam(param.name), defaultParamMap(param))
+      }
+      // copy explicitly set Params
       if (map.contains(param) && to.hasParam(param.name)) {
         to.set(param.name, map(param))
       }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index 050d4170ea017..be95638d81686 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -200,6 +200,14 @@ class ParamsSuite extends SparkFunSuite {
     val inArray = ParamValidators.inArray[Int](Array(1, 2))
     assert(inArray(1) && inArray(2) && !inArray(0))
   }
+
+  test("Params.copyValues") {
+    val t = new TestParams()
+    val t2 = t.copy(ParamMap.empty)
+    assert(!t2.isSet(t2.maxIter))
+    val t3 = t.copy(ParamMap(t.maxIter -> 20))
+    assert(t3.isSet(t3.maxIter))
+  }
 }
 
 object ParamsSuite extends SparkFunSuite {

From 60103ecd3d9c92709a5878be7ebd57012813ab48 Mon Sep 17 00:00:00 2001
From: Brennan Ashton <bashton@brennanashton.com>
Date: Wed, 12 Aug 2015 11:57:30 -0700
Subject: [PATCH 0998/1454] [SPARK-9726] [PYTHON] PySpark DF join no longer
 accepts on=None

rxin

First pull request for Spark so let me know if I am missing anything
The contribution is my original work and I license the work to the project under the project's open source license.

Author: Brennan Ashton <bashton@brennanashton.com>

Closes #8016 from btashton/patch-1.
---
 python/pyspark/sql/dataframe.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 47d5a6a43a84d..09647ff6d0749 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -566,8 +566,7 @@ def join(self, other, on=None, how=None):
 
         if on is None or len(on) == 0:
             jdf = self._jdf.join(other._jdf)
-
-        if isinstance(on[0], basestring):
+        elif isinstance(on[0], basestring):
             jdf = self._jdf.join(other._jdf, self._jseq(on))
         else:
             assert isinstance(on[0], Column), "on should be Column or list of Column"

From 762bacc16ac5e74c8b05a7c1e3e367d1d1633cef Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 12 Aug 2015 13:24:18 -0700
Subject: [PATCH 0999/1454] [SPARK-9766] [ML] [PySpark] check and add miss docs
 for PySpark ML

Check and add miss docs for PySpark ML (this issue only check miss docs for o.a.s.ml not o.a.s.mllib).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8059 from yanboliang/SPARK-9766.
---
 python/pyspark/ml/classification.py | 12 ++++++++++--
 python/pyspark/ml/clustering.py     |  4 +++-
 python/pyspark/ml/evaluation.py     |  3 ++-
 python/pyspark/ml/feature.py        |  9 +++++----
 4 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 5978d8f4d3a01..6702dce5545a9 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -34,6 +34,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
                          HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
     """
     Logistic regression.
+    Currently, this class only supports binary classification.
 
     >>> from pyspark.sql import Row
     >>> from pyspark.mllib.linalg import Vectors
@@ -96,8 +97,8 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
         #  is an L2 penalty. For alpha = 1, it is an L1 penalty.
         self.elasticNetParam = \
             Param(self, "elasticNetParam",
-                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " +
-                  "is an L2 penalty. For alpha = 1, it is an L1 penalty.")
+                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
+                  "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
         #: param for whether to fit an intercept term.
         self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
         #: param for threshold in binary classification prediction, in range [0, 1].
@@ -656,6 +657,13 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
                  HasRawPredictionCol):
     """
     Naive Bayes Classifiers.
+    It supports both Multinomial and Bernoulli NB. Multinomial NB
+    (`http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html`)
+    can handle finitely supported discrete data. For example, by converting documents into
+    TF-IDF vectors, it can be used for document classification. By making every vector a
+    binary (0/1) data, it can also be used as Bernoulli NB
+    (`http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html`).
+    The input feature values must be nonnegative.
 
     >>> from pyspark.sql import Row
     >>> from pyspark.mllib.linalg import Vectors
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index b5e9b6549d9f1..48338713a29ea 100644
--- a/python/pyspark/ml/clustering.py
+++ b/python/pyspark/ml/clustering.py
@@ -37,7 +37,9 @@ def clusterCenters(self):
 @inherit_doc
 class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
     """
-    K-means Clustering
+    K-means clustering with support for multiple parallel runs and a k-means++ like initialization
+    mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
+    they are executed together with joint passes over the data for efficiency.
 
     >>> from pyspark.mllib.linalg import Vectors
     >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 06e809352225b..2734092575ad9 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -23,7 +23,8 @@
 from pyspark.ml.util import keyword_only
 from pyspark.mllib.common import inherit_doc
 
-__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator']
+__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator',
+           'MulticlassClassificationEvaluator']
 
 
 @inherit_doc
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index cb4dfa21298ce..535d55326646c 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -26,10 +26,11 @@
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
-__all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
-           'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
-           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
-           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
+__all__ = ['Binarizer', 'Bucketizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer',
+           'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler',
+           'StandardScalerModel', 'StringIndexer', 'StringIndexerModel', 'Tokenizer',
+           'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel', 'PCA',
+           'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc

From 551def5d6972440365bd7436d484a67138d9a8f3 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 12 Aug 2015 14:27:13 -0700
Subject: [PATCH 1000/1454] [SPARK-9789] [ML] Added logreg threshold param back

Reinstated LogisticRegression.threshold Param for binary compatibility.  Param thresholds overrides threshold, if set.

CC: mengxr dbtsai feynmanliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8079 from jkbradley/logreg-reinstate-threshold.
---
 .../classification/LogisticRegression.scala   | 127 ++++++++++++++----
 .../ml/param/shared/SharedParamsCodeGen.scala |   4 +-
 .../spark/ml/param/shared/sharedParams.scala  |   6 +-
 .../JavaLogisticRegressionSuite.java          |   7 +-
 .../LogisticRegressionSuite.scala             |  33 +++--
 python/pyspark/ml/classification.py           |  98 +++++++++-----
 6 files changed, 199 insertions(+), 76 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index f55134d258857..5bcd7117b668c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -34,8 +34,7 @@ import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Row, SQLContext}
-import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.storage.StorageLevel
 
 /**
@@ -43,44 +42,115 @@ import org.apache.spark.storage.StorageLevel
  */
 private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol
-  with HasStandardization {
+  with HasStandardization with HasThreshold {
 
   /**
-   * Version of setThresholds() for binary classification, available for backwards
-   * compatibility.
+   * Set threshold in binary classification, in range [0, 1].
    *
-   * Calling this with threshold p will effectively call `setThresholds(Array(1-p, p))`.
+   * If the estimated probability of class label 1 is > threshold, then predict 1, else 0.
+   * A high threshold encourages the model to predict 0 more often;
+   * a low threshold encourages the model to predict 1 more often.
+   *
+   * Note: Calling this with threshold p is equivalent to calling `setThresholds(Array(1-p, p))`.
+   *       When [[setThreshold()]] is called, any user-set value for [[thresholds]] will be cleared.
+   *       If both [[threshold]] and [[thresholds]] are set in a ParamMap, then they must be
+   *       equivalent.
+   *
+   * Default is 0.5.
+   * @group setParam
+   */
+  def setThreshold(value: Double): this.type = {
+    if (isSet(thresholds)) clear(thresholds)
+    set(threshold, value)
+  }
+
+  /**
+   * Get threshold for binary classification.
+   *
+   * If [[threshold]] is set, returns that value.
+   * Otherwise, if [[thresholds]] is set with length 2 (i.e., binary classification),
+   * this returns the equivalent threshold: {{{1 / (1 + thresholds(0) / thresholds(1))}}}.
+   * Otherwise, returns [[threshold]] default value.
+   *
+   * @group getParam
+   * @throws IllegalArgumentException if [[thresholds]] is set to an array of length other than 2.
+   */
+  override def getThreshold: Double = {
+    checkThresholdConsistency()
+    if (isSet(thresholds)) {
+      val ts = $(thresholds)
+      require(ts.length == 2, "Logistic Regression getThreshold only applies to" +
+        " binary classification, but thresholds has length != 2.  thresholds: " + ts.mkString(","))
+      1.0 / (1.0 + ts(0) / ts(1))
+    } else {
+      $(threshold)
+    }
+  }
+
+  /**
+   * Set thresholds in multiclass (or binary) classification to adjust the probability of
+   * predicting each class. Array must have length equal to the number of classes, with values >= 0.
+   * The class with largest value p/t is predicted, where p is the original probability of that
+   * class and t is the class' threshold.
+   *
+   * Note: When [[setThresholds()]] is called, any user-set value for [[threshold]] will be cleared.
+   *       If both [[threshold]] and [[thresholds]] are set in a ParamMap, then they must be
+   *       equivalent.
    *
-   * Default is effectively 0.5.
    * @group setParam
    */
-  def setThreshold(value: Double): this.type = set(thresholds, Array(1.0 - value, value))
+  def setThresholds(value: Array[Double]): this.type = {
+    if (isSet(threshold)) clear(threshold)
+    set(thresholds, value)
+  }
 
   /**
-   * Version of [[getThresholds()]] for binary classification, available for backwards
-   * compatibility.
+   * Get thresholds for binary or multiclass classification.
+   *
+   * If [[thresholds]] is set, return its value.
+   * Otherwise, if [[threshold]] is set, return the equivalent thresholds for binary
+   * classification: (1-threshold, threshold).
+   * If neither are set, throw an exception.
    *
-   * Param thresholds must have length 2 (or not be specified).
-   * This returns {{{1 / (1 + thresholds(0) / thresholds(1))}}}.
    * @group getParam
    */
-  def getThreshold: Double = {
-    if (isDefined(thresholds)) {
-      val thresholdValues = $(thresholds)
-      assert(thresholdValues.length == 2, "Logistic Regression getThreshold only applies to" +
-        " binary classification, but thresholds has length != 2." +
-        s"  thresholds: ${thresholdValues.mkString(",")}")
-      1.0 / (1.0 + thresholdValues(0) / thresholdValues(1))
+  override def getThresholds: Array[Double] = {
+    checkThresholdConsistency()
+    if (!isSet(thresholds) && isSet(threshold)) {
+      val t = $(threshold)
+      Array(1-t, t)
     } else {
-      0.5
+      $(thresholds)
+    }
+  }
+
+  /**
+   * If [[threshold]] and [[thresholds]] are both set, ensures they are consistent.
+   * @throws IllegalArgumentException if [[threshold]] and [[thresholds]] are not equivalent
+   */
+  protected def checkThresholdConsistency(): Unit = {
+    if (isSet(threshold) && isSet(thresholds)) {
+      val ts = $(thresholds)
+      require(ts.length == 2, "Logistic Regression found inconsistent values for threshold and" +
+        s" thresholds.  Param threshold is set (${$(threshold)}), indicating binary" +
+        s" classification, but Param thresholds is set with length ${ts.length}." +
+        " Clear one Param value to fix this problem.")
+      val t = 1.0 / (1.0 + ts(0) / ts(1))
+      require(math.abs($(threshold) - t) < 1E-5, "Logistic Regression getThreshold found" +
+        s" inconsistent values for threshold (${$(threshold)}) and thresholds (equivalent to $t)")
     }
   }
+
+  override def validateParams(): Unit = {
+    checkThresholdConsistency()
+  }
 }
 
 /**
  * :: Experimental ::
  * Logistic regression.
- * Currently, this class only supports binary classification.
+ * Currently, this class only supports binary classification.  It will support multiclass
+ * in the future.
  */
 @Experimental
 class LogisticRegression(override val uid: String)
@@ -128,7 +198,7 @@ class LogisticRegression(override val uid: String)
    * Whether to fit an intercept term.
    * Default is true.
    * @group setParam
-   * */
+   */
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
   setDefault(fitIntercept -> true)
 
@@ -140,7 +210,7 @@ class LogisticRegression(override val uid: String)
    * is applied. In R's GLMNET package, the default behavior is true as well.
    * Default is true.
    * @group setParam
-   * */
+   */
   def setStandardization(value: Boolean): this.type = set(standardization, value)
   setDefault(standardization -> true)
 
@@ -148,6 +218,10 @@ class LogisticRegression(override val uid: String)
 
   override def getThreshold: Double = super.getThreshold
 
+  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
+
+  override def getThresholds: Array[Double] = super.getThresholds
+
   override protected def train(dataset: DataFrame): LogisticRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
     val instances = extractLabeledPoints(dataset).map {
@@ -314,6 +388,10 @@ class LogisticRegressionModel private[ml] (
 
   override def getThreshold: Double = super.getThreshold
 
+  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
+
+  override def getThresholds: Array[Double] = super.getThresholds
+
   /** Margin (rawPrediction) for class label 1.  For binary classification only. */
   private val margin: Vector => Double = (features) => {
     BLAS.dot(features, weights) + intercept
@@ -364,6 +442,7 @@ class LogisticRegressionModel private[ml] (
    * The behavior of this can be adjusted using [[thresholds]].
    */
   override protected def predict(features: Vector): Double = {
+    // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden.
     if (score(features) > getThreshold) 1 else 0
   }
 
@@ -393,6 +472,7 @@ class LogisticRegressionModel private[ml] (
   }
 
   override protected def raw2prediction(rawPrediction: Vector): Double = {
+    // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden.
     val t = getThreshold
     val rawThreshold = if (t == 0.0) {
       Double.NegativeInfinity
@@ -405,6 +485,7 @@ class LogisticRegressionModel private[ml] (
   }
 
   override protected def probability2prediction(probability: Vector): Double = {
+    // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden.
     if (probability(1) > getThreshold) 1 else 0
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index da4c076830391..9e12f1856a940 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -45,14 +45,14 @@ private[shared] object SharedParamsCodeGen {
         " These probabilities should be treated as confidences, not precise probabilities.",
         Some("\"probability\"")),
       ParamDesc[Double]("threshold",
-        "threshold in binary classification prediction, in range [0, 1]",
+        "threshold in binary classification prediction, in range [0, 1]", Some("0.5"),
         isValid = "ParamValidators.inRange(0, 1)", finalMethods = false),
       ParamDesc[Array[Double]]("thresholds", "Thresholds in multi-class classification" +
         " to adjust the probability of predicting each class." +
         " Array must have length equal to the number of classes, with values >= 0." +
         " The class with largest value p/t is predicted, where p is the original probability" +
         " of that class and t is the class' threshold.",
-        isValid = "(t: Array[Double]) => t.forall(_ >= 0)"),
+        isValid = "(t: Array[Double]) => t.forall(_ >= 0)", finalMethods = false),
       ParamDesc[String]("inputCol", "input column name"),
       ParamDesc[Array[String]]("inputCols", "input column names"),
       ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")),
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index 23e2b6cc43996..a17d4ea960a90 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -139,7 +139,7 @@ private[ml] trait HasProbabilityCol extends Params {
 }
 
 /**
- * Trait for shared param threshold.
+ * Trait for shared param threshold (default: 0.5).
  */
 private[ml] trait HasThreshold extends Params {
 
@@ -149,6 +149,8 @@ private[ml] trait HasThreshold extends Params {
    */
   final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1))
 
+  setDefault(threshold, 0.5)
+
   /** @group getParam */
   def getThreshold: Double = $(threshold)
 }
@@ -165,7 +167,7 @@ private[ml] trait HasThresholds extends Params {
   final val thresholds: DoubleArrayParam = new DoubleArrayParam(this, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.", (t: Array[Double]) => t.forall(_ >= 0))
 
   /** @group getParam */
-  final def getThresholds: Array[Double] = $(thresholds)
+  def getThresholds: Array[Double] = $(thresholds)
 }
 
 /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index 7e9aa383728f0..618b95b9bd126 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -100,9 +100,7 @@ public void logisticRegressionWithSetters() {
       assert(r.getDouble(0) == 0.0);
     }
     // Call transform with params, and check that the params worked.
-    double[] thresholds = {1.0, 0.0};
-    model.transform(
-      dataset, model.thresholds().w(thresholds), model.probabilityCol().w("myProb"))
+    model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
       .registerTempTable("predNotAllZero");
     DataFrame predNotAllZero = jsql.sql("SELECT prediction, myProb FROM predNotAllZero");
     boolean foundNonZero = false;
@@ -112,9 +110,8 @@ public void logisticRegressionWithSetters() {
     assert(foundNonZero);
 
     // Call fit() with new params, and check as many params as we can.
-    double[] thresholds2 = {0.6, 0.4};
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
-        lr.thresholds().w(thresholds2), lr.probabilityCol().w("theProb"));
+        lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
     LogisticRegression parent2 = (LogisticRegression) model2.parent();
     assert(parent2.getMaxIter() == 5);
     assert(parent2.getRegParam() == 0.1);
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 8c3d4590f5ae9..e354e161c6dee 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -94,12 +94,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   test("setThreshold, getThreshold") {
     val lr = new LogisticRegression
     // default
-    withClue("LogisticRegression should not have thresholds set by default") {
-      intercept[java.util.NoSuchElementException] {
+    assert(lr.getThreshold === 0.5, "LogisticRegression.threshold should default to 0.5")
+    withClue("LogisticRegression should not have thresholds set by default.") {
+      intercept[java.util.NoSuchElementException] { // Note: The exception type may change in future
         lr.getThresholds
       }
     }
-    // Set via thresholds.
+    // Set via threshold.
     // Intuition: Large threshold or large thresholds(1) makes class 0 more likely.
     lr.setThreshold(1.0)
     assert(lr.getThresholds === Array(0.0, 1.0))
@@ -107,10 +108,26 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lr.getThresholds === Array(1.0, 0.0))
     lr.setThreshold(0.5)
     assert(lr.getThresholds === Array(0.5, 0.5))
-    // Test getThreshold
-    lr.setThresholds(Array(0.3, 0.7))
+    // Set via thresholds
+    val lr2 = new LogisticRegression
+    lr2.setThresholds(Array(0.3, 0.7))
     val expectedThreshold = 1.0 / (1.0 + 0.3 / 0.7)
-    assert(lr.getThreshold ~== expectedThreshold relTol 1E-7)
+    assert(lr2.getThreshold ~== expectedThreshold relTol 1E-7)
+    // thresholds and threshold must be consistent
+    lr2.setThresholds(Array(0.1, 0.2, 0.3))
+    withClue("getThreshold should throw error if thresholds has length != 2.") {
+      intercept[IllegalArgumentException] {
+        lr2.getThreshold
+      }
+    }
+    // thresholds and threshold must be consistent: values
+    withClue("fit with ParamMap should throw error if threshold, thresholds do not match.") {
+      intercept[IllegalArgumentException] {
+        val lr2model = lr2.fit(dataset,
+          lr2.thresholds -> Array(0.3, 0.7), lr2.threshold -> (expectedThreshold / 2.0))
+        lr2model.getThreshold
+      }
+    }
   }
 
   test("logistic regression doesn't fit intercept when fitIntercept is off") {
@@ -145,7 +162,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       s" ${predAllZero.count(_ === 0)} of ${dataset.count()} were 0.")
     // Call transform with params, and check that the params worked.
     val predNotAllZero =
-      model.transform(dataset, model.thresholds -> Array(1.0, 0.0),
+      model.transform(dataset, model.threshold -> 0.0,
         model.probabilityCol -> "myProb")
         .select("prediction", "myProb")
         .collect()
@@ -153,8 +170,8 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(predNotAllZero.exists(_ !== 0.0))
 
     // Call fit() with new params, and check as many params as we can.
+    lr.setThresholds(Array(0.6, 0.4))
     val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1,
-      lr.thresholds -> Array(0.6, 0.4),
       lr.probabilityCol -> "theProb")
     val parent2 = model2.parent.asInstanceOf[LogisticRegression]
     assert(parent2.getMaxIter === 5)
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 6702dce5545a9..83f808efc3bf0 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -76,19 +76,21 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
                        " Array must have length equal to the number of classes, with values >= 0." +
                        " The class with largest value p/t is predicted, where p is the original" +
                        " probability of that class and t is the class' threshold.")
+    threshold = Param(Params._dummy(), "threshold",
+                      "Threshold in binary classification prediction, in range [0, 1]." +
+                      " If threshold and thresholds are both set, they must match.")
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 threshold=None, thresholds=None,
+                 threshold=0.5, thresholds=None,
                  probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=None, thresholds=None, \
+                 threshold=0.5, thresholds=None, \
                  probabilityCol="probability", rawPredictionCol="rawPrediction")
-        Param thresholds overrides Param threshold; threshold is provided
-        for backwards compatibility and only applies to binary classification.
+        If the threshold and thresholds Params are both set, they must be equivalent.
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -101,7 +103,11 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                   "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
         #: param for whether to fit an intercept term.
         self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
-        #: param for threshold in binary classification prediction, in range [0, 1].
+        #: param for threshold in binary classification, in range [0, 1].
+        self.threshold = Param(self, "threshold",
+                               "Threshold in binary classification prediction, in range [0, 1]." +
+                               " If threshold and thresholds are both set, they must match.")
+        #: param for thresholds or cutoffs in binary or multiclass classification
         self.thresholds = \
             Param(self, "thresholds",
                   "Thresholds in multi-class classification" +
@@ -110,29 +116,28 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                   " The class with largest value p/t is predicted, where p is the original" +
                   " probability of that class and t is the class' threshold.")
         self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
-                         fitIntercept=True)
+                         fitIntercept=True, threshold=0.5)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
+        self._checkThresholdConsistency()
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  threshold=None, thresholds=None,
+                  threshold=0.5, thresholds=None,
                   probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                  threshold=None, thresholds=None, \
+                  threshold=0.5, thresholds=None, \
                   probabilityCol="probability", rawPredictionCol="rawPrediction")
         Sets params for logistic regression.
-        Param thresholds overrides Param threshold; threshold is provided
-        for backwards compatibility and only applies to binary classification.
+        If the threshold and thresholds Params are both set, they must be equivalent.
         """
-        # Under the hood we use thresholds so translate threshold to thresholds if applicable
-        if thresholds is None and threshold is not None:
-            kwargs[thresholds] = [1-threshold, threshold]
         kwargs = self.setParams._input_kwargs
-        return self._set(**kwargs)
+        self._set(**kwargs)
+        self._checkThresholdConsistency()
+        return self
 
     def _create_model(self, java_model):
         return LogisticRegressionModel(java_model)
@@ -165,44 +170,65 @@ def getFitIntercept(self):
 
     def setThreshold(self, value):
         """
-        Sets the value of :py:attr:`thresholds` using [1-value, value].
+        Sets the value of :py:attr:`threshold`.
+        Clears value of :py:attr:`thresholds` if it has been set.
+        """
+        self._paramMap[self.threshold] = value
+        if self.isSet(self.thresholds):
+            del self._paramMap[self.thresholds]
+        return self
 
-        >>> lr = LogisticRegression()
-        >>> lr.getThreshold()
-        0.5
-        >>> lr.setThreshold(0.6)
-        LogisticRegression_...
-        >>> abs(lr.getThreshold() - 0.6) < 1e-5
-        True
+    def getThreshold(self):
+        """
+        Gets the value of threshold or its default value.
         """
-        return self.setThresholds([1-value, value])
+        self._checkThresholdConsistency()
+        if self.isSet(self.thresholds):
+            ts = self.getOrDefault(self.thresholds)
+            if len(ts) != 2:
+                raise ValueError("Logistic Regression getThreshold only applies to" +
+                                 " binary classification, but thresholds has length != 2." +
+                                 "  thresholds: " + ",".join(ts))
+            return 1.0/(1.0 + ts[0]/ts[1])
+        else:
+            return self.getOrDefault(self.threshold)
 
     def setThresholds(self, value):
         """
         Sets the value of :py:attr:`thresholds`.
+        Clears value of :py:attr:`threshold` if it has been set.
         """
         self._paramMap[self.thresholds] = value
+        if self.isSet(self.threshold):
+            del self._paramMap[self.threshold]
         return self
 
     def getThresholds(self):
         """
-        Gets the value of thresholds or its default value.
+        If :py:attr:`thresholds` is set, return its value.
+        Otherwise, if :py:attr:`threshold` is set, return the equivalent thresholds for binary
+        classification: (1-threshold, threshold).
+        If neither are set, throw an error.
         """
-        return self.getOrDefault(self.thresholds)
+        self._checkThresholdConsistency()
+        if not self.isSet(self.thresholds) and self.isSet(self.threshold):
+            t = self.getOrDefault(self.threshold)
+            return [1.0-t, t]
+        else:
+            return self.getOrDefault(self.thresholds)
 
-    def getThreshold(self):
-        """
-        Gets the value of threshold or its default value.
-        """
-        if self.isDefined(self.thresholds):
-            thresholds = self.getOrDefault(self.thresholds)
-            if len(thresholds) != 2:
+    def _checkThresholdConsistency(self):
+        if self.isSet(self.threshold) and self.isSet(self.thresholds):
+            ts = self.getParam(self.thresholds)
+            if len(ts) != 2:
                 raise ValueError("Logistic Regression getThreshold only applies to" +
                                  " binary classification, but thresholds has length != 2." +
-                                 "  thresholds: " + ",".join(thresholds))
-            return 1.0/(1.0+thresholds[0]/thresholds[1])
-        else:
-            return 0.5
+                                 " thresholds: " + ",".join(ts))
+            t = 1.0/(1.0 + ts[0]/ts[1])
+            t2 = self.getParam(self.threshold)
+            if abs(t2 - t) >= 1E-5:
+                raise ValueError("Logistic Regression getThreshold found inconsistent values for" +
+                                 " threshold (%g) and thresholds (equivalent to %g)" % (t2, t))
 
 
 class LogisticRegressionModel(JavaModel):

From 6f60298b1d7aa97268a42eca1e3b4851a7e88cb5 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 14:28:23 -0700
Subject: [PATCH 1001/1454] [SPARK-8967] [DOC] add Since annotation

Add `Since` as a Scala annotation. The benefit is that we can use it without having explicit JavaDoc. This is useful for inherited methods. The limitation is that is doesn't show up in the generated Java API documentation. This might be fixed by modifying genjavadoc. I think we could leave it as a TODO.

This is how the generated Scala doc looks:

`since` JavaDoc tag:

![screen shot 2015-08-11 at 10 00 37 pm](https://cloud.githubusercontent.com/assets/829644/9230761/fa72865c-40d8-11e5-807e-0f3c815c5acd.png)

`Since` annotation:

![screen shot 2015-08-11 at 10 00 28 pm](https://cloud.githubusercontent.com/assets/829644/9230764/0041d7f4-40d9-11e5-8124-c3f3e5d5b31f.png)

rxin

Author: Xiangrui Meng <meng@databricks.com>

Closes #8131 from mengxr/SPARK-8967.
---
 .../org/apache/spark/annotation/Since.scala   | 28 +++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 core/src/main/scala/org/apache/spark/annotation/Since.scala

diff --git a/core/src/main/scala/org/apache/spark/annotation/Since.scala b/core/src/main/scala/org/apache/spark/annotation/Since.scala
new file mode 100644
index 0000000000000..fa59393c22476
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/annotation/Since.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.annotation
+
+import scala.annotation.StaticAnnotation
+
+/**
+ * A Scala annotation that specifies the Spark version when a definition was added.
+ * Different from the `@since` tag in JavaDoc, this annotation does not require explicit JavaDoc and
+ * hence works for overridden methods that inherit API documentation directly from parents.
+ * The limitation is that it does not show up in the generated Java API documentation.
+ */
+private[spark] class Since(version: String) extends StaticAnnotation

From a17384fa343628cec44437da5b80b9403ecd5838 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 12 Aug 2015 15:27:52 -0700
Subject: [PATCH 1002/1454] [SPARK-9907] [SQL] Python crc32 is mistakenly
 calling md5

Author: Reynold Xin <rxin@databricks.com>

Closes #8138 from rxin/SPARK-9907.
---
 python/pyspark/sql/functions.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 95f46044d324a..e98979533f901 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -885,10 +885,10 @@ def crc32(col):
     returns the value as a bigint.
 
     >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(crc32('a').alias('crc32')).collect()
-    [Row(crc32=u'902fbdd2b1df0c4f70b4a5d23525e932')]
+    [Row(crc32=2743272264)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.md5(_to_java_column(col)))
+    return Column(sc._jvm.functions.crc32(_to_java_column(col)))
 
 
 @ignore_unicode_prefix

From 738f353988dbf02704bd63f5e35d94402c59ed79 Mon Sep 17 00:00:00 2001
From: Niranjan Padmanabhan <niranjan.padmanabhan@cloudera.com>
Date: Wed, 12 Aug 2015 16:10:21 -0700
Subject: [PATCH 1003/1454] [SPARK-9092] Fixed incompatibility when both
 num-executors and dynamic...
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

… allocation are set. Now, dynamic allocation is set to false when num-executors is explicitly specified as an argument. Consequently, executorAllocationManager in not initialized in the SparkContext.

Author: Niranjan Padmanabhan <niranjan.padmanabhan@cloudera.com>

Closes #7657 from neurons/SPARK-9092.
---
 .../scala/org/apache/spark/SparkConf.scala    | 19 +++++++++++++++++++
 .../scala/org/apache/spark/SparkContext.scala |  6 +++++-
 .../org/apache/spark/deploy/SparkSubmit.scala |  4 ++--
 .../scala/org/apache/spark/util/Utils.scala   | 11 +++++++++++
 .../org/apache/spark/SparkContextSuite.scala  |  8 ++++++++
 .../spark/deploy/SparkSubmitSuite.scala       |  1 -
 docs/running-on-yarn.md                       |  2 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  4 ++--
 .../yarn/ApplicationMasterArguments.scala     |  5 -----
 .../org/apache/spark/deploy/yarn/Client.scala |  5 ++++-
 .../spark/deploy/yarn/ClientArguments.scala   |  8 +-------
 .../spark/deploy/yarn/YarnAllocator.scala     |  9 ++++++++-
 .../cluster/YarnClientSchedulerBackend.scala  |  3 ---
 .../deploy/yarn/YarnAllocatorSuite.scala      |  5 +++--
 14 files changed, 64 insertions(+), 26 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 8ff154fb5e334..b344b5e173d67 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -389,6 +389,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     val driverOptsKey = "spark.driver.extraJavaOptions"
     val driverClassPathKey = "spark.driver.extraClassPath"
     val driverLibraryPathKey = "spark.driver.extraLibraryPath"
+    val sparkExecutorInstances = "spark.executor.instances"
 
     // Used by Yarn in 1.1 and before
     sys.props.get("spark.driver.libraryPath").foreach { value =>
@@ -476,6 +477,24 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
         }
       }
     }
+
+    if (!contains(sparkExecutorInstances)) {
+      sys.env.get("SPARK_WORKER_INSTANCES").foreach { value =>
+        val warning =
+          s"""
+             |SPARK_WORKER_INSTANCES was detected (set to '$value').
+             |This is deprecated in Spark 1.0+.
+             |
+             |Please instead use:
+             | - ./spark-submit with --num-executors to specify the number of executors
+             | - Or set SPARK_EXECUTOR_INSTANCES
+             | - spark.executor.instances to configure the number of instances in the spark config.
+        """.stripMargin
+        logWarning(warning)
+
+        set("spark.executor.instances", value)
+      }
+    }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 6aafb4c5644d7..207a0c1bffeb3 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -528,7 +528,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       }
 
     // Optionally scale number of executors dynamically based on workload. Exposed for testing.
-    val dynamicAllocationEnabled = _conf.getBoolean("spark.dynamicAllocation.enabled", false)
+    val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)
+    if (!dynamicAllocationEnabled && _conf.getBoolean("spark.dynamicAllocation.enabled", false)) {
+      logInfo("Dynamic Allocation and num executors both set, thus dynamic allocation disabled.")
+    }
+
     _executorAllocationManager =
       if (dynamicAllocationEnabled) {
         Some(new ExecutorAllocationManager(this, listenerBus, _conf))
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 7ac6cbce4cd1d..02fa3088eded0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -422,7 +422,8 @@ object SparkSubmit {
 
       // Yarn client only
       OptionAssigner(args.queue, YARN, CLIENT, sysProp = "spark.yarn.queue"),
-      OptionAssigner(args.numExecutors, YARN, CLIENT, sysProp = "spark.executor.instances"),
+      OptionAssigner(args.numExecutors, YARN, ALL_DEPLOY_MODES,
+        sysProp = "spark.executor.instances"),
       OptionAssigner(args.files, YARN, CLIENT, sysProp = "spark.yarn.dist.files"),
       OptionAssigner(args.archives, YARN, CLIENT, sysProp = "spark.yarn.dist.archives"),
       OptionAssigner(args.principal, YARN, CLIENT, sysProp = "spark.yarn.principal"),
@@ -433,7 +434,6 @@ object SparkSubmit {
       OptionAssigner(args.driverMemory, YARN, CLUSTER, clOption = "--driver-memory"),
       OptionAssigner(args.driverCores, YARN, CLUSTER, clOption = "--driver-cores"),
       OptionAssigner(args.queue, YARN, CLUSTER, clOption = "--queue"),
-      OptionAssigner(args.numExecutors, YARN, CLUSTER, clOption = "--num-executors"),
       OptionAssigner(args.executorMemory, YARN, CLUSTER, clOption = "--executor-memory"),
       OptionAssigner(args.executorCores, YARN, CLUSTER, clOption = "--executor-cores"),
       OptionAssigner(args.files, YARN, CLUSTER, clOption = "--files"),
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index c4012d0e83f7d..a90d8541366f4 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -2286,6 +2286,17 @@ private[spark] object Utils extends Logging {
     isInDirectory(parent, child.getParentFile)
   }
 
+  /**
+   * Return whether dynamic allocation is enabled in the given conf
+   * Dynamic allocation and explicitly setting the number of executors are inherently
+   * incompatible. In environments where dynamic allocation is turned on by default,
+   * the latter should override the former (SPARK-9092).
+   */
+  def isDynamicAllocationEnabled(conf: SparkConf): Boolean = {
+    conf.contains("spark.dynamicAllocation.enabled") &&
+      conf.getInt("spark.executor.instances", 0) == 0
+  }
+
 }
 
 private [util] class SparkShutdownHookManager {
diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
index 5c57940fa5f77..d4f2ea87650a9 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
@@ -285,4 +285,12 @@ class SparkContextSuite extends SparkFunSuite with LocalSparkContext {
     }
   }
 
+  test("No exception when both num-executors and dynamic allocation set.") {
+    noException should be thrownBy {
+      sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")
+        .set("spark.dynamicAllocation.enabled", "true").set("spark.executor.instances", "6"))
+      assert(sc.executorAllocationManager.isEmpty)
+      assert(sc.getConf.getInt("spark.executor.instances", 0) === 6)
+    }
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 757e0ce3d278b..2456c5d0d49b0 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -159,7 +159,6 @@ class SparkSubmitSuite
     childArgsStr should include ("--executor-cores 5")
     childArgsStr should include ("--arg arg1 --arg arg2")
     childArgsStr should include ("--queue thequeue")
-    childArgsStr should include ("--num-executors 6")
     childArgsStr should include regex ("--jar .*thejar.jar")
     childArgsStr should include regex ("--addJars .*one.jar,.*two.jar,.*three.jar")
     childArgsStr should include regex ("--files .*file1.txt,.*file2.txt")
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index cac08a91b97d9..ec32c419b7c51 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -199,7 +199,7 @@ If you need a reference to the proper location to put log files in the YARN so t
  <td><code>spark.executor.instances</code></td>
   <td>2</td>
   <td>
-    The number of executors. Note that this property is incompatible with <code>spark.dynamicAllocation.enabled</code>.
+    The number of executors. Note that this property is incompatible with <code>spark.dynamicAllocation.enabled</code>. If both <code>spark.dynamicAllocation.enabled</code> and <code>spark.executor.instances</code> are specified, dynamic allocation is turned off and the specified number of <code>spark.executor.instances</code> is used. 
   </td>
 </tr>
 <tr>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 1d67b3ebb51b7..e19940d8d6642 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -64,7 +64,8 @@ private[spark] class ApplicationMaster(
 
   // Default to numExecutors * 2, with minimum of 3
   private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
-    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
+    sparkConf.getInt("spark.yarn.max.worker.failures",
+      math.max(sparkConf.getInt("spark.executor.instances", 0) *  2, 3)))
 
   @volatile private var exitCode = 0
   @volatile private var unregistered = false
@@ -493,7 +494,6 @@ private[spark] class ApplicationMaster(
    */
   private def startUserApplication(): Thread = {
     logInfo("Starting the user application in a separate Thread")
-    System.setProperty("spark.executor.instances", args.numExecutors.toString)
 
     val classpath = Client.getUserClasspath(sparkConf)
     val urls = classpath.map { entry =>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index 37f793763367e..b08412414aa1c 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -29,7 +29,6 @@ class ApplicationMasterArguments(val args: Array[String]) {
   var userArgs: Seq[String] = Nil
   var executorMemory = 1024
   var executorCores = 1
-  var numExecutors = DEFAULT_NUMBER_EXECUTORS
   var propertiesFile: String = null
 
   parseArgs(args.toList)
@@ -63,10 +62,6 @@ class ApplicationMasterArguments(val args: Array[String]) {
           userArgsBuffer += value
           args = tail
 
-        case ("--num-workers" | "--num-executors") :: IntParam(value) :: tail =>
-          numExecutors = value
-          args = tail
-
         case ("--worker-memory" | "--executor-memory") :: MemoryParam(value) :: tail =>
           executorMemory = value
           args = tail
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index b4ba3f0221600..6d63ddaf15852 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -751,7 +751,6 @@ private[spark] class Client(
         userArgs ++ Seq(
           "--executor-memory", args.executorMemory.toString + "m",
           "--executor-cores", args.executorCores.toString,
-          "--num-executors ", args.numExecutors.toString,
           "--properties-file", buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD),
             LOCALIZED_CONF_DIR, SPARK_CONF_FILE))
 
@@ -960,6 +959,10 @@ object Client extends Logging {
     val sparkConf = new SparkConf
 
     val args = new ClientArguments(argStrings, sparkConf)
+    // to maintain backwards-compatibility
+    if (!Utils.isDynamicAllocationEnabled(sparkConf)) {
+      sparkConf.setIfMissing("spark.executor.instances", args.numExecutors.toString)
+    }
     new Client(args, sparkConf).run()
   }
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 20d63d40cf605..4f42ffefa77f9 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -53,8 +53,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
   private val amMemOverheadKey = "spark.yarn.am.memoryOverhead"
   private val driverCoresKey = "spark.driver.cores"
   private val amCoresKey = "spark.yarn.am.cores"
-  private val isDynamicAllocationEnabled =
-    sparkConf.getBoolean("spark.dynamicAllocation.enabled", false)
+  private val isDynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(sparkConf)
 
   parseArgs(args.toList)
   loadEnvironmentArgs()
@@ -196,11 +195,6 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
           if (args(0) == "--num-workers") {
             println("--num-workers is deprecated. Use --num-executors instead.")
           }
-          // Dynamic allocation is not compatible with this option
-          if (isDynamicAllocationEnabled) {
-            throw new IllegalArgumentException("Explicitly setting the number " +
-              "of executors is not compatible with spark.dynamicAllocation.enabled!")
-          }
           numExecutors = value
           args = tail
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 59caa787b6e20..ccf753e69f4b6 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -21,6 +21,8 @@ import java.util.Collections
 import java.util.concurrent._
 import java.util.regex.Pattern
 
+import org.apache.spark.util.Utils
+
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
@@ -86,7 +88,12 @@ private[yarn] class YarnAllocator(
   private var executorIdCounter = 0
   @volatile private var numExecutorsFailed = 0
 
-  @volatile private var targetNumExecutors = args.numExecutors
+  @volatile private var targetNumExecutors =
+    if (Utils.isDynamicAllocationEnabled(sparkConf)) {
+      sparkConf.getInt("spark.dynamicAllocation.initialExecutors", 0)
+    } else {
+      sparkConf.getInt("spark.executor.instances", YarnSparkHadoopUtil.DEFAULT_NUMBER_EXECUTORS)
+    }
 
   // Keep track of which container is running which executor to remove the executors later
   // Visible for testing.
diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index d225061fcd1b4..d06d95140438c 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -81,8 +81,6 @@ private[spark] class YarnClientSchedulerBackend(
     // List of (target Client argument, environment variable, Spark property)
     val optionTuples =
       List(
-        ("--num-executors", "SPARK_WORKER_INSTANCES", "spark.executor.instances"),
-        ("--num-executors", "SPARK_EXECUTOR_INSTANCES", "spark.executor.instances"),
         ("--executor-memory", "SPARK_WORKER_MEMORY", "spark.executor.memory"),
         ("--executor-memory", "SPARK_EXECUTOR_MEMORY", "spark.executor.memory"),
         ("--executor-cores", "SPARK_WORKER_CORES", "spark.executor.cores"),
@@ -92,7 +90,6 @@ private[spark] class YarnClientSchedulerBackend(
       )
     // Warn against the following deprecated environment variables: env var -> suggestion
     val deprecatedEnvVars = Map(
-      "SPARK_WORKER_INSTANCES" -> "SPARK_WORKER_INSTANCES or --num-executors through spark-submit",
       "SPARK_WORKER_MEMORY" -> "SPARK_EXECUTOR_MEMORY or --executor-memory through spark-submit",
       "SPARK_WORKER_CORES" -> "SPARK_EXECUTOR_CORES or --executor-cores through spark-submit")
     optionTuples.foreach { case (optionName, envVar, sparkProp) =>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
index 58318bf9bcc08..5d05f514adde3 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
@@ -87,16 +87,17 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
 
   def createAllocator(maxExecutors: Int = 5): YarnAllocator = {
     val args = Array(
-      "--num-executors", s"$maxExecutors",
       "--executor-cores", "5",
       "--executor-memory", "2048",
       "--jar", "somejar.jar",
       "--class", "SomeClass")
+    val sparkConfClone = sparkConf.clone()
+    sparkConfClone.set("spark.executor.instances", maxExecutors.toString)
     new YarnAllocator(
       "not used",
       mock(classOf[RpcEndpointRef]),
       conf,
-      sparkConf,
+      sparkConfClone,
       rmClient,
       appAttemptId,
       new ApplicationMasterArguments(args),

From ab7e721cfec63155641e81e72b4ad43cf6a7d4c7 Mon Sep 17 00:00:00 2001
From: Michel Lemay <mlemay@gmail.com>
Date: Wed, 12 Aug 2015 16:17:58 -0700
Subject: [PATCH 1004/1454] [SPARK-9826] [CORE] Fix cannot use custom classes
 in log4j.properties

Refactor Utils class and create ShutdownHookManager.

NOTE: Wasn't able to run /dev/run-tests on windows machine.
Manual tests were conducted locally using custom log4j.properties file with Redis appender and logstash formatter (bundled in the fat-jar submitted to spark)

ex:
log4j.rootCategory=WARN,console,redis
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.spark.graphx.Pregel=INFO

log4j.appender.redis=com.ryantenney.log4j.FailoverRedisAppender
log4j.appender.redis.endpoints=hostname:port
log4j.appender.redis.key=mykey
log4j.appender.redis.alwaysBatch=false
log4j.appender.redis.layout=net.logstash.log4j.JSONEventLayoutV1

Author: michellemay <mlemay@gmail.com>

Closes #8109 from michellemay/SPARK-9826.
---
 .../scala/org/apache/spark/SparkContext.scala |   5 +-
 .../spark/deploy/history/HistoryServer.scala  |   4 +-
 .../spark/deploy/worker/ExecutorRunner.scala  |   7 +-
 .../org/apache/spark/rdd/HadoopRDD.scala      |   4 +-
 .../org/apache/spark/rdd/NewHadoopRDD.scala   |   4 +-
 .../apache/spark/rdd/SqlNewHadoopRDD.scala    |   4 +-
 .../spark/storage/DiskBlockManager.scala      |  10 +-
 .../spark/storage/TachyonBlockManager.scala   |   6 +-
 .../spark/util/ShutdownHookManager.scala      | 266 ++++++++++++++++++
 .../util/SparkUncaughtExceptionHandler.scala  |   2 +-
 .../scala/org/apache/spark/util/Utils.scala   | 222 +--------------
 .../hive/thriftserver/HiveThriftServer2.scala |   4 +-
 .../hive/thriftserver/SparkSQLCLIDriver.scala |   4 +-
 .../apache/spark/sql/hive/test/TestHive.scala |   4 +-
 .../spark/streaming/StreamingContext.scala    |   8 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |   5 +-
 16 files changed, 307 insertions(+), 252 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 207a0c1bffeb3..2e01a9a18c784 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -563,7 +563,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Make sure the context is stopped if the user forgets about it. This avoids leaving
     // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM
     // is killed, though.
-    _shutdownHookRef = Utils.addShutdownHook(Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
+    _shutdownHookRef = ShutdownHookManager.addShutdownHook(
+      ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
       logInfo("Invoking stop() from shutdown hook")
       stop()
     }
@@ -1671,7 +1672,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       return
     }
     if (_shutdownHookRef != null) {
-      Utils.removeShutdownHook(_shutdownHookRef)
+      ShutdownHookManager.removeShutdownHook(_shutdownHookRef)
     }
 
     Utils.tryLogNonFatalError {
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index a076a9c3f984d..d4f327cc588fe 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -30,7 +30,7 @@ import org.apache.spark.status.api.v1.{ApiRootResource, ApplicationInfo, Applica
   UIRoot}
 import org.apache.spark.ui.{SparkUI, UIUtils, WebUI}
 import org.apache.spark.ui.JettyUtils._
-import org.apache.spark.util.{SignalLogger, Utils}
+import org.apache.spark.util.{ShutdownHookManager, SignalLogger, Utils}
 
 /**
  * A web server that renders SparkUIs of completed applications.
@@ -238,7 +238,7 @@ object HistoryServer extends Logging {
     val server = new HistoryServer(conf, provider, securityManager, port)
     server.bind()
 
-    Utils.addShutdownHook { () => server.stop() }
+    ShutdownHookManager.addShutdownHook { () => server.stop() }
 
     // Wait until the end of the world... or if the HistoryServer process is manually stopped
     while(true) { Thread.sleep(Int.MaxValue) }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index 29a5042285578..ab3fea475c2a5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -28,7 +28,7 @@ import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.{SecurityManager, SparkConf, Logging}
 import org.apache.spark.deploy.{ApplicationDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.util.logging.FileAppender
 
 /**
@@ -70,7 +70,8 @@ private[deploy] class ExecutorRunner(
     }
     workerThread.start()
     // Shutdown hook that kills actors on shutdown.
-    shutdownHook = Utils.addShutdownHook { () => killProcess(Some("Worker shutting down")) }
+    shutdownHook = ShutdownHookManager.addShutdownHook { () =>
+      killProcess(Some("Worker shutting down")) }
   }
 
   /**
@@ -102,7 +103,7 @@ private[deploy] class ExecutorRunner(
       workerThread = null
       state = ExecutorState.KILLED
       try {
-        Utils.removeShutdownHook(shutdownHook)
+        ShutdownHookManager.removeShutdownHook(shutdownHook)
       } catch {
         case e: IllegalStateException => None
       }
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index f1c17369cb48c..e1f8719eead02 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -44,7 +44,7 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.rdd.HadoopRDD.HadoopMapPartitionsWithSplitRDD
-import org.apache.spark.util.{SerializableConfiguration, NextIterator, Utils}
+import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, NextIterator, Utils}
 import org.apache.spark.scheduler.{HostTaskLocation, HDFSCacheTaskLocation}
 import org.apache.spark.storage.StorageLevel
 
@@ -274,7 +274,7 @@ class HadoopRDD[K, V](
           }
         } catch {
           case e: Exception => {
-            if (!Utils.inShutdown()) {
+            if (!ShutdownHookManager.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
           }
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index f83a051f5da11..6a9c004d65cff 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -33,7 +33,7 @@ import org.apache.spark._
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD
-import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, Utils}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.storage.StorageLevel
 
@@ -186,7 +186,7 @@ class NewHadoopRDD[K, V](
           }
         } catch {
           case e: Exception => {
-            if (!Utils.inShutdown()) {
+            if (!ShutdownHookManager.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
           }
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index 6a95e44c57fec..fa3fecc80cb63 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -33,7 +33,7 @@ import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.{Partition => SparkPartition, _}
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, Utils}
 
 
 private[spark] class SqlNewHadoopPartition(
@@ -212,7 +212,7 @@ private[spark] class SqlNewHadoopRDD[V: ClassTag](
           }
         } catch {
           case e: Exception =>
-            if (!Utils.inShutdown()) {
+            if (!ShutdownHookManager.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
         }
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 56a33d5ca7d60..3f8d26e1d4cab 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -22,7 +22,7 @@ import java.io.{IOException, File}
 
 import org.apache.spark.{SparkConf, Logging}
 import org.apache.spark.executor.ExecutorExitCode
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 
 /**
  * Creates and maintains the logical mapping between logical blocks and physical on-disk
@@ -144,7 +144,7 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
   }
 
   private def addShutdownHook(): AnyRef = {
-    Utils.addShutdownHook(Utils.TEMP_DIR_SHUTDOWN_PRIORITY + 1) { () =>
+    ShutdownHookManager.addShutdownHook(ShutdownHookManager.TEMP_DIR_SHUTDOWN_PRIORITY + 1) { () =>
       logInfo("Shutdown hook called")
       DiskBlockManager.this.doStop()
     }
@@ -154,7 +154,7 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
   private[spark] def stop() {
     // Remove the shutdown hook.  It causes memory leaks if we leave it around.
     try {
-      Utils.removeShutdownHook(shutdownHook)
+      ShutdownHookManager.removeShutdownHook(shutdownHook)
     } catch {
       case e: Exception =>
         logError(s"Exception while removing shutdown hook.", e)
@@ -168,7 +168,9 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
       localDirs.foreach { localDir =>
         if (localDir.isDirectory() && localDir.exists()) {
           try {
-            if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir)
+            if (!ShutdownHookManager.hasRootAsShutdownDeleteDir(localDir)) {
+              Utils.deleteRecursively(localDir)
+            }
           } catch {
             case e: Exception =>
               logError(s"Exception while deleting local spark dir: $localDir", e)
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
index ebad5bc5ab28d..22878783fca67 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
@@ -32,7 +32,7 @@ import tachyon.TachyonURI
 
 import org.apache.spark.Logging
 import org.apache.spark.executor.ExecutorExitCode
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 
 
 /**
@@ -80,7 +80,7 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log
     // in order to avoid having really large inodes at the top level in Tachyon.
     tachyonDirs = createTachyonDirs()
     subDirs = Array.fill(tachyonDirs.length)(new Array[TachyonFile](subDirsPerTachyonDir))
-    tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir))
+    tachyonDirs.foreach(tachyonDir => ShutdownHookManager.registerShutdownDeleteDir(tachyonDir))
   }
 
   override def toString: String = {"ExternalBlockStore-Tachyon"}
@@ -240,7 +240,7 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log
     logDebug("Shutdown hook called")
     tachyonDirs.foreach { tachyonDir =>
       try {
-        if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) {
+        if (!ShutdownHookManager.hasRootAsShutdownDeleteDir(tachyonDir)) {
           Utils.deleteRecursively(tachyonDir, client)
         }
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
new file mode 100644
index 0000000000000..61ff9b89ec1c1
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
@@ -0,0 +1,266 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util
+
+import java.io.File
+import java.util.PriorityQueue
+
+import scala.util.{Failure, Success, Try}
+import tachyon.client.TachyonFile
+
+import org.apache.hadoop.fs.FileSystem
+import org.apache.spark.Logging
+
+/**
+ * Various utility methods used by Spark.
+ */
+private[spark] object ShutdownHookManager extends Logging {
+  val DEFAULT_SHUTDOWN_PRIORITY = 100
+
+  /**
+   * The shutdown priority of the SparkContext instance. This is lower than the default
+   * priority, so that by default hooks are run before the context is shut down.
+   */
+  val SPARK_CONTEXT_SHUTDOWN_PRIORITY = 50
+
+  /**
+   * The shutdown priority of temp directory must be lower than the SparkContext shutdown
+   * priority. Otherwise cleaning the temp directories while Spark jobs are running can
+   * throw undesirable errors at the time of shutdown.
+   */
+  val TEMP_DIR_SHUTDOWN_PRIORITY = 25
+
+  private lazy val shutdownHooks = {
+    val manager = new SparkShutdownHookManager()
+    manager.install()
+    manager
+  }
+
+  private val shutdownDeletePaths = new scala.collection.mutable.HashSet[String]()
+  private val shutdownDeleteTachyonPaths = new scala.collection.mutable.HashSet[String]()
+
+  // Add a shutdown hook to delete the temp dirs when the JVM exits
+  addShutdownHook(TEMP_DIR_SHUTDOWN_PRIORITY) { () =>
+    logInfo("Shutdown hook called")
+    shutdownDeletePaths.foreach { dirPath =>
+      try {
+        logInfo("Deleting directory " + dirPath)
+        Utils.deleteRecursively(new File(dirPath))
+      } catch {
+        case e: Exception => logError(s"Exception while deleting Spark temp dir: $dirPath", e)
+      }
+    }
+  }
+
+  // Register the path to be deleted via shutdown hook
+  def registerShutdownDeleteDir(file: File) {
+    val absolutePath = file.getAbsolutePath()
+    shutdownDeletePaths.synchronized {
+      shutdownDeletePaths += absolutePath
+    }
+  }
+
+  // Register the tachyon path to be deleted via shutdown hook
+  def registerShutdownDeleteDir(tachyonfile: TachyonFile) {
+    val absolutePath = tachyonfile.getPath()
+    shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths += absolutePath
+    }
+  }
+
+  // Remove the path to be deleted via shutdown hook
+  def removeShutdownDeleteDir(file: File) {
+    val absolutePath = file.getAbsolutePath()
+    shutdownDeletePaths.synchronized {
+      shutdownDeletePaths.remove(absolutePath)
+    }
+  }
+
+  // Remove the tachyon path to be deleted via shutdown hook
+  def removeShutdownDeleteDir(tachyonfile: TachyonFile) {
+    val absolutePath = tachyonfile.getPath()
+    shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths.remove(absolutePath)
+    }
+  }
+
+  // Is the path already registered to be deleted via a shutdown hook ?
+  def hasShutdownDeleteDir(file: File): Boolean = {
+    val absolutePath = file.getAbsolutePath()
+    shutdownDeletePaths.synchronized {
+      shutdownDeletePaths.contains(absolutePath)
+    }
+  }
+
+  // Is the path already registered to be deleted via a shutdown hook ?
+  def hasShutdownDeleteTachyonDir(file: TachyonFile): Boolean = {
+    val absolutePath = file.getPath()
+    shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths.contains(absolutePath)
+    }
+  }
+
+  // Note: if file is child of some registered path, while not equal to it, then return true;
+  // else false. This is to ensure that two shutdown hooks do not try to delete each others
+  // paths - resulting in IOException and incomplete cleanup.
+  def hasRootAsShutdownDeleteDir(file: File): Boolean = {
+    val absolutePath = file.getAbsolutePath()
+    val retval = shutdownDeletePaths.synchronized {
+      shutdownDeletePaths.exists { path =>
+        !absolutePath.equals(path) && absolutePath.startsWith(path)
+      }
+    }
+    if (retval) {
+      logInfo("path = " + file + ", already present as root for deletion.")
+    }
+    retval
+  }
+
+  // Note: if file is child of some registered path, while not equal to it, then return true;
+  // else false. This is to ensure that two shutdown hooks do not try to delete each others
+  // paths - resulting in Exception and incomplete cleanup.
+  def hasRootAsShutdownDeleteDir(file: TachyonFile): Boolean = {
+    val absolutePath = file.getPath()
+    val retval = shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths.exists { path =>
+        !absolutePath.equals(path) && absolutePath.startsWith(path)
+      }
+    }
+    if (retval) {
+      logInfo("path = " + file + ", already present as root for deletion.")
+    }
+    retval
+  }
+
+  /**
+   * Detect whether this thread might be executing a shutdown hook. Will always return true if
+   * the current thread is a running a shutdown hook but may spuriously return true otherwise (e.g.
+   * if System.exit was just called by a concurrent thread).
+   *
+   * Currently, this detects whether the JVM is shutting down by Runtime#addShutdownHook throwing
+   * an IllegalStateException.
+   */
+  def inShutdown(): Boolean = {
+    try {
+      val hook = new Thread {
+        override def run() {}
+      }
+      Runtime.getRuntime.addShutdownHook(hook)
+      Runtime.getRuntime.removeShutdownHook(hook)
+    } catch {
+      case ise: IllegalStateException => return true
+    }
+    false
+  }
+
+  /**
+   * Adds a shutdown hook with default priority.
+   *
+   * @param hook The code to run during shutdown.
+   * @return A handle that can be used to unregister the shutdown hook.
+   */
+  def addShutdownHook(hook: () => Unit): AnyRef = {
+    addShutdownHook(DEFAULT_SHUTDOWN_PRIORITY)(hook)
+  }
+
+  /**
+   * Adds a shutdown hook with the given priority. Hooks with lower priority values run
+   * first.
+   *
+   * @param hook The code to run during shutdown.
+   * @return A handle that can be used to unregister the shutdown hook.
+   */
+  def addShutdownHook(priority: Int)(hook: () => Unit): AnyRef = {
+    shutdownHooks.add(priority, hook)
+  }
+
+  /**
+   * Remove a previously installed shutdown hook.
+   *
+   * @param ref A handle returned by `addShutdownHook`.
+   * @return Whether the hook was removed.
+   */
+  def removeShutdownHook(ref: AnyRef): Boolean = {
+    shutdownHooks.remove(ref)
+  }
+
+}
+
+private [util] class SparkShutdownHookManager {
+
+  private val hooks = new PriorityQueue[SparkShutdownHook]()
+  private var shuttingDown = false
+
+  /**
+   * Install a hook to run at shutdown and run all registered hooks in order. Hadoop 1.x does not
+   * have `ShutdownHookManager`, so in that case we just use the JVM's `Runtime` object and hope for
+   * the best.
+   */
+  def install(): Unit = {
+    val hookTask = new Runnable() {
+      override def run(): Unit = runAll()
+    }
+    Try(Utils.classForName("org.apache.hadoop.util.ShutdownHookManager")) match {
+      case Success(shmClass) =>
+        val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get()
+          .asInstanceOf[Int]
+        val shm = shmClass.getMethod("get").invoke(null)
+        shm.getClass().getMethod("addShutdownHook", classOf[Runnable], classOf[Int])
+          .invoke(shm, hookTask, Integer.valueOf(fsPriority + 30))
+
+      case Failure(_) =>
+        Runtime.getRuntime.addShutdownHook(new Thread(hookTask, "Spark Shutdown Hook"));
+    }
+  }
+
+  def runAll(): Unit = synchronized {
+    shuttingDown = true
+    while (!hooks.isEmpty()) {
+      Try(Utils.logUncaughtExceptions(hooks.poll().run()))
+    }
+  }
+
+  def add(priority: Int, hook: () => Unit): AnyRef = synchronized {
+    checkState()
+    val hookRef = new SparkShutdownHook(priority, hook)
+    hooks.add(hookRef)
+    hookRef
+  }
+
+  def remove(ref: AnyRef): Boolean = synchronized {
+    hooks.remove(ref)
+  }
+
+  private def checkState(): Unit = {
+    if (shuttingDown) {
+      throw new IllegalStateException("Shutdown hooks cannot be modified during shutdown.")
+    }
+  }
+
+}
+
+private class SparkShutdownHook(private val priority: Int, hook: () => Unit)
+  extends Comparable[SparkShutdownHook] {
+
+  override def compareTo(other: SparkShutdownHook): Int = {
+    other.priority - priority
+  }
+
+  def run(): Unit = hook()
+
+}
diff --git a/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala b/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala
index ad3db1fbb57ed..7248187247330 100644
--- a/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala
+++ b/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala
@@ -33,7 +33,7 @@ private[spark] object SparkUncaughtExceptionHandler
 
       // We may have been called from a shutdown hook. If so, we must not call System.exit().
       // (If we do, we will deadlock.)
-      if (!Utils.inShutdown()) {
+      if (!ShutdownHookManager.inShutdown()) {
         if (exception.isInstanceOf[OutOfMemoryError]) {
           System.exit(SparkExitCode.OOM)
         } else {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index a90d8541366f4..f2abf227dc129 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -21,7 +21,7 @@ import java.io._
 import java.lang.management.ManagementFactory
 import java.net._
 import java.nio.ByteBuffer
-import java.util.{PriorityQueue, Properties, Locale, Random, UUID}
+import java.util.{Properties, Locale, Random, UUID}
 import java.util.concurrent._
 import javax.net.ssl.HttpsURLConnection
 
@@ -65,21 +65,6 @@ private[spark] object CallSite {
 private[spark] object Utils extends Logging {
   val random = new Random()
 
-  val DEFAULT_SHUTDOWN_PRIORITY = 100
-
-  /**
-   * The shutdown priority of the SparkContext instance. This is lower than the default
-   * priority, so that by default hooks are run before the context is shut down.
-   */
-  val SPARK_CONTEXT_SHUTDOWN_PRIORITY = 50
-
-  /**
-   * The shutdown priority of temp directory must be lower than the SparkContext shutdown
-   * priority. Otherwise cleaning the temp directories while Spark jobs are running can
-   * throw undesirable errors at the time of shutdown.
-   */
-  val TEMP_DIR_SHUTDOWN_PRIORITY = 25
-
   /**
    * Define a default value for driver memory here since this value is referenced across the code
    * base and nearly all files already use Utils.scala
@@ -90,9 +75,6 @@ private[spark] object Utils extends Logging {
   @volatile private var localRootDirs: Array[String] = null
 
 
-  private val shutdownHooks = new SparkShutdownHookManager()
-  shutdownHooks.install()
-
   /** Serialize an object using Java serialization */
   def serialize[T](o: T): Array[Byte] = {
     val bos = new ByteArrayOutputStream()
@@ -205,86 +187,6 @@ private[spark] object Utils extends Logging {
     }
   }
 
-  private val shutdownDeletePaths = new scala.collection.mutable.HashSet[String]()
-  private val shutdownDeleteTachyonPaths = new scala.collection.mutable.HashSet[String]()
-
-  // Add a shutdown hook to delete the temp dirs when the JVM exits
-  addShutdownHook(TEMP_DIR_SHUTDOWN_PRIORITY) { () =>
-    logInfo("Shutdown hook called")
-    shutdownDeletePaths.foreach { dirPath =>
-      try {
-        logInfo("Deleting directory " + dirPath)
-        Utils.deleteRecursively(new File(dirPath))
-      } catch {
-        case e: Exception => logError(s"Exception while deleting Spark temp dir: $dirPath", e)
-      }
-    }
-  }
-
-  // Register the path to be deleted via shutdown hook
-  def registerShutdownDeleteDir(file: File) {
-    val absolutePath = file.getAbsolutePath()
-    shutdownDeletePaths.synchronized {
-      shutdownDeletePaths += absolutePath
-    }
-  }
-
-  // Register the tachyon path to be deleted via shutdown hook
-  def registerShutdownDeleteDir(tachyonfile: TachyonFile) {
-    val absolutePath = tachyonfile.getPath()
-    shutdownDeleteTachyonPaths.synchronized {
-      shutdownDeleteTachyonPaths += absolutePath
-    }
-  }
-
-  // Is the path already registered to be deleted via a shutdown hook ?
-  def hasShutdownDeleteDir(file: File): Boolean = {
-    val absolutePath = file.getAbsolutePath()
-    shutdownDeletePaths.synchronized {
-      shutdownDeletePaths.contains(absolutePath)
-    }
-  }
-
-  // Is the path already registered to be deleted via a shutdown hook ?
-  def hasShutdownDeleteTachyonDir(file: TachyonFile): Boolean = {
-    val absolutePath = file.getPath()
-    shutdownDeleteTachyonPaths.synchronized {
-      shutdownDeleteTachyonPaths.contains(absolutePath)
-    }
-  }
-
-  // Note: if file is child of some registered path, while not equal to it, then return true;
-  // else false. This is to ensure that two shutdown hooks do not try to delete each others
-  // paths - resulting in IOException and incomplete cleanup.
-  def hasRootAsShutdownDeleteDir(file: File): Boolean = {
-    val absolutePath = file.getAbsolutePath()
-    val retval = shutdownDeletePaths.synchronized {
-      shutdownDeletePaths.exists { path =>
-        !absolutePath.equals(path) && absolutePath.startsWith(path)
-      }
-    }
-    if (retval) {
-      logInfo("path = " + file + ", already present as root for deletion.")
-    }
-    retval
-  }
-
-  // Note: if file is child of some registered path, while not equal to it, then return true;
-  // else false. This is to ensure that two shutdown hooks do not try to delete each others
-  // paths - resulting in Exception and incomplete cleanup.
-  def hasRootAsShutdownDeleteDir(file: TachyonFile): Boolean = {
-    val absolutePath = file.getPath()
-    val retval = shutdownDeleteTachyonPaths.synchronized {
-      shutdownDeleteTachyonPaths.exists { path =>
-        !absolutePath.equals(path) && absolutePath.startsWith(path)
-      }
-    }
-    if (retval) {
-      logInfo("path = " + file + ", already present as root for deletion.")
-    }
-    retval
-  }
-
   /**
    * JDK equivalent of `chmod 700 file`.
    *
@@ -333,7 +235,7 @@ private[spark] object Utils extends Logging {
       root: String = System.getProperty("java.io.tmpdir"),
       namePrefix: String = "spark"): File = {
     val dir = createDirectory(root, namePrefix)
-    registerShutdownDeleteDir(dir)
+    ShutdownHookManager.registerShutdownDeleteDir(dir)
     dir
   }
 
@@ -973,9 +875,7 @@ private[spark] object Utils extends Logging {
           if (savedIOException != null) {
             throw savedIOException
           }
-          shutdownDeletePaths.synchronized {
-            shutdownDeletePaths.remove(file.getAbsolutePath)
-          }
+          ShutdownHookManager.removeShutdownDeleteDir(file)
         }
       } finally {
         if (!file.delete()) {
@@ -1478,27 +1378,6 @@ private[spark] object Utils extends Logging {
     serializer.deserialize[T](serializer.serialize(value))
   }
 
-  /**
-   * Detect whether this thread might be executing a shutdown hook. Will always return true if
-   * the current thread is a running a shutdown hook but may spuriously return true otherwise (e.g.
-   * if System.exit was just called by a concurrent thread).
-   *
-   * Currently, this detects whether the JVM is shutting down by Runtime#addShutdownHook throwing
-   * an IllegalStateException.
-   */
-  def inShutdown(): Boolean = {
-    try {
-      val hook = new Thread {
-        override def run() {}
-      }
-      Runtime.getRuntime.addShutdownHook(hook)
-      Runtime.getRuntime.removeShutdownHook(hook)
-    } catch {
-      case ise: IllegalStateException => return true
-    }
-    false
-  }
-
   private def isSpace(c: Char): Boolean = {
     " \t\r\n".indexOf(c) != -1
   }
@@ -2221,37 +2100,6 @@ private[spark] object Utils extends Logging {
     msg.startsWith(BACKUP_STANDALONE_MASTER_PREFIX)
   }
 
-  /**
-   * Adds a shutdown hook with default priority.
-   *
-   * @param hook The code to run during shutdown.
-   * @return A handle that can be used to unregister the shutdown hook.
-   */
-  def addShutdownHook(hook: () => Unit): AnyRef = {
-    addShutdownHook(DEFAULT_SHUTDOWN_PRIORITY)(hook)
-  }
-
-  /**
-   * Adds a shutdown hook with the given priority. Hooks with lower priority values run
-   * first.
-   *
-   * @param hook The code to run during shutdown.
-   * @return A handle that can be used to unregister the shutdown hook.
-   */
-  def addShutdownHook(priority: Int)(hook: () => Unit): AnyRef = {
-    shutdownHooks.add(priority, hook)
-  }
-
-  /**
-   * Remove a previously installed shutdown hook.
-   *
-   * @param ref A handle returned by `addShutdownHook`.
-   * @return Whether the hook was removed.
-   */
-  def removeShutdownHook(ref: AnyRef): Boolean = {
-    shutdownHooks.remove(ref)
-  }
-
   /**
    * To avoid calling `Utils.getCallSite` for every single RDD we create in the body,
    * set a dummy call site that RDDs use instead. This is for performance optimization.
@@ -2299,70 +2147,6 @@ private[spark] object Utils extends Logging {
 
 }
 
-private [util] class SparkShutdownHookManager {
-
-  private val hooks = new PriorityQueue[SparkShutdownHook]()
-  private var shuttingDown = false
-
-  /**
-   * Install a hook to run at shutdown and run all registered hooks in order. Hadoop 1.x does not
-   * have `ShutdownHookManager`, so in that case we just use the JVM's `Runtime` object and hope for
-   * the best.
-   */
-  def install(): Unit = {
-    val hookTask = new Runnable() {
-      override def run(): Unit = runAll()
-    }
-    Try(Utils.classForName("org.apache.hadoop.util.ShutdownHookManager")) match {
-      case Success(shmClass) =>
-        val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get()
-          .asInstanceOf[Int]
-        val shm = shmClass.getMethod("get").invoke(null)
-        shm.getClass().getMethod("addShutdownHook", classOf[Runnable], classOf[Int])
-          .invoke(shm, hookTask, Integer.valueOf(fsPriority + 30))
-
-      case Failure(_) =>
-        Runtime.getRuntime.addShutdownHook(new Thread(hookTask, "Spark Shutdown Hook"));
-    }
-  }
-
-  def runAll(): Unit = synchronized {
-    shuttingDown = true
-    while (!hooks.isEmpty()) {
-      Try(Utils.logUncaughtExceptions(hooks.poll().run()))
-    }
-  }
-
-  def add(priority: Int, hook: () => Unit): AnyRef = synchronized {
-    checkState()
-    val hookRef = new SparkShutdownHook(priority, hook)
-    hooks.add(hookRef)
-    hookRef
-  }
-
-  def remove(ref: AnyRef): Boolean = synchronized {
-    hooks.remove(ref)
-  }
-
-  private def checkState(): Unit = {
-    if (shuttingDown) {
-      throw new IllegalStateException("Shutdown hooks cannot be modified during shutdown.")
-    }
-  }
-
-}
-
-private class SparkShutdownHook(private val priority: Int, hook: () => Unit)
-  extends Comparable[SparkShutdownHook] {
-
-  override def compareTo(other: SparkShutdownHook): Int = {
-    other.priority - priority
-  }
-
-  def run(): Unit = hook()
-
-}
-
 /**
  * A utility class to redirect the child process's stdout or stderr.
  */
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index 9c047347cb58d..2c9fa595b2dad 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -35,7 +35,7 @@ import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.{Logging, SparkContext}
 
 
@@ -76,7 +76,7 @@ object HiveThriftServer2 extends Logging {
     logInfo("Starting SparkContext")
     SparkSQLEnv.init()
 
-    Utils.addShutdownHook { () =>
+    ShutdownHookManager.addShutdownHook { () =>
       SparkSQLEnv.stop()
       uiTab.foreach(_.detach())
     }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index d3886142b388d..7799704c819d9 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -39,7 +39,7 @@ import org.apache.thrift.transport.TSocket
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 
 /**
  * This code doesn't support remote connections in Hive 1.2+, as the underlying CliDriver
@@ -114,7 +114,7 @@ private[hive] object SparkSQLCLIDriver extends Logging {
     SessionState.start(sessionState)
 
     // Clean up after we exit
-    Utils.addShutdownHook { () => SparkSQLEnv.stop() }
+    ShutdownHookManager.addShutdownHook { () => SparkSQLEnv.stop() }
 
     val remoteMode = isRemoteMode(sessionState)
     // "-h" option has been passed, so connect to Hive thrift server.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 296cc5c5e0b04..4eae699ac3b51 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -36,7 +36,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.CacheTableCommand
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.execution.HiveNativeCommand
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.{SparkConf, SparkContext}
 
 /* Implicit conversions */
@@ -154,7 +154,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   val hiveFilesTemp = File.createTempFile("catalystHiveFiles", "")
   hiveFilesTemp.delete()
   hiveFilesTemp.mkdir()
-  Utils.registerShutdownDeleteDir(hiveFilesTemp)
+  ShutdownHookManager.registerShutdownDeleteDir(hiveFilesTemp)
 
   val inRepoTests = if (System.getProperty("user.dir").endsWith("sql" + File.separator + "hive")) {
     new File("src" + File.separator + "test" + File.separator + "resources" + File.separator)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 177e710ace54b..b496d1f341a0b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -44,7 +44,7 @@ import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.receiver.{ActorReceiver, ActorSupervisorStrategy, Receiver}
 import org.apache.spark.streaming.scheduler.{JobScheduler, StreamingListener}
 import org.apache.spark.streaming.ui.{StreamingJobProgressListener, StreamingTab}
-import org.apache.spark.util.{CallSite, Utils}
+import org.apache.spark.util.{CallSite, ShutdownHookManager, Utils}
 
 /**
  * Main entry point for Spark Streaming functionality. It provides methods used to create
@@ -604,7 +604,7 @@ class StreamingContext private[streaming] (
           }
           StreamingContext.setActiveContext(this)
         }
-        shutdownHookRef = Utils.addShutdownHook(
+        shutdownHookRef = ShutdownHookManager.addShutdownHook(
           StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown)
         // Registering Streaming Metrics at the start of the StreamingContext
         assert(env.metricsSystem != null)
@@ -691,7 +691,7 @@ class StreamingContext private[streaming] (
           StreamingContext.setActiveContext(null)
           waiter.notifyStop()
           if (shutdownHookRef != null) {
-            Utils.removeShutdownHook(shutdownHookRef)
+            ShutdownHookManager.removeShutdownHook(shutdownHookRef)
           }
           logInfo("StreamingContext stopped successfully")
       }
@@ -725,7 +725,7 @@ object StreamingContext extends Logging {
    */
   private val ACTIVATION_LOCK = new Object()
 
-  private val SHUTDOWN_HOOK_PRIORITY = Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY + 1
+  private val SHUTDOWN_HOOK_PRIORITY = ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY + 1
 
   private val activeContext = new AtomicReference[StreamingContext](null)
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index e19940d8d6642..6a8ddb37b29e8 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -112,7 +112,8 @@ private[spark] class ApplicationMaster(
       val fs = FileSystem.get(yarnConf)
 
       // This shutdown hook should run *after* the SparkContext is shut down.
-      Utils.addShutdownHook(Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY - 1) { () =>
+      val priority = ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY - 1
+      ShutdownHookManager.addShutdownHook(priority) { () =>
         val maxAppAttempts = client.getMaxRegAttempts(sparkConf, yarnConf)
         val isLastAttempt = client.getAttemptId().getAttemptId() >= maxAppAttempts
 
@@ -199,7 +200,7 @@ private[spark] class ApplicationMaster(
   final def finish(status: FinalApplicationStatus, code: Int, msg: String = null): Unit = {
     synchronized {
       if (!finished) {
-        val inShutdown = Utils.inShutdown()
+        val inShutdown = ShutdownHookManager.inShutdown()
         logInfo(s"Final app status: $status, exitCode: $code" +
           Option(msg).map(msg => s", (reason: $msg)").getOrElse(""))
         exitCode = code

From 7035d880a0cf06910c19b4afd49645124c620f14 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 12 Aug 2015 16:45:15 -0700
Subject: [PATCH 1005/1454] [SPARK-9894] [SQL] Json writer should handle
 MapData.

https://issues.apache.org/jira/browse/SPARK-9894

Author: Yin Huai <yhuai@databricks.com>

Closes #8137 from yhuai/jsonMapData.
---
 .../datasources/json/JacksonGenerator.scala   | 10 +--
 .../sources/JsonHadoopFsRelationSuite.scala   | 78 +++++++++++++++++++
 .../SimpleTextHadoopFsRelationSuite.scala     | 30 -------
 3 files changed, 83 insertions(+), 35 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index 37c2b5a296c15..99ac7730bd1c9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -107,12 +107,12 @@ private[sql] object JacksonGenerator {
         v.foreach(ty, (_, value) => valWriter(ty, value))
         gen.writeEndArray()
 
-      case (MapType(kv, vv, _), v: Map[_, _]) =>
+      case (MapType(kt, vt, _), v: MapData) =>
         gen.writeStartObject()
-        v.foreach { p =>
-          gen.writeFieldName(p._1.toString)
-          valWriter(vv, p._2)
-        }
+        v.foreach(kt, vt, { (k, v) =>
+          gen.writeFieldName(k.toString)
+          valWriter(vt, v)
+        })
         gen.writeEndObject()
 
       case (StructType(ty), v: InternalRow) =>
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
new file mode 100644
index 0000000000000..ed6d512ab36fe
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.sources
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.types._
+
+class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
+  override val dataSourceName: String = "json"
+
+  import sqlContext._
+
+  test("save()/load() - partitioned table - simple queries - partition columns in data") {
+    withTempDir { file =>
+      val basePath = new Path(file.getCanonicalPath)
+      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
+      val qualifiedBasePath = fs.makeQualified(basePath)
+
+      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
+        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
+        sparkContext
+          .parallelize(for (i <- 1 to 3) yield s"""{"a":$i,"b":"val_$i"}""")
+          .saveAsTextFile(partitionDir.toString)
+      }
+
+      val dataSchemaWithPartition =
+        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
+
+      checkQueries(
+        read.format(dataSourceName)
+          .option("dataSchema", dataSchemaWithPartition.json)
+          .load(file.getCanonicalPath))
+    }
+  }
+
+  test("SPARK-9894: save complex types to JSON") {
+    withTempDir { file =>
+      file.delete()
+
+      val schema =
+        new StructType()
+          .add("array", ArrayType(LongType))
+          .add("map", MapType(StringType, new StructType().add("innerField", LongType)))
+
+      val data =
+        Row(Seq(1L, 2L, 3L), Map("m1" -> Row(4L))) ::
+          Row(Seq(5L, 6L, 7L), Map("m2" -> Row(10L))) :: Nil
+      val df = createDataFrame(sparkContext.parallelize(data), schema)
+
+      // Write the data out.
+      df.write.format(dataSourceName).save(file.getCanonicalPath)
+
+      // Read it back and check the result.
+      checkAnswer(
+        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        df
+      )
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index 48c37a1fa1022..e8975e5f5cd08 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -50,33 +50,3 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
     }
   }
 }
-
-class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String =
-    classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource].getCanonicalName
-
-  import sqlContext._
-
-  test("save()/load() - partitioned table - simple queries - partition columns in data") {
-    withTempDir { file =>
-      val basePath = new Path(file.getCanonicalPath)
-      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
-      val qualifiedBasePath = fs.makeQualified(basePath)
-
-      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
-        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
-        sparkContext
-          .parallelize(for (i <- 1 to 3) yield s"""{"a":$i,"b":"val_$i"}""")
-          .saveAsTextFile(partitionDir.toString)
-      }
-
-      val dataSchemaWithPartition =
-        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
-
-      checkQueries(
-        read.format(dataSourceName)
-          .option("dataSchema", dataSchemaWithPartition.json)
-          .load(file.getCanonicalPath))
-    }
-  }
-}

From caa14d9dc9e2eb1102052b22445b63b0e004e3c7 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 16:53:47 -0700
Subject: [PATCH 1006/1454] [SPARK-9913] [MLLIB] LDAUtils should be private

feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8142 from mengxr/SPARK-9913.
---
 .../main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
index f7e5ce1665fe6..a9ba7b60bad08 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
@@ -22,7 +22,7 @@ import breeze.numerics._
 /**
  * Utility methods for LDA.
  */
-object LDAUtils {
+private[clustering] object LDAUtils {
   /**
    * Log Sum Exp with overflow protection using the identity:
    * For any a: \log \sum_{n=1}^N \exp\{x_n\} = a + \log \sum_{n=1}^N \exp\{x_n - a\}

From 6e409bc1357f49de2efdfc4226d074b943fb1153 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 12 Aug 2015 16:54:45 -0700
Subject: [PATCH 1007/1454] [SPARK-9909] [ML] [TRIVIAL] move weightCol to
 shared params

As per the TODO move weightCol to Shared Params.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8144 from holdenk/SPARK-9909-move-weightCol-toSharedParams.
---
 .../ml/param/shared/SharedParamsCodeGen.scala    |  4 +++-
 .../spark/ml/param/shared/sharedParams.scala     | 15 +++++++++++++++
 .../spark/ml/regression/IsotonicRegression.scala | 16 ++--------------
 3 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 9e12f1856a940..8c16c6149b40d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -70,7 +70,9 @@ private[shared] object SharedParamsCodeGen {
         " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.",
         isValid = "ParamValidators.inRange(0, 1)"),
       ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms"),
-      ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization."))
+      ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization."),
+      ParamDesc[String]("weightCol", "weight column name. If this is not set or empty, we treat " +
+        "all instance weights as 1.0."))
 
     val code = genSharedParams(params)
     val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala"
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index a17d4ea960a90..c26768953e3db 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -342,4 +342,19 @@ private[ml] trait HasStepSize extends Params {
   /** @group getParam */
   final def getStepSize: Double = $(stepSize)
 }
+
+/**
+ * Trait for shared param weightCol.
+ */
+private[ml] trait HasWeightCol extends Params {
+
+  /**
+   * Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0..
+   * @group param
+   */
+  final val weightCol: Param[String] = new Param[String](this, "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
+
+  /** @group getParam */
+  final def getWeightCol: String = $(weightCol)
+}
 // scalastyle:on
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index f570590960a62..0f33bae30e622 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -21,7 +21,7 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol}
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
 import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression, IsotonicRegressionModel => MLlibIsotonicRegressionModel}
@@ -35,19 +35,7 @@ import org.apache.spark.storage.StorageLevel
  * Params for isotonic regression.
  */
 private[regression] trait IsotonicRegressionBase extends Params with HasFeaturesCol
-  with HasLabelCol with HasPredictionCol with Logging {
-
-  /**
-   * Param for weight column name (default: none).
-   * @group param
-   */
-  // TODO: Move weightCol to sharedParams.
-  final val weightCol: Param[String] =
-    new Param[String](this, "weightCol",
-      "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
-
-  /** @group getParam */
-  final def getWeightCol: String = $(weightCol)
+  with HasLabelCol with HasPredictionCol with HasWeightCol with Logging {
 
   /**
    * Param for whether the output sequence should be isotonic/increasing (true) or

From e6aef55766d0e2a48e0f9cb6eda0e31a71b962f3 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 17:04:31 -0700
Subject: [PATCH 1008/1454] [SPARK-9912] [MLLIB] QRDecomposition should use
 QType and RType for type names instead of UType and VType

hhbyyh

Author: Xiangrui Meng <meng@databricks.com>

Closes #8140 from mengxr/SPARK-9912.
---
 .../apache/spark/mllib/linalg/SingularValueDecomposition.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index b416d50a5631e..cff5dbeee3e57 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -31,5 +31,5 @@ case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VTyp
  * Represents QR factors.
  */
 @Experimental
-case class QRDecomposition[UType, VType](Q: UType, R: VType)
+case class QRDecomposition[QType, RType](Q: QType, R: RType)
 

From fc1c7fd66e64ccea53b31cd2fbb98bc6d307329c Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 17:06:12 -0700
Subject: [PATCH 1009/1454] [SPARK-9915] [ML] stopWords should use
 StringArrayParam

hhbyyh

Author: Xiangrui Meng <meng@databricks.com>

Closes #8141 from mengxr/SPARK-9915.
---
 .../org/apache/spark/ml/feature/StopWordsRemover.scala      | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
index 3cc41424460f2..5d77ea08db657 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
@@ -19,12 +19,12 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.param.{BooleanParam, ParamMap, StringArrayParam}
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
-import org.apache.spark.ml.param.{ParamMap, BooleanParam, Param}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
-import org.apache.spark.sql.types.{StringType, StructField, ArrayType, StructType}
 import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{ArrayType, StringType, StructField, StructType}
 
 /**
  * stop words list
@@ -100,7 +100,7 @@ class StopWordsRemover(override val uid: String)
    * the stop words set to be filtered out
    * @group param
    */
-  val stopWords: Param[Array[String]] = new Param(this, "stopWords", "stop words")
+  val stopWords: StringArrayParam = new StringArrayParam(this, "stopWords", "stop words")
 
   /** @group setParam */
   def setStopWords(value: Array[String]): this.type = set(stopWords, value)

From 660e6dcff8125b83cc73dbe00c90cbe58744bc66 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 12 Aug 2015 17:07:29 -0700
Subject: [PATCH 1010/1454] [SPARK-9449] [SQL] Include MetastoreRelation's
 inputFiles

Author: Michael Armbrust <michael@databricks.com>

Closes #8119 from marmbrus/metastoreInputFiles.
---
 .../org/apache/spark/sql/DataFrame.scala      | 10 ++++---
 .../spark/sql/execution/FileRelation.scala    | 28 +++++++++++++++++++
 .../apache/spark/sql/sources/interfaces.scala |  6 ++--
 .../org/apache/spark/sql/DataFrameSuite.scala | 26 +++++++++--------
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 16 +++++++++--
 5 files changed, 66 insertions(+), 20 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 27b994f1f0caf..c466d9e6cb349 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -34,10 +34,10 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
-import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
+import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, FileRelation, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
 import org.apache.spark.sql.execution.datasources.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
@@ -1560,8 +1560,10 @@ class DataFrame private[sql](
    */
   def inputFiles: Array[String] = {
     val files: Seq[String] = logicalPlan.collect {
-      case LogicalRelation(fsBasedRelation: HadoopFsRelation) =>
-        fsBasedRelation.paths.toSeq
+      case LogicalRelation(fsBasedRelation: FileRelation) =>
+        fsBasedRelation.inputFiles
+      case fr: FileRelation =>
+        fr.inputFiles
     }.flatten
     files.toSet.toArray
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala
new file mode 100644
index 0000000000000..7a2a9eed5807d
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+/**
+ * An interface for relations that are backed by files.  When a class implements this interface,
+ * the list of paths that it returns will be returned to a user who calls `inputPaths` on any
+ * DataFrame that queries this relation.
+ */
+private[sql] trait FileRelation {
+  /** Returns the list of files that will be read when scanning this relation. */
+  def inputFiles: Array[String]
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 2f8417a48d32e..b3b326fe612c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -31,7 +31,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
-import org.apache.spark.sql.execution.RDDConversions
+import org.apache.spark.sql.execution.{FileRelation, RDDConversions}
 import org.apache.spark.sql.execution.datasources.{PartitioningUtils, PartitionSpec, Partition}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql._
@@ -406,7 +406,7 @@ abstract class OutputWriter {
  */
 @Experimental
 abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[PartitionSpec])
-  extends BaseRelation with Logging {
+  extends BaseRelation with FileRelation with Logging {
 
   override def toString: String = getClass.getSimpleName + paths.mkString("[", ",", "]")
 
@@ -516,6 +516,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
    */
   def paths: Array[String]
 
+  override def inputFiles: Array[String] = cachedLeafStatuses().map(_.getPath.toString).toArray
+
   /**
    * Partition columns.  Can be either defined by [[userDefinedPartitionColumns]] or automatically
    * discovered.  Note that they should always be nullable.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index adbd95197d7ca..2feec29955bc8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -485,21 +485,23 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("inputFiles") {
-    val fakeRelation1 = new ParquetRelation(Array("/my/path", "/my/other/path"),
-      Some(testData.schema), None, Map.empty)(sqlContext)
-    val df1 = DataFrame(sqlContext, LogicalRelation(fakeRelation1))
-    assert(df1.inputFiles.toSet == fakeRelation1.paths.toSet)
+    withTempDir { dir =>
+      val df = Seq((1, 22)).toDF("a", "b")
 
-    val fakeRelation2 = new JSONRelation(
-      None, 1, Some(testData.schema), None, None, Array("/json/path"))(sqlContext)
-    val df2 = DataFrame(sqlContext, LogicalRelation(fakeRelation2))
-    assert(df2.inputFiles.toSet == fakeRelation2.paths.toSet)
+      val parquetDir = new File(dir, "parquet").getCanonicalPath
+      df.write.parquet(parquetDir)
+      val parquetDF = sqlContext.read.parquet(parquetDir)
+      assert(parquetDF.inputFiles.nonEmpty)
 
-    val unionDF = df1.unionAll(df2)
-    assert(unionDF.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
+      val jsonDir = new File(dir, "json").getCanonicalPath
+      df.write.json(jsonDir)
+      val jsonDF = sqlContext.read.json(jsonDir)
+      assert(parquetDF.inputFiles.nonEmpty)
 
-    val filtered = df1.filter("false").unionAll(df2.intersect(df2))
-    assert(filtered.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
+      val unioned = jsonDF.unionAll(parquetDF).inputFiles.sorted
+      val allFiles = (jsonDF.inputFiles ++ parquetDF.inputFiles).toSet.toArray.sorted
+      assert(unioned === allFiles)
+    }
   }
 
   ignore("show") {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index ac9aaed19d566..5e5497837a393 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -38,7 +38,7 @@ import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.{InternalRow, SqlParser, TableIdentifier}
-import org.apache.spark.sql.execution.datasources
+import org.apache.spark.sql.execution.{FileRelation, datasources}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
@@ -739,7 +739,7 @@ private[hive] case class MetastoreRelation
     (databaseName: String, tableName: String, alias: Option[String])
     (val table: HiveTable)
     (@transient sqlContext: SQLContext)
-  extends LeafNode with MultiInstanceRelation {
+  extends LeafNode with MultiInstanceRelation with FileRelation {
 
   override def equals(other: Any): Boolean = other match {
     case relation: MetastoreRelation =>
@@ -888,6 +888,18 @@ private[hive] case class MetastoreRelation
   /** An attribute map for determining the ordinal for non-partition columns. */
   val columnOrdinals = AttributeMap(attributes.zipWithIndex)
 
+  override def inputFiles: Array[String] = {
+    val partLocations = table.getPartitions(Nil).map(_.storage.location).toArray
+    if (partLocations.nonEmpty) {
+      partLocations
+    } else {
+      Array(
+        table.location.getOrElse(
+          sys.error(s"Could not get the location of ${table.qualifiedName}.")))
+    }
+  }
+
+
   override def newInstance(): MetastoreRelation = {
     MetastoreRelation(databaseName, tableName, alias)(table)(sqlContext)
   }

From 8ce60963cb0928058ef7b6e29ff94eb69d1143af Mon Sep 17 00:00:00 2001
From: cody koeninger <cody@koeninger.org>
Date: Wed, 12 Aug 2015 17:44:16 -0700
Subject: [PATCH 1011/1454] =?UTF-8?q?[SPARK-9780]=20[STREAMING]=20[KAFKA]?=
 =?UTF-8?q?=20prevent=20NPE=20if=20KafkaRDD=20instantiation=20=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…fails

Author: cody koeninger <cody@koeninger.org>

Closes #8133 from koeninger/SPARK-9780 and squashes the following commits:

406259d [cody koeninger] [SPARK-9780][Streaming][Kafka] prevent NPE if KafkaRDD instantiation fails
---
 .../scala/org/apache/spark/streaming/kafka/KafkaRDD.scala   | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
index 1a9d78c0d4f59..ea5f842c6cafe 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
@@ -197,7 +197,11 @@ class KafkaRDD[
         .dropWhile(_.offset < requestOffset)
     }
 
-    override def close(): Unit = consumer.close()
+    override def close(): Unit = {
+      if (consumer != null) {
+        consumer.close()
+      }
+    }
 
     override def getNext(): R = {
       if (iter == null || !iter.hasNext) {

From 0d1d146c220f0d47d0e62b368d5b94d3bd9dd197 Mon Sep 17 00:00:00 2001
From: Rohit Agarwal <rohita@qubole.com>
Date: Wed, 12 Aug 2015 17:48:43 -0700
Subject: [PATCH 1012/1454] [SPARK-9724] [WEB UI] Avoid unnecessary redirects
 in the Spark Web UI.

Author: Rohit Agarwal <rohita@qubole.com>

Closes #8014 from mindprince/SPARK-9724 and squashes the following commits:

a7af5ff [Rohit Agarwal] [SPARK-9724] [WEB UI] Inline attachPrefix and attachPrefixForRedirect. Fix logic of attachPrefix
8a977cd [Rohit Agarwal] [SPARK-9724] [WEB UI] Address review comments: Remove unneeded code, update scaladoc.
b257844 [Rohit Agarwal] [SPARK-9724] [WEB UI] Avoid unnecessary redirects in the Spark Web UI.
---
 .../main/scala/org/apache/spark/ui/JettyUtils.scala | 13 ++++++-------
 .../main/scala/org/apache/spark/ui/SparkUI.scala    |  4 ++--
 2 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
index c8356467fab87..779c0ba083596 100644
--- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
@@ -106,7 +106,11 @@ private[spark] object JettyUtils extends Logging {
       path: String,
       servlet: HttpServlet,
       basePath: String): ServletContextHandler = {
-    val prefixedPath = attachPrefix(basePath, path)
+    val prefixedPath = if (basePath == "" && path == "/") {
+      path
+    } else {
+      (basePath + path).stripSuffix("/")
+    }
     val contextHandler = new ServletContextHandler
     val holder = new ServletHolder(servlet)
     contextHandler.setContextPath(prefixedPath)
@@ -121,7 +125,7 @@ private[spark] object JettyUtils extends Logging {
       beforeRedirect: HttpServletRequest => Unit = x => (),
       basePath: String = "",
       httpMethods: Set[String] = Set("GET")): ServletContextHandler = {
-    val prefixedDestPath = attachPrefix(basePath, destPath)
+    val prefixedDestPath = basePath + destPath
     val servlet = new HttpServlet {
       override def doGet(request: HttpServletRequest, response: HttpServletResponse): Unit = {
         if (httpMethods.contains("GET")) {
@@ -246,11 +250,6 @@ private[spark] object JettyUtils extends Logging {
     val (server, boundPort) = Utils.startServiceOnPort[Server](port, connect, conf, serverName)
     ServerInfo(server, boundPort, collection)
   }
-
-  /** Attach a prefix to the given path, but avoid returning an empty path */
-  private def attachPrefix(basePath: String, relativePath: String): String = {
-    if (basePath == "") relativePath else (basePath + relativePath).stripSuffix("/")
-  }
 }
 
 private[spark] case class ServerInfo(
diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
index 3788916cf39bb..d8b90568b7b9a 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
@@ -64,11 +64,11 @@ private[spark] class SparkUI private (
     attachTab(new EnvironmentTab(this))
     attachTab(new ExecutorsTab(this))
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
-    attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath))
+    attachHandler(createRedirectHandler("/", "/jobs/", basePath = basePath))
     attachHandler(ApiRootResource.getServletHandler(this))
     // This should be POST only, but, the YARN AM proxy won't proxy POSTs
     attachHandler(createRedirectHandler(
-      "/stages/stage/kill", "/stages", stagesTab.handleKillRequest,
+      "/stages/stage/kill", "/stages/", stagesTab.handleKillRequest,
       httpMethods = Set("GET", "POST")))
   }
   initialize()

From f4bc01f1f33a93e6affe5c8a3e33ffbd92d03f38 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 12 Aug 2015 18:33:27 -0700
Subject: [PATCH 1013/1454] [SPARK-9855] [SPARKR] Add expression functions into
 SparkR whose params are simple

I added lots of expression functions for SparkR. This PR includes only functions whose params  are only `(Column)` or `(Column, Column)`.  And I think we need to improve how to test those functions. However, it would be better to work on another issue.

## Diff Summary

- Add lots of functions in `functions.R` and their generic in `generic.R`
- Add aliases for `ceiling` and `sign`
- Move expression functions from `column.R` to `functions.R`
- Modify `rdname` from `column` to `functions`

I haven't supported `not` function, because the name has a collesion with `testthat` package. I didn't think of the way  to define it.

## New Supported Functions

```
approxCountDistinct
ascii
base64
bin
bitwiseNOT
ceil (alias: ceiling)
crc32
dayofmonth
dayofyear
explode
factorial
hex
hour
initcap
isNaN
last_day
length
log2
ltrim
md5
minute
month
negate
quarter
reverse
round
rtrim
second
sha1
signum (alias: sign)
size
soundex
to_date
trim
unbase64
unhex
weekofyear
year

datediff
levenshtein
months_between
nanvl
pmod
```

## JIRA
[[SPARK-9855] Add expression functions into SparkR whose params are simple - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9855)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8123 from yu-iskw/SPARK-9855.
---
 R/pkg/DESCRIPTION                |   1 +
 R/pkg/R/column.R                 |  81 --------------
 R/pkg/R/functions.R              | 123 ++++++++++++++++++++
 R/pkg/R/generics.R               | 185 +++++++++++++++++++++++++++++--
 R/pkg/inst/tests/test_sparkSQL.R |  21 ++--
 5 files changed, 309 insertions(+), 102 deletions(-)
 create mode 100644 R/pkg/R/functions.R

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index 4949d86d20c91..83e64897216b1 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -29,6 +29,7 @@ Collate:
     'client.R'
     'context.R'
     'deserialize.R'
+    'functions.R'
     'mllib.R'
     'serialize.R'
     'sparkR.R'
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index eeaf9f193b728..328f595d0805f 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -60,12 +60,6 @@ operators <- list(
 )
 column_functions1 <- c("asc", "desc", "isNull", "isNotNull")
 column_functions2 <- c("like", "rlike", "startsWith", "endsWith", "getField", "getItem", "contains")
-functions <- c("min", "max", "sum", "avg", "mean", "count", "abs", "sqrt",
-               "first", "last", "lower", "upper", "sumDistinct",
-               "acos", "asin", "atan", "cbrt", "ceiling", "cos", "cosh", "exp",
-               "expm1", "floor", "log", "log10", "log1p", "rint", "sign",
-               "sin", "sinh", "tan", "tanh", "toDegrees", "toRadians")
-binary_mathfunctions <- c("atan2", "hypot")
 
 createOperator <- function(op) {
   setMethod(op,
@@ -111,33 +105,6 @@ createColumnFunction2 <- function(name) {
             })
 }
 
-createStaticFunction <- function(name) {
-  setMethod(name,
-            signature(x = "Column"),
-            function(x) {
-              if (name == "ceiling") {
-                  name <- "ceil"
-              }
-              if (name == "sign") {
-                  name <- "signum"
-              }
-              jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc)
-              column(jc)
-            })
-}
-
-createBinaryMathfunctions <- function(name) {
-  setMethod(name,
-            signature(y = "Column"),
-            function(y, x) {
-              if (class(x) == "Column") {
-                x <- x@jc
-              }
-              jc <- callJStatic("org.apache.spark.sql.functions", name, y@jc, x)
-              column(jc)
-            })
-}
-
 createMethods <- function() {
   for (op in names(operators)) {
     createOperator(op)
@@ -148,12 +115,6 @@ createMethods <- function() {
   for (name in column_functions2) {
     createColumnFunction2(name)
   }
-  for (x in functions) {
-    createStaticFunction(x)
-  }
-  for (name in binary_mathfunctions) {
-    createBinaryMathfunctions(name)
-  }
 }
 
 createMethods()
@@ -242,45 +203,3 @@ setMethod("%in%",
             jc <- callJMethod(x@jc, "in", table)
             return(column(jc))
           })
-
-#' Approx Count Distinct
-#'
-#' @rdname column
-#' @return the approximate number of distinct items in a group.
-setMethod("approxCountDistinct",
-          signature(x = "Column"),
-          function(x, rsd = 0.95) {
-            jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc, rsd)
-            column(jc)
-          })
-
-#' Count Distinct
-#'
-#' @rdname column
-#' @return the number of distinct items in a group.
-setMethod("countDistinct",
-          signature(x = "Column"),
-          function(x, ...) {
-            jcol <- lapply(list(...), function (x) {
-              x@jc
-            })
-            jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc,
-                              listToSeq(jcol))
-            column(jc)
-          })
-
-#' @rdname column
-#' @aliases countDistinct
-setMethod("n_distinct",
-          signature(x = "Column"),
-          function(x, ...) {
-            countDistinct(x, ...)
-          })
-
-#' @rdname column
-#' @aliases count
-setMethod("n",
-          signature(x = "Column"),
-          function(x) {
-            count(x)
-          })
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
new file mode 100644
index 0000000000000..a15d2d5da534e
--- /dev/null
+++ b/R/pkg/R/functions.R
@@ -0,0 +1,123 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#' @include generics.R column.R
+NULL
+
+#' @title S4 expression functions for DataFrame column(s)
+#' @description These are expression functions on DataFrame columns
+
+functions1 <- c(
+  "abs", "acos", "approxCountDistinct", "ascii", "asin", "atan",
+  "avg", "base64", "bin", "bitwiseNOT", "cbrt", "ceil", "cos", "cosh", "count",
+  "crc32", "dayofmonth", "dayofyear", "exp", "explode", "expm1", "factorial",
+  "first", "floor", "hex", "hour", "initcap", "isNaN", "last", "last_day",
+  "length", "log", "log10", "log1p", "log2", "lower", "ltrim", "max", "md5",
+  "mean", "min", "minute", "month", "negate", "quarter", "reverse",
+  "rint", "round", "rtrim", "second", "sha1", "signum", "sin", "sinh", "size",
+  "soundex", "sqrt", "sum", "sumDistinct", "tan", "tanh", "toDegrees",
+  "toRadians", "to_date", "trim", "unbase64", "unhex", "upper", "weekofyear",
+  "year")
+functions2 <- c(
+  "atan2", "datediff", "hypot", "levenshtein", "months_between", "nanvl", "pmod")
+
+createFunction1 <- function(name) {
+  setMethod(name,
+            signature(x = "Column"),
+            function(x) {
+              jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc)
+              column(jc)
+            })
+}
+
+createFunction2 <- function(name) {
+  setMethod(name,
+            signature(y = "Column"),
+            function(y, x) {
+              if (class(x) == "Column") {
+                x <- x@jc
+              }
+              jc <- callJStatic("org.apache.spark.sql.functions", name, y@jc, x)
+              column(jc)
+            })
+}
+
+createFunctions <- function() {
+  for (name in functions1) {
+    createFunction1(name)
+  }
+  for (name in functions2) {
+    createFunction2(name)
+  }
+}
+
+createFunctions()
+
+#' Approx Count Distinct
+#'
+#' @rdname functions
+#' @return the approximate number of distinct items in a group.
+setMethod("approxCountDistinct",
+          signature(x = "Column"),
+          function(x, rsd = 0.95) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc, rsd)
+            column(jc)
+          })
+
+#' Count Distinct
+#'
+#' @rdname functions
+#' @return the number of distinct items in a group.
+setMethod("countDistinct",
+          signature(x = "Column"),
+          function(x, ...) {
+            jcol <- lapply(list(...), function (x) {
+              x@jc
+            })
+            jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc,
+                              listToSeq(jcol))
+            column(jc)
+          })
+
+#' @rdname functions
+#' @aliases ceil
+setMethod("ceiling",
+          signature(x = "Column"),
+          function(x) {
+            ceil(x)
+          })
+
+#' @rdname functions
+#' @aliases signum
+setMethod("sign", signature(x = "Column"),
+          function(x) {
+            signum(x)
+          })
+
+#' @rdname functions
+#' @aliases countDistinct
+setMethod("n_distinct", signature(x = "Column"),
+          function(x, ...) {
+            countDistinct(x, ...)
+          })
+
+#' @rdname functions
+#' @aliases count
+setMethod("n", signature(x = "Column"),
+          function(x) {
+            count(x)
+          })
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 379a78b1d833e..f11e7fcb6a07c 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -575,10 +575,6 @@ setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCoun
 #' @export
 setGeneric("asc", function(x) { standardGeneric("asc") })
 
-#' @rdname column
-#' @export
-setGeneric("avg", function(x, ...) { standardGeneric("avg") })
-
 #' @rdname column
 #' @export
 setGeneric("between", function(x, bounds) { standardGeneric("between") })
@@ -587,13 +583,10 @@ setGeneric("between", function(x, bounds) { standardGeneric("between") })
 #' @export
 setGeneric("cast", function(x, dataType) { standardGeneric("cast") })
 
-#' @rdname column
-#' @export
-setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
-
 #' @rdname column
 #' @export
 setGeneric("contains", function(x, ...) { standardGeneric("contains") })
+
 #' @rdname column
 #' @export
 setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") })
@@ -658,22 +651,190 @@ setGeneric("rlike", function(x, ...) { standardGeneric("rlike") })
 #' @export
 setGeneric("startsWith", function(x, ...) { standardGeneric("startsWith") })
 
-#' @rdname column
+
+###################### Expression Function Methods ##########################
+
+#' @rdname functions
+#' @export
+setGeneric("ascii", function(x) { standardGeneric("ascii") })
+
+#' @rdname functions
+#' @export
+setGeneric("avg", function(x, ...) { standardGeneric("avg") })
+
+#' @rdname functions
+#' @export
+setGeneric("base64", function(x) { standardGeneric("base64") })
+
+#' @rdname functions
+#' @export
+setGeneric("bin", function(x) { standardGeneric("bin") })
+
+#' @rdname functions
+#' @export
+setGeneric("bitwiseNOT", function(x) { standardGeneric("bitwiseNOT") })
+
+#' @rdname functions
+#' @export
+setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
+
+#' @rdname functions
+#' @export
+setGeneric("ceil", function(x) { standardGeneric("ceil") })
+
+#' @rdname functions
+#' @export
+setGeneric("crc32", function(x) { standardGeneric("crc32") })
+
+#' @rdname functions
+#' @export
+setGeneric("datediff", function(y, x) { standardGeneric("datediff") })
+
+#' @rdname functions
+#' @export
+setGeneric("dayofmonth", function(x) { standardGeneric("dayofmonth") })
+
+#' @rdname functions
+#' @export
+setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
+
+#' @rdname functions
+#' @export
+setGeneric("explode", function(x) { standardGeneric("explode") })
+
+#' @rdname functions
+#' @export
+setGeneric("hex", function(x) { standardGeneric("hex") })
+
+#' @rdname functions
+#' @export
+setGeneric("hour", function(x) { standardGeneric("hour") })
+
+#' @rdname functions
+#' @export
+setGeneric("initcap", function(x) { standardGeneric("initcap") })
+
+#' @rdname functions
+#' @export
+setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
+
+#' @rdname functions
+#' @export
+setGeneric("last_day", function(x) { standardGeneric("last_day") })
+
+#' @rdname functions
+#' @export
+setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
+
+#' @rdname functions
+#' @export
+setGeneric("lower", function(x) { standardGeneric("lower") })
+
+#' @rdname functions
+#' @export
+setGeneric("ltrim", function(x) { standardGeneric("ltrim") })
+
+#' @rdname functions
+#' @export
+setGeneric("md5", function(x) { standardGeneric("md5") })
+
+#' @rdname functions
+#' @export
+setGeneric("minute", function(x) { standardGeneric("minute") })
+
+#' @rdname functions
+#' @export
+setGeneric("month", function(x) { standardGeneric("month") })
+
+#' @rdname functions
+#' @export
+setGeneric("months_between", function(y, x) { standardGeneric("months_between") })
+
+#' @rdname functions
+#' @export
+setGeneric("nanvl", function(y, x) { standardGeneric("nanvl") })
+
+#' @rdname functions
+#' @export
+setGeneric("negate", function(x) { standardGeneric("negate") })
+
+#' @rdname functions
+#' @export
+setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
+
+#' @rdname functions
+#' @export
+setGeneric("quarter", function(x) { standardGeneric("quarter") })
+
+#' @rdname functions
+#' @export
+setGeneric("reverse", function(x) { standardGeneric("reverse") })
+
+#' @rdname functions
+#' @export
+setGeneric("rtrim", function(x) { standardGeneric("rtrim") })
+
+#' @rdname functions
+#' @export
+setGeneric("second", function(x) { standardGeneric("second") })
+
+#' @rdname functions
+#' @export
+setGeneric("sha1", function(x) { standardGeneric("sha1") })
+
+#' @rdname functions
+#' @export
+setGeneric("signum", function(x) { standardGeneric("signum") })
+
+#' @rdname functions
+#' @export
+setGeneric("size", function(x) { standardGeneric("size") })
+
+#' @rdname functions
+#' @export
+setGeneric("soundex", function(x) { standardGeneric("soundex") })
+
+#' @rdname functions
 #' @export
 setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") })
 
-#' @rdname column
+#' @rdname functions
 #' @export
 setGeneric("toDegrees", function(x) { standardGeneric("toDegrees") })
 
-#' @rdname column
+#' @rdname functions
 #' @export
 setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 
-#' @rdname column
+#' @rdname functions
+#' @export
+setGeneric("to_date", function(x) { standardGeneric("to_date") })
+
+#' @rdname functions
+#' @export
+setGeneric("trim", function(x) { standardGeneric("trim") })
+
+#' @rdname functions
+#' @export
+setGeneric("unbase64", function(x) { standardGeneric("unbase64") })
+
+#' @rdname functions
+#' @export
+setGeneric("unhex", function(x) { standardGeneric("unhex") })
+
+#' @rdname functions
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
 
+#' @rdname functions
+#' @export
+setGeneric("weekofyear", function(x) { standardGeneric("weekofyear") })
+
+#' @rdname functions
+#' @export
+setGeneric("year", function(x) { standardGeneric("year") })
+
+
 #' @rdname glm
 #' @export
 setGeneric("glm")
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 7377fc8f1ca9c..e6d3b21ff825b 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -640,15 +640,18 @@ test_that("column operators", {
 
 test_that("column functions", {
   c <- SparkR:::col("a")
-  c2 <- min(c) + max(c) + sum(c) + avg(c) + count(c) + abs(c) + sqrt(c)
-  c3 <- lower(c) + upper(c) + first(c) + last(c)
-  c4 <- approxCountDistinct(c) + countDistinct(c) + cast(c, "string")
-  c5 <- n(c) + n_distinct(c)
-  c5 <- acos(c) + asin(c) + atan(c) + cbrt(c)
-  c6 <- ceiling(c) + cos(c) + cosh(c) + exp(c) + expm1(c)
-  c7 <- floor(c) + log(c) + log10(c) + log1p(c) + rint(c)
-  c8 <- sign(c) + sin(c) + sinh(c) + tan(c) + tanh(c)
-  c9 <- toDegrees(c) + toRadians(c)
+  c1 <- abs(c) + acos(c) + approxCountDistinct(c) + ascii(c) + asin(c) + atan(c)
+  c2 <- avg(c) + base64(c) + bin(c) + bitwiseNOT(c) + cbrt(c) + ceil(c) + cos(c)
+  c3 <- cosh(c) + count(c) + crc32(c) + dayofmonth(c) + dayofyear(c) + exp(c)
+  c4 <- explode(c) + expm1(c) + factorial(c) + first(c) + floor(c) + hex(c)
+  c5 <- hour(c) + initcap(c) + isNaN(c) + last(c) + last_day(c) + length(c)
+  c6 <- log(c) + (c) + log1p(c) + log2(c) + lower(c) + ltrim(c) + max(c) + md5(c)
+  c7 <- mean(c) + min(c) + minute(c) + month(c) + negate(c) + quarter(c)
+  c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + second(c) + sha1(c)
+  c9 <- signum(c) + sin(c) + sinh(c) + size(c) + soundex(c) + sqrt(c) + sum(c)
+  c10 <- sumDistinct(c) + tan(c) + tanh(c) + toDegrees(c) + toRadians(c)
+  c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c) + weekofyear(c)
+  c12 <- year(c)
 
   df <- jsonFile(sqlContext, jsonPath)
   df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))

From 7b13ed27c1296cf76d0946e400f3449c335c8471 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 12 Aug 2015 18:52:11 -0700
Subject: [PATCH 1014/1454] [SPARK-9870] Disable driver UI and Master REST
 server in SparkSubmitSuite

I think that we should pass additional configuration flags to disable the driver UI and Master REST server in SparkSubmitSuite and HiveSparkSubmitSuite. This might cut down on port-contention-related flakiness in Jenkins.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8124 from JoshRosen/disable-ui-in-sparksubmitsuite.
---
 .../org/apache/spark/deploy/SparkSubmitSuite.scala     |  7 +++++++
 .../apache/spark/sql/hive/HiveSparkSubmitSuite.scala   | 10 +++++++++-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 2456c5d0d49b0..1110ca6051a40 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -324,6 +324,8 @@ class SparkSubmitSuite
       "--class", SimpleApplicationTest.getClass.getName.stripSuffix("$"),
       "--name", "testApp",
       "--master", "local",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       unusedJar.toString)
     runSparkSubmit(args)
   }
@@ -337,6 +339,8 @@ class SparkSubmitSuite
       "--class", JarCreationTest.getClass.getName.stripSuffix("$"),
       "--name", "testApp",
       "--master", "local-cluster[2,1,1024]",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       "--jars", jarsString,
       unusedJar.toString, "SparkSubmitClassA", "SparkSubmitClassB")
     runSparkSubmit(args)
@@ -355,6 +359,7 @@ class SparkSubmitSuite
         "--packages", Seq(main, dep).mkString(","),
         "--repositories", repo,
         "--conf", "spark.ui.enabled=false",
+        "--conf", "spark.master.rest.enabled=false",
         unusedJar.toString,
         "my.great.lib.MyLib", "my.great.dep.MyLib")
       runSparkSubmit(args)
@@ -500,6 +505,8 @@ class SparkSubmitSuite
       "--master", "local",
       "--conf", "spark.driver.extraClassPath=" + systemJar,
       "--conf", "spark.driver.userClassPathFirst=true",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       userJar.toString)
     runSparkSubmit(args)
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index b8d41065d3f02..1e1972d1ac353 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -57,6 +57,8 @@ class HiveSparkSubmitSuite
       "--class", SparkSubmitClassLoaderTest.getClass.getName.stripSuffix("$"),
       "--name", "SparkSubmitClassLoaderTest",
       "--master", "local-cluster[2,1,1024]",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       "--jars", jarsString,
       unusedJar.toString, "SparkSubmitClassA", "SparkSubmitClassB")
     runSparkSubmit(args)
@@ -68,6 +70,8 @@ class HiveSparkSubmitSuite
       "--class", SparkSQLConfTest.getClass.getName.stripSuffix("$"),
       "--name", "SparkSQLConfTest",
       "--master", "local-cluster[2,1,1024]",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       unusedJar.toString)
     runSparkSubmit(args)
   }
@@ -79,7 +83,11 @@ class HiveSparkSubmitSuite
     // the HiveContext code mistakenly overrides the class loader that contains user classes.
     // For more detail, see sql/hive/src/test/resources/regression-test-SPARK-8489/*scala.
     val testJar = "sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar"
-    val args = Seq("--class", "Main", testJar)
+    val args = Seq(
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
+      "--class", "Main",
+      testJar)
     runSparkSubmit(args)
   }
 

From 7c35746c916cf0019367850e75a080d7e739dba0 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 12 Aug 2015 20:02:55 -0700
Subject: [PATCH 1015/1454] [SPARK-9827] [SQL] fix fd leak in
 UnsafeRowSerializer

Currently, UnsafeRowSerializer does not close the InputStream, will cause fd leak if the InputStream has an open fd in it.

TODO: the fd could still be leaked, if any items in the stream is not consumed. Currently it replies on GC to close the fd in this case.

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8116 from davies/fd_leak.
---
 .../sql/execution/UnsafeRowSerializer.scala   |  2 ++
 .../execution/UnsafeRowSerializerSuite.scala  | 31 +++++++++++++++++--
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 3860c4bba9a99..5c18558f9bde7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -108,6 +108,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
       override def asKeyValueIterator: Iterator[(Int, UnsafeRow)] = {
         new Iterator[(Int, UnsafeRow)] {
           private[this] var rowSize: Int = dIn.readInt()
+          if (rowSize == EOF) dIn.close()
 
           override def hasNext: Boolean = rowSize != EOF
 
@@ -119,6 +120,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
             row.pointTo(rowBuffer, Platform.BYTE_ARRAY_OFFSET, numFields, rowSize)
             rowSize = dIn.readInt() // read the next row's size
             if (rowSize == EOF) { // We are returning the last row in this stream
+              dIn.close()
               val _rowTuple = rowTuple
               // Null these out so that the byte array can be garbage collected once the entire
               // iterator has been consumed
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
index 40b47ae18d648..bd02c73a26ace 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+import java.io.{DataOutputStream, ByteArrayInputStream, ByteArrayOutputStream}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.Row
@@ -25,6 +25,18 @@ import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, UnsafeRow}
 import org.apache.spark.sql.types._
 
+
+/**
+ * used to test close InputStream in UnsafeRowSerializer
+ */
+class ClosableByteArrayInputStream(buf: Array[Byte]) extends ByteArrayInputStream(buf) {
+  var closed: Boolean = false
+  override def close(): Unit = {
+    closed = true
+    super.close()
+  }
+}
+
 class UnsafeRowSerializerSuite extends SparkFunSuite {
 
   private def toUnsafeRow(row: Row, schema: Array[DataType]): UnsafeRow = {
@@ -52,8 +64,8 @@ class UnsafeRowSerializerSuite extends SparkFunSuite {
       serializerStream.writeValue(unsafeRow)
     }
     serializerStream.close()
-    val deserializerIter = serializer.deserializeStream(
-      new ByteArrayInputStream(baos.toByteArray)).asKeyValueIterator
+    val input = new ClosableByteArrayInputStream(baos.toByteArray)
+    val deserializerIter = serializer.deserializeStream(input).asKeyValueIterator
     for (expectedRow <- unsafeRows) {
       val actualRow = deserializerIter.next().asInstanceOf[(Integer, UnsafeRow)]._2
       assert(expectedRow.getSizeInBytes === actualRow.getSizeInBytes)
@@ -61,5 +73,18 @@ class UnsafeRowSerializerSuite extends SparkFunSuite {
       assert(expectedRow.getInt(1) === actualRow.getInt(1))
     }
     assert(!deserializerIter.hasNext)
+    assert(input.closed)
+  }
+
+  test("close empty input stream") {
+    val baos = new ByteArrayOutputStream()
+    val dout = new DataOutputStream(baos)
+    dout.writeInt(-1)  // EOF
+    dout.flush()
+    val input = new ClosableByteArrayInputStream(baos.toByteArray)
+    val serializer = new UnsafeRowSerializer(numFields = 2).newInstance()
+    val deserializerIter = serializer.deserializeStream(input).asKeyValueIterator
+    assert(!deserializerIter.hasNext)
+    assert(input.closed)
   }
 }

From 4413d0855aaba5cb00f737dc6934a0b92d9bc05d Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 12 Aug 2015 20:03:55 -0700
Subject: [PATCH 1016/1454] [SPARK-9908] [SQL] When spark.sql.tungsten.enabled
 is false, broadcast join does not work

https://issues.apache.org/jira/browse/SPARK-9908

Author: Yin Huai <yhuai@databricks.com>

Closes #8149 from yhuai/SPARK-9908.
---
 .../apache/spark/sql/execution/joins/HashedRelation.scala   | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 076afe6e4e960..bb333b4d5ed18 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -66,7 +66,8 @@ private[joins] final class GeneralHashedRelation(
     private var hashTable: JavaHashMap[InternalRow, CompactBuffer[InternalRow]])
   extends HashedRelation with Externalizable {
 
-  private def this() = this(null) // Needed for serialization
+  // Needed for serialization (it is public to make Java serialization work)
+  def this() = this(null)
 
   override def get(key: InternalRow): Seq[InternalRow] = hashTable.get(key)
 
@@ -88,7 +89,8 @@ private[joins]
 final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalRow, InternalRow])
   extends HashedRelation with Externalizable {
 
-  private def this() = this(null) // Needed for serialization
+  // Needed for serialization (it is public to make Java serialization work)
+  def this() = this(null)
 
   override def get(key: InternalRow): Seq[InternalRow] = {
     val v = hashTable.get(key)

From d2d5e7fe2df582e1c866334b3014d7cb351f5b70 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 12 Aug 2015 20:43:36 -0700
Subject: [PATCH 1017/1454] [SPARK-9704] [ML] Made ProbabilisticClassifier,
 Identifiable, VectorUDT public APIs

Made ProbabilisticClassifier, Identifiable, VectorUDT public.  All are annotated as DeveloperApi.

CC: mengxr EronWright

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8004 from jkbradley/ml-api-public-items and squashes the following commits:

7ebefda [Joseph K. Bradley] update per code review
7ff0768 [Joseph K. Bradley] attepting to add mima fix
756d84c [Joseph K. Bradley] VectorUDT annotated as AlphaComponent
ae7767d [Joseph K. Bradley] added another warning
94fd553 [Joseph K. Bradley] Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
---
 .../classification/ProbabilisticClassifier.scala |  4 ++--
 .../org/apache/spark/ml/util/Identifiable.scala  | 16 ++++++++++++++--
 .../org/apache/spark/mllib/linalg/Vectors.scala  | 10 ++++------
 project/MimaExcludes.scala                       |  4 ++++
 4 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index 1e50a895a9a05..fdd1851ae5508 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -50,7 +50,7 @@ private[classification] trait ProbabilisticClassifierParams
  * @tparam M  Concrete Model type
  */
 @DeveloperApi
-private[spark] abstract class ProbabilisticClassifier[
+abstract class ProbabilisticClassifier[
     FeaturesType,
     E <: ProbabilisticClassifier[FeaturesType, E, M],
     M <: ProbabilisticClassificationModel[FeaturesType, M]]
@@ -74,7 +74,7 @@ private[spark] abstract class ProbabilisticClassifier[
  * @tparam M  Concrete Model type
  */
 @DeveloperApi
-private[spark] abstract class ProbabilisticClassificationModel[
+abstract class ProbabilisticClassificationModel[
     FeaturesType,
     M <: ProbabilisticClassificationModel[FeaturesType, M]]
   extends ClassificationModel[FeaturesType, M] with ProbabilisticClassifierParams {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala
index ddd34a54503a6..bd213e7362e94 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala
@@ -19,11 +19,19 @@ package org.apache.spark.ml.util
 
 import java.util.UUID
 
+import org.apache.spark.annotation.DeveloperApi
+
 
 /**
+ * :: DeveloperApi ::
+ *
  * Trait for an object with an immutable unique ID that identifies itself and its derivatives.
+ *
+ * WARNING: There have not yet been final discussions on this API, so it may be broken in future
+ *          releases.
  */
-private[spark] trait Identifiable {
+@DeveloperApi
+trait Identifiable {
 
   /**
    * An immutable unique ID for the object and its derivatives.
@@ -33,7 +41,11 @@ private[spark] trait Identifiable {
   override def toString: String = uid
 }
 
-private[spark] object Identifiable {
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+object Identifiable {
 
   /**
    * Returns a random UID that concatenates the given prefix, "_", and 12 random hex chars.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 86c461fa91633..df15d985c814c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -26,7 +26,7 @@ import scala.collection.JavaConverters._
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
@@ -159,15 +159,13 @@ sealed trait Vector extends Serializable {
 }
 
 /**
- * :: DeveloperApi ::
+ * :: AlphaComponent ::
  *
  * User-defined type for [[Vector]] which allows easy interaction with SQL
  * via [[org.apache.spark.sql.DataFrame]].
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@DeveloperApi
-private[spark] class VectorUDT extends UserDefinedType[Vector] {
+@AlphaComponent
+class VectorUDT extends UserDefinedType[Vector] {
 
   override def sqlType: StructType = {
     // type: 0 = sparse, 1 = dense
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 90261ca3d61aa..784f83c10e023 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -178,6 +178,10 @@ object MimaExcludes {
             // SPARK-4751 Dynamic allocation for standalone mode
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.SparkContext.supportDynamicAllocation")
+          ) ++ Seq(
+            // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
+            ProblemFilters.exclude[IncompatibleResultTypeProblem](
+              "org.apache.spark.mllib.linalg.VectorUDT.serialize")
           )
 
         case v if v.startsWith("1.4") =>

From d7053bea985679c514b3add029631ea23e1730ce Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 20:44:40 -0700
Subject: [PATCH 1018/1454] [SPARK-9903] [MLLIB] skip local processing in
 PrefixSpan if there are no small prefixes

There exists a chance that the prefixes keep growing to the maximum pattern length. Then the final local processing step becomes unnecessary. feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8136 from mengxr/SPARK-9903.
---
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 37 +++++++++++--------
 1 file changed, 21 insertions(+), 16 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index ad6715b52f337..dc4ae1d0b69ed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -282,25 +282,30 @@ object PrefixSpan extends Logging {
       largePrefixes = newLargePrefixes
     }
 
-    // Switch to local processing.
-    val bcSmallPrefixes = sc.broadcast(smallPrefixes)
-    val distributedFreqPattern = postfixes.flatMap { postfix =>
-      bcSmallPrefixes.value.values.map { prefix =>
-        (prefix.id, postfix.project(prefix).compressed)
-      }.filter(_._2.nonEmpty)
-    }.groupByKey().flatMap { case (id, projPostfixes) =>
-      val prefix = bcSmallPrefixes.value(id)
-      val localPrefixSpan = new LocalPrefixSpan(minCount, maxPatternLength - prefix.length)
-      // TODO: We collect projected postfixes into memory. We should also compare the performance
-      // TODO: of keeping them on shuffle files.
-      localPrefixSpan.run(projPostfixes.toArray).map { case (pattern, count) =>
-        (prefix.items ++ pattern, count)
+    var freqPatterns = sc.parallelize(localFreqPatterns, 1)
+
+    val numSmallPrefixes = smallPrefixes.size
+    logInfo(s"number of small prefixes for local processing: $numSmallPrefixes")
+    if (numSmallPrefixes > 0) {
+      // Switch to local processing.
+      val bcSmallPrefixes = sc.broadcast(smallPrefixes)
+      val distributedFreqPattern = postfixes.flatMap { postfix =>
+        bcSmallPrefixes.value.values.map { prefix =>
+          (prefix.id, postfix.project(prefix).compressed)
+        }.filter(_._2.nonEmpty)
+      }.groupByKey().flatMap { case (id, projPostfixes) =>
+        val prefix = bcSmallPrefixes.value(id)
+        val localPrefixSpan = new LocalPrefixSpan(minCount, maxPatternLength - prefix.length)
+        // TODO: We collect projected postfixes into memory. We should also compare the performance
+        // TODO: of keeping them on shuffle files.
+        localPrefixSpan.run(projPostfixes.toArray).map { case (pattern, count) =>
+          (prefix.items ++ pattern, count)
+        }
       }
+      // Union local frequent patterns and distributed ones.
+      freqPatterns = freqPatterns ++ distributedFreqPattern
     }
 
-    // Union local frequent patterns and distributed ones.
-    val freqPatterns = (sc.parallelize(localFreqPatterns, 1) ++ distributedFreqPattern)
-      .persist(StorageLevel.MEMORY_AND_DISK)
     freqPatterns
   }
 

From 2fb4901b71cee65d40a43e61e3f4411c30cdefc3 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Wed, 12 Aug 2015 20:59:38 -0700
Subject: [PATCH 1019/1454] [SPARK-9916] [BUILD] [SPARKR] removed left-over
 sparkr.zip copy/create commands from codebase

sparkr.zip is now built by SparkSubmit on a need-to-build basis.

cc shivaram

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8147 from brkyvz/make-dist-fix.
---
 R/install-dev.bat    | 5 -----
 make-distribution.sh | 1 -
 2 files changed, 6 deletions(-)

diff --git a/R/install-dev.bat b/R/install-dev.bat
index f32670b67de96..008a5c668bc45 100644
--- a/R/install-dev.bat
+++ b/R/install-dev.bat
@@ -25,8 +25,3 @@ set SPARK_HOME=%~dp0..
 MKDIR %SPARK_HOME%\R\lib
 
 R.exe CMD INSTALL --library="%SPARK_HOME%\R\lib"  %SPARK_HOME%\R\pkg\
-
-rem Zip the SparkR package so that it can be distributed to worker nodes on YARN
-pushd %SPARK_HOME%\R\lib
-%JAVA_HOME%\bin\jar.exe cfM "%SPARK_HOME%\R\lib\sparkr.zip" SparkR
-popd
diff --git a/make-distribution.sh b/make-distribution.sh
index 4789b0e09cc8a..247a81341e4a4 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -219,7 +219,6 @@ cp -r "$SPARK_HOME/ec2" "$DISTDIR"
 if [ -d "$SPARK_HOME"/R/lib/SparkR ]; then
   mkdir -p "$DISTDIR"/R/lib
   cp -r "$SPARK_HOME/R/lib/SparkR" "$DISTDIR"/R/lib
-  cp "$SPARK_HOME/R/lib/sparkr.zip" "$DISTDIR"/R/lib
 fi
 
 # Download and copy in tachyon, if requested

From 2278219054314f1d31ffc358a59aa5067f9f5de9 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 12 Aug 2015 21:24:15 -0700
Subject: [PATCH 1020/1454] [SPARK-9920] [SQL] The simpleString of
 TungstenAggregate does not show its output

https://issues.apache.org/jira/browse/SPARK-9920

Taking `sqlContext.sql("select i, sum(j1) as sum from testAgg group by i").explain()` as an example, the output of our current master is
```
== Physical Plan ==
TungstenAggregate(key=[i#0], value=[(sum(cast(j1#1 as bigint)),mode=Final,isDistinct=false)]
 TungstenExchange hashpartitioning(i#0)
  TungstenAggregate(key=[i#0], value=[(sum(cast(j1#1 as bigint)),mode=Partial,isDistinct=false)]
   Scan ParquetRelation[file:/user/hive/warehouse/testagg][i#0,j1#1]
```
With this PR, the output will be
```
== Physical Plan ==
TungstenAggregate(key=[i#0], functions=[(sum(cast(j1#1 as bigint)),mode=Final,isDistinct=false)], output=[i#0,sum#18L])
 TungstenExchange hashpartitioning(i#0)
  TungstenAggregate(key=[i#0], functions=[(sum(cast(j1#1 as bigint)),mode=Partial,isDistinct=false)], output=[i#0,currentSum#22L])
   Scan ParquetRelation[file:/user/hive/warehouse/testagg][i#0,j1#1]
```

Author: Yin Huai <yhuai@databricks.com>

Closes #8150 from yhuai/SPARK-9920.
---
 .../spark/sql/execution/aggregate/SortBasedAggregate.scala | 6 +++++-
 .../spark/sql/execution/aggregate/TungstenAggregate.scala  | 7 ++++---
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
index ab26f9c58aa2e..f4c14a9b3556f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
@@ -108,6 +108,10 @@ case class SortBasedAggregate(
 
   override def simpleString: String = {
     val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
-    s"""SortBasedAggregate ${groupingExpressions} ${allAggregateExpressions}"""
+
+    val keyString = groupingExpressions.mkString("[", ",", "]")
+    val functionString = allAggregateExpressions.mkString("[", ",", "]")
+    val outputString = output.mkString("[", ",", "]")
+    s"SortBasedAggregate(key=$keyString, functions=$functionString, output=$outputString)"
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index c40ca973796a6..99f51ba5b6935 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -127,11 +127,12 @@ case class TungstenAggregate(
     testFallbackStartsAt match {
       case None =>
         val keyString = groupingExpressions.mkString("[", ",", "]")
-        val valueString = allAggregateExpressions.mkString("[", ",", "]")
-        s"TungstenAggregate(key=$keyString, value=$valueString"
+        val functionString = allAggregateExpressions.mkString("[", ",", "]")
+        val outputString = output.mkString("[", ",", "]")
+        s"TungstenAggregate(key=$keyString, functions=$functionString, output=$outputString)"
       case Some(fallbackStartsAt) =>
         s"TungstenAggregateWithControlledFallback $groupingExpressions " +
-          s"$allAggregateExpressions fallbackStartsAt=$fallbackStartsAt"
+          s"$allAggregateExpressions $resultExpressions fallbackStartsAt=$fallbackStartsAt"
     }
   }
 }

From a8ab2634c1eee143a4deaf309204df8add727f9e Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 12 Aug 2015 21:26:00 -0700
Subject: [PATCH 1021/1454] [SPARK-9832] [SQL] add a thread-safe lookup for
 BytesToBytseMap

This patch add a thread-safe lookup for BytesToBytseMap, and use that in broadcasted HashedRelation.

Author: Davies Liu <davies@databricks.com>

Closes #8151 from davies/safeLookup.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 30 ++++++++++++++-----
 .../sql/execution/joins/HashedRelation.scala  |  6 ++--
 2 files changed, 26 insertions(+), 10 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 87ed47e88c4ef..5f3a4fcf4d585 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -17,25 +17,24 @@
 
 package org.apache.spark.unsafe.map;
 
-import java.lang.Override;
-import java.lang.UnsupportedOperationException;
+import javax.annotation.Nullable;
 import java.util.Iterator;
 import java.util.LinkedList;
 import java.util.List;
 
-import javax.annotation.Nullable;
-
 import com.google.common.annotations.VisibleForTesting;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import org.apache.spark.shuffle.ShuffleMemoryManager;
-import org.apache.spark.unsafe.*;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.array.LongArray;
 import org.apache.spark.unsafe.bitset.BitSet;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
-import org.apache.spark.unsafe.memory.*;
+import org.apache.spark.unsafe.memory.MemoryBlock;
+import org.apache.spark.unsafe.memory.MemoryLocation;
+import org.apache.spark.unsafe.memory.TaskMemoryManager;
 
 /**
  * An append-only hash map where keys and values are contiguous regions of bytes.
@@ -328,6 +327,20 @@ public Location lookup(
       Object keyBaseObject,
       long keyBaseOffset,
       int keyRowLengthBytes) {
+    safeLookup(keyBaseObject, keyBaseOffset, keyRowLengthBytes, loc);
+    return loc;
+  }
+
+  /**
+   * Looks up a key, and saves the result in provided `loc`.
+   *
+   * This is a thread-safe version of `lookup`, could be used by multiple threads.
+   */
+  public void safeLookup(
+      Object keyBaseObject,
+      long keyBaseOffset,
+      int keyRowLengthBytes,
+      Location loc) {
     assert(bitset != null);
     assert(longArray != null);
 
@@ -343,7 +356,8 @@ public Location lookup(
       }
       if (!bitset.isSet(pos)) {
         // This is a new key.
-        return loc.with(pos, hashcode, false);
+        loc.with(pos, hashcode, false);
+        return;
       } else {
         long stored = longArray.get(pos * 2 + 1);
         if ((int) (stored) == hashcode) {
@@ -361,7 +375,7 @@ public Location lookup(
               keyRowLengthBytes
             );
             if (areEqual) {
-              return loc;
+              return;
             } else {
               if (enablePerfMetrics) {
                 numHashCollisions++;
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index bb333b4d5ed18..ea02076b41a6f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -215,8 +215,10 @@ private[joins] final class UnsafeHashedRelation(
 
     if (binaryMap != null) {
       // Used in Broadcast join
-      val loc = binaryMap.lookup(unsafeKey.getBaseObject, unsafeKey.getBaseOffset,
-        unsafeKey.getSizeInBytes)
+      val map = binaryMap  // avoid the compiler error
+      val loc = new map.Location  // this could be allocated in stack
+      binaryMap.safeLookup(unsafeKey.getBaseObject, unsafeKey.getBaseOffset,
+        unsafeKey.getSizeInBytes, loc)
       if (loc.isDefined) {
         val buffer = CompactBuffer[UnsafeRow]()
 

From 5fc058a1fc5d83ad53feec936475484aef3800b3 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 21:33:38 -0700
Subject: [PATCH 1022/1454] [SPARK-9917] [ML] add getMin/getMax and doc for
 originalMin/origianlMax in MinMaxScaler

hhbyyh

Author: Xiangrui Meng <meng@databricks.com>

Closes #8145 from mengxr/SPARK-9917.
---
 .../org/apache/spark/ml/feature/MinMaxScaler.scala     | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
index b30adf3df48d2..9a473dd23772d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -41,6 +41,9 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H
   val min: DoubleParam = new DoubleParam(this, "min",
     "lower bound of the output feature range")
 
+  /** @group getParam */
+  def getMin: Double = $(min)
+
   /**
    * upper bound after transformation, shared by all features
    * Default: 1.0
@@ -49,6 +52,9 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H
   val max: DoubleParam = new DoubleParam(this, "max",
     "upper bound of the output feature range")
 
+  /** @group getParam */
+  def getMax: Double = $(max)
+
   /** Validates and transforms the input schema. */
   protected def validateAndTransformSchema(schema: StructType): StructType = {
     val inputType = schema($(inputCol)).dataType
@@ -115,6 +121,9 @@ class MinMaxScaler(override val uid: String)
  * :: Experimental ::
  * Model fitted by [[MinMaxScaler]].
  *
+ * @param originalMin min value for each original column during fitting
+ * @param originalMax max value for each original column during fitting
+ *
  * TODO: The transformer does not yet set the metadata in the output column (SPARK-8529).
  */
 @Experimental
@@ -136,7 +145,6 @@ class MinMaxScalerModel private[ml] (
   /** @group setParam */
   def setMax(value: Double): this.type = set(max, value)
 
-
   override def transform(dataset: DataFrame): DataFrame = {
     val originalRange = (originalMax.toBreeze - originalMin.toBreeze).toArray
     val minArray = originalMin.toArray

From df543892122342b97e5137b266959ba97589b3ef Mon Sep 17 00:00:00 2001
From: "shikai.tang" <tar.sky06@gmail.com>
Date: Wed, 12 Aug 2015 21:53:15 -0700
Subject: [PATCH 1023/1454] [SPARK-8922] [DOCUMENTATION, MLLIB] Add @since tags
 to mllib.evaluation

Author: shikai.tang <tar.sky06@gmail.com>

Closes #7429 from mosessky/master.
---
 .../BinaryClassificationMetrics.scala         | 32 ++++++++++++++++---
 .../mllib/evaluation/MulticlassMetrics.scala  |  9 ++++++
 .../mllib/evaluation/MultilabelMetrics.scala  |  4 +++
 .../mllib/evaluation/RankingMetrics.scala     |  4 +++
 .../mllib/evaluation/RegressionMetrics.scala  |  6 ++++
 5 files changed, 50 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index c1d1a224817e8..486741edd6f5a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -41,6 +41,7 @@ import org.apache.spark.sql.DataFrame
  *                of bins may not exactly equal numBins. The last bin in each partition may
  *                be smaller as a result, meaning there may be an extra sample at
  *                partition boundaries.
+ * @since 1.3.0
  */
 @Experimental
 class BinaryClassificationMetrics(
@@ -51,6 +52,7 @@ class BinaryClassificationMetrics(
 
   /**
    * Defaults `numBins` to 0.
+   * @since 1.0.0
    */
   def this(scoreAndLabels: RDD[(Double, Double)]) = this(scoreAndLabels, 0)
 
@@ -61,12 +63,18 @@ class BinaryClassificationMetrics(
   private[mllib] def this(scoreAndLabels: DataFrame) =
     this(scoreAndLabels.map(r => (r.getDouble(0), r.getDouble(1))))
 
-  /** Unpersist intermediate RDDs used in the computation. */
+  /**
+   * Unpersist intermediate RDDs used in the computation.
+   * @since 1.0.0
+   */
   def unpersist() {
     cumulativeCounts.unpersist()
   }
 
-  /** Returns thresholds in descending order. */
+  /**
+   * Returns thresholds in descending order.
+   * @since 1.0.0
+   */
   def thresholds(): RDD[Double] = cumulativeCounts.map(_._1)
 
   /**
@@ -74,6 +82,7 @@ class BinaryClassificationMetrics(
    * which is an RDD of (false positive rate, true positive rate)
    * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
    * @see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
+   * @since 1.0.0
    */
   def roc(): RDD[(Double, Double)] = {
     val rocCurve = createCurve(FalsePositiveRate, Recall)
@@ -85,6 +94,7 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the receiver operating characteristic (ROC) curve.
+   * @since 1.0.0
    */
   def areaUnderROC(): Double = AreaUnderCurve.of(roc())
 
@@ -92,6 +102,7 @@ class BinaryClassificationMetrics(
    * Returns the precision-recall curve, which is an RDD of (recall, precision),
    * NOT (precision, recall), with (0.0, 1.0) prepended to it.
    * @see http://en.wikipedia.org/wiki/Precision_and_recall
+   * @since 1.0.0
    */
   def pr(): RDD[(Double, Double)] = {
     val prCurve = createCurve(Recall, Precision)
@@ -102,6 +113,7 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the precision-recall curve.
+   * @since 1.0.0
    */
   def areaUnderPR(): Double = AreaUnderCurve.of(pr())
 
@@ -110,16 +122,26 @@ class BinaryClassificationMetrics(
    * @param beta the beta factor in F-Measure computation.
    * @return an RDD of (threshold, F-Measure) pairs.
    * @see http://en.wikipedia.org/wiki/F1_score
+   * @since 1.0.0
    */
   def fMeasureByThreshold(beta: Double): RDD[(Double, Double)] = createCurve(FMeasure(beta))
 
-  /** Returns the (threshold, F-Measure) curve with beta = 1.0. */
+  /**
+   * Returns the (threshold, F-Measure) curve with beta = 1.0.
+   * @since 1.0.0
+   */
   def fMeasureByThreshold(): RDD[(Double, Double)] = fMeasureByThreshold(1.0)
 
-  /** Returns the (threshold, precision) curve. */
+  /**
+   * Returns the (threshold, precision) curve.
+   * @since 1.0.0
+   */
   def precisionByThreshold(): RDD[(Double, Double)] = createCurve(Precision)
 
-  /** Returns the (threshold, recall) curve. */
+  /**
+   * Returns the (threshold, recall) curve.
+   * @since 1.0.0
+   */
   def recallByThreshold(): RDD[(Double, Double)] = createCurve(Recall)
 
   private lazy val (
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index 4628dc5690913..dddfa3ea5b800 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -30,6 +30,7 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multiclass classification.
  *
  * @param predictionAndLabels an RDD of (prediction, label) pairs.
+ * @since 1.1.0
  */
 @Experimental
 class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
@@ -64,6 +65,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * predicted classes are in columns,
    * they are ordered by class label ascending,
    * as in "labels"
+   * @since 1.1.0
    */
   def confusionMatrix: Matrix = {
     val n = labels.size
@@ -83,12 +85,14 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns true positive rate for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def truePositiveRate(label: Double): Double = recall(label)
 
   /**
    * Returns false positive rate for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def falsePositiveRate(label: Double): Double = {
     val fp = fpByClass.getOrElse(label, 0)
@@ -98,6 +102,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns precision for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def precision(label: Double): Double = {
     val tp = tpByClass(label)
@@ -108,6 +113,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns recall for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def recall(label: Double): Double = tpByClass(label).toDouble / labelCountByClass(label)
 
@@ -115,6 +121,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * Returns f-measure for a given label (category)
    * @param label the label.
    * @param beta the beta parameter.
+   * @since 1.1.0
    */
   def fMeasure(label: Double, beta: Double): Double = {
     val p = precision(label)
@@ -126,6 +133,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def fMeasure(label: Double): Double = fMeasure(label, 1.0)
 
@@ -179,6 +187,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged f-measure
    * @param beta the beta parameter.
+   * @since 1.1.0
    */
   def weightedFMeasure(beta: Double): Double = labelCountByClass.map { case (category, count) =>
     fMeasure(category, beta) * count.toDouble / labelCount
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index bf6eb1d5bd2ab..77cb1e09bdbb5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multilabel classification.
  * @param predictionAndLabels an RDD of (predictions, labels) pairs,
  * both are non-null Arrays, each with unique elements.
+ * @since 1.2.0
  */
 class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
 
@@ -103,6 +104,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns precision for a given label (category)
    * @param label the label.
+   * @since 1.2.0
    */
   def precision(label: Double): Double = {
     val tp = tpPerClass(label)
@@ -113,6 +115,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns recall for a given label (category)
    * @param label the label.
+   * @since 1.2.0
    */
   def recall(label: Double): Double = {
     val tp = tpPerClass(label)
@@ -123,6 +126,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
+   * @since 1.2.0
    */
   def f1Measure(label: Double): Double = {
     val p = precision(label)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
index 5b5a2a1450f7f..063fbed8cdeea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
@@ -34,6 +34,7 @@ import org.apache.spark.rdd.RDD
  * Java users should use [[RankingMetrics$.of]] to create a [[RankingMetrics]] instance.
  *
  * @param predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs.
+ * @since 1.2.0
  */
 @Experimental
 class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])])
@@ -55,6 +56,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated precision, must be positive
    * @return the average precision at the first k ranking positions
+   * @since 1.2.0
    */
   def precisionAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
@@ -124,6 +126,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated ndcg, must be positive
    * @return the average ndcg at the first k ranking positions
+   * @since 1.2.0
    */
   def ndcgAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
@@ -162,6 +165,7 @@ object RankingMetrics {
   /**
    * Creates a [[RankingMetrics]] instance (for Java users).
    * @param predictionAndLabels a JavaRDD of (predicted ranking, ground truth set) pairs
+   * @since 1.4.0
    */
   def of[E, T <: jl.Iterable[E]](predictionAndLabels: JavaRDD[(T, T)]): RankingMetrics[E] = {
     implicit val tag = JavaSparkContext.fakeClassTag[E]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 408847afa800d..54dfd8c099494 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -29,6 +29,7 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for regression.
  *
  * @param predictionAndObservations an RDD of (prediction, observation) pairs.
+ * @since 1.2.0
  */
 @Experimental
 class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging {
@@ -66,6 +67,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
    * Returns the variance explained by regression.
    * explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n
    * @see [[https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained]]
+   * @since 1.2.0
    */
   def explainedVariance: Double = {
     SSreg / summary.count
@@ -74,6 +76,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean absolute error, which is a risk function corresponding to the
    * expected value of the absolute error loss or l1-norm loss.
+   * @since 1.2.0
    */
   def meanAbsoluteError: Double = {
     summary.normL1(1) / summary.count
@@ -82,6 +85,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean squared error, which is a risk function corresponding to the
    * expected value of the squared error loss or quadratic loss.
+   * @since 1.2.0
    */
   def meanSquaredError: Double = {
     SSerr / summary.count
@@ -90,6 +94,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the root mean squared error, which is defined as the square root of
    * the mean squared error.
+   * @since 1.2.0
    */
   def rootMeanSquaredError: Double = {
     math.sqrt(this.meanSquaredError)
@@ -98,6 +103,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns R^2^, the unadjusted coefficient of determination.
    * @see [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
+   * @since 1.2.0
    */
   def r2: Double = {
     1 - SSerr / SStot

From d7eb371eb6369a34e58a09179efe058c4101de9e Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 22:30:33 -0700
Subject: [PATCH 1024/1454] [SPARK-9914] [ML] define setters explicitly for
 Java and use setParam group in RFormula

The problem with defining setters in the base class is that it doesn't return the correct type in Java.

ericl

Author: Xiangrui Meng <meng@databricks.com>

Closes #8143 from mengxr/SPARK-9914 and squashes the following commits:

d36c887 [Xiangrui Meng] remove setters from model
a49021b [Xiangrui Meng] define setters explicitly for Java and use setParam group
---
 .../scala/org/apache/spark/ml/feature/RFormula.scala  | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index d5360c9217ea9..a752dacd72d95 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -33,11 +33,6 @@ import org.apache.spark.sql.types._
  * Base trait for [[RFormula]] and [[RFormulaModel]].
  */
 private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol {
-  /** @group getParam */
-  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
-
-  /** @group getParam */
-  def setLabelCol(value: String): this.type = set(labelCol, value)
 
   protected def hasLabelCol(schema: StructType): Boolean = {
     schema.map(_.name).contains($(labelCol))
@@ -71,6 +66,12 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
   /** @group getParam */
   def getFormula: String = $(formula)
 
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group setParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
+
   /** Whether the formula specifies fitting an intercept. */
   private[ml] def hasIntercept: Boolean = {
     require(isDefined(formula), "Formula must be defined first.")

From d0b18919d16e6a2f19159516bd2767b60b595279 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Thu, 13 Aug 2015 13:33:39 +0800
Subject: [PATCH 1025/1454] [SPARK-9927] [SQL] Revert 8049 since it's pushing
 wrong filter down

I made a mistake in #8049 by casting literal value to attribute's data type, which would cause simply truncate the literal value and push a wrong filter down.

JIRA: https://issues.apache.org/jira/browse/SPARK-9927

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8157 from yjshen/rever8049.
---
 .../datasources/DataSourceStrategy.scala      | 30 ++--------------
 .../execution/datasources/jdbc/JDBCRDD.scala  |  2 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 35 -------------------
 3 files changed, 3 insertions(+), 64 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 9eea2b0382535..2a4c40db8bb66 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -20,13 +20,13 @@ package org.apache.spark.sql.execution.datasources
 import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, expressions}
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.{TimestampType, DateType, StringType, StructType}
+import org.apache.spark.sql.types.{StringType, StructType}
 import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.{SerializableConfiguration, Utils}
@@ -343,17 +343,11 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
    * and convert them.
    */
   protected[sql] def selectFilters(filters: Seq[Expression]) = {
-    import CatalystTypeConverters._
-
     def translate(predicate: Expression): Option[Filter] = predicate match {
       case expressions.EqualTo(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualTo(a.name, v))
       case expressions.EqualTo(Literal(v, _), a: Attribute) =>
         Some(sources.EqualTo(a.name, v))
-      case expressions.EqualTo(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.EqualTo(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualNullSafe(a.name, v))
@@ -364,41 +358,21 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         Some(sources.GreaterThan(a.name, v))
       case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
         Some(sources.LessThan(a.name, v))
-      case expressions.GreaterThan(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.GreaterThan(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThan(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThan(a.name, v))
       case expressions.LessThan(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThan(a.name, v))
-      case expressions.LessThan(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.LessThan(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
       case expressions.GreaterThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.LessThanOrEqual(a.name, v))
-      case expressions.GreaterThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.GreaterThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.GreaterThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.LessThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThanOrEqual(a.name, v))
       case expressions.LessThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
-      case expressions.LessThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.LessThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.LessThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.GreaterThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.InSet(a: Attribute, set) =>
         Some(sources.In(a.name, set.toArray))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 281943e23fcff..8eab6a0adccc4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -284,7 +284,7 @@ private[sql] class JDBCRDD(
   /**
    * `filters`, but as a WHERE clause suitable for injection into a SQL query.
    */
-  val filterWhereClause: String = {
+  private val filterWhereClause: String = {
     val filterStrings = filters map compileFilter filter (_ != null)
     if (filterStrings.size > 0) {
       val sb = new StringBuilder("WHERE ")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index b9cfae51e809c..e4dcf4c75d208 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -25,8 +25,6 @@ import org.h2.jdbc.JdbcSQLException
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD
-import org.apache.spark.sql.execution.PhysicalRDD
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -150,18 +148,6 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
         |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES', user 'testUser', password 'testPass')
       """.stripMargin.replaceAll("\n", " "))
 
-    conn.prepareStatement("create table test.decimals (a DECIMAL(7, 2), b DECIMAL(4, 0))").
-      executeUpdate()
-    conn.prepareStatement("insert into test.decimals values (12345.67, 1234)").executeUpdate()
-    conn.prepareStatement("insert into test.decimals values (34567.89, 1428)").executeUpdate()
-    conn.commit()
-    sql(
-      s"""
-         |CREATE TEMPORARY TABLE decimals
-         |USING org.apache.spark.sql.jdbc
-         |OPTIONS (url '$url', dbtable 'TEST.DECIMALS', user 'testUser', password 'testPass')
-      """.stripMargin.replaceAll("\n", " "))
-
     conn.prepareStatement(
       s"""
         |create table test.nulltypes (a INT, b BOOLEAN, c TINYINT, d BINARY(20), e VARCHAR(20),
@@ -458,25 +444,4 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
     assert(agg.getCatalystType(0, "", 1, null) === Some(LongType))
     assert(agg.getCatalystType(1, "", 1, null) === Some(StringType))
   }
-
-  test("SPARK-9182: filters are not passed through to jdbc source") {
-    def checkPushedFilter(query: String, filterStr: String): Unit = {
-      val rddOpt = sql(query).queryExecution.executedPlan.collectFirst {
-        case PhysicalRDD(_, rdd: JDBCRDD, _) => rdd
-      }
-      assert(rddOpt.isDefined)
-      val pushedFilterStr = rddOpt.get.filterWhereClause
-      assert(pushedFilterStr.contains(filterStr),
-        s"Expected to push [$filterStr], actually we pushed [$pushedFilterStr]")
-    }
-
-    checkPushedFilter("select * from foobar where NAME = 'fred'", "NAME = 'fred'")
-    checkPushedFilter("select * from inttypes where A > '15'", "A > 15")
-    checkPushedFilter("select * from inttypes where C <= 20", "C <= 20")
-    checkPushedFilter("select * from decimals where A > 1000", "A > 1000.00")
-    checkPushedFilter("select * from decimals where A > 1000 AND A < 2000",
-      "A > 1000.00 AND A < 2000.00")
-    checkPushedFilter("select * from decimals where A = 2000 AND B > 20", "A = 2000.00 AND B > 20")
-    checkPushedFilter("select * from timetypes where B > '1998-09-10'", "B > 1998-09-10")
-  }
 }

From 68f99571492f67596b3656e9f076deeb96616f4a Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 23:04:59 -0700
Subject: [PATCH 1026/1454] [SPARK-9918] [MLLIB] remove runs from k-means and
 rename epsilon to tol

This requires some discussion. I'm not sure whether `runs` is a useful parameter. It certainly complicates the implementation. We might want to optimize the k-means implementation with block matrix operations. In this case, having `runs` may not be worth the trade-off. Also it increases the communication cost in a single job, which might cause other issues.

This PR also renames `epsilon` to `tol` to have consistent naming among algorithms. The Python constructor is updated to include all parameters.

jkbradley yu-iskw

Author: Xiangrui Meng <meng@databricks.com>

Closes #8148 from mengxr/SPARK-9918 and squashes the following commits:

149b9e5 [Xiangrui Meng] fix constructor in Python and rename epsilon to tol
3cc15b3 [Xiangrui Meng] fix test and change initStep to initSteps in python
a0a0274 [Xiangrui Meng] remove runs from k-means in the pipeline API
---
 .../apache/spark/ml/clustering/KMeans.scala   | 51 +++------------
 .../spark/ml/clustering/KMeansSuite.scala     | 12 +---
 python/pyspark/ml/clustering.py               | 63 ++++---------------
 3 files changed, 26 insertions(+), 100 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
index dc192add6ca13..47a18cdb31b53 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.ml.clustering
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.param.{Param, Params, IntParam, DoubleParam, ParamMap}
-import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed}
+import org.apache.spark.ml.param.{Param, Params, IntParam, ParamMap}
+import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel}
@@ -27,14 +27,13 @@ import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.sql.types.{IntegerType, StructType}
 import org.apache.spark.sql.{DataFrame, Row}
-import org.apache.spark.util.Utils
 
 
 /**
  * Common params for KMeans and KMeansModel
  */
-private[clustering] trait KMeansParams
-    extends Params with HasMaxIter with HasFeaturesCol with HasSeed with HasPredictionCol {
+private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFeaturesCol
+  with HasSeed with HasPredictionCol with HasTol {
 
   /**
    * Set the number of clusters to create (k). Must be > 1. Default: 2.
@@ -45,31 +44,6 @@ private[clustering] trait KMeansParams
   /** @group getParam */
   def getK: Int = $(k)
 
-  /**
-   * Param the number of runs of the algorithm to execute in parallel. We initialize the algorithm
-   * this many times with random starting conditions (configured by the initialization mode), then
-   * return the best clustering found over any run. Must be >= 1. Default: 1.
-   * @group param
-   */
-  final val runs = new IntParam(this, "runs",
-    "number of runs of the algorithm to execute in parallel", (value: Int) => value >= 1)
-
-  /** @group getParam */
-  def getRuns: Int = $(runs)
-
-  /**
-   * Param the distance threshold within which we've consider centers to have converged.
-   * If all centers move less than this Euclidean distance, we stop iterating one run.
-   * Must be >= 0.0. Default: 1e-4
-   * @group param
-   */
-  final val epsilon = new DoubleParam(this, "epsilon",
-    "distance threshold within which we've consider centers to have converge",
-    (value: Double) => value >= 0.0)
-
-  /** @group getParam */
-  def getEpsilon: Double = $(epsilon)
-
   /**
    * Param for the initialization algorithm. This can be either "random" to choose random points as
    * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
@@ -136,9 +110,9 @@ class KMeansModel private[ml] (
 
 /**
  * :: Experimental ::
- * K-means clustering with support for multiple parallel runs and a k-means++ like initialization
- * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
- * they are executed together with joint passes over the data for efficiency.
+ * K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
+ *
+ * @see [[http://dx.doi.org/10.14778/2180912.2180915 Bahmani et al., Scalable k-means++.]]
  */
 @Experimental
 class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams {
@@ -146,10 +120,9 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
   setDefault(
     k -> 2,
     maxIter -> 20,
-    runs -> 1,
     initMode -> MLlibKMeans.K_MEANS_PARALLEL,
     initSteps -> 5,
-    epsilon -> 1e-4)
+    tol -> 1e-4)
 
   override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
 
@@ -174,10 +147,7 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
   /** @group setParam */
-  def setRuns(value: Int): this.type = set(runs, value)
-
-  /** @group setParam */
-  def setEpsilon(value: Double): this.type = set(epsilon, value)
+  def setTol(value: Double): this.type = set(tol, value)
 
   /** @group setParam */
   def setSeed(value: Long): this.type = set(seed, value)
@@ -191,8 +161,7 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
       .setInitializationSteps($(initSteps))
       .setMaxIterations($(maxIter))
       .setSeed($(seed))
-      .setEpsilon($(epsilon))
-      .setRuns($(runs))
+      .setEpsilon($(tol))
     val parentModel = algo.run(rdd)
     val model = new KMeansModel(uid, parentModel)
     copyValues(model)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
index 1f15ac02f4008..688b0e31f91dc 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
@@ -52,10 +52,9 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(kmeans.getFeaturesCol === "features")
     assert(kmeans.getPredictionCol === "prediction")
     assert(kmeans.getMaxIter === 20)
-    assert(kmeans.getRuns === 1)
     assert(kmeans.getInitMode === MLlibKMeans.K_MEANS_PARALLEL)
     assert(kmeans.getInitSteps === 5)
-    assert(kmeans.getEpsilon === 1e-4)
+    assert(kmeans.getTol === 1e-4)
   }
 
   test("set parameters") {
@@ -64,21 +63,19 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFeaturesCol("test_feature")
       .setPredictionCol("test_prediction")
       .setMaxIter(33)
-      .setRuns(7)
       .setInitMode(MLlibKMeans.RANDOM)
       .setInitSteps(3)
       .setSeed(123)
-      .setEpsilon(1e-3)
+      .setTol(1e-3)
 
     assert(kmeans.getK === 9)
     assert(kmeans.getFeaturesCol === "test_feature")
     assert(kmeans.getPredictionCol === "test_prediction")
     assert(kmeans.getMaxIter === 33)
-    assert(kmeans.getRuns === 7)
     assert(kmeans.getInitMode === MLlibKMeans.RANDOM)
     assert(kmeans.getInitSteps === 3)
     assert(kmeans.getSeed === 123)
-    assert(kmeans.getEpsilon === 1e-3)
+    assert(kmeans.getTol === 1e-3)
   }
 
   test("parameters validation") {
@@ -91,9 +88,6 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
     intercept[IllegalArgumentException] {
       new KMeans().setInitSteps(0)
     }
-    intercept[IllegalArgumentException] {
-      new KMeans().setRuns(0)
-    }
   }
 
   test("fit & transform") {
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index 48338713a29ea..cb4c16e25a7a3 100644
--- a/python/pyspark/ml/clustering.py
+++ b/python/pyspark/ml/clustering.py
@@ -19,7 +19,6 @@
 from pyspark.ml.wrapper import JavaEstimator, JavaModel
 from pyspark.ml.param.shared import *
 from pyspark.mllib.common import inherit_doc
-from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['KMeans', 'KMeansModel']
 
@@ -35,7 +34,7 @@ def clusterCenters(self):
 
 
 @inherit_doc
-class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
+class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed):
     """
     K-means clustering with support for multiple parallel runs and a k-means++ like initialization
     mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
@@ -45,7 +44,7 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
     >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
     ...         (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
     >>> df = sqlContext.createDataFrame(data, ["features"])
-    >>> kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol("features")
+    >>> kmeans = KMeans(k=2, seed=1)
     >>> model = kmeans.fit(df)
     >>> centers = model.clusterCenters()
     >>> len(centers)
@@ -60,10 +59,6 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
 
     # a placeholder to make it appear in the generated doc
     k = Param(Params._dummy(), "k", "number of clusters to create")
-    epsilon = Param(Params._dummy(), "epsilon",
-                    "distance threshold within which " +
-                    "we've consider centers to have converged")
-    runs = Param(Params._dummy(), "runs", "number of runs of the algorithm to execute in parallel")
     initMode = Param(Params._dummy(), "initMode",
                      "the initialization algorithm. This can be either \"random\" to " +
                      "choose random points as initial cluster centers, or \"k-means||\" " +
@@ -71,21 +66,21 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
     initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode")
 
     @keyword_only
-    def __init__(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initStep=5):
+    def __init__(self, featuresCol="features", predictionCol="prediction", k=2,
+                 initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
+        """
+        __init__(self, featuresCol="features", predictionCol="prediction", k=2, \
+                 initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
+        """
         super(KMeans, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.KMeans", self.uid)
         self.k = Param(self, "k", "number of clusters to create")
-        self.epsilon = Param(self, "epsilon",
-                             "distance threshold within which " +
-                             "we've consider centers to have converged")
-        self.runs = Param(self, "runs", "number of runs of the algorithm to execute in parallel")
-        self.seed = Param(self, "seed", "random seed")
         self.initMode = Param(self, "initMode",
                               "the initialization algorithm. This can be either \"random\" to " +
                               "choose random points as initial cluster centers, or \"k-means||\" " +
                               "to use a parallel variant of k-means++")
         self.initSteps = Param(self, "initSteps", "steps for k-means initialization mode")
-        self._setDefault(k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5)
+        self._setDefault(k=2, initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -93,9 +88,11 @@ def _create_model(self, java_model):
         return KMeansModel(java_model)
 
     @keyword_only
-    def setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+    def setParams(self, featuresCol="features", predictionCol="prediction", k=2,
+                  initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
         """
-        setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+        setParams(self, featuresCol="features", predictionCol="prediction", k=2, \
+                  initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
 
         Sets params for KMeans.
         """
@@ -119,40 +116,6 @@ def getK(self):
         """
         return self.getOrDefault(self.k)
 
-    def setEpsilon(self, value):
-        """
-        Sets the value of :py:attr:`epsilon`.
-
-        >>> algo = KMeans().setEpsilon(1e-5)
-        >>> abs(algo.getEpsilon() - 1e-5) < 1e-5
-        True
-        """
-        self._paramMap[self.epsilon] = value
-        return self
-
-    def getEpsilon(self):
-        """
-        Gets the value of `epsilon`
-        """
-        return self.getOrDefault(self.epsilon)
-
-    def setRuns(self, value):
-        """
-        Sets the value of :py:attr:`runs`.
-
-        >>> algo = KMeans().setRuns(10)
-        >>> algo.getRuns()
-        10
-        """
-        self._paramMap[self.runs] = value
-        return self
-
-    def getRuns(self):
-        """
-        Gets the value of `runs`
-        """
-        return self.getOrDefault(self.runs)
-
     def setInitMode(self, value):
         """
         Sets the value of :py:attr:`initMode`.

From 84a27916a62980c8fcb0977c3a7fdb73c0bd5812 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 13 Aug 2015 15:08:57 +0800
Subject: [PATCH 1027/1454] [SPARK-9885] [SQL] Also pass barrierPrefixes and
 sharedPrefixes to IsolatedClientLoader when hiveMetastoreJars is set to
 maven.

https://issues.apache.org/jira/browse/SPARK-9885

cc marmbrus liancheng

Author: Yin Huai <yhuai@databricks.com>

Closes #8158 from yhuai/classloaderMaven.
---
 .../scala/org/apache/spark/sql/hive/HiveContext.scala |  6 +++++-
 .../spark/sql/hive/client/IsolatedClientLoader.scala  | 11 +++++++++--
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index f17177a771c3b..17762649fd70d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -231,7 +231,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
       // TODO: Support for loading the jars from an already downloaded location.
       logInfo(
         s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using maven.")
-      IsolatedClientLoader.forVersion(hiveMetastoreVersion, allConfig)
+      IsolatedClientLoader.forVersion(
+        version = hiveMetastoreVersion,
+        config = allConfig,
+        barrierPrefixes = hiveMetastoreBarrierPrefixes,
+        sharedPrefixes = hiveMetastoreSharedPrefixes)
     } else {
       // Convert to files and expand any directories.
       val jars =
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index a7d5a991948d9..7856037508412 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -42,11 +42,18 @@ private[hive] object IsolatedClientLoader {
   def forVersion(
       version: String,
       config: Map[String, String] = Map.empty,
-      ivyPath: Option[String] = None): IsolatedClientLoader = synchronized {
+      ivyPath: Option[String] = None,
+      sharedPrefixes: Seq[String] = Seq.empty,
+      barrierPrefixes: Seq[String] = Seq.empty): IsolatedClientLoader = synchronized {
     val resolvedVersion = hiveVersion(version)
     val files = resolvedVersions.getOrElseUpdate(resolvedVersion,
       downloadVersion(resolvedVersion, ivyPath))
-    new IsolatedClientLoader(hiveVersion(version), files, config)
+    new IsolatedClientLoader(
+      version = hiveVersion(version),
+      execJars = files,
+      config = config,
+      sharedPrefixes = sharedPrefixes,
+      barrierPrefixes = barrierPrefixes)
   }
 
   def hiveVersion(version: String): HiveVersion = version match {

From 69930310115501f0de094fe6f5c6c60dade342bd Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 13 Aug 2015 16:16:50 +0800
Subject: [PATCH 1028/1454] [SPARK-9757] [SQL] Fixes persistence of Parquet
 relation with decimal column

PR #7967 enables us to save data source relations to metastore in Hive compatible format when possible. But it fails to persist Parquet relations with decimal column(s) to Hive metastore of versions lower than 1.2.0. This is because `ParquetHiveSerDe` in Hive versions prior to 1.2.0 doesn't support decimal. This PR checks for this case and falls back to Spark SQL specific metastore table format.

Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Lian <lian@databricks.com>

Closes #8130 from liancheng/spark-9757/old-hive-parquet-decimal.
---
 .../apache/spark/sql/types/ArrayType.scala    |  6 +-
 .../org/apache/spark/sql/types/DataType.scala |  5 ++
 .../org/apache/spark/sql/types/MapType.scala  |  6 +-
 .../apache/spark/sql/types/StructType.scala   |  8 ++-
 .../spark/sql/types/DataTypeSuite.scala       | 24 +++++++
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 39 ++++++++---
 .../sql/hive/client/ClientInterface.scala     |  3 +
 .../spark/sql/hive/client/ClientWrapper.scala |  2 +-
 .../spark/sql/hive/client/package.scala       |  2 +-
 .../sql/hive/HiveMetastoreCatalogSuite.scala  | 17 +++--
 .../spark/sql/hive/HiveSparkSubmitSuite.scala | 68 +++++++++++++++++--
 11 files changed, 150 insertions(+), 30 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index 5094058164b2f..5770f59b53077 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -75,6 +75,10 @@ case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataT
 
   override def simpleString: String = s"array<${elementType.simpleString}>"
 
-  private[spark] override def asNullable: ArrayType =
+  override private[spark] def asNullable: ArrayType =
     ArrayType(elementType.asNullable, containsNull = true)
+
+  override private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = {
+    f(this) || elementType.existsRecursively(f)
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index f4428c2e8b202..7bcd623b3f33e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -77,6 +77,11 @@ abstract class DataType extends AbstractDataType {
    */
   private[spark] def asNullable: DataType
 
+  /**
+   * Returns true if any `DataType` of this DataType tree satisfies the given function `f`.
+   */
+  private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = f(this)
+
   override private[sql] def defaultConcreteType: DataType = this
 
   override private[sql] def acceptsType(other: DataType): Boolean = sameType(other)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
index ac34b642827ca..00461e529ca0a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
@@ -62,8 +62,12 @@ case class MapType(
 
   override def simpleString: String = s"map<${keyType.simpleString},${valueType.simpleString}>"
 
-  private[spark] override def asNullable: MapType =
+  override private[spark] def asNullable: MapType =
     MapType(keyType.asNullable, valueType.asNullable, valueContainsNull = true)
+
+  override private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = {
+    f(this) || keyType.existsRecursively(f) || valueType.existsRecursively(f)
+  }
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 9cbc207538d4f..d8968ef806390 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, AttributeReference, Attribute, InterpretedOrdering$}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, InterpretedOrdering}
 
 
 /**
@@ -292,7 +292,7 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
   private[sql] def merge(that: StructType): StructType =
     StructType.merge(this, that).asInstanceOf[StructType]
 
-  private[spark] override def asNullable: StructType = {
+  override private[spark] def asNullable: StructType = {
     val newFields = fields.map {
       case StructField(name, dataType, nullable, metadata) =>
         StructField(name, dataType.asNullable, nullable = true, metadata)
@@ -301,6 +301,10 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
     StructType(newFields)
   }
 
+  override private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = {
+    f(this) || fields.exists(field => field.dataType.existsRecursively(f))
+  }
+
   private[sql] val interpretedOrdering = InterpretedOrdering.forSchema(this.fields.map(_.dataType))
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
index 88b221cd81d74..706ecd29d1355 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
@@ -170,6 +170,30 @@ class DataTypeSuite extends SparkFunSuite {
     }
   }
 
+  test("existsRecursively") {
+    val struct = StructType(
+      StructField("a", LongType) ::
+      StructField("b", FloatType) :: Nil)
+    assert(struct.existsRecursively(_.isInstanceOf[LongType]))
+    assert(struct.existsRecursively(_.isInstanceOf[StructType]))
+    assert(!struct.existsRecursively(_.isInstanceOf[IntegerType]))
+
+    val mapType = MapType(struct, StringType)
+    assert(mapType.existsRecursively(_.isInstanceOf[LongType]))
+    assert(mapType.existsRecursively(_.isInstanceOf[StructType]))
+    assert(mapType.existsRecursively(_.isInstanceOf[StringType]))
+    assert(mapType.existsRecursively(_.isInstanceOf[MapType]))
+    assert(!mapType.existsRecursively(_.isInstanceOf[IntegerType]))
+
+    val arrayType = ArrayType(mapType)
+    assert(arrayType.existsRecursively(_.isInstanceOf[LongType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[StructType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[StringType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[MapType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[ArrayType]))
+    assert(!arrayType.existsRecursively(_.isInstanceOf[IntegerType]))
+  }
+
   def checkDataTypeJsonRepr(dataType: DataType): Unit = {
     test(s"JSON - $dataType") {
       assert(DataType.fromJson(dataType.json) === dataType)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 5e5497837a393..6770462bb0ad3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -33,15 +33,14 @@ import org.apache.hadoop.hive.ql.plan.TableDesc
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.analysis.{Catalog, MultiInstanceRelation, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.{InternalRow, SqlParser, TableIdentifier}
-import org.apache.spark.sql.execution.{FileRelation, datasources}
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
+import org.apache.spark.sql.execution.{FileRelation, datasources}
 import org.apache.spark.sql.hive.client._
-import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
@@ -86,9 +85,9 @@ private[hive] object HiveSerDe {
           serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")))
 
     val key = source.toLowerCase match {
-      case _ if source.startsWith("org.apache.spark.sql.parquet") => "parquet"
-      case _ if source.startsWith("org.apache.spark.sql.orc") => "orc"
-      case _ => source.toLowerCase
+      case s if s.startsWith("org.apache.spark.sql.parquet") => "parquet"
+      case s if s.startsWith("org.apache.spark.sql.orc") => "orc"
+      case s => s
     }
 
     serdeMap.get(key)
@@ -309,11 +308,31 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     val hiveTable = (maybeSerDe, dataSource.relation) match {
       case (Some(serde), relation: HadoopFsRelation)
           if relation.paths.length == 1 && relation.partitionColumns.isEmpty =>
-        logInfo {
-          "Persisting data source relation with a single input path into Hive metastore in Hive " +
-            s"compatible format.  Input path: ${relation.paths.head}"
+        // Hive ParquetSerDe doesn't support decimal type until 1.2.0.
+        val isParquetSerDe = serde.inputFormat.exists(_.toLowerCase.contains("parquet"))
+        val hasDecimalFields = relation.schema.existsRecursively(_.isInstanceOf[DecimalType])
+
+        val hiveParquetSupportsDecimal = client.version match {
+          case org.apache.spark.sql.hive.client.hive.v1_2 => true
+          case _ => false
+        }
+
+        if (isParquetSerDe && !hiveParquetSupportsDecimal && hasDecimalFields) {
+          // If Hive version is below 1.2.0, we cannot save Hive compatible schema to
+          // metastore when the file format is Parquet and the schema has DecimalType.
+          logWarning {
+            "Persisting Parquet relation with decimal field(s) into Hive metastore in Spark SQL " +
+              "specific format, which is NOT compatible with Hive. Because ParquetHiveSerDe in " +
+              s"Hive ${client.version.fullVersion} doesn't support decimal type. See HIVE-6384."
+          }
+          newSparkSQLSpecificMetastoreTable()
+        } else {
+          logInfo {
+            "Persisting data source relation with a single input path into Hive metastore in " +
+              s"Hive compatible format. Input path: ${relation.paths.head}"
+          }
+          newHiveCompatibleMetastoreTable(relation, serde)
         }
-        newHiveCompatibleMetastoreTable(relation, serde)
 
       case (Some(serde), relation: HadoopFsRelation) if relation.partitionColumns.nonEmpty =>
         logWarning {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index a82e152dcda2c..3811c152a7ae6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -88,6 +88,9 @@ private[hive] case class HiveTable(
  */
 private[hive] trait ClientInterface {
 
+  /** Returns the Hive Version of this client. */
+  def version: HiveVersion
+
   /** Returns the configuration for the given key in the current session. */
   def getConf(key: String, defaultValue: String): String
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 3d05b583cf9e0..f49c97de8ff4e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -58,7 +58,7 @@ import org.apache.spark.util.{CircularBuffer, Utils}
  *                        this ClientWrapper.
  */
 private[hive] class ClientWrapper(
-    version: HiveVersion,
+    override val version: HiveVersion,
     config: Map[String, String],
     initClassLoader: ClassLoader)
   extends ClientInterface
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
index 0503691a44249..b1b8439efa011 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
@@ -25,7 +25,7 @@ package object client {
       val exclusions: Seq[String] = Nil)
 
   // scalastyle:off
-  private[client] object hive {
+  private[hive] object hive {
     case object v12 extends HiveVersion("0.12.0")
     case object v13 extends HiveVersion("0.13.1")
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 332c3ec0c28b8..59e65ff97b8e0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -19,13 +19,13 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
-import org.apache.spark.sql.hive.client.{ExternalTable, HiveColumn, ManagedTable}
+import org.apache.spark.sql.hive.client.{ExternalTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.sources.DataSourceTest
 import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types.{DecimalType, StringType, StructType}
 import org.apache.spark.sql.{Row, SaveMode}
 import org.apache.spark.{Logging, SparkFunSuite}
 
@@ -55,7 +55,10 @@ class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
 class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
   override val sqlContext = TestHive
 
-  private val testDF = (1 to 2).map(i => (i, s"val_$i")).toDF("d1", "d2").coalesce(1)
+  private val testDF = range(1, 3).select(
+    ('id + 0.1) cast DecimalType(10, 3) as 'd1,
+    'id cast StringType as 'd2
+  ).coalesce(1)
 
   Seq(
     "parquet" -> (
@@ -88,10 +91,10 @@ class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTes
 
         val columns = hiveTable.schema
         assert(columns.map(_.name) === Seq("d1", "d2"))
-        assert(columns.map(_.hiveType) === Seq("int", "string"))
+        assert(columns.map(_.hiveType) === Seq("decimal(10,3)", "string"))
 
         checkAnswer(table("t"), testDF)
-        assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+        assert(runSqlHive("SELECT * FROM t") === Seq("1.1\t1", "2.1\t2"))
       }
     }
 
@@ -117,10 +120,10 @@ class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTes
 
           val columns = hiveTable.schema
           assert(columns.map(_.name) === Seq("d1", "d2"))
-          assert(columns.map(_.hiveType) === Seq("int", "string"))
+          assert(columns.map(_.hiveType) === Seq("decimal(10,3)", "string"))
 
           checkAnswer(table("t"), testDF)
-          assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+          assert(runSqlHive("SELECT * FROM t") === Seq("1.1\t1", "2.1\t2"))
         }
       }
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 1e1972d1ac353..0c29646114465 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -20,16 +20,18 @@ package org.apache.spark.sql.hive
 import java.io.File
 
 import scala.collection.mutable.ArrayBuffer
-import scala.sys.process.{ProcessLogger, Process}
+import scala.sys.process.{Process, ProcessLogger}
 
+import org.scalatest.Matchers
+import org.scalatest.concurrent.Timeouts
 import org.scalatest.exceptions.TestFailedDueToTimeoutException
+import org.scalatest.time.SpanSugar._
 
 import org.apache.spark._
+import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
+import org.apache.spark.sql.types.DecimalType
 import org.apache.spark.util.{ResetSystemProperties, Utils}
-import org.scalatest.Matchers
-import org.scalatest.concurrent.Timeouts
-import org.scalatest.time.SpanSugar._
 
 /**
  * This suite tests spark-submit with applications using HiveContext.
@@ -50,8 +52,8 @@ class HiveSparkSubmitSuite
     val unusedJar = TestUtils.createJarWithClasses(Seq.empty)
     val jar1 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassA"))
     val jar2 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassB"))
-    val jar3 = TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath()
-    val jar4 = TestHive.getHiveFile("hive-hcatalog-core-0.13.1.jar").getCanonicalPath()
+    val jar3 = TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath
+    val jar4 = TestHive.getHiveFile("hive-hcatalog-core-0.13.1.jar").getCanonicalPath
     val jarsString = Seq(jar1, jar2, jar3, jar4).map(j => j.toString).mkString(",")
     val args = Seq(
       "--class", SparkSubmitClassLoaderTest.getClass.getName.stripSuffix("$"),
@@ -91,6 +93,16 @@ class HiveSparkSubmitSuite
     runSparkSubmit(args)
   }
 
+  test("SPARK-9757 Persist Parquet relation with decimal column") {
+    val unusedJar = TestUtils.createJarWithClasses(Seq.empty)
+    val args = Seq(
+      "--class", SPARK_9757.getClass.getName.stripSuffix("$"),
+      "--name", "SparkSQLConfTest",
+      "--master", "local-cluster[2,1,1024]",
+      unusedJar.toString)
+    runSparkSubmit(args)
+  }
+
   // NOTE: This is an expensive operation in terms of time (10 seconds+). Use sparingly.
   // This is copied from org.apache.spark.deploy.SparkSubmitSuite
   private def runSparkSubmit(args: Seq[String]): Unit = {
@@ -213,7 +225,7 @@ object SparkSQLConfTest extends Logging {
     // before spark.sql.hive.metastore.jars get set, we will see the following exception:
     // Exception in thread "main" java.lang.IllegalArgumentException: Builtin jars can only
     // be used when hive execution version == hive metastore version.
-    // Execution: 0.13.1 != Metastore: 0.12. Specify a vaild path to the correct hive jars
+    // Execution: 0.13.1 != Metastore: 0.12. Specify a valid path to the correct hive jars
     // using $HIVE_METASTORE_JARS or change spark.sql.hive.metastore.version to 0.13.1.
     val conf = new SparkConf() {
       override def getAll: Array[(String, String)] = {
@@ -239,3 +251,45 @@ object SparkSQLConfTest extends Logging {
     sc.stop()
   }
 }
+
+object SPARK_9757 extends QueryTest with Logging {
+  def main(args: Array[String]): Unit = {
+    Utils.configTestLog4j("INFO")
+
+    val sparkContext = new SparkContext(
+      new SparkConf()
+        .set("spark.sql.hive.metastore.version", "0.13.1")
+        .set("spark.sql.hive.metastore.jars", "maven"))
+
+    val hiveContext = new TestHiveContext(sparkContext)
+    import hiveContext.implicits._
+    import org.apache.spark.sql.functions._
+
+    val dir = Utils.createTempDir()
+    dir.delete()
+
+    try {
+      {
+        val df =
+          hiveContext
+            .range(10)
+            .select(('id + 0.1) cast DecimalType(10, 3) as 'dec)
+        df.write.option("path", dir.getCanonicalPath).mode("overwrite").saveAsTable("t")
+        checkAnswer(hiveContext.table("t"), df)
+      }
+
+      {
+        val df =
+          hiveContext
+            .range(10)
+            .select(callUDF("struct", ('id + 0.2) cast DecimalType(10, 3)) as 'dec_struct)
+        df.write.option("path", dir.getCanonicalPath).mode("overwrite").saveAsTable("t")
+        checkAnswer(hiveContext.table("t"), df)
+      }
+    } finally {
+      dir.delete()
+      hiveContext.sql("DROP TABLE t")
+      sparkContext.stop()
+    }
+  }
+}

From 2932e25da4532de9e86b01d08bce0cb680874e70 Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Thu, 13 Aug 2015 09:17:19 -0700
Subject: [PATCH 1029/1454] [SPARK-9073] [ML] spark.ml Models copy() should
 call setParent when there is a parent

Copied ML models must have the same parent of original ones

Author: lewuathe <lewuathe@me.com>
Author: Lewuathe <lewuathe@me.com>

Closes #7447 from Lewuathe/SPARK-9073.
---
 .../examples/ml/JavaDeveloperApiExample.java  |  3 +-
 .../examples/ml/DeveloperApiExample.scala     |  2 +-
 .../scala/org/apache/spark/ml/Pipeline.scala  |  2 +-
 .../DecisionTreeClassifier.scala              |  1 +
 .../ml/classification/GBTClassifier.scala     |  2 +-
 .../classification/LogisticRegression.scala   |  2 +-
 .../spark/ml/classification/OneVsRest.scala   |  2 +-
 .../RandomForestClassifier.scala              |  1 +
 .../apache/spark/ml/feature/Bucketizer.scala  |  4 ++-
 .../org/apache/spark/ml/feature/IDF.scala     |  2 +-
 .../spark/ml/feature/MinMaxScaler.scala       |  2 +-
 .../org/apache/spark/ml/feature/PCA.scala     |  2 +-
 .../spark/ml/feature/StandardScaler.scala     |  2 +-
 .../spark/ml/feature/StringIndexer.scala      |  2 +-
 .../spark/ml/feature/VectorIndexer.scala      |  2 +-
 .../apache/spark/ml/feature/Word2Vec.scala    |  2 +-
 .../apache/spark/ml/recommendation/ALS.scala  |  2 +-
 .../ml/regression/DecisionTreeRegressor.scala |  2 +-
 .../spark/ml/regression/GBTRegressor.scala    |  2 +-
 .../ml/regression/LinearRegression.scala      |  2 +-
 .../ml/regression/RandomForestRegressor.scala |  2 +-
 .../spark/ml/tuning/CrossValidator.scala      |  2 +-
 .../org/apache/spark/ml/PipelineSuite.scala   |  3 ++
 .../DecisionTreeClassifierSuite.scala         |  4 +++
 .../classification/GBTClassifierSuite.scala   |  4 +++
 .../LogisticRegressionSuite.scala             |  4 +++
 .../ml/classification/OneVsRestSuite.scala    |  6 +++-
 .../RandomForestClassifierSuite.scala         |  4 +++
 .../spark/ml/feature/BucketizerSuite.scala    |  1 +
 .../spark/ml/feature/MinMaxScalerSuite.scala  |  4 +++
 .../apache/spark/ml/feature/PCASuite.scala    |  4 +++
 .../spark/ml/feature/StringIndexerSuite.scala |  5 ++++
 .../spark/ml/feature/VectorIndexerSuite.scala |  5 ++++
 .../spark/ml/feature/Word2VecSuite.scala      |  4 +++
 .../spark/ml/recommendation/ALSSuite.scala    |  4 +++
 .../DecisionTreeRegressorSuite.scala          | 11 +++++++
 .../ml/regression/GBTRegressorSuite.scala     |  5 ++++
 .../ml/regression/LinearRegressionSuite.scala |  5 ++++
 .../RandomForestRegressorSuite.scala          |  7 ++++-
 .../spark/ml/tuning/CrossValidatorSuite.scala |  5 ++++
 .../apache/spark/ml/util/MLTestingUtils.scala | 30 +++++++++++++++++++
 41 files changed, 138 insertions(+), 22 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
index 9df26ffca5775..3f1fe900b0008 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
@@ -230,6 +230,7 @@ public Vector predictRaw(Vector features) {
    */
   @Override
   public MyJavaLogisticRegressionModel copy(ParamMap extra) {
-    return copyValues(new MyJavaLogisticRegressionModel(uid(), weights_), extra);
+    return copyValues(new MyJavaLogisticRegressionModel(uid(), weights_), extra)
+      .setParent(parent());
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
index 78f31b4ffe56a..340c3559b15ef 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
@@ -179,7 +179,7 @@ private class MyLogisticRegressionModel(
    * This is used for the default implementation of [[transform()]].
    */
   override def copy(extra: ParamMap): MyLogisticRegressionModel = {
-    copyValues(new MyLogisticRegressionModel(uid, weights), extra)
+    copyValues(new MyLogisticRegressionModel(uid, weights), extra).setParent(parent)
   }
 }
 // scalastyle:on println
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
index aef2c019d2871..a3e59401c5cfb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -198,6 +198,6 @@ class PipelineModel private[ml] (
   }
 
   override def copy(extra: ParamMap): PipelineModel = {
-    new PipelineModel(uid, stages.map(_.copy(extra)))
+    new PipelineModel(uid, stages.map(_.copy(extra))).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 29598f3f05c2d..6f70b96b17ec6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -141,6 +141,7 @@ final class DecisionTreeClassificationModel private[ml] (
 
   override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
     copyValues(new DecisionTreeClassificationModel(uid, rootNode, numClasses), extra)
+      .setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index c3891a9599262..3073a2a61ce83 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -196,7 +196,7 @@ final class GBTClassificationModel(
   }
 
   override def copy(extra: ParamMap): GBTClassificationModel = {
-    copyValues(new GBTClassificationModel(uid, _trees, _treeWeights), extra)
+    copyValues(new GBTClassificationModel(uid, _trees, _treeWeights), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 5bcd7117b668c..21fbe38ca8233 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -468,7 +468,7 @@ class LogisticRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LogisticRegressionModel = {
-    copyValues(new LogisticRegressionModel(uid, weights, intercept), extra)
+    copyValues(new LogisticRegressionModel(uid, weights, intercept), extra).setParent(parent)
   }
 
   override protected def raw2prediction(rawPrediction: Vector): Double = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index 1741f19dc911c..1132d8046df67 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -138,7 +138,7 @@ final class OneVsRestModel private[ml] (
   override def copy(extra: ParamMap): OneVsRestModel = {
     val copied = new OneVsRestModel(
       uid, labelMetadata, models.map(_.copy(extra).asInstanceOf[ClassificationModel[_, _]]))
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 156050aaf7a45..11a6d72468333 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -189,6 +189,7 @@ final class RandomForestClassificationModel private[ml] (
 
   override def copy(extra: ParamMap): RandomForestClassificationModel = {
     copyValues(new RandomForestClassificationModel(uid, _trees, numFeatures, numClasses), extra)
+      .setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
index 67e4785bc3553..cfca494dcf468 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
@@ -90,7 +90,9 @@ final class Bucketizer(override val uid: String)
     SchemaUtils.appendColumn(schema, prepOutputField(schema))
   }
 
-  override def copy(extra: ParamMap): Bucketizer = defaultCopy(extra)
+  override def copy(extra: ParamMap): Bucketizer = {
+    defaultCopy[Bucketizer](extra).setParent(parent)
+  }
 }
 
 private[feature] object Bucketizer {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
index ecde80810580c..938447447a0a2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
@@ -114,6 +114,6 @@ class IDFModel private[ml] (
 
   override def copy(extra: ParamMap): IDFModel = {
     val copied = new IDFModel(uid, idfModel)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
index 9a473dd23772d..1b494ec8b1727 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -173,6 +173,6 @@ class MinMaxScalerModel private[ml] (
 
   override def copy(extra: ParamMap): MinMaxScalerModel = {
     val copied = new MinMaxScalerModel(uid, originalMin, originalMax)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
index 2d3bb680cf309..539084704b653 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
@@ -125,6 +125,6 @@ class PCAModel private[ml] (
 
   override def copy(extra: ParamMap): PCAModel = {
     val copied = new PCAModel(uid, pcaModel)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
index 72b545e5db3e4..f6d0b0c0e9e75 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -136,6 +136,6 @@ class StandardScalerModel private[ml] (
 
   override def copy(extra: ParamMap): StandardScalerModel = {
     val copied = new StandardScalerModel(uid, scaler)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index e4485eb038409..9e4b0f0add612 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -168,7 +168,7 @@ class StringIndexerModel private[ml] (
 
   override def copy(extra: ParamMap): StringIndexerModel = {
     val copied = new StringIndexerModel(uid, labels)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index c73bdccdef5fa..6875aefe065bb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -405,6 +405,6 @@ class VectorIndexerModel private[ml] (
 
   override def copy(extra: ParamMap): VectorIndexerModel = {
     val copied = new VectorIndexerModel(uid, numFeatures, categoryMaps)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 29acc3eb5865f..5af775a4159ad 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -221,6 +221,6 @@ class Word2VecModel private[ml] (
 
   override def copy(extra: ParamMap): Word2VecModel = {
     val copied = new Word2VecModel(uid, wordVectors)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index 2e44cd4cc6a22..7db8ad8d27918 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -219,7 +219,7 @@ class ALSModel private[ml] (
 
   override def copy(extra: ParamMap): ALSModel = {
     val copied = new ALSModel(uid, rank, userFactors, itemFactors)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index dc94a14014542..a2bcd67401d08 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -114,7 +114,7 @@ final class DecisionTreeRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): DecisionTreeRegressionModel = {
-    copyValues(new DecisionTreeRegressionModel(uid, rootNode), extra)
+    copyValues(new DecisionTreeRegressionModel(uid, rootNode), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 5633bc320273a..b66e61f37dd5e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -185,7 +185,7 @@ final class GBTRegressionModel(
   }
 
   override def copy(extra: ParamMap): GBTRegressionModel = {
-    copyValues(new GBTRegressionModel(uid, _trees, _treeWeights), extra)
+    copyValues(new GBTRegressionModel(uid, _trees, _treeWeights), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 92d819bad8654..884003eb38524 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -312,7 +312,7 @@ class LinearRegressionModel private[ml] (
   override def copy(extra: ParamMap): LinearRegressionModel = {
     val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept))
     if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
-    newModel
+    newModel.setParent(parent)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index db75c0d26392f..2f36da371f577 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -151,7 +151,7 @@ final class RandomForestRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): RandomForestRegressionModel = {
-    copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra)
+    copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index f979319cc4b58..4792eb0f0a288 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -160,6 +160,6 @@ class CrossValidatorModel private[ml] (
       uid,
       bestModel.copy(extra).asInstanceOf[Model[_]],
       avgMetrics.clone())
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
index 63d2fa31c7499..1f2c9b75b617b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
@@ -26,6 +26,7 @@ import org.scalatest.mock.MockitoSugar.mock
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.feature.HashingTF
 import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.sql.DataFrame
 
 class PipelineSuite extends SparkFunSuite {
@@ -65,6 +66,8 @@ class PipelineSuite extends SparkFunSuite {
       .setStages(Array(estimator0, transformer1, estimator2, transformer3))
     val pipelineModel = pipeline.fit(dataset0)
 
+    MLTestingUtils.checkCopy(pipelineModel)
+
     assert(pipelineModel.stages.length === 4)
     assert(pipelineModel.stages(0).eq(model0))
     assert(pipelineModel.stages(1).eq(transformer1))
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
index c7bbf1ce07a23..4b7c5d3f23d2c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
@@ -21,6 +21,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.LeafNode
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, DecisionTreeSuite => OldDecisionTreeSuite}
@@ -244,6 +245,9 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     val newData: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
     val newTree = dt.fit(newData)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(newTree)
+
     val predictions = newTree.transform(newData)
       .select(newTree.getPredictionCol, newTree.getRawPredictionCol, newTree.getProbabilityCol)
       .collect()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index d4b5896c12c06..e3909bccaa5ca 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -22,6 +22,7 @@ import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree.LeafNode
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
@@ -92,6 +93,9 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setCheckpointInterval(2)
     val model = gbt.fit(df)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     sc.checkpointDir = None
     Utils.deleteRecursively(tempDir)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index e354e161c6dee..cce39f382f738 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -135,6 +136,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     lr.setFitIntercept(false)
     val model = lr.fit(dataset)
     assert(model.intercept === 0.0)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
   }
 
   test("logistic regression with setters") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index bd8e819f6926c..977f0e0b70c1a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -21,7 +21,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.NominalAttribute
 import org.apache.spark.ml.feature.StringIndexer
 import org.apache.spark.ml.param.{ParamMap, ParamsSuite}
-import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.ml.util.{MLTestingUtils, MetadataUtils}
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
@@ -70,6 +70,10 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(ova.getLabelCol === "label")
     assert(ova.getPredictionCol === "prediction")
     val ovaModel = ova.fit(dataset)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(ovaModel)
+
     assert(ovaModel.models.size === numClasses)
     val transformedDataset = ovaModel.transform(dataset)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index 6ca4b5aa5fde8..b4403ec30049a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -21,6 +21,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.LeafNode
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
@@ -135,6 +136,9 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     val df: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
     val model = rf.fit(df)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     val predictions = model.transform(df)
       .select(rf.getPredictionCol, rf.getRawPredictionCol, rf.getProbabilityCol)
       .collect()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
index ec85e0d151e07..0eba34fda6228 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
@@ -21,6 +21,7 @@ import scala.util.Random
 
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
index c452054bec92f..c04dda41eea34 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{Row, SQLContext}
@@ -51,6 +52,9 @@ class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext {
       .foreach { case Row(vector1: Vector, vector2: Vector) =>
         assert(vector1.equals(vector2), "Transformed vector is different with expected.")
     }
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
   }
 
   test("MinMaxScaler arguments max must be larger than min") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
index d0ae36b28c7a9..30c500f87a769 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Vector, Vectors, DenseMatrix, Matrices}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -56,6 +57,9 @@ class PCASuite extends SparkFunSuite with MLlibTestSparkContext {
       .setK(3)
       .fit(df)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(pca)
+
     pca.transform(df).select("pca_features", "expected").collect().foreach {
       case Row(x: Vector, y: Vector) =>
         assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.")
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index b111036087e6a..2d24914cb91f6 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -21,6 +21,7 @@ import org.apache.spark.SparkException
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
@@ -38,6 +39,10 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setInputCol("label")
       .setOutputCol("labelIndex")
       .fit(df)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(indexer)
+
     val transformed = indexer.transform(df)
     val attr = Attribute.fromStructField(transformed.schema("labelIndex"))
       .asInstanceOf[NominalAttribute]
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
index 03120c828ca96..8cb0a2cf14d37 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
@@ -22,6 +22,7 @@ import scala.beans.{BeanInfo, BeanProperty}
 import org.apache.spark.{Logging, SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
@@ -109,6 +110,10 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext with L
   test("Throws error when given RDDs with different size vectors") {
     val vectorIndexer = getIndexer
     val model = vectorIndexer.fit(densePoints1) // vectors of length 3
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(densePoints1) // should work
     model.transform(sparsePoints1) // should work
     intercept[SparkException] {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
index adcda0e623b25..a2e46f2029956 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -62,6 +63,9 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setSeed(42L)
       .fit(docDF)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(docDF).select("result", "expected").collect().foreach {
       case Row(vector1: Vector, vector2: Vector) =>
         assert(vector1 ~== vector2 absTol 1E-5, "Transformed vector is different with expected.")
diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
index 2e5cfe7027eb6..eadc80e0e62b1 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
@@ -28,6 +28,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.{Logging, SparkException, SparkFunSuite}
 import org.apache.spark.ml.recommendation.ALS._
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -374,6 +375,9 @@ class ALSSuite extends SparkFunSuite with MLlibTestSparkContext with Logging {
       }
     logInfo(s"Test RMSE is $rmse.")
     assert(rmse < targetRMSE)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
   }
 
   test("exact rank-1 matrix") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
index 33aa9d0d62343..b092bcd6a7e86 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree,
   DecisionTreeSuite => OldDecisionTreeSuite}
@@ -61,6 +62,16 @@ class DecisionTreeRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
     compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures)
   }
 
+  test("copied model must have the same parent") {
+    val categoricalFeatures = Map(0 -> 2, 1-> 2)
+    val df = TreeTests.setMetadata(categoricalDataPointsRDD, categoricalFeatures, numClasses = 0)
+    val model = new DecisionTreeRegressor()
+      .setImpurity("variance")
+      .setMaxDepth(2)
+      .setMaxBins(8).fit(df)
+    MLTestingUtils.checkCopy(model)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index dbdce0c9dea54..a68197b59193d 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT}
@@ -82,6 +83,9 @@ class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setMaxDepth(2)
       .setMaxIter(2)
     val model = gbt.fit(df)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
     val preds = model.transform(df)
     val predictions = preds.select("prediction").map(_.getDouble(0))
     // Checks based on SPARK-8736 (to ensure it is not doing classification)
@@ -104,6 +108,7 @@ class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     sc.checkpointDir = None
     Utils.deleteRecursively(tempDir)
+
   }
 
   // TODO: Reinstate test once runWithValidation is implemented  SPARK-7132
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 21ad8225bd9f7..2aaee71ecc734 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{DenseVector, Vectors}
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
 import org.apache.spark.mllib.util.TestingUtils._
@@ -72,6 +73,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lir.getFitIntercept)
     assert(lir.getStandardization)
     val model = lir.fit(dataset)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(dataset)
       .select("label", "prediction")
       .collect()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
index 992ce9562434e..7b1b3f11481de 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
@@ -91,7 +92,11 @@ class RandomForestRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
     val categoricalFeatures = Map.empty[Int, Int]
     val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0)
 
-    val importances = rf.fit(df).featureImportances
+    val model = rf.fit(df)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+    val importances = model.featureImportances
     val mostImportantFeature = importances.argmax
     assert(mostImportantFeature === 1)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
index db64511a76055..aaca08bb61a45 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.tuning
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
@@ -53,6 +54,10 @@ class CrossValidatorSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setEvaluator(eval)
       .setNumFolds(3)
     val cvModel = cv.fit(dataset)
+
+    // copied model must have the same paren.
+    MLTestingUtils.checkCopy(cvModel)
+
     val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression]
     assert(parent.getRegParam === 0.001)
     assert(parent.getMaxIter === 10)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
new file mode 100644
index 0000000000000..d290cc9b06e73
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
@@ -0,0 +1,30 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.util
+
+import org.apache.spark.ml.Model
+import org.apache.spark.ml.param.ParamMap
+
+object MLTestingUtils {
+  def checkCopy(model: Model[_]): Unit = {
+    val copied = model.copy(ParamMap.empty)
+      .asInstanceOf[Model[_]]
+    assert(copied.parent.uid == model.parent.uid)
+    assert(copied.parent == model.parent)
+  }
+}

From 7a539ef3b1792764f866fa88c84c78ad59903f21 Mon Sep 17 00:00:00 2001
From: Rosstin <asterazul@gmail.com>
Date: Thu, 13 Aug 2015 09:18:39 -0700
Subject: [PATCH 1030/1454] [SPARK-8965] [DOCS] Add ml-guide Python Example:
 Estimator, Transformer, and Param

Added ml-guide Python Example: Estimator, Transformer, and Param
/docs/_site/ml-guide.html

Author: Rosstin <asterazul@gmail.com>

Closes #8081 from Rosstin/SPARK-8965.
---
 docs/ml-guide.md | 68 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index b6ca50e98db02..a03ab4356a413 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -355,6 +355,74 @@ jsc.stop();
 {% endhighlight %}
 </div>
 
+<div data-lang="python">
+{% highlight python %}
+from pyspark import SparkContext
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.ml.classification import LogisticRegression
+from pyspark.ml.param import Param, Params
+from pyspark.sql import Row, SQLContext
+
+sc = SparkContext(appName="SimpleParamsExample")
+sqlContext = SQLContext(sc)
+
+# Prepare training data.
+# We use LabeledPoint.
+# Spark SQL can convert RDDs of LabeledPoints into DataFrames.
+training = sc.parallelize([LabeledPoint(1.0, [0.0, 1.1,  0.1]),
+                           LabeledPoint(0.0, [2.0, 1.0, -1.0]),
+                           LabeledPoint(0.0, [2.0, 1.3,  1.0]),
+                           LabeledPoint(1.0, [0.0, 1.2, -0.5])])
+
+# Create a LogisticRegression instance. This instance is an Estimator.
+lr = LogisticRegression(maxIter=10, regParam=0.01)
+# Print out the parameters, documentation, and any default values.
+print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
+
+# Learn a LogisticRegression model. This uses the parameters stored in lr.
+model1 = lr.fit(training.toDF())
+
+# Since model1 is a Model (i.e., a transformer produced by an Estimator),
+# we can view the parameters it used during fit().
+# This prints the parameter (name: value) pairs, where names are unique IDs for this
+# LogisticRegression instance.
+print "Model 1 was fit using parameters: "
+print model1.extractParamMap()
+
+# We may alternatively specify parameters using a Python dictionary as a paramMap
+paramMap = {lr.maxIter: 20}
+paramMap[lr.maxIter] = 30 # Specify 1 Param, overwriting the original maxIter.
+paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55}) # Specify multiple Params.
+
+# You can combine paramMaps, which are python dictionaries.
+paramMap2 = {lr.probabilityCol: "myProbability"} # Change output column name
+paramMapCombined = paramMap.copy()
+paramMapCombined.update(paramMap2)
+
+# Now learn a new model using the paramMapCombined parameters.
+# paramMapCombined overrides all parameters set earlier via lr.set* methods.
+model2 = lr.fit(training.toDF(), paramMapCombined)
+print "Model 2 was fit using parameters: "
+print model2.extractParamMap()
+
+# Prepare test data
+test = sc.parallelize([LabeledPoint(1.0, [-1.0, 1.5,  1.3]),
+                       LabeledPoint(0.0, [ 3.0, 2.0, -0.1]),
+                       LabeledPoint(1.0, [ 0.0, 2.2, -1.5])])
+
+# Make predictions on test data using the Transformer.transform() method.
+# LogisticRegression.transform will only use the 'features' column.
+# Note that model2.transform() outputs a "myProbability" column instead of the usual
+# 'probability' column since we renamed the lr.probabilityCol parameter previously.
+prediction = model2.transform(test.toDF())
+selected = prediction.select("features", "label", "myProbability", "prediction")
+for row in selected.collect():
+    print row
+
+sc.stop()
+{% endhighlight %}
+</div>
+
 </div>
 
 ## Example: Pipeline

From 4b70798c96b0a784b85fda461426ec60f609be12 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 13 Aug 2015 09:31:14 -0700
Subject: [PATCH 1031/1454] [MINOR] [ML] change
 MultilayerPerceptronClassifierModel to
 MultilayerPerceptronClassificationModel

To follow the naming rule of ML, change `MultilayerPerceptronClassifierModel` to `MultilayerPerceptronClassificationModel` like `DecisionTreeClassificationModel`, `GBTClassificationModel` and so on.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8164 from yanboliang/mlp-name.
---
 .../MultilayerPerceptronClassifier.scala         | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 8cd2103d7d5e6..c154561886585 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -131,7 +131,7 @@ private object LabelConverter {
  */
 @Experimental
 class MultilayerPerceptronClassifier(override val uid: String)
-  extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassifierModel]
+  extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]
   with MultilayerPerceptronParams {
 
   def this() = this(Identifiable.randomUID("mlpc"))
@@ -146,7 +146,7 @@ class MultilayerPerceptronClassifier(override val uid: String)
    * @param dataset Training dataset
    * @return Fitted model
    */
-  override protected def train(dataset: DataFrame): MultilayerPerceptronClassifierModel = {
+  override protected def train(dataset: DataFrame): MultilayerPerceptronClassificationModel = {
     val myLayers = $(layers)
     val labels = myLayers.last
     val lpData = extractLabeledPoints(dataset)
@@ -156,13 +156,13 @@ class MultilayerPerceptronClassifier(override val uid: String)
     FeedForwardTrainer.LBFGSOptimizer.setConvergenceTol($(tol)).setNumIterations($(maxIter))
     FeedForwardTrainer.setStackSize($(blockSize))
     val mlpModel = FeedForwardTrainer.train(data)
-    new MultilayerPerceptronClassifierModel(uid, myLayers, mlpModel.weights())
+    new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights())
   }
 }
 
 /**
  * :: Experimental ::
- * Classifier model based on the Multilayer Perceptron.
+ * Classification model based on the Multilayer Perceptron.
  * Each layer has sigmoid activation function, output layer has softmax.
  * @param uid uid
  * @param layers array of layer sizes including input and output layers
@@ -170,11 +170,11 @@ class MultilayerPerceptronClassifier(override val uid: String)
  * @return prediction model
  */
 @Experimental
-class MultilayerPerceptronClassifierModel private[ml] (
+class MultilayerPerceptronClassificationModel private[ml] (
     override val uid: String,
     layers: Array[Int],
     weights: Vector)
-  extends PredictionModel[Vector, MultilayerPerceptronClassifierModel]
+  extends PredictionModel[Vector, MultilayerPerceptronClassificationModel]
   with Serializable {
 
   private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).getInstance(weights)
@@ -187,7 +187,7 @@ class MultilayerPerceptronClassifierModel private[ml] (
     LabelConverter.decodeLabel(mlpModel.predict(features))
   }
 
-  override def copy(extra: ParamMap): MultilayerPerceptronClassifierModel = {
-    copyValues(new MultilayerPerceptronClassifierModel(uid, layers, weights), extra)
+  override def copy(extra: ParamMap): MultilayerPerceptronClassificationModel = {
+    copyValues(new MultilayerPerceptronClassificationModel(uid, layers, weights), extra)
   }
 }

From 65fec798ce52ca6b8b0fe14b78a16712778ad04c Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 13 Aug 2015 10:16:40 -0700
Subject: [PATCH 1032/1454] [MINOR] [DOC] fix mllib pydoc warnings

Switch to correct Sphinx syntax. MechCoder

Author: Xiangrui Meng <meng@databricks.com>

Closes #8169 from mengxr/mllib-pydoc-fix.
---
 python/pyspark/mllib/regression.py | 14 ++++++++++----
 python/pyspark/mllib/util.py       |  1 +
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 5b7afc15ddfba..41946e3674fbe 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -207,8 +207,10 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         Train a linear regression model using Stochastic Gradient
         Descent (SGD).
         This solves the least squares regression formulation
-                f(weights) = 1/n ||A weights-y||^2^
-        (which is the mean squared error).
+
+            f(weights) = 1/(2n) ||A weights - y||^2,
+
+        which is the mean squared error.
         Here the data matrix has n rows, and the input RDD holds the
         set of rows of A, each with its corresponding right hand side
         label y. See also the documentation for the precise formulation.
@@ -334,7 +336,9 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         Stochastic Gradient Descent.
         This solves the l1-regularized least squares regression
         formulation
-            f(weights) = 1/2n ||A weights-y||^2^  + regParam ||weights||_1
+
+            f(weights) = 1/(2n) ||A weights - y||^2  + regParam ||weights||_1.
+
         Here the data matrix has n rows, and the input RDD holds the
         set of rows of A, each with its corresponding right hand side
         label y. See also the documentation for the precise formulation.
@@ -451,7 +455,9 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         Stochastic Gradient Descent.
         This solves the l2-regularized least squares regression
         formulation
-            f(weights) = 1/2n ||A weights-y||^2^  + regParam/2 ||weights||^2^
+
+            f(weights) = 1/(2n) ||A weights - y||^2 + regParam/2 ||weights||^2.
+
         Here the data matrix has n rows, and the input RDD holds the
         set of rows of A, each with its corresponding right hand side
         label y. See also the documentation for the precise formulation.
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 916de2d6fcdbd..10a1e4b3eb0fc 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -300,6 +300,7 @@ def generateLinearInput(intercept, weights, xMean, xVariance,
         :param: seed      Random Seed
         :param: eps       Used to scale the noise. If eps is set high,
                           the amount of gaussian noise added is more.
+
         Returns a list of LabeledPoints of length nPoints
         """
         weights = [float(weight) for weight in weights]

From 8815ba2f674dbb18eb499216df9942b411e10daa Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 13 Aug 2015 11:31:10 -0700
Subject: [PATCH 1033/1454] [SPARK-9649] Fix MasterSuite, third time's a charm

This particular test did not load the default configurations so
it continued to start the REST server, which causes port bind
exceptions.
---
 .../test/scala/org/apache/spark/deploy/master/MasterSuite.scala  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 20d0201a364ab..242bf4b5566eb 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -40,6 +40,7 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     conf.set("spark.deploy.recoveryMode", "CUSTOM")
     conf.set("spark.deploy.recoveryMode.factory",
       classOf[CustomRecoveryModeFactory].getCanonicalName)
+    conf.set("spark.master.rest.enabled", "false")
 
     val instantiationAttempts = CustomRecoveryModeFactory.instantiationAttempts
 

From 864de8eaf4b6ad5c9099f6f29e251c56b029f631 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 13 Aug 2015 13:42:35 -0700
Subject: [PATCH 1034/1454] [SPARK-9661] [MLLIB] [ML] Java compatibility

I skimmed through the docs for various instance of Object and replaced them with Java compaible versions of the same.

1. Some methods in LDAModel.
2. runMiniBatchSGD
3. kolmogorovSmirnovTest

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8126 from MechCoder/java_incop.
---
 .../spark/mllib/clustering/LDAModel.scala     | 27 +++++++++++++++++--
 .../apache/spark/mllib/stat/Statistics.scala  | 16 ++++++++++-
 .../spark/mllib/clustering/JavaLDASuite.java  | 24 +++++++++++++++++
 .../spark/mllib/stat/JavaStatisticsSuite.java | 22 +++++++++++++++
 .../spark/mllib/clustering/LDASuite.scala     | 13 +++++++++
 5 files changed, 99 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 5dc637ebdc133..f31949f13a4cf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -26,7 +26,7 @@ import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.api.java.JavaPairRDD
+import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -228,6 +228,11 @@ class LocalLDAModel private[clustering] (
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
+  /** Java-friendly version of [[logLikelihood]] */
+  def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
+    logLikelihood(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
+  }
+
   /**
    * Calculate an upper bound bound on perplexity.  (Lower is better.)
    * See Equation (16) in original Online LDA paper.
@@ -242,6 +247,11 @@ class LocalLDAModel private[clustering] (
     -logLikelihood(documents) / corpusTokenCount
   }
 
+  /** Java-friendly version of [[logPerplexity]] */
+  def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
+    logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
+  }
+
   /**
    * Estimate the variational likelihood bound of from `documents`:
    *    log p(documents) >= E_q[log p(documents)] - E_q[log q(documents)]
@@ -341,8 +351,14 @@ class LocalLDAModel private[clustering] (
     }
   }
 
-}
+  /** Java-friendly version of [[topicDistributions]] */
+  def topicDistributions(
+      documents: JavaPairRDD[java.lang.Long, Vector]): JavaPairRDD[java.lang.Long, Vector] = {
+    val distributions = topicDistributions(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
+    JavaPairRDD.fromRDD(distributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
+  }
 
+}
 
 @Experimental
 object LocalLDAModel extends Loader[LocalLDAModel] {
@@ -657,6 +673,13 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
+  /** Java-friendly version of [[topTopicsPerDocument]] */
+  def javaTopTopicsPerDocument(
+      k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[java.lang.Double])] = {
+    val topics = topTopicsPerDocument(k)
+    topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[java.lang.Double])]].toJavaRDD()
+  }
+
   // TODO:
   // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ???
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index f84502919e381..24fe48cb8f71f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.stat
 import scala.annotation.varargs
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.api.java.{JavaRDD, JavaDoubleRDD}
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Matrix, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -178,6 +178,9 @@ object Statistics {
     ChiSqTest.chiSquaredFeatures(data)
   }
 
+  /** Java-friendly version of [[chiSqTest()]] */
+  def chiSqTest(data: JavaRDD[LabeledPoint]): Array[ChiSqTestResult] = chiSqTest(data.rdd)
+
   /**
    * Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a
    * continuous distribution. By comparing the largest difference between the empirical cumulative
@@ -212,4 +215,15 @@ object Statistics {
     : KolmogorovSmirnovTestResult = {
     KolmogorovSmirnovTest.testOneSample(data, distName, params: _*)
   }
+
+  /** Java-friendly version of [[kolmogorovSmirnovTest()]] */
+  @varargs
+  def kolmogorovSmirnovTest(
+      data: JavaDoubleRDD,
+      distName: String,
+      params: java.lang.Double*): KolmogorovSmirnovTestResult = {
+    val javaParams = params.asInstanceOf[Seq[Double]]
+    KolmogorovSmirnovTest.testOneSample(data.rdd.asInstanceOf[RDD[Double]],
+      distName, javaParams: _*)
+  }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index d272a42c8576f..427be9430d820 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -124,6 +124,10 @@ public Boolean call(Tuple2<Long, Vector> tuple2) {
         }
     });
     assertEquals(topicDistributions.count(), nonEmptyCorpus.count());
+
+    // Check: javaTopTopicsPerDocuments
+    JavaRDD<scala.Tuple3<java.lang.Long, int[], java.lang.Double[]>> topTopics =
+      model.javaTopTopicsPerDocument(3);
   }
 
   @Test
@@ -160,11 +164,31 @@ public void OnlineOptimizerCompatibility() {
     assertEquals(roundedLocalTopicSummary.length, k);
   }
 
+  @Test
+  public void localLdaMethods() {
+    JavaRDD<Tuple2<Long, Vector>> docs = sc.parallelize(toyData, 2);
+    JavaPairRDD<Long, Vector> pairedDocs = JavaPairRDD.fromJavaRDD(docs);
+
+    // check: topicDistributions
+    assertEquals(toyModel.topicDistributions(pairedDocs).count(), pairedDocs.count());
+
+    // check: logPerplexity
+    double logPerplexity = toyModel.logPerplexity(pairedDocs);
+
+    // check: logLikelihood.
+    ArrayList<Tuple2<Long, Vector>> docsSingleWord = new ArrayList<Tuple2<Long, Vector>>();
+    docsSingleWord.add(new Tuple2<Long, Vector>(Long.valueOf(0), Vectors.dense(1.0, 0.0, 0.0)));
+    JavaPairRDD<Long, Vector> single = JavaPairRDD.fromJavaRDD(sc.parallelize(docsSingleWord));
+    double logLikelihood = toyModel.logLikelihood(single);
+  }
+
   private static int tinyK = LDASuite$.MODULE$.tinyK();
   private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
   private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();
   private static Tuple2<int[], double[]>[] tinyTopicDescription =
       LDASuite$.MODULE$.tinyTopicDescription();
   private JavaPairRDD<Long, Vector> corpus;
+  private LocalLDAModel toyModel = LDASuite$.MODULE$.toyModel();
+  private ArrayList<Tuple2<Long, Vector>> toyData = LDASuite$.MODULE$.javaToyData();
 
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
index 62f7f26b7c98f..eb4e3698624bc 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
@@ -27,7 +27,12 @@
 import static org.junit.Assert.assertEquals;
 
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaDoubleRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.stat.test.ChiSqTestResult;
+import org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult;
 
 public class JavaStatisticsSuite implements Serializable {
   private transient JavaSparkContext sc;
@@ -53,4 +58,21 @@ public void testCorr() {
     // Check default method
     assertEquals(corr1, corr2);
   }
+
+  @Test
+  public void kolmogorovSmirnovTest() {
+    JavaDoubleRDD data = sc.parallelizeDoubles(Lists.newArrayList(0.2, 1.0, -1.0, 2.0));
+    KolmogorovSmirnovTestResult testResult1 = Statistics.kolmogorovSmirnovTest(data, "norm");
+    KolmogorovSmirnovTestResult testResult2 = Statistics.kolmogorovSmirnovTest(
+      data, "norm", 0.0, 1.0);
+  }
+
+  @Test
+  public void chiSqTest() {
+    JavaRDD<LabeledPoint> data = sc.parallelize(Lists.newArrayList(
+      new LabeledPoint(0.0, Vectors.dense(0.1, 2.3)),
+      new LabeledPoint(1.0, Vectors.dense(1.5, 5.1)),
+      new LabeledPoint(0.0, Vectors.dense(2.4, 8.1))));
+    ChiSqTestResult[] testResults = Statistics.chiSqTest(data);
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index ce6a8eb8e8c46..926185e90bcf9 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.mllib.clustering
 
+import java.util.{ArrayList => JArrayList}
+
 import breeze.linalg.{DenseMatrix => BDM, argtopk, max, argmax}
 
 import org.apache.spark.SparkFunSuite
@@ -575,6 +577,17 @@ private[clustering] object LDASuite {
     Vectors.sparse(6, Array(4, 5), Array(1, 1))
   ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
 
+  /** Used in the Java Test Suite */
+  def javaToyData: JArrayList[(java.lang.Long, Vector)] = {
+    val javaData = new JArrayList[(java.lang.Long, Vector)]
+    var i = 0
+    while (i < toyData.size) {
+      javaData.add((toyData(i)._1, toyData(i)._2))
+      i += 1
+    }
+    javaData
+  }
+
   def toyModel: LocalLDAModel = {
     val k = 2
     val vocabSize = 6

From a8d2f4c5f92210a09c846711bd7cc89a43e2edd2 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 14:03:55 -0700
Subject: [PATCH 1035/1454] [SPARK-9942] [PYSPARK] [SQL] ignore exceptions
 while try to import pandas

If pandas is broken (can't be imported, raise other exceptions other than ImportError), pyspark can't be imported, we should ignore all the exceptions.

Author: Davies Liu <davies@databricks.com>

Closes #8173 from davies/fix_pandas.
---
 python/pyspark/sql/context.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 917de24f3536b..0ef46c44644ab 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -39,7 +39,7 @@
 try:
     import pandas
     has_pandas = True
-except ImportError:
+except Exception:
     has_pandas = False
 
 __all__ = ["SQLContext", "HiveContext", "UDFRegistration"]

From c2520f501a200cf794bbe5dc9c385100f518d020 Mon Sep 17 00:00:00 2001
From: hyukjinkwon <gurwls223@gmail.com>
Date: Thu, 13 Aug 2015 16:07:03 -0700
Subject: [PATCH 1036/1454] [SPARK-9935] [SQL] EqualNotNull not processed in
 ORC

https://issues.apache.org/jira/browse/SPARK-9935

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #8163 from HyukjinKwon/master.
---
 .../scala/org/apache/spark/sql/hive/orc/OrcFilters.scala     | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
index 86142e5d66f37..b3d9f7f71a27d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
@@ -107,6 +107,11 @@ private[orc] object OrcFilters extends Logging {
           .filter(isSearchableLiteral)
           .map(builder.equals(attribute, _))
 
+      case EqualNullSafe(attribute, value) =>
+        Option(value)
+          .filter(isSearchableLiteral)
+          .map(builder.nullSafeEquals(attribute, _))
+
       case LessThan(attribute, value) =>
         Option(value)
           .filter(isSearchableLiteral)

From 6c5858bc65c8a8602422b46bfa9cf0a1fb296b88 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 13 Aug 2015 16:52:17 -0700
Subject: [PATCH 1037/1454] [SPARK-9922] [ML] rename StringIndexerReverse to
 IndexToString

What `StringIndexerInverse` does is not strictly associated with `StringIndexer`, and the name is not clearly describing the transformation. Renaming to `IndexToString` might be better.

~~I also changed `invert` to `inverse` without arguments. `inputCol` and `outputCol` could be set after.~~
I also removed `invert`.

jkbradley holdenk

Author: Xiangrui Meng <meng@databricks.com>

Closes #8152 from mengxr/SPARK-9922.
---
 .../spark/ml/feature/StringIndexer.scala      | 34 +++++--------
 .../spark/ml/feature/StringIndexerSuite.scala | 50 +++++++++++++------
 2 files changed, 48 insertions(+), 36 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 9e4b0f0add612..9f6e7b6b6b274 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -24,7 +24,7 @@ import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.Transformer
-import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
+import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DoubleType, NumericType, StringType, StructType}
@@ -59,6 +59,8 @@ private[feature] trait StringIndexerBase extends Params with HasInputCol with Ha
  * If the input column is numeric, we cast it to string and index the string values.
  * The indices are in [0, numLabels), ordered by label frequencies.
  * So the most frequent label gets index 0.
+ *
+ * @see [[IndexToString]] for the inverse transformation
  */
 @Experimental
 class StringIndexer(override val uid: String) extends Estimator[StringIndexerModel]
@@ -170,34 +172,24 @@ class StringIndexerModel private[ml] (
     val copied = new StringIndexerModel(uid, labels)
     copyValues(copied, extra).setParent(parent)
   }
-
-  /**
-   * Return a model to perform the inverse transformation.
-   * Note: By default we keep the original columns during this transformation, so the inverse
-   * should only be used on new columns such as predicted labels.
-   */
-  def invert(inputCol: String, outputCol: String): StringIndexerInverse = {
-    new StringIndexerInverse()
-      .setInputCol(inputCol)
-      .setOutputCol(outputCol)
-      .setLabels(labels)
-  }
 }
 
 /**
  * :: Experimental ::
- * Transform a provided column back to the original input types using either the metadata
- * on the input column, or if provided using the labels supplied by the user.
- * Note: By default we keep the original columns during this transformation,
- * so the inverse should only be used on new columns such as predicted labels.
+ * A [[Transformer]] that maps a column of string indices back to a new column of corresponding
+ * string values using either the ML attributes of the input column, or if provided using the labels
+ * supplied by the user.
+ * All original columns are kept during transformation.
+ *
+ * @see [[StringIndexer]] for converting strings into indices
  */
 @Experimental
-class StringIndexerInverse private[ml] (
+class IndexToString private[ml] (
   override val uid: String) extends Transformer
     with HasInputCol with HasOutputCol {
 
   def this() =
-    this(Identifiable.randomUID("strIdxInv"))
+    this(Identifiable.randomUID("idxToStr"))
 
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
@@ -257,7 +249,7 @@ class StringIndexerInverse private[ml] (
     }
     val indexer = udf { index: Double =>
       val idx = index.toInt
-      if (0 <= idx && idx < values.size) {
+      if (0 <= idx && idx < values.length) {
         values(idx)
       } else {
         throw new SparkException(s"Unseen index: $index ??")
@@ -268,7 +260,7 @@ class StringIndexerInverse private[ml] (
       indexer(dataset($(inputCol)).cast(DoubleType)).as(outputColName))
   }
 
-  override def copy(extra: ParamMap): StringIndexerInverse = {
+  override def copy(extra: ParamMap): IndexToString = {
     defaultCopy(extra)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 2d24914cb91f6..fa918ce64877c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.SparkException
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.col
 
 class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
 
@@ -53,19 +54,6 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
     // a -> 0, b -> 2, c -> 1
     val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0))
     assert(output === expected)
-    // convert reverse our transform
-    val reversed = indexer.invert("labelIndex", "label2")
-      .transform(transformed)
-      .select("id", "label2")
-    assert(df.collect().map(r => (r.getInt(0), r.getString(1))).toSet ===
-      reversed.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
-    // Check invert using only metadata
-    val inverse2 = new StringIndexerInverse()
-      .setInputCol("labelIndex")
-      .setOutputCol("label2")
-    val reversed2 = inverse2.transform(transformed).select("id", "label2")
-    assert(df.collect().map(r => (r.getInt(0), r.getString(1))).toSet ===
-      reversed2.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
   }
 
   test("StringIndexerUnseen") {
@@ -125,4 +113,36 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
     val df = sqlContext.range(0L, 10L)
     assert(indexerModel.transform(df).eq(df))
   }
+
+  test("IndexToString params") {
+    val idxToStr = new IndexToString()
+    ParamsSuite.checkParams(idxToStr)
+  }
+
+  test("IndexToString.transform") {
+    val labels = Array("a", "b", "c")
+    val df0 = sqlContext.createDataFrame(Seq(
+      (0, "a"), (1, "b"), (2, "c"), (0, "a")
+    )).toDF("index", "expected")
+
+    val idxToStr0 = new IndexToString()
+      .setInputCol("index")
+      .setOutputCol("actual")
+      .setLabels(labels)
+    idxToStr0.transform(df0).select("actual", "expected").collect().foreach {
+      case Row(actual, expected) =>
+        assert(actual === expected)
+    }
+
+    val attr = NominalAttribute.defaultAttr.withValues(labels)
+    val df1 = df0.select(col("index").as("indexWithAttr", attr.toMetadata()), col("expected"))
+
+    val idxToStr1 = new IndexToString()
+      .setInputCol("indexWithAttr")
+      .setOutputCol("actual")
+    idxToStr1.transform(df1).select("actual", "expected").collect().foreach {
+      case Row(actual, expected) =>
+        assert(actual === expected)
+    }
+  }
 }

From 693949ba4096c01a0b41da2542ff316823464a16 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 17:33:37 -0700
Subject: [PATCH 1038/1454] [SPARK-8976] [PYSPARK] fix open mode in python3

This bug only happen on Python 3 and Windows.

I tested this manually with python 3 and disable python daemon, no unit test yet.

Author: Davies Liu <davies@databricks.com>

Closes #8181 from davies/open_mode.
---
 python/pyspark/worker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 93df9002be377..42c2f8b75933e 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -146,5 +146,5 @@ def process():
     java_port = int(sys.stdin.readline())
     sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     sock.connect(("127.0.0.1", java_port))
-    sock_file = sock.makefile("a+", 65536)
+    sock_file = sock.makefile("rwb", 65536)
     main(sock_file, sock_file)

From c50f97dafd2d5bf5a8351efcc1c8d3e2b87efc72 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 17:35:11 -0700
Subject: [PATCH 1039/1454] [SPARK-9943] [SQL] deserialized
 UnsafeHashedRelation should be serializable

When the free memory in executor goes low, the cached broadcast objects need to serialized into disk, but currently the deserialized UnsafeHashedRelation can't be serialized , fail with NPE. This PR fixes that.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8174 from davies/serialize_hashed.
---
 .../sql/execution/joins/HashedRelation.scala  | 93 ++++++++++++-------
 .../execution/joins/HashedRelationSuite.scala | 14 +++
 2 files changed, 74 insertions(+), 33 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index ea02076b41a6f..6c0196c21a0d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
-import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
+import org.apache.spark.unsafe.memory.{MemoryLocation, ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.CompactBuffer
 import org.apache.spark.{SparkConf, SparkEnv}
@@ -247,40 +247,67 @@ private[joins] final class UnsafeHashedRelation(
   }
 
   override def writeExternal(out: ObjectOutput): Unit = Utils.tryOrIOException {
-    out.writeInt(hashTable.size())
-
-    val iter = hashTable.entrySet().iterator()
-    while (iter.hasNext) {
-      val entry = iter.next()
-      val key = entry.getKey
-      val values = entry.getValue
-
-      // write all the values as single byte array
-      var totalSize = 0L
-      var i = 0
-      while (i < values.length) {
-        totalSize += values(i).getSizeInBytes + 4 + 4
-        i += 1
+    if (binaryMap != null) {
+      // This could happen when a cached broadcast object need to be dumped into disk to free memory
+      out.writeInt(binaryMap.numElements())
+
+      var buffer = new Array[Byte](64)
+      def write(addr: MemoryLocation, length: Int): Unit = {
+        if (buffer.length < length) {
+          buffer = new Array[Byte](length)
+        }
+        Platform.copyMemory(addr.getBaseObject, addr.getBaseOffset,
+          buffer, Platform.BYTE_ARRAY_OFFSET, length)
+        out.write(buffer, 0, length)
       }
-      assert(totalSize < Integer.MAX_VALUE, "values are too big")
-
-      // [key size] [values size] [key bytes] [values bytes]
-      out.writeInt(key.getSizeInBytes)
-      out.writeInt(totalSize.toInt)
-      out.write(key.getBytes)
-      i = 0
-      while (i < values.length) {
-        // [num of fields] [num of bytes] [row bytes]
-        // write the integer in native order, so they can be read by UNSAFE.getInt()
-        if (ByteOrder.nativeOrder() == ByteOrder.BIG_ENDIAN) {
-          out.writeInt(values(i).numFields())
-          out.writeInt(values(i).getSizeInBytes)
-        } else {
-          out.writeInt(Integer.reverseBytes(values(i).numFields()))
-          out.writeInt(Integer.reverseBytes(values(i).getSizeInBytes))
+
+      val iter = binaryMap.iterator()
+      while (iter.hasNext) {
+        val loc = iter.next()
+        // [key size] [values size] [key bytes] [values bytes]
+        out.writeInt(loc.getKeyLength)
+        out.writeInt(loc.getValueLength)
+        write(loc.getKeyAddress, loc.getKeyLength)
+        write(loc.getValueAddress, loc.getValueLength)
+      }
+
+    } else {
+      assert(hashTable != null)
+      out.writeInt(hashTable.size())
+
+      val iter = hashTable.entrySet().iterator()
+      while (iter.hasNext) {
+        val entry = iter.next()
+        val key = entry.getKey
+        val values = entry.getValue
+
+        // write all the values as single byte array
+        var totalSize = 0L
+        var i = 0
+        while (i < values.length) {
+          totalSize += values(i).getSizeInBytes + 4 + 4
+          i += 1
+        }
+        assert(totalSize < Integer.MAX_VALUE, "values are too big")
+
+        // [key size] [values size] [key bytes] [values bytes]
+        out.writeInt(key.getSizeInBytes)
+        out.writeInt(totalSize.toInt)
+        out.write(key.getBytes)
+        i = 0
+        while (i < values.length) {
+          // [num of fields] [num of bytes] [row bytes]
+          // write the integer in native order, so they can be read by UNSAFE.getInt()
+          if (ByteOrder.nativeOrder() == ByteOrder.BIG_ENDIAN) {
+            out.writeInt(values(i).numFields())
+            out.writeInt(values(i).getSizeInBytes)
+          } else {
+            out.writeInt(Integer.reverseBytes(values(i).numFields()))
+            out.writeInt(Integer.reverseBytes(values(i).getSizeInBytes))
+          }
+          out.write(values(i).getBytes)
+          i += 1
         }
-        out.write(values(i).getBytes)
-        i += 1
       }
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index c635b2d51f464..d33a967093ca5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -102,6 +102,14 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed2.get(toUnsafe(InternalRow(10))) === null)
     assert(hashed2.get(unsafeData(2)) === data2)
     assert(numDataRows.value.value === data.length)
+
+    val os2 = new ByteArrayOutputStream()
+    val out2 = new ObjectOutputStream(os2)
+    hashed2.asInstanceOf[UnsafeHashedRelation].writeExternal(out2)
+    out2.flush()
+    // This depends on that the order of items in BytesToBytesMap.iterator() is exactly the same
+    // as they are inserted
+    assert(java.util.Arrays.equals(os2.toByteArray, os.toByteArray))
   }
 
   test("test serialization empty hash map") {
@@ -119,5 +127,11 @@ class HashedRelationSuite extends SparkFunSuite {
     val toUnsafe = UnsafeProjection.create(schema)
     val row = toUnsafe(InternalRow(0))
     assert(hashed2.get(row) === null)
+
+    val os2 = new ByteArrayOutputStream()
+    val out2 = new ObjectOutputStream(os2)
+    hashed2.writeExternal(out2)
+    out2.flush()
+    assert(java.util.Arrays.equals(os2.toByteArray, os.toByteArray))
   }
 }

From 8187b3ae477e2b2987ae9acc5368d57b1d5653b2 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 13 Aug 2015 17:42:01 -0700
Subject: [PATCH 1040/1454] [SPARK-9580] [SQL] Replace singletons in SQL tests

A fundamental limitation of the existing SQL tests is that *there is simply no way to create your own `SparkContext`*. This is a serious limitation because the user may wish to use a different master or config. As a case in point, `BroadcastJoinSuite` is entirely commented out because there is no way to make it pass with the existing infrastructure.

This patch removes the singletons `TestSQLContext` and `TestData`, and instead introduces a `SharedSQLContext` that starts a context per suite. Unfortunately the singletons were so ingrained in the SQL tests that this patch necessarily needed to touch *all* the SQL test files.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/8111)
<!-- Reviewable:end -->

Author: Andrew Or <andrew@databricks.com>

Closes #8111 from andrewor14/sql-tests-refactor.
---
 project/MimaExcludes.scala                    |  10 +
 project/SparkBuild.scala                      |  16 +-
 .../analysis/AnalysisErrorSuite.scala         |   6 +-
 .../org/apache/spark/sql/SQLContext.scala     |  97 +-----
 .../org/apache/spark/sql/SQLImplicits.scala   | 123 ++++++++
 .../spark/sql/JavaApplySchemaSuite.java       |  10 +-
 .../apache/spark/sql/JavaDataFrameSuite.java  |  39 +--
 .../org/apache/spark/sql/JavaUDFSuite.java    |  10 +-
 .../spark/sql/sources/JavaSaveLoadSuite.java  |  15 +-
 .../apache/spark/sql/CachedTableSuite.scala   |  14 +-
 .../spark/sql/ColumnExpressionSuite.scala     |  37 +--
 .../spark/sql/DataFrameAggregateSuite.scala   |  10 +-
 .../spark/sql/DataFrameFunctionsSuite.scala   |  18 +-
 .../spark/sql/DataFrameImplicitsSuite.scala   |   6 +-
 .../apache/spark/sql/DataFrameJoinSuite.scala |  10 +-
 .../spark/sql/DataFrameNaFunctionsSuite.scala |   6 +-
 .../apache/spark/sql/DataFrameStatSuite.scala |  19 +-
 .../org/apache/spark/sql/DataFrameSuite.scala |  12 +-
 .../spark/sql/DataFrameTungstenSuite.scala    |   8 +-
 .../apache/spark/sql/DateFunctionsSuite.scala |  13 +-
 .../org/apache/spark/sql/JoinSuite.scala      |  47 ++-
 .../apache/spark/sql/JsonFunctionsSuite.scala |   6 +-
 .../apache/spark/sql/ListTablesSuite.scala    |  15 +-
 .../spark/sql/MathExpressionsSuite.scala      |  28 +-
 .../org/apache/spark/sql/QueryTest.scala      |   6 -
 .../scala/org/apache/spark/sql/RowSuite.scala |   7 +-
 .../org/apache/spark/sql/SQLConfSuite.scala   |  19 +-
 .../apache/spark/sql/SQLContextSuite.scala    |  13 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  29 +-
 .../sql/ScalaReflectionRelationSuite.scala    |  17 +-
 .../apache/spark/sql/SerializationSuite.scala |   9 +-
 .../spark/sql/StringFunctionsSuite.scala      |   7 +-
 .../scala/org/apache/spark/sql/TestData.scala | 197 ------------
 .../scala/org/apache/spark/sql/UDFSuite.scala |  39 ++-
 .../spark/sql/UserDefinedTypeSuite.scala      |   9 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |  14 +-
 .../columnar/PartitionBatchPruningSuite.scala |  31 +-
 .../spark/sql/execution/ExchangeSuite.scala   |   3 +-
 .../spark/sql/execution/PlannerSuite.scala    |  39 +--
 .../execution/RowFormatConvertersSuite.scala  |  22 +-
 .../spark/sql/execution/SortSuite.scala       |   3 +-
 .../spark/sql/execution/SparkPlanTest.scala   |  36 +--
 .../sql/execution/TungstenSortSuite.scala     |  21 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala |  14 +-
 .../UnsafeKVExternalSorterSuite.scala         |   7 +-
 .../TungstenAggregationIteratorSuite.scala    |   7 +-
 .../datasources/json/JsonSuite.scala          |  42 ++-
 .../datasources/json/TestJsonData.scala       |  40 ++-
 .../ParquetAvroCompatibilitySuite.scala       |  14 +-
 .../parquet/ParquetCompatibilityTest.scala    |  10 +-
 .../parquet/ParquetFilterSuite.scala          |   8 +-
 .../datasources/parquet/ParquetIOSuite.scala  |   6 +-
 .../ParquetPartitionDiscoverySuite.scala      |  12 +-
 .../ParquetProtobufCompatibilitySuite.scala   |   7 +-
 .../parquet/ParquetQuerySuite.scala           |  43 ++-
 .../parquet/ParquetSchemaSuite.scala          |   6 +-
 .../datasources/parquet/ParquetTest.scala     |  15 +-
 .../ParquetThriftCompatibilitySuite.scala     |   8 +-
 .../sql/execution/debug/DebuggingSuite.scala  |   6 +-
 .../execution/joins/HashedRelationSuite.scala |  10 +-
 .../sql/execution/joins/InnerJoinSuite.scala  | 248 ++++++++-------
 .../sql/execution/joins/OuterJoinSuite.scala  | 125 ++++----
 .../sql/execution/joins/SemiJoinSuite.scala   | 113 +++----
 .../execution/metric/SQLMetricsSuite.scala    |  62 ++--
 .../sql/execution/ui/SQLListenerSuite.scala   |  14 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala |   9 +-
 .../spark/sql/jdbc/JDBCWriteSuite.scala       |  20 +-
 .../sources/CreateTableAsSelectSuite.scala    |  18 +-
 .../sql/sources/DDLSourceLoadSuite.scala      |   3 +-
 .../spark/sql/sources/DDLTestSuite.scala      |  11 +-
 .../spark/sql/sources/DataSourceTest.scala    |  17 +-
 .../spark/sql/sources/FilteredScanSuite.scala |   9 +-
 .../spark/sql/sources/InsertSuite.scala       |  30 +-
 .../sql/sources/PartitionedWriteSuite.scala   |  14 +-
 .../spark/sql/sources/PrunedScanSuite.scala   |  11 +-
 .../spark/sql/sources/SaveLoadSuite.scala     |  26 +-
 .../spark/sql/sources/TableScanSuite.scala    |  15 +-
 .../apache/spark/sql/test/SQLTestData.scala   | 290 ++++++++++++++++++
 .../apache/spark/sql/test/SQLTestUtils.scala  |  92 +++++-
 .../spark/sql/test/SharedSQLContext.scala     |  68 ++++
 .../spark/sql/test/TestSQLContext.scala       |  46 ++-
 .../hive/thriftserver/UISeleniumSuite.scala   |   2 -
 .../sql/hive/HiveMetastoreCatalogSuite.scala  |   5 +-
 .../spark/sql/hive/HiveParquetSuite.scala     |  11 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |   4 +-
 .../spark/sql/hive/MultiDatabaseSuite.scala   |   5 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |   3 +-
 .../org/apache/spark/sql/hive/UDFSuite.scala  |   4 +-
 .../execution/AggregationQuerySuite.scala     |   9 +-
 .../sql/hive/execution/HiveExplainSuite.scala |   4 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |   3 +-
 .../execution/ScriptTransformationSuite.scala |   3 +-
 .../apache/spark/sql/hive/orc/OrcTest.scala   |   4 +-
 .../apache/spark/sql/hive/parquetSuites.scala |   3 +-
 .../CommitFailureTestRelationSuite.scala      |   6 +-
 .../sql/sources/hadoopFsRelationSuites.scala  |   5 +-
 96 files changed, 1460 insertions(+), 1203 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
 rename sql/core/src/{main => test}/scala/org/apache/spark/sql/test/TestSQLContext.scala (54%)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 784f83c10e023..88745dc086a04 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -178,6 +178,16 @@ object MimaExcludes {
             // SPARK-4751 Dynamic allocation for standalone mode
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.SparkContext.supportDynamicAllocation")
+          ) ++ Seq(
+            // SPARK-9580: Remove SQL test singletons
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.LocalSQLContext$SQLSession"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.LocalSQLContext"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.TestSQLContext"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.TestSQLContext$")
           ) ++ Seq(
             // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
             ProblemFilters.exclude[IncompatibleResultTypeProblem](
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 74f815f941d5b..04e0d49b178cf 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -319,6 +319,8 @@ object SQL {
   lazy val settings = Seq(
     initialCommands in console :=
       """
+        |import org.apache.spark.SparkContext
+        |import org.apache.spark.sql.SQLContext
         |import org.apache.spark.sql.catalyst.analysis._
         |import org.apache.spark.sql.catalyst.dsl._
         |import org.apache.spark.sql.catalyst.errors._
@@ -328,9 +330,14 @@ object SQL {
         |import org.apache.spark.sql.catalyst.util._
         |import org.apache.spark.sql.execution
         |import org.apache.spark.sql.functions._
-        |import org.apache.spark.sql.test.TestSQLContext._
-        |import org.apache.spark.sql.types._""".stripMargin,
-    cleanupCommands in console := "sparkContext.stop()"
+        |import org.apache.spark.sql.types._
+        |
+        |val sc = new SparkContext("local[*]", "dev-shell")
+        |val sqlContext = new SQLContext(sc)
+        |import sqlContext.implicits._
+        |import sqlContext._
+      """.stripMargin,
+    cleanupCommands in console := "sc.stop()"
   )
 }
 
@@ -340,8 +347,6 @@ object Hive {
     javaOptions += "-XX:MaxPermSize=256m",
     // Specially disable assertions since some Hive tests fail them
     javaOptions in Test := (javaOptions in Test).value.filterNot(_ == "-ea"),
-    // Multiple queries rely on the TestHive singleton. See comments there for more details.
-    parallelExecution in Test := false,
     // Supporting all SerDes requires us to depend on deprecated APIs, so we turn off the warnings
     // only for this subproject.
     scalacOptions <<= scalacOptions map { currentOpts: Seq[String] =>
@@ -349,6 +354,7 @@ object Hive {
     },
     initialCommands in console :=
       """
+        |import org.apache.spark.SparkContext
         |import org.apache.spark.sql.catalyst.analysis._
         |import org.apache.spark.sql.catalyst.dsl._
         |import org.apache.spark.sql.catalyst.errors._
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 63b475b6366c2..f60d11c988ef8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -17,14 +17,10 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.scalatest.BeforeAndAfter
-
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.Inner
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.types._
@@ -42,7 +38,7 @@ case class UnresolvedTestPlan() extends LeafNode {
   override def output: Seq[Attribute] = Nil
 }
 
-class AnalysisErrorSuite extends AnalysisTest with BeforeAndAfter {
+class AnalysisErrorSuite extends AnalysisTest {
   import TestRelations._
 
   def errorTest(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 4bf00b3399e7a..53de10d5fa9da 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -23,7 +23,6 @@ import java.util.concurrent.atomic.AtomicReference
 
 import scala.collection.JavaConversions._
 import scala.collection.immutable
-import scala.language.implicitConversions
 import scala.reflect.runtime.universe.TypeTag
 import scala.util.control.NonFatal
 
@@ -41,10 +40,9 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
+import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
-import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
 
 /**
@@ -334,97 +332,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @since 1.3.0
    */
   @Experimental
-  object implicits extends Serializable {
-    // scalastyle:on
-
-    /**
-     * Converts $"col name" into an [[Column]].
-     * @since 1.3.0
-     */
-    implicit class StringToColumn(val sc: StringContext) {
-      def $(args: Any*): ColumnName = {
-        new ColumnName(sc.s(args: _*))
-      }
-    }
-
-    /**
-     * An implicit conversion that turns a Scala `Symbol` into a [[Column]].
-     * @since 1.3.0
-     */
-    implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name)
-
-    /**
-     * Creates a DataFrame from an RDD of case classes or tuples.
-     * @since 1.3.0
-     */
-    implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = {
-      DataFrameHolder(self.createDataFrame(rdd))
-    }
-
-    /**
-     * Creates a DataFrame from a local Seq of Product.
-     * @since 1.3.0
-     */
-    implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder =
-    {
-      DataFrameHolder(self.createDataFrame(data))
-    }
-
-    // Do NOT add more implicit conversions. They are likely to break source compatibility by
-    // making existing implicit conversions ambiguous. In particular, RDD[Double] is dangerous
-    // because of [[DoubleRDDFunctions]].
-
-    /**
-     * Creates a single column DataFrame from an RDD[Int].
-     * @since 1.3.0
-     */
-    implicit def intRddToDataFrameHolder(data: RDD[Int]): DataFrameHolder = {
-      val dataType = IntegerType
-      val rows = data.mapPartitions { iter =>
-        val row = new SpecificMutableRow(dataType :: Nil)
-        iter.map { v =>
-          row.setInt(0, v)
-          row: InternalRow
-        }
-      }
-      DataFrameHolder(
-        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
-    }
-
-    /**
-     * Creates a single column DataFrame from an RDD[Long].
-     * @since 1.3.0
-     */
-    implicit def longRddToDataFrameHolder(data: RDD[Long]): DataFrameHolder = {
-      val dataType = LongType
-      val rows = data.mapPartitions { iter =>
-        val row = new SpecificMutableRow(dataType :: Nil)
-        iter.map { v =>
-          row.setLong(0, v)
-          row: InternalRow
-        }
-      }
-      DataFrameHolder(
-        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
-    }
-
-    /**
-     * Creates a single column DataFrame from an RDD[String].
-     * @since 1.3.0
-     */
-    implicit def stringRddToDataFrameHolder(data: RDD[String]): DataFrameHolder = {
-      val dataType = StringType
-      val rows = data.mapPartitions { iter =>
-        val row = new SpecificMutableRow(dataType :: Nil)
-        iter.map { v =>
-          row.update(0, UTF8String.fromString(v))
-          row: InternalRow
-        }
-      }
-      DataFrameHolder(
-        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
-    }
+  object implicits extends SQLImplicits with Serializable {
+    protected override def _sqlContext: SQLContext = self
   }
+  // scalastyle:on
 
   /**
    * :: Experimental ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
new file mode 100644
index 0000000000000..5f82372700f2c
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
@@ -0,0 +1,123 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import scala.language.implicitConversions
+import scala.reflect.runtime.universe.TypeTag
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
+import org.apache.spark.sql.types.StructField
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * A collection of implicit methods for converting common Scala objects into [[DataFrame]]s.
+ */
+private[sql] abstract class SQLImplicits {
+  protected def _sqlContext: SQLContext
+
+  /**
+   * Converts $"col name" into an [[Column]].
+   * @since 1.3.0
+   */
+  implicit class StringToColumn(val sc: StringContext) {
+    def $(args: Any*): ColumnName = {
+      new ColumnName(sc.s(args: _*))
+    }
+  }
+
+  /**
+   * An implicit conversion that turns a Scala `Symbol` into a [[Column]].
+   * @since 1.3.0
+   */
+  implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name)
+
+  /**
+   * Creates a DataFrame from an RDD of case classes or tuples.
+   * @since 1.3.0
+   */
+  implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = {
+    DataFrameHolder(_sqlContext.createDataFrame(rdd))
+  }
+
+  /**
+   * Creates a DataFrame from a local Seq of Product.
+   * @since 1.3.0
+   */
+  implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder =
+  {
+    DataFrameHolder(_sqlContext.createDataFrame(data))
+  }
+
+  // Do NOT add more implicit conversions. They are likely to break source compatibility by
+  // making existing implicit conversions ambiguous. In particular, RDD[Double] is dangerous
+  // because of [[DoubleRDDFunctions]].
+
+  /**
+   * Creates a single column DataFrame from an RDD[Int].
+   * @since 1.3.0
+   */
+  implicit def intRddToDataFrameHolder(data: RDD[Int]): DataFrameHolder = {
+    val dataType = IntegerType
+    val rows = data.mapPartitions { iter =>
+      val row = new SpecificMutableRow(dataType :: Nil)
+      iter.map { v =>
+        row.setInt(0, v)
+        row: InternalRow
+      }
+    }
+    DataFrameHolder(
+      _sqlContext.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+  }
+
+  /**
+   * Creates a single column DataFrame from an RDD[Long].
+   * @since 1.3.0
+   */
+  implicit def longRddToDataFrameHolder(data: RDD[Long]): DataFrameHolder = {
+    val dataType = LongType
+    val rows = data.mapPartitions { iter =>
+      val row = new SpecificMutableRow(dataType :: Nil)
+      iter.map { v =>
+        row.setLong(0, v)
+        row: InternalRow
+      }
+    }
+    DataFrameHolder(
+      _sqlContext.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+  }
+
+  /**
+   * Creates a single column DataFrame from an RDD[String].
+   * @since 1.3.0
+   */
+  implicit def stringRddToDataFrameHolder(data: RDD[String]): DataFrameHolder = {
+    val dataType = StringType
+    val rows = data.mapPartitions { iter =>
+      val row = new SpecificMutableRow(dataType :: Nil)
+      iter.map { v =>
+        row.update(0, UTF8String.fromString(v))
+        row: InternalRow
+      }
+    }
+    DataFrameHolder(
+      _sqlContext.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+  }
+}
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index e912eb835d169..bf693c7c393f6 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -27,6 +27,7 @@
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -34,7 +35,6 @@
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
 import org.apache.spark.sql.SQLContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
@@ -48,14 +48,16 @@ public class JavaApplySchemaSuite implements Serializable {
 
   @Before
   public void setUp() {
-    sqlContext = TestSQLContext$.MODULE$;
-    javaCtx = new JavaSparkContext(sqlContext.sparkContext());
+    SparkContext context = new SparkContext("local[*]", "testing");
+    javaCtx = new JavaSparkContext(context);
+    sqlContext = new SQLContext(context);
   }
 
   @After
   public void tearDown() {
-    javaCtx = null;
+    sqlContext.sparkContext().stop();
     sqlContext = null;
+    javaCtx = null;
   }
 
   public static class Person implements Serializable {
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 7302361ab9fdb..7abdd3db80341 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -17,44 +17,45 @@
 
 package test.org.apache.spark.sql;
 
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Map;
+
+import scala.collection.JavaConversions;
+import scala.collection.Seq;
+
 import com.google.common.collect.ImmutableMap;
 import com.google.common.primitives.Ints;
+import org.junit.*;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.sql.*;
+import static org.apache.spark.sql.functions.*;
 import org.apache.spark.sql.test.TestSQLContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.types.*;
-import org.junit.*;
-
-import scala.collection.JavaConversions;
-import scala.collection.Seq;
-
-import java.io.Serializable;
-import java.util.Arrays;
-import java.util.Comparator;
-import java.util.List;
-import java.util.Map;
-
-import static org.apache.spark.sql.functions.*;
 
 public class JavaDataFrameSuite {
   private transient JavaSparkContext jsc;
-  private transient SQLContext context;
+  private transient TestSQLContext context;
 
   @Before
   public void setUp() {
     // Trigger static initializer of TestData
-    TestData$.MODULE$.testData();
-    jsc = new JavaSparkContext(TestSQLContext.sparkContext());
-    context = TestSQLContext$.MODULE$;
+    SparkContext sc = new SparkContext("local[*]", "testing");
+    jsc = new JavaSparkContext(sc);
+    context = new TestSQLContext(sc);
+    context.loadTestData();
   }
 
   @After
   public void tearDown() {
-    jsc = null;
+    context.sparkContext().stop();
     context = null;
+    jsc = null;
   }
 
   @Test
@@ -230,7 +231,7 @@ public void testCovariance() {
 
   @Test
   public void testSampleBy() {
-    DataFrame df = context.range(0, 100).select(col("id").mod(3).as("key"));
+    DataFrame df = context.range(0, 100, 1, 2).select(col("id").mod(3).as("key"));
     DataFrame sampled = df.stat().<Integer>sampleBy("key", ImmutableMap.of(0, 0.1, 1, 0.2), 0L);
     Row[] actual = sampled.groupBy("key").count().orderBy("key").collect();
     Row[] expected = new Row[] {RowFactory.create(0, 5), RowFactory.create(1, 8)};
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
index 79d92734ff375..bb02b58cca9be 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
@@ -23,12 +23,12 @@
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.api.java.UDF1;
 import org.apache.spark.sql.api.java.UDF2;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.types.DataTypes;
 
 // The test suite itself is Serializable so that anonymous Function implementations can be
@@ -40,12 +40,16 @@ public class JavaUDFSuite implements Serializable {
 
   @Before
   public void setUp() {
-    sqlContext = TestSQLContext$.MODULE$;
-    sc = new JavaSparkContext(sqlContext.sparkContext());
+    SparkContext _sc = new SparkContext("local[*]", "testing");
+    sqlContext = new SQLContext(_sc);
+    sc = new JavaSparkContext(_sc);
   }
 
   @After
   public void tearDown() {
+    sqlContext.sparkContext().stop();
+    sqlContext = null;
+    sc = null;
   }
 
   @SuppressWarnings("unchecked")
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
index 2706e01bd28af..6f9e7f68dc39c 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
@@ -21,13 +21,14 @@
 import java.io.IOException;
 import java.util.*;
 
+import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.*;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.StructField;
@@ -52,8 +53,9 @@ private void checkAnswer(DataFrame actual, List<Row> expected) {
 
   @Before
   public void setUp() throws IOException {
-    sqlContext = TestSQLContext$.MODULE$;
-    sc = new JavaSparkContext(sqlContext.sparkContext());
+    SparkContext _sc = new SparkContext("local[*]", "testing");
+    sqlContext = new SQLContext(_sc);
+    sc = new JavaSparkContext(_sc);
 
     originalDefaultSource = sqlContext.conf().defaultDataSourceName();
     path =
@@ -71,6 +73,13 @@ public void setUp() throws IOException {
     df.registerTempTable("jsonTable");
   }
 
+  @After
+  public void tearDown() {
+    sqlContext.sparkContext().stop();
+    sqlContext = null;
+    sc = null;
+  }
+
   @Test
   public void saveAndLoad() {
     Map<String, String> options = new HashMap<String, String>();
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index a88df91b1001c..af7590c3d3c17 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -18,24 +18,20 @@
 package org.apache.spark.sql
 
 import scala.concurrent.duration._
-import scala.language.{implicitConversions, postfixOps}
+import scala.language.postfixOps
 
 import org.scalatest.concurrent.Eventually._
 
 import org.apache.spark.Accumulators
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.columnar._
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.storage.{StorageLevel, RDDBlockId}
 
-case class BigData(s: String)
+private case class BigData(s: String)
 
-class CachedTableSuite extends QueryTest {
-  TestData // Load test tables.
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.sql
+class CachedTableSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   def rddIdOf(tableName: String): Int = {
     val executedPlan = ctx.table(tableName).queryExecution.executedPlan
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 6a09a3b72c081..ee74e3e83da5a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -21,16 +21,20 @@ import org.scalatest.Matchers._
 
 import org.apache.spark.sql.execution.{Project, TungstenProject}
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.test.SQLTestUtils
 
-class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
-  import org.apache.spark.sql.TestData._
+class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-
-  override def sqlContext(): SQLContext = ctx
+  private lazy val booleanData = {
+    ctx.createDataFrame(ctx.sparkContext.parallelize(
+      Row(false, false) ::
+      Row(false, true) ::
+      Row(true, false) ::
+      Row(true, true) :: Nil),
+      StructType(Seq(StructField("a", BooleanType), StructField("b", BooleanType))))
+  }
 
   test("column names with space") {
     val df = Seq((1, "a")).toDF("name with space", "name.with.dot")
@@ -258,7 +262,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       nullStrings.collect().toSeq.filter(r => r.getString(1) eq null))
 
     checkAnswer(
-      ctx.sql("select isnull(null), isnull(1)"),
+      sql("select isnull(null), isnull(1)"),
       Row(true, false))
   }
 
@@ -268,7 +272,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       nullStrings.collect().toSeq.filter(r => r.getString(1) ne null))
 
     checkAnswer(
-      ctx.sql("select isnotnull(null), isnotnull('a')"),
+      sql("select isnotnull(null), isnotnull('a')"),
       Row(false, true))
   }
 
@@ -289,7 +293,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       Row(true, true) :: Row(true, true) :: Row(false, false) :: Row(false, false) :: Nil)
 
     checkAnswer(
-      ctx.sql("select isnan(15), isnan('invalid')"),
+      sql("select isnan(15), isnan('invalid')"),
       Row(false, false))
   }
 
@@ -309,7 +313,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     )
     testData.registerTempTable("t")
     checkAnswer(
-      ctx.sql(
+      sql(
         "select nanvl(a, 5), nanvl(b, 10), nanvl(10, b), nanvl(c, null), nanvl(d, 10), " +
           " nanvl(b, e), nanvl(e, f) from t"),
       Row(null, 3.0, 10.0, null, Double.PositiveInfinity, 3.0, 1.0)
@@ -433,13 +437,6 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     }
   }
 
-  val booleanData = ctx.createDataFrame(ctx.sparkContext.parallelize(
-    Row(false, false) ::
-      Row(false, true) ::
-      Row(true, false) ::
-      Row(true, true) :: Nil),
-    StructType(Seq(StructField("a", BooleanType), StructField("b", BooleanType))))
-
   test("&&") {
     checkAnswer(
       booleanData.filter($"a" && true),
@@ -523,7 +520,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     )
 
     checkAnswer(
-      ctx.sql("SELECT upper('aB'), ucase('cDe')"),
+      sql("SELECT upper('aB'), ucase('cDe')"),
       Row("AB", "CDE"))
   }
 
@@ -544,7 +541,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     )
 
     checkAnswer(
-      ctx.sql("SELECT lower('aB'), lcase('cDe')"),
+      sql("SELECT lower('aB'), lcase('cDe')"),
       Row("ab", "cde"))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index f9cff7440a76e..72cf7aab0b977 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -17,15 +17,13 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{BinaryType, DecimalType}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.DecimalType
 
 
-class DataFrameAggregateSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("groupBy") {
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 03116a374f3be..9d965258e389d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -17,17 +17,15 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /**
  * Test suite for functions in [[org.apache.spark.sql.functions]].
  */
-class DataFrameFunctionsSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("array with column name") {
     val df = Seq((0, 1)).toDF("a", "b")
@@ -119,11 +117,11 @@ class DataFrameFunctionsSuite extends QueryTest {
 
   test("constant functions") {
     checkAnswer(
-      ctx.sql("SELECT E()"),
+      sql("SELECT E()"),
       Row(scala.math.E)
     )
     checkAnswer(
-      ctx.sql("SELECT PI()"),
+      sql("SELECT PI()"),
       Row(scala.math.Pi)
     )
   }
@@ -153,7 +151,7 @@ class DataFrameFunctionsSuite extends QueryTest {
 
   test("nvl function") {
     checkAnswer(
-      ctx.sql("SELECT nvl(null, 'x'), nvl('y', 'x'), nvl(null, null)"),
+      sql("SELECT nvl(null, 'x'), nvl('y', 'x'), nvl(null, null)"),
       Row("x", "y", null))
   }
 
@@ -222,7 +220,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(-1)
     )
     checkAnswer(
-      ctx.sql("SELECT least(a, 2) as l from testData2 order by l"),
+      sql("SELECT least(a, 2) as l from testData2 order by l"),
       Seq(Row(1), Row(1), Row(2), Row(2), Row(2), Row(2))
     )
   }
@@ -233,7 +231,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(3)
     )
     checkAnswer(
-      ctx.sql("SELECT greatest(a, 2) as g from testData2 order by g"),
+      sql("SELECT greatest(a, 2) as g from testData2 order by g"),
       Seq(Row(2), Row(2), Row(2), Row(2), Row(3), Row(3))
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
index fbb30706a4943..e5d7d63441a6b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.sql
 
-class DataFrameImplicitsSuite extends QueryTest {
+import org.apache.spark.sql.test.SharedSQLContext
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameImplicitsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("RDD of tuples") {
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
index e1c6c706242d2..e2716d7841d85 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.execution.joins.BroadcastHashJoin
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 
-class DataFrameJoinSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameJoinSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("join - join using") {
     val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
@@ -59,7 +57,7 @@ class DataFrameJoinSuite extends QueryTest {
 
     checkAnswer(
       df1.join(df2, $"df1.key" === $"df2.key"),
-      ctx.sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key")
+      sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key")
         .collect().toSeq)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
index dbe3b44ee2c79..cdaa14ac80785 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
@@ -19,11 +19,11 @@ package org.apache.spark.sql
 
 import scala.collection.JavaConversions._
 
+import org.apache.spark.sql.test.SharedSQLContext
 
-class DataFrameNaFunctionsSuite extends QueryTest {
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   def createDF(): DataFrame = {
     Seq[(String, java.lang.Integer, java.lang.Double)](
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 8f5984e4a8ce2..28bdd6f83b687 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -19,20 +19,17 @@ package org.apache.spark.sql
 
 import java.util.Random
 
-import org.scalatest.Matchers._
-
 import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.test.SharedSQLContext
 
-class DataFrameStatSuite extends QueryTest {
-
-  private val sqlCtx = org.apache.spark.sql.test.TestSQLContext
-  import sqlCtx.implicits._
+class DataFrameStatSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   private def toLetter(i: Int): String = (i + 97).toChar.toString
 
   test("sample with replacement") {
     val n = 100
-    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = true, 0.05, seed = 13),
       Seq(5, 10, 52, 73).map(Row(_))
@@ -41,7 +38,7 @@ class DataFrameStatSuite extends QueryTest {
 
   test("sample without replacement") {
     val n = 100
-    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = false, 0.05, seed = 13),
       Seq(16, 23, 88, 100).map(Row(_))
@@ -50,7 +47,7 @@ class DataFrameStatSuite extends QueryTest {
 
   test("randomSplit") {
     val n = 600
-    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
     for (seed <- 1 to 5) {
       val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
       assert(splits.length == 3, "wrong number of splits")
@@ -167,7 +164,7 @@ class DataFrameStatSuite extends QueryTest {
   }
 
   test("Frequent Items 2") {
-    val rows = sqlCtx.sparkContext.parallelize(Seq.empty[Int], 4)
+    val rows = ctx.sparkContext.parallelize(Seq.empty[Int], 4)
     // this is a regression test, where when merging partitions, we omitted values with higher
     // counts than those that existed in the map when the map was full. This test should also fail
     // if anything like SPARK-9614 is observed once again
@@ -185,7 +182,7 @@ class DataFrameStatSuite extends QueryTest {
   }
 
   test("sampleBy") {
-    val df = sqlCtx.range(0, 100).select((col("id") % 3).as("key"))
+    val df = ctx.range(0, 100).select((col("id") % 3).as("key"))
     val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L)
     checkAnswer(
       sampled.groupBy("key").count().orderBy("key"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 2feec29955bc8..10bfa9b64f00d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -23,18 +23,12 @@ import scala.language.postfixOps
 import scala.util.Random
 
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
-import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.execution.datasources.json.JSONRelation
-import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SQLTestUtils}
+import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SharedSQLContext}
 
-class DataFrameSuite extends QueryTest with SQLTestUtils {
-  import org.apache.spark.sql.TestData._
-
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
+class DataFrameSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("analysis error should be eagerly reported") {
     // Eager analysis.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
index bf8ef9a97bc60..77907e91363ec 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /**
@@ -27,10 +27,8 @@ import org.apache.spark.sql.types._
  * This is here for now so I can make sure Tungsten project is tested without refactoring existing
  * end-to-end test infra. In the long run this should just go away.
  */
-class DataFrameTungstenSuite extends QueryTest with SQLTestUtils {
-
-  override lazy val sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
+class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("test simple types") {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 17897caf952a3..9080c53c491ac 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -22,19 +22,18 @@ import java.text.SimpleDateFormat
 
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.unsafe.types.CalendarInterval
 
-class DateFunctionsSuite extends QueryTest {
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-
-  import ctx.implicits._
+class DateFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("function current_date") {
     val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
     val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
     val d1 = DateTimeUtils.fromJavaDate(df1.select(current_date()).collect().head.getDate(0))
     val d2 = DateTimeUtils.fromJavaDate(
-      ctx.sql("""SELECT CURRENT_DATE()""").collect().head.getDate(0))
+      sql("""SELECT CURRENT_DATE()""").collect().head.getDate(0))
     val d3 = DateTimeUtils.millisToDays(System.currentTimeMillis())
     assert(d0 <= d1 && d1 <= d2 && d2 <= d3 && d3 - d0 <= 1)
   }
@@ -44,9 +43,9 @@ class DateFunctionsSuite extends QueryTest {
     val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
     checkAnswer(df1.select(countDistinct(current_timestamp())), Row(1))
     // Execution in one query should return the same value
-    checkAnswer(ctx.sql("""SELECT CURRENT_TIMESTAMP() = CURRENT_TIMESTAMP()"""),
+    checkAnswer(sql("""SELECT CURRENT_TIMESTAMP() = CURRENT_TIMESTAMP()"""),
       Row(true))
-    assert(math.abs(ctx.sql("""SELECT CURRENT_TIMESTAMP()""").collect().head.getTimestamp(
+    assert(math.abs(sql("""SELECT CURRENT_TIMESTAMP()""").collect().head.getTimestamp(
       0).getTime - System.currentTimeMillis()) < 5000)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index ae07eaf91c872..f5c5046a8ed88 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -17,22 +17,15 @@
 
 package org.apache.spark.sql
 
-import org.scalatest.BeforeAndAfterEach
-
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.execution.joins._
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
 
 
-class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
-  // Ensures tables are loaded.
-  TestData
+class JoinSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  override def sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
-  lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.logicalPlanToSparkQuery
+  setupTestData()
 
   test("equi-join is hash-join") {
     val x = testData2.as("x")
@@ -43,7 +36,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
   }
 
   def assertJoin(sqlString: String, c: Class[_]): Any = {
-    val df = ctx.sql(sqlString)
+    val df = sql(sqlString)
     val physical = df.queryExecution.sparkPlan
     val operators = physical.collect {
       case j: ShuffledHashJoin => j
@@ -126,7 +119,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
 
   test("broadcasted hash join operator selection") {
     ctx.cacheManager.clearCache()
-    ctx.sql("CACHE TABLE testData")
+    sql("CACHE TABLE testData")
     for (sortMergeJoinEnabled <- Seq(true, false)) {
       withClue(s"sortMergeJoinEnabled=$sortMergeJoinEnabled") {
         withSQLConf(SQLConf.SORTMERGE_JOIN.key -> s"$sortMergeJoinEnabled") {
@@ -141,12 +134,12 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         }
       }
     }
-    ctx.sql("UNCACHE TABLE testData")
+    sql("UNCACHE TABLE testData")
   }
 
   test("broadcasted hash outer join operator selection") {
     ctx.cacheManager.clearCache()
-    ctx.sql("CACHE TABLE testData")
+    sql("CACHE TABLE testData")
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
         ("SELECT * FROM testData LEFT JOIN testData2 ON key = a",
@@ -167,7 +160,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
           classOf[BroadcastHashOuterJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
     }
-    ctx.sql("UNCACHE TABLE testData")
+    sql("UNCACHE TABLE testData")
   }
 
   test("multiple-key equi-join is hash-join") {
@@ -279,7 +272,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
     // Make sure we are choosing left.outputPartitioning as the
     // outputPartitioning for the outer join operator.
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.N, count(*)
           |FROM upperCaseData l LEFT OUTER JOIN allNulls r ON (l.N = r.a)
@@ -293,7 +286,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         Row(6, 1) :: Nil)
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.a, count(*)
           |FROM upperCaseData l LEFT OUTER JOIN allNulls r ON (l.N = r.a)
@@ -339,7 +332,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
     // Make sure we are choosing right.outputPartitioning as the
     // outputPartitioning for the outer join operator.
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.a, count(*)
           |FROM allNulls l RIGHT OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -348,7 +341,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
       Row(null, 6))
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.N, count(*)
           |FROM allNulls l RIGHT OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -400,7 +393,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
 
     // Make sure we are UnknownPartitioning as the outputPartitioning for the outer join operator.
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.a, count(*)
           |FROM allNulls l FULL OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -409,7 +402,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
       Row(null, 10))
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.N, count(*)
           |FROM allNulls l FULL OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -424,7 +417,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         Row(null, 4) :: Nil)
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.N, count(*)
           |FROM upperCaseData l FULL OUTER JOIN allNulls r ON (l.N = r.a)
@@ -439,7 +432,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         Row(null, 4) :: Nil)
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.a, count(*)
           |FROM upperCaseData l FULL OUTER JOIN allNulls r ON (l.N = r.a)
@@ -450,7 +443,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
 
   test("broadcasted left semi join operator selection") {
     ctx.cacheManager.clearCache()
-    ctx.sql("CACHE TABLE testData")
+    sql("CACHE TABLE testData")
 
     withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "1000000000") {
       Seq(
@@ -469,11 +462,11 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
       }
     }
 
-    ctx.sql("UNCACHE TABLE testData")
+    sql("UNCACHE TABLE testData")
   }
 
   test("left semi join") {
-    val df = ctx.sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a")
+    val df = sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a")
     checkAnswer(df,
       Row(1, 1) ::
         Row(1, 2) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
index 71c26a6f8d367..045fea82e4c89 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.sql
 
-class JsonFunctionsSuite extends QueryTest {
+import org.apache.spark.sql.test.SharedSQLContext
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class JsonFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("function get_json_object") {
     val df: DataFrame = Seq(("""{"name": "alice", "age": 5}""", "")).toDF("a", "b")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
index 2089660c52bf7..babf8835d2545 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
@@ -19,12 +19,11 @@ package org.apache.spark.sql
 
 import org.scalatest.BeforeAndAfter
 
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{BooleanType, StringType, StructField, StructType}
 
-class ListTablesSuite extends QueryTest with BeforeAndAfter {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContext {
+  import testImplicits._
 
   private lazy val df = (1 to 10).map(i => (i, s"str$i")).toDF("key", "value")
 
@@ -42,7 +41,7 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter {
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
-      ctx.sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"),
+      sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
@@ -55,7 +54,7 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter {
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
-      ctx.sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"),
+      sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
@@ -67,13 +66,13 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter {
       StructField("tableName", StringType, false) ::
       StructField("isTemporary", BooleanType, false) :: Nil)
 
-    Seq(ctx.tables(), ctx.sql("SHOW TABLes")).foreach {
+    Seq(ctx.tables(), sql("SHOW TABLes")).foreach {
       case tableDF =>
         assert(expectedSchema === tableDF.schema)
 
         tableDF.registerTempTable("tables")
         checkAnswer(
-          ctx.sql(
+          sql(
             "SELECT isTemporary, tableName from tables WHERE tableName = 'ListTablesSuiteTable'"),
           Row(true, "ListTablesSuiteTable")
         )
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 8cf2ef5957d8d..30289c3c1d097 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -19,18 +19,16 @@ package org.apache.spark.sql
 
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.functions.{log => logarithm}
+import org.apache.spark.sql.test.SharedSQLContext
 
 private object MathExpressionsTestData {
   case class DoubleData(a: java.lang.Double, b: java.lang.Double)
   case class NullDoubles(a: java.lang.Double)
 }
 
-class MathExpressionsSuite extends QueryTest {
-
+class MathExpressionsSuite extends QueryTest with SharedSQLContext {
   import MathExpressionsTestData._
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+  import testImplicits._
 
   private lazy val doubleData = (1 to 10).map(i => DoubleData(i * 0.2 - 1, i * -0.2 + 1)).toDF()
 
@@ -149,7 +147,7 @@ class MathExpressionsSuite extends QueryTest {
   test("toDegrees") {
     testOneToOneMathFunction(toDegrees, math.toDegrees)
     checkAnswer(
-      ctx.sql("SELECT degrees(0), degrees(1), degrees(1.5)"),
+      sql("SELECT degrees(0), degrees(1), degrees(1.5)"),
       Seq((1, 2)).toDF().select(toDegrees(lit(0)), toDegrees(lit(1)), toDegrees(lit(1.5)))
     )
   }
@@ -157,7 +155,7 @@ class MathExpressionsSuite extends QueryTest {
   test("toRadians") {
     testOneToOneMathFunction(toRadians, math.toRadians)
     checkAnswer(
-      ctx.sql("SELECT radians(0), radians(1), radians(1.5)"),
+      sql("SELECT radians(0), radians(1), radians(1.5)"),
       Seq((1, 2)).toDF().select(toRadians(lit(0)), toRadians(lit(1)), toRadians(lit(1.5)))
     )
   }
@@ -169,7 +167,7 @@ class MathExpressionsSuite extends QueryTest {
   test("ceil and ceiling") {
     testOneToOneMathFunction(ceil, math.ceil)
     checkAnswer(
-      ctx.sql("SELECT ceiling(0), ceiling(1), ceiling(1.5)"),
+      sql("SELECT ceiling(0), ceiling(1), ceiling(1.5)"),
       Row(0.0, 1.0, 2.0))
   }
 
@@ -214,7 +212,7 @@ class MathExpressionsSuite extends QueryTest {
 
     val pi = 3.1415
     checkAnswer(
-      ctx.sql(s"SELECT round($pi, -3), round($pi, -2), round($pi, -1), " +
+      sql(s"SELECT round($pi, -3), round($pi, -2), round($pi, -1), " +
         s"round($pi, 0), round($pi, 1), round($pi, 2), round($pi, 3)"),
       Seq(Row(BigDecimal("0E3"), BigDecimal("0E2"), BigDecimal("0E1"), BigDecimal(3),
         BigDecimal("3.1"), BigDecimal("3.14"), BigDecimal("3.142")))
@@ -233,7 +231,7 @@ class MathExpressionsSuite extends QueryTest {
     testOneToOneMathFunction[Double](signum, math.signum)
 
     checkAnswer(
-      ctx.sql("SELECT sign(10), signum(-11)"),
+      sql("SELECT sign(10), signum(-11)"),
       Row(1, -1))
   }
 
@@ -241,7 +239,7 @@ class MathExpressionsSuite extends QueryTest {
     testTwoToOneMathFunction(pow, pow, math.pow)
 
     checkAnswer(
-      ctx.sql("SELECT pow(1, 2), power(2, 1)"),
+      sql("SELECT pow(1, 2), power(2, 1)"),
       Seq((1, 2)).toDF().select(pow(lit(1), lit(2)), pow(lit(2), lit(1)))
     )
   }
@@ -280,7 +278,7 @@ class MathExpressionsSuite extends QueryTest {
   test("log / ln") {
     testOneToOneNonNegativeMathFunction(org.apache.spark.sql.functions.log, math.log)
     checkAnswer(
-      ctx.sql("SELECT ln(0), ln(1), ln(1.5)"),
+      sql("SELECT ln(0), ln(1), ln(1.5)"),
       Seq((1, 2)).toDF().select(logarithm(lit(0)), logarithm(lit(1)), logarithm(lit(1.5)))
     )
   }
@@ -375,7 +373,7 @@ class MathExpressionsSuite extends QueryTest {
       df.select(log2("b") + log2("a")),
       Row(1))
 
-    checkAnswer(ctx.sql("SELECT LOG2(8), LOG2(null)"), Row(3, null))
+    checkAnswer(sql("SELECT LOG2(8), LOG2(null)"), Row(3, null))
   }
 
   test("sqrt") {
@@ -384,13 +382,13 @@ class MathExpressionsSuite extends QueryTest {
       df.select(sqrt("a"), sqrt("b")),
       Row(1.0, 2.0))
 
-    checkAnswer(ctx.sql("SELECT SQRT(4.0), SQRT(null)"), Row(2.0, null))
+    checkAnswer(sql("SELECT SQRT(4.0), SQRT(null)"), Row(2.0, null))
     checkAnswer(df.selectExpr("sqrt(a)", "sqrt(b)", "sqrt(null)"), Row(1.0, 2.0, null))
   }
 
   test("negative") {
     checkAnswer(
-      ctx.sql("SELECT negative(1), negative(0), negative(-1)"),
+      sql("SELECT negative(1), negative(0), negative(-1)"),
       Row(-1, 0, 1))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index 98ba3c99283a1..4adcefb7dc4b1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -71,12 +71,6 @@ class QueryTest extends PlanTest {
     checkAnswer(df, expectedAnswer.collect())
   }
 
-  def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext) {
-    test(sqlString) {
-      checkAnswer(sqlContext.sql(sqlString), expectedAnswer)
-    }
-  }
-
   /**
    * Asserts that a given [[DataFrame]] will be executed using the given number of cached results.
    */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 8a679c7865d6a..795d4e983f27e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -20,13 +20,12 @@ package org.apache.spark.sql
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, SpecificMutableRow}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
-class RowSuite extends SparkFunSuite {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class RowSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   test("create row") {
     val expected = new GenericMutableRow(4)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 75791e9d53c20..7699adadd9cc8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SQLConfSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
 
+class SQLConfSuite extends QueryTest with SharedSQLContext {
   private val testKey = "test.key.0"
   private val testVal = "test.val.0"
 
@@ -52,21 +51,21 @@ class SQLConfSuite extends QueryTest {
 
   test("parse SQL set commands") {
     ctx.conf.clear()
-    ctx.sql(s"set $testKey=$testVal")
+    sql(s"set $testKey=$testVal")
     assert(ctx.getConf(testKey, testVal + "_") === testVal)
     assert(ctx.getConf(testKey, testVal + "_") === testVal)
 
-    ctx.sql("set some.property=20")
+    sql("set some.property=20")
     assert(ctx.getConf("some.property", "0") === "20")
-    ctx.sql("set some.property = 40")
+    sql("set some.property = 40")
     assert(ctx.getConf("some.property", "0") === "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
-    ctx.sql(s"set $key=$vs")
+    sql(s"set $key=$vs")
     assert(ctx.getConf(key, "0") === vs)
 
-    ctx.sql(s"set $key=")
+    sql(s"set $key=")
     assert(ctx.getConf(key, "0") === "")
 
     ctx.conf.clear()
@@ -74,14 +73,14 @@ class SQLConfSuite extends QueryTest {
 
   test("deprecated property") {
     ctx.conf.clear()
-    ctx.sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
+    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
     assert(ctx.conf.numShufflePartitions === 10)
   }
 
   test("invalid conf value") {
     ctx.conf.clear()
     val e = intercept[IllegalArgumentException] {
-      ctx.sql(s"set ${SQLConf.CASE_SENSITIVE.key}=10")
+      sql(s"set ${SQLConf.CASE_SENSITIVE.key}=10")
     }
     assert(e.getMessage === s"${SQLConf.CASE_SENSITIVE.key} should be boolean, but was 10")
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
index c8d8796568a41..007be12950774 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
@@ -17,16 +17,17 @@
 
 package org.apache.spark.sql
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SQLContextSuite extends SparkFunSuite with BeforeAndAfterAll {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+class SQLContextSuite extends SparkFunSuite with SharedSQLContext {
 
   override def afterAll(): Unit = {
-    SQLContext.setLastInstantiatedContext(ctx)
+    try {
+      SQLContext.setLastInstantiatedContext(ctx)
+    } finally {
+      super.afterAll()
+    }
   }
 
   test("getOrCreate instantiates SQLContext") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index b14ef9bab90cb..8c2c328f8191c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -19,28 +19,23 @@ package org.apache.spark.sql
 
 import java.sql.Timestamp
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.TestData._
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 import org.apache.spark.sql.types._
 
 /** A SQL Dialect for testing purpose, and it can not be nested type */
 class MyDialect extends DefaultParserDialect
 
-class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
-  // Make sure the tables are loaded.
-  TestData
+class SQLQuerySuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
-  import sqlContext.sql
+  setupTestData()
 
   test("having clause") {
     Seq(("one", 1), ("two", 2), ("three", 3), ("one", 5)).toDF("k", "v").registerTempTable("hav")
@@ -60,7 +55,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
   }
 
   test("show functions") {
-    checkAnswer(sql("SHOW functions"), FunctionRegistry.builtin.listFunction().sorted.map(Row(_)))
+    checkAnswer(sql("SHOW functions"),
+      FunctionRegistry.builtin.listFunction().sorted.map(Row(_)))
   }
 
   test("describe functions") {
@@ -178,7 +174,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
     val df = Seq(Tuple1(1), Tuple1(2), Tuple1(3)).toDF("index")
     // we except the id is materialized once
-    val idUDF = udf(() => UUID.randomUUID().toString)
+    val idUDF = org.apache.spark.sql.functions.udf(() => UUID.randomUUID().toString)
 
     val dfWithId = df.withColumn("id", idUDF())
     // Make a new DataFrame (actually the same reference to the old one)
@@ -712,9 +708,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
     checkAnswer(
       sql(
-        """
-          |SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3
-        """.stripMargin),
+        "SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3"),
       Row(2, 1, 2, 2, 1))
   }
 
@@ -1161,7 +1155,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     validateMetadata(sql("SELECT * FROM personWithMeta"))
     validateMetadata(sql("SELECT id, name FROM personWithMeta"))
     validateMetadata(sql("SELECT * FROM personWithMeta JOIN salary ON id = personId"))
-    validateMetadata(sql("SELECT name, salary FROM personWithMeta JOIN salary ON id = personId"))
+    validateMetadata(sql(
+      "SELECT name, salary FROM personWithMeta JOIN salary ON id = personId"))
   }
 
   test("SPARK-3371 Renaming a function expression with group by gives error") {
@@ -1627,7 +1622,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       .toDF("num", "str")
     df.registerTempTable("1one")
 
-    checkAnswer(sqlContext.sql("select count(num) from 1one"), Row(10))
+    checkAnswer(sql("select count(num) from 1one"), Row(10))
 
     sqlContext.dropTempTable("1one")
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
index ab6d3dd96d271..295f02f9a7b5d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.sql.{Date, Timestamp}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.SharedSQLContext
 
 case class ReflectData(
     stringField: String,
@@ -71,17 +72,15 @@ case class ComplexReflectData(
     mapFieldContainsNull: Map[Int, Option[Long]],
     dataField: Data)
 
-class ScalaReflectionRelationSuite extends SparkFunSuite {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class ScalaReflectionRelationSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   test("query case class RDD") {
     val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true,
       new java.math.BigDecimal(1), Date.valueOf("1970-01-01"), new Timestamp(12345), Seq(1, 2, 3))
     Seq(data).toDF().registerTempTable("reflectData")
 
-    assert(ctx.sql("SELECT * FROM reflectData").collect().head ===
+    assert(sql("SELECT * FROM reflectData").collect().head ===
       Row("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true,
         new java.math.BigDecimal(1), Date.valueOf("1970-01-01"),
         new Timestamp(12345), Seq(1, 2, 3)))
@@ -91,7 +90,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
     val data = NullReflectData(null, null, null, null, null, null, null)
     Seq(data).toDF().registerTempTable("reflectNullData")
 
-    assert(ctx.sql("SELECT * FROM reflectNullData").collect().head ===
+    assert(sql("SELECT * FROM reflectNullData").collect().head ===
       Row.fromSeq(Seq.fill(7)(null)))
   }
 
@@ -99,7 +98,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
     val data = OptionalReflectData(None, None, None, None, None, None, None)
     Seq(data).toDF().registerTempTable("reflectOptionalData")
 
-    assert(ctx.sql("SELECT * FROM reflectOptionalData").collect().head ===
+    assert(sql("SELECT * FROM reflectOptionalData").collect().head ===
       Row.fromSeq(Seq.fill(7)(null)))
   }
 
@@ -107,7 +106,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
   test("query binary data") {
     Seq(ReflectBinary(Array[Byte](1))).toDF().registerTempTable("reflectBinary")
 
-    val result = ctx.sql("SELECT data FROM reflectBinary")
+    val result = sql("SELECT data FROM reflectBinary")
       .collect().head(0).asInstanceOf[Array[Byte]]
     assert(result.toSeq === Seq[Byte](1))
   }
@@ -126,7 +125,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
         Nested(None, "abc")))
 
     Seq(data).toDF().registerTempTable("reflectComplexData")
-    assert(ctx.sql("SELECT * FROM reflectComplexData").collect().head ===
+    assert(sql("SELECT * FROM reflectComplexData").collect().head ===
       Row(
         Seq(1, 2, 3),
         Seq(1, 2, null),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
index e55c9e460b791..45d0ee4a8e749 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
@@ -19,13 +19,12 @@ package org.apache.spark.sql
 
 import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SerializationSuite extends SparkFunSuite {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+class SerializationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("[SPARK-5235] SQLContext should be serializable") {
-    val sqlContext = new SQLContext(ctx.sparkContext)
-    new JavaSerializer(new SparkConf()).newInstance().serialize(sqlContext)
+    val _sqlContext = new SQLContext(sqlContext.sparkContext)
+    new JavaSerializer(new SparkConf()).newInstance().serialize(_sqlContext)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index ca298b2434410..cc95eede005d7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -18,13 +18,12 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.Decimal
 
 
-class StringFunctionsSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class StringFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("string concat") {
     val df = Seq[(String, String, String)](("a", "b", null)).toDF("a", "b", "c")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
deleted file mode 100644
index bd9729c431f30..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ /dev/null
@@ -1,197 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import org.apache.spark.sql.test.TestSQLContext.implicits._
-import org.apache.spark.sql.test._
-
-
-case class TestData(key: Int, value: String)
-
-object TestData {
-  val testData = TestSQLContext.sparkContext.parallelize(
-    (1 to 100).map(i => TestData(i, i.toString))).toDF()
-  testData.registerTempTable("testData")
-
-  val negativeData = TestSQLContext.sparkContext.parallelize(
-    (1 to 100).map(i => TestData(-i, (-i).toString))).toDF()
-  negativeData.registerTempTable("negativeData")
-
-  case class LargeAndSmallInts(a: Int, b: Int)
-  val largeAndSmallInts =
-    TestSQLContext.sparkContext.parallelize(
-      LargeAndSmallInts(2147483644, 1) ::
-      LargeAndSmallInts(1, 2) ::
-      LargeAndSmallInts(2147483645, 1) ::
-      LargeAndSmallInts(2, 2) ::
-      LargeAndSmallInts(2147483646, 1) ::
-      LargeAndSmallInts(3, 2) :: Nil).toDF()
-  largeAndSmallInts.registerTempTable("largeAndSmallInts")
-
-  case class TestData2(a: Int, b: Int)
-  val testData2 =
-    TestSQLContext.sparkContext.parallelize(
-      TestData2(1, 1) ::
-      TestData2(1, 2) ::
-      TestData2(2, 1) ::
-      TestData2(2, 2) ::
-      TestData2(3, 1) ::
-      TestData2(3, 2) :: Nil, 2).toDF()
-  testData2.registerTempTable("testData2")
-
-  case class DecimalData(a: BigDecimal, b: BigDecimal)
-
-  val decimalData =
-    TestSQLContext.sparkContext.parallelize(
-      DecimalData(1, 1) ::
-      DecimalData(1, 2) ::
-      DecimalData(2, 1) ::
-      DecimalData(2, 2) ::
-      DecimalData(3, 1) ::
-      DecimalData(3, 2) :: Nil).toDF()
-  decimalData.registerTempTable("decimalData")
-
-  case class BinaryData(a: Array[Byte], b: Int)
-  val binaryData =
-    TestSQLContext.sparkContext.parallelize(
-      BinaryData("12".getBytes(), 1) ::
-      BinaryData("22".getBytes(), 5) ::
-      BinaryData("122".getBytes(), 3) ::
-      BinaryData("121".getBytes(), 2) ::
-      BinaryData("123".getBytes(), 4) :: Nil).toDF()
-  binaryData.registerTempTable("binaryData")
-
-  case class TestData3(a: Int, b: Option[Int])
-  val testData3 =
-    TestSQLContext.sparkContext.parallelize(
-      TestData3(1, None) ::
-      TestData3(2, Some(2)) :: Nil).toDF()
-  testData3.registerTempTable("testData3")
-
-  case class UpperCaseData(N: Int, L: String)
-  val upperCaseData =
-    TestSQLContext.sparkContext.parallelize(
-      UpperCaseData(1, "A") ::
-      UpperCaseData(2, "B") ::
-      UpperCaseData(3, "C") ::
-      UpperCaseData(4, "D") ::
-      UpperCaseData(5, "E") ::
-      UpperCaseData(6, "F") :: Nil).toDF()
-  upperCaseData.registerTempTable("upperCaseData")
-
-  case class LowerCaseData(n: Int, l: String)
-  val lowerCaseData =
-    TestSQLContext.sparkContext.parallelize(
-      LowerCaseData(1, "a") ::
-      LowerCaseData(2, "b") ::
-      LowerCaseData(3, "c") ::
-      LowerCaseData(4, "d") :: Nil).toDF()
-  lowerCaseData.registerTempTable("lowerCaseData")
-
-  case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]])
-  val arrayData =
-    TestSQLContext.sparkContext.parallelize(
-      ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) ::
-      ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil)
-  arrayData.toDF().registerTempTable("arrayData")
-
-  case class MapData(data: scala.collection.Map[Int, String])
-  val mapData =
-    TestSQLContext.sparkContext.parallelize(
-      MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
-      MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
-      MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
-      MapData(Map(1 -> "a4", 2 -> "b4")) ::
-      MapData(Map(1 -> "a5")) :: Nil)
-  mapData.toDF().registerTempTable("mapData")
-
-  case class StringData(s: String)
-  val repeatedData =
-    TestSQLContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
-  repeatedData.toDF().registerTempTable("repeatedData")
-
-  val nullableRepeatedData =
-    TestSQLContext.sparkContext.parallelize(
-      List.fill(2)(StringData(null)) ++
-      List.fill(2)(StringData("test")))
-  nullableRepeatedData.toDF().registerTempTable("nullableRepeatedData")
-
-  case class NullInts(a: Integer)
-  val nullInts =
-    TestSQLContext.sparkContext.parallelize(
-      NullInts(1) ::
-      NullInts(2) ::
-      NullInts(3) ::
-      NullInts(null) :: Nil
-    ).toDF()
-  nullInts.registerTempTable("nullInts")
-
-  val allNulls =
-    TestSQLContext.sparkContext.parallelize(
-      NullInts(null) ::
-      NullInts(null) ::
-      NullInts(null) ::
-      NullInts(null) :: Nil).toDF()
-  allNulls.registerTempTable("allNulls")
-
-  case class NullStrings(n: Int, s: String)
-  val nullStrings =
-    TestSQLContext.sparkContext.parallelize(
-      NullStrings(1, "abc") ::
-      NullStrings(2, "ABC") ::
-      NullStrings(3, null) :: Nil).toDF()
-  nullStrings.registerTempTable("nullStrings")
-
-  case class TableName(tableName: String)
-  TestSQLContext
-    .sparkContext
-    .parallelize(TableName("test") :: Nil)
-    .toDF()
-    .registerTempTable("tableName")
-
-  val unparsedStrings =
-    TestSQLContext.sparkContext.parallelize(
-      "1, A1, true, null" ::
-      "2, B2, false, null" ::
-      "3, C3, true, null" ::
-      "4, D4, true, 2147483644" :: Nil)
-
-  case class IntField(i: Int)
-  // An RDD with 4 elements and 8 partitions
-  val withEmptyParts = TestSQLContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
-  withEmptyParts.toDF().registerTempTable("withEmptyParts")
-
-  case class Person(id: Int, name: String, age: Int)
-  case class Salary(personId: Int, salary: Double)
-  val person = TestSQLContext.sparkContext.parallelize(
-    Person(0, "mike", 30) ::
-    Person(1, "jim", 20) :: Nil).toDF()
-  person.registerTempTable("person")
-  val salary = TestSQLContext.sparkContext.parallelize(
-    Salary(0, 2000.0) ::
-    Salary(1, 1000.0) :: Nil).toDF()
-  salary.registerTempTable("salary")
-
-  case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean)
-  val complexData =
-    TestSQLContext.sparkContext.parallelize(
-      ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true)
-        :: ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false)
-        :: Nil).toDF()
-  complexData.registerTempTable("complexData")
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index 183dc3407b3ab..eb275af101e2f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -17,16 +17,13 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 
-case class FunctionResult(f1: String, f2: String)
+private case class FunctionResult(f1: String, f2: String)
 
-class UDFSuite extends QueryTest with SQLTestUtils {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-
-  override def sqlContext(): SQLContext = ctx
+class UDFSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("built-in fixed arity expressions") {
     val df = ctx.emptyDataFrame
@@ -57,7 +54,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
   test("SPARK-8003 spark_partition_id") {
     val df = Seq((1, "Tearing down the walls that divide us")).toDF("id", "saying")
     df.registerTempTable("tmp_table")
-    checkAnswer(ctx.sql("select spark_partition_id() from tmp_table").toDF(), Row(0))
+    checkAnswer(sql("select spark_partition_id() from tmp_table").toDF(), Row(0))
     ctx.dropTempTable("tmp_table")
   }
 
@@ -66,9 +63,9 @@ class UDFSuite extends QueryTest with SQLTestUtils {
       val data = ctx.sparkContext.parallelize(0 to 10, 2).toDF("id")
       data.write.parquet(dir.getCanonicalPath)
       ctx.read.parquet(dir.getCanonicalPath).registerTempTable("test_table")
-      val answer = ctx.sql("select input_file_name() from test_table").head().getString(0)
+      val answer = sql("select input_file_name() from test_table").head().getString(0)
       assert(answer.contains(dir.getCanonicalPath))
-      assert(ctx.sql("select input_file_name() from test_table").distinct().collect().length >= 2)
+      assert(sql("select input_file_name() from test_table").distinct().collect().length >= 2)
       ctx.dropTempTable("test_table")
     }
   }
@@ -91,17 +88,17 @@ class UDFSuite extends QueryTest with SQLTestUtils {
 
   test("Simple UDF") {
     ctx.udf.register("strLenScala", (_: String).length)
-    assert(ctx.sql("SELECT strLenScala('test')").head().getInt(0) === 4)
+    assert(sql("SELECT strLenScala('test')").head().getInt(0) === 4)
   }
 
   test("ZeroArgument UDF") {
     ctx.udf.register("random0", () => { Math.random()})
-    assert(ctx.sql("SELECT random0()").head().getDouble(0) >= 0.0)
+    assert(sql("SELECT random0()").head().getDouble(0) >= 0.0)
   }
 
   test("TwoArgument UDF") {
     ctx.udf.register("strLenScala", (_: String).length + (_: Int))
-    assert(ctx.sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5)
+    assert(sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5)
   }
 
   test("UDF in a WHERE") {
@@ -112,7 +109,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("integerData")
 
     val result =
-      ctx.sql("SELECT * FROM integerData WHERE oneArgFilter(key)")
+      sql("SELECT * FROM integerData WHERE oneArgFilter(key)")
     assert(result.count() === 20)
   }
 
@@ -124,7 +121,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("groupData")
 
     val result =
-      ctx.sql(
+      sql(
         """
          | SELECT g, SUM(v) as s
          | FROM groupData
@@ -143,7 +140,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("groupData")
 
     val result =
-      ctx.sql(
+      sql(
         """
          | SELECT SUM(v)
          | FROM groupData
@@ -163,7 +160,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("groupData")
 
     val result =
-      ctx.sql(
+      sql(
         """
          | SELECT timesHundred(SUM(v)) as v100
          | FROM groupData
@@ -178,7 +175,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     ctx.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
 
     val result =
-      ctx.sql("SELECT returnStruct('test', 'test2') as ret")
+      sql("SELECT returnStruct('test', 'test2') as ret")
         .select($"ret.f1").head().getString(0)
     assert(result === "test")
   }
@@ -186,12 +183,12 @@ class UDFSuite extends QueryTest with SQLTestUtils {
   test("udf that is transformed") {
     ctx.udf.register("makeStruct", (x: Int, y: Int) => (x, y))
     // 1 + 1 is constant folded causing a transformation.
-    assert(ctx.sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2))
+    assert(sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2))
   }
 
   test("type coercion for udf inputs") {
     ctx.udf.register("intExpected", (x: Int) => x)
     // pass a decimal to intExpected.
-    assert(ctx.sql("SELECT intExpected(1.0)").head().getInt(0) === 1)
+    assert(sql("SELECT intExpected(1.0)").head().getInt(0) === 1)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index 9181222f6922b..b6d279ae47268 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -24,6 +24,7 @@ import com.clearspring.analytics.stream.cardinality.HyperLogLog
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions.{OpenHashSetUDT, HyperLogLogUDT}
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.OpenHashSet
@@ -66,10 +67,8 @@ private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] {
   private[spark] override def asNullable: MyDenseVectorUDT = this
 }
 
-class UserDefinedTypeSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   private lazy val pointsRDD = Seq(
     MyLabeledPoint(1.0, new MyDenseVector(Array(0.1, 1.0))),
@@ -94,7 +93,7 @@ class UserDefinedTypeSuite extends QueryTest {
     ctx.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector])
     pointsRDD.registerTempTable("points")
     checkAnswer(
-      ctx.sql("SELECT testType(features) from points"),
+      sql("SELECT testType(features) from points"),
       Seq(Row(true), Row(true)))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 9bca4e7e660d6..952637c5f9cb8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -19,18 +19,16 @@ package org.apache.spark.sql.columnar
 
 import java.sql.{Date, Timestamp}
 
-import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{QueryTest, Row, TestData}
 import org.apache.spark.storage.StorageLevel.MEMORY_ONLY
 
-class InMemoryColumnarQuerySuite extends QueryTest {
-  // Make sure the tables are loaded.
-  TestData
+class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.{logicalPlanToSparkQuery, sql}
+  setupTestData()
 
   test("simple columnar query") {
     val plan = ctx.executePlan(testData.logicalPlan).executedPlan
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
index 2c0879927a129..ab2644eb4581d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
@@ -17,20 +17,19 @@
 
 package org.apache.spark.sql.columnar
 
-import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
-
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 
-class PartitionBatchPruningSuite extends SparkFunSuite with BeforeAndAfterAll with BeforeAndAfter {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class PartitionBatchPruningSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   private lazy val originalColumnBatchSize = ctx.conf.columnBatchSize
   private lazy val originalInMemoryPartitionPruning = ctx.conf.inMemoryPartitionPruning
 
   override protected def beforeAll(): Unit = {
+    super.beforeAll()
     // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch
     ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, 10)
 
@@ -44,19 +43,17 @@ class PartitionBatchPruningSuite extends SparkFunSuite with BeforeAndAfterAll wi
     ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
     // Enable in-memory table scan accumulators
     ctx.setConf("spark.sql.inMemoryTableScanStatistics.enable", "true")
-  }
-
-  override protected def afterAll(): Unit = {
-    ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
-    ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
-  }
-
-  before {
     ctx.cacheTable("pruningData")
   }
 
-  after {
-    ctx.uncacheTable("pruningData")
+  override protected def afterAll(): Unit = {
+    try {
+      ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
+      ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
+      ctx.uncacheTable("pruningData")
+    } finally {
+      super.afterAll()
+    }
   }
 
   // Comparisons
@@ -110,7 +107,7 @@ class PartitionBatchPruningSuite extends SparkFunSuite with BeforeAndAfterAll wi
       expectedQueryResult: => Seq[Int]): Unit = {
 
     test(query) {
-      val df = ctx.sql(query)
+      val df = sql(query)
       val queryExecution = df.queryExecution
 
       assertResult(expectedQueryResult.toArray, s"Wrong query result: $queryExecution") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
index 79e903c2bbd40..8998f5111124c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.plans.physical.SinglePartition
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ExchangeSuite extends SparkPlanTest {
+class ExchangeSuite extends SparkPlanTest with SharedSQLContext {
   test("shuffling UnsafeRows in exchange") {
     val input = (1 to 1000).map(Tuple1.apply)
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 5582caa0d366e..937a108543531 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.{execution, Row, SQLConf}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, Literal, SortOrder}
 import org.apache.spark.sql.catalyst.plans._
@@ -27,19 +27,18 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin}
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
-import org.apache.spark.sql.test.TestSQLContext._
-import org.apache.spark.sql.test.TestSQLContext.implicits._
-import org.apache.spark.sql.test.TestSQLContext.planner._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{SQLContext, Row, SQLConf, execution}
 
 
-class PlannerSuite extends SparkFunSuite with SQLTestUtils {
+class PlannerSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
-  override def sqlContext: SQLContext = TestSQLContext
+  setupTestData()
 
   private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
+    val _ctx = ctx
+    import _ctx.planner._
     val plannedOption = HashAggregation(query).headOption.orElse(Aggregation(query).headOption)
     val planned =
       plannedOption.getOrElse(
@@ -54,6 +53,8 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
   }
 
   test("unions are collapsed") {
+    val _ctx = ctx
+    import _ctx.planner._
     val query = testData.unionAll(testData).unionAll(testData).logicalPlan
     val planned = BasicOperators(query).head
     val logicalUnions = query collect { case u: logical.Union => u }
@@ -81,14 +82,14 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
 
   test("sizeInBytes estimation of limit operator for broadcast hash join optimization") {
     def checkPlan(fieldTypes: Seq[DataType], newThreshold: Int): Unit = {
-      setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold)
+      ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold)
       val fields = fieldTypes.zipWithIndex.map {
         case (dataType, index) => StructField(s"c${index}", dataType, true)
       } :+ StructField("key", IntegerType, true)
       val schema = StructType(fields)
       val row = Row.fromSeq(Seq.fill(fields.size)(null))
-      val rowRDD = org.apache.spark.sql.test.TestSQLContext.sparkContext.parallelize(row :: Nil)
-      createDataFrame(rowRDD, schema).registerTempTable("testLimit")
+      val rowRDD = ctx.sparkContext.parallelize(row :: Nil)
+      ctx.createDataFrame(rowRDD, schema).registerTempTable("testLimit")
 
       val planned = sql(
         """
@@ -102,10 +103,10 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
       assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
       assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
 
-      dropTempTable("testLimit")
+      ctx.dropTempTable("testLimit")
     }
 
-    val origThreshold = conf.autoBroadcastJoinThreshold
+    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
 
     val simpleTypes =
       NullType ::
@@ -137,18 +138,18 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
 
     checkPlan(complexTypes, newThreshold = 901617)
 
-    setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
   }
 
   test("InMemoryRelation statistics propagation") {
-    val origThreshold = conf.autoBroadcastJoinThreshold
-    setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920)
+    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
+    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920)
 
     testData.limit(3).registerTempTable("tiny")
     sql("CACHE TABLE tiny")
 
     val a = testData.as("a")
-    val b = table("tiny").as("b")
+    val b = ctx.table("tiny").as("b")
     val planned = a.join(b, $"a.key" === $"b.key").queryExecution.executedPlan
 
     val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
@@ -157,12 +158,12 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
     assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
     assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
 
-    setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
   }
 
   test("efficient limit -> project -> sort") {
     val query = testData.sort('key).select('value).limit(2).logicalPlan
-    val planned = planner.TakeOrderedAndProject(query)
+    val planned = ctx.planner.TakeOrderedAndProject(query)
     assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index dd08e9025a927..ef6ad59b71fb3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -21,11 +21,11 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute, Literal, IsNull}
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.types.{GenericArrayData, ArrayType, StructType, StringType}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.{GenericArrayData, ArrayType, StringType}
 import org.apache.spark.unsafe.types.UTF8String
 
-class RowFormatConvertersSuite extends SparkPlanTest {
+class RowFormatConvertersSuite extends SparkPlanTest with SharedSQLContext {
 
   private def getConverters(plan: SparkPlan): Seq[SparkPlan] = plan.collect {
     case c: ConvertToUnsafe => c
@@ -39,20 +39,20 @@ class RowFormatConvertersSuite extends SparkPlanTest {
 
   test("planner should insert unsafe->safe conversions when required") {
     val plan = Limit(10, outputsUnsafe)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(preparedPlan.children.head.isInstanceOf[ConvertToSafe])
   }
 
   test("filter can process unsafe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsUnsafe)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).size === 1)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("filter can process safe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsSafe)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).isEmpty)
     assert(!preparedPlan.outputsUnsafeRows)
   }
@@ -67,33 +67,33 @@ class RowFormatConvertersSuite extends SparkPlanTest {
   test("union requires all of its input rows' formats to agree") {
     val plan = Union(Seq(outputsSafe, outputsUnsafe))
     assert(plan.canProcessSafeRows && plan.canProcessUnsafeRows)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process safe rows") {
     val plan = Union(Seq(outputsSafe, outputsSafe))
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(!preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process unsafe rows") {
     val plan = Union(Seq(outputsUnsafe, outputsUnsafe))
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("round trip with ConvertToUnsafe and ConvertToSafe") {
     val input = Seq(("hello", 1), ("world", 2))
     checkAnswer(
-      TestSQLContext.createDataFrame(input),
+      ctx.createDataFrame(input),
       plan => ConvertToSafe(ConvertToUnsafe(plan)),
       input.map(Row.fromTuple)
     )
   }
 
   test("SPARK-9683: copy UTF8String when convert unsafe array/map to safe") {
-    SparkPlan.currentContext.set(TestSQLContext)
+    SparkPlan.currentContext.set(ctx)
     val schema = ArrayType(StringType)
     val rows = (1 to 100).map { i =>
       InternalRow(new GenericArrayData(Array[Any](UTF8String.fromString(i.toString))))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
index a2c10fdaf6cdb..8fa77b0fcb7b7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SortSuite extends SparkPlanTest {
+class SortSuite extends SparkPlanTest with SharedSQLContext {
 
   // This test was originally added as an example of how to use [[SparkPlanTest]];
   // it's not designed to be a comprehensive test of ExternalSort.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index f46855edfe0de..3a87f374d94b0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -17,29 +17,27 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.util._
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{SQLContext, DataFrame, DataFrameHolder, Row}
-
 import scala.language.implicitConversions
 import scala.reflect.runtime.universe.TypeTag
 import scala.util.control.NonFatal
 
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.{DataFrame, DataFrameHolder, Row, SQLContext}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.util._
+
 /**
  * Base class for writing tests for individual physical operators. For an example of how this
  * class's test helper methods can be used, see [[SortSuite]].
  */
-class SparkPlanTest extends SparkFunSuite {
-
-  protected def sqlContext: SQLContext = TestSQLContext
+private[sql] abstract class SparkPlanTest extends SparkFunSuite {
+  protected def _sqlContext: SQLContext
 
   /**
    * Creates a DataFrame from a local Seq of Product.
    */
   implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = {
-    sqlContext.implicits.localSeqToDataFrameHolder(data)
+    _sqlContext.implicits.localSeqToDataFrameHolder(data)
   }
 
   /**
@@ -100,7 +98,7 @@ class SparkPlanTest extends SparkFunSuite {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean = true): Unit = {
-    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, sqlContext) match {
+    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, _sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -124,7 +122,7 @@ class SparkPlanTest extends SparkFunSuite {
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean = true): Unit = {
     SparkPlanTest.checkAnswer(
-        input, planFunction, expectedPlanFunction, sortAnswers, sqlContext) match {
+        input, planFunction, expectedPlanFunction, sortAnswers, _sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -151,13 +149,13 @@ object SparkPlanTest {
       planFunction: SparkPlan => SparkPlan,
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean,
-      sqlContext: SQLContext): Option[String] = {
+      _sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.queryExecution.sparkPlan)
     val expectedOutputPlan = expectedPlanFunction(input.queryExecution.sparkPlan)
 
     val expectedAnswer: Seq[Row] = try {
-      executePlan(expectedOutputPlan, sqlContext)
+      executePlan(expectedOutputPlan, _sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -172,7 +170,7 @@ object SparkPlanTest {
     }
 
     val actualAnswer: Seq[Row] = try {
-      executePlan(outputPlan, sqlContext)
+      executePlan(outputPlan, _sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -212,12 +210,12 @@ object SparkPlanTest {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean,
-      sqlContext: SQLContext): Option[String] = {
+      _sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.map(_.queryExecution.sparkPlan))
 
     val sparkAnswer: Seq[Row] = try {
-      executePlan(outputPlan, sqlContext)
+      executePlan(outputPlan, _sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -280,10 +278,10 @@ object SparkPlanTest {
     }
   }
 
-  private def executePlan(outputPlan: SparkPlan, sqlContext: SQLContext): Seq[Row] = {
+  private def executePlan(outputPlan: SparkPlan, _sqlContext: SQLContext): Seq[Row] = {
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
-    val resolvedPlan = sqlContext.prepareForExecution.execute(
+    val resolvedPlan = _sqlContext.prepareForExecution.execute(
       outputPlan transform {
         case plan: SparkPlan =>
           val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index 88bce0e319f9e..3158458edb831 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -19,25 +19,28 @@ package org.apache.spark.sql.execution
 
 import scala.util.Random
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.{RandomDataGenerator, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /**
  * A test suite that generates randomized data to test the [[TungstenSort]] operator.
  */
-class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
+class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
 
   override def beforeAll(): Unit = {
-    TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
+    super.beforeAll()
+    ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
   }
 
   override def afterAll(): Unit = {
-    TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
+    try {
+      ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
+    } finally {
+      super.afterAll()
+    }
   }
 
   test("sort followed by limit") {
@@ -61,7 +64,7 @@ class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
   }
 
   test("sorting updates peak execution memory") {
-    val sc = TestSQLContext.sparkContext
+    val sc = ctx.sparkContext
     AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "unsafe external sort") {
       checkThatPlansAgree(
         (1 to 100).map(v => Tuple1(v)).toDF("a"),
@@ -80,8 +83,8 @@ class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
   ) {
     test(s"sorting on $dataType with nullable=$nullable, sortOrder=$sortOrder") {
       val inputData = Seq.fill(1000)(randomDataGenerator())
-      val inputDf = TestSQLContext.createDataFrame(
-        TestSQLContext.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
+      val inputDf = ctx.createDataFrame(
+        ctx.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)
       )
       assert(TungstenSort.supportsSchema(inputDf.schema))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index e03473041c3e9..d1f0b2b1fc52f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -26,7 +26,7 @@ import org.scalatest.Matchers
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
 import org.apache.spark.{TaskContextImpl, TaskContext, SparkFunSuite}
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.unsafe.types.UTF8String
@@ -36,7 +36,10 @@ import org.apache.spark.unsafe.types.UTF8String
  *
  * Use [[testWithMemoryLeakDetection]] rather than [[test]] to construct test cases.
  */
-class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
+class UnsafeFixedWidthAggregationMapSuite
+  extends SparkFunSuite
+  with Matchers
+  with SharedSQLContext {
 
   import UnsafeFixedWidthAggregationMap._
 
@@ -171,9 +174,6 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   }
 
   testWithMemoryLeakDetection("test external sorting") {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
-
     // Memory consumption in the beginning of the task.
     val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
 
@@ -233,8 +233,6 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   }
 
   testWithMemoryLeakDetection("test external sorting with an empty map") {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
 
     val map = new UnsafeFixedWidthAggregationMap(
       emptyAggregationBuffer,
@@ -282,8 +280,6 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   }
 
   testWithMemoryLeakDetection("test external sorting with empty records") {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
 
     // Memory consumption in the beginning of the task.
     val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index a9515a03acf2c..d3be568a8758c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -23,15 +23,14 @@ import org.apache.spark._
 import org.apache.spark.sql.{RandomDataGenerator, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, UnsafeRow, UnsafeProjection}
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 
 /**
  * Test suite for [[UnsafeKVExternalSorter]], with randomly generated test data.
  */
-class UnsafeKVExternalSorterSuite extends SparkFunSuite {
-
+class UnsafeKVExternalSorterSuite extends SparkFunSuite with SharedSQLContext {
   private val keyTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
   private val valueTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
 
@@ -109,8 +108,6 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
       inputData: Seq[(InternalRow, InternalRow)],
       pageSize: Long,
       spill: Boolean): Unit = {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
 
     val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
     val shuffleMemMgr = new TestShuffleMemoryManager
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
index ac22c2f3c0a58..5fdb82b067728 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
@@ -21,15 +21,12 @@ import org.apache.spark._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection
 import org.apache.spark.sql.execution.metric.SQLMetrics
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.unsafe.memory.TaskMemoryManager
 
-class TungstenAggregationIteratorSuite extends SparkFunSuite {
+class TungstenAggregationIteratorSuite extends SparkFunSuite with SharedSQLContext {
 
   test("memory acquired on construction") {
-    // set up environment
-    val ctx = TestSQLContext
-
     val taskMemoryManager = new TaskMemoryManager(SparkEnv.get.executorMemoryManager)
     val taskContext = new TaskContextImpl(0, 0, 0, 0, taskMemoryManager, null, Seq.empty)
     TaskContext.setTaskContext(taskContext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 73d5621897819..1174b27732f22 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -24,22 +24,16 @@ import com.fasterxml.jackson.core.JsonFactory
 import org.apache.spark.rdd.RDD
 import org.scalactic.Tolerance._
 
-import org.apache.spark.sql.{SQLContext, QueryTest, Row, SQLConf}
-import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.{QueryTest, Row, SQLConf}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
 import org.apache.spark.sql.execution.datasources.json.InferSchema.compatibleType
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.util.Utils
 
-class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
-
-  protected lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  override def sqlContext: SQLContext = ctx // used by SQLTestUtils
-
-  import ctx.sql
-  import ctx.implicits._
+class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
+  import testImplicits._
 
   test("Type promotion") {
     def checkTypePromotion(expected: Any, actual: Any) {
@@ -596,7 +590,8 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
 
     val schema = StructType(StructField("a", LongType, true) :: Nil)
     val logicalRelation =
-      ctx.read.schema(schema).json(path).queryExecution.analyzed.asInstanceOf[LogicalRelation]
+      ctx.read.schema(schema).json(path)
+        .queryExecution.analyzed.asInstanceOf[LogicalRelation]
     val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation]
     assert(relationWithSchema.paths === Array(path))
     assert(relationWithSchema.schema === schema)
@@ -1040,31 +1035,29 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
   }
 
   test("JSONRelation equality test") {
-    val context = org.apache.spark.sql.test.TestSQLContext
-
     val relation0 = new JSONRelation(
       Some(empty),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation0 = LogicalRelation(relation0)
     val relation1 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation1 = LogicalRelation(relation1)
     val relation2 = new JSONRelation(
       Some(singleRow),
       0.5,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation2 = LogicalRelation(relation2)
     val relation3 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("b", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation3 = LogicalRelation(relation3)
 
     assert(relation0 !== relation1)
@@ -1089,14 +1082,14 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
         .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
 
       val d1 = ResolvedDataSource(
-        context,
+        ctx,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
         options = Map("path" -> path))
 
       val d2 = ResolvedDataSource(
-        context,
+        ctx,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
@@ -1162,11 +1155,12 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
         "abd")
 
         ctx.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
-        checkAnswer(
-          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
-        checkAnswer(
-          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abd'"), Row(5))
-        checkAnswer(sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1"), Row(9))
+        checkAnswer(sql(
+          "SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
+        checkAnswer(sql(
+          "SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abd'"), Row(5))
+        checkAnswer(sql(
+          "SELECT count(a) FROM test_myjson_with_part where d1 = 1"), Row(9))
     })
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
index 6b62c9a003df6..2864181cf91d5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
@@ -20,12 +20,11 @@ package org.apache.spark.sql.execution.datasources.json
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
 
-trait TestJsonData {
-
-  protected def ctx: SQLContext
+private[json] trait TestJsonData {
+  protected def _sqlContext: SQLContext
 
   def primitiveFieldAndType: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"string":"this is a simple string.",
           "integer":10,
           "long":21474836470,
@@ -36,7 +35,7 @@ trait TestJsonData {
       }"""  :: Nil)
 
   def primitiveFieldValueTypeConflict: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"num_num_1":11, "num_num_2":null, "num_num_3": 1.1,
           "num_bool":true, "num_str":13.1, "str_bool":"str1"}""" ::
       """{"num_num_1":null, "num_num_2":21474836470.9, "num_num_3": null,
@@ -47,14 +46,14 @@ trait TestJsonData {
           "num_bool":null, "num_str":92233720368547758070, "str_bool":null}""" :: Nil)
 
   def jsonNullStruct: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"nullstr":"","ip":"27.31.100.29","headers":{"Host":"1.abc.com","Charset":"UTF-8"}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":{}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":""}""" ::
         """{"nullstr":null,"ip":"27.31.100.29","headers":null}""" :: Nil)
 
   def complexFieldValueTypeConflict: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"num_struct":11, "str_array":[1, 2, 3],
           "array":[], "struct_array":[], "struct": {}}""" ::
       """{"num_struct":{"field":false}, "str_array":null,
@@ -65,14 +64,14 @@ trait TestJsonData {
           "array":[7], "struct_array":{"field": true}, "struct": {"field": "str"}}""" :: Nil)
 
   def arrayElementTypeConflict: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"array1": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}],
           "array2": [{"field":214748364700}, {"field":1}]}""" ::
       """{"array3": [{"field":"str"}, {"field":1}]}""" ::
       """{"array3": [1, 2, 3]}""" :: Nil)
 
   def missingFields: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"a":true}""" ::
       """{"b":21474836470}""" ::
       """{"c":[33, 44]}""" ::
@@ -80,7 +79,7 @@ trait TestJsonData {
       """{"e":"str"}""" :: Nil)
 
   def complexFieldAndType1: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"struct":{"field1": true, "field2": 92233720368547758070},
           "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]},
           "arrayOfString":["str1", "str2"],
@@ -96,7 +95,7 @@ trait TestJsonData {
          }"""  :: Nil)
 
   def complexFieldAndType2: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}],
           "complexArrayOfStruct": [
           {
@@ -150,7 +149,7 @@ trait TestJsonData {
       }""" :: Nil)
 
   def mapType1: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"map": {"a": 1}}""" ::
       """{"map": {"b": 2}}""" ::
       """{"map": {"c": 3}}""" ::
@@ -158,7 +157,7 @@ trait TestJsonData {
       """{"map": {"e": null}}""" :: Nil)
 
   def mapType2: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"map": {"a": {"field1": [1, 2, 3, null]}}}""" ::
       """{"map": {"b": {"field2": 2}}}""" ::
       """{"map": {"c": {"field1": [], "field2": 4}}}""" ::
@@ -167,21 +166,21 @@ trait TestJsonData {
       """{"map": {"f": {"field1": null}}}""" :: Nil)
 
   def nullsInArrays: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"field1":[[null], [[["Test"]]]]}""" ::
       """{"field2":[null, [{"Test":1}]]}""" ::
       """{"field3":[[null], [{"Test":"2"}]]}""" ::
       """{"field4":[[null, [1,2,3]]]}""" :: Nil)
 
   def jsonArray: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """[{"a":"str_a_1"}]""" ::
       """[{"a":"str_a_2"}, {"b":"str_b_3"}]""" ::
       """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" ::
       """[]""" :: Nil)
 
   def corruptRecords: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{""" ::
       """""" ::
       """{"a":1, b:2}""" ::
@@ -190,7 +189,7 @@ trait TestJsonData {
       """]""" :: Nil)
 
   def emptyRecords: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{""" ::
         """""" ::
         """{"a": {}}""" ::
@@ -198,9 +197,8 @@ trait TestJsonData {
         """{"b": [{"c": {}}]}""" ::
         """]""" :: Nil)
 
-  lazy val singleRow: RDD[String] =
-    ctx.sparkContext.parallelize(
-      """{"a":123}""" :: Nil)
 
-  def empty: RDD[String] = ctx.sparkContext.parallelize(Seq[String]())
+  lazy val singleRow: RDD[String] = _sqlContext.sparkContext.parallelize("""{"a":123}""" :: Nil)
+
+  def empty: RDD[String] = _sqlContext.sparkContext.parallelize(Seq[String]())
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 866a975ad5404..82d40e2b61a10 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -27,18 +27,16 @@ import org.apache.avro.generic.IndexedRecord
 import org.apache.hadoop.fs.Path
 import org.apache.parquet.avro.AvroParquetWriter
 
-import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat, ParquetEnum, Suit}
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.execution.datasources.parquet.test.avro._
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
+class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
   import ParquetCompatibilityTest._
 
-  override val sqlContext: SQLContext = TestSQLContext
-
   private def withWriter[T <: IndexedRecord]
       (path: String, schema: Schema)
-      (f: AvroParquetWriter[T] => Unit) = {
+      (f: AvroParquetWriter[T] => Unit): Unit = {
     val writer = new AvroParquetWriter[T](new Path(path), schema)
     try f(writer) finally writer.close()
   }
@@ -129,7 +127,7 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
   }
 
   test("SPARK-9407 Don't push down predicates involving Parquet ENUM columns") {
-    import sqlContext.implicits._
+    import testImplicits._
 
     withTempPath { dir =>
       val path = dir.getCanonicalPath
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 0ea64aa2a509b..b3406729fcc5e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -22,16 +22,18 @@ import scala.collection.JavaConversions._
 import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.parquet.hadoop.ParquetFileReader
 import org.apache.parquet.schema.MessageType
-import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.QueryTest
 
-abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with BeforeAndAfterAll {
-  def readParquetSchema(path: String): MessageType = {
+/**
+ * Helper class for testing Parquet compatibility.
+ */
+private[sql] abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest {
+  protected def readParquetSchema(path: String): MessageType = {
     readParquetSchema(path, { path => !path.getName.startsWith("_") })
   }
 
-  def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
+  protected def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
     val fsPath = new Path(path)
     val fs = fsPath.getFileSystem(configuration)
     val parquetFiles = fs.listStatus(fsPath, new PathFilter {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index 7dd9680d8cd65..5b4e568bb9838 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -20,12 +20,13 @@ package org.apache.spark.sql.execution.datasources.parquet
 import org.apache.parquet.filter2.predicate.Operators._
 import org.apache.parquet.filter2.predicate.{FilterPredicate, Operators}
 
+import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
 
 /**
  * A test suite that tests Parquet filter2 API based filter pushdown optimization.
@@ -39,8 +40,7 @@ import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
  * 2. `Tuple1(Option(x))` is used together with `AnyVal` types like `Int` to ensure the inferred
  *    data type is nullable.
  */
-class ParquetFilterSuite extends QueryTest with ParquetTest {
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
+class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   private def checkFilterPredicate(
       df: DataFrame,
@@ -301,7 +301,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest {
   }
 
   test("SPARK-6554: don't push down predicates which reference partition columns") {
-    import sqlContext.implicits._
+    import testImplicits._
 
     withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") {
       withTempPath { dir =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index cb166349fdb26..d819f3ab5e6ab 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -37,6 +37,7 @@ import org.apache.spark.SparkException
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 // Write support class for nested groups: ParquetWriter initializes GroupWriteSupport
@@ -62,9 +63,8 @@ private[parquet] class TestGroupWriteSupport(schema: MessageType) extends WriteS
 /**
  * A test suite that tests basic Parquet I/O.
  */
-class ParquetIOSuite extends QueryTest with ParquetTest {
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
+class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
+  import testImplicits._
 
   /**
    * Writes `data` to a Parquet file, reads it back and check file contents.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
index 73152de244759..ed8bafb10c60b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
@@ -26,13 +26,13 @@ import scala.collection.mutable.ArrayBuffer
 import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
 
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Literal
 import org.apache.spark.sql.execution.datasources.{LogicalRelation, PartitionSpec, Partition, PartitioningUtils}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql._
 import org.apache.spark.unsafe.types.UTF8String
-import PartitioningUtils._
 
 // The data where the partitioning key exists only in the directory structure.
 case class ParquetData(intField: Int, stringField: String)
@@ -40,11 +40,9 @@ case class ParquetData(intField: Int, stringField: String)
 // The data that also includes the partitioning key
 case class ParquetDataWithKey(intField: Int, pi: Int, stringField: String, ps: String)
 
-class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest {
-
-  override lazy val sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
-  import sqlContext.sql
+class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest with SharedSQLContext {
+  import PartitioningUtils._
+  import testImplicits._
 
   val defaultPartitionName = "__HIVE_DEFAULT_PARTITION__"
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
index 981334cf771cf..b290429c2a021 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ParquetProtobufCompatibilitySuite extends ParquetCompatibilityTest {
-  override def sqlContext: SQLContext = TestSQLContext
+class ParquetProtobufCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
 
   private def readParquetProtobufFile(name: String): DataFrame = {
     val url = Thread.currentThread().getContextClassLoader.getResource(name)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index 5e6d9c1cd44a8..e2f2a8c744783 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -21,16 +21,15 @@ import java.io.File
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.sql.types._
 import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
 /**
  * A test suite that tests various Parquet queries.
  */
-class ParquetQuerySuite extends QueryTest with ParquetTest {
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.sql
+class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   test("simple select queries") {
     withParquetTable((0 until 10).map(i => (i, i.toString)), "t") {
@@ -41,22 +40,22 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
 
   test("appending") {
     val data = (0 until 10).map(i => (i, i.toString))
-    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT INTO TABLE t SELECT * FROM tmp")
-      checkAnswer(sqlContext.table("t"), (data ++ data).map(Row.fromTuple))
+      checkAnswer(ctx.table("t"), (data ++ data).map(Row.fromTuple))
     }
-    sqlContext.catalog.unregisterTable(Seq("tmp"))
+    ctx.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("overwriting") {
     val data = (0 until 10).map(i => (i, i.toString))
-    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT OVERWRITE TABLE t SELECT * FROM tmp")
-      checkAnswer(sqlContext.table("t"), data.map(Row.fromTuple))
+      checkAnswer(ctx.table("t"), data.map(Row.fromTuple))
     }
-    sqlContext.catalog.unregisterTable(Seq("tmp"))
+    ctx.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("self-join") {
@@ -119,9 +118,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
     val schema = StructType(List(StructField("d", DecimalType(18, 0), false),
       StructField("time", TimestampType, false)).toArray)
     withTempPath { file =>
-      val df = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(data), schema)
+      val df = ctx.createDataFrame(ctx.sparkContext.parallelize(data), schema)
       df.write.parquet(file.getCanonicalPath)
-      val df2 = sqlContext.read.parquet(file.getCanonicalPath)
+      val df2 = ctx.read.parquet(file.getCanonicalPath)
       checkAnswer(df2, df.collect().toSeq)
     }
   }
@@ -130,12 +129,12 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
         // delete summary files, so if we don't merge part-files, one column will not be included.
         Utils.deleteRecursively(new File(basePath + "/foo=1/_metadata"))
         Utils.deleteRecursively(new File(basePath + "/foo=1/_common_metadata"))
-        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
+        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -154,9 +153,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
-        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
+        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -172,19 +171,19 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
   test("SPARK-8990 DataFrameReader.parquet() should respect user specified options") {
     withTempPath { dir =>
       val basePath = dir.getCanonicalPath
-      sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-      sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
+      ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+      ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
 
       // Disables the global SQL option for schema merging
       withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "false") {
         assertResult(2) {
           // Disables schema merging via data source option
-          sqlContext.read.option("mergeSchema", "false").parquet(basePath).columns.length
+          ctx.read.option("mergeSchema", "false").parquet(basePath).columns.length
         }
 
         assertResult(3) {
           // Enables schema merging via data source option
-          sqlContext.read.option("mergeSchema", "true").parquet(basePath).columns.length
+          ctx.read.option("mergeSchema", "true").parquet(basePath).columns.length
         }
       }
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 971f71e27bfc6..9dcbc1a047bea 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -22,13 +22,11 @@ import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.parquet.schema.MessageTypeParser
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.ScalaReflection
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
-abstract class ParquetSchemaTest extends SparkFunSuite with ParquetTest {
-  val sqlContext = TestSQLContext
+abstract class ParquetSchemaTest extends ParquetTest with SharedSQLContext {
 
   /**
    * Checks whether the reflected Parquet message type for product type `T` conforms `messageType`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
index 3c6e54db4bca7..5dbc7d1630f27 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
@@ -22,9 +22,8 @@ import java.io.File
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.{DataFrame, SaveMode}
+import org.apache.spark.sql.{DataFrame, SaveMode, SQLContext}
 
 /**
  * A helper trait that provides convenient facilities for Parquet testing.
@@ -33,7 +32,9 @@ import org.apache.spark.sql.{DataFrame, SaveMode}
  * convenient to use tuples rather than special case classes when writing test cases/suites.
  * Especially, `Tuple1.apply` can be used to easily wrap a single type/value.
  */
-private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
+private[sql] trait ParquetTest extends SQLTestUtils {
+  protected def _sqlContext: SQLContext
+
   /**
    * Writes `data` to a Parquet file, which is then passed to `f` and will be deleted after `f`
    * returns.
@@ -42,7 +43,7 @@ private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
       (data: Seq[T])
       (f: String => Unit): Unit = {
     withTempPath { file =>
-      sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
+      _sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
       f(file.getCanonicalPath)
     }
   }
@@ -54,7 +55,7 @@ private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
   protected def withParquetDataFrame[T <: Product: ClassTag: TypeTag]
       (data: Seq[T])
       (f: DataFrame => Unit): Unit = {
-    withParquetFile(data)(path => f(sqlContext.read.parquet(path)))
+    withParquetFile(data)(path => f(_sqlContext.read.parquet(path)))
   }
 
   /**
@@ -66,14 +67,14 @@ private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
       (data: Seq[T], tableName: String)
       (f: => Unit): Unit = {
     withParquetDataFrame(data) { df =>
-      sqlContext.registerDataFrameAsTable(df, tableName)
+      _sqlContext.registerDataFrameAsTable(df, tableName)
       withTempTable(tableName)(f)
     }
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
       data: Seq[T], path: File): Unit = {
-    sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
+    _sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
index 92b1d822172d5..b789c5a106e56 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest {
+class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
   import ParquetCompatibilityTest._
 
-  override val sqlContext: SQLContext = TestSQLContext
-
   private val parquetFilePath =
     Thread.currentThread().getContextClassLoader.getResource("parquet-thrift-compat.snappy.parquet")
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
index 239deb7973845..22189477d277d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
@@ -18,10 +18,10 @@
 package org.apache.spark.sql.execution.debug
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.TestData._
-import org.apache.spark.sql.test.TestSQLContext._
+import org.apache.spark.sql.test.SharedSQLContext
+
+class DebuggingSuite extends SparkFunSuite with SharedSQLContext {
 
-class DebuggingSuite extends SparkFunSuite {
   test("DataFrame.debug()") {
     testData.debug()
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index d33a967093ca5..4c9187a9a7106 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -23,12 +23,12 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.metric.SQLMetrics
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 import org.apache.spark.util.collection.CompactBuffer
 
 
-class HashedRelationSuite extends SparkFunSuite {
+class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
 
   // Key is simply the record itself
   private val keyProjection = new Projection {
@@ -37,7 +37,7 @@ class HashedRelationSuite extends SparkFunSuite {
 
   test("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
 
@@ -53,7 +53,7 @@ class HashedRelationSuite extends SparkFunSuite {
 
   test("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
 
@@ -73,7 +73,7 @@ class HashedRelationSuite extends SparkFunSuite {
   test("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
     val toUnsafe = UnsafeProjection.create(schema)
     val unsafeData = data.map(toUnsafe(_).copy()).toArray
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
index ddff7cebcc17d..cc649b9bd4c45 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
@@ -17,97 +17,19 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.{DataFrame, execution, Row, SQLConf}
+import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.logical.Join
-import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
-import org.apache.spark.sql.{SQLConf, execution, Row, DataFrame}
-import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.execution._
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
 
-class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
-
-  private def testInnerJoin(
-      testName: String,
-      leftRows: DataFrame,
-      rightRows: DataFrame,
-      condition: Expression,
-      expectedAnswer: Seq[Product]): Unit = {
-    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
-    ExtractEquiJoinKeys.unapply(join).foreach {
-      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
-
-        def makeBroadcastHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
-          val broadcastHashJoin =
-            execution.joins.BroadcastHashJoin(leftKeys, rightKeys, side, left, right)
-          boundCondition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin)
-        }
-
-        def makeShuffledHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
-          val shuffledHashJoin =
-            execution.joins.ShuffledHashJoin(leftKeys, rightKeys, side, left, right)
-          val filteredJoin =
-            boundCondition.map(Filter(_, shuffledHashJoin)).getOrElse(shuffledHashJoin)
-          EnsureRequirements(sqlContext).apply(filteredJoin)
-        }
-
-        def makeSortMergeJoin(left: SparkPlan, right: SparkPlan) = {
-          val sortMergeJoin =
-            execution.joins.SortMergeJoin(leftKeys, rightKeys, left, right)
-          val filteredJoin = boundCondition.map(Filter(_, sortMergeJoin)).getOrElse(sortMergeJoin)
-          EnsureRequirements(sqlContext).apply(filteredJoin)
-        }
-
-        test(s"$testName using BroadcastHashJoin (build=left)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeBroadcastHashJoin(left, right, joins.BuildLeft),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-
-        test(s"$testName using BroadcastHashJoin (build=right)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeBroadcastHashJoin(left, right, joins.BuildRight),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-
-        test(s"$testName using ShuffledHashJoin (build=left)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeShuffledHashJoin(left, right, joins.BuildLeft),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-
-        test(s"$testName using ShuffledHashJoin (build=right)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeShuffledHashJoin(left, right, joins.BuildRight),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
+class InnerJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-        test(s"$testName using SortMergeJoin") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeSortMergeJoin(left, right),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-    }
-  }
-
-  {
-    val upperCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+  private lazy val myUpperCaseData = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
       Row(1, "A"),
       Row(2, "B"),
       Row(3, "C"),
@@ -117,7 +39,8 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
       Row(null, "G")
     )), new StructType().add("N", IntegerType).add("L", StringType))
 
-    val lowerCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+  private lazy val myLowerCaseData = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
       Row(1, "a"),
       Row(2, "b"),
       Row(3, "c"),
@@ -125,21 +48,7 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
       Row(null, "e")
     )), new StructType().add("n", IntegerType).add("l", StringType))
 
-    testInnerJoin(
-      "inner join, one match per row",
-      upperCaseData,
-      lowerCaseData,
-      (upperCaseData.col("N") === lowerCaseData.col("n")).expr,
-      Seq(
-        (1, "A", 1, "a"),
-        (2, "B", 2, "b"),
-        (3, "C", 3, "c"),
-        (4, "D", 4, "d")
-      )
-    )
-  }
-
-  private val testData2 = Seq(
+  private lazy val myTestData = Seq(
     (1, 1),
     (1, 2),
     (2, 1),
@@ -148,14 +57,139 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
     (3, 2)
   ).toDF("a", "b")
 
+  // Note: the input dataframes and expression must be evaluated lazily because
+  // the SQLContext should be used only within a test to keep SQL tests stable
+  private def testInnerJoin(
+      testName: String,
+      leftRows: => DataFrame,
+      rightRows: => DataFrame,
+      condition: () => Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+
+    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
+      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition()))
+      ExtractEquiJoinKeys.unapply(join)
+    }
+
+    def makeBroadcastHashJoin(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression],
+        boundCondition: Option[Expression],
+        leftPlan: SparkPlan,
+        rightPlan: SparkPlan,
+        side: BuildSide) = {
+      val broadcastHashJoin =
+        execution.joins.BroadcastHashJoin(leftKeys, rightKeys, side, leftPlan, rightPlan)
+      boundCondition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin)
+    }
+
+    def makeShuffledHashJoin(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression],
+        boundCondition: Option[Expression],
+        leftPlan: SparkPlan,
+        rightPlan: SparkPlan,
+        side: BuildSide) = {
+      val shuffledHashJoin =
+        execution.joins.ShuffledHashJoin(leftKeys, rightKeys, side, leftPlan, rightPlan)
+      val filteredJoin =
+        boundCondition.map(Filter(_, shuffledHashJoin)).getOrElse(shuffledHashJoin)
+      EnsureRequirements(sqlContext).apply(filteredJoin)
+    }
+
+    def makeSortMergeJoin(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression],
+        boundCondition: Option[Expression],
+        leftPlan: SparkPlan,
+        rightPlan: SparkPlan) = {
+      val sortMergeJoin =
+        execution.joins.SortMergeJoin(leftKeys, rightKeys, leftPlan, rightPlan)
+      val filteredJoin = boundCondition.map(Filter(_, sortMergeJoin)).getOrElse(sortMergeJoin)
+      EnsureRequirements(sqlContext).apply(filteredJoin)
+    }
+
+    test(s"$testName using BroadcastHashJoin (build=left)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeBroadcastHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildLeft),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using BroadcastHashJoin (build=right)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeBroadcastHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildRight),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using ShuffledHashJoin (build=left)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeShuffledHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildLeft),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using ShuffledHashJoin (build=right)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeShuffledHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildRight),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using SortMergeJoin") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeSortMergeJoin(leftKeys, rightKeys, boundCondition, leftPlan, rightPlan),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+  }
+
+  testInnerJoin(
+    "inner join, one match per row",
+    myUpperCaseData,
+    myLowerCaseData,
+    () => (myUpperCaseData.col("N") === myLowerCaseData.col("n")).expr,
+    Seq(
+      (1, "A", 1, "a"),
+      (2, "B", 2, "b"),
+      (3, "C", 3, "c"),
+      (4, "D", 4, "d")
+    )
+  )
+
   {
-    val left = testData2.where("a = 1")
-    val right = testData2.where("a = 1")
+    lazy val left = myTestData.where("a = 1")
+    lazy val right = myTestData.where("a = 1")
     testInnerJoin(
       "inner join, multiple matches",
       left,
       right,
-      (left.col("a") === right.col("a")).expr,
+      () => (left.col("a") === right.col("a")).expr,
       Seq(
         (1, 1, 1, 1),
         (1, 1, 1, 2),
@@ -166,13 +200,13 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
   }
 
   {
-    val left = testData2.where("a = 1")
-    val right = testData2.where("a = 2")
+    lazy val left = myTestData.where("a = 1")
+    lazy val right = myTestData.where("a = 2")
     testInnerJoin(
       "inner join, no matches",
       left,
       right,
-      (left.col("a") === right.col("a")).expr,
+      () => (left.col("a") === right.col("a")).expr,
       Seq.empty
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index e16f5e39aa2f4..a1a617d7b7398 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -17,28 +17,65 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.{DataFrame, Row, SQLConf}
 import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.Join
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.catalyst.expressions.{And, Expression, LessThan}
+import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{IntegerType, DoubleType, StructType}
-import org.apache.spark.sql.{SQLConf, DataFrame, Row}
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.execution.{EnsureRequirements, joins, SparkPlan, SparkPlanTest}
 
-class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
+class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
+
+  private lazy val left = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(1, 2.0),
+      Row(2, 100.0),
+      Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
+      Row(2, 1.0),
+      Row(3, 3.0),
+      Row(5, 1.0),
+      Row(6, 6.0),
+      Row(null, null)
+    )), new StructType().add("a", IntegerType).add("b", DoubleType))
+
+  private lazy val right = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(0, 0.0),
+      Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
+      Row(2, -1.0),
+      Row(2, -1.0),
+      Row(2, 3.0),
+      Row(3, 2.0),
+      Row(4, 1.0),
+      Row(5, 3.0),
+      Row(7, 7.0),
+      Row(null, null)
+    )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  private lazy val condition = {
+    And((left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
+  }
 
+  // Note: the input dataframes and expression must be evaluated lazily because
+  // the SQLContext should be used only within a test to keep SQL tests stable
   private def testOuterJoin(
       testName: String,
-      leftRows: DataFrame,
-      rightRows: DataFrame,
+      leftRows: => DataFrame,
+      rightRows: => DataFrame,
       joinType: JoinType,
-      condition: Expression,
+      condition: => Expression,
       expectedAnswer: Seq[Product]): Unit = {
-    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
-    ExtractEquiJoinKeys.unapply(join).foreach {
-      case (_, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
-        test(s"$testName using ShuffledHashOuterJoin") {
+
+    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
+      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+      ExtractEquiJoinKeys.unapply(join)
+    }
+
+    test(s"$testName using ShuffledHashOuterJoin") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
           withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
             checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
               EnsureRequirements(sqlContext).apply(
@@ -46,19 +83,23 @@ class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
               expectedAnswer.map(Row.fromTuple),
               sortAnswers = true)
           }
-        }
+      }
+    }
 
-        if (joinType != FullOuter) {
-          test(s"$testName using BroadcastHashOuterJoin") {
+    if (joinType != FullOuter) {
+      test(s"$testName using BroadcastHashOuterJoin") {
+        extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
             withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
               checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
                 BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
                 expectedAnswer.map(Row.fromTuple),
                 sortAnswers = true)
             }
-          }
+        }
+      }
 
-          test(s"$testName using SortMergeOuterJoin") {
+      test(s"$testName using SortMergeOuterJoin") {
+        extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
             withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
               checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
                 EnsureRequirements(sqlContext).apply(
@@ -66,57 +107,9 @@ class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
                 expectedAnswer.map(Row.fromTuple),
                 sortAnswers = false)
             }
-          }
         }
-    }
-
-    test(s"$testName using BroadcastNestedLoopJoin (build=left)") {
-      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-          joins.BroadcastNestedLoopJoin(left, right, joins.BuildLeft, joinType, Some(condition)),
-          expectedAnswer.map(Row.fromTuple),
-          sortAnswers = true)
       }
     }
-
-    test(s"$testName using BroadcastNestedLoopJoin (build=right)") {
-      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-          joins.BroadcastNestedLoopJoin(left, right, joins.BuildRight, joinType, Some(condition)),
-          expectedAnswer.map(Row.fromTuple),
-          sortAnswers = true)
-      }
-    }
-  }
-
-  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(1, 2.0),
-    Row(2, 100.0),
-    Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
-    Row(2, 1.0),
-    Row(3, 3.0),
-    Row(5, 1.0),
-    Row(6, 6.0),
-    Row(null, null)
-  )), new StructType().add("a", IntegerType).add("b", DoubleType))
-
-  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(0, 0.0),
-    Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
-    Row(2, -1.0),
-    Row(2, -1.0),
-    Row(2, 3.0),
-    Row(3, 2.0),
-    Row(4, 1.0),
-    Row(5, 3.0),
-    Row(7, 7.0),
-    Row(null, null)
-  )), new StructType().add("c", IntegerType).add("d", DoubleType))
-
-  val condition = {
-    And(
-      (left.col("a") === right.col("c")).expr,
-      LessThan(left.col("b").expr, right.col("d").expr))
   }
 
   // --- Basic outer joins ------------------------------------------------------------------------
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
index 4503ed251fcb1..baa86e320d986 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
@@ -17,44 +17,80 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.{SQLConf, DataFrame, Row}
 import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.logical.Join
-import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
-import org.apache.spark.sql.{SQLConf, DataFrame, Row}
 import org.apache.spark.sql.catalyst.expressions.{And, LessThan, Expression}
 import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
+
+class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-class SemiJoinSuite extends SparkPlanTest with SQLTestUtils {
+  private lazy val left = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(1, 2.0),
+      Row(1, 2.0),
+      Row(2, 1.0),
+      Row(2, 1.0),
+      Row(3, 3.0),
+      Row(null, null),
+      Row(null, 5.0),
+      Row(6, null)
+    )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
+  private lazy val right = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(2, 3.0),
+      Row(2, 3.0),
+      Row(3, 2.0),
+      Row(4, 1.0),
+      Row(null, null),
+      Row(null, 5.0),
+      Row(6, null)
+    )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  private lazy val condition = {
+    And((left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
+  }
+
+  // Note: the input dataframes and expression must be evaluated lazily because
+  // the SQLContext should be used only within a test to keep SQL tests stable
   private def testLeftSemiJoin(
       testName: String,
-      leftRows: DataFrame,
-      rightRows: DataFrame,
-      condition: Expression,
+      leftRows: => DataFrame,
+      rightRows: => DataFrame,
+      condition: => Expression,
       expectedAnswer: Seq[Product]): Unit = {
-    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
-    ExtractEquiJoinKeys.unapply(join).foreach {
-      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
-        test(s"$testName using LeftSemiJoinHash") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              EnsureRequirements(left.sqlContext).apply(
-                LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
+
+    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
+      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+      ExtractEquiJoinKeys.unapply(join)
+    }
+
+    test(s"$testName using LeftSemiJoinHash") {
+      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            EnsureRequirements(left.sqlContext).apply(
+              LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
         }
+      }
+    }
 
-        test(s"$testName using BroadcastLeftSemiJoinHash") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
+    test(s"$testName using BroadcastLeftSemiJoinHash") {
+      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
         }
+      }
     }
 
     test(s"$testName using LeftSemiJoinBNL") {
@@ -67,33 +103,6 @@ class SemiJoinSuite extends SparkPlanTest with SQLTestUtils {
     }
   }
 
-  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(1, 2.0),
-    Row(1, 2.0),
-    Row(2, 1.0),
-    Row(2, 1.0),
-    Row(3, 3.0),
-    Row(null, null),
-    Row(null, 5.0),
-    Row(6, null)
-  )), new StructType().add("a", IntegerType).add("b", DoubleType))
-
-  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(2, 3.0),
-    Row(2, 3.0),
-    Row(3, 2.0),
-    Row(4, 1.0),
-    Row(null, null),
-    Row(null, 5.0),
-    Row(6, null)
-  )), new StructType().add("c", IntegerType).add("d", DoubleType))
-
-  val condition = {
-    And(
-      (left.col("a") === right.col("c")).expr,
-      LessThan(left.col("b").expr, right.col("d").expr))
-  }
-
   testLeftSemiJoin(
     "basic test",
     left,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index 7383d3f8fe024..80006bf077fe8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -28,17 +28,15 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.ui.SparkPlanGraph
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
-  override val sqlContext = TestSQLContext
-
-  import sqlContext.implicits._
+class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   test("LongSQLMetric should not box Long") {
-    val l = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "long")
+    val l = SQLMetrics.createLongMetric(ctx.sparkContext, "long")
     val f = () => {
       l += 1L
       l.add(1L)
@@ -52,7 +50,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("Normal accumulator should do boxing") {
     // We need this test to make sure BoxingFinder works.
-    val l = TestSQLContext.sparkContext.accumulator(0L)
+    val l = ctx.sparkContext.accumulator(0L)
     val f = () => { l += 1L }
     BoxingFinder.getClassReader(f.getClass).foreach { cl =>
       val boxingFinder = new BoxingFinder()
@@ -73,19 +71,19 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       df: DataFrame,
       expectedNumOfJobs: Int,
       expectedMetrics: Map[Long, (String, Map[String, Any])]): Unit = {
-    val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+    val previousExecutionIds = ctx.listener.executionIdToData.keySet
     df.collect()
-    TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
-    val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+    ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
+    val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
     assert(executionIds.size === 1)
     val executionId = executionIds.head
-    val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+    val jobs = ctx.listener.getExecution(executionId).get.jobs
     // Use "<=" because there is a race condition that we may miss some jobs
     // TODO Change it to "=" once we fix the race condition that missing the JobStarted event.
     assert(jobs.size <= expectedNumOfJobs)
     if (jobs.size == expectedNumOfJobs) {
       // If we can track all jobs, check the metric values
-      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      val metricValues = ctx.listener.getExecutionMetrics(executionId)
       val actualMetrics = SparkPlanGraph(df.queryExecution.executedPlan).nodes.filter { node =>
         expectedMetrics.contains(node.id)
       }.map { node =>
@@ -111,7 +109,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       SQLConf.TUNGSTEN_ENABLED.key -> "false") {
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 1) -> Project(nodeId = 0)
-      val df = TestData.person.select('name)
+      val df = person.select('name)
       testSparkPlanMetrics(df, 1, Map(
         0L ->("Project", Map(
           "number of rows" -> 2L)))
@@ -126,7 +124,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       SQLConf.TUNGSTEN_ENABLED.key -> "true") {
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 1) -> TungstenProject(nodeId = 0)
-      val df = TestData.person.select('name)
+      val df = person.select('name)
       testSparkPlanMetrics(df, 1, Map(
         0L ->("TungstenProject", Map(
           "number of rows" -> 2L)))
@@ -137,7 +135,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
   test("Filter metrics") {
     // Assume the execution plan is
     // PhysicalRDD(nodeId = 1) -> Filter(nodeId = 0)
-    val df = TestData.person.filter('age < 25)
+    val df = person.filter('age < 25)
     testSparkPlanMetrics(df, 1, Map(
       0L -> ("Filter", Map(
         "number of input rows" -> 2L,
@@ -152,7 +150,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       SQLConf.TUNGSTEN_ENABLED.key -> "false") {
       // Assume the execution plan is
       // ... -> Aggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) -> Aggregate(nodeId = 0)
-      val df = TestData.testData2.groupBy().count() // 2 partitions
+      val df = testData2.groupBy().count() // 2 partitions
       testSparkPlanMetrics(df, 1, Map(
         2L -> ("Aggregate", Map(
           "number of input rows" -> 6L,
@@ -163,7 +161,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       )
 
       // 2 partitions and each partition contains 2 keys
-      val df2 = TestData.testData2.groupBy('a).count()
+      val df2 = testData2.groupBy('a).count()
       testSparkPlanMetrics(df2, 1, Map(
         2L -> ("Aggregate", Map(
           "number of input rows" -> 6L,
@@ -185,7 +183,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       // Assume the execution plan is
       // ... -> SortBasedAggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) ->
       // SortBasedAggregate(nodeId = 0)
-      val df = TestData.testData2.groupBy().count() // 2 partitions
+      val df = testData2.groupBy().count() // 2 partitions
       testSparkPlanMetrics(df, 1, Map(
         2L -> ("SortBasedAggregate", Map(
           "number of input rows" -> 6L,
@@ -199,7 +197,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       // ... -> SortBasedAggregate(nodeId = 3) -> TungstenExchange(nodeId = 2)
       // -> ExternalSort(nodeId = 1)-> SortBasedAggregate(nodeId = 0)
       // 2 partitions and each partition contains 2 keys
-      val df2 = TestData.testData2.groupBy('a).count()
+      val df2 = testData2.groupBy('a).count()
       testSparkPlanMetrics(df2, 1, Map(
         3L -> ("SortBasedAggregate", Map(
           "number of input rows" -> 6L,
@@ -219,7 +217,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       // Assume the execution plan is
       // ... -> TungstenAggregate(nodeId = 2) -> Exchange(nodeId = 1)
       // -> TungstenAggregate(nodeId = 0)
-      val df = TestData.testData2.groupBy().count() // 2 partitions
+      val df = testData2.groupBy().count() // 2 partitions
       testSparkPlanMetrics(df, 1, Map(
         2L -> ("TungstenAggregate", Map(
           "number of input rows" -> 6L,
@@ -230,7 +228,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       )
 
       // 2 partitions and each partition contains 2 keys
-      val df2 = TestData.testData2.groupBy('a).count()
+      val df2 = testData2.groupBy('a).count()
       testSparkPlanMetrics(df2, 1, Map(
         2L -> ("TungstenAggregate", Map(
           "number of input rows" -> 6L,
@@ -246,7 +244,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
     // Because SortMergeJoin may skip different rows if the number of partitions is different, this
     // test should use the deterministic number of partitions.
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -268,7 +266,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
     // Because SortMergeOuterJoin may skip different rows if the number of partitions is different,
     // this test should use the deterministic number of partitions.
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -314,7 +312,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("ShuffledHashJoin metrics") {
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -390,7 +388,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("BroadcastNestedLoopJoin metrics") {
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -458,7 +456,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
   }
 
   test("CartesianProduct metrics") {
-    val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+    val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
     testDataForJoin.registerTempTable("testDataForJoin")
     withTempTable("testDataForJoin") {
       // Assume the execution plan is
@@ -476,19 +474,19 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("save metrics") {
     withTempPath { file =>
-      val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+      val previousExecutionIds = ctx.listener.executionIdToData.keySet
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 0)
-      TestData.person.select('name).write.format("json").save(file.getAbsolutePath)
-      TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
-      val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+      person.select('name).write.format("json").save(file.getAbsolutePath)
+      ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
+      val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
       assert(executionIds.size === 1)
       val executionId = executionIds.head
-      val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+      val jobs = ctx.listener.getExecution(executionId).get.jobs
       // Use "<=" because there is a race condition that we may miss some jobs
       // TODO Change "<=" to "=" once we fix the race condition that missing the JobStarted event.
       assert(jobs.size <= 1)
-      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      val metricValues = ctx.listener.getExecutionMetrics(executionId)
       // Because "save" will create a new DataFrame internally, we cannot get the real metric id.
       // However, we still can check the value.
       assert(metricValues.values.toSeq === Seq(2L))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
index 41dd1896c15df..80d1e88956949 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
@@ -25,12 +25,12 @@ import org.apache.spark.sql.execution.metric.LongSQLMetricValue
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.{DataFrame, SQLContext}
 import org.apache.spark.sql.execution.SQLExecution
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SQLListenerSuite extends SparkFunSuite {
+class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   private def createTestDataFrame: DataFrame = {
-    import TestSQLContext.implicits._
     Seq(
       (1, 1),
       (2, 2)
@@ -74,7 +74,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("basic") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     val accumulatorIds =
@@ -212,7 +212,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobSucceeded)") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -241,7 +241,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("onExecutionEnd happens before multiple onJobEnd(JobSucceeded)s") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -281,7 +281,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobFailed)") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index e4dcf4c75d208..0edac0848c3bb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -25,10 +25,13 @@ import org.h2.jdbc.JdbcSQLException
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
+class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext {
+  import testImplicits._
+
   val url = "jdbc:h2:mem:testdb0"
   val urlWithUserAndPass = "jdbc:h2:mem:testdb0;user=testUser;password=testPass"
   var conn: java.sql.Connection = null
@@ -42,10 +45,6 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
       Some(StringType)
   }
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.sql
-
   before {
     Utils.classForName("org.h2.Driver")
     // Extra properties that will be specified for our database. We need these to test
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
index 84b52ca2c733c..5dc3a2c07b8c7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
@@ -23,11 +23,13 @@ import java.util.Properties
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{SaveMode, Row}
+import org.apache.spark.sql.{Row, SaveMode}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
+class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext {
+
   val url = "jdbc:h2:mem:testdb2"
   var conn: java.sql.Connection = null
   val url1 = "jdbc:h2:mem:testdb3"
@@ -37,10 +39,6 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
   properties.setProperty("password", "testPass")
   properties.setProperty("rowId", "false")
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.sql
-
   before {
     Utils.classForName("org.h2.Driver")
     conn = DriverManager.getConnection(url)
@@ -58,14 +56,14 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
       "create table test.people1 (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate()
     conn1.commit()
 
-    ctx.sql(
+    sql(
       s"""
         |CREATE TEMPORARY TABLE PEOPLE
         |USING org.apache.spark.sql.jdbc
         |OPTIONS (url '$url1', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass')
       """.stripMargin.replaceAll("\n", " "))
 
-    ctx.sql(
+    sql(
       s"""
         |CREATE TEMPORARY TABLE PEOPLE1
         |USING org.apache.spark.sql.jdbc
@@ -144,14 +142,14 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
   }
 
   test("INSERT to JDBC Datasource") {
-    ctx.sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
+    sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
 
   test("INSERT to JDBC Datasource with overwrite") {
-    ctx.sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
-    ctx.sql("INSERT OVERWRITE TABLE PEOPLE1 SELECT * FROM PEOPLE")
+    sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
+    sql("INSERT OVERWRITE TABLE PEOPLE1 SELECT * FROM PEOPLE")
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
index 562c279067048..9bc3f6bcf6fce 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
@@ -19,28 +19,32 @@ package org.apache.spark.sql.sources
 
 import java.io.{File, IOException}
 
-import org.scalatest.BeforeAndAfterAll
+import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.execution.datasources.DDLException
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
-
-  import caseInsensitiveContext.sql
 
+class CreateTableAsSelectSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
+  protected override lazy val sql = caseInsensitiveContext.sql _
   private lazy val sparkContext = caseInsensitiveContext.sparkContext
-
-  var path: File = null
+  private var path: File = null
 
   override def beforeAll(): Unit = {
+    super.beforeAll()
     path = Utils.createTempDir()
     val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""))
     caseInsensitiveContext.read.json(rdd).registerTempTable("jt")
   }
 
   override def afterAll(): Unit = {
-    caseInsensitiveContext.dropTempTable("jt")
+    try {
+      caseInsensitiveContext.dropTempTable("jt")
+    } finally {
+      super.afterAll()
+    }
   }
 
   after {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
index 392da0b0826b5..853707c036c9a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
@@ -18,11 +18,12 @@
 package org.apache.spark.sql.sources
 
 import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 
 
 // please note that the META-INF/services had to be modified for the test directory for this to work
-class DDLSourceLoadSuite extends DataSourceTest {
+class DDLSourceLoadSuite extends DataSourceTest with SharedSQLContext {
 
   test("data sources with the same name") {
     intercept[RuntimeException] {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
index 84855ce45e918..5f8514e1a2411 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.sources
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -68,10 +69,12 @@ case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlCo
   }
 }
 
-class DDLTestSuite extends DataSourceTest {
+class DDLTestSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
-  before {
-    caseInsensitiveContext.sql(
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    sql(
       """
       |CREATE TEMPORARY TABLE ddlPeople
       |USING org.apache.spark.sql.sources.DDLScanSource
@@ -105,7 +108,7 @@ class DDLTestSuite extends DataSourceTest {
       ))
 
   test("SPARK-7686 DescribeCommand should have correct physical plan output attributes") {
-    val attributes = caseInsensitiveContext.sql("describe ddlPeople")
+    val attributes = sql("describe ddlPeople")
       .queryExecution.executedPlan.output
     assert(attributes.map(_.name) === Seq("col_name", "data_type", "comment"))
     assert(attributes.map(_.dataType).toSet === Set(StringType))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
index 00cc7d5ea580f..d74d29fb0beb0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
@@ -17,18 +17,23 @@
 
 package org.apache.spark.sql.sources
 
-import org.scalatest.BeforeAndAfter
-
 import org.apache.spark.sql._
-import org.apache.spark.sql.test.TestSQLContext
 
 
-abstract class DataSourceTest extends QueryTest with BeforeAndAfter {
+private[sql] abstract class DataSourceTest extends QueryTest {
+  protected def _sqlContext: SQLContext
+
   // We want to test some edge cases.
-  protected implicit lazy val caseInsensitiveContext = {
-    val ctx = new SQLContext(TestSQLContext.sparkContext)
+  protected lazy val caseInsensitiveContext: SQLContext = {
+    val ctx = new SQLContext(_sqlContext.sparkContext)
     ctx.setConf(SQLConf.CASE_SENSITIVE, false)
     ctx
   }
 
+  protected def sqlTest(sqlString: String, expectedAnswer: Seq[Row]) {
+    test(sqlString) {
+      checkAnswer(caseInsensitiveContext.sql(sqlString), expectedAnswer)
+    }
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
index 5ef365797eace..c81c3d3982805 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
@@ -21,6 +21,7 @@ import scala.language.existentials
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 
@@ -96,11 +97,11 @@ object FiltersPushed {
   var list: Seq[Filter] = Nil
 }
 
-class FilteredScanSuite extends DataSourceTest {
+class FilteredScanSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
-  import caseInsensitiveContext.sql
-
-  before {
+  override def beforeAll(): Unit = {
+    super.beforeAll()
     sql(
       """
         |CREATE TEMPORARY TABLE oneToTenFiltered
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index cdbfaf6455fe4..78bd3e5582964 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -19,20 +19,17 @@ package org.apache.spark.sql.sources
 
 import java.io.File
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.sql.{SaveMode, AnalysisException, Row}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
-
-  import caseInsensitiveContext.sql
-
+class InsertSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
   private lazy val sparkContext = caseInsensitiveContext.sparkContext
-
-  var path: File = null
+  private var path: File = null
 
   override def beforeAll(): Unit = {
+    super.beforeAll()
     path = Utils.createTempDir()
     val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
     caseInsensitiveContext.read.json(rdd).registerTempTable("jt")
@@ -47,9 +44,13 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
   }
 
   override def afterAll(): Unit = {
-    caseInsensitiveContext.dropTempTable("jsonTable")
-    caseInsensitiveContext.dropTempTable("jt")
-    Utils.deleteRecursively(path)
+    try {
+      caseInsensitiveContext.dropTempTable("jsonTable")
+      caseInsensitiveContext.dropTempTable("jt")
+      Utils.deleteRecursively(path)
+    } finally {
+      super.afterAll()
+    }
   }
 
   test("Simple INSERT OVERWRITE a JSONRelation") {
@@ -221,9 +222,10 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
       sql("SELECT a * 2 FROM jsonTable"),
       (1 to 10).map(i => Row(i * 2)).toSeq)
 
-    assertCached(sql("SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"), 2)
-    checkAnswer(
-      sql("SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"),
+    assertCached(sql(
+      "SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"), 2)
+    checkAnswer(sql(
+      "SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"),
       (2 to 10).map(i => Row(i, i - 1)).toSeq)
 
     // Insert overwrite and keep the same schema.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
index c86ddd7c83e53..79b6e9b45c009 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
@@ -19,21 +19,21 @@ package org.apache.spark.sql.sources
 
 import org.apache.spark.sql.{Row, QueryTest}
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class PartitionedWriteSuite extends QueryTest {
-  import TestSQLContext.implicits._
+class PartitionedWriteSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("write many partitions") {
     val path = Utils.createTempDir()
     path.delete()
 
-    val df = TestSQLContext.range(100).select($"id", lit(1).as("data"))
+    val df = ctx.range(100).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      TestSQLContext.read.load(path.getCanonicalPath),
+      ctx.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
@@ -43,12 +43,12 @@ class PartitionedWriteSuite extends QueryTest {
     val path = Utils.createTempDir()
     path.delete()
 
-    val base = TestSQLContext.range(100)
+    val base = ctx.range(100)
     val df = base.unionAll(base).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      TestSQLContext.read.load(path.getCanonicalPath),
+      ctx.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq ++ (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
index 0d5183444af78..a89c5f8007e78 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
@@ -21,6 +21,7 @@ import scala.language.existentials
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 class PrunedScanSource extends RelationProvider {
@@ -51,10 +52,12 @@ case class SimplePrunedScan(from: Int, to: Int)(@transient val sqlContext: SQLCo
   }
 }
 
-class PrunedScanSuite extends DataSourceTest {
+class PrunedScanSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
-  before {
-    caseInsensitiveContext.sql(
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    sql(
       """
         |CREATE TEMPORARY TABLE oneToTenPruned
         |USING org.apache.spark.sql.sources.PrunedScanSource
@@ -114,7 +117,7 @@ class PrunedScanSuite extends DataSourceTest {
 
   def testPruning(sqlString: String, expectedColumns: String*): Unit = {
     test(s"Columns output ${expectedColumns.mkString(",")}: $sqlString") {
-      val queryExecution = caseInsensitiveContext.sql(sqlString).queryExecution
+      val queryExecution = sql(sqlString).queryExecution
       val rawPlan = queryExecution.executedPlan.collect {
         case p: execution.PhysicalRDD => p
       } match {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
index 31730a3d3f8d3..f18546b4c2d9b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
@@ -19,25 +19,22 @@ package org.apache.spark.sql.sources
 
 import java.io.File
 
-import org.scalatest.BeforeAndAfterAll
+import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.{AnalysisException, SaveMode, SQLConf, DataFrame}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
-
-  import caseInsensitiveContext.sql
-
+class SaveLoadSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
+  protected override lazy val sql = caseInsensitiveContext.sql _
   private lazy val sparkContext = caseInsensitiveContext.sparkContext
-
-  var originalDefaultSource: String = null
-
-  var path: File = null
-
-  var df: DataFrame = null
+  private var originalDefaultSource: String = null
+  private var path: File = null
+  private var df: DataFrame = null
 
   override def beforeAll(): Unit = {
+    super.beforeAll()
     originalDefaultSource = caseInsensitiveContext.conf.defaultDataSourceName
 
     path = Utils.createTempDir()
@@ -49,11 +46,14 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
   }
 
   override def afterAll(): Unit = {
-    caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource)
+    try {
+      caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource)
+    } finally {
+      super.afterAll()
+    }
   }
 
   after {
-    caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource)
     Utils.deleteRecursively(path)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index e34e0956d1fdd..12af8068c398f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -22,6 +22,7 @@ import java.sql.{Date, Timestamp}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 class DefaultSource extends SimpleScanSource
@@ -95,8 +96,8 @@ case class AllDataTypesScan(
   }
 }
 
-class TableScanSuite extends DataSourceTest {
-  import caseInsensitiveContext.sql
+class TableScanSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
   private lazy val tableWithSchemaExpected = (1 to 10).map { i =>
     Row(
@@ -122,7 +123,8 @@ class TableScanSuite extends DataSourceTest {
       Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))))
   }.toSeq
 
-  before {
+  override def beforeAll(): Unit = {
+    super.beforeAll()
     sql(
       """
         |CREATE TEMPORARY TABLE oneToTen
@@ -303,9 +305,10 @@ class TableScanSuite extends DataSourceTest {
       sql("SELECT i * 2 FROM oneToTen"),
       (1 to 10).map(i => Row(i * 2)).toSeq)
 
-    assertCached(sql("SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"), 2)
-    checkAnswer(
-      sql("SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"),
+    assertCached(sql(
+      "SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"), 2)
+    checkAnswer(sql(
+      "SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"),
       (2 to 10).map(i => Row(i, i - 1)).toSeq)
 
     // Verify uncaching
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
new file mode 100644
index 0000000000000..1374a97476ca1
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -0,0 +1,290 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.test
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
+
+/**
+ * A collection of sample data used in SQL tests.
+ */
+private[sql] trait SQLTestData { self =>
+  protected def _sqlContext: SQLContext
+
+  // Helper object to import SQL implicits without a concrete SQLContext
+  private object internalImplicits extends SQLImplicits {
+    protected override def _sqlContext: SQLContext = self._sqlContext
+  }
+
+  import internalImplicits._
+  import SQLTestData._
+
+  // Note: all test data should be lazy because the SQLContext is not set up yet.
+
+  protected lazy val testData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      (1 to 100).map(i => TestData(i, i.toString))).toDF()
+    df.registerTempTable("testData")
+    df
+  }
+
+  protected lazy val testData2: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      TestData2(1, 1) ::
+      TestData2(1, 2) ::
+      TestData2(2, 1) ::
+      TestData2(2, 2) ::
+      TestData2(3, 1) ::
+      TestData2(3, 2) :: Nil, 2).toDF()
+    df.registerTempTable("testData2")
+    df
+  }
+
+  protected lazy val testData3: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      TestData3(1, None) ::
+      TestData3(2, Some(2)) :: Nil).toDF()
+    df.registerTempTable("testData3")
+    df
+  }
+
+  protected lazy val negativeData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      (1 to 100).map(i => TestData(-i, (-i).toString))).toDF()
+    df.registerTempTable("negativeData")
+    df
+  }
+
+  protected lazy val largeAndSmallInts: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      LargeAndSmallInts(2147483644, 1) ::
+      LargeAndSmallInts(1, 2) ::
+      LargeAndSmallInts(2147483645, 1) ::
+      LargeAndSmallInts(2, 2) ::
+      LargeAndSmallInts(2147483646, 1) ::
+      LargeAndSmallInts(3, 2) :: Nil).toDF()
+    df.registerTempTable("largeAndSmallInts")
+    df
+  }
+
+  protected lazy val decimalData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      DecimalData(1, 1) ::
+      DecimalData(1, 2) ::
+      DecimalData(2, 1) ::
+      DecimalData(2, 2) ::
+      DecimalData(3, 1) ::
+      DecimalData(3, 2) :: Nil).toDF()
+    df.registerTempTable("decimalData")
+    df
+  }
+
+  protected lazy val binaryData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      BinaryData("12".getBytes, 1) ::
+      BinaryData("22".getBytes, 5) ::
+      BinaryData("122".getBytes, 3) ::
+      BinaryData("121".getBytes, 2) ::
+      BinaryData("123".getBytes, 4) :: Nil).toDF()
+    df.registerTempTable("binaryData")
+    df
+  }
+
+  protected lazy val upperCaseData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      UpperCaseData(1, "A") ::
+      UpperCaseData(2, "B") ::
+      UpperCaseData(3, "C") ::
+      UpperCaseData(4, "D") ::
+      UpperCaseData(5, "E") ::
+      UpperCaseData(6, "F") :: Nil).toDF()
+    df.registerTempTable("upperCaseData")
+    df
+  }
+
+  protected lazy val lowerCaseData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      LowerCaseData(1, "a") ::
+      LowerCaseData(2, "b") ::
+      LowerCaseData(3, "c") ::
+      LowerCaseData(4, "d") :: Nil).toDF()
+    df.registerTempTable("lowerCaseData")
+    df
+  }
+
+  protected lazy val arrayData: RDD[ArrayData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(
+      ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) ::
+      ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil)
+    rdd.toDF().registerTempTable("arrayData")
+    rdd
+  }
+
+  protected lazy val mapData: RDD[MapData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(
+      MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
+      MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
+      MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
+      MapData(Map(1 -> "a4", 2 -> "b4")) ::
+      MapData(Map(1 -> "a5")) :: Nil)
+    rdd.toDF().registerTempTable("mapData")
+    rdd
+  }
+
+  protected lazy val repeatedData: RDD[StringData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
+    rdd.toDF().registerTempTable("repeatedData")
+    rdd
+  }
+
+  protected lazy val nullableRepeatedData: RDD[StringData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(
+      List.fill(2)(StringData(null)) ++
+      List.fill(2)(StringData("test")))
+    rdd.toDF().registerTempTable("nullableRepeatedData")
+    rdd
+  }
+
+  protected lazy val nullInts: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      NullInts(1) ::
+      NullInts(2) ::
+      NullInts(3) ::
+      NullInts(null) :: Nil).toDF()
+    df.registerTempTable("nullInts")
+    df
+  }
+
+  protected lazy val allNulls: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      NullInts(null) ::
+      NullInts(null) ::
+      NullInts(null) ::
+      NullInts(null) :: Nil).toDF()
+    df.registerTempTable("allNulls")
+    df
+  }
+
+  protected lazy val nullStrings: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      NullStrings(1, "abc") ::
+      NullStrings(2, "ABC") ::
+      NullStrings(3, null) :: Nil).toDF()
+    df.registerTempTable("nullStrings")
+    df
+  }
+
+  protected lazy val tableName: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(TableName("test") :: Nil).toDF()
+    df.registerTempTable("tableName")
+    df
+  }
+
+  protected lazy val unparsedStrings: RDD[String] = {
+    _sqlContext.sparkContext.parallelize(
+      "1, A1, true, null" ::
+      "2, B2, false, null" ::
+      "3, C3, true, null" ::
+      "4, D4, true, 2147483644" :: Nil)
+  }
+
+  // An RDD with 4 elements and 8 partitions
+  protected lazy val withEmptyParts: RDD[IntField] = {
+    val rdd = _sqlContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
+    rdd.toDF().registerTempTable("withEmptyParts")
+    rdd
+  }
+
+  protected lazy val person: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      Person(0, "mike", 30) ::
+      Person(1, "jim", 20) :: Nil).toDF()
+    df.registerTempTable("person")
+    df
+  }
+
+  protected lazy val salary: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      Salary(0, 2000.0) ::
+      Salary(1, 1000.0) :: Nil).toDF()
+    df.registerTempTable("salary")
+    df
+  }
+
+  protected lazy val complexData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true) ::
+      ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false) ::
+      Nil).toDF()
+    df.registerTempTable("complexData")
+    df
+  }
+
+  /**
+   * Initialize all test data such that all temp tables are properly registered.
+   */
+  def loadTestData(): Unit = {
+    assert(_sqlContext != null, "attempted to initialize test data before SQLContext.")
+    testData
+    testData2
+    testData3
+    negativeData
+    largeAndSmallInts
+    decimalData
+    binaryData
+    upperCaseData
+    lowerCaseData
+    arrayData
+    mapData
+    repeatedData
+    nullableRepeatedData
+    nullInts
+    allNulls
+    nullStrings
+    tableName
+    unparsedStrings
+    withEmptyParts
+    person
+    salary
+    complexData
+  }
+}
+
+/**
+ * Case classes used in test data.
+ */
+private[sql] object SQLTestData {
+  case class TestData(key: Int, value: String)
+  case class TestData2(a: Int, b: Int)
+  case class TestData3(a: Int, b: Option[Int])
+  case class LargeAndSmallInts(a: Int, b: Int)
+  case class DecimalData(a: BigDecimal, b: BigDecimal)
+  case class BinaryData(a: Array[Byte], b: Int)
+  case class UpperCaseData(N: Int, L: String)
+  case class LowerCaseData(n: Int, l: String)
+  case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]])
+  case class MapData(data: scala.collection.Map[Int, String])
+  case class StringData(s: String)
+  case class IntField(i: Int)
+  case class NullInts(a: Integer)
+  case class NullStrings(n: Int, s: String)
+  case class TableName(tableName: String)
+  case class Person(id: Int, name: String, age: Int)
+  case class Salary(personId: Int, salary: Double)
+  case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index 1066695589778..cdd691e035897 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -21,15 +21,71 @@ import java.io.File
 import java.util.UUID
 
 import scala.util.Try
+import scala.language.implicitConversions
+
+import org.apache.hadoop.conf.Configuration
+import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.util.Utils
 
-trait SQLTestUtils { this: SparkFunSuite =>
-  protected def sqlContext: SQLContext
+/**
+ * Helper trait that should be extended by all SQL test suites.
+ *
+ * This allows subclasses to plugin a custom [[SQLContext]]. It comes with test data
+ * prepared in advance as well as all implicit conversions used extensively by dataframes.
+ * To use implicit methods, import `testImplicits._` instead of through the [[SQLContext]].
+ *
+ * Subclasses should *not* create [[SQLContext]]s in the test suite constructor, which is
+ * prone to leaving multiple overlapping [[org.apache.spark.SparkContext]]s in the same JVM.
+ */
+private[sql] trait SQLTestUtils
+  extends SparkFunSuite
+  with BeforeAndAfterAll
+  with SQLTestData { self =>
+
+  protected def _sqlContext: SQLContext
+
+  // Whether to materialize all test data before the first test is run
+  private var loadTestDataBeforeTests = false
+
+  // Shorthand for running a query using our SQLContext
+  protected lazy val sql = _sqlContext.sql _
+
+  /**
+   * A helper object for importing SQL implicits.
+   *
+   * Note that the alternative of importing `sqlContext.implicits._` is not possible here.
+   * This is because we create the [[SQLContext]] immediately before the first test is run,
+   * but the implicits import is needed in the constructor.
+   */
+  protected object testImplicits extends SQLImplicits {
+    protected override def _sqlContext: SQLContext = self._sqlContext
+  }
+
+  /**
+   * Materialize the test data immediately after the [[SQLContext]] is set up.
+   * This is necessary if the data is accessed by name but not through direct reference.
+   */
+  protected def setupTestData(): Unit = {
+    loadTestDataBeforeTests = true
+  }
 
-  protected def configuration = sqlContext.sparkContext.hadoopConfiguration
+  protected override def beforeAll(): Unit = {
+    super.beforeAll()
+    if (loadTestDataBeforeTests) {
+      loadTestData()
+    }
+  }
+
+  /**
+   * The Hadoop configuration used by the active [[SQLContext]].
+   */
+  protected def configuration: Configuration = {
+    _sqlContext.sparkContext.hadoopConfiguration
+  }
 
   /**
    * Sets all SQL configurations specified in `pairs`, calls `f`, and then restore all SQL
@@ -39,12 +95,12 @@ trait SQLTestUtils { this: SparkFunSuite =>
    */
   protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
     val (keys, values) = pairs.unzip
-    val currentValues = keys.map(key => Try(sqlContext.conf.getConfString(key)).toOption)
-    (keys, values).zipped.foreach(sqlContext.conf.setConfString)
+    val currentValues = keys.map(key => Try(_sqlContext.conf.getConfString(key)).toOption)
+    (keys, values).zipped.foreach(_sqlContext.conf.setConfString)
     try f finally {
       keys.zip(currentValues).foreach {
-        case (key, Some(value)) => sqlContext.conf.setConfString(key, value)
-        case (key, None) => sqlContext.conf.unsetConf(key)
+        case (key, Some(value)) => _sqlContext.conf.setConfString(key, value)
+        case (key, None) => _sqlContext.conf.unsetConf(key)
       }
     }
   }
@@ -76,7 +132,7 @@ trait SQLTestUtils { this: SparkFunSuite =>
    * Drops temporary table `tableName` after calling `f`.
    */
   protected def withTempTable(tableNames: String*)(f: => Unit): Unit = {
-    try f finally tableNames.foreach(sqlContext.dropTempTable)
+    try f finally tableNames.foreach(_sqlContext.dropTempTable)
   }
 
   /**
@@ -85,7 +141,7 @@ trait SQLTestUtils { this: SparkFunSuite =>
   protected def withTable(tableNames: String*)(f: => Unit): Unit = {
     try f finally {
       tableNames.foreach { name =>
-        sqlContext.sql(s"DROP TABLE IF EXISTS $name")
+        _sqlContext.sql(s"DROP TABLE IF EXISTS $name")
       }
     }
   }
@@ -98,12 +154,12 @@ trait SQLTestUtils { this: SparkFunSuite =>
     val dbName = s"db_${UUID.randomUUID().toString.replace('-', '_')}"
 
     try {
-      sqlContext.sql(s"CREATE DATABASE $dbName")
+      _sqlContext.sql(s"CREATE DATABASE $dbName")
     } catch { case cause: Throwable =>
       fail("Failed to create temporary database", cause)
     }
 
-    try f(dbName) finally sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
+    try f(dbName) finally _sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
   }
 
   /**
@@ -111,7 +167,15 @@ trait SQLTestUtils { this: SparkFunSuite =>
    * `f` returns.
    */
   protected def activateDatabase(db: String)(f: => Unit): Unit = {
-    sqlContext.sql(s"USE $db")
-    try f finally sqlContext.sql(s"USE default")
+    _sqlContext.sql(s"USE $db")
+    try f finally _sqlContext.sql(s"USE default")
+  }
+
+  /**
+   * Turn a logical plan into a [[DataFrame]]. This should be removed once we have an easier
+   * way to construct [[DataFrame]] directly out of local data without relying on implicits.
+   */
+  protected implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
+    DataFrame(_sqlContext, plan)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
new file mode 100644
index 0000000000000..3cfd822e2a747
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.test
+
+import org.apache.spark.sql.SQLContext
+
+
+/**
+ * Helper trait for SQL test suites where all tests share a single [[TestSQLContext]].
+ */
+private[sql] trait SharedSQLContext extends SQLTestUtils {
+
+  /**
+   * The [[TestSQLContext]] to use for all tests in this suite.
+   *
+   * By default, the underlying [[org.apache.spark.SparkContext]] will be run in local
+   * mode with the default test configurations.
+   */
+  private var _ctx: TestSQLContext = null
+
+  /**
+   * The [[TestSQLContext]] to use for all tests in this suite.
+   */
+  protected def ctx: TestSQLContext = _ctx
+  protected def sqlContext: TestSQLContext = _ctx
+  protected override def _sqlContext: SQLContext = _ctx
+
+  /**
+   * Initialize the [[TestSQLContext]].
+   */
+  protected override def beforeAll(): Unit = {
+    if (_ctx == null) {
+      _ctx = new TestSQLContext
+    }
+    // Ensure we have initialized the context before calling parent code
+    super.beforeAll()
+  }
+
+  /**
+   * Stop the underlying [[org.apache.spark.SparkContext]], if any.
+   */
+  protected override def afterAll(): Unit = {
+    try {
+      if (_ctx != null) {
+        _ctx.sparkContext.stop()
+        _ctx = null
+      }
+    } finally {
+      super.afterAll()
+    }
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
similarity index 54%
rename from sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
index b3a4231da91c2..92ef2f7d74ba1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -17,40 +17,36 @@
 
 package org.apache.spark.sql.test
 
-import scala.language.implicitConversions
-
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext}
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-
-/** A SQLContext that can be used for local testing. */
-class LocalSQLContext
-  extends SQLContext(
-    new SparkContext("local[2]", "TestSQLContext", new SparkConf()
-      .set("spark.sql.testkey", "true")
-      // SPARK-8910
-      .set("spark.ui.enabled", "false"))) {
-
-  override protected[sql] def createSession(): SQLSession = {
-    new this.SQLSession()
+import org.apache.spark.sql.{SQLConf, SQLContext}
+
+
+/**
+ * A special [[SQLContext]] prepared for testing.
+ */
+private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { self =>
+
+  def this() {
+    this(new SparkContext("local[2]", "test-sql-context",
+      new SparkConf().set("spark.sql.testkey", "true")))
   }
 
+  // Use fewer partitions to speed up testing
+  protected[sql] override def createSession(): SQLSession = new this.SQLSession()
+
+  /** A special [[SQLSession]] that uses fewer shuffle partitions than normal. */
   protected[sql] class SQLSession extends super.SQLSession {
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
-      /** Fewer partitions to speed up testing. */
       override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, 5)
     }
   }
 
-  /**
-   * Turn a logical plan into a [[DataFrame]]. This should be removed once we have an easier way to
-   * construct [[DataFrame]] directly out of local data without relying on implicits.
-   */
-  protected[sql] implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
-    DataFrame(this, plan)
+  // Needed for Java tests
+  def loadTestData(): Unit = {
+    testData.loadTestData()
   }
 
+  private object testData extends SQLTestData {
+    protected override def _sqlContext: SQLContext = self
+  }
 }
-
-object TestSQLContext extends LocalSQLContext
-
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
index 806240e6de458..bf431cd6b0260 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
@@ -27,7 +27,6 @@ import org.scalatest.concurrent.Eventually._
 import org.scalatest.selenium.WebBrowser
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.ui.SparkUICssErrorHandler
 
 class UISeleniumSuite
@@ -36,7 +35,6 @@ class UISeleniumSuite
 
   implicit var webDriver: WebDriver = _
   var server: HiveThriftServer2 = _
-  var hc: HiveContext = _
   val uiPort = 20000 + Random.nextInt(10000)
   override def mode: ServerMode.Value = ServerMode.binary
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 59e65ff97b8e0..574624d501f22 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.sources.DataSourceTest
 import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
 import org.apache.spark.sql.types.{DecimalType, StringType, StructType}
-import org.apache.spark.sql.{Row, SaveMode}
+import org.apache.spark.sql.{Row, SaveMode, SQLContext}
 import org.apache.spark.{Logging, SparkFunSuite}
 
 
@@ -53,7 +53,8 @@ class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
 }
 
 class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  import testImplicits._
 
   private val testDF = range(1, 3).select(
     ('id + 0.1) cast DecimalType(10, 3) as 'd1,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
index 1fa005d5f9a15..fe0db5228de16 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
@@ -19,14 +19,13 @@ package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.execution.datasources.parquet.ParquetTest
-import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.{QueryTest, Row, SQLContext}
 
 case class Cases(lower: String, UPPER: String)
 
 class HiveParquetSuite extends QueryTest with ParquetTest {
-  val sqlContext = TestHive
-
-  import sqlContext._
+  private val ctx = TestHive
+  override def _sqlContext: SQLContext = ctx
 
   test("Case insensitive attribute names") {
     withParquetTable((1 to 4).map(i => Cases(i.toString, i.toString)), "cases") {
@@ -54,7 +53,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
   test("Converting Hive to Parquet Table via saveAsParquetFile") {
     withTempPath { dir =>
       sql("SELECT * FROM src").write.parquet(dir.getCanonicalPath)
-      read.parquet(dir.getCanonicalPath).registerTempTable("p")
+      ctx.read.parquet(dir.getCanonicalPath).registerTempTable("p")
       withTempTable("p") {
         checkAnswer(
           sql("SELECT * FROM src ORDER BY key"),
@@ -67,7 +66,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
     withParquetTable((1 to 10).map(i => (i, s"val_$i")), "t") {
       withTempPath { file =>
         sql("SELECT * FROM t LIMIT 1").write.parquet(file.getCanonicalPath)
-        read.parquet(file.getCanonicalPath).registerTempTable("p")
+        ctx.read.parquet(file.getCanonicalPath).registerTempTable("p")
         withTempTable("p") {
           // let's do three overwrites for good measure
           sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index 7f36a483a3965..20a50586d5201 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -22,7 +22,6 @@ import java.io.{IOException, File}
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapred.InvalidInputException
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.Logging
@@ -42,7 +41,8 @@ import org.apache.spark.util.Utils
  */
 class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll
   with Logging {
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   var jsonFilePath: String = _
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
index 73852f13ad20d..417e8b07917cc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
@@ -22,9 +22,8 @@ import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.{QueryTest, SQLContext, SaveMode}
 
 class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
-  override val sqlContext: SQLContext = TestHive
-
-  import sqlContext.sql
+  override val _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   private val df = sqlContext.range(10).coalesce(1)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 251e0324bfa5f..13452e71a1b3b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -26,7 +26,8 @@ import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
   import ParquetCompatibilityTest.makeNullable
 
-  override val sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   /**
    * Set the staging directory (and hence path to ignore Parquet files under)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
index 9b3ede43ee2d1..7ee1c8d13aa3f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.{Row, QueryTest}
+import org.apache.spark.sql.QueryTest
 
 case class FunctionResult(f1: String, f2: String)
 
 class UDFSuite extends QueryTest {
-
   private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
-  import ctx.implicits._
 
   test("UDF case insensitive") {
     ctx.udf.register("random0", () => { Math.random() })
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 7b5aa4763fd9e..a312f84958248 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -17,17 +17,18 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.sql._
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import org.apache.spark.sql._
-import org.scalatest.BeforeAndAfterAll
 import _root_.test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
 
 abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  protected val sqlContext = _sqlContext
   import sqlContext.implicits._
 
   var originalUseAggregate2: Boolean = _
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 44c5b80392fa5..11d7a872dff09 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -26,8 +26,8 @@ import org.apache.spark.sql.test.SQLTestUtils
  * A set of tests that validates support for Hive Explain command.
  */
 class HiveExplainSuite extends QueryTest with SQLTestUtils {
-
-  def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   test("explain extended command") {
     checkExistence(sql(" explain   select * from src where key=123 "), true,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 79a136ae6f619..8b8f520776e70 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -66,7 +66,8 @@ class MyDialect extends DefaultParserDialect
  * valid, but Hive currently cannot execute it.
  */
 class SQLQuerySuite extends QueryTest with SQLTestUtils {
-  override def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   test("UDTF") {
     sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
index 0875232aede3e..9aca40f15ac15 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
@@ -31,7 +31,8 @@ import org.apache.spark.sql.types.StringType
 
 class ScriptTransformationSuite extends SparkPlanTest {
 
-  override def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   private val noSerdeIOSchema = HiveScriptIOSchema(
     inputRowFormat = Seq.empty,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
index 145965388da01..f7ba20ff41d8d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
@@ -27,8 +27,8 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.test.SQLTestUtils
 
 private[sql] trait OrcTest extends SQLTestUtils { this: SparkFunSuite =>
-  lazy val sqlContext = org.apache.spark.sql.hive.test.TestHive
-
+  protected override def _sqlContext: SQLContext = org.apache.spark.sql.hive.test.TestHive
+  protected val sqlContext = _sqlContext
   import sqlContext.implicits._
   import sqlContext.sparkContext
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index 50f02432dacce..34d3434569f58 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -685,7 +685,8 @@ class ParquetSourceSuite extends ParquetPartitioningTest {
  * A collection of tests for parquet data with various forms of partitioning.
  */
 abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-  override def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  protected val sqlContext = _sqlContext
 
   var partitionedTableDir: File = null
   var normalTableDir: File = null
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
index e976125b3706d..b4640b1616281 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
@@ -18,14 +18,16 @@
 package org.apache.spark.sql.sources
 
 import org.apache.hadoop.fs.Path
-import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 
 
 class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils {
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   // When committing a task, `CommitFailureTestSource` throws an exception for testing purpose.
   val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 2a69d331b6e52..af445626fbe4d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -34,9 +34,8 @@ import org.apache.spark.sql.types._
 
 
 abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
-  override lazy val sqlContext: SQLContext = TestHive
-
-  import sqlContext.sql
+  override def _sqlContext: SQLContext = TestHive
+  protected val sqlContext = _sqlContext
   import sqlContext.implicits._
 
   val dataSourceName: String

From bd35385d53a6b039e0241e3e73092b8b0a8e455a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 21:12:59 -0700
Subject: [PATCH 1041/1454] [SPARK-9945] [SQL] pageSize should be calculated
 from executor.memory

Currently, pageSize of TungstenSort is calculated from driver.memory, it should use executor.memory instead.

Also, in the worst case, the safeFactor could be 4 (because of rounding), increase it to 16.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8175 from davies/page_size.
---
 .../org/apache/spark/shuffle/ShuffleMemoryManager.scala     | 4 +++-
 .../main/scala/org/apache/spark/sql/execution/sort.scala    | 6 +++---
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index 8c3a72644c38a..a0d8abc2eecb3 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -175,7 +175,9 @@ private[spark] object ShuffleMemoryManager {
     val minPageSize = 1L * 1024 * 1024   // 1MB
     val maxPageSize = 64L * minPageSize  // 64MB
     val cores = if (numCores > 0) numCores else Runtime.getRuntime.availableProcessors()
-    val safetyFactor = 8
+    // Because of rounding to next power of 2, we may have safetyFactor as 8 in worst case
+    val safetyFactor = 16
+    // TODO(davies): don't round to next power of 2
     val size = ByteArrayMethods.nextPowerOf2(maxMemory / cores / safetyFactor)
     val default = math.min(maxPageSize, math.max(minPageSize, size))
     conf.getSizeAsBytes("spark.buffer.pageSize", default)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index e316930470127..40ef7c3b53530 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.{SparkEnv, InternalAccumulator, TaskContext}
 import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, OrderedDistribution, Distribution}
+import org.apache.spark.sql.catalyst.plans.physical.{Distribution, OrderedDistribution, UnspecifiedDistribution}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.CompletionIterator
 import org.apache.spark.util.collection.ExternalSorter
+import org.apache.spark.{SparkEnv, InternalAccumulator, TaskContext}
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 // This file defines various sort operators.
@@ -122,7 +122,6 @@ case class TungstenSort(
   protected override def doExecute(): RDD[InternalRow] = {
     val schema = child.schema
     val childOutput = child.output
-    val pageSize = SparkEnv.get.shuffleMemoryManager.pageSizeBytes
 
     /**
      * Set up the sorter in each partition before computing the parent partition.
@@ -143,6 +142,7 @@ case class TungstenSort(
         }
       }
 
+      val pageSize = SparkEnv.get.shuffleMemoryManager.pageSizeBytes
       val sorter = new UnsafeExternalRowSorter(
         schema, ordering, prefixComparator, prefixComputer, pageSize)
       if (testSpillFrequency > 0) {

From 7c7c7529a16c0e79778e522a3df82a0f1c3762a3 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 13 Aug 2015 22:06:09 -0700
Subject: [PATCH 1042/1454] [MINOR] [SQL] Remove canEqual in Row

As `InternalRow` does not extend `Row` now, I think we can remove it.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #8170 from viirya/remove_canequal.
---
 .../main/scala/org/apache/spark/sql/Row.scala | 21 -------------------
 1 file changed, 21 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 40159aaf14d34..ec895af9c3037 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -364,31 +364,10 @@ trait Row extends Serializable {
     false
   }
 
-  /**
-   * Returns true if we can check equality for these 2 rows.
-   * Equality check between external row and internal row is not allowed.
-   * Here we do this check to prevent call `equals` on external row with internal row.
-   */
-  protected def canEqual(other: Row) = {
-    // Note that `Row` is not only the interface of external row but also the parent
-    // of `InternalRow`, so we have to ensure `other` is not a internal row here to prevent
-    // call `equals` on external row with internal row.
-    // `InternalRow` overrides canEqual, and these two canEquals together makes sure that
-    // equality check between external Row and InternalRow will always fail.
-    // In the future, InternalRow should not extend Row. In that case, we can remove these
-    // canEqual methods.
-    !other.isInstanceOf[InternalRow]
-  }
-
   override def equals(o: Any): Boolean = {
     if (!o.isInstanceOf[Row]) return false
     val other = o.asInstanceOf[Row]
 
-    if (!canEqual(other)) {
-      throw new UnsupportedOperationException(
-        "cannot check equality between external and internal rows")
-    }
-
     if (other eq null) return false
 
     if (length != other.length) {

From c8677d73666850b37ff937520e538650632ce304 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 14 Aug 2015 14:41:53 +0800
Subject: [PATCH 1043/1454] [SPARK-9958] [SQL] Make HiveThriftServer2Listener
 thread-safe and update the tab name to "JDBC/ODBC Server"

This PR fixed the thread-safe issue of HiveThriftServer2Listener, and also changed the tab name to "JDBC/ODBC Server" since it's conflict with the new SQL tab.

<img width="1377" alt="thriftserver" src="https://cloud.githubusercontent.com/assets/1000778/9265707/c46f3f2c-4269-11e5-8d7e-888c9113ab4f.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #8185 from zsxwing/SPARK-9958.
---
 .../hive/thriftserver/HiveThriftServer2.scala | 64 +++++++++++--------
 .../thriftserver/ui/ThriftServerPage.scala    | 32 +++++-----
 .../ui/ThriftServerSessionPage.scala          | 38 +++++------
 .../thriftserver/ui/ThriftServerTab.scala     |  4 +-
 4 files changed, 78 insertions(+), 60 deletions(-)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index 2c9fa595b2dad..dd9fef9206d0b 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -152,16 +152,26 @@ object HiveThriftServer2 extends Logging {
     override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = {
       server.stop()
     }
-    var onlineSessionNum: Int = 0
-    val sessionList = new mutable.LinkedHashMap[String, SessionInfo]
-    val executionList = new mutable.LinkedHashMap[String, ExecutionInfo]
-    val retainedStatements =
-      conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT)
-    val retainedSessions =
-      conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT)
-    var totalRunning = 0
-
-    override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
+    private var onlineSessionNum: Int = 0
+    private val sessionList = new mutable.LinkedHashMap[String, SessionInfo]
+    private val executionList = new mutable.LinkedHashMap[String, ExecutionInfo]
+    private val retainedStatements = conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT)
+    private val retainedSessions = conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT)
+    private var totalRunning = 0
+
+    def getOnlineSessionNum: Int = synchronized { onlineSessionNum }
+
+    def getTotalRunning: Int = synchronized { totalRunning }
+
+    def getSessionList: Seq[SessionInfo] = synchronized { sessionList.values.toSeq }
+
+    def getSession(sessionId: String): Option[SessionInfo] = synchronized {
+      sessionList.get(sessionId)
+    }
+
+    def getExecutionList: Seq[ExecutionInfo] = synchronized { executionList.values.toSeq }
+
+    override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized {
       for {
         props <- Option(jobStart.properties)
         groupId <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID))
@@ -173,13 +183,15 @@ object HiveThriftServer2 extends Logging {
     }
 
     def onSessionCreated(ip: String, sessionId: String, userName: String = "UNKNOWN"): Unit = {
-      val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName)
-      sessionList.put(sessionId, info)
-      onlineSessionNum += 1
-      trimSessionIfNecessary()
+      synchronized {
+        val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName)
+        sessionList.put(sessionId, info)
+        onlineSessionNum += 1
+        trimSessionIfNecessary()
+      }
     }
 
-    def onSessionClosed(sessionId: String): Unit = {
+    def onSessionClosed(sessionId: String): Unit = synchronized {
       sessionList(sessionId).finishTimestamp = System.currentTimeMillis
       onlineSessionNum -= 1
       trimSessionIfNecessary()
@@ -190,7 +202,7 @@ object HiveThriftServer2 extends Logging {
         sessionId: String,
         statement: String,
         groupId: String,
-        userName: String = "UNKNOWN"): Unit = {
+        userName: String = "UNKNOWN"): Unit = synchronized {
       val info = new ExecutionInfo(statement, sessionId, System.currentTimeMillis, userName)
       info.state = ExecutionState.STARTED
       executionList.put(id, info)
@@ -200,27 +212,29 @@ object HiveThriftServer2 extends Logging {
       totalRunning += 1
     }
 
-    def onStatementParsed(id: String, executionPlan: String): Unit = {
+    def onStatementParsed(id: String, executionPlan: String): Unit = synchronized {
       executionList(id).executePlan = executionPlan
       executionList(id).state = ExecutionState.COMPILED
     }
 
     def onStatementError(id: String, errorMessage: String, errorTrace: String): Unit = {
-      executionList(id).finishTimestamp = System.currentTimeMillis
-      executionList(id).detail = errorMessage
-      executionList(id).state = ExecutionState.FAILED
-      totalRunning -= 1
-      trimExecutionIfNecessary()
+      synchronized {
+        executionList(id).finishTimestamp = System.currentTimeMillis
+        executionList(id).detail = errorMessage
+        executionList(id).state = ExecutionState.FAILED
+        totalRunning -= 1
+        trimExecutionIfNecessary()
+      }
     }
 
-    def onStatementFinish(id: String): Unit = {
+    def onStatementFinish(id: String): Unit = synchronized {
       executionList(id).finishTimestamp = System.currentTimeMillis
       executionList(id).state = ExecutionState.FINISHED
       totalRunning -= 1
       trimExecutionIfNecessary()
     }
 
-    private def trimExecutionIfNecessary() = synchronized {
+    private def trimExecutionIfNecessary() = {
       if (executionList.size > retainedStatements) {
         val toRemove = math.max(retainedStatements / 10, 1)
         executionList.filter(_._2.finishTimestamp != 0).take(toRemove).foreach { s =>
@@ -229,7 +243,7 @@ object HiveThriftServer2 extends Logging {
       }
     }
 
-    private def trimSessionIfNecessary() = synchronized {
+    private def trimSessionIfNecessary() = {
       if (sessionList.size > retainedSessions) {
         val toRemove = math.max(retainedSessions / 10, 1)
         sessionList.filter(_._2.finishTimestamp != 0).take(toRemove).foreach { s =>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
index 10c83d8b27a2a..e990bd06011ff 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
@@ -39,14 +39,16 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage(""
   /** Render the page */
   def render(request: HttpServletRequest): Seq[Node] = {
     val content =
-      generateBasicStats() ++
-      <br/> ++
-      <h4>
-        {listener.onlineSessionNum} session(s) are online,
-        running {listener.totalRunning} SQL statement(s)
-      </h4> ++
-      generateSessionStatsTable() ++
-      generateSQLStatsTable()
+      listener.synchronized { // make sure all parts in this page are consistent
+        generateBasicStats() ++
+        <br/> ++
+        <h4>
+        {listener.getOnlineSessionNum} session(s) are online,
+        running {listener.getTotalRunning} SQL statement(s)
+        </h4> ++
+        generateSessionStatsTable() ++
+        generateSQLStatsTable()
+      }
     UIUtils.headerSparkPage("JDBC/ODBC Server", content, parent, Some(5000))
   }
 
@@ -65,11 +67,11 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage(""
 
   /** Generate stats of batch statements of the thrift server program */
   private def generateSQLStatsTable(): Seq[Node] = {
-    val numStatement = listener.executionList.size
+    val numStatement = listener.getExecutionList.size
     val table = if (numStatement > 0) {
       val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration",
         "Statement", "State", "Detail")
-      val dataRows = listener.executionList.values
+      val dataRows = listener.getExecutionList
 
       def generateDataRow(info: ExecutionInfo): Seq[Node] = {
         val jobLink = info.jobId.map { id: String =>
@@ -136,15 +138,15 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage(""
 
   /** Generate stats of batch sessions of the thrift server program */
   private def generateSessionStatsTable(): Seq[Node] = {
-    val numBatches = listener.sessionList.size
+    val sessionList = listener.getSessionList
+    val numBatches = sessionList.size
     val table = if (numBatches > 0) {
-      val dataRows =
-        listener.sessionList.values
+      val dataRows = sessionList
       val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration",
         "Total Execute")
       def generateDataRow(session: SessionInfo): Seq[Node] = {
-        val sessionLink = "%s/sql/session?id=%s"
-          .format(UIUtils.prependBaseUri(parent.basePath), session.sessionId)
+        val sessionLink = "%s/%s/session?id=%s"
+          .format(UIUtils.prependBaseUri(parent.basePath), parent.prefix, session.sessionId)
         <tr>
           <td> {session.userName} </td>
           <td> {session.ip} </td>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
index 3b01afa603cea..af16cb31df187 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
@@ -40,21 +40,22 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab)
   def render(request: HttpServletRequest): Seq[Node] = {
     val parameterId = request.getParameter("id")
     require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
-    val sessionStat = listener.sessionList.find(stat => {
-      stat._1 == parameterId
-    }).getOrElse(null)
-    require(sessionStat != null, "Invalid sessionID[" + parameterId + "]")
 
     val content =
-      generateBasicStats() ++
-      <br/> ++
-      <h4>
-        User {sessionStat._2.userName},
-        IP {sessionStat._2.ip},
-        Session created at {formatDate(sessionStat._2.startTimestamp)},
-        Total run {sessionStat._2.totalExecution} SQL
-      </h4> ++
-      generateSQLStatsTable(sessionStat._2.sessionId)
+      listener.synchronized { // make sure all parts in this page are consistent
+        val sessionStat = listener.getSession(parameterId).getOrElse(null)
+        require(sessionStat != null, "Invalid sessionID[" + parameterId + "]")
+
+        generateBasicStats() ++
+        <br/> ++
+        <h4>
+        User {sessionStat.userName},
+        IP {sessionStat.ip},
+        Session created at {formatDate(sessionStat.startTimestamp)},
+        Total run {sessionStat.totalExecution} SQL
+        </h4> ++
+        generateSQLStatsTable(sessionStat.sessionId)
+      }
     UIUtils.headerSparkPage("JDBC/ODBC Session", content, parent, Some(5000))
   }
 
@@ -73,13 +74,13 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab)
 
   /** Generate stats of batch statements of the thrift server program */
   private def generateSQLStatsTable(sessionID: String): Seq[Node] = {
-    val executionList = listener.executionList
-      .filter(_._2.sessionId == sessionID)
+    val executionList = listener.getExecutionList
+      .filter(_.sessionId == sessionID)
     val numStatement = executionList.size
     val table = if (numStatement > 0) {
       val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration",
         "Statement", "State", "Detail")
-      val dataRows = executionList.values.toSeq.sortBy(_.startTimestamp).reverse
+      val dataRows = executionList.sortBy(_.startTimestamp).reverse
 
       def generateDataRow(info: ExecutionInfo): Seq[Node] = {
         val jobLink = info.jobId.map { id: String =>
@@ -146,10 +147,11 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab)
 
   /** Generate stats of batch sessions of the thrift server program */
   private def generateSessionStatsTable(): Seq[Node] = {
-    val numBatches = listener.sessionList.size
+    val sessionList = listener.getSessionList
+    val numBatches = sessionList.size
     val table = if (numBatches > 0) {
       val dataRows =
-        listener.sessionList.values.toSeq.sortBy(_.startTimestamp).reverse.map ( session =>
+        sessionList.sortBy(_.startTimestamp).reverse.map ( session =>
         Seq(
           session.userName,
           session.ip,
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
index 94fd8a6bb60b9..4eabeaa6735e6 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
@@ -27,9 +27,9 @@ import org.apache.spark.{SparkContext, Logging, SparkException}
  * This assumes the given SparkContext has enabled its SparkUI.
  */
 private[thriftserver] class ThriftServerTab(sparkContext: SparkContext)
-  extends SparkUITab(getSparkUI(sparkContext), "sql") with Logging {
+  extends SparkUITab(getSparkUI(sparkContext), "sqlserver") with Logging {
 
-  override val name = "SQL"
+  override val name = "JDBC/ODBC Server"
 
   val parent = getSparkUI(sparkContext)
   val listener = HiveThriftServer2.listener

From a0e1abbd010b9e73d472ce12ff1d987678005d32 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 14 Aug 2015 10:25:11 -0700
Subject: [PATCH 1044/1454] [SPARK-9661] [MLLIB] minor clean-up of SPARK-9661

Some minor clean-ups after SPARK-9661. See my inline comments. MechCoder jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8190 from mengxr/SPARK-9661-fix.
---
 .../spark/mllib/clustering/LDAModel.scala     |  5 +--
 .../apache/spark/mllib/stat/Statistics.scala  |  6 +--
 .../spark/mllib/clustering/JavaLDASuite.java  | 40 +++++++++++--------
 .../spark/mllib/clustering/LDASuite.scala     |  2 +-
 4 files changed, 28 insertions(+), 25 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index f31949f13a4cf..82f05e4a18cee 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -674,10 +674,9 @@ class DistributedLDAModel private[clustering] (
   }
 
   /** Java-friendly version of [[topTopicsPerDocument]] */
-  def javaTopTopicsPerDocument(
-      k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[java.lang.Double])] = {
+  def javaTopTopicsPerDocument(k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[Double])] = {
     val topics = topTopicsPerDocument(k)
-    topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[java.lang.Double])]].toJavaRDD()
+    topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Double])]].toJavaRDD()
   }
 
   // TODO:
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index 24fe48cb8f71f..ef8d78607048f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -221,9 +221,7 @@ object Statistics {
   def kolmogorovSmirnovTest(
       data: JavaDoubleRDD,
       distName: String,
-      params: java.lang.Double*): KolmogorovSmirnovTestResult = {
-    val javaParams = params.asInstanceOf[Seq[Double]]
-    KolmogorovSmirnovTest.testOneSample(data.rdd.asInstanceOf[RDD[Double]],
-      distName, javaParams: _*)
+      params: Double*): KolmogorovSmirnovTestResult = {
+    kolmogorovSmirnovTest(data.rdd.asInstanceOf[RDD[Double]], distName, params: _*)
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index 427be9430d820..6e91cde2eabb5 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -22,12 +22,14 @@
 import java.util.Arrays;
 
 import scala.Tuple2;
+import scala.Tuple3;
 
 import org.junit.After;
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertArrayEquals;
 import org.junit.Before;
 import org.junit.Test;
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
 
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.JavaPairRDD;
@@ -44,9 +46,9 @@ public class JavaLDASuite implements Serializable {
   public void setUp() {
     sc = new JavaSparkContext("local", "JavaLDA");
     ArrayList<Tuple2<Long, Vector>> tinyCorpus = new ArrayList<Tuple2<Long, Vector>>();
-    for (int i = 0; i < LDASuite$.MODULE$.tinyCorpus().length; i++) {
-      tinyCorpus.add(new Tuple2<Long, Vector>((Long)LDASuite$.MODULE$.tinyCorpus()[i]._1(),
-          LDASuite$.MODULE$.tinyCorpus()[i]._2()));
+    for (int i = 0; i < LDASuite.tinyCorpus().length; i++) {
+      tinyCorpus.add(new Tuple2<Long, Vector>((Long)LDASuite.tinyCorpus()[i]._1(),
+          LDASuite.tinyCorpus()[i]._2()));
     }
     JavaRDD<Tuple2<Long, Vector>> tmpCorpus = sc.parallelize(tinyCorpus, 2);
     corpus = JavaPairRDD.fromJavaRDD(tmpCorpus);
@@ -60,7 +62,7 @@ public void tearDown() {
 
   @Test
   public void localLDAModel() {
-    Matrix topics = LDASuite$.MODULE$.tinyTopics();
+    Matrix topics = LDASuite.tinyTopics();
     double[] topicConcentration = new double[topics.numRows()];
     Arrays.fill(topicConcentration, 1.0D / topics.numRows());
     LocalLDAModel model = new LocalLDAModel(topics, Vectors.dense(topicConcentration), 1D, 100D);
@@ -110,8 +112,8 @@ public void distributedLDAModel() {
     assertEquals(roundedLocalTopicSummary.length, k);
 
     // Check: log probabilities
-    assert(model.logLikelihood() < 0.0);
-    assert(model.logPrior() < 0.0);
+    assertTrue(model.logLikelihood() < 0.0);
+    assertTrue(model.logPrior() < 0.0);
 
     // Check: topic distributions
     JavaPairRDD<Long, Vector> topicDistributions = model.javaTopicDistributions();
@@ -126,8 +128,12 @@ public Boolean call(Tuple2<Long, Vector> tuple2) {
     assertEquals(topicDistributions.count(), nonEmptyCorpus.count());
 
     // Check: javaTopTopicsPerDocuments
-    JavaRDD<scala.Tuple3<java.lang.Long, int[], java.lang.Double[]>> topTopics =
-      model.javaTopTopicsPerDocument(3);
+    Tuple3<Long, int[], double[]> topTopics = model.javaTopTopicsPerDocument(3).first();
+    Long docId = topTopics._1(); // confirm doc ID type
+    int[] topicIndices = topTopics._2();
+    double[] topicWeights = topTopics._3();
+    assertEquals(3, topicIndices.length);
+    assertEquals(3, topicWeights.length);
   }
 
   @Test
@@ -177,18 +183,18 @@ public void localLdaMethods() {
 
     // check: logLikelihood.
     ArrayList<Tuple2<Long, Vector>> docsSingleWord = new ArrayList<Tuple2<Long, Vector>>();
-    docsSingleWord.add(new Tuple2<Long, Vector>(Long.valueOf(0), Vectors.dense(1.0, 0.0, 0.0)));
+    docsSingleWord.add(new Tuple2<Long, Vector>(0L, Vectors.dense(1.0, 0.0, 0.0)));
     JavaPairRDD<Long, Vector> single = JavaPairRDD.fromJavaRDD(sc.parallelize(docsSingleWord));
     double logLikelihood = toyModel.logLikelihood(single);
   }
 
-  private static int tinyK = LDASuite$.MODULE$.tinyK();
-  private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
-  private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();
+  private static int tinyK = LDASuite.tinyK();
+  private static int tinyVocabSize = LDASuite.tinyVocabSize();
+  private static Matrix tinyTopics = LDASuite.tinyTopics();
   private static Tuple2<int[], double[]>[] tinyTopicDescription =
-      LDASuite$.MODULE$.tinyTopicDescription();
+      LDASuite.tinyTopicDescription();
   private JavaPairRDD<Long, Vector> corpus;
-  private LocalLDAModel toyModel = LDASuite$.MODULE$.toyModel();
-  private ArrayList<Tuple2<Long, Vector>> toyData = LDASuite$.MODULE$.javaToyData();
+  private LocalLDAModel toyModel = LDASuite.toyModel();
+  private ArrayList<Tuple2<Long, Vector>> toyData = LDASuite.javaToyData();
 
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 926185e90bcf9..99e28499fd316 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -581,7 +581,7 @@ private[clustering] object LDASuite {
   def javaToyData: JArrayList[(java.lang.Long, Vector)] = {
     val javaData = new JArrayList[(java.lang.Long, Vector)]
     var i = 0
-    while (i < toyData.size) {
+    while (i < toyData.length) {
       javaData.add((toyData(i)._1, toyData(i)._2))
       i += 1
     }

From 7ecf0c46990c39df8aeddbd64ca33d01824bcc0a Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 14 Aug 2015 10:48:02 -0700
Subject: [PATCH 1045/1454] [SPARK-9956] [ML] Make trees work with one-category
 features

This modifies DecisionTreeMetadata construction to treat 1-category features as continuous, so that trees do not fail with such features.  It is important for the pipelines API, where VectorIndexer can automatically categorize certain features as categorical.

As stated in the JIRA, this is a temp fix which we can improve upon later by automatically filtering out those features. That will take longer, though, since it will require careful indexing.

Targeted for 1.5 and master

CC: manishamde  mengxr yanboliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8187 from jkbradley/tree-1cat.
---
 .../tree/impl/DecisionTreeMetadata.scala      | 27 ++++++++++++-------
 .../DecisionTreeClassifierSuite.scala         | 13 +++++++++
 2 files changed, 30 insertions(+), 10 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index 9fe264656ede7..21ee49c45788c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -144,21 +144,28 @@ private[spark] object DecisionTreeMetadata extends Logging {
       val maxCategoriesForUnorderedFeature =
         ((math.log(maxPossibleBins / 2 + 1) / math.log(2.0)) + 1).floor.toInt
       strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
-        // Decide if some categorical features should be treated as unordered features,
-        //  which require 2 * ((1 << numCategories - 1) - 1) bins.
-        // We do this check with log values to prevent overflows in case numCategories is large.
-        // The next check is equivalent to: 2 * ((1 << numCategories - 1) - 1) <= maxBins
-        if (numCategories <= maxCategoriesForUnorderedFeature) {
-          unorderedFeatures.add(featureIndex)
-          numBins(featureIndex) = numUnorderedBins(numCategories)
-        } else {
-          numBins(featureIndex) = numCategories
+        // Hack: If a categorical feature has only 1 category, we treat it as continuous.
+        // TODO(SPARK-9957): Handle this properly by filtering out those features.
+        if (numCategories > 1) {
+          // Decide if some categorical features should be treated as unordered features,
+          //  which require 2 * ((1 << numCategories - 1) - 1) bins.
+          // We do this check with log values to prevent overflows in case numCategories is large.
+          // The next check is equivalent to: 2 * ((1 << numCategories - 1) - 1) <= maxBins
+          if (numCategories <= maxCategoriesForUnorderedFeature) {
+            unorderedFeatures.add(featureIndex)
+            numBins(featureIndex) = numUnorderedBins(numCategories)
+          } else {
+            numBins(featureIndex) = numCategories
+          }
         }
       }
     } else {
       // Binary classification or regression
       strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
-        numBins(featureIndex) = numCategories
+        // If a categorical feature has only 1 category, we treat it as continuous: SPARK-9957
+        if (numCategories > 1) {
+          numBins(featureIndex) = numCategories
+        }
       }
     }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
index 4b7c5d3f23d2c..f680d8d3c4cc2 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
@@ -261,6 +261,19 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     }
   }
 
+  test("training with 1-category categorical feature") {
+    val data = sc.parallelize(Seq(
+      LabeledPoint(0, Vectors.dense(0, 2, 3)),
+      LabeledPoint(1, Vectors.dense(0, 3, 1)),
+      LabeledPoint(0, Vectors.dense(0, 2, 2)),
+      LabeledPoint(1, Vectors.dense(0, 3, 9)),
+      LabeledPoint(0, Vectors.dense(0, 2, 6))
+    ))
+    val df = TreeTests.setMetadata(data, Map(0 -> 1), 2)
+    val dt = new DecisionTreeClassifier().setMaxDepth(3)
+    val model = dt.fit(df)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////

From a7317ccdc20d001e5b7f5277b0535923468bfbc6 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Fri, 14 Aug 2015 11:22:10 -0700
Subject: [PATCH 1046/1454] [SPARK-8744] [ML] Add a public constructor to
 StringIndexer

It would be helpful to allow users to pass a pre-computed index to create an indexer, rather than always going through StringIndexer to create the model.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7267 from holdenk/SPARK-8744-StringIndexerModel-should-have-public-constructor.
---
 .../scala/org/apache/spark/ml/feature/StringIndexer.scala     | 4 +++-
 .../org/apache/spark/ml/feature/StringIndexerSuite.scala      | 2 ++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 9f6e7b6b6b274..63475780a6ff9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -102,10 +102,12 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
  * This is a temporary fix for the case when target labels do not exist during prediction.
  */
 @Experimental
-class StringIndexerModel private[ml] (
+class StringIndexerModel (
     override val uid: String,
     labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
 
+  def this(labels: Array[String]) = this(Identifiable.randomUID("strIdx"), labels)
+
   private val labelToIndex: OpenHashMap[String, Double] = {
     val n = labels.length
     val map = new OpenHashMap[String, Double](n)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index fa918ce64877c..0b4c8ba71ee61 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -30,7 +30,9 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
   test("params") {
     ParamsSuite.checkParams(new StringIndexer)
     val model = new StringIndexerModel("indexer", Array("a", "b"))
+    val modelWithoutUid = new StringIndexerModel(Array("a", "b"))
     ParamsSuite.checkParams(model)
+    ParamsSuite.checkParams(modelWithoutUid)
   }
 
   test("StringIndexer") {

From 34d610be854d2a975d9c1e232d87433b85add6fd Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 14 Aug 2015 12:00:01 -0700
Subject: [PATCH 1047/1454] [SPARK-9929] [SQL] support metadata in withColumn

in MLlib sometimes we need to set metadata for the new column, thus we will alias the new column with metadata before call `withColumn` and in `withColumn` we alias this clolumn again. Here I overloaded `withColumn` to allow user set metadata, just like what we did  for `Column.as`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8159 from cloud-fan/withColumn.
---
 .../spark/ml/classification/OneVsRest.scala     |  6 +++---
 .../apache/spark/ml/feature/Bucketizer.scala    |  2 +-
 .../apache/spark/ml/feature/VectorIndexer.scala |  2 +-
 .../apache/spark/ml/feature/VectorSlicer.scala  |  3 +--
 .../scala/org/apache/spark/sql/DataFrame.scala  | 17 +++++++++++++++++
 5 files changed, 23 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index 1132d8046df67..c62e132f5d533 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -131,7 +131,7 @@ final class OneVsRestModel private[ml] (
 
     // output label and label metadata as prediction
     aggregatedDataset
-      .withColumn($(predictionCol), labelUDF(col(accColName)).as($(predictionCol), labelMetadata))
+      .withColumn($(predictionCol), labelUDF(col(accColName)), labelMetadata)
       .drop(accColName)
   }
 
@@ -203,8 +203,8 @@ final class OneVsRest(override val uid: String)
       // TODO: use when ... otherwise after SPARK-7321 is merged
       val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
       val labelColName = "mc2b$" + index
-      val labelUDFWithNewMeta = labelUDF(col($(labelCol))).as(labelColName, newLabelMeta)
-      val trainingDataset = multiclassLabeled.withColumn(labelColName, labelUDFWithNewMeta)
+      val trainingDataset =
+        multiclassLabeled.withColumn(labelColName, labelUDF(col($(labelCol))), newLabelMeta)
       val classifier = getClassifier
       val paramMap = new ParamMap()
       paramMap.put(classifier.labelCol -> labelColName)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
index cfca494dcf468..6fdf25b015b0b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
@@ -75,7 +75,7 @@ final class Bucketizer(override val uid: String)
     }
     val newCol = bucketizer(dataset($(inputCol)))
     val newField = prepOutputField(dataset.schema)
-    dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
+    dataset.withColumn($(outputCol), newCol, newField.metadata)
   }
 
   private def prepOutputField(schema: StructType): StructField = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index 6875aefe065bb..61b925c0fdc07 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -341,7 +341,7 @@ class VectorIndexerModel private[ml] (
     val newField = prepOutputField(dataset.schema)
     val transformUDF = udf { (vector: Vector) => transformFunc(vector) }
     val newCol = transformUDF(dataset($(inputCol)))
-    dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
+    dataset.withColumn($(outputCol), newCol, newField.metadata)
   }
 
   override def transformSchema(schema: StructType): StructType = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
index 772bebeff214b..c5c2272270792 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
@@ -119,8 +119,7 @@ final class VectorSlicer(override val uid: String)
         case features: SparseVector => features.slice(inds)
       }
     }
-    dataset.withColumn($(outputCol),
-      slicer(dataset($(inputCol))).as($(outputCol), outputAttr.toMetadata()))
+    dataset.withColumn($(outputCol), slicer(dataset($(inputCol))), outputAttr.toMetadata())
   }
 
   /** Get the feature indices in order: indices, names */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index c466d9e6cb349..cf75e64e884b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1149,6 +1149,23 @@ class DataFrame private[sql](
     }
   }
 
+  /**
+   * Returns a new [[DataFrame]] by adding a column with metadata.
+   */
+  private[spark] def withColumn(colName: String, col: Column, metadata: Metadata): DataFrame = {
+    val resolver = sqlContext.analyzer.resolver
+    val replaced = schema.exists(f => resolver(f.name, colName))
+    if (replaced) {
+      val colNames = schema.map { field =>
+        val name = field.name
+        if (resolver(name, colName)) col.as(colName, metadata) else Column(name)
+      }
+      select(colNames : _*)
+    } else {
+      select(Column("*"), col.as(colName, metadata))
+    }
+  }
+
   /**
    * Returns a new [[DataFrame]] with a column renamed.
    * This is a no-op if schema doesn't contain existingName.

From 57c2d08800a506614b461b5a505a1dd1a28a8908 Mon Sep 17 00:00:00 2001
From: Neelesh Srinivas Salian <nsalian@cloudera.com>
Date: Fri, 14 Aug 2015 20:03:50 +0100
Subject: [PATCH 1048/1454] [SPARK-9923] [CORE]
 ShuffleMapStage.numAvailableOutputs should be an Int instead of Long

Modified type of ShuffleMapStage.numAvailableOutputs from Long to Int

Author: Neelesh Srinivas Salian <nsalian@cloudera.com>

Closes #8183 from nssalian/SPARK-9923.
---
 .../main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
index 66c75f325fcde..48d8d8e9c4b78 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
@@ -37,7 +37,7 @@ private[spark] class ShuffleMapStage(
 
   override def toString: String = "ShuffleMapStage " + id
 
-  var numAvailableOutputs: Long = 0
+  var numAvailableOutputs: Int = 0
 
   def isAvailable: Boolean = numAvailableOutputs == numPartitions
 

From 3bc55287220b1248e935bf817d880ff176ad4d3b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 12:32:35 -0700
Subject: [PATCH 1049/1454] [SPARK-9946] [SPARK-9589] [SQL] fix NPE and
 thread-safety in TaskMemoryManager

Currently, we access the `page.pageNumer` after it's freed, that could be modified by other thread, cause NPE.

The same TaskMemoryManager could be used by multiple threads (for example, Python UDF and TransportScript), so it should be thread safe to allocate/free memory/page. The underlying Bitset and HashSet are not thread safe, we should put them inside a synchronized block.

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8177 from davies/memory_manager.
---
 .../unsafe/memory/TaskMemoryManager.java      | 44 ++++++++++++-------
 1 file changed, 28 insertions(+), 16 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
index 358bb37250158..ca70d7f4a4311 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
@@ -144,14 +144,16 @@ public MemoryBlock allocatePage(long size) {
   public void freePage(MemoryBlock page) {
     assert (page.pageNumber != -1) :
       "Called freePage() on memory that wasn't allocated with allocatePage()";
-    executorMemoryManager.free(page);
+    assert(allocatedPages.get(page.pageNumber));
+    pageTable[page.pageNumber] = null;
     synchronized (this) {
       allocatedPages.clear(page.pageNumber);
     }
-    pageTable[page.pageNumber] = null;
     if (logger.isTraceEnabled()) {
       logger.trace("Freed page number {} ({} bytes)", page.pageNumber, page.size());
     }
+    // Cannot access a page once it's freed.
+    executorMemoryManager.free(page);
   }
 
   /**
@@ -166,7 +168,9 @@ public void freePage(MemoryBlock page) {
   public MemoryBlock allocate(long size) throws OutOfMemoryError {
     assert(size > 0) : "Size must be positive, but got " + size;
     final MemoryBlock memory = executorMemoryManager.allocate(size);
-    allocatedNonPageMemory.add(memory);
+    synchronized(allocatedNonPageMemory) {
+      allocatedNonPageMemory.add(memory);
+    }
     return memory;
   }
 
@@ -176,8 +180,10 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError {
   public void free(MemoryBlock memory) {
     assert (memory.pageNumber == -1) : "Should call freePage() for pages, not free()";
     executorMemoryManager.free(memory);
-    final boolean wasAlreadyRemoved = !allocatedNonPageMemory.remove(memory);
-    assert (!wasAlreadyRemoved) : "Called free() on memory that was already freed!";
+    synchronized(allocatedNonPageMemory) {
+      final boolean wasAlreadyRemoved = !allocatedNonPageMemory.remove(memory);
+      assert (!wasAlreadyRemoved) : "Called free() on memory that was already freed!";
+    }
   }
 
   /**
@@ -223,9 +229,10 @@ public Object getPage(long pagePlusOffsetAddress) {
     if (inHeap) {
       final int pageNumber = decodePageNumber(pagePlusOffsetAddress);
       assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE);
-      final Object page = pageTable[pageNumber].getBaseObject();
+      final MemoryBlock page = pageTable[pageNumber];
       assert (page != null);
-      return page;
+      assert (page.getBaseObject() != null);
+      return page.getBaseObject();
     } else {
       return null;
     }
@@ -244,7 +251,9 @@ public long getOffsetInPage(long pagePlusOffsetAddress) {
       // converted the absolute address into a relative address. Here, we invert that operation:
       final int pageNumber = decodePageNumber(pagePlusOffsetAddress);
       assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE);
-      return pageTable[pageNumber].getBaseOffset() + offsetInPage;
+      final MemoryBlock page = pageTable[pageNumber];
+      assert (page != null);
+      return page.getBaseOffset() + offsetInPage;
     }
   }
 
@@ -260,14 +269,17 @@ public long cleanUpAllAllocatedMemory() {
         freePage(page);
       }
     }
-    final Iterator<MemoryBlock> iter = allocatedNonPageMemory.iterator();
-    while (iter.hasNext()) {
-      final MemoryBlock memory = iter.next();
-      freedBytes += memory.size();
-      // We don't call free() here because that calls Set.remove, which would lead to a
-      // ConcurrentModificationException here.
-      executorMemoryManager.free(memory);
-      iter.remove();
+
+    synchronized (allocatedNonPageMemory) {
+      final Iterator<MemoryBlock> iter = allocatedNonPageMemory.iterator();
+      while (iter.hasNext()) {
+        final MemoryBlock memory = iter.next();
+        freedBytes += memory.size();
+        // We don't call free() here because that calls Set.remove, which would lead to a
+        // ConcurrentModificationException here.
+        executorMemoryManager.free(memory);
+        iter.remove();
+      }
     }
     return freedBytes;
   }

From ece00566e4d5f38585f2810bef38e526cae7d25e Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 14 Aug 2015 12:37:21 -0700
Subject: [PATCH 1050/1454] [SPARK-9561] Re-enable BroadcastJoinSuite

We can do this now that SPARK-9580 is resolved.

Author: Andrew Or <andrew@databricks.com>

Closes #8208 from andrewor14/reenable-sql-tests.
---
 .../execution/joins/BroadcastJoinSuite.scala  | 145 +++++++++---------
 1 file changed, 69 insertions(+), 76 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
index 0554e11d252ba..53a0e53fd7719 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
@@ -15,80 +15,73 @@
 * limitations under the License.
 */
 
-// TODO: uncomment the test here! It is currently failing due to
-// bad interaction with org.apache.spark.sql.test.TestSQLContext.
+package org.apache.spark.sql.execution.joins
 
-// scalastyle:off
-//package org.apache.spark.sql.execution.joins
-//
-//import scala.reflect.ClassTag
-//
-//import org.scalatest.BeforeAndAfterAll
-//
-//import org.apache.spark.{AccumulatorSuite, SparkConf, SparkContext}
-//import org.apache.spark.sql.functions._
-//import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
-//
-///**
-// * Test various broadcast join operators with unsafe enabled.
-// *
-// * This needs to be its own suite because [[org.apache.spark.sql.test.TestSQLContext]] runs
-// * in local mode, but for tests in this suite we need to run Spark in local-cluster mode.
-// * In particular, the use of [[org.apache.spark.unsafe.map.BytesToBytesMap]] in
-// * [[org.apache.spark.sql.execution.joins.UnsafeHashedRelation]] is not triggered without
-// * serializing the hashed relation, which does not happen in local mode.
-// */
-//class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
-//  private var sc: SparkContext = null
-//  private var sqlContext: SQLContext = null
-//
-//  /**
-//   * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
-//   */
-//  override def beforeAll(): Unit = {
-//    super.beforeAll()
-//    val conf = new SparkConf()
-//      .setMaster("local-cluster[2,1,1024]")
-//      .setAppName("testing")
-//    sc = new SparkContext(conf)
-//    sqlContext = new SQLContext(sc)
-//    sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
-//    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
-//  }
-//
-//  override def afterAll(): Unit = {
-//    sc.stop()
-//    sc = null
-//    sqlContext = null
-//  }
-//
-//  /**
-//   * Test whether the specified broadcast join updates the peak execution memory accumulator.
-//   */
-//  private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
-//    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
-//      val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
-//      val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
-//      // Comparison at the end is for broadcast left semi join
-//      val joinExpression = df1("key") === df2("key") && df1("value") > df2("value")
-//      val df3 = df1.join(broadcast(df2), joinExpression, joinType)
-//      val plan = df3.queryExecution.executedPlan
-//      assert(plan.collect { case p: T => p }.size === 1)
-//      plan.executeCollect()
-//    }
-//  }
-//
-//  test("unsafe broadcast hash join updates peak execution memory") {
-//    testBroadcastJoin[BroadcastHashJoin]("unsafe broadcast hash join", "inner")
-//  }
-//
-//  test("unsafe broadcast hash outer join updates peak execution memory") {
-//    testBroadcastJoin[BroadcastHashOuterJoin]("unsafe broadcast hash outer join", "left_outer")
-//  }
-//
-//  test("unsafe broadcast left semi join updates peak execution memory") {
-//    testBroadcastJoin[BroadcastLeftSemiJoinHash]("unsafe broadcast left semi join", "leftsemi")
-//  }
-//
-//}
-// scalastyle:on
+import scala.reflect.ClassTag
+
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.{AccumulatorSuite, SparkConf, SparkContext}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
+
+/**
+ * Test various broadcast join operators with unsafe enabled.
+ *
+ * Tests in this suite we need to run Spark in local-cluster mode. In particular, the use of
+ * unsafe map in [[org.apache.spark.sql.execution.joins.UnsafeHashedRelation]] is not triggered
+ * without serializing the hashed relation, which does not happen in local mode.
+ */
+class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
+  private var sc: SparkContext = null
+  private var sqlContext: SQLContext = null
+
+  /**
+   * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
+   */
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    val conf = new SparkConf()
+      .setMaster("local-cluster[2,1,1024]")
+      .setAppName("testing")
+    sc = new SparkContext(conf)
+    sqlContext = new SQLContext(sc)
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
+    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
+  }
+
+  override def afterAll(): Unit = {
+    sc.stop()
+    sc = null
+    sqlContext = null
+  }
+
+  /**
+   * Test whether the specified broadcast join updates the peak execution memory accumulator.
+   */
+  private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
+      val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
+      val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
+      // Comparison at the end is for broadcast left semi join
+      val joinExpression = df1("key") === df2("key") && df1("value") > df2("value")
+      val df3 = df1.join(broadcast(df2), joinExpression, joinType)
+      val plan = df3.queryExecution.executedPlan
+      assert(plan.collect { case p: T => p }.size === 1)
+      plan.executeCollect()
+    }
+  }
+
+  test("unsafe broadcast hash join updates peak execution memory") {
+    testBroadcastJoin[BroadcastHashJoin]("unsafe broadcast hash join", "inner")
+  }
+
+  test("unsafe broadcast hash outer join updates peak execution memory") {
+    testBroadcastJoin[BroadcastHashOuterJoin]("unsafe broadcast hash outer join", "left_outer")
+  }
+
+  test("unsafe broadcast left semi join updates peak execution memory") {
+    testBroadcastJoin[BroadcastLeftSemiJoinHash]("unsafe broadcast left semi join", "leftsemi")
+  }
+
+}

From ffa05c84fe75663fc33f3d954d1cb1e084ab3280 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Fri, 14 Aug 2015 12:46:05 -0700
Subject: [PATCH 1051/1454] [SPARK-9828] [PYSPARK] Mutable values should not be
 default arguments

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8110 from MechCoder/spark-9828.
---
 python/pyspark/ml/evaluation.py     |  4 +++-
 python/pyspark/ml/param/__init__.py | 26 +++++++++++++++++---------
 python/pyspark/ml/pipeline.py       |  4 ++--
 python/pyspark/ml/tuning.py         |  8 ++++++--
 python/pyspark/rdd.py               |  5 ++++-
 python/pyspark/sql/readwriter.py    |  8 ++++++--
 python/pyspark/statcounter.py       |  4 +++-
 python/pyspark/streaming/kafka.py   | 12 +++++++++---
 8 files changed, 50 insertions(+), 21 deletions(-)

diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 2734092575ad9..e23ce053baeb7 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -46,7 +46,7 @@ def _evaluate(self, dataset):
         """
         raise NotImplementedError()
 
-    def evaluate(self, dataset, params={}):
+    def evaluate(self, dataset, params=None):
         """
         Evaluates the output with optional parameters.
 
@@ -56,6 +56,8 @@ def evaluate(self, dataset, params={}):
                        params
         :return: metric
         """
+        if params is None:
+            params = dict()
         if isinstance(params, dict):
             if params:
                 return self.copy(params)._evaluate(dataset)
diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py
index 7845536161e07..eeeac49b21980 100644
--- a/python/pyspark/ml/param/__init__.py
+++ b/python/pyspark/ml/param/__init__.py
@@ -60,14 +60,16 @@ class Params(Identifiable):
 
     __metaclass__ = ABCMeta
 
-    #: internal param map for user-supplied values param map
-    _paramMap = {}
+    def __init__(self):
+        super(Params, self).__init__()
+        #: internal param map for user-supplied values param map
+        self._paramMap = {}
 
-    #: internal param map for default values
-    _defaultParamMap = {}
+        #: internal param map for default values
+        self._defaultParamMap = {}
 
-    #: value returned by :py:func:`params`
-    _params = None
+        #: value returned by :py:func:`params`
+        self._params = None
 
     @property
     def params(self):
@@ -155,7 +157,7 @@ def getOrDefault(self, param):
         else:
             return self._defaultParamMap[param]
 
-    def extractParamMap(self, extra={}):
+    def extractParamMap(self, extra=None):
         """
         Extracts the embedded default param values and user-supplied
         values, and then merges them with extra values from input into
@@ -165,12 +167,14 @@ def extractParamMap(self, extra={}):
         :param extra: extra param values
         :return: merged param map
         """
+        if extra is None:
+            extra = dict()
         paramMap = self._defaultParamMap.copy()
         paramMap.update(self._paramMap)
         paramMap.update(extra)
         return paramMap
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
         """
         Creates a copy of this instance with the same uid and some
         extra params. The default implementation creates a
@@ -181,6 +185,8 @@ def copy(self, extra={}):
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
         """
+        if extra is None:
+            extra = dict()
         that = copy.copy(self)
         that._paramMap = self.extractParamMap(extra)
         return that
@@ -233,7 +239,7 @@ def _setDefault(self, **kwargs):
             self._defaultParamMap[getattr(self, param)] = value
         return self
 
-    def _copyValues(self, to, extra={}):
+    def _copyValues(self, to, extra=None):
         """
         Copies param values from this instance to another instance for
         params shared by them.
@@ -241,6 +247,8 @@ def _copyValues(self, to, extra={}):
         :param extra: extra params to be copied
         :return: the target instance with param values copied
         """
+        if extra is None:
+            extra = dict()
         paramMap = self.extractParamMap(extra)
         for p in self.params:
             if p in paramMap and to.hasParam(p.name):
diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
index 9889f56cac9e4..13cf2b0f7bbd9 100644
--- a/python/pyspark/ml/pipeline.py
+++ b/python/pyspark/ml/pipeline.py
@@ -141,7 +141,7 @@ class Pipeline(Estimator):
     @keyword_only
     def __init__(self, stages=None):
         """
-        __init__(self, stages=[])
+        __init__(self, stages=None)
         """
         if stages is None:
             stages = []
@@ -170,7 +170,7 @@ def getStages(self):
     @keyword_only
     def setParams(self, stages=None):
         """
-        setParams(self, stages=[])
+        setParams(self, stages=None)
         Sets params for Pipeline.
         """
         if stages is None:
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 0bf988fd72f14..dcfee6a3170ab 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -227,7 +227,9 @@ def _fit(self, dataset):
         bestModel = est.fit(dataset, epm[bestIndex])
         return CrossValidatorModel(bestModel)
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
+        if extra is None:
+            extra = dict()
         newCV = Params.copy(self, extra)
         if self.isSet(self.estimator):
             newCV.setEstimator(self.getEstimator().copy(extra))
@@ -250,7 +252,7 @@ def __init__(self, bestModel):
     def _transform(self, dataset):
         return self.bestModel.transform(dataset)
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
         """
         Creates a copy of this instance with a randomly generated uid
         and some extra params. This copies the underlying bestModel,
@@ -259,6 +261,8 @@ def copy(self, extra={}):
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
         """
+        if extra is None:
+            extra = dict()
         return CrossValidatorModel(self.bestModel.copy(extra))
 
 
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index fa8e0a0574a62..9ef60a7e2c84b 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -700,7 +700,7 @@ def groupBy(self, f, numPartitions=None):
         return self.map(lambda x: (f(x), x)).groupByKey(numPartitions)
 
     @ignore_unicode_prefix
-    def pipe(self, command, env={}, checkCode=False):
+    def pipe(self, command, env=None, checkCode=False):
         """
         Return an RDD created by piping elements to a forked external process.
 
@@ -709,6 +709,9 @@ def pipe(self, command, env={}, checkCode=False):
 
         :param checkCode: whether or not to check the return value of the shell command.
         """
+        if env is None:
+            env = dict()
+
         def func(iterator):
             pipe = Popen(
                 shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index bf6ac084bbbf8..78247c8fa7372 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -182,7 +182,7 @@ def orc(self, path):
 
     @since(1.4)
     def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
-             predicates=None, properties={}):
+             predicates=None, properties=None):
         """
         Construct a :class:`DataFrame` representing the database table accessible
         via JDBC URL `url` named `table` and connection `properties`.
@@ -208,6 +208,8 @@ def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPar
                            should be included.
         :return: a DataFrame
         """
+        if properties is None:
+            properties = dict()
         jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)()
         for k in properties:
             jprop.setProperty(k, properties[k])
@@ -427,7 +429,7 @@ def orc(self, path, mode=None, partitionBy=None):
         self._jwrite.orc(path)
 
     @since(1.4)
-    def jdbc(self, url, table, mode=None, properties={}):
+    def jdbc(self, url, table, mode=None, properties=None):
         """Saves the content of the :class:`DataFrame` to a external database table via JDBC.
 
         .. note:: Don't create too many partitions in parallel on a large cluster;\
@@ -445,6 +447,8 @@ def jdbc(self, url, table, mode=None, properties={}):
                            arbitrary string tag/value. Normally at least a
                            "user" and "password" property should be included.
         """
+        if properties is None:
+            properties = dict()
         jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)()
         for k in properties:
             jprop.setProperty(k, properties[k])
diff --git a/python/pyspark/statcounter.py b/python/pyspark/statcounter.py
index 944fa414b0c0e..0fee3b2096826 100644
--- a/python/pyspark/statcounter.py
+++ b/python/pyspark/statcounter.py
@@ -30,7 +30,9 @@
 
 class StatCounter(object):
 
-    def __init__(self, values=[]):
+    def __init__(self, values=None):
+        if values is None:
+            values = list()
         self.n = 0    # Running count of our values
         self.mu = 0.0  # Running mean of our values
         self.m2 = 0.0  # Running variance numerator (sum of (x - mean)^2)
diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py
index 33dd596335b47..dc5b7fd878aef 100644
--- a/python/pyspark/streaming/kafka.py
+++ b/python/pyspark/streaming/kafka.py
@@ -35,7 +35,7 @@ def utf8_decoder(s):
 class KafkaUtils(object):
 
     @staticmethod
-    def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
+    def createStream(ssc, zkQuorum, groupId, topics, kafkaParams=None,
                      storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
                      keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
@@ -52,6 +52,8 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder)
         :return: A DStream object
         """
+        if kafkaParams is None:
+            kafkaParams = dict()
         kafkaParams.update({
             "zookeeper.connect": zkQuorum,
             "group.id": groupId,
@@ -77,7 +79,7 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
         return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
 
     @staticmethod
-    def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
+    def createDirectStream(ssc, topics, kafkaParams, fromOffsets=None,
                            keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
         .. note:: Experimental
@@ -105,6 +107,8 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder).
         :return: A DStream object
         """
+        if fromOffsets is None:
+            fromOffsets = dict()
         if not isinstance(topics, list):
             raise TypeError("topics should be list")
         if not isinstance(kafkaParams, dict):
@@ -129,7 +133,7 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
         return KafkaDStream(stream._jdstream, ssc, stream._jrdd_deserializer)
 
     @staticmethod
-    def createRDD(sc, kafkaParams, offsetRanges, leaders={},
+    def createRDD(sc, kafkaParams, offsetRanges, leaders=None,
                   keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
         .. note:: Experimental
@@ -145,6 +149,8 @@ def createRDD(sc, kafkaParams, offsetRanges, leaders={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder)
         :return: A RDD object
         """
+        if leaders is None:
+            leaders = dict()
         if not isinstance(kafkaParams, dict):
             raise TypeError("kafkaParams should be dict")
         if not isinstance(offsetRanges, list):

From 33bae585d4cb25aed2ac32e0d1248f78cc65318b Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Fri, 14 Aug 2015 13:38:25 -0700
Subject: [PATCH 1052/1454] [SPARK-9809] Task crashes because the internal
 accumulators are not properly initialized

When a stage failed and another stage was resubmitted with only part of partitions to compute, all the tasks failed with error message: java.util.NoSuchElementException: key not found: peakExecutionMemory.
This is because the internal accumulators are not properly initialized for this stage while other codes assume the internal accumulators always exist.

Author: Carson Wang <carson.wang@intel.com>

Closes #8090 from carsonwang/SPARK-9809.
---
 .../main/scala/org/apache/spark/scheduler/DAGScheduler.scala   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 7ab5ccf50adb7..f1c63d08766c2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -790,9 +790,10 @@ class DAGScheduler(
       }
     }
 
+    // Create internal accumulators if the stage has no accumulators initialized.
     // Reset internal accumulators only if this stage is not partially submitted
     // Otherwise, we may override existing accumulator values from some tasks
-    if (allPartitions == partitionsToCompute) {
+    if (stage.internalAccumulators.isEmpty || allPartitions == partitionsToCompute) {
       stage.resetInternalAccumulators()
     }
 

From 6518ef63037aa56b541927f99ad26744f91098ce Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 14 Aug 2015 13:42:53 -0700
Subject: [PATCH 1053/1454] [SPARK-9948] Fix flaky AccumulatorSuite - internal
 accumulators

In these tests, we use a custom listener and we assert on fields in the stage / task completion events. However, these events are posted in a separate thread so they're not guaranteed to be posted in time. This commit fixes this flakiness through a job end registration callback.

Author: Andrew Or <andrew@databricks.com>

Closes #8176 from andrewor14/fix-accumulator-suite.
---
 .../org/apache/spark/AccumulatorSuite.scala   | 153 +++++++++++-------
 1 file changed, 92 insertions(+), 61 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index 0eb2293a9d063..5b84acf40be4e 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -182,26 +182,30 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     sc = new SparkContext("local", "test")
     sc.addSparkListener(listener)
     // Have each task add 1 to the internal accumulator
-    sc.parallelize(1 to 100, numPartitions).mapPartitions { iter =>
+    val rdd = sc.parallelize(1 to 100, numPartitions).mapPartitions { iter =>
       TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
       iter
-    }.count()
-    val stageInfos = listener.getCompletedStageInfos
-    val taskInfos = listener.getCompletedTaskInfos
-    assert(stageInfos.size === 1)
-    assert(taskInfos.size === numPartitions)
-    // The accumulator values should be merged in the stage
-    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
-    assert(stageAccum.value.toLong === numPartitions)
-    // The accumulator should be updated locally on each task
-    val taskAccumValues = taskInfos.map { taskInfo =>
-      val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
-      assert(taskAccum.update.isDefined)
-      assert(taskAccum.update.get.toLong === 1)
-      taskAccum.value.toLong
     }
-    // Each task should keep track of the partial value on the way, i.e. 1, 2, ... numPartitions
-    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { _ =>
+      val stageInfos = listener.getCompletedStageInfos
+      val taskInfos = listener.getCompletedTaskInfos
+      assert(stageInfos.size === 1)
+      assert(taskInfos.size === numPartitions)
+      // The accumulator values should be merged in the stage
+      val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+      assert(stageAccum.value.toLong === numPartitions)
+      // The accumulator should be updated locally on each task
+      val taskAccumValues = taskInfos.map { taskInfo =>
+        val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+        assert(taskAccum.update.isDefined)
+        assert(taskAccum.update.get.toLong === 1)
+        taskAccum.value.toLong
+      }
+      // Each task should keep track of the partial value on the way, i.e. 1, 2, ... numPartitions
+      assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+    }
+    rdd.count()
   }
 
   test("internal accumulators in multiple stages") {
@@ -211,7 +215,7 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     sc.addSparkListener(listener)
     // Each stage creates its own set of internal accumulators so the
     // values for the same metric should not be mixed up across stages
-    sc.parallelize(1 to 100, numPartitions)
+    val rdd = sc.parallelize(1 to 100, numPartitions)
       .map { i => (i, i) }
       .mapPartitions { iter =>
         TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
@@ -227,16 +231,20 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
         TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 100
         iter
       }
-      .count()
-    // We ran 3 stages, and the accumulator values should be distinct
-    val stageInfos = listener.getCompletedStageInfos
-    assert(stageInfos.size === 3)
-    val firstStageAccum = findAccumulableInfo(stageInfos(0).accumulables.values, TEST_ACCUMULATOR)
-    val secondStageAccum = findAccumulableInfo(stageInfos(1).accumulables.values, TEST_ACCUMULATOR)
-    val thirdStageAccum = findAccumulableInfo(stageInfos(2).accumulables.values, TEST_ACCUMULATOR)
-    assert(firstStageAccum.value.toLong === numPartitions)
-    assert(secondStageAccum.value.toLong === numPartitions * 10)
-    assert(thirdStageAccum.value.toLong === numPartitions * 2 * 100)
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { _ =>
+      // We ran 3 stages, and the accumulator values should be distinct
+      val stageInfos = listener.getCompletedStageInfos
+      assert(stageInfos.size === 3)
+      val (firstStageAccum, secondStageAccum, thirdStageAccum) =
+        (findAccumulableInfo(stageInfos(0).accumulables.values, TEST_ACCUMULATOR),
+        findAccumulableInfo(stageInfos(1).accumulables.values, TEST_ACCUMULATOR),
+        findAccumulableInfo(stageInfos(2).accumulables.values, TEST_ACCUMULATOR))
+      assert(firstStageAccum.value.toLong === numPartitions)
+      assert(secondStageAccum.value.toLong === numPartitions * 10)
+      assert(thirdStageAccum.value.toLong === numPartitions * 2 * 100)
+    }
+    rdd.count()
   }
 
   test("internal accumulators in fully resubmitted stages") {
@@ -268,7 +276,7 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     // This says use 1 core and retry tasks up to 2 times
     sc = new SparkContext("local[1, 2]", "test")
     sc.addSparkListener(listener)
-    sc.parallelize(1 to 100, numPartitions).mapPartitionsWithIndex { case (i, iter) =>
+    val rdd = sc.parallelize(1 to 100, numPartitions).mapPartitionsWithIndex { case (i, iter) =>
       val taskContext = TaskContext.get()
       taskContext.internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
       // Fail the first attempts of a subset of the tasks
@@ -276,28 +284,32 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
         throw new Exception("Failing a task intentionally.")
       }
       iter
-    }.count()
-    val stageInfos = listener.getCompletedStageInfos
-    val taskInfos = listener.getCompletedTaskInfos
-    assert(stageInfos.size === 1)
-    assert(taskInfos.size === numPartitions + numFailedPartitions)
-    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
-    // We should not double count values in the merged accumulator
-    assert(stageAccum.value.toLong === numPartitions)
-    val taskAccumValues = taskInfos.flatMap { taskInfo =>
-      if (!taskInfo.failed) {
-        // If a task succeeded, its update value should always be 1
-        val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
-        assert(taskAccum.update.isDefined)
-        assert(taskAccum.update.get.toLong === 1)
-        Some(taskAccum.value.toLong)
-      } else {
-        // If a task failed, we should not get its accumulator values
-        assert(taskInfo.accumulables.isEmpty)
-        None
+    }
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { _ =>
+      val stageInfos = listener.getCompletedStageInfos
+      val taskInfos = listener.getCompletedTaskInfos
+      assert(stageInfos.size === 1)
+      assert(taskInfos.size === numPartitions + numFailedPartitions)
+      val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+      // We should not double count values in the merged accumulator
+      assert(stageAccum.value.toLong === numPartitions)
+      val taskAccumValues = taskInfos.flatMap { taskInfo =>
+        if (!taskInfo.failed) {
+          // If a task succeeded, its update value should always be 1
+          val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+          assert(taskAccum.update.isDefined)
+          assert(taskAccum.update.get.toLong === 1)
+          Some(taskAccum.value.toLong)
+        } else {
+          // If a task failed, we should not get its accumulator values
+          assert(taskInfo.accumulables.isEmpty)
+          None
+        }
       }
+      assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
     }
-    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+    rdd.count()
   }
 
 }
@@ -313,20 +325,27 @@ private[spark] object AccumulatorSuite {
       testName: String)(testBody: => Unit): Unit = {
     val listener = new SaveInfoListener
     sc.addSparkListener(listener)
-    // Verify that the accumulator does not already exist
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { jobId =>
+      if (jobId == 0) {
+        // The first job is a dummy one to verify that the accumulator does not already exist
+        val accums = listener.getCompletedStageInfos.flatMap(_.accumulables.values)
+        assert(!accums.exists(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY))
+      } else {
+        // In the subsequent jobs, verify that peak execution memory is updated
+        val accum = listener.getCompletedStageInfos
+          .flatMap(_.accumulables.values)
+          .find(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY)
+          .getOrElse {
+          throw new TestFailedException(
+            s"peak execution memory accumulator not set in '$testName'", 0)
+        }
+        assert(accum.value.toLong > 0)
+      }
+    }
+    // Run the jobs
     sc.parallelize(1 to 10).count()
-    val accums = listener.getCompletedStageInfos.flatMap(_.accumulables.values)
-    assert(!accums.exists(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY))
     testBody
-    // Verify that peak execution memory is updated
-    val accum = listener.getCompletedStageInfos
-      .flatMap(_.accumulables.values)
-      .find(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY)
-      .getOrElse {
-        throw new TestFailedException(
-          s"peak execution memory accumulator not set in '$testName'", 0)
-      }
-    assert(accum.value.toLong > 0)
   }
 }
 
@@ -336,10 +355,22 @@ private[spark] object AccumulatorSuite {
 private class SaveInfoListener extends SparkListener {
   private val completedStageInfos: ArrayBuffer[StageInfo] = new ArrayBuffer[StageInfo]
   private val completedTaskInfos: ArrayBuffer[TaskInfo] = new ArrayBuffer[TaskInfo]
+  private var jobCompletionCallback: (Int => Unit) = null // parameter is job ID
 
   def getCompletedStageInfos: Seq[StageInfo] = completedStageInfos.toArray.toSeq
   def getCompletedTaskInfos: Seq[TaskInfo] = completedTaskInfos.toArray.toSeq
 
+  /** Register a callback to be called on job end. */
+  def registerJobCompletionCallback(callback: (Int => Unit)): Unit = {
+    jobCompletionCallback = callback
+  }
+
+  override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
+    if (jobCompletionCallback != null) {
+      jobCompletionCallback(jobEnd.jobId)
+    }
+  }
+
   override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
     completedStageInfos += stageCompleted.stageInfo
   }

From 9407baa2a7c26f527f2d043715d313d75bd765bb Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Fri, 14 Aug 2015 13:44:38 -0700
Subject: [PATCH 1054/1454] [SPARK-9877] [CORE] Fix StandaloneRestServer NPE
 when submitting application

Detailed exception log can be seen in [SPARK-9877](https://issues.apache.org/jira/browse/SPARK-9877), the problem is when creating `StandaloneRestServer`, `self` (`masterEndpoint`) is null.  So this fix is creating `StandaloneRestServer` when `self` is available.

Author: jerryshao <sshao@hortonworks.com>

Closes #8127 from jerryshao/SPARK-9877.
---
 .../org/apache/spark/deploy/master/Master.scala  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 9217202b69a66..26904d39a9bec 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -127,14 +127,8 @@ private[deploy] class Master(
 
   // Alternative application submission gateway that is stable across Spark versions
   private val restServerEnabled = conf.getBoolean("spark.master.rest.enabled", true)
-  private val restServer =
-    if (restServerEnabled) {
-      val port = conf.getInt("spark.master.rest.port", 6066)
-      Some(new StandaloneRestServer(address.host, port, conf, self, masterUrl))
-    } else {
-      None
-    }
-  private val restServerBoundPort = restServer.map(_.start())
+  private var restServer: Option[StandaloneRestServer] = None
+  private var restServerBoundPort: Option[Int] = None
 
   override def onStart(): Unit = {
     logInfo("Starting Spark master at " + masterUrl)
@@ -148,6 +142,12 @@ private[deploy] class Master(
       }
     }, 0, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS)
 
+    if (restServerEnabled) {
+      val port = conf.getInt("spark.master.rest.port", 6066)
+      restServer = Some(new StandaloneRestServer(address.host, port, conf, self, masterUrl))
+    }
+    restServerBoundPort = restServer.map(_.start())
+
     masterMetricsSystem.registerSource(masterSource)
     masterMetricsSystem.start()
     applicationMetricsSystem.start()

From 11ed2b180ec86523a94679a8b8132fadb911ccd5 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 13:55:29 -0700
Subject: [PATCH 1055/1454] [SPARK-9978] [PYSPARK] [SQL] fix Window.orderBy and
 doc of ntile()

Author: Davies Liu <davies@databricks.com>

Closes #8213 from davies/fix_window.
---
 python/pyspark/sql/functions.py |  7 ++++---
 python/pyspark/sql/tests.py     | 23 +++++++++++++++++++++++
 python/pyspark/sql/window.py    |  2 +-
 3 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index e98979533f901..41dfee9f54f7a 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -530,9 +530,10 @@ def lead(col, count=1, default=None):
 @since(1.4)
 def ntile(n):
     """
-    Window function: returns a group id from 1 to `n` (inclusive) in a round-robin fashion in
-    a window partition. Fow example, if `n` is 3, the first row will get 1, the second row will
-    get 2, the third row will get 3, and the fourth row will get 1...
+    Window function: returns the ntile group id (from 1 to `n` inclusive)
+    in an ordered window partition. Fow example, if `n` is 4, the first
+    quarter of the rows will get value 1, the second quarter will get 2,
+    the third quarter will get 3, and the last quarter will get 4.
 
     This is equivalent to the NTILE function in SQL.
 
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 38c83c427a747..9b748101b5e53 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1124,5 +1124,28 @@ def test_window_functions(self):
         for r, ex in zip(rs, expected):
             self.assertEqual(tuple(r), ex[:len(r)])
 
+    def test_window_functions_without_partitionBy(self):
+        df = self.sqlCtx.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
+        w = Window.orderBy("key", df.value)
+        from pyspark.sql import functions as F
+        sel = df.select(df.value, df.key,
+                        F.max("key").over(w.rowsBetween(0, 1)),
+                        F.min("key").over(w.rowsBetween(0, 1)),
+                        F.count("key").over(w.rowsBetween(float('-inf'), float('inf'))),
+                        F.rowNumber().over(w),
+                        F.rank().over(w),
+                        F.denseRank().over(w),
+                        F.ntile(2).over(w))
+        rs = sorted(sel.collect())
+        expected = [
+            ("1", 1, 1, 1, 4, 1, 1, 1, 1),
+            ("2", 1, 1, 1, 4, 2, 2, 2, 1),
+            ("2", 1, 2, 1, 4, 3, 2, 2, 2),
+            ("2", 2, 2, 2, 4, 4, 4, 3, 2)
+        ]
+        for r, ex in zip(rs, expected):
+            self.assertEqual(tuple(r), ex[:len(r)])
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/python/pyspark/sql/window.py b/python/pyspark/sql/window.py
index c74745c726a0c..eaf4d7e98620a 100644
--- a/python/pyspark/sql/window.py
+++ b/python/pyspark/sql/window.py
@@ -64,7 +64,7 @@ def orderBy(*cols):
         Creates a :class:`WindowSpec` with the partitioning defined.
         """
         sc = SparkContext._active_spark_context
-        jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols))
+        jspec = sc._jvm.org.apache.spark.sql.expressions.Window.orderBy(_to_java_cols(cols))
         return WindowSpec(jspec)
 
 

From 2a6590e510aba3bfc6603d280023128b3f5ac702 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 14 Aug 2015 14:05:03 -0700
Subject: [PATCH 1056/1454] [SPARK-9981] [ML] Made labels public for
 StringIndexerModel

Also added unit test for integration between StringIndexerModel and IndexToString

CC: holdenk We realized we should have left in your unit test (to catch the issue with removing the inverse() method), so this adds it back.  mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8211 from jkbradley/stridx-labels.
---
 .../spark/ml/feature/StringIndexer.scala       |  5 ++++-
 .../spark/ml/feature/StringIndexerSuite.scala  | 18 ++++++++++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 63475780a6ff9..24250e4c4cf92 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -97,14 +97,17 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
 /**
  * :: Experimental ::
  * Model fitted by [[StringIndexer]].
+ *
  * NOTE: During transformation, if the input column does not exist,
  * [[StringIndexerModel.transform]] would return the input dataset unmodified.
  * This is a temporary fix for the case when target labels do not exist during prediction.
+ *
+ * @param labels  Ordered list of labels, corresponding to indices to be assigned
  */
 @Experimental
 class StringIndexerModel (
     override val uid: String,
-    labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
+    val labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
 
   def this(labels: Array[String]) = this(Identifiable.randomUID("strIdx"), labels)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 0b4c8ba71ee61..05e05bdc64bb1 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -147,4 +147,22 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(actual === expected)
     }
   }
+
+  test("StringIndexer, IndexToString are inverses") {
+    val data = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")), 2)
+    val df = sqlContext.createDataFrame(data).toDF("id", "label")
+    val indexer = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("labelIndex")
+      .fit(df)
+    val transformed = indexer.transform(df)
+    val idx2str = new IndexToString()
+      .setInputCol("labelIndex")
+      .setOutputCol("sameLabel")
+      .setLabels(indexer.labels)
+    idx2str.transform(transformed).select("label", "sameLabel").collect().foreach {
+      case Row(a: String, b: String) =>
+        assert(a === b)
+    }
+  }
 }

From 1150a19b188a075166899fdb1e107b2ba1e505d8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 14 Aug 2015 14:09:46 -0700
Subject: [PATCH 1057/1454] [SPARK-8670] [SQL] Nested columns can't be
 referenced in pyspark

This bug is caused by a wrong column-exist-check in `__getitem__` of pyspark dataframe. `DataFrame.apply` accepts not only top level column names, but also nested column name like `a.b`, so we should remove that check from `__getitem__`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8202 from cloud-fan/nested.
---
 python/pyspark/sql/dataframe.py                              | 2 --
 python/pyspark/sql/tests.py                                  | 4 +++-
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 ++
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 09647ff6d0749..da742d7ce7d13 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -722,8 +722,6 @@ def __getitem__(self, item):
         [Row(age=5, name=u'Bob')]
         """
         if isinstance(item, basestring):
-            if item not in self.columns:
-                raise IndexError("no such column: %s" % item)
             jc = self._jdf.apply(item)
             return Column(jc)
         elif isinstance(item, Column):
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 9b748101b5e53..13cf647b66da8 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -770,7 +770,7 @@ def test_access_column(self):
         self.assertTrue(isinstance(df['key'], Column))
         self.assertTrue(isinstance(df[0], Column))
         self.assertRaises(IndexError, lambda: df[2])
-        self.assertRaises(IndexError, lambda: df["bad_key"])
+        self.assertRaises(AnalysisException, lambda: df["bad_key"])
         self.assertRaises(TypeError, lambda: df[{}])
 
     def test_column_name_with_non_ascii(self):
@@ -794,7 +794,9 @@ def test_field_accessor(self):
         df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
         self.assertEqual(1, df.select(df.l[0]).first()[0])
         self.assertEqual(1, df.select(df.r["a"]).first()[0])
+        self.assertEqual(1, df.select(df["r.a"]).first()[0])
         self.assertEqual("b", df.select(df.r["b"]).first()[0])
+        self.assertEqual("b", df.select(df["r.b"]).first()[0])
         self.assertEqual("v", df.select(df.d["k"]).first()[0])
 
     def test_infer_long_type(self):
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index cf75e64e884b4..fd0ead4401193 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -634,6 +634,7 @@ class DataFrame private[sql](
 
   /**
    * Selects column based on the column name and return it as a [[Column]].
+   * Note that the column name can also reference to a nested column like `a.b`.
    * @group dfops
    * @since 1.3.0
    */
@@ -641,6 +642,7 @@ class DataFrame private[sql](
 
   /**
    * Selects column based on the column name and return it as a [[Column]].
+   * Note that the column name can also reference to a nested column like `a.b`.
    * @group dfops
    * @since 1.3.0
    */

From f3bfb711c1742d0915e43bda8230b4d1d22b4190 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 14 Aug 2015 15:10:01 -0700
Subject: [PATCH 1058/1454] [SPARK-9966] [STREAMING] Handle couple of corner
 cases in PIDRateEstimator

1. The rate estimator should not estimate any rate when there are no records in the batch, as there is no data to estimate the rate. In the current state, it estimates and set the rate to zero. That is incorrect.

2. The rate estimator should not never set the rate to zero under any circumstances. Otherwise the system will stop receiving data, and stop generating useful estimates (see reason 1). So the fix is to define a parameters that sets a lower bound on the estimated rate, so that the system always receives some data.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8199 from tdas/SPARK-9966 and squashes the following commits:

829f793 [Tathagata Das] Fixed unit test and added comments
3a994db [Tathagata Das] Added min rate and updated tests in PIDRateEstimator
---
 .../scheduler/rate/PIDRateEstimator.scala     | 46 ++++++++---
 .../scheduler/rate/RateEstimator.scala        |  4 +-
 .../rate/PIDRateEstimatorSuite.scala          | 79 ++++++++++++-------
 3 files changed, 87 insertions(+), 42 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
index 6ae56a68ad88c..84a3ca9d74e58 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.streaming.scheduler.rate
 
+import org.apache.spark.Logging
+
 /**
  * Implements a proportional-integral-derivative (PID) controller which acts on
  * the speed of ingestion of elements into Spark Streaming. A PID controller works
@@ -26,7 +28,7 @@ package org.apache.spark.streaming.scheduler.rate
  *
  * @see https://en.wikipedia.org/wiki/PID_controller
  *
- * @param batchDurationMillis the batch duration, in milliseconds
+ * @param batchIntervalMillis the batch duration, in milliseconds
  * @param proportional how much the correction should depend on the current
  *        error. This term usually provides the bulk of correction and should be positive or zero.
  *        A value too large would make the controller overshoot the setpoint, while a small value
@@ -39,13 +41,17 @@ package org.apache.spark.streaming.scheduler.rate
  *        of future errors, based on current rate of change. This value should be positive or 0.
  *        This term is not used very often, as it impacts stability of the system. The default
  *        value is 0.
+ * @param minRate what is the minimum rate that can be estimated.
+ *        This must be greater than zero, so that the system always receives some data for rate
+ *        estimation to work.
  */
 private[streaming] class PIDRateEstimator(
     batchIntervalMillis: Long,
-    proportional: Double = 1D,
-    integral: Double = .2D,
-    derivative: Double = 0D)
-  extends RateEstimator {
+    proportional: Double,
+    integral: Double,
+    derivative: Double,
+    minRate: Double
+  ) extends RateEstimator with Logging {
 
   private var firstRun: Boolean = true
   private var latestTime: Long = -1L
@@ -64,16 +70,23 @@ private[streaming] class PIDRateEstimator(
   require(
     derivative >= 0,
     s"Derivative term $derivative in PIDRateEstimator should be >= 0.")
+  require(
+    minRate > 0,
+    s"Minimum rate in PIDRateEstimator should be > 0")
 
+  logInfo(s"Created PIDRateEstimator with proportional = $proportional, integral = $integral, " +
+    s"derivative = $derivative, min rate = $minRate")
 
-  def compute(time: Long, // in milliseconds
+  def compute(
+      time: Long, // in milliseconds
       numElements: Long,
       processingDelay: Long, // in milliseconds
       schedulingDelay: Long // in milliseconds
     ): Option[Double] = {
-
+    logTrace(s"\ntime = $time, # records = $numElements, " +
+      s"processing time = $processingDelay, scheduling delay = $schedulingDelay")
     this.synchronized {
-      if (time > latestTime && processingDelay > 0 && batchIntervalMillis > 0) {
+      if (time > latestTime && numElements > 0 && processingDelay > 0) {
 
         // in seconds, should be close to batchDuration
         val delaySinceUpdate = (time - latestTime).toDouble / 1000
@@ -104,21 +117,30 @@ private[streaming] class PIDRateEstimator(
 
         val newRate = (latestRate - proportional * error -
                                     integral * historicalError -
-                                    derivative * dError).max(0.0)
+                                    derivative * dError).max(minRate)
+        logTrace(s"""
+            | latestRate = $latestRate, error = $error
+            | latestError = $latestError, historicalError = $historicalError
+            | delaySinceUpdate = $delaySinceUpdate, dError = $dError
+            """.stripMargin)
+
         latestTime = time
         if (firstRun) {
           latestRate = processingRate
           latestError = 0D
           firstRun = false
-
+          logTrace("First run, rate estimation skipped")
           None
         } else {
           latestRate = newRate
           latestError = error
-
+          logTrace(s"New rate = $newRate")
           Some(newRate)
         }
-      } else None
+      } else {
+        logTrace("Rate estimation skipped")
+        None
+      }
     }
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
index 17ccebc1ed41b..d7210f64fcc36 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.streaming.scheduler.rate
 
 import org.apache.spark.SparkConf
-import org.apache.spark.SparkException
 import org.apache.spark.streaming.Duration
 
 /**
@@ -61,7 +60,8 @@ object RateEstimator {
         val proportional = conf.getDouble("spark.streaming.backpressure.pid.proportional", 1.0)
         val integral = conf.getDouble("spark.streaming.backpressure.pid.integral", 0.2)
         val derived = conf.getDouble("spark.streaming.backpressure.pid.derived", 0.0)
-        new PIDRateEstimator(batchInterval.milliseconds, proportional, integral, derived)
+        val minRate = conf.getDouble("spark.streaming.backpressure.pid.minRate", 100)
+        new PIDRateEstimator(batchInterval.milliseconds, proportional, integral, derived, minRate)
 
       case estimator =>
         throw new IllegalArgumentException(s"Unkown rate estimator: $estimator")
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
index 97c32d8f2d59e..a1af95be81c8e 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
@@ -36,72 +36,89 @@ class PIDRateEstimatorSuite extends SparkFunSuite with Matchers {
 
   test("estimator checks ranges") {
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(0, 1, 2, 3)
+      new PIDRateEstimator(batchIntervalMillis = 0, 1, 2, 3, 10)
     }
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(100, -1, 2, 3)
+      new PIDRateEstimator(100, proportional = -1, 2, 3, 10)
     }
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(100, 0, -1, 3)
+      new PIDRateEstimator(100, 0, integral = -1, 3, 10)
     }
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(100, 0, 0, -1)
+      new PIDRateEstimator(100, 0, 0, derivative = -1, 10)
+    }
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(100, 0, 0, 0, minRate = 0)
+    }
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(100, 0, 0, 0, minRate = -10)
     }
   }
 
-  private def createDefaultEstimator: PIDRateEstimator = {
-    new PIDRateEstimator(20, 1D, 0D, 0D)
-  }
-
-  test("first bound is None") {
-    val p = createDefaultEstimator
+  test("first estimate is None") {
+    val p = createDefaultEstimator()
     p.compute(0, 10, 10, 0) should equal(None)
   }
 
-  test("second bound is rate") {
-    val p = createDefaultEstimator
+  test("second estimate is not None") {
+    val p = createDefaultEstimator()
     p.compute(0, 10, 10, 0)
     // 1000 elements / s
     p.compute(10, 10, 10, 0) should equal(Some(1000))
   }
 
-  test("works even with no time between updates") {
-    val p = createDefaultEstimator
+  test("no estimate when no time difference between successive calls") {
+    val p = createDefaultEstimator()
+    p.compute(0, 10, 10, 0)
+    p.compute(time = 10, 10, 10, 0) shouldNot equal(None)
+    p.compute(time = 10, 10, 10, 0) should equal(None)
+  }
+
+  test("no estimate when no records in previous batch") {
+    val p = createDefaultEstimator()
     p.compute(0, 10, 10, 0)
-    p.compute(10, 10, 10, 0)
-    p.compute(10, 10, 10, 0) should equal(None)
+    p.compute(10, numElements = 0, 10, 0) should equal(None)
+    p.compute(20, numElements = -10, 10, 0) should equal(None)
   }
 
-  test("bound is never negative") {
-    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+  test("no estimate when there is no processing delay") {
+    val p = createDefaultEstimator()
+    p.compute(0, 10, 10, 0)
+    p.compute(10, 10, processingDelay = 0, 0) should equal(None)
+    p.compute(20, 10, processingDelay = -10, 0) should equal(None)
+  }
+
+  test("estimate is never less than min rate") {
+    val minRate = 5D
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D, minRate)
     // prepare a series of batch updates, one every 20ms, 0 processed elements, 2ms of processing
     // this might point the estimator to try and decrease the bound, but we test it never
-    // goes below zero, which would be nonsensical.
+    // goes below the min rate, which would be nonsensical.
     val times = List.tabulate(50)(x => x * 20) // every 20ms
-    val elements = List.fill(50)(0) // no processing
+    val elements = List.fill(50)(1) // no processing
     val proc = List.fill(50)(20) // 20ms of processing
     val sched = List.fill(50)(100) // strictly positive accumulation
     val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
     res.head should equal(None)
-    res.tail should equal(List.fill(49)(Some(0D)))
+    res.tail should equal(List.fill(49)(Some(minRate)))
   }
 
   test("with no accumulated or positive error, |I| > 0, follow the processing speed") {
-    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D, 10)
     // prepare a series of batch updates, one every 20ms with an increasing number of processed
     // elements in each batch, but constant processing time, and no accumulated error. Even though
     // the integral part is non-zero, the estimated rate should follow only the proportional term
     val times = List.tabulate(50)(x => x * 20) // every 20ms
-    val elements = List.tabulate(50)(x => x * 20) // increasing
+    val elements = List.tabulate(50)(x => (x + 1) * 20) // increasing
     val proc = List.fill(50)(20) // 20ms of processing
     val sched = List.fill(50)(0)
     val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
     res.head should equal(None)
-    res.tail should equal(List.tabulate(50)(x => Some(x * 1000D)).tail)
+    res.tail should equal(List.tabulate(50)(x => Some((x + 1) * 1000D)).tail)
   }
 
   test("with no accumulated but some positive error, |I| > 0, follow the processing speed") {
-    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D, 10)
     // prepare a series of batch updates, one every 20ms with an decreasing number of processed
     // elements in each batch, but constant processing time, and no accumulated error. Even though
     // the integral part is non-zero, the estimated rate should follow only the proportional term,
@@ -116,13 +133,14 @@ class PIDRateEstimatorSuite extends SparkFunSuite with Matchers {
   }
 
   test("with some accumulated and some positive error, |I| > 0, stay below the processing speed") {
-    val p = new PIDRateEstimator(20, 1D, .01D, 0D)
+    val minRate = 10D
+    val p = new PIDRateEstimator(20, 1D, .01D, 0D, minRate)
     val times = List.tabulate(50)(x => x * 20) // every 20ms
     val rng = new Random()
-    val elements = List.tabulate(50)(x => rng.nextInt(1000))
+    val elements = List.tabulate(50)(x => rng.nextInt(1000) + 1000)
     val procDelayMs = 20
     val proc = List.fill(50)(procDelayMs) // 20ms of processing
-    val sched = List.tabulate(50)(x => rng.nextInt(19)) // random wait
+    val sched = List.tabulate(50)(x => rng.nextInt(19) + 1) // random wait
     val speeds = elements map ((x) => x.toDouble / procDelayMs * 1000)
 
     val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
@@ -131,7 +149,12 @@ class PIDRateEstimatorSuite extends SparkFunSuite with Matchers {
       res(n) should not be None
       if (res(n).get > 0 && sched(n) > 0) {
         res(n).get should be < speeds(n)
+        res(n).get should be >= minRate
       }
     }
   }
+
+  private def createDefaultEstimator(): PIDRateEstimator = {
+    new PIDRateEstimator(20, 1D, 0D, 0D, 10)
+  }
 }

From 18a761ef7a01a4dfa1dd91abe78cd68f2f8fdb67 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 14 Aug 2015 15:54:14 -0700
Subject: [PATCH 1059/1454] [SPARK-9968] [STREAMING] Reduced time spent within
 synchronized block to prevent lock starvation

When the rate limiter is actually limiting the rate at which data is inserted into the buffer, the synchronized block of BlockGenerator.addData stays blocked for long time. This causes the thread switching the buffer and generating blocks (synchronized with addData) to starve and not generate blocks for seconds. The correct solution is to not block on the rate limiter within the synchronized block for adding data to the buffer.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8204 from tdas/SPARK-9968 and squashes the following commits:

8cbcc1b [Tathagata Das] Removed unused val
a73b645 [Tathagata Das] Reduced time spent within synchronized block
---
 .../streaming/receiver/BlockGenerator.scala   | 40 +++++++++++++++----
 1 file changed, 32 insertions(+), 8 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
index 794dece370b2c..300e820d01777 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
@@ -155,10 +155,17 @@ private[streaming] class BlockGenerator(
   /**
    * Push a single data item into the buffer.
    */
-  def addData(data: Any): Unit = synchronized {
+  def addData(data: Any): Unit = {
     if (state == Active) {
       waitToPush()
-      currentBuffer += data
+      synchronized {
+        if (state == Active) {
+          currentBuffer += data
+        } else {
+          throw new SparkException(
+            "Cannot add data as BlockGenerator has not been started or has been stopped")
+        }
+      }
     } else {
       throw new SparkException(
         "Cannot add data as BlockGenerator has not been started or has been stopped")
@@ -169,11 +176,18 @@ private[streaming] class BlockGenerator(
    * Push a single data item into the buffer. After buffering the data, the
    * `BlockGeneratorListener.onAddData` callback will be called.
    */
-  def addDataWithCallback(data: Any, metadata: Any): Unit = synchronized {
+  def addDataWithCallback(data: Any, metadata: Any): Unit = {
     if (state == Active) {
       waitToPush()
-      currentBuffer += data
-      listener.onAddData(data, metadata)
+      synchronized {
+        if (state == Active) {
+          currentBuffer += data
+          listener.onAddData(data, metadata)
+        } else {
+          throw new SparkException(
+            "Cannot add data as BlockGenerator has not been started or has been stopped")
+        }
+      }
     } else {
       throw new SparkException(
         "Cannot add data as BlockGenerator has not been started or has been stopped")
@@ -185,13 +199,23 @@ private[streaming] class BlockGenerator(
    * `BlockGeneratorListener.onAddData` callback will be called. Note that all the data items
    * are atomically added to the buffer, and are hence guaranteed to be present in a single block.
    */
-  def addMultipleDataWithCallback(dataIterator: Iterator[Any], metadata: Any): Unit = synchronized {
+  def addMultipleDataWithCallback(dataIterator: Iterator[Any], metadata: Any): Unit = {
     if (state == Active) {
+      // Unroll iterator into a temp buffer, and wait for pushing in the process
+      val tempBuffer = new ArrayBuffer[Any]
       dataIterator.foreach { data =>
         waitToPush()
-        currentBuffer += data
+        tempBuffer += data
+      }
+      synchronized {
+        if (state == Active) {
+          currentBuffer ++= tempBuffer
+          listener.onAddData(tempBuffer, metadata)
+        } else {
+          throw new SparkException(
+            "Cannot add data as BlockGenerator has not been started or has been stopped")
+        }
       }
-      listener.onAddData(dataIterator, metadata)
     } else {
       throw new SparkException(
         "Cannot add data as BlockGenerator has not been started or has been stopped")

From 932b24fd144232fb08184f0bd0a46369ecba164e Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 14 Aug 2015 17:35:17 -0700
Subject: [PATCH 1060/1454] [SPARK-9949] [SQL] Fix TakeOrderedAndProject's
 output.

https://issues.apache.org/jira/browse/SPARK-9949

Author: Yin Huai <yhuai@databricks.com>

Closes #8179 from yhuai/SPARK-9949.
---
 .../spark/sql/execution/basicOperators.scala  | 12 ++++++++++-
 .../spark/sql/execution/PlannerSuite.scala    | 20 ++++++++++++++++---
 2 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 247c900baae9e..77b98064a9e16 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -237,7 +237,10 @@ case class TakeOrderedAndProject(
     projectList: Option[Seq[NamedExpression]],
     child: SparkPlan) extends UnaryNode {
 
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = {
+    val projectOutput = projectList.map(_.map(_.toAttribute))
+    projectOutput.getOrElse(child.output)
+  }
 
   override def outputPartitioning: Partitioning = SinglePartition
 
@@ -263,6 +266,13 @@ case class TakeOrderedAndProject(
   protected override def doExecute(): RDD[InternalRow] = sparkContext.makeRDD(collectData(), 1)
 
   override def outputOrdering: Seq[SortOrder] = sortOrder
+
+  override def simpleString: String = {
+    val orderByString = sortOrder.mkString("[", ",", "]")
+    val outputString = output.mkString("[", ",", "]")
+
+    s"TakeOrderedAndProject(limit=$limit, orderBy=$orderByString, output=$outputString)"
+  }
 }
 
 /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 937a108543531..fad93b014c237 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -162,9 +162,23 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("efficient limit -> project -> sort") {
-    val query = testData.sort('key).select('value).limit(2).logicalPlan
-    val planned = ctx.planner.TakeOrderedAndProject(query)
-    assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
+    {
+      val query =
+        testData.select('key, 'value).sort('key).limit(2).logicalPlan
+      val planned = ctx.planner.TakeOrderedAndProject(query)
+      assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
+      assert(planned.head.output === testData.select('key, 'value).logicalPlan.output)
+    }
+
+    {
+      // We need to make sure TakeOrderedAndProject's output is correct when we push a project
+      // into it.
+      val query =
+        testData.select('key, 'value).sort('key).select('value, 'key).limit(2).logicalPlan
+      val planned = ctx.planner.TakeOrderedAndProject(query)
+      assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
+      assert(planned.head.output === testData.select('value, 'key).logicalPlan.output)
+    }
   }
 
   test("PartitioningCollection") {

From e5fd60415fbfea2c5c02602f7ddbc999dd058064 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 14 Aug 2015 20:55:32 -0700
Subject: [PATCH 1061/1454] [SPARK-9934] Deprecate NIO ConnectionManager.

Deprecate NIO ConnectionManager in Spark 1.5.0, before removing it in Spark 1.6.0.

Author: Reynold Xin <rxin@databricks.com>

Closes #8162 from rxin/SPARK-9934.
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala | 2 ++
 docs/configuration.md                               | 3 ++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index a796e72850191..0f1e2e069568d 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -331,6 +331,8 @@ object SparkEnv extends Logging {
         case "netty" =>
           new NettyBlockTransferService(conf, securityManager, numUsableCores)
         case "nio" =>
+          logWarning("NIO-based block transfer service is deprecated, " +
+            "and will be removed in Spark 1.6.0.")
           new NioBlockTransferService(conf, securityManager)
       }
 
diff --git a/docs/configuration.md b/docs/configuration.md
index c60dd16839c02..32147098ae64a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -389,7 +389,8 @@ Apart from these, the following properties are also available, and may be useful
     Implementation to use for transferring shuffle and cached blocks between executors. There
     are two implementations available: <code>netty</code> and <code>nio</code>. Netty-based
     block transfer is intended to be simpler but equally efficient and is the default option
-    starting in 1.2.
+    starting in 1.2, and <code>nio</code> block transfer is deprecated in Spark 1.5.0 and will
+    be removed in Spark 1.6.0.
   </td>
 </tr>
 <tr>

From 37586e5449ff8f892d41f0b6b8fa1de83dd3849e Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 20:56:55 -0700
Subject: [PATCH 1062/1454] [HOTFIX] fix duplicated braces

Author: Davies Liu <davies@databricks.com>

Closes #8219 from davies/fix_typo.
---
 .../main/scala/org/apache/spark/storage/BlockManager.scala  | 2 +-
 .../scala/org/apache/spark/storage/BlockManagerMaster.scala | 6 +++---
 .../main/scala/org/apache/spark/util/ClosureCleaner.scala   | 2 +-
 core/src/main/scala/org/apache/spark/util/Utils.scala       | 2 +-
 .../scala/org/apache/spark/examples/ml/MovieLensALS.scala   | 2 +-
 .../apache/spark/examples/mllib/DecisionTreeRunner.scala    | 2 +-
 .../org/apache/spark/examples/mllib/MovieLensALS.scala      | 2 +-
 .../src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala | 2 +-
 .../spark/sql/catalyst/analysis/HiveTypeCoercion.scala      | 2 +-
 .../spark/sql/execution/datasources/jdbc/JdbcUtils.scala    | 2 +-
 .../org/apache/spark/sql/execution/ui/SQLListener.scala     | 2 +-
 .../spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala   | 2 +-
 .../apache/spark/streaming/scheduler/InputInfoTracker.scala | 2 +-
 13 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 86493673d958d..eedb27942e841 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -222,7 +222,7 @@ private[spark] class BlockManager(
         return
       } catch {
         case e: Exception if i < MAX_ATTEMPTS =>
-          logError(s"Failed to connect to external shuffle server, will retry ${MAX_ATTEMPTS - i}}"
+          logError(s"Failed to connect to external shuffle server, will retry ${MAX_ATTEMPTS - i}"
             + s" more times after waiting $SLEEP_TIME_SECS seconds...", e)
           Thread.sleep(SLEEP_TIME_SECS * 1000)
       }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index f70f701494dbf..2a11f371b9d6e 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -103,7 +103,7 @@ class BlockManagerMaster(
     val future = driverEndpoint.askWithRetry[Future[Seq[Int]]](RemoveRdd(rddId))
     future.onFailure {
       case e: Exception =>
-        logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}", e)
+        logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
       timeout.awaitResult(future)
@@ -115,7 +115,7 @@ class BlockManagerMaster(
     val future = driverEndpoint.askWithRetry[Future[Seq[Boolean]]](RemoveShuffle(shuffleId))
     future.onFailure {
       case e: Exception =>
-        logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}", e)
+        logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
       timeout.awaitResult(future)
@@ -129,7 +129,7 @@ class BlockManagerMaster(
     future.onFailure {
       case e: Exception =>
         logWarning(s"Failed to remove broadcast $broadcastId" +
-          s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}", e)
+          s" with removeFromMaster = $removeFromMaster - ${e.getMessage}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
       timeout.awaitResult(future)
diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index ebead830c6466..150d82b3930ef 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -181,7 +181,7 @@ private[spark] object ClosureCleaner extends Logging {
       return
     }
 
-    logDebug(s"+++ Cleaning closure $func (${func.getClass.getName}}) +++")
+    logDebug(s"+++ Cleaning closure $func (${func.getClass.getName}) +++")
 
     // A list of classes that represents closures enclosed in the given one
     val innerClasses = getInnerClosureClasses(func)
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index f2abf227dc129..fddc24dbfc237 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1366,7 +1366,7 @@ private[spark] object Utils extends Logging {
           file.getAbsolutePath, effectiveStartIndex, effectiveEndIndex))
       }
       sum += fileToLength(file)
-      logDebug(s"After processing file $file, string built is ${stringBuffer.toString}}")
+      logDebug(s"After processing file $file, string built is ${stringBuffer.toString}")
     }
     stringBuffer.toString
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
index cd411397a4b9d..3ae53e57dbdb8 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
@@ -76,7 +76,7 @@ object MovieLensALS {
         .text("path to a MovieLens dataset of movies")
         .action((x, c) => c.copy(movies = x))
       opt[Int]("rank")
-        .text(s"rank, default: ${defaultParams.rank}}")
+        .text(s"rank, default: ${defaultParams.rank}")
         .action((x, c) => c.copy(rank = x))
       opt[Int]("maxIter")
         .text(s"max number of iterations, default: ${defaultParams.maxIter}")
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index 57ffe3dd2524f..cc6bce3cb7c9c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -100,7 +100,7 @@ object DecisionTreeRunner {
         .action((x, c) => c.copy(numTrees = x))
       opt[String]("featureSubsetStrategy")
         .text(s"feature subset sampling strategy" +
-          s" (${RandomForest.supportedFeatureSubsetStrategies.mkString(", ")}}), " +
+          s" (${RandomForest.supportedFeatureSubsetStrategies.mkString(", ")}), " +
           s"default: ${defaultParams.featureSubsetStrategy}")
         .action((x, c) => c.copy(featureSubsetStrategy = x))
       opt[Double]("fracTest")
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
index e43a6f2864c73..69691ae297f64 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -55,7 +55,7 @@ object MovieLensALS {
     val parser = new OptionParser[Params]("MovieLensALS") {
       head("MovieLensALS: an example app for ALS on MovieLens data.")
       opt[Int]("rank")
-        .text(s"rank, default: ${defaultParams.rank}}")
+        .text(s"rank, default: ${defaultParams.rank}")
         .action((x, c) => c.copy(rank = x))
       opt[Int]("numIterations")
         .text(s"number of iterations, default: ${defaultParams.numIterations}")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index 9029093e0fa08..bbbcc8436b7c2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -469,7 +469,7 @@ private[spark] object BLAS extends Serializable with Logging {
     require(A.numCols == x.size,
       s"The columns of A don't match the number of elements of x. A: ${A.numCols}, x: ${x.size}")
     require(A.numRows == y.size,
-      s"The rows of A don't match the number of elements of y. A: ${A.numRows}, y:${y.size}}")
+      s"The rows of A don't match the number of elements of y. A: ${A.numRows}, y:${y.size}")
     if (alpha == 0.0) {
       logDebug("gemv: alpha is equal to 0. Returning y.")
     } else {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 970f3c8282c81..8581d6b496c15 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -164,7 +164,7 @@ object HiveTypeCoercion {
               // Leave the same if the dataTypes match.
               case Some(newType) if a.dataType == newType.dataType => a
               case Some(newType) =>
-                logDebug(s"Promoting $a to $newType in ${q.simpleString}}")
+                logDebug(s"Promoting $a to $newType in ${q.simpleString}")
                 newType
             }
         }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 039c13bf163ca..8ee3b8bda8fc7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -170,7 +170,7 @@ object JdbcUtils extends Logging {
             case BinaryType => "BLOB"
             case TimestampType => "TIMESTAMP"
             case DateType => "DATE"
-            case t: DecimalType => s"DECIMAL(${t.precision}},${t.scale}})"
+            case t: DecimalType => s"DECIMAL(${t.precision},${t.scale})"
             case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
           })
       val nullable = if (field.nullable) "" else "NOT NULL"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
index 0b9bad987c488..5779c71f64e9e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
@@ -162,7 +162,7 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
           // A task of an old stage attempt. Because a new stage is submitted, we can ignore it.
         } else if (stageAttemptID > stageMetrics.stageAttemptId) {
           logWarning(s"A task should not have a higher stageAttemptID ($stageAttemptID) then " +
-            s"what we have seen (${stageMetrics.stageAttemptId}})")
+            s"what we have seen (${stageMetrics.stageAttemptId})")
         } else {
           // TODO We don't know the attemptId. Currently, what we can do is overriding the
           // accumulator updates. However, if there are two same task are running, such as
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
index 31ce8e1ec14d7..620b8a36a2baf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
@@ -84,7 +84,7 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag](
   require(
     blockIds.length == walRecordHandles.length,
     s"Number of block Ids (${blockIds.length}) must be " +
-      s" same as number of WAL record handles (${walRecordHandles.length}})")
+      s" same as number of WAL record handles (${walRecordHandles.length})")
 
   require(
     isBlockIdValid.isEmpty || isBlockIdValid.length == blockIds.length,
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
index 363c03d431f04..deb15d075975c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
@@ -66,7 +66,7 @@ private[streaming] class InputInfoTracker(ssc: StreamingContext) extends Logging
       new mutable.HashMap[Int, StreamInputInfo]())
 
     if (inputInfos.contains(inputInfo.inputStreamId)) {
-      throw new IllegalStateException(s"Input stream ${inputInfo.inputStreamId}} for batch" +
+      throw new IllegalStateException(s"Input stream ${inputInfo.inputStreamId} for batch" +
         s"$batchTime is already added into InputInfoTracker, this is a illegal state")
     }
     inputInfos += ((inputInfo.inputStreamId, inputInfo))

From ec29f2034a3306cc0afdc4c160b42c2eefa0897c Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 14 Aug 2015 20:59:54 -0700
Subject: [PATCH 1063/1454] [SPARK-9634] [SPARK-9323] [SQL] cleanup unnecessary
 Aliases in LogicalPlan at the end of analysis

Also alias the ExtractValue instead of wrapping it with UnresolvedAlias when resolve attribute in LogicalPlan, as this alias will be trimmed if it's unnecessary.

Based on #7957 without the changes to mllib, but instead maintaining earlier behavior when using `withColumn` on expressions that already have metadata.

Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #8215 from marmbrus/pr/7957.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 75 ++++++++++++++++---
 .../expressions/complexTypeCreator.scala      |  2 -
 .../sql/catalyst/optimizer/Optimizer.scala    |  9 ++-
 .../catalyst/plans/logical/LogicalPlan.scala  |  8 +-
 .../plans/logical/basicOperators.scala        |  2 +-
 .../sql/catalyst/analysis/AnalysisSuite.scala | 17 +++++
 .../scala/org/apache/spark/sql/Column.scala   | 16 +++-
 .../spark/sql/ColumnExpressionSuite.scala     |  9 +++
 .../org/apache/spark/sql/DataFrameSuite.scala |  6 ++
 9 files changed, 120 insertions(+), 24 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index a684dbc3afa42..4bc1c1af40bf4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -82,7 +82,9 @@ class Analyzer(
       HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
-      PullOutNondeterministic)
+      PullOutNondeterministic),
+    Batch("Cleanup", fixedPoint,
+      CleanupAliases)
   )
 
   /**
@@ -146,8 +148,6 @@ class Analyzer(
           child match {
             case _: UnresolvedAttribute => u
             case ne: NamedExpression => ne
-            case g: GetStructField => Alias(g, g.field.name)()
-            case g: GetArrayStructFields => Alias(g, g.field.name)()
             case g: Generator if g.resolved && g.elementTypes.size > 1 => MultiAlias(g, Nil)
             case e if !e.resolved => u
             case other => Alias(other, s"_c$i")()
@@ -384,9 +384,7 @@ class Analyzer(
           case u @ UnresolvedAttribute(nameParts) =>
             // Leave unchanged if resolution fails.  Hopefully will be resolved next round.
             val result =
-              withPosition(u) {
-                q.resolveChildren(nameParts, resolver).map(trimUnresolvedAlias).getOrElse(u)
-              }
+              withPosition(u) { q.resolveChildren(nameParts, resolver).getOrElse(u) }
             logDebug(s"Resolving $u to $result")
             result
           case UnresolvedExtractValue(child, fieldExpr) if child.resolved =>
@@ -412,11 +410,6 @@ class Analyzer(
       exprs.exists(_.collect { case _: Star => true }.nonEmpty)
   }
 
-  private def trimUnresolvedAlias(ne: NamedExpression) = ne match {
-    case UnresolvedAlias(child) => child
-    case other => other
-  }
-
   private def resolveSortOrders(ordering: Seq[SortOrder], plan: LogicalPlan, throws: Boolean) = {
     ordering.map { order =>
       // Resolve SortOrder in one round.
@@ -426,7 +419,7 @@ class Analyzer(
       try {
         val newOrder = order transformUp {
           case u @ UnresolvedAttribute(nameParts) =>
-            plan.resolve(nameParts, resolver).map(trimUnresolvedAlias).getOrElse(u)
+            plan.resolve(nameParts, resolver).getOrElse(u)
           case UnresolvedExtractValue(child, fieldName) if child.resolved =>
             ExtractValue(child, fieldName, resolver)
         }
@@ -968,3 +961,61 @@ object EliminateSubQueries extends Rule[LogicalPlan] {
     case Subquery(_, child) => child
   }
 }
+
+/**
+ * Cleans up unnecessary Aliases inside the plan. Basically we only need Alias as a top level
+ * expression in Project(project list) or Aggregate(aggregate expressions) or
+ * Window(window expressions).
+ */
+object CleanupAliases extends Rule[LogicalPlan] {
+  private def trimAliases(e: Expression): Expression = {
+    var stop = false
+    e.transformDown {
+      // CreateStruct is a special case, we need to retain its top level Aliases as they decide the
+      // name of StructField. We also need to stop transform down this expression, or the Aliases
+      // under CreateStruct will be mistakenly trimmed.
+      case c: CreateStruct if !stop =>
+        stop = true
+        c.copy(children = c.children.map(trimNonTopLevelAliases))
+      case c: CreateStructUnsafe if !stop =>
+        stop = true
+        c.copy(children = c.children.map(trimNonTopLevelAliases))
+      case Alias(child, _) if !stop => child
+    }
+  }
+
+  def trimNonTopLevelAliases(e: Expression): Expression = e match {
+    case a: Alias =>
+      Alias(trimAliases(a.child), a.name)(a.exprId, a.qualifiers, a.explicitMetadata)
+    case other => trimAliases(other)
+  }
+
+  override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+    case Project(projectList, child) =>
+      val cleanedProjectList =
+        projectList.map(trimNonTopLevelAliases(_).asInstanceOf[NamedExpression])
+      Project(cleanedProjectList, child)
+
+    case Aggregate(grouping, aggs, child) =>
+      val cleanedAggs = aggs.map(trimNonTopLevelAliases(_).asInstanceOf[NamedExpression])
+      Aggregate(grouping.map(trimAliases), cleanedAggs, child)
+
+    case w @ Window(projectList, windowExprs, partitionSpec, orderSpec, child) =>
+      val cleanedWindowExprs =
+        windowExprs.map(e => trimNonTopLevelAliases(e).asInstanceOf[NamedExpression])
+      Window(projectList, cleanedWindowExprs, partitionSpec.map(trimAliases),
+        orderSpec.map(trimAliases(_).asInstanceOf[SortOrder]), child)
+
+    case other =>
+      var stop = false
+      other transformExpressionsDown {
+        case c: CreateStruct if !stop =>
+          stop = true
+          c.copy(children = c.children.map(trimNonTopLevelAliases))
+        case c: CreateStructUnsafe if !stop =>
+          stop = true
+          c.copy(children = c.children.map(trimNonTopLevelAliases))
+        case Alias(child, _) if !stop => child
+      }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 4a071e663e0d1..298aee3499275 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -75,8 +75,6 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
-  override lazy val resolved: Boolean = childrenResolved
-
   override lazy val dataType: StructType = {
     val fields = children.zipWithIndex.map { case (child, idx) =>
       child match {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 4ab5ac2c61e3c..47b06cae15436 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.optimizer
 
 import scala.collection.immutable.HashSet
-import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
+import org.apache.spark.sql.catalyst.analysis.{CleanupAliases, EliminateSubQueries}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.FullOuter
@@ -260,8 +260,11 @@ object ProjectCollapsing extends Rule[LogicalPlan] {
         val substitutedProjection = projectList1.map(_.transform {
           case a: Attribute => aliasMap.getOrElse(a, a)
         }).asInstanceOf[Seq[NamedExpression]]
-
-        Project(substitutedProjection, child)
+        // collapse 2 projects may introduce unnecessary Aliases, trim them here.
+        val cleanedProjection = substitutedProjection.map(p =>
+          CleanupAliases.trimNonTopLevelAliases(p).asInstanceOf[NamedExpression]
+        )
+        Project(cleanedProjection, child)
       }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index c290e6acb361c..9bb466ac2d29c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -259,13 +259,13 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
       // One match, but we also need to extract the requested nested field.
       case Seq((a, nestedFields)) =>
         // The foldLeft adds ExtractValues for every remaining parts of the identifier,
-        // and wrap it with UnresolvedAlias which will be removed later.
+        // and aliased it with the last part of the name.
         // For example, consider "a.b.c", where "a" is resolved to an existing attribute.
-        // Then this will add ExtractValue("c", ExtractValue("b", a)), and wrap it as
-        // UnresolvedAlias(ExtractValue("c", ExtractValue("b", a))).
+        // Then this will add ExtractValue("c", ExtractValue("b", a)), and alias the final
+        // expression as "c".
         val fieldExprs = nestedFields.foldLeft(a: Expression)((expr, fieldName) =>
           ExtractValue(expr, Literal(fieldName), resolver))
-        Some(UnresolvedAlias(fieldExprs))
+        Some(Alias(fieldExprs, nestedFields.last)())
 
       // No matches.
       case Seq() =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 7c404722d811c..73b8261260acb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -228,7 +228,7 @@ case class Window(
     child: LogicalPlan) extends UnaryNode {
 
   override def output: Seq[Attribute] =
-    (projectList ++ windowExpressions).map(_.toAttribute)
+    projectList ++ windowExpressions.map(_.toAttribute)
 }
 
 /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index c944bc69e25b0..1e0cc81dae974 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -119,4 +119,21 @@ class AnalysisSuite extends AnalysisTest {
           Project(testRelation.output :+ projected, testRelation)))
     checkAnalysis(plan, expected)
   }
+
+  test("SPARK-9634: cleanup unnecessary Aliases in LogicalPlan") {
+    val a = testRelation.output.head
+    var plan = testRelation.select(((a + 1).as("a+1") + 2).as("col"))
+    var expected = testRelation.select((a + 1 + 2).as("col"))
+    checkAnalysis(plan, expected)
+
+    plan = testRelation.groupBy(a.as("a1").as("a2"))((min(a).as("min_a") + 1).as("col"))
+    expected = testRelation.groupBy(a)((min(a) + 1).as("col"))
+    checkAnalysis(plan, expected)
+
+    // CreateStruct is a special case that we should not trim Alias for it.
+    plan = testRelation.select(CreateStruct(Seq(a, (a + 1).as("a+1"))).as("col"))
+    checkAnalysis(plan, plan)
+    plan = testRelation.select(CreateStructUnsafe(Seq(a, (a + 1).as("a+1"))).as("col"))
+    checkAnalysis(plan, plan)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 27bd084847346..807bc8c30c12d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -753,10 +753,16 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    *   df.select($"colA".as("colB"))
    * }}}
    *
+   * If the current column has metadata associated with it, this metadata will be propagated
+   * to the new column.  If this not desired, use `as` with explicitly empty metadata.
+   *
    * @group expr_ops
    * @since 1.3.0
    */
-  def as(alias: String): Column = Alias(expr, alias)()
+  def as(alias: String): Column = expr match {
+    case ne: NamedExpression => Alias(expr, alias)(explicitMetadata = Some(ne.metadata))
+    case other => Alias(other, alias)()
+  }
 
   /**
    * (Scala-specific) Assigns the given aliases to the results of a table generating function.
@@ -789,10 +795,16 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    *   df.select($"colA".as('colB))
    * }}}
    *
+   * If the current column has metadata associated with it, this metadata will be propagated
+   * to the new column.  If this not desired, use `as` with explicitly empty metadata.
+   *
    * @group expr_ops
    * @since 1.3.0
    */
-  def as(alias: Symbol): Column = Alias(expr, alias.name)()
+  def as(alias: Symbol): Column = expr match {
+    case ne: NamedExpression => Alias(expr, alias.name)(explicitMetadata = Some(ne.metadata))
+    case other => Alias(other, alias.name)()
+  }
 
   /**
    * Gives the column an alias with metadata.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index ee74e3e83da5a..37738ec5b3c1d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.catalyst.expressions.NamedExpression
 import org.scalatest.Matchers._
 
 import org.apache.spark.sql.execution.{Project, TungstenProject}
@@ -110,6 +111,14 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
     assert(df.select(df("a").alias("b")).columns.head === "b")
   }
 
+  test("as propagates metadata") {
+    val metadata = new MetadataBuilder
+    metadata.putString("key", "value")
+    val origCol = $"a".as("b", metadata.build())
+    val newCol = origCol.as("c")
+    assert(newCol.expr.asInstanceOf[NamedExpression].metadata.getString("key") === "value")
+  }
+
   test("single explode") {
     val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList")
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 10bfa9b64f00d..cf22797752b97 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -867,4 +867,10 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     val actual = df.sort(rand(seed)).collect().map(_.getInt(0))
     assert(expected === actual)
   }
+
+  test("SPARK-9323: DataFrame.orderBy should support nested column name") {
+    val df = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+      """{"a": {"b": 1}}""" :: Nil))
+    checkAnswer(df.orderBy("a.b"), Row(Row(1)))
+  }
 }

From 6c4fdbec33af287d24cd0995ecbd7191545d05c9 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Fri, 14 Aug 2015 21:03:14 -0700
Subject: [PATCH 1064/1454] [SPARK-8887] [SQL] Explicit define which data types
 can be used as dynamic partition columns

This PR enforce dynamic partition column data type requirements by adding analysis rules.

JIRA: https://issues.apache.org/jira/browse/SPARK-8887

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8201 from yjshen/dynamic_partition_columns.
---
 .../datasources/PartitioningUtils.scala         | 13 +++++++++++++
 .../datasources/ResolvedDataSource.scala        |  5 ++++-
 .../execution/datasources/WriterContainer.scala |  2 +-
 .../spark/sql/execution/datasources/rules.scala |  8 ++++++--
 .../sql/sources/hadoopFsRelationSuites.scala    | 17 +++++++++++++++++
 5 files changed, 41 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
index 66dfcc308ceca..0a2007e15843c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
@@ -26,6 +26,7 @@ import scala.util.Try
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.util.Shell
 
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
 import org.apache.spark.sql.types._
@@ -270,6 +271,18 @@ private[sql] object PartitioningUtils {
   private val upCastingOrder: Seq[DataType] =
     Seq(NullType, IntegerType, LongType, FloatType, DoubleType, StringType)
 
+  def validatePartitionColumnDataTypes(
+      schema: StructType,
+      partitionColumns: Array[String]): Unit = {
+
+    ResolvedDataSource.partitionColumnsSchema(schema, partitionColumns).foreach { field =>
+      field.dataType match {
+        case _: AtomicType => // OK
+        case _ => throw new AnalysisException(s"Cannot use ${field.dataType} for partition column")
+      }
+    }
+  }
+
   /**
    * Given a collection of [[Literal]]s, resolves possible type conflicts by up-casting "lower"
    * types.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
index 7770bbd712f04..8fbaf3a3059db 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
@@ -143,7 +143,7 @@ object ResolvedDataSource extends Logging {
     new ResolvedDataSource(clazz, relation)
   }
 
-  private def partitionColumnsSchema(
+  def partitionColumnsSchema(
       schema: StructType,
       partitionColumns: Array[String]): StructType = {
     StructType(partitionColumns.map { col =>
@@ -179,6 +179,9 @@ object ResolvedDataSource extends Logging {
           val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
           path.makeQualified(fs.getUri, fs.getWorkingDirectory)
         }
+
+        PartitioningUtils.validatePartitionColumnDataTypes(data.schema, partitionColumns)
+
         val dataSchema = StructType(data.schema.filterNot(f => partitionColumns.contains(f.name)))
         val r = dataSource.createRelation(
           sqlContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 2f11f40422402..d36197e50d448 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -287,7 +287,7 @@ private[sql] class DynamicPartitionWriterContainer(
           PartitioningUtils.escapePathName _, StringType, Seq(Cast(c, StringType)), Seq(StringType))
       val str = If(IsNull(c), Literal(defaultPartitionName), escaped)
       val partitionName = Literal(c.name + "=") :: str :: Nil
-      if (i == 0) partitionName else Literal(Path.SEPARATOR_CHAR.toString) :: partitionName
+      if (i == 0) partitionName else Literal(Path.SEPARATOR) :: partitionName
     }
 
     // Returns the partition path given a partition key.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
index 40ca8bf4095d8..9d3d35692ffcc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
@@ -116,6 +116,8 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
+        PartitioningUtils.validatePartitionColumnDataTypes(r.schema, part.keySet.toArray)
+
         // Get all input data source relations of the query.
         val srcRelations = query.collect {
           case LogicalRelation(src: BaseRelation) => src
@@ -138,10 +140,10 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
-      case CreateTableUsingAsSelect(tableName, _, _, _, SaveMode.Overwrite, _, query) =>
+      case CreateTableUsingAsSelect(tableName, _, _, partitionColumns, mode, _, query) =>
         // When the SaveMode is Overwrite, we need to check if the table is an input table of
         // the query. If so, we will throw an AnalysisException to let users know it is not allowed.
-        if (catalog.tableExists(Seq(tableName))) {
+        if (mode == SaveMode.Overwrite && catalog.tableExists(Seq(tableName))) {
           // Need to remove SubQuery operator.
           EliminateSubQueries(catalog.lookupRelation(Seq(tableName))) match {
             // Only do the check if the table is a data source table
@@ -164,6 +166,8 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
+        PartitioningUtils.validatePartitionColumnDataTypes(query.schema, partitionColumns)
+
       case _ => // OK
     }
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index af445626fbe4d..8d0d9218ddd6a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.sources
 
+import java.sql.Date
+
 import scala.collection.JavaConversions._
 
 import org.apache.hadoop.conf.Configuration
@@ -553,6 +555,21 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
+
+  test("SPARK-8887: Explicitly define which data types can be used as dynamic partition columns") {
+    val df = Seq(
+      (1, "v1", Array(1, 2, 3), Map("k1" -> "v1"), Tuple2(1, "4")),
+      (2, "v2", Array(4, 5, 6), Map("k2" -> "v2"), Tuple2(2, "5")),
+      (3, "v3", Array(7, 8, 9), Map("k3" -> "v3"), Tuple2(3, "6"))).toDF("a", "b", "c", "d", "e")
+    withTempDir { file =>
+      intercept[AnalysisException] {
+        df.write.format(dataSourceName).partitionBy("c", "d", "e").save(file.getCanonicalPath)
+      }
+    }
+    intercept[AnalysisException] {
+      df.write.format(dataSourceName).partitionBy("c", "d", "e").saveAsTable("t")
+    }
+  }
 }
 
 // This class is used to test SPARK-8578. We should not use any custom output committer when

From 609ce3c07d4962a9242e488ad0ed48c183896802 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 14 Aug 2015 21:12:11 -0700
Subject: [PATCH 1065/1454] [SPARK-9984] [SQL] Create local physical operator
 interface.

This pull request creates a new operator interface that is more similar to traditional database query iterators (with open/close/next/get).

These local operators are not currently used anywhere, but will become the basis for SPARK-9983 (local physical operators for query execution).

cc zsxwing

Author: Reynold Xin <rxin@databricks.com>

Closes #8212 from rxin/SPARK-9984.
---
 .../sql/execution/local/FilterNode.scala      | 47 ++++++++++
 .../spark/sql/execution/local/LocalNode.scala | 86 +++++++++++++++++++
 .../sql/execution/local/ProjectNode.scala     | 42 +++++++++
 .../sql/execution/local/SeqScanNode.scala     | 49 +++++++++++
 4 files changed, 224 insertions(+)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
new file mode 100644
index 0000000000000..a485a1a1d7ae4
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
@@ -0,0 +1,47 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
+
+
+case class FilterNode(condition: Expression, child: LocalNode) extends UnaryLocalNode {
+
+  private[this] var predicate: (InternalRow) => Boolean = _
+
+  override def output: Seq[Attribute] = child.output
+
+  override def open(): Unit = {
+    child.open()
+    predicate = GeneratePredicate.generate(condition, child.output)
+  }
+
+  override def next(): Boolean = {
+    var found = false
+    while (child.next() && !found) {
+      found = predicate.apply(child.get())
+    }
+    found
+  }
+
+  override def get(): InternalRow = child.get()
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
new file mode 100644
index 0000000000000..341c81438e6d6
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -0,0 +1,86 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A local physical operator, in the form of an iterator.
+ *
+ * Before consuming the iterator, open function must be called.
+ * After consuming the iterator, close function must be called.
+ */
+abstract class LocalNode extends TreeNode[LocalNode] {
+
+  def output: Seq[Attribute]
+
+  /**
+   * Initializes the iterator state. Must be called before calling `next()`.
+   *
+   * Implementations of this must also call the `open()` function of its children.
+   */
+  def open(): Unit
+
+  /**
+   * Advances the iterator to the next tuple. Returns true if there is at least one more tuple.
+   */
+  def next(): Boolean
+
+  /**
+   * Returns the current tuple.
+   */
+  def get(): InternalRow
+
+  /**
+   * Closes the iterator and releases all resources.
+   *
+   * Implementations of this must also call the `close()` function of its children.
+   */
+  def close(): Unit
+
+  /**
+   * Returns the content of the iterator from the beginning to the end in the form of a Scala Seq.
+   */
+  def collect(): Seq[Row] = {
+    val converter = CatalystTypeConverters.createToScalaConverter(StructType.fromAttributes(output))
+    val result = new scala.collection.mutable.ArrayBuffer[Row]
+    open()
+    while (next()) {
+      result += converter.apply(get()).asInstanceOf[Row]
+    }
+    close()
+    result
+  }
+}
+
+
+abstract class LeafLocalNode extends LocalNode {
+  override def children: Seq[LocalNode] = Seq.empty
+}
+
+
+abstract class UnaryLocalNode extends LocalNode {
+
+  def child: LocalNode
+
+  override def children: Seq[LocalNode] = Seq(child)
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
new file mode 100644
index 0000000000000..e574d1473cdcb
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
@@ -0,0 +1,42 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, Attribute, NamedExpression}
+
+
+case class ProjectNode(projectList: Seq[NamedExpression], child: LocalNode) extends UnaryLocalNode {
+
+  private[this] var project: UnsafeProjection = _
+
+  override def output: Seq[Attribute] = projectList.map(_.toAttribute)
+
+  override def open(): Unit = {
+    project = UnsafeProjection.create(projectList, child.output)
+    child.open()
+  }
+
+  override def next(): Boolean = child.next()
+
+  override def get(): InternalRow = {
+    project.apply(child.get())
+  }
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
new file mode 100644
index 0000000000000..994de8afa9a02
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
@@ -0,0 +1,49 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+/**
+ * An operator that scans some local data collection in the form of Scala Seq.
+ */
+case class SeqScanNode(output: Seq[Attribute], data: Seq[InternalRow]) extends LeafLocalNode {
+
+  private[this] var iterator: Iterator[InternalRow] = _
+  private[this] var currentRow: InternalRow = _
+
+  override def open(): Unit = {
+    iterator = data.iterator
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      currentRow = iterator.next()
+      true
+    } else {
+      false
+    }
+  }
+
+  override def get(): InternalRow = currentRow
+
+  override def close(): Unit = {
+    // Do nothing
+  }
+}

From 71a3af8a94f900a26ac7094f22ec1216cab62e15 Mon Sep 17 00:00:00 2001
From: zc he <farseer90718@gmail.com>
Date: Fri, 14 Aug 2015 21:28:50 -0700
Subject: [PATCH 1066/1454] [SPARK-9960] [GRAPHX] sendMessage type fix in
 LabelPropagation.scala

Author: zc he <farseer90718@gmail.com>

Closes #8188 from farseer90718/farseer-patch-1.
---
 .../scala/org/apache/spark/graphx/lib/LabelPropagation.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
index 2bcf8684b8b8e..a3ad6bed1c998 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
@@ -43,7 +43,7 @@ object LabelPropagation {
    */
   def run[VD, ED: ClassTag](graph: Graph[VD, ED], maxSteps: Int): Graph[VertexId, ED] = {
     val lpaGraph = graph.mapVertices { case (vid, _) => vid }
-    def sendMessage(e: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, Map[VertexId, VertexId])] = {
+    def sendMessage(e: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, Map[VertexId, Long])] = {
       Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L)))
     }
     def mergeMessage(count1: Map[VertexId, Long], count2: Map[VertexId, Long])

From 7c1e56825b716a7d703dff38254b4739755ac0c4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 22:30:35 -0700
Subject: [PATCH 1067/1454] [SPARK-9725] [SQL] fix serialization of UTF8String
 across different JVM

The BYTE_ARRAY_OFFSET could be different in JVM with different configurations (for example, different heap size, 24 if heap > 32G, otherwise 16), so offset of UTF8String is not portable, we should handler that during serialization.

Author: Davies Liu <davies@databricks.com>

Closes #8210 from davies/serialize_utf8string.
---
 .../apache/spark/unsafe/types/UTF8String.java | 31 +++++++++++++++----
 1 file changed, 25 insertions(+), 6 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 667c00900f2c5..cbcab958c05a9 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -18,8 +18,7 @@
 package org.apache.spark.unsafe.types;
 
 import javax.annotation.Nonnull;
-import java.io.Serializable;
-import java.io.UnsupportedEncodingException;
+import java.io.*;
 import java.nio.ByteOrder;
 import java.util.Arrays;
 import java.util.Map;
@@ -38,12 +37,13 @@
  * <p>
  * Note: This is not designed for general use cases, should not be used outside SQL.
  */
-public final class UTF8String implements Comparable<UTF8String>, Serializable {
+public final class UTF8String implements Comparable<UTF8String>, Externalizable {
 
+  // These are only updated by readExternal()
   @Nonnull
-  private final Object base;
-  private final long offset;
-  private final int numBytes;
+  private Object base;
+  private long offset;
+  private int numBytes;
 
   public Object getBaseObject() { return base; }
   public long getBaseOffset() { return offset; }
@@ -127,6 +127,11 @@ protected UTF8String(Object base, long offset, int numBytes) {
     this.numBytes = numBytes;
   }
 
+  // for serialization
+  public UTF8String() {
+    this(null, 0, 0);
+  }
+
   /**
    * Writes the content of this string into a memory address, identified by an object and an offset.
    * The target memory address must already been allocated, and have enough space to hold all the
@@ -978,4 +983,18 @@ public UTF8String soundex() {
     }
     return UTF8String.fromBytes(sx);
   }
+
+  public void writeExternal(ObjectOutput out) throws IOException {
+    byte[] bytes = getBytes();
+    out.writeInt(bytes.length);
+    out.write(bytes);
+  }
+
+  public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
+    offset = BYTE_ARRAY_OFFSET;
+    numBytes = in.readInt();
+    base = new byte[numBytes];
+    in.readFully((byte[]) base);
+  }
+
 }

From a85fb6c07fdda5c74d53d6373910dcf5db3ff111 Mon Sep 17 00:00:00 2001
From: Herman van Hovell <hvanhovell@questtec.nl>
Date: Sat, 15 Aug 2015 10:46:04 +0100
Subject: [PATCH 1068/1454] [SPARK-9980] [BUILD] Fix SBT publishLocal error due
 to invalid characters in doc

Tiny modification to a few comments ```sbt publishLocal``` work again.

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #8209 from hvanhovell/SPARK-9980.
---
 .../java/org/apache/spark/unsafe/map/BytesToBytesMap.java | 6 +++---
 .../apache/spark/examples/ml/JavaDeveloperApiExample.java | 4 ++--
 .../examples/streaming/JavaStatefulNetworkWordCount.java  | 2 +-
 .../src/main/java/org/apache/spark/launcher/Main.java     | 4 ++--
 .../apache/spark/launcher/SparkClassCommandBuilder.java   | 2 +-
 .../java/org/apache/spark/launcher/SparkLauncher.java     | 6 +++---
 .../apache/spark/launcher/SparkSubmitCommandBuilder.java  | 4 ++--
 .../apache/spark/launcher/SparkSubmitOptionParser.java    | 8 ++++----
 .../org/apache/spark/unsafe/memory/TaskMemoryManager.java | 2 +-
 9 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 5f3a4fcf4d585..b24eed3952fd6 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -92,9 +92,9 @@ public final class BytesToBytesMap {
 
   /**
    * The maximum number of keys that BytesToBytesMap supports. The hash table has to be
-   * power-of-2-sized and its backing Java array can contain at most (1 << 30) elements, since
-   * that's the largest power-of-2 that's less than Integer.MAX_VALUE. We need two long array
-   * entries per key, giving us a maximum capacity of (1 << 29).
+   * power-of-2-sized and its backing Java array can contain at most (1 &lt;&lt; 30) elements,
+   * since that's the largest power-of-2 that's less than Integer.MAX_VALUE. We need two long array
+   * entries per key, giving us a maximum capacity of (1 &lt;&lt; 29).
    */
   @VisibleForTesting
   static final int MAX_CAPACITY = (1 << 29);
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
index 3f1fe900b0008..a377694507d29 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
@@ -124,7 +124,7 @@ public String uid() {
 
   /**
    * Param for max number of iterations
-   * <p/>
+   * <p>
    * NOTE: The usual way to add a parameter to a model or algorithm is to include:
    * - val myParamName: ParamType
    * - def getMyParamName
@@ -222,7 +222,7 @@ public Vector predictRaw(Vector features) {
   /**
    * Create a copy of the model.
    * The copy is shallow, except for the embedded paramMap, which gets a deep copy.
-   * <p/>
+   * <p>
    * This is used for the defaul implementation of [[transform()]].
    *
    * In Java, we have to make this method public since Java does not understand Scala's protected
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
index 02f58f48b07ab..99b63a2590ae2 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
@@ -45,7 +45,7 @@
  * Usage: JavaStatefulNetworkWordCount <hostname> <port>
  * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
  * data.
- * <p/>
+ * <p>
  * To run this on your local machine, you need to first run a Netcat server
  * `$ nc -lk 9999`
  * and then run the example
diff --git a/launcher/src/main/java/org/apache/spark/launcher/Main.java b/launcher/src/main/java/org/apache/spark/launcher/Main.java
index 62492f9baf3bb..a4e3acc674f36 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/Main.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/Main.java
@@ -32,7 +32,7 @@ class Main {
 
   /**
    * Usage: Main [class] [class args]
-   * <p/>
+   * <p>
    * This CLI works in two different modes:
    * <ul>
    *   <li>"spark-submit": if <i>class</i> is "org.apache.spark.deploy.SparkSubmit", the
@@ -42,7 +42,7 @@ class Main {
    *
    * This class works in tandem with the "bin/spark-class" script on Unix-like systems, and
    * "bin/spark-class2.cmd" batch script on Windows to execute the final command.
-   * <p/>
+   * <p>
    * On Unix-like systems, the output is a list of command arguments, separated by the NULL
    * character. On Windows, the output is a command line suitable for direct execution from the
    * script.
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
index 5f95e2c74f902..931a24cfd4b1d 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
@@ -28,7 +28,7 @@
 
 /**
  * Command builder for internal Spark classes.
- * <p/>
+ * <p>
  * This class handles building the command to launch all internal Spark classes except for
  * SparkSubmit (which is handled by {@link SparkSubmitCommandBuilder} class.
  */
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
index 03c9358bc865d..57993405e47be 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
@@ -193,7 +193,7 @@ public SparkLauncher setMainClass(String mainClass) {
    * Adds a no-value argument to the Spark invocation. If the argument is known, this method
    * validates whether the argument is indeed a no-value argument, and throws an exception
    * otherwise.
-   * <p/>
+   * <p>
    * Use this method with caution. It is possible to create an invalid Spark command by passing
    * unknown arguments to this method, since those are allowed for forward compatibility.
    *
@@ -211,10 +211,10 @@ public SparkLauncher addSparkArg(String arg) {
    * Adds an argument with a value to the Spark invocation. If the argument name corresponds to
    * a known argument, the code validates that the argument actually expects a value, and throws
    * an exception otherwise.
-   * <p/>
+   * <p>
    * It is safe to add arguments modified by other methods in this class (such as
    * {@link #setMaster(String)} - the last invocation will be the one to take effect.
-   * <p/>
+   * <p>
    * Use this method with caution. It is possible to create an invalid Spark command by passing
    * unknown arguments to this method, since those are allowed for forward compatibility.
    *
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
index 4f354cedee66f..fc87814a59ed5 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
@@ -25,11 +25,11 @@
 
 /**
  * Special command builder for handling a CLI invocation of SparkSubmit.
- * <p/>
+ * <p>
  * This builder adds command line parsing compatible with SparkSubmit. It handles setting
  * driver-side options and special parsing behavior needed for the special-casing certain internal
  * Spark applications.
- * <p/>
+ * <p>
  * This class has also some special features to aid launching pyspark.
  */
 class SparkSubmitCommandBuilder extends AbstractCommandBuilder {
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
index 5779eb3fc0f78..6767cc5079649 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
@@ -23,7 +23,7 @@
 
 /**
  * Parser for spark-submit command line options.
- * <p/>
+ * <p>
  * This class encapsulates the parsing code for spark-submit command line options, so that there
  * is a single list of options that needs to be maintained (well, sort of, but it makes it harder
  * to break things).
@@ -80,10 +80,10 @@ class SparkSubmitOptionParser {
    * This is the canonical list of spark-submit options. Each entry in the array contains the
    * different aliases for the same option; the first element of each entry is the "official"
    * name of the option, passed to {@link #handle(String, String)}.
-   * <p/>
+   * <p>
    * Options not listed here nor in the "switch" list below will result in a call to
    * {@link $#handleUnknown(String)}.
-   * <p/>
+   * <p>
    * These two arrays are visible for tests.
    */
   final String[][] opts = {
@@ -130,7 +130,7 @@ class SparkSubmitOptionParser {
 
   /**
    * Parse a list of spark-submit command line options.
-   * <p/>
+   * <p>
    * See SparkSubmitArguments.scala for a more formal description of available options.
    *
    * @throws IllegalArgumentException If an error is found during parsing.
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
index ca70d7f4a4311..97b2c93f0dc37 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
@@ -60,7 +60,7 @@ public class TaskMemoryManager {
 
   /**
    * Maximum supported data page size (in bytes). In principle, the maximum addressable page size is
-   * (1L << OFFSET_BITS) bytes, which is 2+ petabytes. However, the on-heap allocator's maximum page
+   * (1L &lt;&lt; OFFSET_BITS) bytes, which is 2+ petabytes. However, the on-heap allocator's maximum page
    * size is limited by the maximum amount of data that can be stored in a  long[] array, which is
    * (2^32 - 1) * 8 bytes (or 16 gigabytes). Therefore, we cap this at 16 gigabytes.
    */

From 570567258b5839c1e0e28b5182f4c29b119ed4c4 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 15 Aug 2015 14:13:12 -0700
Subject: [PATCH 1069/1454] [SPARK-9955] [SQL] correct error message for
 aggregate

We should skip unresolved `LogicalPlan`s for `PullOutNondeterministic`, as calling `output` on unresolved `LogicalPlan` will produce confusing error message.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8203 from cloud-fan/error-msg and squashes the following commits:

1c67ca7 [Wenchen Fan] move test
7593080 [Wenchen Fan] correct error message for aggregate
---
 .../spark/sql/catalyst/analysis/Analyzer.scala      |  1 +
 .../sql/catalyst/analysis/AnalysisErrorSuite.scala  |  5 +++++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala  | 13 ++++++-------
 3 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 4bc1c1af40bf4..d0eb9c2c90bdf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -924,6 +924,7 @@ class Analyzer(
    */
   object PullOutNondeterministic extends Rule[LogicalPlan] {
     override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+      case p if !p.resolved => p // Skip unresolved nodes.
       case p: Project => p
       case f: Filter => f
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index f60d11c988ef8..7065adce04bf8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -145,6 +145,11 @@ class AnalysisErrorSuite extends AnalysisTest {
     UnresolvedTestPlan(),
     "unresolved" :: Nil)
 
+  errorTest(
+    "SPARK-9955: correct error message for aggregate",
+    // When parse SQL string, we will wrap aggregate expressions with UnresolvedAlias.
+    testRelation2.where('bad_column > 1).groupBy('a)(UnresolvedAlias(max('b))),
+    "cannot resolve 'bad_column'" :: Nil)
 
   test("SPARK-6452 regression test") {
     // CheckAnalysis should throw AnalysisException when Aggregate contains missing attribute(s)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 8c2c328f8191c..c329fdb2a6bb1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1618,12 +1618,11 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-9511: error with table starting with number") {
-    val df = sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
-      .toDF("num", "str")
-    df.registerTempTable("1one")
-
-    checkAnswer(sql("select count(num) from 1one"), Row(10))
-
-    sqlContext.dropTempTable("1one")
+    withTempTable("1one") {
+      sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
+        .toDF("num", "str")
+        .registerTempTable("1one")
+      checkAnswer(sql("select count(num) from 1one"), Row(10))
+    }
   }
 }

From 1db7179fae672fcec7b8de12c374dd384ce51c67 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Sat, 15 Aug 2015 18:48:20 -0700
Subject: [PATCH 1070/1454] [SPARK-9805] [MLLIB] [PYTHON] [STREAMING] Added
 _eventually for ml streaming pyspark tests

Recently, PySpark ML streaming tests have been flaky, most likely because of the batches not being processed in time.  Proposal: Replace the use of _ssc_wait (which waits for a fixed amount of time) with a method which waits for a fixed amount of time but can terminate early based on a termination condition method.  With this, we can extend the waiting period (to make tests less flaky) but also stop early when possible (making tests faster on average, which I verified locally).

CC: mengxr tdas freeman-lab

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8087 from jkbradley/streaming-ml-tests.
---
 python/pyspark/mllib/tests.py | 177 +++++++++++++++++++++++++---------
 1 file changed, 129 insertions(+), 48 deletions(-)

diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 3f5a02af12e39..5097c5e8ba4cd 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -32,6 +32,9 @@
 
 from py4j.protocol import Py4JJavaError
 
+if sys.version > '3':
+    basestring = str
+
 if sys.version_info[:2] <= (2, 6):
     try:
         import unittest2 as unittest
@@ -86,9 +89,42 @@ def tearDown(self):
         self.ssc.stop(False)
 
     @staticmethod
-    def _ssc_wait(start_time, end_time, sleep_time):
-        while time() - start_time < end_time:
+    def _eventually(condition, timeout=30.0, catch_assertions=False):
+        """
+        Wait a given amount of time for a condition to pass, else fail with an error.
+        This is a helper utility for streaming ML tests.
+        :param condition: Function that checks for termination conditions.
+                          condition() can return:
+                           - True: Conditions met. Return without error.
+                           - other value: Conditions not met yet. Continue. Upon timeout,
+                                          include last such value in error message.
+                          Note that this method may be called at any time during
+                          streaming execution (e.g., even before any results
+                          have been created).
+        :param timeout: Number of seconds to wait.  Default 30 seconds.
+        :param catch_assertions: If False (default), do not catch AssertionErrors.
+                                 If True, catch AssertionErrors; continue, but save
+                                 error to throw upon timeout.
+        """
+        start_time = time()
+        lastValue = None
+        while time() - start_time < timeout:
+            if catch_assertions:
+                try:
+                    lastValue = condition()
+                except AssertionError as e:
+                    lastValue = e
+            else:
+                lastValue = condition()
+            if lastValue is True:
+                return
             sleep(0.01)
+        if isinstance(lastValue, AssertionError):
+            raise lastValue
+        else:
+            raise AssertionError(
+                "Test failed due to timeout after %g sec, with last condition returning: %s"
+                % (timeout, lastValue))
 
 
 def _squared_distance(a, b):
@@ -999,10 +1035,13 @@ def test_accuracy_for_single_center(self):
             [self.sc.parallelize(batch, 1) for batch in batches])
         stkm.trainOn(input_stream)
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 10.0, 0.01)
-        self.assertEquals(stkm.latestModel().clusterWeights, [25.0])
+
+        def condition():
+            self.assertEquals(stkm.latestModel().clusterWeights, [25.0])
+            return True
+        self._eventually(condition, catch_assertions=True)
+
         realCenters = array_sum(array(centers), axis=0)
         for i in range(5):
             modelCenters = stkm.latestModel().centers[0][i]
@@ -1027,7 +1066,7 @@ def test_trainOn_model(self):
         stkm.setInitialCenters(
             centers=initCenters, weights=[1.0, 1.0, 1.0, 1.0])
 
-        # Create a toy dataset by setting a tiny offest for each point.
+        # Create a toy dataset by setting a tiny offset for each point.
         offsets = [[0, 0.1], [0, -0.1], [0.1, 0], [-0.1, 0]]
         batches = []
         for offset in offsets:
@@ -1037,14 +1076,15 @@ def test_trainOn_model(self):
         batches = [self.sc.parallelize(batch, 1) for batch in batches]
         input_stream = self.ssc.queueStream(batches)
         stkm.trainOn(input_stream)
-        t = time()
         self.ssc.start()
 
         # Give enough time to train the model.
-        self._ssc_wait(t, 6.0, 0.01)
-        finalModel = stkm.latestModel()
-        self.assertTrue(all(finalModel.centers == array(initCenters)))
-        self.assertEquals(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0])
+        def condition():
+            finalModel = stkm.latestModel()
+            self.assertTrue(all(finalModel.centers == array(initCenters)))
+            self.assertEquals(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0])
+            return True
+        self._eventually(condition, catch_assertions=True)
 
     def test_predictOn_model(self):
         """Test that the model predicts correctly on toy data."""
@@ -1066,10 +1106,13 @@ def update(rdd):
                 result.append(rdd_collect)
 
         predict_val.foreachRDD(update)
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 6.0, 0.01)
-        self.assertEquals(result, [[0], [1], [2], [3]])
+
+        def condition():
+            self.assertEquals(result, [[0], [1], [2], [3]])
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
     def test_trainOn_predictOn(self):
         """Test that prediction happens on the updated model."""
@@ -1095,10 +1138,13 @@ def collect(rdd):
         predict_stream = stkm.predictOn(input_stream)
         predict_stream.foreachRDD(collect)
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 6.0, 0.01)
-        self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
+
+        def condition():
+            self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
 
 class LinearDataGeneratorTests(MLlibTestCase):
@@ -1156,11 +1202,14 @@ def test_parameter_accuracy(self):
         slr.setInitialWeights([0.0])
         slr.trainOn(input_stream)
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 20.0, 0.01)
-        rel = (1.5 - slr.latestModel().weights.array[0]) / 1.5
-        self.assertAlmostEqual(rel, 0.1, 1)
+
+        def condition():
+            rel = (1.5 - slr.latestModel().weights.array[0]) / 1.5
+            self.assertAlmostEqual(rel, 0.1, 1)
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
     def test_convergence(self):
         """
@@ -1179,13 +1228,18 @@ def test_convergence(self):
         input_stream.foreachRDD(
             lambda x: models.append(slr.latestModel().weights[0]))
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 15.0, 0.01)
+
+        def condition():
+            self.assertEquals(len(models), len(input_batches))
+            return True
+
+        # We want all batches to finish for this test.
+        self._eventually(condition, 60.0, catch_assertions=True)
+
         t_models = array(models)
         diff = t_models[1:] - t_models[:-1]
-
-        # Test that weights improve with a small tolerance,
+        # Test that weights improve with a small tolerance
         self.assertTrue(all(diff >= -0.1))
         self.assertTrue(array_sum(diff > 0) > 1)
 
@@ -1208,9 +1262,13 @@ def test_predictions(self):
         predict_stream = slr.predictOnValues(input_stream)
         true_predicted = []
         predict_stream.foreachRDD(lambda x: true_predicted.append(x.collect()))
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 5.0, 0.01)
+
+        def condition():
+            self.assertEquals(len(true_predicted), len(input_batches))
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
         # Test that the accuracy error is no more than 0.4 on each batch.
         for batch in true_predicted:
@@ -1242,12 +1300,17 @@ def collect_errors(rdd):
         ps = slr.predictOnValues(predict_stream)
         ps.foreachRDD(lambda x: collect_errors(x))
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 20.0, 0.01)
 
-        # Test that the improvement in error is atleast 0.3
-        self.assertTrue(errors[1] - errors[-1] > 0.3)
+        def condition():
+            # Test that the improvement in error is > 0.3
+            if len(errors) == len(predict_batches):
+                self.assertGreater(errors[1] - errors[-1], 0.3)
+            if len(errors) >= 3 and errors[1] - errors[-1] > 0.3:
+                return True
+            return "Latest errors: " + ", ".join(map(lambda x: str(x), errors))
+
+        self._eventually(condition)
 
 
 class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
@@ -1274,13 +1337,16 @@ def test_parameter_accuracy(self):
             batches.append(sc.parallelize(batch))
 
         input_stream = self.ssc.queueStream(batches)
-        t = time()
         slr.trainOn(input_stream)
         self.ssc.start()
-        self._ssc_wait(t, 10, 0.01)
-        self.assertArrayAlmostEqual(
-            slr.latestModel().weights.array, [10., 10.], 1)
-        self.assertAlmostEqual(slr.latestModel().intercept, 0.0, 1)
+
+        def condition():
+            self.assertArrayAlmostEqual(
+                slr.latestModel().weights.array, [10., 10.], 1)
+            self.assertAlmostEqual(slr.latestModel().intercept, 0.0, 1)
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
     def test_parameter_convergence(self):
         """Test that the model parameters improve with streaming data."""
@@ -1298,13 +1364,18 @@ def test_parameter_convergence(self):
         input_stream = self.ssc.queueStream(batches)
         input_stream.foreachRDD(
             lambda x: model_weights.append(slr.latestModel().weights[0]))
-        t = time()
         slr.trainOn(input_stream)
         self.ssc.start()
-        self._ssc_wait(t, 10, 0.01)
 
-        model_weights = array(model_weights)
-        diff = model_weights[1:] - model_weights[:-1]
+        def condition():
+            self.assertEquals(len(model_weights), len(batches))
+            return True
+
+        # We want all batches to finish for this test.
+        self._eventually(condition, catch_assertions=True)
+
+        w = array(model_weights)
+        diff = w[1:] - w[:-1]
         self.assertTrue(all(diff >= -0.1))
 
     def test_prediction(self):
@@ -1323,13 +1394,18 @@ def test_prediction(self):
                 sc.parallelize(batch).map(lambda lp: (lp.label, lp.features)))
 
         input_stream = self.ssc.queueStream(batches)
-        t = time()
         output_stream = slr.predictOnValues(input_stream)
         samples = []
         output_stream.foreachRDD(lambda x: samples.append(x.collect()))
 
         self.ssc.start()
-        self._ssc_wait(t, 5, 0.01)
+
+        def condition():
+            self.assertEquals(len(samples), len(batches))
+            return True
+
+        # We want all batches to finish for this test.
+        self._eventually(condition, catch_assertions=True)
 
         # Test that mean absolute error on each batch is less than 0.1
         for batch in samples:
@@ -1350,22 +1426,27 @@ def test_train_prediction(self):
 
         predict_batches = [
             b.map(lambda lp: (lp.label, lp.features)) for b in batches]
-        mean_absolute_errors = []
+        errors = []
 
         def func(rdd):
             true, predicted = zip(*rdd.collect())
-            mean_absolute_errors.append(mean(abs(true) - abs(predicted)))
+            errors.append(mean(abs(true) - abs(predicted)))
 
-        model_weights = []
         input_stream = self.ssc.queueStream(batches)
         output_stream = self.ssc.queueStream(predict_batches)
-        t = time()
         slr.trainOn(input_stream)
         output_stream = slr.predictOnValues(output_stream)
         output_stream.foreachRDD(func)
         self.ssc.start()
-        self._ssc_wait(t, 10, 0.01)
-        self.assertTrue(mean_absolute_errors[1] - mean_absolute_errors[-1] > 2)
+
+        def condition():
+            if len(errors) == len(predict_batches):
+                self.assertGreater(errors[1] - errors[-1], 2)
+            if len(errors) >= 3 and errors[1] - errors[-1] > 2:
+                return True
+            return "Latest errors: " + ", ".join(map(lambda x: str(x), errors))
+
+        self._eventually(condition)
 
 
 class MLUtilsTests(MLlibTestCase):

From 182f9b7a6d3a3ee7ec7de6abc24e296aa794e4e8 Mon Sep 17 00:00:00 2001
From: Kun Xu <viper_kun@163.com>
Date: Sun, 16 Aug 2015 14:44:23 +0800
Subject: [PATCH 1071/1454] [SPARK-9973] [SQL] Correct in-memory columnar
 buffer size

The `initialSize` argument of `ColumnBuilder.initialize()` should be the
number of rows rather than bytes.  However `InMemoryColumnarTableScan`
passes in a byte size, which makes Spark SQL allocate more memory than
necessary when building in-memory columnar buffers.

Author: Kun Xu <viper_kun@163.com>

Closes #8189 from viper-kun/errorSize.
---
 .../apache/spark/sql/columnar/InMemoryColumnarTableScan.scala  | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index d553bb6169ecc..45f15fd04d4e2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -121,8 +121,7 @@ private[sql] case class InMemoryRelation(
         def next(): CachedBatch = {
           val columnBuilders = output.map { attribute =>
             val columnType = ColumnType(attribute.dataType)
-            val initialBufferSize = columnType.defaultSize * batchSize
-            ColumnBuilder(attribute.dataType, initialBufferSize, attribute.name, useCompression)
+            ColumnBuilder(attribute.dataType, batchSize, attribute.name, useCompression)
           }.toArray
 
           var rowCount = 0

From 5f9ce738fe6bab3f0caffad0df1d3876178cf469 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Sun, 16 Aug 2015 00:30:02 -0700
Subject: [PATCH 1072/1454] [SPARK-8844] [SPARKR] head/collect is broken in
 SparkR.

This is a WIP patch for SPARK-8844  for collecting reviews.

This bug is about reading an empty DataFrame. in readCol(),
      lapply(1:numRows, function(x) {
does not take into consideration the case where numRows = 0.

Will add unit test case.

Author: Sun Rui <rui.sun@intel.com>

Closes #7419 from sun-rui/SPARK-8844.
---
 R/pkg/R/deserialize.R            | 16 ++++++++++------
 R/pkg/inst/tests/test_sparkSQL.R | 20 ++++++++++++++++++++
 2 files changed, 30 insertions(+), 6 deletions(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 6d364f77be7ee..33bf13ec9e784 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -176,10 +176,14 @@ readRow <- function(inputCon) {
 
 # Take a single column as Array[Byte] and deserialize it into an atomic vector
 readCol <- function(inputCon, numRows) {
-  # sapply can not work with POSIXlt
-  do.call(c, lapply(1:numRows, function(x) {
-    value <- readObject(inputCon)
-    # Replace NULL with NA so we can coerce to vectors
-    if (is.null(value)) NA else value
-  }))
+  if (numRows > 0) {
+    # sapply can not work with POSIXlt
+    do.call(c, lapply(1:numRows, function(x) {
+      value <- readObject(inputCon)
+      # Replace NULL with NA so we can coerce to vectors
+      if (is.null(value)) NA else value
+    }))
+  } else {
+    vector()
+  }
 }
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index e6d3b21ff825b..c77f633135856 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -408,6 +408,14 @@ test_that("collect() returns a data.frame", {
   expect_equal(names(rdf)[1], "age")
   expect_equal(nrow(rdf), 3)
   expect_equal(ncol(rdf), 2)
+
+  # collect() returns data correctly from a DataFrame with 0 row
+  df0 <- limit(df, 0)
+  rdf <- collect(df0)
+  expect_true(is.data.frame(rdf))
+  expect_equal(names(rdf)[1], "age")
+  expect_equal(nrow(rdf), 0)
+  expect_equal(ncol(rdf), 2)
 })
 
 test_that("limit() returns DataFrame with the correct number of rows", {
@@ -492,6 +500,18 @@ test_that("head() and first() return the correct data", {
 
   testFirst <- first(df)
   expect_equal(nrow(testFirst), 1)
+
+  # head() and first() return the correct data on
+  # a DataFrame with 0 row
+  df0 <- limit(df, 0)
+
+  testHead <- head(df0)
+  expect_equal(nrow(testHead), 0)
+  expect_equal(ncol(testHead), 2)
+
+  testFirst <- first(df0)
+  expect_equal(nrow(testFirst), 0)
+  expect_equal(ncol(testFirst), 2)
 })
 
 test_that("distinct() and unique on DataFrames", {

From cf016075a006034c24c5b758edb279f3e151d25d Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Sun, 16 Aug 2015 00:34:58 -0700
Subject: [PATCH 1073/1454] [SPARK-10008] Ensure shuffle locality doesn't take
 precedence over narrow deps

The shuffle locality patch made the DAGScheduler aware of shuffle data,
but for RDDs that have both narrow and shuffle dependencies, it can
cause them to place tasks based on the shuffle dependency instead of the
narrow one. This case is common in iterative join-based algorithms like
PageRank and ALS, where one RDD is hash-partitioned and one isn't.

Author: Matei Zaharia <matei@databricks.com>

Closes #8220 from mateiz/shuffle-loc-fix.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 37 ++++++++++---------
 .../spark/scheduler/DAGSchedulerSuite.scala   | 26 ++++++++++++-
 2 files changed, 44 insertions(+), 19 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index f1c63d08766c2..dadf83a38296d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1383,33 +1383,36 @@ class DAGScheduler(
       return rddPrefs.map(TaskLocation(_))
     }
 
+    // If the RDD has narrow dependencies, pick the first partition of the first narrow dependency
+    // that has any placement preferences. Ideally we would choose based on transfer sizes,
+    // but this will do for now.
     rdd.dependencies.foreach {
       case n: NarrowDependency[_] =>
-        // If the RDD has narrow dependencies, pick the first partition of the first narrow dep
-        // that has any placement preferences. Ideally we would choose based on transfer sizes,
-        // but this will do for now.
         for (inPart <- n.getParents(partition)) {
           val locs = getPreferredLocsInternal(n.rdd, inPart, visited)
           if (locs != Nil) {
             return locs
           }
         }
-      case s: ShuffleDependency[_, _, _] =>
-        // For shuffle dependencies, pick locations which have at least REDUCER_PREF_LOCS_FRACTION
-        // of data as preferred locations
-        if (shuffleLocalityEnabled &&
-            rdd.partitions.size < SHUFFLE_PREF_REDUCE_THRESHOLD &&
-            s.rdd.partitions.size < SHUFFLE_PREF_MAP_THRESHOLD) {
-          // Get the preferred map output locations for this reducer
-          val topLocsForReducer = mapOutputTracker.getLocationsWithLargestOutputs(s.shuffleId,
-            partition, rdd.partitions.size, REDUCER_PREF_LOCS_FRACTION)
-          if (topLocsForReducer.nonEmpty) {
-            return topLocsForReducer.get.map(loc => TaskLocation(loc.host, loc.executorId))
-          }
-        }
-
       case _ =>
     }
+
+    // If the RDD has shuffle dependencies and shuffle locality is enabled, pick locations that
+    // have at least REDUCER_PREF_LOCS_FRACTION of data as preferred locations
+    if (shuffleLocalityEnabled && rdd.partitions.length < SHUFFLE_PREF_REDUCE_THRESHOLD) {
+      rdd.dependencies.foreach {
+        case s: ShuffleDependency[_, _, _] =>
+          if (s.rdd.partitions.length < SHUFFLE_PREF_MAP_THRESHOLD) {
+            // Get the preferred map output locations for this reducer
+            val topLocsForReducer = mapOutputTracker.getLocationsWithLargestOutputs(s.shuffleId,
+              partition, rdd.partitions.length, REDUCER_PREF_LOCS_FRACTION)
+            if (topLocsForReducer.nonEmpty) {
+              return topLocsForReducer.get.map(loc => TaskLocation(loc.host, loc.executorId))
+            }
+          }
+        case _ =>
+      }
+    }
     Nil
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index b0ca49cbea4f7..a063596d3eae2 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -926,7 +926,7 @@ class DAGSchedulerSuite
     assertLocations(reduceTaskSet, Seq(Seq("hostA")))
     complete(reduceTaskSet, Seq((Success, 42)))
     assert(results === Map(0 -> 42))
-    assertDataStructuresEmpty
+    assertDataStructuresEmpty()
   }
 
   test("reduce task locality preferences should only include machines with largest map outputs") {
@@ -950,7 +950,29 @@ class DAGSchedulerSuite
     assertLocations(reduceTaskSet, Seq(hosts))
     complete(reduceTaskSet, Seq((Success, 42)))
     assert(results === Map(0 -> 42))
-    assertDataStructuresEmpty
+    assertDataStructuresEmpty()
+  }
+
+  test("stages with both narrow and shuffle dependencies use narrow ones for locality") {
+    // Create an RDD that has both a shuffle dependency and a narrow dependency (e.g. for a join)
+    val rdd1 = new MyRDD(sc, 1, Nil)
+    val rdd2 = new MyRDD(sc, 1, Nil, locations = Seq(Seq("hostB")))
+    val shuffleDep = new ShuffleDependency(rdd1, null)
+    val narrowDep = new OneToOneDependency(rdd2)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep, narrowDep))
+    submit(reduceRdd, Array(0))
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", 1))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostA")))
+
+    // Reducer should run where RDD 2 has preferences, even though though it also has a shuffle dep
+    val reduceTaskSet = taskSets(1)
+    assertLocations(reduceTaskSet, Seq(Seq("hostB")))
+    complete(reduceTaskSet, Seq((Success, 42)))
+    assert(results === Map(0 -> 42))
+    assertDataStructuresEmpty()
   }
 
   test("Spark exceptions should include call site in stack trace") {

From ae2370e72f93db8a28b262e8252c55fe1fc9873c Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sun, 16 Aug 2015 10:17:58 -0700
Subject: [PATCH 1074/1454] [SPARK-10005] [SQL] Fixes schema merging for nested
 structs

In case of schema merging, we only handled first level fields when converting Parquet groups to `InternalRow`s. Nested struct fields are not properly handled.

For example, the schema of a Parquet file to be read can be:

```
message individual {
  required group f1 {
    optional binary f11 (utf8);
  }
}
```

while the global schema is:

```
message global {
  required group f1 {
    optional binary f11 (utf8);
    optional int32 f12;
  }
}
```

This PR fixes this issue by padding missing fields when creating actual converters.

Author: Cheng Lian <lian@databricks.com>

Closes #8228 from liancheng/spark-10005/nested-schema-merging.
---
 .../parquet/CatalystReadSupport.scala         | 19 +++--
 .../parquet/CatalystRowConverter.scala        | 70 +++++++++++++++++--
 .../parquet/CatalystSchemaConverter.scala     | 15 +---
 .../parquet/ParquetQuerySuite.scala           | 30 +++++++-
 4 files changed, 112 insertions(+), 22 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 4049795ed3bad..a4679bb2f6389 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -32,6 +32,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.StructType
 
 private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with Logging {
+  // Called after `init()` when initializing Parquet record reader.
   override def prepareForRead(
       conf: Configuration,
       keyValueMetaData: JMap[String, String],
@@ -51,19 +52,30 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
           // available if the target file is written by Spark SQL.
           .orElse(metadata.get(CatalystReadSupport.SPARK_METADATA_KEY))
       }.map(StructType.fromString).getOrElse {
-        logDebug("Catalyst schema not available, falling back to Parquet schema")
+        logInfo("Catalyst schema not available, falling back to Parquet schema")
         toCatalyst.convert(parquetRequestedSchema)
       }
 
-    logDebug(s"Catalyst schema used to read Parquet files: $catalystRequestedSchema")
+    logInfo {
+      s"""Going to read the following fields from the Parquet file:
+         |
+         |Parquet form:
+         |$parquetRequestedSchema
+         |
+         |Catalyst form:
+         |$catalystRequestedSchema
+       """.stripMargin
+    }
+
     new CatalystRecordMaterializer(parquetRequestedSchema, catalystRequestedSchema)
   }
 
+  // Called before `prepareForRead()` when initializing Parquet record reader.
   override def init(context: InitContext): ReadContext = {
     val conf = context.getConfiguration
 
     // If the target file was written by Spark SQL, we should be able to find a serialized Catalyst
-    // schema of this file from its the metadata.
+    // schema of this file from its metadata.
     val maybeRowSchema = Option(conf.get(RowWriteSupport.SPARK_ROW_SCHEMA))
 
     // Optional schema of requested columns, in the form of a string serialized from a Catalyst
@@ -141,7 +153,6 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
         maybeRequestedSchema.map(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA -> _) ++
         maybeRowSchema.map(RowWriteSupport.SPARK_ROW_SCHEMA -> _)
 
-    logInfo(s"Going to read Parquet file with these requested columns: $parquetRequestedSchema")
     new ReadContext(parquetRequestedSchema, metadata)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index ab5a6ddd41cfc..18c5b500209e6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -25,9 +25,10 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
 import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
-import org.apache.parquet.schema.OriginalType.LIST
+import org.apache.parquet.schema.OriginalType.{LIST, INT_32, UTF8}
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.DOUBLE
 import org.apache.parquet.schema.Type.Repetition
-import org.apache.parquet.schema.{GroupType, PrimitiveType, Type}
+import org.apache.parquet.schema.{GroupType, MessageType, PrimitiveType, Type}
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -88,12 +89,54 @@ private[parquet] class CatalystPrimitiveConverter(val updater: ParentContainerUp
 }
 
 /**
- * A [[CatalystRowConverter]] is used to convert Parquet "structs" into Spark SQL [[InternalRow]]s.
- * Since any Parquet record is also a struct, this converter can also be used as root converter.
+ * A [[CatalystRowConverter]] is used to convert Parquet records into Catalyst [[InternalRow]]s.
+ * Since Catalyst `StructType` is also a Parquet record, this converter can be used as root
+ * converter.  Take the following Parquet type as an example:
+ * {{{
+ *   message root {
+ *     required int32 f1;
+ *     optional group f2 {
+ *       required double f21;
+ *       optional binary f22 (utf8);
+ *     }
+ *   }
+ * }}}
+ * 5 converters will be created:
+ *
+ * - a root [[CatalystRowConverter]] for [[MessageType]] `root`, which contains:
+ *   - a [[CatalystPrimitiveConverter]] for required [[INT_32]] field `f1`, and
+ *   - a nested [[CatalystRowConverter]] for optional [[GroupType]] `f2`, which contains:
+ *     - a [[CatalystPrimitiveConverter]] for required [[DOUBLE]] field `f21`, and
+ *     - a [[CatalystStringConverter]] for optional [[UTF8]] string field `f22`
  *
  * When used as a root converter, [[NoopUpdater]] should be used since root converters don't have
  * any "parent" container.
  *
+ * @note Constructor argument [[parquetType]] refers to requested fields of the actual schema of the
+ *       Parquet file being read, while constructor argument [[catalystType]] refers to requested
+ *       fields of the global schema.  The key difference is that, in case of schema merging,
+ *       [[parquetType]] can be a subset of [[catalystType]].  For example, it's possible to have
+ *       the following [[catalystType]]:
+ *       {{{
+ *         new StructType()
+ *           .add("f1", IntegerType, nullable = false)
+ *           .add("f2", StringType, nullable = true)
+ *           .add("f3", new StructType()
+ *             .add("f31", DoubleType, nullable = false)
+ *             .add("f32", IntegerType, nullable = true)
+ *             .add("f33", StringType, nullable = true), nullable = false)
+ *       }}}
+ *       and the following [[parquetType]] (`f2` and `f32` are missing):
+ *       {{{
+ *         message root {
+ *           required int32 f1;
+ *           required group f3 {
+ *             required double f31;
+ *             optional binary f33 (utf8);
+ *           }
+ *         }
+ *       }}}
+ *
  * @param parquetType Parquet schema of Parquet records
  * @param catalystType Spark SQL schema that corresponds to the Parquet record type
  * @param updater An updater which propagates converted field values to the parent container
@@ -126,7 +169,24 @@ private[parquet] class CatalystRowConverter(
 
   // Converters for each field.
   private val fieldConverters: Array[Converter with HasParentContainerUpdater] = {
-    parquetType.getFields.zip(catalystType).zipWithIndex.map {
+    // In case of schema merging, `parquetType` can be a subset of `catalystType`.  We need to pad
+    // those missing fields and create converters for them, although values of these fields are
+    // always null.
+    val paddedParquetFields = {
+      val parquetFields = parquetType.getFields
+      val parquetFieldNames = parquetFields.map(_.getName).toSet
+      val missingFields = catalystType.filterNot(f => parquetFieldNames.contains(f.name))
+
+      // We don't need to worry about feature flag arguments like `assumeBinaryIsString` when
+      // creating the schema converter here, since values of missing fields are always null.
+      val toParquet = new CatalystSchemaConverter()
+
+      (parquetFields ++ missingFields.map(toParquet.convertField)).sortBy { f =>
+        catalystType.indexWhere(_.name == f.getName)
+      }
+    }
+
+    paddedParquetFields.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
         newConverter(parquetFieldType, catalystField.dataType, new RowUpdater(currentRow, ordinal))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index 275646e8181ad..535f0684e97f9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -72,18 +72,9 @@ private[parquet] class CatalystSchemaConverter(
     followParquetFormatSpec = conf.followParquetFormatSpec)
 
   def this(conf: Configuration) = this(
-    assumeBinaryIsString =
-      conf.getBoolean(
-        SQLConf.PARQUET_BINARY_AS_STRING.key,
-        SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get),
-    assumeInt96IsTimestamp =
-      conf.getBoolean(
-        SQLConf.PARQUET_INT96_AS_TIMESTAMP.key,
-        SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get),
-    followParquetFormatSpec =
-      conf.getBoolean(
-        SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key,
-        SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get))
+    assumeBinaryIsString = conf.get(SQLConf.PARQUET_BINARY_AS_STRING.key).toBoolean,
+    assumeInt96IsTimestamp = conf.get(SQLConf.PARQUET_INT96_AS_TIMESTAMP.key).toBoolean,
+    followParquetFormatSpec = conf.get(SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key).toBoolean)
 
   /**
    * Converts Parquet [[MessageType]] `parquetSchema` to a Spark SQL [[StructType]].
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index e2f2a8c744783..b7b70c2bbbd5c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -21,7 +21,7 @@ import java.io.File
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.sql._
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
@@ -201,4 +201,32 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
       assert(Decimal("67123.45") === Decimal(decimal))
     }
   }
+
+  test("SPARK-10005 Schema merging for nested struct") {
+    val sqlContext = _sqlContext
+    import sqlContext.implicits._
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      def append(df: DataFrame): Unit = {
+        df.write.mode(SaveMode.Append).parquet(path)
+      }
+
+      // Note that both the following two DataFrames contain a single struct column with multiple
+      // nested fields.
+      append((1 to 2).map(i => Tuple1((i, i))).toDF())
+      append((1 to 2).map(i => Tuple1((i, i, i))).toDF())
+
+      withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
+        checkAnswer(
+          sqlContext.read.option("mergeSchema", "true").parquet(path),
+          Seq(
+            Row(Row(1, 1, null)),
+            Row(Row(2, 2, null)),
+            Row(Row(1, 1, 1)),
+            Row(Row(2, 2, 2))))
+      }
+    }
+  }
 }

From 26e760581fdf7ca913da93fa80e73b7ddabcedf6 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Sun, 16 Aug 2015 23:33:20 -0700
Subject: [PATCH 1075/1454] [SPARK-9871] [SPARKR] Add expression functions into
 SparkR which have a variable parameter

### Summary

- Add `lit` function
- Add `concat`, `greatest`, `least` functions

I think we need to improve `collect` function in order to implement `struct` function. Since `collect` doesn't work with arguments which includes a nested `list` variable. It seems that a list against `struct` still has `jobj` classes. So it would be better to solve this problem on another issue.

### JIRA
[[SPARK-9871] Add expression functions into SparkR which have a variable parameter - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9871)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8194 from yu-iskw/SPARK-9856.
---
 R/pkg/NAMESPACE                  |  4 +++
 R/pkg/R/functions.R              | 42 ++++++++++++++++++++++++++++++++
 R/pkg/R/generics.R               | 16 ++++++++++++
 R/pkg/inst/tests/test_sparkSQL.R | 13 ++++++++++
 4 files changed, 75 insertions(+)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index b2d92bdf4840e..fd9dfdf60edb3 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -98,6 +98,7 @@ exportMethods("abs",
               "contains",
               "cos",
               "cosh",
+              "concat",
               "countDistinct",
               "desc",
               "endsWith",
@@ -106,10 +107,13 @@ exportMethods("abs",
               "floor",
               "getField",
               "getItem",
+              "greatest",
               "hypot",
               "isNotNull",
               "isNull",
+              "lit",
               "last",
+              "least",
               "like",
               "log",
               "log10",
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index a15d2d5da534e..6eef4d638ee9a 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -67,6 +67,14 @@ createFunctions <- function() {
 
 createFunctions()
 
+#' @rdname functions
+#' @return Creates a Column class of literal value.
+setMethod("lit", signature("ANY"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "lit", ifelse(class(x) == "Column", x@jc, x))
+            column(jc)
+          })
+
 #' Approx Count Distinct
 #'
 #' @rdname functions
@@ -93,6 +101,40 @@ setMethod("countDistinct",
             column(jc)
           })
 
+#' @rdname functions
+#' @return Concatenates multiple input string columns together into a single string column.
+setMethod("concat",
+          signature(x = "Column"),
+          function(x, ...) {
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
+            jc <- callJStatic("org.apache.spark.sql.functions", "concat", listToSeq(jcols))
+            column(jc)
+          })
+
+#' @rdname functions
+#' @return Returns the greatest value of the list of column names, skipping null values.
+#'         This function takes at least 2 parameters. It will return null if all parameters are null.
+setMethod("greatest",
+          signature(x = "Column"),
+          function(x, ...) {
+            stopifnot(length(list(...)) > 0)
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
+            jc <- callJStatic("org.apache.spark.sql.functions", "greatest", listToSeq(jcols))
+            column(jc)
+          })
+
+#' @rdname functions
+#' @return Returns the least value of the list of column names, skipping null values.
+#'         This function takes at least 2 parameters. It will return null iff all parameters are null.
+setMethod("least",
+          signature(x = "Column"),
+          function(x, ...) {
+            stopifnot(length(list(...)) > 0)
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
+            jc <- callJStatic("org.apache.spark.sql.functions", "least", listToSeq(jcols))
+            column(jc)
+          })
+
 #' @rdname functions
 #' @aliases ceil
 setMethod("ceiling",
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index f11e7fcb6a07c..5c1cc98fd9e80 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -682,6 +682,10 @@ setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
 #' @export
 setGeneric("ceil", function(x) { standardGeneric("ceil") })
 
+#' @rdname functions
+#' @export
+setGeneric("concat", function(x, ...) { standardGeneric("concat") })
+
 #' @rdname functions
 #' @export
 setGeneric("crc32", function(x) { standardGeneric("crc32") })
@@ -702,6 +706,10 @@ setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
 #' @export
 setGeneric("explode", function(x) { standardGeneric("explode") })
 
+#' @rdname functions
+#' @export
+setGeneric("greatest", function(x, ...) { standardGeneric("greatest") })
+
 #' @rdname functions
 #' @export
 setGeneric("hex", function(x) { standardGeneric("hex") })
@@ -722,10 +730,18 @@ setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
 #' @export
 setGeneric("last_day", function(x) { standardGeneric("last_day") })
 
+#' @rdname functions
+#' @export
+setGeneric("least", function(x, ...) { standardGeneric("least") })
+
 #' @rdname functions
 #' @export
 setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
 
+#' @rdname functions
+#' @export
+setGeneric("lit", function(x) { standardGeneric("lit") })
+
 #' @rdname functions
 #' @export
 setGeneric("lower", function(x) { standardGeneric("lower") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index c77f633135856..83caba8b5b964 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -580,6 +580,11 @@ test_that("select with column", {
   df2 <- select(df, df$age)
   expect_equal(columns(df2), c("age"))
   expect_equal(count(df2), 3)
+
+  df3 <- select(df, lit("x"))
+  expect_equal(columns(df3), c("x"))
+  expect_equal(count(df3), 3)
+  expect_equal(collect(select(df3, "x"))[[1, 1]], "x")
 })
 
 test_that("selectExpr() on a DataFrame", {
@@ -712,6 +717,14 @@ test_that("string operators", {
   expect_equal(count(where(df, startsWith(df$name, "A"))), 1)
   expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi")
   expect_equal(collect(select(df, cast(df$age, "string")))[[2, 1]], "30")
+  expect_equal(collect(select(df, concat(df$name, lit(":"), df$age)))[[2, 1]], "Andy:30")
+})
+
+test_that("greatest() and least() on a DataFrame", {
+  l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
+  df <- createDataFrame(sqlContext, l)
+  expect_equal(collect(select(df, greatest(df$a, df$b)))[, 1], c(2, 4))
+  expect_equal(collect(select(df, least(df$a, df$b)))[, 1], c(1, 3))
 })
 
 test_that("group by", {

From 3ff81ad2dedc7cc3defb6e418d7c5fb415d56026 Mon Sep 17 00:00:00 2001
From: Calvin Jia <jia.calvin@gmail.com>
Date: Mon, 17 Aug 2015 08:28:16 +0100
Subject: [PATCH 1076/1454] [SPARK-9199] [CORE] Upgrade Tachyon version from
 0.7.0 -> 0.7.1.

Updates the tachyon-client version to the latest release.

The main difference between 0.7.0 and 0.7.1 on the client side is to support running Tachyon on local file system by default.

No new non-Tachyon dependencies are added, and no code changes are required since the client API has not changed.

Author: Calvin Jia <jia.calvin@gmail.com>

Closes #8235 from calvinjia/spark-9199-master.
---
 core/pom.xml         | 2 +-
 make-distribution.sh | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 0e53a79fd2235..4f79d71bf85fa 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -266,7 +266,7 @@
     <dependency>
       <groupId>org.tachyonproject</groupId>
       <artifactId>tachyon-client</artifactId>
-      <version>0.7.0</version>
+      <version>0.7.1</version>
       <exclusions>
         <exclusion>
           <groupId>org.apache.hadoop</groupId>
diff --git a/make-distribution.sh b/make-distribution.sh
index 247a81341e4a4..04ad0052eb24c 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -33,7 +33,7 @@ SPARK_HOME="$(cd "`dirname "$0"`"; pwd)"
 DISTDIR="$SPARK_HOME/dist"
 
 SPARK_TACHYON=false
-TACHYON_VERSION="0.7.0"
+TACHYON_VERSION="0.7.1"
 TACHYON_TGZ="tachyon-${TACHYON_VERSION}-bin.tar.gz"
 TACHYON_URL="https://github.com/amplab/tachyon/releases/download/v${TACHYON_VERSION}/${TACHYON_TGZ}"
 

From f7efda3975d46a8ce4fd720b3730127ea482560b Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 17 Aug 2015 09:58:34 -0700
Subject: [PATCH 1077/1454] [SPARK-9959] [MLLIB] Association Rules Java
 Compatibility

mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #8206 from feynmanliang/SPARK-9959-arules-java.
---
 .../spark/mllib/fpm/AssociationRules.scala    | 30 +++++++++++++++++--
 1 file changed, 28 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 72d0ea0c12e1e..7f4de77044994 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -16,6 +16,7 @@
  */
 package org.apache.spark.mllib.fpm
 
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
@@ -95,8 +96,10 @@ object AssociationRules {
    * :: Experimental ::
    *
    * An association rule between sets of items.
-   * @param antecedent hypotheses of the rule
-   * @param consequent conclusion of the rule
+   * @param antecedent hypotheses of the rule. Java users should call [[Rule#javaAntecedent]]
+   *                   instead.
+   * @param consequent conclusion of the rule. Java users should call [[Rule#javaConsequent]]
+   *                   instead.
    * @tparam Item item type
    *
    * @since 1.5.0
@@ -108,6 +111,11 @@ object AssociationRules {
       freqUnion: Double,
       freqAntecedent: Double) extends Serializable {
 
+    /**
+     * Returns the confidence of the rule.
+     *
+     * @since 1.5.0
+     */
     def confidence: Double = freqUnion.toDouble / freqAntecedent
 
     require(antecedent.toSet.intersect(consequent.toSet).isEmpty, {
@@ -115,5 +123,23 @@ object AssociationRules {
       s"A valid association rule must have disjoint antecedent and " +
         s"consequent but ${sharedItems} is present in both."
     })
+
+    /**
+     * Returns antecedent in a Java List.
+     *
+     * @since 1.5.0
+     */
+    def javaAntecedent: java.util.List[Item] = {
+      antecedent.toList.asJava
+    }
+
+    /**
+     * Returns consequent in a Java List.
+     *
+     * @since 1.5.0
+     */
+    def javaConsequent: java.util.List[Item] = {
+      consequent.toList.asJava
+    }
   }
 }

From 76c155dd4483d58499e5cb66e5e9373bb771dbeb Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 18 Aug 2015 00:59:05 +0800
Subject: [PATCH 1078/1454] [SPARK-7837] [SQL] Avoids double closing output
 writers when commitTask() fails

When inserting data into a `HadoopFsRelation`, if `commitTask()` of the writer container fails, `abortTask()` will be invoked. However, both `commitTask()` and `abortTask()` try to close the output writer(s). The problem is that, closing underlying writers may not be an idempotent operation. E.g., `ParquetRecordWriter.close()` throws NPE when called twice.

Author: Cheng Lian <lian@databricks.com>

Closes #8236 from liancheng/spark-7837/double-closing.
---
 .../datasources/WriterContainer.scala         | 21 +++++++--
 .../datasources/parquet/ParquetIOSuite.scala  | 46 ++++++++++++++++++-
 2 files changed, 61 insertions(+), 6 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index d36197e50d448..e0147079e6997 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -217,6 +217,8 @@ private[sql] class DefaultWriterContainer(
     val writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
     writer.initConverter(dataSchema)
 
+    var writerClosed = false
+
     // If anything below fails, we should abort the task.
     try {
       while (iterator.hasNext) {
@@ -235,7 +237,10 @@ private[sql] class DefaultWriterContainer(
     def commitTask(): Unit = {
       try {
         assert(writer != null, "OutputWriter instance should have been initialized")
-        writer.close()
+        if (!writerClosed) {
+          writer.close()
+          writerClosed = true
+        }
         super.commitTask()
       } catch {
         case cause: Throwable =>
@@ -247,7 +252,10 @@ private[sql] class DefaultWriterContainer(
 
     def abortTask(): Unit = {
       try {
-        writer.close()
+        if (!writerClosed) {
+          writer.close()
+          writerClosed = true
+        }
       } finally {
         super.abortTask()
       }
@@ -275,6 +283,8 @@ private[sql] class DynamicPartitionWriterContainer(
     val outputWriters = new java.util.HashMap[InternalRow, OutputWriter]
     executorSideSetup(taskContext)
 
+    var outputWritersCleared = false
+
     // Returns the partition key given an input row
     val getPartitionKey = UnsafeProjection.create(partitionColumns, inputSchema)
     // Returns the data columns to be written given an input row
@@ -379,8 +389,11 @@ private[sql] class DynamicPartitionWriterContainer(
     }
 
     def clearOutputWriters(): Unit = {
-      outputWriters.asScala.values.foreach(_.close())
-      outputWriters.clear()
+      if (!outputWritersCleared) {
+        outputWriters.asScala.values.foreach(_.close())
+        outputWriters.clear()
+        outputWritersCleared = true
+      }
     }
 
     def commitTask(): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index d819f3ab5e6ab..e6b0a2ea95e38 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -424,7 +424,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
       configuration.set(
         "spark.sql.parquet.output.committer.class",
-        classOf[BogusParquetOutputCommitter].getCanonicalName)
+        classOf[JobCommitFailureParquetOutputCommitter].getCanonicalName)
 
       try {
         val message = intercept[SparkException] {
@@ -450,12 +450,54 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     }.toString
     assert(errorMessage.contains("UnknownHostException"))
   }
+
+  test("SPARK-7837 Do not close output writer twice when commitTask() fails") {
+    val clonedConf = new Configuration(configuration)
+
+    // Using a output committer that always fail when committing a task, so that both
+    // `commitTask()` and `abortTask()` are invoked.
+    configuration.set(
+      "spark.sql.parquet.output.committer.class",
+      classOf[TaskCommitFailureParquetOutputCommitter].getCanonicalName)
+
+    try {
+      // Before fixing SPARK-7837, the following code results in an NPE because both
+      // `commitTask()` and `abortTask()` try to close output writers.
+
+      withTempPath { dir =>
+        val m1 = intercept[SparkException] {
+          sqlContext.range(1).coalesce(1).write.parquet(dir.getCanonicalPath)
+        }.getCause.getMessage
+        assert(m1.contains("Intentional exception for testing purposes"))
+      }
+
+      withTempPath { dir =>
+        val m2 = intercept[SparkException] {
+          val df = sqlContext.range(1).select('id as 'a, 'id as 'b).coalesce(1)
+          df.write.partitionBy("a").parquet(dir.getCanonicalPath)
+        }.getCause.getMessage
+        assert(m2.contains("Intentional exception for testing purposes"))
+      }
+    } finally {
+      // Hadoop 1 doesn't have `Configuration.unset`
+      configuration.clear()
+      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+    }
+  }
 }
 
-class BogusParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
+class JobCommitFailureParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
   extends ParquetOutputCommitter(outputPath, context) {
 
   override def commitJob(jobContext: JobContext): Unit = {
     sys.error("Intentional exception for testing purposes")
   }
 }
+
+class TaskCommitFailureParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
+  extends ParquetOutputCommitter(outputPath, context) {
+
+  override def commitTask(context: TaskAttemptContext): Unit = {
+    sys.error("Intentional exception for testing purposes")
+  }
+}

From ed092a06c28dfa8204b473a5c964a9ef9a6b655e Mon Sep 17 00:00:00 2001
From: Rohit Agarwal <rohita@qubole.com>
Date: Mon, 17 Aug 2015 10:31:57 -0700
Subject: [PATCH 1079/1454] [SPARK-9924] [WEB UI] Don't schedule checkForLogs
 while some of them are already running.

Author: Rohit Agarwal <rohita@qubole.com>

Closes #8153 from mindprince/SPARK-9924.
---
 .../deploy/history/FsHistoryProvider.scala    | 28 ++++++++++++++-----
 1 file changed, 21 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index 53c18ca3ff50c..e573ff16c50a3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -126,11 +126,11 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
     // Disable the background thread during tests.
     if (!conf.contains("spark.testing")) {
       // A task that periodically checks for event log updates on disk.
-      pool.scheduleAtFixedRate(getRunner(checkForLogs), 0, UPDATE_INTERVAL_S, TimeUnit.SECONDS)
+      pool.scheduleWithFixedDelay(getRunner(checkForLogs), 0, UPDATE_INTERVAL_S, TimeUnit.SECONDS)
 
       if (conf.getBoolean("spark.history.fs.cleaner.enabled", false)) {
         // A task that periodically cleans event logs on disk.
-        pool.scheduleAtFixedRate(getRunner(cleanLogs), 0, CLEAN_INTERVAL_S, TimeUnit.SECONDS)
+        pool.scheduleWithFixedDelay(getRunner(cleanLogs), 0, CLEAN_INTERVAL_S, TimeUnit.SECONDS)
       }
     }
   }
@@ -204,11 +204,25 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
           mod1 >= mod2
       }
 
-      logInfos.sliding(20, 20).foreach { batch =>
-        replayExecutor.submit(new Runnable {
-          override def run(): Unit = mergeApplicationListing(batch)
-        })
-      }
+      logInfos.grouped(20)
+        .map { batch =>
+          replayExecutor.submit(new Runnable {
+            override def run(): Unit = mergeApplicationListing(batch)
+          })
+        }
+        .foreach { task =>
+          try {
+            // Wait for all tasks to finish. This makes sure that checkForLogs
+            // is not scheduled again while some tasks are already running in
+            // the replayExecutor.
+            task.get()
+          } catch {
+            case e: InterruptedException =>
+              throw e
+            case e: Exception =>
+              logError("Exception while merging application listings", e)
+          }
+        }
 
       lastModifiedTime = newLastModifiedTime
     } catch {

From f68d024096c90936f9aa4e325141b39f08c72476 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 17 Aug 2015 10:34:22 -0700
Subject: [PATCH 1080/1454] [SPARK-7736] [CORE] [YARN] Make pyspark fail YARN
 app on failure.

The YARN backend doesn't like when user code calls `System.exit`,
since it cannot know the exit status and thus cannot set an
appropriate final status for the application.

So, for pyspark, avoid that call and instead throw an exception with
the exit code. SparkSubmit handles that exception and exits with
the given exit code, while YARN uses the exit code as the failure
code for the Spark app.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7751 from vanzin/SPARK-9416.
---
 .../org/apache/spark/SparkException.scala     |  7 ++++++
 .../apache/spark/deploy/PythonRunner.scala    | 23 +++++++++++++++----
 .../org/apache/spark/deploy/SparkSubmit.scala | 10 ++++++--
 .../spark/deploy/yarn/ApplicationMaster.scala |  8 +++++--
 4 files changed, 40 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkException.scala b/core/src/main/scala/org/apache/spark/SparkException.scala
index 2ebd7a7151a59..977a27bdfe1bf 100644
--- a/core/src/main/scala/org/apache/spark/SparkException.scala
+++ b/core/src/main/scala/org/apache/spark/SparkException.scala
@@ -30,3 +30,10 @@ class SparkException(message: String, cause: Throwable)
  */
 private[spark] class SparkDriverExecutionException(cause: Throwable)
   extends SparkException("Execution error", cause)
+
+/**
+ * Exception thrown when the main user code is run as a child process (e.g. pyspark) and we want
+ * the parent SparkSubmit process to exit with the same exit code.
+ */
+private[spark] case class SparkUserAppException(exitCode: Int)
+  extends SparkException(s"User application exited with $exitCode")
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index c2ed43a5397d6..4277ac2ad13ea 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -24,6 +24,7 @@ import scala.collection.mutable.ArrayBuffer
 import scala.collection.JavaConversions._
 import scala.util.Try
 
+import org.apache.spark.SparkUserAppException
 import org.apache.spark.api.python.PythonUtils
 import org.apache.spark.util.{RedirectThread, Utils}
 
@@ -46,7 +47,14 @@ object PythonRunner {
     // Launch a Py4J gateway server for the process to connect to; this will let it see our
     // Java system properties and such
     val gatewayServer = new py4j.GatewayServer(null, 0)
-    gatewayServer.start()
+    val thread = new Thread(new Runnable() {
+      override def run(): Unit = Utils.logUncaughtExceptions {
+        gatewayServer.start()
+      }
+    })
+    thread.setName("py4j-gateway")
+    thread.setDaemon(true)
+    thread.start()
 
     // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the
     // python directories in SPARK_HOME (if set), and any files in the pyFiles argument
@@ -64,11 +72,18 @@ object PythonRunner {
     env.put("PYTHONUNBUFFERED", "YES") // value is needed to be set to a non-empty string
     env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort)
     builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize
-    val process = builder.start()
+    try {
+      val process = builder.start()
 
-    new RedirectThread(process.getInputStream, System.out, "redirect output").start()
+      new RedirectThread(process.getInputStream, System.out, "redirect output").start()
 
-    System.exit(process.waitFor())
+      val exitCode = process.waitFor()
+      if (exitCode != 0) {
+        throw new SparkUserAppException(exitCode)
+      }
+    } finally {
+      gatewayServer.shutdown()
+    }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 02fa3088eded0..86fcf942c2c4e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -39,8 +39,8 @@ import org.apache.ivy.plugins.matcher.GlobPatternMatcher
 import org.apache.ivy.plugins.repository.file.FileRepository
 import org.apache.ivy.plugins.resolver.{FileSystemResolver, ChainResolver, IBiblioResolver}
 
+import org.apache.spark.{SparkUserAppException, SPARK_VERSION}
 import org.apache.spark.api.r.RUtils
-import org.apache.spark.SPARK_VERSION
 import org.apache.spark.deploy.rest._
 import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils}
 
@@ -672,7 +672,13 @@ object SparkSubmit {
       mainMethod.invoke(null, childArgs.toArray)
     } catch {
       case t: Throwable =>
-        throw findCause(t)
+        findCause(t) match {
+          case SparkUserAppException(exitCode) =>
+            System.exit(exitCode)
+
+          case t: Throwable =>
+            throw t
+        }
     }
   }
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 6a8ddb37b29e8..991b5cec00bd8 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -30,8 +30,8 @@ import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 
 import org.apache.spark.rpc._
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv}
-import org.apache.spark.SparkException
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv,
+  SparkException, SparkUserAppException}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, YarnSchedulerBackend}
@@ -530,6 +530,10 @@ private[spark] class ApplicationMaster(
             e.getCause match {
               case _: InterruptedException =>
                 // Reporter thread can interrupt to stop user class
+              case SparkUserAppException(exitCode) =>
+                val msg = s"User application exited with status $exitCode"
+                logError(msg)
+                finish(FinalApplicationStatus.FAILED, exitCode, msg)
               case cause: Throwable =>
                 logError("User class threw exception: " + cause, cause)
                 finish(FinalApplicationStatus.FAILED,

From a4acdabb103f6d04603163c9555c1ddc413c3b80 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 17 Aug 2015 11:36:18 -0700
Subject: [PATCH 1081/1454] [SPARK-9950] [SQL] Wrong Analysis Error for
 grouping/aggregating on struct fields

This issue has been fixed by https://github.com/apache/spark/pull/8215, this PR added regression test for it.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8222 from cloud-fan/minor and squashes the following commits:

0bbfb1c [Wenchen Fan] fix style...
7e2d8d9 [Wenchen Fan] add test
---
 .../src/test/scala/org/apache/spark/sql/DataFrameSuite.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index cf22797752b97..1e2aaae52c9e8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -873,4 +873,9 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
       """{"a": {"b": 1}}""" :: Nil))
     checkAnswer(df.orderBy("a.b"), Row(Row(1)))
   }
+
+  test("SPARK-9950: correctly analyze grouping/aggregating on struct fields") {
+    val df = Seq(("x", (1, 1)), ("y", (2, 2))).toDF("a", "b")
+    checkAnswer(df.groupBy("b._1").agg(sum("b._2")), Row(1, 1) :: Row(2, 2) :: Nil)
+  }
 }

From f10660fe7b809be2059da4f9781a5743f117a35a Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 17 Aug 2015 11:53:33 -0700
Subject: [PATCH 1082/1454] [SPARK-10036] [SQL] Load JDBC driver in
 DataFrameReader.jdbc and DataFrameWriter.jdbc

This PR uses `JDBCRDD.getConnector` to load JDBC driver before creating connection in `DataFrameReader.jdbc` and `DataFrameWriter.jdbc`.

Author: zsxwing <zsxwing@gmail.com>

Closes #8232 from zsxwing/SPARK-10036 and squashes the following commits:

adf75de [zsxwing] Add extraOptions to the connection properties
57f59d4 [zsxwing] Load JDBC driver in DataFrameReader.jdbc and DataFrameWriter.jdbc
---
 .../scala/org/apache/spark/sql/DataFrameReader.scala   |  8 +++++++-
 .../scala/org/apache/spark/sql/DataFrameWriter.scala   | 10 ++++++++--
 .../spark/sql/execution/datasources/jdbc/JDBCRDD.scala |  5 +++--
 .../sql/execution/datasources/jdbc/JdbcUtils.scala     |  4 ++--
 4 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 9ea955b010017..6dc7bfe333498 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -197,7 +197,13 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
       table: String,
       parts: Array[Partition],
       connectionProperties: Properties): DataFrame = {
-    val relation = JDBCRelation(url, table, parts, connectionProperties)(sqlContext)
+    val props = new Properties()
+    extraOptions.foreach { case (key, value) =>
+      props.put(key, value)
+    }
+    // connectionProperties should override settings in extraOptions
+    props.putAll(connectionProperties)
+    val relation = JDBCRelation(url, table, parts, props)(sqlContext)
     sqlContext.baseRelationToDataFrame(relation)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 5fa11da4c38cd..f0bf1be506411 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -244,7 +244,13 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    *                             should be included.
    */
   def jdbc(url: String, table: String, connectionProperties: Properties): Unit = {
-    val conn = JdbcUtils.createConnection(url, connectionProperties)
+    val props = new Properties()
+    extraOptions.foreach { case (key, value) =>
+      props.put(key, value)
+    }
+    // connectionProperties should override settings in extraOptions
+    props.putAll(connectionProperties)
+    val conn = JdbcUtils.createConnection(url, props)
 
     try {
       var tableExists = JdbcUtils.tableExists(conn, table)
@@ -272,7 +278,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
       conn.close()
     }
 
-    JdbcUtils.saveTable(df, url, table, connectionProperties)
+    JdbcUtils.saveTable(df, url, table, props)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 8eab6a0adccc4..e537d631f4559 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -118,7 +118,7 @@ private[sql] object JDBCRDD extends Logging {
    */
   def resolveTable(url: String, table: String, properties: Properties): StructType = {
     val dialect = JdbcDialects.get(url)
-    val conn: Connection = DriverManager.getConnection(url, properties)
+    val conn: Connection = getConnector(properties.getProperty("driver"), url, properties)()
     try {
       val rs = conn.prepareStatement(s"SELECT * FROM $table WHERE 1=0").executeQuery()
       try {
@@ -171,7 +171,8 @@ private[sql] object JDBCRDD extends Logging {
    * getConnector is run on the driver code, while the function it returns
    * is run on the executor.
    *
-   * @param driver - The class name of the JDBC driver for the given url.
+   * @param driver - The class name of the JDBC driver for the given url, or null if the class name
+   *                 is not necessary.
    * @param url - The JDBC url to connect to.
    *
    * @return A function that loads the driver and connects to the url.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 8ee3b8bda8fc7..2d0e736ee4766 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.jdbc
 
-import java.sql.{Connection, DriverManager, PreparedStatement}
+import java.sql.{Connection, PreparedStatement}
 import java.util.Properties
 
 import scala.util.Try
@@ -36,7 +36,7 @@ object JdbcUtils extends Logging {
    * Establishes a JDBC connection.
    */
   def createConnection(url: String, connectionProperties: Properties): Connection = {
-    DriverManager.getConnection(url, connectionProperties)
+    JDBCRDD.getConnector(connectionProperties.getProperty("driver"), url, connectionProperties)()
   }
 
   /**

From b265e282b62954548740a5767e97ab1678c65194 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Mon, 17 Aug 2015 14:10:19 -0700
Subject: [PATCH 1083/1454] [SPARK-9526] [SQL] Utilize randomized tests to
 reveal potential bugs in sql expressions

JIRA: https://issues.apache.org/jira/browse/SPARK-9526

This PR is a follow up of #7830, aiming at utilizing randomized tests to reveal more potential bugs in sql expression.

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7855 from yjshen/property_check.
---
 .../ArithmeticExpressionSuite.scala           |  42 ++++++
 .../expressions/BitwiseFunctionsSuite.scala   |  17 ++-
 .../ConditionalExpressionSuite.scala          |  13 +-
 .../expressions/DateExpressionsSuite.scala    |  17 +++
 .../expressions/ExpressionEvalHelper.scala    | 112 ++++++++++++++-
 .../expressions/LiteralGenerator.scala        | 128 ++++++++++++++++++
 .../expressions/MathFunctionsSuite.scala      |  45 +++++-
 .../expressions/MiscFunctionsSuite.scala      |   4 +-
 .../catalyst/expressions/PredicateSuite.scala |  19 +++
 .../spark/sql/types/DataTypeTestUtils.scala   |  19 +++
 10 files changed, 410 insertions(+), 6 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index a1f15e4f0f25a..72285c6a24199 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -52,6 +52,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Add(positiveShortLit, negativeShortLit), -1.toShort)
     checkEvaluation(Add(positiveIntLit, negativeIntLit), -1)
     checkEvaluation(Add(positiveLongLit, negativeLongLit), -1L)
+
+    DataTypeTestUtils.numericAndInterval.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Add, tpe, tpe)
+    }
   }
 
   test("- (UnaryMinus)") {
@@ -71,6 +75,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(UnaryMinus(negativeIntLit), - negativeInt)
     checkEvaluation(UnaryMinus(positiveLongLit), - positiveLong)
     checkEvaluation(UnaryMinus(negativeLongLit), - negativeLong)
+
+    DataTypeTestUtils.numericAndInterval.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(UnaryMinus, tpe)
+    }
   }
 
   test("- (Minus)") {
@@ -85,6 +93,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       (positiveShort - negativeShort).toShort)
     checkEvaluation(Subtract(positiveIntLit, negativeIntLit), positiveInt - negativeInt)
     checkEvaluation(Subtract(positiveLongLit, negativeLongLit), positiveLong - negativeLong)
+
+    DataTypeTestUtils.numericAndInterval.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Subtract, tpe, tpe)
+    }
   }
 
   test("* (Multiply)") {
@@ -99,6 +111,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       (positiveShort * negativeShort).toShort)
     checkEvaluation(Multiply(positiveIntLit, negativeIntLit), positiveInt * negativeInt)
     checkEvaluation(Multiply(positiveLongLit, negativeLongLit), positiveLong * negativeLong)
+
+    DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Multiply, tpe, tpe)
+    }
   }
 
   test("/ (Divide) basic") {
@@ -111,6 +127,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Divide(left, Literal.create(null, right.dataType)), null)
       checkEvaluation(Divide(left, Literal(convert(0))), null)  // divide by zero
     }
+
+    DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Divide, tpe, tpe)
+    }
   }
 
   test("/ (Divide) for integral type") {
@@ -144,6 +164,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Remainder(negativeIntLit, negativeIntLit), 0)
     checkEvaluation(Remainder(positiveLongLit, positiveLongLit), 0L)
     checkEvaluation(Remainder(negativeLongLit, negativeLongLit), 0L)
+
+    // TODO: the following lines would fail the test due to inconsistency result of interpret
+    // and codegen for remainder between giant values, seems like a numeric stability issue
+    // DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+    //  checkConsistencyBetweenInterpretedAndCodegen(Remainder, tpe, tpe)
+    // }
   }
 
   test("Abs") {
@@ -161,6 +187,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Abs(negativeIntLit), - negativeInt)
     checkEvaluation(Abs(positiveLongLit), positiveLong)
     checkEvaluation(Abs(negativeLongLit), - negativeLong)
+
+    DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Abs, tpe)
+    }
   }
 
   test("MaxOf basic") {
@@ -175,6 +205,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(MaxOf(positiveShortLit, negativeShortLit), (positiveShort).toShort)
     checkEvaluation(MaxOf(positiveIntLit, negativeIntLit), positiveInt)
     checkEvaluation(MaxOf(positiveLongLit, negativeLongLit), positiveLong)
+
+    DataTypeTestUtils.ordered.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(MaxOf, tpe, tpe)
+    }
   }
 
   test("MaxOf for atomic type") {
@@ -196,6 +230,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(MinOf(positiveShortLit, negativeShortLit), (negativeShort).toShort)
     checkEvaluation(MinOf(positiveIntLit, negativeIntLit), negativeInt)
     checkEvaluation(MinOf(positiveLongLit, negativeLongLit), negativeLong)
+
+    DataTypeTestUtils.ordered.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(MinOf, tpe, tpe)
+    }
   }
 
   test("MinOf for atomic type") {
@@ -222,4 +260,8 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Pmod(positiveInt, negativeInt), positiveInt)
     checkEvaluation(Pmod(positiveLong, negativeLong), positiveLong)
   }
+
+  DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+    checkConsistencyBetweenInterpretedAndCodegen(MinOf, tpe, tpe)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
index 4fc1c06153595..3a310c0e9a7a6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
@@ -45,6 +45,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseNot(negativeIntLit), ~negativeInt)
     checkEvaluation(BitwiseNot(positiveLongLit), ~positiveLong)
     checkEvaluation(BitwiseNot(negativeLongLit), ~negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseNot, dt)
+    }
   }
 
   test("BitwiseAnd") {
@@ -68,6 +72,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       (positiveShort & negativeShort).toShort)
     checkEvaluation(BitwiseAnd(positiveIntLit, negativeIntLit), positiveInt & negativeInt)
     checkEvaluation(BitwiseAnd(positiveLongLit, negativeLongLit), positiveLong & negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseAnd, dt, dt)
+    }
   }
 
   test("BitwiseOr") {
@@ -91,6 +99,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       (positiveShort | negativeShort).toShort)
     checkEvaluation(BitwiseOr(positiveIntLit, negativeIntLit), positiveInt | negativeInt)
     checkEvaluation(BitwiseOr(positiveLongLit, negativeLongLit), positiveLong | negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseOr, dt, dt)
+    }
   }
 
   test("BitwiseXor") {
@@ -110,10 +122,13 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseXor(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseXor(Literal(1), nullLit), null)
     checkEvaluation(BitwiseXor(nullLit, nullLit), null)
-
     checkEvaluation(BitwiseXor(positiveShortLit, negativeShortLit),
       (positiveShort ^ negativeShort).toShort)
     checkEvaluation(BitwiseXor(positiveIntLit, negativeIntLit), positiveInt ^ negativeInt)
     checkEvaluation(BitwiseXor(positiveLongLit, negativeLongLit), positiveLong ^ negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseXor, dt, dt)
+    }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
index d26bcdb2902ab..0df673bb9fa02 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
@@ -66,6 +66,10 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     testIf(_.toLong, TimestampType)
 
     testIf(_.toString, StringType)
+
+    DataTypeTestUtils.propertyCheckSupported.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(If, BooleanType, dt, dt)
+    }
   }
 
   test("case when") {
@@ -176,6 +180,10 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
         Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
         Literal(Timestamp.valueOf("2015-07-01 10:00:00")))),
       Timestamp.valueOf("2015-07-01 08:00:00"), InternalRow.empty)
+
+    DataTypeTestUtils.ordered.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(Least, dt, 2)
+    }
   }
 
   test("function greatest") {
@@ -218,6 +226,9 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
         Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
         Literal(Timestamp.valueOf("2015-07-01 10:00:00")))),
       Timestamp.valueOf("2015-07-01 10:00:00"), InternalRow.empty)
-  }
 
+    DataTypeTestUtils.ordered.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(Greatest, dt, 2)
+    }
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index f9b73f1a75e73..610d39e8493cd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -60,6 +60,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       }
     }
     checkEvaluation(DayOfYear(Literal.create(null, DateType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(DayOfYear, DateType)
   }
 
   test("Year") {
@@ -79,6 +80,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Year, DateType)
   }
 
   test("Quarter") {
@@ -98,6 +100,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Quarter, DateType)
   }
 
   test("Month") {
@@ -117,6 +120,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Month, DateType)
   }
 
   test("Day / DayOfMonth") {
@@ -135,6 +139,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           c.get(Calendar.DAY_OF_MONTH))
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(DayOfMonth, DateType)
   }
 
   test("Seconds") {
@@ -149,6 +154,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(Second(Literal(new Timestamp(c.getTimeInMillis))),
         c.get(Calendar.SECOND))
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Second, TimestampType)
   }
 
   test("WeekOfYear") {
@@ -157,6 +163,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(WeekOfYear(Cast(Literal(sdfDate.format(d)), DateType)), 15)
     checkEvaluation(WeekOfYear(Cast(Literal(ts), DateType)), 45)
     checkEvaluation(WeekOfYear(Cast(Literal("2011-05-06"), DateType)), 18)
+    checkConsistencyBetweenInterpretedAndCodegen(WeekOfYear, DateType)
   }
 
   test("DateFormat") {
@@ -184,6 +191,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Hour, TimestampType)
   }
 
   test("Minute") {
@@ -200,6 +208,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           c.get(Calendar.MINUTE))
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Minute, TimestampType)
   }
 
   test("date_add") {
@@ -218,6 +227,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       DateAdd(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 49627)
     checkEvaluation(
       DateAdd(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -15910)
+    checkConsistencyBetweenInterpretedAndCodegen(DateAdd, DateType, IntegerType)
   }
 
   test("date_sub") {
@@ -236,6 +246,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       DateSub(Literal(Date.valueOf("2016-02-28")), positiveIntLit), -15909)
     checkEvaluation(
       DateSub(Literal(Date.valueOf("2016-02-28")), negativeIntLit), 49628)
+    checkConsistencyBetweenInterpretedAndCodegen(DateSub, DateType, IntegerType)
   }
 
   test("time_add") {
@@ -254,6 +265,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       TimeAdd(Literal.create(null, TimestampType), Literal.create(null, CalendarIntervalType)),
       null)
+    checkConsistencyBetweenInterpretedAndCodegen(TimeAdd, TimestampType, CalendarIntervalType)
   }
 
   test("time_sub") {
@@ -277,6 +289,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       TimeSub(Literal.create(null, TimestampType), Literal.create(null, CalendarIntervalType)),
       null)
+    checkConsistencyBetweenInterpretedAndCodegen(TimeSub, TimestampType, CalendarIntervalType)
   }
 
   test("add_months") {
@@ -296,6 +309,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       AddMonths(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 1014213)
     checkEvaluation(
       AddMonths(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -980528)
+    checkConsistencyBetweenInterpretedAndCodegen(AddMonths, DateType, IntegerType)
   }
 
   test("months_between") {
@@ -320,6 +334,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(MonthsBetween(t, tnull), null)
     checkEvaluation(MonthsBetween(tnull, t), null)
     checkEvaluation(MonthsBetween(tnull, tnull), null)
+    checkConsistencyBetweenInterpretedAndCodegen(MonthsBetween, TimestampType, TimestampType)
   }
 
   test("last_day") {
@@ -337,6 +352,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(LastDay(Literal(Date.valueOf("2016-01-06"))), Date.valueOf("2016-01-31"))
     checkEvaluation(LastDay(Literal(Date.valueOf("2016-02-07"))), Date.valueOf("2016-02-29"))
     checkEvaluation(LastDay(Literal.create(null, DateType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(LastDay, DateType)
   }
 
   test("next_day") {
@@ -370,6 +386,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       ToDate(Literal(Date.valueOf("2015-07-22"))),
       DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-22")))
     checkEvaluation(ToDate(Literal.create(null, DateType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(ToDate, DateType)
   }
 
   test("function trunc") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index a41185b4d8754..465f7d08aa142 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -17,18 +17,21 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.scalacheck.Gen
 import org.scalactic.TripleEqualsSupport.Spread
+import org.scalatest.prop.GeneratorDrivenPropertyChecks
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
+import org.apache.spark.sql.types.DataType
 
 /**
  * A few helper functions for expression evaluation testing. Mixin this trait to use them.
  */
-trait ExpressionEvalHelper {
+trait ExpressionEvalHelper extends GeneratorDrivenPropertyChecks {
   self: SparkFunSuite =>
 
   protected def create_row(values: Any*): InternalRow = {
@@ -211,4 +214,111 @@ trait ExpressionEvalHelper {
       plan(inputRow)).get(0, expression.dataType)
     assert(checkResult(actual, expected))
   }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against unary expressions by feeding them arbitrary literals of `dataType`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: Expression => Expression,
+      dataType: DataType): Unit = {
+    forAll (LiteralGenerator.randomGen(dataType)) { (l: Literal) =>
+      cmpInterpretWithCodegen(EmptyRow, c(l))
+    }
+  }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against binary expressions by feeding them arbitrary literals of `dataType1`
+   * and `dataType2`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: (Expression, Expression) => Expression,
+      dataType1: DataType,
+      dataType2: DataType): Unit = {
+    forAll (
+      LiteralGenerator.randomGen(dataType1),
+      LiteralGenerator.randomGen(dataType2)
+    ) { (l1: Literal, l2: Literal) =>
+      cmpInterpretWithCodegen(EmptyRow, c(l1, l2))
+    }
+  }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against ternary expressions by feeding them arbitrary literals of `dataType1`,
+   * `dataType2` and `dataType3`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: (Expression, Expression, Expression) => Expression,
+      dataType1: DataType,
+      dataType2: DataType,
+      dataType3: DataType): Unit = {
+    forAll (
+      LiteralGenerator.randomGen(dataType1),
+      LiteralGenerator.randomGen(dataType2),
+      LiteralGenerator.randomGen(dataType3)
+    ) { (l1: Literal, l2: Literal, l3: Literal) =>
+      cmpInterpretWithCodegen(EmptyRow, c(l1, l2, l3))
+    }
+  }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against expressions take Seq[Expression] as input by feeding them
+   * arbitrary length Seq of arbitrary literal of `dataType`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: Seq[Expression] => Expression,
+      dataType: DataType,
+      minNumElements: Int = 0): Unit = {
+    forAll (Gen.listOf(LiteralGenerator.randomGen(dataType))) { (literals: Seq[Literal]) =>
+      whenever(literals.size >= minNumElements) {
+        cmpInterpretWithCodegen(EmptyRow, c(literals))
+      }
+    }
+  }
+
+  private def cmpInterpretWithCodegen(inputRow: InternalRow, expr: Expression): Unit = {
+    val interpret = try {
+      evaluate(expr, inputRow)
+    } catch {
+      case e: Exception => fail(s"Exception evaluating $expr", e)
+    }
+
+    val plan = generateProject(
+      GenerateMutableProjection.generate(Alias(expr, s"Optimized($expr)")() :: Nil)(),
+      expr)
+    val codegen = plan(inputRow).get(0, expr.dataType)
+
+    if (!compareResults(interpret, codegen)) {
+      fail(s"Incorrect evaluation: $expr, interpret: $interpret, codegen: $codegen")
+    }
+  }
+
+  /**
+   * Check the equality between result of expression and expected value, it will handle
+   * Array[Byte] and Spread[Double].
+   */
+  private[this] def compareResults(result: Any, expected: Any): Boolean = {
+    (result, expected) match {
+      case (result: Array[Byte], expected: Array[Byte]) =>
+        java.util.Arrays.equals(result, expected)
+      case (result: Double, expected: Spread[Double]) =>
+        expected.isWithin(result)
+      case (result: Double, expected: Double) if result.isNaN && expected.isNaN =>
+        true
+      case (result: Float, expected: Float) if result.isNaN && expected.isNaN =>
+        true
+      case _ => result == expected
+    }
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala
new file mode 100644
index 0000000000000..ee6d25157fc08
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala
@@ -0,0 +1,128 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.sql.{Date, Timestamp}
+
+import org.scalacheck.{Arbitrary, Gen}
+import org.scalatest.Matchers
+import org.scalatest.prop.GeneratorDrivenPropertyChecks
+
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
+
+/**
+ * Property is a high-level specification of behavior that should hold for a range of data points.
+ *
+ * For example, while we are evaluating a deterministic expression for some input, we should always
+ * hold the property that the result never changes, regardless of how we get the result,
+ * via interpreted or codegen.
+ *
+ * In ScalaTest, properties are specified as functions and the data points used to check properties
+ * can be supplied by either tables or generators.
+ *
+ * Generator-driven property checks are performed via integration with ScalaCheck.
+ *
+ * @example {{{
+ *   def toTest(i: Int): Boolean = if (i % 2 == 0) true else false
+ *
+ *   import org.scalacheck.Gen
+ *
+ *   test ("true if param is even") {
+ *     val evenInts = for (n <- Gen.choose(-1000, 1000)) yield 2 * n
+ *     forAll(evenInts) { (i: Int) =>
+ *       assert (toTest(i) === true)
+ *     }
+ *   }
+ * }}}
+ *
+ */
+object LiteralGenerator {
+
+  lazy val byteLiteralGen: Gen[Literal] =
+    for { b <- Arbitrary.arbByte.arbitrary } yield Literal.create(b, ByteType)
+
+  lazy val shortLiteralGen: Gen[Literal] =
+    for { s <- Arbitrary.arbShort.arbitrary } yield Literal.create(s, ShortType)
+
+  lazy val integerLiteralGen: Gen[Literal] =
+    for { i <- Arbitrary.arbInt.arbitrary } yield Literal.create(i, IntegerType)
+
+  lazy val longLiteralGen: Gen[Literal] =
+    for { l <- Arbitrary.arbLong.arbitrary } yield Literal.create(l, LongType)
+
+  lazy val floatLiteralGen: Gen[Literal] =
+    for {
+      f <- Gen.chooseNum(Float.MinValue / 2, Float.MaxValue / 2,
+        Float.NaN, Float.PositiveInfinity, Float.NegativeInfinity)
+    } yield Literal.create(f, FloatType)
+
+  lazy val doubleLiteralGen: Gen[Literal] =
+    for {
+      f <- Gen.chooseNum(Double.MinValue / 2, Double.MaxValue / 2,
+        Double.NaN, Double.PositiveInfinity, Double.NegativeInfinity)
+    } yield Literal.create(f, DoubleType)
+
+  // TODO: decimal type
+
+  lazy val stringLiteralGen: Gen[Literal] =
+    for { s <- Arbitrary.arbString.arbitrary } yield Literal.create(s, StringType)
+
+  lazy val binaryLiteralGen: Gen[Literal] =
+    for { ab <- Gen.listOf[Byte](Arbitrary.arbByte.arbitrary) }
+      yield Literal.create(ab.toArray, BinaryType)
+
+  lazy val booleanLiteralGen: Gen[Literal] =
+    for { b <- Arbitrary.arbBool.arbitrary } yield Literal.create(b, BooleanType)
+
+  lazy val dateLiteralGen: Gen[Literal] =
+    for { d <- Arbitrary.arbInt.arbitrary } yield Literal.create(new Date(d), DateType)
+
+  lazy val timestampLiteralGen: Gen[Literal] =
+    for { t <- Arbitrary.arbLong.arbitrary } yield Literal.create(new Timestamp(t), TimestampType)
+
+  lazy val calendarIntervalLiterGen: Gen[Literal] =
+    for { m <- Arbitrary.arbInt.arbitrary; s <- Arbitrary.arbLong.arbitrary}
+      yield Literal.create(new CalendarInterval(m, s), CalendarIntervalType)
+
+
+  // Sometimes, it would be quite expensive when unlimited value is used,
+  // for example, the `times` arguments for StringRepeat would hang the test 'forever'
+  // if it's tested against Int.MaxValue by ScalaCheck, therefore, use values from a limited
+  // range is more reasonable
+  lazy val limitedIntegerLiteralGen: Gen[Literal] =
+    for { i <- Gen.choose(-100, 100) } yield Literal.create(i, IntegerType)
+
+  def randomGen(dt: DataType): Gen[Literal] = {
+    dt match {
+      case ByteType => byteLiteralGen
+      case ShortType => shortLiteralGen
+      case IntegerType => integerLiteralGen
+      case LongType => longLiteralGen
+      case DoubleType => doubleLiteralGen
+      case FloatType => floatLiteralGen
+      case DateType => dateLiteralGen
+      case TimestampType => timestampLiteralGen
+      case BooleanType => booleanLiteralGen
+      case StringType => stringLiteralGen
+      case BinaryType => binaryLiteralGen
+      case CalendarIntervalType => calendarIntervalLiterGen
+      case dt => throw new IllegalArgumentException(s"not supported type $dt")
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 033792eee6c0f..90c59f240b542 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -27,7 +27,6 @@ import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 import org.apache.spark.sql.types._
 
-
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   import IntegralLiteralTestUtils._
@@ -184,60 +183,74 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("sin") {
     testUnary(Sin, math.sin)
+    checkConsistencyBetweenInterpretedAndCodegen(Sin, DoubleType)
   }
 
   test("asin") {
     testUnary(Asin, math.asin, (-10 to 10).map(_ * 0.1))
     testUnary(Asin, math.asin, (11 to 20).map(_ * 0.1), expectNaN = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Asin, DoubleType)
   }
 
   test("sinh") {
     testUnary(Sinh, math.sinh)
+    checkConsistencyBetweenInterpretedAndCodegen(Sinh, DoubleType)
   }
 
   test("cos") {
     testUnary(Cos, math.cos)
+    checkConsistencyBetweenInterpretedAndCodegen(Cos, DoubleType)
   }
 
   test("acos") {
     testUnary(Acos, math.acos, (-10 to 10).map(_ * 0.1))
     testUnary(Acos, math.acos, (11 to 20).map(_ * 0.1), expectNaN = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Acos, DoubleType)
   }
 
   test("cosh") {
     testUnary(Cosh, math.cosh)
+    checkConsistencyBetweenInterpretedAndCodegen(Cosh, DoubleType)
   }
 
   test("tan") {
     testUnary(Tan, math.tan)
+    checkConsistencyBetweenInterpretedAndCodegen(Tan, DoubleType)
   }
 
   test("atan") {
     testUnary(Atan, math.atan)
+    checkConsistencyBetweenInterpretedAndCodegen(Atan, DoubleType)
   }
 
   test("tanh") {
     testUnary(Tanh, math.tanh)
+    checkConsistencyBetweenInterpretedAndCodegen(Tanh, DoubleType)
   }
 
   test("toDegrees") {
     testUnary(ToDegrees, math.toDegrees)
+    checkConsistencyBetweenInterpretedAndCodegen(Acos, DoubleType)
   }
 
   test("toRadians") {
     testUnary(ToRadians, math.toRadians)
+    checkConsistencyBetweenInterpretedAndCodegen(ToRadians, DoubleType)
   }
 
   test("cbrt") {
     testUnary(Cbrt, math.cbrt)
+    checkConsistencyBetweenInterpretedAndCodegen(Cbrt, DoubleType)
   }
 
   test("ceil") {
     testUnary(Ceil, math.ceil)
+    checkConsistencyBetweenInterpretedAndCodegen(Ceil, DoubleType)
   }
 
   test("floor") {
     testUnary(Floor, math.floor)
+    checkConsistencyBetweenInterpretedAndCodegen(Floor, DoubleType)
   }
 
   test("factorial") {
@@ -247,37 +260,45 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, IntegerType), null, create_row(null))
     checkEvaluation(Factorial(Literal(20)), 2432902008176640000L, EmptyRow)
     checkEvaluation(Factorial(Literal(21)), null, EmptyRow)
+    checkConsistencyBetweenInterpretedAndCodegen(Factorial.apply _, IntegerType)
   }
 
   test("rint") {
     testUnary(Rint, math.rint)
+    checkConsistencyBetweenInterpretedAndCodegen(Rint, DoubleType)
   }
 
   test("exp") {
     testUnary(Exp, math.exp)
+    checkConsistencyBetweenInterpretedAndCodegen(Exp, DoubleType)
   }
 
   test("expm1") {
     testUnary(Expm1, math.expm1)
+    checkConsistencyBetweenInterpretedAndCodegen(Expm1, DoubleType)
   }
 
   test("signum") {
     testUnary[Double, Double](Signum, math.signum)
+    checkConsistencyBetweenInterpretedAndCodegen(Signum, DoubleType)
   }
 
   test("log") {
     testUnary(Log, math.log, (1 to 20).map(_ * 0.1))
     testUnary(Log, math.log, (-5 to 0).map(_ * 0.1), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log, DoubleType)
   }
 
   test("log10") {
     testUnary(Log10, math.log10, (1 to 20).map(_ * 0.1))
     testUnary(Log10, math.log10, (-5 to 0).map(_ * 0.1), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log10, DoubleType)
   }
 
   test("log1p") {
     testUnary(Log1p, math.log1p, (0 to 20).map(_ * 0.1))
     testUnary(Log1p, math.log1p, (-10 to -1).map(_ * 1.0), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log1p, DoubleType)
   }
 
   test("bin") {
@@ -298,12 +319,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(Bin(positiveLongLit), java.lang.Long.toBinaryString(positiveLong))
     checkEvaluation(Bin(negativeLongLit), java.lang.Long.toBinaryString(negativeLong))
+
+    checkConsistencyBetweenInterpretedAndCodegen(Bin, LongType)
   }
 
   test("log2") {
     def f: (Double) => Double = (x: Double) => math.log(x) / math.log(2)
     testUnary(Log2, f, (1 to 20).map(_ * 0.1))
     testUnary(Log2, f, (-5 to 0).map(_ * 1.0), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log2, DoubleType)
   }
 
   test("sqrt") {
@@ -313,11 +337,13 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, create_row(null))
     checkNaN(Sqrt(Literal(-1.0)), EmptyRow)
     checkNaN(Sqrt(Literal(-1.5)), EmptyRow)
+    checkConsistencyBetweenInterpretedAndCodegen(Sqrt, DoubleType)
   }
 
   test("pow") {
     testBinary(Pow, math.pow, (-5 to 5).map(v => (v * 1.0, v * 1.0)))
     testBinary(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), expectNaN = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Pow, DoubleType, DoubleType)
   }
 
   test("shift left") {
@@ -338,6 +364,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(ShiftLeft(positiveLongLit, negativeIntLit), positiveLong << negativeInt)
     checkEvaluation(ShiftLeft(negativeLongLit, positiveIntLit), negativeLong << positiveInt)
     checkEvaluation(ShiftLeft(negativeLongLit, negativeIntLit), negativeLong << negativeInt)
+
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftLeft, IntegerType, IntegerType)
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftLeft, LongType, IntegerType)
   }
 
   test("shift right") {
@@ -358,6 +387,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(ShiftRight(positiveLongLit, negativeIntLit), positiveLong >> negativeInt)
     checkEvaluation(ShiftRight(negativeLongLit, positiveIntLit), negativeLong >> positiveInt)
     checkEvaluation(ShiftRight(negativeLongLit, negativeIntLit), negativeLong >> negativeInt)
+
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRight, IntegerType, IntegerType)
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRight, LongType, IntegerType)
   }
 
   test("shift right unsigned") {
@@ -386,6 +418,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       negativeLong >>> positiveInt)
     checkEvaluation(ShiftRightUnsigned(negativeLongLit, negativeIntLit),
       negativeLong >>> negativeInt)
+
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRightUnsigned, IntegerType, IntegerType)
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRightUnsigned, LongType, IntegerType)
   }
 
   test("hex") {
@@ -400,6 +435,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     // Turn off scala style for non-ascii chars
     checkEvaluation(Hex(Literal("三重的".getBytes("UTF8"))), "E4B889E9878DE79A84")
     // scalastyle:on
+    Seq(LongType, BinaryType, StringType).foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(Hex.apply _, dt)
+    }
   }
 
   test("unhex") {
@@ -413,16 +451,18 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     // Turn off scala style for non-ascii chars
     checkEvaluation(Unhex(Literal("E4B889E9878DE79A84")), "三重的".getBytes("UTF-8"))
     checkEvaluation(Unhex(Literal("三重的")), null)
-
     // scalastyle:on
+    checkConsistencyBetweenInterpretedAndCodegen(Unhex, StringType)
   }
 
   test("hypot") {
     testBinary(Hypot, math.hypot)
+    checkConsistencyBetweenInterpretedAndCodegen(Hypot, DoubleType, DoubleType)
   }
 
   test("atan2") {
     testBinary(Atan2, math.atan2)
+    checkConsistencyBetweenInterpretedAndCodegen(Atan2, DoubleType, DoubleType)
   }
 
   test("binary log") {
@@ -454,6 +494,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       Logarithm(Literal(1.0), Literal(-1.0)),
       null,
       create_row(null))
+    checkConsistencyBetweenInterpretedAndCodegen(Logarithm, DoubleType, DoubleType)
   }
 
   test("round") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
index b524d0af14a67..75d17417e5a02 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
@@ -29,6 +29,7 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Md5(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType)),
       "6ac1e56bc78f031059be7be854522c4c")
     checkEvaluation(Md5(Literal.create(null, BinaryType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(Md5, BinaryType)
   }
 
   test("sha1") {
@@ -37,6 +38,7 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       "5d211bad8f4ee70e16c7d343a838fc344a1ed961")
     checkEvaluation(Sha1(Literal.create(null, BinaryType)), null)
     checkEvaluation(Sha1(Literal("".getBytes)), "da39a3ee5e6b4b0d3255bfef95601890afd80709")
+    checkConsistencyBetweenInterpretedAndCodegen(Sha1, BinaryType)
   }
 
   test("sha2") {
@@ -55,6 +57,6 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Crc32(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType)),
       2180413220L)
     checkEvaluation(Crc32(Literal.create(null, BinaryType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(Crc32, BinaryType)
   }
-
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 7beef71845e43..54c04faddb477 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -73,6 +73,16 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     notTrueTable.foreach { case (v, answer) =>
       checkEvaluation(Not(Literal.create(v, BooleanType)), answer)
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Not, BooleanType)
+  }
+
+  test("AND, OR, EqualTo, EqualNullSafe consistency check") {
+    checkConsistencyBetweenInterpretedAndCodegen(And, BooleanType, BooleanType)
+    checkConsistencyBetweenInterpretedAndCodegen(Or, BooleanType, BooleanType)
+    DataTypeTestUtils.propertyCheckSupported.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(EqualTo, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(EqualNullSafe, dt, dt)
+    }
   }
 
   booleanLogicTest("AND", And,
@@ -180,6 +190,15 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
   private val equalValues2 =
     Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN, true).map(Literal(_))
 
+  test("BinaryComparison consistency check") {
+    DataTypeTestUtils.ordered.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(LessThan, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(LessThanOrEqual, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(GreaterThan, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(GreaterThanOrEqual, dt, dt)
+    }
+  }
+
   test("BinaryComparison: lessThan") {
     for (i <- 0 until smallValues.length) {
       checkEvaluation(LessThan(smallValues(i), largeValues(i)), true)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
index 417df006ab7c2..ed2c641d63e25 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
@@ -46,6 +46,25 @@ object DataTypeTestUtils {
    */
   val numericTypes: Set[NumericType] = integralType ++ fractionalTypes
 
+  // TODO: remove this once we find out how to handle decimal properly in property check
+  val numericTypeWithoutDecimal: Set[DataType] = integralType ++ Set(DoubleType, FloatType)
+
+  /**
+   * Instances of all [[NumericType]]s and [[CalendarIntervalType]]
+   */
+  val numericAndInterval: Set[DataType] = numericTypeWithoutDecimal + CalendarIntervalType
+
+  /**
+   * All the types that support ordering
+   */
+  val ordered: Set[DataType] =
+    numericTypeWithoutDecimal + BooleanType + TimestampType + DateType + StringType + BinaryType
+
+  /**
+   * All the types that we can use in a property check
+   */
+  val propertyCheckSupported: Set[DataType] = ordered
+
   /**
    * Instances of all [[AtomicType]]s.
    */

From 772e7c18fb1a79c0f080408cb43307fe89a4fa04 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 17 Aug 2015 15:30:50 -0700
Subject: [PATCH 1084/1454] [SPARK-9592] [SQL] Fix Last function implemented
 based on AggregateExpression1.

https://issues.apache.org/jira/browse/SPARK-9592

#8113 has the fundamental fix. But, if we want to minimize the number of changed lines, we can go with this one. Then, in 1.6, we merge #8113.

Author: Yin Huai <yhuai@databricks.com>

Closes #8172 from yhuai/lastFix and squashes the following commits:

b28c42a [Yin Huai] Regression test.
af87086 [Yin Huai] Fix last.
---
 .../sql/catalyst/expressions/aggregates.scala     |  9 +++++++--
 .../hive/execution/AggregationQuerySuite.scala    | 15 +++++++++++++++
 2 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 2cf8312ea59aa..5e8298aaaa9cb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -650,6 +650,7 @@ case class FirstFunction(expr: Expression, base: AggregateExpression1) extends A
   var result: Any = null
 
   override def update(input: InternalRow): Unit = {
+    // We ignore null values.
     if (result == null) {
       result = expr.eval(input)
     }
@@ -679,10 +680,14 @@ case class LastFunction(expr: Expression, base: AggregateExpression1) extends Ag
   var result: Any = null
 
   override def update(input: InternalRow): Unit = {
-    result = input
+    val value = expr.eval(input)
+    // We ignore null values.
+    if (value != null) {
+      result = value
+    }
   }
 
   override def eval(input: InternalRow): Any = {
-    if (result != null) expr.eval(result.asInstanceOf[InternalRow]) else null
+    result
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index a312f84958248..119663af1887a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -480,6 +480,21 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
         Row(0, null, 1, 1, null, 0) :: Nil)
   }
 
+  test("test Last implemented based on AggregateExpression1") {
+    // TODO: Remove this test once we remove AggregateExpression1.
+    import org.apache.spark.sql.functions._
+    val df = Seq((1, 1), (2, 2), (3, 3)).toDF("i", "j").repartition(1)
+    withSQLConf(
+      SQLConf.SHUFFLE_PARTITIONS.key -> "1",
+      SQLConf.USE_SQL_AGGREGATE2.key -> "false") {
+
+      checkAnswer(
+        df.groupBy("i").agg(last("j")),
+        df
+      )
+    }
+  }
+
   test("error handling") {
     withSQLConf("spark.sql.useAggregate2" -> "false") {
       val errorMessage = intercept[AnalysisException] {

From fdaf17f63f751f02623414fbc7d0a2f545364050 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 17 Aug 2015 15:42:14 -0700
Subject: [PATCH 1085/1454] [SPARK-10068] [MLLIB] Adds links to MLlib types,
 algos, utilities listing

mengxr jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8255 from feynmanliang/SPARK-10068.
---
 docs/mllib-guide.md | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index eea864eacf7c4..e8000ff478300 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -23,19 +23,19 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
 
 * [Data types](mllib-data-types.html)
 * [Basic statistics](mllib-statistics.html)
-  * summary statistics
-  * correlations
-  * stratified sampling
-  * hypothesis testing
-  * random data generation  
+  * [summary statistics](mllib-statistics.html#summary-statistics)
+  * [correlations](mllib-statistics.html#correlations)
+  * [stratified sampling](mllib-statistics.html#stratified-sampling)
+  * [hypothesis testing](mllib-statistics.html#hypothesis-testing)
+  * [random data generation](mllib-statistics.html#random-data-generation)
 * [Classification and regression](mllib-classification-regression.html)
   * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
   * [naive Bayes](mllib-naive-bayes.html)
   * [decision trees](mllib-decision-tree.html)
-  * [ensembles of trees](mllib-ensembles.html) (Random Forests and Gradient-Boosted Trees)
+  * [ensembles of trees (Random Forests and Gradient-Boosted Trees)](mllib-ensembles.html)
   * [isotonic regression](mllib-isotonic-regression.html)
 * [Collaborative filtering](mllib-collaborative-filtering.html)
-  * alternating least squares (ALS)
+  * [alternating least squares (ALS)](mllib-collaborative-filtering.html#collaborative-filtering)
 * [Clustering](mllib-clustering.html)
   * [k-means](mllib-clustering.html#k-means)
   * [Gaussian mixture](mllib-clustering.html#gaussian-mixture)
@@ -43,19 +43,19 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
   * [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda)
   * [streaming k-means](mllib-clustering.html#streaming-k-means)
 * [Dimensionality reduction](mllib-dimensionality-reduction.html)
-  * singular value decomposition (SVD)
-  * principal component analysis (PCA)
+  * [singular value decomposition (SVD)](mllib-dimensionality-reduction.html#singular-value-decomposition-svd)
+  * [principal component analysis (PCA)](mllib-dimensionality-reduction.html#principal-component-analysis-pca)
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
-  * FP-growth
+  * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
 * [Evaluation Metrics](mllib-evaluation-metrics.html)
 * [Optimization (developer)](mllib-optimization.html)
-  * stochastic gradient descent
-  * limited-memory BFGS (L-BFGS)
+  * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
+  * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
 * [PMML model export](mllib-pmml-model-export.html)
 
 MLlib is under active development.
-The APIs marked `Experimental`/`DeveloperApi` may change in future releases, 
+The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
 and the migration guide below will explain all changes between releases.
 
 # spark.ml: high-level APIs for ML pipelines

From 088b11ec5949e135cb3db2a1ce136837e046c288 Mon Sep 17 00:00:00 2001
From: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.Samavihome>
Date: Mon, 17 Aug 2015 16:00:23 -0700
Subject: [PATCH 1086/1454] [SPARK-8920] [MLLIB] Add @since tags to
 mllib.linalg

Author: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.Samavihome>
Author: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.local>

Closes #7729 from sabhyankar/branch_8920.
---
 .../apache/spark/mllib/linalg/Matrices.scala  | 63 +++++++++++++++++++
 .../linalg/SingularValueDecomposition.scala   |  1 +
 .../apache/spark/mllib/linalg/Vectors.scala   | 60 ++++++++++++++++++
 .../linalg/distributed/BlockMatrix.scala      | 43 +++++++++++--
 .../linalg/distributed/CoordinateMatrix.scala | 28 +++++++--
 .../distributed/DistributedMatrix.scala       |  1 +
 .../linalg/distributed/IndexedRowMatrix.scala | 24 ++++++-
 .../mllib/linalg/distributed/RowMatrix.scala  | 24 ++++++-
 8 files changed, 227 insertions(+), 17 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 1139ce36d50b8..dfa8910fcbc8c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -227,6 +227,7 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
  * @param values matrix entries in column major if not transposed or in row major otherwise
  * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in
  *                     row major.
+ * @since 1.0.0
  */
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class DenseMatrix(
@@ -252,6 +253,7 @@ class DenseMatrix(
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
+   * @since 1.3.0
    */
   def this(numRows: Int, numCols: Int, values: Array[Double]) =
     this(numRows, numCols, values, false)
@@ -276,6 +278,9 @@ class DenseMatrix(
 
   private[mllib] def apply(i: Int): Double = values(i)
 
+  /**
+   * @since 1.3.0
+   */
   override def apply(i: Int, j: Int): Double = values(index(i, j))
 
   private[mllib] def index(i: Int, j: Int): Int = {
@@ -286,6 +291,9 @@ class DenseMatrix(
     values(index(i, j)) = v
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone())
 
   private[spark] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
@@ -301,6 +309,9 @@ class DenseMatrix(
     this
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def transpose: DenseMatrix = new DenseMatrix(numCols, numRows, values, !isTransposed)
 
   private[spark] override def foreachActive(f: (Int, Int, Double) => Unit): Unit = {
@@ -331,13 +342,20 @@ class DenseMatrix(
     }
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def numNonzeros: Int = values.count(_ != 0)
 
+  /**
+   * @since 1.5.0
+   */
   override def numActives: Int = values.length
 
   /**
    * Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed
    * set to false.
+   * @since 1.3.0
    */
   def toSparse: SparseMatrix = {
     val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
@@ -365,6 +383,7 @@ class DenseMatrix(
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
+ * @since 1.3.0
  */
 object DenseMatrix {
 
@@ -373,6 +392,7 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros
+   * @since 1.3.0
    */
   def zeros(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -385,6 +405,7 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones
+   * @since 1.3.0
    */
   def ones(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -396,6 +417,7 @@ object DenseMatrix {
    * Generate an Identity Matrix in `DenseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.3.0
    */
   def eye(n: Int): DenseMatrix = {
     val identity = DenseMatrix.zeros(n, n)
@@ -413,6 +435,7 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.3.0
    */
   def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -426,6 +449,7 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.3.0
    */
   def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -438,6 +462,7 @@ object DenseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `DenseMatrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
+   * @since 1.3.0
    */
   def diag(vector: Vector): DenseMatrix = {
     val n = vector.size
@@ -473,6 +498,7 @@ object DenseMatrix {
  * @param isTransposed whether the matrix is transposed. If true, the matrix can be considered
  *                     Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs,
  *                     and `rowIndices` behave as colIndices, and `values` are stored in row major.
+ * @since 1.2.0
  */
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class SparseMatrix(
@@ -510,6 +536,7 @@ class SparseMatrix(
    * @param rowIndices the row index of the entry. They must be in strictly increasing
    *                   order for each column
    * @param values non-zero matrix entries in column major
+   * @since 1.3.0
    */
   def this(
       numRows: Int,
@@ -532,6 +559,9 @@ class SparseMatrix(
      }
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def apply(i: Int, j: Int): Double = {
     val ind = index(i, j)
     if (ind < 0) 0.0 else values(ind)
@@ -555,6 +585,9 @@ class SparseMatrix(
     }
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def copy: SparseMatrix = {
     new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
   }
@@ -572,6 +605,9 @@ class SparseMatrix(
     this
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def transpose: SparseMatrix =
     new SparseMatrix(numCols, numRows, colPtrs, rowIndices, values, !isTransposed)
 
@@ -605,19 +641,27 @@ class SparseMatrix(
   /**
    * Generate a `DenseMatrix` from the given `SparseMatrix`. The new matrix will have isTransposed
    * set to false.
+   * @since 1.3.0
    */
   def toDense: DenseMatrix = {
     new DenseMatrix(numRows, numCols, toArray)
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def numNonzeros: Int = values.count(_ != 0)
 
+  /**
+   * @since 1.5.0
+   */
   override def numActives: Int = values.length
 
 }
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
+ * @since 1.3.0
  */
 object SparseMatrix {
 
@@ -629,6 +673,7 @@ object SparseMatrix {
    * @param numCols number of columns of the matrix
    * @param entries Array of (i, j, value) tuples
    * @return The corresponding `SparseMatrix`
+   * @since 1.3.0
    */
   def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix = {
     val sortedEntries = entries.toSeq.sortBy(v => (v._2, v._1))
@@ -677,6 +722,7 @@ object SparseMatrix {
    * Generate an Identity Matrix in `SparseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `SparseMatrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.3.0
    */
   def speye(n: Int): SparseMatrix = {
     new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0))
@@ -746,6 +792,7 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.3.0
    */
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
@@ -759,6 +806,7 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.3.0
    */
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
@@ -770,6 +818,7 @@ object SparseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `SparseMatrix` with size `values.length` x `values.length` and non-zero
    *         `values` on the diagonal
+   * @since 1.3.0
    */
   def spdiag(vector: Vector): SparseMatrix = {
     val n = vector.size
@@ -786,6 +835,7 @@ object SparseMatrix {
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.Matrix]].
+ * @since 1.0.0
  */
 object Matrices {
 
@@ -795,6 +845,7 @@ object Matrices {
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
+   * @since 1.0.0
    */
   def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = {
     new DenseMatrix(numRows, numCols, values)
@@ -808,6 +859,7 @@ object Matrices {
    * @param colPtrs the index corresponding to the start of a new column
    * @param rowIndices the row index of the entry
    * @param values non-zero matrix entries in column major
+   * @since 1.2.0
    */
   def sparse(
      numRows: Int,
@@ -841,6 +893,7 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of zeros
+   * @since 1.2.0
    */
   def zeros(numRows: Int, numCols: Int): Matrix = DenseMatrix.zeros(numRows, numCols)
 
@@ -849,6 +902,7 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of ones
+   * @since 1.2.0
    */
   def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows, numCols)
 
@@ -856,6 +910,7 @@ object Matrices {
    * Generate a dense Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.2.0
    */
   def eye(n: Int): Matrix = DenseMatrix.eye(n)
 
@@ -863,6 +918,7 @@ object Matrices {
    * Generate a sparse Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.3.0
    */
   def speye(n: Int): Matrix = SparseMatrix.speye(n)
 
@@ -872,6 +928,7 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.2.0
    */
   def rand(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.rand(numRows, numCols, rng)
@@ -883,6 +940,7 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.3.0
    */
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprand(numRows, numCols, density, rng)
@@ -893,6 +951,7 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.2.0
    */
   def randn(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.randn(numRows, numCols, rng)
@@ -904,6 +963,7 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.3.0
    */
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprandn(numRows, numCols, density, rng)
@@ -913,6 +973,7 @@ object Matrices {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `Matrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
+   * @since 1.2.0
    */
   def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)
 
@@ -922,6 +983,7 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were horizontally concatenated
+   * @since 1.3.0
    */
   def horzcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
@@ -980,6 +1042,7 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were vertically concatenated
+   * @since 1.3.0
    */
   def vertcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index cff5dbeee3e57..8f504f6984cb0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Represents singular value decomposition (SVD) factors.
+ * @since 1.0.0
  */
 @Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index df15d985c814c..52ef7be3b38be 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -240,11 +240,13 @@ class VectorUDT extends UserDefinedType[Vector] {
  * Factory methods for [[org.apache.spark.mllib.linalg.Vector]].
  * We don't use the name `Vector` because Scala imports
  * [[scala.collection.immutable.Vector]] by default.
+ * @since 1.0.0
  */
 object Vectors {
 
   /**
    * Creates a dense vector from its values.
+   * @since 1.0.0
    */
   @varargs
   def dense(firstValue: Double, otherValues: Double*): Vector =
@@ -253,6 +255,7 @@ object Vectors {
   // A dummy implicit is used to avoid signature collision with the one generated by @varargs.
   /**
    * Creates a dense vector from a double array.
+   * @since 1.0.0
    */
   def dense(values: Array[Double]): Vector = new DenseVector(values)
 
@@ -262,6 +265,7 @@ object Vectors {
    * @param size vector size.
    * @param indices index array, must be strictly increasing.
    * @param values value array, must have the same length as indices.
+   * @since 1.0.0
    */
   def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector =
     new SparseVector(size, indices, values)
@@ -271,6 +275,7 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
+   * @since 1.0.0
    */
   def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = {
     require(size > 0, "The size of the requested sparse vector must be greater than 0.")
@@ -292,6 +297,7 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
+   * @since 1.0.0
    */
   def sparse(size: Int, elements: JavaIterable[(JavaInteger, JavaDouble)]): Vector = {
     sparse(size, elements.asScala.map { case (i, x) =>
@@ -304,6 +310,7 @@ object Vectors {
    *
    * @param size vector size
    * @return a zero vector
+   * @since 1.1.0
    */
   def zeros(size: Int): Vector = {
     new DenseVector(new Array[Double](size))
@@ -311,6 +318,7 @@ object Vectors {
 
   /**
    * Parses a string resulted from [[Vector.toString]] into a [[Vector]].
+   * @since 1.1.0
    */
   def parse(s: String): Vector = {
     parseNumeric(NumericParser.parse(s))
@@ -354,6 +362,7 @@ object Vectors {
    * @param vector input vector.
    * @param p norm.
    * @return norm in L^p^ space.
+   * @since 1.3.0
    */
   def norm(vector: Vector, p: Double): Double = {
     require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " +
@@ -406,6 +415,7 @@ object Vectors {
    * @param v1 first Vector.
    * @param v2 second Vector.
    * @return squared distance between two Vectors.
+   * @since 1.3.0
    */
   def sqdist(v1: Vector, v2: Vector): Double = {
     require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +
@@ -519,20 +529,33 @@ object Vectors {
 
 /**
  * A dense vector represented by a value array.
+ * @since 1.0.0
  */
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class DenseVector(val values: Array[Double]) extends Vector {
 
+  /**
+   * @since 1.0.0
+   */
   override def size: Int = values.length
 
   override def toString: String = values.mkString("[", ",", "]")
 
+  /**
+   * @since 1.0.0
+   */
   override def toArray: Array[Double] = values
 
   private[spark] override def toBreeze: BV[Double] = new BDV[Double](values)
 
+  /**
+   * @since 1.0.0
+   */
   override def apply(i: Int): Double = values(i)
 
+  /**
+   * @since 1.1.0
+   */
   override def copy: DenseVector = {
     new DenseVector(values.clone())
   }
@@ -564,8 +587,14 @@ class DenseVector(val values: Array[Double]) extends Vector {
     result
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def numActives: Int = size
 
+  /**
+   * @since 1.4.0
+   */
   override def numNonzeros: Int = {
     // same as values.count(_ != 0.0) but faster
     var nnz = 0
@@ -577,6 +606,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
     nnz
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     val ii = new Array[Int](nnz)
@@ -592,6 +624,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
     new SparseVector(size, ii, vv)
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -611,6 +646,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
   }
 }
 
+/**
+ * @since 1.3.0
+ */
 object DenseVector {
   /** Extracts the value array from a dense vector. */
   def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
@@ -622,6 +660,7 @@ object DenseVector {
  * @param size size of the vector.
  * @param indices index array, assume to be strictly increasing.
  * @param values value array, must have the same length as the index array.
+ * @since 1.0.0
  */
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class SparseVector(
@@ -638,6 +677,9 @@ class SparseVector(
   override def toString: String =
     s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
 
+  /**
+   * @since 1.0.0
+   */
   override def toArray: Array[Double] = {
     val data = new Array[Double](size)
     var i = 0
@@ -649,6 +691,9 @@ class SparseVector(
     data
   }
 
+  /**
+   * @since 1.1.0
+   */
   override def copy: SparseVector = {
     new SparseVector(size, indices.clone(), values.clone())
   }
@@ -689,8 +734,14 @@ class SparseVector(
     result
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def numActives: Int = values.length
 
+  /**
+   * @since 1.4.0
+   */
   override def numNonzeros: Int = {
     var nnz = 0
     values.foreach { v =>
@@ -701,6 +752,9 @@ class SparseVector(
     nnz
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     if (nnz == numActives) {
@@ -720,6 +774,9 @@ class SparseVector(
     }
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -790,6 +847,9 @@ class SparseVector(
   }
 }
 
+/**
+ * @since 1.3.0
+ */
 object SparseVector {
   def unapply(sv: SparseVector): Option[(Int, Array[Int], Array[Double])] =
     Some((sv.size, sv.indices, sv.values))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index 3323ae7b1fba0..cfb6680a18b34 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -128,6 +128,8 @@ private[mllib] object GridPartitioner {
  *              the number of rows will be calculated when `numRows` is invoked.
  * @param nCols Number of columns of this matrix. If the supplied value is less than or equal to
  *              zero, the number of columns will be calculated when `numCols` is invoked.
+ * @since 1.3.0
+ *
  */
 @Experimental
 class BlockMatrix(
@@ -149,6 +151,9 @@ class BlockMatrix(
    *                     rows are not required to have the given number of rows
    * @param colsPerBlock Number of columns that make up each block. The blocks forming the final
    *                     columns are not required to have the given number of columns
+   *
+   * @since 1.3.0
+   *
    */
   def this(
       blocks: RDD[((Int, Int), Matrix)],
@@ -157,11 +162,20 @@ class BlockMatrix(
     this(blocks, rowsPerBlock, colsPerBlock, 0L, 0L)
   }
 
+  /**
+   * @since 1.3.0
+   * */
+
   override def numRows(): Long = {
     if (nRows <= 0L) estimateDim()
     nRows
   }
 
+  /**
+   *
+   * @since 1.3.0
+   */
+
   override def numCols(): Long = {
     if (nCols <= 0L) estimateDim()
     nCols
@@ -192,6 +206,7 @@ class BlockMatrix(
   /**
    * Validates the block matrix info against the matrix data (`blocks`) and throws an exception if
    * any error is found.
+   * @since 1.3.0
    */
   def validate(): Unit = {
     logDebug("Validating BlockMatrix...")
@@ -228,19 +243,25 @@ class BlockMatrix(
     logDebug("BlockMatrix is valid!")
   }
 
-  /** Caches the underlying RDD. */
+  /** Caches the underlying RDD.
+    * @since 1.3.0
+    * */
   def cache(): this.type = {
     blocks.cache()
     this
   }
 
-  /** Persists the underlying RDD with the specified storage level. */
+  /** Persists the underlying RDD with the specified storage level.
+    * @since 1.3.0
+    * */
   def persist(storageLevel: StorageLevel): this.type = {
     blocks.persist(storageLevel)
     this
   }
 
-  /** Converts to CoordinateMatrix. */
+  /** Converts to CoordinateMatrix.
+    * @since 1.3.0
+    * */
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entryRDD = blocks.flatMap { case ((blockRowIndex, blockColIndex), mat) =>
       val rowStart = blockRowIndex.toLong * rowsPerBlock
@@ -254,7 +275,9 @@ class BlockMatrix(
     new CoordinateMatrix(entryRDD, numRows(), numCols())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
+    * @since 1.3.0
+    * */
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     require(numCols() < Int.MaxValue, "The number of columns must be within the integer range. " +
       s"numCols: ${numCols()}")
@@ -262,7 +285,9 @@ class BlockMatrix(
     toCoordinateMatrix().toIndexedRowMatrix()
   }
 
-  /** Collect the distributed matrix on the driver as a `DenseMatrix`. */
+  /** Collect the distributed matrix on the driver as a `DenseMatrix`.
+    * @since 1.3.0
+    * */
   def toLocalMatrix(): Matrix = {
     require(numRows() < Int.MaxValue, "The number of rows of this matrix should be less than " +
       s"Int.MaxValue. Currently numRows: ${numRows()}")
@@ -288,7 +313,10 @@ class BlockMatrix(
   }
 
   /** Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
-    * same underlying data. Is a lazy operation. */
+    * same underlying data. Is a lazy operation.
+    * @since 1.3.0
+    *
+    * */
   def transpose: BlockMatrix = {
     val transposedBlocks = blocks.map { case ((blockRowIndex, blockColIndex), mat) =>
       ((blockColIndex, blockRowIndex), mat.transpose)
@@ -307,6 +335,7 @@ class BlockMatrix(
     * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
     * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
     * also be a [[DenseMatrix]].
+    * @since 1.3.0
     */
   def add(other: BlockMatrix): BlockMatrix = {
     require(numRows() == other.numRows(), "Both matrices must have the same number of rows. " +
@@ -340,6 +369,8 @@ class BlockMatrix(
     * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
     * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
     * some performance issues until support for multiplying two sparse matrices is added.
+    *
+    * @since 1.3.0
     */
   def multiply(other: BlockMatrix): BlockMatrix = {
     require(numCols() == other.numRows(), "The number of columns of A and the number of rows " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 078d1fac44443..2b751e45dd76c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
  * @param i row index
  * @param j column index
  * @param value value of the entry
+ * @since 1.0.0
  */
 @Experimental
 case class MatrixEntry(i: Long, j: Long, value: Double)
@@ -42,6 +43,7 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the max column index plus one.
+ * @since 1.0.0
  */
 @Experimental
 class CoordinateMatrix(
@@ -49,10 +51,14 @@ class CoordinateMatrix(
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically.
+    * @since 1.0.0
+    * */
   def this(entries: RDD[MatrixEntry]) = this(entries, 0L, 0L)
 
-  /** Gets or computes the number of columns. */
+  /** Gets or computes the number of columns.
+    * @since 1.0.0
+    * */
   override def numCols(): Long = {
     if (nCols <= 0L) {
       computeSize()
@@ -60,7 +66,9 @@ class CoordinateMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows. */
+  /** Gets or computes the number of rows.
+    * @since 1.0.0
+    * */
   override def numRows(): Long = {
     if (nRows <= 0L) {
       computeSize()
@@ -68,12 +76,16 @@ class CoordinateMatrix(
     nRows
   }
 
-  /** Transposes this CoordinateMatrix. */
+  /** Transposes this CoordinateMatrix.
+    * @since 1.3.0
+    * */
   def transpose(): CoordinateMatrix = {
     new CoordinateMatrix(entries.map(x => MatrixEntry(x.j, x.i, x.value)), numCols(), numRows())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
+    * @since 1.0.0
+    * */
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     val nl = numCols()
     if (nl > Int.MaxValue) {
@@ -92,12 +104,15 @@ class CoordinateMatrix(
   /**
    * Converts to RowMatrix, dropping row indices after grouping by row index.
    * The number of columns must be within the integer range.
+   * @since 1.0.0
    */
   def toRowMatrix(): RowMatrix = {
     toIndexedRowMatrix().toRowMatrix()
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
+    * @since 1.3.0
+    * */
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -109,6 +124,7 @@ class CoordinateMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
+   * @since 1.3.0
    */
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     require(rowsPerBlock > 0,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
index a0e26ce3bc465..98e90af84abac 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
@@ -21,6 +21,7 @@ import breeze.linalg.{DenseMatrix => BDM}
 
 /**
  * Represents a distributively stored matrix backed by one or more RDDs.
+ * @since 1.0.0
  */
 trait DistributedMatrix extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index 1c33b43ea7a8a..a09f88ce28e58 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -27,6 +27,7 @@ import org.apache.spark.mllib.linalg.SingularValueDecomposition
 /**
  * :: Experimental ::
  * Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
+ * @since 1.0.0
  */
 @Experimental
 case class IndexedRow(index: Long, vector: Vector)
@@ -41,6 +42,7 @@ case class IndexedRow(index: Long, vector: Vector)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
+ * @since 1.0.0
  */
 @Experimental
 class IndexedRowMatrix(
@@ -48,9 +50,15 @@ class IndexedRowMatrix(
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically.
+    * @since 1.0.0
+    * */
   def this(rows: RDD[IndexedRow]) = this(rows, 0L, 0)
 
+  /**
+   *
+   * @since 1.0.0
+   */
   override def numCols(): Long = {
     if (nCols <= 0) {
       // Calling `first` will throw an exception if `rows` is empty.
@@ -59,6 +67,10 @@ class IndexedRowMatrix(
     nCols
   }
 
+  /**
+   *
+   * @since 1.0.0
+   */
   override def numRows(): Long = {
     if (nRows <= 0L) {
       // Reduce will throw an exception if `rows` is empty.
@@ -70,12 +82,15 @@ class IndexedRowMatrix(
   /**
    * Drops row indices and converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]].
+   * @since 1.0.0
    */
   def toRowMatrix(): RowMatrix = {
     new RowMatrix(rows.map(_.vector), 0L, nCols)
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
+    * @since 1.3.0
+    * */
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -87,6 +102,7 @@ class IndexedRowMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
+   * @since 1.3.0
    */
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     // TODO: This implementation may be optimized
@@ -96,6 +112,7 @@ class IndexedRowMatrix(
   /**
    * Converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.CoordinateMatrix]].
+   * @since 1.3.0
    */
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entries = rows.flatMap { row =>
@@ -132,6 +149,7 @@ class IndexedRowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V)
+   * @since 1.0.0
    */
   def computeSVD(
       k: Int,
@@ -158,6 +176,7 @@ class IndexedRowMatrix(
    *
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return an IndexedRowMatrix representing the product, which preserves partitioning
+   * @since 1.0.0
    */
   def multiply(B: Matrix): IndexedRowMatrix = {
     val mat = toRowMatrix().multiply(B)
@@ -169,6 +188,7 @@ class IndexedRowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
+   * @since 1.0.0
    */
   def computeGramianMatrix(): Matrix = {
     toRowMatrix().computeGramianMatrix()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index bfc90c9ef8527..b2e94f2dd6201 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -44,6 +44,7 @@ import org.apache.spark.storage.StorageLevel
  *              be determined by the number of records in the RDD `rows`.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
+ * @since 1.0.0
  */
 @Experimental
 class RowMatrix(
@@ -51,10 +52,14 @@ class RowMatrix(
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix with Logging {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically.
+    * @since 1.0.0
+    * */
   def this(rows: RDD[Vector]) = this(rows, 0L, 0)
 
-  /** Gets or computes the number of columns. */
+  /** Gets or computes the number of columns.
+    * @since 1.0.0
+    * */
   override def numCols(): Long = {
     if (nCols <= 0) {
       try {
@@ -69,7 +74,9 @@ class RowMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows. */
+  /** Gets or computes the number of rows.
+    * @since 1.0.0
+    * */
   override def numRows(): Long = {
     if (nRows <= 0L) {
       nRows = rows.count()
@@ -107,6 +114,7 @@ class RowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
+   * @since 1.0.0
    */
   def computeGramianMatrix(): Matrix = {
     val n = numCols().toInt
@@ -177,6 +185,7 @@ class RowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
+   * @since 1.0.0
    */
   def computeSVD(
       k: Int,
@@ -317,6 +326,7 @@ class RowMatrix(
   /**
    * Computes the covariance matrix, treating each row as an observation.
    * @return a local dense matrix of size n x n
+   * @since 1.0.0
    */
   def computeCovariance(): Matrix = {
     val n = numCols().toInt
@@ -370,6 +380,7 @@ class RowMatrix(
    *
    * @param k number of top principal components.
    * @return a matrix of size n-by-k, whose columns are principal components
+   * @since 1.0.0
    */
   def computePrincipalComponents(k: Int): Matrix = {
     val n = numCols().toInt
@@ -388,6 +399,7 @@ class RowMatrix(
 
   /**
    * Computes column-wise summary statistics.
+   * @since 1.0.0
    */
   def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
     val summary = rows.treeAggregate(new MultivariateOnlineSummarizer)(
@@ -403,6 +415,7 @@ class RowMatrix(
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return a [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] representing the product,
    *         which preserves partitioning
+   * @since 1.0.0
    */
   def multiply(B: Matrix): RowMatrix = {
     val n = numCols().toInt
@@ -435,6 +448,7 @@ class RowMatrix(
    *
    * @return An n x n sparse upper-triangular matrix of cosine similarities between
    *         columns of this matrix.
+   * @since 1.2.0
    */
   def columnSimilarities(): CoordinateMatrix = {
     columnSimilarities(0.0)
@@ -478,6 +492,7 @@ class RowMatrix(
    *                  with the cost vs estimate quality trade-off described above.
    * @return An n x n sparse upper-triangular matrix of cosine similarities
    *         between columns of this matrix.
+   * @since 1.2.0
    */
   def columnSimilarities(threshold: Double): CoordinateMatrix = {
     require(threshold >= 0, s"Threshold cannot be negative: $threshold")
@@ -656,6 +671,9 @@ class RowMatrix(
   }
 }
 
+/**
+ * @since 1.0.0
+ */
 @Experimental
 object RowMatrix {
 

From 52ae952574f5d641a398dd185e09e5a79318c8a9 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 17 Aug 2015 17:25:14 -0700
Subject: [PATCH 1087/1454] [SPARK-9974] [BUILD] [SQL] Makes sure
 com.twitter:parquet-hadoop-bundle:1.6.0 is in SBT assembly jar

PR #7967 enables Spark SQL to persist Parquet tables in Hive compatible format when possible. One of the consequence is that, we have to set input/output classes to `MapredParquetInputFormat`/`MapredParquetOutputFormat`, which rely on com.twitter:parquet-hadoop:1.6.0 bundled with Hive 1.2.1.

When loading such a table in Spark SQL, `o.a.h.h.ql.metadata.Table` first loads these input/output format classes, and thus classes in com.twitter:parquet-hadoop:1.6.0.  However, the scope of this dependency is defined as "runtime", and is not packaged into Spark assembly jar.  This results in a `ClassNotFoundException`.

This issue can be worked around by asking users to add parquet-hadoop 1.6.0 via the `--driver-class-path` option.  However, considering Maven build is immune to this problem, I feel it can be confusing and inconvenient for users.

So this PR fixes this issue by changing scope of parquet-hadoop 1.6.0 to "compile".

Author: Cheng Lian <lian@databricks.com>

Closes #8198 from liancheng/spark-9974/bundle-parquet-1.6.0.
---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index cfd7d32563f2a..9bfca1c4170bb 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1598,7 +1598,7 @@
         <groupId>com.twitter</groupId>
         <artifactId>parquet-hadoop-bundle</artifactId>
         <version>${hive.parquet.version}</version>
-        <scope>runtime</scope>
+        <scope>compile</scope>
       </dependency>
       <dependency>
         <groupId>org.apache.flume</groupId>

From 0076e8212334c613599dcbc2ac23f49e9e50cc44 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 17 Aug 2015 17:25:41 -0700
Subject: [PATCH 1088/1454] [SPARK-9768] [PYSPARK] [ML] Add Python API and user
 guide for ml.feature.ElementwiseProduct

Add Python API, user guide and example for ml.feature.ElementwiseProduct.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8061 from yanboliang/SPARK-9768.
---
 docs/ml-features.md          | 23 ++++++++++---
 python/pyspark/ml/feature.py | 67 +++++++++++++++++++++++++++++++++---
 2 files changed, 81 insertions(+), 9 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index cec2cbe673407..6b2e36b3536dc 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1212,7 +1212,7 @@ v_N
 This example below demonstrates how to transform vectors using a transforming vector value.
 
 <div class="codetabs">
-<div data-lang="scala">
+<div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.feature.ElementwiseProduct
 import org.apache.spark.mllib.linalg.Vectors
@@ -1229,12 +1229,12 @@ val transformer = new ElementwiseProduct()
   .setOutputCol("transformedVector")
 
 // Batch transform the vectors to create new column:
-val transformedData = transformer.transform(dataFrame)
+transformer.transform(dataFrame).show()
 
 {% endhighlight %}
 </div>
 
-<div data-lang="java">
+<div data-lang="java" markdown="1">
 {% highlight java %}
 import com.google.common.collect.Lists;
 
@@ -1267,10 +1267,25 @@ ElementwiseProduct transformer = new ElementwiseProduct()
   .setInputCol("vector")
   .setOutputCol("transformedVector");
 // Batch transform the vectors to create new column:
-DataFrame transformedData = transformer.transform(dataFrame);
+transformer.transform(dataFrame).show();
 
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+{% highlight python %}
+from pyspark.ml.feature import ElementwiseProduct
+from pyspark.mllib.linalg import Vectors
+
+data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)]
+df = sqlContext.createDataFrame(data, ["vector"])
+transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]), 
+                                 inputCol="vector", outputCol="transformedVector")
+transformer.transform(df).show()
+
+{% endhighlight %}
+</div>
+
 </div>
 
 ## VectorAssembler
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 535d55326646c..04b2b2ccc9e55 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -26,11 +26,11 @@
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
-__all__ = ['Binarizer', 'Bucketizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer',
-           'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler',
-           'StandardScalerModel', 'StringIndexer', 'StringIndexerModel', 'Tokenizer',
-           'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel', 'PCA',
-           'PCAModel', 'RFormula', 'RFormulaModel']
+__all__ = ['Binarizer', 'Bucketizer', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
+           'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
+           'StandardScaler', 'StandardScalerModel', 'StringIndexer', 'StringIndexerModel',
+           'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel',
+           'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc
@@ -166,6 +166,63 @@ def getSplits(self):
         return self.getOrDefault(self.splits)
 
 
+@inherit_doc
+class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    Outputs the Hadamard product (i.e., the element-wise product) of each input vector
+    with a provided "weight" vector. In other words, it scales each column of the dataset
+    by a scalar multiplier.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([2.0, 1.0, 3.0]),)], ["values"])
+    >>> ep = ElementwiseProduct(scalingVec=Vectors.dense([1.0, 2.0, 3.0]),
+    ...     inputCol="values", outputCol="eprod")
+    >>> ep.transform(df).head().eprod
+    DenseVector([2.0, 2.0, 9.0])
+    >>> ep.setParams(scalingVec=Vectors.dense([2.0, 3.0, 5.0])).transform(df).head().eprod
+    DenseVector([4.0, 3.0, 15.0])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    scalingVec = Param(Params._dummy(), "scalingVec", "vector for hadamard product, " +
+                       "it must be MLlib Vector type.")
+
+    @keyword_only
+    def __init__(self, scalingVec=None, inputCol=None, outputCol=None):
+        """
+        __init__(self, scalingVec=None, inputCol=None, outputCol=None)
+        """
+        super(ElementwiseProduct, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.ElementwiseProduct",
+                                            self.uid)
+        self.scalingVec = Param(self, "scalingVec", "vector for hadamard product, " +
+                                "it must be MLlib Vector type.")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, scalingVec=None, inputCol=None, outputCol=None):
+        """
+        setParams(self, scalingVec=None, inputCol=None, outputCol=None)
+        Sets params for this ElementwiseProduct.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setScalingVec(self, value):
+        """
+        Sets the value of :py:attr:`scalingVec`.
+        """
+        self._paramMap[self.scalingVec] = value
+        return self
+
+    def getScalingVec(self):
+        """
+        Gets the value of scalingVec or its default value.
+        """
+        return self.getOrDefault(self.scalingVec)
+
+
 @inherit_doc
 class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
     """

From 18523c130548f0438dff8d1f25531fd2ed36e517 Mon Sep 17 00:00:00 2001
From: Prayag Chandran <prayagchandran@gmail.com>
Date: Mon, 17 Aug 2015 17:26:08 -0700
Subject: [PATCH 1089/1454] SPARK-8916 [Documentation, MLlib] Add @since tags
 to mllib.regression

Added since tags to mllib.regression

Author: Prayag Chandran <prayagchandran@gmail.com>

Closes #7518 from prayagchandran/sinceTags and squashes the following commits:

fa4dda2 [Prayag Chandran] Re-formatting
6c6d584 [Prayag Chandran] Corrected a few tags. Removed few unnecessary tags
1a0365f [Prayag Chandran] Reformating and adding a few more tags
89fdb66 [Prayag Chandran] SPARK-8916 [Documentation, MLlib] Add @since tags to mllib.regression
---
 .../GeneralizedLinearAlgorithm.scala          | 26 ++++++++++++++-
 .../mllib/regression/IsotonicRegression.scala | 23 ++++++++++++-
 .../spark/mllib/regression/LabeledPoint.scala |  6 ++++
 .../apache/spark/mllib/regression/Lasso.scala | 21 ++++++++++++
 .../mllib/regression/LinearRegression.scala   | 21 ++++++++++++
 .../mllib/regression/RegressionModel.scala    |  9 ++++++
 .../mllib/regression/RidgeRegression.scala    | 21 ++++++++++++
 .../regression/StreamingLinearAlgorithm.scala | 32 ++++++++++++++++---
 .../StreamingLinearRegressionWithSGD.scala    | 21 ++++++++----
 9 files changed, 168 insertions(+), 12 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 6709bd79bc820..2980b94de35b0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -34,6 +34,8 @@ import org.apache.spark.storage.StorageLevel
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 @DeveloperApi
 abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
@@ -53,6 +55,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    *
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
+   *
+   * @since 1.0.0
    */
   def predict(testData: RDD[Vector]): RDD[Double] = {
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
@@ -71,6 +75,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    *
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
+   *
+   * @since 1.0.0
    */
   def predict(testData: Vector): Double = {
     predictPoint(testData, weights, intercept)
@@ -88,6 +94,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
  * :: DeveloperApi ::
  * GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
  * This class should be extended with an Optimizer to create a new GLM.
+ *
+ * @since 0.8.0
  */
 @DeveloperApi
 abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
@@ -95,7 +103,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   protected val validators: Seq[RDD[LabeledPoint] => Boolean] = List()
 
-  /** The optimizer to solve the problem. */
+  /**
+   * The optimizer to solve the problem.
+   *
+   * @since 1.0.0
+   */
   def optimizer: Optimizer
 
   /** Whether to add intercept (default: false). */
@@ -130,6 +142,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /**
    * The dimension of training features.
+   *
+   * @since 1.4.0
    */
   def getNumFeatures: Int = this.numFeatures
 
@@ -153,12 +167,16 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /**
    * Get if the algorithm uses addIntercept
+   *
+   * @since 1.4.0
    */
   def isAddIntercept: Boolean = this.addIntercept
 
   /**
    * Set if the algorithm should add an intercept. Default false.
    * We set the default to false because adding the intercept will cause memory allocation.
+   *
+   * @since 0.8.0
    */
   def setIntercept(addIntercept: Boolean): this.type = {
     this.addIntercept = addIntercept
@@ -167,6 +185,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /**
    * Set if the algorithm should validate data before training. Default true.
+   *
+   * @since 0.8.0
    */
   def setValidateData(validateData: Boolean): this.type = {
     this.validateData = validateData
@@ -176,6 +196,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Run the algorithm with the configured parameters on an input
    * RDD of LabeledPoint entries.
+   *
+   * @since 0.8.0
    */
   def run(input: RDD[LabeledPoint]): M = {
     if (numFeatures < 0) {
@@ -208,6 +230,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Run the algorithm with the configured parameters on an input RDD
    * of LabeledPoint entries starting from the initial weights provided.
+   *
+   * @since 1.0.0
    */
   def run(input: RDD[LabeledPoint], initialWeights: Vector): M = {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index f3b46c75c05f3..8995591d9e8ce 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -46,6 +46,8 @@ import org.apache.spark.sql.SQLContext
  * @param predictions Array of predictions associated to the boundaries at the same index.
  *                    Results of isotonic regression and therefore monotone.
  * @param isotonic indicates whether this is isotonic or antitonic.
+ *
+ * @since 1.3.0
  */
 @Experimental
 class IsotonicRegressionModel (
@@ -59,7 +61,11 @@ class IsotonicRegressionModel (
   assertOrdered(boundaries)
   assertOrdered(predictions)(predictionOrd)
 
-  /** A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter. */
+  /**
+   * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
+   *
+   * @since 1.4.0
+   */
   def this(boundaries: java.lang.Iterable[Double],
       predictions: java.lang.Iterable[Double],
       isotonic: java.lang.Boolean) = {
@@ -83,6 +89,8 @@ class IsotonicRegressionModel (
    *
    * @param testData Features to be labeled.
    * @return Predicted labels.
+   *
+   * @since 1.3.0
    */
   def predict(testData: RDD[Double]): RDD[Double] = {
     testData.map(predict)
@@ -94,6 +102,8 @@ class IsotonicRegressionModel (
    *
    * @param testData Features to be labeled.
    * @return Predicted labels.
+   *
+   * @since 1.3.0
    */
   def predict(testData: JavaDoubleRDD): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(predict(testData.rdd.retag.asInstanceOf[RDD[Double]]))
@@ -114,6 +124,8 @@ class IsotonicRegressionModel (
    *         3) If testData falls between two values in boundary array then prediction is treated
    *           as piecewise linear function and interpolated value is returned. In case there are
    *           multiple values with the same boundary then the same rules as in 2) are used.
+   *
+   * @since 1.3.0
    */
   def predict(testData: Double): Double = {
 
@@ -148,6 +160,9 @@ class IsotonicRegressionModel (
   /** A convenient method for boundaries called by the Python API. */
   private[mllib] def predictionVector: Vector = Vectors.dense(predictions)
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic)
   }
@@ -155,6 +170,9 @@ class IsotonicRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.4.0
+ */
 object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -200,6 +218,9 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
     }
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def load(sc: SparkContext, path: String): IsotonicRegressionModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index d5fea822ad77b..8b51011eeb297 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -28,6 +28,8 @@ import org.apache.spark.SparkException
  *
  * @param label Label for this data point.
  * @param features List of features for this data point.
+ *
+ * @since 0.8.0
  */
 @BeanInfo
 case class LabeledPoint(label: Double, features: Vector) {
@@ -38,11 +40,15 @@ case class LabeledPoint(label: Double, features: Vector) {
 
 /**
  * Parser for [[org.apache.spark.mllib.regression.LabeledPoint]].
+ *
+ * @since 1.1.0
  */
 object LabeledPoint {
   /**
    * Parses a string resulted from `LabeledPoint#toString` into
    * an [[org.apache.spark.mllib.regression.LabeledPoint]].
+   *
+   * @since 1.1.0
    */
   def parse(s: String): LabeledPoint = {
     if (s.startsWith("(")) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 4f482384f0f38..03eb589b05a0e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -30,6 +30,8 @@ import org.apache.spark.rdd.RDD
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 class LassoModel (
     override val weights: Vector,
@@ -44,6 +46,9 @@ class LassoModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -51,8 +56,14 @@ class LassoModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.3.0
+ */
 object LassoModel extends Loader[LassoModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): LassoModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -106,6 +117,8 @@ class LassoWithSGD private (
 
 /**
  * Top-level methods for calling Lasso.
+ *
+ * @since 0.8.0
  */
 object LassoWithSGD {
 
@@ -123,6 +136,8 @@ object LassoWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   *
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -146,6 +161,8 @@ object LassoWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -167,6 +184,8 @@ object LassoWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -185,6 +204,8 @@ object LassoWithSGD {
    *              matrix A as well as the corresponding right hand side label y
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 9453c4f66c216..fb5c220daaedb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -30,6 +30,8 @@ import org.apache.spark.rdd.RDD
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 class LinearRegressionModel (
     override val weights: Vector,
@@ -44,6 +46,9 @@ class LinearRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -51,8 +56,14 @@ class LinearRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.3.0
+ */
 object LinearRegressionModel extends Loader[LinearRegressionModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): LinearRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -105,6 +116,8 @@ class LinearRegressionWithSGD private[mllib] (
 
 /**
  * Top-level methods for calling LinearRegression.
+ *
+ * @since 0.8.0
  */
 object LinearRegressionWithSGD {
 
@@ -121,6 +134,8 @@ object LinearRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   *
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -142,6 +157,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -161,6 +178,8 @@ object LinearRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of Gradient Descent.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -178,6 +197,8 @@ object LinearRegressionWithSGD {
    *              matrix A as well as the corresponding right hand side label y
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
index 214ac4d0ed7dd..b097fd38fdd82 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
@@ -24,6 +24,9 @@ import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 
+/**
+ * @since 0.8.0
+ */
 @Experimental
 trait RegressionModel extends Serializable {
   /**
@@ -31,6 +34,8 @@ trait RegressionModel extends Serializable {
    *
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
+   *
+   * @since 1.0.0
    */
   def predict(testData: RDD[Vector]): RDD[Double]
 
@@ -39,6 +44,8 @@ trait RegressionModel extends Serializable {
    *
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
+   *
+   * @since 1.0.0
    */
   def predict(testData: Vector): Double
 
@@ -46,6 +53,8 @@ trait RegressionModel extends Serializable {
    * Predict values for examples stored in a JavaRDD.
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
+   *
+   * @since 1.0.0
    */
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 7d28ffad45c92..5bced6b4b7b53 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -31,6 +31,8 @@ import org.apache.spark.rdd.RDD
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 class RidgeRegressionModel (
     override val weights: Vector,
@@ -45,6 +47,9 @@ class RidgeRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -52,8 +57,14 @@ class RidgeRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.3.0
+ */
 object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): RidgeRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -108,6 +119,8 @@ class RidgeRegressionWithSGD private (
 
 /**
  * Top-level methods for calling RidgeRegression.
+ *
+ * @since 0.8.0
  */
 object RidgeRegressionWithSGD {
 
@@ -124,6 +137,8 @@ object RidgeRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -146,6 +161,8 @@ object RidgeRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -166,6 +183,8 @@ object RidgeRegressionWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -183,6 +202,8 @@ object RidgeRegressionWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
index 141052ba813ee..a2ab95c474765 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
@@ -53,6 +53,8 @@ import org.apache.spark.streaming.dstream.DStream
  * It is also ok to call trainOn on different streams; this will update
  * the model using each of the different sources, in sequence.
  *
+ *
+ * @since 1.1.0
  */
 @DeveloperApi
 abstract class StreamingLinearAlgorithm[
@@ -65,7 +67,11 @@ abstract class StreamingLinearAlgorithm[
   /** The algorithm to use for updating. */
   protected val algorithm: A
 
-  /** Return the latest model. */
+  /**
+   * Return the latest model.
+   *
+   * @since 1.1.0
+   */
   def latestModel(): M = {
     model.get
   }
@@ -77,6 +83,8 @@ abstract class StreamingLinearAlgorithm[
    * batch of data from the stream.
    *
    * @param data DStream containing labeled data
+   *
+   * @since 1.3.0
    */
   def trainOn(data: DStream[LabeledPoint]): Unit = {
     if (model.isEmpty) {
@@ -95,7 +103,11 @@ abstract class StreamingLinearAlgorithm[
     }
   }
 
-  /** Java-friendly version of `trainOn`. */
+  /**
+   * Java-friendly version of `trainOn`.
+   *
+   * @since 1.3.0
+   */
   def trainOn(data: JavaDStream[LabeledPoint]): Unit = trainOn(data.dstream)
 
   /**
@@ -103,6 +115,8 @@ abstract class StreamingLinearAlgorithm[
    *
    * @param data DStream containing feature vectors
    * @return DStream containing predictions
+   *
+   * @since 1.1.0
    */
   def predictOn(data: DStream[Vector]): DStream[Double] = {
     if (model.isEmpty) {
@@ -111,7 +125,11 @@ abstract class StreamingLinearAlgorithm[
     data.map{x => model.get.predict(x)}
   }
 
-  /** Java-friendly version of `predictOn`. */
+  /**
+   * Java-friendly version of `predictOn`.
+   *
+   * @since 1.1.0
+   */
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Double] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Double]])
   }
@@ -121,6 +139,8 @@ abstract class StreamingLinearAlgorithm[
    * @param data DStream containing feature vectors
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
+   *
+   * @since 1.1.0
    */
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Double)] = {
     if (model.isEmpty) {
@@ -130,7 +150,11 @@ abstract class StreamingLinearAlgorithm[
   }
 
 
-  /** Java-friendly version of `predictOnValues`. */
+  /**
+   * Java-friendly version of `predictOnValues`.
+   *
+   * @since 1.3.0
+   */
   def predictOnValues[K](data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Double] = {
     implicit val tag = fakeClassTag[K]
     JavaPairDStream.fromPairDStream(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index c6d04464a12ba..537a05274eec2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -39,7 +39,6 @@ import org.apache.spark.mllib.linalg.Vector
  *    .setNumIterations(10)
  *    .setInitialWeights(Vectors.dense(...))
  *    .trainOn(DStream)
- *
  */
 @Experimental
 class StreamingLinearRegressionWithSGD private[mllib] (
@@ -61,31 +60,41 @@ class StreamingLinearRegressionWithSGD private[mllib] (
 
   protected var model: Option[LinearRegressionModel] = None
 
-  /** Set the step size for gradient descent. Default: 0.1. */
+  /**
+   * Set the step size for gradient descent. Default: 0.1.
+   */
   def setStepSize(stepSize: Double): this.type = {
     this.algorithm.optimizer.setStepSize(stepSize)
     this
   }
 
-  /** Set the number of iterations of gradient descent to run per update. Default: 50. */
+  /**
+   * Set the number of iterations of gradient descent to run per update. Default: 50.
+   */
   def setNumIterations(numIterations: Int): this.type = {
     this.algorithm.optimizer.setNumIterations(numIterations)
     this
   }
 
-  /** Set the fraction of each batch to use for updates. Default: 1.0. */
+  /**
+   * Set the fraction of each batch to use for updates. Default: 1.0.
+   */
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
     this
   }
 
-  /** Set the initial weights. */
+  /**
+   * Set the initial weights.
+   */
   def setInitialWeights(initialWeights: Vector): this.type = {
     this.model = Some(algorithm.createModel(initialWeights, 0.0))
     this
   }
 
-  /** Set the convergence tolerance. */
+  /**
+   * Set the convergence tolerance.
+   */
   def setConvergenceTol(tolerance: Double): this.type = {
     this.algorithm.optimizer.setConvergenceTol(tolerance)
     this

From 0b6b01761370629ce387c143a25d41f3a334ff28 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 17 Aug 2015 17:53:24 -0700
Subject: [PATCH 1090/1454] [SPARK-9898] [MLLIB] Prefix Span user guide

Adds user guide for `PrefixSpan`, including Scala and Java example code.

mengxr zhangjiajin

Author: Feynman Liang <fliang@databricks.com>

Closes #8253 from feynmanliang/SPARK-9898.
---
 docs/mllib-frequent-pattern-mining.md | 96 +++++++++++++++++++++++++++
 docs/mllib-guide.md                   |  1 +
 2 files changed, 97 insertions(+)

diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md
index bcc066a185526..8ea4389266484 100644
--- a/docs/mllib-frequent-pattern-mining.md
+++ b/docs/mllib-frequent-pattern-mining.md
@@ -96,3 +96,99 @@ for (FPGrowth.FreqItemset<String> itemset: model.freqItemsets().toJavaRDD().coll
 
 </div>
 </div>
+
+## PrefixSpan
+
+PrefixSpan is a sequential pattern mining algorithm described in
+[Pei et al., Mining Sequential Patterns by Pattern-Growth: The
+PrefixSpan Approach](http://dx.doi.org/10.1109%2FTKDE.2004.77). We refer
+the reader to the referenced paper for formalizing the sequential
+pattern mining problem.
+
+MLlib's PrefixSpan implementation takes the following parameters:
+
+* `minSupport`: the minimum support required to be considered a frequent
+  sequential pattern.
+* `maxPatternLength`: the maximum length of a frequent sequential
+  pattern. Any frequent pattern exceeding this length will not be
+  included in the results.
+* `maxLocalProjDBSize`: the maximum number of items allowed in a
+  prefix-projected database before local iterative processing of the
+  projected databse begins. This parameter should be tuned with respect
+  to the size of your executors.
+
+**Examples**
+
+The following example illustrates PrefixSpan running on the sequences
+(using same notation as Pei et al):
+
+~~~
+  <(12)3>
+  <1(32)(12)>
+  <(12)5>
+  <6>
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`PrefixSpan`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpan) implements the
+PrefixSpan algorithm.
+Calling `PrefixSpan.run` returns a
+[`PrefixSpanModel`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel)
+that stores the frequent sequences with their frequencies.
+
+{% highlight scala %}
+import org.apache.spark.mllib.fpm.PrefixSpan
+
+val sequences = sc.parallelize(Seq(
+    Array(Array(1, 2), Array(3)),
+    Array(Array(1), Array(3, 2), Array(1, 2)),
+    Array(Array(1, 2), Array(5)),
+    Array(Array(6))
+  ), 2).cache()
+val prefixSpan = new PrefixSpan()
+  .setMinSupport(0.5)
+  .setMaxPatternLength(5)
+val model = prefixSpan.run(sequences)
+model.freqSequences.collect().foreach { freqSequence =>
+println(
+  freqSequence.sequence.map(_.mkString("[", ", ", "]")).mkString("[", ", ", "]") + ", " + freqSequence.freq)
+}
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`PrefixSpan`](api/java/org/apache/spark/mllib/fpm/PrefixSpan.html) implements the
+PrefixSpan algorithm.
+Calling `PrefixSpan.run` returns a
+[`PrefixSpanModel`](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html)
+that stores the frequent sequences with their frequencies.
+
+{% highlight java %}
+import java.util.Arrays;
+import java.util.List;
+
+import org.apache.spark.mllib.fpm.PrefixSpan;
+import org.apache.spark.mllib.fpm.PrefixSpanModel;
+
+JavaRDD<List<List<Integer>>> sequences = sc.parallelize(Arrays.asList(
+  Arrays.asList(Arrays.asList(1, 2), Arrays.asList(3)),
+  Arrays.asList(Arrays.asList(1), Arrays.asList(3, 2), Arrays.asList(1, 2)),
+  Arrays.asList(Arrays.asList(1, 2), Arrays.asList(5)),
+  Arrays.asList(Arrays.asList(6))
+), 2);
+PrefixSpan prefixSpan = new PrefixSpan()
+  .setMinSupport(0.5)
+  .setMaxPatternLength(5);
+PrefixSpanModel<Integer> model = prefixSpan.run(sequences);
+for (PrefixSpan.FreqSequence<Integer> freqSeq: model.freqSequences().toJavaRDD().collect()) {
+  System.out.println(freqSeq.javaSequence() + ", " + freqSeq.freq());
+}
+{% endhighlight %}
+
+</div>
+</div>
+
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index e8000ff478300..7851175b98230 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -48,6 +48,7 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
   * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
+  * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
 * [Evaluation Metrics](mllib-evaluation-metrics.html)
 * [Optimization (developer)](mllib-optimization.html)
   * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)

From f9d1a92aa1bac4494022d78559b871149579e6e8 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Mon, 17 Aug 2015 17:57:51 -0700
Subject: [PATCH 1091/1454] [SPARK-7707] User guide and example code for
 KernelDensity

Author: Sandy Ryza <sandy@cloudera.com>

Closes #8230 from sryza/sandy-spark-7707.
---
 docs/mllib-statistics.md | 77 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)

diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index be04d0b4b53a8..80a9d064c0fe5 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -528,5 +528,82 @@ u = RandomRDDs.uniformRDD(sc, 1000000L, 10)
 v = u.map(lambda x: 1.0 + 2.0 * x)
 {% endhighlight %}
 </div>
+</div>
+
+## Kernel density estimation
+
+[Kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation) is a technique
+useful for visualizing empirical probability distributions without requiring assumptions about the
+particular distribution that the observed samples are drawn from. It computes an estimate of the
+probability density function of a random variables, evaluated at a given set of points. It achieves
+this estimate by expressing the PDF of the empirical distribution at a particular point as the the
+mean of PDFs of normal distributions centered around each of the samples.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+[`KernelDensity`](api/scala/index.html#org.apache.spark.mllib.stat.KernelDensity) provides methods
+to compute kernel density estimates from an RDD of samples. The following example demonstrates how
+to do so.
+
+{% highlight scala %}
+import org.apache.spark.mllib.stat.KernelDensity
+import org.apache.spark.rdd.RDD
+
+val data: RDD[Double] = ... // an RDD of sample data
+
+// Construct the density estimator with the sample data and a standard deviation for the Gaussian
+// kernels
+val kd = new KernelDensity()
+  .setSample(data)
+  .setBandwidth(3.0)
+
+// Find density estimates for the given values
+val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+[`KernelDensity`](api/java/index.html#org.apache.spark.mllib.stat.KernelDensity) provides methods
+to compute kernel density estimates from an RDD of samples. The following example demonstrates how
+to do so.
+
+{% highlight java %}
+import org.apache.spark.mllib.stat.KernelDensity;
+import org.apache.spark.rdd.RDD;
+
+RDD<Double> data = ... // an RDD of sample data
+
+// Construct the density estimator with the sample data and a standard deviation for the Gaussian
+// kernels
+KernelDensity kd = new KernelDensity()
+  .setSample(data)
+  .setBandwidth(3.0);
+
+// Find density estimates for the given values
+double[] densities = kd.estimate(new double[] {-1.0, 2.0, 5.0});
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`KernelDensity`](api/python/pyspark.mllib.html#pyspark.mllib.stat.KernelDensity) provides methods
+to compute kernel density estimates from an RDD of samples. The following example demonstrates how
+to do so.
+
+{% highlight python %}
+from pyspark.mllib.stat import KernelDensity
+
+data = ... # an RDD of sample data
+
+# Construct the density estimator with the sample data and a standard deviation for the Gaussian
+# kernels
+kd = KernelDensity()
+kd.setSample(data)
+kd.setBandwidth(3.0)
+
+# Find density estimates for the given values
+densities = kd.estimate([-1.0, 2.0, 5.0])
+{% endhighlight %}
+</div>
 
 </div>

From c90c605dc6a876aef3cc204ac15cd65bab9743ad Mon Sep 17 00:00:00 2001
From: "jose.cambronero" <jose.cambronero@cloudera.com>
Date: Mon, 17 Aug 2015 19:09:45 -0700
Subject: [PATCH 1092/1454] [SPARK-9902] [MLLIB] Add Java and Python examples
 to user guide for 1-sample KS test

added doc examples for python.

Author: jose.cambronero <jose.cambronero@cloudera.com>

Closes #8154 from josepablocam/spark_9902.
---
 docs/mllib-statistics.md | 51 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 47 insertions(+), 4 deletions(-)

diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index 80a9d064c0fe5..6acfc71d7b014 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -438,22 +438,65 @@ run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstra
 and interpret the hypothesis tests.
 
 {% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.mllib.stat.Statistics._
+import org.apache.spark.mllib.stat.Statistics
 
 val data: RDD[Double] = ... // an RDD of sample data
 
 // run a KS test for the sample versus a standard normal distribution
 val testResult = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
 println(testResult) // summary of the test including the p-value, test statistic,
-                      // and null hypothesis
-                      // if our p-value indicates significance, we can reject the null hypothesis
+                    // and null hypothesis
+                    // if our p-value indicates significance, we can reject the null hypothesis
 
 // perform a KS test using a cumulative distribution function of our making
 val myCDF: Double => Double = ...
 val testResult2 = Statistics.kolmogorovSmirnovTest(data, myCDF)
 {% endhighlight %}
 </div>
+
+<div data-lang="java" markdown="1">
+[`Statistics`](api/java/org/apache/spark/mllib/stat/Statistics.html) provides methods to
+run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
+and interpret the hypothesis tests.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+
+import org.apache.spark.mllib.stat.Statistics;
+import org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult;
+
+JavaSparkContext jsc = ...
+JavaDoubleRDD data = jsc.parallelizeDoubles(Arrays.asList(0.2, 1.0, ...));
+KolmogorovSmirnovTestResult testResult = Statistics.kolmogorovSmirnovTest(data, "norm", 0.0, 1.0);
+// summary of the test including the p-value, test statistic,
+// and null hypothesis
+// if our p-value indicates significance, we can reject the null hypothesis
+System.out.println(testResult);
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`Statistics`](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics) provides methods to
+run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
+and interpret the hypothesis tests.
+
+{% highlight python %}
+from pyspark.mllib.stat import Statistics
+
+parallelData = sc.parallelize([1.0, 2.0, ... ])
+
+# run a KS test for the sample versus a standard normal distribution
+testResult = Statistics.kolmogorovSmirnovTest(parallelData, "norm", 0, 1)
+print(testResult) # summary of the test including the p-value, test statistic,
+                  # and null hypothesis
+                  # if our p-value indicates significance, we can reject the null hypothesis
+# Note that the Scala functionality of calling Statistics.kolmogorovSmirnovTest with
+# a lambda to calculate the CDF is not made available in the Python API
+{% endhighlight %}
+</div>
 </div>
 
 

From ee093c8b927e8d488aeb76115c7fb0de96af7720 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 17 Aug 2015 19:35:35 -0700
Subject: [PATCH 1093/1454] [SPARK-10059] [YARN] Explicitly add JSP
 dependencies for tests.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8251 from vanzin/SPARK-10059.
---
 yarn/pom.xml | 22 +++++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)

diff --git a/yarn/pom.xml b/yarn/pom.xml
index 49360c48256ea..15db54e4e7909 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -92,12 +92,28 @@
       <artifactId>jetty-servlet</artifactId>
     </dependency>
     <!-- End of shaded deps. -->
-    
+
+    <!--
+      SPARK-10059: Explicitly add JSP dependencies for tests since the MiniYARN cluster needs them.
+    -->
+    <dependency>
+      <groupId>org.eclipse.jetty.orbit</groupId>
+      <artifactId>javax.servlet.jsp</artifactId>
+      <version>2.2.0.v201112011158</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.eclipse.jetty.orbit</groupId>
+      <artifactId>javax.servlet.jsp.jstl</artifactId>
+      <version>1.2.0.v201105211821</version>
+      <scope>test</scope>
+    </dependency>
+
      <!--
     See SPARK-3710. hadoop-yarn-server-tests in Hadoop 2.2 fails to pull some needed
     dependencies, so they need to be added manually for the tests to work.
     -->
-     
+
     <dependency>
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-yarn-server-tests</artifactId>
@@ -137,7 +153,7 @@
        <scope>test</scope>
      </dependency>
   </dependencies>
-  
+
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>

From e290029a356222bddf4da1be0525a221a5a1630b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 17 Aug 2015 19:40:51 -0700
Subject: [PATCH 1094/1454] [SPARK-7808] [ML] add package doc for ml.feature

This PR adds a short description of `ml.feature` package with code example. The Java package doc will come in a separate PR. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8260 from mengxr/SPARK-7808.
---
 .../org/apache/spark/ml/feature/package.scala | 89 +++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/package.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala
new file mode 100644
index 0000000000000..4571ab26800c0
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml
+
+import org.apache.spark.ml.feature.{HashingTF, IDF, IDFModel, VectorAssembler}
+import org.apache.spark.sql.DataFrame
+
+/**
+ * == Feature transformers ==
+ *
+ * The `ml.feature` package provides common feature transformers that help convert raw data or
+ * features into more suitable forms for model fitting.
+ * Most feature transformers are implemented as [[Transformer]]s, which transform one [[DataFrame]]
+ * into another, e.g., [[HashingTF]].
+ * Some feature transformers are implemented as [[Estimator]]s, because the transformation requires
+ * some aggregated information of the dataset, e.g., document frequencies in [[IDF]].
+ * For those feature transformers, calling [[Estimator!.fit]] is required to obtain the model first,
+ * e.g., [[IDFModel]], in order to apply transformation.
+ * The transformation is usually done by appending new columns to the input [[DataFrame]], so all
+ * input columns are carried over.
+ *
+ * We try to make each transformer minimal, so it becomes flexible to assemble feature
+ * transformation pipelines.
+ * [[Pipeline]] can be used to chain feature transformers, and [[VectorAssembler]] can be used to
+ * combine multiple feature transformations, for example:
+ *
+ * {{{
+ *   import org.apache.spark.ml.feature._
+ *   import org.apache.spark.ml.Pipeline
+ *
+ *   // a DataFrame with three columns: id (integer), text (string), and rating (double).
+ *   val df = sqlContext.createDataFrame(Seq(
+ *     (0, "Hi I heard about Spark", 3.0),
+ *     (1, "I wish Java could use case classes", 4.0),
+ *     (2, "Logistic regression models are neat", 4.0)
+ *   )).toDF("id", "text", "rating")
+ *
+ *   // define feature transformers
+ *   val tok = new RegexTokenizer()
+ *     .setInputCol("text")
+ *     .setOutputCol("words")
+ *   val sw = new StopWordsRemover()
+ *     .setInputCol("words")
+ *     .setOutputCol("filtered_words")
+ *   val tf = new HashingTF()
+ *     .setInputCol("filtered_words")
+ *     .setOutputCol("tf")
+ *     .setNumFeatures(10000)
+ *   val idf = new IDF()
+ *     .setInputCol("tf")
+ *     .setOutputCol("tf_idf")
+ *   val assembler = new VectorAssembler()
+ *     .setInputCols(Array("tf_idf", "rating"))
+ *     .setOutputCol("features")
+ *
+ *   // assemble and fit the feature transformation pipeline
+ *   val pipeline = new Pipeline()
+ *     .setStages(Array(tok, sw, tf, idf, assembler))
+ *   val model = pipeline.fit(df)
+ *
+ *   // save transformed features with raw data
+ *   model.transform(df)
+ *     .select("id", "text", "rating", "features")
+ *     .write.format("parquet").save("/output/path")
+ * }}}
+ *
+ * Some feature transformers implemented in MLlib are inspired by those implemented in scikit-learn.
+ * The major difference is that most scikit-learn feature transformers operate eagerly on the entire
+ * input dataset, while MLlib's feature transformers operate lazily on individual columns,
+ * which is more efficient and flexible to handle large and complex datasets.
+ *
+ * @see [[http://scikit-learn.org/stable/modules/preprocessing.html scikit-learn.preprocessing]]
+ */
+package object feature

From a0910315dae88b033e38a1de07f39ca21f6552ad Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 17 Aug 2015 23:27:11 -0700
Subject: [PATCH 1095/1454] [MINOR] Format the comment of `translate` at
 `functions.scala`

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8265 from yu-iskw/minor-translate-comment.
---
 .../scala/org/apache/spark/sql/functions.scala  | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 79c5f596661d4..435e6319a64c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1863,14 +1863,15 @@ object functions {
   def substring_index(str: Column, delim: String, count: Int): Column =
     SubstringIndex(str.expr, lit(delim).expr, lit(count).expr)
 
-  /* Translate any character in the src by a character in replaceString.
-  * The characters in replaceString is corresponding to the characters in matchingString.
-  * The translate will happen when any character in the string matching with the character
-  * in the matchingString.
-  *
-  * @group string_funcs
-  * @since 1.5.0
-  */
+  /**
+   * Translate any character in the src by a character in replaceString.
+   * The characters in replaceString is corresponding to the characters in matchingString.
+   * The translate will happen when any character in the string matching with the character
+   * in the matchingString.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
   def translate(src: Column, matchingString: String, replaceString: String): Column =
     StringTranslate(src.expr, lit(matchingString).expr, lit(replaceString).expr)
 

From 5af3838d2e59ed83766f85634e26918baa53819f Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 17 Aug 2015 23:27:55 -0700
Subject: [PATCH 1096/1454] [SPARK-10038] [SQL] fix bug in generated unsafe
 projection when there is binary in ArrayData

The type for array of array in Java is slightly different than array of others.

cc cloud-fan

Author: Davies Liu <davies@databricks.com>

Closes #8250 from davies/array_binary.
---
 .../codegen/GenerateUnsafeProjection.scala    | 12 ++++++++---
 .../codegen/GeneratedProjectionSuite.scala    | 21 ++++++++++++++++++-
 2 files changed, 29 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index b2fb913850794..b570fe86db1aa 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -224,7 +224,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     // go through the input array to calculate how many bytes we need.
     val calculateNumBytes = elementType match {
-      case _ if (ctx.isPrimitiveType(elementType)) =>
+      case _ if ctx.isPrimitiveType(elementType) =>
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
@@ -237,6 +237,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       case _ =>
         val writer = getWriter(elementType)
         val elementSize = s"$writer.getSize($elements[$index])"
+        // TODO(davies): avoid the copy
         val unsafeType = elementType match {
           case _: StructType => "UnsafeRow"
           case _: ArrayType => "UnsafeArrayData"
@@ -249,8 +250,13 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           case _ => ""
         }
 
+        val newElements = if (elementType == BinaryType) {
+          s"new byte[$numElements][]"
+        } else {
+          s"new $unsafeType[$numElements]"
+        }
         s"""
-          final $unsafeType[] $elements = new $unsafeType[$numElements];
+          final $unsafeType[] $elements = $newElements;
           for (int $index = 0; $index < $numElements; $index++) {
             ${convertedElement.code}
             if (!${convertedElement.isNull}) {
@@ -262,7 +268,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     }
 
     val writeElement = elementType match {
-      case _ if (ctx.isPrimitiveType(elementType)) =>
+      case _ if ctx.isPrimitiveType(elementType) =>
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
index 8c7ee8720f7bb..098944a9f4fc5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{StringType, IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -79,4 +79,23 @@ class GeneratedProjectionSuite extends SparkFunSuite {
     val row2 = mutableProj(result)
     assert(result === row2)
   }
+
+  test("generated unsafe projection with array of binary") {
+    val row = InternalRow(
+      Array[Byte](1, 2),
+      new GenericArrayData(Array(Array[Byte](1, 2), null, Array[Byte](3, 4))))
+    val fields = (BinaryType :: ArrayType(BinaryType) :: Nil).toArray[DataType]
+
+    val unsafeProj = UnsafeProjection.create(fields)
+    val unsafeRow: UnsafeRow = unsafeProj(row)
+    assert(java.util.Arrays.equals(unsafeRow.getBinary(0), Array[Byte](1, 2)))
+    assert(java.util.Arrays.equals(unsafeRow.getArray(1).getBinary(0), Array[Byte](1, 2)))
+    assert(unsafeRow.getArray(1).isNullAt(1))
+    assert(unsafeRow.getArray(1).getBinary(1) === null)
+    assert(java.util.Arrays.equals(unsafeRow.getArray(1).getBinary(2), Array[Byte](3, 4)))
+
+    val safeProj = FromUnsafeProjection(fields)
+    val row2 = safeProj(unsafeRow)
+    assert(row2 === row)
+  }
 }

From dd0614fd618ad28cb77aecfbd49bb319b98fdba0 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 17 Aug 2015 23:57:02 -0700
Subject: [PATCH 1097/1454] [SPARK-10076] [ML] make
 MultilayerPerceptronClassifier layers and weights public

Fix the issue that ```layers``` and ```weights``` should be public variables of ```MultilayerPerceptronClassificationModel```. Users can not get ```layers``` and ```weights``` from a ```MultilayerPerceptronClassificationModel``` currently.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8263 from yanboliang/mlp-public.
---
 .../ml/classification/MultilayerPerceptronClassifier.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index c154561886585..ccca4ecc004c3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -172,8 +172,8 @@ class MultilayerPerceptronClassifier(override val uid: String)
 @Experimental
 class MultilayerPerceptronClassificationModel private[ml] (
     override val uid: String,
-    layers: Array[Int],
-    weights: Vector)
+    val layers: Array[Int],
+    val weights: Vector)
   extends PredictionModel[Vector, MultilayerPerceptronClassificationModel]
   with Serializable {
 

From c34e9ff0eac2032283b959fe63b47cc30f28d21c Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Tue, 18 Aug 2015 10:31:11 +0100
Subject: [PATCH 1098/1454] [MINOR] fix the comments in
 IndexShuffleBlockResolver

it might be a typo  introduced at the first moment or some leftover after some renaming......

the name of the method accessing the index file is called `getBlockData` now (not `getBlockLocation` as indicated in the comments)

Author: CodingCat <zhunansjtu@gmail.com>

Closes #8238 from CodingCat/minor_1.
---
 .../org/apache/spark/shuffle/IndexShuffleBlockResolver.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
index fae69551e7330..d0163d326dba7 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
@@ -71,7 +71,7 @@ private[spark] class IndexShuffleBlockResolver(conf: SparkConf) extends ShuffleB
 
   /**
    * Write an index file with the offsets of each block, plus a final offset at the end for the
-   * end of the output file. This will be used by getBlockLocation to figure out where each block
+   * end of the output file. This will be used by getBlockData to figure out where each block
    * begins and ends.
    * */
   def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]): Unit = {

From 5723d26d7e677b89383de3fcf2c9a821b68a65b7 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 18 Aug 2015 20:15:33 +0800
Subject: [PATCH 1099/1454] [SPARK-8118] [SQL] Redirects Parquet JUL logger via
 SLF4J

Parquet hard coded a JUL logger which always writes to stdout. This PR redirects it via SLF4j JUL bridge handler, so that we can control Parquet logs via `log4j.properties`.

This solution is inspired by https://github.com/Parquet/parquet-mr/issues/390#issuecomment-46064909.

Author: Cheng Lian <lian@databricks.com>

Closes #8196 from liancheng/spark-8118/redirect-parquet-jul.
---
 conf/log4j.properties.template                |  2 +
 .../datasources/parquet/ParquetRelation.scala | 77 ++++++++++---------
 .../parquet/ParquetTableSupport.scala         |  1 -
 .../parquet/ParquetTypesConverter.scala       |  3 -
 sql/hive/src/test/resources/log4j.properties  |  7 +-
 5 files changed, 47 insertions(+), 43 deletions(-)

diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template
index 27006e45e932b..74c5cea94403a 100644
--- a/conf/log4j.properties.template
+++ b/conf/log4j.properties.template
@@ -10,6 +10,8 @@ log4j.logger.org.spark-project.jetty=WARN
 log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
+log4j.logger.org.apache.parquet=ERROR
+log4j.logger.parquet=ERROR
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
 log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 52fac18ba187a..68169d48ac57c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution.datasources.parquet
 
 import java.net.URI
-import java.util.logging.{Level, Logger => JLogger}
+import java.util.logging.{Logger => JLogger}
 import java.util.{List => JList}
 
 import scala.collection.JavaConversions._
@@ -31,22 +31,22 @@ import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.parquet.filter2.predicate.FilterApi
+import org.apache.parquet.hadoop._
 import org.apache.parquet.hadoop.metadata.CompressionCodecName
 import org.apache.parquet.hadoop.util.ContextUtil
-import org.apache.parquet.hadoop.{ParquetOutputCommitter, ParquetRecordReader, _}
 import org.apache.parquet.schema.MessageType
-import org.apache.parquet.{Log => ParquetLog}
+import org.apache.parquet.{Log => ApacheParquetLog}
+import org.slf4j.bridge.SLF4JBridgeHandler
 
-import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.{SqlNewHadoopPartition, SqlNewHadoopRDD, RDD}
-import org.apache.spark.rdd.RDD._
+import org.apache.spark.rdd.{RDD, SqlNewHadoopPartition, SqlNewHadoopRDD}
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{DataType, StructType}
 import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
 
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
@@ -759,38 +759,39 @@ private[sql] object ParquetRelation extends Logging {
     }.toOption
   }
 
-  def enableLogForwarding() {
-    // Note: the org.apache.parquet.Log class has a static initializer that
-    // sets the java.util.logging Logger for "org.apache.parquet". This
-    // checks first to see if there's any handlers already set
-    // and if not it creates them. If this method executes prior
-    // to that class being loaded then:
-    //  1) there's no handlers installed so there's none to
-    // remove. But when it IS finally loaded the desired affect
-    // of removing them is circumvented.
-    //  2) The parquet.Log static initializer calls setUseParentHandlers(false)
-    // undoing the attempt to override the logging here.
-    //
-    // Therefore we need to force the class to be loaded.
-    // This should really be resolved by Parquet.
-    Utils.classForName(classOf[ParquetLog].getName)
-
-    // Note: Logger.getLogger("parquet") has a default logger
-    // that appends to Console which needs to be cleared.
-    val parquetLogger = JLogger.getLogger(classOf[ParquetLog].getPackage.getName)
-    parquetLogger.getHandlers.foreach(parquetLogger.removeHandler)
-    parquetLogger.setUseParentHandlers(true)
-
-    // Disables a WARN log message in ParquetOutputCommitter.  We first ensure that
-    // ParquetOutputCommitter is loaded and the static LOG field gets initialized.
-    // See https://issues.apache.org/jira/browse/SPARK-5968 for details
-    Utils.classForName(classOf[ParquetOutputCommitter].getName)
-    JLogger.getLogger(classOf[ParquetOutputCommitter].getName).setLevel(Level.OFF)
-
-    // Similar as above, disables a unnecessary WARN log message in ParquetRecordReader.
-    // See https://issues.apache.org/jira/browse/PARQUET-220 for details
-    Utils.classForName(classOf[ParquetRecordReader[_]].getName)
-    JLogger.getLogger(classOf[ParquetRecordReader[_]].getName).setLevel(Level.OFF)
+  // JUL loggers must be held by a strong reference, otherwise they may get destroyed by GC.
+  // However, the root JUL logger used by Parquet isn't properly referenced.  Here we keep
+  // references to loggers in both parquet-mr <= 1.6 and >= 1.7
+  val apacheParquetLogger: JLogger = JLogger.getLogger(classOf[ApacheParquetLog].getPackage.getName)
+  val parquetLogger: JLogger = JLogger.getLogger("parquet")
+
+  // Parquet initializes its own JUL logger in a static block which always prints to stdout.  Here
+  // we redirect the JUL logger via SLF4J JUL bridge handler.
+  val redirectParquetLogsViaSLF4J: Unit = {
+    def redirect(logger: JLogger): Unit = {
+      logger.getHandlers.foreach(logger.removeHandler)
+      logger.setUseParentHandlers(false)
+      logger.addHandler(new SLF4JBridgeHandler)
+    }
+
+    // For parquet-mr 1.7.0 and above versions, which are under `org.apache.parquet` namespace.
+    // scalastyle:off classforname
+    Class.forName(classOf[ApacheParquetLog].getName)
+    // scalastyle:on classforname
+    redirect(JLogger.getLogger(classOf[ApacheParquetLog].getPackage.getName))
+
+    // For parquet-mr 1.6.0 and lower versions bundled with Hive, which are under `parquet`
+    // namespace.
+    try {
+      // scalastyle:off classforname
+      Class.forName("parquet.Log")
+      // scalastyle:on classforname
+      redirect(JLogger.getLogger("parquet"))
+    } catch { case _: Throwable =>
+      // SPARK-9974: com.twitter:parquet-hadoop-bundle:1.6.0 is not packaged into the assembly jar
+      // when Spark is built with SBT. So `parquet.Log` may not be found.  This try/catch block
+      // should be removed after this issue is fixed.
+    }
   }
 
   // The parquet compression short names
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
index 3191cf3d121bb..ed89aa27aa1f0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
@@ -52,7 +52,6 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
     }
 
     log.debug(s"write support initialized for requested schema $attributes")
-    ParquetRelation.enableLogForwarding()
     new WriteSupport.WriteContext(ParquetTypesConverter.convertFromAttributes(attributes), metadata)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 019db34fc666d..42376ef7a9c1f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -104,7 +104,6 @@ private[parquet] object ParquetTypesConverter extends Logging {
       extraMetadata,
       "Spark")
 
-    ParquetRelation.enableLogForwarding()
     ParquetFileWriter.writeMetadataFile(
       conf,
       path,
@@ -140,8 +139,6 @@ private[parquet] object ParquetTypesConverter extends Logging {
           (name(0) == '.' || name(0) == '_') && name != ParquetFileWriter.PARQUET_METADATA_FILE
         }
 
-    ParquetRelation.enableLogForwarding()
-
     // NOTE (lian): Parquet "_metadata" file can be very slow if the file consists of lots of row
     // groups. Since Parquet schema is replicated among all row groups, we only need to touch a
     // single row group to read schema related metadata. Notice that we are making assumptions that
diff --git a/sql/hive/src/test/resources/log4j.properties b/sql/hive/src/test/resources/log4j.properties
index 92eaf1f2795ba..fea3404769d9d 100644
--- a/sql/hive/src/test/resources/log4j.properties
+++ b/sql/hive/src/test/resources/log4j.properties
@@ -48,9 +48,14 @@ log4j.logger.hive.log=OFF
 log4j.additivity.parquet.hadoop.ParquetRecordReader=false
 log4j.logger.parquet.hadoop.ParquetRecordReader=OFF
 
+log4j.additivity.org.apache.parquet.hadoop.ParquetRecordReader=false
+log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
+
+log4j.additivity.org.apache.parquet.hadoop.ParquetOutputCommitter=false
+log4j.logger.org.apache.parquet.hadoop.ParquetOutputCommitter=OFF
+
 log4j.additivity.hive.ql.metadata.Hive=false
 log4j.logger.hive.ql.metadata.Hive=OFF
 
 log4j.additivity.org.apache.hadoop.hive.ql.io.RCFile=false
 log4j.logger.org.apache.hadoop.hive.ql.io.RCFile=ERROR
-

From 1968276af0f681fe51328b7dd795bd21724a5441 Mon Sep 17 00:00:00 2001
From: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 18 Aug 2015 09:10:59 -0700
Subject: [PATCH 1100/1454] [SPARK-10007] [SPARKR] Update `NAMESPACE` file in
 SparkR for simple parameters functions

### JIRA
[[SPARK-10007] Update `NAMESPACE` file in SparkR for simple parameters functions - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10007)

Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8277 from yu-iskw/SPARK-10007.
---
 R/pkg/NAMESPACE | 50 ++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 47 insertions(+), 3 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index fd9dfdf60edb3..607aef26115d5 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -87,48 +87,86 @@ exportMethods("abs",
               "alias",
               "approxCountDistinct",
               "asc",
+              "ascii",
               "asin",
               "atan",
               "atan2",
               "avg",
+              "base64",
               "between",
+              "bin",
+              "bitwiseNOT",
               "cast",
               "cbrt",
+              "ceil",
               "ceiling",
+              "concat",
               "contains",
               "cos",
               "cosh",
-              "concat",
+              "count",
               "countDistinct",
+              "crc32",
+              "datediff",
+              "dayofmonth",
+              "dayofyear",
               "desc",
               "endsWith",
               "exp",
+              "explode",
               "expm1",
+              "factorial",
+              "first",
               "floor",
               "getField",
               "getItem",
               "greatest",
+              "hex",
+              "hour",
               "hypot",
+              "initcap",
+              "isNaN",
               "isNotNull",
               "isNull",
-              "lit",
               "last",
+              "last_day",
               "least",
+              "length",
+              "levenshtein",
               "like",
+              "lit",
               "log",
               "log10",
               "log1p",
+              "log2",
               "lower",
+              "ltrim",
               "max",
+              "md5",
               "mean",
               "min",
+              "minute",
+              "month",
+              "months_between",
               "n",
               "n_distinct",
+              "nanvl",
+              "negate",
+              "pmod",
+              "quarter",
+              "reverse",
               "rint",
               "rlike",
+              "round",
+              "rtrim",
+              "second",
+              "sha1",
               "sign",
+              "signum",
               "sin",
               "sinh",
+              "size",
+              "soundex",
               "sqrt",
               "startsWith",
               "substr",
@@ -138,7 +176,13 @@ exportMethods("abs",
               "tanh",
               "toDegrees",
               "toRadians",
-              "upper")
+              "to_date",
+              "trim",
+              "unbase64",
+              "unhex",
+              "upper",
+              "weekofyear",
+              "year")
 
 exportClasses("GroupedData")
 exportMethods("agg")

From 354f4582b637fa25d3892ec2b12869db50ed83c9 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 18 Aug 2015 11:00:09 -0700
Subject: [PATCH 1101/1454] [SPARK-9028] [ML] Add CountVectorizer as an
 estimator to generate CountVectorizerModel

jira: https://issues.apache.org/jira/browse/SPARK-9028

Add an estimator for CountVectorizerModel. The estimator will extract a vocabulary from document collections according to the term frequency.

I changed the meaning of minCount as a filter across the corpus. This aligns with Word2Vec and the similar parameter in SKlearn.

Author: Yuhao Yang <hhbyyh@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7388 from hhbyyh/cvEstimator.
---
 .../spark/ml/feature/CountVectorizer.scala    | 235 ++++++++++++++++++
 .../ml/feature/CountVectorizerModel.scala     |  82 ------
 .../ml/feature/CountVectorizerSuite.scala     | 167 +++++++++++++
 .../ml/feature/CountVectorizorSuite.scala     |  73 ------
 4 files changed, 402 insertions(+), 155 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
 delete mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
 delete mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
new file mode 100644
index 0000000000000..49028e4b85064
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.util.collection.OpenHashMap
+
+/**
+ * Params for [[CountVectorizer]] and [[CountVectorizerModel]].
+ */
+private[feature] trait CountVectorizerParams extends Params with HasInputCol with HasOutputCol {
+
+  /**
+   * Max size of the vocabulary.
+   * CountVectorizer will build a vocabulary that only considers the top
+   * vocabSize terms ordered by term frequency across the corpus.
+   *
+   * Default: 2^18^
+   * @group param
+   */
+  val vocabSize: IntParam =
+    new IntParam(this, "vocabSize", "max size of the vocabulary", ParamValidators.gt(0))
+
+  /** @group getParam */
+  def getVocabSize: Int = $(vocabSize)
+
+  /**
+   * Specifies the minimum number of different documents a term must appear in to be included
+   * in the vocabulary.
+   * If this is an integer >= 1, this specifies the number of documents the term must appear in;
+   * if this is a double in [0,1), then this specifies the fraction of documents.
+   *
+   * Default: 1
+   * @group param
+   */
+  val minDF: DoubleParam = new DoubleParam(this, "minDF", "Specifies the minimum number of" +
+    " different documents a term must appear in to be included in the vocabulary." +
+    " If this is an integer >= 1, this specifies the number of documents the term must" +
+    " appear in; if this is a double in [0,1), then this specifies the fraction of documents.",
+    ParamValidators.gtEq(0.0))
+
+  /** @group getParam */
+  def getMinDF: Double = $(minDF)
+
+  /** Validates and transforms the input schema. */
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true))
+    SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+  }
+
+  /**
+   * Filter to ignore rare words in a document. For each document, terms with
+   * frequency/count less than the given threshold are ignored.
+   * If this is an integer >= 1, then this specifies a count (of times the term must appear
+   * in the document);
+   * if this is a double in [0,1), then this specifies a fraction (out of the document's token
+   * count).
+   *
+   * Note that the parameter is only used in transform of [[CountVectorizerModel]] and does not
+   * affect fitting.
+   *
+   * Default: 1
+   * @group param
+   */
+  val minTF: DoubleParam = new DoubleParam(this, "minTF", "Filter to ignore rare words in" +
+    " a document. For each document, terms with frequency/count less than the given threshold are" +
+    " ignored. If this is an integer >= 1, then this specifies a count (of times the term must" +
+    " appear in the document); if this is a double in [0,1), then this specifies a fraction (out" +
+    " of the document's token count). Note that the parameter is only used in transform of" +
+    " CountVectorizerModel and does not affect fitting.", ParamValidators.gtEq(0.0))
+
+  setDefault(minTF -> 1)
+
+  /** @group getParam */
+  def getMinTF: Double = $(minTF)
+}
+
+/**
+ * :: Experimental ::
+ * Extracts a vocabulary from document collections and generates a [[CountVectorizerModel]].
+ */
+@Experimental
+class CountVectorizer(override val uid: String)
+  extends Estimator[CountVectorizerModel] with CountVectorizerParams {
+
+  def this() = this(Identifiable.randomUID("cntVec"))
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setVocabSize(value: Int): this.type = set(vocabSize, value)
+
+  /** @group setParam */
+  def setMinDF(value: Double): this.type = set(minDF, value)
+
+  /** @group setParam */
+  def setMinTF(value: Double): this.type = set(minTF, value)
+
+  setDefault(vocabSize -> (1 << 18), minDF -> 1)
+
+  override def fit(dataset: DataFrame): CountVectorizerModel = {
+    transformSchema(dataset.schema, logging = true)
+    val vocSize = $(vocabSize)
+    val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0))
+    val minDf = if ($(minDF) >= 1.0) {
+      $(minDF)
+    } else {
+      $(minDF) * input.cache().count()
+    }
+    val wordCounts: RDD[(String, Long)] = input.flatMap { case (tokens) =>
+      val wc = new OpenHashMap[String, Long]
+      tokens.foreach { w =>
+        wc.changeValue(w, 1L, _ + 1L)
+      }
+      wc.map { case (word, count) => (word, (count, 1)) }
+    }.reduceByKey { case ((wc1, df1), (wc2, df2)) =>
+      (wc1 + wc2, df1 + df2)
+    }.filter { case (word, (wc, df)) =>
+      df >= minDf
+    }.map { case (word, (count, dfCount)) =>
+      (word, count)
+    }.cache()
+    val fullVocabSize = wordCounts.count()
+    val vocab: Array[String] = {
+      val tmpSortedWC: Array[(String, Long)] = if (fullVocabSize <= vocSize) {
+        // Use all terms
+        wordCounts.collect().sortBy(-_._2)
+      } else {
+        // Sort terms to select vocab
+        wordCounts.sortBy(_._2, ascending = false).take(vocSize)
+      }
+      tmpSortedWC.map(_._1)
+    }
+
+    require(vocab.length > 0, "The vocabulary size should be > 0. Lower minDF as necessary.")
+    copyValues(new CountVectorizerModel(uid, vocab).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): CountVectorizer = defaultCopy(extra)
+}
+
+/**
+ * :: Experimental ::
+ * Converts a text document to a sparse vector of token counts.
+ * @param vocabulary An Array over terms. Only the terms in the vocabulary will be counted.
+ */
+@Experimental
+class CountVectorizerModel(override val uid: String, val vocabulary: Array[String])
+  extends Model[CountVectorizerModel] with CountVectorizerParams {
+
+  def this(vocabulary: Array[String]) = {
+    this(Identifiable.randomUID("cntVecModel"), vocabulary)
+    set(vocabSize, vocabulary.length)
+  }
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setMinTF(value: Double): this.type = set(minTF, value)
+
+  /** Dictionary created from [[vocabulary]] and its indices, broadcast once for [[transform()]] */
+  private var broadcastDict: Option[Broadcast[Map[String, Int]]] = None
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    if (broadcastDict.isEmpty) {
+      val dict = vocabulary.zipWithIndex.toMap
+      broadcastDict = Some(dataset.sqlContext.sparkContext.broadcast(dict))
+    }
+    val dictBr = broadcastDict.get
+    val minTf = $(minTF)
+    val vectorizer = udf { (document: Seq[String]) =>
+      val termCounts = new OpenHashMap[Int, Double]
+      var tokenCount = 0L
+      document.foreach { term =>
+        dictBr.value.get(term) match {
+          case Some(index) => termCounts.changeValue(index, 1.0, _ + 1.0)
+          case None => // ignore terms not in the vocabulary
+        }
+        tokenCount += 1
+      }
+      val effectiveMinTF = if (minTf >= 1.0) {
+        minTf
+      } else {
+        tokenCount * minTf
+      }
+      Vectors.sparse(dictBr.value.size, termCounts.filter(_._2 >= effectiveMinTF).toSeq)
+    }
+    dataset.withColumn($(outputCol), vectorizer(col($(inputCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): CountVectorizerModel = {
+    val copied = new CountVectorizerModel(uid, vocabulary).setParent(parent)
+    copyValues(copied, extra)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
deleted file mode 100644
index 6b77de89a0330..0000000000000
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.feature
-
-import scala.collection.mutable
-
-import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{ParamMap, ParamValidators, IntParam}
-import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.linalg.{Vectors, VectorUDT, Vector}
-import org.apache.spark.sql.types.{StringType, ArrayType, DataType}
-
-/**
- * :: Experimental ::
- * Converts a text document to a sparse vector of token counts.
- * @param vocabulary An Array over terms. Only the terms in the vocabulary will be counted.
- */
-@Experimental
-class CountVectorizerModel (override val uid: String, val vocabulary: Array[String])
-  extends UnaryTransformer[Seq[String], Vector, CountVectorizerModel] {
-
-  def this(vocabulary: Array[String]) =
-    this(Identifiable.randomUID("cntVec"), vocabulary)
-
-  /**
-   * Corpus-specific filter to ignore scarce words in a document. For each document, terms with
-   * frequency (count) less than the given threshold are ignored.
-   * Default: 1
-   * @group param
-   */
-  val minTermFreq: IntParam = new IntParam(this, "minTermFreq",
-    "minimum frequency (count) filter used to neglect scarce words (>= 1). For each document, " +
-      "terms with frequency less than the given threshold are ignored.", ParamValidators.gtEq(1))
-
-  /** @group setParam */
-  def setMinTermFreq(value: Int): this.type = set(minTermFreq, value)
-
-  /** @group getParam */
-  def getMinTermFreq: Int = $(minTermFreq)
-
-  setDefault(minTermFreq -> 1)
-
-  override protected def createTransformFunc: Seq[String] => Vector = {
-    val dict = vocabulary.zipWithIndex.toMap
-    document =>
-      val termCounts = mutable.HashMap.empty[Int, Double]
-      document.foreach { term =>
-        dict.get(term) match {
-          case Some(index) => termCounts.put(index, termCounts.getOrElse(index, 0.0) + 1.0)
-          case None => // ignore terms not in the vocabulary
-        }
-      }
-      Vectors.sparse(dict.size, termCounts.filter(_._2 >= $(minTermFreq)).toSeq)
-  }
-
-  override protected def validateInputType(inputType: DataType): Unit = {
-    require(inputType.sameType(ArrayType(StringType)),
-      s"Input type must be ArrayType(StringType) but got $inputType.")
-  }
-
-  override protected def outputDataType: DataType = new VectorUDT()
-
-  override def copy(extra: ParamMap): CountVectorizerModel = {
-    val copied = new CountVectorizerModel(uid, vocabulary)
-    copyValues(copied, extra)
-  }
-}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
new file mode 100644
index 0000000000000..e192fa4850af0
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
@@ -0,0 +1,167 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.sql.Row
+
+class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    ParamsSuite.checkParams(new CountVectorizerModel(Array("empty")))
+  }
+
+  private def split(s: String): Seq[String] = s.split("\\s+")
+
+  test("CountVectorizerModel common cases") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a b c d"),
+        Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0)))),
+      (1, split("a b b c d  a"),
+        Vectors.sparse(4, Seq((0, 2.0), (1, 2.0), (2, 1.0), (3, 1.0)))),
+      (2, split("a"), Vectors.sparse(4, Seq((0, 1.0)))),
+      (3, split(""), Vectors.sparse(4, Seq())), // empty string
+      (4, split("a notInDict d"),
+        Vectors.sparse(4, Seq((0, 1.0), (3, 1.0))))  // with words not in vocabulary
+    )).toDF("id", "words", "expected")
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizer common cases") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a b c d e"),
+        Vectors.sparse(5, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0), (4, 1.0)))),
+      (1, split("a a a a a a"), Vectors.sparse(5, Seq((0, 6.0)))),
+      (2, split("c"), Vectors.sparse(5, Seq((2, 1.0)))),
+      (3, split("b b b b b"), Vectors.sparse(5, Seq((1, 5.0)))))
+    ).toDF("id", "words", "expected")
+    val cv = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .fit(df)
+    assert(cv.vocabulary === Array("a", "b", "c", "d", "e"))
+
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizer vocabSize and minDF") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a b c d"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
+      (1, split("a b c"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
+      (2, split("a b"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
+      (3, split("a"), Vectors.sparse(3, Seq((0, 1.0)))))
+    ).toDF("id", "words", "expected")
+    val cvModel = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setVocabSize(3)  // limit vocab size to 3
+      .fit(df)
+    assert(cvModel.vocabulary === Array("a", "b", "c"))
+
+    // minDF: ignore terms with count less than 3
+    val cvModel2 = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinDF(3)
+      .fit(df)
+    assert(cvModel2.vocabulary === Array("a", "b"))
+
+    cvModel2.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+
+    // minDF: ignore terms with freq < 0.75
+    val cvModel3 = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinDF(3.0 / df.count())
+      .fit(df)
+    assert(cvModel3.vocabulary === Array("a", "b"))
+
+    cvModel3.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizer throws exception when vocab is empty") {
+    intercept[IllegalArgumentException] {
+      val df = sqlContext.createDataFrame(Seq(
+        (0, split("a a b b c c")),
+        (1, split("aa bb cc")))
+      ).toDF("id", "words")
+      val cvModel = new CountVectorizer()
+        .setInputCol("words")
+        .setOutputCol("features")
+        .setVocabSize(3) // limit vocab size to 3
+        .setMinDF(3)
+        .fit(df)
+    }
+  }
+
+  test("CountVectorizerModel with minTF count") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a a a b b c c c d "), Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
+      (1, split("c c c c c c"), Vectors.sparse(4, Seq((2, 6.0)))),
+      (2, split("a"), Vectors.sparse(4, Seq())),
+      (3, split("e e e e e"), Vectors.sparse(4, Seq())))
+    ).toDF("id", "words", "expected")
+
+    // minTF: count
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinTF(3)
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizerModel with minTF freq") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a a a b b c c c d "), Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
+      (1, split("c c c c c c"), Vectors.sparse(4, Seq((2, 6.0)))),
+      (2, split("a"), Vectors.sparse(4, Seq((0, 1.0)))),
+      (3, split("e e e e e"), Vectors.sparse(4, Seq())))
+    ).toDF("id", "words", "expected")
+
+    // minTF: count
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinTF(0.3)
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala
deleted file mode 100644
index e90d9d4ef21ff..0000000000000
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.feature
-
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.ml.param.ParamsSuite
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
-
-class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext {
-
-  test("params") {
-    ParamsSuite.checkParams(new CountVectorizerModel(Array("empty")))
-  }
-
-  test("CountVectorizerModel common cases") {
-    val df = sqlContext.createDataFrame(Seq(
-      (0, "a b c d".split(" ").toSeq,
-        Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0)))),
-      (1, "a b b c d  a".split(" ").toSeq,
-        Vectors.sparse(4, Seq((0, 2.0), (1, 2.0), (2, 1.0), (3, 1.0)))),
-      (2, "a".split(" ").toSeq, Vectors.sparse(4, Seq((0, 1.0)))),
-      (3, "".split(" ").toSeq, Vectors.sparse(4, Seq())), // empty string
-      (4, "a notInDict d".split(" ").toSeq,
-        Vectors.sparse(4, Seq((0, 1.0), (3, 1.0))))  // with words not in vocabulary
-    )).toDF("id", "words", "expected")
-    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
-      .setInputCol("words")
-      .setOutputCol("features")
-    val output = cv.transform(df).collect()
-    output.foreach { p =>
-      val features = p.getAs[Vector]("features")
-      val expected = p.getAs[Vector]("expected")
-      assert(features ~== expected absTol 1e-14)
-    }
-  }
-
-  test("CountVectorizerModel with minTermFreq") {
-    val df = sqlContext.createDataFrame(Seq(
-      (0, "a a a b b c c c d ".split(" ").toSeq, Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
-      (1, "c c c c c c".split(" ").toSeq, Vectors.sparse(4, Seq((2, 6.0)))),
-      (2, "a".split(" ").toSeq, Vectors.sparse(4, Seq())),
-      (3, "e e e e e".split(" ").toSeq, Vectors.sparse(4, Seq())))
-    ).toDF("id", "words", "expected")
-    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
-      .setInputCol("words")
-      .setOutputCol("features")
-      .setMinTermFreq(3)
-    val output = cv.transform(df).collect()
-    output.foreach { p =>
-      val features = p.getAs[Vector]("features")
-      val expected = p.getAs[Vector]("expected")
-      assert(features ~== expected absTol 1e-14)
-    }
-  }
-}
-
-

From c1840a862eb548bc4306e53ee7e9f26986b31832 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 18 Aug 2015 11:36:36 -0700
Subject: [PATCH 1102/1454] [SPARK-7736] [CORE] Fix a race introduced in
 PythonRunner.

The fix for SPARK-7736 introduced a race where a port value of "-1"
could be passed down to the pyspark process, causing it to fail to
connect back to the JVM. This change adds code to fix that race.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8258 from vanzin/SPARK-7736.
---
 .../main/scala/org/apache/spark/deploy/PythonRunner.scala | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index 4277ac2ad13ea..23d01e9cbb9f9 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -52,10 +52,16 @@ object PythonRunner {
         gatewayServer.start()
       }
     })
-    thread.setName("py4j-gateway")
+    thread.setName("py4j-gateway-init")
     thread.setDaemon(true)
     thread.start()
 
+    // Wait until the gateway server has started, so that we know which port is it bound to.
+    // `gatewayServer.start()` will start a new thread and run the server code there, after
+    // initializing the socket, so the thread started above will end as soon as the server is
+    // ready to serve connections.
+    thread.join()
+
     // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the
     // python directories in SPARK_HOME (if set), and any files in the pyFiles argument
     val pathElements = new ArrayBuffer[String]

From f5ea3912900ccdf23e2eb419a342bfe3c0c0b61b Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 18 Aug 2015 12:53:57 -0700
Subject: [PATCH 1103/1454] [SPARK-9900] [MLLIB] User guide for Association
 Rules

Updates FPM user guide to include Association Rules.

Author: Feynman Liang <fliang@databricks.com>

Closes #8207 from feynmanliang/SPARK-9900-arules.
---
 docs/mllib-frequent-pattern-mining.md         | 130 ++++++++++++++++--
 docs/mllib-guide.md                           |   1 +
 .../mllib/fpm/JavaAssociationRulesSuite.java  |   2 +-
 3 files changed, 118 insertions(+), 15 deletions(-)

diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md
index 8ea4389266484..6c06550703d46 100644
--- a/docs/mllib-frequent-pattern-mining.md
+++ b/docs/mllib-frequent-pattern-mining.md
@@ -39,18 +39,30 @@ MLlib's FP-growth implementation takes the following (hyper-)parameters:
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) implements the
-FP-growth algorithm.
-It take a `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type.
+[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth)
+implements the FP-growth algorithm.  It take an `RDD` of transactions,
+where each transaction is an `Iterable` of items of a generic type.
 Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowthModel)
-that stores the frequent itemsets with their frequencies.
+that stores the frequent itemsets with their frequencies.  The following
+example illustrates how to mine frequent itemsets and association rules
+(see [Association
+Rules](mllib-frequent-pattern-mining.html#association-rules) for
+details) from `transactions`.
+
 
 {% highlight scala %}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
 
-val transactions: RDD[Array[String]] = ...
+val transactions: RDD[Array[String]] = sc.parallelize(Seq(
+  "r z h k p",
+  "z y x w v u t s",
+  "s x o n r",
+  "x z y m t s q e",
+  "z",
+  "x z y r q t p")
+  .map(_.split(" ")))
 
 val fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -60,29 +72,48 @@ val model = fpg.run(transactions)
 model.freqItemsets.collect().foreach { itemset =>
   println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
 }
+
+val minConfidence = 0.8
+model.generateAssociationRules(minConfidence).collect().foreach { rule =>
+  println(
+    rule.antecedent.mkString("[", ",", "]")
+      + " => " + rule.consequent .mkString("[", ",", "]")
+      + ", " + rule.confidence)
+}
 {% endhighlight %}
 
 </div>
 
 <div data-lang="java" markdown="1">
 
-[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
-FP-growth algorithm.
-It take an `RDD` of transactions, where each transaction is an `Array` of items of a generic type.
-Calling `FPGrowth.run` with transactions returns an
+[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html)
+implements the FP-growth algorithm.  It take a `JavaRDD` of
+transactions, where each transaction is an `Array` of items of a generic
+type.  Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
-that stores the frequent itemsets with their frequencies.
+that stores the frequent itemsets with their frequencies.  The following
+example illustrates how to mine frequent itemsets and association rules
+(see [Association
+Rules](mllib-frequent-pattern-mining.html#association-rules) for
+details) from `transactions`.
 
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.base.Joiner;
-
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.fpm.AssociationRules;
 import org.apache.spark.mllib.fpm.FPGrowth;
 import org.apache.spark.mllib.fpm.FPGrowthModel;
 
-JavaRDD<List<String>> transactions = ...
+JavaRDD<List<String>> transactions = sc.parallelize(Arrays.asList(
+  Arrays.asList("r z h k p".split(" ")),
+  Arrays.asList("z y x w v u t s".split(" ")),
+  Arrays.asList("s x o n r".split(" ")),
+  Arrays.asList("x z y m t s q e".split(" ")),
+  Arrays.asList("z".split(" ")),
+  Arrays.asList("x z y r q t p".split(" "))), 2);
 
 FPGrowth fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -90,7 +121,78 @@ FPGrowth fpg = new FPGrowth()
 FPGrowthModel<String> model = fpg.run(transactions);
 
 for (FPGrowth.FreqItemset<String> itemset: model.freqItemsets().toJavaRDD().collect()) {
-   System.out.println("[" + Joiner.on(",").join(s.javaItems()) + "], " + s.freq());
+  System.out.println("[" + itemset.javaItems() + "], " + itemset.freq());
+}
+
+double minConfidence = 0.8;
+for (AssociationRules.Rule<String> rule
+    : model.generateAssociationRules(minConfidence).toJavaRDD().collect()) {
+  System.out.println(
+    rule.javaAntecedent() + " => " + rule.javaConsequent() + ", " + rule.confidence());
+}
+{% endhighlight %}
+
+</div>
+</div>
+
+## Association Rules
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[AssociationRules](api/scala/index.html#org.apache.spark.mllib.fpm.AssociationRules)
+implements a parallel rule generation algorithm for constructing rules
+that have a single item as the consequent.
+
+{% highlight scala %}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.fpm.AssociationRules
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
+
+val freqItemsets = sc.parallelize(Seq(
+  new FreqItemset(Array("a"), 15L),
+  new FreqItemset(Array("b"), 35L),
+  new FreqItemset(Array("a", "b"), 12L)
+));
+
+val ar = new AssociationRules()
+  .setMinConfidence(0.8)
+val results = ar.run(freqItemsets)
+
+results.collect().foreach { rule =>
+  println("[" + rule.antecedent.mkString(",")
+    + "=>"
+    + rule.consequent.mkString(",") + "]," + rule.confidence)
+}
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+[AssociationRules](api/java/org/apache/spark/mllib/fpm/AssociationRules.html)
+implements a parallel rule generation algorithm for constructing rules
+that have a single item as the consequent.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.fpm.AssociationRules;
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset;
+
+JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Arrays.asList(
+  new FreqItemset<String>(new String[] {"a"}, 15L),
+  new FreqItemset<String>(new String[] {"b"}, 35L),
+  new FreqItemset<String>(new String[] {"a", "b"}, 12L)
+));
+
+AssociationRules arules = new AssociationRules()
+  .setMinConfidence(0.8);
+JavaRDD<AssociationRules.Rule<String>> results = arules.run(freqItemsets);
+
+for (AssociationRules.Rule<String> rule: results.collect()) {
+  System.out.println(
+    rule.javaAntecedent() + " => " + rule.javaConsequent() + ", " + rule.confidence());
 }
 {% endhighlight %}
 
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 7851175b98230..0573e5d4bc5ff 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -48,6 +48,7 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
   * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
+  * [association rules](mllib-frequent-pattern-mining.html#association-rules)
   * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
 * [Evaluation Metrics](mllib-evaluation-metrics.html)
 * [Optimization (developer)](mllib-optimization.html)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
index b3815ae6039c0..d7c2cb3ae2067 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
@@ -49,7 +49,7 @@ public void runAssociationRules() {
     JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Lists.newArrayList(
       new FreqItemset<String>(new String[] {"a"}, 15L),
       new FreqItemset<String>(new String[] {"b"}, 35L),
-      new FreqItemset<String>(new String[] {"a", "b"}, 18L)
+      new FreqItemset<String>(new String[] {"a", "b"}, 12L)
     ));
 
     JavaRDD<AssociationRules.Rule<String>> results = (new AssociationRules()).run(freqItemsets);

From f4fa61effe34dae2f0eab0bef57b2dee220cf92f Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 18 Aug 2015 12:55:36 -0700
Subject: [PATCH 1104/1454] [SPARK-10029] [MLLIB] [DOC] Add Python examples for
 mllib IsotonicRegression user guide

Add Python examples for mllib IsotonicRegression user guide

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8225 from yanboliang/spark-10029.
---
 docs/mllib-isotonic-regression.md | 35 +++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md
index 5732bc4c7e79e..6aa881f749185 100644
--- a/docs/mllib-isotonic-regression.md
+++ b/docs/mllib-isotonic-regression.md
@@ -160,4 +160,39 @@ model.save(sc.sc(), "myModelPath");
 IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+Data are read from a file where each line has a format label,feature
+i.e. 4710.28,500.00. The data are split to training and testing set.
+Model is created using the training set and a mean squared error is calculated from the predicted
+labels and real labels in the test set.
+
+{% highlight python %}
+import math
+from pyspark.mllib.regression import IsotonicRegression, IsotonicRegressionModel
+
+data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt")
+
+# Create label, feature, weight tuples from input data with weight set to default value 1.0.
+parsedData = data.map(lambda line: tuple([float(x) for x in line.split(',')]) + (1.0,))
+
+# Split data into training (60%) and test (40%) sets.
+training, test = parsedData.randomSplit([0.6, 0.4], 11)
+
+# Create isotonic regression model from training data.
+# Isotonic parameter defaults to true so it is only shown for demonstration
+model = IsotonicRegression.train(training)
+
+# Create tuples of predicted and real labels.
+predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))
+
+# Calculate mean squared error between predicted and real labels.
+meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean()
+print("Mean Squared Error = " + str(meanSquaredError))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = IsotonicRegressionModel.load(sc, "myModelPath")
+{% endhighlight %}
+</div>
 </div>

From 747c2ba8006d5b86f3be8dfa9ace639042a35628 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 18 Aug 2015 12:56:36 -0700
Subject: [PATCH 1105/1454] [SPARK-10032] [PYSPARK] [DOC] Add Python example
 for mllib LDAModel user guide

Add Python example for mllib LDAModel user guide

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8227 from yanboliang/spark-10032.
---
 docs/mllib-clustering.md | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index bb875ae2ae6cb..fd9ab258e196d 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -564,6 +564,34 @@ public class JavaLDAExample {
 {% endhighlight %}
 </div>
 
+<div data-lang="python" markdown="1">
+{% highlight python %}
+from pyspark.mllib.clustering import LDA, LDAModel
+from pyspark.mllib.linalg import Vectors
+
+# Load and parse the data
+data = sc.textFile("data/mllib/sample_lda_data.txt")
+parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
+# Index documents with unique IDs
+corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
+
+# Cluster the documents into three topics using LDA
+ldaModel = LDA.train(corpus, k=3)
+
+# Output topics. Each is a distribution over words (matching word count vectors)
+print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize()) + " words):")
+topics = ldaModel.topicsMatrix()
+for topic in range(3):
+    print("Topic " + str(topic) + ":")
+    for word in range(0, ldaModel.vocabSize()):
+        print(" " + str(topics[word][topic]))
+		
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LDAModel.load(sc, "myModelPath")
+{% endhighlight %}
+</div>
+
 </div>
 
 ## Streaming k-means

From 8bae9015b7e7b4528ca2bc5180771cb95d2aac13 Mon Sep 17 00:00:00 2001
From: Piotr Migdal <pmigdal@gmail.com>
Date: Tue, 18 Aug 2015 12:59:28 -0700
Subject: [PATCH 1106/1454] [SPARK-10085] [MLLIB] [DOCS] removed unnecessary
 numpy array import

See https://issues.apache.org/jira/browse/SPARK-10085

Author: Piotr Migdal <pmigdal@gmail.com>

Closes #8284 from stared/spark-10085.
---
 docs/mllib-linear-methods.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 07655baa414b5..e9b2d276cd389 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -504,7 +504,6 @@ will in the future.
 {% highlight python %}
 from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
 from pyspark.mllib.regression import LabeledPoint
-from numpy import array
 
 # Load and parse the data
 def parsePoint(line):
@@ -676,7 +675,6 @@ Note that the Python API does not yet support model save/load but will in the fu
 
 {% highlight python %}
 from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel
-from numpy import array
 
 # Load and parse the data
 def parsePoint(line):

From bf1d6614dcb8f5974e62e406d9c0f8aac52556d3 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 18 Aug 2015 13:35:45 -0700
Subject: [PATCH 1107/1454] [SPARK-9574] [STREAMING] Remove unnecessary
 contents of spark-streaming-XXX-assembly jars

Removed contents already included in Spark assembly jar from spark-streaming-XXX-assembly jars.

Author: zsxwing <zsxwing@gmail.com>

Closes #8069 from zsxwing/SPARK-9574.
---
 external/flume-assembly/pom.xml     | 11 ++++
 external/kafka-assembly/pom.xml     | 84 +++++++++++++++++++++++++++++
 external/mqtt-assembly/pom.xml      | 74 +++++++++++++++++++++++++
 extras/kinesis-asl-assembly/pom.xml | 79 +++++++++++++++++++++++++++
 pom.xml                             |  2 +-
 5 files changed, 249 insertions(+), 1 deletion(-)

diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
index 13189595d1d6c..e05e4318969ce 100644
--- a/external/flume-assembly/pom.xml
+++ b/external/flume-assembly/pom.xml
@@ -68,6 +68,11 @@
       <artifactId>commons-codec</artifactId>
       <scope>provided</scope>
     </dependency>
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
     <dependency>
       <groupId>commons-net</groupId>
       <artifactId>commons-net</artifactId>
@@ -88,6 +93,12 @@
       <artifactId>avro-ipc</artifactId>
       <scope>provided</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
     <dependency>
       <groupId>org.scala-lang</groupId>
       <artifactId>scala-library</artifactId>
diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml
index 977514fa5a1ec..36342f37bb2ea 100644
--- a/external/kafka-assembly/pom.xml
+++ b/external/kafka-assembly/pom.xml
@@ -47,6 +47,90 @@
       <version>${project.version}</version>
       <scope>provided</scope>
     </dependency>
+    <!--
+      Demote already included in the Spark assembly.
+    -->
+    <dependency>
+      <groupId>commons-codec</groupId>
+      <artifactId>commons-codec</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.jpountz.lz4</groupId>
+      <artifactId>lz4</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.curator</groupId>
+      <artifactId>curator-recipes</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.zookeeper</groupId>
+      <artifactId>zookeeper</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>log4j</groupId>
+      <artifactId>log4j</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.java.dev.jets3t</groupId>
+      <artifactId>jets3t</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.xerial.snappy</groupId>
+      <artifactId>snappy-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
   </dependencies>
 
   <build>
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
index 9c94473053d96..f3e3f93e7ed50 100644
--- a/external/mqtt-assembly/pom.xml
+++ b/external/mqtt-assembly/pom.xml
@@ -47,6 +47,80 @@
       <version>${project.version}</version>
       <scope>provided</scope>
     </dependency>
+    <!--
+      Demote already included in the Spark assembly.
+    -->
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.curator</groupId>
+      <artifactId>curator-recipes</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.zookeeper</groupId>
+      <artifactId>zookeeper</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>log4j</groupId>
+      <artifactId>log4j</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.java.dev.jets3t</groupId>
+      <artifactId>jets3t</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.xerial.snappy</groupId>
+      <artifactId>snappy-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
   </dependencies>
 
   <build>
diff --git a/extras/kinesis-asl-assembly/pom.xml b/extras/kinesis-asl-assembly/pom.xml
index 70d2c9c58f54e..3ca538608f694 100644
--- a/extras/kinesis-asl-assembly/pom.xml
+++ b/extras/kinesis-asl-assembly/pom.xml
@@ -47,6 +47,85 @@
       <version>${project.version}</version>
       <scope>provided</scope>
     </dependency>
+    <!--
+      Demote already included in the Spark assembly.
+    -->
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-databind</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>log4j</groupId>
+      <artifactId>log4j</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.java.dev.jets3t</groupId>
+      <artifactId>jets3t</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-ipc</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.curator</groupId>
+      <artifactId>curator-recipes</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.zookeeper</groupId>
+      <artifactId>zookeeper</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.xerial.snappy</groupId>
+      <artifactId>snappy-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
   </dependencies>
 
   <build>
diff --git a/pom.xml b/pom.xml
index 9bfca1c4170bb..ccfa1ea19b21e 100644
--- a/pom.xml
+++ b/pom.xml
@@ -432,7 +432,7 @@
         <version>${commons-lang3.version}</version>
       </dependency>
       <dependency>
-        <groupId>org.apache.commons</groupId>
+        <groupId>commons-lang</groupId>
         <artifactId>commons-lang</artifactId>
         <version>${commons-lang2.version}</version>
       </dependency>

From 80cb25b228e821a80256546a2f03f73a45cf7645 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 18 Aug 2015 13:50:51 -0700
Subject: [PATCH 1108/1454] [SPARK-10080] [SQL] Fix binary incompatibility for
 $ column interpolation

Turns out that inner classes of inner objects are referenced directly, and thus moving it will break binary compatibility.

Author: Michael Armbrust <michael@databricks.com>

Closes #8281 from marmbrus/binaryCompat.
---
 .../main/scala/org/apache/spark/sql/SQLContext.scala | 11 +++++++++++
 .../scala/org/apache/spark/sql/SQLImplicits.scala    | 10 ----------
 .../org/apache/spark/sql/test/SharedSQLContext.scala | 12 +++++++++++-
 3 files changed, 22 insertions(+), 11 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 53de10d5fa9da..58fe75b59f418 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -334,6 +334,17 @@ class SQLContext(@transient val sparkContext: SparkContext)
   @Experimental
   object implicits extends SQLImplicits with Serializable {
     protected override def _sqlContext: SQLContext = self
+
+    /**
+     * Converts $"col name" into an [[Column]].
+     * @since 1.3.0
+     */
+    // This must live here to preserve binary compatibility with Spark < 1.5.
+    implicit class StringToColumn(val sc: StringContext) {
+      def $(args: Any*): ColumnName = {
+        new ColumnName(sc.s(args: _*))
+      }
+    }
   }
   // scalastyle:on
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
index 5f82372700f2c..47b6f80bed483 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
@@ -33,16 +33,6 @@ import org.apache.spark.unsafe.types.UTF8String
 private[sql] abstract class SQLImplicits {
   protected def _sqlContext: SQLContext
 
-  /**
-   * Converts $"col name" into an [[Column]].
-   * @since 1.3.0
-   */
-  implicit class StringToColumn(val sc: StringContext) {
-    def $(args: Any*): ColumnName = {
-      new ColumnName(sc.s(args: _*))
-    }
-  }
-
   /**
    * An implicit conversion that turns a Scala `Symbol` into a [[Column]].
    * @since 1.3.0
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
index 3cfd822e2a747..8a061b6bc690d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.test
 
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.{ColumnName, SQLContext}
 
 
 /**
@@ -65,4 +65,14 @@ private[sql] trait SharedSQLContext extends SQLTestUtils {
     }
   }
 
+  /**
+   * Converts $"col name" into an [[Column]].
+   * @since 1.3.0
+   */
+  // This must be duplicated here to preserve binary compatibility with Spark < 1.5.
+  implicit class StringToColumn(val sc: StringContext) {
+    def $(args: Any*): ColumnName = {
+      new ColumnName(sc.s(args: _*))
+    }
+  }
 }

From 9b731fad2b43ca18f3c5274062d4c7bc2622ab72 Mon Sep 17 00:00:00 2001
From: Dennis Huo <dhuo@google.com>
Date: Tue, 18 Aug 2015 14:34:20 -0700
Subject: [PATCH 1109/1454] [SPARK-9782] [YARN] Support YARN application tags
 via SparkConf

Add a new test case in yarn/ClientSuite which checks how the various SparkConf
and ClientArguments propagate into the ApplicationSubmissionContext.

Author: Dennis Huo <dhuo@google.com>

Closes #8072 from dennishuo/dhuo-yarn-application-tags.
---
 docs/running-on-yarn.md                       |  8 +++++
 .../org/apache/spark/deploy/yarn/Client.scala | 21 +++++++++++
 .../spark/deploy/yarn/ClientSuite.scala       | 36 +++++++++++++++++++
 3 files changed, 65 insertions(+)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index ec32c419b7c51..8ac26e98da635 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -319,6 +319,14 @@ If you need a reference to the proper location to put log files in the YARN so t
   running against earlier versions, this property will be ignored.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.tags</code></td>
+  <td>(none)</td>
+  <td>
+  Comma-separated list of strings to pass through as YARN application tags appearing
+  in YARN ApplicationReports, which can be used for filtering when querying YARN apps.
+  </td>
+</tr>
 <tr>
   <td><code>spark.yarn.keytab</code></td>
   <td>(none)</td>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 6d63ddaf15852..5c6a716863f66 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -163,6 +163,23 @@ private[spark] class Client(
     appContext.setQueue(args.amQueue)
     appContext.setAMContainerSpec(containerContext)
     appContext.setApplicationType("SPARK")
+    sparkConf.getOption(CONF_SPARK_YARN_APPLICATION_TAGS)
+      .map(StringUtils.getTrimmedStringCollection(_))
+      .filter(!_.isEmpty())
+      .foreach { tagCollection =>
+        try {
+          // The setApplicationTags method was only introduced in Hadoop 2.4+, so we need to use
+          // reflection to set it, printing a warning if a tag was specified but the YARN version
+          // doesn't support it.
+          val method = appContext.getClass().getMethod(
+            "setApplicationTags", classOf[java.util.Set[String]])
+          method.invoke(appContext, new java.util.HashSet[String](tagCollection))
+        } catch {
+          case e: NoSuchMethodException =>
+            logWarning(s"Ignoring $CONF_SPARK_YARN_APPLICATION_TAGS because this version of " +
+              "YARN does not support it")
+        }
+      }
     sparkConf.getOption("spark.yarn.maxAppAttempts").map(_.toInt) match {
       case Some(v) => appContext.setMaxAppAttempts(v)
       case None => logDebug("spark.yarn.maxAppAttempts is not set. " +
@@ -987,6 +1004,10 @@ object Client extends Logging {
   // of the executors
   val CONF_SPARK_YARN_SECONDARY_JARS = "spark.yarn.secondary.jars"
 
+  // Comma-separated list of strings to pass through as YARN application tags appearing
+  // in YARN ApplicationReports, which can be used for filtering when querying YARN.
+  val CONF_SPARK_YARN_APPLICATION_TAGS = "spark.yarn.tags"
+
   // Staging directory is private! -> rwx--------
   val STAGING_DIR_PERMISSION: FsPermission =
     FsPermission.createImmutable(Integer.parseInt("700", 8).toShort)
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
index 837f8d3fa55a7..0a5402c89e764 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
@@ -29,8 +29,11 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce.MRJobConfig
 import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
+import org.apache.hadoop.yarn.api.protocolrecords.GetNewApplicationResponse
 import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.client.api.YarnClientApplication
 import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.util.Records
 import org.mockito.Matchers._
 import org.mockito.Mockito._
 import org.scalatest.{BeforeAndAfterAll, Matchers}
@@ -170,6 +173,39 @@ class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll {
     cp should contain ("/remotePath/my1.jar")
   }
 
+  test("configuration and args propagate through createApplicationSubmissionContext") {
+    val conf = new Configuration()
+    // When parsing tags, duplicates and leading/trailing whitespace should be removed.
+    // Spaces between non-comma strings should be preserved as single tags. Empty strings may or
+    // may not be removed depending on the version of Hadoop being used.
+    val sparkConf = new SparkConf()
+      .set(Client.CONF_SPARK_YARN_APPLICATION_TAGS, ",tag1, dup,tag2 , ,multi word , dup")
+      .set("spark.yarn.maxAppAttempts", "42")
+    val args = new ClientArguments(Array(
+      "--name", "foo-test-app",
+      "--queue", "staging-queue"), sparkConf)
+
+    val appContext = Records.newRecord(classOf[ApplicationSubmissionContext])
+    val getNewApplicationResponse = Records.newRecord(classOf[GetNewApplicationResponse])
+    val containerLaunchContext = Records.newRecord(classOf[ContainerLaunchContext])
+
+    val client = new Client(args, conf, sparkConf)
+    client.createApplicationSubmissionContext(
+      new YarnClientApplication(getNewApplicationResponse, appContext),
+      containerLaunchContext)
+
+    appContext.getApplicationName should be ("foo-test-app")
+    appContext.getQueue should be ("staging-queue")
+    appContext.getAMContainerSpec should be (containerLaunchContext)
+    appContext.getApplicationType should be ("SPARK")
+    appContext.getClass.getMethods.filter(_.getName.equals("getApplicationTags")).foreach{ method =>
+      val tags = method.invoke(appContext).asInstanceOf[java.util.Set[String]]
+      tags should contain allOf ("tag1", "dup", "tag2", "multi word")
+      tags.filter(!_.isEmpty).size should be (4)
+    }
+    appContext.getMaxAppAttempts should be (42)
+  }
+
   object Fixtures {
 
     val knownDefYarnAppCP: Seq[String] =

From fa41e0242f075843beff7dc600d1a6bac004bdc7 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 18 Aug 2015 14:43:05 -0700
Subject: [PATCH 1110/1454] [SPARK-10089] [SQL] Add missing golden files.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8283 from vanzin/SPARK-10089.
---
 ...ry test-0-515e406ffb23f6fd0d8cd34c2b25fbe6 |   3 +
 ...ry test-0-eabbebd5c1d127b1605bfec52d7b7f3f | 500 ++++++++++++++++++
 2 files changed, 503 insertions(+)
 create mode 100644 sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6
 create mode 100644 sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f

diff --git a/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6 b/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6
new file mode 100644
index 0000000000000..9a276bc794c0d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6	
@@ -0,0 +1,3 @@
+476
+172
+622
diff --git a/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f
new file mode 100644
index 0000000000000..444039e75fbac
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f	
@@ -0,0 +1,500 @@
+476
+172
+622
+54
+330
+818
+510
+556
+196
+968
+530
+386
+802
+300
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+448
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+938
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+834
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+834
+978
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+0
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+738
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+544
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+802
+484
+904
+354
+452
+10
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+804
+792
+634
+790
+116
+70
+672
+190
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+336
+68
+458
+466
+286
+944
+644
+996
+320
+390
+84
+642
+860
+238
+978
+916
+156
+152
+82
+446
+984
+298
+898
+436
+456
+276
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+60
+418
+128
+936
+152
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+684
+138
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+226
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+734
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+232
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+256
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+4
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+696
+848
+24
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+402
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+956
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+250
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+328
+848
+374
+764
+10
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+960
+582
+48
+702
+510
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+876
+238
+828
+400
+982
+474
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+496
+958
+610
+834
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+888
+240
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+646
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+956
+356
+936
+620
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+666
+986
+920
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+530
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+36
+924
+984
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+18
+682
+996
+292
+916
+724
+372
+570
+696
+334
+36
+546
+366
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+688
+194
+938
+630
+168
+56
+74
+896
+304
+696
+614
+388
+828
+954
+444
+252
+180
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+400
+194

From 492ac1facbc79ee251d45cff315598ec9935a0e2 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 18 Aug 2015 14:45:19 -0700
Subject: [PATCH 1111/1454] [SPARK-10088] [SQL] Add support for "stored as
 avro" in HiveQL parser.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8282 from vanzin/SPARK-10088.
---
 .../scala/org/apache/spark/sql/hive/HiveQl.scala     | 11 +++++++++++
 .../org/apache/spark/sql/hive/test/TestHive.scala    | 12 ++----------
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index c3f29350101d3..ad33dee555dd2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -729,6 +729,17 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
                 outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat"))
 
+            case "avro" =>
+              tableDesc = tableDesc.copy(
+                inputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat"),
+                outputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.serde2.avro.AvroSerDe"))
+              }
+
             case _ =>
               throw new SemanticException(
                 s"Unrecognized file format in STORED AS clause: ${child.getText}")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 4eae699ac3b51..4da86636ac100 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -25,10 +25,8 @@ import scala.language.implicitConversions
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.exec.FunctionRegistry
-import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat}
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
-import org.apache.hadoop.hive.serde2.avro.AvroSerDe
 
 import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.analysis._
@@ -276,10 +274,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       "INSERT OVERWRITE TABLE serdeins SELECT * FROM src".cmd),
     TestTable("episodes",
       s"""CREATE TABLE episodes (title STRING, air_date STRING, doctor INT)
-         |ROW FORMAT SERDE '${classOf[AvroSerDe].getCanonicalName}'
-         |STORED AS
-         |INPUTFORMAT '${classOf[AvroContainerInputFormat].getCanonicalName}'
-         |OUTPUTFORMAT '${classOf[AvroContainerOutputFormat].getCanonicalName}'
+         |STORED AS avro
          |TBLPROPERTIES (
          |  'avro.schema.literal'='{
          |    "type": "record",
@@ -312,10 +307,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
     TestTable("episodes_part",
       s"""CREATE TABLE episodes_part (title STRING, air_date STRING, doctor INT)
          |PARTITIONED BY (doctor_pt INT)
-         |ROW FORMAT SERDE '${classOf[AvroSerDe].getCanonicalName}'
-         |STORED AS
-         |INPUTFORMAT '${classOf[AvroContainerInputFormat].getCanonicalName}'
-         |OUTPUTFORMAT '${classOf[AvroContainerOutputFormat].getCanonicalName}'
+         |STORED AS avro
          |TBLPROPERTIES (
          |  'avro.schema.literal'='{
          |    "type": "record",

From 1dbffba37a84c62202befd3911d25888f958191d Mon Sep 17 00:00:00 2001
From: Bryan Cutler <bjcutler@us.ibm.com>
Date: Tue, 18 Aug 2015 14:58:30 -0700
Subject: [PATCH 1112/1454] [SPARK-8924] [MLLIB, DOCUMENTATION] Added @since
 tags to mllib.tree

Added since tags to mllib.tree

Author: Bryan Cutler <bjcutler@us.ibm.com>

Closes #7380 from BryanCutler/sinceTag-mllibTree-8924.
---
 .../spark/mllib/tree/DecisionTree.scala       | 13 +++++++
 .../mllib/tree/GradientBoostedTrees.scala     | 10 +++++
 .../spark/mllib/tree/RandomForest.scala       | 10 +++++
 .../spark/mllib/tree/configuration/Algo.scala |  1 +
 .../tree/configuration/BoostingStrategy.scala |  6 +++
 .../tree/configuration/FeatureType.scala      |  1 +
 .../tree/configuration/QuantileStrategy.scala |  1 +
 .../mllib/tree/configuration/Strategy.scala   | 20 +++++++++-
 .../spark/mllib/tree/impurity/Entropy.scala   |  4 ++
 .../spark/mllib/tree/impurity/Gini.scala      |  4 ++
 .../spark/mllib/tree/impurity/Impurity.scala  |  3 ++
 .../spark/mllib/tree/impurity/Variance.scala  |  4 ++
 .../spark/mllib/tree/loss/AbsoluteError.scala |  2 +
 .../spark/mllib/tree/loss/LogLoss.scala       |  2 +
 .../apache/spark/mllib/tree/loss/Loss.scala   |  3 ++
 .../apache/spark/mllib/tree/loss/Losses.scala |  6 +++
 .../spark/mllib/tree/loss/SquaredError.scala  |  2 +
 .../mllib/tree/model/DecisionTreeModel.scala  | 22 +++++++++++
 .../tree/model/InformationGainStats.scala     |  1 +
 .../apache/spark/mllib/tree/model/Node.scala  |  3 ++
 .../spark/mllib/tree/model/Predict.scala      |  1 +
 .../apache/spark/mllib/tree/model/Split.scala |  1 +
 .../mllib/tree/model/treeEnsembleModels.scala | 37 +++++++++++++++++++
 .../org/apache/spark/mllib/tree/package.scala |  1 +
 24 files changed, 157 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index cecd1fed896d5..e5200b86fddd4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -43,6 +43,7 @@ import org.apache.spark.util.random.XORShiftRandom
  * @param strategy The configuration parameters for the tree algorithm which specify the type
  *                 of algorithm (classification, regression, etc.), feature type (continuous,
  *                 categorical), depth of the tree, quantile calculation strategy, etc.
+ * @since 1.0.0
  */
 @Experimental
 class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
@@ -53,6 +54,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
    * Method to train a decision tree model over an RDD
    * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.2.0
    */
   def run(input: RDD[LabeledPoint]): DecisionTreeModel = {
     // Note: random seed will not be used since numTrees = 1.
@@ -62,6 +64,9 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
   }
 }
 
+/**
+ * @since 1.0.0
+ */
 object DecisionTree extends Serializable with Logging {
 
   /**
@@ -79,6 +84,7 @@ object DecisionTree extends Serializable with Logging {
    *                 of algorithm (classification, regression, etc.), feature type (continuous,
    *                 categorical), depth of the tree, quantile calculation strategy, etc.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.0.0
   */
   def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
     new DecisionTree(strategy).run(input)
@@ -100,6 +106,7 @@ object DecisionTree extends Serializable with Logging {
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -127,6 +134,7 @@ object DecisionTree extends Serializable with Logging {
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @param numClasses number of classes for classification. Default value of 2.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.2.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -160,6 +168,7 @@ object DecisionTree extends Serializable with Logging {
    *                                E.g., an entry (n -> k) indicates that feature n is categorical
    *                                with k categories indexed from 0: {0, 1, ..., k-1}.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -192,6 +201,7 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.1.0
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
@@ -207,6 +217,7 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]]
+   * @since 1.1.0
    */
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
@@ -236,6 +247,7 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.1.0
    */
   def trainRegressor(
       input: RDD[LabeledPoint],
@@ -249,6 +261,7 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]]
+   * @since 1.1.0
    */
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index 9ce6faa137c41..143617098637a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -48,6 +48,7 @@ import org.apache.spark.storage.StorageLevel
  *       for other loss functions.
  *
  * @param boostingStrategy Parameters for the gradient boosting algorithm.
+ * @since 1.2.0
  */
 @Experimental
 class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
@@ -57,6 +58,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    * Method to train a gradient boosting model
    * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return a gradient boosted trees model that can be used for prediction
+   * @since 1.2.0
    */
   def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
     val algo = boostingStrategy.treeStrategy.algo
@@ -74,6 +76,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#run]].
+   * @since 1.2.0
    */
   def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = {
     run(input.rdd)
@@ -88,6 +91,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    *                        E.g., these two datasets could be created from an original dataset
    *                        by using [[org.apache.spark.rdd.RDD.randomSplit()]]
    * @return a gradient boosted trees model that can be used for prediction
+   * @since 1.4.0
    */
   def runWithValidation(
       input: RDD[LabeledPoint],
@@ -111,6 +115,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#runWithValidation]].
+   * @since 1.4.0
    */
   def runWithValidation(
       input: JavaRDD[LabeledPoint],
@@ -119,6 +124,9 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
   }
 }
 
+/**
+ * @since 1.2.0
+ */
 object GradientBoostedTrees extends Logging {
 
   /**
@@ -129,6 +137,7 @@ object GradientBoostedTrees extends Logging {
    *              For regression, labels are real numbers.
    * @param boostingStrategy Configuration options for the boosting algorithm.
    * @return a gradient boosted trees model that can be used for prediction
+   * @since 1.2.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -138,6 +147,7 @@ object GradientBoostedTrees extends Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees$#train]]
+   * @since 1.2.0
    */
   def train(
       input: JavaRDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index 069959976a188..9f3230656acc5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -260,6 +260,9 @@ private class RandomForest (
 
 }
 
+/**
+ * @since 1.2.0
+ */
 object RandomForest extends Serializable with Logging {
 
   /**
@@ -276,6 +279,7 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "sqrt".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
+   * @since 1.2.0
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
@@ -313,6 +317,7 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model  that can be used for prediction
+   * @since 1.2.0
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
@@ -332,6 +337,7 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainClassifier]]
+   * @since 1.2.0
    */
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
@@ -362,6 +368,7 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "onethird".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
+   * @since 1.2.0
    */
   def trainRegressor(
       input: RDD[LabeledPoint],
@@ -398,6 +405,7 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
+   * @since 1.2.0
    */
   def trainRegressor(
       input: RDD[LabeledPoint],
@@ -416,6 +424,7 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainRegressor]]
+   * @since 1.2.0
    */
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
@@ -433,6 +442,7 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * List of supported feature subset sampling strategies.
+   * @since 1.2.0
    */
   val supportedFeatureSubsetStrategies: Array[String] =
     Array("auto", "all", "sqrt", "log2", "onethird")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
index b6099259971b7..d9a49aa71fcfb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Enum to select the algorithm for the decision tree
+ * @since 1.0.0
  */
 @Experimental
 object Algo extends Enumeration {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 50fe2ac53da9d..88e5f57e9ab32 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -38,6 +38,7 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  *                      validation input between two iterations is less than the validationTol
  *                      then stop.  Ignored when
  *                      [[org.apache.spark.mllib.tree.GradientBoostedTrees.run()]] is used.
+ * @since 1.2.0
  */
 @Experimental
 case class BoostingStrategy(
@@ -70,6 +71,9 @@ case class BoostingStrategy(
   }
 }
 
+/**
+ * @since 1.2.0
+ */
 @Experimental
 object BoostingStrategy {
 
@@ -77,6 +81,7 @@ object BoostingStrategy {
    * Returns default configuration for the boosting algorithm
    * @param algo Learning goal.  Supported: "Classification" or "Regression"
    * @return Configuration for boosting algorithm
+   * @since 1.2.0
    */
   def defaultParams(algo: String): BoostingStrategy = {
     defaultParams(Algo.fromString(algo))
@@ -88,6 +93,7 @@ object BoostingStrategy {
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Classification]],
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Regression]]
    * @return Configuration for boosting algorithm
+   * @since 1.3.0
    */
   def defaultParams(algo: Algo): BoostingStrategy = {
     val treeStrategy = Strategy.defaultStrategy(algo)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
index f4c877232750f..0684cafa486bd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Enum to describe whether a feature is "continuous" or "categorical"
+ * @since 1.0.0
  */
 @Experimental
 object FeatureType extends Enumeration {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
index 7da976e55a722..2daa63c4d2771 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Enum for selecting the quantile calculation strategy
+ * @since 1.0.0
  */
 @Experimental
 object QuantileStrategy extends Enumeration {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index de2c784809443..7ae25a88bf500 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -66,6 +66,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                           E.g. 10 means that the cache will get checkpointed every 10 updates. If
  *                           the checkpoint directory is not set in
  *                           [[org.apache.spark.SparkContext]], this setting is ignored.
+ * @since 1.0.0
  */
 @Experimental
 class Strategy (
@@ -83,16 +84,23 @@ class Strategy (
     @BeanProperty var useNodeIdCache: Boolean = false,
     @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
 
+  /**
+   * @since 1.2.0
+   */
   def isMulticlassClassification: Boolean = {
     algo == Classification && numClasses > 2
   }
 
+  /**
+   * @since 1.2.0
+   */
   def isMulticlassWithCategoricalFeatures: Boolean = {
     isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
   }
 
   /**
    * Java-friendly constructor for [[org.apache.spark.mllib.tree.configuration.Strategy]]
+   * @since 1.1.0
    */
   def this(
       algo: Algo,
@@ -107,6 +115,7 @@ class Strategy (
 
   /**
    * Sets Algorithm using a String.
+   * @since 1.2.0
    */
   def setAlgo(algo: String): Unit = algo match {
     case "Classification" => setAlgo(Classification)
@@ -115,6 +124,7 @@ class Strategy (
 
   /**
    * Sets categoricalFeaturesInfo using a Java Map.
+   * @since 1.2.0
    */
   def setCategoricalFeaturesInfo(
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer]): Unit = {
@@ -162,7 +172,10 @@ class Strategy (
       s"$subsamplingRate")
   }
 
-  /** Returns a shallow copy of this instance. */
+  /**
+   * Returns a shallow copy of this instance.
+   * @since 1.2.0
+   */
   def copy: Strategy = {
     new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
       quantileCalculationStrategy, categoricalFeaturesInfo, minInstancesPerNode, minInfoGain,
@@ -170,12 +183,16 @@ class Strategy (
   }
 }
 
+/**
+ * @since 1.2.0
+ */
 @Experimental
 object Strategy {
 
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo  "Classification" or "Regression"
+   * @since 1.2.0
    */
   def defaultStrategy(algo: String): Strategy = {
     defaultStrategy(Algo.fromString(algo))
@@ -184,6 +201,7 @@ object Strategy {
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo Algo.Classification or Algo.Regression
+   * @since 1.3.0
    */
   def defaultStrategy(algo: Algo): Strategy = algo match {
     case Algo.Classification =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
index 0768204c33914..0b6c7266dee05 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -23,6 +23,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * :: Experimental ::
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
+ * @since 1.0.0
  */
 @Experimental
 object Entropy extends Impurity {
@@ -35,6 +36,7 @@ object Entropy extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
@@ -62,6 +64,7 @@ object Entropy extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
@@ -70,6 +73,7 @@ object Entropy extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
+   * @since 1.1.0
    */
   def instance: this.type = this
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
index d0077db6832e3..3b0be428833ae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -24,6 +24,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * Class for calculating the
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
  * during binary classification.
+ * @since 1.0.0
  */
 @Experimental
 object Gini extends Impurity {
@@ -34,6 +35,7 @@ object Gini extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
@@ -58,6 +60,7 @@ object Gini extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
@@ -66,6 +69,7 @@ object Gini extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
+   * @since 1.1.0
    */
   def instance: this.type = this
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index 86cee7e430b0a..dd297400058d2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -25,6 +25,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * This trait is used for
  *  (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
  *  (b) calculating impurity values from sufficient statistics.
+ *  @since 1.0.0
  */
 @Experimental
 trait Impurity extends Serializable {
@@ -35,6 +36,7 @@ trait Impurity extends Serializable {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   def calculate(counts: Array[Double], totalCount: Double): Double
@@ -46,6 +48,7 @@ trait Impurity extends Serializable {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   def calculate(count: Double, sum: Double, sumSquares: Double): Double
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
index 04d0cd24e6632..adbe05811f286 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 /**
  * :: Experimental ::
  * Class for calculating variance during regression
+ * @since 1.0.0
  */
 @Experimental
 object Variance extends Impurity {
@@ -32,6 +33,7 @@ object Variance extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double =
@@ -44,6 +46,7 @@ object Variance extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
@@ -57,6 +60,7 @@ object Variance extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
+   * @since 1.0.0
    */
   def instance: this.type = this
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
index 2bdef73c4a8f1..c6e3d0d824dd7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The absolute (L1) error is defined as:
  *  |y - F(x)|
  * where y is the label and F(x) is the model prediction for features x.
+ * @since 1.2.0
  */
 @DeveloperApi
 object AbsoluteError extends Loss {
@@ -40,6 +41,7 @@ object AbsoluteError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
+   * @since 1.2.0
    */
   override def gradient(prediction: Double, label: Double): Double = {
     if (label - prediction < 0) 1.0 else -1.0
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
index 778c24526de70..eee58445a1ec1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
@@ -31,6 +31,7 @@ import org.apache.spark.mllib.util.MLUtils
  * The log loss is defined as:
  *   2 log(1 + exp(-2 y F(x)))
  * where y is a label in {-1, 1} and F(x) is the model prediction for features x.
+ * @since 1.2.0
  */
 @DeveloperApi
 object LogLoss extends Loss {
@@ -42,6 +43,7 @@ object LogLoss extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
+   * @since 1.2.0
    */
   override def gradient(prediction: Double, label: Double): Double = {
     - 4.0 * label / (1.0 + math.exp(2.0 * label * prediction))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
index 64ffccbce073f..7c9fb924645c8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
@@ -26,6 +26,7 @@ import org.apache.spark.rdd.RDD
 /**
  * :: DeveloperApi ::
  * Trait for adding "pluggable" loss functions for the gradient boosting algorithm.
+ * @since 1.2.0
  */
 @DeveloperApi
 trait Loss extends Serializable {
@@ -35,6 +36,7 @@ trait Loss extends Serializable {
    * @param prediction Predicted feature
    * @param label true label.
    * @return Loss gradient.
+   * @since 1.2.0
    */
   def gradient(prediction: Double, label: Double): Double
 
@@ -45,6 +47,7 @@ trait Loss extends Serializable {
    * @param model Model of the weak learner.
    * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return Measure of model error on data
+   * @since 1.2.0
    */
   def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = {
     data.map(point => computeError(model.predict(point.features), point.label)).mean()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
index 42c9ead9884b4..47dc94cde7e03 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
@@ -17,8 +17,14 @@
 
 package org.apache.spark.mllib.tree.loss
 
+/**
+ * @since 1.2.0
+ */
 object Losses {
 
+  /**
+   * @since 1.2.0
+   */
   def fromString(name: String): Loss = name match {
     case "leastSquaresError" => SquaredError
     case "leastAbsoluteError" => AbsoluteError
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
index 011a5d57422f7..ff8903d6956bd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The squared (L2) error is defined as:
  *   (y - F(x))**2
  * where y is the label and F(x) is the model prediction for features x.
+ * @since 1.2.0
  */
 @DeveloperApi
 object SquaredError extends Loss {
@@ -40,6 +41,7 @@ object SquaredError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
+   * @since 1.2.0
    */
   override def gradient(prediction: Double, label: Double): Double = {
     - 2.0 * (label - prediction)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index f2c78bbabff0b..0f386a26601ce 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -40,6 +40,7 @@ import org.apache.spark.util.Utils
  * This model stores the decision tree structure and parameters.
  * @param topNode root node
  * @param algo algorithm type -- classification or regression
+ * @since 1.0.0
  */
 @Experimental
 class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable {
@@ -49,6 +50,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features array representing a single data point
    * @return Double prediction from the trained model
+   * @since 1.0.0
    */
   def predict(features: Vector): Double = {
     topNode.predict(features)
@@ -59,6 +61,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features RDD representing data points to be predicted
    * @return RDD of predictions for each of the given data points
+   * @since 1.0.0
    */
   def predict(features: RDD[Vector]): RDD[Double] = {
     features.map(x => predict(x))
@@ -69,6 +72,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features JavaRDD representing data points to be predicted
    * @return JavaRDD of predictions for each of the given data points
+   * @since 1.2.0
    */
   def predict(features: JavaRDD[Vector]): JavaRDD[Double] = {
     predict(features.rdd)
@@ -76,6 +80,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
 
   /**
    * Get number of nodes in tree, including leaf nodes.
+   * @since 1.1.0
    */
   def numNodes: Int = {
     1 + topNode.numDescendants
@@ -84,6 +89,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   /**
    * Get depth of tree.
    * E.g.: Depth 0 means 1 leaf node.  Depth 1 means 1 internal node and 2 leaf nodes.
+   * @since 1.1.0
    */
   def depth: Int = {
     topNode.subtreeDepth
@@ -109,6 +115,12 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
     header + topNode.subtreeToString(2)
   }
 
+  /**
+   * @param sc  Spark context used to save model data.
+   * @param path  Path specifying the directory in which to save this model.
+   *              If the directory already exists, this method throws an exception.
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     DecisionTreeModel.SaveLoadV1_0.save(sc, path, this)
   }
@@ -116,6 +128,9 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   override protected def formatVersion: String = DecisionTreeModel.formatVersion
 }
 
+/**
+ * @since 1.3.0
+ */
 object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
 
   private[spark] def formatVersion: String = "1.0"
@@ -297,6 +312,13 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
     }
   }
 
+  /**
+   *
+   * @param sc  Spark context used for loading model files.
+   * @param path  Path specifying the directory to which the model was saved.
+   * @return  Model instance
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): DecisionTreeModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index 508bf9c1bdb47..23f0363639120 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
  * @param rightImpurity right node impurity
  * @param leftPredict left node predict
  * @param rightPredict right node predict
+ * @since 1.0.0
  */
 @DeveloperApi
 class InformationGainStats(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index a6d1398fc267b..aca3350c2e535 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -38,6 +38,7 @@ import org.apache.spark.mllib.linalg.Vector
  * @param leftNode  left child
  * @param rightNode right child
  * @param stats information gain stats
+ * @since 1.0.0
  */
 @DeveloperApi
 class Node (
@@ -58,6 +59,7 @@ class Node (
   /**
    * build the left node and right nodes if not leaf
    * @param nodes array of nodes
+   * @since 1.0.0
    */
   @deprecated("build should no longer be used since trees are constructed on-the-fly in training",
     "1.2.0")
@@ -79,6 +81,7 @@ class Node (
    * predict value if node is not leaf
    * @param features feature value
    * @return predicted value
+   * @since 1.1.0
    */
   def predict(features: Vector) : Double = {
     if (isLeaf) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
index 5cbe7c280dbee..be819b59e7048 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -23,6 +23,7 @@ import org.apache.spark.annotation.DeveloperApi
  * Predicted value for a node
  * @param predict predicted value
  * @param prob probability of the label (classification only)
+ * @since 1.2.0
  */
 @DeveloperApi
 class Predict(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
index be6c9b3de5479..18d40530aee1e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -30,6 +30,7 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
  *                  Split left if feature <= threshold, else right.
  * @param featureType type of feature -- categorical or continuous
  * @param categories Split left if categorical feature value is in this set, else right.
+ * @since 1.0.0
  */
 @DeveloperApi
 case class Split(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 905c5fb42bd44..0c629b12a84df 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -45,6 +45,7 @@ import org.apache.spark.util.Utils
  *
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
+ * @since 1.2.0
  */
 @Experimental
 class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel])
@@ -54,6 +55,13 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
 
   require(trees.forall(_.algo == algo))
 
+  /**
+   *
+   * @param sc  Spark context used to save model data.
+   * @param path  Path specifying the directory in which to save this model.
+   *              If the directory already exists, this method throws an exception.
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       RandomForestModel.SaveLoadV1_0.thisClassName)
@@ -62,10 +70,20 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
   override protected def formatVersion: String = RandomForestModel.formatVersion
 }
 
+/**
+ * @since 1.3.0
+ */
 object RandomForestModel extends Loader[RandomForestModel] {
 
   private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion
 
+  /**
+   *
+   * @param sc  Spark context used for loading model files.
+   * @param path  Path specifying the directory to which the model was saved.
+   * @return  Model instance
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): RandomForestModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
@@ -96,6 +114,7 @@ object RandomForestModel extends Loader[RandomForestModel] {
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
  * @param treeWeights tree ensemble weights
+ * @since 1.2.0
  */
 @Experimental
 class GradientBoostedTreesModel(
@@ -107,6 +126,12 @@ class GradientBoostedTreesModel(
 
   require(trees.length == treeWeights.length)
 
+  /**
+   * @param sc  Spark context used to save model data.
+   * @param path  Path specifying the directory in which to save this model.
+   *              If the directory already exists, this method throws an exception.
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       GradientBoostedTreesModel.SaveLoadV1_0.thisClassName)
@@ -118,6 +143,7 @@ class GradientBoostedTreesModel(
    * @param loss evaluation metric.
    * @return an array with index i having the losses or errors for the ensemble
    *         containing the first i+1 trees
+   * @since 1.4.0
    */
   def evaluateEachIteration(
       data: RDD[LabeledPoint],
@@ -159,6 +185,9 @@ class GradientBoostedTreesModel(
   override protected def formatVersion: String = GradientBoostedTreesModel.formatVersion
 }
 
+/**
+ * @since 1.3.0
+ */
 object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
 
   /**
@@ -170,6 +199,7 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to every sample.
+   * @since 1.4.0
    */
   def computeInitialPredictionAndError(
       data: RDD[LabeledPoint],
@@ -193,6 +223,7 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to each sample.
+   * @since 1.4.0
    */
   def updatePredictionError(
     data: RDD[LabeledPoint],
@@ -213,6 +244,12 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
 
   private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion
 
+  /**
+   * @param sc  Spark context used for loading model files.
+   * @param path  Path specifying the directory to which the model was saved.
+   * @return  Model instance
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): GradientBoostedTreesModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
index bcaacc1b1f191..f520b3a1b7c72 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
@@ -24,6 +24,7 @@ package org.apache.spark.mllib
  *  - information loss calculation with entropy and Gini for classification and
  *    variance for regression,
  *  - both continuous and categorical features.
+ *  @since 1.0.0
  */
 package object tree {
 }

From c635a16f64c939182196b46725ef2d00ed107cca Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Tue, 18 Aug 2015 15:30:23 -0700
Subject: [PATCH 1113/1454] [SPARK-10012] [ML] Missing test case for
 Params#arrayLengthGt

Currently there is no test case for `Params#arrayLengthGt`.

Author: lewuathe <lewuathe@me.com>

Closes #8223 from Lewuathe/SPARK-10012.
---
 .../src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index be95638d81686..2c878f8372a47 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -199,6 +199,9 @@ class ParamsSuite extends SparkFunSuite {
 
     val inArray = ParamValidators.inArray[Int](Array(1, 2))
     assert(inArray(1) && inArray(2) && !inArray(0))
+
+    val arrayLengthGt = ParamValidators.arrayLengthGt[Int](2.0)
+    assert(arrayLengthGt(Array(0, 1, 2)) && !arrayLengthGt(Array(0, 1)))
   }
 
   test("Params.copyValues") {

From 9108eff74a2815986fd067b273c2a344b6315405 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 18 Aug 2015 17:00:13 -0700
Subject: [PATCH 1114/1454] [SPARK-10098] [STREAMING] [TEST] Cleanup active
 context after test in FailureSuite

Failures in streaming.FailureSuite can leak StreamingContext and SparkContext which fails all subsequent tests

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8289 from tdas/SPARK-10098.
---
 .../apache/spark/streaming/FailureSuite.scala | 27 ++++++++++++-------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala
index 0c4c06534a693..e82c2fa4e72ad 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala
@@ -17,25 +17,32 @@
 
 package org.apache.spark.streaming
 
-import org.apache.spark.Logging
+import java.io.File
+
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.{SparkFunSuite, Logging}
 import org.apache.spark.util.Utils
 
 /**
  * This testsuite tests master failures at random times while the stream is running using
  * the real clock.
  */
-class FailureSuite extends TestSuiteBase with Logging {
+class FailureSuite extends SparkFunSuite with BeforeAndAfter with Logging {
 
-  val directory = Utils.createTempDir()
-  val numBatches = 30
+  private val batchDuration: Duration = Milliseconds(1000)
+  private val numBatches = 30
+  private var directory: File = null
 
-  override def batchDuration: Duration = Milliseconds(1000)
-
-  override def useManualClock: Boolean = false
+  before {
+    directory = Utils.createTempDir()
+  }
 
-  override def afterFunction() {
-    Utils.deleteRecursively(directory)
-    super.afterFunction()
+  after {
+    if (directory != null) {
+      Utils.deleteRecursively(directory)
+    }
+    StreamingContext.getActive().foreach { _.stop() }
   }
 
   test("multiple failures with map") {

From badf7fa650f9801c70515907fcc26b58d7ec3143 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 18 Aug 2015 17:54:49 -0700
Subject: [PATCH 1115/1454] [SPARK-8473] [SPARK-9889] [ML] User guide and
 example code for DCT

mengxr jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8184 from feynmanliang/SPARK-9889-DCT-docs.
---
 docs/ml-features.md | 71 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 6b2e36b3536dc..28a61933f8238 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -649,6 +649,77 @@ for expanded in polyDF.select("polyFeatures").take(3):
 </div>
 </div>
 
+## Discrete Cosine Transform (DCT)
+
+The [Discrete Cosine
+Transform](https://en.wikipedia.org/wiki/Discrete_cosine_transform)
+transforms a length $N$ real-valued sequence in the time domain into
+another length $N$ real-valued sequence in the frequency domain. A
+[DCT](api/scala/index.html#org.apache.spark.ml.feature.DCT) class
+provides this functionality, implementing the
+[DCT-II](https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II)
+and scaling the result by $1/\sqrt{2}$ such that the representing matrix
+for the transform is unitary. No shift is applied to the transformed
+sequence (e.g. the $0$th element of the transformed sequence is the
+$0$th DCT coefficient and _not_ the $N/2$th).
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.DCT
+import org.apache.spark.mllib.linalg.Vectors
+
+val data = Seq(
+  Vectors.dense(0.0, 1.0, -2.0, 3.0),
+  Vectors.dense(-1.0, 2.0, 4.0, -7.0),
+  Vectors.dense(14.0, -2.0, -5.0, 1.0))
+val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
+val dct = new DCT()
+  .setInputCol("features")
+  .setOutputCol("featuresDCT")
+  .setInverse(false)
+val dctDf = dct.transform(df)
+dctDf.select("featuresDCT").show(3)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.feature.DCT;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Vectors.dense(0.0, 1.0, -2.0, 3.0)),
+  RowFactory.create(Vectors.dense(-1.0, 2.0, 4.0, -7.0)),
+  RowFactory.create(Vectors.dense(14.0, -2.0, -5.0, 1.0))
+));
+StructType schema = new StructType(new StructField[] {
+  new StructField("features", new VectorUDT(), false, Metadata.empty()),
+});
+DataFrame df = jsql.createDataFrame(data, schema);
+DCT dct = new DCT()
+  .setInputCol("features")
+  .setOutputCol("featuresDCT")
+  .setInverse(false);
+DataFrame dctDf = dct.transform(df);
+dctDf.select("featuresDCT").show(3);
+{% endhighlight %}
+</div>
+</div>
+
 ## StringIndexer
 
 `StringIndexer` encodes a string column of labels to a column of label indices.

From 04e0fea79b9acfa3a3cb81dbacb08f9d287b42c3 Mon Sep 17 00:00:00 2001
From: Hossein <hossein@databricks.com>
Date: Tue, 18 Aug 2015 18:02:22 -0700
Subject: [PATCH 1116/1454] Bump SparkR version string to 1.5.0

This patch is against master, but we need to apply it to 1.5 branch as well.

cc shivaram  and rxin

Author: Hossein <hossein@databricks.com>

Closes #8291 from falaki/SparkRVersion1.5.
---
 R/pkg/DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index 83e64897216b1..d0d7201f004a2 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SparkR
 Type: Package
 Title: R frontend for Spark
-Version: 1.4.0
+Version: 1.5.0
 Date: 2013-09-09
 Author: The Apache Software Foundation
 Maintainer: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

From 1f8902964d8af7853c389b3b013b3a9b002c6c06 Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Tue, 18 Aug 2015 18:53:19 -0700
Subject: [PATCH 1117/1454] [SPARK-9969] [YARN] Remove old MR classpath API
 support

Here propose to remove old MRJobConfig#DEFAULT_APPLICATION_CLASSPATH support, since we now move to Yarn stable API.

vanzin and sryza , any opinion on this? If we still want to support old API, I can close it. But as far as I know now major Hadoop releases has moved to stable API.

Author: jerryshao <sshao@hortonworks.com>

Closes #8192 from jerryshao/SPARK-9969.
---
 .../scala/org/apache/spark/deploy/yarn/Client.scala  | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 5c6a716863f66..8672ef601784b 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -1098,20 +1098,10 @@ object Client extends Logging {
     triedDefault.toOption
   }
 
-  /**
-   * In Hadoop 0.23, the MR application classpath comes with the YARN application
-   * classpath. In Hadoop 2.0, it's an array of Strings, and in 2.2+ it's a String.
-   * So we need to use reflection to retrieve it.
-   */
   private[yarn] def getDefaultMRApplicationClasspath: Option[Seq[String]] = {
     val triedDefault = Try[Seq[String]] {
       val field = classOf[MRJobConfig].getField("DEFAULT_MAPREDUCE_APPLICATION_CLASSPATH")
-      val value = if (field.getType == classOf[String]) {
-        StringUtils.getStrings(field.get(null).asInstanceOf[String]).toArray
-      } else {
-        field.get(null).asInstanceOf[Array[String]]
-      }
-      value.toSeq
+      StringUtils.getStrings(field.get(null).asInstanceOf[String]).toSeq
     } recoverWith {
       case e: NoSuchFieldException => Success(Seq.empty[String])
     }

From b4b35f133aecaf84f04e8e444b660a33c6b7894a Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 18 Aug 2015 19:18:05 -0700
Subject: [PATCH 1118/1454] [SPARKR] [MINOR] Get rid of a long line warning

```
R/functions.R:74:1: style: lines should not be more than 100 characters.
            jc <- callJStatic("org.apache.spark.sql.functions", "lit", ifelse(class(x) == "Column", xjc, x))
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8297 from yu-iskw/minor-lint-r.
---
 R/pkg/R/functions.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index 6eef4d638ee9a..e606b20570f53 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -71,7 +71,9 @@ createFunctions()
 #' @return Creates a Column class of literal value.
 setMethod("lit", signature("ANY"),
           function(x) {
-            jc <- callJStatic("org.apache.spark.sql.functions", "lit", ifelse(class(x) == "Column", x@jc, x))
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "lit",
+                              ifelse(class(x) == "Column", x@jc, x))
             column(jc)
           })
 

From 1aeae05bb20f01ab7ccaa62fe905a63e020074b5 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 18 Aug 2015 19:26:38 -0700
Subject: [PATCH 1119/1454] [SPARK-10072] [STREAMING] BlockGenerator can
 deadlock when the queue of generate blocks fills up to capacity

Generated blocks are inserted into an ArrayBlockingQueue, and another thread pulls stuff from the ArrayBlockingQueue and pushes it into BlockManager. Now if that queue fills up to capacity (default is 10 blocks), then the inserting into queue (done in the function updateCurrentBuffer) get blocked inside a synchronized block. However, the thread that is pulling blocks from the queue uses the same lock to check the current (active or stopped) while pulling from the queue. Since the block generating threads is blocked (as the queue is full) on the lock, this thread that is supposed to drain the queue gets blocked. Ergo, deadlock.

Solution: Moved blocking call to ArrayBlockingQueue outside the synchronized to prevent deadlock.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8257 from tdas/SPARK-10072.
---
 .../streaming/receiver/BlockGenerator.scala   | 29 ++++++++++++-------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
index 300e820d01777..421d60ae359f8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
@@ -227,16 +227,21 @@ private[streaming] class BlockGenerator(
   def isStopped(): Boolean = state == StoppedAll
 
   /** Change the buffer to which single records are added to. */
-  private def updateCurrentBuffer(time: Long): Unit = synchronized {
+  private def updateCurrentBuffer(time: Long): Unit = {
     try {
-      val newBlockBuffer = currentBuffer
-      currentBuffer = new ArrayBuffer[Any]
-      if (newBlockBuffer.size > 0) {
-        val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
-        val newBlock = new Block(blockId, newBlockBuffer)
-        listener.onGenerateBlock(blockId)
+      var newBlock: Block = null
+      synchronized {
+        if (currentBuffer.nonEmpty) {
+          val newBlockBuffer = currentBuffer
+          currentBuffer = new ArrayBuffer[Any]
+          val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
+          listener.onGenerateBlock(blockId)
+          newBlock = new Block(blockId, newBlockBuffer)
+        }
+      }
+
+      if (newBlock != null) {
         blocksForPushing.put(newBlock)  // put is blocking when queue is full
-        logDebug("Last element in " + blockId + " is " + newBlockBuffer.last)
       }
     } catch {
       case ie: InterruptedException =>
@@ -250,9 +255,13 @@ private[streaming] class BlockGenerator(
   private def keepPushingBlocks() {
     logInfo("Started block pushing thread")
 
-    def isGeneratingBlocks = synchronized { state == Active || state == StoppedAddingData }
+    def areBlocksBeingGenerated: Boolean = synchronized {
+      state != StoppedGeneratingBlocks
+    }
+
     try {
-      while (isGeneratingBlocks) {
+      // While blocks are being generated, keep polling for to-be-pushed blocks and push them.
+      while (areBlocksBeingGenerated) {
         Option(blocksForPushing.poll(10, TimeUnit.MILLISECONDS)) match {
           case Some(block) => pushBlock(block)
           case None =>

From 90273eff9604439a5a5853077e232d34555c67d7 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 18 Aug 2015 20:15:54 -0700
Subject: [PATCH 1120/1454] [SPARK-10102] [STREAMING] Fix a race condition that
 startReceiver may happen before setting trackerState to Started

Test failure: https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-Maven-with-YARN/HADOOP_PROFILE=hadoop-2.4,label=spark-test/3305/testReport/junit/org.apache.spark.streaming/StreamingContextSuite/stop_gracefully/

There is a race condition that setting `trackerState` to `Started` could happen after calling `startReceiver`. Then `startReceiver` won't start the receivers because it uses `! isTrackerStarted` to check if ReceiverTracker is stopping or stopped. But actually, `trackerState` is `Initialized` and will be changed to `Started` soon.

Therefore, we should use `isTrackerStopping || isTrackerStopped`.

Author: zsxwing <zsxwing@gmail.com>

Closes #8294 from zsxwing/SPARK-9504.
---
 .../spark/streaming/scheduler/ReceiverTracker.scala   | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index e076fb5ea174b..aae3acf7aba3e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -468,8 +468,13 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
      * Start a receiver along with its scheduled executors
      */
     private def startReceiver(receiver: Receiver[_], scheduledExecutors: Seq[String]): Unit = {
+      def shouldStartReceiver: Boolean = {
+        // It's okay to start when trackerState is Initialized or Started
+        !(isTrackerStopping || isTrackerStopped)
+      }
+
       val receiverId = receiver.streamId
-      if (!isTrackerStarted) {
+      if (!shouldStartReceiver) {
         onReceiverJobFinish(receiverId)
         return
       }
@@ -494,14 +499,14 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
       // We will keep restarting the receiver job until ReceiverTracker is stopped
       future.onComplete {
         case Success(_) =>
-          if (!isTrackerStarted) {
+          if (!shouldStartReceiver) {
             onReceiverJobFinish(receiverId)
           } else {
             logInfo(s"Restarting Receiver $receiverId")
             self.send(RestartReceiver(receiver))
           }
         case Failure(e) =>
-          if (!isTrackerStarted) {
+          if (!shouldStartReceiver) {
             onReceiverJobFinish(receiverId)
           } else {
             logError("Receiver has been stopped. Try to restart it.", e)

From a5b5b936596ceb45f5f5b68bf1d6368534fb9470 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 19 Aug 2015 11:21:46 +0800
Subject: [PATCH 1121/1454] [SPARK-9939] [SQL] Resorts to Java process API in
 CliSuite, HiveSparkSubmitSuite and HiveThriftServer2 test suites

Scala process API has a known bug ([SI-8768] [1]), which may be the reason why several test suites which fork sub-processes are flaky.

This PR replaces Scala process API with Java process API in `CliSuite`, `HiveSparkSubmitSuite`, and `HiveThriftServer2` related test suites to see whether it fix these flaky tests.

[1]: https://issues.scala-lang.org/browse/SI-8768

Author: Cheng Lian <lian@databricks.com>

Closes #8168 from liancheng/spark-9939/use-java-process-api.
---
 .../spark/sql/test/ProcessTestUtils.scala     | 37 +++++++++
 sql/hive-thriftserver/pom.xml                 |  7 ++
 .../sql/hive/thriftserver/CliSuite.scala      | 64 ++++++--------
 .../HiveThriftServer2Suites.scala             | 83 ++++++++++---------
 .../spark/sql/hive/HiveSparkSubmitSuite.scala | 49 +++++++----
 5 files changed, 149 insertions(+), 91 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala
new file mode 100644
index 0000000000000..152c9c8459de9
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.test
+
+import java.io.{IOException, InputStream}
+
+import scala.sys.process.BasicIO
+
+object ProcessTestUtils {
+  class ProcessOutputCapturer(stream: InputStream, capture: String => Unit) extends Thread {
+    this.setDaemon(true)
+
+    override def run(): Unit = {
+      try {
+        BasicIO.processFully(capture)(stream)
+      } catch { case _: IOException =>
+        // Ignores the IOException thrown when the process termination, which closes the input
+        // stream abruptly.
+      }
+    }
+  }
+}
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index 2dfbcb2425a37..3566c87dd248c 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -86,6 +86,13 @@
       <artifactId>selenium-java</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-sql_${scala.binary.version}</artifactId>
+      <type>test-jar</type>
+      <version>${project.version}</version>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
index 121b3e077f71f..e59a14ec00d5c 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -18,18 +18,19 @@
 package org.apache.spark.sql.hive.thriftserver
 
 import java.io._
+import java.sql.Timestamp
+import java.util.Date
 
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
 import scala.concurrent.{Await, Promise}
-import scala.sys.process.{Process, ProcessLogger}
-import scala.util.Failure
+import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.scalatest.BeforeAndAfter
 
-import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkFunSuite}
 
 /**
  * A test suite for the `spark-sql` CLI tool.  Note that all test cases share the same temporary
@@ -70,6 +71,9 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
       queriesAndExpectedAnswers: (String, String)*): Unit = {
 
     val (queries, expectedAnswers) = queriesAndExpectedAnswers.unzip
+    // Explicitly adds ENTER for each statement to make sure they are actually entered into the CLI.
+    val queriesString = queries.map(_ + "\n").mkString
+
     val command = {
       val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator)
       val jdbcUrl = s"jdbc:derby:;databaseName=$metastorePath;create=true"
@@ -83,13 +87,14 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
 
     var next = 0
     val foundAllExpectedAnswers = Promise.apply[Unit]()
-    // Explicitly adds ENTER for each statement to make sure they are actually entered into the CLI.
-    val queryStream = new ByteArrayInputStream(queries.map(_ + "\n").mkString.getBytes)
     val buffer = new ArrayBuffer[String]()
     val lock = new Object
 
     def captureOutput(source: String)(line: String): Unit = lock.synchronized {
-      buffer += s"$source> $line"
+      // This test suite sometimes gets extremely slow out of unknown reason on Jenkins.  Here we
+      // add a timestamp to provide more diagnosis information.
+      buffer += s"${new Timestamp(new Date().getTime)} - $source> $line"
+
       // If we haven't found all expected answers and another expected answer comes up...
       if (next < expectedAnswers.size && line.startsWith(expectedAnswers(next))) {
         next += 1
@@ -98,48 +103,27 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
           foundAllExpectedAnswers.trySuccess(())
         }
       } else {
-        errorResponses.foreach( r => {
+        errorResponses.foreach { r =>
           if (line.startsWith(r)) {
             foundAllExpectedAnswers.tryFailure(
               new RuntimeException(s"Failed with error line '$line'"))
-          }})
-      }
-    }
-
-    // Searching expected output line from both stdout and stderr of the CLI process
-    val process = (Process(command, None) #< queryStream).run(
-      ProcessLogger(captureOutput("stdout"), captureOutput("stderr")))
-
-    // catch the output value
-    class exitCodeCatcher extends Runnable {
-      var exitValue = 0
-
-      override def run(): Unit = {
-        try {
-          exitValue = process.exitValue()
-        } catch {
-          case rte: RuntimeException =>
-            // ignored as it will get triggered when the process gets destroyed
-            logDebug("Ignoring exception while waiting for exit code", rte)
-        }
-        if (exitValue != 0) {
-          // process exited: fail fast
-          foundAllExpectedAnswers.tryFailure(
-            new RuntimeException(s"Failed with exit code $exitValue"))
+          }
         }
       }
     }
-    // spin off the code catche thread. No attempt is made to kill this
-    // as it will exit once the launched process terminates.
-    val codeCatcherThread = new Thread(new exitCodeCatcher())
-    codeCatcherThread.start()
+
+    val process = new ProcessBuilder(command: _*).start()
+
+    val stdinWriter = new OutputStreamWriter(process.getOutputStream)
+    stdinWriter.write(queriesString)
+    stdinWriter.flush()
+    stdinWriter.close()
+
+    new ProcessOutputCapturer(process.getInputStream, captureOutput("stdout")).start()
+    new ProcessOutputCapturer(process.getErrorStream, captureOutput("stderr")).start()
 
     try {
-      Await.ready(foundAllExpectedAnswers.future, timeout)
-      foundAllExpectedAnswers.future.value match {
-        case Some(Failure(t)) => throw t
-        case _ =>
-      }
+      Await.result(foundAllExpectedAnswers.future, timeout)
     } catch { case cause: Throwable =>
       val message =
         s"""
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 17e7044c46ecb..ded42bca9971e 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -22,10 +22,9 @@ import java.net.URL
 import java.sql.{Date, DriverManager, SQLException, Statement}
 
 import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.ExecutionContext.Implicits.global
 import scala.concurrent.duration._
 import scala.concurrent.{Await, Promise, future}
-import scala.concurrent.ExecutionContext.Implicits.global
-import scala.sys.process.{Process, ProcessLogger}
 import scala.util.{Random, Try}
 
 import com.google.common.base.Charsets.UTF_8
@@ -38,11 +37,12 @@ import org.apache.hive.service.cli.thrift.TCLIService.Client
 import org.apache.hive.service.cli.thrift.ThriftCLIServiceClient
 import org.apache.thrift.protocol.TBinaryProtocol
 import org.apache.thrift.transport.TSocket
-import org.scalatest.{Ignore, BeforeAndAfterAll}
+import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkFunSuite}
 
 object TestData {
   def getTestDataFilePath(name: String): URL = {
@@ -53,7 +53,6 @@ object TestData {
   val smallKvWithNull = getTestDataFilePath("small_kv_with_null.txt")
 }
 
-@Ignore // SPARK-9606
 class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.binary
 
@@ -380,7 +379,6 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   }
 }
 
-@Ignore // SPARK-9606
 class HiveThriftHttpServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.http
 
@@ -484,7 +482,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
       val tempLog4jConf = Utils.createTempDir().getCanonicalPath
 
       Files.write(
-        """log4j.rootCategory=INFO, console
+        """log4j.rootCategory=DEBUG, console
           |log4j.appender.console=org.apache.log4j.ConsoleAppender
           |log4j.appender.console.target=System.err
           |log4j.appender.console.layout=org.apache.log4j.PatternLayout
@@ -493,7 +491,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
         new File(s"$tempLog4jConf/log4j.properties"),
         UTF_8)
 
-      tempLog4jConf // + File.pathSeparator + sys.props("java.class.path")
+      tempLog4jConf
     }
 
     s"""$startScript
@@ -521,7 +519,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
    */
   val THRIFT_HTTP_SERVICE_LIVE = "Started ThriftHttpCLIService in http"
 
-  val SERVER_STARTUP_TIMEOUT = 1.minute
+  val SERVER_STARTUP_TIMEOUT = 3.minutes
 
   private def startThriftServer(port: Int, attempt: Int) = {
     warehousePath = Utils.createTempDir()
@@ -543,17 +541,22 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
 
     logInfo(s"Trying to start HiveThriftServer2: port=$port, mode=$mode, attempt=$attempt")
 
-    val env = Seq(
-      // Disables SPARK_TESTING to exclude log4j.properties in test directories.
-      "SPARK_TESTING" -> "0",
-      // Points SPARK_PID_DIR to SPARK_HOME, otherwise only 1 Thrift server instance can be started
-      // at a time, which is not Jenkins friendly.
-      "SPARK_PID_DIR" -> pidDir.getCanonicalPath)
-
-    logPath = Process(command, None, env: _*).lines.collectFirst {
-      case line if line.contains(LOG_FILE_MARK) => new File(line.drop(LOG_FILE_MARK.length))
-    }.getOrElse {
-      throw new RuntimeException("Failed to find HiveThriftServer2 log file.")
+    logPath = {
+      val lines = Utils.executeAndGetOutput(
+        command = command,
+        extraEnvironment = Map(
+          // Disables SPARK_TESTING to exclude log4j.properties in test directories.
+          "SPARK_TESTING" -> "0",
+          // Points SPARK_PID_DIR to SPARK_HOME, otherwise only 1 Thrift server instance can be
+          // started at a time, which is not Jenkins friendly.
+          "SPARK_PID_DIR" -> pidDir.getCanonicalPath),
+        redirectStderr = true)
+
+      lines.split("\n").collectFirst {
+        case line if line.contains(LOG_FILE_MARK) => new File(line.drop(LOG_FILE_MARK.length))
+      }.getOrElse {
+        throw new RuntimeException("Failed to find HiveThriftServer2 log file.")
+      }
     }
 
     val serverStarted = Promise[Unit]()
@@ -561,30 +564,36 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
     // Ensures that the following "tail" command won't fail.
     logPath.createNewFile()
     val successLines = Seq(THRIFT_BINARY_SERVICE_LIVE, THRIFT_HTTP_SERVICE_LIVE)
-    val failureLines = Seq("HiveServer2 is stopped", "Exception in thread", "Error:")
-    logTailingProcess =
+
+    logTailingProcess = {
+      val command = s"/usr/bin/env tail -n +0 -f ${logPath.getCanonicalPath}".split(" ")
       // Using "-n +0" to make sure all lines in the log file are checked.
-      Process(s"/usr/bin/env tail -n +0 -f ${logPath.getCanonicalPath}").run(ProcessLogger(
-        (line: String) => {
-          diagnosisBuffer += line
-          successLines.foreach(r => {
-            if (line.contains(r)) {
-              serverStarted.trySuccess(())
-            }
-          })
-          failureLines.foreach(r => {
-            if (line.contains(r)) {
-              serverStarted.tryFailure(new RuntimeException(s"Failed with output '$line'"))
-            }
-          })
-        }))
+      val builder = new ProcessBuilder(command: _*)
+      val captureOutput = (line: String) => diagnosisBuffer.synchronized {
+        diagnosisBuffer += line
+
+        successLines.foreach { r =>
+          if (line.contains(r)) {
+            serverStarted.trySuccess(())
+          }
+        }
+      }
+
+        val process = builder.start()
+
+      new ProcessOutputCapturer(process.getInputStream, captureOutput).start()
+      new ProcessOutputCapturer(process.getErrorStream, captureOutput).start()
+      process
+    }
 
     Await.result(serverStarted.future, SERVER_STARTUP_TIMEOUT)
   }
 
   private def stopThriftServer(): Unit = {
     // The `spark-daemon.sh' script uses kill, which is not synchronous, have to wait for a while.
-    Process(stopScript, None, "SPARK_PID_DIR" -> pidDir.getCanonicalPath).run().exitValue()
+    Utils.executeAndGetOutput(
+      command = Seq(stopScript),
+      extraEnvironment = Map("SPARK_PID_DIR" -> pidDir.getCanonicalPath))
     Thread.sleep(3.seconds.toMillis)
 
     warehousePath.delete()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 0c29646114465..dc2d85f48624c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -18,9 +18,10 @@
 package org.apache.spark.sql.hive
 
 import java.io.File
+import java.sql.Timestamp
+import java.util.Date
 
 import scala.collection.mutable.ArrayBuffer
-import scala.sys.process.{Process, ProcessLogger}
 
 import org.scalatest.Matchers
 import org.scalatest.concurrent.Timeouts
@@ -30,6 +31,7 @@ import org.scalatest.time.SpanSugar._
 import org.apache.spark._
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
+import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 import org.apache.spark.sql.types.DecimalType
 import org.apache.spark.util.{ResetSystemProperties, Utils}
 
@@ -39,6 +41,8 @@ import org.apache.spark.util.{ResetSystemProperties, Utils}
 class HiveSparkSubmitSuite
   extends SparkFunSuite
   with Matchers
+  // This test suite sometimes gets extremely slow out of unknown reason on Jenkins.  Here we
+  // add a timestamp to provide more diagnosis information.
   with ResetSystemProperties
   with Timeouts {
 
@@ -110,28 +114,44 @@ class HiveSparkSubmitSuite
     val history = ArrayBuffer.empty[String]
     val commands = Seq("./bin/spark-submit") ++ args
     val commandLine = commands.mkString("'", "' '", "'")
-    val process = Process(
-      commands,
-      new File(sparkHome),
-      "SPARK_TESTING" -> "1",
-      "SPARK_HOME" -> sparkHome
-    ).run(ProcessLogger(
+
+    val builder = new ProcessBuilder(commands: _*).directory(new File(sparkHome))
+    val env = builder.environment()
+    env.put("SPARK_TESTING", "1")
+    env.put("SPARK_HOME", sparkHome)
+
+    def captureOutput(source: String)(line: String): Unit = {
+      // This test suite has some weird behaviors when executed on Jenkins:
+      //
+      // 1. Sometimes it gets extremely slow out of unknown reason on Jenkins.  Here we add a
+      //    timestamp to provide more diagnosis information.
+      // 2. Log lines are not correctly redirected to unit-tests.log as expected, so here we print
+      //    them out for debugging purposes.
+      val logLine = s"${new Timestamp(new Date().getTime)} - $source> $line"
       // scalastyle:off println
-      (line: String) => { println(s"stdout> $line"); history += s"out> $line"},
-      (line: String) => { println(s"stderr> $line"); history += s"err> $line" }
+      println(logLine)
       // scalastyle:on println
-    ))
+      history += logLine
+    }
+
+    val process = builder.start()
+    new ProcessOutputCapturer(process.getInputStream, captureOutput("stdout")).start()
+    new ProcessOutputCapturer(process.getErrorStream, captureOutput("stderr")).start()
 
     try {
-      val exitCode = failAfter(180.seconds) { process.exitValue() }
+      val exitCode = failAfter(180.seconds) { process.waitFor() }
       if (exitCode != 0) {
         // include logs in output. Note that logging is async and may not have completed
         // at the time this exception is raised
         Thread.sleep(1000)
         val historyLog = history.mkString("\n")
-        fail(s"$commandLine returned with exit code $exitCode." +
-            s" See the log4j logs for more detail." +
-            s"\n$historyLog")
+        fail {
+          s"""spark-submit returned with exit code $exitCode.
+             |Command line: $commandLine
+             |
+             |$historyLog
+           """.stripMargin
+        }
       }
     } catch {
       case to: TestFailedDueToTimeoutException =>
@@ -263,6 +283,7 @@ object SPARK_9757 extends QueryTest with Logging {
 
     val hiveContext = new TestHiveContext(sparkContext)
     import hiveContext.implicits._
+
     import org.apache.spark.sql.functions._
 
     val dir = Utils.createTempDir()

From bf32c1f7f47dd907d787469f979c5859e02ce5e6 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 18 Aug 2015 20:27:36 -0700
Subject: [PATCH 1122/1454] [SPARK-10075] [SPARKR] Add `when` expressino
 function in SparkR

- Add `when` and `otherwise` as `Column` methods
- Add `When` as an expression function
- Add `%otherwise%` infix as an alias of `otherwise`

Since R doesn't support a feature like method chaining, `otherwise(when(condition, value), value)` style is a little annoying for me. If `%otherwise%` looks strange for shivaram, I can remove it. What do you think?

### JIRA
[[SPARK-10075] Add `when` expressino function in SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10075)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8266 from yu-iskw/SPARK-10075.
---
 R/pkg/NAMESPACE                  |  2 ++
 R/pkg/R/column.R                 | 14 ++++++++++++++
 R/pkg/R/functions.R              | 14 ++++++++++++++
 R/pkg/R/generics.R               |  8 ++++++++
 R/pkg/inst/tests/test_sparkSQL.R |  7 +++++++
 5 files changed, 45 insertions(+)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 607aef26115d5..8fa12d5adef8a 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -152,6 +152,7 @@ exportMethods("abs",
               "n_distinct",
               "nanvl",
               "negate",
+              "otherwise",
               "pmod",
               "quarter",
               "reverse",
@@ -182,6 +183,7 @@ exportMethods("abs",
               "unhex",
               "upper",
               "weekofyear",
+              "when",
               "year")
 
 exportClasses("GroupedData")
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 328f595d0805f..5a07ebd308296 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -203,3 +203,17 @@ setMethod("%in%",
             jc <- callJMethod(x@jc, "in", table)
             return(column(jc))
           })
+
+#' otherwise
+#'
+#' If values in the specified column are null, returns the value. 
+#' Can be used in conjunction with `when` to specify a default value for expressions.
+#'
+#' @rdname column
+setMethod("otherwise",
+          signature(x = "Column", value = "ANY"),
+          function(x, value) {
+            value <- ifelse(class(value) == "Column", value@jc, value)
+            jc <- callJMethod(x@jc, "otherwise", value)
+            column(jc)
+          })
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index e606b20570f53..366c230e1e85b 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -165,3 +165,17 @@ setMethod("n", signature(x = "Column"),
           function(x) {
             count(x)
           })
+
+#' when
+#'
+#' Evaluates a list of conditions and returns one of multiple possible result expressions.
+#' For unmatched expressions null is returned.
+#'
+#' @rdname column
+setMethod("when", signature(condition = "Column", value = "ANY"),
+          function(condition, value) {
+              condition <- condition@jc
+              value <- ifelse(class(value) == "Column", value@jc, value)
+              jc <- callJStatic("org.apache.spark.sql.functions", "when", condition, value)
+              column(jc)
+          })
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 5c1cc98fd9e80..338b32e648078 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -651,6 +651,14 @@ setGeneric("rlike", function(x, ...) { standardGeneric("rlike") })
 #' @export
 setGeneric("startsWith", function(x, ...) { standardGeneric("startsWith") })
 
+#' @rdname column
+#' @export
+setGeneric("when", function(condition, value) { standardGeneric("when") })
+
+#' @rdname column
+#' @export
+setGeneric("otherwise", function(x, value) { standardGeneric("otherwise") })
+
 
 ###################### Expression Function Methods ##########################
 
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 83caba8b5b964..841de657df37e 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -727,6 +727,13 @@ test_that("greatest() and least() on a DataFrame", {
   expect_equal(collect(select(df, least(df$a, df$b)))[, 1], c(1, 3))
 })
 
+test_that("when() and otherwise() on a DataFrame", {
+  l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
+  df <- createDataFrame(sqlContext, l)
+  expect_equal(collect(select(df, when(df$a > 1 & df$b > 2, 1)))[, 1], c(NA, 1))
+  expect_equal(collect(select(df, otherwise(when(df$a > 1, 1), 0)))[, 1], c(0, 1))
+})
+
 test_that("group by", {
   df <- jsonFile(sqlContext, jsonPath)
   df1 <- agg(df, name = "max", age = "sum")

From 270ee677750a1f2adaf24b5816857194e61782ff Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 18 Aug 2015 20:39:59 -0700
Subject: [PATCH 1123/1454] [SPARK-10095] [SQL] use public API of BigInteger

In UnsafeRow, we use the private field of BigInteger for better performance, but it actually didn't contribute much (3% in one benchmark) to end-to-end runtime, and make it not portable (may fail on other JVM implementations).

So we should use the public API instead.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8286 from davies/portable_decimal.
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 29 +++++--------------
 .../expressions/UnsafeRowWriters.java         |  9 ++----
 .../org/apache/spark/unsafe/Platform.java     | 18 ------------
 3 files changed, 11 insertions(+), 45 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 7fd94772090df..6c020045c311a 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -273,14 +273,13 @@ public void setDecimal(int ordinal, Decimal value, int precision) {
       } else {
 
         final BigInteger integer = value.toJavaBigDecimal().unscaledValue();
-        final int[] mag = (int[]) Platform.getObjectVolatile(integer,
-          Platform.BIG_INTEGER_MAG_OFFSET);
-        assert(mag.length <= 4);
+        byte[] bytes = integer.toByteArray();
+        assert(bytes.length <= 16);
 
         // Write the bytes to the variable length portion.
         Platform.copyMemory(
-          mag, Platform.INT_ARRAY_OFFSET, baseObject, baseOffset + cursor, mag.length * 4);
-        setLong(ordinal, (cursor << 32) | ((long) (((integer.signum() + 1) << 8) + mag.length)));
+          bytes, Platform.BYTE_ARRAY_OFFSET, baseObject, baseOffset + cursor, bytes.length);
+        setLong(ordinal, (cursor << 32) | ((long) bytes.length));
       }
     }
   }
@@ -375,8 +374,6 @@ public double getDouble(int ordinal) {
     return Platform.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
-  private static byte[] EMPTY = new byte[0];
-
   @Override
   public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (isNullAt(ordinal)) {
@@ -385,20 +382,10 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (precision <= Decimal.MAX_LONG_DIGITS()) {
       return Decimal.apply(getLong(ordinal), precision, scale);
     } else {
-      long offsetAndSize = getLong(ordinal);
-      long offset = offsetAndSize >>> 32;
-      int signum = ((int) (offsetAndSize & 0xfff) >> 8);
-      assert signum >=0 && signum <= 2 : "invalid signum " + signum;
-      int size = (int) (offsetAndSize & 0xff);
-      int[] mag = new int[size];
-      Platform.copyMemory(
-        baseObject, baseOffset + offset, mag, Platform.INT_ARRAY_OFFSET, size * 4);
-
-      // create a BigInteger using signum and mag
-      BigInteger v = new BigInteger(0, EMPTY);  // create the initial object
-      Platform.putInt(v, Platform.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
-      Platform.putObjectVolatile(v, Platform.BIG_INTEGER_MAG_OFFSET, mag);
-      return Decimal.apply(new BigDecimal(v, scale), precision, scale);
+      byte[] bytes = getBinary(ordinal);
+      BigInteger bigInteger = new BigInteger(bytes);
+      BigDecimal javaDecimal = new BigDecimal(bigInteger, scale);
+      return Decimal.apply(javaDecimal, precision, scale);
     }
   }
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 005351f0883e5..2f43db68a750e 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -71,16 +71,13 @@ public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input
       }
 
       final BigInteger integer = input.toJavaBigDecimal().unscaledValue();
-      int signum = integer.signum() + 1;
-      final int[] mag = (int[]) Platform.getObjectVolatile(
-        integer, Platform.BIG_INTEGER_MAG_OFFSET);
-      assert(mag.length <= 4);
+      byte[] bytes = integer.toByteArray();
 
       // Write the bytes to the variable length portion.
       Platform.copyMemory(
-        mag, Platform.INT_ARRAY_OFFSET, base, target.getBaseOffset() + cursor, mag.length * 4);
+        bytes, Platform.BYTE_ARRAY_OFFSET, base, target.getBaseOffset() + cursor, bytes.length);
       // Set the fixed length portion.
-      target.setLong(ordinal, (((long) cursor) << 32) | ((long) ((signum << 8) + mag.length)));
+      target.setLong(ordinal, (((long) cursor) << 32) | (long) bytes.length);
 
       return SIZE;
     }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
index 18343efdc3437..1c16da982923b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
@@ -18,7 +18,6 @@
 package org.apache.spark.unsafe;
 
 import java.lang.reflect.Field;
-import java.math.BigInteger;
 
 import sun.misc.Unsafe;
 
@@ -34,10 +33,6 @@ public final class Platform {
 
   public static final int DOUBLE_ARRAY_OFFSET;
 
-  // Support for resetting final fields while deserializing
-  public static final long BIG_INTEGER_SIGNUM_OFFSET;
-  public static final long BIG_INTEGER_MAG_OFFSET;
-
   public static int getInt(Object object, long offset) {
     return _UNSAFE.getInt(object, offset);
   }
@@ -150,24 +145,11 @@ public static void throwException(Throwable t) {
       INT_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(int[].class);
       LONG_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(long[].class);
       DOUBLE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(double[].class);
-
-      long signumOffset = 0;
-      long magOffset = 0;
-      try {
-        signumOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("signum"));
-        magOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("mag"));
-      } catch (Exception ex) {
-        // should not happen
-      }
-      BIG_INTEGER_SIGNUM_OFFSET = signumOffset;
-      BIG_INTEGER_MAG_OFFSET = magOffset;
     } else {
       BYTE_ARRAY_OFFSET = 0;
       INT_ARRAY_OFFSET = 0;
       LONG_ARRAY_OFFSET = 0;
       DOUBLE_ARRAY_OFFSET = 0;
-      BIG_INTEGER_SIGNUM_OFFSET = 0;
-      BIG_INTEGER_MAG_OFFSET = 0;
     }
   }
 }

From 1ff0580eda90f9247a5233809667f5cebaea290e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 18 Aug 2015 22:08:15 -0700
Subject: [PATCH 1124/1454] [SPARK-10093] [SPARK-10096] [SQL] Avoid
 transformation on executors & fix UDFs on complex types

This is kind of a weird case, but given a sufficiently complex query plan (in this case a TungstenProject with an Exchange underneath), we could have NPEs on the executors due to the time when we were calling transformAllExpressions

In general we should ensure that all transformations occur on the driver and not on the executors. Some reasons for avoid executor side transformations include:

* (this case) Some operator constructors require state such as access to the Spark/SQL conf so doing a makeCopy on the executor can fail.
* (unrelated reason for avoid executor transformations) ExprIds are calculated using an atomic integer, so you can violate their uniqueness constraint by constructing them anywhere other than the driver.

This subsumes #8285.

Author: Reynold Xin <rxin@databricks.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #8295 from rxin/SPARK-10096.
---
 .../expressions/complexTypeCreator.scala      |  8 +++-
 .../spark/sql/execution/basicOperators.scala  | 12 +++--
 .../spark/sql/DataFrameComplexTypeSuite.scala | 46 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala |  9 ++++
 4 files changed, 68 insertions(+), 7 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 298aee3499275..1c546719730b7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -206,7 +206,9 @@ case class CreateStructUnsafe(children: Seq[Expression]) extends Expression {
 
   override def nullable: Boolean = false
 
-  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+  override def eval(input: InternalRow): Any = {
+    InternalRow(children.map(_.eval(input)): _*)
+  }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val eval = GenerateUnsafeProjection.createCode(ctx, children)
@@ -244,7 +246,9 @@ case class CreateNamedStructUnsafe(children: Seq[Expression]) extends Expression
 
   override def nullable: Boolean = false
 
-  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+  override def eval(input: InternalRow): Any = {
+    InternalRow(valExprs.map(_.eval(input)): _*)
+  }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val eval = GenerateUnsafeProjection.createCode(ctx, valExprs)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 77b98064a9e16..3f68b05a24f44 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -75,14 +75,16 @@ case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan)
 
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
+  /** Rewrite the project list to use unsafe expressions as needed. */
+  protected val unsafeProjectList = projectList.map(_ transform {
+    case CreateStruct(children) => CreateStructUnsafe(children)
+    case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
+  })
+
   protected override def doExecute(): RDD[InternalRow] = {
     val numRows = longMetric("numRows")
     child.execute().mapPartitions { iter =>
-      this.transformAllExpressions {
-        case CreateStruct(children) => CreateStructUnsafe(children)
-        case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
-      }
-      val project = UnsafeProjection.create(projectList, child.output)
+      val project = UnsafeProjection.create(unsafeProjectList, child.output)
       iter.map { row =>
         numRows += 1
         project(row)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
new file mode 100644
index 0000000000000..3c359dd840ab7
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
+
+/**
+ * A test suite to test DataFrame/SQL functionalities with complex types (i.e. array, struct, map).
+ */
+class DataFrameComplexTypeSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
+
+  test("UDF on struct") {
+    val f = udf((a: String) => a)
+    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    df.select(struct($"a").as("s")).select(f($"s.a")).collect()
+  }
+
+  test("UDF on named_struct") {
+    val f = udf((a: String) => a)
+    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    df.selectExpr("named_struct('a', a) s").select(f($"s.a")).collect()
+  }
+
+  test("UDF on array") {
+    val f = udf((a: String) => a)
+    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    df.select(array($"a").as("s")).select(f(expr("s[0]"))).collect()
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 1e2aaae52c9e8..284fff184085a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -878,4 +878,13 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     val df = Seq(("x", (1, 1)), ("y", (2, 2))).toDF("a", "b")
     checkAnswer(df.groupBy("b._1").agg(sum("b._2")), Row(1, 1) :: Row(2, 2) :: Nil)
   }
+
+  test("SPARK-10093: Avoid transformations on executors") {
+    val df = Seq((1, 1)).toDF("a", "b")
+    df.where($"a" === 1)
+      .select($"a", $"b", struct($"b"))
+      .orderBy("a")
+      .select(struct($"b"))
+      .collect()
+  }
 }

From de3223872a217c5224ba7136604f6b7753b29108 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 18 Aug 2015 22:11:27 -0700
Subject: [PATCH 1125/1454] [SPARK-9705] [DOC] fix docs about Python version

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8245 from davies/python_doc.
---
 docs/configuration.md     |  6 +++++-
 docs/programming-guide.md | 12 ++++++++++--
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 32147098ae64a..4a6e4dd05b661 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1561,7 +1561,11 @@ The following variables can be set in `spark-env.sh`:
   </tr>
   <tr>
     <td><code>PYSPARK_PYTHON</code></td>
-    <td>Python binary executable to use for PySpark.</td>
+    <td>Python binary executable to use for PySpark in both driver and workers (default is `python`).</td>
+  </tr>
+  <tr>
+    <td><code>PYSPARK_DRIVER_PYTHON</code></td>
+    <td>Python binary executable to use for PySpark in driver only (default is PYSPARK_PYTHON).</td>
   </tr>
   <tr>
     <td><code>SPARK_LOCAL_IP</code></td>
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index ae712d62746f6..982c5eabe652b 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -85,8 +85,8 @@ import org.apache.spark.SparkConf
 
 <div data-lang="python"  markdown="1">
 
-Spark {{site.SPARK_VERSION}} works with Python 2.6 or higher (but not Python 3). It uses the standard CPython interpreter,
-so C libraries like NumPy can be used.
+Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
+so C libraries like NumPy can be used. It also works with PyPy 2.3+.
 
 To run Spark applications in Python, use the `bin/spark-submit` script located in the Spark directory.
 This script will load Spark's Java/Scala libraries and allow you to submit applications to a cluster.
@@ -104,6 +104,14 @@ Finally, you need to import some Spark classes into your program. Add the follow
 from pyspark import SparkContext, SparkConf
 {% endhighlight %}
 
+PySpark requires the same minor version of Python in both driver and workers. It uses the default python version in PATH,
+you can specify which version of Python you want to use by `PYSPARK_PYTHON`, for example:
+
+{% highlight bash %}
+$ PYSPARK_PYTHON=python3.4 bin/pyspark
+$ PYSPARK_PYTHON=/opt/pypy-2.5/bin/pypy bin/spark-submit examples/src/main/python/pi.py
+{% endhighlight %}
+
 </div>
 
 </div>

From 1c843e284818004f16c3f1101c33b510f80722e3 Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Tue, 18 Aug 2015 22:13:52 -0700
Subject: [PATCH 1126/1454] [SPARK-9508] GraphX Pregel docs update with new
 Pregel code

SPARK-9436 simplifies the Pregel code. graphx-programming-guide needs to be modified accordingly since it lists the old Pregel code

Author: Alexander Ulanov <nashb@yandex.ru>

Closes #7831 from avulanov/SPARK-9508-pregel-doc2.
---
 docs/graphx-programming-guide.md | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 99f8c827f767f..c861a763d6222 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -768,16 +768,14 @@ class GraphOps[VD, ED] {
     // Loop until no messages remain or maxIterations is achieved
     var i = 0
     while (activeMessages > 0 && i < maxIterations) {
-      // Receive the messages: -----------------------------------------------------------------------
-      // Run the vertex program on all vertices that receive messages
-      val newVerts = g.vertices.innerJoin(messages)(vprog).cache()
-      // Merge the new vertex values back into the graph
-      g = g.outerJoinVertices(newVerts) { (vid, old, newOpt) => newOpt.getOrElse(old) }.cache()
-      // Send Messages: ------------------------------------------------------------------------------
-      // Vertices that didn't receive a message above don't appear in newVerts and therefore don't
-      // get to send messages.  More precisely the map phase of mapReduceTriplets is only invoked
-      // on edges in the activeDir of vertices in newVerts
-      messages = g.mapReduceTriplets(sendMsg, mergeMsg, Some((newVerts, activeDir))).cache()
+      // Receive the messages and update the vertices.
+      g = g.joinVertices(messages)(vprog).cache()
+      val oldMessages = messages
+      // Send new messages, skipping edges where neither side received a message. We must cache
+      // messages so it can be materialized on the next line, allowing us to uncache the previous
+      // iteration.
+      messages = g.mapReduceTriplets(
+        sendMsg, mergeMsg, Some((oldMessages, activeDirection))).cache()
       activeMessages = messages.count()
       i += 1
     }

From 010b03ed52f35fd4d426d522f8a9927ddc579209 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 18 Aug 2015 22:30:13 -0700
Subject: [PATCH 1127/1454] [SPARK-9952] Fix N^2 loop when
 DAGScheduler.getPreferredLocsInternal accesses cacheLocs

In Scala, `Seq.fill` always seems to return a List. Accessing a list by index is an O(N) operation. Thus, the following code will be really slow (~10 seconds on my machine):

```scala
val numItems = 100000
val s = Seq.fill(numItems)(1)
for (i <- 0 until numItems) s(i)
```

It turns out that we had a loop like this in DAGScheduler code, although it's a little tricky to spot. In `getPreferredLocsInternal`, there's a call to `getCacheLocs(rdd)(partition)`.  The `getCacheLocs` call returns a Seq. If this Seq is a List and the RDD contains many partitions, then indexing into this list will cost O(partitions). Thus, when we loop over our tasks to compute their individual preferred locations we implicitly perform an N^2 loop, reducing scheduling throughput.

This patch fixes this by replacing `Seq` with `Array`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8178 from JoshRosen/dagscheduler-perf.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 22 +++++++++----------
 .../spark/storage/BlockManagerMaster.scala    |  5 +++--
 .../storage/BlockManagerMasterEndpoint.scala  |  3 ++-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  4 ++--
 4 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index dadf83a38296d..684db6646765f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -111,7 +111,7 @@ class DAGScheduler(
    *
    * All accesses to this map should be guarded by synchronizing on it (see SPARK-4454).
    */
-  private val cacheLocs = new HashMap[Int, Seq[Seq[TaskLocation]]]
+  private val cacheLocs = new HashMap[Int, IndexedSeq[Seq[TaskLocation]]]
 
   // For tracking failed nodes, we use the MapOutputTracker's epoch number, which is sent with
   // every task. When we detect a node failing, we note the current epoch number and failed
@@ -205,12 +205,12 @@ class DAGScheduler(
   }
 
   private[scheduler]
-  def getCacheLocs(rdd: RDD[_]): Seq[Seq[TaskLocation]] = cacheLocs.synchronized {
+  def getCacheLocs(rdd: RDD[_]): IndexedSeq[Seq[TaskLocation]] = cacheLocs.synchronized {
     // Note: this doesn't use `getOrElse()` because this method is called O(num tasks) times
     if (!cacheLocs.contains(rdd.id)) {
       // Note: if the storage level is NONE, we don't need to get locations from block manager.
-      val locs: Seq[Seq[TaskLocation]] = if (rdd.getStorageLevel == StorageLevel.NONE) {
-        Seq.fill(rdd.partitions.size)(Nil)
+      val locs: IndexedSeq[Seq[TaskLocation]] = if (rdd.getStorageLevel == StorageLevel.NONE) {
+        IndexedSeq.fill(rdd.partitions.length)(Nil)
       } else {
         val blockIds =
           rdd.partitions.indices.map(index => RDDBlockId(rdd.id, index)).toArray[BlockId]
@@ -302,12 +302,12 @@ class DAGScheduler(
       shuffleDep: ShuffleDependency[_, _, _],
       firstJobId: Int): ShuffleMapStage = {
     val rdd = shuffleDep.rdd
-    val numTasks = rdd.partitions.size
+    val numTasks = rdd.partitions.length
     val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, firstJobId, rdd.creationSite)
     if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) {
       val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId)
       val locs = MapOutputTracker.deserializeMapStatuses(serLocs)
-      for (i <- 0 until locs.size) {
+      for (i <- 0 until locs.length) {
         stage.outputLocs(i) = Option(locs(i)).toList // locs(i) will be null if missing
       }
       stage.numAvailableOutputs = locs.count(_ != null)
@@ -315,7 +315,7 @@ class DAGScheduler(
       // Kind of ugly: need to register RDDs with the cache and map output tracker here
       // since we can't do it in the RDD constructor because # of partitions is unknown
       logInfo("Registering RDD " + rdd.id + " (" + rdd.getCreationSite + ")")
-      mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.size)
+      mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.length)
     }
     stage
   }
@@ -566,7 +566,7 @@ class DAGScheduler(
       properties: Properties): PartialResult[R] = {
     val listener = new ApproximateActionListener(rdd, func, evaluator, timeout)
     val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
-    val partitions = (0 until rdd.partitions.size).toArray
+    val partitions = (0 until rdd.partitions.length).toArray
     val jobId = nextJobId.getAndIncrement()
     eventProcessLoop.post(JobSubmitted(
       jobId, rdd, func2, partitions, callSite, listener, SerializationUtils.clone(properties)))
@@ -718,7 +718,7 @@ class DAGScheduler(
     try {
       // New stage creation may throw an exception if, for example, jobs are run on a
       // HadoopRDD whose underlying HDFS files have been deleted.
-      finalStage = newResultStage(finalRDD, partitions.size, jobId, callSite)
+      finalStage = newResultStage(finalRDD, partitions.length, jobId, callSite)
     } catch {
       case e: Exception =>
         logWarning("Creating new stage failed due to exception - job: " + jobId, e)
@@ -1039,7 +1039,7 @@ class DAGScheduler(
               //       we registered these map outputs.
               mapOutputTracker.registerMapOutputs(
                 shuffleStage.shuffleDep.shuffleId,
-                shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray,
+                shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head),
                 changeEpoch = true)
 
               clearCacheLocs()
@@ -1169,7 +1169,7 @@ class DAGScheduler(
         // TODO: This will be really slow if we keep accumulating shuffle map stages
         for ((shuffleId, stage) <- shuffleToMapStage) {
           stage.removeOutputsOnExecutor(execId)
-          val locs = stage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray
+          val locs = stage.outputLocs.map(list => if (list.isEmpty) null else list.head)
           mapOutputTracker.registerMapOutputs(shuffleId, locs, changeEpoch = true)
         }
         if (shuffleToMapStage.isEmpty) {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index 2a11f371b9d6e..f45bff34d4dbc 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -69,8 +69,9 @@ class BlockManagerMaster(
   }
 
   /** Get locations of multiple blockIds from the driver */
-  def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = {
-    driverEndpoint.askWithRetry[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds))
+  def getLocations(blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
+    driverEndpoint.askWithRetry[IndexedSeq[Seq[BlockManagerId]]](
+      GetLocationsMultipleBlockIds(blockIds))
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
index 5dc0c537cbb62..6fec5240707a6 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
@@ -372,7 +372,8 @@ class BlockManagerMasterEndpoint(
     if (blockLocations.containsKey(blockId)) blockLocations.get(blockId).toSeq else Seq.empty
   }
 
-  private def getLocationsMultipleBlockIds(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = {
+  private def getLocationsMultipleBlockIds(
+      blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
     blockIds.map(blockId => getLocations(blockId))
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index a063596d3eae2..2e8688cf41d99 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -133,11 +133,11 @@ class DAGSchedulerSuite
   val cacheLocations = new HashMap[(Int, Int), Seq[BlockManagerId]]
   // stub out BlockManagerMaster.getLocations to use our cacheLocations
   val blockManagerMaster = new BlockManagerMaster(null, conf, true) {
-      override def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = {
+      override def getLocations(blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
         blockIds.map {
           _.asRDDId.map(id => (id.rddId -> id.splitIndex)).flatMap(key => cacheLocations.get(key)).
             getOrElse(Seq())
-        }.toSeq
+        }.toIndexedSeq
       }
       override def removeExecutor(execId: String) {
         // don't need to propagate to the driver, which we don't have

From bc9a0e03235865d2ec33372f6400dec8c770778a Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 18 Aug 2015 23:37:57 -0700
Subject: [PATCH 1128/1454] [SPARK-9967] [SPARK-10099] [STREAMING] Renamed conf
 spark.streaming.backpressure.{enable-->enabled} and fixed deprecated
 annotations

Small changes
- Renamed conf spark.streaming.backpressure.{enable --> enabled}
- Change Java Deprecated annotations to Scala deprecated annotation with more information.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8299 from tdas/SPARK-9967.
---
 .../scala/org/apache/spark/api/java/JavaRDDLike.scala     | 2 +-
 .../scala/org/apache/spark/deploy/SparkHadoopUtil.scala   | 2 +-
 .../apache/spark/streaming/api/java/JavaDStreamLike.scala | 4 ++--
 .../apache/spark/streaming/scheduler/RateController.scala | 8 ++++----
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index 829fae1d1d9bf..c582488f16fe7 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -354,7 +354,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Return an array that contains all of the elements in this RDD.
    * @deprecated As of Spark 1.0.0, toArray() is deprecated, use {@link #collect()} instead
    */
-  @Deprecated
+  @deprecated("use collect()", "1.0.0")
   def toArray(): JList[T] = collect()
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 7e9dba42bebd8..dda4216c7efe2 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -76,7 +76,7 @@ class SparkHadoopUtil extends Logging {
     }
   }
 
-  @Deprecated
+  @deprecated("use newConfiguration with SparkConf argument", "1.2.0")
   def newConfiguration(): Configuration = newConfiguration(null)
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index 808dcc174cf9a..214cd80108b9b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -291,7 +291,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    *
    * @deprecated  As of release 0.9.0, replaced by foreachRDD
    */
-  @Deprecated
+  @deprecated("Use foreachRDD", "0.9.0")
   def foreach(foreachFunc: JFunction[R, Void]) {
     foreachRDD(foreachFunc)
   }
@@ -302,7 +302,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    *
    * @deprecated  As of release 0.9.0, replaced by foreachRDD
    */
-  @Deprecated
+  @deprecated("Use foreachRDD", "0.9.0")
   def foreach(foreachFunc: JFunction2[R, Time, Void]) {
     foreachRDD(foreachFunc)
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
index 882ca0676b6ad..a46c0c1b25e74 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
@@ -76,9 +76,9 @@ private[streaming] abstract class RateController(val streamUID: Int, rateEstimat
     val elements = batchCompleted.batchInfo.streamIdToInputInfo
 
     for {
-      processingEnd <- batchCompleted.batchInfo.processingEndTime;
-      workDelay <- batchCompleted.batchInfo.processingDelay;
-      waitDelay <- batchCompleted.batchInfo.schedulingDelay;
+      processingEnd <- batchCompleted.batchInfo.processingEndTime
+      workDelay <- batchCompleted.batchInfo.processingDelay
+      waitDelay <- batchCompleted.batchInfo.schedulingDelay
       elems <- elements.get(streamUID).map(_.numRecords)
     } computeAndPublish(processingEnd, elems, workDelay, waitDelay)
   }
@@ -86,5 +86,5 @@ private[streaming] abstract class RateController(val streamUID: Int, rateEstimat
 
 object RateController {
   def isBackPressureEnabled(conf: SparkConf): Boolean =
-    conf.getBoolean("spark.streaming.backpressure.enable", false)
+    conf.getBoolean("spark.streaming.backpressure.enabled", false)
 }

From b23c4d3ffc36e47c057360c611d8ab1a13877699 Mon Sep 17 00:00:00 2001
From: Bill Chambers <wchambers@ischool.berkeley.edu>
Date: Wed, 19 Aug 2015 00:05:01 -0700
Subject: [PATCH 1129/1454] Fix Broken Link

Link was broken because it included tick marks.

Author: Bill Chambers <wchambers@ischool.berkeley.edu>

Closes #8302 from anabranch/patch-1.
---
 docs/sql-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 6c317175d3278..33e7893d7bd0a 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -28,7 +28,7 @@ All of the examples on this page use sample data included in the Spark distribut
 <div data-lang="scala"  markdown="1">
 
 The entry point into all functionality in Spark SQL is the
-[`SQLContext`](api/scala/index.html#org.apache.spark.sql.`SQLContext`) class, or one of its
+[`SQLContext`](api/scala/index.html#org.apache.spark.sql.SQLContext) class, or one of its
 descendants.  To create a basic `SQLContext`, all you need is a SparkContext.
 
 {% highlight scala %}

From f141efeafb42b14b5fcfd9aa8c5275162042349f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Wed, 19 Aug 2015 09:41:09 +0100
Subject: [PATCH 1130/1454] [SPARK-10070] [DOCS] Remove Guava dependencies in
 user guides

`Lists.newArrayList` -> `Arrays.asList`

CC jkbradley feynmanliang

Anybody into replacing usages of `Lists.newArrayList` in the examples / source code too? this method isn't useful in Java 7 and beyond.

Author: Sean Owen <sowen@cloudera.com>

Closes #8272 from srowen/SPARK-10070.
---
 docs/ml-features.md | 52 ++++++++++++++++++++++-----------------------
 docs/ml-guide.md    | 21 ++++++++++--------
 2 files changed, 38 insertions(+), 35 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 28a61933f8238..d82c85ee75a0e 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -55,7 +55,7 @@ rescaledData.select("features", "label").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.HashingTF;
@@ -70,7 +70,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create(0, "Hi I heard about Spark"),
   RowFactory.create(0, "I wish Java could use case classes"),
   RowFactory.create(1, "Logistic regression models are neat")
@@ -153,7 +153,7 @@ result.select("result").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -167,10 +167,10 @@ JavaSparkContext jsc = ...
 SQLContext sqlContext = ...
 
 // Input data: Each row is a bag of words from a sentence or document.
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
-  RowFactory.create(Lists.newArrayList("Hi I heard about Spark".split(" "))),
-  RowFactory.create(Lists.newArrayList("I wish Java could use case classes".split(" "))),
-  RowFactory.create(Lists.newArrayList("Logistic regression models are neat".split(" ")))
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
+  RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
+  RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
 ));
 StructType schema = new StructType(new StructField[]{
   new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
@@ -248,7 +248,7 @@ regexTokenized.select("words", "label").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.RegexTokenizer;
@@ -262,7 +262,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create(0, "Hi I heard about Spark"),
   RowFactory.create(1, "I wish Java could use case classes"),
   RowFactory.create(2, "Logistic,regression,models,are,neat")
@@ -341,7 +341,7 @@ ngramDataFrame.take(3).map(_.getAs[Stream[String]]("ngrams").toList).foreach(pri
 [`NGram`](api/java/org/apache/spark/ml/feature/NGram.html) takes an input column name, an output column name, and an optional length parameter n (n=2 by default).
 
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.NGram;
@@ -354,10 +354,10 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
-  RowFactory.create(0D, Lists.newArrayList("Hi", "I", "heard", "about", "Spark")),
-  RowFactory.create(1D, Lists.newArrayList("I", "wish", "Java", "could", "use", "case", "classes")),
-  RowFactory.create(2D, Lists.newArrayList("Logistic", "regression", "models", "are", "neat"))
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(0.0, Arrays.asList("Hi", "I", "heard", "about", "Spark")),
+  RowFactory.create(1.0, Arrays.asList("I", "wish", "Java", "could", "use", "case", "classes")),
+  RowFactory.create(2.0, Arrays.asList("Logistic", "regression", "models", "are", "neat"))
 ));
 StructType schema = new StructType(new StructField[]{
   new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
@@ -427,7 +427,7 @@ binarizedFeatures.collect().foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.Binarizer;
@@ -439,7 +439,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create(0, 0.1),
   RowFactory.create(1, 0.8),
   RowFactory.create(2, 0.2)
@@ -511,7 +511,7 @@ result.show()
 <div data-lang="java" markdown="1">
 See the [Java API documentation](api/java/org/apache/spark/ml/feature/PCA.html) for API details.
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -529,7 +529,7 @@ import org.apache.spark.sql.types.StructType;
 
 JavaSparkContext jsc = ...
 SQLContext jsql = ...
-JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
   RowFactory.create(Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0})),
   RowFactory.create(Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0)),
   RowFactory.create(Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))
@@ -593,7 +593,7 @@ polyDF.select("polyFeatures").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -614,7 +614,7 @@ PolynomialExpansion polyExpansion = new PolynomialExpansion()
   .setInputCol("features")
   .setOutputCol("polyFeatures")
   .setDegree(3);
-JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
   RowFactory.create(Vectors.dense(-2.0, 2.3)),
   RowFactory.create(Vectors.dense(0.0, 0.0)),
   RowFactory.create(Vectors.dense(0.6, -1.1))
@@ -869,7 +869,7 @@ encoded.select("id", "categoryVec").foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.OneHotEncoder;
@@ -883,7 +883,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
     RowFactory.create(0, "a"),
     RowFactory.create(1, "b"),
     RowFactory.create(2, "c"),
@@ -1206,7 +1206,7 @@ val bucketedData = bucketizer.transform(dataFrame)
 
 <div data-lang="java">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
@@ -1218,7 +1218,7 @@ import org.apache.spark.sql.types.StructType;
 
 double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY};
 
-JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
   RowFactory.create(-0.5),
   RowFactory.create(-0.3),
   RowFactory.create(0.0),
@@ -1307,7 +1307,7 @@ transformer.transform(dataFrame).show()
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.ElementwiseProduct;
@@ -1323,7 +1323,7 @@ import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
 // Create some vector data; also works for sparse vectors
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create("a", Vectors.dense(1.0, 2.0, 3.0)),
   RowFactory.create("b", Vectors.dense(4.0, 5.0, 6.0))
 ));
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index a03ab4356a413..4fe0ea78bbe13 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -274,8 +274,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
@@ -294,7 +295,7 @@ SQLContext jsql = new SQLContext(jsc);
 // Prepare training data.
 // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
 // into DataFrames, where it uses the bean metadata to infer the schema.
-List<LabeledPoint> localTraining = Lists.newArrayList(
+List<LabeledPoint> localTraining = Arrays.asList(
   new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
@@ -335,7 +336,7 @@ LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
 System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
 
 // Prepare test documents.
-List<LabeledPoint> localTest = Lists.newArrayList(
+List<LabeledPoint> localTest = Arrays.asList(
     new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
     new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
     new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
@@ -496,8 +497,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
@@ -546,7 +548,7 @@ JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Lists.newArrayList(
+List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -571,7 +573,7 @@ Pipeline pipeline = new Pipeline()
 PipelineModel model = pipeline.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Lists.newArrayList(
+List<Document> localTest = Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),
@@ -747,8 +749,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
@@ -800,7 +803,7 @@ JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Lists.newArrayList(
+List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -849,7 +852,7 @@ crossval.setNumFolds(2); // Use 3+ in practice
 CrossValidatorModel cvModel = crossval.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Lists.newArrayList(
+List<Document> localTest = Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),

From 865a3df3d578c0442c97d749c81f554b560da406 Mon Sep 17 00:00:00 2001
From: Moussa Taifi <moutai10@gmail.com>
Date: Wed, 19 Aug 2015 09:42:41 +0100
Subject: [PATCH 1131/1454] [DOCS] [SQL] [PYSPARK] Fix typo in ntile function

Fix typo in ntile function.

Author: Moussa Taifi <moutai10@gmail.com>

Closes #8261 from moutai/patch-2.
---
 python/pyspark/sql/functions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 41dfee9f54f7a..4b74a501521a5 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -531,7 +531,7 @@ def lead(col, count=1, default=None):
 def ntile(n):
     """
     Window function: returns the ntile group id (from 1 to `n` inclusive)
-    in an ordered window partition. Fow example, if `n` is 4, the first
+    in an ordered window partition. For example, if `n` is 4, the first
     quarter of the rows will get value 1, the second quarter will get 2,
     the third quarter will get 3, and the last quarter will get 4.
 

From ba2a07e2b6c5a39597b64041cd5bf342ef9631f5 Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Wed, 19 Aug 2015 09:54:03 +0100
Subject: [PATCH 1132/1454] [SPARK-9977] [DOCS] Update documentation for
 StringIndexer

By using `StringIndexer`, we can obtain indexed label on new column. So a following estimator should use this new column through pipeline if it wants to use string indexed label.
I think it is better to make it explicit on documentation.

Author: lewuathe <lewuathe@me.com>

Closes #8205 from Lewuathe/SPARK-9977.
---
 docs/ml-features.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index d82c85ee75a0e..8d56dc32ca3c0 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -725,7 +725,11 @@ dctDf.select("featuresDCT").show(3);
 `StringIndexer` encodes a string column of labels to a column of label indices.
 The indices are in `[0, numLabels)`, ordered by label frequencies.
 So the most frequent label gets index `0`.
-If the input column is numeric, we cast it to string and index the string values.
+If the input column is numeric, we cast it to string and index the string 
+values. When downstream pipeline components such as `Estimator` or 
+`Transformer` make use of this string-indexed label, you must set the input 
+column of the component to this string-indexed column name. In many cases, 
+you can set the input column with `setInputCol`.
 
 **Examples**
 

From 3d16a545007922ee6fa36e5f5c3959406cb46484 Mon Sep 17 00:00:00 2001
From: Han JU <ju.han.felix@gmail.com>
Date: Wed, 19 Aug 2015 13:04:16 +0100
Subject: [PATCH 1133/1454] [SPARK-8949] Print warnings when using preferred
 locations feature

Add warnings according to SPARK-8949 in `SparkContext`
  - warnings in scaladoc
  - log warnings when preferred locations feature is used through `SparkContext`'s constructor

However I didn't found any documentation reference of this feature. Please direct me if you know any reference to this feature.

Author: Han JU <ju.han.felix@gmail.com>

Closes #7874 from darkjh/SPARK-8949.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 2e01a9a18c784..1ddaca8a5ba8c 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -118,9 +118,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * Can be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
    * from a list of input files or InputFormats for the application.
    */
+  @deprecated("Passing in preferred locations has no effect at all, see SPARK-8949", "1.5.0")
   @DeveloperApi
   def this(config: SparkConf, preferredNodeLocationData: Map[String, Set[SplitInfo]]) = {
     this(config)
+    logWarning("Passing in preferred locations has no effect at all, see SPARK-8949")
     this.preferredNodeLocationData = preferredNodeLocationData
   }
 
@@ -153,6 +155,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()) =
   {
     this(SparkContext.updatedConf(new SparkConf(), master, appName, sparkHome, jars, environment))
+    if (preferredNodeLocationData.nonEmpty) {
+      logWarning("Passing in preferred locations has no effect at all, see SPARK-8949")
+    }
     this.preferredNodeLocationData = preferredNodeLocationData
   }
 

From 39e4ebd521defdb68a0787bcd3bde6bc855f5198 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 19 Aug 2015 07:38:27 -0700
Subject: [PATCH 1134/1454] [SPARK-10060] [ML] [DOC] spark.ml DecisionTree user
 guide

New user guide section ml-decision-tree.md, including code examples.

I have run all examples, including the Java ones.

CC: manishamde yanboliang mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8244 from jkbradley/ml-dt-docs.
---
 docs/ml-decision-tree.md | 510 +++++++++++++++++++++++++++++++++++++++
 docs/ml-features.md      |   2 -
 docs/ml-guide.md         |   9 +-
 docs/mllib-ensembles.md  |   2 +-
 docs/mllib-guide.md      |   9 +-
 5 files changed, 519 insertions(+), 13 deletions(-)
 create mode 100644 docs/ml-decision-tree.md

diff --git a/docs/ml-decision-tree.md b/docs/ml-decision-tree.md
new file mode 100644
index 0000000000000..958c6f5e4716c
--- /dev/null
+++ b/docs/ml-decision-tree.md
@@ -0,0 +1,510 @@
+---
+layout: global
+title: Decision Trees - SparkML
+displayTitle: <a href="ml-guide.html">ML</a> - Decision Trees
+---
+
+**Table of Contents**
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+
+# Overview
+
+[Decision trees](http://en.wikipedia.org/wiki/Decision_tree_learning)
+and their ensembles are popular methods for the machine learning tasks of
+classification and regression. Decision trees are widely used since they are easy to interpret,
+handle categorical features, extend to the multiclass classification setting, do not require
+feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble
+algorithms such as random forests and boosting are among the top performers for classification and
+regression tasks.
+
+MLlib supports decision trees for binary and multiclass classification and for regression,
+using both continuous and categorical features. The implementation partitions data by rows,
+allowing distributed training with millions or even billions of instances.
+
+Users can find more information about the decision tree algorithm in the [MLlib Decision Tree guide](mllib-decision-tree.html).  In this section, we demonstrate the Pipelines API for Decision Trees.
+
+The Pipelines API for Decision Trees offers a bit more functionality than the original API.  In particular, for classification, users can get the predicted probability of each class (a.k.a. class conditional probabilities).
+
+Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described in the [Ensembles guide](ml-ensembles.html).
+
+# Inputs and Outputs (Predictions)
+
+We list the input and output (prediction) column types here.
+All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
+
+## Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>labelCol</td>
+      <td>Double</td>
+      <td>"label"</td>
+      <td>Label to predict</td>
+    </tr>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+## Output Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+      <th align="left">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Double</td>
+      <td>"prediction"</td>
+      <td>Predicted label</td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>rawPredictionCol</td>
+      <td>Vector</td>
+      <td>"rawPrediction"</td>
+      <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td>
+      <td>Classification only</td>
+    </tr>
+    <tr>
+      <td>probabilityCol</td>
+      <td>Vector</td>
+      <td>"probability"</td>
+      <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td>
+      <td>Classification only</td>
+    </tr>
+  </tbody>
+</table>
+
+# Examples
+
+The below examples demonstrate the Pipelines API for Decision Trees. The main differences between this API and the [original MLlib Decision Tree API](mllib-decision-tree.html) are:
+
+* support for ML Pipelines
+* separation of Decision Trees for classification vs. regression
+* use of DataFrame metadata to distinguish continuous and categorical features
+
+
+## Classification
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the Decision Tree algorithm can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.classification.DecisionTreeClassifier
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+val labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data)
+// Automatically identify categorical features, and index them.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a DecisionTree model.
+val dt = new DecisionTreeClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+
+// Convert indexed labels back to original labels.
+val labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels)
+
+// Chain indexers and tree in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))
+
+// Train model.  This also runs the indexers.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision")
+val accuracy = evaluator.evaluate(predictions)
+println("Test Error = " + (1.0 - accuracy))
+
+val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel]
+println("Learned classification tree model:\n" + treeModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.DecisionTreeClassifier;
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a DecisionTree model.
+DecisionTreeClassifier dt = new DecisionTreeClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures");
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and tree in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision");
+double accuracy = evaluator.evaluate(predictions);
+System.out.println("Test Error = " + (1.0 - accuracy));
+
+DecisionTreeClassificationModel treeModel =
+  (DecisionTreeClassificationModel)(model.stages()[2]);
+System.out.println("Learned classification tree model:\n" + treeModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier).
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import DecisionTreeClassifier
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# We specify maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a DecisionTree model.
+dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")
+
+# Chain indexers and tree in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = MulticlassClassificationEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+accuracy = evaluator.evaluate(predictions)
+print "Test Error = %g" % (1.0 - accuracy)
+
+treeModel = model.stages[2]
+print treeModel # summary only
+{% endhighlight %}
+</div>
+
+</div>
+
+
+## Regression
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.regression.DecisionTreeRegressor
+import org.apache.spark.ml.regression.DecisionTreeRegressionModel
+import org.apache.spark.ml.feature.VectorIndexer
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Automatically identify categorical features, and index them.
+// Here, we treat features with > 4 distinct values as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a DecisionTree model.
+val dt = new DecisionTreeRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+
+// Chain indexers and tree in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(featureIndexer, dt))
+
+// Train model.  This also runs the indexer.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse")
+// We negate the RMSE value since RegressionEvalutor returns negated RMSE
+// (since evaluation metrics are meant to be maximized by CrossValidator).
+val rmse = - evaluator.evaluate(predictions)
+println("Root Mean Squared Error (RMSE) on test data = " + rmse)
+
+val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel]
+println("Learned regression tree model:\n" + treeModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.ml.regression.DecisionTreeRegressionModel;
+import org.apache.spark.ml.regression.DecisionTreeRegressor;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a DecisionTree model.
+DecisionTreeRegressor dt = new DecisionTreeRegressor()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures");
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and tree in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+RegressionEvaluator evaluator = new RegressionEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse");
+// We negate the RMSE value since RegressionEvalutor returns negated RMSE
+// (since evaluation metrics are meant to be maximized by CrossValidator).
+double rmse = - evaluator.evaluate(predictions);
+System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
+
+DecisionTreeRegressionModel treeModel =
+  (DecisionTreeRegressionModel)(model.stages()[2]);
+System.out.println("Learned regression tree model:\n" + treeModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier).
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.regression import DecisionTreeRegressor
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# We specify maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a DecisionTree model.
+dt = DecisionTreeRegressor(labelCol="indexedLabel", featuresCol="indexedFeatures")
+
+# Chain indexers and tree in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = RegressionEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="rmse")
+# We negate the RMSE value since RegressionEvalutor returns negated RMSE
+# (since evaluation metrics are meant to be maximized by CrossValidator).
+rmse = -evaluator.evaluate(predictions)
+print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
+
+treeModel = model.stages[1]
+print treeModel # summary only
+{% endhighlight %}
+</div>
+
+</div>
diff --git a/docs/ml-features.md b/docs/ml-features.md
index 8d56dc32ca3c0..d0e8eeb7a757e 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,5 +1477,3 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
-# Feature Selectors
-
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 4fe0ea78bbe13..c64fff7c0315a 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -32,10 +32,7 @@ Users should be comfortable using `spark.mllib` features and expect more feature
 Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
 to `spark.ml`.
 
-Guides for sub-packages of `spark.ml` include:
-
-* [Feature Extraction, Transformation, and Selection](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API
-* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API
+See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of `spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
 
 
 **Table of Contents**
@@ -179,10 +176,8 @@ There are now several algorithms in the Pipelines API which are not in the lower
 **Pipelines API Algorithm Guides**
 
 * [Feature Extraction, Transformation, and Selection](ml-features.html)
+* [Decision Trees for Classification and Regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
-
-**Algorithms in `spark.ml`**
-
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
 
 # Code Examples
diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md
index 7521fb14a7bd6..1e00b2083ed73 100644
--- a/docs/mllib-ensembles.md
+++ b/docs/mllib-ensembles.md
@@ -9,7 +9,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Ensembles
 
 An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning)
 is a learning algorithm which creates a model composed of a set of other base models.
-MLlib supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBosotedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest).
+MLlib supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest).
 Both use [decision trees](mllib-decision-tree.html) as their base models.
 
 ## Gradient-Boosted Trees vs. Random Forests
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 0573e5d4bc5ff..6330c977552d1 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -73,11 +73,14 @@ Users should be comfortable using `spark.mllib` features and expect more feature
 Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
 to `spark.ml`.
 
-More detailed guides for `spark.ml` include:
+Guides for `spark.ml` include:
 
 * **[spark.ml programming guide](ml-guide.html)**: overview of the Pipelines API and major concepts
-* [Feature transformers](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API
-* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API
+* Guides on using algorithms within the Pipelines API:
+  * [Feature transformers](ml-features.html), including a few not in the lower-level `spark.mllib` API
+  * [Decision trees](ml-decision-tree.html)
+  * [Ensembles](ml-ensembles.html)
+  * [Linear methods](ml-linear-methods.html)
 
 # Dependencies
 

From 802b5b8791fc2c892810981b2479a04175aa3dcd Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 19 Aug 2015 08:53:34 -0700
Subject: [PATCH 1135/1454] [SPARK-10084] [MLLIB] [DOC] Add Python example for
 mllib FP-growth user guide

1, Add Python example for mllib FP-growth user guide.
2, Correct mistakes of Scala and Java examples.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8279 from yanboliang/spark-10084.
---
 docs/mllib-frequent-pattern-mining.md | 73 ++++++++++++++++++---------
 1 file changed, 50 insertions(+), 23 deletions(-)

diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md
index 6c06550703d46..4d4f5cfdc564e 100644
--- a/docs/mllib-frequent-pattern-mining.md
+++ b/docs/mllib-frequent-pattern-mining.md
@@ -39,9 +39,9 @@ MLlib's FP-growth implementation takes the following (hyper-)parameters:
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth)
-implements the FP-growth algorithm.  It take an `RDD` of transactions,
-where each transaction is an `Iterable` of items of a generic type.
+[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) implements the
+FP-growth algorithm.
+It take a `RDD` of transactions, where each transaction is an `Array` of items of a generic type.
 Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowthModel)
 that stores the frequent itemsets with their frequencies.  The following
@@ -53,16 +53,11 @@ details) from `transactions`.
 
 {% highlight scala %}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
+import org.apache.spark.mllib.fpm.FPGrowth
 
-val transactions: RDD[Array[String]] = sc.parallelize(Seq(
-  "r z h k p",
-  "z y x w v u t s",
-  "s x o n r",
-  "x z y m t s q e",
-  "z",
-  "x z y r q t p")
-  .map(_.split(" ")))
+val data = sc.textFile("data/mllib/sample_fpgrowth.txt")
+
+val transactions: RDD[Array[String]] = data.map(s => s.trim.split(' '))
 
 val fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -86,10 +81,10 @@ model.generateAssociationRules(minConfidence).collect().foreach { rule =>
 
 <div data-lang="java" markdown="1">
 
-[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html)
-implements the FP-growth algorithm.  It take a `JavaRDD` of
-transactions, where each transaction is an `Array` of items of a generic
-type.  Calling `FPGrowth.run` with transactions returns an
+[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
+FP-growth algorithm.
+It take an `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type.
+Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
 that stores the frequent itemsets with their frequencies.  The following
 example illustrates how to mine frequent itemsets and association rules
@@ -107,13 +102,19 @@ import org.apache.spark.mllib.fpm.AssociationRules;
 import org.apache.spark.mllib.fpm.FPGrowth;
 import org.apache.spark.mllib.fpm.FPGrowthModel;
 
-JavaRDD<List<String>> transactions = sc.parallelize(Arrays.asList(
-  Arrays.asList("r z h k p".split(" ")),
-  Arrays.asList("z y x w v u t s".split(" ")),
-  Arrays.asList("s x o n r".split(" ")),
-  Arrays.asList("x z y m t s q e".split(" ")),
-  Arrays.asList("z".split(" ")),
-  Arrays.asList("x z y r q t p".split(" "))), 2);
+SparkConf conf = new SparkConf().setAppName("FP-growth Example");
+JavaSparkContext sc = new JavaSparkContext(conf);
+
+JavaRDD<String> data = sc.textFile("data/mllib/sample_fpgrowth.txt");
+
+JavaRDD<List<String>> transactions = data.map(
+  new Function<String, List<String>>() {
+    public List<String> call(String line) {
+      String[] parts = line.split(" ");
+      return Arrays.asList(parts);
+    }
+  }
+);
 
 FPGrowth fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -133,6 +134,32 @@ for (AssociationRules.Rule<String> rule
 {% endhighlight %}
 
 </div>
+
+<div data-lang="python" markdown="1">
+
+[`FPGrowth`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth) implements the
+FP-growth algorithm.
+It take an `RDD` of transactions, where each transaction is an `List` of items of a generic type.
+Calling `FPGrowth.train` with transactions returns an
+[`FPGrowthModel`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowthModel)
+that stores the frequent itemsets with their frequencies.
+
+{% highlight python %}
+from pyspark.mllib.fpm import FPGrowth
+
+data = sc.textFile("data/mllib/sample_fpgrowth.txt")
+
+transactions = data.map(lambda line: line.strip().split(' '))
+
+model = FPGrowth.train(transactions, minSupport=0.2, numPartitions=10)
+
+result = model.freqItemsets().collect()
+for fi in result:
+    print(fi)
+{% endhighlight %}
+
+</div>
+
 </div>
 
 ## Association Rules

From f3e177917dd093873dca286d2419704a561ba44b Mon Sep 17 00:00:00 2001
From: Carsten Blank <blank@cncengine.com>
Date: Wed, 19 Aug 2015 10:19:24 -0700
Subject: [PATCH 1136/1454] [SPARK-5754] [YARN] Spark/Yarn/Windows
 driver/executor escaping Fix

This is my retry to suggest a fix for using Spark on Yarn on Windows. The former request lacked coding style which I hope to have learned to do better, and wasn't a true solution as I didn't really understand where the problem came from. Albeit being still a bit obscure, I can name the "players" and have come up with a better explaination of why I am suggesting this fix.

I also used vanzin and srowen input to *try* to give a more elegant solution. I am not so sure if that worked out though.

I still hope that this PR is a lot more useful than the last. Also do I hope that this is a _solution_ to the problem that Spark doesn't work on Yarn on Windows. With these changes it works (and I can also explain why!).

I still believe that a Unit Test should be included, kind of like the one I committed the last time. But that was premature, as I want to get the principal 'Go' from vanzin and srowen.

Thanks for your time both of you.

Author: Carsten Blank <blank@cncengine.com>
Author: cbvoxel <blank@cncengine.com>

Closes #8053 from cbvoxel/master.
---
 .../org/apache/spark/deploy/yarn/Client.scala |  2 +-
 .../spark/deploy/yarn/ExecutorRunnable.scala  |  2 +-
 .../deploy/yarn/YarnSparkHadoopUtil.scala     | 56 +++++++++++++++----
 .../launcher/YarnCommandBuilderUtils.scala    | 27 +++++++++
 4 files changed, 75 insertions(+), 12 deletions(-)
 create mode 100644 yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 8672ef601784b..262c6a8d6397f 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -431,7 +431,7 @@ private[spark] class Client(
     }
 
     // Distribute an archive with Hadoop and Spark configuration for the AM.
-    val (_, confLocalizedPath) = distribute(createConfArchive().getAbsolutePath(),
+    val (_, confLocalizedPath) = distribute(createConfArchive().toURI().getPath(),
       resType = LocalResourceType.ARCHIVE,
       destName = Some(LOCALIZED_CONF_DIR),
       appMasterOnly = true)
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 52580deb372c2..4cc50483a17ff 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -217,7 +217,7 @@ class ExecutorRunnable(
       // an inconsistent state.
       // TODO: If the OOM is not recoverable by rescheduling it on different node, then do
       // 'something' to fail job ... akin to blacklisting trackers in mapred ?
-      "-XX:OnOutOfMemoryError='kill %p'") ++
+      YarnSparkHadoopUtil.getOutOfMemoryErrorArgument) ++
       javaOpts ++
       Seq("org.apache.spark.executor.CoarseGrainedExecutorBackend",
         "--driver-url", masterAddress.toString,
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index 68d01c17ef720..445d3dcd266db 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -37,6 +37,7 @@ import org.apache.hadoop.yarn.api.records.{ApplicationAccessType, ContainerId, P
 import org.apache.hadoop.yarn.util.ConverterUtils
 
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.launcher.YarnCommandBuilderUtils
 import org.apache.spark.{SecurityManager, SparkConf, SparkException}
 import org.apache.spark.util.Utils
 
@@ -219,26 +220,61 @@ object YarnSparkHadoopUtil {
     }
   }
 
+  /**
+   * The handler if an OOM Exception is thrown by the JVM must be configured on Windows
+   * differently: the 'taskkill' command should be used, whereas Unix-based systems use 'kill'.
+   *
+   * As the JVM interprets both %p and %%p as the same, we can use either of them. However,
+   * some tests on Windows computers suggest, that the JVM only accepts '%%p'.
+   *
+   * Furthermore, the behavior of the character '%' on the Windows command line differs from
+   * the behavior of '%' in a .cmd file: it gets interpreted as an incomplete environment
+   * variable. Windows .cmd files escape a '%' by '%%'. Thus, the correct way of writing
+   * '%%p' in an escaped way is '%%%%p'.
+   *
+   * @return The correct OOM Error handler JVM option, platform dependent.
+   */
+  def getOutOfMemoryErrorArgument : String = {
+    if (Utils.isWindows) {
+      escapeForShell("-XX:OnOutOfMemoryError=taskkill /F /PID %%%%p")
+    } else {
+      "-XX:OnOutOfMemoryError='kill %p'"
+    }
+  }
+
   /**
    * Escapes a string for inclusion in a command line executed by Yarn. Yarn executes commands
-   * using `bash -c "command arg1 arg2"` and that means plain quoting doesn't really work. The
-   * argument is enclosed in single quotes and some key characters are escaped.
+   * using either
+   *
+   * (Unix-based) `bash -c "command arg1 arg2"` and that means plain quoting doesn't really work.
+   * The argument is enclosed in single quotes and some key characters are escaped.
+   *
+   * (Windows-based) part of a .cmd file in which case windows escaping for each argument must be
+   * applied. Windows is quite lenient, however it is usually Java that causes trouble, needing to
+   * distinguish between arguments starting with '-' and class names. If arguments are surrounded
+   * by ' java takes the following string as is, hence an argument is mistakenly taken as a class
+   * name which happens to start with a '-'. The way to avoid this, is to surround nothing with
+   * a ', but instead with a ".
    *
    * @param arg A single argument.
    * @return Argument quoted for execution via Yarn's generated shell script.
    */
   def escapeForShell(arg: String): String = {
     if (arg != null) {
-      val escaped = new StringBuilder("'")
-      for (i <- 0 to arg.length() - 1) {
-        arg.charAt(i) match {
-          case '$' => escaped.append("\\$")
-          case '"' => escaped.append("\\\"")
-          case '\'' => escaped.append("'\\''")
-          case c => escaped.append(c)
+      if (Utils.isWindows) {
+        YarnCommandBuilderUtils.quoteForBatchScript(arg)
+      } else {
+        val escaped = new StringBuilder("'")
+        for (i <- 0 to arg.length() - 1) {
+          arg.charAt(i) match {
+            case '$' => escaped.append("\\$")
+            case '"' => escaped.append("\\\"")
+            case '\'' => escaped.append("'\\''")
+            case c => escaped.append(c)
+          }
         }
+        escaped.append("'").toString()
       }
-      escaped.append("'").toString()
     } else {
       arg
     }
diff --git a/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala b/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala
new file mode 100644
index 0000000000000..3ac36ef0a1c3f
--- /dev/null
+++ b/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.launcher
+
+/**
+ * Exposes needed methods
+ */
+private[spark] object YarnCommandBuilderUtils {
+  def quoteForBatchScript(arg: String) : String = {
+    CommandBuilderUtils.quoteForBatchScript(arg)
+  }
+}

From 2fcb9cb9552dac1d78dcca5d4d5032b4fa6c985c Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 19 Aug 2015 10:41:14 -0700
Subject: [PATCH 1137/1454] [SPARK-9856] [SPARKR] Add expression functions into
 SparkR whose params are complicated

I added lots of Column functinos into SparkR. And I also added `rand(seed: Int)` and `randn(seed: Int)` in Scala. Since we need such APIs for R integer type.

### JIRA
[[SPARK-9856] Add expression functions into SparkR whose params are complicated - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9856)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8264 from yu-iskw/SPARK-9856-3.
---
 R/pkg/NAMESPACE                               |  28 ++
 R/pkg/R/functions.R                           | 415 ++++++++++++++++++
 R/pkg/R/generics.R                            | 113 +++++
 R/pkg/inst/tests/test_sparkSQL.R              |  98 ++++-
 .../apache/spark/api/r/RBackendHandler.scala  |   1 +
 5 files changed, 649 insertions(+), 6 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 8fa12d5adef8a..111a2dc30db73 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -84,6 +84,7 @@ exportClasses("Column")
 
 exportMethods("abs",
               "acos",
+              "add_months",
               "alias",
               "approxCountDistinct",
               "asc",
@@ -101,12 +102,17 @@ exportMethods("abs",
               "ceil",
               "ceiling",
               "concat",
+              "concat_ws",
               "contains",
+              "conv",
               "cos",
               "cosh",
               "count",
               "countDistinct",
               "crc32",
+              "date_add",
+              "date_format",
+              "date_sub",
               "datediff",
               "dayofmonth",
               "dayofyear",
@@ -115,9 +121,14 @@ exportMethods("abs",
               "exp",
               "explode",
               "expm1",
+              "expr",
               "factorial",
               "first",
               "floor",
+              "format_number",
+              "format_string",
+              "from_unixtime",
+              "from_utc_timestamp",
               "getField",
               "getItem",
               "greatest",
@@ -125,6 +136,7 @@ exportMethods("abs",
               "hour",
               "hypot",
               "initcap",
+              "instr",
               "isNaN",
               "isNotNull",
               "isNull",
@@ -135,11 +147,13 @@ exportMethods("abs",
               "levenshtein",
               "like",
               "lit",
+              "locate",
               "log",
               "log10",
               "log1p",
               "log2",
               "lower",
+              "lpad",
               "ltrim",
               "max",
               "md5",
@@ -152,16 +166,26 @@ exportMethods("abs",
               "n_distinct",
               "nanvl",
               "negate",
+              "next_day",
               "otherwise",
               "pmod",
               "quarter",
+              "rand",
+              "randn",
+              "regexp_extract",
+              "regexp_replace",
               "reverse",
               "rint",
               "rlike",
               "round",
+              "rpad",
               "rtrim",
               "second",
               "sha1",
+              "sha2",
+              "shiftLeft",
+              "shiftRight",
+              "shiftRightUnsigned",
               "sign",
               "signum",
               "sin",
@@ -171,6 +195,7 @@ exportMethods("abs",
               "sqrt",
               "startsWith",
               "substr",
+              "substring_index",
               "sum",
               "sumDistinct",
               "tan",
@@ -178,9 +203,12 @@ exportMethods("abs",
               "toDegrees",
               "toRadians",
               "to_date",
+              "to_utc_timestamp",
+              "translate",
               "trim",
               "unbase64",
               "unhex",
+              "unix_timestamp",
               "upper",
               "weekofyear",
               "when",
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index 366c230e1e85b..5dba0887d150d 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -166,6 +166,421 @@ setMethod("n", signature(x = "Column"),
             count(x)
           })
 
+#' date_format
+#'
+#' Converts a date/timestamp/string to a value of string in the format specified by the date
+#' format given by the second argument.
+#'
+#' A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
+#' pattern letters of `java.text.SimpleDateFormat` can be used.
+#'
+#' NOTE: Use when ever possible specialized functions like `year`. These benefit from a
+#' specialized implementation.
+#'
+#' @rdname functions
+setMethod("date_format", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "date_format", y@jc, x)
+            column(jc)
+          })
+
+#' from_utc_timestamp
+#'
+#' Assumes given timestamp is UTC and converts to given timezone.
+#'
+#' @rdname functions
+setMethod("from_utc_timestamp", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "from_utc_timestamp", y@jc, x)
+            column(jc)
+          })
+
+#' instr
+#'
+#' Locate the position of the first occurrence of substr column in the given string.
+#' Returns null if either of the arguments are null.
+#'
+#' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+#' could not be found in str.
+#'
+#' @rdname functions
+setMethod("instr", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "instr", y@jc, x)
+            column(jc)
+          })
+
+#' next_day
+#'
+#' Given a date column, returns the first date which is later than the value of the date column
+#' that is on the specified day of the week.
+#'
+#' For example, `next <- day('2015-07-27', "Sunday")` returns 2015-08-02 because that is the first
+#' Sunday after 2015-07-27.
+#'
+#' Day of the week parameter is case insensitive, and accepts:
+#' "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".
+#'
+#' @rdname functions
+setMethod("next_day", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "next_day", y@jc, x)
+            column(jc)
+          })
+
+#' to_utc_timestamp
+#'
+#' Assumes given timestamp is in given timezone and converts to UTC.
+#'
+#' @rdname functions
+setMethod("to_utc_timestamp", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "to_utc_timestamp", y@jc, x)
+            column(jc)
+          })
+
+#' add_months
+#'
+#' Returns the date that is numMonths after startDate.
+#'
+#' @rdname functions
+setMethod("add_months", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "add_months", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' date_add
+#'
+#' Returns the date that is `days` days after `start`
+#'
+#' @rdname functions
+setMethod("date_add", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "date_add", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' date_sub
+#'
+#' Returns the date that is `days` days before `start`
+#'
+#' @rdname functions
+setMethod("date_sub", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "date_sub", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' format_number
+#'
+#' Formats numeric column x to a format like '#,###,###.##', rounded to d decimal places,
+#' and returns the result as a string column.
+#'
+#' If d is 0, the result has no decimal point or fractional part.
+#' If d < 0, the result will be null.'
+#'
+#' @rdname functions
+setMethod("format_number", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "format_number",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' sha2
+#'
+#' Calculates the SHA-2 family of hash functions of a binary column and
+#' returns the value as a hex string.
+#'
+#' @rdname functions
+#' @param y column to compute SHA-2 on.
+#' @param x one of 224, 256, 384, or 512.
+setMethod("sha2", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sha2", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' shiftLeft
+#'
+#' Shift the the given value numBits left. If the given value is a long value, this function
+#' will return a long value else it will return an integer value.
+#'
+#' @rdname functions
+setMethod("shiftLeft", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "shiftLeft",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' shiftRight
+#'
+#' Shift the the given value numBits right. If the given value is a long value, it will return
+#' a long value else it will return an integer value.
+#'
+#' @rdname functions
+setMethod("shiftRight", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "shiftRight",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' shiftRightUnsigned
+#'
+#' Unsigned shift the the given value numBits right. If the given value is a long value,
+#' it will return a long value else it will return an integer value.
+#'
+#' @rdname functions
+setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "shiftRightUnsigned",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' concat_ws
+#'
+#' Concatenates multiple input string columns together into a single string column,
+#' using the given separator.
+#'
+#' @rdname functions
+setMethod("concat_ws", signature(sep = "character", x = "Column"),
+          function(sep, x, ...) {
+            jcols <- listToSeq(lapply(list(x, ...), function(x) { x@jc }))
+            jc <- callJStatic("org.apache.spark.sql.functions", "concat_ws", sep, jcols)
+            column(jc)
+          })
+
+#' conv
+#'
+#' Convert a number in a string column from one base to another.
+#'
+#' @rdname functions
+setMethod("conv", signature(x = "Column", fromBase = "numeric", toBase = "numeric"),
+          function(x, fromBase, toBase) {
+            fromBase <- as.integer(fromBase)
+            toBase <- as.integer(toBase)
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "conv",
+                              x@jc, fromBase, toBase)
+            column(jc)
+          })
+
+#' expr
+#'
+#' Parses the expression string into the column that it represents, similar to
+#' DataFrame.selectExpr
+#'
+#' @rdname functions
+setMethod("expr", signature(x = "character"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "expr", x)
+            column(jc)
+          })
+
+#' format_string
+#'
+#' Formats the arguments in printf-style and returns the result as a string column.
+#'
+#' @rdname functions
+setMethod("format_string", signature(format = "character", x = "Column"),
+          function(format, x, ...) {
+            jcols <- listToSeq(lapply(list(x, ...), function(arg) { arg@jc }))
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "format_string",
+                              format, jcols)
+            column(jc)
+          })
+
+#' from_unixtime
+#'
+#' Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
+#' representing the timestamp of that moment in the current system time zone in the given
+#' format.
+#'
+#' @rdname functions
+setMethod("from_unixtime", signature(x = "Column"),
+          function(x, format = "yyyy-MM-dd HH:mm:ss") {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "from_unixtime",
+                              x@jc, format)
+            column(jc)
+          })
+
+#' locate
+#'
+#' Locate the position of the first occurrence of substr.
+#' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+#' could not be found in str.
+#'
+#' @rdname functions
+setMethod("locate", signature(substr = "character", str = "Column"),
+          function(substr, str, pos = 0) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "locate",
+                              substr, str@jc, as.integer(pos))
+            column(jc)
+          })
+
+#' lpad
+#'
+#' Left-pad the string column with
+#'
+#' @rdname functions
+setMethod("lpad", signature(x = "Column", len = "numeric", pad = "character"),
+          function(x, len, pad) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "lpad",
+                              x@jc, as.integer(len), pad)
+            column(jc)
+          })
+
+#' rand
+#'
+#' Generate a random column with i.i.d. samples from U[0.0, 1.0].
+#'
+#' @rdname functions
+setMethod("rand", signature(seed = "missing"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "rand")
+            column(jc)
+          })
+setMethod("rand", signature(seed = "numeric"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "rand", as.integer(seed))
+            column(jc)
+          })
+
+#' randn
+#'
+#' Generate a column with i.i.d. samples from the standard normal distribution.
+#'
+#' @rdname functions
+setMethod("randn", signature(seed = "missing"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "randn")
+            column(jc)
+          })
+setMethod("randn", signature(seed = "numeric"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "randn", as.integer(seed))
+            column(jc)
+          })
+
+#' regexp_extract
+#'
+#' Extract a specific(idx) group identified by a java regex, from the specified string column.
+#'
+#' @rdname functions
+setMethod("regexp_extract",
+          signature(x = "Column", pattern = "character", idx = "numeric"),
+          function(x, pattern, idx) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "regexp_extract",
+                              x@jc, pattern, as.integer(idx))
+            column(jc)
+          })
+
+#' regexp_replace
+#'
+#' Replace all substrings of the specified string value that match regexp with rep.
+#'
+#' @rdname functions
+setMethod("regexp_replace",
+          signature(x = "Column", pattern = "character", replacement = "character"),
+          function(x, pattern, replacement) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "regexp_replace",
+                              x@jc, pattern, replacement)
+            column(jc)
+          })
+
+#' rpad
+#'
+#' Right-padded with pad to a length of len.
+#'
+#' @rdname functions
+setMethod("rpad", signature(x = "Column", len = "numeric", pad = "character"),
+          function(x, len, pad) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "rpad",
+                              x@jc, as.integer(len), pad)
+            column(jc)
+          })
+
+#' substring_index
+#'
+#' Returns the substring from string str before count occurrences of the delimiter delim.
+#' If count is positive, everything the left of the final delimiter (counting from left) is
+#' returned. If count is negative, every to the right of the final delimiter (counting from the
+#' right) is returned. substring <- index performs a case-sensitive match when searching for delim.
+#'
+#' @rdname functions
+setMethod("substring_index",
+          signature(x = "Column", delim = "character", count = "numeric"),
+          function(x, delim, count) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "substring_index",
+                              x@jc, delim, as.integer(count))
+            column(jc)
+          })
+
+#' translate
+#'
+#' Translate any character in the src by a character in replaceString.
+#' The characters in replaceString is corresponding to the characters in matchingString.
+#' The translate will happen when any character in the string matching with the character
+#' in the matchingString.
+#'
+#' @rdname functions
+setMethod("translate",
+          signature(x = "Column", matchingString = "character", replaceString = "character"),
+          function(x, matchingString, replaceString) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "translate", x@jc, matchingString, replaceString)
+            column(jc)
+          })
+
+#' unix_timestamp
+#'
+#' Gets current Unix timestamp in seconds.
+#'
+#' @rdname functions
+setMethod("unix_timestamp", signature(x = "missing", format = "missing"),
+          function(x, format) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp")
+            column(jc)
+          })
+#' unix_timestamp
+#'
+#' Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds),
+#' using the default timezone and the default locale, return null if fail.
+#'
+#' @rdname functions
+setMethod("unix_timestamp", signature(x = "Column", format = "missing"),
+          function(x, format) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc)
+            column(jc)
+          })
+#' unix_timestamp
+#'
+#' Convert time string with given pattern
+#' (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html])
+#' to Unix time stamp (in seconds), return null if fail.
+#'
+#' @rdname functions
+setMethod("unix_timestamp", signature(x = "Column", format = "character"),
+          function(x, format = "yyyy-MM-dd HH:mm:ss") {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc, format)
+            column(jc)
+          })
 #' when
 #'
 #' Evaluates a list of conditions and returns one of multiple possible result expressions.
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 338b32e648078..84cb8dfdaa2dd 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -662,6 +662,10 @@ setGeneric("otherwise", function(x, value) { standardGeneric("otherwise") })
 
 ###################### Expression Function Methods ##########################
 
+#' @rdname functions
+#' @export
+setGeneric("add_months", function(y, x) { standardGeneric("add_months") })
+
 #' @rdname functions
 #' @export
 setGeneric("ascii", function(x) { standardGeneric("ascii") })
@@ -694,6 +698,14 @@ setGeneric("ceil", function(x) { standardGeneric("ceil") })
 #' @export
 setGeneric("concat", function(x, ...) { standardGeneric("concat") })
 
+#' @rdname functions
+#' @export
+setGeneric("concat_ws", function(sep, x, ...) { standardGeneric("concat_ws") })
+
+#' @rdname functions
+#' @export
+setGeneric("conv", function(x, fromBase, toBase) { standardGeneric("conv") })
+
 #' @rdname functions
 #' @export
 setGeneric("crc32", function(x) { standardGeneric("crc32") })
@@ -702,6 +714,18 @@ setGeneric("crc32", function(x) { standardGeneric("crc32") })
 #' @export
 setGeneric("datediff", function(y, x) { standardGeneric("datediff") })
 
+#' @rdname functions
+#' @export
+setGeneric("date_add", function(y, x) { standardGeneric("date_add") })
+
+#' @rdname functions
+#' @export
+setGeneric("date_format", function(y, x) { standardGeneric("date_format") })
+
+#' @rdname functions
+#' @export
+setGeneric("date_sub", function(y, x) { standardGeneric("date_sub") })
+
 #' @rdname functions
 #' @export
 setGeneric("dayofmonth", function(x) { standardGeneric("dayofmonth") })
@@ -714,6 +738,26 @@ setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
 #' @export
 setGeneric("explode", function(x) { standardGeneric("explode") })
 
+#' @rdname functions
+#' @export
+setGeneric("expr", function(x) { standardGeneric("expr") })
+
+#' @rdname functions
+#' @export
+setGeneric("from_utc_timestamp", function(y, x) { standardGeneric("from_utc_timestamp") })
+
+#' @rdname functions
+#' @export
+setGeneric("format_number", function(y, x) { standardGeneric("format_number") })
+
+#' @rdname functions
+#' @export
+setGeneric("format_string", function(format, x, ...) { standardGeneric("format_string") })
+
+#' @rdname functions
+#' @export
+setGeneric("from_unixtime", function(x, ...) { standardGeneric("from_unixtime") })
+
 #' @rdname functions
 #' @export
 setGeneric("greatest", function(x, ...) { standardGeneric("greatest") })
@@ -730,6 +774,10 @@ setGeneric("hour", function(x) { standardGeneric("hour") })
 #' @export
 setGeneric("initcap", function(x) { standardGeneric("initcap") })
 
+#' @rdname functions
+#' @export
+setGeneric("instr", function(y, x) { standardGeneric("instr") })
+
 #' @rdname functions
 #' @export
 setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
@@ -750,10 +798,18 @@ setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
 #' @export
 setGeneric("lit", function(x) { standardGeneric("lit") })
 
+#' @rdname functions
+#' @export
+setGeneric("locate", function(substr, str, ...) { standardGeneric("locate") })
+
 #' @rdname functions
 #' @export
 setGeneric("lower", function(x) { standardGeneric("lower") })
 
+#' @rdname functions
+#' @export
+setGeneric("lpad", function(x, len, pad) { standardGeneric("lpad") })
+
 #' @rdname functions
 #' @export
 setGeneric("ltrim", function(x) { standardGeneric("ltrim") })
@@ -782,6 +838,10 @@ setGeneric("nanvl", function(y, x) { standardGeneric("nanvl") })
 #' @export
 setGeneric("negate", function(x) { standardGeneric("negate") })
 
+#' @rdname functions
+#' @export
+setGeneric("next_day", function(y, x) { standardGeneric("next_day") })
+
 #' @rdname functions
 #' @export
 setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
@@ -790,10 +850,31 @@ setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
 #' @export
 setGeneric("quarter", function(x) { standardGeneric("quarter") })
 
+#' @rdname functions
+#' @export
+setGeneric("rand", function(seed) { standardGeneric("rand") })
+
+#' @rdname functions
+#' @export
+setGeneric("randn", function(seed) { standardGeneric("randn") })
+
+#' @rdname functions
+#' @export
+setGeneric("regexp_extract", function(x, pattern, idx) { standardGeneric("regexp_extract") })
+
+#' @rdname functions
+#' @export
+setGeneric("regexp_replace",
+           function(x, pattern, replacement) { standardGeneric("regexp_replace") })
+
 #' @rdname functions
 #' @export
 setGeneric("reverse", function(x) { standardGeneric("reverse") })
 
+#' @rdname functions
+#' @export
+setGeneric("rpad", function(x, len, pad) { standardGeneric("rpad") })
+
 #' @rdname functions
 #' @export
 setGeneric("rtrim", function(x) { standardGeneric("rtrim") })
@@ -806,6 +887,22 @@ setGeneric("second", function(x) { standardGeneric("second") })
 #' @export
 setGeneric("sha1", function(x) { standardGeneric("sha1") })
 
+#' @rdname functions
+#' @export
+setGeneric("sha2", function(y, x) { standardGeneric("sha2") })
+
+#' @rdname functions
+#' @export
+setGeneric("shiftLeft", function(y, x) { standardGeneric("shiftLeft") })
+
+#' @rdname functions
+#' @export
+setGeneric("shiftRight", function(y, x) { standardGeneric("shiftRight") })
+
+#' @rdname functions
+#' @export
+setGeneric("shiftRightUnsigned", function(y, x) { standardGeneric("shiftRightUnsigned") })
+
 #' @rdname functions
 #' @export
 setGeneric("signum", function(x) { standardGeneric("signum") })
@@ -818,6 +915,10 @@ setGeneric("size", function(x) { standardGeneric("size") })
 #' @export
 setGeneric("soundex", function(x) { standardGeneric("soundex") })
 
+#' @rdname functions
+#' @export
+setGeneric("substring_index", function(x, delim, count) { standardGeneric("substring_index") })
+
 #' @rdname functions
 #' @export
 setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") })
@@ -834,6 +935,14 @@ setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 #' @export
 setGeneric("to_date", function(x) { standardGeneric("to_date") })
 
+#' @rdname functions
+#' @export
+setGeneric("to_utc_timestamp", function(y, x) { standardGeneric("to_utc_timestamp") })
+
+#' @rdname functions
+#' @export
+setGeneric("translate", function(x, matchingString, replaceString) { standardGeneric("translate") })
+
 #' @rdname functions
 #' @export
 setGeneric("trim", function(x) { standardGeneric("trim") })
@@ -846,6 +955,10 @@ setGeneric("unbase64", function(x) { standardGeneric("unbase64") })
 #' @export
 setGeneric("unhex", function(x) { standardGeneric("unhex") })
 
+#' @rdname functions
+#' @export
+setGeneric("unix_timestamp", function(x, format) { standardGeneric("unix_timestamp") })
+
 #' @rdname functions
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 841de657df37e..670017ed34765 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -598,6 +598,11 @@ test_that("selectExpr() on a DataFrame", {
   expect_equal(count(selected2), 3)
 })
 
+test_that("expr() on a DataFrame", {
+  df <- jsonFile(sqlContext, jsonPath)
+  expect_equal(collect(select(df, expr("abs(-123)")))[1, 1], 123)
+})
+
 test_that("column calculation", {
   df <- jsonFile(sqlContext, jsonPath)
   d <- collect(select(df, alias(df$age + 1, "age2")))
@@ -667,16 +672,15 @@ test_that("column functions", {
   c <- SparkR:::col("a")
   c1 <- abs(c) + acos(c) + approxCountDistinct(c) + ascii(c) + asin(c) + atan(c)
   c2 <- avg(c) + base64(c) + bin(c) + bitwiseNOT(c) + cbrt(c) + ceil(c) + cos(c)
-  c3 <- cosh(c) + count(c) + crc32(c) + dayofmonth(c) + dayofyear(c) + exp(c)
+  c3 <- cosh(c) + count(c) + crc32(c) + exp(c)
   c4 <- explode(c) + expm1(c) + factorial(c) + first(c) + floor(c) + hex(c)
   c5 <- hour(c) + initcap(c) + isNaN(c) + last(c) + last_day(c) + length(c)
   c6 <- log(c) + (c) + log1p(c) + log2(c) + lower(c) + ltrim(c) + max(c) + md5(c)
-  c7 <- mean(c) + min(c) + minute(c) + month(c) + negate(c) + quarter(c)
-  c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + second(c) + sha1(c)
+  c7 <- mean(c) + min(c) + month(c) + negate(c) + quarter(c)
+  c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + sha1(c)
   c9 <- signum(c) + sin(c) + sinh(c) + size(c) + soundex(c) + sqrt(c) + sum(c)
   c10 <- sumDistinct(c) + tan(c) + tanh(c) + toDegrees(c) + toRadians(c)
-  c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c) + weekofyear(c)
-  c12 <- year(c)
+  c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c)
 
   df <- jsonFile(sqlContext, jsonPath)
   df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))
@@ -689,8 +693,11 @@ test_that("column functions", {
   expect_equal(collect(df3)[[1, 1]], TRUE)
   expect_equal(collect(df3)[[2, 1]], FALSE)
   expect_equal(collect(df3)[[3, 1]], TRUE)
-})
 
+  df4 <- createDataFrame(sqlContext, list(list(a = "010101")))
+  expect_equal(collect(select(df4, conv(df4$a, 2, 16)))[1, 1], "15")
+})
+#
 test_that("column binary mathfunctions", {
   lines <- c("{\"a\":1, \"b\":5}",
              "{\"a\":2, \"b\":6}",
@@ -709,6 +716,13 @@ test_that("column binary mathfunctions", {
   expect_equal(collect(select(df, hypot(df$a, df$b)))[3, "HYPOT(a, b)"], sqrt(3^2 + 7^2))
   expect_equal(collect(select(df, hypot(df$a, df$b)))[4, "HYPOT(a, b)"], sqrt(4^2 + 8^2))
   ## nolint end
+  expect_equal(collect(select(df, shiftLeft(df$b, 1)))[4, 1], 16)
+  expect_equal(collect(select(df, shiftRight(df$b, 1)))[4, 1], 4)
+  expect_equal(collect(select(df, shiftRightUnsigned(df$b, 1)))[4, 1], 4)
+  expect_equal(class(collect(select(df, rand()))[2, 1]), "numeric")
+  expect_equal(collect(select(df, rand(1)))[1, 1], 0.45, tolerance = 0.01)
+  expect_equal(class(collect(select(df, randn()))[2, 1]), "numeric")
+  expect_equal(collect(select(df, randn(1)))[1, 1], -0.0111, tolerance = 0.01)
 })
 
 test_that("string operators", {
@@ -718,6 +732,78 @@ test_that("string operators", {
   expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi")
   expect_equal(collect(select(df, cast(df$age, "string")))[[2, 1]], "30")
   expect_equal(collect(select(df, concat(df$name, lit(":"), df$age)))[[2, 1]], "Andy:30")
+  expect_equal(collect(select(df, concat_ws(":", df$name)))[[2, 1]], "Andy")
+  expect_equal(collect(select(df, concat_ws(":", df$name, df$age)))[[2, 1]], "Andy:30")
+  expect_equal(collect(select(df, instr(df$name, "i")))[, 1], c(2, 0, 5))
+  expect_equal(collect(select(df, format_number(df$age, 2)))[2, 1], "30.00")
+  expect_equal(collect(select(df, sha1(df$name)))[2, 1],
+               "ab5a000e88b5d9d0fa2575f5c6263eb93452405d")
+  expect_equal(collect(select(df, sha2(df$name, 256)))[2, 1],
+               "80f2aed3c618c423ddf05a2891229fba44942d907173152442cf6591441ed6dc")
+  expect_equal(collect(select(df, format_string("Name:%s", df$name)))[2, 1], "Name:Andy")
+  expect_equal(collect(select(df, format_string("%s, %d", df$name, df$age)))[2, 1], "Andy, 30")
+  expect_equal(collect(select(df, regexp_extract(df$name, "(n.y)", 1)))[2, 1], "ndy")
+  expect_equal(collect(select(df, regexp_replace(df$name, "(n.y)", "ydn")))[2, 1], "Aydn")
+
+  l2 <- list(list(a = "aaads"))
+  df2 <- createDataFrame(sqlContext, l2)
+  expect_equal(collect(select(df2, locate("aa", df2$a)))[1, 1], 1)
+  expect_equal(collect(select(df2, locate("aa", df2$a, 1)))[1, 1], 2)
+  expect_equal(collect(select(df2, lpad(df2$a, 8, "#")))[1, 1], "###aaads")
+  expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###")
+
+  l3 <- list(list(a = "a.b.c.d"))
+  df3 <- createDataFrame(sqlContext, l3)
+  expect_equal(collect(select(df3, substring_index(df3$a, ".", 2)))[1, 1], "a.b")
+  expect_equal(collect(select(df3, substring_index(df3$a, ".", -3)))[1, 1], "b.c.d")
+  expect_equal(collect(select(df3, translate(df3$a, "bc", "12")))[1, 1], "a.1.2.d")
+})
+
+test_that("date functions on a DataFrame", {
+  .originalTimeZone <- Sys.getenv("TZ")
+  Sys.setenv(TZ = "UTC")
+  l <- list(list(a = 1L, b = as.Date("2012-12-13")),
+            list(a = 2L, b = as.Date("2013-12-14")),
+            list(a = 3L, b = as.Date("2014-12-15")))
+  df <- createDataFrame(sqlContext, l)
+  expect_equal(collect(select(df, dayofmonth(df$b)))[, 1], c(13, 14, 15))
+  expect_equal(collect(select(df, dayofyear(df$b)))[, 1], c(348, 348, 349))
+  expect_equal(collect(select(df, weekofyear(df$b)))[, 1], c(50, 50, 51))
+  expect_equal(collect(select(df, year(df$b)))[, 1], c(2012, 2013, 2014))
+  expect_equal(collect(select(df, month(df$b)))[, 1], c(12, 12, 12))
+  expect_equal(collect(select(df, last_day(df$b)))[, 1],
+               c(as.Date("2012-12-31"), as.Date("2013-12-31"), as.Date("2014-12-31")))
+  expect_equal(collect(select(df, next_day(df$b, "MONDAY")))[, 1],
+               c(as.Date("2012-12-17"), as.Date("2013-12-16"), as.Date("2014-12-22")))
+  expect_equal(collect(select(df, date_format(df$b, "y")))[, 1], c("2012", "2013", "2014"))
+  expect_equal(collect(select(df, add_months(df$b, 3)))[, 1],
+               c(as.Date("2013-03-13"), as.Date("2014-03-14"), as.Date("2015-03-15")))
+  expect_equal(collect(select(df, date_add(df$b, 1)))[, 1],
+               c(as.Date("2012-12-14"), as.Date("2013-12-15"), as.Date("2014-12-16")))
+  expect_equal(collect(select(df, date_sub(df$b, 1)))[, 1],
+               c(as.Date("2012-12-12"), as.Date("2013-12-13"), as.Date("2014-12-14")))
+
+  l2 <- list(list(a = 1L, b = as.POSIXlt("2012-12-13 12:34:00", tz = "UTC")),
+            list(a = 2L, b = as.POSIXlt("2014-12-15 01:24:34", tz = "UTC")))
+  df2 <- createDataFrame(sqlContext, l2)
+  expect_equal(collect(select(df2, minute(df2$b)))[, 1], c(34, 24))
+  expect_equal(collect(select(df2, second(df2$b)))[, 1], c(0, 34))
+  expect_equal(collect(select(df2, from_utc_timestamp(df2$b, "JST")))[, 1],
+               c(as.POSIXlt("2012-12-13 21:34:00 UTC"), as.POSIXlt("2014-12-15 10:24:34 UTC")))
+  expect_equal(collect(select(df2, to_utc_timestamp(df2$b, "JST")))[, 1],
+               c(as.POSIXlt("2012-12-13 03:34:00 UTC"), as.POSIXlt("2014-12-14 16:24:34 UTC")))
+  expect_more_than(collect(select(df2, unix_timestamp()))[1, 1], 0)
+  expect_more_than(collect(select(df2, unix_timestamp(df2$b)))[1, 1], 0)
+  expect_more_than(collect(select(df2, unix_timestamp(lit("2015-01-01"), "yyyy-MM-dd")))[1, 1], 0)
+
+  l3 <- list(list(a = 1000), list(a = -1000))
+  df3 <- createDataFrame(sqlContext, l3)
+  result31 <- collect(select(df3, from_unixtime(df3$a)))
+  expect_equal(grep("\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}", result31[, 1], perl = TRUE),
+               c(1, 2))
+  result32 <- collect(select(df3, from_unixtime(df3$a, "yyyy")))
+  expect_equal(grep("\\d{4}", result32[, 1]), c(1, 2))
+  Sys.setenv(TZ = .originalTimeZone)
 })
 
 test_that("greatest() and least() on a DataFrame", {
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index 14dac4ed28ce3..6ce02e2ea336a 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -182,6 +182,7 @@ private[r] class RBackendHandler(server: RBackend)
       if (parameterType.isPrimitive) {
         parameterWrapperType = parameterType match {
           case java.lang.Integer.TYPE => classOf[java.lang.Integer]
+          case java.lang.Long.TYPE => classOf[java.lang.Integer]
           case java.lang.Double.TYPE => classOf[java.lang.Double]
           case java.lang.Boolean.TYPE => classOf[java.lang.Boolean]
           case _ => parameterType

From 5fd53c64bb01de74ae57a7068de85b34adc856cf Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 19 Aug 2015 10:51:59 -0700
Subject: [PATCH 1138/1454] [SPARK-9833] [YARN] Add options to disable
 delegation token retrieval.

This allows skipping the code that tries to talk to Hive and HBase to
fetch delegation tokens, in case that somehow conflicts with the application
being run.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8134 from vanzin/SPARK-9833.
---
 docs/running-on-yarn.md                       | 12 +++++++++
 .../org/apache/spark/deploy/yarn/Client.scala | 27 ++++++++++++++-----
 2 files changed, 33 insertions(+), 6 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 8ac26e98da635..5159ef9e3394e 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -369,6 +369,18 @@ If you need a reference to the proper location to put log files in the YARN so t
   See <code>spark.yarn.config.gatewayPath</code>.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.security.tokens.${service}.enabled</code></td>
+  <td>true</td>
+  <td>
+  Controls whether to retrieve delegation tokens for non-HDFS services when security is enabled.
+  By default, delegation tokens for all supported services are retrieved when those services are
+  configured, but it's possible to disable that behavior if it somehow conflicts with the
+  application being run.
+  <p/>
+  Currently supported services are: hive, hbase
+  </td>
+</tr>
 </table>
 
 # Important notes
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 262c6a8d6397f..bff585b46cbbe 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -285,8 +285,8 @@ private[spark] class Client(
     // multiple times, YARN will fail to launch containers for the app with an internal
     // error.
     val distributedUris = new HashSet[String]
-    obtainTokenForHiveMetastore(hadoopConf, credentials)
-    obtainTokenForHBase(hadoopConf, credentials)
+    obtainTokenForHiveMetastore(sparkConf, hadoopConf, credentials)
+    obtainTokenForHBase(sparkConf, hadoopConf, credentials)
 
     val replication = sparkConf.getInt("spark.yarn.submit.file.replication",
       fs.getDefaultReplication(dst)).toShort
@@ -1239,8 +1239,11 @@ object Client extends Logging {
   /**
    * Obtains token for the Hive metastore and adds them to the credentials.
    */
-  private def obtainTokenForHiveMetastore(conf: Configuration, credentials: Credentials) {
-    if (UserGroupInformation.isSecurityEnabled) {
+  private def obtainTokenForHiveMetastore(
+      sparkConf: SparkConf,
+      conf: Configuration,
+      credentials: Credentials) {
+    if (shouldGetTokens(sparkConf, "hive") && UserGroupInformation.isSecurityEnabled) {
       val mirror = universe.runtimeMirror(getClass.getClassLoader)
 
       try {
@@ -1297,8 +1300,11 @@ object Client extends Logging {
   /**
    * Obtain security token for HBase.
    */
-  def obtainTokenForHBase(conf: Configuration, credentials: Credentials): Unit = {
-    if (UserGroupInformation.isSecurityEnabled) {
+  def obtainTokenForHBase(
+      sparkConf: SparkConf,
+      conf: Configuration,
+      credentials: Credentials): Unit = {
+    if (shouldGetTokens(sparkConf, "hbase") && UserGroupInformation.isSecurityEnabled) {
       val mirror = universe.runtimeMirror(getClass.getClassLoader)
 
       try {
@@ -1394,4 +1400,13 @@ object Client extends Logging {
     components.mkString(Path.SEPARATOR)
   }
 
+  /**
+   * Return whether delegation tokens should be retrieved for the given service when security is
+   * enabled. By default, tokens are retrieved, but that behavior can be changed by setting
+   * a service-specific configuration.
+   */
+  def shouldGetTokens(conf: SparkConf, service: String): Boolean = {
+    conf.getBoolean(s"spark.yarn.security.tokens.${service}.enabled", true)
+  }
+
 }

From 28a98464ea65aa7b35e24fca5ddaa60c2e5d53ee Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 19 Aug 2015 11:35:05 -0700
Subject: [PATCH 1139/1454] [SPARK-10097] Adds `shouldMaximize` flag to
 `ml.evaluation.Evaluator`

Previously, users of evaluator (`CrossValidator` and `TrainValidationSplit`) would only maximize the metric in evaluator, leading to a hacky solution which negated metrics to be minimized and caused erroneous negative values to be reported to the user.

This PR adds a `isLargerBetter` attribute to the `Evaluator` base class, instructing users of `Evaluator` on whether the chosen metric should be maximized or minimized.

CC jkbradley

Author: Feynman Liang <fliang@databricks.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8290 from feynmanliang/SPARK-10097.
---
 .../BinaryClassificationEvaluator.scala       | 20 +++++++++++--------
 .../spark/ml/evaluation/Evaluator.scala       |  7 +++++++
 .../MulticlassClassificationEvaluator.scala   |  8 ++++++++
 .../ml/evaluation/RegressionEvaluator.scala   | 19 ++++++++++--------
 .../spark/ml/tuning/CrossValidator.scala      |  4 +++-
 .../ml/tuning/TrainValidationSplit.scala      |  4 +++-
 .../evaluation/RegressionEvaluatorSuite.scala |  4 ++--
 .../spark/ml/tuning/CrossValidatorSuite.scala |  2 ++
 .../ml/tuning/TrainValidationSplitSuite.scala |  2 ++
 python/pyspark/ml/evaluation.py               |  4 ++--
 10 files changed, 52 insertions(+), 22 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 5d5cb7e94f45b..56419a0a15952 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -40,8 +40,11 @@ class BinaryClassificationEvaluator(override val uid: String)
    * param for metric name in evaluation
    * @group param
    */
-  val metricName: Param[String] = new Param(this, "metricName",
-    "metric name in evaluation (areaUnderROC|areaUnderPR)")
+  val metricName: Param[String] = {
+    val allowedParams = ParamValidators.inArray(Array("areaUnderROC", "areaUnderPR"))
+    new Param(
+      this, "metricName", "metric name in evaluation (areaUnderROC|areaUnderPR)", allowedParams)
+  }
 
   /** @group getParam */
   def getMetricName: String = $(metricName)
@@ -76,16 +79,17 @@ class BinaryClassificationEvaluator(override val uid: String)
       }
     val metrics = new BinaryClassificationMetrics(scoreAndLabels)
     val metric = $(metricName) match {
-      case "areaUnderROC" =>
-        metrics.areaUnderROC()
-      case "areaUnderPR" =>
-        metrics.areaUnderPR()
-      case other =>
-        throw new IllegalArgumentException(s"Does not support metric $other.")
+      case "areaUnderROC" => metrics.areaUnderROC()
+      case "areaUnderPR" => metrics.areaUnderPR()
     }
     metrics.unpersist()
     metric
   }
 
+  override def isLargerBetter: Boolean = $(metricName) match {
+    case "areaUnderROC" => true
+    case "areaUnderPR" => true
+  }
+
   override def copy(extra: ParamMap): BinaryClassificationEvaluator = defaultCopy(extra)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
index e56c946a063e8..13bd3307f8a2f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
@@ -46,5 +46,12 @@ abstract class Evaluator extends Params {
    */
   def evaluate(dataset: DataFrame): Double
 
+  /**
+   * Indicates whether the metric returned by [[evaluate()]] should be maximized (true, default)
+   * or minimized (false).
+   * A given evaluator may support multiple metrics which may be maximized or minimized.
+   */
+  def isLargerBetter: Boolean = true
+
   override def copy(extra: ParamMap): Evaluator
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
index 44f779c1908d7..f73d2345078e6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
@@ -81,5 +81,13 @@ class MulticlassClassificationEvaluator (override val uid: String)
     metric
   }
 
+  override def isLargerBetter: Boolean = $(metricName) match {
+    case "f1" => true
+    case "precision" => true
+    case "recall" => true
+    case "weightedPrecision" => true
+    case "weightedRecall" => true
+  }
+
   override def copy(extra: ParamMap): MulticlassClassificationEvaluator = defaultCopy(extra)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
index 01c000b47514c..d21c88ab9b109 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
@@ -73,17 +73,20 @@ final class RegressionEvaluator(override val uid: String)
       }
     val metrics = new RegressionMetrics(predictionAndLabels)
     val metric = $(metricName) match {
-      case "rmse" =>
-        -metrics.rootMeanSquaredError
-      case "mse" =>
-        -metrics.meanSquaredError
-      case "r2" =>
-        metrics.r2
-      case "mae" =>
-        -metrics.meanAbsoluteError
+      case "rmse" => metrics.rootMeanSquaredError
+      case "mse" => metrics.meanSquaredError
+      case "r2" => metrics.r2
+      case "mae" => metrics.meanAbsoluteError
     }
     metric
   }
 
+  override def isLargerBetter: Boolean = $(metricName) match {
+    case "rmse" => false
+    case "mse" => false
+    case "r2" => true
+    case "mae" => false
+  }
+
   override def copy(extra: ParamMap): RegressionEvaluator = defaultCopy(extra)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index 4792eb0f0a288..0679bfd0f3ffe 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -100,7 +100,9 @@ class CrossValidator(override val uid: String) extends Estimator[CrossValidatorM
     }
     f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1)
     logInfo(s"Average cross-validation metrics: ${metrics.toSeq}")
-    val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
+    val (bestMetric, bestIndex) =
+      if (eval.isLargerBetter) metrics.zipWithIndex.maxBy(_._1)
+      else metrics.zipWithIndex.minBy(_._1)
     logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
     logInfo(s"Best cross-validation metric: $bestMetric.")
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
index c0edc730b6fd6..73a14b8310157 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
@@ -99,7 +99,9 @@ class TrainValidationSplit(override val uid: String) extends Estimator[TrainVali
     validationDataset.unpersist()
 
     logInfo(s"Train validation split metrics: ${metrics.toSeq}")
-    val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
+    val (bestMetric, bestIndex) =
+      if (eval.isLargerBetter) metrics.zipWithIndex.maxBy(_._1)
+      else metrics.zipWithIndex.minBy(_._1)
     logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
     logInfo(s"Best train validation split metric: $bestMetric.")
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
index 5b203784559e2..aa722da323935 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
@@ -63,7 +63,7 @@ class RegressionEvaluatorSuite extends SparkFunSuite with MLlibTestSparkContext
 
     // default = rmse
     val evaluator = new RegressionEvaluator()
-    assert(evaluator.evaluate(predictions) ~== -0.1019382 absTol 0.001)
+    assert(evaluator.evaluate(predictions) ~== 0.1019382 absTol 0.001)
 
     // r2 score
     evaluator.setMetricName("r2")
@@ -71,6 +71,6 @@ class RegressionEvaluatorSuite extends SparkFunSuite with MLlibTestSparkContext
 
     // mae
     evaluator.setMetricName("mae")
-    assert(evaluator.evaluate(predictions) ~== -0.08036075 absTol 0.001)
+    assert(evaluator.evaluate(predictions) ~== 0.08036075 absTol 0.001)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
index aaca08bb61a45..fde02e0c84bc0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
@@ -143,6 +143,8 @@ object CrossValidatorSuite {
       throw new UnsupportedOperationException
     }
 
+    override def isLargerBetter: Boolean = true
+
     override val uid: String = "eval"
 
     override def copy(extra: ParamMap): MyEvaluator = defaultCopy(extra)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
index c8e58f216cceb..ef24e6fb6b80f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
@@ -132,6 +132,8 @@ object TrainValidationSplitSuite {
       throw new UnsupportedOperationException
     }
 
+    override def isLargerBetter: Boolean = true
+
     override val uid: String = "eval"
 
     override def copy(extra: ParamMap): MyEvaluator = defaultCopy(extra)
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index e23ce053baeb7..6b0a9ffde9f42 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -163,11 +163,11 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
     ...
     >>> evaluator = RegressionEvaluator(predictionCol="raw")
     >>> evaluator.evaluate(dataset)
-    -2.842...
+    2.842...
     >>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"})
     0.993...
     >>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"})
-    -2.649...
+    2.649...
     """
     # Because we will maximize evaluation value (ref: `CrossValidator`),
     # when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`),

From d898c33f774b9a3db2fb6aa8f0cb2c2ac6004b58 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 19 Aug 2015 12:39:37 -0700
Subject: [PATCH 1140/1454] [SPARK-10106] [SPARKR] Add `ifelse` Column function
 to SparkR

### JIRA
[[SPARK-10106] Add `ifelse` Column function to SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10106)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8303 from yu-iskw/SPARK-10106.
---
 R/pkg/NAMESPACE                  |  1 +
 R/pkg/R/functions.R              | 19 +++++++++++++++++++
 R/pkg/inst/tests/test_sparkSQL.R |  3 ++-
 3 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 111a2dc30db73..3e5c89d779b7b 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -135,6 +135,7 @@ exportMethods("abs",
               "hex",
               "hour",
               "hypot",
+              "ifelse",
               "initcap",
               "instr",
               "isNaN",
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index 5dba0887d150d..b5879bd9ad553 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -594,3 +594,22 @@ setMethod("when", signature(condition = "Column", value = "ANY"),
               jc <- callJStatic("org.apache.spark.sql.functions", "when", condition, value)
               column(jc)
           })
+
+#' ifelse
+#'
+#' Evaluates a list of conditions and returns `yes` if the conditions are satisfied.
+#' Otherwise `no` is returned for unmatched conditions.
+#'
+#' @rdname column
+setMethod("ifelse",
+          signature(test = "Column", yes = "ANY", no = "ANY"),
+          function(test, yes, no) {
+              test <- test@jc
+              yes <- ifelse(class(yes) == "Column", yes@jc, yes)
+              no <- ifelse(class(no) == "Column", no@jc, no)
+              jc <- callJMethod(callJStatic("org.apache.spark.sql.functions",
+                                            "when",
+                                            test, yes),
+                                "otherwise", no)
+              column(jc)
+          })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 670017ed34765..556b8c5447054 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -813,11 +813,12 @@ test_that("greatest() and least() on a DataFrame", {
   expect_equal(collect(select(df, least(df$a, df$b)))[, 1], c(1, 3))
 })
 
-test_that("when() and otherwise() on a DataFrame", {
+test_that("when(), otherwise() and ifelse() on a DataFrame", {
   l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
   df <- createDataFrame(sqlContext, l)
   expect_equal(collect(select(df, when(df$a > 1 & df$b > 2, 1)))[, 1], c(NA, 1))
   expect_equal(collect(select(df, otherwise(when(df$a > 1, 1), 0)))[, 1], c(0, 1))
+  expect_equal(collect(select(df, ifelse(df$a > 1 & df$b > 2, 0, 1)))[, 1], c(1, 0))
 })
 
 test_that("group by", {

From 5b62bef8cbf73f910513ef3b1f557aa94b384854 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 19 Aug 2015 13:17:26 -0700
Subject: [PATCH 1141/1454] [SPARK-8918] [MLLIB] [DOC] Add @since tags to
 mllib.clustering

This continues the work from #8256. I removed `since` tags from private/protected/local methods/variables (see https://github.com/apache/spark/commit/72fdeb64630470f6f46cf3eed8ffbfe83a7c4659). MechCoder

Closes #8256

Author: Xiangrui Meng <meng@databricks.com>
Author: Xiaoqing Wang <spark445@126.com>
Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8288 from mengxr/SPARK-8918.
---
 .../mllib/clustering/GaussianMixture.scala    | 56 +++++++++++----
 .../clustering/GaussianMixtureModel.scala     | 32 +++++++--
 .../spark/mllib/clustering/KMeans.scala       | 36 +++++++++-
 .../spark/mllib/clustering/KMeansModel.scala  | 37 ++++++++--
 .../apache/spark/mllib/clustering/LDA.scala   | 71 ++++++++++++++++---
 .../spark/mllib/clustering/LDAModel.scala     | 64 +++++++++++++++--
 .../spark/mllib/clustering/LDAOptimizer.scala | 12 +++-
 .../clustering/PowerIterationClustering.scala | 29 +++++++-
 .../mllib/clustering/StreamingKMeans.scala    | 53 +++++++++++---
 9 files changed, 338 insertions(+), 52 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index e459367333d26..bc27b1fe7390b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -62,6 +62,7 @@ class GaussianMixture private (
   /**
    * Constructs a default instance. The default parameters are {k: 2, convergenceTol: 0.01,
    * maxIterations: 100, seed: random}.
+   * @since 1.3.0
    */
   def this() = this(2, 0.01, 100, Utils.random.nextLong())
 
@@ -72,9 +73,11 @@ class GaussianMixture private (
   // default random starting point
   private var initialModel: Option[GaussianMixtureModel] = None
 
-  /** Set the initial GMM starting point, bypassing the random initialization.
-   *  You must call setK() prior to calling this method, and the condition
-   *  (model.k == this.k) must be met; failure will result in an IllegalArgumentException
+  /**
+   * Set the initial GMM starting point, bypassing the random initialization.
+   * You must call setK() prior to calling this method, and the condition
+   * (model.k == this.k) must be met; failure will result in an IllegalArgumentException
+   * @since 1.3.0
    */
   def setInitialModel(model: GaussianMixtureModel): this.type = {
     if (model.k == k) {
@@ -85,30 +88,46 @@ class GaussianMixture private (
     this
   }
 
-  /** Return the user supplied initial GMM, if supplied */
+  /**
+   * Return the user supplied initial GMM, if supplied
+   * @since 1.3.0
+   */
   def getInitialModel: Option[GaussianMixtureModel] = initialModel
 
-  /** Set the number of Gaussians in the mixture model.  Default: 2 */
+  /**
+   * Set the number of Gaussians in the mixture model.  Default: 2
+   * @since 1.3.0
+   */
   def setK(k: Int): this.type = {
     this.k = k
     this
   }
 
-  /** Return the number of Gaussians in the mixture model */
+  /**
+   * Return the number of Gaussians in the mixture model
+   * @since 1.3.0
+   */
   def getK: Int = k
 
-  /** Set the maximum number of iterations to run. Default: 100 */
+  /**
+   * Set the maximum number of iterations to run. Default: 100
+   * @since 1.3.0
+   */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
   }
 
-  /** Return the maximum number of iterations to run */
+  /**
+   * Return the maximum number of iterations to run
+   * @since 1.3.0
+   */
   def getMaxIterations: Int = maxIterations
 
   /**
    * Set the largest change in log-likelihood at which convergence is
    * considered to have occurred.
+   * @since 1.3.0
    */
   def setConvergenceTol(convergenceTol: Double): this.type = {
     this.convergenceTol = convergenceTol
@@ -118,19 +137,29 @@ class GaussianMixture private (
   /**
    * Return the largest change in log-likelihood at which convergence is
    * considered to have occurred.
+   * @since 1.3.0
    */
   def getConvergenceTol: Double = convergenceTol
 
-  /** Set the random seed */
+  /**
+   * Set the random seed
+   * @since 1.3.0
+   */
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
   }
 
-  /** Return the random seed */
+  /**
+   * Return the random seed
+   * @since 1.3.0
+   */
   def getSeed: Long = seed
 
-  /** Perform expectation maximization */
+  /**
+   * Perform expectation maximization
+   * @since 1.3.0
+   */
   def run(data: RDD[Vector]): GaussianMixtureModel = {
     val sc = data.sparkContext
 
@@ -204,7 +233,10 @@ class GaussianMixture private (
     new GaussianMixtureModel(weights, gaussians)
   }
 
-  /** Java-friendly version of [[run()]] */
+  /**
+   * Java-friendly version of [[run()]]
+   * @since 1.3.0
+   */
   def run(data: JavaRDD[Vector]): GaussianMixtureModel = run(data.rdd)
 
   private def updateWeightsAndGaussians(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 76aeebd703d4e..2fa0473737aae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -43,6 +43,7 @@ import org.apache.spark.sql.{SQLContext, Row}
  *                the weight for Gaussian i, and weights.sum == 1
  * @param gaussians Array of MultivariateGaussian where gaussians(i) represents
  *                  the Multivariate Gaussian (Normal) Distribution for Gaussian i
+ * @since 1.3.0
  */
 @Experimental
 class GaussianMixtureModel(
@@ -53,32 +54,48 @@ class GaussianMixtureModel(
 
   override protected def formatVersion = "1.0"
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GaussianMixtureModel.SaveLoadV1_0.save(sc, path, weights, gaussians)
   }
 
-  /** Number of gaussians in mixture */
+  /**
+   * Number of gaussians in mixture
+   * @since 1.3.0
+   */
   def k: Int = weights.length
 
-  /** Maps given points to their cluster indices. */
+  /**
+   * Maps given points to their cluster indices.
+   * @since 1.3.0
+   */
   def predict(points: RDD[Vector]): RDD[Int] = {
     val responsibilityMatrix = predictSoft(points)
     responsibilityMatrix.map(r => r.indexOf(r.max))
   }
 
-  /** Maps given point to its cluster index. */
+  /**
+   * Maps given point to its cluster index.
+   * @since 1.5.0
+   */
   def predict(point: Vector): Int = {
     val r = computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
     r.indexOf(r.max)
   }
 
-  /** Java-friendly version of [[predict()]] */
+  /**
+   * Java-friendly version of [[predict()]]
+   * @since 1.4.0
+   */
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Given the input vectors, return the membership value of each vector
    * to all mixture components.
+   * @since 1.3.0
    */
   def predictSoft(points: RDD[Vector]): RDD[Array[Double]] = {
     val sc = points.sparkContext
@@ -91,6 +108,7 @@ class GaussianMixtureModel(
 
   /**
    * Given the input vector, return the membership values to all mixture components.
+   * @since 1.4.0
    */
   def predictSoft(point: Vector): Array[Double] = {
     computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
@@ -115,6 +133,9 @@ class GaussianMixtureModel(
   }
 }
 
+/**
+ * @since 1.4.0
+ */
 @Experimental
 object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
 
@@ -165,6 +186,9 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
     }
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def load(sc: SparkContext, path: String) : GaussianMixtureModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 0a65403f4ec95..9ef6834e5ea8d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -49,15 +49,20 @@ class KMeans private (
   /**
    * Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1,
    * initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.
+   * @since 0.8.0
    */
   def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4, Utils.random.nextLong())
 
   /**
    * Number of clusters to create (k).
+   * @since 1.4.0
    */
   def getK: Int = k
 
-  /** Set the number of clusters to create (k). Default: 2. */
+  /**
+   * Set the number of clusters to create (k). Default: 2.
+   * @since 0.8.0
+   */
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -65,10 +70,14 @@ class KMeans private (
 
   /**
    * Maximum number of iterations to run.
+   * @since 1.4.0
    */
   def getMaxIterations: Int = maxIterations
 
-  /** Set maximum number of iterations to run. Default: 20. */
+  /**
+   * Set maximum number of iterations to run. Default: 20.
+   * @since 0.8.0
+   */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -76,6 +85,7 @@ class KMeans private (
 
   /**
    * The initialization algorithm. This can be either "random" or "k-means||".
+   * @since 1.4.0
    */
   def getInitializationMode: String = initializationMode
 
@@ -83,6 +93,7 @@ class KMeans private (
    * Set the initialization algorithm. This can be either "random" to choose random points as
    * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
+   * @since 0.8.0
    */
   def setInitializationMode(initializationMode: String): this.type = {
     KMeans.validateInitMode(initializationMode)
@@ -93,6 +104,7 @@ class KMeans private (
   /**
    * :: Experimental ::
    * Number of runs of the algorithm to execute in parallel.
+   * @since 1.4.0
    */
   @Experimental
   def getRuns: Int = runs
@@ -102,6 +114,7 @@ class KMeans private (
    * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
    * this many times with random starting conditions (configured by the initialization mode), then
    * return the best clustering found over any run. Default: 1.
+   * @since 0.8.0
    */
   @Experimental
   def setRuns(runs: Int): this.type = {
@@ -114,12 +127,14 @@ class KMeans private (
 
   /**
    * Number of steps for the k-means|| initialization mode
+   * @since 1.4.0
    */
   def getInitializationSteps: Int = initializationSteps
 
   /**
    * Set the number of steps for the k-means|| initialization mode. This is an advanced
    * setting -- the default of 5 is almost always enough. Default: 5.
+   * @since 0.8.0
    */
   def setInitializationSteps(initializationSteps: Int): this.type = {
     if (initializationSteps <= 0) {
@@ -131,12 +146,14 @@ class KMeans private (
 
   /**
    * The distance threshold within which we've consider centers to have converged.
+   * @since 1.4.0
    */
   def getEpsilon: Double = epsilon
 
   /**
    * Set the distance threshold within which we've consider centers to have converged.
    * If all centers move less than this Euclidean distance, we stop iterating one run.
+   * @since 0.8.0
    */
   def setEpsilon(epsilon: Double): this.type = {
     this.epsilon = epsilon
@@ -145,10 +162,14 @@ class KMeans private (
 
   /**
    * The random seed for cluster initialization.
+   * @since 1.4.0
    */
   def getSeed: Long = seed
 
-  /** Set the random seed for cluster initialization. */
+  /**
+   * Set the random seed for cluster initialization.
+   * @since 1.4.0
+   */
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -162,6 +183,7 @@ class KMeans private (
    * Set the initial starting point, bypassing the random initialization or k-means||
    * The condition model.k == this.k must be met, failure results
    * in an IllegalArgumentException.
+   * @since 1.4.0
    */
   def setInitialModel(model: KMeansModel): this.type = {
     require(model.k == k, "mismatched cluster count")
@@ -172,6 +194,7 @@ class KMeans private (
   /**
    * Train a K-means model on the given set of points; `data` should be cached for high
    * performance, because this is an iterative algorithm.
+   * @since 0.8.0
    */
   def run(data: RDD[Vector]): KMeansModel = {
 
@@ -430,11 +453,14 @@ class KMeans private (
 
 /**
  * Top-level methods for calling K-means clustering.
+ * @since 0.8.0
  */
 object KMeans {
 
   // Initialization mode names
+  /** @since 0.8.0 */
   val RANDOM = "random"
+  /** @since 0.8.0 */
   val K_MEANS_PARALLEL = "k-means||"
 
   /**
@@ -446,6 +472,7 @@ object KMeans {
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
    * @param seed random seed value for cluster initialization
+   * @since 1.3.0
    */
   def train(
       data: RDD[Vector],
@@ -470,6 +497,7 @@ object KMeans {
    * @param maxIterations max number of iterations
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
+   * @since 0.8.0
    */
   def train(
       data: RDD[Vector],
@@ -486,6 +514,7 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
+   * @since 0.8.0
    */
   def train(
       data: RDD[Vector],
@@ -496,6 +525,7 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
+   * @since 0.8.0
    */
   def train(
       data: RDD[Vector],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 96359024fa228..8de2087ceb4df 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -34,35 +34,52 @@ import org.apache.spark.sql.Row
 
 /**
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
+ * @since 0.8.0
  */
 class KMeansModel (
     val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
 
-  /** A Java-friendly constructor that takes an Iterable of Vectors. */
+  /**
+   * A Java-friendly constructor that takes an Iterable of Vectors.
+   * @since 1.4.0
+   */
   def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray)
 
-  /** Total number of clusters. */
+  /**
+   * Total number of clusters.
+   * @since 0.8.0
+   */
   def k: Int = clusterCenters.length
 
-  /** Returns the cluster index that a given point belongs to. */
+  /**
+   * Returns the cluster index that a given point belongs to.
+   * @since 0.8.0
+   */
   def predict(point: Vector): Int = {
     KMeans.findClosest(clusterCentersWithNorm, new VectorWithNorm(point))._1
   }
 
-  /** Maps given points to their cluster indices. */
+  /**
+   * Maps given points to their cluster indices.
+   * @since 1.0.0
+   */
   def predict(points: RDD[Vector]): RDD[Int] = {
     val centersWithNorm = clusterCentersWithNorm
     val bcCentersWithNorm = points.context.broadcast(centersWithNorm)
     points.map(p => KMeans.findClosest(bcCentersWithNorm.value, new VectorWithNorm(p))._1)
   }
 
-  /** Maps given points to their cluster indices. */
+  /**
+   * Maps given points to their cluster indices.
+   * @since 1.0.0
+   */
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Return the K-means cost (sum of squared distances of points to their nearest center) for this
    * model on the given data.
+   * @since 0.8.0
    */
   def computeCost(data: RDD[Vector]): Double = {
     val centersWithNorm = clusterCentersWithNorm
@@ -73,6 +90,9 @@ class KMeansModel (
   private def clusterCentersWithNorm: Iterable[VectorWithNorm] =
     clusterCenters.map(new VectorWithNorm(_))
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     KMeansModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -80,7 +100,14 @@ class KMeansModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.4.0
+ */
 object KMeansModel extends Loader[KMeansModel] {
+
+  /**
+   * @since 1.4.0
+   */
   override def load(sc: SparkContext, path: String): KMeansModel = {
     KMeansModel.SaveLoadV1_0.load(sc, path)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 0fc9b1ac4d716..2a8c6acbaec61 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -43,6 +43,7 @@ import org.apache.spark.util.Utils
  *
  * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent Dirichlet allocation
  *       (Wikipedia)]]
+ * @since 1.3.0
  */
 @Experimental
 class LDA private (
@@ -54,18 +55,25 @@ class LDA private (
     private var checkpointInterval: Int,
     private var ldaOptimizer: LDAOptimizer) extends Logging {
 
+  /**
+   * Constructs a LDA instance with default parameters.
+   * @since 1.3.0
+   */
   def this() = this(k = 10, maxIterations = 20, docConcentration = Vectors.dense(-1),
     topicConcentration = -1, seed = Utils.random.nextLong(), checkpointInterval = 10,
     ldaOptimizer = new EMLDAOptimizer)
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
+   *
+   * @since 1.3.0
    */
   def getK: Int = k
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
    * (default = 10)
+   * @since 1.3.0
    */
   def setK(k: Int): this.type = {
     require(k > 0, s"LDA k (number of clusters) must be > 0, but was set to $k")
@@ -78,6 +86,7 @@ class LDA private (
    * distributions over topics ("theta").
    *
    * This is the parameter to a Dirichlet distribution.
+   * @since 1.5.0
    */
   def getAsymmetricDocConcentration: Vector = this.docConcentration
 
@@ -87,6 +96,7 @@ class LDA private (
    *
    * This method assumes the Dirichlet distribution is symmetric and can be described by a single
    * [[Double]] parameter. It should fail if docConcentration is asymmetric.
+   * @since 1.3.0
    */
   def getDocConcentration: Double = {
     val parameter = docConcentration(0)
@@ -121,6 +131,7 @@ class LDA private (
    *     - Values should be >= 0
    *     - default = uniformly (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
+   * @since 1.5.0
    */
   def setDocConcentration(docConcentration: Vector): this.type = {
     require(docConcentration.size > 0, "docConcentration must have > 0 elements")
@@ -128,22 +139,37 @@ class LDA private (
     this
   }
 
-  /** Replicates a [[Double]] docConcentration to create a symmetric prior. */
+  /**
+   * Replicates a [[Double]] docConcentration to create a symmetric prior.
+   * @since 1.3.0
+   */
   def setDocConcentration(docConcentration: Double): this.type = {
     this.docConcentration = Vectors.dense(docConcentration)
     this
   }
 
-  /** Alias for [[getAsymmetricDocConcentration]] */
+  /**
+   * Alias for [[getAsymmetricDocConcentration]]
+   * @since 1.5.0
+   */
   def getAsymmetricAlpha: Vector = getAsymmetricDocConcentration
 
-  /** Alias for [[getDocConcentration]] */
+  /**
+   * Alias for [[getDocConcentration]]
+   * @since 1.3.0
+   */
   def getAlpha: Double = getDocConcentration
 
-  /** Alias for [[setDocConcentration()]] */
+  /**
+   * Alias for [[setDocConcentration()]]
+   * @since 1.5.0
+   */
   def setAlpha(alpha: Vector): this.type = setDocConcentration(alpha)
 
-  /** Alias for [[setDocConcentration()]] */
+  /**
+   * Alias for [[setDocConcentration()]]
+   * @since 1.3.0
+   */
   def setAlpha(alpha: Double): this.type = setDocConcentration(alpha)
 
   /**
@@ -154,6 +180,7 @@ class LDA private (
    *
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
+   * @since 1.3.0
    */
   def getTopicConcentration: Double = this.topicConcentration
 
@@ -178,36 +205,51 @@ class LDA private (
    *     - Value should be >= 0
    *     - default = (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
+   * @since 1.3.0
    */
   def setTopicConcentration(topicConcentration: Double): this.type = {
     this.topicConcentration = topicConcentration
     this
   }
 
-  /** Alias for [[getTopicConcentration]] */
+  /**
+   * Alias for [[getTopicConcentration]]
+   * @since 1.3.0
+   */
   def getBeta: Double = getTopicConcentration
 
-  /** Alias for [[setTopicConcentration()]] */
+  /**
+   * Alias for [[setTopicConcentration()]]
+   * @since 1.3.0
+   */
   def setBeta(beta: Double): this.type = setTopicConcentration(beta)
 
   /**
    * Maximum number of iterations for learning.
+   * @since 1.3.0
    */
   def getMaxIterations: Int = maxIterations
 
   /**
    * Maximum number of iterations for learning.
    * (default = 20)
+   * @since 1.3.0
    */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
   }
 
-  /** Random seed */
+  /**
+   * Random seed
+   * @since 1.3.0
+   */
   def getSeed: Long = seed
 
-  /** Random seed */
+  /**
+   * Random seed
+   * @since 1.3.0
+   */
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -215,6 +257,7 @@ class LDA private (
 
   /**
    * Period (in iterations) between checkpoints.
+   * @since 1.3.0
    */
   def getCheckpointInterval: Int = checkpointInterval
 
@@ -225,6 +268,7 @@ class LDA private (
    * [[org.apache.spark.SparkContext]], this setting is ignored.
    *
    * @see [[org.apache.spark.SparkContext#setCheckpointDir]]
+   * @since 1.3.0
    */
   def setCheckpointInterval(checkpointInterval: Int): this.type = {
     this.checkpointInterval = checkpointInterval
@@ -236,6 +280,7 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation
+   * @since 1.4.0
    */
   @DeveloperApi
   def getOptimizer: LDAOptimizer = ldaOptimizer
@@ -244,6 +289,7 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
+   * @since 1.4.0
    */
   @DeveloperApi
   def setOptimizer(optimizer: LDAOptimizer): this.type = {
@@ -254,6 +300,7 @@ class LDA private (
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
    * Currently "em", "online" are supported.
+   * @since 1.4.0
    */
   def setOptimizer(optimizerName: String): this.type = {
     this.ldaOptimizer =
@@ -274,6 +321,7 @@ class LDA private (
    *                   (where the vocabulary size is the length of the vector).
    *                   Document IDs must be unique and >= 0.
    * @return  Inferred LDA model
+   * @since 1.3.0
    */
   def run(documents: RDD[(Long, Vector)]): LDAModel = {
     val state = ldaOptimizer.initialize(documents, this)
@@ -289,7 +337,10 @@ class LDA private (
     state.getLDAModel(iterationTimes)
   }
 
-  /** Java-friendly version of [[run()]] */
+  /**
+   * Java-friendly version of [[run()]]
+   * @since 1.3.0
+   */
   def run(documents: JavaPairRDD[java.lang.Long, Vector]): LDAModel = {
     run(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 82f05e4a18cee..b70e380c0393e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -192,12 +192,24 @@ class LocalLDAModel private[clustering] (
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel with Serializable {
 
+  /**
+   * @since 1.3.0
+   */
   override def k: Int = topics.numCols
 
+  /**
+   * @since 1.3.0
+   */
   override def vocabSize: Int = topics.numRows
 
+  /**
+   * @since 1.3.0
+   */
   override def topicsMatrix: Matrix = topics
 
+  /**
+   * @since 1.3.0
+   */
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val brzTopics = topics.toBreeze.toDenseMatrix
     Range(0, k).map { topicIndex =>
@@ -210,6 +222,9 @@ class LocalLDAModel private[clustering] (
 
   override protected def formatVersion = "1.0"
 
+  /**
+   * @since 1.5.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix, docConcentration, topicConcentration,
       gammaShape)
@@ -223,12 +238,16 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating log likelihood
    * @return variational lower bound on the log likelihood of the entire corpus
+   * @since 1.5.0
    */
   def logLikelihood(documents: RDD[(Long, Vector)]): Double = logLikelihoodBound(documents,
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
-  /** Java-friendly version of [[logLikelihood]] */
+  /**
+   * Java-friendly version of [[logLikelihood]]
+   * @since 1.5.0
+   */
   def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logLikelihood(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -239,6 +258,7 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating perplexity
    * @return Variational upper bound on log perplexity per token.
+   * @since 1.5.0
    */
   def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
     val corpusTokenCount = documents
@@ -247,7 +267,9 @@ class LocalLDAModel private[clustering] (
     -logLikelihood(documents) / corpusTokenCount
   }
 
-  /** Java-friendly version of [[logPerplexity]] */
+  /** Java-friendly version of [[logPerplexity]]
+   *  @since 1.5.0
+   */
   def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -325,6 +347,7 @@ class LocalLDAModel private[clustering] (
    * for each document.
    * @param documents documents to predict topic mixture distributions for
    * @return An RDD of (document ID, topic mixture distribution for document)
+   * @since 1.3.0
    */
   // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
   def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = {
@@ -351,7 +374,10 @@ class LocalLDAModel private[clustering] (
     }
   }
 
-  /** Java-friendly version of [[topicDistributions]] */
+  /**
+   * Java-friendly version of [[topicDistributions]]
+   * @since 1.4.1
+   */
   def topicDistributions(
       documents: JavaPairRDD[java.lang.Long, Vector]): JavaPairRDD[java.lang.Long, Vector] = {
     val distributions = topicDistributions(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
@@ -425,6 +451,9 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
     }
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def load(sc: SparkContext, path: String): LocalLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
@@ -481,6 +510,7 @@ class DistributedLDAModel private[clustering] (
    * Convert model to a local model.
    * The local model stores the inferred topics but not the topic distributions for training
    * documents.
+   * @since 1.3.0
    */
   def toLocal: LocalLDAModel = new LocalLDAModel(topicsMatrix, docConcentration, topicConcentration,
     gammaShape)
@@ -491,6 +521,7 @@ class DistributedLDAModel private[clustering] (
    * No guarantees are given about the ordering of the topics.
    *
    * WARNING: This matrix is collected from an RDD. Beware memory usage when vocabSize, k are large.
+   * @since 1.3.0
    */
   override lazy val topicsMatrix: Matrix = {
     // Collect row-major topics
@@ -510,6 +541,9 @@ class DistributedLDAModel private[clustering] (
     Matrices.fromBreeze(brzTopics)
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val numTopics = k
     // Note: N_k is not needed to find the top terms, but it is needed to normalize weights
@@ -548,6 +582,7 @@ class DistributedLDAModel private[clustering] (
    * @return  Array over topics.  Each element represent as a pair of matching arrays:
    *          (IDs for the documents, weights of the topic in these documents).
    *          For each topic, documents are sorted in order of decreasing topic weights.
+   * @since 1.5.0
    */
   def topDocumentsPerTopic(maxDocumentsPerTopic: Int): Array[(Array[Long], Array[Double])] = {
     val numTopics = k
@@ -587,6 +622,7 @@ class DistributedLDAModel private[clustering] (
    *  - This excludes the prior; for that, use [[logPrior]].
    *  - Even with [[logPrior]], this is NOT the same as the data log likelihood given the
    *    hyperparameters.
+   * @since 1.3.0
    */
   lazy val logLikelihood: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
@@ -612,7 +648,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Log probability of the current parameter estimate:
-   *  log P(topics, topic distributions for docs | alpha, eta)
+   * log P(topics, topic distributions for docs | alpha, eta)
+   * @since 1.3.0
    */
   lazy val logPrior: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
@@ -644,6 +681,7 @@ class DistributedLDAModel private[clustering] (
    * ("theta_doc").
    *
    * @return  RDD of (document ID, topic distribution) pairs
+   * @since 1.3.0
    */
   def topicDistributions: RDD[(Long, Vector)] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
@@ -651,7 +689,10 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
-  /** Java-friendly version of [[topicDistributions]] */
+  /**
+   * Java-friendly version of [[topicDistributions]]
+   * @since 1.4.1
+   */
   def javaTopicDistributions: JavaPairRDD[java.lang.Long, Vector] = {
     JavaPairRDD.fromRDD(topicDistributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
   }
@@ -659,6 +700,7 @@ class DistributedLDAModel private[clustering] (
   /**
    * For each document, return the top k weighted topics for that document and their weights.
    * @return RDD of (doc ID, topic indices, topic weights)
+   * @since 1.5.0
    */
   def topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
@@ -673,7 +715,10 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
-  /** Java-friendly version of [[topTopicsPerDocument]] */
+  /**
+   * Java-friendly version of [[topTopicsPerDocument]]
+   * @since 1.5.0
+   */
   def javaTopTopicsPerDocument(k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[Double])] = {
     val topics = topTopicsPerDocument(k)
     topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Double])]].toJavaRDD()
@@ -684,6 +729,10 @@ class DistributedLDAModel private[clustering] (
 
   override protected def formatVersion = "1.0"
 
+  /**
+   * Java-friendly version of [[topicDistributions]]
+   * @since 1.5.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     DistributedLDAModel.SaveLoadV1_0.save(
       sc, path, graph, globalTopicTotals, k, vocabSize, docConcentration, topicConcentration,
@@ -784,6 +833,9 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def load(sc: SparkContext, path: String): DistributedLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index a0008f9c99ad7..360241c8081ac 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -35,6 +35,7 @@ import org.apache.spark.rdd.RDD
  *
  * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
  * hold optimizer-specific parameters for users to set.
+ * @since 1.4.0
  */
 @DeveloperApi
 sealed trait LDAOptimizer {
@@ -73,7 +74,7 @@ sealed trait LDAOptimizer {
  *  - Paper which clearly explains several algorithms, including EM:
  *    Asuncion, Welling, Smyth, and Teh.
  *    "On Smoothing and Inference for Topic Models."  UAI, 2009.
- *
+ * @since 1.4.0
  */
 @DeveloperApi
 final class EMLDAOptimizer extends LDAOptimizer {
@@ -225,6 +226,7 @@ final class EMLDAOptimizer extends LDAOptimizer {
  *
  * Original Online LDA paper:
  *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
+ * @since 1.4.0
  */
 @DeveloperApi
 final class OnlineLDAOptimizer extends LDAOptimizer {
@@ -274,6 +276,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
+   * @since 1.4.0
    */
   def getTau0: Double = this.tau0
 
@@ -281,6 +284,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    * Default: 1024, following the original Online LDA paper.
+   * @since 1.4.0
    */
   def setTau0(tau0: Double): this.type = {
     require(tau0 > 0, s"LDA tau0 must be positive, but was set to $tau0")
@@ -290,6 +294,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Learning rate: exponential decay rate
+   * @since 1.4.0
    */
   def getKappa: Double = this.kappa
 
@@ -297,6 +302,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * Learning rate: exponential decay rate---should be between
    * (0.5, 1.0] to guarantee asymptotic convergence.
    * Default: 0.51, based on the original Online LDA paper.
+   * @since 1.4.0
    */
   def setKappa(kappa: Double): this.type = {
     require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa")
@@ -306,6 +312,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration
+   * @since 1.4.0
    */
   def getMiniBatchFraction: Double = this.miniBatchFraction
 
@@ -318,6 +325,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * maxIterations * miniBatchFraction >= 1.
    *
    * Default: 0.05, i.e., 5% of total documents.
+   * @since 1.4.0
    */
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0,
@@ -329,6 +337,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * Optimize alpha, indicates whether alpha (Dirichlet parameter for document-topic distribution)
    * will be optimized during training.
+   * @since 1.5.0
    */
   def getOptimzeAlpha: Boolean = this.optimizeAlpha
 
@@ -336,6 +345,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * Sets whether to optimize alpha parameter during training.
    *
    * Default: false
+   * @since 1.5.0
    */
   def setOptimzeAlpha(optimizeAlpha: Boolean): this.type = {
     this.optimizeAlpha = optimizeAlpha
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index 407e43a024a2e..b4733ca975152 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -39,12 +39,16 @@ import org.apache.spark.{Logging, SparkContext, SparkException}
  *
  * @param k number of clusters
  * @param assignments an RDD of clustering [[PowerIterationClustering#Assignment]]s
+ * @since 1.3.0
  */
 @Experimental
 class PowerIterationClusteringModel(
     val k: Int,
     val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable {
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     PowerIterationClusteringModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -52,6 +56,9 @@ class PowerIterationClusteringModel(
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.4.0
+ */
 object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] {
   override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
     PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path)
@@ -65,6 +72,9 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
     private[clustering]
     val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel"
 
+    /**
+     * @since 1.4.0
+     */
     def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = {
       val sqlContext = new SQLContext(sc)
       import sqlContext.implicits._
@@ -77,6 +87,9 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
       dataRDD.write.parquet(Loader.dataPath(path))
     }
 
+    /**
+     * @since 1.4.0
+     */
     def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
       implicit val formats = DefaultFormats
       val sqlContext = new SQLContext(sc)
@@ -120,13 +133,16 @@ class PowerIterationClustering private[clustering] (
 
   import org.apache.spark.mllib.clustering.PowerIterationClustering._
 
-  /** Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100,
-   *  initMode: "random"}.
+  /**
+   * Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100,
+   * initMode: "random"}.
+   * @since 1.3.0
    */
   def this() = this(k = 2, maxIterations = 100, initMode = "random")
 
   /**
    * Set the number of clusters.
+   * @since 1.3.0
    */
   def setK(k: Int): this.type = {
     this.k = k
@@ -135,6 +151,7 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * Set maximum number of iterations of the power iteration loop
+   * @since 1.3.0
    */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
@@ -144,6 +161,7 @@ class PowerIterationClustering private[clustering] (
   /**
    * Set the initialization mode. This can be either "random" to use a random vector
    * as vertex properties, or "degree" to use normalized sum similarities. Default: random.
+   * @since 1.3.0
    */
   def setInitializationMode(mode: String): this.type = {
     this.initMode = mode match {
@@ -164,6 +182,7 @@ class PowerIterationClustering private[clustering] (
    *              assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
+   * @since 1.5.0
    */
   def run(graph: Graph[Double, Double]): PowerIterationClusteringModel = {
     val w = normalize(graph)
@@ -185,6 +204,7 @@ class PowerIterationClustering private[clustering] (
    *                     assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
+   * @since 1.3.0
    */
   def run(similarities: RDD[(Long, Long, Double)]): PowerIterationClusteringModel = {
     val w = normalize(similarities)
@@ -197,6 +217,7 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * A Java-friendly version of [[PowerIterationClustering.run]].
+   * @since 1.3.0
    */
   def run(similarities: JavaRDD[(java.lang.Long, java.lang.Long, java.lang.Double)])
     : PowerIterationClusteringModel = {
@@ -221,6 +242,9 @@ class PowerIterationClustering private[clustering] (
   }
 }
 
+/**
+ * @since 1.3.0
+ */
 @Experimental
 object PowerIterationClustering extends Logging {
 
@@ -229,6 +253,7 @@ object PowerIterationClustering extends Logging {
    * Cluster assignment.
    * @param id node id
    * @param cluster assigned cluster id
+   * @since 1.3.0
    */
   @Experimental
   case class Assignment(id: Long, cluster: Int)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index d9b34cec64894..a915804b02c90 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -63,6 +63,7 @@ import org.apache.spark.util.random.XORShiftRandom
  * such that at time t + h the discount applied to the data from t is 0.5.
  * The definition remains the same whether the time unit is given
  * as batches or points.
+ * @since 1.2.0
  *
  */
 @Experimental
@@ -70,7 +71,10 @@ class StreamingKMeansModel(
     override val clusterCenters: Array[Vector],
     val clusterWeights: Array[Double]) extends KMeansModel(clusterCenters) with Logging {
 
-  /** Perform a k-means update on a batch of data. */
+  /**
+   * Perform a k-means update on a batch of data.
+   * @since 1.2.0
+   */
   def update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel = {
 
     // find nearest cluster to each point
@@ -82,6 +86,7 @@ class StreamingKMeansModel(
       (p1._1, p1._2 + p2._2)
     }
     val dim = clusterCenters(0).size
+
     val pointStats: Array[(Int, (Vector, Long))] = closest
       .aggregateByKey((Vectors.zeros(dim), 0L))(mergeContribs, mergeContribs)
       .collect()
@@ -161,6 +166,7 @@ class StreamingKMeansModel(
  *    .setRandomCenters(5, 100.0)
  *    .trainOn(DStream)
  * }}}
+ * @since 1.2.0
  */
 @Experimental
 class StreamingKMeans(
@@ -168,23 +174,33 @@ class StreamingKMeans(
     var decayFactor: Double,
     var timeUnit: String) extends Logging with Serializable {
 
+  /** @since 1.2.0 */
   def this() = this(2, 1.0, StreamingKMeans.BATCHES)
 
   protected var model: StreamingKMeansModel = new StreamingKMeansModel(null, null)
 
-  /** Set the number of clusters. */
+  /**
+   * Set the number of clusters.
+   * @since 1.2.0
+   */
   def setK(k: Int): this.type = {
     this.k = k
     this
   }
 
-  /** Set the decay factor directly (for forgetful algorithms). */
+  /**
+   * Set the decay factor directly (for forgetful algorithms).
+   * @since 1.2.0
+   */
   def setDecayFactor(a: Double): this.type = {
     this.decayFactor = a
     this
   }
 
-  /** Set the half life and time unit ("batches" or "points") for forgetful algorithms. */
+  /**
+   * Set the half life and time unit ("batches" or "points") for forgetful algorithms.
+   * @since 1.2.0
+   */
   def setHalfLife(halfLife: Double, timeUnit: String): this.type = {
     if (timeUnit != StreamingKMeans.BATCHES && timeUnit != StreamingKMeans.POINTS) {
       throw new IllegalArgumentException("Invalid time unit for decay: " + timeUnit)
@@ -195,7 +211,10 @@ class StreamingKMeans(
     this
   }
 
-  /** Specify initial centers directly. */
+  /**
+   * Specify initial centers directly.
+   * @since 1.2.0
+   */
   def setInitialCenters(centers: Array[Vector], weights: Array[Double]): this.type = {
     model = new StreamingKMeansModel(centers, weights)
     this
@@ -207,6 +226,7 @@ class StreamingKMeans(
    * @param dim Number of dimensions
    * @param weight Weight for each center
    * @param seed Random seed
+   * @since 1.2.0
    */
   def setRandomCenters(dim: Int, weight: Double, seed: Long = Utils.random.nextLong): this.type = {
     val random = new XORShiftRandom(seed)
@@ -216,7 +236,10 @@ class StreamingKMeans(
     this
   }
 
-  /** Return the latest model. */
+  /**
+   * Return the latest model.
+   * @since 1.2.0
+   */
   def latestModel(): StreamingKMeansModel = {
     model
   }
@@ -228,6 +251,7 @@ class StreamingKMeans(
    * and updates the model using each batch of data from the stream.
    *
    * @param data DStream containing vector data
+   * @since 1.2.0
    */
   def trainOn(data: DStream[Vector]) {
     assertInitialized()
@@ -236,7 +260,10 @@ class StreamingKMeans(
     }
   }
 
-  /** Java-friendly version of `trainOn`. */
+  /**
+   * Java-friendly version of `trainOn`.
+   * @since 1.4.0
+   */
   def trainOn(data: JavaDStream[Vector]): Unit = trainOn(data.dstream)
 
   /**
@@ -244,13 +271,17 @@ class StreamingKMeans(
    *
    * @param data DStream containing vector data
    * @return DStream containing predictions
+   * @since 1.2.0
    */
   def predictOn(data: DStream[Vector]): DStream[Int] = {
     assertInitialized()
     data.map(model.predict)
   }
 
-  /** Java-friendly version of `predictOn`. */
+  /**
+   * Java-friendly version of `predictOn`.
+   * @since 1.4.0
+   */
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Integer] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Integer]])
   }
@@ -261,13 +292,17 @@ class StreamingKMeans(
    * @param data DStream containing (key, feature vector) pairs
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
+   * @since 1.2.0
    */
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Int)] = {
     assertInitialized()
     data.mapValues(model.predict)
   }
 
-  /** Java-friendly version of `predictOnValues`. */
+  /**
+   * Java-friendly version of `predictOnValues`.
+   * @since 1.4.0
+   */
   def predictOnValues[K](
       data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Integer] = {
     implicit val tag = fakeClassTag[K]

From f3391ff2b8b9c1f1308755dc223776692e3b7725 Mon Sep 17 00:00:00 2001
From: Joshi <rekhajoshm@gmail.com>
Date: Wed, 19 Aug 2015 21:23:02 +0100
Subject: [PATCH 1142/1454] [SPARK-8889] [CORE] Fix for OOM for graph creation

Fix for OOM for graph creation

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #7602 from rekhajoshm/SPARK-8889.
---
 .../spark/ui/scope/RDDOperationGraph.scala    | 23 +++++------
 .../org/apache/spark/ui/UISeleniumSuite.scala | 39 +++++++++++++++++++
 2 files changed, 51 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
index ffea9817c0b08..81f168a447ead 100644
--- a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ui.scope
 
 import scala.collection.mutable
-import scala.collection.mutable.ListBuffer
+import scala.collection.mutable.{StringBuilder, ListBuffer}
 
 import org.apache.spark.Logging
 import org.apache.spark.scheduler.StageInfo
@@ -167,7 +167,7 @@ private[ui] object RDDOperationGraph extends Logging {
   def makeDotFile(graph: RDDOperationGraph): String = {
     val dotFile = new StringBuilder
     dotFile.append("digraph G {\n")
-    dotFile.append(makeDotSubgraph(graph.rootCluster, indent = "  "))
+    makeDotSubgraph(dotFile, graph.rootCluster, indent = "  ")
     graph.edges.foreach { edge => dotFile.append(s"""  ${edge.fromId}->${edge.toId};\n""") }
     dotFile.append("}")
     val result = dotFile.toString()
@@ -180,18 +180,19 @@ private[ui] object RDDOperationGraph extends Logging {
     s"""${node.id} [label="${node.name} [${node.id}]"]"""
   }
 
-  /** Return the dot representation of a subgraph in an RDDOperationGraph. */
-  private def makeDotSubgraph(cluster: RDDOperationCluster, indent: String): String = {
-    val subgraph = new StringBuilder
-    subgraph.append(indent + s"subgraph cluster${cluster.id} {\n")
-    subgraph.append(indent + s"""  label="${cluster.name}";\n""")
+  /** Update the dot representation of the RDDOperationGraph in cluster to subgraph. */
+  private def makeDotSubgraph(
+      subgraph: StringBuilder,
+      cluster: RDDOperationCluster,
+      indent: String): Unit = {
+    subgraph.append(indent).append(s"subgraph cluster${cluster.id} {\n")
+    subgraph.append(indent).append(s"""  label="${cluster.name}";\n""")
     cluster.childNodes.foreach { node =>
-      subgraph.append(indent + s"  ${makeDotNode(node)};\n")
+      subgraph.append(indent).append(s"  ${makeDotNode(node)};\n")
     }
     cluster.childClusters.foreach { cscope =>
-      subgraph.append(makeDotSubgraph(cscope, indent + "  "))
+      makeDotSubgraph(subgraph, cscope, indent + "  ")
     }
-    subgraph.append(indent + "}\n")
-    subgraph.toString()
+    subgraph.append(indent).append("}\n")
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
index 3aa672f8b713c..69888b2694bae 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.ui
 import java.net.{HttpURLConnection, URL}
 import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
 
+import scala.io.Source
 import scala.collection.JavaConversions._
 import scala.xml.Node
 
@@ -603,6 +604,44 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
     }
   }
 
+  test("job stages should have expected dotfile under DAG visualization") {
+    withSpark(newSparkContext()) { sc =>
+      // Create a multi-stage job
+      val rdd =
+        sc.parallelize(Seq(1, 2, 3)).map(identity).groupBy(identity).map(identity).groupBy(identity)
+      rdd.count()
+
+      val stage0 = Source.fromURL(sc.ui.get.appUIAddress +
+        "/stages/stage/?id=0&attempt=0&expandDagViz=true").mkString
+      assert(stage0.contains("digraph G {\n  subgraph clusterstage_0 {\n    " +
+        "label=&quot;Stage 0&quot;;\n    subgraph "))
+      assert(stage0.contains("{\n      label=&quot;parallelize&quot;;\n      " +
+        "0 [label=&quot;ParallelCollectionRDD [0]&quot;];\n    }"))
+      assert(stage0.contains("{\n      label=&quot;map&quot;;\n      " +
+        "1 [label=&quot;MapPartitionsRDD [1]&quot;];\n    }"))
+      assert(stage0.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "2 [label=&quot;MapPartitionsRDD [2]&quot;];\n    }"))
+
+      val stage1 = Source.fromURL(sc.ui.get.appUIAddress +
+        "/stages/stage/?id=1&attempt=0&expandDagViz=true").mkString
+      assert(stage1.contains("digraph G {\n  subgraph clusterstage_1 {\n    " +
+        "label=&quot;Stage 1&quot;;\n    subgraph "))
+      assert(stage1.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "3 [label=&quot;ShuffledRDD [3]&quot;];\n    }"))
+      assert(stage1.contains("{\n      label=&quot;map&quot;;\n      " +
+        "4 [label=&quot;MapPartitionsRDD [4]&quot;];\n    }"))
+      assert(stage1.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "5 [label=&quot;MapPartitionsRDD [5]&quot;];\n    }"))
+
+      val stage2 = Source.fromURL(sc.ui.get.appUIAddress +
+        "/stages/stage/?id=2&attempt=0&expandDagViz=true").mkString
+      assert(stage2.contains("digraph G {\n  subgraph clusterstage_2 {\n    " +
+        "label=&quot;Stage 2&quot;;\n    subgraph "))
+      assert(stage2.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "6 [label=&quot;ShuffledRDD [6]&quot;];\n    }"))
+    }
+  }
+
   def goToUi(sc: SparkContext, path: String): Unit = {
     goToUi(sc.ui.get, path)
   }

From e05da5cb5ea253e6372f648fc8203204f2a8df8d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 19 Aug 2015 13:43:04 -0700
Subject: [PATCH 1143/1454] [SPARK-10107] [SQL] fix NPE in format_number

Author: Davies Liu <davies@databricks.com>

Closes #8305 from davies/format_number.
---
 .../spark/sql/catalyst/expressions/stringOperations.scala     | 2 +-
 .../scala/org/apache/spark/sql/StringFunctionsSuite.scala     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 134f1aa2af9a8..ca044d3e95e38 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -1306,8 +1306,8 @@ case class FormatNumber(x: Expression, d: Expression)
             $df $dFormat = new $df($pattern.toString());
             $lastDValue = $d;
             $numberFormat.applyPattern($dFormat.toPattern());
-            ${ev.primitive} = UTF8String.fromString($numberFormat.format(${typeHelper(num)}));
           }
+          ${ev.primitive} = UTF8String.fromString($numberFormat.format(${typeHelper(num)}));
         } else {
           ${ev.primitive} = null;
           ${ev.isNull} = true;
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index cc95eede005d7..b91438baea06f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -348,9 +348,9 @@ class StringFunctionsSuite extends QueryTest with SharedSQLContext {
     // it will still use the interpretProjection if projection follows by a LocalRelation,
     // hence we add a filter operator.
     // See the optimizer rule `ConvertToLocalRelation`
-    val df2 = Seq((5L, 4), (4L, 3), (3L, 2)).toDF("a", "b")
+    val df2 = Seq((5L, 4), (4L, 3), (4L, 3), (4L, 3), (3L, 2)).toDF("a", "b")
     checkAnswer(
       df2.filter("b>0").selectExpr("format_number(a, b)"),
-      Row("5.0000") :: Row("4.000") :: Row("3.00") :: Nil)
+      Row("5.0000") :: Row("4.000") :: Row("4.000") :: Row("4.000") :: Row("3.00") :: Nil)
   }
 }

From 08887369c890e0dd87eb8b34e8c32bb03307bf24 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 19 Aug 2015 13:56:40 -0700
Subject: [PATCH 1144/1454] [SPARK-10073] [SQL] Python withColumn should
 replace the old column

DataFrame.withColumn in Python should be consistent with the Scala one (replacing the existing column  that has the same name).

cc marmbrus

Author: Davies Liu <davies@databricks.com>

Closes #8300 from davies/with_column.
---
 python/pyspark/sql/dataframe.py                      | 12 ++++++------
 python/pyspark/sql/tests.py                          |  4 ++++
 .../main/scala/org/apache/spark/sql/DataFrame.scala  |  3 ++-
 3 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index da742d7ce7d13..025811f519293 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -1202,7 +1202,9 @@ def freqItems(self, cols, support=None):
     @ignore_unicode_prefix
     @since(1.3)
     def withColumn(self, colName, col):
-        """Returns a new :class:`DataFrame` by adding a column.
+        """
+        Returns a new :class:`DataFrame` by adding a column or replacing the
+        existing column that has the same name.
 
         :param colName: string, name of the new column.
         :param col: a :class:`Column` expression for the new column.
@@ -1210,7 +1212,8 @@ def withColumn(self, colName, col):
         >>> df.withColumn('age2', df.age + 2).collect()
         [Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)]
         """
-        return self.select('*', col.alias(colName))
+        assert isinstance(col, Column), "col should be Column"
+        return DataFrame(self._jdf.withColumn(colName, col._jc), self.sql_ctx)
 
     @ignore_unicode_prefix
     @since(1.3)
@@ -1223,10 +1226,7 @@ def withColumnRenamed(self, existing, new):
         >>> df.withColumnRenamed('age', 'age2').collect()
         [Row(age2=2, name=u'Alice'), Row(age2=5, name=u'Bob')]
         """
-        cols = [Column(_to_java_column(c)).alias(new)
-                if c == existing else c
-                for c in self.columns]
-        return self.select(*cols)
+        return DataFrame(self._jdf.withColumnRenamed(existing, new), self.sql_ctx)
 
     @since(1.4)
     @ignore_unicode_prefix
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 13cf647b66da8..aacfb34c77618 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1035,6 +1035,10 @@ def test_capture_illegalargument_exception(self):
         self.assertRaisesRegexp(IllegalArgumentException, "1024 is not in the permitted values",
                                 lambda: df.select(sha2(df.a, 1024)).collect())
 
+    def test_with_column_with_existing_name(self):
+        keys = self.df.withColumn("key", self.df.key).select("key").collect()
+        self.assertEqual([r.key for r in keys], list(range(100)))
+
 
 class HiveContextSQLTests(ReusedPySparkTestCase):
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index fd0ead4401193..d6688b24ae7d6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1133,7 +1133,8 @@ class DataFrame private[sql](
   /////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Returns a new [[DataFrame]] by adding a column.
+   * Returns a new [[DataFrame]] by adding a column or replacing the existing column that has
+   * the same name.
    * @group dfops
    * @since 1.3.0
    */

From 21bdbe9fe69be47be562de24216a469e5ee64c7b Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 19 Aug 2015 13:57:52 -0700
Subject: [PATCH 1145/1454] [SPARK-9627] [SQL] Stops using Scala runtime
 reflection in DictionaryEncoding

`DictionaryEncoding` uses Scala runtime reflection to avoid boxing costs while building the directory array. However, this code path may hit [SI-6240] [1] and throw exception.

[1]: https://issues.scala-lang.org/browse/SI-6240

Author: Cheng Lian <lian@databricks.com>

Closes #8306 from liancheng/spark-9627/in-memory-cache-scala-reflection.
---
 .../sql/columnar/InMemoryColumnarTableScan.scala  |  1 -
 .../columnar/compression/compressionSchemes.scala | 15 ++++-----------
 2 files changed, 4 insertions(+), 12 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 45f15fd04d4e2..66d429bc06198 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -120,7 +120,6 @@ private[sql] case class InMemoryRelation(
       new Iterator[CachedBatch] {
         def next(): CachedBatch = {
           val columnBuilders = output.map { attribute =>
-            val columnType = ColumnType(attribute.dataType)
             ColumnBuilder(attribute.dataType, batchSize, attribute.name, useCompression)
           }.toArray
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
index c91d960a0932b..ca910a99db082 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
@@ -270,20 +270,13 @@ private[sql] case object DictionaryEncoding extends CompressionScheme {
   class Decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T])
     extends compression.Decoder[T] {
 
-    private val dictionary = {
-      // TODO Can we clean up this mess? Maybe move this to `DataType`?
-      implicit val classTag = {
-        val mirror = runtimeMirror(Utils.getSparkClassLoader)
-        ClassTag[T#InternalType](mirror.runtimeClass(columnType.scalaTag.tpe))
-      }
-
-      Array.fill(buffer.getInt()) {
-        columnType.extract(buffer)
-      }
+    private val dictionary: Array[Any] = {
+      val elementNum = buffer.getInt()
+      Array.fill[Any](elementNum)(columnType.extract(buffer).asInstanceOf[Any])
     }
 
     override def next(row: MutableRow, ordinal: Int): Unit = {
-      columnType.setField(row, ordinal, dictionary(buffer.getShort()))
+      columnType.setField(row, ordinal, dictionary(buffer.getShort()).asInstanceOf[T#InternalType])
     }
 
     override def hasNext: Boolean = buffer.hasRemaining

From 1f4c4fe6dfd8cc52b5fddfd67a31a77edbb1a036 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 19 Aug 2015 14:03:47 -0700
Subject: [PATCH 1146/1454] [SPARK-10090] [SQL] fix decimal scale of division

We should rounding the result of multiply/division of decimal to expected precision/scale, also check overflow.

Author: Davies Liu <davies@databricks.com>

Closes #8287 from davies/decimal_division.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 28 +++++----
 .../spark/sql/catalyst/expressions/Cast.scala | 32 +++++-----
 .../expressions/decimalFunctions.scala        | 38 ++++++++++-
 .../org/apache/spark/sql/types/Decimal.scala  |  4 +-
 .../expressions/DecimalExpressionSuite.scala  | 63 +++++++++++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 23 ++++++-
 6 files changed, 157 insertions(+), 31 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 8581d6b496c15..62c27ee0b9ee0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -371,8 +371,8 @@ object HiveTypeCoercion {
       DecimalType.bounded(range + scale, scale)
     }
 
-    private def changePrecision(e: Expression, dataType: DataType): Expression = {
-      ChangeDecimalPrecision(Cast(e, dataType))
+    private def promotePrecision(e: Expression, dataType: DataType): Expression = {
+      PromotePrecision(Cast(e, dataType))
     }
 
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
@@ -383,36 +383,42 @@ object HiveTypeCoercion {
         case e if !e.childrenResolved => e
 
         // Skip nodes who is already promoted
-        case e: BinaryArithmetic if e.left.isInstanceOf[ChangeDecimalPrecision] => e
+        case e: BinaryArithmetic if e.left.isInstanceOf[PromotePrecision] => e
 
         case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
-          Add(changePrecision(e1, dt), changePrecision(e2, dt))
+          CheckOverflow(Add(promotePrecision(e1, dt), promotePrecision(e2, dt)), dt)
 
         case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
-          Subtract(changePrecision(e1, dt), changePrecision(e2, dt))
+          CheckOverflow(Subtract(promotePrecision(e1, dt), promotePrecision(e2, dt)), dt)
 
         case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          val dt = DecimalType.bounded(p1 + p2 + 1, s1 + s2)
-          Multiply(changePrecision(e1, dt), changePrecision(e2, dt))
+          val resultType = DecimalType.bounded(p1 + p2 + 1, s1 + s2)
+          val widerType = widerDecimalType(p1, s1, p2, s2)
+          CheckOverflow(Multiply(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
+            resultType)
 
         case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          val dt = DecimalType.bounded(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1))
-          Divide(changePrecision(e1, dt), changePrecision(e2, dt))
+          val resultType = DecimalType.bounded(p1 - s1 + s2 + max(6, s1 + p2 + 1),
+            max(6, s1 + p2 + 1))
+          val widerType = widerDecimalType(p1, s1, p2, s2)
+          CheckOverflow(Divide(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
+            resultType)
 
         case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
           // resultType may have lower precision, so we cast them into wider type first.
           val widerType = widerDecimalType(p1, s1, p2, s2)
-          Cast(Remainder(changePrecision(e1, widerType), changePrecision(e2, widerType)),
+          CheckOverflow(Remainder(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
             resultType)
 
         case Pmod(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
           // resultType may have lower precision, so we cast them into wider type first.
           val widerType = widerDecimalType(p1, s1, p2, s2)
-          Cast(Pmod(changePrecision(e1, widerType), changePrecision(e2, widerType)), resultType)
+          CheckOverflow(Pmod(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
+            resultType)
 
         case b @ BinaryComparison(e1 @ DecimalType.Expression(p1, s1),
                                   e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 616b9e0e65b78..2db954257be35 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -447,7 +447,7 @@ case class Cast(child: Expression, dataType: DataType)
     case StringType => castToStringCode(from, ctx)
     case BinaryType => castToBinaryCode(from)
     case DateType => castToDateCode(from, ctx)
-    case decimal: DecimalType => castToDecimalCode(from, decimal)
+    case decimal: DecimalType => castToDecimalCode(from, decimal, ctx)
     case TimestampType => castToTimestampCode(from, ctx)
     case CalendarIntervalType => castToIntervalCode(from)
     case BooleanType => castToBooleanCode(from)
@@ -528,14 +528,18 @@ case class Cast(child: Expression, dataType: DataType)
       }
     """
 
-  private[this] def castToDecimalCode(from: DataType, target: DecimalType): CastFunction = {
+  private[this] def castToDecimalCode(
+      from: DataType,
+      target: DecimalType,
+      ctx: CodeGenContext): CastFunction = {
+    val tmp = ctx.freshName("tmpDecimal")
     from match {
       case StringType =>
         (c, evPrim, evNull) =>
           s"""
             try {
-              Decimal tmpDecimal = Decimal.apply(new java.math.BigDecimal($c.toString()));
-              ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+              Decimal $tmp = Decimal.apply(new java.math.BigDecimal($c.toString()));
+              ${changePrecision(tmp, target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
             }
@@ -543,8 +547,8 @@ case class Cast(child: Expression, dataType: DataType)
       case BooleanType =>
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = $c ? Decimal.apply(1) : Decimal.apply(0);
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            Decimal $tmp = $c ? Decimal.apply(1) : Decimal.apply(0);
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case DateType =>
         // date can't cast to decimal in Hive
@@ -553,29 +557,29 @@ case class Cast(child: Expression, dataType: DataType)
         // Note that we lose precision here.
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = Decimal.apply(
+            Decimal $tmp = Decimal.apply(
               scala.math.BigDecimal.valueOf(${timestampToDoubleCode(c)}));
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case DecimalType() =>
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = $c.clone();
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            Decimal $tmp = $c.clone();
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case x: IntegralType =>
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = Decimal.apply((long) $c);
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            Decimal $tmp = Decimal.apply((long) $c);
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case x: FractionalType =>
         // All other numeric types can be represented precisely as Doubles
         (c, evPrim, evNull) =>
           s"""
             try {
-              Decimal tmpDecimal = Decimal.apply(scala.math.BigDecimal.valueOf((double) $c));
-              ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+              Decimal $tmp = Decimal.apply(scala.math.BigDecimal.valueOf((double) $c));
+              ${changePrecision(tmp, target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
             }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
index adb33e4c8d4a1..b7be12f7aa741 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
@@ -66,10 +66,44 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un
  * An expression used to wrap the children when promote the precision of DecimalType to avoid
  * promote multiple times.
  */
-case class ChangeDecimalPrecision(child: Expression) extends UnaryExpression {
+case class PromotePrecision(child: Expression) extends UnaryExpression {
   override def dataType: DataType = child.dataType
   override def eval(input: InternalRow): Any = child.eval(input)
   override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx)
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = ""
-  override def prettyName: String = "change_decimal_precision"
+  override def prettyName: String = "promote_precision"
+}
+
+/**
+ * Rounds the decimal to given scale and check whether the decimal can fit in provided precision
+ * or not, returns null if not.
+ */
+case class CheckOverflow(child: Expression, dataType: DecimalType) extends UnaryExpression {
+
+  override def nullable: Boolean = true
+
+  override def nullSafeEval(input: Any): Any = {
+    val d = input.asInstanceOf[Decimal].clone()
+    if (d.changePrecision(dataType.precision, dataType.scale)) {
+      d
+    } else {
+      null
+    }
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, eval => {
+      val tmp = ctx.freshName("tmp")
+      s"""
+         | Decimal $tmp = $eval.clone();
+         | if ($tmp.changePrecision(${dataType.precision}, ${dataType.scale})) {
+         |   ${ev.primitive} = $tmp;
+         | } else {
+         |   ${ev.isNull} = true;
+         | }
+       """.stripMargin
+    })
+  }
+
+  override def toString: String = s"CheckOverflow($child, $dataType)"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index d95805c24521c..c988f1d1b972e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -267,7 +267,7 @@ final class Decimal extends Ordered[Decimal] with Serializable {
     if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
       Decimal(longVal + that.longVal, Math.max(precision, that.precision), scale)
     } else {
-      Decimal(toBigDecimal + that.toBigDecimal, precision, scale)
+      Decimal(toBigDecimal + that.toBigDecimal)
     }
   }
 
@@ -275,7 +275,7 @@ final class Decimal extends Ordered[Decimal] with Serializable {
     if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
       Decimal(longVal - that.longVal, Math.max(precision, that.precision), scale)
     } else {
-      Decimal(toBigDecimal - that.toBigDecimal, precision, scale)
+      Decimal(toBigDecimal - that.toBigDecimal)
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala
new file mode 100644
index 0000000000000..511f0307901df
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.types.{LongType, DecimalType, Decimal}
+
+
+class DecimalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
+
+  test("UnscaledValue") {
+    val d1 = Decimal("10.1")
+    checkEvaluation(UnscaledValue(Literal(d1)), 101L)
+    val d2 = Decimal(101, 3, 1)
+    checkEvaluation(UnscaledValue(Literal(d2)), 101L)
+    checkEvaluation(UnscaledValue(Literal.create(null, DecimalType(2, 1))), null)
+  }
+
+  test("MakeDecimal") {
+    checkEvaluation(MakeDecimal(Literal(101L), 3, 1), Decimal("10.1"))
+    checkEvaluation(MakeDecimal(Literal.create(null, LongType), 3, 1), null)
+  }
+
+  test("PromotePrecision") {
+    val d1 = Decimal("10.1")
+    checkEvaluation(PromotePrecision(Literal(d1)), d1)
+    val d2 = Decimal(101, 3, 1)
+    checkEvaluation(PromotePrecision(Literal(d2)), d2)
+    checkEvaluation(PromotePrecision(Literal.create(null, DecimalType(2, 1))), null)
+  }
+
+  test("CheckOverflow") {
+    val d1 = Decimal("10.1")
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 0)), Decimal("10"))
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 1)), d1)
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 2)), d1)
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 3)), null)
+
+    val d2 = Decimal(101, 3, 1)
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 0)), Decimal("10"))
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 1)), d2)
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 2)), d2)
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 3)), null)
+
+    checkEvaluation(CheckOverflow(Literal.create(null, DecimalType(2, 1)), DecimalType(3, 2)), null)
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index c329fdb2a6bb1..141468ca00d67 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -17,16 +17,17 @@
 
 package org.apache.spark.sql
 
+import java.math.MathContext
 import java.sql.Timestamp
 
 import org.apache.spark.AccumulatorSuite
-import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.test.SQLTestData._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /** A SQL Dialect for testing purpose, and it can not be nested type */
@@ -1608,6 +1609,24 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     }
   }
 
+  test("decimal precision with multiply/division") {
+    checkAnswer(sql("select 10.3 * 3.0"), Row(BigDecimal("30.90")))
+    checkAnswer(sql("select 10.3000 * 3.0"), Row(BigDecimal("30.90000")))
+    checkAnswer(sql("select 10.30000 * 30.0"), Row(BigDecimal("309.000000")))
+    checkAnswer(sql("select 10.300000000000000000 * 3.000000000000000000"),
+      Row(BigDecimal("30.900000000000000000000000000000000000", new MathContext(38))))
+    checkAnswer(sql("select 10.300000000000000000 * 3.0000000000000000000"),
+      Row(null))
+
+    checkAnswer(sql("select 10.3 / 3.0"), Row(BigDecimal("3.433333")))
+    checkAnswer(sql("select 10.3000 / 3.0"), Row(BigDecimal("3.4333333")))
+    checkAnswer(sql("select 10.30000 / 30.0"), Row(BigDecimal("0.343333333")))
+    checkAnswer(sql("select 10.300000000000000000 / 3.00000000000000000"),
+      Row(BigDecimal("3.4333333333333333333333333333333333333", new MathContext(38))))
+    checkAnswer(sql("select 10.3000000000000000000 / 3.00000000000000000"),
+      Row(null))
+  }
+
   test("external sorting updates peak execution memory") {
     withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
       val sc = sqlContext.sparkContext

From f3ff4c41d2e32bd0f2419d1c9c68fcd0c2593e41 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 19 Aug 2015 14:15:28 -0700
Subject: [PATCH 1147/1454] [SPARK-9899] [SQL] Disables customized output
 committer when speculation is on

Speculation hates direct output committer, as there are multiple corner cases that may cause data corruption and/or data loss.

Please see this [PR comment] [1] for more details.

[1]: https://github.com/apache/spark/pull/8191#issuecomment-131598385

Author: Cheng Lian <lian@databricks.com>

Closes #8317 from liancheng/spark-9899/speculation-hates-direct-output-committer.
---
 .../datasources/WriterContainer.scala         | 16 ++++++++-
 .../sql/sources/hadoopFsRelationSuites.scala  | 34 +++++++++++++++++++
 2 files changed, 49 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index e0147079e6997..78f48a5cd72c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -58,6 +58,9 @@ private[sql] abstract class BaseWriterContainer(
   // This is only used on driver side.
   @transient private val jobContext: JobContext = job
 
+  private val speculationEnabled: Boolean =
+    relation.sqlContext.sparkContext.conf.getBoolean("spark.speculation", defaultValue = false)
+
   // The following fields are initialized and used on both driver and executor side.
   @transient protected var outputCommitter: OutputCommitter = _
   @transient private var jobId: JobID = _
@@ -126,10 +129,21 @@ private[sql] abstract class BaseWriterContainer(
       // associated with the file output format since it is not safe to use a custom
       // committer for appending. For example, in S3, direct parquet output committer may
       // leave partial data in the destination dir when the the appending job fails.
+      //
+      // See SPARK-8578 for more details
       logInfo(
-        s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName} " +
+        s"Using default output committer ${defaultOutputCommitter.getClass.getCanonicalName} " +
           "for appending.")
       defaultOutputCommitter
+    } else if (speculationEnabled) {
+      // When speculation is enabled, it's not safe to use customized output committer classes,
+      // especially direct output committers (e.g. `DirectParquetOutputCommitter`).
+      //
+      // See SPARK-9899 for more details.
+      logInfo(
+        s"Using default output committer ${defaultOutputCommitter.getClass.getCanonicalName} " +
+          "because spark.speculation is configured to be true.")
+      defaultOutputCommitter
     } else {
       val committerClass = context.getConfiguration.getClass(
         SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 8d0d9218ddd6a..5bbca14bad320 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -570,6 +570,40 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       df.write.format(dataSourceName).partitionBy("c", "d", "e").saveAsTable("t")
     }
   }
+
+  test("SPARK-9899 Disable customized output committer when speculation is on") {
+    val clonedConf = new Configuration(configuration)
+    val speculationEnabled =
+      sqlContext.sparkContext.conf.getBoolean("spark.speculation", defaultValue = false)
+
+    try {
+      withTempPath { dir =>
+        // Enables task speculation
+        sqlContext.sparkContext.conf.set("spark.speculation", "true")
+
+        // Uses a customized output committer which always fails
+        configuration.set(
+          SQLConf.OUTPUT_COMMITTER_CLASS.key,
+          classOf[AlwaysFailOutputCommitter].getName)
+
+        // Code below shouldn't throw since customized output committer should be disabled.
+        val df = sqlContext.range(10).coalesce(1)
+        df.write.format(dataSourceName).save(dir.getCanonicalPath)
+        checkAnswer(
+          sqlContext
+            .read
+            .format(dataSourceName)
+            .option("dataSchema", df.schema.json)
+            .load(dir.getCanonicalPath),
+          df)
+      }
+    } finally {
+      // Hadoop 1 doesn't have `Configuration.unset`
+      configuration.clear()
+      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      sqlContext.sparkContext.conf.set("spark.speculation", speculationEnabled.toString)
+    }
+  }
 }
 
 // This class is used to test SPARK-8578. We should not use any custom output committer when

From 373a376c04320aab228b5c385e2b788809877d3e Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Wed, 19 Aug 2015 14:31:51 -0700
Subject: [PATCH 1148/1454] [SPARK-10083] [SQL] CaseWhen should support type
 coercion of DecimalType and FractionalType

create t1 (a decimal(7, 2), b long);
select case when 1=1 then a else 1.0 end from t1;
select case when 1=1 then a else b end from t1;

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #8270 from adrian-wang/casewhenfractional.
---
 .../sql/catalyst/analysis/HiveTypeCoercion.scala      |  4 ++--
 .../sql/catalyst/analysis/HiveTypeCoercionSuite.scala | 11 +++++++++++
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 62c27ee0b9ee0..f2f2ba2f96552 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -605,7 +605,7 @@ object HiveTypeCoercion {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       case c: CaseWhenLike if c.childrenResolved && !c.valueTypesEqual =>
         logDebug(s"Input values for null casting ${c.valueTypes.mkString(",")}")
-        val maybeCommonType = findTightestCommonTypeAndPromoteToString(c.valueTypes)
+        val maybeCommonType = findWiderCommonType(c.valueTypes)
         maybeCommonType.map { commonType =>
           val castedBranches = c.branches.grouped(2).map {
             case Seq(when, value) if value.dataType != commonType =>
@@ -622,7 +622,7 @@ object HiveTypeCoercion {
 
       case c: CaseKeyWhen if c.childrenResolved && !c.resolved =>
         val maybeCommonType =
-          findTightestCommonTypeAndPromoteToString((c.key +: c.whenList).map(_.dataType))
+          findWiderCommonType((c.key +: c.whenList).map(_.dataType))
         maybeCommonType.map { commonType =>
           val castedBranches = c.branches.grouped(2).map {
             case Seq(whenExpr, thenExpr) if whenExpr.dataType != commonType =>
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index cbdf453f600ab..6f33ab733b615 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -285,6 +285,17 @@ class HiveTypeCoercionSuite extends PlanTest {
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a"))),
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a")))
     )
+    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
+      CaseWhen(Seq(Literal(true), Literal(1.2), Literal.create(1, DecimalType(7, 2)))),
+      CaseWhen(Seq(
+        Literal(true), Literal(1.2), Cast(Literal.create(1, DecimalType(7, 2)), DoubleType)))
+    )
+    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
+      CaseWhen(Seq(Literal(true), Literal(100L), Literal.create(1, DecimalType(7, 2)))),
+      CaseWhen(Seq(
+        Literal(true), Cast(Literal(100L), DecimalType(22, 2)),
+        Cast(Literal.create(1, DecimalType(7, 2)), DecimalType(22, 2))))
+    )
   }
 
   test("type coercion simplification for equal to") {

From e0dd1309ac248375f429639801923570f14de18d Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 19 Aug 2015 14:33:32 -0700
Subject: [PATCH 1149/1454] [SPARK-10119] [CORE] Fix isDynamicAllocationEnabled
 when config is expliticly disabled.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8316 from vanzin/SPARK-10119.
---
 .../main/scala/org/apache/spark/util/Utils.scala   |  2 +-
 .../scala/org/apache/spark/util/UtilsSuite.scala   | 14 ++++++++++++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index fddc24dbfc237..8313312226713 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -2141,7 +2141,7 @@ private[spark] object Utils extends Logging {
    * the latter should override the former (SPARK-9092).
    */
   def isDynamicAllocationEnabled(conf: SparkConf): Boolean = {
-    conf.contains("spark.dynamicAllocation.enabled") &&
+    conf.getBoolean("spark.dynamicAllocation.enabled", false) &&
       conf.getInt("spark.executor.instances", 0) == 0
   }
 
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index 8f7e402d5f2a6..1fb81ad565b41 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -720,4 +720,18 @@ class UtilsSuite extends SparkFunSuite with ResetSystemProperties with Logging {
     assert(Utils.nanSafeCompareFloats(Float.PositiveInfinity, Float.NaN) === -1)
     assert(Utils.nanSafeCompareFloats(Float.NegativeInfinity, Float.NaN) === -1)
   }
+
+  test("isDynamicAllocationEnabled") {
+    val conf = new SparkConf()
+    assert(Utils.isDynamicAllocationEnabled(conf) === false)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.dynamicAllocation.enabled", "false")) === false)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.dynamicAllocation.enabled", "true")) === true)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.executor.instances", "1")) === false)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.executor.instances", "0")) === true)
+  }
+
 }

From b0dbaec4f942a47afde3490b9339ad3bd187024d Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 19 Aug 2015 15:04:56 -0700
Subject: [PATCH 1150/1454] [SPARK-6489] [SQL] add column pruning for Generate

This PR takes over https://github.com/apache/spark/pull/5358

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8268 from cloud-fan/6489.
---
 .../sql/catalyst/expressions/generators.scala |  2 -
 .../sql/catalyst/optimizer/Optimizer.scala    | 16 ++++
 .../optimizer/ColumnPruningSuite.scala        | 84 +++++++++++++++++++
 3 files changed, 100 insertions(+), 2 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index d474853355e5b..c0845e1a0102f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import scala.collection.Map
-
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 47b06cae15436..42457d5318b48 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -165,6 +165,7 @@ object SetOperationPushDown extends Rule[LogicalPlan] {
  *
  *  - Inserting Projections beneath the following operators:
  *   - Aggregate
+ *   - Generate
  *   - Project <- Join
  *   - LeftSemiJoin
  */
@@ -178,6 +179,21 @@ object ColumnPruning extends Rule[LogicalPlan] {
     case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty =>
       a.copy(child = Project(a.references.toSeq, child))
 
+    // Eliminate attributes that are not needed to calculate the Generate.
+    case g: Generate if !g.join && (g.child.outputSet -- g.references).nonEmpty =>
+      g.copy(child = Project(g.references.toSeq, g.child))
+
+    case p @ Project(_, g: Generate) if g.join && p.references.subsetOf(g.generatedSet) =>
+      p.copy(child = g.copy(join = false))
+
+    case p @ Project(projectList, g: Generate) if g.join =>
+      val neededChildOutput = p.references -- g.generatorOutput ++ g.references
+      if (neededChildOutput == g.child.outputSet) {
+        p
+      } else {
+        Project(projectList, g.copy(child = Project(neededChildOutput.toSeq, g.child)))
+      }
+
     case p @ Project(projectList, a @ Aggregate(groupingExpressions, aggregateExpressions, child))
         if (a.outputSet -- p.references).nonEmpty =>
       Project(
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala
new file mode 100644
index 0000000000000..dbebcb86809de
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.expressions.Explode
+import org.apache.spark.sql.catalyst.plans.PlanTest
+import org.apache.spark.sql.catalyst.plans.logical.{Project, LocalRelation, Generate, LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.RuleExecutor
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.types.StringType
+
+class ColumnPruningSuite extends PlanTest {
+
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches = Batch("Column pruning", FixedPoint(100),
+      ColumnPruning) :: Nil
+  }
+
+  test("Column pruning for Generate when Generate.join = false") {
+    val input = LocalRelation('a.int, 'b.array(StringType))
+
+    val query = Generate(Explode('b), false, false, None, 's.string :: Nil, input).analyze
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer =
+      Generate(Explode('b), false, false, None, 's.string :: Nil,
+        Project('b.attr :: Nil, input)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("Column pruning for Generate when Generate.join = true") {
+    val input = LocalRelation('a.int, 'b.int, 'c.array(StringType))
+
+    val query =
+      Project(Seq('a, 's),
+        Generate(Explode('c), true, false, None, 's.string :: Nil,
+          input)).analyze
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer =
+      Project(Seq('a, 's),
+        Generate(Explode('c), true, false, None, 's.string :: Nil,
+          Project(Seq('a, 'c),
+            input))).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("Turn Generate.join to false if possible") {
+    val input = LocalRelation('b.array(StringType))
+
+    val query =
+      Project(('s + 1).as("s+1") :: Nil,
+        Generate(Explode('b), true, false, None, 's.string :: Nil,
+          input)).analyze
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer =
+      Project(('s + 1).as("s+1") :: Nil,
+        Generate(Explode('b), false, false, None, 's.string :: Nil,
+          input)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  // todo: add more tests for column pruning
+}

From 8e0a072f78b4902d5f7ccc6b15232ed202a117f9 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Wed, 19 Aug 2015 15:43:08 -0700
Subject: [PATCH 1151/1454] [SPARK-9895] User Guide for RFormula Feature
 Transformer

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #8293 from ericl/docs-2.
---
 docs/ml-features.md                           | 108 ++++++++++++++++++
 .../apache/spark/ml/feature/RFormula.scala    |   4 +-
 2 files changed, 110 insertions(+), 2 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index d0e8eeb7a757e..6309db97be4d0 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,3 +1477,111 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
+## RFormula
+
+`RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
+
+**Examples**
+
+Assume that we have a DataFrame with the columns `id`, `country`, `hour`, and `clicked`:
+
+~~~
+id | country | hour | clicked
+---|---------|------|---------
+ 7 | "US"    | 18   | 1.0
+ 8 | "CA"    | 12   | 0.0
+ 9 | "NZ"    | 15   | 0.0
+~~~
+
+If we use `RFormula` with a formula string of `clicked ~ country + hour`, which indicates that we want to
+predict `clicked` based on `country` and `hour`, after transformation we should get the following DataFrame:
+
+~~~
+id | country | hour | clicked | features         | label
+---|---------|------|---------|------------------|-------
+ 7 | "US"    | 18   | 1.0     | [0.0, 0.0, 18.0] | 1.0
+ 8 | "CA"    | 12   | 0.0     | [0.0, 1.0, 12.0] | 0.0
+ 9 | "NZ"    | 15   | 0.0     | [1.0, 0.0, 15.0] | 0.0
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`RFormula`](api/scala/index.html#org.apache.spark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight scala %}
+import org.apache.spark.ml.feature.RFormula
+
+val dataset = sqlContext.createDataFrame(Seq(
+  (7, "US", 18, 1.0),
+  (8, "CA", 12, 0.0),
+  (9, "NZ", 15, 0.0)
+)).toDF("id", "country", "hour", "clicked")
+val formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label")
+val output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`RFormula`](api/java/org/apache/spark/ml/feature/RFormula.html) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.RFormula;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+import static org.apache.spark.sql.types.DataTypes.*;
+
+StructType schema = createStructType(new StructField[] {
+  createStructField("id", IntegerType, false),
+  createStructField("country", StringType, false),
+  createStructField("hour", IntegerType, false),
+  createStructField("clicked", DoubleType, false)
+});
+JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(7, "US", 18, 1.0),
+  RowFactory.create(8, "CA", 12, 0.0),
+  RowFactory.create(9, "NZ", 15, 0.0)
+));
+DataFrame dataset = sqlContext.createDataFrame(rdd, schema);
+
+RFormula formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label");
+
+DataFrame output = formula.fit(dataset).transform(dataset);
+output.select("features", "label").show();
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+[`RFormula`](api/python/pyspark.ml.html#pyspark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight python %}
+from pyspark.ml.feature import RFormula
+
+dataset = sqlContext.createDataFrame(
+    [(7, "US", 18, 1.0),
+     (8, "CA", 12, 0.0),
+     (9, "NZ", 15, 0.0)],
+    ["id", "country", "hour", "clicked"])
+formula = RFormula(
+    formula="clicked ~ country + hour",
+    featuresCol="features",
+    labelCol="label")
+output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+</div>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index a752dacd72d95..a7fa50444209b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -42,8 +42,8 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol {
 /**
  * :: Experimental ::
  * Implements the transforms required for fitting a dataset against an R model formula. Currently
- * we support a limited subset of the R operators, including '~' and '+'. Also see the R formula
- * docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+ * we support a limited subset of the R operators, including '.', '~', '+', and '-'. Also see the
+ * R formula docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
  */
 @Experimental
 class RFormula(override val uid: String) extends Estimator[RFormulaModel] with RFormulaBase {

From ba5f7e1842f2c5852b5309910c0d39926643da69 Mon Sep 17 00:00:00 2001
From: hyukjinkwon <gurwls223@gmail.com>
Date: Thu, 20 Aug 2015 08:13:25 +0800
Subject: [PATCH 1152/1454] [SPARK-10035] [SQL] Parquet filters does not
 process EqualNullSafe filter.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

As I talked with Lian,

1. I added EquelNullSafe to ParquetFilters
 - It uses the same equality comparison filter with EqualTo since the Parquet filter performs actually null-safe equality comparison.

2. Updated the test code (ParquetFilterSuite)
 - Convert catalyst.Expression to sources.Filter
 - Removed Cast since only Literal is picked up as a proper Filter in DataSourceStrategy
 - Added EquelNullSafe comparison

3. Removed deprecated createFilter for catalyst.Expression

Author: hyukjinkwon <gurwls223@gmail.com>
Author: 권혁진 <gurwls223@gmail.com>

Closes #8275 from HyukjinKwon/master.
---
 .../datasources/parquet/ParquetFilters.scala  | 113 +++---------------
 .../parquet/ParquetFilterSuite.scala          |  63 ++++------
 2 files changed, 37 insertions(+), 139 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index 63915e0a28655..c74c8388632f5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -22,8 +22,6 @@ import java.nio.ByteBuffer
 
 import com.google.common.io.BaseEncoding
 import org.apache.hadoop.conf.Configuration
-import org.apache.parquet.filter2.compat.FilterCompat
-import org.apache.parquet.filter2.compat.FilterCompat._
 import org.apache.parquet.filter2.predicate.FilterApi._
 import org.apache.parquet.filter2.predicate._
 import org.apache.parquet.io.api.Binary
@@ -39,12 +37,6 @@ import org.apache.spark.unsafe.types.UTF8String
 private[sql] object ParquetFilters {
   val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter"
 
-  def createRecordFilter(filterExpressions: Seq[Expression]): Option[Filter] = {
-    filterExpressions.flatMap { filter =>
-      createFilter(filter)
-    }.reduceOption(FilterApi.and).map(FilterCompat.get)
-  }
-
   case class SetInFilter[T <: Comparable[T]](
     valueSet: Set[T]) extends UserDefinedPredicate[T] with Serializable {
 
@@ -205,6 +197,16 @@ private[sql] object ParquetFilters {
     // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`,
     // which can be casted to `false` implicitly. Please refer to the `eval` method of these
     // operators and the `SimplifyFilters` rule for details.
+
+    // Hyukjin:
+    // I added [[EqualNullSafe]] with [[org.apache.parquet.filter2.predicate.Operators.Eq]].
+    // So, it performs equality comparison identically when given [[sources.Filter]] is [[EqualTo]].
+    // The reason why I did this is, that the actual Parquet filter checks null-safe equality
+    // comparison.
+    // So I added this and maybe [[EqualTo]] should be changed. It still seems fine though, because
+    // physical planning does not set `NULL` to [[EqualTo]] but changes it to [[IsNull]] and etc.
+    // Probably I missed something and obviously this should be changed.
+
     predicate match {
       case sources.IsNull(name) =>
         makeEq.lift(dataTypeOf(name)).map(_(name, null))
@@ -216,6 +218,11 @@ private[sql] object ParquetFilters {
       case sources.Not(sources.EqualTo(name, value)) =>
         makeNotEq.lift(dataTypeOf(name)).map(_(name, value))
 
+      case sources.EqualNullSafe(name, value) =>
+        makeEq.lift(dataTypeOf(name)).map(_(name, value))
+      case sources.Not(sources.EqualNullSafe(name, value)) =>
+        makeNotEq.lift(dataTypeOf(name)).map(_(name, value))
+
       case sources.LessThan(name, value) =>
         makeLt.lift(dataTypeOf(name)).map(_(name, value))
       case sources.LessThanOrEqual(name, value) =>
@@ -273,96 +280,6 @@ private[sql] object ParquetFilters {
     addMethod.invoke(null, classOf[Binary], enumTypeDescriptor)
   }
 
-  /**
-   * Converts Catalyst predicate expressions to Parquet filter predicates.
-   *
-   * @todo This can be removed once we get rid of the old Parquet support.
-   */
-  def createFilter(predicate: Expression): Option[FilterPredicate] = {
-    // NOTE:
-    //
-    // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`,
-    // which can be casted to `false` implicitly. Please refer to the `eval` method of these
-    // operators and the `SimplifyFilters` rule for details.
-    predicate match {
-      case IsNull(NamedExpression(name, dataType)) =>
-        makeEq.lift(dataType).map(_(name, null))
-      case IsNotNull(NamedExpression(name, dataType)) =>
-        makeNotEq.lift(dataType).map(_(name, null))
-
-      case EqualTo(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeEq.lift(dataType).map(_(name, value))
-      case EqualTo(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeEq.lift(dataType).map(_(name, value))
-      case EqualTo(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeEq.lift(dataType).map(_(name, value))
-      case EqualTo(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeEq.lift(dataType).map(_(name, value))
-
-      case Not(EqualTo(NamedExpression(name, _), NonNullLiteral(value, dataType))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-      case Not(EqualTo(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-      case Not(EqualTo(NonNullLiteral(value, dataType), NamedExpression(name, _))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-      case Not(EqualTo(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-
-      case LessThan(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeLt.lift(dataType).map(_(name, value))
-      case LessThan(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeLt.lift(dataType).map(_(name, value))
-      case LessThan(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeGt.lift(dataType).map(_(name, value))
-      case LessThan(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeGt.lift(dataType).map(_(name, value))
-
-      case LessThanOrEqual(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-      case LessThanOrEqual(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-      case LessThanOrEqual(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-      case LessThanOrEqual(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-
-      case GreaterThan(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeGt.lift(dataType).map(_(name, value))
-      case GreaterThan(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeGt.lift(dataType).map(_(name, value))
-      case GreaterThan(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeLt.lift(dataType).map(_(name, value))
-      case GreaterThan(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeLt.lift(dataType).map(_(name, value))
-
-      case GreaterThanOrEqual(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-      case GreaterThanOrEqual(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-      case GreaterThanOrEqual(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-      case GreaterThanOrEqual(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-
-      case And(lhs, rhs) =>
-        (createFilter(lhs) ++ createFilter(rhs)).reduceOption(FilterApi.and)
-
-      case Or(lhs, rhs) =>
-        for {
-          lhsFilter <- createFilter(lhs)
-          rhsFilter <- createFilter(rhs)
-        } yield FilterApi.or(lhsFilter, rhsFilter)
-
-      case Not(pred) =>
-        createFilter(pred).map(FilterApi.not)
-
-      case InSet(NamedExpression(name, dataType), valueSet) =>
-        makeInSet.lift(dataType).map(_(name, valueSet))
-
-      case _ => None
-    }
-  }
-
   /**
    * Note: Inside the Hadoop API we only have access to `Configuration`, not to
    * [[org.apache.spark.SparkContext]], so we cannot use broadcasts to convey
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index 5b4e568bb9838..f067112cfca95 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -24,9 +24,8 @@ import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
-import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.execution.datasources.{DataSourceStrategy, LogicalRelation}
 import org.apache.spark.sql.test.SharedSQLContext
-import org.apache.spark.sql.types._
 
 /**
  * A test suite that tests Parquet filter2 API based filter pushdown optimization.
@@ -55,20 +54,22 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
         .select(output.map(e => Column(e)): _*)
         .where(Column(predicate))
 
-      val maybeAnalyzedPredicate = query.queryExecution.optimizedPlan.collect {
+      val analyzedPredicate = query.queryExecution.optimizedPlan.collect {
         case PhysicalOperation(_, filters, LogicalRelation(_: ParquetRelation)) => filters
-      }.flatten.reduceOption(_ && _)
+      }.flatten
+      assert(analyzedPredicate.nonEmpty)
 
-      assert(maybeAnalyzedPredicate.isDefined)
-      maybeAnalyzedPredicate.foreach { pred =>
-        val maybeFilter = ParquetFilters.createFilter(pred)
+      val selectedFilters = DataSourceStrategy.selectFilters(analyzedPredicate)
+      assert(selectedFilters.nonEmpty)
+
+      selectedFilters.foreach { pred =>
+        val maybeFilter = ParquetFilters.createFilter(df.schema, pred)
         assert(maybeFilter.isDefined, s"Couldn't generate filter predicate for $pred")
         maybeFilter.foreach { f =>
           // Doesn't bother checking type parameters here (e.g. `Eq[Integer]`)
           assert(f.getClass === filterClass)
         }
       }
-
       checker(query, expected)
     }
   }
@@ -109,43 +110,18 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], Seq(Row(true), Row(false)))
 
       checkFilterPredicate('_1 === true, classOf[Eq[_]], true)
+      checkFilterPredicate('_1 <=> true, classOf[Eq[_]], true)
       checkFilterPredicate('_1 !== true, classOf[NotEq[_]], false)
     }
   }
 
-  test("filter pushdown - short") {
-    withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i.toShort)))) { implicit df =>
-      checkFilterPredicate(Cast('_1, IntegerType) === 1, classOf[Eq[_]], 1)
-      checkFilterPredicate(
-        Cast('_1, IntegerType) !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
-
-      checkFilterPredicate(Cast('_1, IntegerType) < 2, classOf[Lt[_]], 1)
-      checkFilterPredicate(Cast('_1, IntegerType) > 3, classOf[Gt[_]], 4)
-      checkFilterPredicate(Cast('_1, IntegerType) <= 1, classOf[LtEq[_]], 1)
-      checkFilterPredicate(Cast('_1, IntegerType) >= 4, classOf[GtEq[_]], 4)
-
-      checkFilterPredicate(Literal(1) === Cast('_1, IntegerType), classOf[Eq[_]], 1)
-      checkFilterPredicate(Literal(2) > Cast('_1, IntegerType), classOf[Lt[_]], 1)
-      checkFilterPredicate(Literal(3) < Cast('_1, IntegerType), classOf[Gt[_]], 4)
-      checkFilterPredicate(Literal(1) >= Cast('_1, IntegerType), classOf[LtEq[_]], 1)
-      checkFilterPredicate(Literal(4) <= Cast('_1, IntegerType), classOf[GtEq[_]], 4)
-
-      checkFilterPredicate(!(Cast('_1, IntegerType) < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate(
-        Cast('_1, IntegerType) > 2 && Cast('_1, IntegerType) < 4, classOf[Operators.And], 3)
-      checkFilterPredicate(
-        Cast('_1, IntegerType) < 2 || Cast('_1, IntegerType) > 3,
-        classOf[Operators.Or],
-        Seq(Row(1), Row(4)))
-    }
-  }
-
   test("filter pushdown - integer") {
     withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i)))) { implicit df =>
       checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row])
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -154,13 +130,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -171,6 +147,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -179,13 +156,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -196,6 +173,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -204,13 +182,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -221,6 +199,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -229,13 +208,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -247,6 +226,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
         '_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(i => Row.apply(i.toString)))
 
       checkFilterPredicate('_1 === "1", classOf[Eq[_]], "1")
+      checkFilterPredicate('_1 <=> "1", classOf[Eq[_]], "1")
       checkFilterPredicate(
         '_1 !== "1", classOf[NotEq[_]], (2 to 4).map(i => Row.apply(i.toString)))
 
@@ -256,13 +236,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= "4", classOf[GtEq[_]], "4")
 
       checkFilterPredicate(Literal("1") === '_1, classOf[Eq[_]], "1")
+      checkFilterPredicate(Literal("1") <=> '_1, classOf[Eq[_]], "1")
       checkFilterPredicate(Literal("2") > '_1, classOf[Lt[_]], "1")
       checkFilterPredicate(Literal("3") < '_1, classOf[Gt[_]], "4")
       checkFilterPredicate(Literal("1") >= '_1, classOf[LtEq[_]], "1")
       checkFilterPredicate(Literal("4") <= '_1, classOf[GtEq[_]], "4")
 
       checkFilterPredicate(!('_1 < "4"), classOf[GtEq[_]], "4")
-      checkFilterPredicate('_1 > "2" && '_1 < "4", classOf[Operators.And], "3")
       checkFilterPredicate('_1 < "2" || '_1 > "3", classOf[Operators.Or], Seq(Row("1"), Row("4")))
     }
   }
@@ -274,6 +254,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
 
     withParquetDataFrame((1 to 4).map(i => Tuple1(i.b))) { implicit df =>
       checkBinaryFilterPredicate('_1 === 1.b, classOf[Eq[_]], 1.b)
+      checkBinaryFilterPredicate('_1 <=> 1.b, classOf[Eq[_]], 1.b)
 
       checkBinaryFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row])
       checkBinaryFilterPredicate(
@@ -288,13 +269,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkBinaryFilterPredicate('_1 >= 4.b, classOf[GtEq[_]], 4.b)
 
       checkBinaryFilterPredicate(Literal(1.b) === '_1, classOf[Eq[_]], 1.b)
+      checkBinaryFilterPredicate(Literal(1.b) <=> '_1, classOf[Eq[_]], 1.b)
       checkBinaryFilterPredicate(Literal(2.b) > '_1, classOf[Lt[_]], 1.b)
       checkBinaryFilterPredicate(Literal(3.b) < '_1, classOf[Gt[_]], 4.b)
       checkBinaryFilterPredicate(Literal(1.b) >= '_1, classOf[LtEq[_]], 1.b)
       checkBinaryFilterPredicate(Literal(4.b) <= '_1, classOf[GtEq[_]], 4.b)
 
       checkBinaryFilterPredicate(!('_1 < 4.b), classOf[GtEq[_]], 4.b)
-      checkBinaryFilterPredicate('_1 > 2.b && '_1 < 4.b, classOf[Operators.And], 3.b)
       checkBinaryFilterPredicate(
         '_1 < 2.b || '_1 > 3.b, classOf[Operators.Or], Seq(Row(1.b), Row(4.b)))
     }

From 2f2686a73f5a2a53ca5b1023e0d7e0e6c9be5896 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 19 Aug 2015 17:35:41 -0700
Subject: [PATCH 1153/1454] [SPARK-9242] [SQL] Audit UDAF interface.

A few minor changes:

1. Improved documentation
2. Rename apply(distinct....) to distinct.
3. Changed MutableAggregationBuffer from a trait to an abstract class.
4. Renamed returnDataType to dataType to be more consistent with other expressions.

And unrelated to UDAFs:

1. Renamed file names in expressions to use suffix "Expressions" to be more consistent.
2. Moved regexp related expressions out to its own file.
3. Renamed StringComparison => StringPredicate.

Author: Reynold Xin <rxin@databricks.com>

Closes #8321 from rxin/SPARK-9242.
---
 ...bitwise.scala => bitwiseExpressions.scala} |   0
 ...als.scala => conditionalExpressions.scala} |   0
 ...ctions.scala => datetimeExpressions.scala} |   0
 ...nctions.scala => decimalExpressions.scala} |   0
 ...nFunctions.scala => jsonExpressions.scala} |   0
 .../{math.scala => mathExpressions.scala}     |   0
 ...lFunctions.scala => nullExpressions.scala} |   0
 .../{random.scala => randomExpressions.scala} |   0
 .../expressions/regexpExpressions.scala       | 346 ++++++++++++++++++
 ...erations.scala => stringExpressions.scala} | 332 +----------------
 .../sql/catalyst/optimizer/Optimizer.scala    |   2 +-
 .../expressions/StringExpressionsSuite.scala  |   2 +-
 .../apache/spark/sql/UDFRegistration.scala    |   1 +
 .../spark/sql/execution/aggregate/udaf.scala  |   2 +-
 .../apache/spark/sql/expressions/udaf.scala   |  44 ++-
 .../spark/sql/hive/JavaDataFrameSuite.java    |   2 +-
 .../spark/sql/hive/aggregate/MyDoubleAvg.java |   2 +-
 .../spark/sql/hive/aggregate/MyDoubleSum.java |   2 +-
 18 files changed, 386 insertions(+), 349 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{bitwise.scala => bitwiseExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{conditionals.scala => conditionalExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{datetimeFunctions.scala => datetimeExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{decimalFunctions.scala => decimalExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{jsonFunctions.scala => jsonExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{math.scala => mathExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{nullFunctions.scala => nullExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{random.scala => randomExpressions.scala} (100%)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{stringOperations.scala => stringExpressions.scala} (74%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwiseExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwiseExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionalExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionalExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala
new file mode 100644
index 0000000000000..6dff28a7cde46
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala
@@ -0,0 +1,346 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.util.regex.{MatchResult, Pattern}
+
+import org.apache.commons.lang3.StringEscapeUtils
+
+import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.catalyst.util.StringUtils
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+
+trait StringRegexExpression extends ImplicitCastInputTypes {
+  self: BinaryExpression =>
+
+  def escape(v: String): String
+  def matches(regex: Pattern, str: String): Boolean
+
+  override def dataType: DataType = BooleanType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+
+  // try cache the pattern for Literal
+  private lazy val cache: Pattern = right match {
+    case x @ Literal(value: String, StringType) => compile(value)
+    case _ => null
+  }
+
+  protected def compile(str: String): Pattern = if (str == null) {
+    null
+  } else {
+    // Let it raise exception if couldn't compile the regex string
+    Pattern.compile(escape(str))
+  }
+
+  protected def pattern(str: String) = if (cache == null) compile(str) else cache
+
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val regex = pattern(input2.asInstanceOf[UTF8String].toString)
+    if(regex == null) {
+      null
+    } else {
+      matches(regex, input1.asInstanceOf[UTF8String].toString)
+    }
+  }
+}
+
+
+/**
+ * Simple RegEx pattern matching function
+ */
+case class Like(left: Expression, right: Expression)
+  extends BinaryExpression with StringRegexExpression with CodegenFallback {
+
+  override def escape(v: String): String = StringUtils.escapeLikeRegex(v)
+
+  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
+
+  override def toString: String = s"$left LIKE $right"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val patternClass = classOf[Pattern].getName
+    val escapeFunc = StringUtils.getClass.getName.stripSuffix("$") + ".escapeLikeRegex"
+    val pattern = ctx.freshName("pattern")
+
+    if (right.foldable) {
+      val rVal = right.eval()
+      if (rVal != null) {
+        val regexStr =
+          StringEscapeUtils.escapeJava(escape(rVal.asInstanceOf[UTF8String].toString()))
+        ctx.addMutableState(patternClass, pattern,
+          s"""$pattern = ${patternClass}.compile("$regexStr");""")
+
+        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
+        val eval = left.gen(ctx)
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = ${eval.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).matches();
+          }
+        """
+      } else {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+        s"""
+          String rightStr = ${eval2}.toString();
+          ${patternClass} $pattern = ${patternClass}.compile($escapeFunc(rightStr));
+          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).matches();
+        """
+      })
+    }
+  }
+}
+
+
+case class RLike(left: Expression, right: Expression)
+  extends BinaryExpression with StringRegexExpression with CodegenFallback {
+
+  override def escape(v: String): String = v
+  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
+  override def toString: String = s"$left RLIKE $right"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val patternClass = classOf[Pattern].getName
+    val pattern = ctx.freshName("pattern")
+
+    if (right.foldable) {
+      val rVal = right.eval()
+      if (rVal != null) {
+        val regexStr =
+          StringEscapeUtils.escapeJava(rVal.asInstanceOf[UTF8String].toString())
+        ctx.addMutableState(patternClass, pattern,
+          s"""$pattern = ${patternClass}.compile("$regexStr");""")
+
+        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
+        val eval = left.gen(ctx)
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = ${eval.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).find(0);
+          }
+        """
+      } else {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+        s"""
+          String rightStr = ${eval2}.toString();
+          ${patternClass} $pattern = ${patternClass}.compile(rightStr);
+          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).find(0);
+        """
+      })
+    }
+  }
+}
+
+
+/**
+ * Splits str around pat (pattern is a regular expression).
+ */
+case class StringSplit(str: Expression, pattern: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = str
+  override def right: Expression = pattern
+  override def dataType: DataType = ArrayType(StringType)
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+
+  override def nullSafeEval(string: Any, regex: Any): Any = {
+    val strings = string.asInstanceOf[UTF8String].split(regex.asInstanceOf[UTF8String], -1)
+    new GenericArrayData(strings.asInstanceOf[Array[Any]])
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val arrayClass = classOf[GenericArrayData].getName
+    nullSafeCodeGen(ctx, ev, (str, pattern) =>
+      // Array in java is covariant, so we don't need to cast UTF8String[] to Object[].
+      s"""${ev.primitive} = new $arrayClass($str.split($pattern, -1));""")
+  }
+
+  override def prettyName: String = "split"
+}
+
+
+/**
+ * Replace all substrings of str that match regexp with rep.
+ *
+ * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
+ */
+case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expression)
+  extends TernaryExpression with ImplicitCastInputTypes {
+
+  // last regex in string, we will update the pattern iff regexp value changed.
+  @transient private var lastRegex: UTF8String = _
+  // last regex pattern, we cache it for performance concern
+  @transient private var pattern: Pattern = _
+  // last replacement string, we don't want to convert a UTF8String => java.langString every time.
+  @transient private var lastReplacement: String = _
+  @transient private var lastReplacementInUTF8: UTF8String = _
+  // result buffer write by Matcher
+  @transient private val result: StringBuffer = new StringBuffer
+
+  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
+    if (!p.equals(lastRegex)) {
+      // regex value changed
+      lastRegex = p.asInstanceOf[UTF8String].clone()
+      pattern = Pattern.compile(lastRegex.toString)
+    }
+    if (!r.equals(lastReplacementInUTF8)) {
+      // replacement string changed
+      lastReplacementInUTF8 = r.asInstanceOf[UTF8String].clone()
+      lastReplacement = lastReplacementInUTF8.toString
+    }
+    val m = pattern.matcher(s.toString())
+    result.delete(0, result.length())
+
+    while (m.find) {
+      m.appendReplacement(result, lastReplacement)
+    }
+    m.appendTail(result)
+
+    UTF8String.fromString(result.toString)
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, StringType)
+  override def children: Seq[Expression] = subject :: regexp :: rep :: Nil
+  override def prettyName: String = "regexp_replace"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastRegex = ctx.freshName("lastRegex")
+    val termPattern = ctx.freshName("pattern")
+
+    val termLastReplacement = ctx.freshName("lastReplacement")
+    val termLastReplacementInUTF8 = ctx.freshName("lastReplacementInUTF8")
+
+    val termResult = ctx.freshName("result")
+
+    val classNamePattern = classOf[Pattern].getCanonicalName
+    val classNameStringBuffer = classOf[java.lang.StringBuffer].getCanonicalName
+
+    ctx.addMutableState("UTF8String", termLastRegex, s"${termLastRegex} = null;")
+    ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
+    ctx.addMutableState("String", termLastReplacement, s"${termLastReplacement} = null;")
+    ctx.addMutableState("UTF8String",
+      termLastReplacementInUTF8, s"${termLastReplacementInUTF8} = null;")
+    ctx.addMutableState(classNameStringBuffer,
+      termResult, s"${termResult} = new $classNameStringBuffer();")
+
+    nullSafeCodeGen(ctx, ev, (subject, regexp, rep) => {
+    s"""
+      if (!$regexp.equals(${termLastRegex})) {
+        // regex value changed
+        ${termLastRegex} = $regexp.clone();
+        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
+      }
+      if (!$rep.equals(${termLastReplacementInUTF8})) {
+        // replacement string changed
+        ${termLastReplacementInUTF8} = $rep.clone();
+        ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
+      }
+      ${termResult}.delete(0, ${termResult}.length());
+      java.util.regex.Matcher m = ${termPattern}.matcher($subject.toString());
+
+      while (m.find()) {
+        m.appendReplacement(${termResult}, ${termLastReplacement});
+      }
+      m.appendTail(${termResult});
+      ${ev.primitive} = UTF8String.fromString(${termResult}.toString());
+      ${ev.isNull} = false;
+    """
+    })
+  }
+}
+
+/**
+ * Extract a specific(idx) group identified by a Java regex.
+ *
+ * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
+ */
+case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expression)
+  extends TernaryExpression with ImplicitCastInputTypes {
+  def this(s: Expression, r: Expression) = this(s, r, Literal(1))
+
+  // last regex in string, we will update the pattern iff regexp value changed.
+  @transient private var lastRegex: UTF8String = _
+  // last regex pattern, we cache it for performance concern
+  @transient private var pattern: Pattern = _
+
+  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
+    if (!p.equals(lastRegex)) {
+      // regex value changed
+      lastRegex = p.asInstanceOf[UTF8String].clone()
+      pattern = Pattern.compile(lastRegex.toString)
+    }
+    val m = pattern.matcher(s.toString)
+    if (m.find) {
+      val mr: MatchResult = m.toMatchResult
+      UTF8String.fromString(mr.group(r.asInstanceOf[Int]))
+    } else {
+      UTF8String.EMPTY_UTF8
+    }
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, IntegerType)
+  override def children: Seq[Expression] = subject :: regexp :: idx :: Nil
+  override def prettyName: String = "regexp_extract"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastRegex = ctx.freshName("lastRegex")
+    val termPattern = ctx.freshName("pattern")
+    val classNamePattern = classOf[Pattern].getCanonicalName
+
+    ctx.addMutableState("UTF8String", termLastRegex, s"${termLastRegex} = null;")
+    ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
+
+    nullSafeCodeGen(ctx, ev, (subject, regexp, idx) => {
+      s"""
+      if (!$regexp.equals(${termLastRegex})) {
+        // regex value changed
+        ${termLastRegex} = $regexp.clone();
+        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
+      }
+      java.util.regex.Matcher m =
+        ${termPattern}.matcher($subject.toString());
+      if (m.find()) {
+        java.util.regex.MatchResult mr = m.toMatchResult();
+        ${ev.primitive} = UTF8String.fromString(mr.group($idx));
+        ${ev.isNull} = false;
+      } else {
+        ${ev.primitive} = UTF8String.EMPTY_UTF8;
+        ${ev.isNull} = false;
+      }"""
+    })
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
similarity index 74%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index ca044d3e95e38..3c23f2ecfb57c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -21,13 +21,9 @@ import java.text.DecimalFormat
 import java.util.Arrays
 import java.util.{Map => JMap, HashMap}
 import java.util.Locale
-import java.util.regex.{MatchResult, Pattern}
-
-import org.apache.commons.lang3.StringEscapeUtils
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.catalyst.util.StringUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -124,143 +120,6 @@ case class ConcatWs(children: Seq[Expression])
   }
 }
 
-
-trait StringRegexExpression extends ImplicitCastInputTypes {
-  self: BinaryExpression =>
-
-  def escape(v: String): String
-  def matches(regex: Pattern, str: String): Boolean
-
-  override def dataType: DataType = BooleanType
-  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
-
-  // try cache the pattern for Literal
-  private lazy val cache: Pattern = right match {
-    case x @ Literal(value: String, StringType) => compile(value)
-    case _ => null
-  }
-
-  protected def compile(str: String): Pattern = if (str == null) {
-    null
-  } else {
-    // Let it raise exception if couldn't compile the regex string
-    Pattern.compile(escape(str))
-  }
-
-  protected def pattern(str: String) = if (cache == null) compile(str) else cache
-
-  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
-    val regex = pattern(input2.asInstanceOf[UTF8String].toString())
-    if(regex == null) {
-      null
-    } else {
-      matches(regex, input1.asInstanceOf[UTF8String].toString())
-    }
-  }
-}
-
-/**
- * Simple RegEx pattern matching function
- */
-case class Like(left: Expression, right: Expression)
-  extends BinaryExpression with StringRegexExpression with CodegenFallback {
-
-  override def escape(v: String): String = StringUtils.escapeLikeRegex(v)
-
-  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
-
-  override def toString: String = s"$left LIKE $right"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val patternClass = classOf[Pattern].getName
-    val escapeFunc = StringUtils.getClass.getName.stripSuffix("$") + ".escapeLikeRegex"
-    val pattern = ctx.freshName("pattern")
-
-    if (right.foldable) {
-      val rVal = right.eval()
-      if (rVal != null) {
-        val regexStr =
-          StringEscapeUtils.escapeJava(escape(rVal.asInstanceOf[UTF8String].toString()))
-        ctx.addMutableState(patternClass, pattern,
-          s"""$pattern = ${patternClass}.compile("$regexStr");""")
-
-        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
-        val eval = left.gen(ctx)
-        s"""
-          ${eval.code}
-          boolean ${ev.isNull} = ${eval.isNull};
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (!${ev.isNull}) {
-            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).matches();
-          }
-        """
-      } else {
-        s"""
-          boolean ${ev.isNull} = true;
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-        """
-      }
-    } else {
-      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
-        s"""
-          String rightStr = ${eval2}.toString();
-          ${patternClass} $pattern = ${patternClass}.compile($escapeFunc(rightStr));
-          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).matches();
-        """
-      })
-    }
-  }
-}
-
-
-case class RLike(left: Expression, right: Expression)
-  extends BinaryExpression with StringRegexExpression with CodegenFallback {
-
-  override def escape(v: String): String = v
-  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
-  override def toString: String = s"$left RLIKE $right"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val patternClass = classOf[Pattern].getName
-    val pattern = ctx.freshName("pattern")
-
-    if (right.foldable) {
-      val rVal = right.eval()
-      if (rVal != null) {
-        val regexStr =
-          StringEscapeUtils.escapeJava(rVal.asInstanceOf[UTF8String].toString())
-        ctx.addMutableState(patternClass, pattern,
-          s"""$pattern = ${patternClass}.compile("$regexStr");""")
-
-        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
-        val eval = left.gen(ctx)
-        s"""
-          ${eval.code}
-          boolean ${ev.isNull} = ${eval.isNull};
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (!${ev.isNull}) {
-            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).find(0);
-          }
-        """
-      } else {
-        s"""
-          boolean ${ev.isNull} = true;
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-        """
-      }
-    } else {
-      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
-        s"""
-          String rightStr = ${eval2}.toString();
-          ${patternClass} $pattern = ${patternClass}.compile(rightStr);
-          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).find(0);
-        """
-      })
-    }
-  }
-}
-
-
 trait String2StringExpression extends ImplicitCastInputTypes {
   self: UnaryExpression =>
 
@@ -305,7 +164,7 @@ case class Lower(child: Expression) extends UnaryExpression with String2StringEx
 }
 
 /** A base trait for functions that compare two strings, returning a boolean. */
-trait StringComparison extends ImplicitCastInputTypes {
+trait StringPredicate extends Predicate with ImplicitCastInputTypes {
   self: BinaryExpression =>
 
   def compare(l: UTF8String, r: UTF8String): Boolean
@@ -322,7 +181,7 @@ trait StringComparison extends ImplicitCastInputTypes {
  * A function that returns true if the string `left` contains the string `right`.
  */
 case class Contains(left: Expression, right: Expression)
-    extends BinaryExpression with Predicate with StringComparison {
+    extends BinaryExpression with StringPredicate {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.contains(r)
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c1, c2) => s"($c1).contains($c2)")
@@ -333,7 +192,7 @@ case class Contains(left: Expression, right: Expression)
  * A function that returns true if the string `left` starts with the string `right`.
  */
 case class StartsWith(left: Expression, right: Expression)
-    extends BinaryExpression with Predicate with StringComparison {
+    extends BinaryExpression with StringPredicate {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.startsWith(r)
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c1, c2) => s"($c1).startsWith($c2)")
@@ -344,7 +203,7 @@ case class StartsWith(left: Expression, right: Expression)
  * A function that returns true if the string `left` ends with the string `right`.
  */
 case class EndsWith(left: Expression, right: Expression)
-    extends BinaryExpression with Predicate with StringComparison {
+    extends BinaryExpression with StringPredicate {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.endsWith(r)
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c1, c2) => s"($c1).endsWith($c2)")
@@ -769,32 +628,6 @@ case class StringSpace(child: Expression)
   override def prettyName: String = "space"
 }
 
-/**
- * Splits str around pat (pattern is a regular expression).
- */
-case class StringSplit(str: Expression, pattern: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes {
-
-  override def left: Expression = str
-  override def right: Expression = pattern
-  override def dataType: DataType = ArrayType(StringType)
-  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
-
-  override def nullSafeEval(string: Any, regex: Any): Any = {
-    val strings = string.asInstanceOf[UTF8String].split(regex.asInstanceOf[UTF8String], -1)
-    new GenericArrayData(strings.asInstanceOf[Array[Any]])
-  }
-
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val arrayClass = classOf[GenericArrayData].getName
-    nullSafeCodeGen(ctx, ev, (str, pattern) =>
-      // Array in java is covariant, so we don't need to cast UTF8String[] to Object[].
-      s"""${ev.primitive} = new $arrayClass($str.split($pattern, -1));""")
-  }
-
-  override def prettyName: String = "split"
-}
-
 object Substring {
   def subStringBinarySQL(bytes: Array[Byte], pos: Int, len: Int): Array[Byte] = {
     if (pos > bytes.length) {
@@ -1048,163 +881,6 @@ case class Encode(value: Expression, charset: Expression)
   }
 }
 
-/**
- * Replace all substrings of str that match regexp with rep.
- *
- * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
- */
-case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expression)
-  extends TernaryExpression with ImplicitCastInputTypes {
-
-  // last regex in string, we will update the pattern iff regexp value changed.
-  @transient private var lastRegex: UTF8String = _
-  // last regex pattern, we cache it for performance concern
-  @transient private var pattern: Pattern = _
-  // last replacement string, we don't want to convert a UTF8String => java.langString every time.
-  @transient private var lastReplacement: String = _
-  @transient private var lastReplacementInUTF8: UTF8String = _
-  // result buffer write by Matcher
-  @transient private val result: StringBuffer = new StringBuffer
-
-  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
-    if (!p.equals(lastRegex)) {
-      // regex value changed
-      lastRegex = p.asInstanceOf[UTF8String].clone()
-      pattern = Pattern.compile(lastRegex.toString)
-    }
-    if (!r.equals(lastReplacementInUTF8)) {
-      // replacement string changed
-      lastReplacementInUTF8 = r.asInstanceOf[UTF8String].clone()
-      lastReplacement = lastReplacementInUTF8.toString
-    }
-    val m = pattern.matcher(s.toString())
-    result.delete(0, result.length())
-
-    while (m.find) {
-      m.appendReplacement(result, lastReplacement)
-    }
-    m.appendTail(result)
-
-    UTF8String.fromString(result.toString)
-  }
-
-  override def dataType: DataType = StringType
-  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, StringType)
-  override def children: Seq[Expression] = subject :: regexp :: rep :: Nil
-  override def prettyName: String = "regexp_replace"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val termLastRegex = ctx.freshName("lastRegex")
-    val termPattern = ctx.freshName("pattern")
-
-    val termLastReplacement = ctx.freshName("lastReplacement")
-    val termLastReplacementInUTF8 = ctx.freshName("lastReplacementInUTF8")
-
-    val termResult = ctx.freshName("result")
-
-    val classNamePattern = classOf[Pattern].getCanonicalName
-    val classNameStringBuffer = classOf[java.lang.StringBuffer].getCanonicalName
-
-    ctx.addMutableState("UTF8String",
-      termLastRegex, s"${termLastRegex} = null;")
-    ctx.addMutableState(classNamePattern,
-      termPattern, s"${termPattern} = null;")
-    ctx.addMutableState("String",
-      termLastReplacement, s"${termLastReplacement} = null;")
-    ctx.addMutableState("UTF8String",
-      termLastReplacementInUTF8, s"${termLastReplacementInUTF8} = null;")
-    ctx.addMutableState(classNameStringBuffer,
-      termResult, s"${termResult} = new $classNameStringBuffer();")
-
-    nullSafeCodeGen(ctx, ev, (subject, regexp, rep) => {
-    s"""
-      if (!$regexp.equals(${termLastRegex})) {
-        // regex value changed
-        ${termLastRegex} = $regexp.clone();
-        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
-      }
-      if (!$rep.equals(${termLastReplacementInUTF8})) {
-        // replacement string changed
-        ${termLastReplacementInUTF8} = $rep.clone();
-        ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
-      }
-      ${termResult}.delete(0, ${termResult}.length());
-      java.util.regex.Matcher m = ${termPattern}.matcher($subject.toString());
-
-      while (m.find()) {
-        m.appendReplacement(${termResult}, ${termLastReplacement});
-      }
-      m.appendTail(${termResult});
-      ${ev.primitive} = UTF8String.fromString(${termResult}.toString());
-      ${ev.isNull} = false;
-    """
-    })
-  }
-}
-
-/**
- * Extract a specific(idx) group identified by a Java regex.
- *
- * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
- */
-case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expression)
-  extends TernaryExpression with ImplicitCastInputTypes {
-  def this(s: Expression, r: Expression) = this(s, r, Literal(1))
-
-  // last regex in string, we will update the pattern iff regexp value changed.
-  @transient private var lastRegex: UTF8String = _
-  // last regex pattern, we cache it for performance concern
-  @transient private var pattern: Pattern = _
-
-  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
-    if (!p.equals(lastRegex)) {
-      // regex value changed
-      lastRegex = p.asInstanceOf[UTF8String].clone()
-      pattern = Pattern.compile(lastRegex.toString)
-    }
-    val m = pattern.matcher(s.toString())
-    if (m.find) {
-      val mr: MatchResult = m.toMatchResult
-      UTF8String.fromString(mr.group(r.asInstanceOf[Int]))
-    } else {
-      UTF8String.EMPTY_UTF8
-    }
-  }
-
-  override def dataType: DataType = StringType
-  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, IntegerType)
-  override def children: Seq[Expression] = subject :: regexp :: idx :: Nil
-  override def prettyName: String = "regexp_extract"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val termLastRegex = ctx.freshName("lastRegex")
-    val termPattern = ctx.freshName("pattern")
-    val classNamePattern = classOf[Pattern].getCanonicalName
-
-    ctx.addMutableState("UTF8String", termLastRegex, s"${termLastRegex} = null;")
-    ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
-
-    nullSafeCodeGen(ctx, ev, (subject, regexp, idx) => {
-      s"""
-      if (!$regexp.equals(${termLastRegex})) {
-        // regex value changed
-        ${termLastRegex} = $regexp.clone();
-        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
-      }
-      java.util.regex.Matcher m =
-        ${termPattern}.matcher($subject.toString());
-      if (m.find()) {
-        java.util.regex.MatchResult mr = m.toMatchResult();
-        ${ev.primitive} = UTF8String.fromString(mr.group($idx));
-        ${ev.isNull} = false;
-      } else {
-        ${ev.primitive} = UTF8String.EMPTY_UTF8;
-        ${ev.isNull} = false;
-      }"""
-    })
-  }
-}
-
 /**
  * Formats the number X to a format like '#,###,###.##', rounded to D decimal places,
  * and returns the result as a string. If D is 0, the result has no decimal point or
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 42457d5318b48..854463dd11c74 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -372,7 +372,7 @@ object NullPropagation extends Rule[LogicalPlan] {
         case _ => e
       }
 
-      case e: StringComparison => e.children match {
+      case e: StringPredicate => e.children match {
         case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType)
         case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType)
         case _ => e
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 426dc272471ae..99e3b13ce8c97 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -673,7 +673,7 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Length(Literal.create(null, BinaryType)), null, create_row(bytes))
   }
 
-  test("number format") {
+  test("format_number / FormatNumber") {
     checkEvaluation(FormatNumber(Literal(4.asInstanceOf[Byte]), Literal(3)), "4.000")
     checkEvaluation(FormatNumber(Literal(4.asInstanceOf[Short]), Literal(3)), "4.000")
     checkEvaluation(FormatNumber(Literal(4.0f), Literal(3)), "4.000")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
index 1f270560d7bc1..fc4d0938c533a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
@@ -56,6 +56,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
 
   /**
    * Register a user-defined aggregate function (UDAF).
+   *
    * @param name the name of the UDAF.
    * @param udaf the UDAF needs to be registered.
    * @return the registered UDAF.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
index 7619f3ec9f0a7..d43d3dd9ffaae 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
@@ -304,7 +304,7 @@ private[sql] case class ScalaUDAF(
 
   override def nullable: Boolean = true
 
-  override def dataType: DataType = udaf.returnDataType
+  override def dataType: DataType = udaf.dataType
 
   override def deterministic: Boolean = udaf.deterministic
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
index 5180871585f25..258afadc76951 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.expressions
 
-import org.apache.spark.sql.catalyst.expressions.ScalaUDF
 import org.apache.spark.sql.catalyst.expressions.aggregate.{Complete, AggregateExpression2}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.{Column, Row}
@@ -26,7 +25,7 @@ import org.apache.spark.annotation.Experimental
 
 /**
  * :: Experimental ::
- * The abstract class for implementing user-defined aggregate functions.
+ * The base class for implementing user-defined aggregate functions (UDAF).
  */
 @Experimental
 abstract class UserDefinedAggregateFunction extends Serializable {
@@ -67,22 +66,35 @@ abstract class UserDefinedAggregateFunction extends Serializable {
   /**
    * The [[DataType]] of the returned value of this [[UserDefinedAggregateFunction]].
    */
-  def returnDataType: DataType
+  def dataType: DataType
 
-  /** Indicates if this function is deterministic. */
+  /**
+   * Returns true iff this function is deterministic, i.e. given the same input,
+   * always return the same output.
+   */
   def deterministic: Boolean
 
   /**
-   *  Initializes the given aggregation buffer. Initial values set by this method should satisfy
-   *  the condition that when merging two buffers with initial values, the new buffer
-   *  still store initial values.
+   * Initializes the given aggregation buffer, i.e. the zero value of the aggregation buffer.
+   *
+   * The contract should be that applying the merge function on two initial buffers should just
+   * return the initial buffer itself, i.e.
+   * `merge(initialBuffer, initialBuffer)` should equal `initialBuffer`.
    */
   def initialize(buffer: MutableAggregationBuffer): Unit
 
-  /** Updates the given aggregation buffer `buffer` with new input data from `input`. */
+  /**
+   * Updates the given aggregation buffer `buffer` with new input data from `input`.
+   *
+   * This is called once per input row.
+   */
   def update(buffer: MutableAggregationBuffer, input: Row): Unit
 
-  /** Merges two aggregation buffers and stores the updated buffer values back to `buffer1`. */
+  /**
+   * Merges two aggregation buffers and stores the updated buffer values back to `buffer1`.
+   *
+   * This is called when we merge two partially aggregated data together.
+   */
   def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit
 
   /**
@@ -92,7 +104,7 @@ abstract class UserDefinedAggregateFunction extends Serializable {
   def evaluate(buffer: Row): Any
 
   /**
-   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
+   * Creates a [[Column]] for this UDAF using given [[Column]]s as input arguments.
    */
   @scala.annotation.varargs
   def apply(exprs: Column*): Column = {
@@ -105,16 +117,16 @@ abstract class UserDefinedAggregateFunction extends Serializable {
   }
 
   /**
-   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
-   * If `isDistinct` is true, this UDAF is working on distinct input values.
+   * Creates a [[Column]] for this UDAF using the distinct values of the given
+   * [[Column]]s as input arguments.
    */
   @scala.annotation.varargs
-  def apply(isDistinct: Boolean, exprs: Column*): Column = {
+  def distinct(exprs: Column*): Column = {
     val aggregateExpression =
       AggregateExpression2(
         ScalaUDAF(exprs.map(_.expr), this),
         Complete,
-        isDistinct = isDistinct)
+        isDistinct = true)
     Column(aggregateExpression)
   }
 }
@@ -122,9 +134,11 @@ abstract class UserDefinedAggregateFunction extends Serializable {
 /**
  * :: Experimental ::
  * A [[Row]] representing an mutable aggregation buffer.
+ *
+ * This is not meant to be extended outside of Spark.
  */
 @Experimental
-trait MutableAggregationBuffer extends Row {
+abstract class MutableAggregationBuffer extends Row {
 
   /** Update the ith value of this buffer. */
   def update(i: Int, value: Any): Unit
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 21b053f07a3ba..a30dfa554eabc 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -92,7 +92,7 @@ public void testUDAF() {
     DataFrame aggregatedDF =
       df.groupBy()
         .agg(
-          udaf.apply(true, col("value")),
+          udaf.distinct(col("value")),
           udaf.apply(col("value")),
           registeredUDAF.apply(col("value")),
           callUDF("mydoublesum", col("value")));
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
index a2247e3da1554..2961b803f14aa 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
@@ -65,7 +65,7 @@ public MyDoubleAvg() {
     return _bufferSchema;
   }
 
-  @Override public DataType returnDataType() {
+  @Override public DataType dataType() {
     return _returnDataType;
   }
 
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
index da29e24d267dd..c71882a6e7bed 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
@@ -60,7 +60,7 @@ public MyDoubleSum() {
     return _bufferSchema;
   }
 
-  @Override public DataType returnDataType() {
+  @Override public DataType dataType() {
     return _returnDataType;
   }
 

From 1f29d502e7ecd6faa185d70dc714f9ea3922fb6d Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 19 Aug 2015 18:36:01 -0700
Subject: [PATCH 1154/1454] [SPARK-9812] [STREAMING] Fix Python 3 compatibility
 issue in PySpark Streaming and some docs

This PR includes the following fixes:
1. Use `range` instead of `xrange` in `queue_stream.py` to support Python 3.
2. Fix the issue that `utf8_decoder` will return `bytes` rather than `str` when receiving an empty `bytes` in Python 3.
3. Fix the commands in docs so that the user can copy them directly to the command line. The previous commands was broken in the middle of a path, so when copying to the command line, the path would be split to two parts by the extra spaces, which forces the user to fix it manually.

Author: zsxwing <zsxwing@gmail.com>

Closes #8315 from zsxwing/SPARK-9812.
---
 .../src/main/python/streaming/direct_kafka_wordcount.py     | 6 +++---
 examples/src/main/python/streaming/flume_wordcount.py       | 5 +++--
 examples/src/main/python/streaming/kafka_wordcount.py       | 5 +++--
 examples/src/main/python/streaming/mqtt_wordcount.py        | 5 +++--
 examples/src/main/python/streaming/queue_stream.py          | 4 ++--
 python/pyspark/streaming/flume.py                           | 4 +++-
 python/pyspark/streaming/kafka.py                           | 4 +++-
 python/pyspark/streaming/kinesis.py                         | 4 +++-
 8 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/examples/src/main/python/streaming/direct_kafka_wordcount.py b/examples/src/main/python/streaming/direct_kafka_wordcount.py
index 6ef188a220c51..ea20678b9acad 100644
--- a/examples/src/main/python/streaming/direct_kafka_wordcount.py
+++ b/examples/src/main/python/streaming/direct_kafka_wordcount.py
@@ -23,8 +23,8 @@
  http://kafka.apache.org/documentation.html#quickstart
 
  and then run the example
-    `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/\
-      spark-streaming-kafka-assembly-*.jar \
+    `$ bin/spark-submit --jars \
+      external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar \
       examples/src/main/python/streaming/direct_kafka_wordcount.py \
       localhost:9092 test`
 """
@@ -37,7 +37,7 @@
 
 if __name__ == "__main__":
     if len(sys.argv) != 3:
-        print >> sys.stderr, "Usage: direct_kafka_wordcount.py <broker_list> <topic>"
+        print("Usage: direct_kafka_wordcount.py <broker_list> <topic>", file=sys.stderr)
         exit(-1)
 
     sc = SparkContext(appName="PythonStreamingDirectKafkaWordCount")
diff --git a/examples/src/main/python/streaming/flume_wordcount.py b/examples/src/main/python/streaming/flume_wordcount.py
index 091b64d8c4af4..d75bc6daac138 100644
--- a/examples/src/main/python/streaming/flume_wordcount.py
+++ b/examples/src/main/python/streaming/flume_wordcount.py
@@ -23,8 +23,9 @@
  https://flume.apache.org/documentation.html
 
  and then run the example
-    `$ bin/spark-submit --jars external/flume-assembly/target/scala-*/\
-      spark-streaming-flume-assembly-*.jar examples/src/main/python/streaming/flume_wordcount.py \
+    `$ bin/spark-submit --jars \
+      external/flume-assembly/target/scala-*/spark-streaming-flume-assembly-*.jar \
+      examples/src/main/python/streaming/flume_wordcount.py \
       localhost 12345
 """
 from __future__ import print_function
diff --git a/examples/src/main/python/streaming/kafka_wordcount.py b/examples/src/main/python/streaming/kafka_wordcount.py
index b178e7899b5e1..8d697f620f467 100644
--- a/examples/src/main/python/streaming/kafka_wordcount.py
+++ b/examples/src/main/python/streaming/kafka_wordcount.py
@@ -23,8 +23,9 @@
  http://kafka.apache.org/documentation.html#quickstart
 
  and then run the example
-    `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/\
-      spark-streaming-kafka-assembly-*.jar examples/src/main/python/streaming/kafka_wordcount.py \
+    `$ bin/spark-submit --jars \
+      external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar \
+      examples/src/main/python/streaming/kafka_wordcount.py \
       localhost:2181 test`
 """
 from __future__ import print_function
diff --git a/examples/src/main/python/streaming/mqtt_wordcount.py b/examples/src/main/python/streaming/mqtt_wordcount.py
index 617ce5ea6775e..abf9c0e21d307 100644
--- a/examples/src/main/python/streaming/mqtt_wordcount.py
+++ b/examples/src/main/python/streaming/mqtt_wordcount.py
@@ -26,8 +26,9 @@
  http://www.eclipse.org/paho/#getting-started
 
  and then run the example
-    `$ bin/spark-submit --jars external/mqtt-assembly/target/scala-*/\
-      spark-streaming-mqtt-assembly-*.jar examples/src/main/python/streaming/mqtt_wordcount.py \
+    `$ bin/spark-submit --jars \
+      external/mqtt-assembly/target/scala-*/spark-streaming-mqtt-assembly-*.jar \
+      examples/src/main/python/streaming/mqtt_wordcount.py \
       tcp://localhost:1883 foo`
 """
 
diff --git a/examples/src/main/python/streaming/queue_stream.py b/examples/src/main/python/streaming/queue_stream.py
index dcd6a0fc6ff91..b3808907f74a6 100644
--- a/examples/src/main/python/streaming/queue_stream.py
+++ b/examples/src/main/python/streaming/queue_stream.py
@@ -36,8 +36,8 @@
     # Create the queue through which RDDs can be pushed to
     # a QueueInputDStream
     rddQueue = []
-    for i in xrange(5):
-        rddQueue += [ssc.sparkContext.parallelize([j for j in xrange(1, 1001)], 10)]
+    for i in range(5):
+        rddQueue += [ssc.sparkContext.parallelize([j for j in range(1, 1001)], 10)]
 
     # Create the QueueInputDStream and use it do some processing
     inputStream = ssc.queueStream(rddQueue)
diff --git a/python/pyspark/streaming/flume.py b/python/pyspark/streaming/flume.py
index cbb573f226bbe..c0cdc50d8d423 100644
--- a/python/pyspark/streaming/flume.py
+++ b/python/pyspark/streaming/flume.py
@@ -31,7 +31,9 @@
 
 def utf8_decoder(s):
     """ Decode the unicode as UTF-8 """
-    return s and s.decode('utf-8')
+    if s is None:
+        return None
+    return s.decode('utf-8')
 
 
 class FlumeUtils(object):
diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py
index dc5b7fd878aef..8a814c64c0423 100644
--- a/python/pyspark/streaming/kafka.py
+++ b/python/pyspark/streaming/kafka.py
@@ -29,7 +29,9 @@
 
 def utf8_decoder(s):
     """ Decode the unicode as UTF-8 """
-    return s and s.decode('utf-8')
+    if s is None:
+        return None
+    return s.decode('utf-8')
 
 
 class KafkaUtils(object):
diff --git a/python/pyspark/streaming/kinesis.py b/python/pyspark/streaming/kinesis.py
index bcfe2703fecf9..34be5880e1708 100644
--- a/python/pyspark/streaming/kinesis.py
+++ b/python/pyspark/streaming/kinesis.py
@@ -26,7 +26,9 @@
 
 def utf8_decoder(s):
     """ Decode the unicode as UTF-8 """
-    return s and s.decode('utf-8')
+    if s is None:
+        return None
+    return s.decode('utf-8')
 
 
 class KinesisUtils(object):

From affc8a887ede9fdc2ca6051833954cd10918c869 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 19 Aug 2015 19:43:09 -0700
Subject: [PATCH 1155/1454] [SPARK-10125] [STREAMING] Fix a potential deadlock
 in JobGenerator.stop

Because `lazy val` uses `this` lock, if JobGenerator.stop and JobGenerator.doCheckpoint (JobGenerator.shouldCheckpoint has not yet been initialized) run at the same time, it may hang.

Here are the stack traces for the deadlock:

```Java
"pool-1-thread-1-ScalaTest-running-StreamingListenerSuite" #11 prio=5 os_prio=31 tid=0x00007fd35d094800 nid=0x5703 in Object.wait() [0x000000012ecaf000]
   java.lang.Thread.State: WAITING (on object monitor)
        at java.lang.Object.wait(Native Method)
        at java.lang.Thread.join(Thread.java:1245)
        - locked <0x00000007b5d8d7f8> (a org.apache.spark.util.EventLoop$$anon$1)
        at java.lang.Thread.join(Thread.java:1319)
        at org.apache.spark.util.EventLoop.stop(EventLoop.scala:81)
        at org.apache.spark.streaming.scheduler.JobGenerator.stop(JobGenerator.scala:155)
        - locked <0x00000007b5d8cea0> (a org.apache.spark.streaming.scheduler.JobGenerator)
        at org.apache.spark.streaming.scheduler.JobScheduler.stop(JobScheduler.scala:95)
        - locked <0x00000007b5d8ced8> (a org.apache.spark.streaming.scheduler.JobScheduler)
        at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:687)

"JobGenerator" #67 daemon prio=5 os_prio=31 tid=0x00007fd35c3b9800 nid=0x9f03 waiting for monitor entry [0x0000000139e4a000]
   java.lang.Thread.State: BLOCKED (on object monitor)
        at org.apache.spark.streaming.scheduler.JobGenerator.shouldCheckpoint$lzycompute(JobGenerator.scala:63)
        - waiting to lock <0x00000007b5d8cea0> (a org.apache.spark.streaming.scheduler.JobGenerator)
        at org.apache.spark.streaming.scheduler.JobGenerator.shouldCheckpoint(JobGenerator.scala:63)
        at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:290)
        at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:182)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:83)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:82)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
```

I can use this patch to produce this deadlock: https://github.com/zsxwing/spark/commit/8a88f28d1331003a65fabef48ae3d22a7c21f05f

And a timeout build in Jenkins due to this deadlock: https://amplab.cs.berkeley.edu/jenkins/job/NewSparkPullRequestBuilder/1654/

This PR initializes `checkpointWriter` before `eventLoop` uses it to avoid this deadlock.

Author: zsxwing <zsxwing@gmail.com>

Closes #8326 from zsxwing/SPARK-10125.
---
 .../org/apache/spark/streaming/scheduler/JobGenerator.scala   | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
index 9f2117ada61c0..2de035d166e7b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
@@ -79,6 +79,10 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging {
   def start(): Unit = synchronized {
     if (eventLoop != null) return // generator has already been started
 
+    // Call checkpointWriter here to initialize it before eventLoop uses it to avoid a deadlock.
+    // See SPARK-10125
+    checkpointWriter
+
     eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {
       override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)
 

From 73431d8afb41b93888d2642a1ce2d011f03fb740 Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Wed, 19 Aug 2015 19:43:26 -0700
Subject: [PATCH 1156/1454] [SPARK-10124] [MESOS] Fix removing queued driver in
 mesos cluster mode.

Currently the spark applications can be queued to the Mesos cluster dispatcher, but when multiple jobs are in queue we don't handle removing jobs from the buffer correctly while iterating and causes null pointer exception.

This patch copies the buffer before iterating them, so exceptions aren't thrown when the jobs are removed.

Author: Timothy Chen <tnachen@gmail.com>

Closes #8322 from tnachen/fix_cluster_mode.
---
 .../cluster/mesos/MesosClusterScheduler.scala | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 64ec2b8e3db15..1206f184fbc82 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -507,14 +507,16 @@ private[spark] class MesosClusterScheduler(
       val driversToRetry = pendingRetryDrivers.filter { d =>
         d.retryState.get.nextRetry.before(currentTime)
       }
+
       scheduleTasks(
-        driversToRetry,
+        copyBuffer(driversToRetry),
         removeFromPendingRetryDrivers,
         currentOffers,
         tasks)
+
       // Then we walk through the queued drivers and try to schedule them.
       scheduleTasks(
-        queuedDrivers,
+        copyBuffer(queuedDrivers),
         removeFromQueuedDrivers,
         currentOffers,
         tasks)
@@ -527,13 +529,14 @@ private[spark] class MesosClusterScheduler(
       .foreach(o => driver.declineOffer(o.getId))
   }
 
+  private def copyBuffer(
+      buffer: ArrayBuffer[MesosDriverDescription]): ArrayBuffer[MesosDriverDescription] = {
+    val newBuffer = new ArrayBuffer[MesosDriverDescription](buffer.size)
+    buffer.copyToBuffer(newBuffer)
+    newBuffer
+  }
+
   def getSchedulerState(): MesosClusterSchedulerState = {
-    def copyBuffer(
-        buffer: ArrayBuffer[MesosDriverDescription]): ArrayBuffer[MesosDriverDescription] = {
-      val newBuffer = new ArrayBuffer[MesosDriverDescription](buffer.size)
-      buffer.copyToBuffer(newBuffer)
-      newBuffer
-    }
     stateLock.synchronized {
       new MesosClusterSchedulerState(
         frameworkId,

From b762f9920f7587d3c08493c49dd2fede62110b88 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 19 Aug 2015 21:15:58 -0700
Subject: [PATCH 1157/1454] [SPARK-10128] [STREAMING] Used correct classloader
 to deserialize WAL data

Recovering Kinesis sequence numbers from WAL leads to classnotfoundexception because the ObjectInputStream does not use the correct classloader and the SequenceNumberRanges class (in streaming-kinesis-asl package) cannot be found (added through spark-submit) while deserializing. The solution is to use `Thread.currentThread().getContextClassLoader` while deserializing.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8328 from tdas/SPARK-10128 and squashes the following commits:

f19b1c2 [Tathagata Das] Used correct classloader to deserialize WAL data
---
 .../spark/streaming/scheduler/ReceivedBlockTracker.scala     | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
index 7720259a5d794..53b96d51c9180 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.streaming.Time
 import org.apache.spark.streaming.util.{WriteAheadLog, WriteAheadLogUtils}
 import org.apache.spark.util.{Clock, Utils}
-import org.apache.spark.{Logging, SparkConf, SparkException}
+import org.apache.spark.{Logging, SparkConf}
 
 /** Trait representing any event in the ReceivedBlockTracker that updates its state. */
 private[streaming] sealed trait ReceivedBlockTrackerLogEvent
@@ -199,7 +199,8 @@ private[streaming] class ReceivedBlockTracker(
       import scala.collection.JavaConversions._
       writeAheadLog.readAll().foreach { byteBuffer =>
         logTrace("Recovering record " + byteBuffer)
-        Utils.deserialize[ReceivedBlockTrackerLogEvent](byteBuffer.array) match {
+        Utils.deserialize[ReceivedBlockTrackerLogEvent](
+          byteBuffer.array, Thread.currentThread().getContextClassLoader) match {
           case BlockAdditionEvent(receivedBlockInfo) =>
             insertAddedBlock(receivedBlockInfo)
           case BatchAllocationEvent(time, allocatedBlocks) =>

From 43e0135421b2262cbb0e06aae53523f663b4f959 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 20 Aug 2015 15:30:31 +0800
Subject: [PATCH 1158/1454] [SPARK-10092] [SQL] Multi-DB support follow up.

https://issues.apache.org/jira/browse/SPARK-10092

This pr is a follow-up one for Multi-DB support. It has the following changes:

* `HiveContext.refreshTable` now accepts `dbName.tableName`.
* `HiveContext.analyze` now accepts `dbName.tableName`.
* `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateTempTableUsing`, `CreateTempTableUsingAsSelect`, `CreateMetastoreDataSource`, and `CreateMetastoreDataSourceAsSelect` all take `TableIdentifier` instead of the string representation of table name.
* When you call `saveAsTable` with a specified database, the data will be saved to the correct location.
* Explicitly do not allow users to create a temporary with a specified database name (users cannot do it before).
* When we save table to metastore, we also check if db name and table name can be accepted by hive (using `MetaStoreUtils.validateName`).

Author: Yin Huai <yhuai@databricks.com>

Closes #8324 from yhuai/saveAsTableDB.
---
 .../spark/sql/catalyst/TableIdentifier.scala  |   4 +-
 .../spark/sql/catalyst/analysis/Catalog.scala |  63 +++++--
 .../apache/spark/sql/DataFrameWriter.scala    |   2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  15 +-
 .../spark/sql/execution/SparkStrategies.scala |  10 +-
 .../sql/execution/datasources/DDLParser.scala |  32 ++--
 .../spark/sql/execution/datasources/ddl.scala |  22 +--
 .../sql/execution/datasources/rules.scala     |   8 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  35 ++++
 .../apache/spark/sql/hive/HiveContext.scala   |  14 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  22 ++-
 .../spark/sql/hive/HiveStrategies.scala       |  12 +-
 .../spark/sql/hive/execution/commands.scala   |  54 ++++--
 .../spark/sql/hive/ListTablesSuite.scala      |   6 -
 .../spark/sql/hive/MultiDatabaseSuite.scala   | 158 +++++++++++++++++-
 .../sql/hive/execution/SQLQuerySuite.scala    |  35 ++++
 16 files changed, 398 insertions(+), 94 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
index aebcdeb9d070f..d701559bf2d9b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
@@ -25,7 +25,9 @@ private[sql] case class TableIdentifier(table: String, database: Option[String]
 
   def toSeq: Seq[String] = database.toSeq :+ table
 
-  override def toString: String = toSeq.map("`" + _ + "`").mkString(".")
+  override def toString: String = quotedString
+
+  def quotedString: String = toSeq.map("`" + _ + "`").mkString(".")
 
   def unquotedString: String = toSeq.mkString(".")
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index 5766e6a2dd51a..503c4f4b20f38 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -23,6 +23,7 @@ import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.{TableIdentifier, CatalystConf, EmptyConf}
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery}
 
@@ -55,12 +56,15 @@ trait Catalog {
 
   def refreshTable(tableIdent: TableIdentifier): Unit
 
+  // TODO: Refactor it in the work of SPARK-10104
   def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit
 
+  // TODO: Refactor it in the work of SPARK-10104
   def unregisterTable(tableIdentifier: Seq[String]): Unit
 
   def unregisterAllTables(): Unit
 
+  // TODO: Refactor it in the work of SPARK-10104
   protected def processTableIdentifier(tableIdentifier: Seq[String]): Seq[String] = {
     if (conf.caseSensitiveAnalysis) {
       tableIdentifier
@@ -69,6 +73,7 @@ trait Catalog {
     }
   }
 
+  // TODO: Refactor it in the work of SPARK-10104
   protected def getDbTableName(tableIdent: Seq[String]): String = {
     val size = tableIdent.size
     if (size <= 2) {
@@ -78,9 +83,22 @@ trait Catalog {
     }
   }
 
+  // TODO: Refactor it in the work of SPARK-10104
   protected def getDBTable(tableIdent: Seq[String]) : (Option[String], String) = {
     (tableIdent.lift(tableIdent.size - 2), tableIdent.last)
   }
+
+  /**
+   * It is not allowed to specifiy database name for tables stored in [[SimpleCatalog]].
+   * We use this method to check it.
+   */
+  protected def checkTableIdentifier(tableIdentifier: Seq[String]): Unit = {
+    if (tableIdentifier.length > 1) {
+      throw new AnalysisException("Specifying database name or other qualifiers are not allowed " +
+        "for temporary tables. If the table name has dots (.) in it, please quote the " +
+        "table name with backticks (`).")
+    }
+  }
 }
 
 class SimpleCatalog(val conf: CatalystConf) extends Catalog {
@@ -89,11 +107,13 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
   override def registerTable(
       tableIdentifier: Seq[String],
       plan: LogicalPlan): Unit = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     tables.put(getDbTableName(tableIdent), plan)
   }
 
   override def unregisterTable(tableIdentifier: Seq[String]): Unit = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     tables.remove(getDbTableName(tableIdent))
   }
@@ -103,6 +123,7 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
   }
 
   override def tableExists(tableIdentifier: Seq[String]): Boolean = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     tables.containsKey(getDbTableName(tableIdent))
   }
@@ -110,6 +131,7 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
   override def lookupRelation(
       tableIdentifier: Seq[String],
       alias: Option[String] = None): LogicalPlan = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     val tableFullName = getDbTableName(tableIdent)
     val table = tables.get(tableFullName)
@@ -149,7 +171,13 @@ trait OverrideCatalog extends Catalog {
 
   abstract override def tableExists(tableIdentifier: Seq[String]): Boolean = {
     val tableIdent = processTableIdentifier(tableIdentifier)
-    overrides.get(getDBTable(tableIdent)) match {
+    // A temporary tables only has a single part in the tableIdentifier.
+    val overriddenTable = if (tableIdentifier.length > 1) {
+      None: Option[LogicalPlan]
+    } else {
+      overrides.get(getDBTable(tableIdent))
+    }
+    overriddenTable match {
       case Some(_) => true
       case None => super.tableExists(tableIdentifier)
     }
@@ -159,7 +187,12 @@ trait OverrideCatalog extends Catalog {
       tableIdentifier: Seq[String],
       alias: Option[String] = None): LogicalPlan = {
     val tableIdent = processTableIdentifier(tableIdentifier)
-    val overriddenTable = overrides.get(getDBTable(tableIdent))
+    // A temporary tables only has a single part in the tableIdentifier.
+    val overriddenTable = if (tableIdentifier.length > 1) {
+      None: Option[LogicalPlan]
+    } else {
+      overrides.get(getDBTable(tableIdent))
+    }
     val tableWithQualifers = overriddenTable.map(r => Subquery(tableIdent.last, r))
 
     // If an alias was specified by the lookup, wrap the plan in a subquery so that attributes are
@@ -171,20 +204,8 @@ trait OverrideCatalog extends Catalog {
   }
 
   abstract override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = {
-    val dbName = if (conf.caseSensitiveAnalysis) {
-      databaseName
-    } else {
-      if (databaseName.isDefined) Some(databaseName.get.toLowerCase) else None
-    }
-
-    val temporaryTables = overrides.filter {
-      // If a temporary table does not have an associated database, we should return its name.
-      case ((None, _), _) => true
-      // If a temporary table does have an associated database, we should return it if the database
-      // matches the given database name.
-      case ((db: Some[String], _), _) if db == dbName => true
-      case _ => false
-    }.map {
+    // We always return all temporary tables.
+    val temporaryTables = overrides.map {
       case ((_, tableName), _) => (tableName, true)
     }.toSeq
 
@@ -194,13 +215,19 @@ trait OverrideCatalog extends Catalog {
   override def registerTable(
       tableIdentifier: Seq[String],
       plan: LogicalPlan): Unit = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     overrides.put(getDBTable(tableIdent), plan)
   }
 
   override def unregisterTable(tableIdentifier: Seq[String]): Unit = {
-    val tableIdent = processTableIdentifier(tableIdentifier)
-    overrides.remove(getDBTable(tableIdent))
+    // A temporary tables only has a single part in the tableIdentifier.
+    // If tableIdentifier has more than one parts, it is not a temporary table
+    // and we do not need to do anything at here.
+    if (tableIdentifier.length == 1) {
+      val tableIdent = processTableIdentifier(tableIdentifier)
+      overrides.remove(getDBTable(tableIdent))
+    }
   }
 
   override def unregisterAllTables(): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index f0bf1be506411..ce8744b53175b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -218,7 +218,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
       case _ =>
         val cmd =
           CreateTableUsingAsSelect(
-            tableIdent.unquotedString,
+            tableIdent,
             source,
             temporary = false,
             partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 58fe75b59f418..126c9c6f839c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -584,9 +584,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
       tableName: String,
       source: String,
       options: Map[String, String]): DataFrame = {
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
     val cmd =
       CreateTableUsing(
-        tableName,
+        tableIdent,
         userSpecifiedSchema = None,
         source,
         temporary = false,
@@ -594,7 +595,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
         allowExisting = false,
         managedIfNoPath = false)
     executePlan(cmd).toRdd
-    table(tableName)
+    table(tableIdent)
   }
 
   /**
@@ -629,9 +630,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
       source: String,
       schema: StructType,
       options: Map[String, String]): DataFrame = {
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
     val cmd =
       CreateTableUsing(
-        tableName,
+        tableIdent,
         userSpecifiedSchema = Some(schema),
         source,
         temporary = false,
@@ -639,7 +641,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
         allowExisting = false,
         managedIfNoPath = false)
     executePlan(cmd).toRdd
-    table(tableName)
+    table(tableIdent)
   }
 
   /**
@@ -724,7 +726,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @since 1.3.0
    */
   def table(tableName: String): DataFrame = {
-    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
+    table(new SqlParser().parseTableIdentifier(tableName))
+  }
+
+  private def table(tableIdent: TableIdentifier): DataFrame = {
     DataFrame(this, catalog.lookupRelation(tableIdent.toSeq))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 1fc870d44b578..4df53687a0731 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -395,22 +395,22 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
   object DDLStrategy extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case CreateTableUsing(tableName, userSpecifiedSchema, provider, true, opts, false, _) =>
+      case CreateTableUsing(tableIdent, userSpecifiedSchema, provider, true, opts, false, _) =>
         ExecutedCommand(
           CreateTempTableUsing(
-            tableName, userSpecifiedSchema, provider, opts)) :: Nil
+            tableIdent, userSpecifiedSchema, provider, opts)) :: Nil
       case c: CreateTableUsing if !c.temporary =>
         sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.")
       case c: CreateTableUsing if c.temporary && c.allowExisting =>
         sys.error("allowExisting should be set to false when creating a temporary table.")
 
-      case CreateTableUsingAsSelect(tableName, provider, true, partitionsCols, mode, opts, query)
+      case CreateTableUsingAsSelect(tableIdent, provider, true, partitionsCols, mode, opts, query)
           if partitionsCols.nonEmpty =>
         sys.error("Cannot create temporary partitioned table.")
 
-      case CreateTableUsingAsSelect(tableName, provider, true, _, mode, opts, query) =>
+      case CreateTableUsingAsSelect(tableIdent, provider, true, _, mode, opts, query) =>
         val cmd = CreateTempTableUsingAsSelect(
-          tableName, provider, Array.empty[String], mode, opts, query)
+          tableIdent, provider, Array.empty[String], mode, opts, query)
         ExecutedCommand(cmd) :: Nil
       case c: CreateTableUsingAsSelect if !c.temporary =>
         sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
index 6c462fa30461b..f7a88b98c0b48 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
@@ -80,9 +80,9 @@ class DDLParser(parseQuery: String => LogicalPlan)
    */
   protected lazy val createTable: Parser[LogicalPlan] = {
     // TODO: Support database.table.
-    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident ~
+    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ tableIdentifier ~
       tableCols.? ~ (USING ~> className) ~ (OPTIONS ~> options).? ~ (AS ~> restInput).? ^^ {
-      case temp ~ allowExisting ~ tableName ~ columns ~ provider ~ opts ~ query =>
+      case temp ~ allowExisting ~ tableIdent ~ columns ~ provider ~ opts ~ query =>
         if (temp.isDefined && allowExisting.isDefined) {
           throw new DDLException(
             "a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause.")
@@ -104,7 +104,7 @@ class DDLParser(parseQuery: String => LogicalPlan)
           }
 
           val queryPlan = parseQuery(query.get)
-          CreateTableUsingAsSelect(tableName,
+          CreateTableUsingAsSelect(tableIdent,
             provider,
             temp.isDefined,
             Array.empty[String],
@@ -114,7 +114,7 @@ class DDLParser(parseQuery: String => LogicalPlan)
         } else {
           val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields)))
           CreateTableUsing(
-            tableName,
+            tableIdent,
             userSpecifiedSchema,
             provider,
             temp.isDefined,
@@ -125,6 +125,12 @@ class DDLParser(parseQuery: String => LogicalPlan)
     }
   }
 
+  // This is the same as tableIdentifier in SqlParser.
+  protected lazy val tableIdentifier: Parser[TableIdentifier] =
+    (ident <~ ".").? ~ ident ^^ {
+      case maybeDbName ~ tableName => TableIdentifier(tableName, maybeDbName)
+    }
+
   protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")"
 
   /*
@@ -132,21 +138,15 @@ class DDLParser(parseQuery: String => LogicalPlan)
    * This will display all columns of table `avroTable` includes column_name,column_type,comment
    */
   protected lazy val describeTable: Parser[LogicalPlan] =
-    (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident  ^^ {
-      case e ~ db ~ tbl =>
-        val tblIdentifier = db match {
-          case Some(dbName) =>
-            Seq(dbName, tbl)
-          case None =>
-            Seq(tbl)
-        }
-        DescribeCommand(UnresolvedRelation(tblIdentifier, None), e.isDefined)
+    (DESCRIBE ~> opt(EXTENDED)) ~ tableIdentifier ^^ {
+      case e ~ tableIdent =>
+        DescribeCommand(UnresolvedRelation(tableIdent.toSeq, None), e.isDefined)
     }
 
   protected lazy val refreshTable: Parser[LogicalPlan] =
-    REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ {
-      case maybeDatabaseName ~ tableName =>
-        RefreshTable(TableIdentifier(tableName, maybeDatabaseName))
+    REFRESH ~> TABLE ~> tableIdentifier ^^ {
+      case tableIndet =>
+        RefreshTable(tableIndet)
     }
 
   protected lazy val options: Parser[Map[String, String]] =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index ecd304c30cdee..31d6b75e13477 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -53,7 +53,7 @@ case class DescribeCommand(
   *                      If it is false, an exception will be thrown
   */
 case class CreateTableUsing(
-    tableName: String,
+    tableIdent: TableIdentifier,
     userSpecifiedSchema: Option[StructType],
     provider: String,
     temporary: Boolean,
@@ -71,8 +71,9 @@ case class CreateTableUsing(
  * can analyze the logical plan that will be used to populate the table.
  * So, [[PreWriteCheck]] can detect cases that are not allowed.
  */
+// TODO: Use TableIdentifier instead of String for tableName (SPARK-10104).
 case class CreateTableUsingAsSelect(
-    tableName: String,
+    tableIdent: TableIdentifier,
     provider: String,
     temporary: Boolean,
     partitionColumns: Array[String],
@@ -80,12 +81,10 @@ case class CreateTableUsingAsSelect(
     options: Map[String, String],
     child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = Seq.empty[Attribute]
-  // TODO: Override resolved after we support databaseName.
-  // override lazy val resolved = databaseName != None && childrenResolved
 }
 
 case class CreateTempTableUsing(
-    tableName: String,
+    tableIdent: TableIdentifier,
     userSpecifiedSchema: Option[StructType],
     provider: String,
     options: Map[String, String]) extends RunnableCommand {
@@ -93,14 +92,16 @@ case class CreateTempTableUsing(
   def run(sqlContext: SQLContext): Seq[Row] = {
     val resolved = ResolvedDataSource(
       sqlContext, userSpecifiedSchema, Array.empty[String], provider, options)
-    sqlContext.registerDataFrameAsTable(
-      DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName)
+    sqlContext.catalog.registerTable(
+      tableIdent.toSeq,
+      DataFrame(sqlContext, LogicalRelation(resolved.relation)).logicalPlan)
+
     Seq.empty[Row]
   }
 }
 
 case class CreateTempTableUsingAsSelect(
-    tableName: String,
+    tableIdent: TableIdentifier,
     provider: String,
     partitionColumns: Array[String],
     mode: SaveMode,
@@ -110,8 +111,9 @@ case class CreateTempTableUsingAsSelect(
   override def run(sqlContext: SQLContext): Seq[Row] = {
     val df = DataFrame(sqlContext, query)
     val resolved = ResolvedDataSource(sqlContext, provider, partitionColumns, mode, options, df)
-    sqlContext.registerDataFrameAsTable(
-      DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName)
+    sqlContext.catalog.registerTable(
+      tableIdent.toSeq,
+      DataFrame(sqlContext, LogicalRelation(resolved.relation)).logicalPlan)
 
     Seq.empty[Row]
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
index 9d3d35692ffcc..16c9138419fa2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
@@ -140,12 +140,12 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
-      case CreateTableUsingAsSelect(tableName, _, _, partitionColumns, mode, _, query) =>
+      case CreateTableUsingAsSelect(tableIdent, _, _, partitionColumns, mode, _, query) =>
         // When the SaveMode is Overwrite, we need to check if the table is an input table of
         // the query. If so, we will throw an AnalysisException to let users know it is not allowed.
-        if (mode == SaveMode.Overwrite && catalog.tableExists(Seq(tableName))) {
+        if (mode == SaveMode.Overwrite && catalog.tableExists(tableIdent.toSeq)) {
           // Need to remove SubQuery operator.
-          EliminateSubQueries(catalog.lookupRelation(Seq(tableName))) match {
+          EliminateSubQueries(catalog.lookupRelation(tableIdent.toSeq)) match {
             // Only do the check if the table is a data source table
             // (the relation is a BaseRelation).
             case l @ LogicalRelation(dest: BaseRelation) =>
@@ -155,7 +155,7 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
               }
               if (srcRelations.contains(dest)) {
                 failAnalysis(
-                  s"Cannot overwrite table $tableName that is also being read from.")
+                  s"Cannot overwrite table $tableIdent that is also being read from.")
               } else {
                 // OK
               }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 141468ca00d67..da50aec17c89e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1644,4 +1644,39 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       checkAnswer(sql("select count(num) from 1one"), Row(10))
     }
   }
+
+  test("specifying database name for a temporary table is not allowed") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df =
+        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+      df
+        .write
+        .format("parquet")
+        .save(path)
+
+      val message = intercept[AnalysisException] {
+        sqlContext.sql(
+          s"""
+          |CREATE TEMPORARY TABLE db.t
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      }.getMessage
+      assert(message.contains("Specifying database name or other qualifiers are not allowed"))
+
+      // If you use backticks to quote the name of a temporary table having dot in it.
+      sqlContext.sql(
+        s"""
+          |CREATE TEMPORARY TABLE `db.t`
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      checkAnswer(sqlContext.table("`db.t`"), df)
+    }
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 17762649fd70d..17cc83087fb1d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -43,7 +43,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
 import org.apache.spark.sql.SQLConf.SQLConfEntry
 import org.apache.spark.sql.SQLConf.SQLConfEntry._
-import org.apache.spark.sql.catalyst.{TableIdentifier, ParserDialect}
+import org.apache.spark.sql.catalyst.{SqlParser, TableIdentifier, ParserDialect}
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUDFs, SetCommand}
@@ -189,6 +189,10 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
     // We instantiate a HiveConf here to read in the hive-site.xml file and then pass the options
     // into the isolated client loader
     val metadataConf = new HiveConf()
+
+    val defaltWarehouseLocation = metadataConf.get("hive.metastore.warehouse.dir")
+    logInfo("defalt warehouse location is " + defaltWarehouseLocation)
+
     // `configure` goes second to override other settings.
     val allConfig = metadataConf.iterator.map(e => e.getKey -> e.getValue).toMap ++ configure
 
@@ -288,12 +292,13 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    * @since 1.3.0
    */
   def refreshTable(tableName: String): Unit = {
-    val tableIdent = TableIdentifier(tableName).withDatabase(catalog.client.currentDatabase)
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
     catalog.refreshTable(tableIdent)
   }
 
   protected[hive] def invalidateTable(tableName: String): Unit = {
-    catalog.invalidateTable(catalog.client.currentDatabase, tableName)
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
+    catalog.invalidateTable(tableIdent)
   }
 
   /**
@@ -307,7 +312,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    */
   @Experimental
   def analyze(tableName: String) {
-    val relation = EliminateSubQueries(catalog.lookupRelation(Seq(tableName)))
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
+    val relation = EliminateSubQueries(catalog.lookupRelation(tableIdent.toSeq))
 
     relation match {
       case relation: MetastoreRelation =>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 6770462bb0ad3..bbe8c1911bf86 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -174,10 +174,13 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     // it is better at here to invalidate the cache to avoid confusing waring logs from the
     // cache loader (e.g. cannot find data source provider, which is only defined for
     // data source table.).
-    invalidateTable(tableIdent.database.getOrElse(client.currentDatabase), tableIdent.table)
+    invalidateTable(tableIdent)
   }
 
-  def invalidateTable(databaseName: String, tableName: String): Unit = {
+  def invalidateTable(tableIdent: TableIdentifier): Unit = {
+    val databaseName = tableIdent.database.getOrElse(client.currentDatabase)
+    val tableName = tableIdent.table
+
     cachedDataSourceTables.invalidate(QualifiedTableName(databaseName, tableName).toLowerCase)
   }
 
@@ -187,6 +190,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
    * Creates a data source table (a table created with USING clause) in Hive's metastore.
    * Returns true when the table has been created. Otherwise, false.
    */
+  // TODO: Remove this in SPARK-10104.
   def createDataSourceTable(
       tableName: String,
       userSpecifiedSchema: Option[StructType],
@@ -203,7 +207,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       isExternal)
   }
 
-  private def createDataSourceTable(
+  def createDataSourceTable(
       tableIdent: TableIdentifier,
       userSpecifiedSchema: Option[StructType],
       partitionColumns: Array[String],
@@ -371,10 +375,16 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
   }
 
   def hiveDefaultTableFilePath(tableName: String): String = {
+    hiveDefaultTableFilePath(new SqlParser().parseTableIdentifier(tableName))
+  }
+
+  def hiveDefaultTableFilePath(tableIdent: TableIdentifier): String = {
     // Code based on: hiveWarehouse.getTablePath(currentDatabase, tableName)
+    val database = tableIdent.database.getOrElse(client.currentDatabase)
+
     new Path(
-      new Path(client.getDatabase(client.currentDatabase).location),
-      tableName.toLowerCase).toString
+      new Path(client.getDatabase(database).location),
+      tableIdent.table.toLowerCase).toString
   }
 
   def tableExists(tableIdentifier: Seq[String]): Boolean = {
@@ -635,7 +645,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
 
           val mode = if (allowExisting) SaveMode.Ignore else SaveMode.ErrorIfExists
           CreateTableUsingAsSelect(
-            desc.name,
+            TableIdentifier(desc.name),
             hive.conf.defaultDataSourceName,
             temporary = false,
             Array.empty[String],
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index cd6cd322c94ed..d38ad9127327d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -83,14 +83,16 @@ private[hive] trait HiveStrategies {
   object HiveDDLStrategy extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case CreateTableUsing(
-          tableName, userSpecifiedSchema, provider, false, opts, allowExisting, managedIfNoPath) =>
-        ExecutedCommand(
+        tableIdent, userSpecifiedSchema, provider, false, opts, allowExisting, managedIfNoPath) =>
+        val cmd =
           CreateMetastoreDataSource(
-            tableName, userSpecifiedSchema, provider, opts, allowExisting, managedIfNoPath)) :: Nil
+            tableIdent, userSpecifiedSchema, provider, opts, allowExisting, managedIfNoPath)
+        ExecutedCommand(cmd) :: Nil
 
-      case CreateTableUsingAsSelect(tableName, provider, false, partitionCols, mode, opts, query) =>
+      case CreateTableUsingAsSelect(
+        tableIdent, provider, false, partitionCols, mode, opts, query) =>
         val cmd =
-          CreateMetastoreDataSourceAsSelect(tableName, provider, partitionCols, mode, opts, query)
+          CreateMetastoreDataSourceAsSelect(tableIdent, provider, partitionCols, mode, opts, query)
         ExecutedCommand(cmd) :: Nil
 
       case _ => Nil
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index 05a78930afe3d..d1699dd536817 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.apache.hadoop.hive.metastore.MetaStoreUtils
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.{TableIdentifier, SqlParser}
 import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
@@ -120,9 +122,10 @@ case class AddFile(path: String) extends RunnableCommand {
   }
 }
 
+// TODO: Use TableIdentifier instead of String for tableName (SPARK-10104).
 private[hive]
 case class CreateMetastoreDataSource(
-    tableName: String,
+    tableIdent: TableIdentifier,
     userSpecifiedSchema: Option[StructType],
     provider: String,
     options: Map[String, String],
@@ -130,9 +133,24 @@ case class CreateMetastoreDataSource(
     managedIfNoPath: Boolean) extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
+    // Since we are saving metadata to metastore, we need to check if metastore supports
+    // the table name and database name we have for this query. MetaStoreUtils.validateName
+    // is the method used by Hive to check if a table name or a database name is valid for
+    // the metastore.
+    if (!MetaStoreUtils.validateName(tableIdent.table)) {
+      throw new AnalysisException(s"Table name ${tableIdent.table} is not a valid name for " +
+        s"metastore. Metastore only accepts table name containing characters, numbers and _.")
+    }
+    if (tableIdent.database.isDefined && !MetaStoreUtils.validateName(tableIdent.database.get)) {
+      throw new AnalysisException(s"Database name ${tableIdent.database.get} is not a valid name " +
+        s"for metastore. Metastore only accepts database name containing " +
+        s"characters, numbers and _.")
+    }
+
+    val tableName = tableIdent.unquotedString
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
 
-    if (hiveContext.catalog.tableExists(tableName :: Nil)) {
+    if (hiveContext.catalog.tableExists(tableIdent.toSeq)) {
       if (allowExisting) {
         return Seq.empty[Row]
       } else {
@@ -144,13 +162,13 @@ case class CreateMetastoreDataSource(
     val optionsWithPath =
       if (!options.contains("path") && managedIfNoPath) {
         isExternal = false
-        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableName))
+        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableIdent))
       } else {
         options
       }
 
     hiveContext.catalog.createDataSourceTable(
-      tableName,
+      tableIdent,
       userSpecifiedSchema,
       Array.empty[String],
       provider,
@@ -161,9 +179,10 @@ case class CreateMetastoreDataSource(
   }
 }
 
+// TODO: Use TableIdentifier instead of String for tableName (SPARK-10104).
 private[hive]
 case class CreateMetastoreDataSourceAsSelect(
-    tableName: String,
+    tableIdent: TableIdentifier,
     provider: String,
     partitionColumns: Array[String],
     mode: SaveMode,
@@ -171,19 +190,34 @@ case class CreateMetastoreDataSourceAsSelect(
     query: LogicalPlan) extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
+    // Since we are saving metadata to metastore, we need to check if metastore supports
+    // the table name and database name we have for this query. MetaStoreUtils.validateName
+    // is the method used by Hive to check if a table name or a database name is valid for
+    // the metastore.
+    if (!MetaStoreUtils.validateName(tableIdent.table)) {
+      throw new AnalysisException(s"Table name ${tableIdent.table} is not a valid name for " +
+        s"metastore. Metastore only accepts table name containing characters, numbers and _.")
+    }
+    if (tableIdent.database.isDefined && !MetaStoreUtils.validateName(tableIdent.database.get)) {
+      throw new AnalysisException(s"Database name ${tableIdent.database.get} is not a valid name " +
+        s"for metastore. Metastore only accepts database name containing " +
+        s"characters, numbers and _.")
+    }
+
+    val tableName = tableIdent.unquotedString
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     var createMetastoreTable = false
     var isExternal = true
     val optionsWithPath =
       if (!options.contains("path")) {
         isExternal = false
-        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableName))
+        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableIdent))
       } else {
         options
       }
 
     var existingSchema = None: Option[StructType]
-    if (sqlContext.catalog.tableExists(Seq(tableName))) {
+    if (sqlContext.catalog.tableExists(tableIdent.toSeq)) {
       // Check if we need to throw an exception or just return.
       mode match {
         case SaveMode.ErrorIfExists =>
@@ -200,7 +234,7 @@ case class CreateMetastoreDataSourceAsSelect(
           val resolved = ResolvedDataSource(
             sqlContext, Some(query.schema.asNullable), partitionColumns, provider, optionsWithPath)
           val createdRelation = LogicalRelation(resolved.relation)
-          EliminateSubQueries(sqlContext.table(tableName).logicalPlan) match {
+          EliminateSubQueries(sqlContext.catalog.lookupRelation(tableIdent.toSeq)) match {
             case l @ LogicalRelation(_: InsertableRelation | _: HadoopFsRelation) =>
               if (l.relation != createdRelation.relation) {
                 val errorDescription =
@@ -249,7 +283,7 @@ case class CreateMetastoreDataSourceAsSelect(
       // the schema of df). It is important since the nullability may be changed by the relation
       // provider (for example, see org.apache.spark.sql.parquet.DefaultSource).
       hiveContext.catalog.createDataSourceTable(
-        tableName,
+        tableIdent,
         Some(resolved.relation.schema),
         partitionColumns,
         provider,
@@ -258,7 +292,7 @@ case class CreateMetastoreDataSourceAsSelect(
     }
 
     // Refresh the cache of the table in the catalog.
-    hiveContext.refreshTable(tableName)
+    hiveContext.catalog.refreshTable(tableIdent)
     Seq.empty[Row]
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
index 1c15997ea8e6d..d3388a9429e41 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
@@ -34,7 +34,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
   override def beforeAll(): Unit = {
     // The catalog in HiveContext is a case insensitive one.
     catalog.registerTable(Seq("ListTablesSuiteTable"), df.logicalPlan)
-    catalog.registerTable(Seq("ListTablesSuiteDB", "InDBListTablesSuiteTable"), df.logicalPlan)
     sql("CREATE TABLE HiveListTablesSuiteTable (key int, value string)")
     sql("CREATE DATABASE IF NOT EXISTS ListTablesSuiteDB")
     sql("CREATE TABLE ListTablesSuiteDB.HiveInDBListTablesSuiteTable (key int, value string)")
@@ -42,7 +41,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
 
   override def afterAll(): Unit = {
     catalog.unregisterTable(Seq("ListTablesSuiteTable"))
-    catalog.unregisterTable(Seq("ListTablesSuiteDB", "InDBListTablesSuiteTable"))
     sql("DROP TABLE IF EXISTS HiveListTablesSuiteTable")
     sql("DROP TABLE IF EXISTS ListTablesSuiteDB.HiveInDBListTablesSuiteTable")
     sql("DROP DATABASE IF EXISTS ListTablesSuiteDB")
@@ -55,7 +53,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
         checkAnswer(
           allTables.filter("tableName = 'listtablessuitetable'"),
           Row("listtablessuitetable", true))
-        assert(allTables.filter("tableName = 'indblisttablessuitetable'").count() === 0)
         checkAnswer(
           allTables.filter("tableName = 'hivelisttablessuitetable'"),
           Row("hivelisttablessuitetable", false))
@@ -69,9 +66,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
         checkAnswer(
           allTables.filter("tableName = 'listtablessuitetable'"),
           Row("listtablessuitetable", true))
-        checkAnswer(
-          allTables.filter("tableName = 'indblisttablessuitetable'"),
-          Row("indblisttablessuitetable", true))
         assert(allTables.filter("tableName = 'hivelisttablessuitetable'").count() === 0)
         checkAnswer(
           allTables.filter("tableName = 'hiveindblisttablessuitetable'"),
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
index 417e8b07917cc..997c667ec0d1b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
@@ -19,14 +19,22 @@ package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.{QueryTest, SQLContext, SaveMode}
+import org.apache.spark.sql.{AnalysisException, QueryTest, SQLContext, SaveMode}
 
 class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
-  override val _sqlContext: SQLContext = TestHive
+  override val _sqlContext: HiveContext = TestHive
   private val sqlContext = _sqlContext
 
   private val df = sqlContext.range(10).coalesce(1)
 
+  private def checkTablePath(dbName: String, tableName: String): Unit = {
+    // val hiveContext = sqlContext.asInstanceOf[HiveContext]
+    val metastoreTable = sqlContext.catalog.client.getTable(dbName, tableName)
+    val expectedPath = sqlContext.catalog.client.getDatabase(dbName).location + "/" + tableName
+
+    assert(metastoreTable.serdeProperties("path") === expectedPath)
+  }
+
   test(s"saveAsTable() to non-default database - with USE - Overwrite") {
     withTempDatabase { db =>
       activateDatabase(db) {
@@ -37,6 +45,8 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df)
+
+      checkTablePath(db, "t")
     }
   }
 
@@ -45,6 +55,58 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
       df.write.mode(SaveMode.Overwrite).saveAsTable(s"$db.t")
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df)
+
+      checkTablePath(db, "t")
+    }
+  }
+
+  test(s"createExternalTable() to non-default database - with USE") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        withTempPath { dir =>
+          val path = dir.getCanonicalPath
+          df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
+
+          sqlContext.createExternalTable("t", path, "parquet")
+          assert(sqlContext.tableNames(db).contains("t"))
+          checkAnswer(sqlContext.table("t"), df)
+
+          sql(
+            s"""
+              |CREATE TABLE t1
+              |USING parquet
+              |OPTIONS (
+              |  path '$path'
+              |)
+            """.stripMargin)
+          assert(sqlContext.tableNames(db).contains("t1"))
+          checkAnswer(sqlContext.table("t1"), df)
+        }
+      }
+    }
+  }
+
+  test(s"createExternalTable() to non-default database - without USE") {
+    withTempDatabase { db =>
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
+        df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
+        sqlContext.createExternalTable(s"$db.t", path, "parquet")
+
+        assert(sqlContext.tableNames(db).contains("t"))
+        checkAnswer(sqlContext.table(s"$db.t"), df)
+
+        sql(
+          s"""
+              |CREATE TABLE $db.t1
+              |USING parquet
+              |OPTIONS (
+              |  path '$path'
+              |)
+            """.stripMargin)
+        assert(sqlContext.tableNames(db).contains("t1"))
+        checkAnswer(sqlContext.table(s"$db.t1"), df)
+      }
     }
   }
 
@@ -59,6 +121,8 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+
+      checkTablePath(db, "t")
     }
   }
 
@@ -68,6 +132,8 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
       df.write.mode(SaveMode.Append).saveAsTable(s"$db.t")
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+
+      checkTablePath(db, "t")
     }
   }
 
@@ -130,7 +196,7 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
     }
   }
 
-  test("Refreshes a table in a non-default database") {
+  test("Refreshes a table in a non-default database - with USE") {
     import org.apache.spark.sql.functions.lit
 
     withTempDatabase { db =>
@@ -151,8 +217,94 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
           sql("ALTER TABLE t ADD PARTITION (p=1)")
           sql("REFRESH TABLE t")
           checkAnswer(sqlContext.table("t"), df.withColumn("p", lit(1)))
+
+          df.write.parquet(s"$path/p=2")
+          sql("ALTER TABLE t ADD PARTITION (p=2)")
+          sqlContext.refreshTable("t")
+          checkAnswer(
+            sqlContext.table("t"),
+            df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
         }
       }
     }
   }
+
+  test("Refreshes a table in a non-default database - without USE") {
+    import org.apache.spark.sql.functions.lit
+
+    withTempDatabase { db =>
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
+
+        sql(
+          s"""CREATE EXTERNAL TABLE $db.t (id BIGINT)
+               |PARTITIONED BY (p INT)
+               |STORED AS PARQUET
+               |LOCATION '$path'
+             """.stripMargin)
+
+        checkAnswer(sqlContext.table(s"$db.t"), sqlContext.emptyDataFrame)
+
+        df.write.parquet(s"$path/p=1")
+        sql(s"ALTER TABLE $db.t ADD PARTITION (p=1)")
+        sql(s"REFRESH TABLE $db.t")
+        checkAnswer(sqlContext.table(s"$db.t"), df.withColumn("p", lit(1)))
+
+        df.write.parquet(s"$path/p=2")
+        sql(s"ALTER TABLE $db.t ADD PARTITION (p=2)")
+        sqlContext.refreshTable(s"$db.t")
+        checkAnswer(
+          sqlContext.table(s"$db.t"),
+          df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
+      }
+    }
+  }
+
+  test("invalid database name and table names") {
+    {
+      val message = intercept[AnalysisException] {
+        df.write.format("parquet").saveAsTable("`d:b`.`t:a`")
+      }.getMessage
+      assert(message.contains("is not a valid name for metastore"))
+    }
+
+    {
+      val message = intercept[AnalysisException] {
+        df.write.format("parquet").saveAsTable("`d:b`.`table`")
+      }.getMessage
+      assert(message.contains("is not a valid name for metastore"))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      {
+        val message = intercept[AnalysisException] {
+          sql(
+            s"""
+            |CREATE TABLE `d:b`.`t:a` (a int)
+            |USING parquet
+            |OPTIONS (
+            |  path '$path'
+            |)
+            """.stripMargin)
+        }.getMessage
+        assert(message.contains("is not a valid name for metastore"))
+      }
+
+      {
+        val message = intercept[AnalysisException] {
+          sql(
+            s"""
+              |CREATE TABLE `d:b`.`table` (a int)
+              |USING parquet
+              |OPTIONS (
+              |  path '$path'
+              |)
+              """.stripMargin)
+        }.getMessage
+        assert(message.contains("is not a valid name for metastore"))
+      }
+    }
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 8b8f520776e70..55ecbd5b5f21d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -1138,4 +1138,39 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
       Row(CalendarInterval.fromString(
         "interval 4 minutes 59 seconds 889 milliseconds 987 microseconds")))
   }
+
+  test("specifying database name for a temporary table is not allowed") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df =
+        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+      df
+        .write
+        .format("parquet")
+        .save(path)
+
+      val message = intercept[AnalysisException] {
+        sqlContext.sql(
+          s"""
+          |CREATE TEMPORARY TABLE db.t
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      }.getMessage
+      assert(message.contains("Specifying database name or other qualifiers are not allowed"))
+
+      // If you use backticks to quote the name of a temporary table having dot in it.
+      sqlContext.sql(
+        s"""
+          |CREATE TEMPORARY TABLE `db.t`
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      checkAnswer(sqlContext.table("`db.t`"), df)
+    }
+  }
 }

From b4f4e91c395cb69ced61d9ff1492d1b814f96828 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 20 Aug 2015 07:53:27 -0700
Subject: [PATCH 1159/1454] [SPARK-10100] [SQL] Eliminate hash table lookup if
 there is no grouping key in aggregation.

This improves performance by ~ 20 - 30% in one of my local test and should fix the performance regression from 1.4 to 1.5 on ss_max.

Author: Reynold Xin <rxin@databricks.com>

Closes #8332 from rxin/SPARK-10100.
---
 .../aggregate/TungstenAggregate.scala         |  2 +-
 .../TungstenAggregationIterator.scala         | 30 +++++++++++++------
 2 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 99f51ba5b6935..ba379d358d206 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -104,7 +104,7 @@ case class TungstenAggregate(
         } else {
           // This is a grouped aggregate and the input iterator is empty,
           // so return an empty iterator.
-          Iterator[UnsafeRow]()
+          Iterator.empty
         }
       } else {
         aggregationIterator.start(parentIterator)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index af7e0fcedbe4e..26fdbc83ef50b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -357,18 +357,30 @@ class TungstenAggregationIterator(
   // sort-based aggregation (by calling switchToSortBasedAggregation).
   private def processInputs(): Unit = {
     assert(inputIter != null, "attempted to process input when iterator was null")
-    while (!sortBased && inputIter.hasNext) {
-      val newInput = inputIter.next()
-      numInputRows += 1
-      val groupingKey = groupProjection.apply(newInput)
+    if (groupingExpressions.isEmpty) {
+      // If there is no grouping expressions, we can just reuse the same buffer over and over again.
+      // Note that it would be better to eliminate the hash map entirely in the future.
+      val groupingKey = groupProjection.apply(null)
       val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
-      if (buffer == null) {
-        // buffer == null means that we could not allocate more memory.
-        // Now, we need to spill the map and switch to sort-based aggregation.
-        switchToSortBasedAggregation(groupingKey, newInput)
-      } else {
+      while (inputIter.hasNext) {
+        val newInput = inputIter.next()
+        numInputRows += 1
         processRow(buffer, newInput)
       }
+    } else {
+      while (!sortBased && inputIter.hasNext) {
+        val newInput = inputIter.next()
+        numInputRows += 1
+        val groupingKey = groupProjection.apply(newInput)
+        val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
+        if (buffer == null) {
+          // buffer == null means that we could not allocate more memory.
+          // Now, we need to spill the map and switch to sort-based aggregation.
+          switchToSortBasedAggregation(groupingKey, newInput)
+        } else {
+          processRow(buffer, newInput)
+        }
+      }
     }
   }
 

From 52c60537a274af5414f6b0340a4bd7488ef35280 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 20 Aug 2015 10:05:31 -0700
Subject: [PATCH 1160/1454] [MINOR] [SQL] Fix sphinx warnings in PySpark SQL

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8171 from MechCoder/sql_sphinx.
---
 python/pyspark/context.py   | 8 ++++----
 python/pyspark/sql/types.py | 4 +++-
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index eb5b0bbbdac4b..1b2a52ad64114 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -302,10 +302,10 @@ def applicationId(self):
         """
         A unique identifier for the Spark application.
         Its format depends on the scheduler implementation.
-        (i.e.
-            in case of local spark app something like 'local-1433865536131'
-            in case of YARN something like 'application_1433865536131_34483'
-        )
+
+        * in case of local spark app something like 'local-1433865536131'
+        * in case of YARN something like 'application_1433865536131_34483'
+
         >>> sc.applicationId  # doctest: +ELLIPSIS
         u'local-...'
         """
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index c083bf89905bf..ed4e5b594bd61 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -467,9 +467,11 @@ def add(self, field, data_type=None, nullable=True, metadata=None):
         """
         Construct a StructType by adding new elements to it to define the schema. The method accepts
         either:
+
             a) A single parameter which is a StructField object.
             b) Between 2 and 4 parameters as (name, data_type, nullable (optional),
-             metadata(optional). The data_type parameter may be either a String or a DataType object
+               metadata(optional). The data_type parameter may be either a String or a
+               DataType object.
 
         >>> struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None)
         >>> struct2 = StructType([StructField("f1", StringType(), True),\

From 39e91fe2fd43044cc734d55625a3c03284b69f09 Mon Sep 17 00:00:00 2001
From: Alex Shkurenko <ashkurenko@enova.com>
Date: Thu, 20 Aug 2015 10:16:38 -0700
Subject: [PATCH 1161/1454] [SPARK-9982] [SPARKR] SparkR DataFrame fail to
 return data of Decimal type

Author: Alex Shkurenko <ashkurenko@enova.com>

Closes #8239 from ashkurenko/master.
---
 core/src/main/scala/org/apache/spark/api/r/SerDe.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index d5b4260bf4529..3c89f24473744 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -181,6 +181,7 @@ private[spark] object SerDe {
   // Boolean -> logical
   // Float -> double
   // Double -> double
+  // Decimal -> double
   // Long -> double
   // Array[Byte] -> raw
   // Date -> Date
@@ -219,6 +220,10 @@ private[spark] object SerDe {
         case "float" | "java.lang.Float" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Float].toDouble)
+        case "decimal" | "java.math.BigDecimal" =>
+          writeType(dos, "double")
+          val javaDecimal = value.asInstanceOf[java.math.BigDecimal]
+          writeDouble(dos, scala.math.BigDecimal(javaDecimal).toDouble)
         case "double" | "java.lang.Double" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Double])

From 85f9a61357994da5023b08b0a8a2eb09388ce7f8 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 20 Aug 2015 11:00:24 -0700
Subject: [PATCH 1162/1454] [SPARK-10136] [SQL] Fixes Parquet support for Avro
 array of primitive array

I caught SPARK-10136 while adding more test cases to `ParquetAvroCompatibilitySuite`. Actual bug fix code lies in `CatalystRowConverter.scala`.

Author: Cheng Lian <lian@databricks.com>

Closes #8341 from liancheng/spark-10136/parquet-avro-nested-primitive-array.
---
 .../parquet/CatalystReadSupport.scala         |   1 -
 .../parquet/CatalystRowConverter.scala        |  24 +-
 sql/core/src/test/avro/parquet-compat.avdl    |  19 +-
 sql/core/src/test/avro/parquet-compat.avpr    |  54 +-
 .../parquet/test/avro/AvroArrayOfArray.java   | 142 +++
 .../parquet/test/avro/AvroMapOfArray.java     | 142 +++
 .../test/avro/AvroNonNullableArrays.java      | 196 +++++
 .../test/avro/AvroOptionalPrimitives.java     | 466 ++++++++++
 .../parquet/test/avro/AvroPrimitives.java     | 461 ++++++++++
 .../parquet/test/avro/CompatibilityTest.java  |   2 +-
 .../parquet/test/avro/ParquetAvroCompat.java  | 821 +-----------------
 .../ParquetAvroCompatibilitySuite.scala       | 227 +++--
 .../parquet/ParquetCompatibilityTest.scala    |   7 +
 13 files changed, 1718 insertions(+), 844 deletions(-)
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index a4679bb2f6389..3f8353af6e2ad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -61,7 +61,6 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
          |
          |Parquet form:
          |$parquetRequestedSchema
-         |
          |Catalyst form:
          |$catalystRequestedSchema
        """.stripMargin
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index 18c5b500209e6..d2c2db51769ba 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -25,11 +25,12 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
 import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
-import org.apache.parquet.schema.OriginalType.{LIST, INT_32, UTF8}
+import org.apache.parquet.schema.OriginalType.{INT_32, LIST, UTF8}
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.DOUBLE
 import org.apache.parquet.schema.Type.Repetition
 import org.apache.parquet.schema.{GroupType, MessageType, PrimitiveType, Type}
 
+import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
@@ -145,7 +146,16 @@ private[parquet] class CatalystRowConverter(
     parquetType: GroupType,
     catalystType: StructType,
     updater: ParentContainerUpdater)
-  extends CatalystGroupConverter(updater) {
+  extends CatalystGroupConverter(updater) with Logging {
+
+  logDebug(
+    s"""Building row converter for the following schema:
+       |
+       |Parquet form:
+       |$parquetType
+       |Catalyst form:
+       |${catalystType.prettyJson}
+     """.stripMargin)
 
   /**
    * Updater used together with field converters within a [[CatalystRowConverter]].  It propagates
@@ -464,9 +474,15 @@ private[parquet] class CatalystRowConverter(
 
       override def getConverter(fieldIndex: Int): Converter = converter
 
-      override def end(): Unit = currentArray += currentElement
+      override def end(): Unit = {
+        converter.updater.end()
+        currentArray += currentElement
+      }
 
-      override def start(): Unit = currentElement = null
+      override def start(): Unit = {
+        converter.updater.start()
+        currentElement = null
+      }
     }
   }
 
diff --git a/sql/core/src/test/avro/parquet-compat.avdl b/sql/core/src/test/avro/parquet-compat.avdl
index 8070d0a9170a3..c5eb5b5164cf4 100644
--- a/sql/core/src/test/avro/parquet-compat.avdl
+++ b/sql/core/src/test/avro/parquet-compat.avdl
@@ -34,7 +34,7 @@ protocol CompatibilityTest {
         string nested_string_column;
     }
 
-    record ParquetAvroCompat {
+    record AvroPrimitives {
         boolean bool_column;
         int int_column;
         long long_column;
@@ -42,7 +42,9 @@ protocol CompatibilityTest {
         double double_column;
         bytes binary_column;
         string string_column;
+    }
 
+    record AvroOptionalPrimitives {
         union { null, boolean } maybe_bool_column;
         union { null, int } maybe_int_column;
         union { null, long } maybe_long_column;
@@ -50,7 +52,22 @@ protocol CompatibilityTest {
         union { null, double } maybe_double_column;
         union { null, bytes } maybe_binary_column;
         union { null, string } maybe_string_column;
+    }
+
+    record AvroNonNullableArrays {
+        array<string> strings_column;
+        union { null, array<int> } maybe_ints_column;
+    }
 
+    record AvroArrayOfArray {
+        array<array<int>> int_arrays_column;
+    }
+
+    record AvroMapOfArray {
+        map<array<int>> string_to_ints_column;
+    }
+
+    record ParquetAvroCompat {
         array<string> strings_column;
         map<int> string_to_int_column;
         map<array<Nested>> complex_column;
diff --git a/sql/core/src/test/avro/parquet-compat.avpr b/sql/core/src/test/avro/parquet-compat.avpr
index 060391765034b..9ad315b74fb41 100644
--- a/sql/core/src/test/avro/parquet-compat.avpr
+++ b/sql/core/src/test/avro/parquet-compat.avpr
@@ -27,7 +27,7 @@
     } ]
   }, {
     "type" : "record",
-    "name" : "ParquetAvroCompat",
+    "name" : "AvroPrimitives",
     "fields" : [ {
       "name" : "bool_column",
       "type" : "boolean"
@@ -49,7 +49,11 @@
     }, {
       "name" : "string_column",
       "type" : "string"
-    }, {
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroOptionalPrimitives",
+    "fields" : [ {
       "name" : "maybe_bool_column",
       "type" : [ "null", "boolean" ]
     }, {
@@ -70,7 +74,53 @@
     }, {
       "name" : "maybe_string_column",
       "type" : [ "null", "string" ]
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroNonNullableArrays",
+    "fields" : [ {
+      "name" : "strings_column",
+      "type" : {
+        "type" : "array",
+        "items" : "string"
+      }
     }, {
+      "name" : "maybe_ints_column",
+      "type" : [ "null", {
+        "type" : "array",
+        "items" : "int"
+      } ]
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroArrayOfArray",
+    "fields" : [ {
+      "name" : "int_arrays_column",
+      "type" : {
+        "type" : "array",
+        "items" : {
+          "type" : "array",
+          "items" : "int"
+        }
+      }
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroMapOfArray",
+    "fields" : [ {
+      "name" : "string_to_ints_column",
+      "type" : {
+        "type" : "map",
+        "values" : {
+          "type" : "array",
+          "items" : "int"
+        }
+      }
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "ParquetAvroCompat",
+    "fields" : [ {
       "name" : "strings_column",
       "type" : {
         "type" : "array",
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java
new file mode 100644
index 0000000000000..ee327827903e5
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java
@@ -0,0 +1,142 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroArrayOfArray extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroArrayOfArray\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"int_arrays_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"array\",\"items\":\"int\"}}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.util.List<java.util.List<java.lang.Integer>> int_arrays_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroArrayOfArray() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroArrayOfArray(java.util.List<java.util.List<java.lang.Integer>> int_arrays_column) {
+    this.int_arrays_column = int_arrays_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return int_arrays_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: int_arrays_column = (java.util.List<java.util.List<java.lang.Integer>>)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'int_arrays_column' field.
+   */
+  public java.util.List<java.util.List<java.lang.Integer>> getIntArraysColumn() {
+    return int_arrays_column;
+  }
+
+  /**
+   * Sets the value of the 'int_arrays_column' field.
+   * @param value the value to set.
+   */
+  public void setIntArraysColumn(java.util.List<java.util.List<java.lang.Integer>> value) {
+    this.int_arrays_column = value;
+  }
+
+  /** Creates a new AvroArrayOfArray RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder();
+  }
+  
+  /** Creates a new AvroArrayOfArray RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder(other);
+  }
+  
+  /** Creates a new AvroArrayOfArray RecordBuilder by copying an existing AvroArrayOfArray instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroArrayOfArray instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroArrayOfArray>
+    implements org.apache.avro.data.RecordBuilder<AvroArrayOfArray> {
+
+    private java.util.List<java.util.List<java.lang.Integer>> int_arrays_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.int_arrays_column)) {
+        this.int_arrays_column = data().deepCopy(fields()[0].schema(), other.int_arrays_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroArrayOfArray instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.SCHEMA$);
+      if (isValidValue(fields()[0], other.int_arrays_column)) {
+        this.int_arrays_column = data().deepCopy(fields()[0].schema(), other.int_arrays_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+
+    /** Gets the value of the 'int_arrays_column' field */
+    public java.util.List<java.util.List<java.lang.Integer>> getIntArraysColumn() {
+      return int_arrays_column;
+    }
+    
+    /** Sets the value of the 'int_arrays_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder setIntArraysColumn(java.util.List<java.util.List<java.lang.Integer>> value) {
+      validate(fields()[0], value);
+      this.int_arrays_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'int_arrays_column' field has been set */
+    public boolean hasIntArraysColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'int_arrays_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder clearIntArraysColumn() {
+      int_arrays_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    @Override
+    public AvroArrayOfArray build() {
+      try {
+        AvroArrayOfArray record = new AvroArrayOfArray();
+        record.int_arrays_column = fieldSetFlags()[0] ? this.int_arrays_column : (java.util.List<java.util.List<java.lang.Integer>>) defaultValue(fields()[0]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java
new file mode 100644
index 0000000000000..727f6a7bf733e
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java
@@ -0,0 +1,142 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroMapOfArray extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroMapOfArray\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"string_to_ints_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"int\"},\"avro.java.string\":\"String\"}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> string_to_ints_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroMapOfArray() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroMapOfArray(java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> string_to_ints_column) {
+    this.string_to_ints_column = string_to_ints_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return string_to_ints_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: string_to_ints_column = (java.util.Map<java.lang.String,java.util.List<java.lang.Integer>>)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'string_to_ints_column' field.
+   */
+  public java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> getStringToIntsColumn() {
+    return string_to_ints_column;
+  }
+
+  /**
+   * Sets the value of the 'string_to_ints_column' field.
+   * @param value the value to set.
+   */
+  public void setStringToIntsColumn(java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> value) {
+    this.string_to_ints_column = value;
+  }
+
+  /** Creates a new AvroMapOfArray RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder();
+  }
+  
+  /** Creates a new AvroMapOfArray RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder(other);
+  }
+  
+  /** Creates a new AvroMapOfArray RecordBuilder by copying an existing AvroMapOfArray instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroMapOfArray instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroMapOfArray>
+    implements org.apache.avro.data.RecordBuilder<AvroMapOfArray> {
+
+    private java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> string_to_ints_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.string_to_ints_column)) {
+        this.string_to_ints_column = data().deepCopy(fields()[0].schema(), other.string_to_ints_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroMapOfArray instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.SCHEMA$);
+      if (isValidValue(fields()[0], other.string_to_ints_column)) {
+        this.string_to_ints_column = data().deepCopy(fields()[0].schema(), other.string_to_ints_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+
+    /** Gets the value of the 'string_to_ints_column' field */
+    public java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> getStringToIntsColumn() {
+      return string_to_ints_column;
+    }
+    
+    /** Sets the value of the 'string_to_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder setStringToIntsColumn(java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> value) {
+      validate(fields()[0], value);
+      this.string_to_ints_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'string_to_ints_column' field has been set */
+    public boolean hasStringToIntsColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'string_to_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder clearStringToIntsColumn() {
+      string_to_ints_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    @Override
+    public AvroMapOfArray build() {
+      try {
+        AvroMapOfArray record = new AvroMapOfArray();
+        record.string_to_ints_column = fieldSetFlags()[0] ? this.string_to_ints_column : (java.util.Map<java.lang.String,java.util.List<java.lang.Integer>>) defaultValue(fields()[0]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java
new file mode 100644
index 0000000000000..934793f42f9c9
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java
@@ -0,0 +1,196 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroNonNullableArrays extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroNonNullableArrays\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"maybe_ints_column\",\"type\":[\"null\",{\"type\":\"array\",\"items\":\"int\"}]}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.util.List<java.lang.String> strings_column;
+  @Deprecated public java.util.List<java.lang.Integer> maybe_ints_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroNonNullableArrays() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroNonNullableArrays(java.util.List<java.lang.String> strings_column, java.util.List<java.lang.Integer> maybe_ints_column) {
+    this.strings_column = strings_column;
+    this.maybe_ints_column = maybe_ints_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return strings_column;
+    case 1: return maybe_ints_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: strings_column = (java.util.List<java.lang.String>)value$; break;
+    case 1: maybe_ints_column = (java.util.List<java.lang.Integer>)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'strings_column' field.
+   */
+  public java.util.List<java.lang.String> getStringsColumn() {
+    return strings_column;
+  }
+
+  /**
+   * Sets the value of the 'strings_column' field.
+   * @param value the value to set.
+   */
+  public void setStringsColumn(java.util.List<java.lang.String> value) {
+    this.strings_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_ints_column' field.
+   */
+  public java.util.List<java.lang.Integer> getMaybeIntsColumn() {
+    return maybe_ints_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_ints_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeIntsColumn(java.util.List<java.lang.Integer> value) {
+    this.maybe_ints_column = value;
+  }
+
+  /** Creates a new AvroNonNullableArrays RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder();
+  }
+  
+  /** Creates a new AvroNonNullableArrays RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder(other);
+  }
+  
+  /** Creates a new AvroNonNullableArrays RecordBuilder by copying an existing AvroNonNullableArrays instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroNonNullableArrays instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroNonNullableArrays>
+    implements org.apache.avro.data.RecordBuilder<AvroNonNullableArrays> {
+
+    private java.util.List<java.lang.String> strings_column;
+    private java.util.List<java.lang.Integer> maybe_ints_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_ints_column)) {
+        this.maybe_ints_column = data().deepCopy(fields()[1].schema(), other.maybe_ints_column);
+        fieldSetFlags()[1] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroNonNullableArrays instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.SCHEMA$);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_ints_column)) {
+        this.maybe_ints_column = data().deepCopy(fields()[1].schema(), other.maybe_ints_column);
+        fieldSetFlags()[1] = true;
+      }
+    }
+
+    /** Gets the value of the 'strings_column' field */
+    public java.util.List<java.lang.String> getStringsColumn() {
+      return strings_column;
+    }
+    
+    /** Sets the value of the 'strings_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder setStringsColumn(java.util.List<java.lang.String> value) {
+      validate(fields()[0], value);
+      this.strings_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'strings_column' field has been set */
+    public boolean hasStringsColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'strings_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder clearStringsColumn() {
+      strings_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_ints_column' field */
+    public java.util.List<java.lang.Integer> getMaybeIntsColumn() {
+      return maybe_ints_column;
+    }
+    
+    /** Sets the value of the 'maybe_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder setMaybeIntsColumn(java.util.List<java.lang.Integer> value) {
+      validate(fields()[1], value);
+      this.maybe_ints_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_ints_column' field has been set */
+    public boolean hasMaybeIntsColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'maybe_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder clearMaybeIntsColumn() {
+      maybe_ints_column = null;
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    @Override
+    public AvroNonNullableArrays build() {
+      try {
+        AvroNonNullableArrays record = new AvroNonNullableArrays();
+        record.strings_column = fieldSetFlags()[0] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[0]);
+        record.maybe_ints_column = fieldSetFlags()[1] ? this.maybe_ints_column : (java.util.List<java.lang.Integer>) defaultValue(fields()[1]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java
new file mode 100644
index 0000000000000..e4d1ead8dd15f
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java
@@ -0,0 +1,466 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroOptionalPrimitives extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroOptionalPrimitives\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.lang.Boolean maybe_bool_column;
+  @Deprecated public java.lang.Integer maybe_int_column;
+  @Deprecated public java.lang.Long maybe_long_column;
+  @Deprecated public java.lang.Float maybe_float_column;
+  @Deprecated public java.lang.Double maybe_double_column;
+  @Deprecated public java.nio.ByteBuffer maybe_binary_column;
+  @Deprecated public java.lang.String maybe_string_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroOptionalPrimitives() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroOptionalPrimitives(java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column) {
+    this.maybe_bool_column = maybe_bool_column;
+    this.maybe_int_column = maybe_int_column;
+    this.maybe_long_column = maybe_long_column;
+    this.maybe_float_column = maybe_float_column;
+    this.maybe_double_column = maybe_double_column;
+    this.maybe_binary_column = maybe_binary_column;
+    this.maybe_string_column = maybe_string_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return maybe_bool_column;
+    case 1: return maybe_int_column;
+    case 2: return maybe_long_column;
+    case 3: return maybe_float_column;
+    case 4: return maybe_double_column;
+    case 5: return maybe_binary_column;
+    case 6: return maybe_string_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: maybe_bool_column = (java.lang.Boolean)value$; break;
+    case 1: maybe_int_column = (java.lang.Integer)value$; break;
+    case 2: maybe_long_column = (java.lang.Long)value$; break;
+    case 3: maybe_float_column = (java.lang.Float)value$; break;
+    case 4: maybe_double_column = (java.lang.Double)value$; break;
+    case 5: maybe_binary_column = (java.nio.ByteBuffer)value$; break;
+    case 6: maybe_string_column = (java.lang.String)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'maybe_bool_column' field.
+   */
+  public java.lang.Boolean getMaybeBoolColumn() {
+    return maybe_bool_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_bool_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeBoolColumn(java.lang.Boolean value) {
+    this.maybe_bool_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_int_column' field.
+   */
+  public java.lang.Integer getMaybeIntColumn() {
+    return maybe_int_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_int_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeIntColumn(java.lang.Integer value) {
+    this.maybe_int_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_long_column' field.
+   */
+  public java.lang.Long getMaybeLongColumn() {
+    return maybe_long_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_long_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeLongColumn(java.lang.Long value) {
+    this.maybe_long_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_float_column' field.
+   */
+  public java.lang.Float getMaybeFloatColumn() {
+    return maybe_float_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_float_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeFloatColumn(java.lang.Float value) {
+    this.maybe_float_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_double_column' field.
+   */
+  public java.lang.Double getMaybeDoubleColumn() {
+    return maybe_double_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_double_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeDoubleColumn(java.lang.Double value) {
+    this.maybe_double_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_binary_column' field.
+   */
+  public java.nio.ByteBuffer getMaybeBinaryColumn() {
+    return maybe_binary_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_binary_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+    this.maybe_binary_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_string_column' field.
+   */
+  public java.lang.String getMaybeStringColumn() {
+    return maybe_string_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_string_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeStringColumn(java.lang.String value) {
+    this.maybe_string_column = value;
+  }
+
+  /** Creates a new AvroOptionalPrimitives RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder();
+  }
+  
+  /** Creates a new AvroOptionalPrimitives RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder(other);
+  }
+  
+  /** Creates a new AvroOptionalPrimitives RecordBuilder by copying an existing AvroOptionalPrimitives instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroOptionalPrimitives instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroOptionalPrimitives>
+    implements org.apache.avro.data.RecordBuilder<AvroOptionalPrimitives> {
+
+    private java.lang.Boolean maybe_bool_column;
+    private java.lang.Integer maybe_int_column;
+    private java.lang.Long maybe_long_column;
+    private java.lang.Float maybe_float_column;
+    private java.lang.Double maybe_double_column;
+    private java.nio.ByteBuffer maybe_binary_column;
+    private java.lang.String maybe_string_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.maybe_bool_column)) {
+        this.maybe_bool_column = data().deepCopy(fields()[0].schema(), other.maybe_bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_int_column)) {
+        this.maybe_int_column = data().deepCopy(fields()[1].schema(), other.maybe_int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.maybe_long_column)) {
+        this.maybe_long_column = data().deepCopy(fields()[2].schema(), other.maybe_long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.maybe_float_column)) {
+        this.maybe_float_column = data().deepCopy(fields()[3].schema(), other.maybe_float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.maybe_double_column)) {
+        this.maybe_double_column = data().deepCopy(fields()[4].schema(), other.maybe_double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.maybe_binary_column)) {
+        this.maybe_binary_column = data().deepCopy(fields()[5].schema(), other.maybe_binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.maybe_string_column)) {
+        this.maybe_string_column = data().deepCopy(fields()[6].schema(), other.maybe_string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroOptionalPrimitives instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.SCHEMA$);
+      if (isValidValue(fields()[0], other.maybe_bool_column)) {
+        this.maybe_bool_column = data().deepCopy(fields()[0].schema(), other.maybe_bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_int_column)) {
+        this.maybe_int_column = data().deepCopy(fields()[1].schema(), other.maybe_int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.maybe_long_column)) {
+        this.maybe_long_column = data().deepCopy(fields()[2].schema(), other.maybe_long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.maybe_float_column)) {
+        this.maybe_float_column = data().deepCopy(fields()[3].schema(), other.maybe_float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.maybe_double_column)) {
+        this.maybe_double_column = data().deepCopy(fields()[4].schema(), other.maybe_double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.maybe_binary_column)) {
+        this.maybe_binary_column = data().deepCopy(fields()[5].schema(), other.maybe_binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.maybe_string_column)) {
+        this.maybe_string_column = data().deepCopy(fields()[6].schema(), other.maybe_string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+
+    /** Gets the value of the 'maybe_bool_column' field */
+    public java.lang.Boolean getMaybeBoolColumn() {
+      return maybe_bool_column;
+    }
+    
+    /** Sets the value of the 'maybe_bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeBoolColumn(java.lang.Boolean value) {
+      validate(fields()[0], value);
+      this.maybe_bool_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_bool_column' field has been set */
+    public boolean hasMaybeBoolColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'maybe_bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeBoolColumn() {
+      maybe_bool_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_int_column' field */
+    public java.lang.Integer getMaybeIntColumn() {
+      return maybe_int_column;
+    }
+    
+    /** Sets the value of the 'maybe_int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeIntColumn(java.lang.Integer value) {
+      validate(fields()[1], value);
+      this.maybe_int_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_int_column' field has been set */
+    public boolean hasMaybeIntColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'maybe_int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeIntColumn() {
+      maybe_int_column = null;
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_long_column' field */
+    public java.lang.Long getMaybeLongColumn() {
+      return maybe_long_column;
+    }
+    
+    /** Sets the value of the 'maybe_long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeLongColumn(java.lang.Long value) {
+      validate(fields()[2], value);
+      this.maybe_long_column = value;
+      fieldSetFlags()[2] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_long_column' field has been set */
+    public boolean hasMaybeLongColumn() {
+      return fieldSetFlags()[2];
+    }
+    
+    /** Clears the value of the 'maybe_long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeLongColumn() {
+      maybe_long_column = null;
+      fieldSetFlags()[2] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_float_column' field */
+    public java.lang.Float getMaybeFloatColumn() {
+      return maybe_float_column;
+    }
+    
+    /** Sets the value of the 'maybe_float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeFloatColumn(java.lang.Float value) {
+      validate(fields()[3], value);
+      this.maybe_float_column = value;
+      fieldSetFlags()[3] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_float_column' field has been set */
+    public boolean hasMaybeFloatColumn() {
+      return fieldSetFlags()[3];
+    }
+    
+    /** Clears the value of the 'maybe_float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeFloatColumn() {
+      maybe_float_column = null;
+      fieldSetFlags()[3] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_double_column' field */
+    public java.lang.Double getMaybeDoubleColumn() {
+      return maybe_double_column;
+    }
+    
+    /** Sets the value of the 'maybe_double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeDoubleColumn(java.lang.Double value) {
+      validate(fields()[4], value);
+      this.maybe_double_column = value;
+      fieldSetFlags()[4] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_double_column' field has been set */
+    public boolean hasMaybeDoubleColumn() {
+      return fieldSetFlags()[4];
+    }
+    
+    /** Clears the value of the 'maybe_double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeDoubleColumn() {
+      maybe_double_column = null;
+      fieldSetFlags()[4] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_binary_column' field */
+    public java.nio.ByteBuffer getMaybeBinaryColumn() {
+      return maybe_binary_column;
+    }
+    
+    /** Sets the value of the 'maybe_binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+      validate(fields()[5], value);
+      this.maybe_binary_column = value;
+      fieldSetFlags()[5] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_binary_column' field has been set */
+    public boolean hasMaybeBinaryColumn() {
+      return fieldSetFlags()[5];
+    }
+    
+    /** Clears the value of the 'maybe_binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeBinaryColumn() {
+      maybe_binary_column = null;
+      fieldSetFlags()[5] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_string_column' field */
+    public java.lang.String getMaybeStringColumn() {
+      return maybe_string_column;
+    }
+    
+    /** Sets the value of the 'maybe_string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeStringColumn(java.lang.String value) {
+      validate(fields()[6], value);
+      this.maybe_string_column = value;
+      fieldSetFlags()[6] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_string_column' field has been set */
+    public boolean hasMaybeStringColumn() {
+      return fieldSetFlags()[6];
+    }
+    
+    /** Clears the value of the 'maybe_string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeStringColumn() {
+      maybe_string_column = null;
+      fieldSetFlags()[6] = false;
+      return this;
+    }
+
+    @Override
+    public AvroOptionalPrimitives build() {
+      try {
+        AvroOptionalPrimitives record = new AvroOptionalPrimitives();
+        record.maybe_bool_column = fieldSetFlags()[0] ? this.maybe_bool_column : (java.lang.Boolean) defaultValue(fields()[0]);
+        record.maybe_int_column = fieldSetFlags()[1] ? this.maybe_int_column : (java.lang.Integer) defaultValue(fields()[1]);
+        record.maybe_long_column = fieldSetFlags()[2] ? this.maybe_long_column : (java.lang.Long) defaultValue(fields()[2]);
+        record.maybe_float_column = fieldSetFlags()[3] ? this.maybe_float_column : (java.lang.Float) defaultValue(fields()[3]);
+        record.maybe_double_column = fieldSetFlags()[4] ? this.maybe_double_column : (java.lang.Double) defaultValue(fields()[4]);
+        record.maybe_binary_column = fieldSetFlags()[5] ? this.maybe_binary_column : (java.nio.ByteBuffer) defaultValue(fields()[5]);
+        record.maybe_string_column = fieldSetFlags()[6] ? this.maybe_string_column : (java.lang.String) defaultValue(fields()[6]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java
new file mode 100644
index 0000000000000..1c2afed16781e
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java
@@ -0,0 +1,461 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroPrimitives extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroPrimitives\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public boolean bool_column;
+  @Deprecated public int int_column;
+  @Deprecated public long long_column;
+  @Deprecated public float float_column;
+  @Deprecated public double double_column;
+  @Deprecated public java.nio.ByteBuffer binary_column;
+  @Deprecated public java.lang.String string_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroPrimitives() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroPrimitives(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column) {
+    this.bool_column = bool_column;
+    this.int_column = int_column;
+    this.long_column = long_column;
+    this.float_column = float_column;
+    this.double_column = double_column;
+    this.binary_column = binary_column;
+    this.string_column = string_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return bool_column;
+    case 1: return int_column;
+    case 2: return long_column;
+    case 3: return float_column;
+    case 4: return double_column;
+    case 5: return binary_column;
+    case 6: return string_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: bool_column = (java.lang.Boolean)value$; break;
+    case 1: int_column = (java.lang.Integer)value$; break;
+    case 2: long_column = (java.lang.Long)value$; break;
+    case 3: float_column = (java.lang.Float)value$; break;
+    case 4: double_column = (java.lang.Double)value$; break;
+    case 5: binary_column = (java.nio.ByteBuffer)value$; break;
+    case 6: string_column = (java.lang.String)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'bool_column' field.
+   */
+  public java.lang.Boolean getBoolColumn() {
+    return bool_column;
+  }
+
+  /**
+   * Sets the value of the 'bool_column' field.
+   * @param value the value to set.
+   */
+  public void setBoolColumn(java.lang.Boolean value) {
+    this.bool_column = value;
+  }
+
+  /**
+   * Gets the value of the 'int_column' field.
+   */
+  public java.lang.Integer getIntColumn() {
+    return int_column;
+  }
+
+  /**
+   * Sets the value of the 'int_column' field.
+   * @param value the value to set.
+   */
+  public void setIntColumn(java.lang.Integer value) {
+    this.int_column = value;
+  }
+
+  /**
+   * Gets the value of the 'long_column' field.
+   */
+  public java.lang.Long getLongColumn() {
+    return long_column;
+  }
+
+  /**
+   * Sets the value of the 'long_column' field.
+   * @param value the value to set.
+   */
+  public void setLongColumn(java.lang.Long value) {
+    this.long_column = value;
+  }
+
+  /**
+   * Gets the value of the 'float_column' field.
+   */
+  public java.lang.Float getFloatColumn() {
+    return float_column;
+  }
+
+  /**
+   * Sets the value of the 'float_column' field.
+   * @param value the value to set.
+   */
+  public void setFloatColumn(java.lang.Float value) {
+    this.float_column = value;
+  }
+
+  /**
+   * Gets the value of the 'double_column' field.
+   */
+  public java.lang.Double getDoubleColumn() {
+    return double_column;
+  }
+
+  /**
+   * Sets the value of the 'double_column' field.
+   * @param value the value to set.
+   */
+  public void setDoubleColumn(java.lang.Double value) {
+    this.double_column = value;
+  }
+
+  /**
+   * Gets the value of the 'binary_column' field.
+   */
+  public java.nio.ByteBuffer getBinaryColumn() {
+    return binary_column;
+  }
+
+  /**
+   * Sets the value of the 'binary_column' field.
+   * @param value the value to set.
+   */
+  public void setBinaryColumn(java.nio.ByteBuffer value) {
+    this.binary_column = value;
+  }
+
+  /**
+   * Gets the value of the 'string_column' field.
+   */
+  public java.lang.String getStringColumn() {
+    return string_column;
+  }
+
+  /**
+   * Sets the value of the 'string_column' field.
+   * @param value the value to set.
+   */
+  public void setStringColumn(java.lang.String value) {
+    this.string_column = value;
+  }
+
+  /** Creates a new AvroPrimitives RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder();
+  }
+  
+  /** Creates a new AvroPrimitives RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder(other);
+  }
+  
+  /** Creates a new AvroPrimitives RecordBuilder by copying an existing AvroPrimitives instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroPrimitives instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroPrimitives>
+    implements org.apache.avro.data.RecordBuilder<AvroPrimitives> {
+
+    private boolean bool_column;
+    private int int_column;
+    private long long_column;
+    private float float_column;
+    private double double_column;
+    private java.nio.ByteBuffer binary_column;
+    private java.lang.String string_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.bool_column)) {
+        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.int_column)) {
+        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.long_column)) {
+        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.float_column)) {
+        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.double_column)) {
+        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.binary_column)) {
+        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.string_column)) {
+        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroPrimitives instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.SCHEMA$);
+      if (isValidValue(fields()[0], other.bool_column)) {
+        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.int_column)) {
+        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.long_column)) {
+        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.float_column)) {
+        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.double_column)) {
+        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.binary_column)) {
+        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.string_column)) {
+        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+
+    /** Gets the value of the 'bool_column' field */
+    public java.lang.Boolean getBoolColumn() {
+      return bool_column;
+    }
+    
+    /** Sets the value of the 'bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setBoolColumn(boolean value) {
+      validate(fields()[0], value);
+      this.bool_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'bool_column' field has been set */
+    public boolean hasBoolColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearBoolColumn() {
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'int_column' field */
+    public java.lang.Integer getIntColumn() {
+      return int_column;
+    }
+    
+    /** Sets the value of the 'int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setIntColumn(int value) {
+      validate(fields()[1], value);
+      this.int_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'int_column' field has been set */
+    public boolean hasIntColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearIntColumn() {
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'long_column' field */
+    public java.lang.Long getLongColumn() {
+      return long_column;
+    }
+    
+    /** Sets the value of the 'long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setLongColumn(long value) {
+      validate(fields()[2], value);
+      this.long_column = value;
+      fieldSetFlags()[2] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'long_column' field has been set */
+    public boolean hasLongColumn() {
+      return fieldSetFlags()[2];
+    }
+    
+    /** Clears the value of the 'long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearLongColumn() {
+      fieldSetFlags()[2] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'float_column' field */
+    public java.lang.Float getFloatColumn() {
+      return float_column;
+    }
+    
+    /** Sets the value of the 'float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setFloatColumn(float value) {
+      validate(fields()[3], value);
+      this.float_column = value;
+      fieldSetFlags()[3] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'float_column' field has been set */
+    public boolean hasFloatColumn() {
+      return fieldSetFlags()[3];
+    }
+    
+    /** Clears the value of the 'float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearFloatColumn() {
+      fieldSetFlags()[3] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'double_column' field */
+    public java.lang.Double getDoubleColumn() {
+      return double_column;
+    }
+    
+    /** Sets the value of the 'double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setDoubleColumn(double value) {
+      validate(fields()[4], value);
+      this.double_column = value;
+      fieldSetFlags()[4] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'double_column' field has been set */
+    public boolean hasDoubleColumn() {
+      return fieldSetFlags()[4];
+    }
+    
+    /** Clears the value of the 'double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearDoubleColumn() {
+      fieldSetFlags()[4] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'binary_column' field */
+    public java.nio.ByteBuffer getBinaryColumn() {
+      return binary_column;
+    }
+    
+    /** Sets the value of the 'binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setBinaryColumn(java.nio.ByteBuffer value) {
+      validate(fields()[5], value);
+      this.binary_column = value;
+      fieldSetFlags()[5] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'binary_column' field has been set */
+    public boolean hasBinaryColumn() {
+      return fieldSetFlags()[5];
+    }
+    
+    /** Clears the value of the 'binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearBinaryColumn() {
+      binary_column = null;
+      fieldSetFlags()[5] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'string_column' field */
+    public java.lang.String getStringColumn() {
+      return string_column;
+    }
+    
+    /** Sets the value of the 'string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setStringColumn(java.lang.String value) {
+      validate(fields()[6], value);
+      this.string_column = value;
+      fieldSetFlags()[6] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'string_column' field has been set */
+    public boolean hasStringColumn() {
+      return fieldSetFlags()[6];
+    }
+    
+    /** Clears the value of the 'string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearStringColumn() {
+      string_column = null;
+      fieldSetFlags()[6] = false;
+      return this;
+    }
+
+    @Override
+    public AvroPrimitives build() {
+      try {
+        AvroPrimitives record = new AvroPrimitives();
+        record.bool_column = fieldSetFlags()[0] ? this.bool_column : (java.lang.Boolean) defaultValue(fields()[0]);
+        record.int_column = fieldSetFlags()[1] ? this.int_column : (java.lang.Integer) defaultValue(fields()[1]);
+        record.long_column = fieldSetFlags()[2] ? this.long_column : (java.lang.Long) defaultValue(fields()[2]);
+        record.float_column = fieldSetFlags()[3] ? this.float_column : (java.lang.Float) defaultValue(fields()[3]);
+        record.double_column = fieldSetFlags()[4] ? this.double_column : (java.lang.Double) defaultValue(fields()[4]);
+        record.binary_column = fieldSetFlags()[5] ? this.binary_column : (java.nio.ByteBuffer) defaultValue(fields()[5]);
+        record.string_column = fieldSetFlags()[6] ? this.string_column : (java.lang.String) defaultValue(fields()[6]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
index 2368323cb36b9..28fdc1dfb911c 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
@@ -8,7 +8,7 @@
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public interface CompatibilityTest {
-  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"types\":[{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]},{\"type\":\"record\",\"name\":\"ParquetEnum\",\"fields\":[{\"name\":\"suit\",\"type\":\"Suit\"}]},{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
+  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"types\":[{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]},{\"type\":\"record\",\"name\":\"ParquetEnum\",\"fields\":[{\"name\":\"suit\",\"type\":\"Suit\"}]},{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"AvroPrimitives\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"AvroOptionalPrimitives\",\"fields\":[{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]}]},{\"type\":\"record\",\"name\":\"AvroNonNullableArrays\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"maybe_ints_column\",\"type\":[\"null\",{\"type\":\"array\",\"items\":\"int\"}]}]},{\"type\":\"record\",\"name\":\"AvroArrayOfArray\",\"fields\":[{\"name\":\"int_arrays_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"array\",\"items\":\"int\"}}}]},{\"type\":\"record\",\"name\":\"AvroMapOfArray\",\"fields\":[{\"name\":\"string_to_ints_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"int\"},\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
 
   @SuppressWarnings("all")
   public interface Callback extends CompatibilityTest {
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
index 681cacbd12c7c..ef12d193f916c 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
@@ -7,22 +7,8 @@
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
-  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
   public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
-  @Deprecated public boolean bool_column;
-  @Deprecated public int int_column;
-  @Deprecated public long long_column;
-  @Deprecated public float float_column;
-  @Deprecated public double double_column;
-  @Deprecated public java.nio.ByteBuffer binary_column;
-  @Deprecated public java.lang.String string_column;
-  @Deprecated public java.lang.Boolean maybe_bool_column;
-  @Deprecated public java.lang.Integer maybe_int_column;
-  @Deprecated public java.lang.Long maybe_long_column;
-  @Deprecated public java.lang.Float maybe_float_column;
-  @Deprecated public java.lang.Double maybe_double_column;
-  @Deprecated public java.nio.ByteBuffer maybe_binary_column;
-  @Deprecated public java.lang.String maybe_string_column;
   @Deprecated public java.util.List<java.lang.String> strings_column;
   @Deprecated public java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
   @Deprecated public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
@@ -37,21 +23,7 @@ public ParquetAvroCompat() {}
   /**
    * All-args constructor.
    */
-  public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column, java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column, java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column) {
-    this.bool_column = bool_column;
-    this.int_column = int_column;
-    this.long_column = long_column;
-    this.float_column = float_column;
-    this.double_column = double_column;
-    this.binary_column = binary_column;
-    this.string_column = string_column;
-    this.maybe_bool_column = maybe_bool_column;
-    this.maybe_int_column = maybe_int_column;
-    this.maybe_long_column = maybe_long_column;
-    this.maybe_float_column = maybe_float_column;
-    this.maybe_double_column = maybe_double_column;
-    this.maybe_binary_column = maybe_binary_column;
-    this.maybe_string_column = maybe_string_column;
+  public ParquetAvroCompat(java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column) {
     this.strings_column = strings_column;
     this.string_to_int_column = string_to_int_column;
     this.complex_column = complex_column;
@@ -61,23 +33,9 @@ public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_co
   // Used by DatumWriter.  Applications should not call. 
   public java.lang.Object get(int field$) {
     switch (field$) {
-    case 0: return bool_column;
-    case 1: return int_column;
-    case 2: return long_column;
-    case 3: return float_column;
-    case 4: return double_column;
-    case 5: return binary_column;
-    case 6: return string_column;
-    case 7: return maybe_bool_column;
-    case 8: return maybe_int_column;
-    case 9: return maybe_long_column;
-    case 10: return maybe_float_column;
-    case 11: return maybe_double_column;
-    case 12: return maybe_binary_column;
-    case 13: return maybe_string_column;
-    case 14: return strings_column;
-    case 15: return string_to_int_column;
-    case 16: return complex_column;
+    case 0: return strings_column;
+    case 1: return string_to_int_column;
+    case 2: return complex_column;
     default: throw new org.apache.avro.AvroRuntimeException("Bad index");
     }
   }
@@ -85,237 +43,13 @@ public java.lang.Object get(int field$) {
   @SuppressWarnings(value="unchecked")
   public void put(int field$, java.lang.Object value$) {
     switch (field$) {
-    case 0: bool_column = (java.lang.Boolean)value$; break;
-    case 1: int_column = (java.lang.Integer)value$; break;
-    case 2: long_column = (java.lang.Long)value$; break;
-    case 3: float_column = (java.lang.Float)value$; break;
-    case 4: double_column = (java.lang.Double)value$; break;
-    case 5: binary_column = (java.nio.ByteBuffer)value$; break;
-    case 6: string_column = (java.lang.String)value$; break;
-    case 7: maybe_bool_column = (java.lang.Boolean)value$; break;
-    case 8: maybe_int_column = (java.lang.Integer)value$; break;
-    case 9: maybe_long_column = (java.lang.Long)value$; break;
-    case 10: maybe_float_column = (java.lang.Float)value$; break;
-    case 11: maybe_double_column = (java.lang.Double)value$; break;
-    case 12: maybe_binary_column = (java.nio.ByteBuffer)value$; break;
-    case 13: maybe_string_column = (java.lang.String)value$; break;
-    case 14: strings_column = (java.util.List<java.lang.String>)value$; break;
-    case 15: string_to_int_column = (java.util.Map<java.lang.String,java.lang.Integer>)value$; break;
-    case 16: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>)value$; break;
+    case 0: strings_column = (java.util.List<java.lang.String>)value$; break;
+    case 1: string_to_int_column = (java.util.Map<java.lang.String,java.lang.Integer>)value$; break;
+    case 2: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>)value$; break;
     default: throw new org.apache.avro.AvroRuntimeException("Bad index");
     }
   }
 
-  /**
-   * Gets the value of the 'bool_column' field.
-   */
-  public java.lang.Boolean getBoolColumn() {
-    return bool_column;
-  }
-
-  /**
-   * Sets the value of the 'bool_column' field.
-   * @param value the value to set.
-   */
-  public void setBoolColumn(java.lang.Boolean value) {
-    this.bool_column = value;
-  }
-
-  /**
-   * Gets the value of the 'int_column' field.
-   */
-  public java.lang.Integer getIntColumn() {
-    return int_column;
-  }
-
-  /**
-   * Sets the value of the 'int_column' field.
-   * @param value the value to set.
-   */
-  public void setIntColumn(java.lang.Integer value) {
-    this.int_column = value;
-  }
-
-  /**
-   * Gets the value of the 'long_column' field.
-   */
-  public java.lang.Long getLongColumn() {
-    return long_column;
-  }
-
-  /**
-   * Sets the value of the 'long_column' field.
-   * @param value the value to set.
-   */
-  public void setLongColumn(java.lang.Long value) {
-    this.long_column = value;
-  }
-
-  /**
-   * Gets the value of the 'float_column' field.
-   */
-  public java.lang.Float getFloatColumn() {
-    return float_column;
-  }
-
-  /**
-   * Sets the value of the 'float_column' field.
-   * @param value the value to set.
-   */
-  public void setFloatColumn(java.lang.Float value) {
-    this.float_column = value;
-  }
-
-  /**
-   * Gets the value of the 'double_column' field.
-   */
-  public java.lang.Double getDoubleColumn() {
-    return double_column;
-  }
-
-  /**
-   * Sets the value of the 'double_column' field.
-   * @param value the value to set.
-   */
-  public void setDoubleColumn(java.lang.Double value) {
-    this.double_column = value;
-  }
-
-  /**
-   * Gets the value of the 'binary_column' field.
-   */
-  public java.nio.ByteBuffer getBinaryColumn() {
-    return binary_column;
-  }
-
-  /**
-   * Sets the value of the 'binary_column' field.
-   * @param value the value to set.
-   */
-  public void setBinaryColumn(java.nio.ByteBuffer value) {
-    this.binary_column = value;
-  }
-
-  /**
-   * Gets the value of the 'string_column' field.
-   */
-  public java.lang.String getStringColumn() {
-    return string_column;
-  }
-
-  /**
-   * Sets the value of the 'string_column' field.
-   * @param value the value to set.
-   */
-  public void setStringColumn(java.lang.String value) {
-    this.string_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_bool_column' field.
-   */
-  public java.lang.Boolean getMaybeBoolColumn() {
-    return maybe_bool_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_bool_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeBoolColumn(java.lang.Boolean value) {
-    this.maybe_bool_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_int_column' field.
-   */
-  public java.lang.Integer getMaybeIntColumn() {
-    return maybe_int_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_int_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeIntColumn(java.lang.Integer value) {
-    this.maybe_int_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_long_column' field.
-   */
-  public java.lang.Long getMaybeLongColumn() {
-    return maybe_long_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_long_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeLongColumn(java.lang.Long value) {
-    this.maybe_long_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_float_column' field.
-   */
-  public java.lang.Float getMaybeFloatColumn() {
-    return maybe_float_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_float_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeFloatColumn(java.lang.Float value) {
-    this.maybe_float_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_double_column' field.
-   */
-  public java.lang.Double getMaybeDoubleColumn() {
-    return maybe_double_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_double_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeDoubleColumn(java.lang.Double value) {
-    this.maybe_double_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_binary_column' field.
-   */
-  public java.nio.ByteBuffer getMaybeBinaryColumn() {
-    return maybe_binary_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_binary_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeBinaryColumn(java.nio.ByteBuffer value) {
-    this.maybe_binary_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_string_column' field.
-   */
-  public java.lang.String getMaybeStringColumn() {
-    return maybe_string_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_string_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeStringColumn(java.lang.String value) {
-    this.maybe_string_column = value;
-  }
-
   /**
    * Gets the value of the 'strings_column' field.
    */
@@ -382,20 +116,6 @@ public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Parqu
   public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<ParquetAvroCompat>
     implements org.apache.avro.data.RecordBuilder<ParquetAvroCompat> {
 
-    private boolean bool_column;
-    private int int_column;
-    private long long_column;
-    private float float_column;
-    private double double_column;
-    private java.nio.ByteBuffer binary_column;
-    private java.lang.String string_column;
-    private java.lang.Boolean maybe_bool_column;
-    private java.lang.Integer maybe_int_column;
-    private java.lang.Long maybe_long_column;
-    private java.lang.Float maybe_float_column;
-    private java.lang.Double maybe_double_column;
-    private java.nio.ByteBuffer maybe_binary_column;
-    private java.lang.String maybe_string_column;
     private java.util.List<java.lang.String> strings_column;
     private java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
     private java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
@@ -408,492 +128,35 @@ private Builder() {
     /** Creates a Builder by copying an existing Builder */
     private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder other) {
       super(other);
-      if (isValidValue(fields()[0], other.bool_column)) {
-        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
         fieldSetFlags()[0] = true;
       }
-      if (isValidValue(fields()[1], other.int_column)) {
-        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+      if (isValidValue(fields()[1], other.string_to_int_column)) {
+        this.string_to_int_column = data().deepCopy(fields()[1].schema(), other.string_to_int_column);
         fieldSetFlags()[1] = true;
       }
-      if (isValidValue(fields()[2], other.long_column)) {
-        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+      if (isValidValue(fields()[2], other.complex_column)) {
+        this.complex_column = data().deepCopy(fields()[2].schema(), other.complex_column);
         fieldSetFlags()[2] = true;
       }
-      if (isValidValue(fields()[3], other.float_column)) {
-        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
-        fieldSetFlags()[3] = true;
-      }
-      if (isValidValue(fields()[4], other.double_column)) {
-        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
-        fieldSetFlags()[4] = true;
-      }
-      if (isValidValue(fields()[5], other.binary_column)) {
-        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
-        fieldSetFlags()[5] = true;
-      }
-      if (isValidValue(fields()[6], other.string_column)) {
-        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
-        fieldSetFlags()[6] = true;
-      }
-      if (isValidValue(fields()[7], other.maybe_bool_column)) {
-        this.maybe_bool_column = data().deepCopy(fields()[7].schema(), other.maybe_bool_column);
-        fieldSetFlags()[7] = true;
-      }
-      if (isValidValue(fields()[8], other.maybe_int_column)) {
-        this.maybe_int_column = data().deepCopy(fields()[8].schema(), other.maybe_int_column);
-        fieldSetFlags()[8] = true;
-      }
-      if (isValidValue(fields()[9], other.maybe_long_column)) {
-        this.maybe_long_column = data().deepCopy(fields()[9].schema(), other.maybe_long_column);
-        fieldSetFlags()[9] = true;
-      }
-      if (isValidValue(fields()[10], other.maybe_float_column)) {
-        this.maybe_float_column = data().deepCopy(fields()[10].schema(), other.maybe_float_column);
-        fieldSetFlags()[10] = true;
-      }
-      if (isValidValue(fields()[11], other.maybe_double_column)) {
-        this.maybe_double_column = data().deepCopy(fields()[11].schema(), other.maybe_double_column);
-        fieldSetFlags()[11] = true;
-      }
-      if (isValidValue(fields()[12], other.maybe_binary_column)) {
-        this.maybe_binary_column = data().deepCopy(fields()[12].schema(), other.maybe_binary_column);
-        fieldSetFlags()[12] = true;
-      }
-      if (isValidValue(fields()[13], other.maybe_string_column)) {
-        this.maybe_string_column = data().deepCopy(fields()[13].schema(), other.maybe_string_column);
-        fieldSetFlags()[13] = true;
-      }
-      if (isValidValue(fields()[14], other.strings_column)) {
-        this.strings_column = data().deepCopy(fields()[14].schema(), other.strings_column);
-        fieldSetFlags()[14] = true;
-      }
-      if (isValidValue(fields()[15], other.string_to_int_column)) {
-        this.string_to_int_column = data().deepCopy(fields()[15].schema(), other.string_to_int_column);
-        fieldSetFlags()[15] = true;
-      }
-      if (isValidValue(fields()[16], other.complex_column)) {
-        this.complex_column = data().deepCopy(fields()[16].schema(), other.complex_column);
-        fieldSetFlags()[16] = true;
-      }
     }
     
     /** Creates a Builder by copying an existing ParquetAvroCompat instance */
     private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat other) {
             super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
-      if (isValidValue(fields()[0], other.bool_column)) {
-        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
         fieldSetFlags()[0] = true;
       }
-      if (isValidValue(fields()[1], other.int_column)) {
-        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+      if (isValidValue(fields()[1], other.string_to_int_column)) {
+        this.string_to_int_column = data().deepCopy(fields()[1].schema(), other.string_to_int_column);
         fieldSetFlags()[1] = true;
       }
-      if (isValidValue(fields()[2], other.long_column)) {
-        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+      if (isValidValue(fields()[2], other.complex_column)) {
+        this.complex_column = data().deepCopy(fields()[2].schema(), other.complex_column);
         fieldSetFlags()[2] = true;
       }
-      if (isValidValue(fields()[3], other.float_column)) {
-        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
-        fieldSetFlags()[3] = true;
-      }
-      if (isValidValue(fields()[4], other.double_column)) {
-        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
-        fieldSetFlags()[4] = true;
-      }
-      if (isValidValue(fields()[5], other.binary_column)) {
-        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
-        fieldSetFlags()[5] = true;
-      }
-      if (isValidValue(fields()[6], other.string_column)) {
-        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
-        fieldSetFlags()[6] = true;
-      }
-      if (isValidValue(fields()[7], other.maybe_bool_column)) {
-        this.maybe_bool_column = data().deepCopy(fields()[7].schema(), other.maybe_bool_column);
-        fieldSetFlags()[7] = true;
-      }
-      if (isValidValue(fields()[8], other.maybe_int_column)) {
-        this.maybe_int_column = data().deepCopy(fields()[8].schema(), other.maybe_int_column);
-        fieldSetFlags()[8] = true;
-      }
-      if (isValidValue(fields()[9], other.maybe_long_column)) {
-        this.maybe_long_column = data().deepCopy(fields()[9].schema(), other.maybe_long_column);
-        fieldSetFlags()[9] = true;
-      }
-      if (isValidValue(fields()[10], other.maybe_float_column)) {
-        this.maybe_float_column = data().deepCopy(fields()[10].schema(), other.maybe_float_column);
-        fieldSetFlags()[10] = true;
-      }
-      if (isValidValue(fields()[11], other.maybe_double_column)) {
-        this.maybe_double_column = data().deepCopy(fields()[11].schema(), other.maybe_double_column);
-        fieldSetFlags()[11] = true;
-      }
-      if (isValidValue(fields()[12], other.maybe_binary_column)) {
-        this.maybe_binary_column = data().deepCopy(fields()[12].schema(), other.maybe_binary_column);
-        fieldSetFlags()[12] = true;
-      }
-      if (isValidValue(fields()[13], other.maybe_string_column)) {
-        this.maybe_string_column = data().deepCopy(fields()[13].schema(), other.maybe_string_column);
-        fieldSetFlags()[13] = true;
-      }
-      if (isValidValue(fields()[14], other.strings_column)) {
-        this.strings_column = data().deepCopy(fields()[14].schema(), other.strings_column);
-        fieldSetFlags()[14] = true;
-      }
-      if (isValidValue(fields()[15], other.string_to_int_column)) {
-        this.string_to_int_column = data().deepCopy(fields()[15].schema(), other.string_to_int_column);
-        fieldSetFlags()[15] = true;
-      }
-      if (isValidValue(fields()[16], other.complex_column)) {
-        this.complex_column = data().deepCopy(fields()[16].schema(), other.complex_column);
-        fieldSetFlags()[16] = true;
-      }
-    }
-
-    /** Gets the value of the 'bool_column' field */
-    public java.lang.Boolean getBoolColumn() {
-      return bool_column;
-    }
-    
-    /** Sets the value of the 'bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBoolColumn(boolean value) {
-      validate(fields()[0], value);
-      this.bool_column = value;
-      fieldSetFlags()[0] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'bool_column' field has been set */
-    public boolean hasBoolColumn() {
-      return fieldSetFlags()[0];
-    }
-    
-    /** Clears the value of the 'bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBoolColumn() {
-      fieldSetFlags()[0] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'int_column' field */
-    public java.lang.Integer getIntColumn() {
-      return int_column;
-    }
-    
-    /** Sets the value of the 'int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setIntColumn(int value) {
-      validate(fields()[1], value);
-      this.int_column = value;
-      fieldSetFlags()[1] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'int_column' field has been set */
-    public boolean hasIntColumn() {
-      return fieldSetFlags()[1];
-    }
-    
-    /** Clears the value of the 'int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearIntColumn() {
-      fieldSetFlags()[1] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'long_column' field */
-    public java.lang.Long getLongColumn() {
-      return long_column;
-    }
-    
-    /** Sets the value of the 'long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setLongColumn(long value) {
-      validate(fields()[2], value);
-      this.long_column = value;
-      fieldSetFlags()[2] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'long_column' field has been set */
-    public boolean hasLongColumn() {
-      return fieldSetFlags()[2];
-    }
-    
-    /** Clears the value of the 'long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearLongColumn() {
-      fieldSetFlags()[2] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'float_column' field */
-    public java.lang.Float getFloatColumn() {
-      return float_column;
-    }
-    
-    /** Sets the value of the 'float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setFloatColumn(float value) {
-      validate(fields()[3], value);
-      this.float_column = value;
-      fieldSetFlags()[3] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'float_column' field has been set */
-    public boolean hasFloatColumn() {
-      return fieldSetFlags()[3];
-    }
-    
-    /** Clears the value of the 'float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearFloatColumn() {
-      fieldSetFlags()[3] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'double_column' field */
-    public java.lang.Double getDoubleColumn() {
-      return double_column;
-    }
-    
-    /** Sets the value of the 'double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setDoubleColumn(double value) {
-      validate(fields()[4], value);
-      this.double_column = value;
-      fieldSetFlags()[4] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'double_column' field has been set */
-    public boolean hasDoubleColumn() {
-      return fieldSetFlags()[4];
-    }
-    
-    /** Clears the value of the 'double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearDoubleColumn() {
-      fieldSetFlags()[4] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'binary_column' field */
-    public java.nio.ByteBuffer getBinaryColumn() {
-      return binary_column;
-    }
-    
-    /** Sets the value of the 'binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBinaryColumn(java.nio.ByteBuffer value) {
-      validate(fields()[5], value);
-      this.binary_column = value;
-      fieldSetFlags()[5] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'binary_column' field has been set */
-    public boolean hasBinaryColumn() {
-      return fieldSetFlags()[5];
-    }
-    
-    /** Clears the value of the 'binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBinaryColumn() {
-      binary_column = null;
-      fieldSetFlags()[5] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'string_column' field */
-    public java.lang.String getStringColumn() {
-      return string_column;
-    }
-    
-    /** Sets the value of the 'string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringColumn(java.lang.String value) {
-      validate(fields()[6], value);
-      this.string_column = value;
-      fieldSetFlags()[6] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'string_column' field has been set */
-    public boolean hasStringColumn() {
-      return fieldSetFlags()[6];
-    }
-    
-    /** Clears the value of the 'string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringColumn() {
-      string_column = null;
-      fieldSetFlags()[6] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_bool_column' field */
-    public java.lang.Boolean getMaybeBoolColumn() {
-      return maybe_bool_column;
-    }
-    
-    /** Sets the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBoolColumn(java.lang.Boolean value) {
-      validate(fields()[7], value);
-      this.maybe_bool_column = value;
-      fieldSetFlags()[7] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_bool_column' field has been set */
-    public boolean hasMaybeBoolColumn() {
-      return fieldSetFlags()[7];
-    }
-    
-    /** Clears the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBoolColumn() {
-      maybe_bool_column = null;
-      fieldSetFlags()[7] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_int_column' field */
-    public java.lang.Integer getMaybeIntColumn() {
-      return maybe_int_column;
-    }
-    
-    /** Sets the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeIntColumn(java.lang.Integer value) {
-      validate(fields()[8], value);
-      this.maybe_int_column = value;
-      fieldSetFlags()[8] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_int_column' field has been set */
-    public boolean hasMaybeIntColumn() {
-      return fieldSetFlags()[8];
-    }
-    
-    /** Clears the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeIntColumn() {
-      maybe_int_column = null;
-      fieldSetFlags()[8] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_long_column' field */
-    public java.lang.Long getMaybeLongColumn() {
-      return maybe_long_column;
-    }
-    
-    /** Sets the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeLongColumn(java.lang.Long value) {
-      validate(fields()[9], value);
-      this.maybe_long_column = value;
-      fieldSetFlags()[9] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_long_column' field has been set */
-    public boolean hasMaybeLongColumn() {
-      return fieldSetFlags()[9];
-    }
-    
-    /** Clears the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeLongColumn() {
-      maybe_long_column = null;
-      fieldSetFlags()[9] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_float_column' field */
-    public java.lang.Float getMaybeFloatColumn() {
-      return maybe_float_column;
-    }
-    
-    /** Sets the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeFloatColumn(java.lang.Float value) {
-      validate(fields()[10], value);
-      this.maybe_float_column = value;
-      fieldSetFlags()[10] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_float_column' field has been set */
-    public boolean hasMaybeFloatColumn() {
-      return fieldSetFlags()[10];
-    }
-    
-    /** Clears the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeFloatColumn() {
-      maybe_float_column = null;
-      fieldSetFlags()[10] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_double_column' field */
-    public java.lang.Double getMaybeDoubleColumn() {
-      return maybe_double_column;
-    }
-    
-    /** Sets the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeDoubleColumn(java.lang.Double value) {
-      validate(fields()[11], value);
-      this.maybe_double_column = value;
-      fieldSetFlags()[11] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_double_column' field has been set */
-    public boolean hasMaybeDoubleColumn() {
-      return fieldSetFlags()[11];
-    }
-    
-    /** Clears the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeDoubleColumn() {
-      maybe_double_column = null;
-      fieldSetFlags()[11] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_binary_column' field */
-    public java.nio.ByteBuffer getMaybeBinaryColumn() {
-      return maybe_binary_column;
-    }
-    
-    /** Sets the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
-      validate(fields()[12], value);
-      this.maybe_binary_column = value;
-      fieldSetFlags()[12] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_binary_column' field has been set */
-    public boolean hasMaybeBinaryColumn() {
-      return fieldSetFlags()[12];
-    }
-    
-    /** Clears the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBinaryColumn() {
-      maybe_binary_column = null;
-      fieldSetFlags()[12] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_string_column' field */
-    public java.lang.String getMaybeStringColumn() {
-      return maybe_string_column;
-    }
-    
-    /** Sets the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeStringColumn(java.lang.String value) {
-      validate(fields()[13], value);
-      this.maybe_string_column = value;
-      fieldSetFlags()[13] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_string_column' field has been set */
-    public boolean hasMaybeStringColumn() {
-      return fieldSetFlags()[13];
-    }
-    
-    /** Clears the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeStringColumn() {
-      maybe_string_column = null;
-      fieldSetFlags()[13] = false;
-      return this;
     }
 
     /** Gets the value of the 'strings_column' field */
@@ -903,21 +166,21 @@ public java.util.List<java.lang.String> getStringsColumn() {
     
     /** Sets the value of the 'strings_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringsColumn(java.util.List<java.lang.String> value) {
-      validate(fields()[14], value);
+      validate(fields()[0], value);
       this.strings_column = value;
-      fieldSetFlags()[14] = true;
+      fieldSetFlags()[0] = true;
       return this; 
     }
     
     /** Checks whether the 'strings_column' field has been set */
     public boolean hasStringsColumn() {
-      return fieldSetFlags()[14];
+      return fieldSetFlags()[0];
     }
     
     /** Clears the value of the 'strings_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringsColumn() {
       strings_column = null;
-      fieldSetFlags()[14] = false;
+      fieldSetFlags()[0] = false;
       return this;
     }
 
@@ -928,21 +191,21 @@ public java.util.Map<java.lang.String,java.lang.Integer> getStringToIntColumn()
     
     /** Sets the value of the 'string_to_int_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
-      validate(fields()[15], value);
+      validate(fields()[1], value);
       this.string_to_int_column = value;
-      fieldSetFlags()[15] = true;
+      fieldSetFlags()[1] = true;
       return this; 
     }
     
     /** Checks whether the 'string_to_int_column' field has been set */
     public boolean hasStringToIntColumn() {
-      return fieldSetFlags()[15];
+      return fieldSetFlags()[1];
     }
     
     /** Clears the value of the 'string_to_int_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringToIntColumn() {
       string_to_int_column = null;
-      fieldSetFlags()[15] = false;
+      fieldSetFlags()[1] = false;
       return this;
     }
 
@@ -953,21 +216,21 @@ public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execut
     
     /** Sets the value of the 'complex_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> value) {
-      validate(fields()[16], value);
+      validate(fields()[2], value);
       this.complex_column = value;
-      fieldSetFlags()[16] = true;
+      fieldSetFlags()[2] = true;
       return this; 
     }
     
     /** Checks whether the 'complex_column' field has been set */
     public boolean hasComplexColumn() {
-      return fieldSetFlags()[16];
+      return fieldSetFlags()[2];
     }
     
     /** Clears the value of the 'complex_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearComplexColumn() {
       complex_column = null;
-      fieldSetFlags()[16] = false;
+      fieldSetFlags()[2] = false;
       return this;
     }
 
@@ -975,23 +238,9 @@ public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroC
     public ParquetAvroCompat build() {
       try {
         ParquetAvroCompat record = new ParquetAvroCompat();
-        record.bool_column = fieldSetFlags()[0] ? this.bool_column : (java.lang.Boolean) defaultValue(fields()[0]);
-        record.int_column = fieldSetFlags()[1] ? this.int_column : (java.lang.Integer) defaultValue(fields()[1]);
-        record.long_column = fieldSetFlags()[2] ? this.long_column : (java.lang.Long) defaultValue(fields()[2]);
-        record.float_column = fieldSetFlags()[3] ? this.float_column : (java.lang.Float) defaultValue(fields()[3]);
-        record.double_column = fieldSetFlags()[4] ? this.double_column : (java.lang.Double) defaultValue(fields()[4]);
-        record.binary_column = fieldSetFlags()[5] ? this.binary_column : (java.nio.ByteBuffer) defaultValue(fields()[5]);
-        record.string_column = fieldSetFlags()[6] ? this.string_column : (java.lang.String) defaultValue(fields()[6]);
-        record.maybe_bool_column = fieldSetFlags()[7] ? this.maybe_bool_column : (java.lang.Boolean) defaultValue(fields()[7]);
-        record.maybe_int_column = fieldSetFlags()[8] ? this.maybe_int_column : (java.lang.Integer) defaultValue(fields()[8]);
-        record.maybe_long_column = fieldSetFlags()[9] ? this.maybe_long_column : (java.lang.Long) defaultValue(fields()[9]);
-        record.maybe_float_column = fieldSetFlags()[10] ? this.maybe_float_column : (java.lang.Float) defaultValue(fields()[10]);
-        record.maybe_double_column = fieldSetFlags()[11] ? this.maybe_double_column : (java.lang.Double) defaultValue(fields()[11]);
-        record.maybe_binary_column = fieldSetFlags()[12] ? this.maybe_binary_column : (java.nio.ByteBuffer) defaultValue(fields()[12]);
-        record.maybe_string_column = fieldSetFlags()[13] ? this.maybe_string_column : (java.lang.String) defaultValue(fields()[13]);
-        record.strings_column = fieldSetFlags()[14] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[14]);
-        record.string_to_int_column = fieldSetFlags()[15] ? this.string_to_int_column : (java.util.Map<java.lang.String,java.lang.Integer>) defaultValue(fields()[15]);
-        record.complex_column = fieldSetFlags()[16] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>) defaultValue(fields()[16]);
+        record.strings_column = fieldSetFlags()[0] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[0]);
+        record.string_to_int_column = fieldSetFlags()[1] ? this.string_to_int_column : (java.util.Map<java.lang.String,java.lang.Integer>) defaultValue(fields()[1]);
+        record.complex_column = fieldSetFlags()[2] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>) defaultValue(fields()[2]);
         return record;
       } catch (Exception e) {
         throw new org.apache.avro.AvroRuntimeException(e);
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 82d40e2b61a10..45db619567a22 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -20,7 +20,8 @@ package org.apache.spark.sql.execution.datasources.parquet
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters.seqAsJavaListConverter
+import scala.collection.JavaConverters.mapAsJavaMapConverter
 
 import org.apache.avro.Schema
 import org.apache.avro.generic.IndexedRecord
@@ -32,48 +33,196 @@ import org.apache.spark.sql.execution.datasources.parquet.test.avro._
 import org.apache.spark.sql.test.SharedSQLContext
 
 class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
-  import ParquetCompatibilityTest._
-
   private def withWriter[T <: IndexedRecord]
       (path: String, schema: Schema)
       (f: AvroParquetWriter[T] => Unit): Unit = {
+    logInfo(
+      s"""Writing Avro records with the following Avro schema into Parquet file:
+         |
+         |${schema.toString(true)}
+       """.stripMargin)
+
     val writer = new AvroParquetWriter[T](new Path(path), schema)
     try f(writer) finally writer.close()
   }
 
-  test("Read Parquet file generated by parquet-avro") {
+  test("required primitives") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
 
-      withWriter[ParquetAvroCompat](path, ParquetAvroCompat.getClassSchema) { writer =>
-        (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
+      withWriter[AvroPrimitives](path, AvroPrimitives.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          writer.write(
+            AvroPrimitives.newBuilder()
+              .setBoolColumn(i % 2 == 0)
+              .setIntColumn(i)
+              .setLongColumn(i.toLong * 10)
+              .setFloatColumn(i.toFloat + 0.1f)
+              .setDoubleColumn(i.toDouble + 0.2d)
+              .setBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes("UTF-8")))
+              .setStringColumn(s"val_$i")
+              .build())
+        }
       }
 
-      logInfo(
-        s"""Schema of the Parquet file written by parquet-avro:
-           |${readParquetSchema(path)}
-       """.stripMargin)
+      logParquetSchema(path)
 
       checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
-        def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
-
         Row(
           i % 2 == 0,
           i,
           i.toLong * 10,
           i.toFloat + 0.1f,
           i.toDouble + 0.2d,
-          s"val_$i".getBytes,
-          s"val_$i",
+          s"val_$i".getBytes("UTF-8"),
+          s"val_$i")
+      })
+    }
+  }
+
+  test("optional primitives") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroOptionalPrimitives](path, AvroOptionalPrimitives.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          val record = if (i % 3 == 0) {
+            AvroOptionalPrimitives.newBuilder()
+              .setMaybeBoolColumn(null)
+              .setMaybeIntColumn(null)
+              .setMaybeLongColumn(null)
+              .setMaybeFloatColumn(null)
+              .setMaybeDoubleColumn(null)
+              .setMaybeBinaryColumn(null)
+              .setMaybeStringColumn(null)
+              .build()
+          } else {
+            AvroOptionalPrimitives.newBuilder()
+              .setMaybeBoolColumn(i % 2 == 0)
+              .setMaybeIntColumn(i)
+              .setMaybeLongColumn(i.toLong * 10)
+              .setMaybeFloatColumn(i.toFloat + 0.1f)
+              .setMaybeDoubleColumn(i.toDouble + 0.2d)
+              .setMaybeBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes("UTF-8")))
+              .setMaybeStringColumn(s"val_$i")
+              .build()
+          }
+
+          writer.write(record)
+        }
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        if (i % 3 == 0) {
+          Row.apply(Seq.fill(7)(null): _*)
+        } else {
+          Row(
+            i % 2 == 0,
+            i,
+            i.toLong * 10,
+            i.toFloat + 0.1f,
+            i.toDouble + 0.2d,
+            s"val_$i".getBytes("UTF-8"),
+            s"val_$i")
+        }
+      })
+    }
+  }
+
+  test("non-nullable arrays") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroNonNullableArrays](path, AvroNonNullableArrays.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          val record = {
+            val builder =
+              AvroNonNullableArrays.newBuilder()
+                .setStringsColumn(Seq.tabulate(3)(i => s"val_$i").asJava)
+
+            if (i % 3 == 0) {
+              builder.setMaybeIntsColumn(null).build()
+            } else {
+              builder.setMaybeIntsColumn(Seq.tabulate(3)(Int.box).asJava).build()
+            }
+          }
+
+          writer.write(record)
+        }
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(
+          Seq.tabulate(3)(i => s"val_$i"),
+          if (i % 3 == 0) null else Seq.tabulate(3)(identity))
+      })
+    }
+  }
+
+  ignore("nullable arrays (parquet-avro 1.7.0 does not properly support this)") {
+    // TODO Complete this test case after upgrading to parquet-mr 1.8+
+  }
+
+  test("SPARK-10136 array of primitive array") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroArrayOfArray](path, AvroArrayOfArray.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          writer.write(AvroArrayOfArray.newBuilder()
+            .setIntArraysColumn(
+              Seq.tabulate(3, 3)((i, j) => i * 3 + j: Integer).map(_.asJava).asJava)
+            .build())
+        }
+      }
 
-          nullable(i % 2 == 0: java.lang.Boolean),
-          nullable(i: Integer),
-          nullable(i.toLong: java.lang.Long),
-          nullable(i.toFloat + 0.1f: java.lang.Float),
-          nullable(i.toDouble + 0.2d: java.lang.Double),
-          nullable(s"val_$i".getBytes),
-          nullable(s"val_$i"),
+      logParquetSchema(path)
 
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(Seq.tabulate(3, 3)((i, j) => i * 3 + j))
+      })
+    }
+  }
+
+  test("map of primitive array") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroMapOfArray](path, AvroMapOfArray.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          writer.write(AvroMapOfArray.newBuilder()
+            .setStringToIntsColumn(
+              Seq.tabulate(3) { i =>
+                i.toString -> Seq.tabulate(3)(j => i + j: Integer).asJava
+              }.toMap.asJava)
+            .build())
+        }
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(Seq.tabulate(3)(i => i.toString -> Seq.tabulate(3)(j => i + j)).toMap)
+      })
+    }
+  }
+
+  test("various complex types") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[ParquetAvroCompat](path, ParquetAvroCompat.getClassSchema) { writer =>
+        (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(
           Seq.tabulate(3)(n => s"arr_${i + n}"),
           Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap,
           Seq.tabulate(3) { n =>
@@ -86,47 +235,27 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest with Shared
   }
 
   def makeParquetAvroCompat(i: Int): ParquetAvroCompat = {
-    def nullable[T <: AnyRef] = makeNullable[T](i) _
-
     def makeComplexColumn(i: Int): JMap[String, JList[Nested]] = {
-      mapAsJavaMap(Seq.tabulate(3) { n =>
-        (i + n).toString -> seqAsJavaList(Seq.tabulate(3) { m =>
+      Seq.tabulate(3) { n =>
+        (i + n).toString -> Seq.tabulate(3) { m =>
           Nested
             .newBuilder()
-            .setNestedIntsColumn(seqAsJavaList(Seq.tabulate(3)(j => i + j + m)))
+            .setNestedIntsColumn(Seq.tabulate(3)(j => i + j + m: Integer).asJava)
             .setNestedStringColumn(s"val_${i + m}")
             .build()
-        })
-      }.toMap)
+        }.asJava
+      }.toMap.asJava
     }
 
     ParquetAvroCompat
       .newBuilder()
-      .setBoolColumn(i % 2 == 0)
-      .setIntColumn(i)
-      .setLongColumn(i.toLong * 10)
-      .setFloatColumn(i.toFloat + 0.1f)
-      .setDoubleColumn(i.toDouble + 0.2d)
-      .setBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes))
-      .setStringColumn(s"val_$i")
-
-      .setMaybeBoolColumn(nullable(i % 2 == 0: java.lang.Boolean))
-      .setMaybeIntColumn(nullable(i: Integer))
-      .setMaybeLongColumn(nullable(i.toLong: java.lang.Long))
-      .setMaybeFloatColumn(nullable(i.toFloat + 0.1f: java.lang.Float))
-      .setMaybeDoubleColumn(nullable(i.toDouble + 0.2d: java.lang.Double))
-      .setMaybeBinaryColumn(nullable(ByteBuffer.wrap(s"val_$i".getBytes)))
-      .setMaybeStringColumn(nullable(s"val_$i"))
-
-      .setStringsColumn(Seq.tabulate(3)(n => s"arr_${i + n}"))
-      .setStringToIntColumn(
-        mapAsJavaMap(Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap))
+      .setStringsColumn(Seq.tabulate(3)(n => s"arr_${i + n}").asJava)
+      .setStringToIntColumn(Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap.asJava)
       .setComplexColumn(makeComplexColumn(i))
-
       .build()
   }
 
-  test("SPARK-9407 Don't push down predicates involving Parquet ENUM columns") {
+  test("SPARK-9407 Push down predicates involving Parquet ENUM columns") {
     import testImplicits._
 
     withTempPath { dir =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index b3406729fcc5e..d85c564e3e8d1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -43,6 +43,13 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
     val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
     footers.head.getParquetMetadata.getFileMetaData.getSchema
   }
+
+  protected def logParquetSchema(path: String): Unit = {
+    logInfo(
+      s"""Schema of the Parquet file written by parquet-avro:
+         |${readParquetSchema(path)}
+       """.stripMargin)
+  }
 }
 
 object ParquetCompatibilityTest {

From 12de348332108f8c0c5bdad1d4cfac89b952b0f8 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Thu, 20 Aug 2015 11:31:03 -0700
Subject: [PATCH 1163/1454] [SPARK-10126] [PROJECT INFRA] Fix typo in
 release-build.sh which broke snapshot publishing for Scala 2.11

The current `release-build.sh` has a typo which breaks snapshot publication for Scala 2.11. We should change the Scala version to 2.11 and clean before building a 2.11 snapshot.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8325 from JoshRosen/fix-2.11-snapshots.
---
 dev/create-release/release-build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/create-release/release-build.sh b/dev/create-release/release-build.sh
index 399c73e7bf6bc..d0b3a54dde1dc 100755
--- a/dev/create-release/release-build.sh
+++ b/dev/create-release/release-build.sh
@@ -225,9 +225,9 @@ if [[ "$1" == "publish-snapshot" ]]; then
 
   $MVN -DzincPort=$ZINC_PORT --settings $tmp_settings -DskipTests $PUBLISH_PROFILES \
     -Phive-thriftserver deploy
-  ./dev/change-scala-version.sh 2.10
+  ./dev/change-scala-version.sh 2.11
   $MVN -DzincPort=$ZINC_PORT -Dscala-2.11 --settings $tmp_settings \
-    -DskipTests $PUBLISH_PROFILES deploy
+    -DskipTests $PUBLISH_PROFILES clean deploy
 
   # Clean-up Zinc nailgun process
   /usr/sbin/lsof -P |grep $ZINC_PORT | grep LISTEN | awk '{ print $2; }' | xargs kill

From 907df2fce00d2cbc9fae371344f05f800e0d2726 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 20 Aug 2015 13:51:54 -0700
Subject: [PATCH 1164/1454] [SQL] [MINOR] remove unnecessary class

This class is identical to `org.apache.spark.sql.execution.datasources.jdbc. DefaultSource` and is not needed.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8334 from cloud-fan/minor.
---
 .../execution/datasources/DefaultSource.scala | 64 -------------------
 1 file changed, 64 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
deleted file mode 100644
index 6e4cc4de7f651..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
-* Licensed to the Apache Software Foundation (ASF) under one or more
-* contributor license agreements.  See the NOTICE file distributed with
-* this work for additional information regarding copyright ownership.
-* The ASF licenses this file to You under the Apache License, Version 2.0
-* (the "License"); you may not use this file except in compliance with
-* the License.  You may obtain a copy of the License at
-*
-*    http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing, software
-* distributed under the License is distributed on an "AS IS" BASIS,
-* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-* See the License for the specific language governing permissions and
-* limitations under the License.
-*/
-
-package org.apache.spark.sql.execution.datasources
-
-import java.util.Properties
-
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.execution.datasources.jdbc.{JDBCRelation, JDBCPartitioningInfo, DriverRegistry}
-import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
-
-
-class DefaultSource extends RelationProvider with DataSourceRegister {
-
-  override def shortName(): String = "jdbc"
-
-  /** Returns a new base relation with the given parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String]): BaseRelation = {
-    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
-    val driver = parameters.getOrElse("driver", null)
-    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
-    val partitionColumn = parameters.getOrElse("partitionColumn", null)
-    val lowerBound = parameters.getOrElse("lowerBound", null)
-    val upperBound = parameters.getOrElse("upperBound", null)
-    val numPartitions = parameters.getOrElse("numPartitions", null)
-
-    if (driver != null) DriverRegistry.register(driver)
-
-    if (partitionColumn != null
-      && (lowerBound == null || upperBound == null || numPartitions == null)) {
-      sys.error("Partitioning incompletely specified")
-    }
-
-    val partitionInfo = if (partitionColumn == null) {
-      null
-    } else {
-      JDBCPartitioningInfo(
-        partitionColumn,
-        lowerBound.toLong,
-        upperBound.toLong,
-        numPartitions.toInt)
-    }
-    val parts = JDBCRelation.columnPartition(partitionInfo)
-    val properties = new Properties() // Additional properties that we will pass to getConnection
-    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
-    JDBCRelation(url, table, parts, properties)(sqlContext)
-  }
-}

From 2a3d98aae285aba39786e9809f96de412a130f39 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 20 Aug 2015 14:47:04 -0700
Subject: [PATCH 1165/1454] [SPARK-10138] [ML] move setters to
 MultilayerPerceptronClassifier and add Java test suite

Otherwise, setters do not return self type. jkbradley avulanov

Author: Xiangrui Meng <meng@databricks.com>

Closes #8342 from mengxr/SPARK-10138.
---
 .../MultilayerPerceptronClassifier.scala      | 54 +++++++-------
 ...vaMultilayerPerceptronClassifierSuite.java | 74 +++++++++++++++++++
 2 files changed, 101 insertions(+), 27 deletions(-)
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index ccca4ecc004c3..1e5b0bc4453e4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -42,9 +42,6 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
     ParamValidators.arrayLengthGt(1)
   )
 
-  /** @group setParam */
-  def setLayers(value: Array[Int]): this.type = set(layers, value)
-
   /** @group getParam */
   final def getLayers: Array[Int] = $(layers)
 
@@ -61,33 +58,9 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
       "it is adjusted to the size of this data. Recommended size is between 10 and 1000",
     ParamValidators.gt(0))
 
-  /** @group setParam */
-  def setBlockSize(value: Int): this.type = set(blockSize, value)
-
   /** @group getParam */
   final def getBlockSize: Int = $(blockSize)
 
-  /**
-   * Set the maximum number of iterations.
-   * Default is 100.
-   * @group setParam
-   */
-  def setMaxIter(value: Int): this.type = set(maxIter, value)
-
-  /**
-   * Set the convergence tolerance of iterations.
-   * Smaller value will lead to higher accuracy with the cost of more iterations.
-   * Default is 1E-4.
-   * @group setParam
-   */
-  def setTol(value: Double): this.type = set(tol, value)
-
-  /**
-   * Set the seed for weights initialization.
-   * @group setParam
-   */
-  def setSeed(value: Long): this.type = set(seed, value)
-
   setDefault(maxIter -> 100, tol -> 1e-4, layers -> Array(1, 1), blockSize -> 128)
 }
 
@@ -136,6 +109,33 @@ class MultilayerPerceptronClassifier(override val uid: String)
 
   def this() = this(Identifiable.randomUID("mlpc"))
 
+  /** @group setParam */
+  def setLayers(value: Array[Int]): this.type = set(layers, value)
+
+  /** @group setParam */
+  def setBlockSize(value: Int): this.type = set(blockSize, value)
+
+  /**
+   * Set the maximum number of iterations.
+   * Default is 100.
+   * @group setParam
+   */
+  def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+  /**
+   * Set the convergence tolerance of iterations.
+   * Smaller value will lead to higher accuracy with the cost of more iterations.
+   * Default is 1E-4.
+   * @group setParam
+   */
+  def setTol(value: Double): this.type = set(tol, value)
+
+  /**
+   * Set the seed for weights initialization.
+   * @group setParam
+   */
+  def setSeed(value: Long): this.type = set(seed, value)
+
   override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra)
 
   /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java
new file mode 100644
index 0000000000000..ec6b4bf3c0f8c
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification;
+
+import java.io.Serializable;
+import java.util.Arrays;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+
+public class JavaMultilayerPerceptronClassifierSuite implements Serializable {
+
+  private transient JavaSparkContext jsc;
+  private transient SQLContext sqlContext;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
+    sqlContext = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+    sqlContext = null;
+  }
+
+  @Test
+  public void testMLPC() {
+    DataFrame dataFrame = sqlContext.createDataFrame(
+      jsc.parallelize(Arrays.asList(
+        new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)),
+        new LabeledPoint(1.0, Vectors.dense(0.0, 1.0)),
+        new LabeledPoint(1.0, Vectors.dense(1.0, 0.0)),
+        new LabeledPoint(0.0, Vectors.dense(1.0, 1.0)))),
+      LabeledPoint.class);
+    MultilayerPerceptronClassifier mlpc = new MultilayerPerceptronClassifier()
+      .setLayers(new int[] {2, 5, 2})
+      .setBlockSize(1)
+      .setSeed(11L)
+      .setMaxIter(100);
+    MultilayerPerceptronClassificationModel model = mlpc.fit(dataFrame);
+    DataFrame result = model.transform(dataFrame);
+    Row[] predictionAndLabels = result.select("prediction", "label").collect();
+    for (Row r: predictionAndLabels) {
+      Assert.assertEquals((int) r.getDouble(0), (int) r.getDouble(1));
+    }
+  }
+}

From 7cfc0750e14f2c1b3847e4720cc02150253525a9 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 20 Aug 2015 14:56:08 -0700
Subject: [PATCH 1166/1454] [SPARK-10108] Add since tags to mllib.feature

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8309 from MechCoder/tags_feature.
---
 .../spark/mllib/feature/ChiSqSelector.scala   | 12 +++++++++---
 .../mllib/feature/ElementwiseProduct.scala    |  4 +++-
 .../spark/mllib/feature/HashingTF.scala       | 11 ++++++++++-
 .../org/apache/spark/mllib/feature/IDF.scala  |  8 +++++++-
 .../spark/mllib/feature/Normalizer.scala      |  5 ++++-
 .../org/apache/spark/mllib/feature/PCA.scala  |  9 ++++++++-
 .../spark/mllib/feature/StandardScaler.scala  | 13 ++++++++++++-
 .../mllib/feature/VectorTransformer.scala     |  6 +++++-
 .../apache/spark/mllib/feature/Word2Vec.scala | 19 ++++++++++++++++++-
 9 files changed, 76 insertions(+), 11 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
index 5f8c1dea237b4..fdd974d7a391e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.feature
 
 import scala.collection.mutable.ArrayBuilder
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.stat.Statistics
@@ -31,8 +31,10 @@ import org.apache.spark.rdd.RDD
  *
  * @param selectedFeatures list of indices to select (filter). Must be ordered asc
  */
+@Since("1.3.0")
 @Experimental
-class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransformer {
+class ChiSqSelectorModel (
+  @Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer {
 
   require(isSorted(selectedFeatures), "Array has to be sorted asc")
 
@@ -52,6 +54,7 @@ class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransf
    * @param vector vector to be transformed.
    * @return transformed vector.
    */
+  @Since("1.3.0")
   override def transform(vector: Vector): Vector = {
     compress(vector, selectedFeatures)
   }
@@ -107,8 +110,10 @@ class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransf
  * @param numTopFeatures number of features that selector will select
  *                       (ordered by statistic value descending)
  */
+@Since("1.3.0")
 @Experimental
-class ChiSqSelector (val numTopFeatures: Int) extends Serializable {
+class ChiSqSelector (
+  @Since("1.3.0") val numTopFeatures: Int) extends Serializable {
 
   /**
    * Returns a ChiSquared feature selector.
@@ -117,6 +122,7 @@ class ChiSqSelector (val numTopFeatures: Int) extends Serializable {
    *             Real-valued features will be treated as categorical for each distinct value.
    *             Apply feature discretizer before using this function.
    */
+  @Since("1.3.0")
   def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel = {
     val indices = Statistics.chiSqTest(data)
       .zipWithIndex.sortBy { case (res, _) => -res.statistic }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
index d67fe6c3ee4f8..33e2d17bb472e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg._
 
 /**
@@ -27,6 +27,7 @@ import org.apache.spark.mllib.linalg._
  * multiplier.
  * @param scalingVec The values used to scale the reference vector's individual components.
  */
+@Since("1.4.0")
 @Experimental
 class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
 
@@ -36,6 +37,7 @@ class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
    * @param vector vector to be transformed.
    * @return transformed vector.
    */
+  @Since("1.4.0")
   override def transform(vector: Vector): Vector = {
     require(vector.size == scalingVec.size,
       s"vector sizes do not match: Expected ${scalingVec.size} but found ${vector.size}")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
index c53475818395f..e47d524b61623 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
@@ -22,7 +22,7 @@ import java.lang.{Iterable => JavaIterable}
 import scala.collection.JavaConverters._
 import scala.collection.mutable
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.rdd.RDD
@@ -34,19 +34,25 @@ import org.apache.spark.util.Utils
  *
  * @param numFeatures number of features (default: 2^20^)
  */
+@Since("1.1.0")
 @Experimental
 class HashingTF(val numFeatures: Int) extends Serializable {
 
+  /**
+   */
+  @Since("1.1.0")
   def this() = this(1 << 20)
 
   /**
    * Returns the index of the input term.
    */
+  @Since("1.1.0")
   def indexOf(term: Any): Int = Utils.nonNegativeMod(term.##, numFeatures)
 
   /**
    * Transforms the input document into a sparse term frequency vector.
    */
+  @Since("1.1.0")
   def transform(document: Iterable[_]): Vector = {
     val termFrequencies = mutable.HashMap.empty[Int, Double]
     document.foreach { term =>
@@ -59,6 +65,7 @@ class HashingTF(val numFeatures: Int) extends Serializable {
   /**
    * Transforms the input document into a sparse term frequency vector (Java version).
    */
+  @Since("1.1.0")
   def transform(document: JavaIterable[_]): Vector = {
     transform(document.asScala)
   }
@@ -66,6 +73,7 @@ class HashingTF(val numFeatures: Int) extends Serializable {
   /**
    * Transforms the input document to term frequency vectors.
    */
+  @Since("1.1.0")
   def transform[D <: Iterable[_]](dataset: RDD[D]): RDD[Vector] = {
     dataset.map(this.transform)
   }
@@ -73,6 +81,7 @@ class HashingTF(val numFeatures: Int) extends Serializable {
   /**
    * Transforms the input document to term frequency vectors (Java version).
    */
+  @Since("1.1.0")
   def transform[D <: JavaIterable[_]](dataset: JavaRDD[D]): JavaRDD[Vector] = {
     dataset.rdd.map(this.transform).toJavaRDD()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
index 3fab7ea79befc..d5353ddd972e0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.feature
 
 import breeze.linalg.{DenseVector => BDV}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.rdd.RDD
@@ -37,6 +37,7 @@ import org.apache.spark.rdd.RDD
  * @param minDocFreq minimum of documents in which a term
  *                   should appear for filtering
  */
+@Since("1.1.0")
 @Experimental
 class IDF(val minDocFreq: Int) {
 
@@ -48,6 +49,7 @@ class IDF(val minDocFreq: Int) {
    * Computes the inverse document frequency.
    * @param dataset an RDD of term frequency vectors
    */
+  @Since("1.1.0")
   def fit(dataset: RDD[Vector]): IDFModel = {
     val idf = dataset.treeAggregate(new IDF.DocumentFrequencyAggregator(
           minDocFreq = minDocFreq))(
@@ -61,6 +63,7 @@ class IDF(val minDocFreq: Int) {
    * Computes the inverse document frequency.
    * @param dataset a JavaRDD of term frequency vectors
    */
+  @Since("1.1.0")
   def fit(dataset: JavaRDD[Vector]): IDFModel = {
     fit(dataset.rdd)
   }
@@ -171,6 +174,7 @@ class IDFModel private[spark] (val idf: Vector) extends Serializable {
    * @param dataset an RDD of term frequency vectors
    * @return an RDD of TF-IDF vectors
    */
+  @Since("1.1.0")
   def transform(dataset: RDD[Vector]): RDD[Vector] = {
     val bcIdf = dataset.context.broadcast(idf)
     dataset.mapPartitions(iter => iter.map(v => IDFModel.transform(bcIdf.value, v)))
@@ -182,6 +186,7 @@ class IDFModel private[spark] (val idf: Vector) extends Serializable {
    * @param v a term frequency vector
    * @return a TF-IDF vector
    */
+  @Since("1.3.0")
   def transform(v: Vector): Vector = IDFModel.transform(idf, v)
 
   /**
@@ -189,6 +194,7 @@ class IDFModel private[spark] (val idf: Vector) extends Serializable {
    * @param dataset a JavaRDD of term frequency vectors
    * @return a JavaRDD of TF-IDF vectors
    */
+  @Since("1.1.0")
   def transform(dataset: JavaRDD[Vector]): JavaRDD[Vector] = {
     transform(dataset.rdd).toJavaRDD()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index 32848e039eb81..0e070257d9fb2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 
 /**
@@ -31,9 +31,11 @@ import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors
  *
  * @param p Normalization in L^p^ space, p = 2 by default.
  */
+@Since("1.1.0")
 @Experimental
 class Normalizer(p: Double) extends VectorTransformer {
 
+  @Since("1.1.0")
   def this() = this(2)
 
   require(p >= 1.0)
@@ -44,6 +46,7 @@ class Normalizer(p: Double) extends VectorTransformer {
    * @param vector vector to be normalized.
    * @return normalized vector. If the norm of the input is zero, it will return the input vector.
    */
+  @Since("1.1.0")
   override def transform(vector: Vector): Vector = {
     val norm = Vectors.norm(vector, p)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
index 2a66263d8b7d6..a48b7bba665d7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
@@ -27,6 +28,7 @@ import org.apache.spark.rdd.RDD
  *
  * @param k number of principal components
  */
+@Since("1.4.0")
 class PCA(val k: Int) {
   require(k >= 1, s"PCA requires a number of principal components k >= 1 but was given $k")
 
@@ -35,6 +37,7 @@ class PCA(val k: Int) {
    *
    * @param sources source vectors
    */
+  @Since("1.4.0")
   def fit(sources: RDD[Vector]): PCAModel = {
     require(k <= sources.first().size,
       s"source vector size is ${sources.first().size} must be greater than k=$k")
@@ -58,7 +61,10 @@ class PCA(val k: Int) {
     new PCAModel(k, pc)
   }
 
-  /** Java-friendly version of [[fit()]] */
+  /**
+   * Java-friendly version of [[fit()]]
+   */
+  @Since("1.4.0")
   def fit(sources: JavaRDD[Vector]): PCAModel = fit(sources.rdd)
 }
 
@@ -76,6 +82,7 @@ class PCAModel private[spark] (val k: Int, val pc: DenseMatrix) extends VectorTr
    *               Vector must be the same length as the source vectors given to [[PCA.fit()]].
    * @return transformed vector. Vector will be of length k.
    */
+  @Since("1.4.0")
   override def transform(vector: Vector): Vector = {
     vector match {
       case dv: DenseVector =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index c73b8f258060d..b95d5a899001e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.feature
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
@@ -32,9 +32,11 @@ import org.apache.spark.rdd.RDD
  *                 dense output, so this does not work on sparse input and will raise an exception.
  * @param withStd True by default. Scales the data to unit standard deviation.
  */
+@Since("1.1.0")
 @Experimental
 class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
 
+  @Since("1.1.0")
   def this() = this(false, true)
 
   if (!(withMean || withStd)) {
@@ -47,6 +49,7 @@ class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
    * @param data The data used to compute the mean and variance to build the transformation model.
    * @return a StandardScalarModel
    */
+  @Since("1.1.0")
   def fit(data: RDD[Vector]): StandardScalerModel = {
     // TODO: skip computation if both withMean and withStd are false
     val summary = data.treeAggregate(new MultivariateOnlineSummarizer)(
@@ -69,6 +72,7 @@ class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
  * @param withStd whether to scale the data to have unit standard deviation
  * @param withMean whether to center the data before scaling
  */
+@Since("1.1.0")
 @Experimental
 class StandardScalerModel (
     val std: Vector,
@@ -76,6 +80,9 @@ class StandardScalerModel (
     var withStd: Boolean,
     var withMean: Boolean) extends VectorTransformer {
 
+  /**
+   */
+  @Since("1.3.0")
   def this(std: Vector, mean: Vector) {
     this(std, mean, withStd = std != null, withMean = mean != null)
     require(this.withStd || this.withMean,
@@ -86,8 +93,10 @@ class StandardScalerModel (
     }
   }
 
+  @Since("1.3.0")
   def this(std: Vector) = this(std, null)
 
+  @Since("1.3.0")
   @DeveloperApi
   def setWithMean(withMean: Boolean): this.type = {
     require(!(withMean && this.mean == null), "cannot set withMean to true while mean is null")
@@ -95,6 +104,7 @@ class StandardScalerModel (
     this
   }
 
+  @Since("1.3.0")
   @DeveloperApi
   def setWithStd(withStd: Boolean): this.type = {
     require(!(withStd && this.std == null),
@@ -115,6 +125,7 @@ class StandardScalerModel (
    * @return Standardized vector. If the std of a column is zero, it will return default `0.0`
    *         for the column with zero std.
    */
+  @Since("1.1.0")
   override def transform(vector: Vector): Vector = {
     require(mean.size == vector.size)
     if (withMean) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala
index 7358c1c84f79c..5778fd1d09254 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
@@ -26,6 +26,7 @@ import org.apache.spark.rdd.RDD
  * :: DeveloperApi ::
  * Trait for transformation of a vector
  */
+@Since("1.1.0")
 @DeveloperApi
 trait VectorTransformer extends Serializable {
 
@@ -35,6 +36,7 @@ trait VectorTransformer extends Serializable {
    * @param vector vector to be transformed.
    * @return transformed vector.
    */
+  @Since("1.1.0")
   def transform(vector: Vector): Vector
 
   /**
@@ -43,6 +45,7 @@ trait VectorTransformer extends Serializable {
    * @param data RDD[Vector] to be transformed.
    * @return transformed RDD[Vector].
    */
+  @Since("1.1.0")
   def transform(data: RDD[Vector]): RDD[Vector] = {
     // Later in #1498 , all RDD objects are sent via broadcasting instead of akka.
     // So it should be no longer necessary to explicitly broadcast `this` object.
@@ -55,6 +58,7 @@ trait VectorTransformer extends Serializable {
    * @param data JavaRDD[Vector] to be transformed.
    * @return transformed JavaRDD[Vector].
    */
+  @Since("1.1.0")
   def transform(data: JavaRDD[Vector]): JavaRDD[Vector] = {
     transform(data.rdd)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index cbbd2b0c8d060..e6f45ae4b01d5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -32,7 +32,7 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Vectors, DenseMatrix, BLAS, DenseVector}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -70,6 +70,7 @@ private case class VocabWord(
  * and
  * Distributed Representations of Words and Phrases and their Compositionality.
  */
+@Since("1.1.0")
 @Experimental
 class Word2Vec extends Serializable with Logging {
 
@@ -83,6 +84,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets vector size (default: 100).
    */
+  @Since("1.1.0")
   def setVectorSize(vectorSize: Int): this.type = {
     this.vectorSize = vectorSize
     this
@@ -91,6 +93,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets initial learning rate (default: 0.025).
    */
+  @Since("1.1.0")
   def setLearningRate(learningRate: Double): this.type = {
     this.learningRate = learningRate
     this
@@ -99,6 +102,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets number of partitions (default: 1). Use a small number for accuracy.
    */
+  @Since("1.1.0")
   def setNumPartitions(numPartitions: Int): this.type = {
     require(numPartitions > 0, s"numPartitions must be greater than 0 but got $numPartitions")
     this.numPartitions = numPartitions
@@ -109,6 +113,7 @@ class Word2Vec extends Serializable with Logging {
    * Sets number of iterations (default: 1), which should be smaller than or equal to number of
    * partitions.
    */
+  @Since("1.1.0")
   def setNumIterations(numIterations: Int): this.type = {
     this.numIterations = numIterations
     this
@@ -117,6 +122,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets random seed (default: a random long integer).
    */
+  @Since("1.1.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -126,6 +132,7 @@ class Word2Vec extends Serializable with Logging {
    * Sets minCount, the minimum number of times a token must appear to be included in the word2vec
    * model's vocabulary (default: 5).
    */
+  @Since("1.3.0")
   def setMinCount(minCount: Int): this.type = {
     this.minCount = minCount
     this
@@ -263,6 +270,7 @@ class Word2Vec extends Serializable with Logging {
    * @param dataset an RDD of words
    * @return a Word2VecModel
    */
+  @Since("1.1.0")
   def fit[S <: Iterable[String]](dataset: RDD[S]): Word2VecModel = {
 
     val words = dataset.flatMap(x => x)
@@ -412,6 +420,7 @@ class Word2Vec extends Serializable with Logging {
    * @param dataset a JavaRDD of words
    * @return a Word2VecModel
    */
+  @Since("1.1.0")
   def fit[S <: JavaIterable[String]](dataset: JavaRDD[S]): Word2VecModel = {
     fit(dataset.rdd.map(_.asScala))
   }
@@ -454,6 +463,7 @@ class Word2VecModel private[mllib] (
     wordVecNorms
   }
 
+  @Since("1.5.0")
   def this(model: Map[String, Array[Float]]) = {
     this(Word2VecModel.buildWordIndex(model), Word2VecModel.buildWordVectors(model))
   }
@@ -469,6 +479,7 @@ class Word2VecModel private[mllib] (
 
   override protected def formatVersion = "1.0"
 
+  @Since("1.4.0")
   def save(sc: SparkContext, path: String): Unit = {
     Word2VecModel.SaveLoadV1_0.save(sc, path, getVectors)
   }
@@ -478,6 +489,7 @@ class Word2VecModel private[mllib] (
    * @param word a word
    * @return vector representation of word
    */
+  @Since("1.1.0")
   def transform(word: String): Vector = {
     wordIndex.get(word) match {
       case Some(ind) =>
@@ -494,6 +506,7 @@ class Word2VecModel private[mllib] (
    * @param num number of synonyms to find
    * @return array of (word, cosineSimilarity)
    */
+  @Since("1.1.0")
   def findSynonyms(word: String, num: Int): Array[(String, Double)] = {
     val vector = transform(word)
     findSynonyms(vector, num)
@@ -505,6 +518,7 @@ class Word2VecModel private[mllib] (
    * @param num number of synonyms to find
    * @return array of (word, cosineSimilarity)
    */
+  @Since("1.1.0")
   def findSynonyms(vector: Vector, num: Int): Array[(String, Double)] = {
     require(num > 0, "Number of similar words should > 0")
     // TODO: optimize top-k
@@ -534,6 +548,7 @@ class Word2VecModel private[mllib] (
   /**
    * Returns a map of words to their vector representations.
    */
+  @Since("1.2.0")
   def getVectors: Map[String, Array[Float]] = {
     wordIndex.map { case (word, ind) =>
       (word, wordVectors.slice(vectorSize * ind, vectorSize * ind + vectorSize))
@@ -541,6 +556,7 @@ class Word2VecModel private[mllib] (
   }
 }
 
+@Since("1.4.0")
 @Experimental
 object Word2VecModel extends Loader[Word2VecModel] {
 
@@ -600,6 +616,7 @@ object Word2VecModel extends Loader[Word2VecModel] {
     }
   }
 
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): Word2VecModel = {
 
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)

From eaafe139f881d6105996373c9b11f2ccd91b5b3e Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Thu, 20 Aug 2015 15:01:31 -0700
Subject: [PATCH 1167/1454] [SPARK-9245] [MLLIB] LDA topic assignments

For each (document, term) pair, return top topic.  Note that instances of (doc, term) pairs within a document (a.k.a. "tokens") are exchangeable, so we should provide an estimate per document-term, rather than per token.

CC: rotationsymmetry mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8329 from jkbradley/lda-topic-assignments.
---
 .../spark/mllib/clustering/LDAModel.scala     | 51 +++++++++++++++++--
 .../spark/mllib/clustering/LDAOptimizer.scala |  2 +-
 .../spark/mllib/clustering/JavaLDASuite.java  |  7 +++
 .../spark/mllib/clustering/LDASuite.scala     | 21 +++++++-
 4 files changed, 74 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index b70e380c0393e..6bc68a4c18b99 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argtopk, normalize, sum}
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax, argtopk, normalize, sum}
 import breeze.numerics.{exp, lgamma}
 import org.apache.hadoop.fs.Path
 import org.json4s.DefaultFormats
@@ -438,7 +438,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       Loader.checkSchema[Data](dataFrame.schema)
       val topics = dataFrame.collect()
       val vocabSize = topics(0).getAs[Vector](0).size
-      val k = topics.size
+      val k = topics.length
 
       val brzTopics = BDM.zeros[Double](vocabSize, k)
       topics.foreach { case Row(vec: Vector, ind: Int) =>
@@ -610,6 +610,50 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
+  /**
+   * Return the top topic for each (doc, term) pair.  I.e., for each document, what is the most
+   * likely topic generating each term?
+   *
+   * @return RDD of (doc ID, assignment of top topic index for each term),
+   *         where the assignment is specified via a pair of zippable arrays
+   *         (term indices, topic indices).  Note that terms will be omitted if not present in
+   *         the document.
+   */
+  lazy val topicAssignments: RDD[(Long, Array[Int], Array[Int])] = {
+    // For reference, compare the below code with the core part of EMLDAOptimizer.next().
+    val eta = topicConcentration
+    val W = vocabSize
+    val alpha = docConcentration(0)
+    val N_k = globalTopicTotals
+    val sendMsg: EdgeContext[TopicCounts, TokenCount, (Array[Int], Array[Int])] => Unit =
+      (edgeContext) => {
+        // E-STEP: Compute gamma_{wjk} (smoothed topic distributions).
+        val scaledTopicDistribution: TopicCounts =
+          computePTopic(edgeContext.srcAttr, edgeContext.dstAttr, N_k, W, eta, alpha)
+        // For this (doc j, term w), send top topic k to doc vertex.
+        val topTopic: Int = argmax(scaledTopicDistribution)
+        val term: Int = index2term(edgeContext.dstId)
+        edgeContext.sendToSrc((Array(term), Array(topTopic)))
+      }
+    val mergeMsg: ((Array[Int], Array[Int]), (Array[Int], Array[Int])) => (Array[Int], Array[Int]) =
+      (terms_topics0, terms_topics1) => {
+        (terms_topics0._1 ++ terms_topics1._1, terms_topics0._2 ++ terms_topics1._2)
+      }
+    // M-STEP: Aggregation computes new N_{kj}, N_{wk} counts.
+    val perDocAssignments =
+      graph.aggregateMessages[(Array[Int], Array[Int])](sendMsg, mergeMsg).filter(isDocumentVertex)
+    perDocAssignments.map { case (docID: Long, (terms: Array[Int], topics: Array[Int])) =>
+      // TODO: Avoid zip, which is inefficient.
+      val (sortedTerms, sortedTopics) = terms.zip(topics).sortBy(_._1).unzip
+      (docID, sortedTerms.toArray, sortedTopics.toArray)
+    }
+  }
+
+  /** Java-friendly version of [[topicAssignments]] */
+  lazy val javaTopicAssignments: JavaRDD[(java.lang.Long, Array[Int], Array[Int])] = {
+    topicAssignments.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Int])]].toJavaRDD()
+  }
+
   // TODO
   // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ???
 
@@ -849,10 +893,9 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
     val classNameV1_0 = SaveLoadV1_0.thisClassName
 
     val model = (loadedClassName, loadedVersion) match {
-      case (className, "1.0") if className == classNameV1_0 => {
+      case (className, "1.0") if className == classNameV1_0 =>
         DistributedLDAModel.SaveLoadV1_0.load(sc, path, vocabSize, docConcentration,
           topicConcentration, iterationTimes.toArray, gammaShape)
-      }
       case _ => throw new Exception(
         s"DistributedLDAModel.load did not recognize model with (className, format version):" +
           s"($loadedClassName, $loadedVersion).  Supported: ($classNameV1_0, 1.0)")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 360241c8081ac..cb517f9689ade 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -167,7 +167,7 @@ final class EMLDAOptimizer extends LDAOptimizer {
         edgeContext.sendToDst((false, scaledTopicDistribution))
         edgeContext.sendToSrc((false, scaledTopicDistribution))
       }
-    // This is a hack to detect whether we could modify the values in-place.
+    // The Boolean is a hack to detect whether we could modify the values in-place.
     // TODO: Add zero/seqOp/combOp option to aggregateMessages. (SPARK-5438)
     val mergeMsg: ((Boolean, TopicCounts), (Boolean, TopicCounts)) => (Boolean, TopicCounts) =
       (m0, m1) => {
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index 6e91cde2eabb5..3fea359a3b46c 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -134,6 +134,13 @@ public Boolean call(Tuple2<Long, Vector> tuple2) {
     double[] topicWeights = topTopics._3();
     assertEquals(3, topicIndices.length);
     assertEquals(3, topicWeights.length);
+
+    // Check: topTopicAssignments
+    Tuple3<Long, int[], int[]> topicAssignment = model.javaTopicAssignments().first();
+    Long docId2 = topicAssignment._1();
+    int[] termIndices2 = topicAssignment._2();
+    int[] topicIndices2 = topicAssignment._3();
+    assertEquals(termIndices2.length, topicIndices2.length);
   }
 
   @Test
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 99e28499fd316..8a714f9b79e02 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -135,17 +135,34 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
 
     // Top 3 documents per topic
-    model.topDocumentsPerTopic(3).zip(topDocsByTopicDistributions(3)).foreach {case (t1, t2) =>
+    model.topDocumentsPerTopic(3).zip(topDocsByTopicDistributions(3)).foreach { case (t1, t2) =>
       assert(t1._1 === t2._1)
       assert(t1._2 === t2._2)
     }
 
     // All documents per topic
     val q = tinyCorpus.length
-    model.topDocumentsPerTopic(q).zip(topDocsByTopicDistributions(q)).foreach {case (t1, t2) =>
+    model.topDocumentsPerTopic(q).zip(topDocsByTopicDistributions(q)).foreach { case (t1, t2) =>
       assert(t1._1 === t2._1)
       assert(t1._2 === t2._2)
     }
+
+    // Check: topTopicAssignments
+    // Make sure it assigns a topic to each term appearing in each doc.
+    val topTopicAssignments: Map[Long, (Array[Int], Array[Int])] =
+      model.topicAssignments.collect().map(x => x._1 -> (x._2, x._3)).toMap
+    assert(topTopicAssignments.keys.max < tinyCorpus.length)
+    tinyCorpus.foreach { case (docID: Long, doc: Vector) =>
+      if (topTopicAssignments.contains(docID)) {
+        val (inds, vals) = topTopicAssignments(docID)
+        assert(inds.length === doc.numNonzeros)
+        // For "term" in actual doc,
+        // check that it has a topic assigned.
+        doc.foreachActive((term, wcnt) => assert(wcnt === 0 || inds.contains(term)))
+      } else {
+        assert(doc.numNonzeros === 0)
+      }
+    }
   }
 
   test("vertex indexing") {

From afe9f03fd964d1e8604d02feee8d6970efbe6009 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Thu, 20 Aug 2015 15:10:13 -0700
Subject: [PATCH 1168/1454] [SPARK-9400] [SQL] codegen for StringLocate

This is based on #7779 , thanks to tarekauel . Fix the conflict and nullability.

Closes #7779 and #8274 .

Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Davies Liu <davies@databricks.com>

Closes #8330 from davies/stringLocate.
---
 .../expressions/stringExpressions.scala       | 28 ++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index 3c23f2ecfb57c..b60d318534a41 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -409,13 +409,14 @@ case class SubstringIndex(strExpr: Expression, delimExpr: Expression, countExpr:
  * in given string after position pos.
  */
 case class StringLocate(substr: Expression, str: Expression, start: Expression)
-  extends TernaryExpression with ImplicitCastInputTypes with CodegenFallback {
+  extends TernaryExpression with ImplicitCastInputTypes {
 
   def this(substr: Expression, str: Expression) = {
     this(substr, str, Literal(0))
   }
 
   override def children: Seq[Expression] = substr :: str :: start :: Nil
+  override def nullable: Boolean = substr.nullable || str.nullable
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType, IntegerType)
 
@@ -441,6 +442,31 @@ case class StringLocate(substr: Expression, str: Expression, start: Expression)
     }
   }
 
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val substrGen = substr.gen(ctx)
+    val strGen = str.gen(ctx)
+    val startGen = start.gen(ctx)
+    s"""
+      int ${ev.primitive} = 0;
+      boolean ${ev.isNull} = false;
+      ${startGen.code}
+      if (!${startGen.isNull}) {
+        ${substrGen.code}
+        if (!${substrGen.isNull}) {
+          ${strGen.code}
+          if (!${strGen.isNull}) {
+            ${ev.primitive} = ${strGen.primitive}.indexOf(${substrGen.primitive},
+              ${startGen.primitive}) + 1;
+          } else {
+            ${ev.isNull} = true;
+          }
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+     """
+  }
+
   override def prettyName: String = "locate"
 }
 

From cdd9a2bb10e20556003843a0f7aaa33acd55f6d2 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 20 Aug 2015 20:01:13 -0700
Subject: [PATCH 1169/1454] [SPARK-10140] [DOC] add target fields to @Since

so constructors parameters and public fields can be annotated. rxin MechCoder

Author: Xiangrui Meng <meng@databricks.com>

Closes #8344 from mengxr/SPARK-10140.2.
---
 core/src/main/scala/org/apache/spark/annotation/Since.scala | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/annotation/Since.scala b/core/src/main/scala/org/apache/spark/annotation/Since.scala
index fa59393c22476..af483e361e339 100644
--- a/core/src/main/scala/org/apache/spark/annotation/Since.scala
+++ b/core/src/main/scala/org/apache/spark/annotation/Since.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.annotation
 
 import scala.annotation.StaticAnnotation
+import scala.annotation.meta._
 
 /**
  * A Scala annotation that specifies the Spark version when a definition was added.
@@ -25,4 +26,5 @@ import scala.annotation.StaticAnnotation
  * hence works for overridden methods that inherit API documentation directly from parents.
  * The limitation is that it does not show up in the generated Java API documentation.
  */
+@param @field @getter @setter @beanGetter @beanSetter
 private[spark] class Since(version: String) extends StaticAnnotation

From dcfe0c5cde953b31c5bfeb6e41d1fc9b333241eb Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Thu, 20 Aug 2015 20:02:27 -0700
Subject: [PATCH 1170/1454] [SPARK-9846] [DOCS] User guide for Multilayer
 Perceptron Classifier

Added user guide for multilayer perceptron classifier:
  - Simplified description of the multilayer perceptron classifier
  - Example code for Scala and Java

Author: Alexander Ulanov <nashb@yandex.ru>

Closes #8262 from avulanov/SPARK-9846-mlpc-docs.
---
 docs/ml-ann.md   | 123 +++++++++++++++++++++++++++++++++++++++++++++++
 docs/ml-guide.md |   1 +
 2 files changed, 124 insertions(+)
 create mode 100644 docs/ml-ann.md

diff --git a/docs/ml-ann.md b/docs/ml-ann.md
new file mode 100644
index 0000000000000..d5ddd92af1e96
--- /dev/null
+++ b/docs/ml-ann.md
@@ -0,0 +1,123 @@
+---
+layout: global
+title: Multilayer perceptron classifier - ML
+displayTitle: <a href="ml-guide.html">ML</a> - Multilayer perceptron classifier
+---
+
+
+`\[
+\newcommand{\R}{\mathbb{R}}
+\newcommand{\E}{\mathbb{E}}
+\newcommand{\x}{\mathbf{x}}
+\newcommand{\y}{\mathbf{y}}
+\newcommand{\wv}{\mathbf{w}}
+\newcommand{\av}{\mathbf{\alpha}}
+\newcommand{\bv}{\mathbf{b}}
+\newcommand{\N}{\mathbb{N}}
+\newcommand{\id}{\mathbf{I}}
+\newcommand{\ind}{\mathbf{1}}
+\newcommand{\0}{\mathbf{0}}
+\newcommand{\unit}{\mathbf{e}}
+\newcommand{\one}{\mathbf{1}}
+\newcommand{\zero}{\mathbf{0}}
+\]`
+
+
+Multilayer perceptron classifier (MLPC) is a classifier based on the [feedforward artificial neural network](https://en.wikipedia.org/wiki/Feedforward_neural_network). 
+MLPC consists of multiple layers of nodes. 
+Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data. All other nodes maps inputs to the outputs 
+by performing linear combination of the inputs with the node's weights `$\wv$` and bias `$\bv$` and applying an activation function. 
+It can be written in matrix form for MLPC with `$K+1$` layers as follows:
+`\[
+\mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T \x+b_1)+b_2)...+b_K)
+\]`
+Nodes in intermediate layers use sigmoid (logistic) function:
+`\[
+\mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}}
+\]`
+Nodes in the output layer use softmax function:
+`\[
+\mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}}
+\]`
+The number of nodes `$N$` in the output layer corresponds to the number of classes. 
+
+MLPC employes backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine.
+
+**Examples**
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.Row
+
+// Load training data
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt").toDF()
+// Split the data into train and test
+val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)
+val train = splits(0)
+val test = splits(1)
+// specify layers for the neural network: 
+// input layer of size 4 (features), two intermediate of size 5 and 4 and output of size 3 (classes)
+val layers = Array[Int](4, 5, 4, 3)
+// create the trainer and set its parameters
+val trainer = new MultilayerPerceptronClassifier()
+  .setLayers(layers)
+  .setBlockSize(128)
+  .setSeed(1234L)
+  .setMaxIter(100)
+// train the model
+val model = trainer.fit(train)
+// compute precision on the test set
+val result = model.transform(test)
+val predictionAndLabels = result.select("prediction", "label")
+val evaluator = new MulticlassClassificationEvaluator()
+  .setMetricName("precision")
+println("Precision:" + evaluator.evaluate(predictionAndLabels))
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel;
+import org.apache.spark.ml.classification.MultilayerPerceptronClassifier;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+
+// Load training data
+String path = "data/mllib/sample_multiclass_classification_data.txt";
+JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+DataFrame dataFrame = sqlContext.createDataFrame(data, LabeledPoint.class);
+// Split the data into train and test
+DataFrame[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
+DataFrame train = splits[0];
+DataFrame test = splits[1];
+// specify layers for the neural network:
+// input layer of size 4 (features), two intermediate of size 5 and 4 and output of size 3 (classes)
+int[] layers = new int[] {4, 5, 4, 3};
+// create the trainer and set its parameters
+MultilayerPerceptronClassifier trainer = new MultilayerPerceptronClassifier()
+  .setLayers(layers)
+  .setBlockSize(128)
+  .setSeed(1234L)
+  .setMaxIter(100);
+// train the model
+MultilayerPerceptronClassificationModel model = trainer.fit(train);
+// compute precision on the test set
+DataFrame result = model.transform(test);
+DataFrame predictionAndLabels = result.select("prediction", "label");
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setMetricName("precision");
+System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels));
+{% endhighlight %}
+</div>
+
+</div>
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index c64fff7c0315a..de8fead3529e4 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -179,6 +179,7 @@ There are now several algorithms in the Pipelines API which are not in the lower
 * [Decision Trees for Classification and Regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
+* [Multilayer perceptron classifier](ml-ann.html)
 
 # Code Examples
 

From bb220f6570aa0b95598b30524224a3e82c1effbc Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Fri, 21 Aug 2015 01:43:49 -0700
Subject: [PATCH 1171/1454] [SPARK-10040] [SQL] Use batch insert for JDBC
 writing

JIRA: https://issues.apache.org/jira/browse/SPARK-10040

We should use batch insert instead of single row in JDBC.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #8273 from viirya/jdbc-insert-batch.
---
 .../execution/datasources/jdbc/JdbcUtils.scala  | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 2d0e736ee4766..26788b2a4fd69 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -88,13 +88,15 @@ object JdbcUtils extends Logging {
       table: String,
       iterator: Iterator[Row],
       rddSchema: StructType,
-      nullTypes: Array[Int]): Iterator[Byte] = {
+      nullTypes: Array[Int],
+      batchSize: Int): Iterator[Byte] = {
     val conn = getConnection()
     var committed = false
     try {
       conn.setAutoCommit(false) // Everything in the same db transaction.
       val stmt = insertStatement(conn, table, rddSchema)
       try {
+        var rowCount = 0
         while (iterator.hasNext) {
           val row = iterator.next()
           val numFields = rddSchema.fields.length
@@ -122,7 +124,15 @@ object JdbcUtils extends Logging {
             }
             i = i + 1
           }
-          stmt.executeUpdate()
+          stmt.addBatch()
+          rowCount += 1
+          if (rowCount % batchSize == 0) {
+            stmt.executeBatch()
+            rowCount = 0
+          }
+        }
+        if (rowCount > 0) {
+          stmt.executeBatch()
         }
       } finally {
         stmt.close()
@@ -211,8 +221,9 @@ object JdbcUtils extends Logging {
     val rddSchema = df.schema
     val driver: String = DriverRegistry.getDriverClassName(url)
     val getConnection: () => Connection = JDBCRDD.getConnector(driver, url, properties)
+    val batchSize = properties.getProperty("batchsize", "1000").toInt
     df.foreachPartition { iterator =>
-      savePartition(getConnection, table, iterator, rddSchema, nullTypes)
+      savePartition(getConnection, table, iterator, rddSchema, nullTypes, batchSize)
     }
   }
 

From 708036c1de52d674ceff30ac465e1dcedeb8dde8 Mon Sep 17 00:00:00 2001
From: Imran Rashid <irashid@cloudera.com>
Date: Fri, 21 Aug 2015 08:41:36 -0500
Subject: [PATCH 1172/1454] [SPARK-9439] [YARN] External shuffle service robust
 to NM restarts using leveldb

https://issues.apache.org/jira/browse/SPARK-9439

In general, Yarn apps should be robust to NodeManager restarts.  However, if you run spark with the external shuffle service on, after a NM restart all shuffles fail, b/c the shuffle service has lost some state with info on each executor.  (Note the shuffle data is perfectly fine on disk across a NM restart, the problem is we've lost the small bit of state that lets us *find* those files.)

The solution proposed here is that the external shuffle service can write out its state to leveldb (backed by a local file) every time an executor is added.  When running with yarn, that file is in the NM's local dir.  Whenever the service is started, it looks for that file, and if it exists, it reads the file and re-registers all executors there.

Nothing is changed in non-yarn modes with this patch.  The service is not given a place to save the state to, so it operates the same as before.  This should make it easy to update other cluster managers as well, by just supplying the right file & the equivalent of yarn's `initializeApplication` -- I'm not familiar enough with those modes to know how to do that.

Author: Imran Rashid <irashid@cloudera.com>

Closes #7943 from squito/leveldb_external_shuffle_service_NM_restart and squashes the following commits:

0d285d3 [Imran Rashid] review feedback
70951d6 [Imran Rashid] Merge branch 'master' into leveldb_external_shuffle_service_NM_restart
5c71c8c [Imran Rashid] save executor to db before registering; style
2499c8c [Imran Rashid] explicit dependency on jackson-annotations
795d28f [Imran Rashid] review feedback
81f80e2 [Imran Rashid] Merge branch 'master' into leveldb_external_shuffle_service_NM_restart
594d520 [Imran Rashid] use json to serialize application executor info
1a7980b [Imran Rashid] version
8267d2a [Imran Rashid] style
e9f99e8 [Imran Rashid] cleanup the handling of bad dbs a little
9378ba3 [Imran Rashid] fail gracefully on corrupt leveldb files
acedb62 [Imran Rashid] switch to writing out one record per executor
79922b7 [Imran Rashid] rely on yarn to call stopApplication; assorted cleanup
12b6a35 [Imran Rashid] save registered executors when apps are removed; add tests
c878fbe [Imran Rashid] better explanation of shuffle service port handling
694934c [Imran Rashid] only open leveldb connection once per service
d596410 [Imran Rashid] store executor data in leveldb
59800b7 [Imran Rashid] Files.move in case renaming is unsupported
32fe5ae [Imran Rashid] Merge branch 'master' into external_shuffle_service_NM_restart
d7450f0 [Imran Rashid] style
f729e2b [Imran Rashid] debugging
4492835 [Imran Rashid] lol, dont use a PrintWriter b/c of scalastyle checks
0a39b98 [Imran Rashid] Merge branch 'master' into external_shuffle_service_NM_restart
55f49fc [Imran Rashid] make sure the service doesnt die if the registered executor file is corrupt; add tests
245db19 [Imran Rashid] style
62586a6 [Imran Rashid] just serialize the whole executors map
bdbbf0d [Imran Rashid] comments, remove some unnecessary changes
857331a [Imran Rashid] better tests & comments
bb9d1e6 [Imran Rashid] formatting
bdc4b32 [Imran Rashid] rename
86e0cb9 [Imran Rashid] for tests, shuffle service finds an open port
23994ff [Imran Rashid] style
7504de8 [Imran Rashid] style
a36729c [Imran Rashid] cleanup
efb6195 [Imran Rashid] proper unit test, and no longer leak if apps stop during NM restart
dd93dc0 [Imran Rashid] test for shuffle service w/ NM restarts
d596969 [Imran Rashid] cleanup imports
0e9d69b [Imran Rashid] better names
9eae119 [Imran Rashid] cleanup lots of duplication
1136f44 [Imran Rashid] test needs to have an actual shuffle
0b588bd [Imran Rashid] more fixes ...
ad122ef [Imran Rashid] more fixes
5e5a7c3 [Imran Rashid] fix build
c69f46b [Imran Rashid] maybe working version, needs tests & cleanup ...
bb3ba49 [Imran Rashid] minor cleanup
36127d3 [Imran Rashid] wip
b9d2ced [Imran Rashid] incomplete setup for external shuffle service tests
---
 .../spark/deploy/ExternalShuffleService.scala |   2 +-
 .../mesos/MesosExternalShuffleService.scala   |   2 +-
 .../apache/spark/storage/BlockManager.scala   |  14 +-
 .../spark/ExternalShuffleServiceSuite.scala   |   2 +-
 network/shuffle/pom.xml                       |  16 ++
 .../shuffle/ExternalShuffleBlockHandler.java  |  37 ++-
 .../shuffle/ExternalShuffleBlockResolver.java | 225 +++++++++++++++--
 .../shuffle/protocol/ExecutorShuffleInfo.java |   8 +-
 .../ExternalShuffleBlockResolverSuite.java    |  35 ++-
 .../shuffle/ExternalShuffleCleanupSuite.java  |   9 +-
 .../ExternalShuffleIntegrationSuite.java      |   2 +-
 .../shuffle/ExternalShuffleSecuritySuite.java |   5 +-
 .../network/yarn/YarnShuffleService.java      |  62 ++++-
 pom.xml                                       |   5 +
 yarn/pom.xml                                  |   6 +
 .../deploy/yarn/BaseYarnClusterSuite.scala    | 193 +++++++++++++++
 .../spark/deploy/yarn/YarnClusterSuite.scala  | 173 +------------
 .../yarn/YarnShuffleIntegrationSuite.scala    | 109 ++++++++
 .../network/shuffle/ShuffleTestAccessor.scala |  71 ++++++
 .../yarn/YarnShuffleServiceSuite.scala        | 233 ++++++++++++++++++
 .../spark/network/yarn/YarnTestAccessor.scala |  37 +++
 21 files changed, 1031 insertions(+), 215 deletions(-)
 create mode 100644 yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
index 20a9faa1784b7..22ef701d833b2 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
@@ -53,7 +53,7 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana
 
   /** Create a new shuffle block handler. Factored out for subclasses to override. */
   protected def newShuffleBlockHandler(conf: TransportConf): ExternalShuffleBlockHandler = {
-    new ExternalShuffleBlockHandler(conf)
+    new ExternalShuffleBlockHandler(conf, null)
   }
 
   /** Starts the external shuffle service if the user has configured us to. */
diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
index 061857476a8a0..12337a940a414 100644
--- a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
@@ -34,7 +34,7 @@ import org.apache.spark.network.util.TransportConf
  * It detects driver termination and calls the cleanup callback to [[ExternalShuffleService]].
  */
 private[mesos] class MesosExternalShuffleBlockHandler(transportConf: TransportConf)
-  extends ExternalShuffleBlockHandler(transportConf) with Logging {
+  extends ExternalShuffleBlockHandler(transportConf, null) with Logging {
 
   // Stores a map of driver socket addresses to app ids
   private val connectedApps = new mutable.HashMap[SocketAddress, String]
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index eedb27942e841..fefaef0ab82c8 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -93,8 +93,17 @@ private[spark] class BlockManager(
 
   // Port used by the external shuffle service. In Yarn mode, this may be already be
   // set through the Hadoop configuration as the server is launched in the Yarn NM.
-  private val externalShuffleServicePort =
-    Utils.getSparkOrYarnConfig(conf, "spark.shuffle.service.port", "7337").toInt
+  private val externalShuffleServicePort = {
+    val tmpPort = Utils.getSparkOrYarnConfig(conf, "spark.shuffle.service.port", "7337").toInt
+    if (tmpPort == 0) {
+      // for testing, we set "spark.shuffle.service.port" to 0 in the yarn config, so yarn finds
+      // an open port.  But we still need to tell our spark apps the right port to use.  So
+      // only if the yarn config has the port set to 0, we prefer the value in the spark config
+      conf.get("spark.shuffle.service.port").toInt
+    } else {
+      tmpPort
+    }
+  }
 
   // Check that we're not using external shuffle service with consolidated shuffle files.
   if (externalShuffleServiceEnabled
@@ -191,6 +200,7 @@ private[spark] class BlockManager(
       executorId, blockTransferService.hostName, blockTransferService.port)
 
     shuffleServerId = if (externalShuffleServiceEnabled) {
+      logInfo(s"external shuffle service port = $externalShuffleServicePort")
       BlockManagerId(executorId, blockTransferService.hostName, externalShuffleServicePort)
     } else {
       blockManagerId
diff --git a/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala b/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
index c38d70252add1..e846a72c888c6 100644
--- a/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
@@ -36,7 +36,7 @@ class ExternalShuffleServiceSuite extends ShuffleSuite with BeforeAndAfterAll {
 
   override def beforeAll() {
     val transportConf = SparkTransportConf.fromSparkConf(conf, numUsableCores = 2)
-    rpcHandler = new ExternalShuffleBlockHandler(transportConf)
+    rpcHandler = new ExternalShuffleBlockHandler(transportConf, null)
     val transportContext = new TransportContext(transportConf, rpcHandler)
     server = transportContext.createServer()
 
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index 532463e96fbb7..3d2edf9d94515 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -43,6 +43,22 @@
       <version>${project.version}</version>
     </dependency>
 
+    <dependency>
+      <groupId>org.fusesource.leveldbjni</groupId>
+      <artifactId>leveldbjni-all</artifactId>
+      <version>1.8</version>
+    </dependency>
+
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-databind</artifactId>
+    </dependency>
+
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-annotations</artifactId>
+    </dependency>
+
     <!-- Provided dependencies -->
     <dependency>
       <groupId>org.slf4j</groupId>
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
index db9dc4f17cee9..0df1dd621f6e9 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
@@ -17,11 +17,12 @@
 
 package org.apache.spark.network.shuffle;
 
+import java.io.File;
+import java.io.IOException;
 import java.util.List;
 
 import com.google.common.annotations.VisibleForTesting;
 import com.google.common.collect.Lists;
-import org.apache.spark.network.util.TransportConf;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -31,10 +32,10 @@
 import org.apache.spark.network.server.OneForOneStreamManager;
 import org.apache.spark.network.server.RpcHandler;
 import org.apache.spark.network.server.StreamManager;
-import org.apache.spark.network.shuffle.protocol.BlockTransferMessage;
-import org.apache.spark.network.shuffle.protocol.OpenBlocks;
-import org.apache.spark.network.shuffle.protocol.RegisterExecutor;
-import org.apache.spark.network.shuffle.protocol.StreamHandle;
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.AppExecId;
+import org.apache.spark.network.shuffle.protocol.*;
+import org.apache.spark.network.util.TransportConf;
+
 
 /**
  * RPC Handler for a server which can serve shuffle blocks from outside of an Executor process.
@@ -46,11 +47,13 @@
 public class ExternalShuffleBlockHandler extends RpcHandler {
   private final Logger logger = LoggerFactory.getLogger(ExternalShuffleBlockHandler.class);
 
-  private final ExternalShuffleBlockResolver blockManager;
+  @VisibleForTesting
+  final ExternalShuffleBlockResolver blockManager;
   private final OneForOneStreamManager streamManager;
 
-  public ExternalShuffleBlockHandler(TransportConf conf) {
-    this(new OneForOneStreamManager(), new ExternalShuffleBlockResolver(conf));
+  public ExternalShuffleBlockHandler(TransportConf conf, File registeredExecutorFile) throws IOException {
+    this(new OneForOneStreamManager(),
+      new ExternalShuffleBlockResolver(conf, registeredExecutorFile));
   }
 
   /** Enables mocking out the StreamManager and BlockManager. */
@@ -105,4 +108,22 @@ public StreamManager getStreamManager() {
   public void applicationRemoved(String appId, boolean cleanupLocalDirs) {
     blockManager.applicationRemoved(appId, cleanupLocalDirs);
   }
+
+  /**
+   * Register an (application, executor) with the given shuffle info.
+   *
+   * The "re-" is meant to highlight the intended use of this method -- when this service is
+   * restarted, this is used to restore the state of executors from before the restart.  Normal
+   * registration will happen via a message handled in receive()
+   *
+   * @param appExecId
+   * @param executorInfo
+   */
+  public void reregisterExecutor(AppExecId appExecId, ExecutorShuffleInfo executorInfo) {
+    blockManager.registerExecutor(appExecId.appId, appExecId.execId, executorInfo);
+  }
+
+  public void close() {
+    blockManager.close();
+  }
 }
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
index 022ed88a16480..79beec4429a99 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
@@ -17,19 +17,24 @@
 
 package org.apache.spark.network.shuffle;
 
-import java.io.DataInputStream;
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.IOException;
-import java.util.Iterator;
-import java.util.Map;
+import java.io.*;
+import java.util.*;
 import java.util.concurrent.ConcurrentMap;
 import java.util.concurrent.Executor;
 import java.util.concurrent.Executors;
 
+import com.fasterxml.jackson.annotation.JsonCreator;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.databind.ObjectMapper;
 import com.google.common.annotations.VisibleForTesting;
+import com.google.common.base.Charsets;
 import com.google.common.base.Objects;
 import com.google.common.collect.Maps;
+import org.fusesource.leveldbjni.JniDBFactory;
+import org.fusesource.leveldbjni.internal.NativeDB;
+import org.iq80.leveldb.DB;
+import org.iq80.leveldb.DBIterator;
+import org.iq80.leveldb.Options;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -52,25 +57,87 @@
 public class ExternalShuffleBlockResolver {
   private static final Logger logger = LoggerFactory.getLogger(ExternalShuffleBlockResolver.class);
 
+  private static final ObjectMapper mapper = new ObjectMapper();
+  /**
+   * This a common prefix to the key for each app registration we stick in leveldb, so they
+   * are easy to find, since leveldb lets you search based on prefix.
+   */
+  private static final String APP_KEY_PREFIX = "AppExecShuffleInfo";
+  private static final StoreVersion CURRENT_VERSION = new StoreVersion(1, 0);
+
   // Map containing all registered executors' metadata.
-  private final ConcurrentMap<AppExecId, ExecutorShuffleInfo> executors;
+  @VisibleForTesting
+  final ConcurrentMap<AppExecId, ExecutorShuffleInfo> executors;
 
   // Single-threaded Java executor used to perform expensive recursive directory deletion.
   private final Executor directoryCleaner;
 
   private final TransportConf conf;
 
-  public ExternalShuffleBlockResolver(TransportConf conf) {
-    this(conf, Executors.newSingleThreadExecutor(
+  @VisibleForTesting
+  final File registeredExecutorFile;
+  @VisibleForTesting
+  final DB db;
+
+  public ExternalShuffleBlockResolver(TransportConf conf, File registeredExecutorFile)
+      throws IOException {
+    this(conf, registeredExecutorFile, Executors.newSingleThreadExecutor(
         // Add `spark` prefix because it will run in NM in Yarn mode.
         NettyUtils.createThreadFactory("spark-shuffle-directory-cleaner")));
   }
 
   // Allows tests to have more control over when directories are cleaned up.
   @VisibleForTesting
-  ExternalShuffleBlockResolver(TransportConf conf, Executor directoryCleaner) {
+  ExternalShuffleBlockResolver(
+      TransportConf conf,
+      File registeredExecutorFile,
+      Executor directoryCleaner) throws IOException {
     this.conf = conf;
-    this.executors = Maps.newConcurrentMap();
+    this.registeredExecutorFile = registeredExecutorFile;
+    if (registeredExecutorFile != null) {
+      Options options = new Options();
+      options.createIfMissing(false);
+      options.logger(new LevelDBLogger());
+      DB tmpDb;
+      try {
+        tmpDb = JniDBFactory.factory.open(registeredExecutorFile, options);
+      } catch (NativeDB.DBException e) {
+        if (e.isNotFound() || e.getMessage().contains(" does not exist ")) {
+          logger.info("Creating state database at " + registeredExecutorFile);
+          options.createIfMissing(true);
+          try {
+            tmpDb = JniDBFactory.factory.open(registeredExecutorFile, options);
+          } catch (NativeDB.DBException dbExc) {
+            throw new IOException("Unable to create state store", dbExc);
+          }
+        } else {
+          // the leveldb file seems to be corrupt somehow.  Lets just blow it away and create a new
+          // one, so we can keep processing new apps
+          logger.error("error opening leveldb file {}.  Creating new file, will not be able to " +
+            "recover state for existing applications", registeredExecutorFile, e);
+          if (registeredExecutorFile.isDirectory()) {
+            for (File f : registeredExecutorFile.listFiles()) {
+              f.delete();
+            }
+          }
+          registeredExecutorFile.delete();
+          options.createIfMissing(true);
+          try {
+            tmpDb = JniDBFactory.factory.open(registeredExecutorFile, options);
+          } catch (NativeDB.DBException dbExc) {
+            throw new IOException("Unable to create state store", dbExc);
+          }
+
+        }
+      }
+      // if there is a version mismatch, we throw an exception, which means the service is unusable
+      checkVersion(tmpDb);
+      executors = reloadRegisteredExecutors(tmpDb);
+      db = tmpDb;
+    } else {
+      db = null;
+      executors = Maps.newConcurrentMap();
+    }
     this.directoryCleaner = directoryCleaner;
   }
 
@@ -81,6 +148,15 @@ public void registerExecutor(
       ExecutorShuffleInfo executorInfo) {
     AppExecId fullId = new AppExecId(appId, execId);
     logger.info("Registered executor {} with {}", fullId, executorInfo);
+    try {
+      if (db != null) {
+        byte[] key = dbAppExecKey(fullId);
+        byte[] value = mapper.writeValueAsString(executorInfo).getBytes(Charsets.UTF_8);
+        db.put(key, value);
+      }
+    } catch (Exception e) {
+      logger.error("Error saving registered executors", e);
+    }
     executors.put(fullId, executorInfo);
   }
 
@@ -136,6 +212,13 @@ public void applicationRemoved(String appId, boolean cleanupLocalDirs) {
       // Only touch executors associated with the appId that was removed.
       if (appId.equals(fullId.appId)) {
         it.remove();
+        if (db != null) {
+          try {
+            db.delete(dbAppExecKey(fullId));
+          } catch (IOException e) {
+            logger.error("Error deleting {} from executor state db", appId, e);
+          }
+        }
 
         if (cleanupLocalDirs) {
           logger.info("Cleaning up executor {}'s {} local dirs", fullId, executor.localDirs.length);
@@ -220,12 +303,23 @@ static File getFile(String[] localDirs, int subDirsPerLocalDir, String filename)
     return new File(new File(localDir, String.format("%02x", subDirId)), filename);
   }
 
+  void close() {
+    if (db != null) {
+      try {
+        db.close();
+      } catch (IOException e) {
+        logger.error("Exception closing leveldb with registered executors", e);
+      }
+    }
+  }
+
   /** Simply encodes an executor's full ID, which is appId + execId. */
-  private static class AppExecId {
-    final String appId;
-    final String execId;
+  public static class AppExecId {
+    public final String appId;
+    public final String execId;
 
-    private AppExecId(String appId, String execId) {
+    @JsonCreator
+    public AppExecId(@JsonProperty("appId") String appId, @JsonProperty("execId") String execId) {
       this.appId = appId;
       this.execId = execId;
     }
@@ -252,4 +346,105 @@ public String toString() {
         .toString();
     }
   }
+
+  private static byte[] dbAppExecKey(AppExecId appExecId) throws IOException {
+    // we stick a common prefix on all the keys so we can find them in the DB
+    String appExecJson = mapper.writeValueAsString(appExecId);
+    String key = (APP_KEY_PREFIX + ";" + appExecJson);
+    return key.getBytes(Charsets.UTF_8);
+  }
+
+  private static AppExecId parseDbAppExecKey(String s) throws IOException {
+    if (!s.startsWith(APP_KEY_PREFIX)) {
+      throw new IllegalArgumentException("expected a string starting with " + APP_KEY_PREFIX);
+    }
+    String json = s.substring(APP_KEY_PREFIX.length() + 1);
+    AppExecId parsed = mapper.readValue(json, AppExecId.class);
+    return parsed;
+  }
+
+  @VisibleForTesting
+  static ConcurrentMap<AppExecId, ExecutorShuffleInfo> reloadRegisteredExecutors(DB db)
+      throws IOException {
+    ConcurrentMap<AppExecId, ExecutorShuffleInfo> registeredExecutors = Maps.newConcurrentMap();
+    if (db != null) {
+      DBIterator itr = db.iterator();
+      itr.seek(APP_KEY_PREFIX.getBytes(Charsets.UTF_8));
+      while (itr.hasNext()) {
+        Map.Entry<byte[], byte[]> e = itr.next();
+        String key = new String(e.getKey(), Charsets.UTF_8);
+        if (!key.startsWith(APP_KEY_PREFIX)) {
+          break;
+        }
+        AppExecId id = parseDbAppExecKey(key);
+        ExecutorShuffleInfo shuffleInfo = mapper.readValue(e.getValue(), ExecutorShuffleInfo.class);
+        registeredExecutors.put(id, shuffleInfo);
+      }
+    }
+    return registeredExecutors;
+  }
+
+  private static class LevelDBLogger implements org.iq80.leveldb.Logger {
+    private static final Logger LOG = LoggerFactory.getLogger(LevelDBLogger.class);
+
+    @Override
+    public void log(String message) {
+      LOG.info(message);
+    }
+  }
+
+  /**
+   * Simple major.minor versioning scheme.  Any incompatible changes should be across major
+   * versions.  Minor version differences are allowed -- meaning we should be able to read
+   * dbs that are either earlier *or* later on the minor version.
+   */
+  private static void checkVersion(DB db) throws IOException {
+    byte[] bytes = db.get(StoreVersion.KEY);
+    if (bytes == null) {
+      storeVersion(db);
+    } else {
+      StoreVersion version = mapper.readValue(bytes, StoreVersion.class);
+      if (version.major != CURRENT_VERSION.major) {
+        throw new IOException("cannot read state DB with version " + version + ", incompatible " +
+          "with current version " + CURRENT_VERSION);
+      }
+      storeVersion(db);
+    }
+  }
+
+  private static void storeVersion(DB db) throws IOException {
+    db.put(StoreVersion.KEY, mapper.writeValueAsBytes(CURRENT_VERSION));
+  }
+
+
+  public static class StoreVersion {
+
+    final static byte[] KEY = "StoreVersion".getBytes(Charsets.UTF_8);
+
+    public final int major;
+    public final int minor;
+
+    @JsonCreator public StoreVersion(@JsonProperty("major") int major, @JsonProperty("minor") int minor) {
+      this.major = major;
+      this.minor = minor;
+    }
+
+    @Override
+    public boolean equals(Object o) {
+      if (this == o) return true;
+      if (o == null || getClass() != o.getClass()) return false;
+
+      StoreVersion that = (StoreVersion) o;
+
+      return major == that.major && minor == that.minor;
+    }
+
+    @Override
+    public int hashCode() {
+      int result = major;
+      result = 31 * result + minor;
+      return result;
+    }
+  }
+
 }
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java
index cadc8e8369c6a..102d4efb8bf3b 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java
@@ -19,6 +19,8 @@
 
 import java.util.Arrays;
 
+import com.fasterxml.jackson.annotation.JsonCreator;
+import com.fasterxml.jackson.annotation.JsonProperty;
 import com.google.common.base.Objects;
 import io.netty.buffer.ByteBuf;
 
@@ -34,7 +36,11 @@ public class ExecutorShuffleInfo implements Encodable {
   /** Shuffle manager (SortShuffleManager or HashShuffleManager) that the executor is using. */
   public final String shuffleManager;
 
-  public ExecutorShuffleInfo(String[] localDirs, int subDirsPerLocalDir, String shuffleManager) {
+  @JsonCreator
+  public ExecutorShuffleInfo(
+      @JsonProperty("localDirs") String[] localDirs,
+      @JsonProperty("subDirsPerLocalDir") int subDirsPerLocalDir,
+      @JsonProperty("shuffleManager") String shuffleManager) {
     this.localDirs = localDirs;
     this.subDirsPerLocalDir = subDirsPerLocalDir;
     this.shuffleManager = shuffleManager;
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
index d02f4f0fdb682..3c6cb367dea46 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
@@ -21,9 +21,12 @@
 import java.io.InputStream;
 import java.io.InputStreamReader;
 
+import com.fasterxml.jackson.databind.ObjectMapper;
 import com.google.common.io.CharStreams;
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo;
 import org.apache.spark.network.util.SystemPropertyConfigProvider;
 import org.apache.spark.network.util.TransportConf;
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.AppExecId;
 import org.junit.AfterClass;
 import org.junit.BeforeClass;
 import org.junit.Test;
@@ -59,8 +62,8 @@ public static void afterAll() {
   }
 
   @Test
-  public void testBadRequests() {
-    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+  public void testBadRequests() throws IOException {
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf, null);
     // Unregistered executor
     try {
       resolver.getBlockData("app0", "exec1", "shuffle_1_1_0");
@@ -91,7 +94,7 @@ public void testBadRequests() {
 
   @Test
   public void testSortShuffleBlocks() throws IOException {
-    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf, null);
     resolver.registerExecutor("app0", "exec0",
       dataContext.createExecutorInfo("org.apache.spark.shuffle.sort.SortShuffleManager"));
 
@@ -110,7 +113,7 @@ public void testSortShuffleBlocks() throws IOException {
 
   @Test
   public void testHashShuffleBlocks() throws IOException {
-    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf, null);
     resolver.registerExecutor("app0", "exec0",
       dataContext.createExecutorInfo("org.apache.spark.shuffle.hash.HashShuffleManager"));
 
@@ -126,4 +129,28 @@ public void testHashShuffleBlocks() throws IOException {
     block1Stream.close();
     assertEquals(hashBlock1, block1);
   }
+
+  @Test
+  public void jsonSerializationOfExecutorRegistration() throws IOException {
+    ObjectMapper mapper = new ObjectMapper();
+    AppExecId appId = new AppExecId("foo", "bar");
+    String appIdJson = mapper.writeValueAsString(appId);
+    AppExecId parsedAppId = mapper.readValue(appIdJson, AppExecId.class);
+    assertEquals(parsedAppId, appId);
+
+    ExecutorShuffleInfo shuffleInfo =
+      new ExecutorShuffleInfo(new String[]{"/bippy", "/flippy"}, 7, "hash");
+    String shuffleJson = mapper.writeValueAsString(shuffleInfo);
+    ExecutorShuffleInfo parsedShuffleInfo =
+      mapper.readValue(shuffleJson, ExecutorShuffleInfo.class);
+    assertEquals(parsedShuffleInfo, shuffleInfo);
+
+    // Intentionally keep these hard-coded strings in here, to check backwards-compatability.
+    // its not legacy yet, but keeping this here in case anybody changes it
+    String legacyAppIdJson = "{\"appId\":\"foo\", \"execId\":\"bar\"}";
+    assertEquals(appId, mapper.readValue(legacyAppIdJson, AppExecId.class));
+    String legacyShuffleJson = "{\"localDirs\": [\"/bippy\", \"/flippy\"], " +
+      "\"subDirsPerLocalDir\": 7, \"shuffleManager\": \"hash\"}";
+    assertEquals(shuffleInfo, mapper.readValue(legacyShuffleJson, ExecutorShuffleInfo.class));
+  }
 }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
index d9d9c1bf2f17a..2f4f1d0df478b 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
@@ -42,7 +42,7 @@ public void noCleanupAndCleanup() throws IOException {
     TestShuffleDataContext dataContext = createSomeData();
 
     ExternalShuffleBlockResolver resolver =
-      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
+      new ExternalShuffleBlockResolver(conf, null, sameThreadExecutor);
     resolver.registerExecutor("app", "exec0", dataContext.createExecutorInfo("shuffleMgr"));
     resolver.applicationRemoved("app", false /* cleanup */);
 
@@ -65,7 +65,8 @@ public void cleanupUsesExecutor() throws IOException {
       @Override public void execute(Runnable runnable) { cleanupCalled.set(true); }
     };
 
-    ExternalShuffleBlockResolver manager = new ExternalShuffleBlockResolver(conf, noThreadExecutor);
+    ExternalShuffleBlockResolver manager =
+      new ExternalShuffleBlockResolver(conf, null, noThreadExecutor);
 
     manager.registerExecutor("app", "exec0", dataContext.createExecutorInfo("shuffleMgr"));
     manager.applicationRemoved("app", true);
@@ -83,7 +84,7 @@ public void cleanupMultipleExecutors() throws IOException {
     TestShuffleDataContext dataContext1 = createSomeData();
 
     ExternalShuffleBlockResolver resolver =
-      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
+      new ExternalShuffleBlockResolver(conf, null, sameThreadExecutor);
 
     resolver.registerExecutor("app", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
     resolver.registerExecutor("app", "exec1", dataContext1.createExecutorInfo("shuffleMgr"));
@@ -99,7 +100,7 @@ public void cleanupOnlyRemovedApp() throws IOException {
     TestShuffleDataContext dataContext1 = createSomeData();
 
     ExternalShuffleBlockResolver resolver =
-      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
+      new ExternalShuffleBlockResolver(conf, null, sameThreadExecutor);
 
     resolver.registerExecutor("app-0", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
     resolver.registerExecutor("app-1", "exec0", dataContext1.createExecutorInfo("shuffleMgr"));
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java
index 39aa49911d9cb..a3f9a38b1aeb9 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java
@@ -92,7 +92,7 @@ public static void beforeAll() throws IOException {
     dataContext1.insertHashShuffleData(1, 0, exec1Blocks);
 
     conf = new TransportConf(new SystemPropertyConfigProvider());
-    handler = new ExternalShuffleBlockHandler(conf);
+    handler = new ExternalShuffleBlockHandler(conf, null);
     TransportContext transportContext = new TransportContext(conf, handler);
     server = transportContext.createServer();
   }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java
index d4ec1956c1e29..aa99efda94948 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java
@@ -43,8 +43,9 @@ public class ExternalShuffleSecuritySuite {
   TransportServer server;
 
   @Before
-  public void beforeEach() {
-    TransportContext context = new TransportContext(conf, new ExternalShuffleBlockHandler(conf));
+  public void beforeEach() throws IOException {
+    TransportContext context =
+      new TransportContext(conf, new ExternalShuffleBlockHandler(conf, null));
     TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf,
         new TestSecretKeyHolder("my-app-id", "secret"));
     this.server = context.createServer(Arrays.asList(bootstrap));
diff --git a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java
index 463f99ef3352d..11ea7f3fd3cfe 100644
--- a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java
+++ b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java
@@ -17,25 +17,21 @@
 
 package org.apache.spark.network.yarn;
 
+import java.io.File;
 import java.nio.ByteBuffer;
 import java.util.List;
 
+import com.google.common.annotations.VisibleForTesting;
 import com.google.common.collect.Lists;
 import org.apache.hadoop.conf.Configuration;
-import org.apache.hadoop.yarn.api.records.ApplicationId;
 import org.apache.hadoop.yarn.api.records.ContainerId;
-import org.apache.hadoop.yarn.server.api.AuxiliaryService;
-import org.apache.hadoop.yarn.server.api.ApplicationInitializationContext;
-import org.apache.hadoop.yarn.server.api.ApplicationTerminationContext;
-import org.apache.hadoop.yarn.server.api.ContainerInitializationContext;
-import org.apache.hadoop.yarn.server.api.ContainerTerminationContext;
+import org.apache.hadoop.yarn.server.api.*;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import org.apache.spark.network.TransportContext;
 import org.apache.spark.network.sasl.SaslServerBootstrap;
 import org.apache.spark.network.sasl.ShuffleSecretManager;
-import org.apache.spark.network.server.RpcHandler;
 import org.apache.spark.network.server.TransportServer;
 import org.apache.spark.network.server.TransportServerBootstrap;
 import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler;
@@ -79,11 +75,26 @@ public class YarnShuffleService extends AuxiliaryService {
   private TransportServer shuffleServer = null;
 
   // Handles registering executors and opening shuffle blocks
-  private ExternalShuffleBlockHandler blockHandler;
+  @VisibleForTesting
+  ExternalShuffleBlockHandler blockHandler;
+
+  // Where to store & reload executor info for recovering state after an NM restart
+  @VisibleForTesting
+  File registeredExecutorFile;
+
+  // just for testing when you want to find an open port
+  @VisibleForTesting
+  static int boundPort = -1;
+
+  // just for integration tests that want to look at this file -- in general not sensible as
+  // a static
+  @VisibleForTesting
+  static YarnShuffleService instance;
 
   public YarnShuffleService() {
     super("spark_shuffle");
     logger.info("Initializing YARN shuffle service for Spark");
+    instance = this;
   }
 
   /**
@@ -100,11 +111,24 @@ private boolean isAuthenticationEnabled() {
    */
   @Override
   protected void serviceInit(Configuration conf) {
+
+    // In case this NM was killed while there were running spark applications, we need to restore
+    // lost state for the existing executors.  We look for an existing file in the NM's local dirs.
+    // If we don't find one, then we choose a file to use to save the state next time.  Even if
+    // an application was stopped while the NM was down, we expect yarn to call stopApplication()
+    // when it comes back
+    registeredExecutorFile =
+      findRegisteredExecutorFile(conf.getStrings("yarn.nodemanager.local-dirs"));
+
     TransportConf transportConf = new TransportConf(new HadoopConfigProvider(conf));
     // If authentication is enabled, set up the shuffle server to use a
     // special RPC handler that filters out unauthenticated fetch requests
     boolean authEnabled = conf.getBoolean(SPARK_AUTHENTICATE_KEY, DEFAULT_SPARK_AUTHENTICATE);
-    blockHandler = new ExternalShuffleBlockHandler(transportConf);
+    try {
+      blockHandler = new ExternalShuffleBlockHandler(transportConf, registeredExecutorFile);
+    } catch (Exception e) {
+      logger.error("Failed to initialize external shuffle service", e);
+    }
 
     List<TransportServerBootstrap> bootstraps = Lists.newArrayList();
     if (authEnabled) {
@@ -116,9 +140,13 @@ protected void serviceInit(Configuration conf) {
       SPARK_SHUFFLE_SERVICE_PORT_KEY, DEFAULT_SPARK_SHUFFLE_SERVICE_PORT);
     TransportContext transportContext = new TransportContext(transportConf, blockHandler);
     shuffleServer = transportContext.createServer(port, bootstraps);
+    // the port should normally be fixed, but for tests its useful to find an open port
+    port = shuffleServer.getPort();
+    boundPort = port;
     String authEnabledString = authEnabled ? "enabled" : "not enabled";
     logger.info("Started YARN shuffle service for Spark on port {}. " +
-      "Authentication is {}.", port, authEnabledString);
+      "Authentication is {}.  Registered executor file is {}", port, authEnabledString,
+      registeredExecutorFile);
   }
 
   @Override
@@ -161,6 +189,16 @@ public void stopContainer(ContainerTerminationContext context) {
     logger.info("Stopping container {}", containerId);
   }
 
+  private File findRegisteredExecutorFile(String[] localDirs) {
+    for (String dir: localDirs) {
+      File f = new File(dir, "registeredExecutors.ldb");
+      if (f.exists()) {
+        return f;
+      }
+    }
+    return new File(localDirs[0], "registeredExecutors.ldb");
+  }
+
   /**
    * Close the shuffle server to clean up any associated state.
    */
@@ -170,6 +208,9 @@ protected void serviceStop() {
       if (shuffleServer != null) {
         shuffleServer.close();
       }
+      if (blockHandler != null) {
+        blockHandler.close();
+      }
     } catch (Exception e) {
       logger.error("Exception when stopping service", e);
     }
@@ -180,5 +221,4 @@ protected void serviceStop() {
   public ByteBuffer getMetaData() {
     return ByteBuffer.allocate(0);
   }
-
 }
diff --git a/pom.xml b/pom.xml
index ccfa1ea19b21e..d5945f2546d38 100644
--- a/pom.xml
+++ b/pom.xml
@@ -655,6 +655,11 @@
         <artifactId>jackson-databind</artifactId>
         <version>${fasterxml.jackson.version}</version>
       </dependency>
+      <dependency>
+        <groupId>com.fasterxml.jackson.core</groupId>
+        <artifactId>jackson-annotations</artifactId>
+        <version>${fasterxml.jackson.version}</version>
+      </dependency>
       <!-- Guava is excluded because of SPARK-6149.  The Guava version referenced in this module is
            15.0, which causes runtime incompatibility issues. -->
       <dependency>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 15db54e4e7909..f6737695307a2 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -38,6 +38,12 @@
       <artifactId>spark-core_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-network-yarn_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
new file mode 100644
index 0000000000000..128e996b71fe5
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
@@ -0,0 +1,193 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import java.io.{File, FileOutputStream, OutputStreamWriter}
+import java.util.Properties
+import java.util.concurrent.TimeUnit
+
+import scala.collection.JavaConversions._
+
+import com.google.common.base.Charsets.UTF_8
+import com.google.common.io.Files
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.server.MiniYARNCluster
+import org.scalatest.{BeforeAndAfterAll, Matchers}
+
+import org.apache.spark._
+import org.apache.spark.util.Utils
+
+abstract class BaseYarnClusterSuite
+  extends SparkFunSuite with BeforeAndAfterAll with Matchers with Logging {
+
+  // log4j configuration for the YARN containers, so that their output is collected
+  // by YARN instead of trying to overwrite unit-tests.log.
+  protected val LOG4J_CONF = """
+    |log4j.rootCategory=DEBUG, console
+    |log4j.appender.console=org.apache.log4j.ConsoleAppender
+    |log4j.appender.console.target=System.err
+    |log4j.appender.console.layout=org.apache.log4j.PatternLayout
+    |log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
+    """.stripMargin
+
+  private var yarnCluster: MiniYARNCluster = _
+  protected var tempDir: File = _
+  private var fakeSparkJar: File = _
+  private var hadoopConfDir: File = _
+  private var logConfDir: File = _
+
+
+  def yarnConfig: YarnConfiguration
+
+  override def beforeAll() {
+    super.beforeAll()
+
+    tempDir = Utils.createTempDir()
+    logConfDir = new File(tempDir, "log4j")
+    logConfDir.mkdir()
+    System.setProperty("SPARK_YARN_MODE", "true")
+
+    val logConfFile = new File(logConfDir, "log4j.properties")
+    Files.write(LOG4J_CONF, logConfFile, UTF_8)
+
+    yarnCluster = new MiniYARNCluster(getClass().getName(), 1, 1, 1)
+    yarnCluster.init(yarnConfig)
+    yarnCluster.start()
+
+    // There's a race in MiniYARNCluster in which start() may return before the RM has updated
+    // its address in the configuration. You can see this in the logs by noticing that when
+    // MiniYARNCluster prints the address, it still has port "0" assigned, although later the
+    // test works sometimes:
+    //
+    //    INFO MiniYARNCluster: MiniYARN ResourceManager address: blah:0
+    //
+    // That log message prints the contents of the RM_ADDRESS config variable. If you check it
+    // later on, it looks something like this:
+    //
+    //    INFO YarnClusterSuite: RM address in configuration is blah:42631
+    //
+    // This hack loops for a bit waiting for the port to change, and fails the test if it hasn't
+    // done so in a timely manner (defined to be 10 seconds).
+    val config = yarnCluster.getConfig()
+    val deadline = System.currentTimeMillis() + TimeUnit.SECONDS.toMillis(10)
+    while (config.get(YarnConfiguration.RM_ADDRESS).split(":")(1) == "0") {
+      if (System.currentTimeMillis() > deadline) {
+        throw new IllegalStateException("Timed out waiting for RM to come up.")
+      }
+      logDebug("RM address still not set in configuration, waiting...")
+      TimeUnit.MILLISECONDS.sleep(100)
+    }
+
+    logInfo(s"RM address in configuration is ${config.get(YarnConfiguration.RM_ADDRESS)}")
+
+    fakeSparkJar = File.createTempFile("sparkJar", null, tempDir)
+    hadoopConfDir = new File(tempDir, Client.LOCALIZED_CONF_DIR)
+    assert(hadoopConfDir.mkdir())
+    File.createTempFile("token", ".txt", hadoopConfDir)
+  }
+
+  override def afterAll() {
+    yarnCluster.stop()
+    System.clearProperty("SPARK_YARN_MODE")
+    super.afterAll()
+  }
+
+  protected def runSpark(
+      clientMode: Boolean,
+      klass: String,
+      appArgs: Seq[String] = Nil,
+      sparkArgs: Seq[String] = Nil,
+      extraClassPath: Seq[String] = Nil,
+      extraJars: Seq[String] = Nil,
+      extraConf: Map[String, String] = Map()): Unit = {
+    val master = if (clientMode) "yarn-client" else "yarn-cluster"
+    val props = new Properties()
+
+    props.setProperty("spark.yarn.jar", "local:" + fakeSparkJar.getAbsolutePath())
+
+    val childClasspath = logConfDir.getAbsolutePath() +
+      File.pathSeparator +
+      sys.props("java.class.path") +
+      File.pathSeparator +
+      extraClassPath.mkString(File.pathSeparator)
+    props.setProperty("spark.driver.extraClassPath", childClasspath)
+    props.setProperty("spark.executor.extraClassPath", childClasspath)
+
+    // SPARK-4267: make sure java options are propagated correctly.
+    props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
+    props.setProperty("spark.executor.extraJavaOptions", "-Dfoo=\"one two three\"")
+
+    yarnCluster.getConfig().foreach { e =>
+      props.setProperty("spark.hadoop." + e.getKey(), e.getValue())
+    }
+
+    sys.props.foreach { case (k, v) =>
+      if (k.startsWith("spark.")) {
+        props.setProperty(k, v)
+      }
+    }
+
+    extraConf.foreach { case (k, v) => props.setProperty(k, v) }
+
+    val propsFile = File.createTempFile("spark", ".properties", tempDir)
+    val writer = new OutputStreamWriter(new FileOutputStream(propsFile), UTF_8)
+    props.store(writer, "Spark properties.")
+    writer.close()
+
+    val extraJarArgs = if (!extraJars.isEmpty()) Seq("--jars", extraJars.mkString(",")) else Nil
+    val mainArgs =
+      if (klass.endsWith(".py")) {
+        Seq(klass)
+      } else {
+        Seq("--class", klass, fakeSparkJar.getAbsolutePath())
+      }
+    val argv =
+      Seq(
+        new File(sys.props("spark.test.home"), "bin/spark-submit").getAbsolutePath(),
+        "--master", master,
+        "--num-executors", "1",
+        "--properties-file", propsFile.getAbsolutePath()) ++
+      extraJarArgs ++
+      sparkArgs ++
+      mainArgs ++
+      appArgs
+
+    Utils.executeAndGetOutput(argv,
+      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()))
+  }
+
+  /**
+   * This is a workaround for an issue with yarn-cluster mode: the Client class will not provide
+   * any sort of error when the job process finishes successfully, but the job itself fails. So
+   * the tests enforce that something is written to a file after everything is ok to indicate
+   * that the job succeeded.
+   */
+  protected def checkResult(result: File): Unit = {
+    checkResult(result, "success")
+  }
+
+  protected def checkResult(result: File, expected: String): Unit = {
+    val resultString = Files.toString(result, UTF_8)
+    resultString should be (expected)
+  }
+
+  protected def mainClassName(klass: Class[_]): String = {
+    klass.getName().stripSuffix("$")
+  }
+
+}
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index eb6e1fd370620..128350b648992 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -17,25 +17,20 @@
 
 package org.apache.spark.deploy.yarn
 
-import java.io.{File, FileOutputStream, OutputStreamWriter}
+import java.io.File
 import java.net.URL
-import java.util.Properties
-import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable
+import scala.collection.JavaConversions._
 
 import com.google.common.base.Charsets.UTF_8
-import com.google.common.io.ByteStreams
-import com.google.common.io.Files
+import com.google.common.io.{ByteStreams, Files}
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.server.MiniYARNCluster
-import org.scalatest.{BeforeAndAfterAll, Matchers}
+import org.scalatest.Matchers
 
 import org.apache.spark._
+import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart, SparkListenerExecutorAdded}
 import org.apache.spark.scheduler.cluster.ExecutorInfo
-import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart,
-  SparkListenerExecutorAdded}
 import org.apache.spark.util.Utils
 
 /**
@@ -43,17 +38,9 @@ import org.apache.spark.util.Utils
  * applications, and require the Spark assembly to be built before they can be successfully
  * run.
  */
-class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matchers with Logging {
-
-  // log4j configuration for the YARN containers, so that their output is collected
-  // by YARN instead of trying to overwrite unit-tests.log.
-  private val LOG4J_CONF = """
-    |log4j.rootCategory=DEBUG, console
-    |log4j.appender.console=org.apache.log4j.ConsoleAppender
-    |log4j.appender.console.target=System.err
-    |log4j.appender.console.layout=org.apache.log4j.PatternLayout
-    |log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
-    """.stripMargin
+class YarnClusterSuite extends BaseYarnClusterSuite {
+
+  override def yarnConfig: YarnConfiguration = new YarnConfiguration()
 
   private val TEST_PYFILE = """
     |import mod1, mod2
@@ -82,65 +69,6 @@ class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matcher
     |    return 42
     """.stripMargin
 
-  private var yarnCluster: MiniYARNCluster = _
-  private var tempDir: File = _
-  private var fakeSparkJar: File = _
-  private var hadoopConfDir: File = _
-  private var logConfDir: File = _
-
-  override def beforeAll() {
-    super.beforeAll()
-
-    tempDir = Utils.createTempDir()
-    logConfDir = new File(tempDir, "log4j")
-    logConfDir.mkdir()
-    System.setProperty("SPARK_YARN_MODE", "true")
-
-    val logConfFile = new File(logConfDir, "log4j.properties")
-    Files.write(LOG4J_CONF, logConfFile, UTF_8)
-
-    yarnCluster = new MiniYARNCluster(getClass().getName(), 1, 1, 1)
-    yarnCluster.init(new YarnConfiguration())
-    yarnCluster.start()
-
-    // There's a race in MiniYARNCluster in which start() may return before the RM has updated
-    // its address in the configuration. You can see this in the logs by noticing that when
-    // MiniYARNCluster prints the address, it still has port "0" assigned, although later the
-    // test works sometimes:
-    //
-    //    INFO MiniYARNCluster: MiniYARN ResourceManager address: blah:0
-    //
-    // That log message prints the contents of the RM_ADDRESS config variable. If you check it
-    // later on, it looks something like this:
-    //
-    //    INFO YarnClusterSuite: RM address in configuration is blah:42631
-    //
-    // This hack loops for a bit waiting for the port to change, and fails the test if it hasn't
-    // done so in a timely manner (defined to be 10 seconds).
-    val config = yarnCluster.getConfig()
-    val deadline = System.currentTimeMillis() + TimeUnit.SECONDS.toMillis(10)
-    while (config.get(YarnConfiguration.RM_ADDRESS).split(":")(1) == "0") {
-      if (System.currentTimeMillis() > deadline) {
-        throw new IllegalStateException("Timed out waiting for RM to come up.")
-      }
-      logDebug("RM address still not set in configuration, waiting...")
-      TimeUnit.MILLISECONDS.sleep(100)
-    }
-
-    logInfo(s"RM address in configuration is ${config.get(YarnConfiguration.RM_ADDRESS)}")
-
-    fakeSparkJar = File.createTempFile("sparkJar", null, tempDir)
-    hadoopConfDir = new File(tempDir, Client.LOCALIZED_CONF_DIR)
-    assert(hadoopConfDir.mkdir())
-    File.createTempFile("token", ".txt", hadoopConfDir)
-  }
-
-  override def afterAll() {
-    yarnCluster.stop()
-    System.clearProperty("SPARK_YARN_MODE")
-    super.afterAll()
-  }
-
   test("run Spark in yarn-client mode") {
     testBasicYarnApp(true)
   }
@@ -174,7 +102,7 @@ class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matcher
   }
 
   private def testBasicYarnApp(clientMode: Boolean): Unit = {
-    var result = File.createTempFile("result", null, tempDir)
+    val result = File.createTempFile("result", null, tempDir)
     runSpark(clientMode, mainClassName(YarnClusterDriver.getClass),
       appArgs = Seq(result.getAbsolutePath()))
     checkResult(result)
@@ -224,89 +152,6 @@ class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matcher
     checkResult(executorResult, "OVERRIDDEN")
   }
 
-  private def runSpark(
-      clientMode: Boolean,
-      klass: String,
-      appArgs: Seq[String] = Nil,
-      sparkArgs: Seq[String] = Nil,
-      extraClassPath: Seq[String] = Nil,
-      extraJars: Seq[String] = Nil,
-      extraConf: Map[String, String] = Map()): Unit = {
-    val master = if (clientMode) "yarn-client" else "yarn-cluster"
-    val props = new Properties()
-
-    props.setProperty("spark.yarn.jar", "local:" + fakeSparkJar.getAbsolutePath())
-
-    val childClasspath = logConfDir.getAbsolutePath() +
-      File.pathSeparator +
-      sys.props("java.class.path") +
-      File.pathSeparator +
-      extraClassPath.mkString(File.pathSeparator)
-    props.setProperty("spark.driver.extraClassPath", childClasspath)
-    props.setProperty("spark.executor.extraClassPath", childClasspath)
-
-    // SPARK-4267: make sure java options are propagated correctly.
-    props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
-    props.setProperty("spark.executor.extraJavaOptions", "-Dfoo=\"one two three\"")
-
-    yarnCluster.getConfig().foreach { e =>
-      props.setProperty("spark.hadoop." + e.getKey(), e.getValue())
-    }
-
-    sys.props.foreach { case (k, v) =>
-      if (k.startsWith("spark.")) {
-        props.setProperty(k, v)
-      }
-    }
-
-    extraConf.foreach { case (k, v) => props.setProperty(k, v) }
-
-    val propsFile = File.createTempFile("spark", ".properties", tempDir)
-    val writer = new OutputStreamWriter(new FileOutputStream(propsFile), UTF_8)
-    props.store(writer, "Spark properties.")
-    writer.close()
-
-    val extraJarArgs = if (!extraJars.isEmpty()) Seq("--jars", extraJars.mkString(",")) else Nil
-    val mainArgs =
-      if (klass.endsWith(".py")) {
-        Seq(klass)
-      } else {
-        Seq("--class", klass, fakeSparkJar.getAbsolutePath())
-      }
-    val argv =
-      Seq(
-        new File(sys.props("spark.test.home"), "bin/spark-submit").getAbsolutePath(),
-        "--master", master,
-        "--num-executors", "1",
-        "--properties-file", propsFile.getAbsolutePath()) ++
-      extraJarArgs ++
-      sparkArgs ++
-      mainArgs ++
-      appArgs
-
-    Utils.executeAndGetOutput(argv,
-      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()))
-  }
-
-  /**
-   * This is a workaround for an issue with yarn-cluster mode: the Client class will not provide
-   * any sort of error when the job process finishes successfully, but the job itself fails. So
-   * the tests enforce that something is written to a file after everything is ok to indicate
-   * that the job succeeded.
-   */
-  private def checkResult(result: File): Unit = {
-    checkResult(result, "success")
-  }
-
-  private def checkResult(result: File, expected: String): Unit = {
-    var resultString = Files.toString(result, UTF_8)
-    resultString should be (expected)
-  }
-
-  private def mainClassName(klass: Class[_]): String = {
-    klass.getName().stripSuffix("$")
-  }
-
 }
 
 private[spark] class SaveExecutorInfo extends SparkListener {
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
new file mode 100644
index 0000000000000..5e8238822b90a
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
@@ -0,0 +1,109 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.deploy.yarn
+
+import java.io.File
+
+import com.google.common.base.Charsets.UTF_8
+import com.google.common.io.Files
+import org.apache.commons.io.FileUtils
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.scalatest.Matchers
+
+import org.apache.spark._
+import org.apache.spark.network.shuffle.ShuffleTestAccessor
+import org.apache.spark.network.yarn.{YarnShuffleService, YarnTestAccessor}
+
+/**
+ * Integration test for the external shuffle service with a yarn mini-cluster
+ */
+class YarnShuffleIntegrationSuite extends BaseYarnClusterSuite {
+
+  override def yarnConfig: YarnConfiguration = {
+    val yarnConfig = new YarnConfiguration()
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
+      classOf[YarnShuffleService].getCanonicalName)
+    yarnConfig.set("spark.shuffle.service.port", "0")
+    yarnConfig
+  }
+
+  test("external shuffle service") {
+    val shuffleServicePort = YarnTestAccessor.getShuffleServicePort
+    val shuffleService = YarnTestAccessor.getShuffleServiceInstance
+
+    val registeredExecFile = YarnTestAccessor.getRegisteredExecutorFile(shuffleService)
+
+    logInfo("Shuffle service port = " + shuffleServicePort)
+    val result = File.createTempFile("result", null, tempDir)
+    runSpark(
+      false,
+      mainClassName(YarnExternalShuffleDriver.getClass),
+      appArgs = Seq(result.getAbsolutePath(), registeredExecFile.getAbsolutePath),
+      extraConf = Map(
+        "spark.shuffle.service.enabled" -> "true",
+        "spark.shuffle.service.port" -> shuffleServicePort.toString
+      )
+    )
+    checkResult(result)
+    assert(YarnTestAccessor.getRegisteredExecutorFile(shuffleService).exists())
+  }
+}
+
+private object YarnExternalShuffleDriver extends Logging with Matchers {
+
+  val WAIT_TIMEOUT_MILLIS = 10000
+
+  def main(args: Array[String]): Unit = {
+    if (args.length != 2) {
+      // scalastyle:off println
+      System.err.println(
+        s"""
+        |Invalid command line: ${args.mkString(" ")}
+        |
+        |Usage: ExternalShuffleDriver [result file] [registed exec file]
+        """.stripMargin)
+      // scalastyle:on println
+      System.exit(1)
+    }
+
+    val sc = new SparkContext(new SparkConf()
+      .setAppName("External Shuffle Test"))
+    val conf = sc.getConf
+    val status = new File(args(0))
+    val registeredExecFile = new File(args(1))
+    logInfo("shuffle service executor file = " + registeredExecFile)
+    var result = "failure"
+    val execStateCopy = new File(registeredExecFile.getAbsolutePath + "_dup")
+    try {
+      val data = sc.parallelize(0 until 100, 10).map { x => (x % 10) -> x }.reduceByKey{ _ + _ }.
+        collect().toSet
+      sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+      data should be ((0 until 10).map{x => x -> (x * 10 + 450)}.toSet)
+      result = "success"
+      // only one process can open a leveldb file at a time, so we copy the files
+      FileUtils.copyDirectory(registeredExecFile, execStateCopy)
+      assert(!ShuffleTestAccessor.reloadRegisteredExecutors(execStateCopy).isEmpty)
+    } finally {
+      sc.stop()
+      FileUtils.deleteDirectory(execStateCopy)
+      Files.write(result, status, UTF_8)
+    }
+  }
+
+}
diff --git a/yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala b/yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala
new file mode 100644
index 0000000000000..aa46ec5100f0e
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.network.shuffle
+
+import java.io.{IOException, File}
+import java.util.concurrent.ConcurrentMap
+
+import com.google.common.annotations.VisibleForTesting
+import org.apache.hadoop.yarn.api.records.ApplicationId
+import org.fusesource.leveldbjni.JniDBFactory
+import org.iq80.leveldb.{DB, Options}
+
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.AppExecId
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo
+
+/**
+ * just a cheat to get package-visible members in tests
+ */
+object ShuffleTestAccessor {
+
+  def getBlockResolver(handler: ExternalShuffleBlockHandler): ExternalShuffleBlockResolver = {
+    handler.blockManager
+  }
+
+  def getExecutorInfo(
+      appId: ApplicationId,
+      execId: String,
+      resolver: ExternalShuffleBlockResolver
+  ): Option[ExecutorShuffleInfo] = {
+    val id = new AppExecId(appId.toString, execId)
+    Option(resolver.executors.get(id))
+  }
+
+  def registeredExecutorFile(resolver: ExternalShuffleBlockResolver): File = {
+    resolver.registeredExecutorFile
+  }
+
+  def shuffleServiceLevelDB(resolver: ExternalShuffleBlockResolver): DB = {
+    resolver.db
+  }
+
+  def reloadRegisteredExecutors(
+    file: File): ConcurrentMap[ExternalShuffleBlockResolver.AppExecId, ExecutorShuffleInfo] = {
+    val options: Options = new Options
+    options.createIfMissing(true)
+    val factory = new JniDBFactory
+    val db = factory.open(file, options)
+    val result = ExternalShuffleBlockResolver.reloadRegisteredExecutors(db)
+    db.close()
+    result
+  }
+
+  def reloadRegisteredExecutors(
+      db: DB): ConcurrentMap[ExternalShuffleBlockResolver.AppExecId, ExecutorShuffleInfo] = {
+    ExternalShuffleBlockResolver.reloadRegisteredExecutors(db)
+  }
+}
diff --git a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
new file mode 100644
index 0000000000000..2f22cbdbeac37
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
@@ -0,0 +1,233 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.network.yarn
+
+import java.io.{DataOutputStream, File, FileOutputStream}
+
+import scala.annotation.tailrec
+
+import org.apache.commons.io.FileUtils
+import org.apache.hadoop.yarn.api.records.ApplicationId
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.server.api.{ApplicationInitializationContext, ApplicationTerminationContext}
+import org.scalatest.{BeforeAndAfterEach, Matchers}
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.network.shuffle.ShuffleTestAccessor
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo
+
+class YarnShuffleServiceSuite extends SparkFunSuite with Matchers with BeforeAndAfterEach {
+  private[yarn] var yarnConfig: YarnConfiguration = new YarnConfiguration
+
+  override def beforeEach(): Unit = {
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
+      classOf[YarnShuffleService].getCanonicalName)
+
+    yarnConfig.get("yarn.nodemanager.local-dirs").split(",").foreach { dir =>
+      val d = new File(dir)
+      if (d.exists()) {
+        FileUtils.deleteDirectory(d)
+      }
+      FileUtils.forceMkdir(d)
+      logInfo(s"creating yarn.nodemanager.local-dirs: $d")
+    }
+  }
+
+  var s1: YarnShuffleService = null
+  var s2: YarnShuffleService = null
+  var s3: YarnShuffleService = null
+
+  override def afterEach(): Unit = {
+    if (s1 != null) {
+      s1.stop()
+      s1 = null
+    }
+    if (s2 != null) {
+      s2.stop()
+      s2 = null
+    }
+    if (s3 != null) {
+      s3.stop()
+      s3 = null
+    }
+  }
+
+  test("executor state kept across NM restart") {
+    s1 = new YarnShuffleService
+    s1.init(yarnConfig)
+    val app1Id = ApplicationId.newInstance(0, 1)
+    val app1Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app1Id, null)
+    s1.initializeApplication(app1Data)
+    val app2Id = ApplicationId.newInstance(0, 2)
+    val app2Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app2Id, null)
+    s1.initializeApplication(app2Data)
+
+    val execStateFile = s1.registeredExecutorFile
+    execStateFile should not be (null)
+    val shuffleInfo1 = new ExecutorShuffleInfo(Array("/foo", "/bar"), 3, "sort")
+    val shuffleInfo2 = new ExecutorShuffleInfo(Array("/bippy"), 5, "hash")
+
+    val blockHandler = s1.blockHandler
+    val blockResolver = ShuffleTestAccessor.getBlockResolver(blockHandler)
+    ShuffleTestAccessor.registeredExecutorFile(blockResolver) should be (execStateFile)
+
+    blockResolver.registerExecutor(app1Id.toString, "exec-1", shuffleInfo1)
+    blockResolver.registerExecutor(app2Id.toString, "exec-2", shuffleInfo2)
+    ShuffleTestAccessor.getExecutorInfo(app1Id, "exec-1", blockResolver) should
+      be (Some(shuffleInfo1))
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", blockResolver) should
+      be (Some(shuffleInfo2))
+
+    if (!execStateFile.exists()) {
+      @tailrec def findExistingParent(file: File): File = {
+        if (file == null) file
+        else if (file.exists()) file
+        else findExistingParent(file.getParentFile())
+      }
+      val existingParent = findExistingParent(execStateFile)
+      assert(false, s"$execStateFile does not exist -- closest existing parent is $existingParent")
+    }
+    assert(execStateFile.exists(), s"$execStateFile did not exist")
+
+    // now we pretend the shuffle service goes down, and comes back up
+    s1.stop()
+    s2 = new YarnShuffleService
+    s2.init(yarnConfig)
+    s2.registeredExecutorFile should be (execStateFile)
+
+    val handler2 = s2.blockHandler
+    val resolver2 = ShuffleTestAccessor.getBlockResolver(handler2)
+
+    // now we reinitialize only one of the apps, and expect yarn to tell us that app2 was stopped
+    // during the restart
+    s2.initializeApplication(app1Data)
+    s2.stopApplication(new ApplicationTerminationContext(app2Id))
+    ShuffleTestAccessor.getExecutorInfo(app1Id, "exec-1", resolver2) should be (Some(shuffleInfo1))
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver2) should be (None)
+
+    // Act like the NM restarts one more time
+    s2.stop()
+    s3 = new YarnShuffleService
+    s3.init(yarnConfig)
+    s3.registeredExecutorFile should be (execStateFile)
+
+    val handler3 = s3.blockHandler
+    val resolver3 = ShuffleTestAccessor.getBlockResolver(handler3)
+
+    // app1 is still running
+    s3.initializeApplication(app1Data)
+    ShuffleTestAccessor.getExecutorInfo(app1Id, "exec-1", resolver3) should be (Some(shuffleInfo1))
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver3) should be (None)
+    s3.stop()
+  }
+
+  test("removed applications should not be in registered executor file") {
+    s1 = new YarnShuffleService
+    s1.init(yarnConfig)
+    val app1Id = ApplicationId.newInstance(0, 1)
+    val app1Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app1Id, null)
+    s1.initializeApplication(app1Data)
+    val app2Id = ApplicationId.newInstance(0, 2)
+    val app2Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app2Id, null)
+    s1.initializeApplication(app2Data)
+
+    val execStateFile = s1.registeredExecutorFile
+    execStateFile should not be (null)
+    val shuffleInfo1 = new ExecutorShuffleInfo(Array("/foo", "/bar"), 3, "sort")
+    val shuffleInfo2 = new ExecutorShuffleInfo(Array("/bippy"), 5, "hash")
+
+    val blockHandler = s1.blockHandler
+    val blockResolver = ShuffleTestAccessor.getBlockResolver(blockHandler)
+    ShuffleTestAccessor.registeredExecutorFile(blockResolver) should be (execStateFile)
+
+    blockResolver.registerExecutor(app1Id.toString, "exec-1", shuffleInfo1)
+    blockResolver.registerExecutor(app2Id.toString, "exec-2", shuffleInfo2)
+
+    val db = ShuffleTestAccessor.shuffleServiceLevelDB(blockResolver)
+    ShuffleTestAccessor.reloadRegisteredExecutors(db) should not be empty
+
+    s1.stopApplication(new ApplicationTerminationContext(app1Id))
+    ShuffleTestAccessor.reloadRegisteredExecutors(db) should not be empty
+    s1.stopApplication(new ApplicationTerminationContext(app2Id))
+    ShuffleTestAccessor.reloadRegisteredExecutors(db) shouldBe empty
+  }
+
+  test("shuffle service should be robust to corrupt registered executor file") {
+    s1 = new YarnShuffleService
+    s1.init(yarnConfig)
+    val app1Id = ApplicationId.newInstance(0, 1)
+    val app1Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app1Id, null)
+    s1.initializeApplication(app1Data)
+
+    val execStateFile = s1.registeredExecutorFile
+    val shuffleInfo1 = new ExecutorShuffleInfo(Array("/foo", "/bar"), 3, "sort")
+
+    val blockHandler = s1.blockHandler
+    val blockResolver = ShuffleTestAccessor.getBlockResolver(blockHandler)
+    ShuffleTestAccessor.registeredExecutorFile(blockResolver) should be (execStateFile)
+
+    blockResolver.registerExecutor(app1Id.toString, "exec-1", shuffleInfo1)
+
+    // now we pretend the shuffle service goes down, and comes back up.  But we'll also
+    // make a corrupt registeredExecutor File
+    s1.stop()
+
+    execStateFile.listFiles().foreach{_.delete()}
+
+    val out = new DataOutputStream(new FileOutputStream(execStateFile + "/CURRENT"))
+    out.writeInt(42)
+    out.close()
+
+    s2 = new YarnShuffleService
+    s2.init(yarnConfig)
+    s2.registeredExecutorFile should be (execStateFile)
+
+    val handler2 = s2.blockHandler
+    val resolver2 = ShuffleTestAccessor.getBlockResolver(handler2)
+
+    // we re-initialize app1, but since the file was corrupt there is nothing we can do about it ...
+    s2.initializeApplication(app1Data)
+    // however, when we initialize a totally new app2, everything is still happy
+    val app2Id = ApplicationId.newInstance(0, 2)
+    val app2Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app2Id, null)
+    s2.initializeApplication(app2Data)
+    val shuffleInfo2 = new ExecutorShuffleInfo(Array("/bippy"), 5, "hash")
+    resolver2.registerExecutor(app2Id.toString, "exec-2", shuffleInfo2)
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver2) should be (Some(shuffleInfo2))
+    s2.stop()
+
+    // another stop & restart should be fine though (eg., we recover from previous corruption)
+    s3 = new YarnShuffleService
+    s3.init(yarnConfig)
+    s3.registeredExecutorFile should be (execStateFile)
+    val handler3 = s3.blockHandler
+    val resolver3 = ShuffleTestAccessor.getBlockResolver(handler3)
+
+    s3.initializeApplication(app2Data)
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver3) should be (Some(shuffleInfo2))
+    s3.stop()
+
+  }
+
+}
diff --git a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala
new file mode 100644
index 0000000000000..db322cd18e150
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.network.yarn
+
+import java.io.File
+
+/**
+ * just a cheat to get package-visible members in tests
+ */
+object YarnTestAccessor {
+  def getShuffleServicePort: Int = {
+    YarnShuffleService.boundPort
+  }
+
+  def getShuffleServiceInstance: YarnShuffleService = {
+    YarnShuffleService.instance
+  }
+
+  def getRegisteredExecutorFile(service: YarnShuffleService): File = {
+    service.registeredExecutorFile
+  }
+
+}

From 3c462f5d87a9654c5a68fd658a40f5062029fd9a Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Fri, 21 Aug 2015 12:21:51 -0700
Subject: [PATCH 1173/1454] [SPARK-10130] [SQL] type coercion for IF should
 have children resolved first

Type coercion for IF should have children resolved first, or we could meet unresolved exception.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #8331 from adrian-wang/spark10130.
---
 .../spark/sql/catalyst/analysis/HiveTypeCoercion.scala     | 1 +
 .../test/scala/org/apache/spark/sql/SQLQuerySuite.scala    | 7 +++++++
 2 files changed, 8 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index f2f2ba2f96552..2cb067f4aac91 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -639,6 +639,7 @@ object HiveTypeCoercion {
    */
   object IfCoercion extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
+      case e if !e.childrenResolved => e
       // Find tightest common type for If, if the true value and false value have different types.
       case i @ If(pred, left, right) if left.dataType != right.dataType =>
         findTightestCommonTypeToString(left.dataType, right.dataType).map { widestType =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index da50aec17c89e..dcb4e83710982 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1679,4 +1679,11 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       checkAnswer(sqlContext.table("`db.t`"), df)
     }
   }
+
+  test("SPARK-10130 type coercion for IF should have children resolved first") {
+    val df = Seq((1, 1), (-1, 1)).toDF("key", "value")
+    df.registerTempTable("src")
+    checkAnswer(
+      sql("SELECT IF(a > 0, a, 0) FROM (SELECT key a FROM src) temp"), Seq(Row(1), Row(0)))
+  }
 }

From d89cc38b33815e7b99fb3389b5038a543527065d Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Fri, 21 Aug 2015 13:10:11 -0700
Subject: [PATCH 1174/1454] [SPARK-10122] [PYSPARK] [STREAMING] Fix
 getOffsetRanges bug in PySpark-Streaming transform function

Details of the bug and explanations can be seen in [SPARK-10122](https://issues.apache.org/jira/browse/SPARK-10122).

tdas , please help to review.

Author: jerryshao <sshao@hortonworks.com>

Closes #8347 from jerryshao/SPARK-10122 and squashes the following commits:

4039b16 [jerryshao] Fix getOffsetRanges in transform() bug
---
 python/pyspark/streaming/dstream.py | 5 ++++-
 python/pyspark/streaming/tests.py   | 4 +++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 8dcb9645cdc6b..698336cfce18d 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -610,7 +610,10 @@ def __init__(self, prev, func):
         self.is_checkpointed = False
         self._jdstream_val = None
 
-        if (isinstance(prev, TransformedDStream) and
+        # Using type() to avoid folding the functions and compacting the DStreams which is not
+        # not strictly a object of TransformedDStream.
+        # Changed here is to avoid bug in KafkaTransformedDStream when calling offsetRanges().
+        if (type(prev) is TransformedDStream and
                 not prev.is_cached and not prev.is_checkpointed):
             prev_func = prev.func
             self.func = lambda t, rdd: func(t, prev_func(t, rdd))
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 6108c845c1efe..214d5be439003 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -850,7 +850,9 @@ def transformWithOffsetRanges(rdd):
                 offsetRanges.append(o)
             return rdd
 
-        stream.transform(transformWithOffsetRanges).foreachRDD(lambda rdd: rdd.count())
+        # Test whether it is ok mixing KafkaTransformedDStream and TransformedDStream together,
+        # only the TransformedDstreams can be folded together.
+        stream.transform(transformWithOffsetRanges).map(lambda kv: kv[1]).count().pprint()
         self.ssc.start()
         self.wait_for(offsetRanges, 1)
 

From f5b028ed2f1ad6de43c8b50ebf480e1b6c047035 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Fri, 21 Aug 2015 14:19:24 -0700
Subject: [PATCH 1175/1454] [SPARK-9864] [DOC] [MLlib] [SQL] Replace since in
 scaladoc to Since annotation

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8352 from MechCoder/since.
---
 .../classification/ClassificationModel.scala  |   8 +-
 .../classification/LogisticRegression.scala   |  30 ++---
 .../mllib/classification/NaiveBayes.scala     |   7 +-
 .../spark/mllib/classification/SVM.scala      |  28 ++---
 .../mllib/clustering/GaussianMixture.scala    |  28 ++---
 .../clustering/GaussianMixtureModel.scala     |  28 ++---
 .../spark/mllib/clustering/KMeans.scala       |  50 ++++-----
 .../spark/mllib/clustering/KMeansModel.scala  |  27 ++---
 .../apache/spark/mllib/clustering/LDA.scala   |  56 ++++-----
 .../spark/mllib/clustering/LDAModel.scala     |  69 +++++-------
 .../spark/mllib/clustering/LDAOptimizer.scala |  24 ++--
 .../clustering/PowerIterationClustering.scala |  38 +++----
 .../mllib/clustering/StreamingKMeans.scala    |  35 +++---
 .../BinaryClassificationMetrics.scala         |  26 ++---
 .../mllib/evaluation/MulticlassMetrics.scala  |  20 ++--
 .../mllib/evaluation/MultilabelMetrics.scala  |   9 +-
 .../mllib/evaluation/RankingMetrics.scala     |  10 +-
 .../mllib/evaluation/RegressionMetrics.scala  |  14 +--
 .../spark/mllib/fpm/AssociationRules.scala    |  20 ++--
 .../org/apache/spark/mllib/fpm/FPGrowth.scala |  22 ++--
 .../apache/spark/mllib/linalg/Matrices.scala  | 106 ++++++++----------
 .../linalg/SingularValueDecomposition.scala   |   4 +-
 .../apache/spark/mllib/linalg/Vectors.scala   |  90 +++++----------
 .../linalg/distributed/BlockMatrix.scala      |  88 ++++++---------
 .../linalg/distributed/CoordinateMatrix.scala |  40 +++----
 .../distributed/DistributedMatrix.scala       |   4 +-
 .../linalg/distributed/IndexedRowMatrix.scala |  38 +++----
 .../mllib/linalg/distributed/RowMatrix.scala  |  39 +++----
 .../spark/mllib/recommendation/ALS.scala      |  22 ++--
 .../MatrixFactorizationModel.scala            |  28 +++--
 .../GeneralizedLinearAlgorithm.scala          |  24 ++--
 .../mllib/regression/IsotonicRegression.scala |  22 ++--
 .../spark/mllib/regression/LabeledPoint.scala |   7 +-
 .../apache/spark/mllib/regression/Lasso.scala |  25 ++---
 .../mllib/regression/LinearRegression.scala   |  25 ++---
 .../mllib/regression/RegressionModel.scala    |  12 +-
 .../mllib/regression/RidgeRegression.scala    |  25 ++---
 .../regression/StreamingLinearAlgorithm.scala |  18 +--
 .../spark/mllib/stat/KernelDensity.scala      |  12 +-
 .../stat/MultivariateOnlineSummarizer.scala   |  24 ++--
 .../stat/MultivariateStatisticalSummary.scala |  19 ++--
 .../apache/spark/mllib/stat/Statistics.scala  |  30 ++---
 .../distribution/MultivariateGaussian.scala   |   8 +-
 .../spark/mllib/tree/DecisionTree.scala       |  28 +++--
 .../mllib/tree/GradientBoostedTrees.scala     |  20 ++--
 .../spark/mllib/tree/RandomForest.scala       |  20 ++--
 .../spark/mllib/tree/configuration/Algo.scala |   4 +-
 .../tree/configuration/BoostingStrategy.scala |  12 +-
 .../tree/configuration/FeatureType.scala      |   4 +-
 .../tree/configuration/QuantileStrategy.scala |   4 +-
 .../mllib/tree/configuration/Strategy.scala   |  24 ++--
 .../spark/mllib/tree/impurity/Entropy.scala   |  10 +-
 .../spark/mllib/tree/impurity/Gini.scala      |  10 +-
 .../spark/mllib/tree/impurity/Impurity.scala  |   8 +-
 .../spark/mllib/tree/impurity/Variance.scala  |  10 +-
 .../spark/mllib/tree/loss/AbsoluteError.scala |   6 +-
 .../spark/mllib/tree/loss/LogLoss.scala       |   6 +-
 .../apache/spark/mllib/tree/loss/Loss.scala   |   8 +-
 .../apache/spark/mllib/tree/loss/Losses.scala |  10 +-
 .../spark/mllib/tree/loss/SquaredError.scala  |   6 +-
 .../mllib/tree/model/DecisionTreeModel.scala  |  22 ++--
 .../tree/model/InformationGainStats.scala     |   4 +-
 .../apache/spark/mllib/tree/model/Node.scala  |   8 +-
 .../spark/mllib/tree/model/Predict.scala      |   4 +-
 .../apache/spark/mllib/tree/model/Split.scala |   4 +-
 .../mllib/tree/model/treeEnsembleModels.scala |  26 ++---
 .../org/apache/spark/mllib/tree/package.scala |   1 -
 .../org/apache/spark/mllib/util/MLUtils.scala |  36 +++---
 68 files changed, 692 insertions(+), 862 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index ba73024e3c04d..a29b425a71fd6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.classification
 
 import org.json4s.{DefaultFormats, JValue}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
@@ -36,8 +36,8 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData RDD representing data points to be predicted
    * @return an RDD[Double] where each entry contains the corresponding prediction
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(testData: RDD[Vector]): RDD[Double]
 
   /**
@@ -45,16 +45,16 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData array representing a single data point
    * @return predicted category from the trained model
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(testData: Vector): Double
 
   /**
    * Predict values for examples stored in a JavaRDD.
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 268642ac6a2f6..e03e662227d14 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.classification
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.classification.impl.GLMClassificationModel
 import org.apache.spark.mllib.linalg.BLAS.dot
 import org.apache.spark.mllib.linalg.{DenseVector, Vector}
@@ -85,8 +85,8 @@ class LogisticRegressionModel (
    * in Binary Logistic Regression. An example with prediction score greater than or equal to
    * this threshold is identified as an positive, and negative otherwise. The default value is 0.5.
    * It is only used for binary classification.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
@@ -97,8 +97,8 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
    * It is only used for binary classification.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def getThreshold: Option[Double] = threshold
 
@@ -106,8 +106,8 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    * It is only used for binary classification.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def clearThreshold(): this.type = {
     threshold = None
@@ -158,9 +158,7 @@ class LogisticRegressionModel (
     }
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures, numClasses, weights, intercept, threshold)
@@ -168,9 +166,7 @@ class LogisticRegressionModel (
 
   override protected def formatVersion: String = "1.0"
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.getOrElse("None")}"
   }
@@ -178,9 +174,7 @@ class LogisticRegressionModel (
 
 object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LogisticRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -261,8 +255,8 @@ object LogisticRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -284,8 +278,8 @@ object LogisticRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
 
    * @param miniBatchFraction Fraction of data to be used per iteration.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -306,8 +300,8 @@ object LogisticRegressionWithSGD {
 
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -324,8 +318,8 @@ object LogisticRegressionWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LogisticRegressionModel = {
@@ -361,8 +355,8 @@ class LogisticRegressionWithLBFGS
    * Set the number of possible outcomes for k classes classification problem in
    * Multinomial Logistic Regression.
    * By default, it is binary logistic regression so k will be set to 2.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def setNumClasses(numClasses: Int): this.type = {
     require(numClasses > 1)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 2df91c09421e9..dab369207cc9a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -25,6 +25,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext, SparkException}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, SparseVector, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -444,8 +445,8 @@ object NaiveBayes {
    *
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def train(input: RDD[LabeledPoint]): NaiveBayesModel = {
     new NaiveBayes().run(input)
   }
@@ -460,8 +461,8 @@ object NaiveBayes {
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
    * @param lambda The smoothing parameter
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
     new NaiveBayes(lambda, Multinomial).run(input)
   }
@@ -483,8 +484,8 @@ object NaiveBayes {
    *
    * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be
    *              multinomial or bernoulli
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
     require(supportedModelTypes.contains(modelType),
       s"NaiveBayes was created with an unknown modelType: $modelType.")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 5b54feeb10467..5f87269863572 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.classification
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.classification.impl.GLMClassificationModel
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
@@ -46,8 +46,8 @@ class SVMModel (
    * Sets the threshold that separates positive predictions from negative predictions. An example
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
@@ -57,16 +57,16 @@ class SVMModel (
   /**
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def getThreshold: Option[Double] = threshold
 
   /**
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def clearThreshold(): this.type = {
     threshold = None
@@ -84,9 +84,7 @@ class SVMModel (
     }
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures = weights.size, numClasses = 2, weights, intercept, threshold)
@@ -94,9 +92,7 @@ class SVMModel (
 
   override protected def formatVersion: String = "1.0"
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = 2, threshold = ${threshold.getOrElse("None")}"
   }
@@ -104,9 +100,7 @@ class SVMModel (
 
 object SVMModel extends Loader[SVMModel] {
 
-   /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): SVMModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -185,8 +179,8 @@ object SVMWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -209,8 +203,8 @@ object SVMWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -231,8 +225,8 @@ object SVMWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -250,8 +244,8 @@ object SVMWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel = {
     train(input, numIterations, 1.0, 0.01, 1.0)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index bc27b1fe7390b..fcc9dfecac54f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable.IndexedSeq
 
 import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, Vector => BV}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, Matrices, Vector, Vectors}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
@@ -62,8 +62,8 @@ class GaussianMixture private (
   /**
    * Constructs a default instance. The default parameters are {k: 2, convergenceTol: 0.01,
    * maxIterations: 100, seed: random}.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(2, 0.01, 100, Utils.random.nextLong())
 
   // number of samples per cluster to use when initializing Gaussians
@@ -77,8 +77,8 @@ class GaussianMixture private (
    * Set the initial GMM starting point, bypassing the random initialization.
    * You must call setK() prior to calling this method, and the condition
    * (model.k == this.k) must be met; failure will result in an IllegalArgumentException
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setInitialModel(model: GaussianMixtureModel): this.type = {
     if (model.k == k) {
       initialModel = Some(model)
@@ -90,14 +90,14 @@ class GaussianMixture private (
 
   /**
    * Return the user supplied initial GMM, if supplied
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getInitialModel: Option[GaussianMixtureModel] = initialModel
 
   /**
    * Set the number of Gaussians in the mixture model.  Default: 2
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -105,14 +105,14 @@ class GaussianMixture private (
 
   /**
    * Return the number of Gaussians in the mixture model
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getK: Int = k
 
   /**
    * Set the maximum number of iterations to run. Default: 100
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -120,15 +120,15 @@ class GaussianMixture private (
 
   /**
    * Return the maximum number of iterations to run
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getMaxIterations: Int = maxIterations
 
   /**
    * Set the largest change in log-likelihood at which convergence is
    * considered to have occurred.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setConvergenceTol(convergenceTol: Double): this.type = {
     this.convergenceTol = convergenceTol
     this
@@ -137,14 +137,14 @@ class GaussianMixture private (
   /**
    * Return the largest change in log-likelihood at which convergence is
    * considered to have occurred.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getConvergenceTol: Double = convergenceTol
 
   /**
    * Set the random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -152,14 +152,14 @@ class GaussianMixture private (
 
   /**
    * Return the random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getSeed: Long = seed
 
   /**
    * Perform expectation maximization
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(data: RDD[Vector]): GaussianMixtureModel = {
     val sc = data.sparkContext
 
@@ -235,8 +235,8 @@ class GaussianMixture private (
 
   /**
    * Java-friendly version of [[run()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(data: JavaRDD[Vector]): GaussianMixtureModel = run(data.rdd)
 
   private def updateWeightsAndGaussians(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 2fa0473737aae..1a10a8b624218 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Matrices, Matrix}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
@@ -43,8 +43,8 @@ import org.apache.spark.sql.{SQLContext, Row}
  *                the weight for Gaussian i, and weights.sum == 1
  * @param gaussians Array of MultivariateGaussian where gaussians(i) represents
  *                  the Multivariate Gaussian (Normal) Distribution for Gaussian i
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class GaussianMixtureModel(
   val weights: Array[Double],
@@ -54,23 +54,21 @@ class GaussianMixtureModel(
 
   override protected def formatVersion = "1.0"
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GaussianMixtureModel.SaveLoadV1_0.save(sc, path, weights, gaussians)
   }
 
   /**
    * Number of gaussians in mixture
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def k: Int = weights.length
 
   /**
    * Maps given points to their cluster indices.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(points: RDD[Vector]): RDD[Int] = {
     val responsibilityMatrix = predictSoft(points)
     responsibilityMatrix.map(r => r.indexOf(r.max))
@@ -78,8 +76,8 @@ class GaussianMixtureModel(
 
   /**
    * Maps given point to its cluster index.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def predict(point: Vector): Int = {
     val r = computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
     r.indexOf(r.max)
@@ -87,16 +85,16 @@ class GaussianMixtureModel(
 
   /**
    * Java-friendly version of [[predict()]]
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Given the input vectors, return the membership value of each vector
    * to all mixture components.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predictSoft(points: RDD[Vector]): RDD[Array[Double]] = {
     val sc = points.sparkContext
     val bcDists = sc.broadcast(gaussians)
@@ -108,8 +106,8 @@ class GaussianMixtureModel(
 
   /**
    * Given the input vector, return the membership values to all mixture components.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predictSoft(point: Vector): Array[Double] = {
     computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
   }
@@ -133,9 +131,7 @@ class GaussianMixtureModel(
   }
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 @Experimental
 object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
 
@@ -186,9 +182,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
     }
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String) : GaussianMixtureModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 9ef6834e5ea8d..3e9545a74bef3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS.{axpy, scal}
 import org.apache.spark.mllib.util.MLUtils
@@ -49,20 +49,20 @@ class KMeans private (
   /**
    * Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1,
    * initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4, Utils.random.nextLong())
 
   /**
    * Number of clusters to create (k).
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getK: Int = k
 
   /**
    * Set the number of clusters to create (k). Default: 2.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -70,14 +70,14 @@ class KMeans private (
 
   /**
    * Maximum number of iterations to run.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getMaxIterations: Int = maxIterations
 
   /**
    * Set maximum number of iterations to run. Default: 20.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -85,16 +85,16 @@ class KMeans private (
 
   /**
    * The initialization algorithm. This can be either "random" or "k-means||".
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getInitializationMode: String = initializationMode
 
   /**
    * Set the initialization algorithm. This can be either "random" to choose random points as
    * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setInitializationMode(initializationMode: String): this.type = {
     KMeans.validateInitMode(initializationMode)
     this.initializationMode = initializationMode
@@ -104,8 +104,8 @@ class KMeans private (
   /**
    * :: Experimental ::
    * Number of runs of the algorithm to execute in parallel.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   @Experimental
   def getRuns: Int = runs
 
@@ -114,8 +114,8 @@ class KMeans private (
    * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
    * this many times with random starting conditions (configured by the initialization mode), then
    * return the best clustering found over any run. Default: 1.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   @Experimental
   def setRuns(runs: Int): this.type = {
     if (runs <= 0) {
@@ -127,15 +127,15 @@ class KMeans private (
 
   /**
    * Number of steps for the k-means|| initialization mode
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getInitializationSteps: Int = initializationSteps
 
   /**
    * Set the number of steps for the k-means|| initialization mode. This is an advanced
    * setting -- the default of 5 is almost always enough. Default: 5.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setInitializationSteps(initializationSteps: Int): this.type = {
     if (initializationSteps <= 0) {
       throw new IllegalArgumentException("Number of initialization steps must be positive")
@@ -146,15 +146,15 @@ class KMeans private (
 
   /**
    * The distance threshold within which we've consider centers to have converged.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getEpsilon: Double = epsilon
 
   /**
    * Set the distance threshold within which we've consider centers to have converged.
    * If all centers move less than this Euclidean distance, we stop iterating one run.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setEpsilon(epsilon: Double): this.type = {
     this.epsilon = epsilon
     this
@@ -162,14 +162,14 @@ class KMeans private (
 
   /**
    * The random seed for cluster initialization.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getSeed: Long = seed
 
   /**
    * Set the random seed for cluster initialization.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -183,8 +183,8 @@ class KMeans private (
    * Set the initial starting point, bypassing the random initialization or k-means||
    * The condition model.k == this.k must be met, failure results
    * in an IllegalArgumentException.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setInitialModel(model: KMeansModel): this.type = {
     require(model.k == k, "mismatched cluster count")
     initialModel = Some(model)
@@ -194,8 +194,8 @@ class KMeans private (
   /**
    * Train a K-means model on the given set of points; `data` should be cached for high
    * performance, because this is an iterative algorithm.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def run(data: RDD[Vector]): KMeansModel = {
 
     if (data.getStorageLevel == StorageLevel.NONE) {
@@ -453,14 +453,14 @@ class KMeans private (
 
 /**
  * Top-level methods for calling K-means clustering.
- * @since 0.8.0
  */
+@Since("0.8.0")
 object KMeans {
 
   // Initialization mode names
-  /** @since 0.8.0 */
+  @Since("0.8.0")
   val RANDOM = "random"
-  /** @since 0.8.0 */
+  @Since("0.8.0")
   val K_MEANS_PARALLEL = "k-means||"
 
   /**
@@ -472,8 +472,8 @@ object KMeans {
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
    * @param seed random seed value for cluster initialization
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def train(
       data: RDD[Vector],
       k: Int,
@@ -497,8 +497,8 @@ object KMeans {
    * @param maxIterations max number of iterations
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       data: RDD[Vector],
       k: Int,
@@ -514,8 +514,8 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       data: RDD[Vector],
       k: Int,
@@ -525,8 +525,8 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       data: RDD[Vector],
       k: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 8de2087ceb4df..e425ecdd481c6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -23,6 +23,7 @@ import org.json4s._
 import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -34,35 +35,35 @@ import org.apache.spark.sql.Row
 
 /**
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
- * @since 0.8.0
  */
+@Since("0.8.0")
 class KMeansModel (
     val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
 
   /**
    * A Java-friendly constructor that takes an Iterable of Vectors.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray)
 
   /**
    * Total number of clusters.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def k: Int = clusterCenters.length
 
   /**
    * Returns the cluster index that a given point belongs to.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(point: Vector): Int = {
     KMeans.findClosest(clusterCentersWithNorm, new VectorWithNorm(point))._1
   }
 
   /**
    * Maps given points to their cluster indices.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(points: RDD[Vector]): RDD[Int] = {
     val centersWithNorm = clusterCentersWithNorm
     val bcCentersWithNorm = points.context.broadcast(centersWithNorm)
@@ -71,16 +72,16 @@ class KMeansModel (
 
   /**
    * Maps given points to their cluster indices.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Return the K-means cost (sum of squared distances of points to their nearest center) for this
    * model on the given data.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def computeCost(data: RDD[Vector]): Double = {
     val centersWithNorm = clusterCentersWithNorm
     val bcCentersWithNorm = data.context.broadcast(centersWithNorm)
@@ -90,9 +91,7 @@ class KMeansModel (
   private def clusterCentersWithNorm: Iterable[VectorWithNorm] =
     clusterCenters.map(new VectorWithNorm(_))
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     KMeansModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -100,14 +99,10 @@ class KMeansModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 object KMeansModel extends Loader[KMeansModel] {
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): KMeansModel = {
     KMeansModel.SaveLoadV1_0.load(sc, path)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 2a8c6acbaec61..92a321afb0ca3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import breeze.linalg.{DenseVector => BDV}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -43,8 +43,8 @@ import org.apache.spark.util.Utils
  *
  * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent Dirichlet allocation
  *       (Wikipedia)]]
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class LDA private (
     private var k: Int,
@@ -57,8 +57,8 @@ class LDA private (
 
   /**
    * Constructs a LDA instance with default parameters.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(k = 10, maxIterations = 20, docConcentration = Vectors.dense(-1),
     topicConcentration = -1, seed = Utils.random.nextLong(), checkpointInterval = 10,
     ldaOptimizer = new EMLDAOptimizer)
@@ -66,15 +66,15 @@ class LDA private (
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getK: Int = k
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
    * (default = 10)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setK(k: Int): this.type = {
     require(k > 0, s"LDA k (number of clusters) must be > 0, but was set to $k")
     this.k = k
@@ -86,8 +86,8 @@ class LDA private (
    * distributions over topics ("theta").
    *
    * This is the parameter to a Dirichlet distribution.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def getAsymmetricDocConcentration: Vector = this.docConcentration
 
   /**
@@ -96,8 +96,8 @@ class LDA private (
    *
    * This method assumes the Dirichlet distribution is symmetric and can be described by a single
    * [[Double]] parameter. It should fail if docConcentration is asymmetric.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getDocConcentration: Double = {
     val parameter = docConcentration(0)
     if (docConcentration.size == 1) {
@@ -131,8 +131,8 @@ class LDA private (
    *     - Values should be >= 0
    *     - default = uniformly (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setDocConcentration(docConcentration: Vector): this.type = {
     require(docConcentration.size > 0, "docConcentration must have > 0 elements")
     this.docConcentration = docConcentration
@@ -141,8 +141,8 @@ class LDA private (
 
   /**
    * Replicates a [[Double]] docConcentration to create a symmetric prior.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setDocConcentration(docConcentration: Double): this.type = {
     this.docConcentration = Vectors.dense(docConcentration)
     this
@@ -150,26 +150,26 @@ class LDA private (
 
   /**
    * Alias for [[getAsymmetricDocConcentration]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def getAsymmetricAlpha: Vector = getAsymmetricDocConcentration
 
   /**
    * Alias for [[getDocConcentration]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getAlpha: Double = getDocConcentration
 
   /**
    * Alias for [[setDocConcentration()]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setAlpha(alpha: Vector): this.type = setDocConcentration(alpha)
 
   /**
    * Alias for [[setDocConcentration()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setAlpha(alpha: Double): this.type = setDocConcentration(alpha)
 
   /**
@@ -180,8 +180,8 @@ class LDA private (
    *
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getTopicConcentration: Double = this.topicConcentration
 
   /**
@@ -205,8 +205,8 @@ class LDA private (
    *     - Value should be >= 0
    *     - default = (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setTopicConcentration(topicConcentration: Double): this.type = {
     this.topicConcentration = topicConcentration
     this
@@ -214,27 +214,27 @@ class LDA private (
 
   /**
    * Alias for [[getTopicConcentration]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getBeta: Double = getTopicConcentration
 
   /**
    * Alias for [[setTopicConcentration()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setBeta(beta: Double): this.type = setTopicConcentration(beta)
 
   /**
    * Maximum number of iterations for learning.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getMaxIterations: Int = maxIterations
 
   /**
    * Maximum number of iterations for learning.
    * (default = 20)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -242,14 +242,14 @@ class LDA private (
 
   /**
    * Random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getSeed: Long = seed
 
   /**
    * Random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -257,8 +257,8 @@ class LDA private (
 
   /**
    * Period (in iterations) between checkpoints.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getCheckpointInterval: Int = checkpointInterval
 
   /**
@@ -268,8 +268,8 @@ class LDA private (
    * [[org.apache.spark.SparkContext]], this setting is ignored.
    *
    * @see [[org.apache.spark.SparkContext#setCheckpointDir]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setCheckpointInterval(checkpointInterval: Int): this.type = {
     this.checkpointInterval = checkpointInterval
     this
@@ -280,8 +280,8 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   @DeveloperApi
   def getOptimizer: LDAOptimizer = ldaOptimizer
 
@@ -289,8 +289,8 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   @DeveloperApi
   def setOptimizer(optimizer: LDAOptimizer): this.type = {
     this.ldaOptimizer = optimizer
@@ -300,8 +300,8 @@ class LDA private (
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
    * Currently "em", "online" are supported.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setOptimizer(optimizerName: String): this.type = {
     this.ldaOptimizer =
       optimizerName.toLowerCase match {
@@ -321,8 +321,8 @@ class LDA private (
    *                   (where the vocabulary size is the length of the vector).
    *                   Document IDs must be unique and >= 0.
    * @return  Inferred LDA model
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(documents: RDD[(Long, Vector)]): LDAModel = {
     val state = ldaOptimizer.initialize(documents, this)
     var iter = 0
@@ -339,8 +339,8 @@ class LDA private (
 
   /**
    * Java-friendly version of [[run()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(documents: JavaPairRDD[java.lang.Long, Vector]): LDAModel = {
     run(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 6bc68a4c18b99..667374a2bc418 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -25,7 +25,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
@@ -192,24 +192,16 @@ class LocalLDAModel private[clustering] (
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel with Serializable {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def k: Int = topics.numCols
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def vocabSize: Int = topics.numRows
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def topicsMatrix: Matrix = topics
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val brzTopics = topics.toBreeze.toDenseMatrix
     Range(0, k).map { topicIndex =>
@@ -222,9 +214,7 @@ class LocalLDAModel private[clustering] (
 
   override protected def formatVersion = "1.0"
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def save(sc: SparkContext, path: String): Unit = {
     LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix, docConcentration, topicConcentration,
       gammaShape)
@@ -238,16 +228,16 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating log likelihood
    * @return variational lower bound on the log likelihood of the entire corpus
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def logLikelihood(documents: RDD[(Long, Vector)]): Double = logLikelihoodBound(documents,
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
   /**
    * Java-friendly version of [[logLikelihood]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logLikelihood(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -258,8 +248,8 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating perplexity
    * @return Variational upper bound on log perplexity per token.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
     val corpusTokenCount = documents
       .map { case (_, termCounts) => termCounts.toArray.sum }
@@ -267,9 +257,8 @@ class LocalLDAModel private[clustering] (
     -logLikelihood(documents) / corpusTokenCount
   }
 
-  /** Java-friendly version of [[logPerplexity]]
-   *  @since 1.5.0
-   */
+  /** Java-friendly version of [[logPerplexity]] */
+  @Since("1.5.0")
   def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -347,8 +336,8 @@ class LocalLDAModel private[clustering] (
    * for each document.
    * @param documents documents to predict topic mixture distributions for
    * @return An RDD of (document ID, topic mixture distribution for document)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
   def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = {
     // Double transpose because dirichletExpectation normalizes by row and we need to normalize
@@ -376,8 +365,8 @@ class LocalLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topicDistributions]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def topicDistributions(
       documents: JavaPairRDD[java.lang.Long, Vector]): JavaPairRDD[java.lang.Long, Vector] = {
     val distributions = topicDistributions(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
@@ -451,9 +440,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def load(sc: SparkContext, path: String): LocalLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
@@ -510,8 +497,8 @@ class DistributedLDAModel private[clustering] (
    * Convert model to a local model.
    * The local model stores the inferred topics but not the topic distributions for training
    * documents.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toLocal: LocalLDAModel = new LocalLDAModel(topicsMatrix, docConcentration, topicConcentration,
     gammaShape)
 
@@ -521,8 +508,8 @@ class DistributedLDAModel private[clustering] (
    * No guarantees are given about the ordering of the topics.
    *
    * WARNING: This matrix is collected from an RDD. Beware memory usage when vocabSize, k are large.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override lazy val topicsMatrix: Matrix = {
     // Collect row-major topics
     val termTopicCounts: Array[(Int, TopicCounts)] =
@@ -541,9 +528,7 @@ class DistributedLDAModel private[clustering] (
     Matrices.fromBreeze(brzTopics)
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val numTopics = k
     // Note: N_k is not needed to find the top terms, but it is needed to normalize weights
@@ -582,8 +567,8 @@ class DistributedLDAModel private[clustering] (
    * @return  Array over topics.  Each element represent as a pair of matching arrays:
    *          (IDs for the documents, weights of the topic in these documents).
    *          For each topic, documents are sorted in order of decreasing topic weights.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def topDocumentsPerTopic(maxDocumentsPerTopic: Int): Array[(Array[Long], Array[Double])] = {
     val numTopics = k
     val topicsInQueues: Array[BoundedPriorityQueue[(Double, Long)]] =
@@ -666,8 +651,8 @@ class DistributedLDAModel private[clustering] (
    *  - This excludes the prior; for that, use [[logPrior]].
    *  - Even with [[logPrior]], this is NOT the same as the data log likelihood given the
    *    hyperparameters.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   lazy val logLikelihood: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
     val alpha = this.docConcentration(0) // To avoid closure capture of enclosing object
@@ -693,8 +678,8 @@ class DistributedLDAModel private[clustering] (
   /**
    * Log probability of the current parameter estimate:
    * log P(topics, topic distributions for docs | alpha, eta)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   lazy val logPrior: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
     val alpha = this.docConcentration(0) // To avoid closure capture of enclosing object
@@ -725,8 +710,8 @@ class DistributedLDAModel private[clustering] (
    * ("theta_doc").
    *
    * @return  RDD of (document ID, topic distribution) pairs
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def topicDistributions: RDD[(Long, Vector)] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
       (docID.toLong, Vectors.fromBreeze(normalize(topicCounts, 1.0)))
@@ -735,8 +720,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topicDistributions]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def javaTopicDistributions: JavaPairRDD[java.lang.Long, Vector] = {
     JavaPairRDD.fromRDD(topicDistributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
   }
@@ -744,8 +729,8 @@ class DistributedLDAModel private[clustering] (
   /**
    * For each document, return the top k weighted topics for that document and their weights.
    * @return RDD of (doc ID, topic indices, topic weights)
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
       val topIndices = argtopk(topicCounts, k)
@@ -761,8 +746,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topTopicsPerDocument]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def javaTopTopicsPerDocument(k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[Double])] = {
     val topics = topTopicsPerDocument(k)
     topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Double])]].toJavaRDD()
@@ -775,8 +760,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topicDistributions]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   override def save(sc: SparkContext, path: String): Unit = {
     DistributedLDAModel.SaveLoadV1_0.save(
       sc, path, graph, globalTopicTotals, k, vocabSize, docConcentration, topicConcentration,
@@ -877,9 +862,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def load(sc: SparkContext, path: String): DistributedLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index cb517f9689ade..5c2aae6403bea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -23,7 +23,7 @@ import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, all, normalize, su
 import breeze.numerics.{trigamma, abs, exp}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
 import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
@@ -35,8 +35,8 @@ import org.apache.spark.rdd.RDD
  *
  * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
  * hold optimizer-specific parameters for users to set.
- * @since 1.4.0
  */
+@Since("1.4.0")
 @DeveloperApi
 sealed trait LDAOptimizer {
 
@@ -74,8 +74,8 @@ sealed trait LDAOptimizer {
  *  - Paper which clearly explains several algorithms, including EM:
  *    Asuncion, Welling, Smyth, and Teh.
  *    "On Smoothing and Inference for Topic Models."  UAI, 2009.
- * @since 1.4.0
  */
+@Since("1.4.0")
 @DeveloperApi
 final class EMLDAOptimizer extends LDAOptimizer {
 
@@ -226,8 +226,8 @@ final class EMLDAOptimizer extends LDAOptimizer {
  *
  * Original Online LDA paper:
  *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
- * @since 1.4.0
  */
+@Since("1.4.0")
 @DeveloperApi
 final class OnlineLDAOptimizer extends LDAOptimizer {
 
@@ -276,16 +276,16 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getTau0: Double = this.tau0
 
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    * Default: 1024, following the original Online LDA paper.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setTau0(tau0: Double): this.type = {
     require(tau0 > 0, s"LDA tau0 must be positive, but was set to $tau0")
     this.tau0 = tau0
@@ -294,16 +294,16 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Learning rate: exponential decay rate
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getKappa: Double = this.kappa
 
   /**
    * Learning rate: exponential decay rate---should be between
    * (0.5, 1.0] to guarantee asymptotic convergence.
    * Default: 0.51, based on the original Online LDA paper.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setKappa(kappa: Double): this.type = {
     require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa")
     this.kappa = kappa
@@ -312,8 +312,8 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getMiniBatchFraction: Double = this.miniBatchFraction
 
   /**
@@ -325,8 +325,8 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * maxIterations * miniBatchFraction >= 1.
    *
    * Default: 0.05, i.e., 5% of total documents.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0,
       s"Online LDA miniBatchFraction must be in range (0,1], but was set to $miniBatchFraction")
@@ -337,16 +337,16 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * Optimize alpha, indicates whether alpha (Dirichlet parameter for document-topic distribution)
    * will be optimized during training.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def getOptimzeAlpha: Boolean = this.optimizeAlpha
 
   /**
    * Sets whether to optimize alpha parameter during training.
    *
    * Default: false
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setOptimzeAlpha(optimizeAlpha: Boolean): this.type = {
     this.optimizeAlpha = optimizeAlpha
     this
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index b4733ca975152..396b36f2f6454 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -21,7 +21,7 @@ import org.json4s.JsonDSL._
 import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
@@ -39,16 +39,14 @@ import org.apache.spark.{Logging, SparkContext, SparkException}
  *
  * @param k number of clusters
  * @param assignments an RDD of clustering [[PowerIterationClustering#Assignment]]s
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class PowerIterationClusteringModel(
     val k: Int,
     val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable {
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     PowerIterationClusteringModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -56,9 +54,7 @@ class PowerIterationClusteringModel(
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] {
   override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
     PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path)
@@ -73,8 +69,8 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
     val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel"
 
     /**
-     * @since 1.4.0
      */
+    @Since("1.4.0")
     def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = {
       val sqlContext = new SQLContext(sc)
       import sqlContext.implicits._
@@ -87,9 +83,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
       dataRDD.write.parquet(Loader.dataPath(path))
     }
 
-    /**
-     * @since 1.4.0
-     */
+    @Since("1.4.0")
     def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
       implicit val formats = DefaultFormats
       val sqlContext = new SQLContext(sc)
@@ -136,14 +130,14 @@ class PowerIterationClustering private[clustering] (
   /**
    * Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100,
    * initMode: "random"}.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(k = 2, maxIterations = 100, initMode = "random")
 
   /**
    * Set the number of clusters.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -151,8 +145,8 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * Set maximum number of iterations of the power iteration loop
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -161,8 +155,8 @@ class PowerIterationClustering private[clustering] (
   /**
    * Set the initialization mode. This can be either "random" to use a random vector
    * as vertex properties, or "degree" to use normalized sum similarities. Default: random.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setInitializationMode(mode: String): this.type = {
     this.initMode = mode match {
       case "random" | "degree" => mode
@@ -182,8 +176,8 @@ class PowerIterationClustering private[clustering] (
    *              assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def run(graph: Graph[Double, Double]): PowerIterationClusteringModel = {
     val w = normalize(graph)
     val w0 = initMode match {
@@ -204,8 +198,8 @@ class PowerIterationClustering private[clustering] (
    *                     assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(similarities: RDD[(Long, Long, Double)]): PowerIterationClusteringModel = {
     val w = normalize(similarities)
     val w0 = initMode match {
@@ -217,8 +211,8 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * A Java-friendly version of [[PowerIterationClustering.run]].
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(similarities: JavaRDD[(java.lang.Long, java.lang.Long, java.lang.Double)])
     : PowerIterationClusteringModel = {
     run(similarities.rdd.asInstanceOf[RDD[(Long, Long, Double)]])
@@ -242,9 +236,7 @@ class PowerIterationClustering private[clustering] (
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 @Experimental
 object PowerIterationClustering extends Logging {
 
@@ -253,8 +245,8 @@ object PowerIterationClustering extends Logging {
    * Cluster assignment.
    * @param id node id
    * @param cluster assigned cluster id
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   case class Assignment(id: Long, cluster: Int)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index a915804b02c90..41f2668ec6a7d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaSparkContext._
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.rdd.RDD
@@ -63,9 +63,8 @@ import org.apache.spark.util.random.XORShiftRandom
  * such that at time t + h the discount applied to the data from t is 0.5.
  * The definition remains the same whether the time unit is given
  * as batches or points.
- * @since 1.2.0
- *
  */
+@Since("1.2.0")
 @Experimental
 class StreamingKMeansModel(
     override val clusterCenters: Array[Vector],
@@ -73,8 +72,8 @@ class StreamingKMeansModel(
 
   /**
    * Perform a k-means update on a batch of data.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel = {
 
     // find nearest cluster to each point
@@ -166,23 +165,23 @@ class StreamingKMeansModel(
  *    .setRandomCenters(5, 100.0)
  *    .trainOn(DStream)
  * }}}
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class StreamingKMeans(
     var k: Int,
     var decayFactor: Double,
     var timeUnit: String) extends Logging with Serializable {
 
-  /** @since 1.2.0 */
+  @Since("1.2.0")
   def this() = this(2, 1.0, StreamingKMeans.BATCHES)
 
   protected var model: StreamingKMeansModel = new StreamingKMeansModel(null, null)
 
   /**
    * Set the number of clusters.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -190,8 +189,8 @@ class StreamingKMeans(
 
   /**
    * Set the decay factor directly (for forgetful algorithms).
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setDecayFactor(a: Double): this.type = {
     this.decayFactor = a
     this
@@ -199,8 +198,8 @@ class StreamingKMeans(
 
   /**
    * Set the half life and time unit ("batches" or "points") for forgetful algorithms.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setHalfLife(halfLife: Double, timeUnit: String): this.type = {
     if (timeUnit != StreamingKMeans.BATCHES && timeUnit != StreamingKMeans.POINTS) {
       throw new IllegalArgumentException("Invalid time unit for decay: " + timeUnit)
@@ -213,8 +212,8 @@ class StreamingKMeans(
 
   /**
    * Specify initial centers directly.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setInitialCenters(centers: Array[Vector], weights: Array[Double]): this.type = {
     model = new StreamingKMeansModel(centers, weights)
     this
@@ -226,8 +225,8 @@ class StreamingKMeans(
    * @param dim Number of dimensions
    * @param weight Weight for each center
    * @param seed Random seed
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setRandomCenters(dim: Int, weight: Double, seed: Long = Utils.random.nextLong): this.type = {
     val random = new XORShiftRandom(seed)
     val centers = Array.fill(k)(Vectors.dense(Array.fill(dim)(random.nextGaussian())))
@@ -238,8 +237,8 @@ class StreamingKMeans(
 
   /**
    * Return the latest model.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def latestModel(): StreamingKMeansModel = {
     model
   }
@@ -251,8 +250,8 @@ class StreamingKMeans(
    * and updates the model using each batch of data from the stream.
    *
    * @param data DStream containing vector data
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainOn(data: DStream[Vector]) {
     assertInitialized()
     data.foreachRDD { (rdd, time) =>
@@ -262,8 +261,8 @@ class StreamingKMeans(
 
   /**
    * Java-friendly version of `trainOn`.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def trainOn(data: JavaDStream[Vector]): Unit = trainOn(data.dstream)
 
   /**
@@ -271,8 +270,8 @@ class StreamingKMeans(
    *
    * @param data DStream containing vector data
    * @return DStream containing predictions
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predictOn(data: DStream[Vector]): DStream[Int] = {
     assertInitialized()
     data.map(model.predict)
@@ -280,8 +279,8 @@ class StreamingKMeans(
 
   /**
    * Java-friendly version of `predictOn`.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Integer] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Integer]])
   }
@@ -292,8 +291,8 @@ class StreamingKMeans(
    * @param data DStream containing (key, feature vector) pairs
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Int)] = {
     assertInitialized()
     data.mapValues(model.predict)
@@ -301,8 +300,8 @@ class StreamingKMeans(
 
   /**
    * Java-friendly version of `predictOnValues`.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predictOnValues[K](
       data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Integer] = {
     implicit val tag = fakeClassTag[K]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index 486741edd6f5a..76ae847921f44 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.evaluation
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.evaluation.binary._
@@ -41,8 +41,8 @@ import org.apache.spark.sql.DataFrame
  *                of bins may not exactly equal numBins. The last bin in each partition may
  *                be smaller as a result, meaning there may be an extra sample at
  *                partition boundaries.
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class BinaryClassificationMetrics(
     val scoreAndLabels: RDD[(Double, Double)],
@@ -52,8 +52,8 @@ class BinaryClassificationMetrics(
 
   /**
    * Defaults `numBins` to 0.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def this(scoreAndLabels: RDD[(Double, Double)]) = this(scoreAndLabels, 0)
 
   /**
@@ -65,16 +65,16 @@ class BinaryClassificationMetrics(
 
   /**
    * Unpersist intermediate RDDs used in the computation.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def unpersist() {
     cumulativeCounts.unpersist()
   }
 
   /**
    * Returns thresholds in descending order.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def thresholds(): RDD[Double] = cumulativeCounts.map(_._1)
 
   /**
@@ -82,8 +82,8 @@ class BinaryClassificationMetrics(
    * which is an RDD of (false positive rate, true positive rate)
    * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
    * @see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def roc(): RDD[(Double, Double)] = {
     val rocCurve = createCurve(FalsePositiveRate, Recall)
     val sc = confusions.context
@@ -94,16 +94,16 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the receiver operating characteristic (ROC) curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def areaUnderROC(): Double = AreaUnderCurve.of(roc())
 
   /**
    * Returns the precision-recall curve, which is an RDD of (recall, precision),
    * NOT (precision, recall), with (0.0, 1.0) prepended to it.
    * @see http://en.wikipedia.org/wiki/Precision_and_recall
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def pr(): RDD[(Double, Double)] = {
     val prCurve = createCurve(Recall, Precision)
     val sc = confusions.context
@@ -113,8 +113,8 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the precision-recall curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def areaUnderPR(): Double = AreaUnderCurve.of(pr())
 
   /**
@@ -122,26 +122,26 @@ class BinaryClassificationMetrics(
    * @param beta the beta factor in F-Measure computation.
    * @return an RDD of (threshold, F-Measure) pairs.
    * @see http://en.wikipedia.org/wiki/F1_score
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def fMeasureByThreshold(beta: Double): RDD[(Double, Double)] = createCurve(FMeasure(beta))
 
   /**
    * Returns the (threshold, F-Measure) curve with beta = 1.0.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def fMeasureByThreshold(): RDD[(Double, Double)] = fMeasureByThreshold(1.0)
 
   /**
    * Returns the (threshold, precision) curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def precisionByThreshold(): RDD[(Double, Double)] = createCurve(Precision)
 
   /**
    * Returns the (threshold, recall) curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def recallByThreshold(): RDD[(Double, Double)] = createCurve(Recall)
 
   private lazy val (
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index dddfa3ea5b800..02e89d921033c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.evaluation
 import scala.collection.Map
 
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
@@ -30,8 +30,8 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multiclass classification.
  *
  * @param predictionAndLabels an RDD of (prediction, label) pairs.
- * @since 1.1.0
  */
+@Since("1.1.0")
 @Experimental
 class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
 
@@ -65,8 +65,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * predicted classes are in columns,
    * they are ordered by class label ascending,
    * as in "labels"
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def confusionMatrix: Matrix = {
     val n = labels.size
     val values = Array.ofDim[Double](n * n)
@@ -85,15 +85,15 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns true positive rate for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def truePositiveRate(label: Double): Double = recall(label)
 
   /**
    * Returns false positive rate for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def falsePositiveRate(label: Double): Double = {
     val fp = fpByClass.getOrElse(label, 0)
     fp.toDouble / (labelCount - labelCountByClass(label))
@@ -102,8 +102,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns precision for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def precision(label: Double): Double = {
     val tp = tpByClass(label)
     val fp = fpByClass.getOrElse(label, 0)
@@ -113,16 +113,16 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns recall for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def recall(label: Double): Double = tpByClass(label).toDouble / labelCountByClass(label)
 
   /**
    * Returns f-measure for a given label (category)
    * @param label the label.
    * @param beta the beta parameter.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def fMeasure(label: Double, beta: Double): Double = {
     val p = precision(label)
     val r = recall(label)
@@ -133,8 +133,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def fMeasure(label: Double): Double = fMeasure(label, 1.0)
 
   /**
@@ -187,8 +187,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged f-measure
    * @param beta the beta parameter.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def weightedFMeasure(beta: Double): Double = labelCountByClass.map { case (category, count) =>
     fMeasure(category, beta) * count.toDouble / labelCount
   }.sum
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index 77cb1e09bdbb5..a0a8d9c56847b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.evaluation
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
 import org.apache.spark.sql.DataFrame
@@ -25,8 +26,8 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multilabel classification.
  * @param predictionAndLabels an RDD of (predictions, labels) pairs,
  * both are non-null Arrays, each with unique elements.
- * @since 1.2.0
  */
+@Since("1.2.0")
 class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
 
   /**
@@ -104,8 +105,8 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns precision for a given label (category)
    * @param label the label.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def precision(label: Double): Double = {
     val tp = tpPerClass(label)
     val fp = fpPerClass.getOrElse(label, 0L)
@@ -115,8 +116,8 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns recall for a given label (category)
    * @param label the label.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def recall(label: Double): Double = {
     val tp = tpPerClass(label)
     val fn = fnPerClass.getOrElse(label, 0L)
@@ -126,8 +127,8 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def f1Measure(label: Double): Double = {
     val p = precision(label)
     val r = recall(label)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
index 063fbed8cdeea..a7f43f0b110f5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
@@ -23,7 +23,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaSparkContext, JavaRDD}
 import org.apache.spark.rdd.RDD
 
@@ -34,8 +34,8 @@ import org.apache.spark.rdd.RDD
  * Java users should use [[RankingMetrics$.of]] to create a [[RankingMetrics]] instance.
  *
  * @param predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])])
   extends Logging with Serializable {
@@ -56,8 +56,8 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated precision, must be positive
    * @return the average precision at the first k ranking positions
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def precisionAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
     predictionAndLabels.map { case (pred, lab) =>
@@ -126,8 +126,8 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated ndcg, must be positive
    * @return the average ndcg at the first k ranking positions
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def ndcgAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
     predictionAndLabels.map { case (pred, lab) =>
@@ -165,8 +165,8 @@ object RankingMetrics {
   /**
    * Creates a [[RankingMetrics]] instance (for Java users).
    * @param predictionAndLabels a JavaRDD of (predicted ranking, ground truth set) pairs
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def of[E, T <: jl.Iterable[E]](predictionAndLabels: JavaRDD[(T, T)]): RankingMetrics[E] = {
     implicit val tag = JavaSparkContext.fakeClassTag[E]
     val rdd = predictionAndLabels.rdd.map { case (predictions, labels) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 54dfd8c099494..36a6c357c3897 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.evaluation
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.Logging
 import org.apache.spark.mllib.linalg.Vectors
@@ -29,8 +29,8 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for regression.
  *
  * @param predictionAndObservations an RDD of (prediction, observation) pairs.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging {
 
@@ -67,8 +67,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
    * Returns the variance explained by regression.
    * explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n
    * @see [[https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def explainedVariance: Double = {
     SSreg / summary.count
   }
@@ -76,8 +76,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean absolute error, which is a risk function corresponding to the
    * expected value of the absolute error loss or l1-norm loss.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def meanAbsoluteError: Double = {
     summary.normL1(1) / summary.count
   }
@@ -85,8 +85,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean squared error, which is a risk function corresponding to the
    * expected value of the squared error loss or quadratic loss.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def meanSquaredError: Double = {
     SSerr / summary.count
   }
@@ -94,8 +94,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the root mean squared error, which is defined as the square root of
    * the mean squared error.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def rootMeanSquaredError: Double = {
     math.sqrt(this.meanSquaredError)
   }
@@ -103,8 +103,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns R^2^, the unadjusted coefficient of determination.
    * @see [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def r2: Double = {
     1 - SSerr / SStot
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 7f4de77044994..ba3b447a83398 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -20,7 +20,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.fpm.AssociationRules.Rule
@@ -33,24 +33,22 @@ import org.apache.spark.rdd.RDD
  * Generates association rules from a [[RDD[FreqItemset[Item]]]. This method only generates
  * association rules which have a single item as the consequent.
  *
- * @since 1.5.0
  */
+@Since("1.5.0")
 @Experimental
 class AssociationRules private[fpm] (
     private var minConfidence: Double) extends Logging with Serializable {
 
   /**
    * Constructs a default instance with default parameters {minConfidence = 0.8}.
-   *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def this() = this(0.8)
 
   /**
    * Sets the minimal confidence (default: `0.8`).
-   *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setMinConfidence(minConfidence: Double): this.type = {
     require(minConfidence >= 0.0 && minConfidence <= 1.0)
     this.minConfidence = minConfidence
@@ -62,8 +60,8 @@ class AssociationRules private[fpm] (
    * @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
    * @return a [[Set[Rule[Item]]] containing the assocation rules.
    *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def run[Item: ClassTag](freqItemsets: RDD[FreqItemset[Item]]): RDD[Rule[Item]] = {
     // For candidate rule X => Y, generate (X, (Y, freq(X union Y)))
     val candidates = freqItemsets.flatMap { itemset =>
@@ -102,8 +100,8 @@ object AssociationRules {
    *                   instead.
    * @tparam Item item type
    *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   @Experimental
   class Rule[Item] private[fpm] (
       val antecedent: Array[Item],
@@ -114,8 +112,8 @@ object AssociationRules {
     /**
      * Returns the confidence of the rule.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def confidence: Double = freqUnion.toDouble / freqAntecedent
 
     require(antecedent.toSet.intersect(consequent.toSet).isEmpty, {
@@ -127,8 +125,8 @@ object AssociationRules {
     /**
      * Returns antecedent in a Java List.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def javaAntecedent: java.util.List[Item] = {
       antecedent.toList.asJava
     }
@@ -136,8 +134,8 @@ object AssociationRules {
     /**
      * Returns consequent in a Java List.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def javaConsequent: java.util.List[Item] = {
       consequent.toList.asJava
     }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index e2370a52f4930..e37f806271680 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -25,7 +25,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.{HashPartitioner, Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.fpm.FPGrowth._
@@ -39,15 +39,15 @@ import org.apache.spark.storage.StorageLevel
  * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
   /**
    * Generates association rules for the [[Item]]s in [[freqItemsets]].
    * @param confidence minimal confidence of the rules produced
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def generateAssociationRules(confidence: Double): RDD[AssociationRules.Rule[Item]] = {
     val associationRules = new AssociationRules(confidence)
     associationRules.run(freqItemsets)
@@ -71,8 +71,8 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
  * @see [[http://en.wikipedia.org/wiki/Association_rule_learning Association rule learning
  *       (Wikipedia)]]
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class FPGrowth private (
     private var minSupport: Double,
@@ -82,15 +82,15 @@ class FPGrowth private (
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
    * as the input data}.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(0.3, -1)
 
   /**
    * Sets the minimal support level (default: `0.3`).
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMinSupport(minSupport: Double): this.type = {
     this.minSupport = minSupport
     this
@@ -99,8 +99,8 @@ class FPGrowth private (
   /**
    * Sets the number of partitions used by parallel FP-growth (default: same as input data).
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setNumPartitions(numPartitions: Int): this.type = {
     this.numPartitions = numPartitions
     this
@@ -111,8 +111,8 @@ class FPGrowth private (
    * @param data input data set, each element contains a transaction
    * @return an [[FPGrowthModel]]
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run[Item: ClassTag](data: RDD[Array[Item]]): FPGrowthModel[Item] = {
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
@@ -213,8 +213,8 @@ class FPGrowth private (
 /**
  * :: Experimental ::
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 object FPGrowth {
 
@@ -224,15 +224,15 @@ object FPGrowth {
    * @param freq frequency
    * @tparam Item item type
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.
      *
-     * @since 1.3.0
      */
+    @Since("1.3.0")
     def javaItems: java.util.List[Item] = {
       items.toList.asJava
     }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index dfa8910fcbc8c..28b5b4637bf17 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable.{ArrayBuilder => MArrayBuilder, HashSet => MHash
 
 import breeze.linalg.{CSCMatrix => BSM, DenseMatrix => BDM, Matrix => BM}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
@@ -227,8 +227,8 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
  * @param values matrix entries in column major if not transposed or in row major otherwise
  * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in
  *                     row major.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class DenseMatrix(
     val numRows: Int,
@@ -253,8 +253,8 @@ class DenseMatrix(
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this(numRows: Int, numCols: Int, values: Array[Double]) =
     this(numRows, numCols, values, false)
 
@@ -278,9 +278,7 @@ class DenseMatrix(
 
   private[mllib] def apply(i: Int): Double = values(i)
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = values(index(i, j))
 
   private[mllib] def index(i: Int, j: Int): Int = {
@@ -291,9 +289,7 @@ class DenseMatrix(
     values(index(i, j)) = v
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone())
 
   private[spark] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
@@ -309,9 +305,7 @@ class DenseMatrix(
     this
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def transpose: DenseMatrix = new DenseMatrix(numCols, numRows, values, !isTransposed)
 
   private[spark] override def foreachActive(f: (Int, Int, Double) => Unit): Unit = {
@@ -342,21 +336,17 @@ class DenseMatrix(
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numNonzeros: Int = values.count(_ != 0)
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numActives: Int = values.length
 
   /**
    * Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed
    * set to false.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toSparse: SparseMatrix = {
     val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
     val colPtrs: Array[Int] = new Array[Int](numCols + 1)
@@ -383,8 +373,8 @@ class DenseMatrix(
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
- * @since 1.3.0
  */
+@Since("1.3.0")
 object DenseMatrix {
 
   /**
@@ -392,8 +382,8 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def zeros(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -405,8 +395,8 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def ones(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -417,8 +407,8 @@ object DenseMatrix {
    * Generate an Identity Matrix in `DenseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def eye(n: Int): DenseMatrix = {
     val identity = DenseMatrix.zeros(n, n)
     var i = 0
@@ -435,8 +425,8 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -449,8 +439,8 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -462,8 +452,8 @@ object DenseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `DenseMatrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def diag(vector: Vector): DenseMatrix = {
     val n = vector.size
     val matrix = DenseMatrix.zeros(n, n)
@@ -498,8 +488,8 @@ object DenseMatrix {
  * @param isTransposed whether the matrix is transposed. If true, the matrix can be considered
  *                     Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs,
  *                     and `rowIndices` behave as colIndices, and `values` are stored in row major.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class SparseMatrix(
     val numRows: Int,
@@ -536,8 +526,8 @@ class SparseMatrix(
    * @param rowIndices the row index of the entry. They must be in strictly increasing
    *                   order for each column
    * @param values non-zero matrix entries in column major
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this(
       numRows: Int,
       numCols: Int,
@@ -560,8 +550,8 @@ class SparseMatrix(
   }
 
   /**
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = {
     val ind = index(i, j)
     if (ind < 0) 0.0 else values(ind)
@@ -585,9 +575,7 @@ class SparseMatrix(
     }
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def copy: SparseMatrix = {
     new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
   }
@@ -605,9 +593,7 @@ class SparseMatrix(
     this
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def transpose: SparseMatrix =
     new SparseMatrix(numCols, numRows, colPtrs, rowIndices, values, !isTransposed)
 
@@ -641,28 +627,24 @@ class SparseMatrix(
   /**
    * Generate a `DenseMatrix` from the given `SparseMatrix`. The new matrix will have isTransposed
    * set to false.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toDense: DenseMatrix = {
     new DenseMatrix(numRows, numCols, toArray)
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numNonzeros: Int = values.count(_ != 0)
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numActives: Int = values.length
 
 }
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
- * @since 1.3.0
  */
+@Since("1.3.0")
 object SparseMatrix {
 
   /**
@@ -673,8 +655,8 @@ object SparseMatrix {
    * @param numCols number of columns of the matrix
    * @param entries Array of (i, j, value) tuples
    * @return The corresponding `SparseMatrix`
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix = {
     val sortedEntries = entries.toSeq.sortBy(v => (v._2, v._1))
     val numEntries = sortedEntries.size
@@ -722,8 +704,8 @@ object SparseMatrix {
    * Generate an Identity Matrix in `SparseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `SparseMatrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def speye(n: Int): SparseMatrix = {
     new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0))
   }
@@ -792,8 +774,8 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
     mat.update(i => rng.nextDouble())
@@ -806,8 +788,8 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
     mat.update(i => rng.nextGaussian())
@@ -818,8 +800,8 @@ object SparseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `SparseMatrix` with size `values.length` x `values.length` and non-zero
    *         `values` on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def spdiag(vector: Vector): SparseMatrix = {
     val n = vector.size
     vector match {
@@ -835,8 +817,8 @@ object SparseMatrix {
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.Matrix]].
- * @since 1.0.0
  */
+@Since("1.0.0")
 object Matrices {
 
   /**
@@ -845,8 +827,8 @@ object Matrices {
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = {
     new DenseMatrix(numRows, numCols, values)
   }
@@ -859,8 +841,8 @@ object Matrices {
    * @param colPtrs the index corresponding to the start of a new column
    * @param rowIndices the row index of the entry
    * @param values non-zero matrix entries in column major
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def sparse(
      numRows: Int,
      numCols: Int,
@@ -893,8 +875,8 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of zeros
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def zeros(numRows: Int, numCols: Int): Matrix = DenseMatrix.zeros(numRows, numCols)
 
   /**
@@ -902,24 +884,24 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of ones
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows, numCols)
 
   /**
    * Generate a dense Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def eye(n: Int): Matrix = DenseMatrix.eye(n)
 
   /**
    * Generate a sparse Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def speye(n: Int): Matrix = SparseMatrix.speye(n)
 
   /**
@@ -928,8 +910,8 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def rand(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.rand(numRows, numCols, rng)
 
@@ -940,8 +922,8 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprand(numRows, numCols, density, rng)
 
@@ -951,8 +933,8 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def randn(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.randn(numRows, numCols, rng)
 
@@ -963,8 +945,8 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprandn(numRows, numCols, density, rng)
 
@@ -973,8 +955,8 @@ object Matrices {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `Matrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)
 
   /**
@@ -983,8 +965,8 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were horizontally concatenated
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def horzcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
       return new DenseMatrix(0, 0, Array[Double]())
@@ -1042,8 +1024,8 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were vertically concatenated
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def vertcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
       return new DenseMatrix(0, 0, Array[Double]())
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index 8f504f6984cb0..a37aca99d5e72 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.linalg
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Represents singular value decomposition (SVD) factors.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 52ef7be3b38be..3d577edbe23e1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -26,7 +26,7 @@ import scala.collection.JavaConverters._
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.{AlphaComponent, Since}
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
@@ -240,14 +240,14 @@ class VectorUDT extends UserDefinedType[Vector] {
  * Factory methods for [[org.apache.spark.mllib.linalg.Vector]].
  * We don't use the name `Vector` because Scala imports
  * [[scala.collection.immutable.Vector]] by default.
- * @since 1.0.0
  */
+@Since("1.0.0")
 object Vectors {
 
   /**
    * Creates a dense vector from its values.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @varargs
   def dense(firstValue: Double, otherValues: Double*): Vector =
     new DenseVector((firstValue +: otherValues).toArray)
@@ -255,8 +255,8 @@ object Vectors {
   // A dummy implicit is used to avoid signature collision with the one generated by @varargs.
   /**
    * Creates a dense vector from a double array.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def dense(values: Array[Double]): Vector = new DenseVector(values)
 
   /**
@@ -265,8 +265,8 @@ object Vectors {
    * @param size vector size.
    * @param indices index array, must be strictly increasing.
    * @param values value array, must have the same length as indices.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector =
     new SparseVector(size, indices, values)
 
@@ -275,8 +275,8 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = {
     require(size > 0, "The size of the requested sparse vector must be greater than 0.")
 
@@ -297,8 +297,8 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, elements: JavaIterable[(JavaInteger, JavaDouble)]): Vector = {
     sparse(size, elements.asScala.map { case (i, x) =>
       (i.intValue(), x.doubleValue())
@@ -310,16 +310,16 @@ object Vectors {
    *
    * @param size vector size
    * @return a zero vector
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def zeros(size: Int): Vector = {
     new DenseVector(new Array[Double](size))
   }
 
   /**
    * Parses a string resulted from [[Vector.toString]] into a [[Vector]].
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def parse(s: String): Vector = {
     parseNumeric(NumericParser.parse(s))
   }
@@ -362,8 +362,8 @@ object Vectors {
    * @param vector input vector.
    * @param p norm.
    * @return norm in L^p^ space.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def norm(vector: Vector, p: Double): Double = {
     require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " +
       s"You specified p=$p.")
@@ -415,8 +415,8 @@ object Vectors {
    * @param v1 first Vector.
    * @param v2 second Vector.
    * @return squared distance between two Vectors.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sqdist(v1: Vector, v2: Vector): Double = {
     require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +
       s"=${v2.size}.")
@@ -529,33 +529,25 @@ object Vectors {
 
 /**
  * A dense vector represented by a value array.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class DenseVector(val values: Array[Double]) extends Vector {
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def size: Int = values.length
 
   override def toString: String = values.mkString("[", ",", "]")
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def toArray: Array[Double] = values
 
   private[spark] override def toBreeze: BV[Double] = new BDV[Double](values)
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def apply(i: Int): Double = values(i)
 
-  /**
-   * @since 1.1.0
-   */
+  @Since("1.1.0")
   override def copy: DenseVector = {
     new DenseVector(values.clone())
   }
@@ -587,14 +579,10 @@ class DenseVector(val values: Array[Double]) extends Vector {
     result
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numActives: Int = size
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numNonzeros: Int = {
     // same as values.count(_ != 0.0) but faster
     var nnz = 0
@@ -606,9 +594,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     nnz
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     val ii = new Array[Int](nnz)
@@ -624,9 +610,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     new SparseVector(size, ii, vv)
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -646,9 +630,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object DenseVector {
   /** Extracts the value array from a dense vector. */
   def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
@@ -660,8 +642,8 @@ object DenseVector {
  * @param size size of the vector.
  * @param indices index array, assume to be strictly increasing.
  * @param values value array, must have the same length as the index array.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class SparseVector(
     override val size: Int,
@@ -677,9 +659,7 @@ class SparseVector(
   override def toString: String =
     s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def toArray: Array[Double] = {
     val data = new Array[Double](size)
     var i = 0
@@ -691,9 +671,7 @@ class SparseVector(
     data
   }
 
-  /**
-   * @since 1.1.0
-   */
+  @Since("1.1.0")
   override def copy: SparseVector = {
     new SparseVector(size, indices.clone(), values.clone())
   }
@@ -734,14 +712,10 @@ class SparseVector(
     result
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numActives: Int = values.length
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numNonzeros: Int = {
     var nnz = 0
     values.foreach { v =>
@@ -752,9 +726,7 @@ class SparseVector(
     nnz
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     if (nnz == numActives) {
@@ -774,9 +746,7 @@ class SparseVector(
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -847,9 +817,7 @@ class SparseVector(
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object SparseVector {
   def unapply(sv: SparseVector): Option[(Int, Array[Int], Array[Double])] =
     Some((sv.size, sv.indices, sv.values))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index cfb6680a18b34..94376c24a7ac6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import breeze.linalg.{DenseMatrix => BDM}
 
 import org.apache.spark.{Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Matrix, SparseMatrix}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -128,9 +128,8 @@ private[mllib] object GridPartitioner {
  *              the number of rows will be calculated when `numRows` is invoked.
  * @param nCols Number of columns of this matrix. If the supplied value is less than or equal to
  *              zero, the number of columns will be calculated when `numCols` is invoked.
- * @since 1.3.0
- *
  */
+@Since("1.3.0")
 @Experimental
 class BlockMatrix(
     val blocks: RDD[((Int, Int), Matrix)],
@@ -151,10 +150,8 @@ class BlockMatrix(
    *                     rows are not required to have the given number of rows
    * @param colsPerBlock Number of columns that make up each block. The blocks forming the final
    *                     columns are not required to have the given number of columns
-   *
-   * @since 1.3.0
-   *
    */
+  @Since("1.3.0")
   def this(
       blocks: RDD[((Int, Int), Matrix)],
       rowsPerBlock: Int,
@@ -162,20 +159,13 @@ class BlockMatrix(
     this(blocks, rowsPerBlock, colsPerBlock, 0L, 0L)
   }
 
-  /**
-   * @since 1.3.0
-   * */
-
+  @Since("1.3.0")
   override def numRows(): Long = {
     if (nRows <= 0L) estimateDim()
     nRows
   }
 
-  /**
-   *
-   * @since 1.3.0
-   */
-
+  @Since("1.3.0")
   override def numCols(): Long = {
     if (nCols <= 0L) estimateDim()
     nCols
@@ -206,8 +196,8 @@ class BlockMatrix(
   /**
    * Validates the block matrix info against the matrix data (`blocks`) and throws an exception if
    * any error is found.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def validate(): Unit = {
     logDebug("Validating BlockMatrix...")
     // check if the matrix is larger than the claimed dimensions
@@ -243,25 +233,22 @@ class BlockMatrix(
     logDebug("BlockMatrix is valid!")
   }
 
-  /** Caches the underlying RDD.
-    * @since 1.3.0
-    * */
+  /** Caches the underlying RDD. */
+  @Since("1.3.0")
   def cache(): this.type = {
     blocks.cache()
     this
   }
 
-  /** Persists the underlying RDD with the specified storage level.
-    * @since 1.3.0
-    * */
+  /** Persists the underlying RDD with the specified storage level. */
+  @Since("1.3.0")
   def persist(storageLevel: StorageLevel): this.type = {
     blocks.persist(storageLevel)
     this
   }
 
-  /** Converts to CoordinateMatrix.
-    * @since 1.3.0
-    * */
+  /** Converts to CoordinateMatrix. */
+  @Since("1.3.0")
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entryRDD = blocks.flatMap { case ((blockRowIndex, blockColIndex), mat) =>
       val rowStart = blockRowIndex.toLong * rowsPerBlock
@@ -275,9 +262,8 @@ class BlockMatrix(
     new CoordinateMatrix(entryRDD, numRows(), numCols())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
-    * @since 1.3.0
-    * */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  @Since("1.3.0")
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     require(numCols() < Int.MaxValue, "The number of columns must be within the integer range. " +
       s"numCols: ${numCols()}")
@@ -285,9 +271,8 @@ class BlockMatrix(
     toCoordinateMatrix().toIndexedRowMatrix()
   }
 
-  /** Collect the distributed matrix on the driver as a `DenseMatrix`.
-    * @since 1.3.0
-    * */
+  /** Collect the distributed matrix on the driver as a `DenseMatrix`. */
+  @Since("1.3.0")
   def toLocalMatrix(): Matrix = {
     require(numRows() < Int.MaxValue, "The number of rows of this matrix should be less than " +
       s"Int.MaxValue. Currently numRows: ${numRows()}")
@@ -312,11 +297,11 @@ class BlockMatrix(
     new DenseMatrix(m, n, values)
   }
 
-  /** Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
-    * same underlying data. Is a lazy operation.
-    * @since 1.3.0
-    *
-    * */
+  /**
+   * Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
+   * same underlying data. Is a lazy operation.
+   */
+  @Since("1.3.0")
   def transpose: BlockMatrix = {
     val transposedBlocks = blocks.map { case ((blockRowIndex, blockColIndex), mat) =>
       ((blockColIndex, blockRowIndex), mat.transpose)
@@ -330,13 +315,14 @@ class BlockMatrix(
     new BDM[Double](localMat.numRows, localMat.numCols, localMat.toArray)
   }
 
-  /** Adds two block matrices together. The matrices must have the same size and matching
-    * `rowsPerBlock` and `colsPerBlock` values. If one of the blocks that are being added are
-    * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
-    * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
-    * also be a [[DenseMatrix]].
-    * @since 1.3.0
-    */
+  /**
+   * Adds two block matrices together. The matrices must have the same size and matching
+   * `rowsPerBlock` and `colsPerBlock` values. If one of the blocks that are being added are
+   * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
+   * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
+   * also be a [[DenseMatrix]].
+   */
+  @Since("1.3.0")
   def add(other: BlockMatrix): BlockMatrix = {
     require(numRows() == other.numRows(), "Both matrices must have the same number of rows. " +
       s"A.numRows: ${numRows()}, B.numRows: ${other.numRows()}")
@@ -364,14 +350,14 @@ class BlockMatrix(
     }
   }
 
-  /** Left multiplies this [[BlockMatrix]] to `other`, another [[BlockMatrix]]. The `colsPerBlock`
-    * of this matrix must equal the `rowsPerBlock` of `other`. If `other` contains
-    * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
-    * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
-    * some performance issues until support for multiplying two sparse matrices is added.
-    *
-    * @since 1.3.0
-    */
+  /**
+   * Left multiplies this [[BlockMatrix]] to `other`, another [[BlockMatrix]]. The `colsPerBlock`
+   * of this matrix must equal the `rowsPerBlock` of `other`. If `other` contains
+   * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
+   * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
+   * some performance issues until support for multiplying two sparse matrices is added.
+   */
+  @Since("1.3.0")
   def multiply(other: BlockMatrix): BlockMatrix = {
     require(numCols() == other.numRows(), "The number of columns of A and the number of rows " +
       s"of B must be equal. A.numCols: ${numCols()}, B.numRows: ${other.numRows()}. If you " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 2b751e45dd76c..4bb27ec840902 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
 
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
  * @param i row index
  * @param j column index
  * @param value value of the entry
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class MatrixEntry(i: Long, j: Long, value: Double)
 
@@ -43,22 +43,20 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the max column index plus one.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class CoordinateMatrix(
     val entries: RDD[MatrixEntry],
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(entries: RDD[MatrixEntry]) = this(entries, 0L, 0L)
 
-  /** Gets or computes the number of columns.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0L) {
       computeSize()
@@ -66,9 +64,8 @@ class CoordinateMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       computeSize()
@@ -76,16 +73,14 @@ class CoordinateMatrix(
     nRows
   }
 
-  /** Transposes this CoordinateMatrix.
-    * @since 1.3.0
-    * */
+  /** Transposes this CoordinateMatrix. */
+  @Since("1.3.0")
   def transpose(): CoordinateMatrix = {
     new CoordinateMatrix(entries.map(x => MatrixEntry(x.j, x.i, x.value)), numCols(), numRows())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
-    * @since 1.0.0
-    * */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  @Since("1.0.0")
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     val nl = numCols()
     if (nl > Int.MaxValue) {
@@ -104,15 +99,14 @@ class CoordinateMatrix(
   /**
    * Converts to RowMatrix, dropping row indices after grouping by row index.
    * The number of columns must be within the integer range.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def toRowMatrix(): RowMatrix = {
     toIndexedRowMatrix().toRowMatrix()
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
-    * @since 1.3.0
-    * */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  @Since("1.3.0")
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -124,8 +118,8 @@ class CoordinateMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     require(rowsPerBlock > 0,
       s"rowsPerBlock needs to be greater than 0. rowsPerBlock: $rowsPerBlock")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
index 98e90af84abac..e51327ebb7b58 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
@@ -19,10 +19,12 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
+import org.apache.spark.annotation.Since
+
 /**
  * Represents a distributively stored matrix backed by one or more RDDs.
- * @since 1.0.0
  */
+@Since("1.0.0")
 trait DistributedMatrix extends Serializable {
 
   /** Gets or computes the number of rows. */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index a09f88ce28e58..6d2c05a47d049 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.SingularValueDecomposition
@@ -27,8 +27,8 @@ import org.apache.spark.mllib.linalg.SingularValueDecomposition
 /**
  * :: Experimental ::
  * Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class IndexedRow(index: Long, vector: Vector)
 
@@ -42,23 +42,19 @@ case class IndexedRow(index: Long, vector: Vector)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class IndexedRowMatrix(
     val rows: RDD[IndexedRow],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(rows: RDD[IndexedRow]) = this(rows, 0L, 0)
 
-  /**
-   *
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0) {
       // Calling `first` will throw an exception if `rows` is empty.
@@ -67,10 +63,7 @@ class IndexedRowMatrix(
     nCols
   }
 
-  /**
-   *
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       // Reduce will throw an exception if `rows` is empty.
@@ -82,15 +75,14 @@ class IndexedRowMatrix(
   /**
    * Drops row indices and converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]].
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def toRowMatrix(): RowMatrix = {
     new RowMatrix(rows.map(_.vector), 0L, nCols)
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
-    * @since 1.3.0
-    * */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  @Since("1.3.0")
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -102,8 +94,8 @@ class IndexedRowMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     // TODO: This implementation may be optimized
     toCoordinateMatrix().toBlockMatrix(rowsPerBlock, colsPerBlock)
@@ -112,8 +104,8 @@ class IndexedRowMatrix(
   /**
    * Converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.CoordinateMatrix]].
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entries = rows.flatMap { row =>
       val rowIndex = row.index
@@ -149,8 +141,8 @@ class IndexedRowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V)
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeSVD(
       k: Int,
       computeU: Boolean = false,
@@ -176,8 +168,8 @@ class IndexedRowMatrix(
    *
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return an IndexedRowMatrix representing the product, which preserves partitioning
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def multiply(B: Matrix): IndexedRowMatrix = {
     val mat = toRowMatrix().multiply(B)
     val indexedRows = rows.map(_.index).zip(mat.rows).map { case (i, v) =>
@@ -188,8 +180,8 @@ class IndexedRowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeGramianMatrix(): Matrix = {
     toRowMatrix().computeGramianMatrix()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index b2e94f2dd6201..78036eba5c3e6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -28,7 +28,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 import org.apache.spark.rdd.RDD
@@ -44,22 +44,20 @@ import org.apache.spark.storage.StorageLevel
  *              be determined by the number of records in the RDD `rows`.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class RowMatrix(
     val rows: RDD[Vector],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix with Logging {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(rows: RDD[Vector]) = this(rows, 0L, 0)
 
-  /** Gets or computes the number of columns.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0) {
       try {
@@ -74,9 +72,8 @@ class RowMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       nRows = rows.count()
@@ -114,8 +111,8 @@ class RowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeGramianMatrix(): Matrix = {
     val n = numCols().toInt
     checkNumColumns(n)
@@ -185,8 +182,8 @@ class RowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeSVD(
       k: Int,
       computeU: Boolean = false,
@@ -326,8 +323,8 @@ class RowMatrix(
   /**
    * Computes the covariance matrix, treating each row as an observation.
    * @return a local dense matrix of size n x n
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeCovariance(): Matrix = {
     val n = numCols().toInt
     checkNumColumns(n)
@@ -380,8 +377,8 @@ class RowMatrix(
    *
    * @param k number of top principal components.
    * @return a matrix of size n-by-k, whose columns are principal components
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computePrincipalComponents(k: Int): Matrix = {
     val n = numCols().toInt
     require(k > 0 && k <= n, s"k = $k out of range (0, n = $n]")
@@ -399,8 +396,8 @@ class RowMatrix(
 
   /**
    * Computes column-wise summary statistics.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
     val summary = rows.treeAggregate(new MultivariateOnlineSummarizer)(
       (aggregator, data) => aggregator.add(data),
@@ -415,8 +412,8 @@ class RowMatrix(
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return a [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] representing the product,
    *         which preserves partitioning
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def multiply(B: Matrix): RowMatrix = {
     val n = numCols().toInt
     val k = B.numCols
@@ -448,8 +445,8 @@ class RowMatrix(
    *
    * @return An n x n sparse upper-triangular matrix of cosine similarities between
    *         columns of this matrix.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def columnSimilarities(): CoordinateMatrix = {
     columnSimilarities(0.0)
   }
@@ -492,8 +489,8 @@ class RowMatrix(
    *                  with the cost vs estimate quality trade-off described above.
    * @return An n x n sparse upper-triangular matrix of cosine similarities
    *         between columns of this matrix.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def columnSimilarities(threshold: Double): CoordinateMatrix = {
     require(threshold >= 0, s"Threshold cannot be negative: $threshold")
 
@@ -671,9 +668,7 @@ class RowMatrix(
   }
 }
 
-/**
- * @since 1.0.0
- */
+@Since("1.0.0")
 @Experimental
 object RowMatrix {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 56c549ef99cb7..b27ef1b949e2e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.recommendation
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.ml.recommendation.{ALS => NewALS}
 import org.apache.spark.rdd.RDD
@@ -26,8 +26,8 @@ import org.apache.spark.storage.StorageLevel
 
 /**
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
- * @since 0.8.0
  */
+@Since("0.8.0")
 case class Rating(user: Int, product: Int, rating: Double)
 
 /**
@@ -255,8 +255,8 @@ class ALS private (
 
 /**
  * Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
- * @since 0.8.0
  */
+@Since("0.8.0")
 object ALS {
   /**
    * Train a matrix factorization model given an RDD of ratings given by users to some products,
@@ -271,8 +271,8 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param seed       random seed
-   * @since 0.9.1
    */
+  @Since("0.9.1")
   def train(
       ratings: RDD[Rating],
       rank: Int,
@@ -296,8 +296,8 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       ratings: RDD[Rating],
       rank: Int,
@@ -319,8 +319,8 @@ object ALS {
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
     : MatrixFactorizationModel = {
     train(ratings, rank, iterations, lambda, -1)
@@ -336,8 +336,8 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
     train(ratings, rank, iterations, 0.01, -1)
@@ -357,8 +357,8 @@ object ALS {
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
    * @param seed       random seed
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(
       ratings: RDD[Rating],
       rank: Int,
@@ -384,8 +384,8 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(
       ratings: RDD[Rating],
       rank: Int,
@@ -409,8 +409,8 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param alpha      confidence parameter
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, lambda, -1, alpha)
@@ -427,8 +427,8 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, 0.01, -1, 1.0)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 261ca9cef0c5b..ba4cfdcd9f1dd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -30,6 +30,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.rdd.MLPairRDDFunctions._
@@ -49,8 +50,8 @@ import org.apache.spark.storage.StorageLevel
  *                     the features computed for this user.
  * @param productFeatures RDD of tuples where each tuple represents the productId
  *                        and the features computed for this product.
- * @since 0.8.0
  */
+@Since("0.8.0")
 class MatrixFactorizationModel(
     val rank: Int,
     val userFeatures: RDD[(Int, Array[Double])],
@@ -74,9 +75,8 @@ class MatrixFactorizationModel(
     }
   }
 
-  /** Predict the rating of one user for one product.
-   * @since 0.8.0
-   */
+  /** Predict the rating of one user for one product. */
+  @Since("0.8.0")
   def predict(user: Int, product: Int): Double = {
     val userVector = userFeatures.lookup(user).head
     val productVector = productFeatures.lookup(product).head
@@ -114,8 +114,8 @@ class MatrixFactorizationModel(
    *
    * @param usersProducts  RDD of (user, product) pairs.
    * @return RDD of Ratings.
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
     // Previously the partitions of ratings are only based on the given products.
     // So if the usersProducts given for prediction contains only few products or
@@ -146,8 +146,8 @@ class MatrixFactorizationModel(
 
   /**
    * Java-friendly version of [[MatrixFactorizationModel.predict]].
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predict(usersProducts: JavaPairRDD[JavaInteger, JavaInteger]): JavaRDD[Rating] = {
     predict(usersProducts.rdd.asInstanceOf[RDD[(Int, Int)]]).toJavaRDD()
   }
@@ -162,8 +162,8 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the user. The score is an opaque value that indicates how strongly
    *  recommended the product is.
-   *  @since 1.1.0
    */
+  @Since("1.1.0")
   def recommendProducts(user: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num)
       .map(t => Rating(user, t._1, t._2))
@@ -179,8 +179,8 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the product. The score is an opaque value that indicates how strongly
    *  recommended the user is.
-   *  @since 1.1.0
    */
+  @Since("1.1.0")
   def recommendUsers(product: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num)
       .map(t => Rating(t._1, product, t._2))
@@ -199,8 +199,8 @@ class MatrixFactorizationModel(
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     MatrixFactorizationModel.SaveLoadV1_0.save(this, path)
   }
@@ -212,8 +212,8 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of
    * rating objects which contains the same userId, recommended productID and a "score" in the
    * rating field. Semantics of score is same as recommendProducts API
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, userFeatures, productFeatures, num).map {
       case (user, top) =>
@@ -230,8 +230,8 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array
    * of rating objects which contains the recommended userId, same productID and a "score" in the
    * rating field. Semantics of score is same as recommendUsers API
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, productFeatures, userFeatures, num).map {
       case (product, top) =>
@@ -241,9 +241,7 @@ class MatrixFactorizationModel(
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -326,8 +324,8 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): MatrixFactorizationModel = {
     val (loadedClassName, formatVersion, _) = loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 2980b94de35b0..509f6a2d169c4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.feature.StandardScaler
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.RDD
@@ -35,8 +35,8 @@ import org.apache.spark.storage.StorageLevel
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @DeveloperApi
 abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
   extends Serializable {
@@ -56,8 +56,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double] = {
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
     // and intercept is needed.
@@ -76,8 +76,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: Vector): Double = {
     predictPoint(testData, weights, intercept)
   }
@@ -95,8 +95,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
  * GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
  * This class should be extended with an Optimizer to create a new GLM.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @DeveloperApi
 abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   extends Logging with Serializable {
@@ -106,8 +106,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * The optimizer to solve the problem.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def optimizer: Optimizer
 
   /** Whether to add intercept (default: false). */
@@ -143,8 +143,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * The dimension of training features.
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getNumFeatures: Int = this.numFeatures
 
   /**
@@ -168,16 +168,16 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Get if the algorithm uses addIntercept
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def isAddIntercept: Boolean = this.addIntercept
 
   /**
    * Set if the algorithm should add an intercept. Default false.
    * We set the default to false because adding the intercept will cause memory allocation.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setIntercept(addIntercept: Boolean): this.type = {
     this.addIntercept = addIntercept
     this
@@ -186,8 +186,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Set if the algorithm should validate data before training. Default true.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setValidateData(validateData: Boolean): this.type = {
     this.validateData = validateData
     this
@@ -197,8 +197,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * Run the algorithm with the configured parameters on an input
    * RDD of LabeledPoint entries.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def run(input: RDD[LabeledPoint]): M = {
     if (numFeatures < 0) {
       numFeatures = input.map(_.features.size).first()
@@ -231,8 +231,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * Run the algorithm with the configured parameters on an input RDD
    * of LabeledPoint entries starting from the initial weights provided.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def run(input: RDD[LabeledPoint], initialWeights: Vector): M = {
 
     if (numFeatures < 0) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index 8995591d9e8ce..31ca7c2f207d9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -29,7 +29,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -47,8 +47,8 @@ import org.apache.spark.sql.SQLContext
  *                    Results of isotonic regression and therefore monotone.
  * @param isotonic indicates whether this is isotonic or antitonic.
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class IsotonicRegressionModel (
     val boundaries: Array[Double],
@@ -64,8 +64,8 @@ class IsotonicRegressionModel (
   /**
    * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def this(boundaries: java.lang.Iterable[Double],
       predictions: java.lang.Iterable[Double],
       isotonic: java.lang.Boolean) = {
@@ -90,8 +90,8 @@ class IsotonicRegressionModel (
    * @param testData Features to be labeled.
    * @return Predicted labels.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: RDD[Double]): RDD[Double] = {
     testData.map(predict)
   }
@@ -103,8 +103,8 @@ class IsotonicRegressionModel (
    * @param testData Features to be labeled.
    * @return Predicted labels.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: JavaDoubleRDD): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(predict(testData.rdd.retag.asInstanceOf[RDD[Double]]))
   }
@@ -125,8 +125,8 @@ class IsotonicRegressionModel (
    *           as piecewise linear function and interpolated value is returned. In case there are
    *           multiple values with the same boundary then the same rules as in 2) are used.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: Double): Double = {
 
     def linearInterpolation(x1: Double, y1: Double, x2: Double, y2: Double, x: Double): Double = {
@@ -160,9 +160,7 @@ class IsotonicRegressionModel (
   /** A convenient method for boundaries called by the Python API. */
   private[mllib] def predictionVector: Vector = Vectors.dense(predictions)
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic)
   }
@@ -170,9 +168,7 @@ class IsotonicRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -219,8 +215,8 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
   }
 
   /**
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): IsotonicRegressionModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index 8b51011eeb297..f7fe1b7b21fca 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -19,6 +19,7 @@ package org.apache.spark.mllib.regression
 
 import scala.beans.BeanInfo
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.SparkException
@@ -29,8 +30,8 @@ import org.apache.spark.SparkException
  * @param label Label for this data point.
  * @param features List of features for this data point.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @BeanInfo
 case class LabeledPoint(label: Double, features: Vector) {
   override def toString: String = {
@@ -41,15 +42,15 @@ case class LabeledPoint(label: Double, features: Vector) {
 /**
  * Parser for [[org.apache.spark.mllib.regression.LabeledPoint]].
  *
- * @since 1.1.0
  */
+@Since("1.1.0")
 object LabeledPoint {
   /**
    * Parses a string resulted from `LabeledPoint#toString` into
    * an [[org.apache.spark.mllib.regression.LabeledPoint]].
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def parse(s: String): LabeledPoint = {
     if (s.startsWith("(")) {
       NumericParser.parse(s) match {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 03eb589b05a0e..556411a366bd2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -31,8 +32,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class LassoModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -46,9 +47,7 @@ class LassoModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -56,14 +55,10 @@ class LassoModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object LassoModel extends Loader[LassoModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LassoModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -118,8 +113,8 @@ class LassoWithSGD private (
 /**
  * Top-level methods for calling Lasso.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object LassoWithSGD {
 
   /**
@@ -137,8 +132,8 @@ object LassoWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -162,8 +157,8 @@ object LassoWithSGD {
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -185,8 +180,8 @@ object LassoWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -205,8 +200,8 @@ object LassoWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LassoModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index fb5c220daaedb..00ab06e3ba738 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -31,8 +32,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class LinearRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -46,9 +47,7 @@ class LinearRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -56,14 +55,10 @@ class LinearRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object LinearRegressionModel extends Loader[LinearRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LinearRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -117,8 +112,8 @@ class LinearRegressionWithSGD private[mllib] (
 /**
  * Top-level methods for calling LinearRegression.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object LinearRegressionWithSGD {
 
   /**
@@ -135,8 +130,8 @@ object LinearRegressionWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -158,8 +153,8 @@ object LinearRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -179,8 +174,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -198,8 +193,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LinearRegressionModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
index b097fd38fdd82..0e72d6591ce83 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
@@ -19,14 +19,12 @@ package org.apache.spark.mllib.regression
 
 import org.json4s.{DefaultFormats, JValue}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 
-/**
- * @since 0.8.0
- */
+@Since("0.8.0")
 @Experimental
 trait RegressionModel extends Serializable {
   /**
@@ -35,8 +33,8 @@ trait RegressionModel extends Serializable {
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double]
 
   /**
@@ -45,8 +43,8 @@ trait RegressionModel extends Serializable {
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: Vector): Double
 
   /**
@@ -54,8 +52,8 @@ trait RegressionModel extends Serializable {
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 5bced6b4b7b53..21a791d98b2cb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -32,8 +33,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class RidgeRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -47,9 +48,7 @@ class RidgeRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -57,14 +56,10 @@ class RidgeRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): RidgeRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -120,8 +115,8 @@ class RidgeRegressionWithSGD private (
 /**
  * Top-level methods for calling RidgeRegression.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object RidgeRegressionWithSGD {
 
   /**
@@ -138,8 +133,8 @@ object RidgeRegressionWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -162,8 +157,8 @@ object RidgeRegressionWithSGD {
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -184,8 +179,8 @@ object RidgeRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -203,8 +198,8 @@ object RidgeRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): RidgeRegressionModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
index a2ab95c474765..cd3ed8a1549db 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.regression
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.streaming.api.java.{JavaDStream, JavaPairDStream}
@@ -54,8 +54,8 @@ import org.apache.spark.streaming.dstream.DStream
  * the model using each of the different sources, in sequence.
  *
  *
- * @since 1.1.0
  */
+@Since("1.1.0")
 @DeveloperApi
 abstract class StreamingLinearAlgorithm[
     M <: GeneralizedLinearModel,
@@ -70,8 +70,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Return the latest model.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def latestModel(): M = {
     model.get
   }
@@ -84,8 +84,8 @@ abstract class StreamingLinearAlgorithm[
    *
    * @param data DStream containing labeled data
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def trainOn(data: DStream[LabeledPoint]): Unit = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting training.")
@@ -106,8 +106,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Java-friendly version of `trainOn`.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def trainOn(data: JavaDStream[LabeledPoint]): Unit = trainOn(data.dstream)
 
   /**
@@ -116,8 +116,8 @@ abstract class StreamingLinearAlgorithm[
    * @param data DStream containing feature vectors
    * @return DStream containing predictions
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predictOn(data: DStream[Vector]): DStream[Double] = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting prediction.")
@@ -128,8 +128,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Java-friendly version of `predictOn`.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Double] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Double]])
   }
@@ -140,8 +140,8 @@ abstract class StreamingLinearAlgorithm[
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Double)] = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting prediction")
@@ -153,8 +153,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Java-friendly version of `predictOnValues`.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predictOnValues[K](data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Double] = {
     implicit val tag = fakeClassTag[K]
     JavaPairDStream.fromPairDStream(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
index 93a6753efd4d9..4a856f7f3434a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.stat
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.rdd.RDD
 
@@ -37,8 +37,8 @@ import org.apache.spark.rdd.RDD
  *   .setBandwidth(3.0)
  * val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
  * }}}
- * @since 1.4.0
  */
+@Since("1.4.0")
 @Experimental
 class KernelDensity extends Serializable {
 
@@ -52,8 +52,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the bandwidth (standard deviation) of the Gaussian kernel (default: `1.0`).
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setBandwidth(bandwidth: Double): this.type = {
     require(bandwidth > 0, s"Bandwidth must be positive, but got $bandwidth.")
     this.bandwidth = bandwidth
@@ -62,8 +62,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the sample to use for density estimation.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setSample(sample: RDD[Double]): this.type = {
     this.sample = sample
     this
@@ -71,8 +71,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the sample to use for density estimation (for Java users).
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setSample(sample: JavaRDD[java.lang.Double]): this.type = {
     this.sample = sample.rdd.asInstanceOf[RDD[Double]]
     this
@@ -80,8 +80,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Estimates probability density function at the given array of points.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def estimate(points: Array[Double]): Array[Double] = {
     val sample = this.sample
     val bandwidth = this.bandwidth
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index 64e4be0ebb97e..51b713e263e0c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.stat
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
@@ -33,8 +33,8 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
  * Reference: [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]]
  * Zero elements (including explicit zero values) are skipped when calling add(),
  * to have time complexity O(nnz) instead of O(n) for each column.
- * @since 1.1.0
  */
+@Since("1.1.0")
 @DeveloperApi
 class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable {
 
@@ -53,8 +53,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    *
    * @param sample The sample in dense/sparse vector format to be added into this summarizer.
    * @return This MultivariateOnlineSummarizer object.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def add(sample: Vector): this.type = {
     if (n == 0) {
       require(sample.size > 0, s"Vector should have dimension larger than zero.")
@@ -109,8 +109,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    *
    * @param other The other MultivariateOnlineSummarizer to be merged.
    * @return This MultivariateOnlineSummarizer object.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def merge(other: MultivariateOnlineSummarizer): this.type = {
     if (this.totalCnt != 0 && other.totalCnt != 0) {
       require(n == other.n, s"Dimensions mismatch when merging with another summarizer. " +
@@ -155,8 +155,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Sample mean of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def mean: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -172,8 +172,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Sample variance of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def variance: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -199,15 +199,15 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Sample size.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def count: Long = totalCnt
 
   /**
    * Number of nonzero elements in each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def numNonzeros: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -217,8 +217,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Maximum value of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def max: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -233,8 +233,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Minimum value of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def min: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -249,8 +249,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * L2 (Euclidian) norm of each dimension.
    *
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def normL2: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -268,8 +268,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * L1 norm of each dimension.
    *
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def normL1: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
index 3bb49f12289e1..39a16fb743d64 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
@@ -17,59 +17,60 @@
 
 package org.apache.spark.mllib.stat
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 
 /**
  * Trait for multivariate statistical summary of a data matrix.
- * @since 1.0.0
  */
+@Since("1.0.0")
 trait MultivariateStatisticalSummary {
 
   /**
    * Sample mean vector.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def mean: Vector
 
   /**
    * Sample variance vector. Should return a zero vector if the sample size is 1.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def variance: Vector
 
   /**
    * Sample size.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def count: Long
 
   /**
    * Number of nonzero elements (including explicitly presented zero values) in each column.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def numNonzeros: Vector
 
   /**
    * Maximum value of each column.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def max: Vector
 
   /**
    * Minimum value of each column.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def min: Vector
 
   /**
    * Euclidean magnitude of each column
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def normL2: Vector
 
   /**
    * L1 norm of each column
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def normL1: Vector
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index ef8d78607048f..84d64a5bfb38e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.stat
 
 import scala.annotation.varargs
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaRDD, JavaDoubleRDD}
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Matrix, Vector}
@@ -32,8 +32,8 @@ import org.apache.spark.rdd.RDD
 /**
  * :: Experimental ::
  * API for statistical functions in MLlib.
- * @since 1.1.0
  */
+@Since("1.1.0")
 @Experimental
 object Statistics {
 
@@ -42,8 +42,8 @@ object Statistics {
    *
    * @param X an RDD[Vector] for which column-wise summary statistics are to be computed.
    * @return [[MultivariateStatisticalSummary]] object containing column-wise summary statistics.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def colStats(X: RDD[Vector]): MultivariateStatisticalSummary = {
     new RowMatrix(X).computeColumnSummaryStatistics()
   }
@@ -54,8 +54,8 @@ object Statistics {
    *
    * @param X an RDD[Vector] for which the correlation matrix is to be computed.
    * @return Pearson correlation matrix comparing columns in X.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(X: RDD[Vector]): Matrix = Correlations.corrMatrix(X)
 
   /**
@@ -71,8 +71,8 @@ object Statistics {
    * @param method String specifying the method to use for computing correlation.
    *               Supported: `pearson` (default), `spearman`
    * @return Correlation matrix comparing columns in X.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(X: RDD[Vector], method: String): Matrix = Correlations.corrMatrix(X, method)
 
   /**
@@ -85,14 +85,14 @@ object Statistics {
    * @param x RDD[Double] of the same cardinality as y.
    * @param y RDD[Double] of the same cardinality as x.
    * @return A Double containing the Pearson correlation between the two input RDD[Double]s
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(x: RDD[Double], y: RDD[Double]): Double = Correlations.corr(x, y)
 
   /**
    * Java-friendly version of [[corr()]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double]): Double =
     corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]])
 
@@ -109,14 +109,14 @@ object Statistics {
    *               Supported: `pearson` (default), `spearman`
    * @return A Double containing the correlation between the two input RDD[Double]s using the
    *         specified method.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(x: RDD[Double], y: RDD[Double], method: String): Double = Correlations.corr(x, y, method)
 
   /**
    * Java-friendly version of [[corr()]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double], method: String): Double =
     corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]], method)
 
@@ -133,8 +133,8 @@ object Statistics {
    *                 `expected` is rescaled if the `expected` sum differs from the `observed` sum.
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(observed: Vector, expected: Vector): ChiSqTestResult = {
     ChiSqTest.chiSquared(observed, expected)
   }
@@ -148,8 +148,8 @@ object Statistics {
    * @param observed Vector containing the observed categorical counts/relative frequencies.
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(observed: Vector): ChiSqTestResult = ChiSqTest.chiSquared(observed)
 
   /**
@@ -159,8 +159,8 @@ object Statistics {
    * @param observed The contingency matrix (containing either counts or relative frequencies).
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(observed: Matrix): ChiSqTestResult = ChiSqTest.chiSquaredMatrix(observed)
 
   /**
@@ -172,13 +172,14 @@ object Statistics {
    *             Real-valued features will be treated as categorical for each distinct value.
    * @return an array containing the ChiSquaredTestResult for every feature against the label.
    *         The order of the elements in the returned array reflects the order of input features.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = {
     ChiSqTest.chiSquaredFeatures(data)
   }
 
   /** Java-friendly version of [[chiSqTest()]] */
+  @Since("1.5.0")
   def chiSqTest(data: JavaRDD[LabeledPoint]): Array[ChiSqTestResult] = chiSqTest(data.rdd)
 
   /**
@@ -194,6 +195,7 @@ object Statistics {
    * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] object containing test
    *        statistic, p-value, and null hypothesis.
    */
+  @Since("1.5.0")
   def kolmogorovSmirnovTest(data: RDD[Double], cdf: Double => Double)
     : KolmogorovSmirnovTestResult = {
     KolmogorovSmirnovTest.testOneSample(data, cdf)
@@ -210,6 +212,7 @@ object Statistics {
    * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] object containing test
    *        statistic, p-value, and null hypothesis.
    */
+  @Since("1.5.0")
   @varargs
   def kolmogorovSmirnovTest(data: RDD[Double], distName: String, params: Double*)
     : KolmogorovSmirnovTestResult = {
@@ -217,6 +220,7 @@ object Statistics {
   }
 
   /** Java-friendly version of [[kolmogorovSmirnovTest()]] */
+  @Since("1.5.0")
   @varargs
   def kolmogorovSmirnovTest(
       data: JavaDoubleRDD,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
index 9aa7763d7890d..bd4d81390bfae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.stat.distribution
 
 import breeze.linalg.{DenseVector => DBV, DenseMatrix => DBM, diag, max, eigSym, Vector => BV}
 
-import org.apache.spark.annotation.DeveloperApi;
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix}
 import org.apache.spark.mllib.util.MLUtils
 
@@ -32,8 +32,8 @@ import org.apache.spark.mllib.util.MLUtils
  *
  * @param mu The mean vector of the distribution
  * @param sigma The covariance matrix of the distribution
- * @since 1.3.0
  */
+@Since("1.3.0")
 @DeveloperApi
 class MultivariateGaussian (
     val mu: Vector,
@@ -62,15 +62,15 @@ class MultivariateGaussian (
   private val (rootSigmaInv: DBM[Double], u: Double) = calculateCovarianceConstants
 
   /** Returns density of this multivariate Gaussian at given point, x
-    * @since 1.3.0
     */
+   @Since("1.3.0")
   def pdf(x: Vector): Double = {
     pdf(x.toBreeze)
   }
 
   /** Returns the log-density of this multivariate Gaussian at given point, x
-    * @since 1.3.0
     */
+   @Since("1.3.0")
   def logpdf(x: Vector): Double = {
     logpdf(x.toBreeze)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index e5200b86fddd4..972841015d4f0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable
 import scala.collection.mutable.ArrayBuilder
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.RandomForest.NodeIndexInfo
@@ -43,8 +43,8 @@ import org.apache.spark.util.random.XORShiftRandom
  * @param strategy The configuration parameters for the tree algorithm which specify the type
  *                 of algorithm (classification, regression, etc.), feature type (continuous,
  *                 categorical), depth of the tree, quantile calculation strategy, etc.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
 
@@ -54,8 +54,8 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
    * Method to train a decision tree model over an RDD
    * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def run(input: RDD[LabeledPoint]): DecisionTreeModel = {
     // Note: random seed will not be used since numTrees = 1.
     val rf = new RandomForest(strategy, numTrees = 1, featureSubsetStrategy = "all", seed = 0)
@@ -64,9 +64,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
   }
 }
 
-/**
- * @since 1.0.0
- */
+@Since("1.0.0")
 object DecisionTree extends Serializable with Logging {
 
   /**
@@ -84,8 +82,8 @@ object DecisionTree extends Serializable with Logging {
    *                 of algorithm (classification, regression, etc.), feature type (continuous,
    *                 categorical), depth of the tree, quantile calculation strategy, etc.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.0.0
-  */
+   */
+ @Since("1.0.0")
   def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
     new DecisionTree(strategy).run(input)
   }
@@ -106,8 +104,8 @@ object DecisionTree extends Serializable with Logging {
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       algo: Algo,
@@ -134,8 +132,8 @@ object DecisionTree extends Serializable with Logging {
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @param numClasses number of classes for classification. Default value of 2.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def train(
       input: RDD[LabeledPoint],
       algo: Algo,
@@ -168,8 +166,8 @@ object DecisionTree extends Serializable with Logging {
    *                                E.g., an entry (n -> k) indicates that feature n is categorical
    *                                with k categories indexed from 0: {0, 1, ..., k-1}.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       algo: Algo,
@@ -201,8 +199,8 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainClassifier(
       input: RDD[LabeledPoint],
       numClasses: Int,
@@ -217,8 +215,8 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
       numClasses: Int,
@@ -247,8 +245,8 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainRegressor(
       input: RDD[LabeledPoint],
       categoricalFeaturesInfo: Map[Int, Int],
@@ -261,8 +259,8 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index 143617098637a..e750408600c33 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.impl.PeriodicRDDCheckpointer
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -48,8 +48,8 @@ import org.apache.spark.storage.StorageLevel
  *       for other loss functions.
  *
  * @param boostingStrategy Parameters for the gradient boosting algorithm.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
   extends Serializable with Logging {
@@ -58,8 +58,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    * Method to train a gradient boosting model
    * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return a gradient boosted trees model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
     val algo = boostingStrategy.treeStrategy.algo
     algo match {
@@ -76,8 +76,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#run]].
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = {
     run(input.rdd)
   }
@@ -91,8 +91,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    *                        E.g., these two datasets could be created from an original dataset
    *                        by using [[org.apache.spark.rdd.RDD.randomSplit()]]
    * @return a gradient boosted trees model that can be used for prediction
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def runWithValidation(
       input: RDD[LabeledPoint],
       validationInput: RDD[LabeledPoint]): GradientBoostedTreesModel = {
@@ -115,8 +115,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#runWithValidation]].
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def runWithValidation(
       input: JavaRDD[LabeledPoint],
       validationInput: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = {
@@ -124,9 +124,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
   }
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 object GradientBoostedTrees extends Logging {
 
   /**
@@ -137,8 +135,8 @@ object GradientBoostedTrees extends Logging {
    *              For regression, labels are real numbers.
    * @param boostingStrategy Configuration options for the boosting algorithm.
    * @return a gradient boosted trees model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def train(
       input: RDD[LabeledPoint],
       boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = {
@@ -147,8 +145,8 @@ object GradientBoostedTrees extends Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees$#train]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def train(
       input: JavaRDD[LabeledPoint],
       boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index 9f3230656acc5..63a902f3eb51e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable
 import scala.collection.JavaConverters._
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Strategy
@@ -260,9 +260,7 @@ private class RandomForest (
 
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 object RandomForest extends Serializable with Logging {
 
   /**
@@ -279,8 +277,8 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "sqrt".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainClassifier(
       input: RDD[LabeledPoint],
       strategy: Strategy,
@@ -317,8 +315,8 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model  that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainClassifier(
       input: RDD[LabeledPoint],
       numClasses: Int,
@@ -337,8 +335,8 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainClassifier]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
       numClasses: Int,
@@ -368,8 +366,8 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "onethird".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainRegressor(
       input: RDD[LabeledPoint],
       strategy: Strategy,
@@ -405,8 +403,8 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainRegressor(
       input: RDD[LabeledPoint],
       categoricalFeaturesInfo: Map[Int, Int],
@@ -424,8 +422,8 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainRegressor]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
@@ -442,8 +440,8 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * List of supported feature subset sampling strategies.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   val supportedFeatureSubsetStrategies: Array[String] =
     Array("auto", "all", "sqrt", "log2", "onethird")
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
index d9a49aa71fcfb..8301ad160836b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Enum to select the algorithm for the decision tree
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Algo extends Enumeration {
   type Algo = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 88e5f57e9ab32..7c569981977b4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.tree.configuration
 
 import scala.beans.BeanProperty
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
 
@@ -38,8 +38,8 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  *                      validation input between two iterations is less than the validationTol
  *                      then stop.  Ignored when
  *                      [[org.apache.spark.mllib.tree.GradientBoostedTrees.run()]] is used.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 case class BoostingStrategy(
     // Required boosting parameters
@@ -71,9 +71,7 @@ case class BoostingStrategy(
   }
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 @Experimental
 object BoostingStrategy {
 
@@ -81,8 +79,8 @@ object BoostingStrategy {
    * Returns default configuration for the boosting algorithm
    * @param algo Learning goal.  Supported: "Classification" or "Regression"
    * @return Configuration for boosting algorithm
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def defaultParams(algo: String): BoostingStrategy = {
     defaultParams(Algo.fromString(algo))
   }
@@ -93,8 +91,8 @@ object BoostingStrategy {
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Classification]],
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Regression]]
    * @return Configuration for boosting algorithm
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def defaultParams(algo: Algo): BoostingStrategy = {
     val treeStrategy = Strategy.defaultStrategy(algo)
     treeStrategy.maxDepth = 3
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
index 0684cafa486bd..bb7c7ee4f964f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Enum to describe whether a feature is "continuous" or "categorical"
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object FeatureType extends Enumeration {
   type FeatureType = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
index 2daa63c4d2771..904e42deebb5f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Enum for selecting the quantile calculation strategy
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object QuantileStrategy extends Enumeration {
   type QuantileStrategy = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 7ae25a88bf500..a58f01ba8544e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.configuration
 import scala.beans.BeanProperty
 import scala.collection.JavaConverters._
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.tree.impurity.{Variance, Entropy, Gini, Impurity}
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
@@ -66,8 +66,8 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                           E.g. 10 means that the cache will get checkpointed every 10 updates. If
  *                           the checkpoint directory is not set in
  *                           [[org.apache.spark.SparkContext]], this setting is ignored.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class Strategy (
     @BeanProperty var algo: Algo,
@@ -85,23 +85,23 @@ class Strategy (
     @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
 
   /**
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def isMulticlassClassification: Boolean = {
     algo == Classification && numClasses > 2
   }
 
   /**
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def isMulticlassWithCategoricalFeatures: Boolean = {
     isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
   }
 
   /**
    * Java-friendly constructor for [[org.apache.spark.mllib.tree.configuration.Strategy]]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def this(
       algo: Algo,
       impurity: Impurity,
@@ -115,8 +115,8 @@ class Strategy (
 
   /**
    * Sets Algorithm using a String.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setAlgo(algo: String): Unit = algo match {
     case "Classification" => setAlgo(Classification)
     case "Regression" => setAlgo(Regression)
@@ -124,8 +124,8 @@ class Strategy (
 
   /**
    * Sets categoricalFeaturesInfo using a Java Map.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setCategoricalFeaturesInfo(
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer]): Unit = {
     this.categoricalFeaturesInfo =
@@ -174,8 +174,8 @@ class Strategy (
 
   /**
    * Returns a shallow copy of this instance.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def copy: Strategy = {
     new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
       quantileCalculationStrategy, categoricalFeaturesInfo, minInstancesPerNode, minInfoGain,
@@ -183,17 +183,15 @@ class Strategy (
   }
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 @Experimental
 object Strategy {
 
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo  "Classification" or "Regression"
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def defaultStrategy(algo: String): Strategy = {
     defaultStrategy(Algo.fromString(algo))
   }
@@ -201,8 +199,8 @@ object Strategy {
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo Algo.Classification or Algo.Regression
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def defaultStrategy(algo: Algo): Strategy = algo match {
     case Algo.Classification =>
       new Strategy(algo = Classification, impurity = Gini, maxDepth = 10,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
index 0b6c7266dee05..73df6b054a8ce 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -17,14 +17,14 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Entropy extends Impurity {
 
@@ -36,8 +36,8 @@ object Entropy extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
     if (totalCount == 0) {
@@ -64,8 +64,8 @@ object Entropy extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Entropy.calculate")
@@ -73,8 +73,8 @@ object Entropy extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def instance: this.type = this
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
index 3b0be428833ae..f21845b21a802 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
  * Class for calculating the
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
  * during binary classification.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Gini extends Impurity {
 
@@ -35,8 +35,8 @@ object Gini extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
     if (totalCount == 0) {
@@ -60,8 +60,8 @@ object Gini extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Gini.calculate")
@@ -69,8 +69,8 @@ object Gini extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def instance: this.type = this
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index dd297400058d2..4637dcceea7f8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
@@ -25,8 +25,8 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * This trait is used for
  *  (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
  *  (b) calculating impurity values from sufficient statistics.
- *  @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 trait Impurity extends Serializable {
 
@@ -36,8 +36,8 @@ trait Impurity extends Serializable {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   def calculate(counts: Array[Double], totalCount: Double): Double
 
@@ -48,8 +48,8 @@ trait Impurity extends Serializable {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   def calculate(count: Double, sum: Double, sumSquares: Double): Double
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
index adbe05811f286..a74197278d6f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
  * Class for calculating variance during regression
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Variance extends Impurity {
 
@@ -33,8 +33,8 @@ object Variance extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double =
      throw new UnsupportedOperationException("Variance.calculate")
@@ -46,8 +46,8 @@ object Variance extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
     if (count == 0) {
@@ -60,8 +60,8 @@ object Variance extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def instance: this.type = this
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
index c6e3d0d824dd7..bab7b8c6cadf2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The absolute (L1) error is defined as:
  *  |y - F(x)|
  * where y is the label and F(x) is the model prediction for features x.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 object AbsoluteError extends Loss {
 
@@ -41,8 +41,8 @@ object AbsoluteError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def gradient(prediction: Double, label: Double): Double = {
     if (label - prediction < 0) 1.0 else -1.0
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
index eee58445a1ec1..b2b4594712f0d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 import org.apache.spark.mllib.util.MLUtils
@@ -31,8 +31,8 @@ import org.apache.spark.mllib.util.MLUtils
  * The log loss is defined as:
  *   2 log(1 + exp(-2 y F(x)))
  * where y is a label in {-1, 1} and F(x) is the model prediction for features x.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 object LogLoss extends Loss {
 
@@ -43,8 +43,8 @@ object LogLoss extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def gradient(prediction: Double, label: Double): Double = {
     - 4.0 * label / (1.0 + math.exp(2.0 * label * prediction))
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
index 7c9fb924645c8..687cde325ffed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 import org.apache.spark.rdd.RDD
@@ -26,8 +26,8 @@ import org.apache.spark.rdd.RDD
 /**
  * :: DeveloperApi ::
  * Trait for adding "pluggable" loss functions for the gradient boosting algorithm.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 trait Loss extends Serializable {
 
@@ -36,8 +36,8 @@ trait Loss extends Serializable {
    * @param prediction Predicted feature
    * @param label true label.
    * @return Loss gradient.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def gradient(prediction: Double, label: Double): Double
 
   /**
@@ -47,8 +47,8 @@ trait Loss extends Serializable {
    * @param model Model of the weak learner.
    * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return Measure of model error on data
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = {
     data.map(point => computeError(model.predict(point.features), point.label)).mean()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
index 47dc94cde7e03..2b112fbe12202 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.mllib.tree.loss
 
-/**
- * @since 1.2.0
- */
+import org.apache.spark.annotation.Since
+
+@Since("1.2.0")
 object Losses {
 
-  /**
-   * @since 1.2.0
-   */
+  @Since("1.2.0")
   def fromString(name: String): Loss = name match {
     case "leastSquaresError" => SquaredError
     case "leastAbsoluteError" => AbsoluteError
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
index ff8903d6956bd..3f7d3d38be16c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The squared (L2) error is defined as:
  *   (y - F(x))**2
  * where y is the label and F(x) is the model prediction for features x.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 object SquaredError extends Loss {
 
@@ -41,8 +41,8 @@ object SquaredError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def gradient(prediction: Double, label: Double): Double = {
     - 2.0 * (label - prediction)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index 0f386a26601ce..3eefd135f7836 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.tree.configuration.{Algo, FeatureType}
@@ -40,8 +40,8 @@ import org.apache.spark.util.Utils
  * This model stores the decision tree structure and parameters.
  * @param topNode root node
  * @param algo algorithm type -- classification or regression
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable {
 
@@ -50,8 +50,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features array representing a single data point
    * @return Double prediction from the trained model
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(features: Vector): Double = {
     topNode.predict(features)
   }
@@ -61,8 +61,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features RDD representing data points to be predicted
    * @return RDD of predictions for each of the given data points
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(features: RDD[Vector]): RDD[Double] = {
     features.map(x => predict(x))
   }
@@ -72,16 +72,16 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features JavaRDD representing data points to be predicted
    * @return JavaRDD of predictions for each of the given data points
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predict(features: JavaRDD[Vector]): JavaRDD[Double] = {
     predict(features.rdd)
   }
 
   /**
    * Get number of nodes in tree, including leaf nodes.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def numNodes: Int = {
     1 + topNode.numDescendants
   }
@@ -89,8 +89,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   /**
    * Get depth of tree.
    * E.g.: Depth 0 means 1 leaf node.  Depth 1 means 1 internal node and 2 leaf nodes.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def depth: Int = {
     topNode.subtreeDepth
   }
@@ -119,8 +119,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     DecisionTreeModel.SaveLoadV1_0.save(sc, path, this)
   }
@@ -128,9 +128,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   override protected def formatVersion: String = DecisionTreeModel.formatVersion
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
 
   private[spark] def formatVersion: String = "1.0"
@@ -317,8 +315,8 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): DecisionTreeModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index 23f0363639120..091a0462c204f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 
 /**
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
  * @param rightImpurity right node impurity
  * @param leftPredict left node predict
  * @param rightPredict right node predict
- * @since 1.0.0
  */
+@Since("1.0.0")
 @DeveloperApi
 class InformationGainStats(
     val gain: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index aca3350c2e535..8c54c55107233 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.Logging
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.linalg.Vector
@@ -38,8 +38,8 @@ import org.apache.spark.mllib.linalg.Vector
  * @param leftNode  left child
  * @param rightNode right child
  * @param stats information gain stats
- * @since 1.0.0
  */
+@Since("1.0.0")
 @DeveloperApi
 class Node (
     val id: Int,
@@ -59,8 +59,8 @@ class Node (
   /**
    * build the left node and right nodes if not leaf
    * @param nodes array of nodes
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @deprecated("build should no longer be used since trees are constructed on-the-fly in training",
     "1.2.0")
   def build(nodes: Array[Node]): Unit = {
@@ -81,8 +81,8 @@ class Node (
    * predict value if node is not leaf
    * @param features feature value
    * @return predicted value
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predict(features: Vector) : Double = {
     if (isLeaf) {
       predict.predict
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
index be819b59e7048..965784051ede5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -17,14 +17,14 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 
 /**
  * Predicted value for a node
  * @param predict predicted value
  * @param prob probability of the label (classification only)
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 class Predict(
     val predict: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
index 18d40530aee1e..45db83ae3a1f3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 import org.apache.spark.mllib.tree.configuration.FeatureType
 import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
@@ -30,8 +30,8 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
  *                  Split left if feature <= threshold, else right.
  * @param featureType type of feature -- categorical or continuous
  * @param categories Split left if categorical feature value is in this set, else right.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @DeveloperApi
 case class Split(
     feature: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 0c629b12a84df..19571447a2c56 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -25,7 +25,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -45,8 +45,8 @@ import org.apache.spark.util.Utils
  *
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel])
   extends TreeEnsembleModel(algo, trees, Array.fill(trees.length)(1.0),
@@ -60,8 +60,8 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       RandomForestModel.SaveLoadV1_0.thisClassName)
@@ -70,9 +70,7 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
   override protected def formatVersion: String = RandomForestModel.formatVersion
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object RandomForestModel extends Loader[RandomForestModel] {
 
   private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion
@@ -82,8 +80,8 @@ object RandomForestModel extends Loader[RandomForestModel] {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): RandomForestModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
@@ -114,8 +112,8 @@ object RandomForestModel extends Loader[RandomForestModel] {
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
  * @param treeWeights tree ensemble weights
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class GradientBoostedTreesModel(
     override val algo: Algo,
@@ -130,8 +128,8 @@ class GradientBoostedTreesModel(
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       GradientBoostedTreesModel.SaveLoadV1_0.thisClassName)
@@ -143,8 +141,8 @@ class GradientBoostedTreesModel(
    * @param loss evaluation metric.
    * @return an array with index i having the losses or errors for the ensemble
    *         containing the first i+1 trees
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def evaluateEachIteration(
       data: RDD[LabeledPoint],
       loss: Loss): Array[Double] = {
@@ -186,8 +184,8 @@ class GradientBoostedTreesModel(
 }
 
 /**
- * @since 1.3.0
  */
+@Since("1.3.0")
 object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
 
   /**
@@ -199,8 +197,8 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to every sample.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def computeInitialPredictionAndError(
       data: RDD[LabeledPoint],
       initTreeWeight: Double,
@@ -223,8 +221,8 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to each sample.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def updatePredictionError(
     data: RDD[LabeledPoint],
     predictionAndError: RDD[(Double, Double)],
@@ -248,8 +246,8 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): GradientBoostedTreesModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
index f520b3a1b7c72..bcaacc1b1f191 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
@@ -24,7 +24,6 @@ package org.apache.spark.mllib
  *  - information loss calculation with entropy and Gini for classification and
  *    variance for regression,
  *  - both continuous and categorical features.
- *  @since 1.0.0
  */
 package object tree {
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 11ed23176fc12..4940974bf4f41 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -21,7 +21,7 @@ import scala.reflect.ClassTag
 
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.PartitionwiseSampledRDD
@@ -64,8 +64,8 @@ object MLUtils {
    *                    feature dimensions.
    * @param minPartitions min number of partitions
    * @return labeled data stored as an RDD[LabeledPoint]
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def loadLibSVMFile(
       sc: SparkContext,
       path: String,
@@ -115,9 +115,7 @@ object MLUtils {
 
   // Convenient methods for `loadLibSVMFile`.
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -130,17 +128,15 @@ object MLUtils {
   /**
    * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
    * partitions.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def loadLibSVMFile(
       sc: SparkContext,
       path: String,
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures, sc.defaultMinPartitions)
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -149,9 +145,7 @@ object MLUtils {
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures)
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -162,8 +156,8 @@ object MLUtils {
   /**
    * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
    * features determined automatically and the default number of partitions.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def loadLibSVMFile(sc: SparkContext, path: String): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, -1)
 
@@ -193,15 +187,15 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return vectors stored as an RDD[Vector]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadVectors(sc: SparkContext, path: String, minPartitions: Int): RDD[Vector] =
     sc.textFile(path, minPartitions).map(Vectors.parse)
 
   /**
    * Loads vectors saved using `RDD[Vector].saveAsTextFile` with the default number of partitions.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadVectors(sc: SparkContext, path: String): RDD[Vector] =
     sc.textFile(path, sc.defaultMinPartitions).map(Vectors.parse)
 
@@ -211,16 +205,16 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return labeled points stored as an RDD[LabeledPoint]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadLabeledPoints(sc: SparkContext, path: String, minPartitions: Int): RDD[LabeledPoint] =
     sc.textFile(path, minPartitions).map(LabeledPoint.parse)
 
   /**
    * Loads labeled points saved using `RDD[LabeledPoint].saveAsTextFile` with the default number of
    * partitions.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint] =
     loadLabeledPoints(sc, dir, sc.defaultMinPartitions)
 
@@ -236,8 +230,8 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1")
   def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
     sc.textFile(dir).map { line =>
@@ -258,8 +252,8 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1")
   def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
     val dataStr = data.map(x => x.label + "," + x.features.toArray.mkString(" "))
@@ -271,8 +265,8 @@ object MLUtils {
    * Return a k element array of pairs of RDDs with the first element of each pair
    * containing the training data, a complement of the validation data and the second
    * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def kFold[T: ClassTag](rdd: RDD[T], numFolds: Int, seed: Int): Array[(RDD[T], RDD[T])] = {
     val numFoldsF = numFolds.toFloat
@@ -287,8 +281,8 @@ object MLUtils {
 
   /**
    * Returns a new vector with `1.0` (bias) appended to the input vector.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def appendBias(vector: Vector): Vector = {
     vector match {
       case dv: DenseVector =>

From e3355090d4030daffed5efb0959bf1d724c13c13 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 21 Aug 2015 14:30:00 -0700
Subject: [PATCH 1176/1454] [SPARK-10143] [SQL] Use parquet's block size (row
 group size) setting as the min split size if necessary.

https://issues.apache.org/jira/browse/SPARK-10143

With this PR, we will set min split size to parquet's block size (row group size) set in the conf if the min split size is smaller. So, we can avoid have too many tasks and even useless tasks for reading parquet data.

I tested it locally. The table I have has 343MB and it is in my local FS. Because I did not set any min/max split size, the default split size was 32MB and the map stage had 11 tasks. But there were only three tasks that actually read data. With my PR, there were only three tasks in the map stage. Here is the difference.

Without this PR:
![image](https://cloud.githubusercontent.com/assets/2072857/9399179/8587dba6-4765-11e5-9189-7ebba52a2b6d.png)

With this PR:
![image](https://cloud.githubusercontent.com/assets/2072857/9399185/a4735d74-4765-11e5-8848-1f1e361a6b4b.png)

Even if the block size setting does match the actual block size of parquet file, I think it is still generally good to use parquet's block size setting if min split size is smaller than this block size.

Tested it on a cluster using
```
val count = sqlContext.table("""store_sales""").groupBy().count().queryExecution.executedPlan(3).execute().count
```
Basically, it reads 0 column of table `store_sales`. My table has 1824 parquet files with size from 80MB to 280MB (1 to 3 row group sizes). Without this patch, in a 16 worker cluster, the job had 5023 tasks and spent 102s. With this patch, the job had 2893 tasks and spent 64s. It is still not as good as using one mapper per file (1824 tasks and 42s), but it is much better than our master.

Author: Yin Huai <yhuai@databricks.com>

Closes #8346 from yhuai/parquetMinSplit.
---
 .../datasources/parquet/ParquetRelation.scala | 41 ++++++++++++++++++-
 1 file changed, 39 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 68169d48ac57c..bbf682aec0f9d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -26,6 +26,7 @@ import scala.collection.mutable
 import scala.util.{Failure, Try}
 
 import com.google.common.base.Objects
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
@@ -281,12 +282,18 @@ private[sql] class ParquetRelation(
     val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
     val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
 
+    // Parquet row group size. We will use this value as the value for
+    // mapreduce.input.fileinputformat.split.minsize and mapred.min.split.size if the value
+    // of these flags are smaller than the parquet row group size.
+    val parquetBlockSize = ParquetOutputFormat.getLongBlockSize(broadcastedConf.value.value)
+
     // Create the function to set variable Parquet confs at both driver and executor side.
     val initLocalJobFuncOpt =
       ParquetRelation.initializeLocalJobFunc(
         requiredColumns,
         filters,
         dataSchema,
+        parquetBlockSize,
         useMetadataCache,
         parquetFilterPushDown,
         assumeBinaryIsString,
@@ -294,7 +301,8 @@ private[sql] class ParquetRelation(
         followParquetFormatSpec) _
 
     // Create the function to set input paths at the driver side.
-    val setInputPaths = ParquetRelation.initializeDriverSideJobFunc(inputFiles) _
+    val setInputPaths =
+      ParquetRelation.initializeDriverSideJobFunc(inputFiles, parquetBlockSize) _
 
     Utils.withDummyCallSite(sqlContext.sparkContext) {
       new SqlNewHadoopRDD(
@@ -482,11 +490,35 @@ private[sql] object ParquetRelation extends Logging {
   // internally.
   private[sql] val METASTORE_SCHEMA = "metastoreSchema"
 
+  /**
+   * If parquet's block size (row group size) setting is larger than the min split size,
+   * we use parquet's block size setting as the min split size. Otherwise, we will create
+   * tasks processing nothing (because a split does not cover the starting point of a
+   * parquet block). See https://issues.apache.org/jira/browse/SPARK-10143 for more information.
+   */
+  private def overrideMinSplitSize(parquetBlockSize: Long, conf: Configuration): Unit = {
+    val minSplitSize =
+      math.max(
+        conf.getLong("mapred.min.split.size", 0L),
+        conf.getLong("mapreduce.input.fileinputformat.split.minsize", 0L))
+    if (parquetBlockSize > minSplitSize) {
+      val message =
+        s"Parquet's block size (row group size) is larger than " +
+          s"mapred.min.split.size/mapreduce.input.fileinputformat.split.minsize. Setting " +
+          s"mapred.min.split.size and mapreduce.input.fileinputformat.split.minsize to " +
+          s"$parquetBlockSize."
+      logDebug(message)
+      conf.set("mapred.min.split.size", parquetBlockSize.toString)
+      conf.set("mapreduce.input.fileinputformat.split.minsize", parquetBlockSize.toString)
+    }
+  }
+
   /** This closure sets various Parquet configurations at both driver side and executor side. */
   private[parquet] def initializeLocalJobFunc(
       requiredColumns: Array[String],
       filters: Array[Filter],
       dataSchema: StructType,
+      parquetBlockSize: Long,
       useMetadataCache: Boolean,
       parquetFilterPushDown: Boolean,
       assumeBinaryIsString: Boolean,
@@ -522,16 +554,21 @@ private[sql] object ParquetRelation extends Logging {
     conf.setBoolean(SQLConf.PARQUET_BINARY_AS_STRING.key, assumeBinaryIsString)
     conf.setBoolean(SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, assumeInt96IsTimestamp)
     conf.setBoolean(SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key, followParquetFormatSpec)
+
+    overrideMinSplitSize(parquetBlockSize, conf)
   }
 
   /** This closure sets input paths at the driver side. */
   private[parquet] def initializeDriverSideJobFunc(
-      inputFiles: Array[FileStatus])(job: Job): Unit = {
+      inputFiles: Array[FileStatus],
+      parquetBlockSize: Long)(job: Job): Unit = {
     // We side the input paths at the driver side.
     logInfo(s"Reading Parquet file(s) from ${inputFiles.map(_.getPath).mkString(", ")}")
     if (inputFiles.nonEmpty) {
       FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*)
     }
+
+    overrideMinSplitSize(parquetBlockSize, job.getConfiguration)
   }
 
   private[parquet] def readSchema(

From f01c4220d2b791f470fa6596ffe11baa51517fbe Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 21 Aug 2015 16:28:00 -0700
Subject: [PATCH 1177/1454] [SPARK-10163] [ML] Allow single-category features
 for GBT models

Removed categorical feature info validation since no longer needed

This is needed to make the ML user guide examples work (in another current PR).

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8367 from jkbradley/gbt-single-cat.
---
 .../org/apache/spark/mllib/tree/configuration/Strategy.scala | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index a58f01ba8544e..b74e3f1f46523 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -158,11 +158,6 @@ class Strategy (
       s"  Valid values are integers >= 0.")
     require(maxBins >= 2, s"DecisionTree Strategy given invalid maxBins parameter: $maxBins." +
       s"  Valid values are integers >= 2.")
-    categoricalFeaturesInfo.foreach { case (feature, arity) =>
-      require(arity >= 2,
-        s"DecisionTree Strategy given invalid categoricalFeaturesInfo setting:" +
-        s" feature $feature has $arity categories.  The number of categories should be >= 2.")
-    }
     require(minInstancesPerNode >= 1,
       s"DecisionTree Strategy requires minInstancesPerNode >= 1 but was given $minInstancesPerNode")
     require(maxMemoryInMB <= 10240,

From 630a994e6a9785d1704f8e7fb604f32f5dea24f8 Mon Sep 17 00:00:00 2001
From: Xusen Yin <yinxusen@gmail.com>
Date: Fri, 21 Aug 2015 16:30:12 -0700
Subject: [PATCH 1178/1454] [SPARK-9893] User guide with Java test suite for
 VectorSlicer

Add user guide for `VectorSlicer`, with Java test suite and Python version VectorSlicer.

Note that Python version does not support selecting by names now.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #8267 from yinxusen/SPARK-9893.
---
 docs/ml-features.md                           | 133 ++++++++++++++++++
 .../ml/feature/JavaVectorSlicerSuite.java     |  85 +++++++++++
 2 files changed, 218 insertions(+)
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 6309db97be4d0..642a4b4c53183 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,6 +1477,139 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
+# Feature Selectors
+
+## VectorSlicer
+
+`VectorSlicer` is a transformer that takes a feature vector and outputs a new feature vector with a
+sub-array of the original features. It is useful for extracting features from a vector column.
+
+`VectorSlicer` accepts a vector column with a specified indices, then outputs a new vector column
+whose values are selected via those indices. There are two types of indices, 
+
+ 1. Integer indices that represents the indices into the vector, `setIndices()`;
+
+ 2. String indices that represents the names of features into the vector, `setNames()`. 
+ *This requires the vector column to have an `AttributeGroup` since the implementation matches on
+ the name field of an `Attribute`.*
+
+Specification by integer and string are both acceptable. Moreover, you can use integer index and 
+string name simultaneously. At least one feature must be selected. Duplicate features are not
+allowed, so there can be no overlap between selected indices and names. Note that if names of
+features are selected, an exception will be threw out when encountering with empty input attributes.
+
+The output vector will order features with the selected indices first (in the order given),
+followed by the selected names (in the order given).
+
+**Examples**
+
+Suppose that we have a DataFrame with the column `userFeatures`:
+
+~~~
+ userFeatures     
+------------------
+ [0.0, 10.0, 0.5] 
+~~~
+
+`userFeatures` is a vector column that contains three user features. Assuming that the first column
+of `userFeatures` are all zeros, so we want to remove it and only the last two columns are selected.
+The `VectorSlicer` selects the last two elements with `setIndices(1, 2)` then produces a new vector
+column named `features`:
+
+~~~
+ userFeatures     | features
+------------------|-----------------------------
+ [0.0, 10.0, 0.5] | [10.0, 0.5]
+~~~
+
+Suppose also that we have a potential input attributes for the `userFeatures`, i.e. 
+`["f1", "f2", "f3"]`, then we can use `setNames("f2", "f3")` to select them.
+
+~~~
+ userFeatures     | features
+------------------|-----------------------------
+ [0.0, 10.0, 0.5] | [10.0, 0.5]
+ ["f1", "f2", "f3"] | ["f2", "f3"]
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`VectorSlicer`](api/scala/index.html#org.apache.spark.ml.feature.VectorSlicer) takes an input
+column name with specified indices or names and an output column name.
+
+{% highlight scala %}
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
+import org.apache.spark.ml.feature.VectorSlicer
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+val data = Array(
+  Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
+  Vectors.dense(-2.0, 2.3, 0.0)
+)
+
+val defaultAttr = NumericAttribute.defaultAttr
+val attrs = Array("f1", "f2", "f3").map(defaultAttr.withName)
+val attrGroup = new AttributeGroup("userFeatures", attrs.asInstanceOf[Array[Attribute]])
+
+val dataRDD = sc.parallelize(data).map(Row.apply)
+val dataset = sqlContext.createDataFrame(dataRDD, StructType(attrGroup.toStructField()))
+
+val slicer = new VectorSlicer().setInputCol("userFeatures").setOutputCol("features")
+
+slicer.setIndices(1).setNames("f3")
+// or slicer.setIndices(Array(1, 2)), or slicer.setNames(Array("f2", "f3"))
+
+val output = slicer.transform(dataset)
+println(output.select("userFeatures", "features").first())
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`VectorSlicer`](api/java/org/apache/spark/ml/feature/VectorSlicer.html) takes an input column name
+with specified indices or names and an output column name.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+import static org.apache.spark.sql.types.DataTypes.*;
+
+Attribute[] attrs = new Attribute[]{
+  NumericAttribute.defaultAttr().withName("f1"),
+  NumericAttribute.defaultAttr().withName("f2"),
+  NumericAttribute.defaultAttr().withName("f3")
+};
+AttributeGroup group = new AttributeGroup("userFeatures", attrs);
+
+JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+  RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
+  RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
+));
+
+DataFrame dataset = jsql.createDataFrame(jrdd, (new StructType()).add(group.toStructField()));
+
+VectorSlicer vectorSlicer = new VectorSlicer()
+  .setInputCol("userFeatures").setOutputCol("features");
+
+vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"});
+// or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"})
+
+DataFrame output = vectorSlicer.transform(dataset);
+
+System.out.println(output.select("userFeatures", "features").first());
+{% endhighlight %}
+</div>
+</div>
+
 ## RFormula
 
 `RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
new file mode 100644
index 0000000000000..56988b9fb29cb
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature;
+
+import com.google.common.collect.Lists;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.attribute.Attribute;
+import org.apache.spark.ml.attribute.AttributeGroup;
+import org.apache.spark.ml.attribute.NumericAttribute;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.StructType;
+
+
+public class JavaVectorSlicerSuite {
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaVectorSlicerSuite");
+    jsql = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  @Test
+  public void vectorSlice() {
+    Attribute[] attrs = new Attribute[]{
+      NumericAttribute.defaultAttr().withName("f1"),
+      NumericAttribute.defaultAttr().withName("f2"),
+      NumericAttribute.defaultAttr().withName("f3")
+    };
+    AttributeGroup group = new AttributeGroup("userFeatures", attrs);
+
+    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+      RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
+      RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
+    ));
+
+    DataFrame dataset = jsql.createDataFrame(jrdd, (new StructType()).add(group.toStructField()));
+
+    VectorSlicer vectorSlicer = new VectorSlicer()
+      .setInputCol("userFeatures").setOutputCol("features");
+
+    vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"});
+
+    DataFrame output = vectorSlicer.transform(dataset);
+
+    for (Row r : output.select("userFeatures", "features").take(2)) {
+      Vector features = r.getAs(1);
+      Assert.assertEquals(features.size(), 2);
+    }
+  }
+}

From 46fcb9e0dbb2b28110f68a3d9f6c0c47bfd197b1 Mon Sep 17 00:00:00 2001
From: Keiji Yoshida <yoshida.keiji.84@gmail.com>
Date: Sat, 22 Aug 2015 02:38:10 -0700
Subject: [PATCH 1179/1454] Update programming-guide.md

Update `lineLengths.persist();` to `lineLengths.persist(StorageLevel.MEMORY_ONLY());` because `JavaRDD#persist` needs a parameter of `StorageLevel`.

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8372 from yosssi/patch-1.
---
 docs/programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 982c5eabe652b..4cf83bb392636 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -549,7 +549,7 @@ returning only its answer to the driver program.
 If we also wanted to use `lineLengths` again later, we could add:
 
 {% highlight java %}
-lineLengths.persist();
+lineLengths.persist(StorageLevel.MEMORY_ONLY());
 {% endhighlight %}
 
 before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.

From 90cb9f05655a25b95b8f9fe81da14e5b9c8bcf44 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sat, 22 Aug 2015 10:16:35 -0700
Subject: [PATCH 1180/1454] [SPARK-9401] [SQL] Fully implement code generation
 for ConcatWs

This PR adds full codegen support for ConcatWs, is a substitute of #7782

JIRA: https://issues.apache.org/jira/browse/SPARK-9401

cc davies

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8353 from yjshen/concatws.
---
 .../expressions/stringExpressions.scala       | 42 +++++++++++++++++--
 1 file changed, 39 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index b60d318534a41..48d02bb534501 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -72,7 +72,7 @@ case class Concat(children: Seq[Expression]) extends Expression with ImplicitCas
  * Returns null if the separator is null. Otherwise, concat_ws skips all null values.
  */
 case class ConcatWs(children: Seq[Expression])
-  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+  extends Expression with ImplicitCastInputTypes {
 
   require(children.nonEmpty, s"$prettyName requires at least one argument.")
 
@@ -114,8 +114,44 @@ case class ConcatWs(children: Seq[Expression])
         boolean ${ev.isNull} = ${ev.primitive} == null;
       """
     } else {
-      // Contains a mix of strings and array<string>s. Fall back to interpreted mode for now.
-      super.genCode(ctx, ev)
+      val array = ctx.freshName("array")
+      val varargNum = ctx.freshName("varargNum")
+      val idxInVararg = ctx.freshName("idxInVararg")
+
+      val evals = children.map(_.gen(ctx))
+      val (varargCount, varargBuild) = children.tail.zip(evals.tail).map { case (child, eval) =>
+        child.dataType match {
+          case StringType =>
+            ("", // we count all the StringType arguments num at once below.
+              s"$array[$idxInVararg ++] = ${eval.isNull} ? (UTF8String) null : ${eval.primitive};")
+          case _: ArrayType =>
+            val size = ctx.freshName("n")
+            (s"""
+              if (!${eval.isNull}) {
+                $varargNum += ${eval.primitive}.numElements();
+              }
+            """,
+            s"""
+            if (!${eval.isNull}) {
+              final int $size = ${eval.primitive}.numElements();
+              for (int j = 0; j < $size; j ++) {
+                $array[$idxInVararg ++] = ${ctx.getValue(eval.primitive, StringType, "j")};
+              }
+            }
+            """)
+        }
+      }.unzip
+
+      evals.map(_.code).mkString("\n") +
+      s"""
+        int $varargNum = ${children.count(_.dataType == StringType) - 1};
+        int $idxInVararg = 0;
+        ${varargCount.mkString("\n")}
+        UTF8String[] $array = new UTF8String[$varargNum];
+        ${varargBuild.mkString("\n")}
+        UTF8String ${ev.primitive} = UTF8String.concatWs(${evals.head.primitive}, $array);
+        boolean ${ev.isNull} = ${ev.primitive} == null;
+      """
     }
   }
 }

From 623c675fde7a3a39957a62c7af26a54f4b01f8ce Mon Sep 17 00:00:00 2001
From: Keiji Yoshida <yoshida.keiji.84@gmail.com>
Date: Sun, 23 Aug 2015 11:04:29 +0100
Subject: [PATCH 1181/1454] Update streaming-programming-guide.md

Update `See the Scala example` to `See the Java example`.

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8376 from yosssi/patch-1.
---
 docs/streaming-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index c59d936b43c88..118ced298f4b0 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1702,7 +1702,7 @@ context.awaitTermination();
 If the `checkpointDirectory` exists, then the context will be recreated from the checkpoint data.
 If the directory does not exist (i.e., running for the first time),
 then the function `contextFactory` will be called to create a new
-context and set up the DStreams. See the Scala example
+context and set up the DStreams. See the Java example
 [JavaRecoverableNetworkWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
 This example appends the word counts of network data into a file.
 

From c6df5f66d9a8b9760f2cd46fcd930f977650c9c5 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Sun, 23 Aug 2015 17:41:49 -0700
Subject: [PATCH 1182/1454] [SPARK-10148] [STREAMING] Display active and
 inactive receiver numbers in Streaming page

Added the active and inactive receiver numbers in the summary section of Streaming page.

<img width="1074" alt="screen shot 2015-08-21 at 2 08 54 pm" src="https://cloud.githubusercontent.com/assets/1000778/9402437/ff2806a2-480f-11e5-8f8e-efdf8e5d514d.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #8351 from zsxwing/receiver-number.
---
 .../spark/streaming/ui/StreamingJobProgressListener.scala | 8 ++++++++
 .../org/apache/spark/streaming/ui/StreamingPage.scala     | 6 ++++++
 2 files changed, 14 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
index b77c555c68b8b..78aeb004e18b1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
@@ -148,6 +148,14 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
     receiverInfos.size
   }
 
+  def numActiveReceivers: Int = synchronized {
+    receiverInfos.count(_._2.active)
+  }
+
+  def numInactiveReceivers: Int = {
+    ssc.graph.getReceiverInputStreams().size - numActiveReceivers
+  }
+
   def numTotalCompletedBatches: Long = synchronized {
     totalCompletedBatches
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
index 87af902428ec8..96d943e75d272 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
@@ -303,6 +303,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
 
     val numCompletedBatches = listener.retainedCompletedBatches.size
     val numActiveBatches = batchTimes.length - numCompletedBatches
+    val numReceivers = listener.numInactiveReceivers + listener.numActiveReceivers
     val table =
       // scalastyle:off
       <table id="stat-table" class="table table-bordered" style="width: auto">
@@ -330,6 +331,11 @@ private[ui] class StreamingPage(parent: StreamingTab)
                 }
               }
               </div>
+              {
+                if (numReceivers > 0) {
+                  <div>Receivers: {listener.numActiveReceivers} / {numReceivers} active</div>
+                }
+              }
               <div>Avg: {eventRateForAllStreams.formattedAvg} events/sec</div>
             </div>
           </td>

From b963c19a803c5a26c9b65655d40ca6621acf8bd4 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Sun, 23 Aug 2015 18:34:07 -0700
Subject: [PATCH 1183/1454] [SPARK-10164] [MLLIB] Fixed GMM distributed
 decomposition bug

GaussianMixture now distributes matrix decompositions for certain problem sizes. Distributed computation actually fails, but this was not tested in unit tests.

This PR adds a unit test which checks this.  It failed previously but works with this fix.

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8370 from jkbradley/gmm-fix.
---
 .../mllib/clustering/GaussianMixture.scala    | 22 +++++++++++++------
 .../clustering/GaussianMixtureSuite.scala     | 22 +++++++++++++++++--
 2 files changed, 35 insertions(+), 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index fcc9dfecac54f..daa947e81d44d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -169,9 +169,7 @@ class GaussianMixture private (
     // Get length of the input vectors
     val d = breezeData.first().length
 
-    // Heuristic to distribute the computation of the [[MultivariateGaussian]]s, approximately when
-    // d > 25 except for when k is very small
-    val distributeGaussians = ((k - 1.0) / k) * d > 25
+    val shouldDistributeGaussians = GaussianMixture.shouldDistributeGaussians(k, d)
 
     // Determine initial weights and corresponding Gaussians.
     // If the user supplied an initial GMM, we use those values, otherwise
@@ -205,15 +203,15 @@ class GaussianMixture private (
       // (often referred to as the "M" step in literature)
       val sumWeights = sums.weights.sum
 
-      if (distributeGaussians) {
+      if (shouldDistributeGaussians) {
         val numPartitions = math.min(k, 1024)
         val tuples =
           Seq.tabulate(k)(i => (sums.means(i), sums.sigmas(i), sums.weights(i)))
         val (ws, gs) = sc.parallelize(tuples, numPartitions).map { case (mean, sigma, weight) =>
           updateWeightsAndGaussians(mean, sigma, weight, sumWeights)
-        }.collect.unzip
-        Array.copy(ws, 0, weights, 0, ws.length)
-        Array.copy(gs, 0, gaussians, 0, gs.length)
+        }.collect().unzip
+        Array.copy(ws.toArray, 0, weights, 0, ws.length)
+        Array.copy(gs.toArray, 0, gaussians, 0, gs.length)
       } else {
         var i = 0
         while (i < k) {
@@ -271,6 +269,16 @@ class GaussianMixture private (
   }
 }
 
+private[clustering] object GaussianMixture {
+  /**
+   * Heuristic to distribute the computation of the [[MultivariateGaussian]]s, approximately when
+   * d > 25 except for when k is very small.
+   * @param k  Number of topics
+   * @param d  Number of features
+   */
+  def shouldDistributeGaussians(k: Int, d: Int): Boolean = ((k - 1.0) / k) * d > 25
+}
+
 // companion class to provide zero constructor for ExpectationSum
 private object ExpectationSum {
   def zero(k: Int, d: Int): ExpectationSum = {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
index b636d02f786e6..a72723eb00daf 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.clustering
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.{Vectors, Matrices}
+import org.apache.spark.mllib.linalg.{Vector, Vectors, Matrices}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -76,6 +76,20 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(gmm.gaussians(1).sigma ~== Esigma(1) absTol 1E-3)
   }
 
+  test("two clusters with distributed decompositions") {
+    val data = sc.parallelize(GaussianTestData.data2, 2)
+
+    val k = 5
+    val d = data.first().size
+    assert(GaussianMixture.shouldDistributeGaussians(k, d))
+
+    val gmm = new GaussianMixture()
+      .setK(k)
+      .run(data)
+
+    assert(gmm.k === k)
+  }
+
   test("single cluster with sparse data") {
     val data = sc.parallelize(Array(
       Vectors.sparse(3, Array(0, 2), Array(4.0, 2.0)),
@@ -116,7 +130,7 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
     val sparseGMM = new GaussianMixture()
       .setK(2)
       .setInitialModel(initialGmm)
-      .run(data)
+      .run(sparseData)
 
     assert(sparseGMM.weights(0) ~== Ew(0) absTol 1E-3)
     assert(sparseGMM.weights(1) ~== Ew(1) absTol 1E-3)
@@ -168,5 +182,9 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
       Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734)
     )
 
+    val data2: Array[Vector] = Array.tabulate(25){ i: Int =>
+      Vectors.dense(Array.tabulate(50)(i + _.toDouble))
+    }
+
   }
 }

From 053d94fcf32268369b5a40837271f15d6af41aa4 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Sun, 23 Aug 2015 19:24:32 -0700
Subject: [PATCH 1184/1454] [SPARK-10142] [STREAMING] Made python checkpoint
 recovery handle non-local checkpoint paths and existing SparkContexts

The current code only checks checkpoint files in local filesystem, and always tries to create a new Python SparkContext (even if one already exists). The solution is to do the following:
1. Use the same code path as Java to check whether a valid checkpoint exists
2. Create a new Python SparkContext only if there no active one.

There is not test for the path as its hard to test with distributed filesystem paths in a local unit test. I am going to test it with a distributed file system manually to verify that this patch works.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8366 from tdas/SPARK-10142 and squashes the following commits:

3afa666 [Tathagata Das] Added tests
2dd4ae5 [Tathagata Das] Added the check to not create a context if one already exists
9bf151b [Tathagata Das] Made python checkpoint recovery use java to find the checkpoint files
---
 python/pyspark/streaming/context.py           | 22 ++++++----
 python/pyspark/streaming/tests.py             | 43 ++++++++++++++++---
 .../apache/spark/streaming/Checkpoint.scala   |  9 ++++
 3 files changed, 58 insertions(+), 16 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index e3ba70e4e5e88..4069d7a149986 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -150,26 +150,30 @@ def getOrCreate(cls, checkpointPath, setupFunc):
         @param checkpointPath: Checkpoint directory used in an earlier streaming program
         @param setupFunc:      Function to create a new context and setup DStreams
         """
-        # TODO: support checkpoint in HDFS
-        if not os.path.exists(checkpointPath) or not os.listdir(checkpointPath):
+        cls._ensure_initialized()
+        gw = SparkContext._gateway
+
+        # Check whether valid checkpoint information exists in the given path
+        if gw.jvm.CheckpointReader.read(checkpointPath).isEmpty():
             ssc = setupFunc()
             ssc.checkpoint(checkpointPath)
             return ssc
 
-        cls._ensure_initialized()
-        gw = SparkContext._gateway
-
         try:
             jssc = gw.jvm.JavaStreamingContext(checkpointPath)
         except Exception:
             print("failed to load StreamingContext from checkpoint", file=sys.stderr)
             raise
 
-        jsc = jssc.sparkContext()
-        conf = SparkConf(_jconf=jsc.getConf())
-        sc = SparkContext(conf=conf, gateway=gw, jsc=jsc)
+        # If there is already an active instance of Python SparkContext use it, or create a new one
+        if not SparkContext._active_spark_context:
+            jsc = jssc.sparkContext()
+            conf = SparkConf(_jconf=jsc.getConf())
+            SparkContext(conf=conf, gateway=gw, jsc=jsc)
+
+        sc = SparkContext._active_spark_context
+
         # update ctx in serializer
-        SparkContext._active_spark_context = sc
         cls._transformerSerializer.ctx = sc
         return StreamingContext(sc, None, jssc)
 
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 214d5be439003..510a4f2b3e472 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -603,6 +603,10 @@ def tearDownClass():
     def tearDown(self):
         if self.ssc is not None:
             self.ssc.stop(True)
+        if self.sc is not None:
+            self.sc.stop()
+        if self.cpd is not None:
+            shutil.rmtree(self.cpd)
 
     def test_get_or_create_and_get_active_or_create(self):
         inputd = tempfile.mkdtemp()
@@ -622,8 +626,12 @@ def setup():
             self.setupCalled = True
             return ssc
 
-        cpd = tempfile.mkdtemp("test_streaming_cps")
-        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        # Verify that getOrCreate() calls setup() in absence of checkpoint files
+        self.cpd = tempfile.mkdtemp("test_streaming_cps")
+        self.setupCalled = False
+        self.ssc = StreamingContext.getOrCreate(self.cpd, setup)
+        self.assertFalse(self.setupCalled)
+
         self.ssc.start()
 
         def check_output(n):
@@ -660,31 +668,52 @@ def check_output(n):
         self.ssc.stop(True, True)
         time.sleep(1)
         self.setupCalled = False
-        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        self.ssc = StreamingContext.getOrCreate(self.cpd, setup)
         self.assertFalse(self.setupCalled)
         self.ssc.start()
         check_output(3)
 
+        # Verify that getOrCreate() uses existing SparkContext
+        self.ssc.stop(True, True)
+        time.sleep(1)
+        sc = SparkContext(SparkConf())
+        self.setupCalled = False
+        self.ssc = StreamingContext.getOrCreate(self.cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.assertTrue(self.ssc.sparkContext == sc)
+
         # Verify the getActiveOrCreate() recovers from checkpoint files
         self.ssc.stop(True, True)
         time.sleep(1)
         self.setupCalled = False
-        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup)
         self.assertFalse(self.setupCalled)
         self.ssc.start()
         check_output(4)
 
         # Verify that getActiveOrCreate() returns active context
         self.setupCalled = False
-        self.assertEquals(StreamingContext.getActiveOrCreate(cpd, setup), self.ssc)
+        self.assertEquals(StreamingContext.getActiveOrCreate(self.cpd, setup), self.ssc)
         self.assertFalse(self.setupCalled)
 
+        # Verify that getActiveOrCreate() uses existing SparkContext
+        self.ssc.stop(True, True)
+        time.sleep(1)
+        self.sc = SparkContext(SparkConf())
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.assertTrue(self.ssc.sparkContext == sc)
+
         # Verify that getActiveOrCreate() calls setup() in absence of checkpoint files
         self.ssc.stop(True, True)
-        shutil.rmtree(cpd)  # delete checkpoint directory
+        shutil.rmtree(self.cpd)  # delete checkpoint directory
+        time.sleep(1)
         self.setupCalled = False
-        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup)
         self.assertTrue(self.setupCalled)
+
+        # Stop everything
         self.ssc.stop(True, True)
 
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 6f6b449accc3c..cd5d960369c05 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -286,6 +286,15 @@ class CheckpointWriter(
 private[streaming]
 object CheckpointReader extends Logging {
 
+  /**
+   * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint
+   * files, then return None, else try to return the latest valid checkpoint object. If no
+   * checkpoint files could be read correctly, then return None.
+   */
+  def read(checkpointDir: String): Option[Checkpoint] = {
+    read(checkpointDir, new SparkConf(), SparkHadoopUtil.get.conf, ignoreReadError = true)
+  }
+
   /**
    * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint
    * files, then return None, else try to return the latest valid checkpoint object. If no

From 4e0395ddb764d092b5b38447af49e196e590e0f0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 24 Aug 2015 12:38:01 -0700
Subject: [PATCH 1185/1454] [SPARK-10168] [STREAMING] Fix the issue that maven
 publishes wrong artifact jars

This PR removed the `outputFile` configuration from pom.xml and updated `tests.py` to search jars for both sbt build and maven build.

I ran ` mvn -Pkinesis-asl -DskipTests clean install` locally, and verified the jars in my local repository were correct. I also checked Python tests for maven build, and it passed all tests.

Author: zsxwing <zsxwing@gmail.com>

Closes #8373 from zsxwing/SPARK-10168 and squashes the following commits:

e0b5818 [zsxwing] Fix the sbt build
c697627 [zsxwing] Add the jar pathes to the exception message
be1d8a5 [zsxwing] Fix the issue that maven publishes wrong artifact jars
---
 external/flume-assembly/pom.xml     |  1 -
 external/kafka-assembly/pom.xml     |  1 -
 external/mqtt-assembly/pom.xml      |  1 -
 extras/kinesis-asl-assembly/pom.xml |  1 -
 python/pyspark/streaming/tests.py   | 47 ++++++++++++++++-------------
 5 files changed, 26 insertions(+), 25 deletions(-)

diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
index e05e4318969ce..561ed4babe5d0 100644
--- a/external/flume-assembly/pom.xml
+++ b/external/flume-assembly/pom.xml
@@ -115,7 +115,6 @@
         <artifactId>maven-shade-plugin</artifactId>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-flume-assembly-${project.version}.jar</outputFile>
           <artifactSet>
             <includes>
               <include>*:*</include>
diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml
index 36342f37bb2ea..6f4e2a89e9af7 100644
--- a/external/kafka-assembly/pom.xml
+++ b/external/kafka-assembly/pom.xml
@@ -142,7 +142,6 @@
       <artifactId>maven-shade-plugin</artifactId>
       <configuration>
         <shadedArtifactAttached>false</shadedArtifactAttached>
-        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-kafka-assembly-${project.version}.jar</outputFile>
         <artifactSet>
           <includes>
             <include>*:*</include>
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
index f3e3f93e7ed50..8412600633734 100644
--- a/external/mqtt-assembly/pom.xml
+++ b/external/mqtt-assembly/pom.xml
@@ -132,7 +132,6 @@
         <artifactId>maven-shade-plugin</artifactId>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-mqtt-assembly-${project.version}.jar</outputFile>
           <artifactSet>
             <includes>
               <include>*:*</include>
diff --git a/extras/kinesis-asl-assembly/pom.xml b/extras/kinesis-asl-assembly/pom.xml
index 3ca538608f694..51af3e6f2225f 100644
--- a/extras/kinesis-asl-assembly/pom.xml
+++ b/extras/kinesis-asl-assembly/pom.xml
@@ -137,7 +137,6 @@
       <artifactId>maven-shade-plugin</artifactId>
       <configuration>
         <shadedArtifactAttached>false</shadedArtifactAttached>
-        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-kinesis-asl-assembly-${project.version}.jar</outputFile>
         <artifactSet>
           <includes>
             <include>*:*</include>
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 510a4f2b3e472..cfea95b0dec71 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -1162,11 +1162,20 @@ def get_output(_, rdd):
             kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
 
 
+# Search jar in the project dir using the jar name_prefix for both sbt build and maven build because
+# the artifact jars are in different directories.
+def search_jar(dir, name_prefix):
+    # We should ignore the following jars
+    ignored_jar_suffixes = ("javadoc.jar", "sources.jar", "test-sources.jar", "tests.jar")
+    jars = (glob.glob(os.path.join(dir, "target/scala-*/" + name_prefix + "-*.jar")) +  # sbt build
+            glob.glob(os.path.join(dir, "target/" + name_prefix + "_*.jar")))  # maven build
+    return [jar for jar in jars if not jar.endswith(ignored_jar_suffixes)]
+
+
 def search_kafka_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     kafka_assembly_dir = os.path.join(SPARK_HOME, "external/kafka-assembly")
-    jars = glob.glob(
-        os.path.join(kafka_assembly_dir, "target/scala-*/spark-streaming-kafka-assembly-*.jar"))
+    jars = search_jar(kafka_assembly_dir, "spark-streaming-kafka-assembly")
     if not jars:
         raise Exception(
             ("Failed to find Spark Streaming kafka assembly jar in %s. " % kafka_assembly_dir) +
@@ -1174,8 +1183,8 @@ def search_kafka_assembly_jar():
             "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or "
             "'build/mvn package' before running this test.")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming Kafka assembly JARs in %s; please "
-                         "remove all but one") % kafka_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming Kafka assembly JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1183,8 +1192,7 @@ def search_kafka_assembly_jar():
 def search_flume_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     flume_assembly_dir = os.path.join(SPARK_HOME, "external/flume-assembly")
-    jars = glob.glob(
-        os.path.join(flume_assembly_dir, "target/scala-*/spark-streaming-flume-assembly-*.jar"))
+    jars = search_jar(flume_assembly_dir, "spark-streaming-flume-assembly")
     if not jars:
         raise Exception(
             ("Failed to find Spark Streaming Flume assembly jar in %s. " % flume_assembly_dir) +
@@ -1192,8 +1200,8 @@ def search_flume_assembly_jar():
             "'build/sbt assembly/assembly streaming-flume-assembly/assembly' or "
             "'build/mvn package' before running this test.")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming Flume assembly JARs in %s; please "
-                        "remove all but one") % flume_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming Flume assembly JARs: %s; please "
+                        "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1201,8 +1209,7 @@ def search_flume_assembly_jar():
 def search_mqtt_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     mqtt_assembly_dir = os.path.join(SPARK_HOME, "external/mqtt-assembly")
-    jars = glob.glob(
-        os.path.join(mqtt_assembly_dir, "target/scala-*/spark-streaming-mqtt-assembly-*.jar"))
+    jars = search_jar(mqtt_assembly_dir, "spark-streaming-mqtt-assembly")
     if not jars:
         raise Exception(
             ("Failed to find Spark Streaming MQTT assembly jar in %s. " % mqtt_assembly_dir) +
@@ -1210,8 +1217,8 @@ def search_mqtt_assembly_jar():
             "'build/sbt assembly/assembly streaming-mqtt-assembly/assembly' or "
             "'build/mvn package' before running this test")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming MQTT assembly JARs in %s; please "
-                         "remove all but one") % mqtt_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming MQTT assembly JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1227,8 +1234,8 @@ def search_mqtt_test_jar():
             "You need to build Spark with "
             "'build/sbt assembly/assembly streaming-mqtt/test:assembly'")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming MQTT test JARs in %s; please "
-                         "remove all but one") % mqtt_test_dir)
+        raise Exception(("Found multiple Spark Streaming MQTT test JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1236,14 +1243,12 @@ def search_mqtt_test_jar():
 def search_kinesis_asl_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     kinesis_asl_assembly_dir = os.path.join(SPARK_HOME, "extras/kinesis-asl-assembly")
-    jars = glob.glob(
-        os.path.join(kinesis_asl_assembly_dir,
-                     "target/scala-*/spark-streaming-kinesis-asl-assembly-*.jar"))
+    jars = search_jar(kinesis_asl_assembly_dir, "spark-streaming-kinesis-asl-assembly")
     if not jars:
         return None
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs in %s; please "
-                         "remove all but one") % kinesis_asl_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1269,8 +1274,8 @@ def search_kinesis_asl_assembly_jar():
                                    mqtt_test_jar, kinesis_asl_assembly_jar)
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
-    testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests,
-                 CheckpointTests, KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests]
+    testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests, CheckpointTests,
+                 KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests, MQTTStreamTests]
 
     if kinesis_jar_present is True:
         testcases.append(KinesisStreamTests)

From 7478c8b66d6a2b1179f20c38b49e27e37b0caec3 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon, 24 Aug 2015 12:40:09 -0700
Subject: [PATCH 1186/1454] [SPARK-9791] [PACKAGE] Change private class to
 private class to prevent unnecessary classes from showing up in the docs

In addition, some random cleanup of import ordering

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8387 from tdas/SPARK-9791 and squashes the following commits:

67f3ee9 [Tathagata Das] Change private class to private[package] class to prevent them from showing up in the docs
---
 .../spark/streaming/flume/FlumeUtils.scala    |  2 +-
 .../apache/spark/streaming/kafka/Broker.scala |  6 ++--
 .../streaming/kafka/KafkaTestUtils.scala      | 10 +++---
 .../spark/streaming/kafka/KafkaUtils.scala    | 36 +++++--------------
 .../spark/streaming/kafka/OffsetRange.scala   |  8 -----
 .../spark/streaming/mqtt/MQTTUtils.scala      |  6 ++--
 .../spark/streaming/mqtt/MQTTTestUtils.scala  |  2 +-
 .../streaming/kinesis/KinesisTestUtils.scala  |  2 +-
 .../spark/streaming/util/WriteAheadLog.java   |  2 ++
 .../util/WriteAheadLogRecordHandle.java       |  2 ++
 .../receiver/ReceivedBlockHandler.scala       |  2 +-
 .../streaming/scheduler/ReceiverTracker.scala |  2 +-
 .../apache/spark/streaming/ui/BatchPage.scala |  2 +-
 13 files changed, 28 insertions(+), 54 deletions(-)

diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
index 095bfb0c73a9a..a65a9b921aafa 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -247,7 +247,7 @@ object FlumeUtils {
  * This is a helper class that wraps the methods in FlumeUtils into more Python-friendly class and
  * function so that it can be easily instantiated and called from Python's FlumeUtils.
  */
-private class FlumeUtilsPythonHelper {
+private[flume] class FlumeUtilsPythonHelper {
 
   def createStream(
       jssc: JavaStreamingContext,
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala
index 5a74febb4bd46..9159051ba06e4 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala
@@ -20,11 +20,9 @@ package org.apache.spark.streaming.kafka
 import org.apache.spark.annotation.Experimental
 
 /**
- * :: Experimental ::
- * Represent the host and port info for a Kafka broker.
- * Differs from the Kafka project's internal kafka.cluster.Broker, which contains a server ID
+ * Represents the host and port info for a Kafka broker.
+ * Differs from the Kafka project's internal kafka.cluster.Broker, which contains a server ID.
  */
-@Experimental
 final class Broker private(
     /** Broker's hostname */
     val host: String,
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
index b608b75952721..79a9db4291bef 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
@@ -20,9 +20,8 @@ package org.apache.spark.streaming.kafka
 import java.io.File
 import java.lang.{Integer => JInt}
 import java.net.InetSocketAddress
-import java.util.{Map => JMap}
-import java.util.Properties
 import java.util.concurrent.TimeoutException
+import java.util.{Map => JMap, Properties}
 
 import scala.annotation.tailrec
 import scala.language.postfixOps
@@ -30,17 +29,16 @@ import scala.util.control.NonFatal
 
 import kafka.admin.AdminUtils
 import kafka.api.Request
-import kafka.common.TopicAndPartition
 import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
 import kafka.serializer.StringEncoder
 import kafka.server.{KafkaConfig, KafkaServer}
 import kafka.utils.{ZKStringSerializer, ZkUtils}
-import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer}
 import org.I0Itec.zkclient.ZkClient
+import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer}
 
-import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.streaming.Time
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkConf}
 
 /**
  * This is a helper class for Kafka test suites. This has the functionality to set up
@@ -48,7 +46,7 @@ import org.apache.spark.util.Utils
  *
  * The reason to put Kafka test utility class in src is to test Python related Kafka APIs.
  */
-private class KafkaTestUtils extends Logging {
+private[kafka] class KafkaTestUtils extends Logging {
 
   // Zookeeper related configurations
   private val zkHost = "localhost"
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
index f3b01bd60b178..388dbb8184106 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
@@ -17,29 +17,25 @@
 
 package org.apache.spark.streaming.kafka
 
-import java.lang.{Integer => JInt}
-import java.lang.{Long => JLong}
-import java.util.{Map => JMap}
-import java.util.{Set => JSet}
-import java.util.{List => JList}
+import java.lang.{Integer => JInt, Long => JLong}
+import java.util.{List => JList, Map => JMap, Set => JSet}
 
-import scala.reflect.ClassTag
 import scala.collection.JavaConversions._
+import scala.reflect.ClassTag
 
 import kafka.common.TopicAndPartition
 import kafka.message.MessageAndMetadata
-import kafka.serializer.{DefaultDecoder, Decoder, StringDecoder}
+import kafka.serializer.{Decoder, DefaultDecoder, StringDecoder}
 
 import org.apache.spark.api.java.function.{Function => JFunction}
-import org.apache.spark.streaming.util.WriteAheadLogUtils
-import org.apache.spark.{SparkContext, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.streaming.api.java.{JavaPairInputDStream, JavaInputDStream, JavaPairReceiverInputDStream, JavaStreamingContext}
+import org.apache.spark.streaming.api.java.{JavaInputDStream, JavaPairInputDStream, JavaPairReceiverInputDStream, JavaStreamingContext}
 import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
-import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
+import org.apache.spark.streaming.util.WriteAheadLogUtils
+import org.apache.spark.{SparkContext, SparkException}
 
 object KafkaUtils {
   /**
@@ -196,7 +192,6 @@ object KafkaUtils {
    * @param offsetRanges Each OffsetRange in the batch corresponds to a
    *   range of offsets for a given Kafka topic/partition
    */
-  @Experimental
   def createRDD[
     K: ClassTag,
     V: ClassTag,
@@ -214,7 +209,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create a RDD from Kafka using offset ranges for each topic and partition. This allows you
    * specify the Kafka leader to connect to (to optimize fetching) and access the message as well
    * as the metadata.
@@ -230,7 +224,6 @@ object KafkaUtils {
    *   in which case leaders will be looked up on the driver.
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createRDD[
     K: ClassTag,
     V: ClassTag,
@@ -268,7 +261,6 @@ object KafkaUtils {
    * @param offsetRanges Each OffsetRange in the batch corresponds to a
    *   range of offsets for a given Kafka topic/partition
    */
-  @Experimental
   def createRDD[K, V, KD <: Decoder[K], VD <: Decoder[V]](
       jsc: JavaSparkContext,
       keyClass: Class[K],
@@ -287,7 +279,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create a RDD from Kafka using offset ranges for each topic and partition. This allows you
    * specify the Kafka leader to connect to (to optimize fetching) and access the message as well
    * as the metadata.
@@ -303,7 +294,6 @@ object KafkaUtils {
    *   in which case leaders will be looked up on the driver.
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createRDD[K, V, KD <: Decoder[K], VD <: Decoder[V], R](
       jsc: JavaSparkContext,
       keyClass: Class[K],
@@ -327,7 +317,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -357,7 +346,6 @@ object KafkaUtils {
    *    starting point of the stream
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createDirectStream[
     K: ClassTag,
     V: ClassTag,
@@ -375,7 +363,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -405,7 +392,6 @@ object KafkaUtils {
    *   to determine where the stream starts (defaults to "largest")
    * @param topics Names of the topics to consume
    */
-  @Experimental
   def createDirectStream[
     K: ClassTag,
     V: ClassTag,
@@ -437,7 +423,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -472,7 +457,6 @@ object KafkaUtils {
    *    starting point of the stream
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createDirectStream[K, V, KD <: Decoder[K], VD <: Decoder[V], R](
       jssc: JavaStreamingContext,
       keyClass: Class[K],
@@ -499,7 +483,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -533,7 +516,6 @@ object KafkaUtils {
    *   to determine where the stream starts (defaults to "largest")
    * @param topics Names of the topics to consume
    */
-  @Experimental
   def createDirectStream[K, V, KD <: Decoder[K], VD <: Decoder[V]](
       jssc: JavaStreamingContext,
       keyClass: Class[K],
@@ -564,7 +546,7 @@ object KafkaUtils {
  * classOf[KafkaUtilsPythonHelper].newInstance(), and the createStream()
  * takes care of known parameters instead of passing them from Python
  */
-private class KafkaUtilsPythonHelper {
+private[kafka] class KafkaUtilsPythonHelper {
   def createStream(
       jssc: JavaStreamingContext,
       kafkaParams: JMap[String, String],
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
index 2f8981d4898bd..8a5f371494511 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
@@ -19,10 +19,7 @@ package org.apache.spark.streaming.kafka
 
 import kafka.common.TopicAndPartition
 
-import org.apache.spark.annotation.Experimental
-
 /**
- * :: Experimental ::
  * Represents any object that has a collection of [[OffsetRange]]s. This can be used access the
  * offset ranges in RDDs generated by the direct Kafka DStream (see
  * [[KafkaUtils.createDirectStream()]]).
@@ -33,13 +30,11 @@ import org.apache.spark.annotation.Experimental
  *   }
  * }}}
  */
-@Experimental
 trait HasOffsetRanges {
   def offsetRanges: Array[OffsetRange]
 }
 
 /**
- * :: Experimental ::
  * Represents a range of offsets from a single Kafka TopicAndPartition. Instances of this class
  * can be created with `OffsetRange.create()`.
  * @param topic Kafka topic name
@@ -47,7 +42,6 @@ trait HasOffsetRanges {
  * @param fromOffset Inclusive starting offset
  * @param untilOffset Exclusive ending offset
  */
-@Experimental
 final class OffsetRange private(
     val topic: String,
     val partition: Int,
@@ -84,10 +78,8 @@ final class OffsetRange private(
 }
 
 /**
- * :: Experimental ::
  * Companion object the provides methods to create instances of [[OffsetRange]].
  */
-@Experimental
 object OffsetRange {
   def create(topic: String, partition: Int, fromOffset: Long, untilOffset: Long): OffsetRange =
     new OffsetRange(topic, partition, fromOffset, untilOffset)
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
index 38a1114863d15..7b8d56d6faf2d 100644
--- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
@@ -21,8 +21,8 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext, JavaDStream}
-import org.apache.spark.streaming.dstream.{ReceiverInputDStream, DStream}
+import org.apache.spark.streaming.api.java.{JavaDStream, JavaReceiverInputDStream, JavaStreamingContext}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
 
 object MQTTUtils {
   /**
@@ -79,7 +79,7 @@ object MQTTUtils {
  * This is a helper class that wraps the methods in MQTTUtils into more Python-friendly class and
  * function so that it can be easily instantiated and called from Python's MQTTUtils.
  */
-private class MQTTUtilsPythonHelper {
+private[mqtt] class MQTTUtilsPythonHelper {
 
   def createStream(
       jssc: JavaStreamingContext,
diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
index 1a371b7008824..1618e2c088b70 100644
--- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
+++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
@@ -33,7 +33,7 @@ import org.apache.spark.{Logging, SparkConf}
 /**
  * Share codes for Scala and Python unit tests
  */
-private class MQTTTestUtils extends Logging {
+private[mqtt] class MQTTTestUtils extends Logging {
 
   private val persistenceDir = Utils.createTempDir()
   private val brokerHost = "localhost"
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index 711aade182945..c8eec13ec7dc7 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -36,7 +36,7 @@ import org.apache.spark.Logging
 /**
  * Shared utility methods for performing Kinesis tests that actually transfer data
  */
-private class KinesisTestUtils extends Logging {
+private[kinesis] class KinesisTestUtils extends Logging {
 
   val endpointUrl = KinesisTestUtils.endpointUrl
   val regionName = RegionUtils.getRegionByEndpoint(endpointUrl).getName()
diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java
index 8c0fdfa9c7478..3738fc1a235c2 100644
--- a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java
+++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java
@@ -21,6 +21,8 @@
 import java.util.Iterator;
 
 /**
+ * :: DeveloperApi ::
+ *
  * This abstract class represents a write ahead log (aka journal) that is used by Spark Streaming
  * to save the received data (by receivers) and associated metadata to a reliable storage, so that
  * they can be recovered after driver failures. See the Spark documentation for more information
diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java
index 02324189b7822..662889e779fb2 100644
--- a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java
+++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java
@@ -18,6 +18,8 @@
 package org.apache.spark.streaming.util;
 
 /**
+ * :: DeveloperApi ::
+ *
  * This abstract class represents a handle that refers to a record written in a
  * {@link org.apache.spark.streaming.util.WriteAheadLog WriteAheadLog}.
  * It must contain all the information necessary for the record to be read and returned by
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala
index c8dd6e06812dc..5f6c5b024085c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala
@@ -222,7 +222,7 @@ private[streaming] object WriteAheadLogBasedBlockHandler {
 /**
  * A utility that will wrap the Iterator to get the count
  */
-private class CountingIterator[T](iterator: Iterator[T]) extends Iterator[T] {
+private[streaming] class CountingIterator[T](iterator: Iterator[T]) extends Iterator[T] {
    private var _count = 0
 
    private def isFullyConsumed: Boolean = !iterator.hasNext
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index aae3acf7aba3e..30d25a64e307a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -546,7 +546,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
  * Function to start the receiver on the worker node. Use a class instead of closure to avoid
  * the serialization issue.
  */
-private class StartReceiverFunc(
+private[streaming] class StartReceiverFunc(
     checkpointDirOption: Option[String],
     serializableHadoopConf: SerializableConfiguration)
   extends (Iterator[Receiver[_]] => Unit) with Serializable {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
index 0c891662c264f..90d1b0fadecfc 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
@@ -28,7 +28,7 @@ import org.apache.spark.ui.{UIUtils => SparkUIUtils, WebUIPage}
 import org.apache.spark.streaming.ui.StreamingJobProgressListener.{SparkJobId, OutputOpId}
 import org.apache.spark.ui.jobs.UIData.JobUIData
 
-private case class SparkJobIdWithUIData(sparkJobId: SparkJobId, jobUIData: Option[JobUIData])
+private[ui] case class SparkJobIdWithUIData(sparkJobId: SparkJobId, jobUIData: Option[JobUIData])
 
 private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
   private val streamingListener = parent.listener

From 9ce0c7ad333f4a3c01207e5e9ed42bcafb99d894 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Mon, 24 Aug 2015 13:48:01 -0700
Subject: [PATCH 1187/1454] [SPARK-7710] [SPARK-7998] [DOCS] Docs for
 DataFrameStatFunctions

This PR contains examples on how to use some of the Stat Functions available for DataFrames under `df.stat`.

rxin

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8378 from brkyvz/update-sql-docs.
---
 .../org/apache/spark/sql/DataFrame.scala      |   2 +-
 .../spark/sql/DataFrameStatFunctions.scala    | 101 ++++++++++++++++++
 2 files changed, 102 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index d6688b24ae7d6..791c10c3d7ce7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -684,7 +684,7 @@ class DataFrame private[sql](
       // make it a NamedExpression.
       case Column(u: UnresolvedAttribute) => UnresolvedAlias(u)
       case Column(expr: NamedExpression) => expr
-      // Leave an unaliased explode with an empty list of names since the analzyer will generate the
+      // Leave an unaliased explode with an empty list of names since the analyzer will generate the
       // correct defaults after the nested expression's type has been resolved.
       case Column(explode: Explode) => MultiAlias(explode, Nil)
       case Column(expr: Expression) => Alias(expr, expr.prettyString)()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index 2e68e358f2f1f..69c984717526d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -39,6 +39,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the second column
    * @return the covariance of the two columns.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.cov("rand1", "rand2")
+   *    res1: Double = 0.065...
+   * }}}
+   *
    * @since 1.4.0
    */
   def cov(col1: String, col2: String): Double = {
@@ -54,6 +61,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2")
+   *    res1: Double = 0.613...
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String, method: String): Double = {
@@ -69,6 +83,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2", "pearson")
+   *    res1: Double = 0.613...
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String): Double = {
@@ -92,6 +113,20 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *             of the DataFrame.
    * @return A DataFrame containing for the contingency table.
    *
+   * {{{
+   *    val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+   *      (3, 3))).toDF("key", "value")
+   *    val ct = df.stat.crosstab("key", "value")
+   *    ct.show()
+   *    +---------+---+---+---+
+   *    |key_value|  1|  2|  3|
+   *    +---------+---+---+---+
+   *    |        2|  2|  0|  1|
+   *    |        1|  1|  1|  0|
+   *    |        3|  0|  1|  1|
+   *    +---------+---+---+---+
+   * }}}
+   *
    * @since 1.4.0
    */
   def crosstab(col1: String, col2: String): DataFrame = {
@@ -112,6 +147,32 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *                than 1e-4.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    val rows = Seq.tabulate(100) { i =>
+   *      if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+   *    }
+   *    val df = sqlContext.createDataFrame(rows).toDF("a", "b")
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    +-----------+-------------+
+   *    |a_freqItems|  b_freqItems|
+   *    +-----------+-------------+
+   *    |    [1, 99]|[-1.0, -99.0]|
+   *    +-----------+-------------+
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+   *    freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+   *    +----------+
+   *    |   freq_ab|
+   *    +----------+
+   *    |  [1,-1.0]|
+   *    |   ...    |
+   *    +----------+
+   * }}}
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Array[String], support: Double): DataFrame = {
@@ -147,6 +208,32 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param cols the names of the columns to search frequent items in.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    val rows = Seq.tabulate(100) { i =>
+   *      if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+   *    }
+   *    val df = sqlContext.createDataFrame(rows).toDF("a", "b")
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    +-----------+-------------+
+   *    |a_freqItems|  b_freqItems|
+   *    +-----------+-------------+
+   *    |    [1, 99]|[-1.0, -99.0]|
+   *    +-----------+-------------+
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+   *    freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+   *    +----------+
+   *    |   freq_ab|
+   *    +----------+
+   *    |  [1,-1.0]|
+   *    |   ...    |
+   *    +----------+
+   * }}}
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Seq[String], support: Double): DataFrame = {
@@ -180,6 +267,20 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @tparam T stratum type
    * @return a new [[DataFrame]] that represents the stratified sample
    *
+   * {{{
+   *    val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+   *      (3, 3))).toDF("key", "value")
+   *    val fractions = Map(1 -> 1.0, 3 -> 0.5)
+   *    df.stat.sampleBy("key", fractions, 36L).show()
+   *    +---+-----+
+   *    |key|value|
+   *    +---+-----+
+   *    |  1|    1|
+   *    |  1|    2|
+   *    |  3|    2|
+   *    +---+-----+
+   * }}}
+   *
    * @since 1.5.0
    */
   def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {

From 662bb9667669cb07cf6d2ccee0d8e76bb561cd89 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 24 Aug 2015 14:10:50 -0700
Subject: [PATCH 1188/1454] [SPARK-10144] [UI] Actually show peak execution
 memory by default

The peak execution memory metric was introduced in SPARK-8735. That was before Tungsten was enabled by default, so it assumed that `spark.sql.unsafe.enabled` must be explicitly set to true. The result is that the memory is not displayed by default.

Author: Andrew Or <andrew@databricks.com>

Closes #8345 from andrewor14/show-memory-default.
---
 .../main/scala/org/apache/spark/ui/jobs/StagePage.scala   | 6 ++----
 .../test/scala/org/apache/spark/ui/StagePageSuite.scala   | 8 ++++++--
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index fb4556b836859..4adc6596ba21c 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -68,8 +68,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
   // if we find that it's okay.
   private val MAX_TIMELINE_TASKS = parent.conf.getInt("spark.ui.timeline.tasks.maximum", 1000)
 
-  private val displayPeakExecutionMemory =
-    parent.conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
+  private val displayPeakExecutionMemory = parent.conf.getBoolean("spark.sql.unsafe.enabled", true)
 
   def render(request: HttpServletRequest): Seq[Node] = {
     progressListener.synchronized {
@@ -1193,8 +1192,7 @@ private[ui] class TaskPagedTable(
     desc: Boolean) extends PagedTable[TaskTableRowData] {
 
   // We only track peak memory used for unsafe operators
-  private val displayPeakExecutionMemory =
-    conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
+  private val displayPeakExecutionMemory = conf.getBoolean("spark.sql.unsafe.enabled", true)
 
   override def tableId: String = "task-table"
 
diff --git a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
index 98f9314f31dff..3388c6dca81f1 100644
--- a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
@@ -33,14 +33,18 @@ class StagePageSuite extends SparkFunSuite with LocalSparkContext {
 
   test("peak execution memory only displayed if unsafe is enabled") {
     val unsafeConf = "spark.sql.unsafe.enabled"
-    val conf = new SparkConf().set(unsafeConf, "true")
+    val conf = new SparkConf(false).set(unsafeConf, "true")
     val html = renderStagePage(conf).toString().toLowerCase
     val targetString = "peak execution memory"
     assert(html.contains(targetString))
     // Disable unsafe and make sure it's not there
-    val conf2 = new SparkConf().set(unsafeConf, "false")
+    val conf2 = new SparkConf(false).set(unsafeConf, "false")
     val html2 = renderStagePage(conf2).toString().toLowerCase
     assert(!html2.contains(targetString))
+    // Avoid setting anything; it should be displayed by default
+    val conf3 = new SparkConf(false)
+    val html3 = renderStagePage(conf3).toString().toLowerCase
+    assert(html3.contains(targetString))
   }
 
   /**

From a2f4cdceba32aaa0df59df335ca0ce1ac73fc6c2 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 24 Aug 2015 14:11:19 -0700
Subject: [PATCH 1189/1454] [SPARK-8580] [SQL] Refactors
 ParquetHiveCompatibilitySuite and adds more test cases

This PR refactors `ParquetHiveCompatibilitySuite` so that it's easier to add new test cases.

Hit two bugs, SPARK-10177 and HIVE-11625, while working on this, added test cases for them and marked as ignored for now. SPARK-10177 will be addressed in a separate PR.

Author: Cheng Lian <lian@databricks.com>

Closes #8392 from liancheng/spark-8580/parquet-hive-compat-tests.
---
 .../hive/ParquetHiveCompatibilitySuite.scala  | 132 ++++++++++++------
 1 file changed, 93 insertions(+), 39 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 13452e71a1b3b..bc30180cf0917 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -17,15 +17,17 @@
 
 package org.apache.spark.sql.hive
 
+import java.sql.Timestamp
+import java.util.{Locale, TimeZone}
+
 import org.apache.hadoop.hive.conf.HiveConf
+import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.execution.datasources.parquet.ParquetCompatibilityTest
+import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
-class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
-  import ParquetCompatibilityTest.makeNullable
-
+class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with BeforeAndAfterAll {
   override def _sqlContext: SQLContext = TestHive
   private val sqlContext = _sqlContext
 
@@ -35,69 +37,121 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
    */
   private val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
 
-  test("Read Parquet file generated by parquet-hive") {
+  private val originalTimeZone = TimeZone.getDefault
+  private val originalLocale = Locale.getDefault
+
+  protected override def beforeAll(): Unit = {
+    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+    Locale.setDefault(Locale.US)
+  }
+
+  override protected def afterAll(): Unit = {
+    TimeZone.setDefault(originalTimeZone)
+    Locale.setDefault(originalLocale)
+  }
+
+  override protected def logParquetSchema(path: String): Unit = {
+    val schema = readParquetSchema(path, { path =>
+      !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
+    })
+
+    logInfo(
+      s"""Schema of the Parquet file written by parquet-avro:
+         |$schema
+       """.stripMargin)
+  }
+
+  private def testParquetHiveCompatibility(row: Row, hiveTypes: String*): Unit = {
     withTable("parquet_compat") {
       withTempPath { dir =>
         val path = dir.getCanonicalPath
 
+        // Hive columns are always nullable, so here we append a all-null row.
+        val rows = row :: Row(Seq.fill(row.length)(null): _*) :: Nil
+
+        // Don't convert Hive metastore Parquet tables to let Hive write those Parquet files.
         withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
           withTempTable("data") {
-            sqlContext.sql(
+            val fields = hiveTypes.zipWithIndex.map { case (typ, index) => s"  col_$index $typ" }
+
+            val ddl =
               s"""CREATE TABLE parquet_compat(
-                 |  bool_column BOOLEAN,
-                 |  byte_column TINYINT,
-                 |  short_column SMALLINT,
-                 |  int_column INT,
-                 |  long_column BIGINT,
-                 |  float_column FLOAT,
-                 |  double_column DOUBLE,
-                 |
-                 |  strings_column ARRAY<STRING>,
-                 |  int_to_string_column MAP<INT, STRING>
+                 |${fields.mkString(",\n")}
                  |)
                  |STORED AS PARQUET
                  |LOCATION '$path'
+               """.stripMargin
+
+            logInfo(
+              s"""Creating testing Parquet table with the following DDL:
+                 |$ddl
                """.stripMargin)
 
+            sqlContext.sql(ddl)
+
             val schema = sqlContext.table("parquet_compat").schema
-            val rowRDD = sqlContext.sparkContext.parallelize(makeRows).coalesce(1)
+            val rowRDD = sqlContext.sparkContext.parallelize(rows).coalesce(1)
             sqlContext.createDataFrame(rowRDD, schema).registerTempTable("data")
             sqlContext.sql("INSERT INTO TABLE parquet_compat SELECT * FROM data")
           }
         }
 
-        val schema = readParquetSchema(path, { path =>
-          !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
-        })
-
-        logInfo(
-          s"""Schema of the Parquet file written by parquet-hive:
-             |$schema
-           """.stripMargin)
+        logParquetSchema(path)
 
         // Unfortunately parquet-hive doesn't add `UTF8` annotation to BINARY when writing strings.
         // Have to assume all BINARY values are strings here.
         withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
-          checkAnswer(sqlContext.read.parquet(path), makeRows)
+          checkAnswer(sqlContext.read.parquet(path), rows)
         }
       }
     }
   }
 
-  def makeRows: Seq[Row] = {
-    (0 until 10).map { i =>
-      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
+  test("simple primitives") {
+    testParquetHiveCompatibility(
+      Row(true, 1.toByte, 2.toShort, 3, 4.toLong, 5.1f, 6.1d, "foo"),
+      "BOOLEAN", "TINYINT", "SMALLINT", "INT", "BIGINT", "FLOAT", "DOUBLE", "STRING")
+  }
 
+  ignore("SPARK-10177 timestamp") {
+    testParquetHiveCompatibility(Row(Timestamp.valueOf("2015-08-24 00:31:00")), "TIMESTAMP")
+  }
+
+  test("array") {
+    testParquetHiveCompatibility(
       Row(
-        nullable(i % 2 == 0: java.lang.Boolean),
-        nullable(i.toByte: java.lang.Byte),
-        nullable((i + 1).toShort: java.lang.Short),
-        nullable(i + 2: Integer),
-        nullable(i.toLong * 10: java.lang.Long),
-        nullable(i.toFloat + 0.1f: java.lang.Float),
-        nullable(i.toDouble + 0.2d: java.lang.Double),
-        nullable(Seq.tabulate(3)(n => s"arr_${i + n}")),
-        nullable(Seq.tabulate(3)(n => (i + n: Integer) -> s"val_${i + n}").toMap))
-    }
+        Seq[Integer](1: Integer, null, 2: Integer, null),
+        Seq[String]("foo", null, "bar", null),
+        Seq[Seq[Integer]](
+          Seq[Integer](1: Integer, null),
+          Seq[Integer](2: Integer, null))),
+      "ARRAY<INT>",
+      "ARRAY<STRING>",
+      "ARRAY<ARRAY<INT>>")
+  }
+
+  test("map") {
+    testParquetHiveCompatibility(
+      Row(
+        Map[Integer, String](
+          (1: Integer) -> "foo",
+          (2: Integer) -> null)),
+      "MAP<INT, STRING>")
+  }
+
+  // HIVE-11625: Parquet map entries with null keys are dropped by Hive
+  ignore("map entries with null keys") {
+    testParquetHiveCompatibility(
+      Row(
+        Map[Integer, String](
+          null.asInstanceOf[Integer] -> "bar",
+          null.asInstanceOf[Integer] -> null)),
+      "MAP<INT, STRING>")
+  }
+
+  test("struct") {
+    testParquetHiveCompatibility(
+      Row(Row(1, Seq("foo", "bar", null))),
+      "STRUCT<f0: INT, f1: ARRAY<STRING>>")
   }
 }

From cb2d2e15844d7ae34b5dd7028b55e11586ed93fa Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 24 Aug 2015 22:35:21 +0100
Subject: [PATCH 1190/1454] [SPARK-9758] [TEST] [SQL] Compilation issue for
 hive test / wrong package?

Move `test.org.apache.spark.sql.hive` package tests to apparent intended `org.apache.spark.sql.hive` as they don't intend to test behavior from outside org.apache.spark.*

Alternate take, per discussion at https://github.com/apache/spark/pull/8051
I think this is what vanzin and I had in mind but also CC rxin to cross-check, as this does indeed depend on whether these tests were accidentally in this package or not. Testing from a `test.org.apache.spark` package is legitimate but didn't seem to be the intent here.

Author: Sean Owen <sowen@cloudera.com>

Closes #8307 from srowen/SPARK-9758.
---
 .../org/apache/spark/sql/hive/JavaDataFrameSuite.java       | 6 ++----
 .../spark/sql/hive/JavaMetastoreDataSourcesSuite.java       | 3 +--
 .../org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java    | 2 +-
 .../org/apache/spark/sql/hive/aggregate/MyDoubleSum.java    | 2 +-
 .../apache/spark/sql/hive/execution/UDFIntegerToString.java | 0
 .../org/apache/spark/sql/hive/execution/UDFListListInt.java | 0
 .../org/apache/spark/sql/hive/execution/UDFListString.java  | 0
 .../apache/spark/sql/hive/execution/UDFStringString.java    | 0
 .../org/apache/spark/sql/hive/execution/UDFTwoListList.java | 0
 .../spark/sql/hive/execution/AggregationQuerySuite.scala    | 2 +-
 10 files changed, 6 insertions(+), 9 deletions(-)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/JavaDataFrameSuite.java (94%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java (98%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java (98%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java (98%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFIntegerToString.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFListListInt.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFListString.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFStringString.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFTwoListList.java (100%)

diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
similarity index 94%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index a30dfa554eabc..019d8a30266e2 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive;
+package org.apache.spark.sql.hive;
 
 import java.io.IOException;
 import java.util.ArrayList;
@@ -31,10 +31,8 @@
 import org.apache.spark.sql.expressions.Window;
 import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
 import static org.apache.spark.sql.functions.*;
-import org.apache.spark.sql.hive.HiveContext;
 import org.apache.spark.sql.hive.test.TestHive$;
-import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
-import test.org.apache.spark.sql.hive.aggregate.MyDoubleSum;
+import org.apache.spark.sql.hive.aggregate.MyDoubleSum;
 
 public class JavaDataFrameSuite {
   private transient JavaSparkContext sc;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
similarity index 98%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
index 15c2c3deb0d83..4192155975c47 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive;
+package org.apache.spark.sql.hive;
 
 import java.io.File;
 import java.io.IOException;
@@ -37,7 +37,6 @@
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.QueryTest$;
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.hive.HiveContext;
 import org.apache.spark.sql.hive.test.TestHive$;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.StructField;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
similarity index 98%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
index 2961b803f14aa..5a167edd89592 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive.aggregate;
+package org.apache.spark.sql.hive.aggregate;
 
 import java.util.ArrayList;
 import java.util.List;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
similarity index 98%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
index c71882a6e7bed..c3b7768e71bf8 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive.aggregate;
+package org.apache.spark.sql.hive.aggregate;
 
 import java.util.ArrayList;
 import java.util.List;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFIntegerToString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFIntegerToString.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFIntegerToString.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFIntegerToString.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListListInt.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListListInt.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListListInt.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListListInt.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListString.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFStringString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFStringString.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFStringString.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFStringString.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFTwoListList.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFTwoListList.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFTwoListList.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFTwoListList.java
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 119663af1887a..4886a85948367 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import _root_.test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
+import org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
 
 abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
   override def _sqlContext: SQLContext = TestHive

From 13db11cb08eb90eb0ea3402c9fe0270aa282f247 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Mon, 24 Aug 2015 15:38:54 -0700
Subject: [PATCH 1191/1454] [SPARK-10061] [DOC] ML ensemble docs

User guide for spark.ml GBTs and Random Forests.
The examples are copied from the decision tree guide and modified to run.

I caught some issues I had somehow missed in the tree guide as well.

I have run all examples, including Java ones.  (Of course, I thought I had previously as well...)

CC: mengxr manishamde yanboliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8369 from jkbradley/ml-ensemble-docs.
---
 docs/ml-decision-tree.md |  75 ++-
 docs/ml-ensembles.md     | 952 ++++++++++++++++++++++++++++++++++++++-
 2 files changed, 976 insertions(+), 51 deletions(-)

diff --git a/docs/ml-decision-tree.md b/docs/ml-decision-tree.md
index 958c6f5e4716c..542819e93e6dc 100644
--- a/docs/ml-decision-tree.md
+++ b/docs/ml-decision-tree.md
@@ -30,7 +30,7 @@ The Pipelines API for Decision Trees offers a bit more functionality than the or
 
 Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described in the [Ensembles guide](ml-ensembles.html).
 
-# Inputs and Outputs (Predictions)
+# Inputs and Outputs
 
 We list the input and output (prediction) column types here.
 All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
@@ -234,7 +234,7 @@ IndexToString labelConverter = new IndexToString()
 
 // Chain indexers and tree in a Pipeline
 Pipeline pipeline = new Pipeline()
-  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+  .setStages(new PipelineStage[] {labelIndexer, featureIndexer, dt, labelConverter});
 
 // Train model.  This also runs the indexers.
 PipelineModel model = pipeline.fit(trainingData);
@@ -315,10 +315,13 @@ print treeModel # summary only
 
 ## Regression
 
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the Decision Tree algorithm can recognize.
+
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
+More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.regression.DecisionTreeRegressor).
 
 {% highlight scala %}
 import org.apache.spark.ml.Pipeline
@@ -347,7 +350,7 @@ val dt = new DecisionTreeRegressor()
   .setLabelCol("label")
   .setFeaturesCol("indexedFeatures")
 
-// Chain indexers and tree in a Pipeline
+// Chain indexer and tree in a Pipeline
 val pipeline = new Pipeline()
   .setStages(Array(featureIndexer, dt))
 
@@ -365,9 +368,7 @@ val evaluator = new RegressionEvaluator()
   .setLabelCol("label")
   .setPredictionCol("prediction")
   .setMetricName("rmse")
-// We negate the RMSE value since RegressionEvalutor returns negated RMSE
-// (since evaluation metrics are meant to be maximized by CrossValidator).
-val rmse = - evaluator.evaluate(predictions)
+val rmse = evaluator.evaluate(predictions)
 println("Root Mean Squared Error (RMSE) on test data = " + rmse)
 
 val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel]
@@ -377,14 +378,15 @@ println("Learned regression tree model:\n" + treeModel.toDebugString)
 
 <div data-lang="java" markdown="1">
 
-More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
+More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/regression/DecisionTreeRegressor.html).
 
 {% highlight java %}
 import org.apache.spark.ml.Pipeline;
 import org.apache.spark.ml.PipelineModel;
 import org.apache.spark.ml.PipelineStage;
 import org.apache.spark.ml.evaluation.RegressionEvaluator;
-import org.apache.spark.ml.feature.*;
+import org.apache.spark.ml.feature.VectorIndexer;
+import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel;
 import org.apache.spark.ml.regression.DecisionTreeRegressor;
 import org.apache.spark.mllib.regression.LabeledPoint;
@@ -396,17 +398,12 @@ import org.apache.spark.sql.DataFrame;
 RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
 DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
 
-// Index labels, adding metadata to the label column.
-// Fit on whole dataset to include all labels in index.
-StringIndexerModel labelIndexer = new StringIndexer()
-  .setInputCol("label")
-  .setOutputCol("indexedLabel")
-  .fit(data);
 // Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
 VectorIndexerModel featureIndexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexedFeatures")
-  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .setMaxCategories(4)
   .fit(data);
 
 // Split the data into training and test sets (30% held out for testing)
@@ -416,61 +413,49 @@ DataFrame testData = splits[1];
 
 // Train a DecisionTree model.
 DecisionTreeRegressor dt = new DecisionTreeRegressor()
-  .setLabelCol("indexedLabel")
   .setFeaturesCol("indexedFeatures");
 
-// Convert indexed labels back to original labels.
-IndexToString labelConverter = new IndexToString()
-  .setInputCol("prediction")
-  .setOutputCol("predictedLabel")
-  .setLabels(labelIndexer.labels());
-
-// Chain indexers and tree in a Pipeline
+// Chain indexer and tree in a Pipeline
 Pipeline pipeline = new Pipeline()
-  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+  .setStages(new PipelineStage[] {featureIndexer, dt});
 
-// Train model.  This also runs the indexers.
+// Train model.  This also runs the indexer.
 PipelineModel model = pipeline.fit(trainingData);
 
 // Make predictions.
 DataFrame predictions = model.transform(testData);
 
 // Select example rows to display.
-predictions.select("predictedLabel", "label", "features").show(5);
+predictions.select("label", "features").show(5);
 
 // Select (prediction, true label) and compute test error
 RegressionEvaluator evaluator = new RegressionEvaluator()
-  .setLabelCol("indexedLabel")
+  .setLabelCol("label")
   .setPredictionCol("prediction")
   .setMetricName("rmse");
-// We negate the RMSE value since RegressionEvalutor returns negated RMSE
-// (since evaluation metrics are meant to be maximized by CrossValidator).
-double rmse = - evaluator.evaluate(predictions);
+double rmse = evaluator.evaluate(predictions);
 System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
 
 DecisionTreeRegressionModel treeModel =
-  (DecisionTreeRegressionModel)(model.stages()[2]);
+  (DecisionTreeRegressionModel)(model.stages()[1]);
 System.out.println("Learned regression tree model:\n" + treeModel.toDebugString());
 {% endhighlight %}
 </div>
 
 <div data-lang="python" markdown="1">
 
-More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier).
+More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor).
 
 {% highlight python %}
 from pyspark.ml import Pipeline
 from pyspark.ml.regression import DecisionTreeRegressor
-from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
 from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
 data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
 
-# Index labels, adding metadata to the label column.
-# Fit on whole dataset to include all labels in index.
-labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
 # Automatically identify categorical features, and index them.
 # We specify maxCategories so features with > 4 distinct values are treated as continuous.
 featureIndexer =\
@@ -480,26 +465,24 @@ featureIndexer =\
 (trainingData, testData) = data.randomSplit([0.7, 0.3])
 
 # Train a DecisionTree model.
-dt = DecisionTreeRegressor(labelCol="indexedLabel", featuresCol="indexedFeatures")
+dt = DecisionTreeRegressor(featuresCol="indexedFeatures")
 
-# Chain indexers and tree in a Pipeline
-pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
+# Chain indexer and tree in a Pipeline
+pipeline = Pipeline(stages=[featureIndexer, dt])
 
-# Train model.  This also runs the indexers.
+# Train model.  This also runs the indexer.
 model = pipeline.fit(trainingData)
 
 # Make predictions.
 predictions = model.transform(testData)
 
 # Select example rows to display.
-predictions.select("prediction", "indexedLabel", "features").show(5)
+predictions.select("prediction", "label", "features").show(5)
 
 # Select (prediction, true label) and compute test error
 evaluator = RegressionEvaluator(
-    labelCol="indexedLabel", predictionCol="prediction", metricName="rmse")
-# We negate the RMSE value since RegressionEvalutor returns negated RMSE
-# (since evaluation metrics are meant to be maximized by CrossValidator).
-rmse = -evaluator.evaluate(predictions)
+    labelCol="label", predictionCol="prediction", metricName="rmse")
+rmse = evaluator.evaluate(predictions)
 print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
 
 treeModel = model.stages[1]
diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md
index 9ff50e95fc479..62749909e01dc 100644
--- a/docs/ml-ensembles.md
+++ b/docs/ml-ensembles.md
@@ -11,11 +11,947 @@ displayTitle: <a href="ml-guide.html">ML</a> - Ensembles
 
 An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning)
 is a learning algorithm which creates a model composed of a set of other base models.
-The Pipelines API supports the following ensemble algorithms: [`OneVsRest`](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest)
 
-## OneVsRest
+## Tree Ensembles
 
-[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently.
+The Pipelines API supports two major tree ensemble algorithms: [Random Forests](http://en.wikipedia.org/wiki/Random_forest) and [Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting).
+Both use [MLlib decision trees](ml-decision-tree.html) as their base models.
+
+Users can find more information about ensemble algorithms in the [MLlib Ensemble guide](mllib-ensembles.html).  In this section, we demonstrate the Pipelines API for ensembles.
+
+The main differences between this API and the [original MLlib ensembles API](mllib-ensembles.html) are:
+* support for ML Pipelines
+* separation of classification vs. regression
+* use of DataFrame metadata to distinguish continuous and categorical features
+* a bit more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification.
+
+### Random Forests
+
+[Random forests](http://en.wikipedia.org/wiki/Random_forest)
+are ensembles of [decision trees](ml-decision-tree.html).
+Random forests combine many decision trees in order to reduce the risk of overfitting.
+MLlib supports random forests for binary and multiclass classification and for regression,
+using both continuous and categorical features.
+
+This section gives examples of using random forests with the Pipelines API.
+For more information on the algorithm, please see the [main MLlib docs on random forests](mllib-ensembles.html).
+
+#### Inputs and Outputs
+
+We list the input and output (prediction) column types here.
+All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
+
+##### Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>labelCol</td>
+      <td>Double</td>
+      <td>"label"</td>
+      <td>Label to predict</td>
+    </tr>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+##### Output Columns (Predictions)
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+      <th align="left">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Double</td>
+      <td>"prediction"</td>
+      <td>Predicted label</td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>rawPredictionCol</td>
+      <td>Vector</td>
+      <td>"rawPrediction"</td>
+      <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td>
+      <td>Classification only</td>
+    </tr>
+    <tr>
+      <td>probabilityCol</td>
+      <td>Vector</td>
+      <td>"probability"</td>
+      <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td>
+      <td>Classification only</td>
+    </tr>
+  </tbody>
+</table>
+
+#### Example: Classification
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.classification.RandomForestClassifier
+import org.apache.spark.ml.classification.RandomForestClassificationModel
+import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+val labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data)
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a RandomForest model.
+val rf = new RandomForestClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+  .setNumTrees(10)
+
+// Convert indexed labels back to original labels.
+val labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels)
+
+// Chain indexers and forest in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))
+
+// Train model.  This also runs the indexers.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision")
+val accuracy = evaluator.evaluate(predictions)
+println("Test Error = " + (1.0 - accuracy))
+
+val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
+println("Learned classification forest model:\n" + rfModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/RandomForestClassifier.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.RandomForestClassifier;
+import org.apache.spark.ml.classification.RandomForestClassificationModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a RandomForest model.
+RandomForestClassifier rf = new RandomForestClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures");
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and forest in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision");
+double accuracy = evaluator.evaluate(predictions);
+System.out.println("Test Error = " + (1.0 - accuracy));
+
+RandomForestClassificationModel rfModel =
+  (RandomForestClassificationModel)(model.stages()[2]);
+System.out.println("Learned classification forest model:\n" + rfModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import RandomForestClassifier
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a RandomForest model.
+rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")
+
+# Chain indexers and forest in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = MulticlassClassificationEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+accuracy = evaluator.evaluate(predictions)
+print "Test Error = %g" % (1.0 - accuracy)
+
+rfModel = model.stages[2]
+print rfModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+#### Example: Regression
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.regression.RandomForestRegressor
+import org.apache.spark.ml.regression.RandomForestRegressionModel
+import org.apache.spark.ml.feature.VectorIndexer
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a RandomForest model.
+val rf = new RandomForestRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+
+// Chain indexer and forest in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(featureIndexer, rf))
+
+// Train model.  This also runs the indexer.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse")
+val rmse = evaluator.evaluate(predictions)
+println("Root Mean Squared Error (RMSE) on test data = " + rmse)
+
+val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel]
+println("Learned regression forest model:\n" + rfModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/RandomForestRegressor.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.feature.VectorIndexer;
+import org.apache.spark.ml.feature.VectorIndexerModel;
+import org.apache.spark.ml.regression.RandomForestRegressionModel;
+import org.apache.spark.ml.regression.RandomForestRegressor;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a RandomForest model.
+RandomForestRegressor rf = new RandomForestRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures");
+
+// Chain indexer and forest in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {featureIndexer, rf});
+
+// Train model.  This also runs the indexer.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+RegressionEvaluator evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse");
+double rmse = evaluator.evaluate(predictions);
+System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
+
+RandomForestRegressionModel rfModel =
+  (RandomForestRegressionModel)(model.stages()[1]);
+System.out.println("Learned regression forest model:\n" + rfModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.regression import RandomForestRegressor
+from pyspark.ml.feature import VectorIndexer
+from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a RandomForest model.
+rf = RandomForestRegressor(featuresCol="indexedFeatures")
+
+# Chain indexer and forest in a Pipeline
+pipeline = Pipeline(stages=[featureIndexer, rf])
+
+# Train model.  This also runs the indexer.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = RegressionEvaluator(
+    labelCol="label", predictionCol="prediction", metricName="rmse")
+rmse = evaluator.evaluate(predictions)
+print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
+
+rfModel = model.stages[1]
+print rfModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+### Gradient-Boosted Trees (GBTs)
+
+[Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting)
+are ensembles of [decision trees](ml-decision-tree.html).
+GBTs iteratively train decision trees in order to minimize a loss function.
+MLlib supports GBTs for binary classification and for regression,
+using both continuous and categorical features.
+
+This section gives examples of using GBTs with the Pipelines API.
+For more information on the algorithm, please see the [main MLlib docs on GBTs](mllib-ensembles.html).
+
+#### Inputs and Outputs
+
+We list the input and output (prediction) column types here.
+All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
+
+##### Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>labelCol</td>
+      <td>Double</td>
+      <td>"label"</td>
+      <td>Label to predict</td>
+    </tr>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+Note that `GBTClassifier` currently only supports binary labels.
+
+##### Output Columns (Predictions)
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+      <th align="left">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Double</td>
+      <td>"prediction"</td>
+      <td>Predicted label</td>
+      <td></td>
+    </tr>
+  </tbody>
+</table>
+
+In the future, `GBTClassifier` will also output columns for `rawPrediction` and `probability`, just as `RandomForestClassifier` does.
+
+#### Example: Classification
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.classification.GBTClassifier
+import org.apache.spark.ml.classification.GBTClassificationModel
+import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+val labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data)
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a GBT model.
+val gbt = new GBTClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10)
+
+// Convert indexed labels back to original labels.
+val labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels)
+
+// Chain indexers and GBT in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter))
+
+// Train model.  This also runs the indexers.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision")
+val accuracy = evaluator.evaluate(predictions)
+println("Test Error = " + (1.0 - accuracy))
+
+val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel]
+println("Learned classification GBT model:\n" + gbtModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/GBTClassifier.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.GBTClassifier;
+import org.apache.spark.ml.classification.GBTClassificationModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a GBT model.
+GBTClassifier gbt = new GBTClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10);
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and GBT in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision");
+double accuracy = evaluator.evaluate(predictions);
+System.out.println("Test Error = " + (1.0 - accuracy));
+
+GBTClassificationModel gbtModel =
+  (GBTClassificationModel)(model.stages()[2]);
+System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import GBTClassifier
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a GBT model.
+gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10)
+
+# Chain indexers and GBT in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = MulticlassClassificationEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+accuracy = evaluator.evaluate(predictions)
+print "Test Error = %g" % (1.0 - accuracy)
+
+gbtModel = model.stages[2]
+print gbtModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+#### Example: Regression
+
+Note: For this example dataset, `GBTRegressor` actually only needs 1 iteration, but that will not
+be true in general.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.regression.GBTRegressor
+import org.apache.spark.ml.regression.GBTRegressionModel
+import org.apache.spark.ml.feature.VectorIndexer
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a GBT model.
+val gbt = new GBTRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10)
+
+// Chain indexer and GBT in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(featureIndexer, gbt))
+
+// Train model.  This also runs the indexer.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse")
+val rmse = evaluator.evaluate(predictions)
+println("Root Mean Squared Error (RMSE) on test data = " + rmse)
+
+val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel]
+println("Learned regression GBT model:\n" + gbtModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/GBTRegressor.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.feature.VectorIndexer;
+import org.apache.spark.ml.feature.VectorIndexerModel;
+import org.apache.spark.ml.regression.GBTRegressionModel;
+import org.apache.spark.ml.regression.GBTRegressor;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a GBT model.
+GBTRegressor gbt = new GBTRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10);
+
+// Chain indexer and GBT in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {featureIndexer, gbt});
+
+// Train model.  This also runs the indexer.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+RegressionEvaluator evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse");
+double rmse = evaluator.evaluate(predictions);
+System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
+
+GBTRegressionModel gbtModel =
+  (GBTRegressionModel)(model.stages()[1]);
+System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.regression import GBTRegressor
+from pyspark.ml.feature import VectorIndexer
+from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a GBT model.
+gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10)
+
+# Chain indexer and GBT in a Pipeline
+pipeline = Pipeline(stages=[featureIndexer, gbt])
+
+# Train model.  This also runs the indexer.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = RegressionEvaluator(
+    labelCol="label", predictionCol="prediction", metricName="rmse")
+rmse = evaluator.evaluate(predictions)
+print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
+
+gbtModel = model.stages[1]
+print gbtModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+
+## One-vs-Rest (a.k.a. One-vs-All)
+
+[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently.  It is also known as "One-vs-All."
 
 `OneVsRest` is implemented as an `Estimator`. For the base classifier it takes instances of `Classifier` and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.
 
@@ -28,6 +964,9 @@ The example below demonstrates how to load the
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest) for more details.
+
 {% highlight scala %}
 import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
@@ -64,9 +1003,12 @@ println("label\tfpr\n")
 }
 {% endhighlight %}
 </div>
+
 <div data-lang="java" markdown="1">
-{% highlight java %}
 
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRest.html) for more details.
+
+{% highlight java %}
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegression;
@@ -88,7 +1030,7 @@ RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(),
   "data/mllib/sample_multiclass_classification_data.txt");
 
 DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
-DataFrame[] splits = dataFrame.randomSplit(new double[]{0.7, 0.3}, 12345);
+DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345);
 DataFrame train = splits[0];
 DataFrame test = splits[1];
 

From d7b4c095271c36fcc7f9ded267ecf5ec66fac803 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 24 Aug 2015 16:17:45 -0700
Subject: [PATCH 1192/1454] [SPARK-10190] Fix NPE in CatalystTypeConverters
 Decimal toScala converter

This adds a missing null check to the Decimal `toScala` converter in `CatalystTypeConverters`, fixing an NPE.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8401 from JoshRosen/SPARK-10190.
---
 .../apache/spark/sql/catalyst/CatalystTypeConverters.scala   | 5 ++++-
 .../spark/sql/catalyst/CatalystTypeConvertersSuite.scala     | 4 +++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 8d0c64eae4774..966623ed017ba 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -329,7 +329,10 @@ object CatalystTypeConverters {
         null
       }
     }
-    override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal
+    override def toScala(catalystValue: Decimal): JavaBigDecimal = {
+      if (catalystValue == null) null
+      else catalystValue.toJavaBigDecimal
+    }
     override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal =
       row.getDecimal(column, dataType.precision, dataType.scale).toJavaBigDecimal
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala
index df0f04563edcf..03bb102c67fe7 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala
@@ -32,7 +32,9 @@ class CatalystTypeConvertersSuite extends SparkFunSuite {
     IntegerType,
     LongType,
     FloatType,
-    DoubleType)
+    DoubleType,
+    DecimalType.SYSTEM_DEFAULT,
+    DecimalType.USER_DEFAULT)
 
   test("null handling in rows") {
     val schema = StructType(simpleTypes.map(t => StructField(t.getClass.getName, t)))

From 2bf338c626e9d97ccc033cfadae8b36a82c66fd1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 24 Aug 2015 18:10:51 -0700
Subject: [PATCH 1193/1454] [SPARK-10165] [SQL] Await child resolution in
 ResolveFunctions

Currently, we eagerly attempt to resolve functions, even before their children are resolved.  However, this is not valid in cases where we need to know the types of the input arguments (i.e. when resolving Hive UDFs).

As a fix, this PR delays function resolution until the functions children are resolved.  This change also necessitates a change to the way we resolve aggregate expressions that are not in aggregate operators (e.g., in `HAVING` or `ORDER BY` clauses).  Specifically, we can't assume that these misplaced functions will be resolved, allowing us to differentiate aggregate functions from normal functions.  To compensate for this change we now attempt to resolve these unresolved expressions in the context of the aggregate operator, before checking to see if any aggregate expressions are present.

Author: Michael Armbrust <michael@databricks.com>

Closes #8371 from marmbrus/hiveUDFResolution.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 116 +++++++++++-------
 .../sql/hive/execution/HiveUDFSuite.scala     |   5 +
 2 files changed, 77 insertions(+), 44 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index d0eb9c2c90bdf..1a5de15c61f86 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -78,7 +78,7 @@ class Analyzer(
       ResolveAliases ::
       ExtractWindowExpressions ::
       GlobalAggregates ::
-      UnresolvedHavingClauseAttributes ::
+      ResolveAggregateFunctions ::
       HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
@@ -452,37 +452,6 @@ class Analyzer(
           logDebug(s"Failed to find $missing in ${p.output.mkString(", ")}")
           s // Nothing we can do here. Return original plan.
         }
-      case s @ Sort(ordering, global, a @ Aggregate(grouping, aggs, child))
-          if !s.resolved && a.resolved =>
-        // A small hack to create an object that will allow us to resolve any references that
-        // refer to named expressions that are present in the grouping expressions.
-        val groupingRelation = LocalRelation(
-          grouping.collect { case ne: NamedExpression => ne.toAttribute }
-        )
-
-        // Find sort attributes that are projected away so we can temporarily add them back in.
-        val (newOrdering, missingAttr) = resolveAndFindMissing(ordering, a, groupingRelation)
-
-        // Find aggregate expressions and evaluate them early, since they can't be evaluated in a
-        // Sort.
-        val (withAggsRemoved, aliasedAggregateList) = newOrdering.map {
-          case aggOrdering if aggOrdering.collect { case a: AggregateExpression => a }.nonEmpty =>
-            val aliased = Alias(aggOrdering.child, "_aggOrdering")()
-            (aggOrdering.copy(child = aliased.toAttribute), Some(aliased))
-
-          case other => (other, None)
-        }.unzip
-
-        val missing = missingAttr ++ aliasedAggregateList.flatten
-
-        if (missing.nonEmpty) {
-          // Add missing grouping exprs and then project them away after the sort.
-          Project(a.output,
-            Sort(withAggsRemoved, global,
-              Aggregate(grouping, aggs ++ missing, child)))
-        } else {
-          s // Nothing we can do here. Return original plan.
-        }
     }
 
     /**
@@ -515,6 +484,7 @@ class Analyzer(
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case q: LogicalPlan =>
         q transformExpressions {
+          case u if !u.childrenResolved => u // Skip until children are resolved.
           case u @ UnresolvedFunction(name, children, isDistinct) =>
             withPosition(u) {
               registry.lookupFunction(name, children) match {
@@ -559,21 +529,79 @@ class Analyzer(
   }
 
   /**
-   * This rule finds expressions in HAVING clause filters that depend on
-   * unresolved attributes.  It pushes these expressions down to the underlying
-   * aggregates and then projects them away above the filter.
+   * This rule finds aggregate expressions that are not in an aggregate operator.  For example,
+   * those in a HAVING clause or ORDER BY clause.  These expressions are pushed down to the
+   * underlying aggregate operator and then projected away after the original operator.
    */
-  object UnresolvedHavingClauseAttributes extends Rule[LogicalPlan] {
+  object ResolveAggregateFunctions extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
-      case filter @ Filter(havingCondition, aggregate @ Aggregate(_, originalAggExprs, _))
-          if aggregate.resolved && containsAggregate(havingCondition) =>
-
-        val evaluatedCondition = Alias(havingCondition, "havingCondition")()
-        val aggExprsWithHaving = evaluatedCondition +: originalAggExprs
+      case filter @ Filter(havingCondition,
+             aggregate @ Aggregate(grouping, originalAggExprs, child))
+          if aggregate.resolved && !filter.resolved =>
+
+        // Try resolving the condition of the filter as though it is in the aggregate clause
+        val aggregatedCondition =
+          Aggregate(grouping, Alias(havingCondition, "havingCondition")() :: Nil, child)
+        val resolvedOperator = execute(aggregatedCondition)
+        def resolvedAggregateFilter =
+          resolvedOperator
+            .asInstanceOf[Aggregate]
+            .aggregateExpressions.head
+
+        // If resolution was successful and we see the filter has an aggregate in it, add it to
+        // the original aggregate operator.
+        if (resolvedOperator.resolved && containsAggregate(resolvedAggregateFilter)) {
+          val aggExprsWithHaving = resolvedAggregateFilter +: originalAggExprs
+
+          Project(aggregate.output,
+            Filter(resolvedAggregateFilter.toAttribute,
+              aggregate.copy(aggregateExpressions = aggExprsWithHaving)))
+        } else {
+          filter
+        }
 
-        Project(aggregate.output,
-          Filter(evaluatedCondition.toAttribute,
-            aggregate.copy(aggregateExpressions = aggExprsWithHaving)))
+      case sort @ Sort(sortOrder, global,
+             aggregate @ Aggregate(grouping, originalAggExprs, child))
+        if aggregate.resolved && !sort.resolved =>
+
+        // Try resolving the ordering as though it is in the aggregate clause.
+        try {
+          val aliasedOrder = sortOrder.map(o => Alias(o.child, "aggOrder")())
+          val aggregatedOrdering = Aggregate(grouping, aliasedOrder, child)
+          val resolvedOperator: Aggregate = execute(aggregatedOrdering).asInstanceOf[Aggregate]
+          def resolvedAggregateOrdering = resolvedOperator.aggregateExpressions
+
+          // Expressions that have an aggregate can be pushed down.
+          val needsAggregate = resolvedAggregateOrdering.exists(containsAggregate)
+
+          // Attribute references, that are missing from the order but are present in the grouping
+          // expressions can also be pushed down.
+          val requiredAttributes = resolvedAggregateOrdering.map(_.references).reduce(_ ++ _)
+          val missingAttributes = requiredAttributes -- aggregate.outputSet
+          val validPushdownAttributes =
+            missingAttributes.filter(a => grouping.exists(a.semanticEquals))
+
+          // If resolution was successful and we see the ordering either has an aggregate in it or
+          // it is missing something that is projected away by the aggregate, add the ordering
+          // the original aggregate operator.
+          if (resolvedOperator.resolved && (needsAggregate || validPushdownAttributes.nonEmpty)) {
+            val evaluatedOrderings: Seq[SortOrder] = sortOrder.zip(resolvedAggregateOrdering).map {
+              case (order, evaluated) => order.copy(child = evaluated.toAttribute)
+            }
+            val aggExprsWithOrdering: Seq[NamedExpression] =
+              resolvedAggregateOrdering ++ originalAggExprs
+
+            Project(aggregate.output,
+              Sort(evaluatedOrderings, global,
+                aggregate.copy(aggregateExpressions = aggExprsWithOrdering)))
+          } else {
+            sort
+          }
+        } catch {
+          // Attempting to resolve in the aggregate can result in ambiguity.  When this happens,
+          // just return the original plan.
+          case ae: AnalysisException => sort
+        }
     }
 
     protected def containsAggregate(condition: Expression): Boolean = {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 10f2902e5eef0..b03a35132325d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -276,6 +276,11 @@ class HiveUDFSuite extends QueryTest {
     checkAnswer(
       sql("SELECT testStringStringUDF(\"hello\", s) FROM stringTable"),
       Seq(Row("hello world"), Row("hello goodbye")))
+
+    checkAnswer(
+      sql("SELECT testStringStringUDF(\"\", testStringStringUDF(\"hello\", s)) FROM stringTable"),
+      Seq(Row(" hello world"), Row(" hello goodbye")))
+
     sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUDF")
 
     TestHive.reset()

From 6511bf559b736d8e23ae398901c8d78938e66869 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 24 Aug 2015 18:17:51 -0700
Subject: [PATCH 1194/1454] [SPARK-10118] [SPARKR] [DOCS] Improve SparkR API
 docs for 1.5 release

cc: shivaram

## Summary

- Modify `tdname` of expression functions. i.e. `ascii`: `rdname functions` => `rdname ascii`
- Replace the dynamical function definitions to the static ones because of thir documentations.

## Generated PDF File
https://drive.google.com/file/d/0B9biIZIU47lLX2t6ZjRoRnBTSEU/view?usp=sharing

## JIRA
[[SPARK-10118] Improve SparkR API docs for 1.5 release - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10118)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8386 from yu-iskw/SPARK-10118.
---
 R/create-docs.sh    |    2 +-
 R/pkg/R/column.R    |    5 +-
 R/pkg/R/functions.R | 1603 +++++++++++++++++++++++++++++++++++++++----
 R/pkg/R/generics.R  |  214 +++---
 4 files changed, 1596 insertions(+), 228 deletions(-)

diff --git a/R/create-docs.sh b/R/create-docs.sh
index 6a4687b06ecb9..d2ae160b50021 100755
--- a/R/create-docs.sh
+++ b/R/create-docs.sh
@@ -39,7 +39,7 @@ pushd $FWDIR
 mkdir -p pkg/html
 pushd pkg/html
 
-Rscript -e 'library(SparkR, lib.loc="../../lib"); library(knitr); knit_rd("SparkR")'
+Rscript -e 'libDir <- "../../lib"; library(SparkR, lib.loc=libDir); library(knitr); knit_rd("SparkR", links = tools::findHTMLlinks(paste(libDir, "SparkR", sep="/")))'
 
 popd
 
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 5a07ebd308296..a1f50c383367c 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -169,8 +169,7 @@ setMethod("between", signature(x = "Column"),
 #'
 #' @rdname column
 #'
-#' @examples
-#' \dontrun{
+#' @examples \dontrun{
 #'   cast(df$age, "string")
 #'   cast(df$name, list(type="array", elementType="byte", containsNull = TRUE))
 #' }
@@ -192,7 +191,7 @@ setMethod("cast",
 #'
 #' @rdname column
 #' @return a matched values as a result of comparing with given values.
-#' \dontrun{
+#' @examples \dontrun{
 #'   filter(df, "age in (10, 30)")
 #'   where(df, df$age %in% c(10, 30))
 #' }
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index b5879bd9ad553..d848730e70433 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -18,69 +18,1298 @@
 #' @include generics.R column.R
 NULL
 
-#' @title S4 expression functions for DataFrame column(s)
-#' @description These are expression functions on DataFrame columns
-
-functions1 <- c(
-  "abs", "acos", "approxCountDistinct", "ascii", "asin", "atan",
-  "avg", "base64", "bin", "bitwiseNOT", "cbrt", "ceil", "cos", "cosh", "count",
-  "crc32", "dayofmonth", "dayofyear", "exp", "explode", "expm1", "factorial",
-  "first", "floor", "hex", "hour", "initcap", "isNaN", "last", "last_day",
-  "length", "log", "log10", "log1p", "log2", "lower", "ltrim", "max", "md5",
-  "mean", "min", "minute", "month", "negate", "quarter", "reverse",
-  "rint", "round", "rtrim", "second", "sha1", "signum", "sin", "sinh", "size",
-  "soundex", "sqrt", "sum", "sumDistinct", "tan", "tanh", "toDegrees",
-  "toRadians", "to_date", "trim", "unbase64", "unhex", "upper", "weekofyear",
-  "year")
-functions2 <- c(
-  "atan2", "datediff", "hypot", "levenshtein", "months_between", "nanvl", "pmod")
-
-createFunction1 <- function(name) {
-  setMethod(name,
-            signature(x = "Column"),
-            function(x) {
-              jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc)
-              column(jc)
-            })
-}
-
-createFunction2 <- function(name) {
-  setMethod(name,
-            signature(y = "Column"),
-            function(y, x) {
-              if (class(x) == "Column") {
-                x <- x@jc
-              }
-              jc <- callJStatic("org.apache.spark.sql.functions", name, y@jc, x)
-              column(jc)
-            })
-}
+#' Creates a \code{Column} of literal value.
+#'
+#' The passed in object is returned directly if it is already a \linkS4class{Column}.
+#' If the object is a Scala Symbol, it is converted into a \linkS4class{Column} also.
+#' Otherwise, a new \linkS4class{Column} is created to represent the literal value.
+#'
+#' @family normal_funcs
+#' @rdname lit
+#' @name lit
+#' @export
+setMethod("lit", signature("ANY"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "lit",
+                              ifelse(class(x) == "Column", x@jc, x))
+            column(jc)
+          })
+
+#' abs
+#'
+#' Computes the absolute value.
+#'
+#' @rdname abs
+#' @name abs
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{abs(df$c)}
+setMethod("abs",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "abs", x@jc)
+            column(jc)
+          })
+
+#' acos
+#'
+#' Computes the cosine inverse of the given value; the returned angle is in the range
+#' 0.0 through pi.
+#'
+#' @rdname acos
+#' @name acos
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{acos(df$c)}
+setMethod("acos",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "acos", x@jc)
+            column(jc)
+          })
+
+#' approxCountDistinct
+#'
+#' Aggregate function: returns the approximate number of distinct items in a group.
+#'
+#' @rdname approxCountDistinct
+#' @name approxCountDistinct
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{approxCountDistinct(df$c)}
+setMethod("approxCountDistinct",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc)
+            column(jc)
+          })
+
+#' ascii
+#'
+#' Computes the numeric value of the first character of the string column, and returns the
+#' result as a int column.
+#'
+#' @rdname ascii
+#' @name ascii
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{\dontrun{ascii(df$c)}}
+setMethod("ascii",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "ascii", x@jc)
+            column(jc)
+          })
+
+#' asin
+#'
+#' Computes the sine inverse of the given value; the returned angle is in the range
+#' -pi/2 through pi/2.
+#'
+#' @rdname asin
+#' @name asin
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{asin(df$c)}
+setMethod("asin",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "asin", x@jc)
+            column(jc)
+          })
+
+#' atan
+#'
+#' Computes the tangent inverse of the given value.
+#'
+#' @rdname atan
+#' @name atan
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{atan(df$c)}
+setMethod("atan",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "atan", x@jc)
+            column(jc)
+          })
+
+#' avg
+#'
+#' Aggregate function: returns the average of the values in a group.
+#'
+#' @rdname avg
+#' @name avg
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{avg(df$c)}
+setMethod("avg",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "avg", x@jc)
+            column(jc)
+          })
+
+#' base64
+#'
+#' Computes the BASE64 encoding of a binary column and returns it as a string column.
+#' This is the reverse of unbase64.
+#'
+#' @rdname base64
+#' @name base64
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{base64(df$c)}
+setMethod("base64",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "base64", x@jc)
+            column(jc)
+          })
+
+#' bin
+#'
+#' An expression that returns the string representation of the binary value of the given long
+#' column. For example, bin("12") returns "1100".
+#'
+#' @rdname bin
+#' @name bin
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{bin(df$c)}
+setMethod("bin",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "bin", x@jc)
+            column(jc)
+          })
+
+#' bitwiseNOT
+#'
+#' Computes bitwise NOT.
+#'
+#' @rdname bitwiseNOT
+#' @name bitwiseNOT
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{bitwiseNOT(df$c)}
+setMethod("bitwiseNOT",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "bitwiseNOT", x@jc)
+            column(jc)
+          })
+
+#' cbrt
+#'
+#' Computes the cube-root of the given value.
+#'
+#' @rdname cbrt
+#' @name cbrt
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{cbrt(df$c)}
+setMethod("cbrt",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "cbrt", x@jc)
+            column(jc)
+          })
+
+#' ceil
+#'
+#' Computes the ceiling of the given value.
+#'
+#' @rdname ceil
+#' @name ceil
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{ceil(df$c)}
+setMethod("ceil",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "ceil", x@jc)
+            column(jc)
+          })
+
+#' cos
+#'
+#' Computes the cosine of the given value.
+#'
+#' @rdname cos
+#' @name cos
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{cos(df$c)}
+setMethod("cos",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "cos", x@jc)
+            column(jc)
+          })
+
+#' cosh
+#'
+#' Computes the hyperbolic cosine of the given value.
+#'
+#' @rdname cosh
+#' @name cosh
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{cosh(df$c)}
+setMethod("cosh",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "cosh", x@jc)
+            column(jc)
+          })
+
+#' count
+#'
+#' Aggregate function: returns the number of items in a group.
+#'
+#' @rdname count
+#' @name count
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{count(df$c)}
+setMethod("count",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "count", x@jc)
+            column(jc)
+          })
+
+#' crc32
+#'
+#' Calculates the cyclic redundancy check value  (CRC32) of a binary column and
+#' returns the value as a bigint.
+#'
+#' @rdname crc32
+#' @name crc32
+#' @family misc_funcs
+#' @export
+#' @examples \dontrun{crc32(df$c)}
+setMethod("crc32",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "crc32", x@jc)
+            column(jc)
+          })
+
+#' dayofmonth
+#'
+#' Extracts the day of the month as an integer from a given date/timestamp/string.
+#'
+#' @rdname dayofmonth
+#' @name dayofmonth
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{dayofmonth(df$c)}
+setMethod("dayofmonth",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "dayofmonth", x@jc)
+            column(jc)
+          })
+
+#' dayofyear
+#'
+#' Extracts the day of the year as an integer from a given date/timestamp/string.
+#'
+#' @rdname dayofyear
+#' @name dayofyear
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{dayofyear(df$c)}
+setMethod("dayofyear",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "dayofyear", x@jc)
+            column(jc)
+          })
+
+#' exp
+#'
+#' Computes the exponential of the given value.
+#'
+#' @rdname exp
+#' @name exp
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{exp(df$c)}
+setMethod("exp",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "exp", x@jc)
+            column(jc)
+          })
+
+#' explode
+#'
+#' Creates a new row for each element in the given array or map column.
+#'
+#' @rdname explode
+#' @name explode
+#' @family collection_funcs
+#' @export
+#' @examples \dontrun{explode(df$c)}
+setMethod("explode",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "explode", x@jc)
+            column(jc)
+          })
+
+#' expm1
+#'
+#' Computes the exponential of the given value minus one.
+#'
+#' @rdname expm1
+#' @name expm1
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{expm1(df$c)}
+setMethod("expm1",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "expm1", x@jc)
+            column(jc)
+          })
+
+#' factorial
+#'
+#' Computes the factorial of the given value.
+#'
+#' @rdname factorial
+#' @name factorial
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{factorial(df$c)}
+setMethod("factorial",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "factorial", x@jc)
+            column(jc)
+          })
+
+#' first
+#'
+#' Aggregate function: returns the first value in a group.
+#'
+#' @rdname first
+#' @name first
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{first(df$c)}
+setMethod("first",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "first", x@jc)
+            column(jc)
+          })
+
+#' floor
+#'
+#' Computes the floor of the given value.
+#'
+#' @rdname floor
+#' @name floor
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{floor(df$c)}
+setMethod("floor",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "floor", x@jc)
+            column(jc)
+          })
+
+#' hex
+#'
+#' Computes hex value of the given column.
+#'
+#' @rdname hex
+#' @name hex
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{hex(df$c)}
+setMethod("hex",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "hex", x@jc)
+            column(jc)
+          })
+
+#' hour
+#'
+#' Extracts the hours as an integer from a given date/timestamp/string.
+#'
+#' @rdname hour
+#' @name hour
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{hour(df$c)}
+setMethod("hour",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "hour", x@jc)
+            column(jc)
+          })
+
+#' initcap
+#'
+#' Returns a new string column by converting the first letter of each word to uppercase.
+#' Words are delimited by whitespace.
+#'
+#' For example, "hello world" will become "Hello World".
+#'
+#' @rdname initcap
+#' @name initcap
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{initcap(df$c)}
+setMethod("initcap",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "initcap", x@jc)
+            column(jc)
+          })
+
+#' isNaN
+#'
+#' Return true iff the column is NaN.
+#'
+#' @rdname isNaN
+#' @name isNaN
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{isNaN(df$c)}
+setMethod("isNaN",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "isNaN", x@jc)
+            column(jc)
+          })
+
+#' last
+#'
+#' Aggregate function: returns the last value in a group.
+#'
+#' @rdname last
+#' @name last
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{last(df$c)}
+setMethod("last",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "last", x@jc)
+            column(jc)
+          })
+
+#' last_day
+#'
+#' Given a date column, returns the last day of the month which the given date belongs to.
+#' For example, input "2015-07-27" returns "2015-07-31" since July 31 is the last day of the
+#' month in July 2015.
+#'
+#' @rdname last_day
+#' @name last_day
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{last_day(df$c)}
+setMethod("last_day",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "last_day", x@jc)
+            column(jc)
+          })
+
+#' length
+#'
+#' Computes the length of a given string or binary column.
+#'
+#' @rdname length
+#' @name length
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{length(df$c)}
+setMethod("length",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "length", x@jc)
+            column(jc)
+          })
+
+#' log
+#'
+#' Computes the natural logarithm of the given value.
+#'
+#' @rdname log
+#' @name log
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log(df$c)}
+setMethod("log",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log", x@jc)
+            column(jc)
+          })
+
+#' log10
+#'
+#' Computes the logarithm of the given value in base 10.
+#'
+#' @rdname log10
+#' @name log10
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log10(df$c)}
+setMethod("log10",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log10", x@jc)
+            column(jc)
+          })
+
+#' log1p
+#'
+#' Computes the natural logarithm of the given value plus one.
+#'
+#' @rdname log1p
+#' @name log1p
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log1p(df$c)}
+setMethod("log1p",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log1p", x@jc)
+            column(jc)
+          })
+
+#' log2
+#'
+#' Computes the logarithm of the given column in base 2.
+#'
+#' @rdname log2
+#' @name log2
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log2(df$c)}
+setMethod("log2",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log2", x@jc)
+            column(jc)
+          })
+
+#' lower
+#'
+#' Converts a string column to lower case.
+#'
+#' @rdname lower
+#' @name lower
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{lower(df$c)}
+setMethod("lower",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "lower", x@jc)
+            column(jc)
+          })
+
+#' ltrim
+#'
+#' Trim the spaces from left end for the specified string value.
+#'
+#' @rdname ltrim
+#' @name ltrim
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{ltrim(df$c)}
+setMethod("ltrim",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "ltrim", x@jc)
+            column(jc)
+          })
+
+#' max
+#'
+#' Aggregate function: returns the maximum value of the expression in a group.
+#'
+#' @rdname max
+#' @name max
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{max(df$c)}
+setMethod("max",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "max", x@jc)
+            column(jc)
+          })
+
+#' md5
+#'
+#' Calculates the MD5 digest of a binary column and returns the value
+#' as a 32 character hex string.
+#'
+#' @rdname md5
+#' @name md5
+#' @family misc_funcs
+#' @export
+#' @examples \dontrun{md5(df$c)}
+setMethod("md5",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "md5", x@jc)
+            column(jc)
+          })
+
+#' mean
+#'
+#' Aggregate function: returns the average of the values in a group.
+#' Alias for avg.
+#'
+#' @rdname mean
+#' @name mean
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{mean(df$c)}
+setMethod("mean",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "mean", x@jc)
+            column(jc)
+          })
+
+#' min
+#'
+#' Aggregate function: returns the minimum value of the expression in a group.
+#'
+#' @rdname min
+#' @name min
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{min(df$c)}
+setMethod("min",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "min", x@jc)
+            column(jc)
+          })
+
+#' minute
+#'
+#' Extracts the minutes as an integer from a given date/timestamp/string.
+#'
+#' @rdname minute
+#' @name minute
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{minute(df$c)}
+setMethod("minute",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "minute", x@jc)
+            column(jc)
+          })
+
+#' month
+#'
+#' Extracts the month as an integer from a given date/timestamp/string.
+#'
+#' @rdname month
+#' @name month
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{month(df$c)}
+setMethod("month",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "month", x@jc)
+            column(jc)
+          })
+
+#' negate
+#'
+#' Unary minus, i.e. negate the expression.
+#'
+#' @rdname negate
+#' @name negate
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{negate(df$c)}
+setMethod("negate",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "negate", x@jc)
+            column(jc)
+          })
 
-createFunctions <- function() {
-  for (name in functions1) {
-    createFunction1(name)
-  }
-  for (name in functions2) {
-    createFunction2(name)
-  }
-}
+#' quarter
+#'
+#' Extracts the quarter as an integer from a given date/timestamp/string.
+#'
+#' @rdname quarter
+#' @name quarter
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{quarter(df$c)}
+setMethod("quarter",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "quarter", x@jc)
+            column(jc)
+          })
 
-createFunctions()
+#' reverse
+#'
+#' Reverses the string column and returns it as a new string column.
+#'
+#' @rdname reverse
+#' @name reverse
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{reverse(df$c)}
+setMethod("reverse",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "reverse", x@jc)
+            column(jc)
+          })
 
-#' @rdname functions
-#' @return Creates a Column class of literal value.
-setMethod("lit", signature("ANY"),
+#' rint
+#'
+#' Returns the double value that is closest in value to the argument and
+#' is equal to a mathematical integer.
+#'
+#' @rdname rint
+#' @name rint
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{rint(df$c)}
+setMethod("rint",
+          signature(x = "Column"),
           function(x) {
-            jc <- callJStatic("org.apache.spark.sql.functions",
-                              "lit",
-                              ifelse(class(x) == "Column", x@jc, x))
+            jc <- callJStatic("org.apache.spark.sql.functions", "rint", x@jc)
+            column(jc)
+          })
+
+#' round
+#'
+#' Returns the value of the column `e` rounded to 0 decimal places.
+#'
+#' @rdname round
+#' @name round
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{round(df$c)}
+setMethod("round",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "round", x@jc)
+            column(jc)
+          })
+
+#' rtrim
+#'
+#' Trim the spaces from right end for the specified string value.
+#'
+#' @rdname rtrim
+#' @name rtrim
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{rtrim(df$c)}
+setMethod("rtrim",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "rtrim", x@jc)
+            column(jc)
+          })
+
+#' second
+#'
+#' Extracts the seconds as an integer from a given date/timestamp/string.
+#'
+#' @rdname second
+#' @name second
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{second(df$c)}
+setMethod("second",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "second", x@jc)
+            column(jc)
+          })
+
+#' sha1
+#'
+#' Calculates the SHA-1 digest of a binary column and returns the value
+#' as a 40 character hex string.
+#'
+#' @rdname sha1
+#' @name sha1
+#' @family misc_funcs
+#' @export
+#' @examples \dontrun{sha1(df$c)}
+setMethod("sha1",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sha1", x@jc)
+            column(jc)
+          })
+
+#' signum
+#'
+#' Computes the signum of the given value.
+#'
+#' @rdname signum
+#' @name signum
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{signum(df$c)}
+setMethod("signum",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "signum", x@jc)
+            column(jc)
+          })
+
+#' sin
+#'
+#' Computes the sine of the given value.
+#'
+#' @rdname sin
+#' @name sin
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{sin(df$c)}
+setMethod("sin",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sin", x@jc)
+            column(jc)
+          })
+
+#' sinh
+#'
+#' Computes the hyperbolic sine of the given value.
+#'
+#' @rdname sinh
+#' @name sinh
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{sinh(df$c)}
+setMethod("sinh",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sinh", x@jc)
+            column(jc)
+          })
+
+#' size
+#'
+#' Returns length of array or map.
+#'
+#' @rdname size
+#' @name size
+#' @family collection_funcs
+#' @export
+#' @examples \dontrun{size(df$c)}
+setMethod("size",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "size", x@jc)
+            column(jc)
+          })
+
+#' soundex
+#'
+#' Return the soundex code for the specified expression.
+#'
+#' @rdname soundex
+#' @name soundex
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{soundex(df$c)}
+setMethod("soundex",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "soundex", x@jc)
+            column(jc)
+          })
+
+#' sqrt
+#'
+#' Computes the square root of the specified float value.
+#'
+#' @rdname sqrt
+#' @name sqrt
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{sqrt(df$c)}
+setMethod("sqrt",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sqrt", x@jc)
+            column(jc)
+          })
+
+#' sum
+#'
+#' Aggregate function: returns the sum of all values in the expression.
+#'
+#' @rdname sum
+#' @name sum
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{sum(df$c)}
+setMethod("sum",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sum", x@jc)
+            column(jc)
+          })
+
+#' sumDistinct
+#'
+#' Aggregate function: returns the sum of distinct values in the expression.
+#'
+#' @rdname sumDistinct
+#' @name sumDistinct
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{sumDistinct(df$c)}
+setMethod("sumDistinct",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sumDistinct", x@jc)
+            column(jc)
+          })
+
+#' tan
+#'
+#' Computes the tangent of the given value.
+#'
+#' @rdname tan
+#' @name tan
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{tan(df$c)}
+setMethod("tan",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "tan", x@jc)
+            column(jc)
+          })
+
+#' tanh
+#'
+#' Computes the hyperbolic tangent of the given value.
+#'
+#' @rdname tanh
+#' @name tanh
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{tanh(df$c)}
+setMethod("tanh",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "tanh", x@jc)
+            column(jc)
+          })
+
+#' toDegrees
+#'
+#' Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
+#'
+#' @rdname toDegrees
+#' @name toDegrees
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{toDegrees(df$c)}
+setMethod("toDegrees",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "toDegrees", x@jc)
+            column(jc)
+          })
+
+#' toRadians
+#'
+#' Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
+#'
+#' @rdname toRadians
+#' @name toRadians
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{toRadians(df$c)}
+setMethod("toRadians",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "toRadians", x@jc)
+            column(jc)
+          })
+
+#' to_date
+#'
+#' Converts the column into DateType.
+#'
+#' @rdname to_date
+#' @name to_date
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{to_date(df$c)}
+setMethod("to_date",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "to_date", x@jc)
+            column(jc)
+          })
+
+#' trim
+#'
+#' Trim the spaces from both ends for the specified string column.
+#'
+#' @rdname trim
+#' @name trim
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{trim(df$c)}
+setMethod("trim",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "trim", x@jc)
+            column(jc)
+          })
+
+#' unbase64
+#'
+#' Decodes a BASE64 encoded string column and returns it as a binary column.
+#' This is the reverse of base64.
+#'
+#' @rdname unbase64
+#' @name unbase64
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{unbase64(df$c)}
+setMethod("unbase64",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unbase64", x@jc)
+            column(jc)
+          })
+
+#' unhex
+#'
+#' Inverse of hex. Interprets each pair of characters as a hexadecimal number
+#' and converts to the byte representation of number.
+#'
+#' @rdname unhex
+#' @name unhex
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{unhex(df$c)}
+setMethod("unhex",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unhex", x@jc)
+            column(jc)
+          })
+
+#' upper
+#'
+#' Converts a string column to upper case.
+#'
+#' @rdname upper
+#' @name upper
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{upper(df$c)}
+setMethod("upper",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "upper", x@jc)
+            column(jc)
+          })
+
+#' weekofyear
+#'
+#' Extracts the week number as an integer from a given date/timestamp/string.
+#'
+#' @rdname weekofyear
+#' @name weekofyear
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{weekofyear(df$c)}
+setMethod("weekofyear",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "weekofyear", x@jc)
+            column(jc)
+          })
+
+#' year
+#'
+#' Extracts the year as an integer from a given date/timestamp/string.
+#'
+#' @rdname year
+#' @name year
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{year(df$c)}
+setMethod("year",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "year", x@jc)
             column(jc)
           })
 
+#' atan2
+#'
+#' Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+#' polar coordinates (r, theta).
+#'
+#' @rdname atan2
+#' @name atan2
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{atan2(df$c, x)}
+setMethod("atan2", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "atan2", y@jc, x)
+            column(jc)
+          })
+
+#' datediff
+#'
+#' Returns the number of days from `start` to `end`.
+#'
+#' @rdname datediff
+#' @name datediff
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{datediff(df$c, x)}
+setMethod("datediff", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "datediff", y@jc, x)
+            column(jc)
+          })
+
+#' hypot
+#'
+#' Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+#'
+#' @rdname hypot
+#' @name hypot
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{hypot(df$c, x)}
+setMethod("hypot", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "hypot", y@jc, x)
+            column(jc)
+          })
+
+#' levenshtein
+#'
+#' Computes the Levenshtein distance of the two given string columns.
+#'
+#' @rdname levenshtein
+#' @name levenshtein
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{levenshtein(df$c, x)}
+setMethod("levenshtein", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "levenshtein", y@jc, x)
+            column(jc)
+          })
+
+#' months_between
+#'
+#' Returns number of months between dates `date1` and `date2`.
+#'
+#' @rdname months_between
+#' @name months_between
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{months_between(df$c, x)}
+setMethod("months_between", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "months_between", y@jc, x)
+            column(jc)
+          })
+
+#' nanvl
+#'
+#' Returns col1 if it is not NaN, or col2 if col1 is NaN.
+#' hhBoth inputs should be floating point columns (DoubleType or FloatType).
+#'
+#' @rdname nanvl
+#' @name nanvl
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{nanvl(df$c, x)}
+setMethod("nanvl", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "nanvl", y@jc, x)
+            column(jc)
+          })
+
+#' pmod
+#'
+#' Returns the positive value of dividend mod divisor.
+#'
+#' @rdname pmod
+#' @name pmod
+#' @docType methods
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{pmod(df$c, x)}
+setMethod("pmod", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "pmod", y@jc, x)
+            column(jc)
+          })
+
+
 #' Approx Count Distinct
 #'
-#' @rdname functions
+#' @family agg_funcs
+#' @rdname approxCountDistinct
+#' @name approxCountDistinct
 #' @return the approximate number of distinct items in a group.
+#' @export
 setMethod("approxCountDistinct",
           signature(x = "Column"),
           function(x, rsd = 0.95) {
@@ -90,8 +1319,11 @@ setMethod("approxCountDistinct",
 
 #' Count Distinct
 #'
-#' @rdname functions
+#' @family agg_funcs
+#' @rdname countDistinct
+#' @name countDistinct
 #' @return the number of distinct items in a group.
+#' @export
 setMethod("countDistinct",
           signature(x = "Column"),
           function(x, ...) {
@@ -103,8 +1335,15 @@ setMethod("countDistinct",
             column(jc)
           })
 
-#' @rdname functions
-#' @return Concatenates multiple input string columns together into a single string column.
+
+#' concat
+#'
+#' Concatenates multiple input string columns together into a single string column.
+#'
+#' @family string_funcs
+#' @rdname concat
+#' @name concat
+#' @export
 setMethod("concat",
           signature(x = "Column"),
           function(x, ...) {
@@ -113,9 +1352,15 @@ setMethod("concat",
             column(jc)
           })
 
-#' @rdname functions
-#' @return Returns the greatest value of the list of column names, skipping null values.
-#'         This function takes at least 2 parameters. It will return null if all parameters are null.
+#' greatest
+#'
+#' Returns the greatest value of the list of column names, skipping null values.
+#' This function takes at least 2 parameters. It will return null if all parameters are null.
+#'
+#' @family normal_funcs
+#' @rdname greatest
+#' @name greatest
+#' @export
 setMethod("greatest",
           signature(x = "Column"),
           function(x, ...) {
@@ -125,9 +1370,15 @@ setMethod("greatest",
             column(jc)
           })
 
-#' @rdname functions
-#' @return Returns the least value of the list of column names, skipping null values.
-#'         This function takes at least 2 parameters. It will return null iff all parameters are null.
+#' least
+#'
+#' Returns the least value of the list of column names, skipping null values.
+#' This function takes at least 2 parameters. It will return null iff all parameters are null.
+#'
+#' @family normal_funcs
+#' @rdname least
+#' @name least
+#' @export
 setMethod("least",
           signature(x = "Column"),
           function(x, ...) {
@@ -137,30 +1388,58 @@ setMethod("least",
             column(jc)
           })
 
-#' @rdname functions
+#' ceiling
+#'
+#' Computes the ceiling of the given value.
+#'
+#' @family math_funcs
+#' @rdname ceil
+#' @name ceil
 #' @aliases ceil
+#' @export
 setMethod("ceiling",
           signature(x = "Column"),
           function(x) {
             ceil(x)
           })
 
-#' @rdname functions
+#' sign
+#'
+#' Computes the signum of the given value.
+#'
+#' @family math_funcs
+#' @rdname signum
+#' @name signum
 #' @aliases signum
+#' @export
 setMethod("sign", signature(x = "Column"),
           function(x) {
             signum(x)
           })
 
-#' @rdname functions
+#' n_distinct
+#'
+#' Aggregate function: returns the number of distinct items in a group.
+#'
+#' @family agg_funcs
+#' @rdname countDistinct
+#' @name countDistinct
 #' @aliases countDistinct
+#' @export
 setMethod("n_distinct", signature(x = "Column"),
           function(x, ...) {
             countDistinct(x, ...)
           })
 
-#' @rdname functions
+#' n
+#'
+#' Aggregate function: returns the number of items in a group.
+#'
+#' @family agg_funcs
+#' @rdname count
+#' @name count
 #' @aliases count
+#' @export
 setMethod("n", signature(x = "Column"),
           function(x) {
             count(x)
@@ -171,13 +1450,16 @@ setMethod("n", signature(x = "Column"),
 #' Converts a date/timestamp/string to a value of string in the format specified by the date
 #' format given by the second argument.
 #'
-#' A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
-#' pattern letters of `java.text.SimpleDateFormat` can be used.
+#' A pattern could be for instance \preformatted{dd.MM.yyyy} and could return a string like '18.03.1993'. All
+#' pattern letters of \code{java.text.SimpleDateFormat} can be used.
 #'
-#' NOTE: Use when ever possible specialized functions like `year`. These benefit from a
+#' NOTE: Use when ever possible specialized functions like \code{year}. These benefit from a
 #' specialized implementation.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname date_format
+#' @name date_format
+#' @export
 setMethod("date_format", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "date_format", y@jc, x)
@@ -188,7 +1470,10 @@ setMethod("date_format", signature(y = "Column", x = "character"),
 #'
 #' Assumes given timestamp is UTC and converts to given timezone.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname from_utc_timestamp
+#' @name from_utc_timestamp
+#' @export
 setMethod("from_utc_timestamp", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "from_utc_timestamp", y@jc, x)
@@ -203,7 +1488,10 @@ setMethod("from_utc_timestamp", signature(y = "Column", x = "character"),
 #' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
 #' could not be found in str.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname instr
+#' @name instr
+#' @export
 setMethod("instr", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "instr", y@jc, x)
@@ -215,13 +1503,16 @@ setMethod("instr", signature(y = "Column", x = "character"),
 #' Given a date column, returns the first date which is later than the value of the date column
 #' that is on the specified day of the week.
 #'
-#' For example, `next <- day('2015-07-27', "Sunday")` returns 2015-08-02 because that is the first
+#' For example, \code{next_day('2015-07-27', "Sunday")} returns 2015-08-02 because that is the first
 #' Sunday after 2015-07-27.
 #'
 #' Day of the week parameter is case insensitive, and accepts:
 #' "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname next_day
+#' @name next_day
+#' @export
 setMethod("next_day", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "next_day", y@jc, x)
@@ -232,7 +1523,10 @@ setMethod("next_day", signature(y = "Column", x = "character"),
 #'
 #' Assumes given timestamp is in given timezone and converts to UTC.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname to_utc_timestamp
+#' @name to_utc_timestamp
+#' @export
 setMethod("to_utc_timestamp", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "to_utc_timestamp", y@jc, x)
@@ -243,7 +1537,11 @@ setMethod("to_utc_timestamp", signature(y = "Column", x = "character"),
 #'
 #' Returns the date that is numMonths after startDate.
 #'
-#' @rdname functions
+#' @name add_months
+#' @family datetime_funcs
+#' @rdname add_months
+#' @name add_months
+#' @export
 setMethod("add_months", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "add_months", y@jc, as.integer(x))
@@ -254,7 +1552,10 @@ setMethod("add_months", signature(y = "Column", x = "numeric"),
 #'
 #' Returns the date that is `days` days after `start`
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname date_add
+#' @name date_add
+#' @export
 setMethod("date_add", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "date_add", y@jc, as.integer(x))
@@ -265,7 +1566,10 @@ setMethod("date_add", signature(y = "Column", x = "numeric"),
 #'
 #' Returns the date that is `days` days before `start`
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname date_sub
+#' @name date_sub
+#' @export
 setMethod("date_sub", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "date_sub", y@jc, as.integer(x))
@@ -280,7 +1584,10 @@ setMethod("date_sub", signature(y = "Column", x = "numeric"),
 #' If d is 0, the result has no decimal point or fractional part.
 #' If d < 0, the result will be null.'
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname format_number
+#' @name format_number
+#' @export
 setMethod("format_number", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -294,9 +1601,12 @@ setMethod("format_number", signature(y = "Column", x = "numeric"),
 #' Calculates the SHA-2 family of hash functions of a binary column and
 #' returns the value as a hex string.
 #'
-#' @rdname functions
 #' @param y column to compute SHA-2 on.
 #' @param x one of 224, 256, 384, or 512.
+#' @family misc_funcs
+#' @rdname sha2
+#' @name sha2
+#' @export
 setMethod("sha2", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "sha2", y@jc, as.integer(x))
@@ -308,7 +1618,10 @@ setMethod("sha2", signature(y = "Column", x = "numeric"),
 #' Shift the the given value numBits left. If the given value is a long value, this function
 #' will return a long value else it will return an integer value.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname shiftLeft
+#' @name shiftLeft
+#' @export
 setMethod("shiftLeft", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -322,7 +1635,10 @@ setMethod("shiftLeft", signature(y = "Column", x = "numeric"),
 #' Shift the the given value numBits right. If the given value is a long value, it will return
 #' a long value else it will return an integer value.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname shiftRight
+#' @name shiftRight
+#' @export
 setMethod("shiftRight", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -336,7 +1652,10 @@ setMethod("shiftRight", signature(y = "Column", x = "numeric"),
 #' Unsigned shift the the given value numBits right. If the given value is a long value,
 #' it will return a long value else it will return an integer value.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname shiftRightUnsigned
+#' @name shiftRightUnsigned
+#' @export
 setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -350,7 +1669,10 @@ setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
 #' Concatenates multiple input string columns together into a single string column,
 #' using the given separator.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname concat_ws
+#' @name concat_ws
+#' @export
 setMethod("concat_ws", signature(sep = "character", x = "Column"),
           function(sep, x, ...) {
             jcols <- listToSeq(lapply(list(x, ...), function(x) { x@jc }))
@@ -362,7 +1684,10 @@ setMethod("concat_ws", signature(sep = "character", x = "Column"),
 #'
 #' Convert a number in a string column from one base to another.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname conv
+#' @name conv
+#' @export
 setMethod("conv", signature(x = "Column", fromBase = "numeric", toBase = "numeric"),
           function(x, fromBase, toBase) {
             fromBase <- as.integer(fromBase)
@@ -378,7 +1703,10 @@ setMethod("conv", signature(x = "Column", fromBase = "numeric", toBase = "numeri
 #' Parses the expression string into the column that it represents, similar to
 #' DataFrame.selectExpr
 #'
-#' @rdname functions
+#' @family normal_funcs
+#' @rdname expr
+#' @name expr
+#' @export
 setMethod("expr", signature(x = "character"),
           function(x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "expr", x)
@@ -389,7 +1717,10 @@ setMethod("expr", signature(x = "character"),
 #'
 #' Formats the arguments in printf-style and returns the result as a string column.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname format_string
+#' @name format_string
+#' @export
 setMethod("format_string", signature(format = "character", x = "Column"),
           function(format, x, ...) {
             jcols <- listToSeq(lapply(list(x, ...), function(arg) { arg@jc }))
@@ -405,7 +1736,10 @@ setMethod("format_string", signature(format = "character", x = "Column"),
 #' representing the timestamp of that moment in the current system time zone in the given
 #' format.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname from_unixtime
+#' @name from_unixtime
+#' @export
 setMethod("from_unixtime", signature(x = "Column"),
           function(x, format = "yyyy-MM-dd HH:mm:ss") {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -420,7 +1754,10 @@ setMethod("from_unixtime", signature(x = "Column"),
 #' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
 #' could not be found in str.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname locate
+#' @name locate
+#' @export
 setMethod("locate", signature(substr = "character", str = "Column"),
           function(substr, str, pos = 0) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -433,7 +1770,10 @@ setMethod("locate", signature(substr = "character", str = "Column"),
 #'
 #' Left-pad the string column with
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname lpad
+#' @name lpad
+#' @export
 setMethod("lpad", signature(x = "Column", len = "numeric", pad = "character"),
           function(x, len, pad) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -446,12 +1786,19 @@ setMethod("lpad", signature(x = "Column", len = "numeric", pad = "character"),
 #'
 #' Generate a random column with i.i.d. samples from U[0.0, 1.0].
 #'
-#' @rdname functions
+#' @family normal_funcs
+#' @rdname rand
+#' @name rand
+#' @export
 setMethod("rand", signature(seed = "missing"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "rand")
             column(jc)
           })
+#' @family normal_funcs
+#' @rdname rand
+#' @name rand
+#' @export
 setMethod("rand", signature(seed = "numeric"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "rand", as.integer(seed))
@@ -462,12 +1809,19 @@ setMethod("rand", signature(seed = "numeric"),
 #'
 #' Generate a column with i.i.d. samples from the standard normal distribution.
 #'
-#' @rdname functions
+#' @family normal_funcs
+#' @rdname randn
+#' @name randn
+#' @export
 setMethod("randn", signature(seed = "missing"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "randn")
             column(jc)
           })
+#' @family normal_funcs
+#' @rdname randn
+#' @name randn
+#' @export
 setMethod("randn", signature(seed = "numeric"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "randn", as.integer(seed))
@@ -478,7 +1832,10 @@ setMethod("randn", signature(seed = "numeric"),
 #'
 #' Extract a specific(idx) group identified by a java regex, from the specified string column.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname regexp_extract
+#' @name regexp_extract
+#' @export
 setMethod("regexp_extract",
           signature(x = "Column", pattern = "character", idx = "numeric"),
           function(x, pattern, idx) {
@@ -492,7 +1849,10 @@ setMethod("regexp_extract",
 #'
 #' Replace all substrings of the specified string value that match regexp with rep.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname regexp_replace
+#' @name regexp_replace
+#' @export
 setMethod("regexp_replace",
           signature(x = "Column", pattern = "character", replacement = "character"),
           function(x, pattern, replacement) {
@@ -506,7 +1866,10 @@ setMethod("regexp_replace",
 #'
 #' Right-padded with pad to a length of len.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname rpad
+#' @name rpad
+#' @export
 setMethod("rpad", signature(x = "Column", len = "numeric", pad = "character"),
           function(x, len, pad) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -522,7 +1885,10 @@ setMethod("rpad", signature(x = "Column", len = "numeric", pad = "character"),
 #' returned. If count is negative, every to the right of the final delimiter (counting from the
 #' right) is returned. substring <- index performs a case-sensitive match when searching for delim.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname substring_index
+#' @name substring_index
+#' @export
 setMethod("substring_index",
           signature(x = "Column", delim = "character", count = "numeric"),
           function(x, delim, count) {
@@ -539,7 +1905,10 @@ setMethod("substring_index",
 #' The translate will happen when any character in the string matching with the character
 #' in the matchingString.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname translate
+#' @name translate
+#' @export
 setMethod("translate",
           signature(x = "Column", matchingString = "character", replaceString = "character"),
           function(x, matchingString, replaceString) {
@@ -552,30 +1921,28 @@ setMethod("translate",
 #'
 #' Gets current Unix timestamp in seconds.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname unix_timestamp
+#' @name unix_timestamp
+#' @export
 setMethod("unix_timestamp", signature(x = "missing", format = "missing"),
           function(x, format) {
             jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp")
             column(jc)
           })
-#' unix_timestamp
-#'
-#' Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds),
-#' using the default timezone and the default locale, return null if fail.
-#'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname unix_timestamp
+#' @name unix_timestamp
+#' @export
 setMethod("unix_timestamp", signature(x = "Column", format = "missing"),
           function(x, format) {
             jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc)
             column(jc)
           })
-#' unix_timestamp
-#'
-#' Convert time string with given pattern
-#' (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html])
-#' to Unix time stamp (in seconds), return null if fail.
-#'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname unix_timestamp
+#' @name unix_timestamp
+#' @export
 setMethod("unix_timestamp", signature(x = "Column", format = "character"),
           function(x, format = "yyyy-MM-dd HH:mm:ss") {
             jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc, format)
@@ -586,7 +1953,10 @@ setMethod("unix_timestamp", signature(x = "Column", format = "character"),
 #' Evaluates a list of conditions and returns one of multiple possible result expressions.
 #' For unmatched expressions null is returned.
 #'
-#' @rdname column
+#' @family normal_funcs
+#' @rdname when
+#' @name when
+#' @export
 setMethod("when", signature(condition = "Column", value = "ANY"),
           function(condition, value) {
               condition <- condition@jc
@@ -597,10 +1967,13 @@ setMethod("when", signature(condition = "Column", value = "ANY"),
 
 #' ifelse
 #'
-#' Evaluates a list of conditions and returns `yes` if the conditions are satisfied.
-#' Otherwise `no` is returned for unmatched conditions.
+#' Evaluates a list of conditions and returns \code{yes} if the conditions are satisfied.
+#' Otherwise \code{no} is returned for unmatched conditions.
 #'
-#' @rdname column
+#' @family normal_funcs
+#' @rdname ifelse
+#' @name ifelse
+#' @export
 setMethod("ifelse",
           signature(test = "Column", yes = "ANY", no = "ANY"),
           function(test, yes, no) {
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 84cb8dfdaa2dd..610a8c31223cd 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -567,10 +567,6 @@ setGeneric("withColumnRenamed",
 
 ###################### Column Methods ##########################
 
-#' @rdname column
-#' @export
-setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCountDistinct") })
-
 #' @rdname column
 #' @export
 setGeneric("asc", function(x) { standardGeneric("asc") })
@@ -587,10 +583,6 @@ setGeneric("cast", function(x, dataType) { standardGeneric("cast") })
 #' @export
 setGeneric("contains", function(x, ...) { standardGeneric("contains") })
 
-#' @rdname column
-#' @export
-setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") })
-
 #' @rdname column
 #' @export
 setGeneric("desc", function(x) { standardGeneric("desc") })
@@ -607,10 +599,6 @@ setGeneric("getField", function(x, ...) { standardGeneric("getField") })
 #' @export
 setGeneric("getItem", function(x, ...) { standardGeneric("getItem") })
 
-#' @rdname column
-#' @export
-setGeneric("hypot", function(y, x) { standardGeneric("hypot") })
-
 #' @rdname column
 #' @export
 setGeneric("isNull", function(x) { standardGeneric("isNull") })
@@ -619,30 +607,10 @@ setGeneric("isNull", function(x) { standardGeneric("isNull") })
 #' @export
 setGeneric("isNotNull", function(x) { standardGeneric("isNotNull") })
 
-#' @rdname column
-#' @export
-setGeneric("last", function(x) { standardGeneric("last") })
-
 #' @rdname column
 #' @export
 setGeneric("like", function(x, ...) { standardGeneric("like") })
 
-#' @rdname column
-#' @export
-setGeneric("lower", function(x) { standardGeneric("lower") })
-
-#' @rdname column
-#' @export
-setGeneric("n", function(x) { standardGeneric("n") })
-
-#' @rdname column
-#' @export
-setGeneric("n_distinct", function(x, ...) { standardGeneric("n_distinct") })
-
-#' @rdname column
-#' @export
-setGeneric("rint", function(x, ...) { standardGeneric("rint") })
-
 #' @rdname column
 #' @export
 setGeneric("rlike", function(x, ...) { standardGeneric("rlike") })
@@ -662,312 +630,340 @@ setGeneric("otherwise", function(x, value) { standardGeneric("otherwise") })
 
 ###################### Expression Function Methods ##########################
 
-#' @rdname functions
+#' @rdname add_months
 #' @export
 setGeneric("add_months", function(y, x) { standardGeneric("add_months") })
 
-#' @rdname functions
+#' @rdname approxCountDistinct
+#' @export
+setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCountDistinct") })
+
+#' @rdname ascii
 #' @export
 setGeneric("ascii", function(x) { standardGeneric("ascii") })
 
-#' @rdname functions
+#' @rdname avg
 #' @export
 setGeneric("avg", function(x, ...) { standardGeneric("avg") })
 
-#' @rdname functions
+#' @rdname base64
 #' @export
 setGeneric("base64", function(x) { standardGeneric("base64") })
 
-#' @rdname functions
+#' @rdname bin
 #' @export
 setGeneric("bin", function(x) { standardGeneric("bin") })
 
-#' @rdname functions
+#' @rdname bitwiseNOT
 #' @export
 setGeneric("bitwiseNOT", function(x) { standardGeneric("bitwiseNOT") })
 
-#' @rdname functions
+#' @rdname cbrt
 #' @export
 setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
 
-#' @rdname functions
+#' @rdname ceil
 #' @export
 setGeneric("ceil", function(x) { standardGeneric("ceil") })
 
-#' @rdname functions
+#' @rdname concat
 #' @export
 setGeneric("concat", function(x, ...) { standardGeneric("concat") })
 
-#' @rdname functions
+#' @rdname concat_ws
 #' @export
 setGeneric("concat_ws", function(sep, x, ...) { standardGeneric("concat_ws") })
 
-#' @rdname functions
+#' @rdname conv
 #' @export
 setGeneric("conv", function(x, fromBase, toBase) { standardGeneric("conv") })
 
-#' @rdname functions
+#' @rdname countDistinct
+#' @export
+setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") })
+
+#' @rdname crc32
 #' @export
 setGeneric("crc32", function(x) { standardGeneric("crc32") })
 
-#' @rdname functions
+#' @rdname datediff
 #' @export
 setGeneric("datediff", function(y, x) { standardGeneric("datediff") })
 
-#' @rdname functions
+#' @rdname date_add
 #' @export
 setGeneric("date_add", function(y, x) { standardGeneric("date_add") })
 
-#' @rdname functions
+#' @rdname date_format
 #' @export
 setGeneric("date_format", function(y, x) { standardGeneric("date_format") })
 
-#' @rdname functions
+#' @rdname date_sub
 #' @export
 setGeneric("date_sub", function(y, x) { standardGeneric("date_sub") })
 
-#' @rdname functions
+#' @rdname dayofmonth
 #' @export
 setGeneric("dayofmonth", function(x) { standardGeneric("dayofmonth") })
 
-#' @rdname functions
+#' @rdname dayofyear
 #' @export
 setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
 
-#' @rdname functions
+#' @rdname explode
 #' @export
 setGeneric("explode", function(x) { standardGeneric("explode") })
 
-#' @rdname functions
+#' @rdname expr
 #' @export
 setGeneric("expr", function(x) { standardGeneric("expr") })
 
-#' @rdname functions
+#' @rdname from_utc_timestamp
 #' @export
 setGeneric("from_utc_timestamp", function(y, x) { standardGeneric("from_utc_timestamp") })
 
-#' @rdname functions
+#' @rdname format_number
 #' @export
 setGeneric("format_number", function(y, x) { standardGeneric("format_number") })
 
-#' @rdname functions
+#' @rdname format_string
 #' @export
 setGeneric("format_string", function(format, x, ...) { standardGeneric("format_string") })
 
-#' @rdname functions
+#' @rdname from_unixtime
 #' @export
 setGeneric("from_unixtime", function(x, ...) { standardGeneric("from_unixtime") })
 
-#' @rdname functions
+#' @rdname greatest
 #' @export
 setGeneric("greatest", function(x, ...) { standardGeneric("greatest") })
 
-#' @rdname functions
+#' @rdname hex
 #' @export
 setGeneric("hex", function(x) { standardGeneric("hex") })
 
-#' @rdname functions
+#' @rdname hour
 #' @export
 setGeneric("hour", function(x) { standardGeneric("hour") })
 
-#' @rdname functions
+#' @rdname hypot
+#' @export
+setGeneric("hypot", function(y, x) { standardGeneric("hypot") })
+
+#' @rdname initcap
 #' @export
 setGeneric("initcap", function(x) { standardGeneric("initcap") })
 
-#' @rdname functions
+#' @rdname instr
 #' @export
 setGeneric("instr", function(y, x) { standardGeneric("instr") })
 
-#' @rdname functions
+#' @rdname isNaN
 #' @export
 setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
 
-#' @rdname functions
+#' @rdname last
+#' @export
+setGeneric("last", function(x) { standardGeneric("last") })
+
+#' @rdname last_day
 #' @export
 setGeneric("last_day", function(x) { standardGeneric("last_day") })
 
-#' @rdname functions
+#' @rdname least
 #' @export
 setGeneric("least", function(x, ...) { standardGeneric("least") })
 
-#' @rdname functions
+#' @rdname levenshtein
 #' @export
 setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
 
-#' @rdname functions
+#' @rdname lit
 #' @export
 setGeneric("lit", function(x) { standardGeneric("lit") })
 
-#' @rdname functions
+#' @rdname locate
 #' @export
 setGeneric("locate", function(substr, str, ...) { standardGeneric("locate") })
 
-#' @rdname functions
+#' @rdname lower
 #' @export
 setGeneric("lower", function(x) { standardGeneric("lower") })
 
-#' @rdname functions
+#' @rdname lpad
 #' @export
 setGeneric("lpad", function(x, len, pad) { standardGeneric("lpad") })
 
-#' @rdname functions
+#' @rdname ltrim
 #' @export
 setGeneric("ltrim", function(x) { standardGeneric("ltrim") })
 
-#' @rdname functions
+#' @rdname md5
 #' @export
 setGeneric("md5", function(x) { standardGeneric("md5") })
 
-#' @rdname functions
+#' @rdname minute
 #' @export
 setGeneric("minute", function(x) { standardGeneric("minute") })
 
-#' @rdname functions
+#' @rdname month
 #' @export
 setGeneric("month", function(x) { standardGeneric("month") })
 
-#' @rdname functions
+#' @rdname months_between
 #' @export
 setGeneric("months_between", function(y, x) { standardGeneric("months_between") })
 
-#' @rdname functions
+#' @rdname count
+#' @export
+setGeneric("n", function(x) { standardGeneric("n") })
+
+#' @rdname nanvl
 #' @export
 setGeneric("nanvl", function(y, x) { standardGeneric("nanvl") })
 
-#' @rdname functions
+#' @rdname negate
 #' @export
 setGeneric("negate", function(x) { standardGeneric("negate") })
 
-#' @rdname functions
+#' @rdname next_day
 #' @export
 setGeneric("next_day", function(y, x) { standardGeneric("next_day") })
 
-#' @rdname functions
+#' @rdname countDistinct
+#' @export
+setGeneric("n_distinct", function(x, ...) { standardGeneric("n_distinct") })
+
+#' @rdname pmod
 #' @export
 setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
 
-#' @rdname functions
+#' @rdname quarter
 #' @export
 setGeneric("quarter", function(x) { standardGeneric("quarter") })
 
-#' @rdname functions
+#' @rdname rand
 #' @export
 setGeneric("rand", function(seed) { standardGeneric("rand") })
 
-#' @rdname functions
+#' @rdname randn
 #' @export
 setGeneric("randn", function(seed) { standardGeneric("randn") })
 
-#' @rdname functions
+#' @rdname regexp_extract
 #' @export
 setGeneric("regexp_extract", function(x, pattern, idx) { standardGeneric("regexp_extract") })
 
-#' @rdname functions
+#' @rdname regexp_replace
 #' @export
 setGeneric("regexp_replace",
            function(x, pattern, replacement) { standardGeneric("regexp_replace") })
 
-#' @rdname functions
+#' @rdname reverse
 #' @export
 setGeneric("reverse", function(x) { standardGeneric("reverse") })
 
-#' @rdname functions
+#' @rdname rint
+#' @export
+setGeneric("rint", function(x, ...) { standardGeneric("rint") })
+
+#' @rdname rpad
 #' @export
 setGeneric("rpad", function(x, len, pad) { standardGeneric("rpad") })
 
-#' @rdname functions
+#' @rdname rtrim
 #' @export
 setGeneric("rtrim", function(x) { standardGeneric("rtrim") })
 
-#' @rdname functions
+#' @rdname second
 #' @export
 setGeneric("second", function(x) { standardGeneric("second") })
 
-#' @rdname functions
+#' @rdname sha1
 #' @export
 setGeneric("sha1", function(x) { standardGeneric("sha1") })
 
-#' @rdname functions
+#' @rdname sha2
 #' @export
 setGeneric("sha2", function(y, x) { standardGeneric("sha2") })
 
-#' @rdname functions
+#' @rdname shiftLeft
 #' @export
 setGeneric("shiftLeft", function(y, x) { standardGeneric("shiftLeft") })
 
-#' @rdname functions
+#' @rdname shiftRight
 #' @export
 setGeneric("shiftRight", function(y, x) { standardGeneric("shiftRight") })
 
-#' @rdname functions
+#' @rdname shiftRightUnsigned
 #' @export
 setGeneric("shiftRightUnsigned", function(y, x) { standardGeneric("shiftRightUnsigned") })
 
-#' @rdname functions
+#' @rdname signum
 #' @export
 setGeneric("signum", function(x) { standardGeneric("signum") })
 
-#' @rdname functions
+#' @rdname size
 #' @export
 setGeneric("size", function(x) { standardGeneric("size") })
 
-#' @rdname functions
+#' @rdname soundex
 #' @export
 setGeneric("soundex", function(x) { standardGeneric("soundex") })
 
-#' @rdname functions
+#' @rdname substring_index
 #' @export
 setGeneric("substring_index", function(x, delim, count) { standardGeneric("substring_index") })
 
-#' @rdname functions
+#' @rdname sumDistinct
 #' @export
 setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") })
 
-#' @rdname functions
+#' @rdname toDegrees
 #' @export
 setGeneric("toDegrees", function(x) { standardGeneric("toDegrees") })
 
-#' @rdname functions
+#' @rdname toRadians
 #' @export
 setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 
-#' @rdname functions
+#' @rdname to_date
 #' @export
 setGeneric("to_date", function(x) { standardGeneric("to_date") })
 
-#' @rdname functions
+#' @rdname to_utc_timestamp
 #' @export
 setGeneric("to_utc_timestamp", function(y, x) { standardGeneric("to_utc_timestamp") })
 
-#' @rdname functions
+#' @rdname translate
 #' @export
 setGeneric("translate", function(x, matchingString, replaceString) { standardGeneric("translate") })
 
-#' @rdname functions
+#' @rdname trim
 #' @export
 setGeneric("trim", function(x) { standardGeneric("trim") })
 
-#' @rdname functions
+#' @rdname unbase64
 #' @export
 setGeneric("unbase64", function(x) { standardGeneric("unbase64") })
 
-#' @rdname functions
+#' @rdname unhex
 #' @export
 setGeneric("unhex", function(x) { standardGeneric("unhex") })
 
-#' @rdname functions
+#' @rdname unix_timestamp
 #' @export
 setGeneric("unix_timestamp", function(x, format) { standardGeneric("unix_timestamp") })
 
-#' @rdname functions
+#' @rdname upper
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
 
-#' @rdname functions
+#' @rdname weekofyear
 #' @export
 setGeneric("weekofyear", function(x) { standardGeneric("weekofyear") })
 
-#' @rdname functions
+#' @rdname year
 #' @export
 setGeneric("year", function(x) { standardGeneric("year") })
 

From 642c43c81c835139e3f35dfd6a215d668a474203 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 24 Aug 2015 19:45:41 -0700
Subject: [PATCH 1195/1454] [SQL] [MINOR] [DOC] Clarify docs for inferring
 DataFrame from RDD of Products

 * Makes `SQLImplicits.rddToDataFrameHolder` scaladoc consistent with `SQLContext.createDataFrame[A <: Product](rdd: RDD[A])` since the former is essentially a wrapper for the latter
 * Clarifies `createDataFrame[A <: Product]` scaladoc to apply for any `RDD[Product]`, not just case classes

Author: Feynman Liang <fliang@databricks.com>

Closes #8406 from feynmanliang/sql-doc-fixes.
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala   | 2 +-
 sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 126c9c6f839c7..a1eea09e0477b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -350,7 +350,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /**
    * :: Experimental ::
-   * Creates a DataFrame from an RDD of case classes.
+   * Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
    *
    * @group dataframes
    * @since 1.3.0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
index 47b6f80bed483..bf03c61088426 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
@@ -40,7 +40,7 @@ private[sql] abstract class SQLImplicits {
   implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name)
 
   /**
-   * Creates a DataFrame from an RDD of case classes or tuples.
+   * Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
    * @since 1.3.0
    */
   implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = {

From a0c0aae1defe5e1e57704065631d201f8e3f6bac Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 25 Aug 2015 12:49:50 +0800
Subject: [PATCH 1196/1454] [SPARK-10121] [SQL] Thrift server always use the
 latest class loader provided by the conf of executionHive's state

https://issues.apache.org/jira/browse/SPARK-10121

Looks like the problem is that if we add a jar through another thread, the thread handling the JDBC session will not get the latest classloader.

Author: Yin Huai <yhuai@databricks.com>

Closes #8368 from yhuai/SPARK-10121.
---
 .../SparkExecuteStatementOperation.scala      |  6 +++
 .../HiveThriftServer2Suites.scala             | 54 +++++++++++++++++++
 2 files changed, 60 insertions(+)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
index 833bf62d47d07..02cc7e5efa521 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
@@ -159,6 +159,12 @@ private[hive] class SparkExecuteStatementOperation(
 
               // User information is part of the metastore client member in Hive
               hiveContext.setSession(currentSqlSession)
+              // Always use the latest class loader provided by executionHive's state.
+              val executionHiveClassLoader =
+                hiveContext.executionHive.state.getConf.getClassLoader
+              sessionHive.getConf.setClassLoader(executionHiveClassLoader)
+              parentSessionState.getConf.setClassLoader(executionHiveClassLoader)
+
               Hive.set(sessionHive)
               SessionState.setCurrentSessionState(parentSessionState)
               try {
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index ded42bca9971e..b72249b3bf8c0 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -377,6 +377,60 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
       rs2.close()
     }
   }
+
+  test("test add jar") {
+    withMultipleConnectionJdbcStatement(
+      {
+        statement =>
+          val jarFile =
+            "../hive/src/test/resources/hive-hcatalog-core-0.13.1.jar"
+              .split("/")
+              .mkString(File.separator)
+
+          statement.executeQuery(s"ADD JAR $jarFile")
+      },
+
+      {
+        statement =>
+          val queries = Seq(
+            "DROP TABLE IF EXISTS smallKV",
+            "CREATE TABLE smallKV(key INT, val STRING)",
+            s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE smallKV",
+            "DROP TABLE IF EXISTS addJar",
+            """CREATE TABLE addJar(key string)
+              |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'
+            """.stripMargin)
+
+          queries.foreach(statement.execute)
+
+          statement.executeQuery(
+            """
+              |INSERT INTO TABLE addJar SELECT 'k1' as key FROM smallKV limit 1
+            """.stripMargin)
+
+          val actualResult =
+            statement.executeQuery("SELECT key FROM addJar")
+          val actualResultBuffer = new collection.mutable.ArrayBuffer[String]()
+          while (actualResult.next()) {
+            actualResultBuffer += actualResult.getString(1)
+          }
+          actualResult.close()
+
+          val expectedResult =
+            statement.executeQuery("SELECT 'k1'")
+          val expectedResultBuffer = new collection.mutable.ArrayBuffer[String]()
+          while (expectedResult.next()) {
+            expectedResultBuffer += expectedResult.getString(1)
+          }
+          expectedResult.close()
+
+          assert(expectedResultBuffer === actualResultBuffer)
+
+          statement.executeQuery("DROP TABLE IF EXISTS addJar")
+          statement.executeQuery("DROP TABLE IF EXISTS smallKV")
+      }
+    )
+  }
 }
 
 class HiveThriftHttpServerSuite extends HiveThriftJdbcTest {

From 5175ca0c85b10045d12c3fb57b1e52278a413ecf Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 24 Aug 2015 23:15:27 -0700
Subject: [PATCH 1197/1454] [SPARK-10178] [SQL] HiveComparisionTest should
 print out dependent tables

In `HiveComparisionTest`s it is possible to fail a query of the form `SELECT * FROM dest1`, where `dest1` is the query that is actually computing the incorrect results.  To aid debugging this patch improves the harness to also print these query plans and their results.

Author: Michael Armbrust <michael@databricks.com>

Closes #8388 from marmbrus/generatedTables.
---
 .../hive/execution/HiveComparisonTest.scala   | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 2bdb0e11878e5..4d45249d9c6b8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.hive.execution
 
 import java.io._
 
+import scala.util.control.NonFatal
+
 import org.scalatest.{BeforeAndAfterAll, GivenWhenThen}
 
 import org.apache.spark.{Logging, SparkFunSuite}
@@ -386,11 +388,45 @@ abstract class HiveComparisonTest
                 hiveCacheFiles.foreach(_.delete())
               }
 
+              // If this query is reading other tables that were created during this test run
+              // also print out the query plans and results for those.
+              val computedTablesMessages: String = try {
+                val tablesRead = new TestHive.QueryExecution(query).executedPlan.collect {
+                  case ts: HiveTableScan => ts.relation.tableName
+                }.toSet
+
+                TestHive.reset()
+                val executions = queryList.map(new TestHive.QueryExecution(_))
+                executions.foreach(_.toRdd)
+                val tablesGenerated = queryList.zip(executions).flatMap {
+                  case (q, e) => e.executedPlan.collect {
+                    case i: InsertIntoHiveTable if tablesRead contains i.table.tableName =>
+                      (q, e, i)
+                  }
+                }
+
+                tablesGenerated.map { case (hiveql, execution, insert) =>
+                  s"""
+                     |=== Generated Table ===
+                     |$hiveql
+                     |$execution
+                     |== Results ==
+                     |${insert.child.execute().collect().mkString("\n")}
+                   """.stripMargin
+                }.mkString("\n")
+
+              } catch {
+                case NonFatal(e) =>
+                  logError("Failed to compute generated tables", e)
+                  s"Couldn't compute dependent tables: $e"
+              }
+
               val errorMessage =
                 s"""
                   |Results do not match for $testCaseName:
                   |$hiveQuery\n${hiveQuery.analyzed.output.map(_.name).mkString("\t")}
                   |$resultComparison
+                  |$computedTablesMessages
                 """.stripMargin
 
               stringToFile(new File(wrongDirectory, testCaseName), errorMessage + consoleTestCase)

From d9c25dec87e6da7d66a47ff94e7eefa008081b9d Mon Sep 17 00:00:00 2001
From: cody koeninger <cody@koeninger.org>
Date: Mon, 24 Aug 2015 23:26:14 -0700
Subject: [PATCH 1198/1454] =?UTF-8?q?[SPARK-9786]=20[STREAMING]=20[KAFKA]?=
 =?UTF-8?q?=20fix=20backpressure=20so=20it=20works=20with=20defa=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…ult maxRatePerPartition setting of 0

Author: cody koeninger <cody@koeninger.org>

Closes #8413 from koeninger/backpressure-testing-master.
---
 .../spark/streaming/kafka/DirectKafkaInputDStream.scala  | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 8a177077775c6..1000094e93cb3 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -95,8 +95,13 @@ class DirectKafkaInputDStream[
 
     val effectiveRateLimitPerPartition = estimatedRateLimit
       .filter(_ > 0)
-      .map(limit => Math.min(maxRateLimitPerPartition, (limit / numPartitions)))
-      .getOrElse(maxRateLimitPerPartition)
+      .map { limit =>
+        if (maxRateLimitPerPartition > 0) {
+          Math.min(maxRateLimitPerPartition, (limit / numPartitions))
+        } else {
+          limit / numPartitions
+        }
+      }.getOrElse(maxRateLimitPerPartition)
 
     if (effectiveRateLimitPerPartition > 0) {
       val secsPerBatch = context.graph.batchDuration.milliseconds.toDouble / 1000

From f023aa2fcc1d1dbb82aee568be0a8f2457c309ae Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 24 Aug 2015 23:34:50 -0700
Subject: [PATCH 1199/1454] [SPARK-10137] [STREAMING] Avoid to restart
 receivers if scheduleReceivers returns balanced results

This PR fixes the following cases for `ReceiverSchedulingPolicy`.

1) Assume there are 4 executors: host1, host2, host3, host4, and 5 receivers: r1, r2, r3, r4, r5. Then `ReceiverSchedulingPolicy.scheduleReceivers` will return (r1 -> host1, r2 -> host2, r3 -> host3, r4 -> host4, r5 -> host1).
Let's assume r1 starts at first on `host1` as `scheduleReceivers` suggested,  and try to register with ReceiverTracker. But the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will return (host2, host3, host4) according to the current executor weights (host1 -> 1.0, host2 -> 0.5, host3 -> 0.5, host4 -> 0.5), so ReceiverTracker will reject `r1`. This is unexpected since r1 is starting exactly where `scheduleReceivers` suggested.

This case can be fixed by ignoring the information of the receiver that is rescheduling in `receiverTrackingInfoMap`.

2) Assume there are 3 executors (host1, host2, host3) and each executors has 3 cores, and 3 receivers: r1, r2, r3. Assume r1 is running on host1. Now r2 is restarting, the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will always return (host1, host2, host3). So it's possible that r2 will be scheduled to host1 by TaskScheduler. r3 is similar. Then at last, it's possible that there are 3 receivers running on host1, while host2 and host3 are idle.

This issue can be fixed by returning only executors that have the minimum wight rather than returning at least 3 executors.

Author: zsxwing <zsxwing@gmail.com>

Closes #8340 from zsxwing/fix-receiver-scheduling.
---
 .../scheduler/ReceiverSchedulingPolicy.scala  |  58 +++++++---
 .../streaming/scheduler/ReceiverTracker.scala | 106 ++++++++++++------
 .../ReceiverSchedulingPolicySuite.scala       |  13 ++-
 3 files changed, 120 insertions(+), 57 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
index ef5b687b5831a..10b5a7f57a802 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
@@ -22,6 +22,36 @@ import scala.collection.mutable
 
 import org.apache.spark.streaming.receiver.Receiver
 
+/**
+ * A class that tries to schedule receivers with evenly distributed. There are two phases for
+ * scheduling receivers.
+ *
+ * - The first phase is global scheduling when ReceiverTracker is starting and we need to schedule
+ *   all receivers at the same time. ReceiverTracker will call `scheduleReceivers` at this phase.
+ *   It will try to schedule receivers with evenly distributed. ReceiverTracker should update its
+ *   receiverTrackingInfoMap according to the results of `scheduleReceivers`.
+ *   `ReceiverTrackingInfo.scheduledExecutors` for each receiver will set to an executor list that
+ *   contains the scheduled locations. Then when a receiver is starting, it will send a register
+ *   request and `ReceiverTracker.registerReceiver` will be called. In
+ *   `ReceiverTracker.registerReceiver`, if a receiver's scheduled executors is set, it should check
+ *   if the location of this receiver is one of the scheduled executors, if not, the register will
+ *   be rejected.
+ * - The second phase is local scheduling when a receiver is restarting. There are two cases of
+ *   receiver restarting:
+ *   - If a receiver is restarting because it's rejected due to the real location and the scheduled
+ *     executors mismatching, in other words, it fails to start in one of the locations that
+ *     `scheduleReceivers` suggested, `ReceiverTracker` should firstly choose the executors that are
+ *     still alive in the list of scheduled executors, then use them to launch the receiver job.
+ *   - If a receiver is restarting without a scheduled executors list, or the executors in the list
+ *     are dead, `ReceiverTracker` should call `rescheduleReceiver`. If so, `ReceiverTracker` should
+ *     not set `ReceiverTrackingInfo.scheduledExecutors` for this executor, instead, it should clear
+ *     it. Then when this receiver is registering, we can know this is a local scheduling, and
+ *     `ReceiverTrackingInfo` should call `rescheduleReceiver` again to check if the launching
+ *     location is matching.
+ *
+ * In conclusion, we should make a global schedule, try to achieve that exactly as long as possible,
+ * otherwise do local scheduling.
+ */
 private[streaming] class ReceiverSchedulingPolicy {
 
   /**
@@ -102,8 +132,7 @@ private[streaming] class ReceiverSchedulingPolicy {
 
   /**
    * Return a list of candidate executors to run the receiver. If the list is empty, the caller can
-   * run this receiver in arbitrary executor. The caller can use `preferredNumExecutors` to require
-   * returning `preferredNumExecutors` executors if possible.
+   * run this receiver in arbitrary executor.
    *
    * This method tries to balance executors' load. Here is the approach to schedule executors
    * for a receiver.
@@ -122,9 +151,8 @@ private[streaming] class ReceiverSchedulingPolicy {
    *         If a receiver is scheduled to an executor but has not yet run, it contributes
    *         `1.0 / #candidate_executors_of_this_receiver` to the executor's weight.</li>
    *     </ul>
-   *     At last, if there are more than `preferredNumExecutors` idle executors (weight = 0),
-   *     returns all idle executors. Otherwise, we only return `preferredNumExecutors` best options
-   *     according to the weights.
+   *     At last, if there are any idle executors (weight = 0), returns all idle executors.
+   *     Otherwise, returns the executors that have the minimum weight.
    *   </li>
    * </ol>
    *
@@ -134,8 +162,7 @@ private[streaming] class ReceiverSchedulingPolicy {
       receiverId: Int,
       preferredLocation: Option[String],
       receiverTrackingInfoMap: Map[Int, ReceiverTrackingInfo],
-      executors: Seq[String],
-      preferredNumExecutors: Int = 3): Seq[String] = {
+      executors: Seq[String]): Seq[String] = {
     if (executors.isEmpty) {
       return Seq.empty
     }
@@ -156,15 +183,18 @@ private[streaming] class ReceiverSchedulingPolicy {
       }
     }.groupBy(_._1).mapValues(_.map(_._2).sum) // Sum weights for each executor
 
-    val idleExecutors = (executors.toSet -- executorWeights.keys).toSeq
-    if (idleExecutors.size >= preferredNumExecutors) {
-      // If there are more than `preferredNumExecutors` idle executors, return all of them
+    val idleExecutors = executors.toSet -- executorWeights.keys
+    if (idleExecutors.nonEmpty) {
       scheduledExecutors ++= idleExecutors
     } else {
-      // If there are less than `preferredNumExecutors` idle executors, return 3 best options
-      scheduledExecutors ++= idleExecutors
-      val sortedExecutors = executorWeights.toSeq.sortBy(_._2).map(_._1)
-      scheduledExecutors ++= (idleExecutors ++ sortedExecutors).take(preferredNumExecutors)
+      // There is no idle executor. So select all executors that have the minimum weight.
+      val sortedExecutors = executorWeights.toSeq.sortBy(_._2)
+      if (sortedExecutors.nonEmpty) {
+        val minWeight = sortedExecutors(0)._2
+        scheduledExecutors ++= sortedExecutors.takeWhile(_._2 == minWeight).map(_._1)
+      } else {
+        // This should not happen since "executors" is not empty
+      }
     }
     scheduledExecutors.toSeq
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 30d25a64e307a..3d532a675db02 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -244,8 +244,21 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     }
 
     if (isTrackerStopping || isTrackerStopped) {
-      false
-    } else if (!scheduleReceiver(streamId).contains(hostPort)) {
+      return false
+    }
+
+    val scheduledExecutors = receiverTrackingInfos(streamId).scheduledExecutors
+    val accetableExecutors = if (scheduledExecutors.nonEmpty) {
+        // This receiver is registering and it's scheduled by
+        // ReceiverSchedulingPolicy.scheduleReceivers. So use "scheduledExecutors" to check it.
+        scheduledExecutors.get
+      } else {
+        // This receiver is scheduled by "ReceiverSchedulingPolicy.rescheduleReceiver", so calling
+        // "ReceiverSchedulingPolicy.rescheduleReceiver" again to check it.
+        scheduleReceiver(streamId)
+      }
+
+    if (!accetableExecutors.contains(hostPort)) {
       // Refuse it since it's scheduled to a wrong executor
       false
     } else {
@@ -426,12 +439,25 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
           startReceiver(receiver, executors)
         }
       case RestartReceiver(receiver) =>
-        val scheduledExecutors = schedulingPolicy.rescheduleReceiver(
-          receiver.streamId,
-          receiver.preferredLocation,
-          receiverTrackingInfos,
-          getExecutors)
-        updateReceiverScheduledExecutors(receiver.streamId, scheduledExecutors)
+        // Old scheduled executors minus the ones that are not active any more
+        val oldScheduledExecutors = getStoredScheduledExecutors(receiver.streamId)
+        val scheduledExecutors = if (oldScheduledExecutors.nonEmpty) {
+            // Try global scheduling again
+            oldScheduledExecutors
+          } else {
+            val oldReceiverInfo = receiverTrackingInfos(receiver.streamId)
+            // Clear "scheduledExecutors" to indicate we are going to do local scheduling
+            val newReceiverInfo = oldReceiverInfo.copy(
+              state = ReceiverState.INACTIVE, scheduledExecutors = None)
+            receiverTrackingInfos(receiver.streamId) = newReceiverInfo
+            schedulingPolicy.rescheduleReceiver(
+              receiver.streamId,
+              receiver.preferredLocation,
+              receiverTrackingInfos,
+              getExecutors)
+          }
+        // Assume there is one receiver restarting at one time, so we don't need to update
+        // receiverTrackingInfos
         startReceiver(receiver, scheduledExecutors)
       case c: CleanupOldBlocks =>
         receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c))
@@ -464,6 +490,24 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         context.reply(true)
     }
 
+    /**
+     * Return the stored scheduled executors that are still alive.
+     */
+    private def getStoredScheduledExecutors(receiverId: Int): Seq[String] = {
+      if (receiverTrackingInfos.contains(receiverId)) {
+        val scheduledExecutors = receiverTrackingInfos(receiverId).scheduledExecutors
+        if (scheduledExecutors.nonEmpty) {
+          val executors = getExecutors.toSet
+          // Only return the alive executors
+          scheduledExecutors.get.filter(executors)
+        } else {
+          Nil
+        }
+      } else {
+        Nil
+      }
+    }
+
     /**
      * Start a receiver along with its scheduled executors
      */
@@ -484,7 +528,23 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         new SerializableConfiguration(ssc.sparkContext.hadoopConfiguration)
 
       // Function to start the receiver on the worker node
-      val startReceiverFunc = new StartReceiverFunc(checkpointDirOption, serializableHadoopConf)
+      val startReceiverFunc: Iterator[Receiver[_]] => Unit =
+        (iterator: Iterator[Receiver[_]]) => {
+          if (!iterator.hasNext) {
+            throw new SparkException(
+              "Could not start receiver as object not found.")
+          }
+          if (TaskContext.get().attemptNumber() == 0) {
+            val receiver = iterator.next()
+            assert(iterator.hasNext == false)
+            val supervisor = new ReceiverSupervisorImpl(
+              receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
+            supervisor.start()
+            supervisor.awaitTermination()
+          } else {
+            // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
+          }
+        }
 
       // Create the RDD using the scheduledExecutors to run the receiver in a Spark job
       val receiverRDD: RDD[Receiver[_]] =
@@ -541,31 +601,3 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
   }
 
 }
-
-/**
- * Function to start the receiver on the worker node. Use a class instead of closure to avoid
- * the serialization issue.
- */
-private[streaming] class StartReceiverFunc(
-    checkpointDirOption: Option[String],
-    serializableHadoopConf: SerializableConfiguration)
-  extends (Iterator[Receiver[_]] => Unit) with Serializable {
-
-  override def apply(iterator: Iterator[Receiver[_]]): Unit = {
-    if (!iterator.hasNext) {
-      throw new SparkException(
-        "Could not start receiver as object not found.")
-    }
-    if (TaskContext.get().attemptNumber() == 0) {
-      val receiver = iterator.next()
-      assert(iterator.hasNext == false)
-      val supervisor = new ReceiverSupervisorImpl(
-        receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
-      supervisor.start()
-      supervisor.awaitTermination()
-    } else {
-      // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
-    }
-  }
-
-}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
index 0418d776ecc9a..b2a51d72bac2b 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
@@ -39,7 +39,7 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
     assert(scheduledExecutors.toSet === Set("host1", "host2"))
   }
 
-  test("rescheduleReceiver: return all idle executors if more than 3 idle executors") {
+  test("rescheduleReceiver: return all idle executors if there are any idle executors") {
     val executors = Seq("host1", "host2", "host3", "host4", "host5")
     // host3 is idle
     val receiverTrackingInfoMap = Map(
@@ -49,16 +49,16 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
     assert(scheduledExecutors.toSet === Set("host2", "host3", "host4", "host5"))
   }
 
-  test("rescheduleReceiver: return 3 best options if less than 3 idle executors") {
+  test("rescheduleReceiver: return all executors that have minimum weight if no idle executors") {
     val executors = Seq("host1", "host2", "host3", "host4", "host5")
-    // Weights: host1 = 1.5, host2 = 0.5, host3 = 1.0
-    // host4 and host5 are idle
+    // Weights: host1 = 1.5, host2 = 0.5, host3 = 1.0, host4 = 0.5, host5 = 0.5
     val receiverTrackingInfoMap = Map(
       0 -> ReceiverTrackingInfo(0, ReceiverState.ACTIVE, None, Some("host1")),
       1 -> ReceiverTrackingInfo(1, ReceiverState.SCHEDULED, Some(Seq("host2", "host3")), None),
-      2 -> ReceiverTrackingInfo(1, ReceiverState.SCHEDULED, Some(Seq("host1", "host3")), None))
+      2 -> ReceiverTrackingInfo(2, ReceiverState.SCHEDULED, Some(Seq("host1", "host3")), None),
+      3 -> ReceiverTrackingInfo(4, ReceiverState.SCHEDULED, Some(Seq("host4", "host5")), None))
     val scheduledExecutors = receiverSchedulingPolicy.rescheduleReceiver(
-      3, None, receiverTrackingInfoMap, executors)
+      4, None, receiverTrackingInfoMap, executors)
     assert(scheduledExecutors.toSet === Set("host2", "host4", "host5"))
   }
 
@@ -127,4 +127,5 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
       assert(executors.isEmpty)
     }
   }
+
 }

From df7041d02d3fd44b08a859f5d77bf6fb726895f0 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 24 Aug 2015 23:38:32 -0700
Subject: [PATCH 1200/1454] [SPARK-10196] [SQL] Correctly saving decimals in
 internal rows to JSON.

https://issues.apache.org/jira/browse/SPARK-10196

Author: Yin Huai <yhuai@databricks.com>

Closes #8408 from yhuai/DecimalJsonSPARK-10196.
---
 .../datasources/json/JacksonGenerator.scala   |  2 +-
 .../sources/JsonHadoopFsRelationSuite.scala   | 27 +++++++++++++++++++
 2 files changed, 28 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index 99ac7730bd1c9..330ba907b2ef9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -95,7 +95,7 @@ private[sql] object JacksonGenerator {
       case (FloatType, v: Float) => gen.writeNumber(v)
       case (DoubleType, v: Double) => gen.writeNumber(v)
       case (LongType, v: Long) => gen.writeNumber(v)
-      case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v)
+      case (DecimalType(), v: Decimal) => gen.writeNumber(v.toJavaBigDecimal)
       case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
       case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
       case (BooleanType, v: Boolean) => gen.writeBoolean(v)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
index ed6d512ab36fe..8ca3a17085194 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.sources
 
+import java.math.BigDecimal
+
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
@@ -75,4 +77,29 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
       )
     }
   }
+
+  test("SPARK-10196: save decimal type to JSON") {
+    withTempDir { file =>
+      file.delete()
+
+      val schema =
+        new StructType()
+          .add("decimal", DecimalType(7, 2))
+
+      val data =
+        Row(new BigDecimal("10.02")) ::
+          Row(new BigDecimal("20000.99")) ::
+          Row(new BigDecimal("10000")) :: Nil
+      val df = createDataFrame(sparkContext.parallelize(data), schema)
+
+      // Write the data out.
+      df.write.format(dataSourceName).save(file.getCanonicalPath)
+
+      // Read it back and check the result.
+      checkAnswer(
+        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        df
+      )
+    }
+  }
 }

From bf03fe68d62f33dda70dff45c3bda1f57b032dfc Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 25 Aug 2015 14:58:42 +0800
Subject: [PATCH 1201/1454] [SPARK-10136] [SQL] A more robust fix for
 SPARK-10136

PR #8341 is a valid fix for SPARK-10136, but it didn't catch the real root cause.  The real problem can be rather tricky to explain, and requires audiences to be pretty familiar with parquet-format spec, especially details of `LIST` backwards-compatibility rules.  Let me have a try to give an explanation here.

The structure of the problematic Parquet schema generated by parquet-avro is something like this:

```
message m {
  <repetition> group f (LIST) {         // Level 1
    repeated group array (LIST) {       // Level 2
      repeated <primitive-type> array;  // Level 3
    }
  }
}
```

(The schema generated by parquet-thrift is structurally similar, just replace the `array` at level 2 with `f_tuple`, and the other one at level 3 with `f_tuple_tuple`.)

This structure consists of two nested legacy 2-level `LIST`-like structures:

1. The repeated group type at level 2 is the element type of the outer array defined at level 1

   This group should map to an `CatalystArrayConverter.ElementConverter` when building converters.

2. The repeated primitive type at level 3 is the element type of the inner array defined at level 2

   This group should also map to an `CatalystArrayConverter.ElementConverter`.

The root cause of SPARK-10136 is that, the group at level 2 isn't properly recognized as the element type of level 1.  Thus, according to parquet-format spec, the repeated primitive at level 3 is left as a so called "unannotated repeated primitive type", and is recognized as a required list of required primitive type, thus a `RepeatedPrimitiveConverter` instead of a `CatalystArrayConverter.ElementConverter` is created for it.

According to  parquet-format spec, unannotated repeated type shouldn't appear in a `LIST`- or `MAP`-annotated group.  PR #8341 fixed this issue by allowing such unannotated repeated type appear in `LIST`-annotated groups, which is a non-standard, hacky, but valid fix.  (I didn't realize this when authoring #8341 though.)

As for the reason why level 2 isn't recognized as a list element type, it's because of the following `LIST` backwards-compatibility rule defined in the parquet-format spec:

> If the repeated field is a group with one field and is named either `array` or uses the `LIST`-annotated group's name with `_tuple` appended then the repeated type is the element type and elements are required.

(The `array` part is for parquet-avro compatibility, while the `_tuple` part is for parquet-thrift.)

This rule is implemented in [`CatalystSchemaConverter.isElementType`] [1], but neglected in [`CatalystRowConverter.isElementType`] [2].  This PR delivers a more robust fix by adding this rule in the latter method.

Note that parquet-avro 1.7.0 also suffers from this issue. Details can be found at [PARQUET-364] [3].

[1]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala#L259-L305
[2]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala#L456-L463
[3]: https://issues.apache.org/jira/browse/PARQUET-364

Author: Cheng Lian <lian@databricks.com>

Closes #8361 from liancheng/spark-10136/proper-version.
---
 .../parquet/CatalystRowConverter.scala         | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index d2c2db51769ba..cbf0704c4a9a4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -415,8 +415,9 @@ private[parquet] class CatalystRowConverter(
     private val elementConverter: Converter = {
       val repeatedType = parquetSchema.getType(0)
       val elementType = catalystSchema.elementType
+      val parentName = parquetSchema.getName
 
-      if (isElementType(repeatedType, elementType)) {
+      if (isElementType(repeatedType, elementType, parentName)) {
         newConverter(repeatedType, elementType, new ParentContainerUpdater {
           override def set(value: Any): Unit = currentArray += value
         })
@@ -453,10 +454,13 @@ private[parquet] class CatalystRowConverter(
      * @see https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules
      */
     // scalastyle:on
-    private def isElementType(parquetRepeatedType: Type, catalystElementType: DataType): Boolean = {
+    private def isElementType(
+        parquetRepeatedType: Type, catalystElementType: DataType, parentName: String): Boolean = {
       (parquetRepeatedType, catalystElementType) match {
         case (t: PrimitiveType, _) => true
         case (t: GroupType, _) if t.getFieldCount > 1 => true
+        case (t: GroupType, _) if t.getFieldCount == 1 && t.getName == "array" => true
+        case (t: GroupType, _) if t.getFieldCount == 1 && t.getName == parentName + "_tuple" => true
         case (t: GroupType, StructType(Array(f))) if f.name == t.getFieldName(0) => true
         case _ => false
       }
@@ -474,15 +478,9 @@ private[parquet] class CatalystRowConverter(
 
       override def getConverter(fieldIndex: Int): Converter = converter
 
-      override def end(): Unit = {
-        converter.updater.end()
-        currentArray += currentElement
-      }
+      override def end(): Unit = currentArray += currentElement
 
-      override def start(): Unit = {
-        converter.updater.start()
-        currentElement = null
-      }
+      override def start(): Unit = currentElement = null
     }
   }
 

From 82268f07abfa658869df2354ae72f8d6ddd119e8 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 25 Aug 2015 00:04:10 -0700
Subject: [PATCH 1202/1454] [SPARK-9293] [SPARK-9813] Analysis should check
 that set operations are only performed on tables with equal numbers of
 columns

This patch adds an analyzer rule to ensure that set operations (union, intersect, and except) are only applied to tables with the same number of columns. Without this rule, there are scenarios where invalid queries can return incorrect results instead of failing with error messages; SPARK-9813 provides one example of this problem. In other cases, the invalid query can crash at runtime with extremely confusing exceptions.

I also performed a bit of cleanup to refactor some of those logical operators' code into a common `SetOperation` base class.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7631 from JoshRosen/SPARK-9293.
---
 .../sql/catalyst/analysis/CheckAnalysis.scala |  6 +++
 .../catalyst/analysis/HiveTypeCoercion.scala  | 14 +++----
 .../plans/logical/basicOperators.scala        | 38 +++++++++----------
 .../analysis/AnalysisErrorSuite.scala         | 18 +++++++++
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  2 +-
 .../hive/execution/InsertIntoHiveTable.scala  |  2 +-
 6 files changed, 48 insertions(+), 32 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 39f554c137c98..7701fd0451041 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -137,6 +137,12 @@ trait CheckAnalysis {
               }
             }
 
+          case s @ SetOperation(left, right) if left.output.length != right.output.length =>
+            failAnalysis(
+              s"${s.nodeName} can only be performed on tables with the same number of columns, " +
+               s"but the left table has ${left.output.length} columns and the right has " +
+               s"${right.output.length}")
+
           case _ => // Fallbacks to the following checks
         }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 2cb067f4aac91..a1aa2a2b2c680 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -203,6 +203,7 @@ object HiveTypeCoercion {
         planName: String,
         left: LogicalPlan,
         right: LogicalPlan): (LogicalPlan, LogicalPlan) = {
+      require(left.output.length == right.output.length)
 
       val castedTypes = left.output.zip(right.output).map {
         case (lhs, rhs) if lhs.dataType != rhs.dataType =>
@@ -229,15 +230,10 @@ object HiveTypeCoercion {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p if p.analyzed => p
 
-      case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
-        val (newLeft, newRight) = widenOutputTypes(u.nodeName, left, right)
-        Union(newLeft, newRight)
-      case e @ Except(left, right) if e.childrenResolved && !e.resolved =>
-        val (newLeft, newRight) = widenOutputTypes(e.nodeName, left, right)
-        Except(newLeft, newRight)
-      case i @ Intersect(left, right) if i.childrenResolved && !i.resolved =>
-        val (newLeft, newRight) = widenOutputTypes(i.nodeName, left, right)
-        Intersect(newLeft, newRight)
+      case s @ SetOperation(left, right) if s.childrenResolved
+          && left.output.length == right.output.length && !s.resolved =>
+        val (newLeft, newRight) = widenOutputTypes(s.nodeName, left, right)
+        s.makeCopy(Array(newLeft, newRight))
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 73b8261260acb..722f69cdca827 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -89,13 +89,21 @@ case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 }
 
-case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
+abstract class SetOperation(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   // TODO: These aren't really the same attributes as nullability etc might change.
-  override def output: Seq[Attribute] = left.output
+  final override def output: Seq[Attribute] = left.output
 
-  override lazy val resolved: Boolean =
+  final override lazy val resolved: Boolean =
     childrenResolved &&
-    left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
+      left.output.length == right.output.length &&
+      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
+}
+
+private[sql] object SetOperation {
+  def unapply(p: SetOperation): Option[(LogicalPlan, LogicalPlan)] = Some((p.left, p.right))
+}
+
+case class Union(left: LogicalPlan, right: LogicalPlan) extends SetOperation(left, right) {
 
   override def statistics: Statistics = {
     val sizeInBytes = left.statistics.sizeInBytes + right.statistics.sizeInBytes
@@ -103,6 +111,10 @@ case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   }
 }
 
+case class Intersect(left: LogicalPlan, right: LogicalPlan) extends SetOperation(left, right)
+
+case class Except(left: LogicalPlan, right: LogicalPlan) extends SetOperation(left, right)
+
 case class Join(
   left: LogicalPlan,
   right: LogicalPlan,
@@ -142,15 +154,6 @@ case class BroadcastHint(child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 }
 
-
-case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
-  override def output: Seq[Attribute] = left.output
-
-  override lazy val resolved: Boolean =
-    childrenResolved &&
-      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
-}
-
 case class InsertIntoTable(
     table: LogicalPlan,
     partition: Map[String, Option[String]],
@@ -160,7 +163,7 @@ case class InsertIntoTable(
   extends LogicalPlan {
 
   override def children: Seq[LogicalPlan] = child :: Nil
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = Seq.empty
 
   assert(overwrite || !ifNotExists)
   override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall {
@@ -440,10 +443,3 @@ case object OneRowRelation extends LeafNode {
   override def statistics: Statistics = Statistics(sizeInBytes = 1)
 }
 
-case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
-  override def output: Seq[Attribute] = left.output
-
-  override lazy val resolved: Boolean =
-    childrenResolved &&
-      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
-}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 7065adce04bf8..fbdd3a7776f50 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -145,6 +145,24 @@ class AnalysisErrorSuite extends AnalysisTest {
     UnresolvedTestPlan(),
     "unresolved" :: Nil)
 
+  errorTest(
+    "union with unequal number of columns",
+    testRelation.unionAll(testRelation2),
+    "union" :: "number of columns" :: testRelation2.output.length.toString ::
+      testRelation.output.length.toString :: Nil)
+
+  errorTest(
+    "intersect with unequal number of columns",
+    testRelation.intersect(testRelation2),
+    "intersect" :: "number of columns" :: testRelation2.output.length.toString ::
+      testRelation.output.length.toString :: Nil)
+
+  errorTest(
+    "except with unequal number of columns",
+    testRelation.except(testRelation2),
+    "except" :: "number of columns" :: testRelation2.output.length.toString ::
+      testRelation.output.length.toString :: Nil)
+
   errorTest(
     "SPARK-9955: correct error message for aggregate",
     // When parse SQL string, we will wrap aggregate expressions with UnresolvedAlias.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index bbe8c1911bf86..98d21aa76d64e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -751,7 +751,7 @@ private[hive] case class InsertIntoHiveTable(
   extends LogicalPlan {
 
   override def children: Seq[LogicalPlan] = child :: Nil
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = Seq.empty
 
   val numDynamicPartitions = partition.values.count(_.isEmpty)
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 12c667e6e92da..62efda613a176 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -61,7 +61,7 @@ case class InsertIntoHiveTable(
     serializer
   }
 
-  def output: Seq[Attribute] = child.output
+  def output: Seq[Attribute] = Seq.empty
 
   def saveAsHiveFile(
       rdd: RDD[InternalRow],

From d4549fe58fa0d781e0e891bceff893420cb1d598 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 25 Aug 2015 00:28:51 -0700
Subject: [PATCH 1203/1454] [SPARK-10214] [SPARKR] [DOCS] Improve SparkR
 Column, DataFrame API docs

cc: shivaram

## Summary

- Add name tags to each methods in DataFrame.R and column.R
- Replace `rdname column` with `rdname {each_func}`. i.e. alias method : `rdname column` =>  `rdname alias`

## Generated PDF File
https://drive.google.com/file/d/0B9biIZIU47lLNHN2aFpnQXlSeGs/view?usp=sharing

## JIRA
[[SPARK-10214] Improve SparkR Column, DataFrame API docs - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10214)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8414 from yu-iskw/SPARK-10214.
---
 R/pkg/R/DataFrame.R | 101 +++++++++++++++++++++++++++++++++++---------
 R/pkg/R/column.R    |  40 ++++++++++++------
 R/pkg/R/generics.R  |   2 +-
 3 files changed, 109 insertions(+), 34 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 895603235011e..10f3c4ea59864 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -27,9 +27,10 @@ setOldClass("jobj")
 #'              \code{jsonFile}, \code{table} etc.
 #' @rdname DataFrame
 #' @seealso jsonFile, table
+#' @docType class
 #'
-#' @param env An R environment that stores bookkeeping states of the DataFrame
-#' @param sdf A Java object reference to the backing Scala DataFrame
+#' @slot env An R environment that stores bookkeeping states of the DataFrame
+#' @slot sdf A Java object reference to the backing Scala DataFrame
 #' @export
 setClass("DataFrame",
          slots = list(env = "environment",
@@ -61,6 +62,7 @@ dataFrame <- function(sdf, isCached = FALSE) {
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname printSchema
+#' @name printSchema
 #' @export
 #' @examples
 #'\dontrun{
@@ -84,6 +86,7 @@ setMethod("printSchema",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname schema
+#' @name schema
 #' @export
 #' @examples
 #'\dontrun{
@@ -106,6 +109,7 @@ setMethod("schema",
 #' @param x A SparkSQL DataFrame
 #' @param extended Logical. If extended is False, explain() only prints the physical plan.
 #' @rdname explain
+#' @name explain
 #' @export
 #' @examples
 #'\dontrun{
@@ -135,6 +139,7 @@ setMethod("explain",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname isLocal
+#' @name isLocal
 #' @export
 #' @examples
 #'\dontrun{
@@ -158,6 +163,7 @@ setMethod("isLocal",
 #' @param numRows The number of rows to print. Defaults to 20.
 #'
 #' @rdname showDF
+#' @name showDF
 #' @export
 #' @examples
 #'\dontrun{
@@ -181,6 +187,7 @@ setMethod("showDF",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname show
+#' @name show
 #' @export
 #' @examples
 #'\dontrun{
@@ -206,6 +213,7 @@ setMethod("show", "DataFrame",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname dtypes
+#' @name dtypes
 #' @export
 #' @examples
 #'\dontrun{
@@ -230,6 +238,8 @@ setMethod("dtypes",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname columns
+#' @name columns
+#' @aliases names
 #' @export
 #' @examples
 #'\dontrun{
@@ -248,7 +258,7 @@ setMethod("columns",
           })
 
 #' @rdname columns
-#' @aliases names,DataFrame,function-method
+#' @name names
 setMethod("names",
           signature(x = "DataFrame"),
           function(x) {
@@ -256,6 +266,7 @@ setMethod("names",
           })
 
 #' @rdname columns
+#' @name names<-
 setMethod("names<-",
           signature(x = "DataFrame"),
           function(x, value) {
@@ -273,6 +284,7 @@ setMethod("names<-",
 #' @param tableName A character vector containing the name of the table
 #'
 #' @rdname registerTempTable
+#' @name registerTempTable
 #' @export
 #' @examples
 #'\dontrun{
@@ -299,6 +311,7 @@ setMethod("registerTempTable",
 #' the existing rows in the table.
 #'
 #' @rdname insertInto
+#' @name insertInto
 #' @export
 #' @examples
 #'\dontrun{
@@ -321,7 +334,8 @@ setMethod("insertInto",
 #'
 #' @param x A SparkSQL DataFrame
 #'
-#' @rdname cache-methods
+#' @rdname cache
+#' @name cache
 #' @export
 #' @examples
 #'\dontrun{
@@ -347,6 +361,7 @@ setMethod("cache",
 #'
 #' @param x The DataFrame to persist
 #' @rdname persist
+#' @name persist
 #' @export
 #' @examples
 #'\dontrun{
@@ -372,6 +387,7 @@ setMethod("persist",
 #' @param x The DataFrame to unpersist
 #' @param blocking Whether to block until all blocks are deleted
 #' @rdname unpersist-methods
+#' @name unpersist
 #' @export
 #' @examples
 #'\dontrun{
@@ -397,6 +413,7 @@ setMethod("unpersist",
 #' @param x A SparkSQL DataFrame
 #' @param numPartitions The number of partitions to use.
 #' @rdname repartition
+#' @name repartition
 #' @export
 #' @examples
 #'\dontrun{
@@ -446,6 +463,7 @@ setMethod("toJSON",
 #' @param x A SparkSQL DataFrame
 #' @param path The directory where the file is saved
 #' @rdname saveAsParquetFile
+#' @name saveAsParquetFile
 #' @export
 #' @examples
 #'\dontrun{
@@ -467,6 +485,7 @@ setMethod("saveAsParquetFile",
 #'
 #' @param x A SparkSQL DataFrame
 #' @rdname distinct
+#' @name distinct
 #' @export
 #' @examples
 #'\dontrun{
@@ -488,7 +507,8 @@ setMethod("distinct",
 #' @description Returns a new DataFrame containing distinct rows in this DataFrame
 #'
 #' @rdname unique
-#' @aliases unique
+#' @name unique
+#' @aliases distinct
 setMethod("unique",
           signature(x = "DataFrame"),
           function(x) {
@@ -526,7 +546,7 @@ setMethod("sample",
           })
 
 #' @rdname sample
-#' @aliases sample
+#' @name sample_frac
 setMethod("sample_frac",
           signature(x = "DataFrame", withReplacement = "logical",
                     fraction = "numeric"),
@@ -541,6 +561,8 @@ setMethod("sample_frac",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname count
+#' @name count
+#' @aliases nrow
 #' @export
 #' @examples
 #'\dontrun{
@@ -574,6 +596,7 @@ setMethod("nrow",
 #' @param x a SparkSQL DataFrame
 #'
 #' @rdname ncol
+#' @name ncol
 #' @export
 #' @examples
 #'\dontrun{
@@ -593,6 +616,7 @@ setMethod("ncol",
 #' @param x a SparkSQL DataFrame
 #'
 #' @rdname dim
+#' @name dim
 #' @export
 #' @examples
 #'\dontrun{
@@ -613,8 +637,8 @@ setMethod("dim",
 #' @param x A SparkSQL DataFrame
 #' @param stringsAsFactors (Optional) A logical indicating whether or not string columns
 #' should be converted to factors. FALSE by default.
-
-#' @rdname collect-methods
+#' @rdname collect
+#' @name collect
 #' @export
 #' @examples
 #'\dontrun{
@@ -650,6 +674,7 @@ setMethod("collect",
 #' @return A new DataFrame containing the number of rows specified.
 #'
 #' @rdname limit
+#' @name limit
 #' @export
 #' @examples
 #' \dontrun{
@@ -669,6 +694,7 @@ setMethod("limit",
 #' Take the first NUM rows of a DataFrame and return a the results as a data.frame
 #'
 #' @rdname take
+#' @name take
 #' @export
 #' @examples
 #'\dontrun{
@@ -696,6 +722,7 @@ setMethod("take",
 #' @return A data.frame
 #'
 #' @rdname head
+#' @name head
 #' @export
 #' @examples
 #'\dontrun{
@@ -717,6 +744,7 @@ setMethod("head",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname first
+#' @name first
 #' @export
 #' @examples
 #'\dontrun{
@@ -732,7 +760,7 @@ setMethod("first",
             take(x, 1)
           })
 
-# toRDD()
+# toRDD
 #
 # Converts a Spark DataFrame to an RDD while preserving column names.
 #
@@ -769,6 +797,7 @@ setMethod("toRDD",
 #' @seealso GroupedData
 #' @aliases group_by
 #' @rdname groupBy
+#' @name groupBy
 #' @export
 #' @examples
 #' \dontrun{
@@ -792,7 +821,7 @@ setMethod("groupBy",
            })
 
 #' @rdname groupBy
-#' @aliases group_by
+#' @name group_by
 setMethod("group_by",
           signature(x = "DataFrame"),
           function(x, ...) {
@@ -804,7 +833,8 @@ setMethod("group_by",
 #' Compute aggregates by specifying a list of columns
 #'
 #' @param x a DataFrame
-#' @rdname DataFrame
+#' @rdname agg
+#' @name agg
 #' @aliases summarize
 #' @export
 setMethod("agg",
@@ -813,8 +843,8 @@ setMethod("agg",
             agg(groupBy(x), ...)
           })
 
-#' @rdname DataFrame
-#' @aliases agg
+#' @rdname agg
+#' @name summarize
 setMethod("summarize",
           signature(x = "DataFrame"),
           function(x, ...) {
@@ -890,12 +920,14 @@ getColumn <- function(x, c) {
 }
 
 #' @rdname select
+#' @name $
 setMethod("$", signature(x = "DataFrame"),
           function(x, name) {
             getColumn(x, name)
           })
 
 #' @rdname select
+#' @name $<-
 setMethod("$<-", signature(x = "DataFrame"),
           function(x, name, value) {
             stopifnot(class(value) == "Column" || is.null(value))
@@ -923,6 +955,7 @@ setMethod("$<-", signature(x = "DataFrame"),
           })
 
 #' @rdname select
+#' @name [[
 setMethod("[[", signature(x = "DataFrame"),
           function(x, i) {
             if (is.numeric(i)) {
@@ -933,6 +966,7 @@ setMethod("[[", signature(x = "DataFrame"),
           })
 
 #' @rdname select
+#' @name [
 setMethod("[", signature(x = "DataFrame", i = "missing"),
           function(x, i, j, ...) {
             if (is.numeric(j)) {
@@ -1008,6 +1042,7 @@ setMethod("select",
 #' @param ... Additional expressions
 #' @return A DataFrame
 #' @rdname selectExpr
+#' @name selectExpr
 #' @export
 #' @examples
 #'\dontrun{
@@ -1034,6 +1069,8 @@ setMethod("selectExpr",
 #' @param col A Column expression.
 #' @return A DataFrame with the new column added.
 #' @rdname withColumn
+#' @name withColumn
+#' @aliases mutate
 #' @export
 #' @examples
 #'\dontrun{
@@ -1057,7 +1094,7 @@ setMethod("withColumn",
 #' @param col a named argument of the form name = col
 #' @return A new DataFrame with the new columns added.
 #' @rdname withColumn
-#' @aliases withColumn
+#' @name mutate
 #' @export
 #' @examples
 #'\dontrun{
@@ -1094,6 +1131,7 @@ setMethod("mutate",
 #' @param newCol The new column name.
 #' @return A DataFrame with the column name changed.
 #' @rdname withColumnRenamed
+#' @name withColumnRenamed
 #' @export
 #' @examples
 #'\dontrun{
@@ -1124,6 +1162,7 @@ setMethod("withColumnRenamed",
 #' @param newCol A named pair of the form new_column_name = existing_column
 #' @return A DataFrame with the column name changed.
 #' @rdname withColumnRenamed
+#' @name rename
 #' @aliases withColumnRenamed
 #' @export
 #' @examples
@@ -1165,6 +1204,8 @@ setClassUnion("characterOrColumn", c("character", "Column"))
 #' @param ... Additional sorting fields
 #' @return A DataFrame where all elements are sorted.
 #' @rdname arrange
+#' @name arrange
+#' @aliases orderby
 #' @export
 #' @examples
 #'\dontrun{
@@ -1191,7 +1232,7 @@ setMethod("arrange",
           })
 
 #' @rdname arrange
-#' @aliases orderBy,DataFrame,function-method
+#' @name orderby
 setMethod("orderBy",
           signature(x = "DataFrame", col = "characterOrColumn"),
           function(x, col) {
@@ -1207,6 +1248,7 @@ setMethod("orderBy",
 #' or a string containing a SQL statement
 #' @return A DataFrame containing only the rows that meet the condition.
 #' @rdname filter
+#' @name filter
 #' @export
 #' @examples
 #'\dontrun{
@@ -1228,7 +1270,7 @@ setMethod("filter",
           })
 
 #' @rdname filter
-#' @aliases where,DataFrame,function-method
+#' @name where
 setMethod("where",
           signature(x = "DataFrame", condition = "characterOrColumn"),
           function(x, condition) {
@@ -1247,6 +1289,7 @@ setMethod("where",
 #' 'inner', 'outer', 'left_outer', 'right_outer', 'semijoin'. The default joinType is "inner".
 #' @return A DataFrame containing the result of the join operation.
 #' @rdname join
+#' @name join
 #' @export
 #' @examples
 #'\dontrun{
@@ -1279,8 +1322,9 @@ setMethod("join",
             dataFrame(sdf)
           })
 
-#' rdname merge
-#' aliases join
+#' @rdname merge
+#' @name merge
+#' @aliases join
 setMethod("merge",
           signature(x = "DataFrame", y = "DataFrame"),
           function(x, y, joinExpr = NULL, joinType = NULL, ...) {
@@ -1298,6 +1342,7 @@ setMethod("merge",
 #' @param y A Spark DataFrame
 #' @return A DataFrame containing the result of the union.
 #' @rdname unionAll
+#' @name unionAll
 #' @export
 #' @examples
 #'\dontrun{
@@ -1319,6 +1364,7 @@ setMethod("unionAll",
 #' @description Returns a new DataFrame containing rows of all parameters.
 #
 #' @rdname rbind
+#' @name rbind
 #' @aliases unionAll
 setMethod("rbind",
           signature(... = "DataFrame"),
@@ -1339,6 +1385,7 @@ setMethod("rbind",
 #' @param y A Spark DataFrame
 #' @return A DataFrame containing the result of the intersect.
 #' @rdname intersect
+#' @name intersect
 #' @export
 #' @examples
 #'\dontrun{
@@ -1364,6 +1411,7 @@ setMethod("intersect",
 #' @param y A Spark DataFrame
 #' @return A DataFrame containing the result of the except operation.
 #' @rdname except
+#' @name except
 #' @export
 #' @examples
 #'\dontrun{
@@ -1403,6 +1451,8 @@ setMethod("except",
 #' @param mode One of 'append', 'overwrite', 'error', 'ignore'
 #'
 #' @rdname write.df
+#' @name write.df
+#' @aliases saveDF
 #' @export
 #' @examples
 #'\dontrun{
@@ -1435,7 +1485,7 @@ setMethod("write.df",
           })
 
 #' @rdname write.df
-#' @aliases saveDF
+#' @name saveDF
 #' @export
 setMethod("saveDF",
           signature(df = "DataFrame", path = "character"),
@@ -1466,6 +1516,7 @@ setMethod("saveDF",
 #' @param mode One of 'append', 'overwrite', 'error', 'ignore'
 #'
 #' @rdname saveAsTable
+#' @name saveAsTable
 #' @export
 #' @examples
 #'\dontrun{
@@ -1505,6 +1556,8 @@ setMethod("saveAsTable",
 #' @param ... Additional expressions
 #' @return A DataFrame
 #' @rdname describe
+#' @name describe
+#' @aliases summary
 #' @export
 #' @examples
 #'\dontrun{
@@ -1525,6 +1578,7 @@ setMethod("describe",
           })
 
 #' @rdname describe
+#' @name describe
 setMethod("describe",
           signature(x = "DataFrame"),
           function(x) {
@@ -1538,7 +1592,7 @@ setMethod("describe",
 #' @description Computes statistics for numeric columns of the DataFrame
 #'
 #' @rdname summary
-#' @aliases describe
+#' @name summary
 setMethod("summary",
           signature(x = "DataFrame"),
           function(x) {
@@ -1562,6 +1616,8 @@ setMethod("summary",
 #' @return A DataFrame
 #'
 #' @rdname nafunctions
+#' @name dropna
+#' @aliases na.omit
 #' @export
 #' @examples
 #'\dontrun{
@@ -1588,7 +1644,8 @@ setMethod("dropna",
             dataFrame(sdf)
           })
 
-#' @aliases dropna
+#' @rdname nafunctions
+#' @name na.omit
 #' @export
 setMethod("na.omit",
           signature(x = "DataFrame"),
@@ -1615,6 +1672,7 @@ setMethod("na.omit",
 #' @return A DataFrame
 #'
 #' @rdname nafunctions
+#' @name fillna
 #' @export
 #' @examples
 #'\dontrun{
@@ -1685,6 +1743,7 @@ setMethod("fillna",
 #'         occurrences will have zero as their counts.
 #'
 #' @rdname statfunctions
+#' @name crosstab
 #' @export
 #' @examples
 #' \dontrun{
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index a1f50c383367c..4805096f3f9c5 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -24,10 +24,9 @@ setOldClass("jobj")
 
 #' @title S4 class that represents a DataFrame column
 #' @description The column class supports unary, binary operations on DataFrame columns
-
 #' @rdname column
 #'
-#' @param jc reference to JVM DataFrame column
+#' @slot jc reference to JVM DataFrame column
 #' @export
 setClass("Column",
          slots = list(jc = "jobj"))
@@ -46,6 +45,7 @@ col <- function(x) {
 }
 
 #' @rdname show
+#' @name show
 setMethod("show", "Column",
           function(object) {
             cat("Column", callJMethod(object@jc, "toString"), "\n")
@@ -122,8 +122,11 @@ createMethods()
 #' alias
 #'
 #' Set a new name for a column
-
-#' @rdname column
+#'
+#' @rdname alias
+#' @name alias
+#' @family colum_func
+#' @export
 setMethod("alias",
           signature(object = "Column"),
           function(object, data) {
@@ -138,7 +141,9 @@ setMethod("alias",
 #'
 #' An expression that returns a substring.
 #'
-#' @rdname column
+#' @rdname substr
+#' @name substr
+#' @family colum_func
 #'
 #' @param start starting position
 #' @param stop ending position
@@ -152,7 +157,9 @@ setMethod("substr", signature(x = "Column"),
 #'
 #' Test if the column is between the lower bound and upper bound, inclusive.
 #'
-#' @rdname column
+#' @rdname between
+#' @name between
+#' @family colum_func
 #'
 #' @param bounds lower and upper bounds
 setMethod("between", signature(x = "Column"),
@@ -167,7 +174,9 @@ setMethod("between", signature(x = "Column"),
 
 #' Casts the column to a different data type.
 #'
-#' @rdname column
+#' @rdname cast
+#' @name cast
+#' @family colum_func
 #'
 #' @examples \dontrun{
 #'   cast(df$age, "string")
@@ -189,11 +198,15 @@ setMethod("cast",
 
 #' Match a column with given values.
 #'
-#' @rdname column
+#' @rdname match
+#' @name %in%
+#' @aliases %in%
 #' @return a matched values as a result of comparing with given values.
-#' @examples \dontrun{
-#'   filter(df, "age in (10, 30)")
-#'   where(df, df$age %in% c(10, 30))
+#' @export
+#' @examples
+#' \dontrun{
+#' filter(df, "age in (10, 30)")
+#' where(df, df$age %in% c(10, 30))
 #' }
 setMethod("%in%",
           signature(x = "Column"),
@@ -208,7 +221,10 @@ setMethod("%in%",
 #' If values in the specified column are null, returns the value. 
 #' Can be used in conjunction with `when` to specify a default value for expressions.
 #'
-#' @rdname column
+#' @rdname otherwise
+#' @name otherwise
+#' @family colum_func
+#' @export
 setMethod("otherwise",
           signature(x = "Column", value = "ANY"),
           function(x, value) {
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 610a8c31223cd..a829d46c1894c 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -441,7 +441,7 @@ setGeneric("filter", function(x, condition) { standardGeneric("filter") })
 #' @export
 setGeneric("group_by", function(x, ...) { standardGeneric("group_by") })
 
-#' @rdname DataFrame
+#' @rdname groupBy
 #' @export
 setGeneric("groupBy", function(x, ...) { standardGeneric("groupBy") })
 

From 57b960bf3706728513f9e089455a533f0244312e Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 25 Aug 2015 08:32:20 +0100
Subject: [PATCH 1204/1454] [SPARK-6196] [BUILD] Remove MapR profiles in favor
 of hadoop-provided

Follow up to https://github.com/apache/spark/pull/7047

pwendell mentioned that MapR should use `hadoop-provided` now, and indeed the new build script does not produce `mapr3`/`mapr4` artifacts anymore. Hence the action seems to be to remove the profiles, which are now not used.

CC trystanleftwich

Author: Sean Owen <sowen@cloudera.com>

Closes #8338 from srowen/SPARK-6196.
---
 pom.xml | 38 --------------------------------------
 1 file changed, 38 deletions(-)

diff --git a/pom.xml b/pom.xml
index d5945f2546d38..0716016523ee1 100644
--- a/pom.xml
+++ b/pom.xml
@@ -2386,44 +2386,6 @@
       </modules>
     </profile>
 
-    <profile>
-      <id>mapr3</id>
-      <properties>
-        <hadoop.version>1.0.3-mapr-3.0.3</hadoop.version>
-        <yarn.version>2.4.1-mapr-1408</yarn.version>
-        <hbase.version>0.98.4-mapr-1408</hbase.version>
-        <zookeeper.version>3.4.5-mapr-1406</zookeeper.version>
-      </properties>
-    </profile>
-
-    <profile>
-      <id>mapr4</id>
-      <properties>
-        <hadoop.version>2.4.1-mapr-1408</hadoop.version>
-        <yarn.version>2.4.1-mapr-1408</yarn.version>
-        <hbase.version>0.98.4-mapr-1408</hbase.version>
-        <zookeeper.version>3.4.5-mapr-1406</zookeeper.version>
-      </properties>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.curator</groupId>
-          <artifactId>curator-recipes</artifactId>
-          <version>${curator.version}</version>
-          <exclusions>
-            <exclusion>
-              <groupId>org.apache.zookeeper</groupId>
-              <artifactId>zookeeper</artifactId>
-            </exclusion>
-          </exclusions>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.zookeeper</groupId>
-          <artifactId>zookeeper</artifactId>
-          <version>3.4.5-mapr-1406</version>
-        </dependency>
-      </dependencies>
-    </profile>
-
     <profile>
       <id>hive-thriftserver</id>
       <modules>

From 1fc37581a52530bac5d555dbf14927a5780c3b75 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 25 Aug 2015 00:35:51 -0700
Subject: [PATCH 1205/1454] [SPARK-10210] [STREAMING] Filter out non-existent
 blocks before creating BlockRDD

When write ahead log is not enabled, a recovered streaming driver still tries to run jobs using pre-failure block ids, and fails as the block do not exists in-memory any more (and cannot be recovered as receiver WAL is not enabled).

This occurs because the driver-side WAL of ReceivedBlockTracker is recovers that past block information, and ReceiveInputDStream creates BlockRDDs even if those blocks do not exist.

The solution in this PR is to filter out block ids that do not exist before creating the BlockRDD. In addition, it adds unit tests to verify other logic in ReceiverInputDStream.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8405 from tdas/SPARK-10210.
---
 .../dstream/ReceiverInputDStream.scala        |  10 +-
 .../rdd/WriteAheadLogBackedBlockRDD.scala     |   2 +-
 .../streaming/ReceiverInputDStreamSuite.scala | 156 ++++++++++++++++++
 3 files changed, 166 insertions(+), 2 deletions(-)
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index a15800917c6f4..6c139f32da31d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -116,7 +116,15 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
             logWarning("Some blocks have Write Ahead Log information; this is unexpected")
           }
         }
-        new BlockRDD[T](ssc.sc, blockIds)
+        val validBlockIds = blockIds.filter { id =>
+          ssc.sparkContext.env.blockManager.master.contains(id)
+        }
+        if (validBlockIds.size != blockIds.size) {
+          logWarning("Some blocks could not be recovered as they were not found in memory. " +
+            "To prevent such data loss, enabled Write Ahead Log (see programming guide " +
+            "for more details.")
+        }
+        new BlockRDD[T](ssc.sc, validBlockIds)
       }
     } else {
       // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
index 620b8a36a2baf..e081ffe46f502 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
@@ -75,7 +75,7 @@ private[streaming]
 class WriteAheadLogBackedBlockRDD[T: ClassTag](
     @transient sc: SparkContext,
     @transient blockIds: Array[BlockId],
-    @transient walRecordHandles: Array[WriteAheadLogRecordHandle],
+    @transient val walRecordHandles: Array[WriteAheadLogRecordHandle],
     @transient isBlockIdValid: Array[Boolean] = Array.empty,
     storeInBlockManager: Boolean = false,
     storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala
new file mode 100644
index 0000000000000..6d388d9624d92
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.rdd.BlockRDD
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
+import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
+import org.apache.spark.streaming.receiver.{BlockManagerBasedStoreResult, Receiver, WriteAheadLogBasedStoreResult}
+import org.apache.spark.streaming.scheduler.ReceivedBlockInfo
+import org.apache.spark.streaming.util.{WriteAheadLogRecordHandle, WriteAheadLogUtils}
+import org.apache.spark.{SparkConf, SparkEnv}
+
+class ReceiverInputDStreamSuite extends TestSuiteBase with BeforeAndAfterAll {
+
+  override def afterAll(): Unit = {
+    StreamingContext.getActive().map { _.stop() }
+  }
+
+  testWithoutWAL("createBlockRDD creates empty BlockRDD when no block info") { receiverStream =>
+    val rdd = receiverStream.createBlockRDD(Time(0), Seq.empty)
+    assert(rdd.isInstanceOf[BlockRDD[_]])
+    assert(!rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+    assert(rdd.isEmpty())
+  }
+
+  testWithoutWAL("createBlockRDD creates correct BlockRDD with block info") { receiverStream =>
+    val blockInfos = Seq.fill(5) { createBlockInfo(withWALInfo = false) }
+    val blockIds = blockInfos.map(_.blockId)
+
+    // Verify that there are some blocks that are present, and some that are not
+    require(blockIds.forall(blockId => SparkEnv.get.blockManager.master.contains(blockId)))
+
+    val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+    assert(rdd.isInstanceOf[BlockRDD[_]])
+    assert(!rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+    val blockRDD = rdd.asInstanceOf[BlockRDD[_]]
+    assert(blockRDD.blockIds.toSeq === blockIds)
+  }
+
+  testWithoutWAL("createBlockRDD filters non-existent blocks before creating BlockRDD") {
+    receiverStream =>
+      val presentBlockInfos = Seq.fill(2)(createBlockInfo(withWALInfo = false, createBlock = true))
+      val absentBlockInfos = Seq.fill(3)(createBlockInfo(withWALInfo = false, createBlock = false))
+      val blockInfos = presentBlockInfos ++ absentBlockInfos
+      val blockIds = blockInfos.map(_.blockId)
+
+      // Verify that there are some blocks that are present, and some that are not
+      require(blockIds.exists(blockId => SparkEnv.get.blockManager.master.contains(blockId)))
+      require(blockIds.exists(blockId => !SparkEnv.get.blockManager.master.contains(blockId)))
+
+      val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+      assert(rdd.isInstanceOf[BlockRDD[_]])
+      val blockRDD = rdd.asInstanceOf[BlockRDD[_]]
+      assert(blockRDD.blockIds.toSeq === presentBlockInfos.map { _.blockId})
+  }
+
+  testWithWAL("createBlockRDD creates empty WALBackedBlockRDD when no block info") {
+    receiverStream =>
+      val rdd = receiverStream.createBlockRDD(Time(0), Seq.empty)
+      assert(rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+      assert(rdd.isEmpty())
+  }
+
+  testWithWAL(
+    "createBlockRDD creates correct WALBackedBlockRDD with all block info having WAL info") {
+    receiverStream =>
+      val blockInfos = Seq.fill(5) { createBlockInfo(withWALInfo = true) }
+      val blockIds = blockInfos.map(_.blockId)
+      val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+      assert(rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+      val blockRDD = rdd.asInstanceOf[WriteAheadLogBackedBlockRDD[_]]
+      assert(blockRDD.blockIds.toSeq === blockIds)
+      assert(blockRDD.walRecordHandles.toSeq === blockInfos.map { _.walRecordHandleOption.get })
+  }
+
+  testWithWAL("createBlockRDD creates BlockRDD when some block info dont have WAL info") {
+    receiverStream =>
+      val blockInfos1 = Seq.fill(2) { createBlockInfo(withWALInfo = true) }
+      val blockInfos2 = Seq.fill(3) { createBlockInfo(withWALInfo = false) }
+      val blockInfos = blockInfos1 ++ blockInfos2
+      val blockIds = blockInfos.map(_.blockId)
+      val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+      assert(rdd.isInstanceOf[BlockRDD[_]])
+      val blockRDD = rdd.asInstanceOf[BlockRDD[_]]
+      assert(blockRDD.blockIds.toSeq === blockIds)
+  }
+
+
+  private def testWithoutWAL(msg: String)(body: ReceiverInputDStream[_] => Unit): Unit = {
+    test(s"Without WAL enabled: $msg") {
+      runTest(enableWAL = false, body)
+    }
+  }
+
+  private def testWithWAL(msg: String)(body: ReceiverInputDStream[_] => Unit): Unit = {
+    test(s"With WAL enabled: $msg") {
+      runTest(enableWAL = true, body)
+    }
+  }
+
+  private def runTest(enableWAL: Boolean, body: ReceiverInputDStream[_] => Unit): Unit = {
+    val conf = new SparkConf()
+    conf.setMaster("local[4]").setAppName("ReceiverInputDStreamSuite")
+    conf.set(WriteAheadLogUtils.RECEIVER_WAL_ENABLE_CONF_KEY, enableWAL.toString)
+    require(WriteAheadLogUtils.enableReceiverLog(conf) === enableWAL)
+    val ssc = new StreamingContext(conf, Seconds(1))
+    val receiverStream = new ReceiverInputDStream[Int](ssc) {
+      override def getReceiver(): Receiver[Int] = null
+    }
+    withStreamingContext(ssc) { ssc =>
+      body(receiverStream)
+    }
+  }
+
+  /**
+   * Create a block info for input to the ReceiverInputDStream.createBlockRDD
+   * @param withWALInfo Create block with WAL info in it
+   * @param createBlock Actually create the block in the BlockManager
+   * @return
+   */
+  private def createBlockInfo(
+      withWALInfo: Boolean,
+      createBlock: Boolean = true): ReceivedBlockInfo = {
+    val blockId = new StreamBlockId(0, Random.nextLong())
+    if (createBlock) {
+      SparkEnv.get.blockManager.putSingle(blockId, 1, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      require(SparkEnv.get.blockManager.master.contains(blockId))
+    }
+    val storeResult = if (withWALInfo) {
+      new WriteAheadLogBasedStoreResult(blockId, None, new WriteAheadLogRecordHandle { })
+    } else {
+      new BlockManagerBasedStoreResult(blockId, None)
+    }
+    new ReceivedBlockInfo(0, None, None, storeResult)
+  }
+}

From 2f493f7e3924b769160a16f73cccbebf21973b91 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 25 Aug 2015 16:00:44 +0800
Subject: [PATCH 1206/1454] [SPARK-10177] [SQL] fix reading Timestamp in
 parquet from Hive

We misunderstood the Julian days and nanoseconds of the day in parquet (as TimestampType) from Hive/Impala, they are overlapped, so can't be added together directly.

In order to avoid the confusing rounding when do the converting, we use `2440588` as the Julian Day of epoch of unix timestamp (which should be 2440587.5).

Author: Davies Liu <davies@databricks.com>
Author: Cheng Lian <lian@databricks.com>

Closes #8400 from davies/timestamp_parquet.
---
 .../spark/sql/catalyst/util/DateTimeUtils.scala     |  7 ++++---
 .../sql/catalyst/util/DateTimeUtilsSuite.scala      | 13 +++++++++----
 .../sql/hive/ParquetHiveCompatibilitySuite.scala    |  2 +-
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 672620460c3c5..d652fce3fd9b6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -37,7 +37,8 @@ object DateTimeUtils {
   type SQLTimestamp = Long
 
   // see http://stackoverflow.com/questions/466321/convert-unix-timestamp-to-julian
-  final val JULIAN_DAY_OF_EPOCH = 2440587  // and .5
+  // it's 2440587.5, rounding up to compatible with Hive
+  final val JULIAN_DAY_OF_EPOCH = 2440588
   final val SECONDS_PER_DAY = 60 * 60 * 24L
   final val MICROS_PER_SECOND = 1000L * 1000L
   final val NANOS_PER_SECOND = MICROS_PER_SECOND * 1000L
@@ -183,7 +184,7 @@ object DateTimeUtils {
    */
   def fromJulianDay(day: Int, nanoseconds: Long): SQLTimestamp = {
     // use Long to avoid rounding errors
-    val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY - SECONDS_PER_DAY / 2
+    val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY
     seconds * MICROS_PER_SECOND + nanoseconds / 1000L
   }
 
@@ -191,7 +192,7 @@ object DateTimeUtils {
    * Returns Julian day and nanoseconds in a day from the number of microseconds
    */
   def toJulianDay(us: SQLTimestamp): (Int, Long) = {
-    val seconds = us / MICROS_PER_SECOND + SECONDS_PER_DAY / 2
+    val seconds = us / MICROS_PER_SECOND
     val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
     val secondsInDay = seconds % SECONDS_PER_DAY
     val nanos = (us % MICROS_PER_SECOND) * 1000L
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index d18fa4df13355..1596bb79fa94b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -49,13 +49,18 @@ class DateTimeUtilsSuite extends SparkFunSuite {
   test("us and julian day") {
     val (d, ns) = toJulianDay(0)
     assert(d === JULIAN_DAY_OF_EPOCH)
-    assert(ns === SECONDS_PER_DAY / 2 * NANOS_PER_SECOND)
+    assert(ns === 0)
     assert(fromJulianDay(d, ns) == 0L)
 
-    val t = new Timestamp(61394778610000L) // (2015, 6, 11, 10, 10, 10, 100)
+    val t = Timestamp.valueOf("2015-06-11 10:10:10.100")
     val (d1, ns1) = toJulianDay(fromJavaTimestamp(t))
-    val t2 = toJavaTimestamp(fromJulianDay(d1, ns1))
-    assert(t.equals(t2))
+    val t1 = toJavaTimestamp(fromJulianDay(d1, ns1))
+    assert(t.equals(t1))
+
+    val t2 = Timestamp.valueOf("2015-06-11 20:10:10.100")
+    val (d2, ns2) = toJulianDay(fromJavaTimestamp(t2))
+    val t22 = toJavaTimestamp(fromJulianDay(d2, ns2))
+    assert(t2.equals(t22))
   }
 
   test("SPARK-6785: java date conversion before and after epoch") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index bc30180cf0917..91d7a48208e8d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -113,7 +113,7 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with Before
       "BOOLEAN", "TINYINT", "SMALLINT", "INT", "BIGINT", "FLOAT", "DOUBLE", "STRING")
   }
 
-  ignore("SPARK-10177 timestamp") {
+  test("SPARK-10177 timestamp") {
     testParquetHiveCompatibility(Row(Timestamp.valueOf("2015-08-24 00:31:00")), "TIMESTAMP")
   }
 

From 7bc9a8c6249300ded31ea931c463d0a8f798e193 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 25 Aug 2015 01:06:36 -0700
Subject: [PATCH 1207/1454] [SPARK-10195] [SQL] Data sources Filter should not
 expose internal types

Spark SQL's data sources API exposes Catalyst's internal types through its Filter interfaces. This is a problem because types like UTF8String are not stable developer APIs and should not be exposed to third-parties.

This issue caused incompatibilities when upgrading our `spark-redshift` library to work against Spark 1.5.0.  To avoid these issues in the future we should only expose public types through these Filter objects. This patch accomplishes this by using CatalystTypeConverters to add the appropriate conversions.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8403 from JoshRosen/datasources-internal-vs-external-types.
---
 .../datasources/DataSourceStrategy.scala      | 67 ++++++++++---------
 .../execution/datasources/jdbc/JDBCRDD.scala  |  2 +-
 .../datasources/parquet/ParquetFilters.scala  | 19 +++---
 .../spark/sql/sources/FilteredScanSuite.scala |  7 ++
 4 files changed, 54 insertions(+), 41 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 2a4c40db8bb66..6c1ef6a6df887 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -20,7 +20,8 @@ package org.apache.spark.sql.execution.datasources
 import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
-import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.catalyst.CatalystTypeConverters.convertToScala
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, expressions}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
@@ -344,45 +345,47 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
    */
   protected[sql] def selectFilters(filters: Seq[Expression]) = {
     def translate(predicate: Expression): Option[Filter] = predicate match {
-      case expressions.EqualTo(a: Attribute, Literal(v, _)) =>
-        Some(sources.EqualTo(a.name, v))
-      case expressions.EqualTo(Literal(v, _), a: Attribute) =>
-        Some(sources.EqualTo(a.name, v))
-
-      case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
-        Some(sources.EqualNullSafe(a.name, v))
-      case expressions.EqualNullSafe(Literal(v, _), a: Attribute) =>
-        Some(sources.EqualNullSafe(a.name, v))
-
-      case expressions.GreaterThan(a: Attribute, Literal(v, _)) =>
-        Some(sources.GreaterThan(a.name, v))
-      case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
-        Some(sources.LessThan(a.name, v))
-
-      case expressions.LessThan(a: Attribute, Literal(v, _)) =>
-        Some(sources.LessThan(a.name, v))
-      case expressions.LessThan(Literal(v, _), a: Attribute) =>
-        Some(sources.GreaterThan(a.name, v))
-
-      case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, _)) =>
-        Some(sources.GreaterThanOrEqual(a.name, v))
-      case expressions.GreaterThanOrEqual(Literal(v, _), a: Attribute) =>
-        Some(sources.LessThanOrEqual(a.name, v))
-
-      case expressions.LessThanOrEqual(a: Attribute, Literal(v, _)) =>
-        Some(sources.LessThanOrEqual(a.name, v))
-      case expressions.LessThanOrEqual(Literal(v, _), a: Attribute) =>
-        Some(sources.GreaterThanOrEqual(a.name, v))
+      case expressions.EqualTo(a: Attribute, Literal(v, t)) =>
+        Some(sources.EqualTo(a.name, convertToScala(v, t)))
+      case expressions.EqualTo(Literal(v, t), a: Attribute) =>
+        Some(sources.EqualTo(a.name, convertToScala(v, t)))
+
+      case expressions.EqualNullSafe(a: Attribute, Literal(v, t)) =>
+        Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))
+      case expressions.EqualNullSafe(Literal(v, t), a: Attribute) =>
+        Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))
+
+      case expressions.GreaterThan(a: Attribute, Literal(v, t)) =>
+        Some(sources.GreaterThan(a.name, convertToScala(v, t)))
+      case expressions.GreaterThan(Literal(v, t), a: Attribute) =>
+        Some(sources.LessThan(a.name, convertToScala(v, t)))
+
+      case expressions.LessThan(a: Attribute, Literal(v, t)) =>
+        Some(sources.LessThan(a.name, convertToScala(v, t)))
+      case expressions.LessThan(Literal(v, t), a: Attribute) =>
+        Some(sources.GreaterThan(a.name, convertToScala(v, t)))
+
+      case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, t)) =>
+        Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))
+      case expressions.GreaterThanOrEqual(Literal(v, t), a: Attribute) =>
+        Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))
+
+      case expressions.LessThanOrEqual(a: Attribute, Literal(v, t)) =>
+        Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))
+      case expressions.LessThanOrEqual(Literal(v, t), a: Attribute) =>
+        Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))
 
       case expressions.InSet(a: Attribute, set) =>
-        Some(sources.In(a.name, set.toArray))
+        val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
+        Some(sources.In(a.name, set.toArray.map(toScala)))
 
       // Because we only convert In to InSet in Optimizer when there are more than certain
       // items. So it is possible we still get an In expression here that needs to be pushed
       // down.
       case expressions.In(a: Attribute, list) if !list.exists(!_.isInstanceOf[Literal]) =>
         val hSet = list.map(e => e.eval(EmptyRow))
-        Some(sources.In(a.name, hSet.toArray))
+        val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
+        Some(sources.In(a.name, hSet.toArray.map(toScala)))
 
       case expressions.IsNull(a: Attribute) =>
         Some(sources.IsNull(a.name))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index e537d631f4559..730d88b024cb1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -262,7 +262,7 @@ private[sql] class JDBCRDD(
    * Converts value to SQL expression.
    */
   private def compileValue(value: Any): Any = value match {
-    case stringValue: UTF8String => s"'${escapeSql(stringValue.toString)}'"
+    case stringValue: String => s"'${escapeSql(stringValue)}'"
     case _ => value
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index c74c8388632f5..c6b3fe7900da8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -32,7 +32,6 @@ import org.apache.spark.SparkEnv
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.sources
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 
 private[sql] object ParquetFilters {
   val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter"
@@ -65,7 +64,7 @@ private[sql] object ParquetFilters {
     case StringType =>
       (n: String, v: Any) => FilterApi.eq(
         binaryColumn(n),
-        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull)
+        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[String].getBytes("utf-8"))).orNull)
     case BinaryType =>
       (n: String, v: Any) => FilterApi.eq(
         binaryColumn(n),
@@ -86,7 +85,7 @@ private[sql] object ParquetFilters {
     case StringType =>
       (n: String, v: Any) => FilterApi.notEq(
         binaryColumn(n),
-        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull)
+        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[String].getBytes("utf-8"))).orNull)
     case BinaryType =>
       (n: String, v: Any) => FilterApi.notEq(
         binaryColumn(n),
@@ -104,7 +103,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.lt(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.lt(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -121,7 +121,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.ltEq(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.ltEq(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -138,7 +139,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.gt(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.gt(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -155,7 +157,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.gtEq(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.gtEq(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -177,7 +180,7 @@ private[sql] object ParquetFilters {
     case StringType =>
       (n: String, v: Set[Any]) =>
         FilterApi.userDefined(binaryColumn(n),
-          SetInFilter(v.map(e => Binary.fromByteArray(e.asInstanceOf[UTF8String].getBytes))))
+          SetInFilter(v.map(s => Binary.fromByteArray(s.asInstanceOf[String].getBytes("utf-8")))))
     case BinaryType =>
       (n: String, v: Set[Any]) =>
         FilterApi.userDefined(binaryColumn(n),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
index c81c3d3982805..68ce37c00077e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.sources
 import scala.language.existentials
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.sql._
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
@@ -78,6 +79,9 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL
       case StringStartsWith("c", v) => _.startsWith(v)
       case StringEndsWith("c", v) => _.endsWith(v)
       case StringContains("c", v) => _.contains(v)
+      case EqualTo("c", v: String) => _.equals(v)
+      case EqualTo("c", v: UTF8String) => sys.error("UTF8String should not appear in filters")
+      case In("c", values) => (s: String) => values.map(_.asInstanceOf[String]).toSet.contains(s)
       case _ => (c: String) => true
     }
 
@@ -237,6 +241,9 @@ class FilteredScanSuite extends DataSourceTest with SharedSQLContext {
   testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%eE%'", 1)
   testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%Ee%'", 0)
 
+  testPushDown("SELECT c FROM oneToTenFiltered WHERE c = 'aaaaaAAAAA'", 1)
+  testPushDown("SELECT c FROM oneToTenFiltered WHERE c IN ('aaaaaAAAAA', 'foo')", 1)
+
   def testPushDown(sqlString: String, expectedCount: Int): Unit = {
     test(s"PushDown Returns $expectedCount: $sqlString") {
       val queryExecution = sql(sqlString).queryExecution

From 0e6368ffaec1965d0c7f89420e04a974675c7f6e Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 25 Aug 2015 16:19:34 +0800
Subject: [PATCH 1208/1454] [SPARK-10197] [SQL] Add null check in wrapperFor
 (inside HiveInspectors).

https://issues.apache.org/jira/browse/SPARK-10197

Author: Yin Huai <yhuai@databricks.com>

Closes #8407 from yhuai/ORCSPARK-10197.
---
 .../spark/sql/hive/HiveInspectors.scala       | 29 +++++++++++++++----
 .../spark/sql/hive/orc/OrcSourceSuite.scala   | 29 +++++++++++++++++++
 2 files changed, 53 insertions(+), 5 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 9824dad239596..64fffdbf9b020 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -370,17 +370,36 @@ private[hive] trait HiveInspectors {
   protected def wrapperFor(oi: ObjectInspector, dataType: DataType): Any => Any = oi match {
     case _: JavaHiveVarcharObjectInspector =>
       (o: Any) =>
-        val s = o.asInstanceOf[UTF8String].toString
-        new HiveVarchar(s, s.size)
+        if (o != null) {
+          val s = o.asInstanceOf[UTF8String].toString
+          new HiveVarchar(s, s.size)
+        } else {
+          null
+        }
 
     case _: JavaHiveDecimalObjectInspector =>
-      (o: Any) => HiveDecimal.create(o.asInstanceOf[Decimal].toJavaBigDecimal)
+      (o: Any) =>
+        if (o != null) {
+          HiveDecimal.create(o.asInstanceOf[Decimal].toJavaBigDecimal)
+        } else {
+          null
+        }
 
     case _: JavaDateObjectInspector =>
-      (o: Any) => DateTimeUtils.toJavaDate(o.asInstanceOf[Int])
+      (o: Any) =>
+        if (o != null) {
+          DateTimeUtils.toJavaDate(o.asInstanceOf[Int])
+        } else {
+          null
+        }
 
     case _: JavaTimestampObjectInspector =>
-      (o: Any) => DateTimeUtils.toJavaTimestamp(o.asInstanceOf[Long])
+      (o: Any) =>
+        if (o != null) {
+          DateTimeUtils.toJavaTimestamp(o.asInstanceOf[Long])
+        } else {
+          null
+        }
 
     case soi: StandardStructObjectInspector =>
       val schema = dataType.asInstanceOf[StructType]
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
index 82e08caf46457..80c38084f293d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
@@ -121,6 +121,35 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
       sql("SELECT * FROM normal_orc_as_source"),
       (6 to 10).map(i => Row(i, s"part-$i")))
   }
+
+  test("write null values") {
+    sql("DROP TABLE IF EXISTS orcNullValues")
+
+    val df = sql(
+      """
+        |SELECT
+        |  CAST(null as TINYINT),
+        |  CAST(null as SMALLINT),
+        |  CAST(null as INT),
+        |  CAST(null as BIGINT),
+        |  CAST(null as FLOAT),
+        |  CAST(null as DOUBLE),
+        |  CAST(null as DECIMAL(7,2)),
+        |  CAST(null as TIMESTAMP),
+        |  CAST(null as DATE),
+        |  CAST(null as STRING),
+        |  CAST(null as VARCHAR(10))
+        |FROM orc_temp_table limit 1
+      """.stripMargin)
+
+    df.write.format("orc").saveAsTable("orcNullValues")
+
+    checkAnswer(
+      sql("SELECT * FROM orcNullValues"),
+      Row.fromSeq(Seq.fill(11)(null)))
+
+    sql("DROP TABLE IF EXISTS orcNullValues")
+  }
 }
 
 class OrcSourceSuite extends OrcSuite {

From 5c14890159a5711072bf395f662b2433a389edf9 Mon Sep 17 00:00:00 2001
From: "Zhang, Liye" <liye.zhang@intel.com>
Date: Tue, 25 Aug 2015 11:48:55 +0100
Subject: [PATCH 1209/1454] [DOC] add missing parameters in SparkContext.scala
 for scala doc

Author: Zhang, Liye <liye.zhang@intel.com>

Closes #8412 from liyezhang556520/minorDoc.
---
 .../scala/org/apache/spark/SparkContext.scala     | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 1ddaca8a5ba8c..9849aff85d72e 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -114,6 +114,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * :: DeveloperApi ::
    * Alternative constructor for setting preferred locations where Spark will create executors.
    *
+   * @param config a [[org.apache.spark.SparkConf]] object specifying other Spark parameters
    * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on.
    * Can be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
    * from a list of input files or InputFormats for the application.
@@ -145,6 +146,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @param jars Collection of JARs to send to the cluster. These can be paths on the local file
    *             system or HDFS, HTTP, HTTPS, or FTP URLs.
    * @param environment Environment variables to set on worker nodes.
+   * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on.
+   * Can be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
+   * from a list of input files or InputFormats for the application.
    */
   def this(
       master: String,
@@ -841,6 +845,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
    * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
    *       in a directory rather than `.../path/` or `.../path`
+   *
+   * @param path Directory to the input data files, the path can be comma separated paths as the
+   *             list of inputs.
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
   def wholeTextFiles(
@@ -889,6 +896,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @note Small files are preferred; very large files may cause bad performance.
    * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
    *       in a directory rather than `.../path/` or `.../path`
+   *
+   * @param path Directory to the input data files, the path can be comma separated paths as the
+   *             list of inputs.
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
   @Experimental
@@ -918,8 +928,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * '''Note:''' We ensure that the byte array for each record in the resulting RDD
    * has the provided record length.
    *
-   * @param path Directory to the input data files
+   * @param path Directory to the input data files, the path can be comma separated paths as the
+   *             list of inputs.
    * @param recordLength The length at which to split the records
+   * @param conf Configuration for setting up the dataset.
+   *
    * @return An RDD of data with values, represented as byte arrays
    */
   @Experimental

From 7f1e507bf7e82bff323c5dec3c1ee044687c4173 Mon Sep 17 00:00:00 2001
From: ehnalis <zoltan.zvara@gmail.com>
Date: Tue, 25 Aug 2015 12:30:06 +0100
Subject: [PATCH 1210/1454] Fixed a typo in DAGScheduler.

Author: ehnalis <zoltan.zvara@gmail.com>

Closes #8308 from ehnalis/master.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 27 ++++++++++++++-----
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 684db6646765f..daf9b0f95273e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -152,17 +152,24 @@ class DAGScheduler(
   // may lead to more delay in scheduling if those locations are busy.
   private[scheduler] val REDUCER_PREF_LOCS_FRACTION = 0.2
 
-  // Called by TaskScheduler to report task's starting.
+  /**
+   * Called by the TaskSetManager to report task's starting.
+   */
   def taskStarted(task: Task[_], taskInfo: TaskInfo) {
     eventProcessLoop.post(BeginEvent(task, taskInfo))
   }
 
-  // Called to report that a task has completed and results are being fetched remotely.
+  /**
+   * Called by the TaskSetManager to report that a task has completed
+   * and results are being fetched remotely.
+   */
   def taskGettingResult(taskInfo: TaskInfo) {
     eventProcessLoop.post(GettingResultEvent(taskInfo))
   }
 
-  // Called by TaskScheduler to report task completions or failures.
+  /**
+   * Called by the TaskSetManager to report task completions or failures.
+   */
   def taskEnded(
       task: Task[_],
       reason: TaskEndReason,
@@ -188,18 +195,24 @@ class DAGScheduler(
       BlockManagerHeartbeat(blockManagerId), new RpcTimeout(600 seconds, "BlockManagerHeartbeat"))
   }
 
-  // Called by TaskScheduler when an executor fails.
+  /**
+   * Called by TaskScheduler implementation when an executor fails.
+   */
   def executorLost(execId: String): Unit = {
     eventProcessLoop.post(ExecutorLost(execId))
   }
 
-  // Called by TaskScheduler when a host is added
+  /**
+   * Called by TaskScheduler implementation when a host is added.
+   */
   def executorAdded(execId: String, host: String): Unit = {
     eventProcessLoop.post(ExecutorAdded(execId, host))
   }
 
-  // Called by TaskScheduler to cancel an entire TaskSet due to either repeated failures or
-  // cancellation of the job itself.
+  /**
+   * Called by the TaskSetManager to cancel an entire TaskSet due to either repeated failures or
+   * cancellation of the job itself.
+   */
   def taskSetFailed(taskSet: TaskSet, reason: String, exception: Option[Throwable]): Unit = {
     eventProcessLoop.post(TaskSetFailed(taskSet, reason, exception))
   }

From 69c9c177160e32a2fbc9b36ecc52156077fca6fc Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 25 Aug 2015 12:33:13 +0100
Subject: [PATCH 1211/1454] [SPARK-9613] [CORE] Ban use of JavaConversions and
 migrate all existing uses to JavaConverters

Replace `JavaConversions` implicits with `JavaConverters`

Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.

Author: Sean Owen <sowen@cloudera.com>

Closes #8033 from srowen/SPARK-9613.
---
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |   4 +-
 .../org/apache/spark/MapOutputTracker.scala   |   4 +-
 .../scala/org/apache/spark/SSLOptions.scala   |  11 +-
 .../scala/org/apache/spark/SparkContext.scala |   4 +-
 .../scala/org/apache/spark/TestUtils.scala    |   9 +-
 .../apache/spark/api/java/JavaHadoopRDD.scala |   4 +-
 .../spark/api/java/JavaNewHadoopRDD.scala     |   4 +-
 .../apache/spark/api/java/JavaPairRDD.scala   |  19 ++-
 .../apache/spark/api/java/JavaRDDLike.scala   |  75 +++++-------
 .../spark/api/java/JavaSparkContext.scala     |  20 ++--
 .../spark/api/python/PythonHadoopUtil.scala   |  28 ++---
 .../apache/spark/api/python/PythonRDD.scala   |  26 ++---
 .../apache/spark/api/python/PythonUtils.scala |  15 ++-
 .../api/python/PythonWorkerFactory.scala      |  11 +-
 .../apache/spark/api/python/SerDeUtil.scala   |   3 +-
 .../WriteInputFormatTestDataGenerator.scala   |   8 +-
 .../scala/org/apache/spark/api/r/RRDD.scala   |  13 ++-
 .../scala/org/apache/spark/api/r/RUtils.scala |   5 +-
 .../scala/org/apache/spark/api/r/SerDe.scala  |   4 +-
 .../spark/broadcast/TorrentBroadcast.scala    |   4 +-
 .../spark/deploy/ExternalShuffleService.scala |   8 +-
 .../apache/spark/deploy/PythonRunner.scala    |   4 +-
 .../apache/spark/deploy/RPackageUtils.scala   |   4 +-
 .../org/apache/spark/deploy/RRunner.scala     |   4 +-
 .../spark/deploy/SparkCuratorUtil.scala       |   4 +-
 .../apache/spark/deploy/SparkHadoopUtil.scala |  19 +--
 .../spark/deploy/SparkSubmitArguments.scala   |   6 +-
 .../master/ZooKeeperPersistenceEngine.scala   |   6 +-
 .../spark/deploy/worker/CommandUtils.scala    |   5 +-
 .../spark/deploy/worker/DriverRunner.scala    |   8 +-
 .../spark/deploy/worker/ExecutorRunner.scala  |   7 +-
 .../apache/spark/deploy/worker/Worker.scala   |   1 -
 .../org/apache/spark/executor/Executor.scala  |   6 +-
 .../spark/executor/ExecutorSource.scala       |   4 +-
 .../spark/executor/MesosExecutorBackend.scala |   6 +-
 .../spark/input/PortableDataStream.scala      |  11 +-
 .../input/WholeTextFileInputFormat.scala      |   8 +-
 .../spark/launcher/WorkerCommandBuilder.scala |   4 +-
 .../apache/spark/metrics/MetricsConfig.scala  |  22 ++--
 .../network/netty/NettyBlockRpcServer.scala   |   4 +-
 .../netty/NettyBlockTransferService.scala     |   6 +-
 .../apache/spark/network/nio/Connection.scala |   4 +-
 .../spark/partial/GroupedCountEvaluator.scala |  10 +-
 .../spark/partial/GroupedMeanEvaluator.scala  |  10 +-
 .../spark/partial/GroupedSumEvaluator.scala   |  10 +-
 .../apache/spark/rdd/PairRDDFunctions.scala   |   6 +-
 .../scala/org/apache/spark/rdd/PipedRDD.scala |   6 +-
 .../org/apache/spark/rdd/SubtractedRDD.scala  |   4 +-
 .../spark/scheduler/InputFormatInfo.scala     |   4 +-
 .../org/apache/spark/scheduler/Pool.scala     |  10 +-
 .../mesos/CoarseMesosSchedulerBackend.scala   |  20 ++--
 .../mesos/MesosClusterPersistenceEngine.scala |   4 +-
 .../cluster/mesos/MesosClusterScheduler.scala |  14 +--
 .../cluster/mesos/MesosSchedulerBackend.scala |  22 ++--
 .../cluster/mesos/MesosSchedulerUtils.scala   |  25 ++--
 .../spark/serializer/KryoSerializer.scala     |  10 +-
 .../shuffle/FileShuffleBlockResolver.scala    |   8 +-
 .../storage/BlockManagerMasterEndpoint.scala  |   8 +-
 .../org/apache/spark/util/AkkaUtils.scala     |   4 +-
 .../org/apache/spark/util/ListenerBus.scala   |   7 +-
 .../spark/util/MutableURLClassLoader.scala    |   2 -
 .../spark/util/TimeStampedHashMap.scala       |  10 +-
 .../spark/util/TimeStampedHashSet.scala       |   4 +-
 .../scala/org/apache/spark/util/Utils.scala   |  20 ++--
 .../apache/spark/util/collection/Utils.scala  |   4 +-
 .../java/org/apache/spark/JavaAPISuite.java   |   6 +-
 .../org/apache/spark/SparkConfSuite.scala     |   7 +-
 .../spark/deploy/LogUrlsStandaloneSuite.scala |   1 -
 .../spark/deploy/RPackageUtilsSuite.scala     |   8 +-
 .../deploy/worker/ExecutorRunnerTest.scala    |   5 +-
 .../spark/scheduler/SparkListenerSuite.scala  |   9 +-
 .../mesos/MesosSchedulerBackendSuite.scala    |  15 +--
 .../serializer/KryoSerializerSuite.scala      |   3 +-
 .../org/apache/spark/ui/UISeleniumSuite.scala |  21 ++--
 .../spark/examples/CassandraCQLTest.scala     |  15 +--
 .../apache/spark/examples/CassandraTest.scala |   6 +-
 .../spark/examples/DriverSubmissionTest.scala |   6 +-
 .../pythonconverters/AvroConverters.scala     |  16 +--
 .../CassandraConverters.scala                 |  14 ++-
 .../pythonconverters/HBaseConverters.scala    |   5 +-
 .../streaming/flume/sink/SparkSinkSuite.scala |   4 +-
 .../streaming/flume/EventTransformer.scala    |   4 +-
 .../streaming/flume/FlumeBatchFetcher.scala   |   3 +-
 .../streaming/flume/FlumeInputDStream.scala   |   7 +-
 .../flume/FlumePollingInputDStream.scala      |   6 +-
 .../streaming/flume/FlumeTestUtils.scala      |  10 +-
 .../spark/streaming/flume/FlumeUtils.scala    |   8 +-
 .../flume/PollingFlumeTestUtils.scala         |  16 ++-
 .../flume/FlumePollingStreamSuite.scala       |   8 +-
 .../streaming/flume/FlumeStreamSuite.scala    |   2 +-
 .../streaming/kafka/KafkaTestUtils.scala      |   4 +-
 .../spark/streaming/kafka/KafkaUtils.scala    |  35 +++---
 .../spark/streaming/zeromq/ZeroMQUtils.scala  |  15 ++-
 .../kinesis/KinesisBackedBlockRDD.scala       |   4 +-
 .../streaming/kinesis/KinesisReceiver.scala   |   4 +-
 .../streaming/kinesis/KinesisTestUtils.scala  |   3 +-
 .../kinesis/KinesisReceiverSuite.scala        |  12 +-
 .../mllib/util/LinearDataGenerator.scala      |   4 +-
 .../ml/classification/JavaOneVsRestSuite.java |   7 +-
 .../LogisticRegressionSuite.scala             |   4 +-
 .../spark/mllib/classification/SVMSuite.scala |   4 +-
 .../optimization/GradientDescentSuite.scala   |   4 +-
 .../spark/mllib/recommendation/ALSSuite.scala |   4 +-
 project/SparkBuild.scala                      |   8 +-
 python/pyspark/sql/column.py                  |  12 ++
 python/pyspark/sql/dataframe.py               |   4 +-
 scalastyle-config.xml                         |   7 ++
 .../main/scala/org/apache/spark/sql/Row.scala |  12 +-
 .../spark/sql/catalyst/analysis/Catalog.scala |   4 +-
 .../spark/sql/DataFrameNaFunctions.scala      |   8 +-
 .../apache/spark/sql/DataFrameReader.scala    |   4 +-
 .../apache/spark/sql/DataFrameWriter.scala    |   4 +-
 .../org/apache/spark/sql/GroupedData.scala    |   4 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  13 ++-
 .../org/apache/spark/sql/SQLContext.scala     |   8 +-
 .../datasources/ResolvedDataSource.scala      |   4 +-
 .../parquet/CatalystReadSupport.scala         |   8 +-
 .../parquet/CatalystRowConverter.scala        |   4 +-
 .../parquet/CatalystSchemaConverter.scala     |   4 +-
 .../datasources/parquet/ParquetRelation.scala |  13 ++-
 .../parquet/ParquetTypesConverter.scala       |   4 +-
 .../joins/ShuffledHashOuterJoin.scala         |   6 +-
 .../spark/sql/execution/pythonUDFs.scala      |  11 +-
 .../apache/spark/sql/JavaDataFrameSuite.java  |   8 +-
 .../spark/sql/DataFrameNaFunctionsSuite.scala |   6 +-
 .../org/apache/spark/sql/QueryTest.scala      |   4 +-
 .../ParquetAvroCompatibilitySuite.scala       |   3 +-
 .../parquet/ParquetCompatibilityTest.scala    |   7 +-
 .../datasources/parquet/ParquetIOSuite.scala  |  25 ++--
 .../SparkExecuteStatementOperation.scala      |  10 +-
 .../hive/thriftserver/SparkSQLCLIDriver.scala |  16 +--
 .../thriftserver/SparkSQLCLIService.scala     |   6 +-
 .../hive/thriftserver/SparkSQLDriver.scala    |  14 +--
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |   4 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   4 +-
 .../spark/sql/hive/HiveInspectors.scala       |  40 +++----
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  12 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    | 110 ++++++++++--------
 .../org/apache/spark/sql/hive/HiveShim.scala  |   5 +-
 .../spark/sql/hive/client/ClientWrapper.scala |  27 ++---
 .../spark/sql/hive/client/HiveShim.scala      |  14 +--
 .../execution/DescribeHiveTableCommand.scala  |   8 +-
 .../sql/hive/execution/HiveTableScan.scala    |   9 +-
 .../hive/execution/InsertIntoHiveTable.scala  |  12 +-
 .../hive/execution/ScriptTransformation.scala |  12 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |   9 +-
 .../spark/sql/hive/orc/OrcRelation.scala      |   8 +-
 .../apache/spark/sql/hive/test/TestHive.scala |  11 +-
 .../spark/sql/hive/client/FiltersSuite.scala  |   4 +-
 .../sql/hive/execution/HiveUDFSuite.scala     |  29 +++--
 .../sql/hive/execution/PruningSuite.scala     |   7 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |   4 +-
 .../sql/sources/hadoopFsRelationSuites.scala  |   8 +-
 .../streaming/api/java/JavaDStreamLike.scala  |  12 +-
 .../streaming/api/java/JavaPairDStream.scala  |  28 ++---
 .../api/java/JavaStreamingContext.scala       |  32 ++---
 .../streaming/api/python/PythonDStream.scala  |   5 +-
 .../spark/streaming/receiver/Receiver.scala   |   6 +-
 .../streaming/scheduler/JobScheduler.scala    |   4 +-
 .../scheduler/ReceivedBlockTracker.scala      |   4 +-
 .../util/FileBasedWriteAheadLog.scala         |   4 +-
 .../spark/streaming/JavaTestUtils.scala       |  24 ++--
 .../streaming/util/WriteAheadLogSuite.scala   |   4 +-
 .../spark/tools/GenerateMIMAIgnore.scala      |   6 +-
 .../org/apache/spark/deploy/yarn/Client.scala |  13 ++-
 .../spark/deploy/yarn/ExecutorRunnable.scala  |  24 ++--
 .../spark/deploy/yarn/YarnAllocator.scala     |  19 ++-
 .../spark/deploy/yarn/YarnRMClient.scala      |   8 +-
 .../deploy/yarn/BaseYarnClusterSuite.scala    |   6 +-
 .../spark/deploy/yarn/ClientSuite.scala       |   8 +-
 .../spark/deploy/yarn/YarnClusterSuite.scala  |   5 +-
 171 files changed, 863 insertions(+), 880 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index 2389c28b28395..fdb309e365f69 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -24,7 +24,7 @@
 
 import scala.Option;
 import scala.Product2;
-import scala.collection.JavaConversions;
+import scala.collection.JavaConverters;
 import scala.collection.immutable.Map;
 import scala.reflect.ClassTag;
 import scala.reflect.ClassTag$;
@@ -160,7 +160,7 @@ public long getPeakMemoryUsedBytes() {
    */
   @VisibleForTesting
   public void write(Iterator<Product2<K, V>> records) throws IOException {
-    write(JavaConversions.asScalaIterator(records));
+    write(JavaConverters.asScalaIteratorConverter(records).asScala());
   }
 
   @Override
diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index 92218832d256f..a387592783850 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -21,8 +21,8 @@ import java.io._
 import java.util.concurrent.ConcurrentHashMap
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map}
-import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
 
 import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, RpcEndpoint}
@@ -398,7 +398,7 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf)
  */
 private[spark] class MapOutputTrackerWorker(conf: SparkConf) extends MapOutputTracker(conf) {
   protected val mapStatuses: Map[Int, Array[MapStatus]] =
-    new ConcurrentHashMap[Int, Array[MapStatus]]
+    new ConcurrentHashMap[Int, Array[MapStatus]]().asScala
 }
 
 private[spark] object MapOutputTracker extends Logging {
diff --git a/core/src/main/scala/org/apache/spark/SSLOptions.scala b/core/src/main/scala/org/apache/spark/SSLOptions.scala
index 32df42d57dbd6..3b9c885bf97a7 100644
--- a/core/src/main/scala/org/apache/spark/SSLOptions.scala
+++ b/core/src/main/scala/org/apache/spark/SSLOptions.scala
@@ -17,9 +17,11 @@
 
 package org.apache.spark
 
-import java.io.{File, FileInputStream}
-import java.security.{KeyStore, NoSuchAlgorithmException}
-import javax.net.ssl.{KeyManager, KeyManagerFactory, SSLContext, TrustManager, TrustManagerFactory}
+import java.io.File
+import java.security.NoSuchAlgorithmException
+import javax.net.ssl.SSLContext
+
+import scala.collection.JavaConverters._
 
 import com.typesafe.config.{Config, ConfigFactory, ConfigValueFactory}
 import org.eclipse.jetty.util.ssl.SslContextFactory
@@ -79,7 +81,6 @@ private[spark] case class SSLOptions(
    * object. It can be used then to compose the ultimate Akka configuration.
    */
   def createAkkaConfig: Option[Config] = {
-    import scala.collection.JavaConversions._
     if (enabled) {
       Some(ConfigFactory.empty()
         .withValue("akka.remote.netty.tcp.security.key-store",
@@ -97,7 +98,7 @@ private[spark] case class SSLOptions(
         .withValue("akka.remote.netty.tcp.security.protocol",
           ConfigValueFactory.fromAnyRef(protocol.getOrElse("")))
         .withValue("akka.remote.netty.tcp.security.enabled-algorithms",
-          ConfigValueFactory.fromIterable(supportedAlgorithms.toSeq))
+          ConfigValueFactory.fromIterable(supportedAlgorithms.asJava))
         .withValue("akka.remote.netty.tcp.enable-ssl",
           ConfigValueFactory.fromAnyRef(true)))
     } else {
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 9849aff85d72e..f3da04a7f55d0 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -26,8 +26,8 @@ import java.util.{Arrays, Properties, UUID}
 import java.util.concurrent.atomic.{AtomicReference, AtomicBoolean, AtomicInteger}
 import java.util.UUID.randomUUID
 
+import scala.collection.JavaConverters._
 import scala.collection.{Map, Set}
-import scala.collection.JavaConversions._
 import scala.collection.generic.Growable
 import scala.collection.mutable.HashMap
 import scala.reflect.{ClassTag, classTag}
@@ -1546,7 +1546,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   def getAllPools: Seq[Schedulable] = {
     assertNotStopped()
     // TODO(xiajunluan): We should take nested pools into account
-    taskScheduler.rootPool.schedulableQueue.toSeq
+    taskScheduler.rootPool.schedulableQueue.asScala.toSeq
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala
index a1ebbecf93b7b..888763a3e8ebf 100644
--- a/core/src/main/scala/org/apache/spark/TestUtils.scala
+++ b/core/src/main/scala/org/apache/spark/TestUtils.scala
@@ -19,11 +19,12 @@ package org.apache.spark
 
 import java.io.{ByteArrayInputStream, File, FileInputStream, FileOutputStream}
 import java.net.{URI, URL}
+import java.nio.charset.StandardCharsets
+import java.util.Arrays
 import java.util.jar.{JarEntry, JarOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
-import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.{ByteStreams, Files}
 import javax.tools.{JavaFileObject, SimpleJavaFileObject, ToolProvider}
 
@@ -71,7 +72,7 @@ private[spark] object TestUtils {
     files.foreach { case (k, v) =>
       val entry = new JarEntry(k)
       jarStream.putNextEntry(entry)
-      ByteStreams.copy(new ByteArrayInputStream(v.getBytes(UTF_8)), jarStream)
+      ByteStreams.copy(new ByteArrayInputStream(v.getBytes(StandardCharsets.UTF_8)), jarStream)
     }
     jarStream.close()
     jarFile.toURI.toURL
@@ -125,7 +126,7 @@ private[spark] object TestUtils {
     } else {
       Seq()
     }
-    compiler.getTask(null, null, null, options, null, Seq(sourceFile)).call()
+    compiler.getTask(null, null, null, options.asJava, null, Arrays.asList(sourceFile)).call()
 
     val fileName = className + ".class"
     val result = new File(fileName)
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala
index 0ae0b4ec042e2..891bcddeac286 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.api.java
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.hadoop.mapred.InputSplit
@@ -37,7 +37,7 @@ class JavaHadoopRDD[K, V](rdd: HadoopRDD[K, V])
   def mapPartitionsWithInputSplit[R](
       f: JFunction2[InputSplit, java.util.Iterator[(K, V)], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] = {
-    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, asJavaIterator(b)),
+    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, b.asJava).asScala,
       preservesPartitioning)(fakeClassTag))(fakeClassTag)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala
index ec4f3964d75e0..0f49279f3e647 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.api.java
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.hadoop.mapreduce.InputSplit
@@ -37,7 +37,7 @@ class JavaNewHadoopRDD[K, V](rdd: NewHadoopRDD[K, V])
   def mapPartitionsWithInputSplit[R](
       f: JFunction2[InputSplit, java.util.Iterator[(K, V)], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] = {
-    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, asJavaIterator(b)),
+    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, b.asJava).asScala,
       preservesPartitioning)(fakeClassTag))(fakeClassTag)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 8441bb3a3047e..fb787979c1820 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -20,7 +20,7 @@ package org.apache.spark.api.java
 import java.util.{Comparator, List => JList, Map => JMap}
 import java.lang.{Iterable => JIterable}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -142,7 +142,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   def sampleByKey(withReplacement: Boolean,
       fractions: JMap[K, Double],
       seed: Long): JavaPairRDD[K, V] =
-    new JavaPairRDD[K, V](rdd.sampleByKey(withReplacement, fractions, seed))
+    new JavaPairRDD[K, V](rdd.sampleByKey(withReplacement, fractions.asScala, seed))
 
   /**
    * Return a subset of this RDD sampled by key (via stratified sampling).
@@ -173,7 +173,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   def sampleByKeyExact(withReplacement: Boolean,
       fractions: JMap[K, Double],
       seed: Long): JavaPairRDD[K, V] =
-    new JavaPairRDD[K, V](rdd.sampleByKeyExact(withReplacement, fractions, seed))
+    new JavaPairRDD[K, V](rdd.sampleByKeyExact(withReplacement, fractions.asScala, seed))
 
   /**
    * ::Experimental::
@@ -768,7 +768,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return the list of values in the RDD for key `key`. This operation is done efficiently if the
    * RDD has a known partitioner by only searching the partition that the key maps to.
    */
-  def lookup(key: K): JList[V] = seqAsJavaList(rdd.lookup(key))
+  def lookup(key: K): JList[V] = rdd.lookup(key).asJava
 
   /** Output the RDD to any Hadoop-supported file system. */
   def saveAsHadoopFile[F <: OutputFormat[_, _]](
@@ -987,30 +987,27 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
 object JavaPairRDD {
   private[spark]
   def groupByResultToJava[K: ClassTag, T](rdd: RDD[(K, Iterable[T])]): RDD[(K, JIterable[T])] = {
-    rddToPairRDDFunctions(rdd).mapValues(asJavaIterable)
+    rddToPairRDDFunctions(rdd).mapValues(_.asJava)
   }
 
   private[spark]
   def cogroupResultToJava[K: ClassTag, V, W](
       rdd: RDD[(K, (Iterable[V], Iterable[W]))]): RDD[(K, (JIterable[V], JIterable[W]))] = {
-    rddToPairRDDFunctions(rdd).mapValues(x => (asJavaIterable(x._1), asJavaIterable(x._2)))
+    rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava))
   }
 
   private[spark]
   def cogroupResult2ToJava[K: ClassTag, V, W1, W2](
       rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))])
       : RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2]))] = {
-    rddToPairRDDFunctions(rdd)
-      .mapValues(x => (asJavaIterable(x._1), asJavaIterable(x._2), asJavaIterable(x._3)))
+    rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava, x._3.asJava))
   }
 
   private[spark]
   def cogroupResult3ToJava[K: ClassTag, V, W1, W2, W3](
       rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))])
   : RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3]))] = {
-    rddToPairRDDFunctions(rdd)
-      .mapValues(x =>
-        (asJavaIterable(x._1), asJavaIterable(x._2), asJavaIterable(x._3), asJavaIterable(x._4)))
+    rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava, x._3.asJava, x._4.asJava))
   }
 
   def fromRDD[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]): JavaPairRDD[K, V] = {
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index c582488f16fe7..fc817cdd6a3f8 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -21,7 +21,6 @@ import java.{lang => jl}
 import java.lang.{Iterable => JIterable, Long => JLong}
 import java.util.{Comparator, List => JList, Iterator => JIterator}
 
-import scala.collection.JavaConversions._
 import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
@@ -59,10 +58,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def rdd: RDD[T]
 
   @deprecated("Use partitions() instead.", "1.1.0")
-  def splits: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq)
+  def splits: JList[Partition] = rdd.partitions.toSeq.asJava
 
   /** Set of partitions in this RDD. */
-  def partitions: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq)
+  def partitions: JList[Partition] = rdd.partitions.toSeq.asJava
 
   /** The partitioner of this RDD. */
   def partitioner: Optional[Partitioner] = JavaUtils.optionToOptional(rdd.partitioner)
@@ -82,7 +81,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * subclasses of RDD.
    */
   def iterator(split: Partition, taskContext: TaskContext): java.util.Iterator[T] =
-    asJavaIterator(rdd.iterator(split, taskContext))
+    rdd.iterator(split, taskContext).asJava
 
   // Transformations (return a new RDD)
 
@@ -99,7 +98,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsWithIndex[R](
       f: JFunction2[jl.Integer, java.util.Iterator[T], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] =
-    new JavaRDD(rdd.mapPartitionsWithIndex(((a, b) => f(a, asJavaIterator(b))),
+    new JavaRDD(rdd.mapPartitionsWithIndex((a, b) => f.call(a, b.asJava).asScala,
         preservesPartitioning)(fakeClassTag))(fakeClassTag)
 
   /**
@@ -153,7 +152,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    */
   def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaRDD[U] = {
     def fn: (Iterator[T]) => Iterator[U] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaRDD.fromRDD(rdd.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U])
   }
@@ -164,7 +163,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U],
       preservesPartitioning: Boolean): JavaRDD[U] = {
     def fn: (Iterator[T]) => Iterator[U] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaRDD.fromRDD(
       rdd.mapPartitions(fn, preservesPartitioning)(fakeClassTag[U]))(fakeClassTag[U])
@@ -175,7 +174,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    */
   def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]]): JavaDoubleRDD = {
     def fn: (Iterator[T]) => Iterator[jl.Double] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaDoubleRDD(rdd.mapPartitions(fn).map((x: jl.Double) => x.doubleValue()))
   }
@@ -186,7 +185,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2]):
   JavaPairRDD[K2, V2] = {
     def fn: (Iterator[T]) => Iterator[(K2, V2)] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaPairRDD.fromRDD(rdd.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2])
   }
@@ -197,7 +196,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]],
       preservesPartitioning: Boolean): JavaDoubleRDD = {
     def fn: (Iterator[T]) => Iterator[jl.Double] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaDoubleRDD(rdd.mapPartitions(fn, preservesPartitioning)
       .map(x => x.doubleValue()))
@@ -209,7 +208,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2],
       preservesPartitioning: Boolean): JavaPairRDD[K2, V2] = {
     def fn: (Iterator[T]) => Iterator[(K2, V2)] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaPairRDD.fromRDD(
       rdd.mapPartitions(fn, preservesPartitioning))(fakeClassTag[K2], fakeClassTag[V2])
@@ -219,14 +218,14 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Applies a function f to each partition of this RDD.
    */
   def foreachPartition(f: VoidFunction[java.util.Iterator[T]]) {
-    rdd.foreachPartition((x => f.call(asJavaIterator(x))))
+    rdd.foreachPartition((x => f.call(x.asJava)))
   }
 
   /**
    * Return an RDD created by coalescing all elements within each partition into an array.
    */
   def glom(): JavaRDD[JList[T]] =
-    new JavaRDD(rdd.glom().map(x => new java.util.ArrayList[T](x.toSeq)))
+    new JavaRDD(rdd.glom().map(_.toSeq.asJava))
 
   /**
    * Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of
@@ -266,13 +265,13 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Return an RDD created by piping elements to a forked external process.
    */
   def pipe(command: JList[String]): JavaRDD[String] =
-    rdd.pipe(asScalaBuffer(command))
+    rdd.pipe(command.asScala)
 
   /**
    * Return an RDD created by piping elements to a forked external process.
    */
   def pipe(command: JList[String], env: java.util.Map[String, String]): JavaRDD[String] =
-    rdd.pipe(asScalaBuffer(command), mapAsScalaMap(env))
+    rdd.pipe(command.asScala, env.asScala)
 
   /**
    * Zips this RDD with another one, returning key-value pairs with the first element in each RDD,
@@ -294,8 +293,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
       other: JavaRDDLike[U, _],
       f: FlatMapFunction2[java.util.Iterator[T], java.util.Iterator[U], V]): JavaRDD[V] = {
     def fn: (Iterator[T], Iterator[U]) => Iterator[V] = {
-      (x: Iterator[T], y: Iterator[U]) => asScalaIterator(
-        f.call(asJavaIterator(x), asJavaIterator(y)).iterator())
+      (x: Iterator[T], y: Iterator[U]) => f.call(x.asJava, y.asJava).iterator().asScala
     }
     JavaRDD.fromRDD(
       rdd.zipPartitions(other.rdd)(fn)(other.classTag, fakeClassTag[V]))(fakeClassTag[V])
@@ -333,22 +331,16 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   /**
    * Return an array that contains all of the elements in this RDD.
    */
-  def collect(): JList[T] = {
-    import scala.collection.JavaConversions._
-    val arr: java.util.Collection[T] = rdd.collect().toSeq
-    new java.util.ArrayList(arr)
-  }
+  def collect(): JList[T] =
+    rdd.collect().toSeq.asJava
 
   /**
    * Return an iterator that contains all of the elements in this RDD.
    *
    * The iterator will consume as much memory as the largest partition in this RDD.
    */
-  def toLocalIterator(): JIterator[T] = {
-     import scala.collection.JavaConversions._
-     rdd.toLocalIterator
-  }
-
+  def toLocalIterator(): JIterator[T] =
+     asJavaIteratorConverter(rdd.toLocalIterator).asJava
 
   /**
    * Return an array that contains all of the elements in this RDD.
@@ -363,9 +355,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def collectPartitions(partitionIds: Array[Int]): Array[JList[T]] = {
     // This is useful for implementing `take` from other language frontends
     // like Python where the data is serialized.
-    import scala.collection.JavaConversions._
     val res = context.runJob(rdd, (it: Iterator[T]) => it.toArray, partitionIds)
-    res.map(x => new java.util.ArrayList(x.toSeq)).toArray
+    res.map(_.toSeq.asJava)
   }
 
   /**
@@ -489,20 +480,14 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * it will be slow if a lot of partitions are required. In that case, use collect() to get the
    * whole RDD instead.
    */
-  def take(num: Int): JList[T] = {
-    import scala.collection.JavaConversions._
-    val arr: java.util.Collection[T] = rdd.take(num).toSeq
-    new java.util.ArrayList(arr)
-  }
+  def take(num: Int): JList[T] =
+    rdd.take(num).toSeq.asJava
 
   def takeSample(withReplacement: Boolean, num: Int): JList[T] =
     takeSample(withReplacement, num, Utils.random.nextLong)
 
-  def takeSample(withReplacement: Boolean, num: Int, seed: Long): JList[T] = {
-    import scala.collection.JavaConversions._
-    val arr: java.util.Collection[T] = rdd.takeSample(withReplacement, num, seed).toSeq
-    new java.util.ArrayList(arr)
-  }
+  def takeSample(withReplacement: Boolean, num: Int, seed: Long): JList[T] =
+    rdd.takeSample(withReplacement, num, seed).toSeq.asJava
 
   /**
    * Return the first element in this RDD.
@@ -582,10 +567,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * @return an array of top elements
    */
   def top(num: Int, comp: Comparator[T]): JList[T] = {
-    import scala.collection.JavaConversions._
-    val topElems = rdd.top(num)(Ordering.comparatorToOrdering(comp))
-    val arr: java.util.Collection[T] = topElems.toSeq
-    new java.util.ArrayList(arr)
+    rdd.top(num)(Ordering.comparatorToOrdering(comp)).toSeq.asJava
   }
 
   /**
@@ -607,10 +589,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * @return an array of top elements
    */
   def takeOrdered(num: Int, comp: Comparator[T]): JList[T] = {
-    import scala.collection.JavaConversions._
-    val topElems = rdd.takeOrdered(num)(Ordering.comparatorToOrdering(comp))
-    val arr: java.util.Collection[T] = topElems.toSeq
-    new java.util.ArrayList(arr)
+    rdd.takeOrdered(num)(Ordering.comparatorToOrdering(comp)).toSeq.asJava
   }
 
   /**
@@ -696,7 +675,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * applies a function f to each partition of this RDD.
    */
   def foreachPartitionAsync(f: VoidFunction[java.util.Iterator[T]]): JavaFutureAction[Void] = {
-    new JavaFutureActionWrapper[Unit, Void](rdd.foreachPartitionAsync(x => f.call(x)),
+    new JavaFutureActionWrapper[Unit, Void](rdd.foreachPartitionAsync(x => f.call(x.asJava)),
       { x => null.asInstanceOf[Void] })
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index 02e49a853c5f7..609496ccdfef1 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -21,8 +21,7 @@ import java.io.Closeable
 import java.util
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConversions
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -104,7 +103,7 @@ class JavaSparkContext(val sc: SparkContext)
    */
   def this(master: String, appName: String, sparkHome: String, jars: Array[String],
       environment: JMap[String, String]) =
-    this(new SparkContext(master, appName, sparkHome, jars.toSeq, environment, Map()))
+    this(new SparkContext(master, appName, sparkHome, jars.toSeq, environment.asScala, Map()))
 
   private[spark] val env = sc.env
 
@@ -118,7 +117,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   def appName: String = sc.appName
 
-  def jars: util.List[String] = sc.jars
+  def jars: util.List[String] = sc.jars.asJava
 
   def startTime: java.lang.Long = sc.startTime
 
@@ -142,7 +141,7 @@ class JavaSparkContext(val sc: SparkContext)
   /** Distribute a local Scala collection to form an RDD. */
   def parallelize[T](list: java.util.List[T], numSlices: Int): JavaRDD[T] = {
     implicit val ctag: ClassTag[T] = fakeClassTag
-    sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices)
+    sc.parallelize(list.asScala, numSlices)
   }
 
   /** Get an RDD that has no partitions or elements. */
@@ -161,7 +160,7 @@ class JavaSparkContext(val sc: SparkContext)
   : JavaPairRDD[K, V] = {
     implicit val ctagK: ClassTag[K] = fakeClassTag
     implicit val ctagV: ClassTag[V] = fakeClassTag
-    JavaPairRDD.fromRDD(sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices))
+    JavaPairRDD.fromRDD(sc.parallelize(list.asScala, numSlices))
   }
 
   /** Distribute a local Scala collection to form an RDD. */
@@ -170,8 +169,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   /** Distribute a local Scala collection to form an RDD. */
   def parallelizeDoubles(list: java.util.List[java.lang.Double], numSlices: Int): JavaDoubleRDD =
-    JavaDoubleRDD.fromRDD(sc.parallelize(JavaConversions.asScalaBuffer(list).map(_.doubleValue()),
-      numSlices))
+    JavaDoubleRDD.fromRDD(sc.parallelize(list.asScala.map(_.doubleValue()), numSlices))
 
   /** Distribute a local Scala collection to form an RDD. */
   def parallelizeDoubles(list: java.util.List[java.lang.Double]): JavaDoubleRDD =
@@ -519,7 +517,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   /** Build the union of two or more RDDs. */
   override def union[T](first: JavaRDD[T], rest: java.util.List[JavaRDD[T]]): JavaRDD[T] = {
-    val rdds: Seq[RDD[T]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.rdd)
+    val rdds: Seq[RDD[T]] = (Seq(first) ++ rest.asScala).map(_.rdd)
     implicit val ctag: ClassTag[T] = first.classTag
     sc.union(rdds)
   }
@@ -527,7 +525,7 @@ class JavaSparkContext(val sc: SparkContext)
   /** Build the union of two or more RDDs. */
   override def union[K, V](first: JavaPairRDD[K, V], rest: java.util.List[JavaPairRDD[K, V]])
       : JavaPairRDD[K, V] = {
-    val rdds: Seq[RDD[(K, V)]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.rdd)
+    val rdds: Seq[RDD[(K, V)]] = (Seq(first) ++ rest.asScala).map(_.rdd)
     implicit val ctag: ClassTag[(K, V)] = first.classTag
     implicit val ctagK: ClassTag[K] = first.kClassTag
     implicit val ctagV: ClassTag[V] = first.vClassTag
@@ -536,7 +534,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   /** Build the union of two or more RDDs. */
   override def union(first: JavaDoubleRDD, rest: java.util.List[JavaDoubleRDD]): JavaDoubleRDD = {
-    val rdds: Seq[RDD[Double]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.srdd)
+    val rdds: Seq[RDD[Double]] = (Seq(first) ++ rest.asScala).map(_.srdd)
     new JavaDoubleRDD(sc.union(rdds))
   }
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
index b959b683d1674..a7dfa1d257cf2 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
@@ -17,15 +17,17 @@
 
 package org.apache.spark.api.python
 
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
-import org.apache.spark.util.{SerializableConfiguration, Utils}
-import org.apache.spark.{Logging, SparkException}
+import scala.collection.JavaConverters._
+import scala.util.{Failure, Success, Try}
+
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io._
-import scala.util.{Failure, Success, Try}
-import org.apache.spark.annotation.Experimental
 
+import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 /**
  * :: Experimental ::
@@ -68,7 +70,6 @@ private[python] class WritableToJavaConverter(
    * object representation
    */
   private def convertWritable(writable: Writable): Any = {
-    import collection.JavaConversions._
     writable match {
       case iw: IntWritable => iw.get()
       case dw: DoubleWritable => dw.get()
@@ -89,9 +90,7 @@ private[python] class WritableToJavaConverter(
         aw.get().map(convertWritable(_))
       case mw: MapWritable =>
         val map = new java.util.HashMap[Any, Any]()
-        mw.foreach { case (k, v) =>
-          map.put(convertWritable(k), convertWritable(v))
-        }
+        mw.asScala.foreach { case (k, v) => map.put(convertWritable(k), convertWritable(v)) }
         map
       case w: Writable => WritableUtils.clone(w, conf.value.value)
       case other => other
@@ -122,7 +121,6 @@ private[python] class JavaToWritableConverter extends Converter[Any, Writable] {
    * supported out-of-the-box.
    */
   private def convertToWritable(obj: Any): Writable = {
-    import collection.JavaConversions._
     obj match {
       case i: java.lang.Integer => new IntWritable(i)
       case d: java.lang.Double => new DoubleWritable(d)
@@ -134,7 +132,7 @@ private[python] class JavaToWritableConverter extends Converter[Any, Writable] {
       case null => NullWritable.get()
       case map: java.util.Map[_, _] =>
         val mapWritable = new MapWritable()
-        map.foreach { case (k, v) =>
+        map.asScala.foreach { case (k, v) =>
           mapWritable.put(convertToWritable(k), convertToWritable(v))
         }
         mapWritable
@@ -161,9 +159,8 @@ private[python] object PythonHadoopUtil {
    * Convert a [[java.util.Map]] of properties to a [[org.apache.hadoop.conf.Configuration]]
    */
   def mapToConf(map: java.util.Map[String, String]): Configuration = {
-    import collection.JavaConversions._
     val conf = new Configuration()
-    map.foreach{ case (k, v) => conf.set(k, v) }
+    map.asScala.foreach { case (k, v) => conf.set(k, v) }
     conf
   }
 
@@ -172,9 +169,8 @@ private[python] object PythonHadoopUtil {
    * any matching keys in left
    */
   def mergeConfs(left: Configuration, right: Configuration): Configuration = {
-    import collection.JavaConversions._
     val copy = new Configuration(left)
-    right.iterator().foreach(entry => copy.set(entry.getKey, entry.getValue))
+    right.asScala.foreach(entry => copy.set(entry.getKey, entry.getValue))
     copy
   }
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 2a56bf28d7027..b4d152b336602 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -21,7 +21,7 @@ import java.io._
 import java.net._
 import java.util.{Collections, ArrayList => JArrayList, List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.language.existentials
 
@@ -66,11 +66,11 @@ private[spark] class PythonRDD(
     val env = SparkEnv.get
     val localdir = env.blockManager.diskBlockManager.localDirs.map(
       f => f.getPath()).mkString(",")
-    envVars += ("SPARK_LOCAL_DIRS" -> localdir) // it's also used in monitor thread
+    envVars.put("SPARK_LOCAL_DIRS", localdir) // it's also used in monitor thread
     if (reuse_worker) {
-      envVars += ("SPARK_REUSE_WORKER" -> "1")
+      envVars.put("SPARK_REUSE_WORKER", "1")
     }
-    val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
+    val worker: Socket = env.createPythonWorker(pythonExec, envVars.asScala.toMap)
     // Whether is the worker released into idle pool
     @volatile var released = false
 
@@ -150,7 +150,7 @@ private[spark] class PythonRDD(
               // Check whether the worker is ready to be re-used.
               if (stream.readInt() == SpecialLengths.END_OF_STREAM) {
                 if (reuse_worker) {
-                  env.releasePythonWorker(pythonExec, envVars.toMap, worker)
+                  env.releasePythonWorker(pythonExec, envVars.asScala.toMap, worker)
                   released = true
                 }
               }
@@ -217,13 +217,13 @@ private[spark] class PythonRDD(
         // sparkFilesDir
         PythonRDD.writeUTF(SparkFiles.getRootDirectory, dataOut)
         // Python includes (*.zip and *.egg files)
-        dataOut.writeInt(pythonIncludes.length)
-        for (include <- pythonIncludes) {
+        dataOut.writeInt(pythonIncludes.size())
+        for (include <- pythonIncludes.asScala) {
           PythonRDD.writeUTF(include, dataOut)
         }
         // Broadcast variables
         val oldBids = PythonRDD.getWorkerBroadcasts(worker)
-        val newBids = broadcastVars.map(_.id).toSet
+        val newBids = broadcastVars.asScala.map(_.id).toSet
         // number of different broadcasts
         val toRemove = oldBids.diff(newBids)
         val cnt = toRemove.size + newBids.diff(oldBids).size
@@ -233,7 +233,7 @@ private[spark] class PythonRDD(
           dataOut.writeLong(- bid - 1)  // bid >= 0
           oldBids.remove(bid)
         }
-        for (broadcast <- broadcastVars) {
+        for (broadcast <- broadcastVars.asScala) {
           if (!oldBids.contains(broadcast.id)) {
             // send new broadcast
             dataOut.writeLong(broadcast.id)
@@ -287,7 +287,7 @@ private[spark] class PythonRDD(
       if (!context.isCompleted) {
         try {
           logWarning("Incomplete task interrupted: Attempting to kill Python Worker")
-          env.destroyPythonWorker(pythonExec, envVars.toMap, worker)
+          env.destroyPythonWorker(pythonExec, envVars.asScala.toMap, worker)
         } catch {
           case e: Exception =>
             logError("Exception when trying to kill worker", e)
@@ -358,10 +358,10 @@ private[spark] object PythonRDD extends Logging {
     type ByteArray = Array[Byte]
     type UnrolledPartition = Array[ByteArray]
     val allPartitions: Array[UnrolledPartition] =
-      sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions)
+      sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions.asScala)
     val flattenedPartition: UnrolledPartition = Array.concat(allPartitions: _*)
     serveIterator(flattenedPartition.iterator,
-      s"serve RDD ${rdd.id} with partitions ${partitions.mkString(",")}")
+      s"serve RDD ${rdd.id} with partitions ${partitions.asScala.mkString(",")}")
   }
 
   /**
@@ -819,7 +819,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort:
       val in = socket.getInputStream
       val out = new DataOutputStream(new BufferedOutputStream(socket.getOutputStream, bufferSize))
       out.writeInt(val2.size)
-      for (array <- val2) {
+      for (array <- val2.asScala) {
         out.writeInt(array.length)
         out.write(array)
       }
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
index 90dacaeb93429..31e534f160eeb 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.api.python
 
-import java.io.{File}
+import java.io.File
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.SparkContext
@@ -51,7 +51,14 @@ private[spark] object PythonUtils {
    * Convert list of T into seq of T (for calling API with varargs)
    */
   def toSeq[T](vs: JList[T]): Seq[T] = {
-    vs.toList.toSeq
+    vs.asScala
+  }
+
+  /**
+   * Convert list of T into a (Scala) List of T
+   */
+  def toList[T](vs: JList[T]): List[T] = {
+    vs.asScala.toList
   }
 
   /**
@@ -65,6 +72,6 @@ private[spark] object PythonUtils {
    * Convert java map of K, V into Map of K, V (for calling API with varargs)
    */
   def toScalaMap[K, V](jm: java.util.Map[K, V]): Map[K, V] = {
-    jm.toMap
+    jm.asScala.toMap
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index e314408c067e9..7039b734d2e40 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -19,9 +19,10 @@ package org.apache.spark.api.python
 
 import java.io.{DataOutputStream, DataInputStream, InputStream, OutputStreamWriter}
 import java.net.{InetAddress, ServerSocket, Socket, SocketException}
+import java.util.Arrays
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark._
 import org.apache.spark.util.{RedirectThread, Utils}
@@ -108,9 +109,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
       serverSocket = new ServerSocket(0, 1, InetAddress.getByAddress(Array(127, 0, 0, 1)))
 
       // Create and start the worker
-      val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.worker"))
+      val pb = new ProcessBuilder(Arrays.asList(pythonExec, "-m", "pyspark.worker"))
       val workerEnv = pb.environment()
-      workerEnv.putAll(envVars)
+      workerEnv.putAll(envVars.asJava)
       workerEnv.put("PYTHONPATH", pythonPath)
       // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u:
       workerEnv.put("PYTHONUNBUFFERED", "YES")
@@ -151,9 +152,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
 
       try {
         // Create and start the daemon
-        val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.daemon"))
+        val pb = new ProcessBuilder(Arrays.asList(pythonExec, "-m", "pyspark.daemon"))
         val workerEnv = pb.environment()
-        workerEnv.putAll(envVars)
+        workerEnv.putAll(envVars.asJava)
         workerEnv.put("PYTHONPATH", pythonPath)
         // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u:
         workerEnv.put("PYTHONUNBUFFERED", "YES")
diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
index 1f1debcf84ad4..fd27276e70bfe 100644
--- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
@@ -22,7 +22,6 @@ import java.util.{ArrayList => JArrayList}
 
 import org.apache.spark.api.java.JavaRDD
 
-import scala.collection.JavaConversions._
 import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.Failure
@@ -214,7 +213,7 @@ private[spark] object SerDeUtil extends Logging {
         new AutoBatchedPickler(cleaned)
       } else {
         val pickle = new Pickler
-        cleaned.grouped(batchSize).map(batched => pickle.dumps(seqAsJavaList(batched)))
+        cleaned.grouped(batchSize).map(batched => pickle.dumps(batched.asJava))
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
index 8f30ff9202c83..ee1fb056f0d96 100644
--- a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
@@ -20,6 +20,8 @@ package org.apache.spark.api.python
 import java.io.{DataOutput, DataInput}
 import java.{util => ju}
 
+import scala.collection.JavaConverters._
+
 import com.google.common.base.Charsets.UTF_8
 
 import org.apache.hadoop.io._
@@ -62,10 +64,9 @@ private[python] class TestInputKeyConverter extends Converter[Any, Any] {
 }
 
 private[python] class TestInputValueConverter extends Converter[Any, Any] {
-  import collection.JavaConversions._
   override def convert(obj: Any): ju.List[Double] = {
     val m = obj.asInstanceOf[MapWritable]
-    seqAsJavaList(m.keySet.map(w => w.asInstanceOf[DoubleWritable].get()).toSeq)
+    m.keySet.asScala.map(_.asInstanceOf[DoubleWritable].get()).toSeq.asJava
   }
 }
 
@@ -76,9 +77,8 @@ private[python] class TestOutputKeyConverter extends Converter[Any, Any] {
 }
 
 private[python] class TestOutputValueConverter extends Converter[Any, Any] {
-  import collection.JavaConversions._
   override def convert(obj: Any): DoubleWritable = {
-    new DoubleWritable(obj.asInstanceOf[java.util.Map[Double, _]].keySet().head)
+    new DoubleWritable(obj.asInstanceOf[java.util.Map[Double, _]].keySet().iterator().next())
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
index 1cf2824f862ee..9d5bbb5d609f3 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
@@ -19,9 +19,10 @@ package org.apache.spark.api.r
 
 import java.io._
 import java.net.{InetAddress, ServerSocket}
+import java.util.Arrays
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.io.Source
 import scala.reflect.ClassTag
 import scala.util.Try
@@ -365,11 +366,11 @@ private[r] object RRDD {
       sparkConf.setIfMissing("spark.master", "local")
     }
 
-    for ((name, value) <- sparkEnvirMap) {
-      sparkConf.set(name.asInstanceOf[String], value.asInstanceOf[String])
+    for ((name, value) <- sparkEnvirMap.asScala) {
+      sparkConf.set(name.toString, value.toString)
     }
-    for ((name, value) <- sparkExecutorEnvMap) {
-      sparkConf.setExecutorEnv(name.asInstanceOf[String], value.asInstanceOf[String])
+    for ((name, value) <- sparkExecutorEnvMap.asScala) {
+      sparkConf.setExecutorEnv(name.toString, value.toString)
     }
 
     val jsc = new JavaSparkContext(sparkConf)
@@ -395,7 +396,7 @@ private[r] object RRDD {
     val rOptions = "--vanilla"
     val rLibDir = RUtils.sparkRPackagePath(isDriver = false)
     val rExecScript = rLibDir + "/SparkR/worker/" + script
-    val pb = new ProcessBuilder(List(rCommand, rOptions, rExecScript))
+    val pb = new ProcessBuilder(Arrays.asList(rCommand, rOptions, rExecScript))
     // Unset the R_TESTS environment variable for workers.
     // This is set by R CMD check as startup.Rs
     // (http://svn.r-project.org/R/trunk/src/library/tools/R/testing.R)
diff --git a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
index 427b2bc7cbcbb..9e807cc52f18c 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.api.r
 
 import java.io.File
-
-import scala.collection.JavaConversions._
+import java.util.Arrays
 
 import org.apache.spark.{SparkEnv, SparkException}
 
@@ -68,7 +67,7 @@ private[spark] object RUtils {
   /** Check if R is installed before running tests that use R commands. */
   def isRInstalled: Boolean = {
     try {
-      val builder = new ProcessBuilder(Seq("R", "--version"))
+      val builder = new ProcessBuilder(Arrays.asList("R", "--version"))
       builder.start().waitFor() == 0
     } catch {
       case e: Exception => false
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 3c89f24473744..dbbbcf40c1e96 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -20,7 +20,7 @@ package org.apache.spark.api.r
 import java.io.{DataInputStream, DataOutputStream}
 import java.sql.{Timestamp, Date, Time}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 /**
  * Utility functions to serialize, deserialize objects to / from R
@@ -165,7 +165,7 @@ private[spark] object SerDe {
         val valueType = readObjectType(in)
         readTypedObject(in, valueType)
       })
-      mapAsJavaMap(keys.zip(values).toMap)
+      keys.zip(values).toMap.asJava
     } else {
       new java.util.HashMap[Object, Object]()
     }
diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
index a0c9b5e63c744..7e3764d802fe1 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
@@ -20,7 +20,7 @@ package org.apache.spark.broadcast
 import java.io._
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions.asJavaEnumeration
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 import scala.util.Random
 
@@ -210,7 +210,7 @@ private object TorrentBroadcast extends Logging {
       compressionCodec: Option[CompressionCodec]): T = {
     require(blocks.nonEmpty, "Cannot unblockify an empty array of blocks")
     val is = new SequenceInputStream(
-      asJavaEnumeration(blocks.iterator.map(block => new ByteBufferInputStream(block))))
+      blocks.iterator.map(new ByteBufferInputStream(_)).asJavaEnumeration)
     val in: InputStream = compressionCodec.map(c => c.compressedInputStream(is)).getOrElse(is)
     val ser = serializer.newInstance()
     val serIn = ser.deserializeStream(in)
diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
index 22ef701d833b2..6840a3ae831f0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
@@ -19,13 +19,13 @@ package org.apache.spark.deploy
 
 import java.util.concurrent.CountDownLatch
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.{Logging, SparkConf, SecurityManager}
 import org.apache.spark.network.TransportContext
 import org.apache.spark.network.netty.SparkTransportConf
 import org.apache.spark.network.sasl.SaslServerBootstrap
-import org.apache.spark.network.server.TransportServer
+import org.apache.spark.network.server.{TransportServerBootstrap, TransportServer}
 import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler
 import org.apache.spark.network.util.TransportConf
 import org.apache.spark.util.Utils
@@ -67,13 +67,13 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana
   def start() {
     require(server == null, "Shuffle server already started")
     logInfo(s"Starting shuffle service on port $port with useSasl = $useSasl")
-    val bootstraps =
+    val bootstraps: Seq[TransportServerBootstrap] =
       if (useSasl) {
         Seq(new SaslServerBootstrap(transportConf, securityManager))
       } else {
         Nil
       }
-    server = transportContext.createServer(port, bootstraps)
+    server = transportContext.createServer(port, bootstraps.asJava)
   }
 
   /** Clean up all shuffle files associated with an application that has exited. */
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index 23d01e9cbb9f9..d85327603f64d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -21,7 +21,7 @@ import java.net.URI
 import java.io.File
 
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Try
 
 import org.apache.spark.SparkUserAppException
@@ -71,7 +71,7 @@ object PythonRunner {
     val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*)
 
     // Launch Python process
-    val builder = new ProcessBuilder(Seq(pythonExec, formattedPythonFile) ++ otherArgs)
+    val builder = new ProcessBuilder((Seq(pythonExec, formattedPythonFile) ++ otherArgs).asJava)
     val env = builder.environment()
     env.put("PYTHONPATH", pythonPath)
     // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u:
diff --git a/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
index ed1e972955679..4b28866dcaa7c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
@@ -22,7 +22,7 @@ import java.util.jar.JarFile
 import java.util.logging.Level
 import java.util.zip.{ZipEntry, ZipOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.io.{ByteStreams, Files}
 
@@ -110,7 +110,7 @@ private[deploy] object RPackageUtils extends Logging {
       print(s"Building R package with the command: $installCmd", printStream)
     }
     try {
-      val builder = new ProcessBuilder(installCmd)
+      val builder = new ProcessBuilder(installCmd.asJava)
       builder.redirectErrorStream(true)
       val env = builder.environment()
       env.clear()
diff --git a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
index c0cab22fa8252..05b954ce36998 100644
--- a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
@@ -20,7 +20,7 @@ package org.apache.spark.deploy
 import java.io._
 import java.util.concurrent.{Semaphore, TimeUnit}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.Path
 
@@ -68,7 +68,7 @@ object RRunner {
     if (initialized.tryAcquire(backendTimeout, TimeUnit.SECONDS)) {
       // Launch R
       val returnCode = try {
-        val builder = new ProcessBuilder(Seq(rCommand, rFileNormalized) ++ otherArgs)
+        val builder = new ProcessBuilder((Seq(rCommand, rFileNormalized) ++ otherArgs).asJava)
         val env = builder.environment()
         env.put("EXISTING_SPARKR_BACKEND_PORT", sparkRBackendPort.toString)
         val rPackageDir = RUtils.sparkRPackagePath(isDriver = true)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala
index b8d3993540220..8d5e716e6aea4 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.deploy
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.curator.framework.{CuratorFramework, CuratorFrameworkFactory}
 import org.apache.curator.retry.ExponentialBackoffRetry
@@ -57,7 +57,7 @@ private[spark] object SparkCuratorUtil extends Logging {
 
   def deleteRecursive(zk: CuratorFramework, path: String) {
     if (zk.checkExists().forPath(path) != null) {
-      for (child <- zk.getChildren.forPath(path)) {
+      for (child <- zk.getChildren.forPath(path).asScala) {
         zk.delete().forPath(path + "/" + child)
       }
       zk.delete().forPath(path)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index dda4216c7efe2..f7723ef5bde4c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -22,7 +22,7 @@ import java.lang.reflect.Method
 import java.security.PrivilegedExceptionAction
 import java.util.{Arrays, Comparator}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.util.control.NonFatal
@@ -71,7 +71,7 @@ class SparkHadoopUtil extends Logging {
   }
 
   def transferCredentials(source: UserGroupInformation, dest: UserGroupInformation) {
-    for (token <- source.getTokens()) {
+    for (token <- source.getTokens.asScala) {
       dest.addToken(token)
     }
   }
@@ -175,8 +175,8 @@ class SparkHadoopUtil extends Logging {
   }
 
   private def getFileSystemThreadStatistics(): Seq[AnyRef] = {
-    val stats = FileSystem.getAllStatistics()
-    stats.map(Utils.invoke(classOf[Statistics], _, "getThreadStatistics"))
+    FileSystem.getAllStatistics.asScala.map(
+      Utils.invoke(classOf[Statistics], _, "getThreadStatistics"))
   }
 
   private def getFileSystemThreadStatisticsMethod(methodName: String): Method = {
@@ -306,12 +306,13 @@ class SparkHadoopUtil extends Logging {
     val renewalInterval =
       sparkConf.getLong("spark.yarn.token.renewal.interval", (24 hours).toMillis)
 
-    credentials.getAllTokens.filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
+    credentials.getAllTokens.asScala
+      .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
       .map { t =>
-      val identifier = new DelegationTokenIdentifier()
-      identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier)))
-      (identifier.getIssueDate + fraction * renewalInterval).toLong - now
-    }.foldLeft(0L)(math.max)
+        val identifier = new DelegationTokenIdentifier()
+        identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier)))
+        (identifier.getIssueDate + fraction * renewalInterval).toLong - now
+      }.foldLeft(0L)(math.max)
   }
 
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 3f3c6627c21fb..18a1c52ae53fb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -23,7 +23,7 @@ import java.net.URI
 import java.util.{List => JList}
 import java.util.jar.JarFile
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.io.Source
 
@@ -94,7 +94,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
 
   // Set parameters from command line arguments
   try {
-    parse(args.toList)
+    parse(args.asJava)
   } catch {
     case e: IllegalArgumentException =>
       SparkSubmit.printErrorAndExit(e.getMessage())
@@ -458,7 +458,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
   }
 
   override protected def handleExtraArgs(extra: JList[String]): Unit = {
-    childArgs ++= extra
+    childArgs ++= extra.asScala
   }
 
   private def printUsageAndExit(exitCode: Int, unknownParam: Any = null): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
index 563831cc6b8dd..540e802420ce0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.master
 
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.curator.framework.CuratorFramework
@@ -49,8 +49,8 @@ private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serializer
   }
 
   override def read[T: ClassTag](prefix: String): Seq[T] = {
-    val file = zk.getChildren.forPath(WORKING_DIR).filter(_.startsWith(prefix))
-    file.map(deserializeFromFile[T]).flatten
+    zk.getChildren.forPath(WORKING_DIR).asScala
+      .filter(_.startsWith(prefix)).map(deserializeFromFile[T]).flatten
   }
 
   override def close() {
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
index 45a3f43045437..ce02ee203a4bd 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
@@ -18,9 +18,8 @@
 package org.apache.spark.deploy.worker
 
 import java.io.{File, FileOutputStream, InputStream, IOException}
-import java.lang.System._
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.Map
 
 import org.apache.spark.Logging
@@ -62,7 +61,7 @@ object CommandUtils extends Logging {
     // SPARK-698: do not call the run.cmd script, as process.destroy()
     // fails to kill a process tree on Windows
     val cmd = new WorkerCommandBuilder(sparkHome, memory, command).buildCommand()
-    cmd.toSeq ++ Seq(command.mainClass) ++ command.arguments
+    cmd.asScala ++ Seq(command.mainClass) ++ command.arguments
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
index ec51c3d935d8e..89159ff5e2b3c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.worker
 
 import java.io._
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
@@ -172,8 +172,8 @@ private[deploy] class DriverRunner(
       CommandUtils.redirectStream(process.getInputStream, stdout)
 
       val stderr = new File(baseDir, "stderr")
-      val header = "Launch Command: %s\n%s\n\n".format(
-        builder.command.mkString("\"", "\" \"", "\""), "=" * 40)
+      val formattedCommand = builder.command.asScala.mkString("\"", "\" \"", "\"")
+      val header = "Launch Command: %s\n%s\n\n".format(formattedCommand, "=" * 40)
       Files.append(header, stderr, UTF_8)
       CommandUtils.redirectStream(process.getErrorStream, stderr)
     }
@@ -229,6 +229,6 @@ private[deploy] trait ProcessBuilderLike {
 private[deploy] object ProcessBuilderLike {
   def apply(processBuilder: ProcessBuilder): ProcessBuilderLike = new ProcessBuilderLike {
     override def start(): Process = processBuilder.start()
-    override def command: Seq[String] = processBuilder.command()
+    override def command: Seq[String] = processBuilder.command().asScala
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index ab3fea475c2a5..3aef0515cbf6e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.worker
 
 import java.io._
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
@@ -129,7 +129,8 @@ private[deploy] class ExecutorRunner(
       val builder = CommandUtils.buildProcessBuilder(appDesc.command, new SecurityManager(conf),
         memory, sparkHome.getAbsolutePath, substituteVariables)
       val command = builder.command()
-      logInfo("Launch command: " + command.mkString("\"", "\" \"", "\""))
+      val formattedCommand = command.asScala.mkString("\"", "\" \"", "\"")
+      logInfo(s"Launch command: $formattedCommand")
 
       builder.directory(executorDir)
       builder.environment.put("SPARK_EXECUTOR_DIRS", appLocalDirs.mkString(File.pathSeparator))
@@ -145,7 +146,7 @@ private[deploy] class ExecutorRunner(
 
       process = builder.start()
       val header = "Spark Executor Command: %s\n%s\n\n".format(
-        command.mkString("\"", "\" \"", "\""), "=" * 40)
+        formattedCommand, "=" * 40)
 
       // Redirect its stdout and stderr to files
       val stdout = new File(executorDir, "stdout")
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 79b1536d94016..770927c80f7a4 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -24,7 +24,6 @@ import java.util.{UUID, Date}
 import java.util.concurrent._
 import java.util.concurrent.{Future => JFuture, ScheduledFuture => JScheduledFuture}
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, HashSet, LinkedHashMap}
 import scala.concurrent.ExecutionContext
 import scala.util.Random
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 42a85e42ea2b6..c3491bb8b1cf3 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -23,7 +23,7 @@ import java.net.URL
 import java.nio.ByteBuffer
 import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.util.control.NonFatal
 
@@ -147,7 +147,7 @@ private[spark] class Executor(
 
   /** Returns the total amount of time this JVM process has spent in garbage collection. */
   private def computeTotalGcTime(): Long = {
-    ManagementFactory.getGarbageCollectorMXBeans.map(_.getCollectionTime).sum
+    ManagementFactory.getGarbageCollectorMXBeans.asScala.map(_.getCollectionTime).sum
   }
 
   class TaskRunner(
@@ -425,7 +425,7 @@ private[spark] class Executor(
     val tasksMetrics = new ArrayBuffer[(Long, TaskMetrics)]()
     val curGCTime = computeTotalGcTime()
 
-    for (taskRunner <- runningTasks.values()) {
+    for (taskRunner <- runningTasks.values().asScala) {
       if (taskRunner.task != null) {
         taskRunner.task.metrics.foreach { metrics =>
           metrics.updateShuffleReadMetrics()
diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala
index 293c512f8b70c..d16f4a1fc4e3b 100644
--- a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala
+++ b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala
@@ -19,7 +19,7 @@ package org.apache.spark.executor
 
 import java.util.concurrent.ThreadPoolExecutor
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.codahale.metrics.{Gauge, MetricRegistry}
 import org.apache.hadoop.fs.FileSystem
@@ -30,7 +30,7 @@ private[spark]
 class ExecutorSource(threadPool: ThreadPoolExecutor, executorId: String) extends Source {
 
   private def fileStats(scheme: String) : Option[FileSystem.Statistics] =
-    FileSystem.getAllStatistics().find(s => s.getScheme.equals(scheme))
+    FileSystem.getAllStatistics.asScala.find(s => s.getScheme.equals(scheme))
 
   private def registerFileSystemStat[T](
         scheme: String, name: String, f: FileSystem.Statistics => T, defaultValue: T) = {
diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
index cfd672e1d8a97..0474fd2ccc12e 100644
--- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
@@ -19,7 +19,7 @@ package org.apache.spark.executor
 
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.mesos.protobuf.ByteString
 import org.apache.mesos.{Executor => MesosExecutor, ExecutorDriver, MesosExecutorDriver}
@@ -28,7 +28,7 @@ import org.apache.mesos.Protos.{TaskStatus => MesosTaskStatus, _}
 import org.apache.spark.{Logging, TaskState, SparkConf, SparkEnv}
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.scheduler.cluster.mesos.{MesosTaskLaunchData}
+import org.apache.spark.scheduler.cluster.mesos.MesosTaskLaunchData
 import org.apache.spark.util.{SignalLogger, Utils}
 
 private[spark] class MesosExecutorBackend
@@ -55,7 +55,7 @@ private[spark] class MesosExecutorBackend
       slaveInfo: SlaveInfo) {
 
     // Get num cores for this task from ExecutorInfo, created in MesosSchedulerBackend.
-    val cpusPerTask = executorInfo.getResourcesList
+    val cpusPerTask = executorInfo.getResourcesList.asScala
       .find(_.getName == "cpus")
       .map(_.getScalar.getValue.toInt)
       .getOrElse(0)
diff --git a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
index 6cda7772f77bc..a5ad47293f1c2 100644
--- a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
+++ b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
@@ -19,7 +19,7 @@ package org.apache.spark.input
 
 import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.io.ByteStreams
 import org.apache.hadoop.conf.Configuration
@@ -44,12 +44,9 @@ private[spark] abstract class StreamFileInputFormat[T]
    * which is set through setMaxSplitSize
    */
   def setMinPartitions(context: JobContext, minPartitions: Int) {
-    val files = listStatus(context)
-    val totalLen = files.map { file =>
-      if (file.isDir) 0L else file.getLen
-    }.sum
-
-    val maxSplitSize = Math.ceil(totalLen * 1.0 / files.length).toLong
+    val files = listStatus(context).asScala
+    val totalLen = files.map(file => if (file.isDir) 0L else file.getLen).sum
+    val maxSplitSize = Math.ceil(totalLen * 1.0 / files.size).toLong
     super.setMaxSplitSize(maxSplitSize)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
index aaef7c74eea33..1ba34a11414a2 100644
--- a/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
+++ b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.input
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce.InputSplit
@@ -52,10 +52,8 @@ private[spark] class WholeTextFileInputFormat
    * which is set through setMaxSplitSize
    */
   def setMinPartitions(context: JobContext, minPartitions: Int) {
-    val files = listStatus(context)
-    val totalLen = files.map { file =>
-      if (file.isDir) 0L else file.getLen
-    }.sum
+    val files = listStatus(context).asScala
+    val totalLen = files.map(file => if (file.isDir) 0L else file.getLen).sum
     val maxSplitSize = Math.ceil(totalLen * 1.0 /
       (if (minPartitions == 0) 1 else minPartitions)).toLong
     super.setMaxSplitSize(maxSplitSize)
diff --git a/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala b/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala
index 9be98723aed14..0c096656f9236 100644
--- a/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala
+++ b/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala
@@ -20,7 +20,7 @@ package org.apache.spark.launcher
 import java.io.File
 import java.util.{HashMap => JHashMap, List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.deploy.Command
 
@@ -32,7 +32,7 @@ import org.apache.spark.deploy.Command
 private[spark] class WorkerCommandBuilder(sparkHome: String, memoryMb: Int, command: Command)
     extends AbstractCommandBuilder {
 
-  childEnv.putAll(command.environment)
+  childEnv.putAll(command.environment.asJava)
   childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, sparkHome)
 
   override def buildCommand(env: JMap[String, String]): JList[String] = {
diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala
index d7495551ad233..dd2d325d87034 100644
--- a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala
+++ b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala
@@ -20,6 +20,7 @@ package org.apache.spark.metrics
 import java.io.{FileInputStream, InputStream}
 import java.util.Properties
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.matching.Regex
 
@@ -58,25 +59,20 @@ private[spark] class MetricsConfig(conf: SparkConf) extends Logging {
 
     propertyCategories = subProperties(properties, INSTANCE_REGEX)
     if (propertyCategories.contains(DEFAULT_PREFIX)) {
-      import scala.collection.JavaConversions._
-
-      val defaultProperty = propertyCategories(DEFAULT_PREFIX)
-      for { (inst, prop) <- propertyCategories
-            if (inst != DEFAULT_PREFIX)
-            (k, v) <- defaultProperty
-            if (prop.getProperty(k) == null) } {
-        prop.setProperty(k, v)
+      val defaultProperty = propertyCategories(DEFAULT_PREFIX).asScala
+      for((inst, prop) <- propertyCategories if (inst != DEFAULT_PREFIX);
+          (k, v) <- defaultProperty if (prop.get(k) == null)) {
+        prop.put(k, v)
       }
     }
   }
 
   def subProperties(prop: Properties, regex: Regex): mutable.HashMap[String, Properties] = {
     val subProperties = new mutable.HashMap[String, Properties]
-    import scala.collection.JavaConversions._
-    prop.foreach { kv =>
-      if (regex.findPrefixOf(kv._1).isDefined) {
-        val regex(prefix, suffix) = kv._1
-        subProperties.getOrElseUpdate(prefix, new Properties).setProperty(suffix, kv._2)
+    prop.asScala.foreach { kv =>
+      if (regex.findPrefixOf(kv._1.toString).isDefined) {
+        val regex(prefix, suffix) = kv._1.toString
+        subProperties.getOrElseUpdate(prefix, new Properties).setProperty(suffix, kv._2.toString)
       }
     }
     subProperties
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
index b089da8596e2b..7c170a742fb64 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
@@ -19,7 +19,7 @@ package org.apache.spark.network.netty
 
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.Logging
 import org.apache.spark.network.BlockDataManager
@@ -55,7 +55,7 @@ class NettyBlockRpcServer(
       case openBlocks: OpenBlocks =>
         val blocks: Seq[ManagedBuffer] =
           openBlocks.blockIds.map(BlockId.apply).map(blockManager.getBlockData)
-        val streamId = streamManager.registerStream(blocks.iterator)
+        val streamId = streamManager.registerStream(blocks.iterator.asJava)
         logTrace(s"Registered streamId $streamId with ${blocks.size} buffers")
         responseContext.onSuccess(new StreamHandle(streamId, blocks.size).toByteArray)
 
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index d650d5fe73087..ff8aae9ebe9f0 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.network.netty
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.{Future, Promise}
 
 import org.apache.spark.{SecurityManager, SparkConf}
@@ -58,7 +58,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
         securityManager.isSaslEncryptionEnabled()))
     }
     transportContext = new TransportContext(transportConf, rpcHandler)
-    clientFactory = transportContext.createClientFactory(clientBootstrap.toList)
+    clientFactory = transportContext.createClientFactory(clientBootstrap.toSeq.asJava)
     server = createServer(serverBootstrap.toList)
     appId = conf.getAppId
     logInfo("Server created on " + server.getPort)
@@ -67,7 +67,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
   /** Creates and binds the TransportServer, possibly trying multiple ports. */
   private def createServer(bootstraps: List[TransportServerBootstrap]): TransportServer = {
     def startService(port: Int): (TransportServer, Int) = {
-      val server = transportContext.createServer(port, bootstraps)
+      val server = transportContext.createServer(port, bootstraps.asJava)
       (server, server.getPort)
     }
 
diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
index 1499da07bb83b..8d9ebadaf79d4 100644
--- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
@@ -23,7 +23,7 @@ import java.nio.channels._
 import java.util.concurrent.ConcurrentLinkedQueue
 import java.util.LinkedList
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.util.control.NonFatal
 
@@ -145,7 +145,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
   }
 
   def callOnExceptionCallbacks(e: Throwable) {
-    onExceptionCallbacks foreach {
+    onExceptionCallbacks.asScala.foreach {
       callback =>
         try {
           callback(this, e)
diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala
index 91b07ce3af1b6..5afce75680f94 100644
--- a/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.partial
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions.mapAsScalaMap
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.HashMap
 import scala.reflect.ClassTag
@@ -48,9 +48,9 @@ private[spark] class GroupedCountEvaluator[T : ClassTag](totalOutputs: Int, conf
     if (outputsMerged == totalOutputs) {
       val result = new JHashMap[T, BoundedDouble](sums.size)
       sums.foreach { case (key, sum) =>
-        result(key) = new BoundedDouble(sum, 1.0, sum, sum)
+        result.put(key, new BoundedDouble(sum, 1.0, sum, sum))
       }
-      result
+      result.asScala
     } else if (outputsMerged == 0) {
       new HashMap[T, BoundedDouble]
     } else {
@@ -64,9 +64,9 @@ private[spark] class GroupedCountEvaluator[T : ClassTag](totalOutputs: Int, conf
         val stdev = math.sqrt(variance)
         val low = mean - confFactor * stdev
         val high = mean + confFactor * stdev
-        result(key) = new BoundedDouble(mean, confidence, low, high)
+        result.put(key, new BoundedDouble(mean, confidence, low, high))
       }
-      result
+      result.asScala
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
index af26c3d59ac02..a164040684803 100644
--- a/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.partial
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions.mapAsScalaMap
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.HashMap
 
@@ -55,9 +55,9 @@ private[spark] class GroupedMeanEvaluator[T](totalOutputs: Int, confidence: Doub
       while (iter.hasNext) {
         val entry = iter.next()
         val mean = entry.getValue.mean
-        result(entry.getKey) = new BoundedDouble(mean, 1.0, mean, mean)
+        result.put(entry.getKey, new BoundedDouble(mean, 1.0, mean, mean))
       }
-      result
+      result.asScala
     } else if (outputsMerged == 0) {
       new HashMap[T, BoundedDouble]
     } else {
@@ -72,9 +72,9 @@ private[spark] class GroupedMeanEvaluator[T](totalOutputs: Int, confidence: Doub
         val confFactor = studentTCacher.get(counter.count)
         val low = mean - confFactor * stdev
         val high = mean + confFactor * stdev
-        result(entry.getKey) = new BoundedDouble(mean, confidence, low, high)
+        result.put(entry.getKey, new BoundedDouble(mean, confidence, low, high))
       }
-      result
+      result.asScala
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala
index 442fb86227d86..54a1beab3514b 100644
--- a/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.partial
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions.mapAsScalaMap
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.HashMap
 
@@ -55,9 +55,9 @@ private[spark] class GroupedSumEvaluator[T](totalOutputs: Int, confidence: Doubl
       while (iter.hasNext) {
         val entry = iter.next()
         val sum = entry.getValue.sum
-        result(entry.getKey) = new BoundedDouble(sum, 1.0, sum, sum)
+        result.put(entry.getKey, new BoundedDouble(sum, 1.0, sum, sum))
       }
-      result
+      result.asScala
     } else if (outputsMerged == 0) {
       new HashMap[T, BoundedDouble]
     } else {
@@ -80,9 +80,9 @@ private[spark] class GroupedSumEvaluator[T](totalOutputs: Int, confidence: Doubl
         val confFactor = studentTCacher.get(counter.count)
         val low = sumEstimate - confFactor * sumStdev
         val high = sumEstimate + confFactor * sumStdev
-        result(entry.getKey) = new BoundedDouble(sumEstimate, confidence, low, high)
+        result.put(entry.getKey, new BoundedDouble(sumEstimate, confidence, low, high))
       }
-      result
+      result.asScala
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 326fafb230a40..4e5f2e8a5d467 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -22,7 +22,7 @@ import java.text.SimpleDateFormat
 import java.util.{Date, HashMap => JHashMap}
 
 import scala.collection.{Map, mutable}
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 import scala.util.DynamicVariable
@@ -312,14 +312,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     } : Iterator[JHashMap[K, V]]
 
     val mergeMaps = (m1: JHashMap[K, V], m2: JHashMap[K, V]) => {
-      m2.foreach { pair =>
+      m2.asScala.foreach { pair =>
         val old = m1.get(pair._1)
         m1.put(pair._1, if (old == null) pair._2 else cleanedF(old, pair._2))
       }
       m1
     } : JHashMap[K, V]
 
-    self.mapPartitions(reducePartition).reduce(mergeMaps)
+    self.mapPartitions(reducePartition).reduce(mergeMaps).asScala
   }
 
   /** Alias for reduceByKeyLocally */
diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
index 3bb9998e1db44..afbe566b76566 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
@@ -23,7 +23,7 @@ import java.io.IOException
 import java.io.PrintWriter
 import java.util.StringTokenizer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
@@ -72,7 +72,7 @@ private[spark] class PipedRDD[T: ClassTag](
   }
 
   override def compute(split: Partition, context: TaskContext): Iterator[String] = {
-    val pb = new ProcessBuilder(command)
+    val pb = new ProcessBuilder(command.asJava)
     // Add the environmental variables to the process.
     val currentEnvVars = pb.environment()
     envVars.foreach { case (variable, value) => currentEnvVars.put(variable, value) }
@@ -81,7 +81,7 @@ private[spark] class PipedRDD[T: ClassTag](
     // so the user code can access the input filename
     if (split.isInstanceOf[HadoopPartition]) {
       val hadoopSplit = split.asInstanceOf[HadoopPartition]
-      currentEnvVars.putAll(hadoopSplit.getPipeEnvVars())
+      currentEnvVars.putAll(hadoopSplit.getPipeEnvVars().asJava)
     }
 
     // When spark.worker.separated.working.directory option is turned on, each
diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
index f7cb1791d4ac6..9a4fa301b06e3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
@@ -19,7 +19,7 @@ package org.apache.spark.rdd
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
@@ -125,7 +125,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
     integrate(0, t => getSeq(t._1) += t._2)
     // the second dep is rdd2; remove all of its keys
     integrate(1, t => map.remove(t._1))
-    map.iterator.map { t => t._2.iterator.map { (t._1, _) } }.flatten
+    map.asScala.iterator.map(t => t._2.iterator.map((t._1, _))).flatten
   }
 
   override def clearDependencies() {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala
index bac37bfdaa23f..0e438ab4366d9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.immutable.Set
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
@@ -107,7 +107,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl
 
     val retval = new ArrayBuffer[SplitInfo]()
     val list = instance.getSplits(job)
-    for (split <- list) {
+    for (split <- list.asScala) {
       retval ++= SplitInfo.toSplitInfo(inputFormatClazz, path, split)
     }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
index 174b73221afc0..5821afea98982 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
@@ -19,7 +19,7 @@ package org.apache.spark.scheduler
 
 import java.util.concurrent.{ConcurrentHashMap, ConcurrentLinkedQueue}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.Logging
@@ -74,7 +74,7 @@ private[spark] class Pool(
     if (schedulableNameToSchedulable.containsKey(schedulableName)) {
       return schedulableNameToSchedulable.get(schedulableName)
     }
-    for (schedulable <- schedulableQueue) {
+    for (schedulable <- schedulableQueue.asScala) {
       val sched = schedulable.getSchedulableByName(schedulableName)
       if (sched != null) {
         return sched
@@ -84,12 +84,12 @@ private[spark] class Pool(
   }
 
   override def executorLost(executorId: String, host: String) {
-    schedulableQueue.foreach(_.executorLost(executorId, host))
+    schedulableQueue.asScala.foreach(_.executorLost(executorId, host))
   }
 
   override def checkSpeculatableTasks(): Boolean = {
     var shouldRevive = false
-    for (schedulable <- schedulableQueue) {
+    for (schedulable <- schedulableQueue.asScala) {
       shouldRevive |= schedulable.checkSpeculatableTasks()
     }
     shouldRevive
@@ -98,7 +98,7 @@ private[spark] class Pool(
   override def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] = {
     var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]
     val sortedSchedulableQueue =
-      schedulableQueue.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator)
+      schedulableQueue.asScala.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator)
     for (schedulable <- sortedSchedulableQueue) {
       sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index d6e1e9e5bebc2..452c32d5411cd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.util.concurrent.locks.ReentrantLock
 import java.util.{Collections, List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{HashMap, HashSet}
 
 import com.google.common.collect.HashBiMap
@@ -233,7 +233,7 @@ private[spark] class CoarseMesosSchedulerBackend(
   override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) {
     stateLock.synchronized {
       val filters = Filters.newBuilder().setRefuseSeconds(5).build()
-      for (offer <- offers) {
+      for (offer <- offers.asScala) {
         val offerAttributes = toAttributeMap(offer.getAttributesList)
         val meetsConstraints = matchesAttributeRequirements(slaveOfferConstraints, offerAttributes)
         val slaveId = offer.getSlaveId.getValue
@@ -251,21 +251,21 @@ private[spark] class CoarseMesosSchedulerBackend(
           val cpusToUse = math.min(cpus, maxCores - totalCoresAcquired)
           totalCoresAcquired += cpusToUse
           val taskId = newMesosTaskId()
-          taskIdToSlaveId(taskId) = slaveId
+          taskIdToSlaveId.put(taskId, slaveId)
           slaveIdsWithExecutors += slaveId
           coresByTaskId(taskId) = cpusToUse
           // Gather cpu resources from the available resources and use them in the task.
           val (remainingResources, cpuResourcesToUse) =
             partitionResources(offer.getResourcesList, "cpus", cpusToUse)
           val (_, memResourcesToUse) =
-            partitionResources(remainingResources, "mem", calculateTotalMemory(sc))
+            partitionResources(remainingResources.asJava, "mem", calculateTotalMemory(sc))
           val taskBuilder = MesosTaskInfo.newBuilder()
             .setTaskId(TaskID.newBuilder().setValue(taskId.toString).build())
             .setSlaveId(offer.getSlaveId)
             .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave, taskId))
             .setName("Task " + taskId)
-            .addAllResources(cpuResourcesToUse)
-            .addAllResources(memResourcesToUse)
+            .addAllResources(cpuResourcesToUse.asJava)
+            .addAllResources(memResourcesToUse.asJava)
 
           sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image =>
             MesosSchedulerBackendUtil
@@ -314,9 +314,9 @@ private[spark] class CoarseMesosSchedulerBackend(
       }
 
       if (TaskState.isFinished(TaskState.fromMesos(state))) {
-        val slaveId = taskIdToSlaveId(taskId)
+        val slaveId = taskIdToSlaveId.get(taskId)
         slaveIdsWithExecutors -= slaveId
-        taskIdToSlaveId -= taskId
+        taskIdToSlaveId.remove(taskId)
         // Remove the cores we have remembered for this task, if it's in the hashmap
         for (cores <- coresByTaskId.get(taskId)) {
           totalCoresAcquired -= cores
@@ -361,7 +361,7 @@ private[spark] class CoarseMesosSchedulerBackend(
     stateLock.synchronized {
       if (slaveIdsWithExecutors.contains(slaveId)) {
         val slaveIdToTaskId = taskIdToSlaveId.inverse()
-        if (slaveIdToTaskId.contains(slaveId)) {
+        if (slaveIdToTaskId.containsKey(slaveId)) {
           val taskId: Int = slaveIdToTaskId.get(slaveId)
           taskIdToSlaveId.remove(taskId)
           removeExecutor(sparkExecutorId(slaveId, taskId.toString), reason)
@@ -411,7 +411,7 @@ private[spark] class CoarseMesosSchedulerBackend(
     val slaveIdToTaskId = taskIdToSlaveId.inverse()
     for (executorId <- executorIds) {
       val slaveId = executorId.split("/")(0)
-      if (slaveIdToTaskId.contains(slaveId)) {
+      if (slaveIdToTaskId.containsKey(slaveId)) {
         mesosDriver.killTask(
           TaskID.newBuilder().setValue(slaveIdToTaskId.get(slaveId).toString).build())
         pendingRemovedSlaveIds += slaveId
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala
index 3efc536f1456c..e0c547dce6d07 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler.cluster.mesos
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.curator.framework.CuratorFramework
 import org.apache.zookeeper.CreateMode
@@ -129,6 +129,6 @@ private[spark] class ZookeeperMesosClusterPersistenceEngine(
   }
 
   override def fetchAll[T](): Iterable[T] = {
-    zk.getChildren.forPath(WORKING_DIR).map(fetch[T]).flatten
+    zk.getChildren.forPath(WORKING_DIR).asScala.flatMap(fetch[T])
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 1206f184fbc82..07da9242b9922 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.util.concurrent.locks.ReentrantLock
 import java.util.{Collections, Date, List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -350,7 +350,7 @@ private[spark] class MesosClusterScheduler(
         }
         // TODO: Page the status updates to avoid trying to reconcile
         // a large amount of tasks at once.
-        driver.reconcileTasks(statuses)
+        driver.reconcileTasks(statuses.toSeq.asJava)
       }
     }
   }
@@ -493,10 +493,10 @@ private[spark] class MesosClusterScheduler(
   }
 
   override def resourceOffers(driver: SchedulerDriver, offers: JList[Offer]): Unit = {
-    val currentOffers = offers.map { o =>
+    val currentOffers = offers.asScala.map(o =>
       new ResourceOffer(
         o, getResource(o.getResourcesList, "cpus"), getResource(o.getResourcesList, "mem"))
-    }.toList
+    ).toList
     logTrace(s"Received offers from Mesos: \n${currentOffers.mkString("\n")}")
     val tasks = new mutable.HashMap[OfferID, ArrayBuffer[TaskInfo]]()
     val currentTime = new Date()
@@ -521,10 +521,10 @@ private[spark] class MesosClusterScheduler(
         currentOffers,
         tasks)
     }
-    tasks.foreach { case (offerId, tasks) =>
-      driver.launchTasks(Collections.singleton(offerId), tasks)
+    tasks.foreach { case (offerId, taskInfos) =>
+      driver.launchTasks(Collections.singleton(offerId), taskInfos.asJava)
     }
-    offers
+    offers.asScala
       .filter(o => !tasks.keySet.contains(o.getId))
       .foreach(o => driver.declineOffer(o.getId))
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 5c20606d58715..2e424054be785 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -20,7 +20,7 @@ package org.apache.spark.scheduler.cluster.mesos
 import java.io.File
 import java.util.{ArrayList => JArrayList, Collections, List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{HashMap, HashSet}
 
 import org.apache.mesos.{Scheduler => MScheduler, _}
@@ -129,14 +129,12 @@ private[spark] class MesosSchedulerBackend(
     val (resourcesAfterCpu, usedCpuResources) =
       partitionResources(availableResources, "cpus", scheduler.CPUS_PER_TASK)
     val (resourcesAfterMem, usedMemResources) =
-      partitionResources(resourcesAfterCpu, "mem", calculateTotalMemory(sc))
+      partitionResources(resourcesAfterCpu.asJava, "mem", calculateTotalMemory(sc))
 
-    builder.addAllResources(usedCpuResources)
-    builder.addAllResources(usedMemResources)
+    builder.addAllResources(usedCpuResources.asJava)
+    builder.addAllResources(usedMemResources.asJava)
 
-    sc.conf.getOption("spark.mesos.uris").map { uris =>
-      setupUris(uris, command)
-    }
+    sc.conf.getOption("spark.mesos.uris").foreach(setupUris(_, command))
 
     val executorInfo = builder
       .setExecutorId(ExecutorID.newBuilder().setValue(execId).build())
@@ -148,7 +146,7 @@ private[spark] class MesosSchedulerBackend(
         .setupContainerBuilderDockerInfo(image, sc.conf, executorInfo.getContainerBuilder())
     }
 
-    (executorInfo.build(), resourcesAfterMem)
+    (executorInfo.build(), resourcesAfterMem.asJava)
   }
 
   /**
@@ -193,7 +191,7 @@ private[spark] class MesosSchedulerBackend(
 
   private def getTasksSummary(tasks: JArrayList[MesosTaskInfo]): String = {
     val builder = new StringBuilder
-    tasks.foreach { t =>
+    tasks.asScala.foreach { t =>
       builder.append("Task id: ").append(t.getTaskId.getValue).append("\n")
         .append("Slave id: ").append(t.getSlaveId.getValue).append("\n")
         .append("Task resources: ").append(t.getResourcesList).append("\n")
@@ -211,7 +209,7 @@ private[spark] class MesosSchedulerBackend(
   override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) {
     inClassLoader() {
       // Fail-fast on offers we know will be rejected
-      val (usableOffers, unUsableOffers) = offers.partition { o =>
+      val (usableOffers, unUsableOffers) = offers.asScala.partition { o =>
         val mem = getResource(o.getResourcesList, "mem")
         val cpus = getResource(o.getResourcesList, "cpus")
         val slaveId = o.getSlaveId.getValue
@@ -323,10 +321,10 @@ private[spark] class MesosSchedulerBackend(
       .setSlaveId(SlaveID.newBuilder().setValue(slaveId).build())
       .setExecutor(executorInfo)
       .setName(task.name)
-      .addAllResources(cpuResources)
+      .addAllResources(cpuResources.asJava)
       .setData(MesosTaskLaunchData(task.serializedTask, task.attemptNumber).toByteString)
       .build()
-    (taskInfo, finalResources)
+    (taskInfo, finalResources.asJava)
   }
 
   override def statusUpdate(d: SchedulerDriver, status: TaskStatus) {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
index 5b854aa5c2754..860c8e097b3b9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.scheduler.cluster.mesos
 import java.util.{List => JList}
 import java.util.concurrent.CountDownLatch
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.util.control.NonFatal
 
@@ -137,7 +137,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
   protected def getResource(res: JList[Resource], name: String): Double = {
     // A resource can have multiple values in the offer since it can either be from
     // a specific role or wildcard.
-    res.filter(_.getName == name).map(_.getScalar.getValue).sum
+    res.asScala.filter(_.getName == name).map(_.getScalar.getValue).sum
   }
 
   protected def markRegistered(): Unit = {
@@ -169,7 +169,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
       amountToUse: Double): (List[Resource], List[Resource]) = {
     var remain = amountToUse
     var requestedResources = new ArrayBuffer[Resource]
-    val remainingResources = resources.map {
+    val remainingResources = resources.asScala.map {
       case r => {
         if (remain > 0 &&
           r.getType == Value.Type.SCALAR &&
@@ -214,7 +214,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
    * @return
    */
   protected def toAttributeMap(offerAttributes: JList[Attribute]): Map[String, GeneratedMessage] = {
-    offerAttributes.map(attr => {
+    offerAttributes.asScala.map(attr => {
       val attrValue = attr.getType match {
         case Value.Type.SCALAR => attr.getScalar
         case Value.Type.RANGES => attr.getRanges
@@ -253,7 +253,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
             requiredValues.map(_.toLong).exists(offerRange.contains(_))
           case Some(offeredValue: Value.Set) =>
             // check if the specified required values is a subset of offered set
-            requiredValues.subsetOf(offeredValue.getItemList.toSet)
+            requiredValues.subsetOf(offeredValue.getItemList.asScala.toSet)
           case Some(textValue: Value.Text) =>
             // check if the specified value is equal, if multiple values are specified
             // we succeed if any of them match.
@@ -299,14 +299,13 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
       Map()
     } else {
       try {
-        Map() ++ mapAsScalaMap(splitter.split(constraintsVal)).map {
-          case (k, v) =>
-            if (v == null || v.isEmpty) {
-              (k, Set[String]())
-            } else {
-              (k, v.split(',').toSet)
-            }
-        }
+        splitter.split(constraintsVal).asScala.toMap.mapValues(v =>
+          if (v == null || v.isEmpty) {
+            Set[String]()
+          } else {
+            v.split(',').toSet
+          }
+        )
       } catch {
         case NonFatal(e) =>
           throw new IllegalArgumentException(s"Bad constraint string: $constraintsVal", e)
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 0ff7562e912ca..048a938507277 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -21,6 +21,7 @@ import java.io.{EOFException, IOException, InputStream, OutputStream}
 import java.nio.ByteBuffer
 import javax.annotation.Nullable
 
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.{Kryo, KryoException}
@@ -373,16 +374,15 @@ private class JavaIterableWrapperSerializer
   override def read(kryo: Kryo, in: KryoInput, clz: Class[java.lang.Iterable[_]])
     : java.lang.Iterable[_] = {
     kryo.readClassAndObject(in) match {
-      case scalaIterable: Iterable[_] =>
-        scala.collection.JavaConversions.asJavaIterable(scalaIterable)
-      case javaIterable: java.lang.Iterable[_] =>
-        javaIterable
+      case scalaIterable: Iterable[_] => scalaIterable.asJava
+      case javaIterable: java.lang.Iterable[_] => javaIterable
     }
   }
 }
 
 private object JavaIterableWrapperSerializer extends Logging {
-  // The class returned by asJavaIterable (scala.collection.convert.Wrappers$IterableWrapper).
+  // The class returned by JavaConverters.asJava
+  // (scala.collection.convert.Wrappers$IterableWrapper).
   val wrapperClass =
     scala.collection.convert.WrapAsJava.asJavaIterable(Seq(1)).getClass
 
diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
index f6a96d81e7aa9..c057de9b3f4df 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.util.concurrent.ConcurrentLinkedQueue
 import java.util.concurrent.atomic.AtomicInteger
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.{Logging, SparkConf, SparkEnv}
 import org.apache.spark.executor.ShuffleWriteMetrics
@@ -210,11 +210,13 @@ private[spark] class FileShuffleBlockResolver(conf: SparkConf)
     shuffleStates.get(shuffleId) match {
       case Some(state) =>
         if (consolidateShuffleFiles) {
-          for (fileGroup <- state.allFileGroups; file <- fileGroup.files) {
+          for (fileGroup <- state.allFileGroups.asScala;
+               file <- fileGroup.files) {
             file.delete()
           }
         } else {
-          for (mapId <- state.completedMapTasks; reduceId <- 0 until state.numBuckets) {
+          for (mapId <- state.completedMapTasks.asScala;
+               reduceId <- 0 until state.numBuckets) {
             val blockId = new ShuffleBlockId(shuffleId, mapId, reduceId)
             blockManager.diskBlockManager.getFile(blockId).delete()
           }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
index 6fec5240707a6..7db6035553ae6 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
@@ -21,7 +21,7 @@ import java.util.{HashMap => JHashMap}
 
 import scala.collection.immutable.HashSet
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.{ExecutionContext, Future}
 
 import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, ThreadSafeRpcEndpoint}
@@ -133,7 +133,7 @@ class BlockManagerMasterEndpoint(
 
     // Find all blocks for the given RDD, remove the block from both blockLocations and
     // the blockManagerInfo that is tracking the blocks.
-    val blocks = blockLocations.keys.flatMap(_.asRDDId).filter(_.rddId == rddId)
+    val blocks = blockLocations.asScala.keys.flatMap(_.asRDDId).filter(_.rddId == rddId)
     blocks.foreach { blockId =>
       val bms: mutable.HashSet[BlockManagerId] = blockLocations.get(blockId)
       bms.foreach(bm => blockManagerInfo.get(bm).foreach(_.removeBlock(blockId)))
@@ -242,7 +242,7 @@ class BlockManagerMasterEndpoint(
 
   private def storageStatus: Array[StorageStatus] = {
     blockManagerInfo.map { case (blockManagerId, info) =>
-      new StorageStatus(blockManagerId, info.maxMem, info.blocks)
+      new StorageStatus(blockManagerId, info.maxMem, info.blocks.asScala)
     }.toArray
   }
 
@@ -292,7 +292,7 @@ class BlockManagerMasterEndpoint(
           if (askSlaves) {
             info.slaveEndpoint.ask[Seq[BlockId]](getMatchingBlockIds)
           } else {
-            Future { info.blocks.keys.filter(filter).toSeq }
+            Future { info.blocks.asScala.keys.filter(filter).toSeq }
           }
         future
       }
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index 78e7ddc27d1c7..1738258a0c794 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.util
 
-import scala.collection.JavaConversions.mapAsJavaMap
+import scala.collection.JavaConverters._
 
 import akka.actor.{ActorRef, ActorSystem, ExtendedActorSystem}
 import akka.pattern.ask
@@ -92,7 +92,7 @@ private[spark] object AkkaUtils extends Logging {
     val akkaSslConfig = securityManager.akkaSSLOptions.createAkkaConfig
         .getOrElse(ConfigFactory.empty())
 
-    val akkaConf = ConfigFactory.parseMap(conf.getAkkaConf.toMap[String, String])
+    val akkaConf = ConfigFactory.parseMap(conf.getAkkaConf.toMap.asJava)
       .withFallback(akkaSslConfig).withFallback(ConfigFactory.parseString(
       s"""
       |akka.daemonic = on
diff --git a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala
index a725767d08cc2..13cb516b583e9 100644
--- a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala
+++ b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala
@@ -19,12 +19,11 @@ package org.apache.spark.util
 
 import java.util.concurrent.CopyOnWriteArrayList
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 import scala.util.control.NonFatal
 
 import org.apache.spark.Logging
-import org.apache.spark.scheduler.SparkListener
 
 /**
  * An event bus which posts events to its listeners.
@@ -46,7 +45,7 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging {
    * `postToAll` in the same thread for all events.
    */
   final def postToAll(event: E): Unit = {
-    // JavaConversions will create a JIterableWrapper if we use some Scala collection functions.
+    // JavaConverters can create a JIterableWrapper if we use asScala.
     // However, this method will be called frequently. To avoid the wrapper cost, here ewe use
     // Java Iterator directly.
     val iter = listeners.iterator
@@ -69,7 +68,7 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging {
 
   private[spark] def findListenersByClass[T <: L : ClassTag](): Seq[T] = {
     val c = implicitly[ClassTag[T]].runtimeClass
-    listeners.filter(_.getClass == c).map(_.asInstanceOf[T]).toSeq
+    listeners.asScala.filter(_.getClass == c).map(_.asInstanceOf[T]).toSeq
   }
 
 }
diff --git a/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala b/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala
index 169489df6c1ea..a1c33212cdb2b 100644
--- a/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala
+++ b/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala
@@ -21,8 +21,6 @@ import java.net.{URLClassLoader, URL}
 import java.util.Enumeration
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.JavaConversions._
-
 /**
  * URL class loader that exposes the `addURL` and `getURLs` methods in URLClassLoader.
  */
diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala
index 8de75ba9a9c92..d7e5143c30953 100644
--- a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala
@@ -21,7 +21,8 @@ import java.util.Set
 import java.util.Map.Entry
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.{JavaConversions, mutable}
+import scala.collection.JavaConverters._
+import scala.collection.mutable
 
 import org.apache.spark.Logging
 
@@ -50,8 +51,7 @@ private[spark] class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = fa
   }
 
   def iterator: Iterator[(A, B)] = {
-    val jIterator = getEntrySet.iterator
-    JavaConversions.asScalaIterator(jIterator).map(kv => (kv.getKey, kv.getValue.value))
+    getEntrySet.iterator.asScala.map(kv => (kv.getKey, kv.getValue.value))
   }
 
   def getEntrySet: Set[Entry[A, TimeStampedValue[B]]] = internalMap.entrySet
@@ -90,9 +90,7 @@ private[spark] class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = fa
   }
 
   override def filter(p: ((A, B)) => Boolean): mutable.Map[A, B] = {
-    JavaConversions.mapAsScalaConcurrentMap(internalMap)
-      .map { case (k, TimeStampedValue(v, t)) => (k, v) }
-      .filter(p)
+    internalMap.asScala.map { case (k, TimeStampedValue(v, t)) => (k, v) }.filter(p)
   }
 
   override def empty: mutable.Map[A, B] = new TimeStampedHashMap[A, B]()
diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala
index 7cd8f28b12dd6..65efeb1f4c19c 100644
--- a/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala
+++ b/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala
@@ -19,7 +19,7 @@ package org.apache.spark.util
 
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.JavaConversions
+import scala.collection.JavaConverters._
 import scala.collection.mutable.Set
 
 private[spark] class TimeStampedHashSet[A] extends Set[A] {
@@ -31,7 +31,7 @@ private[spark] class TimeStampedHashSet[A] extends Set[A] {
 
   def iterator: Iterator[A] = {
     val jIterator = internalMap.entrySet().iterator()
-    JavaConversions.asScalaIterator(jIterator).map(_.getKey)
+    jIterator.asScala.map(_.getKey)
   }
 
   override def + (elem: A): Set[A] = {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 8313312226713..2bab4af2e73ab 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -25,7 +25,7 @@ import java.util.{Properties, Locale, Random, UUID}
 import java.util.concurrent._
 import javax.net.ssl.HttpsURLConnection
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
@@ -748,12 +748,12 @@ private[spark] object Utils extends Logging {
         // getNetworkInterfaces returns ifs in reverse order compared to ifconfig output order
         // on unix-like system. On windows, it returns in index order.
         // It's more proper to pick ip address following system output order.
-        val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.toList
+        val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.asScala.toSeq
         val reOrderedNetworkIFs = if (isWindows) activeNetworkIFs else activeNetworkIFs.reverse
 
         for (ni <- reOrderedNetworkIFs) {
-          val addresses = ni.getInetAddresses.toList
-            .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress)
+          val addresses = ni.getInetAddresses.asScala
+            .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress).toSeq
           if (addresses.nonEmpty) {
             val addr = addresses.find(_.isInstanceOf[Inet4Address]).getOrElse(addresses.head)
             // because of Inet6Address.toHostName may add interface at the end if it knows about it
@@ -1498,10 +1498,8 @@ private[spark] object Utils extends Logging {
     * properties which have been set explicitly, as well as those for which only a default value
     * has been defined. */
   def getSystemProperties: Map[String, String] = {
-    val sysProps = for (key <- System.getProperties.stringPropertyNames()) yield
-      (key, System.getProperty(key))
-
-    sysProps.toMap
+    System.getProperties.stringPropertyNames().asScala
+      .map(key => (key, System.getProperty(key))).toMap
   }
 
   /**
@@ -1812,7 +1810,8 @@ private[spark] object Utils extends Logging {
     try {
       val properties = new Properties()
       properties.load(inReader)
-      properties.stringPropertyNames().map(k => (k, properties(k).trim)).toMap
+      properties.stringPropertyNames().asScala.map(
+        k => (k, properties.getProperty(k).trim)).toMap
     } catch {
       case e: IOException =>
         throw new SparkException(s"Failed when loading Spark properties from $filename", e)
@@ -1941,7 +1940,8 @@ private[spark] object Utils extends Logging {
           return true
         }
         isBindCollision(e.getCause)
-      case e: MultiException => e.getThrowables.exists(isBindCollision)
+      case e: MultiException =>
+        e.getThrowables.asScala.exists(isBindCollision)
       case e: Exception => isBindCollision(e.getCause)
       case _ => false
     }
diff --git a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala
index bdbca00a00622..4939b600dbfbd 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.util.collection
 
-import scala.collection.JavaConversions.{collectionAsScalaIterable, asJavaIterator}
+import scala.collection.JavaConverters._
 
 import com.google.common.collect.{Ordering => GuavaOrdering}
 
@@ -34,6 +34,6 @@ private[spark] object Utils {
     val ordering = new GuavaOrdering[T] {
       override def compare(l: T, r: T): Int = ord.compare(l, r)
     }
-    collectionAsScalaIterable(ordering.leastOf(asJavaIterator(input), num)).iterator
+    ordering.leastOf(input.asJava, num).iterator.asScala
   }
 }
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index ffe4b4baffb2a..ebd3d61ae7324 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -24,10 +24,10 @@
 import java.util.*;
 import java.util.concurrent.*;
 
-import scala.collection.JavaConversions;
 import scala.Tuple2;
 import scala.Tuple3;
 import scala.Tuple4;
+import scala.collection.JavaConverters;
 
 import com.google.common.collect.ImmutableMap;
 import com.google.common.collect.Iterables;
@@ -1473,7 +1473,9 @@ public Integer call(Integer v1, Integer v2) throws Exception {
     Assert.assertEquals(expected, results);
 
     Partitioner defaultPartitioner = Partitioner.defaultPartitioner(
-        combinedRDD.rdd(), JavaConversions.asScalaBuffer(Lists.<RDD<?>>newArrayList()));
+        combinedRDD.rdd(),
+        JavaConverters.collectionAsScalaIterableConverter(
+            Collections.<RDD<?>>emptyList()).asScala().toSeq());
     combinedRDD = originalRDD.keyBy(keyFunction)
         .combineByKey(
              createCombinerFunction,
diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
index 90cb7da94e88a..ff9a92cc0a421 100644
--- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark
 
 import java.util.concurrent.{TimeUnit, Executors}
 
+import scala.collection.JavaConverters._
 import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.util.{Try, Random}
@@ -148,7 +149,6 @@ class SparkConfSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
   }
 
   test("Thread safeness - SPARK-5425") {
-    import scala.collection.JavaConversions._
     val executor = Executors.newSingleThreadScheduledExecutor()
     val sf = executor.scheduleAtFixedRate(new Runnable {
       override def run(): Unit =
@@ -163,8 +163,9 @@ class SparkConfSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
       }
     } finally {
       executor.shutdownNow()
-      for (key <- System.getProperties.stringPropertyNames() if key.startsWith("spark.5425."))
-        System.getProperties.remove(key)
+      val sysProps = System.getProperties
+      for (key <- sysProps.stringPropertyNames().asScala if key.startsWith("spark.5425."))
+        sysProps.remove(key)
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
index cbd2aee10c0e2..86eb41dd7e5d7 100644
--- a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
@@ -19,7 +19,6 @@ package org.apache.spark.deploy
 
 import java.net.URL
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.io.Source
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
index 47a64081e297e..1ed4bae3ca21e 100644
--- a/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
@@ -21,14 +21,14 @@ import java.io.{PrintStream, OutputStream, File}
 import java.net.URI
 import java.util.jar.Attributes.Name
 import java.util.jar.{JarFile, Manifest}
-import java.util.zip.{ZipEntry, ZipFile}
+import java.util.zip.ZipFile
 
-import org.scalatest.BeforeAndAfterEach
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.io.Files
 import org.apache.commons.io.FileUtils
+import org.scalatest.BeforeAndAfterEach
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.api.r.RUtils
@@ -142,7 +142,7 @@ class RPackageUtilsSuite extends SparkFunSuite with BeforeAndAfterEach {
       IvyTestUtils.writeFile(fakePackageDir, "DESCRIPTION", "abc")
       val finalZip = RPackageUtils.zipRLibraries(tempDir, "sparkr.zip")
       assert(finalZip.exists())
-      val entries = new ZipFile(finalZip).entries().toSeq.map(_.getName)
+      val entries = new ZipFile(finalZip).entries().asScala.map(_.getName).toSeq
       assert(entries.contains("/test.R"))
       assert(entries.contains("/SparkR/abc.R"))
       assert(entries.contains("/SparkR/DESCRIPTION"))
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
index bed6f3ea61241..98664dc1101e6 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
@@ -19,8 +19,6 @@ package org.apache.spark.deploy.worker
 
 import java.io.File
 
-import scala.collection.JavaConversions._
-
 import org.apache.spark.deploy.{ApplicationDescription, Command, ExecutorState}
 import org.apache.spark.{SecurityManager, SparkConf, SparkFunSuite}
 
@@ -36,6 +34,7 @@ class ExecutorRunnerTest extends SparkFunSuite {
       ExecutorState.RUNNING)
     val builder = CommandUtils.buildProcessBuilder(
       appDesc.command, new SecurityManager(conf), 512, sparkHome, er.substituteVariables)
-    assert(builder.command().last === appId)
+    val builderCommand = builder.command()
+    assert(builderCommand.get(builderCommand.size() - 1) === appId)
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 730535ece7878..a9652d7e7d0b0 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.scheduler
 import java.util.concurrent.Semaphore
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.scalatest.Matchers
 
@@ -365,10 +365,9 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match
       .set("spark.extraListeners", classOf[ListenerThatAcceptsSparkConf].getName + "," +
         classOf[BasicJobCounter].getName)
     sc = new SparkContext(conf)
-    sc.listenerBus.listeners.collect { case x: BasicJobCounter => x}.size should be (1)
-    sc.listenerBus.listeners.collect {
-      case x: ListenerThatAcceptsSparkConf => x
-    }.size should be (1)
+    sc.listenerBus.listeners.asScala.count(_.isInstanceOf[BasicJobCounter]) should be (1)
+    sc.listenerBus.listeners.asScala
+      .count(_.isInstanceOf[ListenerThatAcceptsSparkConf]) should be (1)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
index 5ed30f64d705f..319b3173e7a6e 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
@@ -18,10 +18,11 @@
 package org.apache.spark.scheduler.cluster.mesos
 
 import java.nio.ByteBuffer
-import java.util
+import java.util.Arrays
+import java.util.Collection
 import java.util.Collections
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -61,7 +62,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
 
     val mesosSchedulerBackend = new MesosSchedulerBackend(taskScheduler, sc, "master")
 
-    val resources = List(
+    val resources = Arrays.asList(
       mesosSchedulerBackend.createResource("cpus", 4),
       mesosSchedulerBackend.createResource("mem", 1024))
     // uri is null.
@@ -98,7 +99,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     val backend = new MesosSchedulerBackend(taskScheduler, sc, "master")
 
     val (execInfo, _) = backend.createExecutorInfo(
-      List(backend.createResource("cpus", 4)), "mockExecutor")
+      Arrays.asList(backend.createResource("cpus", 4)), "mockExecutor")
     assert(execInfo.getContainer.getDocker.getImage.equals("spark/mock"))
     val portmaps = execInfo.getContainer.getDocker.getPortMappingsList
     assert(portmaps.get(0).getHostPort.equals(80))
@@ -179,7 +180,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     when(taskScheduler.resourceOffers(expectedWorkerOffers)).thenReturn(Seq(Seq(taskDesc)))
     when(taskScheduler.CPUS_PER_TASK).thenReturn(2)
 
-    val capture = ArgumentCaptor.forClass(classOf[util.Collection[TaskInfo]])
+    val capture = ArgumentCaptor.forClass(classOf[Collection[TaskInfo]])
     when(
       driver.launchTasks(
         Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)),
@@ -279,7 +280,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     when(taskScheduler.resourceOffers(expectedWorkerOffers)).thenReturn(Seq(Seq(taskDesc)))
     when(taskScheduler.CPUS_PER_TASK).thenReturn(1)
 
-    val capture = ArgumentCaptor.forClass(classOf[util.Collection[TaskInfo]])
+    val capture = ArgumentCaptor.forClass(classOf[Collection[TaskInfo]])
     when(
       driver.launchTasks(
         Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)),
@@ -304,7 +305,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     assert(cpusDev.getName.equals("cpus"))
     assert(cpusDev.getScalar.getValue.equals(1.0))
     assert(cpusDev.getRole.equals("dev"))
-    val executorResources = taskInfo.getExecutor.getResourcesList
+    val executorResources = taskInfo.getExecutor.getResourcesList.asScala
     assert(executorResources.exists { r =>
       r.getName.equals("mem") && r.getScalar.getValue.equals(484.0) && r.getRole.equals("prod")
     })
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index 23a1fdb0f5009..8d1c9d17e977e 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.serializer
 
 import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.reflect.ClassTag
 
@@ -173,7 +174,7 @@ class KryoSerializerSuite extends SparkFunSuite with SharedSparkContext {
   test("asJavaIterable") {
     // Serialize a collection wrapped by asJavaIterable
     val ser = new KryoSerializer(conf).newInstance()
-    val a = ser.serialize(scala.collection.convert.WrapAsJava.asJavaIterable(Seq(12345)))
+    val a = ser.serialize(Seq(12345).asJava)
     val b = ser.deserialize[java.lang.Iterable[Int]](a)
     assert(b.iterator().next() === 12345)
 
diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
index 69888b2694bae..22e30ecaf0533 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
@@ -21,7 +21,6 @@ import java.net.{HttpURLConnection, URL}
 import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
 
 import scala.io.Source
-import scala.collection.JavaConversions._
 import scala.xml.Node
 
 import com.gargoylesoftware.htmlunit.DefaultCssErrorHandler
@@ -341,15 +340,15 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
         // The completed jobs table should have two rows. The first row will be the most recent job:
         val firstRow = find(cssSelector("tbody tr")).get.underlying
         val firstRowColumns = firstRow.findElements(By.tagName("td"))
-        firstRowColumns(0).getText should be ("1")
-        firstRowColumns(4).getText should be ("1/1 (2 skipped)")
-        firstRowColumns(5).getText should be ("8/8 (16 skipped)")
+        firstRowColumns.get(0).getText should be ("1")
+        firstRowColumns.get(4).getText should be ("1/1 (2 skipped)")
+        firstRowColumns.get(5).getText should be ("8/8 (16 skipped)")
         // The second row is the first run of the job, where nothing was skipped:
         val secondRow = findAll(cssSelector("tbody tr")).toSeq(1).underlying
         val secondRowColumns = secondRow.findElements(By.tagName("td"))
-        secondRowColumns(0).getText should be ("0")
-        secondRowColumns(4).getText should be ("3/3")
-        secondRowColumns(5).getText should be ("24/24")
+        secondRowColumns.get(0).getText should be ("0")
+        secondRowColumns.get(4).getText should be ("3/3")
+        secondRowColumns.get(5).getText should be ("24/24")
       }
     }
   }
@@ -502,8 +501,8 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
         for {
           (row, idx) <- rows.zipWithIndex
           columns = row.findElements(By.tagName("td"))
-          id = columns(0).getText()
-          description = columns(1).getText()
+          id = columns.get(0).getText()
+          description = columns.get(1).getText()
         } {
           id should be (expJobInfo(idx)._1)
           description should include (expJobInfo(idx)._2)
@@ -547,8 +546,8 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
         for {
           (row, idx) <- rows.zipWithIndex
           columns = row.findElements(By.tagName("td"))
-          id = columns(0).getText()
-          description = columns(1).getText()
+          id = columns.get(0).getText()
+          description = columns.get(1).getText()
         } {
           id should be (expStageInfo(idx)._1)
           description should include (expStageInfo(idx)._2)
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index 36832f51d2ad4..fa07c1e5017cd 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -19,10 +19,7 @@
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
-
-import scala.collection.JavaConversions._
-import scala.collection.mutable.ListBuffer
-import scala.collection.immutable.Map
+import java.util.Collections
 
 import org.apache.cassandra.hadoop.ConfigHelper
 import org.apache.cassandra.hadoop.cql3.CqlPagingInputFormat
@@ -32,7 +29,6 @@ import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 
 
 /*
@@ -121,12 +117,9 @@ object CassandraCQLTest {
 
     val casoutputCF = aggregatedRDD.map {
       case (productId, saleCount) => {
-        val outColFamKey = Map("prod_id" -> ByteBufferUtil.bytes(productId))
-        val outKey: java.util.Map[String, ByteBuffer] = outColFamKey
-        var outColFamVal = new ListBuffer[ByteBuffer]
-        outColFamVal += ByteBufferUtil.bytes(saleCount)
-        val outVal: java.util.List[ByteBuffer] = outColFamVal
-       (outKey, outVal)
+        val outKey = Collections.singletonMap("prod_id", ByteBufferUtil.bytes(productId))
+        val outVal = Collections.singletonList(ByteBufferUtil.bytes(saleCount))
+        (outKey, outVal)
       }
     }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
index 96ef3e198e380..2e56d24c60c33 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
@@ -19,10 +19,9 @@
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
+import java.util.Arrays
 import java.util.SortedMap
 
-import scala.collection.JavaConversions._
-
 import org.apache.cassandra.db.IColumn
 import org.apache.cassandra.hadoop.ColumnFamilyOutputFormat
 import org.apache.cassandra.hadoop.ConfigHelper
@@ -32,7 +31,6 @@ import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 
 /*
  * This example demonstrates using Spark with Cassandra with the New Hadoop API and Cassandra
@@ -118,7 +116,7 @@ object CassandraTest {
 
         val outputkey = ByteBufferUtil.bytes(word + "-COUNT-" + System.currentTimeMillis)
 
-        val mutations: java.util.List[Mutation] = new Mutation() :: new Mutation() :: Nil
+        val mutations = Arrays.asList(new Mutation(), new Mutation())
         mutations.get(0).setColumn_or_supercolumn(new ColumnOrSuperColumn())
         mutations.get(0).column_or_supercolumn.setColumn(colWord)
         mutations.get(1).setColumn_or_supercolumn(new ColumnOrSuperColumn())
diff --git a/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
index c42df2b8845d2..bec61f3cd4296 100644
--- a/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
@@ -18,7 +18,7 @@
 // scalastyle:off println
 package org.apache.spark.examples
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.util.Utils
 
@@ -36,10 +36,10 @@ object DriverSubmissionTest {
     val properties = Utils.getSystemProperties
 
     println("Environment variables containing SPARK_TEST:")
-    env.filter{case (k, v) => k.contains("SPARK_TEST")}.foreach(println)
+    env.asScala.filter { case (k, _) => k.contains("SPARK_TEST")}.foreach(println)
 
     println("System properties containing spark.test:")
-    properties.filter{case (k, v) => k.toString.contains("spark.test")}.foreach(println)
+    properties.filter { case (k, _) => k.toString.contains("spark.test") }.foreach(println)
 
     for (i <- 1 until numSecondsToSleep) {
       println(s"Alive for $i out of $numSecondsToSleep seconds")
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala
index 3ebb112fc069e..805184e740f06 100644
--- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala
@@ -19,7 +19,7 @@ package org.apache.spark.examples.pythonconverters
 
 import java.util.{Collection => JCollection, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.avro.generic.{GenericFixed, IndexedRecord}
 import org.apache.avro.mapred.AvroWrapper
@@ -58,7 +58,7 @@ object AvroConversionUtil extends Serializable {
     val map = new java.util.HashMap[String, Any]
     obj match {
       case record: IndexedRecord =>
-        record.getSchema.getFields.zipWithIndex.foreach { case (f, i) =>
+        record.getSchema.getFields.asScala.zipWithIndex.foreach { case (f, i) =>
           map.put(f.name, fromAvro(record.get(i), f.schema))
         }
       case other => throw new SparkException(
@@ -68,9 +68,9 @@ object AvroConversionUtil extends Serializable {
   }
 
   def unpackMap(obj: Any, schema: Schema): JMap[String, Any] = {
-    obj.asInstanceOf[JMap[_, _]].map { case (key, value) =>
+    obj.asInstanceOf[JMap[_, _]].asScala.map { case (key, value) =>
       (key.toString, fromAvro(value, schema.getValueType))
-    }
+    }.asJava
   }
 
   def unpackFixed(obj: Any, schema: Schema): Array[Byte] = {
@@ -91,17 +91,17 @@ object AvroConversionUtil extends Serializable {
 
   def unpackArray(obj: Any, schema: Schema): JCollection[Any] = obj match {
     case c: JCollection[_] =>
-      c.map(fromAvro(_, schema.getElementType))
+      c.asScala.map(fromAvro(_, schema.getElementType)).toSeq.asJava
     case arr: Array[_] if arr.getClass.getComponentType.isPrimitive =>
-      arr.toSeq
+      arr.toSeq.asJava.asInstanceOf[JCollection[Any]]
     case arr: Array[_] =>
-      arr.map(fromAvro(_, schema.getElementType)).toSeq
+      arr.map(fromAvro(_, schema.getElementType)).toSeq.asJava
     case other => throw new SparkException(
       s"Unknown ARRAY type ${other.getClass.getName}")
   }
 
   def unpackUnion(obj: Any, schema: Schema): Any = {
-    schema.getTypes.toList match {
+    schema.getTypes.asScala.toList match {
       case List(s) => fromAvro(obj, s)
       case List(n, s) if n.getType == NULL => fromAvro(obj, s)
       case List(s, n) if n.getType == NULL => fromAvro(obj, s)
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
index 83feb5703b908..00ce47af4813d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.examples.pythonconverters
 
-import org.apache.spark.api.python.Converter
 import java.nio.ByteBuffer
+
+import scala.collection.JavaConverters._
+
 import org.apache.cassandra.utils.ByteBufferUtil
-import collection.JavaConversions._
 
+import org.apache.spark.api.python.Converter
 
 /**
  * Implementation of [[org.apache.spark.api.python.Converter]] that converts Cassandra
@@ -30,7 +32,7 @@ import collection.JavaConversions._
 class CassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, Int]] {
   override def convert(obj: Any): java.util.Map[String, Int] = {
     val result = obj.asInstanceOf[java.util.Map[String, ByteBuffer]]
-    mapAsJavaMap(result.mapValues(bb => ByteBufferUtil.toInt(bb)))
+    result.asScala.mapValues(ByteBufferUtil.toInt).asJava
   }
 }
 
@@ -41,7 +43,7 @@ class CassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, Int]
 class CassandraCQLValueConverter extends Converter[Any, java.util.Map[String, String]] {
   override def convert(obj: Any): java.util.Map[String, String] = {
     val result = obj.asInstanceOf[java.util.Map[String, ByteBuffer]]
-    mapAsJavaMap(result.mapValues(bb => ByteBufferUtil.string(bb)))
+    result.asScala.mapValues(ByteBufferUtil.string).asJava
   }
 }
 
@@ -52,7 +54,7 @@ class CassandraCQLValueConverter extends Converter[Any, java.util.Map[String, St
 class ToCassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, ByteBuffer]] {
   override def convert(obj: Any): java.util.Map[String, ByteBuffer] = {
     val input = obj.asInstanceOf[java.util.Map[String, Int]]
-    mapAsJavaMap(input.mapValues(i => ByteBufferUtil.bytes(i)))
+    input.asScala.mapValues(ByteBufferUtil.bytes).asJava
   }
 }
 
@@ -63,6 +65,6 @@ class ToCassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, By
 class ToCassandraCQLValueConverter extends Converter[Any, java.util.List[ByteBuffer]] {
   override def convert(obj: Any): java.util.List[ByteBuffer] = {
     val input = obj.asInstanceOf[java.util.List[String]]
-    seqAsJavaList(input.map(s => ByteBufferUtil.bytes(s)))
+    input.asScala.map(ByteBufferUtil.bytes).asJava
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala
index 90d48a64106c7..0a25ee7ae56f4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.examples.pythonconverters
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.parsing.json.JSONObject
 
 import org.apache.spark.api.python.Converter
@@ -33,7 +33,6 @@ import org.apache.hadoop.hbase.CellUtil
  */
 class HBaseResultToStringConverter extends Converter[Any, String] {
   override def convert(obj: Any): String = {
-    import collection.JavaConverters._
     val result = obj.asInstanceOf[Result]
     val output = result.listCells.asScala.map(cell =>
         Map(
@@ -77,7 +76,7 @@ class StringToImmutableBytesWritableConverter extends Converter[Any, ImmutableBy
  */
 class StringListToPutConverter extends Converter[Any, Put] {
   override def convert(obj: Any): Put = {
-    val output = obj.asInstanceOf[java.util.ArrayList[String]].map(Bytes.toBytes(_)).toArray
+    val output = obj.asInstanceOf[java.util.ArrayList[String]].asScala.map(Bytes.toBytes).toArray
     val put = new Put(output(0))
     put.add(output(1), output(2), output(3))
   }
diff --git a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
index fa43629d49771..d2654700ea729 100644
--- a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
+++ b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
@@ -20,7 +20,7 @@ import java.net.InetSocketAddress
 import java.util.concurrent.atomic.AtomicInteger
 import java.util.concurrent.{TimeUnit, CountDownLatch, Executors}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.{ExecutionContext, Future}
 import scala.util.{Failure, Success}
 
@@ -166,7 +166,7 @@ class SparkSinkSuite extends FunSuite {
     channelContext.put("capacity", channelCapacity.toString)
     channelContext.put("transactionCapacity", 1000.toString)
     channelContext.put("keep-alive", 0.toString)
-    channelContext.putAll(overrides)
+    channelContext.putAll(overrides.asJava)
     channel.setName(scala.util.Random.nextString(10))
     channel.configure(channelContext)
 
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
index 65c49c131518b..48df27b26867f 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.flume
 
 import java.io.{ObjectOutput, ObjectInput}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.util.Utils
 import org.apache.spark.Logging
@@ -60,7 +60,7 @@ private[streaming] object EventTransformer extends Logging {
     out.write(body)
     val numHeaders = headers.size()
     out.writeInt(numHeaders)
-    for ((k, v) <- headers) {
+    for ((k, v) <- headers.asScala) {
       val keyBuff = Utils.serialize(k.toString)
       out.writeInt(keyBuff.length)
       out.write(keyBuff)
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
index 88cc2aa3bf022..b9d4e762ca05d 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
@@ -16,7 +16,6 @@
  */
 package org.apache.spark.streaming.flume
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.base.Throwables
@@ -155,7 +154,7 @@ private[flume] class FlumeBatchFetcher(receiver: FlumePollingReceiver) extends R
     val buffer = new ArrayBuffer[SparkFlumeEvent](events.size())
     var j = 0
     while (j < events.size()) {
-      val event = events(j)
+      val event = events.get(j)
       val sparkFlumeEvent = new SparkFlumeEvent()
       sparkFlumeEvent.event.setBody(event.getBody)
       sparkFlumeEvent.event.setHeaders(event.getHeaders)
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 1e32a365a1eee..2bf99cb3cba1f 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -22,7 +22,7 @@ import java.io.{ObjectInput, ObjectOutput, Externalizable}
 import java.nio.ByteBuffer
 import java.util.concurrent.Executors
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.flume.source.avro.AvroSourceProtocol
@@ -99,7 +99,7 @@ class SparkFlumeEvent() extends Externalizable {
 
     val numHeaders = event.getHeaders.size()
     out.writeInt(numHeaders)
-    for ((k, v) <- event.getHeaders) {
+    for ((k, v) <- event.getHeaders.asScala) {
       val keyBuff = Utils.serialize(k.toString)
       out.writeInt(keyBuff.length)
       out.write(keyBuff)
@@ -127,8 +127,7 @@ class FlumeEventServer(receiver : FlumeReceiver) extends AvroSourceProtocol {
   }
 
   override def appendBatch(events : java.util.List[AvroFlumeEvent]) : Status = {
-    events.foreach (event =>
-      receiver.store(SparkFlumeEvent.fromAvroFlumeEvent(event)))
+    events.asScala.foreach(event => receiver.store(SparkFlumeEvent.fromAvroFlumeEvent(event)))
     Status.OK
   }
 }
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
index 583e7dca317ad..0bc46209b8369 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.flume
 import java.net.InetSocketAddress
 import java.util.concurrent.{LinkedBlockingQueue, Executors}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import com.google.common.util.concurrent.ThreadFactoryBuilder
@@ -94,9 +94,7 @@ private[streaming] class FlumePollingReceiver(
   override def onStop(): Unit = {
     logInfo("Shutting down Flume Polling Receiver")
     receiverExecutor.shutdownNow()
-    connections.foreach(connection => {
-      connection.transceiver.close()
-    })
+    connections.asScala.foreach(_.transceiver.close())
     channelFactory.releaseExternalResources()
   }
 
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
index 9d9c3b189415f..70018c86f92be 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
@@ -19,9 +19,9 @@ package org.apache.spark.streaming.flume
 
 import java.net.{InetSocketAddress, ServerSocket}
 import java.nio.ByteBuffer
-import java.util.{List => JList}
+import java.util.Collections
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import org.apache.avro.ipc.NettyTransceiver
@@ -59,13 +59,13 @@ private[flume] class FlumeTestUtils {
   }
 
   /** Send data to the flume receiver */
-  def writeInput(input: JList[String], enableCompression: Boolean): Unit = {
+  def writeInput(input: Seq[String], enableCompression: Boolean): Unit = {
     val testAddress = new InetSocketAddress("localhost", testPort)
 
     val inputEvents = input.map { item =>
       val event = new AvroFlumeEvent
       event.setBody(ByteBuffer.wrap(item.getBytes(UTF_8)))
-      event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header"))
+      event.setHeaders(Collections.singletonMap("test", "header"))
       event
     }
 
@@ -88,7 +88,7 @@ private[flume] class FlumeTestUtils {
     }
 
     // Send data
-    val status = client.appendBatch(inputEvents.toList)
+    val status = client.appendBatch(inputEvents.asJava)
     if (status != avro.Status.OK) {
       throw new AssertionError("Sent events unsuccessfully")
     }
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
index a65a9b921aafa..c719b80aca7ed 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -21,7 +21,7 @@ import java.net.InetSocketAddress
 import java.io.{DataOutputStream, ByteArrayOutputStream}
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.api.java.function.PairFunction
 import org.apache.spark.api.python.PythonRDD
@@ -268,8 +268,8 @@ private[flume] class FlumeUtilsPythonHelper {
       maxBatchSize: Int,
       parallelism: Int
     ): JavaPairDStream[Array[Byte], Array[Byte]] = {
-    assert(hosts.length == ports.length)
-    val addresses = hosts.zip(ports).map {
+    assert(hosts.size() == ports.size())
+    val addresses = hosts.asScala.zip(ports.asScala).map {
       case (host, port) => new InetSocketAddress(host, port)
     }
     val dstream = FlumeUtils.createPollingStream(
@@ -286,7 +286,7 @@ private object FlumeUtilsPythonHelper {
     val output = new DataOutputStream(byteStream)
     try {
       output.writeInt(map.size)
-      map.foreach { kv =>
+      map.asScala.foreach { kv =>
         PythonRDD.writeUTF(kv._1.toString, output)
         PythonRDD.writeUTF(kv._2.toString, output)
       }
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
index 91d63d49dbec3..a2ab320957db3 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
@@ -18,9 +18,8 @@
 package org.apache.spark.streaming.flume
 
 import java.util.concurrent._
-import java.util.{List => JList, Map => JMap}
+import java.util.{Map => JMap, Collections}
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.base.Charsets.UTF_8
@@ -77,7 +76,7 @@ private[flume] class PollingFlumeTestUtils {
   /**
    * Start 2 sinks and return the ports
    */
-  def startMultipleSinks(): JList[Int] = {
+  def startMultipleSinks(): Seq[Int] = {
     channels.clear()
     sinks.clear()
 
@@ -138,8 +137,7 @@ private[flume] class PollingFlumeTestUtils {
   /**
    * A Python-friendly method to assert the output
    */
-  def assertOutput(
-      outputHeaders: JList[JMap[String, String]], outputBodies: JList[String]): Unit = {
+  def assertOutput(outputHeaders: Seq[JMap[String, String]], outputBodies: Seq[String]): Unit = {
     require(outputHeaders.size == outputBodies.size)
     val eventSize = outputHeaders.size
     if (eventSize != totalEventsPerChannel * channels.size) {
@@ -149,12 +147,12 @@ private[flume] class PollingFlumeTestUtils {
     var counter = 0
     for (k <- 0 until channels.size; i <- 0 until totalEventsPerChannel) {
       val eventBodyToVerify = s"${channels(k).getName}-$i"
-      val eventHeaderToVerify: JMap[String, String] = Map[String, String](s"test-$i" -> "header")
+      val eventHeaderToVerify: JMap[String, String] = Collections.singletonMap(s"test-$i", "header")
       var found = false
       var j = 0
       while (j < eventSize && !found) {
-        if (eventBodyToVerify == outputBodies.get(j) &&
-          eventHeaderToVerify == outputHeaders.get(j)) {
+        if (eventBodyToVerify == outputBodies(j) &&
+          eventHeaderToVerify == outputHeaders(j)) {
           found = true
           counter += 1
         }
@@ -195,7 +193,7 @@ private[flume] class PollingFlumeTestUtils {
         tx.begin()
         for (j <- 0 until eventsPerBatch) {
           channel.put(EventBuilder.withBody(s"${channel.getName}-$t".getBytes(UTF_8),
-            Map[String, String](s"test-$t" -> "header")))
+            Collections.singletonMap(s"test-$t", "header")))
           t += 1
         }
         tx.commit()
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
index d5f9a0aa38f9f..ff2fb8eed204c 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.flume
 
 import java.net.InetSocketAddress
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
 import scala.concurrent.duration._
 import scala.language.postfixOps
@@ -116,9 +116,9 @@ class FlumePollingStreamSuite extends SparkFunSuite with BeforeAndAfter with Log
       // The eventually is required to ensure that all data in the batch has been processed.
       eventually(timeout(10 seconds), interval(100 milliseconds)) {
         val flattenOutputBuffer = outputBuffer.flatten
-        val headers = flattenOutputBuffer.map(_.event.getHeaders.map {
-          case kv => (kv._1.toString, kv._2.toString)
-        }).map(mapAsJavaMap)
+        val headers = flattenOutputBuffer.map(_.event.getHeaders.asScala.map {
+          case (key, value) => (key.toString, value.toString)
+        }).map(_.asJava)
         val bodies = flattenOutputBuffer.map(e => new String(e.event.getBody.array(), UTF_8))
         utils.assertOutput(headers, bodies)
       }
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
index 5bc4cdf65306c..5ffb60bd602f9 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.flume
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
 import scala.concurrent.duration._
 import scala.language.postfixOps
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
index 79a9db4291bef..c9fd715d3d554 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
@@ -24,6 +24,7 @@ import java.util.concurrent.TimeoutException
 import java.util.{Map => JMap, Properties}
 
 import scala.annotation.tailrec
+import scala.collection.JavaConverters._
 import scala.language.postfixOps
 import scala.util.control.NonFatal
 
@@ -159,8 +160,7 @@ private[kafka] class KafkaTestUtils extends Logging {
 
   /** Java-friendly function for sending messages to the Kafka broker */
   def sendMessages(topic: String, messageToFreq: JMap[String, JInt]): Unit = {
-    import scala.collection.JavaConversions._
-    sendMessages(topic, Map(messageToFreq.mapValues(_.intValue()).toSeq: _*))
+    sendMessages(topic, Map(messageToFreq.asScala.mapValues(_.intValue()).toSeq: _*))
   }
 
   /** Send the messages to the Kafka broker */
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
index 388dbb8184106..3128222077537 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.kafka
 import java.lang.{Integer => JInt, Long => JLong}
 import java.util.{List => JList, Map => JMap, Set => JSet}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import kafka.common.TopicAndPartition
@@ -96,7 +96,7 @@ object KafkaUtils {
       groupId: String,
       topics: JMap[String, JInt]
     ): JavaPairReceiverInputDStream[String, String] = {
-    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*))
+    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.asScala.mapValues(_.intValue()).toSeq: _*))
   }
 
   /**
@@ -115,7 +115,7 @@ object KafkaUtils {
       topics: JMap[String, JInt],
       storageLevel: StorageLevel
     ): JavaPairReceiverInputDStream[String, String] = {
-    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*),
+    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.asScala.mapValues(_.intValue()).toSeq: _*),
       storageLevel)
   }
 
@@ -149,7 +149,10 @@ object KafkaUtils {
     implicit val valueCmd: ClassTag[T] = ClassTag(valueDecoderClass)
 
     createStream[K, V, U, T](
-      jssc.ssc, kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel)
+      jssc.ssc,
+      kafkaParams.asScala.toMap,
+      Map(topics.asScala.mapValues(_.intValue()).toSeq: _*),
+      storageLevel)
   }
 
   /** get leaders for the given offset ranges, or throw an exception */
@@ -275,7 +278,7 @@ object KafkaUtils {
     implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass)
     implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass)
     new JavaPairRDD(createRDD[K, V, KD, VD](
-      jsc.sc, Map(kafkaParams.toSeq: _*), offsetRanges))
+      jsc.sc, Map(kafkaParams.asScala.toSeq: _*), offsetRanges))
   }
 
   /**
@@ -311,9 +314,9 @@ object KafkaUtils {
     implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass)
     implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass)
     implicit val recordCmt: ClassTag[R] = ClassTag(recordClass)
-    val leaderMap = Map(leaders.toSeq: _*)
+    val leaderMap = Map(leaders.asScala.toSeq: _*)
     createRDD[K, V, KD, VD, R](
-      jsc.sc, Map(kafkaParams.toSeq: _*), offsetRanges, leaderMap, messageHandler.call _)
+      jsc.sc, Map(kafkaParams.asScala.toSeq: _*), offsetRanges, leaderMap, messageHandler.call(_))
   }
 
   /**
@@ -476,8 +479,8 @@ object KafkaUtils {
     val cleanedHandler = jssc.sparkContext.clean(messageHandler.call _)
     createDirectStream[K, V, KD, VD, R](
       jssc.ssc,
-      Map(kafkaParams.toSeq: _*),
-      Map(fromOffsets.mapValues { _.longValue() }.toSeq: _*),
+      Map(kafkaParams.asScala.toSeq: _*),
+      Map(fromOffsets.asScala.mapValues(_.longValue()).toSeq: _*),
       cleanedHandler
     )
   }
@@ -531,8 +534,8 @@ object KafkaUtils {
     implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass)
     createDirectStream[K, V, KD, VD](
       jssc.ssc,
-      Map(kafkaParams.toSeq: _*),
-      Set(topics.toSeq: _*)
+      Map(kafkaParams.asScala.toSeq: _*),
+      Set(topics.asScala.toSeq: _*)
     )
   }
 }
@@ -602,10 +605,10 @@ private[kafka] class KafkaUtilsPythonHelper {
     ): JavaPairInputDStream[Array[Byte], Array[Byte]] = {
 
     if (!fromOffsets.isEmpty) {
-      import scala.collection.JavaConversions._
-      val topicsFromOffsets = fromOffsets.keySet().map(_.topic)
-      if (topicsFromOffsets != topics.toSet) {
-        throw new IllegalStateException(s"The specified topics: ${topics.toSet.mkString(" ")} " +
+      val topicsFromOffsets = fromOffsets.keySet().asScala.map(_.topic)
+      if (topicsFromOffsets != topics.asScala.toSet) {
+        throw new IllegalStateException(
+          s"The specified topics: ${topics.asScala.toSet.mkString(" ")} " +
           s"do not equal to the topic from offsets: ${topicsFromOffsets.mkString(" ")}")
       }
     }
@@ -663,6 +666,6 @@ private[kafka] class KafkaUtilsPythonHelper {
         "with this RDD, please call this method only on a Kafka RDD.")
 
     val kafkaRDD = kafkaRDDs.head.asInstanceOf[KafkaRDD[_, _, _, _, _]]
-    kafkaRDD.offsetRanges.toSeq
+    kafkaRDD.offsetRanges.toSeq.asJava
   }
 }
diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
index 0469d0af8864a..4ea218eaa4de1 100644
--- a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
+++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
@@ -18,15 +18,17 @@
 package org.apache.spark.streaming.zeromq
 
 import scala.reflect.ClassTag
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
+
 import akka.actor.{Props, SupervisorStrategy}
 import akka.util.ByteString
 import akka.zeromq.Subscribe
+
 import org.apache.spark.api.java.function.{Function => JFunction}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
 import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext}
-import org.apache.spark.streaming.dstream.{ReceiverInputDStream}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
 import org.apache.spark.streaming.receiver.ActorSupervisorStrategy
 
 object ZeroMQUtils {
@@ -75,7 +77,8 @@ object ZeroMQUtils {
     ): JavaReceiverInputDStream[T] = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val fn = (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).toIterator
+    val fn =
+      (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).iterator().asScala
     createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel, supervisorStrategy)
   }
 
@@ -99,7 +102,8 @@ object ZeroMQUtils {
     ): JavaReceiverInputDStream[T] = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val fn = (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).toIterator
+    val fn =
+      (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).iterator().asScala
     createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel)
   }
 
@@ -122,7 +126,8 @@ object ZeroMQUtils {
     ): JavaReceiverInputDStream[T] = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val fn = (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).toIterator
+    val fn =
+      (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).iterator().asScala
     createStream[T](jssc.ssc, publisherUrl, subscribe, fn)
   }
 }
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
index a003ddf325e6e..5d32fa699ae5b 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.kinesis
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.control.NonFatal
 
 import com.amazonaws.auth.{AWSCredentials, DefaultAWSCredentialsProviderChain}
@@ -213,7 +213,7 @@ class KinesisSequenceRangeIterator(
       s"getting records using shard iterator") {
         client.getRecords(getRecordsRequest)
       }
-    (getRecordsResult.getRecords.iterator(), getRecordsResult.getNextShardIterator)
+    (getRecordsResult.getRecords.iterator().asScala, getRecordsResult.getNextShardIterator)
   }
 
   /**
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
index 22324e821ce94..6e0988c1af8a1 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
@@ -18,7 +18,7 @@ package org.apache.spark.streaming.kinesis
 
 import java.util.UUID
 
-import scala.collection.JavaConversions.asScalaIterator
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.control.NonFatal
 
@@ -202,7 +202,7 @@ private[kinesis] class KinesisReceiver(
   /** Add records of the given shard to the current block being generated */
   private[kinesis] def addRecords(shardId: String, records: java.util.List[Record]): Unit = {
     if (records.size > 0) {
-      val dataIterator = records.iterator().map { record =>
+      val dataIterator = records.iterator().asScala.map { record =>
         val byteBuffer = record.getData()
         val byteArray = new Array[Byte](byteBuffer.remaining())
         byteBuffer.get(byteArray)
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index c8eec13ec7dc7..634bf94521079 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming.kinesis
 import java.nio.ByteBuffer
 import java.util.concurrent.TimeUnit
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 import scala.util.{Failure, Random, Success, Try}
@@ -115,7 +116,7 @@ private[kinesis] class KinesisTestUtils extends Logging {
    * Expose a Python friendly API.
    */
   def pushData(testData: java.util.List[Int]): Unit = {
-    pushData(scala.collection.JavaConversions.asScalaBuffer(testData))
+    pushData(testData.asScala)
   }
 
   def deleteStream(): Unit = {
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
index ceb135e0651aa..3d136aec2e702 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
@@ -17,10 +17,10 @@
 package org.apache.spark.streaming.kinesis
 
 import java.nio.ByteBuffer
+import java.nio.charset.StandardCharsets
+import java.util.Arrays
 
-import scala.collection.JavaConversions.seqAsJavaList
-
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.{InvalidStateException, KinesisClientLibDependencyException, ShutdownException, ThrottlingException}
+import com.amazonaws.services.kinesis.clientlibrary.exceptions._
 import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorCheckpointer
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
@@ -47,10 +47,10 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   val someSeqNum = Some(seqNum)
 
   val record1 = new Record()
-  record1.setData(ByteBuffer.wrap("Spark In Action".getBytes()))
+  record1.setData(ByteBuffer.wrap("Spark In Action".getBytes(StandardCharsets.UTF_8)))
   val record2 = new Record()
-  record2.setData(ByteBuffer.wrap("Learning Spark".getBytes()))
-  val batch = List[Record](record1, record2)
+  record2.setData(ByteBuffer.wrap("Learning Spark".getBytes(StandardCharsets.UTF_8)))
+  val batch = Arrays.asList(record1, record2)
 
   var receiverMock: KinesisReceiver = _
   var checkpointerMock: IRecordProcessorCheckpointer = _
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index 87eeb5db05d26..7a1c7796065ee 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.util
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
@@ -52,7 +52,7 @@ object LinearDataGenerator {
       nPoints: Int,
       seed: Int,
       eps: Double): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateLinearInput(intercept, weights, nPoints, seed, eps))
+    generateLinearInput(intercept, weights, nPoints, seed, eps).asJava
   }
 
   /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
index a1ee554152372..2744e020e9e49 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
@@ -20,7 +20,7 @@
 import java.io.Serializable;
 import java.util.List;
 
-import static scala.collection.JavaConversions.seqAsJavaList;
+import scala.collection.JavaConverters;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -55,8 +55,9 @@ public void setUp() {
 
         double[] xMean = {5.843, 3.057, 3.758, 1.199};
         double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
-        List<LabeledPoint> points = seqAsJavaList(generateMultinomialLogisticInput(
-                weights, xMean, xVariance, true, nPoints, 42));
+        List<LabeledPoint> points = JavaConverters.asJavaListConverter(
+            generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
+        ).asJava();
         datasetRDD = jsc.parallelize(points, 2);
         dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class);
     }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 2473510e13514..8d14bb6572155 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 import scala.util.control.Breaks._
 
@@ -38,7 +38,7 @@ object LogisticRegressionSuite {
     scale: Double,
     nPoints: Int,
     seed: Int): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateLogisticInput(offset, scale, nPoints, seed))
+    generateLogisticInput(offset, scale, nPoints, seed).asJava
   }
 
   // Generate input of the form Y = logistic(offset + scale*X)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
index b1d78cba9e3dc..ee3c85d09a463 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 
 import org.jblas.DoubleMatrix
@@ -35,7 +35,7 @@ object SVMSuite {
     weights: Array[Double],
     nPoints: Int,
     seed: Int): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateSVMInput(intercept, weights, nPoints, seed))
+    generateSVMInput(intercept, weights, nPoints, seed).asJava
   }
 
   // Generate noisy input of the form Y = signum(x.dot(weights) + intercept + noise)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
index 13b754a03943a..36ac7d267243d 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.optimization
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 
 import org.scalatest.Matchers
@@ -35,7 +35,7 @@ object GradientDescentSuite {
       scale: Double,
       nPoints: Int,
       seed: Int): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateGDInput(offset, scale, nPoints, seed))
+    generateGDInput(offset, scale, nPoints, seed).asJava
   }
 
   // Generate input of the form Y = logistic(offset + scale * X)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
index 05b87728d6fdb..045135f7f8d60 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.recommendation
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.math.abs
 import scala.util.Random
 
@@ -38,7 +38,7 @@ object ALSSuite {
       negativeWeights: Boolean): (java.util.List[Rating], DoubleMatrix, DoubleMatrix) = {
     val (sampledRatings, trueRatings, truePrefs) =
       generateRatings(users, products, features, samplingRate, implicitPrefs)
-    (seqAsJavaList(sampledRatings), trueRatings, truePrefs)
+    (sampledRatings.asJava, trueRatings, truePrefs)
   }
 
   def generateRatings(
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 04e0d49b178cf..ea52bfd67944a 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -18,13 +18,13 @@
 import java.io._
 
 import scala.util.Properties
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import sbt._
 import sbt.Classpaths.publishTask
 import sbt.Keys._
 import sbtunidoc.Plugin.UnidocKeys.unidocGenjavadocVersion
-import com.typesafe.sbt.pom.{loadEffectivePom, PomBuild, SbtPomKeys}
+import com.typesafe.sbt.pom.{PomBuild, SbtPomKeys}
 import net.virtualvoid.sbt.graph.Plugin.graphSettings
 
 import spray.revolver.RevolverPlugin._
@@ -120,7 +120,7 @@ object SparkBuild extends PomBuild {
     case _ =>
   }
 
-  override val userPropertiesMap = System.getProperties.toMap
+  override val userPropertiesMap = System.getProperties.asScala.toMap
 
   lazy val MavenCompile = config("m2r") extend(Compile)
   lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy")
@@ -559,7 +559,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true",
     javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test += "-Dderby.system.durability=test",
-    javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark")
+    javaOptions in Test ++= System.getProperties.asScala.filter(_._1.startsWith("spark"))
       .map { case (k,v) => s"-D$k=$v" }.toSeq,
     javaOptions in Test += "-ea",
     javaOptions in Test ++= "-Xmx3g -Xss4096k -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g"
diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 8af8637cf948d..0948f9b27cd38 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -61,6 +61,18 @@ def _to_seq(sc, cols, converter=None):
     return sc._jvm.PythonUtils.toSeq(cols)
 
 
+def _to_list(sc, cols, converter=None):
+    """
+    Convert a list of Column (or names) into a JVM (Scala) List of Column.
+
+    An optional `converter` could be used to convert items in `cols`
+    into JVM Column objects.
+    """
+    if converter:
+        cols = [converter(c) for c in cols]
+    return sc._jvm.PythonUtils.toList(cols)
+
+
 def _unary_op(name, doc="unary operator"):
     """ Create a method for given unary operator """
     def _(self):
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 025811f519293..e269ef4304f3f 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -32,7 +32,7 @@
 from pyspark.traceback_utils import SCCallSiteSync
 from pyspark.sql import since
 from pyspark.sql.types import _parse_datatype_json_string
-from pyspark.sql.column import Column, _to_seq, _to_java_column
+from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
 from pyspark.sql.readwriter import DataFrameWriter
 from pyspark.sql.types import *
 
@@ -494,7 +494,7 @@ def randomSplit(self, weights, seed=None):
             if w < 0.0:
                 raise ValueError("Weights must be positive. Found weight value: %s" % w)
         seed = seed if seed is not None else random.randint(0, sys.maxsize)
-        rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed))
+        rdd_array = self._jdf.randomSplit(_to_list(self.sql_ctx._sc, weights), long(seed))
         return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array]
 
     @property
diff --git a/scalastyle-config.xml b/scalastyle-config.xml
index b5e2e882d2254..68fdb4141cf27 100644
--- a/scalastyle-config.xml
+++ b/scalastyle-config.xml
@@ -161,6 +161,13 @@ This file is divided into 3 sections:
     ]]></customMessage>
   </check>
 
+  <!-- As of SPARK-9613 JavaConversions should be replaced with JavaConverters -->
+  <check customId="javaconversions" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
+    <parameters><parameter name="regex">JavaConversions</parameter></parameters>
+    <customMessage>Instead of importing implicits in scala.collection.JavaConversions._, import
+    scala.collection.JavaConverters._ and use .asScala / .asJava methods</customMessage>
+  </check>
+
   <!-- ================================================================================ -->
   <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
   <!-- ================================================================================ -->
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index ec895af9c3037..cfd9cb0e62598 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import scala.collection.JavaConverters._
+
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.sql.types.StructType
@@ -280,9 +282,8 @@ trait Row extends Serializable {
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getList[T](i: Int): java.util.List[T] = {
-    scala.collection.JavaConversions.seqAsJavaList(getSeq[T](i))
-  }
+  def getList[T](i: Int): java.util.List[T] =
+    getSeq[T](i).asJava
 
   /**
    * Returns the value at position i of map type as a Scala Map.
@@ -296,9 +297,8 @@ trait Row extends Serializable {
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getJavaMap[K, V](i: Int): java.util.Map[K, V] = {
-    scala.collection.JavaConversions.mapAsJavaMap(getMap[K, V](i))
-  }
+  def getJavaMap[K, V](i: Int): java.util.Map[K, V] =
+    getMap[K, V](i).asJava
 
   /**
    * Returns the value at position i of struct type as an [[Row]] object.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index 503c4f4b20f38..4cc9a5520a085 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.analysis
 
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -147,7 +147,7 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
 
   override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = {
     val result = ArrayBuffer.empty[(String, Boolean)]
-    for (name <- tables.keySet()) {
+    for (name <- tables.keySet().asScala) {
       result += ((name, true))
     }
     result
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index a4fd4cf3b330b..77a42c0873a6b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql
 
 import java.{lang => jl}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.expressions._
@@ -209,7 +209,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    *
    * @since 1.3.1
    */
-  def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.toSeq)
+  def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.asScala.toSeq)
 
   /**
    * (Scala-specific) Returns a new [[DataFrame]] that replaces null values.
@@ -254,7 +254,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def replace[T](col: String, replacement: java.util.Map[T, T]): DataFrame = {
-    replace[T](col, replacement.toMap : Map[T, T])
+    replace[T](col, replacement.asScala.toMap)
   }
 
   /**
@@ -277,7 +277,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def replace[T](cols: Array[String], replacement: java.util.Map[T, T]): DataFrame = {
-    replace(cols.toSeq, replacement.toMap)
+    replace(cols.toSeq, replacement.asScala.toMap)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 6dc7bfe333498..97a8b6518a832 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql
 
 import java.util.Properties
 
+import scala.collection.JavaConverters._
+
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.Experimental
@@ -90,7 +92,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
    * @since 1.4.0
    */
   def options(options: java.util.Map[String, String]): DataFrameReader = {
-    this.options(scala.collection.JavaConversions.mapAsScalaMap(options))
+    this.options(options.asScala)
     this
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index ce8744b53175b..b2a66dd417b4c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql
 
 import java.util.Properties
 
+import scala.collection.JavaConverters._
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.{SqlParser, TableIdentifier}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
@@ -109,7 +111,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    * @since 1.4.0
    */
   def options(options: java.util.Map[String, String]): DataFrameWriter = {
-    this.options(scala.collection.JavaConversions.mapAsScalaMap(options))
+    this.options(options.asScala)
     this
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
index 99d557b03a033..ee31d83cce42c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 
 import org.apache.spark.annotation.Experimental
@@ -188,7 +188,7 @@ class GroupedData protected[sql](
    * @since 1.3.0
    */
   def agg(exprs: java.util.Map[String, String]): DataFrame = {
-    agg(exprs.toMap)
+    agg(exprs.asScala.toMap)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index e9de14f025502..e6f7619519e6a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql
 import java.util.Properties
 
 import scala.collection.immutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.parquet.hadoop.ParquetOutputCommitter
 
@@ -531,7 +531,7 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   /** Set Spark SQL configuration properties. */
   def setConf(props: Properties): Unit = settings.synchronized {
-    props.foreach { case (k, v) => setConfString(k, v) }
+    props.asScala.foreach { case (k, v) => setConfString(k, v) }
   }
 
   /** Set the given Spark SQL configuration property using a `string` value. */
@@ -601,24 +601,25 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
    * Return all the configuration properties that have been set (i.e. not the default).
    * This creates a new copy of the config properties in the form of a Map.
    */
-  def getAllConfs: immutable.Map[String, String] = settings.synchronized { settings.toMap }
+  def getAllConfs: immutable.Map[String, String] =
+    settings.synchronized { settings.asScala.toMap }
 
   /**
    * Return all the configuration definitions that have been defined in [[SQLConf]]. Each
    * definition contains key, defaultValue and doc.
    */
   def getAllDefinedConfs: Seq[(String, String, String)] = sqlConfEntries.synchronized {
-    sqlConfEntries.values.filter(_.isPublic).map { entry =>
+    sqlConfEntries.values.asScala.filter(_.isPublic).map { entry =>
       (entry.key, entry.defaultValueString, entry.doc)
     }.toSeq
   }
 
   private[spark] def unsetConf(key: String): Unit = {
-    settings -= key
+    settings.remove(key)
   }
 
   private[spark] def unsetConf(entry: SQLConfEntry[_]): Unit = {
-    settings -= entry.key
+    settings.remove(entry.key)
   }
 
   private[spark] def clear(): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index a1eea09e0477b..4e8414af50b44 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -21,7 +21,7 @@ import java.beans.Introspector
 import java.util.Properties
 import java.util.concurrent.atomic.AtomicReference
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.immutable
 import scala.reflect.runtime.universe.TypeTag
 import scala.util.control.NonFatal
@@ -225,7 +225,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     conf.setConf(properties)
     // After we have populated SQLConf, we call setConf to populate other confs in the subclass
     // (e.g. hiveconf in HiveContext).
-    properties.foreach {
+    properties.asScala.foreach {
       case (key, value) => setConf(key, value)
     }
   }
@@ -567,7 +567,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       tableName: String,
       source: String,
       options: java.util.Map[String, String]): DataFrame = {
-    createExternalTable(tableName, source, options.toMap)
+    createExternalTable(tableName, source, options.asScala.toMap)
   }
 
   /**
@@ -612,7 +612,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       source: String,
       schema: StructType,
       options: java.util.Map[String, String]): DataFrame = {
-    createExternalTable(tableName, source, schema, options.toMap)
+    createExternalTable(tableName, source, schema, options.asScala.toMap)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
index 8fbaf3a3059db..011724436621d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.datasources
 
 import java.util.ServiceLoader
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.{existentials, implicitConversions}
 import scala.util.{Success, Failure, Try}
 
@@ -55,7 +55,7 @@ object ResolvedDataSource extends Logging {
     val loader = Utils.getContextOrSparkClassLoader
     val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
 
-    serviceLoader.iterator().filter(_.shortName().equalsIgnoreCase(provider)).toList match {
+    serviceLoader.asScala.filter(_.shortName().equalsIgnoreCase(provider)).toList match {
       /** the provider format did not match any given registered aliases */
       case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
         case Success(dataSource) => dataSource
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 3f8353af6e2ad..0a6bb44445f6e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.datasources.parquet
 
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConversions.{iterableAsScalaIterable, mapAsJavaMap, mapAsScalaMap}
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
@@ -44,7 +44,7 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
     val parquetRequestedSchema = readContext.getRequestedSchema
 
     val catalystRequestedSchema =
-      Option(readContext.getReadSupportMetadata).map(_.toMap).flatMap { metadata =>
+      Option(readContext.getReadSupportMetadata).map(_.asScala).flatMap { metadata =>
         metadata
           // First tries to read requested schema, which may result from projections
           .get(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
@@ -123,7 +123,7 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
       maybeRequestedSchema.fold(context.getFileSchema) { schemaString =>
         val toParquet = new CatalystSchemaConverter(conf)
         val fileSchema = context.getFileSchema.asGroupType()
-        val fileFieldNames = fileSchema.getFields.map(_.getName).toSet
+        val fileFieldNames = fileSchema.getFields.asScala.map(_.getName).toSet
 
         StructType
           // Deserializes the Catalyst schema of requested columns
@@ -152,7 +152,7 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
         maybeRequestedSchema.map(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA -> _) ++
         maybeRowSchema.map(RowWriteSupport.SPARK_ROW_SCHEMA -> _)
 
-    new ReadContext(parquetRequestedSchema, metadata)
+    new ReadContext(parquetRequestedSchema, metadata.asJava)
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index cbf0704c4a9a4..f682ca0d8ff4f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.datasources.parquet
 import java.math.{BigDecimal, BigInteger}
 import java.nio.ByteOrder
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
@@ -183,7 +183,7 @@ private[parquet] class CatalystRowConverter(
     // those missing fields and create converters for them, although values of these fields are
     // always null.
     val paddedParquetFields = {
-      val parquetFields = parquetType.getFields
+      val parquetFields = parquetType.getFields.asScala
       val parquetFieldNames = parquetFields.map(_.getName).toSet
       val missingFields = catalystType.filterNot(f => parquetFieldNames.contains(f.name))
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index 535f0684e97f9..be6c0545f5a0a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.schema.OriginalType._
@@ -82,7 +82,7 @@ private[parquet] class CatalystSchemaConverter(
   def convert(parquetSchema: MessageType): StructType = convert(parquetSchema.asGroupType())
 
   private def convert(parquetSchema: GroupType): StructType = {
-    val fields = parquetSchema.getFields.map { field =>
+    val fields = parquetSchema.getFields.asScala.map { field =>
       field.getRepetition match {
         case OPTIONAL =>
           StructField(field.getName, convertField(field), nullable = true)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index bbf682aec0f9d..64982f37cf872 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -21,7 +21,7 @@ import java.net.URI
 import java.util.logging.{Logger => JLogger}
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.{Failure, Try}
 
@@ -336,7 +336,7 @@ private[sql] class ParquetRelation(
         override def getPartitions: Array[SparkPartition] = {
           val inputFormat = new ParquetInputFormat[InternalRow] {
             override def listStatus(jobContext: JobContext): JList[FileStatus] = {
-              if (cacheMetadata) cachedStatuses else super.listStatus(jobContext)
+              if (cacheMetadata) cachedStatuses.asJava else super.listStatus(jobContext)
             }
           }
 
@@ -344,7 +344,8 @@ private[sql] class ParquetRelation(
           val rawSplits = inputFormat.getSplits(jobContext)
 
           Array.tabulate[SparkPartition](rawSplits.size) { i =>
-            new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
+            new SqlNewHadoopPartition(
+              id, i, rawSplits.get(i).asInstanceOf[InputSplit with Writable])
           }
         }
       }.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
@@ -588,7 +589,7 @@ private[sql] object ParquetRelation extends Logging {
       val metadata = footer.getParquetMetadata.getFileMetaData
       val serializedSchema = metadata
         .getKeyValueMetaData
-        .toMap
+        .asScala.toMap
         .get(CatalystReadSupport.SPARK_METADATA_KEY)
       if (serializedSchema.isEmpty) {
         // Falls back to Parquet schema if no Spark SQL schema found.
@@ -745,7 +746,7 @@ private[sql] object ParquetRelation extends Logging {
           // Reads footers in multi-threaded manner within each task
           val footers =
             ParquetFileReader.readAllFootersInParallel(
-              serializedConf.value, fakeFileStatuses, skipRowGroups)
+              serializedConf.value, fakeFileStatuses.asJava, skipRowGroups).asScala
 
           // Converter used to convert Parquet `MessageType` to Spark SQL `StructType`
           val converter =
@@ -772,7 +773,7 @@ private[sql] object ParquetRelation extends Logging {
     val fileMetaData = footer.getParquetMetadata.getFileMetaData
     fileMetaData
       .getKeyValueMetaData
-      .toMap
+      .asScala.toMap
       .get(CatalystReadSupport.SPARK_METADATA_KEY)
       .flatMap(deserializeSchemaString)
       .getOrElse(converter.convert(fileMetaData.getSchema))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 42376ef7a9c1f..142301fe87cb6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.IOException
+import java.util.{Collections, Arrays}
 
-import scala.collection.JavaConversions._
 import scala.util.Try
 
 import org.apache.hadoop.conf.Configuration
@@ -107,7 +107,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
     ParquetFileWriter.writeMetadataFile(
       conf,
       path,
-      new Footer(path, new ParquetMetadata(metaData, Nil)) :: Nil)
+      Arrays.asList(new Footer(path, new ParquetMetadata(metaData, Collections.emptyList()))))
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index ed282f98b7d71..d800c7456bdac 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.joins
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
@@ -92,9 +92,9 @@ case class ShuffledHashOuterJoin(
         case FullOuter =>
           // TODO(davies): use UnsafeRow
           val leftHashTable =
-            buildHashTable(leftIter, numLeftRows, newProjection(leftKeys, left.output))
+            buildHashTable(leftIter, numLeftRows, newProjection(leftKeys, left.output)).asScala
           val rightHashTable =
-            buildHashTable(rightIter, numRightRows, newProjection(rightKeys, right.output))
+            buildHashTable(rightIter, numRightRows, newProjection(rightKeys, right.output)).asScala
           (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
             fullOuterIterator(key,
               leftHashTable.getOrElse(key, EMPTY_LIST),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 59f8b079ab333..5a58d846ad80b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution
 import java.io.OutputStream
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import net.razorvine.pickle._
 
@@ -196,14 +196,15 @@ object EvaluatePython {
     case (c, BinaryType) if c.getClass.isArray && c.getClass.getComponentType.getName == "byte" => c
 
     case (c: java.util.List[_], ArrayType(elementType, _)) =>
-      new GenericArrayData(c.map { e => fromJava(e, elementType)}.toArray)
+      new GenericArrayData(c.asScala.map { e => fromJava(e, elementType)}.toArray)
 
     case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
       new GenericArrayData(c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType)))
 
     case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) =>
-      val keys = c.keysIterator.map(fromJava(_, keyType)).toArray
-      val values = c.valuesIterator.map(fromJava(_, valueType)).toArray
+      val keyValues = c.asScala.toSeq
+      val keys = keyValues.map(kv => fromJava(kv._1, keyType)).toArray
+      val values = keyValues.map(kv => fromJava(kv._2, valueType)).toArray
       ArrayBasedMapData(keys, values)
 
     case (c, StructType(fields)) if c.getClass.isArray =>
@@ -367,7 +368,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
       val pickle = new Unpickler
       iter.flatMap { pickedResult =>
         val unpickledBatch = pickle.loads(pickedResult)
-        unpickledBatch.asInstanceOf[java.util.ArrayList[Any]]
+        unpickledBatch.asInstanceOf[java.util.ArrayList[Any]].asScala
       }
     }.mapPartitions { iter =>
       val row = new GenericMutableRow(1)
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 7abdd3db80341..4867cebf5328c 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -23,7 +23,7 @@
 import java.util.List;
 import java.util.Map;
 
-import scala.collection.JavaConversions;
+import scala.collection.JavaConverters;
 import scala.collection.Seq;
 
 import com.google.common.collect.ImmutableMap;
@@ -96,7 +96,7 @@ public void testVarargMethods() {
     df.groupBy().agg(countDistinct("key", "value"));
     df.groupBy().agg(countDistinct(col("key"), col("value")));
     df.select(coalesce(col("key")));
-    
+
     // Varargs with mathfunctions
     DataFrame df2 = context.table("testData2");
     df2.select(exp("a"), exp("b"));
@@ -172,7 +172,7 @@ public void testCreateDataFrameFromJavaBeans() {
     Seq<Integer> outputBuffer = (Seq<Integer>) first.getJavaMap(2).get("hello");
     Assert.assertArrayEquals(
       bean.getC().get("hello"),
-      Ints.toArray(JavaConversions.seqAsJavaList(outputBuffer)));
+      Ints.toArray(JavaConverters.seqAsJavaListConverter(outputBuffer).asJava()));
     Seq<String> d = first.getAs(3);
     Assert.assertEquals(bean.getD().size(), d.length());
     for (int i = 0; i < d.length(); i++) {
@@ -206,7 +206,7 @@ public void testCrosstab() {
       count++;
     }
   }
-  
+
   @Test
   public void testFrequentItems() {
     DataFrame df = context.table("testData2");
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
index cdaa14ac80785..329ffb66083b1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.sql.test.SharedSQLContext
 
@@ -153,11 +153,11 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
 
     // Test Java version
     checkAnswer(
-      df.na.fill(mapAsJavaMap(Map(
+      df.na.fill(Map(
         "a" -> "test",
         "c" -> 1,
         "d" -> 2.2
-      ))),
+      ).asJava),
       Row("test", null, 1, 2.2))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index 4adcefb7dc4b1..3649c2a97b5ef 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql
 
 import java.util.{Locale, TimeZone}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.util._
@@ -145,7 +145,7 @@ object QueryTest {
   }
 
   def checkAnswer(df: DataFrame, expectedAnswer: java.util.List[Row]): String = {
-    checkAnswer(df, expectedAnswer.toSeq) match {
+    checkAnswer(df, expectedAnswer.asScala) match {
       case Some(errorMessage) => errorMessage
       case None => null
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 45db619567a22..bd7cf8c10abef 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -20,8 +20,7 @@ package org.apache.spark.sql.execution.datasources.parquet
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConverters.seqAsJavaListConverter
-import scala.collection.JavaConverters.mapAsJavaMapConverter
+import scala.collection.JavaConverters._
 
 import org.apache.avro.Schema
 import org.apache.avro.generic.IndexedRecord
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index d85c564e3e8d1..df68432faeeb3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.parquet.hadoop.ParquetFileReader
@@ -40,8 +40,9 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
       override def accept(path: Path): Boolean = pathFilter(path)
     }).toSeq
 
-    val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
-    footers.head.getParquetMetadata.getFileMetaData.getSchema
+    val footers =
+      ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles.asJava, true)
+    footers.iterator().next().getParquetMetadata.getFileMetaData.getSchema
   }
 
   protected def logParquetSchema(path: String): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index e6b0a2ea95e38..08d2b9dee99b0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConversions._
+import java.util.Collections
+
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
@@ -28,7 +30,7 @@ import org.apache.parquet.example.data.simple.SimpleGroup
 import org.apache.parquet.example.data.{Group, GroupWriter}
 import org.apache.parquet.hadoop.api.WriteSupport
 import org.apache.parquet.hadoop.api.WriteSupport.WriteContext
-import org.apache.parquet.hadoop.metadata.{CompressionCodecName, FileMetaData, ParquetMetadata}
+import org.apache.parquet.hadoop.metadata.{BlockMetaData, CompressionCodecName, FileMetaData, ParquetMetadata}
 import org.apache.parquet.hadoop.{Footer, ParquetFileWriter, ParquetOutputCommitter, ParquetWriter}
 import org.apache.parquet.io.api.RecordConsumer
 import org.apache.parquet.schema.{MessageType, MessageTypeParser}
@@ -205,9 +207,8 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   test("compression codec") {
     def compressionCodecFor(path: String): String = {
       val codecs = ParquetTypesConverter
-        .readMetaData(new Path(path), Some(configuration))
-        .getBlocks
-        .flatMap(_.getColumns)
+        .readMetaData(new Path(path), Some(configuration)).getBlocks.asScala
+        .flatMap(_.getColumns.asScala)
         .map(_.getCodec.name())
         .distinct
 
@@ -348,14 +349,16 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       """.stripMargin)
 
     withTempPath { location =>
-      val extraMetadata = Map(CatalystReadSupport.SPARK_METADATA_KEY -> sparkSchema.toString)
+      val extraMetadata = Collections.singletonMap(
+        CatalystReadSupport.SPARK_METADATA_KEY, sparkSchema.toString)
       val fileMetadata = new FileMetaData(parquetSchema, extraMetadata, "Spark")
       val path = new Path(location.getCanonicalPath)
 
       ParquetFileWriter.writeMetadataFile(
         sqlContext.sparkContext.hadoopConfiguration,
         path,
-        new Footer(path, new ParquetMetadata(fileMetadata, Nil)) :: Nil)
+        Collections.singletonList(
+          new Footer(path, new ParquetMetadata(fileMetadata, Collections.emptyList()))))
 
       assertResult(sqlContext.read.parquet(path.toString).schema) {
         StructType(
@@ -386,7 +389,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -410,7 +413,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -434,7 +437,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       } finally {
         // Hadoop 1 doesn't have `Configuration.unset`
         configuration.clear()
-        clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+        clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
       }
     }
   }
@@ -481,7 +484,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
index 02cc7e5efa521..306f98bcb5344 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
@@ -20,9 +20,9 @@ package org.apache.spark.sql.hive.thriftserver
 import java.security.PrivilegedExceptionAction
 import java.sql.{Date, Timestamp}
 import java.util.concurrent.RejectedExecutionException
-import java.util.{Map => JMap, UUID}
+import java.util.{Arrays, Map => JMap, UUID}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, Map => SMap}
 import scala.util.control.NonFatal
 
@@ -126,13 +126,13 @@ private[hive] class SparkExecuteStatementOperation(
 
   def getResultSetSchema: TableSchema = {
     if (result == null || result.queryExecution.analyzed.output.size == 0) {
-      new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
+      new TableSchema(Arrays.asList(new FieldSchema("Result", "string", "")))
     } else {
       logInfo(s"Result Schema: ${result.queryExecution.analyzed.output}")
       val schema = result.queryExecution.analyzed.output.map { attr =>
         new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
       }
-      new TableSchema(schema)
+      new TableSchema(schema.asJava)
     }
   }
 
@@ -298,7 +298,7 @@ private[hive] class SparkExecuteStatementOperation(
       sqlOperationConf = new HiveConf(sqlOperationConf)
 
       // apply overlay query specific settings, if any
-      getConfOverlay().foreach { case (k, v) =>
+      getConfOverlay().asScala.foreach { case (k, v) =>
         try {
           sqlOperationConf.verifyAndSet(k, v)
         } catch {
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index 7799704c819d9..a29df567983b1 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
-import scala.collection.JavaConversions._
-
 import java.io._
 import java.util.{ArrayList => JArrayList, Locale}
 
+import scala.collection.JavaConverters._
+
 import jline.console.ConsoleReader
 import jline.console.history.FileHistory
 
@@ -101,9 +101,9 @@ private[hive] object SparkSQLCLIDriver extends Logging {
 
     // Set all properties specified via command line.
     val conf: HiveConf = sessionState.getConf
-    sessionState.cmdProperties.entrySet().foreach { item =>
-      val key = item.getKey.asInstanceOf[String]
-      val value = item.getValue.asInstanceOf[String]
+    sessionState.cmdProperties.entrySet().asScala.foreach { item =>
+      val key = item.getKey.toString
+      val value = item.getValue.toString
       // We do not propagate metastore options to the execution copy of hive.
       if (key != "javax.jdo.option.ConnectionURL") {
         conf.set(key, value)
@@ -316,15 +316,15 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
 
           if (HiveConf.getBoolVar(conf, HiveConf.ConfVars.HIVE_CLI_PRINT_HEADER)) {
             // Print the column names.
-            Option(driver.getSchema.getFieldSchemas).map { fields =>
-              out.println(fields.map(_.getName).mkString("\t"))
+            Option(driver.getSchema.getFieldSchemas).foreach { fields =>
+              out.println(fields.asScala.map(_.getName).mkString("\t"))
             }
           }
 
           var counter = 0
           try {
             while (!out.checkError() && driver.getResults(res)) {
-              res.foreach{ l =>
+              res.asScala.foreach { l =>
                 counter += 1
                 out.println(l)
               }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
index 644165acf70a7..5ad8c54f296d5 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
@@ -21,6 +21,8 @@ import java.io.IOException
 import java.util.{List => JList}
 import javax.security.auth.login.LoginException
 
+import scala.collection.JavaConverters._
+
 import org.apache.commons.logging.Log
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.shims.Utils
@@ -34,8 +36,6 @@ import org.apache.hive.service.{AbstractService, Service, ServiceException}
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 
-import scala.collection.JavaConversions._
-
 private[hive] class SparkSQLCLIService(hiveServer: HiveServer2, hiveContext: HiveContext)
   extends CLIService(hiveServer)
   with ReflectedCompositeService {
@@ -76,7 +76,7 @@ private[thriftserver] trait ReflectedCompositeService { this: AbstractService =>
   def initCompositeService(hiveConf: HiveConf) {
     // Emulating `CompositeService.init(hiveConf)`
     val serviceList = getAncestorField[JList[Service]](this, 2, "serviceList")
-    serviceList.foreach(_.init(hiveConf))
+    serviceList.asScala.foreach(_.init(hiveConf))
 
     // Emulating `AbstractService.init(hiveConf)`
     invoke(classOf[AbstractService], this, "ensureCurrentState", classOf[STATE] -> STATE.NOTINITED)
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
index 77272aecf2835..2619286afc148 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
-import java.util.{ArrayList => JArrayList, List => JList}
+import java.util.{Arrays, ArrayList => JArrayList, List => JList}
+
+import scala.collection.JavaConverters._
 
 import org.apache.commons.lang3.exception.ExceptionUtils
 import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema}
@@ -27,8 +29,6 @@ import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse
 import org.apache.spark.Logging
 import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
 
-import scala.collection.JavaConversions._
-
 private[hive] class SparkSQLDriver(
     val context: HiveContext = SparkSQLEnv.hiveContext)
   extends Driver
@@ -43,14 +43,14 @@ private[hive] class SparkSQLDriver(
   private def getResultSetSchema(query: context.QueryExecution): Schema = {
     val analyzed = query.analyzed
     logDebug(s"Result Schema: ${analyzed.output}")
-    if (analyzed.output.size == 0) {
-      new Schema(new FieldSchema("Response code", "string", "") :: Nil, null)
+    if (analyzed.output.isEmpty) {
+      new Schema(Arrays.asList(new FieldSchema("Response code", "string", "")), null)
     } else {
       val fieldSchemas = analyzed.output.map { attr =>
         new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
       }
 
-      new Schema(fieldSchemas, null)
+      new Schema(fieldSchemas.asJava, null)
     }
   }
 
@@ -79,7 +79,7 @@ private[hive] class SparkSQLDriver(
     if (hiveResponse == null) {
       false
     } else {
-      res.asInstanceOf[JArrayList[String]].addAll(hiveResponse)
+      res.asInstanceOf[JArrayList[String]].addAll(hiveResponse.asJava)
       hiveResponse = null
       true
     }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
index 1d41c46131828..bacf6cc458fd5 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver
 
 import java.io.PrintStream
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.scheduler.StatsReportListener
 import org.apache.spark.sql.hive.HiveContext
@@ -64,7 +64,7 @@ private[hive] object SparkSQLEnv extends Logging {
       hiveContext.setConf("spark.sql.hive.version", HiveContext.hiveExecutionVersion)
 
       if (log.isDebugEnabled) {
-        hiveContext.hiveconf.getAllProperties.toSeq.sorted.foreach { case (k, v) =>
+        hiveContext.hiveconf.getAllProperties.asScala.toSeq.sorted.foreach { case (k, v) =>
           logDebug(s"HiveConf var: $k=$v")
         }
       }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 17cc83087fb1d..c0a458fa9ab8d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -22,7 +22,7 @@ import java.net.{URL, URLClassLoader}
 import java.sql.Timestamp
 import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.HashMap
 import scala.language.implicitConversions
 import scala.concurrent.duration._
@@ -194,7 +194,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
     logInfo("defalt warehouse location is " + defaltWarehouseLocation)
 
     // `configure` goes second to override other settings.
-    val allConfig = metadataConf.iterator.map(e => e.getKey -> e.getValue).toMap ++ configure
+    val allConfig = metadataConf.asScala.map(e => e.getKey -> e.getValue).toMap ++ configure
 
     val isolatedLoader = if (hiveMetastoreJars == "builtin") {
       if (hiveExecutionVersion != hiveMetastoreVersion) {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 64fffdbf9b020..cfe2bb05ad89e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import scala.collection.JavaConverters._
+
 import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
 import org.apache.hadoop.hive.serde2.objectinspector.primitive._
 import org.apache.hadoop.hive.serde2.objectinspector.{StructField => HiveStructField, _}
@@ -31,9 +33,6 @@ import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, types}
 import org.apache.spark.unsafe.types.UTF8String
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 /**
  * 1. The Underlying data type in catalyst and in Hive
  * In catalyst:
@@ -290,13 +289,13 @@ private[hive] trait HiveInspectors {
       DateTimeUtils.fromJavaDate(poi.getWritableConstantValue.get())
     case mi: StandardConstantMapObjectInspector =>
       // take the value from the map inspector object, rather than the input data
-      val map = mi.getWritableConstantValue
-      val keys = map.keysIterator.map(unwrap(_, mi.getMapKeyObjectInspector)).toArray
-      val values = map.valuesIterator.map(unwrap(_, mi.getMapValueObjectInspector)).toArray
+      val keyValues = mi.getWritableConstantValue.asScala.toSeq
+      val keys = keyValues.map(kv => unwrap(kv._1, mi.getMapKeyObjectInspector)).toArray
+      val values = keyValues.map(kv => unwrap(kv._2, mi.getMapValueObjectInspector)).toArray
       ArrayBasedMapData(keys, values)
     case li: StandardConstantListObjectInspector =>
       // take the value from the list inspector object, rather than the input data
-      val values = li.getWritableConstantValue
+      val values = li.getWritableConstantValue.asScala
         .map(unwrap(_, li.getListElementObjectInspector))
         .toArray
       new GenericArrayData(values)
@@ -342,7 +341,7 @@ private[hive] trait HiveInspectors {
     case li: ListObjectInspector =>
       Option(li.getList(data))
         .map { l =>
-          val values = l.map(unwrap(_, li.getListElementObjectInspector)).toArray
+          val values = l.asScala.map(unwrap(_, li.getListElementObjectInspector)).toArray
           new GenericArrayData(values)
         }
         .orNull
@@ -351,15 +350,16 @@ private[hive] trait HiveInspectors {
       if (map == null) {
         null
       } else {
-        val keys = map.keysIterator.map(unwrap(_, mi.getMapKeyObjectInspector)).toArray
-        val values = map.valuesIterator.map(unwrap(_, mi.getMapValueObjectInspector)).toArray
+        val keyValues = map.asScala.toSeq
+        val keys = keyValues.map(kv => unwrap(kv._1, mi.getMapKeyObjectInspector)).toArray
+        val values = keyValues.map(kv => unwrap(kv._2, mi.getMapValueObjectInspector)).toArray
         ArrayBasedMapData(keys, values)
       }
     // currently, hive doesn't provide the ConstantStructObjectInspector
     case si: StructObjectInspector =>
       val allRefs = si.getAllStructFieldRefs
-      InternalRow.fromSeq(
-        allRefs.map(r => unwrap(si.getStructFieldData(data, r), r.getFieldObjectInspector)))
+      InternalRow.fromSeq(allRefs.asScala.map(
+        r => unwrap(si.getStructFieldData(data, r), r.getFieldObjectInspector)))
   }
 
 
@@ -403,14 +403,14 @@ private[hive] trait HiveInspectors {
 
     case soi: StandardStructObjectInspector =>
       val schema = dataType.asInstanceOf[StructType]
-      val wrappers = soi.getAllStructFieldRefs.zip(schema.fields).map { case (ref, field) =>
-        wrapperFor(ref.getFieldObjectInspector, field.dataType)
+      val wrappers = soi.getAllStructFieldRefs.asScala.zip(schema.fields).map {
+        case (ref, field) => wrapperFor(ref.getFieldObjectInspector, field.dataType)
       }
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
           val row = o.asInstanceOf[InternalRow]
-          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
+          soi.getAllStructFieldRefs.asScala.zip(wrappers).zipWithIndex.foreach {
             case ((field, wrapper), i) =>
               soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
           }
@@ -537,7 +537,7 @@ private[hive] trait HiveInspectors {
       // 1. create the pojo (most likely) object
       val result = x.create()
       var i = 0
-      while (i < fieldRefs.length) {
+      while (i < fieldRefs.size) {
         // 2. set the property for the pojo
         val tpe = structType(i).dataType
         x.setStructFieldData(
@@ -552,9 +552,9 @@ private[hive] trait HiveInspectors {
       val fieldRefs = x.getAllStructFieldRefs
       val structType = dataType.asInstanceOf[StructType]
       val row = a.asInstanceOf[InternalRow]
-      val result = new java.util.ArrayList[AnyRef](fieldRefs.length)
+      val result = new java.util.ArrayList[AnyRef](fieldRefs.size)
       var i = 0
-      while (i < fieldRefs.length) {
+      while (i < fieldRefs.size) {
         val tpe = structType(i).dataType
         result.add(wrap(row.get(i, tpe), fieldRefs.get(i).getFieldObjectInspector, tpe))
         i += 1
@@ -712,10 +712,10 @@ private[hive] trait HiveInspectors {
 
   def inspectorToDataType(inspector: ObjectInspector): DataType = inspector match {
     case s: StructObjectInspector =>
-      StructType(s.getAllStructFieldRefs.map(f => {
+      StructType(s.getAllStructFieldRefs.asScala.map(f =>
         types.StructField(
           f.getFieldName, inspectorToDataType(f.getFieldObjectInspector), nullable = true)
-      }))
+      ))
     case l: ListObjectInspector => ArrayType(inspectorToDataType(l.getListElementObjectInspector))
     case m: MapObjectInspector =>
       MapType(
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 98d21aa76d64e..b8da0840ae569 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 
 import com.google.common.base.Objects
@@ -483,7 +483,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       // are empty.
       val partitions = metastoreRelation.getHiveQlPartitions().map { p =>
         val location = p.getLocation
-        val values = InternalRow.fromSeq(p.getValues.zip(partitionColumnDataTypes).map {
+        val values = InternalRow.fromSeq(p.getValues.asScala.zip(partitionColumnDataTypes).map {
           case (rawValue, dataType) => Cast(Literal(rawValue), dataType).eval(null)
         })
         ParquetPartition(values, location)
@@ -798,9 +798,9 @@ private[hive] case class MetastoreRelation
 
     val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
     tTable.setSd(sd)
-    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
     tTable.setPartitionKeys(
-      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
 
     table.location.foreach(sd.setLocation)
     table.inputFormat.foreach(sd.setInputFormat)
@@ -852,11 +852,11 @@ private[hive] case class MetastoreRelation
       val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
       tPartition.setDbName(databaseName)
       tPartition.setTableName(tableName)
-      tPartition.setValues(p.values)
+      tPartition.setValues(p.values.asJava)
 
       val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
       tPartition.setSd(sd)
-      sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+      sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
 
       sd.setLocation(p.storage.location)
       sd.setInputFormat(p.storage.inputFormat)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index ad33dee555dd2..d5cd7e98b5267 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -20,6 +20,9 @@ package org.apache.spark.sql.hive
 import java.sql.Date
 import java.util.Locale
 
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -48,10 +51,6 @@ import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
 import org.apache.spark.util.random.RandomSampler
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-import scala.collection.mutable.ArrayBuffer
-
 /**
  * Used when we need to start parsing the AST before deciding that we are going to pass the command
  * back for Hive to execute natively.  Will be replaced with a native command that contains the
@@ -202,7 +201,7 @@ private[hive] object HiveQl extends Logging {
      * Returns a scala.Seq equivalent to [s] or Nil if [s] is null.
      */
     private def nilIfEmpty[A](s: java.util.List[A]): Seq[A] =
-      Option(s).map(_.toSeq).getOrElse(Nil)
+      Option(s).map(_.asScala).getOrElse(Nil)
 
     /**
      * Returns this ASTNode with the text changed to `newText`.
@@ -217,7 +216,7 @@ private[hive] object HiveQl extends Logging {
      */
     def withChildren(newChildren: Seq[ASTNode]): ASTNode = {
       (1 to n.getChildCount).foreach(_ => n.deleteChild(0))
-      n.addChildren(newChildren)
+      n.addChildren(newChildren.asJava)
       n
     }
 
@@ -323,11 +322,11 @@ private[hive] object HiveQl extends Logging {
     assert(tree.asInstanceOf[ASTNode].getText == "TOK_CREATETABLE", "Only CREATE TABLE supported.")
     val tableOps = tree.getChildren
     val colList =
-      tableOps
+      tableOps.asScala
         .find(_.asInstanceOf[ASTNode].getText == "TOK_TABCOLLIST")
         .getOrElse(sys.error("No columnList!")).getChildren
 
-    colList.map(nodeToAttribute)
+    colList.asScala.map(nodeToAttribute)
   }
 
   /** Extractor for matching Hive's AST Tokens. */
@@ -337,7 +336,7 @@ private[hive] object HiveQl extends Logging {
       case t: ASTNode =>
         CurrentOrigin.setPosition(t.getLine, t.getCharPositionInLine)
         Some((t.getText,
-          Option(t.getChildren).map(_.toList).getOrElse(Nil).asInstanceOf[Seq[ASTNode]]))
+          Option(t.getChildren).map(_.asScala.toList).getOrElse(Nil).asInstanceOf[Seq[ASTNode]]))
       case _ => None
     }
   }
@@ -424,7 +423,9 @@ private[hive] object HiveQl extends Logging {
 
   protected def extractDbNameTableName(tableNameParts: Node): (Option[String], String) = {
     val (db, tableName) =
-      tableNameParts.getChildren.map { case Token(part, Nil) => cleanIdentifier(part) } match {
+      tableNameParts.getChildren.asScala.map {
+        case Token(part, Nil) => cleanIdentifier(part)
+      } match {
         case Seq(tableOnly) => (None, tableOnly)
         case Seq(databaseName, table) => (Some(databaseName), table)
       }
@@ -433,7 +434,9 @@ private[hive] object HiveQl extends Logging {
   }
 
   protected def extractTableIdent(tableNameParts: Node): Seq[String] = {
-    tableNameParts.getChildren.map { case Token(part, Nil) => cleanIdentifier(part) } match {
+    tableNameParts.getChildren.asScala.map {
+      case Token(part, Nil) => cleanIdentifier(part)
+    } match {
       case Seq(tableOnly) => Seq(tableOnly)
       case Seq(databaseName, table) => Seq(databaseName, table)
       case other => sys.error("Hive only supports tables names like 'tableName' " +
@@ -624,7 +627,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
           val cols = BaseSemanticAnalyzer.getColumns(list, true)
           if (cols != null) {
             tableDesc = tableDesc.copy(
-              schema = cols.map { field =>
+              schema = cols.asScala.map { field =>
                 HiveColumn(field.getName, field.getType, field.getComment)
               })
           }
@@ -636,7 +639,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
           val cols = BaseSemanticAnalyzer.getColumns(list(0), false)
           if (cols != null) {
             tableDesc = tableDesc.copy(
-              partitionColumns = cols.map { field =>
+              partitionColumns = cols.asScala.map { field =>
                 HiveColumn(field.getName, field.getType, field.getComment)
               })
           }
@@ -672,7 +675,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             case _ => assert(false)
           }
           tableDesc = tableDesc.copy(
-            serdeProperties = tableDesc.serdeProperties ++ serdeParams)
+            serdeProperties = tableDesc.serdeProperties ++ serdeParams.asScala)
         case Token("TOK_TABLELOCATION", child :: Nil) =>
           var location = BaseSemanticAnalyzer.unescapeSQLString(child.getText)
           location = EximUtil.relativeToAbsolutePath(hiveConf, location)
@@ -684,7 +687,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             val serdeParams = new java.util.HashMap[String, String]()
             BaseSemanticAnalyzer.readProps(
               (child.getChild(1).getChild(0)).asInstanceOf[ASTNode], serdeParams)
-            tableDesc = tableDesc.copy(serdeProperties = tableDesc.serdeProperties ++ serdeParams)
+            tableDesc = tableDesc.copy(
+              serdeProperties = tableDesc.serdeProperties ++ serdeParams.asScala)
           }
         case Token("TOK_FILEFORMAT_GENERIC", child :: Nil) =>
           child.getText().toLowerCase(Locale.ENGLISH) match {
@@ -847,7 +851,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         }
 
         val withWhere = whereClause.map { whereNode =>
-          val Seq(whereExpr) = whereNode.getChildren.toSeq
+          val Seq(whereExpr) = whereNode.getChildren.asScala
           Filter(nodeToExpr(whereExpr), relations)
         }.getOrElse(relations)
 
@@ -856,7 +860,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         // Script transformations are expressed as a select clause with a single expression of type
         // TOK_TRANSFORM
-        val transformation = select.getChildren.head match {
+        val transformation = select.getChildren.iterator().next() match {
           case Token("TOK_SELEXPR",
                  Token("TOK_TRANSFORM",
                    Token("TOK_EXPLIST", inputExprs) ::
@@ -925,10 +929,10 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         val withLateralView = lateralViewClause.map { lv =>
           val Token("TOK_SELECT",
-          Token("TOK_SELEXPR", clauses) :: Nil) = lv.getChildren.head
+          Token("TOK_SELEXPR", clauses) :: Nil) = lv.getChildren.iterator().next()
 
-          val alias =
-            getClause("TOK_TABALIAS", clauses).getChildren.head.asInstanceOf[ASTNode].getText
+          val alias = getClause("TOK_TABALIAS", clauses).getChildren.iterator().next()
+            .asInstanceOf[ASTNode].getText
 
           val (generator, attributes) = nodesToGenerator(clauses)
             Generate(
@@ -944,7 +948,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         // (if there is a group by) or a script transformation.
         val withProject: LogicalPlan = transformation.getOrElse {
           val selectExpressions =
-            select.getChildren.flatMap(selExprNodeToExpr).map(UnresolvedAlias(_)).toSeq
+            select.getChildren.asScala.flatMap(selExprNodeToExpr).map(UnresolvedAlias)
           Seq(
             groupByClause.map(e => e match {
               case Token("TOK_GROUPBY", children) =>
@@ -973,7 +977,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         // Handle HAVING clause.
         val withHaving = havingClause.map { h =>
-          val havingExpr = h.getChildren.toSeq match { case Seq(hexpr) => nodeToExpr(hexpr) }
+          val havingExpr = h.getChildren.asScala match { case Seq(hexpr) => nodeToExpr(hexpr) }
           // Note that we added a cast to boolean. If the expression itself is already boolean,
           // the optimizer will get rid of the unnecessary cast.
           Filter(Cast(havingExpr, BooleanType), withProject)
@@ -983,32 +987,42 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         val withDistinct =
           if (selectDistinctClause.isDefined) Distinct(withHaving) else withHaving
 
-        // Handle ORDER BY, SORT BY, DISTRIBETU BY, and CLUSTER BY clause.
+        // Handle ORDER BY, SORT BY, DISTRIBUTE BY, and CLUSTER BY clause.
         val withSort =
           (orderByClause, sortByClause, distributeByClause, clusterByClause) match {
             case (Some(totalOrdering), None, None, None) =>
-              Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withDistinct)
+              Sort(totalOrdering.getChildren.asScala.map(nodeToSortOrder), true, withDistinct)
             case (None, Some(perPartitionOrdering), None, None) =>
-              Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withDistinct)
+              Sort(
+                perPartitionOrdering.getChildren.asScala.map(nodeToSortOrder),
+                false, withDistinct)
             case (None, None, Some(partitionExprs), None) =>
-              RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct)
+              RepartitionByExpression(
+                partitionExprs.getChildren.asScala.map(nodeToExpr), withDistinct)
             case (None, Some(perPartitionOrdering), Some(partitionExprs), None) =>
-              Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false,
-                RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct))
+              Sort(
+                perPartitionOrdering.getChildren.asScala.map(nodeToSortOrder), false,
+                RepartitionByExpression(
+                  partitionExprs.getChildren.asScala.map(nodeToExpr),
+                  withDistinct))
             case (None, None, None, Some(clusterExprs)) =>
-              Sort(clusterExprs.getChildren.map(nodeToExpr).map(SortOrder(_, Ascending)), false,
-                RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withDistinct))
+              Sort(
+                clusterExprs.getChildren.asScala.map(nodeToExpr).map(SortOrder(_, Ascending)),
+                false,
+                RepartitionByExpression(
+                  clusterExprs.getChildren.asScala.map(nodeToExpr),
+                  withDistinct))
             case (None, None, None, None) => withDistinct
             case _ => sys.error("Unsupported set of ordering / distribution clauses.")
           }
 
         val withLimit =
-          limitClause.map(l => nodeToExpr(l.getChildren.head))
+          limitClause.map(l => nodeToExpr(l.getChildren.iterator().next()))
             .map(Limit(_, withSort))
             .getOrElse(withSort)
 
         // Collect all window specifications defined in the WINDOW clause.
-        val windowDefinitions = windowClause.map(_.getChildren.toSeq.collect {
+        val windowDefinitions = windowClause.map(_.getChildren.asScala.collect {
           case Token("TOK_WINDOWDEF",
           Token(windowName, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) =>
             windowName -> nodesToWindowSpecification(spec)
@@ -1063,7 +1077,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       val Token("TOK_SELECT",
             Token("TOK_SELEXPR", clauses) :: Nil) = selectClause
 
-      val alias = getClause("TOK_TABALIAS", clauses).getChildren.head.asInstanceOf[ASTNode].getText
+      val alias = getClause("TOK_TABALIAS", clauses).getChildren.iterator().next()
+        .asInstanceOf[ASTNode].getText
 
       val (generator, attributes) = nodesToGenerator(clauses)
         Generate(
@@ -1092,7 +1107,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       }
 
       val tableIdent =
-        tableNameParts.getChildren.map{ case Token(part, Nil) => cleanIdentifier(part)} match {
+        tableNameParts.getChildren.asScala.map {
+          case Token(part, Nil) => cleanIdentifier(part)
+        } match {
           case Seq(tableOnly) => Seq(tableOnly)
           case Seq(databaseName, table) => Seq(databaseName, table)
           case other => sys.error("Hive only supports tables names like 'tableName' " +
@@ -1139,7 +1156,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
       val isPreserved = tableOrdinals.map(i => (i - 1 < 0) || joinArgs(i - 1).getText == "PRESERVE")
       val tables = tableOrdinals.map(i => nodeToRelation(joinArgs(i)))
-      val joinExpressions = tableOrdinals.map(i => joinArgs(i + 1).getChildren.map(nodeToExpr))
+      val joinExpressions =
+        tableOrdinals.map(i => joinArgs(i + 1).getChildren.asScala.map(nodeToExpr))
 
       val joinConditions = joinExpressions.sliding(2).map {
         case Seq(c1, c2) =>
@@ -1164,7 +1182,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             joinType = joinType.remove(joinType.length - 1))
       }
 
-      val groups = (0 until joinExpressions.head.size).map(i => Coalesce(joinExpressions.map(_(i))))
+      val groups = joinExpressions.head.indices.map(i => Coalesce(joinExpressions.map(_(i))))
 
       // Unique join is not really the same as an outer join so we must group together results where
       // the joinExpressions are the same, taking the First of each value is only okay because the
@@ -1229,7 +1247,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
       val tableIdent = extractTableIdent(tableNameParts)
 
-      val partitionKeys = partitionClause.map(_.getChildren.map {
+      val partitionKeys = partitionClause.map(_.getChildren.asScala.map {
         // Parse partitions. We also make keys case insensitive.
         case Token("TOK_PARTVAL", Token(key, Nil) :: Token(value, Nil) :: Nil) =>
           cleanIdentifier(key.toLowerCase) -> Some(PlanUtils.stripQuotes(value))
@@ -1249,7 +1267,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
       val tableIdent = extractTableIdent(tableNameParts)
 
-      val partitionKeys = partitionClause.map(_.getChildren.map {
+      val partitionKeys = partitionClause.map(_.getChildren.asScala.map {
         // Parse partitions. We also make keys case insensitive.
         case Token("TOK_PARTVAL", Token(key, Nil) :: Token(value, Nil) :: Nil) =>
           cleanIdentifier(key.toLowerCase) -> Some(PlanUtils.stripQuotes(value))
@@ -1590,18 +1608,18 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         val (partitionByClause :: orderByClause :: sortByClause :: clusterByClause :: Nil) =
           getClauses(
             Seq("TOK_DISTRIBUTEBY", "TOK_ORDERBY", "TOK_SORTBY", "TOK_CLUSTERBY"),
-            partitionAndOrdering.getChildren.toSeq.asInstanceOf[Seq[ASTNode]])
+            partitionAndOrdering.getChildren.asScala.asInstanceOf[Seq[ASTNode]])
 
         (partitionByClause, orderByClause.orElse(sortByClause), clusterByClause) match {
           case (Some(partitionByExpr), Some(orderByExpr), None) =>
-            (partitionByExpr.getChildren.map(nodeToExpr),
-              orderByExpr.getChildren.map(nodeToSortOrder))
+            (partitionByExpr.getChildren.asScala.map(nodeToExpr),
+              orderByExpr.getChildren.asScala.map(nodeToSortOrder))
           case (Some(partitionByExpr), None, None) =>
-            (partitionByExpr.getChildren.map(nodeToExpr), Nil)
+            (partitionByExpr.getChildren.asScala.map(nodeToExpr), Nil)
           case (None, Some(orderByExpr), None) =>
-            (Nil, orderByExpr.getChildren.map(nodeToSortOrder))
+            (Nil, orderByExpr.getChildren.asScala.map(nodeToSortOrder))
           case (None, None, Some(clusterByExpr)) =>
-            val expressions = clusterByExpr.getChildren.map(nodeToExpr)
+            val expressions = clusterByExpr.getChildren.asScala.map(nodeToExpr)
             (expressions, expressions.map(SortOrder(_, Ascending)))
           case _ =>
             throw new NotImplementedError(
@@ -1639,7 +1657,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
           }
 
           rowFrame.orElse(rangeFrame).map { frame =>
-            frame.getChildren.toList match {
+            frame.getChildren.asScala.toList match {
               case precedingNode :: followingNode :: Nil =>
                 SpecifiedWindowFrame(
                   frameType,
@@ -1701,7 +1719,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       case other => sys.error(s"Non ASTNode encountered: $other")
     }
 
-    Option(node.getChildren).map(_.toList).getOrElse(Nil).foreach(dumpTree(_, builder, indent + 1))
+    Option(node.getChildren).map(_.asScala).getOrElse(Nil).foreach(dumpTree(_, builder, indent + 1))
     builder
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
index 267074f3ad102..004805f3aed0b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -22,8 +22,7 @@ import java.rmi.server.UID
 
 import org.apache.avro.Schema
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -73,7 +72,7 @@ private[hive] object HiveShim {
    */
   def appendReadColumns(conf: Configuration, ids: Seq[Integer], names: Seq[String]) {
     if (ids != null && ids.nonEmpty) {
-      ColumnProjectionUtils.appendReadColumns(conf, ids)
+      ColumnProjectionUtils.appendReadColumns(conf, ids.asJava)
     }
     if (names != null && names.nonEmpty) {
       appendReadColumnNames(conf, names)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index f49c97de8ff4e..4d1e3ed9198e6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -21,7 +21,7 @@ import java.io.{File, PrintStream}
 import java.util.{Map => JMap}
 import javax.annotation.concurrent.GuardedBy
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.reflectiveCalls
 
 import org.apache.hadoop.fs.Path
@@ -305,10 +305,11 @@ private[hive] class ClientWrapper(
       HiveTable(
         name = h.getTableName,
         specifiedDatabase = Option(h.getDbName),
-        schema = h.getCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
-        partitionColumns = h.getPartCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
-        properties = h.getParameters.toMap,
-        serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap,
+        schema = h.getCols.asScala.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
+        partitionColumns = h.getPartCols.asScala.map(f =>
+          HiveColumn(f.getName, f.getType, f.getComment)),
+        properties = h.getParameters.asScala.toMap,
+        serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.asScala.toMap,
         tableType = h.getTableType match {
           case HTableType.MANAGED_TABLE => ManagedTable
           case HTableType.EXTERNAL_TABLE => ExternalTable
@@ -334,9 +335,9 @@ private[hive] class ClientWrapper(
   private def toQlTable(table: HiveTable): metadata.Table = {
     val qlTable = new metadata.Table(table.database, table.name)
 
-    qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+    qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
     qlTable.setPartCols(
-      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
     table.properties.foreach { case (k, v) => qlTable.setProperty(k, v) }
     table.serdeProperties.foreach { case (k, v) => qlTable.setSerdeParam(k, v) }
 
@@ -366,13 +367,13 @@ private[hive] class ClientWrapper(
   private def toHivePartition(partition: metadata.Partition): HivePartition = {
     val apiPartition = partition.getTPartition
     HivePartition(
-      values = Option(apiPartition.getValues).map(_.toSeq).getOrElse(Seq.empty),
+      values = Option(apiPartition.getValues).map(_.asScala).getOrElse(Seq.empty),
       storage = HiveStorageDescriptor(
         location = apiPartition.getSd.getLocation,
         inputFormat = apiPartition.getSd.getInputFormat,
         outputFormat = apiPartition.getSd.getOutputFormat,
         serde = apiPartition.getSd.getSerdeInfo.getSerializationLib,
-        serdeProperties = apiPartition.getSd.getSerdeInfo.getParameters.toMap))
+        serdeProperties = apiPartition.getSd.getSerdeInfo.getParameters.asScala.toMap))
   }
 
   override def getPartitionOption(
@@ -397,7 +398,7 @@ private[hive] class ClientWrapper(
   }
 
   override def listTables(dbName: String): Seq[String] = withHiveState {
-    client.getAllTables(dbName)
+    client.getAllTables(dbName).asScala
   }
 
   /**
@@ -514,17 +515,17 @@ private[hive] class ClientWrapper(
   }
 
   def reset(): Unit = withHiveState {
-    client.getAllTables("default").foreach { t =>
+    client.getAllTables("default").asScala.foreach { t =>
         logDebug(s"Deleting table $t")
         val table = client.getTable("default", t)
-        client.getIndexes("default", t, 255).foreach { index =>
+        client.getIndexes("default", t, 255).asScala.foreach { index =>
           shim.dropIndex(client, "default", t, index.getIndexName)
         }
         if (!table.isIndexTable) {
           client.dropTable("default", t)
         }
       }
-      client.getAllDatabases.filterNot(_ == "default").foreach { db =>
+      client.getAllDatabases.asScala.filterNot(_ == "default").foreach { db =>
         logDebug(s"Dropping Database: $db")
         client.dropDatabase(db, true, false, true)
       }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 8fc8935b1dc3c..48bbb21e6c1de 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -23,7 +23,7 @@ import java.net.URI
 import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, Set => JSet}
 import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.hive.conf.HiveConf
@@ -201,7 +201,7 @@ private[client] class Shim_v0_12 extends Shim with Logging {
     setDataLocationMethod.invoke(table, new URI(loc))
 
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
-    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
+    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].asScala.toSeq
 
   override def getPartitionsByFilter(
       hive: Hive,
@@ -220,7 +220,7 @@ private[client] class Shim_v0_12 extends Shim with Logging {
   override def getDriverResults(driver: Driver): Seq[String] = {
     val res = new JArrayList[String]()
     getDriverResultsMethod.invoke(driver, res)
-    res.toSeq
+    res.asScala
   }
 
   override def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long = {
@@ -310,7 +310,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
     setDataLocationMethod.invoke(table, new Path(loc))
 
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
-    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
+    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].asScala.toSeq
 
   /**
    * Converts catalyst expression to the format that Hive's getPartitionsByFilter() expects, i.e.
@@ -320,7 +320,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
    */
   def convertFilters(table: Table, filters: Seq[Expression]): String = {
     // hive varchar is treated as catalyst string, but hive varchar can't be pushed down.
-    val varcharKeys = table.getPartitionKeys
+    val varcharKeys = table.getPartitionKeys.asScala
       .filter(col => col.getType.startsWith(serdeConstants.VARCHAR_TYPE_NAME))
       .map(col => col.getName).toSet
 
@@ -354,7 +354,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
         getPartitionsByFilterMethod.invoke(hive, table, filter).asInstanceOf[JArrayList[Partition]]
       }
 
-    partitions.toSeq
+    partitions.asScala.toSeq
   }
 
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
@@ -363,7 +363,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
   override def getDriverResults(driver: Driver): Seq[String] = {
     val res = new JArrayList[Object]()
     getDriverResultsMethod.invoke(driver, res)
-    res.map { r =>
+    res.asScala.map { r =>
       r match {
         case s: String => s
         case a: Array[Object] => a(0).asInstanceOf[String]
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
index 5f0ed5393d191..441b6b6033e1f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.execution
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 
@@ -39,8 +39,8 @@ case class DescribeHiveTableCommand(
     // Trying to mimic the format of Hive's output. But not exactly the same.
     var results: Seq[(String, String, String)] = Nil
 
-    val columns: Seq[FieldSchema] = table.hiveQlTable.getCols
-    val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols
+    val columns: Seq[FieldSchema] = table.hiveQlTable.getCols.asScala
+    val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols.asScala
     results ++= columns.map(field => (field.getName, field.getType, field.getComment))
     if (partitionColumns.nonEmpty) {
       val partColumnInfo =
@@ -48,7 +48,7 @@ case class DescribeHiveTableCommand(
       results ++=
         partColumnInfo ++
           Seq(("# Partition Information", "", "")) ++
-          Seq((s"# ${output.get(0).name}", output.get(1).name, output.get(2).name)) ++
+          Seq((s"# ${output(0).name}", output(1).name, output(2).name)) ++
           partColumnInfo
     }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index ba7eb15a1c0c6..806d2b9b0b7d4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.execution
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition}
@@ -98,7 +98,7 @@ case class HiveTableScan(
       .asInstanceOf[StructObjectInspector]
 
     val columnTypeNames = structOI
-      .getAllStructFieldRefs
+      .getAllStructFieldRefs.asScala
       .map(_.getFieldObjectInspector)
       .map(TypeInfoUtils.getTypeInfoFromObjectInspector(_).getTypeName)
       .mkString(",")
@@ -118,9 +118,8 @@ case class HiveTableScan(
       case None => partitions
       case Some(shouldKeep) => partitions.filter { part =>
         val dataTypes = relation.partitionKeys.map(_.dataType)
-        val castedValues = for ((value, dataType) <- part.getValues.zip(dataTypes)) yield {
-          castFromString(value, dataType)
-        }
+        val castedValues = part.getValues.asScala.zip(dataTypes)
+          .map { case (value, dataType) => castFromString(value, dataType) }
 
         // Only partitioned values are needed here, since the predicate has already been bound to
         // partition key attribute references.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 62efda613a176..58f7fa640e8a9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -19,9 +19,10 @@ package org.apache.spark.sql.hive.execution
 
 import java.util
 
+import scala.collection.JavaConverters._
+
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.hadoop.hive.metastore.MetaStoreUtils
 import org.apache.hadoop.hive.ql.plan.TableDesc
 import org.apache.hadoop.hive.ql.{Context, ErrorMsg}
 import org.apache.hadoop.hive.serde2.Serializer
@@ -38,8 +39,6 @@ import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc}
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.{SparkException, TaskContext}
-
-import scala.collection.JavaConversions._
 import org.apache.spark.util.SerializableJobConf
 
 private[hive]
@@ -94,7 +93,8 @@ case class InsertIntoHiveTable(
           ObjectInspectorCopyOption.JAVA)
         .asInstanceOf[StructObjectInspector]
 
-      val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector).toArray
+      val fieldOIs = standardOI.getAllStructFieldRefs.asScala
+        .map(_.getFieldObjectInspector).toArray
       val dataTypes: Array[DataType] = child.output.map(_.dataType).toArray
       val wrappers = fieldOIs.zip(dataTypes).map { case (f, dt) => wrapperFor(f, dt)}
       val outputData = new Array[Any](fieldOIs.length)
@@ -198,7 +198,7 @@ case class InsertIntoHiveTable(
 
       // loadPartition call orders directories created on the iteration order of the this map
       val orderedPartitionSpec = new util.LinkedHashMap[String, String]()
-      table.hiveQlTable.getPartCols().foreach { entry =>
+      table.hiveQlTable.getPartCols.asScala.foreach { entry =>
         orderedPartitionSpec.put(entry.getName, partitionSpec.get(entry.getName).getOrElse(""))
       }
 
@@ -226,7 +226,7 @@ case class InsertIntoHiveTable(
         val oldPart =
           catalog.client.getPartitionOption(
             catalog.client.getTable(table.databaseName, table.tableName),
-            partitionSpec)
+            partitionSpec.asJava)
 
         if (oldPart.isEmpty || !ifNotExists) {
             catalog.client.loadPartition(
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index ade27454b9d29..c7651daffe36e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -21,7 +21,7 @@ import java.io._
 import java.util.Properties
 import javax.annotation.Nullable
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.control.NonFatal
 
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -61,7 +61,7 @@ case class ScriptTransformation(
   protected override def doExecute(): RDD[InternalRow] = {
     def processIterator(inputIterator: Iterator[InternalRow]): Iterator[InternalRow] = {
       val cmd = List("/bin/bash", "-c", script)
-      val builder = new ProcessBuilder(cmd)
+      val builder = new ProcessBuilder(cmd.asJava)
 
       val proc = builder.start()
       val inputStream = proc.getInputStream
@@ -172,10 +172,10 @@ case class ScriptTransformation(
             val fieldList = outputSoi.getAllStructFieldRefs()
             var i = 0
             while (i < dataList.size()) {
-              if (dataList(i) == null) {
+              if (dataList.get(i) == null) {
                 mutableRow.setNullAt(i)
               } else {
-                mutableRow(i) = unwrap(dataList(i), fieldList(i).getFieldObjectInspector)
+                mutableRow(i) = unwrap(dataList.get(i), fieldList.get(i).getFieldObjectInspector)
               }
               i += 1
             }
@@ -307,7 +307,7 @@ case class HiveScriptIOSchema (
       val serde = initSerDe(serdeClass, columns, columnTypes, inputSerdeProps)
       val fieldObjectInspectors = columnTypes.map(toInspector)
       val objectInspector = ObjectInspectorFactory
-        .getStandardStructObjectInspector(columns, fieldObjectInspectors)
+        .getStandardStructObjectInspector(columns.asJava, fieldObjectInspectors.asJava)
         .asInstanceOf[ObjectInspector]
       (serde, objectInspector)
     }
@@ -342,7 +342,7 @@ case class HiveScriptIOSchema (
     propsMap = propsMap + (serdeConstants.LIST_COLUMN_TYPES -> columnTypesNames)
 
     val properties = new Properties()
-    properties.putAll(propsMap)
+    properties.putAll(propsMap.asJava)
     serde.initialize(null, properties)
 
     serde
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 7182246e466a4..cad02373e5ba1 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive
 
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Try
 
 import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ConstantObjectInspector}
@@ -81,8 +81,7 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
 
   /* List all of the registered function names. */
   override def listFunction(): Seq[String] = {
-    val a = FunctionRegistry.getFunctionNames ++ underlying.listFunction()
-    a.toList.sorted
+    (FunctionRegistry.getFunctionNames.asScala ++ underlying.listFunction()).toList.sorted
   }
 
   /* Get the class of the registered function by specified name. */
@@ -116,7 +115,7 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
 
   @transient
   private lazy val method =
-    function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo))
+    function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo).asJava)
 
   @transient
   private lazy val arguments = children.map(toInspector).toArray
@@ -541,7 +540,7 @@ private[hive] case class HiveGenericUDTF(
   @transient
   protected lazy val collector = new UDTFCollector
 
-  lazy val elementTypes = outputInspector.getAllStructFieldRefs.map {
+  lazy val elementTypes = outputInspector.getAllStructFieldRefs.asScala.map {
     field => (inspectorToDataType(field.getFieldObjectInspector), true)
   }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 9f4f8b5789afe..1cff5cf9c3543 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.hive.orc
 
 import java.util.Properties
 
+import scala.collection.JavaConverters._
+
 import com.google.common.base.Objects
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
@@ -43,9 +45,6 @@ import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{Row, SQLContext}
 import org.apache.spark.util.SerializableConfiguration
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
   override def shortName(): String = "orc"
@@ -97,7 +96,8 @@ private[orc] class OrcOutputWriter(
   private val reusableOutputBuffer = new Array[Any](dataSchema.length)
 
   // Used to convert Catalyst values into Hadoop `Writable`s.
-  private val wrappers = structOI.getAllStructFieldRefs.zip(dataSchema.fields.map(_.dataType))
+  private val wrappers = structOI.getAllStructFieldRefs.asScala
+    .zip(dataSchema.fields.map(_.dataType))
     .map { case (ref, dt) =>
       wrapperFor(ref.getFieldObjectInspector, dt)
     }.toArray
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 4da86636ac100..572eaebe81ac2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.hive.test
 import java.io.File
 import java.util.{Set => JavaSet}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.language.implicitConversions
 
@@ -37,9 +38,6 @@ import org.apache.spark.sql.hive.execution.HiveNativeCommand
 import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.{SparkConf, SparkContext}
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 // SPARK-3729: Test key required to check for initialization errors with config.
 object TestHive
   extends TestHiveContext(
@@ -405,7 +403,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   def reset() {
     try {
       // HACK: Hive is too noisy by default.
-      org.apache.log4j.LogManager.getCurrentLoggers.foreach { log =>
+      org.apache.log4j.LogManager.getCurrentLoggers.asScala.foreach { log =>
         log.asInstanceOf[org.apache.log4j.Logger].setLevel(org.apache.log4j.Level.WARN)
       }
 
@@ -415,9 +413,8 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       catalog.client.reset()
       catalog.unregisterAllTables()
 
-      FunctionRegistry.getFunctionNames.filterNot(originalUDFs.contains(_)).foreach { udfName =>
-        FunctionRegistry.unregisterTemporaryUDF(udfName)
-      }
+      FunctionRegistry.getFunctionNames.asScala.filterNot(originalUDFs.contains(_)).
+        foreach { udfName => FunctionRegistry.unregisterTemporaryUDF(udfName) }
 
       // Some tests corrupt this value on purpose, which breaks the RESET call below.
       hiveconf.set("fs.default.name", new File(".").toURI.toString)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
index 0efcf80bd4ea7..5e7b93d457106 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.client
 
-import scala.collection.JavaConversions._
+import java.util.Collections
 
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -38,7 +38,7 @@ class FiltersSuite extends SparkFunSuite with Logging {
   private val varCharCol = new FieldSchema()
   varCharCol.setName("varchar")
   varCharCol.setType(serdeConstants.VARCHAR_TYPE_NAME)
-  testTable.setPartCols(varCharCol :: Nil)
+  testTable.setPartCols(Collections.singletonList(varCharCol))
 
   filterTest("string filter",
     (a("stringcol", StringType) > Literal("test")) :: Nil,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index b03a35132325d..9c10ffe1113dc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.hive.execution
 
 import java.io.{DataInput, DataOutput}
-import java.util
-import java.util.Properties
+import java.util.{ArrayList, Arrays, Properties}
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.hive.ql.udf.generic.{GenericUDAFAverage, GenericUDF}
@@ -33,8 +32,6 @@ import org.apache.spark.sql.hive.test.TestHive
 
 import org.apache.spark.util.Utils
 
-import scala.collection.JavaConversions._
-
 case class Fields(f1: Int, f2: Int, f3: Int, f4: Int, f5: Int)
 
 // Case classes for the custom UDF's.
@@ -326,11 +323,11 @@ class PairSerDe extends AbstractSerDe {
   override def getObjectInspector: ObjectInspector = {
     ObjectInspectorFactory
       .getStandardStructObjectInspector(
-        Seq("pair"),
-        Seq(ObjectInspectorFactory.getStandardStructObjectInspector(
-          Seq("id", "value"),
-          Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
-              PrimitiveObjectInspectorFactory.javaIntObjectInspector))
+        Arrays.asList("pair"),
+        Arrays.asList(ObjectInspectorFactory.getStandardStructObjectInspector(
+          Arrays.asList("id", "value"),
+          Arrays.asList(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
+                        PrimitiveObjectInspectorFactory.javaIntObjectInspector))
     ))
   }
 
@@ -343,10 +340,10 @@ class PairSerDe extends AbstractSerDe {
   override def deserialize(value: Writable): AnyRef = {
     val pair = value.asInstanceOf[TestPair]
 
-    val row = new util.ArrayList[util.ArrayList[AnyRef]]
-    row.add(new util.ArrayList[AnyRef](2))
-    row(0).add(Integer.valueOf(pair.entry._1))
-    row(0).add(Integer.valueOf(pair.entry._2))
+    val row = new ArrayList[ArrayList[AnyRef]]
+    row.add(new ArrayList[AnyRef](2))
+    row.get(0).add(Integer.valueOf(pair.entry._1))
+    row.get(0).add(Integer.valueOf(pair.entry._2))
 
     row
   }
@@ -355,9 +352,9 @@ class PairSerDe extends AbstractSerDe {
 class PairUDF extends GenericUDF {
   override def initialize(p1: Array[ObjectInspector]): ObjectInspector =
     ObjectInspectorFactory.getStandardStructObjectInspector(
-      Seq("id", "value"),
-      Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
-        PrimitiveObjectInspectorFactory.javaIntObjectInspector)
+      Arrays.asList("id", "value"),
+      Arrays.asList(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
+                    PrimitiveObjectInspectorFactory.javaIntObjectInspector)
   )
 
   override def evaluate(args: Array[DeferredObject]): AnyRef = {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index 3bf8f3ac20480..210d566745415 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -17,13 +17,12 @@
 
 package org.apache.spark.sql.hive.execution
 
+import scala.collection.JavaConverters._
+
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.hive.test.TestHive
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 /**
  * A set of test cases that validate partition and column pruning.
  */
@@ -161,7 +160,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
       assert(actualOutputColumns === expectedOutputColumns, "Output columns mismatch")
       assert(actualScannedColumns === expectedScannedColumns, "Scanned columns mismatch")
 
-      val actualPartitions = actualPartValues.map(_.toSeq.mkString(",")).sorted
+      val actualPartitions = actualPartValues.map(_.asScala.mkString(",")).sorted
       val expectedPartitions = expectedPartValues.map(_.mkString(",")).sorted
 
       assert(actualPartitions === expectedPartitions, "Partitions selected do not match")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 55ecbd5b5f21d..1ff1d9a2934cc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.execution
 
 import java.sql.{Date, Timestamp}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.DefaultParserDialect
@@ -164,7 +164,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   test("show functions") {
     val allFunctions =
       (FunctionRegistry.builtin.listFunction().toSet[String] ++
-        org.apache.hadoop.hive.ql.exec.FunctionRegistry.getFunctionNames).toList.sorted
+        org.apache.hadoop.hive.ql.exec.FunctionRegistry.getFunctionNames.asScala).toList.sorted
     checkAnswer(sql("SHOW functions"), allFunctions.map(Row(_)))
     checkAnswer(sql("SHOW functions abs"), Row("abs"))
     checkAnswer(sql("SHOW functions 'abs'"), Row("abs"))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 5bbca14bad320..7966b43596e75 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -17,9 +17,7 @@
 
 package org.apache.spark.sql.sources
 
-import java.sql.Date
-
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
@@ -552,7 +550,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -600,7 +598,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
       sqlContext.sparkContext.conf.set("spark.speculation", speculationEnabled.toString)
     }
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index 214cd80108b9b..edfa474677f15 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.streaming.api.java
 
-import java.util
 import java.lang.{Long => JLong}
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -145,8 +144,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * an array.
    */
   def glom(): JavaDStream[JList[T]] =
-    new JavaDStream(dstream.glom().map(x => new java.util.ArrayList[T](x.toSeq)))
-
+    new JavaDStream(dstream.glom().map(_.toSeq.asJava))
 
 
   /** Return the [[org.apache.spark.streaming.StreamingContext]] associated with this DStream */
@@ -191,7 +189,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    */
   def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaDStream[U] = {
     def fn: (Iterator[T]) => Iterator[U] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaDStream(dstream.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U])
   }
@@ -204,7 +202,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
   def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2])
   : JavaPairDStream[K2, V2] = {
     def fn: (Iterator[T]) => Iterator[(K2, V2)] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaPairDStream(dstream.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2])
   }
@@ -282,7 +280,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
    */
   def slice(fromTime: Time, toTime: Time): JList[R] = {
-    new util.ArrayList(dstream.slice(fromTime, toTime).map(wrapRDD(_)).toSeq)
+    dstream.slice(fromTime, toTime).map(wrapRDD).asJava
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
index 26383e420101e..e2aec6c2f63e7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.api.java
 import java.lang.{Long => JLong, Iterable => JIterable}
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -116,14 +116,14 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    * generate the RDDs with Spark's default number of partitions.
    */
   def groupByKey(): JavaPairDStream[K, JIterable[V]] =
-    dstream.groupByKey().mapValues(asJavaIterable _)
+    dstream.groupByKey().mapValues(_.asJava)
 
   /**
    * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to
    * generate the RDDs with `numPartitions` partitions.
    */
   def groupByKey(numPartitions: Int): JavaPairDStream[K, JIterable[V]] =
-    dstream.groupByKey(numPartitions).mapValues(asJavaIterable _)
+    dstream.groupByKey(numPartitions).mapValues(_.asJava)
 
   /**
    * Return a new DStream by applying `groupByKey` on each RDD of `this` DStream.
@@ -132,7 +132,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    * is used to control the partitioning of each RDD.
    */
   def groupByKey(partitioner: Partitioner): JavaPairDStream[K, JIterable[V]] =
-    dstream.groupByKey(partitioner).mapValues(asJavaIterable _)
+    dstream.groupByKey(partitioner).mapValues(_.asJava)
 
   /**
    * Return a new DStream by applying `reduceByKey` to each RDD. The values for each key are
@@ -197,7 +197,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    *                       batching interval
    */
   def groupByKeyAndWindow(windowDuration: Duration): JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration).mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration).mapValues(_.asJava)
   }
 
   /**
@@ -212,7 +212,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    */
   def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration)
   : JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration, slideDuration).mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration, slideDuration).mapValues(_.asJava)
   }
 
   /**
@@ -228,8 +228,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    */
   def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int)
     : JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration, slideDuration, numPartitions)
-      .mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration, slideDuration, numPartitions).mapValues(_.asJava)
   }
 
   /**
@@ -248,8 +247,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
       slideDuration: Duration,
       partitioner: Partitioner
     ): JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration, slideDuration, partitioner)
-      .mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration, slideDuration, partitioner).mapValues(_.asJava)
   }
 
   /**
@@ -431,7 +429,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
   private def convertUpdateStateFunction[S](in: JFunction2[JList[V], Optional[S], Optional[S]]):
   (Seq[V], Option[S]) => Option[S] = {
     val scalaFunc: (Seq[V], Option[S]) => Option[S] = (values, state) => {
-      val list: JList[V] = values
+      val list: JList[V] = values.asJava
       val scalaState: Optional[S] = JavaUtils.optionToOptional(state)
       val result: Optional[S] = in.apply(list, scalaState)
       result.isPresent match {
@@ -539,7 +537,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    */
   def cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (JIterable[V], JIterable[W])] = {
     implicit val cm: ClassTag[W] = fakeClassTag
-    dstream.cogroup(other.dstream).mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2))))
+    dstream.cogroup(other.dstream).mapValues(t => (t._1.asJava, t._2.asJava))
   }
 
   /**
@@ -551,8 +549,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
       numPartitions: Int
     ): JavaPairDStream[K, (JIterable[V], JIterable[W])] = {
     implicit val cm: ClassTag[W] = fakeClassTag
-    dstream.cogroup(other.dstream, numPartitions)
-           .mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2))))
+    dstream.cogroup(other.dstream, numPartitions).mapValues(t => (t._1.asJava, t._2.asJava))
   }
 
   /**
@@ -564,8 +561,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
       partitioner: Partitioner
     ): JavaPairDStream[K, (JIterable[V], JIterable[W])] = {
     implicit val cm: ClassTag[W] = fakeClassTag
-    dstream.cogroup(other.dstream, partitioner)
-           .mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2))))
+    dstream.cogroup(other.dstream, partitioner).mapValues(t => (t._1.asJava, t._2.asJava))
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 35cc3ce5cf468..13f371f29603a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -21,7 +21,7 @@ import java.lang.{Boolean => JBoolean}
 import java.io.{Closeable, InputStream}
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import akka.actor.{Props, SupervisorStrategy}
@@ -115,7 +115,13 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     sparkHome: String,
     jars: Array[String],
     environment: JMap[String, String]) =
-    this(new StreamingContext(master, appName, batchDuration, sparkHome, jars, environment))
+    this(new StreamingContext(
+      master,
+      appName,
+      batchDuration,
+      sparkHome,
+      jars,
+      environment.asScala))
 
   /**
    * Create a JavaStreamingContext using an existing JavaSparkContext.
@@ -197,7 +203,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
       converter: JFunction[InputStream, java.lang.Iterable[T]],
       storageLevel: StorageLevel)
   : JavaReceiverInputDStream[T] = {
-    def fn: (InputStream) => Iterator[T] = (x: InputStream) => converter.call(x).toIterator
+    def fn: (InputStream) => Iterator[T] = (x: InputStream) => converter.call(x).iterator().asScala
     implicit val cmt: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     ssc.socketStream(hostname, port, fn, storageLevel)
@@ -432,7 +438,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     val sQueue = new scala.collection.mutable.Queue[RDD[T]]
-    sQueue.enqueue(queue.map(_.rdd).toSeq: _*)
+    sQueue.enqueue(queue.asScala.map(_.rdd).toSeq: _*)
     ssc.queueStream(sQueue)
   }
 
@@ -456,7 +462,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     val sQueue = new scala.collection.mutable.Queue[RDD[T]]
-    sQueue.enqueue(queue.map(_.rdd).toSeq: _*)
+    sQueue.enqueue(queue.asScala.map(_.rdd).toSeq: _*)
     ssc.queueStream(sQueue, oneAtATime)
   }
 
@@ -481,7 +487,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     val sQueue = new scala.collection.mutable.Queue[RDD[T]]
-    sQueue.enqueue(queue.map(_.rdd).toSeq: _*)
+    sQueue.enqueue(queue.asScala.map(_.rdd).toSeq: _*)
     ssc.queueStream(sQueue, oneAtATime, defaultRDD.rdd)
   }
 
@@ -500,7 +506,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
    * Create a unified DStream from multiple DStreams of the same type and same slide duration.
    */
   def union[T](first: JavaDStream[T], rest: JList[JavaDStream[T]]): JavaDStream[T] = {
-    val dstreams: Seq[DStream[T]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream)
+    val dstreams: Seq[DStream[T]] = (Seq(first) ++ rest.asScala).map(_.dstream)
     implicit val cm: ClassTag[T] = first.classTag
     ssc.union(dstreams)(cm)
   }
@@ -512,7 +518,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
       first: JavaPairDStream[K, V],
       rest: JList[JavaPairDStream[K, V]]
     ): JavaPairDStream[K, V] = {
-    val dstreams: Seq[DStream[(K, V)]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream)
+    val dstreams: Seq[DStream[(K, V)]] = (Seq(first) ++ rest.asScala).map(_.dstream)
     implicit val cm: ClassTag[(K, V)] = first.classTag
     implicit val kcm: ClassTag[K] = first.kManifest
     implicit val vcm: ClassTag[V] = first.vManifest
@@ -534,12 +540,11 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     ): JavaDStream[T] = {
     implicit val cmt: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val scalaDStreams = dstreams.map(_.dstream).toSeq
     val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => {
-      val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList
+      val jrdds = rdds.map(JavaRDD.fromRDD(_)).asJava
       transformFunc.call(jrdds, time).rdd
     }
-    ssc.transform(scalaDStreams, scalaTransformFunc)
+    ssc.transform(dstreams.asScala.map(_.dstream).toSeq, scalaTransformFunc)
   }
 
   /**
@@ -559,12 +564,11 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]]
     implicit val cmv: ClassTag[V] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
-    val scalaDStreams = dstreams.map(_.dstream).toSeq
     val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => {
-      val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList
+      val jrdds = rdds.map(JavaRDD.fromRDD(_)).asJava
       transformFunc.call(jrdds, time).rdd
     }
-    ssc.transform(scalaDStreams, scalaTransformFunc)
+    ssc.transform(dstreams.asScala.map(_.dstream).toSeq, scalaTransformFunc)
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index d06401245ff17..2c373640d2fd9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -20,14 +20,13 @@ package org.apache.spark.streaming.api.python
 import java.io.{ObjectInputStream, ObjectOutputStream}
 import java.lang.reflect.Proxy
 import java.util.{ArrayList => JArrayList, List => JList}
-import scala.collection.JavaConversions._
+
 import scala.collection.JavaConverters._
 import scala.language.existentials
 
 import py4j.GatewayServer
 
 import org.apache.spark.api.java._
-import org.apache.spark.api.python._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Interval, Duration, Time}
@@ -161,7 +160,7 @@ private[python] object PythonDStream {
    */
   def toRDDQueue(rdds: JArrayList[JavaRDD[Array[Byte]]]): java.util.Queue[JavaRDD[Array[Byte]]] = {
     val queue = new java.util.LinkedList[JavaRDD[Array[Byte]]]
-    rdds.forall(queue.add(_))
+    rdds.asScala.foreach(queue.add)
     queue
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index 554aae0117b24..2252e28f22af8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.receiver
 import java.nio.ByteBuffer
 
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.annotation.DeveloperApi
@@ -144,12 +144,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataIterator: java.util.Iterator[T], metadata: Any) {
-    supervisor.pushIterator(dataIterator, Some(metadata), None)
+    supervisor.pushIterator(dataIterator.asScala, Some(metadata), None)
   }
 
   /** Store an iterator of received data as a data block into Spark's memory. */
   def store(dataIterator: java.util.Iterator[T]) {
-    supervisor.pushIterator(dataIterator, None, None)
+    supervisor.pushIterator(dataIterator.asScala, None, None)
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
index 6d4cdc4aa6b10..0cd39594ee923 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.scheduler
 
 import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.{Failure, Success}
 
 import org.apache.spark.Logging
@@ -128,7 +128,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
   }
 
   def getPendingTimes(): Seq[Time] = {
-    jobSets.keySet.toSeq
+    jobSets.asScala.keys.toSeq
   }
 
   def reportError(msg: String, e: Throwable) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
index 53b96d51c9180..f2711d1355e60 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
@@ -19,6 +19,7 @@ package org.apache.spark.streaming.scheduler
 
 import java.nio.ByteBuffer
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.language.implicitConversions
 
@@ -196,8 +197,7 @@ private[streaming] class ReceivedBlockTracker(
 
     writeAheadLogOption.foreach { writeAheadLog =>
       logInfo(s"Recovering from write ahead logs in ${checkpointDirOption.get}")
-      import scala.collection.JavaConversions._
-      writeAheadLog.readAll().foreach { byteBuffer =>
+      writeAheadLog.readAll().asScala.foreach { byteBuffer =>
         logTrace("Recovering record " + byteBuffer)
         Utils.deserialize[ReceivedBlockTrackerLogEvent](
           byteBuffer.array, Thread.currentThread().getContextClassLoader) match {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala
index fe6328b1ce727..9f4a4d6806ab5 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala
@@ -19,6 +19,7 @@ package org.apache.spark.streaming.util
 import java.nio.ByteBuffer
 import java.util.{Iterator => JIterator}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.{Await, ExecutionContext, Future}
 import scala.language.postfixOps
@@ -118,7 +119,6 @@ private[streaming] class FileBasedWriteAheadLog(
    * hence the implementation is kept simple.
    */
   def readAll(): JIterator[ByteBuffer] = synchronized {
-    import scala.collection.JavaConversions._
     val logFilesToRead = pastLogs.map{ _.path} ++ currentLogPath
     logInfo("Reading from the logs: " + logFilesToRead.mkString("\n"))
 
@@ -126,7 +126,7 @@ private[streaming] class FileBasedWriteAheadLog(
       logDebug(s"Creating log reader with $file")
       val reader = new FileBasedWriteAheadLogReader(file, hadoopConf)
       CompletionIterator[ByteBuffer, Iterator[ByteBuffer]](reader, reader.close _)
-    } flatMap { x => x }
+    }.flatten.asJava
   }
 
   /**
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
index bb80bff6dc2e6..57b50bdfd6520 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
@@ -17,16 +17,13 @@
 
 package org.apache.spark.streaming
 
-import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
+import java.util.{List => JList}
+
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
-import java.util.{List => JList}
-import org.apache.spark.streaming.api.java.{JavaPairDStream, JavaDStreamLike, JavaDStream, JavaStreamingContext}
-import org.apache.spark.streaming._
-import java.util.ArrayList
-import collection.JavaConversions._
 import org.apache.spark.api.java.JavaRDDLike
-import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.streaming.api.java.{JavaDStreamLike, JavaDStream, JavaStreamingContext}
 
 /** Exposes streaming test functionality in a Java-friendly way. */
 trait JavaTestBase extends TestSuiteBase {
@@ -39,7 +36,7 @@ trait JavaTestBase extends TestSuiteBase {
       ssc: JavaStreamingContext,
       data: JList[JList[T]],
       numPartitions: Int) = {
-    val seqData = data.map(Seq(_:_*))
+    val seqData = data.asScala.map(_.asScala)
 
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
@@ -72,9 +69,7 @@ trait JavaTestBase extends TestSuiteBase {
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
     ssc.getState()
     val res = runStreams[V](ssc.ssc, numBatches, numExpectedOutput)
-    val out = new ArrayList[JList[V]]()
-    res.map(entry => out.append(new ArrayList[V](entry)))
-    out
+    res.map(_.asJava).asJava
   }
 
   /**
@@ -90,12 +85,7 @@ trait JavaTestBase extends TestSuiteBase {
     implicit val cm: ClassTag[V] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
     val res = runStreamsWithPartitions[V](ssc.ssc, numBatches, numExpectedOutput)
-    val out = new ArrayList[JList[JList[V]]]()
-    res.map{entry =>
-      val lists = entry.map(new ArrayList[V](_))
-      out.append(new ArrayList[JList[V]](lists))
-    }
-    out
+    res.map(entry => entry.map(_.asJava).asJava).asJava
   }
 }
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala
index 325ff7c74c39d..5e49fd00769ad 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala
@@ -20,6 +20,7 @@ import java.io._
 import java.nio.ByteBuffer
 import java.util
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
 import scala.language.{implicitConversions, postfixOps}
@@ -417,9 +418,8 @@ object WriteAheadLogSuite {
 
   /** Read all the data in the log file in a directory using the WriteAheadLog class. */
   def readDataUsingWriteAheadLog(logDirectory: String): Seq[String] = {
-    import scala.collection.JavaConversions._
     val wal = new FileBasedWriteAheadLog(new SparkConf(), logDirectory, hadoopConf, 1, 1)
-    val data = wal.readAll().map(byteBufferToString).toSeq
+    val data = wal.readAll().asScala.map(byteBufferToString).toSeq
     wal.close()
     data
   }
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index 9418beb6b3e3a..a0524cabff2d4 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -22,7 +22,7 @@ import java.io.File
 import java.util.jar.JarFile
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.runtime.universe.runtimeMirror
 import scala.reflect.runtime.{universe => unv}
 import scala.util.Try
@@ -161,7 +161,7 @@ object GenerateMIMAIgnore {
     val path = packageName.replace('.', '/')
     val resources = classLoader.getResources(path)
 
-    val jars = resources.filter(x => x.getProtocol == "jar")
+    val jars = resources.asScala.filter(_.getProtocol == "jar")
       .map(_.getFile.split(":")(1).split("!")(0)).toSeq
 
     jars.flatMap(getClassesFromJar(_, path))
@@ -175,7 +175,7 @@ object GenerateMIMAIgnore {
   private def getClassesFromJar(jarPath: String, packageName: String) = {
     import scala.collection.mutable
     val jar = new JarFile(new File(jarPath))
-    val enums = jar.entries().map(_.getName).filter(_.startsWith(packageName))
+    val enums = jar.entries().asScala.map(_.getName).filter(_.startsWith(packageName))
     val classes = mutable.HashSet[Class[_]]()
     for (entry <- enums if entry.endsWith(".class")) {
       try {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index bff585b46cbbe..e9a02baafd28e 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -25,7 +25,7 @@ import java.security.PrivilegedExceptionAction
 import java.util.{Properties, UUID}
 import java.util.zip.{ZipEntry, ZipOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, ListBuffer, Map}
 import scala.reflect.runtime.universe
 import scala.util.{Try, Success, Failure}
@@ -511,7 +511,7 @@ private[spark] class Client(
     val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + stagingDirPath
     YarnSparkHadoopUtil.get.obtainTokensForNamenodes(
       nns, hadoopConf, creds, Some(sparkConf.get("spark.yarn.principal")))
-    val t = creds.getAllTokens
+    val t = creds.getAllTokens.asScala
       .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
       .head
     val newExpiration = t.renew(hadoopConf)
@@ -650,8 +650,8 @@ private[spark] class Client(
     distCacheMgr.setDistArchivesEnv(launchEnv)
 
     val amContainer = Records.newRecord(classOf[ContainerLaunchContext])
-    amContainer.setLocalResources(localResources)
-    amContainer.setEnvironment(launchEnv)
+    amContainer.setLocalResources(localResources.asJava)
+    amContainer.setEnvironment(launchEnv.asJava)
 
     val javaOpts = ListBuffer[String]()
 
@@ -782,7 +782,7 @@ private[spark] class Client(
 
     // TODO: it would be nicer to just make sure there are no null commands here
     val printableCommands = commands.map(s => if (s == null) "null" else s).toList
-    amContainer.setCommands(printableCommands)
+    amContainer.setCommands(printableCommands.asJava)
 
     logDebug("===============================================================================")
     logDebug("YARN AM launch context:")
@@ -797,7 +797,8 @@ private[spark] class Client(
 
     // send the acl settings into YARN to control who has access via YARN interfaces
     val securityManager = new SecurityManager(sparkConf)
-    amContainer.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityManager))
+    amContainer.setApplicationACLs(
+      YarnSparkHadoopUtil.getApplicationAclsForYarn(securityManager).asJava)
     setupSecurityToken(amContainer)
     UserGroupInformation.getCurrentUser().addCredentials(credentials)
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 4cc50483a17ff..9abd09b3cc7a5 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -20,14 +20,13 @@ package org.apache.spark.deploy.yarn
 import java.io.File
 import java.net.URI
 import java.nio.ByteBuffer
+import java.util.Collections
 
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
-import org.apache.spark.util.Utils
-
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{HashMap, ListBuffer}
 
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.DataOutputBuffer
 import org.apache.hadoop.security.UserGroupInformation
@@ -40,6 +39,7 @@ import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.network.util.JavaUtils
+import org.apache.spark.util.Utils
 
 class ExecutorRunnable(
     container: Container,
@@ -74,9 +74,9 @@ class ExecutorRunnable(
       .asInstanceOf[ContainerLaunchContext]
 
     val localResources = prepareLocalResources
-    ctx.setLocalResources(localResources)
+    ctx.setLocalResources(localResources.asJava)
 
-    ctx.setEnvironment(env)
+    ctx.setEnvironment(env.asJava)
 
     val credentials = UserGroupInformation.getCurrentUser().getCredentials()
     val dob = new DataOutputBuffer()
@@ -96,8 +96,9 @@ class ExecutorRunnable(
       |===============================================================================
       """.stripMargin)
 
-    ctx.setCommands(commands)
-    ctx.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr))
+    ctx.setCommands(commands.asJava)
+    ctx.setApplicationACLs(
+      YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr).asJava)
 
     // If external shuffle service is enabled, register with the Yarn shuffle service already
     // started on the NodeManager and, if authentication is enabled, provide it with our secret
@@ -112,7 +113,7 @@ class ExecutorRunnable(
           // Authentication is not enabled, so just provide dummy metadata
           ByteBuffer.allocate(0)
         }
-      ctx.setServiceData(Map[String, ByteBuffer]("spark_shuffle" -> secretBytes))
+      ctx.setServiceData(Collections.singletonMap("spark_shuffle", secretBytes))
     }
 
     // Send the start request to the ContainerManager
@@ -314,7 +315,8 @@ class ExecutorRunnable(
       env("SPARK_LOG_URL_STDOUT") = s"$baseUrl/stdout?start=-4096"
     }
 
-    System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k, v) => env(k) = v }
+    System.getenv().asScala.filterKeys(_.startsWith("SPARK"))
+      .foreach { case (k, v) => env(k) = v }
     env
   }
 }
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index ccf753e69f4b6..5f897cbcb4e9f 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -21,9 +21,7 @@ import java.util.Collections
 import java.util.concurrent._
 import java.util.regex.Pattern
 
-import org.apache.spark.util.Utils
-
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
 import com.google.common.util.concurrent.ThreadFactoryBuilder
@@ -39,8 +37,8 @@ import org.apache.log4j.{Level, Logger}
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
 import org.apache.spark.rpc.RpcEndpointRef
-import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.util.Utils
 
 /**
  * YarnAllocator is charged with requesting containers from the YARN ResourceManager and deciding
@@ -164,7 +162,7 @@ private[yarn] class YarnAllocator(
    * Number of container requests at the given location that have not yet been fulfilled.
    */
   private def getNumPendingAtLocation(location: String): Int =
-    amClient.getMatchingRequests(RM_REQUEST_PRIORITY, location, resource).map(_.size).sum
+    amClient.getMatchingRequests(RM_REQUEST_PRIORITY, location, resource).asScala.map(_.size).sum
 
   /**
    * Request as many executors from the ResourceManager as needed to reach the desired total. If
@@ -231,14 +229,14 @@ private[yarn] class YarnAllocator(
           numExecutorsRunning,
           allocateResponse.getAvailableResources))
 
-      handleAllocatedContainers(allocatedContainers)
+      handleAllocatedContainers(allocatedContainers.asScala)
     }
 
     val completedContainers = allocateResponse.getCompletedContainersStatuses()
     if (completedContainers.size > 0) {
       logDebug("Completed %d containers".format(completedContainers.size))
 
-      processCompletedContainers(completedContainers)
+      processCompletedContainers(completedContainers.asScala)
 
       logDebug("Finished processing %d completed containers. Current running executor count: %d."
         .format(completedContainers.size, numExecutorsRunning))
@@ -271,7 +269,7 @@ private[yarn] class YarnAllocator(
         val request = createContainerRequest(resource, locality.nodes, locality.racks)
         amClient.addContainerRequest(request)
         val nodes = request.getNodes
-        val hostStr = if (nodes == null || nodes.isEmpty) "Any" else nodes.last
+        val hostStr = if (nodes == null || nodes.isEmpty) "Any" else nodes.asScala.last
         logInfo(s"Container request (host: $hostStr, capability: $resource)")
       }
     } else if (missing < 0) {
@@ -280,7 +278,8 @@ private[yarn] class YarnAllocator(
 
       val matchingRequests = amClient.getMatchingRequests(RM_REQUEST_PRIORITY, ANY_HOST, resource)
       if (!matchingRequests.isEmpty) {
-        matchingRequests.head.take(numToCancel).foreach(amClient.removeContainerRequest)
+        matchingRequests.iterator().next().asScala
+          .take(numToCancel).foreach(amClient.removeContainerRequest)
       } else {
         logWarning("Expected to find pending requests, but found none.")
       }
@@ -459,7 +458,7 @@ private[yarn] class YarnAllocator(
         }
       }
 
-      if (allocatedContainerToHostMap.containsKey(containerId)) {
+      if (allocatedContainerToHostMap.contains(containerId)) {
         val host = allocatedContainerToHostMap.get(containerId).get
         val containerSet = allocatedHostToContainersMap.get(host).get
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
index 4999f9c06210a..df042bf291de7 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
@@ -19,17 +19,15 @@ package org.apache.spark.deploy.yarn
 
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.{Map, Set}
 import scala.util.Try
 
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.yarn.api.ApplicationConstants
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.util.ConverterUtils
 import org.apache.hadoop.yarn.webapp.util.WebAppUtils
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
@@ -108,8 +106,8 @@ private[spark] class YarnRMClient(args: ApplicationMasterArguments) extends Logg
       val method = classOf[WebAppUtils].getMethod("getProxyHostsAndPortsForAmFilter",
         classOf[Configuration])
       val proxies = method.invoke(null, conf).asInstanceOf[JList[String]]
-      val hosts = proxies.map { proxy => proxy.split(":")(0) }
-      val uriBases = proxies.map { proxy => prefix + proxy + proxyBase }
+      val hosts = proxies.asScala.map { proxy => proxy.split(":")(0) }
+      val uriBases = proxies.asScala.map { proxy => prefix + proxy + proxyBase }
       Map("PROXY_HOSTS" -> hosts.mkString(","), "PROXY_URI_BASES" -> uriBases.mkString(","))
     } catch {
       case e: NoSuchMethodException =>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
index 128e996b71fe5..b4f8049bff577 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
@@ -21,7 +21,7 @@ import java.io.{File, FileOutputStream, OutputStreamWriter}
 import java.util.Properties
 import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
@@ -132,7 +132,7 @@ abstract class BaseYarnClusterSuite
     props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
     props.setProperty("spark.executor.extraJavaOptions", "-Dfoo=\"one two three\"")
 
-    yarnCluster.getConfig().foreach { e =>
+    yarnCluster.getConfig.asScala.foreach { e =>
       props.setProperty("spark.hadoop." + e.getKey(), e.getValue())
     }
 
@@ -149,7 +149,7 @@ abstract class BaseYarnClusterSuite
     props.store(writer, "Spark properties.")
     writer.close()
 
-    val extraJarArgs = if (!extraJars.isEmpty()) Seq("--jars", extraJars.mkString(",")) else Nil
+    val extraJarArgs = if (extraJars.nonEmpty) Seq("--jars", extraJars.mkString(",")) else Nil
     val mainArgs =
       if (klass.endsWith(".py")) {
         Seq(klass)
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
index 0a5402c89e764..e7f2501e7899f 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
@@ -20,8 +20,8 @@ package org.apache.spark.deploy.yarn
 import java.io.File
 import java.net.URI
 
-import scala.collection.JavaConversions._
-import scala.collection.mutable.{ HashMap => MutableHashMap }
+import scala.collection.JavaConverters._
+import scala.collection.mutable.{HashMap => MutableHashMap}
 import scala.reflect.ClassTag
 import scala.util.Try
 
@@ -38,7 +38,7 @@ import org.mockito.Matchers._
 import org.mockito.Mockito._
 import org.scalatest.{BeforeAndAfterAll, Matchers}
 
-import org.apache.spark.{SparkConf, SparkException, SparkFunSuite}
+import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.util.Utils
 
 class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll {
@@ -201,7 +201,7 @@ class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll {
     appContext.getClass.getMethods.filter(_.getName.equals("getApplicationTags")).foreach{ method =>
       val tags = method.invoke(appContext).asInstanceOf[java.util.Set[String]]
       tags should contain allOf ("tag1", "dup", "tag2", "multi word")
-      tags.filter(!_.isEmpty).size should be (4)
+      tags.asScala.filter(_.nonEmpty).size should be (4)
     }
     appContext.getMaxAppAttempts should be (42)
   }
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 128350b648992..5a4ea2ea2f4ff 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -21,7 +21,6 @@ import java.io.File
 import java.net.URL
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.{ByteStreams, Files}
@@ -216,8 +215,8 @@ private object YarnClusterDriver extends Logging with Matchers {
       assert(listener.driverLogs.nonEmpty)
       val driverLogs = listener.driverLogs.get
       assert(driverLogs.size === 2)
-      assert(driverLogs.containsKey("stderr"))
-      assert(driverLogs.containsKey("stdout"))
+      assert(driverLogs.contains("stderr"))
+      assert(driverLogs.contains("stdout"))
       val urlStr = driverLogs("stderr")
       // Ensure that this is a valid URL, else this will throw an exception
       new URL(urlStr)

From 5c08c86bfa43462fb2ca5f7c5980ddfb44dd57f8 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 25 Aug 2015 10:22:54 -0700
Subject: [PATCH 1212/1454] [SPARK-10198] [SQL] Turn off partition verification
 by default

Author: Michael Armbrust <michael@databricks.com>

Closes #8404 from marmbrus/turnOffPartitionVerification.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  2 +-
 .../spark/sql/hive/QueryPartitionSuite.scala  | 64 ++++++++++---------
 2 files changed, 35 insertions(+), 31 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index e6f7619519e6a..9de75f4c4d084 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -312,7 +312,7 @@ private[spark] object SQLConf {
     doc = "When true, enable filter pushdown for ORC files.")
 
   val HIVE_VERIFY_PARTITION_PATH = booleanConf("spark.sql.hive.verifyPartitionPath",
-    defaultValue = Some(true),
+    defaultValue = Some(false),
     doc = "<TODO>")
 
   val HIVE_METASTORE_PARTITION_PRUNING = booleanConf("spark.sql.hive.metastorePartitionPruning",
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
index 017bc2adc103b..1cc8a93e83411 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
@@ -18,50 +18,54 @@
 package org.apache.spark.sql.hive
 
 import com.google.common.io.Files
+import org.apache.spark.sql.test.SQLTestUtils
 
 import org.apache.spark.sql.{QueryTest, _}
 import org.apache.spark.util.Utils
 
 
-class QueryPartitionSuite extends QueryTest {
+class QueryPartitionSuite extends QueryTest with SQLTestUtils {
 
   private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
   import ctx.implicits._
-  import ctx.sql
+
+  protected def _sqlContext = ctx
 
   test("SPARK-5068: query data when path doesn't exist"){
-    val testData = ctx.sparkContext.parallelize(
-      (1 to 10).map(i => TestData(i, i.toString))).toDF()
-    testData.registerTempTable("testData")
+    withSQLConf((SQLConf.HIVE_VERIFY_PARTITION_PATH.key, "true")) {
+      val testData = ctx.sparkContext.parallelize(
+        (1 to 10).map(i => TestData(i, i.toString))).toDF()
+      testData.registerTempTable("testData")
 
-    val tmpDir = Files.createTempDir()
-    // create the table for test
-    sql(s"CREATE TABLE table_with_partition(key int,value string) " +
-      s"PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='1') " +
-      "SELECT key,value FROM testData")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='2') " +
-      "SELECT key,value FROM testData")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='3') " +
-      "SELECT key,value FROM testData")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='4') " +
-      "SELECT key,value FROM testData")
+      val tmpDir = Files.createTempDir()
+      // create the table for test
+      sql(s"CREATE TABLE table_with_partition(key int,value string) " +
+        s"PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='1') " +
+        "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='2') " +
+        "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='3') " +
+        "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='4') " +
+        "SELECT key,value FROM testData")
 
-    // test for the exist path
-    checkAnswer(sql("select key,value from table_with_partition"),
-      testData.toDF.collect ++ testData.toDF.collect
-        ++ testData.toDF.collect ++ testData.toDF.collect)
+      // test for the exist path
+      checkAnswer(sql("select key,value from table_with_partition"),
+        testData.toDF.collect ++ testData.toDF.collect
+          ++ testData.toDF.collect ++ testData.toDF.collect)
 
-    // delete the path of one partition
-    tmpDir.listFiles
-      .find { f => f.isDirectory && f.getName().startsWith("ds=") }
-      .foreach { f => Utils.deleteRecursively(f) }
+      // delete the path of one partition
+      tmpDir.listFiles
+        .find { f => f.isDirectory && f.getName().startsWith("ds=") }
+        .foreach { f => Utils.deleteRecursively(f) }
 
-    // test for after delete the path
-    checkAnswer(sql("select key,value from table_with_partition"),
-      testData.toDF.collect ++ testData.toDF.collect ++ testData.toDF.collect)
+      // test for after delete the path
+      checkAnswer(sql("select key,value from table_with_partition"),
+        testData.toDF.collect ++ testData.toDF.collect ++ testData.toDF.collect)
 
-    sql("DROP TABLE table_with_partition")
-    sql("DROP TABLE createAndInsertTest")
+      sql("DROP TABLE table_with_partition")
+      sql("DROP TABLE createAndInsertTest")
+    }
   }
 }

From b37f0cc1b4c064d6f09edb161250fa8b783de52a Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 25 Aug 2015 10:54:03 -0700
Subject: [PATCH 1213/1454] [SPARK-8531] [ML] Update ML user guide for
 MinMaxScaler

jira: https://issues.apache.org/jira/browse/SPARK-8531

Update ML user guide for MinMaxScaler

Author: Yuhao Yang <hhbyyh@gmail.com>
Author: unknown <yuhaoyan@yuhaoyan-MOBL1.ccr.corp.intel.com>

Closes #7211 from hhbyyh/minmaxdoc.
---
 docs/ml-features.md | 71 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 642a4b4c53183..62de4838981cb 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1133,6 +1133,7 @@ val scaledData = scalerModel.transform(dataFrame)
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.StandardScaler;
+import org.apache.spark.ml.feature.StandardScalerModel;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
@@ -1173,6 +1174,76 @@ scaledData = scalerModel.transform(dataFrame)
 </div>
 </div>
 
+## MinMaxScaler
+
+`MinMaxScaler` transforms a dataset of `Vector` rows, rescaling each feature to a specific range (often [0, 1]).  It takes parameters:
+
+* `min`: 0.0 by default. Lower bound after transformation, shared by all features.
+* `max`: 1.0 by default. Upper bound after transformation, shared by all features.
+
+`MinMaxScaler` computes summary statistics on a data set and produces a `MinMaxScalerModel`. The model can then transform each feature individually such that it is in the given range.
+
+The rescaled value for a feature E is calculated as,
+`\begin{equation}
+  Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
+\end{equation}`
+For the case `E_{max} == E_{min}`, `Rescaled(e_i) = 0.5 * (max + min)`
+
+Note that since zero values will probably be transformed to non-zero values, output of the transformer will be DenseVector even for sparse input.
+
+The following example demonstrates how to load a dataset in libsvm format and then rescale each feature to [0, 1].
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+More details can be found in the API docs for
+[MinMaxScaler](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScaler) and
+[MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).
+{% highlight scala %}
+import org.apache.spark.ml.feature.MinMaxScaler
+import org.apache.spark.mllib.util.MLUtils
+
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+val dataFrame = sqlContext.createDataFrame(data)
+val scaler = new MinMaxScaler()
+  .setInputCol("features")
+  .setOutputCol("scaledFeatures")
+
+// Compute summary statistics and generate MinMaxScalerModel
+val scalerModel = scaler.fit(dataFrame)
+
+// rescale each feature to range [min, max].
+val scaledData = scalerModel.transform(dataFrame)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+More details can be found in the API docs for
+[MinMaxScaler](api/java/org/apache/spark/ml/feature/MinMaxScaler.html) and
+[MinMaxScalerModel](api/java/org/apache/spark/ml/feature/MinMaxScalerModel.html).
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.MinMaxScaler;
+import org.apache.spark.ml.feature.MinMaxScalerModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.sql.DataFrame;
+
+JavaRDD<LabeledPoint> data =
+  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
+DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+MinMaxScaler scaler = new MinMaxScaler()
+  .setInputCol("features")
+  .setOutputCol("scaledFeatures");
+
+// Compute summary statistics and generate MinMaxScalerModel
+MinMaxScalerModel scalerModel = scaler.fit(dataFrame);
+
+// rescale each feature to range [min, max].
+DataFrame scaledData = scalerModel.transform(dataFrame);
+{% endhighlight %}
+</div>
+</div>
+
 ## Bucketizer
 
 `Bucketizer` transforms a column of continuous features to a column of feature buckets, where the buckets are specified by users. It takes a parameter:

From 881208a8e849facf54166bdd69d3634407f952e7 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 11:58:47 -0700
Subject: [PATCH 1214/1454] [SPARK-10230] [MLLIB] Rename optimizeAlpha to
 optimizeDocConcentration

See [discussion](https://github.com/apache/spark/pull/8254#discussion_r37837770)

CC jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8422 from feynmanliang/SPARK-10230.
---
 .../spark/mllib/clustering/LDAOptimizer.scala    | 16 ++++++++--------
 .../apache/spark/mllib/clustering/LDASuite.scala |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 5c2aae6403bea..38486e949bbcf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -258,7 +258,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   private var tau0: Double = 1024
   private var kappa: Double = 0.51
   private var miniBatchFraction: Double = 0.05
-  private var optimizeAlpha: Boolean = false
+  private var optimizeDocConcentration: Boolean = false
 
   // internal data structure
   private var docs: RDD[(Long, Vector)] = null
@@ -335,20 +335,20 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   }
 
   /**
-   * Optimize alpha, indicates whether alpha (Dirichlet parameter for document-topic distribution)
-   * will be optimized during training.
+   * Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for
+   * document-topic distribution) will be optimized during training.
    */
   @Since("1.5.0")
-  def getOptimzeAlpha: Boolean = this.optimizeAlpha
+  def getOptimizeDocConcentration: Boolean = this.optimizeDocConcentration
 
   /**
-   * Sets whether to optimize alpha parameter during training.
+   * Sets whether to optimize docConcentration parameter during training.
    *
    * Default: false
    */
   @Since("1.5.0")
-  def setOptimzeAlpha(optimizeAlpha: Boolean): this.type = {
-    this.optimizeAlpha = optimizeAlpha
+  def setOptimizeDocConcentration(optimizeDocConcentration: Boolean): this.type = {
+    this.optimizeDocConcentration = optimizeDocConcentration
     this
   }
 
@@ -458,7 +458,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
     // Note that this is an optimization to avoid batch.count
     updateLambda(batchResult, (miniBatchFraction * corpusSize).ceil.toInt)
-    if (optimizeAlpha) updateAlpha(gammat)
+    if (optimizeDocConcentration) updateAlpha(gammat)
     this
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 8a714f9b79e02..746a76a7e5fa1 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -423,7 +423,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     val k = 2
     val docs = sc.parallelize(toyData)
     val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
-      .setGammaShape(100).setOptimzeAlpha(true).setSampleWithReplacement(false)
+      .setGammaShape(100).setOptimizeDocConcentration(true).setSampleWithReplacement(false)
     val lda = new LDA().setK(k)
       .setDocConcentration(1D / k)
       .setTopicConcentration(0.01)

From 16a2be1a84c0a274a60c0a584faaf58b55d4942b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 12:16:23 -0700
Subject: [PATCH 1215/1454] [SPARK-10231] [MLLIB] update @Since annotation for
 mllib.classification

Update `Since` annotation in `mllib.classification`:

1. add version to classes, objects, constructors, and public variables declared in constructors
2. correct some versions
3. remove `Since` on `toString`

MechCoder dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8421 from mengxr/SPARK-10231 and squashes the following commits:

b2dce80 [Xiangrui Meng] update @Since annotation for mllib.classification
---
 .../classification/ClassificationModel.scala  |  7 +++--
 .../classification/LogisticRegression.scala   | 20 +++++++++----
 .../mllib/classification/NaiveBayes.scala     | 28 +++++++++++++++----
 .../spark/mllib/classification/SVM.scala      | 15 ++++++----
 .../StreamingLogisticRegressionWithSGD.scala  |  9 +++++-
 5 files changed, 58 insertions(+), 21 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index a29b425a71fd6..85a413243b049 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -30,6 +30,7 @@ import org.apache.spark.rdd.RDD
  * belongs. The categories are represented by double values: 0.0, 1.0, 2.0, etc.
  */
 @Experimental
+@Since("0.8.0")
 trait ClassificationModel extends Serializable {
   /**
    * Predict values for the given data set using the model trained.
@@ -37,7 +38,7 @@ trait ClassificationModel extends Serializable {
    * @param testData RDD representing data points to be predicted
    * @return an RDD[Double] where each entry contains the corresponding prediction
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double]
 
   /**
@@ -46,7 +47,7 @@ trait ClassificationModel extends Serializable {
    * @param testData array representing a single data point
    * @return predicted category from the trained model
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def predict(testData: Vector): Double
 
   /**
@@ -54,7 +55,7 @@ trait ClassificationModel extends Serializable {
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index e03e662227d14..5ceff5b2259ea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -41,11 +41,12 @@ import org.apache.spark.rdd.RDD
  *                   Multinomial Logistic Regression. By default, it is binary logistic regression
  *                   so numClasses will be set to 2.
  */
-class LogisticRegressionModel (
-    override val weights: Vector,
-    override val intercept: Double,
-    val numFeatures: Int,
-    val numClasses: Int)
+@Since("0.8.0")
+class LogisticRegressionModel @Since("1.3.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("1.0.0") override val intercept: Double,
+    @Since("1.3.0") val numFeatures: Int,
+    @Since("1.3.0") val numClasses: Int)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable
   with Saveable with PMMLExportable {
 
@@ -75,6 +76,7 @@ class LogisticRegressionModel (
   /**
    * Constructs a [[LogisticRegressionModel]] with weights and intercept for binary classification.
    */
+  @Since("1.0.0")
   def this(weights: Vector, intercept: Double) = this(weights, intercept, weights.size, 2)
 
   private var threshold: Option[Double] = Some(0.5)
@@ -166,12 +168,12 @@ class LogisticRegressionModel (
 
   override protected def formatVersion: String = "1.0"
 
-  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.getOrElse("None")}"
   }
 }
 
+@Since("1.3.0")
 object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
 
   @Since("1.3.0")
@@ -207,6 +209,7 @@ object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
  * for k classes multi-label classification problem.
  * Using [[LogisticRegressionWithLBFGS]] is recommended over this.
  */
+@Since("0.8.0")
 class LogisticRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -216,6 +219,7 @@ class LogisticRegressionWithSGD private[mllib] (
 
   private val gradient = new LogisticGradient()
   private val updater = new SquaredL2Updater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -227,6 +231,7 @@ class LogisticRegressionWithSGD private[mllib] (
    * Construct a LogisticRegression object with default parameters: {stepSize: 1.0,
    * numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
@@ -238,6 +243,7 @@ class LogisticRegressionWithSGD private[mllib] (
  * Top-level methods for calling Logistic Regression using Stochastic Gradient Descent.
  * NOTE: Labels used in Logistic Regression should be {0, 1}
  */
+@Since("0.8.0")
 object LogisticRegressionWithSGD {
   // NOTE(shivaram): We use multiple train methods instead of default arguments to support
   // Java programs.
@@ -333,11 +339,13 @@ object LogisticRegressionWithSGD {
  * NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1}
  * for k classes multi-label classification problem.
  */
+@Since("1.1.0")
 class LogisticRegressionWithLBFGS
   extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable {
 
   this.setFeatureScaling(true)
 
+  @Since("1.1.0")
   override val optimizer = new LBFGS(new LogisticGradient, new SquaredL2Updater)
 
   override protected val validators = List(multiLabelValidator)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index dab369207cc9a..a956084ae06e8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -41,11 +41,12 @@ import org.apache.spark.sql.{DataFrame, SQLContext}
  *              where D is number of features
  * @param modelType The type of NB model to fit  can be "multinomial" or "bernoulli"
  */
+@Since("0.9.0")
 class NaiveBayesModel private[spark] (
-    val labels: Array[Double],
-    val pi: Array[Double],
-    val theta: Array[Array[Double]],
-    val modelType: String)
+    @Since("1.0.0") val labels: Array[Double],
+    @Since("0.9.0") val pi: Array[Double],
+    @Since("0.9.0") val theta: Array[Array[Double]],
+    @Since("1.4.0") val modelType: String)
   extends ClassificationModel with Serializable with Saveable {
 
   import NaiveBayes.{Bernoulli, Multinomial, supportedModelTypes}
@@ -83,6 +84,7 @@ class NaiveBayesModel private[spark] (
       throw new UnknownError(s"Invalid modelType: $modelType.")
   }
 
+  @Since("1.0.0")
   override def predict(testData: RDD[Vector]): RDD[Double] = {
     val bcModel = testData.context.broadcast(this)
     testData.mapPartitions { iter =>
@@ -91,6 +93,7 @@ class NaiveBayesModel private[spark] (
     }
   }
 
+  @Since("1.0.0")
   override def predict(testData: Vector): Double = {
     modelType match {
       case Multinomial =>
@@ -107,6 +110,7 @@ class NaiveBayesModel private[spark] (
    * @return an RDD[Vector] where each entry contains the predicted posterior class probabilities,
    *         in the same order as class labels
    */
+  @Since("1.5.0")
   def predictProbabilities(testData: RDD[Vector]): RDD[Vector] = {
     val bcModel = testData.context.broadcast(this)
     testData.mapPartitions { iter =>
@@ -122,6 +126,7 @@ class NaiveBayesModel private[spark] (
    * @return predicted posterior class probabilities from the trained model,
    *         in the same order as class labels
    */
+  @Since("1.5.0")
   def predictProbabilities(testData: Vector): Vector = {
     modelType match {
       case Multinomial =>
@@ -158,6 +163,7 @@ class NaiveBayesModel private[spark] (
     new DenseVector(scaledProbs.map(_ / probSum))
   }
 
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     val data = NaiveBayesModel.SaveLoadV2_0.Data(labels, pi, theta, modelType)
     NaiveBayesModel.SaveLoadV2_0.save(sc, path, data)
@@ -166,6 +172,7 @@ class NaiveBayesModel private[spark] (
   override protected def formatVersion: String = "2.0"
 }
 
+@Since("1.3.0")
 object NaiveBayesModel extends Loader[NaiveBayesModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -199,6 +206,7 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
       dataRDD.write.parquet(dataPath(path))
     }
 
+    @Since("1.3.0")
     def load(sc: SparkContext, path: String): NaiveBayesModel = {
       val sqlContext = new SQLContext(sc)
       // Load Parquet data.
@@ -301,30 +309,35 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
  * document classification.  By making every vector a 0-1 vector, it can also be used as
  * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). The input feature values must be nonnegative.
  */
-
+@Since("0.9.0")
 class NaiveBayes private (
     private var lambda: Double,
     private var modelType: String) extends Serializable with Logging {
 
   import NaiveBayes.{Bernoulli, Multinomial}
 
+  @Since("1.4.0")
   def this(lambda: Double) = this(lambda, NaiveBayes.Multinomial)
 
+  @Since("0.9.0")
   def this() = this(1.0, NaiveBayes.Multinomial)
 
   /** Set the smoothing parameter. Default: 1.0. */
+  @Since("0.9.0")
   def setLambda(lambda: Double): NaiveBayes = {
     this.lambda = lambda
     this
   }
 
   /** Get the smoothing parameter. */
+  @Since("1.4.0")
   def getLambda: Double = lambda
 
   /**
    * Set the model type using a string (case-sensitive).
    * Supported options: "multinomial" (default) and "bernoulli".
    */
+  @Since("1.4.0")
   def setModelType(modelType: String): NaiveBayes = {
     require(NaiveBayes.supportedModelTypes.contains(modelType),
       s"NaiveBayes was created with an unknown modelType: $modelType.")
@@ -333,6 +346,7 @@ class NaiveBayes private (
   }
 
   /** Get the model type. */
+  @Since("1.4.0")
   def getModelType: String = this.modelType
 
   /**
@@ -340,6 +354,7 @@ class NaiveBayes private (
    *
    * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    */
+  @Since("0.9.0")
   def run(data: RDD[LabeledPoint]): NaiveBayesModel = {
     val requireNonnegativeValues: Vector => Unit = (v: Vector) => {
       val values = v match {
@@ -423,6 +438,7 @@ class NaiveBayes private (
 /**
  * Top-level methods for calling naive Bayes.
  */
+@Since("0.9.0")
 object NaiveBayes {
 
   /** String name for multinomial model type. */
@@ -485,7 +501,7 @@ object NaiveBayes {
    * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be
    *              multinomial or bernoulli
    */
-  @Since("0.9.0")
+  @Since("1.4.0")
   def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
     require(supportedModelTypes.contains(modelType),
       s"NaiveBayes was created with an unknown modelType: $modelType.")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 5f87269863572..896565cd90e89 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -33,9 +33,10 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class SVMModel (
-    override val weights: Vector,
-    override val intercept: Double)
+@Since("0.8.0")
+class SVMModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable
   with Saveable with PMMLExportable {
 
@@ -47,7 +48,7 @@ class SVMModel (
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
    */
-  @Since("1.3.0")
+  @Since("1.0.0")
   @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
@@ -92,12 +93,12 @@ class SVMModel (
 
   override protected def formatVersion: String = "1.0"
 
-  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = 2, threshold = ${threshold.getOrElse("None")}"
   }
 }
 
+@Since("1.3.0")
 object SVMModel extends Loader[SVMModel] {
 
   @Since("1.3.0")
@@ -132,6 +133,7 @@ object SVMModel extends Loader[SVMModel] {
  * regularization is used, which can be changed via [[SVMWithSGD.optimizer]].
  * NOTE: Labels used in SVM should be {0, 1}.
  */
+@Since("0.8.0")
 class SVMWithSGD private (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -141,6 +143,7 @@ class SVMWithSGD private (
 
   private val gradient = new HingeGradient()
   private val updater = new SquaredL2Updater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -152,6 +155,7 @@ class SVMWithSGD private (
    * Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParm: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {
@@ -162,6 +166,7 @@ class SVMWithSGD private (
 /**
  * Top-level methods for calling SVM. NOTE: Labels used in SVM should be {0, 1}.
  */
+@Since("0.8.0")
 object SVMWithSGD {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
index 7d33df3221fbf..75630054d1368 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
 
@@ -44,6 +44,7 @@ import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
  * }}}
  */
 @Experimental
+@Since("1.3.0")
 class StreamingLogisticRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -58,6 +59,7 @@ class StreamingLogisticRegressionWithSGD private[mllib] (
    * Initial weights must be set before using trainOn or predictOn
    * (see `StreamingLinearAlgorithm`)
    */
+  @Since("1.3.0")
   def this() = this(0.1, 50, 1.0, 0.0)
 
   protected val algorithm = new LogisticRegressionWithSGD(
@@ -66,30 +68,35 @@ class StreamingLogisticRegressionWithSGD private[mllib] (
   protected var model: Option[LogisticRegressionModel] = None
 
   /** Set the step size for gradient descent. Default: 0.1. */
+  @Since("1.3.0")
   def setStepSize(stepSize: Double): this.type = {
     this.algorithm.optimizer.setStepSize(stepSize)
     this
   }
 
   /** Set the number of iterations of gradient descent to run per update. Default: 50. */
+  @Since("1.3.0")
   def setNumIterations(numIterations: Int): this.type = {
     this.algorithm.optimizer.setNumIterations(numIterations)
     this
   }
 
   /** Set the fraction of each batch to use for updates. Default: 1.0. */
+  @Since("1.3.0")
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
     this
   }
 
   /** Set the regularization parameter. Default: 0.0. */
+  @Since("1.3.0")
   def setRegParam(regParam: Double): this.type = {
     this.algorithm.optimizer.setRegParam(regParam)
     this
   }
 
   /** Set the initial weights. Default: [0.0, 0.0]. */
+  @Since("1.3.0")
   def setInitialWeights(initialWeights: Vector): this.type = {
     this.model = Some(algorithm.createModel(initialWeights, 0.0))
     this

From 71a138cd0e0a14e8426f97877e3b52a562bbd02c Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Tue, 25 Aug 2015 13:14:10 -0700
Subject: [PATCH 1216/1454] [SPARK-10048] [SPARKR] Support arbitrary nested
 Java array in serde.

This PR:
1. supports transferring arbitrary nested array from JVM to R side in SerDe;
2. based on 1, collect() implemenation is improved. Now it can support collecting data of complex types
   from a DataFrame.

Author: Sun Rui <rui.sun@intel.com>

Closes #8276 from sun-rui/SPARK-10048.
---
 R/pkg/R/DataFrame.R                           | 55 +++++++++---
 R/pkg/R/deserialize.R                         | 72 +++++++---------
 R/pkg/R/serialize.R                           | 10 +--
 R/pkg/inst/tests/test_Serde.R                 | 77 +++++++++++++++++
 R/pkg/inst/worker/worker.R                    |  4 +-
 .../apache/spark/api/r/RBackendHandler.scala  |  7 ++
 .../scala/org/apache/spark/api/r/SerDe.scala  | 86 +++++++++++--------
 .../org/apache/spark/sql/api/r/SQLUtils.scala | 32 +------
 8 files changed, 216 insertions(+), 127 deletions(-)
 create mode 100644 R/pkg/inst/tests/test_Serde.R

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 10f3c4ea59864..ae1d912cf6da1 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -652,18 +652,49 @@ setMethod("dim",
 setMethod("collect",
           signature(x = "DataFrame"),
           function(x, stringsAsFactors = FALSE) {
-            # listCols is a list of raw vectors, one per column
-            listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf)
-            cols <- lapply(listCols, function(col) {
-              objRaw <- rawConnection(col)
-              numRows <- readInt(objRaw)
-              col <- readCol(objRaw, numRows)
-              close(objRaw)
-              col
-            })
-            names(cols) <- columns(x)
-            do.call(cbind.data.frame, list(cols, stringsAsFactors = stringsAsFactors))
-          })
+            names <- columns(x)
+            ncol <- length(names)
+            if (ncol <= 0) {
+              # empty data.frame with 0 columns and 0 rows
+              data.frame()
+            } else {
+              # listCols is a list of columns
+              listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf)
+              stopifnot(length(listCols) == ncol)
+              
+              # An empty data.frame with 0 columns and number of rows as collected
+              nrow <- length(listCols[[1]])
+              if (nrow <= 0) {
+                df <- data.frame()
+              } else {
+                df <- data.frame(row.names = 1 : nrow)                
+              }
+              
+              # Append columns one by one
+              for (colIndex in 1 : ncol) {
+                # Note: appending a column of list type into a data.frame so that
+                # data of complex type can be held. But getting a cell from a column
+                # of list type returns a list instead of a vector. So for columns of
+                # non-complex type, append them as vector.
+                col <- listCols[[colIndex]]
+                if (length(col) <= 0) {
+                  df[[names[colIndex]]] <- col
+                } else {
+                  # TODO: more robust check on column of primitive types
+                  vec <- do.call(c, col)
+                  if (class(vec) != "list") {
+                    df[[names[colIndex]]] <- vec                  
+                  } else {
+                    # For columns of complex type, be careful to access them.
+                    # Get a column of complex type returns a list.
+                    # Get a cell from a column of complex type returns a list instead of a vector.
+                    df[[names[colIndex]]] <- col
+                 }
+              }
+            }
+            df
+          }
+        })
 
 #' Limit
 #'
diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 33bf13ec9e784..6cf628e3007de 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -48,6 +48,7 @@ readTypedObject <- function(con, type) {
     "r" = readRaw(con),
     "D" = readDate(con),
     "t" = readTime(con),
+    "a" = readArray(con),
     "l" = readList(con),
     "n" = NULL,
     "j" = getJobj(readString(con)),
@@ -85,8 +86,7 @@ readTime <- function(con) {
   as.POSIXct(t, origin = "1970-01-01")
 }
 
-# We only support lists where all elements are of same type
-readList <- function(con) {
+readArray <- function(con) {
   type <- readType(con)
   len <- readInt(con)
   if (len > 0) {
@@ -100,6 +100,25 @@ readList <- function(con) {
   }
 }
 
+# Read a list. Types of each element may be different.
+# Null objects are read as NA.
+readList <- function(con) {
+  len <- readInt(con)
+  if (len > 0) {
+    l <- vector("list", len)
+    for (i in 1:len) {
+      elem <- readObject(con)
+      if (is.null(elem)) {
+        elem <- NA
+      }
+      l[[i]] <- elem
+    }
+    l
+  } else {
+    list()
+  }
+}
+
 readRaw <- function(con) {
   dataLen <- readInt(con)
   readBin(con, raw(), as.integer(dataLen), endian = "big")
@@ -132,18 +151,19 @@ readDeserialize <- function(con) {
   }
 }
 
-readDeserializeRows <- function(inputCon) {
-  # readDeserializeRows will deserialize a DataOutputStream composed of
-  # a list of lists. Since the DOS is one continuous stream and
-  # the number of rows varies, we put the readRow function in a while loop
-  # that termintates when the next row is empty.
+readMultipleObjects <- function(inputCon) {
+  # readMultipleObjects will read multiple continuous objects from
+  # a DataOutputStream. There is no preceding field telling the count
+  # of the objects, so the number of objects varies, we try to read
+  # all objects in a loop until the end of the stream.
   data <- list()
   while(TRUE) {
-    row <- readRow(inputCon)
-    if (length(row) == 0) {
+    # If reaching the end of the stream, type returned should be "".
+    type <- readType(inputCon)
+    if (type == "") {
       break
     }
-    data[[length(data) + 1L]] <- row
+    data[[length(data) + 1L]] <- readTypedObject(inputCon, type)
   }
   data # this is a list of named lists now
 }
@@ -155,35 +175,5 @@ readRowList <- function(obj) {
   # deserialize the row.
   rawObj <- rawConnection(obj, "r+")
   on.exit(close(rawObj))
-  readRow(rawObj)
-}
-
-readRow <- function(inputCon) {
-  numCols <- readInt(inputCon)
-  if (length(numCols) > 0 && numCols > 0) {
-    lapply(1:numCols, function(x) {
-      obj <- readObject(inputCon)
-      if (is.null(obj)) {
-        NA
-      } else {
-        obj
-      }
-    }) # each row is a list now
-  } else {
-    list()
-  }
-}
-
-# Take a single column as Array[Byte] and deserialize it into an atomic vector
-readCol <- function(inputCon, numRows) {
-  if (numRows > 0) {
-    # sapply can not work with POSIXlt
-    do.call(c, lapply(1:numRows, function(x) {
-      value <- readObject(inputCon)
-      # Replace NULL with NA so we can coerce to vectors
-      if (is.null(value)) NA else value
-    }))
-  } else {
-    vector()
-  }
+  readObject(rawObj)
 }
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R
index 311021e5d8473..e3676f57f907f 100644
--- a/R/pkg/R/serialize.R
+++ b/R/pkg/R/serialize.R
@@ -110,18 +110,10 @@ writeRowSerialize <- function(outputCon, rows) {
 serializeRow <- function(row) {
   rawObj <- rawConnection(raw(0), "wb")
   on.exit(close(rawObj))
-  writeRow(rawObj, row)
+  writeGenericList(rawObj, row)
   rawConnectionValue(rawObj)
 }
 
-writeRow <- function(con, row) {
-  numCols <- length(row)
-  writeInt(con, numCols)
-  for (i in 1:numCols) {
-    writeObject(con, row[[i]])
-  }
-}
-
 writeRaw <- function(con, batch) {
   writeInt(con, length(batch))
   writeBin(batch, con, endian = "big")
diff --git a/R/pkg/inst/tests/test_Serde.R b/R/pkg/inst/tests/test_Serde.R
new file mode 100644
index 0000000000000..009db85da2beb
--- /dev/null
+++ b/R/pkg/inst/tests/test_Serde.R
@@ -0,0 +1,77 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+context("SerDe functionality")
+
+sc <- sparkR.init()
+
+test_that("SerDe of primitive types", {
+  x <- callJStatic("SparkRHandler", "echo", 1L)
+  expect_equal(x, 1L)
+  expect_equal(class(x), "integer")
+  
+  x <- callJStatic("SparkRHandler", "echo", 1)
+  expect_equal(x, 1)
+  expect_equal(class(x), "numeric")
+
+  x <- callJStatic("SparkRHandler", "echo", TRUE)
+  expect_true(x)
+  expect_equal(class(x), "logical")
+  
+  x <- callJStatic("SparkRHandler", "echo", "abc")
+  expect_equal(x, "abc")
+  expect_equal(class(x), "character")  
+})
+
+test_that("SerDe of list of primitive types", {
+  x <- list(1L, 2L, 3L)
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "integer")
+
+  x <- list(1, 2, 3)
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "numeric")
+  
+  x <- list(TRUE, FALSE)
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "logical")
+  
+  x <- list("a", "b", "c")
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "character")
+  
+  # Empty list
+  x <- list()
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+})
+
+test_that("SerDe of list of lists", {
+  x <- list(list(1L, 2L, 3L), list(1, 2, 3),
+            list(TRUE, FALSE), list("a", "b", "c"))
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+
+  # List of empty lists
+  x <- list(list(), list())
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+})
diff --git a/R/pkg/inst/worker/worker.R b/R/pkg/inst/worker/worker.R
index 7e3b5fc403b25..0c3b0d1f4be20 100644
--- a/R/pkg/inst/worker/worker.R
+++ b/R/pkg/inst/worker/worker.R
@@ -94,7 +94,7 @@ if (isEmpty != 0) {
     } else if (deserializer == "string") {
       data <- as.list(readLines(inputCon))
     } else if (deserializer == "row") {
-      data <- SparkR:::readDeserializeRows(inputCon)
+      data <- SparkR:::readMultipleObjects(inputCon)
     }
     # Timing reading input data for execution
     inputElap <- elapsedSecs()
@@ -120,7 +120,7 @@ if (isEmpty != 0) {
     } else if (deserializer == "string") {
       data <- readLines(inputCon)
     } else if (deserializer == "row") {
-      data <- SparkR:::readDeserializeRows(inputCon)
+      data <- SparkR:::readMultipleObjects(inputCon)
     }
     # Timing reading input data for execution
     inputElap <- elapsedSecs()
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index 6ce02e2ea336a..bb82f3285f1d9 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -53,6 +53,13 @@ private[r] class RBackendHandler(server: RBackend)
 
     if (objId == "SparkRHandler") {
       methodName match {
+        // This function is for test-purpose only
+        case "echo" =>
+          val args = readArgs(numArgs, dis)
+          assert(numArgs == 1)
+
+          writeInt(dos, 0)
+          writeObject(dos, args(0))
         case "stopBackend" =>
           writeInt(dos, 0)
           writeType(dos, "void")
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index dbbbcf40c1e96..26ad4f1d4697e 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -149,6 +149,10 @@ private[spark] object SerDe {
       case 'b' => readBooleanArr(dis)
       case 'j' => readStringArr(dis).map(x => JVMObjectTracker.getObject(x))
       case 'r' => readBytesArr(dis)
+      case 'l' => {
+        val len = readInt(dis)
+        (0 until len).map(_ => readList(dis)).toArray
+      }
       case _ => throw new IllegalArgumentException(s"Invalid array type $arrType")
     }
   }
@@ -200,6 +204,9 @@ private[spark] object SerDe {
       case "date" => dos.writeByte('D')
       case "time" => dos.writeByte('t')
       case "raw" => dos.writeByte('r')
+      // Array of primitive types
+      case "array" => dos.writeByte('a')
+      // Array of objects
       case "list" => dos.writeByte('l')
       case "jobj" => dos.writeByte('j')
       case _ => throw new IllegalArgumentException(s"Invalid type $typeStr")
@@ -211,26 +218,35 @@ private[spark] object SerDe {
       writeType(dos, "void")
     } else {
       value.getClass.getName match {
+        case "java.lang.Character" =>
+          writeType(dos, "character")
+          writeString(dos, value.asInstanceOf[Character].toString)
         case "java.lang.String" =>
           writeType(dos, "character")
           writeString(dos, value.asInstanceOf[String])
-        case "long" | "java.lang.Long" =>
+        case "java.lang.Long" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Long].toDouble)
-        case "float" | "java.lang.Float" =>
+        case "java.lang.Float" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Float].toDouble)
-        case "decimal" | "java.math.BigDecimal" =>
+        case "java.math.BigDecimal" =>
           writeType(dos, "double")
           val javaDecimal = value.asInstanceOf[java.math.BigDecimal]
           writeDouble(dos, scala.math.BigDecimal(javaDecimal).toDouble)
-        case "double" | "java.lang.Double" =>
+        case "java.lang.Double" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Double])
-        case "int" | "java.lang.Integer" =>
+        case "java.lang.Byte" =>
+          writeType(dos, "integer")
+          writeInt(dos, value.asInstanceOf[Byte].toInt)
+        case "java.lang.Short" =>
+          writeType(dos, "integer")
+          writeInt(dos, value.asInstanceOf[Short].toInt)
+        case "java.lang.Integer" =>
           writeType(dos, "integer")
           writeInt(dos, value.asInstanceOf[Int])
-        case "boolean" | "java.lang.Boolean" =>
+        case "java.lang.Boolean" =>
           writeType(dos, "logical")
           writeBoolean(dos, value.asInstanceOf[Boolean])
         case "java.sql.Date" =>
@@ -242,43 +258,48 @@ private[spark] object SerDe {
         case "java.sql.Timestamp" =>
           writeType(dos, "time")
           writeTime(dos, value.asInstanceOf[Timestamp])
+
+        // Handle arrays
+
+        // Array of primitive types
+
+        // Special handling for byte array
         case "[B" =>
           writeType(dos, "raw")
           writeBytes(dos, value.asInstanceOf[Array[Byte]])
-        // TODO: Types not handled right now include
-        // byte, char, short, float
 
-        // Handle arrays
-        case "[Ljava.lang.String;" =>
-          writeType(dos, "list")
-          writeStringArr(dos, value.asInstanceOf[Array[String]])
+        case "[C" =>
+          writeType(dos, "array")
+          writeStringArr(dos, value.asInstanceOf[Array[Char]].map(_.toString))
+        case "[S" =>
+          writeType(dos, "array")
+          writeIntArr(dos, value.asInstanceOf[Array[Short]].map(_.toInt))
         case "[I" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeIntArr(dos, value.asInstanceOf[Array[Int]])
         case "[J" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeDoubleArr(dos, value.asInstanceOf[Array[Long]].map(_.toDouble))
+        case "[F" =>
+          writeType(dos, "array")
+          writeDoubleArr(dos, value.asInstanceOf[Array[Float]].map(_.toDouble))
         case "[D" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeDoubleArr(dos, value.asInstanceOf[Array[Double]])
         case "[Z" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeBooleanArr(dos, value.asInstanceOf[Array[Boolean]])
-        case "[[B" =>
+
+        // Array of objects, null objects use "void" type
+        case c if c.startsWith("[") =>
           writeType(dos, "list")
-          writeBytesArr(dos, value.asInstanceOf[Array[Array[Byte]]])
-        case otherName =>
-          // Handle array of objects
-          if (otherName.startsWith("[L")) {
-            val objArr = value.asInstanceOf[Array[Object]]
-            writeType(dos, "list")
-            writeType(dos, "jobj")
-            dos.writeInt(objArr.length)
-            objArr.foreach(o => writeJObj(dos, o))
-          } else {
-            writeType(dos, "jobj")
-            writeJObj(dos, value)
-          }
+          val array = value.asInstanceOf[Array[Object]]
+          writeInt(dos, array.length)
+          array.foreach(elem => writeObject(dos, elem))
+
+        case _ =>
+          writeType(dos, "jobj")
+          writeJObj(dos, value)
       }
     }
   }
@@ -350,11 +371,6 @@ private[spark] object SerDe {
     value.foreach(v => writeString(out, v))
   }
 
-  def writeBytesArr(out: DataOutputStream, value: Array[Array[Byte]]): Unit = {
-    writeType(out, "raw")
-    out.writeInt(value.length)
-    value.foreach(v => writeBytes(out, v))
-  }
 }
 
 private[r] object SerializationFormats {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
index 92861ab038f19..7f3defec3d42e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
@@ -98,27 +98,17 @@ private[r] object SQLUtils {
     val bos = new ByteArrayOutputStream()
     val dos = new DataOutputStream(bos)
 
-    SerDe.writeInt(dos, row.length)
-    (0 until row.length).map { idx =>
-      val obj: Object = row(idx).asInstanceOf[Object]
-      SerDe.writeObject(dos, obj)
-    }
+    val cols = (0 until row.length).map(row(_).asInstanceOf[Object]).toArray
+    SerDe.writeObject(dos, cols)
     bos.toByteArray()
   }
 
-  def dfToCols(df: DataFrame): Array[Array[Byte]] = {
+  def dfToCols(df: DataFrame): Array[Array[Any]] = {
     // localDF is Array[Row]
     val localDF = df.collect()
     val numCols = df.columns.length
-    // dfCols is Array[Array[Any]]
-    val dfCols = convertRowsToColumns(localDF, numCols)
-
-    dfCols.map { col =>
-      colToRBytes(col)
-    }
-  }
 
-  def convertRowsToColumns(localDF: Array[Row], numCols: Int): Array[Array[Any]] = {
+    // result is Array[Array[Any]]
     (0 until numCols).map { colIdx =>
       localDF.map { row =>
         row(colIdx)
@@ -126,20 +116,6 @@ private[r] object SQLUtils {
     }.toArray
   }
 
-  def colToRBytes(col: Array[Any]): Array[Byte] = {
-    val numRows = col.length
-    val bos = new ByteArrayOutputStream()
-    val dos = new DataOutputStream(bos)
-
-    SerDe.writeInt(dos, numRows)
-
-    col.map { item =>
-      val obj: Object = item.asInstanceOf[Object]
-      SerDe.writeObject(dos, obj)
-    }
-    bos.toByteArray()
-  }
-
   def saveMode(mode: String): SaveMode = {
     mode match {
       case "append" => SaveMode.Append

From c0e9ff1588b4d9313cc6ec6e00e5c7663eb67910 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 13:21:05 -0700
Subject: [PATCH 1217/1454] [SPARK-9800] Adds docs for
 GradientDescent$.runMiniBatchSGD alias

* Adds doc for alias of runMIniBatchSGD documenting default value for convergeTol
* Cleans up a note in code

Author: Feynman Liang <fliang@databricks.com>

Closes #8425 from feynmanliang/SPARK-9800.
---
 .../apache/spark/mllib/optimization/GradientDescent.scala    | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index 8f0d1e4aa010a..3b663b5defb03 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -235,7 +235,7 @@ object GradientDescent extends Logging {
 
       if (miniBatchSize > 0) {
         /**
-         * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration
+         * lossSum is computed using the weights from the previous iteration
          * and regVal is the regularization value computed in the previous iteration as well.
          */
         stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
@@ -264,6 +264,9 @@ object GradientDescent extends Logging {
 
   }
 
+  /**
+   * Alias of [[runMiniBatchSGD]] with convergenceTol set to default value of 0.001.
+   */
   def runMiniBatchSGD(
       data: RDD[(Double, Vector)],
       gradient: Gradient,

From c619c7552f22d28cfa321ce671fc9ca854dd655f Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 13:22:38 -0700
Subject: [PATCH 1218/1454] [SPARK-10237] [MLLIB] update since versions in
 mllib.fpm

Same as #8421 but for `mllib.fpm`.

cc feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8429 from mengxr/SPARK-10237.
---
 .../spark/mllib/fpm/AssociationRules.scala    |  7 ++++--
 .../org/apache/spark/mllib/fpm/FPGrowth.scala |  9 ++++++--
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 23 ++++++++++++++++---
 3 files changed, 32 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index ba3b447a83398..95c688c86a7e4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -82,12 +82,15 @@ class AssociationRules private[fpm] (
     }.filter(_.confidence >= minConfidence)
   }
 
+  /** Java-friendly version of [[run]]. */
+  @Since("1.5.0")
   def run[Item](freqItemsets: JavaRDD[FreqItemset[Item]]): JavaRDD[Rule[Item]] = {
     val tag = fakeClassTag[Item]
     run(freqItemsets.rdd)(tag)
   }
 }
 
+@Since("1.5.0")
 object AssociationRules {
 
   /**
@@ -104,8 +107,8 @@ object AssociationRules {
   @Since("1.5.0")
   @Experimental
   class Rule[Item] private[fpm] (
-      val antecedent: Array[Item],
-      val consequent: Array[Item],
+      @Since("1.5.0") val antecedent: Array[Item],
+      @Since("1.5.0") val consequent: Array[Item],
       freqUnion: Double,
       freqAntecedent: Double) extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index e37f806271680..aea5c4f8a8a7d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -42,7 +42,8 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("1.3.0")
 @Experimental
-class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
+class FPGrowthModel[Item: ClassTag] @Since("1.3.0") (
+    @Since("1.3.0") val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
   /**
    * Generates association rules for the [[Item]]s in [[freqItemsets]].
    * @param confidence minimal confidence of the rules produced
@@ -126,6 +127,8 @@ class FPGrowth private (
     new FPGrowthModel(freqItemsets)
   }
 
+  /** Java-friendly version of [[run]]. */
+  @Since("1.3.0")
   def run[Item, Basket <: JavaIterable[Item]](data: JavaRDD[Basket]): FPGrowthModel[Item] = {
     implicit val tag = fakeClassTag[Item]
     run(data.rdd.map(_.asScala.toArray))
@@ -226,7 +229,9 @@ object FPGrowth {
    *
    */
   @Since("1.3.0")
-  class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
+  class FreqItemset[Item] @Since("1.3.0") (
+      @Since("1.3.0") val items: Array[Item],
+      @Since("1.3.0") val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index dc4ae1d0b69ed..97916daa2e9ad 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -25,7 +25,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.rdd.RDD
@@ -51,6 +51,7 @@ import org.apache.spark.storage.StorageLevel
  *       (Wikipedia)]]
  */
 @Experimental
+@Since("1.5.0")
 class PrefixSpan private (
     private var minSupport: Double,
     private var maxPatternLength: Int,
@@ -61,17 +62,20 @@ class PrefixSpan private (
    * Constructs a default instance with default parameters
    * {minSupport: `0.1`, maxPatternLength: `10`, maxLocalProjDBSize: `32000000L`}.
    */
+  @Since("1.5.0")
   def this() = this(0.1, 10, 32000000L)
 
   /**
    * Get the minimal support (i.e. the frequency of occurrence before a pattern is considered
    * frequent).
    */
+  @Since("1.5.0")
   def getMinSupport: Double = minSupport
 
   /**
    * Sets the minimal support level (default: `0.1`).
    */
+  @Since("1.5.0")
   def setMinSupport(minSupport: Double): this.type = {
     require(minSupport >= 0 && minSupport <= 1,
       s"The minimum support value must be in [0, 1], but got $minSupport.")
@@ -82,11 +86,13 @@ class PrefixSpan private (
   /**
    * Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
    */
+  @Since("1.5.0")
   def getMaxPatternLength: Int = maxPatternLength
 
   /**
    * Sets maximal pattern length (default: `10`).
    */
+  @Since("1.5.0")
   def setMaxPatternLength(maxPatternLength: Int): this.type = {
     // TODO: support unbounded pattern length when maxPatternLength = 0
     require(maxPatternLength >= 1,
@@ -98,12 +104,14 @@ class PrefixSpan private (
   /**
    * Gets the maximum number of items allowed in a projected database before local processing.
    */
+  @Since("1.5.0")
   def getMaxLocalProjDBSize: Long = maxLocalProjDBSize
 
   /**
    * Sets the maximum number of items (including delimiters used in the internal storage format)
    * allowed in a projected database before local processing (default: `32000000L`).
    */
+  @Since("1.5.0")
   def setMaxLocalProjDBSize(maxLocalProjDBSize: Long): this.type = {
     require(maxLocalProjDBSize >= 0L,
       s"The maximum local projected database size must be nonnegative, but got $maxLocalProjDBSize")
@@ -116,6 +124,7 @@ class PrefixSpan private (
    * @param data sequences of itemsets.
    * @return a [[PrefixSpanModel]] that contains the frequent patterns
    */
+  @Since("1.5.0")
   def run[Item: ClassTag](data: RDD[Array[Array[Item]]]): PrefixSpanModel[Item] = {
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
@@ -202,6 +211,7 @@ class PrefixSpan private (
    * @tparam Sequence sequence type, which is an Iterable of Itemsets
    * @return a [[PrefixSpanModel]] that contains the frequent sequential patterns
    */
+  @Since("1.5.0")
   def run[Item, Itemset <: jl.Iterable[Item], Sequence <: jl.Iterable[Itemset]](
       data: JavaRDD[Sequence]): PrefixSpanModel[Item] = {
     implicit val tag = fakeClassTag[Item]
@@ -211,6 +221,7 @@ class PrefixSpan private (
 }
 
 @Experimental
+@Since("1.5.0")
 object PrefixSpan extends Logging {
 
   /**
@@ -535,10 +546,14 @@ object PrefixSpan extends Logging {
    * @param freq frequency
    * @tparam Item item type
    */
-  class FreqSequence[Item](val sequence: Array[Array[Item]], val freq: Long) extends Serializable {
+  @Since("1.5.0")
+  class FreqSequence[Item] @Since("1.5.0") (
+      @Since("1.5.0") val sequence: Array[Array[Item]],
+      @Since("1.5.0") val freq: Long) extends Serializable {
     /**
      * Returns sequence as a Java List of lists for Java users.
      */
+    @Since("1.5.0")
     def javaSequence: ju.List[ju.List[Item]] = sequence.map(_.toList.asJava).toList.asJava
   }
 }
@@ -548,5 +563,7 @@ object PrefixSpan extends Logging {
  * @param freqSequences frequent sequences
  * @tparam Item item type
  */
-class PrefixSpanModel[Item](val freqSequences: RDD[PrefixSpan.FreqSequence[Item]])
+@Since("1.5.0")
+class PrefixSpanModel[Item] @Since("1.5.0") (
+    @Since("1.5.0") val freqSequences: RDD[PrefixSpan.FreqSequence[Item]])
   extends Serializable

From 9205907876cf65695e56c2a94bedd83df3675c03 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 13:23:15 -0700
Subject: [PATCH 1219/1454] [SPARK-9797] [MLLIB] [DOC]
 StreamingLinearRegressionWithSGD.setConvergenceTol default value

Adds default convergence tolerance (0.001, set in `GradientDescent.convergenceTol`) to `setConvergenceTol`'s scaladoc

Author: Feynman Liang <fliang@databricks.com>

Closes #8424 from feynmanliang/SPARK-9797.
---
 .../mllib/regression/StreamingLinearRegressionWithSGD.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index 537a05274eec2..26654e4a06838 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -93,7 +93,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   }
 
   /**
-   * Set the convergence tolerance.
+   * Set the convergence tolerance. Default: 0.001.
    */
   def setConvergenceTol(tolerance: Double): this.type = {
     this.algorithm.optimizer.setConvergenceTol(tolerance)

From 00ae4be97f7b205432db2967ba6d506286ef2ca6 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 14:11:38 -0700
Subject: [PATCH 1220/1454] [SPARK-10239] [SPARK-10244] [MLLIB] update since
 versions in mllib.pmml and mllib.util

Same as #8421 but for `mllib.pmml` and `mllib.util`.

cc dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8430 from mengxr/SPARK-10239 and squashes the following commits:

a189acf [Xiangrui Meng] update since versions in mllib.pmml and mllib.util
---
 .../org/apache/spark/mllib/pmml/PMMLExportable.scala   |  7 ++++++-
 .../org/apache/spark/mllib/util/DataValidators.scala   |  7 +++++--
 .../apache/spark/mllib/util/KMeansDataGenerator.scala  |  5 ++++-
 .../apache/spark/mllib/util/LinearDataGenerator.scala  | 10 ++++++++--
 .../mllib/util/LogisticRegressionDataGenerator.scala   |  5 ++++-
 .../org/apache/spark/mllib/util/MFDataGenerator.scala  |  4 +++-
 .../scala/org/apache/spark/mllib/util/MLUtils.scala    |  2 ++
 .../org/apache/spark/mllib/util/SVMDataGenerator.scala |  6 ++++--
 .../org/apache/spark/mllib/util/modelSaveLoad.scala    |  6 +++++-
 9 files changed, 41 insertions(+), 11 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala
index 5e882d4ebb10b..274ac7c99553b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala
@@ -23,7 +23,7 @@ import javax.xml.transform.stream.StreamResult
 import org.jpmml.model.JAXBUtil
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
 
 /**
@@ -33,6 +33,7 @@ import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
  * developed by the Data Mining Group (www.dmg.org).
  */
 @DeveloperApi
+@Since("1.4.0")
 trait PMMLExportable {
 
   /**
@@ -48,6 +49,7 @@ trait PMMLExportable {
    * Export the model to a local file in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(localPath: String): Unit = {
     toPMML(new StreamResult(new File(localPath)))
   }
@@ -57,6 +59,7 @@ trait PMMLExportable {
    * Export the model to a directory on a distributed file system in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(sc: SparkContext, path: String): Unit = {
     val pmml = toPMML()
     sc.parallelize(Array(pmml), 1).saveAsTextFile(path)
@@ -67,6 +70,7 @@ trait PMMLExportable {
    * Export the model to the OutputStream in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(outputStream: OutputStream): Unit = {
     toPMML(new StreamResult(outputStream))
   }
@@ -76,6 +80,7 @@ trait PMMLExportable {
    * Export the model to a String in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(): String = {
     val writer = new StringWriter
     toPMML(new StreamResult(writer))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
index be335a1aca58a..dffe6e78939e8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
@@ -17,16 +17,17 @@
 
 package org.apache.spark.mllib.util
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.Logging
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
  * A collection of methods used to validate data before applying ML algorithms.
  */
 @DeveloperApi
+@Since("0.8.0")
 object DataValidators extends Logging {
 
   /**
@@ -34,6 +35,7 @@ object DataValidators extends Logging {
    *
    * @return True if labels are all zero or one, false otherwise.
    */
+  @Since("1.0.0")
   val binaryLabelValidator: RDD[LabeledPoint] => Boolean = { data =>
     val numInvalid = data.filter(x => x.label != 1.0 && x.label != 0.0).count()
     if (numInvalid != 0) {
@@ -48,6 +50,7 @@ object DataValidators extends Logging {
    *
    * @return True if labels are all in the range of {0, 1, ..., k-1}, false otherwise.
    */
+  @Since("1.3.0")
   def multiLabelValidator(k: Int): RDD[LabeledPoint] => Boolean = { data =>
     val numInvalid = data.filter(x =>
       x.label - x.label.toInt != 0.0 || x.label < 0 || x.label > k - 1).count()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
index e6bcff48b022c..00fd1606a369c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
@@ -19,8 +19,8 @@ package org.apache.spark.mllib.util
 
 import scala.util.Random
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.rdd.RDD
 
 /**
@@ -30,6 +30,7 @@ import org.apache.spark.rdd.RDD
  * cluster with scale 1 around each center.
  */
 @DeveloperApi
+@Since("0.8.0")
 object KMeansDataGenerator {
 
   /**
@@ -42,6 +43,7 @@ object KMeansDataGenerator {
    * @param r Scaling factor for the distribution of the initial centers
    * @param numPartitions Number of partitions of the generated RDD; default 2
    */
+  @Since("0.8.0")
   def generateKMeansRDD(
       sc: SparkContext,
       numPoints: Int,
@@ -62,6 +64,7 @@ object KMeansDataGenerator {
     }
   }
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 6) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index 7a1c7796065ee..d0ba454f379a9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -22,11 +22,11 @@ import scala.util.Random
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
@@ -35,6 +35,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
  * response variable `Y`.
  */
 @DeveloperApi
+@Since("0.8.0")
 object LinearDataGenerator {
 
   /**
@@ -46,6 +47,7 @@ object LinearDataGenerator {
    * @param seed Random seed
    * @return Java List of input.
    */
+  @Since("0.8.0")
   def generateLinearInputAsList(
       intercept: Double,
       weights: Array[Double],
@@ -68,6 +70,7 @@ object LinearDataGenerator {
    * @param eps Epsilon scaling factor.
    * @return Seq of input.
    */
+  @Since("0.8.0")
   def generateLinearInput(
       intercept: Double,
       weights: Array[Double],
@@ -92,6 +95,7 @@ object LinearDataGenerator {
    * @param eps Epsilon scaling factor.
    * @return Seq of input.
    */
+  @Since("0.8.0")
   def generateLinearInput(
       intercept: Double,
       weights: Array[Double],
@@ -132,6 +136,7 @@ object LinearDataGenerator {
    *
    * @return RDD of LabeledPoint containing sample data.
    */
+  @Since("0.8.0")
   def generateLinearRDD(
       sc: SparkContext,
       nexamples: Int,
@@ -151,6 +156,7 @@ object LinearDataGenerator {
     data
   }
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 2) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
index c09cbe69bb971..33477ee20ebbd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.util
 
 import scala.util.Random
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -31,6 +31,7 @@ import org.apache.spark.mllib.linalg.Vectors
  * with probability `probOne` and scales features for positive examples by `eps`.
  */
 @DeveloperApi
+@Since("0.8.0")
 object LogisticRegressionDataGenerator {
 
   /**
@@ -43,6 +44,7 @@ object LogisticRegressionDataGenerator {
    * @param nparts Number of partitions of the generated RDD. Default value is 2.
    * @param probOne Probability that a label is 1 (and not 0). Default value is 0.5.
    */
+  @Since("0.8.0")
   def generateLogisticRDD(
     sc: SparkContext,
     nexamples: Int,
@@ -62,6 +64,7 @@ object LogisticRegressionDataGenerator {
     data
   }
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length != 5) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
index 16f430599a515..906bd30563bd0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
@@ -23,7 +23,7 @@ import scala.language.postfixOps
 import scala.util.Random
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix}
 import org.apache.spark.rdd.RDD
 
@@ -52,7 +52,9 @@ import org.apache.spark.rdd.RDD
  *   testSampFact   (Double) Percentage of training data to use as test data.
  */
 @DeveloperApi
+@Since("0.8.0")
 object MFDataGenerator {
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 2) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 4940974bf4f41..81c2f0ce6e12c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -36,6 +36,7 @@ import org.apache.spark.streaming.dstream.DStream
 /**
  * Helper methods to load, save and pre-process data used in ML Lib.
  */
+@Since("0.8.0")
 object MLUtils {
 
   private[mllib] lazy val EPSILON = {
@@ -168,6 +169,7 @@ object MLUtils {
    *
    * @see [[org.apache.spark.mllib.util.MLUtils#loadLibSVMFile]]
    */
+  @Since("1.0.0")
   def saveAsLibSVMFile(data: RDD[LabeledPoint], dir: String) {
     // TODO: allow to specify label precision and feature precision.
     val dataStr = data.map { case LabeledPoint(label, features) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
index ad20b7694a779..cde5979396178 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
@@ -21,11 +21,11 @@ import scala.util.Random
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
@@ -33,8 +33,10 @@ import org.apache.spark.mllib.regression.LabeledPoint
  * for the features and adds Gaussian noise with weight 0.1 to generate labels.
  */
 @DeveloperApi
+@Since("0.8.0")
 object SVMDataGenerator {
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 2) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala
index 30d642c754b7c..4d71d534a0774 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala
@@ -24,7 +24,7 @@ import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 
@@ -35,6 +35,7 @@ import org.apache.spark.sql.types.{DataType, StructField, StructType}
  * This should be inherited by the class which implements model instances.
  */
 @DeveloperApi
+@Since("1.3.0")
 trait Saveable {
 
   /**
@@ -50,6 +51,7 @@ trait Saveable {
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
    */
+  @Since("1.3.0")
   def save(sc: SparkContext, path: String): Unit
 
   /** Current version of model save/load format. */
@@ -64,6 +66,7 @@ trait Saveable {
  * This should be inherited by an object paired with the model class.
  */
 @DeveloperApi
+@Since("1.3.0")
 trait Loader[M <: Saveable] {
 
   /**
@@ -75,6 +78,7 @@ trait Loader[M <: Saveable] {
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
    */
+  @Since("1.3.0")
   def load(sc: SparkContext, path: String): M
 
 }

From ec89bd840a6862751999d612f586a962cae63f6d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 25 Aug 2015 14:55:34 -0700
Subject: [PATCH 1221/1454] [SPARK-10245] [SQL] Fix decimal literals with
 precision < scale

In BigDecimal or java.math.BigDecimal, the precision could be smaller than scale, for example, BigDecimal("0.001") has precision = 1 and scale = 3. But DecimalType require that the precision should be larger than scale, so we should use the maximum of precision and scale when inferring the schema from decimal literal.

Author: Davies Liu <davies@databricks.com>

Closes #8428 from davies/smaller_decimal.
---
 .../spark/sql/catalyst/expressions/literals.scala      |  7 ++++---
 .../catalyst/expressions/LiteralExpressionSuite.scala  |  8 +++++---
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala     | 10 ++++++++++
 3 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index 34bad23802ba4..8c0c5d5b1e31e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -36,9 +36,10 @@ object Literal {
     case s: Short => Literal(s, ShortType)
     case s: String => Literal(UTF8String.fromString(s), StringType)
     case b: Boolean => Literal(b, BooleanType)
-    case d: BigDecimal => Literal(Decimal(d), DecimalType(d.precision, d.scale))
-    case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType(d.precision(), d.scale()))
-    case d: Decimal => Literal(d, DecimalType(d.precision, d.scale))
+    case d: BigDecimal => Literal(Decimal(d), DecimalType(Math.max(d.precision, d.scale), d.scale))
+    case d: java.math.BigDecimal =>
+      Literal(Decimal(d), DecimalType(Math.max(d.precision, d.scale), d.scale()))
+    case d: Decimal => Literal(d, DecimalType(Math.max(d.precision, d.scale), d.scale))
     case t: Timestamp => Literal(DateTimeUtils.fromJavaTimestamp(t), TimestampType)
     case d: Date => Literal(DateTimeUtils.fromJavaDate(d), DateType)
     case a: Array[Byte] => Literal(a, BinaryType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
index f6404d21611e5..015eb1897fb8c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
@@ -83,12 +83,14 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("decimal") {
-    List(0.0, 1.2, 1.1111, 5).foreach { d =>
+    List(-0.0001, 0.0, 0.001, 1.2, 1.1111, 5).foreach { d =>
       checkEvaluation(Literal(Decimal(d)), Decimal(d))
       checkEvaluation(Literal(Decimal(d.toInt)), Decimal(d.toInt))
       checkEvaluation(Literal(Decimal(d.toLong)), Decimal(d.toLong))
-      checkEvaluation(Literal(Decimal((d * 1000L).toLong, 10, 1)),
-        Decimal((d * 1000L).toLong, 10, 1))
+      checkEvaluation(Literal(Decimal((d * 1000L).toLong, 10, 3)),
+        Decimal((d * 1000L).toLong, 10, 3))
+      checkEvaluation(Literal(BigDecimal(d.toString)), Decimal(d))
+      checkEvaluation(Literal(new java.math.BigDecimal(d.toString)), Decimal(d))
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index dcb4e83710982..aa07665c6b705 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1627,6 +1627,16 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       Row(null))
   }
 
+  test("precision smaller than scale") {
+    checkAnswer(sql("select 10.00"), Row(BigDecimal("10.00")))
+    checkAnswer(sql("select 1.00"), Row(BigDecimal("1.00")))
+    checkAnswer(sql("select 0.10"), Row(BigDecimal("0.10")))
+    checkAnswer(sql("select 0.01"), Row(BigDecimal("0.01")))
+    checkAnswer(sql("select 0.001"), Row(BigDecimal("0.001")))
+    checkAnswer(sql("select -0.01"), Row(BigDecimal("-0.01")))
+    checkAnswer(sql("select -0.001"), Row(BigDecimal("-0.001")))
+  }
+
   test("external sorting updates peak execution memory") {
     withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
       val sc = sqlContext.sparkContext

From 7467b52ed07f174d93dfc4cb544dc4b69a2c2826 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 25 Aug 2015 15:19:41 -0700
Subject: [PATCH 1222/1454] [SPARK-10215] [SQL] Fix precision of division
 (follow the rule in Hive)

Follow the rule in Hive for decimal division. see https://github.com/apache/hive/blob/ac755ebe26361a4647d53db2a28500f71697b276/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFOPDivide.java#L113

cc chenghao-intel

Author: Davies Liu <davies@databricks.com>

Closes #8415 from davies/decimal_div2.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 10 ++++++--
 .../sql/catalyst/analysis/AnalysisSuite.scala |  9 +++----
 .../analysis/DecimalPrecisionSuite.scala      |  8 +++---
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 25 +++++++++++++++++--
 4 files changed, 39 insertions(+), 13 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index a1aa2a2b2c680..87c11abbad490 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -396,8 +396,14 @@ object HiveTypeCoercion {
             resultType)
 
         case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          val resultType = DecimalType.bounded(p1 - s1 + s2 + max(6, s1 + p2 + 1),
-            max(6, s1 + p2 + 1))
+          var intDig = min(DecimalType.MAX_SCALE, p1 - s1 + s2)
+          var decDig = min(DecimalType.MAX_SCALE, max(6, s1 + p2 + 1))
+          val diff = (intDig + decDig) - DecimalType.MAX_SCALE
+          if (diff > 0) {
+            decDig -= diff / 2 + 1
+            intDig = DecimalType.MAX_SCALE - decDig
+          }
+          val resultType = DecimalType.bounded(intDig + decDig, decDig)
           val widerType = widerDecimalType(p1, s1, p2, s2)
           CheckOverflow(Divide(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
             resultType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 1e0cc81dae974..820b336aac759 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -17,15 +17,14 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.catalyst.SimpleCatalystConf
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.dsl.plans._
 
 class AnalysisSuite extends AnalysisTest {
-  import TestRelations._
+  import org.apache.spark.sql.catalyst.analysis.TestRelations._
 
   test("union project *") {
     val plan = (1 to 100)
@@ -96,7 +95,7 @@ class AnalysisSuite extends AnalysisTest {
     assert(pl(1).dataType == DoubleType)
     assert(pl(2).dataType == DoubleType)
     // StringType will be promoted into Decimal(38, 18)
-    assert(pl(3).dataType == DecimalType(38, 29))
+    assert(pl(3).dataType == DecimalType(38, 22))
     assert(pl(4).dataType == DoubleType)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
index fc11627da6fd1..b4ad618c23e39 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
@@ -136,10 +136,10 @@ class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter {
     checkType(Multiply(i, u), DecimalType(38, 18))
     checkType(Multiply(u, u), DecimalType(38, 36))
 
-    checkType(Divide(u, d1), DecimalType(38, 21))
-    checkType(Divide(u, d2), DecimalType(38, 24))
-    checkType(Divide(u, i), DecimalType(38, 29))
-    checkType(Divide(u, u), DecimalType(38, 38))
+    checkType(Divide(u, d1), DecimalType(38, 18))
+    checkType(Divide(u, d2), DecimalType(38, 19))
+    checkType(Divide(u, i), DecimalType(38, 23))
+    checkType(Divide(u, u), DecimalType(38, 18))
 
     checkType(Remainder(d1, u), DecimalType(19, 18))
     checkType(Remainder(d2, u), DecimalType(21, 18))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index aa07665c6b705..9e172b2c264cb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1622,9 +1622,30 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     checkAnswer(sql("select 10.3000 / 3.0"), Row(BigDecimal("3.4333333")))
     checkAnswer(sql("select 10.30000 / 30.0"), Row(BigDecimal("0.343333333")))
     checkAnswer(sql("select 10.300000000000000000 / 3.00000000000000000"),
-      Row(BigDecimal("3.4333333333333333333333333333333333333", new MathContext(38))))
+      Row(BigDecimal("3.433333333333333333333333333", new MathContext(38))))
     checkAnswer(sql("select 10.3000000000000000000 / 3.00000000000000000"),
-      Row(null))
+      Row(BigDecimal("3.4333333333333333333333333333", new MathContext(38))))
+  }
+
+  test("SPARK-10215 Div of Decimal returns null") {
+    val d = Decimal(1.12321)
+    val df = Seq((d, 1)).toDF("a", "b")
+
+    checkAnswer(
+      df.selectExpr("b * a / b"),
+      Seq(Row(d.toBigDecimal)))
+    checkAnswer(
+      df.selectExpr("b * a / b / b"),
+      Seq(Row(d.toBigDecimal)))
+    checkAnswer(
+      df.selectExpr("b * a + b"),
+      Seq(Row(BigDecimal(2.12321))))
+    checkAnswer(
+      df.selectExpr("b * a - b"),
+      Seq(Row(BigDecimal(0.12321))))
+    checkAnswer(
+      df.selectExpr("b * a * b"),
+      Seq(Row(d.toBigDecimal)))
   }
 
   test("precision smaller than scale") {

From 125205cdb35530cdb4a8fff3e1ee49cf4a299583 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 17:39:20 -0700
Subject: [PATCH 1223/1454] [SPARK-9888] [MLLIB] User guide for new LDA
 features

 * Adds two new sections to LDA's user guide; one for each optimizer/model
 * Documents new features added to LDA (e.g. topXXXperXXX, asymmetric priors, hyperpam optimization)
 * Cleans up a TODO and sets a default parameter in LDA code

jkbradley hhbyyh

Author: Feynman Liang <fliang@databricks.com>

Closes #8254 from feynmanliang/SPARK-9888.
---
 docs/mllib-clustering.md                      | 135 +++++++++++++++---
 .../spark/mllib/clustering/LDAModel.scala     |   1 -
 .../spark/mllib/clustering/LDASuite.scala     |   1 +
 3 files changed, 117 insertions(+), 20 deletions(-)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index fd9ab258e196d..3fb35d3c50b06 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -438,28 +438,125 @@ sameModel = PowerIterationClusteringModel.load(sc, "myModelPath")
 is a topic model which infers topics from a collection of text documents.
 LDA can be thought of as a clustering algorithm as follows:
 
-* Topics correspond to cluster centers, and documents correspond to examples (rows) in a dataset.
-* Topics and documents both exist in a feature space, where feature vectors are vectors of word counts.
-* Rather than estimating a clustering using a traditional distance, LDA uses a function based
- on a statistical model of how text documents are generated.
-
-LDA takes in a collection of documents as vectors of word counts.
-It supports different inference algorithms via `setOptimizer` function. EMLDAOptimizer learns clustering using [expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
-on the likelihood function and yields comprehensive results, while OnlineLDAOptimizer uses iterative mini-batch sampling for [online variational inference](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf) and is generally memory friendly. After fitting on the documents, LDA provides:
-
-* Topics: Inferred topics, each of which is a probability distribution over terms (words).
-* Topic distributions for documents: For each non empty document in the training set, LDA gives a probability distribution over topics. (EM only). Note that for empty documents, we don't create the topic distributions. (EM only)
+* Topics correspond to cluster centers, and documents correspond to
+examples (rows) in a dataset.
+* Topics and documents both exist in a feature space, where feature
+vectors are vectors of word counts (bag of words).
+* Rather than estimating a clustering using a traditional distance, LDA
+uses a function based on a statistical model of how text documents are
+generated.
+
+LDA supports different inference algorithms via `setOptimizer` function.
+`EMLDAOptimizer` learns clustering using
+[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
+on the likelihood function and yields comprehensive results, while
+`OnlineLDAOptimizer` uses iterative mini-batch sampling for [online
+variational
+inference](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf)
+and is generally memory friendly.
 
-LDA takes the following parameters:
+LDA takes in a collection of documents as vectors of word counts and the
+following parameters (set using the builder pattern):
 
 * `k`: Number of topics (i.e., cluster centers)
-* `maxIterations`: Limit on the number of iterations of EM used for learning
-* `docConcentration`: Hyperparameter for prior over documents' distributions over topics. Currently must be > 1, where larger values encourage smoother inferred distributions.
-* `topicConcentration`: Hyperparameter for prior over topics' distributions over terms (words). Currently must be > 1, where larger values encourage smoother inferred distributions.
-* `checkpointInterval`: If using checkpointing (set in the Spark configuration), this parameter specifies the frequency with which checkpoints will be created.  If `maxIterations` is large, using checkpointing can help reduce shuffle file sizes on disk and help with failure recovery.
-
-*Note*: LDA is a new feature with some missing functionality.  In particular, it does not yet
-support prediction on new documents, and it does not have a Python API.  These will be added in the future.
+* `optimizer`: Optimizer to use for learning the LDA model, either
+`EMLDAOptimizer` or `OnlineLDAOptimizer`
+* `docConcentration`: Dirichlet parameter for prior over documents'
+distributions over topics. Larger values encourage smoother inferred
+distributions.
+* `topicConcentration`: Dirichlet parameter for prior over topics'
+distributions over terms (words). Larger values encourage smoother
+inferred distributions.
+* `maxIterations`: Limit on the number of iterations.
+* `checkpointInterval`: If using checkpointing (set in the Spark
+configuration), this parameter specifies the frequency with which
+checkpoints will be created.  If `maxIterations` is large, using
+checkpointing can help reduce shuffle file sizes on disk and help with
+failure recovery.
+
+
+All of MLlib's LDA models support:
+
+* `describeTopics`: Returns topics as arrays of most important terms and
+term weights
+* `topicsMatrix`: Returns a `vocabSize` by `k` matrix where each column
+is a topic
+
+*Note*: LDA is still an experimental feature under active development.
+As a result, certain features are only available in one of the two
+optimizers / models generated by the optimizer. Currently, a distributed
+model can be converted into a local model, but not vice-versa.
+
+The following discussion will describe each optimizer/model pair
+separately.
+
+**Expectation Maximization**
+
+Implemented in
+[`EMLDAOptimizer`](api/scala/index.html#org.apache.spark.mllib.clustering.EMLDAOptimizer)
+and
+[`DistributedLDAModel`](api/scala/index.html#org.apache.spark.mllib.clustering.DistributedLDAModel).
+
+For the parameters provided to `LDA`:
+
+* `docConcentration`: Only symmetric priors are supported, so all values
+in the provided `k`-dimensional vector must be identical. All values
+must also be $> 1.0$. Providing `Vector(-1)` results in default behavior
+(uniform `k` dimensional vector with value $(50 / k) + 1$
+* `topicConcentration`: Only symmetric priors supported. Values must be
+$> 1.0$. Providing `-1` results in defaulting to a value of $0.1 + 1$.
+* `maxIterations`: The maximum number of EM iterations.
+
+`EMLDAOptimizer` produces a `DistributedLDAModel`, which stores not only
+the inferred topics but also the full training corpus and topic
+distributions for each document in the training corpus. A
+`DistributedLDAModel` supports:
+
+ * `topTopicsPerDocument`: The top topics and their weights for
+ each document in the training corpus
+ * `topDocumentsPerTopic`: The top documents for each topic and
+ the corresponding weight of the topic in the documents.
+ * `logPrior`: log probability of the estimated topics and
+ document-topic distributions given the hyperparameters
+ `docConcentration` and `topicConcentration`
+ * `logLikelihood`: log likelihood of the training corpus, given the
+ inferred topics and document-topic distributions
+
+**Online Variational Bayes**
+
+Implemented in
+[`OnlineLDAOptimizer`](api/scala/org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html)
+and
+[`LocalLDAModel`](api/scala/org/apache/spark/mllib/clustering/LocalLDAModel.html).
+
+For the parameters provided to `LDA`:
+
+* `docConcentration`: Asymmetric priors can be used by passing in a
+vector with values equal to the Dirichlet parameter in each of the `k`
+dimensions. Values should be $>= 0$. Providing `Vector(-1)` results in
+default behavior (uniform `k` dimensional vector with value $(1.0 / k)$)
+* `topicConcentration`: Only symmetric priors supported. Values must be
+$>= 0$. Providing `-1` results in defaulting to a value of $(1.0 / k)$.
+* `maxIterations`: Maximum number of minibatches to submit.
+
+In addition, `OnlineLDAOptimizer` accepts the following parameters:
+
+* `miniBatchFraction`: Fraction of corpus sampled and used at each
+iteration
+* `optimizeDocConcentration`: If set to true, performs maximum-likelihood
+estimation of the hyperparameter `docConcentration` (aka `alpha`)
+after each minibatch and sets the optimized `docConcentration` in the
+returned `LocalLDAModel`
+* `tau0` and `kappa`: Used for learning-rate decay, which is computed by
+$(\tau_0 + iter)^{-\kappa}$ where $iter$ is the current number of iterations.
+
+`OnlineLDAOptimizer` produces a `LocalLDAModel`, which only stores the
+inferred topics. A `LocalLDAModel` supports:
+
+* `logLikelihood(documents)`: Calculates a lower bound on the provided
+`documents` given the inferred topics.
+* `logPerplexity(documents)`: Calculates an upper bound on the
+perplexity of the provided `documents` given the inferred topics.
 
 **Examples**
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 667374a2bc418..432bbedc8d6f8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -435,7 +435,6 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       }
       val topicsMat = Matrices.fromBreeze(brzTopics)
 
-      // TODO: initialize with docConcentration, topicConcentration, and gammaShape after SPARK-9940
       new LocalLDAModel(topicsMat, docConcentration, topicConcentration, gammaShape)
     }
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 746a76a7e5fa1..37fb69d68f6be 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -68,6 +68,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     // Train a model
     val lda = new LDA()
     lda.setK(k)
+      .setOptimizer(new EMLDAOptimizer)
       .setDocConcentration(topicSmoothing)
       .setTopicConcentration(termSmoothing)
       .setMaxIterations(5)

From 8668ead2e7097b9591069599fbfccf67c53db659 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 18:17:54 -0700
Subject: [PATCH 1224/1454] [SPARK-10233] [MLLIB] update since version in
 mllib.evaluation

Same as #8421 but for `mllib.evaluation`.

cc avulanov

Author: Xiangrui Meng <meng@databricks.com>

Closes #8423 from mengxr/SPARK-10233.
---
 .../evaluation/BinaryClassificationMetrics.scala     |  8 ++++----
 .../spark/mllib/evaluation/MulticlassMetrics.scala   | 11 ++++++++++-
 .../spark/mllib/evaluation/MultilabelMetrics.scala   | 12 +++++++++++-
 .../spark/mllib/evaluation/RegressionMetrics.scala   |  3 ++-
 4 files changed, 27 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index 76ae847921f44..508fe532b1306 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -42,11 +42,11 @@ import org.apache.spark.sql.DataFrame
  *                be smaller as a result, meaning there may be an extra sample at
  *                partition boundaries.
  */
-@Since("1.3.0")
+@Since("1.0.0")
 @Experimental
-class BinaryClassificationMetrics(
-    val scoreAndLabels: RDD[(Double, Double)],
-    val numBins: Int) extends Logging {
+class BinaryClassificationMetrics @Since("1.3.0") (
+    @Since("1.3.0") val scoreAndLabels: RDD[(Double, Double)],
+    @Since("1.3.0") val numBins: Int) extends Logging {
 
   require(numBins >= 0, "numBins must be nonnegative")
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index 02e89d921033c..00e837661dfc2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -33,7 +33,7 @@ import org.apache.spark.sql.DataFrame
  */
 @Since("1.1.0")
 @Experimental
-class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
+class MulticlassMetrics @Since("1.1.0") (predictionAndLabels: RDD[(Double, Double)]) {
 
   /**
    * An auxiliary constructor taking a DataFrame.
@@ -140,6 +140,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns precision
    */
+  @Since("1.1.0")
   lazy val precision: Double = tpByClass.values.sum.toDouble / labelCount
 
   /**
@@ -148,23 +149,27 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * because sum of all false positives is equal to sum
    * of all false negatives)
    */
+  @Since("1.1.0")
   lazy val recall: Double = precision
 
   /**
    * Returns f-measure
    * (equals to precision and recall because precision equals recall)
    */
+  @Since("1.1.0")
   lazy val fMeasure: Double = precision
 
   /**
    * Returns weighted true positive rate
    * (equals to precision, recall and f-measure)
    */
+  @Since("1.1.0")
   lazy val weightedTruePositiveRate: Double = weightedRecall
 
   /**
    * Returns weighted false positive rate
    */
+  @Since("1.1.0")
   lazy val weightedFalsePositiveRate: Double = labelCountByClass.map { case (category, count) =>
     falsePositiveRate(category) * count.toDouble / labelCount
   }.sum
@@ -173,6 +178,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * Returns weighted averaged recall
    * (equals to precision, recall and f-measure)
    */
+  @Since("1.1.0")
   lazy val weightedRecall: Double = labelCountByClass.map { case (category, count) =>
     recall(category) * count.toDouble / labelCount
   }.sum
@@ -180,6 +186,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged precision
    */
+  @Since("1.1.0")
   lazy val weightedPrecision: Double = labelCountByClass.map { case (category, count) =>
     precision(category) * count.toDouble / labelCount
   }.sum
@@ -196,6 +203,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged f1-measure
    */
+  @Since("1.1.0")
   lazy val weightedFMeasure: Double = labelCountByClass.map { case (category, count) =>
     fMeasure(category, 1.0) * count.toDouble / labelCount
   }.sum
@@ -203,5 +211,6 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns the sequence of labels in ascending order
    */
+  @Since("1.1.0")
   lazy val labels: Array[Double] = tpByClass.keys.toArray.sorted
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index a0a8d9c56847b..c100b3c9ec14a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.DataFrame
  * both are non-null Arrays, each with unique elements.
  */
 @Since("1.2.0")
-class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
+class MultilabelMetrics @Since("1.2.0") (predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
 
   /**
    * An auxiliary constructor taking a DataFrame.
@@ -46,6 +46,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns subset accuracy
    * (for equal sets of labels)
    */
+  @Since("1.2.0")
   lazy val subsetAccuracy: Double = predictionAndLabels.filter { case (predictions, labels) =>
     predictions.deep == labels.deep
   }.count().toDouble / numDocs
@@ -53,6 +54,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns accuracy
    */
+  @Since("1.2.0")
   lazy val accuracy: Double = predictionAndLabels.map { case (predictions, labels) =>
     labels.intersect(predictions).size.toDouble /
       (labels.size + predictions.size - labels.intersect(predictions).size)}.sum / numDocs
@@ -61,6 +63,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns Hamming-loss
    */
+  @Since("1.2.0")
   lazy val hammingLoss: Double = predictionAndLabels.map { case (predictions, labels) =>
     labels.size + predictions.size - 2 * labels.intersect(predictions).size
   }.sum / (numDocs * numLabels)
@@ -68,6 +71,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns document-based precision averaged by the number of documents
    */
+  @Since("1.2.0")
   lazy val precision: Double = predictionAndLabels.map { case (predictions, labels) =>
     if (predictions.size > 0) {
       predictions.intersect(labels).size.toDouble / predictions.size
@@ -79,6 +83,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns document-based recall averaged by the number of documents
    */
+  @Since("1.2.0")
   lazy val recall: Double = predictionAndLabels.map { case (predictions, labels) =>
     labels.intersect(predictions).size.toDouble / labels.size
   }.sum / numDocs
@@ -86,6 +91,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns document-based f1-measure averaged by the number of documents
    */
+  @Since("1.2.0")
   lazy val f1Measure: Double = predictionAndLabels.map { case (predictions, labels) =>
     2.0 * predictions.intersect(labels).size / (predictions.size + labels.size)
   }.sum / numDocs
@@ -143,6 +149,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns micro-averaged label-based precision
    * (equals to micro-averaged document-based precision)
    */
+  @Since("1.2.0")
   lazy val microPrecision: Double = {
     val sumFp = fpPerClass.foldLeft(0L){ case(cum, (_, fp)) => cum + fp}
     sumTp.toDouble / (sumTp + sumFp)
@@ -152,6 +159,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns micro-averaged label-based recall
    * (equals to micro-averaged document-based recall)
    */
+  @Since("1.2.0")
   lazy val microRecall: Double = {
     val sumFn = fnPerClass.foldLeft(0.0){ case(cum, (_, fn)) => cum + fn}
     sumTp.toDouble / (sumTp + sumFn)
@@ -161,10 +169,12 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns micro-averaged label-based f1-measure
    * (equals to micro-averaged document-based f1-measure)
    */
+  @Since("1.2.0")
   lazy val microF1Measure: Double = 2.0 * sumTp / (2 * sumTp + sumFnClass + sumFpClass)
 
   /**
    * Returns the sequence of labels in ascending order
    */
+  @Since("1.2.0")
   lazy val labels: Array[Double] = tpPerClass.keys.toArray.sorted
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 36a6c357c3897..799ebb980ef01 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -32,7 +32,8 @@ import org.apache.spark.sql.DataFrame
  */
 @Since("1.2.0")
 @Experimental
-class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging {
+class RegressionMetrics @Since("1.2.0") (
+    predictionAndObservations: RDD[(Double, Double)]) extends Logging {
 
   /**
    * An auxiliary constructor taking a DataFrame.

From ab431f8a970b85fba34ccb506c0f8815e55c63bf Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 20:07:56 -0700
Subject: [PATCH 1225/1454] [SPARK-10238] [MLLIB] update since versions in
 mllib.linalg

Same as #8421 but for `mllib.linalg`.

cc dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8440 from mengxr/SPARK-10238 and squashes the following commits:

b38437e [Xiangrui Meng] update since versions in mllib.linalg
---
 .../apache/spark/mllib/linalg/Matrices.scala  | 44 ++++++++++++-------
 .../linalg/SingularValueDecomposition.scala   |  1 +
 .../apache/spark/mllib/linalg/Vectors.scala   | 25 ++++++++---
 .../linalg/distributed/BlockMatrix.scala      | 10 +++--
 .../linalg/distributed/CoordinateMatrix.scala |  4 +-
 .../distributed/DistributedMatrix.scala       |  2 +
 .../linalg/distributed/IndexedRowMatrix.scala |  4 +-
 .../mllib/linalg/distributed/RowMatrix.scala  |  5 ++-
 8 files changed, 64 insertions(+), 31 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 28b5b4637bf17..c02ba426fcc3a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -32,18 +32,23 @@ import org.apache.spark.sql.types._
  * Trait for a local matrix.
  */
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
+@Since("1.0.0")
 sealed trait Matrix extends Serializable {
 
   /** Number of rows. */
+  @Since("1.0.0")
   def numRows: Int
 
   /** Number of columns. */
+  @Since("1.0.0")
   def numCols: Int
 
   /** Flag that keeps track whether the matrix is transposed or not. False by default. */
+  @Since("1.3.0")
   val isTransposed: Boolean = false
 
   /** Converts to a dense array in column major. */
+  @Since("1.0.0")
   def toArray: Array[Double] = {
     val newArray = new Array[Double](numRows * numCols)
     foreachActive { (i, j, v) =>
@@ -56,6 +61,7 @@ sealed trait Matrix extends Serializable {
   private[mllib] def toBreeze: BM[Double]
 
   /** Gets the (i, j)-th element. */
+  @Since("1.3.0")
   def apply(i: Int, j: Int): Double
 
   /** Return the index for the (i, j)-th element in the backing array. */
@@ -65,12 +71,15 @@ sealed trait Matrix extends Serializable {
   private[mllib] def update(i: Int, j: Int, v: Double): Unit
 
   /** Get a deep copy of the matrix. */
+  @Since("1.2.0")
   def copy: Matrix
 
   /** Transpose the Matrix. Returns a new `Matrix` instance sharing the same underlying data. */
+  @Since("1.3.0")
   def transpose: Matrix
 
   /** Convenience method for `Matrix`-`DenseMatrix` multiplication. */
+  @Since("1.2.0")
   def multiply(y: DenseMatrix): DenseMatrix = {
     val C: DenseMatrix = DenseMatrix.zeros(numRows, y.numCols)
     BLAS.gemm(1.0, this, y, 0.0, C)
@@ -78,11 +87,13 @@ sealed trait Matrix extends Serializable {
   }
 
   /** Convenience method for `Matrix`-`DenseVector` multiplication. For binary compatibility. */
+  @Since("1.2.0")
   def multiply(y: DenseVector): DenseVector = {
     multiply(y.asInstanceOf[Vector])
   }
 
   /** Convenience method for `Matrix`-`Vector` multiplication. */
+  @Since("1.4.0")
   def multiply(y: Vector): DenseVector = {
     val output = new DenseVector(new Array[Double](numRows))
     BLAS.gemv(1.0, this, y, 0.0, output)
@@ -93,6 +104,7 @@ sealed trait Matrix extends Serializable {
   override def toString: String = toBreeze.toString()
 
   /** A human readable representation of the matrix with maximum lines and width */
+  @Since("1.4.0")
   def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth)
 
   /** Map the values of this matrix using a function. Generates a new matrix. Performs the
@@ -118,11 +130,13 @@ sealed trait Matrix extends Serializable {
   /**
    * Find the number of non-zero active values.
    */
+  @Since("1.5.0")
   def numNonzeros: Int
 
   /**
    * Find the number of values stored explicitly. These values can be zero as well.
    */
+  @Since("1.5.0")
   def numActives: Int
 }
 
@@ -230,11 +244,11 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
  */
 @Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
-class DenseMatrix(
-    val numRows: Int,
-    val numCols: Int,
-    val values: Array[Double],
-    override val isTransposed: Boolean) extends Matrix {
+class DenseMatrix @Since("1.3.0") (
+    @Since("1.0.0") val numRows: Int,
+    @Since("1.0.0") val numCols: Int,
+    @Since("1.0.0") val values: Array[Double],
+    @Since("1.3.0") override val isTransposed: Boolean) extends Matrix {
 
   require(values.length == numRows * numCols, "The number of values supplied doesn't match the " +
     s"size of the matrix! values.length: ${values.length}, numRows * numCols: ${numRows * numCols}")
@@ -254,7 +268,7 @@ class DenseMatrix(
    * @param numCols number of columns
    * @param values matrix entries in column major
    */
-  @Since("1.3.0")
+  @Since("1.0.0")
   def this(numRows: Int, numCols: Int, values: Array[Double]) =
     this(numRows, numCols, values, false)
 
@@ -491,13 +505,13 @@ object DenseMatrix {
  */
 @Since("1.2.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
-class SparseMatrix(
-    val numRows: Int,
-    val numCols: Int,
-    val colPtrs: Array[Int],
-    val rowIndices: Array[Int],
-    val values: Array[Double],
-    override val isTransposed: Boolean) extends Matrix {
+class SparseMatrix @Since("1.3.0") (
+    @Since("1.2.0") val numRows: Int,
+    @Since("1.2.0") val numCols: Int,
+    @Since("1.2.0") val colPtrs: Array[Int],
+    @Since("1.2.0") val rowIndices: Array[Int],
+    @Since("1.2.0") val values: Array[Double],
+    @Since("1.3.0") override val isTransposed: Boolean) extends Matrix {
 
   require(values.length == rowIndices.length, "The number of row indices and values don't match! " +
     s"values.length: ${values.length}, rowIndices.length: ${rowIndices.length}")
@@ -527,7 +541,7 @@ class SparseMatrix(
    *                   order for each column
    * @param values non-zero matrix entries in column major
    */
-  @Since("1.3.0")
+  @Since("1.2.0")
   def this(
       numRows: Int,
       numCols: Int,
@@ -549,8 +563,6 @@ class SparseMatrix(
      }
   }
 
-  /**
-   */
   @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = {
     val ind = index(i, j)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index a37aca99d5e72..4dcf8f28f2023 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -31,6 +31,7 @@ case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VTyp
  * :: Experimental ::
  * Represents QR factors.
  */
+@Since("1.5.0")
 @Experimental
 case class QRDecomposition[QType, RType](Q: QType, R: RType)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 3d577edbe23e1..06ebb15869909 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -38,16 +38,19 @@ import org.apache.spark.sql.types._
  * Note: Users should not implement this interface.
  */
 @SQLUserDefinedType(udt = classOf[VectorUDT])
+@Since("1.0.0")
 sealed trait Vector extends Serializable {
 
   /**
    * Size of the vector.
    */
+  @Since("1.0.0")
   def size: Int
 
   /**
    * Converts the instance to a double array.
    */
+  @Since("1.0.0")
   def toArray: Array[Double]
 
   override def equals(other: Any): Boolean = {
@@ -99,11 +102,13 @@ sealed trait Vector extends Serializable {
    * Gets the value of the ith element.
    * @param i index
    */
+  @Since("1.1.0")
   def apply(i: Int): Double = toBreeze(i)
 
   /**
    * Makes a deep copy of this vector.
    */
+  @Since("1.1.0")
   def copy: Vector = {
     throw new NotImplementedError(s"copy is not implemented for ${this.getClass}.")
   }
@@ -121,26 +126,31 @@ sealed trait Vector extends Serializable {
    * Number of active entries.  An "active entry" is an element which is explicitly stored,
    * regardless of its value.  Note that inactive entries have value 0.
    */
+  @Since("1.4.0")
   def numActives: Int
 
   /**
    * Number of nonzero elements. This scans all active values and count nonzeros.
    */
+  @Since("1.4.0")
   def numNonzeros: Int
 
   /**
    * Converts this vector to a sparse vector with all explicit zeros removed.
    */
+  @Since("1.4.0")
   def toSparse: SparseVector
 
   /**
    * Converts this vector to a dense vector.
    */
+  @Since("1.4.0")
   def toDense: DenseVector = new DenseVector(this.toArray)
 
   /**
    * Returns a vector in either dense or sparse format, whichever uses less storage.
    */
+  @Since("1.4.0")
   def compressed: Vector = {
     val nnz = numNonzeros
     // A dense vector needs 8 * size + 8 bytes, while a sparse vector needs 12 * nnz + 20 bytes.
@@ -155,6 +165,7 @@ sealed trait Vector extends Serializable {
    * Find the index of a maximal element.  Returns the first maximal element in case of a tie.
    * Returns -1 if vector has length 0.
    */
+  @Since("1.5.0")
   def argmax: Int
 }
 
@@ -532,7 +543,8 @@ object Vectors {
  */
 @Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
-class DenseVector(val values: Array[Double]) extends Vector {
+class DenseVector @Since("1.0.0") (
+    @Since("1.0.0") val values: Array[Double]) extends Vector {
 
   @Since("1.0.0")
   override def size: Int = values.length
@@ -632,7 +644,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
 
 @Since("1.3.0")
 object DenseVector {
+
   /** Extracts the value array from a dense vector. */
+  @Since("1.3.0")
   def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
 }
 
@@ -645,10 +659,10 @@ object DenseVector {
  */
 @Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
-class SparseVector(
-    override val size: Int,
-    val indices: Array[Int],
-    val values: Array[Double]) extends Vector {
+class SparseVector @Since("1.0.0") (
+    @Since("1.0.0") override val size: Int,
+    @Since("1.0.0") val indices: Array[Int],
+    @Since("1.0.0") val values: Array[Double]) extends Vector {
 
   require(indices.length == values.length, "Sparse vectors require that the dimension of the" +
     s" indices match the dimension of the values. You provided ${indices.length} indices and " +
@@ -819,6 +833,7 @@ class SparseVector(
 
 @Since("1.3.0")
 object SparseVector {
+  @Since("1.3.0")
   def unapply(sv: SparseVector): Option[(Int, Array[Int], Array[Double])] =
     Some((sv.size, sv.indices, sv.values))
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index 94376c24a7ac6..a33b6137cf9cc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -131,10 +131,10 @@ private[mllib] object GridPartitioner {
  */
 @Since("1.3.0")
 @Experimental
-class BlockMatrix(
-    val blocks: RDD[((Int, Int), Matrix)],
-    val rowsPerBlock: Int,
-    val colsPerBlock: Int,
+class BlockMatrix @Since("1.3.0") (
+    @Since("1.3.0") val blocks: RDD[((Int, Int), Matrix)],
+    @Since("1.3.0") val rowsPerBlock: Int,
+    @Since("1.3.0") val colsPerBlock: Int,
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix with Logging {
 
@@ -171,7 +171,9 @@ class BlockMatrix(
     nCols
   }
 
+  @Since("1.3.0")
   val numRowBlocks = math.ceil(numRows() * 1.0 / rowsPerBlock).toInt
+  @Since("1.3.0")
   val numColBlocks = math.ceil(numCols() * 1.0 / colsPerBlock).toInt
 
   private[mllib] def createPartitioner(): GridPartitioner =
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 4bb27ec840902..644f293d88a75 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -46,8 +46,8 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  */
 @Since("1.0.0")
 @Experimental
-class CoordinateMatrix(
-    val entries: RDD[MatrixEntry],
+class CoordinateMatrix @Since("1.0.0") (
+    @Since("1.0.0") val entries: RDD[MatrixEntry],
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
index e51327ebb7b58..db3433a5e2456 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
@@ -28,9 +28,11 @@ import org.apache.spark.annotation.Since
 trait DistributedMatrix extends Serializable {
 
   /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   def numRows(): Long
 
   /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   def numCols(): Long
 
   /** Collects data and assembles a local dense breeze matrix (for test only). */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index 6d2c05a47d049..b20ea0dc50da5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -45,8 +45,8 @@ case class IndexedRow(index: Long, vector: Vector)
  */
 @Since("1.0.0")
 @Experimental
-class IndexedRowMatrix(
-    val rows: RDD[IndexedRow],
+class IndexedRowMatrix @Since("1.0.0") (
+    @Since("1.0.0") val rows: RDD[IndexedRow],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 78036eba5c3e6..9a423ddafdc09 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -47,8 +47,8 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("1.0.0")
 @Experimental
-class RowMatrix(
-    val rows: RDD[Vector],
+class RowMatrix @Since("1.0.0") (
+    @Since("1.0.0") val rows: RDD[Vector],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix with Logging {
 
@@ -519,6 +519,7 @@ class RowMatrix(
    * @param computeQ whether to computeQ
    * @return QRDecomposition(Q, R), Q = null if computeQ = false.
    */
+  @Since("1.5.0")
   def tallSkinnyQR(computeQ: Boolean = false): QRDecomposition[RowMatrix, Matrix] = {
     val col = numCols().toInt
     // split rows horizontally into smaller matrices, and compute QR for each of them

From c3a54843c0c8a14059da4e6716c1ad45c69bbe6c Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:31:23 -0700
Subject: [PATCH 1226/1454] [SPARK-10240] [SPARK-10242] [MLLIB] update since
 versions in mlilb.random and mllib.stat

The same as #8241 but for `mllib.stat` and `mllib.random`.

cc feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8439 from mengxr/SPARK-10242.
---
 .../mllib/random/RandomDataGenerator.scala    | 43 ++++++++++--
 .../spark/mllib/random/RandomRDDs.scala       | 69 ++++++++++++++++---
 .../distribution/MultivariateGaussian.scala   |  6 +-
 .../spark/mllib/stat/test/TestResult.scala    | 24 ++++---
 4 files changed, 117 insertions(+), 25 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
index 9349ecaa13f56..a2d85a68cd327 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.random
 import org.apache.commons.math3.distribution.{ExponentialDistribution,
   GammaDistribution, LogNormalDistribution, PoissonDistribution}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
 
 /**
@@ -28,17 +28,20 @@ import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
  * Trait for random data generators that generate i.i.d. data.
  */
 @DeveloperApi
+@Since("1.1.0")
 trait RandomDataGenerator[T] extends Pseudorandom with Serializable {
 
   /**
    * Returns an i.i.d. sample as a generic type from an underlying distribution.
    */
+  @Since("1.1.0")
   def nextValue(): T
 
   /**
    * Returns a copy of the RandomDataGenerator with a new instance of the rng object used in the
    * class when applicable for non-locking concurrent usage.
    */
+  @Since("1.1.0")
   def copy(): RandomDataGenerator[T]
 }
 
@@ -47,17 +50,21 @@ trait RandomDataGenerator[T] extends Pseudorandom with Serializable {
  * Generates i.i.d. samples from U[0.0, 1.0]
  */
 @DeveloperApi
+@Since("1.1.0")
 class UniformGenerator extends RandomDataGenerator[Double] {
 
   // XORShiftRandom for better performance. Thread safety isn't necessary here.
   private val random = new XORShiftRandom()
 
+  @Since("1.1.0")
   override def nextValue(): Double = {
     random.nextDouble()
   }
 
+  @Since("1.1.0")
   override def setSeed(seed: Long): Unit = random.setSeed(seed)
 
+  @Since("1.1.0")
   override def copy(): UniformGenerator = new UniformGenerator()
 }
 
@@ -66,17 +73,21 @@ class UniformGenerator extends RandomDataGenerator[Double] {
  * Generates i.i.d. samples from the standard normal distribution.
  */
 @DeveloperApi
+@Since("1.1.0")
 class StandardNormalGenerator extends RandomDataGenerator[Double] {
 
   // XORShiftRandom for better performance. Thread safety isn't necessary here.
   private val random = new XORShiftRandom()
 
+  @Since("1.1.0")
   override def nextValue(): Double = {
       random.nextGaussian()
   }
 
+  @Since("1.1.0")
   override def setSeed(seed: Long): Unit = random.setSeed(seed)
 
+  @Since("1.1.0")
   override def copy(): StandardNormalGenerator = new StandardNormalGenerator()
 }
 
@@ -87,16 +98,21 @@ class StandardNormalGenerator extends RandomDataGenerator[Double] {
  * @param mean mean for the Poisson distribution.
  */
 @DeveloperApi
-class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] {
+@Since("1.1.0")
+class PoissonGenerator @Since("1.1.0") (
+    @Since("1.1.0") val mean: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new PoissonDistribution(mean)
 
+  @Since("1.1.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.1.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.1.0")
   override def copy(): PoissonGenerator = new PoissonGenerator(mean)
 }
 
@@ -107,16 +123,21 @@ class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] {
  * @param mean mean for the exponential distribution.
  */
 @DeveloperApi
-class ExponentialGenerator(val mean: Double) extends RandomDataGenerator[Double] {
+@Since("1.3.0")
+class ExponentialGenerator @Since("1.3.0") (
+    @Since("1.3.0") val mean: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new ExponentialDistribution(mean)
 
+  @Since("1.3.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.3.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.3.0")
   override def copy(): ExponentialGenerator = new ExponentialGenerator(mean)
 }
 
@@ -128,16 +149,22 @@ class ExponentialGenerator(val mean: Double) extends RandomDataGenerator[Double]
  * @param scale scale for the gamma distribution
  */
 @DeveloperApi
-class GammaGenerator(val shape: Double, val scale: Double) extends RandomDataGenerator[Double] {
+@Since("1.3.0")
+class GammaGenerator @Since("1.3.0") (
+    @Since("1.3.0") val shape: Double,
+    @Since("1.3.0") val scale: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new GammaDistribution(shape, scale)
 
+  @Since("1.3.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.3.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.3.0")
   override def copy(): GammaGenerator = new GammaGenerator(shape, scale)
 }
 
@@ -150,15 +177,21 @@ class GammaGenerator(val shape: Double, val scale: Double) extends RandomDataGen
  * @param std standard deviation for the log normal distribution
  */
 @DeveloperApi
-class LogNormalGenerator(val mean: Double, val std: Double) extends RandomDataGenerator[Double] {
+@Since("1.3.0")
+class LogNormalGenerator @Since("1.3.0") (
+    @Since("1.3.0") val mean: Double,
+    @Since("1.3.0") val std: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new LogNormalDistribution(mean, std)
 
+  @Since("1.3.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.3.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.3.0")
   override def copy(): LogNormalGenerator = new LogNormalGenerator(mean, std)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
index 174d5e0f6c9f0..4dd5ea214d678 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.random
 import scala.reflect.ClassTag
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD, JavaSparkContext}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.rdd.{RandomRDD, RandomVectorRDD}
@@ -32,6 +32,7 @@ import org.apache.spark.util.Utils
  * Generator methods for creating RDDs comprised of `i.i.d.` samples from some distribution.
  */
 @Experimental
+@Since("1.1.0")
 object RandomRDDs {
 
   /**
@@ -46,6 +47,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ `U(0.0, 1.0)`.
    */
+  @Since("1.1.0")
   def uniformRDD(
       sc: SparkContext,
       size: Long,
@@ -58,6 +60,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#uniformRDD]].
    */
+  @Since("1.1.0")
   def uniformJavaRDD(
       jsc: JavaSparkContext,
       size: Long,
@@ -69,6 +72,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size, numPartitions))
   }
@@ -76,6 +80,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size))
   }
@@ -92,6 +97,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ N(0.0, 1.0).
    */
+  @Since("1.1.0")
   def normalRDD(
       sc: SparkContext,
       size: Long,
@@ -104,6 +110,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#normalRDD]].
    */
+  @Since("1.1.0")
   def normalJavaRDD(
       jsc: JavaSparkContext,
       size: Long,
@@ -115,6 +122,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size, numPartitions))
   }
@@ -122,6 +130,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size))
   }
@@ -137,6 +146,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.1.0")
   def poissonRDD(
       sc: SparkContext,
       mean: Double,
@@ -150,6 +160,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#poissonRDD]].
    */
+  @Since("1.1.0")
   def poissonJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -162,6 +173,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -173,6 +185,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size))
   }
@@ -188,6 +201,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.3.0")
   def exponentialRDD(
       sc: SparkContext,
       mean: Double,
@@ -201,6 +215,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#exponentialRDD]].
    */
+  @Since("1.3.0")
   def exponentialJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -213,6 +228,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#exponentialJavaRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -224,6 +240,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#exponentialJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(exponentialRDD(jsc.sc, mean, size))
   }
@@ -240,6 +257,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.3.0")
   def gammaRDD(
       sc: SparkContext,
       shape: Double,
@@ -254,6 +272,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#gammaRDD]].
    */
+  @Since("1.3.0")
   def gammaJavaRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -267,6 +286,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -279,11 +299,12 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaRDD(
-    jsc: JavaSparkContext,
-    shape: Double,
-    scale: Double,
-    size: Long): JavaDoubleRDD = {
+      jsc: JavaSparkContext,
+      shape: Double,
+      scale: Double,
+      size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(gammaRDD(jsc.sc, shape, scale, size))
   }
 
@@ -299,6 +320,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.3.0")
   def logNormalRDD(
       sc: SparkContext,
       mean: Double,
@@ -313,6 +335,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#logNormalRDD]].
    */
+  @Since("1.3.0")
   def logNormalJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -326,6 +349,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#logNormalJavaRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -338,11 +362,12 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#logNormalJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaRDD(
-    jsc: JavaSparkContext,
-    mean: Double,
-    std: Double,
-    size: Long): JavaDoubleRDD = {
+      jsc: JavaSparkContext,
+      mean: Double,
+      std: Double,
+      size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(logNormalRDD(jsc.sc, mean, std, size))
   }
 
@@ -359,6 +384,7 @@ object RandomRDDs {
    * @return RDD[Double] comprised of `i.i.d.` samples produced by generator.
    */
   @DeveloperApi
+  @Since("1.1.0")
   def randomRDD[T: ClassTag](
       sc: SparkContext,
       generator: RandomDataGenerator[T],
@@ -381,6 +407,7 @@ object RandomRDDs {
    * @param seed Seed for the RNG that generates the seed for the generator in each partition.
    * @return RDD[Vector] with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
    */
+  @Since("1.1.0")
   def uniformVectorRDD(
       sc: SparkContext,
       numRows: Long,
@@ -394,6 +421,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#uniformVectorRDD]].
    */
+  @Since("1.1.0")
   def uniformJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -406,6 +434,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaVectorRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -417,6 +446,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -435,6 +465,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ `N(0.0, 1.0)`.
    */
+  @Since("1.1.0")
   def normalVectorRDD(
       sc: SparkContext,
       numRows: Long,
@@ -448,6 +479,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#normalVectorRDD]].
    */
+  @Since("1.1.0")
   def normalJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -460,6 +492,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaVectorRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def normalJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -471,6 +504,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def normalJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -491,6 +525,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples.
    */
+  @Since("1.3.0")
   def logNormalVectorRDD(
       sc: SparkContext,
       mean: Double,
@@ -507,6 +542,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#logNormalVectorRDD]].
    */
+  @Since("1.3.0")
   def logNormalJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -521,6 +557,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#logNormalJavaVectorRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -535,6 +572,7 @@ object RandomRDDs {
    * [[RandomRDDs#logNormalJavaVectorRDD]] with the default number of partitions and
    * the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -556,6 +594,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.1.0")
   def poissonVectorRDD(
       sc: SparkContext,
       mean: Double,
@@ -570,6 +609,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#poissonVectorRDD]].
    */
+  @Since("1.1.0")
   def poissonJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -583,6 +623,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaVectorRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -595,6 +636,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -615,6 +657,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean).
    */
+  @Since("1.3.0")
   def exponentialVectorRDD(
       sc: SparkContext,
       mean: Double,
@@ -630,6 +673,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#exponentialVectorRDD]].
    */
+  @Since("1.3.0")
   def exponentialJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -643,6 +687,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#exponentialJavaVectorRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -656,6 +701,7 @@ object RandomRDDs {
    * [[RandomRDDs#exponentialJavaVectorRDD]] with the default number of partitions
    * and the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -678,6 +724,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean).
    */
+  @Since("1.3.0")
   def gammaVectorRDD(
       sc: SparkContext,
       shape: Double,
@@ -693,6 +740,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#gammaVectorRDD]].
    */
+  @Since("1.3.0")
   def gammaJavaVectorRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -707,6 +755,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaVectorRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaVectorRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -720,6 +769,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaVectorRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -744,6 +794,7 @@ object RandomRDDs {
    * @return RDD[Vector] with vectors containing `i.i.d.` samples produced by generator.
    */
   @DeveloperApi
+  @Since("1.1.0")
   def randomVectorRDD(sc: SparkContext,
       generator: RandomDataGenerator[Double],
       numRows: Long,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
index bd4d81390bfae..92a5af708d04b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
@@ -35,9 +35,9 @@ import org.apache.spark.mllib.util.MLUtils
  */
 @Since("1.3.0")
 @DeveloperApi
-class MultivariateGaussian (
-    val mu: Vector,
-    val sigma: Matrix) extends Serializable {
+class MultivariateGaussian @Since("1.3.0") (
+    @Since("1.3.0") val mu: Vector,
+    @Since("1.3.0") val sigma: Matrix) extends Serializable {
 
   require(sigma.numCols == sigma.numRows, "Covariance matrix must be square")
   require(mu.size == sigma.numCols, "Mean vector length must match covariance matrix size")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
index f44be13706695..d01b3707be944 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.stat.test
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
@@ -25,28 +25,33 @@ import org.apache.spark.annotation.Experimental
  * @tparam DF Return type of `degreesOfFreedom`.
  */
 @Experimental
+@Since("1.1.0")
 trait TestResult[DF] {
 
   /**
    * The probability of obtaining a test statistic result at least as extreme as the one that was
    * actually observed, assuming that the null hypothesis is true.
    */
+  @Since("1.1.0")
   def pValue: Double
 
   /**
    * Returns the degree(s) of freedom of the hypothesis test.
    * Return type should be Number(e.g. Int, Double) or tuples of Numbers for toString compatibility.
    */
+  @Since("1.1.0")
   def degreesOfFreedom: DF
 
   /**
    * Test statistic.
    */
+  @Since("1.1.0")
   def statistic: Double
 
   /**
    * Null hypothesis of the test.
    */
+  @Since("1.1.0")
   def nullHypothesis: String
 
   /**
@@ -78,11 +83,12 @@ trait TestResult[DF] {
  * Object containing the test results for the chi-squared hypothesis test.
  */
 @Experimental
+@Since("1.1.0")
 class ChiSqTestResult private[stat] (override val pValue: Double,
-    override val degreesOfFreedom: Int,
-    override val statistic: Double,
-    val method: String,
-    override val nullHypothesis: String) extends TestResult[Int] {
+    @Since("1.1.0") override val degreesOfFreedom: Int,
+    @Since("1.1.0") override val statistic: Double,
+    @Since("1.1.0") val method: String,
+    @Since("1.1.0") override val nullHypothesis: String) extends TestResult[Int] {
 
   override def toString: String = {
     "Chi squared test summary:\n" +
@@ -96,11 +102,13 @@ class ChiSqTestResult private[stat] (override val pValue: Double,
  * Object containing the test results for the Kolmogorov-Smirnov test.
  */
 @Experimental
+@Since("1.5.0")
 class KolmogorovSmirnovTestResult private[stat] (
-    override val pValue: Double,
-    override val statistic: Double,
-    override val nullHypothesis: String) extends TestResult[Int] {
+    @Since("1.5.0") override val pValue: Double,
+    @Since("1.5.0") override val statistic: Double,
+    @Since("1.5.0") override val nullHypothesis: String) extends TestResult[Int] {
 
+  @Since("1.5.0")
   override val degreesOfFreedom = 0
 
   override def toString: String = {

From d703372f86d6a59383ba8569fcd9d379849cffbf Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:33:48 -0700
Subject: [PATCH 1227/1454] [SPARK-10234] [MLLIB] update since version in
 mllib.clustering

Same as #8421 but for `mllib.clustering`.

cc feynmanliang yu-iskw

Author: Xiangrui Meng <meng@databricks.com>

Closes #8435 from mengxr/SPARK-10234.
---
 .../mllib/clustering/GaussianMixture.scala    |  1 +
 .../clustering/GaussianMixtureModel.scala     |  8 +++---
 .../spark/mllib/clustering/KMeans.scala       |  1 +
 .../spark/mllib/clustering/KMeansModel.scala  |  4 +--
 .../spark/mllib/clustering/LDAModel.scala     | 28 ++++++++++++++-----
 .../clustering/PowerIterationClustering.scala | 10 +++++--
 .../mllib/clustering/StreamingKMeans.scala    | 15 +++++-----
 7 files changed, 44 insertions(+), 23 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index daa947e81d44d..f82bd82c20371 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -53,6 +53,7 @@ import org.apache.spark.util.Utils
  * @param maxIterations The maximum number of iterations to perform
  */
 @Experimental
+@Since("1.3.0")
 class GaussianMixture private (
     private var k: Int,
     private var convergenceTol: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 1a10a8b624218..7f6163e04bf17 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -46,9 +46,9 @@ import org.apache.spark.sql.{SQLContext, Row}
  */
 @Since("1.3.0")
 @Experimental
-class GaussianMixtureModel(
-  val weights: Array[Double],
-  val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable {
+class GaussianMixtureModel @Since("1.3.0") (
+  @Since("1.3.0") val weights: Array[Double],
+  @Since("1.3.0") val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable {
 
   require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match")
 
@@ -178,7 +178,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
           (weight, new MultivariateGaussian(mu, sigma))
       }.unzip
 
-      return new GaussianMixtureModel(weights.toArray, gaussians.toArray)
+      new GaussianMixtureModel(weights.toArray, gaussians.toArray)
     }
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 3e9545a74bef3..46920fffe6e1a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -37,6 +37,7 @@ import org.apache.spark.util.random.XORShiftRandom
  * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given
  * to it should be cached by the user.
  */
+@Since("0.8.0")
 class KMeans private (
     private var k: Int,
     private var maxIterations: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index e425ecdd481c6..a741584982725 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -37,8 +37,8 @@ import org.apache.spark.sql.Row
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
  */
 @Since("0.8.0")
-class KMeansModel (
-    val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
+class KMeansModel @Since("1.1.0") (@Since("1.0.0") val clusterCenters: Array[Vector])
+  extends Saveable with Serializable with PMMLExportable {
 
   /**
    * A Java-friendly constructor that takes an Iterable of Vectors.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 432bbedc8d6f8..15129e0dd5a91 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -43,12 +43,15 @@ import org.apache.spark.util.BoundedPriorityQueue
  * including local and distributed data structures.
  */
 @Experimental
+@Since("1.3.0")
 abstract class LDAModel private[clustering] extends Saveable {
 
   /** Number of topics */
+  @Since("1.3.0")
   def k: Int
 
   /** Vocabulary size (number of terms or terms in the vocabulary) */
+  @Since("1.3.0")
   def vocabSize: Int
 
   /**
@@ -57,6 +60,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    *
    * This is the parameter to a Dirichlet distribution.
    */
+  @Since("1.5.0")
   def docConcentration: Vector
 
   /**
@@ -68,6 +72,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
    */
+  @Since("1.5.0")
   def topicConcentration: Double
 
   /**
@@ -81,6 +86,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    * This is a matrix of size vocabSize x k, where each column is a topic.
    * No guarantees are given about the ordering of the topics.
    */
+  @Since("1.3.0")
   def topicsMatrix: Matrix
 
   /**
@@ -91,6 +97,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    *          (term indices, term weights in topic).
    *          Each topic's terms are sorted in order of decreasing weight.
    */
+  @Since("1.3.0")
   def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])]
 
   /**
@@ -102,6 +109,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    *          (term indices, term weights in topic).
    *          Each topic's terms are sorted in order of decreasing weight.
    */
+  @Since("1.3.0")
   def describeTopics(): Array[(Array[Int], Array[Double])] = describeTopics(vocabSize)
 
   /* TODO (once LDA can be trained with Strings or given a dictionary)
@@ -185,10 +193,11 @@ abstract class LDAModel private[clustering] extends Saveable {
  * @param topics Inferred topics (vocabSize x k matrix).
  */
 @Experimental
+@Since("1.3.0")
 class LocalLDAModel private[clustering] (
-    val topics: Matrix,
-    override val docConcentration: Vector,
-    override val topicConcentration: Double,
+    @Since("1.3.0") val topics: Matrix,
+    @Since("1.5.0") override val docConcentration: Vector,
+    @Since("1.5.0") override val topicConcentration: Double,
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel with Serializable {
 
@@ -376,6 +385,7 @@ class LocalLDAModel private[clustering] (
 }
 
 @Experimental
+@Since("1.5.0")
 object LocalLDAModel extends Loader[LocalLDAModel] {
 
   private object SaveLoadV1_0 {
@@ -479,13 +489,14 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
  * than the [[LocalLDAModel]].
  */
 @Experimental
+@Since("1.3.0")
 class DistributedLDAModel private[clustering] (
     private[clustering] val graph: Graph[LDA.TopicCounts, LDA.TokenCount],
     private[clustering] val globalTopicTotals: LDA.TopicCounts,
-    val k: Int,
-    val vocabSize: Int,
-    override val docConcentration: Vector,
-    override val topicConcentration: Double,
+    @Since("1.3.0") val k: Int,
+    @Since("1.3.0") val vocabSize: Int,
+    @Since("1.5.0") override val docConcentration: Vector,
+    @Since("1.5.0") override val topicConcentration: Double,
     private[spark] val iterationTimes: Array[Double],
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel {
@@ -603,6 +614,7 @@ class DistributedLDAModel private[clustering] (
    *         (term indices, topic indices).  Note that terms will be omitted if not present in
    *         the document.
    */
+  @Since("1.5.0")
   lazy val topicAssignments: RDD[(Long, Array[Int], Array[Int])] = {
     // For reference, compare the below code with the core part of EMLDAOptimizer.next().
     val eta = topicConcentration
@@ -634,6 +646,7 @@ class DistributedLDAModel private[clustering] (
   }
 
   /** Java-friendly version of [[topicAssignments]] */
+  @Since("1.5.0")
   lazy val javaTopicAssignments: JavaRDD[(java.lang.Long, Array[Int], Array[Int])] = {
     topicAssignments.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Int])]].toJavaRDD()
   }
@@ -770,6 +783,7 @@ class DistributedLDAModel private[clustering] (
 
 
 @Experimental
+@Since("1.5.0")
 object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
   private object SaveLoadV1_0 {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index 396b36f2f6454..da234bdbb29e6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -42,9 +42,10 @@ import org.apache.spark.{Logging, SparkContext, SparkException}
  */
 @Since("1.3.0")
 @Experimental
-class PowerIterationClusteringModel(
-    val k: Int,
-    val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable {
+class PowerIterationClusteringModel @Since("1.3.0") (
+    @Since("1.3.0") val k: Int,
+    @Since("1.3.0") val assignments: RDD[PowerIterationClustering.Assignment])
+  extends Saveable with Serializable {
 
   @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
@@ -56,6 +57,8 @@ class PowerIterationClusteringModel(
 
 @Since("1.4.0")
 object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] {
+
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
     PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path)
   }
@@ -120,6 +123,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
  * @see [[http://en.wikipedia.org/wiki/Spectral_clustering Spectral clustering (Wikipedia)]]
  */
 @Experimental
+@Since("1.3.0")
 class PowerIterationClustering private[clustering] (
     private var k: Int,
     private var maxIterations: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index 41f2668ec6a7d..1d50ffec96faf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -66,9 +66,10 @@ import org.apache.spark.util.random.XORShiftRandom
  */
 @Since("1.2.0")
 @Experimental
-class StreamingKMeansModel(
-    override val clusterCenters: Array[Vector],
-    val clusterWeights: Array[Double]) extends KMeansModel(clusterCenters) with Logging {
+class StreamingKMeansModel @Since("1.2.0") (
+    @Since("1.2.0") override val clusterCenters: Array[Vector],
+    @Since("1.2.0") val clusterWeights: Array[Double])
+  extends KMeansModel(clusterCenters) with Logging {
 
   /**
    * Perform a k-means update on a batch of data.
@@ -168,10 +169,10 @@ class StreamingKMeansModel(
  */
 @Since("1.2.0")
 @Experimental
-class StreamingKMeans(
-    var k: Int,
-    var decayFactor: Double,
-    var timeUnit: String) extends Logging with Serializable {
+class StreamingKMeans @Since("1.2.0") (
+    @Since("1.2.0") var k: Int,
+    @Since("1.2.0") var decayFactor: Double,
+    @Since("1.2.0") var timeUnit: String) extends Logging with Serializable {
 
   @Since("1.2.0")
   def this() = this(2, 1.0, StreamingKMeans.BATCHES)

From fb7e12fe2e14af8de4c206ca8096b2e8113bfddc Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:35:49 -0700
Subject: [PATCH 1228/1454] [SPARK-10243] [MLLIB] update since versions in
 mllib.tree

Same as #8421 but for `mllib.tree`.

cc jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8442 from mengxr/SPARK-10236.
---
 .../spark/mllib/tree/DecisionTree.scala       |  3 +-
 .../mllib/tree/GradientBoostedTrees.scala     |  2 +-
 .../spark/mllib/tree/configuration/Algo.scala |  2 ++
 .../tree/configuration/BoostingStrategy.scala | 12 ++++----
 .../tree/configuration/FeatureType.scala      |  2 ++
 .../tree/configuration/QuantileStrategy.scala |  2 ++
 .../mllib/tree/configuration/Strategy.scala   | 29 ++++++++++---------
 .../mllib/tree/model/DecisionTreeModel.scala  |  5 +++-
 .../apache/spark/mllib/tree/model/Node.scala  | 18 ++++++------
 .../spark/mllib/tree/model/Predict.scala      |  6 ++--
 .../apache/spark/mllib/tree/model/Split.scala |  8 ++---
 .../mllib/tree/model/treeEnsembleModels.scala | 12 ++++----
 12 files changed, 57 insertions(+), 44 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 972841015d4f0..4a77d4adcd865 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -46,7 +46,8 @@ import org.apache.spark.util.random.XORShiftRandom
  */
 @Since("1.0.0")
 @Experimental
-class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
+class DecisionTree @Since("1.0.0") (private val strategy: Strategy)
+  extends Serializable with Logging {
 
   strategy.assertValid()
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index e750408600c33..95ed48cea6716 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -51,7 +51,7 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("1.2.0")
 @Experimental
-class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
+class GradientBoostedTrees @Since("1.2.0") (private val boostingStrategy: BoostingStrategy)
   extends Serializable with Logging {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
index 8301ad160836b..853c7319ec44d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -26,7 +26,9 @@ import org.apache.spark.annotation.{Experimental, Since}
 @Since("1.0.0")
 @Experimental
 object Algo extends Enumeration {
+  @Since("1.0.0")
   type Algo = Value
+  @Since("1.0.0")
   val Classification, Regression = Value
 
   private[mllib] def fromString(name: String): Algo = name match {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 7c569981977b4..b5c72fba3ede1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -41,14 +41,14 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  */
 @Since("1.2.0")
 @Experimental
-case class BoostingStrategy(
+case class BoostingStrategy @Since("1.4.0") (
     // Required boosting parameters
-    @BeanProperty var treeStrategy: Strategy,
-    @BeanProperty var loss: Loss,
+    @Since("1.2.0") @BeanProperty var treeStrategy: Strategy,
+    @Since("1.2.0") @BeanProperty var loss: Loss,
     // Optional boosting parameters
-    @BeanProperty var numIterations: Int = 100,
-    @BeanProperty var learningRate: Double = 0.1,
-    @BeanProperty var validationTol: Double = 1e-5) extends Serializable {
+    @Since("1.2.0") @BeanProperty var numIterations: Int = 100,
+    @Since("1.2.0") @BeanProperty var learningRate: Double = 0.1,
+    @Since("1.4.0") @BeanProperty var validationTol: Double = 1e-5) extends Serializable {
 
   /**
    * Check validity of parameters.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
index bb7c7ee4f964f..4e0cd473def06 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -26,6 +26,8 @@ import org.apache.spark.annotation.{Experimental, Since}
 @Since("1.0.0")
 @Experimental
 object FeatureType extends Enumeration {
+  @Since("1.0.0")
   type FeatureType = Value
+  @Since("1.0.0")
   val Continuous, Categorical = Value
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
index 904e42deebb5f..8262db8a4f111 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -26,6 +26,8 @@ import org.apache.spark.annotation.{Experimental, Since}
 @Since("1.0.0")
 @Experimental
 object QuantileStrategy extends Enumeration {
+  @Since("1.0.0")
   type QuantileStrategy = Value
+  @Since("1.0.0")
   val Sort, MinMax, ApproxHist = Value
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index b74e3f1f46523..89cc13b7c06cf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -69,20 +69,20 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  */
 @Since("1.0.0")
 @Experimental
-class Strategy (
-    @BeanProperty var algo: Algo,
-    @BeanProperty var impurity: Impurity,
-    @BeanProperty var maxDepth: Int,
-    @BeanProperty var numClasses: Int = 2,
-    @BeanProperty var maxBins: Int = 32,
-    @BeanProperty var quantileCalculationStrategy: QuantileStrategy = Sort,
-    @BeanProperty var categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
-    @BeanProperty var minInstancesPerNode: Int = 1,
-    @BeanProperty var minInfoGain: Double = 0.0,
-    @BeanProperty var maxMemoryInMB: Int = 256,
-    @BeanProperty var subsamplingRate: Double = 1,
-    @BeanProperty var useNodeIdCache: Boolean = false,
-    @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
+class Strategy @Since("1.3.0") (
+    @Since("1.0.0") @BeanProperty var algo: Algo,
+    @Since("1.0.0") @BeanProperty var impurity: Impurity,
+    @Since("1.0.0") @BeanProperty var maxDepth: Int,
+    @Since("1.2.0") @BeanProperty var numClasses: Int = 2,
+    @Since("1.0.0") @BeanProperty var maxBins: Int = 32,
+    @Since("1.0.0") @BeanProperty var quantileCalculationStrategy: QuantileStrategy = Sort,
+    @Since("1.0.0") @BeanProperty var categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
+    @Since("1.2.0") @BeanProperty var minInstancesPerNode: Int = 1,
+    @Since("1.2.0") @BeanProperty var minInfoGain: Double = 0.0,
+    @Since("1.0.0") @BeanProperty var maxMemoryInMB: Int = 256,
+    @Since("1.2.0") @BeanProperty var subsamplingRate: Double = 1,
+    @Since("1.2.0") @BeanProperty var useNodeIdCache: Boolean = false,
+    @Since("1.2.0") @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
 
   /**
    */
@@ -206,6 +206,7 @@ object Strategy {
   }
 
   @deprecated("Use Strategy.defaultStrategy instead.", "1.5.0")
+  @Since("1.2.0")
   def defaultStategy(algo: Algo): Strategy = defaultStrategy(algo)
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index 3eefd135f7836..e1bf23f4c34bb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -43,7 +43,9 @@ import org.apache.spark.util.Utils
  */
 @Since("1.0.0")
 @Experimental
-class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable {
+class DecisionTreeModel @Since("1.0.0") (
+    @Since("1.0.0") val topNode: Node,
+    @Since("1.0.0") val algo: Algo) extends Serializable with Saveable {
 
   /**
    * Predict values for a single data point using the model trained.
@@ -110,6 +112,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   /**
    * Print the full model to a string.
    */
+  @Since("1.2.0")
   def toDebugString: String = {
     val header = toString + "\n"
     header + topNode.subtreeToString(2)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index 8c54c55107233..ea6e5aa5d94e7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -41,15 +41,15 @@ import org.apache.spark.mllib.linalg.Vector
  */
 @Since("1.0.0")
 @DeveloperApi
-class Node (
-    val id: Int,
-    var predict: Predict,
-    var impurity: Double,
-    var isLeaf: Boolean,
-    var split: Option[Split],
-    var leftNode: Option[Node],
-    var rightNode: Option[Node],
-    var stats: Option[InformationGainStats]) extends Serializable with Logging {
+class Node @Since("1.2.0") (
+    @Since("1.0.0") val id: Int,
+    @Since("1.0.0") var predict: Predict,
+    @Since("1.2.0") var impurity: Double,
+    @Since("1.0.0") var isLeaf: Boolean,
+    @Since("1.0.0") var split: Option[Split],
+    @Since("1.0.0") var leftNode: Option[Node],
+    @Since("1.0.0") var rightNode: Option[Node],
+    @Since("1.0.0") var stats: Option[InformationGainStats]) extends Serializable with Logging {
 
   override def toString: String = {
     s"id = $id, isLeaf = $isLeaf, predict = $predict, impurity = $impurity, " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
index 965784051ede5..06ceff19d8633 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -26,9 +26,9 @@ import org.apache.spark.annotation.{DeveloperApi, Since}
  */
 @Since("1.2.0")
 @DeveloperApi
-class Predict(
-    val predict: Double,
-    val prob: Double = 0.0) extends Serializable {
+class Predict @Since("1.2.0") (
+    @Since("1.2.0") val predict: Double,
+    @Since("1.2.0") val prob: Double = 0.0) extends Serializable {
 
   override def toString: String = s"$predict (prob = $prob)"
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
index 45db83ae3a1f3..b85a66c05a81d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -34,10 +34,10 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 @Since("1.0.0")
 @DeveloperApi
 case class Split(
-    feature: Int,
-    threshold: Double,
-    featureType: FeatureType,
-    categories: List[Double]) {
+    @Since("1.0.0") feature: Int,
+    @Since("1.0.0") threshold: Double,
+    @Since("1.0.0") featureType: FeatureType,
+    @Since("1.0.0") categories: List[Double]) {
 
   override def toString: String = {
     s"Feature = $feature, threshold = $threshold, featureType = $featureType, " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 19571447a2c56..df5b8feab5d5d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -48,7 +48,9 @@ import org.apache.spark.util.Utils
  */
 @Since("1.2.0")
 @Experimental
-class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel])
+class RandomForestModel @Since("1.2.0") (
+    @Since("1.2.0") override val algo: Algo,
+    @Since("1.2.0") override val trees: Array[DecisionTreeModel])
   extends TreeEnsembleModel(algo, trees, Array.fill(trees.length)(1.0),
     combiningStrategy = if (algo == Classification) Vote else Average)
   with Saveable {
@@ -115,10 +117,10 @@ object RandomForestModel extends Loader[RandomForestModel] {
  */
 @Since("1.2.0")
 @Experimental
-class GradientBoostedTreesModel(
-    override val algo: Algo,
-    override val trees: Array[DecisionTreeModel],
-    override val treeWeights: Array[Double])
+class GradientBoostedTreesModel @Since("1.2.0") (
+    @Since("1.2.0") override val algo: Algo,
+    @Since("1.2.0") override val trees: Array[DecisionTreeModel],
+    @Since("1.2.0") override val treeWeights: Array[Double])
   extends TreeEnsembleModel(algo, trees, treeWeights, combiningStrategy = Sum)
   with Saveable {
 

From 4657fa1f37d41dd4c7240a960342b68c7c591f48 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:49:33 -0700
Subject: [PATCH 1229/1454] [SPARK-10235] [MLLIB] update since versions in
 mllib.regression

Same as #8421 but for `mllib.regression`.

cc freeman-lab dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8426 from mengxr/SPARK-10235 and squashes the following commits:

6cd28e4 [Xiangrui Meng] update since versions in mllib.regression
---
 .../regression/GeneralizedLinearAlgorithm.scala  |  6 ++++--
 .../mllib/regression/IsotonicRegression.scala    | 16 +++++++++-------
 .../spark/mllib/regression/LabeledPoint.scala    |  5 +++--
 .../apache/spark/mllib/regression/Lasso.scala    |  9 ++++++---
 .../mllib/regression/LinearRegression.scala      |  9 ++++++---
 .../spark/mllib/regression/RidgeRegression.scala | 12 +++++++-----
 .../regression/StreamingLinearAlgorithm.scala    |  8 +++-----
 .../StreamingLinearRegressionWithSGD.scala       | 11 +++++++++--
 8 files changed, 47 insertions(+), 29 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 509f6a2d169c4..7e3b4d5648fe3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -38,7 +38,9 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("0.8.0")
 @DeveloperApi
-abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
+abstract class GeneralizedLinearModel @Since("1.0.0") (
+    @Since("1.0.0") val weights: Vector,
+    @Since("0.8.0") val intercept: Double)
   extends Serializable {
 
   /**
@@ -107,7 +109,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * The optimizer to solve the problem.
    *
    */
-  @Since("1.0.0")
+  @Since("0.8.0")
   def optimizer: Optimizer
 
   /** Whether to add intercept (default: false). */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index 31ca7c2f207d9..877d31ba41303 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -50,10 +50,10 @@ import org.apache.spark.sql.SQLContext
  */
 @Since("1.3.0")
 @Experimental
-class IsotonicRegressionModel (
-    val boundaries: Array[Double],
-    val predictions: Array[Double],
-    val isotonic: Boolean) extends Serializable with Saveable {
+class IsotonicRegressionModel @Since("1.3.0") (
+    @Since("1.3.0") val boundaries: Array[Double],
+    @Since("1.3.0") val predictions: Array[Double],
+    @Since("1.3.0") val isotonic: Boolean) extends Serializable with Saveable {
 
   private val predictionOrd = if (isotonic) Ordering[Double] else Ordering[Double].reverse
 
@@ -63,7 +63,6 @@ class IsotonicRegressionModel (
 
   /**
    * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
-   *
    */
   @Since("1.4.0")
   def this(boundaries: java.lang.Iterable[Double],
@@ -214,8 +213,6 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
     }
   }
 
-  /**
-   */
   @Since("1.4.0")
   override def load(sc: SparkContext, path: String): IsotonicRegressionModel = {
     implicit val formats = DefaultFormats
@@ -256,6 +253,7 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
  * @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]
  */
 @Experimental
+@Since("1.3.0")
 class IsotonicRegression private (private var isotonic: Boolean) extends Serializable {
 
   /**
@@ -263,6 +261,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    *
    * @return New instance of IsotonicRegression.
    */
+  @Since("1.3.0")
   def this() = this(true)
 
   /**
@@ -271,6 +270,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    * @param isotonic Isotonic (increasing) or antitonic (decreasing) sequence.
    * @return This instance of IsotonicRegression.
    */
+  @Since("1.3.0")
   def setIsotonic(isotonic: Boolean): this.type = {
     this.isotonic = isotonic
     this
@@ -286,6 +286,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    *              the algorithm is executed.
    * @return Isotonic regression model.
    */
+  @Since("1.3.0")
   def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel = {
     val preprocessedInput = if (isotonic) {
       input
@@ -311,6 +312,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    *              the algorithm is executed.
    * @return Isotonic regression model.
    */
+  @Since("1.3.0")
   def run(input: JavaRDD[(JDouble, JDouble, JDouble)]): IsotonicRegressionModel = {
     run(input.rdd.retag.asInstanceOf[RDD[(Double, Double, Double)]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index f7fe1b7b21fca..c284ad2325374 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -29,11 +29,12 @@ import org.apache.spark.SparkException
  *
  * @param label Label for this data point.
  * @param features List of features for this data point.
- *
  */
 @Since("0.8.0")
 @BeanInfo
-case class LabeledPoint(label: Double, features: Vector) {
+case class LabeledPoint @Since("1.0.0") (
+    @Since("0.8.0") label: Double,
+    @Since("1.0.0") features: Vector) {
   override def toString: String = {
     s"($label,$features)"
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 556411a366bd2..a9aba173fa0e3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -34,9 +34,9 @@ import org.apache.spark.rdd.RDD
  *
  */
 @Since("0.8.0")
-class LassoModel (
-    override val weights: Vector,
-    override val intercept: Double)
+class LassoModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable with Saveable with PMMLExportable {
 
@@ -84,6 +84,7 @@ object LassoModel extends Loader[LassoModel] {
  * its corresponding right hand side label y.
  * See also the documentation for the precise formulation.
  */
+@Since("0.8.0")
 class LassoWithSGD private (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -93,6 +94,7 @@ class LassoWithSGD private (
 
   private val gradient = new LeastSquaresGradient()
   private val updater = new L1Updater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -103,6 +105,7 @@ class LassoWithSGD private (
    * Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 00ab06e3ba738..4996ace5df85d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -34,9 +34,9 @@ import org.apache.spark.rdd.RDD
  *
  */
 @Since("0.8.0")
-class LinearRegressionModel (
-    override val weights: Vector,
-    override val intercept: Double)
+class LinearRegressionModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable
   with Saveable with PMMLExportable {
 
@@ -85,6 +85,7 @@ object LinearRegressionModel extends Loader[LinearRegressionModel] {
  * its corresponding right hand side label y.
  * See also the documentation for the precise formulation.
  */
+@Since("0.8.0")
 class LinearRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -93,6 +94,7 @@ class LinearRegressionWithSGD private[mllib] (
 
   private val gradient = new LeastSquaresGradient()
   private val updater = new SimpleUpdater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -102,6 +104,7 @@ class LinearRegressionWithSGD private[mllib] (
    * Construct a LinearRegression object with default parameters: {stepSize: 1.0,
    * numIterations: 100, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 1.0)
 
   override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 21a791d98b2cb..0a44ff559d55b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -35,9 +35,9 @@ import org.apache.spark.rdd.RDD
  *
  */
 @Since("0.8.0")
-class RidgeRegressionModel (
-    override val weights: Vector,
-    override val intercept: Double)
+class RidgeRegressionModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable with Saveable with PMMLExportable {
 
@@ -85,6 +85,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
  * its corresponding right hand side label y.
  * See also the documentation for the precise formulation.
  */
+@Since("0.8.0")
 class RidgeRegressionWithSGD private (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -94,7 +95,7 @@ class RidgeRegressionWithSGD private (
 
   private val gradient = new LeastSquaresGradient()
   private val updater = new SquaredL2Updater()
-
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -105,6 +106,7 @@ class RidgeRegressionWithSGD private (
    * Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {
@@ -134,7 +136,7 @@ object RidgeRegressionWithSGD {
    *        the number of features in the data.
    *
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
index cd3ed8a1549db..73948b2d9851a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.Logging
 import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.streaming.api.java.{JavaDStream, JavaPairDStream}
 import org.apache.spark.streaming.dstream.DStream
 
@@ -83,9 +83,8 @@ abstract class StreamingLinearAlgorithm[
    * batch of data from the stream.
    *
    * @param data DStream containing labeled data
-   *
    */
-  @Since("1.3.0")
+  @Since("1.1.0")
   def trainOn(data: DStream[LabeledPoint]): Unit = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting training.")
@@ -105,7 +104,6 @@ abstract class StreamingLinearAlgorithm[
 
   /**
    * Java-friendly version of `trainOn`.
-   *
    */
   @Since("1.3.0")
   def trainOn(data: JavaDStream[LabeledPoint]): Unit = trainOn(data.dstream)
@@ -129,7 +127,7 @@ abstract class StreamingLinearAlgorithm[
    * Java-friendly version of `predictOn`.
    *
    */
-  @Since("1.1.0")
+  @Since("1.3.0")
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Double] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Double]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index 26654e4a06838..fe1d487cdd078 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.Vector
 
 /**
@@ -41,6 +41,7 @@ import org.apache.spark.mllib.linalg.Vector
  *    .trainOn(DStream)
  */
 @Experimental
+@Since("1.1.0")
 class StreamingLinearRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -54,8 +55,10 @@ class StreamingLinearRegressionWithSGD private[mllib] (
    * Initial weights must be set before using trainOn or predictOn
    * (see `StreamingLinearAlgorithm`)
    */
+  @Since("1.1.0")
   def this() = this(0.1, 50, 1.0)
 
+  @Since("1.1.0")
   val algorithm = new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction)
 
   protected var model: Option[LinearRegressionModel] = None
@@ -63,6 +66,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the step size for gradient descent. Default: 0.1.
    */
+  @Since("1.1.0")
   def setStepSize(stepSize: Double): this.type = {
     this.algorithm.optimizer.setStepSize(stepSize)
     this
@@ -71,6 +75,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the number of iterations of gradient descent to run per update. Default: 50.
    */
+  @Since("1.1.0")
   def setNumIterations(numIterations: Int): this.type = {
     this.algorithm.optimizer.setNumIterations(numIterations)
     this
@@ -79,6 +84,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the fraction of each batch to use for updates. Default: 1.0.
    */
+  @Since("1.1.0")
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
     this
@@ -87,6 +93,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the initial weights.
    */
+  @Since("1.1.0")
   def setInitialWeights(initialWeights: Vector): this.type = {
     this.model = Some(algorithm.createModel(initialWeights, 0.0))
     this
@@ -95,9 +102,9 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the convergence tolerance. Default: 0.001.
    */
+  @Since("1.5.0")
   def setConvergenceTol(tolerance: Double): this.type = {
     this.algorithm.optimizer.setConvergenceTol(tolerance)
     this
   }
-
 }

From 321d7759691bed9867b1f0470f12eab2faa50aff Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 23:45:41 -0700
Subject: [PATCH 1230/1454] [SPARK-10236] [MLLIB] update since versions in
 mllib.feature

Same as #8421 but for `mllib.feature`.

cc dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8449 from mengxr/SPARK-10236.feature and squashes the following commits:

0e8d658 [Xiangrui Meng] remove unnecessary comment
ad70b03 [Xiangrui Meng] update since versions in mllib.feature
---
 .../mllib/clustering/PowerIterationClustering.scala  |  2 --
 .../apache/spark/mllib/feature/ChiSqSelector.scala   |  4 ++--
 .../spark/mllib/feature/ElementwiseProduct.scala     |  3 ++-
 .../scala/org/apache/spark/mllib/feature/IDF.scala   |  6 ++++--
 .../org/apache/spark/mllib/feature/Normalizer.scala  |  2 +-
 .../scala/org/apache/spark/mllib/feature/PCA.scala   |  7 +++++--
 .../apache/spark/mllib/feature/StandardScaler.scala  | 12 ++++++------
 .../org/apache/spark/mllib/feature/Word2Vec.scala    |  1 +
 8 files changed, 21 insertions(+), 16 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index da234bdbb29e6..6c76e26fd1626 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -71,8 +71,6 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
     private[clustering]
     val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel"
 
-    /**
-     */
     @Since("1.4.0")
     def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = {
       val sqlContext = new SQLContext(sc)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
index fdd974d7a391e..4743cfd1a2c3f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
@@ -33,7 +33,7 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.3.0")
 @Experimental
-class ChiSqSelectorModel (
+class ChiSqSelectorModel @Since("1.3.0") (
   @Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer {
 
   require(isSorted(selectedFeatures), "Array has to be sorted asc")
@@ -112,7 +112,7 @@ class ChiSqSelectorModel (
  */
 @Since("1.3.0")
 @Experimental
-class ChiSqSelector (
+class ChiSqSelector @Since("1.3.0") (
   @Since("1.3.0") val numTopFeatures: Int) extends Serializable {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
index 33e2d17bb472e..d0a6cf61687a8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
@@ -29,7 +29,8 @@ import org.apache.spark.mllib.linalg._
  */
 @Since("1.4.0")
 @Experimental
-class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
+class ElementwiseProduct @Since("1.4.0") (
+    @Since("1.4.0") val scalingVec: Vector) extends VectorTransformer {
 
   /**
    * Does the hadamard product transformation.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
index d5353ddd972e0..68078ccfa3d60 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
@@ -39,8 +39,9 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.1.0")
 @Experimental
-class IDF(val minDocFreq: Int) {
+class IDF @Since("1.2.0") (@Since("1.2.0") val minDocFreq: Int) {
 
+  @Since("1.1.0")
   def this() = this(0)
 
   // TODO: Allow different IDF formulations.
@@ -162,7 +163,8 @@ private object IDF {
  * Represents an IDF model that can transform term frequency vectors.
  */
 @Experimental
-class IDFModel private[spark] (val idf: Vector) extends Serializable {
+@Since("1.1.0")
+class IDFModel private[spark] (@Since("1.1.0") val idf: Vector) extends Serializable {
 
   /**
    * Transforms term frequency (TF) vectors to TF-IDF vectors.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index 0e070257d9fb2..8d5a22520d6b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -33,7 +33,7 @@ import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors
  */
 @Since("1.1.0")
 @Experimental
-class Normalizer(p: Double) extends VectorTransformer {
+class Normalizer @Since("1.1.0") (p: Double) extends VectorTransformer {
 
   @Since("1.1.0")
   def this() = this(2)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
index a48b7bba665d7..ecb3c1e6c1c83 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
@@ -29,7 +29,7 @@ import org.apache.spark.rdd.RDD
  * @param k number of principal components
  */
 @Since("1.4.0")
-class PCA(val k: Int) {
+class PCA @Since("1.4.0") (@Since("1.4.0") val k: Int) {
   require(k >= 1, s"PCA requires a number of principal components k >= 1 but was given $k")
 
   /**
@@ -74,7 +74,10 @@ class PCA(val k: Int) {
  * @param k number of principal components.
  * @param pc a principal components Matrix. Each column is one principal component.
  */
-class PCAModel private[spark] (val k: Int, val pc: DenseMatrix) extends VectorTransformer {
+@Since("1.4.0")
+class PCAModel private[spark] (
+    @Since("1.4.0") val k: Int,
+    @Since("1.4.0") val pc: DenseMatrix) extends VectorTransformer {
   /**
    * Transform a vector by computed Principal Components.
    *
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index b95d5a899001e..f018b453bae7e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -34,7 +34,7 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.1.0")
 @Experimental
-class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
+class StandardScaler @Since("1.1.0") (withMean: Boolean, withStd: Boolean) extends Logging {
 
   @Since("1.1.0")
   def this() = this(false, true)
@@ -74,11 +74,11 @@ class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
  */
 @Since("1.1.0")
 @Experimental
-class StandardScalerModel (
-    val std: Vector,
-    val mean: Vector,
-    var withStd: Boolean,
-    var withMean: Boolean) extends VectorTransformer {
+class StandardScalerModel @Since("1.3.0") (
+    @Since("1.3.0") val std: Vector,
+    @Since("1.1.0") val mean: Vector,
+    @Since("1.3.0") var withStd: Boolean,
+    @Since("1.3.0") var withMean: Boolean) extends VectorTransformer {
 
   /**
    */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index e6f45ae4b01d5..36b124c5d2966 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -436,6 +436,7 @@ class Word2Vec extends Serializable with Logging {
  *                    (i * vectorSize, i * vectorSize + vectorSize)
  */
 @Experimental
+@Since("1.1.0")
 class Word2VecModel private[mllib] (
     private val wordIndex: Map[String, Int],
     private val wordVectors: Array[Float]) extends Serializable with Saveable {

From 75d4773aa50e24972c533e8b48697fde586429eb Mon Sep 17 00:00:00 2001
From: felixcheung <felixcheung_m@hotmail.com>
Date: Tue, 25 Aug 2015 23:48:16 -0700
Subject: [PATCH 1231/1454] [SPARK-9316] [SPARKR] Add support for filtering
 using `[` (synonym for filter / select)

Add support for
```
   df[df$name == "Smith", c(1,2)]
   df[df$age %in% c(19, 30), 1:2]
```

shivaram

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #8394 from felixcheung/rsubset.
---
 R/pkg/R/DataFrame.R              | 22 +++++++++++++++++++++-
 R/pkg/inst/tests/test_sparkSQL.R | 27 +++++++++++++++++++++++++++
 2 files changed, 48 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index ae1d912cf6da1..a5162de705f8f 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -985,9 +985,11 @@ setMethod("$<-", signature(x = "DataFrame"),
             x
           })
 
+setClassUnion("numericOrcharacter", c("numeric", "character"))
+
 #' @rdname select
 #' @name [[
-setMethod("[[", signature(x = "DataFrame"),
+setMethod("[[", signature(x = "DataFrame", i = "numericOrcharacter"),
           function(x, i) {
             if (is.numeric(i)) {
               cols <- columns(x)
@@ -1010,6 +1012,20 @@ setMethod("[", signature(x = "DataFrame", i = "missing"),
             select(x, j)
           })
 
+#' @rdname select
+#' @name [
+setMethod("[", signature(x = "DataFrame", i = "Column"),
+          function(x, i, j, ...) {
+            # It could handle i as "character" but it seems confusing and not required
+            # https://stat.ethz.ch/R-manual/R-devel/library/base/html/Extract.data.frame.html
+            filtered <- filter(x, i)
+            if (!missing(j)) {
+              filtered[, j]
+            } else {
+              filtered
+            }
+          })
+
 #' Select
 #'
 #' Selects a set of columns with names or Column expressions.
@@ -1028,8 +1044,12 @@ setMethod("[", signature(x = "DataFrame", i = "missing"),
 #'   # Columns can also be selected using `[[` and `[`
 #'   df[[2]] == df[["age"]]
 #'   df[,2] == df[,"age"]
+#'   df[,c("name", "age")]
 #'   # Similar to R data frames columns can also be selected using `$`
 #'   df$age
+#'   # It can also be subset on rows and Columns
+#'   df[df$name == "Smith", c(1,2)]
+#'   df[df$age %in% c(19, 30), 1:2]
 #' }
 setMethod("select", signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 556b8c5447054..ee48a3dc0cc05 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -587,6 +587,33 @@ test_that("select with column", {
   expect_equal(collect(select(df3, "x"))[[1, 1]], "x")
 })
 
+test_that("subsetting", {
+  # jsonFile returns columns in random order
+  df <- select(jsonFile(sqlContext, jsonPath), "name", "age")
+  filtered <- df[df$age > 20,]
+  expect_equal(count(filtered), 1)
+  expect_equal(columns(filtered), c("name", "age"))
+  expect_equal(collect(filtered)$name, "Andy")
+
+  df2 <- df[df$age == 19, 1]
+  expect_is(df2, "DataFrame")
+  expect_equal(count(df2), 1)
+  expect_equal(columns(df2), c("name"))
+  expect_equal(collect(df2)$name, "Justin")
+
+  df3 <- df[df$age > 20, 2]
+  expect_equal(count(df3), 1)
+  expect_equal(columns(df3), c("age"))
+
+  df4 <- df[df$age %in% c(19, 30), 1:2]
+  expect_equal(count(df4), 2)
+  expect_equal(columns(df4), c("name", "age"))
+  
+  df5 <- df[df$age %in% c(19), c(1,2)]
+  expect_equal(count(df5), 1)
+  expect_equal(columns(df5), c("name", "age"))
+})
+
 test_that("selectExpr() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   selected <- selectExpr(df, "age * 2")

From bb1640529725c6c38103b95af004f8bd90eeee5c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 26 Aug 2015 00:37:04 -0700
Subject: [PATCH 1232/1454] Closes #8443


From 6519fd06cc8175c9182ef16cf8a37d7f255eb846 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 26 Aug 2015 11:47:05 -0700
Subject: [PATCH 1233/1454] [SPARK-9665] [MLLIB] audit MLlib API annotations

I only found `ml.NaiveBayes` missing `Experimental` annotation. This PR doesn't cover Python APIs.

cc jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8452 from mengxr/SPARK-9665.
---
 .../apache/spark/ml/classification/NaiveBayes.scala  | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 97cbaf1fa8761..69cb88a7e6718 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkException
-import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
-import org.apache.spark.ml.param.{ParamMap, ParamValidators, Param, DoubleParam}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.PredictorParams
+import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes}
-import org.apache.spark.mllib.classification.{NaiveBayesModel => OldNaiveBayesModel}
+import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes, NaiveBayesModel => OldNaiveBayesModel}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
@@ -59,6 +59,7 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
 }
 
 /**
+ * :: Experimental ::
  * Naive Bayes Classifiers.
  * It supports both Multinomial NB
  * ([[http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html]])
@@ -68,6 +69,7 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
  * ([[http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html]]).
  * The input feature values must be nonnegative.
  */
+@Experimental
 class NaiveBayes(override val uid: String)
   extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel]
   with NaiveBayesParams {
@@ -101,11 +103,13 @@ class NaiveBayes(override val uid: String)
 }
 
 /**
+ * :: Experimental ::
  * Model produced by [[NaiveBayes]]
  * @param pi log of class priors, whose dimension is C (number of classes)
  * @param theta log of class conditional probabilities, whose dimension is C (number of classes)
  *              by D (number of features)
  */
+@Experimental
 class NaiveBayesModel private[ml] (
     override val uid: String,
     val pi: Vector,

From de7209c256aaf79a0978cfcf6e98bb013267b93a Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Wed, 26 Aug 2015 12:19:36 -0700
Subject: [PATCH 1234/1454] HOTFIX: Increase PRB timeout

---
 dev/run-tests-jenkins | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index c4d39d95d5890..f144c053046c5 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -48,8 +48,8 @@ COMMIT_URL="https://github.com/apache/spark/commit/${ghprbActualCommit}"
 SHORT_COMMIT_HASH="${ghprbActualCommit:0:7}"
 
 # format: http://linux.die.net/man/1/timeout
-# must be less than the timeout configured on Jenkins (currently 180m)
-TESTS_TIMEOUT="175m"
+# must be less than the timeout configured on Jenkins (currently 300m)
+TESTS_TIMEOUT="250m"
 
 # Array to capture all tests to run on the pull request. These tests are held under the
 #+ dev/tests/ directory.

From 086d4681df3ebfccfc04188262c10482f44553b0 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 26 Aug 2015 14:02:19 -0700
Subject: [PATCH 1235/1454] [SPARK-10241] [MLLIB] update since versions in
 mllib.recommendation

Same as #8421 but for `mllib.recommendation`.

cc srowen coderxiang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8432 from mengxr/SPARK-10241.
---
 .../spark/mllib/recommendation/ALS.scala      | 22 ++++++++++++++++++-
 .../MatrixFactorizationModel.scala            |  8 +++----
 2 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index b27ef1b949e2e..33aaf853e599d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -28,7 +28,10 @@ import org.apache.spark.storage.StorageLevel
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
  */
 @Since("0.8.0")
-case class Rating(user: Int, product: Int, rating: Double)
+case class Rating @Since("0.8.0") (
+    @Since("0.8.0") user: Int,
+    @Since("0.8.0") product: Int,
+    @Since("0.8.0") rating: Double)
 
 /**
  * Alternating Least Squares matrix factorization.
@@ -59,6 +62,7 @@ case class Rating(user: Int, product: Int, rating: Double)
  * indicated user
  * preferences rather than explicit ratings given to items.
  */
+@Since("0.8.0")
 class ALS private (
     private var numUserBlocks: Int,
     private var numProductBlocks: Int,
@@ -74,6 +78,7 @@ class ALS private (
    * Constructs an ALS instance with default parameters: {numBlocks: -1, rank: 10, iterations: 10,
    * lambda: 0.01, implicitPrefs: false, alpha: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(-1, -1, 10, 10, 0.01, false, 1.0)
 
   /** If true, do alternating nonnegative least squares. */
@@ -90,6 +95,7 @@ class ALS private (
    * Set the number of blocks for both user blocks and product blocks to parallelize the computation
    * into; pass -1 for an auto-configured number of blocks. Default: -1.
    */
+  @Since("0.8.0")
   def setBlocks(numBlocks: Int): this.type = {
     this.numUserBlocks = numBlocks
     this.numProductBlocks = numBlocks
@@ -99,6 +105,7 @@ class ALS private (
   /**
    * Set the number of user blocks to parallelize the computation.
    */
+  @Since("1.1.0")
   def setUserBlocks(numUserBlocks: Int): this.type = {
     this.numUserBlocks = numUserBlocks
     this
@@ -107,30 +114,35 @@ class ALS private (
   /**
    * Set the number of product blocks to parallelize the computation.
    */
+  @Since("1.1.0")
   def setProductBlocks(numProductBlocks: Int): this.type = {
     this.numProductBlocks = numProductBlocks
     this
   }
 
   /** Set the rank of the feature matrices computed (number of features). Default: 10. */
+  @Since("0.8.0")
   def setRank(rank: Int): this.type = {
     this.rank = rank
     this
   }
 
   /** Set the number of iterations to run. Default: 10. */
+  @Since("0.8.0")
   def setIterations(iterations: Int): this.type = {
     this.iterations = iterations
     this
   }
 
   /** Set the regularization parameter, lambda. Default: 0.01. */
+  @Since("0.8.0")
   def setLambda(lambda: Double): this.type = {
     this.lambda = lambda
     this
   }
 
   /** Sets whether to use implicit preference. Default: false. */
+  @Since("0.8.1")
   def setImplicitPrefs(implicitPrefs: Boolean): this.type = {
     this.implicitPrefs = implicitPrefs
     this
@@ -139,12 +151,14 @@ class ALS private (
   /**
    * Sets the constant used in computing confidence in implicit ALS. Default: 1.0.
    */
+  @Since("0.8.1")
   def setAlpha(alpha: Double): this.type = {
     this.alpha = alpha
     this
   }
 
   /** Sets a random seed to have deterministic results. */
+  @Since("1.0.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -154,6 +168,7 @@ class ALS private (
    * Set whether the least-squares problems solved at each iteration should have
    * nonnegativity constraints.
    */
+  @Since("1.1.0")
   def setNonnegative(b: Boolean): this.type = {
     this.nonnegative = b
     this
@@ -166,6 +181,7 @@ class ALS private (
    * set `spark.rdd.compress` to `true` to reduce the space requirement, at the cost of speed.
    */
   @DeveloperApi
+  @Since("1.1.0")
   def setIntermediateRDDStorageLevel(storageLevel: StorageLevel): this.type = {
     require(storageLevel != StorageLevel.NONE,
       "ALS is not designed to run without persisting intermediate RDDs.")
@@ -181,6 +197,7 @@ class ALS private (
    * at the cost of speed.
    */
   @DeveloperApi
+  @Since("1.3.0")
   def setFinalRDDStorageLevel(storageLevel: StorageLevel): this.type = {
     this.finalRDDStorageLevel = storageLevel
     this
@@ -194,6 +211,7 @@ class ALS private (
    * this setting is ignored.
    */
   @DeveloperApi
+  @Since("1.4.0")
   def setCheckpointInterval(checkpointInterval: Int): this.type = {
     this.checkpointInterval = checkpointInterval
     this
@@ -203,6 +221,7 @@ class ALS private (
    * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples.
    * Returns a MatrixFactorizationModel with feature vectors for each user and product.
    */
+  @Since("0.8.0")
   def run(ratings: RDD[Rating]): MatrixFactorizationModel = {
     val sc = ratings.context
 
@@ -250,6 +269,7 @@ class ALS private (
   /**
    * Java-friendly version of [[ALS.run]].
    */
+  @Since("1.3.0")
   def run(ratings: JavaRDD[Rating]): MatrixFactorizationModel = run(ratings.rdd)
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index ba4cfdcd9f1dd..46562eb2ad0f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -52,10 +52,10 @@ import org.apache.spark.storage.StorageLevel
  *                        and the features computed for this product.
  */
 @Since("0.8.0")
-class MatrixFactorizationModel(
-    val rank: Int,
-    val userFeatures: RDD[(Int, Array[Double])],
-    val productFeatures: RDD[(Int, Array[Double])])
+class MatrixFactorizationModel @Since("0.8.0") (
+    @Since("0.8.0") val rank: Int,
+    @Since("0.8.0") val userFeatures: RDD[(Int, Array[Double])],
+    @Since("0.8.0") val productFeatures: RDD[(Int, Array[Double])])
   extends Saveable with Serializable with Logging {
 
   require(rank > 0)

From d41d6c48207159490c1e1d9cc54015725cfa41b2 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 26 Aug 2015 16:04:44 -0700
Subject: [PATCH 1236/1454] [SPARK-10305] [SQL] fix create DataFrame from
 Python class

cc jkbradley

Author: Davies Liu <davies@databricks.com>

Closes #8470 from davies/fix_create_df.
---
 python/pyspark/sql/tests.py | 12 ++++++++++++
 python/pyspark/sql/types.py |  6 ++++++
 2 files changed, 18 insertions(+)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index aacfb34c77618..cd32e26c64f22 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -145,6 +145,12 @@ class PythonOnlyPoint(ExamplePoint):
     __UDT__ = PythonOnlyUDT()
 
 
+class MyObject(object):
+    def __init__(self, key, value):
+        self.key = key
+        self.value = value
+
+
 class DataTypeTests(unittest.TestCase):
     # regression test for SPARK-6055
     def test_data_type_eq(self):
@@ -383,6 +389,12 @@ def test_infer_nested_schema(self):
         df = self.sqlCtx.inferSchema(rdd)
         self.assertEquals(Row(field1=1, field2=u'row1'), df.first())
 
+    def test_create_dataframe_from_objects(self):
+        data = [MyObject(1, "1"), MyObject(2, "2")]
+        df = self.sqlCtx.createDataFrame(data)
+        self.assertEqual(df.dtypes, [("key", "bigint"), ("value", "string")])
+        self.assertEqual(df.first(), Row(key=1, value="1"))
+
     def test_select_null_literal(self):
         df = self.sqlCtx.sql("select null as col")
         self.assertEquals(Row(col=None), df.first())
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index ed4e5b594bd61..94e581a78364c 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -537,6 +537,9 @@ def toInternal(self, obj):
                 return tuple(f.toInternal(obj.get(n)) for n, f in zip(self.names, self.fields))
             elif isinstance(obj, (tuple, list)):
                 return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
+            elif hasattr(obj, "__dict__"):
+                d = obj.__dict__
+                return tuple(f.toInternal(d.get(n)) for n, f in zip(self.names, self.fields))
             else:
                 raise ValueError("Unexpected tuple %r with StructType" % obj)
         else:
@@ -544,6 +547,9 @@ def toInternal(self, obj):
                 return tuple(obj.get(n) for n in self.names)
             elif isinstance(obj, (list, tuple)):
                 return tuple(obj)
+            elif hasattr(obj, "__dict__"):
+                d = obj.__dict__
+                return tuple(d.get(n) for n in self.names)
             else:
                 raise ValueError("Unexpected tuple %r with StructType" % obj)
 

From ad7f0f160be096c0fdae6e6cf7e3b6ba4a606de7 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Wed, 26 Aug 2015 18:13:07 -0700
Subject: [PATCH 1237/1454] [SPARK-10308] [SPARKR] Add %in% to the exported
 namespace

I also checked all the other functions defined in column.R, functions.R and DataFrame.R and everything else looked fine.

cc yu-iskw

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8473 from shivaram/in-namespace.
---
 R/pkg/NAMESPACE | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 3e5c89d779b7b..5286c01986204 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -47,12 +47,12 @@ exportMethods("arrange",
               "join",
               "limit",
               "merge",
+              "mutate",
+              "na.omit",
               "names",
               "ncol",
               "nrow",
               "orderBy",
-              "mutate",
-              "names",
               "persist",
               "printSchema",
               "rbind",
@@ -82,7 +82,8 @@ exportMethods("arrange",
 
 exportClasses("Column")
 
-exportMethods("abs",
+exportMethods("%in%",
+              "abs",
               "acos",
               "add_months",
               "alias",

From 773ca037a43d464ce7f16fe693ca6034f09a35b7 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 26 Aug 2015 18:14:32 -0700
Subject: [PATCH 1238/1454] [MINOR] [SPARKR] Fix some validation problems in
 SparkR

Getting rid of some validation problems in SparkR
https://github.com/apache/spark/pull/7883

cc shivaram

```
inst/tests/test_Serde.R:26:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:34:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:37:38: style: Trailing whitespace is superfluous.
  expect_equal(class(x), "character")
                                     ^~
inst/tests/test_Serde.R:50:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:55:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:60:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_sparkSQL.R:611:1: style: Trailing whitespace is superfluous.

^~
R/DataFrame.R:664:1: style: Trailing whitespace is superfluous.

^~~~~~~~~~~~~~
R/DataFrame.R:670:55: style: Trailing whitespace is superfluous.
                df <- data.frame(row.names = 1 : nrow)
                                                      ^~~~~~~~~~~~~~~~
R/DataFrame.R:672:1: style: Trailing whitespace is superfluous.

^~~~~~~~~~~~~~
R/DataFrame.R:686:49: style: Trailing whitespace is superfluous.
                    df[[names[colIndex]]] <- vec
                                                ^~~~~~~~~~~~~~~~~~
```

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8474 from yu-iskw/minor-fix-sparkr.
---
 R/pkg/R/DataFrame.R              |  8 ++++----
 R/pkg/inst/tests/test_Serde.R    | 12 ++++++------
 R/pkg/inst/tests/test_sparkSQL.R |  2 +-
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index a5162de705f8f..dd8126aebf467 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -661,15 +661,15 @@ setMethod("collect",
               # listCols is a list of columns
               listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf)
               stopifnot(length(listCols) == ncol)
-              
+
               # An empty data.frame with 0 columns and number of rows as collected
               nrow <- length(listCols[[1]])
               if (nrow <= 0) {
                 df <- data.frame()
               } else {
-                df <- data.frame(row.names = 1 : nrow)                
+                df <- data.frame(row.names = 1 : nrow)
               }
-              
+
               # Append columns one by one
               for (colIndex in 1 : ncol) {
                 # Note: appending a column of list type into a data.frame so that
@@ -683,7 +683,7 @@ setMethod("collect",
                   # TODO: more robust check on column of primitive types
                   vec <- do.call(c, col)
                   if (class(vec) != "list") {
-                    df[[names[colIndex]]] <- vec                  
+                    df[[names[colIndex]]] <- vec
                   } else {
                     # For columns of complex type, be careful to access them.
                     # Get a column of complex type returns a list.
diff --git a/R/pkg/inst/tests/test_Serde.R b/R/pkg/inst/tests/test_Serde.R
index 009db85da2beb..dddce54d70443 100644
--- a/R/pkg/inst/tests/test_Serde.R
+++ b/R/pkg/inst/tests/test_Serde.R
@@ -23,7 +23,7 @@ test_that("SerDe of primitive types", {
   x <- callJStatic("SparkRHandler", "echo", 1L)
   expect_equal(x, 1L)
   expect_equal(class(x), "integer")
-  
+
   x <- callJStatic("SparkRHandler", "echo", 1)
   expect_equal(x, 1)
   expect_equal(class(x), "numeric")
@@ -31,10 +31,10 @@ test_that("SerDe of primitive types", {
   x <- callJStatic("SparkRHandler", "echo", TRUE)
   expect_true(x)
   expect_equal(class(x), "logical")
-  
+
   x <- callJStatic("SparkRHandler", "echo", "abc")
   expect_equal(x, "abc")
-  expect_equal(class(x), "character")  
+  expect_equal(class(x), "character")
 })
 
 test_that("SerDe of list of primitive types", {
@@ -47,17 +47,17 @@ test_that("SerDe of list of primitive types", {
   y <- callJStatic("SparkRHandler", "echo", x)
   expect_equal(x, y)
   expect_equal(class(y[[1]]), "numeric")
-  
+
   x <- list(TRUE, FALSE)
   y <- callJStatic("SparkRHandler", "echo", x)
   expect_equal(x, y)
   expect_equal(class(y[[1]]), "logical")
-  
+
   x <- list("a", "b", "c")
   y <- callJStatic("SparkRHandler", "echo", x)
   expect_equal(x, y)
   expect_equal(class(y[[1]]), "character")
-  
+
   # Empty list
   x <- list()
   y <- callJStatic("SparkRHandler", "echo", x)
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index ee48a3dc0cc05..8e22c56824b16 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -608,7 +608,7 @@ test_that("subsetting", {
   df4 <- df[df$age %in% c(19, 30), 1:2]
   expect_equal(count(df4), 2)
   expect_equal(columns(df4), c("name", "age"))
-  
+
   df5 <- df[df$age %in% c(19), c(1,2)]
   expect_equal(count(df5), 1)
   expect_equal(columns(df5), c("name", "age"))

From 0fac144f6bd835395059154532d72cdb5dc7ef8d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 26 Aug 2015 18:14:54 -0700
Subject: [PATCH 1239/1454] [SPARK-9424] [SQL] Parquet programming guide
 updates for 1.5

Author: Cheng Lian <lian@databricks.com>

Closes #8467 from liancheng/spark-9424/parquet-docs-for-1.5.
---
 docs/sql-programming-guide.md | 45 ++++++++++++++++++++++++++++-------
 1 file changed, 37 insertions(+), 8 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 33e7893d7bd0a..e64190b9b209d 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1124,6 +1124,13 @@ a simple schema, and gradually add more columns to the schema as needed.  In thi
 up with multiple Parquet files with different but mutually compatible schemas.  The Parquet data
 source is now able to automatically detect this case and merge schemas of all these files.
 
+Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we
+turned it off by default starting from 1.5.0.  You may enable it by
+
+1. setting data source option `mergeSchema` to `true` when reading Parquet files (as shown in the
+   examples below), or
+2. setting the global SQL option `spark.sql.parquet.mergeSchema` to `true`.
+
 <div class="codetabs">
 
 <div data-lang="scala"  markdown="1">
@@ -1143,7 +1150,7 @@ val df2 = sc.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple")
 df2.write.parquet("data/test_table/key=2")
 
 // Read the partitioned table
-val df3 = sqlContext.read.parquet("data/test_table")
+val df3 = sqlContext.read.option("mergeSchema", "true").parquet("data/test_table")
 df3.printSchema()
 
 // The final schema consists of all 3 columns in the Parquet files together
@@ -1165,16 +1172,16 @@ df3.printSchema()
 # Create a simple DataFrame, stored into a partition directory
 df1 = sqlContext.createDataFrame(sc.parallelize(range(1, 6))\
                                    .map(lambda i: Row(single=i, double=i * 2)))
-df1.save("data/test_table/key=1", "parquet")
+df1.write.parquet("data/test_table/key=1")
 
 # Create another DataFrame in a new partition directory,
 # adding a new column and dropping an existing column
 df2 = sqlContext.createDataFrame(sc.parallelize(range(6, 11))
                                    .map(lambda i: Row(single=i, triple=i * 3)))
-df2.save("data/test_table/key=2", "parquet")
+df2.write.parquet("data/test_table/key=2")
 
 # Read the partitioned table
-df3 = sqlContext.load("data/test_table", "parquet")
+df3 = sqlContext.read.option("mergeSchema", "true").parquet("data/test_table")
 df3.printSchema()
 
 # The final schema consists of all 3 columns in the Parquet files together
@@ -1201,7 +1208,7 @@ saveDF(df1, "data/test_table/key=1", "parquet", "overwrite")
 saveDF(df2, "data/test_table/key=2", "parquet", "overwrite")
 
 # Read the partitioned table
-df3 <- loadDF(sqlContext, "data/test_table", "parquet")
+df3 <- loadDF(sqlContext, "data/test_table", "parquet", mergeSchema="true")
 printSchema(df3)
 
 # The final schema consists of all 3 columns in the Parquet files together
@@ -1301,7 +1308,7 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
   <td><code>spark.sql.parquet.binaryAsString</code></td>
   <td>false</td>
   <td>
-    Some other Parquet-producing systems, in particular Impala and older versions of Spark SQL, do
+    Some other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do
     not differentiate between binary data and strings when writing out the Parquet schema. This
     flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
   </td>
@@ -1310,8 +1317,7 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
   <td><code>spark.sql.parquet.int96AsTimestamp</code></td>
   <td>true</td>
   <td>
-    Some Parquet-producing systems, in particular Impala, store Timestamp into INT96. Spark would also
-    store Timestamp as INT96 because we need to avoid precision lost of the nanoseconds field. This
+    Some Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96.  This
     flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
   </td>
 </tr>
@@ -1355,6 +1361,9 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
     <p>
       <b>Note:</b>
       <ul>
+        <li>
+          This option is automatically ignored if <code>spark.speculation</code> is turned on.
+        </li>
         <li>
           This option must be set via Hadoop <code>Configuration</code> rather than Spark
           <code>SQLConf</code>.
@@ -1371,6 +1380,26 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
     </p>
   </td>
 </tr>
+<tr>
+  <td><code>spark.sql.parquet.mergeSchema</code></td>
+  <td><code>false</code></td>
+  <td>
+    <p>
+      When true, the Parquet data source merges schemas collected from all data files, otherwise the
+      schema is picked from the summary file or a random data file if no summary file is available.
+    </p>
+  </td>
+</tr>
+<tr>
+  <td><code>spark.sql.parquet.mergeSchema</code></td>
+  <td><code>false</code></td>
+  <td>
+    <p>
+      When true, the Parquet data source merges schemas collected from all data files, otherwise the
+      schema is picked from the summary file or a random data file if no summary file is available.
+    </p>
+  </td>
+</tr>
 </table>
 
 ## JSON Datasets

From ce97834dc0cc55eece0e909a4061ca6f2123f60d Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 26 Aug 2015 22:19:11 -0700
Subject: [PATCH 1240/1454] [SPARK-9964] [PYSPARK] [SQL] PySpark
 DataFrameReader accept RDD of String for JSON

PySpark DataFrameReader should could accept an RDD of Strings (like the Scala version does) for JSON, rather than only taking a path.
If this PR is merged, it should be duplicated to cover the other input types (not just JSON).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8444 from yanboliang/spark-9964.
---
 python/pyspark/sql/readwriter.py | 28 ++++++++++++++++++++++------
 1 file changed, 22 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 78247c8fa7372..3fa6895880a97 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -15,8 +15,14 @@
 # limitations under the License.
 #
 
+import sys
+
+if sys.version >= '3':
+    basestring = unicode = str
+
 from py4j.java_gateway import JavaClass
 
+from pyspark import RDD
 from pyspark.sql import since
 from pyspark.sql.column import _to_seq
 from pyspark.sql.types import *
@@ -125,23 +131,33 @@ def load(self, path=None, format=None, schema=None, **options):
     @since(1.4)
     def json(self, path, schema=None):
         """
-        Loads a JSON file (one object per line) and returns the result as
-        a :class`DataFrame`.
+        Loads a JSON file (one object per line) or an RDD of Strings storing JSON objects
+        (one object per record) and returns the result as a :class`DataFrame`.
 
         If the ``schema`` parameter is not specified, this function goes
         through the input once to determine the input schema.
 
-        :param path: string, path to the JSON dataset.
+        :param path: string represents path to the JSON dataset,
+                     or RDD of Strings storing JSON objects.
         :param schema: an optional :class:`StructType` for the input schema.
 
-        >>> df = sqlContext.read.json('python/test_support/sql/people.json')
-        >>> df.dtypes
+        >>> df1 = sqlContext.read.json('python/test_support/sql/people.json')
+        >>> df1.dtypes
+        [('age', 'bigint'), ('name', 'string')]
+        >>> rdd = sc.textFile('python/test_support/sql/people.json')
+        >>> df2 = sqlContext.read.json(rdd)
+        >>> df2.dtypes
         [('age', 'bigint'), ('name', 'string')]
 
         """
         if schema is not None:
             self.schema(schema)
-        return self._df(self._jreader.json(path))
+        if isinstance(path, basestring):
+            return self._df(self._jreader.json(path))
+        elif isinstance(path, RDD):
+            return self._df(self._jreader.json(path._jrdd))
+        else:
+            raise TypeError("path can be only string or RDD")
 
     @since(1.4)
     def table(self, tableName):

From e936cf8088a06d6aefce44305f3904bbeb17b432 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Wed, 26 Aug 2015 22:27:31 -0700
Subject: [PATCH 1241/1454] [SPARK-10219] [SPARKR] Fix varargsToEnv and add
 test case

cc sun-rui davies

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8475 from shivaram/varargs-fix.
---
 R/pkg/R/utils.R                  | 3 ++-
 R/pkg/inst/tests/test_sparkSQL.R | 6 ++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 4f9f4d9cad2a8..3babcb519378e 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -314,7 +314,8 @@ convertEnvsToList <- function(keys, vals) {
 
 # Utility function to capture the varargs into environment object
 varargsToEnv <- function(...) {
-  pairs <- as.list(substitute(list(...)))[-1L]
+  # Based on http://stackoverflow.com/a/3057419/4577954
+  pairs <- list(...)
   env <- new.env()
   for (name in names(pairs)) {
     env[[name]] <- pairs[[name]]
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 8e22c56824b16..4b672e115f924 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -1060,6 +1060,12 @@ test_that("parquetFile works with multiple input paths", {
   parquetDF <- parquetFile(sqlContext, parquetPath, parquetPath2)
   expect_is(parquetDF, "DataFrame")
   expect_equal(count(parquetDF), count(df) * 2)
+
+  # Test if varargs works with variables
+  saveMode <- "overwrite"
+  mergeSchema <- "true"
+  parquetPath3 <- tempfile(pattern = "parquetPath3", fileext = ".parquet")
+  write.df(df, parquetPath2, "parquet", mode = saveMode, mergeSchema = mergeSchema)
 })
 
 test_that("describe() and summarize() on a DataFrame", {

From de0278286cf6db8df53b0b68918ea114f2c77f1f Mon Sep 17 00:00:00 2001
From: Ram Sriharsha <rsriharsha@hw11853.local>
Date: Wed, 26 Aug 2015 23:12:55 -0700
Subject: [PATCH 1242/1454] =?UTF-8?q?[SPARK-10251]=20[CORE]=20some=20commo?=
 =?UTF-8?q?n=20types=20are=20not=20registered=20for=20Kryo=20Serializat?=
 =?UTF-8?q?=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…ion by default

Author: Ram Sriharsha <rsriharsha@hw11853.local>

Closes #8465 from harsha2010/SPARK-10251.
---
 .../spark/serializer/KryoSerializer.scala     | 35 ++++++++++++++++++-
 .../serializer/KryoSerializerSuite.scala      | 30 ++++++++++++++++
 2 files changed, 64 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 048a938507277..b977711e7d5ad 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -22,6 +22,7 @@ import java.nio.ByteBuffer
 import javax.annotation.Nullable
 
 import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.{Kryo, KryoException}
@@ -38,7 +39,7 @@ import org.apache.spark.network.nio.{GetBlock, GotBlock, PutBlock}
 import org.apache.spark.network.util.ByteUnit
 import org.apache.spark.scheduler.{CompressedMapStatus, HighlyCompressedMapStatus}
 import org.apache.spark.storage._
-import org.apache.spark.util.{BoundedPriorityQueue, SerializableConfiguration, SerializableJobConf}
+import org.apache.spark.util.{Utils, BoundedPriorityQueue, SerializableConfiguration, SerializableJobConf}
 import org.apache.spark.util.collection.CompactBuffer
 
 /**
@@ -131,6 +132,38 @@ class KryoSerializer(conf: SparkConf)
     // our code override the generic serializers in Chill for things like Seq
     new AllScalaRegistrar().apply(kryo)
 
+    // Register types missed by Chill.
+    // scalastyle:off
+    kryo.register(classOf[Array[Tuple1[Any]]])
+    kryo.register(classOf[Array[Tuple2[Any, Any]]])
+    kryo.register(classOf[Array[Tuple3[Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple4[Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple5[Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple6[Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple7[Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple8[Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple9[Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple10[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple11[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple12[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple13[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple14[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple15[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple16[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple17[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple18[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple19[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple20[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple21[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple22[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+
+    // scalastyle:on
+
+    kryo.register(None.getClass)
+    kryo.register(Nil.getClass)
+    kryo.register(Utils.classForName("scala.collection.immutable.$colon$colon"))
+    kryo.register(classOf[ArrayBuffer[Any]])
+
     kryo.setClassLoader(classLoader)
     kryo
   }
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index 8d1c9d17e977e..e428414cf6e85 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -150,6 +150,36 @@ class KryoSerializerSuite extends SparkFunSuite with SharedSparkContext {
       mutable.HashMap(1->"one", 2->"two", 3->"three")))
   }
 
+  test("Bug: SPARK-10251") {
+    val ser = new KryoSerializer(conf.clone.set("spark.kryo.registrationRequired", "true"))
+      .newInstance()
+    def check[T: ClassTag](t: T) {
+      assert(ser.deserialize[T](ser.serialize(t)) === t)
+    }
+    check((1, 3))
+    check(Array((1, 3)))
+    check(List((1, 3)))
+    check(List[Int]())
+    check(List[Int](1, 2, 3))
+    check(List[String]())
+    check(List[String]("x", "y", "z"))
+    check(None)
+    check(Some(1))
+    check(Some("hi"))
+    check(1 -> 1)
+    check(mutable.ArrayBuffer(1, 2, 3))
+    check(mutable.ArrayBuffer("1", "2", "3"))
+    check(mutable.Map())
+    check(mutable.Map(1 -> "one", 2 -> "two"))
+    check(mutable.Map("one" -> 1, "two" -> 2))
+    check(mutable.HashMap(1 -> "one", 2 -> "two"))
+    check(mutable.HashMap("one" -> 1, "two" -> 2))
+    check(List(Some(mutable.HashMap(1->1, 2->2)), None, Some(mutable.HashMap(3->4))))
+    check(List(
+      mutable.HashMap("one" -> 1, "two" -> 2),
+      mutable.HashMap(1->"one", 2->"two", 3->"three")))
+  }
+
   test("ranges") {
     val ser = new KryoSerializer(conf).newInstance()
     def check[T: ClassTag](t: T) {

From 9625d13d575c97bbff264f6a94838aae72c9202d Mon Sep 17 00:00:00 2001
From: Moussa Taifi <moutai10@gmail.com>
Date: Thu, 27 Aug 2015 10:34:47 +0100
Subject: [PATCH 1243/1454] [DOCS] [STREAMING] [KAFKA] Fix typo in exactly once
 semantics

Fix Typo in exactly once semantics
[Semantics of output operations] link

Author: Moussa Taifi <moutai10@gmail.com>

Closes #8468 from moutai/patch-3.
---
 docs/streaming-kafka-integration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md
index 7571e22575efd..5db39ae54a274 100644
--- a/docs/streaming-kafka-integration.md
+++ b/docs/streaming-kafka-integration.md
@@ -82,7 +82,7 @@ This approach has the following advantages over the receiver-based approach (i.e
 
 - *Efficiency:* Achieving zero-data loss in the first approach required the data to be stored in a Write Ahead Log, which further replicated the data. This is actually inefficient as the data effectively gets replicated twice - once by Kafka, and a second time by the Write Ahead Log. This second approach eliminates the problem as there is no receiver, and hence no need for Write Ahead Logs. As long as you have sufficient Kafka retention, messages can be recovered from Kafka.
 
-- *Exactly-once semantics:* The first approach uses Kafka's high level API to store consumed offsets in Zookeeper. This is traditionally the way to consume data from Kafka. While this approach (in combination with write ahead logs) can ensure zero data loss (i.e. at-least once semantics), there is a small chance some records may get consumed twice under some failures. This occurs because of inconsistencies between data reliably received by Spark Streaming and offsets tracked by Zookeeper. Hence, in this second approach, we use simple Kafka API that does not use Zookeeper. Offsets are tracked by Spark Streaming within its checkpoints. This eliminates inconsistencies between Spark Streaming and Zookeeper/Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures. In order to achieve exactly-once semantics for output of your results, your output operation that saves the data to an external data store must be either idempotent, or an atomic transaction that saves results and offsets (see [Semanitcs of output operations](streaming-programming-guide.html#semantics-of-output-operations) in the main programming guide for further information).
+- *Exactly-once semantics:* The first approach uses Kafka's high level API to store consumed offsets in Zookeeper. This is traditionally the way to consume data from Kafka. While this approach (in combination with write ahead logs) can ensure zero data loss (i.e. at-least once semantics), there is a small chance some records may get consumed twice under some failures. This occurs because of inconsistencies between data reliably received by Spark Streaming and offsets tracked by Zookeeper. Hence, in this second approach, we use simple Kafka API that does not use Zookeeper. Offsets are tracked by Spark Streaming within its checkpoints. This eliminates inconsistencies between Spark Streaming and Zookeeper/Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures. In order to achieve exactly-once semantics for output of your results, your output operation that saves the data to an external data store must be either idempotent, or an atomic transaction that saves results and offsets (see [Semantics of output operations](streaming-programming-guide.html#semantics-of-output-operations) in the main programming guide for further information).
 
 Note that one disadvantage of this approach is that it does not update offsets in Zookeeper, hence Zookeeper-based Kafka monitoring tools will not show progress. However, you can access the offsets processed by this approach in each batch and update Zookeeper yourself (see below).
 

From 1650f6f56ed4b7f1a7f645c9e8d5ac533464bd78 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 10:44:44 +0100
Subject: [PATCH 1244/1454] [SPARK-10254] [ML] Removes Guava dependencies in
 spark.ml.feature JavaTests

* Replaces `com.google.common` dependencies with `java.util.Arrays`
* Small clean up in `JavaNormalizerSuite`

Author: Feynman Liang <fliang@databricks.com>

Closes #8445 from feynmanliang/SPARK-10254.
---
 .../apache/spark/ml/feature/JavaBucketizerSuite.java  |  5 +++--
 .../org/apache/spark/ml/feature/JavaDCTSuite.java     |  5 +++--
 .../apache/spark/ml/feature/JavaHashingTFSuite.java   |  5 +++--
 .../apache/spark/ml/feature/JavaNormalizerSuite.java  | 11 +++++------
 .../org/apache/spark/ml/feature/JavaPCASuite.java     |  4 ++--
 .../ml/feature/JavaPolynomialExpansionSuite.java      |  5 +++--
 .../spark/ml/feature/JavaStandardScalerSuite.java     |  4 ++--
 .../apache/spark/ml/feature/JavaTokenizerSuite.java   |  6 ++++--
 .../spark/ml/feature/JavaVectorIndexerSuite.java      |  5 ++---
 .../spark/ml/feature/JavaVectorSlicerSuite.java       |  4 ++--
 .../apache/spark/ml/feature/JavaWord2VecSuite.java    | 11 ++++++-----
 11 files changed, 35 insertions(+), 30 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java
index d5bd230a957a1..47d68de599da2 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -54,7 +55,7 @@ public void tearDown() {
   public void bucketizerTest() {
     double[] splits = {-0.5, 0.0, 0.5};
 
-    JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
       RowFactory.create(-0.5),
       RowFactory.create(-0.3),
       RowFactory.create(0.0),
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
index 845eed61c45c6..0f6ec64d97d36 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import edu.emory.mathcs.jtransforms.dct.DoubleDCT_1D;
 import org.junit.After;
 import org.junit.Assert;
@@ -56,7 +57,7 @@ public void tearDown() {
   @Test
   public void javaCompatibilityTest() {
     double[] input = new double[] {1D, 2D, 3D, 4D};
-    JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
       RowFactory.create(Vectors.dense(input))
     ));
     DataFrame dataset = jsql.createDataFrame(data, new StructType(new StructField[]{
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
index 599e9cfd23ad4..03dd5369bddf7 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -54,7 +55,7 @@ public void tearDown() {
 
   @Test
   public void hashingTF() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
       RowFactory.create(0.0, "Hi I heard about Spark"),
       RowFactory.create(0.0, "I wish Java could use case classes"),
       RowFactory.create(1.0, "Logistic regression models are neat")
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
index d82f3b7e8c076..e17d549c5059b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
@@ -17,15 +17,15 @@
 
 package org.apache.spark.ml.feature;
 
-import java.util.List;
+import java.util.Arrays;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
 
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 
@@ -48,13 +48,12 @@ public void tearDown() {
   @Test
   public void normalizer() {
     // The tests are to check Java compatibility.
-    List<VectorIndexerSuite.FeatureData> points = Lists.newArrayList(
+    JavaRDD<VectorIndexerSuite.FeatureData> points = jsc.parallelize(Arrays.asList(
       new VectorIndexerSuite.FeatureData(Vectors.dense(0.0, -2.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 3.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 4.0))
-    );
-    DataFrame dataFrame = jsql.createDataFrame(jsc.parallelize(points, 2),
-      VectorIndexerSuite.FeatureData.class);
+    ));
+    DataFrame dataFrame = jsql.createDataFrame(points, VectorIndexerSuite.FeatureData.class);
     Normalizer normalizer = new Normalizer()
       .setInputCol("features")
       .setOutputCol("normFeatures");
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
index 5cf43fec6f29e..e8f329f9cf29e 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.ml.feature;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -78,7 +78,7 @@ public Vector getExpected() {
 
   @Test
   public void testPCA() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0}),
       Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
       Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
index 5e8211c2c5118..834fedbb59e1b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -59,7 +60,7 @@ public void polynomialExpansionTest() {
       .setOutputCol("polyFeatures")
       .setDegree(3);
 
-    JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
       RowFactory.create(
         Vectors.dense(-2.0, 2.3),
         Vectors.dense(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17)
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
index 74eb2733f06ef..ed74363f59e34 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
@@ -17,9 +17,9 @@
 
 package org.apache.spark.ml.feature;
 
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -48,7 +48,7 @@ public void tearDown() {
   @Test
   public void standardScaler() {
     // The tests are to check Java compatibility.
-    List<VectorIndexerSuite.FeatureData> points = Lists.newArrayList(
+    List<VectorIndexerSuite.FeatureData> points = Arrays.asList(
       new VectorIndexerSuite.FeatureData(Vectors.dense(0.0, -2.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 3.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 4.0))
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java
index 3806f650025b2..02309ce63219a 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -54,7 +55,8 @@ public void regexTokenizer() {
       .setGaps(true)
       .setMinTokenLength(3);
 
-    JavaRDD<TokenizerTestData> rdd = jsc.parallelize(Lists.newArrayList(
+
+    JavaRDD<TokenizerTestData> rdd = jsc.parallelize(Arrays.asList(
       new TokenizerTestData("Test of tok.", new String[] {"Test", "tok."}),
       new TokenizerTestData("Te,st.  punct", new String[] {"Te,st.", "punct"})
     ));
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
index c7ae5468b9429..bfcca62fa1c98 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 
@@ -26,8 +27,6 @@
 import org.junit.Before;
 import org.junit.Test;
 
-import com.google.common.collect.Lists;
-
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.feature.VectorIndexerSuite.FeatureData;
 import org.apache.spark.mllib.linalg.Vectors;
@@ -52,7 +51,7 @@ public void tearDown() {
   @Test
   public void vectorIndexerAPI() {
     // The tests are to check Java compatibility.
-    List<FeatureData> points = Lists.newArrayList(
+    List<FeatureData> points = Arrays.asList(
       new FeatureData(Vectors.dense(0.0, -2.0)),
       new FeatureData(Vectors.dense(1.0, 3.0)),
       new FeatureData(Vectors.dense(1.0, 4.0))
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
index 56988b9fb29cb..f953361427586 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -63,7 +63,7 @@ public void vectorSlice() {
     };
     AttributeGroup group = new AttributeGroup("userFeatures", attrs);
 
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
       RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
       RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
     ));
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
index 39c70157f83c0..70f5ad9432212 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -50,10 +51,10 @@ public void tearDown() {
 
   @Test
   public void testJavaWord2Vec() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
-      RowFactory.create(Lists.newArrayList("Hi I heard about Spark".split(" "))),
-      RowFactory.create(Lists.newArrayList("I wish Java could use case classes".split(" "))),
-      RowFactory.create(Lists.newArrayList("Logistic regression models are neat".split(" ")))
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+      RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
+      RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
+      RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
     ));
     StructType schema = new StructType(new StructField[]{
       new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())

From 75d62307946283b03bec6aaf1bdd4f2b08c93915 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 10:45:35 +0100
Subject: [PATCH 1245/1454] [SPARK-10255] [ML] Removes Guava dependencies from
 spark.ml.param JavaTests

Author: Feynman Liang <fliang@databricks.com>

Closes #8446 from feynmanliang/SPARK-10255.
---
 .../java/org/apache/spark/ml/param/JavaParamsSuite.java    | 7 ++++---
 .../java/org/apache/spark/ml/param/JavaTestParams.java     | 5 ++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java
index 9890155e9f865..fa777f3d42a9a 100644
--- a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.param;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -61,7 +62,7 @@ public void testParamValidate() {
     ParamValidators.ltEq(1.0);
     ParamValidators.inRange(0, 1, true, false);
     ParamValidators.inRange(0, 1);
-    ParamValidators.inArray(Lists.newArrayList(0, 1, 3));
-    ParamValidators.inArray(Lists.newArrayList("a", "b"));
+    ParamValidators.inArray(Arrays.asList(0, 1, 3));
+    ParamValidators.inArray(Arrays.asList("a", "b"));
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
index dc6ce8061f62b..65841182df9b4 100644
--- a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
+++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
@@ -17,10 +17,9 @@
 
 package org.apache.spark.ml.param;
 
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.collect.Lists;
-
 import org.apache.spark.ml.util.Identifiable$;
 
 /**
@@ -89,7 +88,7 @@ private void init() {
     myIntParam_ = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0));
     myDoubleParam_ = new DoubleParam(this, "myDoubleParam", "this is a double param",
       ParamValidators.inRange(0.0, 1.0));
-    List<String> validStrings = Lists.newArrayList("a", "b");
+    List<String> validStrings = Arrays.asList("a", "b");
     myStringParam_ = new Param<String>(this, "myStringParam", "this is a string param",
       ParamValidators.inArray(validStrings));
     myDoubleArrayParam_ =

From 1a446f75b6cac46caea0217a66abeb226946ac71 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 10:46:18 +0100
Subject: [PATCH 1246/1454] [SPARK-10256] [ML] Removes guava dependency from
 spark.ml.classification JavaTests

Author: Feynman Liang <fliang@databricks.com>

Closes #8447 from feynmanliang/SPARK-10256.
---
 .../apache/spark/ml/classification/JavaNaiveBayesSuite.java   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
index a700c9cddb206..8fd7bf55a2e5d 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -18,8 +18,8 @@
 package org.apache.spark.ml.classification;
 
 import java.io.Serializable;
+import java.util.Arrays;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -74,7 +74,7 @@ public void naiveBayesDefaultParams() {
 
   @Test
   public void testNaiveBayes() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
       RowFactory.create(0.0, Vectors.dense(1.0, 0.0, 0.0)),
       RowFactory.create(0.0, Vectors.dense(2.0, 0.0, 0.0)),
       RowFactory.create(1.0, Vectors.dense(0.0, 1.0, 0.0)),

From b02e8187225d1765f67ce38864dfaca487be8a44 Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek@japila.pl>
Date: Thu, 27 Aug 2015 11:07:37 +0100
Subject: [PATCH 1247/1454] [SPARK-9613] [HOTFIX] Fix usage of JavaConverters
 removed in Scala 2.11

Fix for [JavaConverters.asJavaListConverter](http://www.scala-lang.org/api/2.10.5/index.html#scala.collection.JavaConverters$) being removed in 2.11.7 and hence the build fails with the 2.11 profile enabled. Tested with the default 2.10 and 2.11 profiles. BUILD SUCCESS in both cases.

Build for 2.10:

    ./build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -DskipTests clean install

and 2.11:

    ./dev/change-scala-version.sh 2.11
    ./build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -Dscala-2.11 -DskipTests clean install

Author: Jacek Laskowski <jacek@japila.pl>

Closes #8479 from jaceklaskowski/SPARK-9613-hotfix.
---
 .../org/apache/spark/ml/classification/JavaOneVsRestSuite.java  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
index 2744e020e9e49..253cabf0133d0 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
@@ -55,7 +55,7 @@ public void setUp() {
 
         double[] xMean = {5.843, 3.057, 3.758, 1.199};
         double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
-        List<LabeledPoint> points = JavaConverters.asJavaListConverter(
+        List<LabeledPoint> points = JavaConverters.seqAsJavaListConverter(
             generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
         ).asJava();
         datasetRDD = jsc.parallelize(points, 2);

From e1f4de4a7d15d4ca4b5c64ff929ac3980f5d706f Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 18:46:41 +0100
Subject: [PATCH 1248/1454] [SPARK-10257] [MLLIB] Removes Guava from all
 spark.mllib Java tests

* Replaces instances of `Lists.newArrayList` with `Arrays.asList`
* Replaces `commons.lang.StringUtils` over `com.google.collections.Strings`
* Replaces `List` interface over `ArrayList` implementations

This PR along with #8445 #8446 #8447 completely removes all `com.google.collections.Lists` dependencies within mllib's Java tests.

Author: Feynman Liang <fliang@databricks.com>

Closes #8451 from feynmanliang/SPARK-10257.
---
 .../JavaStreamingLogisticRegressionSuite.java | 10 +++----
 .../clustering/JavaGaussianMixtureSuite.java  |  4 +--
 .../mllib/clustering/JavaKMeansSuite.java     |  9 +++----
 .../clustering/JavaStreamingKMeansSuite.java  | 10 +++----
 .../spark/mllib/feature/JavaTfIdfSuite.java   | 19 +++++++------
 .../mllib/feature/JavaWord2VecSuite.java      |  6 ++---
 .../mllib/fpm/JavaAssociationRulesSuite.java  |  5 ++--
 .../spark/mllib/fpm/JavaFPGrowthSuite.java    | 17 ++++++------
 .../spark/mllib/linalg/JavaVectorsSuite.java  |  5 ++--
 .../mllib/random/JavaRandomRDDsSuite.java     | 27 ++++++++++---------
 .../mllib/recommendation/JavaALSSuite.java    |  5 ++--
 .../JavaIsotonicRegressionSuite.java          |  7 ++---
 .../JavaStreamingLinearRegressionSuite.java   | 10 +++----
 .../spark/mllib/stat/JavaStatisticsSuite.java | 11 ++++----
 14 files changed, 71 insertions(+), 74 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java
index 55787f8606d48..c9e5ee22f3273 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.classification;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -60,16 +60,16 @@ public void tearDown() {
   @Test
   @SuppressWarnings("unchecked")
   public void javaAPI() {
-    List<LabeledPoint> trainingBatch = Lists.newArrayList(
+    List<LabeledPoint> trainingBatch = Arrays.asList(
       new LabeledPoint(1.0, Vectors.dense(1.0)),
       new LabeledPoint(0.0, Vectors.dense(0.0)));
     JavaDStream<LabeledPoint> training =
-      attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2);
-    List<Tuple2<Integer, Vector>> testBatch = Lists.newArrayList(
+      attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
+    List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
       new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
       new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
-      attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2));
+      attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingLogisticRegressionWithSGD slr = new StreamingLogisticRegressionWithSGD()
       .setNumIterations(2)
       .setInitialWeights(Vectors.dense(0.0));
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java
index 467a7a69e8f30..123f78da54e34 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java
@@ -18,9 +18,9 @@
 package org.apache.spark.mllib.clustering;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -48,7 +48,7 @@ public void tearDown() {
 
   @Test
   public void runGaussianMixture() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
index 31676e64025d0..ad06676c72ac6 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.clustering;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import org.junit.After;
@@ -25,8 +26,6 @@
 import org.junit.Test;
 import static org.junit.Assert.*;
 
-import com.google.common.collect.Lists;
-
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.linalg.Vector;
@@ -48,7 +47,7 @@ public void tearDown() {
 
   @Test
   public void runKMeansUsingStaticMethods() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
@@ -67,7 +66,7 @@ public void runKMeansUsingStaticMethods() {
 
   @Test
   public void runKMeansUsingConstructor() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
@@ -90,7 +89,7 @@ public void runKMeansUsingConstructor() {
 
   @Test
   public void testPredictJavaRDD() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java
index 3b0e879eec77f..d644766d1e54d 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.clustering;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -60,16 +60,16 @@ public void tearDown() {
   @Test
   @SuppressWarnings("unchecked")
   public void javaAPI() {
-    List<Vector> trainingBatch = Lists.newArrayList(
+    List<Vector> trainingBatch = Arrays.asList(
       Vectors.dense(1.0),
       Vectors.dense(0.0));
     JavaDStream<Vector> training =
-      attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2);
-    List<Tuple2<Integer, Vector>> testBatch = Lists.newArrayList(
+      attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
+    List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
       new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
       new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
-      attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2));
+      attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingKMeans skmeans = new StreamingKMeans()
       .setK(1)
       .setDecayFactor(1.0)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java
index fbc26167ce66f..8a320afa4b13d 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java
@@ -18,14 +18,13 @@
 package org.apache.spark.mllib.feature;
 
 import java.io.Serializable;
-import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
-import com.google.common.collect.Lists;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -50,10 +49,10 @@ public void tfIdf() {
     // The tests are to check Java compatibility.
     HashingTF tf = new HashingTF();
     @SuppressWarnings("unchecked")
-    JavaRDD<ArrayList<String>> documents = sc.parallelize(Lists.newArrayList(
-      Lists.newArrayList("this is a sentence".split(" ")),
-      Lists.newArrayList("this is another sentence".split(" ")),
-      Lists.newArrayList("this is still a sentence".split(" "))), 2);
+    JavaRDD<List<String>> documents = sc.parallelize(Arrays.asList(
+      Arrays.asList("this is a sentence".split(" ")),
+      Arrays.asList("this is another sentence".split(" ")),
+      Arrays.asList("this is still a sentence".split(" "))), 2);
     JavaRDD<Vector> termFreqs = tf.transform(documents);
     termFreqs.collect();
     IDF idf = new IDF();
@@ -70,10 +69,10 @@ public void tfIdfMinimumDocumentFrequency() {
     // The tests are to check Java compatibility.
     HashingTF tf = new HashingTF();
     @SuppressWarnings("unchecked")
-    JavaRDD<ArrayList<String>> documents = sc.parallelize(Lists.newArrayList(
-      Lists.newArrayList("this is a sentence".split(" ")),
-      Lists.newArrayList("this is another sentence".split(" ")),
-      Lists.newArrayList("this is still a sentence".split(" "))), 2);
+    JavaRDD<List<String>> documents = sc.parallelize(Arrays.asList(
+      Arrays.asList("this is a sentence".split(" ")),
+      Arrays.asList("this is another sentence".split(" ")),
+      Arrays.asList("this is still a sentence".split(" "))), 2);
     JavaRDD<Vector> termFreqs = tf.transform(documents);
     termFreqs.collect();
     IDF idf = new IDF(2);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java
index fb7afe8c6434b..e13ed07e283dd 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.feature;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import com.google.common.base.Strings;
 import org.junit.After;
 import org.junit.Assert;
@@ -51,8 +51,8 @@ public void tearDown() {
   public void word2Vec() {
     // The tests are to check Java compatibility.
     String sentence = Strings.repeat("a b ", 100) + Strings.repeat("a c ", 10);
-    List<String> words = Lists.newArrayList(sentence.split(" "));
-    List<List<String>> localDoc = Lists.newArrayList(words, words);
+    List<String> words = Arrays.asList(sentence.split(" "));
+    List<List<String>> localDoc = Arrays.asList(words, words);
     JavaRDD<List<String>> doc = sc.parallelize(localDoc);
     Word2Vec word2vec = new Word2Vec()
       .setVectorSize(10)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
index d7c2cb3ae2067..2bef7a8609757 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
@@ -17,17 +17,16 @@
 package org.apache.spark.mllib.fpm;
 
 import java.io.Serializable;
+import java.util.Arrays;
 
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
-import com.google.common.collect.Lists;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset;
 
-
 public class JavaAssociationRulesSuite implements Serializable {
   private transient JavaSparkContext sc;
 
@@ -46,7 +45,7 @@ public void tearDown() {
   public void runAssociationRules() {
 
     @SuppressWarnings("unchecked")
-    JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Lists.newArrayList(
+    JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Arrays.asList(
       new FreqItemset<String>(new String[] {"a"}, 15L),
       new FreqItemset<String>(new String[] {"b"}, 35L),
       new FreqItemset<String>(new String[] {"a", "b"}, 12L)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
index 9ce2c52dca8b6..154f75d75e4a6 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
@@ -18,13 +18,12 @@
 package org.apache.spark.mllib.fpm;
 
 import java.io.Serializable;
-import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
-import com.google.common.collect.Lists;
 import static org.junit.Assert.*;
 
 import org.apache.spark.api.java.JavaRDD;
@@ -48,13 +47,13 @@ public void tearDown() {
   public void runFPGrowth() {
 
     @SuppressWarnings("unchecked")
-    JavaRDD<ArrayList<String>> rdd = sc.parallelize(Lists.newArrayList(
-      Lists.newArrayList("r z h k p".split(" ")),
-      Lists.newArrayList("z y x w v u t s".split(" ")),
-      Lists.newArrayList("s x o n r".split(" ")),
-      Lists.newArrayList("x z y m t s q e".split(" ")),
-      Lists.newArrayList("z".split(" ")),
-      Lists.newArrayList("x z y r q t p".split(" "))), 2);
+    JavaRDD<List<String>> rdd = sc.parallelize(Arrays.asList(
+      Arrays.asList("r z h k p".split(" ")),
+      Arrays.asList("z y x w v u t s".split(" ")),
+      Arrays.asList("s x o n r".split(" ")),
+      Arrays.asList("x z y m t s q e".split(" ")),
+      Arrays.asList("z".split(" ")),
+      Arrays.asList("x z y r q t p".split(" "))), 2);
 
     FPGrowthModel<String> model = new FPGrowth()
       .setMinSupport(0.5)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java
index 1421067dc61ed..77c8c6274f374 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java
@@ -18,11 +18,10 @@
 package org.apache.spark.mllib.linalg;
 
 import java.io.Serializable;
+import java.util.Arrays;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
-
 import org.junit.Test;
 import static org.junit.Assert.*;
 
@@ -37,7 +36,7 @@ public void denseArrayConstruction() {
   @Test
   public void sparseArrayConstruction() {
     @SuppressWarnings("unchecked")
-    Vector v = Vectors.sparse(3, Lists.<Tuple2<Integer, Double>>newArrayList(
+    Vector v = Vectors.sparse(3, Arrays.asList(
         new Tuple2<Integer, Double>(0, 2.0),
         new Tuple2<Integer, Double>(2, 3.0)));
     assertArrayEquals(new double[]{2.0, 0.0, 3.0}, v.toArray(), 0.0);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
index fcc13c00cbdc5..33d81b1e9592b 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.mllib.random;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.apache.spark.api.java.JavaRDD;
 import org.junit.Assert;
 import org.junit.After;
@@ -51,7 +52,7 @@ public void testUniformRDD() {
     JavaDoubleRDD rdd1 = uniformJavaRDD(sc, m);
     JavaDoubleRDD rdd2 = uniformJavaRDD(sc, m, p);
     JavaDoubleRDD rdd3 = uniformJavaRDD(sc, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -64,7 +65,7 @@ public void testNormalRDD() {
     JavaDoubleRDD rdd1 = normalJavaRDD(sc, m);
     JavaDoubleRDD rdd2 = normalJavaRDD(sc, m, p);
     JavaDoubleRDD rdd3 = normalJavaRDD(sc, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -79,7 +80,7 @@ public void testLNormalRDD() {
     JavaDoubleRDD rdd1 = logNormalJavaRDD(sc, mean, std, m);
     JavaDoubleRDD rdd2 = logNormalJavaRDD(sc, mean, std, m, p);
     JavaDoubleRDD rdd3 = logNormalJavaRDD(sc, mean, std, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -93,7 +94,7 @@ public void testPoissonRDD() {
     JavaDoubleRDD rdd1 = poissonJavaRDD(sc, mean, m);
     JavaDoubleRDD rdd2 = poissonJavaRDD(sc, mean, m, p);
     JavaDoubleRDD rdd3 = poissonJavaRDD(sc, mean, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -107,7 +108,7 @@ public void testExponentialRDD() {
     JavaDoubleRDD rdd1 = exponentialJavaRDD(sc, mean, m);
     JavaDoubleRDD rdd2 = exponentialJavaRDD(sc, mean, m, p);
     JavaDoubleRDD rdd3 = exponentialJavaRDD(sc, mean, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -122,7 +123,7 @@ public void testGammaRDD() {
     JavaDoubleRDD rdd1 = gammaJavaRDD(sc, shape, scale, m);
     JavaDoubleRDD rdd2 = gammaJavaRDD(sc, shape, scale, m, p);
     JavaDoubleRDD rdd3 = gammaJavaRDD(sc, shape, scale, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -138,7 +139,7 @@ public void testUniformVectorRDD() {
     JavaRDD<Vector> rdd1 = uniformJavaVectorRDD(sc, m, n);
     JavaRDD<Vector> rdd2 = uniformJavaVectorRDD(sc, m, n, p);
     JavaRDD<Vector> rdd3 = uniformJavaVectorRDD(sc, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -154,7 +155,7 @@ public void testNormalVectorRDD() {
     JavaRDD<Vector> rdd1 = normalJavaVectorRDD(sc, m, n);
     JavaRDD<Vector> rdd2 = normalJavaVectorRDD(sc, m, n, p);
     JavaRDD<Vector> rdd3 = normalJavaVectorRDD(sc, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -172,7 +173,7 @@ public void testLogNormalVectorRDD() {
     JavaRDD<Vector> rdd1 = logNormalJavaVectorRDD(sc, mean, std, m, n);
     JavaRDD<Vector> rdd2 = logNormalJavaVectorRDD(sc, mean, std, m, n, p);
     JavaRDD<Vector> rdd3 = logNormalJavaVectorRDD(sc, mean, std, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -189,7 +190,7 @@ public void testPoissonVectorRDD() {
     JavaRDD<Vector> rdd1 = poissonJavaVectorRDD(sc, mean, m, n);
     JavaRDD<Vector> rdd2 = poissonJavaVectorRDD(sc, mean, m, n, p);
     JavaRDD<Vector> rdd3 = poissonJavaVectorRDD(sc, mean, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -206,7 +207,7 @@ public void testExponentialVectorRDD() {
     JavaRDD<Vector> rdd1 = exponentialJavaVectorRDD(sc, mean, m, n);
     JavaRDD<Vector> rdd2 = exponentialJavaVectorRDD(sc, mean, m, n, p);
     JavaRDD<Vector> rdd3 = exponentialJavaVectorRDD(sc, mean, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -224,7 +225,7 @@ public void testGammaVectorRDD() {
     JavaRDD<Vector> rdd1 = gammaJavaVectorRDD(sc, shape, scale, m, n);
     JavaRDD<Vector> rdd2 = gammaJavaVectorRDD(sc, shape, scale, m, n, p);
     JavaRDD<Vector> rdd3 = gammaJavaVectorRDD(sc, shape, scale, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
index af688c504cf1e..271dda4662e0d 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
@@ -18,12 +18,12 @@
 package org.apache.spark.mllib.recommendation;
 
 import java.io.Serializable;
+import java.util.ArrayList;
 import java.util.List;
 
 import scala.Tuple2;
 import scala.Tuple3;
 
-import com.google.common.collect.Lists;
 import org.jblas.DoubleMatrix;
 import org.junit.After;
 import org.junit.Assert;
@@ -56,8 +56,7 @@ void validatePrediction(
       double matchThreshold,
       boolean implicitPrefs,
       DoubleMatrix truePrefs) {
-    List<Tuple2<Integer, Integer>> localUsersProducts =
-      Lists.newArrayListWithCapacity(users * products);
+    List<Tuple2<Integer, Integer>> localUsersProducts = new ArrayList(users * products);
     for (int u=0; u < users; ++u) {
       for (int p=0; p < products; ++p) {
         localUsersProducts.add(new Tuple2<Integer, Integer>(u, p));
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
index d38fc91ace3cf..32c2f4f3395b7 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
@@ -18,11 +18,12 @@
 package org.apache.spark.mllib.regression;
 
 import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple3;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -36,7 +37,7 @@ public class JavaIsotonicRegressionSuite implements Serializable {
   private transient JavaSparkContext sc;
 
   private List<Tuple3<Double, Double, Double>> generateIsotonicInput(double[] labels) {
-    List<Tuple3<Double, Double, Double>> input = Lists.newArrayList();
+    ArrayList<Tuple3<Double, Double, Double>> input = new ArrayList(labels.length);
 
     for (int i = 1; i <= labels.length; i++) {
       input.add(new Tuple3<Double, Double, Double>(labels[i-1], (double) i, 1d));
@@ -77,7 +78,7 @@ public void testIsotonicRegressionPredictionsJavaRDD() {
     IsotonicRegressionModel model =
       runIsotonicRegression(new double[]{1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12});
 
-    JavaDoubleRDD testRDD = sc.parallelizeDoubles(Lists.newArrayList(0.0, 1.0, 9.5, 12.0, 13.0));
+    JavaDoubleRDD testRDD = sc.parallelizeDoubles(Arrays.asList(0.0, 1.0, 9.5, 12.0, 13.0));
     List<Double> predictions = model.predict(testRDD).collect();
 
     Assert.assertTrue(predictions.get(0) == 1d);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java
index 899c4ea607869..dbf6488d41085 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.regression;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -59,16 +59,16 @@ public void tearDown() {
   @Test
   @SuppressWarnings("unchecked")
   public void javaAPI() {
-    List<LabeledPoint> trainingBatch = Lists.newArrayList(
+    List<LabeledPoint> trainingBatch = Arrays.asList(
       new LabeledPoint(1.0, Vectors.dense(1.0)),
       new LabeledPoint(0.0, Vectors.dense(0.0)));
     JavaDStream<LabeledPoint> training =
-      attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2);
-    List<Tuple2<Integer, Vector>> testBatch = Lists.newArrayList(
+      attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
+    List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
       new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
       new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
-      attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2));
+      attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingLinearRegressionWithSGD slr = new StreamingLinearRegressionWithSGD()
       .setNumIterations(2)
       .setInitialWeights(Vectors.dense(0.0));
diff --git a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
index eb4e3698624bc..4795809e47a46 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
@@ -19,7 +19,8 @@
 
 import java.io.Serializable;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -50,8 +51,8 @@ public void tearDown() {
 
   @Test
   public void testCorr() {
-    JavaRDD<Double> x = sc.parallelize(Lists.newArrayList(1.0, 2.0, 3.0, 4.0));
-    JavaRDD<Double> y = sc.parallelize(Lists.newArrayList(1.1, 2.2, 3.1, 4.3));
+    JavaRDD<Double> x = sc.parallelize(Arrays.asList(1.0, 2.0, 3.0, 4.0));
+    JavaRDD<Double> y = sc.parallelize(Arrays.asList(1.1, 2.2, 3.1, 4.3));
 
     Double corr1 = Statistics.corr(x, y);
     Double corr2 = Statistics.corr(x, y, "pearson");
@@ -61,7 +62,7 @@ public void testCorr() {
 
   @Test
   public void kolmogorovSmirnovTest() {
-    JavaDoubleRDD data = sc.parallelizeDoubles(Lists.newArrayList(0.2, 1.0, -1.0, 2.0));
+    JavaDoubleRDD data = sc.parallelizeDoubles(Arrays.asList(0.2, 1.0, -1.0, 2.0));
     KolmogorovSmirnovTestResult testResult1 = Statistics.kolmogorovSmirnovTest(data, "norm");
     KolmogorovSmirnovTestResult testResult2 = Statistics.kolmogorovSmirnovTest(
       data, "norm", 0.0, 1.0);
@@ -69,7 +70,7 @@ public void kolmogorovSmirnovTest() {
 
   @Test
   public void chiSqTest() {
-    JavaRDD<LabeledPoint> data = sc.parallelize(Lists.newArrayList(
+    JavaRDD<LabeledPoint> data = sc.parallelize(Arrays.asList(
       new LabeledPoint(0.0, Vectors.dense(0.1, 2.3)),
       new LabeledPoint(1.0, Vectors.dense(1.5, 5.1)),
       new LabeledPoint(0.0, Vectors.dense(2.4, 8.1))));

From fdd466bed7a7151dd066d732ef98d225f4acda4a Mon Sep 17 00:00:00 2001
From: Vyacheslav Baranov <slavik.baranov@gmail.com>
Date: Thu, 27 Aug 2015 18:56:18 +0100
Subject: [PATCH 1249/1454] [SPARK-10182] [MLLIB] GeneralizedLinearModel
 doesn't unpersist cached data

`GeneralizedLinearModel` creates a cached RDD when building a model. It's inconvenient, since these RDDs flood the memory when building several models in a row, so useful data might get evicted from the cache.

The proposed solution is to always cache the dataset & remove the warning. There's a caveat though: input dataset gets evaluated twice, in line 270 when fitting `StandardScaler` for the first time, and when running optimizer for the second time. So, it might worth to return removed warning.

Another possible solution is to disable caching entirely & return removed warning. I don't really know what approach is better.

Author: Vyacheslav Baranov <slavik.baranov@gmail.com>

Closes #8395 from SlavikBaranov/SPARK-10182.
---
 .../spark/mllib/regression/GeneralizedLinearAlgorithm.scala  | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 7e3b4d5648fe3..8f657bfb9c730 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -359,6 +359,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
         + " parent RDDs are also uncached.")
     }
 
+    // Unpersist cached data
+    if (data.getStorageLevel != StorageLevel.NONE) {
+      data.unpersist(false)
+    }
+
     createModel(weights, intercept)
   }
 }

From dc86a227e4fc8a9d8c3e8c68da8dff9298447fd0 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 27 Aug 2015 11:45:15 -0700
Subject: [PATCH 1250/1454] [SPARK-9148] [SPARK-10252] [SQL] Update SQL
 Programming Guide

Author: Michael Armbrust <michael@databricks.com>

Closes #8441 from marmbrus/documentation.
---
 docs/sql-programming-guide.md | 92 +++++++++++++++++++++++++++--------
 1 file changed, 73 insertions(+), 19 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index e64190b9b209d..99fec6c7785af 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -11,7 +11,7 @@ title: Spark SQL and DataFrames
 
 Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine.
 
-For how to enable Hive support, please refer to the [Hive Tables](#hive-tables) section.
+Spark SQL can also be used to read data from an existing Hive installation.  For more on how to configure this feature, please refer to the [Hive Tables](#hive-tables) section.
 
 # DataFrames
 
@@ -213,6 +213,11 @@ df.groupBy("age").count().show()
 // 30   1
 {% endhighlight %}
 
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/scala/index.html#org.apache.spark.sql.DataFrame).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/scala/index.html#org.apache.spark.sql.DataFrame).
+
+
 </div>
 
 <div data-lang="java" markdown="1">
@@ -263,6 +268,10 @@ df.groupBy("age").count().show();
 // 30   1
 {% endhighlight %}
 
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/java/org/apache/spark/sql/DataFrame.html).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/java/org/apache/spark/sql/functions.html).
+
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -320,6 +329,10 @@ df.groupBy("age").count().show()
 
 {% endhighlight %}
 
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/python/pyspark.sql.html#pyspark.sql.DataFrame).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/python/pyspark.sql.html#module-pyspark.sql.functions).
+
 </div>
 
 <div data-lang="r"  markdown="1">
@@ -370,10 +383,13 @@ showDF(count(groupBy(df, "age")))
 
 {% endhighlight %}
 
-</div>
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/R/index.html).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/R/index.html).
 
 </div>
 
+</div>
 
 ## Running SQL Queries Programmatically
 
@@ -870,12 +886,11 @@ saveDF(select(df, "name", "age"), "namesAndAges.parquet", "parquet")
 
 Save operations can optionally take a `SaveMode`, that specifies how to handle existing data if
 present.  It is important to realize that these save modes do not utilize any locking and are not
-atomic.  Thus, it is not safe to have multiple writers attempting to write to the same location.
-Additionally, when performing a `Overwrite`, the data will be deleted before writing out the
+atomic.  Additionally, when performing a `Overwrite`, the data will be deleted before writing out the
 new data.
 
 <table class="table">
-<tr><th>Scala/Java</th><th>Python</th><th>Meaning</th></tr>
+<tr><th>Scala/Java</th><th>Any Language</th><th>Meaning</th></tr>
 <tr>
   <td><code>SaveMode.ErrorIfExists</code> (default)</td>
   <td><code>"error"</code> (default)</td>
@@ -1671,12 +1686,12 @@ results <- collect(sql(sqlContext, "FROM src SELECT key, value"))
 ### Interacting with Different Versions of Hive Metastore
 
 One of the most important pieces of Spark SQL's Hive support is interaction with Hive metastore,
-which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below.
+which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary 
+build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below.
+Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL
+will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc).
 
-Internally, Spark SQL uses two Hive clients, one for executing native Hive commands like `SET`
-and `DESCRIBE`, the other dedicated for communicating with Hive metastore. The former uses Hive
-jars of version 0.13.1, which are bundled with Spark 1.4.0. The latter uses Hive jars of the
-version specified by users. An isolated classloader is used here to avoid dependency conflicts.
+The following options can be used to configure the version of Hive that is used to retrieve metadata:
 
 <table class="table">
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
@@ -1685,7 +1700,7 @@ version specified by users. An isolated classloader is used here to avoid depend
     <td><code>0.13.1</code></td>
     <td>
       Version of the Hive metastore. Available
-      options are <code>0.12.0</code> and <code>0.13.1</code>. Support for more versions is coming in the future.
+      options are <code>0.12.0</code> through <code>1.2.1</code>.
     </td>
   </tr>
   <tr>
@@ -1696,12 +1711,16 @@ version specified by users. An isolated classloader is used here to avoid depend
       property can be one of three options:
       <ol>
         <li><code>builtin</code></li>
-        Use Hive 0.13.1, which is bundled with the Spark assembly jar when <code>-Phive</code> is
+        Use Hive 1.2.1, which is bundled with the Spark assembly jar when <code>-Phive</code> is
         enabled. When this option is chosen, <code>spark.sql.hive.metastore.version</code> must be
-        either <code>0.13.1</code> or not defined.
+        either <code>1.2.1</code> or not defined.
         <li><code>maven</code></li>
-        Use Hive jars of specified version downloaded from Maven repositories.
-        <li>A classpath in the standard format for both Hive and Hadoop.</li>
+        Use Hive jars of specified version downloaded from Maven repositories.  This configuration
+        is not generally recommended for production deployments. 
+        <li>A classpath in the standard format for the JVM.  This classpath must include all of Hive 
+        and its dependencies, including the correct version of Hadoop.  These jars only need to be
+        present on the driver, but if you are running in yarn cluster mode then you must ensure
+        they are packaged with you application.</li>
       </ol>
     </td>
   </tr>
@@ -2017,6 +2036,28 @@ options.
 
 # Migration Guide
 
+## Upgrading From Spark SQL 1.4 to 1.5
+
+ - Optimized execution using manually managed memory (Tungsten) is now enabled by default, along with
+   code generation for expression evaluation.  These features can both be disabled by setting
+   `spark.sql.tungsten.enabled` to `false.
+ - Parquet schema merging is no longer enabled by default.  It can be re-enabled by setting 
+   `spark.sql.parquet.mergeSchema` to `true`.
+ - Resolution of strings to columns in python now supports using dots (`.`) to qualify the column or 
+   access nested values.  For example `df['table.column.nestedField']`.  However, this means that if 
+   your column name contains any dots you must now escape them using backticks (e.g., ``table.`column.with.dots`.nested``).   
+ - In-memory columnar storage partition pruning is on by default. It can be disabled by setting
+   `spark.sql.inMemoryColumnarStorage.partitionPruning` to `false`.
+ - Unlimited precision decimal columns are no longer supported, instead Spark SQL enforces a maximum
+   precision of 38.  When inferring schema from `BigDecimal` objects, a precision of (38, 18) is now
+   used. When no precision is specified in DDL then the default remains `Decimal(10, 0)`.
+ - Timestamps are now stored at a precision of 1us, rather than 1ns
+ - In the `sql` dialect, floating point numbers are now parsed as decimal.  HiveQL parsing remains
+   unchanged.
+ - The canonical name of SQL/DataFrame functions are now lower case (e.g. sum vs SUM).
+ - It has been determined that using the DirectOutputCommitter when speculation is enabled is unsafe
+   and thus this output committer will not be used when speculation is on, independent of configuration.
+
 ## Upgrading from Spark SQL 1.3 to 1.4
 
 #### DataFrame data reader/writer interface
@@ -2038,7 +2079,8 @@ See the API docs for `SQLContext.read` (
 
 #### DataFrame.groupBy retains grouping columns
 
-Based on user feedback, we changed the default behavior of `DataFrame.groupBy().agg()` to retain the grouping columns in the resulting `DataFrame`. To keep the behavior in 1.3, set `spark.sql.retainGroupColumns` to `false`.
+Based on user feedback, we changed the default behavior of `DataFrame.groupBy().agg()` to retain the
+grouping columns in the resulting `DataFrame`. To keep the behavior in 1.3, set `spark.sql.retainGroupColumns` to `false`.
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
@@ -2175,7 +2217,7 @@ Python UDF registration is unchanged.
 When using DataTypes in Python you will need to construct them (i.e. `StringType()`) instead of
 referencing a singleton.
 
-## Migration Guide for Shark User
+## Migration Guide for Shark Users
 
 ### Scheduling
 To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
@@ -2251,6 +2293,7 @@ Spark SQL supports the vast majority of Hive features, such as:
 * User defined functions (UDF)
 * User defined aggregation functions (UDAF)
 * User defined serialization formats (SerDes)
+* Window functions
 * Joins
   * `JOIN`
   * `{LEFT|RIGHT|FULL} OUTER JOIN`
@@ -2261,7 +2304,7 @@ Spark SQL supports the vast majority of Hive features, such as:
   * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
 * Sampling
 * Explain
-* Partitioned tables
+* Partitioned tables including dynamic partition insertion
 * View
 * All Hive DDL Functions, including:
   * `CREATE TABLE`
@@ -2323,8 +2366,9 @@ releases of Spark SQL.
   Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
   metadata. Spark SQL does not support that.
 
+# Reference
 
-# Data Types
+## Data Types
 
 Spark SQL and DataFrames support the following data types:
 
@@ -2937,3 +2981,13 @@ from pyspark.sql.types import *
 
 </div>
 
+## NaN Semantics
+
+There is specially handling for not-a-number (NaN) when dealing with `float` or `double` types that
+does not exactly match standard floating point semantics.
+Specifically:
+
+ - NaN = NaN returns true.
+ - In aggregations all NaN values are grouped together.
+ - NaN is treated as a normal value in join keys.
+ - NaN values go last when in ascending order, larger than any other numeric value.

From 84baa5e9b5edc8c55871fbed5057324450bf097f Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Thu, 27 Aug 2015 20:19:09 +0100
Subject: [PATCH 1251/1454] [SPARK-10315] remove document on
 spark.akka.failure-detector.threshold

https://issues.apache.org/jira/browse/SPARK-10315

this parameter is not used any longer and there is some mistake in the current document , should be 'akka.remote.watch-failure-detector.threshold'

Author: CodingCat <zhunansjtu@gmail.com>

Closes #8483 from CodingCat/SPARK_10315.
---
 docs/configuration.md | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 4a6e4dd05b661..77c5cbc7b3196 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -906,16 +906,6 @@ Apart from these, the following properties are also available, and may be useful
 #### Networking
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
-<tr>
-  <td><code>spark.akka.failure-detector.threshold</code></td>
-  <td>300.0</td>
-  <td>
-     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
-     enabled again, if you plan to use this feature (Not recommended). This maps to akka's
-     `akka.remote.transport-failure-detector.threshold`. Tune this in combination of
-     `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
-  </td>
-</tr>
 <tr>
   <td><code>spark.akka.frameSize</code></td>
   <td>128</td>

From 6185cdd2afcd492b77ff225b477b3624e3bc7bb2 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Thu, 27 Aug 2015 13:57:20 -0700
Subject: [PATCH 1252/1454] [SPARK-9901] User guide for RowMatrix
 Tall-and-skinny QR

jira: https://issues.apache.org/jira/browse/SPARK-9901

The jira covers only the document update. I can further provide example code for QR (like the ones for SVD and PCA) in a separate PR.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8462 from hhbyyh/qrDoc.
---
 docs/mllib-data-types.md | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index f0e8d5495675d..065bf4727624f 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -337,7 +337,10 @@ limited by the integer range but it should be much smaller in practice.
 <div data-lang="scala" markdown="1">
 
 A [`RowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
-created from an `RDD[Vector]` instance.  Then we can compute its column summary statistics.
+created from an `RDD[Vector]` instance.  Then we can compute its column summary statistics and decompositions.
+[QR decomposition](https://en.wikipedia.org/wiki/QR_decomposition) is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix.
+For [singular value decomposition (SVD)](https://en.wikipedia.org/wiki/Singular_value_decomposition) and [principal component analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis), please refer to [Dimensionality reduction](mllib-dimensionality-reduction.html).
+
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Vector
@@ -350,6 +353,9 @@ val mat: RowMatrix = new RowMatrix(rows)
 // Get its size.
 val m = mat.numRows()
 val n = mat.numCols()
+
+// QR decomposition 
+val qrResult = mat.tallSkinnyQR(true)
 {% endhighlight %}
 </div>
 
@@ -370,6 +376,9 @@ RowMatrix mat = new RowMatrix(rows.rdd());
 // Get its size.
 long m = mat.numRows();
 long n = mat.numCols();
+
+// QR decomposition 
+QRDecomposition<RowMatrix, Matrix> result = mat.tallSkinnyQR(true);
 {% endhighlight %}
 </div>
 

From c94ecdfc5b3c0fe6c38a170dc2af9259354dc9e3 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 27 Aug 2015 15:33:43 -0700
Subject: [PATCH 1253/1454] [SPARK-9906] [ML] User guide for
 LogisticRegressionSummary

User guide for LogisticRegression summaries

Author: MechCoder <manojkumarsivaraj334@gmail.com>
Author: Manoj Kumar <mks542@nyu.edu>
Author: Feynman Liang <fliang@databricks.com>

Closes #8197 from MechCoder/log_summary_user_guide.
---
 docs/ml-linear-methods.md | 149 ++++++++++++++++++++++++++++++++++----
 1 file changed, 133 insertions(+), 16 deletions(-)

diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
index 1ac83d94c9e81..2761aeb789621 100644
--- a/docs/ml-linear-methods.md
+++ b/docs/ml-linear-methods.md
@@ -23,20 +23,41 @@ displayTitle: <a href="ml-guide.html">ML</a> - Linear Methods
 \]`
 
 
-In MLlib, we implement popular linear methods such as logistic regression and linear least squares with L1 or L2 regularization. Refer to [the linear methods in mllib](mllib-linear-methods.html) for details. In `spark.ml`, we also include Pipelines API for [Elastic net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid of L1 and L2 regularization proposed in [this paper](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf). Mathematically it is defined as a linear combination of the L1-norm and the L2-norm:
+In MLlib, we implement popular linear methods such as logistic
+regression and linear least squares with $L_1$ or $L_2$ regularization.
+Refer to [the linear methods in mllib](mllib-linear-methods.html) for
+details.  In `spark.ml`, we also include Pipelines API for [Elastic
+net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid
+of $L_1$ and $L_2$ regularization proposed in [Zou et al, Regularization
+and variable selection via the elastic
+net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
+Mathematically, it is defined as a convex combination of the $L_1$ and
+the $L_2$ regularization terms:
 `\[
-\alpha \|\wv\|_1 + (1-\alpha) \frac{1}{2}\|\wv\|_2^2, \alpha \in [0, 1].
+\alpha~\lambda \|\wv\|_1 + (1-\alpha) \frac{\lambda}{2}\|\wv\|_2^2, \alpha \in [0, 1], \lambda \geq 0.
 \]`
-By setting $\alpha$ properly, it contains both L1 and L2 regularization as special cases. For example, if a [linear regression](https://en.wikipedia.org/wiki/Linear_regression) model is trained with the elastic net parameter $\alpha$ set to $1$, it is equivalent to a [Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model. On the other hand, if $\alpha$ is set to $0$, the trained model reduces to a [ridge regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model. We implement Pipelines API for both linear regression and logistic regression with elastic net regularization.
-
-**Examples**
+By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$
+regularization as special cases. For example, if a [linear
+regression](https://en.wikipedia.org/wiki/Linear_regression) model is
+trained with the elastic net parameter $\alpha$ set to $1$, it is
+equivalent to a
+[Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model.
+On the other hand, if $\alpha$ is set to $0$, the trained model reduces
+to a [ridge
+regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model.
+We implement Pipelines API for both linear regression and logistic
+regression with elastic net regularization.
+
+## Example: Logistic Regression
+
+The following example shows how to train a logistic regression model
+with elastic net regularization. `elasticNetParam` corresponds to
+$\alpha$ and `regParam` corresponds to $\lambda$.
 
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
-
 {% highlight scala %}
-
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.mllib.util.MLUtils
 
@@ -53,15 +74,11 @@ val lrModel = lr.fit(training)
 
 // Print the weights and intercept for logistic regression
 println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
-
 {% endhighlight %}
-
 </div>
 
 <div data-lang="java" markdown="1">
-
 {% highlight java %}
-
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
 import org.apache.spark.mllib.regression.LabeledPoint;
@@ -99,9 +116,7 @@ public class LogisticRegressionWithElasticNetExample {
 </div>
 
 <div data-lang="python" markdown="1">
-
 {% highlight python %}
-
 from pyspark.ml.classification import LogisticRegression
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.util import MLUtils
@@ -118,12 +133,114 @@ lrModel = lr.fit(training)
 print("Weights: " + str(lrModel.weights))
 print("Intercept: " + str(lrModel.intercept))
 {% endhighlight %}
+</div>
 
 </div>
 
+The `spark.ml` implementation of logistic regression also supports
+extracting a summary of the model over the training set. Note that the
+predictions and metrics which are stored as `Dataframe` in
+`BinaryLogisticRegressionSummary` are annotated `@transient` and hence
+only available on the driver.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+[`LogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionTrainingSummary)
+provides a summary for a
+[`LogisticRegressionModel`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionModel).
+Currently, only binary classification is supported and the
+summary must be explicitly cast to
+[`BinaryLogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary).
+This will likely change when multiclass classification is supported.
+
+Continuing the earlier example:
+
+{% highlight scala %}
+// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
+val trainingSummary = lrModel.summary
+
+// Obtain the loss per iteration.
+val objectiveHistory = trainingSummary.objectiveHistory
+objectiveHistory.foreach(loss => println(loss))
+
+// Obtain the metrics useful to judge performance on test data.
+// We cast the summary to a BinaryLogisticRegressionSummary since the problem is a
+// binary classification problem.
+val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary]
+
+// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
+val roc = binarySummary.roc
+roc.show()
+roc.select("FPR").show()
+println(binarySummary.areaUnderROC)
+
+// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
+// this selected threshold.
+val fMeasure = binarySummary.fMeasureByThreshold
+val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
+val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure).
+  select("threshold").head().getDouble(0)
+logReg.setThreshold(bestThreshold)
+logReg.fit(logRegDataFrame)
+{% endhighlight %}
 </div>
 
-### Optimization
+<div data-lang="java" markdown="1">
+[`LogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/LogisticRegressionTrainingSummary.html)
+provides a summary for a
+[`LogisticRegressionModel`](api/java/org/apache/spark/ml/classification/LogisticRegressionModel.html).
+Currently, only binary classification is supported and the
+summary must be explicitly cast to
+[`BinaryLogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/BinaryLogisticRegressionTrainingSummary.html).
+This will likely change when multiclass classification is supported.
+
+Continuing the earlier example:
+
+{% highlight java %}
+// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
+LogisticRegressionTrainingSummary trainingSummary = logRegModel.summary();
+
+// Obtain the loss per iteration.
+double[] objectiveHistory = trainingSummary.objectiveHistory();
+for (double lossPerIteration : objectiveHistory) {
+  System.out.println(lossPerIteration);
+}
+
+// Obtain the metrics useful to judge performance on test data.
+// We cast the summary to a BinaryLogisticRegressionSummary since the problem is a
+// binary classification problem.
+BinaryLogisticRegressionSummary binarySummary = (BinaryLogisticRegressionSummary) trainingSummary;
+
+// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
+DataFrame roc = binarySummary.roc();
+roc.show();
+roc.select("FPR").show();
+System.out.println(binarySummary.areaUnderROC());
+
+// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
+// this selected threshold.
+DataFrame fMeasure = binarySummary.fMeasureByThreshold();
+double maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0);
+double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure)).
+  select("threshold").head().getDouble(0);
+logReg.setThreshold(bestThreshold);
+logReg.fit(logRegDataFrame);
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+Logistic regression model summary is not yet supported in Python.
+</div>
+
+</div>
+
+# Optimization
+
+The optimization algorithm underlying the implementation is called
+[Orthant-Wise Limited-memory
+QuasiNewton](http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf)
+(OWL-QN). It is an extension of L-BFGS that can effectively handle L1
+regularization and elastic net.
 
-The optimization algorithm underlies the implementation is called [Orthant-Wise Limited-memory QuasiNewton](http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf)
-(OWL-QN). It is an extension of L-BFGS that can effectively handle L1 regularization and elastic net.

From 5bfe9e1111d9862084586549a7dc79476f67bab9 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 16:10:37 -0700
Subject: [PATCH 1254/1454] [SPARK-9680] [MLLIB] [DOC] StopWordsRemovers user
 guide and Java compatibility test

* Adds user guide for ml.feature.StopWordsRemovers, ran code examples on my machine
* Cleans up scaladocs for public methods
* Adds test for Java compatibility
* Follow up Python user guide code example is tracked by SPARK-10249

Author: Feynman Liang <fliang@databricks.com>

Closes #8436 from feynmanliang/SPARK-10230.
---
 docs/ml-features.md                           | 102 +++++++++++++++++-
 .../ml/feature/JavaStopWordsRemoverSuite.java |  72 +++++++++++++
 2 files changed, 171 insertions(+), 3 deletions(-)
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 62de4838981cb..89a9bad570446 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -306,15 +306,111 @@ regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern=
 </div>
 </div>
 
+## StopWordsRemover
+[Stop words](https://en.wikipedia.org/wiki/Stop_words) are words which
+should be excluded from the input, typically because the words appear
+frequently and don't carry as much meaning.
+
+`StopWordsRemover` takes as input a sequence of strings (e.g. the output
+of a [Tokenizer](ml-features.html#tokenizer)) and drops all the stop
+words from the input sequences. The list of stopwords is specified by
+the `stopWords` parameter.  We provide [a list of stop
+words](http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words) by
+default, accessible by calling `getStopWords` on a newly instantiated
+`StopWordsRemover` instance.
 
-## $n$-gram
+**Examples**
 
-An [n-gram](https://en.wikipedia.org/wiki/N-gram) is a sequence of $n$ tokens (typically words) for some integer $n$. The `NGram` class can be used to transform input features into $n$-grams.
+Assume that we have the following DataFrame with columns `id` and `raw`:
 
-`NGram` takes as input a sequence of strings (e.g. the output of a [Tokenizer](ml-features.html#tokenizer).  The parameter `n` is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of $n$ consecutive words.  If the input sequence contains fewer than `n` strings, no output is produced.
+~~~~
+ id | raw
+----|----------
+ 0  | [I, saw, the, red, baloon]
+ 1  | [Mary, had, a, little, lamb]
+~~~~
+
+Applying `StopWordsRemover` with `raw` as the input column and `filtered` as the output
+column, we should get the following:
+
+~~~~
+ id | raw                         | filtered
+----|-----------------------------|--------------------
+ 0  | [I, saw, the, red, baloon]  |  [saw, red, baloon]
+ 1  | [Mary, had, a, little, lamb]|[Mary, little, lamb]
+~~~~
+
+In `filtered`, the stop words "I", "the", "had", and "a" have been
+filtered out.
 
 <div class="codetabs">
+
 <div data-lang="scala" markdown="1">
+
+[`StopWordsRemover`](api/scala/index.html#org.apache.spark.ml.feature.StopWordsRemover)
+takes an input column name, an output column name, a list of stop words,
+and a boolean indicating if the matches should be case sensitive (false
+by default).
+
+{% highlight scala %}
+import org.apache.spark.ml.feature.StopWordsRemover
+
+val remover = new StopWordsRemover()
+  .setInputCol("raw")
+  .setOutputCol("filtered")
+val dataSet = sqlContext.createDataFrame(Seq(
+  (0, Seq("I", "saw", "the", "red", "baloon")),
+  (1, Seq("Mary", "had", "a", "little", "lamb"))
+)).toDF("id", "raw")
+
+remover.transform(dataSet).show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`StopWordsRemover`](api/java/org/apache/spark/ml/feature/StopWordsRemover.html)
+takes an input column name, an output column name, a list of stop words,
+and a boolean indicating if the matches should be case sensitive (false
+by default).
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.StopWordsRemover;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+StopWordsRemover remover = new StopWordsRemover()
+  .setInputCol("raw")
+  .setOutputCol("filtered");
+
+JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
+  RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
+));
+StructType schema = new StructType(new StructField[] {
+  new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
+});
+DataFrame dataset = jsql.createDataFrame(rdd, schema);
+
+remover.transform(dataset).show();
+{% endhighlight %}
+</div>
+</div>
+
+## $n$-gram
+
+An [n-gram](https://en.wikipedia.org/wiki/N-gram) is a sequence of $n$ tokens (typically words) for some integer $n$. The `NGram` class can be used to transform input features into $n$-grams.
+
+`NGram` takes as input a sequence of strings (e.g. the output of a [Tokenizer](ml-features.html#tokenizer)).  The parameter `n` is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of $n$ consecutive words.  If the input sequence contains fewer than `n` strings, no output is produced.
+
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java
new file mode 100644
index 0000000000000..76cdd0fae84ab
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature;
+
+import java.util.Arrays;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+
+public class JavaStopWordsRemoverSuite {
+
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaStopWordsRemoverSuite");
+    jsql = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  @Test
+  public void javaCompatibilityTest() {
+    StopWordsRemover remover = new StopWordsRemover()
+      .setInputCol("raw")
+      .setOutputCol("filtered");
+
+    JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+      RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
+      RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
+    ));
+    StructType schema = new StructType(new StructField[] {
+      new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
+    });
+    DataFrame dataset = jsql.createDataFrame(rdd, schema);
+
+    remover.transform(dataset).collect();
+  }
+}

From b3dd569ad40905f8861a547a1e25ed3ca8e1d272 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 27 Aug 2015 16:11:25 -0700
Subject: [PATCH 1255/1454] [SPARK-10287] [SQL] Fixes JSONRelation refreshing
 on read path

https://issues.apache.org/jira/browse/SPARK-10287

After porting json to HadoopFsRelation, it seems hard to keep the behavior of picking up new files automatically for JSON. This PR removes this behavior, so JSON is consistent with others (ORC and Parquet).

Author: Yin Huai <yhuai@databricks.com>

Closes #8469 from yhuai/jsonRefresh.
---
 docs/sql-programming-guide.md                     |  6 ++++++
 .../execution/datasources/json/JSONRelation.scala |  9 ---------
 .../org/apache/spark/sql/sources/interfaces.scala |  2 +-
 .../apache/spark/sql/sources/InsertSuite.scala    | 15 ---------------
 4 files changed, 7 insertions(+), 25 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 99fec6c7785af..e8eb88488ee24 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -2057,6 +2057,12 @@ options.
  - The canonical name of SQL/DataFrame functions are now lower case (e.g. sum vs SUM).
  - It has been determined that using the DirectOutputCommitter when speculation is enabled is unsafe
    and thus this output committer will not be used when speculation is on, independent of configuration.
+ - JSON data source will not automatically load new files that are created by other applications
+   (i.e. files that are not inserted to the dataset through Spark SQL).
+   For a JSON persistent table (i.e. the metadata of the table is stored in Hive Metastore),
+   users can use `REFRESH TABLE` SQL command or `HiveContext`'s `refreshTable` method
+   to include those new files to the table. For a DataFrame representing a JSON dataset, users need to recreate
+   the DataFrame and the new DataFrame will include new files.
 
 ## Upgrading from Spark SQL 1.3 to 1.4
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index 114c8b211891e..ab8ca5f748f24 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -111,15 +111,6 @@ private[sql] class JSONRelation(
     jsonSchema
   }
 
-  override private[sql] def buildScan(
-      requiredColumns: Array[String],
-      filters: Array[Filter],
-      inputPaths: Array[String],
-      broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = {
-    refresh()
-    super.buildScan(requiredColumns, filters, inputPaths, broadcastedConf)
-  }
-
   override def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index b3b326fe612c7..dff726b33fc74 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -562,7 +562,7 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
     })
   }
 
-  private[sql] def buildScan(
+  final private[sql] def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
       inputPaths: Array[String],
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 78bd3e5582964..084d83f6e9bff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -167,21 +167,6 @@ class InsertSuite extends DataSourceTest with SharedSQLContext {
     )
   }
 
-  test("save directly to the path of a JSON table") {
-    caseInsensitiveContext.table("jt").selectExpr("a * 5 as a", "b")
-      .write.mode(SaveMode.Overwrite).json(path.toString)
-    checkAnswer(
-      sql("SELECT a, b FROM jsonTable"),
-      (1 to 10).map(i => Row(i * 5, s"str$i"))
-    )
-
-    caseInsensitiveContext.table("jt").write.mode(SaveMode.Overwrite).json(path.toString)
-    checkAnswer(
-      sql("SELECT a, b FROM jsonTable"),
-      (1 to 10).map(i => Row(i, s"str$i"))
-    )
-  }
-
   test("it is not allowed to write to a table while querying it.") {
     val message = intercept[AnalysisException] {
       sql(

From 54cda0deb6bebf1470f16ba5bcc6c4fb842bdac1 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 27 Aug 2015 16:38:00 -0700
Subject: [PATCH 1256/1454] [SPARK-10321] sizeInBytes in HadoopFsRelation

Having sizeInBytes in HadoopFsRelation to enable broadcast join.

cc marmbrus

Author: Davies Liu <davies@databricks.com>

Closes #8490 from davies/sizeInByte.
---
 .../main/scala/org/apache/spark/sql/sources/interfaces.scala    | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index dff726b33fc74..7b030b7d73bd5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -518,6 +518,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
 
   override def inputFiles: Array[String] = cachedLeafStatuses().map(_.getPath.toString).toArray
 
+  override def sizeInBytes: Long = cachedLeafStatuses().map(_.getLen).sum
+
   /**
    * Partition columns.  Can be either defined by [[userDefinedPartitionColumns]] or automatically
    * discovered.  Note that they should always be nullable.

From 1f90c5e2198bcf49e115d97ec300c17c1be4dcb4 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Thu, 27 Aug 2015 19:38:53 -0700
Subject: [PATCH 1257/1454] [SPARK-8505] [SPARKR] Add settings to kick `lint-r`
 from `./dev/run-test.py`

JoshRosen we'd like to check the SparkR source code with the `dev/lint-r` script on the Jenkins. I tried to incorporate the script into `dev/run-test.py`. Could you review it when you have time?

shivaram I modified `dev/lint-r` and `dev/lint-r.R` to install lintr package into a local directory(`R/lib/`) and to exit with a lint status. Could you review it?

- [[SPARK-8505] Add settings to kick `lint-r` from `./dev/run-test.py` - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8505)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7883 from yu-iskw/SPARK-8505.
---
 dev/lint-r             | 11 +++++++++++
 dev/lint-r.R           | 12 +++++++-----
 dev/run-tests-codes.sh | 13 +++++++------
 dev/run-tests-jenkins  |  2 ++
 dev/run-tests.py       | 21 ++++++++++++++++++++-
 5 files changed, 47 insertions(+), 12 deletions(-)

diff --git a/dev/lint-r b/dev/lint-r
index 7d5f4cd31153d..c15d57aad86da 100755
--- a/dev/lint-r
+++ b/dev/lint-r
@@ -28,3 +28,14 @@ if ! type "Rscript" > /dev/null; then
 fi
 
 `which Rscript` --vanilla "$SPARK_ROOT_DIR/dev/lint-r.R" "$SPARK_ROOT_DIR" | tee "$LINT_R_REPORT_FILE_NAME"
+
+NUM_LINES=`wc -l < "$LINT_R_REPORT_FILE_NAME"`
+if [ "$NUM_LINES" = "0" ] ; then
+  lint_status=0
+  echo "lintr checks passed."
+else
+  lint_status=1
+  echo "lintr checks failed."
+fi
+
+exit "$lint_status"
diff --git a/dev/lint-r.R b/dev/lint-r.R
index 48bd6246096ae..999eef571b824 100644
--- a/dev/lint-r.R
+++ b/dev/lint-r.R
@@ -17,8 +17,14 @@
 
 argv <- commandArgs(TRUE)
 SPARK_ROOT_DIR <- as.character(argv[1])
+LOCAL_LIB_LOC <- file.path(SPARK_ROOT_DIR, "R", "lib")
 
-# Installs lintr from Github.
+# Checks if SparkR is installed in a local directory.
+if (! library(SparkR, lib.loc = LOCAL_LIB_LOC, logical.return = TRUE)) {
+  stop("You should install SparkR in a local directory with `R/install-dev.sh`.")
+}
+
+# Installs lintr from Github in a local directory.
 # NOTE: The CRAN's version is too old to adapt to our rules.
 if ("lintr" %in% row.names(installed.packages())  == FALSE) {
   devtools::install_github("jimhester/lintr")
@@ -27,9 +33,5 @@ if ("lintr" %in% row.names(installed.packages())  == FALSE) {
 library(lintr)
 library(methods)
 library(testthat)
-if (! library(SparkR, lib.loc = file.path(SPARK_ROOT_DIR, "R", "lib"), logical.return = TRUE)) {
-  stop("You should install SparkR in a local directory with `R/install-dev.sh`.")
-}
-
 path.to.package <- file.path(SPARK_ROOT_DIR, "R", "pkg")
 lint_package(path.to.package, cache = FALSE)
diff --git a/dev/run-tests-codes.sh b/dev/run-tests-codes.sh
index f4b238e1b78a7..1f16790522e76 100644
--- a/dev/run-tests-codes.sh
+++ b/dev/run-tests-codes.sh
@@ -21,9 +21,10 @@ readonly BLOCK_GENERAL=10
 readonly BLOCK_RAT=11
 readonly BLOCK_SCALA_STYLE=12
 readonly BLOCK_PYTHON_STYLE=13
-readonly BLOCK_DOCUMENTATION=14
-readonly BLOCK_BUILD=15
-readonly BLOCK_MIMA=16
-readonly BLOCK_SPARK_UNIT_TESTS=17
-readonly BLOCK_PYSPARK_UNIT_TESTS=18
-readonly BLOCK_SPARKR_UNIT_TESTS=19
+readonly BLOCK_R_STYLE=14
+readonly BLOCK_DOCUMENTATION=15
+readonly BLOCK_BUILD=16
+readonly BLOCK_MIMA=17
+readonly BLOCK_SPARK_UNIT_TESTS=18
+readonly BLOCK_PYSPARK_UNIT_TESTS=19
+readonly BLOCK_SPARKR_UNIT_TESTS=20
diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index f144c053046c5..39cf54f78104c 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -210,6 +210,8 @@ done
       failing_test="Scala style tests"
     elif [ "$test_result" -eq "$BLOCK_PYTHON_STYLE" ]; then
       failing_test="Python style tests"
+    elif [ "$test_result" -eq "$BLOCK_R_STYLE" ]; then
+      failing_test="R style tests"
     elif [ "$test_result" -eq "$BLOCK_DOCUMENTATION" ]; then
       failing_test="to generate documentation"
     elif [ "$test_result" -eq "$BLOCK_BUILD" ]; then
diff --git a/dev/run-tests.py b/dev/run-tests.py
index f689425ee40b6..4fd703a7c219f 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -209,6 +209,18 @@ def run_python_style_checks():
     run_cmd([os.path.join(SPARK_HOME, "dev", "lint-python")])
 
 
+def run_sparkr_style_checks():
+    set_title_and_block("Running R style checks", "BLOCK_R_STYLE")
+
+    if which("R"):
+        # R style check should be executed after `install-dev.sh`.
+        # Since warnings about `no visible global function definition` appear
+        # without the installation. SEE ALSO: SPARK-9121.
+        run_cmd([os.path.join(SPARK_HOME, "dev", "lint-r")])
+    else:
+        print("Ignoring SparkR style check as R was not found in PATH")
+
+
 def build_spark_documentation():
     set_title_and_block("Building Spark Documentation", "BLOCK_DOCUMENTATION")
     os.environ["PRODUCTION"] = "1 jekyll build"
@@ -387,7 +399,6 @@ def run_sparkr_tests():
     set_title_and_block("Running SparkR tests", "BLOCK_SPARKR_UNIT_TESTS")
 
     if which("R"):
-        run_cmd([os.path.join(SPARK_HOME, "R", "install-dev.sh")])
         run_cmd([os.path.join(SPARK_HOME, "R", "run-tests.sh")])
     else:
         print("Ignoring SparkR tests as R was not found in PATH")
@@ -438,6 +449,12 @@ def main():
     if java_version.minor < 8:
         print("[warn] Java 8 tests will not run because JDK version is < 1.8.")
 
+    # install SparkR
+    if which("R"):
+        run_cmd([os.path.join(SPARK_HOME, "R", "install-dev.sh")])
+    else:
+        print("Can't install SparkR as R is was not found in PATH")
+
     if os.environ.get("AMPLAB_JENKINS"):
         # if we're on the Amplab Jenkins build servers setup variables
         # to reflect the environment settings
@@ -485,6 +502,8 @@ def main():
         run_scala_style_checks()
     if not changed_files or any(f.endswith(".py") for f in changed_files):
         run_python_style_checks()
+    if not changed_files or any(f.endswith(".R") for f in changed_files):
+        run_sparkr_style_checks()
 
     # determine if docs were changed and if we're inside the amplab environment
     # note - the below commented out until *all* Jenkins workers can get `jekyll` installed

From 30734d45fbbb269437c062241a9161e198805a76 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 27 Aug 2015 21:44:06 -0700
Subject: [PATCH 1258/1454] [SPARK-9911] [DOC] [ML] Update Userguide for
 Evaluator

I added a small note about the different types of evaluator and the metrics used.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8304 from MechCoder/multiclass_evaluator.
---
 docs/ml-guide.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index de8fead3529e4..01bf5ee18e328 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -643,6 +643,13 @@ An important task in ML is *model selection*, or using data to find the best mod
 Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator).
 `CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
 `CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
+
+The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator)
+for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator)
+for binary data or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
+for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the setMetric
+method in each of these evaluators.
+
 The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
 `CrossValidator` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
 
@@ -708,9 +715,12 @@ val pipeline = new Pipeline()
 // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
 // This will allow us to jointly choose parameters for all Pipeline stages.
 // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
+// used is areaUnderROC.
 val crossval = new CrossValidator()
   .setEstimator(pipeline)
   .setEvaluator(new BinaryClassificationEvaluator)
+
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
@@ -831,9 +841,12 @@ Pipeline pipeline = new Pipeline()
 // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
 // This will allow us to jointly choose parameters for all Pipeline stages.
 // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
+// used is areaUnderROC.
 CrossValidator crossval = new CrossValidator()
     .setEstimator(pipeline)
     .setEvaluator(new BinaryClassificationEvaluator());
+
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.

From af0e1249b1c881c0fa7a921fd21fd2c27214b980 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 21:55:20 -0700
Subject: [PATCH 1259/1454] [SPARK-9905] [ML] [DOC] Adds
 LinearRegressionSummary user guide

* Adds user guide for `LinearRegressionSummary`
* Fixes unresolved issues in  #8197

CC jkbradley mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #8491 from feynmanliang/SPARK-9905.
---
 docs/ml-linear-methods.md | 140 ++++++++++++++++++++++++++++++++++----
 1 file changed, 127 insertions(+), 13 deletions(-)

diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
index 2761aeb789621..cdd9d4999fa1b 100644
--- a/docs/ml-linear-methods.md
+++ b/docs/ml-linear-methods.md
@@ -34,7 +34,7 @@ net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
 Mathematically, it is defined as a convex combination of the $L_1$ and
 the $L_2$ regularization terms:
 `\[
-\alpha~\lambda \|\wv\|_1 + (1-\alpha) \frac{\lambda}{2}\|\wv\|_2^2, \alpha \in [0, 1], \lambda \geq 0.
+\alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0
 \]`
 By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$
 regularization as special cases. For example, if a [linear
@@ -95,7 +95,7 @@ public class LogisticRegressionWithElasticNetExample {
 
     SparkContext sc = new SparkContext(conf);
     SQLContext sql = new SQLContext(sc);
-    String path = "sample_libsvm_data.txt";
+    String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
     DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
@@ -103,7 +103,7 @@ public class LogisticRegressionWithElasticNetExample {
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
       .setRegParam(0.3)
-      .setElasticNetParam(0.8)
+      .setElasticNetParam(0.8);
 
     // Fit the model
     LogisticRegressionModel lrModel = lr.fit(training);
@@ -158,10 +158,12 @@ This will likely change when multiclass classification is supported.
 Continuing the earlier example:
 
 {% highlight scala %}
+import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
+
 // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
 val trainingSummary = lrModel.summary
 
-// Obtain the loss per iteration.
+// Obtain the objective per iteration.
 val objectiveHistory = trainingSummary.objectiveHistory
 objectiveHistory.foreach(loss => println(loss))
 
@@ -173,17 +175,14 @@ val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary
 // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
 val roc = binarySummary.roc
 roc.show()
-roc.select("FPR").show()
 println(binarySummary.areaUnderROC)
 
-// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
-// this selected threshold.
+// Set the model threshold to maximize F-Measure
 val fMeasure = binarySummary.fMeasureByThreshold
 val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
 val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure).
   select("threshold").head().getDouble(0)
-logReg.setThreshold(bestThreshold)
-logReg.fit(logRegDataFrame)
+lrModel.setThreshold(bestThreshold)
 {% endhighlight %}
 </div>
 
@@ -199,8 +198,12 @@ This will likely change when multiclass classification is supported.
 Continuing the earlier example:
 
 {% highlight java %}
+import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary;
+import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary;
+import org.apache.spark.sql.functions;
+
 // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
-LogisticRegressionTrainingSummary trainingSummary = logRegModel.summary();
+LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();
 
 // Obtain the loss per iteration.
 double[] objectiveHistory = trainingSummary.objectiveHistory();
@@ -222,20 +225,131 @@ System.out.println(binarySummary.areaUnderROC());
 // Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
 // this selected threshold.
 DataFrame fMeasure = binarySummary.fMeasureByThreshold();
-double maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0);
+double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0);
 double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure)).
   select("threshold").head().getDouble(0);
-logReg.setThreshold(bestThreshold);
-logReg.fit(logRegDataFrame);
+lrModel.setThreshold(bestThreshold);
 {% endhighlight %}
 </div>
 
+<!--- TODO: Add python model summaries once implemented -->
 <div data-lang="python" markdown="1">
 Logistic regression model summary is not yet supported in Python.
 </div>
 
 </div>
 
+## Example: Linear Regression
+
+The interface for working with linear regression models and model
+summaries is similar to the logistic regression case. The following
+example demonstrates training an elastic net regularized linear
+regression model and extracting model summary statistics.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.mllib.util.MLUtils
+
+// Load training data
+val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+val lr = new LinearRegression()
+  .setMaxIter(10)
+  .setRegParam(0.3)
+  .setElasticNetParam(0.8)
+
+// Fit the model
+val lrModel = lr.fit(training)
+
+// Print the weights and intercept for linear regression
+println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
+
+// Summarize the model over the training set and print out some metrics
+val trainingSummary = lrModel.summary
+println(s"numIterations: ${trainingSummary.totalIterations}")
+println(s"objectiveHistory: ${trainingSummary.objectiveHistory.toList}")
+trainingSummary.residuals.show()
+println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")
+println(s"r2: ${trainingSummary.r2}")
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import org.apache.spark.ml.regression.LinearRegression;
+import org.apache.spark.ml.regression.LinearRegressionModel;
+import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class LinearRegressionWithElasticNetExample {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf()
+      .setAppName("Linear Regression with Elastic Net Example");
+
+    SparkContext sc = new SparkContext(conf);
+    SQLContext sql = new SQLContext(sc);
+    String path = "data/mllib/sample_libsvm_data.txt";
+
+    // Load training data
+    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+
+    LinearRegression lr = new LinearRegression()
+      .setMaxIter(10)
+      .setRegParam(0.3)
+      .setElasticNetParam(0.8);
+
+    // Fit the model
+    LinearRegressionModel lrModel = lr.fit(training);
+
+    // Print the weights and intercept for linear regression
+    System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
+
+    // Summarize the model over the training set and print out some metrics
+    LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
+    System.out.println("numIterations: " + trainingSummary.totalIterations());
+    System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory()));
+    trainingSummary.residuals().show();
+    System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError());
+    System.out.println("r2: " + trainingSummary.r2());
+  }
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+<!--- TODO: Add python model summaries once implemented -->
+{% highlight python %}
+from pyspark.ml.regression import LinearRegression
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.util import MLUtils
+
+# Load training data
+training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
+
+# Fit the model
+lrModel = lr.fit(training)
+
+# Print the weights and intercept for linear regression
+print("Weights: " + str(lrModel.weights))
+print("Intercept: " + str(lrModel.intercept))
+
+# Linear regression model summary is not yet supported in Python.
+{% endhighlight %}
+</div>
+
+</div>
+
 # Optimization
 
 The optimization algorithm underlying the implementation is called

From 89b943438512fcfb239c268b43431397de46cbcf Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 27 Aug 2015 22:30:01 -0700
Subject: [PATCH 1260/1454] [SPARK-SQL] [MINOR] Fixes some typos in HiveContext

Author: Cheng Lian <lian@databricks.com>

Closes #8481 from liancheng/hive-context-typo.
---
 .../scala/org/apache/spark/sql/hive/HiveContext.scala     | 8 ++++----
 .../scala/org/apache/spark/sql/hive/test/TestHive.scala   | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index c0a458fa9ab8d..2e791cea96b41 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -171,11 +171,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    * Overrides default Hive configurations to avoid breaking changes to Spark SQL users.
    *  - allow SQL11 keywords to be used as identifiers
    */
-  private[sql] def defaultOverides() = {
+  private[sql] def defaultOverrides() = {
     setConf(ConfVars.HIVE_SUPPORT_SQL11_RESERVED_KEYWORDS.varname, "false")
   }
 
-  defaultOverides()
+  defaultOverrides()
 
   /**
    * The copy of the Hive client that is used to retrieve metadata from the Hive MetaStore.
@@ -190,8 +190,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
     // into the isolated client loader
     val metadataConf = new HiveConf()
 
-    val defaltWarehouseLocation = metadataConf.get("hive.metastore.warehouse.dir")
-    logInfo("defalt warehouse location is " + defaltWarehouseLocation)
+    val defaultWarehouseLocation = metadataConf.get("hive.metastore.warehouse.dir")
+    logInfo("default warehouse location is " + defaultWarehouseLocation)
 
     // `configure` goes second to override other settings.
     val allConfig = metadataConf.asScala.map(e => e.getKey -> e.getValue).toMap ++ configure
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 572eaebe81ac2..57fea5d8db343 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -434,7 +434,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         case (k, v) =>
           metadataHive.runSqlHive(s"SET $k=$v")
       }
-      defaultOverides()
+      defaultOverrides()
 
       runSqlHive("USE default")
 

From 7583681e6b0824d7eed471dc4d8fa0b2addf9ffc Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Thu, 27 Aug 2015 23:59:30 -0700
Subject: [PATCH 1261/1454] [SPARK-10188] [PYSPARK] Pyspark CrossValidator with
 RMSE selects incorrect model

* Added isLargerBetter() method to Pyspark Evaluator to match the Scala version.
* JavaEvaluator delegates isLargerBetter() to underlying Scala object.
* Added check for isLargerBetter() in CrossValidator to determine whether to use argmin or argmax.
* Added test cases for where smaller is better (RMSE) and larger is better (R-Squared).

(This contribution is my original work and that I license the work to the project under Sparks' open source license)

Author: noelsmith <mail@noelsmith.com>

Closes #8399 from noel-smith/pyspark-rmse-xval-fix.
---
 python/pyspark/ml/evaluation.py | 12 +++++
 python/pyspark/ml/tests.py      | 87 +++++++++++++++++++++++++++++++++
 python/pyspark/ml/tuning.py     |  6 ++-
 3 files changed, 104 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 6b0a9ffde9f42..cb3b07947e488 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -66,6 +66,14 @@ def evaluate(self, dataset, params=None):
         else:
             raise ValueError("Params must be a param map but got %s." % type(params))
 
+    def isLargerBetter(self):
+        """
+        Indicates whether the metric returned by :py:meth:`evaluate` should be maximized
+        (True, default) or minimized (False).
+        A given evaluator may support multiple metrics which may be maximized or minimized.
+        """
+        return True
+
 
 @inherit_doc
 class JavaEvaluator(Evaluator, JavaWrapper):
@@ -85,6 +93,10 @@ def _evaluate(self, dataset):
         self._transfer_params_to_java()
         return self._java_obj.evaluate(dataset._jdf)
 
+    def isLargerBetter(self):
+        self._transfer_params_to_java()
+        return self._java_obj.isLargerBetter()
+
 
 @inherit_doc
 class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index c151d21fd661a..60e4237293adc 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -32,11 +32,14 @@
 
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
 from pyspark.sql import DataFrame, SQLContext
+from pyspark.sql.functions import rand
+from pyspark.ml.evaluation import RegressionEvaluator
 from pyspark.ml.param import Param, Params
 from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
 from pyspark.ml.util import keyword_only
 from pyspark.ml import Estimator, Model, Pipeline, Transformer
 from pyspark.ml.feature import *
+from pyspark.ml.tuning import ParamGridBuilder, CrossValidator, CrossValidatorModel
 from pyspark.mllib.linalg import DenseVector
 
 
@@ -264,5 +267,89 @@ def test_ngram(self):
         self.assertEquals(transformedDF.head().output, ["a b c d", "b c d e"])
 
 
+class HasInducedError(Params):
+
+    def __init__(self):
+        super(HasInducedError, self).__init__()
+        self.inducedError = Param(self, "inducedError",
+                                  "Uniformly-distributed error added to feature")
+
+    def getInducedError(self):
+        return self.getOrDefault(self.inducedError)
+
+
+class InducedErrorModel(Model, HasInducedError):
+
+    def __init__(self):
+        super(InducedErrorModel, self).__init__()
+
+    def _transform(self, dataset):
+        return dataset.withColumn("prediction",
+                                  dataset.feature + (rand(0) * self.getInducedError()))
+
+
+class InducedErrorEstimator(Estimator, HasInducedError):
+
+    def __init__(self, inducedError=1.0):
+        super(InducedErrorEstimator, self).__init__()
+        self._set(inducedError=inducedError)
+
+    def _fit(self, dataset):
+        model = InducedErrorModel()
+        self._copyValues(model)
+        return model
+
+
+class CrossValidatorTests(PySparkTestCase):
+
+    def test_fit_minimize_metric(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([
+            (10, 10.0),
+            (50, 50.0),
+            (100, 100.0),
+            (500, 500.0)] * 10,
+            ["feature", "label"])
+
+        iee = InducedErrorEstimator()
+        evaluator = RegressionEvaluator(metricName="rmse")
+
+        grid = (ParamGridBuilder()
+                .addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
+                .build())
+        cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
+        cvModel = cv.fit(dataset)
+        bestModel = cvModel.bestModel
+        bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
+
+        self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
+                         "Best model should have zero induced error")
+        self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0")
+
+    def test_fit_maximize_metric(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([
+            (10, 10.0),
+            (50, 50.0),
+            (100, 100.0),
+            (500, 500.0)] * 10,
+            ["feature", "label"])
+
+        iee = InducedErrorEstimator()
+        evaluator = RegressionEvaluator(metricName="r2")
+
+        grid = (ParamGridBuilder()
+                .addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
+                .build())
+        cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
+        cvModel = cv.fit(dataset)
+        bestModel = cvModel.bestModel
+        bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
+
+        self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
+                         "Best model should have zero induced error")
+        self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index dcfee6a3170ab..cae778869e9c5 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -223,7 +223,11 @@ def _fit(self, dataset):
                 # TODO: duplicate evaluator to take extra params from input
                 metric = eva.evaluate(model.transform(validation, epm[j]))
                 metrics[j] += metric
-        bestIndex = np.argmax(metrics)
+
+        if eva.isLargerBetter():
+            bestIndex = np.argmax(metrics)
+        else:
+            bestIndex = np.argmin(metrics)
         bestModel = est.fit(dataset, epm[bestIndex])
         return CrossValidatorModel(bestModel)
 

From 2f99c37273c1d82e2ba39476e4429ea4aaba7ec6 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Fri, 28 Aug 2015 00:37:50 -0700
Subject: [PATCH 1262/1454] [SPARK-10328] [SPARKR] Fix generic for na.omit

S3 function is at https://stat.ethz.ch/R-manual/R-patched/library/stats/html/na.fail.html

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Author: Shivaram Venkataraman <shivaram.venkataraman@gmail.com>
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8495 from shivaram/na-omit-fix.
---
 R/pkg/R/DataFrame.R              |  6 +++---
 R/pkg/R/generics.R               |  2 +-
 R/pkg/inst/tests/test_sparkSQL.R | 23 ++++++++++++++++++++++-
 dev/lint-r                       |  2 +-
 4 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index dd8126aebf467..74de7c81e35a6 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1699,9 +1699,9 @@ setMethod("dropna",
 #' @name na.omit
 #' @export
 setMethod("na.omit",
-          signature(x = "DataFrame"),
-          function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
-            dropna(x, how, minNonNulls, cols)
+          signature(object = "DataFrame"),
+          function(object, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
+            dropna(object, how, minNonNulls, cols)
           })
 
 #' fillna
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index a829d46c1894c..b578b8789d2c5 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -413,7 +413,7 @@ setGeneric("dropna",
 #' @rdname nafunctions
 #' @export
 setGeneric("na.omit",
-           function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
+           function(object, ...) {
              standardGeneric("na.omit")
            })
 
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 4b672e115f924..933b11c8ee7e2 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -1083,7 +1083,7 @@ test_that("describe() and summarize() on a DataFrame", {
   expect_equal(collect(stats2)[5, "age"], "30")
 })
 
-test_that("dropna() on a DataFrame", {
+test_that("dropna() and na.omit() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPathNa)
   rows <- collect(df)
 
@@ -1092,6 +1092,8 @@ test_that("dropna() on a DataFrame", {
   expected <- rows[!is.na(rows$name),]
   actual <- collect(dropna(df, cols = "name"))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, cols = "name"))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age),]
   actual <- collect(dropna(df, cols = "age"))
@@ -1101,48 +1103,67 @@ test_that("dropna() on a DataFrame", {
   expect_identical(expected$age, actual$age)
   expect_identical(expected$height, actual$height)
   expect_identical(expected$name, actual$name)
+  actual <- collect(na.omit(df, cols = "age"))
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
   actual <- collect(dropna(df, cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df))
+  expect_identical(expected, actual)
 
   # drop with how
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) | !is.na(rows$height) | !is.na(rows$name),]
   actual <- collect(dropna(df, "all"))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "all"))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df, "any"))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "any"))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
   actual <- collect(dropna(df, "any", cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "any", cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) | !is.na(rows$height),]
   actual <- collect(dropna(df, "all", cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "all", cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   # drop with threshold
 
   expected <- rows[as.integer(!is.na(rows$age)) + as.integer(!is.na(rows$height)) >= 2,]
   actual <- collect(dropna(df, minNonNulls = 2, cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, minNonNulls = 2, cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   expected <- rows[as.integer(!is.na(rows$age)) +
                    as.integer(!is.na(rows$height)) +
                    as.integer(!is.na(rows$name)) >= 3,]
   actual <- collect(dropna(df, minNonNulls = 3, cols = c("name", "age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, minNonNulls = 3, cols = c("name", "age", "height")))
+  expect_identical(expected, actual)
 })
 
 test_that("fillna() on a DataFrame", {
diff --git a/dev/lint-r b/dev/lint-r
index c15d57aad86da..bfda0bca15eb7 100755
--- a/dev/lint-r
+++ b/dev/lint-r
@@ -29,7 +29,7 @@ fi
 
 `which Rscript` --vanilla "$SPARK_ROOT_DIR/dev/lint-r.R" "$SPARK_ROOT_DIR" | tee "$LINT_R_REPORT_FILE_NAME"
 
-NUM_LINES=`wc -l < "$LINT_R_REPORT_FILE_NAME"`
+NUM_LINES=`wc -l < "$LINT_R_REPORT_FILE_NAME" | awk '{print $1}'`
 if [ "$NUM_LINES" = "0" ] ; then
   lint_status=0
   echo "lintr checks passed."

From 4eeda8d472498acd40ef54723d1be9924a273d76 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Fri, 28 Aug 2015 00:50:26 -0700
Subject: [PATCH 1263/1454] [SPARK-10260] [ML] Add @Since annotation to
 ml.clustering

### JIRA
[[SPARK-10260] Add Since annotation to ml.clustering - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10260)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8455 from yu-iskw/SPARK-10260.
---
 .../apache/spark/ml/clustering/KMeans.scala   | 32 +++++++++++++++++--
 1 file changed, 29 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
index 47a18cdb31b53..f40ab71fb22a6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.clustering
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Since, Experimental}
 import org.apache.spark.ml.param.{Param, Params, IntParam, ParamMap}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
@@ -39,9 +39,11 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
    * Set the number of clusters to create (k). Must be > 1. Default: 2.
    * @group param
    */
+  @Since("1.5.0")
   final val k = new IntParam(this, "k", "number of clusters to create", (x: Int) => x > 1)
 
   /** @group getParam */
+  @Since("1.5.0")
   def getK: Int = $(k)
 
   /**
@@ -50,10 +52,12 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
    * @group expertParam
    */
+  @Since("1.5.0")
   final val initMode = new Param[String](this, "initMode", "initialization algorithm",
     (value: String) => MLlibKMeans.validateInitMode(value))
 
   /** @group expertGetParam */
+  @Since("1.5.0")
   def getInitMode: String = $(initMode)
 
   /**
@@ -61,10 +65,12 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
    * setting -- the default of 5 is almost always enough. Must be > 0. Default: 5.
    * @group expertParam
    */
+  @Since("1.5.0")
   final val initSteps = new IntParam(this, "initSteps", "number of steps for k-means||",
     (value: Int) => value > 0)
 
   /** @group expertGetParam */
+  @Since("1.5.0")
   def getInitSteps: Int = $(initSteps)
 
   /**
@@ -84,27 +90,32 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
  *
  * @param parentModel a model trained by spark.mllib.clustering.KMeans.
  */
+@Since("1.5.0")
 @Experimental
 class KMeansModel private[ml] (
-    override val uid: String,
+    @Since("1.5.0") override val uid: String,
     private val parentModel: MLlibKMeansModel) extends Model[KMeansModel] with KMeansParams {
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): KMeansModel = {
     val copied = new KMeansModel(uid, parentModel)
     copyValues(copied, extra)
   }
 
+  @Since("1.5.0")
   override def transform(dataset: DataFrame): DataFrame = {
     val predictUDF = udf((vector: Vector) => predict(vector))
     dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
   }
 
+  @Since("1.5.0")
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }
 
   private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
 
+  @Since("1.5.0")
   def clusterCenters: Array[Vector] = parentModel.clusterCenters
 }
 
@@ -114,8 +125,11 @@ class KMeansModel private[ml] (
  *
  * @see [[http://dx.doi.org/10.14778/2180912.2180915 Bahmani et al., Scalable k-means++.]]
  */
+@Since("1.5.0")
 @Experimental
-class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams {
+class KMeans @Since("1.5.0") (
+    @Since("1.5.0") override val uid: String)
+  extends Estimator[KMeansModel] with KMeansParams {
 
   setDefault(
     k -> 2,
@@ -124,34 +138,45 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
     initSteps -> 5,
     tol -> 1e-4)
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
 
+  @Since("1.5.0")
   def this() = this(Identifiable.randomUID("kmeans"))
 
   /** @group setParam */
+  @Since("1.5.0")
   def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setK(value: Int): this.type = set(k, value)
 
   /** @group expertSetParam */
+  @Since("1.5.0")
   def setInitMode(value: String): this.type = set(initMode, value)
 
   /** @group expertSetParam */
+  @Since("1.5.0")
   def setInitSteps(value: Int): this.type = set(initSteps, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setTol(value: Double): this.type = set(tol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setSeed(value: Long): this.type = set(seed, value)
 
+  @Since("1.5.0")
   override def fit(dataset: DataFrame): KMeansModel = {
     val rdd = dataset.select(col($(featuresCol))).map { case Row(point: Vector) => point }
 
@@ -167,6 +192,7 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
     copyValues(model)
   }
 
+  @Since("1.5.0")
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }

From cc39803062119c1d14611dc227b9ed0ed1284d38 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Fri, 28 Aug 2015 09:32:23 +0100
Subject: [PATCH 1264/1454] [SPARK-10295] [CORE] Dynamic allocation in Mesos
 does not release when RDDs are cached

Remove obsolete warning about dynamic allocation not working with cached RDDs

See discussion in https://issues.apache.org/jira/browse/SPARK-10295

Author: Sean Owen <sowen@cloudera.com>

Closes #8489 from srowen/SPARK-10295.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index f3da04a7f55d0..738887076b0d1 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1590,11 +1590,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * Register an RDD to be persisted in memory and/or disk storage
    */
   private[spark] def persistRDD(rdd: RDD[_]) {
-    _executorAllocationManager.foreach { _ =>
-      logWarning(
-        s"Dynamic allocation currently does not support cached RDDs. Cached data for RDD " +
-        s"${rdd.id} will be lost when executors are removed.")
-    }
     persistentRdds(rdd.id) = rdd
   }
 

From 18294cd8710427076caa86bfac596de67089d57e Mon Sep 17 00:00:00 2001
From: Keiji Yoshida <yoshida.keiji.84@gmail.com>
Date: Fri, 28 Aug 2015 09:36:50 +0100
Subject: [PATCH 1265/1454] Fix DynamodDB/DynamoDB typo in Kinesis Integration
 doc

Fix DynamodDB/DynamoDB typo in Kinesis Integration doc

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8501 from yosssi/patch-1.
---
 docs/streaming-kinesis-integration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md
index a7bcaec6fcd84..238a911a9199f 100644
--- a/docs/streaming-kinesis-integration.md
+++ b/docs/streaming-kinesis-integration.md
@@ -91,7 +91,7 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 
 	- Kinesis data processing is ordered per partition and occurs at-least once per message.
 
-	- Multiple applications can read from the same Kinesis stream.  Kinesis will maintain the application-specific shard and checkpoint info in DynamodDB.
+	- Multiple applications can read from the same Kinesis stream.  Kinesis will maintain the application-specific shard and checkpoint info in DynamoDB.
 
 	- A single Kinesis stream shard is processed by one input DStream at a time.
 

From 71a077f6c16c8816eae13341f645ba50d997f63d Mon Sep 17 00:00:00 2001
From: Dharmesh Kakadia <dharmeshkakadia@users.noreply.github.com>
Date: Fri, 28 Aug 2015 09:38:35 +0100
Subject: [PATCH 1266/1454] typo in comment

Author: Dharmesh Kakadia <dharmeshkakadia@users.noreply.github.com>

Closes #8497 from dharmeshkakadia/patch-2.
---
 .../apache/spark/network/shuffle/protocol/RegisterExecutor.java | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java
index cca8b17c4f129..167ef33104227 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java
@@ -27,7 +27,7 @@
 
 /**
  * Initial registration message between an executor and its local shuffle server.
- * Returns nothing (empty bye array).
+ * Returns nothing (empty byte array).
  */
 public class RegisterExecutor extends BlockTransferMessage {
   public final String appId;

From 1502a0f6c5d2f85a331b29d3bf50002911ea393e Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Fri, 28 Aug 2015 09:32:52 -0500
Subject: [PATCH 1267/1454] [YARN] [MINOR] Avoid hard code port number in
 YarnShuffleService test

Current port number is fixed as default (7337) in test, this will introduce port contention exception, better to change to a random number in unit test.

squito , seems you're author of this unit test, mind taking a look at this fix? Thanks a lot.

```
[info] - executor state kept across NM restart *** FAILED *** (597 milliseconds)
[info]   org.apache.hadoop.service.ServiceStateException: java.net.BindException: Address already in use
[info]   at org.apache.hadoop.service.ServiceStateException.convert(ServiceStateException.java:59)
[info]   at org.apache.hadoop.service.AbstractService.init(AbstractService.java:172)
[info]   at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply$mcV$sp(YarnShuffleServiceSuite.scala:72)
[info]   at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply(YarnShuffleServiceSuite.scala:70)
[info]   at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply(YarnShuffleServiceSuite.scala:70)
[info]   at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
[info]   at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
[info]   at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:22)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:20)
[info]   at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
[info]   at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:42)
...
```

Author: jerryshao <sshao@hortonworks.com>

Closes #8502 from jerryshao/avoid-hardcode-port.
---
 .../org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
index 2f22cbdbeac37..6aa8c814cd4f0 100644
--- a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
@@ -37,6 +37,7 @@ class YarnShuffleServiceSuite extends SparkFunSuite with Matchers with BeforeAnd
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
       classOf[YarnShuffleService].getCanonicalName)
+    yarnConfig.setInt("spark.shuffle.service.port", 0)
 
     yarnConfig.get("yarn.nodemanager.local-dirs").split(",").foreach { dir =>
       val d = new File(dir)

From e2a843090cb031f6aa774f6d9c031a7f0f732ee1 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Fri, 28 Aug 2015 08:00:44 -0700
Subject: [PATCH 1268/1454] [SPARK-9890] [DOC] [ML] User guide for
 CountVectorizer

jira: https://issues.apache.org/jira/browse/SPARK-9890

document with Scala and java examples

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8487 from hhbyyh/cvDoc.
---
 docs/ml-features.md | 109 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 89a9bad570446..90654d1e5a248 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -211,6 +211,115 @@ for feature in result.select("result").take(3):
 </div>
 </div>
 
+## CountVectorizer
+
+`CountVectorizer` and `CountVectorizerModel` aim to help convert a collection of text documents
+ to vectors of token counts. When an a-priori dictionary is not available, `CountVectorizer` can
+ be used as an `Estimator` to extract the vocabulary and generates a `CountVectorizerModel`. The
+ model produces sparse representations for the documents over the vocabulary, which can then be
+ passed to other algorithms like LDA.
+
+ During the fitting process, `CountVectorizer` will select the top `vocabSize` words ordered by
+ term frequency across the corpus. An optional parameter "minDF" also affect the fitting process
+ by specifying the minimum number (or fraction if < 1.0) of documents a term must appear in to be
+ included in the vocabulary.
+
+**Examples**
+
+Assume that we have the following DataFrame with columns `id` and `texts`:
+
+~~~~
+ id | texts
+----|----------
+ 0  | Array("a", "b", "c")
+ 1  | Array("a", "b", "b", "c", "a")
+~~~~
+
+each row in`texts` is a document of type Array[String].
+Invoking fit of `CountVectorizer` produces a `CountVectorizerModel` with vocabulary (a, b, c),
+then the output column "vector" after transformation contains:
+
+~~~~
+ id | texts                           | vector
+----|---------------------------------|---------------
+ 0  | Array("a", "b", "c")            | (3,[0,1,2],[1.0,1.0,1.0])
+ 1  | Array("a", "b", "b", "c", "a")  | (3,[0,1,2],[2.0,2.0,1.0])
+~~~~
+
+each vector represents the token counts of the document over the vocabulary.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+More details can be found in the API docs for
+[CountVectorizer](api/scala/index.html#org.apache.spark.ml.feature.CountVectorizer) and
+[CountVectorizerModel](api/scala/index.html#org.apache.spark.ml.feature.CountVectorizerModel).
+{% highlight scala %}
+import org.apache.spark.ml.feature.CountVectorizer
+import org.apache.spark.mllib.util.CountVectorizerModel
+
+val df = sqlContext.createDataFrame(Seq(
+  (0, Array("a", "b", "c")),
+  (1, Array("a", "b", "b", "c", "a"))
+)).toDF("id", "words")
+
+// fit a CountVectorizerModel from the corpus
+val cvModel: CountVectorizerModel = new CountVectorizer()
+  .setInputCol("words")
+  .setOutputCol("features")
+  .setVocabSize(3)
+  .setMinDF(2) // a term must appear in more or equal to 2 documents to be included in the vocabulary
+  .fit(df)
+
+// alternatively, define CountVectorizerModel with a-priori vocabulary
+val cvm = new CountVectorizerModel(Array("a", "b", "c"))
+  .setInputCol("words")
+  .setOutputCol("features")
+
+cvModel.transform(df).select("features").show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+More details can be found in the API docs for
+[CountVectorizer](api/java/org/apache/spark/ml/feature/CountVectorizer.html) and
+[CountVectorizerModel](api/java/org/apache/spark/ml/feature/CountVectorizerModel.html).
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.CountVectorizer;
+import org.apache.spark.ml.feature.CountVectorizerModel;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+
+// Input data: Each row is a bag of words from a sentence or document.
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Arrays.asList("a", "b", "c")),
+  RowFactory.create(Arrays.asList("a", "b", "b", "c", "a"))
+));
+StructType schema = new StructType(new StructField [] {
+  new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
+});
+DataFrame df = sqlContext.createDataFrame(jrdd, schema);
+
+// fit a CountVectorizerModel from the corpus
+CountVectorizerModel cvModel = new CountVectorizer()
+  .setInputCol("text")
+  .setOutputCol("feature")
+  .setVocabSize(3)
+  .setMinDF(2) // a term must appear in more or equal to 2 documents to be included in the vocabulary
+  .fit(df);
+
+// alternatively, define CountVectorizerModel with a-priori vocabulary
+CountVectorizerModel cvm = new CountVectorizerModel(new String[]{"a", "b", "c"})
+  .setInputCol("text")
+  .setOutputCol("feature");
+
+cvModel.transform(df).show();
+{% endhighlight %}
+</div>
+</div>
+
 # Feature Transformers
 
 ## Tokenizer

From 499e8e154bdcc9d7b2f685b159e0ddb4eae48fe4 Mon Sep 17 00:00:00 2001
From: Luciano Resende <lresende@apache.org>
Date: Fri, 28 Aug 2015 09:13:21 -0700
Subject: [PATCH 1269/1454] [SPARK-8952] [SPARKR] - Wrap normalizePath calls
 with suppressWarnings

This is based on davies comment on SPARK-8952 which suggests to only call normalizePath() when path starts with '~'

Author: Luciano Resende <lresende@apache.org>

Closes #8343 from lresende/SPARK-8952.
---
 R/pkg/R/SQLContext.R | 4 ++--
 R/pkg/R/sparkR.R     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 110117a18ccbc..1bc6445311473 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -201,7 +201,7 @@ setMethod("toDF", signature(x = "RDD"),
 
 jsonFile <- function(sqlContext, path) {
   # Allow the user to have a more flexible definiton of the text file path
-  path <- normalizePath(path)
+  path <- suppressWarnings(normalizePath(path))
   # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
   sdf <- callJMethod(sqlContext, "jsonFile", path)
@@ -251,7 +251,7 @@ jsonRDD <- function(sqlContext, rdd, schema = NULL, samplingRatio = 1.0) {
 # TODO: Implement saveasParquetFile and write examples for both
 parquetFile <- function(sqlContext, ...) {
   # Allow the user to have a more flexible definiton of the text file path
-  paths <- lapply(list(...), normalizePath)
+  paths <- lapply(list(...), function(x) suppressWarnings(normalizePath(x)))
   sdf <- callJMethod(sqlContext, "parquetFile", paths)
   dataFrame(sdf)
 }
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index e83104f116422..3c57a44db257d 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -160,7 +160,7 @@ sparkR.init <- function(
   })
 
   if (nchar(sparkHome) != 0) {
-    sparkHome <- normalizePath(sparkHome)
+    sparkHome <- suppressWarnings(normalizePath(sparkHome))
   }
 
   sparkEnvirMap <- new.env()

From d3f87dc39480f075170817bbd00142967a938078 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 28 Aug 2015 11:51:42 -0700
Subject: [PATCH 1270/1454] [SPARK-10325] Override hashCode() for public Row

This commit fixes an issue where the public SQL `Row` class did not override `hashCode`, causing it to violate the hashCode() + equals() contract. To fix this, I simply ported the `hashCode` implementation from the 1.4.x version of `Row`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8500 from JoshRosen/SPARK-10325 and squashes the following commits:

51ffea1 [Josh Rosen] Override hashCode() for public Row.
---
 .../src/main/scala/org/apache/spark/sql/Row.scala   | 13 +++++++++++++
 .../test/scala/org/apache/spark/sql/RowSuite.scala  |  9 +++++++++
 2 files changed, 22 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index cfd9cb0e62598..ed2fdf9f2f7cf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql
 
 import scala.collection.JavaConverters._
+import scala.util.hashing.MurmurHash3
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericRow
@@ -410,6 +411,18 @@ trait Row extends Serializable {
     true
   }
 
+  override def hashCode: Int = {
+    // Using Scala's Seq hash code implementation.
+    var n = 0
+    var h = MurmurHash3.seqSeed
+    val len = length
+    while (n < len) {
+      h = MurmurHash3.mix(h, apply(n).##)
+      n += 1
+    }
+    MurmurHash3.finalizeHash(h, n)
+  }
+
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 795d4e983f27e..77ccd6f775e50 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -85,4 +85,13 @@ class RowSuite extends SparkFunSuite with SharedSQLContext {
     val r2 = Row(Double.NaN)
     assert(r1 === r2)
   }
+
+  test("equals and hashCode") {
+    val r1 = Row("Hello")
+    val r2 = Row("Hello")
+    assert(r1 === r2)
+    assert(r1.hashCode() === r2.hashCode())
+    val r3 = Row("World")
+    assert(r3.hashCode() != r1.hashCode())
+  }
 }

From c53c902fa9c458200245f919067b41dde9cd9418 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 28 Aug 2015 12:33:40 -0700
Subject: [PATCH 1271/1454] [SPARK-9284] [TESTS] Allow all tests to run without
 an assembly.

This change aims at speeding up the dev cycle a little bit, by making
sure that all tests behave the same w.r.t. where the code to be tested
is loaded from. Namely, that means that tests don't rely on the assembly
anymore, rather loading all needed classes from the build directories.

The main change is to make sure all build directories (classes and test-classes)
are added to the classpath of child processes when running tests.

YarnClusterSuite required some custom code since the executors are run
differently (i.e. not through the launcher library, like standalone and
Mesos do).

I also found a couple of tests that could leak a SparkContext on failure,
and added code to handle those.

With this patch, it's possible to run the following command from a clean
source directory and have all tests pass:

  mvn -Pyarn -Phadoop-2.4 -Phive-thriftserver install

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7629 from vanzin/SPARK-9284.
---
 bin/spark-class                               | 16 ++++----
 .../spark/launcher/SparkLauncherSuite.java    |  0
 .../spark/broadcast/BroadcastSuite.scala      | 10 ++++-
 .../launcher/AbstractCommandBuilder.java      | 28 ++++++++------
 pom.xml                                       |  8 ++++
 project/SparkBuild.scala                      |  2 +
 .../deploy/yarn/BaseYarnClusterSuite.scala    | 37 +++++++++++++------
 .../spark/deploy/yarn/YarnClusterSuite.scala  | 20 ++++++++--
 .../yarn/YarnShuffleIntegrationSuite.scala    |  2 +-
 .../spark/launcher/TestClasspathBuilder.scala | 36 ++++++++++++++++++
 10 files changed, 122 insertions(+), 37 deletions(-)
 rename {launcher => core}/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java (100%)
 create mode 100644 yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala

diff --git a/bin/spark-class b/bin/spark-class
index 2b59e5df5736f..e38e08dec40e4 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -43,17 +43,19 @@ else
 fi
 
 num_jars="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" | wc -l)"
-if [ "$num_jars" -eq "0" -a -z "$SPARK_ASSEMBLY_JAR" ]; then
+if [ "$num_jars" -eq "0" -a -z "$SPARK_ASSEMBLY_JAR" -a "$SPARK_PREPEND_CLASSES" != "1" ]; then
   echo "Failed to find Spark assembly in $ASSEMBLY_DIR." 1>&2
   echo "You need to build Spark before running this program." 1>&2
   exit 1
 fi
-ASSEMBLY_JARS="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" || true)"
-if [ "$num_jars" -gt "1" ]; then
-  echo "Found multiple Spark assembly jars in $ASSEMBLY_DIR:" 1>&2
-  echo "$ASSEMBLY_JARS" 1>&2
-  echo "Please remove all but one jar." 1>&2
-  exit 1
+if [ -d "$ASSEMBLY_DIR" ]; then
+  ASSEMBLY_JARS="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" || true)"
+  if [ "$num_jars" -gt "1" ]; then
+    echo "Found multiple Spark assembly jars in $ASSEMBLY_DIR:" 1>&2
+    echo "$ASSEMBLY_JARS" 1>&2
+    echo "Please remove all but one jar." 1>&2
+    exit 1
+  fi
 fi
 
 SPARK_ASSEMBLY_JAR="${ASSEMBLY_DIR}/${ASSEMBLY_JARS}"
diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java b/core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
similarity index 100%
rename from launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
rename to core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
index 48e74f06f79b1..fb7a8ae3f9d41 100644
--- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
+++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
@@ -310,8 +310,14 @@ class BroadcastSuite extends SparkFunSuite with LocalSparkContext {
       val _sc =
         new SparkContext("local-cluster[%d, 1, 1024]".format(numSlaves), "test", broadcastConf)
       // Wait until all salves are up
-      _sc.jobProgressListener.waitUntilExecutorsUp(numSlaves, 10000)
-      _sc
+      try {
+        _sc.jobProgressListener.waitUntilExecutorsUp(numSlaves, 10000)
+        _sc
+      } catch {
+        case e: Throwable =>
+          _sc.stop()
+          throw e
+      }
     } else {
       new SparkContext("local", "test", broadcastConf)
     }
diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
index 5e793a5c48775..0a237ee73b670 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
@@ -169,9 +169,11 @@ List<String> buildClassPath(String appClassPath) throws IOException {
         "streaming", "tools", "sql/catalyst", "sql/core", "sql/hive", "sql/hive-thriftserver",
         "yarn", "launcher");
       if (prependClasses) {
-        System.err.println(
-          "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark classes ahead of " +
-          "assembly.");
+        if (!isTesting) {
+          System.err.println(
+            "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark classes ahead of " +
+            "assembly.");
+        }
         for (String project : projects) {
           addToClassPath(cp, String.format("%s/%s/target/scala-%s/classes", sparkHome, project,
             scala));
@@ -200,7 +202,7 @@ List<String> buildClassPath(String appClassPath) throws IOException {
     // For the user code case, we fall back to looking for the Spark assembly under SPARK_HOME.
     // That duplicates some of the code in the shell scripts that look for the assembly, though.
     String assembly = getenv(ENV_SPARK_ASSEMBLY);
-    if (assembly == null && isEmpty(getenv("SPARK_TESTING"))) {
+    if (assembly == null && !isTesting) {
       assembly = findAssembly();
     }
     addToClassPath(cp, assembly);
@@ -215,12 +217,14 @@ List<String> buildClassPath(String appClassPath) throws IOException {
       libdir = new File(sparkHome, "lib_managed/jars");
     }
 
-    checkState(libdir.isDirectory(), "Library directory '%s' does not exist.",
-      libdir.getAbsolutePath());
-    for (File jar : libdir.listFiles()) {
-      if (jar.getName().startsWith("datanucleus-")) {
-        addToClassPath(cp, jar.getAbsolutePath());
+    if (libdir.isDirectory()) {
+      for (File jar : libdir.listFiles()) {
+        if (jar.getName().startsWith("datanucleus-")) {
+          addToClassPath(cp, jar.getAbsolutePath());
+        }
       }
+    } else {
+      checkState(isTesting, "Library directory '%s' does not exist.", libdir.getAbsolutePath());
     }
 
     addToClassPath(cp, getenv("HADOOP_CONF_DIR"));
@@ -256,15 +260,15 @@ String getScalaVersion() {
       return scala;
     }
     String sparkHome = getSparkHome();
-    File scala210 = new File(sparkHome, "assembly/target/scala-2.10");
-    File scala211 = new File(sparkHome, "assembly/target/scala-2.11");
+    File scala210 = new File(sparkHome, "launcher/target/scala-2.10");
+    File scala211 = new File(sparkHome, "launcher/target/scala-2.11");
     checkState(!scala210.isDirectory() || !scala211.isDirectory(),
       "Presence of build for both scala versions (2.10 and 2.11) detected.\n" +
       "Either clean one of them or set SPARK_SCALA_VERSION in your environment.");
     if (scala210.isDirectory()) {
       return "2.10";
     } else {
-      checkState(scala211.isDirectory(), "Cannot find any assembly build directories.");
+      checkState(scala211.isDirectory(), "Cannot find any build directories.");
       return "2.11";
     }
   }
diff --git a/pom.xml b/pom.xml
index 0716016523ee1..88ebceca769e9 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1421,6 +1421,10 @@
             <groupId>org.apache.thrift</groupId>
             <artifactId>libthrift</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>com.google.guava</groupId>
             <artifactId>guava</artifactId>
@@ -1892,6 +1896,8 @@
                 launched by the tests have access to the correct test-time classpath.
               -->
               <SPARK_DIST_CLASSPATH>${test_classpath}</SPARK_DIST_CLASSPATH>
+              <SPARK_PREPEND_CLASSES>1</SPARK_PREPEND_CLASSES>
+              <SPARK_TESTING>1</SPARK_TESTING>
               <JAVA_HOME>${test.java.home}</JAVA_HOME>
             </environmentVariables>
             <systemProperties>
@@ -1929,6 +1935,8 @@
                 launched by the tests have access to the correct test-time classpath.
               -->
               <SPARK_DIST_CLASSPATH>${test_classpath}</SPARK_DIST_CLASSPATH>
+              <SPARK_PREPEND_CLASSES>1</SPARK_PREPEND_CLASSES>
+              <SPARK_TESTING>1</SPARK_TESTING>
               <JAVA_HOME>${test.java.home}</JAVA_HOME>
             </environmentVariables>
             <systemProperties>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index ea52bfd67944a..901cfa538d23e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -547,6 +547,8 @@ object TestSettings {
     envVars in Test ++= Map(
       "SPARK_DIST_CLASSPATH" ->
         (fullClasspath in Test).value.files.map(_.getAbsolutePath).mkString(":").stripSuffix(":"),
+      "SPARK_PREPEND_CLASSES" -> "1",
+      "SPARK_TESTING" -> "1",
       "JAVA_HOME" -> sys.env.get("JAVA_HOME").getOrElse(sys.props("java.home"))),
     javaOptions in Test += s"-Djava.io.tmpdir=$testTempDir",
     javaOptions in Test += "-Dspark.test.home=" + sparkHome,
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
index b4f8049bff577..17c59ff06e0c1 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
@@ -30,6 +30,7 @@ import org.apache.hadoop.yarn.server.MiniYARNCluster
 import org.scalatest.{BeforeAndAfterAll, Matchers}
 
 import org.apache.spark._
+import org.apache.spark.launcher.TestClasspathBuilder
 import org.apache.spark.util.Utils
 
 abstract class BaseYarnClusterSuite
@@ -43,6 +44,9 @@ abstract class BaseYarnClusterSuite
     |log4j.appender.console.target=System.err
     |log4j.appender.console.layout=org.apache.log4j.PatternLayout
     |log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
+    |log4j.logger.org.apache.hadoop=WARN
+    |log4j.logger.org.eclipse.jetty=WARN
+    |log4j.logger.org.spark-project.jetty=WARN
     """.stripMargin
 
   private var yarnCluster: MiniYARNCluster = _
@@ -51,8 +55,7 @@ abstract class BaseYarnClusterSuite
   private var hadoopConfDir: File = _
   private var logConfDir: File = _
 
-
-  def yarnConfig: YarnConfiguration
+  def newYarnConfig(): YarnConfiguration
 
   override def beforeAll() {
     super.beforeAll()
@@ -65,8 +68,14 @@ abstract class BaseYarnClusterSuite
     val logConfFile = new File(logConfDir, "log4j.properties")
     Files.write(LOG4J_CONF, logConfFile, UTF_8)
 
+    // Disable the disk utilization check to avoid the test hanging when people's disks are
+    // getting full.
+    val yarnConf = newYarnConfig()
+    yarnConf.set("yarn.nodemanager.disk-health-checker.max-disk-utilization-per-disk-percentage",
+      "100.0")
+
     yarnCluster = new MiniYARNCluster(getClass().getName(), 1, 1, 1)
-    yarnCluster.init(yarnConfig)
+    yarnCluster.init(yarnConf)
     yarnCluster.start()
 
     // There's a race in MiniYARNCluster in which start() may return before the RM has updated
@@ -114,19 +123,23 @@ abstract class BaseYarnClusterSuite
       sparkArgs: Seq[String] = Nil,
       extraClassPath: Seq[String] = Nil,
       extraJars: Seq[String] = Nil,
-      extraConf: Map[String, String] = Map()): Unit = {
+      extraConf: Map[String, String] = Map(),
+      extraEnv: Map[String, String] = Map()): Unit = {
     val master = if (clientMode) "yarn-client" else "yarn-cluster"
     val props = new Properties()
 
     props.setProperty("spark.yarn.jar", "local:" + fakeSparkJar.getAbsolutePath())
 
-    val childClasspath = logConfDir.getAbsolutePath() +
-      File.pathSeparator +
-      sys.props("java.class.path") +
-      File.pathSeparator +
-      extraClassPath.mkString(File.pathSeparator)
-    props.setProperty("spark.driver.extraClassPath", childClasspath)
-    props.setProperty("spark.executor.extraClassPath", childClasspath)
+    val testClasspath = new TestClasspathBuilder()
+      .buildClassPath(
+        logConfDir.getAbsolutePath() +
+        File.pathSeparator +
+        extraClassPath.mkString(File.pathSeparator))
+      .asScala
+      .mkString(File.pathSeparator)
+
+    props.setProperty("spark.driver.extraClassPath", testClasspath)
+    props.setProperty("spark.executor.extraClassPath", testClasspath)
 
     // SPARK-4267: make sure java options are propagated correctly.
     props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
@@ -168,7 +181,7 @@ abstract class BaseYarnClusterSuite
       appArgs
 
     Utils.executeAndGetOutput(argv,
-      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()))
+      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()) ++ extraEnv)
   }
 
   /**
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 5a4ea2ea2f4ff..b5a42fd6afd98 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -28,7 +28,9 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.scalatest.Matchers
 
 import org.apache.spark._
-import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart, SparkListenerExecutorAdded}
+import org.apache.spark.launcher.TestClasspathBuilder
+import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart,
+  SparkListenerExecutorAdded}
 import org.apache.spark.scheduler.cluster.ExecutorInfo
 import org.apache.spark.util.Utils
 
@@ -39,7 +41,7 @@ import org.apache.spark.util.Utils
  */
 class YarnClusterSuite extends BaseYarnClusterSuite {
 
-  override def yarnConfig: YarnConfiguration = new YarnConfiguration()
+  override def newYarnConfig(): YarnConfiguration = new YarnConfiguration()
 
   private val TEST_PYFILE = """
     |import mod1, mod2
@@ -111,6 +113,17 @@ class YarnClusterSuite extends BaseYarnClusterSuite {
     val primaryPyFile = new File(tempDir, "test.py")
     Files.write(TEST_PYFILE, primaryPyFile, UTF_8)
 
+    // When running tests, let's not assume the user has built the assembly module, which also
+    // creates the pyspark archive. Instead, let's use PYSPARK_ARCHIVES_PATH to point at the
+    // needed locations.
+    val sparkHome = sys.props("spark.test.home");
+    val pythonPath = Seq(
+        s"$sparkHome/python/lib/py4j-0.8.2.1-src.zip",
+        s"$sparkHome/python")
+    val extraEnv = Map(
+      "PYSPARK_ARCHIVES_PATH" -> pythonPath.map("local:" + _).mkString(File.pathSeparator),
+      "PYTHONPATH" -> pythonPath.mkString(File.pathSeparator))
+
     val moduleDir =
       if (clientMode) {
         // In client-mode, .py files added with --py-files are not visible in the driver.
@@ -130,7 +143,8 @@ class YarnClusterSuite extends BaseYarnClusterSuite {
 
     runSpark(clientMode, primaryPyFile.getAbsolutePath(),
       sparkArgs = Seq("--py-files", pyFiles),
-      appArgs = Seq(result.getAbsolutePath()))
+      appArgs = Seq(result.getAbsolutePath()),
+      extraEnv = extraEnv)
     checkResult(result)
   }
 
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
index 5e8238822b90a..8d9c9b3004eda 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
@@ -34,7 +34,7 @@ import org.apache.spark.network.yarn.{YarnShuffleService, YarnTestAccessor}
  */
 class YarnShuffleIntegrationSuite extends BaseYarnClusterSuite {
 
-  override def yarnConfig: YarnConfiguration = {
+  override def newYarnConfig(): YarnConfiguration = {
     val yarnConfig = new YarnConfiguration()
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
diff --git a/yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala b/yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala
new file mode 100644
index 0000000000000..da9e8e21a26ae
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.launcher
+
+import java.util.{List => JList, Map => JMap}
+
+/**
+ * Exposes AbstractCommandBuilder to the YARN tests, so that they can build classpaths the same
+ * way other cluster managers do.
+ */
+private[spark] class TestClasspathBuilder extends AbstractCommandBuilder {
+
+  childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, sys.props("spark.test.home"))
+
+  override def buildClassPath(extraCp: String): JList[String] = super.buildClassPath(extraCp)
+
+  /** Not used by the YARN tests. */
+  override def buildCommand(env: JMap[String, String]): JList[String] =
+    throw new UnsupportedOperationException()
+
+}

From 45723214e694b9a440723e9504c562e6393709f3 Mon Sep 17 00:00:00 2001
From: Shuo Xiang <sxiang@pinterest.com>
Date: Fri, 28 Aug 2015 13:09:13 -0700
Subject: [PATCH 1272/1454] [SPARK-10336][example] fix not being able to set
 intercept in LR example

`fitIntercept` is a command line option but not set in the main program.

dbtsai

Author: Shuo Xiang <sxiang@pinterest.com>

Closes #8510 from coderxiang/intercept and squashes the following commits:

57c9b7d [Shuo Xiang] fix not being able to set intercept in LR example
---
 .../org/apache/spark/examples/ml/LogisticRegressionExample.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
index 7682557127b51..8e3760ddb50a9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
@@ -136,6 +136,7 @@ object LogisticRegressionExample {
       .setElasticNetParam(params.elasticNetParam)
       .setMaxIter(params.maxIter)
       .setTol(params.tol)
+      .setFitIntercept(params.fitIntercept)
 
     stages += lor
     val pipeline = new Pipeline().setStages(stages.toArray)

From 88032ecaf0455886aed7a66b30af80dae7f6cff7 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 28 Aug 2015 13:53:31 -0700
Subject: [PATCH 1273/1454] [SPARK-9671] [MLLIB] re-org user guide and add
 migration guide

This PR updates the MLlib user guide and adds migration guide for 1.4->1.5.

* merge migration guide for `spark.mllib` and `spark.ml` packages
* remove dependency section from `spark.ml` guide
* move the paragraph about `spark.mllib` and `spark.ml` to the top and recommend `spark.ml`
* move Sam's talk to footnote to make the section focus on dependencies

Minor changes to code examples and other wording will be in a separate PR.

jkbradley srowen feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8498 from mengxr/SPARK-9671.
---
 docs/ml-guide.md               |  52 ++------------
 docs/mllib-guide.md            | 119 ++++++++++++++++-----------------
 docs/mllib-migration-guides.md |  30 +++++++++
 3 files changed, 95 insertions(+), 106 deletions(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 01bf5ee18e328..ce53400b6ee56 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -21,19 +21,11 @@ title: Spark ML Programming Guide
 \]`
 
 
-Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of
-high-level APIs that help users create and tune practical machine learning pipelines.
-
-*Graduated from Alpha!*  The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`.
-
-Note that we will keep supporting and adding features to `spark.mllib` along with the
-development of `spark.ml`.
-Users should be comfortable using `spark.mllib` features and expect more features coming.
-Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
-to `spark.ml`.
-
-See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of `spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
-
+The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
+[DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
+machine learning pipelines.
+See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of
+`spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
 
 **Table of Contents**
 
@@ -171,7 +163,7 @@ This is useful if there are two algorithms with the `maxIter` parameter in a `Pi
 
 # Algorithm Guides
 
-There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
+There are now several algorithms in the Pipelines API which are not in the `spark.mllib` API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
 
 **Pipelines API Algorithm Guides**
 
@@ -880,35 +872,3 @@ jsc.stop();
 </div>
 
 </div>
-
-# Dependencies
-
-Spark ML currently depends on MLlib and has the same dependencies.
-Please see the [MLlib Dependencies guide](mllib-guide.html#dependencies) for more info.
-
-Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not bring additional dependencies.
-
-# Migration Guide
-
-## From 1.3 to 1.4
-
-Several major API changes occurred, including:
-* `Param` and other APIs for specifying parameters
-* `uid` unique IDs for Pipeline components
-* Reorganization of certain classes
-Since the `spark.ml` API was an Alpha Component in Spark 1.3, we do not list all changes here.
-
-However, now that `spark.ml` is no longer an Alpha Component, we will provide details on any API changes for future releases.
-
-## From 1.2 to 1.3
-
-The main API changes are from Spark SQL.  We list the most important changes here:
-
-* The old [SchemaRDD](http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD) has been replaced with [DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a somewhat modified API.  All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.
-* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint` into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an instance of `SQLContext`.  These implicits have been moved, so we now call `import sqlContext.implicits._`.
-* Java APIs for SQL have also changed accordingly.  Please see the examples above and the [Spark SQL Programming Guide](sql-programming-guide.html) for details.
-
-Other changes were in `LogisticRegression`:
-
-* The `scoreCol` output column (with default value "score") was renamed to be `probabilityCol` (with default value "probability").  The type was originally `Double` (for the probability of class 1.0), but it is now `Vector` (for the probability of each class, to support multiclass classification in the future).
-* In Spark 1.2, `LogisticRegressionModel` did not include an intercept.  In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for [spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS).  The option to use an intercept will be added in the future.
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 6330c977552d1..876dcfd40ed7b 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -5,21 +5,28 @@ displayTitle: Machine Learning Library (MLlib) Guide
 description: MLlib machine learning library overview for Spark SPARK_VERSION_SHORT
 ---
 
-MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities,
-including classification, regression, clustering, collaborative
-filtering, dimensionality reduction, as well as underlying optimization primitives.
-Guides for individual algorithms are listed below.
+MLlib is Spark's machine learning (ML) library.
+Its goal is to make practical machine learning scalable and easy.
+It consists of common learning algorithms and utilities, including classification, regression,
+clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization
+primitives and higher-level pipeline APIs.
 
-The API is divided into 2 parts:
+It divides into two packages:
 
-* [The original `spark.mllib` API](mllib-guide.html#mllib-types-algorithms-and-utilities) is the primary API.
-* [The "Pipelines" `spark.ml` API](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) is a higher-level API for constructing ML workflows.
+* [`spark.mllib`](mllib-guide.html#mllib-types-algorithms-and-utilities) contains the original API
+  built on top of RDDs.
+* [`spark.ml`](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) provides higher-level API
+  built on top of DataFrames for constructing ML pipelines.
 
-We list major functionality from both below, with links to detailed guides.
+Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.
+But we will keep supporting `spark.mllib` along with the development of `spark.ml`.
+Users should be comfortable using `spark.mllib` features and expect more features coming.
+Developers should contribute new algorithms to `spark.ml` if they fit the ML pipeline concept well,
+e.g., feature extractors and transformers.
 
-# MLlib types, algorithms and utilities
+We list major functionality from both below, with links to detailed guides.
 
-This lists functionality included in `spark.mllib`, the main MLlib API.
+# spark.mllib: data types, algorithms, and utilities
 
 * [Data types](mllib-data-types.html)
 * [Basic statistics](mllib-statistics.html)
@@ -56,71 +63,63 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
   * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
 * [PMML model export](mllib-pmml-model-export.html)
 
-MLlib is under active development.
-The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
-and the migration guide below will explain all changes between releases.
-
 # spark.ml: high-level APIs for ML pipelines
 
-Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of
-high-level APIs that help users create and tune practical machine learning pipelines.
-
-*Graduated from Alpha!*  The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`.
-
-Note that we will keep supporting and adding features to `spark.mllib` along with the
-development of `spark.ml`.
-Users should be comfortable using `spark.mllib` features and expect more features coming.
-Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
-to `spark.ml`.
-
-Guides for `spark.ml` include:
+**[spark.ml programming guide](ml-guide.html)** provides an overview of the Pipelines API and major
+concepts. It also contains sections on using algorithms within the Pipelines API, for example:
 
-* **[spark.ml programming guide](ml-guide.html)**: overview of the Pipelines API and major concepts
-* Guides on using algorithms within the Pipelines API:
-  * [Feature transformers](ml-features.html), including a few not in the lower-level `spark.mllib` API
-  * [Decision trees](ml-decision-tree.html)
-  * [Ensembles](ml-ensembles.html)
-  * [Linear methods](ml-linear-methods.html)
+* [Feature Extraction, Transformation, and Selection](ml-features.html)
+* [Decision Trees for Classification and Regression](ml-decision-tree.html)
+* [Ensembles](ml-ensembles.html)
+* [Linear methods with elastic net regularization](ml-linear-methods.html)
+* [Multilayer perceptron classifier](ml-ann.html)
 
 # Dependencies
 
-MLlib uses the linear algebra package
-[Breeze](http://www.scalanlp.org/), which depends on
-[netlib-java](https://github.com/fommil/netlib-java) for optimised
-numerical processing. If natives are not available at runtime, you
-will see a warning message and a pure JVM implementation will be used
-instead.
+MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), which depends on
+[netlib-java](https://github.com/fommil/netlib-java) for optimised numerical processing.
+If natives libraries[^1] are not available at runtime, you will see a warning message and a pure JVM
+implementation will be used instead.
 
-To learn more about the benefits and background of system optimised
-natives, you may wish to watch Sam Halliday's ScalaX talk on
-[High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/)).
+Due to licensing issues with runtime proprietary binaries, we do not include `netlib-java`'s native
+proxies by default.
+To configure `netlib-java` / Breeze to use system optimised binaries, include
+`com.github.fommil.netlib:all:1.1.2` (or build Spark with `-Pnetlib-lgpl`) as a dependency of your
+project and read the [netlib-java](https://github.com/fommil/netlib-java) documentation for your
+platform's additional installation instructions.
 
-Due to licensing issues with runtime proprietary binaries, we do not
-include `netlib-java`'s native proxies by default. To configure
-`netlib-java` / Breeze to use system optimised binaries, include
-`com.github.fommil.netlib:all:1.1.2` (or build Spark with
-`-Pnetlib-lgpl`) as a dependency of your project and read the
-[netlib-java](https://github.com/fommil/netlib-java) documentation for
-your platform's additional installation instructions.
+To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.
 
-To use MLlib in Python, you will need [NumPy](http://www.numpy.org)
-version 1.4 or newer.
+[^1]: To learn more about the benefits and background of system optimised natives, you may wish to
+    watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/).
 
----
+# Migration guide
 
-# Migration Guide
+MLlib is under active development.
+The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
+and the migration guide below will explain all changes between releases.
+
+## From 1.4 to 1.5
 
-For the `spark.ml` package, please see the [spark.ml Migration Guide](ml-guide.html#migration-guide).
+In the `spark.mllib` package, there are no break API changes but several behavior changes:
 
-## From 1.3 to 1.4
+* [SPARK-9005](https://issues.apache.org/jira/browse/SPARK-9005):
+  `RegressionMetrics.explainedVariance` returns the average regression sum of squares.
+* [SPARK-8600](https://issues.apache.org/jira/browse/SPARK-8600): `NaiveBayesModel.labels` become
+  sorted.
+* [SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382): `GradientDescent` has a default
+  convergence tolerance `1e-3`, and hence iterations might end earlier than 1.4.
 
-In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs:
+In the `spark.ml` package, there exists one break API change and one behavior change:
 
-* Gradient-Boosted Trees
-    * *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed.  This is only an issues for users who wrote their own losses for GBTs.
-    * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields.  This could be an issue for users who use `BoostingStrategy` to set GBT parameters.
-* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed.  It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`.  The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm.
+* [SPARK-9268](https://issues.apache.org/jira/browse/SPARK-9268): Java's varargs support is removed
+  from `Params.setDefault` due to a
+  [Scala compiler bug](https://issues.scala-lang.org/browse/SI-9013).
+* [SPARK-10097](https://issues.apache.org/jira/browse/SPARK-10097): `Evaluator.isLargerBetter` is
+  added to indicate metric ordering. Metrics like RMSE no longer flip signs as in 1.4.
 
-## Previous Spark Versions
+## Previous Spark versions
 
 Earlier migration guides are archived [on this page](mllib-migration-guides.html).
+
+---
diff --git a/docs/mllib-migration-guides.md b/docs/mllib-migration-guides.md
index 8df68d81f3c78..774b85d1f773a 100644
--- a/docs/mllib-migration-guides.md
+++ b/docs/mllib-migration-guides.md
@@ -7,6 +7,25 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT
 
 The migration guide for the current Spark version is kept on the [MLlib Programming Guide main page](mllib-guide.html#migration-guide).
 
+## From 1.3 to 1.4
+
+In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs:
+
+* Gradient-Boosted Trees
+    * *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed.  This is only an issues for users who wrote their own losses for GBTs.
+    * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields.  This could be an issue for users who use `BoostingStrategy` to set GBT parameters.
+* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed.  It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`.  The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm.
+
+In the `spark.ml` package, several major API changes occurred, including:
+
+* `Param` and other APIs for specifying parameters
+* `uid` unique IDs for Pipeline components
+* Reorganization of certain classes
+
+Since the `spark.ml` API was an alpha component in Spark 1.3, we do not list all changes here.
+However, since 1.4 `spark.ml` is no longer an alpha component, we will provide details on any API
+changes for future releases.
+
 ## From 1.2 to 1.3
 
 In the `spark.mllib` package, there were several breaking changes.  The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental.
@@ -23,6 +42,17 @@ In the `spark.mllib` package, there were several breaking changes.  The first ch
 * In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2.
   So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2.
 
+In the `spark.ml` package, the main API changes are from Spark SQL.  We list the most important changes here:
+
+* The old [SchemaRDD](http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD) has been replaced with [DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a somewhat modified API.  All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.
+* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint` into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an instance of `SQLContext`.  These implicits have been moved, so we now call `import sqlContext.implicits._`.
+* Java APIs for SQL have also changed accordingly.  Please see the examples above and the [Spark SQL Programming Guide](sql-programming-guide.html) for details.
+
+Other changes were in `LogisticRegression`:
+
+* The `scoreCol` output column (with default value "score") was renamed to be `probabilityCol` (with default value "probability").  The type was originally `Double` (for the probability of class 1.0), but it is now `Vector` (for the probability of each class, to support multiclass classification in the future).
+* In Spark 1.2, `LogisticRegressionModel` did not include an intercept.  In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for [spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS).  The option to use an intercept will be added in the future.
+
 ## From 1.1 to 1.2
 
 The only API changes in MLlib v1.2 are in

From bb7f35239385ec74b5ee69631b5480fbcee253e4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 28 Aug 2015 14:38:20 -0700
Subject: [PATCH 1274/1454] [SPARK-10323] [SQL] fix nullability of
 In/InSet/ArrayContain

After this PR, In/InSet/ArrayContain will return null if value is null, instead of false. They also will return null even if there is a null in the set/array.

Author: Davies Liu <davies@databricks.com>

Closes #8492 from davies/fix_in.
---
 .../expressions/collectionOperations.scala    | 62 ++++++++--------
 .../sql/catalyst/expressions/predicates.scala | 71 +++++++++++++++----
 .../sql/catalyst/optimizer/Optimizer.scala    |  6 --
 .../CollectionFunctionsSuite.scala            | 12 +++-
 .../catalyst/expressions/PredicateSuite.scala | 49 +++++++++----
 .../optimizer/ConstantFoldingSuite.scala      | 21 +-----
 .../spark/sql/DataFrameFunctionsSuite.scala   | 14 ++--
 7 files changed, 138 insertions(+), 97 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 646afa4047d84..7b8c5b723ded4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -19,9 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 import java.util.Comparator
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{
-  CodegenFallback, CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, CodegenFallback, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 
 /**
@@ -145,46 +143,42 @@ case class ArrayContains(left: Expression, right: Expression)
     }
   }
 
-  override def nullable: Boolean = false
+  override def nullable: Boolean = {
+    left.nullable || right.nullable || left.dataType.asInstanceOf[ArrayType].containsNull
+  }
 
-  override def eval(input: InternalRow): Boolean = {
-    val arr = left.eval(input)
-    if (arr == null) {
-      false
-    } else {
-      val value = right.eval(input)
-      if (value == null) {
-        false
-      } else {
-        arr.asInstanceOf[ArrayData].foreach(right.dataType, (i, v) =>
-          if (v == value) return true
-        )
-        false
+  override def nullSafeEval(arr: Any, value: Any): Any = {
+    var hasNull = false
+    arr.asInstanceOf[ArrayData].foreach(right.dataType, (i, v) =>
+      if (v == null) {
+        hasNull = true
+      } else if (v == value) {
+        return true
       }
+    )
+    if (hasNull) {
+      null
+    } else {
+      false
     }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val arrGen = left.gen(ctx)
-    val elementGen = right.gen(ctx)
-    val i = ctx.freshName("i")
-    val getValue = ctx.getValue(arrGen.primitive, right.dataType, i)
-    s"""
-      ${arrGen.code}
-      boolean ${ev.isNull} = false;
-      boolean ${ev.primitive} = false;
-      if (!${arrGen.isNull}) {
-        ${elementGen.code}
-        if (!${elementGen.isNull}) {
-          for (int $i = 0; $i < ${arrGen.primitive}.numElements(); $i ++) {
-            if (${ctx.genEqual(right.dataType, elementGen.primitive, getValue)}) {
-              ${ev.primitive} = true;
-              break;
-            }
-          }
+    nullSafeCodeGen(ctx, ev, (arr, value) => {
+      val i = ctx.freshName("i")
+      val getValue = ctx.getValue(arr, right.dataType, i)
+      s"""
+      for (int $i = 0; $i < $arr.numElements(); $i ++) {
+        if ($arr.isNullAt($i)) {
+          ${ev.isNull} = true;
+        } else if (${ctx.genEqual(right.dataType, value, getValue)}) {
+          ${ev.isNull} = false;
+          ${ev.primitive} = true;
+          break;
         }
       }
      """
+    })
   }
 
   override def prettyName: String = "array_contains"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index fe7dffb815987..65706dba7d975 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -17,11 +17,9 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import scala.collection.mutable
-
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
@@ -103,6 +101,8 @@ case class Not(child: Expression)
 case class In(value: Expression, list: Seq[Expression]) extends Predicate
     with ImplicitCastInputTypes {
 
+  require(list != null, "list should not be null")
+
   override def inputTypes: Seq[AbstractDataType] = value.dataType +: list.map(_.dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
@@ -116,12 +116,31 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
 
   override def children: Seq[Expression] = value +: list
 
-  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
+  override def nullable: Boolean = children.exists(_.nullable)
+  override def foldable: Boolean = children.forall(_.foldable)
+
   override def toString: String = s"$value IN ${list.mkString("(", ",", ")")}"
 
   override def eval(input: InternalRow): Any = {
     val evaluatedValue = value.eval(input)
-    list.exists(e => e.eval(input) == evaluatedValue)
+    if (evaluatedValue == null) {
+      null
+    } else {
+      var hasNull = false
+      list.foreach { e =>
+        val v = e.eval(input)
+        if (v == evaluatedValue) {
+          return true
+        } else if (v == null) {
+          hasNull = true
+        }
+      }
+      if (hasNull) {
+        null
+      } else {
+        false
+      }
+    }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -131,7 +150,10 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
       s"""
         if (!${ev.primitive}) {
           ${x.code}
-          if (${ctx.genEqual(value.dataType, valueGen.primitive, x.primitive)}) {
+          if (${x.isNull}) {
+            ${ev.isNull} = true;
+          } else if (${ctx.genEqual(value.dataType, valueGen.primitive, x.primitive)}) {
+            ${ev.isNull} = false;
             ${ev.primitive} = true;
           }
         }
@@ -139,8 +161,10 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
     s"""
       ${valueGen.code}
       boolean ${ev.primitive} = false;
-      boolean ${ev.isNull} = false;
-      $listCode
+      boolean ${ev.isNull} = ${valueGen.isNull};
+      if (!${ev.isNull}) {
+        $listCode
+      }
     """
   }
 }
@@ -151,11 +175,22 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
  */
 case class InSet(child: Expression, hset: Set[Any]) extends UnaryExpression with Predicate {
 
-  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
+  require(hset != null, "hset could not be null")
+
   override def toString: String = s"$child INSET ${hset.mkString("(", ",", ")")}"
 
-  override def eval(input: InternalRow): Any = {
-    hset.contains(child.eval(input))
+  @transient private[this] lazy val hasNull: Boolean = hset.contains(null)
+
+  override def nullable: Boolean = child.nullable || hasNull
+
+  protected override def nullSafeEval(value: Any): Any = {
+    if (hset.contains(value)) {
+      true
+    } else if (hasNull) {
+      null
+    } else {
+      false
+    }
   }
 
   def getHSet(): Set[Any] = hset
@@ -166,12 +201,20 @@ case class InSet(child: Expression, hset: Set[Any]) extends UnaryExpression with
     val childGen = child.gen(ctx)
     ctx.references += this
     val hsetTerm = ctx.freshName("hset")
+    val hasNullTerm = ctx.freshName("hasNull")
     ctx.addMutableState(setName, hsetTerm,
       s"$hsetTerm = (($InSetName)expressions[${ctx.references.size - 1}]).getHSet();")
+    ctx.addMutableState("boolean", hasNullTerm, s"$hasNullTerm = $hsetTerm.contains(null);")
     s"""
       ${childGen.code}
-      boolean ${ev.isNull} = false;
-      boolean ${ev.primitive} = $hsetTerm.contains(${childGen.primitive});
+      boolean ${ev.isNull} = ${childGen.isNull};
+      boolean ${ev.primitive} = false;
+      if (!${ev.isNull}) {
+        ${ev.primitive} = $hsetTerm.contains(${childGen.primitive});
+        if (!${ev.primitive} && $hasNullTerm) {
+          ${ev.isNull} = true;
+        }
+      }
      """
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 854463dd11c74..a430000bef653 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -395,12 +395,6 @@ object ConstantFolding extends Rule[LogicalPlan] {
 
       // Fold expressions that are foldable.
       case e if e.foldable => Literal.create(e.eval(EmptyRow), e.dataType)
-
-      // Fold "literal in (item1, item2, ..., literal, ...)" into true directly.
-      case In(Literal(v, _), list) if list.exists {
-          case Literal(candidate, _) if candidate == v => true
-          case _ => false
-        } => Literal.create(true, BooleanType)
     }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
index 95f0e38212a1a..a3e81888dfd0d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -70,14 +70,20 @@ class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val a0 = Literal.create(Seq(1, 2, 3), ArrayType(IntegerType))
     val a1 = Literal.create(Seq[String](null, ""), ArrayType(StringType))
     val a2 = Literal.create(Seq(null), ArrayType(LongType))
+    val a3 = Literal.create(null, ArrayType(StringType))
 
     checkEvaluation(ArrayContains(a0, Literal(1)), true)
     checkEvaluation(ArrayContains(a0, Literal(0)), false)
-    checkEvaluation(ArrayContains(a0, Literal(null)), false)
+    checkEvaluation(ArrayContains(a0, Literal.create(null, IntegerType)), null)
 
     checkEvaluation(ArrayContains(a1, Literal("")), true)
-    checkEvaluation(ArrayContains(a1, Literal(null)), false)
+    checkEvaluation(ArrayContains(a1, Literal("a")), null)
+    checkEvaluation(ArrayContains(a1, Literal.create(null, StringType)), null)
 
-    checkEvaluation(ArrayContains(a2, Literal(null)), false)
+    checkEvaluation(ArrayContains(a2, Literal(1L)), null)
+    checkEvaluation(ArrayContains(a2, Literal.create(null, LongType)), null)
+
+    checkEvaluation(ArrayContains(a3, Literal("")), null)
+    checkEvaluation(ArrayContains(a3, Literal.create(null, StringType)), null)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 54c04faddb477..03e7611fce8ff 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import scala.collection.immutable.HashSet
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.RandomDataGenerator
 import org.apache.spark.sql.types._
 
@@ -119,6 +118,12 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
       (null, null, null) :: Nil)
 
   test("IN") {
+    checkEvaluation(In(Literal.create(null, IntegerType), Seq(Literal(1), Literal(2))), null)
+    checkEvaluation(In(Literal.create(null, IntegerType), Seq(Literal.create(null, IntegerType))),
+      null)
+    checkEvaluation(In(Literal(1), Seq(Literal.create(null, IntegerType))), null)
+    checkEvaluation(In(Literal(1), Seq(Literal(1), Literal.create(null, IntegerType))), true)
+    checkEvaluation(In(Literal(2), Seq(Literal(1), Literal.create(null, IntegerType))), null)
     checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true)
     checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true)
     checkEvaluation(In(Literal(3), Seq(Literal(1), Literal(2))), false)
@@ -126,14 +131,18 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
       And(In(Literal(1), Seq(Literal(1), Literal(2))), In(Literal(2), Seq(Literal(1), Literal(2)))),
       true)
 
-    checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("^Ba*n"))), true)
+    val ns = Literal.create(null, StringType)
+    checkEvaluation(In(ns, Seq(Literal("1"), Literal("2"))), null)
+    checkEvaluation(In(ns, Seq(ns)), null)
+    checkEvaluation(In(Literal("a"), Seq(ns)), null)
+    checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("^Ba*n"), ns)), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^Ba*n"))), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^n"))), false)
 
     val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
       LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
     primitiveTypes.map { t =>
-      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val dataGen = RandomDataGenerator.forType(t, nullable = true).get
       val inputData = Seq.fill(10) {
         val value = dataGen.apply()
         value match {
@@ -142,9 +151,17 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
           case _ => value
         }
       }
-      val input = inputData.map(Literal(_))
-      checkEvaluation(In(input(0), input.slice(1, 10)),
-        inputData.slice(1, 10).contains(inputData(0)))
+      val input = inputData.map(Literal.create(_, t))
+      val expected = if (inputData(0) == null) {
+        null
+      } else if (inputData.slice(1, 10).contains(inputData(0))) {
+        true
+      } else if (inputData.slice(1, 10).contains(null)) {
+        null
+      } else {
+        false
+      }
+      checkEvaluation(In(input(0), input.slice(1, 10)), expected)
     }
   }
 
@@ -158,15 +175,15 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(InSet(one, hS), true)
     checkEvaluation(InSet(two, hS), true)
     checkEvaluation(InSet(two, nS), true)
-    checkEvaluation(InSet(nl, nS), true)
     checkEvaluation(InSet(three, hS), false)
-    checkEvaluation(InSet(three, nS), false)
-    checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true)
+    checkEvaluation(InSet(three, nS), null)
+    checkEvaluation(InSet(nl, hS), null)
+    checkEvaluation(InSet(nl, nS), null)
 
     val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
       LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
     primitiveTypes.map { t =>
-      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val dataGen = RandomDataGenerator.forType(t, nullable = true).get
       val inputData = Seq.fill(10) {
         val value = dataGen.apply()
         value match {
@@ -176,8 +193,16 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
       val input = inputData.map(Literal(_))
-      checkEvaluation(InSet(input(0), inputData.slice(1, 10).toSet),
-        inputData.slice(1, 10).contains(inputData(0)))
+      val expected = if (inputData(0) == null) {
+        null
+      } else if (inputData.slice(1, 10).contains(inputData(0))) {
+        true
+      } else if (inputData.slice(1, 10).contains(null)) {
+        null
+      } else {
+        false
+      }
+      checkEvaluation(InSet(input(0), inputData.slice(1, 10).toSet), expected)
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index ec3b2f1edfa05..e67606288f514 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -250,29 +250,14 @@ class ConstantFoldingSuite extends PlanTest {
   }
 
   test("Constant folding test: Fold In(v, list) into true or false") {
-    var originalQuery =
+    val originalQuery =
       testRelation
         .select('a)
         .where(In(Literal(1), Seq(Literal(1), Literal(2))))
 
-    var optimized = Optimize.execute(originalQuery.analyze)
-
-    var correctAnswer =
-      testRelation
-        .select('a)
-        .where(Literal(true))
-        .analyze
-
-    comparePlans(optimized, correctAnswer)
-
-    originalQuery =
-      testRelation
-        .select('a)
-        .where(In(Literal(1), Seq(Literal(1), 'a.attr)))
-
-    optimized = Optimize.execute(originalQuery.analyze)
+    val optimized = Optimize.execute(originalQuery.analyze)
 
-    correctAnswer =
+    val correctAnswer =
       testRelation
         .select('a)
         .where(Literal(true))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 9d965258e389d..3a3f19af1473b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -366,10 +366,6 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
       df.selectExpr("array_contains(a, 1)"),
       Seq(Row(true), Row(false))
     )
-    checkAnswer(
-      df.select(array_contains(array(lit(2), lit(null)), 1)),
-      Seq(Row(false), Row(false))
-    )
 
     // In hive, this errors because null has no type information
     intercept[AnalysisException] {
@@ -382,15 +378,13 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
       df.selectExpr("array_contains(null, 1)")
     }
 
-    // In hive, if either argument has a matching type has a null value, return false, even if
-    // the first argument array contains a null and the second argument is null
     checkAnswer(
-      df.selectExpr("array_contains(array(array(1), null)[1], 1)"),
-      Seq(Row(false), Row(false))
+      df.selectExpr("array_contains(array(array(1), null)[0], 1)"),
+      Seq(Row(true), Row(true))
     )
     checkAnswer(
-      df.selectExpr("array_contains(array(0, null), array(1, null)[1])"),
-      Seq(Row(false), Row(false))
+      df.selectExpr("array_contains(array(1, null), array(1, null)[0])"),
+      Seq(Row(true), Row(true))
     )
   }
 }

From 2a4e00ca4d4e7a148b4ff8ce0ad1c6d517cee55f Mon Sep 17 00:00:00 2001
From: felixcheung <felixcheung_m@hotmail.com>
Date: Fri, 28 Aug 2015 18:35:01 -0700
Subject: [PATCH 1275/1454] [SPARK-9803] [SPARKR] Add subset and transform +
 tests

Add subset and transform
Also reorganize `[` & `[[` to subset instead of select

Note: for transform, transform is very similar to mutate. Spark doesn't seem to replace existing column with the name in mutate (ie. `mutate(df, age = df$age + 2)` - returned DataFrame has 2 columns with the same name 'age'), so therefore not doing that for now in transform.
Though it is clearly stated it should replace column with matching name (should I open a JIRA for mutate/transform?)

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #8503 from felixcheung/rsubset_transform.
---
 R/pkg/NAMESPACE                  |  2 +
 R/pkg/R/DataFrame.R              | 70 +++++++++++++++++++++++++-------
 R/pkg/R/generics.R               | 10 ++++-
 R/pkg/inst/tests/test_sparkSQL.R | 20 ++++++++-
 4 files changed, 85 insertions(+), 17 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 5286c01986204..9d39630706436 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -69,9 +69,11 @@ exportMethods("arrange",
               "selectExpr",
               "show",
               "showDF",
+              "subset",
               "summarize",
               "summary",
               "take",
+              "transform",
               "unionAll",
               "unique",
               "unpersist",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 74de7c81e35a6..8a00238b41d60 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -987,7 +987,7 @@ setMethod("$<-", signature(x = "DataFrame"),
 
 setClassUnion("numericOrcharacter", c("numeric", "character"))
 
-#' @rdname select
+#' @rdname subset
 #' @name [[
 setMethod("[[", signature(x = "DataFrame", i = "numericOrcharacter"),
           function(x, i) {
@@ -998,7 +998,7 @@ setMethod("[[", signature(x = "DataFrame", i = "numericOrcharacter"),
             getColumn(x, i)
           })
 
-#' @rdname select
+#' @rdname subset
 #' @name [
 setMethod("[", signature(x = "DataFrame", i = "missing"),
           function(x, i, j, ...) {
@@ -1012,7 +1012,7 @@ setMethod("[", signature(x = "DataFrame", i = "missing"),
             select(x, j)
           })
 
-#' @rdname select
+#' @rdname subset
 #' @name [
 setMethod("[", signature(x = "DataFrame", i = "Column"),
           function(x, i, j, ...) {
@@ -1020,12 +1020,43 @@ setMethod("[", signature(x = "DataFrame", i = "Column"),
             # https://stat.ethz.ch/R-manual/R-devel/library/base/html/Extract.data.frame.html
             filtered <- filter(x, i)
             if (!missing(j)) {
-              filtered[, j]
+              filtered[, j, ...]
             } else {
               filtered
             }
           })
 
+#' Subset
+#'
+#' Return subsets of DataFrame according to given conditions
+#' @param x A DataFrame
+#' @param subset A logical expression to filter on rows
+#' @param select expression for the single Column or a list of columns to select from the DataFrame
+#' @return A new DataFrame containing only the rows that meet the condition with selected columns
+#' @export
+#' @rdname subset
+#' @name subset
+#' @aliases [
+#' @family subsetting functions
+#' @examples
+#' \dontrun{
+#'   # Columns can be selected using `[[` and `[`
+#'   df[[2]] == df[["age"]]
+#'   df[,2] == df[,"age"]
+#'   df[,c("name", "age")]
+#'   # Or to filter rows
+#'   df[df$age > 20,]
+#'   # DataFrame can be subset on both rows and Columns
+#'   df[df$name == "Smith", c(1,2)]
+#'   df[df$age %in% c(19, 30), 1:2]
+#'   subset(df, df$age %in% c(19, 30), 1:2)
+#'   subset(df, df$age %in% c(19), select = c(1,2))
+#' }
+setMethod("subset", signature(x = "DataFrame"),
+          function(x, subset, select, ...) {
+            x[subset, select, ...]
+          })
+
 #' Select
 #'
 #' Selects a set of columns with names or Column expressions.
@@ -1034,6 +1065,8 @@ setMethod("[", signature(x = "DataFrame", i = "Column"),
 #' @return A new DataFrame with selected columns
 #' @export
 #' @rdname select
+#' @name select
+#' @family subsetting functions
 #' @examples
 #' \dontrun{
 #'   select(df, "*")
@@ -1041,15 +1074,8 @@ setMethod("[", signature(x = "DataFrame", i = "Column"),
 #'   select(df, df$name, df$age + 1)
 #'   select(df, c("col1", "col2"))
 #'   select(df, list(df$name, df$age + 1))
-#'   # Columns can also be selected using `[[` and `[`
-#'   df[[2]] == df[["age"]]
-#'   df[,2] == df[,"age"]
-#'   df[,c("name", "age")]
 #'   # Similar to R data frames columns can also be selected using `$`
 #'   df$age
-#'   # It can also be subset on rows and Columns
-#'   df[df$name == "Smith", c(1,2)]
-#'   df[df$age %in% c(19, 30), 1:2]
 #' }
 setMethod("select", signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
@@ -1121,7 +1147,7 @@ setMethod("selectExpr",
 #' @return A DataFrame with the new column added.
 #' @rdname withColumn
 #' @name withColumn
-#' @aliases mutate
+#' @aliases mutate transform
 #' @export
 #' @examples
 #'\dontrun{
@@ -1141,11 +1167,12 @@ setMethod("withColumn",
 #'
 #' Return a new DataFrame with the specified columns added.
 #'
-#' @param x A DataFrame
+#' @param .data A DataFrame
 #' @param col a named argument of the form name = col
 #' @return A new DataFrame with the new columns added.
 #' @rdname withColumn
 #' @name mutate
+#' @aliases withColumn transform
 #' @export
 #' @examples
 #'\dontrun{
@@ -1155,10 +1182,12 @@ setMethod("withColumn",
 #' df <- jsonFile(sqlContext, path)
 #' newDF <- mutate(df, newCol = df$col1 * 5, newCol2 = df$col1 * 2)
 #' names(newDF) # Will contain newCol, newCol2
+#' newDF2 <- transform(df, newCol = df$col1 / 5, newCol2 = df$col1 * 2)
 #' }
 setMethod("mutate",
-          signature(x = "DataFrame"),
-          function(x, ...) {
+          signature(.data = "DataFrame"),
+          function(.data, ...) {
+            x <- .data
             cols <- list(...)
             stopifnot(length(cols) > 0)
             stopifnot(class(cols[[1]]) == "Column")
@@ -1173,6 +1202,16 @@ setMethod("mutate",
             do.call(select, c(x, x$"*", cols))
           })
 
+#' @export
+#' @rdname withColumn
+#' @name transform
+#' @aliases withColumn mutate
+setMethod("transform",
+          signature(`_data` = "DataFrame"),
+          function(`_data`, ...) {
+            mutate(`_data`, ...)
+          })
+
 #' WithColumnRenamed
 #'
 #' Rename an existing column in a DataFrame.
@@ -1300,6 +1339,7 @@ setMethod("orderBy",
 #' @return A DataFrame containing only the rows that meet the condition.
 #' @rdname filter
 #' @name filter
+#' @family subsetting functions
 #' @export
 #' @examples
 #'\dontrun{
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index b578b8789d2c5..43dd8d283ab6b 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -467,7 +467,7 @@ setGeneric("merge")
 
 #' @rdname withColumn
 #' @export
-setGeneric("mutate", function(x, ...) {standardGeneric("mutate") })
+setGeneric("mutate", function(.data, ...) {standardGeneric("mutate") })
 
 #' @rdname arrange
 #' @export
@@ -507,6 +507,10 @@ setGeneric("saveAsTable", function(df, tableName, source, mode, ...) {
   standardGeneric("saveAsTable")
 })
 
+#' @rdname withColumn
+#' @export
+setGeneric("transform", function(`_data`, ...) {standardGeneric("transform") })
+
 #' @rdname write.df
 #' @export
 setGeneric("write.df", function(df, path, ...) { standardGeneric("write.df") })
@@ -531,6 +535,10 @@ setGeneric("selectExpr", function(x, expr, ...) { standardGeneric("selectExpr")
 #' @export
 setGeneric("showDF", function(x,...) { standardGeneric("showDF") })
 
+# @rdname subset
+# @export
+setGeneric("subset", function(x, subset, select, ...) { standardGeneric("subset") })
+
 #' @rdname agg
 #' @export
 setGeneric("summarize", function(x,...) { standardGeneric("summarize") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 933b11c8ee7e2..0da5e38654732 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -612,6 +612,10 @@ test_that("subsetting", {
   df5 <- df[df$age %in% c(19), c(1,2)]
   expect_equal(count(df5), 1)
   expect_equal(columns(df5), c("name", "age"))
+
+  df6 <- subset(df, df$age %in% c(30), c(1,2))
+  expect_equal(count(df6), 1)
+  expect_equal(columns(df6), c("name", "age"))
 })
 
 test_that("selectExpr() on a DataFrame", {
@@ -1028,7 +1032,7 @@ test_that("withColumn() and withColumnRenamed()", {
   expect_equal(columns(newDF2)[1], "newerAge")
 })
 
-test_that("mutate(), rename() and names()", {
+test_that("mutate(), transform(), rename() and names()", {
   df <- jsonFile(sqlContext, jsonPath)
   newDF <- mutate(df, newAge = df$age + 2)
   expect_equal(length(columns(newDF)), 3)
@@ -1042,6 +1046,20 @@ test_that("mutate(), rename() and names()", {
   names(newDF2) <- c("newerName", "evenNewerAge")
   expect_equal(length(names(newDF2)), 2)
   expect_equal(names(newDF2)[1], "newerName")
+
+  transformedDF <- transform(df, newAge = -df$age, newAge2 = df$age / 2)
+  expect_equal(length(columns(transformedDF)), 4)
+  expect_equal(columns(transformedDF)[3], "newAge")
+  expect_equal(columns(transformedDF)[4], "newAge2")
+  expect_equal(first(filter(transformedDF, transformedDF$name == "Andy"))$newAge, -30)
+
+  # test if transform on local data frames works
+  # ensure the proper signature is used - otherwise this will fail to run
+  attach(airquality)
+  result <- transform(Ozone, logOzone = log(Ozone))
+  expect_equal(nrow(result), 153)
+  expect_equal(ncol(result), 2)
+  detach(airquality)
 })
 
 test_that("write.df() on DataFrame and works with parquetFile", {

From e8ea5bafee9ca734edf62021145d0c2d5491cba8 Mon Sep 17 00:00:00 2001
From: martinzapletal <zapletal-martin@email.cz>
Date: Fri, 28 Aug 2015 21:03:48 -0700
Subject: [PATCH 1276/1454] [SPARK-9910] [ML] User guide for train validation
 split

Author: martinzapletal <zapletal-martin@email.cz>

Closes #8377 from zapletal-martin/SPARK-9910.
---
 docs/ml-guide.md                              | 117 ++++++++++++++++++
 .../ml/JavaTrainValidationSplitExample.java   |  90 ++++++++++++++
 .../ml/TrainValidationSplitExample.scala      |  80 ++++++++++++
 3 files changed, 287 insertions(+)
 create mode 100644 examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index ce53400b6ee56..a92a285f3af85 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -872,3 +872,120 @@ jsc.stop();
 </div>
 
 </div>
+
+## Example: Model Selection via Train Validation Split
+In addition to  `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
+`TrainValidationSplit` only evaluates each combination of parameters once as opposed to k times in
+ case of `CrossValidator`. It is therefore less expensive,
+ but will not produce as reliable results when the training dataset is not sufficiently large..
+
+`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
+and an `Evaluator`.
+It begins by splitting the dataset into two parts using `trainRatio` parameter
+which are used as separate training and test datasets. For example with `$trainRatio=0.75$` (default),
+`TrainValidationSplit` will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation.
+Similar to `CrossValidator`, `TrainValidationSplit` also iterates through the set of `ParamMap`s.
+For each combination of parameters, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
+The `ParamMap` which produces the best evaluation metric is selected as the best option.
+`TrainValidationSplit` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
+import org.apache.spark.mllib.util.MLUtils
+
+// Prepare training and test data.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
+
+val lr = new LinearRegression()
+
+// We use a ParamGridBuilder to construct a grid of parameters to search over.
+// TrainValidationSplit will try all combinations of values and determine best model using
+// the evaluator.
+val paramGrid = new ParamGridBuilder()
+  .addGrid(lr.regParam, Array(0.1, 0.01))
+  .addGrid(lr.fitIntercept, Array(true, false))
+  .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
+  .build()
+
+// In this case the estimator is simply the linear regression.
+// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+val trainValidationSplit = new TrainValidationSplit()
+  .setEstimator(lr)
+  .setEvaluator(new RegressionEvaluator)
+  .setEstimatorParamMaps(paramGrid)
+
+// 80% of the data will be used for training and the remaining 20% for validation.
+trainValidationSplit.setTrainRatio(0.8)
+
+// Run train validation split, and choose the best set of parameters.
+val model = trainValidationSplit.fit(training)
+
+// Make predictions on test data. model is the model with combination of parameters
+// that performed best.
+model.transform(test)
+  .select("features", "label", "prediction")
+  .show()
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.ml.regression.LinearRegression;
+import org.apache.spark.ml.tuning.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+DataFrame data = jsql.createDataFrame(
+  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"),
+  LabeledPoint.class);
+
+// Prepare training and test data.
+DataFrame[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345);
+DataFrame training = splits[0];
+DataFrame test = splits[1];
+
+LinearRegression lr = new LinearRegression();
+
+// We use a ParamGridBuilder to construct a grid of parameters to search over.
+// TrainValidationSplit will try all combinations of values and determine best model using
+// the evaluator.
+ParamMap[] paramGrid = new ParamGridBuilder()
+  .addGrid(lr.regParam(), new double[] {0.1, 0.01})
+  .addGrid(lr.fitIntercept())
+  .addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0})
+  .build();
+
+// In this case the estimator is simply the linear regression.
+// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
+  .setEstimator(lr)
+  .setEvaluator(new RegressionEvaluator())
+  .setEstimatorParamMaps(paramGrid);
+
+// 80% of the data will be used for training and the remaining 20% for validation.
+trainValidationSplit.setTrainRatio(0.8);
+
+// Run train validation split, and choose the best set of parameters.
+TrainValidationSplitModel model = trainValidationSplit.fit(training);
+
+// Make predictions on test data. model is the model with combination of parameters
+// that performed best.
+model.transform(test)
+  .select("features", "label", "prediction")
+  .show();
+
+{% endhighlight %}
+</div>
+
+</div>
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
new file mode 100644
index 0000000000000..23f834ab4332b
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.ml;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.ml.regression.LinearRegression;
+import org.apache.spark.ml.tuning.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+/**
+ * A simple example demonstrating model selection using TrainValidationSplit.
+ *
+ * The example is based on {@link org.apache.spark.examples.ml.JavaSimpleParamsExample}
+ * using linear regression.
+ *
+ * Run with
+ * {{{
+ * bin/run-example ml.JavaTrainValidationSplitExample
+ * }}}
+ */
+public class JavaTrainValidationSplitExample {
+
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("JavaTrainValidationSplitExample");
+    JavaSparkContext jsc = new JavaSparkContext(conf);
+    SQLContext jsql = new SQLContext(jsc);
+
+    DataFrame data = jsql.createDataFrame(
+      MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"),
+      LabeledPoint.class);
+
+    // Prepare training and test data.
+    DataFrame[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345);
+    DataFrame training = splits[0];
+    DataFrame test = splits[1];
+
+    LinearRegression lr = new LinearRegression();
+
+    // We use a ParamGridBuilder to construct a grid of parameters to search over.
+    // TrainValidationSplit will try all combinations of values and determine best model using
+    // the evaluator.
+    ParamMap[] paramGrid = new ParamGridBuilder()
+      .addGrid(lr.regParam(), new double[] {0.1, 0.01})
+      .addGrid(lr.fitIntercept())
+      .addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0})
+      .build();
+
+    // In this case the estimator is simply the linear regression.
+    // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+    TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEvaluator(new RegressionEvaluator())
+      .setEstimatorParamMaps(paramGrid);
+
+    // 80% of the data will be used for training and the remaining 20% for validation.
+    trainValidationSplit.setTrainRatio(0.8);
+
+    // Run train validation split, and choose the best set of parameters.
+    TrainValidationSplitModel model = trainValidationSplit.fit(training);
+
+    // Make predictions on test data. model is the model with combination of parameters
+    // that performed best.
+    model.transform(test)
+      .select("features", "label", "prediction")
+      .show();
+
+    jsc.stop();
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala
new file mode 100644
index 0000000000000..1abdf219b1c00
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.ml
+
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.{SparkConf, SparkContext}
+
+/**
+ * A simple example demonstrating model selection using TrainValidationSplit.
+ *
+ * The example is based on [[SimpleParamsExample]] using linear regression.
+ * Run with
+ * {{{
+ * bin/run-example ml.TrainValidationSplitExample
+ * }}}
+ */
+object TrainValidationSplitExample {
+
+  def main(args: Array[String]): Unit = {
+    val conf = new SparkConf().setAppName("TrainValidationSplitExample")
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    // Prepare training and test data.
+    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+    val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
+
+    val lr = new LinearRegression()
+
+    // We use a ParamGridBuilder to construct a grid of parameters to search over.
+    // TrainValidationSplit will try all combinations of values and determine best model using
+    // the evaluator.
+    val paramGrid = new ParamGridBuilder()
+      .addGrid(lr.regParam, Array(0.1, 0.01))
+      .addGrid(lr.fitIntercept, Array(true, false))
+      .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
+      .build()
+
+    // In this case the estimator is simply the linear regression.
+    // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+    val trainValidationSplit = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEvaluator(new RegressionEvaluator)
+      .setEstimatorParamMaps(paramGrid)
+
+    // 80% of the data will be used for training and the remaining 20% for validation.
+    trainValidationSplit.setTrainRatio(0.8)
+
+    // Run train validation split, and choose the best set of parameters.
+    val model = trainValidationSplit.fit(training)
+
+    // Make predictions on test data. model is the model with combination of parameters
+    // that performed best.
+    model.transform(test)
+      .select("features", "label", "prediction")
+      .show()
+
+    sc.stop()
+  }
+}

From 5369be806848f43cb87c76504258c4e7de930c90 Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Sat, 29 Aug 2015 13:20:22 -0700
Subject: [PATCH 1277/1454] [SPARK-10350] [DOC] [SQL] Removed duplicated option
 description from SQL guide

Author: GuoQiang Li <witgo@qq.com>

Closes #8520 from witgo/SPARK-10350.
---
 docs/sql-programming-guide.md | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index e8eb88488ee24..6a1b0fbfa1eb3 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1405,16 +1405,6 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
     </p>
   </td>
 </tr>
-<tr>
-  <td><code>spark.sql.parquet.mergeSchema</code></td>
-  <td><code>false</code></td>
-  <td>
-    <p>
-      When true, the Parquet data source merges schemas collected from all data files, otherwise the
-      schema is picked from the summary file or a random data file if no summary file is available.
-    </p>
-  </td>
-</tr>
 </table>
 
 ## JSON Datasets

From 24ffa85c002a095ffb270175ec838995d3ed5469 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sat, 29 Aug 2015 13:24:32 -0700
Subject: [PATCH 1278/1454] [SPARK-10289] [SQL] A direct write API for testing
 Parquet

This PR introduces a direct write API for testing Parquet. It's a DSL flavored version of the [`writeDirect` method] [1] comes with parquet-avro testing code. With this API, it's much easier to construct arbitrary Parquet structures. It's especially useful when adding regression tests for various compatibility corner cases.

Sample usage of this API can be found in the new test case added in `ParquetThriftCompatibilitySuite`.

[1]: https://github.com/apache/parquet-mr/blob/apache-parquet-1.8.1/parquet-avro/src/test/java/org/apache/parquet/avro/TestArrayCompatibility.java#L945-L972

Author: Cheng Lian <lian@databricks.com>

Closes #8454 from liancheng/spark-10289/parquet-testing-direct-write-api.
---
 .../parquet/ParquetCompatibilityTest.scala    |  84 +++++++++++++--
 .../ParquetThriftCompatibilitySuite.scala     | 100 +++++++++++++++---
 2 files changed, 160 insertions(+), 24 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index df68432faeeb3..91f3ce4d34c8b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -17,11 +17,15 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConverters._
+import scala.collection.JavaConverters.{collectionAsScalaIterableConverter, mapAsJavaMapConverter, seqAsJavaListConverter}
 
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{Path, PathFilter}
-import org.apache.parquet.hadoop.ParquetFileReader
-import org.apache.parquet.schema.MessageType
+import org.apache.parquet.hadoop.api.WriteSupport
+import org.apache.parquet.hadoop.api.WriteSupport.WriteContext
+import org.apache.parquet.hadoop.{ParquetFileReader, ParquetWriter}
+import org.apache.parquet.io.api.RecordConsumer
+import org.apache.parquet.schema.{MessageType, MessageTypeParser}
 
 import org.apache.spark.sql.QueryTest
 
@@ -38,11 +42,10 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
     val fs = fsPath.getFileSystem(configuration)
     val parquetFiles = fs.listStatus(fsPath, new PathFilter {
       override def accept(path: Path): Boolean = pathFilter(path)
-    }).toSeq
+    }).toSeq.asJava
 
-    val footers =
-      ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles.asJava, true)
-    footers.iterator().next().getParquetMetadata.getFileMetaData.getSchema
+    val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
+    footers.asScala.head.getParquetMetadata.getFileMetaData.getSchema
   }
 
   protected def logParquetSchema(path: String): Unit = {
@@ -53,8 +56,69 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
   }
 }
 
-object ParquetCompatibilityTest {
-  def makeNullable[T <: AnyRef](i: Int)(f: => T): T = {
-    if (i % 3 == 0) null.asInstanceOf[T] else f
+private[sql] object ParquetCompatibilityTest {
+  implicit class RecordConsumerDSL(consumer: RecordConsumer) {
+    def message(f: => Unit): Unit = {
+      consumer.startMessage()
+      f
+      consumer.endMessage()
+    }
+
+    def group(f: => Unit): Unit = {
+      consumer.startGroup()
+      f
+      consumer.endGroup()
+    }
+
+    def field(name: String, index: Int)(f: => Unit): Unit = {
+      consumer.startField(name, index)
+      f
+      consumer.endField(name, index)
+    }
+  }
+
+  /**
+   * A testing Parquet [[WriteSupport]] implementation used to write manually constructed Parquet
+   * records with arbitrary structures.
+   */
+  private class DirectWriteSupport(schema: MessageType, metadata: Map[String, String])
+    extends WriteSupport[RecordConsumer => Unit] {
+
+    private var recordConsumer: RecordConsumer = _
+
+    override def init(configuration: Configuration): WriteContext = {
+      new WriteContext(schema, metadata.asJava)
+    }
+
+    override def write(recordWriter: RecordConsumer => Unit): Unit = {
+      recordWriter.apply(recordConsumer)
+    }
+
+    override def prepareForWrite(recordConsumer: RecordConsumer): Unit = {
+      this.recordConsumer = recordConsumer
+    }
+  }
+
+  /**
+   * Writes arbitrary messages conforming to a given `schema` to a Parquet file located by `path`.
+   * Records are produced by `recordWriters`.
+   */
+  def writeDirect(path: String, schema: String, recordWriters: (RecordConsumer => Unit)*): Unit = {
+    writeDirect(path, schema, Map.empty[String, String], recordWriters: _*)
+  }
+
+  /**
+   * Writes arbitrary messages conforming to a given `schema` to a Parquet file located by `path`
+   * with given user-defined key-value `metadata`. Records are produced by `recordWriters`.
+   */
+  def writeDirect(
+      path: String,
+      schema: String,
+      metadata: Map[String, String],
+      recordWriters: (RecordConsumer => Unit)*): Unit = {
+    val messageType = MessageTypeParser.parseMessageType(schema)
+    val writeSupport = new DirectWriteSupport(messageType, metadata)
+    val parquetWriter = new ParquetWriter[RecordConsumer => Unit](new Path(path), writeSupport)
+    try recordWriters.foreach(parquetWriter.write) finally parquetWriter.close()
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
index b789c5a106e56..88a3d878f97fe 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
@@ -33,11 +33,9 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with Shar
        """.stripMargin)
 
     checkAnswer(sqlContext.read.parquet(parquetFilePath.toString), (0 until 10).map { i =>
-      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
-
       val suits = Array("SPADES", "HEARTS", "DIAMONDS", "CLUBS")
 
-      Row(
+      val nonNullablePrimitiveValues = Seq(
         i % 2 == 0,
         i.toByte,
         (i + 1).toShort,
@@ -50,18 +48,15 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with Shar
         s"val_$i",
         s"val_$i",
         // Thrift ENUM values are converted to Parquet binaries containing UTF-8 strings
-        suits(i % 4),
-
-        nullable(i % 2 == 0: java.lang.Boolean),
-        nullable(i.toByte: java.lang.Byte),
-        nullable((i + 1).toShort: java.lang.Short),
-        nullable(i + 2: Integer),
-        nullable((i * 10).toLong: java.lang.Long),
-        nullable(i.toDouble + 0.2d: java.lang.Double),
-        nullable(s"val_$i"),
-        nullable(s"val_$i"),
-        nullable(suits(i % 4)),
+        suits(i % 4))
+
+      val nullablePrimitiveValues = if (i % 3 == 0) {
+        Seq.fill(nonNullablePrimitiveValues.length)(null)
+      } else {
+        nonNullablePrimitiveValues
+      }
 
+      val complexValues = Seq(
         Seq.tabulate(3)(n => s"arr_${i + n}"),
         // Thrift `SET`s are converted to Parquet `LIST`s
         Seq(i),
@@ -71,6 +66,83 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with Shar
             Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
           }
         }.toMap)
+
+      Row(nonNullablePrimitiveValues ++ nullablePrimitiveValues ++ complexValues: _*)
     })
   }
+
+  test("SPARK-10136 list of primitive list") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      // This Parquet schema is translated from the following Thrift schema:
+      //
+      //   struct ListOfPrimitiveList {
+      //     1: list<list<i32>> f;
+      //   }
+      val schema =
+        s"""message ListOfPrimitiveList {
+           |  required group f (LIST) {
+           |    repeated group f_tuple (LIST) {
+           |      repeated int32 f_tuple_tuple;
+           |    }
+           |  }
+           |}
+         """.stripMargin
+
+      writeDirect(path, schema, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.group {
+              rc.field("f_tuple", 0) {
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(0)
+                    rc.addInteger(1)
+                  }
+                }
+
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(2)
+                    rc.addInteger(3)
+                  }
+                }
+              }
+            }
+          }
+        }
+      }, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.group {
+              rc.field("f_tuple", 0) {
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(4)
+                    rc.addInteger(5)
+                  }
+                }
+
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(6)
+                    rc.addInteger(7)
+                  }
+                }
+              }
+            }
+          }
+        }
+      })
+
+      logParquetSchema(path)
+
+      checkAnswer(
+        sqlContext.read.parquet(path),
+        Seq(
+          Row(Seq(Seq(0, 1), Seq(2, 3))),
+          Row(Seq(Seq(4, 5), Seq(6, 7)))))
+    }
+  }
 }

From 5c3d16a9b91bb9a458d3ba141f7bef525cf3d285 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 29 Aug 2015 13:26:01 -0700
Subject: [PATCH 1279/1454] [SPARK-10344] [SQL] Add tests for extraStrategies

Actually using this API requires access to a lot of classes that we might make private by accident.  I've added some tests to prevent this.

Author: Michael Armbrust <michael@databricks.com>

Closes #8516 from marmbrus/extraStrategiesTests.
---
 .../spark/sql/ExtraStrategiesSuite.scala      | 67 +++++++++++++++++++
 .../spark/sql/test/SharedSQLContext.scala     |  2 +-
 2 files changed, 68 insertions(+), 1 deletion(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
new file mode 100644
index 0000000000000..8d2f45d70308b
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package test.org.apache.spark.sql
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Literal, GenericInternalRow, Attribute}
+import org.apache.spark.sql.catalyst.plans.logical.{Project, LogicalPlan}
+import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.{Row, Strategy, QueryTest}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.unsafe.types.UTF8String
+
+case class FastOperator(output: Seq[Attribute]) extends SparkPlan {
+
+  override protected def doExecute(): RDD[InternalRow] = {
+    val str = Literal("so fast").value
+    val row = new GenericInternalRow(Array[Any](str))
+    sparkContext.parallelize(Seq(row))
+  }
+
+  override def children: Seq[SparkPlan] = Nil
+}
+
+object TestStrategy extends Strategy {
+  def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+    case Project(Seq(attr), _) if attr.name == "a" =>
+      FastOperator(attr.toAttribute :: Nil) :: Nil
+    case _ => Nil
+  }
+}
+
+class ExtraStrategiesSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
+
+  test("insert an extraStrategy") {
+    try {
+      sqlContext.experimental.extraStrategies = TestStrategy :: Nil
+
+      val df = sqlContext.sparkContext.parallelize(Seq(("so slow", 1))).toDF("a", "b")
+      checkAnswer(
+        df.select("a"),
+        Row("so fast"))
+
+      checkAnswer(
+        df.select("a", "b"),
+        Row("so slow", 1))
+    } finally {
+      sqlContext.experimental.extraStrategies = Nil
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
index 8a061b6bc690d..d23c6a0732669 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -23,7 +23,7 @@ import org.apache.spark.sql.{ColumnName, SQLContext}
 /**
  * Helper trait for SQL test suites where all tests share a single [[TestSQLContext]].
  */
-private[sql] trait SharedSQLContext extends SQLTestUtils {
+trait SharedSQLContext extends SQLTestUtils {
 
   /**
    * The [[TestSQLContext]] to use for all tests in this suite.

From 277148b285748e863f2b9fdf6cf12963977f91ca Mon Sep 17 00:00:00 2001
From: wangwei <wangwei82@huawei.com>
Date: Sat, 29 Aug 2015 13:29:50 -0700
Subject: [PATCH 1280/1454] [SPARK-10226] [SQL] Fix exclamation mark issue in
 SparkSQL

When I tested the latest version of spark with exclamation mark, I got some errors. Then I reseted the spark version and found that commit id "a2409d1c8e8ddec04b529ac6f6a12b5993f0eeda" brought the bug. With jline version changing from 0.9.94 to 2.12 after this commit, exclamation mark would be treated as a special character in ConsoleReader.

Author: wangwei <wangwei82@huawei.com>

Closes #8420 from small-wang/jline-SPARK-10226.
---
 .../apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index a29df567983b1..b5073961a1c84 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -171,6 +171,7 @@ private[hive] object SparkSQLCLIDriver extends Logging {
 
     val reader = new ConsoleReader()
     reader.setBellEnabled(false)
+    reader.setExpandEvents(false)
     // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
     CliDriver.getCommandCompleter.foreach((e) => reader.addCompleter(e))
 

From 6a6f3c91ee1f63dd464eb03d156d02c1a5887d88 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 29 Aug 2015 13:36:25 -0700
Subject: [PATCH 1281/1454] [SPARK-10330] Use SparkHadoopUtil
 TaskAttemptContext reflection methods in more places

SparkHadoopUtil contains methods that use reflection to work around TaskAttemptContext binary incompatibilities between Hadoop 1.x and 2.x. We should use these methods in more places.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8499 from JoshRosen/use-hadoop-reflection-in-more-places.
---
 .../sql/execution/datasources/WriterContainer.scala    | 10 +++++++---
 .../sql/execution/datasources/json/JSONRelation.scala  |  7 +++++--
 .../datasources/parquet/ParquetRelation.scala          |  7 +++++--
 .../org/apache/spark/sql/hive/orc/OrcRelation.scala    |  9 ++++++---
 .../apache/spark/sql/sources/SimpleTextRelation.scala  |  7 +++++--
 5 files changed, 28 insertions(+), 12 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 78f48a5cd72c7..879fd69863211 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -25,6 +25,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter}
 import org.apache.spark._
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.sql._
@@ -145,7 +146,8 @@ private[sql] abstract class BaseWriterContainer(
           "because spark.speculation is configured to be true.")
       defaultOutputCommitter
     } else {
-      val committerClass = context.getConfiguration.getClass(
+      val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+      val committerClass = configuration.getClass(
         SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
 
       Option(committerClass).map { clazz =>
@@ -227,7 +229,8 @@ private[sql] class DefaultWriterContainer(
 
   def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
     executorSideSetup(taskContext)
-    taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path", outputPath)
+    val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(taskAttemptContext)
+    configuration.set("spark.sql.sources.output.path", outputPath)
     val writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
     writer.initConverter(dataSchema)
 
@@ -395,7 +398,8 @@ private[sql] class DynamicPartitionWriterContainer(
     def newOutputWriter(key: InternalRow): OutputWriter = {
       val partitionPath = getPartitionString(key).getString(0)
       val path = new Path(getWorkPath, partitionPath)
-      taskAttemptContext.getConfiguration.set(
+      val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(taskAttemptContext)
+      configuration.set(
         "spark.sql.sources.output.path", new Path(outputPath, partitionPath).toString)
       val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
       newWriter.initConverter(dataSchema)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index ab8ca5f748f24..7a49157d9e72c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -30,6 +30,7 @@ import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
 
 import org.apache.spark.Logging
 import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -169,8 +170,10 @@ private[json] class JsonOutputWriter(
   private val recordWriter: RecordWriter[NullWritable, Text] = {
     new TextOutputFormat[NullWritable, Text]() {
       override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-        val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
-        val split = context.getTaskAttemptID.getTaskID.getId
+        val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+        val uniqueWriteJobId = configuration.get("spark.sql.sources.writeJobUUID")
+        val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+        val split = taskAttemptId.getTaskID.getId
         new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
       }
     }.getRecordWriter(context)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 64982f37cf872..c6bbc392cad4c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -40,6 +40,7 @@ import org.apache.parquet.{Log => ApacheParquetLog}
 import org.slf4j.bridge.SLF4JBridgeHandler
 
 import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{RDD, SqlNewHadoopPartition, SqlNewHadoopRDD}
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
@@ -81,8 +82,10 @@ private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext
         //     `FileOutputCommitter.getWorkPath()`, which points to the base directory of all
         //     partitions in the case of dynamic partitioning.
         override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-          val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
-          val split = context.getTaskAttemptID.getTaskID.getId
+          val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+          val uniqueWriteJobId = configuration.get("spark.sql.sources.writeJobUUID")
+          val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+          val split = taskAttemptId.getTaskID.getId
           new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
         }
       }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 1cff5cf9c3543..4eeca9aec12bd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -34,6 +34,7 @@ import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
 import org.apache.spark.Logging
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.{HadoopRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
@@ -77,7 +78,8 @@ private[orc] class OrcOutputWriter(
     }.mkString(":"))
 
     val serde = new OrcSerde
-    serde.initialize(context.getConfiguration, table)
+    val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+    serde.initialize(configuration, table)
     serde
   }
 
@@ -109,9 +111,10 @@ private[orc] class OrcOutputWriter(
   private lazy val recordWriter: RecordWriter[NullWritable, Writable] = {
     recordWriterInstantiated = true
 
-    val conf = context.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
     val uniqueWriteJobId = conf.get("spark.sql.sources.writeJobUUID")
-    val partition = context.getTaskAttemptID.getTaskID.getId
+    val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+    val partition = taskAttemptId.getTaskID.getId
     val filename = f"part-r-$partition%05d-$uniqueWriteJobId.orc"
 
     new OrcOutputFormat().getRecordWriter(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
index e8141923a9b5c..527ca7a81cad8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
@@ -27,6 +27,7 @@ import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, TextOutputForma
 import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
 import org.apache.spark.sql.types.{DataType, StructType}
@@ -53,8 +54,10 @@ class AppendingTextOutputFormat(outputFile: Path) extends TextOutputFormat[NullW
   numberFormat.setGroupingUsed(false)
 
   override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-    val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
-    val split = context.getTaskAttemptID.getTaskID.getId
+    val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+    val uniqueWriteJobId = configuration.get("spark.sql.sources.writeJobUUID")
+    val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+    val split = taskAttemptId.getTaskID.getId
     val name = FileOutputFormat.getOutputName(context)
     new Path(outputFile, s"$name-${numberFormat.format(split)}-$uniqueWriteJobId")
   }

From 097a7e36e0bf7290b1879331375bacc905583bd3 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Sat, 29 Aug 2015 16:39:40 -0700
Subject: [PATCH 1282/1454] [SPARK-10339] [SPARK-10334] [SPARK-10301] [SQL]
 Partitioned table scan can OOM driver and throw a better error message when
 users need to enable parquet schema merging

This fixes the problem that scanning partitioned table causes driver have a high memory pressure and takes down the cluster. Also, with this fix, we will be able to correctly show the query plan of a query consuming partitioned tables.

https://issues.apache.org/jira/browse/SPARK-10339
https://issues.apache.org/jira/browse/SPARK-10334

Finally, this PR squeeze in a "quick fix" for SPARK-10301. It is not a real fix, but it just throw a better error message to let user know what to do.

Author: Yin Huai <yhuai@databricks.com>

Closes #8515 from yhuai/partitionedTableScan.
---
 .../datasources/DataSourceStrategy.scala      | 85 ++++++++++---------
 .../parquet/CatalystRowConverter.scala        |  7 ++
 .../ParquetHadoopFsRelationSuite.scala        | 15 +++-
 3 files changed, 65 insertions(+), 42 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 6c1ef6a6df887..c58213155daa8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{StringType, StructType}
 import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
@@ -121,7 +122,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       projections: Seq[NamedExpression],
       filters: Seq[Expression],
       partitionColumns: StructType,
-      partitions: Array[Partition]) = {
+      partitions: Array[Partition]): SparkPlan = {
     val relation = logicalRelation.relation.asInstanceOf[HadoopFsRelation]
 
     // Because we are creating one RDD per partition, we need to have a shared HadoopConf.
@@ -130,49 +131,51 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     val confBroadcast =
       relation.sqlContext.sparkContext.broadcast(new SerializableConfiguration(sharedHadoopConf))
 
-    // Builds RDD[Row]s for each selected partition.
-    val perPartitionRows = partitions.map { case Partition(partitionValues, dir) =>
-      // The table scan operator (PhysicalRDD) which retrieves required columns from data files.
-      // Notice that the schema of data files, represented by `relation.dataSchema`, may contain
-      // some partition column(s).
-      val scan =
-        pruneFilterProject(
-          logicalRelation,
-          projections,
-          filters,
-          (columns: Seq[Attribute], filters) => {
-            val partitionColNames = partitionColumns.fieldNames
-
-            // Don't scan any partition columns to save I/O.  Here we are being optimistic and
-            // assuming partition columns data stored in data files are always consistent with those
-            // partition values encoded in partition directory paths.
-            val needed = columns.filterNot(a => partitionColNames.contains(a.name))
-            val dataRows =
-              relation.buildScan(needed.map(_.name).toArray, filters, Array(dir), confBroadcast)
-
-            // Merges data values with partition values.
-            mergeWithPartitionValues(
-              relation.schema,
-              columns.map(_.name).toArray,
-              partitionColNames,
-              partitionValues,
-              toCatalystRDD(logicalRelation, needed, dataRows))
-          })
-
-      scan.execute()
-    }
+    // Now, we create a scan builder, which will be used by pruneFilterProject. This scan builder
+    // will union all partitions and attach partition values if needed.
+    val scanBuilder = {
+      (columns: Seq[Attribute], filters: Array[Filter]) => {
+        // Builds RDD[Row]s for each selected partition.
+        val perPartitionRows = partitions.map { case Partition(partitionValues, dir) =>
+          val partitionColNames = partitionColumns.fieldNames
+
+          // Don't scan any partition columns to save I/O.  Here we are being optimistic and
+          // assuming partition columns data stored in data files are always consistent with those
+          // partition values encoded in partition directory paths.
+          val needed = columns.filterNot(a => partitionColNames.contains(a.name))
+          val dataRows =
+            relation.buildScan(needed.map(_.name).toArray, filters, Array(dir), confBroadcast)
+
+          // Merges data values with partition values.
+          mergeWithPartitionValues(
+            relation.schema,
+            columns.map(_.name).toArray,
+            partitionColNames,
+            partitionValues,
+            toCatalystRDD(logicalRelation, needed, dataRows))
+        }
+
+        val unionedRows =
+          if (perPartitionRows.length == 0) {
+            relation.sqlContext.emptyResult
+          } else {
+            new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
+          }
 
-    val unionedRows =
-      if (perPartitionRows.length == 0) {
-        relation.sqlContext.emptyResult
-      } else {
-        new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
+        unionedRows
       }
+    }
+
+    // Create the scan operator. If needed, add Filter and/or Project on top of the scan.
+    // The added Filter/Project is on top of the unioned RDD. We do not want to create
+    // one Filter/Project for every partition.
+    val sparkPlan = pruneFilterProject(
+      logicalRelation,
+      projections,
+      filters,
+      scanBuilder)
 
-    execution.PhysicalRDD.createFromDataSource(
-      projections.map(_.toAttribute),
-      unionedRows,
-      logicalRelation.relation)
+    sparkPlan
   }
 
   // TODO: refactor this thing. It is very complicated because it does projection internally.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index f682ca0d8ff4f..fe13dfbbed385 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -196,6 +196,13 @@ private[parquet] class CatalystRowConverter(
       }
     }
 
+    if (paddedParquetFields.length != catalystType.length) {
+      throw new UnsupportedOperationException(
+        "A Parquet file's schema has different number of fields with the table schema. " +
+          "Please enable schema merging by setting \"mergeSchema\" to true when load " +
+          "a Parquet dataset or set spark.sql.parquet.mergeSchema to true in SQLConf.")
+    }
+
     paddedParquetFields.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index cb4cedddbfddd..06dadbb5feab0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -23,7 +23,7 @@ import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.{AnalysisException, SaveMode}
+import org.apache.spark.sql.{execution, AnalysisException, SaveMode}
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 
@@ -136,4 +136,17 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
       assert(fs.exists(commonSummaryPath))
     }
   }
+
+  test("SPARK-10334 Projections and filters should be kept in physical plan") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      sqlContext.range(2).select('id as 'a, 'id as 'b).write.partitionBy("b").parquet(path)
+      val df = sqlContext.read.parquet(path).filter('a === 0).select('b)
+      val physicalPlan = df.queryExecution.executedPlan
+
+      assert(physicalPlan.collect { case p: execution.Project => p }.length === 1)
+      assert(physicalPlan.collect { case p: execution.Filter => p }.length === 1)
+    }
+  }
 }

From 13f5f8ec97c6886346641b73bd99004e0d70836c Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Sat, 29 Aug 2015 18:10:44 -0700
Subject: [PATCH 1283/1454] [SPARK-9986] [SPARK-9991] [SPARK-9993] [SQL] Create
 a simple test framework for local operators

This PR includes the following changes:
- Add `LocalNodeTest` for local operator tests and add unit tests for FilterNode and ProjectNode.
- Add `LimitNode` and `UnionNode` and their unit tests to show how to use `LocalNodeTest`. (SPARK-9991, SPARK-9993)

Author: zsxwing <zsxwing@gmail.com>

Closes #8464 from zsxwing/local-execution.
---
 .../sql/execution/local/FilterNode.scala      |   6 +-
 .../spark/sql/execution/local/LimitNode.scala |  45 ++++++
 .../spark/sql/execution/local/LocalNode.scala |  13 +-
 .../sql/execution/local/ProjectNode.scala     |   4 +-
 .../sql/execution/local/SeqScanNode.scala     |   2 +-
 .../spark/sql/execution/local/UnionNode.scala |  72 +++++++++
 .../spark/sql/execution/SparkPlanTest.scala   |  46 +-----
 .../sql/execution/local/FilterNodeSuite.scala |  41 +++++
 .../sql/execution/local/LimitNodeSuite.scala  |  39 +++++
 .../sql/execution/local/LocalNodeTest.scala   | 146 ++++++++++++++++++
 .../execution/local/ProjectNodeSuite.scala    |  44 ++++++
 .../sql/execution/local/UnionNodeSuite.scala  |  52 +++++++
 .../apache/spark/sql/test/SQLTestData.scala   |   8 +
 .../apache/spark/sql/test/SQLTestUtils.scala  |  46 +++++-
 14 files changed, 509 insertions(+), 55 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
index a485a1a1d7ae4..81dd37c7da733 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
@@ -35,13 +35,13 @@ case class FilterNode(condition: Expression, child: LocalNode) extends UnaryLoca
 
   override def next(): Boolean = {
     var found = false
-    while (child.next() && !found) {
-      found = predicate.apply(child.get())
+    while (!found && child.next()) {
+      found = predicate.apply(child.fetch())
     }
     found
   }
 
-  override def get(): InternalRow = child.get()
+  override def fetch(): InternalRow = child.fetch()
 
   override def close(): Unit = child.close()
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
new file mode 100644
index 0000000000000..fffc52abf6dd5
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
@@ -0,0 +1,45 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+
+case class LimitNode(limit: Int, child: LocalNode) extends UnaryLocalNode {
+
+  private[this] var count = 0
+
+  override def output: Seq[Attribute] = child.output
+
+  override def open(): Unit = child.open()
+
+  override def close(): Unit = child.close()
+
+  override def fetch(): InternalRow = child.fetch()
+
+  override def next(): Boolean = {
+    if (count < limit) {
+      count += 1
+      child.next()
+    } else {
+      false
+    }
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index 341c81438e6d6..1c4469acbf264 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -48,10 +48,10 @@ abstract class LocalNode extends TreeNode[LocalNode] {
   /**
    * Returns the current tuple.
    */
-  def get(): InternalRow
+  def fetch(): InternalRow
 
   /**
-   * Closes the iterator and releases all resources.
+   * Closes the iterator and releases all resources. It should be idempotent.
    *
    * Implementations of this must also call the `close()` function of its children.
    */
@@ -64,10 +64,13 @@ abstract class LocalNode extends TreeNode[LocalNode] {
     val converter = CatalystTypeConverters.createToScalaConverter(StructType.fromAttributes(output))
     val result = new scala.collection.mutable.ArrayBuffer[Row]
     open()
-    while (next()) {
-      result += converter.apply(get()).asInstanceOf[Row]
+    try {
+      while (next()) {
+        result += converter.apply(fetch()).asInstanceOf[Row]
+      }
+    } finally {
+      close()
     }
-    close()
     result
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
index e574d1473cdcb..9b8a4fe493026 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
@@ -34,8 +34,8 @@ case class ProjectNode(projectList: Seq[NamedExpression], child: LocalNode) exte
 
   override def next(): Boolean = child.next()
 
-  override def get(): InternalRow = {
-    project.apply(child.get())
+  override def fetch(): InternalRow = {
+    project.apply(child.fetch())
   }
 
   override def close(): Unit = child.close()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
index 994de8afa9a02..242cb66e07b7f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
@@ -41,7 +41,7 @@ case class SeqScanNode(output: Seq[Attribute], data: Seq[InternalRow]) extends L
     }
   }
 
-  override def get(): InternalRow = currentRow
+  override def fetch(): InternalRow = currentRow
 
   override def close(): Unit = {
     // Do nothing
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
new file mode 100644
index 0000000000000..ba4aa7671aebd
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
@@ -0,0 +1,72 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+case class UnionNode(children: Seq[LocalNode]) extends LocalNode {
+
+  override def output: Seq[Attribute] = children.head.output
+
+  private[this] var currentChild: LocalNode = _
+
+  private[this] var nextChildIndex: Int = _
+
+  override def open(): Unit = {
+    currentChild = children.head
+    currentChild.open()
+    nextChildIndex = 1
+  }
+
+  private def advanceToNextChild(): Boolean = {
+    var found = false
+    var exit = false
+    while (!exit && !found) {
+      if (currentChild != null) {
+        currentChild.close()
+      }
+      if (nextChildIndex >= children.size) {
+        found = false
+        exit = true
+      } else {
+        currentChild = children(nextChildIndex)
+        nextChildIndex += 1
+        currentChild.open()
+        found = currentChild.next()
+      }
+    }
+    found
+  }
+
+  override def close(): Unit = {
+    if (currentChild != null) {
+      currentChild.close()
+    }
+  }
+
+  override def fetch(): InternalRow = currentChild.fetch()
+
+  override def next(): Boolean = {
+    if (currentChild.next()) {
+      true
+    } else {
+      advanceToNextChild()
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index 3a87f374d94b0..5ab8f44faebf6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -24,7 +24,7 @@ import scala.util.control.NonFatal
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.{DataFrame, DataFrameHolder, Row, SQLContext}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.test.SQLTestUtils
 
 /**
  * Base class for writing tests for individual physical operators. For an example of how this
@@ -184,7 +184,7 @@ object SparkPlanTest {
         return Some(errorMessage)
     }
 
-    compareAnswers(actualAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
+    SQLTestUtils.compareAnswers(actualAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
       s"""
          | Results do not match.
          | Actual result Spark plan:
@@ -229,7 +229,7 @@ object SparkPlanTest {
         return Some(errorMessage)
     }
 
-    compareAnswers(sparkAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
+    SQLTestUtils.compareAnswers(sparkAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
       s"""
          | Results do not match for Spark plan:
          | $outputPlan
@@ -238,46 +238,6 @@ object SparkPlanTest {
     }
   }
 
-  private def compareAnswers(
-      sparkAnswer: Seq[Row],
-      expectedAnswer: Seq[Row],
-      sort: Boolean): Option[String] = {
-    def prepareAnswer(answer: Seq[Row]): Seq[Row] = {
-      // Converts data to types that we can do equality comparison using Scala collections.
-      // For BigDecimal type, the Scala type has a better definition of equality test (similar to
-      // Java's java.math.BigDecimal.compareTo).
-      // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for
-      // equality test.
-      // This function is copied from Catalyst's QueryTest
-      val converted: Seq[Row] = answer.map { s =>
-        Row.fromSeq(s.toSeq.map {
-          case d: java.math.BigDecimal => BigDecimal(d)
-          case b: Array[Byte] => b.toSeq
-          case o => o
-        })
-      }
-      if (sort) {
-        converted.sortBy(_.toString())
-      } else {
-        converted
-      }
-    }
-    if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) {
-      val errorMessage =
-        s"""
-           | == Results ==
-           | ${sideBySide(
-              s"== Expected Answer - ${expectedAnswer.size} ==" +:
-              prepareAnswer(expectedAnswer).map(_.toString()),
-              s"== Actual Answer - ${sparkAnswer.size} ==" +:
-              prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")}
-      """.stripMargin
-      Some(errorMessage)
-    } else {
-      None
-    }
-  }
-
   private def executePlan(outputPlan: SparkPlan, _sqlContext: SQLContext): Seq[Row] = {
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
new file mode 100644
index 0000000000000..07209f3779248
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
@@ -0,0 +1,41 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class FilterNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    val condition = (testData.col("key") % 2) === 0
+    checkAnswer(
+      testData,
+      node => FilterNode(condition.expr, node),
+      testData.filter(condition).collect()
+    )
+  }
+
+  test("empty") {
+    val condition = (emptyTestData.col("key") % 2) === 0
+    checkAnswer(
+      emptyTestData,
+      node => FilterNode(condition.expr, node),
+      emptyTestData.filter(condition).collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
new file mode 100644
index 0000000000000..523c02f4a6014
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
@@ -0,0 +1,39 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class LimitNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    checkAnswer(
+      testData,
+      node => LimitNode(10, node),
+      testData.limit(10).collect()
+    )
+  }
+
+  test("empty") {
+    checkAnswer(
+      emptyTestData,
+      node => LimitNode(10, node),
+      emptyTestData.limit(10).collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
new file mode 100644
index 0000000000000..95f06081bd0a8
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
@@ -0,0 +1,146 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import scala.util.control.NonFatal
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.test.SQLTestUtils
+
+class LocalNodeTest extends SparkFunSuite {
+
+  /**
+   * Runs the LocalNode and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param nodeFunction a function which accepts the input LocalNode and uses it to instantiate
+   *                     the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  protected def checkAnswer(
+      input: DataFrame,
+      nodeFunction: LocalNode => LocalNode,
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean = true): Unit = {
+    doCheckAnswer(
+      input :: Nil,
+      nodes => nodeFunction(nodes.head),
+      expectedAnswer,
+      sortAnswers)
+  }
+
+  /**
+   * Runs the LocalNode and makes sure the answer matches the expected result.
+   * @param left the left input data to be used.
+   * @param right the right input data to be used.
+   * @param nodeFunction a function which accepts the input LocalNode and uses it to instantiate
+   *                     the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  protected def checkAnswer2(
+      left: DataFrame,
+      right: DataFrame,
+      nodeFunction: (LocalNode, LocalNode) => LocalNode,
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean = true): Unit = {
+    doCheckAnswer(
+      left :: right :: Nil,
+      nodes => nodeFunction(nodes(0), nodes(1)),
+      expectedAnswer,
+      sortAnswers)
+  }
+
+  /**
+   * Runs the `LocalNode`s and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param nodeFunction a function which accepts a sequence of input `LocalNode`s and uses them to
+   *                     instantiate the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  protected def doCheckAnswer(
+    input: Seq[DataFrame],
+    nodeFunction: Seq[LocalNode] => LocalNode,
+    expectedAnswer: Seq[Row],
+    sortAnswers: Boolean = true): Unit = {
+    LocalNodeTest.checkAnswer(
+      input.map(dataFrameToSeqScanNode), nodeFunction, expectedAnswer, sortAnswers) match {
+      case Some(errorMessage) => fail(errorMessage)
+      case None =>
+    }
+  }
+
+  protected def dataFrameToSeqScanNode(df: DataFrame): SeqScanNode = {
+    new SeqScanNode(
+      df.queryExecution.sparkPlan.output,
+      df.queryExecution.toRdd.map(_.copy()).collect())
+  }
+
+}
+
+/**
+ * Helper methods for writing tests of individual local physical operators.
+ */
+object LocalNodeTest {
+
+  /**
+   * Runs the `LocalNode`s and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param nodeFunction a function which accepts the input `LocalNode`s and uses them to
+   *                     instantiate the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  def checkAnswer(
+    input: Seq[SeqScanNode],
+    nodeFunction: Seq[LocalNode] => LocalNode,
+    expectedAnswer: Seq[Row],
+    sortAnswers: Boolean): Option[String] = {
+
+    val outputNode = nodeFunction(input)
+
+    val outputResult: Seq[Row] = try {
+      outputNode.collect()
+    } catch {
+      case NonFatal(e) =>
+        val errorMessage =
+          s"""
+              | Exception thrown while executing local plan:
+              | $outputNode
+              | == Exception ==
+              | $e
+              | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
+          """.stripMargin
+        return Some(errorMessage)
+    }
+
+    SQLTestUtils.compareAnswers(outputResult, expectedAnswer, sortAnswers).map { errorMessage =>
+      s"""
+          | Results do not match for local plan:
+          | $outputNode
+          | $errorMessage
+       """.stripMargin
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
new file mode 100644
index 0000000000000..ffcf092e2c66a
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
@@ -0,0 +1,44 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class ProjectNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    val output = testData.queryExecution.sparkPlan.output
+    val columns = Seq(output(1), output(0))
+    checkAnswer(
+      testData,
+      node => ProjectNode(columns, node),
+      testData.select("value", "key").collect()
+    )
+  }
+
+  test("empty") {
+    val output = emptyTestData.queryExecution.sparkPlan.output
+    val columns = Seq(output(1), output(0))
+    checkAnswer(
+      emptyTestData,
+      node => ProjectNode(columns, node),
+      emptyTestData.select("value", "key").collect()
+    )
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
new file mode 100644
index 0000000000000..34670287c3e1d
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
@@ -0,0 +1,52 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    checkAnswer2(
+      testData,
+      testData,
+      (node1, node2) => UnionNode(Seq(node1, node2)),
+      testData.unionAll(testData).collect()
+    )
+  }
+
+  test("empty") {
+    checkAnswer2(
+      emptyTestData,
+      emptyTestData,
+      (node1, node2) => UnionNode(Seq(node1, node2)),
+      emptyTestData.unionAll(emptyTestData).collect()
+    )
+  }
+
+  test("complicated union") {
+    val dfs = Seq(testData, emptyTestData, emptyTestData, testData, testData, emptyTestData,
+      emptyTestData, emptyTestData, testData, emptyTestData)
+    doCheckAnswer(
+      dfs,
+      nodes => UnionNode(nodes),
+      dfs.reduce(_.unionAll(_)).collect()
+    )
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
index 1374a97476ca1..3fc02df954e23 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -36,6 +36,13 @@ private[sql] trait SQLTestData { self =>
 
   // Note: all test data should be lazy because the SQLContext is not set up yet.
 
+  protected lazy val emptyTestData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      Seq.empty[Int].map(i => TestData(i, i.toString))).toDF()
+    df.registerTempTable("emptyTestData")
+    df
+  }
+
   protected lazy val testData: DataFrame = {
     val df = _sqlContext.sparkContext.parallelize(
       (1 to 100).map(i => TestData(i, i.toString))).toDF()
@@ -240,6 +247,7 @@ private[sql] trait SQLTestData { self =>
    */
   def loadTestData(): Unit = {
     assert(_sqlContext != null, "attempted to initialize test data before SQLContext.")
+    emptyTestData
     testData
     testData2
     testData3
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index cdd691e035897..dc08306ad9cb4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -27,8 +27,9 @@ import org.apache.hadoop.conf.Configuration
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
+import org.apache.spark.sql.{DataFrame, Row, SQLContext, SQLImplicits}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.util.Utils
 
 /**
@@ -179,3 +180,46 @@ private[sql] trait SQLTestUtils
     DataFrame(_sqlContext, plan)
   }
 }
+
+private[sql] object SQLTestUtils {
+
+  def compareAnswers(
+      sparkAnswer: Seq[Row],
+      expectedAnswer: Seq[Row],
+      sort: Boolean): Option[String] = {
+    def prepareAnswer(answer: Seq[Row]): Seq[Row] = {
+      // Converts data to types that we can do equality comparison using Scala collections.
+      // For BigDecimal type, the Scala type has a better definition of equality test (similar to
+      // Java's java.math.BigDecimal.compareTo).
+      // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for
+      // equality test.
+      // This function is copied from Catalyst's QueryTest
+      val converted: Seq[Row] = answer.map { s =>
+        Row.fromSeq(s.toSeq.map {
+          case d: java.math.BigDecimal => BigDecimal(d)
+          case b: Array[Byte] => b.toSeq
+          case o => o
+        })
+      }
+      if (sort) {
+        converted.sortBy(_.toString())
+      } else {
+        converted
+      }
+    }
+    if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) {
+      val errorMessage =
+        s"""
+           | == Results ==
+           | ${sideBySide(
+          s"== Expected Answer - ${expectedAnswer.size} ==" +:
+            prepareAnswer(expectedAnswer).map(_.toString()),
+          s"== Actual Answer - ${sparkAnswer.size} ==" +:
+            prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")}
+      """.stripMargin
+      Some(errorMessage)
+    } else {
+      None
+    }
+  }
+}

From 905fbe498bdd29116468628e6a2a553c1fd57165 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sat, 29 Aug 2015 23:26:23 -0700
Subject: [PATCH 1284/1454] [SPARK-10348] [MLLIB] updates ml-guide

* replace `ML Dataset` by `DataFrame` to unify the abstraction
* ML algorithms -> pipeline components to describe the main concept
* remove Scala API doc links from the main guide
* `Section Title` -> `Section tile` to be consistent with other section titles in MLlib guide
* modified lines break at 100 chars or periods

jkbradley feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8517 from mengxr/SPARK-10348.
---
 docs/ml-guide.md    | 118 +++++++++++++++++++++++++++-----------------
 docs/mllib-guide.md |  12 ++---
 2 files changed, 78 insertions(+), 52 deletions(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index a92a285f3af85..4ba07542bfb40 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -24,61 +24,74 @@ title: Spark ML Programming Guide
 The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
 [DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
 machine learning pipelines.
-See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of
+See the [algorithm guides](#algorithm-guides) section below for guides on sub-packages of
 `spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
 
-**Table of Contents**
+**Table of contents**
 
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-# Main Concepts
+# Main concepts
 
-Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow.  This section covers the key concepts introduced by the Spark ML API.
+Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
+algorithms into a single pipeline, or workflow.
+This section covers the key concepts introduced by the Spark ML API, where the pipeline concept is
+mostly inspired by the [scikit-learn](http://scikit-learn.org/) project.
 
-* **[ML Dataset](ml-guide.html#ml-dataset)**: Spark ML uses the [`DataFrame`](api/scala/index.html#org.apache.spark.sql.DataFrame) from Spark SQL as a dataset which can hold a variety of data types.
-E.g., a dataset could have different columns storing text, feature vectors, true labels, and predictions.
+* **[`DataFrame`](ml-guide.html#dataframe)**: Spark ML uses `DataFrame` from Spark SQL as an ML
+  dataset, which can hold a variety of data types.
+  E.g., a `DataFrame` could have different columns storing text, feature vectors, true labels, and predictions.
 
 * **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `DataFrame` into another `DataFrame`.
-E.g., an ML model is a `Transformer` which transforms an RDD with features into an RDD with predictions.
+E.g., an ML model is a `Transformer` which transforms `DataFrame` with features into a `DataFrame` with predictions.
 
 * **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `DataFrame` to produce a `Transformer`.
-E.g., a learning algorithm is an `Estimator` which trains on a dataset and produces a model.
+E.g., a learning algorithm is an `Estimator` which trains on a `DataFrame` and produces a model.
 
 * **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow.
 
-* **[`Param`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.
+* **[`Parameter`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.
 
-## ML Dataset
+## DataFrame
 
 Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
-Spark ML adopts the [`DataFrame`](api/scala/index.html#org.apache.spark.sql.DataFrame) from Spark SQL in order to support a variety of data types under a unified Dataset concept.
+Spark ML adopts the `DataFrame` from Spark SQL in order to support a variety of data types.
 
 `DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types.
-In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) types.
+In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](mllib-data-types.html#local-vector) types.
 
 A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`.  See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
 
 Columns in a `DataFrame` are named.  The code examples below use names such as "text," "features," and "label."
 
-## ML Algorithms
+## Pipeline components
 
 ### Transformers
 
-A [`Transformer`](api/scala/index.html#org.apache.spark.ml.Transformer) is an abstraction which includes feature transformers and learned models.  Technically, a `Transformer` implements a method `transform()` which converts one `DataFrame` into another, generally by appending one or more columns.
+A `Transformer` is an abstraction that includes feature transformers and learned models.
+Technically, a `Transformer` implements a method `transform()`, which converts one `DataFrame` into
+another, generally by appending one or more columns.
 For example:
 
-* A feature transformer might take a dataset, read a column (e.g., text), convert it into a new column (e.g., feature vectors), append the new column to the dataset, and output the updated dataset.
-* A learning model might take a dataset, read the column containing feature vectors, predict the label for each feature vector, append the labels as a new column, and output the updated dataset.
+* A feature transformer might take a `DataFrame`, read a column (e.g., text), map it into a new
+  column (e.g., feature vectors), and output a new `DataFrame` with the mapped column appended.
+* A learning model might take a `DataFrame`, read the column containing feature vectors, predict the
+  label for each feature vector, and output a new `DataFrame` with predicted labels appended as a
+  column.
 
 ### Estimators
 
-An [`Estimator`](api/scala/index.html#org.apache.spark.ml.Estimator) abstracts the concept of a learning algorithm or any algorithm which fits or trains on data.  Technically, an `Estimator` implements a method `fit()` which accepts a `DataFrame` and produces a `Transformer`.
-For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling `fit()` trains a `LogisticRegressionModel`, which is a `Transformer`.
+An `Estimator` abstracts the concept of a learning algorithm or any algorithm that fits or trains on
+data.
+Technically, an `Estimator` implements a method `fit()`, which accepts a `DataFrame` and produces a
+`Model`, which is a `Transformer`.
+For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling
+`fit()` trains a `LogisticRegressionModel`, which is a `Model` and hence a `Transformer`.
 
-### Properties of ML Algorithms
+### Properties of pipeline components
 
-`Transformer`s and `Estimator`s are both stateless.  In the future, stateful algorithms may be supported via alternative concepts.
+`Transformer.transform()`s and `Estimator.fit()`s are both stateless.  In the future, stateful algorithms may be supported via alternative concepts.
 
 Each instance of a `Transformer` or `Estimator` has a unique ID, which is useful in specifying parameters (discussed below).
 
@@ -91,15 +104,16 @@ E.g., a simple text document processing workflow might include several stages:
 * Convert each document's words into a numerical feature vector.
 * Learn a prediction model using the feature vectors and labels.
 
-Spark ML represents such a workflow as a [`Pipeline`](api/scala/index.html#org.apache.spark.ml.Pipeline),
-which consists of a sequence of [`PipelineStage`s](api/scala/index.html#org.apache.spark.ml.PipelineStage) (`Transformer`s and `Estimator`s) to be run in a specific order.  We will use this simple workflow as a running example in this section.
+Spark ML represents such a workflow as a `Pipeline`, which consists of a sequence of
+`PipelineStage`s (`Transformer`s and `Estimator`s) to be run in a specific order.
+We will use this simple workflow as a running example in this section.
 
-### How It Works
+### How it works
 
 A `Pipeline` is specified as a sequence of stages, and each stage is either a `Transformer` or an `Estimator`.
-These stages are run in order, and the input dataset is modified as it passes through each stage.
-For `Transformer` stages, the `transform()` method is called on the dataset.
-For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the dataset.
+These stages are run in order, and the input `DataFrame` is transformed as it passes through each stage.
+For `Transformer` stages, the `transform()` method is called on the `DataFrame`.
+For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the `DataFrame`.
 
 We illustrate this for the simple text document workflow.  The figure below is for the *training time* usage of a `Pipeline`.
 
@@ -115,14 +129,17 @@ We illustrate this for the simple text document workflow.  The figure below is f
 Above, the top row represents a `Pipeline` with three stages.
 The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red).
 The bottom row represents data flowing through the pipeline, where cylinders indicate `DataFrame`s.
-The `Pipeline.fit()` method is called on the original dataset which has raw text documents and labels.
-The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words into the dataset.
-The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the dataset.
+The `Pipeline.fit()` method is called on the original `DataFrame`, which has raw text documents and labels.
+The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words to the `DataFrame`.
+The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the `DataFrame`.
 Now, since `LogisticRegression` is an `Estimator`, the `Pipeline` first calls `LogisticRegression.fit()` to produce a `LogisticRegressionModel`.
-If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()` method on the dataset before passing the dataset to the next stage.
+If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()`
+method on the `DataFrame` before passing the `DataFrame` to the next stage.
 
 A `Pipeline` is an `Estimator`.
-Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel` which is a `Transformer`.  This `PipelineModel` is used at *test time*; the figure below illustrates this usage.
+Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel`, which is a
+`Transformer`.
+This `PipelineModel` is used at *test time*; the figure below illustrates this usage.
 
 <p style="text-align: center;">
   <img
@@ -134,7 +151,8 @@ Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel` wh
 </p>
 
 In the figure above, the `PipelineModel` has the same number of stages as the original `Pipeline`, but all `Estimator`s in the original `Pipeline` have become `Transformer`s.
-When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed through the `Pipeline` in order.
+When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed
+through the fitted pipeline in order.
 Each stage's `transform()` method updates the dataset and passes it to the next stage.
 
 `Pipeline`s and `PipelineModel`s help to ensure that training and test data go through identical feature processing steps.
@@ -143,40 +161,48 @@ Each stage's `transform()` method updates the dataset and passes it to the next
 
 *DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array.  The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage.  It is possible to create non-linear `Pipeline`s as long as the data flow graph forms a Directed Acyclic Graph (DAG).  This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters).  If the `Pipeline` forms a DAG, then the stages must be specified in topological order.
 
-*Runtime checking*: Since `Pipeline`s can operate on datasets with varied types, they cannot use compile-time type checking.  `Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.  This type checking is done using the dataset *schema*, a description of the data types of columns in the `DataFrame`.
+*Runtime checking*: Since `Pipeline`s can operate on `DataFrame`s with varied types, they cannot use
+compile-time type checking.
+`Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
+This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.
 
 ## Parameters
 
 Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
 
-A [`Param`](api/scala/index.html#org.apache.spark.ml.param.Param) is a named parameter with self-contained documentation.
-A [`ParamMap`](api/scala/index.html#org.apache.spark.ml.param.ParamMap) is a set of (parameter, value) pairs.
+A `Param` is a named parameter with self-contained documentation.
+A `ParamMap` is a set of (parameter, value) pairs.
 
 There are two main ways to pass parameters to an algorithm:
 
-1. Set parameters for an instance.  E.g., if `lr` is an instance of `LogisticRegression`, one could call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.  This API resembles the API used in MLlib.
+1. Set parameters for an instance.  E.g., if `lr` is an instance of `LogisticRegression`, one could
+   call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.
+   This API resembles the API used in `spark.mllib` package.
 2. Pass a `ParamMap` to `fit()` or `transform()`.  Any parameters in the `ParamMap` will override parameters previously specified via setter methods.
 
 Parameters belong to specific instances of `Estimator`s and `Transformer`s.
 For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
 This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
 
-# Algorithm Guides
+# Algorithm guides
 
 There are now several algorithms in the Pipelines API which are not in the `spark.mllib` API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
 
-**Pipelines API Algorithm Guides**
-
-* [Feature Extraction, Transformation, and Selection](ml-features.html)
-* [Decision Trees for Classification and Regression](ml-decision-tree.html)
+* [Feature extraction, transformation, and selection](ml-features.html)
+* [Decision Trees for classification and regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
 * [Multilayer perceptron classifier](ml-ann.html)
 
-# Code Examples
+# Code examples
 
 This section gives code examples illustrating the functionality discussed above.
-There is not yet documentation for specific algorithms in Spark ML.  For more info, please refer to the [API Documentation](api/scala/index.html#org.apache.spark.ml.package).  Spark ML algorithms are currently wrappers for MLlib algorithms, and the [MLlib programming guide](mllib-guide.html) has details on specific algorithms.
+For more info, please refer to the API documentation
+([Scala](api/scala/index.html#org.apache.spark.ml.package),
+[Java](api/java/org/apache/spark/ml/package-summary.html),
+and [Python](api/python/pyspark.ml.html)).
+Some Spark ML algorithms are wrappers for `spark.mllib` algorithms, and the
+[MLlib programming guide](mllib-guide.html) has details on specific algorithms.
 
 ## Example: Estimator, Transformer, and Param
 
@@ -627,7 +653,7 @@ sc.stop()
 
 </div>
 
-## Example: Model Selection via Cross-Validation
+## Example: model selection via cross-validation
 
 An important task in ML is *model selection*, or using data to find the best model or parameters for a given task.  This is also called *tuning*.
 `Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately.
@@ -873,11 +899,11 @@ jsc.stop();
 
 </div>
 
-## Example: Model Selection via Train Validation Split
+## Example: model selection via train validation split
 In addition to  `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
 `TrainValidationSplit` only evaluates each combination of parameters once as opposed to k times in
  case of `CrossValidator`. It is therefore less expensive,
- but will not produce as reliable results when the training dataset is not sufficiently large..
+ but will not produce as reliable results when the training dataset is not sufficiently large.
 
 `TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
 and an `Evaluator`.
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 876dcfd40ed7b..257f7cc7603fa 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -14,9 +14,9 @@ primitives and higher-level pipeline APIs.
 It divides into two packages:
 
 * [`spark.mllib`](mllib-guide.html#mllib-types-algorithms-and-utilities) contains the original API
-  built on top of RDDs.
+  built on top of [RDDs](programming-guide.html#resilient-distributed-datasets-rdds).
 * [`spark.ml`](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) provides higher-level API
-  built on top of DataFrames for constructing ML pipelines.
+  built on top of [DataFrames](sql-programming-guide.html#dataframes) for constructing ML pipelines.
 
 Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.
 But we will keep supporting `spark.mllib` along with the development of `spark.ml`.
@@ -57,19 +57,19 @@ We list major functionality from both below, with links to detailed guides.
   * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
   * [association rules](mllib-frequent-pattern-mining.html#association-rules)
   * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
-* [Evaluation Metrics](mllib-evaluation-metrics.html)
+* [Evaluation metrics](mllib-evaluation-metrics.html)
+* [PMML model export](mllib-pmml-model-export.html)
 * [Optimization (developer)](mllib-optimization.html)
   * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
   * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
-* [PMML model export](mllib-pmml-model-export.html)
 
 # spark.ml: high-level APIs for ML pipelines
 
 **[spark.ml programming guide](ml-guide.html)** provides an overview of the Pipelines API and major
 concepts. It also contains sections on using algorithms within the Pipelines API, for example:
 
-* [Feature Extraction, Transformation, and Selection](ml-features.html)
-* [Decision Trees for Classification and Regression](ml-decision-tree.html)
+* [Feature extraction, transformation, and selection](ml-features.html)
+* [Decision trees for classification and regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
 * [Multilayer perceptron classifier](ml-ann.html)

From ca69fc8efda8a3e5442ffa16692a2b1eb86b7673 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sat, 29 Aug 2015 23:57:09 -0700
Subject: [PATCH 1285/1454] [SPARK-10331] [MLLIB] Update example code in
 ml-guide

* The example code was added in 1.2, before `createDataFrame`. This PR switches to `createDataFrame`. Java code still uses JavaBean.
* assume `sqlContext` is available
* fix some minor issues from previous code review

jkbradley srowen feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8518 from mengxr/SPARK-10331.
---
 docs/ml-guide.md | 362 +++++++++++++++++++----------------------------
 1 file changed, 147 insertions(+), 215 deletions(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 4ba07542bfb40..78c93a95c7807 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -212,26 +212,18 @@ This example covers the concepts of `Estimator`, `Transformer`, and `Param`.
 
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.Row
 
-val conf = new SparkConf().setAppName("SimpleParamsExample")
-val sc = new SparkContext(conf)
-val sqlContext = new SQLContext(sc)
-import sqlContext.implicits._
-
-// Prepare training data.
-// We use LabeledPoint, which is a case class.  Spark SQL can convert RDDs of case classes
-// into DataFrames, where it uses the case class metadata to infer the schema.
-val training = sc.parallelize(Seq(
-  LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
-  LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
-  LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
-  LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
+// Prepare training data from a list of (label, features) tuples.
+val training = sqlContext.createDataFrame(Seq(
+  (1.0, Vectors.dense(0.0, 1.1, 0.1)),
+  (0.0, Vectors.dense(2.0, 1.0, -1.0)),
+  (0.0, Vectors.dense(2.0, 1.3, 1.0)),
+  (1.0, Vectors.dense(0.0, 1.2, -0.5))
+)).toDF("label", "features")
 
 // Create a LogisticRegression instance.  This instance is an Estimator.
 val lr = new LogisticRegression()
@@ -243,7 +235,7 @@ lr.setMaxIter(10)
   .setRegParam(0.01)
 
 // Learn a LogisticRegression model.  This uses the parameters stored in lr.
-val model1 = lr.fit(training.toDF)
+val model1 = lr.fit(training)
 // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
 // we can view the parameters it used during fit().
 // This prints the parameter (name: value) pairs, where names are unique IDs for this
@@ -253,8 +245,8 @@ println("Model 1 was fit using parameters: " + model1.parent.extractParamMap)
 // We may alternatively specify parameters using a ParamMap,
 // which supports several methods for specifying parameters.
 val paramMap = ParamMap(lr.maxIter -> 20)
-paramMap.put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
-paramMap.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
+  .put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
+  .put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
 
 // One can also combine ParamMaps.
 val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
@@ -262,27 +254,27 @@ val paramMapCombined = paramMap ++ paramMap2
 
 // Now learn a new model using the paramMapCombined parameters.
 // paramMapCombined overrides all parameters set earlier via lr.set* methods.
-val model2 = lr.fit(training.toDF, paramMapCombined)
+val model2 = lr.fit(training, paramMapCombined)
 println("Model 2 was fit using parameters: " + model2.parent.extractParamMap)
 
 // Prepare test data.
-val test = sc.parallelize(Seq(
-  LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
-  LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
-  LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))
+val test = sqlContext.createDataFrame(Seq(
+  (1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+  (0.0, Vectors.dense(3.0, 2.0, -0.1)),
+  (1.0, Vectors.dense(0.0, 2.2, -1.5))
+)).toDF("label", "features")
 
 // Make predictions on test data using the Transformer.transform() method.
 // LogisticRegression.transform will only use the 'features' column.
 // Note that model2.transform() outputs a 'myProbability' column instead of the usual
 // 'probability' column since we renamed the lr.probabilityCol parameter previously.
-model2.transform(test.toDF)
+model2.transform(test)
   .select("features", "label", "myProbability", "prediction")
   .collect()
   .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>
     println(s"($features, $label) -> prob=$prob, prediction=$prediction")
   }
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -291,30 +283,23 @@ sc.stop()
 import java.util.Arrays;
 import java.util.List;
 
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
 import org.apache.spark.ml.param.ParamMap;
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
-import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
-SparkConf conf = new SparkConf().setAppName("JavaSimpleParamsExample");
-JavaSparkContext jsc = new JavaSparkContext(conf);
-SQLContext jsql = new SQLContext(jsc);
-
 // Prepare training data.
 // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
 // into DataFrames, where it uses the bean metadata to infer the schema.
-List<LabeledPoint> localTraining = Arrays.asList(
+DataFrame training = sqlContext.createDataFrame(Arrays.asList(
   new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
-  new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
-DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class);
+  new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))
+), LabeledPoint.class);
 
 // Create a LogisticRegression instance.  This instance is an Estimator.
 LogisticRegression lr = new LogisticRegression();
@@ -334,14 +319,14 @@ LogisticRegressionModel model1 = lr.fit(training);
 System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
 
 // We may alternatively specify parameters using a ParamMap.
-ParamMap paramMap = new ParamMap();
-paramMap.put(lr.maxIter().w(20)); // Specify 1 Param.
-paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
-paramMap.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
+ParamMap paramMap = new ParamMap()
+  .put(lr.maxIter().w(20)) // Specify 1 Param.
+  .put(lr.maxIter(), 30) // This overwrites the original maxIter.
+  .put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
 
 // One can also combine ParamMaps.
-ParamMap paramMap2 = new ParamMap();
-paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name
+ParamMap paramMap2 = new ParamMap()
+  .put(lr.probabilityCol().w("myProbability")); // Change output column name
 ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
 
 // Now learn a new model using the paramMapCombined parameters.
@@ -350,11 +335,11 @@ LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
 System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
 
 // Prepare test documents.
-List<LabeledPoint> localTest = Arrays.asList(
-    new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
-    new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
-    new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
-DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class);
+DataFrame test = sqlContext.createDataFrame(Arrays.asList(
+  new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+  new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
+  new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))
+), LabeledPoint.class);
 
 // Make predictions on test documents using the Transformer.transform() method.
 // LogisticRegression.transform will only use the 'features' column.
@@ -366,28 +351,21 @@ for (Row r: results.select("features", "label", "myProbability", "prediction").c
       + ", prediction=" + r.get(3));
 }
 
-jsc.stop();
 {% endhighlight %}
 </div>
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark import SparkContext
-from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.linalg import Vectors
 from pyspark.ml.classification import LogisticRegression
 from pyspark.ml.param import Param, Params
-from pyspark.sql import Row, SQLContext
 
-sc = SparkContext(appName="SimpleParamsExample")
-sqlContext = SQLContext(sc)
-
-# Prepare training data.
-# We use LabeledPoint.
-# Spark SQL can convert RDDs of LabeledPoints into DataFrames.
-training = sc.parallelize([LabeledPoint(1.0, [0.0, 1.1,  0.1]),
-                           LabeledPoint(0.0, [2.0, 1.0, -1.0]),
-                           LabeledPoint(0.0, [2.0, 1.3,  1.0]),
-                           LabeledPoint(1.0, [0.0, 1.2, -0.5])])
+# Prepare training data from a list of (label, features) tuples.
+training = sqlContext.createDataFrame([
+    (1.0, Vectors.dense([0.0, 1.1, 0.1])),
+    (0.0, Vectors.dense([2.0, 1.0, -1.0])),
+    (0.0, Vectors.dense([2.0, 1.3, 1.0])),
+    (1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
 
 # Create a LogisticRegression instance. This instance is an Estimator.
 lr = LogisticRegression(maxIter=10, regParam=0.01)
@@ -395,7 +373,7 @@ lr = LogisticRegression(maxIter=10, regParam=0.01)
 print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
 
 # Learn a LogisticRegression model. This uses the parameters stored in lr.
-model1 = lr.fit(training.toDF())
+model1 = lr.fit(training)
 
 # Since model1 is a Model (i.e., a transformer produced by an Estimator),
 # we can view the parameters it used during fit().
@@ -416,25 +394,25 @@ paramMapCombined.update(paramMap2)
 
 # Now learn a new model using the paramMapCombined parameters.
 # paramMapCombined overrides all parameters set earlier via lr.set* methods.
-model2 = lr.fit(training.toDF(), paramMapCombined)
+model2 = lr.fit(training, paramMapCombined)
 print "Model 2 was fit using parameters: "
 print model2.extractParamMap()
 
 # Prepare test data
-test = sc.parallelize([LabeledPoint(1.0, [-1.0, 1.5,  1.3]),
-                       LabeledPoint(0.0, [ 3.0, 2.0, -0.1]),
-                       LabeledPoint(1.0, [ 0.0, 2.2, -1.5])])
+test = sqlContext.createDataFrame([
+    (1.0, Vectors.dense([-1.0, 1.5, 1.3])),
+    (0.0, Vectors.dense([3.0, 2.0, -0.1])),
+    (1.0, Vectors.dense([0.0, 2.2, -1.5]))], ["label", "features"])
 
 # Make predictions on test data using the Transformer.transform() method.
 # LogisticRegression.transform will only use the 'features' column.
 # Note that model2.transform() outputs a "myProbability" column instead of the usual
 # 'probability' column since we renamed the lr.probabilityCol parameter previously.
-prediction = model2.transform(test.toDF())
+prediction = model2.transform(test)
 selected = prediction.select("features", "label", "myProbability", "prediction")
 for row in selected.collect():
     print row
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -448,30 +426,19 @@ This example follows the simple text document `Pipeline` illustrated in the figu
 
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.sql.{Row, SQLContext}
-
-// Labeled and unlabeled instance types.
-// Spark SQL can infer schema from case classes.
-case class LabeledDocument(id: Long, text: String, label: Double)
-case class Document(id: Long, text: String)
+import org.apache.spark.sql.Row
 
-// Set up contexts.  Import implicit conversions to DataFrame from sqlContext.
-val conf = new SparkConf().setAppName("SimpleTextClassificationPipeline")
-val sc = new SparkContext(conf)
-val sqlContext = new SQLContext(sc)
-import sqlContext.implicits._
-
-// Prepare training documents, which are labeled.
-val training = sc.parallelize(Seq(
-  LabeledDocument(0L, "a b c d e spark", 1.0),
-  LabeledDocument(1L, "b d", 0.0),
-  LabeledDocument(2L, "spark f g h", 1.0),
-  LabeledDocument(3L, "hadoop mapreduce", 0.0)))
+// Prepare training documents from a list of (id, text, label) tuples.
+val training = sqlContext.createDataFrame(Seq(
+  (0L, "a b c d e spark", 1.0),
+  (1L, "b d", 0.0),
+  (2L, "spark f g h", 1.0),
+  (3L, "hadoop mapreduce", 0.0)
+)).toDF("id", "text", "label")
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 val tokenizer = new Tokenizer()
@@ -488,14 +455,15 @@ val pipeline = new Pipeline()
   .setStages(Array(tokenizer, hashingTF, lr))
 
 // Fit the pipeline to training documents.
-val model = pipeline.fit(training.toDF)
+val model = pipeline.fit(training)
 
-// Prepare test documents, which are unlabeled.
-val test = sc.parallelize(Seq(
-  Document(4L, "spark i j k"),
-  Document(5L, "l m n"),
-  Document(6L, "mapreduce spark"),
-  Document(7L, "apache hadoop")))
+// Prepare test documents, which are unlabeled (id, text) tuples.
+val test = sqlContext.createDataFrame(Seq(
+  (4L, "spark i j k"),
+  (5L, "l m n"),
+  (6L, "mapreduce spark"),
+  (7L, "apache hadoop")
+)).toDF("id", "text")
 
 // Make predictions on test documents.
 model.transform(test.toDF)
@@ -505,7 +473,6 @@ model.transform(test.toDF)
     println(s"($id, $text) --> prob=$prob, prediction=$prediction")
   }
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -514,8 +481,6 @@ sc.stop()
 import java.util.Arrays;
 import java.util.List;
 
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
 import org.apache.spark.ml.PipelineModel;
 import org.apache.spark.ml.PipelineStage;
@@ -524,7 +489,6 @@ import org.apache.spark.ml.feature.HashingTF;
 import org.apache.spark.ml.feature.Tokenizer;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
 
 // Labeled and unlabeled instance types.
 // Spark SQL can infer schema from Java Beans.
@@ -556,18 +520,13 @@ public class LabeledDocument extends Document implements Serializable {
   public void setLabel(double label) { this.label = label; }
 }
 
-// Set up contexts.
-SparkConf conf = new SparkConf().setAppName("JavaSimpleTextClassificationPipeline");
-JavaSparkContext jsc = new JavaSparkContext(conf);
-SQLContext jsql = new SQLContext(jsc);
-
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Arrays.asList(
+DataFrame training = sqlContext.createDataFrame(Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
-  new LabeledDocument(3L, "hadoop mapreduce", 0.0));
-DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class);
+  new LabeledDocument(3L, "hadoop mapreduce", 0.0)
+), LabeledDocument.class);
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 Tokenizer tokenizer = new Tokenizer()
@@ -587,12 +546,12 @@ Pipeline pipeline = new Pipeline()
 PipelineModel model = pipeline.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Arrays.asList(
+DataFrame test = sqlContext.createDataFrame(Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),
-  new Document(7L, "apache hadoop"));
-DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);
+  new Document(7L, "apache hadoop")
+), Document.class);
 
 // Make predictions on test documents.
 DataFrame predictions = model.transform(test);
@@ -601,28 +560,23 @@ for (Row r: predictions.select("id", "text", "probability", "prediction").collec
       + ", prediction=" + r.get(3));
 }
 
-jsc.stop();
 {% endhighlight %}
 </div>
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark import SparkContext
 from pyspark.ml import Pipeline
 from pyspark.ml.classification import LogisticRegression
 from pyspark.ml.feature import HashingTF, Tokenizer
-from pyspark.sql import Row, SQLContext
-
-sc = SparkContext(appName="SimpleTextClassificationPipeline")
-sqlContext = SQLContext(sc)
+from pyspark.sql import Row
 
-# Prepare training documents, which are labeled.
+# Prepare training documents from a list of (id, text, label) tuples.
 LabeledDocument = Row("id", "text", "label")
-training = sc.parallelize([(0L, "a b c d e spark", 1.0),
-                           (1L, "b d", 0.0),
-                           (2L, "spark f g h", 1.0),
-                           (3L, "hadoop mapreduce", 0.0)]) \
-    .map(lambda x: LabeledDocument(*x)).toDF()
+training = sqlContext.createDataFrame([
+    (0L, "a b c d e spark", 1.0),
+    (1L, "b d", 0.0),
+    (2L, "spark f g h", 1.0),
+    (3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
 
 # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
 tokenizer = Tokenizer(inputCol="text", outputCol="words")
@@ -633,13 +587,12 @@ pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
 # Fit the pipeline to training documents.
 model = pipeline.fit(training)
 
-# Prepare test documents, which are unlabeled.
-Document = Row("id", "text")
-test = sc.parallelize([(4L, "spark i j k"),
-                       (5L, "l m n"),
-                       (6L, "mapreduce spark"),
-                       (7L, "apache hadoop")]) \
-    .map(lambda x: Document(*x)).toDF()
+# Prepare test documents, which are unlabeled (id, text) tuples.
+test = sqlContext.createDataFrame([
+    (4L, "spark i j k"),
+    (5L, "l m n"),
+    (6L, "mapreduce spark"),
+    (7L, "apache hadoop")], ["id", "text"])
 
 # Make predictions on test documents and print columns of interest.
 prediction = model.transform(test)
@@ -647,7 +600,6 @@ selected = prediction.select("id", "text", "prediction")
 for row in selected.collect():
     print(row)
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -664,8 +616,8 @@ Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/
 
 The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator)
 for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator)
-for binary data or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
-for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the setMetric
+for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
+for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetric`
 method in each of these evaluators.
 
 The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
@@ -684,39 +636,29 @@ However, it is also a well-established method for choosing parameters which is m
 
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
 import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.sql.{Row, SQLContext}
-
-// Labeled and unlabeled instance types.
-// Spark SQL can infer schema from case classes.
-case class LabeledDocument(id: Long, text: String, label: Double)
-case class Document(id: Long, text: String)
-
-val conf = new SparkConf().setAppName("CrossValidatorExample")
-val sc = new SparkContext(conf)
-val sqlContext = new SQLContext(sc)
-import sqlContext.implicits._
-
-// Prepare training documents, which are labeled.
-val training = sc.parallelize(Seq(
-  LabeledDocument(0L, "a b c d e spark", 1.0),
-  LabeledDocument(1L, "b d", 0.0),
-  LabeledDocument(2L, "spark f g h", 1.0),
-  LabeledDocument(3L, "hadoop mapreduce", 0.0),
-  LabeledDocument(4L, "b spark who", 1.0),
-  LabeledDocument(5L, "g d a y", 0.0),
-  LabeledDocument(6L, "spark fly", 1.0),
-  LabeledDocument(7L, "was mapreduce", 0.0),
-  LabeledDocument(8L, "e spark program", 1.0),
-  LabeledDocument(9L, "a e c l", 0.0),
-  LabeledDocument(10L, "spark compile", 1.0),
-  LabeledDocument(11L, "hadoop software", 0.0)))
+import org.apache.spark.sql.Row
+
+// Prepare training data from a list of (id, text, label) tuples.
+val training = sqlContext.createDataFrame(Seq(
+  (0L, "a b c d e spark", 1.0),
+  (1L, "b d", 0.0),
+  (2L, "spark f g h", 1.0),
+  (3L, "hadoop mapreduce", 0.0),
+  (4L, "b spark who", 1.0),
+  (5L, "g d a y", 0.0),
+  (6L, "spark fly", 1.0),
+  (7L, "was mapreduce", 0.0),
+  (8L, "e spark program", 1.0),
+  (9L, "a e c l", 0.0),
+  (10L, "spark compile", 1.0),
+  (11L, "hadoop software", 0.0)
+)).toDF("id", "text", "label")
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 val tokenizer = new Tokenizer()
@@ -730,15 +672,6 @@ val lr = new LogisticRegression()
 val pipeline = new Pipeline()
   .setStages(Array(tokenizer, hashingTF, lr))
 
-// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
-// This will allow us to jointly choose parameters for all Pipeline stages.
-// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
-// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
-// used is areaUnderROC.
-val crossval = new CrossValidator()
-  .setEstimator(pipeline)
-  .setEvaluator(new BinaryClassificationEvaluator)
-
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
@@ -746,28 +679,37 @@ val paramGrid = new ParamGridBuilder()
   .addGrid(hashingTF.numFeatures, Array(10, 100, 1000))
   .addGrid(lr.regParam, Array(0.1, 0.01))
   .build()
-crossval.setEstimatorParamMaps(paramGrid)
-crossval.setNumFolds(2) // Use 3+ in practice
+
+// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
+// This will allow us to jointly choose parameters for all Pipeline stages.
+// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
+// is areaUnderROC.
+val cv = new CrossValidator()
+  .setEstimator(pipeline)
+  .setEvaluator(new BinaryClassificationEvaluator)
+  .setEstimatorParamMaps(paramGrid)
+  .setNumFolds(2) // Use 3+ in practice
 
 // Run cross-validation, and choose the best set of parameters.
-val cvModel = crossval.fit(training.toDF)
+val cvModel = cv.fit(training)
 
-// Prepare test documents, which are unlabeled.
-val test = sc.parallelize(Seq(
-  Document(4L, "spark i j k"),
-  Document(5L, "l m n"),
-  Document(6L, "mapreduce spark"),
-  Document(7L, "apache hadoop")))
+// Prepare test documents, which are unlabeled (id, text) tuples.
+val test = sqlContext.createDataFrame(Seq(
+  (4L, "spark i j k"),
+  (5L, "l m n"),
+  (6L, "mapreduce spark"),
+  (7L, "apache hadoop")
+)).toDF("id", "text")
 
 // Make predictions on test documents. cvModel uses the best model found (lrModel).
-cvModel.transform(test.toDF)
+cvModel.transform(test)
   .select("id", "text", "probability", "prediction")
   .collect()
   .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
-  println(s"($id, $text) --> prob=$prob, prediction=$prediction")
-}
+    println(s"($id, $text) --> prob=$prob, prediction=$prediction")
+  }
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -776,8 +718,6 @@ sc.stop()
 import java.util.Arrays;
 import java.util.List;
 
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
 import org.apache.spark.ml.PipelineStage;
 import org.apache.spark.ml.classification.LogisticRegression;
@@ -790,7 +730,6 @@ import org.apache.spark.ml.tuning.CrossValidatorModel;
 import org.apache.spark.ml.tuning.ParamGridBuilder;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
 
 // Labeled and unlabeled instance types.
 // Spark SQL can infer schema from Java Beans.
@@ -822,12 +761,9 @@ public class LabeledDocument extends Document implements Serializable {
   public void setLabel(double label) { this.label = label; }
 }
 
-SparkConf conf = new SparkConf().setAppName("JavaCrossValidatorExample");
-JavaSparkContext jsc = new JavaSparkContext(conf);
-SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Arrays.asList(
+DataFrame training = sqlContext.createDataFrame(Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -839,8 +775,8 @@ List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(8L, "e spark program", 1.0),
   new LabeledDocument(9L, "a e c l", 0.0),
   new LabeledDocument(10L, "spark compile", 1.0),
-  new LabeledDocument(11L, "hadoop software", 0.0));
-DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class);
+  new LabeledDocument(11L, "hadoop software", 0.0)
+), LabeledDocument.class);
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 Tokenizer tokenizer = new Tokenizer()
@@ -856,15 +792,6 @@ LogisticRegression lr = new LogisticRegression()
 Pipeline pipeline = new Pipeline()
   .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
 
-// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
-// This will allow us to jointly choose parameters for all Pipeline stages.
-// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
-// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
-// used is areaUnderROC.
-CrossValidator crossval = new CrossValidator()
-    .setEstimator(pipeline)
-    .setEvaluator(new BinaryClassificationEvaluator());
-
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
@@ -872,19 +799,28 @@ ParamMap[] paramGrid = new ParamGridBuilder()
     .addGrid(hashingTF.numFeatures(), new int[]{10, 100, 1000})
     .addGrid(lr.regParam(), new double[]{0.1, 0.01})
     .build();
-crossval.setEstimatorParamMaps(paramGrid);
-crossval.setNumFolds(2); // Use 3+ in practice
+
+// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
+// This will allow us to jointly choose parameters for all Pipeline stages.
+// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
+// is areaUnderROC.
+CrossValidator cv = new CrossValidator()
+  .setEstimator(pipeline)
+  .setEvaluator(new BinaryClassificationEvaluator())
+  .setEstimatorParamMaps(paramGrid)
+  .setNumFolds(2); // Use 3+ in practice
 
 // Run cross-validation, and choose the best set of parameters.
-CrossValidatorModel cvModel = crossval.fit(training);
+CrossValidatorModel cvModel = cv.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Arrays.asList(
+DataFrame test = sqlContext.createDataFrame(Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),
-  new Document(7L, "apache hadoop"));
-DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);
+  new Document(7L, "apache hadoop")
+), Document.class);
 
 // Make predictions on test documents. cvModel uses the best model found (lrModel).
 DataFrame predictions = cvModel.transform(test);
@@ -893,7 +829,6 @@ for (Row r: predictions.select("id", "text", "probability", "prediction").collec
       + ", prediction=" + r.get(3));
 }
 
-jsc.stop();
 {% endhighlight %}
 </div>
 
@@ -935,7 +870,7 @@ val lr = new LinearRegression()
 // the evaluator.
 val paramGrid = new ParamGridBuilder()
   .addGrid(lr.regParam, Array(0.1, 0.01))
-  .addGrid(lr.fitIntercept, Array(true, false))
+  .addGrid(lr.fitIntercept)
   .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
   .build()
 
@@ -945,9 +880,8 @@ val trainValidationSplit = new TrainValidationSplit()
   .setEstimator(lr)
   .setEvaluator(new RegressionEvaluator)
   .setEstimatorParamMaps(paramGrid)
-
-// 80% of the data will be used for training and the remaining 20% for validation.
-trainValidationSplit.setTrainRatio(0.8)
+  // 80% of the data will be used for training and the remaining 20% for validation.
+  .setTrainRatio(0.8)
 
 // Run train validation split, and choose the best set of parameters.
 val model = trainValidationSplit.fit(training)
@@ -972,12 +906,12 @@ import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
-DataFrame data = jsql.createDataFrame(
+DataFrame data = sqlContext.createDataFrame(
   MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"),
   LabeledPoint.class);
 
 // Prepare training and test data.
-DataFrame[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345);
+DataFrame[] splits = data.randomSplit(new double[] {0.9, 0.1}, 12345);
 DataFrame training = splits[0];
 DataFrame test = splits[1];
 
@@ -997,10 +931,8 @@ ParamMap[] paramGrid = new ParamGridBuilder()
 TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
   .setEstimator(lr)
   .setEvaluator(new RegressionEvaluator())
-  .setEstimatorParamMaps(paramGrid);
-
-// 80% of the data will be used for training and the remaining 20% for validation.
-trainValidationSplit.setTrainRatio(0.8);
+  .setEstimatorParamMaps(paramGrid)
+  .setTrainRatio(0.8); // 80% for training and the remaining 20% for validation
 
 // Run train validation split, and choose the best set of parameters.
 TrainValidationSplitModel model = trainValidationSplit.fit(training);

From 1bfd9347822df65e76201c4c471a26488d722319 Mon Sep 17 00:00:00 2001
From: ihainan <ihainan72@gmail.com>
Date: Sun, 30 Aug 2015 08:26:14 +0100
Subject: [PATCH 1286/1454] [SPARK-10184] [CORE] Optimization for bounds
 determination in RangePartitioner

JIRA Issue: https://issues.apache.org/jira/browse/SPARK-10184

Change `cumWeight > target` to `cumWeight >= target` in `RangePartitioner.determineBounds` method to make the output partitions more balanced.

Author: ihainan <ihainan72@gmail.com>

Closes #8397 from ihainan/opt_for_rangepartitioner.
---
 core/src/main/scala/org/apache/spark/Partitioner.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 4b9d59975bdc2..29e581bb57cbc 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -291,7 +291,7 @@ private[spark] object RangePartitioner {
     while ((i < numCandidates) && (j < partitions - 1)) {
       val (key, weight) = ordered(i)
       cumWeight += weight
-      if (cumWeight > target) {
+      if (cumWeight >= target) {
         // Skip duplicate values.
         if (previousBound.isEmpty || ordering.gt(key, previousBound.get)) {
           bounds += key

From 8d2ab75d3b71b632f2394f2453af32f417cb45e5 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Sun, 30 Aug 2015 12:21:15 -0700
Subject: [PATCH 1287/1454] [SPARK-10353] [MLLIB] BLAS gemm not scaling when
 beta = 0.0 for some subset of matrix multiplications

mengxr jkbradley rxin

It would be great if this fix made it into RC3!

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8525 from brkyvz/blas-scaling.
---
 .../org/apache/spark/mllib/linalg/BLAS.scala  | 26 +++++++------------
 .../apache/spark/mllib/linalg/BLASSuite.scala |  5 ++++
 2 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index bbbcc8436b7c2..ab475af264dd3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -305,6 +305,8 @@ private[spark] object BLAS extends Serializable with Logging {
       "The matrix C cannot be the product of a transpose() call. C.isTransposed must be false.")
     if (alpha == 0.0 && beta == 1.0) {
       logDebug("gemm: alpha is equal to 0 and beta is equal to 1. Returning C.")
+    } else if (alpha == 0.0) {
+      f2jBLAS.dscal(C.values.length, beta, C.values, 1)
     } else {
       A match {
         case sparse: SparseMatrix => gemm(alpha, sparse, B, beta, C)
@@ -408,8 +410,8 @@ private[spark] object BLAS extends Serializable with Logging {
         }
       }
     } else {
-      // Scale matrix first if `beta` is not equal to 0.0
-      if (beta != 0.0) {
+      // Scale matrix first if `beta` is not equal to 1.0
+      if (beta != 1.0) {
         f2jBLAS.dscal(C.values.length, beta, C.values, 1)
       }
       // Perform matrix multiplication and add to C. The rows of A are multiplied by the columns of
@@ -470,8 +472,10 @@ private[spark] object BLAS extends Serializable with Logging {
       s"The columns of A don't match the number of elements of x. A: ${A.numCols}, x: ${x.size}")
     require(A.numRows == y.size,
       s"The rows of A don't match the number of elements of y. A: ${A.numRows}, y:${y.size}")
-    if (alpha == 0.0) {
-      logDebug("gemv: alpha is equal to 0. Returning y.")
+    if (alpha == 0.0 && beta == 1.0) {
+      logDebug("gemv: alpha is equal to 0 and beta is equal to 1. Returning y.")
+    } else if (alpha == 0.0) {
+      scal(beta, y)
     } else {
       (A, x) match {
         case (smA: SparseMatrix, dvx: DenseVector) =>
@@ -526,11 +530,6 @@ private[spark] object BLAS extends Serializable with Logging {
     val xValues = x.values
     val yValues = y.values
 
-    if (alpha == 0.0) {
-      scal(beta, y)
-      return
-    }
-
     if (A.isTransposed) {
       var rowCounterForA = 0
       while (rowCounterForA < mA) {
@@ -581,11 +580,6 @@ private[spark] object BLAS extends Serializable with Logging {
     val Arows = if (!A.isTransposed) A.rowIndices else A.colPtrs
     val Acols = if (!A.isTransposed) A.colPtrs else A.rowIndices
 
-    if (alpha == 0.0) {
-      scal(beta, y)
-      return
-    }
-
     if (A.isTransposed) {
       var rowCounter = 0
       while (rowCounter < mA) {
@@ -604,7 +598,7 @@ private[spark] object BLAS extends Serializable with Logging {
         rowCounter += 1
       }
     } else {
-      scal(beta, y)
+      if (beta != 1.0) scal(beta, y)
 
       var colCounterForA = 0
       var k = 0
@@ -659,7 +653,7 @@ private[spark] object BLAS extends Serializable with Logging {
         rowCounter += 1
       }
     } else {
-      scal(beta, y)
+      if (beta != 1.0) scal(beta, y)
       // Perform matrix-vector multiplication and add to y
       var colCounterForA = 0
       while (colCounterForA < nA) {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
index d119e0b50a393..8db5c8424abe9 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
@@ -204,6 +204,7 @@ class BLASSuite extends SparkFunSuite {
     val C14 = C1.copy
     val C15 = C1.copy
     val C16 = C1.copy
+    val C17 = C1.copy
     val expected2 = new DenseMatrix(4, 2, Array(2.0, 1.0, 4.0, 2.0, 4.0, 0.0, 4.0, 3.0))
     val expected3 = new DenseMatrix(4, 2, Array(2.0, 2.0, 4.0, 2.0, 8.0, 0.0, 6.0, 6.0))
     val expected4 = new DenseMatrix(4, 2, Array(5.0, 0.0, 10.0, 5.0, 0.0, 0.0, 5.0, 0.0))
@@ -217,6 +218,10 @@ class BLASSuite extends SparkFunSuite {
     assert(C2 ~== expected2 absTol 1e-15)
     assert(C3 ~== expected3 absTol 1e-15)
     assert(C4 ~== expected3 absTol 1e-15)
+    gemm(1.0, dA, B, 0.0, C17)
+    assert(C17 ~== expected absTol 1e-15)
+    gemm(1.0, sA, B, 0.0, C17)
+    assert(C17 ~== expected absTol 1e-15)
 
     withClue("columns of A don't match the rows of B") {
       intercept[Exception] {

From 35e896a79bb5e72d63b82b047f46f4f6fa2e1970 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Sun, 30 Aug 2015 21:39:16 -0700
Subject: [PATCH 1288/1454] SPARK-9545, SPARK-9547: Use Maven in PRB if title
 contains "[test-maven]"

This is just some small glue code to actually make use of the
AMPLAB_JENKINS_BUILD_TOOL switch. As far as I can tell, we actually
don't currently use the Maven support in the tool even though it exists.
This patch switches to Maven when the PR title contains "test-maven".

There are a few small other pieces of cleanup in the patch as well.

Author: Patrick Wendell <patrick@databricks.com>

Closes #7878 from pwendell/maven-tests.
---
 dev/run-tests-jenkins | 18 ++++++++++++++++--
 dev/run-tests.py      | 28 ++++++++++++++++++++++++++--
 2 files changed, 42 insertions(+), 4 deletions(-)

diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index 39cf54f78104c..3be78575e70f1 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -164,8 +164,9 @@ pr_message=""
 current_pr_head="`git rev-parse HEAD`"
 
 echo "HEAD:  `git rev-parse HEAD`"
-echo "GHPRB: $ghprbActualCommit"
-echo "SHA1:  $sha1"
+echo "\$ghprbActualCommit: $ghprbActualCommit"
+echo "\$sha1:  $sha1"
+echo "\$ghprbPullTitle: $ghprbPullTitle"
 
 # Run pull request tests
 for t in "${PR_TESTS[@]}"; do
@@ -189,6 +190,19 @@ done
 {
   # Marks this build is a pull request build.
   export AMP_JENKINS_PRB=true
+  if [[ $ghprbPullTitle == *"test-maven"* ]]; then
+    export AMPLAB_JENKINS_BUILD_TOOL="maven"
+  fi
+  if [[ $ghprbPullTitle == *"test-hadoop1.0"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop1.0"
+  elif [[ $ghprbPullTitle == *"test-hadoop2.0"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop2.0"
+  elif [[ $ghprbPullTitle == *"test-hadoop2.2"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop2.2"
+  elif [[ $ghprbPullTitle == *"test-hadoop2.3"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop2.3"
+  fi
+
   timeout "${TESTS_TIMEOUT}" ./dev/run-tests
   test_result="$?"
 
diff --git a/dev/run-tests.py b/dev/run-tests.py
index 4fd703a7c219f..d8b22e1665e7b 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -21,6 +21,7 @@
 import itertools
 from optparse import OptionParser
 import os
+import random
 import re
 import sys
 import subprocess
@@ -239,11 +240,32 @@ def build_spark_documentation():
     os.chdir(SPARK_HOME)
 
 
+def get_zinc_port():
+    """
+    Get a randomized port on which to start Zinc
+    """
+    return random.randrange(3030, 4030)
+
+
+def kill_zinc_on_port(zinc_port):
+    """
+    Kill the Zinc process running on the given port, if one exists.
+    """
+    cmd = ("/usr/sbin/lsof -P |grep %s | grep LISTEN "
+           "| awk '{ print $2; }' | xargs kill") % zinc_port
+    subprocess.check_call(cmd, shell=True)
+
+
 def exec_maven(mvn_args=()):
     """Will call Maven in the current directory with the list of mvn_args passed
     in and returns the subprocess for any further processing"""
 
-    run_cmd([os.path.join(SPARK_HOME, "build", "mvn")] + mvn_args)
+    zinc_port = get_zinc_port()
+    os.environ["ZINC_PORT"] = "%s" % zinc_port
+    zinc_flag = "-DzincPort=%s" % zinc_port
+    flags = [os.path.join(SPARK_HOME, "build", "mvn"), "--force", zinc_flag]
+    run_cmd(flags + mvn_args)
+    kill_zinc_on_port(zinc_port)
 
 
 def exec_sbt(sbt_args=()):
@@ -514,7 +536,9 @@ def main():
     build_apache_spark(build_tool, hadoop_version)
 
     # backwards compatibility checks
-    detect_binary_inop_with_mima()
+    if build_tool == "sbt":
+        # Note: compatiblity tests only supported in sbt for now
+        detect_binary_inop_with_mima()
 
     # run the test suites
     run_scala_tests(build_tool, hadoop_version, test_modules)

From 8694c3ad7dcafca9563649e93b7a08076748d6f2 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Sun, 30 Aug 2015 23:12:56 -0700
Subject: [PATCH 1289/1454] [SPARK-10351] [SQL] Fixes UTF8String.fromAddress to
 handle off-heap memory

CC rxin marmbrus

Author: Feynman Liang <fliang@databricks.com>

Closes #8523 from feynmanliang/SPARK-10351.
---
 .../test/scala/org/apache/spark/sql/UnsafeRowSuite.scala | 9 +++++----
 .../java/org/apache/spark/unsafe/types/UTF8String.java   | 6 +-----
 2 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index 219435dff5bc8..2476b10e3cf9e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -43,12 +43,12 @@ class UnsafeRowSuite extends SparkFunSuite {
     val arrayBackedUnsafeRow: UnsafeRow =
       UnsafeProjection.create(Array[DataType](StringType, StringType, IntegerType)).apply(row)
     assert(arrayBackedUnsafeRow.getBaseObject.isInstanceOf[Array[Byte]])
-    val bytesFromArrayBackedRow: Array[Byte] = {
+    val (bytesFromArrayBackedRow, field0StringFromArrayBackedRow): (Array[Byte], String) = {
       val baos = new ByteArrayOutputStream()
       arrayBackedUnsafeRow.writeToStream(baos, null)
-      baos.toByteArray
+      (baos.toByteArray, arrayBackedUnsafeRow.getString(0))
     }
-    val bytesFromOffheapRow: Array[Byte] = {
+    val (bytesFromOffheapRow, field0StringFromOffheapRow): (Array[Byte], String) = {
       val offheapRowPage = MemoryAllocator.UNSAFE.allocate(arrayBackedUnsafeRow.getSizeInBytes)
       try {
         Platform.copyMemory(
@@ -69,13 +69,14 @@ class UnsafeRowSuite extends SparkFunSuite {
         val baos = new ByteArrayOutputStream()
         val writeBuffer = new Array[Byte](1024)
         offheapUnsafeRow.writeToStream(baos, writeBuffer)
-        baos.toByteArray
+        (baos.toByteArray, offheapUnsafeRow.getString(0))
       } finally {
         MemoryAllocator.UNSAFE.free(offheapRowPage)
       }
     }
 
     assert(bytesFromArrayBackedRow === bytesFromOffheapRow)
+    assert(field0StringFromArrayBackedRow === field0StringFromOffheapRow)
   }
 
   test("calling getDouble() and getFloat() on null columns") {
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index cbcab958c05a9..216aeea60d1c8 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -90,11 +90,7 @@ public static UTF8String fromBytes(byte[] bytes, int offset, int numBytes) {
    * Creates an UTF8String from given address (base and offset) and length.
    */
   public static UTF8String fromAddress(Object base, long offset, int numBytes) {
-    if (base != null) {
-      return new UTF8String(base, offset, numBytes);
-    } else {
-      return null;
-    }
+    return new UTF8String(base, offset, numBytes);
   }
 
   /**

From f0f563a3c43fc9683e6920890cce44611c0c5f4b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sun, 30 Aug 2015 23:20:03 -0700
Subject: [PATCH 1290/1454] [SPARK-100354] [MLLIB] fix some apparent memory
 issues in k-means|| initializaiton

* do not cache first cost RDD
* change following cost RDD cache level to MEMORY_AND_DISK
* remove Vector wrapper to save a object per instance

Further improvements will be addressed in SPARK-10329

cc: yu-iskw HuJiayin

Author: Xiangrui Meng <meng@databricks.com>

Closes #8526 from mengxr/SPARK-10354.
---
 .../spark/mllib/clustering/KMeans.scala       | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 46920fffe6e1a..7168aac32c997 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -369,7 +369,7 @@ class KMeans private (
   : Array[Array[VectorWithNorm]] = {
     // Initialize empty centers and point costs.
     val centers = Array.tabulate(runs)(r => ArrayBuffer.empty[VectorWithNorm])
-    var costs = data.map(_ => Vectors.dense(Array.fill(runs)(Double.PositiveInfinity))).cache()
+    var costs = data.map(_ => Array.fill(runs)(Double.PositiveInfinity))
 
     // Initialize each run's first center to a random point.
     val seed = new XORShiftRandom(this.seed).nextInt()
@@ -394,21 +394,28 @@ class KMeans private (
       val bcNewCenters = data.context.broadcast(newCenters)
       val preCosts = costs
       costs = data.zip(preCosts).map { case (point, cost) =>
-        Vectors.dense(
           Array.tabulate(runs) { r =>
             math.min(KMeans.pointCost(bcNewCenters.value(r), point), cost(r))
-          })
-      }.cache()
+          }
+        }.persist(StorageLevel.MEMORY_AND_DISK)
       val sumCosts = costs
-        .aggregate(Vectors.zeros(runs))(
+        .aggregate(new Array[Double](runs))(
           seqOp = (s, v) => {
             // s += v
-            axpy(1.0, v, s)
+            var r = 0
+            while (r < runs) {
+              s(r) += v(r)
+              r += 1
+            }
             s
           },
           combOp = (s0, s1) => {
             // s0 += s1
-            axpy(1.0, s1, s0)
+            var r = 0
+            while (r < runs) {
+              s0(r) += s1(r)
+              r += 1
+            }
             s0
           }
         )

From 72f6dbf7b0c8b271f5f9c762374422c69c8ab43d Mon Sep 17 00:00:00 2001
From: EugenCepoi <cepoi.eugen@gmail.com>
Date: Mon, 31 Aug 2015 13:24:35 -0500
Subject: [PATCH 1291/1454] [SPARK-8730] Fixes - Deser objects containing a
 primitive class attribute

Author: EugenCepoi <cepoi.eugen@gmail.com>

Closes #7122 from EugenCepoi/master.
---
 .../spark/serializer/JavaSerializer.scala     | 27 +++++++++++++++----
 .../serializer/JavaSerializerSuite.scala      | 18 +++++++++++++
 2 files changed, 40 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
index 4a5274b46b7a0..b463a71d5bd7d 100644
--- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
@@ -62,17 +62,34 @@ private[spark] class JavaDeserializationStream(in: InputStream, loader: ClassLoa
   extends DeserializationStream {
 
   private val objIn = new ObjectInputStream(in) {
-    override def resolveClass(desc: ObjectStreamClass): Class[_] = {
-      // scalastyle:off classforname
-      Class.forName(desc.getName, false, loader)
-      // scalastyle:on classforname
-    }
+    override def resolveClass(desc: ObjectStreamClass): Class[_] =
+      try {
+        // scalastyle:off classforname
+        Class.forName(desc.getName, false, loader)
+        // scalastyle:on classforname
+      } catch {
+        case e: ClassNotFoundException =>
+          JavaDeserializationStream.primitiveMappings.get(desc.getName).getOrElse(throw e)
+      }
   }
 
   def readObject[T: ClassTag](): T = objIn.readObject().asInstanceOf[T]
   def close() { objIn.close() }
 }
 
+private object JavaDeserializationStream {
+  val primitiveMappings = Map[String, Class[_]](
+    "boolean" -> classOf[Boolean],
+    "byte" -> classOf[Byte],
+    "char" -> classOf[Char],
+    "short" -> classOf[Short],
+    "int" -> classOf[Int],
+    "long" -> classOf[Long],
+    "float" -> classOf[Float],
+    "double" -> classOf[Double],
+    "void" -> classOf[Void]
+  )
+}
 
 private[spark] class JavaSerializerInstance(
     counterReset: Int, extraDebugInfo: Boolean, defaultClassLoader: ClassLoader)
diff --git a/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala
index 329a2b6dad831..20f45670bc2ba 100644
--- a/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala
@@ -25,4 +25,22 @@ class JavaSerializerSuite extends SparkFunSuite {
     val instance = serializer.newInstance()
     instance.deserialize[JavaSerializer](instance.serialize(serializer))
   }
+
+  test("Deserialize object containing a primitive Class as attribute") {
+    val serializer = new JavaSerializer(new SparkConf())
+    val instance = serializer.newInstance()
+    instance.deserialize[JavaSerializer](instance.serialize(new ContainsPrimitiveClass()))
+  }
+}
+
+private class ContainsPrimitiveClass extends Serializable {
+  val intClass = classOf[Int]
+  val longClass = classOf[Long]
+  val shortClass = classOf[Short]
+  val charClass = classOf[Char]
+  val doubleClass = classOf[Double]
+  val floatClass = classOf[Float]
+  val booleanClass = classOf[Boolean]
+  val byteClass = classOf[Byte]
+  val voidClass = classOf[Void]
 }

From 4a5fe091658b1d06f427e404a11a84fc84f953c5 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 31 Aug 2015 12:19:11 -0700
Subject: [PATCH 1292/1454] [SPARK-10369] [STREAMING] Don't remove
 ReceiverTrackingInfo when deregisterReceivering since we may reuse it later

`deregisterReceiver` should not remove `ReceiverTrackingInfo`. Otherwise, it will throw `java.util.NoSuchElementException: key not found` when restarting it.

Author: zsxwing <zsxwing@gmail.com>

Closes #8538 from zsxwing/SPARK-10369.
---
 .../streaming/scheduler/ReceiverTracker.scala |  4 +-
 .../scheduler/ReceiverTrackerSuite.scala      | 51 +++++++++++++++++++
 2 files changed, 53 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 3d532a675db02..f86fd44b48719 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -291,7 +291,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         ReceiverTrackingInfo(
           streamId, ReceiverState.INACTIVE, None, None, None, None, Some(errorInfo))
     }
-    receiverTrackingInfos -= streamId
+    receiverTrackingInfos(streamId) = newReceiverTrackingInfo
     listenerBus.post(StreamingListenerReceiverStopped(newReceiverTrackingInfo.toReceiverInfo))
     val messageWithError = if (error != null && !error.isEmpty) {
       s"$message - $error"
@@ -483,7 +483,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         context.reply(true)
       // Local messages
       case AllReceiverIds =>
-        context.reply(receiverTrackingInfos.keys.toSeq)
+        context.reply(receiverTrackingInfos.filter(_._2.state != ReceiverState.INACTIVE).keys.toSeq)
       case StopAllReceivers =>
         assert(isTrackerStopping || isTrackerStopped)
         stopReceivers()
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index dd292ba4dd949..45138b748ecab 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -60,6 +60,26 @@ class ReceiverTrackerSuite extends TestSuiteBase {
       }
     }
   }
+
+  test("should restart receiver after stopping it") {
+    withStreamingContext(new StreamingContext(conf, Milliseconds(100))) { ssc =>
+      @volatile var startTimes = 0
+      ssc.addStreamingListener(new StreamingListener {
+        override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = {
+          startTimes += 1
+        }
+      })
+      val input = ssc.receiverStream(new StoppableReceiver)
+      val output = new TestOutputStream(input)
+      output.register()
+      ssc.start()
+      StoppableReceiver.shouldStop = true
+      eventually(timeout(10 seconds), interval(10 millis)) {
+        // The receiver is stopped once, so if it's restarted, it should be started twice.
+        assert(startTimes === 2)
+      }
+    }
+  }
 }
 
 /** An input DStream with for testing rate controlling */
@@ -132,3 +152,34 @@ private[streaming] object RateTestReceiver {
 
   def getActive(): Option[RateTestReceiver] = Option(activeReceiver)
 }
+
+/**
+ * A custom receiver that could be stopped via StoppableReceiver.shouldStop
+ */
+class StoppableReceiver extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
+
+  var receivingThreadOption: Option[Thread] = None
+
+  def onStart() {
+    val thread = new Thread() {
+      override def run() {
+        while (!StoppableReceiver.shouldStop) {
+          Thread.sleep(10)
+        }
+        StoppableReceiver.this.stop("stop")
+      }
+    }
+    thread.start()
+  }
+
+  def onStop() {
+    StoppableReceiver.shouldStop = true
+    receivingThreadOption.foreach(_.join())
+    // Reset it so as to restart it
+    StoppableReceiver.shouldStop = false
+  }
+}
+
+object StoppableReceiver {
+  @volatile var shouldStop = false
+}

From a2d5c72091b1c602694dbca823a7b26f86b02864 Mon Sep 17 00:00:00 2001
From: sureshthalamati <suresh.thalamati@gmail.com>
Date: Mon, 31 Aug 2015 12:39:58 -0700
Subject: [PATCH 1293/1454] [SPARK-10170] [SQL] Add DB2 JDBC dialect support.

Data frame write to DB2 database is failing because by default JDBC data source implementation is generating a table schema with DB2 unsupported data types TEXT for String, and BIT1(1) for Boolean.

This patch registers DB2 JDBC Dialect that maps String, Boolean to valid DB2 data types.

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #8393 from sureshthalamati/db2_dialect_spark-10170.
---
 .../apache/spark/sql/jdbc/JdbcDialects.scala   | 18 ++++++++++++++++++
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala  |  7 +++++++
 2 files changed, 25 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
index 8849fc2f1f0ef..c6d05c9b83b98 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
@@ -125,6 +125,7 @@ object JdbcDialects {
 
   registerDialect(MySQLDialect)
   registerDialect(PostgresDialect)
+  registerDialect(DB2Dialect)
 
   /**
    * Fetch the JdbcDialect class corresponding to a given database url.
@@ -222,3 +223,20 @@ case object MySQLDialect extends JdbcDialect {
     s"`$colName`"
   }
 }
+
+/**
+ * :: DeveloperApi ::
+ * Default DB2 dialect, mapping string/boolean on write to valid DB2 types.
+ * By default string, and boolean gets mapped to db2 invalid types TEXT, and BIT(1).
+ */
+@DeveloperApi
+case object DB2Dialect extends JdbcDialect {
+
+  override def canHandle(url: String): Boolean = url.startsWith("jdbc:db2")
+
+  override def getJDBCType(dt: DataType): Option[JdbcType] = dt match {
+    case StringType => Some(JdbcType("CLOB", java.sql.Types.CLOB))
+    case BooleanType => Some(JdbcType("CHAR(1)", java.sql.Types.CHAR))
+    case _ => None
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 0edac0848c3bb..d8c9a08d84c61 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -407,6 +407,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   test("Default jdbc dialect registration") {
     assert(JdbcDialects.get("jdbc:mysql://127.0.0.1/db") == MySQLDialect)
     assert(JdbcDialects.get("jdbc:postgresql://127.0.0.1/db") == PostgresDialect)
+    assert(JdbcDialects.get("jdbc:db2://127.0.0.1/db") == DB2Dialect)
     assert(JdbcDialects.get("test.invalid") == NoopDialect)
   }
 
@@ -443,4 +444,10 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
     assert(agg.getCatalystType(0, "", 1, null) === Some(LongType))
     assert(agg.getCatalystType(1, "", 1, null) === Some(StringType))
   }
+
+  test("DB2Dialect type mapping") {
+    val db2Dialect = JdbcDialects.get("jdbc:db2://127.0.0.1/db")
+    assert(db2Dialect.getJDBCType(StringType).map(_.databaseTypeDefinition).get == "CLOB")
+    assert(db2Dialect.getJDBCType(BooleanType).map(_.databaseTypeDefinition).get == "CHAR(1)")
+  }
 }

From 23e39cc7b1bb7f1087c4706234c9b5165a571357 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 31 Aug 2015 15:49:25 -0700
Subject: [PATCH 1294/1454] [SPARK-9954] [MLLIB] use first 128 nonzeros to
 compute Vector.hashCode

This could help reduce hash collisions, e.g., in `RDD[Vector].repartition`. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8182 from mengxr/SPARK-9954.
---
 .../apache/spark/mllib/linalg/Vectors.scala   | 38 ++++++++++---------
 1 file changed, 21 insertions(+), 17 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 06ebb15869909..3642e9286504f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -71,20 +71,22 @@ sealed trait Vector extends Serializable {
   }
 
   /**
-   * Returns a hash code value for the vector. The hash code is based on its size and its nonzeros
-   * in the first 16 entries, using a hash algorithm similar to [[java.util.Arrays.hashCode]].
+   * Returns a hash code value for the vector. The hash code is based on its size and its first 128
+   * nonzero entries, using a hash algorithm similar to [[java.util.Arrays.hashCode]].
    */
   override def hashCode(): Int = {
     // This is a reference implementation. It calls return in foreachActive, which is slow.
     // Subclasses should override it with optimized implementation.
     var result: Int = 31 + size
+    var nnz = 0
     this.foreachActive { (index, value) =>
-      if (index < 16) {
+      if (nnz < Vectors.MAX_HASH_NNZ) {
         // ignore explicit 0 for comparison between sparse and dense
         if (value != 0) {
           result = 31 * result + index
           val bits = java.lang.Double.doubleToLongBits(value)
           result = 31 * result + (bits ^ (bits >>> 32)).toInt
+          nnz += 1
         }
       } else {
         return result
@@ -536,6 +538,9 @@ object Vectors {
     }
     allEqual
   }
+
+  /** Max number of nonzero entries used in computing hash code. */
+  private[linalg] val MAX_HASH_NNZ = 128
 }
 
 /**
@@ -578,13 +583,15 @@ class DenseVector @Since("1.0.0") (
   override def hashCode(): Int = {
     var result: Int = 31 + size
     var i = 0
-    val end = math.min(values.length, 16)
-    while (i < end) {
+    val end = values.length
+    var nnz = 0
+    while (i < end && nnz < Vectors.MAX_HASH_NNZ) {
       val v = values(i)
       if (v != 0.0) {
         result = 31 * result + i
         val bits = java.lang.Double.doubleToLongBits(values(i))
         result = 31 * result + (bits ^ (bits >>> 32)).toInt
+        nnz += 1
       }
       i += 1
     }
@@ -707,19 +714,16 @@ class SparseVector @Since("1.0.0") (
   override def hashCode(): Int = {
     var result: Int = 31 + size
     val end = values.length
-    var continue = true
     var k = 0
-    while ((k < end) & continue) {
-      val i = indices(k)
-      if (i < 16) {
-        val v = values(k)
-        if (v != 0.0) {
-          result = 31 * result + i
-          val bits = java.lang.Double.doubleToLongBits(v)
-          result = 31 * result + (bits ^ (bits >>> 32)).toInt
-        }
-      } else {
-        continue = false
+    var nnz = 0
+    while (k < end && nnz < Vectors.MAX_HASH_NNZ) {
+      val v = values(k)
+      if (v != 0.0) {
+        val i = indices(k)
+        result = 31 * result + i
+        val bits = java.lang.Double.doubleToLongBits(v)
+        result = 31 * result + (bits ^ (bits >>> 32)).toInt
+        nnz += 1
       }
       k += 1
     }

From 5b3245d6dff65972fc39c73f90d5cbdf84d19129 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 31 Aug 2015 15:50:41 -0700
Subject: [PATCH 1295/1454] [SPARK-8472] [ML] [PySpark] Python API for DCT

Add Python API for ml.feature.DCT.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8485 from yanboliang/spark-8472.
---
 python/pyspark/ml/feature.py | 65 +++++++++++++++++++++++++++++++++++-
 1 file changed, 64 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 04b2b2ccc9e55..59300a607815b 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -26,7 +26,7 @@
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
-__all__ = ['Binarizer', 'Bucketizer', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
+__all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
            'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
            'StandardScaler', 'StandardScalerModel', 'StringIndexer', 'StringIndexerModel',
            'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel',
@@ -166,6 +166,69 @@ def getSplits(self):
         return self.getOrDefault(self.splits)
 
 
+@inherit_doc
+class DCT(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    A feature transformer that takes the 1D discrete cosine transform
+    of a real vector. No zero padding is performed on the input vector.
+    It returns a real vector of the same length representing the DCT.
+    The return vector is scaled such that the transform matrix is
+    unitary (aka scaled DCT-II).
+
+    More information on
+    `https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia`.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df1 = sqlContext.createDataFrame([(Vectors.dense([5.0, 8.0, 6.0]),)], ["vec"])
+    >>> dct = DCT(inverse=False, inputCol="vec", outputCol="resultVec")
+    >>> df2 = dct.transform(df1)
+    >>> df2.head().resultVec
+    DenseVector([10.969..., -0.707..., -2.041...])
+    >>> df3 = DCT(inverse=True, inputCol="resultVec", outputCol="origVec").transform(df2)
+    >>> df3.head().origVec
+    DenseVector([5.0, 8.0, 6.0])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    inverse = Param(Params._dummy(), "inverse", "Set transformer to perform inverse DCT, " +
+                    "default False.")
+
+    @keyword_only
+    def __init__(self, inverse=False, inputCol=None, outputCol=None):
+        """
+        __init__(self, inverse=False, inputCol=None, outputCol=None)
+        """
+        super(DCT, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.DCT", self.uid)
+        self.inverse = Param(self, "inverse", "Set transformer to perform inverse DCT, " +
+                             "default False.")
+        self._setDefault(inverse=False)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inverse=False, inputCol=None, outputCol=None):
+        """
+        setParams(self, inverse=False, inputCol=None, outputCol=None)
+        Sets params for this DCT.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setInverse(self, value):
+        """
+        Sets the value of :py:attr:`inverse`.
+        """
+        self._paramMap[self.inverse] = value
+        return self
+
+    def getInverse(self):
+        """
+        Gets the value of inverse or its default value.
+        """
+        return self.getOrDefault(self.inverse)
+
+
 @inherit_doc
 class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol):
     """

From 540bdee93103a73736d282b95db6a8cda8f6a2b1 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 31 Aug 2015 15:55:22 -0700
Subject: [PATCH 1296/1454] [SPARK-10341] [SQL] fix memory starving in unsafe
 SMJ

In SMJ, the first ExternalSorter could consume all the memory before spilling, then the second can not even acquire the first page.

Before we have a better memory allocator, SMJ should call prepare() before call any compute() of it's children.

cc rxin JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8511 from davies/smj_memory.
---
 .../rdd/MapPartitionsWithPreparationRDD.scala | 21 +++++++++++++++++--
 .../spark/rdd/ZippedPartitionsRDD.scala       | 13 ++++++++++++
 ...MapPartitionsWithPreparationRDDSuite.scala | 14 +++++++++----
 3 files changed, 42 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
index b475bd8d79f85..1f2213d0c4346 100644
--- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.rdd
 
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
 import org.apache.spark.{Partition, Partitioner, TaskContext}
@@ -38,12 +39,28 @@ private[spark] class MapPartitionsWithPreparationRDD[U: ClassTag, T: ClassTag, M
 
   override def getPartitions: Array[Partition] = firstParent[T].partitions
 
+  // In certain join operations, prepare can be called on the same partition multiple times.
+  // In this case, we need to ensure that each call to compute gets a separate prepare argument.
+  private[this] var preparedArguments: ArrayBuffer[M] = new ArrayBuffer[M]
+
+  /**
+   * Prepare a partition for a single call to compute.
+   */
+  def prepare(): Unit = {
+    preparedArguments += preparePartition()
+  }
+
   /**
    * Prepare a partition before computing it from its parent.
    */
   override def compute(partition: Partition, context: TaskContext): Iterator[U] = {
-    val preparedArgument = preparePartition()
+    val prepared =
+      if (preparedArguments.isEmpty) {
+        preparePartition()
+      } else {
+        preparedArguments.remove(0)
+      }
     val parentIterator = firstParent[T].iterator(partition, context)
-    executePartition(context, partition.index, preparedArgument, parentIterator)
+    executePartition(context, partition.index, prepared, parentIterator)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
index 81f40ad33aa5d..b3c64394abc76 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
@@ -73,6 +73,16 @@ private[spark] abstract class ZippedPartitionsBaseRDD[V: ClassTag](
     super.clearDependencies()
     rdds = null
   }
+
+  /**
+   * Call the prepare method of every parent that has one.
+   * This is needed for reserving execution memory in advance.
+   */
+  protected def tryPrepareParents(): Unit = {
+    rdds.collect {
+      case rdd: MapPartitionsWithPreparationRDD[_, _, _] => rdd.prepare()
+    }
+  }
 }
 
 private[spark] class ZippedPartitionsRDD2[A: ClassTag, B: ClassTag, V: ClassTag](
@@ -84,6 +94,7 @@ private[spark] class ZippedPartitionsRDD2[A: ClassTag, B: ClassTag, V: ClassTag]
   extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2), preservesPartitioning) {
 
   override def compute(s: Partition, context: TaskContext): Iterator[V] = {
+    tryPrepareParents()
     val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
     f(rdd1.iterator(partitions(0), context), rdd2.iterator(partitions(1), context))
   }
@@ -107,6 +118,7 @@ private[spark] class ZippedPartitionsRDD3
   extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3), preservesPartitioning) {
 
   override def compute(s: Partition, context: TaskContext): Iterator[V] = {
+    tryPrepareParents()
     val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
     f(rdd1.iterator(partitions(0), context),
       rdd2.iterator(partitions(1), context),
@@ -134,6 +146,7 @@ private[spark] class ZippedPartitionsRDD4
   extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3, rdd4), preservesPartitioning) {
 
   override def compute(s: Partition, context: TaskContext): Iterator[V] = {
+    tryPrepareParents()
     val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
     f(rdd1.iterator(partitions(0), context),
       rdd2.iterator(partitions(1), context),
diff --git a/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
index c16930e7d6491..e281e817e493d 100644
--- a/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
@@ -46,11 +46,17 @@ class MapPartitionsWithPreparationRDDSuite extends SparkFunSuite with LocalSpark
     }
 
     // Verify that the numbers are pushed in the order expected
-    val result = {
-      new MapPartitionsWithPreparationRDD[Int, Int, Unit](
-        parent, preparePartition, executePartition).collect()
-    }
+    val rdd = new MapPartitionsWithPreparationRDD[Int, Int, Unit](
+      parent, preparePartition, executePartition)
+    val result = rdd.collect()
     assert(result === Array(10, 20, 30))
+
+    TestObject.things.clear()
+    // Zip two of these RDDs, both should be prepared before the parent is executed
+    val rdd2 = new MapPartitionsWithPreparationRDD[Int, Int, Unit](
+      parent, preparePartition, executePartition)
+    val result2 = rdd.zipPartitions(rdd2)((a, b) => a).collect()
+    assert(result2 === Array(10, 10, 20, 30, 20, 30))
   }
 
 }

From fe16fd0b8b717f01151bc659ec3299dab091c97a Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 31 Aug 2015 16:06:38 -0700
Subject: [PATCH 1297/1454] [SPARK-10349] [ML] OneVsRest use 'when ...
 otherwise' not UDF to generate new label at binary reduction

Currently OneVsRest use UDF to generate new binary label during training.
Considering that [SPARK-7321](https://issues.apache.org/jira/browse/SPARK-7321) has been merged, we can use ```when ... otherwise``` which will be more efficiency.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8519 from yanboliang/spark-10349.
---
 .../org/apache/spark/ml/classification/OneVsRest.scala | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index c62e132f5d533..debc164bf2432 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -91,7 +91,6 @@ final class OneVsRestModel private[ml] (
     // add an accumulator column to store predictions of all the models
     val accColName = "mbc$acc" + UUID.randomUUID().toString
     val initUDF = udf { () => Map[Int, Double]() }
-    val mapType = MapType(IntegerType, DoubleType, valueContainsNull = false)
     val newDataset = dataset.withColumn(accColName, initUDF())
 
     // persist if underlying dataset is not persistent.
@@ -195,16 +194,11 @@ final class OneVsRest(override val uid: String)
 
     // create k columns, one for each binary classifier.
     val models = Range(0, numClasses).par.map { index =>
-      val labelUDF = udf { (label: Double) =>
-        if (label.toInt == index) 1.0 else 0.0
-      }
-
       // generate new label metadata for the binary problem.
-      // TODO: use when ... otherwise after SPARK-7321 is merged
       val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
       val labelColName = "mc2b$" + index
-      val trainingDataset =
-        multiclassLabeled.withColumn(labelColName, labelUDF(col($(labelCol))), newLabelMeta)
+      val trainingDataset = multiclassLabeled.withColumn(
+        labelColName, when(col($(labelCol)) === index.toDouble, 1.0).otherwise(0.0), newLabelMeta)
       val classifier = getClassifier
       val paramMap = new ParamMap()
       paramMap.put(classifier.labelCol -> labelColName)

From 52ea399e6ee37b7c44aae7709863e006fca88906 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 31 Aug 2015 16:11:27 -0700
Subject: [PATCH 1298/1454] [SPARK-10355] [ML] [PySpark] Add Python API for
 SQLTransformer

Add Python API for SQLTransformer

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8527 from yanboliang/spark-10355.
---
 python/pyspark/ml/feature.py | 57 ++++++++++++++++++++++++++++++++++--
 1 file changed, 54 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 59300a607815b..0626281e200a1 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -28,9 +28,9 @@
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
            'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
-           'StandardScaler', 'StandardScalerModel', 'StringIndexer', 'StringIndexerModel',
-           'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel',
-           'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
+           'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StringIndexer',
+           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec',
+           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc
@@ -743,6 +743,57 @@ def getPattern(self):
         return self.getOrDefault(self.pattern)
 
 
+@inherit_doc
+class SQLTransformer(JavaTransformer):
+    """
+    Implements the transforms which are defined by SQL statement.
+    Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
+    where '__THIS__' represents the underlying table of the input dataset.
+
+    >>> df = sqlContext.createDataFrame([(0, 1.0, 3.0), (2, 2.0, 5.0)], ["id", "v1", "v2"])
+    >>> sqlTrans = SQLTransformer(
+    ...     statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
+    >>> sqlTrans.transform(df).head()
+    Row(id=0, v1=1.0, v2=3.0, v3=4.0, v4=3.0)
+    """
+
+    # a placeholder to make it appear in the generated doc
+    statement = Param(Params._dummy(), "statement", "SQL statement")
+
+    @keyword_only
+    def __init__(self, statement=None):
+        """
+        __init__(self, statement=None)
+        """
+        super(SQLTransformer, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.SQLTransformer", self.uid)
+        self.statement = Param(self, "statement", "SQL statement")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, statement=None):
+        """
+        setParams(self, statement=None)
+        Sets params for this SQLTransformer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setStatement(self, value):
+        """
+        Sets the value of :py:attr:`statement`.
+        """
+        self._paramMap[self.statement] = value
+        return self
+
+    def getStatement(self):
+        """
+        Gets the value of statement or its default value.
+        """
+        return self.getOrDefault(self.statement)
+
+
 @inherit_doc
 class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol):
     """

From d65656c455d19b83c6412571873586b458aa355e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 31 Aug 2015 18:09:24 -0700
Subject: [PATCH 1299/1454] [SPARK-10378][SQL][Test] Remove
 HashJoinCompatibilitySuite.

They don't bring much value since we now have better unit test coverage for hash joins. This will also help reduce the test time.

Author: Reynold Xin <rxin@databricks.com>

Closes #8542 from rxin/SPARK-10378.
---
 .../HashJoinCompatibilitySuite.scala          | 169 ------------------
 1 file changed, 169 deletions(-)
 delete mode 100644 sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala
deleted file mode 100644
index 1a5ba20404c4e..0000000000000
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala
+++ /dev/null
@@ -1,169 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.execution
-
-import java.io.File
-
-import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.hive.test.TestHive
-
-/**
- * Runs the test cases that are included in the hive distribution with hash joins.
- */
-class HashJoinCompatibilitySuite extends HiveCompatibilitySuite {
-  override def beforeAll() {
-    super.beforeAll()
-    TestHive.setConf(SQLConf.SORTMERGE_JOIN, false)
-  }
-
-  override def afterAll() {
-    TestHive.setConf(SQLConf.SORTMERGE_JOIN, true)
-    super.afterAll()
-  }
-
-  override def whiteList = Seq(
-    "auto_join0",
-    "auto_join1",
-    "auto_join10",
-    "auto_join11",
-    "auto_join12",
-    "auto_join13",
-    "auto_join14",
-    "auto_join14_hadoop20",
-    "auto_join15",
-    "auto_join17",
-    "auto_join18",
-    "auto_join19",
-    "auto_join2",
-    "auto_join20",
-    "auto_join21",
-    "auto_join22",
-    "auto_join23",
-    "auto_join24",
-    "auto_join25",
-    "auto_join26",
-    "auto_join27",
-    "auto_join28",
-    "auto_join3",
-    "auto_join30",
-    "auto_join31",
-    "auto_join32",
-    "auto_join4",
-    "auto_join5",
-    "auto_join6",
-    "auto_join7",
-    "auto_join8",
-    "auto_join9",
-    "auto_join_filters",
-    "auto_join_nulls",
-    "auto_join_reordering_values",
-    "auto_smb_mapjoin_14",
-    "auto_sortmerge_join_1",
-    "auto_sortmerge_join_10",
-    "auto_sortmerge_join_11",
-    "auto_sortmerge_join_12",
-    "auto_sortmerge_join_13",
-    "auto_sortmerge_join_14",
-    "auto_sortmerge_join_15",
-    "auto_sortmerge_join_16",
-    "auto_sortmerge_join_2",
-    "auto_sortmerge_join_3",
-    "auto_sortmerge_join_4",
-    "auto_sortmerge_join_5",
-    "auto_sortmerge_join_6",
-    "auto_sortmerge_join_7",
-    "auto_sortmerge_join_8",
-    "auto_sortmerge_join_9",
-    "correlationoptimizer1",
-    "correlationoptimizer10",
-    "correlationoptimizer11",
-    "correlationoptimizer13",
-    "correlationoptimizer14",
-    "correlationoptimizer15",
-    "correlationoptimizer2",
-    "correlationoptimizer3",
-    "correlationoptimizer4",
-    "correlationoptimizer6",
-    "correlationoptimizer7",
-    "correlationoptimizer8",
-    "correlationoptimizer9",
-    "join0",
-    "join1",
-    "join10",
-    "join11",
-    "join12",
-    "join13",
-    "join14",
-    "join14_hadoop20",
-    "join15",
-    "join16",
-    "join17",
-    "join18",
-    "join19",
-    "join2",
-    "join20",
-    "join21",
-    "join22",
-    "join23",
-    "join24",
-    "join25",
-    "join26",
-    "join27",
-    "join28",
-    "join29",
-    "join3",
-    "join30",
-    "join31",
-    "join32",
-    "join32_lessSize",
-    "join33",
-    "join34",
-    "join35",
-    "join36",
-    "join37",
-    "join38",
-    "join39",
-    "join4",
-    "join40",
-    "join41",
-    "join5",
-    "join6",
-    "join7",
-    "join8",
-    "join9",
-    "join_1to1",
-    "join_array",
-    "join_casesensitive",
-    "join_empty",
-    "join_filters",
-    "join_hive_626",
-    "join_map_ppr",
-    "join_nulls",
-    "join_nullsafe",
-    "join_rc",
-    "join_reorder2",
-    "join_reorder3",
-    "join_reorder4",
-    "join_star"
-  )
-
-  // Only run those query tests in the realWhileList (do not try other ignored query files).
-  override def testCases: Seq[(String, File)] = super.testCases.filter {
-    case (name, _) => realWhiteList.contains(name)
-  }
-}

From 391e6be0ae883f3ea0fab79463eb8b618af79afb Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 1 Sep 2015 16:52:59 +0800
Subject: [PATCH 1300/1454] [SPARK-10301] [SQL] Fixes schema merging for nested
 structs

This PR can be quite challenging to review.  I'm trying to give a detailed description of the problem as well as its solution here.

When reading Parquet files, we need to specify a potentially nested Parquet schema (of type `MessageType`) as requested schema for column pruning.  This Parquet schema is translated from a Catalyst schema (of type `StructType`), which is generated by the query planner and represents all requested columns.  However, this translation can be fairly complicated because of several reasons:

1.  Requested schema must conform to the real schema of the physical file to be read.

    This means we have to tailor the actual file schema of every individual physical Parquet file to be read according to the given Catalyst schema.  Fortunately we are already doing this in Spark 1.5 by pushing request schema conversion to executor side in PR #7231.

1.  Support for schema merging.

    A single Parquet dataset may consist of multiple physical Parquet files come with different but compatible schemas.  This means we may request for a column path that doesn't exist in a physical Parquet file.  All requested column paths can be nested.  For example, for a Parquet file schema

    ```
    message root {
      required group f0 {
        required group f00 {
          required int32 f000;
          required binary f001 (UTF8);
        }
      }
    }
    ```

    we may request for column paths defined in the following schema:

    ```
    message root {
      required group f0 {
        required group f00 {
          required binary f001 (UTF8);
          required float f002;
        }
      }

      optional double f1;
    }
    ```

    Notice that we pruned column path `f0.f00.f000`, but added `f0.f00.f002` and `f1`.

    The good news is that Parquet handles non-existing column paths properly and always returns null for them.

1.  The map from `StructType` to `MessageType` is a one-to-many map.

    This is the most unfortunate part.

    Due to historical reasons (dark histories!), schemas of Parquet files generated by different libraries have different "flavors".  For example, to handle a schema with a single non-nullable column, whose type is an array of non-nullable integers, parquet-protobuf generates the following Parquet schema:

    ```
    message m0 {
      repeated int32 f;
    }
    ```

    while parquet-avro generates another version:

    ```
    message m1 {
      required group f (LIST) {
        repeated int32 array;
      }
    }
    ```

    and parquet-thrift spills this:

    ```
    message m1 {
      required group f (LIST) {
        repeated int32 f_tuple;
      }
    }
    ```

    All of them can be mapped to the following _unique_ Catalyst schema:

    ```
    StructType(
      StructField(
        "f",
        ArrayType(IntegerType, containsNull = false),
        nullable = false))
    ```

    This greatly complicates Parquet requested schema construction, since the path of a given column varies in different cases.  To read the array elements from files with the above schemas, we must use `f` for `m0`, `f.array` for `m1`, and `f.f_tuple` for `m2`.

In earlier Spark versions, we didn't try to fix this issue properly.  Spark 1.4 and prior versions simply translate the Catalyst schema in a way more or less compatible with parquet-hive and parquet-avro, but is broken in many other cases.  Earlier revisions of Spark 1.5 only try to tailor the Parquet file schema at the first level, and ignore nested ones.  This caused [SPARK-10301] [spark-10301] as well as [SPARK-10005] [spark-10005].  In PR #8228, I tried to avoid the hard part of the problem and made a minimum change in `CatalystRowConverter` to fix SPARK-10005.  However, when taking SPARK-10301 into consideration, keeping hacking `CatalystRowConverter` doesn't seem to be a good idea.  So this PR is an attempt to fix the problem in a proper way.

For a given physical Parquet file with schema `ps` and a compatible Catalyst requested schema `cs`, we use the following algorithm to tailor `ps` to get the result Parquet requested schema `ps'`:

For a leaf column path `c` in `cs`:

- if `c` exists in `cs` and a corresponding Parquet column path `c'` can be found in `ps`, `c'` should be included in `ps'`;
- otherwise, we convert `c` to a Parquet column path `c"` using `CatalystSchemaConverter`, and include `c"` in `ps'`;
- no other column paths should exist in `ps'`.

Then comes the most tedious part:

> Given `cs`, `ps`, and `c`, how to locate `c'` in `ps`?

Unfortunately, there's no quick answer, and we have to enumerate all possible structures defined in parquet-format spec.  They are:

1.  the standard structure of nested types, and
1.  cases defined in all backwards-compatibility rules for `LIST` and `MAP`.

The core part of this PR is `CatalystReadSupport.clipParquetType()`, which tailors a given Parquet file schema according to a requested schema in its Catalyst form.  Backwards-compatibility rules of `LIST` and `MAP` are covered in `clipParquetListType()` and `clipParquetMapType()` respectively.  The column path selection algorithm is implemented in `clipParquetGroupFields()`.

With this PR, we no longer need to do schema tailoring in `CatalystReadSupport` and `CatalystRowConverter`.  Another benefit is that, now we can also read Parquet datasets consist of files with different physical Parquet schema but share the same logical schema, for example, files generated by different Parquet libraries.  This situation is illustrated by [this test case] [test-case].

[spark-10301]: https://issues.apache.org/jira/browse/SPARK-10301
[spark-10005]: https://issues.apache.org/jira/browse/SPARK-10005
[test-case]: https://github.com/liancheng/spark/commit/38644d8a45175cbdf20d2ace021c2c2544a50ab3#diff-a9b98e28ce3ae30641829dffd1173be2R26

Author: Cheng Lian <lian@databricks.com>

Closes #8509 from liancheng/spark-10301/fix-parquet-requested-schema.
---
 .../parquet/CatalystReadSupport.scala         | 235 +++++++++----
 .../parquet/CatalystRowConverter.scala        |  51 +--
 .../parquet/CatalystSchemaConverter.scala     |  14 +-
 .../ParquetAvroCompatibilitySuite.scala       |   1 +
 .../ParquetInteroperabilitySuite.scala        |  90 +++++
 .../parquet/ParquetQuerySuite.scala           |  77 +++++
 .../parquet/ParquetSchemaSuite.scala          | 310 ++++++++++++++++++
 7 files changed, 653 insertions(+), 125 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 0a6bb44445f6e..dc4ff06df6f22 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -19,17 +19,18 @@ package org.apache.spark.sql.execution.datasources.parquet
 
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConverters._
+import scala.collection.JavaConverters.{collectionAsScalaIterableConverter, mapAsJavaMapConverter, mapAsScalaMapConverter}
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
 import org.apache.parquet.hadoop.api.{InitContext, ReadSupport}
 import org.apache.parquet.io.api.RecordMaterializer
-import org.apache.parquet.schema.MessageType
+import org.apache.parquet.schema.Type.Repetition
+import org.apache.parquet.schema._
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types._
 
 private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with Logging {
   // Called after `init()` when initializing Parquet record reader.
@@ -81,70 +82,10 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
     // `StructType` containing all requested columns.
     val maybeRequestedSchema = Option(conf.get(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA))
 
-    // Below we construct a Parquet schema containing all requested columns.  This schema tells
-    // Parquet which columns to read.
-    //
-    // If `maybeRequestedSchema` is defined, we assemble an equivalent Parquet schema.  Otherwise,
-    // we have to fallback to the full file schema which contains all columns in the file.
-    // Obviously this may waste IO bandwidth since it may read more columns than requested.
-    //
-    // Two things to note:
-    //
-    // 1. It's possible that some requested columns don't exist in the target Parquet file.  For
-    //    example, in the case of schema merging, the globally merged schema may contain extra
-    //    columns gathered from other Parquet files.  These columns will be simply filled with nulls
-    //    when actually reading the target Parquet file.
-    //
-    // 2. When `maybeRequestedSchema` is available, we can't simply convert the Catalyst schema to
-    //    Parquet schema using `CatalystSchemaConverter`, because the mapping is not unique due to
-    //    non-standard behaviors of some Parquet libraries/tools.  For example, a Parquet file
-    //    containing a single integer array field `f1` may have the following legacy 2-level
-    //    structure:
-    //
-    //      message root {
-    //        optional group f1 (LIST) {
-    //          required INT32 element;
-    //        }
-    //      }
-    //
-    //    while `CatalystSchemaConverter` may generate a standard 3-level structure:
-    //
-    //      message root {
-    //        optional group f1 (LIST) {
-    //          repeated group list {
-    //            required INT32 element;
-    //          }
-    //        }
-    //      }
-    //
-    //    Apparently, we can't use the 2nd schema to read the target Parquet file as they have
-    //    different physical structures.
     val parquetRequestedSchema =
       maybeRequestedSchema.fold(context.getFileSchema) { schemaString =>
-        val toParquet = new CatalystSchemaConverter(conf)
-        val fileSchema = context.getFileSchema.asGroupType()
-        val fileFieldNames = fileSchema.getFields.asScala.map(_.getName).toSet
-
-        StructType
-          // Deserializes the Catalyst schema of requested columns
-          .fromString(schemaString)
-          .map { field =>
-            if (fileFieldNames.contains(field.name)) {
-              // If the field exists in the target Parquet file, extracts the field type from the
-              // full file schema and makes a single-field Parquet schema
-              new MessageType("root", fileSchema.getType(field.name))
-            } else {
-              // Otherwise, just resorts to `CatalystSchemaConverter`
-              toParquet.convert(StructType(Array(field)))
-            }
-          }
-          // Merges all single-field Parquet schemas to form a complete schema for all requested
-          // columns.  Note that it's possible that no columns are requested at all (e.g., count
-          // some partition column of a partitioned Parquet table). That's why `fold` is used here
-          // and always fallback to an empty Parquet schema.
-          .fold(new MessageType("root")) {
-            _ union _
-          }
+        val catalystRequestedSchema = StructType.fromString(schemaString)
+        CatalystReadSupport.clipParquetSchema(context.getFileSchema, catalystRequestedSchema)
       }
 
     val metadata =
@@ -160,4 +101,168 @@ private[parquet] object CatalystReadSupport {
   val SPARK_ROW_REQUESTED_SCHEMA = "org.apache.spark.sql.parquet.row.requested_schema"
 
   val SPARK_METADATA_KEY = "org.apache.spark.sql.parquet.row.metadata"
+
+  /**
+   * Tailors `parquetSchema` according to `catalystSchema` by removing column paths don't exist
+   * in `catalystSchema`, and adding those only exist in `catalystSchema`.
+   */
+  def clipParquetSchema(parquetSchema: MessageType, catalystSchema: StructType): MessageType = {
+    val clippedParquetFields = clipParquetGroupFields(parquetSchema.asGroupType(), catalystSchema)
+    Types.buildMessage().addFields(clippedParquetFields: _*).named("root")
+  }
+
+  private def clipParquetType(parquetType: Type, catalystType: DataType): Type = {
+    catalystType match {
+      case t: ArrayType if !isPrimitiveCatalystType(t.elementType) =>
+        // Only clips array types with nested type as element type.
+        clipParquetListType(parquetType.asGroupType(), t.elementType)
+
+      case t: MapType if !isPrimitiveCatalystType(t.valueType) =>
+        // Only clips map types with nested type as value type.
+        clipParquetMapType(parquetType.asGroupType(), t.keyType, t.valueType)
+
+      case t: StructType =>
+        clipParquetGroup(parquetType.asGroupType(), t)
+
+      case _ =>
+        parquetType
+    }
+  }
+
+  /**
+   * Whether a Catalyst [[DataType]] is primitive.  Primitive [[DataType]] is not equivalent to
+   * [[AtomicType]].  For example, [[CalendarIntervalType]] is primitive, but it's not an
+   * [[AtomicType]].
+   */
+  private def isPrimitiveCatalystType(dataType: DataType): Boolean = {
+    dataType match {
+      case _: ArrayType | _: MapType | _: StructType => false
+      case _ => true
+    }
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[ArrayType]].  The element type
+   * of the [[ArrayType]] should also be a nested type, namely an [[ArrayType]], a [[MapType]], or a
+   * [[StructType]].
+   */
+  private def clipParquetListType(parquetList: GroupType, elementType: DataType): Type = {
+    // Precondition of this method, should only be called for lists with nested element types.
+    assert(!isPrimitiveCatalystType(elementType))
+
+    // Unannotated repeated group should be interpreted as required list of required element, so
+    // list element type is just the group itself.  Clip it.
+    if (parquetList.getOriginalType == null && parquetList.isRepetition(Repetition.REPEATED)) {
+      clipParquetType(parquetList, elementType)
+    } else {
+      assert(
+        parquetList.getOriginalType == OriginalType.LIST,
+        "Invalid Parquet schema. " +
+          "Original type of annotated Parquet lists must be LIST: " +
+          parquetList.toString)
+
+      assert(
+        parquetList.getFieldCount == 1 && parquetList.getType(0).isRepetition(Repetition.REPEATED),
+        "Invalid Parquet schema. " +
+          "LIST-annotated group should only have exactly one repeated field: " +
+          parquetList)
+
+      // Precondition of this method, should only be called for lists with nested element types.
+      assert(!parquetList.getType(0).isPrimitive)
+
+      val repeatedGroup = parquetList.getType(0).asGroupType()
+
+      // If the repeated field is a group with multiple fields, or the repeated field is a group
+      // with one field and is named either "array" or uses the LIST-annotated group's name with
+      // "_tuple" appended then the repeated type is the element type and elements are required.
+      // Build a new LIST-annotated group with clipped `repeatedGroup` as element type and the
+      // only field.
+      if (
+        repeatedGroup.getFieldCount > 1 ||
+        repeatedGroup.getName == "array" ||
+        repeatedGroup.getName == parquetList.getName + "_tuple"
+      ) {
+        Types
+          .buildGroup(parquetList.getRepetition)
+          .as(OriginalType.LIST)
+          .addField(clipParquetType(repeatedGroup, elementType))
+          .named(parquetList.getName)
+      } else {
+        // Otherwise, the repeated field's type is the element type with the repeated field's
+        // repetition.
+        Types
+          .buildGroup(parquetList.getRepetition)
+          .as(OriginalType.LIST)
+          .addField(
+            Types
+              .repeatedGroup()
+              .addField(clipParquetType(repeatedGroup.getType(0), elementType))
+              .named(repeatedGroup.getName))
+          .named(parquetList.getName)
+      }
+    }
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[MapType]].  The value type
+   * of the [[MapType]] should also be a nested type, namely an [[ArrayType]], a [[MapType]], or a
+   * [[StructType]].  Note that key type of any [[MapType]] is always a primitive type.
+   */
+  private def clipParquetMapType(
+      parquetMap: GroupType, keyType: DataType, valueType: DataType): GroupType = {
+    // Precondition of this method, should only be called for maps with nested value types.
+    assert(!isPrimitiveCatalystType(valueType))
+
+    val repeatedGroup = parquetMap.getType(0).asGroupType()
+    val parquetKeyType = repeatedGroup.getType(0)
+    val parquetValueType = repeatedGroup.getType(1)
+
+    val clippedRepeatedGroup =
+      Types
+        .repeatedGroup()
+        .as(repeatedGroup.getOriginalType)
+        .addField(parquetKeyType)
+        .addField(clipParquetType(parquetValueType, valueType))
+        .named(repeatedGroup.getName)
+
+    Types
+      .buildGroup(parquetMap.getRepetition)
+      .as(parquetMap.getOriginalType)
+      .addField(clippedRepeatedGroup)
+      .named(parquetMap.getName)
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[StructType]].
+   *
+   * @return A clipped [[GroupType]], which has at least one field.
+   * @note Parquet doesn't allow creating empty [[GroupType]] instances except for empty
+   *       [[MessageType]].  Because it's legal to construct an empty requested schema for column
+   *       pruning.
+   */
+  private def clipParquetGroup(parquetRecord: GroupType, structType: StructType): GroupType = {
+    val clippedParquetFields = clipParquetGroupFields(parquetRecord, structType)
+    Types
+      .buildGroup(parquetRecord.getRepetition)
+      .as(parquetRecord.getOriginalType)
+      .addFields(clippedParquetFields: _*)
+      .named(parquetRecord.getName)
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[StructType]].
+   *
+   * @return A list of clipped [[GroupType]] fields, which can be empty.
+   */
+  private def clipParquetGroupFields(
+      parquetRecord: GroupType, structType: StructType): Seq[Type] = {
+    val parquetFieldMap = parquetRecord.getFields.asScala.map(f => f.getName -> f).toMap
+    val toParquet = new CatalystSchemaConverter(followParquetFormatSpec = true)
+    structType.map { f =>
+      parquetFieldMap
+        .get(f.name)
+        .map(clipParquetType(_, f.dataType))
+        .getOrElse(toParquet.convertField(f))
+    }
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index fe13dfbbed385..f17e794b76650 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -113,31 +113,6 @@ private[parquet] class CatalystPrimitiveConverter(val updater: ParentContainerUp
  * When used as a root converter, [[NoopUpdater]] should be used since root converters don't have
  * any "parent" container.
  *
- * @note Constructor argument [[parquetType]] refers to requested fields of the actual schema of the
- *       Parquet file being read, while constructor argument [[catalystType]] refers to requested
- *       fields of the global schema.  The key difference is that, in case of schema merging,
- *       [[parquetType]] can be a subset of [[catalystType]].  For example, it's possible to have
- *       the following [[catalystType]]:
- *       {{{
- *         new StructType()
- *           .add("f1", IntegerType, nullable = false)
- *           .add("f2", StringType, nullable = true)
- *           .add("f3", new StructType()
- *             .add("f31", DoubleType, nullable = false)
- *             .add("f32", IntegerType, nullable = true)
- *             .add("f33", StringType, nullable = true), nullable = false)
- *       }}}
- *       and the following [[parquetType]] (`f2` and `f32` are missing):
- *       {{{
- *         message root {
- *           required int32 f1;
- *           required group f3 {
- *             required double f31;
- *             optional binary f33 (utf8);
- *           }
- *         }
- *       }}}
- *
  * @param parquetType Parquet schema of Parquet records
  * @param catalystType Spark SQL schema that corresponds to the Parquet record type
  * @param updater An updater which propagates converted field values to the parent container
@@ -179,31 +154,7 @@ private[parquet] class CatalystRowConverter(
 
   // Converters for each field.
   private val fieldConverters: Array[Converter with HasParentContainerUpdater] = {
-    // In case of schema merging, `parquetType` can be a subset of `catalystType`.  We need to pad
-    // those missing fields and create converters for them, although values of these fields are
-    // always null.
-    val paddedParquetFields = {
-      val parquetFields = parquetType.getFields.asScala
-      val parquetFieldNames = parquetFields.map(_.getName).toSet
-      val missingFields = catalystType.filterNot(f => parquetFieldNames.contains(f.name))
-
-      // We don't need to worry about feature flag arguments like `assumeBinaryIsString` when
-      // creating the schema converter here, since values of missing fields are always null.
-      val toParquet = new CatalystSchemaConverter()
-
-      (parquetFields ++ missingFields.map(toParquet.convertField)).sortBy { f =>
-        catalystType.indexWhere(_.name == f.getName)
-      }
-    }
-
-    if (paddedParquetFields.length != catalystType.length) {
-      throw new UnsupportedOperationException(
-        "A Parquet file's schema has different number of fields with the table schema. " +
-          "Please enable schema merging by setting \"mergeSchema\" to true when load " +
-          "a Parquet dataset or set spark.sql.parquet.mergeSchema to true in SQLConf.")
-    }
-
-    paddedParquetFields.zip(catalystType).zipWithIndex.map {
+    parquetType.getFields.asScala.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
         newConverter(parquetFieldType, catalystField.dataType, new RowUpdater(currentRow, ordinal))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index be6c0545f5a0a..a21ab1dbb25d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -55,16 +55,10 @@ import org.apache.spark.sql.{AnalysisException, SQLConf}
  *        to old style non-standard behaviors.
  */
 private[parquet] class CatalystSchemaConverter(
-    private val assumeBinaryIsString: Boolean,
-    private val assumeInt96IsTimestamp: Boolean,
-    private val followParquetFormatSpec: Boolean) {
-
-  // Only used when constructing converter for converting Spark SQL schema to Parquet schema, in
-  // which case `assumeInt96IsTimestamp` and `assumeBinaryIsString` are irrelevant.
-  def this() = this(
-    assumeBinaryIsString = SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get,
-    assumeInt96IsTimestamp = SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get,
-    followParquetFormatSpec = SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get)
+    assumeBinaryIsString: Boolean = SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get,
+    assumeInt96IsTimestamp: Boolean = SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get,
+    followParquetFormatSpec: Boolean = SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get
+) {
 
   def this(conf: SQLConf) = this(
     assumeBinaryIsString = conf.isParquetBinaryAsString,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index bd7cf8c10abef..36b929ee1f409 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
+import java.io.File
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala
new file mode 100644
index 0000000000000..83b65fb419ed3
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import java.io.File
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.test.SharedSQLContext
+
+class ParquetInteroperabilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
+  test("parquet files with different physical schemas but share the same logical schema") {
+    import ParquetCompatibilityTest._
+
+    // This test case writes two Parquet files, both representing the following Catalyst schema
+    //
+    //   StructType(
+    //     StructField(
+    //       "f",
+    //       ArrayType(IntegerType, containsNull = false),
+    //       nullable = false))
+    //
+    // The first Parquet file comes with parquet-avro style 2-level LIST-annotated group, while the
+    // other one comes with parquet-protobuf style 1-level unannotated primitive field.
+    withTempDir { dir =>
+      val avroStylePath = new File(dir, "avro-style").getCanonicalPath
+      val protobufStylePath = new File(dir, "protobuf-style").getCanonicalPath
+
+      val avroStyleSchema =
+        """message avro_style {
+          |  required group f (LIST) {
+          |    repeated int32 array;
+          |  }
+          |}
+        """.stripMargin
+
+      writeDirect(avroStylePath, avroStyleSchema, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.group {
+              rc.field("array", 0) {
+                rc.addInteger(0)
+                rc.addInteger(1)
+              }
+            }
+          }
+        }
+      })
+
+      logParquetSchema(avroStylePath)
+
+      val protobufStyleSchema =
+        """message protobuf_style {
+          |  repeated int32 f;
+          |}
+        """.stripMargin
+
+      writeDirect(protobufStylePath, protobufStyleSchema, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.addInteger(2)
+            rc.addInteger(3)
+          }
+        }
+      })
+
+      logParquetSchema(protobufStylePath)
+
+      checkAnswer(
+        sqlContext.read.parquet(dir.getCanonicalPath),
+        Seq(
+          Row(Seq(0, 1)),
+          Row(Seq(2, 3))))
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index b7b70c2bbbd5c..a379523d67f80 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -229,4 +229,81 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
       }
     }
   }
+
+  test("SPARK-10301 Clipping nested structs in requested schema") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .coalesce(1)
+
+      df.write.mode("append").parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s", new StructType().add("a", LongType, nullable = true), nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0)))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df1 = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .coalesce(1)
+
+      val df2 = sqlContext
+        .range(1, 2)
+        .selectExpr("NAMED_STRUCT('b', id, 'c', id) AS s")
+        .coalesce(1)
+
+      df1.write.parquet(path)
+      df2.write.mode(SaveMode.Append).parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s",
+          new StructType()
+            .add("a", LongType, nullable = true)
+            .add("c", LongType, nullable = true),
+          nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Seq(
+          Row(Row(0, null)),
+          Row(Row(null, 1))))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', ARRAY(NAMED_STRUCT('b', id, 'c', id))) AS s")
+        .coalesce(1)
+
+      df.write.parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s",
+          new StructType()
+            .add(
+              "a",
+              ArrayType(
+                new StructType()
+                  .add("b", LongType, nullable = true)
+                  .add("d", StringType, nullable = true),
+                containsNull = true),
+              nullable = true),
+          nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(Seq(Row(0, null)))))
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 9dcbc1a047bea..28c59a4abdd76 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -22,6 +22,7 @@ import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.parquet.schema.MessageTypeParser
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
@@ -941,4 +942,313 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       |  optional fixed_len_byte_array(8) f1 (DECIMAL(18, 3));
       |}
     """.stripMargin)
+
+  private def testSchemaClipping(
+      testName: String,
+      parquetSchema: String,
+      catalystSchema: StructType,
+      expectedSchema: String): Unit = {
+    test(s"Clipping - $testName") {
+      val expected = MessageTypeParser.parseMessageType(expectedSchema)
+      val actual = CatalystReadSupport.clipParquetSchema(
+        MessageTypeParser.parseMessageType(parquetSchema), catalystSchema)
+
+      try {
+        expected.checkContains(actual)
+        actual.checkContains(expected)
+      } catch { case cause: Throwable =>
+        fail(
+          s"""Expected clipped schema:
+             |$expected
+             |Actual clipped schema:
+             |$actual
+           """.stripMargin,
+          cause)
+      }
+    }
+  }
+
+  testSchemaClipping(
+    "simple nested struct",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional int32 f00;
+        |    optional int32 f01;
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f0Type = new StructType().add("f00", IntegerType, nullable = true)
+      new StructType()
+        .add("f0", f0Type, nullable = false)
+        .add("f1", IntegerType, nullable = true)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional int32 f00;
+        |  }
+        |  optional int32 f1;
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-protobuf style array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    repeated binary f00 (UTF8);
+        |    repeated group f01 {
+        |      optional int32 f010;
+        |      optional double f011;
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f11Type = new StructType().add("f011", DoubleType, nullable = true)
+      val f01Type = ArrayType(StringType, containsNull = false)
+      val f0Type = new StructType()
+        .add("f00", f01Type, nullable = false)
+        .add("f01", f11Type, nullable = false)
+      val f1Type = ArrayType(IntegerType, containsNull = true)
+      new StructType()
+        .add("f0", f0Type, nullable = false)
+        .add("f1", f1Type, nullable = true)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    repeated binary f00 (UTF8);
+        |    repeated group f01 {
+        |      optional double f011;
+        |    }
+        |  }
+        |
+        |  optional group f1 (LIST) {
+        |    repeated group list {
+        |      optional int32 element;
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-thrift style array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary f00_tuple (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group f01_tuple {
+        |        optional int32 f010;
+        |        optional double f011;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f11ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
+        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary f00_tuple (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group f01_tuple {
+        |        optional double f011;
+        |        optional int64 f012;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-avro style array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary array (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group array {
+        |        optional int32 f010;
+        |        optional double f011;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f11ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
+        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary array (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group array {
+        |        optional double f011;
+        |        optional int64 f012;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-hive style array",
+
+    parquetSchema =
+      """message root {
+        |  optional group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group bag {
+        |        optional binary array_element;
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group bag {
+        |        optional group array_element {
+        |          optional int32 f010;
+        |          optional double f011;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f01ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = true), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = true), nullable = true)
+
+      new StructType().add("f0", f0Type, nullable = true)
+    },
+
+    expectedSchema =
+      """message root {
+        |  optional group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group bag {
+        |        optional binary array_element;
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group bag {
+        |        optional group array_element {
+        |          optional double f011;
+        |          optional int64 f012;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "2-level list of required struct",
+
+    parquetSchema =
+      s"""message root {
+         |  required group f0 {
+         |    required group f00 (LIST) {
+         |      repeated group element {
+         |        required int32 f000;
+         |        optional int64 f001;
+         |      }
+         |    }
+         |  }
+         |}
+       """.stripMargin,
+
+    catalystSchema = {
+      val f00ElementType =
+        new StructType()
+          .add("f001", LongType, nullable = true)
+          .add("f002", DoubleType, nullable = false)
+
+      val f00Type = ArrayType(f00ElementType, containsNull = false)
+      val f0Type = new StructType().add("f00", f00Type, nullable = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      s"""message root {
+         |  required group f0 {
+         |    required group f00 (LIST) {
+         |      repeated group element {
+         |        optional int64 f001;
+         |        required double f002;
+         |      }
+         |    }
+         |  }
+         |}
+       """.stripMargin)
+
+  testSchemaClipping(
+    "empty requested schema",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    required int32 f00;
+        |    required int64 f01;
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = new StructType(),
+
+    expectedSchema = "message root {}")
 }

From e6e483cc4de740c46398385b03ffe0e662edae39 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 1 Sep 2015 10:48:57 -0700
Subject: [PATCH 1301/1454] [SPARK-9679] [ML] [PYSPARK] Add Python API for Stop
 Words Remover

Add a python API for the Stop Words Remover.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8118 from holdenk/SPARK-9679-python-StopWordsRemover.
---
 .../spark/ml/feature/StopWordsRemover.scala   |  6 +-
 .../ml/feature/StopWordsRemoverSuite.scala    |  2 +-
 python/pyspark/ml/feature.py                  | 73 ++++++++++++++++++-
 python/pyspark/ml/tests.py                    | 20 ++++-
 4 files changed, 93 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
index 5d77ea08db657..7da430c7d16df 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
@@ -29,14 +29,14 @@ import org.apache.spark.sql.types.{ArrayType, StringType, StructField, StructTyp
 /**
  * stop words list
  */
-private object StopWords {
+private[spark] object StopWords {
 
   /**
    * Use the same default stopwords list as scikit-learn.
    * The original list can be found from "Glasgow Information Retrieval Group"
    * [[http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words]]
    */
-  val EnglishStopWords = Array( "a", "about", "above", "across", "after", "afterwards", "again",
+  val English = Array( "a", "about", "above", "across", "after", "afterwards", "again",
     "against", "all", "almost", "alone", "along", "already", "also", "although", "always",
     "am", "among", "amongst", "amoungst", "amount", "an", "and", "another",
     "any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are",
@@ -121,7 +121,7 @@ class StopWordsRemover(override val uid: String)
   /** @group getParam */
   def getCaseSensitive: Boolean = $(caseSensitive)
 
-  setDefault(stopWords -> StopWords.EnglishStopWords, caseSensitive -> false)
+  setDefault(stopWords -> StopWords.English, caseSensitive -> false)
 
   override def transform(dataset: DataFrame): DataFrame = {
     val outputSchema = transformSchema(dataset.schema)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
index f01306f89cb5f..e0d433f566c25 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
@@ -65,7 +65,7 @@ class StopWordsRemoverSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("StopWordsRemover with additional words") {
-    val stopWords = StopWords.EnglishStopWords ++ Array("python", "scala")
+    val stopWords = StopWords.English ++ Array("python", "scala")
     val remover = new StopWordsRemover()
       .setInputCol("raw")
       .setOutputCol("filtered")
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 0626281e200a1..d955307e27efd 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -22,7 +22,7 @@
 from pyspark.rdd import ignore_unicode_prefix
 from pyspark.ml.param.shared import *
 from pyspark.ml.util import keyword_only
-from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer
+from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer, _jvm
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
@@ -30,7 +30,7 @@
            'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
            'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StringIndexer',
            'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec',
-           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
+           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel', 'StopWordsRemover']
 
 
 @inherit_doc
@@ -933,6 +933,75 @@ class StringIndexerModel(JavaModel):
     """
 
 
+class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    A feature transformer that filters out stop words from input.
+    Note: null values from input array are preserved unless adding null to stopWords explicitly.
+    """
+    # a placeholder to make the stopwords show up in generated doc
+    stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out")
+    caseSensitive = Param(Params._dummy(), "caseSensitive", "whether to do a case sensitive " +
+                          "comparison over the stop words")
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None, stopWords=None,
+                 caseSensitive=False):
+        """
+        __init__(self, inputCol=None, outputCol=None, stopWords=None,\
+                 caseSensitive=false)
+        """
+        super(StopWordsRemover, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StopWordsRemover",
+                                            self.uid)
+        self.stopWords = Param(self, "stopWords", "The words to be filtered out")
+        self.caseSensitive = Param(self, "caseSensitive", "whether to do a case " +
+                                   "sensitive comparison over the stop words")
+        stopWordsObj = _jvm().org.apache.spark.ml.feature.StopWords
+        defaultStopWords = stopWordsObj.English()
+        self._setDefault(stopWords=defaultStopWords)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None, stopWords=None,
+                  caseSensitive=False):
+        """
+        setParams(self, inputCol="input", outputCol="output", stopWords=None,\
+                  caseSensitive=false)
+        Sets params for this StopWordRemover.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setStopWords(self, value):
+        """
+        Specify the stopwords to be filtered.
+        """
+        self._paramMap[self.stopWords] = value
+        return self
+
+    def getStopWords(self):
+        """
+        Get the stopwords.
+        """
+        return self.getOrDefault(self.stopWords)
+
+    def setCaseSensitive(self, value):
+        """
+        Set whether to do a case sensitive comparison over the stop words
+        """
+        self._paramMap[self.caseSensitive] = value
+        return self
+
+    def getCaseSensitive(self):
+        """
+        Get whether to do a case sensitive comparison over the stop words.
+        """
+        return self.getOrDefault(self.caseSensitive)
+
+
 @inherit_doc
 @ignore_unicode_prefix
 class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 60e4237293adc..b892318f50bd9 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -31,7 +31,7 @@
     import unittest
 
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
-from pyspark.sql import DataFrame, SQLContext
+from pyspark.sql import DataFrame, SQLContext, Row
 from pyspark.sql.functions import rand
 from pyspark.ml.evaluation import RegressionEvaluator
 from pyspark.ml.param import Param, Params
@@ -258,7 +258,7 @@ def test_idf(self):
     def test_ngram(self):
         sqlContext = SQLContext(self.sc)
         dataset = sqlContext.createDataFrame([
-            ([["a", "b", "c", "d", "e"]])], ["input"])
+            Row(input=["a", "b", "c", "d", "e"])])
         ngram0 = NGram(n=4, inputCol="input", outputCol="output")
         self.assertEqual(ngram0.getN(), 4)
         self.assertEqual(ngram0.getInputCol(), "input")
@@ -266,6 +266,22 @@ def test_ngram(self):
         transformedDF = ngram0.transform(dataset)
         self.assertEquals(transformedDF.head().output, ["a b c d", "b c d e"])
 
+    def test_stopwordsremover(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([Row(input=["a", "panda"])])
+        stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output")
+        # Default
+        self.assertEquals(stopWordRemover.getInputCol(), "input")
+        transformedDF = stopWordRemover.transform(dataset)
+        self.assertEquals(transformedDF.head().output, ["panda"])
+        # Custom
+        stopwords = ["panda"]
+        stopWordRemover.setStopWords(stopwords)
+        self.assertEquals(stopWordRemover.getInputCol(), "input")
+        self.assertEquals(stopWordRemover.getStopWords(), stopwords)
+        transformedDF = stopWordRemover.transform(dataset)
+        self.assertEquals(transformedDF.head().output, ["a"])
+
 
 class HasInducedError(Params):
 

From 3f63bd6023edcc9af268933a235f34e10bc3d2ba Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 1 Sep 2015 20:06:01 +0100
Subject: [PATCH 1302/1454] [SPARK-10398] [DOCS] Migrate Spark download page to
 use new lua mirroring scripts

Migrate Apache download closer.cgi refs to new closer.lua

This is the bit of the change that affects the project docs; I'm implementing the changes to the Apache site separately.

Author: Sean Owen <sowen@cloudera.com>

Closes #8557 from srowen/SPARK-10398.
---
 docker/spark-mesos/Dockerfile | 2 +-
 docs/running-on-mesos.md      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docker/spark-mesos/Dockerfile b/docker/spark-mesos/Dockerfile
index b90aef3655dee..fb3f267fe5c78 100644
--- a/docker/spark-mesos/Dockerfile
+++ b/docker/spark-mesos/Dockerfile
@@ -24,7 +24,7 @@ RUN apt-get update && \
     apt-get install -y python libnss3 openjdk-7-jre-headless curl
 
 RUN mkdir /opt/spark && \
-    curl http://www.apache.org/dyn/closer.cgi/spark/spark-1.4.0/spark-1.4.0-bin-hadoop2.4.tgz \
+    curl http://www.apache.org/dyn/closer.lua/spark/spark-1.4.0/spark-1.4.0-bin-hadoop2.4.tgz \
     | tar -xzC /opt
 ENV SPARK_HOME /opt/spark
 ENV MESOS_NATIVE_JAVA_LIBRARY /usr/local/lib/libmesos.so
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index cfd219ab02e26..f36921ae30c2f 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -45,7 +45,7 @@ frameworks.  You can install Mesos either from source or using prebuilt packages
 To install Apache Mesos from source, follow these steps:
 
 1. Download a Mesos release from a
-   [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/)
+   [mirror](http://www.apache.org/dyn/closer.lua/mesos/{{site.MESOS_VERSION}}/)
 2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling and
    installing Mesos
 

From ec012805337926e56343be2761a1037296446880 Mon Sep 17 00:00:00 2001
From: zhuol <zhuol@yahoo-inc.com>
Date: Tue, 1 Sep 2015 11:14:59 -1000
Subject: [PATCH 1303/1454] [SPARK-4223] [CORE] Support * in acls.

SPARK-4223.

Currently we support setting view and modify acls but you have to specify a list of users. It would be nice to support * meaning all users have access.

Manual tests to verify that: "*" works for any user in:
a. Spark ui: view and kill stage.     Done.
b. Spark history server.                  Done.
c. Yarn application killing.  Done.

Author: zhuol <zhuol@yahoo-inc.com>

Closes #8398 from zhuoliu/4223.
---
 .../org/apache/spark/SecurityManager.scala    | 26 ++++++++++--
 .../apache/spark/SecurityManagerSuite.scala   | 41 +++++++++++++++++++
 docs/configuration.md                         |  9 ++--
 3 files changed, 69 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala
index 673ef49e7c1c5..746d2081d4393 100644
--- a/core/src/main/scala/org/apache/spark/SecurityManager.scala
+++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala
@@ -310,7 +310,16 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
     setViewAcls(Set[String](defaultUser), allowedUsers)
   }
 
-  def getViewAcls: String = viewAcls.mkString(",")
+  /**
+   * Checking the existence of "*" is necessary as YARN can't recognize the "*" in "defaultuser,*"
+   */
+  def getViewAcls: String = {
+    if (viewAcls.contains("*")) {
+      "*"
+    } else {
+      viewAcls.mkString(",")
+    }
+  }
 
   /**
    * Admin acls should be set before the view or modify acls.  If you modify the admin
@@ -321,7 +330,16 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
     logInfo("Changing modify acls to: " + modifyAcls.mkString(","))
   }
 
-  def getModifyAcls: String = modifyAcls.mkString(",")
+  /**
+   * Checking the existence of "*" is necessary as YARN can't recognize the "*" in "defaultuser,*"
+   */
+  def getModifyAcls: String = {
+    if (modifyAcls.contains("*")) {
+      "*"
+    } else {
+      modifyAcls.mkString(",")
+    }
+  }
 
   /**
    * Admin acls should be set before the view or modify acls.  If you modify the admin
@@ -394,7 +412,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
   def checkUIViewPermissions(user: String): Boolean = {
     logDebug("user=" + user + " aclsEnabled=" + aclsEnabled() + " viewAcls=" +
       viewAcls.mkString(","))
-    !aclsEnabled || user == null || viewAcls.contains(user)
+    !aclsEnabled || user == null || viewAcls.contains(user) || viewAcls.contains("*")
   }
 
   /**
@@ -409,7 +427,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
   def checkModifyPermissions(user: String): Boolean = {
     logDebug("user=" + user + " aclsEnabled=" + aclsEnabled() + " modifyAcls=" +
       modifyAcls.mkString(","))
-    !aclsEnabled || user == null || modifyAcls.contains(user)
+    !aclsEnabled || user == null || modifyAcls.contains(user) || modifyAcls.contains("*")
   }
 
 
diff --git a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
index f34aefca4eb18..f29160d834082 100644
--- a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
@@ -125,6 +125,47 @@ class SecurityManagerSuite extends SparkFunSuite {
 
   }
 
+  test("set security with * in acls") {
+    val conf = new SparkConf
+    conf.set("spark.ui.acls.enable", "true")
+    conf.set("spark.admin.acls", "user1,user2")
+    conf.set("spark.ui.view.acls", "*")
+    conf.set("spark.modify.acls", "user4")
+
+    val securityManager = new SecurityManager(conf)
+    assert(securityManager.aclsEnabled() === true)
+
+    // check for viewAcls with *
+    assert(securityManager.checkUIViewPermissions("user1") === true)
+    assert(securityManager.checkUIViewPermissions("user5") === true)
+    assert(securityManager.checkUIViewPermissions("user6") === true)
+    assert(securityManager.checkModifyPermissions("user4") === true)
+    assert(securityManager.checkModifyPermissions("user7") === false)
+    assert(securityManager.checkModifyPermissions("user8") === false)
+
+    // check for modifyAcls with *
+    securityManager.setModifyAcls(Set("user4"), "*")
+    assert(securityManager.checkModifyPermissions("user7") === true)
+    assert(securityManager.checkModifyPermissions("user8") === true)
+
+    securityManager.setAdminAcls("user1,user2")
+    securityManager.setModifyAcls(Set("user1"), "user2")
+    securityManager.setViewAcls(Set("user1"), "user2")
+    assert(securityManager.checkUIViewPermissions("user5") === false)
+    assert(securityManager.checkUIViewPermissions("user6") === false)
+    assert(securityManager.checkModifyPermissions("user7") === false)
+    assert(securityManager.checkModifyPermissions("user8") === false)
+
+    // check for adminAcls with *
+    securityManager.setAdminAcls("user1,*")
+    securityManager.setModifyAcls(Set("user1"), "user2")
+    securityManager.setViewAcls(Set("user1"), "user2")
+    assert(securityManager.checkUIViewPermissions("user5") === true)
+    assert(securityManager.checkUIViewPermissions("user6") === true)
+    assert(securityManager.checkModifyPermissions("user7") === true)
+    assert(securityManager.checkModifyPermissions("user8") === true)
+  }
+
   test("ssl on setup") {
     val conf = SSLSampleConfigs.sparkSSLConfig()
     val expectedAlgorithms = Set(
diff --git a/docs/configuration.md b/docs/configuration.md
index 77c5cbc7b3196..fb0315ce7c3cc 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1286,7 +1286,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>
     Comma separated list of users/administrators that have view and modify access to all Spark jobs.
     This can be used if you run on a shared cluster and have a set of administrators or devs who
-    help debug when things work.
+    help debug when things work. Putting a "*" in the list means any user can have the priviledge
+    of admin.
   </td>
 </tr>
 <tr>
@@ -1327,7 +1328,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>Empty</td>
   <td>
     Comma separated list of users that have modify access to the Spark job. By default only the
-    user that started the Spark job has access to modify it (kill it for example).
+    user that started the Spark job has access to modify it (kill it for example). Putting a "*" in
+    the list means any user can have access to modify it.
   </td>
 </tr>
 <tr>
@@ -1349,7 +1351,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>Empty</td>
   <td>
     Comma separated list of users that have view access to the Spark web ui. By default only the
-    user that started the Spark job has view access.
+    user that started the Spark job has view access. Putting a "*" in the list means any user can
+    have view access to this Spark job.
   </td>
 </tr>
 </table>

From bf550a4b551b6dd18fea3eb3f70497f9a6ad8e6c Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Tue, 1 Sep 2015 14:34:59 -0700
Subject: [PATCH 1304/1454] [SPARK-10162] [SQL] Fix the timezone omitting for
 PySpark Dataframe filter function

This PR addresses [SPARK-10162](https://issues.apache.org/jira/browse/SPARK-10162)
The issue is with DataFrame filter() function, if datetime.datetime is passed to it:
* Timezone information of this datetime is ignored
* This datetime is assumed to be in local timezone, which depends on the OS timezone setting

Fix includes both code change and regression test. Problem reproduction code on master:
```python
import pytz
from datetime import datetime
from pyspark.sql import *
from pyspark.sql.types import *
sqc = SQLContext(sc)
df = sqc.createDataFrame([], StructType([StructField("dt", TimestampType())]))

m1 = pytz.timezone('UTC')
m2 = pytz.timezone('Etc/GMT+3')

df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
```
It gives the same timestamp ignoring time zone:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946713600000000)
 Scan PhysicalRDD[dt#0]

>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946713600000000)
 Scan PhysicalRDD[dt#0]
```
After the fix:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946684800000000)
 Scan PhysicalRDD[dt#0]

>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946695600000000)
 Scan PhysicalRDD[dt#0]
```
PR [8536](https://github.com/apache/spark/pull/8536) was occasionally closed by me dropping the repo

Author: 0x0FFF <programmerag@gmail.com>

Closes #8555 from 0x0FFF/SPARK-10162.
---
 python/pyspark/sql/tests.py | 26 ++++++++++++++++++--------
 python/pyspark/sql/types.py |  7 +++++--
 2 files changed, 23 insertions(+), 10 deletions(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index cd32e26c64f22..59a891bd7c420 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -50,16 +50,17 @@
 from pyspark.sql.utils import AnalysisException, IllegalArgumentException
 
 
-class UTC(datetime.tzinfo):
-    """UTC"""
-    ZERO = datetime.timedelta(0)
+class UTCOffsetTimezone(datetime.tzinfo):
+    """
+    Specifies timezone in UTC offset
+    """
+
+    def __init__(self, offset=0):
+        self.ZERO = datetime.timedelta(hours=offset)
 
     def utcoffset(self, dt):
         return self.ZERO
 
-    def tzname(self, dt):
-        return "UTC"
-
     def dst(self, dt):
         return self.ZERO
 
@@ -841,13 +842,22 @@ def test_filter_with_datetime(self):
         self.assertEqual(0, df.filter(df.date > date).count())
         self.assertEqual(0, df.filter(df.time > time).count())
 
+    def test_filter_with_datetime_timezone(self):
+        dt1 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(0))
+        dt2 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(1))
+        row = Row(date=dt1)
+        df = self.sqlCtx.createDataFrame([row])
+        self.assertEqual(0, df.filter(df.date == dt2).count())
+        self.assertEqual(1, df.filter(df.date > dt2).count())
+        self.assertEqual(0, df.filter(df.date < dt2).count())
+
     def test_time_with_timezone(self):
         day = datetime.date.today()
         now = datetime.datetime.now()
         ts = time.mktime(now.timetuple())
         # class in __main__ is not serializable
-        from pyspark.sql.tests import UTC
-        utc = UTC()
+        from pyspark.sql.tests import UTCOffsetTimezone
+        utc = UTCOffsetTimezone()
         utcnow = datetime.datetime.utcfromtimestamp(ts)  # without microseconds
         # add microseconds to utcnow (keeping year,month,day,hour,minute,second)
         utcnow = datetime.datetime(*(utcnow.timetuple()[:6] + (now.microsecond, utc)))
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 94e581a78364c..f84d08d7098ad 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1290,8 +1290,11 @@ def can_convert(self, obj):
 
     def convert(self, obj, gateway_client):
         Timestamp = JavaClass("java.sql.Timestamp", gateway_client)
-        return Timestamp(int(time.mktime(obj.timetuple())) * 1000 + obj.microsecond // 1000)
-
+        seconds = (calendar.timegm(obj.utctimetuple()) if obj.tzinfo
+                   else time.mktime(obj.timetuple()))
+        t = Timestamp(int(seconds) * 1000)
+        t.setNanos(obj.microsecond * 1000)
+        return t
 
 # datetime is a subclass of date, we should register DatetimeConverter first
 register_input_converter(DatetimeConverter())

From 00d9af5e190475affffb8b50467fcddfc40f50dc Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Tue, 1 Sep 2015 14:58:49 -0700
Subject: [PATCH 1305/1454] [SPARK-10392] [SQL] Pyspark - Wrong DateType
 support on JDBC connection

This PR addresses issue [SPARK-10392](https://issues.apache.org/jira/browse/SPARK-10392)
The problem is that for "start of epoch" date (01 Jan 1970) PySpark class DateType returns 0 instead of the `datetime.date` due to implementation of its return statement

Issue reproduction on master:
```
>>> from pyspark.sql.types import *
>>> a = DateType()
>>> a.fromInternal(0)
0
>>> a.fromInternal(1)
datetime.date(1970, 1, 2)
```

Author: 0x0FFF <programmerag@gmail.com>

Closes #8556 from 0x0FFF/SPARK-10392.
---
 python/pyspark/sql/tests.py | 5 +++++
 python/pyspark/sql/types.py | 6 ++++--
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 59a891bd7c420..fc778631d93a3 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -168,6 +168,11 @@ def test_decimal_type(self):
         t3 = DecimalType(8)
         self.assertNotEqual(t2, t3)
 
+    # regression test for SPARK-10392
+    def test_datetype_equal_zero(self):
+        dt = DateType()
+        self.assertEqual(dt.fromInternal(0), datetime.date(1970, 1, 1))
+
 
 class SQLTests(ReusedPySparkTestCase):
 
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index f84d08d7098ad..8bd58d69eeecd 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -168,10 +168,12 @@ def needConversion(self):
         return True
 
     def toInternal(self, d):
-        return d and d.toordinal() - self.EPOCH_ORDINAL
+        if d is not None:
+            return d.toordinal() - self.EPOCH_ORDINAL
 
     def fromInternal(self, v):
-        return v and datetime.date.fromordinal(v + self.EPOCH_ORDINAL)
+        if v is not None:
+            return datetime.date.fromordinal(v + self.EPOCH_ORDINAL)
 
 
 class TimestampType(AtomicType):

From c3b881a7d7e4736f7131ff002a80e25def1f63af Mon Sep 17 00:00:00 2001
From: Chuan Shao <shaochuan@huawei.com>
Date: Wed, 2 Sep 2015 11:02:27 -0700
Subject: [PATCH 1306/1454] [SPARK-7336] [HISTORYSERVER] Fix bug that
 applications status incorrect on JobHistory UI.

Author: ArcherShao <shaochuan@huawei.com>

Closes #5886 from ArcherShao/SPARK-7336.
---
 .../deploy/history/FsHistoryProvider.scala    | 27 +++++++++++++++----
 1 file changed, 22 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index e573ff16c50a3..a5755eac36396 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.deploy.history
 
 import java.io.{BufferedInputStream, FileNotFoundException, InputStream, IOException, OutputStream}
+import java.util.UUID
 import java.util.concurrent.{ExecutorService, Executors, TimeUnit}
 import java.util.zip.{ZipEntry, ZipOutputStream}
 
@@ -73,7 +74,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
   // The modification time of the newest log detected during the last scan. This is used
   // to ignore logs that are older during subsequent scans, to avoid processing data that
   // is already known.
-  private var lastModifiedTime = -1L
+  private var lastScanTime = -1L
 
   // Mapping of application IDs to their metadata, in descending end time order. Apps are inserted
   // into the map in order, so the LinkedHashMap maintains the correct ordering.
@@ -179,15 +180,14 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
    */
   private[history] def checkForLogs(): Unit = {
     try {
+      val newLastScanTime = getNewLastScanTime()
       val statusList = Option(fs.listStatus(new Path(logDir))).map(_.toSeq)
         .getOrElse(Seq[FileStatus]())
-      var newLastModifiedTime = lastModifiedTime
       val logInfos: Seq[FileStatus] = statusList
         .filter { entry =>
           try {
             getModificationTime(entry).map { time =>
-              newLastModifiedTime = math.max(newLastModifiedTime, time)
-              time >= lastModifiedTime
+              time >= lastScanTime
             }.getOrElse(false)
           } catch {
             case e: AccessControlException =>
@@ -224,12 +224,29 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
           }
         }
 
-      lastModifiedTime = newLastModifiedTime
+      lastScanTime = newLastScanTime
     } catch {
       case e: Exception => logError("Exception in checking for event log updates", e)
     }
   }
 
+  private def getNewLastScanTime(): Long = {
+    val fileName = "." + UUID.randomUUID().toString
+    val path = new Path(logDir, fileName)
+    val fos = fs.create(path)
+
+    try {
+      fos.close()
+      fs.getFileStatus(path).getModificationTime
+    } catch {
+      case e: Exception =>
+        logError("Exception encountered when attempting to update last scan time", e)
+        lastScanTime
+    } finally {
+      fs.delete(path)
+    }
+  }
+
   override def writeEventLogs(
       appId: String,
       attemptId: Option[String],

From 56c4c172e99a5e14f4bc3308e7ff36d94113b63e Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 2 Sep 2015 11:13:17 -0700
Subject: [PATCH 1307/1454] [SPARK-10034] [SQL] add regression test for Sort on
 Aggregate

Before #8371, there was a bug for `Sort` on `Aggregate` that we can't use aggregate expressions named `_aggOrdering` and can't use more than one ordering expressions which contains aggregate functions. The reason of this bug is that: The aggregate expression in `SortOrder` never get resolved, we alias it with `_aggOrdering` and call `toAttribute` which gives us an `UnresolvedAttribute`. So actually we are referencing aggregate expression by name, not by exprId like we thought. And if there is already an aggregate expression named `_aggOrdering` or there are more than one ordering expressions having aggregate functions, we will have conflict names and can't search by name.

However, after #8371 got merged, the `SortOrder`s are guaranteed to be resolved and we are always referencing aggregate expression by exprId. The Bug doesn't exist anymore and this PR add regression tests for it.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8231 from cloud-fan/sort-agg.
---
 .../scala/org/apache/spark/sql/DataFrameSuite.scala    |  8 ++++++++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala     | 10 ++++++++++
 2 files changed, 18 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 284fff184085a..a4871e247cff7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -887,4 +887,12 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
       .select(struct($"b"))
       .collect()
   }
+
+  test("SPARK-10034: Sort on Aggregate with aggregation expression named 'aggOrdering'") {
+    val df = Seq(1 -> 2).toDF("i", "j")
+    val query = df.groupBy('i)
+      .agg(max('j).as("aggOrdering"))
+      .orderBy(sum('j))
+    checkAnswer(query, Row(1, 2))
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 9e172b2c264cb..28201073a2d7b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1490,6 +1490,16 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
         """.stripMargin),
       Row(3) :: Row(7) :: Row(11) :: Row(15) :: Nil)
 
+    checkAnswer(
+      sql(
+        """
+          |SELECT sum(b)
+          |FROM orderByData
+          |GROUP BY a
+          |ORDER BY sum(b), max(b)
+        """.stripMargin),
+      Row(3) :: Row(7) :: Row(11) :: Row(15) :: Nil)
+
     checkAnswer(
       sql(
         """

From fc48307797912dc1d53893dce741ddda8630957b Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 2 Sep 2015 11:32:27 -0700
Subject: [PATCH 1308/1454] [SPARK-10389] [SQL] support order by non-attribute
 grouping expression on Aggregate

For example, we can write `SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY key + 1` in PostgreSQL, and we should support this in Spark SQL.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8548 from cloud-fan/support-order-by-non-attribute.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 72 ++++++++++---------
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 19 +++--
 2 files changed, 52 insertions(+), 39 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 1a5de15c61f86..591747b45c376 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -560,43 +560,47 @@ class Analyzer(
           filter
         }
 
-      case sort @ Sort(sortOrder, global,
-             aggregate @ Aggregate(grouping, originalAggExprs, child))
+      case sort @ Sort(sortOrder, global, aggregate: Aggregate)
         if aggregate.resolved && !sort.resolved =>
 
         // Try resolving the ordering as though it is in the aggregate clause.
         try {
-          val aliasedOrder = sortOrder.map(o => Alias(o.child, "aggOrder")())
-          val aggregatedOrdering = Aggregate(grouping, aliasedOrder, child)
-          val resolvedOperator: Aggregate = execute(aggregatedOrdering).asInstanceOf[Aggregate]
-          def resolvedAggregateOrdering = resolvedOperator.aggregateExpressions
-
-          // Expressions that have an aggregate can be pushed down.
-          val needsAggregate = resolvedAggregateOrdering.exists(containsAggregate)
-
-          // Attribute references, that are missing from the order but are present in the grouping
-          // expressions can also be pushed down.
-          val requiredAttributes = resolvedAggregateOrdering.map(_.references).reduce(_ ++ _)
-          val missingAttributes = requiredAttributes -- aggregate.outputSet
-          val validPushdownAttributes =
-            missingAttributes.filter(a => grouping.exists(a.semanticEquals))
-
-          // If resolution was successful and we see the ordering either has an aggregate in it or
-          // it is missing something that is projected away by the aggregate, add the ordering
-          // the original aggregate operator.
-          if (resolvedOperator.resolved && (needsAggregate || validPushdownAttributes.nonEmpty)) {
-            val evaluatedOrderings: Seq[SortOrder] = sortOrder.zip(resolvedAggregateOrdering).map {
-              case (order, evaluated) => order.copy(child = evaluated.toAttribute)
-            }
-            val aggExprsWithOrdering: Seq[NamedExpression] =
-              resolvedAggregateOrdering ++ originalAggExprs
-
-            Project(aggregate.output,
-              Sort(evaluatedOrderings, global,
-                aggregate.copy(aggregateExpressions = aggExprsWithOrdering)))
-          } else {
-            sort
+          val aliasedOrdering = sortOrder.map(o => Alias(o.child, "aggOrder")())
+          val aggregatedOrdering = aggregate.copy(aggregateExpressions = aliasedOrdering)
+          val resolvedAggregate: Aggregate = execute(aggregatedOrdering).asInstanceOf[Aggregate]
+          val resolvedAliasedOrdering: Seq[Alias] =
+            resolvedAggregate.aggregateExpressions.asInstanceOf[Seq[Alias]]
+
+          // If we pass the analysis check, then the ordering expressions should only reference to
+          // aggregate expressions or grouping expressions, and it's safe to push them down to
+          // Aggregate.
+          checkAnalysis(resolvedAggregate)
+
+          val originalAggExprs = aggregate.aggregateExpressions.map(
+            CleanupAliases.trimNonTopLevelAliases(_).asInstanceOf[NamedExpression])
+
+          // If the ordering expression is same with original aggregate expression, we don't need
+          // to push down this ordering expression and can reference the original aggregate
+          // expression instead.
+          val needsPushDown = ArrayBuffer.empty[NamedExpression]
+          val evaluatedOrderings = resolvedAliasedOrdering.zip(sortOrder).map {
+            case (evaluated, order) =>
+              val index = originalAggExprs.indexWhere {
+                case Alias(child, _) => child semanticEquals evaluated.child
+                case other => other semanticEquals evaluated.child
+              }
+
+              if (index == -1) {
+                needsPushDown += evaluated
+                order.copy(child = evaluated.toAttribute)
+              } else {
+                order.copy(child = originalAggExprs(index).toAttribute)
+              }
           }
+
+          Project(aggregate.output,
+            Sort(evaluatedOrderings, global,
+              aggregate.copy(aggregateExpressions = originalAggExprs ++ needsPushDown)))
         } catch {
           // Attempting to resolve in the aggregate can result in ambiguity.  When this happens,
           // just return the original plan.
@@ -605,9 +609,7 @@ class Analyzer(
     }
 
     protected def containsAggregate(condition: Expression): Boolean = {
-      condition
-        .collect { case ae: AggregateExpression => ae }
-        .nonEmpty
+      condition.find(_.isInstanceOf[AggregateExpression]).isDefined
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 28201073a2d7b..0ef25fe0faef0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1722,9 +1722,20 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-10130 type coercion for IF should have children resolved first") {
-    val df = Seq((1, 1), (-1, 1)).toDF("key", "value")
-    df.registerTempTable("src")
-    checkAnswer(
-      sql("SELECT IF(a > 0, a, 0) FROM (SELECT key a FROM src) temp"), Seq(Row(1), Row(0)))
+    withTempTable("src") {
+      Seq((1, 1), (-1, 1)).toDF("key", "value").registerTempTable("src")
+      checkAnswer(
+        sql("SELECT IF(a > 0, a, 0) FROM (SELECT key a FROM src) temp"), Seq(Row(1), Row(0)))
+    }
+  }
+
+  test("SPARK-10389: order by non-attribute grouping expression on Aggregate") {
+    withTempTable("src") {
+      Seq((1, 1), (-1, 1)).toDF("key", "value").registerTempTable("src")
+      checkAnswer(sql("SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY key + 1"),
+        Seq(Row(1), Row(1)))
+      checkAnswer(sql("SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY (key + 1) * 2"),
+        Seq(Row(1), Row(1)))
+    }
   }
 }

From 2da3a9e98e5d129d4507b5db01bba5ee9558d28e Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 2 Sep 2015 12:53:24 -0700
Subject: [PATCH 1309/1454] [SPARK-10004] [SHUFFLE] Perform auth checks when
 clients read shuffle data.

To correctly isolate applications, when requests to read shuffle data
arrive at the shuffle service, proper authorization checks need to
be performed. This change makes sure that only the application that
created the shuffle data can read from it.

Such checks are only enabled when "spark.authenticate" is enabled,
otherwise there's no secure way to make sure that the client is really
who it says it is.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8218 from vanzin/SPARK-10004.
---
 .../network/netty/NettyBlockRpcServer.scala   |   3 +-
 .../netty/NettyBlockTransferService.scala     |   2 +-
 network/common/pom.xml                        |   4 +
 .../spark/network/client/TransportClient.java |  22 +++
 .../network/sasl/SaslClientBootstrap.java     |   2 +
 .../spark/network/sasl/SaslRpcHandler.java    |   1 +
 .../server/OneForOneStreamManager.java        |  31 +++-
 .../spark/network/server/StreamManager.java   |   9 +
 .../server/TransportRequestHandler.java       |   1 +
 .../shuffle/ExternalShuffleBlockHandler.java  |  16 +-
 .../network/sasl/SaslIntegrationSuite.java    | 163 +++++++++++++++---
 .../ExternalShuffleBlockHandlerSuite.java     |   2 +-
 project/MimaExcludes.scala                    |   1 +
 13 files changed, 221 insertions(+), 36 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
index 7c170a742fb64..76968249fb625 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
@@ -38,6 +38,7 @@ import org.apache.spark.storage.{BlockId, StorageLevel}
  * is equivalent to one Spark-level shuffle block.
  */
 class NettyBlockRpcServer(
+    appId: String,
     serializer: Serializer,
     blockManager: BlockDataManager)
   extends RpcHandler with Logging {
@@ -55,7 +56,7 @@ class NettyBlockRpcServer(
       case openBlocks: OpenBlocks =>
         val blocks: Seq[ManagedBuffer] =
           openBlocks.blockIds.map(BlockId.apply).map(blockManager.getBlockData)
-        val streamId = streamManager.registerStream(blocks.iterator.asJava)
+        val streamId = streamManager.registerStream(appId, blocks.iterator.asJava)
         logTrace(s"Registered streamId $streamId with ${blocks.size} buffers")
         responseContext.onSuccess(new StreamHandle(streamId, blocks.size).toByteArray)
 
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index ff8aae9ebe9f0..d5ad2c9ad00e8 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -49,7 +49,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
   private[this] var appId: String = _
 
   override def init(blockDataManager: BlockDataManager): Unit = {
-    val rpcHandler = new NettyBlockRpcServer(serializer, blockDataManager)
+    val rpcHandler = new NettyBlockRpcServer(conf.getAppId, serializer, blockDataManager)
     var serverBootstrap: Option[TransportServerBootstrap] = None
     var clientBootstrap: Option[TransportClientBootstrap] = None
     if (authEnabled) {
diff --git a/network/common/pom.xml b/network/common/pom.xml
index 7dc3068ab8cb7..4141fcb8267a5 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -48,6 +48,10 @@
       <artifactId>slf4j-api</artifactId>
       <scope>provided</scope>
     </dependency>
+    <dependency>
+      <groupId>com.google.code.findbugs</groupId>
+      <artifactId>jsr305</artifactId>
+    </dependency>
     <!--
       Promote Guava to "compile" so that maven-shade-plugin picks it up (for packaging the Optional
       class exposed in the Java API). The plugin will then remove this dependency from the published
diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java b/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
index e8e7f06247d3e..df841288a02e3 100644
--- a/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
+++ b/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
@@ -23,6 +23,7 @@
 import java.util.UUID;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.TimeUnit;
+import javax.annotation.Nullable;
 
 import com.google.common.base.Objects;
 import com.google.common.base.Preconditions;
@@ -70,6 +71,7 @@ public class TransportClient implements Closeable {
 
   private final Channel channel;
   private final TransportResponseHandler handler;
+  @Nullable private String clientId;
 
   public TransportClient(Channel channel, TransportResponseHandler handler) {
     this.channel = Preconditions.checkNotNull(channel);
@@ -84,6 +86,25 @@ public SocketAddress getSocketAddress() {
     return channel.remoteAddress();
   }
 
+  /**
+   * Returns the ID used by the client to authenticate itself when authentication is enabled.
+   *
+   * @return The client ID, or null if authentication is disabled.
+   */
+  public String getClientId() {
+    return clientId;
+  }
+
+  /**
+   * Sets the authenticated client ID. This is meant to be used by the authentication layer.
+   *
+   * Trying to set a different client ID after it's been set will result in an exception.
+   */
+  public void setClientId(String id) {
+    Preconditions.checkState(clientId == null, "Client ID has already been set.");
+    this.clientId = id;
+  }
+
   /**
    * Requests a single chunk from the remote side, from the pre-negotiated streamId.
    *
@@ -207,6 +228,7 @@ public void close() {
   public String toString() {
     return Objects.toStringHelper(this)
       .add("remoteAdress", channel.remoteAddress())
+      .add("clientId", clientId)
       .add("isActive", isActive())
       .toString();
   }
diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java
index 185ba2ef3bb1f..69923769d44b4 100644
--- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java
+++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java
@@ -77,6 +77,8 @@ public void doBootstrap(TransportClient client, Channel channel) {
         payload = saslClient.response(response);
       }
 
+      client.setClientId(appId);
+
       if (encrypt) {
         if (!SparkSaslServer.QOP_AUTH_CONF.equals(saslClient.getNegotiatedProperty(Sasl.QOP))) {
           throw new RuntimeException(
diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java
index be6165caf3c74..3f2ebe32887b8 100644
--- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java
+++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java
@@ -81,6 +81,7 @@ public void receive(TransportClient client, byte[] message, RpcResponseCallback
 
     if (saslServer == null) {
       // First message in the handshake, setup the necessary state.
+      client.setClientId(saslMessage.appId);
       saslServer = new SparkSaslServer(saslMessage.appId, secretKeyHolder,
         conf.saslServerAlwaysEncrypt());
     }
diff --git a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java
index c95e64e8e2cda..e671854da1cae 100644
--- a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java
+++ b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java
@@ -24,13 +24,13 @@
 import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.atomic.AtomicLong;
 
+import com.google.common.base.Preconditions;
 import io.netty.channel.Channel;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import org.apache.spark.network.buffer.ManagedBuffer;
-
-import com.google.common.base.Preconditions;
+import org.apache.spark.network.client.TransportClient;
 
 /**
  * StreamManager which allows registration of an Iterator&lt;ManagedBuffer&gt;, which are individually
@@ -44,6 +44,7 @@ public class OneForOneStreamManager extends StreamManager {
 
   /** State of a single stream. */
   private static class StreamState {
+    final String appId;
     final Iterator<ManagedBuffer> buffers;
 
     // The channel associated to the stream
@@ -53,7 +54,8 @@ private static class StreamState {
     // that the caller only requests each chunk one at a time, in order.
     int curChunk = 0;
 
-    StreamState(Iterator<ManagedBuffer> buffers) {
+    StreamState(String appId, Iterator<ManagedBuffer> buffers) {
+      this.appId = appId;
       this.buffers = Preconditions.checkNotNull(buffers);
     }
   }
@@ -109,15 +111,34 @@ public void connectionTerminated(Channel channel) {
     }
   }
 
+  @Override
+  public void checkAuthorization(TransportClient client, long streamId) {
+    if (client.getClientId() != null) {
+      StreamState state = streams.get(streamId);
+      Preconditions.checkArgument(state != null, "Unknown stream ID.");
+      if (!client.getClientId().equals(state.appId)) {
+        throw new SecurityException(String.format(
+          "Client %s not authorized to read stream %d (app %s).",
+          client.getClientId(),
+          streamId,
+          state.appId));
+      }
+    }
+  }
+
   /**
    * Registers a stream of ManagedBuffers which are served as individual chunks one at a time to
    * callers. Each ManagedBuffer will be release()'d after it is transferred on the wire. If a
    * client connection is closed before the iterator is fully drained, then the remaining buffers
    * will all be release()'d.
+   *
+   * If an app ID is provided, only callers who've authenticated with the given app ID will be
+   * allowed to fetch from this stream.
    */
-  public long registerStream(Iterator<ManagedBuffer> buffers) {
+  public long registerStream(String appId, Iterator<ManagedBuffer> buffers) {
     long myStreamId = nextStreamId.getAndIncrement();
-    streams.put(myStreamId, new StreamState(buffers));
+    streams.put(myStreamId, new StreamState(appId, buffers));
     return myStreamId;
   }
+
 }
diff --git a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java
index 929f789bf9d24..aaa677c965640 100644
--- a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java
+++ b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java
@@ -20,6 +20,7 @@
 import io.netty.channel.Channel;
 
 import org.apache.spark.network.buffer.ManagedBuffer;
+import org.apache.spark.network.client.TransportClient;
 
 /**
  * The StreamManager is used to fetch individual chunks from a stream. This is used in
@@ -60,4 +61,12 @@ public void registerChannel(Channel channel, long streamId) { }
    * to read from the associated streams again, so any state can be cleaned up.
    */
   public void connectionTerminated(Channel channel) { }
+
+  /**
+   * Verify that the client is authorized to read from the given stream.
+   *
+   * @throws SecurityException If client is not authorized.
+   */
+  public void checkAuthorization(TransportClient client, long streamId) { }
+
 }
diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java
index e5159ab56d0d4..df6027805838d 100644
--- a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java
+++ b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java
@@ -97,6 +97,7 @@ private void processFetchRequest(final ChunkFetchRequest req) {
 
     ManagedBuffer buf;
     try {
+      streamManager.checkAuthorization(reverseClient, req.streamChunkId.streamId);
       streamManager.registerChannel(channel, req.streamChunkId.streamId);
       buf = streamManager.getChunk(req.streamChunkId.streamId, req.streamChunkId.chunkIndex);
     } catch (Exception e) {
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
index 0df1dd621f6e9..3ddf5c3c39189 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
@@ -58,7 +58,7 @@ public ExternalShuffleBlockHandler(TransportConf conf, File registeredExecutorFi
 
   /** Enables mocking out the StreamManager and BlockManager. */
   @VisibleForTesting
-  ExternalShuffleBlockHandler(
+  public ExternalShuffleBlockHandler(
       OneForOneStreamManager streamManager,
       ExternalShuffleBlockResolver blockManager) {
     this.streamManager = streamManager;
@@ -77,17 +77,19 @@ protected void handleMessage(
       RpcResponseCallback callback) {
     if (msgObj instanceof OpenBlocks) {
       OpenBlocks msg = (OpenBlocks) msgObj;
-      List<ManagedBuffer> blocks = Lists.newArrayList();
+      checkAuth(client, msg.appId);
 
+      List<ManagedBuffer> blocks = Lists.newArrayList();
       for (String blockId : msg.blockIds) {
         blocks.add(blockManager.getBlockData(msg.appId, msg.execId, blockId));
       }
-      long streamId = streamManager.registerStream(blocks.iterator());
+      long streamId = streamManager.registerStream(client.getClientId(), blocks.iterator());
       logger.trace("Registered streamId {} with {} buffers", streamId, msg.blockIds.length);
       callback.onSuccess(new StreamHandle(streamId, msg.blockIds.length).toByteArray());
 
     } else if (msgObj instanceof RegisterExecutor) {
       RegisterExecutor msg = (RegisterExecutor) msgObj;
+      checkAuth(client, msg.appId);
       blockManager.registerExecutor(msg.appId, msg.execId, msg.executorInfo);
       callback.onSuccess(new byte[0]);
 
@@ -126,4 +128,12 @@ public void reregisterExecutor(AppExecId appExecId, ExecutorShuffleInfo executor
   public void close() {
     blockManager.close();
   }
+
+  private void checkAuth(TransportClient client, String appId) {
+    if (client.getClientId() != null && !client.getClientId().equals(appId)) {
+      throw new SecurityException(String.format(
+        "Client for %s not authorized for application %s.", client.getClientId(), appId));
+    }
+  }
+
 }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java
index 382f613ecbb1b..5cb0e4d4a6458 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java
@@ -19,6 +19,7 @@
 
 import java.io.IOException;
 import java.util.Arrays;
+import java.util.concurrent.atomic.AtomicReference;
 
 import com.google.common.collect.Lists;
 import org.junit.After;
@@ -27,9 +28,12 @@
 import org.junit.Test;
 
 import static org.junit.Assert.*;
+import static org.mockito.Mockito.*;
 
 import org.apache.spark.network.TestUtils;
 import org.apache.spark.network.TransportContext;
+import org.apache.spark.network.buffer.ManagedBuffer;
+import org.apache.spark.network.client.ChunkReceivedCallback;
 import org.apache.spark.network.client.RpcResponseCallback;
 import org.apache.spark.network.client.TransportClient;
 import org.apache.spark.network.client.TransportClientBootstrap;
@@ -39,44 +43,39 @@
 import org.apache.spark.network.server.StreamManager;
 import org.apache.spark.network.server.TransportServer;
 import org.apache.spark.network.server.TransportServerBootstrap;
+import org.apache.spark.network.shuffle.BlockFetchingListener;
 import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler;
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver;
+import org.apache.spark.network.shuffle.OneForOneBlockFetcher;
+import org.apache.spark.network.shuffle.protocol.BlockTransferMessage;
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo;
+import org.apache.spark.network.shuffle.protocol.OpenBlocks;
+import org.apache.spark.network.shuffle.protocol.RegisterExecutor;
+import org.apache.spark.network.shuffle.protocol.StreamHandle;
 import org.apache.spark.network.util.SystemPropertyConfigProvider;
 import org.apache.spark.network.util.TransportConf;
 
 public class SaslIntegrationSuite {
-  static ExternalShuffleBlockHandler handler;
   static TransportServer server;
   static TransportConf conf;
   static TransportContext context;
+  static SecretKeyHolder secretKeyHolder;
 
   TransportClientFactory clientFactory;
 
-  /** Provides a secret key holder which always returns the given secret key. */
-  static class TestSecretKeyHolder implements SecretKeyHolder {
-
-    private final String secretKey;
-
-    TestSecretKeyHolder(String secretKey) {
-      this.secretKey = secretKey;
-    }
-
-    @Override
-    public String getSaslUser(String appId) {
-      return "user";
-    }
-    @Override
-    public String getSecretKey(String appId) {
-      return secretKey;
-    }
-  }
-
-
   @BeforeClass
   public static void beforeAll() throws IOException {
-    SecretKeyHolder secretKeyHolder = new TestSecretKeyHolder("good-key");
     conf = new TransportConf(new SystemPropertyConfigProvider());
     context = new TransportContext(conf, new TestRpcHandler());
 
+    secretKeyHolder = mock(SecretKeyHolder.class);
+    when(secretKeyHolder.getSaslUser(eq("app-1"))).thenReturn("app-1");
+    when(secretKeyHolder.getSecretKey(eq("app-1"))).thenReturn("app-1");
+    when(secretKeyHolder.getSaslUser(eq("app-2"))).thenReturn("app-2");
+    when(secretKeyHolder.getSecretKey(eq("app-2"))).thenReturn("app-2");
+    when(secretKeyHolder.getSaslUser(anyString())).thenReturn("other-app");
+    when(secretKeyHolder.getSecretKey(anyString())).thenReturn("correct-password");
+
     TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, secretKeyHolder);
     server = context.createServer(Arrays.asList(bootstrap));
   }
@@ -99,7 +98,7 @@ public void afterEach() {
   public void testGoodClient() throws IOException {
     clientFactory = context.createClientFactory(
       Lists.<TransportClientBootstrap>newArrayList(
-        new SaslClientBootstrap(conf, "app-id", new TestSecretKeyHolder("good-key"))));
+        new SaslClientBootstrap(conf, "app-1", secretKeyHolder)));
 
     TransportClient client = clientFactory.createClient(TestUtils.getLocalHost(), server.getPort());
     String msg = "Hello, World!";
@@ -109,13 +108,17 @@ public void testGoodClient() throws IOException {
 
   @Test
   public void testBadClient() {
+    SecretKeyHolder badKeyHolder = mock(SecretKeyHolder.class);
+    when(badKeyHolder.getSaslUser(anyString())).thenReturn("other-app");
+    when(badKeyHolder.getSecretKey(anyString())).thenReturn("wrong-password");
     clientFactory = context.createClientFactory(
       Lists.<TransportClientBootstrap>newArrayList(
-        new SaslClientBootstrap(conf, "app-id", new TestSecretKeyHolder("bad-key"))));
+        new SaslClientBootstrap(conf, "unknown-app", badKeyHolder)));
 
     try {
       // Bootstrap should fail on startup.
       clientFactory.createClient(TestUtils.getLocalHost(), server.getPort());
+      fail("Connection should have failed.");
     } catch (Exception e) {
       assertTrue(e.getMessage(), e.getMessage().contains("Mismatched response"));
     }
@@ -149,7 +152,7 @@ public void testNoSaslServer() {
     TransportContext context = new TransportContext(conf, handler);
     clientFactory = context.createClientFactory(
       Lists.<TransportClientBootstrap>newArrayList(
-        new SaslClientBootstrap(conf, "app-id", new TestSecretKeyHolder("key"))));
+        new SaslClientBootstrap(conf, "app-1", secretKeyHolder)));
     TransportServer server = context.createServer();
     try {
       clientFactory.createClient(TestUtils.getLocalHost(), server.getPort());
@@ -160,6 +163,110 @@ public void testNoSaslServer() {
     }
   }
 
+  /**
+   * This test is not actually testing SASL behavior, but testing that the shuffle service
+   * performs correct authorization checks based on the SASL authentication data.
+   */
+  @Test
+  public void testAppIsolation() throws Exception {
+    // Start a new server with the correct RPC handler to serve block data.
+    ExternalShuffleBlockResolver blockResolver = mock(ExternalShuffleBlockResolver.class);
+    ExternalShuffleBlockHandler blockHandler = new ExternalShuffleBlockHandler(
+      new OneForOneStreamManager(), blockResolver);
+    TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, secretKeyHolder);
+    TransportContext blockServerContext = new TransportContext(conf, blockHandler);
+    TransportServer blockServer = blockServerContext.createServer(Arrays.asList(bootstrap));
+
+    TransportClient client1 = null;
+    TransportClient client2 = null;
+    TransportClientFactory clientFactory2 = null;
+    try {
+      // Create a client, and make a request to fetch blocks from a different app.
+      clientFactory = blockServerContext.createClientFactory(
+        Lists.<TransportClientBootstrap>newArrayList(
+          new SaslClientBootstrap(conf, "app-1", secretKeyHolder)));
+      client1 = clientFactory.createClient(TestUtils.getLocalHost(),
+        blockServer.getPort());
+
+      final AtomicReference<Throwable> exception = new AtomicReference<>();
+
+      BlockFetchingListener listener = new BlockFetchingListener() {
+        @Override
+        public synchronized void onBlockFetchSuccess(String blockId, ManagedBuffer data) {
+          notifyAll();
+        }
+
+        @Override
+        public synchronized void onBlockFetchFailure(String blockId, Throwable t) {
+          exception.set(t);
+          notifyAll();
+        }
+      };
+
+      String[] blockIds = new String[] { "shuffle_2_3_4", "shuffle_6_7_8" };
+      OneForOneBlockFetcher fetcher = new OneForOneBlockFetcher(client1, "app-2", "0",
+        blockIds, listener);
+      synchronized (listener) {
+        fetcher.start();
+        listener.wait();
+      }
+      checkSecurityException(exception.get());
+
+      // Register an executor so that the next steps work.
+      ExecutorShuffleInfo executorInfo = new ExecutorShuffleInfo(
+        new String[] { System.getProperty("java.io.tmpdir") }, 1,
+        "org.apache.spark.shuffle.sort.SortShuffleManager");
+      RegisterExecutor regmsg = new RegisterExecutor("app-1", "0", executorInfo);
+      client1.sendRpcSync(regmsg.toByteArray(), 10000);
+
+      // Make a successful request to fetch blocks, which creates a new stream. But do not actually
+      // fetch any blocks, to keep the stream open.
+      OpenBlocks openMessage = new OpenBlocks("app-1", "0", blockIds);
+      byte[] response = client1.sendRpcSync(openMessage.toByteArray(), 10000);
+      StreamHandle stream = (StreamHandle) BlockTransferMessage.Decoder.fromByteArray(response);
+      long streamId = stream.streamId;
+
+      // Create a second client, authenticated with a different app ID, and try to read from
+      // the stream created for the previous app.
+      clientFactory2 = blockServerContext.createClientFactory(
+        Lists.<TransportClientBootstrap>newArrayList(
+          new SaslClientBootstrap(conf, "app-2", secretKeyHolder)));
+      client2 = clientFactory2.createClient(TestUtils.getLocalHost(),
+        blockServer.getPort());
+
+      ChunkReceivedCallback callback = new ChunkReceivedCallback() {
+        @Override
+        public synchronized void onSuccess(int chunkIndex, ManagedBuffer buffer) {
+          notifyAll();
+        }
+
+        @Override
+        public synchronized void onFailure(int chunkIndex, Throwable t) {
+          exception.set(t);
+          notifyAll();
+        }
+      };
+
+      exception.set(null);
+      synchronized (callback) {
+        client2.fetchChunk(streamId, 0, callback);
+        callback.wait();
+      }
+      checkSecurityException(exception.get());
+    } finally {
+      if (client1 != null) {
+        client1.close();
+      }
+      if (client2 != null) {
+        client2.close();
+      }
+      if (clientFactory2 != null) {
+        clientFactory2.close();
+      }
+      blockServer.close();
+    }
+  }
+
   /** RPC handler which simply responds with the message it received. */
   public static class TestRpcHandler extends RpcHandler {
     @Override
@@ -172,4 +279,10 @@ public StreamManager getStreamManager() {
       return new OneForOneStreamManager();
     }
   }
+
+  private void checkSecurityException(Throwable t) {
+    assertNotNull("No exception was caught.", t);
+    assertTrue("Expected SecurityException.",
+      t.getMessage().contains(SecurityException.class.getName()));
+  }
 }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
index 1d197497b7c8f..e61390cf57061 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
@@ -93,7 +93,7 @@ public void testOpenShuffleBlocks() {
     @SuppressWarnings("unchecked")
     ArgumentCaptor<Iterator<ManagedBuffer>> stream = (ArgumentCaptor<Iterator<ManagedBuffer>>)
         (ArgumentCaptor<?>) ArgumentCaptor.forClass(Iterator.class);
-    verify(streamManager, times(1)).registerStream(stream.capture());
+    verify(streamManager, times(1)).registerStream(anyString(), stream.capture());
     Iterator<ManagedBuffer> buffers = stream.getValue();
     assertEquals(block0Marker, buffers.next());
     assertEquals(block1Marker, buffers.next());
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 88745dc086a04..714ce3cd9b1de 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -37,6 +37,7 @@ object MimaExcludes {
         case v if v.startsWith("1.5") =>
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),
+            MimaBuild.excludeSparkPackage("network"),
             // These are needed if checking against the sbt build, since they are part of
             // the maven-generated artifacts in 1.3.
             excludePackage("org.spark-project.jetty"),

From 6cd98c1878a9c5c6475ed5974643021ab27862a7 Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Wed, 2 Sep 2015 13:36:36 -0700
Subject: [PATCH 1310/1454] [SPARK-10417] [SQL] Iterating through Column
 results in infinite loop

`pyspark.sql.column.Column` object has `__getitem__` method, which makes it iterable for Python. In fact it has `__getitem__` to address the case when the column might be a list or dict, for you to be able to access certain element of it in DF API. The ability to iterate over it is just a side effect that might cause confusion for the people getting familiar with Spark DF (as you might iterate this way on Pandas DF for instance)

Issue reproduction:
```
df = sqlContext.jsonRDD(sc.parallelize(['{"name": "El Magnifico"}']))
for i in df["name"]: print i
```

Author: 0x0FFF <programmerag@gmail.com>

Closes #8574 from 0x0FFF/SPARK-10417.
---
 python/pyspark/sql/column.py | 3 +++
 python/pyspark/sql/tests.py  | 9 +++++++++
 2 files changed, 12 insertions(+)

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 0948f9b27cd38..56e75e8caee88 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -226,6 +226,9 @@ def __getattr__(self, item):
             raise AttributeError(item)
         return self.getField(item)
 
+    def __iter__(self):
+        raise TypeError("Column is not iterable")
+
     # string methods
     rlike = _bin_op("rlike")
     like = _bin_op("like")
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index fc778631d93a3..eb449e8679fa0 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1066,6 +1066,15 @@ def test_with_column_with_existing_name(self):
         keys = self.df.withColumn("key", self.df.key).select("key").collect()
         self.assertEqual([r.key for r in keys], list(range(100)))
 
+    # regression test for SPARK-10417
+    def test_column_iterator(self):
+
+        def foo():
+            for x in self.df.key:
+                break
+
+        self.assertRaises(TypeError, foo)
+
 
 class HiveContextSQLTests(ReusedPySparkTestCase):
 

From 03f3e91ff21707d8a1c7057a00f1b1cd8b743e3f Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 2 Sep 2015 21:00:13 -0700
Subject: [PATCH 1311/1454] [SPARK-10422] [SQL] String column in
 InMemoryColumnarCache needs to override clone method

https://issues.apache.org/jira/browse/SPARK-10422

Author: Yin Huai <yhuai@databricks.com>

Closes #8578 from yhuai/SPARK-10422.
---
 .../spark/sql/columnar/ColumnType.scala       |  2 ++
 .../columnar/InMemoryColumnarQuerySuite.scala | 20 +++++++++++++++++++
 2 files changed, 22 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 531a8244d55d1..ab482a3636121 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -339,6 +339,8 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
   override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     setField(to, toOrdinal, getField(from, fromOrdinal))
   }
+
+  override def clone(v: UTF8String): UTF8String = v.clone()
 }
 
 private[sql] object DATE extends NativeColumnType(DateType, 8, 4) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 952637c5f9cb8..83db9b6510b36 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -191,4 +191,24 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       ctx.table("InMemoryCache_different_data_types").collect())
     ctx.dropTempTable("InMemoryCache_different_data_types")
   }
+
+  test("SPARK-10422: String column in InMemoryColumnarCache needs to override clone method") {
+    val df =
+      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+    val cached = df.cache()
+    // count triggers the caching action. It should not throw.
+    cached.count()
+
+    // Make sure, the DataFrame is indeed cached.
+    assert(sqlContext.cacheManager.lookupCachedData(cached).nonEmpty)
+
+    // Check result.
+    checkAnswer(
+      cached,
+      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+    )
+
+    // Drop the cache.
+    cached.unpersist()
+  }
 }

From 44948a2e9dcad5cd8d1eb749f469e49c5750b5ba Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 2 Sep 2015 21:19:42 -0700
Subject: [PATCH 1312/1454] [SPARK-9723] [ML] params getordefault should throw
 more useful error

Params.getOrDefault should throw a more meaningful exception than what you get from a bad key lookup.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8567 from holdenk/SPARK-9723-params-getordefault-should-throw-more-useful-error.
---
 .../scala/org/apache/spark/ml/param/params.scala  |  3 ++-
 .../org/apache/spark/ml/param/ParamsSuite.scala   | 15 ++++++++++-----
 .../org/apache/spark/ml/param/TestParams.scala    |  5 +++--
 3 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 91c0a5631319d..de32b7218c277 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -461,7 +461,8 @@ trait Params extends Identifiable with Serializable {
    */
   final def getOrDefault[T](param: Param[T]): T = {
     shouldOwn(param)
-    get(param).orElse(getDefault(param)).get
+    get(param).orElse(getDefault(param)).getOrElse(
+      throw new NoSuchElementException(s"Failed to find a default value for ${param.name}"))
   }
 
   /** An alias for [[getOrDefault()]]. */
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index 2c878f8372a47..dfab82c8b67ad 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -40,6 +40,10 @@ class ParamsSuite extends SparkFunSuite {
 
     assert(inputCol.toString === s"${uid}__inputCol")
 
+    intercept[java.util.NoSuchElementException] {
+      solver.getOrDefault(solver.handleInvalid)
+    }
+
     intercept[IllegalArgumentException] {
       solver.setMaxIter(-1)
     }
@@ -102,12 +106,13 @@ class ParamsSuite extends SparkFunSuite {
 
   test("params") {
     val solver = new TestParams()
-    import solver.{maxIter, inputCol}
+    import solver.{handleInvalid, maxIter, inputCol}
 
     val params = solver.params
-    assert(params.length === 2)
-    assert(params(0).eq(inputCol), "params must be ordered by name")
-    assert(params(1).eq(maxIter))
+    assert(params.length === 3)
+    assert(params(0).eq(handleInvalid), "params must be ordered by name")
+    assert(params(1).eq(inputCol), "params must be ordered by name")
+    assert(params(2).eq(maxIter))
 
     assert(!solver.isSet(maxIter))
     assert(solver.isDefined(maxIter))
@@ -122,7 +127,7 @@ class ParamsSuite extends SparkFunSuite {
     assert(solver.explainParam(maxIter) ===
       "maxIter: maximum number of iterations (>= 0) (default: 10, current: 100)")
     assert(solver.explainParams() ===
-      Seq(inputCol, maxIter).map(solver.explainParam).mkString("\n"))
+      Seq(handleInvalid, inputCol, maxIter).map(solver.explainParam).mkString("\n"))
 
     assert(solver.getParam("inputCol").eq(inputCol))
     assert(solver.getParam("maxIter").eq(maxIter))
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
index 2759248344531..9d23547f28447 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
@@ -17,11 +17,12 @@
 
 package org.apache.spark.ml.param
 
-import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter}
+import org.apache.spark.ml.param.shared.{HasHandleInvalid, HasInputCol, HasMaxIter}
 import org.apache.spark.ml.util.Identifiable
 
 /** A subclass of Params for testing. */
-class TestParams(override val uid: String) extends Params with HasMaxIter with HasInputCol {
+class TestParams(override val uid: String) extends Params with HasHandleInvalid with HasMaxIter
+    with HasInputCol {
 
   def this() = this(Identifiable.randomUID("testParams"))
 

From 4bd85d06e0334c49be18c4612b04d013b37f189c Mon Sep 17 00:00:00 2001
From: Ilya Ganelin <ilya.ganelin@capitalone.com>
Date: Wed, 2 Sep 2015 22:07:50 -0700
Subject: [PATCH 1313/1454] [SPARK-5945] Spark should not retry a stage
 infinitely on a FetchFailedException

The ```Stage``` class now tracks whether there were a sufficient number of consecutive failures of that stage to trigger an abort.

To avoid an infinite loop of stage retries, we abort the job completely after 4 consecutive stage failures for one stage. We still allow more than 4 consecutive stage failures if there is an intervening successful attempt for the stage, so that in very long-lived applications, where a stage may get reused many times, we don't abort the job after failures that have been recovered from successfully.

I've added test cases to exercise the most obvious scenarios.

Author: Ilya Ganelin <ilya.ganelin@capitalone.com>

Closes #5636 from ilganeli/SPARK-5945.
---
 .../apache/spark/scheduler/DAGScheduler.scala |  13 +-
 .../org/apache/spark/scheduler/Stage.scala    |  30 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   | 282 +++++++++++++++++-
 3 files changed, 320 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index daf9b0f95273e..d673cb0946639 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1101,7 +1101,6 @@ class DAGScheduler(
             s" ${task.stageAttemptId} and there is a more recent attempt for that stage " +
             s"(attempt ID ${failedStage.latestInfo.attemptId}) running")
         } else {
-
           // It is likely that we receive multiple FetchFailed for a single stage (because we have
           // multiple tasks running concurrently on different executors). In that case, it is
           // possible the fetch failure has already been handled by the scheduler.
@@ -1117,6 +1116,11 @@ class DAGScheduler(
           if (disallowStageRetryForTest) {
             abortStage(failedStage, "Fetch failure will not retry stage due to testing config",
               None)
+          } else if (failedStage.failedOnFetchAndShouldAbort(task.stageAttemptId)) {
+            abortStage(failedStage, s"$failedStage (${failedStage.name}) " +
+              s"has failed the maximum allowable number of " +
+              s"times: ${Stage.MAX_CONSECUTIVE_FETCH_FAILURES}. " +
+              s"Most recent failure reason: ${failureMessage}", None)
           } else if (failedStages.isEmpty) {
             // Don't schedule an event to resubmit failed stages if failed isn't empty, because
             // in that case the event will already have been scheduled.
@@ -1240,10 +1244,17 @@ class DAGScheduler(
     if (errorMessage.isEmpty) {
       logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
       stage.latestInfo.completionTime = Some(clock.getTimeMillis())
+
+      // Clear failure count for this stage, now that it's succeeded.
+      // We only limit consecutive failures of stage attempts,so that if a stage is
+      // re-used many times in a long-running job, unrelated failures don't eventually cause the
+      // stage to be aborted.
+      stage.clearFailures()
     } else {
       stage.latestInfo.stageFailed(errorMessage.get)
       logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime))
     }
+
     outputCommitCoordinator.stageEnd(stage.id)
     listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
     runningStages -= stage
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 1cf06856ffbc2..c086535782c23 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -46,7 +46,7 @@ import org.apache.spark.util.CallSite
  * be updated for each attempt.
  *
  */
-private[spark] abstract class Stage(
+private[scheduler] abstract class Stage(
     val id: Int,
     val rdd: RDD[_],
     val numTasks: Int,
@@ -92,6 +92,29 @@ private[spark] abstract class Stage(
    */
   private var _latestInfo: StageInfo = StageInfo.fromStage(this, nextAttemptId)
 
+  /**
+   * Set of stage attempt IDs that have failed with a FetchFailure. We keep track of these
+   * failures in order to avoid endless retries if a stage keeps failing with a FetchFailure.
+   * We keep track of each attempt ID that has failed to avoid recording duplicate failures if
+   * multiple tasks from the same stage attempt fail (SPARK-5945).
+   */
+  private val fetchFailedAttemptIds = new HashSet[Int]
+
+  private[scheduler] def clearFailures() : Unit = {
+    fetchFailedAttemptIds.clear()
+  }
+
+  /**
+   * Check whether we should abort the failedStage due to multiple consecutive fetch failures.
+   *
+   * This method updates the running set of failed stage attempts and returns
+   * true if the number of failures exceeds the allowable number of failures.
+   */
+  private[scheduler] def failedOnFetchAndShouldAbort(stageAttemptId: Int): Boolean = {
+    fetchFailedAttemptIds.add(stageAttemptId)
+    fetchFailedAttemptIds.size >= Stage.MAX_CONSECUTIVE_FETCH_FAILURES
+  }
+
   /** Creates a new attempt for this stage by creating a new StageInfo with a new attempt ID. */
   def makeNewStageAttempt(
       numPartitionsToCompute: Int,
@@ -110,3 +133,8 @@ private[spark] abstract class Stage(
     case _ => false
   }
 }
+
+private[scheduler] object Stage {
+  // The number of consecutive failures allowed before a stage is aborted
+  val MAX_CONSECUTIVE_FETCH_FAILURES = 4
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 2e8688cf41d99..62957c66974fb 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -26,11 +26,11 @@ import org.scalatest.concurrent.Timeouts
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark._
+import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster}
 import org.apache.spark.util.CallSite
-import org.apache.spark.executor.TaskMetrics
 
 class DAGSchedulerEventProcessLoopTester(dagScheduler: DAGScheduler)
   extends DAGSchedulerEventProcessLoop(dagScheduler) {
@@ -473,6 +473,282 @@ class DAGSchedulerSuite
     assertDataStructuresEmpty()
   }
 
+
+  // Helper function to validate state when creating tests for task failures
+  private def checkStageId(stageId: Int, attempt: Int, stageAttempt: TaskSet) {
+    assert(stageAttempt.stageId === stageId)
+    assert(stageAttempt.stageAttemptId == attempt)
+  }
+
+
+  // Helper functions to extract commonly used code in Fetch Failure test cases
+  private def setupStageAbortTest(sc: SparkContext) {
+    sc.listenerBus.addListener(new EndListener())
+    ended = false
+    jobResult = null
+  }
+
+  // Create a new Listener to confirm that the listenerBus sees the JobEnd message
+  // when we abort the stage. This message will also be consumed by the EventLoggingListener
+  // so this will propagate up to the user.
+  var ended = false
+  var jobResult : JobResult = null
+
+  class EndListener extends SparkListener {
+    override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
+      jobResult = jobEnd.jobResult
+      ended = true
+    }
+  }
+
+  /**
+   * Common code to get the next stage attempt, confirm it's the one we expect, and complete it
+   * successfully.
+   *
+   * @param stageId - The current stageId
+   * @param attemptIdx - The current attempt count
+   * @param numShufflePartitions - The number of partitions in the next stage
+   */
+  private def completeShuffleMapStageSuccessfully(
+      stageId: Int,
+      attemptIdx: Int,
+      numShufflePartitions: Int): Unit = {
+    val stageAttempt = taskSets.last
+    checkStageId(stageId, attemptIdx, stageAttempt)
+    complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map {
+      case (task, idx) =>
+        (Success, makeMapStatus("host" + ('A' + idx).toChar, numShufflePartitions))
+    }.toSeq)
+  }
+
+  /**
+   * Common code to get the next stage attempt, confirm it's the one we expect, and complete it
+   * with all FetchFailure.
+   *
+   * @param stageId - The current stageId
+   * @param attemptIdx - The current attempt count
+   * @param shuffleDep - The shuffle dependency of the stage with a fetch failure
+   */
+  private def completeNextStageWithFetchFailure(
+      stageId: Int,
+      attemptIdx: Int,
+      shuffleDep: ShuffleDependency[_, _, _]): Unit = {
+    val stageAttempt = taskSets.last
+    checkStageId(stageId, attemptIdx, stageAttempt)
+    complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map { case (task, idx) =>
+      (FetchFailed(makeBlockManagerId("hostA"), shuffleDep.shuffleId, 0, idx, "ignored"), null)
+    }.toSeq)
+  }
+
+  /**
+   * Common code to get the next result stage attempt, confirm it's the one we expect, and
+   * complete it with a success where we return 42.
+   *
+   * @param stageId - The current stageId
+   * @param attemptIdx - The current attempt count
+   */
+  private def completeNextResultStageWithSuccess(stageId: Int, attemptIdx: Int): Unit = {
+    val stageAttempt = taskSets.last
+    checkStageId(stageId, attemptIdx, stageAttempt)
+    assert(scheduler.stageIdToStage(stageId).isInstanceOf[ResultStage])
+    complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map(_ => (Success, 42)).toSeq)
+  }
+
+  /**
+   * In this test, we simulate a job where many tasks in the same stage fail. We want to show
+   * that many fetch failures inside a single stage attempt do not trigger an abort
+   * on their own, but only when there are enough failing stage attempts.
+   */
+  test("Single stage fetch failure should not abort the stage.") {
+    setupStageAbortTest(sc)
+
+    val parts = 8
+    val shuffleMapRdd = new MyRDD(sc, parts, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, parts, List(shuffleDep))
+    submit(reduceRdd, (0 until parts).toArray)
+
+    completeShuffleMapStageSuccessfully(0, 0, numShufflePartitions = parts)
+
+    completeNextStageWithFetchFailure(1, 0, shuffleDep)
+
+    // Resubmit and confirm that now all is well
+    scheduler.resubmitFailedStages()
+
+    assert(scheduler.runningStages.nonEmpty)
+    assert(!ended)
+
+    // Complete stage 0 and then stage 1 with a "42"
+    completeShuffleMapStageSuccessfully(0, 1, numShufflePartitions = parts)
+    completeNextResultStageWithSuccess(1, 1)
+
+    // Confirm job finished succesfully
+    sc.listenerBus.waitUntilEmpty(1000)
+    assert(ended === true)
+    assert(results === (0 until parts).map { idx => idx -> 42 }.toMap)
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test we simulate a job failure where the first stage completes successfully and
+   * the second stage fails due to a fetch failure. Multiple successive fetch failures of a stage
+   * trigger an overall job abort to avoid endless retries.
+   */
+  test("Multiple consecutive stage fetch failures should lead to job being aborted.") {
+    setupStageAbortTest(sc)
+
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+    submit(reduceRdd, Array(0, 1))
+
+    for (attempt <- 0 until Stage.MAX_CONSECUTIVE_FETCH_FAILURES) {
+      // Complete all the tasks for the current attempt of stage 0 successfully
+      completeShuffleMapStageSuccessfully(0, attempt, numShufflePartitions = 2)
+
+      // Now we should have a new taskSet, for a new attempt of stage 1.
+      // Fail all these tasks with FetchFailure
+      completeNextStageWithFetchFailure(1, attempt, shuffleDep)
+
+      // this will trigger a resubmission of stage 0, since we've lost some of its
+      // map output, for the next iteration through the loop
+      scheduler.resubmitFailedStages()
+
+      if (attempt < Stage.MAX_CONSECUTIVE_FETCH_FAILURES - 1) {
+        assert(scheduler.runningStages.nonEmpty)
+        assert(!ended)
+      } else {
+        // Stage should have been aborted and removed from running stages
+        assertDataStructuresEmpty()
+        sc.listenerBus.waitUntilEmpty(1000)
+        assert(ended)
+        jobResult match {
+          case JobFailed(reason) =>
+            assert(reason.getMessage.contains("ResultStage 1 () has failed the maximum"))
+          case other => fail(s"expected JobFailed, not $other")
+        }
+      }
+    }
+  }
+
+  /**
+   * In this test, we create a job with two consecutive shuffles, and simulate 2 failures for each
+   * shuffle fetch. In total In total, the job has had four failures overall but not four failures
+   * for a particular stage, and as such should not be aborted.
+   */
+  test("Failures in different stages should not trigger an overall abort") {
+    setupStageAbortTest(sc)
+
+    val shuffleOneRdd = new MyRDD(sc, 2, Nil).cache()
+    val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null)
+    val shuffleTwoRdd = new MyRDD(sc, 2, List(shuffleDepOne)).cache()
+    val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null)
+    val finalRdd = new MyRDD(sc, 1, List(shuffleDepTwo))
+    submit(finalRdd, Array(0))
+
+    // In the first two iterations, Stage 0 succeeds and stage 1 fails. In the next two iterations,
+    // stage 2 fails.
+    for (attempt <- 0 until Stage.MAX_CONSECUTIVE_FETCH_FAILURES) {
+      // Complete all the tasks for the current attempt of stage 0 successfully
+      completeShuffleMapStageSuccessfully(0, attempt, numShufflePartitions = 2)
+
+      if (attempt < Stage.MAX_CONSECUTIVE_FETCH_FAILURES / 2) {
+        // Now we should have a new taskSet, for a new attempt of stage 1.
+        // Fail all these tasks with FetchFailure
+        completeNextStageWithFetchFailure(1, attempt, shuffleDepOne)
+      } else {
+        completeShuffleMapStageSuccessfully(1, attempt, numShufflePartitions = 1)
+
+        // Fail stage 2
+        completeNextStageWithFetchFailure(2, attempt - Stage.MAX_CONSECUTIVE_FETCH_FAILURES / 2,
+          shuffleDepTwo)
+      }
+
+      // this will trigger a resubmission of stage 0, since we've lost some of its
+      // map output, for the next iteration through the loop
+      scheduler.resubmitFailedStages()
+    }
+
+    completeShuffleMapStageSuccessfully(0, 4, numShufflePartitions = 2)
+    completeShuffleMapStageSuccessfully(1, 4, numShufflePartitions = 1)
+
+    // Succeed stage2 with a "42"
+    completeNextResultStageWithSuccess(2, Stage.MAX_CONSECUTIVE_FETCH_FAILURES/2)
+
+    assert(results === Map(0 -> 42))
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test we demonstrate that only consecutive failures trigger a stage abort. A stage may
+   * fail multiple times, succeed, then fail a few more times (because its run again by downstream
+   * dependencies). The total number of failed attempts for one stage will go over the limit,
+   * but that doesn't matter, since they have successes in the middle.
+   */
+  test("Non-consecutive stage failures don't trigger abort") {
+    setupStageAbortTest(sc)
+
+    val shuffleOneRdd = new MyRDD(sc, 2, Nil).cache()
+    val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null)
+    val shuffleTwoRdd = new MyRDD(sc, 2, List(shuffleDepOne)).cache()
+    val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null)
+    val finalRdd = new MyRDD(sc, 1, List(shuffleDepTwo))
+    submit(finalRdd, Array(0))
+
+    // First, execute stages 0 and 1, failing stage 1 up to MAX-1 times.
+    for (attempt <- 0 until Stage.MAX_CONSECUTIVE_FETCH_FAILURES - 1) {
+      // Make each task in stage 0 success
+      completeShuffleMapStageSuccessfully(0, attempt, numShufflePartitions = 2)
+
+      // Now we should have a new taskSet, for a new attempt of stage 1.
+      // Fail these tasks with FetchFailure
+      completeNextStageWithFetchFailure(1, attempt, shuffleDepOne)
+
+      scheduler.resubmitFailedStages()
+
+      // Confirm we have not yet aborted
+      assert(scheduler.runningStages.nonEmpty)
+      assert(!ended)
+    }
+
+    // Rerun stage 0 and 1 to step through the task set
+    completeShuffleMapStageSuccessfully(0, 3, numShufflePartitions = 2)
+    completeShuffleMapStageSuccessfully(1, 3, numShufflePartitions = 1)
+
+    // Fail stage 2 so that stage 1 is resubmitted when we call scheduler.resubmitFailedStages()
+    completeNextStageWithFetchFailure(2, 0, shuffleDepTwo)
+
+    scheduler.resubmitFailedStages()
+
+    // Rerun stage 0 to step through the task set
+    completeShuffleMapStageSuccessfully(0, 4, numShufflePartitions = 2)
+
+    // Now again, fail stage 1 (up to MAX_FAILURES) but confirm that this doesn't trigger an abort
+    // since we succeeded in between.
+    completeNextStageWithFetchFailure(1, 4, shuffleDepOne)
+
+    scheduler.resubmitFailedStages()
+
+    // Confirm we have not yet aborted
+    assert(scheduler.runningStages.nonEmpty)
+    assert(!ended)
+
+    // Next, succeed all and confirm output
+    // Rerun stage 0 + 1
+    completeShuffleMapStageSuccessfully(0, 5, numShufflePartitions = 2)
+    completeShuffleMapStageSuccessfully(1, 5, numShufflePartitions = 1)
+
+    // Succeed stage 2 and verify results
+    completeNextResultStageWithSuccess(2, 1)
+
+    assertDataStructuresEmpty()
+    sc.listenerBus.waitUntilEmpty(1000)
+    assert(ended === true)
+    assert(results === Map(0 -> 42))
+  }
+
   test("trivial shuffle with multiple fetch failures") {
     val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
@@ -810,7 +1086,7 @@ class DAGSchedulerSuite
     submit(finalRdd, Array(0))
     cacheLocations(shuffleTwoRdd.id -> 0) = Seq(makeBlockManagerId("hostD"))
     cacheLocations(shuffleTwoRdd.id -> 1) = Seq(makeBlockManagerId("hostC"))
-    // complete stage 2
+    // complete stage 0
     complete(taskSets(0), Seq(
         (Success, makeMapStatus("hostA", 2)),
         (Success, makeMapStatus("hostB", 2))))
@@ -818,7 +1094,7 @@ class DAGSchedulerSuite
     complete(taskSets(1), Seq(
         (Success, makeMapStatus("hostA", 1)),
         (Success, makeMapStatus("hostB", 1))))
-    // pretend stage 0 failed because hostA went down
+    // pretend stage 2 failed because hostA went down
     complete(taskSets(2), Seq(
         (FetchFailed(makeBlockManagerId("hostA"), shuffleDepTwo.shuffleId, 0, 0, "ignored"), null)))
     // TODO assert this:

From 0985d2c30e031f80892987f7c3581d15dd210303 Mon Sep 17 00:00:00 2001
From: "navis.ryu" <navis@apache.org>
Date: Wed, 2 Sep 2015 22:11:11 -0700
Subject: [PATCH 1314/1454] [SPARK-8707] RDD#toDebugString fails if any cached
 RDD has invalid partitions

Added numPartitions(evaluate: Boolean) to RDD. With "evaluate=true" the method is same with "partitions.length". With "evaluate=false", it checks checked-out or already evaluated partitions in the RDD to get number of partition. If it's not those cases, returns -1. RDDInfo.partitionNum calls numPartition only when it's accessed.

Author: navis.ryu <navis@apache.org>

Closes #7127 from navis/SPARK-8707.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 6 +++++-
 core/src/main/scala/org/apache/spark/rdd/RDD.scala      | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 738887076b0d1..cbfe8bf31c3d6 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1516,8 +1516,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    */
   @DeveloperApi
   def getRDDStorageInfo: Array[RDDInfo] = {
+    getRDDStorageInfo(_ => true)
+  }
+
+  private[spark] def getRDDStorageInfo(filter: RDD[_] => Boolean): Array[RDDInfo] = {
     assertNotStopped()
-    val rddInfos = persistentRdds.values.map(RDDInfo.fromRdd).toArray
+    val rddInfos = persistentRdds.values.filter(filter).map(RDDInfo.fromRdd).toArray
     StorageUtils.updateRddInfo(rddInfos, getExecutorStorageStatus)
     rddInfos.filter(_.isCached)
   }
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 081c721f23687..7dd2bc5d7cd72 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1666,7 +1666,7 @@ abstract class RDD[T: ClassTag](
       import Utils.bytesToString
 
       val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else ""
-      val storageInfo = rdd.context.getRDDStorageInfo.filter(_.id == rdd.id).map(info =>
+      val storageInfo = rdd.context.getRDDStorageInfo(_.id == rdd.id).map(info =>
         "    CachedPartitions: %d; MemorySize: %s; ExternalBlockStoreSize: %s; DiskSize: %s".format(
           info.numCachedPartitions, bytesToString(info.memSize),
           bytesToString(info.externalBlockStoreSize), bytesToString(info.diskSize)))

From f6c447f87592286a6f58aee5e0b2dc8dcb470d0c Mon Sep 17 00:00:00 2001
From: Evan Racah <ejracah@gmail.com>
Date: Wed, 2 Sep 2015 22:13:18 -0700
Subject: [PATCH 1315/1454] Removed code duplication in
 ShuffleBlockFetcherIterator

Added fetchUpToMaxBytes() to prevent having to update both code blocks when a change is made.

Author: Evan Racah <ejracah@gmail.com>

Closes #8514 from eracah/master.
---
 .../storage/ShuffleBlockFetcherIterator.scala  | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
index a759ceb96ec1e..0d0448feb5b06 100644
--- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
@@ -260,10 +260,7 @@ final class ShuffleBlockFetcherIterator(
     fetchRequests ++= Utils.randomize(remoteRequests)
 
     // Send out initial requests for blocks, up to our maxBytesInFlight
-    while (fetchRequests.nonEmpty &&
-      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
-      sendRequest(fetchRequests.dequeue())
-    }
+    fetchUpToMaxBytes()
 
     val numFetches = remoteRequests.size - fetchRequests.size
     logInfo("Started " + numFetches + " remote fetches in" + Utils.getUsedTimeMs(startTime))
@@ -296,10 +293,7 @@ final class ShuffleBlockFetcherIterator(
       case _ =>
     }
     // Send fetch requests up to maxBytesInFlight
-    while (fetchRequests.nonEmpty &&
-      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
-      sendRequest(fetchRequests.dequeue())
-    }
+    fetchUpToMaxBytes()
 
     result match {
       case FailureFetchResult(blockId, address, e) =>
@@ -315,6 +309,14 @@ final class ShuffleBlockFetcherIterator(
     }
   }
 
+  private def fetchUpToMaxBytes(): Unit = {
+    // Send fetch requests up to maxBytesInFlight
+    while (fetchRequests.nonEmpty &&
+      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
+      sendRequest(fetchRequests.dequeue())
+    }
+  }
+
   private def throwFetchFailedException(blockId: BlockId, address: BlockManagerId, e: Throwable) = {
     blockId match {
       case ShuffleBlockId(shufId, mapId, reduceId) =>

From 3ddb9b32335154e47890a0c761e0dfea3ccaac7b Mon Sep 17 00:00:00 2001
From: Imran Rashid <irashid@cloudera.com>
Date: Wed, 2 Sep 2015 22:14:50 -0700
Subject: [PATCH 1316/1454] [SPARK-10247] [CORE] improve readability of a test
 case in DAGSchedulerSuite

This is pretty minor, just trying to improve the readability of `DAGSchedulerSuite`, I figure every bit helps.  Before whenever I read this test, I never knew what "should work" and "should be ignored" really meant -- this adds some asserts & updates comments to make it more clear.  Also some reformatting per a suggestion from markhamstra on https://github.com/apache/spark/pull/7699

Author: Imran Rashid <irashid@cloudera.com>

Closes #8434 from squito/SPARK-10247.
---
 .../spark/scheduler/DAGSchedulerSuite.scala   | 57 +++++++++++++++----
 1 file changed, 47 insertions(+), 10 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 62957c66974fb..80f64de44307e 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -926,27 +926,64 @@ class DAGSchedulerSuite
     val shuffleId = shuffleDep.shuffleId
     val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
     submit(reduceRdd, Array(0, 1))
+
     // pretend we were told hostA went away
     val oldEpoch = mapOutputTracker.getEpoch
     runEvent(ExecutorLost("exec-hostA"))
     val newEpoch = mapOutputTracker.getEpoch
     assert(newEpoch > oldEpoch)
+
+    // now start completing some tasks in the shuffle map stage, under different hosts
+    // and epochs, and make sure scheduler updates its state correctly
     val taskSet = taskSets(0)
+    val shuffleStage = scheduler.stageIdToStage(taskSet.stageId).asInstanceOf[ShuffleMapStage]
+    assert(shuffleStage.numAvailableOutputs === 0)
+
     // should be ignored for being too old
-    runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
-    // should work because it's a non-failed host
-    runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostB",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
+    runEvent(CompletionEvent(
+      taskSet.tasks(0),
+      Success,
+      makeMapStatus("hostA", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 0)
+
+    // should work because it's a non-failed host (so the available map outputs will increase)
+    runEvent(CompletionEvent(
+      taskSet.tasks(0),
+      Success,
+      makeMapStatus("hostB", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 1)
+
     // should be ignored for being too old
-    runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
-    // should work because it's a new epoch
+    runEvent(CompletionEvent(
+      taskSet.tasks(0),
+      Success,
+      makeMapStatus("hostA", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 1)
+
+    // should work because it's a new epoch, which will increase the number of available map
+    // outputs, and also finish the stage
     taskSet.tasks(1).epoch = newEpoch
-    runEvent(CompletionEvent(taskSet.tasks(1), Success, makeMapStatus("hostA",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
+    runEvent(CompletionEvent(
+      taskSet.tasks(1),
+      Success,
+      makeMapStatus("hostA", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 2)
     assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
            HashSet(makeBlockManagerId("hostB"), makeBlockManagerId("hostA")))
+
+    // finish the next stage normally, which completes the job
     complete(taskSets(1), Seq((Success, 42), (Success, 43)))
     assert(results === Map(0 -> 42, 1 -> 43))
     assertDataStructuresEmpty()

From 62b4690d6b3016f41292b640ac28644ef31e299d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 2 Sep 2015 22:15:54 -0700
Subject: [PATCH 1317/1454] [SPARK-10379] preserve first page in
 UnsafeShuffleExternalSorter

Author: Davies Liu <davies@databricks.com>

Closes #8543 from davies/preserve_page.
---
 .../spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java    | 4 ++++
 .../apache/spark/rdd/MapPartitionsWithPreparationRDD.scala   | 2 +-
 .../spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java       | 5 +++--
 3 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 3d1ef0c48adc5..e73ba39468828 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -122,6 +122,10 @@ public UnsafeShuffleExternalSorter(
     this.maxRecordSizeBytes = pageSizeBytes - 4;
     this.writeMetrics = writeMetrics;
     initializeForWriting();
+
+    // preserve first page to ensure that we have at least one page to work with. Otherwise,
+    // other operators in the same task may starve this sorter (SPARK-9709).
+    acquireNewPageIfNecessary(pageSizeBytes);
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
index 1f2213d0c4346..417ff5278db2a 100644
--- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
@@ -41,7 +41,7 @@ private[spark] class MapPartitionsWithPreparationRDD[U: ClassTag, T: ClassTag, M
 
   // In certain join operations, prepare can be called on the same partition multiple times.
   // In this case, we need to ensure that each call to compute gets a separate prepare argument.
-  private[this] var preparedArguments: ArrayBuffer[M] = new ArrayBuffer[M]
+  private[this] val preparedArguments: ArrayBuffer[M] = new ArrayBuffer[M]
 
   /**
    * Prepare a partition for a single call to compute.
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 94650be536b5f..a266b0c36e0fa 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -530,8 +530,9 @@ public void testPeakMemoryUsed() throws Exception {
       for (int i = 0; i < numRecordsPerPage * 10; i++) {
         writer.insertRecordIntoSorter(new Tuple2<Object, Object>(1, 1));
         newPeakMemory = writer.getPeakMemoryUsedBytes();
-        if (i % numRecordsPerPage == 0) {
-          // We allocated a new page for this record, so peak memory should change
+        if (i % numRecordsPerPage == 0 && i != 0) {
+          // The first page is allocated in constructor, another page will be allocated after
+          // every numRecordsPerPage records (peak memory should change).
           assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
           assertEquals(previousPeakMemory, newPeakMemory);

From 0349b5b4383cf813bea4e1053bcc4e0268603743 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 2 Sep 2015 22:17:39 -0700
Subject: [PATCH 1318/1454] [SPARK-10411] [SQL] Move visualization above
 explain output and hide explain by default

New screenshots after this fix:

<img width="627" alt="s1" src="https://cloud.githubusercontent.com/assets/1000778/9625782/4b2dba36-518b-11e5-9104-c713ff026e3d.png">

Default:
<img width="462" alt="s2" src="https://cloud.githubusercontent.com/assets/1000778/9625817/92366e50-518b-11e5-9981-cdfb774d66b8.png">

After clicking `+details`:
<img width="377" alt="s3" src="https://cloud.githubusercontent.com/assets/1000778/9625784/4ba24342-518b-11e5-8522-846a16a95d44.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #8570 from zsxwing/SPARK-10411.
---
 .../sql/execution/ui/ExecutionPage.scala      | 27 +++++++++++++++----
 1 file changed, 22 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
index f0b56c2eb7a53..a4dbd2e1978d0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
@@ -74,16 +74,14 @@ private[sql] class ExecutionPage(parent: SQLTab) extends WebUIPage("execution")
                 }}
               </li>
             }}
-            <li>
-              <strong>Detail: </strong><br/>
-              <pre>{executionUIData.physicalPlanDescription}</pre>
-            </li>
           </ul>
         </div>
 
       val metrics = listener.getExecutionMetrics(executionId)
 
-      summary ++ planVisualization(metrics, executionUIData.physicalPlanGraph)
+      summary ++
+        planVisualization(metrics, executionUIData.physicalPlanGraph) ++
+        physicalPlanDescription(executionUIData.physicalPlanDescription)
     }.getOrElse {
       <div>No information to display for Plan {executionId}</div>
     }
@@ -124,4 +122,23 @@ private[sql] class ExecutionPage(parent: SQLTab) extends WebUIPage("execution")
 
   private def jobURL(jobId: Long): String =
     "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
+
+  private def physicalPlanDescription(physicalPlanDescription: String): Seq[Node] = {
+    <div>
+      <span style="cursor: pointer;" onclick="clickPhysicalPlanDetails();">
+        <span id="physical-plan-details-arrow" class="arrow-closed"></span>
+        <a>Details</a>
+      </span>
+    </div>
+    <div id="physical-plan-details" style="display: none;">
+      <pre>{physicalPlanDescription}</pre>
+    </div>
+    <script>
+      function clickPhysicalPlanDetails() {{
+        $('#physical-plan-details').toggle();
+        $('#physical-plan-details-arrow').toggleClass('arrow-open').toggleClass('arrow-closed');
+      }}
+    </script>
+    <br/>
+  }
 }

From 67580f1f574d272af3712fd91458f3c87368c2e4 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 3 Sep 2015 09:30:54 +0100
Subject: [PATCH 1319/1454] [SPARK-10332] [CORE] Fix yarn spark executor
 validation

From Jira:
Running spark-submit with yarn with number-executors equal to 0 when not using dynamic allocation should error out.
In spark 1.5.0 it continues and ends up hanging.
yarn.ClientArguments still has the check so something else must have changed.
spark-submit --master yarn --deploy-mode cluster --class org.apache.spark.examples.SparkPi --num-executors 0 ....
spark 1.4.1 errors with:
java.lang.IllegalArgumentException:
Number of executors was 0, but must be at least 1
(or 0 if dynamic executor allocation is enabled).

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8580 from holdenk/SPARK-10332-spark-submit-to-yarn-executors-0-message.
---
 .../scala/org/apache/spark/deploy/yarn/ClientArguments.scala   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 4f42ffefa77f9..54f62e6b723ac 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -96,6 +96,9 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
       }
 
       numExecutors = initialNumExecutors
+    } else {
+      val numExecutorsConf = "spark.executor.instances"
+      numExecutors = sparkConf.getInt(numExecutorsConf, numExecutors)
     }
     principal = Option(principal)
       .orElse(sparkConf.getOption("spark.yarn.principal"))

From 3abc0d512541158d11b181e2d9fa126d1371d5c0 Mon Sep 17 00:00:00 2001
From: WangTaoTheTonic <wangtao111@huawei.com>
Date: Thu, 3 Sep 2015 12:56:36 -0700
Subject: [PATCH 1320/1454] [SPARK-9596] [SQL] treat hadoop classes as shared
 one in IsolatedClientLoader

https://issues.apache.org/jira/browse/SPARK-9596

Author: WangTaoTheTonic <wangtao111@huawei.com>

Closes #7931 from WangTaoTheTonic/SPARK-9596.
---
 .../org/apache/spark/sql/hive/client/IsolatedClientLoader.scala  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index 7856037508412..1fe4cba9571f3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -131,6 +131,7 @@ private[hive] class IsolatedClientLoader(
     name.contains("slf4j") ||
     name.contains("log4j") ||
     name.startsWith("org.apache.spark.") ||
+    (name.startsWith("org.apache.hadoop.") && !name.startsWith("org.apache.hadoop.hive.")) ||
     name.startsWith("scala.") ||
     (name.startsWith("com.google") && !name.startsWith("com.google.cloud")) ||
     name.startsWith("java.lang.") ||

From af0e3125cb1d48b1fc0e44c42b6880d67a9f1a85 Mon Sep 17 00:00:00 2001
From: CHOIJAEHONG <redrock07@naver.com>
Date: Thu, 3 Sep 2015 13:38:26 -0700
Subject: [PATCH 1321/1454] [SPARK-8951] [SPARKR] support Unicode characters in
 collect()

Spark gives an error message and does not show the output when a field of the result DataFrame contains characters in CJK.
I changed SerDe.scala in order that Spark support Unicode characters when writes a string to R.

Author: CHOIJAEHONG <redrock07@naver.com>

Closes #7494 from CHOIJAEHONG1/SPARK-8951.
---
 R/pkg/R/deserialize.R                         |  6 +++--
 R/pkg/R/serialize.R                           |  2 +-
 R/pkg/inst/tests/test_sparkSQL.R              | 26 +++++++++++++++++++
 .../scala/org/apache/spark/api/r/SerDe.scala  |  9 +++----
 4 files changed, 35 insertions(+), 8 deletions(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 6cf628e3007de..88f18613fd7b1 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -57,8 +57,10 @@ readTypedObject <- function(con, type) {
 
 readString <- function(con) {
   stringLen <- readInt(con)
-  string <- readBin(con, raw(), stringLen, endian = "big")
-  rawToChar(string)
+  raw <- readBin(con, raw(), stringLen, endian = "big")
+  string <- rawToChar(raw)
+  Encoding(string) <- "UTF-8"
+  string 
 }
 
 readInt <- function(con) {
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R
index e3676f57f907f..91e6b3e5609b5 100644
--- a/R/pkg/R/serialize.R
+++ b/R/pkg/R/serialize.R
@@ -79,7 +79,7 @@ writeJobj <- function(con, value) {
 writeString <- function(con, value) {
   utfVal <- enc2utf8(value)
   writeInt(con, as.integer(nchar(utfVal, type = "bytes") + 1))
-  writeBin(utfVal, con, endian = "big")
+  writeBin(utfVal, con, endian = "big", useBytes=TRUE)
 }
 
 writeInt <- function(con, value) {
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 0da5e38654732..6d331f9883d55 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -431,6 +431,32 @@ test_that("collect() and take() on a DataFrame return the same number of rows an
   expect_equal(ncol(collect(df)), ncol(take(df, 10)))
 })
 
+test_that("collect() support Unicode characters", {
+  markUtf8 <- function(s) {
+    Encoding(s) <- "UTF-8"
+    s
+  }
+
+  lines <- c("{\"name\":\"안녕하세요\"}",
+             "{\"name\":\"您好\", \"age\":30}",
+             "{\"name\":\"こんにちは\", \"age\":19}",
+             "{\"name\":\"Xin chào\"}")
+
+  jsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
+  writeLines(lines, jsonPath)
+
+  df <- read.df(sqlContext, jsonPath, "json")
+  rdf <- collect(df)
+  expect_true(is.data.frame(rdf))
+  expect_equal(rdf$name[1], markUtf8("안녕하세요"))
+  expect_equal(rdf$name[2], markUtf8("您好"))
+  expect_equal(rdf$name[3], markUtf8("こんにちは"))
+  expect_equal(rdf$name[4], markUtf8("Xin chào"))
+
+  df1 <- createDataFrame(sqlContext, rdf)
+  expect_equal(collect(where(df1, df1$name == markUtf8("您好")))$name, markUtf8("您好"))
+})
+
 test_that("multiple pipeline transformations result in an RDD with the correct values", {
   df <- jsonFile(sqlContext, jsonPath)
   first <- lapply(df, function(row) {
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 26ad4f1d4697e..190e193427af8 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -329,12 +329,11 @@ private[spark] object SerDe {
     out.writeDouble((value.getTime / 1000).toDouble + value.getNanos.toDouble / 1e9)
   }
 
-  // NOTE: Only works for ASCII right now
   def writeString(out: DataOutputStream, value: String): Unit = {
-    val len = value.length
-    out.writeInt(len + 1) // For the \0
-    out.writeBytes(value)
-    out.writeByte(0)
+    val utf8 = value.getBytes("UTF-8")
+    val len = utf8.length
+    out.writeInt(len)
+    out.write(utf8, 0, len)
   }
 
   def writeBytes(out: DataOutputStream, value: Array[Byte]): Unit = {

From 49aff7b9add4a70fb229c93f15394387bde142b8 Mon Sep 17 00:00:00 2001
From: Tom Graves <tgraves@yahoo-inc.com>
Date: Thu, 3 Sep 2015 13:46:16 -0700
Subject: [PATCH 1322/1454] [SPARK-10432] spark.port.maxRetries documentation
 is unclear

Author: Tom Graves <tgraves@yahoo-inc.com>

Closes #8585 from tgravescs/SPARK-10432.
---
 docs/configuration.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index fb0315ce7c3cc..29a36bd67f28b 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1015,7 +1015,11 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.port.maxRetries</code></td>
   <td>16</td>
   <td>
-    Default maximum number of retries when binding to a port before giving up.
+    Maximum number of retries when binding to a port before giving up.
+    When a port is given a specific value (non 0), each subsequent retry will
+    increment the port used in the previous attempt by 1 before retrying. This 
+    essentially allows it to try a range of ports from the start port specified 
+    to port + maxRetries.
   </td>
 </tr>
 <tr>

From d911c682f00cd5c438568c548098e03d3e7ea05c Mon Sep 17 00:00:00 2001
From: robbins <robbins@uk.ibm.com>
Date: Thu, 3 Sep 2015 13:47:22 -0700
Subject: [PATCH 1323/1454] [SPARK-10431] [CORE] Fix intermittent test failure.
 Wait for event queue to be clear

Author: robbins <robbins@uk.ibm.com>

Closes #8582 from robbinspg/InputOutputMetricsSuite.
---
 .../org/apache/spark/metrics/InputOutputMetricsSuite.scala    | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
index d3218a548efc7..44eb5a0469122 100644
--- a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
@@ -286,6 +286,10 @@ class InputOutputMetricsSuite extends SparkFunSuite with SharedSparkContext
   private def runAndReturnMetrics(job: => Unit,
       collector: (SparkListenerTaskEnd) => Option[Long]): Long = {
     val taskMetrics = new ArrayBuffer[Long]()
+
+    // Avoid receiving earlier taskEnd events
+    sc.listenerBus.waitUntilEmpty(500)
+
     sc.addSparkListener(new SparkListener() {
       override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
         collector(taskEnd).foreach(taskMetrics += _)

From 754f853b02e9fd221f138c2446445fd56e3f3fb3 Mon Sep 17 00:00:00 2001
From: robbins <robbins@uk.ibm.com>
Date: Thu, 3 Sep 2015 13:48:35 -0700
Subject: [PATCH 1324/1454] [SPARK-9869] [STREAMING] Wait for all event
 notifications before asserting results

Author: robbins <robbins@uk.ibm.com>

Closes #8589 from robbinspg/InputStreamSuite-fix.
---
 .../scala/org/apache/spark/streaming/InputStreamsSuite.scala   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index ec2852d9a0206..047e38ef90998 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -76,6 +76,9 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
           fail("Timeout: cannot finish all batches in 30 seconds")
         }
 
+        // Ensure progress listener has been notified of all events
+        ssc.scheduler.listenerBus.waitUntilEmpty(500)
+
         // Verify all "InputInfo"s have been reported
         assert(ssc.progressListener.numTotalReceivedRecords === input.size)
         assert(ssc.progressListener.numTotalProcessedRecords === input.size)

From e62f4a46f4396ae1e064e3d2ebfa2434f549b090 Mon Sep 17 00:00:00 2001
From: Pat Shields <yeoldefortran@gmail.com>
Date: Thu, 3 Sep 2015 13:52:47 -0700
Subject: [PATCH 1325/1454] =?UTF-8?q?[SPARK-9672]=20[MESOS]=20Don=E2=80=99?=
 =?UTF-8?q?t=20include=20SPARK=5FENV=5FLOADED=20when=20passing=20env=20var?=
 =?UTF-8?q?s?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This contribution is my original work and I license the work to the project under the project's open source license.

Author: Pat Shields <yeoldefortran@gmail.com>

Closes #7979 from pashields/env-loading-on-driver.
---
 .../deploy/rest/RestSubmissionClient.scala      | 17 +++++++++++++----
 .../deploy/rest/StandaloneRestSubmitSuite.scala | 12 ++++++++++++
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala
index 1fe956320a1b8..957a928bc402b 100644
--- a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala
@@ -392,15 +392,14 @@ private[spark] object RestSubmissionClient {
       mainClass: String,
       appArgs: Array[String],
       conf: SparkConf,
-      env: Map[String, String] = sys.env): SubmitRestProtocolResponse = {
+      env: Map[String, String] = Map()): SubmitRestProtocolResponse = {
     val master = conf.getOption("spark.master").getOrElse {
       throw new IllegalArgumentException("'spark.master' must be set.")
     }
     val sparkProperties = conf.getAll.toMap
-    val environmentVariables = env.filter { case (k, _) => k.startsWith("SPARK_") }
     val client = new RestSubmissionClient(master)
     val submitRequest = client.constructSubmitRequest(
-      appResource, mainClass, appArgs, sparkProperties, environmentVariables)
+      appResource, mainClass, appArgs, sparkProperties, env)
     client.createSubmission(submitRequest)
   }
 
@@ -413,6 +412,16 @@ private[spark] object RestSubmissionClient {
     val mainClass = args(1)
     val appArgs = args.slice(2, args.size)
     val conf = new SparkConf
-    run(appResource, mainClass, appArgs, conf)
+    val env = filterSystemEnvironment(sys.env)
+    run(appResource, mainClass, appArgs, conf, env)
+  }
+
+  /**
+   * Filter non-spark environment variables from any environment.
+   */
+  private[rest] def filterSystemEnvironment(env: Map[String, String]): Map[String, String] = {
+    env.filter { case (k, _) =>
+      (k.startsWith("SPARK_") && k != "SPARK_ENV_LOADED") || k.startsWith("MESOS_")
+    }
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
index 96e456d889ac3..9693e32bf6af6 100644
--- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
@@ -366,6 +366,18 @@ class StandaloneRestSubmitSuite extends SparkFunSuite with BeforeAndAfterEach {
     assert(conn3.getResponseCode === HttpServletResponse.SC_INTERNAL_SERVER_ERROR)
   }
 
+  test("client does not send 'SPARK_ENV_LOADED' env var by default") {
+    val environmentVariables = Map("SPARK_VAR" -> "1", "SPARK_ENV_LOADED" -> "1")
+    val filteredVariables = RestSubmissionClient.filterSystemEnvironment(environmentVariables)
+    assert(filteredVariables == Map("SPARK_VAR" -> "1"))
+  }
+
+  test("client includes mesos env vars") {
+    val environmentVariables = Map("SPARK_VAR" -> "1", "MESOS_VAR" -> "1", "OTHER_VAR" -> "1")
+    val filteredVariables = RestSubmissionClient.filterSystemEnvironment(environmentVariables)
+    assert(filteredVariables == Map("SPARK_VAR" -> "1", "MESOS_VAR" -> "1"))
+  }
+
   /* --------------------- *
    |     Helper methods    |
    * --------------------- */

From 11ef32c5a1fad84574733ade1e9d50a94275842b Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Thu, 3 Sep 2015 13:54:58 -0700
Subject: [PATCH 1326/1454] [SPARK-10430] [CORE] Added hashCode methods in
 AccumulableInfo and RDDOperationScope

Author: Vinod K C <vinod.kc@huawei.com>

Closes #8581 from vinodkc/fix_RDDOperationScope_Hashcode.
---
 .../scala/org/apache/spark/rdd/RDDOperationScope.scala   | 3 +++
 .../org/apache/spark/scheduler/AccumulableInfo.scala     | 8 +++++++-
 .../org/apache/spark/rdd/RDDOperationScopeSuite.scala    | 7 +++++++
 .../org/apache/spark/scheduler/DAGSchedulerSuite.scala   | 9 +++++++++
 4 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
index 44667281c1063..540cbd688b63b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
@@ -23,6 +23,7 @@ import com.fasterxml.jackson.annotation.{JsonIgnore, JsonInclude, JsonPropertyOr
 import com.fasterxml.jackson.annotation.JsonInclude.Include
 import com.fasterxml.jackson.databind.ObjectMapper
 import com.fasterxml.jackson.module.scala.DefaultScalaModule
+import com.google.common.base.Objects
 
 import org.apache.spark.{Logging, SparkContext}
 
@@ -67,6 +68,8 @@ private[spark] class RDDOperationScope(
     }
   }
 
+  override def hashCode(): Int = Objects.hashCode(id, name, parent)
+
   override def toString: String = toJson
 }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
index 11d123eec43ca..b6bff64ee368e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
@@ -34,9 +34,15 @@ class AccumulableInfo private[spark] (
   override def equals(other: Any): Boolean = other match {
     case acc: AccumulableInfo =>
       this.id == acc.id && this.name == acc.name &&
-        this.update == acc.update && this.value == acc.value
+        this.update == acc.update && this.value == acc.value &&
+        this.internal == acc.internal
     case _ => false
   }
+
+  override def hashCode(): Int = {
+    val state = Seq(id, name, update, value, internal)
+    state.map(_.hashCode).reduceLeft(31 * _ + _)
+  }
 }
 
 object AccumulableInfo {
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
index f65349e3e3585..16a92f54f9368 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
@@ -38,6 +38,13 @@ class RDDOperationScopeSuite extends SparkFunSuite with BeforeAndAfter {
     sc.stop()
   }
 
+  test("equals and hashCode") {
+    val opScope1 = new RDDOperationScope("scope1", id = "1")
+    val opScope2 = new RDDOperationScope("scope1", id = "1")
+    assert(opScope1 === opScope2)
+    assert(opScope1.hashCode() === opScope2.hashCode())
+  }
+
   test("getAllScopes") {
     assert(scope1.getAllScopes === Seq(scope1))
     assert(scope2.getAllScopes === Seq(scope1, scope2))
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 80f64de44307e..2c3aa2cf90d3c 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -285,6 +285,15 @@ class DAGSchedulerSuite
     assertDataStructuresEmpty()
   }
 
+  test("equals and hashCode AccumulableInfo") {
+    val accInfo1 = new AccumulableInfo(1, " Accumulable " + 1, Some("delta" + 1), "val" + 1, true)
+    val accInfo2 = new AccumulableInfo(1, " Accumulable " + 1, Some("delta" + 1), "val" + 1, false)
+    val accInfo3 = new AccumulableInfo(1, " Accumulable " + 1, Some("delta" + 1), "val" + 1, false)
+    assert(accInfo1 !== accInfo2)
+    assert(accInfo2 === accInfo3)
+    assert(accInfo2.hashCode() === accInfo3.hashCode())
+  }
+
   test("cache location preferences w/ dependency") {
     val baseRdd = new MyRDD(sc, 1, Nil).cache()
     val finalRdd = new MyRDD(sc, 1, List(new OneToOneDependency(baseRdd)))

From db4c130f9e8e2195c7cd8332ccb668217df55507 Mon Sep 17 00:00:00 2001
From: jeanlyn <jeanlyn92@gmail.com>
Date: Thu, 3 Sep 2015 13:56:11 -0700
Subject: [PATCH 1327/1454] [SPARK-9591] [CORE] Job may fail for exception
 during getting remote block

[SPARK-9591](https://issues.apache.org/jira/browse/SPARK-9591)
When we getting the broadcast variable, we can fetch the block form several location,but now when connecting the lost blockmanager(idle for enough time removed by driver when using dynamic resource allocate and so on) will cause task fail,and the worse case will cause the job fail.

Author: jeanlyn <jeanlyn92@gmail.com>

Closes #7927 from jeanlyn/catch_exception.
---
 .../spark/storage/BlockFetchException.scala   | 24 ++++++++++++
 .../apache/spark/storage/BlockManager.scala   | 21 ++++++++++-
 .../spark/storage/BlockManagerSuite.scala     | 37 +++++++++++++++++++
 3 files changed, 80 insertions(+), 2 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala
new file mode 100644
index 0000000000000..f6e46ae9a481a
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala
@@ -0,0 +1,24 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import org.apache.spark.SparkException
+
+private[spark]
+case class BlockFetchException(messages: String, throwable: Throwable)
+  extends SparkException(messages, throwable)
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index fefaef0ab82c8..d31aa68eb6954 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -23,6 +23,7 @@ import java.nio.{ByteBuffer, MappedByteBuffer}
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.concurrent.{ExecutionContext, Await, Future}
 import scala.concurrent.duration._
+import scala.util.control.NonFatal
 import scala.util.Random
 
 import sun.nio.ch.DirectBuffer
@@ -600,10 +601,26 @@ private[spark] class BlockManager(
   private def doGetRemote(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {
     require(blockId != null, "BlockId is null")
     val locations = Random.shuffle(master.getLocations(blockId))
+    var numFetchFailures = 0
     for (loc <- locations) {
       logDebug(s"Getting remote block $blockId from $loc")
-      val data = blockTransferService.fetchBlockSync(
-        loc.host, loc.port, loc.executorId, blockId.toString).nioByteBuffer()
+      val data = try {
+        blockTransferService.fetchBlockSync(
+          loc.host, loc.port, loc.executorId, blockId.toString).nioByteBuffer()
+      } catch {
+        case NonFatal(e) =>
+          numFetchFailures += 1
+          if (numFetchFailures == locations.size) {
+            // An exception is thrown while fetching this block from all locations
+            throw new BlockFetchException(s"Failed to fetch block from" +
+              s" ${locations.size} locations. Most recent failure cause:", e)
+          } else {
+            // This location failed, so we retry fetch from a different one by returning null here
+            logWarning(s"Failed to fetch remote block $blockId " +
+              s"from $loc (failed attempt $numFetchFailures)", e)
+            null
+          }
+      }
 
       if (data != null) {
         if (asBlockResult) {
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index f480fd107a0c2..e5b54d66c8157 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -47,6 +47,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
   private val conf = new SparkConf(false)
   var store: BlockManager = null
   var store2: BlockManager = null
+  var store3: BlockManager = null
   var rpcEnv: RpcEnv = null
   var master: BlockManagerMaster = null
   conf.set("spark.authenticate", "false")
@@ -99,6 +100,10 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
       store2.stop()
       store2 = null
     }
+    if (store3 != null) {
+      store3.stop()
+      store3 = null
+    }
     rpcEnv.shutdown()
     rpcEnv.awaitTermination()
     rpcEnv = null
@@ -443,6 +448,38 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     assert(list2DiskGet.get.readMethod === DataReadMethod.Disk)
   }
 
+  test("SPARK-9591: getRemoteBytes from another location when Exception throw") {
+    val origTimeoutOpt = conf.getOption("spark.network.timeout")
+    try {
+      conf.set("spark.network.timeout", "2s")
+      store = makeBlockManager(8000, "executor1")
+      store2 = makeBlockManager(8000, "executor2")
+      store3 = makeBlockManager(8000, "executor3")
+      val list1 = List(new Array[Byte](4000))
+      store2.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      store3.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      var list1Get = store.getRemoteBytes("list1")
+      assert(list1Get.isDefined, "list1Get expected to be fetched")
+      // block manager exit
+      store2.stop()
+      store2 = null
+      list1Get = store.getRemoteBytes("list1")
+      // get `list1` block
+      assert(list1Get.isDefined, "list1Get expected to be fetched")
+      store3.stop()
+      store3 = null
+      // exception throw because there is no locations
+      intercept[BlockFetchException] {
+        list1Get = store.getRemoteBytes("list1")
+      }
+    } finally {
+      origTimeoutOpt match {
+        case Some(t) => conf.set("spark.network.timeout", t)
+        case None => conf.remove("spark.network.timeout")
+      }
+    }
+  }
+
   test("in-memory LRU storage") {
     store = makeBlockManager(12000)
     val a1 = new Array[Byte](4000)

From 08b07509746667791db7dbfa1b329b0e221044bb Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 3 Sep 2015 13:57:20 -0700
Subject: [PATCH 1328/1454] [SPARK-10435] Spark submit should fail fast for
 Mesos cluster mode with R

It's not supported yet so we should error with a clear message.

Author: Andrew Or <andrew@databricks.com>

Closes #8590 from andrewor14/mesos-cluster-r-guard.
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 86fcf942c2c4e..36e9750b86636 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -322,6 +322,9 @@ object SparkSubmit {
       case (MESOS, CLUSTER) if args.isPython =>
         printErrorAndExit("Cluster deploy mode is currently not supported for python " +
           "applications on Mesos clusters.")
+      case (MESOS, CLUSTER) if args.isR =>
+        printErrorAndExit("Cluster deploy mode is currently not supported for R " +
+          "applications on Mesos clusters.")
       case (STANDALONE, CLUSTER) if args.isPython =>
         printErrorAndExit("Cluster deploy mode is currently not supported for python " +
           "applications on standalone clusters.")

From 208fbca102c269c52eaf84bdde9838474ded276b Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 3 Sep 2015 15:10:30 -0700
Subject: [PATCH 1329/1454] [SPARK-10421] [BUILD] Exclude curator artifacts
 from tachyon dependencies.

This avoids them being mistakenly pulled instead of the newer ones that
Spark actually uses. Spark only depends on these artifacts transitively,
so sometimes maven just decides to pick tachyon's version of the
dependency for whatever reason.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8577 from vanzin/SPARK-10421.
---
 core/pom.xml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/core/pom.xml b/core/pom.xml
index 4f79d71bf85fa..a46292c13bcc0 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -272,6 +272,14 @@
           <groupId>org.apache.hadoop</groupId>
           <artifactId>hadoop-client</artifactId>
         </exclusion>
+        <exclusion>
+          <groupId>org.apache.curator</groupId>
+          <artifactId>curator-client</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.apache.curator</groupId>
+          <artifactId>curator-framework</artifactId>
+        </exclusion>
         <exclusion>
           <groupId>org.apache.curator</groupId>
           <artifactId>curator-recipes</artifactId>

From cf42138643d1d4bf464f1d700457309d9e537721 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 3 Sep 2015 17:55:10 -0700
Subject: [PATCH 1330/1454] [SPARK-10003] Improve readability of DAGScheduler

Note: this is not intended to be in Spark 1.5!

This patch rewrites some code in the `DAGScheduler` to make it more readable. In particular
- there were blocks of code that are unnecessary and removed for simplicity
- there were abstractions that are unnecessary and made the code hard to navigate
- other minor changes

Author: Andrew Or <andrew@databricks.com>

Closes #8217 from andrewor14/dag-scheduler-readability and squashes the following commits:

57abca3 [Andrew Or] Move comment back into if case
574fb1e [Andrew Or] Merge branch 'master' of github.com:apache/spark into dag-scheduler-readability
64a9ed2 [Andrew Or] Remove unnecessary code + minor code rewrites
---
 .../apache/spark/scheduler/DAGScheduler.scala | 46 ++++---------------
 1 file changed, 9 insertions(+), 37 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index d673cb0946639..09e963f5cdf60 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -250,11 +250,12 @@ class DAGScheduler(
       case Some(stage) => stage
       case None =>
         // We are going to register ancestor shuffle dependencies
-        registerShuffleDependencies(shuffleDep, firstJobId)
+        getAncestorShuffleDependencies(shuffleDep.rdd).foreach { dep =>
+          shuffleToMapStage(dep.shuffleId) = newOrUsedShuffleStage(dep, firstJobId)
+        }
         // Then register current shuffleDep
         val stage = newOrUsedShuffleStage(shuffleDep, firstJobId)
         shuffleToMapStage(shuffleDep.shuffleId) = stage
-
         stage
     }
   }
@@ -365,16 +366,6 @@ class DAGScheduler(
     parents.toList
   }
 
-  /** Find ancestor missing shuffle dependencies and register into shuffleToMapStage */
-  private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], firstJobId: Int) {
-    val parentsWithNoMapStage = getAncestorShuffleDependencies(shuffleDep.rdd)
-    while (parentsWithNoMapStage.nonEmpty) {
-      val currentShufDep = parentsWithNoMapStage.pop()
-      val stage = newOrUsedShuffleStage(currentShufDep, firstJobId)
-      shuffleToMapStage(currentShufDep.shuffleId) = stage
-    }
-  }
-
   /** Find ancestor shuffle dependencies that are not registered in shuffleToMapStage yet */
   private def getAncestorShuffleDependencies(rdd: RDD[_]): Stack[ShuffleDependency[_, _, _]] = {
     val parents = new Stack[ShuffleDependency[_, _, _]]
@@ -391,11 +382,9 @@ class DAGScheduler(
               if (!shuffleToMapStage.contains(shufDep.shuffleId)) {
                 parents.push(shufDep)
               }
-
-              waitingForVisit.push(shufDep.rdd)
             case _ =>
-              waitingForVisit.push(dep.rdd)
           }
+          waitingForVisit.push(dep.rdd)
         }
       }
     }
@@ -1052,10 +1041,11 @@ class DAGScheduler(
               //       we registered these map outputs.
               mapOutputTracker.registerMapOutputs(
                 shuffleStage.shuffleDep.shuffleId,
-                shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head),
+                shuffleStage.outputLocs.map(_.headOption.orNull),
                 changeEpoch = true)
 
               clearCacheLocs()
+
               if (shuffleStage.outputLocs.contains(Nil)) {
                 // Some tasks had failed; let's resubmit this shuffleStage
                 // TODO: Lower-level scheduler should also deal with this
@@ -1064,27 +1054,9 @@ class DAGScheduler(
                   shuffleStage.outputLocs.zipWithIndex.filter(_._1.isEmpty)
                       .map(_._2).mkString(", "))
                 submitStage(shuffleStage)
-              } else {
-                val newlyRunnable = new ArrayBuffer[Stage]
-                for (shuffleStage <- waitingStages) {
-                  logInfo("Missing parents for " + shuffleStage + ": " +
-                    getMissingParentStages(shuffleStage))
-                }
-                for (shuffleStage <- waitingStages if getMissingParentStages(shuffleStage).isEmpty)
-                {
-                  newlyRunnable += shuffleStage
-                }
-                waitingStages --= newlyRunnable
-                runningStages ++= newlyRunnable
-                for {
-                  shuffleStage <- newlyRunnable.sortBy(_.id)
-                  jobId <- activeJobForStage(shuffleStage)
-                } {
-                  logInfo("Submitting " + shuffleStage + " (" +
-                    shuffleStage.rdd + "), which is now runnable")
-                  submitMissingTasks(shuffleStage, jobId)
-                }
               }
+
+              // Note: newly runnable stages will be submitted below when we submit waiting stages
             }
           }
 
@@ -1186,7 +1158,7 @@ class DAGScheduler(
         // TODO: This will be really slow if we keep accumulating shuffle map stages
         for ((shuffleId, stage) <- shuffleToMapStage) {
           stage.removeOutputsOnExecutor(execId)
-          val locs = stage.outputLocs.map(list => if (list.isEmpty) null else list.head)
+          val locs = stage.outputLocs.map(_.headOption.orNull)
           mapOutputTracker.registerMapOutputs(shuffleId, locs, changeEpoch = true)
         }
         if (shuffleToMapStage.isEmpty) {

From 143e521d1a0e16af18083524ceb4399cbe6372bc Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Fri, 4 Sep 2015 11:24:47 -0700
Subject: [PATCH 1331/1454] [MINOR] Minor style fix in SparkR

`dev/lintr-r` passes on my machine now

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8601 from shivaram/sparkr-style-fix.
---
 R/pkg/R/deserialize.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 88f18613fd7b1..d1858ec227b56 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -60,7 +60,7 @@ readString <- function(con) {
   raw <- readBin(con, raw(), stringLen, endian = "big")
   string <- rawToChar(raw)
   Encoding(string) <- "UTF-8"
-  string 
+  string
 }
 
 readInt <- function(con) {

From 804a0126e0cc982cc9f22cc76ba7b874ebbef5dd Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 4 Sep 2015 14:59:56 -0700
Subject: [PATCH 1332/1454] MAINTENANCE: Automated closing of pull requests.
 This commit exists to close the following pull requests on Github:

Closes #1890 (requested by andrewor14, JoshRosen)
Closes #3558 (requested by JoshRosen, marmbrus)
Closes #3890 (requested by marmbrus)
Closes #3895 (requested by andrewor14, marmbrus)
Closes #4055 (requested by andrewor14)
Closes #4105 (requested by andrewor14)
Closes #4812 (requested by marmbrus)
Closes #5109 (requested by andrewor14)
Closes #5178 (requested by andrewor14)
Closes #5298 (requested by marmbrus)
Closes #5393 (requested by marmbrus)
Closes #5449 (requested by andrewor14)
Closes #5468 (requested by marmbrus)
Closes #5715 (requested by marmbrus)
Closes #6192 (requested by marmbrus)
Closes #6319 (requested by marmbrus)
Closes #6326 (requested by marmbrus)
Closes #6349 (requested by marmbrus)
Closes #6380 (requested by andrewor14)
Closes #6554 (requested by marmbrus)
Closes #6696 (requested by marmbrus)
Closes #6868 (requested by marmbrus)
Closes #6951 (requested by marmbrus)
Closes #7129 (requested by marmbrus)
Closes #7188 (requested by marmbrus)
Closes #7358 (requested by marmbrus)
Closes #7379 (requested by marmbrus)
Closes #7628 (requested by marmbrus)
Closes #7715 (requested by marmbrus)
Closes #7782 (requested by marmbrus)
Closes #7914 (requested by andrewor14)
Closes #8051 (requested by andrewor14)
Closes #8269 (requested by andrewor14)
Closes #8448 (requested by andrewor14)
Closes #8576 (requested by andrewor14)

From c3c0e431a6280fbcf726ac9bc4db0e1b5a862be8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 4 Sep 2015 15:17:37 -0700
Subject: [PATCH 1333/1454] [SPARK-10176] [SQL] Show partially analyzed plans
 when checkAnswer fails to analyze

This PR takes over https://github.com/apache/spark/pull/8389.

This PR improves `checkAnswer` to print the partially analyzed plan in addition to the user friendly error message, in order to aid debugging failing tests.

In doing so, I ran into a conflict with the various ways that we bring a SQLContext into the tests. Depending on the trait we refer to the current context as `sqlContext`, `_sqlContext`, `ctx` or `hiveContext` with access modifiers `public`, `protected` and `private` depending on the defining class.

I propose we refactor as follows:

1. All tests should only refer to a `protected sqlContext` when testing general features, and `protected hiveContext` when it is a method that only exists on a `HiveContext`.
2. All tests should only import `testImplicits._` (i.e., don't import `TestHive.implicits._`)

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8584 from cloud-fan/cleanupTests.
---
 .../spark/sql/catalyst/plans/PlanTest.scala   |   1 -
 .../apache/spark/sql/CachedTableSuite.scala   | 156 ++++++-------
 .../spark/sql/ColumnExpressionSuite.scala     |  16 +-
 .../spark/sql/DataFrameAggregateSuite.scala   |   4 +-
 .../spark/sql/DataFrameComplexTypeSuite.scala |   6 +-
 .../spark/sql/DataFrameImplicitsSuite.scala   |   8 +-
 .../apache/spark/sql/DataFrameStatSuite.scala |  10 +-
 .../org/apache/spark/sql/DataFrameSuite.scala |  14 +-
 .../spark/sql/DataFrameTungstenSuite.scala    |   6 +-
 .../spark/sql/ExtraStrategiesSuite.scala      |   2 +-
 .../org/apache/spark/sql/JoinSuite.scala      |  12 +-
 .../apache/spark/sql/ListTablesSuite.scala    |  20 +-
 .../org/apache/spark/sql/QueryTest.scala      |  27 ++-
 .../scala/org/apache/spark/sql/RowSuite.scala |   2 +-
 .../org/apache/spark/sql/SQLConfSuite.scala   |  44 ++--
 .../apache/spark/sql/SQLContextSuite.scala    |  12 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  40 ++--
 .../apache/spark/sql/SerializationSuite.scala |   2 +-
 .../spark/sql/StringFunctionsSuite.scala      |  47 ++--
 .../scala/org/apache/spark/sql/UDFSuite.scala |  42 ++--
 .../spark/sql/UserDefinedTypeSuite.scala      |   6 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |  41 ++--
 .../columnar/PartitionBatchPruningSuite.scala |  20 +-
 .../spark/sql/execution/ExchangeSuite.scala   |   2 +
 .../spark/sql/execution/PlannerSuite.scala    |  99 ++++----
 .../execution/RowFormatConvertersSuite.scala  |  16 +-
 .../spark/sql/execution/SortSuite.scala       |   1 +
 .../spark/sql/execution/SparkPlanTest.scala   |  27 +--
 .../sql/execution/TungstenSortSuite.scala     |  12 +-
 .../TungstenAggregationIteratorSuite.scala    |   2 +-
 .../datasources/json/JsonSuite.scala          | 214 +++++++++---------
 .../datasources/json/TestJsonData.scala       |  34 +--
 .../parquet/ParquetCompatibilityTest.scala    |   5 +-
 .../datasources/parquet/ParquetIOSuite.scala  |  52 ++---
 .../ParquetPartitionDiscoverySuite.scala      |   2 +-
 .../parquet/ParquetQuerySuite.scala           |  42 ++--
 .../datasources/parquet/ParquetTest.scala     |   9 +-
 .../execution/joins/BroadcastJoinSuite.scala  |  10 +-
 .../execution/joins/HashedRelationSuite.scala |   6 +-
 .../sql/execution/joins/InnerJoinSuite.scala  |   9 +-
 .../sql/execution/joins/OuterJoinSuite.scala  |   8 +-
 .../sql/execution/joins/SemiJoinSuite.scala   |   8 +-
 .../execution/metric/SQLMetricsSuite.scala    |  24 +-
 .../sql/execution/ui/SQLListenerSuite.scala   |   8 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala |  20 +-
 .../spark/sql/jdbc/JDBCWriteSuite.scala       |  52 ++---
 .../sources/CreateTableAsSelectSuite.scala    |   2 -
 .../spark/sql/sources/DataSourceTest.scala    |   4 +-
 .../spark/sql/sources/InsertSuite.scala       |   3 +-
 .../sql/sources/PartitionedWriteSuite.scala   |   8 +-
 .../spark/sql/sources/SaveLoadSuite.scala     |   1 -
 .../apache/spark/sql/test/SQLTestData.scala   |  52 ++---
 .../apache/spark/sql/test/SQLTestUtils.scala  |  41 ++--
 .../spark/sql/test/SharedSQLContext.scala     |  17 +-
 .../spark/sql/test/TestSQLContext.scala       |   2 +-
 .../apache/spark/sql/hive/test/TestHive.scala |   7 +-
 .../spark/sql/hive/CachedTableSuite.scala     |  20 +-
 .../spark/sql/hive/ErrorPositionSuite.scala   |   8 +-
 .../hive/HiveDataFrameAnalyticsSuite.scala    |  13 +-
 .../sql/hive/HiveDataFrameJoinSuite.scala     |   6 +-
 .../sql/hive/HiveDataFrameWindowSuite.scala   |   7 +-
 .../sql/hive/HiveMetastoreCatalogSuite.scala  |  20 +-
 .../spark/sql/hive/HiveParquetSuite.scala     |  12 +-
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |  11 +-
 .../sql/hive/InsertIntoHiveTableSuite.scala   |  35 ++-
 .../spark/sql/hive/ListTablesSuite.scala      |  12 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  13 +-
 .../spark/sql/hive/MultiDatabaseSuite.scala   |  20 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |  24 +-
 .../spark/sql/hive/QueryPartitionSuite.scala  |  18 +-
 .../spark/sql/hive/StatisticsSuite.scala      |  42 ++--
 .../org/apache/spark/sql/hive/UDFSuite.scala  |  16 +-
 .../execution/AggregationQuerySuite.scala     |  19 +-
 .../hive/execution/HiveComparisonTest.scala   |   4 +-
 .../sql/hive/execution/HiveExplainSuite.scala |  11 +-
 .../HiveOperatorQueryableSuite.scala          |   8 +-
 .../sql/hive/execution/HivePlanTest.scala     |   8 +-
 .../sql/hive/execution/HiveUDFSuite.scala     |  54 ++---
 .../sql/hive/execution/SQLQuerySuite.scala    |  39 ++--
 .../execution/ScriptTransformationSuite.scala |  17 +-
 .../hive/orc/OrcHadoopFsRelationSuite.scala   |   7 +-
 .../hive/orc/OrcPartitionDiscoverySuite.scala |  23 +-
 .../spark/sql/hive/orc/OrcSourceSuite.scala   |  10 +-
 .../apache/spark/sql/hive/orc/OrcTest.scala   |   9 +-
 .../apache/spark/sql/hive/parquetSuites.scala |  16 +-
 .../CommitFailureTestRelationSuite.scala      |   9 +-
 .../sources/JsonHadoopFsRelationSuite.scala   |  12 +-
 .../ParquetHadoopFsRelationSuite.scala        |  15 +-
 .../SimpleTextHadoopFsRelationSuite.scala     |   4 +-
 .../sql/sources/hadoopFsRelationSuites.scala  |  28 ++-
 90 files changed, 908 insertions(+), 999 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
index 765c1e2dda99f..f76a903dcc9cf 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
@@ -26,7 +26,6 @@ import org.apache.spark.sql.catalyst.util._
  * Provides helper methods for comparing plans.
  */
 class PlanTest extends SparkFunSuite {
-
   /**
    * Since attribute references are given globally unique ids during analysis,
    * we must normalize them to check if two different queries are identical.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index af7590c3d3c17..3a3541a8429b6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -34,7 +34,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   import testImplicits._
 
   def rddIdOf(tableName: String): Int = {
-    val executedPlan = ctx.table(tableName).queryExecution.executedPlan
+    val executedPlan = sqlContext.table(tableName).queryExecution.executedPlan
     executedPlan.collect {
       case InMemoryColumnarTableScan(_, _, relation) =>
         relation.cachedColumnBuffers.id
@@ -44,7 +44,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   }
 
   def isMaterialized(rddId: Int): Boolean = {
-    ctx.sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty
+    sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty
   }
 
   test("withColumn doesn't invalidate cached dataframe") {
@@ -69,41 +69,41 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   test("cache temp table") {
     testData.select('key).registerTempTable("tempTable")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"), 0)
-    ctx.cacheTable("tempTable")
+    sqlContext.cacheTable("tempTable")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"))
-    ctx.uncacheTable("tempTable")
+    sqlContext.uncacheTable("tempTable")
   }
 
   test("unpersist an uncached table will not raise exception") {
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = true)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = false)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.persist()
-    assert(None != ctx.cacheManager.lookupCachedData(testData))
+    assert(None != sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = true)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = false)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
   }
 
   test("cache table as select") {
     sql("CACHE TABLE tempTable AS SELECT key FROM testData")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"))
-    ctx.uncacheTable("tempTable")
+    sqlContext.uncacheTable("tempTable")
   }
 
   test("uncaching temp table") {
     testData.select('key).registerTempTable("tempTable1")
     testData.select('key).registerTempTable("tempTable2")
-    ctx.cacheTable("tempTable1")
+    sqlContext.cacheTable("tempTable1")
 
     assertCached(sql("SELECT COUNT(*) FROM tempTable1"))
     assertCached(sql("SELECT COUNT(*) FROM tempTable2"))
 
     // Is this valid?
-    ctx.uncacheTable("tempTable2")
+    sqlContext.uncacheTable("tempTable2")
 
     // Should this be cached?
     assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0)
@@ -111,103 +111,103 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("too big for memory") {
     val data = "*" * 1000
-    ctx.sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF()
+    sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF()
       .registerTempTable("bigData")
-    ctx.table("bigData").persist(StorageLevel.MEMORY_AND_DISK)
-    assert(ctx.table("bigData").count() === 200000L)
-    ctx.table("bigData").unpersist(blocking = true)
+    sqlContext.table("bigData").persist(StorageLevel.MEMORY_AND_DISK)
+    assert(sqlContext.table("bigData").count() === 200000L)
+    sqlContext.table("bigData").unpersist(blocking = true)
   }
 
   test("calling .cache() should use in-memory columnar caching") {
-    ctx.table("testData").cache()
-    assertCached(ctx.table("testData"))
-    ctx.table("testData").unpersist(blocking = true)
+    sqlContext.table("testData").cache()
+    assertCached(sqlContext.table("testData"))
+    sqlContext.table("testData").unpersist(blocking = true)
   }
 
   test("calling .unpersist() should drop in-memory columnar cache") {
-    ctx.table("testData").cache()
-    ctx.table("testData").count()
-    ctx.table("testData").unpersist(blocking = true)
-    assertCached(ctx.table("testData"), 0)
+    sqlContext.table("testData").cache()
+    sqlContext.table("testData").count()
+    sqlContext.table("testData").unpersist(blocking = true)
+    assertCached(sqlContext.table("testData"), 0)
   }
 
   test("isCached") {
-    ctx.cacheTable("testData")
+    sqlContext.cacheTable("testData")
 
-    assertCached(ctx.table("testData"))
-    assert(ctx.table("testData").queryExecution.withCachedData match {
+    assertCached(sqlContext.table("testData"))
+    assert(sqlContext.table("testData").queryExecution.withCachedData match {
       case _: InMemoryRelation => true
       case _ => false
     })
 
-    ctx.uncacheTable("testData")
-    assert(!ctx.isCached("testData"))
-    assert(ctx.table("testData").queryExecution.withCachedData match {
+    sqlContext.uncacheTable("testData")
+    assert(!sqlContext.isCached("testData"))
+    assert(sqlContext.table("testData").queryExecution.withCachedData match {
       case _: InMemoryRelation => false
       case _ => true
     })
   }
 
   test("SPARK-1669: cacheTable should be idempotent") {
-    assume(!ctx.table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
+    assume(!sqlContext.table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
 
-    ctx.cacheTable("testData")
-    assertCached(ctx.table("testData"))
+    sqlContext.cacheTable("testData")
+    assertCached(sqlContext.table("testData"))
 
     assertResult(1, "InMemoryRelation not found, testData should have been cached") {
-      ctx.table("testData").queryExecution.withCachedData.collect {
+      sqlContext.table("testData").queryExecution.withCachedData.collect {
         case r: InMemoryRelation => r
       }.size
     }
 
-    ctx.cacheTable("testData")
+    sqlContext.cacheTable("testData")
     assertResult(0, "Double InMemoryRelations found, cacheTable() is not idempotent") {
-      ctx.table("testData").queryExecution.withCachedData.collect {
+      sqlContext.table("testData").queryExecution.withCachedData.collect {
         case r @ InMemoryRelation(_, _, _, _, _: InMemoryColumnarTableScan, _) => r
       }.size
     }
 
-    ctx.uncacheTable("testData")
+    sqlContext.uncacheTable("testData")
   }
 
   test("read from cached table and uncache") {
-    ctx.cacheTable("testData")
-    checkAnswer(ctx.table("testData"), testData.collect().toSeq)
-    assertCached(ctx.table("testData"))
+    sqlContext.cacheTable("testData")
+    checkAnswer(sqlContext.table("testData"), testData.collect().toSeq)
+    assertCached(sqlContext.table("testData"))
 
-    ctx.uncacheTable("testData")
-    checkAnswer(ctx.table("testData"), testData.collect().toSeq)
-    assertCached(ctx.table("testData"), 0)
+    sqlContext.uncacheTable("testData")
+    checkAnswer(sqlContext.table("testData"), testData.collect().toSeq)
+    assertCached(sqlContext.table("testData"), 0)
   }
 
   test("correct error on uncache of non-cached table") {
     intercept[IllegalArgumentException] {
-      ctx.uncacheTable("testData")
+      sqlContext.uncacheTable("testData")
     }
   }
 
   test("SELECT star from cached table") {
     sql("SELECT * FROM testData").registerTempTable("selectStar")
-    ctx.cacheTable("selectStar")
+    sqlContext.cacheTable("selectStar")
     checkAnswer(
       sql("SELECT * FROM selectStar WHERE key = 1"),
       Seq(Row(1, "1")))
-    ctx.uncacheTable("selectStar")
+    sqlContext.uncacheTable("selectStar")
   }
 
   test("Self-join cached") {
     val unCachedAnswer =
       sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key").collect()
-    ctx.cacheTable("testData")
+    sqlContext.cacheTable("testData")
     checkAnswer(
       sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key"),
       unCachedAnswer.toSeq)
-    ctx.uncacheTable("testData")
+    sqlContext.uncacheTable("testData")
   }
 
   test("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
     sql("CACHE TABLE testData")
-    assertCached(ctx.table("testData"))
+    assertCached(sqlContext.table("testData"))
 
     val rddId = rddIdOf("testData")
     assert(
@@ -215,7 +215,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
       "Eagerly cached in-memory table should have already been materialized")
 
     sql("UNCACHE TABLE testData")
-    assert(!ctx.isCached("testData"), "Table 'testData' should not be cached")
+    assert(!sqlContext.isCached("testData"), "Table 'testData' should not be cached")
 
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
@@ -224,14 +224,14 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("CACHE TABLE tableName AS SELECT * FROM anotherTable") {
     sql("CACHE TABLE testCacheTable AS SELECT * FROM testData")
-    assertCached(ctx.table("testCacheTable"))
+    assertCached(sqlContext.table("testCacheTable"))
 
     val rddId = rddIdOf("testCacheTable")
     assert(
       isMaterialized(rddId),
       "Eagerly cached in-memory table should have already been materialized")
 
-    ctx.uncacheTable("testCacheTable")
+    sqlContext.uncacheTable("testCacheTable")
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
     }
@@ -239,14 +239,14 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("CACHE TABLE tableName AS SELECT ...") {
     sql("CACHE TABLE testCacheTable AS SELECT key FROM testData LIMIT 10")
-    assertCached(ctx.table("testCacheTable"))
+    assertCached(sqlContext.table("testCacheTable"))
 
     val rddId = rddIdOf("testCacheTable")
     assert(
       isMaterialized(rddId),
       "Eagerly cached in-memory table should have already been materialized")
 
-    ctx.uncacheTable("testCacheTable")
+    sqlContext.uncacheTable("testCacheTable")
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
     }
@@ -254,7 +254,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("CACHE LAZY TABLE tableName") {
     sql("CACHE LAZY TABLE testData")
-    assertCached(ctx.table("testData"))
+    assertCached(sqlContext.table("testData"))
 
     val rddId = rddIdOf("testData")
     assert(
@@ -266,7 +266,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
       isMaterialized(rddId),
       "Lazily cached in-memory table should have been materialized")
 
-    ctx.uncacheTable("testData")
+    sqlContext.uncacheTable("testData")
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
     }
@@ -274,7 +274,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("InMemoryRelation statistics") {
     sql("CACHE TABLE testData")
-    ctx.table("testData").queryExecution.withCachedData.collect {
+    sqlContext.table("testData").queryExecution.withCachedData.collect {
       case cached: InMemoryRelation =>
         val actualSizeInBytes = (1 to 100).map(i => INT.defaultSize + i.toString.length + 4).sum
         assert(cached.statistics.sizeInBytes === actualSizeInBytes)
@@ -283,46 +283,48 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("Drops temporary table") {
     testData.select('key).registerTempTable("t1")
-    ctx.table("t1")
-    ctx.dropTempTable("t1")
-    assert(intercept[RuntimeException](ctx.table("t1")).getMessage.startsWith("Table Not Found"))
+    sqlContext.table("t1")
+    sqlContext.dropTempTable("t1")
+    assert(
+      intercept[RuntimeException](sqlContext.table("t1")).getMessage.startsWith("Table Not Found"))
   }
 
   test("Drops cached temporary table") {
     testData.select('key).registerTempTable("t1")
     testData.select('key).registerTempTable("t2")
-    ctx.cacheTable("t1")
+    sqlContext.cacheTable("t1")
 
-    assert(ctx.isCached("t1"))
-    assert(ctx.isCached("t2"))
+    assert(sqlContext.isCached("t1"))
+    assert(sqlContext.isCached("t2"))
 
-    ctx.dropTempTable("t1")
-    assert(intercept[RuntimeException](ctx.table("t1")).getMessage.startsWith("Table Not Found"))
-    assert(!ctx.isCached("t2"))
+    sqlContext.dropTempTable("t1")
+    assert(
+      intercept[RuntimeException](sqlContext.table("t1")).getMessage.startsWith("Table Not Found"))
+    assert(!sqlContext.isCached("t2"))
   }
 
   test("Clear all cache") {
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
-    ctx.cacheTable("t1")
-    ctx.cacheTable("t2")
-    ctx.clearCache()
-    assert(ctx.cacheManager.isEmpty)
+    sqlContext.cacheTable("t1")
+    sqlContext.cacheTable("t2")
+    sqlContext.clearCache()
+    assert(sqlContext.cacheManager.isEmpty)
 
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
-    ctx.cacheTable("t1")
-    ctx.cacheTable("t2")
+    sqlContext.cacheTable("t1")
+    sqlContext.cacheTable("t2")
     sql("Clear CACHE")
-    assert(ctx.cacheManager.isEmpty)
+    assert(sqlContext.cacheManager.isEmpty)
   }
 
   test("Clear accumulators when uncacheTable to prevent memory leaking") {
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
 
-    ctx.cacheTable("t1")
-    ctx.cacheTable("t2")
+    sqlContext.cacheTable("t1")
+    sqlContext.cacheTable("t2")
 
     sql("SELECT * FROM t1").count()
     sql("SELECT * FROM t2").count()
@@ -331,8 +333,8 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
     Accumulators.synchronized {
       val accsSize = Accumulators.originals.size
-      ctx.uncacheTable("t1")
-      ctx.uncacheTable("t2")
+      sqlContext.uncacheTable("t1")
+      sqlContext.uncacheTable("t2")
       assert((accsSize - 2) == Accumulators.originals.size)
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 37738ec5b3c1d..4e988f074b113 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -29,7 +29,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   import testImplicits._
 
   private lazy val booleanData = {
-    ctx.createDataFrame(ctx.sparkContext.parallelize(
+    sqlContext.createDataFrame(sparkContext.parallelize(
       Row(false, false) ::
       Row(false, true) ::
       Row(true, false) ::
@@ -286,7 +286,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("isNaN") {
-    val testData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+    val testData = sqlContext.createDataFrame(sparkContext.parallelize(
       Row(Double.NaN, Float.NaN) ::
       Row(math.log(-1), math.log(-3).toFloat) ::
       Row(null, null) ::
@@ -307,7 +307,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("nanvl") {
-    val testData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+    val testData = sqlContext.createDataFrame(sparkContext.parallelize(
       Row(null, 3.0, Double.NaN, Double.PositiveInfinity, 1.0f, 4) :: Nil),
       StructType(Seq(StructField("a", DoubleType), StructField("b", DoubleType),
         StructField("c", DoubleType), StructField("d", DoubleType),
@@ -350,7 +350,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("!==") {
-    val nullData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+    val nullData = sqlContext.createDataFrame(sparkContext.parallelize(
       Row(1, 1) ::
       Row(1, 2) ::
       Row(1, null) ::
@@ -411,7 +411,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("between") {
-    val testData = ctx.sparkContext.parallelize(
+    val testData = sparkContext.parallelize(
       (0, 1, 2) ::
       (1, 2, 3) ::
       (2, 1, 0) ::
@@ -556,7 +556,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
 
   test("monotonicallyIncreasingId") {
     // Make sure we have 2 partitions, each with 2 records.
-    val df = ctx.sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
+    val df = sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
       Iterator(Tuple1(1), Tuple1(2))
     }.toDF("a")
     checkAnswer(
@@ -567,7 +567,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
 
   test("sparkPartitionId") {
     // Make sure we have 2 partitions, each with 2 records.
-    val df = ctx.sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
+    val df = sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
       Iterator(Tuple1(1), Tuple1(2))
     }.toDF("a")
     checkAnswer(
@@ -578,7 +578,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
 
   test("InputFileName") {
     withTempPath { dir =>
-      val data = sqlContext.sparkContext.parallelize(0 to 10).toDF("id")
+      val data = sparkContext.parallelize(0 to 10).toDF("id")
       data.write.parquet(dir.getCanonicalPath)
       val answer = sqlContext.read.parquet(dir.getCanonicalPath).select(inputFileName())
         .head.getString(0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index 72cf7aab0b977..c0950b09b14ad 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -66,12 +66,12 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
       Seq(Row(1, 3), Row(2, 3), Row(3, 3))
     )
 
-    ctx.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, false)
+    sqlContext.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, false)
     checkAnswer(
       testData2.groupBy("a").agg(sum($"b")),
       Seq(Row(3), Row(3), Row(3))
     )
-    ctx.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, true)
+    sqlContext.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, true)
   }
 
   test("agg without groups") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
index 3c359dd840ab7..09f7b507670c9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
@@ -28,19 +28,19 @@ class DataFrameComplexTypeSuite extends QueryTest with SharedSQLContext {
 
   test("UDF on struct") {
     val f = udf((a: String) => a)
-    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    val df = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
     df.select(struct($"a").as("s")).select(f($"s.a")).collect()
   }
 
   test("UDF on named_struct") {
     val f = udf((a: String) => a)
-    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    val df = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
     df.selectExpr("named_struct('a', a) s").select(f($"s.a")).collect()
   }
 
   test("UDF on array") {
     val f = udf((a: String) => a)
-    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    val df = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
     df.select(array($"a").as("s")).select(f(expr("s[0]"))).collect()
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
index e5d7d63441a6b..094efbaeadcd5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
@@ -24,7 +24,7 @@ class DataFrameImplicitsSuite extends QueryTest with SharedSQLContext {
 
   test("RDD of tuples") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"),
+      sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"),
       (1 to 10).map(i => Row(i, i.toString)))
   }
 
@@ -36,19 +36,19 @@ class DataFrameImplicitsSuite extends QueryTest with SharedSQLContext {
 
   test("RDD[Int]") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1 to 10).toDF("intCol"),
+      sparkContext.parallelize(1 to 10).toDF("intCol"),
       (1 to 10).map(i => Row(i)))
   }
 
   test("RDD[Long]") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1L to 10L).toDF("longCol"),
+      sparkContext.parallelize(1L to 10L).toDF("longCol"),
       (1L to 10L).map(i => Row(i)))
   }
 
   test("RDD[String]") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1 to 10).map(_.toString).toDF("stringCol"),
+      sparkContext.parallelize(1 to 10).map(_.toString).toDF("stringCol"),
       (1 to 10).map(i => Row(i.toString)))
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 28bdd6f83b687..6524abcf5e97f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -29,7 +29,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
 
   test("sample with replacement") {
     val n = 100
-    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = true, 0.05, seed = 13),
       Seq(5, 10, 52, 73).map(Row(_))
@@ -38,7 +38,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
 
   test("sample without replacement") {
     val n = 100
-    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = false, 0.05, seed = 13),
       Seq(16, 23, 88, 100).map(Row(_))
@@ -47,7 +47,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
 
   test("randomSplit") {
     val n = 600
-    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = sparkContext.parallelize(1 to n, 2).toDF("id")
     for (seed <- 1 to 5) {
       val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
       assert(splits.length == 3, "wrong number of splits")
@@ -164,7 +164,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
   }
 
   test("Frequent Items 2") {
-    val rows = ctx.sparkContext.parallelize(Seq.empty[Int], 4)
+    val rows = sparkContext.parallelize(Seq.empty[Int], 4)
     // this is a regression test, where when merging partitions, we omitted values with higher
     // counts than those that existed in the map when the map was full. This test should also fail
     // if anything like SPARK-9614 is observed once again
@@ -182,7 +182,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
   }
 
   test("sampleBy") {
-    val df = ctx.range(0, 100).select((col("id") % 3).as("key"))
+    val df = sqlContext.range(0, 100).select((col("id") % 3).as("key"))
     val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L)
     checkAnswer(
       sampled.groupBy("key").count().orderBy("key"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index a4871e247cff7..b5b9f11785074 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -345,7 +345,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("replace column using withColumn") {
-    val df2 = sqlContext.sparkContext.parallelize(Array(1, 2, 3)).toDF("x")
+    val df2 = sparkContext.parallelize(Array(1, 2, 3)).toDF("x")
     val df3 = df2.withColumn("x", df2("x") + 1)
     checkAnswer(
       df3.select("x"),
@@ -506,7 +506,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
 
   test("showString: truncate = [true, false]") {
     val longString = Array.fill(21)("1").mkString
-    val df = sqlContext.sparkContext.parallelize(Seq("1", longString)).toDF()
+    val df = sparkContext.parallelize(Seq("1", longString)).toDF()
     val expectedAnswerForFalse = """+---------------------+
                                    ||_1                   |
                                    |+---------------------+
@@ -596,7 +596,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") {
-    val rowRDD = sqlContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0))))
+    val rowRDD = sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0))))
     val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false)))
     val df = sqlContext.createDataFrame(rowRDD, schema)
     df.rdd.collect()
@@ -619,14 +619,14 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-7551: support backticks for DataFrame attribute resolution") {
-    val df = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    val df = sqlContext.read.json(sparkContext.makeRDD(
       """{"a.b": {"c": {"d..e": {"f": 1}}}}""" :: Nil))
     checkAnswer(
       df.select(df("`a.b`.c.`d..e`.`f`")),
       Row(1)
     )
 
-    val df2 = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    val df2 = sqlContext.read.json(sparkContext.makeRDD(
       """{"a  b": {"c": {"d  e": {"f": 1}}}}""" :: Nil))
     checkAnswer(
       df2.select(df2("`a  b`.c.d  e.f")),
@@ -646,7 +646,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-7324 dropDuplicates") {
-    val testData = sqlContext.sparkContext.parallelize(
+    val testData = sparkContext.parallelize(
       (2, 1, 2) :: (1, 1, 1) ::
       (1, 2, 1) :: (2, 1, 2) ::
       (2, 2, 2) :: (2, 2, 1) ::
@@ -869,7 +869,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-9323: DataFrame.orderBy should support nested column name") {
-    val df = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    val df = sqlContext.read.json(sparkContext.makeRDD(
       """{"a": {"b": 1}}""" :: Nil))
     checkAnswer(df.orderBy("a.b"), Row(Row(1)))
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
index 77907e91363ec..7ae12a7895f7e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
@@ -32,7 +32,7 @@ class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
 
   test("test simple types") {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
-      val df = sqlContext.sparkContext.parallelize(Seq((1, 2))).toDF("a", "b")
+      val df = sparkContext.parallelize(Seq((1, 2))).toDF("a", "b")
       assert(df.select(struct("a", "b")).first().getStruct(0) === Row(1, 2))
     }
   }
@@ -40,7 +40,7 @@ class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
   test("test struct type") {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
       val struct = Row(1, 2L, 3.0F, 3.0)
-      val data = sqlContext.sparkContext.parallelize(Seq(Row(1, struct)))
+      val data = sparkContext.parallelize(Seq(Row(1, struct)))
 
       val schema = new StructType()
         .add("a", IntegerType)
@@ -60,7 +60,7 @@ class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
       val innerStruct = Row(1, "abcd")
       val outerStruct = Row(1, 2L, 3.0F, 3.0, innerStruct, "efg")
-      val data = sqlContext.sparkContext.parallelize(Seq(Row(1, outerStruct)))
+      val data = sparkContext.parallelize(Seq(Row(1, outerStruct)))
 
       val schema = new StructType()
         .add("a", IntegerType)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
index 8d2f45d70308b..78a98798eff64 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
@@ -52,7 +52,7 @@ class ExtraStrategiesSuite extends QueryTest with SharedSQLContext {
     try {
       sqlContext.experimental.extraStrategies = TestStrategy :: Nil
 
-      val df = sqlContext.sparkContext.parallelize(Seq(("so slow", 1))).toDF("a", "b")
+      val df = sparkContext.parallelize(Seq(("so slow", 1))).toDF("a", "b")
       checkAnswer(
         df.select("a"),
         Row("so fast"))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index f5c5046a8ed88..b05435bad5c5a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -31,7 +31,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.optimizedPlan
-    val planned = ctx.planner.EquiJoinSelection(join)
+    val planned = sqlContext.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -59,7 +59,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
 
     Seq(
       ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]),
@@ -118,7 +118,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("broadcasted hash join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
     sql("CACHE TABLE testData")
     for (sortMergeJoinEnabled <- Seq(true, false)) {
       withClue(s"sortMergeJoinEnabled=$sortMergeJoinEnabled") {
@@ -138,7 +138,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("broadcasted hash outer join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
     sql("CACHE TABLE testData")
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
@@ -167,7 +167,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.optimizedPlan
-    val planned = ctx.planner.EquiJoinSelection(join)
+    val planned = sqlContext.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -442,7 +442,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("broadcasted left semi join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
     sql("CACHE TABLE testData")
 
     withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "1000000000") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
index babf8835d2545..eab0fbb196eb6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
@@ -32,33 +32,33 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContex
   }
 
   after {
-    ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
+    sqlContext.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
   }
 
   test("get all tables") {
     checkAnswer(
-      ctx.tables().filter("tableName = 'ListTablesSuiteTable'"),
+      sqlContext.tables().filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
       sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
-    ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
-    assert(ctx.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
+    sqlContext.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
+    assert(sqlContext.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
   }
 
   test("getting all Tables with a database name has no impact on returned table names") {
     checkAnswer(
-      ctx.tables("DB").filter("tableName = 'ListTablesSuiteTable'"),
+      sqlContext.tables("DB").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
       sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
-    ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
-    assert(ctx.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
+    sqlContext.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
+    assert(sqlContext.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
   }
 
   test("query the returned DataFrame of tables") {
@@ -66,7 +66,7 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContex
       StructField("tableName", StringType, false) ::
       StructField("isTemporary", BooleanType, false) :: Nil)
 
-    Seq(ctx.tables(), sql("SHOW TABLes")).foreach {
+    Seq(sqlContext.tables(), sql("SHOW TABLes")).foreach {
       case tableDF =>
         assert(expectedSchema === tableDF.schema)
 
@@ -77,9 +77,9 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContex
           Row(true, "ListTablesSuiteTable")
         )
         checkAnswer(
-          ctx.tables().filter("tableName = 'tables'").select("tableName", "isTemporary"),
+          sqlContext.tables().filter("tableName = 'tables'").select("tableName", "isTemporary"),
           Row("tables", true))
-        ctx.dropTempTable("tables")
+        sqlContext.dropTempTable("tables")
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index 3649c2a97b5ef..cada03e9ac6bb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -25,7 +25,9 @@ import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.columnar.InMemoryRelation
 
-class QueryTest extends PlanTest {
+abstract class QueryTest extends PlanTest {
+
+  protected def sqlContext: SQLContext
 
   // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
   TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
@@ -56,18 +58,33 @@ class QueryTest extends PlanTest {
    * @param df the [[DataFrame]] to be executed
    * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
    */
-  protected def checkAnswer(df: DataFrame, expectedAnswer: Seq[Row]): Unit = {
-    QueryTest.checkAnswer(df, expectedAnswer) match {
+  protected def checkAnswer(df: => DataFrame, expectedAnswer: Seq[Row]): Unit = {
+    val analyzedDF = try df catch {
+      case ae: AnalysisException =>
+        val currentValue = sqlContext.conf.dataFrameEagerAnalysis
+        sqlContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, false)
+        val partiallyAnalzyedPlan = df.queryExecution.analyzed
+        sqlContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, currentValue)
+        fail(
+          s"""
+             |Failed to analyze query: $ae
+             |$partiallyAnalzyedPlan
+             |
+             |${stackTraceToString(ae)}
+             |""".stripMargin)
+    }
+
+    QueryTest.checkAnswer(analyzedDF, expectedAnswer) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
   }
 
-  protected def checkAnswer(df: DataFrame, expectedAnswer: Row): Unit = {
+  protected def checkAnswer(df: => DataFrame, expectedAnswer: Row): Unit = {
     checkAnswer(df, Seq(expectedAnswer))
   }
 
-  protected def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = {
+  protected def checkAnswer(df: => DataFrame, expectedAnswer: DataFrame): Unit = {
     checkAnswer(df, expectedAnswer.collect())
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 77ccd6f775e50..3ba14d7602a62 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -57,7 +57,7 @@ class RowSuite extends SparkFunSuite with SharedSQLContext {
 
   test("serialize w/ kryo") {
     val row = Seq((1, Seq(1), Map(1 -> 1), BigDecimal(1))).toDF().first()
-    val serializer = new SparkSqlSerializer(ctx.sparkContext.getConf)
+    val serializer = new SparkSqlSerializer(sparkContext.getConf)
     val instance = serializer.newInstance()
     val ser = instance.serialize(row)
     val de = instance.deserialize(ser).asInstanceOf[Row]
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 7699adadd9cc8..c35b31c96df48 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -27,58 +27,58 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
   test("propagate from spark conf") {
     // We create a new context here to avoid order dependence with other tests that might call
     // clear().
-    val newContext = new SQLContext(ctx.sparkContext)
+    val newContext = new SQLContext(sparkContext)
     assert(newContext.getConf("spark.sql.testkey", "false") === "true")
   }
 
   test("programmatic ways of basic setting and getting") {
-    ctx.conf.clear()
-    assert(ctx.getAllConfs.size === 0)
+    sqlContext.conf.clear()
+    assert(sqlContext.getAllConfs.size === 0)
 
-    ctx.setConf(testKey, testVal)
-    assert(ctx.getConf(testKey) === testVal)
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
-    assert(ctx.getAllConfs.contains(testKey))
+    sqlContext.setConf(testKey, testVal)
+    assert(sqlContext.getConf(testKey) === testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getAllConfs.contains(testKey))
 
     // Tests SQLConf as accessed from a SQLContext is mutable after
     // the latter is initialized, unlike SparkConf inside a SparkContext.
-    assert(ctx.getConf(testKey) == testVal)
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
-    assert(ctx.getAllConfs.contains(testKey))
+    assert(sqlContext.getConf(testKey) == testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getAllConfs.contains(testKey))
 
-    ctx.conf.clear()
+    sqlContext.conf.clear()
   }
 
   test("parse SQL set commands") {
-    ctx.conf.clear()
+    sqlContext.conf.clear()
     sql(s"set $testKey=$testVal")
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
 
     sql("set some.property=20")
-    assert(ctx.getConf("some.property", "0") === "20")
+    assert(sqlContext.getConf("some.property", "0") === "20")
     sql("set some.property = 40")
-    assert(ctx.getConf("some.property", "0") === "40")
+    assert(sqlContext.getConf("some.property", "0") === "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
     sql(s"set $key=$vs")
-    assert(ctx.getConf(key, "0") === vs)
+    assert(sqlContext.getConf(key, "0") === vs)
 
     sql(s"set $key=")
-    assert(ctx.getConf(key, "0") === "")
+    assert(sqlContext.getConf(key, "0") === "")
 
-    ctx.conf.clear()
+    sqlContext.conf.clear()
   }
 
   test("deprecated property") {
-    ctx.conf.clear()
+    sqlContext.conf.clear()
     sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-    assert(ctx.conf.numShufflePartitions === 10)
+    assert(sqlContext.conf.numShufflePartitions === 10)
   }
 
   test("invalid conf value") {
-    ctx.conf.clear()
+    sqlContext.conf.clear()
     val e = intercept[IllegalArgumentException] {
       sql(s"set ${SQLConf.CASE_SENSITIVE.key}=10")
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
index 007be12950774..dd88ae3700ab9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
@@ -24,7 +24,7 @@ class SQLContextSuite extends SparkFunSuite with SharedSQLContext {
 
   override def afterAll(): Unit = {
     try {
-      SQLContext.setLastInstantiatedContext(ctx)
+      SQLContext.setLastInstantiatedContext(sqlContext)
     } finally {
       super.afterAll()
     }
@@ -32,18 +32,18 @@ class SQLContextSuite extends SparkFunSuite with SharedSQLContext {
 
   test("getOrCreate instantiates SQLContext") {
     SQLContext.clearLastInstantiatedContext()
-    val sqlContext = SQLContext.getOrCreate(ctx.sparkContext)
+    val sqlContext = SQLContext.getOrCreate(sparkContext)
     assert(sqlContext != null, "SQLContext.getOrCreate returned null")
-    assert(SQLContext.getOrCreate(ctx.sparkContext).eq(sqlContext),
+    assert(SQLContext.getOrCreate(sparkContext).eq(sqlContext),
       "SQLContext created by SQLContext.getOrCreate not returned by SQLContext.getOrCreate")
   }
 
   test("getOrCreate gets last explicitly instantiated SQLContext") {
     SQLContext.clearLastInstantiatedContext()
-    val sqlContext = new SQLContext(ctx.sparkContext)
-    assert(SQLContext.getOrCreate(ctx.sparkContext) != null,
+    val sqlContext = new SQLContext(sparkContext)
+    assert(SQLContext.getOrCreate(sparkContext) != null,
       "SQLContext.getOrCreate after explicitly created SQLContext returned null")
-    assert(SQLContext.getOrCreate(ctx.sparkContext).eq(sqlContext),
+    assert(SQLContext.getOrCreate(sparkContext).eq(sqlContext),
       "SQLContext.getOrCreate after explicitly created SQLContext did not return the context")
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 0ef25fe0faef0..05f2000459599 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -147,14 +147,14 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SQL Dialect Switching to a new SQL parser") {
-    val newContext = new SQLContext(sqlContext.sparkContext)
+    val newContext = new SQLContext(sparkContext)
     newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName())
     assert(newContext.getSQLDialect().getClass === classOf[MyDialect])
     assert(newContext.sql("SELECT 1").collect() === Array(Row(1)))
   }
 
   test("SQL Dialect Switch to an invalid parser with alias") {
-    val newContext = new SQLContext(sqlContext.sparkContext)
+    val newContext = new SQLContext(sparkContext)
     newContext.sql("SET spark.sql.dialect=MyTestClass")
     intercept[DialectException] {
       newContext.sql("SELECT 1")
@@ -196,7 +196,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("grouping on nested fields") {
-    sqlContext.read.json(sqlContext.sparkContext.parallelize(
+    sqlContext.read.json(sparkContext.parallelize(
       """{"nested": {"attribute": 1}, "value": 2}""" :: Nil))
      .registerTempTable("rows")
 
@@ -215,7 +215,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("SPARK-6201 IN type conversion") {
     sqlContext.read.json(
-      sqlContext.sparkContext.parallelize(
+      sparkContext.parallelize(
         Seq("{\"a\": \"1\"}}", "{\"a\": \"2\"}}", "{\"a\": \"3\"}}")))
       .registerTempTable("d")
 
@@ -1342,7 +1342,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-3483 Special chars in column names") {
-    val data = sqlContext.sparkContext.parallelize(
+    val data = sparkContext.parallelize(
       Seq("""{"key?number1": "value1", "key.number2": "value2"}"""))
     sqlContext.read.json(data).registerTempTable("records")
     sql("SELECT `key?number1`, `key.number2` FROM records")
@@ -1385,13 +1385,13 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-4322 Grouping field with struct field as sub expression") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD("""{"a": {"b": [{"c": 1}]}}""" :: Nil))
+    sqlContext.read.json(sparkContext.makeRDD("""{"a": {"b": [{"c": 1}]}}""" :: Nil))
       .registerTempTable("data")
     checkAnswer(sql("SELECT a.b[0].c FROM data GROUP BY a.b[0].c"), Row(1))
     sqlContext.dropTempTable("data")
 
     sqlContext.read.json(
-      sqlContext.sparkContext.makeRDD("""{"a": {"b": 1}}""" :: Nil)).registerTempTable("data")
+      sparkContext.makeRDD("""{"a": {"b": 1}}""" :: Nil)).registerTempTable("data")
     checkAnswer(sql("SELECT a.b + 1 FROM data GROUP BY a.b + 1"), Row(2))
     sqlContext.dropTempTable("data")
   }
@@ -1412,10 +1412,10 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("Supporting relational operator '<=>' in Spark SQL") {
     val nullCheckData1 = TestData(1, "1") :: TestData(2, null) :: Nil
-    val rdd1 = sqlContext.sparkContext.parallelize((0 to 1).map(i => nullCheckData1(i)))
+    val rdd1 = sparkContext.parallelize((0 to 1).map(i => nullCheckData1(i)))
     rdd1.toDF().registerTempTable("nulldata1")
     val nullCheckData2 = TestData(1, "1") :: TestData(2, null) :: Nil
-    val rdd2 = sqlContext.sparkContext.parallelize((0 to 1).map(i => nullCheckData2(i)))
+    val rdd2 = sparkContext.parallelize((0 to 1).map(i => nullCheckData2(i)))
     rdd2.toDF().registerTempTable("nulldata2")
     checkAnswer(sql("SELECT nulldata1.key FROM nulldata1 join " +
       "nulldata2 on nulldata1.value <=> nulldata2.value"),
@@ -1424,7 +1424,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("Multi-column COUNT(DISTINCT ...)") {
     val data = TestData(1, "val_1") :: TestData(2, "val_2") :: Nil
-    val rdd = sqlContext.sparkContext.parallelize((0 to 1).map(i => data(i)))
+    val rdd = sparkContext.parallelize((0 to 1).map(i => data(i)))
     rdd.toDF().registerTempTable("distinctData")
     checkAnswer(sql("SELECT COUNT(DISTINCT key,value) FROM distinctData"), Row(2))
   }
@@ -1432,14 +1432,14 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   test("SPARK-4699 case sensitivity SQL query") {
     sqlContext.setConf(SQLConf.CASE_SENSITIVE, false)
     val data = TestData(1, "val_1") :: TestData(2, "val_2") :: Nil
-    val rdd = sqlContext.sparkContext.parallelize((0 to 1).map(i => data(i)))
+    val rdd = sparkContext.parallelize((0 to 1).map(i => data(i)))
     rdd.toDF().registerTempTable("testTable1")
     checkAnswer(sql("SELECT VALUE FROM TESTTABLE1 where KEY = 1"), Row("val_1"))
     sqlContext.setConf(SQLConf.CASE_SENSITIVE, true)
   }
 
   test("SPARK-6145: ORDER BY test for nested fields") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    sqlContext.read.json(sparkContext.makeRDD(
         """{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil))
       .registerTempTable("nestedOrder")
 
@@ -1452,14 +1452,14 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-6145: special cases") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    sqlContext.read.json(sparkContext.makeRDD(
       """{"a": {"b": [1]}, "b": [{"a": 1}], "_c0": {"a": 1}}""" :: Nil)).registerTempTable("t")
     checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY _c0.a"), Row(1))
     checkAnswer(sql("SELECT b[0].a FROM t ORDER BY _c0.a"), Row(1))
   }
 
   test("SPARK-6898: complete support for special chars in column names") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    sqlContext.read.json(sparkContext.makeRDD(
       """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil))
       .registerTempTable("t")
 
@@ -1543,7 +1543,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("SPARK-7067: order by queries for complex ExtractValue chain") {
     withTempTable("t") {
-      sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+      sqlContext.read.json(sparkContext.makeRDD(
         """{"a": {"b": [{"c": 1}]}, "b": [{"d": 1}]}""" :: Nil)).registerTempTable("t")
       checkAnswer(sql("SELECT a.b FROM t ORDER BY b[0].d"), Row(Seq(Row(1))))
     }
@@ -1610,8 +1610,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("aggregation with codegen updates peak execution memory") {
     withSQLConf((SQLConf.CODEGEN_ENABLED.key, "true")) {
-      val sc = sqlContext.sparkContext
-      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "aggregation with codegen") {
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sparkContext, "aggregation with codegen") {
         testCodeGen(
           "SELECT key, count(value) FROM testData GROUP BY key",
           (1 to 100).map(i => Row(i, 1)))
@@ -1670,8 +1669,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("external sorting updates peak execution memory") {
     withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
-      val sc = sqlContext.sparkContext
-      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external sort") {
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sparkContext, "external sort") {
         sortTest()
       }
     }
@@ -1679,7 +1677,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("SPARK-9511: error with table starting with number") {
     withTempTable("1one") {
-      sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
+      sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
         .toDF("num", "str")
         .registerTempTable("1one")
       checkAnswer(sql("select count(num) from 1one"), Row(10))
@@ -1690,7 +1688,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
       val df =
-        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+        sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
       df
         .write
         .format("parquet")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
index 45d0ee4a8e749..ddab918629645 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.test.SharedSQLContext
 class SerializationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("[SPARK-5235] SQLContext should be serializable") {
-    val _sqlContext = new SQLContext(sqlContext.sparkContext)
+    val _sqlContext = new SQLContext(sparkContext)
     new JavaSerializer(new SparkConf()).newInstance().serialize(_sqlContext)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index b91438baea06f..e12e6bea30260 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -268,9 +268,7 @@ class StringFunctionsSuite extends QueryTest with SharedSQLContext {
       Row(3, 4))
 
     intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("length(c)"), // int type of the argument is unacceptable
-        Row("5.0000"))
+      df.selectExpr("length(c)") // int type of the argument is unacceptable
     }
   }
 
@@ -284,63 +282,46 @@ class StringFunctionsSuite extends QueryTest with SharedSQLContext {
   }
 
   test("number format function") {
-    val tuple =
-      ("aa", 1.asInstanceOf[Byte], 2.asInstanceOf[Short],
-        3.13223f, 4, 5L, 6.48173d, Decimal(7.128381))
-    val df =
-      Seq(tuple)
-        .toDF(
-          "a", // string "aa"
-          "b", // byte    1
-          "c", // short   2
-          "d", // float   3.13223f
-          "e", // integer 4
-          "f", // long    5L
-          "g", // double  6.48173d
-          "h") // decimal 7.128381
-
-    checkAnswer(
-      df.select(format_number($"f", 4)),
+    val df = sqlContext.range(1)
+
+    checkAnswer(
+      df.select(format_number(lit(5L), 4)),
       Row("5.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(b, e)"), // convert the 1st argument to integer
+      df.select(format_number(lit(1.toByte), 4)), // convert the 1st argument to integer
       Row("1.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(c, e)"), // convert the 1st argument to integer
+      df.select(format_number(lit(2.toShort), 4)), // convert the 1st argument to integer
       Row("2.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(d, e)"), // convert the 1st argument to double
+      df.select(format_number(lit(3.1322.toFloat), 4)), // convert the 1st argument to double
       Row("3.1322"))
 
     checkAnswer(
-      df.selectExpr("format_number(e, e)"), // not convert anything
+      df.select(format_number(lit(4), 4)), // not convert anything
       Row("4.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(f, e)"), // not convert anything
+      df.select(format_number(lit(5L), 4)), // not convert anything
       Row("5.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(g, e)"), // not convert anything
+      df.select(format_number(lit(6.48173), 4)), // not convert anything
       Row("6.4817"))
 
     checkAnswer(
-      df.selectExpr("format_number(h, e)"), // not convert anything
+      df.select(format_number(lit(BigDecimal(7.128381)), 4)), // not convert anything
       Row("7.1284"))
 
     intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("format_number(a, e)"), // string type of the 1st argument is unacceptable
-        Row("5.0000"))
+      df.select(format_number(lit("aa"), 4)) // string type of the 1st argument is unacceptable
     }
 
     intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("format_number(e, g)"), // decimal type of the 2nd argument is unacceptable
-        Row("5.0000"))
+      df.selectExpr("format_number(4, 6.48173)") // non-integral type 2nd argument is unacceptable
     }
 
     // for testing the mutable state of the expression in code gen.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index eb275af101e2f..e0435a0dba6ad 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -26,7 +26,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   import testImplicits._
 
   test("built-in fixed arity expressions") {
-    val df = ctx.emptyDataFrame
+    val df = sqlContext.emptyDataFrame
     df.selectExpr("rand()", "randn()", "rand(5)", "randn(50)")
   }
 
@@ -55,23 +55,23 @@ class UDFSuite extends QueryTest with SharedSQLContext {
     val df = Seq((1, "Tearing down the walls that divide us")).toDF("id", "saying")
     df.registerTempTable("tmp_table")
     checkAnswer(sql("select spark_partition_id() from tmp_table").toDF(), Row(0))
-    ctx.dropTempTable("tmp_table")
+    sqlContext.dropTempTable("tmp_table")
   }
 
   test("SPARK-8005 input_file_name") {
     withTempPath { dir =>
-      val data = ctx.sparkContext.parallelize(0 to 10, 2).toDF("id")
+      val data = sparkContext.parallelize(0 to 10, 2).toDF("id")
       data.write.parquet(dir.getCanonicalPath)
-      ctx.read.parquet(dir.getCanonicalPath).registerTempTable("test_table")
+      sqlContext.read.parquet(dir.getCanonicalPath).registerTempTable("test_table")
       val answer = sql("select input_file_name() from test_table").head().getString(0)
       assert(answer.contains(dir.getCanonicalPath))
       assert(sql("select input_file_name() from test_table").distinct().collect().length >= 2)
-      ctx.dropTempTable("test_table")
+      sqlContext.dropTempTable("test_table")
     }
   }
 
   test("error reporting for incorrect number of arguments") {
-    val df = ctx.emptyDataFrame
+    val df = sqlContext.emptyDataFrame
     val e = intercept[AnalysisException] {
       df.selectExpr("substr('abcd', 2, 3, 4)")
     }
@@ -79,7 +79,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("error reporting for undefined functions") {
-    val df = ctx.emptyDataFrame
+    val df = sqlContext.emptyDataFrame
     val e = intercept[AnalysisException] {
       df.selectExpr("a_function_that_does_not_exist()")
     }
@@ -87,24 +87,24 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("Simple UDF") {
-    ctx.udf.register("strLenScala", (_: String).length)
+    sqlContext.udf.register("strLenScala", (_: String).length)
     assert(sql("SELECT strLenScala('test')").head().getInt(0) === 4)
   }
 
   test("ZeroArgument UDF") {
-    ctx.udf.register("random0", () => { Math.random()})
+    sqlContext.udf.register("random0", () => { Math.random()})
     assert(sql("SELECT random0()").head().getDouble(0) >= 0.0)
   }
 
   test("TwoArgument UDF") {
-    ctx.udf.register("strLenScala", (_: String).length + (_: Int))
+    sqlContext.udf.register("strLenScala", (_: String).length + (_: Int))
     assert(sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5)
   }
 
   test("UDF in a WHERE") {
-    ctx.udf.register("oneArgFilter", (n: Int) => { n > 80 })
+    sqlContext.udf.register("oneArgFilter", (n: Int) => { n > 80 })
 
-    val df = ctx.sparkContext.parallelize(
+    val df = sparkContext.parallelize(
       (1 to 100).map(i => TestData(i, i.toString))).toDF()
     df.registerTempTable("integerData")
 
@@ -114,7 +114,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDF in a HAVING") {
-    ctx.udf.register("havingFilter", (n: Long) => { n > 5 })
+    sqlContext.udf.register("havingFilter", (n: Long) => { n > 5 })
 
     val df = Seq(("red", 1), ("red", 2), ("blue", 10),
       ("green", 100), ("green", 200)).toDF("g", "v")
@@ -133,7 +133,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDF in a GROUP BY") {
-    ctx.udf.register("groupFunction", (n: Int) => { n > 10 })
+    sqlContext.udf.register("groupFunction", (n: Int) => { n > 10 })
 
     val df = Seq(("red", 1), ("red", 2), ("blue", 10),
       ("green", 100), ("green", 200)).toDF("g", "v")
@@ -150,10 +150,10 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDFs everywhere") {
-    ctx.udf.register("groupFunction", (n: Int) => { n > 10 })
-    ctx.udf.register("havingFilter", (n: Long) => { n > 2000 })
-    ctx.udf.register("whereFilter", (n: Int) => { n < 150 })
-    ctx.udf.register("timesHundred", (n: Long) => { n * 100 })
+    sqlContext.udf.register("groupFunction", (n: Int) => { n > 10 })
+    sqlContext.udf.register("havingFilter", (n: Long) => { n > 2000 })
+    sqlContext.udf.register("whereFilter", (n: Int) => { n < 150 })
+    sqlContext.udf.register("timesHundred", (n: Long) => { n * 100 })
 
     val df = Seq(("red", 1), ("red", 2), ("blue", 10),
       ("green", 100), ("green", 200)).toDF("g", "v")
@@ -172,7 +172,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("struct UDF") {
-    ctx.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
+    sqlContext.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
 
     val result =
       sql("SELECT returnStruct('test', 'test2') as ret")
@@ -181,13 +181,13 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("udf that is transformed") {
-    ctx.udf.register("makeStruct", (x: Int, y: Int) => (x, y))
+    sqlContext.udf.register("makeStruct", (x: Int, y: Int) => (x, y))
     // 1 + 1 is constant folded causing a transformation.
     assert(sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2))
   }
 
   test("type coercion for udf inputs") {
-    ctx.udf.register("intExpected", (x: Int) => x)
+    sqlContext.udf.register("intExpected", (x: Int) => x)
     // pass a decimal to intExpected.
     assert(sql("SELECT intExpected(1.0)").head().getInt(0) === 1)
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index b6d279ae47268..fa8f9c8e00089 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -90,7 +90,7 @@ class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDTs and UDFs") {
-    ctx.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector])
+    sqlContext.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector])
     pointsRDD.registerTempTable("points")
     checkAnswer(
       sql("SELECT testType(features) from points"),
@@ -148,8 +148,8 @@ class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
       StructField("vec", new MyDenseVectorUDT, false)
     ))
 
-    val stringRDD = ctx.sparkContext.parallelize(data)
-    val jsonRDD = ctx.read.schema(schema).json(stringRDD)
+    val stringRDD = sparkContext.parallelize(data)
+    val jsonRDD = sqlContext.read.schema(schema).json(stringRDD)
     checkAnswer(
       jsonRDD,
       Row(1, new MyDenseVector(Array(1.1, 2.2, 3.3, 4.4))) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 83db9b6510b36..cd3644eb9c099 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -31,7 +31,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
   setupTestData()
 
   test("simple columnar query") {
-    val plan = ctx.executePlan(testData.logicalPlan).executedPlan
+    val plan = sqlContext.executePlan(testData.logicalPlan).executedPlan
     val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None)
 
     checkAnswer(scan, testData.collect().toSeq)
@@ -39,16 +39,16 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
 
   test("default size avoids broadcast") {
     // TODO: Improve this test when we have better statistics
-    ctx.sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString))
+    sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString))
       .toDF().registerTempTable("sizeTst")
-    ctx.cacheTable("sizeTst")
+    sqlContext.cacheTable("sizeTst")
     assert(
-      ctx.table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes >
-        ctx.conf.autoBroadcastJoinThreshold)
+      sqlContext.table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes >
+        sqlContext.conf.autoBroadcastJoinThreshold)
   }
 
   test("projection") {
-    val plan = ctx.executePlan(testData.select('value, 'key).logicalPlan).executedPlan
+    val plan = sqlContext.executePlan(testData.select('value, 'key).logicalPlan).executedPlan
     val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None)
 
     checkAnswer(scan, testData.collect().map {
@@ -57,7 +57,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-1436 regression: in-memory columns must be able to be accessed multiple times") {
-    val plan = ctx.executePlan(testData.logicalPlan).executedPlan
+    val plan = sqlContext.executePlan(testData.logicalPlan).executedPlan
     val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None)
 
     checkAnswer(scan, testData.collect().toSeq)
@@ -69,7 +69,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT * FROM repeatedData"),
       repeatedData.collect().toSeq.map(Row.fromTuple))
 
-    ctx.cacheTable("repeatedData")
+    sqlContext.cacheTable("repeatedData")
 
     checkAnswer(
       sql("SELECT * FROM repeatedData"),
@@ -81,7 +81,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT * FROM nullableRepeatedData"),
       nullableRepeatedData.collect().toSeq.map(Row.fromTuple))
 
-    ctx.cacheTable("nullableRepeatedData")
+    sqlContext.cacheTable("nullableRepeatedData")
 
     checkAnswer(
       sql("SELECT * FROM nullableRepeatedData"),
@@ -96,7 +96,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT time FROM timestamps"),
       timestamps.collect().toSeq)
 
-    ctx.cacheTable("timestamps")
+    sqlContext.cacheTable("timestamps")
 
     checkAnswer(
       sql("SELECT time FROM timestamps"),
@@ -108,7 +108,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT * FROM withEmptyParts"),
       withEmptyParts.collect().toSeq.map(Row.fromTuple))
 
-    ctx.cacheTable("withEmptyParts")
+    sqlContext.cacheTable("withEmptyParts")
 
     checkAnswer(
       sql("SELECT * FROM withEmptyParts"),
@@ -157,7 +157,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
 
     // Create a RDD for the schema
     val rdd =
-      ctx.sparkContext.parallelize((1 to 100), 10).map { i =>
+      sparkContext.parallelize((1 to 100), 10).map { i =>
         Row(
           s"str${i}: test cache.",
           s"binary${i}: test cache.".getBytes("UTF-8"),
@@ -177,24 +177,24 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
           (0 to i).map(j => s"map_key_$j" -> (Long.MaxValue - j)).toMap,
           Row((i - 0.25).toFloat, Seq(true, false, null)))
       }
-    ctx.createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types")
+    sqlContext.createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types")
     // Cache the table.
     sql("cache table InMemoryCache_different_data_types")
     // Make sure the table is indeed cached.
-    val tableScan = ctx.table("InMemoryCache_different_data_types").queryExecution.executedPlan
+    sqlContext.table("InMemoryCache_different_data_types").queryExecution.executedPlan
     assert(
-      ctx.isCached("InMemoryCache_different_data_types"),
+      sqlContext.isCached("InMemoryCache_different_data_types"),
       "InMemoryCache_different_data_types should be cached.")
     // Issue a query and check the results.
     checkAnswer(
       sql(s"SELECT DISTINCT ${allColumns} FROM InMemoryCache_different_data_types"),
-      ctx.table("InMemoryCache_different_data_types").collect())
-    ctx.dropTempTable("InMemoryCache_different_data_types")
+      sqlContext.table("InMemoryCache_different_data_types").collect())
+    sqlContext.dropTempTable("InMemoryCache_different_data_types")
   }
 
   test("SPARK-10422: String column in InMemoryColumnarCache needs to override clone method") {
-    val df =
-      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+    val df = sqlContext.range(1, 100).selectExpr("id % 10 as id")
+      .rdd.map(id => Tuple1(s"str_$id")).toDF("i")
     val cached = df.cache()
     // count triggers the caching action. It should not throw.
     cached.count()
@@ -205,7 +205,8 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
     // Check result.
     checkAnswer(
       cached,
-      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+      sqlContext.range(1, 100).selectExpr("id % 10 as id")
+        .rdd.map(id => Tuple1(s"str_$id")).toDF("i")
     )
 
     // Drop the cache.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
index ab2644eb4581d..6b7401464f46f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
@@ -25,32 +25,32 @@ import org.apache.spark.sql.test.SQLTestData._
 class PartitionBatchPruningSuite extends SparkFunSuite with SharedSQLContext {
   import testImplicits._
 
-  private lazy val originalColumnBatchSize = ctx.conf.columnBatchSize
-  private lazy val originalInMemoryPartitionPruning = ctx.conf.inMemoryPartitionPruning
+  private lazy val originalColumnBatchSize = sqlContext.conf.columnBatchSize
+  private lazy val originalInMemoryPartitionPruning = sqlContext.conf.inMemoryPartitionPruning
 
   override protected def beforeAll(): Unit = {
     super.beforeAll()
     // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch
-    ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, 10)
+    sqlContext.setConf(SQLConf.COLUMN_BATCH_SIZE, 10)
 
-    val pruningData = ctx.sparkContext.makeRDD((1 to 100).map { key =>
+    val pruningData = sparkContext.makeRDD((1 to 100).map { key =>
       val string = if (((key - 1) / 10) % 2 == 0) null else key.toString
       TestData(key, string)
     }, 5).toDF()
     pruningData.registerTempTable("pruningData")
 
     // Enable in-memory partition pruning
-    ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
+    sqlContext.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
     // Enable in-memory table scan accumulators
-    ctx.setConf("spark.sql.inMemoryTableScanStatistics.enable", "true")
-    ctx.cacheTable("pruningData")
+    sqlContext.setConf("spark.sql.inMemoryTableScanStatistics.enable", "true")
+    sqlContext.cacheTable("pruningData")
   }
 
   override protected def afterAll(): Unit = {
     try {
-      ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
-      ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
-      ctx.uncacheTable("pruningData")
+      sqlContext.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
+      sqlContext.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
+      sqlContext.uncacheTable("pruningData")
     } finally {
       super.afterAll()
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
index 8998f5111124c..911d12e93e503 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
@@ -22,6 +22,8 @@ import org.apache.spark.sql.catalyst.plans.physical.SinglePartition
 import org.apache.spark.sql.test.SharedSQLContext
 
 class ExchangeSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
+
   test("shuffling UnsafeRows in exchange") {
     val input = (1 to 1000).map(Tuple1.apply)
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index fad93b014c237..cafa1d5154788 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{execution, Row, SQLConf}
 import org.apache.spark.sql.catalyst.InternalRow
@@ -31,14 +30,14 @@ import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 
-class PlannerSuite extends SparkFunSuite with SharedSQLContext {
+class PlannerSuite extends SharedSQLContext {
   import testImplicits._
 
   setupTestData()
 
   private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
-    val _ctx = ctx
-    import _ctx.planner._
+    val planner = sqlContext.planner
+    import planner._
     val plannedOption = HashAggregation(query).headOption.orElse(Aggregation(query).headOption)
     val planned =
       plannedOption.getOrElse(
@@ -53,8 +52,8 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("unions are collapsed") {
-    val _ctx = ctx
-    import _ctx.planner._
+    val planner = sqlContext.planner
+    import planner._
     val query = testData.unionAll(testData).unionAll(testData).logicalPlan
     val planned = BasicOperators(query).head
     val logicalUnions = query collect { case u: logical.Union => u }
@@ -81,33 +80,30 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("sizeInBytes estimation of limit operator for broadcast hash join optimization") {
-    def checkPlan(fieldTypes: Seq[DataType], newThreshold: Int): Unit = {
-      ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold)
-      val fields = fieldTypes.zipWithIndex.map {
-        case (dataType, index) => StructField(s"c${index}", dataType, true)
-      } :+ StructField("key", IntegerType, true)
-      val schema = StructType(fields)
-      val row = Row.fromSeq(Seq.fill(fields.size)(null))
-      val rowRDD = ctx.sparkContext.parallelize(row :: Nil)
-      ctx.createDataFrame(rowRDD, schema).registerTempTable("testLimit")
-
-      val planned = sql(
-        """
-          |SELECT l.a, l.b
-          |FROM testData2 l JOIN (SELECT * FROM testLimit LIMIT 1) r ON (l.a = r.key)
-        """.stripMargin).queryExecution.executedPlan
-
-      val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
-      val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
-
-      assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
-      assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
-
-      ctx.dropTempTable("testLimit")
+    def checkPlan(fieldTypes: Seq[DataType]): Unit = {
+      withTempTable("testLimit") {
+        val fields = fieldTypes.zipWithIndex.map {
+          case (dataType, index) => StructField(s"c${index}", dataType, true)
+        } :+ StructField("key", IntegerType, true)
+        val schema = StructType(fields)
+        val row = Row.fromSeq(Seq.fill(fields.size)(null))
+        val rowRDD = sparkContext.parallelize(row :: Nil)
+        sqlContext.createDataFrame(rowRDD, schema).registerTempTable("testLimit")
+
+        val planned = sql(
+          """
+            |SELECT l.a, l.b
+            |FROM testData2 l JOIN (SELECT * FROM testLimit LIMIT 1) r ON (l.a = r.key)
+          """.stripMargin).queryExecution.executedPlan
+
+        val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
+        val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
+
+        assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
+        assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
+      }
     }
 
-    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
-
     val simpleTypes =
       NullType ::
       BooleanType ::
@@ -124,7 +120,9 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
       StringType ::
       BinaryType :: Nil
 
-    checkPlan(simpleTypes, newThreshold = 16434)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "16434") {
+      checkPlan(simpleTypes)
+    }
 
     val complexTypes =
       ArrayType(DoubleType, true) ::
@@ -136,36 +134,37 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
         StructField("b", ArrayType(DoubleType), nullable = false),
         StructField("c", DoubleType, nullable = false))) :: Nil
 
-    checkPlan(complexTypes, newThreshold = 901617)
-
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "901617") {
+      checkPlan(complexTypes)
+    }
   }
 
   test("InMemoryRelation statistics propagation") {
-    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920)
-
-    testData.limit(3).registerTempTable("tiny")
-    sql("CACHE TABLE tiny")
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "81920") {
+      withTempTable("tiny") {
+        testData.limit(3).registerTempTable("tiny")
+        sql("CACHE TABLE tiny")
 
-    val a = testData.as("a")
-    val b = ctx.table("tiny").as("b")
-    val planned = a.join(b, $"a.key" === $"b.key").queryExecution.executedPlan
+        val a = testData.as("a")
+        val b = sqlContext.table("tiny").as("b")
+        val planned = a.join(b, $"a.key" === $"b.key").queryExecution.executedPlan
 
-    val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
-    val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
+        val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
+        val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
 
-    assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
-    assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
+        assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
+        assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
 
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+        sqlContext.clearCache()
+      }
+    }
   }
 
   test("efficient limit -> project -> sort") {
     {
       val query =
         testData.select('key, 'value).sort('key).limit(2).logicalPlan
-      val planned = ctx.planner.TakeOrderedAndProject(query)
+      val planned = sqlContext.planner.TakeOrderedAndProject(query)
       assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
       assert(planned.head.output === testData.select('key, 'value).logicalPlan.output)
     }
@@ -175,7 +174,7 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
       // into it.
       val query =
         testData.select('key, 'value).sort('key).select('value, 'key).limit(2).logicalPlan
-      val planned = ctx.planner.TakeOrderedAndProject(query)
+      val planned = sqlContext.planner.TakeOrderedAndProject(query)
       assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
       assert(planned.head.output === testData.select('value, 'key).logicalPlan.output)
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index ef6ad59b71fb3..4492e37ad01ff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -39,20 +39,20 @@ class RowFormatConvertersSuite extends SparkPlanTest with SharedSQLContext {
 
   test("planner should insert unsafe->safe conversions when required") {
     val plan = Limit(10, outputsUnsafe)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(preparedPlan.children.head.isInstanceOf[ConvertToSafe])
   }
 
   test("filter can process unsafe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsUnsafe)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).size === 1)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("filter can process safe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsSafe)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).isEmpty)
     assert(!preparedPlan.outputsUnsafeRows)
   }
@@ -67,33 +67,33 @@ class RowFormatConvertersSuite extends SparkPlanTest with SharedSQLContext {
   test("union requires all of its input rows' formats to agree") {
     val plan = Union(Seq(outputsSafe, outputsUnsafe))
     assert(plan.canProcessSafeRows && plan.canProcessUnsafeRows)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process safe rows") {
     val plan = Union(Seq(outputsSafe, outputsSafe))
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(!preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process unsafe rows") {
     val plan = Union(Seq(outputsUnsafe, outputsUnsafe))
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("round trip with ConvertToUnsafe and ConvertToSafe") {
     val input = Seq(("hello", 1), ("world", 2))
     checkAnswer(
-      ctx.createDataFrame(input),
+      sqlContext.createDataFrame(input),
       plan => ConvertToSafe(ConvertToUnsafe(plan)),
       input.map(Row.fromTuple)
     )
   }
 
   test("SPARK-9683: copy UTF8String when convert unsafe array/map to safe") {
-    SparkPlan.currentContext.set(ctx)
+    SparkPlan.currentContext.set(sqlContext)
     val schema = ArrayType(StringType)
     val rows = (1 to 100).map { i =>
       InternalRow(new GenericArrayData(Array[Any](UTF8String.fromString(i.toString))))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
index 8fa77b0fcb7b7..3073d492e613b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
@@ -22,6 +22,7 @@ import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.test.SharedSQLContext
 
 class SortSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
 
   // This test was originally added as an example of how to use [[SparkPlanTest]];
   // it's not designed to be a comprehensive test of ExternalSort.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index 5ab8f44faebf6..de45ae4635fb7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -31,14 +31,7 @@ import org.apache.spark.sql.test.SQLTestUtils
  * class's test helper methods can be used, see [[SortSuite]].
  */
 private[sql] abstract class SparkPlanTest extends SparkFunSuite {
-  protected def _sqlContext: SQLContext
-
-  /**
-   * Creates a DataFrame from a local Seq of Product.
-   */
-  implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = {
-    _sqlContext.implicits.localSeqToDataFrameHolder(data)
-  }
+  protected def sqlContext: SQLContext
 
   /**
    * Runs the plan and makes sure the answer matches the expected result.
@@ -98,7 +91,7 @@ private[sql] abstract class SparkPlanTest extends SparkFunSuite {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean = true): Unit = {
-    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, _sqlContext) match {
+    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -122,7 +115,7 @@ private[sql] abstract class SparkPlanTest extends SparkFunSuite {
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean = true): Unit = {
     SparkPlanTest.checkAnswer(
-        input, planFunction, expectedPlanFunction, sortAnswers, _sqlContext) match {
+        input, planFunction, expectedPlanFunction, sortAnswers, sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -149,13 +142,13 @@ object SparkPlanTest {
       planFunction: SparkPlan => SparkPlan,
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean,
-      _sqlContext: SQLContext): Option[String] = {
+      sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.queryExecution.sparkPlan)
     val expectedOutputPlan = expectedPlanFunction(input.queryExecution.sparkPlan)
 
     val expectedAnswer: Seq[Row] = try {
-      executePlan(expectedOutputPlan, _sqlContext)
+      executePlan(expectedOutputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -170,7 +163,7 @@ object SparkPlanTest {
     }
 
     val actualAnswer: Seq[Row] = try {
-      executePlan(outputPlan, _sqlContext)
+      executePlan(outputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -210,12 +203,12 @@ object SparkPlanTest {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean,
-      _sqlContext: SQLContext): Option[String] = {
+      sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.map(_.queryExecution.sparkPlan))
 
     val sparkAnswer: Seq[Row] = try {
-      executePlan(outputPlan, _sqlContext)
+      executePlan(outputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -238,10 +231,10 @@ object SparkPlanTest {
     }
   }
 
-  private def executePlan(outputPlan: SparkPlan, _sqlContext: SQLContext): Seq[Row] = {
+  private def executePlan(outputPlan: SparkPlan, sqlContext: SQLContext): Seq[Row] = {
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
-    val resolvedPlan = _sqlContext.prepareForExecution.execute(
+    val resolvedPlan = sqlContext.prepareForExecution.execute(
       outputPlan transform {
         case plan: SparkPlan =>
           val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index 3158458edb831..7a0f0dfd2b7f1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -29,15 +29,16 @@ import org.apache.spark.sql.types._
  * A test suite that generates randomized data to test the [[TungstenSort]] operator.
  */
 class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
 
   override def beforeAll(): Unit = {
     super.beforeAll()
-    ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
+    sqlContext.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
   }
 
   override def afterAll(): Unit = {
     try {
-      ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
+      sqlContext.conf.unsetConf(SQLConf.CODEGEN_ENABLED)
     } finally {
       super.afterAll()
     }
@@ -64,8 +65,7 @@ class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
   }
 
   test("sorting updates peak execution memory") {
-    val sc = ctx.sparkContext
-    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "unsafe external sort") {
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sparkContext, "unsafe external sort") {
       checkThatPlansAgree(
         (1 to 100).map(v => Tuple1(v)).toDF("a"),
         (child: SparkPlan) => TungstenSort('a.asc :: Nil, true, child),
@@ -83,8 +83,8 @@ class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
   ) {
     test(s"sorting on $dataType with nullable=$nullable, sortOrder=$sortOrder") {
       val inputData = Seq.fill(1000)(randomDataGenerator())
-      val inputDf = ctx.createDataFrame(
-        ctx.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
+      val inputDf = sqlContext.createDataFrame(
+        sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)
       )
       assert(TungstenSort.supportsSchema(inputDf.schema))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
index 5fdb82b067728..afda0d29f6d91 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
@@ -37,7 +37,7 @@ class TungstenAggregationIteratorSuite extends SparkFunSuite with SharedSQLConte
       val newMutableProjection = (expr: Seq[Expression], schema: Seq[Attribute]) => {
         () => new InterpretedMutableProjection(expr, schema)
       }
-      val dummyAccum = SQLMetrics.createLongMetric(ctx.sparkContext, "dummy")
+      val dummyAccum = SQLMetrics.createLongMetric(sparkContext, "dummy")
       iter = new TungstenAggregationIterator(Seq.empty, Seq.empty, Seq.empty, 0,
         Seq.empty, newMutableProjection, Seq.empty, None, dummyAccum, dummyAccum)
       val numPages = iter.getHashMap.getNumDataPages
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 1174b27732f22..6a18cc6d27138 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -215,7 +215,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Complex field and type inferring with null in sampling") {
-    val jsonDF = ctx.read.json(jsonNullStruct)
+    val jsonDF = sqlContext.read.json(jsonNullStruct)
     val expectedSchema = StructType(
       StructField("headers", StructType(
         StructField("Charset", StringType, true) ::
@@ -234,7 +234,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Primitive field and type inferring") {
-    val jsonDF = ctx.read.json(primitiveFieldAndType)
+    val jsonDF = sqlContext.read.json(primitiveFieldAndType)
 
     val expectedSchema = StructType(
       StructField("bigInteger", DecimalType(20, 0), true) ::
@@ -262,7 +262,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Complex field and type inferring") {
-    val jsonDF = ctx.read.json(complexFieldAndType1)
+    val jsonDF = sqlContext.read.json(complexFieldAndType1)
 
     val expectedSchema = StructType(
       StructField("arrayOfArray1", ArrayType(ArrayType(StringType, true), true), true) ::
@@ -361,7 +361,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("GetField operation on complex data type") {
-    val jsonDF = ctx.read.json(complexFieldAndType1)
+    val jsonDF = sqlContext.read.json(complexFieldAndType1)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -377,7 +377,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Type conflict in primitive field values") {
-    val jsonDF = ctx.read.json(primitiveFieldValueTypeConflict)
+    val jsonDF = sqlContext.read.json(primitiveFieldValueTypeConflict)
 
     val expectedSchema = StructType(
       StructField("num_bool", StringType, true) ::
@@ -449,7 +449,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   ignore("Type conflict in primitive field values (Ignored)") {
-    val jsonDF = ctx.read.json(primitiveFieldValueTypeConflict)
+    val jsonDF = sqlContext.read.json(primitiveFieldValueTypeConflict)
     jsonDF.registerTempTable("jsonTable")
 
     // Right now, the analyzer does not promote strings in a boolean expression.
@@ -502,7 +502,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Type conflict in complex field values") {
-    val jsonDF = ctx.read.json(complexFieldValueTypeConflict)
+    val jsonDF = sqlContext.read.json(complexFieldValueTypeConflict)
 
     val expectedSchema = StructType(
       StructField("array", ArrayType(LongType, true), true) ::
@@ -526,7 +526,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Type conflict in array elements") {
-    val jsonDF = ctx.read.json(arrayElementTypeConflict)
+    val jsonDF = sqlContext.read.json(arrayElementTypeConflict)
 
     val expectedSchema = StructType(
       StructField("array1", ArrayType(StringType, true), true) ::
@@ -554,7 +554,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Handling missing fields") {
-    val jsonDF = ctx.read.json(missingFields)
+    val jsonDF = sqlContext.read.json(missingFields)
 
     val expectedSchema = StructType(
       StructField("a", BooleanType, true) ::
@@ -573,9 +573,9 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     val dir = Utils.createTempDir()
     dir.delete()
     val path = dir.getCanonicalFile.toURI.toString
-    ctx.sparkContext.parallelize(1 to 100)
+    sparkContext.parallelize(1 to 100)
       .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
-    val jsonDF = ctx.read.option("samplingRatio", "0.49").json(path)
+    val jsonDF = sqlContext.read.option("samplingRatio", "0.49").json(path)
 
     val analyzed = jsonDF.queryExecution.analyzed
     assert(
@@ -590,7 +590,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
     val schema = StructType(StructField("a", LongType, true) :: Nil)
     val logicalRelation =
-      ctx.read.schema(schema).json(path)
+      sqlContext.read.schema(schema).json(path)
         .queryExecution.analyzed.asInstanceOf[LogicalRelation]
     val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation]
     assert(relationWithSchema.paths === Array(path))
@@ -603,7 +603,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     dir.delete()
     val path = dir.getCanonicalPath
     primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
-    val jsonDF = ctx.read.json(path)
+    val jsonDF = sqlContext.read.json(path)
 
     val expectedSchema = StructType(
       StructField("bigInteger", DecimalType(20, 0), true) ::
@@ -672,7 +672,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       StructField("null", StringType, true) ::
       StructField("string", StringType, true) :: Nil)
 
-    val jsonDF1 = ctx.read.schema(schema).json(path)
+    val jsonDF1 = sqlContext.read.schema(schema).json(path)
 
     assert(schema === jsonDF1.schema)
 
@@ -689,7 +689,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       "this is a simple string.")
     )
 
-    val jsonDF2 = ctx.read.schema(schema).json(primitiveFieldAndType)
+    val jsonDF2 = sqlContext.read.schema(schema).json(primitiveFieldAndType)
 
     assert(schema === jsonDF2.schema)
 
@@ -710,7 +710,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   test("Applying schemas with MapType") {
     val schemaWithSimpleMap = StructType(
       StructField("map", MapType(StringType, IntegerType, true), false) :: Nil)
-    val jsonWithSimpleMap = ctx.read.schema(schemaWithSimpleMap).json(mapType1)
+    val jsonWithSimpleMap = sqlContext.read.schema(schemaWithSimpleMap).json(mapType1)
 
     jsonWithSimpleMap.registerTempTable("jsonWithSimpleMap")
 
@@ -738,7 +738,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     val schemaWithComplexMap = StructType(
       StructField("map", MapType(StringType, innerStruct, true), false) :: Nil)
 
-    val jsonWithComplexMap = ctx.read.schema(schemaWithComplexMap).json(mapType2)
+    val jsonWithComplexMap = sqlContext.read.schema(schemaWithComplexMap).json(mapType2)
 
     jsonWithComplexMap.registerTempTable("jsonWithComplexMap")
 
@@ -764,7 +764,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-2096 Correctly parse dot notations") {
-    val jsonDF = ctx.read.json(complexFieldAndType2)
+    val jsonDF = sqlContext.read.json(complexFieldAndType2)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -782,7 +782,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-3390 Complex arrays") {
-    val jsonDF = ctx.read.json(complexFieldAndType2)
+    val jsonDF = sqlContext.read.json(complexFieldAndType2)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -805,7 +805,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-3308 Read top level JSON arrays") {
-    val jsonDF = ctx.read.json(jsonArray)
+    val jsonDF = sqlContext.read.json(jsonArray)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -823,64 +823,63 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
   test("Corrupt records") {
     // Test if we can query corrupt records.
-    val oldColumnNameOfCorruptRecord = ctx.conf.columnNameOfCorruptRecord
-    ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed")
-
-    val jsonDF = ctx.read.json(corruptRecords)
-    jsonDF.registerTempTable("jsonTable")
-
-    val schema = StructType(
-      StructField("_unparsed", StringType, true) ::
-      StructField("a", StringType, true) ::
-      StructField("b", StringType, true) ::
-      StructField("c", StringType, true) :: Nil)
-
-    assert(schema === jsonDF.schema)
-
-    // In HiveContext, backticks should be used to access columns starting with a underscore.
-    checkAnswer(
-      sql(
-        """
-          |SELECT a, b, c, _unparsed
-          |FROM jsonTable
-        """.stripMargin),
-      Row(null, null, null, "{") ::
-        Row(null, null, null, "") ::
-        Row(null, null, null, """{"a":1, b:2}""") ::
-        Row(null, null, null, """{"a":{, b:3}""") ::
-        Row("str_a_4", "str_b_4", "str_c_4", null) ::
-        Row(null, null, null, "]") :: Nil
-    )
-
-    checkAnswer(
-      sql(
-        """
-          |SELECT a, b, c
-          |FROM jsonTable
-          |WHERE _unparsed IS NULL
-        """.stripMargin),
-      Row("str_a_4", "str_b_4", "str_c_4")
-    )
-
-    checkAnswer(
-      sql(
-        """
-          |SELECT _unparsed
-          |FROM jsonTable
-          |WHERE _unparsed IS NOT NULL
-        """.stripMargin),
-      Row("{") ::
-        Row("") ::
-        Row("""{"a":1, b:2}""") ::
-        Row("""{"a":{, b:3}""") ::
-        Row("]") :: Nil
-    )
-
-    ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord)
+    withSQLConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD.key -> "_unparsed") {
+      withTempTable("jsonTable") {
+        val jsonDF = sqlContext.read.json(corruptRecords)
+        jsonDF.registerTempTable("jsonTable")
+
+        val schema = StructType(
+          StructField("_unparsed", StringType, true) ::
+          StructField("a", StringType, true) ::
+          StructField("b", StringType, true) ::
+          StructField("c", StringType, true) :: Nil)
+
+        assert(schema === jsonDF.schema)
+
+        // In HiveContext, backticks should be used to access columns starting with a underscore.
+        checkAnswer(
+          sql(
+            """
+              |SELECT a, b, c, _unparsed
+              |FROM jsonTable
+            """.stripMargin),
+          Row(null, null, null, "{") ::
+            Row(null, null, null, "") ::
+            Row(null, null, null, """{"a":1, b:2}""") ::
+            Row(null, null, null, """{"a":{, b:3}""") ::
+            Row("str_a_4", "str_b_4", "str_c_4", null) ::
+            Row(null, null, null, "]") :: Nil
+        )
+
+        checkAnswer(
+          sql(
+            """
+              |SELECT a, b, c
+              |FROM jsonTable
+              |WHERE _unparsed IS NULL
+            """.stripMargin),
+          Row("str_a_4", "str_b_4", "str_c_4")
+        )
+
+        checkAnswer(
+          sql(
+            """
+              |SELECT _unparsed
+              |FROM jsonTable
+              |WHERE _unparsed IS NOT NULL
+            """.stripMargin),
+          Row("{") ::
+            Row("") ::
+            Row("""{"a":1, b:2}""") ::
+            Row("""{"a":{, b:3}""") ::
+            Row("]") :: Nil
+        )
+      }
+    }
   }
 
   test("SPARK-4068: nulls in arrays") {
-    val jsonDF = ctx.read.json(nullsInArrays)
+    val jsonDF = sqlContext.read.json(nullsInArrays)
     jsonDF.registerTempTable("jsonTable")
 
     val schema = StructType(
@@ -926,7 +925,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       Row(values(0).toInt, values(1), values(2).toBoolean, r.split(",").toList, v5)
     }
 
-    val df1 = ctx.createDataFrame(rowRDD1, schema1)
+    val df1 = sqlContext.createDataFrame(rowRDD1, schema1)
     df1.registerTempTable("applySchema1")
     val df2 = df1.toDF
     val result = df2.toJSON.collect()
@@ -949,7 +948,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       Row(Row(values(0).toInt, values(2).toBoolean), Map(values(1) -> v4))
     }
 
-    val df3 = ctx.createDataFrame(rowRDD2, schema2)
+    val df3 = sqlContext.createDataFrame(rowRDD2, schema2)
     df3.registerTempTable("applySchema2")
     val df4 = df3.toDF
     val result2 = df4.toJSON.collect()
@@ -957,8 +956,8 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     assert(result2(1) === "{\"f1\":{\"f11\":2,\"f12\":false},\"f2\":{\"B2\":null}}")
     assert(result2(3) === "{\"f1\":{\"f11\":4,\"f12\":true},\"f2\":{\"D4\":2147483644}}")
 
-    val jsonDF = ctx.read.json(primitiveFieldAndType)
-    val primTable = ctx.read.json(jsonDF.toJSON)
+    val jsonDF = sqlContext.read.json(primitiveFieldAndType)
+    val primTable = sqlContext.read.json(jsonDF.toJSON)
     primTable.registerTempTable("primativeTable")
     checkAnswer(
         sql("select * from primativeTable"),
@@ -970,8 +969,8 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
         "this is a simple string.")
       )
 
-    val complexJsonDF = ctx.read.json(complexFieldAndType1)
-    val compTable = ctx.read.json(complexJsonDF.toJSON)
+    val complexJsonDF = sqlContext.read.json(complexFieldAndType1)
+    val compTable = sqlContext.read.json(complexJsonDF.toJSON)
     compTable.registerTempTable("complexTable")
     // Access elements of a primitive array.
     checkAnswer(
@@ -1039,25 +1038,25 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       Some(empty),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation0 = LogicalRelation(relation0)
     val relation1 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation1 = LogicalRelation(relation1)
     val relation2 = new JSONRelation(
       Some(singleRow),
       0.5,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation2 = LogicalRelation(relation2)
     val relation3 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("b", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation3 = LogicalRelation(relation3)
 
     assert(relation0 !== relation1)
@@ -1078,18 +1077,18 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
     withTempPath(dir => {
       val path = dir.getCanonicalFile.toURI.toString
-      ctx.sparkContext.parallelize(1 to 100)
+      sparkContext.parallelize(1 to 100)
         .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
 
       val d1 = ResolvedDataSource(
-        ctx,
+        sqlContext,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
         options = Map("path" -> path))
 
       val d2 = ResolvedDataSource(
-        ctx,
+        sqlContext,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
@@ -1105,24 +1104,21 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-7565 MapType in JsonRDD") {
-    val oldColumnNameOfCorruptRecord = ctx.conf.columnNameOfCorruptRecord
-    ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed")
-
-    val schemaWithSimpleMap = StructType(
-      StructField("map", MapType(StringType, IntegerType, true), false) :: Nil)
-    try {
-      val temp = Utils.createTempDir().getPath
-
-      val df = ctx.read.schema(schemaWithSimpleMap).json(mapType1)
-      df.write.mode("overwrite").parquet(temp)
-      // order of MapType is not defined
-      assert(ctx.read.parquet(temp).count() == 5)
-
-      val df2 = ctx.read.json(corruptRecords)
-      df2.write.mode("overwrite").parquet(temp)
-      checkAnswer(ctx.read.parquet(temp), df2.collect())
-    } finally {
-      ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord)
+    withSQLConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD.key -> "_unparsed") {
+      withTempDir { dir =>
+        val schemaWithSimpleMap = StructType(
+          StructField("map", MapType(StringType, IntegerType, true), false) :: Nil)
+        val df = sqlContext.read.schema(schemaWithSimpleMap).json(mapType1)
+
+        val path = dir.getAbsolutePath
+        df.write.mode("overwrite").parquet(path)
+        // order of MapType is not defined
+        assert(sqlContext.read.parquet(path).count() == 5)
+
+        val df2 = sqlContext.read.json(corruptRecords)
+        df2.write.mode("overwrite").parquet(path)
+        checkAnswer(sqlContext.read.parquet(path), df2.collect())
+      }
     }
   }
 
@@ -1142,19 +1138,19 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       val d1 = new File(root, "d1=1")
       // root/dt=1/col1=abc
       val p1_col1 = makePartition(
-        ctx.sparkContext.parallelize(2 to 5).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        sparkContext.parallelize(2 to 5).map(i => s"""{"a": 1, "b": "str$i"}"""),
         d1,
         "col1",
         "abc")
 
       // root/dt=1/col1=abd
       val p2 = makePartition(
-        ctx.sparkContext.parallelize(6 to 10).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        sparkContext.parallelize(6 to 10).map(i => s"""{"a": 1, "b": "str$i"}"""),
         d1,
         "col1",
         "abd")
 
-        ctx.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
+        sqlContext.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
         checkAnswer(sql(
           "SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
         checkAnswer(sql(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
index 2864181cf91d5..713d1da1cb515 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
@@ -21,10 +21,10 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
 
 private[json] trait TestJsonData {
-  protected def _sqlContext: SQLContext
+  protected def sqlContext: SQLContext
 
   def primitiveFieldAndType: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"string":"this is a simple string.",
           "integer":10,
           "long":21474836470,
@@ -35,7 +35,7 @@ private[json] trait TestJsonData {
       }"""  :: Nil)
 
   def primitiveFieldValueTypeConflict: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"num_num_1":11, "num_num_2":null, "num_num_3": 1.1,
           "num_bool":true, "num_str":13.1, "str_bool":"str1"}""" ::
       """{"num_num_1":null, "num_num_2":21474836470.9, "num_num_3": null,
@@ -46,14 +46,14 @@ private[json] trait TestJsonData {
           "num_bool":null, "num_str":92233720368547758070, "str_bool":null}""" :: Nil)
 
   def jsonNullStruct: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"nullstr":"","ip":"27.31.100.29","headers":{"Host":"1.abc.com","Charset":"UTF-8"}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":{}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":""}""" ::
         """{"nullstr":null,"ip":"27.31.100.29","headers":null}""" :: Nil)
 
   def complexFieldValueTypeConflict: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"num_struct":11, "str_array":[1, 2, 3],
           "array":[], "struct_array":[], "struct": {}}""" ::
       """{"num_struct":{"field":false}, "str_array":null,
@@ -64,14 +64,14 @@ private[json] trait TestJsonData {
           "array":[7], "struct_array":{"field": true}, "struct": {"field": "str"}}""" :: Nil)
 
   def arrayElementTypeConflict: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"array1": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}],
           "array2": [{"field":214748364700}, {"field":1}]}""" ::
       """{"array3": [{"field":"str"}, {"field":1}]}""" ::
       """{"array3": [1, 2, 3]}""" :: Nil)
 
   def missingFields: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"a":true}""" ::
       """{"b":21474836470}""" ::
       """{"c":[33, 44]}""" ::
@@ -79,7 +79,7 @@ private[json] trait TestJsonData {
       """{"e":"str"}""" :: Nil)
 
   def complexFieldAndType1: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"struct":{"field1": true, "field2": 92233720368547758070},
           "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]},
           "arrayOfString":["str1", "str2"],
@@ -95,7 +95,7 @@ private[json] trait TestJsonData {
          }"""  :: Nil)
 
   def complexFieldAndType2: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}],
           "complexArrayOfStruct": [
           {
@@ -149,7 +149,7 @@ private[json] trait TestJsonData {
       }""" :: Nil)
 
   def mapType1: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"map": {"a": 1}}""" ::
       """{"map": {"b": 2}}""" ::
       """{"map": {"c": 3}}""" ::
@@ -157,7 +157,7 @@ private[json] trait TestJsonData {
       """{"map": {"e": null}}""" :: Nil)
 
   def mapType2: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"map": {"a": {"field1": [1, 2, 3, null]}}}""" ::
       """{"map": {"b": {"field2": 2}}}""" ::
       """{"map": {"c": {"field1": [], "field2": 4}}}""" ::
@@ -166,21 +166,21 @@ private[json] trait TestJsonData {
       """{"map": {"f": {"field1": null}}}""" :: Nil)
 
   def nullsInArrays: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"field1":[[null], [[["Test"]]]]}""" ::
       """{"field2":[null, [{"Test":1}]]}""" ::
       """{"field3":[[null], [{"Test":"2"}]]}""" ::
       """{"field4":[[null, [1,2,3]]]}""" :: Nil)
 
   def jsonArray: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """[{"a":"str_a_1"}]""" ::
       """[{"a":"str_a_2"}, {"b":"str_b_3"}]""" ::
       """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" ::
       """[]""" :: Nil)
 
   def corruptRecords: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{""" ::
       """""" ::
       """{"a":1, b:2}""" ::
@@ -189,7 +189,7 @@ private[json] trait TestJsonData {
       """]""" :: Nil)
 
   def emptyRecords: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{""" ::
         """""" ::
         """{"a": {}}""" ::
@@ -198,7 +198,7 @@ private[json] trait TestJsonData {
         """]""" :: Nil)
 
 
-  lazy val singleRow: RDD[String] = _sqlContext.sparkContext.parallelize("""{"a":123}""" :: Nil)
+  lazy val singleRow: RDD[String] = sqlContext.sparkContext.parallelize("""{"a":123}""" :: Nil)
 
-  def empty: RDD[String] = _sqlContext.sparkContext.parallelize(Seq[String]())
+  def empty: RDD[String] = sqlContext.sparkContext.parallelize(Seq[String]())
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 91f3ce4d34c8b..0835bd123049b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -39,12 +39,13 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
 
   protected def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
     val fsPath = new Path(path)
-    val fs = fsPath.getFileSystem(configuration)
+    val fs = fsPath.getFileSystem(hadoopConfiguration)
     val parquetFiles = fs.listStatus(fsPath, new PathFilter {
       override def accept(path: Path): Boolean = pathFilter(path)
     }).toSeq.asJava
 
-    val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
+    val footers =
+      ParquetFileReader.readAllFootersInParallel(hadoopConfiguration, parquetFiles, true)
     footers.asScala.head.getParquetMetadata.getFileMetaData.getSchema
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index 08d2b9dee99b0..cd552e83372f1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -101,7 +101,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   test("fixed-length decimals") {
     def makeDecimalRDD(decimal: DecimalType): DataFrame =
-      sqlContext.sparkContext
+      sparkContext
         .parallelize(0 to 1000)
         .map(i => Tuple1(i / 100.0))
         .toDF()
@@ -119,7 +119,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   test("date type") {
     def makeDateRDD(): DataFrame =
-      sqlContext.sparkContext
+      sparkContext
         .parallelize(0 to 1000)
         .map(i => Tuple1(DateTimeUtils.toJavaDate(i)))
         .toDF()
@@ -207,7 +207,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   test("compression codec") {
     def compressionCodecFor(path: String): String = {
       val codecs = ParquetTypesConverter
-        .readMetaData(new Path(path), Some(configuration)).getBlocks.asScala
+        .readMetaData(new Path(path), Some(hadoopConfiguration)).getBlocks.asScala
         .flatMap(_.getColumns.asScala)
         .map(_.getCodec.name())
         .distinct
@@ -277,14 +277,14 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   test("write metadata") {
     withTempPath { file =>
       val path = new Path(file.toURI.toString)
-      val fs = FileSystem.getLocal(configuration)
+      val fs = FileSystem.getLocal(hadoopConfiguration)
       val attributes = ScalaReflection.attributesFor[(Int, String)]
-      ParquetTypesConverter.writeMetaData(attributes, path, configuration)
+      ParquetTypesConverter.writeMetaData(attributes, path, hadoopConfiguration)
 
       assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_COMMON_METADATA_FILE)))
       assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)))
 
-      val metaData = ParquetTypesConverter.readMetaData(path, Some(configuration))
+      val metaData = ParquetTypesConverter.readMetaData(path, Some(hadoopConfiguration))
       val actualSchema = metaData.getFileMetaData.getSchema
       val expectedSchema = ParquetTypesConverter.convertFromAttributes(attributes)
 
@@ -355,7 +355,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       val path = new Path(location.getCanonicalPath)
 
       ParquetFileWriter.writeMetadataFile(
-        sqlContext.sparkContext.hadoopConfiguration,
+        sparkContext.hadoopConfiguration,
         path,
         Collections.singletonList(
           new Footer(path, new ParquetMetadata(fileMetadata, Collections.emptyList()))))
@@ -370,12 +370,12 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   }
 
   test("SPARK-6352 DirectParquetOutputCommitter") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
 
     // Write to a parquet file and let it fail.
     // _temporary should be missing if direct output committer works.
     try {
-      configuration.set("spark.sql.parquet.output.committer.class",
+      hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
         classOf[DirectParquetOutputCommitter].getCanonicalName)
       sqlContext.udf.register("div0", (x: Int) => x / 0)
       withTempPath { dir =>
@@ -383,23 +383,23 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
           sqlContext.sql("select div0(1)").write.parquet(dir.getCanonicalPath)
         }
         val path = new Path(dir.getCanonicalPath, "_temporary")
-        val fs = path.getFileSystem(configuration)
+        val fs = path.getFileSystem(hadoopConfiguration)
         assert(!fs.exists(path))
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 
   test("SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
 
     // Write to a parquet file and let it fail.
     // _temporary should be missing if direct output committer works.
     try {
-      configuration.set("spark.sql.parquet.output.committer.class",
+      hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
         "org.apache.spark.sql.parquet.DirectParquetOutputCommitter")
       sqlContext.udf.register("div0", (x: Int) => x / 0)
       withTempPath { dir =>
@@ -407,25 +407,25 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
           sqlContext.sql("select div0(1)").write.parquet(dir.getCanonicalPath)
         }
         val path = new Path(dir.getCanonicalPath, "_temporary")
-        val fs = path.getFileSystem(configuration)
+        val fs = path.getFileSystem(hadoopConfiguration)
         assert(!fs.exists(path))
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 
 
   test("SPARK-8121: spark.sql.parquet.output.committer.class shouldn't be overridden") {
     withTempPath { dir =>
-      val clonedConf = new Configuration(configuration)
+      val clonedConf = new Configuration(hadoopConfiguration)
 
-      configuration.set(
+      hadoopConfiguration.set(
         SQLConf.OUTPUT_COMMITTER_CLASS.key, classOf[ParquetOutputCommitter].getCanonicalName)
 
-      configuration.set(
+      hadoopConfiguration.set(
         "spark.sql.parquet.output.committer.class",
         classOf[JobCommitFailureParquetOutputCommitter].getCanonicalName)
 
@@ -436,8 +436,8 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
         assert(message === "Intentional exception for testing purposes")
       } finally {
         // Hadoop 1 doesn't have `Configuration.unset`
-        configuration.clear()
-        clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+        hadoopConfiguration.clear()
+        clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
       }
     }
   }
@@ -455,11 +455,11 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   }
 
   test("SPARK-7837 Do not close output writer twice when commitTask() fails") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
 
     // Using a output committer that always fail when committing a task, so that both
     // `commitTask()` and `abortTask()` are invoked.
-    configuration.set(
+    hadoopConfiguration.set(
       "spark.sql.parquet.output.committer.class",
       classOf[TaskCommitFailureParquetOutputCommitter].getCanonicalName)
 
@@ -483,8 +483,8 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
index ed8bafb10c60b..7bac8609e1b91 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
@@ -517,7 +517,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest with Sha
     }
 
     val schema = StructType(partitionColumns :+ StructField(s"i", StringType))
-    val df = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(row :: Nil), schema)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(row :: Nil), schema)
 
     withTempPath { dir =>
       df.write.format("parquet").partitionBy(partitionColumns.map(_.name): _*).save(dir.toString)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index a379523d67f80..9edbb522684eb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -30,6 +30,7 @@ import org.apache.spark.util.Utils
  * A test suite that tests various Parquet queries.
  */
 class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext {
+  import testImplicits._
 
   test("simple select queries") {
     withParquetTable((0 until 10).map(i => (i, i.toString)), "t") {
@@ -40,22 +41,22 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
 
   test("appending") {
     val data = (0 until 10).map(i => (i, i.toString))
-    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT INTO TABLE t SELECT * FROM tmp")
-      checkAnswer(ctx.table("t"), (data ++ data).map(Row.fromTuple))
+      checkAnswer(sqlContext.table("t"), (data ++ data).map(Row.fromTuple))
     }
-    ctx.catalog.unregisterTable(Seq("tmp"))
+    sqlContext.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("overwriting") {
     val data = (0 until 10).map(i => (i, i.toString))
-    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT OVERWRITE TABLE t SELECT * FROM tmp")
-      checkAnswer(ctx.table("t"), data.map(Row.fromTuple))
+      checkAnswer(sqlContext.table("t"), data.map(Row.fromTuple))
     }
-    ctx.catalog.unregisterTable(Seq("tmp"))
+    sqlContext.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("self-join") {
@@ -118,9 +119,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     val schema = StructType(List(StructField("d", DecimalType(18, 0), false),
       StructField("time", TimestampType, false)).toArray)
     withTempPath { file =>
-      val df = ctx.createDataFrame(ctx.sparkContext.parallelize(data), schema)
+      val df = sqlContext.createDataFrame(sparkContext.parallelize(data), schema)
       df.write.parquet(file.getCanonicalPath)
-      val df2 = ctx.read.parquet(file.getCanonicalPath)
+      val df2 = sqlContext.read.parquet(file.getCanonicalPath)
       checkAnswer(df2, df.collect().toSeq)
     }
   }
@@ -129,12 +130,12 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
         // delete summary files, so if we don't merge part-files, one column will not be included.
         Utils.deleteRecursively(new File(basePath + "/foo=1/_metadata"))
         Utils.deleteRecursively(new File(basePath + "/foo=1/_common_metadata"))
-        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
+        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -153,9 +154,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
-        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
+        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -171,19 +172,19 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
   test("SPARK-8990 DataFrameReader.parquet() should respect user specified options") {
     withTempPath { dir =>
       val basePath = dir.getCanonicalPath
-      ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-      ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
+      sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+      sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
 
       // Disables the global SQL option for schema merging
       withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "false") {
         assertResult(2) {
           // Disables schema merging via data source option
-          ctx.read.option("mergeSchema", "false").parquet(basePath).columns.length
+          sqlContext.read.option("mergeSchema", "false").parquet(basePath).columns.length
         }
 
         assertResult(3) {
           // Enables schema merging via data source option
-          ctx.read.option("mergeSchema", "true").parquet(basePath).columns.length
+          sqlContext.read.option("mergeSchema", "true").parquet(basePath).columns.length
         }
       }
     }
@@ -193,7 +194,7 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     withTempPath { dir =>
       val basePath = dir.getCanonicalPath
       val schema = StructType(Array(StructField("name", DecimalType(10, 5), false)))
-      val rowRDD = sqlContext.sparkContext.parallelize(Array(Row(Decimal("67123.45"))))
+      val rowRDD = sparkContext.parallelize(Array(Row(Decimal("67123.45"))))
       val df = sqlContext.createDataFrame(rowRDD, schema)
       df.write.parquet(basePath)
 
@@ -203,9 +204,6 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
   }
 
   test("SPARK-10005 Schema merging for nested struct") {
-    val sqlContext = _sqlContext
-    import sqlContext.implicits._
-
     withTempPath { dir =>
       val path = dir.getCanonicalPath
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
index 5dbc7d1630f27..442fafb12f200 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
@@ -33,7 +33,6 @@ import org.apache.spark.sql.{DataFrame, SaveMode, SQLContext}
  * Especially, `Tuple1.apply` can be used to easily wrap a single type/value.
  */
 private[sql] trait ParquetTest extends SQLTestUtils {
-  protected def _sqlContext: SQLContext
 
   /**
    * Writes `data` to a Parquet file, which is then passed to `f` and will be deleted after `f`
@@ -43,7 +42,7 @@ private[sql] trait ParquetTest extends SQLTestUtils {
       (data: Seq[T])
       (f: String => Unit): Unit = {
     withTempPath { file =>
-      _sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
+      sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
       f(file.getCanonicalPath)
     }
   }
@@ -55,7 +54,7 @@ private[sql] trait ParquetTest extends SQLTestUtils {
   protected def withParquetDataFrame[T <: Product: ClassTag: TypeTag]
       (data: Seq[T])
       (f: DataFrame => Unit): Unit = {
-    withParquetFile(data)(path => f(_sqlContext.read.parquet(path)))
+    withParquetFile(data)(path => f(sqlContext.read.parquet(path)))
   }
 
   /**
@@ -67,14 +66,14 @@ private[sql] trait ParquetTest extends SQLTestUtils {
       (data: Seq[T], tableName: String)
       (f: => Unit): Unit = {
     withParquetDataFrame(data) { df =>
-      _sqlContext.registerDataFrameAsTable(df, tableName)
+      sqlContext.registerDataFrameAsTable(df, tableName)
       withTempTable(tableName)(f)
     }
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
       data: Seq[T], path: File): Unit = {
-    _sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
+    sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
index 53a0e53fd7719..dcbfdca71acb6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
@@ -33,8 +33,7 @@ import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
  * without serializing the hashed relation, which does not happen in local mode.
  */
 class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
-  private var sc: SparkContext = null
-  private var sqlContext: SQLContext = null
+  protected var sqlContext: SQLContext = null
 
   /**
    * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
@@ -44,15 +43,14 @@ class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
     val conf = new SparkConf()
       .setMaster("local-cluster[2,1,1024]")
       .setAppName("testing")
-    sc = new SparkContext(conf)
+    val sc = new SparkContext(conf)
     sqlContext = new SQLContext(sc)
     sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
     sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
   }
 
   override def afterAll(): Unit = {
-    sc.stop()
-    sc = null
+    sqlContext.sparkContext.stop()
     sqlContext = null
   }
 
@@ -60,7 +58,7 @@ class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
    * Test whether the specified broadcast join updates the peak execution memory accumulator.
    */
   private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
-    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sqlContext.sparkContext, name) {
       val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
       val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
       // Comparison at the end is for broadcast left semi join
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 4c9187a9a7106..e5fd9e277fc61 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -37,7 +37,7 @@ class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
 
@@ -53,7 +53,7 @@ class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
 
@@ -73,7 +73,7 @@ class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
   test("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(sparkContext, "data")
     val toUnsafe = UnsafeProjection.create(schema)
     val unsafeData = data.map(toUnsafe(_).copy()).toArray
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
index cc649b9bd4c45..4174ee055021d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
@@ -27,9 +27,10 @@ import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
 
 class InnerJoinSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
 
-  private lazy val myUpperCaseData = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val myUpperCaseData = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, "A"),
       Row(2, "B"),
       Row(3, "C"),
@@ -39,8 +40,8 @@ class InnerJoinSuite extends SparkPlanTest with SharedSQLContext {
       Row(null, "G")
     )), new StructType().add("N", IntegerType).add("L", StringType))
 
-  private lazy val myLowerCaseData = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val myLowerCaseData = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, "a"),
       Row(2, "b"),
       Row(3, "c"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index a1a617d7b7398..c2e0bdac17968 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -28,8 +28,8 @@ import org.apache.spark.sql.types.{IntegerType, DoubleType, StructType}
 
 class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-  private lazy val left = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val left = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, 2.0),
       Row(2, 100.0),
       Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
@@ -40,8 +40,8 @@ class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
       Row(null, null)
     )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
-  private lazy val right = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val right = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(0, 0.0),
       Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
       Row(2, -1.0),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
index baa86e320d986..3afd762942bcf 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
@@ -28,8 +28,8 @@ import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
 
 class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-  private lazy val left = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val left = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, 2.0),
       Row(1, 2.0),
       Row(2, 1.0),
@@ -40,8 +40,8 @@ class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {
       Row(6, null)
     )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
-  private lazy val right = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val right = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(2, 3.0),
       Row(2, 3.0),
       Row(3, 2.0),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index 80006bf077fe8..6afffae161ef6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -36,7 +36,7 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
   import testImplicits._
 
   test("LongSQLMetric should not box Long") {
-    val l = SQLMetrics.createLongMetric(ctx.sparkContext, "long")
+    val l = SQLMetrics.createLongMetric(sparkContext, "long")
     val f = () => {
       l += 1L
       l.add(1L)
@@ -50,7 +50,7 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
 
   test("Normal accumulator should do boxing") {
     // We need this test to make sure BoxingFinder works.
-    val l = ctx.sparkContext.accumulator(0L)
+    val l = sparkContext.accumulator(0L)
     val f = () => { l += 1L }
     BoxingFinder.getClassReader(f.getClass).foreach { cl =>
       val boxingFinder = new BoxingFinder()
@@ -71,19 +71,19 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
       df: DataFrame,
       expectedNumOfJobs: Int,
       expectedMetrics: Map[Long, (String, Map[String, Any])]): Unit = {
-    val previousExecutionIds = ctx.listener.executionIdToData.keySet
+    val previousExecutionIds = sqlContext.listener.executionIdToData.keySet
     df.collect()
-    ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
-    val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
+    sparkContext.listenerBus.waitUntilEmpty(10000)
+    val executionIds = sqlContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
     assert(executionIds.size === 1)
     val executionId = executionIds.head
-    val jobs = ctx.listener.getExecution(executionId).get.jobs
+    val jobs = sqlContext.listener.getExecution(executionId).get.jobs
     // Use "<=" because there is a race condition that we may miss some jobs
     // TODO Change it to "=" once we fix the race condition that missing the JobStarted event.
     assert(jobs.size <= expectedNumOfJobs)
     if (jobs.size == expectedNumOfJobs) {
       // If we can track all jobs, check the metric values
-      val metricValues = ctx.listener.getExecutionMetrics(executionId)
+      val metricValues = sqlContext.listener.getExecutionMetrics(executionId)
       val actualMetrics = SparkPlanGraph(df.queryExecution.executedPlan).nodes.filter { node =>
         expectedMetrics.contains(node.id)
       }.map { node =>
@@ -474,19 +474,19 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
 
   test("save metrics") {
     withTempPath { file =>
-      val previousExecutionIds = ctx.listener.executionIdToData.keySet
+      val previousExecutionIds = sqlContext.listener.executionIdToData.keySet
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 0)
       person.select('name).write.format("json").save(file.getAbsolutePath)
-      ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
-      val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
+      sparkContext.listenerBus.waitUntilEmpty(10000)
+      val executionIds = sqlContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
       assert(executionIds.size === 1)
       val executionId = executionIds.head
-      val jobs = ctx.listener.getExecution(executionId).get.jobs
+      val jobs = sqlContext.listener.getExecution(executionId).get.jobs
       // Use "<=" because there is a race condition that we may miss some jobs
       // TODO Change "<=" to "=" once we fix the race condition that missing the JobStarted event.
       assert(jobs.size <= 1)
-      val metricValues = ctx.listener.getExecutionMetrics(executionId)
+      val metricValues = sqlContext.listener.getExecutionMetrics(executionId)
       // Because "save" will create a new DataFrame internally, we cannot get the real metric id.
       // However, we still can check the value.
       assert(metricValues.values.toSeq === Seq(2L))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
index 80d1e88956949..2bbb41ca777b7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
@@ -74,7 +74,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("basic") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     val accumulatorIds =
@@ -212,7 +212,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobSucceeded)") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -241,7 +241,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("onExecutionEnd happens before multiple onJobEnd(JobSucceeded)s") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -281,7 +281,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobFailed)") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index d8c9a08d84c61..ed710689cc670 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -255,26 +255,26 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("Basic API") {
-    assert(ctx.read.jdbc(
+    assert(sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.PEOPLE", new Properties).collect().length === 3)
   }
 
   test("Basic API with FetchSize") {
     val properties = new Properties
     properties.setProperty("fetchSize", "2")
-    assert(ctx.read.jdbc(
+    assert(sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.PEOPLE", properties).collect().length === 3)
   }
 
   test("Partitioning via JDBCPartitioningInfo API") {
     assert(
-      ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3, new Properties)
+      sqlContext.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3, new Properties)
       .collect().length === 3)
   }
 
   test("Partitioning via list-of-where-clauses API") {
     val parts = Array[String]("THEID < 2", "THEID >= 2")
-    assert(ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties)
+    assert(sqlContext.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties)
       .collect().length === 3)
   }
 
@@ -330,9 +330,9 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("test DATE types") {
-    val rows = ctx.read.jdbc(
+    val rows = sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect()
-    val cachedRows = ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
+    val cachedRows = sqlContext.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
       .cache().collect()
     assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01"))
     assert(rows(1).getAs[java.sql.Date](1) === null)
@@ -340,8 +340,8 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("test DATE types in cache") {
-    val rows = ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect()
-    ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
+    val rows = sqlContext.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect()
+    sqlContext.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
       .cache().registerTempTable("mycached_date")
     val cachedRows = sql("select * from mycached_date").collect()
     assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01"))
@@ -349,7 +349,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("test types for null value") {
-    val rows = ctx.read.jdbc(
+    val rows = sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.NULLTYPES", new Properties).collect()
     assert((0 to 14).forall(i => rows(0).isNullAt(i)))
   }
@@ -396,7 +396,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
 
   test("Remap types via JdbcDialects") {
     JdbcDialects.registerDialect(testH2Dialect)
-    val df = ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", new Properties)
+    val df = sqlContext.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", new Properties)
     assert(df.schema.filter(_.dataType != org.apache.spark.sql.types.StringType).isEmpty)
     val rows = df.collect()
     assert(rows(0).get(0).isInstanceOf[String])
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
index 5dc3a2c07b8c7..e23ee6693133b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
@@ -22,13 +22,12 @@ import java.util.Properties
 
 import org.scalatest.BeforeAndAfter
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.{Row, SaveMode}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext {
+class JDBCWriteSuite extends SharedSQLContext with BeforeAndAfter {
 
   val url = "jdbc:h2:mem:testdb2"
   var conn: java.sql.Connection = null
@@ -76,8 +75,6 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLCon
     conn1.close()
   }
 
-  private lazy val sc = ctx.sparkContext
-
   private lazy val arr2x2 = Array[Row](Row.apply("dave", 42), Row.apply("mary", 222))
   private lazy val arr1x2 = Array[Row](Row.apply("fred", 3))
   private lazy val schema2 = StructType(
@@ -91,49 +88,50 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLCon
       StructField("seq", IntegerType) :: Nil)
 
   test("Basic CREATE") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
 
     df.write.jdbc(url, "TEST.BASICCREATETEST", new Properties)
-    assert(2 === ctx.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).count)
-    assert(2 === ctx.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).count)
+    assert(
+      2 === sqlContext.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).collect()(0).length)
   }
 
   test("CREATE with overwrite") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x3), schema3)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x3), schema3)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr1x2), schema2)
 
     df.write.jdbc(url1, "TEST.DROPTEST", properties)
-    assert(2 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).count)
-    assert(3 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).count)
+    assert(3 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
 
     df2.write.mode(SaveMode.Overwrite).jdbc(url1, "TEST.DROPTEST", properties)
-    assert(1 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
+    assert(1 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
   }
 
   test("CREATE then INSERT to append") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr1x2), schema2)
 
     df.write.jdbc(url, "TEST.APPENDTEST", new Properties)
     df2.write.mode(SaveMode.Append).jdbc(url, "TEST.APPENDTEST", new Properties)
-    assert(3 === ctx.read.jdbc(url, "TEST.APPENDTEST", new Properties).count)
-    assert(2 === ctx.read.jdbc(url, "TEST.APPENDTEST", new Properties).collect()(0).length)
+    assert(3 === sqlContext.read.jdbc(url, "TEST.APPENDTEST", new Properties).count)
+    assert(2 === sqlContext.read.jdbc(url, "TEST.APPENDTEST", new Properties).collect()(0).length)
   }
 
   test("CREATE then INSERT to truncate") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr1x2), schema2)
 
     df.write.jdbc(url1, "TEST.TRUNCATETEST", properties)
     df2.write.mode(SaveMode.Overwrite).jdbc(url1, "TEST.TRUNCATETEST", properties)
-    assert(1 === ctx.read.jdbc(url1, "TEST.TRUNCATETEST", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.TRUNCATETEST", properties).collect()(0).length)
+    assert(1 === sqlContext.read.jdbc(url1, "TEST.TRUNCATETEST", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.TRUNCATETEST", properties).collect()(0).length)
   }
 
   test("Incompatible INSERT to append") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr2x3), schema3)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr2x3), schema3)
 
     df.write.jdbc(url, "TEST.INCOMPATIBLETEST", new Properties)
     intercept[org.apache.spark.SparkException] {
@@ -143,14 +141,14 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLCon
 
   test("INSERT to JDBC Datasource") {
     sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
 
   test("INSERT to JDBC Datasource with overwrite") {
     sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
     sql("INSERT OVERWRITE TABLE PEOPLE1 SELECT * FROM PEOPLE")
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
index 9bc3f6bcf6fce..6fc9febe49707 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
@@ -26,10 +26,8 @@ import org.apache.spark.sql.execution.datasources.DDLException
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-
 class CreateTableAsSelectSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
   protected override lazy val sql = caseInsensitiveContext.sql _
-  private lazy val sparkContext = caseInsensitiveContext.sparkContext
   private var path: File = null
 
   override def beforeAll(): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
index d74d29fb0beb0..af04079ec895a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
@@ -19,13 +19,11 @@ package org.apache.spark.sql.sources
 
 import org.apache.spark.sql._
 
-
 private[sql] abstract class DataSourceTest extends QueryTest {
-  protected def _sqlContext: SQLContext
 
   // We want to test some edge cases.
   protected lazy val caseInsensitiveContext: SQLContext = {
-    val ctx = new SQLContext(_sqlContext.sparkContext)
+    val ctx = new SQLContext(sqlContext.sparkContext)
     ctx.setConf(SQLConf.CASE_SENSITIVE, false)
     ctx
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 084d83f6e9bff..5b70d258d6ce3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -19,13 +19,12 @@ package org.apache.spark.sql.sources
 
 import java.io.File
 
-import org.apache.spark.sql.{SaveMode, AnalysisException, Row}
+import org.apache.spark.sql.{AnalysisException, Row}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
 class InsertSuite extends DataSourceTest with SharedSQLContext {
   protected override lazy val sql = caseInsensitiveContext.sql _
-  private lazy val sparkContext = caseInsensitiveContext.sparkContext
   private var path: File = null
 
   override def beforeAll(): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
index 79b6e9b45c009..c9791879ec74c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
@@ -29,11 +29,11 @@ class PartitionedWriteSuite extends QueryTest with SharedSQLContext {
     val path = Utils.createTempDir()
     path.delete()
 
-    val df = ctx.range(100).select($"id", lit(1).as("data"))
+    val df = sqlContext.range(100).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      ctx.read.load(path.getCanonicalPath),
+      sqlContext.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
@@ -43,12 +43,12 @@ class PartitionedWriteSuite extends QueryTest with SharedSQLContext {
     val path = Utils.createTempDir()
     path.delete()
 
-    val base = ctx.range(100)
+    val base = sqlContext.range(100)
     val df = base.unionAll(base).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      ctx.read.load(path.getCanonicalPath),
+      sqlContext.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq ++ (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
index f18546b4c2d9b..10d261368993d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
@@ -28,7 +28,6 @@ import org.apache.spark.util.Utils
 
 class SaveLoadSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
   protected override lazy val sql = caseInsensitiveContext.sql _
-  private lazy val sparkContext = caseInsensitiveContext.sparkContext
   private var originalDefaultSource: String = null
   private var path: File = null
   private var df: DataFrame = null
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
index 3fc02df954e23..520dea7f7dd92 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -24,11 +24,11 @@ import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
  * A collection of sample data used in SQL tests.
  */
 private[sql] trait SQLTestData { self =>
-  protected def _sqlContext: SQLContext
+  protected def sqlContext: SQLContext
 
   // Helper object to import SQL implicits without a concrete SQLContext
   private object internalImplicits extends SQLImplicits {
-    protected override def _sqlContext: SQLContext = self._sqlContext
+    protected override def _sqlContext: SQLContext = self.sqlContext
   }
 
   import internalImplicits._
@@ -37,21 +37,21 @@ private[sql] trait SQLTestData { self =>
   // Note: all test data should be lazy because the SQLContext is not set up yet.
 
   protected lazy val emptyTestData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       Seq.empty[Int].map(i => TestData(i, i.toString))).toDF()
     df.registerTempTable("emptyTestData")
     df
   }
 
   protected lazy val testData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       (1 to 100).map(i => TestData(i, i.toString))).toDF()
     df.registerTempTable("testData")
     df
   }
 
   protected lazy val testData2: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       TestData2(1, 1) ::
       TestData2(1, 2) ::
       TestData2(2, 1) ::
@@ -63,7 +63,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val testData3: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       TestData3(1, None) ::
       TestData3(2, Some(2)) :: Nil).toDF()
     df.registerTempTable("testData3")
@@ -71,14 +71,14 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val negativeData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       (1 to 100).map(i => TestData(-i, (-i).toString))).toDF()
     df.registerTempTable("negativeData")
     df
   }
 
   protected lazy val largeAndSmallInts: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       LargeAndSmallInts(2147483644, 1) ::
       LargeAndSmallInts(1, 2) ::
       LargeAndSmallInts(2147483645, 1) ::
@@ -90,7 +90,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val decimalData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       DecimalData(1, 1) ::
       DecimalData(1, 2) ::
       DecimalData(2, 1) ::
@@ -102,7 +102,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val binaryData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       BinaryData("12".getBytes, 1) ::
       BinaryData("22".getBytes, 5) ::
       BinaryData("122".getBytes, 3) ::
@@ -113,7 +113,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val upperCaseData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       UpperCaseData(1, "A") ::
       UpperCaseData(2, "B") ::
       UpperCaseData(3, "C") ::
@@ -125,7 +125,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val lowerCaseData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       LowerCaseData(1, "a") ::
       LowerCaseData(2, "b") ::
       LowerCaseData(3, "c") ::
@@ -135,7 +135,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val arrayData: RDD[ArrayData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(
+    val rdd = sqlContext.sparkContext.parallelize(
       ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) ::
       ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil)
     rdd.toDF().registerTempTable("arrayData")
@@ -143,7 +143,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val mapData: RDD[MapData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(
+    val rdd = sqlContext.sparkContext.parallelize(
       MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
       MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
       MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
@@ -154,13 +154,13 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val repeatedData: RDD[StringData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
+    val rdd = sqlContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
     rdd.toDF().registerTempTable("repeatedData")
     rdd
   }
 
   protected lazy val nullableRepeatedData: RDD[StringData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(
+    val rdd = sqlContext.sparkContext.parallelize(
       List.fill(2)(StringData(null)) ++
       List.fill(2)(StringData("test")))
     rdd.toDF().registerTempTable("nullableRepeatedData")
@@ -168,7 +168,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val nullInts: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       NullInts(1) ::
       NullInts(2) ::
       NullInts(3) ::
@@ -178,7 +178,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val allNulls: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       NullInts(null) ::
       NullInts(null) ::
       NullInts(null) ::
@@ -188,7 +188,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val nullStrings: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       NullStrings(1, "abc") ::
       NullStrings(2, "ABC") ::
       NullStrings(3, null) :: Nil).toDF()
@@ -197,13 +197,13 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val tableName: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(TableName("test") :: Nil).toDF()
+    val df = sqlContext.sparkContext.parallelize(TableName("test") :: Nil).toDF()
     df.registerTempTable("tableName")
     df
   }
 
   protected lazy val unparsedStrings: RDD[String] = {
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       "1, A1, true, null" ::
       "2, B2, false, null" ::
       "3, C3, true, null" ::
@@ -212,13 +212,13 @@ private[sql] trait SQLTestData { self =>
 
   // An RDD with 4 elements and 8 partitions
   protected lazy val withEmptyParts: RDD[IntField] = {
-    val rdd = _sqlContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
+    val rdd = sqlContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
     rdd.toDF().registerTempTable("withEmptyParts")
     rdd
   }
 
   protected lazy val person: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       Person(0, "mike", 30) ::
       Person(1, "jim", 20) :: Nil).toDF()
     df.registerTempTable("person")
@@ -226,7 +226,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val salary: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       Salary(0, 2000.0) ::
       Salary(1, 1000.0) :: Nil).toDF()
     df.registerTempTable("salary")
@@ -234,7 +234,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val complexData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true) ::
       ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false) ::
       Nil).toDF()
@@ -246,7 +246,7 @@ private[sql] trait SQLTestData { self =>
    * Initialize all test data such that all temp tables are properly registered.
    */
   def loadTestData(): Unit = {
-    assert(_sqlContext != null, "attempted to initialize test data before SQLContext.")
+    assert(sqlContext != null, "attempted to initialize test data before SQLContext.")
     emptyTestData
     testData
     testData2
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index dc08306ad9cb4..9214569f18e93 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -27,7 +27,7 @@ import org.apache.hadoop.conf.Configuration
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{DataFrame, Row, SQLContext, SQLImplicits}
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.util.Utils
@@ -47,13 +47,13 @@ private[sql] trait SQLTestUtils
   with BeforeAndAfterAll
   with SQLTestData { self =>
 
-  protected def _sqlContext: SQLContext
+  protected def sparkContext = sqlContext.sparkContext
 
   // Whether to materialize all test data before the first test is run
   private var loadTestDataBeforeTests = false
 
   // Shorthand for running a query using our SQLContext
-  protected lazy val sql = _sqlContext.sql _
+  protected lazy val sql = sqlContext.sql _
 
   /**
    * A helper object for importing SQL implicits.
@@ -63,7 +63,14 @@ private[sql] trait SQLTestUtils
    * but the implicits import is needed in the constructor.
    */
   protected object testImplicits extends SQLImplicits {
-    protected override def _sqlContext: SQLContext = self._sqlContext
+    protected override def _sqlContext: SQLContext = self.sqlContext
+
+    // This must live here to preserve binary compatibility with Spark < 1.5.
+    implicit class StringToColumn(val sc: StringContext) {
+      def $(args: Any*): ColumnName = {
+        new ColumnName(sc.s(args: _*))
+      }
+    }
   }
 
   /**
@@ -84,8 +91,8 @@ private[sql] trait SQLTestUtils
   /**
    * The Hadoop configuration used by the active [[SQLContext]].
    */
-  protected def configuration: Configuration = {
-    _sqlContext.sparkContext.hadoopConfiguration
+  protected def hadoopConfiguration: Configuration = {
+    sparkContext.hadoopConfiguration
   }
 
   /**
@@ -96,12 +103,12 @@ private[sql] trait SQLTestUtils
    */
   protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
     val (keys, values) = pairs.unzip
-    val currentValues = keys.map(key => Try(_sqlContext.conf.getConfString(key)).toOption)
-    (keys, values).zipped.foreach(_sqlContext.conf.setConfString)
+    val currentValues = keys.map(key => Try(sqlContext.conf.getConfString(key)).toOption)
+    (keys, values).zipped.foreach(sqlContext.conf.setConfString)
     try f finally {
       keys.zip(currentValues).foreach {
-        case (key, Some(value)) => _sqlContext.conf.setConfString(key, value)
-        case (key, None) => _sqlContext.conf.unsetConf(key)
+        case (key, Some(value)) => sqlContext.conf.setConfString(key, value)
+        case (key, None) => sqlContext.conf.unsetConf(key)
       }
     }
   }
@@ -133,7 +140,7 @@ private[sql] trait SQLTestUtils
    * Drops temporary table `tableName` after calling `f`.
    */
   protected def withTempTable(tableNames: String*)(f: => Unit): Unit = {
-    try f finally tableNames.foreach(_sqlContext.dropTempTable)
+    try f finally tableNames.foreach(sqlContext.dropTempTable)
   }
 
   /**
@@ -142,7 +149,7 @@ private[sql] trait SQLTestUtils
   protected def withTable(tableNames: String*)(f: => Unit): Unit = {
     try f finally {
       tableNames.foreach { name =>
-        _sqlContext.sql(s"DROP TABLE IF EXISTS $name")
+        sqlContext.sql(s"DROP TABLE IF EXISTS $name")
       }
     }
   }
@@ -155,12 +162,12 @@ private[sql] trait SQLTestUtils
     val dbName = s"db_${UUID.randomUUID().toString.replace('-', '_')}"
 
     try {
-      _sqlContext.sql(s"CREATE DATABASE $dbName")
+      sqlContext.sql(s"CREATE DATABASE $dbName")
     } catch { case cause: Throwable =>
       fail("Failed to create temporary database", cause)
     }
 
-    try f(dbName) finally _sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
+    try f(dbName) finally sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
   }
 
   /**
@@ -168,8 +175,8 @@ private[sql] trait SQLTestUtils
    * `f` returns.
    */
   protected def activateDatabase(db: String)(f: => Unit): Unit = {
-    _sqlContext.sql(s"USE $db")
-    try f finally _sqlContext.sql(s"USE default")
+    sqlContext.sql(s"USE $db")
+    try f finally sqlContext.sql(s"USE default")
   }
 
   /**
@@ -177,7 +184,7 @@ private[sql] trait SQLTestUtils
    * way to construct [[DataFrame]] directly out of local data without relying on implicits.
    */
   protected implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
-    DataFrame(_sqlContext, plan)
+    DataFrame(sqlContext, plan)
   }
 }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
index d23c6a0732669..963d10eed62ed 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.test
 
-import org.apache.spark.sql.{ColumnName, SQLContext}
+import org.apache.spark.sql.SQLContext
 
 
 /**
@@ -36,9 +36,7 @@ trait SharedSQLContext extends SQLTestUtils {
   /**
    * The [[TestSQLContext]] to use for all tests in this suite.
    */
-  protected def ctx: TestSQLContext = _ctx
-  protected def sqlContext: TestSQLContext = _ctx
-  protected override def _sqlContext: SQLContext = _ctx
+  protected def sqlContext: SQLContext = _ctx
 
   /**
    * Initialize the [[TestSQLContext]].
@@ -64,15 +62,4 @@ trait SharedSQLContext extends SQLTestUtils {
       super.afterAll()
     }
   }
-
-  /**
-   * Converts $"col name" into an [[Column]].
-   * @since 1.3.0
-   */
-  // This must be duplicated here to preserve binary compatibility with Spark < 1.5.
-  implicit class StringToColumn(val sc: StringContext) {
-    def $(args: Any*): ColumnName = {
-      new ColumnName(sc.s(args: _*))
-    }
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
index 92ef2f7d74ba1..d99d191ebe81e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -47,6 +47,6 @@ private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { sel
   }
 
   private object testData extends SQLTestData {
-    protected override def _sqlContext: SQLContext = self
+    protected override def sqlContext: SQLContext = self
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 57fea5d8db343..77f43f92705d8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -29,7 +29,7 @@ import org.apache.hadoop.hive.ql.exec.FunctionRegistry
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
 
-import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.{SQLContext, SQLConf}
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.CacheTableCommand
@@ -51,6 +51,11 @@ object TestHive
         // SPARK-8910
         .set("spark.ui.enabled", "false")))
 
+trait TestHiveSingleton {
+  protected val sqlContext: SQLContext = TestHive
+  protected val hiveContext: TestHiveContext = TestHive
+}
+
 /**
  * A locally running test instance of Spark's Hive execution engine.
  *
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
index 39d315aaeab57..9adb3780a2c55 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
@@ -19,14 +19,14 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
-import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.{SaveMode, AnalysisException, DataFrame, QueryTest}
+import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.{AnalysisException, QueryTest, SaveMode}
 import org.apache.spark.storage.RDDBlockId
 import org.apache.spark.util.Utils
 
-class CachedTableSuite extends QueryTest {
+class CachedTableSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext._
 
   def rddIdOf(tableName: String): Int = {
     val executedPlan = table(tableName).queryExecution.executedPlan
@@ -95,18 +95,18 @@ class CachedTableSuite extends QueryTest {
 
   test("correct error on uncache of non-cached table") {
     intercept[IllegalArgumentException] {
-      TestHive.uncacheTable("src")
+      hiveContext.uncacheTable("src")
     }
   }
 
   test("'CACHE TABLE' and 'UNCACHE TABLE' HiveQL statement") {
-    TestHive.sql("CACHE TABLE src")
+    sql("CACHE TABLE src")
     assertCached(table("src"))
-    assert(TestHive.isCached("src"), "Table 'src' should be cached")
+    assert(hiveContext.isCached("src"), "Table 'src' should be cached")
 
-    TestHive.sql("UNCACHE TABLE src")
+    sql("UNCACHE TABLE src")
     assertCached(table("src"), 0)
-    assert(!TestHive.isCached("src"), "Table 'src' should not be cached")
+    assert(!hiveContext.isCached("src"), "Table 'src' should not be cached")
   }
 
   test("CACHE TABLE tableName AS SELECT * FROM anotherTable") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
index 30f5313d2b812..cf737836939f9 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
@@ -22,12 +22,12 @@ import scala.util.Try
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.catalyst.util.quietly
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.{AnalysisException, QueryTest}
 
 
-class ErrorPositionSuite extends QueryTest with BeforeAndAfter {
+class ErrorPositionSuite extends QueryTest with TestHiveSingleton with BeforeAndAfter {
+  import hiveContext.implicits._
 
   before {
     Seq((1, 1, 1)).toDF("a", "a", "b").registerTempTable("dupAttributes")
@@ -122,7 +122,7 @@ class ErrorPositionSuite extends QueryTest with BeforeAndAfter {
 
     test(name) {
       val error = intercept[AnalysisException] {
-        quietly(sql(query))
+        quietly(hiveContext.sql(query))
       }
 
       assert(!error.getMessage.contains("Seq("))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala
index fb10f8583da99..2e5cae415e54b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala
@@ -19,24 +19,25 @@ package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.{DataFrame, QueryTest}
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.scalatest.BeforeAndAfterAll
 
 // TODO ideally we should put the test suite into the package `sql`, as
 // `hive` package is optional in compiling, however, `SQLContext.sql` doesn't
 // support the `cube` or `rollup` yet.
-class HiveDataFrameAnalyticsSuite extends QueryTest with BeforeAndAfterAll {
+class HiveDataFrameAnalyticsSuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext.implicits._
+  import hiveContext.sql
+
   private var testData: DataFrame = _
 
   override def beforeAll() {
     testData = Seq((1, 2), (2, 4)).toDF("a", "b")
-    TestHive.registerDataFrameAsTable(testData, "mytable")
+    hiveContext.registerDataFrameAsTable(testData, "mytable")
   }
 
   override def afterAll(): Unit = {
-    TestHive.dropTempTable("mytable")
+    hiveContext.dropTempTable("mytable")
   }
 
   test("rollup") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
index 52e782768cb75..f621367eb553b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
@@ -18,10 +18,10 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.{Row, QueryTest}
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-
-class HiveDataFrameJoinSuite extends QueryTest {
+class HiveDataFrameJoinSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext.implicits._
 
   // We should move this into SQL package if we make case sensitivity configurable in SQL.
   test("join - self join auto resolve ambiguity with case insensitivity") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
index c177cbdd991cf..2c98f1c3cc49c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
@@ -20,10 +20,11 @@ package org.apache.spark.sql.hive
 import org.apache.spark.sql.{Row, QueryTest}
 import org.apache.spark.sql.expressions.Window
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-class HiveDataFrameWindowSuite extends QueryTest {
+class HiveDataFrameWindowSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext.implicits._
+  import hiveContext.sql
 
   test("reuse window partitionBy") {
     val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 574624d501f22..107457f79ec03 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -19,18 +19,15 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.{QueryTest, Row, SaveMode}
 import org.apache.spark.sql.hive.client.{ExternalTable, ManagedTable}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.sources.DataSourceTest
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
 import org.apache.spark.sql.types.{DecimalType, StringType, StructType}
-import org.apache.spark.sql.{Row, SaveMode, SQLContext}
-import org.apache.spark.{Logging, SparkFunSuite}
 
-
-class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
+class HiveMetastoreCatalogSuite extends SparkFunSuite with TestHiveSingleton {
+  import hiveContext.implicits._
 
   test("struct field should accept underscore in sub-column name") {
     val hiveTypeStr = "struct<a: int, b_1: string, c: string>"
@@ -46,14 +43,15 @@ class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
   }
 
   test("duplicated metastore relations") {
-    val df = sql("SELECT * FROM src")
+    val df = hiveContext.sql("SELECT * FROM src")
     logInfo(df.queryExecution.toString)
     df.as('a).join(df.as('b), $"a.key" === $"b.key")
   }
 }
 
-class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
+class DataSourceWithHiveMetastoreCatalogSuite
+  extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext._
   import testImplicits._
 
   private val testDF = range(1, 3).select(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
index fe0db5228de16..5596ec6882ea2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
@@ -17,15 +17,13 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.execution.datasources.parquet.ParquetTest
-import org.apache.spark.sql.{QueryTest, Row, SQLContext}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.{QueryTest, Row}
 
 case class Cases(lower: String, UPPER: String)
 
-class HiveParquetSuite extends QueryTest with ParquetTest {
-  private val ctx = TestHive
-  override def _sqlContext: SQLContext = ctx
+class HiveParquetSuite extends QueryTest with ParquetTest with TestHiveSingleton {
 
   test("Case insensitive attribute names") {
     withParquetTable((1 to 4).map(i => Cases(i.toString, i.toString)), "cases") {
@@ -53,7 +51,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
   test("Converting Hive to Parquet Table via saveAsParquetFile") {
     withTempPath { dir =>
       sql("SELECT * FROM src").write.parquet(dir.getCanonicalPath)
-      ctx.read.parquet(dir.getCanonicalPath).registerTempTable("p")
+      hiveContext.read.parquet(dir.getCanonicalPath).registerTempTable("p")
       withTempTable("p") {
         checkAnswer(
           sql("SELECT * FROM src ORDER BY key"),
@@ -66,7 +64,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
     withParquetTable((1 to 10).map(i => (i, s"val_$i")), "t") {
       withTempPath { file =>
         sql("SELECT * FROM t LIMIT 1").write.parquet(file.getCanonicalPath)
-        ctx.read.parquet(file.getCanonicalPath).registerTempTable("p")
+        hiveContext.read.parquet(file.getCanonicalPath).registerTempTable("p")
         withTempTable("p") {
           // let's do three overwrites for good measure
           sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index dc2d85f48624c..84f3db44ecd8d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -29,7 +29,7 @@ import org.scalatest.exceptions.TestFailedDueToTimeoutException
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark._
-import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.{SQLContext, QueryTest}
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
 import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 import org.apache.spark.sql.types.DecimalType
@@ -272,7 +272,11 @@ object SparkSQLConfTest extends Logging {
   }
 }
 
-object SPARK_9757 extends QueryTest with Logging {
+object SPARK_9757 extends QueryTest {
+  import org.apache.spark.sql.functions._
+
+  protected var sqlContext: SQLContext = _
+
   def main(args: Array[String]): Unit = {
     Utils.configTestLog4j("INFO")
 
@@ -282,10 +286,9 @@ object SPARK_9757 extends QueryTest with Logging {
         .set("spark.sql.hive.metastore.jars", "maven"))
 
     val hiveContext = new TestHiveContext(sparkContext)
+    sqlContext = hiveContext
     import hiveContext.implicits._
 
-    import org.apache.spark.sql.functions._
-
     val dir = Utils.createTempDir()
     dir.delete()
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
index d33e81227db88..80a61f82fd24f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
@@ -24,28 +24,25 @@ import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.execution.QueryExecutionException
 import org.apache.spark.sql.{QueryTest, _}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-/* Implicits */
-import org.apache.spark.sql.hive.test.TestHive._
-
 case class TestData(key: Int, value: String)
 
 case class ThreeCloumntable(key: Int, value: String, key1: String)
 
-class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
-  import org.apache.spark.sql.hive.test.TestHive.implicits._
-
+class InsertIntoHiveTableSuite extends QueryTest with TestHiveSingleton with BeforeAndAfter {
+  import hiveContext.implicits._
+  import hiveContext.sql
 
-  val testData = TestHive.sparkContext.parallelize(
+  val testData = hiveContext.sparkContext.parallelize(
     (1 to 100).map(i => TestData(i, i.toString))).toDF()
 
   before {
     // Since every we are doing tests for DDL statements,
     // it is better to reset before every test.
-    TestHive.reset()
+    hiveContext.reset()
     // Register the testData, which will be used in every test.
     testData.registerTempTable("testData")
   }
@@ -96,9 +93,9 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
 
   test("SPARK-4052: scala.collection.Map as value type of MapType") {
     val schema = StructType(StructField("m", MapType(StringType, StringType), true) :: Nil)
-    val rowRDD = TestHive.sparkContext.parallelize(
+    val rowRDD = hiveContext.sparkContext.parallelize(
       (1 to 100).map(i => Row(scala.collection.mutable.HashMap(s"key$i" -> s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithMapValue")
     sql("CREATE TABLE hiveTableWithMapValue(m MAP <STRING, STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithMapValue SELECT m FROM tableWithMapValue")
@@ -169,8 +166,8 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("Insert ArrayType.containsNull == false") {
     val schema = StructType(Seq(
       StructField("a", ArrayType(StringType, containsNull = false))))
-    val rowRDD = TestHive.sparkContext.parallelize((1 to 100).map(i => Row(Seq(s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val rowRDD = hiveContext.sparkContext.parallelize((1 to 100).map(i => Row(Seq(s"value$i"))))
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithArrayValue")
     sql("CREATE TABLE hiveTableWithArrayValue(a Array <STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithArrayValue SELECT a FROM tableWithArrayValue")
@@ -185,9 +182,9 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("Insert MapType.valueContainsNull == false") {
     val schema = StructType(Seq(
       StructField("m", MapType(StringType, StringType, valueContainsNull = false))))
-    val rowRDD = TestHive.sparkContext.parallelize(
+    val rowRDD = hiveContext.sparkContext.parallelize(
       (1 to 100).map(i => Row(Map(s"key$i" -> s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithMapValue")
     sql("CREATE TABLE hiveTableWithMapValue(m Map <STRING, STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithMapValue SELECT m FROM tableWithMapValue")
@@ -202,9 +199,9 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("Insert StructType.fields.exists(_.nullable == false)") {
     val schema = StructType(Seq(
       StructField("s", StructType(Seq(StructField("f", StringType, nullable = false))))))
-    val rowRDD = TestHive.sparkContext.parallelize(
+    val rowRDD = hiveContext.sparkContext.parallelize(
       (1 to 100).map(i => Row(Row(s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithStructValue")
     sql("CREATE TABLE hiveTableWithStructValue(s Struct <f: STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithStructValue SELECT s FROM tableWithStructValue")
@@ -217,11 +214,11 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   }
 
   test("SPARK-5498:partition schema does not match table schema") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       (1 to 10).map(i => TestData(i, i.toString))).toDF()
     testData.registerTempTable("testData")
 
-    val testDatawithNull = TestHive.sparkContext.parallelize(
+    val testDatawithNull = hiveContext.sparkContext.parallelize(
       (1 to 10).map(i => ThreeCloumntable(i, i.toString, null))).toDF()
 
     val tmpDir = Utils.createTempDir()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
index d3388a9429e41..579631df772b5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
@@ -19,17 +19,15 @@ package org.apache.spark.sql.hive
 
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.Row
 
-class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
+class ListTablesSuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext._
+  import hiveContext.implicits._
 
-  import org.apache.spark.sql.hive.test.TestHive.implicits._
-
-  val df =
-    sparkContext.parallelize((1 to 10).map(i => (i, s"str$i"))).toDF("key", "value")
+  val df = sparkContext.parallelize((1 to 10).map(i => (i, s"str$i"))).toDF("key", "value")
 
   override def beforeAll(): Unit = {
     // The catalog in HiveContext is a case insensitive one.
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index 20a50586d5201..bf0db084906cd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -22,15 +22,11 @@ import java.io.{IOException, File}
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.fs.Path
-import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.Logging
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
@@ -39,10 +35,9 @@ import org.apache.spark.util.Utils
 /**
  * Tests for persisting tables created though the data sources API into the metastore.
  */
-class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll
-  with Logging {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext._
+  import hiveContext.implicits._
 
   var jsonFilePath: String = _
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
index 997c667ec0d1b..f16c257ab5ab4 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
@@ -17,20 +17,16 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.{AnalysisException, QueryTest, SQLContext, SaveMode}
+import org.apache.spark.sql.{AnalysisException, QueryTest, SaveMode}
 
-class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
-  override val _sqlContext: HiveContext = TestHive
-  private val sqlContext = _sqlContext
-
-  private val df = sqlContext.range(10).coalesce(1)
+class MultiDatabaseSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  private lazy val df = sqlContext.range(10).coalesce(1)
 
   private def checkTablePath(dbName: String, tableName: String): Unit = {
-    // val hiveContext = sqlContext.asInstanceOf[HiveContext]
-    val metastoreTable = sqlContext.catalog.client.getTable(dbName, tableName)
-    val expectedPath = sqlContext.catalog.client.getDatabase(dbName).location + "/" + tableName
+    val metastoreTable = hiveContext.catalog.client.getTable(dbName, tableName)
+    val expectedPath = hiveContext.catalog.client.getDatabase(dbName).location + "/" + tableName
 
     assert(metastoreTable.serdeProperties("path") === expectedPath)
   }
@@ -220,7 +216,7 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
           df.write.parquet(s"$path/p=2")
           sql("ALTER TABLE t ADD PARTITION (p=2)")
-          sqlContext.refreshTable("t")
+          hiveContext.refreshTable("t")
           checkAnswer(
             sqlContext.table("t"),
             df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
@@ -252,7 +248,7 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
         df.write.parquet(s"$path/p=2")
         sql(s"ALTER TABLE $db.t ADD PARTITION (p=2)")
-        sqlContext.refreshTable(s"$db.t")
+        hiveContext.refreshTable(s"$db.t")
         checkAnswer(
           sqlContext.table(s"$db.t"),
           df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 91d7a48208e8d..49aab85cf1aaf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -18,38 +18,20 @@
 package org.apache.spark.sql.hive
 
 import java.sql.Timestamp
-import java.util.{Locale, TimeZone}
 
 import org.apache.hadoop.hive.conf.HiveConf
-import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.execution.datasources.parquet.ParquetCompatibilityTest
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.{Row, SQLConf, SQLContext}
-
-class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with BeforeAndAfterAll {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+import org.apache.spark.sql.{Row, SQLConf}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
+class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with TestHiveSingleton {
   /**
    * Set the staging directory (and hence path to ignore Parquet files under)
    * to that set by [[HiveConf.ConfVars.STAGINGDIR]].
    */
   private val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
 
-  private val originalTimeZone = TimeZone.getDefault
-  private val originalLocale = Locale.getDefault
-
-  protected override def beforeAll(): Unit = {
-    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
-    Locale.setDefault(Locale.US)
-  }
-
-  override protected def afterAll(): Unit = {
-    TimeZone.setDefault(originalTimeZone)
-    Locale.setDefault(originalLocale)
-  }
-
   override protected def logParquetSchema(path: String): Unit = {
     val schema = readParquetSchema(path, { path =>
       !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
index 1cc8a93e83411..f542a5a02508c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
@@ -18,22 +18,18 @@
 package org.apache.spark.sql.hive
 
 import com.google.common.io.Files
-import org.apache.spark.sql.test.SQLTestUtils
 
-import org.apache.spark.sql.{QueryTest, _}
 import org.apache.spark.util.Utils
+import org.apache.spark.sql.{QueryTest, _}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.test.SQLTestUtils
 
+class QueryPartitionSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext.implicits._
 
-class QueryPartitionSuite extends QueryTest with SQLTestUtils {
-
-  private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
-  import ctx.implicits._
-
-  protected def _sqlContext = ctx
-
-  test("SPARK-5068: query data when path doesn't exist"){
+  test("SPARK-5068: query data when path doesn't exist") {
     withSQLConf((SQLConf.HIVE_VERIFY_PARTITION_PATH.key, "true")) {
-      val testData = ctx.sparkContext.parallelize(
+      val testData = sparkContext.parallelize(
         (1 to 10).map(i => TestData(i, i.toString))).toDF()
       testData.registerTempTable("testData")
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
index e4fec7e2c8a2a..6a692d6fce562 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -17,24 +17,15 @@
 
 package org.apache.spark.sql.hive
 
-import org.scalatest.BeforeAndAfterAll
-
 import scala.reflect.ClassTag
 
 import org.apache.spark.sql.{Row, SQLConf, QueryTest}
 import org.apache.spark.sql.execution.joins._
 import org.apache.spark.sql.hive.execution._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
-
-  private lazy val ctx: HiveContext = {
-    val ctx = org.apache.spark.sql.hive.test.TestHive
-    ctx.reset()
-    ctx.cacheTables = false
-    ctx
-  }
-
-  import ctx.sql
+class StatisticsSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext.sql
 
   test("parse analyze commands") {
     def assertAnalyzeCommand(analyzeCommand: String, c: Class[_]) {
@@ -54,9 +45,6 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
       }
     }
 
-    // Ensure session state is initialized.
-    ctx.parseSql("use default")
-
     assertAnalyzeCommand(
       "ANALYZE TABLE Table1 COMPUTE STATISTICS",
       classOf[HiveNativeCommand])
@@ -80,7 +68,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
 
   test("analyze MetastoreRelations") {
     def queryTotalSize(tableName: String): BigInt =
-      ctx.catalog.lookupRelation(Seq(tableName)).statistics.sizeInBytes
+      hiveContext.catalog.lookupRelation(Seq(tableName)).statistics.sizeInBytes
 
     // Non-partitioned table
     sql("CREATE TABLE analyzeTable (key STRING, value STRING)").collect()
@@ -114,7 +102,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
         |SELECT * FROM src
       """.stripMargin).collect()
 
-    assert(queryTotalSize("analyzeTable_part") === ctx.conf.defaultSizeInBytes)
+    assert(queryTotalSize("analyzeTable_part") === hiveContext.conf.defaultSizeInBytes)
 
     sql("ANALYZE TABLE analyzeTable_part COMPUTE STATISTICS noscan")
 
@@ -125,9 +113,9 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
     // Try to analyze a temp table
     sql("""SELECT * FROM src""").registerTempTable("tempTable")
     intercept[UnsupportedOperationException] {
-      ctx.analyze("tempTable")
+      hiveContext.analyze("tempTable")
     }
-    ctx.catalog.unregisterTable(Seq("tempTable"))
+    hiveContext.catalog.unregisterTable(Seq("tempTable"))
   }
 
   test("estimates the size of a test MetastoreRelation") {
@@ -155,8 +143,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
       val sizes = df.queryExecution.analyzed.collect {
         case r if ct.runtimeClass.isAssignableFrom(r.getClass) => r.statistics.sizeInBytes
       }
-      assert(sizes.size === 2 && sizes(0) <= ctx.conf.autoBroadcastJoinThreshold
-        && sizes(1) <= ctx.conf.autoBroadcastJoinThreshold,
+      assert(sizes.size === 2 && sizes(0) <= hiveContext.conf.autoBroadcastJoinThreshold
+        && sizes(1) <= hiveContext.conf.autoBroadcastJoinThreshold,
         s"query should contain two relations, each of which has size smaller than autoConvertSize")
 
       // Using `sparkPlan` because for relevant patterns in HashJoin to be
@@ -167,8 +155,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
 
       checkAnswer(df, expectedAnswer) // check correctness of output
 
-      ctx.conf.settings.synchronized {
-        val tmp = ctx.conf.autoBroadcastJoinThreshold
+      hiveContext.conf.settings.synchronized {
+        val tmp = hiveContext.conf.autoBroadcastJoinThreshold
 
         sql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1""")
         df = sql(query)
@@ -211,8 +199,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
         .isAssignableFrom(r.getClass) =>
         r.statistics.sizeInBytes
     }
-    assert(sizes.size === 2 && sizes(1) <= ctx.conf.autoBroadcastJoinThreshold
-      && sizes(0) <= ctx.conf.autoBroadcastJoinThreshold,
+    assert(sizes.size === 2 && sizes(1) <= hiveContext.conf.autoBroadcastJoinThreshold
+      && sizes(0) <= hiveContext.conf.autoBroadcastJoinThreshold,
       s"query should contain two relations, each of which has size smaller than autoConvertSize")
 
     // Using `sparkPlan` because for relevant patterns in HashJoin to be
@@ -225,8 +213,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
 
     checkAnswer(df, answer) // check correctness of output
 
-    ctx.conf.settings.synchronized {
-      val tmp = ctx.conf.autoBroadcastJoinThreshold
+    hiveContext.conf.settings.synchronized {
+      val tmp = hiveContext.conf.autoBroadcastJoinThreshold
 
       sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1")
       df = sql(leftSemiJoinQuery)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
index 7ee1c8d13aa3f..3ab4576811194 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
@@ -18,18 +18,18 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 case class FunctionResult(f1: String, f2: String)
 
-class UDFSuite extends QueryTest {
-  private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
+class UDFSuite extends QueryTest with TestHiveSingleton {
 
   test("UDF case insensitive") {
-    ctx.udf.register("random0", () => { Math.random() })
-    ctx.udf.register("RANDOM1", () => { Math.random() })
-    ctx.udf.register("strlenScala", (_: String).length + (_: Int))
-    assert(ctx.sql("SELECT RANDOM0() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
-    assert(ctx.sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
-    assert(ctx.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5)
+    hiveContext.udf.register("random0", () => { Math.random() })
+    hiveContext.udf.register("RANDOM1", () => { Math.random() })
+    hiveContext.udf.register("strlenScala", (_: String).length + (_: Int))
+    assert(hiveContext.sql("SELECT RANDOM0() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
+    assert(hiveContext.sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
+    assert(hiveContext.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5)
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 4886a85948367..b126ec455fc69 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -17,19 +17,15 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.aggregate
-import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
 import org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-  override def _sqlContext: SQLContext = TestHive
-  protected val sqlContext = _sqlContext
-  import sqlContext.implicits._
+abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import testImplicits._
 
   var originalUseAggregate2: Boolean = _
 
@@ -69,7 +65,7 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
     data2.write.saveAsTable("agg2")
 
     val emptyDF = sqlContext.createDataFrame(
-      sqlContext.sparkContext.emptyRDD[Row],
+      sparkContext.emptyRDD[Row],
       StructType(StructField("key", StringType) :: StructField("value", IntegerType) :: Nil))
     emptyDF.registerTempTable("emptyTable")
 
@@ -597,7 +593,7 @@ class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQue
     sqlContext.conf.unsetConf("spark.sql.TungstenAggregate.testFallbackStartsAt")
   }
 
-  override protected def checkAnswer(actual: DataFrame, expectedAnswer: Seq[Row]): Unit = {
+  override protected def checkAnswer(actual: => DataFrame, expectedAnswer: Seq[Row]): Unit = {
     (0 to 2).foreach { fallbackStartsAt =>
       sqlContext.setConf(
         "spark.sql.TungstenAggregate.testFallbackStartsAt",
@@ -605,6 +601,7 @@ class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQue
 
       // Create a new df to make sure its physical operator picks up
       // spark.sql.TungstenAggregate.testFallbackStartsAt.
+      // todo: remove it?
       val newActual = DataFrame(sqlContext, actual.logicalPlan)
 
       QueryTest.checkAnswer(newActual, expectedAnswer) match {
@@ -626,12 +623,12 @@ class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQue
   }
 
   // Override it to make sure we call the actually overridden checkAnswer.
-  override protected def checkAnswer(df: DataFrame, expectedAnswer: Row): Unit = {
+  override protected def checkAnswer(df: => DataFrame, expectedAnswer: Row): Unit = {
     checkAnswer(df, Seq(expectedAnswer))
   }
 
   // Override it to make sure we call the actually overridden checkAnswer.
-  override protected def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = {
+  override protected def checkAnswer(df: => DataFrame, expectedAnswer: DataFrame): Unit = {
     checkAnswer(df, expectedAnswer.collect())
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 4d45249d9c6b8..aa95ba94fa873 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -23,7 +23,7 @@ import scala.util.control.NonFatal
 
 import org.scalatest.{BeforeAndAfterAll, GivenWhenThen}
 
-import org.apache.spark.{Logging, SparkFunSuite}
+import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.util._
@@ -42,7 +42,7 @@ import org.apache.spark.sql.hive.test.TestHive
  * configured using system properties.
  */
 abstract class HiveComparisonTest
-  extends SparkFunSuite with BeforeAndAfterAll with GivenWhenThen with Logging {
+  extends SparkFunSuite with BeforeAndAfterAll with GivenWhenThen {
 
   /**
    * When set, any cache files that result in test failures will be deleted.  Used when the test
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 11d7a872dff09..94162da4eae1a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -17,17 +17,14 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.{SQLContext, QueryTest}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 /**
  * A set of tests that validates support for Hive Explain command.
  */
-class HiveExplainSuite extends QueryTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class HiveExplainSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
 
   test("explain extended command") {
     checkExistence(sql(" explain   select * from src where key=123 "), true,
@@ -83,7 +80,7 @@ class HiveExplainSuite extends QueryTest with SQLTestUtils {
   test("SPARK-6212: The EXPLAIN output of CTAS only shows the analyzed plan") {
     withTempTable("jt") {
       val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
-      read.json(rdd).registerTempTable("jt")
+      hiveContext.read.json(rdd).registerTempTable("jt")
       val outputs = sql(
         s"""
            |EXPLAIN EXTENDED
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala
index efbef68cd4447..0d4c7f86b315a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala
@@ -18,14 +18,16 @@
 package org.apache.spark.sql.hive.execution
 
 import org.apache.spark.sql.{Row, QueryTest}
-import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.hive.test.{TestHive, TestHiveSingleton}
 
 /**
  * A set of tests that validates commands can also be queried by like a table
  */
-class HiveOperatorQueryableSuite extends QueryTest {
+class HiveOperatorQueryableSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext._
+
   test("SPARK-5324 query result of describe command") {
-    loadTestTable("src")
+    hiveContext.loadTestTable("src")
 
     // register a describe command to be a temp table
     sql("desc src").registerTempTable("mydesc")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
index ba56a8a6b689c..cd055f9eca37e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
@@ -21,11 +21,11 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.expressions.Window
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-class HivePlanTest extends QueryTest {
-  import TestHive._
-  import TestHive.implicits._
+class HivePlanTest extends QueryTest with TestHiveSingleton {
+  import hiveContext.sql
+  import hiveContext.implicits._
 
   test("udf constant folding") {
     Seq.empty[Tuple1[Int]].toDF("a").registerTempTable("t")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 9c10ffe1113dc..d9ba895e1eceb 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectIns
 import org.apache.hadoop.hive.serde2.{AbstractSerDe, SerDeStats}
 import org.apache.hadoop.io.Writable
 import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 import org.apache.spark.util.Utils
 
@@ -43,10 +43,10 @@ case class ListStringCaseClass(l: Seq[String])
 /**
  * A test suite for Hive custom UDFs.
  */
-class HiveUDFSuite extends QueryTest {
+class HiveUDFSuite extends QueryTest with TestHiveSingleton {
 
-  import TestHive.{udf, sql}
-  import TestHive.implicits._
+  import hiveContext.{udf, sql}
+  import hiveContext.implicits._
 
   test("spark sql udf test that returns a struct") {
     udf.register("getStruct", (_: Int) => Fields(1, 2, 3, 4, 5))
@@ -123,12 +123,12 @@ class HiveUDFSuite extends QueryTest {
           |           "value", value)).value FROM src
         """.stripMargin), Seq(Row("val_0")))
     }
-    val codegenDefault = TestHive.getConf(SQLConf.CODEGEN_ENABLED)
-    TestHive.setConf(SQLConf.CODEGEN_ENABLED, true)
+    val codegenDefault = hiveContext.getConf(SQLConf.CODEGEN_ENABLED)
+    hiveContext.setConf(SQLConf.CODEGEN_ENABLED, true)
     testOrderInStruct()
-    TestHive.setConf(SQLConf.CODEGEN_ENABLED, false)
+    hiveContext.setConf(SQLConf.CODEGEN_ENABLED, false)
     testOrderInStruct()
-    TestHive.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
+    hiveContext.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
   }
 
   test("SPARK-6409 UDAFAverage test") {
@@ -137,7 +137,7 @@ class HiveUDFSuite extends QueryTest {
       sql("SELECT test_avg(1), test_avg(substr(value,5)) FROM src"),
       Seq(Row(1.0, 260.182)))
     sql("DROP TEMPORARY FUNCTION IF EXISTS test_avg")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("SPARK-2693 udaf aggregates test") {
@@ -157,7 +157,7 @@ class HiveUDFSuite extends QueryTest {
    }
 
   test("UDFIntegerToString") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       IntegerCaseClass(1) :: IntegerCaseClass(2) :: Nil).toDF()
     testData.registerTempTable("integerTable")
 
@@ -168,11 +168,11 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row("1"), Row("2")))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFIntegerToString")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToListString") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToListString AS '${classOf[UDFToListString].getName}'")
@@ -183,11 +183,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListString")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToListInt") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToListInt AS '${classOf[UDFToListInt].getName}'")
@@ -198,11 +198,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListInt")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToStringIntMap") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToStringIntMap " +
@@ -214,11 +214,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToStringIntMap")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToIntIntMap") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToIntIntMap " +
@@ -230,11 +230,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToIntIntMap")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFListListInt") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       ListListIntCaseClass(Nil) ::
       ListListIntCaseClass(Seq((1, 2, 3))) ::
       ListListIntCaseClass(Seq((4, 5, 6), (7, 8, 9))) :: Nil).toDF()
@@ -246,11 +246,11 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row(0), Row(2), Row(13)))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListListInt")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFListString") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       ListStringCaseClass(Seq("a", "b", "c")) ::
       ListStringCaseClass(Seq("d", "e")) :: Nil).toDF()
     testData.registerTempTable("listStringTable")
@@ -261,11 +261,11 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row("a,b,c"), Row("d,e")))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListString")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFStringString") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       StringCaseClass("world") :: StringCaseClass("goodbye") :: Nil).toDF()
     testData.registerTempTable("stringTable")
 
@@ -280,11 +280,11 @@ class HiveUDFSuite extends QueryTest {
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUDF")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFTwoListList") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       ListListIntCaseClass(Nil) ::
       ListListIntCaseClass(Seq((1, 2, 3))) ::
       ListListIntCaseClass(Seq((4, 5, 6), (7, 8, 9))) ::
@@ -297,7 +297,7 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row("0, 0"), Row("2, 2"), Row("13, 13")))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFTwoListList")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 1ff1d9a2934cc..8126d02335217 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -26,9 +26,7 @@ import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.analysis.{FunctionRegistry, EliminateSubQueries}
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.datasources.LogicalRelation
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
@@ -65,12 +63,12 @@ class MyDialect extends DefaultParserDialect
  * Hive to generate them (in contrast to HiveQuerySuite).  Often this is because the query is
  * valid, but Hive currently cannot execute it.
  */
-class SQLQuerySuite extends QueryTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class SQLQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext._
+  import hiveContext.implicits._
 
   test("UDTF") {
-    sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
+    sql(s"ADD JAR ${hiveContext.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
     // The function source code can be found at:
     // https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF
     sql(
@@ -509,19 +507,19 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
     checkAnswer(
       sql("SELECT f1.f2.f3 FROM nested"),
       Row(1))
-    checkAnswer(sql("CREATE TABLE test_ctas_1234 AS SELECT * from nested"),
-      Seq.empty[Row])
+
+    sql("CREATE TABLE test_ctas_1234 AS SELECT * from nested")
     checkAnswer(
       sql("SELECT * FROM test_ctas_1234"),
       sql("SELECT * FROM nested").collect().toSeq)
 
     intercept[AnalysisException] {
-      sql("CREATE TABLE test_ctas_12345 AS SELECT * from notexists").collect()
+      sql("CREATE TABLE test_ctas_1234 AS SELECT * from notexists").collect()
     }
   }
 
   test("test CTAS") {
-    checkAnswer(sql("CREATE TABLE test_ctas_123 AS SELECT key, value FROM src"), Seq.empty[Row])
+    sql("CREATE TABLE test_ctas_123 AS SELECT key, value FROM src")
     checkAnswer(
       sql("SELECT key, value FROM test_ctas_123 ORDER BY key"),
       sql("SELECT key, value FROM src ORDER BY key").collect().toSeq)
@@ -614,7 +612,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
     val rowRdd = sparkContext.parallelize(row :: Nil)
 
-    TestHive.createDataFrame(rowRdd, schema).registerTempTable("testTable")
+    hiveContext.createDataFrame(rowRdd, schema).registerTempTable("testTable")
 
     sql(
       """CREATE TABLE nullValuesInInnerComplexTypes
@@ -1044,10 +1042,10 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
     val thread = new Thread {
       override def run() {
         // To make sure this test works, this jar should not be loaded in another place.
-        TestHive.sql(
-          s"ADD JAR ${TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath()}")
+        sql(
+          s"ADD JAR ${hiveContext.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath()}")
         try {
-          TestHive.sql(
+          sql(
             """
               |CREATE TEMPORARY FUNCTION example_max
               |AS 'org.apache.hadoop.hive.contrib.udaf.example.UDAFExampleMax'
@@ -1097,21 +1095,21 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
   test("SPARK-8588 HiveTypeCoercion.inConversion fires too early") {
     val df =
-      TestHive.createDataFrame(Seq((1, "2014-01-01"), (2, "2015-01-01"), (3, "2016-01-01")))
+      createDataFrame(Seq((1, "2014-01-01"), (2, "2015-01-01"), (3, "2016-01-01")))
     df.toDF("id", "datef").registerTempTable("test_SPARK8588")
     checkAnswer(
-      TestHive.sql(
+      sql(
         """
           |select id, concat(year(datef))
           |from test_SPARK8588 where concat(year(datef), ' year') in ('2015 year', '2014 year')
         """.stripMargin),
       Row(1, "2014") :: Row(2, "2015") :: Nil
     )
-    TestHive.dropTempTable("test_SPARK8588")
+    dropTempTable("test_SPARK8588")
   }
 
   test("SPARK-9371: fix the support for special chars in column names for hive context") {
-    TestHive.read.json(TestHive.sparkContext.makeRDD(
+    read.json(sparkContext.makeRDD(
       """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil))
       .registerTempTable("t")
 
@@ -1142,8 +1140,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   test("specifying database name for a temporary table is not allowed") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
-      val df =
-        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+      val df = sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
       df
         .write
         .format("parquet")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
index 9aca40f15ac15..cb8d0fca8e693 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
@@ -22,17 +22,14 @@ import org.scalatest.exceptions.TestFailedException
 
 import org.apache.spark.TaskContext
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan, SparkPlanTest}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.types.StringType
 
-class ScriptTransformationSuite extends SparkPlanTest {
-
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class ScriptTransformationSuite extends SparkPlanTest with TestHiveSingleton {
+  import hiveContext.implicits._
 
   private val noSerdeIOSchema = HiveScriptIOSchema(
     inputRowFormat = Seq.empty,
@@ -59,7 +56,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
         output = Seq(AttributeReference("a", StringType)()),
         child = child,
         ioschema = noSerdeIOSchema
-      )(TestHive),
+      )(hiveContext),
       rowsDf.collect())
   }
 
@@ -73,7 +70,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
         output = Seq(AttributeReference("a", StringType)()),
         child = child,
         ioschema = serdeIOSchema
-      )(TestHive),
+      )(hiveContext),
       rowsDf.collect())
   }
 
@@ -88,7 +85,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
           output = Seq(AttributeReference("a", StringType)()),
           child = ExceptionInjectingOperator(child),
           ioschema = noSerdeIOSchema
-        )(TestHive),
+        )(hiveContext),
         rowsDf.collect())
     }
     assert(e.getMessage().contains("intentional exception"))
@@ -105,7 +102,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
           output = Seq(AttributeReference("a", StringType)()),
           child = ExceptionInjectingOperator(child),
           ioschema = serdeIOSchema
-        )(TestHive),
+        )(hiveContext),
         rowsDf.collect())
     }
     assert(e.getMessage().contains("intentional exception"))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
index deec0048d24b8..9a299c3f9d1f3 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
@@ -24,10 +24,9 @@ import org.apache.spark.sql.sources.HadoopFsRelationTest
 import org.apache.spark.sql.types._
 
 class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = classOf[DefaultSource].getCanonicalName
+  import testImplicits._
 
-  import sqlContext._
-  import sqlContext.implicits._
+  override val dataSourceName: String = classOf[DefaultSource].getCanonicalName
 
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
@@ -48,7 +47,7 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.options(Map(
+        hiveContext.read.options(Map(
           "path" -> file.getCanonicalPath,
           "dataSchema" -> dataSchemaWithPartition.json)).format(dataSourceName).load())
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
index a46ca9a2c9706..52e09f9496f05 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
@@ -18,19 +18,17 @@
 package org.apache.spark.sql.hive.orc
 
 import java.io.File
-import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.util.Utils
-import org.scalatest.BeforeAndAfterAll
 
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
+import org.scalatest.BeforeAndAfterAll
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
+
+import org.apache.spark.sql._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.util.Utils
+
 // The data where the partitioning key exists only in the directory structure.
 case class OrcParData(intField: Int, stringField: String)
 
@@ -38,7 +36,10 @@ case class OrcParData(intField: Int, stringField: String)
 case class OrcParDataWithKey(intField: Int, pi: Int, stringField: String, ps: String)
 
 // TODO This test suite duplicates ParquetPartitionDiscoverySuite a lot
-class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
+class OrcPartitionDiscoverySuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext._
+  import hiveContext.implicits._
+
   val defaultPartitionName = ConfVars.DEFAULTPARTITIONNAME.defaultStrVal
 
   def withTempDir(f: File => Unit): Unit = {
@@ -58,7 +59,7 @@ class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
   }
 
   protected def withTempTable(tableName: String)(f: => Unit): Unit = {
-    try f finally TestHive.dropTempTable(tableName)
+    try f finally hiveContext.dropTempTable(tableName)
   }
 
   protected def makePartitionDir(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
index 80c38084f293d..7a34cf731b4c5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
@@ -21,12 +21,14 @@ import java.io.File
 
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 case class OrcData(intField: Int, stringField: String)
 
-abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
+abstract class OrcSuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext._
+
   var orcTableDir: File = null
   var orcTableAsDir: File = null
 
@@ -156,7 +158,7 @@ class OrcSourceSuite extends OrcSuite {
   override def beforeAll(): Unit = {
     super.beforeAll()
 
-    sql(
+    hiveContext.sql(
       s"""CREATE TEMPORARY TABLE normal_orc_source
          |USING org.apache.spark.sql.hive.orc
          |OPTIONS (
@@ -164,7 +166,7 @@ class OrcSourceSuite extends OrcSuite {
          |)
        """.stripMargin)
 
-    sql(
+    hiveContext.sql(
       s"""CREATE TEMPORARY TABLE normal_orc_as_source
          |USING org.apache.spark.sql.hive.orc
          |OPTIONS (
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
index f7ba20ff41d8d..88a0ed511749f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
@@ -22,15 +22,12 @@ import java.io.File
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
 import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-private[sql] trait OrcTest extends SQLTestUtils { this: SparkFunSuite =>
-  protected override def _sqlContext: SQLContext = org.apache.spark.sql.hive.test.TestHive
-  protected val sqlContext = _sqlContext
-  import sqlContext.implicits._
-  import sqlContext.sparkContext
+private[sql] trait OrcTest extends SQLTestUtils with TestHiveSingleton {
+  import testImplicits._
 
   /**
    * Writes `data` to a Orc file, which is then passed to `f` and will be deleted after `f`
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index 34d3434569f58..6842ec2b5eb37 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -19,15 +19,11 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation}
 import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD}
 import org.apache.spark.sql.hive.execution.HiveTableScan
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
@@ -58,6 +54,8 @@ case class ParquetDataWithKeyAndComplexTypes(
  * built in parquet support.
  */
 class ParquetMetastoreSuite extends ParquetPartitioningTest {
+  import hiveContext._
+
   override def beforeAll(): Unit = {
     super.beforeAll()
     dropTables("partitioned_parquet",
@@ -536,6 +534,9 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
  * A suite of tests for the Parquet support through the data sources API.
  */
 class ParquetSourceSuite extends ParquetPartitioningTest {
+  import testImplicits._
+  import hiveContext._
+
   override def beforeAll(): Unit = {
     super.beforeAll()
     dropTables("partitioned_parquet",
@@ -684,9 +685,8 @@ class ParquetSourceSuite extends ParquetPartitioningTest {
 /**
  * A collection of tests for parquet data with various forms of partitioning.
  */
-abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-  override def _sqlContext: SQLContext = TestHive
-  protected val sqlContext = _sqlContext
+abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import testImplicits._
 
   var partitionedTableDir: File = null
   var normalTableDir: File = null
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
index b4640b1616281..dc0531a6d4bc5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
@@ -18,16 +18,13 @@
 package org.apache.spark.sql.sources
 
 import org.apache.hadoop.fs.Path
-import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.SparkException
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.SQLTestUtils
 
 
-class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class CommitFailureTestRelationSuite extends SQLTestUtils with TestHiveSingleton  {
 
   // When committing a task, `CommitFailureTestSource` throws an exception for testing purpose.
   val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
index 8ca3a17085194..1945b15002337 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -28,8 +28,6 @@ import org.apache.spark.sql.types._
 class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = "json"
 
-  import sqlContext._
-
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
@@ -47,7 +45,7 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.format(dataSourceName)
+        hiveContext.read.format(dataSourceName)
           .option("dataSchema", dataSchemaWithPartition.json)
           .load(file.getCanonicalPath))
     }
@@ -65,14 +63,14 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
       val data =
         Row(Seq(1L, 2L, 3L), Map("m1" -> Row(4L))) ::
           Row(Seq(5L, 6L, 7L), Map("m2" -> Row(10L))) :: Nil
-      val df = createDataFrame(sparkContext.parallelize(data), schema)
+      val df = hiveContext.createDataFrame(sparkContext.parallelize(data), schema)
 
       // Write the data out.
       df.write.format(dataSourceName).save(file.getCanonicalPath)
 
       // Read it back and check the result.
       checkAnswer(
-        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        hiveContext.read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
         df
       )
     }
@@ -90,14 +88,14 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
         Row(new BigDecimal("10.02")) ::
           Row(new BigDecimal("20000.99")) ::
           Row(new BigDecimal("10000")) :: Nil
-      val df = createDataFrame(sparkContext.parallelize(data), schema)
+      val df = hiveContext.createDataFrame(sparkContext.parallelize(data), schema)
 
       // Write the data out.
       df.write.format(dataSourceName).save(file.getCanonicalPath)
 
       // Read it back and check the result.
       checkAnswer(
-        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        hiveContext.read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
         df
       )
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index 06dadbb5feab0..08c3c17973043 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -28,10 +28,9 @@ import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 
 class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = "parquet"
+  import testImplicits._
 
-  import sqlContext._
-  import sqlContext.implicits._
+  override val dataSourceName: String = "parquet"
 
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
@@ -51,7 +50,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.format(dataSourceName)
+        hiveContext.read.format(dataSourceName)
           .option("dataSchema", dataSchemaWithPartition.json)
           .load(file.getCanonicalPath))
     }
@@ -69,7 +68,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
         .format("parquet")
         .save(s"${dir.getCanonicalPath}/_temporary")
 
-      checkAnswer(read.format("parquet").load(dir.getCanonicalPath), df.collect())
+      checkAnswer(hiveContext.read.format("parquet").load(dir.getCanonicalPath), df.collect())
     }
   }
 
@@ -97,7 +96,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
 
       // This shouldn't throw anything.
       df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
-      checkAnswer(read.format("parquet").load(path), df)
+      checkAnswer(hiveContext.read.format("parquet").load(path), df)
     }
   }
 
@@ -107,7 +106,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
         // Parquet doesn't allow field names with spaces.  Here we are intentionally making an
         // exception thrown from the `ParquetRelation2.prepareForWriteJob()` method to trigger
         // the bug.  Please refer to spark-8079 for more details.
-        range(1, 10)
+        hiveContext.range(1, 10)
           .withColumnRenamed("id", "a b")
           .write
           .format("parquet")
@@ -125,7 +124,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
       val summaryPath = new Path(path, "_metadata")
       val commonSummaryPath = new Path(path, "_common_metadata")
 
-      val fs = summaryPath.getFileSystem(configuration)
+      val fs = summaryPath.getFileSystem(hadoopConfiguration)
       fs.delete(summaryPath, true)
       fs.delete(commonSummaryPath, true)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index e8975e5f5cd08..1125ca670107b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -25,8 +25,6 @@ import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
 
-  import sqlContext._
-
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
@@ -44,7 +42,7 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.format(dataSourceName)
+        hiveContext.read.format(dataSourceName)
           .option("dataSchema", dataSchemaWithPartition.json)
           .load(file.getCanonicalPath))
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 7966b43596e75..2ad2618dfc436 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -28,14 +28,12 @@ import org.apache.parquet.hadoop.ParquetOutputCommitter
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.LogicalRelation
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 
 
-abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  protected val sqlContext = _sqlContext
+abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with TestHiveSingleton {
   import sqlContext.implicits._
 
   val dataSourceName: String
@@ -504,17 +502,17 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-8578 specified custom output committer will not be used to append data") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
     try {
       val df = sqlContext.range(1, 10).toDF("i")
       withTempPath { dir =>
         df.write.mode("append").format(dataSourceName).save(dir.getCanonicalPath)
-        configuration.set(
+        hadoopConfiguration.set(
           SQLConf.OUTPUT_COMMITTER_CLASS.key,
           classOf[AlwaysFailOutputCommitter].getName)
         // Since Parquet has its own output committer setting, also set it
         // to AlwaysFailParquetOutputCommitter at here.
-        configuration.set("spark.sql.parquet.output.committer.class",
+        hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
           classOf[AlwaysFailParquetOutputCommitter].getName)
         // Because there data already exists,
         // this append should succeed because we will use the output committer associated
@@ -533,12 +531,12 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
         }
       }
       withTempPath { dir =>
-        configuration.set(
+        hadoopConfiguration.set(
           SQLConf.OUTPUT_COMMITTER_CLASS.key,
           classOf[AlwaysFailOutputCommitter].getName)
         // Since Parquet has its own output committer setting, also set it
         // to AlwaysFailParquetOutputCommitter at here.
-        configuration.set("spark.sql.parquet.output.committer.class",
+        hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
           classOf[AlwaysFailParquetOutputCommitter].getName)
         // Because there is no existing data,
         // this append will fail because AlwaysFailOutputCommitter is used when we do append
@@ -549,8 +547,8 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -570,7 +568,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-9899 Disable customized output committer when speculation is on") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
     val speculationEnabled =
       sqlContext.sparkContext.conf.getBoolean("spark.speculation", defaultValue = false)
 
@@ -580,7 +578,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
         sqlContext.sparkContext.conf.set("spark.speculation", "true")
 
         // Uses a customized output committer which always fails
-        configuration.set(
+        hadoopConfiguration.set(
           SQLConf.OUTPUT_COMMITTER_CLASS.key,
           classOf[AlwaysFailOutputCommitter].getName)
 
@@ -597,8 +595,8 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
       sqlContext.sparkContext.conf.set("spark.speculation", speculationEnabled.toString)
     }
   }

From 3339e6f674264e387ab6c4f9c356bd3bc7167221 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 4 Sep 2015 15:20:20 -0700
Subject: [PATCH 1334/1454] [SPARK-10450] [SQL] Minor improvements to
 readability / style / typos etc.

Author: Andrew Or <andrew@databricks.com>

Closes #8603 from andrewor14/minor-sql-changes.
---
 .../sql/catalyst/expressions/predicates.scala    |  2 +-
 .../sql/catalyst/planning/QueryPlanner.scala     |  2 +-
 .../spark/sql/catalyst/planning/patterns.scala   |  8 ++++----
 .../spark/sql/execution/SparkStrategies.scala    | 16 ++++++++--------
 .../sql/execution/joins/SortMergeJoin.scala      |  2 +-
 5 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 65706dba7d975..daefc016bc91c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -67,7 +67,7 @@ trait PredicateHelper {
 
   /**
    * Returns true if `expr` can be evaluated using only the output of `plan`.  This method
-   * can be used to determine when is is acceptable to move expression evaluation within a query
+   * can be used to determine when it is acceptable to move expression evaluation within a query
    * plan.
    *
    * For example consider a join between two relations R(a, b) and S(c, d).
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
index 73a21884a4710..56a3dd02f9ba3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
@@ -51,7 +51,7 @@ abstract class QueryPlanner[PhysicalPlan <: TreeNode[PhysicalPlan]] {
    * filled in automatically by the QueryPlanner using the other execution strategies that are
    * available.
    */
-  protected def planLater(plan: LogicalPlan) = this.plan(plan).next()
+  protected def planLater(plan: LogicalPlan): PhysicalPlan = this.plan(plan).next()
 
   def plan(plan: LogicalPlan): Iterator[PhysicalPlan] = {
     // Obviously a lot to do here still...
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index b9ca712c1ee1c..e8abcd63f7d85 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -195,8 +195,9 @@ object ExtractEquiJoinKeys extends Logging with PredicateHelper {
       // as join keys.
       val (joinPredicates, otherPredicates) =
         condition.map(splitConjunctivePredicates).getOrElse(Nil).partition {
-          case EqualTo(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
-            (canEvaluate(l, right) && canEvaluate(r, left)) => true
+          case EqualTo(l, r) =>
+            (canEvaluate(l, left) && canEvaluate(r, right)) ||
+            (canEvaluate(l, right) && canEvaluate(r, left))
           case _ => false
         }
 
@@ -204,10 +205,9 @@ object ExtractEquiJoinKeys extends Logging with PredicateHelper {
         case EqualTo(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
         case EqualTo(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
       }
-      val leftKeys = joinKeys.map(_._1)
-      val rightKeys = joinKeys.map(_._2)
 
       if (joinKeys.nonEmpty) {
+        val (leftKeys, rightKeys) = joinKeys.unzip
         logDebug(s"leftKeys:$leftKeys | rightKeys:$rightKeys")
         Some((joinType, leftKeys, rightKeys, otherPredicates.reduceOption(And), left, right))
       } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 4df53687a0731..2170bc73a0fd6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -87,7 +87,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         left: LogicalPlan,
         right: LogicalPlan,
         condition: Option[Expression],
-        side: joins.BuildSide) = {
+        side: joins.BuildSide): Seq[SparkPlan] = {
       val broadcastHashJoin = execution.joins.BroadcastHashJoin(
         leftKeys, rightKeys, side, planLater(left), planLater(right))
       condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
@@ -123,12 +123,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       // --- Outer joins --------------------------------------------------------------------------
 
       case ExtractEquiJoinKeys(
-             LeftOuter, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
+          LeftOuter, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, LeftOuter, condition, planLater(left), planLater(right)) :: Nil
 
       case ExtractEquiJoinKeys(
-             RightOuter, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
+          RightOuter, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
 
@@ -156,11 +156,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       // Aggregations that can be performed in two phases, before and after the shuffle.
       case PartialAggregation(
-             namedGroupingAttributes,
-             rewrittenAggregateExpressions,
-             groupingExpressions,
-             partialComputation,
-             child) if !canBeConvertedToNewAggregation(plan) =>
+          namedGroupingAttributes,
+          rewrittenAggregateExpressions,
+          groupingExpressions,
+          partialComputation,
+          child) if !canBeConvertedToNewAggregation(plan) =>
         execution.Aggregate(
           partial = false,
           namedGroupingAttributes,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 6b7322671d6b4..906f20d2a7289 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -238,7 +238,7 @@ private[joins] class SortMergeJoinScanner(
    * Advances the streamed input iterator and buffers all rows from the buffered input that
    * have matching keys.
    * @return true if the streamed iterator returned a row, false otherwise. If this returns true,
-   *         then [getStreamedRow and [[getBufferedMatches]] can be called to produce the outer
+   *         then [[getStreamedRow]] and [[getBufferedMatches]] can be called to produce the outer
    *         join results.
    */
   final def findNextOuterJoinRows(): Boolean = {

From b087d23e28004f1bfdf6d2cd3ff34ae58c8132df Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Fri, 4 Sep 2015 15:21:11 -0700
Subject: [PATCH 1335/1454] [SPARK-9669] [MESOS] Support PySpark on Mesos
 cluster mode.

Support running pyspark with cluster mode on Mesos!
This doesn't upload any scripts, so if running in a remote Mesos requires the user to specify the script from a available URI.

Author: Timothy Chen <tnachen@gmail.com>

Closes #8349 from tnachen/mesos_python.
---
 .../org/apache/spark/deploy/SparkSubmit.scala | 13 ++++--
 .../cluster/mesos/MesosClusterScheduler.scala | 42 +++++++++++++------
 docs/running-on-mesos.md                      |  2 +
 3 files changed, 41 insertions(+), 16 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 36e9750b86636..ad92f5635af35 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -319,9 +319,6 @@ object SparkSubmit {
 
     // The following modes are not supported or applicable
     (clusterManager, deployMode) match {
-      case (MESOS, CLUSTER) if args.isPython =>
-        printErrorAndExit("Cluster deploy mode is currently not supported for python " +
-          "applications on Mesos clusters.")
       case (MESOS, CLUSTER) if args.isR =>
         printErrorAndExit("Cluster deploy mode is currently not supported for R " +
           "applications on Mesos clusters.")
@@ -554,7 +551,15 @@ object SparkSubmit {
     if (isMesosCluster) {
       assert(args.useRest, "Mesos cluster mode is only supported through the REST submission API")
       childMainClass = "org.apache.spark.deploy.rest.RestSubmissionClient"
-      childArgs += (args.primaryResource, args.mainClass)
+      if (args.isPython) {
+        // Second argument is main class
+        childArgs += (args.primaryResource, "")
+        if (args.pyFiles != null) {
+          sysProps("spark.submit.pyFiles") = args.pyFiles
+        }
+      } else {
+        childArgs += (args.primaryResource, args.mainClass)
+      }
       if (args.childArgs != null) {
         childArgs ++= args.childArgs
       }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 07da9242b9922..a6d9374eb9e8c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -29,7 +29,6 @@ import org.apache.mesos.Protos.Environment.Variable
 import org.apache.mesos.Protos.TaskStatus.Reason
 import org.apache.mesos.Protos.{TaskState => MesosTaskState, _}
 import org.apache.mesos.{Scheduler, SchedulerDriver}
-
 import org.apache.spark.deploy.mesos.MesosDriverDescription
 import org.apache.spark.deploy.rest.{CreateSubmissionResponse, KillSubmissionResponse, SubmissionStatusResponse}
 import org.apache.spark.metrics.MetricsSystem
@@ -375,21 +374,20 @@ private[spark] class MesosClusterScheduler(
     val executorOpts = desc.schedulerProperties.map { case (k, v) => s"-D$k=$v" }.mkString(" ")
     envBuilder.addVariables(
       Variable.newBuilder().setName("SPARK_EXECUTOR_OPTS").setValue(executorOpts))
-    val cmdOptions = generateCmdOption(desc).mkString(" ")
     val dockerDefined = desc.schedulerProperties.contains("spark.mesos.executor.docker.image")
     val executorUri = desc.schedulerProperties.get("spark.executor.uri")
       .orElse(desc.command.environment.get("SPARK_EXECUTOR_URI"))
-    val appArguments = desc.command.arguments.mkString(" ")
-    val (executable, jar) = if (dockerDefined) {
+    // Gets the path to run spark-submit, and the path to the Mesos sandbox.
+    val (executable, sandboxPath) = if (dockerDefined) {
       // Application jar is automatically downloaded in the mounted sandbox by Mesos,
       // and the path to the mounted volume is stored in $MESOS_SANDBOX env variable.
-      ("./bin/spark-submit", s"$$MESOS_SANDBOX/${desc.jarUrl.split("/").last}")
+      ("./bin/spark-submit", "$MESOS_SANDBOX")
     } else if (executorUri.isDefined) {
       builder.addUris(CommandInfo.URI.newBuilder().setValue(executorUri.get).build())
       val folderBasename = executorUri.get.split('/').last.split('.').head
       val cmdExecutable = s"cd $folderBasename*; $prefixEnv bin/spark-submit"
-      val cmdJar = s"../${desc.jarUrl.split("/").last}"
-      (cmdExecutable, cmdJar)
+      // Sandbox path points to the parent folder as we chdir into the folderBasename.
+      (cmdExecutable, "..")
     } else {
       val executorSparkHome = desc.schedulerProperties.get("spark.mesos.executor.home")
         .orElse(conf.getOption("spark.home"))
@@ -398,30 +396,50 @@ private[spark] class MesosClusterScheduler(
           throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
         }
       val cmdExecutable = new File(executorSparkHome, "./bin/spark-submit").getCanonicalPath
-      val cmdJar = desc.jarUrl.split("/").last
-      (cmdExecutable, cmdJar)
+      // Sandbox points to the current directory by default with Mesos.
+      (cmdExecutable, ".")
     }
-    builder.setValue(s"$executable $cmdOptions $jar $appArguments")
+    val primaryResource = new File(sandboxPath, desc.jarUrl.split("/").last).toString()
+    val cmdOptions = generateCmdOption(desc, sandboxPath).mkString(" ")
+    val appArguments = desc.command.arguments.mkString(" ")
+    builder.setValue(s"$executable $cmdOptions $primaryResource $appArguments")
     builder.setEnvironment(envBuilder.build())
     conf.getOption("spark.mesos.uris").map { uris =>
       setupUris(uris, builder)
     }
+    desc.schedulerProperties.get("spark.mesos.uris").map { uris =>
+      setupUris(uris, builder)
+    }
+    desc.schedulerProperties.get("spark.submit.pyFiles").map { pyFiles =>
+      setupUris(pyFiles, builder)
+    }
     builder.build()
   }
 
-  private def generateCmdOption(desc: MesosDriverDescription): Seq[String] = {
+  private def generateCmdOption(desc: MesosDriverDescription, sandboxPath: String): Seq[String] = {
     var options = Seq(
       "--name", desc.schedulerProperties("spark.app.name"),
-      "--class", desc.command.mainClass,
       "--master", s"mesos://${conf.get("spark.master")}",
       "--driver-cores", desc.cores.toString,
       "--driver-memory", s"${desc.mem}M")
+
+    // Assume empty main class means we're running python
+    if (!desc.command.mainClass.equals("")) {
+      options ++= Seq("--class", desc.command.mainClass)
+    }
+
     desc.schedulerProperties.get("spark.executor.memory").map { v =>
       options ++= Seq("--executor-memory", v)
     }
     desc.schedulerProperties.get("spark.cores.max").map { v =>
       options ++= Seq("--total-executor-cores", v)
     }
+    desc.schedulerProperties.get("spark.submit.pyFiles").map { pyFiles =>
+      val formattedFiles = pyFiles.split(",")
+        .map { path => new File(sandboxPath, path.split("/").last).toString() }
+        .mkString(",")
+      options ++= Seq("--py-files", formattedFiles)
+    }
     options
   }
 
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index f36921ae30c2f..247e6ecfbdb86 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -157,6 +157,8 @@ From the client, you can submit a job to Mesos cluster by running `spark-submit`
 to the url of the MesosClusterDispatcher (e.g: mesos://dispatcher:7077). You can view driver statuses on the
 Spark cluster Web UI.
 
+Note that jars or python files that are passed to spark-submit should be URIs reachable by Mesos slaves.
+
 # Mesos Run Modes
 
 Spark can run over Mesos in two modes: "fine-grained" (default) and "coarse-grained".

From 2e1c17553d179f2d26a165805622cc01f92081b9 Mon Sep 17 00:00:00 2001
From: robbins <robbins@uk.ibm.com>
Date: Fri, 4 Sep 2015 15:23:29 -0700
Subject: [PATCH 1336/1454] [SPARK-10454] [SPARK CORE] wait for empty event
 queue

Author: robbins <robbins@uk.ibm.com>

Closes #8605 from robbinspg/DAGSchedulerSuite-fix.
---
 .../scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 2c3aa2cf90d3c..1b9ff740ff530 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -812,6 +812,7 @@ class DAGSchedulerSuite
     }
 
     // The map stage should have been submitted.
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
     assert(countSubmittedMapStageAttempts() === 1)
 
     complete(taskSets(0), Seq(

From eafe37236c56b41ab32adf6610743e1e9a0f230f Mon Sep 17 00:00:00 2001
From: xutingjun <xutingjun@huawei.com>
Date: Fri, 4 Sep 2015 15:40:02 -0700
Subject: [PATCH 1337/1454] [SPARK-10311] [STREAMING] Reload appId and
 attemptId when app starts with checkpoint file in cluster mode

Author: xutingjun <xutingjun@huawei.com>

Closes #8477 from XuTingjun/streaming-attempt.
---
 .../src/main/scala/org/apache/spark/streaming/Checkpoint.scala  | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index cd5d960369c05..3985e1a3d9dfa 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -49,6 +49,8 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
     // Reload properties for the checkpoint application since user wants to set a reload property
     // or spark had changed its value and user wants to set it back.
     val propertiesToReload = List(
+      "spark.yarn.app.id",
+      "spark.yarn.app.attemptId",
       "spark.driver.host",
       "spark.driver.port",
       "spark.master",

From 22eab706f4a1459100f9e97fc557a7f3c88ca10e Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Fri, 4 Sep 2015 17:32:35 -0700
Subject: [PATCH 1338/1454] [SPARK-10402] [DOCS] [ML] Add defaults to the
 scaladoc for params in ml/

We should make sure the scaladoc for params includes their default values through the models in ml/

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8591 from holdenk/SPARK-10402-add-scaladoc-for-default-values-of-params-in-ml.
---
 .../ml/classification/MultilayerPerceptronClassifier.scala    | 2 ++
 .../spark/ml/evaluation/BinaryClassificationEvaluator.scala   | 1 +
 .../main/scala/org/apache/spark/ml/feature/Binarizer.scala    | 1 +
 mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala    | 1 +
 .../scala/org/apache/spark/ml/feature/StopWordsRemover.scala  | 2 ++
 .../scala/org/apache/spark/ml/feature/StringIndexer.scala     | 4 ++--
 .../scala/org/apache/spark/ml/feature/VectorIndexer.scala     | 1 +
 .../main/scala/org/apache/spark/ml/feature/VectorSlicer.scala | 2 ++
 .../src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala | 3 +++
 .../org/apache/spark/ml/regression/IsotonicRegression.scala   | 1 +
 10 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 1e5b0bc4453e4..82fc80c58054f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -32,6 +32,7 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
   with HasSeed with HasMaxIter with HasTol {
   /**
    * Layer sizes including input size and output size.
+   * Default: Array(1, 1)
    * @group param
    */
   final val layers: IntArrayParam = new IntArrayParam(this, "layers",
@@ -50,6 +51,7 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
    * Data is stacked within partitions. If block size is more than remaining data in
    * a partition then it is adjusted to the size of this data.
    * Recommended size is between 10 and 1000.
+   * Default: 128
    * @group expertParam
    */
   final val blockSize: IntParam = new IntParam(this, "blockSize",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 56419a0a15952..08df2919a8a87 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -38,6 +38,7 @@ class BinaryClassificationEvaluator(override val uid: String)
 
   /**
    * param for metric name in evaluation
+   * Default: areaUnderROC
    * @group param
    */
   val metricName: Param[String] = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
index 46314854d5e3a..edad754436455 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
@@ -41,6 +41,7 @@ final class Binarizer(override val uid: String)
    * Param for threshold used to binarize continuous features.
    * The features greater than the threshold, will be binarized to 1.0.
    * The features equal to or less than the threshold, will be binarized to 0.0.
+   * Default: 0.0
    * @group param
    */
   val threshold: DoubleParam =
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
index 938447447a0a2..4c36df75d8aa0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
@@ -35,6 +35,7 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol
 
   /**
    * The minimum of documents in which a term should appear.
+   * Default: 0
    * @group param
    */
   final val minDocFreq = new IntParam(
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
index 7da430c7d16df..2a79582625e9a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
@@ -98,6 +98,7 @@ class StopWordsRemover(override val uid: String)
 
   /**
    * the stop words set to be filtered out
+   * Default: [[StopWords.English]]
    * @group param
    */
   val stopWords: StringArrayParam = new StringArrayParam(this, "stopWords", "stop words")
@@ -110,6 +111,7 @@ class StopWordsRemover(override val uid: String)
 
   /**
    * whether to do a case sensitive comparison over the stop words
+   * Default: false
    * @group param
    */
   val caseSensitive: BooleanParam = new BooleanParam(this, "caseSensitive",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 24250e4c4cf92..77aeed0ab0370 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -212,8 +212,8 @@ class IndexToString private[ml] (
 
   /**
    * Param for array of labels.
-   * Optional labels to be provided by the user, if not supplied column
-   * metadata is read for labels.
+   * Optional labels to be provided by the user.
+   * Default: Empty array, in which case column metadata is used for labels.
    * @group param
    */
   final val labels: StringArrayParam = new StringArrayParam(this, "labels",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index 61b925c0fdc07..52e0599e38d83 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -43,6 +43,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
    * Must be >= 2.
    *
    * (default = 20)
+   * @group param
    */
   val maxCategories = new IntParam(this, "maxCategories",
     "Threshold for the number of values a categorical feature can take (>= 2)." +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
index c5c2272270792..fb3387d4aa9be 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
@@ -49,6 +49,7 @@ final class VectorSlicer(override val uid: String)
   /**
    * An array of indices to select features from a vector column.
    * There can be no overlap with [[names]].
+   * Default: Empty array
    * @group param
    */
   val indices = new IntArrayParam(this, "indices",
@@ -67,6 +68,7 @@ final class VectorSlicer(override val uid: String)
    * An array of feature names to select features from a vector column.
    * These names must be specified by ML [[org.apache.spark.ml.attribute.Attribute]]s.
    * There can be no overlap with [[indices]].
+   * Default: Empty Array
    * @group param
    */
   val names = new StringArrayParam(this, "names",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 5af775a4159ad..9edab3af913ca 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -39,6 +39,7 @@ private[feature] trait Word2VecBase extends Params
 
   /**
    * The dimension of the code that you want to transform from words.
+   * Default: 100
    * @group param
    */
   final val vectorSize = new IntParam(
@@ -50,6 +51,7 @@ private[feature] trait Word2VecBase extends Params
 
   /**
    * Number of partitions for sentences of words.
+   * Default: 1
    * @group param
    */
   final val numPartitions = new IntParam(
@@ -62,6 +64,7 @@ private[feature] trait Word2VecBase extends Params
   /**
    * The minimum number of times a token must appear to be included in the word2vec model's
    * vocabulary.
+   * Default: 5
    * @group param
    */
   final val minCount = new IntParam(this, "minCount", "the minimum number of times a token must " +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index 0f33bae30e622..d43a3447d3975 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -40,6 +40,7 @@ private[regression] trait IsotonicRegressionBase extends Params with HasFeatures
   /**
    * Param for whether the output sequence should be isotonic/increasing (true) or
    * antitonic/decreasing (false).
+   * Default: true
    * @group param
    */
   final val isotonic: BooleanParam =

From 47058ca5db3135a33feea9f485fcda455e430054 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 4 Sep 2015 18:58:25 -0700
Subject: [PATCH 1339/1454] [SPARK-9925] [SQL] [TESTS] Set
 SQLConf.SHUFFLE_PARTITIONS.key correctly for tests

This PR fix the failed test and conflict for #8155

https://issues.apache.org/jira/browse/SPARK-9925

Closes #8155

Author: Yin Huai <yhuai@databricks.com>
Author: Davies Liu <davies@databricks.com>

Closes #8602 from davies/shuffle_partitions.
---
 .../org/apache/spark/sql/SQLConfSuite.scala   | 19 ++++++++++----
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 21 ++++++++++-----
 .../spark/sql/test/TestSQLContext.scala       | 26 +++++++++++++++++--
 .../apache/spark/sql/hive/test/TestHive.scala | 26 +++++++++++++++++--
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |  5 +++-
 .../hive/execution/ConcurrentHiveSuite.scala  |  4 ++-
 .../sql/hive/execution/HiveQuerySuite.scala   | 10 ++++---
 7 files changed, 90 insertions(+), 21 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index c35b31c96df48..589ba86a84144 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.{TestSQLContext, SharedSQLContext}
 
 
 class SQLConfSuite extends QueryTest with SharedSQLContext {
@@ -32,8 +32,12 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
   }
 
   test("programmatic ways of basic setting and getting") {
+    // Set a conf first.
+    sqlContext.setConf(testKey, testVal)
+    // Clear the conf.
     sqlContext.conf.clear()
-    assert(sqlContext.getAllConfs.size === 0)
+    // After clear, only overrideConfs used by unit test should be in the SQLConf.
+    assert(sqlContext.getAllConfs === TestSQLContext.overrideConfs)
 
     sqlContext.setConf(testKey, testVal)
     assert(sqlContext.getConf(testKey) === testVal)
@@ -42,7 +46,7 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
 
     // Tests SQLConf as accessed from a SQLContext is mutable after
     // the latter is initialized, unlike SparkConf inside a SparkContext.
-    assert(sqlContext.getConf(testKey) == testVal)
+    assert(sqlContext.getConf(testKey) === testVal)
     assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
     assert(sqlContext.getAllConfs.contains(testKey))
 
@@ -73,8 +77,13 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
 
   test("deprecated property") {
     sqlContext.conf.clear()
-    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-    assert(sqlContext.conf.numShufflePartitions === 10)
+    val original = sqlContext.conf.numShufflePartitions
+    try{
+      sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
+      assert(ctx.conf.numShufflePartitions === 10)
+    } finally {
+      sql(s"set ${SQLConf.SHUFFLE_PARTITIONS}=$original")
+    }
   }
 
   test("invalid conf value") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 05f2000459599..664b7a1512faf 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.test.SQLTestData._
-import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.{SharedSQLContext, TestSQLContext}
 import org.apache.spark.sql.types._
 
 /** A SQL Dialect for testing purpose, and it can not be nested type */
@@ -991,21 +991,30 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     val nonexistentKey = "nonexistent"
 
     // "set" itself returns all config variables currently specified in SQLConf.
-    assert(sql("SET").collect().size == 0)
+    assert(sql("SET").collect().size === TestSQLContext.overrideConfs.size)
+    sql("SET").collect().foreach { row =>
+      val key = row.getString(0)
+      val value = row.getString(1)
+      assert(
+        TestSQLContext.overrideConfs.contains(key),
+        s"$key should exist in SQLConf.")
+      assert(
+        TestSQLContext.overrideConfs(key) === value,
+        s"The value of $key should be ${TestSQLContext.overrideConfs(key)} instead of $value.")
+    }
+    val overrideConfs = sql("SET").collect()
 
     // "set key=val"
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Row(testKey, testVal)
+      overrideConfs ++ Seq(Row(testKey, testVal))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
-      Seq(
-        Row(testKey, testVal),
-        Row(testKey + testKey, testVal + testVal))
+      overrideConfs ++ Seq(Row(testKey, testVal), Row(testKey + testKey, testVal + testVal))
     )
 
     // "set key"
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
index d99d191ebe81e..10e633f3cde46 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -31,13 +31,24 @@ private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { sel
       new SparkConf().set("spark.sql.testkey", "true")))
   }
 
-  // Use fewer partitions to speed up testing
+  // Make sure we set those test specific confs correctly when we create
+  // the SQLConf as well as when we call clear.
   protected[sql] override def createSession(): SQLSession = new this.SQLSession()
 
   /** A special [[SQLSession]] that uses fewer shuffle partitions than normal. */
   protected[sql] class SQLSession extends super.SQLSession {
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
-      override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, 5)
+
+      clear()
+
+      override def clear(): Unit = {
+        super.clear()
+
+        // Make sure we start with the default test configs even after clear
+        TestSQLContext.overrideConfs.map {
+          case (key, value) => setConfString(key, value)
+        }
+      }
     }
   }
 
@@ -50,3 +61,14 @@ private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { sel
     protected override def sqlContext: SQLContext = self
   }
 }
+
+private[sql] object TestSQLContext {
+
+  /**
+   * A map used to store all confs that need to be overridden in sql/core unit tests.
+   */
+  val overrideConfs: Map[String, String] =
+    Map(
+      // Fewer shuffle partitions to speed up testing.
+      SQLConf.SHUFFLE_PARTITIONS.key -> "5")
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 77f43f92705d8..be335a47dcabd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -116,18 +116,28 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   override def executePlan(plan: LogicalPlan): this.QueryExecution =
     new this.QueryExecution(plan)
 
+  // Make sure we set those test specific confs correctly when we create
+  // the SQLConf as well as when we call clear.
   override protected[sql] def createSession(): SQLSession = {
     new this.SQLSession()
   }
 
   protected[hive] class SQLSession extends super.SQLSession {
-    /** Fewer partitions to speed up testing. */
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
-      override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, 5)
       // TODO as in unit test, conf.clear() probably be called, all of the value will be cleared.
       // The super.getConf(SQLConf.DIALECT) is "sql" by default, we need to set it as "hiveql"
       override def dialect: String = super.getConf(SQLConf.DIALECT, "hiveql")
       override def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE, false)
+
+      clear()
+
+      override def clear(): Unit = {
+        super.clear()
+
+        TestHiveContext.overrideConfs.map {
+          case (key, value) => setConfString(key, value)
+        }
+      }
     }
   }
 
@@ -455,3 +465,15 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
     }
   }
 }
+
+private[hive] object TestHiveContext {
+
+  /**
+   * A map used to store all confs that need to be overridden in sql/hive unit tests.
+   */
+  val overrideConfs: Map[String, String] =
+    Map(
+      // Fewer shuffle partitions to speed up testing.
+      SQLConf.SHUFFLE_PARTITIONS.key -> "5"
+    )
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 84f3db44ecd8d..97df249bdb6d6 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -173,6 +173,7 @@ object SparkSubmitClassLoaderTest extends Logging {
   def main(args: Array[String]) {
     Utils.configTestLog4j("INFO")
     val conf = new SparkConf()
+    conf.set("spark.ui.enabled", "false")
     val sc = new SparkContext(conf)
     val hiveContext = new TestHiveContext(sc)
     val df = hiveContext.createDataFrame((1 to 100).map(i => (i, i))).toDF("i", "j")
@@ -264,6 +265,7 @@ object SparkSQLConfTest extends Logging {
       // For this simple test, we do not really clone this object.
       override def clone: SparkConf = this
     }
+    conf.set("spark.ui.enabled", "false")
     val sc = new SparkContext(conf)
     val hiveContext = new TestHiveContext(sc)
     // Run a simple command to make sure all lazy vals in hiveContext get instantiated.
@@ -283,7 +285,8 @@ object SPARK_9757 extends QueryTest {
     val sparkContext = new SparkContext(
       new SparkConf()
         .set("spark.sql.hive.metastore.version", "0.13.1")
-        .set("spark.sql.hive.metastore.jars", "maven"))
+        .set("spark.sql.hive.metastore.jars", "maven")
+        .set("spark.ui.enabled", "false"))
 
     val hiveContext = new TestHiveContext(sparkContext)
     sqlContext = hiveContext
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala
index b0d3dd44daedc..e38d1eb5779fe 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala
@@ -25,8 +25,10 @@ class ConcurrentHiveSuite extends SparkFunSuite with BeforeAndAfterAll {
   ignore("multiple instances not supported") {
     test("Multiple Hive Instances") {
       (1 to 10).map { i =>
+        val conf = new SparkConf()
+        conf.set("spark.ui.enabled", "false")
         val ts =
-          new TestHiveContext(new SparkContext("local", s"TestSQLContext$i", new SparkConf()))
+          new TestHiveContext(new SparkContext("local", s"TestSQLContext$i", conf))
         ts.executeSql("SHOW TABLES").toRdd.collect()
         ts.executeSql("SELECT * FROM src").toRdd.collect()
         ts.executeSql("SHOW TABLES").toRdd.collect()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 83f9f3eaa3a5e..fe63ad5683195 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -31,6 +31,7 @@ import org.apache.spark.sql.{AnalysisException, DataFrame, Row}
 import org.apache.spark.sql.catalyst.expressions.Cast
 import org.apache.spark.sql.catalyst.plans.logical.Project
 import org.apache.spark.sql.hive._
+import org.apache.spark.sql.hive.test.TestHiveContext
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 
@@ -1104,18 +1105,19 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
 
     // "SET" itself returns all config variables currently specified in SQLConf.
     // TODO: Should we be listing the default here always? probably...
-    assert(sql("SET").collect().size == 0)
+    assert(sql("SET").collect().size === TestHiveContext.overrideConfs.size)
 
+    val defaults = collectResults(sql("SET"))
     assertResult(Set(testKey -> testVal)) {
       collectResults(sql(s"SET $testKey=$testVal"))
     }
 
-    assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal))(collectResults(sql("SET")))
+    assert(hiveconf.get(testKey, "") === testVal)
+    assertResult(defaults ++ Set(testKey -> testVal))(collectResults(sql("SET")))
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+    assertResult(defaults ++ Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
       collectResults(sql("SET"))
     }
 

From 6c751940ea8449b7540ba956a00ed19ba0af599b Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Fri, 4 Sep 2015 22:57:52 -1000
Subject: [PATCH 1340/1454] [HOTFIX] [SQL] Fixes compilation error

Jenkins master builders are currently broken by a merge conflict between PR #8584 and PR #8155.

Author: Cheng Lian <lian@databricks.com>

Closes #8614 from liancheng/hotfix/fix-pr-8155-8584-conflict.
---
 sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 589ba86a84144..3d2bd236ceead 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -80,7 +80,7 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
     val original = sqlContext.conf.numShufflePartitions
     try{
       sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-      assert(ctx.conf.numShufflePartitions === 10)
+      assert(sqlContext.conf.numShufflePartitions === 10)
     } finally {
       sql(s"set ${SQLConf.SHUFFLE_PARTITIONS}=$original")
     }

From 7a4f326c00fb33c384b4fb927310d687ec063329 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 4 Sep 2015 23:16:39 -1000
Subject: [PATCH 1341/1454] [SPARK-10440] [STREAMING] [DOCS] Update python API
 stuff in the programming guides and python docs

- Fixed information around Python API tags in streaming programming guides
- Added missing stuff in python docs

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8595 from tdas/SPARK-10440.
---
 docs/streaming-flume-integration.md |  2 --
 docs/streaming-programming-guide.md | 14 ++++----------
 python/docs/index.rst               |  8 ++++++++
 python/docs/pyspark.streaming.rst   | 21 +++++++++++++++++++++
 4 files changed, 33 insertions(+), 12 deletions(-)

diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md
index de0461010daec..383d954409ce4 100644
--- a/docs/streaming-flume-integration.md
+++ b/docs/streaming-flume-integration.md
@@ -5,8 +5,6 @@ title: Spark Streaming + Flume Integration Guide
 
 [Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Here we explain how to configure Flume and Spark Streaming to receive data from Flume. There are two approaches to this.
 
-<span class="badge" style="background-color: grey">Python API</span> Flume is not yet available in the Python API.
-
 ## Approach 1: Flume-style Push-based Approach
 Flume is designed to push data between Flume agents. In this approach, Spark Streaming essentially sets up a receiver that acts an Avro agent for Flume, to which Flume can push the data. Here are the configuration steps.
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 118ced298f4b0..a1acf83f75245 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -50,13 +50,7 @@ all of which are presented in this guide.
 You will find tabs throughout this guide that let you choose between code snippets of
 different languages.
 
-**Note:** Python API for Spark Streaming has been introduced in Spark 1.2. It has all the DStream
-transformations and almost all the output operations available in Scala and Java interfaces.
-However, it only has support for basic sources like text files and text data over sockets.
-APIs for additional sources, like Kafka and Flume, will be available in the future.
-Further information about available features in the Python API are mentioned throughout this
-document; look out for the tag
-<span class="badge" style="background-color: grey">Python API</span>.
+**Note:** There are a few APIs that are either different or not available in Python. Throughout this guide, you will find the tag <span class="badge" style="background-color: grey">Python API</span> highlighting these differences.
 
 ***************************************************************************************************
 
@@ -683,7 +677,7 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea
 {:.no_toc}
 
 <span class="badge" style="background-color: grey">Python API</span> As of Spark {{site.SPARK_VERSION_SHORT}},
-out of these sources, *only* Kafka, Flume and MQTT are available in the Python API. We will add more advanced sources in the Python API in future.
+out of these sources, Kafka, Kinesis, Flume and MQTT are available in the Python API.
 
 This category of sources require interfacing with external non-Spark libraries, some of them with
 complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts
@@ -725,9 +719,9 @@ Some of these advanced sources are as follows.
 
 - **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kafka 0.8.2.1. See the [Kafka Integration Guide](streaming-kafka-integration.html) for more details.
 
-- **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Flume 1.4.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details.
+- **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Flume 1.6.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details.
 
-- **Kinesis:** See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details.
+- **Kinesis:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kinesis Client Library 1.2.1. See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details.
 
 - **Twitter:** Spark Streaming's TwitterUtils uses Twitter4j 3.0.3 to get the public stream of tweets using
   [Twitter's Streaming API](https://dev.twitter.com/docs/streaming-apis). Authentication information
diff --git a/python/docs/index.rst b/python/docs/index.rst
index f7eede9c3c82a..306ffdb0e0f13 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -29,6 +29,14 @@ Core classes:
 
     A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
 
+    :class:`pyspark.streaming.StreamingContext`
+
+    Main entry point for Spark Streaming functionality.
+
+    :class:`pyspark.streaming.DStream`
+
+    A Discretized Stream (DStream), the basic abstraction in Spark Streaming.
+
     :class:`pyspark.sql.SQLContext`
 
     Main entry point for DataFrame and SQL functionality.
diff --git a/python/docs/pyspark.streaming.rst b/python/docs/pyspark.streaming.rst
index 50822c93faba1..fc52a647543e7 100644
--- a/python/docs/pyspark.streaming.rst
+++ b/python/docs/pyspark.streaming.rst
@@ -15,3 +15,24 @@ pyspark.streaming.kafka module
     :members:
     :undoc-members:
     :show-inheritance:
+
+pyspark.streaming.kinesis module
+--------------------------------
+.. automodule:: pyspark.streaming.kinesis
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.streaming.flume.module
+------------------------------
+.. automodule:: pyspark.streaming.flume
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.streaming.mqtt module
+-----------------------------
+.. automodule:: pyspark.streaming.mqtt
+    :members:
+    :undoc-members:
+    :show-inheritance:

From bca8c072bd710beda6cfac1533a67f32f579b134 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sat, 5 Sep 2015 17:50:12 +0800
Subject: [PATCH 1342/1454] [SPARK-10434] [SQL] Fixes Parquet schema of arrays
 that may contain null

To keep full compatibility of Parquet write path with Spark 1.4, we should rename the innermost field name of arrays that may contain null from "array_element" to "array".

Please refer to [SPARK-10434] [1] for more details.

[1]: https://issues.apache.org/jira/browse/SPARK-10434

Author: Cheng Lian <lian@databricks.com>

Closes #8586 from liancheng/spark-10434/fix-parquet-array-type.
---
 .../parquet/CatalystSchemaConverter.scala           | 13 +++++++------
 .../datasources/parquet/ParquetSchemaSuite.scala    |  6 +++---
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index a21ab1dbb25d1..2d237da81c20d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -426,13 +426,14 @@ private[parquet] class CatalystSchemaConverter(
       // ArrayType and MapType (for Spark versions <= 1.4.x)
       // ===================================================
 
-      // Spark 1.4.x and prior versions convert ArrayType with nullable elements into a 3-level
-      // LIST structure.  This behavior mimics parquet-hive (1.6.0rc3).  Note that this case is
-      // covered by the backwards-compatibility rules implemented in `isElementType()`.
+      // Spark 1.4.x and prior versions convert `ArrayType` with nullable elements into a 3-level
+      // `LIST` structure.  This behavior is somewhat a hybrid of parquet-hive and parquet-avro
+      // (1.6.0rc3): the 3-level structure is similar to parquet-hive while the 3rd level element
+      // field name "array" is borrowed from parquet-avro.
       case ArrayType(elementType, nullable @ true) if !followParquetFormatSpec =>
         // <list-repetition> group <name> (LIST) {
         //   optional group bag {
-        //     repeated <element-type> element;
+        //     repeated <element-type> array;
         //   }
         // }
         ConversionPatterns.listType(
@@ -441,8 +442,8 @@ private[parquet] class CatalystSchemaConverter(
           Types
             .buildGroup(REPEATED)
             // "array_element" is the name chosen by parquet-hive (1.7.0 and prior version)
-            .addField(convertField(StructField("array_element", elementType, nullable)))
-            .named(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME))
+            .addField(convertField(StructField("array", elementType, nullable)))
+            .named("bag"))
 
       // Spark 1.4.x and prior versions convert ArrayType with non-nullable elements into a 2-level
       // LIST structure.  This behavior mimics parquet-avro (1.6.0rc3).  Note that this case is
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 28c59a4abdd76..5331d7c035236 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -197,7 +197,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |message root {
       |  optional group _1 (LIST) {
       |    repeated group bag {
-      |      optional int32 array_element;
+      |      optional int32 array;
       |    }
       |  }
       |}
@@ -266,7 +266,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |        optional binary _1 (UTF8);
       |        optional group _2 (LIST) {
       |          repeated group bag {
-      |            optional group array_element {
+      |            optional group array {
       |              required int32 _1;
       |              required double _2;
       |            }
@@ -645,7 +645,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     """message root {
       |  optional group f1 (LIST) {
       |    repeated group bag {
-      |      optional int32 array_element;
+      |      optional int32 array;
       |    }
       |  }
       |}

From 871764c6ce531af5b1ac7ccccb32e7a903b59a2a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Sat, 5 Sep 2015 00:04:00 -1000
Subject: [PATCH 1343/1454] [SPARK-10013] [ML] [JAVA] [TEST] remove java assert
 from java unit tests

From Jira: We should use assertTrue, etc. instead to make sure the asserts are not ignored in tests.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8607 from holdenk/SPARK-10013-remove-java-assert-from-java-unit-tests.
---
 .../JavaLogisticRegressionSuite.java          | 51 ++++++++++---------
 .../classification/JavaNaiveBayesSuite.java   | 13 ++---
 .../regression/JavaLinearRegressionSuite.java |  2 +-
 .../spark/mllib/linalg/JavaMatricesSuite.java | 40 +++++++--------
 4 files changed, 54 insertions(+), 52 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index 618b95b9bd126..fd22eb6dca018 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -22,6 +22,7 @@
 import java.util.List;
 
 import org.junit.After;
+import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
@@ -63,16 +64,16 @@ public void tearDown() {
   @Test
   public void logisticRegressionDefaultParams() {
     LogisticRegression lr = new LogisticRegression();
-    assert(lr.getLabelCol().equals("label"));
+    Assert.assertEquals(lr.getLabelCol(), "label");
     LogisticRegressionModel model = lr.fit(dataset);
     model.transform(dataset).registerTempTable("prediction");
     DataFrame predictions = jsql.sql("SELECT label, probability, prediction FROM prediction");
     predictions.collectAsList();
     // Check defaults
-    assert(model.getThreshold() == 0.5);
-    assert(model.getFeaturesCol().equals("features"));
-    assert(model.getPredictionCol().equals("prediction"));
-    assert(model.getProbabilityCol().equals("probability"));
+    Assert.assertEquals(0.5, model.getThreshold(), eps);
+    Assert.assertEquals("features", model.getFeaturesCol());
+    Assert.assertEquals("prediction", model.getPredictionCol());
+    Assert.assertEquals("probability", model.getProbabilityCol());
   }
 
   @Test
@@ -85,19 +86,19 @@ public void logisticRegressionWithSetters() {
       .setProbabilityCol("myProbability");
     LogisticRegressionModel model = lr.fit(dataset);
     LogisticRegression parent = (LogisticRegression) model.parent();
-    assert(parent.getMaxIter() == 10);
-    assert(parent.getRegParam() == 1.0);
-    assert(parent.getThresholds()[0] == 0.4);
-    assert(parent.getThresholds()[1] == 0.6);
-    assert(parent.getThreshold() == 0.6);
-    assert(model.getThreshold() == 0.6);
+    Assert.assertEquals(10, parent.getMaxIter());
+    Assert.assertEquals(1.0, parent.getRegParam(), eps);
+    Assert.assertEquals(0.4, parent.getThresholds()[0], eps);
+    Assert.assertEquals(0.6, parent.getThresholds()[1], eps);
+    Assert.assertEquals(0.6, parent.getThreshold(), eps);
+    Assert.assertEquals(0.6, model.getThreshold(), eps);
 
     // Modify model params, and check that the params worked.
     model.setThreshold(1.0);
     model.transform(dataset).registerTempTable("predAllZero");
     DataFrame predAllZero = jsql.sql("SELECT prediction, myProbability FROM predAllZero");
     for (Row r: predAllZero.collectAsList()) {
-      assert(r.getDouble(0) == 0.0);
+      Assert.assertEquals(0.0, r.getDouble(0), eps);
     }
     // Call transform with params, and check that the params worked.
     model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
@@ -107,17 +108,17 @@ public void logisticRegressionWithSetters() {
     for (Row r: predNotAllZero.collectAsList()) {
       if (r.getDouble(0) != 0.0) foundNonZero = true;
     }
-    assert(foundNonZero);
+    Assert.assertTrue(foundNonZero);
 
     // Call fit() with new params, and check as many params as we can.
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
         lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
     LogisticRegression parent2 = (LogisticRegression) model2.parent();
-    assert(parent2.getMaxIter() == 5);
-    assert(parent2.getRegParam() == 0.1);
-    assert(parent2.getThreshold() == 0.4);
-    assert(model2.getThreshold() == 0.4);
-    assert(model2.getProbabilityCol().equals("theProb"));
+    Assert.assertEquals(5, parent2.getMaxIter());
+    Assert.assertEquals(0.1, parent2.getRegParam(), eps);
+    Assert.assertEquals(0.4, parent2.getThreshold(), eps);
+    Assert.assertEquals(0.4, model2.getThreshold(), eps);
+    Assert.assertEquals("theProb", model2.getProbabilityCol());
   }
 
   @SuppressWarnings("unchecked")
@@ -125,18 +126,18 @@ public void logisticRegressionWithSetters() {
   public void logisticRegressionPredictorClassifierMethods() {
     LogisticRegression lr = new LogisticRegression();
     LogisticRegressionModel model = lr.fit(dataset);
-    assert(model.numClasses() == 2);
+    Assert.assertEquals(2, model.numClasses());
 
     model.transform(dataset).registerTempTable("transformed");
     DataFrame trans1 = jsql.sql("SELECT rawPrediction, probability FROM transformed");
     for (Row row: trans1.collect()) {
       Vector raw = (Vector)row.get(0);
       Vector prob = (Vector)row.get(1);
-      assert(raw.size() == 2);
-      assert(prob.size() == 2);
+      Assert.assertEquals(raw.size(), 2);
+      Assert.assertEquals(prob.size(), 2);
       double probFromRaw1 = 1.0 / (1.0 + Math.exp(-raw.apply(1)));
-      assert(Math.abs(prob.apply(1) - probFromRaw1) < eps);
-      assert(Math.abs(prob.apply(0) - (1.0 - probFromRaw1)) < eps);
+      Assert.assertEquals(0, Math.abs(prob.apply(1) - probFromRaw1), eps);
+      Assert.assertEquals(0, Math.abs(prob.apply(0) - (1.0 - probFromRaw1)), eps);
     }
 
     DataFrame trans2 = jsql.sql("SELECT prediction, probability FROM transformed");
@@ -145,7 +146,7 @@ public void logisticRegressionPredictorClassifierMethods() {
       Vector prob = (Vector)row.get(1);
       double probOfPred = prob.apply((int)pred);
       for (int i = 0; i < prob.size(); ++i) {
-        assert(probOfPred >= prob.apply(i));
+        Assert.assertTrue(probOfPred >= prob.apply(i));
       }
     }
   }
@@ -156,6 +157,6 @@ public void logisticRegressionTrainingSummary() {
     LogisticRegressionModel model = lr.fit(dataset);
 
     LogisticRegressionTrainingSummary summary = model.summary();
-    assert(summary.totalIterations() == summary.objectiveHistory().length);
+    Assert.assertEquals(summary.totalIterations(), summary.objectiveHistory().length);
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
index 8fd7bf55a2e5d..075a62c493f17 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -23,6 +23,7 @@
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
+import static org.junit.Assert.assertEquals;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -58,18 +59,18 @@ public void validatePrediction(DataFrame predictionAndLabels) {
     for (Row r : predictionAndLabels.collect()) {
       double prediction = r.getAs(0);
       double label = r.getAs(1);
-      assert(prediction == label);
+      assertEquals(label, prediction, 1E-5);
     }
   }
 
   @Test
   public void naiveBayesDefaultParams() {
     NaiveBayes nb = new NaiveBayes();
-    assert(nb.getLabelCol() == "label");
-    assert(nb.getFeaturesCol() == "features");
-    assert(nb.getPredictionCol() == "prediction");
-    assert(nb.getSmoothing() == 1.0);
-    assert(nb.getModelType() == "multinomial");
+    assertEquals("label", nb.getLabelCol());
+    assertEquals("features", nb.getFeaturesCol());
+    assertEquals("prediction", nb.getPredictionCol());
+    assertEquals(1.0, nb.getSmoothing(), 1E-5);
+    assertEquals("multinomial", nb.getModelType());
   }
 
   @Test
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
index d591a456864e4..91c589d00abd5 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
@@ -60,7 +60,7 @@ public void tearDown() {
   @Test
   public void linearRegressionDefaultParams() {
     LinearRegression lr = new LinearRegression();
-    assert(lr.getLabelCol().equals("label"));
+    assertEquals("label", lr.getLabelCol());
     LinearRegressionModel model = lr.fit(dataset);
     model.transform(dataset).registerTempTable("prediction");
     DataFrame predictions = jsql.sql("SELECT label, prediction FROM prediction");
diff --git a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java
index 3349c5022423a..8beea102efd01 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java
@@ -80,10 +80,10 @@ public void diagonalMatrixConstruction() {
         assertArrayEquals(sd.toArray(), s.toArray(), 0.0);
         assertArrayEquals(s.toArray(), ss.toArray(), 0.0);
         assertArrayEquals(s.values(), ss.values(), 0.0);
-        assert(s.values().length == 2);
-        assert(ss.values().length == 2);
-        assert(s.colPtrs().length == 4);
-        assert(ss.colPtrs().length == 4);
+        assertEquals(2, s.values().length);
+        assertEquals(2, ss.values().length);
+        assertEquals(4, s.colPtrs().length);
+        assertEquals(4, ss.colPtrs().length);
     }
 
     @Test
@@ -137,27 +137,27 @@ public void concatenateMatrices() {
         Matrix deHorz2 = Matrices.horzcat(new Matrix[]{spMat1, deMat2});
         Matrix deHorz3 = Matrices.horzcat(new Matrix[]{deMat1, spMat2});
 
-        assert(deHorz1.numRows() == 3);
-        assert(deHorz2.numRows() == 3);
-        assert(deHorz3.numRows() == 3);
-        assert(spHorz.numRows() == 3);
-        assert(deHorz1.numCols() == 5);
-        assert(deHorz2.numCols() == 5);
-        assert(deHorz3.numCols() == 5);
-        assert(spHorz.numCols() == 5);
+        assertEquals(3, deHorz1.numRows());
+        assertEquals(3, deHorz2.numRows());
+        assertEquals(3, deHorz3.numRows());
+        assertEquals(3, spHorz.numRows());
+        assertEquals(5, deHorz1.numCols());
+        assertEquals(5, deHorz2.numCols());
+        assertEquals(5, deHorz3.numCols());
+        assertEquals(5, spHorz.numCols());
 
         Matrix spVert = Matrices.vertcat(new Matrix[]{spMat1, spMat3});
         Matrix deVert1 = Matrices.vertcat(new Matrix[]{deMat1, deMat3});
         Matrix deVert2 = Matrices.vertcat(new Matrix[]{spMat1, deMat3});
         Matrix deVert3 = Matrices.vertcat(new Matrix[]{deMat1, spMat3});
 
-        assert(deVert1.numRows() == 5);
-        assert(deVert2.numRows() == 5);
-        assert(deVert3.numRows() == 5);
-        assert(spVert.numRows() == 5);
-        assert(deVert1.numCols() == 2);
-        assert(deVert2.numCols() == 2);
-        assert(deVert3.numCols() == 2);
-        assert(spVert.numCols() == 2);
+        assertEquals(5, deVert1.numRows());
+        assertEquals(5, deVert2.numRows());
+        assertEquals(5, deVert3.numRows());
+        assertEquals(5, spVert.numRows());
+        assertEquals(2, deVert1.numCols());
+        assertEquals(2, deVert2.numCols());
+        assertEquals(2, deVert3.numCols());
+        assertEquals(2, spVert.numCols());
     }
 }

From 5ffe752b59e468d55363f1f24b17a3677927ca8f Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 7 Sep 2015 10:42:30 -1000
Subject: [PATCH 1344/1454] [SPARK-9767] Remove ConnectionManager.

We introduced the Netty network module for shuffle in Spark 1.2, and has turned it on by default for 3 releases. The old ConnectionManager is difficult to maintain. If we merge the patch now, by the time it is released, it would be 1 yr for which ConnectionManager is off by default. It's time to remove it.

Author: Reynold Xin <rxin@databricks.com>

Closes #8161 from rxin/SPARK-9767.
---
 .../scala/org/apache/spark/SparkEnv.scala     |   11 +-
 .../netty/NettyBlockTransferService.scala     |    8 +-
 .../spark/network/nio/BlockMessage.scala      |  175 ---
 .../spark/network/nio/BlockMessageArray.scala |  140 --
 .../spark/network/nio/BufferMessage.scala     |  114 --
 .../apache/spark/network/nio/Connection.scala |  619 --------
 .../spark/network/nio/ConnectionId.scala      |   36 -
 .../spark/network/nio/ConnectionManager.scala | 1157 ---------------
 .../network/nio/ConnectionManagerId.scala     |   37 -
 .../apache/spark/network/nio/Message.scala    |  114 --
 .../spark/network/nio/MessageChunk.scala      |   41 -
 .../network/nio/MessageChunkHeader.scala      |   83 --
 .../network/nio/NioBlockTransferService.scala |  217 ---
 .../spark/network/nio/SecurityMessage.scala   |  160 ---
 .../spark/serializer/KryoSerializer.scala     |    4 -
 .../network/nio/ConnectionManagerSuite.scala  |  296 ----
 .../BlockManagerReplicationSuite.scala        |    6 +-
 .../spark/storage/BlockManagerSuite.scala     |   10 +-
 docs/configuration.md                         |   11 -
 project/MimaExcludes.scala                    | 1257 +++++++++--------
 .../streaming/ReceivedBlockHandlerSuite.scala |   10 +-
 21 files changed, 651 insertions(+), 3855 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/Connection.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/Message.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
 delete mode 100644 core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 0f1e2e069568d..c6fef7f91f00c 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -33,7 +33,6 @@ import org.apache.spark.broadcast.BroadcastManager
 import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.network.BlockTransferService
 import org.apache.spark.network.netty.NettyBlockTransferService
-import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.rpc.{RpcEndpointRef, RpcEndpoint, RpcEnv}
 import org.apache.spark.rpc.akka.AkkaRpcEnv
 import org.apache.spark.scheduler.{OutputCommitCoordinator, LiveListenerBus}
@@ -326,15 +325,7 @@ object SparkEnv extends Logging {
 
     val shuffleMemoryManager = ShuffleMemoryManager.create(conf, numUsableCores)
 
-    val blockTransferService =
-      conf.get("spark.shuffle.blockTransferService", "netty").toLowerCase match {
-        case "netty" =>
-          new NettyBlockTransferService(conf, securityManager, numUsableCores)
-        case "nio" =>
-          logWarning("NIO-based block transfer service is deprecated, " +
-            "and will be removed in Spark 1.6.0.")
-          new NioBlockTransferService(conf, securityManager)
-      }
+    val blockTransferService = new NettyBlockTransferService(conf, securityManager, numUsableCores)
 
     val blockManagerMaster = new BlockManagerMaster(registerOrLookupEndpoint(
       BlockManagerMaster.DRIVER_ENDPOINT_NAME,
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index d5ad2c9ad00e8..4b851bcb36597 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -149,7 +149,11 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
   }
 
   override def close(): Unit = {
-    server.close()
-    clientFactory.close()
+    if (server != null) {
+      server.close()
+    }
+    if (clientFactory != null) {
+      clientFactory.close()
+    }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
deleted file mode 100644
index 79cb0640c8672..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
+++ /dev/null
@@ -1,175 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import org.apache.spark.storage.{BlockId, StorageLevel, TestBlockId}
-
-import scala.collection.mutable.{ArrayBuffer, StringBuilder}
-
-// private[spark] because we need to register them in Kryo
-private[spark] case class GetBlock(id: BlockId)
-private[spark] case class GotBlock(id: BlockId, data: ByteBuffer)
-private[spark] case class PutBlock(id: BlockId, data: ByteBuffer, level: StorageLevel)
-
-private[nio] class BlockMessage() {
-  // Un-initialized: typ = 0
-  // GetBlock: typ = 1
-  // GotBlock: typ = 2
-  // PutBlock: typ = 3
-  private var typ: Int = BlockMessage.TYPE_NON_INITIALIZED
-  private var id: BlockId = null
-  private var data: ByteBuffer = null
-  private var level: StorageLevel = null
-
-  def set(getBlock: GetBlock) {
-    typ = BlockMessage.TYPE_GET_BLOCK
-    id = getBlock.id
-  }
-
-  def set(gotBlock: GotBlock) {
-    typ = BlockMessage.TYPE_GOT_BLOCK
-    id = gotBlock.id
-    data = gotBlock.data
-  }
-
-  def set(putBlock: PutBlock) {
-    typ = BlockMessage.TYPE_PUT_BLOCK
-    id = putBlock.id
-    data = putBlock.data
-    level = putBlock.level
-  }
-
-  def set(buffer: ByteBuffer) {
-    typ = buffer.getInt()
-    val idLength = buffer.getInt()
-    val idBuilder = new StringBuilder(idLength)
-    for (i <- 1 to idLength) {
-      idBuilder += buffer.getChar()
-    }
-    id = BlockId(idBuilder.toString)
-
-    if (typ == BlockMessage.TYPE_PUT_BLOCK) {
-
-      val booleanInt = buffer.getInt()
-      val replication = buffer.getInt()
-      level = StorageLevel(booleanInt, replication)
-
-      val dataLength = buffer.getInt()
-      data = ByteBuffer.allocate(dataLength)
-      if (dataLength != buffer.remaining) {
-        throw new Exception("Error parsing buffer")
-      }
-      data.put(buffer)
-      data.flip()
-    } else if (typ == BlockMessage.TYPE_GOT_BLOCK) {
-
-      val dataLength = buffer.getInt()
-      data = ByteBuffer.allocate(dataLength)
-      if (dataLength != buffer.remaining) {
-        throw new Exception("Error parsing buffer")
-      }
-      data.put(buffer)
-      data.flip()
-    }
-
-  }
-
-  def set(bufferMsg: BufferMessage) {
-    val buffer = bufferMsg.buffers.apply(0)
-    buffer.clear()
-    set(buffer)
-  }
-
-  def getType: Int = typ
-  def getId: BlockId = id
-  def getData: ByteBuffer = data
-  def getLevel: StorageLevel = level
-
-  def toBufferMessage: BufferMessage = {
-    val buffers = new ArrayBuffer[ByteBuffer]()
-    var buffer = ByteBuffer.allocate(4 + 4 + id.name.length * 2)
-    buffer.putInt(typ).putInt(id.name.length)
-    id.name.foreach((x: Char) => buffer.putChar(x))
-    buffer.flip()
-    buffers += buffer
-
-    if (typ == BlockMessage.TYPE_PUT_BLOCK) {
-      buffer = ByteBuffer.allocate(8).putInt(level.toInt).putInt(level.replication)
-      buffer.flip()
-      buffers += buffer
-
-      buffer = ByteBuffer.allocate(4).putInt(data.remaining)
-      buffer.flip()
-      buffers += buffer
-
-      buffers += data
-    } else if (typ == BlockMessage.TYPE_GOT_BLOCK) {
-      buffer = ByteBuffer.allocate(4).putInt(data.remaining)
-      buffer.flip()
-      buffers += buffer
-
-      buffers += data
-    }
-
-    Message.createBufferMessage(buffers)
-  }
-
-  override def toString: String = {
-    "BlockMessage [type = " + typ + ", id = " + id + ", level = " + level +
-    ", data = " + (if (data != null) data.remaining.toString else "null") + "]"
-  }
-}
-
-private[nio] object BlockMessage {
-  val TYPE_NON_INITIALIZED: Int = 0
-  val TYPE_GET_BLOCK: Int = 1
-  val TYPE_GOT_BLOCK: Int = 2
-  val TYPE_PUT_BLOCK: Int = 3
-
-  def fromBufferMessage(bufferMessage: BufferMessage): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(bufferMessage)
-    newBlockMessage
-  }
-
-  def fromByteBuffer(buffer: ByteBuffer): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(buffer)
-    newBlockMessage
-  }
-
-  def fromGetBlock(getBlock: GetBlock): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(getBlock)
-    newBlockMessage
-  }
-
-  def fromGotBlock(gotBlock: GotBlock): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(gotBlock)
-    newBlockMessage
-  }
-
-  def fromPutBlock(putBlock: PutBlock): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(putBlock)
-    newBlockMessage
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
deleted file mode 100644
index f1c9ea8b64ca3..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
+++ /dev/null
@@ -1,140 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import org.apache.spark._
-import org.apache.spark.storage.{StorageLevel, TestBlockId}
-
-import scala.collection.mutable.ArrayBuffer
-
-private[nio]
-class BlockMessageArray(var blockMessages: Seq[BlockMessage])
-  extends Seq[BlockMessage] with Logging {
-
-  def this(bm: BlockMessage) = this(Array(bm))
-
-  def this() = this(null.asInstanceOf[Seq[BlockMessage]])
-
-  def apply(i: Int): BlockMessage = blockMessages(i)
-
-  def iterator: Iterator[BlockMessage] = blockMessages.iterator
-
-  def length: Int = blockMessages.length
-
-  def set(bufferMessage: BufferMessage) {
-    val startTime = System.currentTimeMillis
-    val newBlockMessages = new ArrayBuffer[BlockMessage]()
-    val buffer = bufferMessage.buffers(0)
-    buffer.clear()
-    while (buffer.remaining() > 0) {
-      val size = buffer.getInt()
-      logDebug("Creating block message of size " + size + " bytes")
-      val newBuffer = buffer.slice()
-      newBuffer.clear()
-      newBuffer.limit(size)
-      logDebug("Trying to convert buffer " + newBuffer + " to block message")
-      val newBlockMessage = BlockMessage.fromByteBuffer(newBuffer)
-      logDebug("Created " + newBlockMessage)
-      newBlockMessages += newBlockMessage
-      buffer.position(buffer.position() + size)
-    }
-    val finishTime = System.currentTimeMillis
-    logDebug("Converted block message array from buffer message in " +
-      (finishTime - startTime) / 1000.0  + " s")
-    this.blockMessages = newBlockMessages
-  }
-
-  def toBufferMessage: BufferMessage = {
-    val buffers = new ArrayBuffer[ByteBuffer]()
-
-    blockMessages.foreach(blockMessage => {
-      val bufferMessage = blockMessage.toBufferMessage
-      logDebug("Adding " + blockMessage)
-      val sizeBuffer = ByteBuffer.allocate(4).putInt(bufferMessage.size)
-      sizeBuffer.flip
-      buffers += sizeBuffer
-      buffers ++= bufferMessage.buffers
-      logDebug("Added " + bufferMessage)
-    })
-
-    logDebug("Buffer list:")
-    buffers.foreach((x: ByteBuffer) => logDebug("" + x))
-    Message.createBufferMessage(buffers)
-  }
-}
-
-private[nio] object BlockMessageArray extends Logging {
-
-  def fromBufferMessage(bufferMessage: BufferMessage): BlockMessageArray = {
-    val newBlockMessageArray = new BlockMessageArray()
-    newBlockMessageArray.set(bufferMessage)
-    newBlockMessageArray
-  }
-
-  def main(args: Array[String]) {
-    val blockMessages =
-      (0 until 10).map { i =>
-        if (i % 2 == 0) {
-          val buffer = ByteBuffer.allocate(100)
-          buffer.clear()
-          BlockMessage.fromPutBlock(PutBlock(TestBlockId(i.toString), buffer,
-            StorageLevel.MEMORY_ONLY_SER))
-        } else {
-          BlockMessage.fromGetBlock(GetBlock(TestBlockId(i.toString)))
-        }
-      }
-    val blockMessageArray = new BlockMessageArray(blockMessages)
-    logDebug("Block message array created")
-
-    val bufferMessage = blockMessageArray.toBufferMessage
-    logDebug("Converted to buffer message")
-
-    val totalSize = bufferMessage.size
-    val newBuffer = ByteBuffer.allocate(totalSize)
-    newBuffer.clear()
-    bufferMessage.buffers.foreach(buffer => {
-      assert (0 == buffer.position())
-      newBuffer.put(buffer)
-      buffer.rewind()
-    })
-    newBuffer.flip
-    val newBufferMessage = Message.createBufferMessage(newBuffer)
-    logDebug("Copied to new buffer message, size = " + newBufferMessage.size)
-
-    val newBlockMessageArray = BlockMessageArray.fromBufferMessage(newBufferMessage)
-    logDebug("Converted back to block message array")
-    // scalastyle:off println
-    newBlockMessageArray.foreach(blockMessage => {
-      blockMessage.getType match {
-        case BlockMessage.TYPE_PUT_BLOCK => {
-          val pB = PutBlock(blockMessage.getId, blockMessage.getData, blockMessage.getLevel)
-          println(pB)
-        }
-        case BlockMessage.TYPE_GET_BLOCK => {
-          val gB = new GetBlock(blockMessage.getId)
-          println(gB)
-        }
-      }
-    })
-    // scalastyle:on println
-  }
-}
-
-
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
deleted file mode 100644
index 9a9e22b0c2366..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.ArrayBuffer
-
-import org.apache.spark.storage.BlockManager
-
-
-private[nio]
-class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: Int)
-  extends Message(Message.BUFFER_MESSAGE, id_) {
-
-  val initialSize = currentSize()
-  var gotChunkForSendingOnce = false
-
-  def size: Int = initialSize
-
-  def currentSize(): Int = {
-    if (buffers == null || buffers.isEmpty) {
-      0
-    } else {
-      buffers.map(_.remaining).reduceLeft(_ + _)
-    }
-  }
-
-  def getChunkForSending(maxChunkSize: Int): Option[MessageChunk] = {
-    if (maxChunkSize <= 0) {
-      throw new Exception("Max chunk size is " + maxChunkSize)
-    }
-
-    val security = if (isSecurityNeg) 1 else 0
-    if (size == 0 && !gotChunkForSendingOnce) {
-      val newChunk = new MessageChunk(
-        new MessageChunkHeader(typ, id, 0, 0, ackId, hasError, security, senderAddress), null)
-      gotChunkForSendingOnce = true
-      return Some(newChunk)
-    }
-
-    while(!buffers.isEmpty) {
-      val buffer = buffers(0)
-      if (buffer.remaining == 0) {
-        BlockManager.dispose(buffer)
-        buffers -= buffer
-      } else {
-        val newBuffer = if (buffer.remaining <= maxChunkSize) {
-          buffer.duplicate()
-        } else {
-          buffer.slice().limit(maxChunkSize).asInstanceOf[ByteBuffer]
-        }
-        buffer.position(buffer.position + newBuffer.remaining)
-        val newChunk = new MessageChunk(new MessageChunkHeader(
-          typ, id, size, newBuffer.remaining, ackId,
-          hasError, security, senderAddress), newBuffer)
-        gotChunkForSendingOnce = true
-        return Some(newChunk)
-      }
-    }
-    None
-  }
-
-  def getChunkForReceiving(chunkSize: Int): Option[MessageChunk] = {
-    // STRONG ASSUMPTION: BufferMessage created when receiving data has ONLY ONE data buffer
-    if (buffers.size > 1) {
-      throw new Exception("Attempting to get chunk from message with multiple data buffers")
-    }
-    val buffer = buffers(0)
-    val security = if (isSecurityNeg) 1 else 0
-    if (buffer.remaining > 0) {
-      if (buffer.remaining < chunkSize) {
-        throw new Exception("Not enough space in data buffer for receiving chunk")
-      }
-      val newBuffer = buffer.slice().limit(chunkSize).asInstanceOf[ByteBuffer]
-      buffer.position(buffer.position + newBuffer.remaining)
-      val newChunk = new MessageChunk(new MessageChunkHeader(
-          typ, id, size, newBuffer.remaining, ackId, hasError, security, senderAddress), newBuffer)
-      return Some(newChunk)
-    }
-    None
-  }
-
-  def flip() {
-    buffers.foreach(_.flip)
-  }
-
-  def hasAckId(): Boolean = ackId != 0
-
-  def isCompletelyReceived: Boolean = !buffers(0).hasRemaining
-
-  override def toString: String = {
-    if (hasAckId) {
-      "BufferAckMessage(aid = " + ackId + ", id = " + id + ", size = " + size + ")"
-    } else {
-      "BufferMessage(id = " + id + ", size = " + size + ")"
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
deleted file mode 100644
index 8d9ebadaf79d4..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
+++ /dev/null
@@ -1,619 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net._
-import java.nio._
-import java.nio.channels._
-import java.util.concurrent.ConcurrentLinkedQueue
-import java.util.LinkedList
-
-import scala.collection.JavaConverters._
-import scala.collection.mutable.{ArrayBuffer, HashMap}
-import scala.util.control.NonFatal
-
-import org.apache.spark._
-import org.apache.spark.network.sasl.{SparkSaslClient, SparkSaslServer}
-
-private[nio]
-abstract class Connection(val channel: SocketChannel, val selector: Selector,
-    val socketRemoteConnectionManagerId: ConnectionManagerId, val connectionId: ConnectionId,
-    val securityMgr: SecurityManager)
-  extends Logging {
-
-  var sparkSaslServer: SparkSaslServer = null
-  var sparkSaslClient: SparkSaslClient = null
-
-  def this(channel_ : SocketChannel, selector_ : Selector, id_ : ConnectionId,
-      securityMgr_ : SecurityManager) = {
-    this(channel_, selector_,
-      ConnectionManagerId.fromSocketAddress(
-        channel_.socket.getRemoteSocketAddress.asInstanceOf[InetSocketAddress]),
-        id_, securityMgr_)
-  }
-
-  channel.configureBlocking(false)
-  channel.socket.setTcpNoDelay(true)
-  channel.socket.setReuseAddress(true)
-  channel.socket.setKeepAlive(true)
-  /* channel.socket.setReceiveBufferSize(32768) */
-
-  @volatile private var closed = false
-  var onCloseCallback: Connection => Unit = null
-  val onExceptionCallbacks = new ConcurrentLinkedQueue[(Connection, Throwable) => Unit]
-  var onKeyInterestChangeCallback: (Connection, Int) => Unit = null
-
-  val remoteAddress = getRemoteAddress()
-
-  def isSaslComplete(): Boolean
-
-  def resetForceReregister(): Boolean
-
-  // Read channels typically do not register for write and write does not for read
-  // Now, we do have write registering for read too (temporarily), but this is to detect
-  // channel close NOT to actually read/consume data on it !
-  // How does this work if/when we move to SSL ?
-
-  // What is the interest to register with selector for when we want this connection to be selected
-  def registerInterest()
-
-  // What is the interest to register with selector for when we want this connection to
-  // be de-selected
-  // Traditionally, 0 - but in our case, for example, for close-detection on SendingConnection hack,
-  // it will be SelectionKey.OP_READ (until we fix it properly)
-  def unregisterInterest()
-
-  // On receiving a read event, should we change the interest for this channel or not ?
-  // Will be true for ReceivingConnection, false for SendingConnection.
-  def changeInterestForRead(): Boolean
-
-  private def disposeSasl() {
-    if (sparkSaslServer != null) {
-      sparkSaslServer.dispose()
-    }
-
-    if (sparkSaslClient != null) {
-      sparkSaslClient.dispose()
-    }
-  }
-
-  // On receiving a write event, should we change the interest for this channel or not ?
-  // Will be false for ReceivingConnection, true for SendingConnection.
-  // Actually, for now, should not get triggered for ReceivingConnection
-  def changeInterestForWrite(): Boolean
-
-  def getRemoteConnectionManagerId(): ConnectionManagerId = {
-    socketRemoteConnectionManagerId
-  }
-
-  def key(): SelectionKey = channel.keyFor(selector)
-
-  def getRemoteAddress(): InetSocketAddress = {
-    channel.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress]
-  }
-
-  // Returns whether we have to register for further reads or not.
-  def read(): Boolean = {
-    throw new UnsupportedOperationException(
-      "Cannot read on connection of type " + this.getClass.toString)
-  }
-
-  // Returns whether we have to register for further writes or not.
-  def write(): Boolean = {
-    throw new UnsupportedOperationException(
-      "Cannot write on connection of type " + this.getClass.toString)
-  }
-
-  def close() {
-    closed = true
-    val k = key()
-    if (k != null) {
-      k.cancel()
-    }
-    channel.close()
-    disposeSasl()
-    callOnCloseCallback()
-  }
-
-  protected def isClosed: Boolean = closed
-
-  def onClose(callback: Connection => Unit) {
-    onCloseCallback = callback
-  }
-
-  def onException(callback: (Connection, Throwable) => Unit) {
-    onExceptionCallbacks.add(callback)
-  }
-
-  def onKeyInterestChange(callback: (Connection, Int) => Unit) {
-    onKeyInterestChangeCallback = callback
-  }
-
-  def callOnExceptionCallbacks(e: Throwable) {
-    onExceptionCallbacks.asScala.foreach {
-      callback =>
-        try {
-          callback(this, e)
-        } catch {
-          case NonFatal(e) => {
-            logWarning("Ignored error in onExceptionCallback", e)
-          }
-        }
-    }
-  }
-
-  def callOnCloseCallback() {
-    if (onCloseCallback != null) {
-      onCloseCallback(this)
-    } else {
-      logWarning("Connection to " + getRemoteConnectionManagerId() +
-        " closed and OnExceptionCallback not registered")
-    }
-
-  }
-
-  def changeConnectionKeyInterest(ops: Int) {
-    if (onKeyInterestChangeCallback != null) {
-      onKeyInterestChangeCallback(this, ops)
-    } else {
-      throw new Exception("OnKeyInterestChangeCallback not registered")
-    }
-  }
-
-  def printRemainingBuffer(buffer: ByteBuffer) {
-    val bytes = new Array[Byte](buffer.remaining)
-    val curPosition = buffer.position
-    buffer.get(bytes)
-    bytes.foreach(x => print(x + " "))
-    buffer.position(curPosition)
-    print(" (" + bytes.length + ")")
-  }
-
-  def printBuffer(buffer: ByteBuffer, position: Int, length: Int) {
-    val bytes = new Array[Byte](length)
-    val curPosition = buffer.position
-    buffer.position(position)
-    buffer.get(bytes)
-    bytes.foreach(x => print(x + " "))
-    print(" (" + position + ", " + length + ")")
-    buffer.position(curPosition)
-  }
-}
-
-
-private[nio]
-class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
-    remoteId_ : ConnectionManagerId, id_ : ConnectionId,
-    securityMgr_ : SecurityManager)
-  extends Connection(SocketChannel.open, selector_, remoteId_, id_, securityMgr_) {
-
-  def isSaslComplete(): Boolean = {
-    if (sparkSaslClient != null) sparkSaslClient.isComplete() else false
-  }
-
-  private class Outbox {
-    val messages = new LinkedList[Message]()
-    val defaultChunkSize = 65536
-    var nextMessageToBeUsed = 0
-
-    def addMessage(message: Message) {
-      messages.synchronized {
-        messages.add(message)
-        logDebug("Added [" + message + "] to outbox for sending to " +
-          "[" + getRemoteConnectionManagerId() + "]")
-      }
-    }
-
-    def getChunk(): Option[MessageChunk] = {
-      messages.synchronized {
-        while (!messages.isEmpty) {
-          /* nextMessageToBeUsed = nextMessageToBeUsed % messages.size */
-          /* val message = messages(nextMessageToBeUsed) */
-
-          val message = if (securityMgr.isAuthenticationEnabled() && !isSaslComplete()) {
-            // only allow sending of security messages until sasl is complete
-            var pos = 0
-            var securityMsg: Message = null
-            while (pos < messages.size() && securityMsg == null) {
-              if (messages.get(pos).isSecurityNeg) {
-                securityMsg = messages.remove(pos)
-              }
-              pos = pos + 1
-            }
-            // didn't find any security messages and auth isn't completed so return
-            if (securityMsg == null) return None
-            securityMsg
-          } else {
-            messages.removeFirst()
-          }
-
-          val chunk = message.getChunkForSending(defaultChunkSize)
-          if (chunk.isDefined) {
-            messages.add(message)
-            nextMessageToBeUsed = nextMessageToBeUsed + 1
-            if (!message.started) {
-              logDebug(
-                "Starting to send [" + message + "] to [" + getRemoteConnectionManagerId() + "]")
-              message.started = true
-              message.startTime = System.currentTimeMillis
-            }
-            logTrace(
-              "Sending chunk from [" + message + "] to [" + getRemoteConnectionManagerId() + "]")
-            return chunk
-          } else {
-            message.finishTime = System.currentTimeMillis
-            logDebug("Finished sending [" + message + "] to [" + getRemoteConnectionManagerId() +
-              "] in "  + message.timeTaken )
-          }
-        }
-      }
-      None
-    }
-  }
-
-  // outbox is used as a lock - ensure that it is always used as a leaf (since methods which
-  // lock it are invoked in context of other locks)
-  private val outbox = new Outbox()
-  /*
-    This is orthogonal to whether we have pending bytes to write or not - and satisfies a slightly
-    different purpose. This flag is to see if we need to force reregister for write even when we
-    do not have any pending bytes to write to socket.
-    This can happen due to a race between adding pending buffers, and checking for existing of
-    data as detailed in https://github.com/mesos/spark/pull/791
-   */
-  private var needForceReregister = false
-
-  val currentBuffers = new ArrayBuffer[ByteBuffer]()
-
-  /* channel.socket.setSendBufferSize(256 * 1024) */
-
-  override def getRemoteAddress(): InetSocketAddress = address
-
-  val DEFAULT_INTEREST = SelectionKey.OP_READ
-
-  override def registerInterest() {
-    // Registering read too - does not really help in most cases, but for some
-    // it does - so let us keep it for now.
-    changeConnectionKeyInterest(SelectionKey.OP_WRITE | DEFAULT_INTEREST)
-  }
-
-  override def unregisterInterest() {
-    changeConnectionKeyInterest(DEFAULT_INTEREST)
-  }
-
-  def registerAfterAuth(): Unit = {
-    outbox.synchronized {
-      needForceReregister = true
-    }
-    if (channel.isConnected) {
-      registerInterest()
-    }
-  }
-
-  def send(message: Message) {
-    outbox.synchronized {
-      outbox.addMessage(message)
-      needForceReregister = true
-    }
-    if (channel.isConnected) {
-      registerInterest()
-    }
-  }
-
-  // return previous value after resetting it.
-  def resetForceReregister(): Boolean = {
-    outbox.synchronized {
-      val result = needForceReregister
-      needForceReregister = false
-      result
-    }
-  }
-
-  // MUST be called within the selector loop
-  def connect() {
-    try {
-      channel.register(selector, SelectionKey.OP_CONNECT)
-      channel.connect(address)
-      logInfo("Initiating connection to [" + address + "]")
-    } catch {
-      case e: Exception =>
-        logError("Error connecting to " + address, e)
-        callOnExceptionCallbacks(e)
-    }
-  }
-
-  def finishConnect(force: Boolean): Boolean = {
-    try {
-      // Typically, this should finish immediately since it was triggered by a connect
-      // selection - though need not necessarily always complete successfully.
-      val connected = channel.finishConnect
-      if (!force && !connected) {
-        logInfo(
-          "finish connect failed [" + address + "], " + outbox.messages.size + " messages pending")
-        return false
-      }
-
-      // Fallback to previous behavior - assume finishConnect completed
-      // This will happen only when finishConnect failed for some repeated number of times
-      // (10 or so)
-      // Is highly unlikely unless there was an unclean close of socket, etc
-      registerInterest()
-      logInfo("Connected to [" + address + "], " + outbox.messages.size + " messages pending")
-    } catch {
-      case e: Exception => {
-        logWarning("Error finishing connection to " + address, e)
-        callOnExceptionCallbacks(e)
-      }
-    }
-    true
-  }
-
-  override def write(): Boolean = {
-    try {
-      while (true) {
-        if (currentBuffers.size == 0) {
-          outbox.synchronized {
-            outbox.getChunk() match {
-              case Some(chunk) => {
-                val buffers = chunk.buffers
-                // If we have 'seen' pending messages, then reset flag - since we handle that as
-                // normal registering of event (below)
-                if (needForceReregister && buffers.exists(_.remaining() > 0)) resetForceReregister()
-
-                currentBuffers ++= buffers
-              }
-              case None => {
-                // changeConnectionKeyInterest(0)
-                /* key.interestOps(0) */
-                return false
-              }
-            }
-          }
-        }
-
-        if (currentBuffers.size > 0) {
-          val buffer = currentBuffers(0)
-          val remainingBytes = buffer.remaining
-          val writtenBytes = channel.write(buffer)
-          if (buffer.remaining == 0) {
-            currentBuffers -= buffer
-          }
-          if (writtenBytes < remainingBytes) {
-            // re-register for write.
-            return true
-          }
-        }
-      }
-    } catch {
-      case e: Exception => {
-        logWarning("Error writing in connection to " + getRemoteConnectionManagerId(), e)
-        callOnExceptionCallbacks(e)
-        close()
-        return false
-      }
-    }
-    // should not happen - to keep scala compiler happy
-    true
-  }
-
-  // This is a hack to determine if remote socket was closed or not.
-  // SendingConnection DOES NOT expect to receive any data - if it does, it is an error
-  // For a bunch of cases, read will return -1 in case remote socket is closed : hence we
-  // register for reads to determine that.
-  override def read(): Boolean = {
-    // We don't expect the other side to send anything; so, we just read to detect an error or EOF.
-    try {
-      val length = channel.read(ByteBuffer.allocate(1))
-      if (length == -1) { // EOF
-        close()
-      } else if (length > 0) {
-        logWarning(
-          "Unexpected data read from SendingConnection to " + getRemoteConnectionManagerId())
-      }
-    } catch {
-      case e: Exception =>
-        logError("Exception while reading SendingConnection to " + getRemoteConnectionManagerId(),
-          e)
-        callOnExceptionCallbacks(e)
-        close()
-    }
-
-    false
-  }
-
-  override def changeInterestForRead(): Boolean = false
-
-  override def changeInterestForWrite(): Boolean = ! isClosed
-}
-
-
-// Must be created within selector loop - else deadlock
-private[spark] class ReceivingConnection(
-    channel_ : SocketChannel,
-    selector_ : Selector,
-    id_ : ConnectionId,
-    securityMgr_ : SecurityManager)
-    extends Connection(channel_, selector_, id_, securityMgr_) {
-
-  def isSaslComplete(): Boolean = {
-    if (sparkSaslServer != null) sparkSaslServer.isComplete() else false
-  }
-
-  class Inbox() {
-    val messages = new HashMap[Int, BufferMessage]()
-
-    def getChunk(header: MessageChunkHeader): Option[MessageChunk] = {
-
-      def createNewMessage: BufferMessage = {
-        val newMessage = Message.create(header).asInstanceOf[BufferMessage]
-        newMessage.started = true
-        newMessage.startTime = System.currentTimeMillis
-        newMessage.isSecurityNeg = header.securityNeg == 1
-        logDebug(
-          "Starting to receive [" + newMessage + "] from [" + getRemoteConnectionManagerId() + "]")
-        messages += ((newMessage.id, newMessage))
-        newMessage
-      }
-
-      val message = messages.getOrElseUpdate(header.id, createNewMessage)
-      logTrace(
-        "Receiving chunk of [" + message + "] from [" + getRemoteConnectionManagerId() + "]")
-      message.getChunkForReceiving(header.chunkSize)
-    }
-
-    def getMessageForChunk(chunk: MessageChunk): Option[BufferMessage] = {
-      messages.get(chunk.header.id)
-    }
-
-    def removeMessage(message: Message) {
-      messages -= message.id
-    }
-  }
-
-  @volatile private var inferredRemoteManagerId: ConnectionManagerId = null
-
-  override def getRemoteConnectionManagerId(): ConnectionManagerId = {
-    val currId = inferredRemoteManagerId
-    if (currId != null) currId else super.getRemoteConnectionManagerId()
-  }
-
-  // The receiver's remote address is the local socket on remote side : which is NOT
-  // the connection manager id of the receiver.
-  // We infer that from the messages we receive on the receiver socket.
-  private def processConnectionManagerId(header: MessageChunkHeader) {
-    val currId = inferredRemoteManagerId
-    if (header.address == null || currId != null) return
-
-    val managerId = ConnectionManagerId.fromSocketAddress(header.address)
-
-    if (managerId != null) {
-      inferredRemoteManagerId = managerId
-    }
-  }
-
-
-  val inbox = new Inbox()
-  val headerBuffer: ByteBuffer = ByteBuffer.allocate(MessageChunkHeader.HEADER_SIZE)
-  var onReceiveCallback: (Connection, Message) => Unit = null
-  var currentChunk: MessageChunk = null
-
-  channel.register(selector, SelectionKey.OP_READ)
-
-  override def read(): Boolean = {
-    try {
-      while (true) {
-        if (currentChunk == null) {
-          val headerBytesRead = channel.read(headerBuffer)
-          if (headerBytesRead == -1) {
-            close()
-            return false
-          }
-          if (headerBuffer.remaining > 0) {
-            // re-register for read event ...
-            return true
-          }
-          headerBuffer.flip
-          if (headerBuffer.remaining != MessageChunkHeader.HEADER_SIZE) {
-            throw new Exception(
-              "Unexpected number of bytes (" + headerBuffer.remaining + ") in the header")
-          }
-          val header = MessageChunkHeader.create(headerBuffer)
-          headerBuffer.clear()
-
-          processConnectionManagerId(header)
-
-          header.typ match {
-            case Message.BUFFER_MESSAGE => {
-              if (header.totalSize == 0) {
-                if (onReceiveCallback != null) {
-                  onReceiveCallback(this, Message.create(header))
-                }
-                currentChunk = null
-                // re-register for read event ...
-                return true
-              } else {
-                currentChunk = inbox.getChunk(header).orNull
-              }
-            }
-            case _ => throw new Exception("Message of unknown type received")
-          }
-        }
-
-        if (currentChunk == null) throw new Exception("No message chunk to receive data")
-
-        val bytesRead = channel.read(currentChunk.buffer)
-        if (bytesRead == 0) {
-          // re-register for read event ...
-          return true
-        } else if (bytesRead == -1) {
-          close()
-          return false
-        }
-
-        /* logDebug("Read " + bytesRead + " bytes for the buffer") */
-
-        if (currentChunk.buffer.remaining == 0) {
-          /* println("Filled buffer at " + System.currentTimeMillis) */
-          val bufferMessage = inbox.getMessageForChunk(currentChunk).get
-          if (bufferMessage.isCompletelyReceived) {
-            bufferMessage.flip()
-            bufferMessage.finishTime = System.currentTimeMillis
-            logDebug("Finished receiving [" + bufferMessage + "] from " +
-              "[" + getRemoteConnectionManagerId() + "] in " + bufferMessage.timeTaken)
-            if (onReceiveCallback != null) {
-              onReceiveCallback(this, bufferMessage)
-            }
-            inbox.removeMessage(bufferMessage)
-          }
-          currentChunk = null
-        }
-      }
-    } catch {
-      case e: Exception => {
-        logWarning("Error reading from connection to " + getRemoteConnectionManagerId(), e)
-        callOnExceptionCallbacks(e)
-        close()
-        return false
-      }
-    }
-    // should not happen - to keep scala compiler happy
-    true
-  }
-
-  def onReceive(callback: (Connection, Message) => Unit) {onReceiveCallback = callback}
-
-  // override def changeInterestForRead(): Boolean = ! isClosed
-  override def changeInterestForRead(): Boolean = true
-
-  override def changeInterestForWrite(): Boolean = {
-    throw new IllegalStateException("Unexpected invocation right now")
-  }
-
-  override def registerInterest() {
-    // Registering read too - does not really help in most cases, but for some
-    // it does - so let us keep it for now.
-    changeConnectionKeyInterest(SelectionKey.OP_READ)
-  }
-
-  override def unregisterInterest() {
-    changeConnectionKeyInterest(0)
-  }
-
-  // For read conn, always false.
-  override def resetForceReregister(): Boolean = false
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
deleted file mode 100644
index b3b281ff465f1..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-private[nio] case class ConnectionId(connectionManagerId: ConnectionManagerId, uniqId: Int) {
-  override def toString: String = {
-    connectionManagerId.host + "_" + connectionManagerId.port + "_" + uniqId
-  }
-}
-
-private[nio] object ConnectionId {
-
-  def createConnectionIdFromString(connectionIdString: String): ConnectionId = {
-    val res = connectionIdString.split("_").map(_.trim())
-    if (res.size != 3) {
-      throw new Exception("Error converting ConnectionId string: " + connectionIdString +
-        " to a ConnectionId Object")
-    }
-    new ConnectionId(new ConnectionManagerId(res(0), res(1).toInt), res(2).toInt)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
deleted file mode 100644
index 9143918790381..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
+++ /dev/null
@@ -1,1157 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.io.IOException
-import java.lang.ref.WeakReference
-import java.net._
-import java.nio._
-import java.nio.channels._
-import java.nio.channels.spi._
-import java.util.concurrent.atomic.AtomicInteger
-import java.util.concurrent.{LinkedBlockingDeque, ThreadPoolExecutor, TimeUnit}
-
-import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, SynchronizedMap, SynchronizedQueue}
-import scala.concurrent.duration._
-import scala.concurrent.{Await, ExecutionContext, Future, Promise}
-import scala.language.postfixOps
-
-import com.google.common.base.Charsets.UTF_8
-import io.netty.util.{Timeout, TimerTask, HashedWheelTimer}
-
-import org.apache.spark._
-import org.apache.spark.network.sasl.{SparkSaslClient, SparkSaslServer}
-import org.apache.spark.util.{ThreadUtils, Utils}
-
-import scala.util.Try
-import scala.util.control.NonFatal
-
-private[nio] class ConnectionManager(
-    port: Int,
-    conf: SparkConf,
-    securityManager: SecurityManager,
-    name: String = "Connection manager")
-  extends Logging {
-
-  /**
-   * Used by sendMessageReliably to track messages being sent.
-   * @param message the message that was sent
-   * @param connectionManagerId the connection manager that sent this message
-   * @param completionHandler callback that's invoked when the send has completed or failed
-   */
-  class MessageStatus(
-      val message: Message,
-      val connectionManagerId: ConnectionManagerId,
-      completionHandler: Try[Message] => Unit) {
-
-    def success(ackMessage: Message) {
-      if (ackMessage == null) {
-        failure(new NullPointerException)
-      }
-      else {
-        completionHandler(scala.util.Success(ackMessage))
-      }
-    }
-
-    def failWithoutAck() {
-      completionHandler(scala.util.Failure(new IOException("Failed without being ACK'd")))
-    }
-
-    def failure(e: Throwable) {
-      completionHandler(scala.util.Failure(e))
-    }
-  }
-
-  private val selector = SelectorProvider.provider.openSelector()
-  private val ackTimeoutMonitor =
-    new HashedWheelTimer(ThreadUtils.namedThreadFactory("AckTimeoutMonitor"))
-
-  private val ackTimeout =
-    conf.getTimeAsSeconds("spark.core.connection.ack.wait.timeout",
-      conf.get("spark.network.timeout", "120s"))
-
-  // Get the thread counts from the Spark Configuration.
-  //
-  // Even though the ThreadPoolExecutor constructor takes both a minimum and maximum value,
-  // we only query for the minimum value because we are using LinkedBlockingDeque.
-  //
-  // The JavaDoc for ThreadPoolExecutor points out that when using a LinkedBlockingDeque (which is
-  // an unbounded queue) no more than corePoolSize threads will ever be created, so only the "min"
-  // parameter is necessary.
-  private val handlerThreadCount = conf.getInt("spark.core.connection.handler.threads.min", 20)
-  private val ioThreadCount = conf.getInt("spark.core.connection.io.threads.min", 4)
-  private val connectThreadCount = conf.getInt("spark.core.connection.connect.threads.min", 1)
-
-  private val handleMessageExecutor = new ThreadPoolExecutor(
-    handlerThreadCount,
-    handlerThreadCount,
-    conf.getInt("spark.core.connection.handler.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable](),
-    ThreadUtils.namedThreadFactory("handle-message-executor")) {
-
-    override def afterExecute(r: Runnable, t: Throwable): Unit = {
-      super.afterExecute(r, t)
-      if (t != null && NonFatal(t)) {
-        logError("Error in handleMessageExecutor is not handled properly", t)
-      }
-    }
-  }
-
-  private val handleReadWriteExecutor = new ThreadPoolExecutor(
-    ioThreadCount,
-    ioThreadCount,
-    conf.getInt("spark.core.connection.io.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable](),
-    ThreadUtils.namedThreadFactory("handle-read-write-executor")) {
-
-    override def afterExecute(r: Runnable, t: Throwable): Unit = {
-      super.afterExecute(r, t)
-      if (t != null && NonFatal(t)) {
-        logError("Error in handleReadWriteExecutor is not handled properly", t)
-      }
-    }
-  }
-
-  // Use a different, yet smaller, thread pool - infrequently used with very short lived tasks :
-  // which should be executed asap
-  private val handleConnectExecutor = new ThreadPoolExecutor(
-    connectThreadCount,
-    connectThreadCount,
-    conf.getInt("spark.core.connection.connect.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable](),
-    ThreadUtils.namedThreadFactory("handle-connect-executor")) {
-
-    override def afterExecute(r: Runnable, t: Throwable): Unit = {
-      super.afterExecute(r, t)
-      if (t != null && NonFatal(t)) {
-        logError("Error in handleConnectExecutor is not handled properly", t)
-      }
-    }
-  }
-
-  private val serverChannel = ServerSocketChannel.open()
-  // used to track the SendingConnections waiting to do SASL negotiation
-  private val connectionsAwaitingSasl = new HashMap[ConnectionId, SendingConnection]
-    with SynchronizedMap[ConnectionId, SendingConnection]
-  private val connectionsByKey =
-    new HashMap[SelectionKey, Connection] with SynchronizedMap[SelectionKey, Connection]
-  private val connectionsById = new HashMap[ConnectionManagerId, SendingConnection]
-    with SynchronizedMap[ConnectionManagerId, SendingConnection]
-  // Tracks sent messages for which we are awaiting acknowledgements.  Entries are added to this
-  // map when messages are sent and are removed when acknowledgement messages are received or when
-  // acknowledgement timeouts expire
-  private val messageStatuses = new HashMap[Int, MessageStatus]  // [MessageId, MessageStatus]
-  private val keyInterestChangeRequests = new SynchronizedQueue[(SelectionKey, Int)]
-  private val registerRequests = new SynchronizedQueue[SendingConnection]
-
-  implicit val futureExecContext = ExecutionContext.fromExecutor(
-    ThreadUtils.newDaemonCachedThreadPool("Connection manager future execution context"))
-
-  @volatile
-  private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message] = null
-
-  private val authEnabled = securityManager.isAuthenticationEnabled()
-
-  serverChannel.configureBlocking(false)
-  serverChannel.socket.setReuseAddress(true)
-  serverChannel.socket.setReceiveBufferSize(256 * 1024)
-
-  private def startService(port: Int): (ServerSocketChannel, Int) = {
-    serverChannel.socket.bind(new InetSocketAddress(port))
-    (serverChannel, serverChannel.socket.getLocalPort)
-  }
-  Utils.startServiceOnPort[ServerSocketChannel](port, startService, conf, name)
-  serverChannel.register(selector, SelectionKey.OP_ACCEPT)
-
-  val id = new ConnectionManagerId(Utils.localHostName, serverChannel.socket.getLocalPort)
-  logInfo("Bound socket to port " + serverChannel.socket.getLocalPort() + " with id = " + id)
-
-  // used in combination with the ConnectionManagerId to create unique Connection ids
-  // to be able to track asynchronous messages
-  private val idCount: AtomicInteger = new AtomicInteger(1)
-
-  private val writeRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]()
-  private val readRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]()
-
-  @volatile private var isActive = true
-  private val selectorThread = new Thread("connection-manager-thread") {
-    override def run(): Unit = ConnectionManager.this.run()
-  }
-  selectorThread.setDaemon(true)
-  // start this thread last, since it invokes run(), which accesses members above
-  selectorThread.start()
-
-  private def triggerWrite(key: SelectionKey) {
-    val conn = connectionsByKey.getOrElse(key, null)
-    if (conn == null) return
-
-    writeRunnableStarted.synchronized {
-      // So that we do not trigger more write events while processing this one.
-      // The write method will re-register when done.
-      if (conn.changeInterestForWrite()) conn.unregisterInterest()
-      if (writeRunnableStarted.contains(key)) {
-        // key.interestOps(key.interestOps() & ~ SelectionKey.OP_WRITE)
-        return
-      }
-
-      writeRunnableStarted += key
-    }
-    handleReadWriteExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          var register: Boolean = false
-          try {
-            register = conn.write()
-          } finally {
-            writeRunnableStarted.synchronized {
-              writeRunnableStarted -= key
-              val needReregister = register || conn.resetForceReregister()
-              if (needReregister && conn.changeInterestForWrite()) {
-                conn.registerInterest()
-              }
-            }
-          }
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when writing to " + conn.getRemoteConnectionManagerId(), e)
-            conn.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    } )
-  }
-
-
-  private def triggerRead(key: SelectionKey) {
-    val conn = connectionsByKey.getOrElse(key, null)
-    if (conn == null) return
-
-    readRunnableStarted.synchronized {
-      // So that we do not trigger more read events while processing this one.
-      // The read method will re-register when done.
-      if (conn.changeInterestForRead())conn.unregisterInterest()
-      if (readRunnableStarted.contains(key)) {
-        return
-      }
-
-      readRunnableStarted += key
-    }
-    handleReadWriteExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          var register: Boolean = false
-          try {
-            register = conn.read()
-          } finally {
-            readRunnableStarted.synchronized {
-              readRunnableStarted -= key
-              if (register && conn.changeInterestForRead()) {
-                conn.registerInterest()
-              }
-            }
-          }
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when reading from " + conn.getRemoteConnectionManagerId(), e)
-            conn.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    } )
-  }
-
-  private def triggerConnect(key: SelectionKey) {
-    val conn = connectionsByKey.getOrElse(key, null).asInstanceOf[SendingConnection]
-    if (conn == null) return
-
-    // prevent other events from being triggered
-    // Since we are still trying to connect, we do not need to do the additional steps in
-    // triggerWrite
-    conn.changeConnectionKeyInterest(0)
-
-    handleConnectExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          var tries: Int = 10
-          while (tries >= 0) {
-            if (conn.finishConnect(false)) return
-            // Sleep ?
-            Thread.sleep(1)
-            tries -= 1
-          }
-
-          // fallback to previous behavior : we should not really come here since this method was
-          // triggered since channel became connectable : but at times, the first finishConnect need
-          // not succeed : hence the loop to retry a few 'times'.
-          conn.finishConnect(true)
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when finishConnect for " + conn.getRemoteConnectionManagerId(), e)
-            conn.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    } )
-  }
-
-  // MUST be called within selector loop - else deadlock.
-  private def triggerForceCloseByException(key: SelectionKey, e: Exception) {
-    try {
-      key.interestOps(0)
-    } catch {
-      // ignore exceptions
-      case e: Exception => logDebug("Ignoring exception", e)
-    }
-
-    val conn = connectionsByKey.getOrElse(key, null)
-    if (conn == null) return
-
-    // Pushing to connect threadpool
-    handleConnectExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          conn.callOnExceptionCallbacks(e)
-        } catch {
-          // ignore exceptions
-          case NonFatal(e) => logDebug("Ignoring exception", e)
-        }
-        try {
-          conn.close()
-        } catch {
-          // ignore exceptions
-          case NonFatal(e) => logDebug("Ignoring exception", e)
-        }
-      }
-    })
-  }
-
-
-  def run() {
-    try {
-      while (isActive) {
-        while (!registerRequests.isEmpty) {
-          val conn: SendingConnection = registerRequests.dequeue()
-          addListeners(conn)
-          conn.connect()
-          addConnection(conn)
-        }
-
-        while(!keyInterestChangeRequests.isEmpty) {
-          val (key, ops) = keyInterestChangeRequests.dequeue()
-
-          try {
-            if (key.isValid) {
-              val connection = connectionsByKey.getOrElse(key, null)
-              if (connection != null) {
-                val lastOps = key.interestOps()
-                key.interestOps(ops)
-
-                // hot loop - prevent materialization of string if trace not enabled.
-                if (isTraceEnabled()) {
-                  def intToOpStr(op: Int): String = {
-                    val opStrs = ArrayBuffer[String]()
-                    if ((op & SelectionKey.OP_READ) != 0) opStrs += "READ"
-                    if ((op & SelectionKey.OP_WRITE) != 0) opStrs += "WRITE"
-                    if ((op & SelectionKey.OP_CONNECT) != 0) opStrs += "CONNECT"
-                    if ((op & SelectionKey.OP_ACCEPT) != 0) opStrs += "ACCEPT"
-                    if (opStrs.size > 0) opStrs.reduceLeft(_ + " | " + _) else " "
-                  }
-
-                  logTrace("Changed key for connection to [" +
-                    connection.getRemoteConnectionManagerId()  + "] changed from [" +
-                      intToOpStr(lastOps) + "] to [" + intToOpStr(ops) + "]")
-                }
-              }
-            } else {
-              logInfo("Key not valid ? " + key)
-              throw new CancelledKeyException()
-            }
-          } catch {
-            case e: CancelledKeyException => {
-              logInfo("key already cancelled ? " + key, e)
-              triggerForceCloseByException(key, e)
-            }
-            case e: Exception => {
-              logError("Exception processing key " + key, e)
-              triggerForceCloseByException(key, e)
-            }
-          }
-        }
-
-        val selectedKeysCount =
-          try {
-            selector.select()
-          } catch {
-            // Explicitly only dealing with CancelledKeyException here since other exceptions
-            // should be dealt with differently.
-            case e: CancelledKeyException =>
-              // Some keys within the selectors list are invalid/closed. clear them.
-              val allKeys = selector.keys().iterator()
-
-              while (allKeys.hasNext) {
-                val key = allKeys.next()
-                try {
-                  if (! key.isValid) {
-                    logInfo("Key not valid ? " + key)
-                    throw new CancelledKeyException()
-                  }
-                } catch {
-                  case e: CancelledKeyException => {
-                    logInfo("key already cancelled ? " + key, e)
-                    triggerForceCloseByException(key, e)
-                  }
-                  case e: Exception => {
-                    logError("Exception processing key " + key, e)
-                    triggerForceCloseByException(key, e)
-                  }
-                }
-              }
-              0
-
-            case e: ClosedSelectorException =>
-              logDebug("Failed select() as selector is closed.", e)
-              return
-          }
-
-        if (selectedKeysCount == 0) {
-          logDebug("Selector selected " + selectedKeysCount + " of " + selector.keys.size +
-            " keys")
-        }
-        if (selectorThread.isInterrupted) {
-          logInfo("Selector thread was interrupted!")
-          return
-        }
-
-        if (0 != selectedKeysCount) {
-          val selectedKeys = selector.selectedKeys().iterator()
-          while (selectedKeys.hasNext) {
-            val key = selectedKeys.next
-            selectedKeys.remove()
-            try {
-              if (key.isValid) {
-                if (key.isAcceptable) {
-                  acceptConnection(key)
-                } else
-                if (key.isConnectable) {
-                  triggerConnect(key)
-                } else
-                if (key.isReadable) {
-                  triggerRead(key)
-                } else
-                if (key.isWritable) {
-                  triggerWrite(key)
-                }
-              } else {
-                logInfo("Key not valid ? " + key)
-                throw new CancelledKeyException()
-              }
-            } catch {
-              // weird, but we saw this happening - even though key.isValid was true,
-              // key.isAcceptable would throw CancelledKeyException.
-              case e: CancelledKeyException => {
-                logInfo("key already cancelled ? " + key, e)
-                triggerForceCloseByException(key, e)
-              }
-              case e: Exception => {
-                logError("Exception processing key " + key, e)
-                triggerForceCloseByException(key, e)
-              }
-            }
-          }
-        }
-      }
-    } catch {
-      case e: Exception => logError("Error in select loop", e)
-    }
-  }
-
-  def acceptConnection(key: SelectionKey) {
-    val serverChannel = key.channel.asInstanceOf[ServerSocketChannel]
-
-    var newChannel = serverChannel.accept()
-
-    // accept them all in a tight loop. non blocking accept with no processing, should be fine
-    while (newChannel != null) {
-      try {
-        val newConnectionId = new ConnectionId(id, idCount.getAndIncrement.intValue)
-        val newConnection = new ReceivingConnection(newChannel, selector, newConnectionId,
-          securityManager)
-        newConnection.onReceive(receiveMessage)
-        addListeners(newConnection)
-        addConnection(newConnection)
-        logInfo("Accepted connection from [" + newConnection.remoteAddress + "]")
-      } catch {
-        // might happen in case of issues with registering with selector
-        case e: Exception => logError("Error in accept loop", e)
-      }
-
-      newChannel = serverChannel.accept()
-    }
-  }
-
-  private def addListeners(connection: Connection) {
-    connection.onKeyInterestChange(changeConnectionKeyInterest)
-    connection.onException(handleConnectionError)
-    connection.onClose(removeConnection)
-  }
-
-  def addConnection(connection: Connection) {
-    connectionsByKey += ((connection.key, connection))
-  }
-
-  def removeConnection(connection: Connection) {
-    connectionsByKey -= connection.key
-
-    try {
-      connection match {
-        case sendingConnection: SendingConnection =>
-          val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
-          logInfo("Removing SendingConnection to " + sendingConnectionManagerId)
-
-          connectionsById -= sendingConnectionManagerId
-          connectionsAwaitingSasl -= connection.connectionId
-
-          messageStatuses.synchronized {
-            messageStatuses.values.filter(_.connectionManagerId == sendingConnectionManagerId)
-              .foreach(status => {
-                logInfo("Notifying " + status)
-                status.failWithoutAck()
-              })
-
-            messageStatuses.retain((i, status) => {
-              status.connectionManagerId != sendingConnectionManagerId
-            })
-          }
-        case receivingConnection: ReceivingConnection =>
-          val remoteConnectionManagerId = receivingConnection.getRemoteConnectionManagerId()
-          logInfo("Removing ReceivingConnection to " + remoteConnectionManagerId)
-
-          val sendingConnectionOpt = connectionsById.get(remoteConnectionManagerId)
-          if (!sendingConnectionOpt.isDefined) {
-            logError(s"Corresponding SendingConnection to ${remoteConnectionManagerId} not found")
-            return
-          }
-
-          val sendingConnection = sendingConnectionOpt.get
-          connectionsById -= remoteConnectionManagerId
-          sendingConnection.close()
-
-          val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
-
-          assert(sendingConnectionManagerId == remoteConnectionManagerId)
-
-          messageStatuses.synchronized {
-            for (s <- messageStatuses.values
-                 if s.connectionManagerId == sendingConnectionManagerId) {
-              logInfo("Notifying " + s)
-              s.failWithoutAck()
-            }
-
-            messageStatuses.retain((i, status) => {
-              status.connectionManagerId != sendingConnectionManagerId
-            })
-          }
-        case _ => logError("Unsupported type of connection.")
-      }
-    } finally {
-      // So that the selection keys can be removed.
-      wakeupSelector()
-    }
-  }
-
-  def handleConnectionError(connection: Connection, e: Throwable) {
-    logInfo("Handling connection error on connection to " +
-      connection.getRemoteConnectionManagerId())
-    removeConnection(connection)
-  }
-
-  def changeConnectionKeyInterest(connection: Connection, ops: Int) {
-    keyInterestChangeRequests += ((connection.key, ops))
-    // so that registrations happen !
-    wakeupSelector()
-  }
-
-  def receiveMessage(connection: Connection, message: Message) {
-    val connectionManagerId = ConnectionManagerId.fromSocketAddress(message.senderAddress)
-    logDebug("Received [" + message + "] from [" + connectionManagerId + "]")
-    val runnable = new Runnable() {
-      val creationTime = System.currentTimeMillis
-      def run() {
-        try {
-          logDebug("Handler thread delay is " + (System.currentTimeMillis - creationTime) + " ms")
-          handleMessage(connectionManagerId, message, connection)
-          logDebug("Handling delay is " + (System.currentTimeMillis - creationTime) + " ms")
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when handling messages from " +
-              connection.getRemoteConnectionManagerId(), e)
-            connection.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    }
-    handleMessageExecutor.execute(runnable)
-    /* handleMessage(connection, message) */
-  }
-
-  private def handleClientAuthentication(
-      waitingConn: SendingConnection,
-      securityMsg: SecurityMessage,
-      connectionId : ConnectionId) {
-    if (waitingConn.isSaslComplete()) {
-      logDebug("Client sasl completed for id: "  + waitingConn.connectionId)
-      connectionsAwaitingSasl -= waitingConn.connectionId
-      waitingConn.registerAfterAuth()
-      wakeupSelector()
-      return
-    } else {
-      var replyToken : Array[Byte] = null
-      try {
-        replyToken = waitingConn.sparkSaslClient.response(securityMsg.getToken)
-        if (waitingConn.isSaslComplete()) {
-          logDebug("Client sasl completed after evaluate for id: " + waitingConn.connectionId)
-          connectionsAwaitingSasl -= waitingConn.connectionId
-          waitingConn.registerAfterAuth()
-          wakeupSelector()
-          return
-        }
-        val securityMsgResp = SecurityMessage.fromResponse(replyToken,
-          securityMsg.getConnectionId.toString)
-        val message = securityMsgResp.toBufferMessage
-        if (message == null) throw new IOException("Error creating security message")
-        sendSecurityMessage(waitingConn.getRemoteConnectionManagerId(), message)
-      } catch {
-        case e: Exception =>
-          logError("Error handling sasl client authentication", e)
-          waitingConn.close()
-          throw new IOException("Error evaluating sasl response: ", e)
-      }
-    }
-  }
-
-  private def handleServerAuthentication(
-      connection: Connection,
-      securityMsg: SecurityMessage,
-      connectionId: ConnectionId) {
-    if (!connection.isSaslComplete()) {
-      logDebug("saslContext not established")
-      var replyToken : Array[Byte] = null
-      try {
-        connection.synchronized {
-          if (connection.sparkSaslServer == null) {
-            logDebug("Creating sasl Server")
-            connection.sparkSaslServer = new SparkSaslServer(conf.getAppId, securityManager, false)
-          }
-        }
-        replyToken = connection.sparkSaslServer.response(securityMsg.getToken)
-        if (connection.isSaslComplete()) {
-          logDebug("Server sasl completed: " + connection.connectionId +
-            " for: " + connectionId)
-        } else {
-          logDebug("Server sasl not completed: " + connection.connectionId +
-            " for: " + connectionId)
-        }
-        if (replyToken != null) {
-          val securityMsgResp = SecurityMessage.fromResponse(replyToken,
-            securityMsg.getConnectionId)
-          val message = securityMsgResp.toBufferMessage
-          if (message == null) throw new Exception("Error creating security Message")
-          sendSecurityMessage(connection.getRemoteConnectionManagerId(), message)
-        }
-      } catch {
-        case e: Exception => {
-          logError("Error in server auth negotiation: " + e)
-          // It would probably be better to send an error message telling other side auth failed
-          // but for now just close
-          connection.close()
-        }
-      }
-    } else {
-      logDebug("connection already established for this connection id: " + connection.connectionId)
-    }
-  }
-
-
-  private def handleAuthentication(conn: Connection, bufferMessage: BufferMessage): Boolean = {
-    if (bufferMessage.isSecurityNeg) {
-      logDebug("This is security neg message")
-
-      // parse as SecurityMessage
-      val securityMsg = SecurityMessage.fromBufferMessage(bufferMessage)
-      val connectionId = ConnectionId.createConnectionIdFromString(securityMsg.getConnectionId)
-
-      connectionsAwaitingSasl.get(connectionId) match {
-        case Some(waitingConn) => {
-          // Client - this must be in response to us doing Send
-          logDebug("Client handleAuth for id: " +  waitingConn.connectionId)
-          handleClientAuthentication(waitingConn, securityMsg, connectionId)
-        }
-        case None => {
-          // Server - someone sent us something and we haven't authenticated yet
-          logDebug("Server handleAuth for id: " + connectionId)
-          handleServerAuthentication(conn, securityMsg, connectionId)
-        }
-      }
-      return true
-    } else {
-      if (!conn.isSaslComplete()) {
-        // We could handle this better and tell the client we need to do authentication
-        // negotiation, but for now just ignore them.
-        logError("message sent that is not security negotiation message on connection " +
-                 "not authenticated yet, ignoring it!!")
-        return true
-      }
-    }
-    false
-  }
-
-  private def handleMessage(
-      connectionManagerId: ConnectionManagerId,
-      message: Message,
-      connection: Connection) {
-    logDebug("Handling [" + message + "] from [" + connectionManagerId + "]")
-    message match {
-      case bufferMessage: BufferMessage => {
-        if (authEnabled) {
-          val res = handleAuthentication(connection, bufferMessage)
-          if (res) {
-            // message was security negotiation so skip the rest
-            logDebug("After handleAuth result was true, returning")
-            return
-          }
-        }
-        if (bufferMessage.hasAckId()) {
-          messageStatuses.synchronized {
-            messageStatuses.get(bufferMessage.ackId) match {
-              case Some(status) => {
-                messageStatuses -= bufferMessage.ackId
-                status.success(message)
-              }
-              case None => {
-                /**
-                 * We can fall down on this code because of following 2 cases
-                 *
-                 * (1) Invalid ack sent due to buggy code.
-                 *
-                 * (2) Late-arriving ack for a SendMessageStatus
-                 *     To avoid unwilling late-arriving ack
-                 *     caused by long pause like GC, you can set
-                 *     larger value than default to spark.core.connection.ack.wait.timeout
-                 */
-                logWarning(s"Could not find reference for received ack Message ${message.id}")
-              }
-            }
-          }
-        } else {
-          var ackMessage : Option[Message] = None
-          try {
-            ackMessage = if (onReceiveCallback != null) {
-              logDebug("Calling back")
-              onReceiveCallback(bufferMessage, connectionManagerId)
-            } else {
-              logDebug("Not calling back as callback is null")
-              None
-            }
-
-            if (ackMessage.isDefined) {
-              if (!ackMessage.get.isInstanceOf[BufferMessage]) {
-                logDebug("Response to " + bufferMessage + " is not a buffer message, it is of type "
-                  + ackMessage.get.getClass)
-              } else if (!ackMessage.get.asInstanceOf[BufferMessage].hasAckId) {
-                logDebug("Response to " + bufferMessage + " does not have ack id set")
-                ackMessage.get.asInstanceOf[BufferMessage].ackId = bufferMessage.id
-              }
-            }
-          } catch {
-            case e: Exception => {
-              logError(s"Exception was thrown while processing message", e)
-              ackMessage = Some(Message.createErrorMessage(e, bufferMessage.id))
-            }
-          } finally {
-            sendMessage(connectionManagerId, ackMessage.getOrElse {
-              Message.createBufferMessage(bufferMessage.id)
-            })
-          }
-        }
-      }
-      case _ => throw new Exception("Unknown type message received")
-    }
-  }
-
-  private def checkSendAuthFirst(connManagerId: ConnectionManagerId, conn: SendingConnection) {
-    // see if we need to do sasl before writing
-    // this should only be the first negotiation as the Client!!!
-    if (!conn.isSaslComplete()) {
-      conn.synchronized {
-        if (conn.sparkSaslClient == null) {
-          conn.sparkSaslClient = new SparkSaslClient(conf.getAppId, securityManager, false)
-          var firstResponse: Array[Byte] = null
-          try {
-            firstResponse = conn.sparkSaslClient.firstToken()
-            val securityMsg = SecurityMessage.fromResponse(firstResponse,
-              conn.connectionId.toString())
-            val message = securityMsg.toBufferMessage
-            if (message == null) throw new Exception("Error creating security message")
-            connectionsAwaitingSasl += ((conn.connectionId, conn))
-            sendSecurityMessage(connManagerId, message)
-            logDebug("adding connectionsAwaitingSasl id: " + conn.connectionId +
-              " to: " + connManagerId)
-          } catch {
-            case e: Exception => {
-              logError("Error getting first response from the SaslClient.", e)
-              conn.close()
-              throw new Exception("Error getting first response from the SaslClient")
-            }
-          }
-        }
-      }
-    } else {
-      logDebug("Sasl already established ")
-    }
-  }
-
-  // allow us to add messages to the inbox for doing sasl negotiating
-  private def sendSecurityMessage(connManagerId: ConnectionManagerId, message: Message) {
-    def startNewConnection(): SendingConnection = {
-      val inetSocketAddress = new InetSocketAddress(connManagerId.host, connManagerId.port)
-      val newConnectionId = new ConnectionId(id, idCount.getAndIncrement.intValue)
-      val newConnection = new SendingConnection(inetSocketAddress, selector, connManagerId,
-        newConnectionId, securityManager)
-      logInfo("creating new sending connection for security! " + newConnectionId )
-      registerRequests.enqueue(newConnection)
-
-      newConnection
-    }
-    // I removed the lookupKey stuff as part of merge ... should I re-add it ?
-    // We did not find it useful in our test-env ...
-    // If we do re-add it, we should consistently use it everywhere I guess ?
-    message.senderAddress = id.toSocketAddress()
-    logTrace("Sending Security [" + message + "] to [" + connManagerId + "]")
-    val connection = connectionsById.getOrElseUpdate(connManagerId, startNewConnection())
-
-    // send security message until going connection has been authenticated
-    connection.send(message)
-
-    wakeupSelector()
-  }
-
-  private def sendMessage(connectionManagerId: ConnectionManagerId, message: Message) {
-    def startNewConnection(): SendingConnection = {
-      val inetSocketAddress = new InetSocketAddress(connectionManagerId.host,
-        connectionManagerId.port)
-      val newConnectionId = new ConnectionId(id, idCount.getAndIncrement.intValue)
-      val newConnection = new SendingConnection(inetSocketAddress, selector, connectionManagerId,
-        newConnectionId, securityManager)
-      newConnection.onException {
-        case (conn, e) => {
-          logError("Exception while sending message.", e)
-          reportSendingMessageFailure(message.id, e)
-        }
-      }
-      logTrace("creating new sending connection: " + newConnectionId)
-      registerRequests.enqueue(newConnection)
-
-      newConnection
-    }
-    val connection = connectionsById.getOrElseUpdate(connectionManagerId, startNewConnection())
-
-    message.senderAddress = id.toSocketAddress()
-    logDebug("Before Sending [" + message + "] to [" + connectionManagerId + "]" + " " +
-      "connectionid: "  + connection.connectionId)
-
-    if (authEnabled) {
-      try {
-        checkSendAuthFirst(connectionManagerId, connection)
-      } catch {
-        case NonFatal(e) => {
-          reportSendingMessageFailure(message.id, e)
-        }
-      }
-    }
-    logDebug("Sending [" + message + "] to [" + connectionManagerId + "]")
-    connection.send(message)
-    wakeupSelector()
-  }
-
-  private def reportSendingMessageFailure(messageId: Int, e: Throwable): Unit = {
-    // need to tell sender it failed
-    messageStatuses.synchronized {
-      val s = messageStatuses.get(messageId)
-      s match {
-        case Some(msgStatus) => {
-          messageStatuses -= messageId
-          logInfo("Notifying " + msgStatus.connectionManagerId)
-          msgStatus.failure(e)
-        }
-        case None => {
-          logError("no messageStatus for failed message id: " + messageId)
-        }
-      }
-    }
-  }
-
-  private def wakeupSelector() {
-    selector.wakeup()
-  }
-
-  /**
-   * Send a message and block until an acknowledgment is received or an error occurs.
-   * @param connectionManagerId the message's destination
-   * @param message the message being sent
-   * @return a Future that either returns the acknowledgment message or captures an exception.
-   */
-  def sendMessageReliably(connectionManagerId: ConnectionManagerId, message: Message)
-      : Future[Message] = {
-    val promise = Promise[Message]()
-
-    // It's important that the TimerTask doesn't capture a reference to `message`, which can cause
-    // memory leaks since cancelled TimerTasks won't necessarily be garbage collected until the time
-    // at which they would originally be scheduled to run.  Therefore, extract the message id
-    // from outside of the TimerTask closure (see SPARK-4393 for more context).
-    val messageId = message.id
-    // Keep a weak reference to the promise so that the completed promise may be garbage-collected
-    val promiseReference = new WeakReference(promise)
-    val timeoutTask: TimerTask = new TimerTask {
-      override def run(timeout: Timeout): Unit = {
-        messageStatuses.synchronized {
-          messageStatuses.remove(messageId).foreach { s =>
-            val e = new IOException("sendMessageReliably failed because ack " +
-              s"was not received within $ackTimeout sec")
-            val p = promiseReference.get
-            if (p != null) {
-              // Attempt to fail the promise with a Timeout exception
-              if (!p.tryFailure(e)) {
-                // If we reach here, then someone else has already signalled success or failure
-                // on this promise, so log a warning:
-                logError("Ignore error because promise is completed", e)
-              }
-            } else {
-              // The WeakReference was empty, which should never happen because
-              // sendMessageReliably's caller should have a strong reference to promise.future;
-              logError("Promise was garbage collected; this should never happen!", e)
-            }
-          }
-        }
-      }
-    }
-
-    val timeoutTaskHandle = ackTimeoutMonitor.newTimeout(timeoutTask, ackTimeout, TimeUnit.SECONDS)
-
-    val status = new MessageStatus(message, connectionManagerId, s => {
-      timeoutTaskHandle.cancel()
-      s match {
-        case scala.util.Failure(e) =>
-          // Indicates a failure where we either never sent or never got ACK'd
-          if (!promise.tryFailure(e)) {
-            logWarning("Ignore error because promise is completed", e)
-          }
-        case scala.util.Success(ackMessage) =>
-          if (ackMessage.hasError) {
-            val errorMsgByteBuf = ackMessage.asInstanceOf[BufferMessage].buffers.head
-            val errorMsgBytes = new Array[Byte](errorMsgByteBuf.limit())
-            errorMsgByteBuf.get(errorMsgBytes)
-            val errorMsg = new String(errorMsgBytes, UTF_8)
-            val e = new IOException(
-              s"sendMessageReliably failed with ACK that signalled a remote error: $errorMsg")
-            if (!promise.tryFailure(e)) {
-              logWarning("Ignore error because promise is completed", e)
-            }
-          } else {
-            if (!promise.trySuccess(ackMessage)) {
-              logWarning("Drop ackMessage because promise is completed")
-            }
-          }
-      }
-    })
-    messageStatuses.synchronized {
-      messageStatuses += ((message.id, status))
-    }
-
-    sendMessage(connectionManagerId, message)
-    promise.future
-  }
-
-  def onReceiveMessage(callback: (Message, ConnectionManagerId) => Option[Message]) {
-    onReceiveCallback = callback
-  }
-
-  def stop() {
-    isActive = false
-    ackTimeoutMonitor.stop()
-    selector.close()
-    selectorThread.interrupt()
-    selectorThread.join()
-    val connections = connectionsByKey.values
-    connections.foreach(_.close())
-    if (connectionsByKey.size != 0) {
-      logWarning("All connections not cleaned up")
-    }
-    handleMessageExecutor.shutdown()
-    handleReadWriteExecutor.shutdown()
-    handleConnectExecutor.shutdown()
-    logInfo("ConnectionManager stopped")
-  }
-}
-
-
-private[spark] object ConnectionManager {
-  import scala.concurrent.ExecutionContext.Implicits.global
-
-  def main(args: Array[String]) {
-    val conf = new SparkConf
-    val manager = new ConnectionManager(9999, conf, new SecurityManager(conf))
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      // scalastyle:off println
-      println("Received [" + msg + "] from [" + id + "]")
-      // scalastyle:on println
-      None
-    })
-
-    /* testSequentialSending(manager) */
-    /* System.gc() */
-
-    /* testParallelSending(manager) */
-    /* System.gc() */
-
-    /* testParallelDecreasingSending(manager) */
-    /* System.gc() */
-
-    testContinuousSending(manager)
-    System.gc()
-  }
-
-  // scalastyle:off println
-  def testSequentialSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Sequential Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      Await.result(manager.sendMessageReliably(manager.id, bufferMessage), Duration.Inf)
-    })
-    println("--------------------------")
-    println()
-  }
-
-  def testParallelSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Parallel Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    val startTime = System.currentTimeMillis
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      manager.sendMessageReliably(manager.id, bufferMessage)
-    }).foreach(f => {
-      f.onFailure {
-        case e => println("Failed due to " + e)
-      }
-      Await.ready(f, 1 second)
-    })
-    val finishTime = System.currentTimeMillis
-
-    val mb = size * count / 1024.0 / 1024.0
-    val ms = finishTime - startTime
-    val tput = mb * 1000.0 / ms
-    println("--------------------------")
-    println("Started at " + startTime + ", finished at " + finishTime)
-    println("Sent " + count + " messages of size " + size + " in " + ms + " ms " +
-      "(" + tput + " MB/s)")
-    println("--------------------------")
-    println()
-  }
-
-  def testParallelDecreasingSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Parallel Decreasing Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-    val buffers = Array.tabulate(count) { i =>
-      val bufferLen = size * (i + 1)
-      val bufferContent = Array.tabulate[Byte](bufferLen)(x => x.toByte)
-      ByteBuffer.allocate(bufferLen).put(bufferContent)
-    }
-    buffers.foreach(_.flip)
-    val mb = buffers.map(_.remaining).reduceLeft(_ + _) / 1024.0 / 1024.0
-
-    val startTime = System.currentTimeMillis
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffers(count - 1 - i).duplicate)
-      manager.sendMessageReliably(manager.id, bufferMessage)
-    }).foreach(f => {
-      f.onFailure {
-        case e => println("Failed due to " + e)
-      }
-      Await.ready(f, 1 second)
-    })
-    val finishTime = System.currentTimeMillis
-
-    val ms = finishTime - startTime
-    val tput = mb * 1000.0 / ms
-    println("--------------------------")
-    /* println("Started at " + startTime + ", finished at " + finishTime) */
-    println("Sent " + mb + " MB in " + ms + " ms (" + tput + " MB/s)")
-    println("--------------------------")
-    println()
-  }
-
-  def testContinuousSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Continuous Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    val startTime = System.currentTimeMillis
-    while(true) {
-      (0 until count).map(i => {
-          val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-          manager.sendMessageReliably(manager.id, bufferMessage)
-        }).foreach(f => {
-          f.onFailure {
-            case e => println("Failed due to " + e)
-          }
-          Await.ready(f, 1 second)
-        })
-      val finishTime = System.currentTimeMillis
-      Thread.sleep(1000)
-      val mb = size * count / 1024.0 / 1024.0
-      val ms = finishTime - startTime
-      val tput = mb * 1000.0 / ms
-      println("Sent " + mb + " MB in " + ms + " ms (" + tput + " MB/s)")
-      println("--------------------------")
-      println()
-    }
-  }
-  // scalastyle:on println
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
deleted file mode 100644
index 1cd13d887c6f6..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net.InetSocketAddress
-
-import org.apache.spark.util.Utils
-
-private[nio] case class ConnectionManagerId(host: String, port: Int) {
-  // DEBUG code
-  Utils.checkHost(host)
-  assert (port > 0)
-
-  def toSocketAddress(): InetSocketAddress = new InetSocketAddress(host, port)
-}
-
-
-private[nio] object ConnectionManagerId {
-  def fromSocketAddress(socketAddress: InetSocketAddress): ConnectionManagerId = {
-    new ConnectionManagerId(socketAddress.getHostName, socketAddress.getPort)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/Message.scala b/core/src/main/scala/org/apache/spark/network/nio/Message.scala
deleted file mode 100644
index 85d2fe2bf9c20..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/Message.scala
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net.InetSocketAddress
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.ArrayBuffer
-
-import com.google.common.base.Charsets.UTF_8
-
-import org.apache.spark.util.Utils
-
-private[nio] abstract class Message(val typ: Long, val id: Int) {
-  var senderAddress: InetSocketAddress = null
-  var started = false
-  var startTime = -1L
-  var finishTime = -1L
-  var isSecurityNeg = false
-  var hasError = false
-
-  def size: Int
-
-  def getChunkForSending(maxChunkSize: Int): Option[MessageChunk]
-
-  def getChunkForReceiving(chunkSize: Int): Option[MessageChunk]
-
-  def timeTaken(): String = (finishTime - startTime).toString + " ms"
-
-  override def toString: String = {
-    this.getClass.getSimpleName + "(id = " + id + ", size = " + size + ")"
-  }
-}
-
-
-private[nio] object Message {
-  val BUFFER_MESSAGE = 1111111111L
-
-  var lastId = 1
-
-  def getNewId(): Int = synchronized {
-    lastId += 1
-    if (lastId == 0) {
-      lastId += 1
-    }
-    lastId
-  }
-
-  def createBufferMessage(dataBuffers: Seq[ByteBuffer], ackId: Int): BufferMessage = {
-    if (dataBuffers == null) {
-      return new BufferMessage(getNewId(), new ArrayBuffer[ByteBuffer], ackId)
-    }
-    if (dataBuffers.exists(_ == null)) {
-      throw new Exception("Attempting to create buffer message with null buffer")
-    }
-    new BufferMessage(getNewId(), new ArrayBuffer[ByteBuffer] ++= dataBuffers, ackId)
-  }
-
-  def createBufferMessage(dataBuffers: Seq[ByteBuffer]): BufferMessage =
-    createBufferMessage(dataBuffers, 0)
-
-  def createBufferMessage(dataBuffer: ByteBuffer, ackId: Int): BufferMessage = {
-    if (dataBuffer == null) {
-      createBufferMessage(Array(ByteBuffer.allocate(0)), ackId)
-    } else {
-      createBufferMessage(Array(dataBuffer), ackId)
-    }
-  }
-
-  def createBufferMessage(dataBuffer: ByteBuffer): BufferMessage =
-    createBufferMessage(dataBuffer, 0)
-
-  def createBufferMessage(ackId: Int): BufferMessage = {
-    createBufferMessage(new Array[ByteBuffer](0), ackId)
-  }
-
-  /**
-   * Create a "negative acknowledgment" to notify a sender that an error occurred
-   * while processing its message.  The exception's stacktrace will be formatted
-   * as a string, serialized into a byte array, and sent as the message payload.
-   */
-  def createErrorMessage(exception: Exception, ackId: Int): BufferMessage = {
-    val exceptionString = Utils.exceptionString(exception)
-    val serializedExceptionString = ByteBuffer.wrap(exceptionString.getBytes(UTF_8))
-    val errorMessage = createBufferMessage(serializedExceptionString, ackId)
-    errorMessage.hasError = true
-    errorMessage
-  }
-
-  def create(header: MessageChunkHeader): Message = {
-    val newMessage: Message = header.typ match {
-      case BUFFER_MESSAGE => new BufferMessage(header.id,
-        ArrayBuffer(ByteBuffer.allocate(header.totalSize)), header.other)
-    }
-    newMessage.hasError = header.hasError
-    newMessage.senderAddress = header.address
-    newMessage
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
deleted file mode 100644
index a4568e849fa13..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.ArrayBuffer
-
-private[nio]
-class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) {
-
-  val size: Int = if (buffer == null) 0 else buffer.remaining
-
-  lazy val buffers: ArrayBuffer[ByteBuffer] = {
-    val ab = new ArrayBuffer[ByteBuffer]()
-    ab += header.buffer
-    if (buffer != null) {
-      ab += buffer
-    }
-    ab
-  }
-
-  override def toString: String = {
-    "" + this.getClass.getSimpleName + " (id = " + header.id + ", size = " + size + ")"
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
deleted file mode 100644
index 7b3da4bb9d5ee..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net.{InetAddress, InetSocketAddress}
-import java.nio.ByteBuffer
-
-private[nio] class MessageChunkHeader(
-    val typ: Long,
-    val id: Int,
-    val totalSize: Int,
-    val chunkSize: Int,
-    val other: Int,
-    val hasError: Boolean,
-    val securityNeg: Int,
-    val address: InetSocketAddress) {
-  lazy val buffer = {
-    // No need to change this, at 'use' time, we do a reverse lookup of the hostname.
-    // Refer to network.Connection
-    val ip = address.getAddress.getAddress()
-    val port = address.getPort()
-    ByteBuffer.
-      allocate(MessageChunkHeader.HEADER_SIZE).
-      putLong(typ).
-      putInt(id).
-      putInt(totalSize).
-      putInt(chunkSize).
-      putInt(other).
-      put(if (hasError) 1.asInstanceOf[Byte] else 0.asInstanceOf[Byte]).
-      putInt(securityNeg).
-      putInt(ip.size).
-      put(ip).
-      putInt(port).
-      position(MessageChunkHeader.HEADER_SIZE).
-      flip.asInstanceOf[ByteBuffer]
-  }
-
-  override def toString: String = {
-    "" + this.getClass.getSimpleName + ":" + id + " of type " + typ +
-      " and sizes " + totalSize + " / " + chunkSize + " bytes, securityNeg: " + securityNeg
-  }
-
-}
-
-
-private[nio] object MessageChunkHeader {
-  val HEADER_SIZE = 45
-
-  def create(buffer: ByteBuffer): MessageChunkHeader = {
-    if (buffer.remaining != HEADER_SIZE) {
-      throw new IllegalArgumentException("Cannot convert buffer data to Message")
-    }
-    val typ = buffer.getLong()
-    val id = buffer.getInt()
-    val totalSize = buffer.getInt()
-    val chunkSize = buffer.getInt()
-    val other = buffer.getInt()
-    val hasError = buffer.get() != 0
-    val securityNeg = buffer.getInt()
-    val ipSize = buffer.getInt()
-    val ipBytes = new Array[Byte](ipSize)
-    buffer.get(ipBytes)
-    val ip = InetAddress.getByAddress(ipBytes)
-    val port = buffer.getInt()
-    new MessageChunkHeader(typ, id, totalSize, chunkSize, other, hasError, securityNeg,
-      new InetSocketAddress(ip, port))
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
deleted file mode 100644
index b2aec160635c7..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
+++ /dev/null
@@ -1,217 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import org.apache.spark.network._
-import org.apache.spark.network.buffer.{ManagedBuffer, NioManagedBuffer}
-import org.apache.spark.network.shuffle.BlockFetchingListener
-import org.apache.spark.storage.{BlockId, StorageLevel}
-import org.apache.spark.util.Utils
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
-
-import scala.concurrent.Future
-
-
-/**
- * A [[BlockTransferService]] implementation based on [[ConnectionManager]], a custom
- * implementation using Java NIO.
- */
-final class NioBlockTransferService(conf: SparkConf, securityManager: SecurityManager)
-  extends BlockTransferService with Logging {
-
-  private var cm: ConnectionManager = _
-
-  private var blockDataManager: BlockDataManager = _
-
-  /**
-   * Port number the service is listening on, available only after [[init]] is invoked.
-   */
-  override def port: Int = {
-    checkInit()
-    cm.id.port
-  }
-
-  /**
-   * Host name the service is listening on, available only after [[init]] is invoked.
-   */
-  override def hostName: String = {
-    checkInit()
-    cm.id.host
-  }
-
-  /**
-   * Initialize the transfer service by giving it the BlockDataManager that can be used to fetch
-   * local blocks or put local blocks.
-   */
-  override def init(blockDataManager: BlockDataManager): Unit = {
-    this.blockDataManager = blockDataManager
-    cm = new ConnectionManager(
-      conf.getInt("spark.blockManager.port", 0),
-      conf,
-      securityManager,
-      "Connection manager for block manager")
-    cm.onReceiveMessage(onBlockMessageReceive)
-  }
-
-  /**
-   * Tear down the transfer service.
-   */
-  override def close(): Unit = {
-    if (cm != null) {
-      cm.stop()
-    }
-  }
-
-  override def fetchBlocks(
-      host: String,
-      port: Int,
-      execId: String,
-      blockIds: Array[String],
-      listener: BlockFetchingListener): Unit = {
-    checkInit()
-
-    val cmId = new ConnectionManagerId(host, port)
-    val blockMessageArray = new BlockMessageArray(blockIds.map { blockId =>
-      BlockMessage.fromGetBlock(GetBlock(BlockId(blockId)))
-    })
-
-    val future = cm.sendMessageReliably(cmId, blockMessageArray.toBufferMessage)
-
-    // Register the listener on success/failure future callback.
-    future.onSuccess { case message =>
-      val bufferMessage = message.asInstanceOf[BufferMessage]
-      val blockMessageArray = BlockMessageArray.fromBufferMessage(bufferMessage)
-
-      // SPARK-4064: In some cases(eg. Remote block was removed) blockMessageArray may be empty.
-      if (blockMessageArray.isEmpty) {
-        blockIds.foreach { id =>
-          listener.onBlockFetchFailure(id, new SparkException(s"Received empty message from $cmId"))
-        }
-      } else {
-        for (blockMessage: BlockMessage <- blockMessageArray) {
-          val msgType = blockMessage.getType
-          if (msgType != BlockMessage.TYPE_GOT_BLOCK) {
-            if (blockMessage.getId != null) {
-              listener.onBlockFetchFailure(blockMessage.getId.toString,
-                new SparkException(s"Unexpected message $msgType received from $cmId"))
-            }
-          } else {
-            val blockId = blockMessage.getId
-            val networkSize = blockMessage.getData.limit()
-            listener.onBlockFetchSuccess(
-              blockId.toString, new NioManagedBuffer(blockMessage.getData))
-          }
-        }
-      }
-    }(cm.futureExecContext)
-
-    future.onFailure { case exception =>
-      blockIds.foreach { blockId =>
-        listener.onBlockFetchFailure(blockId, exception)
-      }
-    }(cm.futureExecContext)
-  }
-
-  /**
-   * Upload a single block to a remote node, available only after [[init]] is invoked.
-   *
-   * This call blocks until the upload completes, or throws an exception upon failures.
-   */
-  override def uploadBlock(
-      hostname: String,
-      port: Int,
-      execId: String,
-      blockId: BlockId,
-      blockData: ManagedBuffer,
-      level: StorageLevel)
-    : Future[Unit] = {
-    checkInit()
-    val msg = PutBlock(blockId, blockData.nioByteBuffer(), level)
-    val blockMessageArray = new BlockMessageArray(BlockMessage.fromPutBlock(msg))
-    val remoteCmId = new ConnectionManagerId(hostName, port)
-    val reply = cm.sendMessageReliably(remoteCmId, blockMessageArray.toBufferMessage)
-    reply.map(x => ())(cm.futureExecContext)
-  }
-
-  private def checkInit(): Unit = if (cm == null) {
-    throw new IllegalStateException(getClass.getName + " has not been initialized")
-  }
-
-  private def onBlockMessageReceive(msg: Message, id: ConnectionManagerId): Option[Message] = {
-    logDebug("Handling message " + msg)
-    msg match {
-      case bufferMessage: BufferMessage =>
-        try {
-          logDebug("Handling as a buffer message " + bufferMessage)
-          val blockMessages = BlockMessageArray.fromBufferMessage(bufferMessage)
-          logDebug("Parsed as a block message array")
-          val responseMessages = blockMessages.map(processBlockMessage).filter(_ != None).map(_.get)
-          Some(new BlockMessageArray(responseMessages).toBufferMessage)
-        } catch {
-          case e: Exception =>
-            logError("Exception handling buffer message", e)
-            Some(Message.createErrorMessage(e, msg.id))
-        }
-
-      case otherMessage: Any =>
-        val errorMsg = s"Received unknown message type: ${otherMessage.getClass.getName}"
-        logError(errorMsg)
-        Some(Message.createErrorMessage(new UnsupportedOperationException(errorMsg), msg.id))
-    }
-  }
-
-  private def processBlockMessage(blockMessage: BlockMessage): Option[BlockMessage] = {
-    blockMessage.getType match {
-      case BlockMessage.TYPE_PUT_BLOCK =>
-        val msg = PutBlock(blockMessage.getId, blockMessage.getData, blockMessage.getLevel)
-        logDebug("Received [" + msg + "]")
-        putBlock(msg.id, msg.data, msg.level)
-        None
-
-      case BlockMessage.TYPE_GET_BLOCK =>
-        val msg = new GetBlock(blockMessage.getId)
-        logDebug("Received [" + msg + "]")
-        val buffer = getBlock(msg.id)
-        if (buffer == null) {
-          return None
-        }
-        Some(BlockMessage.fromGotBlock(GotBlock(msg.id, buffer)))
-
-      case _ => None
-    }
-  }
-
-  private def putBlock(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel) {
-    val startTimeMs = System.currentTimeMillis()
-    logDebug("PutBlock " + blockId + " started from " + startTimeMs + " with data: " + bytes)
-    blockDataManager.putBlockData(blockId, new NioManagedBuffer(bytes), level)
-    logDebug("PutBlock " + blockId + " used " + Utils.getUsedTimeMs(startTimeMs)
-      + " with data size: " + bytes.limit)
-  }
-
-  private def getBlock(blockId: BlockId): ByteBuffer = {
-    val startTimeMs = System.currentTimeMillis()
-    logDebug("GetBlock " + blockId + " started from " + startTimeMs)
-    val buffer = blockDataManager.getBlockData(blockId)
-    logDebug("GetBlock " + blockId + " used " + Utils.getUsedTimeMs(startTimeMs)
-      + " and got buffer " + buffer)
-    buffer.nioByteBuffer()
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
deleted file mode 100644
index 232c552f9865d..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.{ArrayBuffer, StringBuilder}
-
-import org.apache.spark._
-
-/**
- * SecurityMessage is class that contains the connectionId and sasl token
- * used in SASL negotiation. SecurityMessage has routines for converting
- * it to and from a BufferMessage so that it can be sent by the ConnectionManager
- * and easily consumed by users when received.
- * The api was modeled after BlockMessage.
- *
- * The connectionId is the connectionId of the client side. Since
- * message passing is asynchronous and its possible for the server side (receiving)
- * to get multiple different types of messages on the same connection the connectionId
- * is used to know which connnection the security message is intended for.
- *
- * For instance, lets say we are node_0. We need to send data to node_1. The node_0 side
- * is acting as a client and connecting to node_1. SASL negotiation has to occur
- * between node_0 and node_1 before node_1 trusts node_0 so node_0 sends a security message.
- * node_1 receives the message from node_0 but before it can process it and send a response,
- * some thread on node_1 decides it needs to send data to node_0 so it connects to node_0
- * and sends a security message of its own to authenticate as a client. Now node_0 gets
- * the message and it needs to decide if this message is in response to it being a client
- * (from the first send) or if its just node_1 trying to connect to it to send data.  This
- * is where the connectionId field is used. node_0 can lookup the connectionId to see if
- * it is in response to it being a client or if its in response to someone sending other data.
- *
- * The format of a SecurityMessage as its sent is:
- *   - Length of the ConnectionId
- *   - ConnectionId
- *   - Length of the token
- *   - Token
- */
-private[nio] class SecurityMessage extends Logging {
-
-  private var connectionId: String = null
-  private var token: Array[Byte] = null
-
-  def set(byteArr: Array[Byte], newconnectionId: String) {
-    if (byteArr == null) {
-      token = new Array[Byte](0)
-    } else {
-      token = byteArr
-    }
-    connectionId = newconnectionId
-  }
-
-  /**
-   * Read the given buffer and set the members of this class.
-   */
-  def set(buffer: ByteBuffer) {
-    val idLength = buffer.getInt()
-    val idBuilder = new StringBuilder(idLength)
-    for (i <- 1 to idLength) {
-        idBuilder += buffer.getChar()
-    }
-    connectionId = idBuilder.toString()
-
-    val tokenLength = buffer.getInt()
-    token = new Array[Byte](tokenLength)
-    if (tokenLength > 0) {
-      buffer.get(token, 0, tokenLength)
-    }
-  }
-
-  def set(bufferMsg: BufferMessage) {
-    val buffer = bufferMsg.buffers.apply(0)
-    buffer.clear()
-    set(buffer)
-  }
-
-  def getConnectionId: String = {
-    return connectionId
-  }
-
-  def getToken: Array[Byte] = {
-    return token
-  }
-
-  /**
-   * Create a BufferMessage that can be sent by the ConnectionManager containing
-   * the security information from this class.
-   * @return BufferMessage
-   */
-  def toBufferMessage: BufferMessage = {
-    val buffers = new ArrayBuffer[ByteBuffer]()
-
-    // 4 bytes for the length of the connectionId
-    // connectionId is of type char so multiple the length by 2 to get number of bytes
-    // 4 bytes for the length of token
-    // token is a byte buffer so just take the length
-    var buffer = ByteBuffer.allocate(4 + connectionId.length() * 2 + 4 + token.length)
-    buffer.putInt(connectionId.length())
-    connectionId.foreach((x: Char) => buffer.putChar(x))
-    buffer.putInt(token.length)
-
-    if (token.length > 0) {
-      buffer.put(token)
-    }
-    buffer.flip()
-    buffers += buffer
-
-    var message = Message.createBufferMessage(buffers)
-    logDebug("message total size is : " + message.size)
-    message.isSecurityNeg = true
-    return message
-  }
-
-  override def toString: String = {
-    "SecurityMessage [connId= " + connectionId + ", Token = " + token + "]"
-  }
-}
-
-private[nio] object SecurityMessage {
-
-  /**
-   * Convert the given BufferMessage to a SecurityMessage by parsing the contents
-   * of the BufferMessage and populating the SecurityMessage fields.
-   * @param bufferMessage is a BufferMessage that was received
-   * @return new SecurityMessage
-   */
-  def fromBufferMessage(bufferMessage: BufferMessage): SecurityMessage = {
-    val newSecurityMessage = new SecurityMessage()
-    newSecurityMessage.set(bufferMessage)
-    newSecurityMessage
-  }
-
-  /**
-   * Create a SecurityMessage to send from a given saslResponse.
-   * @param response is the response to a challenge from the SaslClient or Saslserver
-   * @param connectionId the client connectionId we are negotiation authentication for
-   * @return a new SecurityMessage
-   */
-  def fromResponse(response : Array[Byte], connectionId : String) : SecurityMessage = {
-    val newSecurityMessage = new SecurityMessage()
-    newSecurityMessage.set(response, connectionId)
-    newSecurityMessage
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index b977711e7d5ad..c5195c1143a8f 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -35,7 +35,6 @@ import org.roaringbitmap.{ArrayContainer, BitmapContainer, RoaringArray, Roaring
 import org.apache.spark._
 import org.apache.spark.api.python.PythonBroadcast
 import org.apache.spark.broadcast.HttpBroadcast
-import org.apache.spark.network.nio.{GetBlock, GotBlock, PutBlock}
 import org.apache.spark.network.util.ByteUnit
 import org.apache.spark.scheduler.{CompressedMapStatus, HighlyCompressedMapStatus}
 import org.apache.spark.storage._
@@ -362,9 +361,6 @@ private[serializer] object KryoSerializer {
   private val toRegister: Seq[Class[_]] = Seq(
     ByteBuffer.allocate(1).getClass,
     classOf[StorageLevel],
-    classOf[PutBlock],
-    classOf[GotBlock],
-    classOf[GetBlock],
     classOf[CompressedMapStatus],
     classOf[HighlyCompressedMapStatus],
     classOf[RoaringBitmap],
diff --git a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala
deleted file mode 100644
index 5e364cc0edeb2..0000000000000
--- a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala
+++ /dev/null
@@ -1,296 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.io.IOException
-import java.nio._
-
-import scala.concurrent.duration._
-import scala.concurrent.{Await, TimeoutException}
-import scala.language.postfixOps
-
-import org.apache.spark.{SecurityManager, SparkConf, SparkFunSuite}
-import org.apache.spark.util.Utils
-
-/**
-  * Test the ConnectionManager with various security settings.
-  */
-class ConnectionManagerSuite extends SparkFunSuite {
-
-  test("security default off") {
-    val conf = new SparkConf
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var receivedMessage = false
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      receivedMessage = true
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    Await.result(manager.sendMessageReliably(manager.id, bufferMessage), 10 seconds)
-
-    assert(receivedMessage == true)
-
-    manager.stop()
-  }
-
-  test("security on same password") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "true")
-    conf.set("spark.authenticate.secret", "good")
-    conf.set("spark.app.id", "app-id")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-    val managerServer = new ConnectionManager(0, conf, securityManager)
-    var numReceivedServerMessages = 0
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val count = 10
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      Await.result(manager.sendMessageReliably(managerServer.id, bufferMessage), 10 seconds)
-    })
-
-    assert(numReceivedServerMessages == 10)
-    assert(numReceivedMessages == 0)
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("security mismatch password") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "true")
-    conf.set("spark.app.id", "app-id")
-    conf.set("spark.authenticate.secret", "good")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-
-    val badconf = conf.clone.set("spark.authenticate.secret", "bad")
-    val badsecurityManager = new SecurityManager(badconf)
-    val managerServer = new ConnectionManager(0, badconf, badsecurityManager)
-    var numReceivedServerMessages = 0
-
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    // Expect managerServer to close connection, which we'll report as an error:
-    intercept[IOException] {
-      Await.result(manager.sendMessageReliably(managerServer.id, bufferMessage), 10 seconds)
-    }
-
-    assert(numReceivedServerMessages == 0)
-    assert(numReceivedMessages == 0)
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("security mismatch auth off") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "false")
-    conf.set("spark.authenticate.secret", "good")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-
-    val badconf = new SparkConf
-    badconf.set("spark.authenticate", "true")
-    badconf.set("spark.authenticate.secret", "good")
-    val badsecurityManager = new SecurityManager(badconf)
-    val managerServer = new ConnectionManager(0, badconf, badsecurityManager)
-    var numReceivedServerMessages = 0
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    (0 until 1).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      manager.sendMessageReliably(managerServer.id, bufferMessage)
-    }).foreach(f => {
-      try {
-        val g = Await.result(f, 1 second)
-        assert(false)
-      } catch {
-        case i: IOException =>
-          assert(true)
-        case e: TimeoutException => {
-          // we should timeout here since the client can't do the negotiation
-          assert(true)
-        }
-      }
-    })
-
-    assert(numReceivedServerMessages == 0)
-    assert(numReceivedMessages == 0)
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("security auth off") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "false")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-
-    val badconf = new SparkConf
-    badconf.set("spark.authenticate", "false")
-    val badsecurityManager = new SecurityManager(badconf)
-    val managerServer = new ConnectionManager(0, badconf, badsecurityManager)
-    var numReceivedServerMessages = 0
-
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    (0 until 10).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      manager.sendMessageReliably(managerServer.id, bufferMessage)
-    }).foreach(f => {
-      try {
-        val g = Await.result(f, 1 second)
-      } catch {
-        case e: Exception => {
-          assert(false)
-        }
-      }
-    })
-    assert(numReceivedServerMessages == 10)
-    assert(numReceivedMessages == 0)
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("Ack error message") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "false")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    val managerServer = new ConnectionManager(0, conf, securityManager)
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      throw new Exception("Custom exception text")
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer)
-
-    val future = manager.sendMessageReliably(managerServer.id, bufferMessage)
-
-    val exception = intercept[IOException] {
-      Await.result(future, 1 second)
-    }
-    assert(Utils.exceptionString(exception).contains("Custom exception text"))
-
-    manager.stop()
-    managerServer.stop()
-
-  }
-
-  test("sendMessageReliably timeout") {
-    val clientConf = new SparkConf
-    clientConf.set("spark.authenticate", "false")
-    val ackTimeoutS = 30
-    clientConf.set("spark.core.connection.ack.wait.timeout", s"${ackTimeoutS}s")
-
-    val clientSecurityManager = new SecurityManager(clientConf)
-    val manager = new ConnectionManager(0, clientConf, clientSecurityManager)
-
-    val serverConf = new SparkConf
-    serverConf.set("spark.authenticate", "false")
-    val serverSecurityManager = new SecurityManager(serverConf)
-    val managerServer = new ConnectionManager(0, serverConf, serverSecurityManager)
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      // sleep 60 sec > ack timeout for simulating server slow down or hang up
-      Thread.sleep(ackTimeoutS * 3 * 1000)
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-
-    val future = manager.sendMessageReliably(managerServer.id, bufferMessage)
-
-    // Future should throw IOException in 30 sec.
-    // Otherwise TimeoutExcepton is thrown from Await.result.
-    // We expect TimeoutException is not thrown.
-    intercept[IOException] {
-      Await.result(future, (ackTimeoutS * 2) second)
-    }
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-}
-
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala
index 0f5ba46f69c2f..eb5af70d57aec 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala
@@ -26,10 +26,10 @@ import org.mockito.Mockito.{mock, when}
 import org.scalatest.{BeforeAndAfter, Matchers}
 import org.scalatest.concurrent.Eventually._
 
+import org.apache.spark.network.netty.NettyBlockTransferService
 import org.apache.spark.rpc.RpcEnv
 import org.apache.spark._
 import org.apache.spark.network.BlockTransferService
-import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.shuffle.hash.HashShuffleManager
@@ -38,7 +38,7 @@ import org.apache.spark.storage.StorageLevel._
 /** Testsuite that tests block replication in BlockManager */
 class BlockManagerReplicationSuite extends SparkFunSuite with Matchers with BeforeAndAfter {
 
-  private val conf = new SparkConf(false)
+  private val conf = new SparkConf(false).set("spark.app.id", "test")
   var rpcEnv: RpcEnv = null
   var master: BlockManagerMaster = null
   val securityMgr = new SecurityManager(conf)
@@ -59,7 +59,7 @@ class BlockManagerReplicationSuite extends SparkFunSuite with Matchers with Befo
   private def makeBlockManager(
       maxMem: Long,
       name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = {
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     val store = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf,
       mapOutputTracker, shuffleManager, transfer, securityMgr, 0)
     store.initialize("app-id")
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index e5b54d66c8157..34bb4952e7246 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -30,10 +30,10 @@ import org.scalatest._
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.concurrent.Timeouts._
 
+import org.apache.spark.network.netty.NettyBlockTransferService
 import org.apache.spark.rpc.RpcEnv
 import org.apache.spark._
 import org.apache.spark.executor.DataReadMethod
-import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.{JavaSerializer, KryoSerializer}
 import org.apache.spark.shuffle.hash.HashShuffleManager
@@ -44,7 +44,7 @@ import org.apache.spark.util._
 class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterEach
   with PrivateMethodTester with ResetSystemProperties {
 
-  private val conf = new SparkConf(false)
+  private val conf = new SparkConf(false).set("spark.app.id", "test")
   var store: BlockManager = null
   var store2: BlockManager = null
   var store3: BlockManager = null
@@ -66,7 +66,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
   private def makeBlockManager(
       maxMem: Long,
       name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = {
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     val manager = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf,
       mapOutputTracker, shuffleManager, transfer, securityMgr, 0)
     manager.initialize("app-id")
@@ -819,7 +819,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
 
   test("block store put failure") {
     // Use Java serializer so we can create an unserializable error.
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     store = new BlockManager(SparkContext.DRIVER_IDENTIFIER, rpcEnv, master,
       new JavaSerializer(conf), 1200, conf, mapOutputTracker, shuffleManager, transfer, securityMgr,
       0)
@@ -833,7 +833,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
 
     // Make sure get a1 doesn't hang and returns None.
     failAfter(1 second) {
-      assert(store.getSingle("a1") == None, "a1 should not be in store")
+      assert(store.getSingle("a1").isEmpty, "a1 should not be in store")
     }
   }
 
diff --git a/docs/configuration.md b/docs/configuration.md
index 29a36bd67f28b..a2cc7a37e2240 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -382,17 +382,6 @@ Apart from these, the following properties are also available, and may be useful
     overhead per reduce task, so keep it small unless you have a large amount of memory.
   </td>
 </tr>
-<tr>
-  <td><code>spark.shuffle.blockTransferService</code></td>
-  <td>netty</td>
-  <td>
-    Implementation to use for transferring shuffle and cached blocks between executors. There
-    are two implementations available: <code>netty</code> and <code>nio</code>. Netty-based
-    block transfer is intended to be simpler but equally efficient and is the default option
-    starting in 1.2, and <code>nio</code> block transfer is deprecated in Spark 1.5.0 and will
-    be removed in Spark 1.6.0.
-  </td>
-</tr>
 <tr>
   <td><code>spark.shuffle.compress</code></td>
   <td>true</td>
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 714ce3cd9b1de..3b8b6c8ffa375 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -32,635 +32,638 @@ import com.typesafe.tools.mima.core.ProblemFilters._
  * MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
  */
 object MimaExcludes {
-    def excludes(version: String) =
-      version match {
-        case v if v.startsWith("1.5") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("network"),
-            // These are needed if checking against the sbt build, since they are part of
-            // the maven-generated artifacts in 1.3.
-            excludePackage("org.spark-project.jetty"),
-            MimaBuild.excludeSparkPackage("unused"),
-            // JavaRDDLike is not meant to be extended by user programs
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.partitioner"),
-            // Modification of private static method
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.streaming.kafka.KafkaUtils.org$apache$spark$streaming$kafka$KafkaUtils$$leadersForRanges"),
-            // Mima false positive (was a private[spark] class)
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.util.collection.PairIterator"),
-            // Removing a testing method from a private class
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"),
-            // While private MiMa is still not happy about the changes,
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.ml.regression.LeastSquaresAggregator.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.ml.regression.LeastSquaresCostFun.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.ml.classification.LogisticCostFun.this"),
-            // SQL execution is considered private.
-            excludePackage("org.apache.spark.sql.execution"),
-            // The old JSON RDD is removed in favor of streaming Jackson
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
-            // local function inside a method
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.sql.UDFRegistration.org$apache$spark$sql$UDFRegistration$$builder$24")
-          ) ++ Seq(
-            // SPARK-8479 Add numNonzeros and numActives to Matrix.
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.numNonzeros"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.numActives")
-          ) ++ Seq(
-            // SPARK-8914 Remove RDDApi
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.RDDApi")
-          ) ++ Seq(
-            // SPARK-7292 Provide operator to truncate lineage cheaply
-            ProblemFilters.exclude[AbstractClassProblem](
-              "org.apache.spark.rdd.RDDCheckpointData"),
-            ProblemFilters.exclude[AbstractClassProblem](
-              "org.apache.spark.rdd.CheckpointRDD")
-          ) ++ Seq(
-            // SPARK-8701 Add input metadata in the batch page.
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.streaming.scheduler.InputInfo$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.streaming.scheduler.InputInfo")
-          ) ++ Seq(
-            // SPARK-6797 Support YARN modes for SparkR
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.PairwiseRRDD.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.RRDD.createRWorker"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.RRDD.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.StringRRDD.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.BaseRRDD.this")
-          ) ++ Seq(
-            // SPARK-7422 add argmax for sparse vectors
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.argmax")
-          ) ++ Seq(
-            // SPARK-8906 Move all internal data source classes into execution.datasources
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopPartition"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DefaultWriterContainer"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DynamicPartitionWriterContainer"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.BaseWriterContainer"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CaseInsensitiveMap"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException"),
-            // SPARK-9763 Minimize exposure of internal SQL classes
-            excludePackage("org.apache.spark.sql.parquet"),
-            excludePackage("org.apache.spark.sql.json"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$JDBCConversion"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$DriverWrapper"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DefaultSource"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation")
-          ) ++ Seq(
-            // SPARK-4751 Dynamic allocation for standalone mode
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.SparkContext.supportDynamicAllocation")
-          ) ++ Seq(
-            // SPARK-9580: Remove SQL test singletons
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.LocalSQLContext$SQLSession"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.LocalSQLContext"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.TestSQLContext"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.TestSQLContext$")
-          ) ++ Seq(
-            // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.mllib.linalg.VectorUDT.serialize")
-          )
+  def excludes(version: String) = version match {
+    case v if v.startsWith("1.6") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("network")
+        )
+    case v if v.startsWith("1.5") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("network"),
+        MimaBuild.excludeSparkPackage("deploy"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in 1.3.
+        excludePackage("org.spark-project.jetty"),
+        MimaBuild.excludeSparkPackage("unused"),
+        // JavaRDDLike is not meant to be extended by user programs
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.partitioner"),
+        // Modification of private static method
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.streaming.kafka.KafkaUtils.org$apache$spark$streaming$kafka$KafkaUtils$$leadersForRanges"),
+        // Mima false positive (was a private[spark] class)
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.util.collection.PairIterator"),
+        // Removing a testing method from a private class
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"),
+        // While private MiMa is still not happy about the changes,
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.regression.LeastSquaresAggregator.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.regression.LeastSquaresCostFun.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.classification.LogisticCostFun.this"),
+        // SQL execution is considered private.
+        excludePackage("org.apache.spark.sql.execution"),
+        // The old JSON RDD is removed in favor of streaming Jackson
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
+        // local function inside a method
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.sql.UDFRegistration.org$apache$spark$sql$UDFRegistration$$builder$24")
+      ) ++ Seq(
+        // SPARK-8479 Add numNonzeros and numActives to Matrix.
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.numNonzeros"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.numActives")
+      ) ++ Seq(
+        // SPARK-8914 Remove RDDApi
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.RDDApi")
+      ) ++ Seq(
+        // SPARK-7292 Provide operator to truncate lineage cheaply
+        ProblemFilters.exclude[AbstractClassProblem](
+          "org.apache.spark.rdd.RDDCheckpointData"),
+        ProblemFilters.exclude[AbstractClassProblem](
+          "org.apache.spark.rdd.CheckpointRDD")
+      ) ++ Seq(
+        // SPARK-8701 Add input metadata in the batch page.
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.streaming.scheduler.InputInfo$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.streaming.scheduler.InputInfo")
+      ) ++ Seq(
+        // SPARK-6797 Support YARN modes for SparkR
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.PairwiseRRDD.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.RRDD.createRWorker"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.RRDD.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.StringRRDD.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.BaseRRDD.this")
+      ) ++ Seq(
+        // SPARK-7422 add argmax for sparse vectors
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.argmax")
+      ) ++ Seq(
+        // SPARK-8906 Move all internal data source classes into execution.datasources
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopPartition"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DefaultWriterContainer"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DynamicPartitionWriterContainer"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.BaseWriterContainer"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CaseInsensitiveMap"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException"),
+        // SPARK-9763 Minimize exposure of internal SQL classes
+        excludePackage("org.apache.spark.sql.parquet"),
+        excludePackage("org.apache.spark.sql.json"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$JDBCConversion"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$DriverWrapper"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DefaultSource"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation")
+      ) ++ Seq(
+        // SPARK-4751 Dynamic allocation for standalone mode
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.SparkContext.supportDynamicAllocation")
+      ) ++ Seq(
+        // SPARK-9580: Remove SQL test singletons
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.LocalSQLContext$SQLSession"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.LocalSQLContext"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.TestSQLContext"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.TestSQLContext$")
+      ) ++ Seq(
+        // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.mllib.linalg.VectorUDT.serialize")
+      )
 
-        case v if v.startsWith("1.4") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("ml"),
-            // SPARK-7910 Adding a method to get the partioner to JavaRDD,
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitioner"),
-            // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff"),
-            // These are needed if checking against the sbt build, since they are part of
-            // the maven-generated artifacts in 1.3.
-            excludePackage("org.spark-project.jetty"),
-            MimaBuild.excludeSparkPackage("unused"),
-            ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.rdd.JdbcRDD.compute"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint")
-          ) ++ Seq(
-            // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though
-            // the stage class is defined as private[spark]
-            ProblemFilters.exclude[AbstractClassProblem]("org.apache.spark.scheduler.Stage")
-          ) ++ Seq(
-            // SPARK-6510 Add a Graph#minus method acting as Set#difference
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus")
-          ) ++ Seq(
-            // SPARK-6492 Fix deadlock in SparkContext.stop()
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" +
-                "apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK")
-          )++ Seq(
-            // SPARK-6693 add tostring with max lines and width for matrix
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.toString")
-          )++ Seq(
-            // SPARK-6703 Add getOrCreate method to SparkContext
-            ProblemFilters.exclude[IncompatibleResultTypeProblem]
-                ("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext")
-          )++ Seq(
-            // SPARK-7090 Introduce LDAOptimizer to LDA to further improve extensibility
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.mllib.clustering.LDA$EMOptimizer")
-          ) ++ Seq(
-            // SPARK-6756 add toSparse, toDense, numActives, numNonzeros, and compressed to Vector
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.compressed"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.toDense"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.numNonzeros"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.toSparse"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.numActives"),
-            // SPARK-7681 add SparseVector support for gemv
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.multiply"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.DenseMatrix.multiply"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.SparseMatrix.multiply")
-          ) ++ Seq(
-            // Execution should never be included as its always internal.
-            MimaBuild.excludeSparkPackage("sql.execution"),
-            // This `protected[sql]` method was removed in 1.3.1
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.sql.SQLContext.checkAnalysis"),
-            // These `private[sql]` class were removed in 1.4.0:
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.execution.AddExchange"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.execution.AddExchange$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.PartitionSpec"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.PartitionSpec$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.Partition"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.Partition$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache"),
-            // These test support classes were moved out of src/main and into src/test:
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTestData"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTestData$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.TestGroupWriteSupport"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CacheManager"),
-            // TODO: Remove the following rule once ParquetTest has been moved to src/test.
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTest")
-          ) ++ Seq(
-            // SPARK-7530 Added StreamingContext.getState()
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.StreamingContext.state_=")
-          ) ++ Seq(
-            // SPARK-7081 changed ShuffleWriter from a trait to an abstract class and removed some
-            // unnecessary type bounds in order to fix some compiler warnings that occurred when
-            // implementing this interface in Java. Note that ShuffleWriter is private[spark].
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.shuffle.ShuffleWriter")
-          ) ++ Seq(
-            // SPARK-6888 make jdbc driver handling user definable
-            // This patch renames some classes to API friendly names.
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.PostgresQuirks"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.NoQuirks"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.MySQLQuirks")
-          )
+    case v if v.startsWith("1.4") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("ml"),
+        // SPARK-7910 Adding a method to get the partioner to JavaRDD,
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitioner"),
+        // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in 1.3.
+        excludePackage("org.spark-project.jetty"),
+        MimaBuild.excludeSparkPackage("unused"),
+        ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.rdd.JdbcRDD.compute"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint")
+      ) ++ Seq(
+        // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though
+        // the stage class is defined as private[spark]
+        ProblemFilters.exclude[AbstractClassProblem]("org.apache.spark.scheduler.Stage")
+      ) ++ Seq(
+        // SPARK-6510 Add a Graph#minus method acting as Set#difference
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus")
+      ) ++ Seq(
+        // SPARK-6492 Fix deadlock in SparkContext.stop()
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" +
+            "apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK")
+      )++ Seq(
+        // SPARK-6693 add tostring with max lines and width for matrix
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.toString")
+      )++ Seq(
+        // SPARK-6703 Add getOrCreate method to SparkContext
+        ProblemFilters.exclude[IncompatibleResultTypeProblem]
+            ("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext")
+      )++ Seq(
+        // SPARK-7090 Introduce LDAOptimizer to LDA to further improve extensibility
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.mllib.clustering.LDA$EMOptimizer")
+      ) ++ Seq(
+        // SPARK-6756 add toSparse, toDense, numActives, numNonzeros, and compressed to Vector
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.compressed"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.toDense"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.numNonzeros"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.toSparse"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.numActives"),
+        // SPARK-7681 add SparseVector support for gemv
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.multiply"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.DenseMatrix.multiply"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.SparseMatrix.multiply")
+      ) ++ Seq(
+        // Execution should never be included as its always internal.
+        MimaBuild.excludeSparkPackage("sql.execution"),
+        // This `protected[sql]` method was removed in 1.3.1
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.sql.SQLContext.checkAnalysis"),
+        // These `private[sql]` class were removed in 1.4.0:
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.execution.AddExchange"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.execution.AddExchange$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.PartitionSpec"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.PartitionSpec$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.Partition"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.Partition$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache"),
+        // These test support classes were moved out of src/main and into src/test:
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetTestData"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetTestData$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.TestGroupWriteSupport"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CacheManager"),
+        // TODO: Remove the following rule once ParquetTest has been moved to src/test.
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetTest")
+      ) ++ Seq(
+        // SPARK-7530 Added StreamingContext.getState()
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.StreamingContext.state_=")
+      ) ++ Seq(
+        // SPARK-7081 changed ShuffleWriter from a trait to an abstract class and removed some
+        // unnecessary type bounds in order to fix some compiler warnings that occurred when
+        // implementing this interface in Java. Note that ShuffleWriter is private[spark].
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.shuffle.ShuffleWriter")
+      ) ++ Seq(
+        // SPARK-6888 make jdbc driver handling user definable
+        // This patch renames some classes to API friendly names.
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.PostgresQuirks"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.NoQuirks"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.MySQLQuirks")
+      )
 
-        case v if v.startsWith("1.3") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("ml"),
-            // These are needed if checking against the sbt build, since they are part of
-            // the maven-generated artifacts in the 1.2 build.
-            MimaBuild.excludeSparkPackage("unused"),
-            ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional")
-          ) ++ Seq(
-            // SPARK-2321
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.SparkStageInfoImpl.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.SparkStageInfo.submissionTime")
-          ) ++ Seq(
-            // SPARK-4614
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrices.randn"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrices.rand")
-          ) ++ Seq(
-            // SPARK-5321
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.SparseMatrix.transposeMultiply"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.transpose"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.DenseMatrix.transposeMultiply"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Matrix." +
-                "org$apache$spark$mllib$linalg$Matrix$_setter_$isTransposed_="),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.isTransposed"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.foreachActive")
-          ) ++ Seq(
-            // SPARK-5540
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.solveLeastSquares"),
-            // SPARK-5536
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateBlock")
-          ) ++ Seq(
-            // SPARK-3325
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.api.java.JavaDStreamLike.print"),
-            // SPARK-2757
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.streaming.flume.sink.SparkAvroCallbackHandler." +
-                "removeAndGetProcessor")
-          ) ++ Seq(
-            // SPARK-5123 (SparkSQL data type change) - alpha component only
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.ml.feature.HashingTF.outputDataType"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.ml.feature.Tokenizer.outputDataType"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.ml.feature.Tokenizer.validateInputType"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.ml.classification.LogisticRegressionModel.validateAndTransformSchema"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.ml.classification.LogisticRegression.validateAndTransformSchema")
-          ) ++ Seq(
-            // SPARK-4014
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.TaskContext.taskAttemptId"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.TaskContext.attemptNumber")
-          ) ++ Seq(
-            // SPARK-5166 Spark SQL API stabilization
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Transformer.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Estimator.fit"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Transformer.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Pipeline.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.PipelineModel.transform"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Estimator.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Evaluator.evaluate"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Evaluator.evaluate"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidator.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidatorModel.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScaler.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScalerModel.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionModel.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegression.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.evaluation.BinaryClassificationEvaluator.evaluate")
-          ) ++ Seq(
-            // SPARK-5270
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.isEmpty")
-          ) ++ Seq(
-            // SPARK-5430
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.treeReduce"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.treeAggregate")
-          ) ++ Seq(
-            // SPARK-5297 Java FileStream do not work with custom key/values
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.api.java.JavaStreamingContext.fileStream")
-          ) ++ Seq(
-            // SPARK-5315 Spark Streaming Java API returns Scala DStream
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.api.java.JavaDStreamLike.reduceByWindow")
-          ) ++ Seq(
-            // SPARK-5461 Graph should have isCheckpointed, getCheckpointFiles methods
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.graphx.Graph.getCheckpointFiles"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.graphx.Graph.isCheckpointed")
-          ) ++ Seq(
-            // SPARK-4789 Standardize ML Prediction APIs
-            ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.linalg.VectorUDT"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.serialize"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.sqlType")
-          ) ++ Seq(
-            // SPARK-5814
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$wrapDoubleArray"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$fillFullMatrix"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$iterations"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$computeYtY"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeLinkRDDs"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$alpha"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$randomFactor"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeInLinkBlock"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$dspr"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$lambda"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$implicitPrefs"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$rank")
-          ) ++ Seq(
-            // SPARK-4682
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.RealClock"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.Clock"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.TestClock")
-          ) ++ Seq(
-            // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff")
-          )
+    case v if v.startsWith("1.3") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("ml"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in the 1.2 build.
+        MimaBuild.excludeSparkPackage("unused"),
+        ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional")
+      ) ++ Seq(
+        // SPARK-2321
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.SparkStageInfoImpl.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.SparkStageInfo.submissionTime")
+      ) ++ Seq(
+        // SPARK-4614
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrices.randn"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrices.rand")
+      ) ++ Seq(
+        // SPARK-5321
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.SparseMatrix.transposeMultiply"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.transpose"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.DenseMatrix.transposeMultiply"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Matrix." +
+            "org$apache$spark$mllib$linalg$Matrix$_setter_$isTransposed_="),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.isTransposed"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.foreachActive")
+      ) ++ Seq(
+        // SPARK-5540
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.solveLeastSquares"),
+        // SPARK-5536
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateBlock")
+      ) ++ Seq(
+        // SPARK-3325
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.api.java.JavaDStreamLike.print"),
+        // SPARK-2757
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.streaming.flume.sink.SparkAvroCallbackHandler." +
+            "removeAndGetProcessor")
+      ) ++ Seq(
+        // SPARK-5123 (SparkSQL data type change) - alpha component only
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.ml.feature.HashingTF.outputDataType"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.ml.feature.Tokenizer.outputDataType"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.ml.feature.Tokenizer.validateInputType"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.ml.classification.LogisticRegressionModel.validateAndTransformSchema"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.ml.classification.LogisticRegression.validateAndTransformSchema")
+      ) ++ Seq(
+        // SPARK-4014
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.TaskContext.taskAttemptId"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.TaskContext.attemptNumber")
+      ) ++ Seq(
+        // SPARK-5166 Spark SQL API stabilization
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Transformer.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Estimator.fit"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Transformer.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Pipeline.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.PipelineModel.transform"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Estimator.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Evaluator.evaluate"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Evaluator.evaluate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidator.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidatorModel.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScaler.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScalerModel.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionModel.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegression.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.evaluation.BinaryClassificationEvaluator.evaluate")
+      ) ++ Seq(
+        // SPARK-5270
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.isEmpty")
+      ) ++ Seq(
+        // SPARK-5430
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.treeReduce"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.treeAggregate")
+      ) ++ Seq(
+        // SPARK-5297 Java FileStream do not work with custom key/values
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.api.java.JavaStreamingContext.fileStream")
+      ) ++ Seq(
+        // SPARK-5315 Spark Streaming Java API returns Scala DStream
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.api.java.JavaDStreamLike.reduceByWindow")
+      ) ++ Seq(
+        // SPARK-5461 Graph should have isCheckpointed, getCheckpointFiles methods
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.graphx.Graph.getCheckpointFiles"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.graphx.Graph.isCheckpointed")
+      ) ++ Seq(
+        // SPARK-4789 Standardize ML Prediction APIs
+        ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.linalg.VectorUDT"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.serialize"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.sqlType")
+      ) ++ Seq(
+        // SPARK-5814
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$wrapDoubleArray"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$fillFullMatrix"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$iterations"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$computeYtY"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeLinkRDDs"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$alpha"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$randomFactor"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeInLinkBlock"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$dspr"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$lambda"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$implicitPrefs"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$rank")
+      ) ++ Seq(
+        // SPARK-4682
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.RealClock"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.Clock"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.TestClock")
+      ) ++ Seq(
+        // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff")
+      )
 
-        case v if v.startsWith("1.2") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("graphx")
-          ) ++
-          MimaBuild.excludeSparkClass("mllib.linalg.Matrix") ++
-          MimaBuild.excludeSparkClass("mllib.linalg.Vector") ++
-          Seq(
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.scheduler.TaskLocation"),
-            // Added normL1 and normL2 to trait MultivariateStatisticalSummary
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL2"),
-            // MapStatus should be private[spark]
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.scheduler.MapStatus"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.network.netty.PathResolver"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.network.netty.client.BlockClientListener"),
+    case v if v.startsWith("1.2") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("graphx")
+      ) ++
+      MimaBuild.excludeSparkClass("mllib.linalg.Matrix") ++
+      MimaBuild.excludeSparkClass("mllib.linalg.Vector") ++
+      Seq(
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.scheduler.TaskLocation"),
+        // Added normL1 and normL2 to trait MultivariateStatisticalSummary
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL2"),
+        // MapStatus should be private[spark]
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.scheduler.MapStatus"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.network.netty.PathResolver"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.network.netty.client.BlockClientListener"),
 
-            // TaskContext was promoted to Abstract class
-            ProblemFilters.exclude[AbstractClassProblem](
-              "org.apache.spark.TaskContext"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.util.collection.SortDataFormat")
-          ) ++ Seq(
-            // Adding new methods to the JavaRDDLike trait:
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.takeAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.foreachPartitionAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.countAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.collectAsync")
-          ) ++ Seq(
-            // SPARK-3822
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.SparkContext.org$apache$spark$SparkContext$$createTaskScheduler")
-          ) ++ Seq(
-            // SPARK-1209
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.hadoop.mapred.SparkHadoopMapRedUtil"),
-            ProblemFilters.exclude[MissingTypesProblem](
-              "org.apache.spark.rdd.PairRDDFunctions")
-          ) ++ Seq(
-            // SPARK-4062
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.kafka.KafkaReceiver#MessageHandler.this")
-          )
+        // TaskContext was promoted to Abstract class
+        ProblemFilters.exclude[AbstractClassProblem](
+          "org.apache.spark.TaskContext"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.util.collection.SortDataFormat")
+      ) ++ Seq(
+        // Adding new methods to the JavaRDDLike trait:
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.takeAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.foreachPartitionAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.countAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.collectAsync")
+      ) ++ Seq(
+        // SPARK-3822
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.SparkContext.org$apache$spark$SparkContext$$createTaskScheduler")
+      ) ++ Seq(
+        // SPARK-1209
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.hadoop.mapred.SparkHadoopMapRedUtil"),
+        ProblemFilters.exclude[MissingTypesProblem](
+          "org.apache.spark.rdd.PairRDDFunctions")
+      ) ++ Seq(
+        // SPARK-4062
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.kafka.KafkaReceiver#MessageHandler.this")
+      )
 
-        case v if v.startsWith("1.1") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("graphx")
-          ) ++
-          Seq(
-            // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
-            // Should probably mark this as Experimental
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
-            // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
-            // for countApproxDistinct* functions, which does not work in Java. We later removed
-            // them, and use the following to tell Mima to not care about them.
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.DiskStore.getValues"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.MemoryStore.Entry")
-          ) ++
-          Seq(
-            // Serializer interface change. See SPARK-3045.
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.DeserializationStream"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.Serializer"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.SerializationStream"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.SerializerInstance")
-          )++
-          Seq(
-            // Renamed putValues -> putArray + putIterator
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.MemoryStore.putValues"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.DiskStore.putValues"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.TachyonStore.putValues")
-          ) ++
-          Seq(
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.flume.FlumeReceiver.this"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.streaming.kafka.KafkaUtils.createStream"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.streaming.kafka.KafkaReceiver.this")
-          ) ++
-          Seq( // Ignore some private methods in ALS.
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
-            ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
-              "org.apache.spark.mllib.recommendation.ALS.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
-          ) ++
-          MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
-          MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
-          MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
-          MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
-          MimaBuild.excludeSparkClass("storage.Values") ++
-          MimaBuild.excludeSparkClass("storage.Entry") ++
-          MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
-          // Class was missing "@DeveloperApi" annotation in 1.0.
-          MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
-          Seq(
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.tree.impurity.Variance.calculate")
-          ) ++
-          Seq( // Package-private classes removed in SPARK-2341
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser$")
-          ) ++
-          Seq( // package-private classes removed in MLlib
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.org$apache$spark$mllib$regression$GeneralizedLinearAlgorithm$$prependOne")
-          ) ++
-          Seq( // new Vector methods in MLlib (binary compatible assuming users do not implement Vector)
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Vector.copy")
-          ) ++
-          Seq( // synthetic methods generated in LabeledPoint
-            ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.regression.LabeledPoint$"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.regression.LabeledPoint.apply"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.regression.LabeledPoint.toString")
-          ) ++
-          Seq ( // Scala 2.11 compatibility fix
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.StreamingContext.<init>$default$2")
-          )
-        case v if v.startsWith("1.0") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("api.java"),
-            MimaBuild.excludeSparkPackage("mllib"),
-            MimaBuild.excludeSparkPackage("streaming")
-          ) ++
-          MimaBuild.excludeSparkClass("rdd.ClassTags") ++
-          MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
-          MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
-          MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
-          MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
-          MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
-          MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
-          MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
-          MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
-          MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
-          MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
-          MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
-          MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
-        case _ => Seq()
-      }
-}
+    case v if v.startsWith("1.1") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("graphx")
+      ) ++
+      Seq(
+        // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
+        // Should probably mark this as Experimental
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
+        // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
+        // for countApproxDistinct* functions, which does not work in Java. We later removed
+        // them, and use the following to tell Mima to not care about them.
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.DiskStore.getValues"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.MemoryStore.Entry")
+      ) ++
+      Seq(
+        // Serializer interface change. See SPARK-3045.
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.DeserializationStream"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.Serializer"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.SerializationStream"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.SerializerInstance")
+      )++
+      Seq(
+        // Renamed putValues -> putArray + putIterator
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.MemoryStore.putValues"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.DiskStore.putValues"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.TachyonStore.putValues")
+      ) ++
+      Seq(
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.flume.FlumeReceiver.this"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.streaming.kafka.KafkaUtils.createStream"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.streaming.kafka.KafkaReceiver.this")
+      ) ++
+      Seq( // Ignore some private methods in ALS.
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
+        ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
+          "org.apache.spark.mllib.recommendation.ALS.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
+      ) ++
+      MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
+      MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
+      MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
+      MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
+      MimaBuild.excludeSparkClass("storage.Values") ++
+      MimaBuild.excludeSparkClass("storage.Entry") ++
+      MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
+      // Class was missing "@DeveloperApi" annotation in 1.0.
+      MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
+      Seq(
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Variance.calculate")
+      ) ++
+      Seq( // Package-private classes removed in SPARK-2341
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser$")
+      ) ++
+      Seq( // package-private classes removed in MLlib
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.org$apache$spark$mllib$regression$GeneralizedLinearAlgorithm$$prependOne")
+      ) ++
+      Seq( // new Vector methods in MLlib (binary compatible assuming users do not implement Vector)
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Vector.copy")
+      ) ++
+      Seq( // synthetic methods generated in LabeledPoint
+        ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.regression.LabeledPoint$"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.regression.LabeledPoint.apply"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.regression.LabeledPoint.toString")
+      ) ++
+      Seq ( // Scala 2.11 compatibility fix
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.StreamingContext.<init>$default$2")
+      )
+    case v if v.startsWith("1.0") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("api.java"),
+        MimaBuild.excludeSparkPackage("mllib"),
+        MimaBuild.excludeSparkPackage("streaming")
+      ) ++
+      MimaBuild.excludeSparkClass("rdd.ClassTags") ++
+      MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
+      MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
+      MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
+      MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
+      MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
+      MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
+      MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
+      MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
+      MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
+      MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
+      MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
+      MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
+    case _ => Seq()
+  }
+}
\ No newline at end of file
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala
index 6c0c926755c20..13cfe29d7b304 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala
@@ -29,7 +29,7 @@ import org.scalatest.{BeforeAndAfter, Matchers}
 import org.scalatest.concurrent.Eventually._
 
 import org.apache.spark._
-import org.apache.spark.network.nio.NioBlockTransferService
+import org.apache.spark.network.netty.NettyBlockTransferService
 import org.apache.spark.rpc.RpcEnv
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.KryoSerializer
@@ -47,7 +47,9 @@ class ReceivedBlockHandlerSuite
   with Matchers
   with Logging {
 
-  val conf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1")
+  val conf = new SparkConf()
+    .set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1")
+    .set("spark.app.id", "streaming-test")
   val hadoopConf = new Configuration()
   val streamId = 1
   val securityMgr = new SecurityManager(conf)
@@ -184,7 +186,7 @@ class ReceivedBlockHandlerSuite
   }
 
   test("Test Block - isFullyConsumed") {
-    val sparkConf = new SparkConf()
+    val sparkConf = new SparkConf().set("spark.app.id", "streaming-test")
     sparkConf.set("spark.storage.unrollMemoryThreshold", "512")
     // spark.storage.unrollFraction set to 0.4 for BlockManager
     sparkConf.set("spark.storage.unrollFraction", "0.4")
@@ -251,7 +253,7 @@ class ReceivedBlockHandlerSuite
       maxMem: Long,
       conf: SparkConf,
       name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = {
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     val manager = new BlockManager(name, rpcEnv, blockManagerMaster, serializer, maxMem, conf,
       mapOutputTracker, shuffleManager, transfer, securityMgr, 0)
     manager.initialize("app-id")

From 9d8e838d883ed21f9ef562e7e3ac074c7e4adb88 Mon Sep 17 00:00:00 2001
From: Stephen Hopper <shopper@shopper-osx.local>
Date: Tue, 8 Sep 2015 14:36:34 +0100
Subject: [PATCH 1345/1454] =?UTF-8?q?[DOC]=20Added=20R=20to=20the=20list?=
 =?UTF-8?q?=20of=20languages=20with=20"high-level=20API"=20support=20in=20?=
 =?UTF-8?q?the=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

… main README.

Author: Stephen Hopper <shopper@shopper-osx.local>

Closes #8646 from enragedginger/master.
---
 README.md           |  4 ++--
 docs/quick-start.md | 16 ++++++++--------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 380422ca00dbe..76e29b4235666 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # Apache Spark
 
 Spark is a fast and general cluster computing system for Big Data. It provides
-high-level APIs in Scala, Java, and Python, and an optimized engine that
+high-level APIs in Scala, Java, Python, and R, and an optimized engine that
 supports general computation graphs for data analysis. It also supports a
 rich set of higher-level tools including Spark SQL for SQL and DataFrames,
 MLlib for machine learning, GraphX for graph processing,
@@ -94,5 +94,5 @@ distribution.
 
 ## Configuration
 
-Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)
+Please refer to the [Configuration Guide](http://spark.apache.org/docs/latest/configuration.html)
 in the online documentation for an overview on how to configure Spark.
diff --git a/docs/quick-start.md b/docs/quick-start.md
index ce2cc9d2169cd..d481fe0ea6d70 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -126,7 +126,7 @@ scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (w
 wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight scala %}
 scala> wordCounts.collect()
@@ -163,7 +163,7 @@ One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can i
 >>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight python %}
 >>> wordCounts.collect()
@@ -217,13 +217,13 @@ a cluster, as described in the [programming guide](programming-guide.html#initia
 </div>
 
 # Self-Contained Applications
-Now say we wanted to write a self-contained application using the Spark API. We will walk through a
-simple application in both Scala (with SBT), Java (with Maven), and Python.
+Suppose we wish to write a self-contained application using the Spark API. We will walk through a
+simple application in Scala (with sbt), Java (with Maven), and Python.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-We'll create a very simple Spark application in Scala. So simple, in fact, that it's
+We'll create a very simple Spark application in Scala--so simple, in fact, that it's
 named `SimpleApp.scala`:
 
 {% highlight scala %}
@@ -259,7 +259,7 @@ object which contains information about our
 application. 
 
 Our application depends on the Spark API, so we'll also include an sbt configuration file, 
-`simple.sbt` which explains that Spark is a dependency. This file also adds a repository that 
+`simple.sbt`, which explains that Spark is a dependency. This file also adds a repository that 
 Spark depends on:
 
 {% highlight scala %}
@@ -302,7 +302,7 @@ Lines with a: 46, Lines with b: 23
 
 </div>
 <div data-lang="java" markdown="1">
-This example will use Maven to compile an application jar, but any similar build system will work.
+This example will use Maven to compile an application JAR, but any similar build system will work.
 
 We'll create a very simple Spark application, `SimpleApp.java`:
 
@@ -374,7 +374,7 @@ $ find .
 Now, we can package the application using Maven and execute it with `./bin/spark-submit`.
 
 {% highlight bash %}
-# Package a jar containing your application
+# Package a JAR containing your application
 $ mvn package
 ...
 [INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar

From 6ceed852ab716d8acc46ce90cba9cfcff6d3616f Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek@japila.pl>
Date: Tue, 8 Sep 2015 14:38:10 +0100
Subject: [PATCH 1346/1454] Docs small fixes

Author: Jacek Laskowski <jacek@japila.pl>

Closes #8629 from jaceklaskowski/docs-fixes.
---
 docs/building-spark.md   | 23 +++++++++++------------
 docs/cluster-overview.md | 15 ++++++++-------
 2 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/docs/building-spark.md b/docs/building-spark.md
index f133eb96d9a21..4db32cfd628bc 100644
--- a/docs/building-spark.md
+++ b/docs/building-spark.md
@@ -61,12 +61,13 @@ If you don't run this, you may see errors like the following:
 You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
 
 **Note:**
-* *For Java 8 and above this step is not required.*
-* *If using `build/mvn` and `MAVEN_OPTS` were not already set, the script will automate this for you.*
+
+* For Java 8 and above this step is not required.
+* If using `build/mvn` with no `MAVEN_OPTS` set, the script will automate this for you.
 
 # Specifying the Hadoop Version
 
-Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
+Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the `hadoop.version` property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
 
 <table class="table">
   <thead>
@@ -91,7 +92,7 @@ mvn -Dhadoop.version=1.2.1 -Phadoop-1 -DskipTests clean package
 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -Phadoop-1 -DskipTests clean package
 {% endhighlight %}
 
-You can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". Spark only supports YARN versions 2.2.0 and later.
+You can enable the `yarn` profile and optionally set the `yarn.version` property if it is different from `hadoop.version`. Spark only supports YARN versions 2.2.0 and later.
 
 Examples:
 
@@ -125,7 +126,7 @@ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -Dskip
 # Building for Scala 2.11
 To produce a Spark package compiled with Scala 2.11, use the `-Dscala-2.11` property:
 
-    dev/change-scala-version.sh 2.11
+    ./dev/change-scala-version.sh 2.11
     mvn -Pyarn -Phadoop-2.4 -Dscala-2.11 -DskipTests clean package
 
 Spark does not yet support its JDBC component for Scala 2.11.
@@ -163,11 +164,9 @@ the `spark-parent` module).
 
 Thus, the full flow for running continuous-compilation of the `core` submodule may look more like:
 
-```
- $ mvn install
- $ cd core
- $ mvn scala:cc
-```
+    $ mvn install
+    $ cd core
+    $ mvn scala:cc
 
 # Building Spark with IntelliJ IDEA or Eclipse
 
@@ -193,11 +192,11 @@ then ship it over to the cluster. We are investigating the exact cause for this.
 
 # Packaging without Hadoop Dependencies for YARN
 
-The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.
+The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with `yarn.application.classpath`.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.
 
 # Building with SBT
 
-Maven is the official recommendation for packaging Spark, and is the "build of reference".
+Maven is the official build tool recommended for packaging Spark, and is the *build of reference*.
 But SBT is supported for day-to-day development since it can provide much faster iterative
 compilation. More advanced developers may wish to use SBT.
 
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index 7079de546e2f5..faaf154d243f5 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -5,18 +5,19 @@ title: Cluster Mode Overview
 
 This document gives a short overview of how Spark runs on clusters, to make it easier to understand
 the components involved. Read through the [application submission guide](submitting-applications.html)
-to submit applications to a cluster.
+to learn about launching applications on a cluster.
 
 # Components
 
-Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext
+Spark applications run as independent sets of processes on a cluster, coordinated by the `SparkContext`
 object in your main program (called the _driver program_).
+
 Specifically, to run on a cluster, the SparkContext can connect to several types of _cluster managers_
-(either Spark's own standalone cluster manager or Mesos/YARN), which allocate resources across
+(either Spark's own standalone cluster manager, Mesos or YARN), which allocate resources across
 applications. Once connected, Spark acquires *executors* on nodes in the cluster, which are
 processes that run computations and store data for your application.
 Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to
-the executors. Finally, SparkContext sends *tasks* for the executors to run.
+the executors. Finally, SparkContext sends *tasks* to the executors to run.
 
 <p style="text-align: center;">
   <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" />
@@ -33,9 +34,9 @@ There are several useful things to note about this architecture:
 2. Spark is agnostic to the underlying cluster manager. As long as it can acquire executor
    processes, and these communicate with each other, it is relatively easy to run it even on a
    cluster manager that also supports other applications (e.g. Mesos/YARN).
-3. The driver program must listen for and accept incoming connections from its executors throughout 
-   its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config 
-   section](configuration.html#networking)). As such, the driver program must be network 
+3. The driver program must listen for and accept incoming connections from its executors throughout
+   its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config
+   section](configuration.html#networking)). As such, the driver program must be network
    addressable from the worker nodes.
 4. Because the driver schedules tasks on the cluster, it should be run close to the worker
    nodes, preferably on the same local area network. If you'd like to send requests to the

From 990c9f79c28db501018a0a3af446ff879962475d Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Tue, 8 Sep 2015 23:07:34 +0800
Subject: [PATCH 1347/1454] [SPARK-9170] [SQL] Use OrcStructInspector to be
 case preserving when writing ORC files

JIRA: https://issues.apache.org/jira/browse/SPARK-9170

`StandardStructObjectInspector` will implicitly lowercase column names. But I think Orc format doesn't have such requirement. In fact, there is a `OrcStructInspector` specified for Orc format. We should use it when serialize rows to Orc file. It can be case preserving when writing ORC files.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7520 from viirya/use_orcstruct.
---
 .../spark/sql/hive/orc/OrcRelation.scala      | 47 ++++++++++---------
 .../spark/sql/hive/orc/OrcQuerySuite.scala    | 14 ++++++
 2 files changed, 40 insertions(+), 21 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 4eeca9aec12bd..7e89109259955 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -25,9 +25,9 @@ import com.google.common.base.Objects
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.hadoop.hive.ql.io.orc.{OrcInputFormat, OrcOutputFormat, OrcSerde, OrcSplit}
-import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
-import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
+import org.apache.hadoop.hive.ql.io.orc.{OrcInputFormat, OrcOutputFormat, OrcSerde, OrcSplit, OrcStruct}
+import org.apache.hadoop.hive.serde2.objectinspector.SettableStructObjectInspector
+import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfoUtils, StructTypeInfo}
 import org.apache.hadoop.io.{NullWritable, Writable}
 import org.apache.hadoop.mapred.{InputFormat => MapRedInputFormat, JobConf, OutputFormat => MapRedOutputFormat, RecordWriter, Reporter}
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
@@ -89,21 +89,10 @@ private[orc] class OrcOutputWriter(
       TypeInfoUtils.getTypeInfoFromTypeString(
         HiveMetastoreTypes.toMetastoreType(dataSchema))
 
-    TypeInfoUtils
-      .getStandardJavaObjectInspectorFromTypeInfo(typeInfo)
-      .asInstanceOf[StructObjectInspector]
+    OrcStruct.createObjectInspector(typeInfo.asInstanceOf[StructTypeInfo])
+      .asInstanceOf[SettableStructObjectInspector]
   }
 
-  // Used to hold temporary `Writable` fields of the next row to be written.
-  private val reusableOutputBuffer = new Array[Any](dataSchema.length)
-
-  // Used to convert Catalyst values into Hadoop `Writable`s.
-  private val wrappers = structOI.getAllStructFieldRefs.asScala
-    .zip(dataSchema.fields.map(_.dataType))
-    .map { case (ref, dt) =>
-      wrapperFor(ref.getFieldObjectInspector, dt)
-    }.toArray
-
   // `OrcRecordWriter.close()` creates an empty file if no rows are written at all.  We use this
   // flag to decide whether `OrcRecordWriter.close()` needs to be called.
   private var recordWriterInstantiated = false
@@ -127,16 +116,32 @@ private[orc] class OrcOutputWriter(
 
   override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
 
-  override protected[sql] def writeInternal(row: InternalRow): Unit = {
+  private def wrapOrcStruct(
+      struct: OrcStruct,
+      oi: SettableStructObjectInspector,
+      row: InternalRow): Unit = {
+    val fieldRefs = oi.getAllStructFieldRefs
     var i = 0
-    while (i < row.numFields) {
-      reusableOutputBuffer(i) = wrappers(i)(row.get(i, dataSchema(i).dataType))
+    while (i < fieldRefs.size) {
+      oi.setStructFieldData(
+        struct,
+        fieldRefs.get(i),
+        wrap(
+          row.get(i, dataSchema(i).dataType),
+          fieldRefs.get(i).getFieldObjectInspector,
+          dataSchema(i).dataType))
       i += 1
     }
+  }
+
+  val cachedOrcStruct = structOI.create().asInstanceOf[OrcStruct]
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = {
+    wrapOrcStruct(cachedOrcStruct, structOI, row)
 
     recordWriter.write(
       NullWritable.get(),
-      serializer.serialize(reusableOutputBuffer, structOI))
+      serializer.serialize(cachedOrcStruct, structOI))
   }
 
   override def close(): Unit = {
@@ -259,7 +264,7 @@ private[orc] case class OrcTableScan(
     maybeStructOI.map { soi =>
       val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map {
         case (attr, ordinal) =>
-          soi.getStructFieldRef(attr.name.toLowerCase) -> ordinal
+          soi.getStructFieldRef(attr.name) -> ordinal
       }.unzip
       val unwrappers = fieldRefs.map(unwrapperFor)
       // Map each tuple to a row object
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
index 744d462938141..8bc33fcf5d906 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
@@ -287,6 +287,20 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
     }
   }
 
+  test("SPARK-9170: Don't implicitly lowercase of user-provided columns") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      sqlContext.range(0, 10).select('id as "Acol").write.format("orc").save(path)
+      sqlContext.read.format("orc").load(path).schema("Acol")
+      intercept[IllegalArgumentException] {
+        sqlContext.read.format("orc").load(path).schema("acol")
+      }
+      checkAnswer(sqlContext.read.format("orc").load(path).select("acol").sort("acol"),
+        (0 until 10).map(Row(_)))
+    }
+  }
+
   test("SPARK-8501: Avoids discovery schema from empty ORC files") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath

From 5b2192e846b843d8a0cb9427d19bb677431194a0 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 8 Sep 2015 11:11:35 -0700
Subject: [PATCH 1348/1454] [SPARK-10480] [ML] Fix
 ML.LinearRegressionModel.copy()

This PR fix two model ```copy()``` related issues:
[SPARK-10480](https://issues.apache.org/jira/browse/SPARK-10480)
```ML.LinearRegressionModel.copy()``` ignored argument ```extra```, it will not take effect when users setting this parameter.
[SPARK-10479](https://issues.apache.org/jira/browse/SPARK-10479)
```ML.LogisticRegressionModel.copy()``` should copy model summary if available.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8641 from yanboliang/linear-regression-copy.
---
 .../apache/spark/ml/classification/LogisticRegression.scala   | 4 +++-
 .../org/apache/spark/ml/regression/LinearRegression.scala     | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 21fbe38ca8233..a460262b87e43 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -468,7 +468,9 @@ class LogisticRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LogisticRegressionModel = {
-    copyValues(new LogisticRegressionModel(uid, weights, intercept), extra).setParent(parent)
+    val newModel = copyValues(new LogisticRegressionModel(uid, weights, intercept), extra)
+    if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
+    newModel.setParent(parent)
   }
 
   override protected def raw2prediction(rawPrediction: Vector): Double = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 884003eb38524..e4602d36ccc87 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -310,7 +310,7 @@ class LinearRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LinearRegressionModel = {
-    val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept))
+    val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept), extra)
     if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
     newModel.setParent(parent)
   }

From 5fd57955ef477347408f68eb1cb6ad1881fdb6e0 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 8 Sep 2015 12:05:41 -0700
Subject: [PATCH 1349/1454] [SPARK-10316] [SQL] respect nondeterministic
 expressions in PhysicalOperation

We did a lot of special handling for non-deterministic expressions in `Optimizer`. However, `PhysicalOperation` just collects all Projects and Filters and mess it up. We should respect the operators order caused by non-deterministic expressions in `PhysicalOperation`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8486 from cloud-fan/fix.
---
 .../sql/catalyst/planning/patterns.scala      | 38 ++++---------------
 .../org/apache/spark/sql/DataFrameSuite.scala | 12 ++++++
 2 files changed, 20 insertions(+), 30 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index e8abcd63f7d85..53537799517ce 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -17,35 +17,12 @@
 
 package org.apache.spark.sql.catalyst.planning
 
-import scala.annotation.tailrec
-
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNodeRef
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 
-/**
- * A pattern that matches any number of filter operations on top of another relational operator.
- * Adjacent filter operators are collected and their conditions are broken up and returned as a
- * sequence of conjunctive predicates.
- *
- * @return A tuple containing a sequence of conjunctive predicates that should be used to filter the
- *         output and a relational operator.
- */
-object FilteredOperation extends PredicateHelper {
-  type ReturnType = (Seq[Expression], LogicalPlan)
-
-  def unapply(plan: LogicalPlan): Option[ReturnType] = Some(collectFilters(Nil, plan))
-
-  @tailrec
-  private def collectFilters(filters: Seq[Expression], plan: LogicalPlan): ReturnType = plan match {
-    case Filter(condition, child) =>
-      collectFilters(filters ++ splitConjunctivePredicates(condition), child)
-    case other => (filters, other)
-  }
-}
-
 /**
  * A pattern that matches any number of project or filter operations on top of another relational
  * operator.  All filter operators are collected and their conditions are broken up and returned
@@ -62,8 +39,9 @@ object PhysicalOperation extends PredicateHelper {
   }
 
   /**
-   * Collects projects and filters, in-lining/substituting aliases if necessary.  Here are two
-   * examples for alias in-lining/substitution.  Before:
+   * Collects all deterministic projects and filters, in-lining/substituting aliases if necessary.
+   * Here are two examples for alias in-lining/substitution.
+   * Before:
    * {{{
    *   SELECT c1 FROM (SELECT key AS c1 FROM t1) t2 WHERE c1 > 10
    *   SELECT c1 AS c2 FROM (SELECT key AS c1 FROM t1) t2 WHERE c1 > 10
@@ -74,15 +52,15 @@ object PhysicalOperation extends PredicateHelper {
    *   SELECT key AS c2 FROM t1 WHERE key > 10
    * }}}
    */
-  def collectProjectsAndFilters(plan: LogicalPlan):
+  private def collectProjectsAndFilters(plan: LogicalPlan):
       (Option[Seq[NamedExpression]], Seq[Expression], LogicalPlan, Map[Attribute, Expression]) =
     plan match {
-      case Project(fields, child) =>
+      case Project(fields, child) if fields.forall(_.deterministic) =>
         val (_, filters, other, aliases) = collectProjectsAndFilters(child)
         val substitutedFields = fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
         (Some(substitutedFields), filters, other, collectAliases(substitutedFields))
 
-      case Filter(condition, child) =>
+      case Filter(condition, child) if condition.deterministic =>
         val (fields, filters, other, aliases) = collectProjectsAndFilters(child)
         val substitutedCondition = substitute(aliases)(condition)
         (fields, filters ++ splitConjunctivePredicates(substitutedCondition), other, aliases)
@@ -91,11 +69,11 @@ object PhysicalOperation extends PredicateHelper {
         (None, Nil, other, Map.empty)
     }
 
-  def collectAliases(fields: Seq[Expression]): Map[Attribute, Expression] = fields.collect {
+  private def collectAliases(fields: Seq[Expression]): Map[Attribute, Expression] = fields.collect {
     case a @ Alias(child, _) => a.toAttribute -> child
   }.toMap
 
-  def substitute(aliases: Map[Attribute, Expression])(expr: Expression): Expression = {
+  private def substitute(aliases: Map[Attribute, Expression])(expr: Expression): Expression = {
     expr.transform {
       case a @ Alias(ref: AttributeReference, name) =>
         aliases.get(ref).map(Alias(_, name)(a.exprId, a.qualifiers)).getOrElse(a)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index b5b9f11785074..dbed4fc247140 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -22,6 +22,8 @@ import java.io.File
 import scala.language.postfixOps
 import scala.util.Random
 
+import org.scalatest.Matchers._
+
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
@@ -895,4 +897,14 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
       .orderBy(sum('j))
     checkAnswer(query, Row(1, 2))
   }
+
+  test("SPARK-10316: respect non-deterministic expressions in PhysicalOperation") {
+    val input = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+      (1 to 10).map(i => s"""{"id": $i}""")))
+
+    val df = input.select($"id", rand(0).as('r))
+    df.as("a").join(df.filter($"r" < 0.5).as("b"), $"a.id" === $"b.id").collect().foreach { row =>
+      assert(row.getDouble(1) - row.getDouble(3) === 0.0 +- 0.001)
+    }
+  }
 }

From f7b55dbfc3343cad988e2490478fce1a11343c73 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 8 Sep 2015 12:48:21 -0700
Subject: [PATCH 1350/1454] [SPARK-10470] [ML] ml.IsotonicRegressionModel.copy
 should set parent

Copied model must have the same parent, but ml.IsotonicRegressionModel.copy did not set parent.
Here fix it and add test case.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8637 from yanboliang/spark-10470.
---
 .../org/apache/spark/ml/regression/IsotonicRegression.scala  | 2 +-
 .../apache/spark/ml/regression/IsotonicRegressionSuite.scala | 5 +++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index d43a3447d3975..2ff500f291abc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -203,7 +203,7 @@ class IsotonicRegressionModel private[ml] (
   def predictions: Vector = Vectors.dense(oldModel.predictions)
 
   override def copy(extra: ParamMap): IsotonicRegressionModel = {
-    copyValues(new IsotonicRegressionModel(uid, oldModel), extra)
+    copyValues(new IsotonicRegressionModel(uid, oldModel), extra).setParent(parent)
   }
 
   override def transform(dataset: DataFrame): DataFrame = {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
index c0ab00b68a2f3..59f4193abc8f0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Row}
@@ -89,6 +90,10 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(ir.getFeatureIndex === 0)
 
     val model = ir.fit(dataset)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(dataset)
       .select("label", "features", "prediction", "weight")
       .collect()

From 7a9dcbc91d55dbc0cbf4812319bde65f4509b467 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 8 Sep 2015 14:10:12 -0700
Subject: [PATCH 1351/1454] [SPARK-10441] [SQL] Save data correctly to json.

https://issues.apache.org/jira/browse/SPARK-10441

Author: Yin Huai <yhuai@databricks.com>

Closes #8597 from yhuai/timestampJson.
---
 .../spark/sql/RandomDataGenerator.scala       | 41 +++++++++-
 .../datasources/json/JacksonGenerator.scala   | 11 ++-
 .../datasources/json/JacksonParser.scala      | 31 ++++++++
 .../hive/orc/OrcHadoopFsRelationSuite.scala   |  8 ++
 .../sources/JsonHadoopFsRelationSuite.scala   |  8 ++
 .../ParquetHadoopFsRelationSuite.scala        |  9 ++-
 .../SimpleTextHadoopFsRelationSuite.scala     | 19 ++++-
 .../sql/sources/SimpleTextRelation.scala      |  7 +-
 .../sql/sources/hadoopFsRelationSuites.scala  | 79 +++++++++++++++++++
 9 files changed, 205 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
index 11e0c120f4072..4025cbcec1019 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -23,6 +23,8 @@ import java.math.MathContext
 
 import scala.util.Random
 
+import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
 
@@ -84,6 +86,7 @@ object RandomDataGenerator {
    * random data generator is defined for that data type. The generated values will use an external
    * representation of the data type; for example, the random generator for [[DateType]] will return
    * instances of [[java.sql.Date]] and the generator for [[StructType]] will return a [[Row]].
+   * For a [[UserDefinedType]] for a class X, an instance of class X is returned.
    *
    * @param dataType the type to generate values for
    * @param nullable whether null values should be generated
@@ -106,7 +109,22 @@ object RandomDataGenerator {
       })
       case BooleanType => Some(() => rand.nextBoolean())
       case DateType => Some(() => new java.sql.Date(rand.nextInt()))
-      case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
+      case TimestampType =>
+        val generator =
+          () => {
+            var milliseconds = rand.nextLong() % 253402329599999L
+            // -62135740800000L is the number of milliseconds before January 1, 1970, 00:00:00 GMT
+            // for "0001-01-01 00:00:00.000000". We need to find a
+            // number that is greater or equals to this number as a valid timestamp value.
+            while (milliseconds < -62135740800000L) {
+              // 253402329599999L is the the number of milliseconds since
+              // January 1, 1970, 00:00:00 GMT for "9999-12-31 23:59:59.999999".
+              milliseconds = rand.nextLong() % 253402329599999L
+            }
+            // DateTimeUtils.toJavaTimestamp takes microsecond.
+            DateTimeUtils.toJavaTimestamp(milliseconds * 1000)
+          }
+        Some(generator)
       case CalendarIntervalType => Some(() => {
         val months = rand.nextInt(1000)
         val ns = rand.nextLong()
@@ -159,6 +177,27 @@ object RandomDataGenerator {
           None
         }
       }
+      case udt: UserDefinedType[_] => {
+        val maybeSqlTypeGenerator = forType(udt.sqlType, nullable, seed)
+        // Because random data generator at here returns scala value, we need to
+        // convert it to catalyst value to call udt's deserialize.
+        val toCatalystType = CatalystTypeConverters.createToCatalystConverter(udt.sqlType)
+
+        if (maybeSqlTypeGenerator.isDefined) {
+          val sqlTypeGenerator = maybeSqlTypeGenerator.get
+          val generator = () => {
+            val generatedScalaValue = sqlTypeGenerator.apply()
+            if (generatedScalaValue == null) {
+              null
+            } else {
+              udt.deserialize(toCatalystType(generatedScalaValue))
+            }
+          }
+          Some(generator)
+        } else {
+          None
+        }
+      }
       case unsupportedType => None
     }
     // Handle nullability by wrapping the non-null value generator:
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index 330ba907b2ef9..f65c7bbd6e29d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.execution.datasources.json
 
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 
 import scala.collection.Map
 
@@ -89,7 +90,7 @@ private[sql] object JacksonGenerator {
     def valWriter: (DataType, Any) => Unit = {
       case (_, null) | (NullType, _) => gen.writeNull()
       case (StringType, v) => gen.writeString(v.toString)
-      case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
+      case (TimestampType, v: Long) => gen.writeString(DateTimeUtils.toJavaTimestamp(v).toString)
       case (IntegerType, v: Int) => gen.writeNumber(v)
       case (ShortType, v: Short) => gen.writeNumber(v)
       case (FloatType, v: Float) => gen.writeNumber(v)
@@ -99,8 +100,12 @@ private[sql] object JacksonGenerator {
       case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
       case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
       case (BooleanType, v: Boolean) => gen.writeBoolean(v)
-      case (DateType, v) => gen.writeString(v.toString)
-      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v))
+      case (DateType, v: Int) => gen.writeString(DateTimeUtils.toJavaDate(v).toString)
+      // For UDT values, they should be in the SQL type's corresponding value type.
+      // We should not see values in the user-defined class at here.
+      // For example, VectorUDT's SQL type is an array of double. So, we should expect that v is
+      // an ArrayData at here, instead of a Vector.
+      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, v)
 
       case (ArrayType(ty, _), v: ArrayData) =>
         gen.writeStartArray()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
index cd68bd667c5c4..ff4d8c04e8eaf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
@@ -81,9 +81,37 @@ private[sql] object JacksonParser {
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) =>
         parser.getFloatValue
 
+      case (VALUE_STRING, FloatType) =>
+        // Special case handling for NaN and Infinity.
+        val value = parser.getText
+        val lowerCaseValue = value.toLowerCase()
+        if (lowerCaseValue.equals("nan") ||
+          lowerCaseValue.equals("infinity") ||
+          lowerCaseValue.equals("-infinity") ||
+          lowerCaseValue.equals("inf") ||
+          lowerCaseValue.equals("-inf")) {
+          value.toFloat
+        } else {
+          sys.error(s"Cannot parse $value as FloatType.")
+        }
+
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
         parser.getDoubleValue
 
+      case (VALUE_STRING, DoubleType) =>
+        // Special case handling for NaN and Infinity.
+        val value = parser.getText
+        val lowerCaseValue = value.toLowerCase()
+        if (lowerCaseValue.equals("nan") ||
+          lowerCaseValue.equals("infinity") ||
+          lowerCaseValue.equals("-infinity") ||
+          lowerCaseValue.equals("inf") ||
+          lowerCaseValue.equals("-inf")) {
+          value.toDouble
+        } else {
+          sys.error(s"Cannot parse $value as DoubleType.")
+        }
+
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, dt: DecimalType) =>
         Decimal(parser.getDecimalValue, dt.precision, dt.scale)
 
@@ -126,6 +154,9 @@ private[sql] object JacksonParser {
 
       case (_, udt: UserDefinedType[_]) =>
         convertField(factory, parser, udt.sqlType)
+
+      case (token, dataType) =>
+        sys.error(s"Failed to parse a value for data type $dataType (current token: $token).")
     }
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
index 9a299c3f9d1f3..92043d66c914f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
@@ -28,6 +28,14 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
 
   override val dataSourceName: String = classOf[DefaultSource].getCanonicalName
 
+  // ORC does not play well with NullType and UDT.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: CalendarIntervalType => false
+    case _: UserDefinedType[_] => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
index 1945b15002337..ef37787137d07 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -28,6 +28,14 @@ import org.apache.spark.sql.types._
 class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = "json"
 
+  // JSON does not write data of NullType and does not play well with BinaryType.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: BinaryType => false
+    case _: CalendarIntervalType => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index 08c3c17973043..e2d754e806403 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -24,7 +24,7 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.{execution, AnalysisException, SaveMode}
-import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 
 
 class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
@@ -32,6 +32,13 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
 
   override val dataSourceName: String = "parquet"
 
+  // Parquet does not play well with NullType.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: CalendarIntervalType => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index 1125ca670107b..a3a124488d983 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -20,11 +20,28 @@ package org.apache.spark.sql.sources
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
 
+  // We have a very limited number of supported types at here since it is just for a
+  // test relation and we do very basic testing at here.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: BinaryType => false
+    // We are using random data generator and the generated strings are not really valid string.
+    case _: StringType => false
+    case _: BooleanType => false // see https://issues.apache.org/jira/browse/SPARK-10442
+    case _: CalendarIntervalType => false
+    case _: DateType => false
+    case _: TimestampType => false
+    case _: ArrayType => false
+    case _: MapType => false
+    case _: StructType => false
+    case _: UserDefinedType[_] => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
index 527ca7a81cad8..aeaaa3e1c5220 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
@@ -68,7 +68,9 @@ class SimpleTextOutputWriter(path: String, context: TaskAttemptContext) extends
     new AppendingTextOutputFormat(new Path(path)).getRecordWriter(context)
 
   override def write(row: Row): Unit = {
-    val serialized = row.toSeq.map(_.toString).mkString(",")
+    val serialized = row.toSeq.map { v =>
+      if (v == null) "" else v.toString
+    }.mkString(",")
     recordWriter.write(null, new Text(serialized))
   }
 
@@ -112,7 +114,8 @@ class SimpleTextRelation(
     val fields = dataSchema.map(_.dataType)
 
     sparkContext.textFile(inputStatuses.map(_.getPath).mkString(",")).map { record =>
-      Row(record.split(",").zip(fields).map { case (value, dataType) =>
+      Row(record.split(",", -1).zip(fields).map { case (v, dataType) =>
+        val value = if (v == "") null else v
         // `Cast`ed values are always of Catalyst types (i.e. UTF8String instead of String, etc.)
         val catalystValue = Cast(Literal(value), dataType).eval()
         // Here we're converting Catalyst values to Scala values to test `needsConversion`
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 2ad2618dfc436..24f43cf7c15ca 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -38,6 +38,8 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
 
   val dataSourceName: String
 
+  protected def supportsDataType(dataType: DataType): Boolean = true
+
   val dataSchema =
     StructType(
       Seq(
@@ -98,6 +100,83 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
     }
   }
 
+  test("test all data types") {
+    withTempPath { file =>
+      // Create the schema.
+      val struct =
+        StructType(
+          StructField("f1", FloatType, true) ::
+            StructField("f2", ArrayType(BooleanType), true) :: Nil)
+      // TODO: add CalendarIntervalType to here once we can save it out.
+      val dataTypes =
+        Seq(
+          StringType, BinaryType, NullType, BooleanType,
+          ByteType, ShortType, IntegerType, LongType,
+          FloatType, DoubleType, DecimalType(25, 5), DecimalType(6, 5),
+          DateType, TimestampType,
+          ArrayType(IntegerType), MapType(StringType, LongType), struct,
+          new MyDenseVectorUDT())
+      val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
+        StructField(s"col$index", dataType, nullable = true)
+      }
+      val schema = StructType(fields)
+
+      // Generate data at the driver side. We need to materialize the data first and then
+      // create RDD.
+      val maybeDataGenerator =
+        RandomDataGenerator.forType(
+          dataType = schema,
+          nullable = true,
+          seed = Some(System.nanoTime()))
+      val dataGenerator =
+        maybeDataGenerator
+          .getOrElse(fail(s"Failed to create data generator for schema $schema"))
+      val data = (1 to 10).map { i =>
+        dataGenerator.apply() match {
+          case row: Row => row
+          case null => Row.fromSeq(Seq.fill(schema.length)(null))
+          case other =>
+            fail(s"Row or null is expected to be generated, " +
+              s"but a ${other.getClass.getCanonicalName} is generated.")
+        }
+      }
+
+      // Create a DF for the schema with random data.
+      val rdd = sqlContext.sparkContext.parallelize(data, 10)
+      val df = sqlContext.createDataFrame(rdd, schema)
+
+      // All columns that have supported data types of this source.
+      val supportedColumns = schema.fields.collect {
+        case StructField(name, dataType, _, _) if supportsDataType(dataType) => name
+      }
+      val selectedColumns = util.Random.shuffle(supportedColumns.toSeq)
+
+      val dfToBeSaved = df.selectExpr(selectedColumns: _*)
+
+      // Save the data out.
+      dfToBeSaved
+        .write
+        .format(dataSourceName)
+        .option("dataSchema", dfToBeSaved.schema.json) // This option is just used by tests.
+        .save(file.getCanonicalPath)
+
+      val loadedDF =
+        sqlContext
+          .read
+          .format(dataSourceName)
+          .schema(dfToBeSaved.schema)
+          .option("dataSchema", dfToBeSaved.schema.json) // This option is just used by tests.
+          .load(file.getCanonicalPath)
+          .selectExpr(selectedColumns: _*)
+
+      // Read the data back.
+      checkAnswer(
+        loadedDF,
+        dfToBeSaved
+      )
+    }
+  }
+
   test("save()/load() - non-partitioned table - Overwrite") {
     withTempPath { file =>
       testDF.write.mode(SaveMode.Overwrite).format(dataSourceName).save(file.getCanonicalPath)

From e6f8d3686016a305a747c5bcc85f46fd4c0cbe83 Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Tue, 8 Sep 2015 14:44:05 -0700
Subject: [PATCH 1352/1454] [SPARK-10468] [ MLLIB ] Verify schema before
 Dataframe select API call

Loader.checkSchema was called to verify the schema after dataframe.select(...).
Schema verification should be done before dataframe.select(...)

Author: Vinod K C <vinod.kc@huawei.com>

Closes #8636 from vinodkc/fix_GaussianMixtureModel_load_verification.
---
 .../apache/spark/mllib/clustering/GaussianMixtureModel.scala  | 3 +--
 .../main/scala/org/apache/spark/mllib/feature/Word2Vec.scala  | 4 +---
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 7f6163e04bf17..a5902190d4637 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -168,10 +168,9 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
       val dataPath = Loader.dataPath(path)
       val sqlContext = new SQLContext(sc)
       val dataFrame = sqlContext.read.parquet(dataPath)
-      val dataArray = dataFrame.select("weight", "mu", "sigma").collect()
-
       // Check schema explicitly since erasure makes it hard to use match-case for checking.
       Loader.checkSchema[Data](dataFrame.schema)
+      val dataArray = dataFrame.select("weight", "mu", "sigma").collect()
 
       val (weights, gaussians) = dataArray.map {
         case Row(weight: Double, mu: Vector, sigma: Matrix) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index 36b124c5d2966..58857c338f546 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -590,12 +590,10 @@ object Word2VecModel extends Loader[Word2VecModel] {
       val dataPath = Loader.dataPath(path)
       val sqlContext = new SQLContext(sc)
       val dataFrame = sqlContext.read.parquet(dataPath)
-
-      val dataArray = dataFrame.select("word", "vector").collect()
-
       // Check schema explicitly since erasure makes it hard to use match-case for checking.
       Loader.checkSchema[Data](dataFrame.schema)
 
+      val dataArray = dataFrame.select("word", "vector").collect()
       val word2VecMap = dataArray.map(i => (i.getString(0), i.getSeq[Float](1).toArray)).toMap
       new Word2VecModel(word2VecMap)
     }

From 52b24a602ad615a7f6aa427aefb1c7444c05d298 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 8 Sep 2015 14:54:43 -0700
Subject: [PATCH 1353/1454] [SPARK-10492] [STREAMING] [DOCUMENTATION] Update
 Streaming documentation about rate limiting and backpressure

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8656 from tdas/SPARK-10492 and squashes the following commits:

986cdd6 [Tathagata Das] Added information on backpressure
---
 docs/configuration.md               | 13 +++++++++++++
 docs/streaming-programming-guide.md | 13 ++++++++++++-
 2 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index a2cc7a37e2240..e287591f3fda1 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1433,6 +1433,19 @@ Apart from these, the following properties are also available, and may be useful
 #### Spark Streaming
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.streaming.backpressure.enabled</code></td>
+  <td>false</td>
+  <td>
+    Enables or disables Spark Streaming's internal backpressure mechanism (since 1.5).
+    This enables the Spark Streaming to control the receiving rate based on the 
+    current batch scheduling delays and processing times so that the system receives
+    only as fast as the system can process. Internally, this dynamically sets the 
+    maximum receiving rate of receivers. This rate is upper bounded by the values
+    `spark.streaming.receiver.maxRate` and `spark.streaming.kafka.maxRatePerPartition`
+    if they are set (see below).
+  </td>
+</tr>
 <tr>
   <td><code>spark.streaming.blockInterval</code></td>
   <td>200ms</td>
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index a1acf83f75245..c751dbb41785a 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1807,7 +1807,7 @@ To run a Spark Streaming applications, you need to have the following.
     + *Mesos* - [Marathon](https://github.com/mesosphere/marathon) has been used to achieve this
       with Mesos.
 
-- *[Since Spark 1.2] Configuring write ahead logs* - Since Spark 1.2,
+- *Configuring write ahead logs* - Since Spark 1.2,
   we have introduced _write ahead logs_ for achieving strong
   fault-tolerance guarantees. If enabled,  all the data received from a receiver gets written into
   a write ahead log in the configuration checkpoint directory. This prevents data loss on driver
@@ -1822,6 +1822,17 @@ To run a Spark Streaming applications, you need to have the following.
   stored in a replicated storage system. This can be done by setting the storage level for the
   input stream to `StorageLevel.MEMORY_AND_DISK_SER`.
 
+- *Setting the max receiving rate* - If the cluster resources is not large enough for the streaming
+  application to process data as fast as it is being received, the receivers can be rate limited
+  by setting a maximum rate limit in terms of records / sec.
+  See the [configuration parameters](configuration.html#spark-streaming)
+  `spark.streaming.receiver.maxRate` for receivers and `spark.streaming.kafka.maxRatePerPartition`
+  for Direct Kafka approach. In Spark 1.5, we have introduced a feature called *backpressure* that
+  eliminate the need to set this rate limit, as Spark Streaming automatically figures out the
+  rate limits and dynamically adjusts them if the processing conditions change. This backpressure
+  can be enabled by setting the [configuration parameter](configuration.html#spark-streaming)
+  `spark.streaming.backpressure.enabled` to `true`.
+
 ### Upgrading Application Code
 {:.no_toc}
 

From d637a666d5932002c8ce0bd23c06064fbfdc1c97 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 8 Sep 2015 16:16:50 -0700
Subject: [PATCH 1354/1454] [SPARK-10327] [SQL] Cache Table is not working
 while subquery has alias in its project list

```scala
    import org.apache.spark.sql.hive.execution.HiveTableScan
    sql("select key, value, key + 1 from src").registerTempTable("abc")
    cacheTable("abc")

    val sparkPlan = sql(
      """select a.key, b.key, c.key from
        |abc a join abc b on a.key=b.key
        |join abc c on a.key=c.key""".stripMargin).queryExecution.sparkPlan

    assert(sparkPlan.collect { case e: InMemoryColumnarTableScan => e }.size === 3) // failed
    assert(sparkPlan.collect { case e: HiveTableScan => e }.size === 0) // failed
```

The actual plan is:

```
== Parsed Logical Plan ==
'Project [unresolvedalias('a.key),unresolvedalias('b.key),unresolvedalias('c.key)]
 'Join Inner, Some(('a.key = 'c.key))
  'Join Inner, Some(('a.key = 'b.key))
   'UnresolvedRelation [abc], Some(a)
   'UnresolvedRelation [abc], Some(b)
  'UnresolvedRelation [abc], Some(c)

== Analyzed Logical Plan ==
key: int, key: int, key: int
Project [key#14,key#61,key#66]
 Join Inner, Some((key#14 = key#66))
  Join Inner, Some((key#14 = key#61))
   Subquery a
    Subquery abc
     Project [key#14,value#15,(key#14 + 1) AS _c2#16]
      MetastoreRelation default, src, None
   Subquery b
    Subquery abc
     Project [key#61,value#62,(key#61 + 1) AS _c2#58]
      MetastoreRelation default, src, None
  Subquery c
   Subquery abc
    Project [key#66,value#67,(key#66 + 1) AS _c2#63]
     MetastoreRelation default, src, None

== Optimized Logical Plan ==
Project [key#14,key#61,key#66]
 Join Inner, Some((key#14 = key#66))
  Project [key#14,key#61]
   Join Inner, Some((key#14 = key#61))
    Project [key#14]
     InMemoryRelation [key#14,value#15,_c2#16], true, 10000, StorageLevel(true, true, false, true, 1), (Project [key#14,value#15,(key#14 + 1) AS _c2#16]), Some(abc)
    Project [key#61]
     MetastoreRelation default, src, None
  Project [key#66]
   MetastoreRelation default, src, None

== Physical Plan ==
TungstenProject [key#14,key#61,key#66]
 BroadcastHashJoin [key#14], [key#66], BuildRight
  TungstenProject [key#14,key#61]
   BroadcastHashJoin [key#14], [key#61], BuildRight
    ConvertToUnsafe
     InMemoryColumnarTableScan [key#14], (InMemoryRelation [key#14,value#15,_c2#16], true, 10000, StorageLevel(true, true, false, true, 1), (Project [key#14,value#15,(key#14 + 1) AS _c2#16]), Some(abc))
    ConvertToUnsafe
     HiveTableScan [key#61], (MetastoreRelation default, src, None)
  ConvertToUnsafe
   HiveTableScan [key#66], (MetastoreRelation default, src, None)
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #8494 from chenghao-intel/weird_cache.
---
 .../sql/catalyst/plans/logical/LogicalPlan.scala | 15 ++++++++++++---
 .../org/apache/spark/sql/CachedTableSuite.scala  | 16 ++++++++++++++++
 2 files changed, 28 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 9bb466ac2d29c..8f8747e105932 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -135,16 +135,25 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
   /** Args that have cleaned such that differences in expression id should not affect equality */
   protected lazy val cleanArgs: Seq[Any] = {
     val input = children.flatMap(_.output)
+    def cleanExpression(e: Expression) = e match {
+      case a: Alias =>
+        // As the root of the expression, Alias will always take an arbitrary exprId, we need
+        // to erase that for equality testing.
+        val cleanedExprId = Alias(a.child, a.name)(ExprId(-1), a.qualifiers)
+        BindReferences.bindReference(cleanedExprId, input, allowFailures = true)
+      case other => BindReferences.bindReference(other, input, allowFailures = true)
+    }
+
     productIterator.map {
       // Children are checked using sameResult above.
       case tn: TreeNode[_] if containsChild(tn) => null
-      case e: Expression => BindReferences.bindReference(e, input, allowFailures = true)
+      case e: Expression => cleanExpression(e)
       case s: Option[_] => s.map {
-        case e: Expression => BindReferences.bindReference(e, input, allowFailures = true)
+        case e: Expression => cleanExpression(e)
         case other => other
       }
       case s: Seq[_] => s.map {
-        case e: Expression => BindReferences.bindReference(e, input, allowFailures = true)
+        case e: Expression => cleanExpression(e)
         case other => other
       }
       case other => other
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index 3a3541a8429b6..84e66b50cf1d7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.execution.PhysicalRDD
+
 import scala.concurrent.duration._
 import scala.language.postfixOps
 
@@ -338,4 +340,18 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
       assert((accsSize - 2) == Accumulators.originals.size)
     }
   }
+
+  test("SPARK-10327 Cache Table is not working while subquery has alias in its project list") {
+    ctx.sparkContext.parallelize((1, 1) :: (2, 2) :: Nil)
+      .toDF("key", "value").selectExpr("key", "value", "key+1").registerTempTable("abc")
+    ctx.cacheTable("abc")
+
+    val sparkPlan = sql(
+      """select a.key, b.key, c.key from
+        |abc a join abc b on a.key=b.key
+        |join abc c on a.key=c.key""".stripMargin).queryExecution.sparkPlan
+
+    assert(sparkPlan.collect { case e: InMemoryColumnarTableScan => e }.size === 3)
+    assert(sparkPlan.collect { case e: PhysicalRDD => e }.size === 0)
+  }
 }

From 2143d592c802ec8f83a1eb5ce9b33ad8e48d7196 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 8 Sep 2015 16:51:45 -0700
Subject: [PATCH 1355/1454] [HOTFIX] Fix build break caused by #8494

Author: Michael Armbrust <michael@databricks.com>

Closes #8659 from marmbrus/testBuildBreak.
---
 .../test/scala/org/apache/spark/sql/CachedTableSuite.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index 84e66b50cf1d7..356d4ff3fa837 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -342,9 +342,9 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-10327 Cache Table is not working while subquery has alias in its project list") {
-    ctx.sparkContext.parallelize((1, 1) :: (2, 2) :: Nil)
+    sparkContext.parallelize((1, 1) :: (2, 2) :: Nil)
       .toDF("key", "value").selectExpr("key", "value", "key+1").registerTempTable("abc")
-    ctx.cacheTable("abc")
+    sqlContext.cacheTable("abc")
 
     val sparkPlan = sql(
       """select a.key, b.key, c.key from

From ae74c3fa84885dac18f39368e6dda48a2f7a25a5 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 8 Sep 2015 17:36:00 -0700
Subject: [PATCH 1356/1454] [RELEASE] Add more contributors & only show names
 in release notes.

Author: Reynold Xin <rxin@databricks.com>

Closes #8660 from rxin/contrib.
---
 dev/create-release/generate-contributors.py | 20 +++++++++------
 dev/create-release/known_translations       | 27 +++++++++++++++++++++
 2 files changed, 39 insertions(+), 8 deletions(-)

diff --git a/dev/create-release/generate-contributors.py b/dev/create-release/generate-contributors.py
index 8aaa250bd7e29..db9c680a4bad3 100755
--- a/dev/create-release/generate-contributors.py
+++ b/dev/create-release/generate-contributors.py
@@ -178,13 +178,16 @@ def populate(issue_type, components):
             author_info[author][issue_type].add(component)
     # Find issues and components associated with this commit
     for issue in issues:
-        jira_issue = jira_client.issue(issue)
-        jira_type = jira_issue.fields.issuetype.name
-        jira_type = translate_issue_type(jira_type, issue, warnings)
-        jira_components = [translate_component(c.name, _hash, warnings)\
-          for c in jira_issue.fields.components]
-        all_components = set(jira_components + commit_components)
-        populate(jira_type, all_components)
+        try:
+            jira_issue = jira_client.issue(issue)
+            jira_type = jira_issue.fields.issuetype.name
+            jira_type = translate_issue_type(jira_type, issue, warnings)
+            jira_components = [translate_component(c.name, _hash, warnings)\
+              for c in jira_issue.fields.components]
+            all_components = set(jira_components + commit_components)
+            populate(jira_type, all_components)
+        except Exception as e:
+            print "Unexpected error:", e
     # For docs without an associated JIRA, manually add it ourselves
     if is_docs(title) and not issues:
         populate("documentation", commit_components)
@@ -223,7 +226,8 @@ def populate(issue_type, components):
     # E.g. andrewor14/SPARK-3425/SPARK-1157/SPARK-6672
     if author in invalid_authors and invalid_authors[author]:
         author = author + "/" + "/".join(invalid_authors[author])
-    line = " * %s -- %s" % (author, contribution)
+    #line = " * %s -- %s" % (author, contribution)
+    line = author
     contributors_file.write(line + "\n")
 contributors_file.close()
 print "Contributors list is successfully written to %s!" % contributors_file_name
diff --git a/dev/create-release/known_translations b/dev/create-release/known_translations
index e462302f28423..3563fe3cc3c03 100644
--- a/dev/create-release/known_translations
+++ b/dev/create-release/known_translations
@@ -138,3 +138,30 @@ lee19 - Lee
 lockwobr - Brian Lockwood
 navis - Navis Ryu
 pparkkin - Paavo Parkkinen
+HyukjinKwon - Hyukjin Kwon
+JDrit - Joseph Batchik
+JuhongPark - Juhong Park
+KaiXinXiaoLei - KaiXinXIaoLei
+NamelessAnalyst - NamelessAnalyst
+alyaxey - Alex Slusarenko
+baishuo - Shuo Bai
+fe2s - Oleksiy Dyagilev
+felixcheung - Felix Cheung
+feynmanliang - Feynman Liang
+josepablocam - Jose Cambronero
+kai-zeng - Kai Zeng
+mosessky - mosessky
+msannell - Michael Sannella
+nishkamravi2 - Nishkam Ravi
+noel-smith - Noel Smith
+petz2000 - Patrick Baier
+qiansl127 - Shilei Qian
+rahulpalamuttam - Rahul Palamuttam
+rowan000 - Rowan Chattaway
+sarutak - Kousuke Saruta
+sethah - Seth Hendrickson
+small-wang - Wang Wei
+stanzhai - Stan Zhai
+tien-dungle - Tien-Dung Le
+xuchenCN - Xu Chen
+zhangjiajin - Zhang JiaJin

From 820913f554bef610d07ca2dadaead657f916ae63 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 8 Sep 2015 20:39:15 -0700
Subject: [PATCH 1357/1454] [SPARK-10071] [STREAMING] Output a warning when
 writing QueueInputDStream and throw a better exception when reading
 QueueInputDStream

Output a warning when serializing QueueInputDStream rather than throwing an exception to allow unit tests use it. Moreover, this PR also throws an better exception when deserializing QueueInputDStream to make the user find out the problem easily. The previous exception is hard to understand: https://issues.apache.org/jira/browse/SPARK-8553

Author: zsxwing <zsxwing@gmail.com>

Closes #8624 from zsxwing/SPARK-10071 and squashes the following commits:

847cfa8 [zsxwing] Output a warning when writing QueueInputDStream and throw a better exception when reading QueueInputDStream
---
 .../apache/spark/streaming/Checkpoint.scala   |  6 ++--
 .../streaming/dstream/QueueInputDStream.scala |  9 ++++--
 .../streaming/StreamingContextSuite.scala     | 28 +++++++++++++------
 3 files changed, 30 insertions(+), 13 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 3985e1a3d9dfa..27024ecfd9101 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -321,7 +321,7 @@ object CheckpointReader extends Logging {
 
     // Try to read the checkpoint files in the order
     logInfo("Checkpoint files found: " + checkpointFiles.mkString(","))
-    val compressionCodec = CompressionCodec.createCodec(conf)
+    var readError: Exception = null
     checkpointFiles.foreach(file => {
       logInfo("Attempting to load checkpoint from file " + file)
       try {
@@ -332,13 +332,15 @@ object CheckpointReader extends Logging {
         return Some(cp)
       } catch {
         case e: Exception =>
+          readError = e
           logWarning("Error reading checkpoint from file " + file, e)
       }
     })
 
     // If none of checkpoint files could be read, then throw exception
     if (!ignoreReadError) {
-      throw new SparkException(s"Failed to read checkpoint from directory $checkpointPath")
+      throw new SparkException(
+        s"Failed to read checkpoint from directory $checkpointPath", readError)
     }
     None
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
index a2f5d82a79bd3..bab78a3536b47 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.dstream
 
-import java.io.{NotSerializableException, ObjectOutputStream}
+import java.io.{NotSerializableException, ObjectInputStream, ObjectOutputStream}
 
 import scala.collection.mutable.{ArrayBuffer, Queue}
 import scala.reflect.ClassTag
@@ -37,8 +37,13 @@ class QueueInputDStream[T: ClassTag](
 
   override def stop() { }
 
+  private def readObject(in: ObjectInputStream): Unit = {
+    throw new NotSerializableException("queueStream doesn't support checkpointing. " +
+      "Please don't use queueStream when checkpointing is enabled.")
+  }
+
   private def writeObject(oos: ObjectOutputStream): Unit = {
-    throw new NotSerializableException("queueStream doesn't support checkpointing")
+    logWarning("queueStream doesn't support checkpointing")
   }
 
   override def compute(validTime: Time): Option[RDD[T]] = {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 7423ef6bcb6ea..d26894e88fc26 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -30,7 +30,7 @@ import org.scalatest.concurrent.Timeouts
 import org.scalatest.exceptions.TestFailedDueToTimeoutException
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.{Logging, SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark._
 import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.metrics.source.Source
 import org.apache.spark.storage.StorageLevel
@@ -726,16 +726,26 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
   }
 
   test("queueStream doesn't support checkpointing") {
-    val checkpointDir = Utils.createTempDir()
-    ssc = new StreamingContext(master, appName, batchDuration)
-    val rdd = ssc.sparkContext.parallelize(1 to 10)
-    ssc.queueStream[Int](Queue(rdd)).print()
-    ssc.checkpoint(checkpointDir.getAbsolutePath)
-    val e = intercept[NotSerializableException] {
-      ssc.start()
+    val checkpointDirectory = Utils.createTempDir().getAbsolutePath()
+    def creatingFunction(): StreamingContext = {
+      val _ssc = new StreamingContext(conf, batchDuration)
+      val rdd = _ssc.sparkContext.parallelize(1 to 10)
+      _ssc.checkpoint(checkpointDirectory)
+      _ssc.queueStream[Int](Queue(rdd)).register()
+      _ssc
+    }
+    ssc = StreamingContext.getOrCreate(checkpointDirectory, creatingFunction _)
+    ssc.start()
+    eventually(timeout(10000 millis)) {
+      assert(Checkpoint.getCheckpointFiles(checkpointDirectory).size > 1)
+    }
+    ssc.stop()
+    val e = intercept[SparkException] {
+      ssc = StreamingContext.getOrCreate(checkpointDirectory, creatingFunction _)
     }
     // StreamingContext.validate changes the message, so use "contains" here
-    assert(e.getMessage.contains("queueStream doesn't support checkpointing"))
+    assert(e.getCause.getMessage.contains("queueStream doesn't support checkpointing. " +
+      "Please don't use queueStream when checkpointing is enabled."))
   }
 
   def addInputStream(s: StreamingContext): DStream[Int] = {

From 52fe32f6ac7a04fa9b4478fda1307c5b0c61c4a2 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 8 Sep 2015 20:51:20 -0700
Subject: [PATCH 1358/1454] [SPARK-9834] [MLLIB] implement weighted least
 squares via normal equation

The goal of this PR is to have a weighted least squares implementation that takes the normal equation approach, and hence to be able to provide R-like summary statistics and support IRLS (used by GLMs). The tests match R's lm and glmnet.

There are couple TODOs that can be addressed in future PRs:
* consolidate summary statistics aggregators
* move `dspr` to `BLAS`
* etc

It would be nice to have this merged first because it blocks couple other features.

dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8588 from mengxr/SPARK-9834.
---
 .../spark/ml/optim/WeightedLeastSquares.scala | 296 ++++++++++++++++++
 .../org/apache/spark/mllib/linalg/BLAS.scala  |   7 +
 .../mllib/linalg/distributed/RowMatrix.scala  |   3 +-
 .../ml/optim/WeightedLeastSquaresSuite.scala  | 133 ++++++++
 4 files changed, 438 insertions(+), 1 deletion(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
new file mode 100644
index 0000000000000..a99e2ac4c6913
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
@@ -0,0 +1,296 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.optim
+
+import com.github.fommil.netlib.LAPACK.{getInstance => lapack}
+import org.netlib.util.intW
+
+import org.apache.spark.Logging
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.rdd.RDD
+
+/**
+ * Model fitted by [[WeightedLeastSquares]].
+ * @param coefficients model coefficients
+ * @param intercept model intercept
+ */
+private[ml] class WeightedLeastSquaresModel(
+    val coefficients: DenseVector,
+    val intercept: Double) extends Serializable
+
+/**
+ * Weighted least squares solver via normal equation.
+ * Given weighted observations (w,,i,,, a,,i,,, b,,i,,), we use the following weighted least squares
+ * formulation:
+ *
+ * min,,x,z,, 1/2 sum,,i,, w,,i,, (a,,i,,^T^ x + z - b,,i,,)^2^ / sum,,i,, w_i
+ *   + 1/2 lambda / delta sum,,j,, (sigma,,j,, x,,j,,)^2^,
+ *
+ * where lambda is the regularization parameter, and delta and sigma,,j,, are controlled by
+ * [[standardizeLabel]] and [[standardizeFeatures]], respectively.
+ *
+ * Set [[regParam]] to 0.0 and turn off both [[standardizeFeatures]] and [[standardizeLabel]] to
+ * match R's `lm`.
+ * Turn on [[standardizeLabel]] to match R's `glmnet`.
+ *
+ * @param fitIntercept whether to fit intercept. If false, z is 0.0.
+ * @param regParam L2 regularization parameter (lambda)
+ * @param standardizeFeatures whether to standardize features. If true, sigma_,,j,, is the
+ *                            population standard deviation of the j-th column of A. Otherwise,
+ *                            sigma,,j,, is 1.0.
+ * @param standardizeLabel whether to standardize label. If true, delta is the population standard
+ *                         deviation of the label column b. Otherwise, delta is 1.0.
+ */
+private[ml] class WeightedLeastSquares(
+    val fitIntercept: Boolean,
+    val regParam: Double,
+    val standardizeFeatures: Boolean,
+    val standardizeLabel: Boolean) extends Logging with Serializable {
+  import WeightedLeastSquares._
+
+  require(regParam >= 0.0, s"regParam cannot be negative: $regParam")
+  if (regParam == 0.0) {
+    logWarning("regParam is zero, which might cause numerical instability and overfitting.")
+  }
+
+  /**
+   * Creates a [[WeightedLeastSquaresModel]] from an RDD of [[Instance]]s.
+   */
+  def fit(instances: RDD[Instance]): WeightedLeastSquaresModel = {
+    val summary = instances.treeAggregate(new Aggregator)(_.add(_), _.merge(_))
+    summary.validate()
+    logInfo(s"Number of instances: ${summary.count}.")
+    val triK = summary.triK
+    val bBar = summary.bBar
+    val bStd = summary.bStd
+    val aBar = summary.aBar
+    val aVar = summary.aVar
+    val abBar = summary.abBar
+    val aaBar = summary.aaBar
+    val aaValues = aaBar.values
+
+    if (fitIntercept) {
+      // shift centers
+      // A^T A - aBar aBar^T
+      RowMatrix.dspr(-1.0, aBar, aaValues)
+      // A^T b - bBar aBar
+      BLAS.axpy(-bBar, aBar, abBar)
+    }
+
+    // add regularization to diagonals
+    var i = 0
+    var j = 2
+    while (i < triK) {
+      var lambda = regParam
+      if (standardizeFeatures) {
+        lambda *= aVar(j - 2)
+      }
+      if (standardizeLabel) {
+        // TODO: handle the case when bStd = 0
+        lambda /= bStd
+      }
+      aaValues(i) += lambda
+      i += j
+      j += 1
+    }
+
+    val x = choleskySolve(aaBar.values, abBar)
+
+    // compute intercept
+    val intercept = if (fitIntercept) {
+      bBar - BLAS.dot(aBar, x)
+    } else {
+      0.0
+    }
+
+    new WeightedLeastSquaresModel(x, intercept)
+  }
+
+  /**
+   * Solves a symmetric positive definite linear system via Cholesky factorization.
+   * The input arguments are modified in-place to store the factorization and the solution.
+   * @param A the upper triangular part of A
+   * @param bx right-hand side
+   * @return the solution vector
+   */
+  // TODO: SPARK-10490 - consolidate this and the Cholesky solver in ALS
+  private def choleskySolve(A: Array[Double], bx: DenseVector): DenseVector = {
+    val k = bx.size
+    val info = new intW(0)
+    lapack.dppsv("U", k, 1, A, bx.values, k, info)
+    val code = info.`val`
+    assert(code == 0, s"lapack.dpotrs returned $code.")
+    bx
+  }
+}
+
+private[ml] object WeightedLeastSquares {
+
+  /**
+   * Case class for weighted observations.
+   * @param w weight, must be positive
+   * @param a features
+   * @param b label
+   */
+  case class Instance(w: Double, a: Vector, b: Double) {
+    require(w >= 0.0, s"Weight cannot be negative: $w.")
+  }
+
+  /**
+   * Aggregator to provide necessary summary statistics for solving [[WeightedLeastSquares]].
+   */
+  // TODO: consolidate aggregates for summary statistics
+  private class Aggregator extends Serializable {
+    var initialized: Boolean = false
+    var k: Int = _
+    var count: Long = _
+    var triK: Int = _
+    private var wSum: Double = _
+    private var wwSum: Double = _
+    private var bSum: Double = _
+    private var bbSum: Double = _
+    private var aSum: DenseVector = _
+    private var abSum: DenseVector = _
+    private var aaSum: DenseVector = _
+
+    private def init(k: Int): Unit = {
+      require(k <= 4096, "In order to take the normal equation approach efficiently, " +
+        s"we set the max number of features to 4096 but got $k.")
+      this.k = k
+      triK = k * (k + 1) / 2
+      count = 0L
+      wSum = 0.0
+      wwSum = 0.0
+      bSum = 0.0
+      bbSum = 0.0
+      aSum = new DenseVector(Array.ofDim(k))
+      abSum = new DenseVector(Array.ofDim(k))
+      aaSum = new DenseVector(Array.ofDim(triK))
+      initialized = true
+    }
+
+    /**
+     * Adds an instance.
+     */
+    def add(instance: Instance): this.type = {
+      val Instance(w, a, b) = instance
+      val ak = a.size
+      if (!initialized) {
+        init(ak)
+        initialized = true
+      }
+      assert(ak == k, s"Dimension mismatch. Expect vectors of size $k but got $ak.")
+      count += 1L
+      wSum += w
+      wwSum += w * w
+      bSum += w * b
+      bbSum += w * b * b
+      BLAS.axpy(w, a, aSum)
+      BLAS.axpy(w * b, a, abSum)
+      RowMatrix.dspr(w, a, aaSum.values)
+      this
+    }
+
+    /**
+     * Merges another [[Aggregator]].
+     */
+    def merge(other: Aggregator): this.type = {
+      if (!other.initialized) {
+        this
+      } else {
+        if (!initialized) {
+          init(other.k)
+        }
+        assert(k == other.k, s"dimension mismatch: this.k = $k but other.k = ${other.k}")
+        count += other.count
+        wSum += other.wSum
+        wwSum += other.wwSum
+        bSum += other.bSum
+        bbSum += other.bbSum
+        BLAS.axpy(1.0, other.aSum, aSum)
+        BLAS.axpy(1.0, other.abSum, abSum)
+        BLAS.axpy(1.0, other.aaSum, aaSum)
+        this
+      }
+    }
+
+    /**
+     * Validates that we have seen observations.
+     */
+    def validate(): Unit = {
+      assert(initialized, "Training dataset is empty.")
+      assert(wSum > 0.0, "Sum of weights cannot be zero.")
+    }
+
+    /**
+     * Weighted mean of features.
+     */
+    def aBar: DenseVector = {
+      val output = aSum.copy
+      BLAS.scal(1.0 / wSum, output)
+      output
+    }
+
+    /**
+     * Weighted mean of labels.
+     */
+    def bBar: Double = bSum / wSum
+
+    /**
+     * Weighted population standard deviation of labels.
+     */
+    def bStd: Double = math.sqrt(bbSum / wSum - bBar * bBar)
+
+    /**
+     * Weighted mean of (label * features).
+     */
+    def abBar: DenseVector = {
+      val output = abSum.copy
+      BLAS.scal(1.0 / wSum, output)
+      output
+    }
+
+    /**
+     * Weighted mean of (features * features^T^).
+     */
+    def aaBar: DenseVector = {
+      val output = aaSum.copy
+      BLAS.scal(1.0 / wSum, output)
+      output
+    }
+
+    /**
+     * Weighted population variance of features.
+     */
+    def aVar: DenseVector = {
+      val variance = Array.ofDim[Double](k)
+      var i = 0
+      var j = 2
+      val aaValues = aaSum.values
+      while (i < triK) {
+        val l = j - 2
+        val aw = aSum(l) / wSum
+        variance(l) = aaValues(i) / wSum - aw * aw
+        i += j
+        j += 1
+      }
+      new DenseVector(variance)
+    }
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index ab475af264dd3..9ee81eda8a8c0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -92,6 +92,13 @@ private[spark] object BLAS extends Serializable with Logging {
     }
   }
 
+  /** Y += a * x */
+  private[spark] def axpy(a: Double, X: DenseMatrix, Y: DenseMatrix): Unit = {
+    require(X.numRows == Y.numRows && X.numCols == Y.numCols, "Dimension mismatch: " +
+      s"size(X) = ${(X.numRows, X.numCols)} but size(Y) = ${(Y.numRows, Y.numCols)}.")
+    f2jBLAS.daxpy(X.numRows * X.numCols, a, X.values, 1, Y.values, 1)
+  }
+
   /**
    * dot(x, y)
    */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 9a423ddafdc09..83779ac88989b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -678,7 +678,8 @@ object RowMatrix {
    *
    * @param U the upper triangular part of the matrix packed in an array (column major)
    */
-  private def dspr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
+  // TODO: SPARK-10491 - move this method to linalg.BLAS
+  private[spark] def dspr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
     // TODO: Find a better home (breeze?) for this method.
     val n = v.size
     v match {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
new file mode 100644
index 0000000000000..652f3adb984d3
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
@@ -0,0 +1,133 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.optim
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.optim.WeightedLeastSquares.Instance
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.rdd.RDD
+
+class WeightedLeastSquaresSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  private var instances: RDD[Instance] = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    /*
+       R code:
+
+       A <- matrix(c(0, 1, 2, 3, 5, 7, 11, 13), 4, 2)
+       b <- c(17, 19, 23, 29)
+       w <- c(1, 2, 3, 4)
+     */
+    instances = sc.parallelize(Seq(
+      Instance(1.0, Vectors.dense(0.0, 5.0).toSparse, 17.0),
+      Instance(2.0, Vectors.dense(1.0, 7.0), 19.0),
+      Instance(3.0, Vectors.dense(2.0, 11.0), 23.0),
+      Instance(4.0, Vectors.dense(3.0, 13.0), 29.0)
+    ), 2)
+  }
+
+  test("WLS against lm") {
+    /*
+       R code:
+
+       df <- as.data.frame(cbind(A, b))
+       for (formula in c(b ~ . -1, b ~ .)) {
+         model <- lm(formula, data=df, weights=w)
+         print(as.vector(coef(model)))
+       }
+
+       [1] -3.727121  3.009983
+       [1] 18.08  6.08 -0.60
+     */
+
+    val expected = Seq(
+      Vectors.dense(0.0, -3.727121, 3.009983),
+      Vectors.dense(18.08, 6.08, -0.60))
+
+    var idx = 0
+    for (fitIntercept <- Seq(false, true)) {
+      val wls = new WeightedLeastSquares(
+        fitIntercept, regParam = 0.0, standardizeFeatures = false, standardizeLabel = false)
+        .fit(instances)
+      val actual = Vectors.dense(wls.intercept, wls.coefficients(0), wls.coefficients(1))
+      assert(actual ~== expected(idx) absTol 1e-4)
+      idx += 1
+    }
+  }
+
+  test("WLS against glmnet") {
+    /*
+       R code:
+
+       library(glmnet)
+
+       for (intercept in c(FALSE, TRUE)) {
+         for (lambda in c(0.0, 0.1, 1.0)) {
+           for (standardize in c(FALSE, TRUE)) {
+             model <- glmnet(A, b, weights=w, intercept=intercept, lambda=lambda,
+                             standardize=standardize, alpha=0, thresh=1E-14)
+             print(as.vector(coef(model)))
+           }
+         }
+       }
+
+       [1]  0.000000 -3.727117  3.009982
+       [1]  0.000000 -3.727117  3.009982
+       [1]  0.000000 -3.307532  2.924206
+       [1]  0.000000 -2.914790  2.840627
+       [1]  0.000000 -1.526575  2.558158
+       [1] 0.00000000 0.06984238 2.20488344
+       [1] 18.0799727  6.0799832 -0.5999941
+       [1] 18.0799727  6.0799832 -0.5999941
+       [1] 13.5356178  3.2714044  0.3770744
+       [1] 14.064629  3.565802  0.269593
+       [1] 10.1238013  0.9708569  1.1475466
+       [1] 13.1860638  2.1761382  0.6213134
+     */
+
+    val expected = Seq(
+      Vectors.dense(0.0, -3.727117, 3.009982),
+      Vectors.dense(0.0, -3.727117, 3.009982),
+      Vectors.dense(0.0, -3.307532, 2.924206),
+      Vectors.dense(0.0, -2.914790, 2.840627),
+      Vectors.dense(0.0, -1.526575, 2.558158),
+      Vectors.dense(0.0, 0.06984238, 2.20488344),
+      Vectors.dense(18.0799727, 6.0799832, -0.5999941),
+      Vectors.dense(18.0799727, 6.0799832, -0.5999941),
+      Vectors.dense(13.5356178, 3.2714044, 0.3770744),
+      Vectors.dense(14.064629, 3.565802, 0.269593),
+      Vectors.dense(10.1238013, 0.9708569, 1.1475466),
+      Vectors.dense(13.1860638, 2.1761382, 0.6213134))
+
+    var idx = 0
+    for (fitIntercept <- Seq(false, true);
+         regParam <- Seq(0.0, 0.1, 1.0);
+         standardizeFeatures <- Seq(false, true)) {
+      val wls = new WeightedLeastSquares(
+        fitIntercept, regParam, standardizeFeatures, standardizeLabel = true)
+        .fit(instances)
+      val actual = Vectors.dense(wls.intercept, wls.coefficients(0), wls.coefficients(1))
+      assert(actual ~== expected(idx) absTol 1e-4)
+      idx += 1
+    }
+  }
+}

From a1573489a37def97b7c26b798898ffbbdc4defa8 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 8 Sep 2015 20:54:02 -0700
Subject: [PATCH 1359/1454] [SPARK-10464] [MLLIB] Add WeibullGenerator for
 RandomDataGenerator

Add WeibullGenerator for RandomDataGenerator.
#8611 need use WeibullGenerator to generate random data based on Weibull distribution.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8622 from yanboliang/spark-10464.
---
 .../mllib/random/RandomDataGenerator.scala    | 27 +++++++++++++++++--
 .../random/RandomDataGeneratorSuite.scala     | 16 ++++++++++-
 2 files changed, 40 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
index a2d85a68cd327..9eab7efc160da 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
@@ -17,8 +17,7 @@
 
 package org.apache.spark.mllib.random
 
-import org.apache.commons.math3.distribution.{ExponentialDistribution,
-  GammaDistribution, LogNormalDistribution, PoissonDistribution}
+import org.apache.commons.math3.distribution._
 
 import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
@@ -195,3 +194,27 @@ class LogNormalGenerator @Since("1.3.0") (
   @Since("1.3.0")
   override def copy(): LogNormalGenerator = new LogNormalGenerator(mean, std)
 }
+
+/**
+ * :: DeveloperApi ::
+ * Generates i.i.d. samples from the Weibull distribution with the
+ * given shape and scale parameter.
+ *
+ * @param alpha shape parameter for the Weibull distribution.
+ * @param beta scale parameter for the Weibull distribution.
+ */
+@DeveloperApi
+class WeibullGenerator(
+    val alpha: Double,
+    val beta: Double) extends RandomDataGenerator[Double] {
+
+  private val rng = new WeibullDistribution(alpha, beta)
+
+  override def nextValue(): Double = rng.sample()
+
+  override def setSeed(seed: Long): Unit = {
+    rng.reseedRandomGenerator(seed)
+  }
+
+  override def copy(): WeibullGenerator = new WeibullGenerator(alpha, beta)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
index a5ca1518f82f5..8416771552fd3 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.random
 
-import scala.math
+import org.apache.commons.math3.special.Gamma
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.util.StatCounter
@@ -136,4 +136,18 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
         distributionChecks(gamma, expectedMean, expectedStd, 0.1)
     }
   }
+
+  test("WeibullGenerator") {
+    List((1.0, 2.0), (2.0, 3.0), (2.5, 3.5), (10.4, 2.222)).map {
+      case (alpha: Double, beta: Double) =>
+        val weibull = new WeibullGenerator(alpha, beta)
+        apiChecks(weibull)
+
+        val expectedMean = math.exp(Gamma.logGamma(1 + (1 / alpha))) * beta
+        val expectedVariance = math.exp(
+          Gamma.logGamma(1 + (2 / alpha))) * beta * beta - expectedMean * expectedMean
+        val expectedStd = math.sqrt(expectedVariance)
+        distributionChecks(weibull, expectedMean, expectedStd, 0.1)
+    }
+  }
 }

From 3a11e50e21ececbec9708eb487b08196f195cd87 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 8 Sep 2015 20:56:22 -0700
Subject: [PATCH 1360/1454] [SPARK-10373] [PYSPARK] move @since into pyspark
 from sql

cc mengxr

Author: Davies Liu <davies@databricks.com>

Closes #8657 from davies/move_since.
---
 python/pyspark/__init__.py       | 16 ++++++++++++++++
 python/pyspark/sql/__init__.py   | 15 ---------------
 python/pyspark/sql/column.py     |  2 +-
 python/pyspark/sql/context.py    |  2 +-
 python/pyspark/sql/dataframe.py  |  2 +-
 python/pyspark/sql/functions.py  |  3 +--
 python/pyspark/sql/group.py      |  2 +-
 python/pyspark/sql/readwriter.py |  3 +--
 python/pyspark/sql/window.py     |  3 +--
 9 files changed, 23 insertions(+), 25 deletions(-)

diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py
index 5f70ac6ed8fe6..8475dfb1c6ad0 100644
--- a/python/pyspark/__init__.py
+++ b/python/pyspark/__init__.py
@@ -48,6 +48,22 @@
 from pyspark.status import *
 from pyspark.profiler import Profiler, BasicProfiler
 
+
+def since(version):
+    """
+    A decorator that annotates a function to append the version of Spark the function was added.
+    """
+    import re
+    indent_p = re.compile(r'\n( +)')
+
+    def deco(f):
+        indents = indent_p.findall(f.__doc__)
+        indent = ' ' * (min(len(m) for m in indents) if indents else 0)
+        f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
+        return f
+    return deco
+
+
 # for back compatibility
 from pyspark.sql import SQLContext, HiveContext, SchemaRDD, Row
 
diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py
index ad9c891ba1c04..98eaf52866d23 100644
--- a/python/pyspark/sql/__init__.py
+++ b/python/pyspark/sql/__init__.py
@@ -44,21 +44,6 @@
 from __future__ import absolute_import
 
 
-def since(version):
-    """
-    A decorator that annotates a function to append the version of Spark the function was added.
-    """
-    import re
-    indent_p = re.compile(r'\n( +)')
-
-    def deco(f):
-        indents = indent_p.findall(f.__doc__)
-        indent = ' ' * (min(len(m) for m in indents) if indents else 0)
-        f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
-        return f
-    return deco
-
-
 from pyspark.sql.types import Row
 from pyspark.sql.context import SQLContext, HiveContext
 from pyspark.sql.column import Column
diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 56e75e8caee88..573f65f5bf096 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -22,9 +22,9 @@
     basestring = str
     long = int
 
+from pyspark import since
 from pyspark.context import SparkContext
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.sql import since
 from pyspark.sql.types import *
 
 __all__ = ["DataFrame", "Column", "SchemaRDD", "DataFrameNaFunctions",
diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 0ef46c44644ab..89c8c6e0d94f1 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -26,9 +26,9 @@
 
 from py4j.protocol import Py4JError
 
+from pyspark import since
 from pyspark.rdd import RDD, _prepare_for_python_RDD, ignore_unicode_prefix
 from pyspark.serializers import AutoBatchedSerializer, PickleSerializer
-from pyspark.sql import since
 from pyspark.sql.types import Row, StringType, StructType, _verify_type, \
     _infer_schema, _has_nulltype, _merge_type, _create_converter
 from pyspark.sql.dataframe import DataFrame
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index e269ef4304f3f..c5bf55791240b 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -26,11 +26,11 @@
 else:
     from itertools import imap as map
 
+from pyspark import since
 from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix
 from pyspark.serializers import BatchedSerializer, PickleSerializer, UTF8Deserializer
 from pyspark.storagelevel import StorageLevel
 from pyspark.traceback_utils import SCCallSiteSync
-from pyspark.sql import since
 from pyspark.sql.types import _parse_datatype_json_string
 from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
 from pyspark.sql.readwriter import DataFrameWriter
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 4b74a501521a5..26b8662718a60 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -24,10 +24,9 @@
 if sys.version < "3":
     from itertools import imap as map
 
-from pyspark import SparkContext
+from pyspark import since, SparkContext
 from pyspark.rdd import _prepare_for_python_RDD, ignore_unicode_prefix
 from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
-from pyspark.sql import since
 from pyspark.sql.types import StringType
 from pyspark.sql.column import Column, _to_java_column, _to_seq
 
diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py
index 04594d5a836ce..71c0bccc5eeff 100644
--- a/python/pyspark/sql/group.py
+++ b/python/pyspark/sql/group.py
@@ -15,8 +15,8 @@
 # limitations under the License.
 #
 
+from pyspark import since
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.sql import since
 from pyspark.sql.column import Column, _to_seq
 from pyspark.sql.dataframe import DataFrame
 from pyspark.sql.types import *
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 3fa6895880a97..f43d8bf646a9e 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -22,8 +22,7 @@
 
 from py4j.java_gateway import JavaClass
 
-from pyspark import RDD
-from pyspark.sql import since
+from pyspark import RDD, since
 from pyspark.sql.column import _to_seq
 from pyspark.sql.types import *
 
diff --git a/python/pyspark/sql/window.py b/python/pyspark/sql/window.py
index eaf4d7e98620a..57bbe340bbd4d 100644
--- a/python/pyspark/sql/window.py
+++ b/python/pyspark/sql/window.py
@@ -17,8 +17,7 @@
 
 import sys
 
-from pyspark import SparkContext
-from pyspark.sql import since
+from pyspark import since, SparkContext
 from pyspark.sql.column import _to_seq, _to_java_column
 
 __all__ = ["Window", "WindowSpec"]

From 0e2f2163314972f6be18e3453c64314d1bee7bb9 Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Tue, 8 Sep 2015 21:26:20 -0700
Subject: [PATCH 1361/1454] [SPARK-10094] Pyspark ML Feature transformers
 marked as experimental

Modified class-level docstrings to mark all feature transformers in pyspark.ml as experimental.

Author: noelsmith <mail@noelsmith.com>

Closes #8623 from noel-smith/SPARK-10094-mark-pyspark-ml-trans-exp.
---
 python/pyspark/ml/feature.py | 52 ++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index d955307e27efd..a7c5b2b62e764 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -36,6 +36,8 @@
 @inherit_doc
 class Binarizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Binarize a column of continuous features given a threshold.
 
     >>> df = sqlContext.createDataFrame([(0.5,)], ["values"])
@@ -92,6 +94,8 @@ def getThreshold(self):
 @inherit_doc
 class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Maps a column of continuous features to a column of feature buckets.
 
     >>> df = sqlContext.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"])
@@ -169,6 +173,8 @@ def getSplits(self):
 @inherit_doc
 class DCT(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A feature transformer that takes the 1D discrete cosine transform
     of a real vector. No zero padding is performed on the input vector.
     It returns a real vector of the same length representing the DCT.
@@ -232,6 +238,8 @@ def getInverse(self):
 @inherit_doc
 class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Outputs the Hadamard product (i.e., the element-wise product) of each input vector
     with a provided "weight" vector. In other words, it scales each column of the dataset
     by a scalar multiplier.
@@ -289,6 +297,8 @@ def getScalingVec(self):
 @inherit_doc
 class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
     """
+    .. note:: Experimental
+
     Maps a sequence of terms to their term frequencies using the
     hashing trick.
 
@@ -327,6 +337,8 @@ def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None):
 @inherit_doc
 class IDF(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Compute the Inverse Document Frequency (IDF) given a collection of documents.
 
     >>> from pyspark.mllib.linalg import DenseVector
@@ -387,6 +399,8 @@ def _create_model(self, java_model):
 
 class IDFModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by IDF.
     """
 
@@ -395,6 +409,8 @@ class IDFModel(JavaModel):
 @ignore_unicode_prefix
 class NGram(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A feature transformer that converts the input array of strings into an array of n-grams. Null
     values in the input array are ignored.
     It returns an array of n-grams where each n-gram is represented by a space-separated string of
@@ -463,6 +479,8 @@ def getN(self):
 @inherit_doc
 class Normalizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
      Normalize a vector to have unit norm using the given p-norm.
 
     >>> from pyspark.mllib.linalg import Vectors
@@ -519,6 +537,8 @@ def getP(self):
 @inherit_doc
 class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A one-hot encoder that maps a column of category indices to a
     column of binary vectors, with at most a single one-value per row
     that indicates the input category index.
@@ -591,6 +611,8 @@ def getDropLast(self):
 @inherit_doc
 class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion,
     which is available at `http://en.wikipedia.org/wiki/Polynomial_expansion`, "In mathematics, an
     expansion of a product of sums expresses it as a sum of products by using the fact that
@@ -649,6 +671,8 @@ def getDegree(self):
 @ignore_unicode_prefix
 class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A regex based tokenizer that extracts tokens either by using the
     provided regex pattern (in Java dialect) to split the text
     (default) or repeatedly matching the regex (if gaps is false).
@@ -746,6 +770,8 @@ def getPattern(self):
 @inherit_doc
 class SQLTransformer(JavaTransformer):
     """
+    .. note:: Experimental
+
     Implements the transforms which are defined by SQL statement.
     Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
     where '__THIS__' represents the underlying table of the input dataset.
@@ -797,6 +823,8 @@ def getStatement(self):
 @inherit_doc
 class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Standardizes features by removing the mean and scaling to unit variance using column summary
     statistics on the samples in the training set.
 
@@ -870,6 +898,8 @@ def _create_model(self, java_model):
 
 class StandardScalerModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by StandardScaler.
     """
 
@@ -891,6 +921,8 @@ def mean(self):
 @inherit_doc
 class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A label indexer that maps a string column of labels to an ML column of label indices.
     If the input column is numeric, we cast it to string and index the string values.
     The indices are in [0, numLabels), ordered by label frequencies.
@@ -929,6 +961,8 @@ def _create_model(self, java_model):
 
 class StringIndexerModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by StringIndexer.
     """
 
@@ -1006,6 +1040,8 @@ def getCaseSensitive(self):
 @ignore_unicode_prefix
 class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A tokenizer that converts the input string to lowercase and then
     splits it by white spaces.
 
@@ -1051,6 +1087,8 @@ def setParams(self, inputCol=None, outputCol=None):
 @inherit_doc
 class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol):
     """
+    .. note:: Experimental
+
     A feature transformer that merges multiple columns into a vector column.
 
     >>> df = sqlContext.createDataFrame([(1, 0, 3)], ["a", "b", "c"])
@@ -1087,6 +1125,8 @@ def setParams(self, inputCols=None, outputCol=None):
 @inherit_doc
 class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Class for indexing categorical feature columns in a dataset of [[Vector]].
 
     This has 2 usage modes:
@@ -1186,6 +1226,8 @@ def _create_model(self, java_model):
 
 class VectorIndexerModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by VectorIndexer.
     """
 
@@ -1194,6 +1236,8 @@ class VectorIndexerModel(JavaModel):
 @ignore_unicode_prefix
 class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further
     natural language processing or machine learning process.
 
@@ -1307,6 +1351,8 @@ def _create_model(self, java_model):
 
 class Word2VecModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by Word2Vec.
     """
 
@@ -1332,6 +1378,8 @@ def findSynonyms(self, word, num):
 @inherit_doc
 class PCA(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     PCA trains a model to project vectors to a low-dimensional space using PCA.
 
     >>> from pyspark.mllib.linalg import Vectors
@@ -1387,6 +1435,8 @@ def _create_model(self, java_model):
 
 class PCAModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by PCA.
     """
 
@@ -1470,6 +1520,8 @@ def _create_model(self, java_model):
 
 class RFormulaModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by :py:class:`RFormula`.
     """
 

From 2f6fd5256c6650868916a3eefaa0beb091187cbb Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 8 Sep 2015 22:13:05 -0700
Subject: [PATCH 1362/1454] [SPARK-9654] [ML] [PYSPARK] Add IndexToString to
 PySpark

Adds IndexToString to PySpark.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7976 from holdenk/SPARK-9654-add-string-indexer-inverse-in-pyspark.
---
 .../spark/ml/feature/StringIndexer.scala      |  2 +-
 python/pyspark/ml/feature.py                  | 74 ++++++++++++++++++-
 python/pyspark/ml/wrapper.py                  |  3 +-
 3 files changed, 73 insertions(+), 6 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 77aeed0ab0370..b6482ffe0b2ee 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -102,7 +102,7 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
  * [[StringIndexerModel.transform]] would return the input dataset unmodified.
  * This is a temporary fix for the case when target labels do not exist during prediction.
  *
- * @param labels  Ordered list of labels, corresponding to indices to be assigned
+ * @param labels  Ordered list of labels, corresponding to indices to be assigned.
  */
 @Experimental
 class StringIndexerModel (
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index a7c5b2b62e764..8c26cfbd5a47d 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -27,10 +27,11 @@
 from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
-           'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
-           'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StringIndexer',
-           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec',
-           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel', 'StopWordsRemover']
+           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion',
+           'RegexTokenizer', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel',
+           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
+           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel',
+           'StopWordsRemover']
 
 
 @inherit_doc
@@ -934,6 +935,11 @@ class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     >>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
     ...     key=lambda x: x[0])
     [(0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)]
+    >>> inverter = IndexToString(inputCol="indexed", outputCol="label2", labels=model.labels())
+    >>> itd = inverter.transform(td)
+    >>> sorted(set([(i[0], str(i[1])) for i in itd.select(itd.id, itd.label2).collect()]),
+    ...     key=lambda x: x[0])
+    [(0, 'a'), (1, 'b'), (2, 'c'), (3, 'a'), (4, 'a'), (5, 'c')]
     """
 
     @keyword_only
@@ -965,6 +971,66 @@ class StringIndexerModel(JavaModel):
 
     Model fitted by StringIndexer.
     """
+    @property
+    def labels(self):
+        """
+        Ordered list of labels, corresponding to indices to be assigned.
+        """
+        return self._java_obj.labels
+
+
+@inherit_doc
+class IndexToString(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    A :py:class:`Transformer` that maps a column of string indices back to a new column of
+    corresponding string values using either the ML attributes of the input column, or if
+    provided using the labels supplied by the user.
+    All original columns are kept during transformation.
+    See L{StringIndexer} for converting strings into indices.
+    """
+
+    # a placeholder to make the labels show up in generated doc
+    labels = Param(Params._dummy(), "labels",
+                   "Optional array of labels to be provided by the user, if not supplied or " +
+                   "empty, column metadata is read for labels")
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None, labels=None):
+        """
+        __init__(self, inputCol=None, outputCol=None, labels=None)
+        """
+        super(IndexToString, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IndexToString",
+                                            self.uid)
+        self.labels = Param(self, "labels",
+                            "Optional array of labels to be provided by the user, if not " +
+                            "supplied or empty, column metadata is read for labels")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None, labels=None):
+        """
+        setParams(self, inputCol=None, outputCol=None, labels=None)
+        Sets params for this IndexToString.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setLabels(self, value):
+        """
+        Sets the value of :py:attr:`labels`.
+        """
+        self._paramMap[self.labels] = value
+        return self
+
+    def getLabels(self):
+        """
+        Gets the value of :py:attr:`labels` or its default value.
+        """
+        return self.getOrDefault(self.labels)
 
 
 class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol):
diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index 253705bde913e..8218c7c5f801c 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -136,7 +136,8 @@ def _fit(self, dataset):
 class JavaTransformer(Transformer, JavaWrapper):
     """
     Base class for :py:class:`Transformer`s that wrap Java/Scala
-    implementations.
+    implementations. Subclasses should ensure they have the transformer Java object
+    available as _java_obj.
     """
 
     __metaclass__ = ABCMeta

From 91a577d2778ab5946f0c40cb80c89de24e3d10e8 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 8 Sep 2015 22:33:23 -0700
Subject: [PATCH 1363/1454] [SPARK-10249] [ML] [DOC] Add Python Code Example to
 StopWordsRemover User Guide

jira: https://issues.apache.org/jira/browse/SPARK-10249

update user guide since python support added.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8620 from hhbyyh/swPyDocExample.
---
 docs/ml-features.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 90654d1e5a248..58b31a5a5cc47 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -512,6 +512,25 @@ DataFrame dataset = jsql.createDataFrame(rdd, schema);
 remover.transform(dataset).show();
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+[`StopWordsRemover`](api/python/pyspark.ml.html#pyspark.ml.feature.StopWordsRemover)
+takes an input column name, an output column name, a list of stop words,
+and a boolean indicating if the matches should be case sensitive (false
+by default).
+
+{% highlight python %}
+from pyspark.ml.feature import StopWordsRemover
+
+sentenceData = sqlContext.createDataFrame([
+  (0, ["I", "saw", "the", "red", "baloon"]),
+  (1, ["Mary", "had", "a", "little", "lamb"])
+], ["label", "raw"])
+
+remover = StopWordsRemover(inputCol="raw", outputCol="filtered")
+remover.transform(sentenceData).show(truncate=False)
+{% endhighlight %}
+</div>
 </div>
 
 ## $n$-gram

From c1bc4f439f54625c01a585691e5293cd9961eb0c Mon Sep 17 00:00:00 2001
From: Luc Bourlier <luc.bourlier@typesafe.com>
Date: Wed, 9 Sep 2015 09:57:58 +0100
Subject: [PATCH 1364/1454] [SPARK-10227] fatal warnings with sbt on Scala 2.11

The bulk of the changes are on `transient` annotation on class parameter. Often the compiler doesn't generate a field for this parameters, so the the transient annotation would be unnecessary.
But if the class parameter are used in methods, then fields are created. So it is safer to keep the annotations.

The remainder are some potential bugs, and deprecated syntax.

Author: Luc Bourlier <luc.bourlier@typesafe.com>

Closes #8433 from skyluc/issue/sbt-2.11.
---
 .../scala/org/apache/spark/Accumulators.scala |  2 +-
 .../scala/org/apache/spark/Dependency.scala   |  2 +-
 .../scala/org/apache/spark/Partitioner.scala  |  4 +-
 .../org/apache/spark/SparkHadoopWriter.scala  |  2 +-
 .../apache/spark/api/python/PythonRDD.scala   |  4 +-
 .../spark/input/PortableDataStream.scala      |  4 +-
 .../netty/NettyBlockTransferService.scala     |  2 +-
 .../org/apache/spark/rdd/BinaryFileRDD.scala  |  6 +--
 .../scala/org/apache/spark/rdd/BlockRDD.scala |  4 +-
 .../org/apache/spark/rdd/CartesianRDD.scala   |  4 +-
 .../org/apache/spark/rdd/CheckpointRDD.scala  |  2 +-
 .../org/apache/spark/rdd/HadoopRDD.scala      |  8 ++--
 .../apache/spark/rdd/LocalCheckpointRDD.scala |  2 +-
 .../spark/rdd/LocalRDDCheckpointData.scala    |  2 +-
 .../org/apache/spark/rdd/NewHadoopRDD.scala   | 18 ++++----
 .../spark/rdd/ParallelCollectionRDD.scala     |  4 +-
 .../spark/rdd/PartitionPruningRDD.scala       |  6 +--
 .../spark/rdd/PartitionwiseSampledRDD.scala   |  4 +-
 .../apache/spark/rdd/RDDCheckpointData.scala  |  2 +-
 .../spark/rdd/ReliableCheckpointRDD.scala     |  2 +-
 .../spark/rdd/ReliableRDDCheckpointData.scala |  2 +-
 .../apache/spark/rdd/SqlNewHadoopRDD.scala    |  6 +--
 .../scala/org/apache/spark/rdd/UnionRDD.scala |  4 +-
 .../spark/rdd/ZippedPartitionsRDD.scala       |  2 +-
 .../apache/spark/rdd/ZippedWithIndexRDD.scala |  2 +-
 .../org/apache/spark/rpc/RpcEndpointRef.scala |  2 +-
 .../apache/spark/rpc/akka/AkkaRpcEnv.scala    | 21 ++++++---
 .../apache/spark/scheduler/ResultTask.scala   |  2 +-
 .../shuffle/unsafe/UnsafeShuffleManager.scala |  2 +-
 .../apache/spark/util/ClosureCleaner.scala    |  2 +-
 .../spark/util/ShutdownHookManager.scala      |  4 +-
 .../util/logging/RollingFileAppender.scala    | 46 +++++++++----------
 .../streaming/flume/FlumeInputDStream.scala   |  2 +-
 .../flume/FlumePollingInputDStream.scala      |  2 +-
 .../kafka/DirectKafkaInputDStream.scala       |  4 +-
 .../streaming/kafka/KafkaInputDStream.scala   |  2 +-
 .../streaming/mqtt/MQTTInputDStream.scala     |  2 +-
 .../twitter/TwitterInputDStream.scala         |  2 +-
 .../org/apache/spark/graphx/EdgeRDD.scala     |  4 +-
 .../scala/org/apache/spark/graphx/Graph.scala |  6 +--
 .../org/apache/spark/graphx/VertexRDD.scala   |  4 +-
 .../apache/spark/mllib/rdd/RandomRDD.scala    | 12 ++---
 .../org/apache/spark/repl/SparkILoop.scala    |  2 +-
 .../expressions/stringExpressions.scala       |  2 +-
 .../spark/sql/types/AbstractDataType.scala    |  2 +-
 .../datasources/WriterContainer.scala         | 10 ++--
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  2 +-
 .../apache/spark/sql/hive/TableReader.scala   |  8 ++--
 .../hive/execution/ScriptTransformation.scala |  2 +-
 .../spark/sql/hive/hiveWriterContainers.scala | 10 ++--
 .../apache/spark/streaming/Checkpoint.scala   |  2 +-
 .../streaming/api/python/PythonDStream.scala  | 12 ++---
 .../streaming/dstream/FileInputDStream.scala  |  2 +-
 .../streaming/dstream/InputDStream.scala      |  2 +-
 .../dstream/PluggableInputDStream.scala       |  2 +-
 .../streaming/dstream/QueueInputDStream.scala |  4 +-
 .../streaming/dstream/RawInputDStream.scala   |  2 +-
 .../dstream/ReceiverInputDStream.scala        |  2 +-
 .../dstream/SocketInputDStream.scala          |  2 +-
 .../rdd/WriteAheadLogBackedBlockRDD.scala     | 22 ++++-----
 60 files changed, 158 insertions(+), 151 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index c39c8667d013e..5592b75afb75b 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -47,7 +47,7 @@ import org.apache.spark.util.Utils
  * @tparam T partial data that can be added in
  */
 class Accumulable[R, T] private[spark] (
-    @transient initialValue: R,
+    initialValue: R,
     param: AccumulableParam[R, T],
     val name: Option[String],
     internal: Boolean)
diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index fc8cdde9348ee..cfeeb3902c033 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -66,7 +66,7 @@ abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
  */
 @DeveloperApi
 class ShuffleDependency[K, V, C](
-    @transient _rdd: RDD[_ <: Product2[K, V]],
+    @transient private val _rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
     val serializer: Option[Serializer] = None,
     val keyOrdering: Option[Ordering[K]] = None,
diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 29e581bb57cbc..e4df7af81a6d2 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -104,8 +104,8 @@ class HashPartitioner(partitions: Int) extends Partitioner {
  * the value of `partitions`.
  */
 class RangePartitioner[K : Ordering : ClassTag, V](
-    @transient partitions: Int,
-    @transient rdd: RDD[_ <: Product2[K, V]],
+    partitions: Int,
+    rdd: RDD[_ <: Product2[K, V]],
     private var ascending: Boolean = true)
   extends Partitioner {
 
diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
index f5dd36cbcfe6d..ae5926dd534a6 100644
--- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
+++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
@@ -37,7 +37,7 @@ import org.apache.spark.util.SerializableJobConf
  * a filename to write to, etc, exactly like in a Hadoop MapReduce job.
  */
 private[spark]
-class SparkHadoopWriter(@transient jobConf: JobConf)
+class SparkHadoopWriter(jobConf: JobConf)
   extends Logging
   with SparkHadoopMapRedUtil
   with Serializable {
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index b4d152b336602..69da180593bb5 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -41,7 +41,7 @@ import org.apache.spark.util.{SerializableConfiguration, Utils}
 import scala.util.control.NonFatal
 
 private[spark] class PythonRDD(
-    @transient parent: RDD[_],
+    parent: RDD[_],
     command: Array[Byte],
     envVars: JMap[String, String],
     pythonIncludes: JList[String],
@@ -785,7 +785,7 @@ class BytesToString extends org.apache.spark.api.java.function.Function[Array[By
  * Internal class that acts as an `AccumulatorParam` for Python accumulators. Inside, it
  * collects a list of pickled strings that we pass to Python through a socket.
  */
-private class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int)
+private class PythonAccumulatorParam(@transient private val serverHost: String, serverPort: Int)
   extends AccumulatorParam[JList[Array[Byte]]] {
 
   Utils.checkHost(serverHost, "Expected hostname")
diff --git a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
index a5ad47293f1c2..e2ffc3b64e5db 100644
--- a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
+++ b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
@@ -131,8 +131,8 @@ private[spark] class StreamInputFormat extends StreamFileInputFormat[PortableDat
  */
 @Experimental
 class PortableDataStream(
-    @transient isplit: CombineFileSplit,
-    @transient context: TaskAttemptContext,
+    isplit: CombineFileSplit,
+    context: TaskAttemptContext,
     index: Integer)
   extends Serializable {
 
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index 4b851bcb36597..70a42f9045e6b 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -137,7 +137,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
       new RpcResponseCallback {
         override def onSuccess(response: Array[Byte]): Unit = {
           logTrace(s"Successfully uploaded block $blockId")
-          result.success()
+          result.success((): Unit)
         }
         override def onFailure(e: Throwable): Unit = {
           logError(s"Error while uploading block $blockId", e)
diff --git a/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala
index 1f755db485812..6fec00dcd0d85 100644
--- a/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala
@@ -28,7 +28,7 @@ private[spark] class BinaryFileRDD[T](
     inputFormatClass: Class[_ <: StreamFileInputFormat[T]],
     keyClass: Class[String],
     valueClass: Class[T],
-    @transient conf: Configuration,
+    conf: Configuration,
     minPartitions: Int)
   extends NewHadoopRDD[String, T](sc, inputFormatClass, keyClass, valueClass, conf) {
 
@@ -36,10 +36,10 @@ private[spark] class BinaryFileRDD[T](
     val inputFormat = inputFormatClass.newInstance
     inputFormat match {
       case configurable: Configurable =>
-        configurable.setConf(conf)
+        configurable.setConf(getConf)
       case _ =>
     }
-    val jobContext = newJobContext(conf, jobId)
+    val jobContext = newJobContext(getConf, jobId)
     inputFormat.setMinPartitions(jobContext, minPartitions)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
index 922030263756b..fc1710fbad0a3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
@@ -28,7 +28,7 @@ private[spark] class BlockRDDPartition(val blockId: BlockId, idx: Int) extends P
 }
 
 private[spark]
-class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds: Array[BlockId])
+class BlockRDD[T: ClassTag](sc: SparkContext, @transient val blockIds: Array[BlockId])
   extends RDD[T](sc, Nil) {
 
   @transient lazy val _locations = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get)
@@ -64,7 +64,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds
    */
   private[spark] def removeBlocks() {
     blockIds.foreach { blockId =>
-      sc.env.blockManager.master.removeBlock(blockId)
+      sparkContext.env.blockManager.master.removeBlock(blockId)
     }
     _isValid = false
   }
diff --git a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala
index c1d6971787572..18e8cddbc40db 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala
@@ -27,8 +27,8 @@ import org.apache.spark.util.Utils
 private[spark]
 class CartesianPartition(
     idx: Int,
-    @transient rdd1: RDD[_],
-    @transient rdd2: RDD[_],
+    @transient private val rdd1: RDD[_],
+    @transient private val rdd2: RDD[_],
     s1Index: Int,
     s2Index: Int
   ) extends Partition {
diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
index 72fe215dae73e..b0364623af4cf 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
@@ -29,7 +29,7 @@ private[spark] class CheckpointRDDPartition(val index: Int) extends Partition
 /**
  * An RDD that recovers checkpointed data from storage.
  */
-private[spark] abstract class CheckpointRDD[T: ClassTag](@transient sc: SparkContext)
+private[spark] abstract class CheckpointRDD[T: ClassTag](sc: SparkContext)
   extends RDD[T](sc, Nil) {
 
   // CheckpointRDD should not be checkpointed again
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index e1f8719eead02..8f2655d63b797 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -51,7 +51,7 @@ import org.apache.spark.storage.StorageLevel
 /**
  * A Spark split class that wraps around a Hadoop InputSplit.
  */
-private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSplit)
+private[spark] class HadoopPartition(rddId: Int, idx: Int, s: InputSplit)
   extends Partition {
 
   val inputSplit = new SerializableWritable[InputSplit](s)
@@ -99,7 +99,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp
  */
 @DeveloperApi
 class HadoopRDD[K, V](
-    @transient sc: SparkContext,
+    sc: SparkContext,
     broadcastedConf: Broadcast[SerializableConfiguration],
     initLocalJobConfFuncOpt: Option[JobConf => Unit],
     inputFormatClass: Class[_ <: InputFormat[K, V]],
@@ -109,7 +109,7 @@ class HadoopRDD[K, V](
   extends RDD[(K, V)](sc, Nil) with Logging {
 
   if (initLocalJobConfFuncOpt.isDefined) {
-    sc.clean(initLocalJobConfFuncOpt.get)
+    sparkContext.clean(initLocalJobConfFuncOpt.get)
   }
 
   def this(
@@ -137,7 +137,7 @@ class HadoopRDD[K, V](
   // used to build JobTracker ID
   private val createTime = new Date()
 
-  private val shouldCloneJobConf = sc.conf.getBoolean("spark.hadoop.cloneConf", false)
+  private val shouldCloneJobConf = sparkContext.conf.getBoolean("spark.hadoop.cloneConf", false)
 
   // Returns a JobConf that will be used on slaves to obtain input splits for Hadoop reads.
   protected def getJobConf(): JobConf = {
diff --git a/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
index daa5779d688cc..bfe19195fcd37 100644
--- a/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
@@ -35,7 +35,7 @@ import org.apache.spark.storage.RDDBlockId
  * @param numPartitions the number of partitions in the checkpointed RDD
  */
 private[spark] class LocalCheckpointRDD[T: ClassTag](
-    @transient sc: SparkContext,
+    sc: SparkContext,
     rddId: Int,
     numPartitions: Int)
   extends CheckpointRDD[T](sc) {
diff --git a/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
index d6fad896845f6..c115e0ff74d3c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
@@ -31,7 +31,7 @@ import org.apache.spark.util.Utils
  * is written to the local, ephemeral block storage that lives in each executor. This is useful
  * for use cases where RDDs build up long lineages that need to be truncated often (e.g. GraphX).
  */
-private[spark] class LocalRDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+private[spark] class LocalRDDCheckpointData[T: ClassTag](@transient private val rdd: RDD[T])
   extends RDDCheckpointData[T](rdd) with Logging {
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index 6a9c004d65cff..174979aaeb231 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -40,7 +40,7 @@ import org.apache.spark.storage.StorageLevel
 private[spark] class NewHadoopPartition(
     rddId: Int,
     val index: Int,
-    @transient rawSplit: InputSplit with Writable)
+    rawSplit: InputSplit with Writable)
   extends Partition {
 
   val serializableHadoopSplit = new SerializableWritable(rawSplit)
@@ -68,14 +68,14 @@ class NewHadoopRDD[K, V](
     inputFormatClass: Class[_ <: InputFormat[K, V]],
     keyClass: Class[K],
     valueClass: Class[V],
-    @transient conf: Configuration)
+    @transient private val _conf: Configuration)
   extends RDD[(K, V)](sc, Nil)
   with SparkHadoopMapReduceUtil
   with Logging {
 
   // A Hadoop Configuration can be about 10 KB, which is pretty big, so broadcast it
-  private val confBroadcast = sc.broadcast(new SerializableConfiguration(conf))
-  // private val serializableConf = new SerializableWritable(conf)
+  private val confBroadcast = sc.broadcast(new SerializableConfiguration(_conf))
+  // private val serializableConf = new SerializableWritable(_conf)
 
   private val jobTrackerId: String = {
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
@@ -88,10 +88,10 @@ class NewHadoopRDD[K, V](
     val inputFormat = inputFormatClass.newInstance
     inputFormat match {
       case configurable: Configurable =>
-        configurable.setConf(conf)
+        configurable.setConf(_conf)
       case _ =>
     }
-    val jobContext = newJobContext(conf, jobId)
+    val jobContext = newJobContext(_conf, jobId)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
     for (i <- 0 until rawSplits.size) {
@@ -262,7 +262,7 @@ private[spark] class WholeTextFileRDD(
     inputFormatClass: Class[_ <: WholeTextFileInputFormat],
     keyClass: Class[String],
     valueClass: Class[String],
-    @transient conf: Configuration,
+    conf: Configuration,
     minPartitions: Int)
   extends NewHadoopRDD[String, String](sc, inputFormatClass, keyClass, valueClass, conf) {
 
@@ -270,10 +270,10 @@ private[spark] class WholeTextFileRDD(
     val inputFormat = inputFormatClass.newInstance
     inputFormat match {
       case configurable: Configurable =>
-        configurable.setConf(conf)
+        configurable.setConf(getConf)
       case _ =>
     }
-    val jobContext = newJobContext(conf, jobId)
+    val jobContext = newJobContext(getConf, jobId)
     inputFormat.setMinPartitions(jobContext, minPartitions)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
index e2394e28f8d26..582fa93afe34e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
@@ -83,8 +83,8 @@ private[spark] class ParallelCollectionPartition[T: ClassTag](
 }
 
 private[spark] class ParallelCollectionRDD[T: ClassTag](
-    @transient sc: SparkContext,
-    @transient data: Seq[T],
+    sc: SparkContext,
+    @transient private val data: Seq[T],
     numSlices: Int,
     locationPrefs: Map[Int, Seq[String]])
     extends RDD[T](sc, Nil) {
diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala
index a00f4c1cdff91..d6a37e8cc5dac 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala
@@ -32,7 +32,7 @@ private[spark] class PartitionPruningRDDPartition(idx: Int, val parentSplit: Par
  * Represents a dependency between the PartitionPruningRDD and its parent. In this
  * case, the child RDD contains a subset of partitions of the parents'.
  */
-private[spark] class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boolean)
+private[spark] class PruneDependency[T](rdd: RDD[T], partitionFilterFunc: Int => Boolean)
   extends NarrowDependency[T](rdd) {
 
   @transient
@@ -55,8 +55,8 @@ private[spark] class PruneDependency[T](rdd: RDD[T], @transient partitionFilterF
  */
 @DeveloperApi
 class PartitionPruningRDD[T: ClassTag](
-    @transient prev: RDD[T],
-    @transient partitionFilterFunc: Int => Boolean)
+    prev: RDD[T],
+    partitionFilterFunc: Int => Boolean)
   extends RDD[T](prev.context, List(new PruneDependency(prev, partitionFilterFunc))) {
 
   override def compute(split: Partition, context: TaskContext): Iterator[T] = {
diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
index a637d6f15b7e5..3b1acacf409b9 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
@@ -47,8 +47,8 @@ class PartitionwiseSampledRDDPartition(val prev: Partition, val seed: Long)
 private[spark] class PartitionwiseSampledRDD[T: ClassTag, U: ClassTag](
     prev: RDD[T],
     sampler: RandomSampler[T, U],
-    @transient preservesPartitioning: Boolean,
-    @transient seed: Long = Utils.random.nextLong)
+    preservesPartitioning: Boolean,
+    @transient private val seed: Long = Utils.random.nextLong)
   extends RDD[U](prev) {
 
   @transient override val partitioner = if (preservesPartitioning) prev.partitioner else None
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index 0e43520870c0a..429514b4f6bee 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -36,7 +36,7 @@ private[spark] object CheckpointState extends Enumeration {
  * as well as, manages the post-checkpoint state by providing the updated partitions,
  * iterator and preferred locations of the checkpointed RDD.
  */
-private[spark] abstract class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+private[spark] abstract class RDDCheckpointData[T: ClassTag](@transient private val rdd: RDD[T])
   extends Serializable {
 
   import CheckpointState._
diff --git a/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
index 35d8b0bfd18c5..1c3b5da19ceba 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
@@ -32,7 +32,7 @@ import org.apache.spark.util.{SerializableConfiguration, Utils}
  * An RDD that reads from checkpoint files previously written to reliable storage.
  */
 private[spark] class ReliableCheckpointRDD[T: ClassTag](
-    @transient sc: SparkContext,
+    sc: SparkContext,
     val checkpointPath: String)
   extends CheckpointRDD[T](sc) {
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
index 1df8eef5ff2b9..e9f6060301ba3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
@@ -28,7 +28,7 @@ import org.apache.spark.util.SerializableConfiguration
  * An implementation of checkpointing that writes the RDD data to reliable storage.
  * This allows drivers to be restarted on failure with previously computed state.
  */
-private[spark] class ReliableRDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+private[spark] class ReliableRDDCheckpointData[T: ClassTag](@transient private val rdd: RDD[T])
   extends RDDCheckpointData[T](rdd) with Logging {
 
   // The directory to which the associated RDD has been checkpointed to
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index fa3fecc80cb63..9babe56267e08 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -39,7 +39,7 @@ import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, Ut
 private[spark] class SqlNewHadoopPartition(
     rddId: Int,
     val index: Int,
-    @transient rawSplit: InputSplit with Writable)
+    rawSplit: InputSplit with Writable)
   extends SparkPartition {
 
   val serializableHadoopSplit = new SerializableWritable(rawSplit)
@@ -61,9 +61,9 @@ private[spark] class SqlNewHadoopPartition(
  * changes based on [[org.apache.spark.rdd.HadoopRDD]].
  */
 private[spark] class SqlNewHadoopRDD[V: ClassTag](
-    @transient sc : SparkContext,
+    sc : SparkContext,
     broadcastedConf: Broadcast[SerializableConfiguration],
-    @transient initDriverSideJobFuncOpt: Option[Job => Unit],
+    @transient private val initDriverSideJobFuncOpt: Option[Job => Unit],
     initLocalJobFuncOpt: Option[Job => Unit],
     inputFormatClass: Class[_ <: InputFormat[Void, V]],
     valueClass: Class[V])
diff --git a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala
index 3986645350a82..66cf4369da2ef 100644
--- a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala
@@ -37,9 +37,9 @@ import org.apache.spark.util.Utils
  */
 private[spark] class UnionPartition[T: ClassTag](
     idx: Int,
-    @transient rdd: RDD[T],
+    @transient private val rdd: RDD[T],
     val parentRddIndex: Int,
-    @transient parentRddPartitionIndex: Int)
+    @transient private val parentRddPartitionIndex: Int)
   extends Partition {
 
   var parentPartition: Partition = rdd.partitions(parentRddPartitionIndex)
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
index b3c64394abc76..70bf04de6400d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
@@ -26,7 +26,7 @@ import org.apache.spark.util.Utils
 
 private[spark] class ZippedPartitionsPartition(
     idx: Int,
-    @transient rdds: Seq[RDD[_]],
+    @transient private val rdds: Seq[RDD[_]],
     @transient val preferredLocations: Seq[String])
   extends Partition {
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
index e277ae28d588f..32931d59acb18 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
@@ -37,7 +37,7 @@ class ZippedWithIndexRDDPartition(val prev: Partition, val startIndex: Long)
  * @tparam T parent RDD item type
  */
 private[spark]
-class ZippedWithIndexRDD[T: ClassTag](@transient prev: RDD[T]) extends RDD[(T, Long)](prev) {
+class ZippedWithIndexRDD[T: ClassTag](prev: RDD[T]) extends RDD[(T, Long)](prev) {
 
   /** The start index of each partition. */
   @transient private val startIndices: Array[Long] = {
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
index 7409ac8859991..f25710bb5bd6e 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
@@ -26,7 +26,7 @@ import org.apache.spark.{SparkException, Logging, SparkConf}
 /**
  * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe.
  */
-private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
+private[spark] abstract class RpcEndpointRef(conf: SparkConf)
   extends Serializable with Logging {
 
   private[this] val maxRetries = RpcUtils.numRetries(conf)
diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
index fc17542abf81d..ad67e1c5ad4d5 100644
--- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
@@ -87,9 +87,9 @@ private[spark] class AkkaRpcEnv private[akka] (
 
   override def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef = {
     @volatile var endpointRef: AkkaRpcEndpointRef = null
-    // Use lazy because the Actor needs to use `endpointRef`.
+    // Use defered function because the Actor needs to use `endpointRef`.
     // So `actorRef` should be created after assigning `endpointRef`.
-    lazy val actorRef = actorSystem.actorOf(Props(new Actor with ActorLogReceive with Logging {
+    val actorRef = () => actorSystem.actorOf(Props(new Actor with ActorLogReceive with Logging {
 
       assert(endpointRef != null)
 
@@ -272,13 +272,20 @@ private[akka] class ErrorMonitor extends Actor with ActorLogReceive with Logging
 }
 
 private[akka] class AkkaRpcEndpointRef(
-    @transient defaultAddress: RpcAddress,
-    @transient _actorRef: => ActorRef,
-    @transient conf: SparkConf,
-    @transient initInConstructor: Boolean = true)
+    @transient private val defaultAddress: RpcAddress,
+    @transient private val _actorRef: () => ActorRef,
+    conf: SparkConf,
+    initInConstructor: Boolean)
   extends RpcEndpointRef(conf) with Logging {
 
-  lazy val actorRef = _actorRef
+  def this(
+      defaultAddress: RpcAddress,
+      _actorRef: ActorRef,
+      conf: SparkConf) = {
+    this(defaultAddress, () => _actorRef, conf, true)
+  }
+
+  lazy val actorRef = _actorRef()
 
   override lazy val address: RpcAddress = {
     val akkaAddress = actorRef.path.address
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index c4dc080e2b22b..fb693721a9cb6 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -44,7 +44,7 @@ private[spark] class ResultTask[T, U](
     stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
-    @transient locs: Seq[TaskLocation],
+    locs: Seq[TaskLocation],
     val outputId: Int,
     internalAccumulators: Seq[Accumulator[Long]])
   extends Task[U](stageId, stageAttemptId, partition.index, internalAccumulators)
diff --git a/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala
index df7bbd64247dd..75f22f642b9d1 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala
@@ -159,7 +159,7 @@ private[spark] class UnsafeShuffleManager(conf: SparkConf) extends ShuffleManage
       mapId: Int,
       context: TaskContext): ShuffleWriter[K, V] = {
     handle match {
-      case unsafeShuffleHandle: UnsafeShuffleHandle[K, V] =>
+      case unsafeShuffleHandle: UnsafeShuffleHandle[K @unchecked, V @unchecked] =>
         numMapsForShufflesThatUsedNewPath.putIfAbsent(handle.shuffleId, unsafeShuffleHandle.numMaps)
         val env = SparkEnv.get
         new UnsafeShuffleWriter(
diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index 150d82b3930ef..1b49dca9dc78b 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -94,7 +94,7 @@ private[spark] object ClosureCleaner extends Logging {
     if (cls.isPrimitive) {
       cls match {
         case java.lang.Boolean.TYPE => new java.lang.Boolean(false)
-        case java.lang.Character.TYPE => new java.lang.Character('\0')
+        case java.lang.Character.TYPE => new java.lang.Character('\u0000')
         case java.lang.Void.TYPE =>
           // This should not happen because `Foo(void x) {}` does not compile.
           throw new IllegalStateException("Unexpected void parameter in constructor")
diff --git a/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
index 61ff9b89ec1c1..db4a8b304ec3e 100644
--- a/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
+++ b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
@@ -217,7 +217,9 @@ private [util] class SparkShutdownHookManager {
     }
     Try(Utils.classForName("org.apache.hadoop.util.ShutdownHookManager")) match {
       case Success(shmClass) =>
-        val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get()
+        val fsPriority = classOf[FileSystem]
+          .getField("SHUTDOWN_HOOK_PRIORITY")
+          .get(null) // static field, the value is not used
           .asInstanceOf[Int]
         val shm = shmClass.getMethod("get").invoke(null)
         shm.getClass().getMethod("addShutdownHook", classOf[Runnable], classOf[Int])
diff --git a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
index 7138b4b8e4533..1e8476c4a047e 100644
--- a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
+++ b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
@@ -79,32 +79,30 @@ private[spark] class RollingFileAppender(
     val rolloverSuffix = rollingPolicy.generateRolledOverFileSuffix()
     val rolloverFile = new File(
       activeFile.getParentFile, activeFile.getName + rolloverSuffix).getAbsoluteFile
-    try {
-      logDebug(s"Attempting to rollover file $activeFile to file $rolloverFile")
-      if (activeFile.exists) {
-        if (!rolloverFile.exists) {
-          Files.move(activeFile, rolloverFile)
-          logInfo(s"Rolled over $activeFile to $rolloverFile")
-        } else {
-          // In case the rollover file name clashes, make a unique file name.
-          // The resultant file names are long and ugly, so this is used only
-          // if there is a name collision. This can be avoided by the using
-          // the right pattern such that name collisions do not occur.
-          var i = 0
-          var altRolloverFile: File = null
-          do {
-            altRolloverFile = new File(activeFile.getParent,
-              s"${activeFile.getName}$rolloverSuffix--$i").getAbsoluteFile
-            i += 1
-          } while (i < 10000 && altRolloverFile.exists)
-
-          logWarning(s"Rollover file $rolloverFile already exists, " +
-            s"rolled over $activeFile to file $altRolloverFile")
-          Files.move(activeFile, altRolloverFile)
-        }
+    logDebug(s"Attempting to rollover file $activeFile to file $rolloverFile")
+    if (activeFile.exists) {
+      if (!rolloverFile.exists) {
+        Files.move(activeFile, rolloverFile)
+        logInfo(s"Rolled over $activeFile to $rolloverFile")
       } else {
-        logWarning(s"File $activeFile does not exist")
+        // In case the rollover file name clashes, make a unique file name.
+        // The resultant file names are long and ugly, so this is used only
+        // if there is a name collision. This can be avoided by the using
+        // the right pattern such that name collisions do not occur.
+        var i = 0
+        var altRolloverFile: File = null
+        do {
+          altRolloverFile = new File(activeFile.getParent,
+            s"${activeFile.getName}$rolloverSuffix--$i").getAbsoluteFile
+          i += 1
+        } while (i < 10000 && altRolloverFile.exists)
+
+        logWarning(s"Rollover file $rolloverFile already exists, " +
+          s"rolled over $activeFile to file $altRolloverFile")
+        Files.move(activeFile, altRolloverFile)
       }
+    } else {
+      logWarning(s"File $activeFile does not exist")
     }
   }
 
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 2bf99cb3cba1f..c8780aa83bdbd 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -43,7 +43,7 @@ import org.jboss.netty.handler.codec.compression._
 
 private[streaming]
 class FlumeInputDStream[T: ClassTag](
-  @transient ssc_ : StreamingContext,
+  ssc_ : StreamingContext,
   host: String,
   port: Int,
   storageLevel: StorageLevel,
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
index 0bc46209b8369..3b936d88abd3e 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
@@ -46,7 +46,7 @@ import org.apache.spark.streaming.flume.sink._
  * @tparam T Class type of the object of this stream
  */
 private[streaming] class FlumePollingInputDStream[T: ClassTag](
-    @transient _ssc: StreamingContext,
+    _ssc: StreamingContext,
     val addresses: Seq[InetSocketAddress],
     val maxBatchSize: Int,
     val parallelism: Int,
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 1000094e93cb3..8a087474d3169 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -58,7 +58,7 @@ class DirectKafkaInputDStream[
   U <: Decoder[K]: ClassTag,
   T <: Decoder[V]: ClassTag,
   R: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     val kafkaParams: Map[String, String],
     val fromOffsets: Map[TopicAndPartition, Long],
     messageHandler: MessageAndMetadata[K, V] => R
@@ -79,7 +79,7 @@ class DirectKafkaInputDStream[
   override protected[streaming] val rateController: Option[RateController] = {
     if (RateController.isBackPressureEnabled(ssc.conf)) {
       Some(new DirectKafkaRateController(id,
-        RateEstimator.create(ssc.conf, ssc_.graph.batchDuration)))
+        RateEstimator.create(ssc.conf, context.graph.batchDuration)))
     } else {
       None
     }
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
index 04b2dc10d39ea..38730fecf332a 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
@@ -48,7 +48,7 @@ class KafkaInputDStream[
   V: ClassTag,
   U <: Decoder[_]: ClassTag,
   T <: Decoder[_]: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     kafkaParams: Map[String, String],
     topics: Map[String, Int],
     useReliableReceiver: Boolean,
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
index 7c2f18cb35bda..116c170489e96 100644
--- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
@@ -38,7 +38,7 @@ import org.apache.spark.streaming.receiver.Receiver
 
 private[streaming]
 class MQTTInputDStream(
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     brokerUrl: String,
     topic: String,
     storageLevel: StorageLevel
diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
index 7cf02d85d73d3..d7de74b350543 100644
--- a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
@@ -39,7 +39,7 @@ import org.apache.spark.streaming.receiver.Receiver
 */
 private[streaming]
 class TwitterInputDStream(
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     twitterAuth: Option[Authorization],
     filters: Seq[String],
     storageLevel: StorageLevel
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index 4611a3ace219b..ee7302a1edbf6 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -38,8 +38,8 @@ import org.apache.spark.graphx.impl.EdgeRDDImpl
  * `impl.ReplicatedVertexView`.
  */
 abstract class EdgeRDD[ED](
-    @transient sc: SparkContext,
-    @transient deps: Seq[Dependency[_]]) extends RDD[Edge[ED]](sc, deps) {
+    sc: SparkContext,
+    deps: Seq[Dependency[_]]) extends RDD[Edge[ED]](sc, deps) {
 
   // scalastyle:off structural.type
   private[graphx] def partitionsRDD: RDD[(PartitionID, EdgePartition[ED, VD])] forSome { type VD }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index db73a8abc5733..869caa340f52b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -46,7 +46,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * @note vertex ids are unique.
    * @return an RDD containing the vertices in this graph
    */
-  @transient val vertices: VertexRDD[VD]
+  val vertices: VertexRDD[VD]
 
   /**
    * An RDD containing the edges and their associated attributes.  The entries in the RDD contain
@@ -59,7 +59,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * along with their vertex data.
    *
    */
-  @transient val edges: EdgeRDD[ED]
+  val edges: EdgeRDD[ED]
 
   /**
    * An RDD containing the edge triplets, which are edges along with the vertex data associated with
@@ -77,7 +77,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * val numInvalid = graph.triplets.map(e => if (e.src.data == e.dst.data) 1 else 0).sum
    * }}}
    */
-  @transient val triplets: RDD[EdgeTriplet[VD, ED]]
+  val triplets: RDD[EdgeTriplet[VD, ED]]
 
   /**
    * Caches the vertices and edges associated with this graph at the specified storage level,
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index a9f04b559c3d1..1ef7a78fbcd00 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -55,8 +55,8 @@ import org.apache.spark.graphx.impl.VertexRDDImpl
  * @tparam VD the vertex attribute associated with each vertex in the set.
  */
 abstract class VertexRDD[VD](
-    @transient sc: SparkContext,
-    @transient deps: Seq[Dependency[_]]) extends RDD[(VertexId, VD)](sc, deps) {
+    sc: SparkContext,
+    deps: Seq[Dependency[_]]) extends RDD[(VertexId, VD)](sc, deps) {
 
   implicit protected def vdTag: ClassTag[VD]
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
index 910eff9540a47..f8cea7ecea6bf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
@@ -35,11 +35,11 @@ private[mllib] class RandomRDDPartition[T](override val index: Int,
 }
 
 // These two classes are necessary since Range objects in Scala cannot have size > Int.MaxValue
-private[mllib] class RandomRDD[T: ClassTag](@transient sc: SparkContext,
+private[mllib] class RandomRDD[T: ClassTag](sc: SparkContext,
     size: Long,
     numPartitions: Int,
-    @transient rng: RandomDataGenerator[T],
-    @transient seed: Long = Utils.random.nextLong) extends RDD[T](sc, Nil) {
+    @transient private val rng: RandomDataGenerator[T],
+    @transient private val seed: Long = Utils.random.nextLong) extends RDD[T](sc, Nil) {
 
   require(size > 0, "Positive RDD size required.")
   require(numPartitions > 0, "Positive number of partitions required")
@@ -56,12 +56,12 @@ private[mllib] class RandomRDD[T: ClassTag](@transient sc: SparkContext,
   }
 }
 
-private[mllib] class RandomVectorRDD(@transient sc: SparkContext,
+private[mllib] class RandomVectorRDD(sc: SparkContext,
     size: Long,
     vectorSize: Int,
     numPartitions: Int,
-    @transient rng: RandomDataGenerator[Double],
-    @transient seed: Long = Utils.random.nextLong) extends RDD[Vector](sc, Nil) {
+    @transient private val rng: RandomDataGenerator[Double],
+    @transient private val seed: Long = Utils.random.nextLong) extends RDD[Vector](sc, Nil) {
 
   require(size > 0, "Positive RDD size required.")
   require(numPartitions > 0, "Positive number of partitions required")
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index bf609ff0f65fc..33d262558b1fc 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -118,5 +118,5 @@ object SparkILoop {
       }
     }
   }
-  def run(lines: List[String]): String = run(lines map (_ + "\n") mkString)
+  def run(lines: List[String]): String = run(lines.map(_ + "\n").mkString)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index 48d02bb534501..a09d5b6e3ad14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -255,7 +255,7 @@ object StringTranslate {
     val dict = new HashMap[Character, Character]()
     var i = 0
     while (i < matching.length()) {
-      val rep = if (i < replace.length()) replace.charAt(i) else '\0'
+      val rep = if (i < replace.length()) replace.charAt(i) else '\u0000'
       if (null == dict.get(matching.charAt(i))) {
         dict.put(matching.charAt(i), rep)
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index e0667c629486d..1d2d007c2b4d2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -126,7 +126,7 @@ protected[sql] object AnyDataType extends AbstractDataType {
  */
 protected[sql] abstract class AtomicType extends DataType {
   private[sql] type InternalType
-  @transient private[sql] val tag: TypeTag[InternalType]
+  private[sql] val tag: TypeTag[InternalType]
   private[sql] val ordering: Ordering[InternalType]
 
   @transient private[sql] val classTag = ScalaReflectionLock.synchronized {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 879fd69863211..9a573db0c023a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -39,7 +39,7 @@ import org.apache.spark.util.SerializableConfiguration
 
 private[sql] abstract class BaseWriterContainer(
     @transient val relation: HadoopFsRelation,
-    @transient job: Job,
+    @transient private val job: Job,
     isAppend: Boolean)
   extends SparkHadoopMapReduceUtil
   with Logging
@@ -222,8 +222,8 @@ private[sql] abstract class BaseWriterContainer(
  * A writer that writes all of the rows in a partition to a single file.
  */
 private[sql] class DefaultWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
+    relation: HadoopFsRelation,
+    job: Job,
     isAppend: Boolean)
   extends BaseWriterContainer(relation, job, isAppend) {
 
@@ -286,8 +286,8 @@ private[sql] class DefaultWriterContainer(
  * writer externally sorts the remaining rows and then writes out them out one file at a time.
  */
 private[sql] class DynamicPartitionWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
+    relation: HadoopFsRelation,
+    job: Job,
     partitionColumns: Seq[Attribute],
     dataColumns: Seq[Attribute],
     inputSchema: Seq[Attribute],
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index b8da0840ae569..0a5569b0a4446 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -767,7 +767,7 @@ private[hive] case class InsertIntoHiveTable(
 private[hive] case class MetastoreRelation
     (databaseName: String, tableName: String, alias: Option[String])
     (val table: HiveTable)
-    (@transient sqlContext: SQLContext)
+    (@transient private val sqlContext: SQLContext)
   extends LeafNode with MultiInstanceRelation with FileRelation {
 
   override def equals(other: Any): Boolean = other match {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index dc355690852bd..e35468a624c3e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -54,10 +54,10 @@ private[hive] sealed trait TableReader {
  */
 private[hive]
 class HadoopTableReader(
-    @transient attributes: Seq[Attribute],
-    @transient relation: MetastoreRelation,
-    @transient sc: HiveContext,
-    @transient hiveExtraConf: HiveConf)
+    @transient private val attributes: Seq[Attribute],
+    @transient private val relation: MetastoreRelation,
+    @transient private val sc: HiveContext,
+    hiveExtraConf: HiveConf)
   extends TableReader with Logging {
 
   // Hadoop honors "mapred.map.tasks" as hint, but will ignore when mapred.job.tracker is "local".
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index c7651daffe36e..32bddbaeaeaf9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -53,7 +53,7 @@ case class ScriptTransformation(
     script: String,
     output: Seq[Attribute],
     child: SparkPlan,
-    ioschema: HiveScriptIOSchema)(@transient sc: HiveContext)
+    ioschema: HiveScriptIOSchema)(@transient private val sc: HiveContext)
   extends UnaryNode {
 
   override def otherCopyArgs: Seq[HiveContext] = sc :: Nil
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 8dc796b056a72..29a6f08f40728 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -45,7 +45,7 @@ import org.apache.spark.util.SerializableJobConf
  * It is based on [[SparkHadoopWriter]].
  */
 private[hive] class SparkHiveWriterContainer(
-    @transient jobConf: JobConf,
+    jobConf: JobConf,
     fileSinkConf: FileSinkDesc)
   extends Logging
   with SparkHadoopMapRedUtil
@@ -163,7 +163,7 @@ private[spark] object SparkHiveDynamicPartitionWriterContainer {
 }
 
 private[spark] class SparkHiveDynamicPartitionWriterContainer(
-    @transient jobConf: JobConf,
+    jobConf: JobConf,
     fileSinkConf: FileSinkDesc,
     dynamicPartColNames: Array[String])
   extends SparkHiveWriterContainer(jobConf, fileSinkConf) {
@@ -194,10 +194,10 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
     // Better solution is to add a step similar to what Hive FileSinkOperator.jobCloseOp does:
     // calling something like Utilities.mvFileToFinalPath to cleanup the output directory and then
     // load it with loadDynamicPartitions/loadPartition/loadTable.
-    val oldMarker = jobConf.getBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, true)
-    jobConf.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, false)
+    val oldMarker = conf.value.getBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, true)
+    conf.value.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, false)
     super.commitJob()
-    jobConf.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, oldMarker)
+    conf.value.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, oldMarker)
   }
 
   override def getLocalFileWriter(row: InternalRow, schema: StructType)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 27024ecfd9101..8a6050f5227bf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -32,7 +32,7 @@ import org.apache.spark.streaming.scheduler.JobGenerator
 
 
 private[streaming]
-class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
+class Checkpoint(ssc: StreamingContext, val checkpointTime: Time)
   extends Logging with Serializable {
   val master = ssc.sc.master
   val framework = ssc.sc.appName
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 2c373640d2fd9..dfc569451df86 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -170,7 +170,7 @@ private[python] object PythonDStream {
  */
 private[python] abstract class PythonDStream(
     parent: DStream[_],
-    @transient pfunc: PythonTransformFunction)
+    pfunc: PythonTransformFunction)
   extends DStream[Array[Byte]] (parent.ssc) {
 
   val func = new TransformFunction(pfunc)
@@ -187,7 +187,7 @@ private[python] abstract class PythonDStream(
  */
 private[python] class PythonTransformedDStream (
     parent: DStream[_],
-    @transient pfunc: PythonTransformFunction)
+    pfunc: PythonTransformFunction)
   extends PythonDStream(parent, pfunc) {
 
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
@@ -206,7 +206,7 @@ private[python] class PythonTransformedDStream (
 private[python] class PythonTransformed2DStream(
     parent: DStream[_],
     parent2: DStream[_],
-    @transient pfunc: PythonTransformFunction)
+    pfunc: PythonTransformFunction)
   extends DStream[Array[Byte]] (parent.ssc) {
 
   val func = new TransformFunction(pfunc)
@@ -230,7 +230,7 @@ private[python] class PythonTransformed2DStream(
  */
 private[python] class PythonStateDStream(
     parent: DStream[Array[Byte]],
-    @transient reduceFunc: PythonTransformFunction)
+    reduceFunc: PythonTransformFunction)
   extends PythonDStream(parent, reduceFunc) {
 
   super.persist(StorageLevel.MEMORY_ONLY)
@@ -252,8 +252,8 @@ private[python] class PythonStateDStream(
  */
 private[python] class PythonReducedWindowedDStream(
     parent: DStream[Array[Byte]],
-    @transient preduceFunc: PythonTransformFunction,
-    @transient pinvReduceFunc: PythonTransformFunction,
+    preduceFunc: PythonTransformFunction,
+    @transient private val pinvReduceFunc: PythonTransformFunction,
     _windowDuration: Duration,
     _slideDuration: Duration)
   extends PythonDStream(parent, preduceFunc) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
index c358f5b5bd70b..40208a64861fb 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
@@ -70,7 +70,7 @@ import org.apache.spark.util.{SerializableConfiguration, TimeStampedHashMap, Uti
  */
 private[streaming]
 class FileInputDStream[K, V, F <: NewInputFormat[K, V]](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     directory: String,
     filter: Path => Boolean = FileInputDStream.defaultFilter,
     newFilesOnly: Boolean = true,
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
index a6c4cd220e42f..95994c983c0cc 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
@@ -39,7 +39,7 @@ import org.apache.spark.util.Utils
  *
  * @param ssc_ Streaming context that will execute this input stream
  */
-abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext)
+abstract class InputDStream[T: ClassTag] (ssc_ : StreamingContext)
   extends DStream[T](ssc_) {
 
   private[streaming] var lastValidTime: Time = null
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala
index 186e1bf03a944..002aac9f43617 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala
@@ -23,7 +23,7 @@ import org.apache.spark.streaming.receiver.Receiver
 
 private[streaming]
 class PluggableInputDStream[T: ClassTag](
-  @transient ssc_ : StreamingContext,
+  ssc_ : StreamingContext,
   receiver: Receiver[T]) extends ReceiverInputDStream[T](ssc_) {
 
   def getReceiver(): Receiver[T] = {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
index bab78a3536b47..a2685046e03d4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
@@ -27,7 +27,7 @@ import org.apache.spark.streaming.{Time, StreamingContext}
 
 private[streaming]
 class QueueInputDStream[T: ClassTag](
-    @transient ssc: StreamingContext,
+    ssc: StreamingContext,
     val queue: Queue[RDD[T]],
     oneAtATime: Boolean,
     defaultRDD: RDD[T]
@@ -57,7 +57,7 @@ class QueueInputDStream[T: ClassTag](
       if (oneAtATime) {
         Some(buffer.head)
       } else {
-        Some(new UnionRDD(ssc.sc, buffer.toSeq))
+        Some(new UnionRDD(context.sc, buffer.toSeq))
       }
     } else if (defaultRDD != null) {
       Some(defaultRDD)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala
index e2925b9e03ec3..5a9eda7c12776 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala
@@ -39,7 +39,7 @@ import org.apache.spark.streaming.receiver.Receiver
  */
 private[streaming]
 class RawInputDStream[T: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     host: String,
     port: Int,
     storageLevel: StorageLevel
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index 6c139f32da31d..87c20afd5c13c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -38,7 +38,7 @@ import org.apache.spark.streaming.{StreamingContext, Time}
  * @param ssc_ Streaming context that will execute this input stream
  * @tparam T Class type of the object of this stream
  */
-abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingContext)
+abstract class ReceiverInputDStream[T: ClassTag](ssc_ : StreamingContext)
   extends InputDStream[T](ssc_) {
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
index 5ce5b7aae6e69..de84e0c9a498d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
@@ -32,7 +32,7 @@ import org.apache.spark.streaming.receiver.Receiver
 
 private[streaming]
 class SocketInputDStream[T: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     host: String,
     port: Int,
     bytesToObjects: InputStream => Iterator[T],
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
index e081ffe46f502..f811784b25c82 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
@@ -61,7 +61,7 @@ class WriteAheadLogBackedBlockRDDPartition(
  *
  *
  * @param sc SparkContext
- * @param blockIds Ids of the blocks that contains this RDD's data
+ * @param _blockIds Ids of the blocks that contains this RDD's data
  * @param walRecordHandles Record handles in write ahead logs that contain this RDD's data
  * @param isBlockIdValid Whether the block Ids are valid (i.e., the blocks are present in the Spark
  *                         executors). If not, then block lookups by the block ids will be skipped.
@@ -73,23 +73,23 @@ class WriteAheadLogBackedBlockRDDPartition(
  */
 private[streaming]
 class WriteAheadLogBackedBlockRDD[T: ClassTag](
-    @transient sc: SparkContext,
-    @transient blockIds: Array[BlockId],
+    sc: SparkContext,
+    @transient private val _blockIds: Array[BlockId],
     @transient val walRecordHandles: Array[WriteAheadLogRecordHandle],
-    @transient isBlockIdValid: Array[Boolean] = Array.empty,
+    @transient private val isBlockIdValid: Array[Boolean] = Array.empty,
     storeInBlockManager: Boolean = false,
     storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER)
-  extends BlockRDD[T](sc, blockIds) {
+  extends BlockRDD[T](sc, _blockIds) {
 
   require(
-    blockIds.length == walRecordHandles.length,
-    s"Number of block Ids (${blockIds.length}) must be " +
+    _blockIds.length == walRecordHandles.length,
+    s"Number of block Ids (${_blockIds.length}) must be " +
       s" same as number of WAL record handles (${walRecordHandles.length})")
 
   require(
-    isBlockIdValid.isEmpty || isBlockIdValid.length == blockIds.length,
+    isBlockIdValid.isEmpty || isBlockIdValid.length == _blockIds.length,
     s"Number of elements in isBlockIdValid (${isBlockIdValid.length}) must be " +
-      s" same as number of block Ids (${blockIds.length})")
+      s" same as number of block Ids (${_blockIds.length})")
 
   // Hadoop configuration is not serializable, so broadcast it as a serializable.
   @transient private val hadoopConfig = sc.hadoopConfiguration
@@ -99,9 +99,9 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag](
 
   override def getPartitions: Array[Partition] = {
     assertValid()
-    Array.tabulate(blockIds.length) { i =>
+    Array.tabulate(_blockIds.length) { i =>
       val isValid = if (isBlockIdValid.length == 0) true else isBlockIdValid(i)
-      new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), isValid, walRecordHandles(i))
+      new WriteAheadLogBackedBlockRDDPartition(i, _blockIds(i), isValid, walRecordHandles(i))
     }
   }
 

From 2ddeb63126d26149eda197e85b7b26ef16a6e97c Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Wed, 9 Sep 2015 09:29:10 -0700
Subject: [PATCH 1365/1454] [SPARK-10117] [MLLIB] Implement SQL data source API
 for reading LIBSVM data

It is convenient to implement data source API for LIBSVM format to have a better integration with DataFrames and ML pipeline API.

Two option is implemented.
* `numFeatures`: Specify the dimension of features vector
* `featuresType`: Specify the type of output vector. `sparse` is default.

Author: lewuathe <lewuathe@me.com>

Closes #8537 from Lewuathe/SPARK-10117 and squashes the following commits:

986999d [lewuathe] Change unit test phrase
11d513f [lewuathe] Fix some reviews
21600a4 [lewuathe] Merge branch 'master' into SPARK-10117
9ce63c7 [lewuathe] Rewrite service loader file
1fdd2df [lewuathe] Merge branch 'SPARK-10117' of github.com:Lewuathe/spark into SPARK-10117
ba3657c [lewuathe] Merge branch 'master' into SPARK-10117
0ea1c1c [lewuathe] LibSVMRelation is registered into META-INF
4f40891 [lewuathe] Improve test suites
5ab62ab [lewuathe] Merge branch 'master' into SPARK-10117
8660d0e [lewuathe] Fix Java unit test
b56a948 [lewuathe] Merge branch 'master' into SPARK-10117
2c12894 [lewuathe] Remove unnecessary tag
7d693c2 [lewuathe] Resolv conflict
62010af [lewuathe] Merge branch 'master' into SPARK-10117
a97ee97 [lewuathe] Fix some points
aef9564 [lewuathe] Fix
70ee4dd [lewuathe] Add Java test
3fd8dce [lewuathe] [SPARK-10117] Implement SQL data source API for reading LIBSVM data
40d3027 [lewuathe] Add Java test
7056d4a [lewuathe] Merge branch 'master' into SPARK-10117
99accaa [lewuathe] [SPARK-10117] Implement SQL data source API for reading LIBSVM data
---
 ...pache.spark.sql.sources.DataSourceRegister |  1 +
 .../ml/source/libsvm/LibSVMRelation.scala     | 99 +++++++++++++++++++
 .../ml/source/JavaLibSVMRelationSuite.java    | 80 +++++++++++++++
 .../spark/ml/source/LibSVMRelationSuite.scala | 76 ++++++++++++++
 4 files changed, 256 insertions(+)
 create mode 100644 mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala

diff --git a/mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..f632dd603c449
--- /dev/null
+++ b/mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1 @@
+org.apache.spark.ml.source.libsvm.DefaultSource
diff --git a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
new file mode 100644
index 0000000000000..b12cb62a4ef15
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
@@ -0,0 +1,99 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.source.libsvm
+
+import com.google.common.base.Objects
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Since
+import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types.{StructType, StructField, DoubleType}
+import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.sources._
+
+/**
+ * LibSVMRelation provides the DataFrame constructed from LibSVM format data.
+ * @param path File path of LibSVM format
+ * @param numFeatures The number of features
+ * @param vectorType The type of vector. It can be 'sparse' or 'dense'
+ * @param sqlContext The Spark SQLContext
+ */
+private[ml] class LibSVMRelation(val path: String, val numFeatures: Int, val vectorType: String)
+    (@transient val sqlContext: SQLContext)
+  extends BaseRelation with TableScan with Logging with Serializable {
+
+  override def schema: StructType = StructType(
+    StructField("label", DoubleType, nullable = false) ::
+      StructField("features", new VectorUDT(), nullable = false) :: Nil
+  )
+
+  override def buildScan(): RDD[Row] = {
+    val sc = sqlContext.sparkContext
+    val baseRdd = MLUtils.loadLibSVMFile(sc, path, numFeatures)
+
+    baseRdd.map { pt =>
+      val features = if (vectorType == "dense") pt.features.toDense else pt.features.toSparse
+      Row(pt.label, features)
+    }
+  }
+
+  override def hashCode(): Int = {
+    Objects.hashCode(path, schema)
+  }
+
+  override def equals(other: Any): Boolean = other match {
+    case that: LibSVMRelation => (this.path == that.path) && this.schema.equals(that.schema)
+    case _ => false
+  }
+
+}
+
+/**
+ * This is used for creating DataFrame from LibSVM format file.
+ * The LibSVM file path must be specified to DefaultSource.
+ */
+@Since("1.6.0")
+class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  @Since("1.6.0")
+  override def shortName(): String = "libsvm"
+
+  private def checkPath(parameters: Map[String, String]): String = {
+    require(parameters.contains("path"), "'path' must be specified")
+    parameters.get("path").get
+  }
+
+  /**
+   * Returns a new base relation with the given parameters.
+   * Note: the parameters' keywords are case insensitive and this insensitivity is enforced
+   * by the Map that is passed to the function.
+   */
+  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String])
+    : BaseRelation = {
+    val path = checkPath(parameters)
+    val numFeatures = parameters.getOrElse("numFeatures", "-1").toInt
+    /**
+     * featuresType can be selected "dense" or "sparse".
+     * This parameter decides the type of returned feature vector.
+     */
+    val vectorType = parameters.getOrElse("vectorType", "sparse")
+    new LibSVMRelation(path, numFeatures, vectorType)(sqlContext)
+  }
+}
diff --git a/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java b/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
new file mode 100644
index 0000000000000..11fa4eec0ccf0
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.source;
+
+import java.io.File;
+import java.io.IOException;
+
+import com.google.common.base.Charsets;
+import com.google.common.io.Files;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.DenseVector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.util.Utils;
+
+
+/**
+ * Test LibSVMRelation in Java.
+ */
+public class JavaLibSVMRelationSuite {
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+  private transient DataFrame dataset;
+
+  private File tmpDir;
+  private File path;
+
+  @Before
+  public void setUp() throws IOException {
+    jsc = new JavaSparkContext("local", "JavaLibSVMRelationSuite");
+    jsql = new SQLContext(jsc);
+
+    tmpDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
+    path = new File(tmpDir.getPath(), "part-00000");
+
+    String s = "1 1:1.0 3:2.0 5:3.0\n0\n0 2:4.0 4:5.0 6:6.0";
+    Files.write(s, path, Charsets.US_ASCII);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+    Utils.deleteRecursively(tmpDir);
+  }
+
+  @Test
+  public void verifyLibSVMDF() {
+    dataset = jsql.read().format("libsvm").option("vectorType", "dense").load(path.getPath());
+    Assert.assertEquals("label", dataset.columns()[0]);
+    Assert.assertEquals("features", dataset.columns()[1]);
+    Row r = dataset.first();
+    Assert.assertEquals(1.0, r.getDouble(0), 1e-15);
+    DenseVector v = r.getAs(1);
+    Assert.assertEquals(Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0), v);
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
new file mode 100644
index 0000000000000..8ed134128c8d2
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.source
+
+import java.io.File
+
+import com.google.common.base.Charsets
+import com.google.common.io.Files
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.{SparseVector, Vectors, DenseVector}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.util.Utils
+
+class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
+  var path: String = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    val lines =
+      """
+        |1 1:1.0 3:2.0 5:3.0
+        |0
+        |0 2:4.0 4:5.0 6:6.0
+      """.stripMargin
+    val tempDir = Utils.createTempDir()
+    val file = new File(tempDir.getPath, "part-00000")
+    Files.write(lines, file, Charsets.US_ASCII)
+    path = tempDir.toURI.toString
+  }
+
+  test("select as sparse vector") {
+    val df = sqlContext.read.format("libsvm").load(path)
+    assert(df.columns(0) == "label")
+    assert(df.columns(1) == "features")
+    val row1 = df.first()
+    assert(row1.getDouble(0) == 1.0)
+    val v = row1.getAs[SparseVector](1)
+    assert(v == Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
+  }
+
+  test("select as dense vector") {
+    val df = sqlContext.read.format("libsvm").options(Map("vectorType" -> "dense"))
+      .load(path)
+    assert(df.columns(0) == "label")
+    assert(df.columns(1) == "features")
+    assert(df.count() == 3)
+    val row1 = df.first()
+    assert(row1.getDouble(0) == 1.0)
+    val v = row1.getAs[DenseVector](1)
+    assert(v == Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0))
+  }
+
+  test("select a vector with specifying the longer dimension") {
+    val df = sqlContext.read.option("numFeatures", "100").format("libsvm")
+      .load(path)
+    val row1 = df.first()
+    val v = row1.getAs[SparseVector](1)
+    assert(v == Vectors.sparse(100, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
+  }
+}

From c0052d8d09eebadadb5ed35ac512caaf73919551 Mon Sep 17 00:00:00 2001
From: Jeff Zhang <zjffdu@apache.org>
Date: Wed, 9 Sep 2015 10:26:53 -0700
Subject: [PATCH 1366/1454] =?UTF-8?q?[SPARK-10481]=20[YARN]=20SPARK=5FPREP?=
 =?UTF-8?q?END=5FCLASSES=20make=20spark-yarn=20related=20jar=20could=20n?=
 =?UTF-8?q?=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Throw a more readable exception. Please help review. Thanks

Author: Jeff Zhang <zjffdu@apache.org>

Closes #8649 from zjffdu/SPARK-10481.
---
 .../src/main/scala/org/apache/spark/deploy/yarn/Client.scala | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index e9a02baafd28e..a2c4bc2f5480b 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -1045,7 +1045,10 @@ object Client extends Logging {
           s"in favor of the $CONF_SPARK_JAR configuration variable.")
       System.getenv(ENV_SPARK_JAR)
     } else {
-      SparkContext.jarOfClass(this.getClass).head
+      SparkContext.jarOfClass(this.getClass).getOrElse(throw new SparkException("Could not "
+        + "find jar containing Spark classes. The jar can be defined using the "
+        + "spark.yarn.jar configuration option. If testing Spark, either set that option or "
+        + "make sure SPARK_PREPEND_CLASSES is not set."))
     }
   }
 

From 71da1633c4dcc4e748fbe3b3236af90032b695ae Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 9 Sep 2015 10:57:29 -0700
Subject: [PATCH 1367/1454] [SPARK-10461] [SQL] make sure `input.primitive` is
 always variable name not code at `GenerateUnsafeProjection`

When we generate unsafe code inside `createCodeForXXX`, we always assign the `input.primitive` to a temp variable in case `input.primitive` is expression code.

This PR did some refactor to make sure `input.primitive` is always variable name, and some other typo and style fixes.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8613 from cloud-fan/minor.
---
 .../expressions/codegen/CodeGenerator.scala   | 10 +--
 .../codegen/GenerateMutableProjection.scala   |  2 -
 .../codegen/GenerateProjection.scala          | 12 +--
 .../codegen/GenerateUnsafeProjection.scala    | 85 ++++++++++---------
 .../expressions/complexTypeCreator.scala      | 33 ++++---
 5 files changed, 75 insertions(+), 67 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index bf96248feaef7..da3103b4ebb6b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -84,11 +84,11 @@ class CodeGenContext {
   /**
    * Holding all the functions those will be added into generated class.
    */
-  val addedFuntions: mutable.Map[String, String] =
+  val addedFunctions: mutable.Map[String, String] =
     mutable.Map.empty[String, String]
 
   def addNewFunction(funcName: String, funcCode: String): Unit = {
-    addedFuntions += ((funcName, funcCode))
+    addedFunctions += ((funcName, funcCode))
   }
 
   final val JAVA_BOOLEAN = "boolean"
@@ -298,8 +298,8 @@ class CodeGenContext {
            |  $body
            |}
          """.stripMargin
-         addNewFunction(name, code)
-         name
+        addNewFunction(name, code)
+        name
       }
 
       functions.map(name => s"$name($row);").mkString("\n")
@@ -337,7 +337,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   }
 
   protected def declareAddedFunctions(ctx: CodeGenContext): String = {
-    ctx.addedFuntions.map { case (funcName, funcCode) => funcCode }.mkString("\n")
+    ctx.addedFunctions.map { case (funcName, funcCode) => funcCode }.mkString("\n")
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index b4d4df8934bd4..793023b9fbed3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
 import org.apache.spark.sql.types.DecimalType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c744e84d822e8..2164ddf03d1b2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -48,7 +48,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
     val columns = expressions.zipWithIndex.map {
       case (e, i) =>
         s"private ${ctx.javaType(e.dataType)} c$i = ${ctx.defaultValue(e.dataType)};\n"
-    }.mkString("\n      ")
+    }.mkString("\n")
 
     val initColumns = expressions.zipWithIndex.map {
       case (e, i) =>
@@ -67,18 +67,18 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
 
     val getCases = (0 until expressions.size).map { i =>
       s"case $i: return c$i;"
-    }.mkString("\n        ")
+    }.mkString("\n")
 
     val updateCases = expressions.zipWithIndex.map { case (e, i) =>
       s"case $i: { c$i = (${ctx.boxedType(e.dataType)})value; return;}"
-    }.mkString("\n        ")
+    }.mkString("\n")
 
     val specificAccessorFunctions = ctx.primitiveTypes.map { jt =>
       val cases = expressions.zipWithIndex.flatMap {
         case (e, i) if ctx.javaType(e.dataType) == jt =>
           Some(s"case $i: return c$i;")
         case _ => None
-      }.mkString("\n        ")
+      }.mkString("\n")
       if (cases.length > 0) {
         val getter = "get" + ctx.primitiveTypeName(jt)
         s"""
@@ -103,7 +103,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         case (e, i) if ctx.javaType(e.dataType) == jt =>
           Some(s"case $i: { c$i = value; return; }")
         case _ => None
-      }.mkString("\n        ")
+      }.mkString("\n")
       if (cases.length > 0) {
         val setter = "set" + ctx.primitiveTypeName(jt)
         s"""
@@ -152,7 +152,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
 
     val copyColumns = expressions.zipWithIndex.map { case (e, i) =>
         s"""if (!nullBits[$i]) arr[$i] = c$i;"""
-    }.mkString("\n      ")
+    }.mkString("\n")
 
     val code = s"""
     public SpecificProjection generate($exprType[] expr) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index b570fe86db1aa..03c5f449bf9ac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -134,7 +134,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     ctx.addMutableState("byte[]", buffer, s"this.$buffer = new byte[$fixedSize];")
     val cursor = ctx.freshName("cursor")
     ctx.addMutableState("int", cursor, s"this.$cursor = 0;")
-    val tmp = ctx.freshName("tmpBuffer")
+    val tmpBuffer = ctx.freshName("tmpBuffer")
 
     val convertedFields = inputTypes.zip(inputs).zipWithIndex.map { case ((dt, input), i) =>
       val ev = createConvertCode(ctx, input, dt)
@@ -144,10 +144,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           int $numBytes = $cursor + (${genAdditionalSize(dt, ev)});
           if ($buffer.length < $numBytes) {
             // This will not happen frequently, because the buffer is re-used.
-            byte[] $tmp = new byte[$numBytes * 2];
+            byte[] $tmpBuffer = new byte[$numBytes * 2];
             Platform.copyMemory($buffer, Platform.BYTE_ARRAY_OFFSET,
-              $tmp, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
-            $buffer = $tmp;
+              $tmpBuffer, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
+            $buffer = $tmpBuffer;
           }
           $output.pointTo($buffer, Platform.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $numBytes);
          """
@@ -207,20 +207,22 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     val buffer = ctx.freshName("buffer")
     ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
     val outputIsNull = ctx.freshName("isNull")
-    val tmp = ctx.freshName("tmp")
     val numElements = ctx.freshName("numElements")
     val fixedSize = ctx.freshName("fixedSize")
     val numBytes = ctx.freshName("numBytes")
     val elements = ctx.freshName("elements")
     val cursor = ctx.freshName("cursor")
     val index = ctx.freshName("index")
+    val elementName = ctx.freshName("elementName")
 
-    val element = GeneratedExpressionCode(
-      code = "",
-      isNull = s"$tmp.isNullAt($index)",
-      primitive = s"${ctx.getValue(tmp, elementType, index)}"
-    )
-    val convertedElement: GeneratedExpressionCode = createConvertCode(ctx, element, elementType)
+    val element = {
+      val code = s"${ctx.javaType(elementType)} $elementName = " +
+        s"${ctx.getValue(input.primitive, elementType, index)};"
+      val isNull = s"${input.primitive}.isNullAt($index)"
+      GeneratedExpressionCode(code, isNull, elementName)
+    }
+
+    val convertedElement = createConvertCode(ctx, element, elementType)
 
     // go through the input array to calculate how many bytes we need.
     val calculateNumBytes = elementType match {
@@ -272,6 +274,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
+          ${convertedElement.code}
           Platform.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -280,6 +283,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
+          ${convertedElement.code}
           Platform.putLong(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -307,11 +311,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       ${input.code}
       final boolean $outputIsNull = ${input.isNull};
       if (!$outputIsNull) {
-        final ArrayData $tmp = ${input.primitive};
-        if ($tmp instanceof UnsafeArrayData) {
-          $output = (UnsafeArrayData) $tmp;
+        if (${input.primitive} instanceof UnsafeArrayData) {
+          $output = (UnsafeArrayData) ${input.primitive};
         } else {
-          final int $numElements = $tmp.numElements();
+          final int $numElements = ${input.primitive}.numElements();
           final int $fixedSize = 4 * $numElements;
           int $numBytes = $fixedSize;
 
@@ -350,29 +353,31 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       valueType: DataType): GeneratedExpressionCode = {
     val output = ctx.freshName("convertedMap")
     val outputIsNull = ctx.freshName("isNull")
-    val tmp = ctx.freshName("tmp")
-
-    val keyArray = GeneratedExpressionCode(
-      code = "",
-      isNull = "false",
-      primitive = s"$tmp.keyArray()"
-    )
-    val valueArray = GeneratedExpressionCode(
-      code = "",
-      isNull = "false",
-      primitive = s"$tmp.valueArray()"
-    )
-    val convertedKeys: GeneratedExpressionCode = createCodeForArray(ctx, keyArray, keyType)
-    val convertedValues: GeneratedExpressionCode = createCodeForArray(ctx, valueArray, valueType)
+    val keyArrayName = ctx.freshName("keyArrayName")
+    val valueArrayName = ctx.freshName("valueArrayName")
+
+    val keyArray = {
+      val code = s"ArrayData $keyArrayName = ${input.primitive}.keyArray();"
+      val isNull = "false"
+      GeneratedExpressionCode(code, isNull, keyArrayName)
+    }
+
+    val valueArray = {
+      val code = s"ArrayData $valueArrayName = ${input.primitive}.valueArray();"
+      val isNull = "false"
+      GeneratedExpressionCode(code, isNull, valueArrayName)
+    }
+
+    val convertedKeys = createCodeForArray(ctx, keyArray, keyType)
+    val convertedValues = createCodeForArray(ctx, valueArray, valueType)
 
     val code = s"""
       ${input.code}
       final boolean $outputIsNull = ${input.isNull};
       UnsafeMapData $output = null;
       if (!$outputIsNull) {
-        final MapData $tmp = ${input.primitive};
-        if ($tmp instanceof UnsafeMapData) {
-          $output = (UnsafeMapData) $tmp;
+        if (${input.primitive} instanceof UnsafeMapData) {
+          $output = (UnsafeMapData) ${input.primitive};
         } else {
           ${convertedKeys.code}
           ${convertedValues.code}
@@ -393,22 +398,22 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     case t: StructType =>
       val output = ctx.freshName("convertedStruct")
       val outputIsNull = ctx.freshName("isNull")
-      val tmp = ctx.freshName("tmp")
       val fieldTypes = t.fields.map(_.dataType)
       val fieldEvals = fieldTypes.zipWithIndex.map { case (dt, i) =>
-        val getFieldCode = ctx.getValue(tmp, dt, i.toString)
-        val fieldIsNull = s"$tmp.isNullAt($i)"
-        GeneratedExpressionCode("", fieldIsNull, getFieldCode)
+        val fieldName = ctx.freshName("fieldName")
+        val code = s"${ctx.javaType(dt)} $fieldName = " +
+          s"${ctx.getValue(input.primitive, dt, i.toString)};"
+        val isNull = s"${input.primitive}.isNullAt($i)"
+        GeneratedExpressionCode(code, isNull, fieldName)
       }
-      val converter = createCodeForStruct(ctx, tmp, fieldEvals, fieldTypes)
+      val converter = createCodeForStruct(ctx, input.primitive, fieldEvals, fieldTypes)
       val code = s"""
         ${input.code}
          UnsafeRow $output = null;
          final boolean $outputIsNull = ${input.isNull};
          if (!$outputIsNull) {
-           final InternalRow $tmp = ${input.primitive};
-           if ($tmp instanceof UnsafeRow) {
-             $output = (UnsafeRow) $tmp;
+           if (${input.primitive} instanceof UnsafeRow) {
+             $output = (UnsafeRow) ${input.primitive};
            } else {
              ${converter.code}
              $output = ${converter.primitive};
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 1c546719730b7..82eab5fb3d03a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -48,21 +48,22 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val arrayClass = classOf[GenericArrayData].getName
+    val values = ctx.freshName("values")
     s"""
       final boolean ${ev.isNull} = false;
-      final Object[] values = new Object[${children.size}];
+      final Object[] $values = new Object[${children.size}];
     """ +
       children.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
           if (${eval.isNull}) {
-            values[$i] = null;
+            $values[$i] = null;
           } else {
-            values[$i] = ${eval.primitive};
+            $values[$i] = ${eval.primitive};
           }
          """
       }.mkString("\n") +
-      s"final ${ctx.javaType(dataType)} ${ev.primitive} = new $arrayClass(values);"
+      s"final ArrayData ${ev.primitive} = new $arrayClass($values);"
   }
 
   override def prettyName: String = "array"
@@ -94,21 +95,23 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val rowClass = classOf[GenericMutableRow].getName
+    val rowClass = classOf[GenericInternalRow].getName
+    val values = ctx.freshName("values")
     s"""
       boolean ${ev.isNull} = false;
-      final $rowClass ${ev.primitive} = new $rowClass(${children.size});
+      final Object[] $values = new Object[${children.size}];
     """ +
       children.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
           if (${eval.isNull}) {
-            ${ev.primitive}.update($i, null);
+            $values[$i] = null;
           } else {
-            ${ev.primitive}.update($i, ${eval.primitive});
+            $values[$i] = ${eval.primitive};
           }
          """
-      }.mkString("\n")
+      }.mkString("\n") +
+      s"final InternalRow ${ev.primitive} = new $rowClass($values);"
   }
 
   override def prettyName: String = "struct"
@@ -161,21 +164,23 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val rowClass = classOf[GenericMutableRow].getName
+    val rowClass = classOf[GenericInternalRow].getName
+    val values = ctx.freshName("values")
     s"""
       boolean ${ev.isNull} = false;
-      final $rowClass ${ev.primitive} = new $rowClass(${valExprs.size});
+      final Object[] $values = new Object[${valExprs.size}];
     """ +
       valExprs.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
           if (${eval.isNull}) {
-            ${ev.primitive}.update($i, null);
+            $values[$i] = null;
           } else {
-            ${ev.primitive}.update($i, ${eval.primitive});
+            $values[$i] = ${eval.primitive};
           }
          """
-      }.mkString("\n")
+      }.mkString("\n") +
+      s"final InternalRow ${ev.primitive} = new $rowClass($values);"
   }
 
   override def prettyName: String = "named_struct"

From 45de518742446ddfbd4816c9d0f8501139f9bc2d Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 9 Sep 2015 16:02:27 -0700
Subject: [PATCH 1368/1454] [SPARK-9730] [SQL] Add Full Outer Join support for
 SortMergeJoin

This PR is based on #8383 , thanks to viirya

JIRA: https://issues.apache.org/jira/browse/SPARK-9730

This patch adds the Full Outer Join support for SortMergeJoin. A new class SortMergeFullJoinScanner is added to scan rows from left and right iterators. FullOuterIterator is simply a wrapper of type RowIterator to consume joined rows from SortMergeFullJoinScanner.

Closes #8383

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Davies Liu <davies@databricks.com>

Closes #8579 from davies/smj_fullouter.
---
 .../apache/spark/util/collection/BitSet.scala |  11 +
 .../spark/sql/execution/SparkStrategies.scala |   9 +-
 .../execution/joins/SortMergeOuterJoin.scala  | 227 +++++++++++++++++-
 .../org/apache/spark/sql/JoinSuite.scala      |   2 +-
 .../sql/execution/joins/OuterJoinSuite.scala  |  44 ++--
 5 files changed, 259 insertions(+), 34 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala
index 9c15b1188d91c..7ab67fc3a2de9 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala
@@ -32,6 +32,17 @@ class BitSet(numBits: Int) extends Serializable {
    */
   def capacity: Int = numWords * 64
 
+  /**
+   * Clear all set bits.
+   */
+  def clear(): Unit = {
+    var i = 0
+    while (i < numWords) {
+      words(i) = 0L
+      i += 1
+    }
+  }
+
   /**
    * Set all the bits up to a given index
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 2170bc73a0fd6..4572d5efc92bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -132,15 +132,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
 
-      case ExtractEquiJoinKeys(LeftOuter, leftKeys, rightKeys, condition, left, right)
+      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
         if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
         joins.SortMergeOuterJoin(
-          leftKeys, rightKeys, LeftOuter, condition, planLater(left), planLater(right)) :: Nil
-
-      case ExtractEquiJoinKeys(RightOuter, leftKeys, rightKeys, condition, left, right)
-        if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
-        joins.SortMergeOuterJoin(
-          leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
+          leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil
 
       case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right) =>
         joins.ShuffledHashOuterJoin(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
index dea9e5e580a1e..ab20ee573ab5d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -17,20 +17,21 @@
 
 package org.apache.spark.sql.execution.joins
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
+import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.util.collection.BitSet
 
 /**
  * :: DeveloperApi ::
  * Performs an sort merge outer join of two child relations.
- *
- * Note: this does not support full outer join yet; see SPARK-9730 for progress on this.
  */
 @DeveloperApi
 case class SortMergeOuterJoin(
@@ -52,6 +53,8 @@ case class SortMergeOuterJoin(
         left.output ++ right.output.map(_.withNullability(true))
       case RightOuter =>
         left.output.map(_.withNullability(true)) ++ right.output
+      case FullOuter =>
+        (left.output ++ right.output).map(_.withNullability(true))
       case x =>
         throw new IllegalArgumentException(
           s"${getClass.getSimpleName} should not take $x as the JoinType")
@@ -62,6 +65,7 @@ case class SortMergeOuterJoin(
     // For left and right outer joins, the output is partitioned by the streamed input's join keys.
     case LeftOuter => left.outputPartitioning
     case RightOuter => right.outputPartitioning
+    case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
     case x =>
       throw new IllegalArgumentException(
         s"${getClass.getSimpleName} should not take $x as the JoinType")
@@ -71,6 +75,8 @@ case class SortMergeOuterJoin(
     // For left and right outer joins, the output is ordered by the streamed input's join keys.
     case LeftOuter => requiredOrders(leftKeys)
     case RightOuter => requiredOrders(rightKeys)
+    // there are null rows in both streams, so there is no order
+    case FullOuter => Nil
     case x => throw new IllegalArgumentException(
       s"SortMergeOuterJoin should not take $x as the JoinType")
   }
@@ -165,6 +171,26 @@ case class SortMergeOuterJoin(
           new RightOuterIterator(
             smjScanner, leftNullRow, boundCondition, resultProj, numOutputRows).toScala
 
+        case FullOuter =>
+          val leftNullRow = new GenericInternalRow(left.output.length)
+          val rightNullRow = new GenericInternalRow(right.output.length)
+          val smjScanner = new SortMergeFullOuterJoinScanner(
+            leftKeyGenerator = createLeftKeyGenerator(),
+            rightKeyGenerator = createRightKeyGenerator(),
+            keyOrdering,
+            leftIter = RowIterator.fromScala(leftIter),
+            numLeftRows,
+            rightIter = RowIterator.fromScala(rightIter),
+            numRightRows,
+            boundCondition,
+            leftNullRow,
+            rightNullRow)
+
+          new FullOuterIterator(
+            smjScanner,
+            resultProj,
+            numOutputRows).toScala
+
         case x =>
           throw new IllegalArgumentException(
             s"SortMergeOuterJoin should not take $x as the JoinType")
@@ -271,3 +297,196 @@ private class RightOuterIterator(
 
   override def getRow: InternalRow = resultProj(joinedRow)
 }
+
+private class SortMergeFullOuterJoinScanner(
+    leftKeyGenerator: Projection,
+    rightKeyGenerator: Projection,
+    keyOrdering: Ordering[InternalRow],
+    leftIter: RowIterator,
+    numLeftRows: LongSQLMetric,
+    rightIter: RowIterator,
+    numRightRows: LongSQLMetric,
+    boundCondition: InternalRow => Boolean,
+    leftNullRow: InternalRow,
+    rightNullRow: InternalRow)  {
+  private[this] val joinedRow: JoinedRow = new JoinedRow()
+  private[this] var leftRow: InternalRow = _
+  private[this] var leftRowKey: InternalRow = _
+  private[this] var rightRow: InternalRow = _
+  private[this] var rightRowKey: InternalRow = _
+
+  private[this] var leftIndex: Int = 0
+  private[this] var rightIndex: Int = 0
+  private[this] val leftMatches: ArrayBuffer[InternalRow] = new ArrayBuffer[InternalRow]
+  private[this] val rightMatches: ArrayBuffer[InternalRow] = new ArrayBuffer[InternalRow]
+  private[this] var leftMatched: BitSet = new BitSet(1)
+  private[this] var rightMatched: BitSet = new BitSet(1)
+
+  advancedLeft()
+  advancedRight()
+
+  // --- Private methods --------------------------------------------------------------------------
+
+  /**
+   * Advance the left iterator and compute the new row's join key.
+   * @return true if the left iterator returned a row and false otherwise.
+   */
+  private def advancedLeft(): Boolean = {
+    if (leftIter.advanceNext()) {
+      leftRow = leftIter.getRow
+      leftRowKey = leftKeyGenerator(leftRow)
+      numLeftRows += 1
+      true
+    } else {
+      leftRow = null
+      leftRowKey = null
+      false
+    }
+  }
+
+  /**
+   * Advance the right iterator and compute the new row's join key.
+   * @return true if the right iterator returned a row and false otherwise.
+   */
+  private def advancedRight(): Boolean = {
+    if (rightIter.advanceNext()) {
+      rightRow = rightIter.getRow
+      rightRowKey = rightKeyGenerator(rightRow)
+      numRightRows += 1
+      true
+    } else {
+      rightRow = null
+      rightRowKey = null
+      false
+    }
+  }
+
+  /**
+   * Populate the left and right buffers with rows matching the provided key.
+   * This consumes rows from both iterators until their keys are different from the matching key.
+   */
+  private def findMatchingRows(matchingKey: InternalRow): Unit = {
+    leftMatches.clear()
+    rightMatches.clear()
+    leftIndex = 0
+    rightIndex = 0
+
+    while (leftRowKey != null && keyOrdering.compare(leftRowKey, matchingKey) == 0) {
+      leftMatches += leftRow.copy()
+      advancedLeft()
+    }
+    while (rightRowKey != null && keyOrdering.compare(rightRowKey, matchingKey) == 0) {
+      rightMatches += rightRow.copy()
+      advancedRight()
+    }
+
+    if (leftMatches.size <= leftMatched.capacity) {
+      leftMatched.clear()
+    } else {
+      leftMatched = new BitSet(leftMatches.size)
+    }
+    if (rightMatches.size <= rightMatched.capacity) {
+      rightMatched.clear()
+    } else {
+      rightMatched = new BitSet(rightMatches.size)
+    }
+  }
+
+  /**
+   * Scan the left and right buffers for the next valid match.
+   *
+   * Note: this method mutates `joinedRow` to point to the latest matching rows in the buffers.
+   * If a left row has no valid matches on the right, or a right row has no valid matches on the
+   * left, then the row is joined with the null row and the result is considered a valid match.
+   *
+   * @return true if a valid match is found, false otherwise.
+   */
+  private def scanNextInBuffered(): Boolean = {
+    while (leftIndex < leftMatches.size) {
+      while (rightIndex < rightMatches.size) {
+        joinedRow(leftMatches(leftIndex), rightMatches(rightIndex))
+        if (boundCondition(joinedRow)) {
+          leftMatched.set(leftIndex)
+          rightMatched.set(rightIndex)
+          rightIndex += 1
+          return true
+        }
+        rightIndex += 1
+      }
+      rightIndex = 0
+      if (!leftMatched.get(leftIndex)) {
+        // the left row has never matched any right row, join it with null row
+        joinedRow(leftMatches(leftIndex), rightNullRow)
+        leftIndex += 1
+        return true
+      }
+      leftIndex += 1
+    }
+
+    while (rightIndex < rightMatches.size) {
+      if (!rightMatched.get(rightIndex)) {
+        // the right row has never matched any left row, join it with null row
+        joinedRow(leftNullRow, rightMatches(rightIndex))
+        rightIndex += 1
+        return true
+      }
+      rightIndex += 1
+    }
+
+    // There are no more valid matches in the left and right buffers
+    false
+  }
+
+  // --- Public methods --------------------------------------------------------------------------
+
+  def getJoinedRow(): JoinedRow = joinedRow
+
+  def advanceNext(): Boolean = {
+    // If we already buffered some matching rows, use them directly
+    if (leftIndex <= leftMatches.size || rightIndex <= rightMatches.size) {
+      if (scanNextInBuffered()) {
+        return true
+      }
+    }
+
+    if (leftRow != null && (leftRowKey.anyNull || rightRow == null)) {
+      joinedRow(leftRow.copy(), rightNullRow)
+      advancedLeft()
+      true
+    } else if (rightRow != null && (rightRowKey.anyNull || leftRow == null)) {
+      joinedRow(leftNullRow, rightRow.copy())
+      advancedRight()
+      true
+    } else if (leftRow != null && rightRow != null) {
+      // Both rows are present and neither have null values,
+      // so we populate the buffers with rows matching the next key
+      val comp = keyOrdering.compare(leftRowKey, rightRowKey)
+      if (comp <= 0) {
+        findMatchingRows(leftRowKey.copy())
+      } else {
+        findMatchingRows(rightRowKey.copy())
+      }
+      scanNextInBuffered()
+      true
+    } else {
+      // Both iterators have been consumed
+      false
+    }
+  }
+}
+
+private class FullOuterIterator(
+    smjScanner: SortMergeFullOuterJoinScanner,
+    resultProj: InternalRow => InternalRow,
+    numRows: LongSQLMetric
+  ) extends RowIterator {
+  private[this] val joinedRow: JoinedRow = smjScanner.getJoinedRow()
+
+  override def advanceNext(): Boolean = {
+    val r = smjScanner.advanceNext()
+    if (r) numRows += 1
+    r
+  }
+
+  override def getRow: InternalRow = resultProj(joinedRow)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index b05435bad5c5a..7a027e13089e3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -83,7 +83,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
       ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
         classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData full outer join testData2 ON key = a",
-        classOf[ShuffledHashOuterJoin]),
+        classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData left JOIN testData2 ON (key * a != key + a)",
         classOf[BroadcastNestedLoopJoin]),
       ("SELECT * FROM testData right JOIN testData2 ON (key * a != key + a)",
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index c2e0bdac17968..09e0237a7cc50 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -76,37 +76,37 @@ class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
 
     test(s"$testName using ShuffledHashOuterJoin") {
       extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              EnsureRequirements(sqlContext).apply(
-                ShuffledHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            EnsureRequirements(sqlContext).apply(
+              ShuffledHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
       }
     }
 
     if (joinType != FullOuter) {
       test(s"$testName using BroadcastHashOuterJoin") {
         extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
-            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-                BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
-                expectedAnswer.map(Row.fromTuple),
-                sortAnswers = true)
-            }
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
         }
       }
+    }
 
-      test(s"$testName using SortMergeOuterJoin") {
-        extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
-            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-                EnsureRequirements(sqlContext).apply(
-                  SortMergeOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
-                expectedAnswer.map(Row.fromTuple),
-                sortAnswers = false)
-            }
+    test(s"$testName using SortMergeOuterJoin") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            EnsureRequirements(sqlContext).apply(
+              SortMergeOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
         }
       }
     }

From 56a0fe5c6e4ae2929c48fae2d6225558d020e5f9 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 9 Sep 2015 18:02:33 -0700
Subject: [PATCH 1369/1454] [SPARK-9772] [PYSPARK] [ML] Add Python API for
 ml.feature.VectorSlicer

Add Python API for ml.feature.VectorSlicer.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8102 from yanboliang/SPARK-9772.
---
 python/pyspark/ml/feature.py | 95 ++++++++++++++++++++++++++++++++++--
 1 file changed, 90 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 8c26cfbd5a47d..1c423486be8d9 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -27,11 +27,11 @@
 from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
-           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion',
-           'RegexTokenizer', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel',
-           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
-           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel',
-           'StopWordsRemover']
+           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel',
+           'PolynomialExpansion', 'RegexTokenizer', 'RFormula', 'RFormulaModel', 'SQLTransformer',
+           'StandardScaler', 'StandardScalerModel', 'StopWordsRemover', 'StringIndexer',
+           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'VectorSlicer',
+           'Word2Vec', 'Word2VecModel']
 
 
 @inherit_doc
@@ -1298,6 +1298,91 @@ class VectorIndexerModel(JavaModel):
     """
 
 
+@inherit_doc
+class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    This class takes a feature vector and outputs a new feature vector with a subarray
+    of the original features.
+
+    The subset of features can be specified with either indices (`setIndices()`)
+    or names (`setNames()`).  At least one feature must be selected. Duplicate features
+    are not allowed, so there can be no overlap between selected indices and names.
+
+    The output vector will order features with the selected indices first (in the order given),
+    followed by the selected names (in the order given).
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([
+    ...     (Vectors.dense([-2.0, 2.3, 0.0, 0.0, 1.0]),),
+    ...     (Vectors.dense([0.0, 0.0, 0.0, 0.0, 0.0]),),
+    ...     (Vectors.dense([0.6, -1.1, -3.0, 4.5, 3.3]),)], ["features"])
+    >>> vs = VectorSlicer(inputCol="features", outputCol="sliced", indices=[1, 4])
+    >>> vs.transform(df).head().sliced
+    DenseVector([2.3, 1.0])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    indices = Param(Params._dummy(), "indices", "An array of indices to select features from " +
+                    "a vector column. There can be no overlap with names.")
+    names = Param(Params._dummy(), "names", "An array of feature names to select features from " +
+                  "a vector column. These names must be specified by ML " +
+                  "org.apache.spark.ml.attribute.Attribute. There can be no overlap with " +
+                  "indices.")
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None, indices=None, names=None):
+        """
+        __init__(self, inputCol=None, outputCol=None, indices=None, names=None)
+        """
+        super(VectorSlicer, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorSlicer", self.uid)
+        self.indices = Param(self, "indices", "An array of indices to select features from " +
+                             "a vector column. There can be no overlap with names.")
+        self.names = Param(self, "names", "An array of feature names to select features from " +
+                           "a vector column. These names must be specified by ML " +
+                           "org.apache.spark.ml.attribute.Attribute. There can be no overlap " +
+                           "with indices.")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None, indices=None, names=None):
+        """
+        setParams(self, inputCol=None, outputCol=None, indices=None, names=None):
+        Sets params for this VectorSlicer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setIndices(self, value):
+        """
+        Sets the value of :py:attr:`indices`.
+        """
+        self._paramMap[self.indices] = value
+        return self
+
+    def getIndices(self):
+        """
+        Gets the value of indices or its default value.
+        """
+        return self.getOrDefault(self.indices)
+
+    def setNames(self, value):
+        """
+        Sets the value of :py:attr:`names`.
+        """
+        self._paramMap[self.names] = value
+        return self
+
+    def getNames(self):
+        """
+        Gets the value of names or its default value.
+        """
+        return self.getOrDefault(self.names)
+
+
 @inherit_doc
 @ignore_unicode_prefix
 class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol):

From 1dc7548c598c4eb4ecc7d5bb8962a735bbd2c0f7 Mon Sep 17 00:00:00 2001
From: Sean Paradiso <seanparadiso@gmail.com>
Date: Wed, 9 Sep 2015 22:09:33 -0700
Subject: [PATCH 1370/1454] [MINOR] [MLLIB] [ML] [DOC] fixed typo: label for
 negative result should be 0.0 (original: 1.0)

Small typo in the example for `LabelledPoint` in the MLLib docs.

Author: Sean Paradiso <seanparadiso@gmail.com>

Closes #8680 from sparadiso/docs_mllib_smalltypo.
---
 docs/mllib-data-types.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index 065bf4727624f..d8c7bdc63c70e 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -144,7 +144,7 @@ import org.apache.spark.mllib.regression.LabeledPoint;
 LabeledPoint pos = new LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0));
 
 // Create a labeled point with a negative label and a sparse feature vector.
-LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new double[] {1.0, 3.0}));
+LabeledPoint neg = new LabeledPoint(0.0, Vectors.sparse(3, new int[] {0, 2}, new double[] {1.0, 3.0}));
 {% endhighlight %}
 </div>
 

From 48817cc111a9705f40b7c842315eee24291c2198 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 10 Sep 2015 16:42:12 +0200
Subject: [PATCH 1371/1454] [SPARK-10497] [BUILD] [TRIVIAL] Handle both
 locations for JIRAError with python-jira

Location of JIRAError has moved between old and new versions of python-jira package.
Longer term it probably makes sense to pin to specific versions (as mentioned in https://issues.apache.org/jira/browse/SPARK-10498 ) but for now, making release tools works with both new and old versions of python-jira.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8661 from holdenk/SPARK-10497-release-utils-does-not-work-with-new-jira-python.
---
 dev/create-release/releaseutils.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/dev/create-release/releaseutils.py b/dev/create-release/releaseutils.py
index 51ab25a6a5bd8..7f152b7f53559 100755
--- a/dev/create-release/releaseutils.py
+++ b/dev/create-release/releaseutils.py
@@ -24,7 +24,11 @@
 
 try:
     from jira.client import JIRA
-    from jira.exceptions import JIRAError
+    # Old versions have JIRAError in exceptions package, new (0.5+) in utils.
+    try:
+        from jira.exceptions import JIRAError
+    except ImportError:
+        from jira.utils import JIRAError
 except ImportError:
     print "This tool requires the jira-python library"
     print "Install using 'sudo pip install jira'"

From 4f1daa1ef6b36440962f3c8faea3928599e33784 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 10 Sep 2015 10:04:38 -0700
Subject: [PATCH 1372/1454] [SPARK-10065] [SQL] avoid the extra copy when
 generate unsafe array

The reason for this extra copy is that we iterate the array twice: calculate elements data size and copy elements to array buffer.

A simple solution is to follow `createCodeForStruct`, we can dynamically grow the buffer when needed and thus don't need to know the data size ahead.

This PR also include some typo and style fixes, and did some minor refactor to make sure `input.primitive` is always variable name not code when generate unsafe code.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8496 from cloud-fan/avoid-copy.
---
 .../codegen/GenerateUnsafeProjection.scala    | 84 ++++++-------------
 1 file changed, 24 insertions(+), 60 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 03c5f449bf9ac..55562facf9652 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -206,11 +206,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     ctx.addMutableState("UnsafeArrayData", output, s"$output = new UnsafeArrayData();")
     val buffer = ctx.freshName("buffer")
     ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
+    val tmpBuffer = ctx.freshName("tmpBuffer")
     val outputIsNull = ctx.freshName("isNull")
     val numElements = ctx.freshName("numElements")
     val fixedSize = ctx.freshName("fixedSize")
     val numBytes = ctx.freshName("numBytes")
-    val elements = ctx.freshName("elements")
     val cursor = ctx.freshName("cursor")
     val index = ctx.freshName("index")
     val elementName = ctx.freshName("elementName")
@@ -224,57 +224,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val convertedElement = createConvertCode(ctx, element, elementType)
 
-    // go through the input array to calculate how many bytes we need.
-    val calculateNumBytes = elementType match {
-      case _ if ctx.isPrimitiveType(elementType) =>
-        // Should we do word align?
-        val elementSize = elementType.defaultSize
-        s"""
-          $numBytes += $elementSize * $numElements;
-        """
-      case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
-        s"""
-          $numBytes += 8 * $numElements;
-        """
-      case _ =>
-        val writer = getWriter(elementType)
-        val elementSize = s"$writer.getSize($elements[$index])"
-        // TODO(davies): avoid the copy
-        val unsafeType = elementType match {
-          case _: StructType => "UnsafeRow"
-          case _: ArrayType => "UnsafeArrayData"
-          case _: MapType => "UnsafeMapData"
-          case _ => ctx.javaType(elementType)
-        }
-        val copy = elementType match {
-          // We reuse the buffer during conversion, need copy it before process next element.
-          case _: StructType | _: ArrayType | _: MapType => ".copy()"
-          case _ => ""
-        }
-
-        val newElements = if (elementType == BinaryType) {
-          s"new byte[$numElements][]"
-        } else {
-          s"new $unsafeType[$numElements]"
-        }
-        s"""
-          final $unsafeType[] $elements = $newElements;
-          for (int $index = 0; $index < $numElements; $index++) {
-            ${convertedElement.code}
-            if (!${convertedElement.isNull}) {
-              $elements[$index] = ${convertedElement.primitive}$copy;
-              $numBytes += $elementSize;
-            }
-          }
-        """
-    }
-
     val writeElement = elementType match {
       case _ if ctx.isPrimitiveType(elementType) =>
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
-          ${convertedElement.code}
           Platform.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -283,7 +237,6 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
-          ${convertedElement.code}
           Platform.putLong(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -296,15 +249,23 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           $cursor += $writer.write(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
-            $elements[$index]);
+            ${convertedElement.primitive});
         """
     }
 
-    val checkNull = elementType match {
-      case _ if ctx.isPrimitiveType(elementType) => s"${convertedElement.isNull}"
-      case t: DecimalType => s"$elements[$index] == null" +
-        s" || !$elements[$index].changePrecision(${t.precision}, ${t.scale})"
-      case _ => s"$elements[$index] == null"
+    val checkNull = convertedElement.isNull + (elementType match {
+      case t: DecimalType =>
+        s" || !${convertedElement.primitive}.changePrecision(${t.precision}, ${t.scale})"
+      case _ => ""
+    })
+
+    val elementSize = elementType match {
+      // Should we do word align for primitive types?
+      case _ if ctx.isPrimitiveType(elementType) => elementType.defaultSize.toString
+      case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS => "8"
+      case _ =>
+        val writer = getWriter(elementType)
+        s"$writer.getSize(${convertedElement.primitive})"
     }
 
     val code = s"""
@@ -318,18 +279,21 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           final int $fixedSize = 4 * $numElements;
           int $numBytes = $fixedSize;
 
-          $calculateNumBytes
-
-          if ($numBytes > $buffer.length) {
-            $buffer = new byte[$numBytes];
-          }
-
           int $cursor = $fixedSize;
           for (int $index = 0; $index < $numElements; $index++) {
+            ${convertedElement.code}
             if ($checkNull) {
               // If element is null, write the negative value address into offset region.
               Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, -$cursor);
             } else {
+              $numBytes += $elementSize;
+              if ($buffer.length < $numBytes) {
+                // This will not happen frequently, because the buffer is re-used.
+                byte[] $tmpBuffer = new byte[$numBytes * 2];
+                Platform.copyMemory($buffer, Platform.BYTE_ARRAY_OFFSET,
+                  $tmpBuffer, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
+                $buffer = $tmpBuffer;
+              }
               Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, $cursor);
               $writeElement
             }

From f892d927d7246856dd3ea617b2942873359454bc Mon Sep 17 00:00:00 2001
From: Yash Datta <Yash.Datta@guavus.com>
Date: Thu, 10 Sep 2015 10:34:00 -0700
Subject: [PATCH 1373/1454] [SPARK-7142] [SQL] Minor enhancement to
 BooleanSimplification Optimizer rule

Use these in the optimizer as well:

            A and (not(A) or B) => A and B
            not(A and B) => not(A) or not(B)
            not(A or B) => not(A) and not(B)

Author: Yash Datta <Yash.Datta@guavus.com>

Closes #5700 from saucam/bool_simp.
---
 .../spark/sql/catalyst/optimizer/Optimizer.scala |  9 +++++++++
 .../optimizer/BooleanSimplificationSuite.scala   | 16 ++++++++++++++++
 2 files changed, 25 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index a430000bef653..d9b50f3c97da0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -434,6 +434,11 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         case (_, Literal(false, BooleanType)) => Literal(false)
         // a && a  =>  a
         case (l, r) if l fastEquals r => l
+        // a && (not(a) || b) => a && b
+        case (l, Or(l1, r)) if (Not(l) fastEquals l1) => And(l, r)
+        case (l, Or(r, l1)) if (Not(l) fastEquals l1) => And(l, r)
+        case (Or(l, l1), r) if (l1 fastEquals Not(r)) => And(l, r)
+        case (Or(l1, l), r) if (l1 fastEquals Not(r)) => And(l, r)
         // (a || b) && (a || c)  =>  a || (b && c)
         case _ =>
           // 1. Split left and right to get the disjunctive predicates,
@@ -512,6 +517,10 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         case LessThan(l, r) => GreaterThanOrEqual(l, r)
         // not(l <= r)  =>  l > r
         case LessThanOrEqual(l, r) => GreaterThan(l, r)
+        // not(l || r) => not(l) && not(r)
+        case Or(l, r) => And(Not(l), Not(r))
+        // not(l && r) => not(l) or not(r)
+        case And(l, r) => Or(Not(l), Not(r))
         // not(not(e))  =>  e
         case Not(e) => e
         case _ => not
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
index 1877cff1334bd..cde346e99eb17 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
@@ -89,6 +89,22 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper {
       ('a === 'b || 'b > 3 && 'a > 3 && 'a < 5))
   }
 
+  test("a && (!a || b)") {
+    checkCondition(('a && (!('a) || 'b )), ('a && 'b))
+
+    checkCondition(('a && ('b || !('a) )), ('a && 'b))
+
+    checkCondition(((!('a) || 'b ) && 'a), ('b && 'a))
+
+    checkCondition((('b || !('a) ) && 'a), ('b && 'a))
+  }
+
+  test("!(a && b) , !(a || b)") {
+    checkCondition((!('a && 'b)), (!('a) || !('b)))
+
+    checkCondition(!('a || 'b), (!('a) && !('b)))
+  }
+
   private val caseInsensitiveAnalyzer =
     new Analyzer(EmptyCatalog, EmptyFunctionRegistry, new SimpleCatalystConf(false))
 

From 49da38e5f728e05e8e929c4dcd37145ba060151d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 10 Sep 2015 11:01:08 -0700
Subject: [PATCH 1374/1454] [SPARK-10301] [SPARK-10428] [SQL] Addresses
 comments of PR #8583 and #8509 for master

Author: Cheng Lian <lian@databricks.com>

Closes #8670 from liancheng/spark-10301/address-pr-comments.
---
 .../parquet/CatalystReadSupport.scala         |  20 +-
 .../parquet/CatalystRowConverter.scala        |  10 +
 .../parquet/ParquetQuerySuite.scala           | 291 ++++++++++++++++--
 .../parquet/ParquetSchemaSuite.scala          | 246 ++++++++++++++-
 4 files changed, 522 insertions(+), 45 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index dc4ff06df6f22..5a8166fac5418 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -117,14 +117,18 @@ private[parquet] object CatalystReadSupport {
         // Only clips array types with nested type as element type.
         clipParquetListType(parquetType.asGroupType(), t.elementType)
 
-      case t: MapType if !isPrimitiveCatalystType(t.valueType) =>
-        // Only clips map types with nested type as value type.
+      case t: MapType
+        if !isPrimitiveCatalystType(t.keyType) ||
+           !isPrimitiveCatalystType(t.valueType) =>
+        // Only clips map types with nested key type or value type
         clipParquetMapType(parquetType.asGroupType(), t.keyType, t.valueType)
 
       case t: StructType =>
         clipParquetGroup(parquetType.asGroupType(), t)
 
       case _ =>
+        // UDTs and primitive types are not clipped.  For UDTs, a clipped version might not be able
+        // to be mapped to desired user-space types.  So UDTs shouldn't participate schema merging.
         parquetType
     }
   }
@@ -204,14 +208,14 @@ private[parquet] object CatalystReadSupport {
   }
 
   /**
-   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[MapType]].  The value type
-   * of the [[MapType]] should also be a nested type, namely an [[ArrayType]], a [[MapType]], or a
-   * [[StructType]].  Note that key type of any [[MapType]] is always a primitive type.
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[MapType]].  Either key type or
+   * value type of the [[MapType]] must be a nested type, namely an [[ArrayType]], a [[MapType]], or
+   * a [[StructType]].
    */
   private def clipParquetMapType(
       parquetMap: GroupType, keyType: DataType, valueType: DataType): GroupType = {
-    // Precondition of this method, should only be called for maps with nested value types.
-    assert(!isPrimitiveCatalystType(valueType))
+    // Precondition of this method, only handles maps with nested key types or value types.
+    assert(!isPrimitiveCatalystType(keyType) || !isPrimitiveCatalystType(valueType))
 
     val repeatedGroup = parquetMap.getType(0).asGroupType()
     val parquetKeyType = repeatedGroup.getType(0)
@@ -221,7 +225,7 @@ private[parquet] object CatalystReadSupport {
       Types
         .repeatedGroup()
         .as(repeatedGroup.getOriginalType)
-        .addField(parquetKeyType)
+        .addField(clipParquetType(parquetKeyType, keyType))
         .addField(clipParquetType(parquetValueType, valueType))
         .named(repeatedGroup.getName)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index f17e794b76650..2ff2fda3610b8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -123,6 +123,16 @@ private[parquet] class CatalystRowConverter(
     updater: ParentContainerUpdater)
   extends CatalystGroupConverter(updater) with Logging {
 
+  assert(
+    parquetType.getFieldCount == catalystType.length,
+    s"""Field counts of the Parquet schema and the Catalyst schema don't match:
+       |
+       |Parquet schema:
+       |$parquetType
+       |Catalyst schema:
+       |${catalystType.prettyJson}
+     """.stripMargin)
+
   logDebug(
     s"""Building row converter for the following schema:
        |
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index 9edbb522684eb..1c1cfa34ad04b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -22,6 +22,9 @@ import java.io.File
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
+import org.apache.spark.sql.execution.datasources.parquet.TestingUDT.{NestedStruct, NestedStructUDT}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
@@ -228,54 +231,168 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     }
   }
 
-  test("SPARK-10301 Clipping nested structs in requested schema") {
+  test("SPARK-10301 requested schema clipping - same schema") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'b', id + 1) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, 1L)))
+    }
+  }
+
+  // This test case is ignored because of parquet-mr bug PARQUET-370
+  ignore("SPARK-10301 requested schema clipping - schemas with disjoint sets of fields") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'b', id + 1) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(null, null)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - requested schema contains physical schema") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'b', id + 1) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true)
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, 1L, null, null)))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'd', id + 3) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true)
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, null, null, 3L)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - physical schema contains requested schema") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
       val df = sqlContext
         .range(1)
-        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2, 'd', id + 3) AS s")
         .coalesce(1)
 
-      df.write.mode("append").parquet(path)
+      df.write.parquet(path)
 
-      val userDefinedSchema = new StructType()
-        .add("s", new StructType().add("a", LongType, nullable = true), nullable = true)
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true),
+            nullable = true)
 
       checkAnswer(
         sqlContext.read.schema(userDefinedSchema).parquet(path),
-        Row(Row(0)))
+        Row(Row(0L, 1L)))
     }
 
     withTempPath { dir =>
       val path = dir.getCanonicalPath
-
-      val df1 = sqlContext
+      val df = sqlContext
         .range(1)
-        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2, 'd', id + 3) AS s")
         .coalesce(1)
 
-      val df2 = sqlContext
-        .range(1, 2)
-        .selectExpr("NAMED_STRUCT('b', id, 'c', id) AS s")
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, 3L)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - schemas overlap but don't contain each other") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2) AS s")
         .coalesce(1)
 
-      df1.write.parquet(path)
-      df2.write.mode(SaveMode.Append).parquet(path)
+      df.write.parquet(path)
 
-      val userDefinedSchema = new StructType()
-        .add("s",
-          new StructType()
-            .add("a", LongType, nullable = true)
-            .add("c", LongType, nullable = true),
-          nullable = true)
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("b", LongType, nullable = true)
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
 
       checkAnswer(
         sqlContext.read.schema(userDefinedSchema).parquet(path),
-        Seq(
-          Row(Row(0, null)),
-          Row(Row(null, 1))))
+        Row(Row(1L, 2L, null)))
     }
+  }
 
+  test("SPARK-10301 requested schema clipping - deeply nested struct") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
 
@@ -304,4 +421,132 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
         Row(Row(Seq(Row(0, null)))))
     }
   }
+
+  test("SPARK-10301 requested schema clipping - out of order") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df1 = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2) AS s")
+        .coalesce(1)
+
+      val df2 = sqlContext
+        .range(1, 2)
+        .selectExpr("NAMED_STRUCT('c', id + 2, 'b', id + 1, 'd', id + 3) AS s")
+        .coalesce(1)
+
+      df1.write.parquet(path)
+      df2.write.mode(SaveMode.Append).parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s",
+          new StructType()
+            .add("a", LongType, nullable = true)
+            .add("b", LongType, nullable = true)
+            .add("d", LongType, nullable = true),
+          nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Seq(
+          Row(Row(0, 1, null)),
+          Row(Row(null, 2, 4))))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - schema merging") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df1 = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'c', id + 2) AS s")
+        .coalesce(1)
+
+      val df2 = sqlContext
+        .range(1, 2)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2) AS s")
+        .coalesce(1)
+
+      df1.write.mode(SaveMode.Append).parquet(path)
+      df2.write.mode(SaveMode.Append).parquet(path)
+
+      checkAnswer(
+        sqlContext
+          .read
+          .option("mergeSchema", "true")
+          .parquet(path)
+          .selectExpr("s.a", "s.b", "s.c"),
+        Seq(
+          Row(0, null, 2),
+          Row(1, 2, 3)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - UDT") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df = sqlContext
+        .range(1)
+        .selectExpr(
+          """NAMED_STRUCT(
+            |  'f0', CAST(id AS STRING),
+            |  'f1', NAMED_STRUCT(
+            |    'a', CAST(id + 1 AS INT),
+            |    'b', CAST(id + 2 AS LONG),
+            |    'c', CAST(id + 3.5 AS DOUBLE)
+            |  )
+            |) AS s
+          """.stripMargin)
+        .coalesce(1)
+
+      df.write.mode(SaveMode.Append).parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("f1", new NestedStructUDT, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(NestedStruct(1, 2L, 3.5D))))
+    }
+  }
+}
+
+object TestingUDT {
+  @SQLUserDefinedType(udt = classOf[NestedStructUDT])
+  case class NestedStruct(a: Integer, b: Long, c: Double)
+
+  class NestedStructUDT extends UserDefinedType[NestedStruct] {
+    override def sqlType: DataType =
+      new StructType()
+        .add("a", IntegerType, nullable = true)
+        .add("b", LongType, nullable = false)
+        .add("c", DoubleType, nullable = false)
+
+    override def serialize(obj: Any): Any = {
+      val row = new SpecificMutableRow(sqlType.asInstanceOf[StructType].map(_.dataType))
+      obj match {
+        case n: NestedStruct =>
+          row.setInt(0, n.a)
+          row.setLong(1, n.b)
+          row.setDouble(2, n.c)
+      }
+    }
+
+    override def userClass: Class[NestedStruct] = classOf[NestedStruct]
+
+    override def deserialize(datum: Any): NestedStruct = {
+      datum match {
+        case row: InternalRow =>
+          NestedStruct(row.getInt(0), row.getLong(1), row.getDouble(2))
+      }
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 5331d7c035236..5a8f772c32289 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -1012,12 +1012,17 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       """.stripMargin,
 
     catalystSchema = {
-      val f11Type = new StructType().add("f011", DoubleType, nullable = true)
-      val f01Type = ArrayType(StringType, containsNull = false)
+      val f00Type = ArrayType(StringType, containsNull = false)
+      val f01Type = ArrayType(
+        new StructType()
+          .add("f011", DoubleType, nullable = true),
+        containsNull = false)
+
       val f0Type = new StructType()
-        .add("f00", f01Type, nullable = false)
-        .add("f01", f11Type, nullable = false)
+        .add("f00", f00Type, nullable = false)
+        .add("f01", f01Type, nullable = false)
       val f1Type = ArrayType(IntegerType, containsNull = true)
+
       new StructType()
         .add("f0", f0Type, nullable = false)
         .add("f1", f1Type, nullable = true)
@@ -1046,7 +1051,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     parquetSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary f00_tuple (UTF8);
         |    }
         |
@@ -1061,13 +1066,13 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       """.stripMargin,
 
     catalystSchema = {
-      val f11ElementType = new StructType()
+      val f01ElementType = new StructType()
         .add("f011", DoubleType, nullable = true)
         .add("f012", LongType, nullable = true)
 
       val f0Type = new StructType()
-        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
-        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = false), nullable = true)
 
       new StructType().add("f0", f0Type, nullable = false)
     },
@@ -1075,7 +1080,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     expectedSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary f00_tuple (UTF8);
         |    }
         |
@@ -1095,7 +1100,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     parquetSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary array (UTF8);
         |    }
         |
@@ -1110,13 +1115,13 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       """.stripMargin,
 
     catalystSchema = {
-      val f11ElementType = new StructType()
+      val f01ElementType = new StructType()
         .add("f011", DoubleType, nullable = true)
         .add("f012", LongType, nullable = true)
 
       val f0Type = new StructType()
-        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
-        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = false), nullable = true)
 
       new StructType().add("f0", f0Type, nullable = false)
     },
@@ -1124,7 +1129,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     expectedSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary array (UTF8);
         |    }
         |
@@ -1236,6 +1241,63 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
          |}
        """.stripMargin)
 
+  testSchemaClipping(
+    "standard array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group list {
+        |        required binary element (UTF8);
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group list {
+        |        required group element {
+        |          optional int32 f010;
+        |          optional double f011;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f01ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = false), nullable = true)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group list {
+        |        required binary element (UTF8);
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group list {
+        |        required group element {
+        |          optional double f011;
+        |          optional int64 f012;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
   testSchemaClipping(
     "empty requested schema",
 
@@ -1251,4 +1313,160 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     catalystSchema = new StructType(),
 
     expectedSchema = "message root {}")
+
+  testSchemaClipping(
+    "disjoint field sets",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    required int32 f00;
+        |    required int64 f01;
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema =
+      new StructType()
+        .add(
+          "f0",
+          new StructType()
+            .add("f02", FloatType, nullable = true)
+            .add("f03", DoubleType, nullable = true),
+          nullable = true),
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional float f02;
+        |    optional double f03;
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-avro style map",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group map (MAP_KEY_VALUE) {
+        |      required int32 key;
+        |      required group value {
+        |        required int32 value_f0;
+        |        required int64 value_f1;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val valueType =
+        new StructType()
+          .add("value_f1", LongType, nullable = false)
+          .add("value_f2", DoubleType, nullable = false)
+
+      val f0Type = MapType(IntegerType, valueType, valueContainsNull = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group map (MAP_KEY_VALUE) {
+        |      required int32 key;
+        |      required group value {
+        |        required int64 value_f1;
+        |        required double value_f2;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "standard map",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required int32 key;
+        |      required group value {
+        |        required int32 value_f0;
+        |        required int64 value_f1;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val valueType =
+        new StructType()
+          .add("value_f1", LongType, nullable = false)
+          .add("value_f2", DoubleType, nullable = false)
+
+      val f0Type = MapType(IntegerType, valueType, valueContainsNull = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required int32 key;
+        |      required group value {
+        |        required int64 value_f1;
+        |        required double value_f2;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "standard map with complex key",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required group key {
+        |        required int32 value_f0;
+        |        required int64 value_f1;
+        |      }
+        |      required int32 value;
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val keyType =
+        new StructType()
+          .add("value_f1", LongType, nullable = false)
+          .add("value_f2", DoubleType, nullable = false)
+
+      val f0Type = MapType(keyType, IntegerType, valueContainsNull = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required group key {
+        |        required int64 value_f1;
+        |        required double value_f2;
+        |      }
+        |      required int32 value;
+        |    }
+        |  }
+        |}
+      """.stripMargin)
 }

From e04811137680f937669cdcc78771227aeb7cd849 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 10 Sep 2015 11:48:43 -0700
Subject: [PATCH 1375/1454] [SPARK-10466] [SQL] UnsafeRow SerDe exception with
 data spill

Data Spill with UnsafeRow causes assert failure.

```
java.lang.AssertionError: assertion failed
	at scala.Predef$.assert(Predef.scala:165)
	at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$2.writeKey(UnsafeRowSerializer.scala:75)
	at org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:180)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2$$anonfun$apply$1.apply(ExternalSorter.scala:688)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2$$anonfun$apply$1.apply(ExternalSorter.scala:687)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2.apply(ExternalSorter.scala:687)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2.apply(ExternalSorter.scala:683)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at org.apache.spark.util.collection.ExternalSorter.writePartitionedFile(ExternalSorter.scala:683)
	at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:80)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
	at org.apache.spark.scheduler.Task.run(Task.scala:88)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
```

To reproduce that with code (thanks andrewor14):
```scala
bin/spark-shell --master local
  --conf spark.shuffle.memoryFraction=0.005
  --conf spark.shuffle.sort.bypassMergeThreshold=0

sc.parallelize(1 to 2 * 1000 * 1000, 10)
  .map { i => (i, i) }.toDF("a", "b").groupBy("b").avg().count()
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #8635 from chenghao-intel/unsafe_spill.
---
 .../util/collection/ExternalSorter.scala      |  6 ++
 .../sql/execution/UnsafeRowSerializer.scala   |  2 +-
 .../execution/UnsafeRowSerializerSuite.scala  | 64 +++++++++++++++++--
 3 files changed, 67 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index 19287edbaf166..138c05dff19e4 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -188,6 +188,12 @@ private[spark] class ExternalSorter[K, V, C](
 
   private val spills = new ArrayBuffer[SpilledFile]
 
+  /**
+   * Number of files this sorter has spilled so far.
+   * Exposed for testing.
+   */
+  private[spark] def numSpills: Int = spills.size
+
   override def insertAll(records: Iterator[Product2[K, V]]): Unit = {
     // TODO: stop combining if we find that the reduction factor isn't high
     val shouldCombine = aggregator.isDefined
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 5c18558f9bde7..e060c06d9e2a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -72,7 +72,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
     override def writeKey[T: ClassTag](key: T): SerializationStream = {
       // The key is only needed on the map side when computing partition ids. It does not need to
       // be shuffled.
-      assert(key.isInstanceOf[Int])
+      assert(null == key || key.isInstanceOf[Int])
       this
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
index bd02c73a26ace..0113d052e338d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
@@ -17,13 +17,17 @@
 
 package org.apache.spark.sql.execution
 
-import java.io.{DataOutputStream, ByteArrayInputStream, ByteArrayOutputStream}
+import java.io.{File, DataOutputStream, ByteArrayInputStream, ByteArrayOutputStream}
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.executor.ShuffleWriteMetrics
+import org.apache.spark.storage.ShuffleBlockId
+import org.apache.spark.util.collection.ExternalSorter
+import org.apache.spark.util.Utils
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, UnsafeRow}
 import org.apache.spark.sql.types._
+import org.apache.spark._
 
 
 /**
@@ -40,9 +44,15 @@ class ClosableByteArrayInputStream(buf: Array[Byte]) extends ByteArrayInputStrea
 class UnsafeRowSerializerSuite extends SparkFunSuite {
 
   private def toUnsafeRow(row: Row, schema: Array[DataType]): UnsafeRow = {
-    val internalRow = CatalystTypeConverters.convertToCatalyst(row).asInstanceOf[InternalRow]
+    val converter = unsafeRowConverter(schema)
+    converter(row)
+  }
+
+  private def unsafeRowConverter(schema: Array[DataType]): Row => UnsafeRow = {
     val converter = UnsafeProjection.create(schema)
-    converter.apply(internalRow)
+    (row: Row) => {
+      converter(CatalystTypeConverters.convertToCatalyst(row).asInstanceOf[InternalRow])
+    }
   }
 
   test("toUnsafeRow() test helper method") {
@@ -87,4 +97,50 @@ class UnsafeRowSerializerSuite extends SparkFunSuite {
     assert(!deserializerIter.hasNext)
     assert(input.closed)
   }
+
+  test("SPARK-10466: external sorter spilling with unsafe row serializer") {
+    var sc: SparkContext = null
+    var outputFile: File = null
+    val oldEnv = SparkEnv.get // save the old SparkEnv, as it will be overwritten
+    Utils.tryWithSafeFinally {
+      val conf = new SparkConf()
+        .set("spark.shuffle.spill.initialMemoryThreshold", "1024")
+        .set("spark.shuffle.sort.bypassMergeThreshold", "0")
+        .set("spark.shuffle.memoryFraction", "0.0001")
+
+      sc = new SparkContext("local", "test", conf)
+      outputFile = File.createTempFile("test-unsafe-row-serializer-spill", "")
+      // prepare data
+      val converter = unsafeRowConverter(Array(IntegerType))
+      val data = (1 to 1000).iterator.map { i =>
+        (i, converter(Row(i)))
+      }
+      val sorter = new ExternalSorter[Int, UnsafeRow, UnsafeRow](
+        partitioner = Some(new HashPartitioner(10)),
+        serializer = Some(new UnsafeRowSerializer(numFields = 1)))
+
+      // Ensure we spilled something and have to merge them later
+      assert(sorter.numSpills === 0)
+      sorter.insertAll(data)
+      assert(sorter.numSpills > 0)
+
+      // Merging spilled files should not throw assertion error
+      val taskContext =
+        new TaskContextImpl(0, 0, 0, 0, null, null, InternalAccumulator.create(sc))
+      taskContext.taskMetrics.shuffleWriteMetrics = Some(new ShuffleWriteMetrics)
+      sorter.writePartitionedFile(ShuffleBlockId(0, 0, 0), taskContext, outputFile)
+    } {
+      // Clean up
+      if (sc != null) {
+        sc.stop()
+      }
+
+      // restore the spark env
+      SparkEnv.set(oldEnv)
+
+      if (outputFile != null) {
+        outputFile.delete()
+      }
+    }
+  }
 }

From a76bde9dae54c4641e21f3c1ceb4870e3dc91881 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 10 Sep 2015 11:49:53 -0700
Subject: [PATCH 1376/1454] [SPARK-10469] [DOC] Try and document the three
 options

From JIRA:
Add documentation for tungsten-sort.
From the mailing list "I saw a new "spark.shuffle.manager=tungsten-sort" implemented in
https://issues.apache.org/jira/browse/SPARK-7081, but it can't be found its
corresponding description in
http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/configuration.html(Currenlty
there are only 'sort' and 'hash' two options)."

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8638 from holdenk/SPARK-10469-document-tungsten-sort.
---
 docs/configuration.md | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index e287591f3fda1..0b1a273916314 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -447,9 +447,12 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.shuffle.manager</code></td>
   <td>sort</td>
   <td>
-    Implementation to use for shuffling data. There are two implementations available:
-    <code>sort</code> and <code>hash</code>. Sort-based shuffle is more memory-efficient and is
-    the default option starting in 1.2.
+    Implementation to use for shuffling data. There are three implementations available:
+    <code>sort</code>, <code>hash</code> and the new (1.5+) <code>tungsten-sort</code>.
+    Sort-based shuffle is more memory-efficient and is the default option starting in 1.2.
+    Tungsten-sort is similar to the sort based shuffle, with a direct binary cache-friendly
+    implementation with a fall back to regular sort based shuffle if its requirements are not
+    met.
   </td>
 </tr>
 <tr>

From af3bc59d1f5d9d952c2d7ad1af599c49f1dbdaf0 Mon Sep 17 00:00:00 2001
From: mcheah <mcheah@palantir.com>
Date: Thu, 10 Sep 2015 11:58:54 -0700
Subject: [PATCH 1377/1454] [SPARK-8167] Make tasks that fail from YARN
 preemption not fail job

The architecture is that, in YARN mode, if the driver detects that an executor has disconnected, it asks the ApplicationMaster why the executor died. If the ApplicationMaster is aware that the executor died because of preemption, all tasks associated with that executor are not marked as failed. The executor
is still removed from the driver's list of available executors, however.

There's a few open questions:
1. Should standalone mode have a similar "get executor loss reason" as well? I localized this change as much as possible to affect only YARN, but there could be a valid case to differentiate executor losses in standalone mode as well.
2. I make a pretty strong assumption in YarnAllocator that getExecutorLossReason(executorId) will only be called once per executor id; I do this so that I can remove the metadata from the in-memory map to avoid object accumulation. It's not clear if I'm being overly zealous to save space, however.

cc vanzin specifically for review because it collided with some earlier YARN scheduling work.
cc JoshRosen because it's similar to output commit coordination we did in the past
cc andrewor14 for our discussion on how to get executor exit codes and loss reasons

Author: mcheah <mcheah@palantir.com>

Closes #8007 from mccheah/feature/preemption-handling.
---
 .../org/apache/spark/TaskEndReason.scala      | 18 +++-
 .../spark/scheduler/ExecutorLossReason.scala  | 14 ++-
 .../org/apache/spark/scheduler/Pool.scala     |  4 +-
 .../apache/spark/scheduler/Schedulable.scala  |  2 +-
 .../spark/scheduler/TaskSchedulerImpl.scala   |  9 +-
 .../spark/scheduler/TaskSetManager.scala      | 21 +++--
 .../cluster/CoarseGrainedClusterMessage.scala |  8 +-
 .../CoarseGrainedSchedulerBackend.scala       | 24 +++--
 .../cluster/SparkDeploySchedulerBackend.scala |  6 +-
 .../cluster/YarnSchedulerBackend.scala        | 77 ++++++++++++++--
 .../mesos/CoarseMesosSchedulerBackend.scala   |  4 +-
 .../cluster/mesos/MesosSchedulerBackend.scala |  2 +-
 .../org/apache/spark/util/JsonProtocol.scala  |  9 +-
 .../spark/scheduler/TaskSetManagerSuite.scala | 33 ++++++-
 .../apache/spark/util/JsonProtocolSuite.scala | 10 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  7 ++
 .../spark/deploy/yarn/YarnAllocator.scala     | 92 ++++++++++++++-----
 17 files changed, 261 insertions(+), 79 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index 934d00dc708b9..2ae878b3e6087 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -48,6 +48,8 @@ case object Success extends TaskEndReason
 sealed trait TaskFailedReason extends TaskEndReason {
   /** Error message displayed in the web UI. */
   def toErrorString: String
+
+  def shouldEventuallyFailJob: Boolean = true
 }
 
 /**
@@ -194,6 +196,12 @@ case object TaskKilled extends TaskFailedReason {
 case class TaskCommitDenied(jobID: Int, partitionID: Int, attemptID: Int) extends TaskFailedReason {
   override def toErrorString: String = s"TaskCommitDenied (Driver denied task commit)" +
     s" for job: $jobID, partition: $partitionID, attempt: $attemptID"
+  /**
+   * If a task failed because its attempt to commit was denied, do not count this failure
+   * towards failing the stage. This is intended to prevent spurious stage failures in cases
+   * where many speculative tasks are launched and denied to commit.
+   */
+  override def shouldEventuallyFailJob: Boolean = false
 }
 
 /**
@@ -202,8 +210,14 @@ case class TaskCommitDenied(jobID: Int, partitionID: Int, attemptID: Int) extend
  * the task crashed the JVM.
  */
 @DeveloperApi
-case class ExecutorLostFailure(execId: String) extends TaskFailedReason {
-  override def toErrorString: String = s"ExecutorLostFailure (executor ${execId} lost)"
+case class ExecutorLostFailure(execId: String, isNormalExit: Boolean = false)
+  extends TaskFailedReason {
+  override def toErrorString: String = {
+    val exitBehavior = if (isNormalExit) "normally" else "abnormally"
+    s"ExecutorLostFailure (executor ${execId} exited ${exitBehavior})"
+  }
+
+  override def shouldEventuallyFailJob: Boolean = !isNormalExit
 }
 
 /**
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala b/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala
index 2bc43a9186449..0a98c69b89ea5 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala
@@ -23,16 +23,20 @@ import org.apache.spark.executor.ExecutorExitCode
  * Represents an explanation for a executor or whole slave failing or exiting.
  */
 private[spark]
-class ExecutorLossReason(val message: String) {
+class ExecutorLossReason(val message: String) extends Serializable {
   override def toString: String = message
 }
 
 private[spark]
-case class ExecutorExited(val exitCode: Int)
-  extends ExecutorLossReason(ExecutorExitCode.explainExitCode(exitCode)) {
+case class ExecutorExited(exitCode: Int, isNormalExit: Boolean, reason: String)
+  extends ExecutorLossReason(reason)
+
+private[spark] object ExecutorExited {
+  def apply(exitCode: Int, isNormalExit: Boolean): ExecutorExited = {
+    ExecutorExited(exitCode, isNormalExit, ExecutorExitCode.explainExitCode(exitCode))
+  }
 }
 
 private[spark]
 case class SlaveLost(_message: String = "Slave lost")
-  extends ExecutorLossReason(_message) {
-}
+  extends ExecutorLossReason(_message)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
index 5821afea98982..551e39a81b695 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
@@ -83,8 +83,8 @@ private[spark] class Pool(
     null
   }
 
-  override def executorLost(executorId: String, host: String) {
-    schedulableQueue.asScala.foreach(_.executorLost(executorId, host))
+  override def executorLost(executorId: String, host: String, reason: ExecutorLossReason) {
+    schedulableQueue.asScala.foreach(_.executorLost(executorId, host, reason))
   }
 
   override def checkSpeculatableTasks(): Boolean = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
index a87ef030e69c2..ab00bc8f0bf4e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
@@ -42,7 +42,7 @@ private[spark] trait Schedulable {
   def addSchedulable(schedulable: Schedulable): Unit
   def removeSchedulable(schedulable: Schedulable): Unit
   def getSchedulableByName(name: String): Schedulable
-  def executorLost(executorId: String, host: String): Unit
+  def executorLost(executorId: String, host: String, reason: ExecutorLossReason): Unit
   def checkSpeculatableTasks(): Boolean
   def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager]
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 1705e7f962de2..1c7bfe89c02ac 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -332,7 +332,8 @@ private[spark] class TaskSchedulerImpl(
           // We lost this entire executor, so remember that it's gone
           val execId = taskIdToExecutorId(tid)
           if (activeExecutorIds.contains(execId)) {
-            removeExecutor(execId)
+            removeExecutor(execId,
+              SlaveLost(s"Task $tid was lost, so marking the executor as lost as well."))
             failedExecutor = Some(execId)
           }
         }
@@ -464,7 +465,7 @@ private[spark] class TaskSchedulerImpl(
       if (activeExecutorIds.contains(executorId)) {
         val hostPort = executorIdToHost(executorId)
         logError("Lost executor %s on %s: %s".format(executorId, hostPort, reason))
-        removeExecutor(executorId)
+        removeExecutor(executorId, reason)
         failedExecutor = Some(executorId)
       } else {
          // We may get multiple executorLost() calls with different loss reasons. For example, one
@@ -482,7 +483,7 @@ private[spark] class TaskSchedulerImpl(
   }
 
   /** Remove an executor from all our data structures and mark it as lost */
-  private def removeExecutor(executorId: String) {
+  private def removeExecutor(executorId: String, reason: ExecutorLossReason) {
     activeExecutorIds -= executorId
     val host = executorIdToHost(executorId)
     val execs = executorsByHost.getOrElse(host, new HashSet)
@@ -497,7 +498,7 @@ private[spark] class TaskSchedulerImpl(
       }
     }
     executorIdToHost -= executorId
-    rootPool.executorLost(executorId, host)
+    rootPool.executorLost(executorId, host, reason)
   }
 
   def executorAdded(execId: String, host: String) {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index 818b95d67f6be..62af9031b9f8b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -709,6 +709,11 @@ private[spark] class TaskSetManager(
         }
         ef.exception
 
+      case e: ExecutorLostFailure if e.isNormalExit =>
+        logInfo(s"Task $tid failed because while it was being computed, its executor" +
+          s" exited normally. Not marking the task as failed.")
+        None
+
       case e: TaskFailedReason =>  // TaskResultLost, TaskKilled, and others
         logWarning(failureReason)
         None
@@ -722,10 +727,9 @@ private[spark] class TaskSetManager(
       put(info.executorId, clock.getTimeMillis())
     sched.dagScheduler.taskEnded(tasks(index), reason, null, null, info, taskMetrics)
     addPendingTask(index)
-    if (!isZombie && state != TaskState.KILLED && !reason.isInstanceOf[TaskCommitDenied]) {
-      // If a task failed because its attempt to commit was denied, do not count this failure
-      // towards failing the stage. This is intended to prevent spurious stage failures in cases
-      // where many speculative tasks are launched and denied to commit.
+    if (!isZombie && state != TaskState.KILLED
+        && reason.isInstanceOf[TaskFailedReason]
+        && reason.asInstanceOf[TaskFailedReason].shouldEventuallyFailJob) {
       assert (null != failureReason)
       numFailures(index) += 1
       if (numFailures(index) >= maxTaskFailures) {
@@ -778,7 +782,7 @@ private[spark] class TaskSetManager(
   }
 
   /** Called by TaskScheduler when an executor is lost so we can re-enqueue our tasks */
-  override def executorLost(execId: String, host: String) {
+  override def executorLost(execId: String, host: String, reason: ExecutorLossReason) {
     logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id)
 
     // Re-enqueue pending tasks for this host based on the status of the cluster. Note
@@ -809,9 +813,12 @@ private[spark] class TaskSetManager(
         }
       }
     }
-    // Also re-enqueue any tasks that were running on the node
     for ((tid, info) <- taskInfos if info.running && info.executorId == execId) {
-      handleFailedTask(tid, TaskState.FAILED, ExecutorLostFailure(execId))
+      val isNormalExit: Boolean = reason match {
+        case exited: ExecutorExited => exited.isNormalExit
+        case _ => false
+      }
+      handleFailedTask(tid, TaskState.FAILED, ExecutorLostFailure(info.executorId, isNormalExit))
     }
     // recalculate valid locality levels and waits when executor is lost
     recomputeLocality()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
index 06f5438433b6e..d94743677783f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
@@ -21,6 +21,7 @@ import java.nio.ByteBuffer
 
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.scheduler.ExecutorLossReason
 import org.apache.spark.util.{SerializableBuffer, Utils}
 
 private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable
@@ -70,7 +71,8 @@ private[spark] object CoarseGrainedClusterMessages {
 
   case object StopExecutors extends CoarseGrainedClusterMessage
 
-  case class RemoveExecutor(executorId: String, reason: String) extends CoarseGrainedClusterMessage
+  case class RemoveExecutor(executorId: String, reason: ExecutorLossReason)
+    extends CoarseGrainedClusterMessage
 
   case class SetupDriver(driver: RpcEndpointRef) extends CoarseGrainedClusterMessage
 
@@ -92,6 +94,10 @@ private[spark] object CoarseGrainedClusterMessages {
       hostToLocalTaskCount: Map[String, Int])
     extends CoarseGrainedClusterMessage
 
+  // Check if an executor was force-killed but for a normal reason.
+  // This could be the case if the executor is preempted, for instance.
+  case class GetExecutorLossReason(executorId: String) extends CoarseGrainedClusterMessage
+
   case class KillExecutors(executorIds: Seq[String]) extends CoarseGrainedClusterMessage
 
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 5730a87f960a0..18771f79b44bb 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -26,6 +26,7 @@ import org.apache.spark.rpc._
 import org.apache.spark.{ExecutorAllocationClient, Logging, SparkEnv, SparkException, TaskState}
 import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.ENDPOINT_NAME
 import org.apache.spark.util.{ThreadUtils, SerializableBuffer, AkkaUtils, Utils}
 
 /**
@@ -82,7 +83,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
 
     override protected def log = CoarseGrainedSchedulerBackend.this.log
 
-    private val addressToExecutorId = new HashMap[RpcAddress, String]
+    protected val addressToExecutorId = new HashMap[RpcAddress, String]
 
     private val reviveThread =
       ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-revive-thread")
@@ -128,6 +129,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+
       case RegisterExecutor(executorId, executorRef, hostPort, cores, logUrls) =>
         Utils.checkHostPort(hostPort, "Host port expected " + hostPort)
         if (executorDataMap.contains(executorId)) {
@@ -185,8 +187,9 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     override def onDisconnected(remoteAddress: RpcAddress): Unit = {
-      addressToExecutorId.get(remoteAddress).foreach(removeExecutor(_,
-        "remote Rpc client disassociated"))
+      addressToExecutorId
+        .get(remoteAddress)
+        .foreach(removeExecutor(_, SlaveLost("remote Rpc client disassociated")))
     }
 
     // Make fake resource offers on just one executor
@@ -227,7 +230,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     // Remove a disconnected slave from the cluster
-    def removeExecutor(executorId: String, reason: String): Unit = {
+    def removeExecutor(executorId: String, reason: ExecutorLossReason): Unit = {
       executorDataMap.get(executorId) match {
         case Some(executorInfo) =>
           // This must be synchronized because variables mutated
@@ -239,9 +242,9 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
           }
           totalCoreCount.addAndGet(-executorInfo.totalCores)
           totalRegisteredExecutors.addAndGet(-1)
-          scheduler.executorLost(executorId, SlaveLost(reason))
+          scheduler.executorLost(executorId, reason)
           listenerBus.post(
-            SparkListenerExecutorRemoved(System.currentTimeMillis(), executorId, reason))
+            SparkListenerExecutorRemoved(System.currentTimeMillis(), executorId, reason.toString))
         case None => logInfo(s"Asked to remove non-existent executor $executorId")
       }
     }
@@ -263,8 +266,11 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     // TODO (prashant) send conf instead of properties
-    driverEndpoint = rpcEnv.setupEndpoint(
-      CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties))
+    driverEndpoint = rpcEnv.setupEndpoint(ENDPOINT_NAME, createDriverEndpoint(properties))
+  }
+
+  protected def createDriverEndpoint(properties: Seq[(String, String)]): DriverEndpoint = {
+    new DriverEndpoint(rpcEnv, properties)
   }
 
   def stopExecutors() {
@@ -304,7 +310,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
   }
 
   // Called by subclasses when notified of a lost worker
-  def removeExecutor(executorId: String, reason: String) {
+  def removeExecutor(executorId: String, reason: ExecutorLossReason) {
     try {
       driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, reason))
     } catch {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index bbe51b4a09a22..27491ecf8b97d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -23,7 +23,7 @@ import org.apache.spark.rpc.RpcAddress
 import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv}
 import org.apache.spark.deploy.{ApplicationDescription, Command}
 import org.apache.spark.deploy.client.{AppClient, AppClientListener}
-import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl}
+import org.apache.spark.scheduler._
 import org.apache.spark.util.Utils
 
 private[spark] class SparkDeploySchedulerBackend(
@@ -135,11 +135,11 @@ private[spark] class SparkDeploySchedulerBackend(
 
   override def executorRemoved(fullId: String, message: String, exitStatus: Option[Int]) {
     val reason: ExecutorLossReason = exitStatus match {
-      case Some(code) => ExecutorExited(code)
+      case Some(code) => ExecutorExited(code, isNormalExit = true, message)
       case None => SlaveLost(message)
     }
     logInfo("Executor %s removed: %s".format(fullId, message))
-    removeExecutor(fullId.split("/")(1), reason.toString)
+    removeExecutor(fullId.split("/")(1), reason)
   }
 
   override def sufficientResourcesRegistered(): Boolean = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
index 044f6288fabdd..6a4b536dee191 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.scheduler.cluster
 
+import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.{Future, ExecutionContext}
 
 import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.rpc._
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
-import org.apache.spark.scheduler.TaskSchedulerImpl
+import org.apache.spark.scheduler._
 import org.apache.spark.ui.JettyUtils
 import org.apache.spark.util.{ThreadUtils, RpcUtils}
 
@@ -43,8 +44,10 @@ private[spark] abstract class YarnSchedulerBackend(
 
   protected var totalExpectedExecutors = 0
 
-  private val yarnSchedulerEndpoint = rpcEnv.setupEndpoint(
-    YarnSchedulerBackend.ENDPOINT_NAME, new YarnSchedulerEndpoint(rpcEnv))
+  private val yarnSchedulerEndpoint = new YarnSchedulerEndpoint(rpcEnv)
+
+  private val yarnSchedulerEndpointRef = rpcEnv.setupEndpoint(
+    YarnSchedulerBackend.ENDPOINT_NAME, yarnSchedulerEndpoint)
 
   private implicit val askTimeout = RpcUtils.askRpcTimeout(sc.conf)
 
@@ -53,7 +56,7 @@ private[spark] abstract class YarnSchedulerBackend(
    * This includes executors already pending or running.
    */
   override def doRequestTotalExecutors(requestedTotal: Int): Boolean = {
-    yarnSchedulerEndpoint.askWithRetry[Boolean](
+    yarnSchedulerEndpointRef.askWithRetry[Boolean](
       RequestExecutors(requestedTotal, localityAwareTasks, hostToLocalTaskCount))
   }
 
@@ -61,7 +64,7 @@ private[spark] abstract class YarnSchedulerBackend(
    * Request that the ApplicationMaster kill the specified executors.
    */
   override def doKillExecutors(executorIds: Seq[String]): Boolean = {
-    yarnSchedulerEndpoint.askWithRetry[Boolean](KillExecutors(executorIds))
+    yarnSchedulerEndpointRef.askWithRetry[Boolean](KillExecutors(executorIds))
   }
 
   override def sufficientResourcesRegistered(): Boolean = {
@@ -90,6 +93,41 @@ private[spark] abstract class YarnSchedulerBackend(
     }
   }
 
+  override def createDriverEndpoint(properties: Seq[(String, String)]): DriverEndpoint = {
+    new YarnDriverEndpoint(rpcEnv, properties)
+  }
+
+  /**
+   * Override the DriverEndpoint to add extra logic for the case when an executor is disconnected.
+   * This endpoint communicates with the executors and queries the AM for an executor's exit
+   * status when the executor is disconnected.
+   */
+  private class YarnDriverEndpoint(rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)])
+      extends DriverEndpoint(rpcEnv, sparkProperties) {
+
+    /**
+     * When onDisconnected is received at the driver endpoint, the superclass DriverEndpoint
+     * handles it by assuming the Executor was lost for a bad reason and removes the executor
+     * immediately.
+     *
+     * In YARN's case however it is crucial to talk to the application master and ask why the
+     * executor had exited. In particular, the executor may have exited due to the executor
+     * having been preempted. If the executor "exited normally" according to the application
+     * master then we pass that information down to the TaskSetManager to inform the
+     * TaskSetManager that tasks on that lost executor should not count towards a job failure.
+     *
+     * TODO there's a race condition where while we are querying the ApplicationMaster for
+     * the executor loss reason, there is the potential that tasks will be scheduled on
+     * the executor that failed. We should fix this by having this onDisconnected event
+     * also "blacklist" executors so that tasks are not assigned to them.
+     */
+    override def onDisconnected(rpcAddress: RpcAddress): Unit = {
+      addressToExecutorId.get(rpcAddress).foreach { executorId =>
+        yarnSchedulerEndpoint.handleExecutorDisconnectedFromDriver(executorId, rpcAddress)
+      }
+    }
+  }
+
   /**
    * An [[RpcEndpoint]] that communicates with the ApplicationMaster.
    */
@@ -101,6 +139,33 @@ private[spark] abstract class YarnSchedulerBackend(
       ThreadUtils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-thread-pool")
     implicit val askAmExecutor = ExecutionContext.fromExecutor(askAmThreadPool)
 
+    private[YarnSchedulerBackend] def handleExecutorDisconnectedFromDriver(
+        executorId: String,
+        executorRpcAddress: RpcAddress): Unit = {
+      amEndpoint match {
+        case Some(am) =>
+          val lossReasonRequest = GetExecutorLossReason(executorId)
+          val future = am.ask[ExecutorLossReason](lossReasonRequest, askTimeout)
+          future onSuccess {
+            case reason: ExecutorLossReason => {
+              driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, reason))
+            }
+          }
+          future onFailure {
+            case NonFatal(e) => {
+              logWarning(s"Attempted to get executor loss reason" +
+                s" for executor id ${executorId} at RPC address ${executorRpcAddress}," +
+                s" but got no response. Marking as slave lost.", e)
+              driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, SlaveLost()))
+            }
+            case t => throw t
+          }
+        case None =>
+          logWarning("Attempted to check for an executor loss reason" +
+            " before the AM has registered!")
+      }
+    }
+
     override def receive: PartialFunction[Any, Unit] = {
       case RegisterClusterManager(am) =>
         logInfo(s"ApplicationMaster registered as $am")
@@ -113,6 +178,7 @@ private[spark] abstract class YarnSchedulerBackend(
         removeExecutor(executorId, reason)
     }
 
+
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
       case r: RequestExecutors =>
         amEndpoint match {
@@ -143,7 +209,6 @@ private[spark] abstract class YarnSchedulerBackend(
             logWarning("Attempted to kill executors before the AM has registered!")
             context.reply(false)
         }
-
     }
 
     override def onDisconnected(remoteAddress: RpcAddress): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 452c32d5411cd..65df8874774ca 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -32,7 +32,7 @@ import org.apache.spark.{SecurityManager, SparkContext, SparkEnv, SparkException
 import org.apache.spark.network.netty.SparkTransportConf
 import org.apache.spark.network.shuffle.mesos.MesosExternalShuffleClient
 import org.apache.spark.rpc.RpcAddress
-import org.apache.spark.scheduler.TaskSchedulerImpl
+import org.apache.spark.scheduler.{SlaveLost, TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
 
@@ -364,7 +364,7 @@ private[spark] class CoarseMesosSchedulerBackend(
         if (slaveIdToTaskId.containsKey(slaveId)) {
           val taskId: Int = slaveIdToTaskId.get(slaveId)
           taskIdToSlaveId.remove(taskId)
-          removeExecutor(sparkExecutorId(slaveId, taskId.toString), reason)
+          removeExecutor(sparkExecutorId(slaveId, taskId.toString), SlaveLost(reason))
         }
         // TODO: This assumes one Spark executor per Mesos slave,
         // which may no longer be true after SPARK-5095
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 2e424054be785..18da6d2491280 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -390,7 +390,7 @@ private[spark] class MesosSchedulerBackend(
                             slaveId: SlaveID, status: Int) {
     logInfo("Executor lost: %s, marking slave %s as lost".format(executorId.getValue,
                                                                  slaveId.getValue))
-    recordSlaveLost(d, slaveId, ExecutorExited(status))
+    recordSlaveLost(d, slaveId, ExecutorExited(status, isNormalExit = false))
   }
 
   override def killTask(taskId: Long, executorId: String, interruptThread: Boolean): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index cbc94fd6d54d9..24f78744ad74c 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -362,8 +362,9 @@ private[spark] object JsonProtocol {
         ("Stack Trace" -> stackTrace) ~
         ("Full Stack Trace" -> exceptionFailure.fullStackTrace) ~
         ("Metrics" -> metrics)
-      case ExecutorLostFailure(executorId) =>
-        ("Executor ID" -> executorId)
+      case ExecutorLostFailure(executorId, isNormalExit) =>
+        ("Executor ID" -> executorId) ~
+        ("Normal Exit" -> isNormalExit)
       case _ => Utils.emptyJson
     }
     ("Reason" -> reason) ~ json
@@ -794,8 +795,10 @@ private[spark] object JsonProtocol {
       case `taskResultLost` => TaskResultLost
       case `taskKilled` => TaskKilled
       case `executorLostFailure` =>
+        val isNormalExit = Utils.jsonOption(json \ "Normal Exit").
+          map(_.extract[Boolean])
         val executorId = Utils.jsonOption(json \ "Executor ID").map(_.extract[String])
-        ExecutorLostFailure(executorId.getOrElse("Unknown"))
+        ExecutorLostFailure(executorId.getOrElse("Unknown"), isNormalExit.getOrElse(false))
       case `unknownReason` => UnknownReason
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index edbdb485c5ea4..f0eadf240943e 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -334,7 +334,7 @@ class TaskSetManagerSuite extends SparkFunSuite with LocalSparkContext with Logg
 
     // Now mark host2 as dead
     sched.removeExecutor("exec2")
-    manager.executorLost("exec2", "host2")
+    manager.executorLost("exec2", "host2", SlaveLost())
 
     // nothing should be chosen
     assert(manager.resourceOffer("exec1", "host1", ANY) === None)
@@ -504,13 +504,36 @@ class TaskSetManagerSuite extends SparkFunSuite with LocalSparkContext with Logg
       Array(PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY)))
     // test if the valid locality is recomputed when the executor is lost
     sched.removeExecutor("execC")
-    manager.executorLost("execC", "host2")
+    manager.executorLost("execC", "host2", SlaveLost())
     assert(manager.myLocalityLevels.sameElements(Array(NODE_LOCAL, NO_PREF, ANY)))
     sched.removeExecutor("execD")
-    manager.executorLost("execD", "host1")
+    manager.executorLost("execD", "host1", SlaveLost())
     assert(manager.myLocalityLevels.sameElements(Array(NO_PREF, ANY)))
   }
 
+  test("Executors are added but exit normally while running tasks") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc)
+    val taskSet = FakeTask.createTaskSet(4,
+      Seq(TaskLocation("host1", "execA")),
+      Seq(TaskLocation("host1", "execB")),
+      Seq(TaskLocation("host2", "execC")),
+      Seq())
+    val manager = new TaskSetManager(sched, taskSet, 1, new ManualClock)
+    sched.addExecutor("execA", "host1")
+    manager.executorAdded()
+    sched.addExecutor("execC", "host2")
+    manager.executorAdded()
+    assert(manager.resourceOffer("exec1", "host1", ANY).isDefined)
+    sched.removeExecutor("execA")
+    manager.executorLost("execA", "host1", ExecutorExited(143, true, "Normal termination"))
+    assert(!sched.taskSetsFailed.contains(taskSet.id))
+    assert(manager.resourceOffer("execC", "host2", ANY).isDefined)
+    sched.removeExecutor("execC")
+    manager.executorLost("execC", "host2", ExecutorExited(1, false, "Abnormal termination"))
+    assert(sched.taskSetsFailed.contains(taskSet.id))
+  }
+
   test("test RACK_LOCAL tasks") {
     // Assign host1 to rack1
     FakeRackUtil.assignHostToRack("host1", "rack1")
@@ -721,8 +744,8 @@ class TaskSetManagerSuite extends SparkFunSuite with LocalSparkContext with Logg
     assert(manager.resourceOffer("execB.2", "host2", ANY) !== None)
     sched.removeExecutor("execA")
     sched.removeExecutor("execB.2")
-    manager.executorLost("execA", "host1")
-    manager.executorLost("execB.2", "host2")
+    manager.executorLost("execA", "host1", SlaveLost())
+    manager.executorLost("execB.2", "host2", SlaveLost())
     clock.advance(LOCALITY_WAIT_MS * 4)
     sched.addExecutor("execC", "host3")
     manager.executorAdded()
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 343a4139b0ca8..47e548ef0d442 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -151,7 +151,7 @@ class JsonProtocolSuite extends SparkFunSuite {
     testTaskEndReason(exceptionFailure)
     testTaskEndReason(TaskResultLost)
     testTaskEndReason(TaskKilled)
-    testTaskEndReason(ExecutorLostFailure("100"))
+    testTaskEndReason(ExecutorLostFailure("100", true))
     testTaskEndReason(UnknownReason)
 
     // BlockId
@@ -295,10 +295,10 @@ class JsonProtocolSuite extends SparkFunSuite {
 
   test("ExecutorLostFailure backward compatibility") {
     // ExecutorLostFailure in Spark 1.1.0 does not have an "Executor ID" property.
-    val executorLostFailure = ExecutorLostFailure("100")
+    val executorLostFailure = ExecutorLostFailure("100", true)
     val oldEvent = JsonProtocol.taskEndReasonToJson(executorLostFailure)
       .removeField({ _._1 == "Executor ID" })
-    val expectedExecutorLostFailure = ExecutorLostFailure("Unknown")
+    val expectedExecutorLostFailure = ExecutorLostFailure("Unknown", true)
     assert(expectedExecutorLostFailure === JsonProtocol.taskEndReasonFromJson(oldEvent))
   }
 
@@ -577,8 +577,10 @@ class JsonProtocolSuite extends SparkFunSuite {
         assertOptionEquals(r1.metrics, r2.metrics, assertTaskMetricsEquals)
       case (TaskResultLost, TaskResultLost) =>
       case (TaskKilled, TaskKilled) =>
-      case (ExecutorLostFailure(execId1), ExecutorLostFailure(execId2)) =>
+      case (ExecutorLostFailure(execId1, isNormalExit1),
+          ExecutorLostFailure(execId2, isNormalExit2)) =>
         assert(execId1 === execId2)
+        assert(isNormalExit1 === isNormalExit2)
       case (UnknownReason, UnknownReason) =>
       case _ => fail("Task end reasons don't match in types!")
     }
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 991b5cec00bd8..93621b44c9183 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -590,6 +590,13 @@ private[spark] class ApplicationMaster(
           case None => logWarning("Container allocator is not ready to kill executors yet.")
         }
         context.reply(true)
+
+      case GetExecutorLossReason(eid) =>
+        Option(allocator) match {
+          case Some(a) => a.enqueueGetLossReasonRequest(eid, context)
+          case None => logWarning(s"Container allocator is not ready to find" +
+            s" executor loss reasons yet.")
+        }
     }
 
     override def onDisconnected(remoteAddress: RpcAddress): Unit = {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 5f897cbcb4e9f..fd88b8b7fe3b9 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -21,8 +21,9 @@ import java.util.Collections
 import java.util.concurrent._
 import java.util.regex.Pattern
 
-import scala.collection.JavaConverters._
+import scala.collection.mutable
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
+import scala.collection.JavaConverters._
 
 import com.google.common.util.concurrent.ThreadFactoryBuilder
 
@@ -36,8 +37,9 @@ import org.apache.log4j.{Level, Logger}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
-import org.apache.spark.rpc.RpcEndpointRef
-import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef}
+import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason}
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveExecutor
 import org.apache.spark.util.Utils
 
 /**
@@ -93,6 +95,11 @@ private[yarn] class YarnAllocator(
       sparkConf.getInt("spark.executor.instances", YarnSparkHadoopUtil.DEFAULT_NUMBER_EXECUTORS)
     }
 
+  // Executor loss reason requests that are pending - maps from executor ID for inquiry to a
+  // list of requesters that should be responded to once we find out why the given executor
+  // was lost.
+  private val pendingLossReasonRequests = new HashMap[String, mutable.Buffer[RpcCallContext]]
+
   // Keep track of which container is running which executor to remove the executors later
   // Visible for testing.
   private[yarn] val executorIdToContainer = new HashMap[String, Container]
@@ -235,9 +242,7 @@ private[yarn] class YarnAllocator(
     val completedContainers = allocateResponse.getCompletedContainersStatuses()
     if (completedContainers.size > 0) {
       logDebug("Completed %d containers".format(completedContainers.size))
-
       processCompletedContainers(completedContainers.asScala)
-
       logDebug("Finished processing %d completed containers. Current running executor count: %d."
         .format(completedContainers.size, numExecutorsRunning))
     }
@@ -429,7 +434,7 @@ private[yarn] class YarnAllocator(
     for (completedContainer <- completedContainers) {
       val containerId = completedContainer.getContainerId
       val alreadyReleased = releasedContainers.remove(containerId)
-      if (!alreadyReleased) {
+      val exitReason = if (!alreadyReleased) {
         // Decrement the number of executors running. The next iteration of
         // the ApplicationMaster's reporting thread will take care of allocating.
         numExecutorsRunning -= 1
@@ -440,22 +445,42 @@ private[yarn] class YarnAllocator(
         // Hadoop 2.2.X added a ContainerExitStatus we should switch to use
         // there are some exit status' we shouldn't necessarily count against us, but for
         // now I think its ok as none of the containers are expected to exit
-        if (completedContainer.getExitStatus == ContainerExitStatus.PREEMPTED) {
-          logInfo("Container preempted: " + containerId)
-        } else if (completedContainer.getExitStatus == -103) { // vmem limit exceeded
-          logWarning(memLimitExceededLogMessage(
-            completedContainer.getDiagnostics,
-            VMEM_EXCEEDED_PATTERN))
-        } else if (completedContainer.getExitStatus == -104) { // pmem limit exceeded
-          logWarning(memLimitExceededLogMessage(
-            completedContainer.getDiagnostics,
-            PMEM_EXCEEDED_PATTERN))
-        } else if (completedContainer.getExitStatus != 0) {
-          logInfo("Container marked as failed: " + containerId +
-            ". Exit status: " + completedContainer.getExitStatus +
-            ". Diagnostics: " + completedContainer.getDiagnostics)
-          numExecutorsFailed += 1
+        val exitStatus = completedContainer.getExitStatus
+        val (isNormalExit, containerExitReason) = exitStatus match {
+          case ContainerExitStatus.SUCCESS =>
+            (true, s"Executor for container $containerId exited normally.")
+          case ContainerExitStatus.PREEMPTED =>
+            // Preemption should count as a normal exit, since YARN preempts containers merely
+            // to do resource sharing, and tasks that fail due to preempted executors could
+            // just as easily finish on any other executor. See SPARK-8167.
+            (true, s"Container $containerId was preempted.")
+          // Should probably still count memory exceeded exit codes towards task failures
+          case VMEM_EXCEEDED_EXIT_CODE =>
+            (false, memLimitExceededLogMessage(
+              completedContainer.getDiagnostics,
+              VMEM_EXCEEDED_PATTERN))
+          case PMEM_EXCEEDED_EXIT_CODE =>
+            (false, memLimitExceededLogMessage(
+              completedContainer.getDiagnostics,
+              PMEM_EXCEEDED_PATTERN))
+          case unknown =>
+            numExecutorsFailed += 1
+            (false, "Container marked as failed: " + containerId +
+              ". Exit status: " + completedContainer.getExitStatus +
+              ". Diagnostics: " + completedContainer.getDiagnostics)
+
+        }
+        if (isNormalExit) {
+          logInfo(containerExitReason)
+        } else {
+          logWarning(containerExitReason)
         }
+        ExecutorExited(0, isNormalExit, containerExitReason)
+      } else {
+        // If we have already released this container, then it must mean
+        // that the driver has explicitly requested it to be killed
+        ExecutorExited(completedContainer.getExitStatus, isNormalExit = true,
+          s"Container $containerId exited from explicit termination request.")
       }
 
       if (allocatedContainerToHostMap.contains(containerId)) {
@@ -474,18 +499,35 @@ private[yarn] class YarnAllocator(
 
       containerIdToExecutorId.remove(containerId).foreach { eid =>
         executorIdToContainer.remove(eid)
-
+        pendingLossReasonRequests.remove(eid).foreach { pendingRequests =>
+          // Notify application of executor loss reasons so it can decide whether it should abort
+          pendingRequests.foreach(_.reply(exitReason))
+        }
         if (!alreadyReleased) {
           // The executor could have gone away (like no route to host, node failure, etc)
           // Notify backend about the failure of the executor
           numUnexpectedContainerRelease += 1
-          driverRef.send(RemoveExecutor(eid,
-            s"Yarn deallocated the executor $eid (container $containerId)"))
+          driverRef.send(RemoveExecutor(eid, exitReason))
         }
       }
     }
   }
 
+  /**
+   * Register that some RpcCallContext has asked the AM why the executor was lost. Note that
+   * we can only find the loss reason to send back in the next call to allocateResources().
+   */
+  private[yarn] def enqueueGetLossReasonRequest(
+      eid: String,
+      context: RpcCallContext): Unit = synchronized {
+    if (executorIdToContainer.contains(eid)) {
+      pendingLossReasonRequests
+        .getOrElseUpdate(eid, new ArrayBuffer[RpcCallContext]) += context
+    } else {
+      logWarning(s"Tried to get the loss reason for non-existent executor $eid")
+    }
+  }
+
   private def internalReleaseContainer(container: Container): Unit = {
     releasedContainers.add(container.getId())
     amClient.releaseAssignedContainer(container.getId())
@@ -501,6 +543,8 @@ private object YarnAllocator {
     Pattern.compile(s"$MEM_REGEX of $MEM_REGEX physical memory used")
   val VMEM_EXCEEDED_PATTERN =
     Pattern.compile(s"$MEM_REGEX of $MEM_REGEX virtual memory used")
+  val VMEM_EXCEEDED_EXIT_CODE = -103
+  val PMEM_EXCEEDED_EXIT_CODE = -104
 
   def memLimitExceededLogMessage(diagnostics: String, pattern: Pattern): String = {
     val matcher = pattern.matcher(diagnostics)

From f0562e8cdbab7ce40f3186da98595312252f8b5c Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Thu, 10 Sep 2015 12:00:21 -0700
Subject: [PATCH 1378/1454] [SPARK-6350] [MESOS] Fine-grained mode scheduler
 respects mesosExecutor.cores

This is a regression introduced in #4960, this commit fixes it and adds a test.

tnachen andrewor14 please review, this should be an easy one.

Author: Iulian Dragos <jaguarul@gmail.com>

Closes #8653 from dragos/issue/mesos/fine-grained-maxExecutorCores.
---
 .../cluster/mesos/MesosSchedulerBackend.scala |  3 +-
 .../mesos/MesosSchedulerBackendSuite.scala    | 33 ++++++++++++++++++-
 2 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 18da6d2491280..8edf7007a5daf 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -32,7 +32,6 @@ import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.cluster.ExecutorInfo
 import org.apache.spark.util.Utils
 
-
 /**
  * A SchedulerBackend for running fine-grained tasks on Mesos. Each Spark task is mapped to a
  * separate Mesos task, allowing multiple applications to share cluster nodes both in space (tasks
@@ -127,7 +126,7 @@ private[spark] class MesosSchedulerBackend(
     }
     val builder = MesosExecutorInfo.newBuilder()
     val (resourcesAfterCpu, usedCpuResources) =
-      partitionResources(availableResources, "cpus", scheduler.CPUS_PER_TASK)
+      partitionResources(availableResources, "cpus", mesosExecutorCores)
     val (resourcesAfterMem, usedMemResources) =
       partitionResources(resourcesAfterCpu.asJava, "mem", calculateTotalMemory(sc))
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
index 319b3173e7a6e..c4dc560031207 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
@@ -42,6 +42,38 @@ import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkFunSui
 
 class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext with MockitoSugar {
 
+  test("Use configured mesosExecutor.cores for ExecutorInfo") {
+    val mesosExecutorCores = 3
+    val conf = new SparkConf
+    conf.set("spark.mesos.mesosExecutor.cores", mesosExecutorCores.toString)
+
+    val listenerBus = mock[LiveListenerBus]
+    listenerBus.post(
+      SparkListenerExecutorAdded(anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty)))
+
+    val sc = mock[SparkContext]
+    when(sc.getSparkHome()).thenReturn(Option("/spark-home"))
+
+    when(sc.conf).thenReturn(conf)
+    when(sc.executorEnvs).thenReturn(new mutable.HashMap[String, String])
+    when(sc.executorMemory).thenReturn(100)
+    when(sc.listenerBus).thenReturn(listenerBus)
+    val taskScheduler = mock[TaskSchedulerImpl]
+    when(taskScheduler.CPUS_PER_TASK).thenReturn(2)
+
+    val mesosSchedulerBackend = new MesosSchedulerBackend(taskScheduler, sc, "master")
+
+    val resources = Arrays.asList(
+      mesosSchedulerBackend.createResource("cpus", 4),
+      mesosSchedulerBackend.createResource("mem", 1024))
+    // uri is null.
+    val (executorInfo, _) = mesosSchedulerBackend.createExecutorInfo(resources, "test-id")
+    val executorResources = executorInfo.getResourcesList
+    val cpus = executorResources.asScala.find(_.getName.equals("cpus")).get.getScalar.getValue
+
+    assert(cpus === mesosExecutorCores)
+  }
+
   test("check spark-class location correctly") {
     val conf = new SparkConf
     conf.set("spark.mesos.executor.home" , "/mesos-home")
@@ -263,7 +295,6 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
       .setSlaveId(SlaveID.newBuilder().setValue(s"s${id.toString}"))
       .setHostname(s"host${id.toString}").build()
 
-
     val mesosOffers = new java.util.ArrayList[Offer]
     mesosOffers.add(offer)
 

From a5ef2d0600d5e23ca05fabc1005bb81e5ada0727 Mon Sep 17 00:00:00 2001
From: Akash Mishra <akash.mishra20@gmail.com>
Date: Thu, 10 Sep 2015 12:03:11 -0700
Subject: [PATCH 1379/1454] [SPARK-10514] [MESOS] waiting for min no of total
 cores acquired by Spark by implementing the sufficientResourcesRegistered
 method

spark.scheduler.minRegisteredResourcesRatio configuration parameter works for YARN mode but not for Mesos Coarse grained mode.

If the parameter specified default value of 0 will be set for spark.scheduler.minRegisteredResourcesRatio in base class and this method will always return true.

There are no existing test for YARN mode too. Hence not added test for the same.

Author: Akash Mishra <akash.mishra20@gmail.com>

Closes #8672 from SleepyThread/master.
---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala          | 4 ++++
 docs/configuration.md                                        | 5 +++--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 65df8874774ca..65cb5016cfcc9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -222,6 +222,10 @@ private[spark] class CoarseMesosSchedulerBackend(
     markRegistered()
   }
 
+  override def sufficientResourcesRegistered(): Boolean = {
+    totalCoresAcquired >= maxCores * minRegisteredRatio
+  }
+
   override def disconnected(d: SchedulerDriver) {}
 
   override def reregistered(d: SchedulerDriver, masterInfo: MasterInfo) {}
diff --git a/docs/configuration.md b/docs/configuration.md
index 0b1a273916314..1a701f18881fe 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1112,10 +1112,11 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.scheduler.minRegisteredResourcesRatio</code></td>
-  <td>0.8 for YARN mode; 0.0 otherwise</td>
+  <td>0.8 for YARN mode; 0.0 for standalone mode and Mesos coarse-grained mode</td>
   <td>
     The minimum ratio of registered resources (registered resources / total expected resources)
-    (resources are executors in yarn mode, CPU cores in standalone mode)
+    (resources are executors in yarn mode, CPU cores in standalone mode and Mesos coarsed-grained
+     mode ['spark.cores.max' value is total expected resources for Mesos coarse-grained mode] )
     to wait for before scheduling begins. Specified as a double between 0.0 and 1.0.
     Regardless of whether the minimum ratio of resources has been reached,
     the maximum amount of time it will wait before scheduling begins is controlled by config

From d88abb7e212fb55f9b0398a0f76a753c86b85cf1 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 10 Sep 2015 12:06:49 -0700
Subject: [PATCH 1380/1454] [SPARK-9990] [SQL] Create local hash join operator

This PR includes the following changes:
- Add SQLConf to LocalNode
- Add HashJoinNode
- Add ConvertToUnsafeNode and ConvertToSafeNode.scala to test unsafe hash join.

Author: zsxwing <zsxwing@gmail.com>

Closes #8535 from zsxwing/SPARK-9990.
---
 .../sql/execution/joins/HashedRelation.scala  |   4 +-
 .../execution/local/ConvertToSafeNode.scala   |  40 +++++
 .../execution/local/ConvertToUnsafeNode.scala |  40 +++++
 .../sql/execution/local/FilterNode.scala      |   4 +-
 .../sql/execution/local/HashJoinNode.scala    | 137 ++++++++++++++++++
 .../spark/sql/execution/local/LimitNode.scala |   3 +-
 .../spark/sql/execution/local/LocalNode.scala |  83 ++++++++++-
 .../sql/execution/local/ProjectNode.scala     |   4 +-
 .../sql/execution/local/SeqScanNode.scala     |   4 +-
 .../spark/sql/execution/local/UnionNode.scala |   3 +-
 .../sql/execution/local/FilterNodeSuite.scala |   4 +-
 .../execution/local/HashJoinNodeSuite.scala   | 130 +++++++++++++++++
 .../sql/execution/local/LimitNodeSuite.scala  |   4 +-
 .../sql/execution/local/LocalNodeTest.scala   |   9 +-
 .../execution/local/ProjectNodeSuite.scala    |   4 +-
 .../sql/execution/local/UnionNodeSuite.scala  |   6 +-
 16 files changed, 455 insertions(+), 24 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 6c0196c21a0d1..0cff21ca618b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -38,7 +38,7 @@ import org.apache.spark.{SparkConf, SparkEnv}
  * Interface for a hashed relation by some key. Use [[HashedRelation.apply]] to create a concrete
  * object.
  */
-private[joins] sealed trait HashedRelation {
+private[execution] sealed trait HashedRelation {
   def get(key: InternalRow): Seq[InternalRow]
 
   // This is a helper method to implement Externalizable, and is used by
@@ -111,7 +111,7 @@ final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalR
 // TODO(rxin): a version of [[HashedRelation]] backed by arrays for consecutive integer keys.
 
 
-private[joins] object HashedRelation {
+private[execution] object HashedRelation {
 
   def apply(
       input: Iterator[InternalRow],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala
new file mode 100644
index 0000000000000..b31c5a863832e
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala
@@ -0,0 +1,40 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, FromUnsafeProjection, Projection}
+
+case class ConvertToSafeNode(conf: SQLConf, child: LocalNode) extends UnaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = child.output
+
+  private[this] var convertToSafe: Projection = _
+
+  override def open(): Unit = {
+    child.open()
+    convertToSafe = FromUnsafeProjection(child.schema)
+  }
+
+  override def next(): Boolean = child.next()
+
+  override def fetch(): InternalRow = convertToSafe(child.fetch())
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala
new file mode 100644
index 0000000000000..de2f4e661ab44
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala
@@ -0,0 +1,40 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Projection, UnsafeProjection}
+
+case class ConvertToUnsafeNode(conf: SQLConf, child: LocalNode) extends UnaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = child.output
+
+  private[this] var convertToUnsafe: Projection = _
+
+  override def open(): Unit = {
+    child.open()
+    convertToUnsafe = UnsafeProjection.create(child.schema)
+  }
+
+  override def next(): Boolean = child.next()
+
+  override def fetch(): InternalRow = convertToUnsafe(child.fetch())
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
index 81dd37c7da733..dd1113b6726cf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
@@ -17,12 +17,14 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
 import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
 
 
-case class FilterNode(condition: Expression, child: LocalNode) extends UnaryLocalNode {
+case class FilterNode(conf: SQLConf, condition: Expression, child: LocalNode)
+  extends UnaryLocalNode(conf) {
 
   private[this] var predicate: (InternalRow) => Boolean = _
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
new file mode 100644
index 0000000000000..a3e68d6a7c341
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
@@ -0,0 +1,137 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.joins._
+import org.apache.spark.sql.execution.metric.SQLMetrics
+
+/**
+ * Much of this code is similar to [[org.apache.spark.sql.execution.joins.HashJoin]].
+ */
+case class HashJoinNode(
+    conf: SQLConf,
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    buildSide: BuildSide,
+    left: LocalNode,
+    right: LocalNode) extends BinaryLocalNode(conf) {
+
+  private[this] lazy val (buildNode, buildKeys, streamedNode, streamedKeys) = buildSide match {
+    case BuildLeft => (left, leftKeys, right, rightKeys)
+    case BuildRight => (right, rightKeys, left, leftKeys)
+  }
+
+  private[this] var currentStreamedRow: InternalRow = _
+  private[this] var currentHashMatches: Seq[InternalRow] = _
+  private[this] var currentMatchPosition: Int = -1
+
+  private[this] var joinRow: JoinedRow = _
+  private[this] var resultProjection: (InternalRow) => InternalRow = _
+
+  private[this] var hashed: HashedRelation = _
+  private[this] var joinKeys: Projection = _
+
+  override def output: Seq[Attribute] = left.output ++ right.output
+
+  private[this] def isUnsafeMode: Boolean = {
+    (codegenEnabled && unsafeEnabled
+      && UnsafeProjection.canSupport(buildKeys)
+      && UnsafeProjection.canSupport(schema))
+  }
+
+  private[this] def buildSideKeyGenerator: Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(buildKeys, buildNode.output)
+    } else {
+      newMutableProjection(buildKeys, buildNode.output)()
+    }
+  }
+
+  private[this] def streamSideKeyGenerator: Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(streamedKeys, streamedNode.output)
+    } else {
+      newMutableProjection(streamedKeys, streamedNode.output)()
+    }
+  }
+
+  override def open(): Unit = {
+    buildNode.open()
+    hashed = HashedRelation.apply(
+      new LocalNodeIterator(buildNode), SQLMetrics.nullLongMetric, buildSideKeyGenerator)
+    streamedNode.open()
+    joinRow = new JoinedRow
+    resultProjection = {
+      if (isUnsafeMode) {
+        UnsafeProjection.create(schema)
+      } else {
+        identity[InternalRow]
+      }
+    }
+    joinKeys = streamSideKeyGenerator
+  }
+
+  override def next(): Boolean = {
+    currentMatchPosition += 1
+    if (currentHashMatches == null || currentMatchPosition >= currentHashMatches.size) {
+      fetchNextMatch()
+    } else {
+      true
+    }
+  }
+
+  /**
+   * Populate `currentHashMatches` with build-side rows matching the next streamed row.
+   * @return whether matches are found such that subsequent calls to `fetch` are valid.
+   */
+  private def fetchNextMatch(): Boolean = {
+    currentHashMatches = null
+    currentMatchPosition = -1
+
+    while (currentHashMatches == null && streamedNode.next()) {
+      currentStreamedRow = streamedNode.fetch()
+      val key = joinKeys(currentStreamedRow)
+      if (!key.anyNull) {
+        currentHashMatches = hashed.get(key)
+      }
+    }
+
+    if (currentHashMatches == null) {
+      false
+    } else {
+      currentMatchPosition = 0
+      true
+    }
+  }
+
+  override def fetch(): InternalRow = {
+    val ret = buildSide match {
+      case BuildRight => joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition))
+      case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow)
+    }
+    resultProjection(ret)
+  }
+
+  override def close(): Unit = {
+    left.close()
+    right.close()
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
index fffc52abf6dd5..401b10a5ed307 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
@@ -17,11 +17,12 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 
 
-case class LimitNode(limit: Int, child: LocalNode) extends UnaryLocalNode {
+case class LimitNode(conf: SQLConf, limit: Int, child: LocalNode) extends UnaryLocalNode(conf) {
 
   private[this] var count = 0
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index 1c4469acbf264..c4f8ae304db39 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -17,9 +17,13 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.sql.Row
+import scala.util.control.NonFatal
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.{SQLConf, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types.StructType
 
@@ -29,7 +33,15 @@ import org.apache.spark.sql.types.StructType
  * Before consuming the iterator, open function must be called.
  * After consuming the iterator, close function must be called.
  */
-abstract class LocalNode extends TreeNode[LocalNode] {
+abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging {
+
+  protected val codegenEnabled: Boolean = conf.codegenEnabled
+
+  protected val unsafeEnabled: Boolean = conf.unsafeEnabled
+
+  lazy val schema: StructType = StructType.fromAttributes(output)
+
+  private[this] lazy val isTesting: Boolean = sys.props.contains("spark.testing")
 
   def output: Seq[Attribute]
 
@@ -73,17 +85,78 @@ abstract class LocalNode extends TreeNode[LocalNode] {
     }
     result
   }
+
+  protected def newMutableProjection(
+      expressions: Seq[Expression],
+      inputSchema: Seq[Attribute]): () => MutableProjection = {
+    log.debug(
+      s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
+    if (codegenEnabled) {
+      try {
+        GenerateMutableProjection.generate(expressions, inputSchema)
+      } catch {
+        case NonFatal(e) =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate mutable projection, fallback to interpreted", e)
+            () => new InterpretedMutableProjection(expressions, inputSchema)
+          }
+      }
+    } else {
+      () => new InterpretedMutableProjection(expressions, inputSchema)
+    }
+  }
+
 }
 
 
-abstract class LeafLocalNode extends LocalNode {
+abstract class LeafLocalNode(conf: SQLConf) extends LocalNode(conf) {
   override def children: Seq[LocalNode] = Seq.empty
 }
 
 
-abstract class UnaryLocalNode extends LocalNode {
+abstract class UnaryLocalNode(conf: SQLConf) extends LocalNode(conf) {
 
   def child: LocalNode
 
   override def children: Seq[LocalNode] = Seq(child)
 }
+
+abstract class BinaryLocalNode(conf: SQLConf) extends LocalNode(conf) {
+
+  def left: LocalNode
+
+  def right: LocalNode
+
+  override def children: Seq[LocalNode] = Seq(left, right)
+}
+
+/**
+ * An thin wrapper around a [[LocalNode]] that provides an `Iterator` interface.
+ */
+private[local] class LocalNodeIterator(localNode: LocalNode) extends Iterator[InternalRow] {
+  private var nextRow: InternalRow = _
+
+  override def hasNext: Boolean = {
+    if (nextRow == null) {
+      val res = localNode.next()
+      if (res) {
+        nextRow = localNode.fetch()
+      }
+      res
+    } else {
+      true
+    }
+  }
+
+  override def next(): InternalRow = {
+    if (hasNext) {
+      val res = nextRow
+      nextRow = null
+      res
+    } else {
+      throw new NoSuchElementException
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
index 9b8a4fe493026..11529d6dd9b83 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, Attribute, NamedExpression}
 
 
-case class ProjectNode(projectList: Seq[NamedExpression], child: LocalNode) extends UnaryLocalNode {
+case class ProjectNode(conf: SQLConf, projectList: Seq[NamedExpression], child: LocalNode)
+  extends UnaryLocalNode(conf) {
 
   private[this] var project: UnsafeProjection = _
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
index 242cb66e07b7f..b8467f6ae58e0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
@@ -17,13 +17,15 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 
 /**
  * An operator that scans some local data collection in the form of Scala Seq.
  */
-case class SeqScanNode(output: Seq[Attribute], data: Seq[InternalRow]) extends LeafLocalNode {
+case class SeqScanNode(conf: SQLConf, output: Seq[Attribute], data: Seq[InternalRow])
+  extends LeafLocalNode(conf) {
 
   private[this] var iterator: Iterator[InternalRow] = _
   private[this] var currentRow: InternalRow = _
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
index ba4aa7671aebd..0f2b8303e7372 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
@@ -17,10 +17,11 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 
-case class UnionNode(children: Seq[LocalNode]) extends LocalNode {
+case class UnionNode(conf: SQLConf, children: Seq[LocalNode]) extends LocalNode(conf) {
 
   override def output: Seq[Attribute] = children.head.output
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
index 07209f3779248..a12670e347c25 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
@@ -25,7 +25,7 @@ class FilterNodeSuite extends LocalNodeTest with SharedSQLContext {
     val condition = (testData.col("key") % 2) === 0
     checkAnswer(
       testData,
-      node => FilterNode(condition.expr, node),
+      node => FilterNode(conf, condition.expr, node),
       testData.filter(condition).collect()
     )
   }
@@ -34,7 +34,7 @@ class FilterNodeSuite extends LocalNodeTest with SharedSQLContext {
     val condition = (emptyTestData.col("key") % 2) === 0
     checkAnswer(
       emptyTestData,
-      node => FilterNode(condition.expr, node),
+      node => FilterNode(conf, condition.expr, node),
       emptyTestData.filter(condition).collect()
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
new file mode 100644
index 0000000000000..43b6f06aead88
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
@@ -0,0 +1,130 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.execution.joins
+
+class HashJoinNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def wrapForUnsafe(
+      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
+    if (conf.unsafeEnabled) {
+      (left: LocalNode, right: LocalNode) => {
+        val _left = ConvertToUnsafeNode(conf, left)
+        val _right = ConvertToUnsafeNode(conf, right)
+        val r = f(_left, _right)
+        ConvertToSafeNode(conf, r)
+      }
+    } else {
+      f
+    }
+  }
+
+  def joinSuite(suiteName: String, confPairs: (String, String)*): Unit = {
+    test(s"$suiteName: inner join with one match per row") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => HashJoinNode(
+              conf,
+              Seq(upperCaseData.col("N").expr),
+              Seq(lowerCaseData.col("n").expr),
+              joins.BuildLeft,
+              node1,
+              node2)
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N").collect()
+        )
+      }
+    }
+
+    test(s"$suiteName: inner join with multiple matches") {
+      withSQLConf(confPairs: _*) {
+        val x = testData2.where($"a" === 1).as("x")
+        val y = testData2.where($"a" === 1).as("y")
+        checkAnswer2(
+          x,
+          y,
+          wrapForUnsafe(
+            (node1, node2) => HashJoinNode(
+              conf,
+              Seq(x.col("a").expr),
+              Seq(y.col("a").expr),
+              joins.BuildLeft,
+              node1,
+              node2)
+          ),
+          x.join(y).where($"x.a" === $"y.a").collect()
+        )
+      }
+    }
+
+    test(s"$suiteName: inner join, no matches") {
+      withSQLConf(confPairs: _*) {
+        val x = testData2.where($"a" === 1).as("x")
+        val y = testData2.where($"a" === 2).as("y")
+        checkAnswer2(
+          x,
+          y,
+          wrapForUnsafe(
+            (node1, node2) => HashJoinNode(
+              conf,
+              Seq(x.col("a").expr),
+              Seq(y.col("a").expr),
+              joins.BuildLeft,
+              node1,
+              node2)
+          ),
+          Nil
+        )
+      }
+    }
+
+    test(s"$suiteName: big inner join, 4 matches per row") {
+      withSQLConf(confPairs: _*) {
+        val bigData = testData.unionAll(testData).unionAll(testData).unionAll(testData)
+        val bigDataX = bigData.as("x")
+        val bigDataY = bigData.as("y")
+
+        checkAnswer2(
+          bigDataX,
+          bigDataY,
+          wrapForUnsafe(
+            (node1, node2) =>
+              HashJoinNode(
+                conf,
+                Seq(bigDataX.col("key").expr),
+                Seq(bigDataY.col("key").expr),
+                joins.BuildLeft,
+                node1,
+                node2)
+          ),
+          bigDataX.join(bigDataY).where($"x.key" === $"y.key").collect())
+      }
+    }
+  }
+
+  joinSuite(
+    "general", SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
+  joinSuite("tungsten", SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
index 523c02f4a6014..3b183902007e4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
@@ -24,7 +24,7 @@ class LimitNodeSuite extends LocalNodeTest with SharedSQLContext {
   test("basic") {
     checkAnswer(
       testData,
-      node => LimitNode(10, node),
+      node => LimitNode(conf, 10, node),
       testData.limit(10).collect()
     )
   }
@@ -32,7 +32,7 @@ class LimitNodeSuite extends LocalNodeTest with SharedSQLContext {
   test("empty") {
     checkAnswer(
       emptyTestData,
-      node => LimitNode(10, node),
+      node => LimitNode(conf, 10, node),
       emptyTestData.limit(10).collect()
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
index 95f06081bd0a8..b95d4ea7f8f2a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
@@ -20,10 +20,12 @@ package org.apache.spark.sql.execution.local
 import scala.util.control.NonFatal
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{DataFrame, Row}
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.{DataFrame, Row, SQLConf}
+import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils}
 
-class LocalNodeTest extends SparkFunSuite {
+class LocalNodeTest extends SparkFunSuite with SharedSQLContext {
+
+  def conf: SQLConf = sqlContext.conf
 
   /**
    * Runs the LocalNode and makes sure the answer matches the expected result.
@@ -92,6 +94,7 @@ class LocalNodeTest extends SparkFunSuite {
 
   protected def dataFrameToSeqScanNode(df: DataFrame): SeqScanNode = {
     new SeqScanNode(
+      conf,
       df.queryExecution.sparkPlan.output,
       df.queryExecution.toRdd.map(_.copy()).collect())
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
index ffcf092e2c66a..38e0a230c46d8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
@@ -26,7 +26,7 @@ class ProjectNodeSuite extends LocalNodeTest with SharedSQLContext {
     val columns = Seq(output(1), output(0))
     checkAnswer(
       testData,
-      node => ProjectNode(columns, node),
+      node => ProjectNode(conf, columns, node),
       testData.select("value", "key").collect()
     )
   }
@@ -36,7 +36,7 @@ class ProjectNodeSuite extends LocalNodeTest with SharedSQLContext {
     val columns = Seq(output(1), output(0))
     checkAnswer(
       emptyTestData,
-      node => ProjectNode(columns, node),
+      node => ProjectNode(conf, columns, node),
       emptyTestData.select("value", "key").collect()
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
index 34670287c3e1d..eedd7320900f9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
@@ -25,7 +25,7 @@ class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
     checkAnswer2(
       testData,
       testData,
-      (node1, node2) => UnionNode(Seq(node1, node2)),
+      (node1, node2) => UnionNode(conf, Seq(node1, node2)),
       testData.unionAll(testData).collect()
     )
   }
@@ -34,7 +34,7 @@ class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
     checkAnswer2(
       emptyTestData,
       emptyTestData,
-      (node1, node2) => UnionNode(Seq(node1, node2)),
+      (node1, node2) => UnionNode(conf, Seq(node1, node2)),
       emptyTestData.unionAll(emptyTestData).collect()
     )
   }
@@ -44,7 +44,7 @@ class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
       emptyTestData, emptyTestData, testData, emptyTestData)
     doCheckAnswer(
       dfs,
-      nodes => UnionNode(nodes),
+      nodes => UnionNode(conf, nodes),
       dfs.reduce(_.unionAll(_)).collect()
     )
   }

From 45e3be5c138d983f40f619735d60bf7eb78c9bf0 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Thu, 10 Sep 2015 12:21:13 -0700
Subject: [PATCH 1381/1454] [SPARK-10049] [SPARKR] Support collecting data of
 ArraryType in DataFrame.

this PR :
1.  Enhance reflection in RBackend. Automatically matching a Java array to Scala Seq when finding methods. Util functions like seq(), listToSeq() in R side can be removed, as they will conflict with the Serde logic that transferrs a Scala seq to R side.

2.  Enhance the SerDe to support transferring  a Scala seq to R side. Data of ArrayType in DataFrame
after collection is observed to be of Scala Seq type.

3.  Support ArrayType in createDataFrame().

Author: Sun Rui <rui.sun@intel.com>

Closes #8458 from sun-rui/SPARK-10049.
---
 R/pkg/R/DataFrame.R                           |  26 ++--
 R/pkg/R/SQLContext.R                          |   4 +-
 R/pkg/R/column.R                              |   3 +-
 R/pkg/R/functions.R                           |  12 +-
 R/pkg/R/group.R                               |   4 +-
 R/pkg/R/schema.R                              |  54 +++++---
 R/pkg/R/utils.R                               |  10 --
 R/pkg/inst/tests/test_sparkSQL.R              |  44 ++++++-
 .../apache/spark/api/r/RBackendHandler.scala  | 121 +++++++++++++-----
 .../scala/org/apache/spark/api/r/SerDe.scala  | 109 ++++++++--------
 .../org/apache/spark/sql/api/r/SQLUtils.scala |  14 +-
 11 files changed, 250 insertions(+), 151 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 8a00238b41d60..c3c1893487334 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -271,7 +271,7 @@ setMethod("names<-",
           signature(x = "DataFrame"),
           function(x, value) {
             if (!is.null(value)) {
-              sdf <- callJMethod(x@sdf, "toDF", listToSeq(as.list(value)))
+              sdf <- callJMethod(x@sdf, "toDF", as.list(value))
               dataFrame(sdf)
             }
           })
@@ -843,10 +843,10 @@ setMethod("groupBy",
            function(x, ...) {
              cols <- list(...)
              if (length(cols) >= 1 && class(cols[[1]]) == "character") {
-               sgd <- callJMethod(x@sdf, "groupBy", cols[[1]], listToSeq(cols[-1]))
+               sgd <- callJMethod(x@sdf, "groupBy", cols[[1]], cols[-1])
              } else {
                jcol <- lapply(cols, function(c) { c@jc })
-               sgd <- callJMethod(x@sdf, "groupBy", listToSeq(jcol))
+               sgd <- callJMethod(x@sdf, "groupBy", jcol)
              }
              groupedData(sgd)
            })
@@ -1079,7 +1079,7 @@ setMethod("subset", signature(x = "DataFrame"),
 #' }
 setMethod("select", signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
-            sdf <- callJMethod(x@sdf, "select", col, toSeq(...))
+            sdf <- callJMethod(x@sdf, "select", col, list(...))
             dataFrame(sdf)
           })
 
@@ -1090,7 +1090,7 @@ setMethod("select", signature(x = "DataFrame", col = "Column"),
             jcols <- lapply(list(col, ...), function(c) {
               c@jc
             })
-            sdf <- callJMethod(x@sdf, "select", listToSeq(jcols))
+            sdf <- callJMethod(x@sdf, "select", jcols)
             dataFrame(sdf)
           })
 
@@ -1106,7 +1106,7 @@ setMethod("select",
                 col(c)@jc
               }
             })
-            sdf <- callJMethod(x@sdf, "select", listToSeq(cols))
+            sdf <- callJMethod(x@sdf, "select", cols)
             dataFrame(sdf)
           })
 
@@ -1133,7 +1133,7 @@ setMethod("selectExpr",
           signature(x = "DataFrame", expr = "character"),
           function(x, expr, ...) {
             exprList <- list(expr, ...)
-            sdf <- callJMethod(x@sdf, "selectExpr", listToSeq(exprList))
+            sdf <- callJMethod(x@sdf, "selectExpr", exprList)
             dataFrame(sdf)
           })
 
@@ -1311,12 +1311,12 @@ setMethod("arrange",
           signature(x = "DataFrame", col = "characterOrColumn"),
           function(x, col, ...) {
             if (class(col) == "character") {
-              sdf <- callJMethod(x@sdf, "sort", col, toSeq(...))
+              sdf <- callJMethod(x@sdf, "sort", col, list(...))
             } else if (class(col) == "Column") {
               jcols <- lapply(list(col, ...), function(c) {
                 c@jc
               })
-              sdf <- callJMethod(x@sdf, "sort", listToSeq(jcols))
+              sdf <- callJMethod(x@sdf, "sort", jcols)
             }
             dataFrame(sdf)
           })
@@ -1664,7 +1664,7 @@ setMethod("describe",
           signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
             colList <- list(col, ...)
-            sdf <- callJMethod(x@sdf, "describe", listToSeq(colList))
+            sdf <- callJMethod(x@sdf, "describe", colList)
             dataFrame(sdf)
           })
 
@@ -1674,7 +1674,7 @@ setMethod("describe",
           signature(x = "DataFrame"),
           function(x) {
             colList <- as.list(c(columns(x)))
-            sdf <- callJMethod(x@sdf, "describe", listToSeq(colList))
+            sdf <- callJMethod(x@sdf, "describe", colList)
             dataFrame(sdf)
           })
 
@@ -1731,7 +1731,7 @@ setMethod("dropna",
 
             naFunctions <- callJMethod(x@sdf, "na")
             sdf <- callJMethod(naFunctions, "drop",
-                               as.integer(minNonNulls), listToSeq(as.list(cols)))
+                               as.integer(minNonNulls), as.list(cols))
             dataFrame(sdf)
           })
 
@@ -1815,7 +1815,7 @@ setMethod("fillna",
             sdf <- if (length(cols) == 0) {
               callJMethod(naFunctions, "fill", value)
             } else {
-              callJMethod(naFunctions, "fill", value, listToSeq(as.list(cols)))
+              callJMethod(naFunctions, "fill", value, as.list(cols))
             }
             dataFrame(sdf)
           })
diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 1bc6445311473..4ac057d0f2d83 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -49,7 +49,7 @@ infer_type <- function(x) {
     stopifnot(length(x) > 0)
     names <- names(x)
     if (is.null(names)) {
-      list(type = "array", elementType = infer_type(x[[1]]), containsNull = TRUE)
+      paste0("array<", infer_type(x[[1]]), ">")
     } else {
       # StructType
       types <- lapply(x, infer_type)
@@ -59,7 +59,7 @@ infer_type <- function(x) {
       do.call(structType, fields)
     }
   } else if (length(x) > 1) {
-    list(type = "array", elementType = type, containsNull = TRUE)
+    paste0("array<", infer_type(x[[1]]), ">")
   } else {
     type
   }
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 4805096f3f9c5..42e9d12179db7 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -211,8 +211,7 @@ setMethod("cast",
 setMethod("%in%",
           signature(x = "Column"),
           function(x, table) {
-            table <- listToSeq(as.list(table))
-            jc <- callJMethod(x@jc, "in", table)
+            jc <- callJMethod(x@jc, "in", as.list(table))
             return(column(jc))
           })
 
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index d848730e70433..94687edb05442 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -1331,7 +1331,7 @@ setMethod("countDistinct",
               x@jc
             })
             jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc,
-                              listToSeq(jcol))
+                              jcol)
             column(jc)
           })
 
@@ -1348,7 +1348,7 @@ setMethod("concat",
           signature(x = "Column"),
           function(x, ...) {
             jcols <- lapply(list(x, ...), function(x) { x@jc })
-            jc <- callJStatic("org.apache.spark.sql.functions", "concat", listToSeq(jcols))
+            jc <- callJStatic("org.apache.spark.sql.functions", "concat", jcols)
             column(jc)
           })
 
@@ -1366,7 +1366,7 @@ setMethod("greatest",
           function(x, ...) {
             stopifnot(length(list(...)) > 0)
             jcols <- lapply(list(x, ...), function(x) { x@jc })
-            jc <- callJStatic("org.apache.spark.sql.functions", "greatest", listToSeq(jcols))
+            jc <- callJStatic("org.apache.spark.sql.functions", "greatest", jcols)
             column(jc)
           })
 
@@ -1384,7 +1384,7 @@ setMethod("least",
           function(x, ...) {
             stopifnot(length(list(...)) > 0)
             jcols <- lapply(list(x, ...), function(x) { x@jc })
-            jc <- callJStatic("org.apache.spark.sql.functions", "least", listToSeq(jcols))
+            jc <- callJStatic("org.apache.spark.sql.functions", "least", jcols)
             column(jc)
           })
 
@@ -1675,7 +1675,7 @@ setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
 #' @export
 setMethod("concat_ws", signature(sep = "character", x = "Column"),
           function(sep, x, ...) {
-            jcols <- listToSeq(lapply(list(x, ...), function(x) { x@jc }))
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
             jc <- callJStatic("org.apache.spark.sql.functions", "concat_ws", sep, jcols)
             column(jc)
           })
@@ -1723,7 +1723,7 @@ setMethod("expr", signature(x = "character"),
 #' @export
 setMethod("format_string", signature(format = "character", x = "Column"),
           function(format, x, ...) {
-            jcols <- listToSeq(lapply(list(x, ...), function(arg) { arg@jc }))
+            jcols <- lapply(list(x, ...), function(arg) { arg@jc })
             jc <- callJStatic("org.apache.spark.sql.functions",
                               "format_string",
                               format, jcols)
diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R
index 576ac72f40fc0..4cab1a69f601a 100644
--- a/R/pkg/R/group.R
+++ b/R/pkg/R/group.R
@@ -102,7 +102,7 @@ setMethod("agg",
                 }
               }
               jcols <- lapply(cols, function(c) { c@jc })
-              sdf <- callJMethod(x@sgd, "agg", jcols[[1]], listToSeq(jcols[-1]))
+              sdf <- callJMethod(x@sgd, "agg", jcols[[1]], jcols[-1])
             } else {
               stop("agg can only support Column or character")
             }
@@ -124,7 +124,7 @@ createMethod <- function(name) {
   setMethod(name,
             signature(x = "GroupedData"),
             function(x, ...) {
-              sdf <- callJMethod(x@sgd, name, toSeq(...))
+              sdf <- callJMethod(x@sgd, name, list(...))
               dataFrame(sdf)
             })
 }
diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R
index 79c744ef29c23..62d4f73878d29 100644
--- a/R/pkg/R/schema.R
+++ b/R/pkg/R/schema.R
@@ -56,7 +56,7 @@ structType.structField <- function(x, ...) {
   })
   stObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils",
                        "createStructType",
-                       listToSeq(sfObjList))
+                       sfObjList)
   structType(stObj)
 }
 
@@ -114,6 +114,35 @@ structField.jobj <- function(x) {
   obj
 }
 
+checkType <- function(type) {
+  primtiveTypes <- c("byte",
+                     "integer",
+                     "float",
+                     "double",
+                     "numeric",
+                     "character",
+                     "string",
+                     "binary",
+                     "raw",
+                     "logical",
+                     "boolean",
+                     "timestamp",
+                     "date")
+  if (type %in% primtiveTypes) {
+    return()
+  } else {
+    m <- regexec("^array<(.*)>$", type)
+    matchedStrings <- regmatches(type, m)
+    if (length(matchedStrings[[1]]) >= 2) {
+      elemType <- matchedStrings[[1]][2]
+      checkType(elemType)
+      return()
+    }
+  }
+
+  stop(paste("Unsupported type for Dataframe:", type))
+}
+
 structField.character <- function(x, type, nullable = TRUE) {
   if (class(x) != "character") {
     stop("Field name must be a string.")
@@ -124,28 +153,13 @@ structField.character <- function(x, type, nullable = TRUE) {
   if (class(nullable) != "logical") {
     stop("nullable must be either TRUE or FALSE")
   }
-  options <- c("byte",
-               "integer",
-               "float",
-               "double",
-               "numeric",
-               "character",
-               "string",
-               "binary",
-               "raw",
-               "logical",
-               "boolean",
-               "timestamp",
-               "date")
-  dataType <- if (type %in% options) {
-    type
-  } else {
-    stop(paste("Unsupported type for Dataframe:", type))
-  }
+
+  checkType(type)
+
   sfObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils",
                        "createStructField",
                        x,
-                       dataType,
+                       type,
                        nullable)
   structField(sfObj)
 }
diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 3babcb519378e..69a2bc728f842 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -361,16 +361,6 @@ numToInt <- function(num) {
   as.integer(num)
 }
 
-# create a Seq in JVM
-toSeq <- function(...) {
-  callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", list(...))
-}
-
-# create a Seq in JVM from a list
-listToSeq <- function(l) {
-  callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", l)
-}
-
 # Utility function to recursively traverse the Abstract Syntax Tree (AST) of a
 # user defined function (UDF), and to examine variables in the UDF to decide
 # if their values should be included in the new function environment.
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 6d331f9883d55..1ccfde59176f5 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -49,6 +49,14 @@ mockLinesNa <- c("{\"name\":\"Bob\",\"age\":16,\"height\":176.5}",
 jsonPathNa <- tempfile(pattern="sparkr-test", fileext=".tmp")
 writeLines(mockLinesNa, jsonPathNa)
 
+# For test complex types in DataFrame
+mockLinesComplexType <-
+  c("{\"c1\":[1, 2, 3], \"c2\":[\"a\", \"b\", \"c\"], \"c3\":[1.0, 2.0, 3.0]}",
+    "{\"c1\":[4, 5, 6], \"c2\":[\"d\", \"e\", \"f\"], \"c3\":[4.0, 5.0, 6.0]}",
+    "{\"c1\":[7, 8, 9], \"c2\":[\"g\", \"h\", \"i\"], \"c3\":[7.0, 8.0, 9.0]}")
+complexTypeJsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
+writeLines(mockLinesComplexType, complexTypeJsonPath)
+
 test_that("infer types", {
   expect_equal(infer_type(1L), "integer")
   expect_equal(infer_type(1.0), "double")
@@ -56,10 +64,8 @@ test_that("infer types", {
   expect_equal(infer_type(TRUE), "boolean")
   expect_equal(infer_type(as.Date("2015-03-11")), "date")
   expect_equal(infer_type(as.POSIXlt("2015-03-11 12:13:04.043")), "timestamp")
-  expect_equal(infer_type(c(1L, 2L)),
-               list(type = "array", elementType = "integer", containsNull = TRUE))
-  expect_equal(infer_type(list(1L, 2L)),
-               list(type = "array", elementType = "integer", containsNull = TRUE))
+  expect_equal(infer_type(c(1L, 2L)), "array<integer>")
+  expect_equal(infer_type(list(1L, 2L)), "array<integer>")
   testStruct <- infer_type(list(a = 1L, b = "2"))
   expect_equal(class(testStruct), "structType")
   checkStructField(testStruct$fields()[[1]], "a", "IntegerType", TRUE)
@@ -236,8 +242,7 @@ test_that("create DataFrame with different data types", {
   expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE))
 })
 
-# TODO: enable this test after fix serialization for nested object
-#test_that("create DataFrame with nested array and struct", {
+test_that("create DataFrame with nested array and struct", {
 #  e <- new.env()
 #  assign("n", 3L, envir = e)
 #  l <- list(1:10, list("a", "b"), e, list(a="aa", b=3L))
@@ -247,7 +252,32 @@ test_that("create DataFrame with different data types", {
 #  expect_equal(count(df), 1)
 #  ldf <- collect(df)
 #  expect_equal(ldf[1,], l[[1]])
-#})
+
+
+  #  ArrayType only for now
+  l <- list(as.list(1:10), list("a", "b"))
+  df <- createDataFrame(sqlContext, list(l), c("a", "b"))
+  expect_equal(dtypes(df), list(c("a", "array<int>"), c("b", "array<string>")))
+  expect_equal(count(df), 1)
+  ldf <- collect(df)
+  expect_equal(names(ldf), c("a", "b"))
+  expect_equal(ldf[1, 1][[1]], l[[1]])
+  expect_equal(ldf[1, 2][[1]], l[[2]])
+})
+
+test_that("Collect DataFrame with complex types", {
+  # only ArrayType now
+  # TODO: tests for StructType and MapType after they are supported
+  df <- jsonFile(sqlContext, complexTypeJsonPath)
+
+  ldf <- collect(df)
+  expect_equal(nrow(ldf), 3)
+  expect_equal(ncol(ldf), 3)
+  expect_equal(names(ldf), c("c1", "c2", "c3"))
+  expect_equal(ldf$c1, list(list(1, 2, 3), list(4, 5, 6), list (7, 8, 9)))
+  expect_equal(ldf$c2, list(list("a", "b", "c"), list("d", "e", "f"), list ("g", "h", "i")))
+  expect_equal(ldf$c3, list(list(1.0, 2.0, 3.0), list(4.0, 5.0, 6.0), list (7.0, 8.0, 9.0)))
+})
 
 test_that("jsonFile() on a local file returns a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index bb82f3285f1d9..2a792d81994fd 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -125,10 +125,11 @@ private[r] class RBackendHandler(server: RBackend)
       val methods = cls.getMethods
       val selectedMethods = methods.filter(m => m.getName == methodName)
       if (selectedMethods.length > 0) {
-        val methods = selectedMethods.filter { x =>
-          matchMethod(numArgs, args, x.getParameterTypes)
-        }
-        if (methods.isEmpty) {
+        val index = findMatchedSignature(
+          selectedMethods.map(_.getParameterTypes),
+          args)
+
+        if (index.isEmpty) {
           logWarning(s"cannot find matching method ${cls}.$methodName. "
             + s"Candidates are:")
           selectedMethods.foreach { method =>
@@ -136,18 +137,29 @@ private[r] class RBackendHandler(server: RBackend)
           }
           throw new Exception(s"No matched method found for $cls.$methodName")
         }
-        val ret = methods.head.invoke(obj, args : _*)
+
+        val ret = selectedMethods(index.get).invoke(obj, args : _*)
 
         // Write status bit
         writeInt(dos, 0)
         writeObject(dos, ret.asInstanceOf[AnyRef])
       } else if (methodName == "<init>") {
         // methodName should be "<init>" for constructor
-        val ctor = cls.getConstructors.filter { x =>
-          matchMethod(numArgs, args, x.getParameterTypes)
-        }.head
+        val ctors = cls.getConstructors
+        val index = findMatchedSignature(
+          ctors.map(_.getParameterTypes),
+          args)
 
-        val obj = ctor.newInstance(args : _*)
+        if (index.isEmpty) {
+          logWarning(s"cannot find matching constructor for ${cls}. "
+            + s"Candidates are:")
+          ctors.foreach { ctor =>
+            logWarning(s"$cls(${ctor.getParameterTypes.mkString(",")})")
+          }
+          throw new Exception(s"No matched constructor found for $cls")
+        }
+
+        val obj = ctors(index.get).newInstance(args : _*)
 
         writeInt(dos, 0)
         writeObject(dos, obj.asInstanceOf[AnyRef])
@@ -166,40 +178,79 @@ private[r] class RBackendHandler(server: RBackend)
 
   // Read a number of arguments from the data input stream
   def readArgs(numArgs: Int, dis: DataInputStream): Array[java.lang.Object] = {
-    (0 until numArgs).map { arg =>
+    (0 until numArgs).map { _ =>
       readObject(dis)
     }.toArray
   }
 
-  // Checks if the arguments passed in args matches the parameter types.
-  // NOTE: Currently we do exact match. We may add type conversions later.
-  def matchMethod(
-      numArgs: Int,
-      args: Array[java.lang.Object],
-      parameterTypes: Array[Class[_]]): Boolean = {
-    if (parameterTypes.length != numArgs) {
-      return false
-    }
+  // Find a matching method signature in an array of signatures of constructors
+  // or methods of the same name according to the passed arguments. Arguments
+  // may be converted in order to match a signature.
+  //
+  // Note that in Java reflection, constructors and normal methods are of different
+  // classes, and share no parent class that provides methods for reflection uses.
+  // There is no unified way to handle them in this function. So an array of signatures
+  // is passed in instead of an array of candidate constructors or methods.
+  //
+  // Returns an Option[Int] which is the index of the matched signature in the array.
+  def findMatchedSignature(
+      parameterTypesOfMethods: Array[Array[Class[_]]],
+      args: Array[Object]): Option[Int] = {
+    val numArgs = args.length
+
+    for (index <- 0 until parameterTypesOfMethods.length) {
+      val parameterTypes = parameterTypesOfMethods(index)
+
+      if (parameterTypes.length == numArgs) {
+        var argMatched = true
+        var i = 0
+        while (i < numArgs && argMatched) {
+          val parameterType = parameterTypes(i)
+
+          if (parameterType == classOf[Seq[Any]] && args(i).getClass.isArray) {
+            // The case that the parameter type is a Scala Seq and the argument
+            // is a Java array is considered matching. The array will be converted
+            // to a Seq later if this method is matched.
+          } else {
+            var parameterWrapperType = parameterType
+
+            // Convert native parameters to Object types as args is Array[Object] here
+            if (parameterType.isPrimitive) {
+              parameterWrapperType = parameterType match {
+                case java.lang.Integer.TYPE => classOf[java.lang.Integer]
+                case java.lang.Long.TYPE => classOf[java.lang.Integer]
+                case java.lang.Double.TYPE => classOf[java.lang.Double]
+                case java.lang.Boolean.TYPE => classOf[java.lang.Boolean]
+                case _ => parameterType
+              }
+            }
+            if (!parameterWrapperType.isInstance(args(i))) {
+              argMatched = false
+            }
+          }
 
-    for (i <- 0 to numArgs - 1) {
-      val parameterType = parameterTypes(i)
-      var parameterWrapperType = parameterType
-
-      // Convert native parameters to Object types as args is Array[Object] here
-      if (parameterType.isPrimitive) {
-        parameterWrapperType = parameterType match {
-          case java.lang.Integer.TYPE => classOf[java.lang.Integer]
-          case java.lang.Long.TYPE => classOf[java.lang.Integer]
-          case java.lang.Double.TYPE => classOf[java.lang.Double]
-          case java.lang.Boolean.TYPE => classOf[java.lang.Boolean]
-          case _ => parameterType
+          i = i + 1
+        }
+
+        if (argMatched) {
+          // For now, we return the first matching method.
+          // TODO: find best method in matching methods.
+
+          // Convert args if needed
+          val parameterTypes = parameterTypesOfMethods(index)
+
+          (0 until numArgs).map { i =>
+            if (parameterTypes(i) == classOf[Seq[Any]] && args(i).getClass.isArray) {
+              // Convert a Java array to scala Seq
+              args(i) = args(i).asInstanceOf[Array[_]].toSeq
+            }
+          }
+
+          return Some(index)
         }
-      }
-      if (!parameterWrapperType.isInstance(args(i))) {
-        return false
       }
     }
-    true
+    None
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 190e193427af8..3c92bb7a1c73c 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -21,6 +21,7 @@ import java.io.{DataInputStream, DataOutputStream}
 import java.sql.{Timestamp, Date, Time}
 
 import scala.collection.JavaConverters._
+import scala.collection.mutable.WrappedArray
 
 /**
  * Utility functions to serialize, deserialize objects to / from R
@@ -213,89 +214,97 @@ private[spark] object SerDe {
     }
   }
 
-  def writeObject(dos: DataOutputStream, value: Object): Unit = {
-    if (value == null) {
+  def writeObject(dos: DataOutputStream, obj: Object): Unit = {
+    if (obj == null) {
       writeType(dos, "void")
     } else {
-      value.getClass.getName match {
-        case "java.lang.Character" =>
+      // Convert ArrayType collected from DataFrame to Java array
+      // Collected data of ArrayType from a DataFrame is observed to be of
+      // type "scala.collection.mutable.WrappedArray"
+      val value =
+        if (obj.isInstanceOf[WrappedArray[_]]) {
+          obj.asInstanceOf[WrappedArray[_]].toArray
+        } else {
+          obj
+        }
+
+      value match {
+        case v: java.lang.Character =>
           writeType(dos, "character")
-          writeString(dos, value.asInstanceOf[Character].toString)
-        case "java.lang.String" =>
+          writeString(dos, v.toString)
+        case v: java.lang.String =>
           writeType(dos, "character")
-          writeString(dos, value.asInstanceOf[String])
-        case "java.lang.Long" =>
+          writeString(dos, v)
+        case v: java.lang.Long =>
           writeType(dos, "double")
-          writeDouble(dos, value.asInstanceOf[Long].toDouble)
-        case "java.lang.Float" =>
+          writeDouble(dos, v.toDouble)
+        case v: java.lang.Float =>
           writeType(dos, "double")
-          writeDouble(dos, value.asInstanceOf[Float].toDouble)
-        case "java.math.BigDecimal" =>
+          writeDouble(dos, v.toDouble)
+        case v: java.math.BigDecimal =>
           writeType(dos, "double")
-          val javaDecimal = value.asInstanceOf[java.math.BigDecimal]
-          writeDouble(dos, scala.math.BigDecimal(javaDecimal).toDouble)
-        case "java.lang.Double" =>
+          writeDouble(dos, scala.math.BigDecimal(v).toDouble)
+        case v: java.lang.Double =>
           writeType(dos, "double")
-          writeDouble(dos, value.asInstanceOf[Double])
-        case "java.lang.Byte" =>
+          writeDouble(dos, v)
+        case v: java.lang.Byte =>
           writeType(dos, "integer")
-          writeInt(dos, value.asInstanceOf[Byte].toInt)
-        case "java.lang.Short" =>
+          writeInt(dos, v.toInt)
+        case v: java.lang.Short =>
           writeType(dos, "integer")
-          writeInt(dos, value.asInstanceOf[Short].toInt)
-        case "java.lang.Integer" =>
+          writeInt(dos, v.toInt)
+        case v: java.lang.Integer =>
           writeType(dos, "integer")
-          writeInt(dos, value.asInstanceOf[Int])
-        case "java.lang.Boolean" =>
+          writeInt(dos, v)
+        case v: java.lang.Boolean =>
           writeType(dos, "logical")
-          writeBoolean(dos, value.asInstanceOf[Boolean])
-        case "java.sql.Date" =>
+          writeBoolean(dos, v)
+        case v: java.sql.Date =>
           writeType(dos, "date")
-          writeDate(dos, value.asInstanceOf[Date])
-        case "java.sql.Time" =>
+          writeDate(dos, v)
+        case v: java.sql.Time =>
           writeType(dos, "time")
-          writeTime(dos, value.asInstanceOf[Time])
-        case "java.sql.Timestamp" =>
+          writeTime(dos, v)
+        case v: java.sql.Timestamp =>
           writeType(dos, "time")
-          writeTime(dos, value.asInstanceOf[Timestamp])
+          writeTime(dos, v)
 
         // Handle arrays
 
         // Array of primitive types
 
         // Special handling for byte array
-        case "[B" =>
+        case v: Array[Byte] =>
           writeType(dos, "raw")
-          writeBytes(dos, value.asInstanceOf[Array[Byte]])
+          writeBytes(dos, v)
 
-        case "[C" =>
+        case v: Array[Char] =>
           writeType(dos, "array")
-          writeStringArr(dos, value.asInstanceOf[Array[Char]].map(_.toString))
-        case "[S" =>
+          writeStringArr(dos, v.map(_.toString))
+        case v: Array[Short] =>
           writeType(dos, "array")
-          writeIntArr(dos, value.asInstanceOf[Array[Short]].map(_.toInt))
-        case "[I" =>
+          writeIntArr(dos, v.map(_.toInt))
+        case v: Array[Int] =>
           writeType(dos, "array")
-          writeIntArr(dos, value.asInstanceOf[Array[Int]])
-        case "[J" =>
+          writeIntArr(dos, v)
+        case v: Array[Long] =>
           writeType(dos, "array")
-          writeDoubleArr(dos, value.asInstanceOf[Array[Long]].map(_.toDouble))
-        case "[F" =>
+          writeDoubleArr(dos, v.map(_.toDouble))
+        case v: Array[Float] =>
           writeType(dos, "array")
-          writeDoubleArr(dos, value.asInstanceOf[Array[Float]].map(_.toDouble))
-        case "[D" =>
+          writeDoubleArr(dos, v.map(_.toDouble))
+        case v: Array[Double] =>
           writeType(dos, "array")
-          writeDoubleArr(dos, value.asInstanceOf[Array[Double]])
-        case "[Z" =>
+          writeDoubleArr(dos, v)
+        case v: Array[Boolean] =>
           writeType(dos, "array")
-          writeBooleanArr(dos, value.asInstanceOf[Array[Boolean]])
+          writeBooleanArr(dos, v)
 
         // Array of objects, null objects use "void" type
-        case c if c.startsWith("[") =>
+        case v: Array[Object] =>
           writeType(dos, "list")
-          val array = value.asInstanceOf[Array[Object]]
-          writeInt(dos, array.length)
-          array.foreach(elem => writeObject(dos, elem))
+          writeInt(dos, v.length)
+          v.foreach(elem => writeObject(dos, elem))
 
         case _ =>
           writeType(dos, "jobj")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
index 7f3defec3d42e..d4b834adb6e39 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
@@ -26,6 +26,8 @@ import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, NamedExpres
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, DataFrame, GroupedData, Row, SQLContext, SaveMode}
 
+import scala.util.matching.Regex
+
 private[r] object SQLUtils {
   def createSQLContext(jsc: JavaSparkContext): SQLContext = {
     new SQLContext(jsc)
@@ -35,14 +37,15 @@ private[r] object SQLUtils {
     new JavaSparkContext(sqlCtx.sparkContext)
   }
 
-  def toSeq[T](arr: Array[T]): Seq[T] = {
-    arr.toSeq
-  }
-
   def createStructType(fields : Seq[StructField]): StructType = {
     StructType(fields)
   }
 
+  // Support using regex in string interpolation
+  private[this] implicit class RegexContext(sc: StringContext) {
+    def r: Regex = new Regex(sc.parts.mkString, sc.parts.tail.map(_ => "x"): _*)
+  }
+
   def getSQLDataType(dataType: String): DataType = {
     dataType match {
       case "byte" => org.apache.spark.sql.types.ByteType
@@ -58,6 +61,9 @@ private[r] object SQLUtils {
       case "boolean" => org.apache.spark.sql.types.BooleanType
       case "timestamp" => org.apache.spark.sql.types.TimestampType
       case "date" => org.apache.spark.sql.types.DateType
+      case r"\Aarray<(.*)${elemType}>\Z" => {
+        org.apache.spark.sql.types.ArrayType(getSQLDataType(elemType))
+      }
       case _ => throw new IllegalArgumentException(s"Invaid type $dataType")
     }
   }

From 3db72554be3f13478ccd5915e746491982163298 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 10 Sep 2015 13:22:35 -0700
Subject: [PATCH 1382/1454] [SPARK-10443] [SQL] Refactor SortMergeOuterJoin to
 reduce duplication

`LeftOutputIterator` and `RightOutputIterator` are symmetrically identical and can share a lot of code. If someone makes a change in one but forgets to do the same thing in the other we'll end up with inconsistent behavior. This patch also adds inline comments to clarify the intention of the code.

Author: Andrew Or <andrew@databricks.com>

Closes #8596 from andrewor14/smoj-cleanup.
---
 .../execution/joins/SortMergeOuterJoin.scala  | 138 ++++++++++--------
 1 file changed, 77 insertions(+), 61 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
index ab20ee573ab5d..c117dff9c8b1d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -199,99 +199,115 @@ case class SortMergeOuterJoin(
   }
 }
 
-
+/**
+ * An iterator for outputting rows in left outer join.
+ */
 private class LeftOuterIterator(
     smjScanner: SortMergeJoinScanner,
     rightNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
     resultProj: InternalRow => InternalRow,
-    numRows: LongSQLMetric
-  ) extends RowIterator {
-  private[this] val joinedRow: JoinedRow = new JoinedRow()
-  private[this] var rightIdx: Int = 0
-  assert(smjScanner.getBufferedMatches.length == 0)
-
-  private def advanceLeft(): Boolean = {
-    rightIdx = 0
-    if (smjScanner.findNextOuterJoinRows()) {
-      joinedRow.withLeft(smjScanner.getStreamedRow)
-      if (smjScanner.getBufferedMatches.isEmpty) {
-        // There are no matching right rows, so return nulls for the right row
-        joinedRow.withRight(rightNullRow)
-      } else {
-        // Find the next row from the right input that satisfied the bound condition
-        if (!advanceRightUntilBoundConditionSatisfied()) {
-          joinedRow.withRight(rightNullRow)
-        }
-      }
-      true
-    } else {
-      // Left input has been exhausted
-      false
-    }
-  }
-
-  private def advanceRightUntilBoundConditionSatisfied(): Boolean = {
-    var foundMatch: Boolean = false
-    while (!foundMatch && rightIdx < smjScanner.getBufferedMatches.length) {
-      foundMatch = boundCondition(joinedRow.withRight(smjScanner.getBufferedMatches(rightIdx)))
-      rightIdx += 1
-    }
-    foundMatch
-  }
-
-  override def advanceNext(): Boolean = {
-    val r = advanceRightUntilBoundConditionSatisfied() || advanceLeft()
-    if (r) numRows += 1
-    r
-  }
+    numOutputRows: LongSQLMetric)
+  extends OneSideOuterIterator(
+    smjScanner, rightNullRow, boundCondition, resultProj, numOutputRows) {
 
-  override def getRow: InternalRow = resultProj(joinedRow)
+  protected override def setStreamSideOutput(row: InternalRow): Unit = joinedRow.withLeft(row)
+  protected override def setBufferedSideOutput(row: InternalRow): Unit = joinedRow.withRight(row)
 }
 
+/**
+ * An iterator for outputting rows in right outer join.
+ */
 private class RightOuterIterator(
     smjScanner: SortMergeJoinScanner,
     leftNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
     resultProj: InternalRow => InternalRow,
-    numRows: LongSQLMetric
-  ) extends RowIterator {
-  private[this] val joinedRow: JoinedRow = new JoinedRow()
-  private[this] var leftIdx: Int = 0
+    numOutputRows: LongSQLMetric)
+  extends OneSideOuterIterator(
+    smjScanner, leftNullRow, boundCondition, resultProj, numOutputRows) {
+
+  protected override def setStreamSideOutput(row: InternalRow): Unit = joinedRow.withRight(row)
+  protected override def setBufferedSideOutput(row: InternalRow): Unit = joinedRow.withLeft(row)
+}
+
+/**
+ * An abstract iterator for sharing code between [[LeftOuterIterator]] and [[RightOuterIterator]].
+ *
+ * Each [[OneSideOuterIterator]] has a streamed side and a buffered side. Each row on the
+ * streamed side will output 0 or many rows, one for each matching row on the buffered side.
+ * If there are no matches, then the buffered side of the joined output will be a null row.
+ *
+ * In left outer join, the left is the streamed side and the right is the buffered side.
+ * In right outer join, the right is the streamed side and the left is the buffered side.
+ *
+ * @param smjScanner a scanner that streams rows and buffers any matching rows
+ * @param bufferedSideNullRow the default row to return when a streamed row has no matches
+ * @param boundCondition an additional filter condition for buffered rows
+ * @param resultProj how the output should be projected
+ * @param numOutputRows an accumulator metric for the number of rows output
+ */
+private abstract class OneSideOuterIterator(
+    smjScanner: SortMergeJoinScanner,
+    bufferedSideNullRow: InternalRow,
+    boundCondition: InternalRow => Boolean,
+    resultProj: InternalRow => InternalRow,
+    numOutputRows: LongSQLMetric) extends RowIterator {
+
+  // A row to store the joined result, reused many times
+  protected[this] val joinedRow: JoinedRow = new JoinedRow()
+
+  // Index of the buffered rows, reset to 0 whenever we advance to a new streamed row
+  private[this] var bufferIndex: Int = 0
+
+  // This iterator is initialized lazily so there should be no matches initially
   assert(smjScanner.getBufferedMatches.length == 0)
 
-  private def advanceRight(): Boolean = {
-    leftIdx = 0
+  // Set output methods to be overridden by subclasses
+  protected def setStreamSideOutput(row: InternalRow): Unit
+  protected def setBufferedSideOutput(row: InternalRow): Unit
+
+  /**
+   * Advance to the next row on the stream side and populate the buffer with matches.
+   * @return whether there are more rows in the stream to consume.
+   */
+  private def advanceStream(): Boolean = {
+    bufferIndex = 0
     if (smjScanner.findNextOuterJoinRows()) {
-      joinedRow.withRight(smjScanner.getStreamedRow)
+      setStreamSideOutput(smjScanner.getStreamedRow)
       if (smjScanner.getBufferedMatches.isEmpty) {
-        // There are no matching left rows, so return nulls for the left row
-        joinedRow.withLeft(leftNullRow)
+        // There are no matching rows in the buffer, so return the null row
+        setBufferedSideOutput(bufferedSideNullRow)
       } else {
-        // Find the next row from the left input that satisfied the bound condition
-        if (!advanceLeftUntilBoundConditionSatisfied()) {
-          joinedRow.withLeft(leftNullRow)
+        // Find the next row in the buffer that satisfied the bound condition
+        if (!advanceBufferUntilBoundConditionSatisfied()) {
+          setBufferedSideOutput(bufferedSideNullRow)
         }
       }
       true
     } else {
-      // Right input has been exhausted
+      // Stream has been exhausted
       false
     }
   }
 
-  private def advanceLeftUntilBoundConditionSatisfied(): Boolean = {
+  /**
+   * Advance to the next row in the buffer that satisfies the bound condition.
+   * @return whether there is such a row in the current buffer.
+   */
+  private def advanceBufferUntilBoundConditionSatisfied(): Boolean = {
     var foundMatch: Boolean = false
-    while (!foundMatch && leftIdx < smjScanner.getBufferedMatches.length) {
-      foundMatch = boundCondition(joinedRow.withLeft(smjScanner.getBufferedMatches(leftIdx)))
-      leftIdx += 1
+    while (!foundMatch && bufferIndex < smjScanner.getBufferedMatches.length) {
+      setBufferedSideOutput(smjScanner.getBufferedMatches(bufferIndex))
+      foundMatch = boundCondition(joinedRow)
+      bufferIndex += 1
     }
     foundMatch
   }
 
   override def advanceNext(): Boolean = {
-    val r = advanceLeftUntilBoundConditionSatisfied() || advanceRight()
-    if (r) numRows += 1
+    val r = advanceBufferUntilBoundConditionSatisfied() || advanceStream()
+    if (r) numOutputRows += 1
     r
   }
 

From 4204757714b36364611fb63bc008cf90fc53d8df Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Thu, 10 Sep 2015 13:43:13 -0700
Subject: [PATCH 1383/1454] Add 1.5 to master branch EC2 scripts

This change brings it to par with `branch-1.5` (and 1.5.0 release)

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8704 from shivaram/ec2-1.5-update.
---
 ec2/spark_ec2.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 11fd7ee0ec8df..3a2361c6d6d2b 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -51,7 +51,7 @@
     raw_input = input
     xrange = range
 
-SPARK_EC2_VERSION = "1.4.0"
+SPARK_EC2_VERSION = "1.5.0"
 SPARK_EC2_DIR = os.path.dirname(os.path.realpath(__file__))
 
 VALID_SPARK_VERSIONS = set([
@@ -71,6 +71,8 @@
     "1.3.0",
     "1.3.1",
     "1.4.0",
+    "1.4.1",
+    "1.5.0"
 ])
 
 SPARK_TACHYON_MAP = {
@@ -84,6 +86,8 @@
     "1.3.0": "0.5.0",
     "1.3.1": "0.5.0",
     "1.4.0": "0.6.4",
+    "1.4.1": "0.6.4",
+    "1.5.0": "0.7.1"
 }
 
 DEFAULT_SPARK_VERSION = SPARK_EC2_VERSION
@@ -91,7 +95,7 @@
 
 # Default location to get the spark-ec2 scripts (and ami-list) from
 DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/amplab/spark-ec2"
-DEFAULT_SPARK_EC2_BRANCH = "branch-1.4"
+DEFAULT_SPARK_EC2_BRANCH = "branch-1.5"
 
 
 def setup_external_libs(libs):

From 89562a172fd3efa032f60714d600407c6cfe2c2f Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 10 Sep 2015 13:54:20 -0700
Subject: [PATCH 1384/1454] [SPARK-7544] [SQL] [PySpark] pyspark.sql.types.Row
 implements __getitem__

pyspark.sql.types.Row implements ```__getitem__```

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8333 from yanboliang/spark-7544.
---
 python/pyspark/sql/types.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 8bd58d69eeecd..1f86894855cbe 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1176,6 +1176,8 @@ class Row(tuple):
     >>> row = Row(name="Alice", age=11)
     >>> row
     Row(age=11, name='Alice')
+    >>> row['name'], row['age']
+    ('Alice', 11)
     >>> row.name, row.age
     ('Alice', 11)
 
@@ -1243,6 +1245,19 @@ def __call__(self, *args):
         """create new Row object"""
         return _create_row(self, args)
 
+    def __getitem__(self, item):
+        if isinstance(item, (int, slice)):
+            return super(Row, self).__getitem__(item)
+        try:
+            # it will be slow when it has many fields,
+            # but this will not be used in normal cases
+            idx = self.__fields__.index(item)
+            return super(Row, self).__getitem__(idx)
+        except IndexError:
+            raise KeyError(item)
+        except ValueError:
+            raise ValueError(item)
+
     def __getattr__(self, item):
         if item.startswith("__"):
             raise AttributeError(item)

From 0eabea8a058ad60411c1384930ba12c1c638f5f1 Mon Sep 17 00:00:00 2001
From: Matt Massie <massie@cs.berkeley.edu>
Date: Thu, 10 Sep 2015 17:24:33 -0700
Subject: [PATCH 1385/1454] [SPARK-9043] Serialize key, value and combiner
 classes in ShuffleDependency

ShuffleManager implementations are currently not given type information for
the key, value and combiner classes. Serialization of shuffle objects relies
on objects being JavaSerializable, with methods defined for reading/writing
the object or, alternatively, serialization via Kryo which uses reflection.

Serialization systems like Avro, Thrift and Protobuf generate classes with
zero argument constructors and explicit schema information
(e.g. IndexedRecords in Avro have get, put and getSchema methods).

By serializing the key, value and combiner class names in ShuffleDependency,
shuffle implementations will have access to schema information when
registerShuffle() is called.

Author: Matt Massie <massie@cs.berkeley.edu>

Closes #7403 from massie/shuffle-classtags.
---
 .../scala/org/apache/spark/bagel/Bagel.scala  |  2 +-
 .../scala/org/apache/spark/Dependency.scala   | 11 ++-
 .../apache/spark/api/java/JavaPairRDD.scala   |  2 +-
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |  5 +-
 .../apache/spark/rdd/PairRDDFunctions.scala   | 90 ++++++++++++++++---
 .../org/apache/spark/rdd/ShuffledRDD.scala    |  4 +-
 .../org/apache/spark/rdd/SubtractedRDD.scala  |  8 +-
 .../org/apache/spark/CheckpointSuite.scala    |  2 +-
 .../shuffle/ShuffleDependencySuite.scala      | 67 ++++++++++++++
 9 files changed, 168 insertions(+), 23 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala

diff --git a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
index ef0bb2ac13f08..4e6b7686f771d 100644
--- a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
+++ b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
@@ -78,7 +78,7 @@ object Bagel extends Logging {
       val startTime = System.currentTimeMillis
 
       val aggregated = agg(verts, aggregator)
-      val combinedMsgs = msgs.combineByKey(
+      val combinedMsgs = msgs.combineByKeyWithClassTag(
         combiner.createCombiner _, combiner.mergeMsg _, combiner.mergeCombiners _, partitioner)
       val grouped = combinedMsgs.groupWith(verts)
       val superstep_ = superstep  // Create a read-only copy of superstep for capture in closure
diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index cfeeb3902c033..9aafc9eb1cde7 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
@@ -65,7 +67,7 @@ abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
  * @param mapSideCombine whether to perform partial aggregation (also known as map-side combine)
  */
 @DeveloperApi
-class ShuffleDependency[K, V, C](
+class ShuffleDependency[K: ClassTag, V: ClassTag, C: ClassTag](
     @transient private val _rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
     val serializer: Option[Serializer] = None,
@@ -76,6 +78,13 @@ class ShuffleDependency[K, V, C](
 
   override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]]
 
+  private[spark] val keyClassName: String = reflect.classTag[K].runtimeClass.getName
+  private[spark] val valueClassName: String = reflect.classTag[V].runtimeClass.getName
+  // Note: It's possible that the combiner class tag is null, if the combineByKey
+  // methods in PairRDDFunctions are used instead of combineByKeyWithClassTag.
+  private[spark] val combinerClassName: Option[String] =
+    Option(reflect.classTag[C]).map(_.runtimeClass.getName)
+
   val shuffleId: Int = _rdd.context.newShuffleId()
 
   val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index fb787979c1820..8344f6368ac48 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -239,7 +239,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
       mapSideCombine: Boolean,
       serializer: Serializer): JavaPairRDD[K, C] = {
       implicit val ctag: ClassTag[C] = fakeClassTag
-    fromRDD(rdd.combineByKey(
+    fromRDD(rdd.combineByKeyWithClassTag(
       createCombiner,
       mergeValue,
       mergeCombiners,
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 9c617fc719cb5..7bad749d58327 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -22,6 +22,7 @@ import scala.language.existentials
 import java.io.{IOException, ObjectOutputStream}
 
 import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
 
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
@@ -74,7 +75,9 @@ private[spark] class CoGroupPartition(
  * @param part partitioner used to partition the shuffle output
  */
 @DeveloperApi
-class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: Partitioner)
+class CoGroupedRDD[K: ClassTag](
+    @transient var rdds: Seq[RDD[_ <: Product2[K, _]]],
+    part: Partitioner)
   extends RDD[(K, Array[Iterable[_]])](rdds.head.context, Nil) {
 
   // For example, `(k, a) cogroup (k, b)` produces k -> Array(ArrayBuffer as, ArrayBuffer bs).
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 4e5f2e8a5d467..c59f0d4aa75a0 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -57,7 +57,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   with SparkHadoopMapReduceUtil
   with Serializable
 {
+
   /**
+   * :: Experimental ::
    * Generic function to combine the elements for each key using a custom set of aggregation
    * functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined type" C
    * Note that V and C can be different -- for example, one might group an RDD of type
@@ -70,12 +72,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * In addition, users can control the partitioning of the output RDD, and whether to perform
    * map-side aggregation (if a mapper can produce multiple items with the same key).
    */
-  def combineByKey[C](createCombiner: V => C,
+  @Experimental
+  def combineByKeyWithClassTag[C](
+      createCombiner: V => C,
       mergeValue: (C, V) => C,
       mergeCombiners: (C, C) => C,
       partitioner: Partitioner,
       mapSideCombine: Boolean = true,
-      serializer: Serializer = null): RDD[(K, C)] = self.withScope {
+      serializer: Serializer = null)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
     require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0
     if (keyClass.isArray) {
       if (mapSideCombine) {
@@ -103,13 +107,50 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   }
 
   /**
-   * Simplified version of combineByKey that hash-partitions the output RDD.
+   * Generic function to combine the elements for each key using a custom set of aggregation
+   * functions. This method is here for backward compatibility. It does not provide combiner
+   * classtag information to the shuffle.
+   *
+   * @see [[combineByKeyWithClassTag]]
    */
-  def combineByKey[C](createCombiner: V => C,
+  def combineByKey[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C,
+      partitioner: Partitioner,
+      mapSideCombine: Boolean = true,
+      serializer: Serializer = null): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners,
+      partitioner, mapSideCombine, serializer)(null)
+  }
+
+  /**
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
+   * This method is here for backward compatibility. It does not provide combiner
+   * classtag information to the shuffle.
+   *
+   * @see [[combineByKeyWithClassTag]]
+   */
+  def combineByKey[C](
+      createCombiner: V => C,
       mergeValue: (C, V) => C,
       mergeCombiners: (C, C) => C,
       numPartitions: Int): RDD[(K, C)] = self.withScope {
-    combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners, numPartitions)(null)
+  }
+
+  /**
+   * :: Experimental ::
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
+   */
+  @Experimental
+  def combineByKeyWithClassTag[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C,
+      numPartitions: Int)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners,
+      new HashPartitioner(numPartitions))
   }
 
   /**
@@ -133,7 +174,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
 
     // We will clean the combiner closure later in `combineByKey`
     val cleanedSeqOp = self.context.clean(seqOp)
-    combineByKey[U]((v: V) => cleanedSeqOp(createZero(), v), cleanedSeqOp, combOp, partitioner)
+    combineByKeyWithClassTag[U]((v: V) => cleanedSeqOp(createZero(), v),
+      cleanedSeqOp, combOp, partitioner)
   }
 
   /**
@@ -182,7 +224,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val createZero = () => cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray))
 
     val cleanedFunc = self.context.clean(func)
-    combineByKey[V]((v: V) => cleanedFunc(createZero(), v), cleanedFunc, cleanedFunc, partitioner)
+    combineByKeyWithClassTag[V]((v: V) => cleanedFunc(createZero(), v),
+      cleanedFunc, cleanedFunc, partitioner)
   }
 
   /**
@@ -268,7 +311,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * "combiner" in MapReduce.
    */
   def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope {
-    combineByKey[V]((v: V) => v, func, func, partitioner)
+    combineByKeyWithClassTag[V]((v: V) => v, func, func, partitioner)
   }
 
   /**
@@ -392,7 +435,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       h1
     }
 
-    combineByKey(createHLL, mergeValueHLL, mergeHLL, partitioner).mapValues(_.cardinality())
+    combineByKeyWithClassTag(createHLL, mergeValueHLL, mergeHLL, partitioner)
+      .mapValues(_.cardinality())
   }
 
   /**
@@ -466,7 +510,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val createCombiner = (v: V) => CompactBuffer(v)
     val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
     val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
-    val bufs = combineByKey[CompactBuffer[V]](
+    val bufs = combineByKeyWithClassTag[CompactBuffer[V]](
       createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false)
     bufs.asInstanceOf[RDD[(K, Iterable[V])]]
   }
@@ -565,12 +609,30 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   }
 
   /**
-   * Simplified version of combineByKey that hash-partitions the resulting RDD using the
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
+   * existing partitioner/parallelism level. This method is here for backward compatibility. It
+   * does not provide combiner classtag information to the shuffle.
+   *
+   * @see [[combineByKeyWithClassTag]]
+   */
+  def combineByKey[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners)(null)
+  }
+
+  /**
+   * :: Experimental ::
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
    * existing partitioner/parallelism level.
    */
-  def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C)
-    : RDD[(K, C)] = self.withScope {
-    combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
+  @Experimental
+  def combineByKeyWithClassTag[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
index 2dc47f95937cb..cb15d912bbfb5 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.rdd
 
+import scala.reflect.ClassTag
+
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.serializer.Serializer
@@ -37,7 +39,7 @@ private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
  */
 // TODO: Make this return RDD[Product2[K, C]] or have some way to configure mutable pairs
 @DeveloperApi
-class ShuffledRDD[K, V, C](
+class ShuffledRDD[K: ClassTag, V: ClassTag, C: ClassTag](
     @transient var prev: RDD[_ <: Product2[K, V]],
     part: Partitioner)
   extends RDD[(K, C)](prev.context, Nil) {
diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
index 9a4fa301b06e3..25ec685eff5ab 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
@@ -63,15 +63,17 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
   }
 
   override def getDependencies: Seq[Dependency[_]] = {
-    Seq(rdd1, rdd2).map { rdd =>
+    def rddDependency[T1: ClassTag, T2: ClassTag](rdd: RDD[_ <: Product2[T1, T2]])
+      : Dependency[_] = {
       if (rdd.partitioner == Some(part)) {
         logDebug("Adding one-to-one dependency with " + rdd)
         new OneToOneDependency(rdd)
       } else {
         logDebug("Adding shuffle dependency with " + rdd)
-        new ShuffleDependency(rdd, part, serializer)
+        new ShuffleDependency[T1, T2, Any](rdd, part, serializer)
       }
     }
+    Seq(rddDependency[K, V](rdd1), rddDependency[K, W](rdd2))
   }
 
   override def getPartitions: Array[Partition] = {
@@ -105,7 +107,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
         seq
       }
     }
-    def integrate(depNum: Int, op: Product2[K, V] => Unit) = {
+    def integrate(depNum: Int, op: Product2[K, V] => Unit): Unit = {
       dependencies(depNum) match {
         case oneToOneDependency: OneToOneDependency[_] =>
           val dependencyPartition = partition.narrowDeps(depNum).get.split
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index d343bb95cb68c..4d70bfed909b6 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -483,7 +483,7 @@ class FatPairRDD(parent: RDD[Int], _partitioner: Partitioner) extends RDD[(Int,
 object CheckpointSuite {
   // This is a custom cogroup function that does not use mapValues like
   // the PairRDDFunctions.cogroup()
-  def cogroup[K, V](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner)
+  def cogroup[K: ClassTag, V: ClassTag](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner)
     : RDD[(K, Array[Iterable[V]])] = {
     new CoGroupedRDD[K](
       Seq(first.asInstanceOf[RDD[(K, _)]], second.asInstanceOf[RDD[(K, _)]]),
diff --git a/core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala b/core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala
new file mode 100644
index 0000000000000..4d5f599fb12ab
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.shuffle
+
+import org.apache.spark._
+
+case class KeyClass()
+
+case class ValueClass()
+
+case class CombinerClass()
+
+class ShuffleDependencySuite extends SparkFunSuite with LocalSparkContext {
+
+  val conf = new SparkConf(loadDefaults = false)
+
+  test("key, value, and combiner classes correct in shuffle dependency without aggregation") {
+    sc = new SparkContext("local", "test", conf.clone())
+    val rdd = sc.parallelize(1 to 5, 4)
+      .map(key => (KeyClass(), ValueClass()))
+      .groupByKey()
+    val dep = rdd.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
+    assert(!dep.mapSideCombine, "Test requires that no map-side aggregator is defined")
+    assert(dep.keyClassName == classOf[KeyClass].getName)
+    assert(dep.valueClassName == classOf[ValueClass].getName)
+  }
+
+  test("key, value, and combiner classes available in shuffle dependency with aggregation") {
+    sc = new SparkContext("local", "test", conf.clone())
+    val rdd = sc.parallelize(1 to 5, 4)
+      .map(key => (KeyClass(), ValueClass()))
+      .aggregateByKey(CombinerClass())({ case (a, b) => a }, { case (a, b) => a })
+    val dep = rdd.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
+    assert(dep.mapSideCombine && dep.aggregator.isDefined, "Test requires map-side aggregation")
+    assert(dep.keyClassName == classOf[KeyClass].getName)
+    assert(dep.valueClassName == classOf[ValueClass].getName)
+    assert(dep.combinerClassName == Some(classOf[CombinerClass].getName))
+  }
+
+  test("combineByKey null combiner class tag handled correctly") {
+    sc = new SparkContext("local", "test", conf.clone())
+    val rdd = sc.parallelize(1 to 5, 4)
+      .map(key => (KeyClass(), ValueClass()))
+      .combineByKey((v: ValueClass) => v,
+        (c: AnyRef, v: ValueClass) => c,
+        (c1: AnyRef, c2: AnyRef) => c1)
+    val dep = rdd.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
+    assert(dep.keyClassName == classOf[KeyClass].getName)
+    assert(dep.valueClassName == classOf[ValueClass].getName)
+    assert(dep.combinerClassName == None)
+  }
+
+}

From 339a527141984bfb182862b0987d3c4690c9ede1 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 10 Sep 2015 20:34:00 -0700
Subject: [PATCH 1386/1454] [SPARK-10023] [ML] [PySpark] Unified
 DecisionTreeParams checkpointInterval between Scala and Python API.

"checkpointInterval" is member of DecisionTreeParams in Scala API which is inconsistency with Python API, we should unified them.
```
member of DecisionTreeParams <-> Scala API
shared param for all ML Transformer/Estimator <-> Python API
```
Proposal:
"checkpointInterval" is also used by ALS, so we make it shared params at Scala.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8528 from yanboliang/spark-10023.
---
 .../DecisionTreeClassifier.scala              |  1 +
 .../ml/param/shared/SharedParamsCodeGen.scala |  3 +-
 .../spark/ml/param/shared/sharedParams.scala  |  4 +--
 .../org/apache/spark/ml/tree/treeParams.scala | 32 +++++++------------
 4 files changed, 16 insertions(+), 24 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 6f70b96b17ec6..0a75d5d22280f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.shared.HasCheckpointInterval
 import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 8c16c6149b40d..e9e99ed1db40e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -56,7 +56,8 @@ private[shared] object SharedParamsCodeGen {
       ParamDesc[String]("inputCol", "input column name"),
       ParamDesc[Array[String]]("inputCols", "input column names"),
       ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")),
-      ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)",
+      ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1). E.g. 10 means that " +
+        "the cache will get checkpointed every 10 iterations.",
         isValid = "ParamValidators.gtEq(1)"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
       ParamDesc[String]("handleInvalid", "how to handle invalid entries. Options are skip (which " +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index c26768953e3db..30092170863ad 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -223,10 +223,10 @@ private[ml] trait HasOutputCol extends Params {
 private[ml] trait HasCheckpointInterval extends Params {
 
   /**
-   * Param for checkpoint interval (>= 1).
+   * Param for checkpoint interval (>= 1). E.g. 10 means that the cache will get checkpointed every 10 iterations..
    * @group param
    */
-  final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1)", ParamValidators.gtEq(1))
+  final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1). E.g. 10 means that the cache will get checkpointed every 10 iterations.", ParamValidators.gtEq(1))
 
   /** @group getParam */
   final def getCheckpointInterval: Int = $(checkpointInterval)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index dbd8d31571d2e..d29f5253c9c3f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.tree
 import org.apache.spark.ml.classification.ClassifierParams
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed, HasThresholds}
+import org.apache.spark.ml.param.shared.{HasCheckpointInterval, HasMaxIter, HasSeed, HasThresholds}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance}
 import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
@@ -30,7 +30,7 @@ import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
  *
  * Note: Marked as private and DeveloperApi since this may be made public in the future.
  */
-private[ml] trait DecisionTreeParams extends PredictorParams {
+private[ml] trait DecisionTreeParams extends PredictorParams with HasCheckpointInterval {
 
   /**
    * Maximum depth of the tree (>= 0).
@@ -96,21 +96,6 @@ private[ml] trait DecisionTreeParams extends PredictorParams {
     " algorithm will cache node IDs for each instance. Caching can speed up training of deeper" +
     " trees.")
 
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be >= 1.
-   * (default = 10)
-   * @group expertParam
-   */
-  final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "Specifies" +
-    " how often to checkpoint the cached node IDs.  E.g. 10 means that the cache will get" +
-    " checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the" +
-    " checkpoint directory is set in the SparkContext. Must be >= 1.",
-    ParamValidators.gtEq(1))
-
   setDefault(maxDepth -> 5, maxBins -> 32, minInstancesPerNode -> 1, minInfoGain -> 0.0,
     maxMemoryInMB -> 256, cacheNodeIds -> false, checkpointInterval -> 10)
 
@@ -150,12 +135,17 @@ private[ml] trait DecisionTreeParams extends PredictorParams {
   /** @group expertGetParam */
   final def getCacheNodeIds: Boolean = $(cacheNodeIds)
 
-  /** @group expertSetParam */
+  /**
+   * Specifies how often to checkpoint the cached node IDs.
+   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
+   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
+   * [[org.apache.spark.SparkContext]].
+   * Must be >= 1.
+   * (default = 10)
+   * @group expertSetParam
+   */
   def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
 
-  /** @group expertGetParam */
-  final def getCheckpointInterval: Int = $(checkpointInterval)
-
   /** (private[ml]) Create a Strategy instance to use with the old API. */
   private[ml] def getOldStrategy(
       categoricalFeatures: Map[Int, Int],

From a140dd77c62255d6f7f6817a2517d47feb8540d4 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 10 Sep 2015 20:43:38 -0700
Subject: [PATCH 1387/1454] [SPARK-10027] [ML] [PySpark] Add Python API missing
 methods for ml.feature

Missing method of ml.feature are listed here:
```StringIndexer``` lacks of parameter ```handleInvalid```.
```StringIndexerModel``` lacks of method ```labels```.
```VectorIndexerModel``` lacks of methods ```numFeatures``` and ```categoryMaps```.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8313 from yanboliang/spark-10027.
---
 python/pyspark/ml/feature.py                  | 31 ++++++++++++++++---
 .../ml/param/_shared_params_code_gen.py       |  5 ++-
 python/pyspark/ml/param/shared.py             | 31 +++++++++++++++++--
 3 files changed, 59 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 1c423486be8d9..71dc636b83eac 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -920,7 +920,7 @@ def mean(self):
 
 
 @inherit_doc
-class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
+class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol, HasHandleInvalid):
     """
     .. note:: Experimental
 
@@ -943,19 +943,20 @@ class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     """
 
     @keyword_only
-    def __init__(self, inputCol=None, outputCol=None):
+    def __init__(self, inputCol=None, outputCol=None, handleInvalid="error"):
         """
-        __init__(self, inputCol=None, outputCol=None)
+        __init__(self, inputCol=None, outputCol=None, handleInvalid="error")
         """
         super(StringIndexer, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StringIndexer", self.uid)
+        self._setDefault(handleInvalid="error")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
-    def setParams(self, inputCol=None, outputCol=None):
+    def setParams(self, inputCol=None, outputCol=None, handleInvalid="error"):
         """
-        setParams(self, inputCol=None, outputCol=None)
+        setParams(self, inputCol=None, outputCol=None, handleInvalid="error")
         Sets params for this StringIndexer.
         """
         kwargs = self.setParams._input_kwargs
@@ -1235,6 +1236,10 @@ class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     >>> model = indexer.fit(df)
     >>> model.transform(df).head().indexed
     DenseVector([1.0, 0.0])
+    >>> model.numFeatures
+    2
+    >>> model.categoryMaps
+    {0: {0.0: 0, -1.0: 1}}
     >>> indexer.setParams(outputCol="test").fit(df).transform(df).collect()[1].test
     DenseVector([0.0, 1.0])
     >>> params = {indexer.maxCategories: 3, indexer.outputCol: "vector"}
@@ -1297,6 +1302,22 @@ class VectorIndexerModel(JavaModel):
     Model fitted by VectorIndexer.
     """
 
+    @property
+    def numFeatures(self):
+        """
+        Number of features, i.e., length of Vectors which this transforms.
+        """
+        return self._call_java("numFeatures")
+
+    @property
+    def categoryMaps(self):
+        """
+        Feature value index.  Keys are categorical feature indices (column indices).
+        Values are maps from original features values to 0-based category indices.
+        If a feature is not in this map, it is treated as continuous.
+        """
+        return self._call_java("javaCategoryMaps")
+
 
 @inherit_doc
 class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol):
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 69efc424ec4ef..926375e44871d 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -121,7 +121,10 @@ def get$Name(self):
         ("checkpointInterval", "checkpoint interval (>= 1)", None),
         ("seed", "random seed", "hash(type(self).__name__)"),
         ("tol", "the convergence tolerance for iterative algorithms", None),
-        ("stepSize", "Step size to be used for each iteration of optimization.", None)]
+        ("stepSize", "Step size to be used for each iteration of optimization.", None),
+        ("handleInvalid", "how to handle invalid entries. Options are skip (which will filter " +
+         "out rows with bad values), or error (which will throw an errror). More options may be " +
+         "added later.", None)]
     code = []
     for name, doc, defaultValueStr in shared:
         param_code = _gen_param_header(name, doc, defaultValueStr)
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 595124726366d..682170aee85fb 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -432,6 +432,33 @@ def getStepSize(self):
         return self.getOrDefault(self.stepSize)
 
 
+class HasHandleInvalid(Params):
+    """
+    Mixin for param handleInvalid: how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.")
+
+    def __init__(self):
+        super(HasHandleInvalid, self).__init__()
+        #: param for how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.
+        self.handleInvalid = Param(self, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.")
+
+    def setHandleInvalid(self, value):
+        """
+        Sets the value of :py:attr:`handleInvalid`.
+        """
+        self._paramMap[self.handleInvalid] = value
+        return self
+
+    def getHandleInvalid(self):
+        """
+        Gets the value of handleInvalid or its default value.
+        """
+        return self.getOrDefault(self.handleInvalid)
+
+
 class DecisionTreeParams(Params):
     """
     Mixin for Decision Tree parameters.
@@ -444,7 +471,7 @@ class DecisionTreeParams(Params):
     minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.")
     maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
     cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-
+    
 
     def __init__(self):
         super(DecisionTreeParams, self).__init__()
@@ -460,7 +487,7 @@ def __init__(self):
         self.maxMemoryInMB = Param(self, "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
         #: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.
         self.cacheNodeIds = Param(self, "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-
+        
     def setMaxDepth(self, value):
         """
         Sets the value of :py:attr:`maxDepth`.

From e1d7f64296d8164cc4c935aa524af664f63bf9c1 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Fri, 11 Sep 2015 18:26:56 +0800
Subject: [PATCH 1388/1454] [SPARK-10472] [SQL] Fixes DataType.typeName for UDT

Before this fix, `MyDenseVectorUDT.typeName` gives `mydensevecto`, which is not desirable.

Author: Cheng Lian <lian@databricks.com>

Closes #8640 from liancheng/spark-10472/udt-type-name.
---
 .../main/scala/org/apache/spark/sql/types/DataType.scala    | 4 +++-
 .../scala/org/apache/spark/sql/UserDefinedTypeSuite.scala   | 6 ++++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index 7bcd623b3f33e..4b54c31dcc27a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -51,7 +51,9 @@ abstract class DataType extends AbstractDataType {
   def defaultSize: Int
 
   /** Name of the type used in JSON serialization. */
-  def typeName: String = this.getClass.getSimpleName.stripSuffix("$").dropRight(4).toLowerCase
+  def typeName: String = {
+    this.getClass.getSimpleName.stripSuffix("$").stripSuffix("Type").stripSuffix("UDT").toLowerCase
+  }
 
   private[sql] def jsonValue: JValue = typeName
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index fa8f9c8e00089..46d87843dfa4d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -157,4 +157,10 @@ class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
         Nil
     )
   }
+
+  test("SPARK-10472 UserDefinedType.typeName") {
+    assert(IntegerType.typeName === "integer")
+    assert(new MyDenseVectorUDT().typeName === "mydensevector")
+    assert(new OpenHashSetUDT(IntegerType).typeName === "openhashset")
+  }
 }

From 9bbe33f318c866c0b13088917542715062f0787f Mon Sep 17 00:00:00 2001
From: Ahir Reddy <ahirreddy@gmail.com>
Date: Fri, 11 Sep 2015 13:06:14 +0100
Subject: [PATCH 1389/1454] [SPARK-10556] Remove explicit Scala version for sbt
 project build files

Previously, project/plugins.sbt explicitly set scalaVersion to 2.10.4. This can cause issues when using a version of sbt that is compiled against a different version of Scala (for example sbt 0.13.9 uses 2.10.5). Removing this explicit setting will cause build files to be compiled and run against the same version of Scala that sbt is compiled against.

Note that this only applies to the project build files (items in project/), it is distinct from the version of Scala we target for the actual spark compilation.

Author: Ahir Reddy <ahirreddy@gmail.com>

Closes #8709 from ahirreddy/sbt-scala-version-fix.
---
 project/plugins.sbt | 2 --
 1 file changed, 2 deletions(-)

diff --git a/project/plugins.sbt b/project/plugins.sbt
index 51820460ca1a0..c06687d8f197b 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -1,5 +1,3 @@
-scalaVersion := "2.10.4"
-
 resolvers += Resolver.url("artifactory", url("http://scalasbt.artifactoryonline.com/scalasbt/sbt-plugin-releases"))(Resolver.ivyStylePatterns)
 
 resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/"

From c268ca4ddde2f5213b2e3985dcaaac5900aea71c Mon Sep 17 00:00:00 2001
From: y-shimizu <y.shimizu0429@gmail.com>
Date: Fri, 11 Sep 2015 08:27:30 -0700
Subject: [PATCH 1390/1454] [SPARK-10518] [DOCS] Update code examples in
 spark.ml user guide to use LIBSVM data source instead of MLUtils

I fixed to use LIBSVM data source in the example code in spark.ml instead of MLUtils

Author: y-shimizu <y.shimizu0429@gmail.com>

Closes #8697 from y-shimizu/SPARK-10518.
---
 docs/ml-ensembles.md      | 65 ++++++++++++---------------------------
 docs/ml-features.md       | 64 +++++++++++++-------------------------
 docs/ml-linear-methods.md | 22 ++++---------
 3 files changed, 47 insertions(+), 104 deletions(-)

diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md
index 62749909e01dc..58f566c9b4b55 100644
--- a/docs/ml-ensembles.md
+++ b/docs/ml-ensembles.md
@@ -121,10 +121,9 @@ import org.apache.spark.ml.classification.RandomForestClassifier
 import org.apache.spark.ml.classification.RandomForestClassificationModel
 import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -193,14 +192,11 @@ import org.apache.spark.ml.classification.RandomForestClassifier;
 import org.apache.spark.ml.classification.RandomForestClassificationModel;
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
 import org.apache.spark.ml.feature.*;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -268,10 +264,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.classification import RandomForestClassifier
 from pyspark.ml.feature import StringIndexer, VectorIndexer
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Index labels, adding metadata to the label column.
 # Fit on whole dataset to include all labels in index.
@@ -327,10 +322,9 @@ import org.apache.spark.ml.regression.RandomForestRegressor
 import org.apache.spark.ml.regression.RandomForestRegressionModel
 import org.apache.spark.ml.feature.VectorIndexer
 import org.apache.spark.ml.evaluation.RegressionEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -387,14 +381,11 @@ import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.RandomForestRegressionModel;
 import org.apache.spark.ml.regression.RandomForestRegressor;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -450,10 +441,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.regression import RandomForestRegressor
 from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Automatically identify categorical features, and index them.
 # Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -576,10 +566,9 @@ import org.apache.spark.ml.classification.GBTClassifier
 import org.apache.spark.ml.classification.GBTClassificationModel
 import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -648,14 +637,10 @@ import org.apache.spark.ml.classification.GBTClassifier;
 import org.apache.spark.ml.classification.GBTClassificationModel;
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
 import org.apache.spark.ml.feature.*;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt");
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -724,10 +709,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.classification import GBTClassifier
 from pyspark.ml.feature import StringIndexer, VectorIndexer
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Index labels, adding metadata to the label column.
 # Fit on whole dataset to include all labels in index.
@@ -783,10 +767,9 @@ import org.apache.spark.ml.regression.GBTRegressor
 import org.apache.spark.ml.regression.GBTRegressionModel
 import org.apache.spark.ml.feature.VectorIndexer
 import org.apache.spark.ml.evaluation.RegressionEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -844,14 +827,10 @@ import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.GBTRegressionModel;
 import org.apache.spark.ml.regression.GBTRegressor;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt");
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -908,10 +887,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.regression import GBTRegressor
 from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Automatically identify categorical features, and index them.
 # Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -970,15 +948,14 @@ Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifie
 {% highlight scala %}
 import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.{Row, SQLContext}
 
 val sqlContext = new SQLContext(sc)
 
 // parse data into dataframe
-val data = MLUtils.loadLibSVMFile(sc, 
-  "data/mllib/sample_multiclass_classification_data.txt")
-val Array(train, test) = data.toDF().randomSplit(Array(0.7, 0.3))
+val data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_multiclass_classification_data.txt")
+val Array(train, test) = data.randomSplit(Array(0.7, 0.3))
 
 // instantiate multiclass learner and train
 val ovr = new OneVsRest().setClassifier(new LogisticRegression)
@@ -1016,9 +993,6 @@ import org.apache.spark.ml.classification.OneVsRest;
 import org.apache.spark.ml.classification.OneVsRestModel;
 import org.apache.spark.mllib.evaluation.MulticlassMetrics;
 import org.apache.spark.mllib.linalg.Matrix;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 
@@ -1026,10 +1000,9 @@ SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample");
 JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
-RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(),
-  "data/mllib/sample_multiclass_classification_data.txt");
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_multiclass_classification_data.txt");
 
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
 DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345);
 DataFrame train = splits[0];
 DataFrame test = splits[1];
diff --git a/docs/ml-features.md b/docs/ml-features.md
index 58b31a5a5cc47..a414c21b5c280 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1179,9 +1179,9 @@ In the example below, we read in a dataset of labeled points and then use `Vecto
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.feature.VectorIndexer
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val indexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexed")
@@ -1200,16 +1200,12 @@ val indexedData = indexerModel.transform(data)
 {% highlight java %}
 import java.util.Map;
 
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(),
-  "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame data = sqlContext.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 VectorIndexer indexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexed")
@@ -1230,9 +1226,9 @@ DataFrame indexedData = indexerModel.transform(data);
 <div data-lang="python" markdown="1">
 {% highlight python %}
 from pyspark.ml.feature import VectorIndexer
-from pyspark.mllib.util import MLUtils
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10)
 indexerModel = indexer.fit(data)
 
@@ -1253,10 +1249,9 @@ The following example demonstrates how to load a dataset in libsvm format and th
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.ml.feature.Normalizer
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 
 // Normalize each Vector using $L^1$ norm.
 val normalizer = new Normalizer()
@@ -1272,15 +1267,11 @@ val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.Positi
 
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.Normalizer;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Normalize each Vector using $L^1$ norm.
 Normalizer normalizer = new Normalizer()
@@ -1297,11 +1288,10 @@ DataFrame lInfNormData =
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark.mllib.util import MLUtils
 from pyspark.ml.feature import Normalizer
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-dataFrame = sqlContext.createDataFrame(data)
+dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 
 # Normalize each Vector using $L^1$ norm.
 normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0)
@@ -1335,10 +1325,9 @@ The following example demonstrates how to load a dataset in libsvm format and th
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.ml.feature.StandardScaler
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val scaler = new StandardScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1355,16 +1344,12 @@ val scaledData = scalerModel.transform(dataFrame)
 
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.StandardScaler;
 import org.apache.spark.ml.feature.StandardScalerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 StandardScaler scaler = new StandardScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1381,11 +1366,10 @@ DataFrame scaledData = scalerModel.transform(dataFrame);
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark.mllib.util import MLUtils
 from pyspark.ml.feature import StandardScaler
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-dataFrame = sqlContext.createDataFrame(data)
+dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",
                         withStd=True, withMean=False)
 
@@ -1424,10 +1408,9 @@ More details can be found in the API docs for
 [MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).
 {% highlight scala %}
 import org.apache.spark.ml.feature.MinMaxScaler
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val scaler = new MinMaxScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1448,13 +1431,10 @@ More details can be found in the API docs for
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.MinMaxScaler;
 import org.apache.spark.ml.feature.MinMaxScalerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 MinMaxScaler scaler = new MinMaxScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures");
diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
index cdd9d4999fa1b..4e94e2f9c708d 100644
--- a/docs/ml-linear-methods.md
+++ b/docs/ml-linear-methods.md
@@ -59,10 +59,9 @@ $\alpha$ and `regParam` corresponds to $\lambda$.
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.mllib.util.MLUtils
 
 // Load training data
-val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 val lr = new LogisticRegression()
   .setMaxIter(10)
@@ -81,8 +80,6 @@ println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
 {% highlight java %}
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.SparkConf;
 import org.apache.spark.SparkContext;
 import org.apache.spark.sql.DataFrame;
@@ -98,7 +95,7 @@ public class LogisticRegressionWithElasticNetExample {
     String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
-    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+    DataFrame training = sqlContext.read.format("libsvm").load(path);
 
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
@@ -118,11 +115,9 @@ public class LogisticRegressionWithElasticNetExample {
 <div data-lang="python" markdown="1">
 {% highlight python %}
 from pyspark.ml.classification import LogisticRegression
-from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib.util import MLUtils
 
 # Load training data
-training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
 
@@ -251,10 +246,9 @@ regression model and extracting model summary statistics.
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.regression.LinearRegression
-import org.apache.spark.mllib.util.MLUtils
 
 // Load training data
-val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 val lr = new LinearRegression()
   .setMaxIter(10)
@@ -283,8 +277,6 @@ import org.apache.spark.ml.regression.LinearRegression;
 import org.apache.spark.ml.regression.LinearRegressionModel;
 import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
 import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.SparkConf;
 import org.apache.spark.SparkContext;
 import org.apache.spark.sql.DataFrame;
@@ -300,7 +292,7 @@ public class LinearRegressionWithElasticNetExample {
     String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
-    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+    DataFrame training = sqlContext.read.format("libsvm").load(path);
 
     LinearRegression lr = new LinearRegression()
       .setMaxIter(10)
@@ -329,11 +321,9 @@ public class LinearRegressionWithElasticNetExample {
 <!--- TODO: Add python model summaries once implemented -->
 {% highlight python %}
 from pyspark.ml.regression import LinearRegression
-from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib.util import MLUtils
 
 # Load training data
-training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
 

From b656e6134fc5cd27e1fe6b6ab30fd7633cab0b14 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 11 Sep 2015 08:50:35 -0700
Subject: [PATCH 1391/1454] [SPARK-10026] [ML] [PySpark] Implement some common
 Params for regression in PySpark

LinearRegression and LogisticRegression lack of some Params for Python, and some Params are not shared classes which lead we need to write them for each class. These kinds of Params are list here:
```scala
HasElasticNetParam
HasFitIntercept
HasStandardization
HasThresholds
```
Here we implement them in shared params at Python side and make LinearRegression/LogisticRegression parameters peer with Scala one.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8508 from yanboliang/spark-10026.
---
 python/pyspark/ml/classification.py           |  75 ++----------
 .../ml/param/_shared_params_code_gen.py       |  11 +-
 python/pyspark/ml/param/shared.py             | 111 ++++++++++++++++++
 python/pyspark/ml/regression.py               |  42 ++-----
 4 files changed, 143 insertions(+), 96 deletions(-)

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 83f808efc3bf0..22bdd1b322aca 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -31,7 +31,8 @@
 
 @inherit_doc
 class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                         HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
+                         HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
+                         HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds):
     """
     Logistic regression.
     Currently, this class only supports binary classification.
@@ -65,17 +66,6 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
     """
 
     # a placeholder to make it appear in the generated doc
-    elasticNetParam = \
-        Param(Params._dummy(), "elasticNetParam",
-              "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
-              "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-    fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
-    thresholds = Param(Params._dummy(), "thresholds",
-                       "Thresholds in multi-class classification" +
-                       " to adjust the probability of predicting each class." +
-                       " Array must have length equal to the number of classes, with values >= 0." +
-                       " The class with largest value p/t is predicted, where p is the original" +
-                       " probability of that class and t is the class' threshold.")
     threshold = Param(Params._dummy(), "threshold",
                       "Threshold in binary classification prediction, in range [0, 1]." +
                       " If threshold and thresholds are both set, they must match.")
@@ -83,40 +73,23 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 threshold=0.5, thresholds=None,
-                 probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                 threshold=0.5, thresholds=None, probabilityCol="probability",
+                 rawPredictionCol="rawPrediction", standardization=True):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, thresholds=None, \
-                 probabilityCol="probability", rawPredictionCol="rawPrediction")
+                 threshold=0.5, thresholds=None, probabilityCol="probability", \
+                 rawPredictionCol="rawPrediction", standardization=True)
         If the threshold and thresholds Params are both set, they must be equivalent.
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.classification.LogisticRegression", self.uid)
-        #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty
-        #  is an L2 penalty. For alpha = 1, it is an L1 penalty.
-        self.elasticNetParam = \
-            Param(self, "elasticNetParam",
-                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
-                  "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-        #: param for whether to fit an intercept term.
-        self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
         #: param for threshold in binary classification, in range [0, 1].
         self.threshold = Param(self, "threshold",
                                "Threshold in binary classification prediction, in range [0, 1]." +
                                " If threshold and thresholds are both set, they must match.")
-        #: param for thresholds or cutoffs in binary or multiclass classification
-        self.thresholds = \
-            Param(self, "thresholds",
-                  "Thresholds in multi-class classification" +
-                  " to adjust the probability of predicting each class." +
-                  " Array must have length equal to the number of classes, with values >= 0." +
-                  " The class with largest value p/t is predicted, where p is the original" +
-                  " probability of that class and t is the class' threshold.")
-        self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
-                         fitIntercept=True, threshold=0.5)
+        self._setDefault(maxIter=100, regParam=0.1, tol=1E-6, threshold=0.5)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
         self._checkThresholdConsistency()
@@ -124,13 +97,13 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  threshold=0.5, thresholds=None,
-                  probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                  threshold=0.5, thresholds=None, probabilityCol="probability",
+                  rawPredictionCol="rawPrediction", standardization=True):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                  threshold=0.5, thresholds=None, \
-                  probabilityCol="probability", rawPredictionCol="rawPrediction")
+                  threshold=0.5, thresholds=None, probabilityCol="probability", \
+                  rawPredictionCol="rawPrediction", standardization=True)
         Sets params for logistic regression.
         If the threshold and thresholds Params are both set, they must be equivalent.
         """
@@ -142,32 +115,6 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
     def _create_model(self, java_model):
         return LogisticRegressionModel(java_model)
 
-    def setElasticNetParam(self, value):
-        """
-        Sets the value of :py:attr:`elasticNetParam`.
-        """
-        self._paramMap[self.elasticNetParam] = value
-        return self
-
-    def getElasticNetParam(self):
-        """
-        Gets the value of elasticNetParam or its default value.
-        """
-        return self.getOrDefault(self.elasticNetParam)
-
-    def setFitIntercept(self, value):
-        """
-        Sets the value of :py:attr:`fitIntercept`.
-        """
-        self._paramMap[self.fitIntercept] = value
-        return self
-
-    def getFitIntercept(self):
-        """
-        Gets the value of fitIntercept or its default value.
-        """
-        return self.getOrDefault(self.fitIntercept)
-
     def setThreshold(self, value):
         """
         Sets the value of :py:attr:`threshold`.
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 926375e44871d..5b39e5dd4e25b 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -124,7 +124,16 @@ def get$Name(self):
         ("stepSize", "Step size to be used for each iteration of optimization.", None),
         ("handleInvalid", "how to handle invalid entries. Options are skip (which will filter " +
          "out rows with bad values), or error (which will throw an errror). More options may be " +
-         "added later.", None)]
+         "added later.", None),
+        ("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
+         "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", "0.0"),
+        ("fitIntercept", "whether to fit an intercept term.", "True"),
+        ("standardization", "whether to standardize the training features before fitting the " +
+         "model.", "True"),
+        ("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
+         "predicting each class. Array must have length equal to the number of classes, with " +
+         "values >= 0. The class with largest value p/t is predicted, where p is the original " +
+         "probability of that class and t is the class' threshold.", None)]
     code = []
     for name, doc, defaultValueStr in shared:
         param_code = _gen_param_header(name, doc, defaultValueStr)
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 682170aee85fb..af1218128602b 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -459,6 +459,117 @@ def getHandleInvalid(self):
         return self.getOrDefault(self.handleInvalid)
 
 
+class HasElasticNetParam(Params):
+    """
+    Mixin for param elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    elasticNetParam = Param(Params._dummy(), "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
+
+    def __init__(self):
+        super(HasElasticNetParam, self).__init__()
+        #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
+        self.elasticNetParam = Param(self, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
+        self._setDefault(elasticNetParam=0.0)
+
+    def setElasticNetParam(self, value):
+        """
+        Sets the value of :py:attr:`elasticNetParam`.
+        """
+        self._paramMap[self.elasticNetParam] = value
+        return self
+
+    def getElasticNetParam(self):
+        """
+        Gets the value of elasticNetParam or its default value.
+        """
+        return self.getOrDefault(self.elasticNetParam)
+
+
+class HasFitIntercept(Params):
+    """
+    Mixin for param fitIntercept: whether to fit an intercept term..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
+
+    def __init__(self):
+        super(HasFitIntercept, self).__init__()
+        #: param for whether to fit an intercept term.
+        self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
+        self._setDefault(fitIntercept=True)
+
+    def setFitIntercept(self, value):
+        """
+        Sets the value of :py:attr:`fitIntercept`.
+        """
+        self._paramMap[self.fitIntercept] = value
+        return self
+
+    def getFitIntercept(self):
+        """
+        Gets the value of fitIntercept or its default value.
+        """
+        return self.getOrDefault(self.fitIntercept)
+
+
+class HasStandardization(Params):
+    """
+    Mixin for param standardization: whether to standardize the training features before fitting the model..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    standardization = Param(Params._dummy(), "standardization", "whether to standardize the training features before fitting the model.")
+
+    def __init__(self):
+        super(HasStandardization, self).__init__()
+        #: param for whether to standardize the training features before fitting the model.
+        self.standardization = Param(self, "standardization", "whether to standardize the training features before fitting the model.")
+        self._setDefault(standardization=True)
+
+    def setStandardization(self, value):
+        """
+        Sets the value of :py:attr:`standardization`.
+        """
+        self._paramMap[self.standardization] = value
+        return self
+
+    def getStandardization(self):
+        """
+        Gets the value of standardization or its default value.
+        """
+        return self.getOrDefault(self.standardization)
+
+
+class HasThresholds(Params):
+    """
+    Mixin for param thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")
+
+    def __init__(self):
+        super(HasThresholds, self).__init__()
+        #: param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.
+        self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")
+
+    def setThresholds(self, value):
+        """
+        Sets the value of :py:attr:`thresholds`.
+        """
+        self._paramMap[self.thresholds] = value
+        return self
+
+    def getThresholds(self):
+        """
+        Gets the value of thresholds or its default value.
+        """
+        return self.getOrDefault(self.thresholds)
+
+
 class DecisionTreeParams(Params):
     """
     Mixin for Decision Tree parameters.
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 44f60a769566d..a9503608b7f25 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -28,7 +28,8 @@
 
 @inherit_doc
 class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                       HasRegParam, HasTol):
+                       HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
+                       HasStandardization):
     """
     Linear regression.
 
@@ -63,38 +64,30 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     TypeError: Method setParams forces keyword arguments.
     """
 
-    # a placeholder to make it appear in the generated doc
-    elasticNetParam = \
-        Param(Params._dummy(), "elasticNetParam",
-              "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
-              "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
+                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+                 standardization=True):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
+                 standardization=True)
         """
         super(LinearRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.regression.LinearRegression", self.uid)
-        #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty
-        #  is an L2 penalty. For alpha = 1, it is an L1 penalty.
-        self.elasticNetParam = \
-            Param(self, "elasticNetParam",
-                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " +
-                  "is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-        self._setDefault(maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+        self._setDefault(maxIter=100, regParam=0.0, tol=1e-6)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
+                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+                  standardization=True):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
+                  standardization=True)
         Sets params for linear regression.
         """
         kwargs = self.setParams._input_kwargs
@@ -103,19 +96,6 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
     def _create_model(self, java_model):
         return LinearRegressionModel(java_model)
 
-    def setElasticNetParam(self, value):
-        """
-        Sets the value of :py:attr:`elasticNetParam`.
-        """
-        self._paramMap[self.elasticNetParam] = value
-        return self
-
-    def getElasticNetParam(self):
-        """
-        Gets the value of elasticNetParam or its default value.
-        """
-        return self.getOrDefault(self.elasticNetParam)
-
 
 class LinearRegressionModel(JavaModel):
     """

From b01b26260625f0ba14e5f3010207666d62d93864 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 11 Sep 2015 08:52:28 -0700
Subject: [PATCH 1392/1454] [SPARK-9773] [ML] [PySpark] Add Python API for
 MultilayerPerceptronClassifier

Add Python API for ```MultilayerPerceptronClassifier```.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8067 from yanboliang/SPARK-9773.
---
 .../MultilayerPerceptronClassifier.scala      |   9 ++
 python/pyspark/ml/classification.py           | 132 +++++++++++++++++-
 2 files changed, 140 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 82fc80c58054f..5f60dea91fcfa 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.ml.classification
 
+import scala.collection.JavaConverters._
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param.shared.{HasTol, HasMaxIter, HasSeed}
 import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
@@ -181,6 +183,13 @@ class MultilayerPerceptronClassificationModel private[ml] (
 
   private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).getInstance(weights)
 
+  /**
+   * Returns layers in a Java List.
+   */
+  private[ml] def javaLayers: java.util.List[Int] = {
+    layers.toList.asJava
+  }
+
   /**
    * Predict label for the given features.
    * This internal method is used to implement [[transform()]] and output [[predictionCol]].
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 22bdd1b322aca..88815e561f572 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -26,7 +26,8 @@
 __all__ = ['LogisticRegression', 'LogisticRegressionModel', 'DecisionTreeClassifier',
            'DecisionTreeClassificationModel', 'GBTClassifier', 'GBTClassificationModel',
            'RandomForestClassifier', 'RandomForestClassificationModel', 'NaiveBayes',
-           'NaiveBayesModel']
+           'NaiveBayesModel', 'MultilayerPerceptronClassifier',
+           'MultilayerPerceptronClassificationModel']
 
 
 @inherit_doc
@@ -755,6 +756,135 @@ def theta(self):
         return self._call_java("theta")
 
 
+@inherit_doc
+class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
+                                     HasMaxIter, HasTol, HasSeed):
+    """
+    Classifier trainer based on the Multilayer Perceptron.
+    Each layer has sigmoid activation function, output layer has softmax.
+    Number of inputs has to be equal to the size of feature vectors.
+    Number of outputs has to be equal to the total number of labels.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([
+    ...     (0.0, Vectors.dense([0.0, 0.0])),
+    ...     (1.0, Vectors.dense([0.0, 1.0])),
+    ...     (1.0, Vectors.dense([1.0, 0.0])),
+    ...     (0.0, Vectors.dense([1.0, 1.0]))], ["label", "features"])
+    >>> mlp = MultilayerPerceptronClassifier(maxIter=100, layers=[2, 5, 2], blockSize=1, seed=11)
+    >>> model = mlp.fit(df)
+    >>> model.layers
+    [2, 5, 2]
+    >>> model.weights.size
+    27
+    >>> testDF = sqlContext.createDataFrame([
+    ...     (Vectors.dense([1.0, 0.0]),),
+    ...     (Vectors.dense([0.0, 0.0]),)], ["features"])
+    >>> model.transform(testDF).show()
+    +---------+----------+
+    | features|prediction|
+    +---------+----------+
+    |[1.0,0.0]|       1.0|
+    |[0.0,0.0]|       0.0|
+    +---------+----------+
+    ...
+    """
+
+    # a placeholder to make it appear in the generated doc
+    layers = Param(Params._dummy(), "layers", "Sizes of layers from input layer to output layer " +
+                   "E.g., Array(780, 100, 10) means 780 inputs, one hidden layer with 100 " +
+                   "neurons and output layer of 10 neurons, default is [1, 1].")
+    blockSize = Param(Params._dummy(), "blockSize", "Block size for stacking input data in " +
+                      "matrices. Data is stacked within partitions. If block size is more than " +
+                      "remaining data in a partition then it is adjusted to the size of this " +
+                      "data. Recommended size is between 10 and 1000, default is 128.")
+
+    @keyword_only
+    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128):
+        """
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 maxIter=100, tol=1e-4, seed=None, layers=[1, 1], blockSize=128)
+        """
+        super(MultilayerPerceptronClassifier, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.MultilayerPerceptronClassifier", self.uid)
+        self.layers = Param(self, "layers", "Sizes of layers from input layer to output layer " +
+                            "E.g., Array(780, 100, 10) means 780 inputs, one hidden layer with " +
+                            "100 neurons and output layer of 10 neurons, default is [1, 1].")
+        self.blockSize = Param(self, "blockSize", "Block size for stacking input data in " +
+                               "matrices. Data is stacked within partitions. If block size is " +
+                               "more than remaining data in a partition then it is adjusted to " +
+                               "the size of this data. Recommended size is between 10 and 1000, " +
+                               "default is 128.")
+        self._setDefault(maxIter=100, tol=1E-4, layers=[1, 1], blockSize=128)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128):
+        """
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxIter=100, tol=1e-4, seed=None, layers=[1, 1], blockSize=128)
+        Sets params for MultilayerPerceptronClassifier.
+        """
+        kwargs = self.setParams._input_kwargs
+        if layers is None:
+            return self._set(**kwargs).setLayers([1, 1])
+        else:
+            return self._set(**kwargs)
+
+    def _create_model(self, java_model):
+        return MultilayerPerceptronClassificationModel(java_model)
+
+    def setLayers(self, value):
+        """
+        Sets the value of :py:attr:`layers`.
+        """
+        self._paramMap[self.layers] = value
+        return self
+
+    def getLayers(self):
+        """
+        Gets the value of layers or its default value.
+        """
+        return self.getOrDefault(self.layers)
+
+    def setBlockSize(self, value):
+        """
+        Sets the value of :py:attr:`blockSize`.
+        """
+        self._paramMap[self.blockSize] = value
+        return self
+
+    def getBlockSize(self):
+        """
+        Gets the value of blockSize or its default value.
+        """
+        return self.getOrDefault(self.blockSize)
+
+
+class MultilayerPerceptronClassificationModel(JavaModel):
+    """
+    Model fitted by MultilayerPerceptronClassifier.
+    """
+
+    @property
+    def layers(self):
+        """
+        array of layer sizes including input and output layers.
+        """
+        return self._call_java("javaLayers")
+
+    @property
+    def weights(self):
+        """
+        vector of initial weights for the model that consists of the weights of layers.
+        """
+        return self._call_java("weights")
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

From 960d2d0ac6b5a22242a922f87f745f7d1f736181 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 11 Sep 2015 08:53:40 -0700
Subject: [PATCH 1393/1454] [SPARK-10537] [ML] document LIBSVM source options
 in public API doc and some minor improvements

We should document options in public API doc. Otherwise, it is hard to find out the options without looking at the code. I tried to make `DefaultSource` private and put the documentation to package doc. However, since then there exists no public class under `source.libsvm`, the Java package doc doesn't show up in the generated html file (http://bugs.java.com/bugdatabase/view_bug.do?bug_id=4492654). So I put the doc to `DefaultSource` instead. There are several minor updates in this PR:

1. Do `vectorType == "sparse"` only once.
2. Update `hashCode` and `equals`.
3. Remove inherited doc.
4. Delete temp dir in `afterAll`.

Lewuathe

Author: Xiangrui Meng <meng@databricks.com>

Closes #8699 from mengxr/SPARK-10537.
---
 .../ml/source/libsvm/LibSVMRelation.scala     | 71 ++++++++++++-------
 .../{ => libsvm}/JavaLibSVMRelationSuite.java | 24 +++----
 .../{ => libsvm}/LibSVMRelationSuite.scala    | 14 ++--
 3 files changed, 66 insertions(+), 43 deletions(-)
 rename mllib/src/test/java/org/apache/spark/ml/source/{ => libsvm}/JavaLibSVMRelationSuite.java (79%)
 rename mllib/src/test/scala/org/apache/spark/ml/source/{ => libsvm}/LibSVMRelationSuite.scala (88%)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
index b12cb62a4ef15..1f627777fc68d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
@@ -21,12 +21,12 @@ import com.google.common.base.Objects
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Since
-import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{StructType, StructField, DoubleType}
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.{DataFrameReader, DataFrame, Row, SQLContext}
 import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 
 /**
  * LibSVMRelation provides the DataFrame constructed from LibSVM format data.
@@ -35,7 +35,7 @@ import org.apache.spark.sql.sources._
  * @param vectorType The type of vector. It can be 'sparse' or 'dense'
  * @param sqlContext The Spark SQLContext
  */
-private[ml] class LibSVMRelation(val path: String, val numFeatures: Int, val vectorType: String)
+private[libsvm] class LibSVMRelation(val path: String, val numFeatures: Int, val vectorType: String)
     (@transient val sqlContext: SQLContext)
   extends BaseRelation with TableScan with Logging with Serializable {
 
@@ -47,27 +47,56 @@ private[ml] class LibSVMRelation(val path: String, val numFeatures: Int, val vec
   override def buildScan(): RDD[Row] = {
     val sc = sqlContext.sparkContext
     val baseRdd = MLUtils.loadLibSVMFile(sc, path, numFeatures)
-
+    val sparse = vectorType == "sparse"
     baseRdd.map { pt =>
-      val features = if (vectorType == "dense") pt.features.toDense else pt.features.toSparse
+      val features = if (sparse) pt.features.toSparse else pt.features.toDense
       Row(pt.label, features)
     }
   }
 
   override def hashCode(): Int = {
-    Objects.hashCode(path, schema)
+    Objects.hashCode(path, Double.box(numFeatures), vectorType)
   }
 
   override def equals(other: Any): Boolean = other match {
-    case that: LibSVMRelation => (this.path == that.path) && this.schema.equals(that.schema)
-    case _ => false
+    case that: LibSVMRelation =>
+      path == that.path &&
+        numFeatures == that.numFeatures &&
+        vectorType == that.vectorType
+    case _ =>
+      false
   }
-
 }
 
 /**
- * This is used for creating DataFrame from LibSVM format file.
- * The LibSVM file path must be specified to DefaultSource.
+ * `libsvm` package implements Spark SQL data source API for loading LIBSVM data as [[DataFrame]].
+ * The loaded [[DataFrame]] has two columns: `label` containing labels stored as doubles and
+ * `features` containing feature vectors stored as [[Vector]]s.
+ *
+ * To use LIBSVM data source, you need to set "libsvm" as the format in [[DataFrameReader]] and
+ * optionally specify options, for example:
+ * {{{
+ *   // Scala
+ *   val df = sqlContext.read.format("libsvm")
+ *     .option("numFeatures", "780")
+ *     .load("data/mllib/sample_libsvm_data.txt")
+ *
+ *   // Java
+ *   DataFrame df = sqlContext.read.format("libsvm")
+ *     .option("numFeatures, "780")
+ *     .load("data/mllib/sample_libsvm_data.txt");
+ * }}}
+ *
+ * LIBSVM data source supports the following options:
+ *  - "numFeatures": number of features.
+ *    If unspecified or nonpositive, the number of features will be determined automatically at the
+ *    cost of one additional pass.
+ *    This is also useful when the dataset is already split into multiple files and you want to load
+ *    them separately, because some features may not present in certain files, which leads to
+ *    inconsistent feature dimensions.
+ *  - "vectorType": feature vector type, "sparse" (default) or "dense".
+ *
+ *  @see [[https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ LIBSVM datasets]]
  */
 @Since("1.6.0")
 class DefaultSource extends RelationProvider with DataSourceRegister {
@@ -75,24 +104,12 @@ class DefaultSource extends RelationProvider with DataSourceRegister {
   @Since("1.6.0")
   override def shortName(): String = "libsvm"
 
-  private def checkPath(parameters: Map[String, String]): String = {
-    require(parameters.contains("path"), "'path' must be specified")
-    parameters.get("path").get
-  }
-
-  /**
-   * Returns a new base relation with the given parameters.
-   * Note: the parameters' keywords are case insensitive and this insensitivity is enforced
-   * by the Map that is passed to the function.
-   */
+  @Since("1.6.0")
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String])
     : BaseRelation = {
-    val path = checkPath(parameters)
+    val path = parameters.getOrElse("path",
+      throw new IllegalArgumentException("'path' must be specified"))
     val numFeatures = parameters.getOrElse("numFeatures", "-1").toInt
-    /**
-     * featuresType can be selected "dense" or "sparse".
-     * This parameter decides the type of returned feature vector.
-     */
     val vectorType = parameters.getOrElse("vectorType", "sparse")
     new LibSVMRelation(path, numFeatures, vectorType)(sqlContext)
   }
diff --git a/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java b/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
similarity index 79%
rename from mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
rename to mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
index 11fa4eec0ccf0..2976b38e45031 100644
--- a/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.source;
+package org.apache.spark.ml.source.libsvm;
 
 import java.io.File;
 import java.io.IOException;
@@ -42,34 +42,34 @@
  */
 public class JavaLibSVMRelationSuite {
   private transient JavaSparkContext jsc;
-  private transient SQLContext jsql;
-  private transient DataFrame dataset;
+  private transient SQLContext sqlContext;
 
-  private File tmpDir;
-  private File path;
+  private File tempDir;
+  private String path;
 
   @Before
   public void setUp() throws IOException {
     jsc = new JavaSparkContext("local", "JavaLibSVMRelationSuite");
-    jsql = new SQLContext(jsc);
-
-    tmpDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
-    path = new File(tmpDir.getPath(), "part-00000");
+    sqlContext = new SQLContext(jsc);
 
+    tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
+    File file = new File(tempDir, "part-00000");
     String s = "1 1:1.0 3:2.0 5:3.0\n0\n0 2:4.0 4:5.0 6:6.0";
-    Files.write(s, path, Charsets.US_ASCII);
+    Files.write(s, file, Charsets.US_ASCII);
+    path = tempDir.toURI().toString();
   }
 
   @After
   public void tearDown() {
     jsc.stop();
     jsc = null;
-    Utils.deleteRecursively(tmpDir);
+    Utils.deleteRecursively(tempDir);
   }
 
   @Test
   public void verifyLibSVMDF() {
-    dataset = jsql.read().format("libsvm").option("vectorType", "dense").load(path.getPath());
+    DataFrame dataset = sqlContext.read().format("libsvm").option("vectorType", "dense")
+      .load(path);
     Assert.assertEquals("label", dataset.columns()[0]);
     Assert.assertEquals("features", dataset.columns()[1]);
     Row r = dataset.first();
diff --git a/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
similarity index 88%
rename from mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
rename to mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
index 8ed134128c8d2..997f574e51f6a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.source
+package org.apache.spark.ml.source.libsvm
 
 import java.io.File
 
@@ -23,11 +23,12 @@ import com.google.common.base.Charsets
 import com.google.common.io.Files
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.{SparseVector, Vectors, DenseVector}
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.util.Utils
 
 class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
+  var tempDir: File = _
   var path: String = _
 
   override def beforeAll(): Unit = {
@@ -38,12 +39,17 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
         |0
         |0 2:4.0 4:5.0 6:6.0
       """.stripMargin
-    val tempDir = Utils.createTempDir()
-    val file = new File(tempDir.getPath, "part-00000")
+    tempDir = Utils.createTempDir()
+    val file = new File(tempDir, "part-00000")
     Files.write(lines, file, Charsets.US_ASCII)
     path = tempDir.toURI.toString
   }
 
+  override def afterAll(): Unit = {
+    Utils.deleteRecursively(tempDir)
+    super.afterAll()
+  }
+
   test("select as sparse vector") {
     val df = sqlContext.read.format("libsvm").load(path)
     assert(df.columns(0) == "label")

From 2e3a280754a28dc36a71b9ff988e34cbf457f6c3 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 11 Sep 2015 08:55:35 -0700
Subject: [PATCH 1394/1454] [MINOR] [MLLIB] [ML] [DOC] Minor doc fixes for
 StringIndexer and MetadataUtils
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Changes:
* Make Scala doc for StringIndexerInverse clearer.  Also remove Scala doc from transformSchema, so that the doc is inherited.
* MetadataUtils.scala: “ Helper utilities for tree-based algorithms” —> not just trees anymore

CC: holdenk mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8679 from jkbradley/doc-fixes-1.5.
---
 .../spark/ml/feature/StringIndexer.scala      | 31 +++++++------------
 .../apache/spark/ml/util/MetadataUtils.scala  |  2 +-
 python/pyspark/ml/feature.py                  | 16 +++++-----
 3 files changed, 20 insertions(+), 29 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index b6482ffe0b2ee..3a4ab9a857648 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -181,10 +181,10 @@ class StringIndexerModel (
 
 /**
  * :: Experimental ::
- * A [[Transformer]] that maps a column of string indices back to a new column of corresponding
- * string values using either the ML attributes of the input column, or if provided using the labels
- * supplied by the user.
- * All original columns are kept during transformation.
+ * A [[Transformer]] that maps a column of indices back to a new column of corresponding
+ * string values.
+ * The index-string mapping is either from the ML attributes of the input column,
+ * or from user-supplied labels (which take precedence over ML attributes).
  *
  * @see [[StringIndexer]] for converting strings into indices
  */
@@ -202,32 +202,23 @@ class IndexToString private[ml] (
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  /**
-   * Optional labels to be provided by the user, if not supplied column
-   * metadata is read for labels. The default value is an empty array,
-   * but the empty array is ignored and column metadata used instead.
-   * @group setParam
-   */
+  /** @group setParam */
   def setLabels(value: Array[String]): this.type = set(labels, value)
 
   /**
-   * Param for array of labels.
-   * Optional labels to be provided by the user.
-   * Default: Empty array, in which case column metadata is used for labels.
+   * Optional param for array of labels specifying index-string mapping.
+   *
+   * Default: Empty array, in which case [[inputCol]] metadata is used for labels.
    * @group param
    */
   final val labels: StringArrayParam = new StringArrayParam(this, "labels",
-    "array of labels, if not provided metadata from inputCol is used instead.")
+    "Optional array of labels specifying index-string mapping." +
+      " If not provided or if empty, then metadata from inputCol is used instead.")
   setDefault(labels, Array.empty[String])
 
-  /**
-   * Optional labels to be provided by the user, if not supplied column
-   * metadata is read for labels.
-   * @group getParam
-   */
+  /** @group getParam */
   final def getLabels: Array[String] = $(labels)
 
-  /** Transform the schema for the inverse transformation */
   override def transformSchema(schema: StructType): StructType = {
     val inputColName = $(inputCol)
     val inputDataType = schema(inputColName).dataType
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
index fcb517b5f735e..96a38a3bde960 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
@@ -25,7 +25,7 @@ import org.apache.spark.sql.types.StructField
 
 
 /**
- * Helper utilities for tree-based algorithms
+ * Helper utilities for algorithms using ML metadata
  */
 private[spark] object MetadataUtils {
 
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 71dc636b83eac..97cbee73a05ed 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -985,17 +985,17 @@ class IndexToString(JavaTransformer, HasInputCol, HasOutputCol):
     """
     .. note:: Experimental
 
-    A :py:class:`Transformer` that maps a column of string indices back to a new column of
-    corresponding string values using either the ML attributes of the input column, or if
-    provided using the labels supplied by the user.
-    All original columns are kept during transformation.
+    A :py:class:`Transformer` that maps a column of indices back to a new column of
+    corresponding string values.
+    The index-string mapping is either from the ML attributes of the input column,
+    or from user-supplied labels (which take precedence over ML attributes).
     See L{StringIndexer} for converting strings into indices.
     """
 
     # a placeholder to make the labels show up in generated doc
     labels = Param(Params._dummy(), "labels",
-                   "Optional array of labels to be provided by the user, if not supplied or " +
-                   "empty, column metadata is read for labels")
+                   "Optional array of labels specifying index-string mapping." +
+                   " If not provided or if empty, then metadata from inputCol is used instead.")
 
     @keyword_only
     def __init__(self, inputCol=None, outputCol=None, labels=None):
@@ -1006,8 +1006,8 @@ def __init__(self, inputCol=None, outputCol=None, labels=None):
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IndexToString",
                                             self.uid)
         self.labels = Param(self, "labels",
-                            "Optional array of labels to be provided by the user, if not " +
-                            "supplied or empty, column metadata is read for labels")
+                            "Optional array of labels specifying index-string mapping. If not" +
+                            " provided or if empty, then metadata from inputCol is used instead.")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 

From 6ce0886eb0916a985db142c0b6d2c2b14db5063d Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 11 Sep 2015 09:42:53 -0700
Subject: [PATCH 1395/1454] [SPARK-10540] [SQL] Ignore HadoopFsRelationTest's
 "test all data types" if it is too flaky

If hadoopFsRelationSuites's "test all data types" is too flaky we can disable it for now.

https://issues.apache.org/jira/browse/SPARK-10540

Author: Yin Huai <yhuai@databricks.com>

Closes #8705 from yhuai/SPARK-10540-ignore.
---
 .../org/apache/spark/sql/sources/hadoopFsRelationSuites.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 24f43cf7c15ca..13223c61584b2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -100,7 +100,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
     }
   }
 
-  test("test all data types") {
+  ignore("test all data types") {
     withTempPath { file =>
       // Create the schema.
       val struct =

From 5f46444765a377696af76af6e2c77ab14bfdab8e Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Fri, 11 Sep 2015 10:32:35 -0700
Subject: [PATCH 1396/1454] [SPARK-8530] [ML] add python API for MinMaxScaler

jira: https://issues.apache.org/jira/browse/SPARK-8530

add python API for MinMaxScaler
jira for MinMaxScaler: https://issues.apache.org/jira/browse/SPARK-7514

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7150 from hhbyyh/pythonMinMax.
---
 python/pyspark/ml/feature.py | 104 +++++++++++++++++++++++++++++++++--
 1 file changed, 99 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 97cbee73a05ed..92db8df80280b 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -27,11 +27,11 @@
 from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
-           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel',
-           'PolynomialExpansion', 'RegexTokenizer', 'RFormula', 'RFormulaModel', 'SQLTransformer',
-           'StandardScaler', 'StandardScalerModel', 'StopWordsRemover', 'StringIndexer',
-           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'VectorSlicer',
-           'Word2Vec', 'Word2VecModel']
+           'IndexToString', 'MinMaxScaler', 'MinMaxScalerModel', 'NGram', 'Normalizer',
+           'OneHotEncoder', 'PCA', 'PCAModel', 'PolynomialExpansion', 'RegexTokenizer',
+           'RFormula', 'RFormulaModel', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel',
+           'StopWordsRemover', 'StringIndexer', 'StringIndexerModel', 'Tokenizer',
+           'VectorAssembler', 'VectorIndexer', 'VectorSlicer', 'Word2Vec', 'Word2VecModel']
 
 
 @inherit_doc
@@ -406,6 +406,100 @@ class IDFModel(JavaModel):
     """
 
 
+@inherit_doc
+class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    Rescale each feature individually to a common range [min, max] linearly using column summary
+    statistics, which is also known as min-max normalization or Rescaling. The rescaled value for
+    feature E is calculated as,
+
+    Rescaled(e_i) = (e_i - E_min) / (E_max - E_min) * (max - min) + min
+
+    For the case E_max == E_min, Rescaled(e_i) = 0.5 * (max + min)
+
+    Note that since zero values will probably be transformed to non-zero values, output of the
+    transformer will be DenseVector even for sparse input.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
+    >>> mmScaler = MinMaxScaler(inputCol="a", outputCol="scaled")
+    >>> model = mmScaler.fit(df)
+    >>> model.transform(df).show()
+    +-----+------+
+    |    a|scaled|
+    +-----+------+
+    |[0.0]| [0.0]|
+    |[2.0]| [1.0]|
+    +-----+------+
+    ...
+    """
+
+    # a placeholder to make it appear in the generated doc
+    min = Param(Params._dummy(), "min", "Lower bound of the output feature range")
+    max = Param(Params._dummy(), "max", "Upper bound of the output feature range")
+
+    @keyword_only
+    def __init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
+        """
+        __init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
+        """
+        super(MinMaxScaler, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.MinMaxScaler", self.uid)
+        self.min = Param(self, "min", "Lower bound of the output feature range")
+        self.max = Param(self, "max", "Upper bound of the output feature range")
+        self._setDefault(min=0.0, max=1.0)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
+        """
+        setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
+        Sets params for this MinMaxScaler.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setMin(self, value):
+        """
+        Sets the value of :py:attr:`min`.
+        """
+        self._paramMap[self.min] = value
+        return self
+
+    def getMin(self):
+        """
+        Gets the value of min or its default value.
+        """
+        return self.getOrDefault(self.min)
+
+    def setMax(self, value):
+        """
+        Sets the value of :py:attr:`max`.
+        """
+        self._paramMap[self.max] = value
+        return self
+
+    def getMax(self):
+        """
+        Gets the value of max or its default value.
+        """
+        return self.getOrDefault(self.max)
+
+    def _create_model(self, java_model):
+        return MinMaxScalerModel(java_model)
+
+
+class MinMaxScalerModel(JavaModel):
+    """
+    .. note:: Experimental
+
+    Model fitted by :py:class:`MinMaxScaler`.
+    """
+
+
 @inherit_doc
 @ignore_unicode_prefix
 class NGram(JavaTransformer, HasInputCol, HasOutputCol):

From b231ab8938ae3c4fc2089cfc69c0d8164807d533 Mon Sep 17 00:00:00 2001
From: tedyu <yuzhihong@gmail.com>
Date: Fri, 11 Sep 2015 21:45:45 +0100
Subject: [PATCH 1397/1454] [SPARK-10546] Check partitionId's range in
 ExternalSorter#spill()

See this thread for background:
http://search-hadoop.com/m/q3RTt0rWvIkHAE81

We should check the range of partition Id and provide meaningful message through exception.

Alternatively, we can use abs() and modulo to force the partition Id into legitimate range. However, expectation is that user should correct the logic error in his / her code.

Author: tedyu <yuzhihong@gmail.com>

Closes #8703 from tedyu/master.
---
 .../scala/org/apache/spark/util/collection/ExternalSorter.scala | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index 138c05dff19e4..31230d5978b2a 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -297,6 +297,8 @@ private[spark] class ExternalSorter[K, V, C](
       val it = collection.destructiveSortedWritablePartitionedIterator(comparator)
       while (it.hasNext) {
         val partitionId = it.nextPartition()
+        require(partitionId >= 0 && partitionId < numPartitions,
+          s"partition Id: ${partitionId} should be in the range [0, ${numPartitions})")
         it.writeNext(writer)
         elementsPerPartition(partitionId) += 1
         objectsWritten += 1

From c373866774c082885a50daaf7c83f3a14b0cd714 Mon Sep 17 00:00:00 2001
From: Icaro Medeiros <icaro.medeiros@gmail.com>
Date: Fri, 11 Sep 2015 21:46:52 +0100
Subject: [PATCH 1398/1454] [PYTHON] Fixed typo in exception message

Just fixing a typo in exception message, raised when attempting to pickle SparkContext.

Author: Icaro Medeiros <icaro.medeiros@gmail.com>

Closes #8724 from icaromedeiros/master.
---
 python/pyspark/context.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 1b2a52ad64114..a0a1ccbeefb09 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -255,7 +255,7 @@ def __getnewargs__(self):
         # This method is called when attempting to pickle SparkContext, which is always an error:
         raise Exception(
             "It appears that you are attempting to reference SparkContext from a broadcast "
-            "variable, action, or transforamtion. SparkContext can only be used on the driver, "
+            "variable, action, or transformation. SparkContext can only be used on the driver, "
             "not in code that it run on workers. For more information, see SPARK-5063."
         )
 

From d5d647380f93f4773f9cb85ea6544892d409b5a1 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 11 Sep 2015 14:15:16 -0700
Subject: [PATCH 1399/1454] [SPARK-10442] [SQL] fix string to boolean cast

When we cast string to boolean in hive, it returns `true` if the length of string is > 0, and spark SQL follows this behavior.

However, this behavior is very different from other SQL systems:

1. [presto](https://github.com/facebook/presto/blob/master/presto-main/src/main/java/com/facebook/presto/type/VarcharOperators.java#L89-L118) will return `true` for 't' 'true' '1', `false` for 'f' 'false' '0', throw exception for others.
2. [redshift](http://docs.aws.amazon.com/redshift/latest/dg/r_Boolean_type.html) will return `true` for 't' 'true' 'y' 'yes' '1', `false` for 'f' 'false' 'n' 'no' '0', null for others.
3. [postgresql](http://www.postgresql.org/docs/devel/static/datatype-boolean.html) will return `true` for 't' 'true' 'y' 'yes' 'on' '1', `false` for 'f' 'false' 'n' 'no' 'off' '0', throw exception for others.
4. [vertica](https://my.vertica.com/docs/5.0/HTML/Master/2983.htm) will return `true` for 't' 'true' 'y' 'yes' '1', `false` for 'f' 'false' 'n' 'no' '0', null for others.
5. [impala](http://www.cloudera.com/content/cloudera/en/documentation/cloudera-impala/latest/topics/impala_boolean.html) throw exception when try to cast string to boolean.
6. mysql, oracle, sqlserver don't have boolean type

Whether we should change the cast behavior according to other SQL system or not is not decided yet, this PR is a test to see if we changed, how many compatibility tests will fail.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8698 from cloud-fan/string2boolean.
---
 .../spark/sql/catalyst/expressions/Cast.scala | 24 +++++++-
 .../spark/sql/catalyst/util/StringUtils.scala |  8 +++
 .../sql/catalyst/expressions/CastSuite.scala  | 61 ++++++++++++-------
 .../sql/sources/hadoopFsRelationSuites.scala  | 13 ++++
 4 files changed, 82 insertions(+), 24 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 2db954257be35..f0bce388d959a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -22,7 +22,7 @@ import java.math.{BigDecimal => JavaBigDecimal}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.catalyst.util.{StringUtils, DateTimeUtils}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
@@ -140,7 +140,15 @@ case class Cast(child: Expression, dataType: DataType)
   // UDFToBoolean
   private[this] def castToBoolean(from: DataType): Any => Any = from match {
     case StringType =>
-      buildCast[UTF8String](_, _.numBytes() != 0)
+      buildCast[UTF8String](_, s => {
+        if (StringUtils.isTrueString(s)) {
+          true
+        } else if (StringUtils.isFalseString(s)) {
+          false
+        } else {
+          null
+        }
+      })
     case TimestampType =>
       buildCast[Long](_, t => t != 0)
     case DateType =>
@@ -646,7 +654,17 @@ case class Cast(child: Expression, dataType: DataType)
 
   private[this] def castToBooleanCode(from: DataType): CastFunction = from match {
     case StringType =>
-      (c, evPrim, evNull) => s"$evPrim = $c.numBytes() != 0;"
+      val stringUtils = StringUtils.getClass.getName.stripSuffix("$")
+      (c, evPrim, evNull) =>
+        s"""
+          if ($stringUtils.isTrueString($c)) {
+            $evPrim = true;
+          } else if ($stringUtils.isFalseString($c)) {
+            $evPrim = false;
+          } else {
+            $evNull = true;
+          }
+        """
     case TimestampType =>
       (c, evPrim, evNull) => s"$evPrim = $c != 0;"
     case DateType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
index 9ddfb3a0d3759..c2eeb3c5650ab 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.catalyst.util
 
 import java.util.regex.Pattern
 
+import org.apache.spark.unsafe.types.UTF8String
+
 object StringUtils {
 
   // replace the _ with .{1} exactly match 1 time of any character
@@ -44,4 +46,10 @@ object StringUtils {
       v
     }
   }
+
+  private[this] val trueStrings = Set("t", "true", "y", "yes", "1").map(UTF8String.fromString)
+  private[this] val falseStrings = Set("f", "false", "n", "no", "0").map(UTF8String.fromString)
+
+  def isTrueString(s: UTF8String): Boolean = trueStrings.contains(s.toLowerCase)
+  def isFalseString(s: UTF8String): Boolean = falseStrings.contains(s.toLowerCase)
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 1ad70733eae03..f4db4da7646f8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -503,9 +503,9 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("cast from array") {
-    val array = Literal.create(Seq("123", "abc", "", null),
+    val array = Literal.create(Seq("123", "true", "f", null),
       ArrayType(StringType, containsNull = true))
-    val array_notNull = Literal.create(Seq("123", "abc", ""),
+    val array_notNull = Literal.create(Seq("123", "true", "f"),
       ArrayType(StringType, containsNull = false))
 
     checkNullCast(ArrayType(StringType), ArrayType(IntegerType))
@@ -522,7 +522,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(array, ArrayType(BooleanType, containsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Seq(true, true, false, null))
+      checkEvaluation(ret, Seq(null, true, false, null))
     }
     {
       val ret = cast(array, ArrayType(BooleanType, containsNull = false))
@@ -541,12 +541,12 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(array_notNull, ArrayType(BooleanType, containsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Seq(true, true, false))
+      checkEvaluation(ret, Seq(null, true, false))
     }
     {
       val ret = cast(array_notNull, ArrayType(BooleanType, containsNull = false))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Seq(true, true, false))
+      checkEvaluation(ret, Seq(null, true, false))
     }
 
     {
@@ -557,10 +557,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("cast from map") {
     val map = Literal.create(
-      Map("a" -> "123", "b" -> "abc", "c" -> "", "d" -> null),
+      Map("a" -> "123", "b" -> "true", "c" -> "f", "d" -> null),
       MapType(StringType, StringType, valueContainsNull = true))
     val map_notNull = Literal.create(
-      Map("a" -> "123", "b" -> "abc", "c" -> ""),
+      Map("a" -> "123", "b" -> "true", "c" -> "f"),
       MapType(StringType, StringType, valueContainsNull = false))
 
     checkNullCast(MapType(StringType, IntegerType), MapType(StringType, StringType))
@@ -577,7 +577,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(map, MapType(StringType, BooleanType, valueContainsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false, "d" -> null))
+      checkEvaluation(ret, Map("a" -> null, "b" -> true, "c" -> false, "d" -> null))
     }
     {
       val ret = cast(map, MapType(StringType, BooleanType, valueContainsNull = false))
@@ -600,12 +600,12 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false))
+      checkEvaluation(ret, Map("a" -> null, "b" -> true, "c" -> false))
     }
     {
       val ret = cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = false))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false))
+      checkEvaluation(ret, Map("a" -> null, "b" -> true, "c" -> false))
     }
     {
       val ret = cast(map_notNull, MapType(IntegerType, StringType, valueContainsNull = true))
@@ -630,8 +630,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     val struct = Literal.create(
       InternalRow(
         UTF8String.fromString("123"),
-        UTF8String.fromString("abc"),
-        UTF8String.fromString(""),
+        UTF8String.fromString("true"),
+        UTF8String.fromString("f"),
         null),
       StructType(Seq(
         StructField("a", StringType, nullable = true),
@@ -641,8 +641,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     val struct_notNull = Literal.create(
       InternalRow(
         UTF8String.fromString("123"),
-        UTF8String.fromString("abc"),
-        UTF8String.fromString("")),
+        UTF8String.fromString("true"),
+        UTF8String.fromString("f")),
       StructType(Seq(
         StructField("a", StringType, nullable = false),
         StructField("b", StringType, nullable = false),
@@ -672,7 +672,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
         StructField("c", BooleanType, nullable = true),
         StructField("d", BooleanType, nullable = true))))
       assert(ret.resolved === true)
-      checkEvaluation(ret, InternalRow(true, true, false, null))
+      checkEvaluation(ret, InternalRow(null, true, false, null))
     }
     {
       val ret = cast(struct, StructType(Seq(
@@ -704,7 +704,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
         StructField("b", BooleanType, nullable = true),
         StructField("c", BooleanType, nullable = true))))
       assert(ret.resolved === true)
-      checkEvaluation(ret, InternalRow(true, true, false))
+      checkEvaluation(ret, InternalRow(null, true, false))
     }
     {
       val ret = cast(struct_notNull, StructType(Seq(
@@ -712,7 +712,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
         StructField("b", BooleanType, nullable = true),
         StructField("c", BooleanType, nullable = false))))
       assert(ret.resolved === true)
-      checkEvaluation(ret, InternalRow(true, true, false))
+      checkEvaluation(ret, InternalRow(null, true, false))
     }
 
     {
@@ -731,8 +731,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("complex casting") {
     val complex = Literal.create(
       Row(
-        Seq("123", "abc", ""),
-        Map("a" ->"123", "b" -> "abc", "c" -> ""),
+        Seq("123", "true", "f"),
+        Map("a" ->"123", "b" -> "true", "c" -> "f"),
         Row(0)),
       StructType(Seq(
         StructField("a",
@@ -755,11 +755,11 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     assert(ret.resolved === true)
     checkEvaluation(ret, Row(
       Seq(123, null, null),
-      Map("a" -> true, "b" -> true, "c" -> false),
+      Map("a" -> null, "b" -> true, "c" -> false),
       Row(0L)))
   }
 
-  test("case between string and interval") {
+  test("cast between string and interval") {
     import org.apache.spark.unsafe.types.CalendarInterval
 
     checkEvaluation(Cast(Literal("interval -3 month 7 hours"), CalendarIntervalType),
@@ -769,4 +769,23 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       StringType),
       "interval 1 years 3 months -3 days")
   }
+
+  test("cast string to boolean") {
+    checkCast("t", true)
+    checkCast("true", true)
+    checkCast("tRUe", true)
+    checkCast("y", true)
+    checkCast("yes", true)
+    checkCast("1", true)
+
+    checkCast("f", false)
+    checkCast("false", false)
+    checkCast("FAlsE", false)
+    checkCast("n", false)
+    checkCast("no", false)
+    checkCast("0", false)
+
+    checkEvaluation(cast("abc", BooleanType), null)
+    checkEvaluation(cast("", BooleanType), null)
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 13223c61584b2..8ffcef85668d6 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -375,6 +375,19 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
     }
   }
 
+  test("saveAsTable()/load() - partitioned table - boolean type") {
+    sqlContext.range(2)
+      .select('id, ('id % 2 === 0).as("b"))
+      .write.partitionBy("b").saveAsTable("t")
+
+    withTable("t") {
+      checkAnswer(
+        sqlContext.table("t").sort('id),
+        Row(0, true) :: Row(1, false) :: Nil
+      )
+    }
+  }
+
   test("saveAsTable()/load() - partitioned table - Overwrite") {
     partitionedTestDF.write
       .format(dataSourceName)

From 1eede3b254ee3793841c92971707094ac8afee35 Mon Sep 17 00:00:00 2001
From: Yash Datta <Yash.Datta@guavus.com>
Date: Fri, 11 Sep 2015 14:55:15 -0700
Subject: [PATCH 1400/1454] [SPARK-7142] [SQL] Minor enhancement to
 BooleanSimplification Optimizer rule. Incorporate review comments

Adding changes suggested by cloud-fan  in #5700

cc marmbrus

Author: Yash Datta <Yash.Datta@guavus.com>

Closes #8716 from saucam/bool_simp.
---
 .../apache/spark/sql/catalyst/optimizer/Optimizer.scala   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index d9b50f3c97da0..0f4caec7451a2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -435,10 +435,10 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         // a && a  =>  a
         case (l, r) if l fastEquals r => l
         // a && (not(a) || b) => a && b
-        case (l, Or(l1, r)) if (Not(l) fastEquals l1) => And(l, r)
-        case (l, Or(r, l1)) if (Not(l) fastEquals l1) => And(l, r)
-        case (Or(l, l1), r) if (l1 fastEquals Not(r)) => And(l, r)
-        case (Or(l1, l), r) if (l1 fastEquals Not(r)) => And(l, r)
+        case (l, Or(l1, r)) if (Not(l) == l1) => And(l, r)
+        case (l, Or(r, l1)) if (Not(l) == l1) => And(l, r)
+        case (Or(l, l1), r) if (l1 == Not(r)) => And(l, r)
+        case (Or(l1, l), r) if (l1 == Not(r)) => And(l, r)
         // (a || b) && (a || c)  =>  a || (b && c)
         case _ =>
           // 1. Split left and right to get the disjunctive predicates,

From e626ac5f5c27dcc74113070f2fec03682bcd12bd Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 11 Sep 2015 15:00:13 -0700
Subject: [PATCH 1401/1454] [SPARK-9992] [SPARK-9994] [SPARK-9998] [SQL]
 Implement the local TopK, sample and intersect operators

This PR is in conflict with #8535. I will update this one when #8535 gets merged.

Author: zsxwing <zsxwing@gmail.com>

Closes #8573 from zsxwing/more-local-operators.
---
 .../spark/sql/execution/basicOperators.scala  |  2 +-
 .../sql/execution/local/IntersectNode.scala   | 63 ++++++++++++++
 .../spark/sql/execution/local/LocalNode.scala |  5 ++
 .../sql/execution/local/SampleNode.scala      | 82 +++++++++++++++++++
 .../local/TakeOrderedAndProjectNode.scala     | 73 +++++++++++++++++
 .../execution/local/IntersectNodeSuite.scala  | 35 ++++++++
 .../sql/execution/local/SampleNodeSuite.scala | 40 +++++++++
 .../TakeOrderedAndProjectNodeSuite.scala      | 54 ++++++++++++
 8 files changed, 353 insertions(+), 1 deletion(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 3f68b05a24f44..bf6d44c098ee3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -138,7 +138,7 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
  *                   will be ub - lb.
  * @param withReplacement Whether to sample with replacement.
  * @param seed the random seed
- * @param child the QueryPlan
+ * @param child the SparkPlan
  */
 @DeveloperApi
 case class Sample(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala
new file mode 100644
index 0000000000000..740d485f8d9e6
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala
@@ -0,0 +1,63 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import scala.collection.mutable
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+case class IntersectNode(conf: SQLConf, left: LocalNode, right: LocalNode)
+  extends BinaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = left.output
+
+  private[this] var leftRows: mutable.HashSet[InternalRow] = _
+
+  private[this] var currentRow: InternalRow = _
+
+  override def open(): Unit = {
+    left.open()
+    leftRows = mutable.HashSet[InternalRow]()
+    while (left.next()) {
+      leftRows += left.fetch().copy()
+    }
+    left.close()
+    right.open()
+  }
+
+  override def next(): Boolean = {
+    currentRow = null
+    while (currentRow == null && right.next()) {
+      currentRow = right.fetch()
+      if (!leftRows.contains(currentRow)) {
+        currentRow = null
+      }
+    }
+    currentRow != null
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = {
+    left.close()
+    right.close()
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index c4f8ae304db39..a2c275db9b35d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -69,6 +69,11 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
    */
   def close(): Unit
 
+  /**
+   * Returns the content through the [[Iterator]] interface.
+   */
+  final def asIterator: Iterator[InternalRow] = new LocalNodeIterator(this)
+
   /**
    * Returns the content of the iterator from the beginning to the end in the form of a Scala Seq.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
new file mode 100644
index 0000000000000..abf3df1c0c2af
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import java.util.Random
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.util.random.{BernoulliCellSampler, PoissonSampler}
+
+/**
+ * Sample the dataset.
+ *
+ * @param conf the SQLConf
+ * @param lowerBound Lower-bound of the sampling probability (usually 0.0)
+ * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled
+ *                   will be ub - lb.
+ * @param withReplacement Whether to sample with replacement.
+ * @param seed the random seed
+ * @param child the LocalNode
+ */
+case class SampleNode(
+    conf: SQLConf,
+    lowerBound: Double,
+    upperBound: Double,
+    withReplacement: Boolean,
+    seed: Long,
+    child: LocalNode) extends UnaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = child.output
+
+  private[this] var iterator: Iterator[InternalRow] = _
+
+  private[this] var currentRow: InternalRow = _
+
+  override def open(): Unit = {
+    child.open()
+    val (sampler, _seed) = if (withReplacement) {
+        val random = new Random(seed)
+        // Disable gap sampling since the gap sampling method buffers two rows internally,
+        // requiring us to copy the row, which is more expensive than the random number generator.
+        (new PoissonSampler[InternalRow](upperBound - lowerBound, useGapSamplingIfPossible = false),
+          // Use the seed for partition 0 like PartitionwiseSampledRDD to generate the same result
+          // of DataFrame
+          random.nextLong())
+      } else {
+        (new BernoulliCellSampler[InternalRow](lowerBound, upperBound), seed)
+      }
+    sampler.setSeed(_seed)
+    iterator = sampler.sample(child.asIterator)
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      currentRow = iterator.next()
+      true
+    } else {
+      false
+    }
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = child.close()
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
new file mode 100644
index 0000000000000..53f1dcc65d8cf
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.util.BoundedPriorityQueue
+
+case class TakeOrderedAndProjectNode(
+    conf: SQLConf,
+    limit: Int,
+    sortOrder: Seq[SortOrder],
+    projectList: Option[Seq[NamedExpression]],
+    child: LocalNode) extends UnaryLocalNode(conf) {
+
+  private[this] var projection: Option[Projection] = _
+  private[this] var ord: InterpretedOrdering = _
+  private[this] var iterator: Iterator[InternalRow] = _
+  private[this] var currentRow: InternalRow = _
+
+  override def output: Seq[Attribute] = {
+    val projectOutput = projectList.map(_.map(_.toAttribute))
+    projectOutput.getOrElse(child.output)
+  }
+
+  override def open(): Unit = {
+    child.open()
+    projection = projectList.map(new InterpretedProjection(_, child.output))
+    ord = new InterpretedOrdering(sortOrder, child.output)
+    // Priority keeps the largest elements, so let's reverse the ordering.
+    val queue = new BoundedPriorityQueue[InternalRow](limit)(ord.reverse)
+    while (child.next()) {
+      queue += child.fetch()
+    }
+    // Close it eagerly since we don't need it.
+    child.close()
+    iterator = queue.iterator
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      val _currentRow = iterator.next()
+      currentRow = projection match {
+        case Some(p) => p(_currentRow)
+        case None => _currentRow
+      }
+      true
+    } else {
+      false
+    }
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = child.close()
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
new file mode 100644
index 0000000000000..7deaa375fcfc2
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
@@ -0,0 +1,35 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+class IntersectNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  test("basic") {
+    val input1 = (1 to 10).map(i => (i, i.toString)).toDF("key", "value")
+    val input2 = (1 to 10).filter(_ % 2 == 0).map(i => (i, i.toString)).toDF("key", "value")
+
+    checkAnswer2(
+      input1,
+      input2,
+      (node1, node2) => IntersectNode(conf, node1, node2),
+      input1.intersect(input2).collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
new file mode 100644
index 0000000000000..87a7da453999c
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+class SampleNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def testSample(withReplacement: Boolean): Unit = {
+    test(s"withReplacement: $withReplacement") {
+      val seed = 0L
+      val input = sqlContext.sparkContext.
+        parallelize((1 to 10).map(i => (i, i.toString)), 1). // Should be only 1 partition
+        toDF("key", "value")
+      checkAnswer(
+        input,
+        node => SampleNode(conf, 0.0, 0.3, withReplacement, seed, node),
+        input.sample(withReplacement, 0.3, seed).collect()
+      )
+    }
+  }
+
+  testSample(withReplacement = true)
+  testSample(withReplacement = false)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala
new file mode 100644
index 0000000000000..ff28b24eeff14
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala
@@ -0,0 +1,54 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.Column
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Expression, SortOrder}
+
+class TakeOrderedAndProjectNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def columnToSortOrder(sortExprs: Column*): Seq[SortOrder] = {
+    val sortOrder: Seq[SortOrder] = sortExprs.map { col =>
+      col.expr match {
+        case expr: SortOrder =>
+          expr
+        case expr: Expression =>
+          SortOrder(expr, Ascending)
+      }
+    }
+    sortOrder
+  }
+
+  private def testTakeOrderedAndProjectNode(desc: Boolean): Unit = {
+    val testCaseName = if (desc) "desc" else "asc"
+    test(testCaseName) {
+      val input = (1 to 10).map(i => (i, i.toString)).toDF("key", "value")
+      val sortColumn = if (desc) input.col("key").desc else input.col("key")
+      checkAnswer(
+        input,
+        node => TakeOrderedAndProjectNode(conf, 5, columnToSortOrder(sortColumn), None, node),
+        input.sort(sortColumn).limit(5).collect()
+      )
+    }
+  }
+
+  testTakeOrderedAndProjectNode(desc = false)
+  testTakeOrderedAndProjectNode(desc = true)
+}

From c2af42b5f32287ff595ad027a8191d4b75702d8d Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 11 Sep 2015 15:01:37 -0700
Subject: [PATCH 1402/1454] [SPARK-9990] [SQL] Local hash join follow-ups

1. Hide `LocalNodeIterator` behind the `LocalNode#asIterator` method
2. Add tests for this

Author: Andrew Or <andrew@databricks.com>

Closes #8708 from andrewor14/local-hash-join-follow-up.
---
 .../sql/execution/joins/HashedRelation.scala  |   7 +-
 .../sql/execution/local/HashJoinNode.scala    |   3 +-
 .../spark/sql/execution/local/LocalNode.scala |   4 +-
 .../sql/execution/local/LocalNodeSuite.scala  | 116 ++++++++++++++++++
 4 files changed, 125 insertions(+), 5 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 0cff21ca618b4..bc255b27502b2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -25,7 +25,8 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
-import org.apache.spark.sql.execution.metric.LongSQLMetric
+import org.apache.spark.sql.execution.local.LocalNode
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
 import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{MemoryLocation, ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
@@ -113,6 +114,10 @@ final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalR
 
 private[execution] object HashedRelation {
 
+  def apply(localNode: LocalNode, keyGenerator: Projection): HashedRelation = {
+    apply(localNode.asIterator, SQLMetrics.nullLongMetric, keyGenerator)
+  }
+
   def apply(
       input: Iterator[InternalRow],
       numInputRows: LongSQLMetric,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
index a3e68d6a7c341..e7b24e3fca2b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
@@ -75,8 +75,7 @@ case class HashJoinNode(
 
   override def open(): Unit = {
     buildNode.open()
-    hashed = HashedRelation.apply(
-      new LocalNodeIterator(buildNode), SQLMetrics.nullLongMetric, buildSideKeyGenerator)
+    hashed = HashedRelation(buildNode, buildSideKeyGenerator)
     streamedNode.open()
     joinRow = new JoinedRow
     resultProjection = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index a2c275db9b35d..e540ef8555eb6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -77,7 +77,7 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
   /**
    * Returns the content of the iterator from the beginning to the end in the form of a Scala Seq.
    */
-  def collect(): Seq[Row] = {
+  final def collect(): Seq[Row] = {
     val converter = CatalystTypeConverters.createToScalaConverter(StructType.fromAttributes(output))
     val result = new scala.collection.mutable.ArrayBuffer[Row]
     open()
@@ -140,7 +140,7 @@ abstract class BinaryLocalNode(conf: SQLConf) extends LocalNode(conf) {
 /**
  * An thin wrapper around a [[LocalNode]] that provides an `Iterator` interface.
  */
-private[local] class LocalNodeIterator(localNode: LocalNode) extends Iterator[InternalRow] {
+private class LocalNodeIterator(localNode: LocalNode) extends Iterator[InternalRow] {
   private var nextRow: InternalRow = _
 
   override def hasNext: Boolean = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala
new file mode 100644
index 0000000000000..b89fa46f8b3b4
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala
@@ -0,0 +1,116 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.IntegerType
+
+class LocalNodeSuite extends SparkFunSuite {
+  private val data = (1 to 100).toArray
+
+  test("basic open, next, fetch, close") {
+    val node = new DummyLocalNode(data)
+    assert(!node.isOpen)
+    node.open()
+    assert(node.isOpen)
+    data.foreach { i =>
+      assert(node.next())
+      // fetch should be idempotent
+      val fetched = node.fetch()
+      assert(node.fetch() === fetched)
+      assert(node.fetch() === fetched)
+      assert(node.fetch().numFields === 1)
+      assert(node.fetch().getInt(0) === i)
+    }
+    assert(!node.next())
+    node.close()
+    assert(!node.isOpen)
+  }
+
+  test("asIterator") {
+    val node = new DummyLocalNode(data)
+    val iter = node.asIterator
+    node.open()
+    data.foreach { i =>
+      // hasNext should be idempotent
+      assert(iter.hasNext)
+      assert(iter.hasNext)
+      val item = iter.next()
+      assert(item.numFields === 1)
+      assert(item.getInt(0) === i)
+    }
+    intercept[NoSuchElementException] {
+      iter.next()
+    }
+    node.close()
+  }
+
+  test("collect") {
+    val node = new DummyLocalNode(data)
+    node.open()
+    val collected = node.collect()
+    assert(collected.size === data.size)
+    assert(collected.forall(_.size === 1))
+    assert(collected.map(_.getInt(0)) === data)
+    node.close()
+  }
+
+}
+
+/**
+ * A dummy [[LocalNode]] that just returns one row per integer in the input.
+ */
+private case class DummyLocalNode(conf: SQLConf, input: Array[Int]) extends LocalNode(conf) {
+  private var index = Int.MinValue
+
+  def this(input: Array[Int]) {
+    this(new SQLConf, input)
+  }
+
+  def isOpen: Boolean = {
+    index != Int.MinValue
+  }
+
+  override def output: Seq[Attribute] = {
+    Seq(AttributeReference("something", IntegerType)())
+  }
+
+  override def children: Seq[LocalNode] = Seq.empty
+
+  override def open(): Unit = {
+    index = -1
+  }
+
+  override def next(): Boolean = {
+    index += 1
+    index < input.size
+  }
+
+  override def fetch(): InternalRow = {
+    assert(index >= 0 && index < input.size)
+    val values = Array(input(index).asInstanceOf[Any])
+    new GenericInternalRow(values)
+  }
+
+  override def close(): Unit = {
+    index = Int.MinValue
+  }
+}

From d74c6a143cbd060c25bf14a8d306841b3ec55d03 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 11 Sep 2015 15:02:59 -0700
Subject: [PATCH 1403/1454] [SPARK-10564] ThreadingSuite: assertion failures in
 threads don't fail the test

This commit ensures if an assertion fails within a thread, it will ultimately fail the test. Otherwise we end up potentially masking real bugs by not propagating assertion failures properly.

Author: Andrew Or <andrew@databricks.com>

Closes #8723 from andrewor14/fix-threading-suite.
---
 .../org/apache/spark/ThreadingSuite.scala     | 68 ++++++++++++-------
 1 file changed, 45 insertions(+), 23 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
index 48509f0759a3b..cda2b245526f7 100644
--- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
@@ -119,23 +119,30 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     val nums = sc.parallelize(1 to 2, 2)
     val sem = new Semaphore(0)
     ThreadingSuiteState.clear()
+    var throwable: Option[Throwable] = None
     for (i <- 0 until 2) {
       new Thread {
         override def run() {
-          val ans = nums.map(number => {
-            val running = ThreadingSuiteState.runningThreads
-            running.getAndIncrement()
-            val time = System.currentTimeMillis()
-            while (running.get() != 4 && System.currentTimeMillis() < time + 1000) {
-              Thread.sleep(100)
-            }
-            if (running.get() != 4) {
-              ThreadingSuiteState.failed.set(true)
-            }
-            number
-          }).collect()
-          assert(ans.toList === List(1, 2))
-          sem.release()
+          try {
+            val ans = nums.map(number => {
+              val running = ThreadingSuiteState.runningThreads
+              running.getAndIncrement()
+              val time = System.currentTimeMillis()
+              while (running.get() != 4 && System.currentTimeMillis() < time + 1000) {
+                Thread.sleep(100)
+              }
+              if (running.get() != 4) {
+                ThreadingSuiteState.failed.set(true)
+              }
+              number
+            }).collect()
+            assert(ans.toList === List(1, 2))
+          } catch {
+            case t: Throwable =>
+              throwable = Some(t)
+          } finally {
+            sem.release()
+          }
         }
       }.start()
     }
@@ -145,18 +152,25 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
                 ThreadingSuiteState.runningThreads.get() + "); failing test")
       fail("One or more threads didn't see runningThreads = 4")
     }
+    throwable.foreach { t => throw t }
   }
 
   test("set local properties in different thread") {
     sc = new SparkContext("local", "test")
     val sem = new Semaphore(0)
-
+    var throwable: Option[Throwable] = None
     val threads = (1 to 5).map { i =>
       new Thread() {
         override def run() {
-          sc.setLocalProperty("test", i.toString)
-          assert(sc.getLocalProperty("test") === i.toString)
-          sem.release()
+          try {
+            sc.setLocalProperty("test", i.toString)
+            assert(sc.getLocalProperty("test") === i.toString)
+          } catch {
+            case t: Throwable =>
+              throwable = Some(t)
+          } finally {
+            sem.release()
+          }
         }
       }
     }
@@ -165,20 +179,27 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
 
     sem.acquire(5)
     assert(sc.getLocalProperty("test") === null)
+    throwable.foreach { t => throw t }
   }
 
   test("set and get local properties in parent-children thread") {
     sc = new SparkContext("local", "test")
     sc.setLocalProperty("test", "parent")
     val sem = new Semaphore(0)
-
+    var throwable: Option[Throwable] = None
     val threads = (1 to 5).map { i =>
       new Thread() {
         override def run() {
-          assert(sc.getLocalProperty("test") === "parent")
-          sc.setLocalProperty("test", i.toString)
-          assert(sc.getLocalProperty("test") === i.toString)
-          sem.release()
+          try {
+            assert(sc.getLocalProperty("test") === "parent")
+            sc.setLocalProperty("test", i.toString)
+            assert(sc.getLocalProperty("test") === i.toString)
+          } catch {
+            case t: Throwable =>
+              throwable = Some(t)
+          } finally {
+            sem.release()
+          }
         }
       }
     }
@@ -188,6 +209,7 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     sem.acquire(5)
     assert(sc.getLocalProperty("test") === "parent")
     assert(sc.getLocalProperty("Foo") === null)
+    throwable.foreach { t => throw t }
   }
 
   test("mutations to local properties should not affect submitted jobs (SPARK-6629)") {

From c34fc19765bdf55365cdce78d9ba11b220b73bb6 Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Fri, 11 Sep 2015 15:19:04 -0700
Subject: [PATCH 1404/1454] [SPARK-9014] [SQL] Allow Python spark API to use
 built-in exponential operator

This PR addresses (SPARK-9014)[https://issues.apache.org/jira/browse/SPARK-9014]
Added functionality: `Column` object in Python now supports exponential operator `**`
Example:
```
from pyspark.sql import *
df = sqlContext.createDataFrame([Row(a=2)])
df.select(3**df.a,df.a**3,df.a**df.a).collect()
```
Outputs:
```
[Row(POWER(3.0, a)=9.0, POWER(a, 3.0)=8.0, POWER(a, a)=4.0)]
```

Author: 0x0FFF <programmerag@gmail.com>

Closes #8658 from 0x0FFF/SPARK-9014.
---
 python/pyspark/sql/column.py | 13 +++++++++++++
 python/pyspark/sql/tests.py  |  2 +-
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 573f65f5bf096..9ca8e1f264cfa 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -91,6 +91,17 @@ def _(self):
     return _
 
 
+def _bin_func_op(name, reverse=False, doc="binary function"):
+    def _(self, other):
+        sc = SparkContext._active_spark_context
+        fn = getattr(sc._jvm.functions, name)
+        jc = other._jc if isinstance(other, Column) else _create_column_from_literal(other)
+        njc = fn(self._jc, jc) if not reverse else fn(jc, self._jc)
+        return Column(njc)
+    _.__doc__ = doc
+    return _
+
+
 def _bin_op(name, doc="binary operator"):
     """ Create a method for given binary operator
     """
@@ -151,6 +162,8 @@ def __init__(self, jc):
     __rdiv__ = _reverse_op("divide")
     __rtruediv__ = _reverse_op("divide")
     __rmod__ = _reverse_op("mod")
+    __pow__ = _bin_func_op("pow")
+    __rpow__ = _bin_func_op("pow", reverse=True)
 
     # logistic operators
     __eq__ = _bin_op("equalTo")
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index eb449e8679fa0..f2172b7a27d88 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -568,7 +568,7 @@ def test_column_operators(self):
         cs = self.df.value
         c = ci == cs
         self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
-        rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci)
+        rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci), (1 ** ci), (ci ** 1)
         self.assertTrue(all(isinstance(c, Column) for c in rcc))
         cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7]
         self.assertTrue(all(isinstance(c, Column) for c in cb))

From 6d8367807cb62c2cb139cee1d039dc8b12c63385 Mon Sep 17 00:00:00 2001
From: Daniel Imfeld <daniel@danielimfeld.com>
Date: Sat, 12 Sep 2015 09:19:59 +0100
Subject: [PATCH 1405/1454] [SPARK-10566] [CORE] SnappyCompressionCodec init
 exception handling masks important error information

When throwing an IllegalArgumentException in SnappyCompressionCodec.init, chain the existing exception. This allows potentially important debugging info to be passed to the user.

Manual testing shows the exception chained properly, and the test suite still looks fine as well.

This contribution is my original work and I license the work to the project under the project's open source license.

Author: Daniel Imfeld <daniel@danielimfeld.com>

Closes #8725 from dimfeld/dimfeld-patch-1.
---
 core/src/main/scala/org/apache/spark/io/CompressionCodec.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
index 607d5a321efca..9dc36704a676d 100644
--- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
+++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
@@ -148,7 +148,7 @@ class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec {
   try {
     Snappy.getNativeLibraryVersion
   } catch {
-    case e: Error => throw new IllegalArgumentException
+    case e: Error => throw new IllegalArgumentException(e)
   }
 
   override def compressedOutputStream(s: OutputStream): OutputStream = {

From 8285e3b0d3dc0eff669eba993742dfe0401116f9 Mon Sep 17 00:00:00 2001
From: Nithin Asokan <Nithin.Asokan@Cerner.com>
Date: Sat, 12 Sep 2015 09:50:49 +0100
Subject: [PATCH 1406/1454] [SPARK-10554] [CORE] Fix NPE with ShutdownHook

https://issues.apache.org/jira/browse/SPARK-10554

Fixes NPE when ShutdownHook tries to cleanup temporary folders

Author: Nithin Asokan <Nithin.Asokan@Cerner.com>

Closes #8720 from nasokan/SPARK-10554.
---
 .../scala/org/apache/spark/storage/DiskBlockManager.scala     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 3f8d26e1d4cab..f7e84a2c2e14c 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -164,7 +164,9 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
 
   private def doStop(): Unit = {
     // Only perform cleanup if an external service is not serving our shuffle files.
-    if (!blockManager.externalShuffleServiceEnabled || blockManager.blockManagerId.isDriver) {
+    // Also blockManagerId could be null if block manager is not initialized properly.
+    if (!blockManager.externalShuffleServiceEnabled ||
+      (blockManager.blockManagerId != null && blockManager.blockManagerId.isDriver)) {
       localDirs.foreach { localDir =>
         if (localDir.isDirectory() && localDir.exists()) {
           try {

From 22730ad54d681ad30e63fe910e8d89360853177d Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sat, 12 Sep 2015 10:40:10 +0100
Subject: [PATCH 1407/1454] [SPARK-10547] [TEST] Streamline / improve style of
 Java API tests

Fix a few Java API test style issues: unused generic types, exceptions, wrong assert argument order

Author: Sean Owen <sowen@cloudera.com>

Closes #8706 from srowen/SPARK-10547.
---
 .../java/org/apache/spark/JavaAPISuite.java   | 451 ++++++-----
 .../kafka/JavaDirectKafkaStreamSuite.java     |  24 +-
 .../streaming/kafka/JavaKafkaRDDSuite.java    |  17 +-
 .../streaming/kafka/JavaKafkaStreamSuite.java |  14 +-
 .../twitter/JavaTwitterStreamSuite.java       |   4 +-
 .../java/org/apache/spark/Java8APISuite.java  |  46 +-
 .../spark/sql/JavaApplySchemaSuite.java       |  39 +-
 .../apache/spark/sql/JavaDataFrameSuite.java  |  29 +-
 .../org/apache/spark/sql/JavaRowSuite.java    |  15 +-
 .../org/apache/spark/sql/JavaUDFSuite.java    |   9 +-
 .../spark/sql/sources/JavaSaveLoadSuite.java  |  10 +-
 .../spark/sql/hive/JavaDataFrameSuite.java    |   8 +-
 .../hive/JavaMetastoreDataSourcesSuite.java   |  12 +-
 .../apache/spark/streaming/JavaAPISuite.java  | 752 +++++++++---------
 .../spark/streaming/JavaReceiverAPISuite.java |  86 +-
 15 files changed, 755 insertions(+), 761 deletions(-)

diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index ebd3d61ae7324..fd8f7f39b7cc8 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -90,7 +90,7 @@ public void sparkContextUnion() {
     JavaRDD<String> sUnion = sc.union(s1, s2);
     Assert.assertEquals(4, sUnion.count());
     // List
-    List<JavaRDD<String>> list = new ArrayList<JavaRDD<String>>();
+    List<JavaRDD<String>> list = new ArrayList<>();
     list.add(s2);
     sUnion = sc.union(s1, list);
     Assert.assertEquals(4, sUnion.count());
@@ -103,9 +103,9 @@ public void sparkContextUnion() {
     Assert.assertEquals(4, dUnion.count());
 
     // Union of JavaPairRDDs
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(1, 2));
-    pairs.add(new Tuple2<Integer, Integer>(3, 4));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(1, 2));
+    pairs.add(new Tuple2<>(3, 4));
     JavaPairRDD<Integer, Integer> p1 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> p2 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> pUnion = sc.union(p1, p2);
@@ -133,9 +133,9 @@ public void intersection() {
     JavaDoubleRDD dIntersection = d1.intersection(d2);
     Assert.assertEquals(2, dIntersection.count());
 
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(1, 2));
-    pairs.add(new Tuple2<Integer, Integer>(3, 4));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(1, 2));
+    pairs.add(new Tuple2<>(3, 4));
     JavaPairRDD<Integer, Integer> p1 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> p2 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> pIntersection = p1.intersection(p2);
@@ -165,47 +165,49 @@ public void randomSplit() {
 
   @Test
   public void sortByKey() {
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(0, 4));
-    pairs.add(new Tuple2<Integer, Integer>(3, 2));
-    pairs.add(new Tuple2<Integer, Integer>(-1, 1));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(0, 4));
+    pairs.add(new Tuple2<>(3, 2));
+    pairs.add(new Tuple2<>(-1, 1));
 
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
 
     // Default comparator
     JavaPairRDD<Integer, Integer> sortedRDD = rdd.sortByKey();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     List<Tuple2<Integer, Integer>> sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(2));
 
     // Custom comparator
     sortedRDD = rdd.sortByKey(Collections.<Integer>reverseOrder(), false);
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(2));
   }
 
   @SuppressWarnings("unchecked")
   @Test
   public void repartitionAndSortWithinPartitions() {
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(0, 5));
-    pairs.add(new Tuple2<Integer, Integer>(3, 8));
-    pairs.add(new Tuple2<Integer, Integer>(2, 6));
-    pairs.add(new Tuple2<Integer, Integer>(0, 8));
-    pairs.add(new Tuple2<Integer, Integer>(3, 8));
-    pairs.add(new Tuple2<Integer, Integer>(1, 3));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(0, 5));
+    pairs.add(new Tuple2<>(3, 8));
+    pairs.add(new Tuple2<>(2, 6));
+    pairs.add(new Tuple2<>(0, 8));
+    pairs.add(new Tuple2<>(3, 8));
+    pairs.add(new Tuple2<>(1, 3));
 
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
 
     Partitioner partitioner = new Partitioner() {
+      @Override
       public int numPartitions() {
         return 2;
       }
+      @Override
       public int getPartition(Object key) {
-        return ((Integer)key).intValue() % 2;
+        return (Integer) key % 2;
       }
     };
 
@@ -214,10 +216,10 @@ public int getPartition(Object key) {
     Assert.assertTrue(repartitioned.partitioner().isPresent());
     Assert.assertEquals(repartitioned.partitioner().get(), partitioner);
     List<List<Tuple2<Integer, Integer>>> partitions = repartitioned.glom().collect();
-    Assert.assertEquals(partitions.get(0), Arrays.asList(new Tuple2<Integer, Integer>(0, 5),
-        new Tuple2<Integer, Integer>(0, 8), new Tuple2<Integer, Integer>(2, 6)));
-    Assert.assertEquals(partitions.get(1), Arrays.asList(new Tuple2<Integer, Integer>(1, 3),
-        new Tuple2<Integer, Integer>(3, 8), new Tuple2<Integer, Integer>(3, 8)));
+    Assert.assertEquals(partitions.get(0),
+        Arrays.asList(new Tuple2<>(0, 5), new Tuple2<>(0, 8), new Tuple2<>(2, 6)));
+    Assert.assertEquals(partitions.get(1),
+        Arrays.asList(new Tuple2<>(1, 3), new Tuple2<>(3, 8), new Tuple2<>(3, 8)));
   }
 
   @Test
@@ -228,35 +230,37 @@ public void emptyRDD() {
 
   @Test
   public void sortBy() {
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(0, 4));
-    pairs.add(new Tuple2<Integer, Integer>(3, 2));
-    pairs.add(new Tuple2<Integer, Integer>(-1, 1));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(0, 4));
+    pairs.add(new Tuple2<>(3, 2));
+    pairs.add(new Tuple2<>(-1, 1));
 
     JavaRDD<Tuple2<Integer, Integer>> rdd = sc.parallelize(pairs);
 
     // compare on first value
     JavaRDD<Tuple2<Integer, Integer>> sortedRDD = rdd.sortBy(new Function<Tuple2<Integer, Integer>, Integer>() {
-      public Integer call(Tuple2<Integer, Integer> t) throws Exception {
+      @Override
+      public Integer call(Tuple2<Integer, Integer> t) {
         return t._1();
       }
     }, true, 2);
 
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     List<Tuple2<Integer, Integer>> sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(2));
 
     // compare on second value
     sortedRDD = rdd.sortBy(new Function<Tuple2<Integer, Integer>, Integer>() {
-      public Integer call(Tuple2<Integer, Integer> t) throws Exception {
+      @Override
+      public Integer call(Tuple2<Integer, Integer> t) {
         return t._2();
       }
     }, true, 2);
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(2));
   }
 
   @Test
@@ -265,7 +269,7 @@ public void foreach() {
     JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
     rdd.foreach(new VoidFunction<String>() {
       @Override
-      public void call(String s) throws IOException {
+      public void call(String s) {
         accum.add(1);
       }
     });
@@ -278,7 +282,7 @@ public void foreachPartition() {
     JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
     rdd.foreachPartition(new VoidFunction<Iterator<String>>() {
       @Override
-      public void call(Iterator<String> iter) throws IOException {
+      public void call(Iterator<String> iter) {
         while (iter.hasNext()) {
           iter.next();
           accum.add(1);
@@ -301,7 +305,7 @@ public void zipWithUniqueId() {
     List<Integer> dataArray = Arrays.asList(1, 2, 3, 4);
     JavaPairRDD<Integer, Long> zip = sc.parallelize(dataArray).zipWithUniqueId();
     JavaRDD<Long> indexes = zip.values();
-    Assert.assertEquals(4, new HashSet<Long>(indexes.collect()).size());
+    Assert.assertEquals(4, new HashSet<>(indexes.collect()).size());
   }
 
   @Test
@@ -317,10 +321,10 @@ public void zipWithIndex() {
   @Test
   public void lookup() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
-      ));
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
+    ));
     Assert.assertEquals(2, categories.lookup("Oranges").size());
     Assert.assertEquals(2, Iterables.size(categories.groupByKey().lookup("Oranges").get(0)));
   }
@@ -390,18 +394,17 @@ public String call(Tuple2<Integer, Integer> x) {
   @Test
   public void cogroup() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
       ));
     JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 2),
-      new Tuple2<String, Integer>("Apples", 3)
+      new Tuple2<>("Oranges", 2),
+      new Tuple2<>("Apples", 3)
     ));
     JavaPairRDD<String, Tuple2<Iterable<String>, Iterable<Integer>>> cogrouped =
         categories.cogroup(prices);
-    Assert.assertEquals("[Fruit, Citrus]",
-                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
     Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
 
     cogrouped.collect();
@@ -411,23 +414,22 @@ public void cogroup() {
   @Test
   public void cogroup3() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
       ));
     JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 2),
-      new Tuple2<String, Integer>("Apples", 3)
+      new Tuple2<>("Oranges", 2),
+      new Tuple2<>("Apples", 3)
     ));
     JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 21),
-      new Tuple2<String, Integer>("Apples", 42)
+      new Tuple2<>("Oranges", 21),
+      new Tuple2<>("Apples", 42)
     ));
 
     JavaPairRDD<String, Tuple3<Iterable<String>, Iterable<Integer>, Iterable<Integer>>> cogrouped =
         categories.cogroup(prices, quantities);
-    Assert.assertEquals("[Fruit, Citrus]",
-                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
     Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
     Assert.assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3()));
 
@@ -439,27 +441,26 @@ public void cogroup3() {
   @Test
   public void cogroup4() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
       ));
     JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 2),
-      new Tuple2<String, Integer>("Apples", 3)
+      new Tuple2<>("Oranges", 2),
+      new Tuple2<>("Apples", 3)
     ));
     JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 21),
-      new Tuple2<String, Integer>("Apples", 42)
+      new Tuple2<>("Oranges", 21),
+      new Tuple2<>("Apples", 42)
     ));
     JavaPairRDD<String, String> countries = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Oranges", "BR"),
-      new Tuple2<String, String>("Apples", "US")
+      new Tuple2<>("Oranges", "BR"),
+      new Tuple2<>("Apples", "US")
     ));
 
     JavaPairRDD<String, Tuple4<Iterable<String>, Iterable<Integer>, Iterable<Integer>, Iterable<String>>> cogrouped =
         categories.cogroup(prices, quantities, countries);
-    Assert.assertEquals("[Fruit, Citrus]",
-                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
     Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
     Assert.assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3()));
     Assert.assertEquals("[BR]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._4()));
@@ -471,16 +472,16 @@ public void cogroup4() {
   @Test
   public void leftOuterJoin() {
     JavaPairRDD<Integer, Integer> rdd1 = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(1, 2),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(1, 1),
+      new Tuple2<>(1, 2),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(3, 1)
       ));
     JavaPairRDD<Integer, Character> rdd2 = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<Integer, Character>(1, 'x'),
-      new Tuple2<Integer, Character>(2, 'y'),
-      new Tuple2<Integer, Character>(2, 'z'),
-      new Tuple2<Integer, Character>(4, 'w')
+      new Tuple2<>(1, 'x'),
+      new Tuple2<>(2, 'y'),
+      new Tuple2<>(2, 'z'),
+      new Tuple2<>(4, 'w')
     ));
     List<Tuple2<Integer,Tuple2<Integer,Optional<Character>>>> joined =
       rdd1.leftOuterJoin(rdd2).collect();
@@ -548,11 +549,11 @@ public Integer call(Integer a, Integer b) {
   public void aggregateByKey() {
     JavaPairRDD<Integer, Integer> pairs = sc.parallelizePairs(
       Arrays.asList(
-        new Tuple2<Integer, Integer>(1, 1),
-        new Tuple2<Integer, Integer>(1, 1),
-        new Tuple2<Integer, Integer>(3, 2),
-        new Tuple2<Integer, Integer>(5, 1),
-        new Tuple2<Integer, Integer>(5, 3)), 2);
+        new Tuple2<>(1, 1),
+        new Tuple2<>(1, 1),
+        new Tuple2<>(3, 2),
+        new Tuple2<>(5, 1),
+        new Tuple2<>(5, 3)), 2);
 
     Map<Integer, Set<Integer>> sets = pairs.aggregateByKey(new HashSet<Integer>(),
       new Function2<Set<Integer>, Integer, Set<Integer>>() {
@@ -570,20 +571,20 @@ public Set<Integer> call(Set<Integer> a, Set<Integer> b) {
         }
       }).collectAsMap();
     Assert.assertEquals(3, sets.size());
-    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(1)), sets.get(1));
-    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(2)), sets.get(3));
-    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(1, 3)), sets.get(5));
+    Assert.assertEquals(new HashSet<>(Arrays.asList(1)), sets.get(1));
+    Assert.assertEquals(new HashSet<>(Arrays.asList(2)), sets.get(3));
+    Assert.assertEquals(new HashSet<>(Arrays.asList(1, 3)), sets.get(5));
   }
 
   @SuppressWarnings("unchecked")
   @Test
   public void foldByKey() {
     List<Tuple2<Integer, Integer>> pairs = Arrays.asList(
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(3, 2),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(2, 1),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(1, 1),
+      new Tuple2<>(3, 2),
+      new Tuple2<>(3, 1)
     );
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> sums = rdd.foldByKey(0,
@@ -602,11 +603,11 @@ public Integer call(Integer a, Integer b) {
   @Test
   public void reduceByKey() {
     List<Tuple2<Integer, Integer>> pairs = Arrays.asList(
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(3, 2),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(2, 1),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(1, 1),
+      new Tuple2<>(3, 2),
+      new Tuple2<>(3, 1)
     );
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> counts = rdd.reduceByKey(
@@ -690,7 +691,7 @@ public void cartesian() {
     JavaDoubleRDD doubleRDD = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0));
     JavaRDD<String> stringRDD = sc.parallelize(Arrays.asList("Hello", "World"));
     JavaPairRDD<String, Double> cartesian = stringRDD.cartesian(doubleRDD);
-    Assert.assertEquals(new Tuple2<String, Double>("Hello", 1.0), cartesian.first());
+    Assert.assertEquals(new Tuple2<>("Hello", 1.0), cartesian.first());
   }
 
   @Test
@@ -743,6 +744,7 @@ public void javaDoubleRDDHistoGram() {
   }
 
   private static class DoubleComparator implements Comparator<Double>, Serializable {
+    @Override
     public int compare(Double o1, Double o2) {
       return o1.compareTo(o2);
     }
@@ -766,14 +768,14 @@ public void min() {
   public void naturalMax() {
     JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0));
     double max = rdd.max();
-    Assert.assertTrue(4.0 == max);
+    Assert.assertEquals(4.0, max, 0.0);
   }
 
   @Test
   public void naturalMin() {
     JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0));
     double max = rdd.min();
-    Assert.assertTrue(1.0 == max);
+    Assert.assertEquals(1.0, max, 0.0);
   }
 
   @Test
@@ -809,7 +811,7 @@ public void reduceOnJavaDoubleRDD() {
     JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0));
     double sum = rdd.reduce(new Function2<Double, Double, Double>() {
       @Override
-      public Double call(Double v1, Double v2) throws Exception {
+      public Double call(Double v1, Double v2) {
         return v1 + v2;
       }
     });
@@ -844,7 +846,7 @@ public double call(Integer x) {
         new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer x) {
-            return new Tuple2<Integer, Integer>(x, x);
+            return new Tuple2<>(x, x);
           }
         }).cache();
     pairs.collect();
@@ -870,26 +872,25 @@ public Iterable<String> call(String x) {
     Assert.assertEquals("Hello", words.first());
     Assert.assertEquals(11, words.count());
 
-    JavaPairRDD<String, String> pairs = rdd.flatMapToPair(
+    JavaPairRDD<String, String> pairsRDD = rdd.flatMapToPair(
       new PairFlatMapFunction<String, String, String>() {
-
         @Override
         public Iterable<Tuple2<String, String>> call(String s) {
-          List<Tuple2<String, String>> pairs = new LinkedList<Tuple2<String, String>>();
+          List<Tuple2<String, String>> pairs = new LinkedList<>();
           for (String word : s.split(" ")) {
-            pairs.add(new Tuple2<String, String>(word, word));
+            pairs.add(new Tuple2<>(word, word));
           }
           return pairs;
         }
       }
     );
-    Assert.assertEquals(new Tuple2<String, String>("Hello", "Hello"), pairs.first());
-    Assert.assertEquals(11, pairs.count());
+    Assert.assertEquals(new Tuple2<>("Hello", "Hello"), pairsRDD.first());
+    Assert.assertEquals(11, pairsRDD.count());
 
     JavaDoubleRDD doubles = rdd.flatMapToDouble(new DoubleFlatMapFunction<String>() {
       @Override
       public Iterable<Double> call(String s) {
-        List<Double> lengths = new LinkedList<Double>();
+        List<Double> lengths = new LinkedList<>();
         for (String word : s.split(" ")) {
           lengths.add((double) word.length());
         }
@@ -897,36 +898,36 @@ public Iterable<Double> call(String s) {
       }
     });
     Assert.assertEquals(5.0, doubles.first(), 0.01);
-    Assert.assertEquals(11, pairs.count());
+    Assert.assertEquals(11, pairsRDD.count());
   }
 
   @SuppressWarnings("unchecked")
   @Test
   public void mapsFromPairsToPairs() {
-      List<Tuple2<Integer, String>> pairs = Arrays.asList(
-              new Tuple2<Integer, String>(1, "a"),
-              new Tuple2<Integer, String>(2, "aa"),
-              new Tuple2<Integer, String>(3, "aaa")
-      );
-      JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs);
-
-      // Regression test for SPARK-668:
-      JavaPairRDD<String, Integer> swapped = pairRDD.flatMapToPair(
-          new PairFlatMapFunction<Tuple2<Integer, String>, String, Integer>() {
-          @Override
-          public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) {
-              return Collections.singletonList(item.swap());
-          }
+    List<Tuple2<Integer, String>> pairs = Arrays.asList(
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
+    );
+    JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs);
+
+    // Regression test for SPARK-668:
+    JavaPairRDD<String, Integer> swapped = pairRDD.flatMapToPair(
+      new PairFlatMapFunction<Tuple2<Integer, String>, String, Integer>() {
+        @Override
+        public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) {
+          return Collections.singletonList(item.swap());
+        }
       });
-      swapped.collect();
+    swapped.collect();
 
-      // There was never a bug here, but it's worth testing:
-      pairRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
-          @Override
-          public Tuple2<String, Integer> call(Tuple2<Integer, String> item) {
-              return item.swap();
-          }
-      }).collect();
+    // There was never a bug here, but it's worth testing:
+    pairRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
+      @Override
+      public Tuple2<String, Integer> call(Tuple2<Integer, String> item) {
+        return item.swap();
+      }
+    }).collect();
   }
 
   @Test
@@ -953,7 +954,7 @@ public void mapPartitionsWithIndex() {
     JavaRDD<Integer> partitionSums = rdd.mapPartitionsWithIndex(
       new Function2<Integer, Iterator<Integer>, Iterator<Integer>>() {
         @Override
-        public Iterator<Integer> call(Integer index, Iterator<Integer> iter) throws Exception {
+        public Iterator<Integer> call(Integer index, Iterator<Integer> iter) {
           int sum = 0;
           while (iter.hasNext()) {
             sum += iter.next();
@@ -972,8 +973,8 @@ public void repartition() {
     JavaRDD<Integer> repartitioned1 = in1.repartition(4);
     List<List<Integer>> result1 = repartitioned1.glom().collect();
     Assert.assertEquals(4, result1.size());
-    for (List<Integer> l: result1) {
-      Assert.assertTrue(l.size() > 0);
+    for (List<Integer> l : result1) {
+      Assert.assertFalse(l.isEmpty());
     }
 
     // Growing number of partitions
@@ -982,7 +983,7 @@ public void repartition() {
     List<List<Integer>> result2 = repartitioned2.glom().collect();
     Assert.assertEquals(2, result2.size());
     for (List<Integer> l: result2) {
-      Assert.assertTrue(l.size() > 0);
+      Assert.assertFalse(l.isEmpty());
     }
   }
 
@@ -994,9 +995,9 @@ public void persist() {
     Assert.assertEquals(20, doubleRDD.sum(), 0.1);
 
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs);
     pairRDD = pairRDD.persist(StorageLevel.DISK_ONLY());
@@ -1046,7 +1047,7 @@ public void wholeTextFiles() throws Exception {
     Files.write(content1, new File(tempDirName + "/part-00000"));
     Files.write(content2, new File(tempDirName + "/part-00001"));
 
-    Map<String, String> container = new HashMap<String, String>();
+    Map<String, String> container = new HashMap<>();
     container.put(tempDirName+"/part-00000", new Text(content1).toString());
     container.put(tempDirName+"/part-00001", new Text(content2).toString());
 
@@ -1075,16 +1076,16 @@ public void textFilesCompressed() throws IOException {
   public void sequenceFile() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
 
@@ -1093,7 +1094,7 @@ public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
       Text.class).mapToPair(new PairFunction<Tuple2<IntWritable, Text>, Integer, String>() {
       @Override
       public Tuple2<Integer, String> call(Tuple2<IntWritable, Text> pair) {
-        return new Tuple2<Integer, String>(pair._1().get(), pair._2().toString());
+        return new Tuple2<>(pair._1().get(), pair._2().toString());
       }
     });
     Assert.assertEquals(pairs, readRDD.collect());
@@ -1110,7 +1111,7 @@ public void binaryFiles() throws Exception {
     FileOutputStream fos1 = new FileOutputStream(file1);
 
     FileChannel channel1 = fos1.getChannel();
-    ByteBuffer bbuf = java.nio.ByteBuffer.wrap(content1);
+    ByteBuffer bbuf = ByteBuffer.wrap(content1);
     channel1.write(bbuf);
     channel1.close();
     JavaPairRDD<String, PortableDataStream> readRDD = sc.binaryFiles(tempDirName, 3);
@@ -1131,14 +1132,14 @@ public void binaryFilesCaching() throws Exception {
     FileOutputStream fos1 = new FileOutputStream(file1);
 
     FileChannel channel1 = fos1.getChannel();
-    ByteBuffer bbuf = java.nio.ByteBuffer.wrap(content1);
+    ByteBuffer bbuf = ByteBuffer.wrap(content1);
     channel1.write(bbuf);
     channel1.close();
 
     JavaPairRDD<String, PortableDataStream> readRDD = sc.binaryFiles(tempDirName).cache();
     readRDD.foreach(new VoidFunction<Tuple2<String,PortableDataStream>>() {
       @Override
-      public void call(Tuple2<String, PortableDataStream> pair) throws Exception {
+      public void call(Tuple2<String, PortableDataStream> pair) {
         pair._2().toArray(); // force the file to read
       }
     });
@@ -1162,7 +1163,7 @@ public void binaryRecords() throws Exception {
     FileChannel channel1 = fos1.getChannel();
 
     for (int i = 0; i < numOfCopies; i++) {
-      ByteBuffer bbuf = java.nio.ByteBuffer.wrap(content1);
+      ByteBuffer bbuf = ByteBuffer.wrap(content1);
       channel1.write(bbuf);
     }
     channel1.close();
@@ -1180,24 +1181,23 @@ public void binaryRecords() throws Exception {
   public void writeWithNewAPIHadoopFile() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
-    }).saveAsNewAPIHadoopFile(outputDir, IntWritable.class, Text.class,
-      org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class);
+    }).saveAsNewAPIHadoopFile(
+        outputDir, IntWritable.class, Text.class,
+        org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class);
 
-    JavaPairRDD<IntWritable, Text> output = sc.sequenceFile(outputDir, IntWritable.class,
-      Text.class);
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-      String>() {
+    JavaPairRDD<IntWritable, Text> output = sc.sequenceFile(outputDir, IntWritable.class, Text.class);
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1210,24 +1210,23 @@ public String call(Tuple2<IntWritable, Text> x) {
   public void readWithNewAPIHadoopFile() throws IOException {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
 
     JavaPairRDD<IntWritable, Text> output = sc.newAPIHadoopFile(outputDir,
-      org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.class, IntWritable.class,
-      Text.class, new Job().getConfiguration());
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-      String>() {
+        org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.class,
+        IntWritable.class, Text.class, new Job().getConfiguration());
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1251,9 +1250,9 @@ public void objectFilesOfInts() {
   public void objectFilesOfComplexTypes() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
     rdd.saveAsObjectFile(outputDir);
@@ -1267,23 +1266,22 @@ public void objectFilesOfComplexTypes() {
   public void hadoopFile() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
 
     JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir,
-      SequenceFileInputFormat.class, IntWritable.class, Text.class);
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-      String>() {
+        SequenceFileInputFormat.class, IntWritable.class, Text.class);
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1296,16 +1294,16 @@ public String call(Tuple2<IntWritable, Text> x) {
   public void hadoopFileCompressed() {
     String outputDir = new File(tempDir, "output_compressed").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-        new Tuple2<Integer, String>(1, "a"),
-        new Tuple2<Integer, String>(2, "aa"),
-        new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class,
         DefaultCodec.class);
@@ -1313,8 +1311,7 @@ public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
     JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir,
         SequenceFileInputFormat.class, IntWritable.class, Text.class);
 
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-        String>() {
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1414,8 +1411,8 @@ public String call(Integer t) {
         return t.toString();
       }
     }).collect();
-    Assert.assertEquals(new Tuple2<String, Integer>("1", 1), s.get(0));
-    Assert.assertEquals(new Tuple2<String, Integer>("2", 2), s.get(1));
+    Assert.assertEquals(new Tuple2<>("1", 1), s.get(0));
+    Assert.assertEquals(new Tuple2<>("2", 2), s.get(1));
   }
 
   @Test
@@ -1448,20 +1445,20 @@ public void combineByKey() {
     JavaRDD<Integer> originalRDD = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6));
     Function<Integer, Integer> keyFunction = new Function<Integer, Integer>() {
       @Override
-      public Integer call(Integer v1) throws Exception {
+      public Integer call(Integer v1) {
         return v1 % 3;
       }
     };
     Function<Integer, Integer> createCombinerFunction = new Function<Integer, Integer>() {
       @Override
-      public Integer call(Integer v1) throws Exception {
+      public Integer call(Integer v1) {
         return v1;
       }
     };
 
     Function2<Integer, Integer, Integer> mergeValueFunction = new Function2<Integer, Integer, Integer>() {
       @Override
-      public Integer call(Integer v1, Integer v2) throws Exception {
+      public Integer call(Integer v1, Integer v2) {
         return v1 + v2;
       }
     };
@@ -1496,21 +1493,21 @@ public void mapOnPairRDD() {
         new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer i) {
-            return new Tuple2<Integer, Integer>(i, i % 2);
+            return new Tuple2<>(i, i % 2);
           }
         });
     JavaPairRDD<Integer, Integer> rdd3 = rdd2.mapToPair(
         new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {
-      @Override
-      public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) {
-        return new Tuple2<Integer, Integer>(in._2(), in._1());
-      }
-    });
+          @Override
+          public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) {
+            return new Tuple2<>(in._2(), in._1());
+          }
+        });
     Assert.assertEquals(Arrays.asList(
-        new Tuple2<Integer, Integer>(1, 1),
-        new Tuple2<Integer, Integer>(0, 2),
-        new Tuple2<Integer, Integer>(1, 3),
-        new Tuple2<Integer, Integer>(0, 4)), rdd3.collect());
+        new Tuple2<>(1, 1),
+        new Tuple2<>(0, 2),
+        new Tuple2<>(1, 3),
+        new Tuple2<>(0, 4)), rdd3.collect());
 
   }
 
@@ -1523,7 +1520,7 @@ public void collectPartitions() {
         new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer i) {
-            return new Tuple2<Integer, Integer>(i, i % 2);
+            return new Tuple2<>(i, i % 2);
           }
         });
 
@@ -1534,23 +1531,23 @@ public Tuple2<Integer, Integer> call(Integer i) {
     Assert.assertEquals(Arrays.asList(3, 4), parts[0]);
     Assert.assertEquals(Arrays.asList(5, 6, 7), parts[1]);
 
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(1, 1),
-                                      new Tuple2<Integer, Integer>(2, 0)),
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(1, 1),
+                                      new Tuple2<>(2, 0)),
                         rdd2.collectPartitions(new int[] {0})[0]);
 
     List<Tuple2<Integer,Integer>>[] parts2 = rdd2.collectPartitions(new int[] {1, 2});
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(3, 1),
-                                      new Tuple2<Integer, Integer>(4, 0)),
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(3, 1),
+                                      new Tuple2<>(4, 0)),
                         parts2[0]);
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(5, 1),
-                                      new Tuple2<Integer, Integer>(6, 0),
-                                      new Tuple2<Integer, Integer>(7, 1)),
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(5, 1),
+                                      new Tuple2<>(6, 0),
+                                      new Tuple2<>(7, 1)),
                         parts2[1]);
   }
 
   @Test
   public void countApproxDistinct() {
-    List<Integer> arrayData = new ArrayList<Integer>();
+    List<Integer> arrayData = new ArrayList<>();
     int size = 100;
     for (int i = 0; i < 100000; i++) {
       arrayData.add(i % size);
@@ -1561,15 +1558,15 @@ public void countApproxDistinct() {
 
   @Test
   public void countApproxDistinctByKey() {
-    List<Tuple2<Integer, Integer>> arrayData = new ArrayList<Tuple2<Integer, Integer>>();
+    List<Tuple2<Integer, Integer>> arrayData = new ArrayList<>();
     for (int i = 10; i < 100; i++) {
       for (int j = 0; j < i; j++) {
-        arrayData.add(new Tuple2<Integer, Integer>(i, j));
+        arrayData.add(new Tuple2<>(i, j));
       }
     }
     double relativeSD = 0.001;
     JavaPairRDD<Integer, Integer> pairRdd = sc.parallelizePairs(arrayData);
-    List<Tuple2<Integer, Object>> res =  pairRdd.countApproxDistinctByKey(8, 0).collect();
+    List<Tuple2<Integer, Object>> res =  pairRdd.countApproxDistinctByKey(relativeSD, 8).collect();
     for (Tuple2<Integer, Object> resItem : res) {
       double count = (double)resItem._1();
       Long resCount = (Long)resItem._2();
@@ -1587,7 +1584,7 @@ public void collectAsMapWithIntArrayValues() {
         new PairFunction<Integer, Integer, int[]>() {
           @Override
           public Tuple2<Integer, int[]> call(Integer x) {
-            return new Tuple2<Integer, int[]>(x, new int[] { x });
+            return new Tuple2<>(x, new int[]{x});
           }
         });
     pairRDD.collect();  // Works fine
@@ -1598,7 +1595,7 @@ public Tuple2<Integer, int[]> call(Integer x) {
   @Test
   public void collectAsMapAndSerialize() throws Exception {
     JavaPairRDD<String,Integer> rdd =
-        sc.parallelizePairs(Arrays.asList(new Tuple2<String,Integer>("foo", 1)));
+        sc.parallelizePairs(Arrays.asList(new Tuple2<>("foo", 1)));
     Map<String,Integer> map = rdd.collectAsMap();
     ByteArrayOutputStream bytes = new ByteArrayOutputStream();
     new ObjectOutputStream(bytes).writeObject(map);
@@ -1615,7 +1612,7 @@ public void sampleByKey() {
       new PairFunction<Integer, Integer, Integer>() {
         @Override
         public Tuple2<Integer, Integer> call(Integer i) {
-          return new Tuple2<Integer, Integer>(i % 2, 1);
+          return new Tuple2<>(i % 2, 1);
         }
       });
     Map<Integer, Object> fractions = Maps.newHashMap();
@@ -1623,12 +1620,12 @@ public Tuple2<Integer, Integer> call(Integer i) {
     fractions.put(1, 1.0);
     JavaPairRDD<Integer, Integer> wr = rdd2.sampleByKey(true, fractions, 1L);
     Map<Integer, Long> wrCounts = (Map<Integer, Long>) (Object) wr.countByKey();
-    Assert.assertTrue(wrCounts.size() == 2);
+    Assert.assertEquals(2, wrCounts.size());
     Assert.assertTrue(wrCounts.get(0) > 0);
     Assert.assertTrue(wrCounts.get(1) > 0);
     JavaPairRDD<Integer, Integer> wor = rdd2.sampleByKey(false, fractions, 1L);
     Map<Integer, Long> worCounts = (Map<Integer, Long>) (Object) wor.countByKey();
-    Assert.assertTrue(worCounts.size() == 2);
+    Assert.assertEquals(2, worCounts.size());
     Assert.assertTrue(worCounts.get(0) > 0);
     Assert.assertTrue(worCounts.get(1) > 0);
   }
@@ -1641,7 +1638,7 @@ public void sampleByKeyExact() {
       new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer i) {
-              return new Tuple2<Integer, Integer>(i % 2, 1);
+              return new Tuple2<>(i % 2, 1);
           }
       });
     Map<Integer, Object> fractions = Maps.newHashMap();
@@ -1649,25 +1646,25 @@ public Tuple2<Integer, Integer> call(Integer i) {
     fractions.put(1, 1.0);
     JavaPairRDD<Integer, Integer> wrExact = rdd2.sampleByKeyExact(true, fractions, 1L);
     Map<Integer, Long> wrExactCounts = (Map<Integer, Long>) (Object) wrExact.countByKey();
-    Assert.assertTrue(wrExactCounts.size() == 2);
+    Assert.assertEquals(2, wrExactCounts.size());
     Assert.assertTrue(wrExactCounts.get(0) == 2);
     Assert.assertTrue(wrExactCounts.get(1) == 4);
     JavaPairRDD<Integer, Integer> worExact = rdd2.sampleByKeyExact(false, fractions, 1L);
     Map<Integer, Long> worExactCounts = (Map<Integer, Long>) (Object) worExact.countByKey();
-    Assert.assertTrue(worExactCounts.size() == 2);
+    Assert.assertEquals(2, worExactCounts.size());
     Assert.assertTrue(worExactCounts.get(0) == 2);
     Assert.assertTrue(worExactCounts.get(1) == 4);
   }
 
   private static class SomeCustomClass implements Serializable {
-    public SomeCustomClass() {
+    SomeCustomClass() {
       // Intentionally left blank
     }
   }
 
   @Test
   public void collectUnderlyingScalaRDD() {
-    List<SomeCustomClass> data = new ArrayList<SomeCustomClass>();
+    List<SomeCustomClass> data = new ArrayList<>();
     for (int i = 0; i < 100; i++) {
       data.add(new SomeCustomClass());
     }
@@ -1679,7 +1676,7 @@ public void collectUnderlyingScalaRDD() {
   private static final class BuggyMapFunction<T> implements Function<T, T> {
 
     @Override
-    public T call(T x) throws Exception {
+    public T call(T x) {
       throw new IllegalStateException("Custom exception!");
     }
   }
@@ -1716,7 +1713,7 @@ public void foreachAsync() throws Exception {
     JavaFutureAction<Void> future = rdd.foreachAsync(
         new VoidFunction<Integer>() {
           @Override
-          public void call(Integer integer) throws Exception {
+          public void call(Integer integer) {
             // intentionally left blank.
           }
         }
@@ -1745,7 +1742,7 @@ public void testAsyncActionCancellation() throws Exception {
     JavaRDD<Integer> rdd = sc.parallelize(data, 1);
     JavaFutureAction<Void> future = rdd.foreachAsync(new VoidFunction<Integer>() {
       @Override
-      public void call(Integer integer) throws Exception {
+      public void call(Integer integer) throws InterruptedException {
         Thread.sleep(10000);  // To ensure that the job won't finish before it's cancelled.
       }
     });
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
index 9db07d0507fea..fbdfbf7e509b3 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
@@ -75,11 +75,11 @@ public void testKafkaStream() throws InterruptedException {
     String[] topic1data = createTopicAndSendData(topic1);
     String[] topic2data = createTopicAndSendData(topic2);
 
-    HashSet<String> sent = new HashSet<String>();
+    Set<String> sent = new HashSet<>();
     sent.addAll(Arrays.asList(topic1data));
     sent.addAll(Arrays.asList(topic2data));
 
-    HashMap<String, String> kafkaParams = new HashMap<String, String>();
+    Map<String, String> kafkaParams = new HashMap<>();
     kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress());
     kafkaParams.put("auto.offset.reset", "smallest");
 
@@ -95,17 +95,17 @@ public void testKafkaStream() throws InterruptedException {
         // Make sure you can get offset ranges from the rdd
         new Function<JavaPairRDD<String, String>, JavaPairRDD<String, String>>() {
           @Override
-          public JavaPairRDD<String, String> call(JavaPairRDD<String, String> rdd) throws Exception {
+          public JavaPairRDD<String, String> call(JavaPairRDD<String, String> rdd) {
             OffsetRange[] offsets = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
             offsetRanges.set(offsets);
-            Assert.assertEquals(offsets[0].topic(), topic1);
+            Assert.assertEquals(topic1, offsets[0].topic());
             return rdd;
           }
         }
     ).map(
         new Function<Tuple2<String, String>, String>() {
           @Override
-          public String call(Tuple2<String, String> kv) throws Exception {
+          public String call(Tuple2<String, String> kv) {
             return kv._2();
           }
         }
@@ -119,10 +119,10 @@ public String call(Tuple2<String, String> kv) throws Exception {
         StringDecoder.class,
         String.class,
         kafkaParams,
-        topicOffsetToMap(topic2, (long) 0),
+        topicOffsetToMap(topic2, 0L),
         new Function<MessageAndMetadata<String, String>, String>() {
           @Override
-          public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception {
+          public String call(MessageAndMetadata<String, String> msgAndMd) {
             return msgAndMd.message();
           }
         }
@@ -133,7 +133,7 @@ public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception
     unifiedStream.foreachRDD(
         new Function<JavaRDD<String>, Void>() {
           @Override
-          public Void call(JavaRDD<String> rdd) throws Exception {
+          public Void call(JavaRDD<String> rdd) {
             result.addAll(rdd.collect());
             for (OffsetRange o : offsetRanges.get()) {
               System.out.println(
@@ -155,14 +155,14 @@ public Void call(JavaRDD<String> rdd) throws Exception {
     ssc.stop();
   }
 
-  private HashSet<String> topicToSet(String topic) {
-    HashSet<String> topicSet = new HashSet<String>();
+  private static Set<String> topicToSet(String topic) {
+    Set<String> topicSet = new HashSet<>();
     topicSet.add(topic);
     return topicSet;
   }
 
-  private HashMap<TopicAndPartition, Long> topicOffsetToMap(String topic, Long offsetToStart) {
-    HashMap<TopicAndPartition, Long> topicMap = new HashMap<TopicAndPartition, Long>();
+  private static Map<TopicAndPartition, Long> topicOffsetToMap(String topic, Long offsetToStart) {
+    Map<TopicAndPartition, Long> topicMap = new HashMap<>();
     topicMap.put(new TopicAndPartition(topic, 0), offsetToStart);
     return topicMap;
   }
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java
index a9dc6e50613ca..afcc6cfccd39a 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java
@@ -19,6 +19,7 @@
 
 import java.io.Serializable;
 import java.util.HashMap;
+import java.util.Map;
 
 import scala.Tuple2;
 
@@ -66,10 +67,10 @@ public void testKafkaRDD() throws InterruptedException {
     String topic1 = "topic1";
     String topic2 = "topic2";
 
-    String[] topic1data = createTopicAndSendData(topic1);
-    String[] topic2data = createTopicAndSendData(topic2);
+    createTopicAndSendData(topic1);
+    createTopicAndSendData(topic2);
 
-    HashMap<String, String> kafkaParams = new HashMap<String, String>();
+    Map<String, String> kafkaParams = new HashMap<>();
     kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress());
 
     OffsetRange[] offsetRanges = {
@@ -77,8 +78,8 @@ public void testKafkaRDD() throws InterruptedException {
       OffsetRange.create(topic2, 0, 0, 1)
     };
 
-    HashMap<TopicAndPartition, Broker> emptyLeaders = new HashMap<TopicAndPartition, Broker>();
-    HashMap<TopicAndPartition, Broker> leaders = new HashMap<TopicAndPartition, Broker>();
+    Map<TopicAndPartition, Broker> emptyLeaders = new HashMap<>();
+    Map<TopicAndPartition, Broker> leaders = new HashMap<>();
     String[] hostAndPort = kafkaTestUtils.brokerAddress().split(":");
     Broker broker = Broker.create(hostAndPort[0], Integer.parseInt(hostAndPort[1]));
     leaders.put(new TopicAndPartition(topic1, 0), broker);
@@ -95,7 +96,7 @@ public void testKafkaRDD() throws InterruptedException {
     ).map(
         new Function<Tuple2<String, String>, String>() {
           @Override
-          public String call(Tuple2<String, String> kv) throws Exception {
+          public String call(Tuple2<String, String> kv) {
             return kv._2();
           }
         }
@@ -113,7 +114,7 @@ public String call(Tuple2<String, String> kv) throws Exception {
         emptyLeaders,
         new Function<MessageAndMetadata<String, String>, String>() {
           @Override
-          public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception {
+          public String call(MessageAndMetadata<String, String> msgAndMd) {
             return msgAndMd.message();
           }
         }
@@ -131,7 +132,7 @@ public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception
         leaders,
         new Function<MessageAndMetadata<String, String>, String>() {
           @Override
-          public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception {
+          public String call(MessageAndMetadata<String, String> msgAndMd) {
             return msgAndMd.message();
           }
         }
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
index e4c659215b767..1e69de46cd35d 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
@@ -67,10 +67,10 @@ public void tearDown() {
   @Test
   public void testKafkaStream() throws InterruptedException {
     String topic = "topic1";
-    HashMap<String, Integer> topics = new HashMap<String, Integer>();
+    Map<String, Integer> topics = new HashMap<>();
     topics.put(topic, 1);
 
-    HashMap<String, Integer> sent = new HashMap<String, Integer>();
+    Map<String, Integer> sent = new HashMap<>();
     sent.put("a", 5);
     sent.put("b", 3);
     sent.put("c", 10);
@@ -78,7 +78,7 @@ public void testKafkaStream() throws InterruptedException {
     kafkaTestUtils.createTopic(topic);
     kafkaTestUtils.sendMessages(topic, sent);
 
-    HashMap<String, String> kafkaParams = new HashMap<String, String>();
+    Map<String, String> kafkaParams = new HashMap<>();
     kafkaParams.put("zookeeper.connect", kafkaTestUtils.zkAddress());
     kafkaParams.put("group.id", "test-consumer-" + random.nextInt(10000));
     kafkaParams.put("auto.offset.reset", "smallest");
@@ -97,7 +97,7 @@ public void testKafkaStream() throws InterruptedException {
     JavaDStream<String> words = stream.map(
       new Function<Tuple2<String, String>, String>() {
         @Override
-        public String call(Tuple2<String, String> tuple2) throws Exception {
+        public String call(Tuple2<String, String> tuple2) {
           return tuple2._2();
         }
       }
@@ -106,7 +106,7 @@ public String call(Tuple2<String, String> tuple2) throws Exception {
     words.countByValue().foreachRDD(
       new Function<JavaPairRDD<String, Long>, Void>() {
         @Override
-        public Void call(JavaPairRDD<String, Long> rdd) throws Exception {
+        public Void call(JavaPairRDD<String, Long> rdd) {
           List<Tuple2<String, Long>> ret = rdd.collect();
           for (Tuple2<String, Long> r : ret) {
             if (result.containsKey(r._1())) {
@@ -130,8 +130,8 @@ public Void call(JavaPairRDD<String, Long> rdd) throws Exception {
       Thread.sleep(200);
     }
     Assert.assertEquals(sent.size(), result.size());
-    for (String k : sent.keySet()) {
-      Assert.assertEquals(sent.get(k).intValue(), result.get(k).intValue());
+    for (Map.Entry<String, Integer> e : sent.entrySet()) {
+      Assert.assertEquals(e.getValue().intValue(), result.get(e.getKey()).intValue());
     }
   }
 }
diff --git a/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
index e46b4e5c7531d..26ec8af455bcf 100644
--- a/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
+++ b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
@@ -17,8 +17,6 @@
 
 package org.apache.spark.streaming.twitter;
 
-import java.util.Arrays;
-
 import org.junit.Test;
 import twitter4j.Status;
 import twitter4j.auth.Authorization;
@@ -30,7 +28,7 @@
 public class JavaTwitterStreamSuite extends LocalJavaStreamingContext {
   @Test
   public void testTwitterStream() {
-    String[] filters = (String[])Arrays.<String>asList("filter1", "filter2").toArray();
+    String[] filters = { "filter1", "filter2" };
     Authorization auth = NullAuthorization.getInstance();
 
     // tests the API, does not actually test data receiving
diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
index 729bc0459ce52..14975265ab2ce 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
@@ -77,7 +77,7 @@ public void call(String s) {
   public void foreach() {
     foreachCalls = 0;
     JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
-    rdd.foreach((x) -> foreachCalls++);
+    rdd.foreach(x -> foreachCalls++);
     Assert.assertEquals(2, foreachCalls);
   }
 
@@ -180,7 +180,7 @@ public void map() {
     JavaPairRDD<Integer, Integer> pairs = rdd.mapToPair(x -> new Tuple2<>(x, x))
       .cache();
     pairs.collect();
-    JavaRDD<String> strings = rdd.map(x -> x.toString()).cache();
+    JavaRDD<String> strings = rdd.map(Object::toString).cache();
     strings.collect();
   }
 
@@ -195,7 +195,9 @@ public void flatMap() {
 
     JavaPairRDD<String, String> pairs = rdd.flatMapToPair(s -> {
       List<Tuple2<String, String>> pairs2 = new LinkedList<>();
-      for (String word : s.split(" ")) pairs2.add(new Tuple2<>(word, word));
+      for (String word : s.split(" ")) {
+        pairs2.add(new Tuple2<>(word, word));
+      }
       return pairs2;
     });
 
@@ -204,11 +206,12 @@ public void flatMap() {
 
     JavaDoubleRDD doubles = rdd.flatMapToDouble(s -> {
       List<Double> lengths = new LinkedList<>();
-      for (String word : s.split(" ")) lengths.add(word.length() * 1.0);
+      for (String word : s.split(" ")) {
+        lengths.add((double) word.length());
+      }
       return lengths;
     });
 
-    Double x = doubles.first();
     Assert.assertEquals(5.0, doubles.first(), 0.01);
     Assert.assertEquals(11, pairs.count());
   }
@@ -228,7 +231,7 @@ public void mapsFromPairsToPairs() {
     swapped.collect();
 
     // There was never a bug here, but it's worth testing:
-    pairRDD.map(item -> item.swap()).collect();
+    pairRDD.map(Tuple2::swap).collect();
   }
 
   @Test
@@ -282,11 +285,11 @@ public void zipPartitions() {
     FlatMapFunction2<Iterator<Integer>, Iterator<String>, Integer> sizesFn =
       (Iterator<Integer> i, Iterator<String> s) -> {
         int sizeI = 0;
-        int sizeS = 0;
         while (i.hasNext()) {
           sizeI += 1;
           i.next();
         }
+        int sizeS = 0;
         while (s.hasNext()) {
           sizeS += 1;
           s.next();
@@ -301,30 +304,31 @@ public void zipPartitions() {
   public void accumulators() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
 
-    final Accumulator<Integer> intAccum = sc.intAccumulator(10);
-    rdd.foreach(x -> intAccum.add(x));
+    Accumulator<Integer> intAccum = sc.intAccumulator(10);
+    rdd.foreach(intAccum::add);
     Assert.assertEquals((Integer) 25, intAccum.value());
 
-    final Accumulator<Double> doubleAccum = sc.doubleAccumulator(10.0);
+    Accumulator<Double> doubleAccum = sc.doubleAccumulator(10.0);
     rdd.foreach(x -> doubleAccum.add((double) x));
     Assert.assertEquals((Double) 25.0, doubleAccum.value());
 
     // Try a custom accumulator type
     AccumulatorParam<Float> floatAccumulatorParam = new AccumulatorParam<Float>() {
+      @Override
       public Float addInPlace(Float r, Float t) {
         return r + t;
       }
-
+      @Override
       public Float addAccumulator(Float r, Float t) {
         return r + t;
       }
-
+      @Override
       public Float zero(Float initialValue) {
         return 0.0f;
       }
     };
 
-    final Accumulator<Float> floatAccum = sc.accumulator(10.0f, floatAccumulatorParam);
+    Accumulator<Float> floatAccum = sc.accumulator(10.0f, floatAccumulatorParam);
     rdd.foreach(x -> floatAccum.add((float) x));
     Assert.assertEquals((Float) 25.0f, floatAccum.value());
 
@@ -336,7 +340,7 @@ public Float zero(Float initialValue) {
   @Test
   public void keyBy() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2));
-    List<Tuple2<String, Integer>> s = rdd.keyBy(x -> x.toString()).collect();
+    List<Tuple2<String, Integer>> s = rdd.keyBy(Object::toString).collect();
     Assert.assertEquals(new Tuple2<>("1", 1), s.get(0));
     Assert.assertEquals(new Tuple2<>("2", 2), s.get(1));
   }
@@ -349,7 +353,7 @@ public void mapOnPairRDD() {
     JavaPairRDD<Integer, Integer> rdd3 =
       rdd2.mapToPair(in -> new Tuple2<>(in._2(), in._1()));
     Assert.assertEquals(Arrays.asList(
-      new Tuple2<Integer, Integer>(1, 1),
+      new Tuple2<>(1, 1),
       new Tuple2<>(0, 2),
       new Tuple2<>(1, 3),
       new Tuple2<>(0, 4)), rdd3.collect());
@@ -361,7 +365,7 @@ public void collectPartitions() {
 
     JavaPairRDD<Integer, Integer> rdd2 =
       rdd1.mapToPair(i -> new Tuple2<>(i, i % 2));
-    List[] parts = rdd1.collectPartitions(new int[]{0});
+    List<Integer>[] parts = rdd1.collectPartitions(new int[]{0});
     Assert.assertEquals(Arrays.asList(1, 2), parts[0]);
 
     parts = rdd1.collectPartitions(new int[]{1, 2});
@@ -371,19 +375,19 @@ public void collectPartitions() {
     Assert.assertEquals(Arrays.asList(new Tuple2<>(1, 1), new Tuple2<>(2, 0)),
       rdd2.collectPartitions(new int[]{0})[0]);
 
-    parts = rdd2.collectPartitions(new int[]{1, 2});
-    Assert.assertEquals(Arrays.asList(new Tuple2<>(3, 1), new Tuple2<>(4, 0)), parts[0]);
+    List<Tuple2<Integer, Integer>>[] parts2 = rdd2.collectPartitions(new int[]{1, 2});
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(3, 1), new Tuple2<>(4, 0)), parts2[0]);
     Assert.assertEquals(Arrays.asList(new Tuple2<>(5, 1), new Tuple2<>(6, 0), new Tuple2<>(7, 1)),
-      parts[1]);
+      parts2[1]);
   }
 
   @Test
   public void collectAsMapWithIntArrayValues() {
     // Regression test for SPARK-1040
-    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(new Integer[]{1}));
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1));
     JavaPairRDD<Integer, int[]> pairRDD =
       rdd.mapToPair(x -> new Tuple2<>(x, new int[]{x}));
     pairRDD.collect();  // Works fine
-    Map<Integer, int[]> map = pairRDD.collectAsMap();  // Used to crash with ClassCastException
+    pairRDD.collectAsMap();  // Used to crash with ClassCastException
   }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index bf693c7c393f6..7b50aad4ad498 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -18,6 +18,7 @@
 package test.org.apache.spark.sql;
 
 import java.io.Serializable;
+import java.math.BigDecimal;
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.List;
@@ -83,7 +84,7 @@ public void setAge(int age) {
 
   @Test
   public void applySchema() {
-    List<Person> personList = new ArrayList<Person>(2);
+    List<Person> personList = new ArrayList<>(2);
     Person person1 = new Person();
     person1.setName("Michael");
     person1.setAge(29);
@@ -95,12 +96,13 @@ public void applySchema() {
 
     JavaRDD<Row> rowRDD = javaCtx.parallelize(personList).map(
       new Function<Person, Row>() {
+        @Override
         public Row call(Person person) throws Exception {
           return RowFactory.create(person.getName(), person.getAge());
         }
       });
 
-    List<StructField> fields = new ArrayList<StructField>(2);
+    List<StructField> fields = new ArrayList<>(2);
     fields.add(DataTypes.createStructField("name", DataTypes.StringType, false));
     fields.add(DataTypes.createStructField("age", DataTypes.IntegerType, false));
     StructType schema = DataTypes.createStructType(fields);
@@ -118,7 +120,7 @@ public Row call(Person person) throws Exception {
 
   @Test
   public void dataFrameRDDOperations() {
-    List<Person> personList = new ArrayList<Person>(2);
+    List<Person> personList = new ArrayList<>(2);
     Person person1 = new Person();
     person1.setName("Michael");
     person1.setAge(29);
@@ -129,27 +131,28 @@ public void dataFrameRDDOperations() {
     personList.add(person2);
 
     JavaRDD<Row> rowRDD = javaCtx.parallelize(personList).map(
-            new Function<Person, Row>() {
-              public Row call(Person person) throws Exception {
-                return RowFactory.create(person.getName(), person.getAge());
-              }
-            });
-
-    List<StructField> fields = new ArrayList<StructField>(2);
-    fields.add(DataTypes.createStructField("name", DataTypes.StringType, false));
+        new Function<Person, Row>() {
+          @Override
+          public Row call(Person person) {
+            return RowFactory.create(person.getName(), person.getAge());
+          }
+        });
+
+    List<StructField> fields = new ArrayList<>(2);
+    fields.add(DataTypes.createStructField("", DataTypes.StringType, false));
     fields.add(DataTypes.createStructField("age", DataTypes.IntegerType, false));
     StructType schema = DataTypes.createStructType(fields);
 
     DataFrame df = sqlContext.applySchema(rowRDD, schema);
     df.registerTempTable("people");
     List<String> actual = sqlContext.sql("SELECT * FROM people").toJavaRDD().map(new Function<Row, String>() {
-
+      @Override
       public String call(Row row) {
-        return row.getString(0) + "_" + row.get(1).toString();
+        return row.getString(0) + "_" + row.get(1);
       }
     }).collect();
 
-    List<String> expected = new ArrayList<String>(2);
+    List<String> expected = new ArrayList<>(2);
     expected.add("Michael_29");
     expected.add("Yin_28");
 
@@ -165,7 +168,7 @@ public void applySchemaToJSON() {
       "{\"string\":\"this is another simple string.\", \"integer\":11, \"long\":21474836469, " +
         "\"bigInteger\":92233720368547758069, \"double\":1.7976931348623157E305, " +
         "\"boolean\":false, \"null\":null}"));
-    List<StructField> fields = new ArrayList<StructField>(7);
+    List<StructField> fields = new ArrayList<>(7);
     fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(20, 0),
       true));
     fields.add(DataTypes.createStructField("boolean", DataTypes.BooleanType, true));
@@ -175,10 +178,10 @@ public void applySchemaToJSON() {
     fields.add(DataTypes.createStructField("null", DataTypes.StringType, true));
     fields.add(DataTypes.createStructField("string", DataTypes.StringType, true));
     StructType expectedSchema = DataTypes.createStructType(fields);
-    List<Row> expectedResult = new ArrayList<Row>(2);
+    List<Row> expectedResult = new ArrayList<>(2);
     expectedResult.add(
       RowFactory.create(
-        new java.math.BigDecimal("92233720368547758070"),
+        new BigDecimal("92233720368547758070"),
         true,
         1.7976931348623157E308,
         10,
@@ -187,7 +190,7 @@ public void applySchemaToJSON() {
         "this is a simple string."));
     expectedResult.add(
       RowFactory.create(
-        new java.math.BigDecimal("92233720368547758069"),
+        new BigDecimal("92233720368547758069"),
         false,
         1.7976931348623157E305,
         11,
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 4867cebf5328c..d981ce947f435 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -61,7 +61,7 @@ public void tearDown() {
   @Test
   public void testExecution() {
     DataFrame df = context.table("testData").filter("key = 1");
-    Assert.assertEquals(df.select("key").collect()[0].get(0), 1);
+    Assert.assertEquals(1, df.select("key").collect()[0].get(0));
   }
 
   /**
@@ -119,7 +119,7 @@ public void testShow() {
 
   public static class Bean implements Serializable {
     private double a = 0.0;
-    private Integer[] b = new Integer[]{0, 1};
+    private Integer[] b = { 0, 1 };
     private Map<String, int[]> c = ImmutableMap.of("hello", new int[] { 1, 2 });
     private List<String> d = Arrays.asList("floppy", "disk");
 
@@ -161,7 +161,7 @@ public void testCreateDataFrameFromJavaBeans() {
       schema.apply("d"));
     Row first = df.select("a", "b", "c", "d").first();
     Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0);
-    // Now Java lists and maps are converetd to Scala Seq's and Map's. Once we get a Seq below,
+    // Now Java lists and maps are converted to Scala Seq's and Map's. Once we get a Seq below,
     // verify that it has the expected length, and contains expected elements.
     Seq<Integer> result = first.getAs(1);
     Assert.assertEquals(bean.getB().length, result.length());
@@ -180,7 +180,8 @@ public void testCreateDataFrameFromJavaBeans() {
     }
   }
 
-  private static Comparator<Row> CrosstabRowComparator = new Comparator<Row>() {
+  private static final Comparator<Row> crosstabRowComparator = new Comparator<Row>() {
+    @Override
     public int compare(Row row1, Row row2) {
       String item1 = row1.getString(0);
       String item2 = row2.getString(0);
@@ -193,16 +194,16 @@ public void testCrosstab() {
     DataFrame df = context.table("testData2");
     DataFrame crosstab = df.stat().crosstab("a", "b");
     String[] columnNames = crosstab.schema().fieldNames();
-    Assert.assertEquals(columnNames[0], "a_b");
-    Assert.assertEquals(columnNames[1], "1");
-    Assert.assertEquals(columnNames[2], "2");
+    Assert.assertEquals("a_b", columnNames[0]);
+    Assert.assertEquals("1", columnNames[1]);
+    Assert.assertEquals("2", columnNames[2]);
     Row[] rows = crosstab.collect();
-    Arrays.sort(rows, CrosstabRowComparator);
+    Arrays.sort(rows, crosstabRowComparator);
     Integer count = 1;
     for (Row row : rows) {
       Assert.assertEquals(row.get(0).toString(), count.toString());
-      Assert.assertEquals(row.getLong(1), 1L);
-      Assert.assertEquals(row.getLong(2), 1L);
+      Assert.assertEquals(1L, row.getLong(1));
+      Assert.assertEquals(1L, row.getLong(2));
       count++;
     }
   }
@@ -210,7 +211,7 @@ public void testCrosstab() {
   @Test
   public void testFrequentItems() {
     DataFrame df = context.table("testData2");
-    String[] cols = new String[]{"a"};
+    String[] cols = {"a"};
     DataFrame results = df.stat().freqItems(cols, 0.2);
     Assert.assertTrue(results.collect()[0].getSeq(0).contains(1));
   }
@@ -219,14 +220,14 @@ public void testFrequentItems() {
   public void testCorrelation() {
     DataFrame df = context.table("testData2");
     Double pearsonCorr = df.stat().corr("a", "b", "pearson");
-    Assert.assertTrue(Math.abs(pearsonCorr) < 1e-6);
+    Assert.assertTrue(Math.abs(pearsonCorr) < 1.0e-6);
   }
 
   @Test
   public void testCovariance() {
     DataFrame df = context.table("testData2");
     Double result = df.stat().cov("a", "b");
-    Assert.assertTrue(Math.abs(result) < 1e-6);
+    Assert.assertTrue(Math.abs(result) < 1.0e-6);
   }
 
   @Test
@@ -234,7 +235,7 @@ public void testSampleBy() {
     DataFrame df = context.range(0, 100, 1, 2).select(col("id").mod(3).as("key"));
     DataFrame sampled = df.stat().<Integer>sampleBy("key", ImmutableMap.of(0, 0.1, 1, 0.2), 0L);
     Row[] actual = sampled.groupBy("key").count().orderBy("key").collect();
-    Row[] expected = new Row[] {RowFactory.create(0, 5), RowFactory.create(1, 8)};
+    Row[] expected = {RowFactory.create(0, 5), RowFactory.create(1, 8)};
     Assert.assertArrayEquals(expected, actual);
   }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java
index 4ce1d1dddb26a..3ab4db2a035d3 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java
@@ -18,6 +18,7 @@
 package test.org.apache.spark.sql;
 
 import java.math.BigDecimal;
+import java.nio.charset.StandardCharsets;
 import java.sql.Date;
 import java.sql.Timestamp;
 import java.util.Arrays;
@@ -52,12 +53,12 @@ public void setUp() {
     shortValue = (short)32767;
     intValue = 2147483647;
     longValue = 9223372036854775807L;
-    floatValue = (float)3.4028235E38;
+    floatValue = 3.4028235E38f;
     doubleValue = 1.7976931348623157E308;
     decimalValue = new BigDecimal("1.7976931348623157E328");
     booleanValue = true;
     stringValue = "this is a string";
-    binaryValue = stringValue.getBytes();
+    binaryValue = stringValue.getBytes(StandardCharsets.UTF_8);
     dateValue = Date.valueOf("2014-06-30");
     timestampValue = Timestamp.valueOf("2014-06-30 09:20:00.0");
   }
@@ -123,8 +124,8 @@ public void constructSimpleRow() {
     Assert.assertEquals(binaryValue, simpleRow.get(16));
     Assert.assertEquals(dateValue, simpleRow.get(17));
     Assert.assertEquals(timestampValue, simpleRow.get(18));
-    Assert.assertEquals(true, simpleRow.isNullAt(19));
-    Assert.assertEquals(null, simpleRow.get(19));
+    Assert.assertTrue(simpleRow.isNullAt(19));
+    Assert.assertNull(simpleRow.get(19));
   }
 
   @Test
@@ -134,7 +135,7 @@ public void constructComplexRow() {
       stringValue + " (1)", stringValue + " (2)", stringValue + "(3)");
 
     // Simple map
-    Map<String, Long> simpleMap = new HashMap<String, Long>();
+    Map<String, Long> simpleMap = new HashMap<>();
     simpleMap.put(stringValue + " (1)", longValue);
     simpleMap.put(stringValue + " (2)", longValue - 1);
     simpleMap.put(stringValue + " (3)", longValue - 2);
@@ -149,7 +150,7 @@ public void constructComplexRow() {
     List<Row> arrayOfRows = Arrays.asList(simpleStruct);
 
     // Complex map
-    Map<List<Row>, Row> complexMap = new HashMap<List<Row>, Row>();
+    Map<List<Row>, Row> complexMap = new HashMap<>();
     complexMap.put(arrayOfRows, simpleStruct);
 
     // Complex struct
@@ -167,7 +168,7 @@ public void constructComplexRow() {
     Assert.assertEquals(arrayOfMaps, complexStruct.get(3));
     Assert.assertEquals(arrayOfRows, complexStruct.get(4));
     Assert.assertEquals(complexMap, complexStruct.get(5));
-    Assert.assertEquals(null, complexStruct.get(6));
+    Assert.assertNull(complexStruct.get(6));
 
     // A very complex row
     Row complexRow = RowFactory.create(arrayOfMaps, arrayOfRows, complexMap, complexStruct);
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
index bb02b58cca9be..4a78dca7fea66 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
@@ -20,6 +20,7 @@
 import java.io.Serializable;
 
 import org.junit.After;
+import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
@@ -61,13 +62,13 @@ public void udf1Test() {
 
     sqlContext.udf().register("stringLengthTest", new UDF1<String, Integer>() {
       @Override
-      public Integer call(String str) throws Exception {
+      public Integer call(String str) {
         return str.length();
       }
     }, DataTypes.IntegerType);
 
     Row result = sqlContext.sql("SELECT stringLengthTest('test')").head();
-    assert(result.getInt(0) == 4);
+    Assert.assertEquals(4, result.getInt(0));
   }
 
   @SuppressWarnings("unchecked")
@@ -81,12 +82,12 @@ public void udf2Test() {
 
     sqlContext.udf().register("stringLengthTest", new UDF2<String, String, Integer>() {
       @Override
-      public Integer call(String str1, String str2) throws Exception {
+      public Integer call(String str1, String str2) {
         return str1.length() + str2.length();
       }
     }, DataTypes.IntegerType);
 
     Row result = sqlContext.sql("SELECT stringLengthTest('test', 'test2')").head();
-    assert(result.getInt(0) == 9);
+    Assert.assertEquals(9, result.getInt(0));
   }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
index 6f9e7f68dc39c..9e241f20987c0 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
@@ -44,7 +44,7 @@ public class JavaSaveLoadSuite {
   File path;
   DataFrame df;
 
-  private void checkAnswer(DataFrame actual, List<Row> expected) {
+  private static void checkAnswer(DataFrame actual, List<Row> expected) {
     String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected);
     if (errorMessage != null) {
       Assert.fail(errorMessage);
@@ -64,7 +64,7 @@ public void setUp() throws IOException {
       path.delete();
     }
 
-    List<String> jsonObjects = new ArrayList<String>(10);
+    List<String> jsonObjects = new ArrayList<>(10);
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}");
     }
@@ -82,7 +82,7 @@ public void tearDown() {
 
   @Test
   public void saveAndLoad() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write().mode(SaveMode.ErrorIfExists).format("json").options(options).save();
     DataFrame loadedDF = sqlContext.read().format("json").options(options).load();
@@ -91,11 +91,11 @@ public void saveAndLoad() {
 
   @Test
   public void saveAndLoadWithSchema() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write().format("json").mode(SaveMode.ErrorIfExists).options(options).save();
 
-    List<StructField> fields = new ArrayList<StructField>();
+    List<StructField> fields = new ArrayList<>();
     fields.add(DataTypes.createStructField("b", DataTypes.StringType, true));
     StructType schema = DataTypes.createStructType(fields);
     DataFrame loadedDF = sqlContext.read().format("json").schema(schema).options(options).load();
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 019d8a30266e2..b4bf9eef8fca5 100644
--- a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -40,7 +40,7 @@ public class JavaDataFrameSuite {
 
   DataFrame df;
 
-  private void checkAnswer(DataFrame actual, List<Row> expected) {
+  private static void checkAnswer(DataFrame actual, List<Row> expected) {
     String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected);
     if (errorMessage != null) {
       Assert.fail(errorMessage);
@@ -52,7 +52,7 @@ public void setUp() throws IOException {
     hc = TestHive$.MODULE$;
     sc = new JavaSparkContext(hc.sparkContext());
 
-    List<String> jsonObjects = new ArrayList<String>(10);
+    List<String> jsonObjects = new ArrayList<>(10);
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"key\":" + i + ", \"value\":\"str" + i + "\"}");
     }
@@ -71,7 +71,7 @@ public void tearDown() throws IOException {
   @Test
   public void saveTableAndQueryIt() {
     checkAnswer(
-      df.select(functions.avg("key").over(
+      df.select(avg("key").over(
         Window.partitionBy("value").orderBy("key").rowsBetween(-1, 1))),
       hc.sql("SELECT avg(key) " +
         "OVER (PARTITION BY value " +
@@ -95,7 +95,7 @@ public void testUDAF() {
           registeredUDAF.apply(col("value")),
           callUDF("mydoublesum", col("value")));
 
-    List<Row> expectedResult = new ArrayList<Row>();
+    List<Row> expectedResult = new ArrayList<>();
     expectedResult.add(RowFactory.create(4950.0, 9900.0, 9900.0, 9900.0));
     checkAnswer(
       aggregatedDF,
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
index 4192155975c47..c8d272794d10b 100644
--- a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
@@ -53,7 +53,7 @@ public class JavaMetastoreDataSourcesSuite {
   FileSystem fs;
   DataFrame df;
 
-  private void checkAnswer(DataFrame actual, List<Row> expected) {
+  private static void checkAnswer(DataFrame actual, List<Row> expected) {
     String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected);
     if (errorMessage != null) {
       Assert.fail(errorMessage);
@@ -77,7 +77,7 @@ public void setUp() throws IOException {
       fs.delete(hiveManagedPath, true);
     }
 
-    List<String> jsonObjects = new ArrayList<String>(10);
+    List<String> jsonObjects = new ArrayList<>(10);
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}");
     }
@@ -97,7 +97,7 @@ public void tearDown() throws IOException {
 
   @Test
   public void saveExternalTableAndQueryIt() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write()
       .format("org.apache.spark.sql.json")
@@ -120,7 +120,7 @@ public void saveExternalTableAndQueryIt() {
 
   @Test
   public void saveExternalTableWithSchemaAndQueryIt() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write()
       .format("org.apache.spark.sql.json")
@@ -132,7 +132,7 @@ public void saveExternalTableWithSchemaAndQueryIt() {
       sqlContext.sql("SELECT * FROM javaSavedTable"),
       df.collectAsList());
 
-    List<StructField> fields = new ArrayList<StructField>();
+    List<StructField> fields = new ArrayList<>();
     fields.add(DataTypes.createStructField("b", DataTypes.StringType, true));
     StructType schema = DataTypes.createStructType(fields);
     DataFrame loadedDF =
@@ -148,7 +148,7 @@ public void saveExternalTableWithSchemaAndQueryIt() {
 
   @Test
   public void saveTableAndQueryIt() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     df.write()
       .format("org.apache.spark.sql.json")
       .mode(SaveMode.Append)
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
index e0718f73aa13f..c5217149224e4 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
@@ -18,24 +18,22 @@
 package org.apache.spark.streaming;
 
 import java.io.*;
-import java.lang.Iterable;
 import java.nio.charset.Charset;
 import java.util.*;
 import java.util.concurrent.atomic.AtomicBoolean;
 
+import scala.Tuple2;
+
+import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.LongWritable;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
 
-import scala.Tuple2;
-
 import org.junit.Assert;
-import static org.junit.Assert.*;
 import org.junit.Test;
 
 import com.google.common.base.Optional;
-import com.google.common.collect.Lists;
 import com.google.common.io.Files;
 import com.google.common.collect.Sets;
 
@@ -54,14 +52,14 @@
 // see http://stackoverflow.com/questions/758570/.
 public class JavaAPISuite extends LocalJavaStreamingContext implements Serializable {
 
-  public void equalIterator(Iterator<?> a, Iterator<?> b) {
+  public static void equalIterator(Iterator<?> a, Iterator<?> b) {
     while (a.hasNext() && b.hasNext()) {
       Assert.assertEquals(a.next(), b.next());
     }
     Assert.assertEquals(a.hasNext(), b.hasNext());
   }
 
-  public void equalIterable(Iterable<?> a, Iterable<?> b) {
+  public static void equalIterable(Iterable<?> a, Iterable<?> b) {
       equalIterator(a.iterator(), b.iterator());
   }
 
@@ -74,14 +72,14 @@ public void testInitialization() {
   @Test
   public void testContextState() {
     List<List<Integer>> inputData = Arrays.asList(Arrays.asList(1, 2, 3, 4));
-    Assert.assertTrue(ssc.getState() == StreamingContextState.INITIALIZED);
+    Assert.assertEquals(StreamingContextState.INITIALIZED, ssc.getState());
     JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaTestUtils.attachTestOutputStream(stream);
-    Assert.assertTrue(ssc.getState() == StreamingContextState.INITIALIZED);
+    Assert.assertEquals(StreamingContextState.INITIALIZED, ssc.getState());
     ssc.start();
-    Assert.assertTrue(ssc.getState() == StreamingContextState.ACTIVE);
+    Assert.assertEquals(StreamingContextState.ACTIVE, ssc.getState());
     ssc.stop();
-    Assert.assertTrue(ssc.getState() == StreamingContextState.STOPPED);
+    Assert.assertEquals(StreamingContextState.STOPPED, ssc.getState());
   }
 
   @SuppressWarnings("unchecked")
@@ -118,7 +116,7 @@ public void testMap() {
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<Integer> letterCount = stream.map(new Function<String, Integer>() {
         @Override
-        public Integer call(String s) throws Exception {
+        public Integer call(String s) {
           return s.length();
         }
     });
@@ -180,7 +178,7 @@ public void testWindowWithSlideDuration() {
   public void testFilter() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<String>> expected = Arrays.asList(
         Arrays.asList("giants"),
@@ -189,7 +187,7 @@ public void testFilter() {
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<String> filtered = stream.filter(new Function<String, Boolean>() {
       @Override
-      public Boolean call(String s) throws Exception {
+      public Boolean call(String s) {
         return s.contains("a");
       }
     });
@@ -243,11 +241,11 @@ public void testRepartitionFewerPartitions() {
   public void testGlom() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<List<String>>> expected = Arrays.asList(
         Arrays.asList(Arrays.asList("giants", "dodgers")),
-        Arrays.asList(Arrays.asList("yankees", "red socks")));
+        Arrays.asList(Arrays.asList("yankees", "red sox")));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<List<String>> glommed = stream.glom();
@@ -262,22 +260,22 @@ public void testGlom() {
   public void testMapPartitions() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<String>> expected = Arrays.asList(
         Arrays.asList("GIANTSDODGERS"),
-        Arrays.asList("YANKEESRED SOCKS"));
+        Arrays.asList("YANKEESRED SOX"));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<String> mapped = stream.mapPartitions(
         new FlatMapFunction<Iterator<String>, String>() {
           @Override
           public Iterable<String> call(Iterator<String> in) {
-            String out = "";
+            StringBuilder out = new StringBuilder();
             while (in.hasNext()) {
-              out = out + in.next().toUpperCase();
+              out.append(in.next().toUpperCase(Locale.ENGLISH));
             }
-            return Lists.newArrayList(out);
+            return Arrays.asList(out.toString());
           }
         });
     JavaTestUtils.attachTestOutputStream(mapped);
@@ -286,16 +284,16 @@ public Iterable<String> call(Iterator<String> in) {
     Assert.assertEquals(expected, result);
   }
 
-  private class IntegerSum implements Function2<Integer, Integer, Integer> {
+  private static class IntegerSum implements Function2<Integer, Integer, Integer> {
     @Override
-    public Integer call(Integer i1, Integer i2) throws Exception {
+    public Integer call(Integer i1, Integer i2) {
       return i1 + i2;
     }
   }
 
-  private class IntegerDifference implements Function2<Integer, Integer, Integer> {
+  private static class IntegerDifference implements Function2<Integer, Integer, Integer> {
     @Override
-    public Integer call(Integer i1, Integer i2) throws Exception {
+    public Integer call(Integer i1, Integer i2) {
       return i1 - i2;
     }
   }
@@ -347,13 +345,13 @@ private void testReduceByWindow(boolean withInverse) {
         Arrays.asList(24));
 
     JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
-    JavaDStream<Integer> reducedWindowed = null;
+    JavaDStream<Integer> reducedWindowed;
     if (withInverse) {
       reducedWindowed = stream.reduceByWindow(new IntegerSum(),
-        new IntegerDifference(), new Duration(2000), new Duration(1000));
+                                              new IntegerDifference(), new Duration(2000), new Duration(1000));
     } else {
       reducedWindowed = stream.reduceByWindow(new IntegerSum(),
-        new Duration(2000), new Duration(1000));
+                                              new Duration(2000), new Duration(1000));
     }
     JavaTestUtils.attachTestOutputStream(reducedWindowed);
     List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 4, 4);
@@ -378,11 +376,11 @@ public void testQueueStream() {
         Arrays.asList(7,8,9));
 
     JavaSparkContext jsc = new JavaSparkContext(ssc.ssc().sc());
-    JavaRDD<Integer> rdd1 = ssc.sparkContext().parallelize(Arrays.asList(1, 2, 3));
-    JavaRDD<Integer> rdd2 = ssc.sparkContext().parallelize(Arrays.asList(4, 5, 6));
-    JavaRDD<Integer> rdd3 = ssc.sparkContext().parallelize(Arrays.asList(7,8,9));
+    JavaRDD<Integer> rdd1 = jsc.parallelize(Arrays.asList(1, 2, 3));
+    JavaRDD<Integer> rdd2 = jsc.parallelize(Arrays.asList(4, 5, 6));
+    JavaRDD<Integer> rdd3 = jsc.parallelize(Arrays.asList(7,8,9));
 
-    LinkedList<JavaRDD<Integer>> rdds = Lists.newLinkedList();
+    Queue<JavaRDD<Integer>> rdds = new LinkedList<>();
     rdds.add(rdd1);
     rdds.add(rdd2);
     rdds.add(rdd3);
@@ -410,10 +408,10 @@ public void testTransform() {
     JavaDStream<Integer> transformed = stream.transform(
       new Function<JavaRDD<Integer>, JavaRDD<Integer>>() {
         @Override
-        public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception {
+        public JavaRDD<Integer> call(JavaRDD<Integer> in) {
           return in.map(new Function<Integer, Integer>() {
             @Override
-            public Integer call(Integer i) throws Exception {
+            public Integer call(Integer i) {
               return i + 2;
             }
           });
@@ -435,70 +433,70 @@ public void testVariousTransform() {
     JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
 
     List<List<Tuple2<String, Integer>>> pairInputData =
-        Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1)));
+        Arrays.asList(Arrays.asList(new Tuple2<>("x", 1)));
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(
         JavaTestUtils.attachTestInputStream(ssc, pairInputData, 1));
 
-    JavaDStream<Integer> transformed1 = stream.transform(
+    stream.transform(
         new Function<JavaRDD<Integer>, JavaRDD<Integer>>() {
           @Override
-          public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception {
+          public JavaRDD<Integer> call(JavaRDD<Integer> in) {
             return null;
           }
         }
     );
 
-    JavaDStream<Integer> transformed2 = stream.transform(
+    stream.transform(
       new Function2<JavaRDD<Integer>, Time, JavaRDD<Integer>>() {
-        @Override public JavaRDD<Integer> call(JavaRDD<Integer> in, Time time) throws Exception {
+        @Override public JavaRDD<Integer> call(JavaRDD<Integer> in, Time time) {
           return null;
         }
       }
     );
 
-    JavaPairDStream<String, Integer> transformed3 = stream.transformToPair(
+    stream.transformToPair(
         new Function<JavaRDD<Integer>, JavaPairRDD<String, Integer>>() {
-          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in) throws Exception {
+          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<String, Integer> transformed4 = stream.transformToPair(
+    stream.transformToPair(
         new Function2<JavaRDD<Integer>, Time, JavaPairRDD<String, Integer>>() {
-          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in, Time time) throws Exception {
+          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Integer> pairTransformed1 = pairStream.transform(
+    pairStream.transform(
         new Function<JavaPairRDD<String, Integer>, JavaRDD<Integer>>() {
-          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in) throws Exception {
+          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in) {
             return null;
           }
         }
     );
 
-    JavaDStream<Integer> pairTransformed2 = pairStream.transform(
+    pairStream.transform(
         new Function2<JavaPairRDD<String, Integer>, Time, JavaRDD<Integer>>() {
-          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in, Time time) throws Exception {
+          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<String, String> pairTransformed3 = pairStream.transformToPair(
+    pairStream.transformToPair(
         new Function<JavaPairRDD<String, Integer>, JavaPairRDD<String, String>>() {
-          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in) throws Exception {
+          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<String, String> pairTransformed4 = pairStream.transformToPair(
+    pairStream.transformToPair(
         new Function2<JavaPairRDD<String, Integer>, Time, JavaPairRDD<String, String>>() {
-          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in, Time time) throws Exception {
+          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in, Time time) {
             return null;
           }
         }
@@ -511,32 +509,32 @@ public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception {
   public void testTransformWith() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
+            new Tuple2<>("california", "dodgers"),
+            new Tuple2<>("new york", "yankees")),
         Arrays.asList(
-            new Tuple2<String, String>("california", "sharks"),
-            new Tuple2<String, String>("new york", "rangers")));
+            new Tuple2<>("california", "sharks"),
+            new Tuple2<>("new york", "rangers")));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, String>("california", "giants"),
-            new Tuple2<String, String>("new york", "mets")),
+            new Tuple2<>("california", "giants"),
+            new Tuple2<>("new york", "mets")),
         Arrays.asList(
-            new Tuple2<String, String>("california", "ducks"),
-            new Tuple2<String, String>("new york", "islanders")));
+            new Tuple2<>("california", "ducks"),
+            new Tuple2<>("new york", "islanders")));
 
 
     List<HashSet<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList(
         Sets.newHashSet(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("dodgers", "giants")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("yankees", "mets"))),
+            new Tuple2<>("california",
+                         new Tuple2<>("dodgers", "giants")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("yankees", "mets"))),
         Sets.newHashSet(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("sharks", "ducks")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("rangers", "islanders"))));
+            new Tuple2<>("california",
+                         new Tuple2<>("sharks", "ducks")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("rangers", "islanders"))));
 
     JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream(
         ssc, stringStringKVStream1, 1);
@@ -552,14 +550,12 @@ public void testTransformWith() {
             JavaPairRDD<String, String>,
             JavaPairRDD<String, String>,
             Time,
-            JavaPairRDD<String, Tuple2<String, String>>
-          >() {
+            JavaPairRDD<String, Tuple2<String, String>>>() {
           @Override
           public JavaPairRDD<String, Tuple2<String, String>> call(
               JavaPairRDD<String, String> rdd1,
               JavaPairRDD<String, String> rdd2,
-              Time time
-          ) throws Exception {
+              Time time) {
             return rdd1.join(rdd2);
           }
         }
@@ -567,9 +563,9 @@ public JavaPairRDD<String, Tuple2<String, String>> call(
 
     JavaTestUtils.attachTestOutputStream(joined);
     List<List<Tuple2<String, Tuple2<String, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2);
-    List<HashSet<Tuple2<String, Tuple2<String, String>>>> unorderedResult = Lists.newArrayList();
+    List<HashSet<Tuple2<String, Tuple2<String, String>>>> unorderedResult = new ArrayList<>();
     for (List<Tuple2<String, Tuple2<String, String>>> res: result) {
-        unorderedResult.add(Sets.newHashSet(res));
+      unorderedResult.add(Sets.newHashSet(res));
     }
 
     Assert.assertEquals(expected, unorderedResult);
@@ -587,89 +583,89 @@ public void testVariousTransformWith() {
     JavaDStream<String> stream2 = JavaTestUtils.attachTestInputStream(ssc, inputData2, 1);
 
     List<List<Tuple2<String, Integer>>> pairInputData1 =
-        Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1)));
+        Arrays.asList(Arrays.asList(new Tuple2<>("x", 1)));
     List<List<Tuple2<Double, Character>>> pairInputData2 =
-        Arrays.asList(Arrays.asList(new Tuple2<Double, Character>(1.0, 'x')));
+        Arrays.asList(Arrays.asList(new Tuple2<>(1.0, 'x')));
     JavaPairDStream<String, Integer> pairStream1 = JavaPairDStream.fromJavaDStream(
         JavaTestUtils.attachTestInputStream(ssc, pairInputData1, 1));
     JavaPairDStream<Double, Character> pairStream2 = JavaPairDStream.fromJavaDStream(
         JavaTestUtils.attachTestInputStream(ssc, pairInputData2, 1));
 
-    JavaDStream<Double> transformed1 = stream1.transformWith(
+    stream1.transformWith(
         stream2,
         new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Double> transformed2 = stream1.transformWith(
+    stream1.transformWith(
         pairStream1,
         new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> transformed3 = stream1.transformWithToPair(
+    stream1.transformWithToPair(
         stream2,
         new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> transformed4 = stream1.transformWithToPair(
+    stream1.transformWithToPair(
         pairStream1,
         new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Double> pairTransformed1 = pairStream1.transformWith(
+    pairStream1.transformWith(
         stream2,
         new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Double> pairTransformed2_ = pairStream1.transformWith(
+    pairStream1.transformWith(
         pairStream1,
         new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> pairTransformed3 = pairStream1.transformWithToPair(
+    pairStream1.transformWithToPair(
         stream2,
         new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> pairTransformed4 = pairStream1.transformWithToPair(
+    pairStream1.transformWithToPair(
         pairStream2,
         new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<Double, Character>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<Double, Character> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<Double, Character> rdd2, Time time) {
             return null;
           }
         }
@@ -690,13 +686,13 @@ public void testStreamingContextTransform(){
     );
 
     List<List<Tuple2<Integer, String>>> pairStream1input = Arrays.asList(
-        Arrays.asList(new Tuple2<Integer, String>(1, "x")),
-        Arrays.asList(new Tuple2<Integer, String>(2, "y"))
+        Arrays.asList(new Tuple2<>(1, "x")),
+        Arrays.asList(new Tuple2<>(2, "y"))
     );
 
     List<List<Tuple2<Integer, Tuple2<Integer, String>>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(1, new Tuple2<Integer, String>(1, "x"))),
-        Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(2, new Tuple2<Integer, String>(2, "y")))
+        Arrays.asList(new Tuple2<>(1, new Tuple2<>(1, "x"))),
+        Arrays.asList(new Tuple2<>(2, new Tuple2<>(2, "y")))
     );
 
     JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, stream1input, 1);
@@ -707,7 +703,7 @@ public void testStreamingContextTransform(){
     List<JavaDStream<?>> listOfDStreams1 = Arrays.<JavaDStream<?>>asList(stream1, stream2);
 
     // This is just to test whether this transform to JavaStream compiles
-    JavaDStream<Long> transformed1 = ssc.transform(
+    ssc.transform(
       listOfDStreams1,
       new Function2<List<JavaRDD<?>>, Time, JavaRDD<Long>>() {
         @Override
@@ -733,8 +729,8 @@ public JavaPairRDD<Integer, Tuple2<Integer, String>> call(List<JavaRDD<?>> listO
           JavaPairRDD<Integer, String> prdd3 = JavaPairRDD.fromJavaRDD(rdd3);
           PairFunction<Integer, Integer, Integer> mapToTuple = new PairFunction<Integer, Integer, Integer>() {
             @Override
-            public Tuple2<Integer, Integer> call(Integer i) throws Exception {
-              return new Tuple2<Integer, Integer>(i, i);
+            public Tuple2<Integer, Integer> call(Integer i) {
+              return new Tuple2<>(i, i);
             }
           };
           return rdd1.union(rdd2).mapToPair(mapToTuple).join(prdd3);
@@ -763,7 +759,7 @@ public void testFlatMap() {
     JavaDStream<String> flatMapped = stream.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
-        return Lists.newArrayList(x.split("(?!^)"));
+        return Arrays.asList(x.split("(?!^)"));
       }
     });
     JavaTestUtils.attachTestOutputStream(flatMapped);
@@ -782,39 +778,39 @@ public void testPairFlatMap() {
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, String>(6, "g"),
-            new Tuple2<Integer, String>(6, "i"),
-            new Tuple2<Integer, String>(6, "a"),
-            new Tuple2<Integer, String>(6, "n"),
-            new Tuple2<Integer, String>(6, "t"),
-            new Tuple2<Integer, String>(6, "s")),
+            new Tuple2<>(6, "g"),
+            new Tuple2<>(6, "i"),
+            new Tuple2<>(6, "a"),
+            new Tuple2<>(6, "n"),
+            new Tuple2<>(6, "t"),
+            new Tuple2<>(6, "s")),
         Arrays.asList(
-            new Tuple2<Integer, String>(7, "d"),
-            new Tuple2<Integer, String>(7, "o"),
-            new Tuple2<Integer, String>(7, "d"),
-            new Tuple2<Integer, String>(7, "g"),
-            new Tuple2<Integer, String>(7, "e"),
-            new Tuple2<Integer, String>(7, "r"),
-            new Tuple2<Integer, String>(7, "s")),
+            new Tuple2<>(7, "d"),
+            new Tuple2<>(7, "o"),
+            new Tuple2<>(7, "d"),
+            new Tuple2<>(7, "g"),
+            new Tuple2<>(7, "e"),
+            new Tuple2<>(7, "r"),
+            new Tuple2<>(7, "s")),
         Arrays.asList(
-            new Tuple2<Integer, String>(9, "a"),
-            new Tuple2<Integer, String>(9, "t"),
-            new Tuple2<Integer, String>(9, "h"),
-            new Tuple2<Integer, String>(9, "l"),
-            new Tuple2<Integer, String>(9, "e"),
-            new Tuple2<Integer, String>(9, "t"),
-            new Tuple2<Integer, String>(9, "i"),
-            new Tuple2<Integer, String>(9, "c"),
-            new Tuple2<Integer, String>(9, "s")));
+            new Tuple2<>(9, "a"),
+            new Tuple2<>(9, "t"),
+            new Tuple2<>(9, "h"),
+            new Tuple2<>(9, "l"),
+            new Tuple2<>(9, "e"),
+            new Tuple2<>(9, "t"),
+            new Tuple2<>(9, "i"),
+            new Tuple2<>(9, "c"),
+            new Tuple2<>(9, "s")));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<Integer, String> flatMapped = stream.flatMapToPair(
       new PairFlatMapFunction<String, Integer, String>() {
         @Override
-        public Iterable<Tuple2<Integer, String>> call(String in) throws Exception {
-          List<Tuple2<Integer, String>> out = Lists.newArrayList();
+        public Iterable<Tuple2<Integer, String>> call(String in) {
+          List<Tuple2<Integer, String>> out = new ArrayList<>();
           for (String letter: in.split("(?!^)")) {
-            out.add(new Tuple2<Integer, String>(in.length(), letter));
+            out.add(new Tuple2<>(in.length(), letter));
           }
           return out;
         }
@@ -859,13 +855,13 @@ public void testUnion() {
    */
   public static <T> void assertOrderInvariantEquals(
       List<List<T>> expected, List<List<T>> actual) {
-    List<Set<T>> expectedSets = new ArrayList<Set<T>>();
+    List<Set<T>> expectedSets = new ArrayList<>();
     for (List<T> list: expected) {
-      expectedSets.add(Collections.unmodifiableSet(new HashSet<T>(list)));
+      expectedSets.add(Collections.unmodifiableSet(new HashSet<>(list)));
     }
-    List<Set<T>> actualSets = new ArrayList<Set<T>>();
+    List<Set<T>> actualSets = new ArrayList<>();
     for (List<T> list: actual) {
-      actualSets.add(Collections.unmodifiableSet(new HashSet<T>(list)));
+      actualSets.add(Collections.unmodifiableSet(new HashSet<>(list)));
     }
     Assert.assertEquals(expectedSets, actualSets);
   }
@@ -877,25 +873,25 @@ public static <T> void assertOrderInvariantEquals(
   public void testPairFilter() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("giants", 6)),
-        Arrays.asList(new Tuple2<String, Integer>("yankees", 7)));
+        Arrays.asList(new Tuple2<>("giants", 6)),
+        Arrays.asList(new Tuple2<>("yankees", 7)));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = stream.mapToPair(
         new PairFunction<String, String, Integer>() {
           @Override
-          public Tuple2<String, Integer> call(String in) throws Exception {
-            return new Tuple2<String, Integer>(in, in.length());
+          public Tuple2<String, Integer> call(String in) {
+            return new Tuple2<>(in, in.length());
           }
         });
 
     JavaPairDStream<String, Integer> filtered = pairStream.filter(
         new Function<Tuple2<String, Integer>, Boolean>() {
       @Override
-      public Boolean call(Tuple2<String, Integer> in) throws Exception {
+      public Boolean call(Tuple2<String, Integer> in) {
         return in._1().contains("a");
       }
     });
@@ -906,28 +902,28 @@ public Boolean call(Tuple2<String, Integer> in) throws Exception {
   }
 
   @SuppressWarnings("unchecked")
-  private List<List<Tuple2<String, String>>> stringStringKVStream = Arrays.asList(
-      Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-          new Tuple2<String, String>("california", "giants"),
-          new Tuple2<String, String>("new york", "yankees"),
-          new Tuple2<String, String>("new york", "mets")),
-      Arrays.asList(new Tuple2<String, String>("california", "sharks"),
-          new Tuple2<String, String>("california", "ducks"),
-          new Tuple2<String, String>("new york", "rangers"),
-          new Tuple2<String, String>("new york", "islanders")));
+  private final List<List<Tuple2<String, String>>> stringStringKVStream = Arrays.asList(
+      Arrays.asList(new Tuple2<>("california", "dodgers"),
+                    new Tuple2<>("california", "giants"),
+                    new Tuple2<>("new york", "yankees"),
+                    new Tuple2<>("new york", "mets")),
+      Arrays.asList(new Tuple2<>("california", "sharks"),
+                    new Tuple2<>("california", "ducks"),
+                    new Tuple2<>("new york", "rangers"),
+                    new Tuple2<>("new york", "islanders")));
 
   @SuppressWarnings("unchecked")
-  private List<List<Tuple2<String, Integer>>> stringIntKVStream = Arrays.asList(
+  private final List<List<Tuple2<String, Integer>>> stringIntKVStream = Arrays.asList(
       Arrays.asList(
-          new Tuple2<String, Integer>("california", 1),
-          new Tuple2<String, Integer>("california", 3),
-          new Tuple2<String, Integer>("new york", 4),
-          new Tuple2<String, Integer>("new york", 1)),
+          new Tuple2<>("california", 1),
+          new Tuple2<>("california", 3),
+          new Tuple2<>("new york", 4),
+          new Tuple2<>("new york", 1)),
       Arrays.asList(
-          new Tuple2<String, Integer>("california", 5),
-          new Tuple2<String, Integer>("california", 5),
-          new Tuple2<String, Integer>("new york", 3),
-          new Tuple2<String, Integer>("new york", 1)));
+          new Tuple2<>("california", 5),
+          new Tuple2<>("california", 5),
+          new Tuple2<>("new york", 3),
+          new Tuple2<>("new york", 1)));
 
   @SuppressWarnings("unchecked")
   @Test
@@ -936,22 +932,22 @@ public void testPairMap() { // Maps pair -> pair of different type
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-                new Tuple2<Integer, String>(1, "california"),
-                new Tuple2<Integer, String>(3, "california"),
-                new Tuple2<Integer, String>(4, "new york"),
-                new Tuple2<Integer, String>(1, "new york")),
+            new Tuple2<>(1, "california"),
+            new Tuple2<>(3, "california"),
+            new Tuple2<>(4, "new york"),
+            new Tuple2<>(1, "new york")),
         Arrays.asList(
-                new Tuple2<Integer, String>(5, "california"),
-                new Tuple2<Integer, String>(5, "california"),
-                new Tuple2<Integer, String>(3, "new york"),
-                new Tuple2<Integer, String>(1, "new york")));
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(3, "new york"),
+            new Tuple2<>(1, "new york")));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
     JavaPairDStream<Integer, String> reversed = pairStream.mapToPair(
         new PairFunction<Tuple2<String, Integer>, Integer, String>() {
           @Override
-          public Tuple2<Integer, String> call(Tuple2<String, Integer> in) throws Exception {
+          public Tuple2<Integer, String> call(Tuple2<String, Integer> in) {
             return in.swap();
           }
         });
@@ -969,23 +965,23 @@ public void testPairMapPartitions() { // Maps pair -> pair of different type
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, String>(1, "california"),
-            new Tuple2<Integer, String>(3, "california"),
-            new Tuple2<Integer, String>(4, "new york"),
-            new Tuple2<Integer, String>(1, "new york")),
+            new Tuple2<>(1, "california"),
+            new Tuple2<>(3, "california"),
+            new Tuple2<>(4, "new york"),
+            new Tuple2<>(1, "new york")),
         Arrays.asList(
-            new Tuple2<Integer, String>(5, "california"),
-            new Tuple2<Integer, String>(5, "california"),
-            new Tuple2<Integer, String>(3, "new york"),
-            new Tuple2<Integer, String>(1, "new york")));
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(3, "new york"),
+            new Tuple2<>(1, "new york")));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
     JavaPairDStream<Integer, String> reversed = pairStream.mapPartitionsToPair(
         new PairFlatMapFunction<Iterator<Tuple2<String, Integer>>, Integer, String>() {
           @Override
-          public Iterable<Tuple2<Integer, String>> call(Iterator<Tuple2<String, Integer>> in) throws Exception {
-            LinkedList<Tuple2<Integer, String>> out = new LinkedList<Tuple2<Integer, String>>();
+          public Iterable<Tuple2<Integer, String>> call(Iterator<Tuple2<String, Integer>> in) {
+            List<Tuple2<Integer, String>> out = new LinkedList<>();
             while (in.hasNext()) {
               Tuple2<String, Integer> next = in.next();
               out.add(next.swap());
@@ -1014,7 +1010,7 @@ public void testPairMap2() { // Maps pair -> single
     JavaDStream<Integer> reversed = pairStream.map(
         new Function<Tuple2<String, Integer>, Integer>() {
           @Override
-          public Integer call(Tuple2<String, Integer> in) throws Exception {
+          public Integer call(Tuple2<String, Integer> in) {
             return in._2();
           }
         });
@@ -1030,23 +1026,23 @@ public Integer call(Tuple2<String, Integer> in) throws Exception {
   public void testPairToPairFlatMapWithChangingTypes() { // Maps pair -> pair
     List<List<Tuple2<String, Integer>>> inputData = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Integer>("hi", 1),
-            new Tuple2<String, Integer>("ho", 2)),
+            new Tuple2<>("hi", 1),
+            new Tuple2<>("ho", 2)),
         Arrays.asList(
-            new Tuple2<String, Integer>("hi", 1),
-            new Tuple2<String, Integer>("ho", 2)));
+            new Tuple2<>("hi", 1),
+            new Tuple2<>("ho", 2)));
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, String>(1, "h"),
-            new Tuple2<Integer, String>(1, "i"),
-            new Tuple2<Integer, String>(2, "h"),
-            new Tuple2<Integer, String>(2, "o")),
+            new Tuple2<>(1, "h"),
+            new Tuple2<>(1, "i"),
+            new Tuple2<>(2, "h"),
+            new Tuple2<>(2, "o")),
         Arrays.asList(
-            new Tuple2<Integer, String>(1, "h"),
-            new Tuple2<Integer, String>(1, "i"),
-            new Tuple2<Integer, String>(2, "h"),
-            new Tuple2<Integer, String>(2, "o")));
+            new Tuple2<>(1, "h"),
+            new Tuple2<>(1, "i"),
+            new Tuple2<>(2, "h"),
+            new Tuple2<>(2, "o")));
 
     JavaDStream<Tuple2<String, Integer>> stream =
         JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
@@ -1054,10 +1050,10 @@ public void testPairToPairFlatMapWithChangingTypes() { // Maps pair -> pair
     JavaPairDStream<Integer, String> flatMapped = pairStream.flatMapToPair(
         new PairFlatMapFunction<Tuple2<String, Integer>, Integer, String>() {
           @Override
-          public Iterable<Tuple2<Integer, String>> call(Tuple2<String, Integer> in) throws Exception {
-            List<Tuple2<Integer, String>> out = new LinkedList<Tuple2<Integer, String>>();
+          public Iterable<Tuple2<Integer, String>> call(Tuple2<String, Integer> in) {
+            List<Tuple2<Integer, String>> out = new LinkedList<>();
             for (Character s : in._1().toCharArray()) {
-              out.add(new Tuple2<Integer, String>(in._2(), s.toString()));
+              out.add(new Tuple2<>(in._2(), s.toString()));
             }
             return out;
           }
@@ -1075,11 +1071,11 @@ public void testPairGroupByKey() {
 
     List<List<Tuple2<String, List<String>>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, List<String>>("california", Arrays.asList("dodgers", "giants")),
-            new Tuple2<String, List<String>>("new york", Arrays.asList("yankees", "mets"))),
+            new Tuple2<>("california", Arrays.asList("dodgers", "giants")),
+            new Tuple2<>("new york", Arrays.asList("yankees", "mets"))),
         Arrays.asList(
-            new Tuple2<String, List<String>>("california", Arrays.asList("sharks", "ducks")),
-            new Tuple2<String, List<String>>("new york", Arrays.asList("rangers", "islanders"))));
+            new Tuple2<>("california", Arrays.asList("sharks", "ducks")),
+            new Tuple2<>("new york", Arrays.asList("rangers", "islanders"))));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, String> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1111,11 +1107,11 @@ public void testPairReduceByKey() {
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
+            new Tuple2<>("california", 4),
+            new Tuple2<>("new york", 5)),
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+            new Tuple2<>("california", 10),
+            new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1136,20 +1132,20 @@ public void testCombineByKey() {
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
+            new Tuple2<>("california", 4),
+            new Tuple2<>("new york", 5)),
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+            new Tuple2<>("california", 10),
+            new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
 
-    JavaPairDStream<String, Integer> combined = pairStream.<Integer>combineByKey(
+    JavaPairDStream<String, Integer> combined = pairStream.combineByKey(
         new Function<Integer, Integer>() {
           @Override
-          public Integer call(Integer i) throws Exception {
+          public Integer call(Integer i) {
             return i;
           }
         }, new IntegerSum(), new IntegerSum(), new HashPartitioner(2));
@@ -1170,13 +1166,13 @@ public void testCountByValue() {
 
     List<List<Tuple2<String, Long>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Long>("hello", 1L),
-            new Tuple2<String, Long>("world", 1L)),
+            new Tuple2<>("hello", 1L),
+            new Tuple2<>("world", 1L)),
         Arrays.asList(
-            new Tuple2<String, Long>("hello", 1L),
-            new Tuple2<String, Long>("moon", 1L)),
+            new Tuple2<>("hello", 1L),
+            new Tuple2<>("moon", 1L)),
         Arrays.asList(
-            new Tuple2<String, Long>("hello", 1L)));
+            new Tuple2<>("hello", 1L)));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Long> counted = stream.countByValue();
@@ -1193,16 +1189,16 @@ public void testGroupByKeyAndWindow() {
 
     List<List<Tuple2<String, List<Integer>>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<String, List<Integer>>("california", Arrays.asList(1, 3)),
-        new Tuple2<String, List<Integer>>("new york", Arrays.asList(1, 4))
+        new Tuple2<>("california", Arrays.asList(1, 3)),
+        new Tuple2<>("new york", Arrays.asList(1, 4))
       ),
       Arrays.asList(
-        new Tuple2<String, List<Integer>>("california", Arrays.asList(1, 3, 5, 5)),
-        new Tuple2<String, List<Integer>>("new york", Arrays.asList(1, 1, 3, 4))
+        new Tuple2<>("california", Arrays.asList(1, 3, 5, 5)),
+        new Tuple2<>("new york", Arrays.asList(1, 1, 3, 4))
       ),
       Arrays.asList(
-        new Tuple2<String, List<Integer>>("california", Arrays.asList(5, 5)),
-        new Tuple2<String, List<Integer>>("new york", Arrays.asList(1, 3))
+        new Tuple2<>("california", Arrays.asList(5, 5)),
+        new Tuple2<>("new york", Arrays.asList(1, 3))
       )
     );
 
@@ -1220,16 +1216,16 @@ public void testGroupByKeyAndWindow() {
     }
   }
 
-  private HashSet<Tuple2<String, HashSet<Integer>>> convert(List<Tuple2<String, List<Integer>>> listOfTuples) {
-    List<Tuple2<String, HashSet<Integer>>> newListOfTuples = new ArrayList<Tuple2<String, HashSet<Integer>>>();
+  private static Set<Tuple2<String, HashSet<Integer>>> convert(List<Tuple2<String, List<Integer>>> listOfTuples) {
+    List<Tuple2<String, HashSet<Integer>>> newListOfTuples = new ArrayList<>();
     for (Tuple2<String, List<Integer>> tuple: listOfTuples) {
       newListOfTuples.add(convert(tuple));
     }
-    return new HashSet<Tuple2<String, HashSet<Integer>>>(newListOfTuples);
+    return new HashSet<>(newListOfTuples);
   }
 
-  private Tuple2<String, HashSet<Integer>> convert(Tuple2<String, List<Integer>> tuple) {
-    return new Tuple2<String, HashSet<Integer>>(tuple._1(), new HashSet<Integer>(tuple._2()));
+  private static Tuple2<String, HashSet<Integer>> convert(Tuple2<String, List<Integer>> tuple) {
+    return new Tuple2<>(tuple._1(), new HashSet<>(tuple._2()));
   }
 
   @SuppressWarnings("unchecked")
@@ -1238,12 +1234,12 @@ public void testReduceByKeyAndWindow() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+        Arrays.asList(new Tuple2<>("california", 4),
+                      new Tuple2<>("new york", 5)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)),
+        Arrays.asList(new Tuple2<>("california", 10),
+                      new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1262,12 +1258,12 @@ public void testUpdateStateByKey() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)));
+        Arrays.asList(new Tuple2<>("california", 4),
+                      new Tuple2<>("new york", 5)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1278,10 +1274,10 @@ public void testUpdateStateByKey() {
           public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
             int out = 0;
             if (state.isPresent()) {
-              out = out + state.get();
+              out += state.get();
             }
             for (Integer v : values) {
-              out = out + v;
+              out += v;
             }
             return Optional.of(out);
           }
@@ -1298,19 +1294,19 @@ public void testUpdateStateByKeyWithInitial() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<Tuple2<String, Integer>> initial = Arrays.asList (
-            new Tuple2<String, Integer> ("california", 1),
-            new Tuple2<String, Integer> ("new york", 2));
+        new Tuple2<>("california", 1),
+            new Tuple2<>("new york", 2));
 
     JavaRDD<Tuple2<String, Integer>> tmpRDD = ssc.sparkContext().parallelize(initial);
     JavaPairRDD<String, Integer> initialRDD = JavaPairRDD.fromJavaRDD (tmpRDD);
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 5),
-            new Tuple2<String, Integer>("new york", 7)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 15),
-            new Tuple2<String, Integer>("new york", 11)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 15),
-            new Tuple2<String, Integer>("new york", 11)));
+        Arrays.asList(new Tuple2<>("california", 5),
+                      new Tuple2<>("new york", 7)),
+        Arrays.asList(new Tuple2<>("california", 15),
+                      new Tuple2<>("new york", 11)),
+        Arrays.asList(new Tuple2<>("california", 15),
+                      new Tuple2<>("new york", 11)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1321,10 +1317,10 @@ public void testUpdateStateByKeyWithInitial() {
           public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
             int out = 0;
             if (state.isPresent()) {
-              out = out + state.get();
+              out += state.get();
             }
             for (Integer v : values) {
-              out = out + v;
+              out += v;
             }
             return Optional.of(out);
           }
@@ -1341,19 +1337,19 @@ public void testReduceByKeyAndWindowWithInverse() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+        Arrays.asList(new Tuple2<>("california", 4),
+                      new Tuple2<>("new york", 5)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)),
+        Arrays.asList(new Tuple2<>("california", 10),
+                      new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
 
     JavaPairDStream<String, Integer> reduceWindowed =
         pairStream.reduceByKeyAndWindow(new IntegerSum(), new IntegerDifference(),
-            new Duration(2000), new Duration(1000));
+                                        new Duration(2000), new Duration(1000));
     JavaTestUtils.attachTestOutputStream(reduceWindowed);
     List<List<Tuple2<String, Integer>>> result = JavaTestUtils.runStreams(ssc, 3, 3);
 
@@ -1370,15 +1366,15 @@ public void testCountByValueAndWindow() {
 
     List<HashSet<Tuple2<String, Long>>> expected = Arrays.asList(
         Sets.newHashSet(
-            new Tuple2<String, Long>("hello", 1L),
-            new Tuple2<String, Long>("world", 1L)),
+            new Tuple2<>("hello", 1L),
+            new Tuple2<>("world", 1L)),
         Sets.newHashSet(
-            new Tuple2<String, Long>("hello", 2L),
-            new Tuple2<String, Long>("world", 1L),
-            new Tuple2<String, Long>("moon", 1L)),
+            new Tuple2<>("hello", 2L),
+            new Tuple2<>("world", 1L),
+            new Tuple2<>("moon", 1L)),
         Sets.newHashSet(
-            new Tuple2<String, Long>("hello", 2L),
-            new Tuple2<String, Long>("moon", 1L)));
+            new Tuple2<>("hello", 2L),
+            new Tuple2<>("moon", 1L)));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1386,7 +1382,7 @@ public void testCountByValueAndWindow() {
       stream.countByValueAndWindow(new Duration(2000), new Duration(1000));
     JavaTestUtils.attachTestOutputStream(counted);
     List<List<Tuple2<String, Long>>> result = JavaTestUtils.runStreams(ssc, 3, 3);
-    List<HashSet<Tuple2<String, Long>>> unorderedResult = Lists.newArrayList();
+    List<Set<Tuple2<String, Long>>> unorderedResult = new ArrayList<>();
     for (List<Tuple2<String, Long>> res: result) {
       unorderedResult.add(Sets.newHashSet(res));
     }
@@ -1399,27 +1395,27 @@ public void testCountByValueAndWindow() {
   public void testPairTransform() {
     List<List<Tuple2<Integer, Integer>>> inputData = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(2, 5)),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(1, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(2, 5)),
         Arrays.asList(
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(1, 5)));
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(1, 5)));
 
     List<List<Tuple2<Integer, Integer>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5)),
+            new Tuple2<>(1, 5),
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5)),
         Arrays.asList(
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5)));
+            new Tuple2<>(1, 5),
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5)));
 
     JavaDStream<Tuple2<Integer, Integer>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1428,7 +1424,7 @@ public void testPairTransform() {
     JavaPairDStream<Integer, Integer> sorted = pairStream.transformToPair(
         new Function<JavaPairRDD<Integer, Integer>, JavaPairRDD<Integer, Integer>>() {
           @Override
-          public JavaPairRDD<Integer, Integer> call(JavaPairRDD<Integer, Integer> in) throws Exception {
+          public JavaPairRDD<Integer, Integer> call(JavaPairRDD<Integer, Integer> in) {
             return in.sortByKey();
           }
         });
@@ -1444,15 +1440,15 @@ public JavaPairRDD<Integer, Integer> call(JavaPairRDD<Integer, Integer> in) thro
   public void testPairToNormalRDDTransform() {
     List<List<Tuple2<Integer, Integer>>> inputData = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(2, 5)),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(1, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(2, 5)),
         Arrays.asList(
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(1, 5)));
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(1, 5)));
 
     List<List<Integer>> expected = Arrays.asList(
         Arrays.asList(3,1,4,2),
@@ -1465,11 +1461,11 @@ public void testPairToNormalRDDTransform() {
     JavaDStream<Integer> firstParts = pairStream.transform(
         new Function<JavaPairRDD<Integer, Integer>, JavaRDD<Integer>>() {
           @Override
-          public JavaRDD<Integer> call(JavaPairRDD<Integer, Integer> in) throws Exception {
+          public JavaRDD<Integer> call(JavaPairRDD<Integer, Integer> in) {
             return in.map(new Function<Tuple2<Integer, Integer>, Integer>() {
               @Override
-              public Integer call(Tuple2<Integer, Integer> in) {
-                return in._1();
+              public Integer call(Tuple2<Integer, Integer> in2) {
+                return in2._1();
               }
             });
           }
@@ -1487,14 +1483,14 @@ public void testMapValues() {
     List<List<Tuple2<String, String>>> inputData = stringStringKVStream;
 
     List<List<Tuple2<String, String>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "DODGERS"),
-            new Tuple2<String, String>("california", "GIANTS"),
-            new Tuple2<String, String>("new york", "YANKEES"),
-            new Tuple2<String, String>("new york", "METS")),
-        Arrays.asList(new Tuple2<String, String>("california", "SHARKS"),
-            new Tuple2<String, String>("california", "DUCKS"),
-            new Tuple2<String, String>("new york", "RANGERS"),
-            new Tuple2<String, String>("new york", "ISLANDERS")));
+        Arrays.asList(new Tuple2<>("california", "DODGERS"),
+                      new Tuple2<>("california", "GIANTS"),
+                      new Tuple2<>("new york", "YANKEES"),
+                      new Tuple2<>("new york", "METS")),
+        Arrays.asList(new Tuple2<>("california", "SHARKS"),
+                      new Tuple2<>("california", "DUCKS"),
+                      new Tuple2<>("new york", "RANGERS"),
+                      new Tuple2<>("new york", "ISLANDERS")));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1502,8 +1498,8 @@ public void testMapValues() {
 
     JavaPairDStream<String, String> mapped = pairStream.mapValues(new Function<String, String>() {
       @Override
-      public String call(String s) throws Exception {
-        return s.toUpperCase();
+      public String call(String s) {
+        return s.toUpperCase(Locale.ENGLISH);
       }
     });
 
@@ -1519,22 +1515,22 @@ public void testFlatMapValues() {
     List<List<Tuple2<String, String>>> inputData = stringStringKVStream;
 
     List<List<Tuple2<String, String>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers1"),
-            new Tuple2<String, String>("california", "dodgers2"),
-            new Tuple2<String, String>("california", "giants1"),
-            new Tuple2<String, String>("california", "giants2"),
-            new Tuple2<String, String>("new york", "yankees1"),
-            new Tuple2<String, String>("new york", "yankees2"),
-            new Tuple2<String, String>("new york", "mets1"),
-            new Tuple2<String, String>("new york", "mets2")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks1"),
-            new Tuple2<String, String>("california", "sharks2"),
-            new Tuple2<String, String>("california", "ducks1"),
-            new Tuple2<String, String>("california", "ducks2"),
-            new Tuple2<String, String>("new york", "rangers1"),
-            new Tuple2<String, String>("new york", "rangers2"),
-            new Tuple2<String, String>("new york", "islanders1"),
-            new Tuple2<String, String>("new york", "islanders2")));
+        Arrays.asList(new Tuple2<>("california", "dodgers1"),
+                      new Tuple2<>("california", "dodgers2"),
+                      new Tuple2<>("california", "giants1"),
+                      new Tuple2<>("california", "giants2"),
+                      new Tuple2<>("new york", "yankees1"),
+                      new Tuple2<>("new york", "yankees2"),
+                      new Tuple2<>("new york", "mets1"),
+                      new Tuple2<>("new york", "mets2")),
+        Arrays.asList(new Tuple2<>("california", "sharks1"),
+                      new Tuple2<>("california", "sharks2"),
+                      new Tuple2<>("california", "ducks1"),
+                      new Tuple2<>("california", "ducks2"),
+                      new Tuple2<>("new york", "rangers1"),
+                      new Tuple2<>("new york", "rangers2"),
+                      new Tuple2<>("new york", "islanders1"),
+                      new Tuple2<>("new york", "islanders2")));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1545,7 +1541,7 @@ public void testFlatMapValues() {
         new Function<String, Iterable<String>>() {
           @Override
           public Iterable<String> call(String in) {
-            List<String> out = new ArrayList<String>();
+            List<String> out = new ArrayList<>();
             out.add(in + "1");
             out.add(in + "2");
             return out;
@@ -1562,29 +1558,29 @@ public Iterable<String> call(String in) {
   @Test
   public void testCoGroup() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks"),
-            new Tuple2<String, String>("new york", "rangers")));
+        Arrays.asList(new Tuple2<>("california", "dodgers"),
+                      new Tuple2<>("new york", "yankees")),
+        Arrays.asList(new Tuple2<>("california", "sharks"),
+                      new Tuple2<>("new york", "rangers")));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "giants"),
-            new Tuple2<String, String>("new york", "mets")),
-        Arrays.asList(new Tuple2<String, String>("california", "ducks"),
-            new Tuple2<String, String>("new york", "islanders")));
+        Arrays.asList(new Tuple2<>("california", "giants"),
+                      new Tuple2<>("new york", "mets")),
+        Arrays.asList(new Tuple2<>("california", "ducks"),
+                      new Tuple2<>("new york", "islanders")));
 
 
     List<List<Tuple2<String, Tuple2<List<String>, List<String>>>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("california",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("dodgers"), Arrays.asList("giants"))),
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("new york",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("yankees"), Arrays.asList("mets")))),
+            new Tuple2<>("california",
+                         new Tuple2<>(Arrays.asList("dodgers"), Arrays.asList("giants"))),
+            new Tuple2<>("new york",
+                         new Tuple2<>(Arrays.asList("yankees"), Arrays.asList("mets")))),
         Arrays.asList(
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("california",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("sharks"), Arrays.asList("ducks"))),
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("new york",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("rangers"), Arrays.asList("islanders")))));
+            new Tuple2<>("california",
+                         new Tuple2<>(Arrays.asList("sharks"), Arrays.asList("ducks"))),
+            new Tuple2<>("new york",
+                         new Tuple2<>(Arrays.asList("rangers"), Arrays.asList("islanders")))));
 
 
     JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream(
@@ -1620,29 +1616,29 @@ public void testCoGroup() {
   @Test
   public void testJoin() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks"),
-            new Tuple2<String, String>("new york", "rangers")));
+        Arrays.asList(new Tuple2<>("california", "dodgers"),
+                      new Tuple2<>("new york", "yankees")),
+        Arrays.asList(new Tuple2<>("california", "sharks"),
+                      new Tuple2<>("new york", "rangers")));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "giants"),
-            new Tuple2<String, String>("new york", "mets")),
-        Arrays.asList(new Tuple2<String, String>("california", "ducks"),
-            new Tuple2<String, String>("new york", "islanders")));
+        Arrays.asList(new Tuple2<>("california", "giants"),
+                      new Tuple2<>("new york", "mets")),
+        Arrays.asList(new Tuple2<>("california", "ducks"),
+                      new Tuple2<>("new york", "islanders")));
 
 
     List<List<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("dodgers", "giants")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("yankees", "mets"))),
+            new Tuple2<>("california",
+                         new Tuple2<>("dodgers", "giants")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("yankees", "mets"))),
         Arrays.asList(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("sharks", "ducks")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("rangers", "islanders"))));
+            new Tuple2<>("california",
+                         new Tuple2<>("sharks", "ducks")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("rangers", "islanders"))));
 
 
     JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream(
@@ -1664,13 +1660,13 @@ public void testJoin() {
   @Test
   public void testLeftOuterJoin() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks") ));
+        Arrays.asList(new Tuple2<>("california", "dodgers"),
+                      new Tuple2<>("new york", "yankees")),
+        Arrays.asList(new Tuple2<>("california", "sharks") ));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "giants") ),
-        Arrays.asList(new Tuple2<String, String>("new york", "islanders") )
+        Arrays.asList(new Tuple2<>("california", "giants") ),
+        Arrays.asList(new Tuple2<>("new york", "islanders") )
 
     );
 
@@ -1713,7 +1709,7 @@ public void testCheckpointMasterRecovery() throws InterruptedException {
     JavaDStream<String> stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<Integer> letterCount = stream.map(new Function<String, Integer>() {
       @Override
-      public Integer call(String s) throws Exception {
+      public Integer call(String s) {
         return s.length();
       }
     });
@@ -1752,6 +1748,7 @@ public void testContextGetOrCreate() throws InterruptedException {
     // (used to detect the new context)
     final AtomicBoolean newContextCreated = new AtomicBoolean(false);
     Function0<JavaStreamingContext> creatingFunc = new Function0<JavaStreamingContext>() {
+      @Override
       public JavaStreamingContext call() {
         newContextCreated.set(true);
         return new JavaStreamingContext(conf, Seconds.apply(1));
@@ -1765,20 +1762,20 @@ public JavaStreamingContext call() {
 
     newContextCreated.set(false);
     ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc,
-        new org.apache.hadoop.conf.Configuration(), true);
+        new Configuration(), true);
     Assert.assertTrue("new context not created", newContextCreated.get());
     ssc.stop();
 
     newContextCreated.set(false);
     ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc,
-        new org.apache.hadoop.conf.Configuration());
+        new Configuration());
     Assert.assertTrue("old context not recovered", !newContextCreated.get());
     ssc.stop();
 
     newContextCreated.set(false);
     JavaSparkContext sc = new JavaSparkContext(conf);
     ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc,
-        new org.apache.hadoop.conf.Configuration());
+        new Configuration());
     Assert.assertTrue("old context not recovered", !newContextCreated.get());
     ssc.stop();
   }
@@ -1800,7 +1797,7 @@ public void testCheckpointofIndividualStream() throws InterruptedException {
     JavaDStream stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream letterCount = stream.map(new Function<String, Integer>() {
       @Override
-      public Integer call(String s) throws Exception {
+      public Integer call(String s) {
         return s.length();
       }
     });
@@ -1818,29 +1815,26 @@ public Integer call(String s) throws Exception {
   // InputStream functionality is deferred to the existing Scala tests.
   @Test
   public void testSocketTextStream() {
-      JavaReceiverInputDStream<String> test = ssc.socketTextStream("localhost", 12345);
+    ssc.socketTextStream("localhost", 12345);
   }
 
   @Test
   public void testSocketString() {
-
-    class Converter implements Function<InputStream, Iterable<String>> {
-      public Iterable<String> call(InputStream in) throws IOException {
-        BufferedReader reader = new BufferedReader(new InputStreamReader(in));
-        List<String> out = new ArrayList<String>();
-        while (true) {
-          String line = reader.readLine();
-          if (line == null) { break; }
-          out.add(line);
-        }
-        return out;
-      }
-    }
-
-    JavaDStream<String> test = ssc.socketStream(
+    ssc.socketStream(
       "localhost",
       12345,
-      new Converter(),
+      new Function<InputStream, Iterable<String>>() {
+        @Override
+        public Iterable<String> call(InputStream in) throws IOException {
+          List<String> out = new ArrayList<>();
+          try (BufferedReader reader = new BufferedReader(new InputStreamReader(in))) {
+            for (String line; (line = reader.readLine()) != null;) {
+              out.add(line);
+            }
+          }
+          return out;
+        }
+      },
       StorageLevel.MEMORY_ONLY());
   }
 
@@ -1870,7 +1864,7 @@ public void testFileStream() throws IOException {
       TextInputFormat.class,
       new Function<Path, Boolean>() {
         @Override
-        public Boolean call(Path v1) throws Exception {
+        public Boolean call(Path v1) {
           return Boolean.TRUE;
         }
       },
@@ -1879,7 +1873,7 @@ public Boolean call(Path v1) throws Exception {
     JavaDStream<String> test = inputStream.map(
       new Function<Tuple2<LongWritable, Text>, String>() {
         @Override
-        public String call(Tuple2<LongWritable, Text> v1) throws Exception {
+        public String call(Tuple2<LongWritable, Text> v1) {
           return v1._2().toString();
         }
     });
@@ -1892,19 +1886,15 @@ public String call(Tuple2<LongWritable, Text> v1) throws Exception {
 
   @Test
   public void testRawSocketStream() {
-    JavaReceiverInputDStream<String> test = ssc.rawSocketStream("localhost", 12345);
+    ssc.rawSocketStream("localhost", 12345);
   }
 
-  private List<List<String>> fileTestPrepare(File testDir) throws IOException {
+  private static List<List<String>> fileTestPrepare(File testDir) throws IOException {
     File existingFile = new File(testDir, "0");
     Files.write("0\n", existingFile, Charset.forName("UTF-8"));
-    assertTrue(existingFile.setLastModified(1000) && existingFile.lastModified() == 1000);
-
-    List<List<String>> expected = Arrays.asList(
-      Arrays.asList("0")
-    );
-
-    return expected;
+    Assert.assertTrue(existingFile.setLastModified(1000));
+    Assert.assertEquals(1000, existingFile.lastModified());
+    return Arrays.asList(Arrays.asList("0"));
   }
 
   @SuppressWarnings("unchecked")
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
index 1b0787fe69dec..ec2bffd6a5b97 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
@@ -36,7 +36,6 @@
 import java.io.Serializable;
 import java.net.ConnectException;
 import java.net.Socket;
-import java.util.concurrent.atomic.AtomicInteger;
 import java.util.concurrent.atomic.AtomicLong;
 
 public class JavaReceiverAPISuite implements Serializable {
@@ -64,16 +63,16 @@ public void testReceiver() throws InterruptedException {
         ssc.receiverStream(new JavaSocketReceiver("localhost", server.port()));
       JavaDStream<String> mapped = input.map(new Function<String, String>() {
         @Override
-        public String call(String v1) throws Exception {
+        public String call(String v1) {
           return v1 + ".";
         }
       });
       mapped.foreachRDD(new Function<JavaRDD<String>, Void>() {
         @Override
-        public Void call(JavaRDD<String> rdd) throws Exception {
-        long count = rdd.count();
-        dataCounter.addAndGet(count);
-        return null;
+        public Void call(JavaRDD<String> rdd) {
+          long count = rdd.count();
+          dataCounter.addAndGet(count);
+          return null;
         }
       });
 
@@ -83,7 +82,7 @@ public Void call(JavaRDD<String> rdd) throws Exception {
 
       Thread.sleep(200);
       for (int i = 0; i < 6; i++) {
-        server.send("" + i + "\n"); // \n to make sure these are separate lines
+        server.send(i + "\n"); // \n to make sure these are separate lines
         Thread.sleep(100);
       }
       while (dataCounter.get() == 0 && System.currentTimeMillis() - startTime < timeout) {
@@ -95,50 +94,49 @@ public Void call(JavaRDD<String> rdd) throws Exception {
       server.stop();
     }
   }
-}
 
-class JavaSocketReceiver extends Receiver<String> {
+  private static class JavaSocketReceiver extends Receiver<String> {
 
-  String host = null;
-  int port = -1;
+    String host = null;
+    int port = -1;
 
-  public JavaSocketReceiver(String host_ , int port_) {
-    super(StorageLevel.MEMORY_AND_DISK());
-    host = host_;
-    port = port_;
-  }
+    JavaSocketReceiver(String host_ , int port_) {
+      super(StorageLevel.MEMORY_AND_DISK());
+      host = host_;
+      port = port_;
+    }
 
-  @Override
-  public void onStart() {
-    new Thread()  {
-      @Override public void run() {
-        receive();
-      }
-    }.start();
-  }
+    @Override
+    public void onStart() {
+      new Thread()  {
+        @Override public void run() {
+          receive();
+        }
+      }.start();
+    }
 
-  @Override
-  public void onStop() {
-  }
+    @Override
+    public void onStop() {
+    }
 
-  private void receive() {
-    Socket socket = null;
-    try {
-      socket = new Socket(host, port);
-      BufferedReader in = new BufferedReader(new InputStreamReader(socket.getInputStream()));
-      String userInput;
-      while ((userInput = in.readLine()) != null) {
-        store(userInput);
+    private void receive() {
+      try {
+        Socket socket = new Socket(host, port);
+        BufferedReader in = new BufferedReader(new InputStreamReader(socket.getInputStream()));
+        String userInput;
+        while ((userInput = in.readLine()) != null) {
+          store(userInput);
+        }
+        in.close();
+        socket.close();
+      } catch(ConnectException ce) {
+        ce.printStackTrace();
+        restart("Could not connect", ce);
+      } catch(Throwable t) {
+        t.printStackTrace();
+        restart("Error receiving data", t);
       }
-      in.close();
-      socket.close();
-    } catch(ConnectException ce) {
-      ce.printStackTrace();
-      restart("Could not connect", ce);
-    } catch(Throwable t) {
-      t.printStackTrace();
-      restart("Error receiving data", t);
     }
   }
-}
 
+}

From f4a22808e03fa12bfe1bfc82cf713cfda7e063a9 Mon Sep 17 00:00:00 2001
From: JihongMa <linlin200605@gmail.com>
Date: Sat, 12 Sep 2015 10:17:15 -0700
Subject: [PATCH 1408/1454] [SPARK-6548] Adding stddev to DataFrame functions

Adding STDDEV support for DataFrame using 1-pass online /parallel algorithm to compute variance. Please review the code change.

Author: JihongMa <linlin200605@gmail.com>
Author: Jihong MA <linlin200605@gmail.com>
Author: Jihong MA <jihongma@jihongs-mbp.usca.ibm.com>
Author: Jihong MA <jihongma@Jihongs-MacBook-Pro.local>

Closes #6297 from JihongMA/SPARK-SQL.
---
 R/pkg/inst/tests/test_sparkSQL.R              |   2 +-
 python/pyspark/sql/dataframe.py               |  36 +--
 .../catalyst/analysis/FunctionRegistry.scala  |   3 +
 .../catalyst/analysis/HiveTypeCoercion.scala  |   3 +
 .../spark/sql/catalyst/dsl/package.scala      |   3 +
 .../expressions/aggregate/functions.scala     | 143 ++++++++++
 .../expressions/aggregate/utils.scala         |  18 ++
 .../sql/catalyst/expressions/aggregates.scala | 245 ++++++++++++++++++
 .../org/apache/spark/sql/DataFrame.scala      |   6 +-
 .../org/apache/spark/sql/GroupedData.scala    |  39 +++
 .../org/apache/spark/sql/functions.scala      |  27 ++
 .../apache/spark/sql/JavaDataFrameSuite.java  |   1 +
 .../spark/sql/DataFrameAggregateSuite.scala   |  33 +++
 .../org/apache/spark/sql/DataFrameSuite.scala |   2 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  42 ++-
 .../execution/AggregationQuerySuite.scala     |  35 ---
 16 files changed, 574 insertions(+), 64 deletions(-)

diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 1ccfde59176f5..98d4402d368e1 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -1147,7 +1147,7 @@ test_that("describe() and summarize() on a DataFrame", {
   stats <- describe(df, "age")
   expect_equal(collect(stats)[1, "summary"], "count")
   expect_equal(collect(stats)[2, "age"], "24.5")
-  expect_equal(collect(stats)[3, "age"], "5.5")
+  expect_equal(collect(stats)[3, "age"], "7.7781745930520225")
   stats <- describe(df)
   expect_equal(collect(stats)[4, "name"], "Andy")
   expect_equal(collect(stats)[5, "age"], "30")
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index c5bf55791240b..fb995fa3a76b5 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -653,25 +653,25 @@ def describe(self, *cols):
         guarantee about the backward compatibility of the schema of the resulting DataFrame.
 
         >>> df.describe().show()
-        +-------+---+
-        |summary|age|
-        +-------+---+
-        |  count|  2|
-        |   mean|3.5|
-        | stddev|1.5|
-        |    min|  2|
-        |    max|  5|
-        +-------+---+
+        +-------+------------------+
+        |summary|               age|
+        +-------+------------------+
+        |  count|                 2|
+        |   mean|               3.5|
+        | stddev|2.1213203435596424|
+        |    min|                 2|
+        |    max|                 5|
+        +-------+------------------+
         >>> df.describe(['age', 'name']).show()
-        +-------+---+-----+
-        |summary|age| name|
-        +-------+---+-----+
-        |  count|  2|    2|
-        |   mean|3.5| null|
-        | stddev|1.5| null|
-        |    min|  2|Alice|
-        |    max|  5|  Bob|
-        +-------+---+-----+
+        +-------+------------------+-----+
+        |summary|               age| name|
+        +-------+------------------+-----+
+        |  count|                 2|    2|
+        |   mean|               3.5| null|
+        | stddev|2.1213203435596424| null|
+        |    min|                 2|Alice|
+        |    max|                 5|  Bob|
+        +-------+------------------+-----+
         """
         if len(cols) == 1 and isinstance(cols[0], list):
             cols = cols[0]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index cd5a90d788151..11b4866bf264b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -168,6 +168,9 @@ object FunctionRegistry {
     expression[Last]("last"),
     expression[Max]("max"),
     expression[Min]("min"),
+    expression[Stddev]("stddev"),
+    expression[StddevPop]("stddev_pop"),
+    expression[StddevSamp]("stddev_samp"),
     expression[Sum]("sum"),
 
     // string functions
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 87c11abbad490..87a3845b2d9e5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -297,6 +297,9 @@ object HiveTypeCoercion {
       case Sum(e @ StringType()) => Sum(Cast(e, DoubleType))
       case SumDistinct(e @ StringType()) => Sum(Cast(e, DoubleType))
       case Average(e @ StringType()) => Average(Cast(e, DoubleType))
+      case Stddev(e @ StringType()) => Stddev(Cast(e, DoubleType))
+      case StddevPop(e @ StringType()) => StddevPop(Cast(e, DoubleType))
+      case StddevSamp(e @ StringType()) => StddevSamp(Cast(e, DoubleType))
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index a7e3a49327655..699c4cc63d09a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -159,6 +159,9 @@ package object dsl {
     def lower(e: Expression): Expression = Lower(e)
     def sqrt(e: Expression): Expression = Sqrt(e)
     def abs(e: Expression): Expression = Abs(e)
+    def stddev(e: Expression): Expression = Stddev(e)
+    def stddev_pop(e: Expression): Expression = StddevPop(e)
+    def stddev_samp(e: Expression): Expression = StddevSamp(e)
 
     implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s: String = sym.name }
     // TODO more implicit class for literal?
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
index a73024d6adba1..02cd0ac0db118 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
@@ -249,6 +249,149 @@ case class Min(child: Expression) extends AlgebraicAggregate {
   override val evaluateExpression = min
 }
 
+// Compute the sample standard deviation of a column
+case class Stddev(child: Expression) extends StddevAgg(child) {
+
+  override def isSample: Boolean = true
+  override def prettyName: String = "stddev"
+}
+
+// Compute the population standard deviation of a column
+case class StddevPop(child: Expression) extends StddevAgg(child) {
+
+  override def isSample: Boolean = false
+  override def prettyName: String = "stddev_pop"
+}
+
+// Compute the sample standard deviation of a column
+case class StddevSamp(child: Expression) extends StddevAgg(child) {
+
+  override def isSample: Boolean = true
+  override def prettyName: String = "stddev_samp"
+}
+
+// Compute standard deviation based on online algorithm specified here:
+// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+abstract class StddevAgg(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  def isSample: Boolean
+
+  // Return data type.
+  override def dataType: DataType = resultType
+
+  // Expected input data type.
+  // TODO: Right now, we replace old aggregate functions (based on AggregateExpression1) to the
+  // new version at planning time (after analysis phase). For now, NullType is added at here
+  // to make it resolved when we have cases like `select stddev(null)`.
+  // We can use our analyzer to cast NullType to the default data type of the NumericType once
+  // we remove the old aggregate functions. Then, we will not need NullType at here.
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(NumericType, NullType))
+
+  private val resultType = DoubleType
+
+  private val preCount = AttributeReference("preCount", resultType)()
+  private val currentCount = AttributeReference("currentCount", resultType)()
+  private val preAvg = AttributeReference("preAvg", resultType)()
+  private val currentAvg = AttributeReference("currentAvg", resultType)()
+  private val currentMk = AttributeReference("currentMk", resultType)()
+
+  override val bufferAttributes = preCount :: currentCount :: preAvg ::
+                                  currentAvg :: currentMk :: Nil
+
+  override val initialValues = Seq(
+    /* preCount = */ Cast(Literal(0), resultType),
+    /* currentCount = */ Cast(Literal(0), resultType),
+    /* preAvg = */ Cast(Literal(0), resultType),
+    /* currentAvg = */ Cast(Literal(0), resultType),
+    /* currentMk = */ Cast(Literal(0), resultType)
+  )
+
+  override val updateExpressions = {
+
+    // update average
+    // avg = avg + (value - avg)/count
+    def avgAdd: Expression = {
+      currentAvg + ((Cast(child, resultType) - currentAvg) / currentCount)
+    }
+
+    // update sum of square of difference from mean
+    // Mk = Mk + (value - preAvg) * (value - updatedAvg)
+    def mkAdd: Expression = {
+      val delta1 = Cast(child, resultType) - preAvg
+      val delta2 = Cast(child, resultType) - currentAvg
+      currentMk + (delta1 * delta2)
+    }
+
+    Seq(
+      /* preCount = */ If(IsNull(child), preCount, currentCount),
+      /* currentCount = */ If(IsNull(child), currentCount,
+                           Add(currentCount, Cast(Literal(1), resultType))),
+      /* preAvg = */ If(IsNull(child), preAvg, currentAvg),
+      /* currentAvg = */ If(IsNull(child), currentAvg, avgAdd),
+      /* currentMk = */ If(IsNull(child), currentMk, mkAdd)
+    )
+  }
+
+  override val mergeExpressions = {
+
+    // count merge
+    def countMerge: Expression = {
+      currentCount.left + currentCount.right
+    }
+
+    // average merge
+    def avgMerge: Expression = {
+      ((currentAvg.left * preCount) + (currentAvg.right * currentCount.right)) /
+      (preCount + currentCount.right)
+    }
+
+    // update sum of square differences
+    def mkMerge: Expression = {
+      val avgDelta = currentAvg.right - preAvg
+      val mkDelta = (avgDelta * avgDelta) * (preCount * currentCount.right) /
+        (preCount + currentCount.right)
+
+      currentMk.left + currentMk.right + mkDelta
+    }
+
+    Seq(
+      /* preCount = */ If(IsNull(currentCount.left),
+                         Cast(Literal(0), resultType), currentCount.left),
+      /* currentCount = */ If(IsNull(currentCount.left), currentCount.right,
+                             If(IsNull(currentCount.right), currentCount.left, countMerge)),
+      /* preAvg = */ If(IsNull(currentAvg.left), Cast(Literal(0), resultType), currentAvg.left),
+      /* currentAvg = */ If(IsNull(currentAvg.left), currentAvg.right,
+                           If(IsNull(currentAvg.right), currentAvg.left, avgMerge)),
+      /* currentMk = */ If(IsNull(currentMk.left), currentMk.right,
+                          If(IsNull(currentMk.right), currentMk.left, mkMerge))
+    )
+  }
+
+  override val evaluateExpression = {
+    // when currentCount == 0, return null
+    // when currentCount == 1, return 0
+    // when currentCount >1
+    // stddev_samp = sqrt (currentMk/(currentCount -1))
+    // stddev_pop = sqrt (currentMk/currentCount)
+    val varCol = {
+      if (isSample) {
+        currentMk / Cast((currentCount - Cast(Literal(1), resultType)), resultType)
+      }
+      else {
+        currentMk / currentCount
+      }
+    }
+
+    If(EqualTo(currentCount, Cast(Literal(0), resultType)), Cast(Literal(null), resultType),
+      If(EqualTo(currentCount, Cast(Literal(1), resultType)), Cast(Literal(0), resultType),
+        Cast(Sqrt(varCol), resultType)))
+  }
+}
+
 case class Sum(child: Expression) extends AlgebraicAggregate {
 
   override def children: Seq[Expression] = child :: Nil
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
index 4a43318a95490..ce3dddad87f55 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
@@ -85,6 +85,24 @@ object Utils {
             mode = aggregate.Complete,
             isDistinct = false)
 
+        case expressions.Stddev(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Stddev(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.StddevPop(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.StddevPop(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.StddevSamp(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.StddevSamp(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
         case expressions.Sum(child) =>
           aggregate.AggregateExpression2(
             aggregateFunction = aggregate.Sum(child),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 5e8298aaaa9cb..f1c47f39043c8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -691,3 +691,248 @@ case class LastFunction(expr: Expression, base: AggregateExpression1) extends Ag
     result
   }
 }
+
+// Compute standard deviation based on online algorithm specified here:
+// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+abstract class StddevAgg1(child: Expression) extends UnaryExpression with PartialAggregate1 {
+  override def nullable: Boolean = true
+  override def dataType: DataType = DoubleType
+
+  def isSample: Boolean
+
+  override def asPartial: SplitEvaluation = {
+    val partialStd = Alias(ComputePartialStd(child), "PartialStddev")()
+    SplitEvaluation(MergePartialStd(partialStd.toAttribute, isSample), partialStd :: Nil)
+  }
+
+  override def newInstance(): StddevFunction = new StddevFunction(child, this, isSample)
+
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForNumericExpr(child.dataType, "function stddev")
+
+}
+
+// Compute the sample standard deviation of a column
+case class Stddev(child: Expression) extends StddevAgg1(child) {
+
+  override def toString: String = s"STDDEV($child)"
+  override def isSample: Boolean = true
+}
+
+// Compute the population standard deviation of a column
+case class StddevPop(child: Expression) extends StddevAgg1(child) {
+
+  override def toString: String = s"STDDEV_POP($child)"
+  override def isSample: Boolean = false
+}
+
+// Compute the sample standard deviation of a column
+case class StddevSamp(child: Expression) extends StddevAgg1(child) {
+
+  override def toString: String = s"STDDEV_SAMP($child)"
+  override def isSample: Boolean = true
+}
+
+case class ComputePartialStd(child: Expression) extends UnaryExpression with AggregateExpression1 {
+    def this() = this(null)
+
+    override def children: Seq[Expression] = child :: Nil
+    override def nullable: Boolean = false
+    override def dataType: DataType = ArrayType(DoubleType)
+    override def toString: String = s"computePartialStddev($child)"
+    override def newInstance(): ComputePartialStdFunction =
+      new ComputePartialStdFunction(child, this)
+}
+
+case class ComputePartialStdFunction (
+    expr: Expression,
+    base: AggregateExpression1
+) extends AggregateFunction1 {
+  def this() = this(null, null)  // Required for serialization
+
+  private val computeType = DoubleType
+  private val zero = Cast(Literal(0), computeType)
+  private var partialCount: Long = 0L
+
+  // the mean of data processed so far
+  private val partialAvg: MutableLiteral = MutableLiteral(zero.eval(null), computeType)
+
+  // update average based on this formula:
+  // avg = avg + (value - avg)/count
+  private def avgAddFunction (value: Literal): Expression = {
+    val delta = Subtract(Cast(value, computeType), partialAvg)
+    Add(partialAvg, Divide(delta, Cast(Literal(partialCount), computeType)))
+  }
+
+  // the sum of squares of difference from mean
+  private val partialMk: MutableLiteral = MutableLiteral(zero.eval(null), computeType)
+
+  // update sum of square of difference from mean based on following formula:
+  // Mk = Mk + (value - preAvg) * (value - updatedAvg)
+  private def mkAddFunction(value: Literal, prePartialAvg: MutableLiteral): Expression = {
+    val delta1 = Subtract(Cast(value, computeType), prePartialAvg)
+    val delta2 = Subtract(Cast(value, computeType), partialAvg)
+    Add(partialMk, Multiply(delta1, delta2))
+  }
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      val exprValue = Literal.create(evaluatedExpr, expr.dataType)
+      val prePartialAvg = partialAvg.copy()
+      partialCount += 1
+      partialAvg.update(avgAddFunction(exprValue), input)
+      partialMk.update(mkAddFunction(exprValue, prePartialAvg), input)
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    new GenericArrayData(Array(Cast(Literal(partialCount), computeType).eval(null),
+        partialAvg.eval(null),
+        partialMk.eval(null)))
+  }
+}
+
+case class MergePartialStd(
+    child: Expression,
+    isSample: Boolean
+) extends UnaryExpression with AggregateExpression1 {
+  def this() = this(null, false) // required for serialization
+
+  override def children: Seq[Expression] = child:: Nil
+  override def nullable: Boolean = false
+  override def dataType: DataType = DoubleType
+  override def toString: String = s"MergePartialStd($child)"
+  override def newInstance(): MergePartialStdFunction = {
+    new MergePartialStdFunction(child, this, isSample)
+  }
+}
+
+case class MergePartialStdFunction(
+    expr: Expression,
+    base: AggregateExpression1,
+    isSample: Boolean
+) extends AggregateFunction1 {
+  def this() = this (null, null, false) // Required for serialization
+
+  private val computeType = DoubleType
+  private val zero = Cast(Literal(0), computeType)
+  private val combineCount = MutableLiteral(zero.eval(null), computeType)
+  private val combineAvg = MutableLiteral(zero.eval(null), computeType)
+  private val combineMk = MutableLiteral(zero.eval(null), computeType)
+
+  private def avgUpdateFunction(preCount: Expression,
+                                partialCount: Expression,
+                                partialAvg: Expression): Expression = {
+    Divide(Add(Multiply(combineAvg, preCount),
+               Multiply(partialAvg, partialCount)),
+           Add(preCount, partialCount))
+  }
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input).asInstanceOf[ArrayData]
+
+    if (evaluatedExpr != null) {
+      val exprValue = evaluatedExpr.toArray(computeType)
+      val (partialCount, partialAvg, partialMk) =
+        (Literal.create(exprValue(0), computeType),
+         Literal.create(exprValue(1), computeType),
+         Literal.create(exprValue(2), computeType))
+
+      if (Cast(partialCount, LongType).eval(null).asInstanceOf[Long] > 0) {
+        val preCount = combineCount.copy()
+        combineCount.update(Add(combineCount, partialCount), input)
+
+        val preAvg = combineAvg.copy()
+        val avgDelta = Subtract(partialAvg, preAvg)
+        val mkDelta = Multiply(Multiply(avgDelta, avgDelta),
+                               Divide(Multiply(preCount, partialCount),
+                                      combineCount))
+
+        // update average based on following formula
+        // (combineAvg * preCount + partialAvg * partialCount) / (preCount + partialCount)
+        combineAvg.update(avgUpdateFunction(preCount, partialCount, partialAvg), input)
+
+        // update sum of square differences from mean based on following formula
+        // (combineMk + partialMk + (avgDelta * avgDelta) * (preCount * partialCount/combineCount)
+        combineMk.update(Add(combineMk, Add(partialMk, mkDelta)), input)
+      }
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val count: Long = Cast(combineCount, LongType).eval(null).asInstanceOf[Long]
+
+    if (count == 0) null
+    else if (count < 2) zero.eval(null)
+    else {
+      // when total count > 2
+      // stddev_samp = sqrt (combineMk/(combineCount -1))
+      // stddev_pop = sqrt (combineMk/combineCount)
+      val varCol = {
+        if (isSample) {
+          Divide(combineMk, Cast(Literal(count - 1), computeType))
+        }
+        else {
+          Divide(combineMk, Cast(Literal(count), computeType))
+        }
+      }
+      Sqrt(varCol).eval(null)
+    }
+  }
+}
+
+case class StddevFunction(
+    expr: Expression,
+    base: AggregateExpression1,
+    isSample: Boolean
+) extends AggregateFunction1 {
+
+  def this() = this(null, null, false) // Required for serialization
+
+  private val computeType = DoubleType
+  private var curCount: Long = 0L
+  private val zero = Cast(Literal(0), computeType)
+  private val curAvg = MutableLiteral(zero.eval(null), computeType)
+  private val curMk = MutableLiteral(zero.eval(null), computeType)
+
+  private def curAvgAddFunction(value: Literal): Expression = {
+    val delta = Subtract(Cast(value, computeType), curAvg)
+    Add(curAvg, Divide(delta, Cast(Literal(curCount), computeType)))
+  }
+  private def curMkAddFunction(value: Literal, preAvg: MutableLiteral): Expression = {
+    val delta1 = Subtract(Cast(value, computeType), preAvg)
+    val delta2 = Subtract(Cast(value, computeType), curAvg)
+    Add(curMk, Multiply(delta1, delta2))
+  }
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      val preAvg: MutableLiteral = curAvg.copy()
+      val exprValue = Literal.create(evaluatedExpr, expr.dataType)
+      curCount += 1L
+      curAvg.update(curAvgAddFunction(exprValue), input)
+      curMk.update(curMkAddFunction(exprValue, preAvg), input)
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    if (curCount == 0) null
+    else if (curCount < 2) zero.eval(null)
+    else {
+      // when total count > 2,
+      // stddev_samp = sqrt(curMk/(curCount - 1))
+      // stddev_pop = sqrt(curMk/curCount)
+      val varCol = {
+        if (isSample) {
+          Divide(curMk, Cast(Literal(curCount - 1), computeType))
+        }
+        else {
+          Divide(curMk, Cast(Literal(curCount), computeType))
+        }
+      }
+      Sqrt(varCol).eval(null)
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 791c10c3d7ce7..1a687b2374f14 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1288,15 +1288,11 @@ class DataFrame private[sql](
   @scala.annotation.varargs
   def describe(cols: String*): DataFrame = {
 
-    // TODO: Add stddev as an expression, and remove it from here.
-    def stddevExpr(expr: Expression): Expression =
-      Sqrt(Subtract(Average(Multiply(expr, expr)), Multiply(Average(expr), Average(expr))))
-
     // The list of summary statistics to compute, in the form of expressions.
     val statistics = List[(String, Expression => Expression)](
       "count" -> Count,
       "mean" -> Average,
-      "stddev" -> stddevExpr,
+      "stddev" -> Stddev,
       "min" -> Min,
       "max" -> Max)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
index ee31d83cce42c..102b802ad0a0a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
@@ -124,6 +124,9 @@ class GroupedData protected[sql](
       case "avg" | "average" | "mean" => Average
       case "max" => Max
       case "min" => Min
+      case "stddev" => Stddev
+      case "stddev_pop" => StddevPop
+      case "stddev_samp" => StddevSamp
       case "sum" => Sum
       case "count" | "size" =>
         // Turn count(*) into count(1)
@@ -283,6 +286,42 @@ class GroupedData protected[sql](
     aggregateNumericColumns(colNames : _*)(Min)
   }
 
+  /**
+   * Compute the sample standard deviation for each numeric columns for each group.
+   * The resulting [[DataFrame]] will also contain the grouping columns.
+   * When specified columns are given, only compute the stddev for them.
+   *
+   * @since 1.6.0
+   */
+  @scala.annotation.varargs
+  def stddev(colNames: String*): DataFrame = {
+    aggregateNumericColumns(colNames : _*)(Stddev)
+  }
+
+  /**
+   * Compute the population standard deviation for each numeric columns for each group.
+   * The resulting [[DataFrame]] will also contain the grouping columns.
+   * When specified columns are given, only compute the stddev for them.
+   *
+   * @since 1.6.0
+   */
+  @scala.annotation.varargs
+  def stddev_pop(colNames: String*): DataFrame = {
+    aggregateNumericColumns(colNames : _*)(StddevPop)
+  }
+
+  /**
+   * Compute the sample standard deviation for each numeric columns for each group.
+   * The resulting [[DataFrame]] will also contain the grouping columns.
+   * When specified columns are given, only compute the stddev for them.
+   *
+   * @since 1.6.0
+   */
+  @scala.annotation.varargs
+  def stddev_samp(colNames: String*): DataFrame = {
+    aggregateNumericColumns(colNames : _*)(StddevSamp)
+  }
+
   /**
    * Compute the sum for each numeric columns for each group.
    * The resulting [[DataFrame]] will also contain the grouping columns.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 435e6319a64c4..60d9c509104d5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -294,6 +294,33 @@ object functions {
    */
   def min(columnName: String): Column = min(Column(columnName))
 
+  /**
+   * Aggregate function: returns the unbiased sample standard deviation
+   * of the expression in a group.
+   *
+   * @group agg_funcs
+   * @since 1.6.0
+   */
+  def stddev(e: Column): Column = Stddev(e.expr)
+
+  /**
+   * Aggregate function: returns the population standard deviation of
+   * the expression in a group.
+   *
+   * @group agg_funcs
+   * @since 1.6.0
+   */
+  def stddev_pop(e: Column): Column = StddevPop(e.expr)
+
+  /**
+   * Aggregate function: returns the unbiased sample standard deviation of
+   * the expression in a group.
+   *
+   * @group agg_funcs
+   * @since 1.6.0
+   */
+  def stddev_samp(e: Column): Column = StddevSamp(e.expr)
+
   /**
    * Aggregate function: returns the sum of all values in the expression.
    *
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index d981ce947f435..5f9abd4999ce0 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -90,6 +90,7 @@ public void testVarargMethods() {
     df.groupBy().mean("key");
     df.groupBy().max("key");
     df.groupBy().min("key");
+    df.groupBy().stddev("key");
     df.groupBy().sum("key");
 
     // Varargs in column expressions
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index c0950b09b14ad..f5ef9ffd7f4f2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -175,6 +175,39 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
       Row(0, null))
   }
 
+  test("stddev") {
+    val testData2ADev = math.sqrt(4/5.0)
+
+    checkAnswer(
+      testData2.agg(stddev('a)),
+      Row(testData2ADev))
+
+    checkAnswer(
+      testData2.agg(stddev_pop('a)),
+      Row(math.sqrt(4/6.0)))
+
+    checkAnswer(
+      testData2.agg(stddev_samp('a)),
+      Row(testData2ADev))
+  }
+
+  test("zero stddev") {
+    val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
+    assert(emptyTableData.count() == 0)
+
+    checkAnswer(
+    emptyTableData.agg(stddev('a)),
+    Row(null))
+
+    checkAnswer(
+    emptyTableData.agg(stddev_pop('a)),
+    Row(null))
+
+    checkAnswer(
+    emptyTableData.agg(stddev_samp('a)),
+    Row(null))
+  }
+
   test("zero sum") {
     val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index dbed4fc247140..c167999af580e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -436,7 +436,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     val describeResult = Seq(
       Row("count", "4", "4"),
       Row("mean", "33.0", "178.0"),
-      Row("stddev", "16.583123951777", "10.0"),
+      Row("stddev", "19.148542155126762", "11.547005383792516"),
       Row("min", "16", "164"),
       Row("max", "60", "192"))
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 664b7a1512faf..962b100b532c9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -328,6 +328,13 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       testCodeGen(
         "SELECT min(key) FROM testData3x",
         Row(1) :: Nil)
+      // STDDEV
+      testCodeGen(
+        "SELECT a, stddev(b), stddev_pop(b) FROM testData2 GROUP BY a",
+        (1 to 3).map(i => Row(i, math.sqrt(0.5), math.sqrt(0.25))))
+      testCodeGen(
+        "SELECT stddev(b), stddev_pop(b), stddev_samp(b) FROM testData2",
+        Row(math.sqrt(1.5 / 5), math.sqrt(1.5 / 6), math.sqrt(1.5 / 5)) :: Nil)
       // Some combinations.
       testCodeGen(
         """
@@ -348,8 +355,8 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
         Row(100, 1, 50.5, 300, 100) :: Nil)
       // Aggregate with Code generation handling all null values
       testCodeGen(
-        "SELECT  sum('a'), avg('a'), count(null) FROM testData",
-        Row(null, null, 0) :: Nil)
+        "SELECT  sum('a'), avg('a'), stddev('a'), count(null) FROM testData",
+        Row(null, null, null, 0) :: Nil)
     } finally {
       sqlContext.dropTempTable("testData3x")
       sqlContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue)
@@ -515,8 +522,8 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("aggregates with nulls") {
     checkAnswer(
-      sql("SELECT MIN(a), MAX(a), AVG(a), SUM(a), COUNT(a) FROM nullInts"),
-      Row(1, 3, 2, 6, 3)
+      sql("SELECT MIN(a), MAX(a), AVG(a), STDDEV(a), SUM(a), COUNT(a) FROM nullInts"),
+      Row(1, 3, 2, 1, 6, 3)
     )
   }
 
@@ -722,6 +729,33 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     }
   }
 
+  test("stddev") {
+    checkAnswer(
+      sql("SELECT STDDEV(a) FROM testData2"),
+      Row(math.sqrt(4/5.0))
+    )
+  }
+
+  test("stddev_pop") {
+    checkAnswer(
+      sql("SELECT STDDEV_POP(a) FROM testData2"),
+      Row(math.sqrt(4/6.0))
+    )
+  }
+
+  test("stddev_samp") {
+    checkAnswer(
+      sql("SELECT STDDEV_SAMP(a) FROM testData2"),
+      Row(math.sqrt(4/5.0))
+    )
+  }
+
+  test("stddev agg") {
+    checkAnswer(
+      sql("SELECT a, stddev(b), stddev_pop(b), stddev_samp(b) FROM testData2 GROUP BY a"),
+      (1 to 3).map(i => Row(i, math.sqrt(1/2.0), math.sqrt(1/4.0), math.sqrt(1/2.0))))
+  }
+
   test("inner join where, one match per row") {
     checkAnswer(
       sql("SELECT * FROM upperCaseData JOIN lowerCaseData WHERE n = N"),
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index b126ec455fc69..a73b1bd52c09f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -507,41 +507,6 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Te
       }.getMessage
       assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
     }
-
-    // TODO: once we support Hive UDAF in the new interface,
-    // we can remove the following two tests.
-    withSQLConf("spark.sql.useAggregate2" -> "true") {
-      val errorMessage = intercept[AnalysisException] {
-        sqlContext.sql(
-          """
-            |SELECT
-            |  key,
-            |  mydoublesum(value + 1.5 * key),
-            |  stddev_samp(value)
-            |FROM agg1
-            |GROUP BY key
-          """.stripMargin).collect()
-      }.getMessage
-      assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
-
-      // This will fall back to the old aggregate
-      val newAggregateOperators = sqlContext.sql(
-        """
-          |SELECT
-          |  key,
-          |  sum(value + 1.5 * key),
-          |  stddev_samp(value)
-          |FROM agg1
-          |GROUP BY key
-        """.stripMargin).queryExecution.executedPlan.collect {
-        case agg: aggregate.SortBasedAggregate => agg
-        case agg: aggregate.TungstenAggregate => agg
-      }
-      val message =
-        "We should fallback to the old aggregation code path if " +
-          "there is any aggregate function that cannot be converted to the new interface."
-      assert(newAggregateOperators.isEmpty, message)
-    }
   }
 }
 

From b3a7480ab0821ab38f710de96e3ac4a13f62dbca Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 12 Sep 2015 16:23:55 -0700
Subject: [PATCH 1409/1454] [SPARK-10330] Add Scalastyle rule to require use of
 SparkHadoopUtil JobContext methods

This is a followup to #8499 which adds a Scalastyle rule to mandate the use of SparkHadoopUtil's JobContext accessor methods and fixes the existing violations.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8521 from JoshRosen/SPARK-10330-part2.
---
 .../src/main/scala/org/apache/spark/SparkContext.scala |  6 +++---
 .../org/apache/spark/deploy/SparkHadoopUtil.scala      |  4 ++++
 .../scala/org/apache/spark/rdd/PairRDDFunctions.scala  |  8 +++++---
 .../scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala   |  2 +-
 core/src/test/scala/org/apache/spark/FileSuite.scala   |  6 ++++--
 .../org/apache/spark/examples/CassandraCQLTest.scala   |  3 +++
 .../org/apache/spark/examples/CassandraTest.scala      |  2 ++
 scalastyle-config.xml                                  |  8 ++++++++
 .../sql/execution/datasources/WriterContainer.scala    |  8 ++++++--
 .../sql/execution/datasources/json/JSONRelation.scala  |  2 +-
 .../datasources/parquet/CatalystReadSupport.scala      |  6 +++++-
 .../parquet/DirectParquetOutputCommitter.scala         |  6 +++++-
 .../datasources/parquet/ParquetRelation.scala          | 10 +++++++---
 .../datasources/parquet/ParquetTypesConverter.scala    |  6 +++++-
 .../org/apache/spark/sql/hive/orc/OrcRelation.scala    |  4 ++--
 15 files changed, 61 insertions(+), 20 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index cbfe8bf31c3d6..e27b3c4962221 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -858,7 +858,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking
     // comma separated files as input. (see SPARK-7155)
     NewFileInputFormat.setInputPaths(job, path)
-    val updateConf = job.getConfiguration
+    val updateConf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     new WholeTextFileRDD(
       this,
       classOf[WholeTextFileInputFormat],
@@ -910,7 +910,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking
     // comma separated files as input. (see SPARK-7155)
     NewFileInputFormat.setInputPaths(job, path)
-    val updateConf = job.getConfiguration
+    val updateConf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     new BinaryFileRDD(
       this,
       classOf[StreamInputFormat],
@@ -1092,7 +1092,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Use setInputPaths so that newAPIHadoopFile aligns with hadoopFile/textFile in taking
     // comma separated files as input. (see SPARK-7155)
     NewFileInputFormat.setInputPaths(job, path)
-    val updatedConf = job.getConfiguration
+    val updatedConf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     new NewHadoopRDD(this, fClass, kClass, vClass, updatedConf).setName(path)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index f7723ef5bde4c..a0b7365df900a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -192,7 +192,9 @@ class SparkHadoopUtil extends Logging {
    * while it's interface in Hadoop 2.+.
    */
   def getConfigurationFromJobContext(context: JobContext): Configuration = {
+    // scalastyle:off jobconfig
     val method = context.getClass.getMethod("getConfiguration")
+    // scalastyle:on jobconfig
     method.invoke(context).asInstanceOf[Configuration]
   }
 
@@ -204,7 +206,9 @@ class SparkHadoopUtil extends Logging {
    */
   def getTaskAttemptIDFromTaskAttemptContext(
       context: MapReduceTaskAttemptContext): MapReduceTaskAttemptID = {
+    // scalastyle:off jobconfig
     val method = context.getClass.getMethod("getTaskAttemptID")
+    // scalastyle:on jobconfig
     method.invoke(context).asInstanceOf[MapReduceTaskAttemptID]
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index c59f0d4aa75a0..199d79b811d65 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -996,8 +996,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     job.setOutputKeyClass(keyClass)
     job.setOutputValueClass(valueClass)
     job.setOutputFormatClass(outputFormatClass)
-    job.getConfiguration.set("mapred.output.dir", path)
-    saveAsNewAPIHadoopDataset(job.getConfiguration)
+    val jobConfiguration = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
+    jobConfiguration.set("mapred.output.dir", path)
+    saveAsNewAPIHadoopDataset(jobConfiguration)
   }
 
   /**
@@ -1064,7 +1065,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
     val jobtrackerID = formatter.format(new Date())
     val stageId = self.id
-    val wrappedConf = new SerializableConfiguration(job.getConfiguration)
+    val jobConfiguration = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
+    val wrappedConf = new SerializableConfiguration(jobConfiguration)
     val outfmt = job.getOutputFormatClass
     val jobFormat = outfmt.newInstance
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index 9babe56267e08..0228c54e0511c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -86,7 +86,7 @@ private[spark] class SqlNewHadoopRDD[V: ClassTag](
     if (isDriverSide) {
       initDriverSideJobFuncOpt.map(f => f(job))
     }
-    job.getConfiguration
+    SparkHadoopUtil.get.getConfigurationFromJobContext(job)
   }
 
   private val jobTrackerId: String = {
diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala
index 418763f4e5ffa..fdb00aafc4a48 100644
--- a/core/src/test/scala/org/apache/spark/FileSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark
 
 import java.io.{File, FileWriter}
 
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.input.PortableDataStream
 import org.apache.spark.storage.StorageLevel
 
@@ -506,8 +507,9 @@ class FileSuite extends SparkFunSuite with LocalSparkContext {
     job.setOutputKeyClass(classOf[String])
     job.setOutputValueClass(classOf[String])
     job.setOutputFormatClass(classOf[NewTextOutputFormat[String, String]])
-    job.getConfiguration.set("mapred.output.dir", tempDir.getPath + "/outputDataset_new")
-    randomRDD.saveAsNewAPIHadoopDataset(job.getConfiguration)
+    val jobConfig = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
+    jobConfig.set("mapred.output.dir", tempDir.getPath + "/outputDataset_new")
+    randomRDD.saveAsNewAPIHadoopDataset(jobConfig)
     assert(new File(tempDir.getPath + "/outputDataset_new/part-r-00000").exists() === true)
   }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index fa07c1e5017cd..d1b9b8d398dd8 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -16,6 +16,7 @@
  */
 
  // scalastyle:off println
+ // scalastyle:off jobcontext
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
@@ -81,6 +82,7 @@ object CassandraCQLTest {
 
     val job = new Job()
     job.setInputFormatClass(classOf[CqlPagingInputFormat])
+    val configuration = job.getConfiguration
     ConfigHelper.setInputInitialAddress(job.getConfiguration(), cHost)
     ConfigHelper.setInputRpcPort(job.getConfiguration(), cPort)
     ConfigHelper.setInputColumnFamily(job.getConfiguration(), KeySpace, InputColumnFamily)
@@ -135,3 +137,4 @@ object CassandraCQLTest {
   }
 }
 // scalastyle:on println
+// scalastyle:on jobcontext
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
index 2e56d24c60c33..1e679bfb55343 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
@@ -16,6 +16,7 @@
  */
 
 // scalastyle:off println
+// scalastyle:off jobcontext
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
@@ -130,6 +131,7 @@ object CassandraTest {
   }
 }
 // scalastyle:on println
+// scalastyle:on jobcontext
 
 /*
 create keyspace casDemo;
diff --git a/scalastyle-config.xml b/scalastyle-config.xml
index 68fdb4141cf27..64a0c71bbef2a 100644
--- a/scalastyle-config.xml
+++ b/scalastyle-config.xml
@@ -168,6 +168,14 @@ This file is divided into 3 sections:
     scala.collection.JavaConverters._ and use .asScala / .asJava methods</customMessage>
   </check>
 
+  <!-- As of SPARK-10330 JobContext methods should not be called directly -->
+  <check customId="jobcontext" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
+    <parameters><parameter name="regex">^getConfiguration$|^getTaskAttemptID$</parameter></parameters>
+    <customMessage>Instead of calling .getConfiguration() or .getTaskAttemptID() directly,
+    use SparkHadoopUtil's getConfigurationFromJobContext() and getTaskAttemptIDFromTaskAttemptContext() methods.
+    </customMessage>
+  </check>
+
   <!-- ================================================================================ -->
   <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
   <!-- ================================================================================ -->
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 9a573db0c023a..f8ef674ed29c1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -47,7 +47,8 @@ private[sql] abstract class BaseWriterContainer(
 
   protected val dataSchema = relation.dataSchema
 
-  protected val serializableConf = new SerializableConfiguration(job.getConfiguration)
+  protected val serializableConf =
+    new SerializableConfiguration(SparkHadoopUtil.get.getConfigurationFromJobContext(job))
 
   // This UUID is used to avoid output file name collision between different appending write jobs.
   // These jobs may belong to different SparkContext instances. Concrete data source implementations
@@ -89,7 +90,8 @@ private[sql] abstract class BaseWriterContainer(
     // This UUID is sent to executor side together with the serialized `Configuration` object within
     // the `Job` instance.  `OutputWriters` on the executor side should use this UUID to generate
     // unique task output files.
-    job.getConfiguration.set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
+    SparkHadoopUtil.get.getConfigurationFromJobContext(job).
+      set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
 
     // Order of the following two lines is important.  For Hadoop 1, TaskAttemptContext constructor
     // clones the Configuration object passed in.  If we initialize the TaskAttemptContext first,
@@ -182,7 +184,9 @@ private[sql] abstract class BaseWriterContainer(
   private def setupIDs(jobId: Int, splitId: Int, attemptId: Int): Unit = {
     this.jobId = SparkHadoopWriter.createJobID(new Date, jobId)
     this.taskId = new TaskID(this.jobId, true, splitId)
+    // scalastyle:off jobcontext
     this.taskAttemptId = new TaskAttemptID(taskId, attemptId)
+    // scalastyle:on jobcontext
   }
 
   private def setupConf(): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index 7a49157d9e72c..8ee0127c3bde8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -81,7 +81,7 @@ private[sql] class JSONRelation(
 
   private def createBaseRdd(inputPaths: Array[FileStatus]): RDD[String] = {
     val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
-    val conf = job.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
 
     val paths = inputPaths.map(_.getPath)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 5a8166fac5418..8c819f1a48cd6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -72,7 +72,11 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
 
   // Called before `prepareForRead()` when initializing Parquet record reader.
   override def init(context: InitContext): ReadContext = {
-    val conf = context.getConfiguration
+    val conf = {
+      // scalastyle:off jobcontext
+      context.getConfiguration
+      // scalastyle:on jobcontext
+    }
 
     // If the target file was written by Spark SQL, we should be able to find a serialized Catalyst
     // schema of this file from its metadata.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
index 2c6b914328b60..de1fd0166ac5a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
@@ -53,7 +53,11 @@ private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: T
   override def setupTask(taskContext: TaskAttemptContext): Unit = {}
 
   override def commitJob(jobContext: JobContext) {
-    val configuration = ContextUtil.getConfiguration(jobContext)
+    val configuration = {
+      // scalastyle:off jobcontext
+      ContextUtil.getConfiguration(jobContext)
+      // scalastyle:on jobcontext
+    }
     val fileSystem = outputPath.getFileSystem(configuration)
 
     if (configuration.getBoolean(ParquetOutputFormat.ENABLE_JOB_SUMMARY, true)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index c6bbc392cad4c..953fcab126970 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -211,7 +211,11 @@ private[sql] class ParquetRelation(
   override def sizeInBytes: Long = metadataCache.dataStatuses.map(_.getLen).sum
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
-    val conf = ContextUtil.getConfiguration(job)
+    val conf = {
+      // scalastyle:off jobcontext
+      ContextUtil.getConfiguration(job)
+      // scalastyle:on jobcontext
+    }
 
     // SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible
     val committerClassname = conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key)
@@ -528,7 +532,7 @@ private[sql] object ParquetRelation extends Logging {
       assumeBinaryIsString: Boolean,
       assumeInt96IsTimestamp: Boolean,
       followParquetFormatSpec: Boolean)(job: Job): Unit = {
-    val conf = job.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     conf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[CatalystReadSupport].getName)
 
     // Try to push down filters when filter push-down is enabled.
@@ -572,7 +576,7 @@ private[sql] object ParquetRelation extends Logging {
       FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*)
     }
 
-    overrideMinSplitSize(parquetBlockSize, job.getConfiguration)
+    overrideMinSplitSize(parquetBlockSize, SparkHadoopUtil.get.getConfigurationFromJobContext(job))
   }
 
   private[parquet] def readSchema(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 142301fe87cb6..b647bb6116afa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -123,7 +123,11 @@ private[parquet] object ParquetTypesConverter extends Logging {
       throw new IllegalArgumentException("Unable to read Parquet metadata: path is null")
     }
     val job = new Job()
-    val conf = configuration.getOrElse(ContextUtil.getConfiguration(job))
+    val conf = {
+      // scalastyle:off jobcontext
+      configuration.getOrElse(ContextUtil.getConfiguration(job))
+      // scalastyle:on jobcontext
+    }
     val fs: FileSystem = origPath.getFileSystem(conf)
     if (fs == null) {
       throw new IllegalArgumentException(s"Incorrectly formatted Parquet metadata path $origPath")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 7e89109259955..d1f30e188eafb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -208,7 +208,7 @@ private[sql] class OrcRelation(
   }
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
-    job.getConfiguration match {
+    SparkHadoopUtil.get.getConfigurationFromJobContext(job) match {
       case conf: JobConf =>
         conf.setOutputFormat(classOf[OrcOutputFormat])
       case conf =>
@@ -289,7 +289,7 @@ private[orc] case class OrcTableScan(
 
   def execute(): RDD[InternalRow] = {
     val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
-    val conf = job.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
 
     // Tries to push down filters if ORC filter push-down is enabled
     if (sqlContext.conf.orcFilterPushDown) {

From 1dc614b874badde0eee60def46fb47f608bc4759 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 13 Sep 2015 08:36:46 +0100
Subject: [PATCH 1410/1454] [SPARK-10222] [GRAPHX] [DOCS] More thoroughly
 deprecate Bagel in favor of GraphX

Finish deprecating Bagel; remove reference to nonexistent example

Author: Sean Owen <sowen@cloudera.com>

Closes #8731 from srowen/SPARK-10222.
---
 .../src/main/scala/org/apache/spark/bagel/Bagel.scala  |  6 ++++++
 docs/bagel-programming-guide.md                        | 10 +---------
 docs/index.md                                          |  1 -
 pom.xml                                                |  2 +-
 4 files changed, 8 insertions(+), 11 deletions(-)

diff --git a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
index 4e6b7686f771d..8399033ac61ec 100644
--- a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
+++ b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
@@ -22,6 +22,7 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 object Bagel extends Logging {
   val DEFAULT_STORAGE_LEVEL = StorageLevel.MEMORY_AND_DISK
 
@@ -270,18 +271,21 @@ object Bagel extends Logging {
   }
 }
 
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Combiner[M, C] {
   def createCombiner(msg: M): C
   def mergeMsg(combiner: C, msg: M): C
   def mergeCombiners(a: C, b: C): C
 }
 
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Aggregator[V, A] {
   def createAggregator(vert: V): A
   def mergeAggregators(a: A, b: A): A
 }
 
 /** Default combiner that simply appends messages together (i.e. performs no aggregation) */
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 class DefaultCombiner[M: Manifest] extends Combiner[M, Array[M]] with Serializable {
   def createCombiner(msg: M): Array[M] =
     Array(msg)
@@ -297,6 +301,7 @@ class DefaultCombiner[M: Manifest] extends Combiner[M, Array[M]] with Serializab
  * Subclasses may store state along with each vertex and must
  * inherit from java.io.Serializable or scala.Serializable.
  */
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Vertex {
   def active: Boolean
 }
@@ -307,6 +312,7 @@ trait Vertex {
  * Subclasses may contain a payload to deliver to the target vertex
  * and must inherit from java.io.Serializable or scala.Serializable.
  */
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Message[K] {
   def targetId: K
 }
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index c2fe6b0e286ce..347ca4a7af989 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -4,7 +4,7 @@ displayTitle: Bagel Programming Guide
 title: Bagel
 ---
 
-**Bagel will soon be superseded by [GraphX](graphx-programming-guide.html); we recommend that new users try GraphX instead.**
+**Bagel is deprecated, and superseded by [GraphX](graphx-programming-guide.html).**
 
 Bagel is a Spark implementation of Google's [Pregel](http://portal.acm.org/citation.cfm?id=1807184) graph processing framework. Bagel currently supports basic graph computation, combiners, and aggregators.
 
@@ -157,11 +157,3 @@ trait Message[K] {
   def targetId: K
 }
 {% endhighlight %}
-
-# Where to Go from Here
-
-Two example jobs, PageRank and shortest path, are included in `examples/src/main/scala/org/apache/spark/examples/bagel`. You can run them by passing the class name to the `bin/run-example` script included in Spark; e.g.:
-
-    ./bin/run-example org.apache.spark.examples.bagel.WikipediaPageRank
-
-Each example program prints usage help when run without any arguments.
diff --git a/docs/index.md b/docs/index.md
index d85cf12defefd..c0dc2b8d7412a 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -90,7 +90,6 @@ options for deployment:
   * [Spark SQL and DataFrames](sql-programming-guide.html): support for structured data and relational queries
   * [MLlib](mllib-guide.html): built-in machine learning library
   * [GraphX](graphx-programming-guide.html): Spark's new API for graph processing
-  * [Bagel (Pregel on Spark)](bagel-programming-guide.html): older, simple graph processing model
 
 **API Docs:**
 
diff --git a/pom.xml b/pom.xml
index 88ebceca769e9..421357e141572 100644
--- a/pom.xml
+++ b/pom.xml
@@ -87,7 +87,7 @@
 
   <modules>
     <module>core</module>
-    <module>bagel</module>
+    <module>bagel</module> <!-- Deprecated -->
     <module>graphx</module>
     <module>mllib</module>
     <module>tools</module>

From d81565465cc6d4f38b4ed78036cded630c700388 Mon Sep 17 00:00:00 2001
From: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>
Date: Mon, 14 Sep 2015 09:18:46 +0100
Subject: [PATCH 1411/1454] [SPARK-9720] [ML] Identifiable types need UID in
 toString methods

A few Identifiable types did override their toString method but without using the parent implementation. As a consequence, the uid was not present anymore in the toString result. It is the default behaviour.

This patch is a quick fix. The question of enforcement is still up.

No tests have been written to verify the toString method behaviour. That would be long to do because all types should be tested and not only those which have a regression now.

It is possible to enforce the condition using the compiler by making the toString method final but that would introduce unwanted potential API breaking changes (see jira).

Author: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>

Closes #8062 from BertrandDechoux/SPARK-9720.
---
 .../spark/ml/classification/DecisionTreeClassifier.scala      | 2 +-
 .../org/apache/spark/ml/classification/GBTClassifier.scala    | 2 +-
 .../scala/org/apache/spark/ml/classification/NaiveBayes.scala | 2 +-
 .../spark/ml/classification/RandomForestClassifier.scala      | 2 +-
 .../src/main/scala/org/apache/spark/ml/feature/RFormula.scala | 4 ++--
 .../apache/spark/ml/regression/DecisionTreeRegressor.scala    | 2 +-
 .../scala/org/apache/spark/ml/regression/GBTRegressor.scala   | 2 +-
 .../apache/spark/ml/regression/RandomForestRegressor.scala    | 2 +-
 8 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 0a75d5d22280f..b8eb49f9bdb48 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -146,7 +146,7 @@ final class DecisionTreeClassificationModel private[ml] (
   }
 
   override def toString: String = {
-    s"DecisionTreeClassificationModel of depth $depth with $numNodes nodes"
+    s"DecisionTreeClassificationModel (uid=$uid) of depth $depth with $numNodes nodes"
   }
 
   /** (private[ml]) Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 3073a2a61ce83..ad8683648b975 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -200,7 +200,7 @@ final class GBTClassificationModel(
   }
 
   override def toString: String = {
-    s"GBTClassificationModel with $numTrees trees"
+    s"GBTClassificationModel (uid=$uid) with $numTrees trees"
   }
 
   /** (private[ml]) Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 69cb88a7e6718..082ea1ffad58f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -198,7 +198,7 @@ class NaiveBayesModel private[ml] (
   }
 
   override def toString: String = {
-    s"NaiveBayesModel with ${pi.size} classes"
+    s"NaiveBayesModel (uid=$uid) with ${pi.size} classes"
   }
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 11a6d72468333..a6ebee1bb10af 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -193,7 +193,7 @@ final class RandomForestClassificationModel private[ml] (
   }
 
   override def toString: String = {
-    s"RandomForestClassificationModel with $numTrees trees"
+    s"RandomForestClassificationModel (uid=$uid) with $numTrees trees"
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index a7fa50444209b..dcd6fe3c406a4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -129,7 +129,7 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
 
   override def copy(extra: ParamMap): RFormula = defaultCopy(extra)
 
-  override def toString: String = s"RFormula(${get(formula)})"
+  override def toString: String = s"RFormula(${get(formula)}) (uid=$uid)"
 }
 
 /**
@@ -171,7 +171,7 @@ class RFormulaModel private[feature](
   override def copy(extra: ParamMap): RFormulaModel = copyValues(
     new RFormulaModel(uid, resolvedFormula, pipelineModel))
 
-  override def toString: String = s"RFormulaModel(${resolvedFormula})"
+  override def toString: String = s"RFormulaModel(${resolvedFormula}) (uid=$uid)"
 
   private def transformLabel(dataset: DataFrame): DataFrame = {
     val labelName = resolvedFormula.label
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index a2bcd67401d08..d9a244bea28d2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -118,7 +118,7 @@ final class DecisionTreeRegressionModel private[ml] (
   }
 
   override def toString: String = {
-    s"DecisionTreeRegressionModel of depth $depth with $numNodes nodes"
+    s"DecisionTreeRegressionModel (uid=$uid) of depth $depth with $numNodes nodes"
   }
 
   /** Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index b66e61f37dd5e..d841ecb9e58d6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -189,7 +189,7 @@ final class GBTRegressionModel(
   }
 
   override def toString: String = {
-    s"GBTRegressionModel with $numTrees trees"
+    s"GBTRegressionModel (uid=$uid) with $numTrees trees"
   }
 
   /** (private[ml]) Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 2f36da371f577..ddb7214416a69 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -155,7 +155,7 @@ final class RandomForestRegressionModel private[ml] (
   }
 
   override def toString: String = {
-    s"RandomForestRegressionModel with $numTrees trees"
+    s"RandomForestRegressionModel (uid=$uid) with $numTrees trees"
   }
 
   /**

From 32407bfd2bdbf84d65cacfa7554dae6a2332bc37 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 14 Sep 2015 11:51:39 -0700
Subject: [PATCH 1412/1454] [SPARK-9899] [SQL] log warning for direct output
 committer with speculation enabled

This is a follow-up of https://github.com/apache/spark/pull/8317.

When speculation is enabled, there may be multiply tasks writing to the same path. Generally it's OK as we will write to a temporary directory first and only one task can commit the temporary directory to target path.

However, when we use direct output committer, tasks will write data to target path directly without temporary directory. This causes problems like corrupted data. Please see [PR comment](https://github.com/apache/spark/pull/8191#issuecomment-131598385) for more details.

Unfortunately, we don't have a simple flag to tell if a output committer will write to temporary directory or not, so for safety, we have to disable any customized output committer when `speculation` is true.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8687 from cloud-fan/direct-committer.
---
 .../apache/spark/rdd/PairRDDFunctions.scala   | 44 ++++++++++++++++---
 .../hive/execution/InsertIntoHiveTable.scala  | 17 ++++++-
 .../spark/sql/hive/hiveWriterContainers.scala |  1 -
 3 files changed, 53 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 199d79b811d65..a981b63942e6d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -1018,6 +1018,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   /**
    * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class
    * supporting the key and value types K and V in this RDD.
+   *
+   * Note that, we should make sure our tasks are idempotent when speculation is enabled, i.e. do
+   * not use output committer that writes data directly.
+   * There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad
+   * result of using direct output committer with speculation enabled.
    */
   def saveAsHadoopFile(
       path: String,
@@ -1030,10 +1035,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val hadoopConf = conf
     hadoopConf.setOutputKeyClass(keyClass)
     hadoopConf.setOutputValueClass(valueClass)
-    // Doesn't work in Scala 2.9 due to what may be a generics bug
-    // TODO: Should we uncomment this for Scala 2.10?
-    // conf.setOutputFormat(outputFormatClass)
-    hadoopConf.set("mapred.output.format.class", outputFormatClass.getName)
+    conf.setOutputFormat(outputFormatClass)
     for (c <- codec) {
       hadoopConf.setCompressMapOutput(true)
       hadoopConf.set("mapred.output.compress", "true")
@@ -1047,6 +1049,19 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
     }
 
+    // When speculation is on and output committer class name contains "Direct", we should warn
+    // users that they may loss data if they are using a direct output committer.
+    val speculationEnabled = self.conf.getBoolean("spark.speculation", false)
+    val outputCommitterClass = hadoopConf.get("mapred.output.committer.class", "")
+    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
+      val warningMessage =
+        s"$outputCommitterClass may be an output committer that writes data directly to " +
+          "the final location. Because speculation is enabled, this output committer may " +
+          "cause data loss (see the case in SPARK-10063). If possible, please use a output " +
+          "committer that does not have this behavior (e.g. FileOutputCommitter)."
+      logWarning(warningMessage)
+    }
+
     FileOutputFormat.setOutputPath(hadoopConf,
       SparkHadoopWriter.createPathFromString(path, hadoopConf))
     saveAsHadoopDataset(hadoopConf)
@@ -1057,6 +1072,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Configuration object for that storage system. The Conf should set an OutputFormat and any
    * output paths required (e.g. a table name to write to) in the same way as it would be
    * configured for a Hadoop MapReduce job.
+   *
+   * Note that, we should make sure our tasks are idempotent when speculation is enabled, i.e. do
+   * not use output committer that writes data directly.
+   * There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad
+   * result of using direct output committer with speculation enabled.
    */
   def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
@@ -1115,6 +1135,20 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = true, 0, 0)
     val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId)
     val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext)
+
+    // When speculation is on and output committer class name contains "Direct", we should warn
+    // users that they may loss data if they are using a direct output committer.
+    val speculationEnabled = self.conf.getBoolean("spark.speculation", false)
+    val outputCommitterClass = jobCommitter.getClass.getSimpleName
+    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
+      val warningMessage =
+        s"$outputCommitterClass may be an output committer that writes data directly to " +
+          "the final location. Because speculation is enabled, this output committer may " +
+          "cause data loss (see the case in SPARK-10063). If possible, please use a output " +
+          "committer that does not have this behavior (e.g. FileOutputCommitter)."
+      logWarning(warningMessage)
+    }
+
     jobCommitter.setupJob(jobTaskContext)
     self.context.runJob(self, writeShard)
     jobCommitter.commitJob(jobTaskContext)
@@ -1129,7 +1163,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
     val hadoopConf = conf
-    val wrappedConf = new SerializableConfiguration(hadoopConf)
     val outputFormatInstance = hadoopConf.getOutputFormat
     val keyClass = hadoopConf.getOutputKeyClass
     val valueClass = hadoopConf.getOutputValueClass
@@ -1157,7 +1190,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     writer.preSetup()
 
     val writeToFile = (context: TaskContext, iter: Iterator[(K, V)]) => {
-      val config = wrappedConf.value
       // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
       // around by taking a mod. We expect that no task will be attempted 2 billion times.
       val taskAttemptId = (context.taskAttemptId % Int.MaxValue).toInt
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 58f7fa640e8a9..0c700bdb370ac 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.hive.ql.{Context, ErrorMsg}
 import org.apache.hadoop.hive.serde2.Serializer
 import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
 import org.apache.hadoop.hive.serde2.objectinspector._
-import org.apache.hadoop.mapred.{FileOutputFormat, JobConf}
+import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
@@ -62,7 +62,7 @@ case class InsertIntoHiveTable(
 
   def output: Seq[Attribute] = Seq.empty
 
-  def saveAsHiveFile(
+  private def saveAsHiveFile(
       rdd: RDD[InternalRow],
       valueClass: Class[_],
       fileSinkConf: FileSinkDesc,
@@ -178,6 +178,19 @@ case class InsertIntoHiveTable(
     val jobConf = new JobConf(sc.hiveconf)
     val jobConfSer = new SerializableJobConf(jobConf)
 
+    // When speculation is on and output committer class name contains "Direct", we should warn
+    // users that they may loss data if they are using a direct output committer.
+    val speculationEnabled = sqlContext.sparkContext.conf.getBoolean("spark.speculation", false)
+    val outputCommitterClass = jobConf.get("mapred.output.committer.class", "")
+    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
+      val warningMessage =
+        s"$outputCommitterClass may be an output committer that writes data directly to " +
+          "the final location. Because speculation is enabled, this output committer may " +
+          "cause data loss (see the case in SPARK-10063). If possible, please use a output " +
+          "committer that does not have this behavior (e.g. FileOutputCommitter)."
+      logWarning(warningMessage)
+    }
+
     val writerContainer = if (numDynamicPartitions > 0) {
       val dynamicPartColNames = partitionColumnNames.takeRight(numDynamicPartitions)
       new SparkHiveDynamicPartitionWriterContainer(jobConf, fileSinkConf, dynamicPartColNames)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 29a6f08f40728..4ca8042d22367 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -32,7 +32,6 @@ import org.apache.hadoop.mapred._
 import org.apache.hadoop.hive.common.FileUtils
 
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
-import org.apache.spark.sql.Row
 import org.apache.spark.{Logging, SerializableWritable, SparkHadoopWriter}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils

From cf2821ef5fd9965eb6256e8e8b3f1e00c0788098 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 14 Sep 2015 12:06:23 -0700
Subject: [PATCH 1413/1454] [SPARK-10584] [DOC] [SQL] Documentation about
 spark.sql.hive.metastore.version is wrong.

The default value of hive metastore version is 1.2.1 but the documentation says the value of `spark.sql.hive.metastore.version` is 0.13.1.
Also, we cannot get the default value by `sqlContext.getConf("spark.sql.hive.metastore.version")`.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #8739 from sarutak/SPARK-10584.
---
 docs/sql-programming-guide.md                         |  2 +-
 .../scala/org/apache/spark/sql/hive/HiveContext.scala | 11 +++++++----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 6a1b0fbfa1eb3..a0b911d207243 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1687,7 +1687,7 @@ The following options can be used to configure the version of Hive that is used
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
   <tr>
     <td><code>spark.sql.hive.metastore.version</code></td>
-    <td><code>0.13.1</code></td>
+    <td><code>1.2.1</code></td>
     <td>
       Version of the Hive metastore. Available
       options are <code>0.12.0</code> through <code>1.2.1</code>.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 2e791cea96b41..d37ba5ddc2d80 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -111,8 +111,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    * this does not necessarily need to be the same version of Hive that is used internally by
    * Spark SQL for execution.
    */
-  protected[hive] def hiveMetastoreVersion: String =
-    getConf(HIVE_METASTORE_VERSION, hiveExecutionVersion)
+  protected[hive] def hiveMetastoreVersion: String = getConf(HIVE_METASTORE_VERSION)
 
   /**
    * The location of the jars that should be used to instantiate the HiveMetastoreClient.  This
@@ -202,7 +201,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
           "Builtin jars can only be used when hive execution version == hive metastore version. " +
           s"Execution: ${hiveExecutionVersion} != Metastore: ${hiveMetastoreVersion}. " +
           "Specify a vaild path to the correct hive jars using $HIVE_METASTORE_JARS " +
-          s"or change $HIVE_METASTORE_VERSION to $hiveExecutionVersion.")
+          s"or change ${HIVE_METASTORE_VERSION.key} to $hiveExecutionVersion.")
       }
 
       // We recursively find all jars in the class loader chain,
@@ -606,7 +605,11 @@ private[hive] object HiveContext {
   /** The version of hive used internally by Spark SQL. */
   val hiveExecutionVersion: String = "1.2.1"
 
-  val HIVE_METASTORE_VERSION: String = "spark.sql.hive.metastore.version"
+  val HIVE_METASTORE_VERSION = stringConf("spark.sql.hive.metastore.version",
+    defaultValue = Some(hiveExecutionVersion),
+    doc = "Version of the Hive metastore. Available options are " +
+        s"<code>0.12.0</code> through <code>$hiveExecutionVersion</code>.")
+
   val HIVE_METASTORE_JARS = stringConf("spark.sql.hive.metastore.jars",
     defaultValue = Some("builtin"),
     doc = s"""

From ce6f3f163bc667cb5da9ab4331c8bad10cc0d701 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 14 Sep 2015 12:08:52 -0700
Subject: [PATCH 1414/1454] [SPARK-10194] [MLLIB] [PYSPARK] SGD algorithms need
 convergenceTol parameter in Python

[SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382) added a ```convergenceTol``` parameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8457 from yanboliang/spark-10194.
---
 .../mllib/api/python/PythonMLLibAPI.scala     | 20 +++++++++---
 python/pyspark/mllib/classification.py        | 17 +++++++---
 python/pyspark/mllib/regression.py            | 32 ++++++++++++-------
 3 files changed, 48 insertions(+), 21 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index f585aacd452e0..69ce7f50709a1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -132,7 +132,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       regParam: Double,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val lrAlg = new LinearRegressionWithSGD()
     lrAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -141,6 +142,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     lrAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       lrAlg,
@@ -159,7 +161,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       miniBatchFraction: Double,
       initialWeights: Vector,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val lassoAlg = new LassoWithSGD()
     lassoAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -168,6 +171,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     trainRegressionModel(
       lassoAlg,
       data,
@@ -185,7 +189,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       miniBatchFraction: Double,
       initialWeights: Vector,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val ridgeAlg = new RidgeRegressionWithSGD()
     ridgeAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -194,6 +199,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     trainRegressionModel(
       ridgeAlg,
       data,
@@ -212,7 +218,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       initialWeights: Vector,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val SVMAlg = new SVMWithSGD()
     SVMAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -221,6 +228,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     SVMAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       SVMAlg,
@@ -240,7 +248,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       regParam: Double,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val LogRegAlg = new LogisticRegressionWithSGD()
     LogRegAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -249,6 +258,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     LogRegAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       LogRegAlg,
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 8f27c446a66e8..cb4ee83678081 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -241,7 +241,7 @@ class LogisticRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
               initialWeights=None, regParam=0.01, regType="l2", intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a logistic regression model on the given data.
 
@@ -274,11 +274,13 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
                                  float(step), float(miniBatchFraction), i, float(regParam), regType,
-                                 bool(intercept), bool(validateData))
+                                 bool(intercept), bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
 
@@ -439,7 +441,7 @@ class SVMWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
               miniBatchFraction=1.0, initialWeights=None, regType="l2",
-              intercept=False, validateData=True):
+              intercept=False, validateData=True, convergenceTol=0.001):
         """
         Train a support vector machine on the given data.
 
@@ -472,11 +474,13 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step),
                                  float(regParam), float(miniBatchFraction), i, regType,
-                                 bool(intercept), bool(validateData))
+                                 bool(intercept), bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, SVMModel, data, initialWeights)
 
@@ -600,12 +604,15 @@ class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
     :param miniBatchFraction: Fraction of data on which SGD is run for each
                               iteration.
     :param regParam: L2 Regularization parameter.
+    :param convergenceTol: A condition which decides iteration termination.
     """
-    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01):
+    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01,
+                 convergenceTol=0.001):
         self.stepSize = stepSize
         self.numIterations = numIterations
         self.regParam = regParam
         self.miniBatchFraction = miniBatchFraction
+        self.convergenceTol = convergenceTol
         self._model = None
         super(StreamingLogisticRegressionWithSGD, self).__init__(
             model=self._model)
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 41946e3674fbe..256b7537fef6b 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -28,7 +28,8 @@
            'LinearRegressionModel', 'LinearRegressionWithSGD',
            'RidgeRegressionModel', 'RidgeRegressionWithSGD',
            'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel',
-           'IsotonicRegression']
+           'IsotonicRegression', 'StreamingLinearAlgorithm',
+           'StreamingLinearRegressionWithSGD']
 
 
 class LabeledPoint(object):
@@ -202,7 +203,7 @@ class LinearRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
               initialWeights=None, regParam=0.0, regType=None, intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a linear regression model using Stochastic Gradient
         Descent (SGD).
@@ -244,11 +245,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations),
                                  float(step), float(miniBatchFraction), i, float(regParam),
-                                 regType, bool(intercept), bool(validateData))
+                                 regType, bool(intercept), bool(validateData),
+                                 float(convergenceTol))
 
         return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights)
 
@@ -330,7 +334,7 @@ class LassoWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
               miniBatchFraction=1.0, initialWeights=None, intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a regression model with L1-regularization using
         Stochastic Gradient Descent.
@@ -362,11 +366,13 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step),
                                  float(regParam), float(miniBatchFraction), i, bool(intercept),
-                                 bool(validateData))
+                                 bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, LassoModel, data, initialWeights)
 
@@ -449,7 +455,7 @@ class RidgeRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
               miniBatchFraction=1.0, initialWeights=None, intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a regression model with L2-regularization using
         Stochastic Gradient Descent.
@@ -481,11 +487,13 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step),
                                  float(regParam), float(miniBatchFraction), i, bool(intercept),
-                                 bool(validateData))
+                                 bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)
 
@@ -636,15 +644,17 @@ class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm):
     After training on a batch of data, the weights obtained at the end of
     training are used as initial weights for the next batch.
 
-    :param: stepSize Step size for each iteration of gradient descent.
-    :param: numIterations Total number of iterations run.
-    :param: miniBatchFraction Fraction of data on which SGD is run for each
+    :param stepSize: Step size for each iteration of gradient descent.
+    :param numIterations: Total number of iterations run.
+    :param miniBatchFraction: Fraction of data on which SGD is run for each
                               iteration.
+    :param convergenceTol: A condition which decides iteration termination.
     """
-    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0):
+    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, convergenceTol=0.001):
         self.stepSize = stepSize
         self.numIterations = numIterations
         self.miniBatchFraction = miniBatchFraction
+        self.convergenceTol = convergenceTol
         self._model = None
         super(StreamingLinearRegressionWithSGD, self).__init__(
             model=self._model)

From 8a634e9bcc671167613fb575c6c0c054fb4b3479 Mon Sep 17 00:00:00 2001
From: Nick Pritchard <nicholas.pritchard@falkonry.com>
Date: Mon, 14 Sep 2015 13:27:45 -0700
Subject: [PATCH 1415/1454] [SPARK-10573] [ML] IndexToString output schema
 should be StringType

Fixes bug where IndexToString output schema was DoubleType. Correct me if I'm wrong, but it doesn't seem like the output needs to have any "ML Attribute" metadata.

Author: Nick Pritchard <nicholas.pritchard@falkonry.com>

Closes #8751 from pnpritchard/SPARK-10573.
---
 .../scala/org/apache/spark/ml/feature/StringIndexer.scala | 5 ++---
 .../org/apache/spark/ml/feature/StringIndexerSuite.scala  | 8 ++++++++
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 3a4ab9a857648..2b1592930e77b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -27,7 +27,7 @@ import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{DoubleType, NumericType, StringType, StructType}
+import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashMap
 
 /**
@@ -229,8 +229,7 @@ class IndexToString private[ml] (
     val outputColName = $(outputCol)
     require(inputFields.forall(_.name != outputColName),
       s"Output column $outputColName already exists.")
-    val attr = NominalAttribute.defaultAttr.withName($(outputCol))
-    val outputFields = inputFields :+ attr.toStructField()
+    val outputFields = inputFields :+ StructField($(outputCol), StringType)
     StructType(outputFields)
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 05e05bdc64bb1..ddcdb5f4212be 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.feature
 
+import org.apache.spark.sql.types.{StringType, StructType, StructField, DoubleType}
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
@@ -165,4 +166,11 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(a === b)
     }
   }
+
+  test("IndexToString.transformSchema (SPARK-10573)") {
+    val idxToStr = new IndexToString().setInputCol("input").setOutputCol("output")
+    val inSchema = StructType(Seq(StructField("input", DoubleType)))
+    val outSchema = idxToStr.transformSchema(inSchema)
+    assert(outSchema("output").dataType === StringType)
+  }
 }

From 7e32387ae6303fd1cd32389d47df87170b841c67 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 14 Sep 2015 14:10:54 -0700
Subject: [PATCH 1416/1454] [SPARK-10522] [SQL] Nanoseconds of Timestamp in
 Parquet should be positive

Or Hive can't read it back correctly.

Thanks vanzin for report this.

Author: Davies Liu <davies@databricks.com>

Closes #8674 from davies/positive_nano.
---
 .../spark/sql/catalyst/util/DateTimeUtils.scala | 12 +++++++-----
 .../sql/catalyst/util/DateTimeUtilsSuite.scala  | 17 ++++++++---------
 2 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index d652fce3fd9b6..687ca000d12bb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -42,6 +42,7 @@ object DateTimeUtils {
   final val SECONDS_PER_DAY = 60 * 60 * 24L
   final val MICROS_PER_SECOND = 1000L * 1000L
   final val NANOS_PER_SECOND = MICROS_PER_SECOND * 1000L
+  final val MICROS_PER_DAY = MICROS_PER_SECOND * SECONDS_PER_DAY
 
   final val MILLIS_PER_DAY = SECONDS_PER_DAY * 1000L
 
@@ -190,13 +191,14 @@ object DateTimeUtils {
 
   /**
    * Returns Julian day and nanoseconds in a day from the number of microseconds
+   *
+   * Note: support timestamp since 4717 BC (without negative nanoseconds, compatible with Hive).
    */
   def toJulianDay(us: SQLTimestamp): (Int, Long) = {
-    val seconds = us / MICROS_PER_SECOND
-    val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
-    val secondsInDay = seconds % SECONDS_PER_DAY
-    val nanos = (us % MICROS_PER_SECOND) * 1000L
-    (day.toInt, secondsInDay * NANOS_PER_SECOND + nanos)
+    val julian_us = us + JULIAN_DAY_OF_EPOCH * MICROS_PER_DAY
+    val day = julian_us / MICROS_PER_DAY
+    val micros = julian_us % MICROS_PER_DAY
+    (day.toInt, micros * 1000L)
   }
 
   /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 1596bb79fa94b..6b9a11f0ff743 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -52,15 +52,14 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     assert(ns === 0)
     assert(fromJulianDay(d, ns) == 0L)
 
-    val t = Timestamp.valueOf("2015-06-11 10:10:10.100")
-    val (d1, ns1) = toJulianDay(fromJavaTimestamp(t))
-    val t1 = toJavaTimestamp(fromJulianDay(d1, ns1))
-    assert(t.equals(t1))
-
-    val t2 = Timestamp.valueOf("2015-06-11 20:10:10.100")
-    val (d2, ns2) = toJulianDay(fromJavaTimestamp(t2))
-    val t22 = toJavaTimestamp(fromJulianDay(d2, ns2))
-    assert(t2.equals(t22))
+    Seq(Timestamp.valueOf("2015-06-11 10:10:10.100"),
+      Timestamp.valueOf("2015-06-11 20:10:10.100"),
+      Timestamp.valueOf("1900-06-11 20:10:10.100")).foreach { t =>
+      val (d, ns) = toJulianDay(fromJavaTimestamp(t))
+      assert(ns > 0)
+      val t1 = toJavaTimestamp(fromJulianDay(d, ns))
+      assert(t.equals(t1))
+    }
   }
 
   test("SPARK-6785: java date conversion before and after epoch") {

From 64f04154e3078ec7340da97e3c2b07cf24e89098 Mon Sep 17 00:00:00 2001
From: Edoardo Vacchi <uncommonnonsense@gmail.com>
Date: Mon, 14 Sep 2015 14:56:04 -0700
Subject: [PATCH 1417/1454] [SPARK-6981] [SQL] Factor out SparkPlanner and
 QueryExecution from SQLContext

Alternative to PR #6122; in this case the refactored out classes are replaced by inner classes with the same name for backwards binary compatibility

   * process in a lighter-weight, backwards-compatible way

Author: Edoardo Vacchi <uncommonnonsense@gmail.com>

Closes #6356 from evacchi/sqlctx-refactoring-lite.
---
 .../org/apache/spark/sql/DataFrame.scala      |   4 +-
 .../org/apache/spark/sql/SQLContext.scala     | 138 ++----------------
 .../spark/sql/execution/QueryExecution.scala  |  85 +++++++++++
 .../spark/sql/execution/SQLExecution.scala    |   2 +-
 .../spark/sql/execution/SparkPlanner.scala    |  92 ++++++++++++
 .../spark/sql/execution/SparkStrategies.scala |   2 +-
 6 files changed, 195 insertions(+), 128 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 1a687b2374f14..3e61123c145cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -37,7 +37,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
-import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, FileRelation, LogicalRDD, SQLExecution}
+import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, FileRelation, LogicalRDD, QueryExecution, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
 import org.apache.spark.sql.execution.datasources.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
@@ -114,7 +114,7 @@ private[sql] object DataFrame {
 @Experimental
 class DataFrame private[sql](
     @transient val sqlContext: SQLContext,
-    @DeveloperApi @transient val queryExecution: SQLContext#QueryExecution) extends Serializable {
+    @DeveloperApi @transient val queryExecution: QueryExecution) extends Serializable {
 
   // Note for Spark contributors: if adding or updating any action in `DataFrame`, please make sure
   // you wrap it with `withNewExecutionId` if this actions doesn't call other action.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 4e8414af50b44..e3fdd782e6ff6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -38,6 +38,10 @@ import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer}
 import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
+import org.apache.spark.sql.execution.{Filter, _}
+import org.apache.spark.sql.{execution => sparkexecution}
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
@@ -188,9 +192,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   protected[sql] def parseSql(sql: String): LogicalPlan = ddlParser.parse(sql, false)
 
-  protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql))
+  protected[sql] def executeSql(sql: String):
+    org.apache.spark.sql.execution.QueryExecution = executePlan(parseSql(sql))
 
-  protected[sql] def executePlan(plan: LogicalPlan) = new this.QueryExecution(plan)
+  protected[sql] def executePlan(plan: LogicalPlan) =
+    new sparkexecution.QueryExecution(this, plan)
 
   @transient
   protected[sql] val tlSession = new ThreadLocal[SQLSession]() {
@@ -781,77 +787,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
     }.toArray
   }
 
-  protected[sql] class SparkPlanner extends SparkStrategies {
-    val sparkContext: SparkContext = self.sparkContext
-
-    val sqlContext: SQLContext = self
-
-    def codegenEnabled: Boolean = self.conf.codegenEnabled
-
-    def unsafeEnabled: Boolean = self.conf.unsafeEnabled
-
-    def numPartitions: Int = self.conf.numShufflePartitions
-
-    def strategies: Seq[Strategy] =
-      experimental.extraStrategies ++ (
-      DataSourceStrategy ::
-      DDLStrategy ::
-      TakeOrderedAndProject ::
-      HashAggregation ::
-      Aggregation ::
-      LeftSemiJoin ::
-      EquiJoinSelection ::
-      InMemoryScans ::
-      BasicOperators ::
-      CartesianProduct ::
-      BroadcastNestedLoopJoin :: Nil)
-
-    /**
-     * Used to build table scan operators where complex projection and filtering are done using
-     * separate physical operators.  This function returns the given scan operator with Project and
-     * Filter nodes added only when needed.  For example, a Project operator is only used when the
-     * final desired output requires complex expressions to be evaluated or when columns can be
-     * further eliminated out after filtering has been done.
-     *
-     * The `prunePushedDownFilters` parameter is used to remove those filters that can be optimized
-     * away by the filter pushdown optimization.
-     *
-     * The required attributes for both filtering and expression evaluation are passed to the
-     * provided `scanBuilder` function so that it can avoid unnecessary column materialization.
-     */
-    def pruneFilterProject(
-        projectList: Seq[NamedExpression],
-        filterPredicates: Seq[Expression],
-        prunePushedDownFilters: Seq[Expression] => Seq[Expression],
-        scanBuilder: Seq[Attribute] => SparkPlan): SparkPlan = {
-
-      val projectSet = AttributeSet(projectList.flatMap(_.references))
-      val filterSet = AttributeSet(filterPredicates.flatMap(_.references))
-      val filterCondition =
-        prunePushedDownFilters(filterPredicates).reduceLeftOption(catalyst.expressions.And)
-
-      // Right now we still use a projection even if the only evaluation is applying an alias
-      // to a column.  Since this is a no-op, it could be avoided. However, using this
-      // optimization with the current implementation would change the output schema.
-      // TODO: Decouple final output schema from expression evaluation so this copy can be
-      // avoided safely.
-
-      if (AttributeSet(projectList.map(_.toAttribute)) == projectSet &&
-          filterSet.subsetOf(projectSet)) {
-        // When it is possible to just use column pruning to get the right projection and
-        // when the columns of this projection are enough to evaluate all filter conditions,
-        // just do a scan followed by a filter, with no extra project.
-        val scan = scanBuilder(projectList.asInstanceOf[Seq[Attribute]])
-        filterCondition.map(Filter(_, scan)).getOrElse(scan)
-      } else {
-        val scan = scanBuilder((projectSet ++ filterSet).toSeq)
-        Project(projectList, filterCondition.map(Filter(_, scan)).getOrElse(scan))
-      }
-    }
-  }
+  @deprecated("use org.apache.spark.sql.SparkPlanner", "1.6.0")
+  protected[sql] class SparkPlanner extends sparkexecution.SparkPlanner(this)
 
   @transient
-  protected[sql] val planner = new SparkPlanner
+  protected[sql] val planner: sparkexecution.SparkPlanner = new sparkexecution.SparkPlanner(this)
 
   @transient
   protected[sql] lazy val emptyResult = sparkContext.parallelize(Seq.empty[InternalRow], 1)
@@ -898,59 +838,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
     protected[sql] lazy val conf: SQLConf = new SQLConf
   }
 
-  /**
-   * :: DeveloperApi ::
-   * The primary workflow for executing relational queries using Spark.  Designed to allow easy
-   * access to the intermediate phases of query execution for developers.
-   */
-  @DeveloperApi
-  protected[sql] class QueryExecution(val logical: LogicalPlan) {
-    def assertAnalyzed(): Unit = analyzer.checkAnalysis(analyzed)
-
-    lazy val analyzed: LogicalPlan = analyzer.execute(logical)
-    lazy val withCachedData: LogicalPlan = {
-      assertAnalyzed()
-      cacheManager.useCachedData(analyzed)
-    }
-    lazy val optimizedPlan: LogicalPlan = optimizer.execute(withCachedData)
-
-    // TODO: Don't just pick the first one...
-    lazy val sparkPlan: SparkPlan = {
-      SparkPlan.currentContext.set(self)
-      planner.plan(optimizedPlan).next()
-    }
-    // executedPlan should not be used to initialize any SparkPlan. It should be
-    // only used for execution.
-    lazy val executedPlan: SparkPlan = prepareForExecution.execute(sparkPlan)
-
-    /** Internal version of the RDD. Avoids copies and has no schema */
-    lazy val toRdd: RDD[InternalRow] = executedPlan.execute()
-
-    protected def stringOrError[A](f: => A): String =
-      try f.toString catch { case e: Throwable => e.toString }
-
-    def simpleString: String =
-      s"""== Physical Plan ==
-         |${stringOrError(executedPlan)}
-      """.stripMargin.trim
-
-    override def toString: String = {
-      def output =
-        analyzed.output.map(o => s"${o.name}: ${o.dataType.simpleString}").mkString(", ")
-
-      s"""== Parsed Logical Plan ==
-         |${stringOrError(logical)}
-         |== Analyzed Logical Plan ==
-         |${stringOrError(output)}
-         |${stringOrError(analyzed)}
-         |== Optimized Logical Plan ==
-         |${stringOrError(optimizedPlan)}
-         |== Physical Plan ==
-         |${stringOrError(executedPlan)}
-         |Code Generation: ${stringOrError(executedPlan.codegenEnabled)}
-      """.stripMargin.trim
-    }
-  }
+  @deprecated("use org.apache.spark.sql.QueryExecution", "1.6.0")
+  protected[sql] class QueryExecution(logical: LogicalPlan)
+    extends sparkexecution.QueryExecution(this, logical)
 
   /**
    * Parses the data type in our internal string representation. The data type string should
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala
new file mode 100644
index 0000000000000..7bb4133a29059
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.annotation.{Experimental, DeveloperApi}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.{InternalRow, optimizer}
+import org.apache.spark.sql.{SQLContext, Row}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+
+/**
+ * :: DeveloperApi ::
+ * The primary workflow for executing relational queries using Spark.  Designed to allow easy
+ * access to the intermediate phases of query execution for developers.
+ */
+@DeveloperApi
+class QueryExecution(val sqlContext: SQLContext, val logical: LogicalPlan) {
+  val analyzer = sqlContext.analyzer
+  val optimizer = sqlContext.optimizer
+  val planner = sqlContext.planner
+  val cacheManager = sqlContext.cacheManager
+  val prepareForExecution = sqlContext.prepareForExecution
+
+  def assertAnalyzed(): Unit = analyzer.checkAnalysis(analyzed)
+
+  lazy val analyzed: LogicalPlan = analyzer.execute(logical)
+  lazy val withCachedData: LogicalPlan = {
+    assertAnalyzed()
+    cacheManager.useCachedData(analyzed)
+  }
+  lazy val optimizedPlan: LogicalPlan = optimizer.execute(withCachedData)
+
+  // TODO: Don't just pick the first one...
+  lazy val sparkPlan: SparkPlan = {
+    SparkPlan.currentContext.set(sqlContext)
+    planner.plan(optimizedPlan).next()
+  }
+  // executedPlan should not be used to initialize any SparkPlan. It should be
+  // only used for execution.
+  lazy val executedPlan: SparkPlan = prepareForExecution.execute(sparkPlan)
+
+  /** Internal version of the RDD. Avoids copies and has no schema */
+  lazy val toRdd: RDD[InternalRow] = executedPlan.execute()
+
+  protected def stringOrError[A](f: => A): String =
+    try f.toString catch { case e: Throwable => e.toString }
+
+  def simpleString: String =
+    s"""== Physical Plan ==
+       |${stringOrError(executedPlan)}
+      """.stripMargin.trim
+
+
+  override def toString: String = {
+    def output =
+      analyzed.output.map(o => s"${o.name}: ${o.dataType.simpleString}").mkString(", ")
+
+    s"""== Parsed Logical Plan ==
+       |${stringOrError(logical)}
+       |== Analyzed Logical Plan ==
+       |${stringOrError(output)}
+       |${stringOrError(analyzed)}
+       |== Optimized Logical Plan ==
+       |${stringOrError(optimizedPlan)}
+       |== Physical Plan ==
+       |${stringOrError(executedPlan)}
+       |Code Generation: ${stringOrError(executedPlan.codegenEnabled)}
+    """.stripMargin.trim
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
index cee58218a885b..1422e15549c94 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
@@ -37,7 +37,7 @@ private[sql] object SQLExecution {
    * we can connect them with an execution.
    */
   def withNewExecutionId[T](
-      sqlContext: SQLContext, queryExecution: SQLContext#QueryExecution)(body: => T): T = {
+      sqlContext: SQLContext, queryExecution: QueryExecution)(body: => T): T = {
     val sc = sqlContext.sparkContext
     val oldExecutionId = sc.getLocalProperty(EXECUTION_ID_KEY)
     if (oldExecutionId == null) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala
new file mode 100644
index 0000000000000..b346f43faebe2
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala
@@ -0,0 +1,92 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.datasources.DataSourceStrategy
+
+@Experimental
+class SparkPlanner(val sqlContext: SQLContext) extends SparkStrategies {
+  val sparkContext: SparkContext = sqlContext.sparkContext
+
+  def codegenEnabled: Boolean = sqlContext.conf.codegenEnabled
+
+  def unsafeEnabled: Boolean = sqlContext.conf.unsafeEnabled
+
+  def numPartitions: Int = sqlContext.conf.numShufflePartitions
+
+  def strategies: Seq[Strategy] =
+    sqlContext.experimental.extraStrategies ++ (
+      DataSourceStrategy ::
+      DDLStrategy ::
+      TakeOrderedAndProject ::
+      HashAggregation ::
+      Aggregation ::
+      LeftSemiJoin ::
+      EquiJoinSelection ::
+      InMemoryScans ::
+      BasicOperators ::
+      CartesianProduct ::
+      BroadcastNestedLoopJoin :: Nil)
+
+  /**
+   * Used to build table scan operators where complex projection and filtering are done using
+   * separate physical operators.  This function returns the given scan operator with Project and
+   * Filter nodes added only when needed.  For example, a Project operator is only used when the
+   * final desired output requires complex expressions to be evaluated or when columns can be
+   * further eliminated out after filtering has been done.
+   *
+   * The `prunePushedDownFilters` parameter is used to remove those filters that can be optimized
+   * away by the filter pushdown optimization.
+   *
+   * The required attributes for both filtering and expression evaluation are passed to the
+   * provided `scanBuilder` function so that it can avoid unnecessary column materialization.
+   */
+  def pruneFilterProject(
+      projectList: Seq[NamedExpression],
+      filterPredicates: Seq[Expression],
+      prunePushedDownFilters: Seq[Expression] => Seq[Expression],
+      scanBuilder: Seq[Attribute] => SparkPlan): SparkPlan = {
+
+    val projectSet = AttributeSet(projectList.flatMap(_.references))
+    val filterSet = AttributeSet(filterPredicates.flatMap(_.references))
+    val filterCondition =
+      prunePushedDownFilters(filterPredicates).reduceLeftOption(catalyst.expressions.And)
+
+    // Right now we still use a projection even if the only evaluation is applying an alias
+    // to a column.  Since this is a no-op, it could be avoided. However, using this
+    // optimization with the current implementation would change the output schema.
+    // TODO: Decouple final output schema from expression evaluation so this copy can be
+    // avoided safely.
+
+    if (AttributeSet(projectList.map(_.toAttribute)) == projectSet &&
+        filterSet.subsetOf(projectSet)) {
+      // When it is possible to just use column pruning to get the right projection and
+      // when the columns of this projection are enough to evaluate all filter conditions,
+      // just do a scan followed by a filter, with no extra project.
+      val scan = scanBuilder(projectList.asInstanceOf[Seq[Attribute]])
+      filterCondition.map(Filter(_, scan)).getOrElse(scan)
+    } else {
+      val scan = scanBuilder((projectSet ++ filterSet).toSeq)
+      Project(projectList, filterCondition.map(Filter(_, scan)).getOrElse(scan))
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 4572d5efc92bb..5e40d77689045 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -31,7 +31,7 @@ import org.apache.spark.sql.types._
 import org.apache.spark.sql.{SQLContext, Strategy, execution}
 
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
-  self: SQLContext#SparkPlanner =>
+  self: SparkPlanner =>
 
   object LeftSemiJoin extends Strategy with PredicateHelper {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {

From 217e4964444f4e07b894b1bca768a0cbbe799ea0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 14 Sep 2015 15:00:27 -0700
Subject: [PATCH 1418/1454] [SPARK-9996] [SPARK-9997] [SQL] Add local expand
 and NestedLoopJoin operators

This PR is in conflict with #8535 and #8573. Will update this one when they are merged.

Author: zsxwing <zsxwing@gmail.com>

Closes #8642 from zsxwing/expand-nest-join.
---
 .../sql/execution/local/ExpandNode.scala      |  60 +++++
 .../spark/sql/execution/local/LocalNode.scala |  55 +++-
 .../execution/local/NestedLoopJoinNode.scala  | 156 ++++++++++++
 .../sql/execution/local/ExpandNodeSuite.scala |  51 ++++
 .../execution/local/HashJoinNodeSuite.scala   |  14 -
 .../sql/execution/local/LocalNodeTest.scala   |  14 +
 .../local/NestedLoopJoinNodeSuite.scala       | 239 ++++++++++++++++++
 7 files changed, 574 insertions(+), 15 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala
new file mode 100644
index 0000000000000..2aff156d18b54
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala
@@ -0,0 +1,60 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Projection}
+
+case class ExpandNode(
+    conf: SQLConf,
+    projections: Seq[Seq[Expression]],
+    output: Seq[Attribute],
+    child: LocalNode) extends UnaryLocalNode(conf) {
+
+  assert(projections.size > 0)
+
+  private[this] var result: InternalRow = _
+  private[this] var idx: Int = _
+  private[this] var input: InternalRow = _
+  private[this] var groups: Array[Projection] = _
+
+  override def open(): Unit = {
+    child.open()
+    groups = projections.map(ee => newProjection(ee, child.output)).toArray
+    idx = groups.length
+  }
+
+  override def next(): Boolean = {
+    if (idx >= groups.length) {
+      if (child.next()) {
+        input = child.fetch()
+        idx = 0
+      } else {
+        return false
+      }
+    }
+    result = groups(idx)(input)
+    idx += 1
+    true
+  }
+
+  override def fetch(): InternalRow = result
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index e540ef8555eb6..9840080e16953 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -23,7 +23,7 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.{SQLConf, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types.StructType
 
@@ -69,6 +69,18 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
    */
   def close(): Unit
 
+  /** Specifies whether this operator outputs UnsafeRows */
+  def outputsUnsafeRows: Boolean = false
+
+  /** Specifies whether this operator is capable of processing UnsafeRows */
+  def canProcessUnsafeRows: Boolean = false
+
+  /**
+   * Specifies whether this operator is capable of processing Java-object-based Rows (i.e. rows
+   * that are not UnsafeRows).
+   */
+  def canProcessSafeRows: Boolean = true
+
   /**
    * Returns the content through the [[Iterator]] interface.
    */
@@ -91,6 +103,28 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
     result
   }
 
+  protected def newProjection(
+      expressions: Seq[Expression],
+      inputSchema: Seq[Attribute]): Projection = {
+    log.debug(
+      s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
+    if (codegenEnabled) {
+      try {
+        GenerateProjection.generate(expressions, inputSchema)
+      } catch {
+        case NonFatal(e) =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate projection, fallback to interpret", e)
+            new InterpretedProjection(expressions, inputSchema)
+          }
+      }
+    } else {
+      new InterpretedProjection(expressions, inputSchema)
+    }
+  }
+
   protected def newMutableProjection(
       expressions: Seq[Expression],
       inputSchema: Seq[Attribute]): () => MutableProjection = {
@@ -113,6 +147,25 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
     }
   }
 
+  protected def newPredicate(
+      expression: Expression,
+      inputSchema: Seq[Attribute]): (InternalRow) => Boolean = {
+    if (codegenEnabled) {
+      try {
+        GeneratePredicate.generate(expression, inputSchema)
+      } catch {
+        case NonFatal(e) =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate predicate, fallback to interpreted", e)
+            InterpretedPredicate.create(expression, inputSchema)
+          }
+      }
+    } else {
+      InterpretedPredicate.create(expression, inputSchema)
+    }
+  }
 }
 
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala
new file mode 100644
index 0000000000000..7321fc66b4dde
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.{FullOuter, RightOuter, LeftOuter, JoinType}
+import org.apache.spark.sql.execution.joins.{BuildLeft, BuildRight, BuildSide}
+import org.apache.spark.util.collection.{BitSet, CompactBuffer}
+
+case class NestedLoopJoinNode(
+    conf: SQLConf,
+    left: LocalNode,
+    right: LocalNode,
+    buildSide: BuildSide,
+    joinType: JoinType,
+    condition: Option[Expression]) extends BinaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = {
+    joinType match {
+      case LeftOuter =>
+        left.output ++ right.output.map(_.withNullability(true))
+      case RightOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output
+      case FullOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output.map(_.withNullability(true))
+      case x =>
+        throw new IllegalArgumentException(
+          s"NestedLoopJoin should not take $x as the JoinType")
+    }
+  }
+
+  private[this] def genResultProjection: InternalRow => InternalRow = {
+    if (outputsUnsafeRows) {
+      UnsafeProjection.create(schema)
+    } else {
+      identity[InternalRow]
+    }
+  }
+
+  private[this] var currentRow: InternalRow = _
+
+  private[this] var iterator: Iterator[InternalRow] = _
+
+  override def open(): Unit = {
+    val (streamed, build) = buildSide match {
+      case BuildRight => (left, right)
+      case BuildLeft => (right, left)
+    }
+    build.open()
+    val buildRelation = new CompactBuffer[InternalRow]
+    while (build.next()) {
+      buildRelation += build.fetch().copy()
+    }
+    build.close()
+
+    val boundCondition =
+      newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
+
+    val leftNulls = new GenericMutableRow(left.output.size)
+    val rightNulls = new GenericMutableRow(right.output.size)
+    val joinedRow = new JoinedRow
+    val matchedBuildTuples = new BitSet(buildRelation.size)
+    val resultProj = genResultProjection
+    streamed.open()
+
+    // streamedRowMatches also contains null rows if using outer join
+    val streamedRowMatches: Iterator[InternalRow] = streamed.asIterator.flatMap { streamedRow =>
+      val matchedRows = new CompactBuffer[InternalRow]
+
+      var i = 0
+      var streamRowMatched = false
+
+      // Scan the build relation to look for matches for each streamed row
+      while (i < buildRelation.size) {
+        val buildRow = buildRelation(i)
+        buildSide match {
+          case BuildRight => joinedRow(streamedRow, buildRow)
+          case BuildLeft => joinedRow(buildRow, streamedRow)
+        }
+        if (boundCondition(joinedRow)) {
+          matchedRows += resultProj(joinedRow).copy()
+          streamRowMatched = true
+          matchedBuildTuples.set(i)
+        }
+        i += 1
+      }
+
+      // If this row had no matches and we're using outer join, join it with the null rows
+      if (!streamRowMatched) {
+        (joinType, buildSide) match {
+          case (LeftOuter | FullOuter, BuildRight) =>
+            matchedRows += resultProj(joinedRow(streamedRow, rightNulls)).copy()
+          case (RightOuter | FullOuter, BuildLeft) =>
+            matchedRows += resultProj(joinedRow(leftNulls, streamedRow)).copy()
+          case _ =>
+        }
+      }
+
+      matchedRows.iterator
+    }
+
+    // If we're using outer join, find rows on the build side that didn't match anything
+    // and join them with the null row
+    lazy val unmatchedBuildRows: Iterator[InternalRow] = {
+      var i = 0
+      buildRelation.filter { row =>
+        val r = !matchedBuildTuples.get(i)
+        i += 1
+        r
+      }.iterator
+    }
+    iterator = (joinType, buildSide) match {
+      case (RightOuter | FullOuter, BuildRight) =>
+        streamedRowMatches ++
+          unmatchedBuildRows.map { buildRow => resultProj(joinedRow(leftNulls, buildRow)) }
+      case (LeftOuter | FullOuter, BuildLeft) =>
+        streamedRowMatches ++
+          unmatchedBuildRows.map { buildRow => resultProj(joinedRow(buildRow, rightNulls)) }
+      case _ => streamedRowMatches
+    }
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      currentRow = iterator.next()
+      true
+    } else {
+      false
+    }
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = {
+    left.close()
+    right.close()
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
new file mode 100644
index 0000000000000..cfa7f3f6dcb97
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
@@ -0,0 +1,51 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+class ExpandNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  test("expand") {
+    val input = Seq((1, 1), (2, 2), (3, 3), (4, 4), (5, 5)).toDF("key", "value")
+    checkAnswer(
+      input,
+      node =>
+        ExpandNode(conf, Seq(
+          Seq(
+            input.col("key") + input.col("value"), input.col("key") - input.col("value")
+          ).map(_.expr),
+          Seq(
+            input.col("key") * input.col("value"), input.col("key") / input.col("value")
+          ).map(_.expr)
+        ), node.output, node),
+      Seq(
+        (2, 0),
+        (1, 1),
+        (4, 0),
+        (4, 1),
+        (6, 0),
+        (9, 1),
+        (8, 0),
+        (16, 1),
+        (10, 0),
+        (25, 1)
+      ).toDF().collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
index 43b6f06aead88..78d891351f4a9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
@@ -24,20 +24,6 @@ class HashJoinNodeSuite extends LocalNodeTest {
 
   import testImplicits._
 
-  private def wrapForUnsafe(
-      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
-    if (conf.unsafeEnabled) {
-      (left: LocalNode, right: LocalNode) => {
-        val _left = ConvertToUnsafeNode(conf, left)
-        val _right = ConvertToUnsafeNode(conf, right)
-        val r = f(_left, _right)
-        ConvertToSafeNode(conf, r)
-      }
-    } else {
-      f
-    }
-  }
-
   def joinSuite(suiteName: String, confPairs: (String, String)*): Unit = {
     test(s"$suiteName: inner join with one match per row") {
       withSQLConf(confPairs: _*) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
index b95d4ea7f8f2a..86dd28064cc6a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
@@ -27,6 +27,20 @@ class LocalNodeTest extends SparkFunSuite with SharedSQLContext {
 
   def conf: SQLConf = sqlContext.conf
 
+  protected def wrapForUnsafe(
+      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
+    if (conf.unsafeEnabled) {
+      (left: LocalNode, right: LocalNode) => {
+        val _left = ConvertToUnsafeNode(conf, left)
+        val _right = ConvertToUnsafeNode(conf, right)
+        val r = f(_left, _right)
+        ConvertToSafeNode(conf, r)
+      }
+    } else {
+      f
+    }
+  }
+
   /**
    * Runs the LocalNode and makes sure the answer matches the expected result.
    * @param input the input data to be used.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala
new file mode 100644
index 0000000000000..b1ef26ba82f16
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala
@@ -0,0 +1,239 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, RightOuter}
+import org.apache.spark.sql.execution.joins.{BuildLeft, BuildRight, BuildSide}
+
+class NestedLoopJoinNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def joinSuite(
+      suiteName: String, buildSide: BuildSide, confPairs: (String, String)*): Unit = {
+    test(s"$suiteName: left outer join") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some((upperCaseData.col("N") === lowerCaseData.col("n")).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N", "left").collect())
+
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some(
+                (upperCaseData.col("N") === lowerCaseData.col("n") &&
+                  lowerCaseData.col("n") > 1).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"n" > 1, "left").collect())
+
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some(
+                (upperCaseData.col("N") === lowerCaseData.col("n") &&
+                  upperCaseData.col("N") > 1).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"N" > 1, "left").collect())
+
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some(
+                (upperCaseData.col("N") === lowerCaseData.col("n") &&
+                  lowerCaseData.col("l") > upperCaseData.col("L")).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"l" > $"L", "left").collect())
+      }
+    }
+
+    test(s"$suiteName: right outer join") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N", "right").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("n") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > 1, "right").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                upperCaseData.col("N") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > 1, "right").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("l") > upperCaseData.col("L")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"l" > $"L", "right").collect())
+      }
+    }
+
+    test(s"$suiteName: full outer join") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N", "full").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("n") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > 1, "full").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                upperCaseData.col("N") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > 1, "full").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("l") > upperCaseData.col("L")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"l" > $"L", "full").collect())
+      }
+    }
+  }
+
+  joinSuite(
+    "general-build-left",
+    BuildLeft,
+    SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
+  joinSuite(
+    "general-build-right",
+    BuildRight,
+    SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
+  joinSuite(
+    "tungsten-build-left",
+    BuildLeft,
+    SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
+  joinSuite(
+    "tungsten-build-right",
+    BuildRight,
+    SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
+}

From 16b6d18613e150c7038c613992d80a7828413e66 Mon Sep 17 00:00:00 2001
From: Erick Tryzelaar <erick.tryzelaar@gmail.com>
Date: Mon, 14 Sep 2015 15:02:38 -0700
Subject: [PATCH 1419/1454] [SPARK-10594] [YARN] Remove reference to
 --num-executors, add --properties-file

`ApplicationMaster` no longer has the `--num-executors` flag, and had an undocumented `--properties-file` configuration option.

cc srowen

Author: Erick Tryzelaar <erick.tryzelaar@gmail.com>

Closes #8754 from erickt/master.
---
 .../apache/spark/deploy/yarn/ApplicationMasterArguments.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index b08412414aa1c..17d9943c795e3 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -105,9 +105,9 @@ class ApplicationMasterArguments(val args: Array[String]) {
       |                       place on the PYTHONPATH for Python apps.
       |  --args ARGS          Arguments to be passed to your application's main class.
       |                       Multiple invocations are possible, each will be passed in order.
-      |  --num-executors NUM    Number of executors to start (Default: 2)
       |  --executor-cores NUM   Number of cores for the executors (Default: 1)
       |  --executor-memory MEM  Memory per executor (e.g. 1000M, 2G) (Default: 1G)
+      |  --properties-file FILE Path to a custom Spark properties file.
       """.stripMargin)
     // scalastyle:on println
     System.exit(exitCode)

From 4e2242bb41dda922573046c00c5142745632f95f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 14 Sep 2015 15:03:51 -0700
Subject: [PATCH 1420/1454] [SPARK-10576] [BUILD] Move .java files out of
 src/main/scala

Move .java files in `src/main/scala` to `src/main/java` root, except for `package-info.java` (to stay next to package.scala)

Author: Sean Owen <sowen@cloudera.com>

Closes #8736 from srowen/SPARK-10576.
---
 .../org/apache/spark/annotation/AlphaComponent.java               | 0
 .../{scala => java}/org/apache/spark/annotation/DeveloperApi.java | 0
 .../{scala => java}/org/apache/spark/annotation/Experimental.java | 0
 .../main/{scala => java}/org/apache/spark/annotation/Private.java | 0
 .../{scala => java}/org/apache/spark/graphx/TripletFields.java    | 0
 .../org/apache/spark/graphx/impl/EdgeActiveness.java              | 0
 .../org/apache/spark/sql/types/SQLUserDefinedType.java            | 0
 .../org/apache/spark/streaming/StreamingContextState.java         | 0
 8 files changed, 0 insertions(+), 0 deletions(-)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/AlphaComponent.java (100%)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/DeveloperApi.java (100%)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/Experimental.java (100%)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/Private.java (100%)
 rename graphx/src/main/{scala => java}/org/apache/spark/graphx/TripletFields.java (100%)
 rename graphx/src/main/{scala => java}/org/apache/spark/graphx/impl/EdgeActiveness.java (100%)
 rename sql/catalyst/src/main/{scala => java}/org/apache/spark/sql/types/SQLUserDefinedType.java (100%)
 rename streaming/src/main/{scala => java}/org/apache/spark/streaming/StreamingContextState.java (100%)

diff --git a/core/src/main/scala/org/apache/spark/annotation/AlphaComponent.java b/core/src/main/java/org/apache/spark/annotation/AlphaComponent.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/AlphaComponent.java
rename to core/src/main/java/org/apache/spark/annotation/AlphaComponent.java
diff --git a/core/src/main/scala/org/apache/spark/annotation/DeveloperApi.java b/core/src/main/java/org/apache/spark/annotation/DeveloperApi.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/DeveloperApi.java
rename to core/src/main/java/org/apache/spark/annotation/DeveloperApi.java
diff --git a/core/src/main/scala/org/apache/spark/annotation/Experimental.java b/core/src/main/java/org/apache/spark/annotation/Experimental.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/Experimental.java
rename to core/src/main/java/org/apache/spark/annotation/Experimental.java
diff --git a/core/src/main/scala/org/apache/spark/annotation/Private.java b/core/src/main/java/org/apache/spark/annotation/Private.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/Private.java
rename to core/src/main/java/org/apache/spark/annotation/Private.java
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/TripletFields.java b/graphx/src/main/java/org/apache/spark/graphx/TripletFields.java
similarity index 100%
rename from graphx/src/main/scala/org/apache/spark/graphx/TripletFields.java
rename to graphx/src/main/java/org/apache/spark/graphx/TripletFields.java
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeActiveness.java b/graphx/src/main/java/org/apache/spark/graphx/impl/EdgeActiveness.java
similarity index 100%
rename from graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeActiveness.java
rename to graphx/src/main/java/org/apache/spark/graphx/impl/EdgeActiveness.java
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java b/sql/catalyst/src/main/java/org/apache/spark/sql/types/SQLUserDefinedType.java
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java
rename to sql/catalyst/src/main/java/org/apache/spark/sql/types/SQLUserDefinedType.java
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContextState.java b/streaming/src/main/java/org/apache/spark/streaming/StreamingContextState.java
similarity index 100%
rename from streaming/src/main/scala/org/apache/spark/streaming/StreamingContextState.java
rename to streaming/src/main/java/org/apache/spark/streaming/StreamingContextState.java

From ffbbc2c58b9bf1e2abc2ea797feada6821ab4de8 Mon Sep 17 00:00:00 2001
From: Tom Graves <tgraves@yahoo-inc.com>
Date: Mon, 14 Sep 2015 15:05:19 -0700
Subject: [PATCH 1421/1454] [SPARK-10549] scala 2.11 spark on yarn with
 security - Repl doesn't work

Make this lazy so that it can set the yarn mode before creating the securityManager.

Author: Tom Graves <tgraves@yahoo-inc.com>
Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>

Closes #8719 from tgravescs/SPARK-10549.
---
 .../scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
index be31eb2eda546..627148df80c11 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
@@ -35,7 +35,8 @@ object Main extends Logging {
   s.processArguments(List("-Yrepl-class-based",
     "-Yrepl-outdir", s"${outputDir.getAbsolutePath}",
     "-classpath", getAddedJars.mkString(File.pathSeparator)), true)
-  val classServer = new HttpServer(conf, outputDir, new SecurityManager(conf))
+  // the creation of SecurityManager has to be lazy so SPARK_YARN_MODE is set if needed
+  lazy val classServer = new HttpServer(conf, outputDir, new SecurityManager(conf))
   var sparkContext: SparkContext = _
   var sqlContext: SQLContext = _
   var interp = new SparkILoop // this is a public var because tests reset it.

From fd1e8cddf2635c55fec2ac6e1f1c221c9685af0f Mon Sep 17 00:00:00 2001
From: Forest Fang <forest.fang@outlook.com>
Date: Mon, 14 Sep 2015 15:07:13 -0700
Subject: [PATCH 1422/1454] [SPARK-10543] [CORE] Peak Execution Memory Quantile
 should be Per-task Basis

Read `PEAK_EXECUTION_MEMORY` using `update` to get per task partial value instead of cumulative value.

I tested with this workload:

```scala
val size = 1000
val repetitions = 10
val data = sc.parallelize(1 to size, 5).map(x => (util.Random.nextInt(size / repetitions),util.Random.nextDouble)).toDF("key", "value")
val res = data.toDF.groupBy("key").agg(sum("value")).count
```

Before:
![image](https://cloud.githubusercontent.com/assets/4317392/9828197/07dd6874-58b8-11e5-9bd9-6ba927c38b26.png)

After:
![image](https://cloud.githubusercontent.com/assets/4317392/9828151/a5ddff30-58b7-11e5-8d31-eda5dc4eae79.png)

Tasks view:
![image](https://cloud.githubusercontent.com/assets/4317392/9828199/17dc2b84-58b8-11e5-92a8-be89ce4d29d1.png)

cc andrewor14 I appreciate if you can give feedback on this since I think you introduced display of this metric.

Author: Forest Fang <forest.fang@outlook.com>

Closes #8726 from saurfang/stagepage.
---
 .../org/apache/spark/ui/jobs/StagePage.scala  |  2 +-
 .../org/apache/spark/ui/StagePageSuite.scala  | 29 ++++++++++++++-----
 2 files changed, 23 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 4adc6596ba21c..2b71f55b7bb4f 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -368,7 +368,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
           val peakExecutionMemory = validTasks.map { case TaskUIData(info, _, _) =>
             info.accumulables
               .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
-              .map { acc => acc.value.toLong }
+              .map { acc => acc.update.getOrElse("0").toLong }
               .getOrElse(0L)
               .toDouble
           }
diff --git a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
index 3388c6dca81f1..86699e7f56953 100644
--- a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
@@ -23,7 +23,7 @@ import scala.xml.Node
 
 import org.mockito.Mockito.{mock, when, RETURNS_SMART_NULLS}
 
-import org.apache.spark.{LocalSparkContext, SparkConf, SparkFunSuite, Success}
+import org.apache.spark._
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.ui.jobs.{JobProgressListener, StagePage, StagesTab}
@@ -47,6 +47,14 @@ class StagePageSuite extends SparkFunSuite with LocalSparkContext {
     assert(html3.contains(targetString))
   }
 
+  test("SPARK-10543: peak execution memory should be per-task rather than cumulative") {
+    val unsafeConf = "spark.sql.unsafe.enabled"
+    val conf = new SparkConf(false).set(unsafeConf, "true")
+    val html = renderStagePage(conf).toString().toLowerCase
+    // verify min/25/50/75/max show task value not cumulative values
+    assert(html.contains("<td>10.0 b</td>" * 5))
+  }
+
   /**
    * Render a stage page started with the given conf and return the HTML.
    * This also runs a dummy stage to populate the page with useful content.
@@ -67,12 +75,19 @@ class StagePageSuite extends SparkFunSuite with LocalSparkContext {
 
     // Simulate a stage in job progress listener
     val stageInfo = new StageInfo(0, 0, "dummy", 1, Seq.empty, Seq.empty, "details")
-    val taskInfo = new TaskInfo(0, 0, 0, 0, "0", "localhost", TaskLocality.ANY, false)
-    jobListener.onStageSubmitted(SparkListenerStageSubmitted(stageInfo))
-    jobListener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo))
-    taskInfo.markSuccessful()
-    jobListener.onTaskEnd(
-      SparkListenerTaskEnd(0, 0, "result", Success, taskInfo, TaskMetrics.empty))
+    // Simulate two tasks to test PEAK_EXECUTION_MEMORY correctness
+    (1 to 2).foreach {
+      taskId =>
+        val taskInfo = new TaskInfo(taskId, taskId, 0, 0, "0", "localhost", TaskLocality.ANY, false)
+        val peakExecutionMemory = 10
+        taskInfo.accumulables += new AccumulableInfo(0, InternalAccumulator.PEAK_EXECUTION_MEMORY,
+          Some(peakExecutionMemory.toString), (peakExecutionMemory * taskId).toString, true)
+        jobListener.onStageSubmitted(SparkListenerStageSubmitted(stageInfo))
+        jobListener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo))
+        taskInfo.markSuccessful()
+        jobListener.onTaskEnd(
+          SparkListenerTaskEnd(0, 0, "result", Success, taskInfo, TaskMetrics.empty))
+    }
     jobListener.onStageCompleted(SparkListenerStageCompleted(stageInfo))
     page.render(request)
   }

From 7b6c856367b9c36348e80e83959150da9656c4dd Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 14 Sep 2015 15:09:43 -0700
Subject: [PATCH 1423/1454] [SPARK-10564] ThreadingSuite: assertion failures in
 threads don't fail the test (round 2)

This is a follow-up patch to #8723. I missed one case there.

Author: Andrew Or <andrew@databricks.com>

Closes #8727 from andrewor14/fix-threading-suite.
---
 .../org/apache/spark/ThreadingSuite.scala     | 23 ++++++++++++-------
 1 file changed, 15 insertions(+), 8 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
index cda2b245526f7..a96a4ce201c21 100644
--- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
@@ -147,12 +147,12 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
       }.start()
     }
     sem.acquire(2)
+    throwable.foreach { t => throw t }
     if (ThreadingSuiteState.failed.get()) {
       logError("Waited 1 second without seeing runningThreads = 4 (it was " +
                 ThreadingSuiteState.runningThreads.get() + "); failing test")
       fail("One or more threads didn't see runningThreads = 4")
     }
-    throwable.foreach { t => throw t }
   }
 
   test("set local properties in different thread") {
@@ -178,8 +178,8 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     threads.foreach(_.start())
 
     sem.acquire(5)
-    assert(sc.getLocalProperty("test") === null)
     throwable.foreach { t => throw t }
+    assert(sc.getLocalProperty("test") === null)
   }
 
   test("set and get local properties in parent-children thread") {
@@ -207,15 +207,16 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     threads.foreach(_.start())
 
     sem.acquire(5)
+    throwable.foreach { t => throw t }
     assert(sc.getLocalProperty("test") === "parent")
     assert(sc.getLocalProperty("Foo") === null)
-    throwable.foreach { t => throw t }
   }
 
   test("mutations to local properties should not affect submitted jobs (SPARK-6629)") {
     val jobStarted = new Semaphore(0)
     val jobEnded = new Semaphore(0)
     @volatile var jobResult: JobResult = null
+    var throwable: Option[Throwable] = None
 
     sc = new SparkContext("local", "test")
     sc.setJobGroup("originalJobGroupId", "description")
@@ -232,14 +233,19 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     // Create a new thread which will inherit the current thread's properties
     val thread = new Thread() {
       override def run(): Unit = {
-        assert(sc.getLocalProperty(SparkContext.SPARK_JOB_GROUP_ID) === "originalJobGroupId")
-        // Sleeps for a total of 10 seconds, but allows cancellation to interrupt the task
         try {
-          sc.parallelize(1 to 100).foreach { x =>
-            Thread.sleep(100)
+          assert(sc.getLocalProperty(SparkContext.SPARK_JOB_GROUP_ID) === "originalJobGroupId")
+          // Sleeps for a total of 10 seconds, but allows cancellation to interrupt the task
+          try {
+            sc.parallelize(1 to 100).foreach { x =>
+              Thread.sleep(100)
+            }
+          } catch {
+            case s: SparkException => // ignored so that we don't print noise in test logs
           }
         } catch {
-          case s: SparkException => // ignored so that we don't print noise in test logs
+          case t: Throwable =>
+            throwable = Some(t)
         }
       }
     }
@@ -252,6 +258,7 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     // modification of the properties object should not affect the properties of running jobs
     sc.cancelJobGroup("originalJobGroupId")
     jobEnded.tryAcquire(10, TimeUnit.SECONDS)
+    throwable.foreach { t => throw t }
     assert(jobResult.isInstanceOf[JobFailed])
   }
 }

From 1a0955250bb65cd6f5818ad60efb62ea4b45d18e Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Mon, 14 Sep 2015 21:47:40 -0400
Subject: [PATCH 1424/1454] [SPARK-9851] Support submitting map stages
 individually in DAGScheduler

This patch adds support for submitting map stages in a DAG individually so that we can make downstream decisions after seeing statistics about their output, as part of SPARK-9850. I also added more comments to many of the key classes in DAGScheduler. By itself, the patch is not super useful except maybe to switch between a shuffle and broadcast join, but with the other subtasks of SPARK-9850 we'll be able to do more interesting decisions.

The main entry point is SparkContext.submitMapStage, which lets you run a map stage and see stats about the map output sizes. Other stats could also be collected through accumulators. See AdaptiveSchedulingSuite for a short example.

Author: Matei Zaharia <matei@databricks.com>

Closes #8180 from mateiz/spark-9851.
---
 .../apache/spark/MapOutputStatistics.scala    |  27 ++
 .../org/apache/spark/MapOutputTracker.scala   |  49 +++-
 .../scala/org/apache/spark/SparkContext.scala |  17 ++
 .../apache/spark/scheduler/ActiveJob.scala    |  34 ++-
 .../apache/spark/scheduler/DAGScheduler.scala | 251 +++++++++++++++---
 .../spark/scheduler/DAGSchedulerEvent.scala   |  10 +
 .../apache/spark/scheduler/ResultStage.scala  |  17 +-
 .../spark/scheduler/ShuffleMapStage.scala     |  13 +-
 .../org/apache/spark/scheduler/Stage.scala    |  26 +-
 .../scala/org/apache/spark/FailureSuite.scala |  21 ++
 .../scheduler/AdaptiveSchedulingSuite.scala   |  65 +++++
 .../spark/scheduler/DAGSchedulerSuite.scala   | 243 ++++++++++++++++-
 12 files changed, 710 insertions(+), 63 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/MapOutputStatistics.scala
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/MapOutputStatistics.scala b/core/src/main/scala/org/apache/spark/MapOutputStatistics.scala
new file mode 100644
index 0000000000000..f8a6f1d0d8cbb
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/MapOutputStatistics.scala
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Holds statistics about the output sizes in a map stage. May become a DeveloperApi in the future.
+ *
+ * @param shuffleId ID of the shuffle
+ * @param bytesByPartitionId approximate number of output bytes for each map output partition
+ *   (may be inexact due to use of compressed map statuses)
+ */
+private[spark] class MapOutputStatistics(val shuffleId: Int, val bytesByPartitionId: Array[Long])
diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index a387592783850..94eb8daa85c53 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -18,6 +18,7 @@
 package org.apache.spark
 
 import java.io._
+import java.util.Arrays
 import java.util.concurrent.ConcurrentHashMap
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
@@ -132,13 +133,43 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
    *         describing the shuffle blocks that are stored at that block manager.
    */
   def getMapSizesByExecutorId(shuffleId: Int, reduceId: Int)
-  : Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
+      : Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
     logDebug(s"Fetching outputs for shuffle $shuffleId, reduce $reduceId")
-    val startTime = System.currentTimeMillis
+    val statuses = getStatuses(shuffleId)
+    // Synchronize on the returned array because, on the driver, it gets mutated in place
+    statuses.synchronized {
+      return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, statuses)
+    }
+  }
 
+  /**
+   * Return statistics about all of the outputs for a given shuffle.
+   */
+  def getStatistics(dep: ShuffleDependency[_, _, _]): MapOutputStatistics = {
+    val statuses = getStatuses(dep.shuffleId)
+    // Synchronize on the returned array because, on the driver, it gets mutated in place
+    statuses.synchronized {
+      val totalSizes = new Array[Long](dep.partitioner.numPartitions)
+      for (s <- statuses) {
+        for (i <- 0 until totalSizes.length) {
+          totalSizes(i) += s.getSizeForBlock(i)
+        }
+      }
+      new MapOutputStatistics(dep.shuffleId, totalSizes)
+    }
+  }
+
+  /**
+   * Get or fetch the array of MapStatuses for a given shuffle ID. NOTE: clients MUST synchronize
+   * on this array when reading it, because on the driver, we may be changing it in place.
+   *
+   * (It would be nice to remove this restriction in the future.)
+   */
+  private def getStatuses(shuffleId: Int): Array[MapStatus] = {
     val statuses = mapStatuses.get(shuffleId).orNull
     if (statuses == null) {
       logInfo("Don't have map outputs for shuffle " + shuffleId + ", fetching them")
+      val startTime = System.currentTimeMillis
       var fetchedStatuses: Array[MapStatus] = null
       fetching.synchronized {
         // Someone else is fetching it; wait for them to be done
@@ -160,7 +191,7 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
       }
 
       if (fetchedStatuses == null) {
-        // We won the race to fetch the output locs; do so
+        // We won the race to fetch the statuses; do so
         logInfo("Doing the fetch; tracker endpoint = " + trackerEndpoint)
         // This try-finally prevents hangs due to timeouts:
         try {
@@ -175,22 +206,18 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
           }
         }
       }
-      logDebug(s"Fetching map output location for shuffle $shuffleId, reduce $reduceId took " +
+      logDebug(s"Fetching map output statuses for shuffle $shuffleId took " +
         s"${System.currentTimeMillis - startTime} ms")
 
       if (fetchedStatuses != null) {
-        fetchedStatuses.synchronized {
-          return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, fetchedStatuses)
-        }
+        return fetchedStatuses
       } else {
         logError("Missing all output locations for shuffle " + shuffleId)
         throw new MetadataFetchFailedException(
-          shuffleId, reduceId, "Missing all output locations for shuffle " + shuffleId)
+          shuffleId, -1, "Missing all output locations for shuffle " + shuffleId)
       }
     } else {
-      statuses.synchronized {
-        return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, statuses)
-      }
+      return statuses
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index e27b3c4962221..dee6091ce3caf 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1984,6 +1984,23 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     new SimpleFutureAction(waiter, resultFunc)
   }
 
+  /**
+   * Submit a map stage for execution. This is currently an internal API only, but might be
+   * promoted to DeveloperApi in the future.
+   */
+  private[spark] def submitMapStage[K, V, C](dependency: ShuffleDependency[K, V, C])
+      : SimpleFutureAction[MapOutputStatistics] = {
+    assertNotStopped()
+    val callSite = getCallSite()
+    var result: MapOutputStatistics = null
+    val waiter = dagScheduler.submitMapStage(
+      dependency,
+      (r: MapOutputStatistics) => { result = r },
+      callSite,
+      localProperties.get)
+    new SimpleFutureAction[MapOutputStatistics](waiter, result)
+  }
+
   /**
    * Cancel active jobs for the specified group. See [[org.apache.spark.SparkContext.setJobGroup]]
    * for more information.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
index 50a69379412d2..a3d2db31301b3 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
@@ -23,18 +23,42 @@ import org.apache.spark.TaskContext
 import org.apache.spark.util.CallSite
 
 /**
- * Tracks information about an active job in the DAGScheduler.
+ * A running job in the DAGScheduler. Jobs can be of two types: a result job, which computes a
+ * ResultStage to execute an action, or a map-stage job, which computes the map outputs for a
+ * ShuffleMapStage before any downstream stages are submitted. The latter is used for adaptive
+ * query planning, to look at map output statistics before submitting later stages. We distinguish
+ * between these two types of jobs using the finalStage field of this class.
+ *
+ * Jobs are only tracked for "leaf" stages that clients directly submitted, through DAGScheduler's
+ * submitJob or submitMapStage methods. However, either type of job may cause the execution of
+ * other earlier stages (for RDDs in the DAG it depends on), and multiple jobs may share some of
+ * these previous stages. These dependencies are managed inside DAGScheduler.
+ *
+ * @param jobId A unique ID for this job.
+ * @param finalStage The stage that this job computes (either a ResultStage for an action or a
+ *   ShuffleMapStage for submitMapStage).
+ * @param callSite Where this job was initiated in the user's program (shown on UI).
+ * @param listener A listener to notify if tasks in this job finish or the job fails.
+ * @param properties Scheduling properties attached to the job, such as fair scheduler pool name.
  */
 private[spark] class ActiveJob(
     val jobId: Int,
-    val finalStage: ResultStage,
-    val func: (TaskContext, Iterator[_]) => _,
-    val partitions: Array[Int],
+    val finalStage: Stage,
     val callSite: CallSite,
     val listener: JobListener,
     val properties: Properties) {
 
-  val numPartitions = partitions.length
+  /**
+   * Number of partitions we need to compute for this job. Note that result stages may not need
+   * to compute all partitions in their target RDD, for actions like first() and lookup().
+   */
+  val numPartitions = finalStage match {
+    case r: ResultStage => r.partitions.length
+    case m: ShuffleMapStage => m.rdd.partitions.length
+  }
+
+  /** Which partitions of the stage have finished */
   val finished = Array.fill[Boolean](numPartitions)(false)
+
   var numFinished = 0
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 09e963f5cdf60..b4f90e8347894 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -45,17 +45,65 @@ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
  * The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of
  * stages for each job, keeps track of which RDDs and stage outputs are materialized, and finds a
  * minimal schedule to run the job. It then submits stages as TaskSets to an underlying
- * TaskScheduler implementation that runs them on the cluster.
+ * TaskScheduler implementation that runs them on the cluster. A TaskSet contains fully independent
+ * tasks that can run right away based on the data that's already on the cluster (e.g. map output
+ * files from previous stages), though it may fail if this data becomes unavailable.
  *
- * In addition to coming up with a DAG of stages, this class also determines the preferred
+ * Spark stages are created by breaking the RDD graph at shuffle boundaries. RDD operations with
+ * "narrow" dependencies, like map() and filter(), are pipelined together into one set of tasks
+ * in each stage, but operations with shuffle dependencies require multiple stages (one to write a
+ * set of map output files, and another to read those files after a barrier). In the end, every
+ * stage will have only shuffle dependencies on other stages, and may compute multiple operations
+ * inside it. The actual pipelining of these operations happens in the RDD.compute() functions of
+ * various RDDs (MappedRDD, FilteredRDD, etc).
+ *
+ * In addition to coming up with a DAG of stages, the DAGScheduler also determines the preferred
  * locations to run each task on, based on the current cache status, and passes these to the
  * low-level TaskScheduler. Furthermore, it handles failures due to shuffle output files being
  * lost, in which case old stages may need to be resubmitted. Failures *within* a stage that are
  * not caused by shuffle file loss are handled by the TaskScheduler, which will retry each task
  * a small number of times before cancelling the whole stage.
  *
+ * When looking through this code, there are several key concepts:
+ *
+ *  - Jobs (represented by [[ActiveJob]]) are the top-level work items submitted to the scheduler.
+ *    For example, when the user calls an action, like count(), a job will be submitted through
+ *    submitJob. Each Job may require the execution of multiple stages to build intermediate data.
+ *
+ *  - Stages ([[Stage]]) are sets of tasks that compute intermediate results in jobs, where each
+ *    task computes the same function on partitions of the same RDD. Stages are separated at shuffle
+ *    boundaries, which introduce a barrier (where we must wait for the previous stage to finish to
+ *    fetch outputs). There are two types of stages: [[ResultStage]], for the final stage that
+ *    executes an action, and [[ShuffleMapStage]], which writes map output files for a shuffle.
+ *    Stages are often shared across multiple jobs, if these jobs reuse the same RDDs.
+ *
+ *  - Tasks are individual units of work, each sent to one machine.
+ *
+ *  - Cache tracking: the DAGScheduler figures out which RDDs are cached to avoid recomputing them
+ *    and likewise remembers which shuffle map stages have already produced output files to avoid
+ *    redoing the map side of a shuffle.
+ *
+ *  - Preferred locations: the DAGScheduler also computes where to run each task in a stage based
+ *    on the preferred locations of its underlying RDDs, or the location of cached or shuffle data.
+ *
+ *  - Cleanup: all data structures are cleared when the running jobs that depend on them finish,
+ *    to prevent memory leaks in a long-running application.
+ *
+ * To recover from failures, the same stage might need to run multiple times, which are called
+ * "attempts". If the TaskScheduler reports that a task failed because a map output file from a
+ * previous stage was lost, the DAGScheduler resubmits that lost stage. This is detected through a
+ * CompletionEvent with FetchFailed, or an ExecutorLost event. The DAGScheduler will wait a small
+ * amount of time to see whether other nodes or tasks fail, then resubmit TaskSets for any lost
+ * stage(s) that compute the missing tasks. As part of this process, we might also have to create
+ * Stage objects for old (finished) stages where we previously cleaned up the Stage object. Since
+ * tasks from the old attempt of a stage could still be running, care must be taken to map any
+ * events received in the correct Stage object.
+ *
  * Here's a checklist to use when making or reviewing changes to this class:
  *
+ *  - All data structures should be cleared when the jobs involving them end to avoid indefinite
+ *    accumulation of state in long-running programs.
+ *
  *  - When adding a new data structure, update `DAGSchedulerSuite.assertDataStructuresEmpty` to
  *    include the new structure. This will help to catch memory leaks.
  */
@@ -295,12 +343,12 @@ class DAGScheduler(
    */
   private def newResultStage(
       rdd: RDD[_],
-      numTasks: Int,
+      func: (TaskContext, Iterator[_]) => _,
+      partitions: Array[Int],
       jobId: Int,
       callSite: CallSite): ResultStage = {
     val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId)
-    val stage: ResultStage = new ResultStage(id, rdd, numTasks, parentStages, jobId, callSite)
-
+    val stage = new ResultStage(id, rdd, func, partitions, parentStages, jobId, callSite)
     stageIdToStage(id) = stage
     updateJobIdStageIdMaps(jobId, stage)
     stage
@@ -500,12 +548,25 @@ class DAGScheduler(
     jobIdToStageIds -= job.jobId
     jobIdToActiveJob -= job.jobId
     activeJobs -= job
-    job.finalStage.resultOfJob = None
+    job.finalStage match {
+      case r: ResultStage =>
+        r.resultOfJob = None
+      case m: ShuffleMapStage =>
+        m.mapStageJobs = m.mapStageJobs.filter(_ != job)
+    }
   }
 
   /**
-   * Submit a job to the job scheduler and get a JobWaiter object back. The JobWaiter object
+   * Submit an action job to the scheduler and get a JobWaiter object back. The JobWaiter object
    * can be used to block until the the job finishes executing or can be used to cancel the job.
+   *
+   * @param rdd target RDD to run tasks on
+   * @param func a function to run on each partition of the RDD
+   * @param partitions set of partitions to run on; some jobs may not want to compute on all
+   *   partitions of the target RDD, e.g. for operations like first()
+   * @param callSite where in the user program this job was called
+   * @param resultHandler callback to pass each result to
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
    */
   def submitJob[T, U](
       rdd: RDD[T],
@@ -524,6 +585,7 @@ class DAGScheduler(
 
     val jobId = nextJobId.getAndIncrement()
     if (partitions.size == 0) {
+      // Return immediately if the job is running 0 tasks
       return new JobWaiter[U](this, jobId, 0, resultHandler)
     }
 
@@ -536,6 +598,18 @@ class DAGScheduler(
     waiter
   }
 
+  /**
+   * Run an action job on the given RDD and pass all the results to the resultHandler function as
+   * they arrive. Throws an exception if the job fials, or returns normally if successful.
+   *
+   * @param rdd target RDD to run tasks on
+   * @param func a function to run on each partition of the RDD
+   * @param partitions set of partitions to run on; some jobs may not want to compute on all
+   *   partitions of the target RDD, e.g. for operations like first()
+   * @param callSite where in the user program this job was called
+   * @param resultHandler callback to pass each result to
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
+   */
   def runJob[T, U](
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
@@ -559,6 +633,17 @@ class DAGScheduler(
     }
   }
 
+  /**
+   * Run an approximate job on the given RDD and pass all the results to an ApproximateEvaluator
+   * as they arrive. Returns a partial result object from the evaluator.
+   *
+   * @param rdd target RDD to run tasks on
+   * @param func a function to run on each partition of the RDD
+   * @param evaluator [[ApproximateEvaluator]] to receive the partial results
+   * @param callSite where in the user program this job was called
+   * @param timeout maximum time to wait for the job, in milliseconds
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
+   */
   def runApproximateJob[T, U, R](
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
@@ -575,6 +660,41 @@ class DAGScheduler(
     listener.awaitResult()    // Will throw an exception if the job fails
   }
 
+  /**
+   * Submit a shuffle map stage to run independently and get a JobWaiter object back. The waiter
+   * can be used to block until the the job finishes executing or can be used to cancel the job.
+   * This method is used for adaptive query planning, to run map stages and look at statistics
+   * about their outputs before submitting downstream stages.
+   *
+   * @param dependency the ShuffleDependency to run a map stage for
+   * @param callback function called with the result of the job, which in this case will be a
+   *   single MapOutputStatistics object showing how much data was produced for each partition
+   * @param callSite where in the user program this job was submitted
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
+   */
+  def submitMapStage[K, V, C](
+      dependency: ShuffleDependency[K, V, C],
+      callback: MapOutputStatistics => Unit,
+      callSite: CallSite,
+      properties: Properties): JobWaiter[MapOutputStatistics] = {
+
+    val rdd = dependency.rdd
+    val jobId = nextJobId.getAndIncrement()
+    if (rdd.partitions.length == 0) {
+      throw new SparkException("Can't run submitMapStage on RDD with 0 partitions")
+    }
+
+    // We create a JobWaiter with only one "task", which will be marked as complete when the whole
+    // map stage has completed, and will be passed the MapOutputStatistics for that stage.
+    // This makes it easier to avoid race conditions between the user code and the map output
+    // tracker that might result if we told the user the stage had finished, but then they queries
+    // the map output tracker and some node failures had caused the output statistics to be lost.
+    val waiter = new JobWaiter(this, jobId, 1, (i: Int, r: MapOutputStatistics) => callback(r))
+    eventProcessLoop.post(MapStageSubmitted(
+      jobId, dependency, callSite, waiter, SerializationUtils.clone(properties)))
+    waiter
+  }
+
   /**
    * Cancel a job that is running or waiting in the queue.
    */
@@ -583,6 +703,9 @@ class DAGScheduler(
     eventProcessLoop.post(JobCancelled(jobId))
   }
 
+  /**
+   * Cancel all jobs in the given job group ID.
+   */
   def cancelJobGroup(groupId: String): Unit = {
     logInfo("Asked to cancel job group " + groupId)
     eventProcessLoop.post(JobGroupCancelled(groupId))
@@ -720,31 +843,77 @@ class DAGScheduler(
     try {
       // New stage creation may throw an exception if, for example, jobs are run on a
       // HadoopRDD whose underlying HDFS files have been deleted.
-      finalStage = newResultStage(finalRDD, partitions.length, jobId, callSite)
+      finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
     } catch {
       case e: Exception =>
         logWarning("Creating new stage failed due to exception - job: " + jobId, e)
         listener.jobFailed(e)
         return
     }
-    if (finalStage != null) {
-      val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
-      clearCacheLocs()
-      logInfo("Got job %s (%s) with %d output partitions".format(
-        job.jobId, callSite.shortForm, partitions.length))
-      logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
-      logInfo("Parents of final stage: " + finalStage.parents)
-      logInfo("Missing parents: " + getMissingParentStages(finalStage))
-      val jobSubmissionTime = clock.getTimeMillis()
-      jobIdToActiveJob(jobId) = job
-      activeJobs += job
-      finalStage.resultOfJob = Some(job)
-      val stageIds = jobIdToStageIds(jobId).toArray
-      val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
-      listenerBus.post(
-        SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
-      submitStage(finalStage)
+
+    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
+    clearCacheLocs()
+    logInfo("Got job %s (%s) with %d output partitions".format(
+      job.jobId, callSite.shortForm, partitions.length))
+    logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
+    logInfo("Parents of final stage: " + finalStage.parents)
+    logInfo("Missing parents: " + getMissingParentStages(finalStage))
+
+    val jobSubmissionTime = clock.getTimeMillis()
+    jobIdToActiveJob(jobId) = job
+    activeJobs += job
+    finalStage.resultOfJob = Some(job)
+    val stageIds = jobIdToStageIds(jobId).toArray
+    val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
+    listenerBus.post(
+      SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
+    submitStage(finalStage)
+
+    submitWaitingStages()
+  }
+
+  private[scheduler] def handleMapStageSubmitted(jobId: Int,
+      dependency: ShuffleDependency[_, _, _],
+      callSite: CallSite,
+      listener: JobListener,
+      properties: Properties) {
+    // Submitting this map stage might still require the creation of some parent stages, so make
+    // sure that happens.
+    var finalStage: ShuffleMapStage = null
+    try {
+      // New stage creation may throw an exception if, for example, jobs are run on a
+      // HadoopRDD whose underlying HDFS files have been deleted.
+      finalStage = getShuffleMapStage(dependency, jobId)
+    } catch {
+      case e: Exception =>
+        logWarning("Creating new stage failed due to exception - job: " + jobId, e)
+        listener.jobFailed(e)
+        return
+    }
+
+    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
+    clearCacheLocs()
+    logInfo("Got map stage job %s (%s) with %d output partitions".format(
+      jobId, callSite.shortForm, dependency.rdd.partitions.size))
+    logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
+    logInfo("Parents of final stage: " + finalStage.parents)
+    logInfo("Missing parents: " + getMissingParentStages(finalStage))
+
+    val jobSubmissionTime = clock.getTimeMillis()
+    jobIdToActiveJob(jobId) = job
+    activeJobs += job
+    finalStage.mapStageJobs = job :: finalStage.mapStageJobs
+    val stageIds = jobIdToStageIds(jobId).toArray
+    val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
+    listenerBus.post(
+      SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
+    submitStage(finalStage)
+
+    // If the whole stage has already finished, tell the listener and remove it
+    if (!finalStage.outputLocs.contains(Nil)) {
+      markMapStageJobAsFinished(job, mapOutputTracker.getStatistics(dependency))
     }
+
     submitWaitingStages()
   }
 
@@ -814,7 +983,7 @@ class DAGScheduler(
         case s: ResultStage =>
           val job = s.resultOfJob.get
           partitionsToCompute.map { id =>
-            val p = job.partitions(id)
+            val p = s.partitions(id)
             (id, getPreferredLocs(stage.rdd, p))
           }.toMap
       }
@@ -844,7 +1013,7 @@ class DAGScheduler(
         case stage: ShuffleMapStage =>
           closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array()
         case stage: ResultStage =>
-          closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func): AnyRef).array()
+          closureSerializer.serialize((stage.rdd, stage.func): AnyRef).array()
       }
 
       taskBinary = sc.broadcast(taskBinaryBytes)
@@ -875,7 +1044,7 @@ class DAGScheduler(
         case stage: ResultStage =>
           val job = stage.resultOfJob.get
           partitionsToCompute.map { id =>
-            val p: Int = job.partitions(id)
+            val p: Int = stage.partitions(id)
             val part = stage.rdd.partitions(p)
             val locs = taskIdToLocations(id)
             new ResultTask(stage.id, stage.latestInfo.attemptId,
@@ -1052,13 +1221,21 @@ class DAGScheduler(
                 logInfo("Resubmitting " + shuffleStage + " (" + shuffleStage.name +
                   ") because some of its tasks had failed: " +
                   shuffleStage.outputLocs.zipWithIndex.filter(_._1.isEmpty)
-                      .map(_._2).mkString(", "))
+                    .map(_._2).mkString(", "))
                 submitStage(shuffleStage)
+              } else {
+                // Mark any map-stage jobs waiting on this stage as finished
+                if (shuffleStage.mapStageJobs.nonEmpty) {
+                  val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep)
+                  for (job <- shuffleStage.mapStageJobs) {
+                    markMapStageJobAsFinished(job, stats)
+                  }
+                }
               }
 
               // Note: newly runnable stages will be submitted below when we submit waiting stages
             }
-          }
+        }
 
       case Resubmitted =>
         logInfo("Resubmitted " + task + ", so marking it as still running")
@@ -1412,6 +1589,17 @@ class DAGScheduler(
     Nil
   }
 
+  /** Mark a map stage job as finished with the given output stats, and report to its listener. */
+  def markMapStageJobAsFinished(job: ActiveJob, stats: MapOutputStatistics): Unit = {
+    // In map stage jobs, we only create a single "task", which is to finish all of the stage
+    // (including reusing any previous map outputs, etc); so we just mark task 0 as done
+    job.finished(0) = true
+    job.numFinished += 1
+    job.listener.taskSucceeded(0, stats)
+    cleanupStateForJobAndIndependentStages(job)
+    listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobSucceeded))
+  }
+
   def stop() {
     logInfo("Stopping DAGScheduler")
     messageScheduler.shutdownNow()
@@ -1445,6 +1633,9 @@ private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler
     case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
       dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
 
+    case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
+      dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
+
     case StageCancelled(stageId) =>
       dagScheduler.handleStageCancellation(stageId)
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index f72a52e85dc15..dda3b6cc7f960 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -35,6 +35,7 @@ import org.apache.spark.util.CallSite
  */
 private[scheduler] sealed trait DAGSchedulerEvent
 
+/** A result-yielding job was submitted on a target RDD */
 private[scheduler] case class JobSubmitted(
     jobId: Int,
     finalRDD: RDD[_],
@@ -45,6 +46,15 @@ private[scheduler] case class JobSubmitted(
     properties: Properties = null)
   extends DAGSchedulerEvent
 
+/** A map stage as submitted to run as a separate job */
+private[scheduler] case class MapStageSubmitted(
+  jobId: Int,
+  dependency: ShuffleDependency[_, _, _],
+  callSite: CallSite,
+  listener: JobListener,
+  properties: Properties = null)
+  extends DAGSchedulerEvent
+
 private[scheduler] case class StageCancelled(stageId: Int) extends DAGSchedulerEvent
 
 private[scheduler] case class JobCancelled(jobId: Int) extends DAGSchedulerEvent
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala
index bf81b9aca4810..c0451da1f0247 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala
@@ -17,23 +17,30 @@
 
 package org.apache.spark.scheduler
 
+import org.apache.spark.TaskContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.CallSite
 
 /**
- * The ResultStage represents the final stage in a job.
+ * ResultStages apply a function on some partitions of an RDD to compute the result of an action.
+ * The ResultStage object captures the function to execute, `func`, which will be applied to each
+ * partition, and the set of partition IDs, `partitions`. Some stages may not run on all partitions
+ * of the RDD, for actions like first() and lookup().
  */
 private[spark] class ResultStage(
     id: Int,
     rdd: RDD[_],
-    numTasks: Int,
+    val func: (TaskContext, Iterator[_]) => _,
+    val partitions: Array[Int],
     parents: List[Stage],
     firstJobId: Int,
     callSite: CallSite)
-  extends Stage(id, rdd, numTasks, parents, firstJobId, callSite) {
+  extends Stage(id, rdd, partitions.length, parents, firstJobId, callSite) {
 
-  // The active job for this result stage. Will be empty if the job has already finished
-  // (e.g., because the job was cancelled).
+  /**
+   * The active job for this result stage. Will be empty if the job has already finished
+   * (e.g., because the job was cancelled).
+   */
   var resultOfJob: Option[ActiveJob] = None
 
   override def toString: String = "ResultStage " + id
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
index 48d8d8e9c4b78..7d92960876403 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
@@ -23,7 +23,15 @@ import org.apache.spark.storage.BlockManagerId
 import org.apache.spark.util.CallSite
 
 /**
- * The ShuffleMapStage represents the intermediate stages in a job.
+ * ShuffleMapStages are intermediate stages in the execution DAG that produce data for a shuffle.
+ * They occur right before each shuffle operation, and might contain multiple pipelined operations
+ * before that (e.g. map and filter). When executed, they save map output files that can later be
+ * fetched by reduce tasks. The `shuffleDep` field describes the shuffle each stage is part of,
+ * and variables like `outputLocs` and `numAvailableOutputs` track how many map outputs are ready.
+ *
+ * ShuffleMapStages can also be submitted independently as jobs with DAGScheduler.submitMapStage.
+ * For such stages, the ActiveJobs that submitted them are tracked in `mapStageJobs`. Note that
+ * there can be multiple ActiveJobs trying to compute the same shuffle map stage.
  */
 private[spark] class ShuffleMapStage(
     id: Int,
@@ -37,6 +45,9 @@ private[spark] class ShuffleMapStage(
 
   override def toString: String = "ShuffleMapStage " + id
 
+  /** Running map-stage jobs that were submitted to execute this stage independently (if any) */
+  var mapStageJobs: List[ActiveJob] = Nil
+
   var numAvailableOutputs: Int = 0
 
   def isAvailable: Boolean = numAvailableOutputs == numPartitions
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index c086535782c23..b37eccbd0f7b8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -24,27 +24,33 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.util.CallSite
 
 /**
- * A stage is a set of independent tasks all computing the same function that need to run as part
+ * A stage is a set of parallel tasks all computing the same function that need to run as part
  * of a Spark job, where all the tasks have the same shuffle dependencies. Each DAG of tasks run
  * by the scheduler is split up into stages at the boundaries where shuffle occurs, and then the
  * DAGScheduler runs these stages in topological order.
  *
  * Each Stage can either be a shuffle map stage, in which case its tasks' results are input for
- * another stage, or a result stage, in which case its tasks directly compute the action that
- * initiated a job (e.g. count(), save(), etc). For shuffle map stages, we also track the nodes
- * that each output partition is on.
+ * other stage(s), or a result stage, in which case its tasks directly compute a Spark action
+ * (e.g. count(), save(), etc) by running a function on an RDD. For shuffle map stages, we also
+ * track the nodes that each output partition is on.
  *
  * Each Stage also has a firstJobId, identifying the job that first submitted the stage.  When FIFO
  * scheduling is used, this allows Stages from earlier jobs to be computed first or recovered
  * faster on failure.
  *
- * The callSite provides a location in user code which relates to the stage. For a shuffle map
- * stage, the callSite gives the user code that created the RDD being shuffled. For a result
- * stage, the callSite gives the user code that executes the associated action (e.g. count()).
- *
- * A single stage can consist of multiple attempts. In that case, the latestInfo field will
- * be updated for each attempt.
+ * Finally, a single stage can be re-executed in multiple attempts due to fault recovery. In that
+ * case, the Stage object will track multiple StageInfo objects to pass to listeners or the web UI.
+ * The latest one will be accessible through latestInfo.
  *
+ * @param id Unique stage ID
+ * @param rdd RDD that this stage runs on: for a shuffle map stage, it's the RDD we run map tasks
+ *   on, while for a result stage, it's the target RDD that we ran an action on
+ * @param numTasks Total number of tasks in stage; result stages in particular may not need to
+ *   compute all partitions, e.g. for first(), lookup(), and take().
+ * @param parents List of stages that this stage depends on (through shuffle dependencies).
+ * @param firstJobId ID of the first job this stage was part of, for FIFO scheduling.
+ * @param callSite Location in the user program associated with this stage: either where the target
+ *   RDD was created, for a shuffle map stage, or where the action for a result stage was called.
  */
 private[scheduler] abstract class Stage(
     val id: Int,
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index aa50a49c50232..f58756e6f6179 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -217,6 +217,27 @@ class FailureSuite extends SparkFunSuite with LocalSparkContext {
     FailureSuiteState.clear()
   }
 
+  // Run a 3-task map stage where one task fails once.
+  test("failure in tasks in a submitMapStage") {
+    sc = new SparkContext("local[1,2]", "test")
+    val rdd = sc.makeRDD(1 to 3, 3).map { x =>
+      FailureSuiteState.synchronized {
+        FailureSuiteState.tasksRun += 1
+        if (x == 1 && FailureSuiteState.tasksFailed == 0) {
+          FailureSuiteState.tasksFailed += 1
+          throw new Exception("Intentional task failure")
+        }
+      }
+      (x, x)
+    }
+    val dep = new ShuffleDependency[Int, Int, Int](rdd, new HashPartitioner(2))
+    sc.submitMapStage(dep).get()
+    FailureSuiteState.synchronized {
+      assert(FailureSuiteState.tasksRun === 4)
+    }
+    FailureSuiteState.clear()
+  }
+
   // TODO: Need to add tests with shuffle fetch failures.
 }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala
new file mode 100644
index 0000000000000..3fe28027c3c21
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala
@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import org.apache.spark.rdd.{ShuffledRDDPartition, RDD, ShuffledRDD}
+import org.apache.spark._
+
+object AdaptiveSchedulingSuiteState {
+  var tasksRun = 0
+
+  def clear(): Unit = {
+    tasksRun = 0
+  }
+}
+
+/** A special ShuffledRDD where we can pass a ShuffleDependency object to use */
+class CustomShuffledRDD[K, V, C](@transient dep: ShuffleDependency[K, V, C])
+  extends RDD[(K, C)](dep.rdd.context, Seq(dep)) {
+
+  override def compute(split: Partition, context: TaskContext): Iterator[(K, C)] = {
+    val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, C]]
+    SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, split.index + 1, context)
+      .read()
+      .asInstanceOf[Iterator[(K, C)]]
+  }
+
+  override def getPartitions: Array[Partition] = {
+    Array.tabulate[Partition](dep.partitioner.numPartitions)(i => new ShuffledRDDPartition(i))
+  }
+}
+
+class AdaptiveSchedulingSuite extends SparkFunSuite with LocalSparkContext {
+  test("simple use of submitMapStage") {
+    try {
+      sc = new SparkContext("local[1,2]", "test")
+      val rdd = sc.parallelize(1 to 3, 3).map { x =>
+        AdaptiveSchedulingSuiteState.tasksRun += 1
+        (x, x)
+      }
+      val dep = new ShuffleDependency[Int, Int, Int](rdd, new HashPartitioner(2))
+      val shuffled = new CustomShuffledRDD[Int, Int, Int](dep)
+      sc.submitMapStage(dep).get()
+      assert(AdaptiveSchedulingSuiteState.tasksRun == 3)
+      assert(shuffled.collect().toSet == Set((1, 1), (2, 2), (3, 3)))
+      assert(AdaptiveSchedulingSuiteState.tasksRun == 3)
+    } finally {
+      AdaptiveSchedulingSuiteState.clear()
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 1b9ff740ff530..1c55f90ad9b44 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -152,6 +152,14 @@ class DAGSchedulerSuite
     override def jobFailed(exception: Exception) = { failure = exception }
   }
 
+  /** A simple helper class for creating custom JobListeners */
+  class SimpleListener extends JobListener {
+    val results = new HashMap[Int, Any]
+    var failure: Exception = null
+    override def taskSucceeded(index: Int, result: Any): Unit = results.put(index, result)
+    override def jobFailed(exception: Exception): Unit = { failure = exception }
+  }
+
   before {
     sc = new SparkContext("local", "DAGSchedulerSuite")
     sparkListener.submittedStageInfos.clear()
@@ -229,7 +237,7 @@ class DAGSchedulerSuite
     }
   }
 
-  /** Sends the rdd to the scheduler for scheduling and returns the job id. */
+  /** Submits a job to the scheduler and returns the job id. */
   private def submit(
       rdd: RDD[_],
       partitions: Array[Int],
@@ -240,6 +248,15 @@ class DAGSchedulerSuite
     jobId
   }
 
+  /** Submits a map stage to the scheduler and returns the job id. */
+  private def submitMapStage(
+      shuffleDep: ShuffleDependency[_, _, _],
+      listener: JobListener = jobListener): Int = {
+    val jobId = scheduler.nextJobId.getAndIncrement()
+    runEvent(MapStageSubmitted(jobId, shuffleDep, CallSite("", ""), listener))
+    jobId
+  }
+
   /** Sends TaskSetFailed to the scheduler. */
   private def failed(taskSet: TaskSet, message: String) {
     runEvent(TaskSetFailed(taskSet, message, None))
@@ -1313,6 +1330,230 @@ class DAGSchedulerSuite
     assert(stackTraceString.contains("org.scalatest.FunSuite"))
   }
 
+  test("simple map stage submission") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(1))
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep))
+
+    // Submit a map stage by itself
+    submitMapStage(shuffleDep)
+    assert(results.size === 0)  // No results yet
+    completeShuffleMapStageSuccessfully(0, 0, 1)
+    assert(results.size === 1)
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Submit a reduce job that depends on this map stage; it should directly do the reduce
+    submit(reduceRdd, Array(0))
+    completeNextResultStageWithSuccess(2, 0)
+    assert(results === Map(0 -> 42))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Check that if we submit the map stage again, no tasks run
+    submitMapStage(shuffleDep)
+    assert(results.size === 1)
+    assertDataStructuresEmpty()
+  }
+
+  test("map stage submission with reduce stage also depending on the data") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(1))
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep))
+
+    // Submit the map stage by itself
+    submitMapStage(shuffleDep)
+
+    // Submit a reduce job that depends on this map stage
+    submit(reduceRdd, Array(0))
+
+    // Complete tasks for the map stage
+    completeShuffleMapStageSuccessfully(0, 0, 1)
+    assert(results.size === 1)
+    results.clear()
+
+    // Complete tasks for the reduce stage
+    completeNextResultStageWithSuccess(1, 0)
+    assert(results === Map(0 -> 42))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Check that if we submit the map stage again, no tasks run
+    submitMapStage(shuffleDep)
+    assert(results.size === 1)
+    assertDataStructuresEmpty()
+  }
+
+  test("map stage submission with fetch failure") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(2))
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+
+    // Submit a map stage by itself
+    submitMapStage(shuffleDep)
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", reduceRdd.partitions.size)),
+      (Success, makeMapStatus("hostB", reduceRdd.partitions.size))))
+    assert(results.size === 1)
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Submit a reduce job that depends on this map stage, but where one reduce will fail a fetch
+    submit(reduceRdd, Array(0, 1))
+    complete(taskSets(1), Seq(
+      (Success, 42),
+      (FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0, "ignored"), null)))
+    // Ask the scheduler to try it again; TaskSet 2 will rerun the map task that we couldn't fetch
+    // from, then TaskSet 3 will run the reduce stage
+    scheduler.resubmitFailedStages()
+    complete(taskSets(2), Seq((Success, makeMapStatus("hostA", reduceRdd.partitions.size))))
+    complete(taskSets(3), Seq((Success, 43)))
+    assert(results === Map(0 -> 42, 1 -> 43))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Run another reduce job without a failure; this should just work
+    submit(reduceRdd, Array(0, 1))
+    complete(taskSets(4), Seq(
+      (Success, 44),
+      (Success, 45)))
+    assert(results === Map(0 -> 44, 1 -> 45))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Resubmit the map stage; this should also just work
+    submitMapStage(shuffleDep)
+    assert(results.size === 1)
+    results.clear()
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test, we have three RDDs with shuffle dependencies, and we submit map stage jobs
+   * that are waiting on each one, as well as a reduce job on the last one. We test that all of
+   * these jobs complete even if there are some fetch failures in both shuffles.
+   */
+  test("map stage submission with multiple shared stages and failures") {
+    val rdd1 = new MyRDD(sc, 2, Nil)
+    val dep1 = new ShuffleDependency(rdd1, new HashPartitioner(2))
+    val rdd2 = new MyRDD(sc, 2, List(dep1))
+    val dep2 = new ShuffleDependency(rdd2, new HashPartitioner(2))
+    val rdd3 = new MyRDD(sc, 2, List(dep2))
+
+    val listener1 = new SimpleListener
+    val listener2 = new SimpleListener
+    val listener3 = new SimpleListener
+
+    submitMapStage(dep1, listener1)
+    submitMapStage(dep2, listener2)
+    submit(rdd3, Array(0, 1), listener = listener3)
+
+    // Complete the first stage
+    assert(taskSets(0).stageId === 0)
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", rdd1.partitions.size)),
+      (Success, makeMapStatus("hostB", rdd1.partitions.size))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostA"), makeBlockManagerId("hostB")))
+    assert(listener1.results.size === 1)
+
+    // When attempting the second stage, show a fetch failure
+    assert(taskSets(1).stageId === 1)
+    complete(taskSets(1), Seq(
+      (Success, makeMapStatus("hostA", rdd2.partitions.size)),
+      (FetchFailed(makeBlockManagerId("hostA"), dep1.shuffleId, 0, 0, "ignored"), null)))
+    scheduler.resubmitFailedStages()
+    assert(listener2.results.size === 0)    // Second stage listener should not have a result yet
+
+    // Stage 0 should now be running as task set 2; make its task succeed
+    assert(taskSets(2).stageId === 0)
+    complete(taskSets(2), Seq(
+      (Success, makeMapStatus("hostC", rdd2.partitions.size))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostC"), makeBlockManagerId("hostB")))
+    assert(listener2.results.size === 0)    // Second stage listener should still not have a result
+
+    // Stage 1 should now be running as task set 3; make its first task succeed
+    assert(taskSets(3).stageId === 1)
+    complete(taskSets(3), Seq(
+      (Success, makeMapStatus("hostB", rdd2.partitions.size)),
+      (Success, makeMapStatus("hostD", rdd2.partitions.size))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep2.shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostB"), makeBlockManagerId("hostD")))
+    assert(listener2.results.size === 1)
+
+    // Finally, the reduce job should be running as task set 4; make it see a fetch failure,
+    // then make it run again and succeed
+    assert(taskSets(4).stageId === 2)
+    complete(taskSets(4), Seq(
+      (Success, 52),
+      (FetchFailed(makeBlockManagerId("hostD"), dep2.shuffleId, 0, 0, "ignored"), null)))
+    scheduler.resubmitFailedStages()
+
+    // TaskSet 5 will rerun stage 1's lost task, then TaskSet 6 will rerun stage 2
+    assert(taskSets(5).stageId === 1)
+    complete(taskSets(5), Seq(
+      (Success, makeMapStatus("hostE", rdd2.partitions.size))))
+    complete(taskSets(6), Seq(
+      (Success, 53)))
+    assert(listener3.results === Map(0 -> 52, 1 -> 53))
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test, we run a map stage where one of the executors fails but we still receive a
+   * "zombie" complete message from that executor. We want to make sure the stage is not reported
+   * as done until all tasks have completed.
+   */
+  test("map stage submission with executor failure late map task completions") {
+    val shuffleMapRdd = new MyRDD(sc, 3, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(2))
+
+    submitMapStage(shuffleDep)
+
+    val oldTaskSet = taskSets(0)
+    runEvent(CompletionEvent(oldTaskSet.tasks(0), Success, makeMapStatus("hostA", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+
+    // Pretend host A was lost
+    val oldEpoch = mapOutputTracker.getEpoch
+    runEvent(ExecutorLost("exec-hostA"))
+    val newEpoch = mapOutputTracker.getEpoch
+    assert(newEpoch > oldEpoch)
+
+    // Suppose we also get a completed event from task 1 on the same host; this should be ignored
+    runEvent(CompletionEvent(oldTaskSet.tasks(1), Success, makeMapStatus("hostA", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+
+    // A completion from another task should work because it's a non-failed host
+    runEvent(CompletionEvent(oldTaskSet.tasks(2), Success, makeMapStatus("hostB", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+
+    // Now complete tasks in the second task set
+    val newTaskSet = taskSets(1)
+    assert(newTaskSet.tasks.size === 2)     // Both tasks 0 and 1 were on on hostA
+    runEvent(CompletionEvent(newTaskSet.tasks(0), Success, makeMapStatus("hostB", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+    runEvent(CompletionEvent(newTaskSet.tasks(1), Success, makeMapStatus("hostB", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 1)    // Map stage job should now finally be complete
+    assertDataStructuresEmpty()
+
+    // Also test that a reduce stage using this shuffled data can immediately run
+    val reduceRDD = new MyRDD(sc, 2, List(shuffleDep))
+    results.clear()
+    submit(reduceRDD, Array(0, 1))
+    complete(taskSets(2), Seq((Success, 42), (Success, 43)))
+    assert(results === Map(0 -> 42, 1 -> 43))
+    results.clear()
+    assertDataStructuresEmpty()
+  }
+
   /**
    * Assert that the supplied TaskSet has exactly the given hosts as its preferred locations.
    * Note that this checks only the host and not the executor ID.

From 55204181004c105c7a3e8c31a099b37e48bfd953 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 14 Sep 2015 19:46:34 -0700
Subject: [PATCH 1425/1454] [SPARK-10542] [PYSPARK] fix serialize namedtuple

Author: Davies Liu <davies@databricks.com>

Closes #8707 from davies/fix_namedtuple.
---
 python/pyspark/cloudpickle.py | 15 ++++++++++++++-
 python/pyspark/serializers.py |  1 +
 python/pyspark/tests.py       |  5 +++++
 3 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index 3b647985801b7..95b3abc74244b 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -350,6 +350,11 @@ def save_global(self, obj, name=None, pack=struct.pack):
             if new_override:
                 d['__new__'] = obj.__new__
 
+            # workaround for namedtuple (hijacked by PySpark)
+            if getattr(obj, '_is_namedtuple_', False):
+                self.save_reduce(_load_namedtuple, (obj.__name__, obj._fields))
+                return
+
             self.save(_load_class)
             self.save_reduce(typ, (obj.__name__, obj.__bases__, {"__doc__": obj.__doc__}), obj=obj)
             d.pop('__doc__', None)
@@ -382,7 +387,7 @@ def save_instancemethod(self, obj):
             self.save_reduce(types.MethodType, (obj.__func__, obj.__self__), obj=obj)
         else:
             self.save_reduce(types.MethodType, (obj.__func__, obj.__self__, obj.__self__.__class__),
-                         obj=obj)
+                             obj=obj)
     dispatch[types.MethodType] = save_instancemethod
 
     def save_inst(self, obj):
@@ -744,6 +749,14 @@ def _load_class(cls, d):
     return cls
 
 
+def _load_namedtuple(name, fields):
+    """
+    Loads a class generated by namedtuple
+    """
+    from collections import namedtuple
+    return namedtuple(name, fields)
+
+
 """Constructors for 3rd party libraries
 Note: These can never be renamed due to client compatibility issues"""
 
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 411b4dbf481f1..2a1326947f4f5 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -359,6 +359,7 @@ def _hack_namedtuple(cls):
     def __reduce__(self):
         return (_restore, (name, fields, tuple(self)))
     cls.__reduce__ = __reduce__
+    cls._is_namedtuple_ = True
     return cls
 
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 8bfed074c9052..647504c32f156 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -218,6 +218,11 @@ def test_namedtuple(self):
         p2 = loads(dumps(p1, 2))
         self.assertEqual(p1, p2)
 
+        from pyspark.cloudpickle import dumps
+        P2 = loads(dumps(P))
+        p3 = P2(1, 3)
+        self.assertEqual(p1, p3)
+
     def test_itemgetter(self):
         from operator import itemgetter
         ser = CloudPickleSerializer()

From 4ae4d54794778042b2cc983e52757edac02412ab Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 14 Sep 2015 21:37:43 -0700
Subject: [PATCH 1426/1454] [SPARK-9793] [MLLIB] [PYSPARK] PySpark DenseVector,
 SparseVector implement __eq__ and __hash__ correctly

PySpark DenseVector, SparseVector ```__eq__``` method should use semantics equality, and DenseVector can compared with SparseVector.
Implement PySpark DenseVector, SparseVector ```__hash__``` method based on the first 16 entries. That will make PySpark Vector objects can be used in collections.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8166 from yanboliang/spark-9793.
---
 python/pyspark/mllib/linalg/__init__.py | 90 ++++++++++++++++++++-----
 python/pyspark/mllib/tests.py           | 32 +++++++++
 2 files changed, 107 insertions(+), 15 deletions(-)

diff --git a/python/pyspark/mllib/linalg/__init__.py b/python/pyspark/mllib/linalg/__init__.py
index 334dc8e38bb8f..380f86e9b44f8 100644
--- a/python/pyspark/mllib/linalg/__init__.py
+++ b/python/pyspark/mllib/linalg/__init__.py
@@ -25,6 +25,7 @@
 
 import sys
 import array
+import struct
 
 if sys.version >= '3':
     basestring = str
@@ -122,6 +123,13 @@ def _format_float_list(l):
     return [_format_float(x) for x in l]
 
 
+def _double_to_long_bits(value):
+    if np.isnan(value):
+        value = float('nan')
+    # pack double into 64 bits, then unpack as long int
+    return struct.unpack('Q', struct.pack('d', value))[0]
+
+
 class VectorUDT(UserDefinedType):
     """
     SQL user-defined type (UDT) for Vector.
@@ -404,11 +412,31 @@ def __repr__(self):
         return "DenseVector([%s])" % (', '.join(_format_float(i) for i in self.array))
 
     def __eq__(self, other):
-        return isinstance(other, DenseVector) and np.array_equal(self.array, other.array)
+        if isinstance(other, DenseVector):
+            return np.array_equal(self.array, other.array)
+        elif isinstance(other, SparseVector):
+            if len(self) != other.size:
+                return False
+            return Vectors._equals(list(xrange(len(self))), self.array, other.indices, other.values)
+        return False
 
     def __ne__(self, other):
         return not self == other
 
+    def __hash__(self):
+        size = len(self)
+        result = 31 + size
+        nnz = 0
+        i = 0
+        while i < size and nnz < 128:
+            if self.array[i] != 0:
+                result = 31 * result + i
+                bits = _double_to_long_bits(self.array[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
     def __getattr__(self, item):
         return getattr(self.array, item)
 
@@ -704,20 +732,14 @@ def __repr__(self):
         return "SparseVector({0}, {{{1}}})".format(self.size, entries)
 
     def __eq__(self, other):
-        """
-        Test SparseVectors for equality.
-
-        >>> v1 = SparseVector(4, [(1, 1.0), (3, 5.5)])
-        >>> v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
-        >>> v1 == v2
-        True
-        >>> v1 != v2
-        False
-        """
-        return (isinstance(other, self.__class__)
-                and other.size == self.size
-                and np.array_equal(other.indices, self.indices)
-                and np.array_equal(other.values, self.values))
+        if isinstance(other, SparseVector):
+            return other.size == self.size and np.array_equal(other.indices, self.indices) \
+                and np.array_equal(other.values, self.values)
+        elif isinstance(other, DenseVector):
+            if self.size != len(other):
+                return False
+            return Vectors._equals(self.indices, self.values, list(xrange(len(other))), other.array)
+        return False
 
     def __getitem__(self, index):
         inds = self.indices
@@ -739,6 +761,19 @@ def __getitem__(self, index):
     def __ne__(self, other):
         return not self.__eq__(other)
 
+    def __hash__(self):
+        result = 31 + self.size
+        nnz = 0
+        i = 0
+        while i < len(self.values) and nnz < 128:
+            if self.values[i] != 0:
+                result = 31 * result + int(self.indices[i])
+                bits = _double_to_long_bits(self.values[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
 
 class Vectors(object):
 
@@ -841,6 +876,31 @@ def parse(s):
     def zeros(size):
         return DenseVector(np.zeros(size))
 
+    @staticmethod
+    def _equals(v1_indices, v1_values, v2_indices, v2_values):
+        """
+        Check equality between sparse/dense vectors,
+        v1_indices and v2_indices assume to be strictly increasing.
+        """
+        v1_size = len(v1_values)
+        v2_size = len(v2_values)
+        k1 = 0
+        k2 = 0
+        all_equal = True
+        while all_equal:
+            while k1 < v1_size and v1_values[k1] == 0:
+                k1 += 1
+            while k2 < v2_size and v2_values[k2] == 0:
+                k2 += 1
+
+            if k1 >= v1_size or k2 >= v2_size:
+                return k1 >= v1_size and k2 >= v2_size
+
+            all_equal = v1_indices[k1] == v2_indices[k2] and v1_values[k1] == v2_values[k2]
+            k1 += 1
+            k2 += 1
+        return all_equal
+
 
 class Matrix(object):
 
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 5097c5e8ba4cd..636f9a06cab7b 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -194,6 +194,38 @@ def test_squared_distance(self):
         self.assertEquals(3.0, _squared_distance(sv, arr))
         self.assertEquals(3.0, _squared_distance(sv, narr))
 
+    def test_hash(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertEquals(hash(v1), hash(v2))
+        self.assertEquals(hash(v1), hash(v3))
+        self.assertEquals(hash(v2), hash(v3))
+        self.assertFalse(hash(v1) == hash(v4))
+        self.assertFalse(hash(v2) == hash(v4))
+
+    def test_eq(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
+        v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
+        v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertEquals(v1, v2)
+        self.assertEquals(v1, v3)
+        self.assertFalse(v2 == v4)
+        self.assertFalse(v1 == v5)
+        self.assertFalse(v1 == v6)
+
+    def test_equals(self):
+        indices = [1, 2, 4]
+        values = [1., 3., 2.]
+        self.assertTrue(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 1., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 2., 2.]))
+
     def test_conversion(self):
         # numpy arrays should be automatically upcast to float64
         # tests for fix of [SPARK-5089]

From 610971ecfe858b1a48ce69b25614afe52bcbe77f Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Mon, 14 Sep 2015 21:58:52 -0700
Subject: [PATCH 1427/1454] [SPARK-10273] Add @since annotation to
 pyspark.mllib.feature

Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings).

Added since to methods + "versionadded::" to classes (derived from the git file history in pyspark).

Author: noelsmith <mail@noelsmith.com>

Closes #8633 from noel-smith/SPARK-10273-since-mllib-feature.
---
 python/pyspark/mllib/feature.py | 58 ++++++++++++++++++++++++++++++++-
 1 file changed, 57 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index f921e3ad1a314..7b077b058c3fd 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -30,7 +30,7 @@
 
 from py4j.protocol import Py4JJavaError
 
-from pyspark import SparkContext
+from pyspark import SparkContext, since
 from pyspark.rdd import RDD, ignore_unicode_prefix
 from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
 from pyspark.mllib.linalg import (
@@ -84,11 +84,14 @@ class Normalizer(VectorTransformer):
     >>> nor2 = Normalizer(float("inf"))
     >>> nor2.transform(v)
     DenseVector([0.0, 0.5, 1.0])
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, p=2.0):
         assert p >= 1.0, "p should be greater than 1.0"
         self.p = float(p)
 
+    @since('1.2.0')
     def transform(self, vector):
         """
         Applies unit length normalization on a vector.
@@ -133,7 +136,11 @@ class StandardScalerModel(JavaVectorTransformer):
     .. note:: Experimental
 
     Represents a StandardScaler model that can transform vectors.
+
+    .. versionadded:: 1.2.0
     """
+
+    @since('1.2.0')
     def transform(self, vector):
         """
         Applies standardization transformation on a vector.
@@ -149,6 +156,7 @@ def transform(self, vector):
         """
         return JavaVectorTransformer.transform(self, vector)
 
+    @since('1.4.0')
     def setWithMean(self, withMean):
         """
         Setter of the boolean which decides
@@ -157,6 +165,7 @@ def setWithMean(self, withMean):
         self.call("setWithMean", withMean)
         return self
 
+    @since('1.4.0')
     def setWithStd(self, withStd):
         """
         Setter of the boolean which decides
@@ -189,6 +198,8 @@ class StandardScaler(object):
     >>> for r in result.collect(): r
     DenseVector([-0.7071, 0.7071, -0.7071])
     DenseVector([0.7071, -0.7071, 0.7071])
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, withMean=False, withStd=True):
         if not (withMean or withStd):
@@ -196,6 +207,7 @@ def __init__(self, withMean=False, withStd=True):
         self.withMean = withMean
         self.withStd = withStd
 
+    @since('1.2.0')
     def fit(self, dataset):
         """
         Computes the mean and variance and stores as a model to be used
@@ -215,7 +227,11 @@ class ChiSqSelectorModel(JavaVectorTransformer):
     .. note:: Experimental
 
     Represents a Chi Squared selector model.
+
+    .. versionadded:: 1.4.0
     """
+
+    @since('1.4.0')
     def transform(self, vector):
         """
         Applies transformation on a vector.
@@ -245,10 +261,13 @@ class ChiSqSelector(object):
     SparseVector(1, {0: 6.0})
     >>> model.transform(DenseVector([8.0, 9.0, 5.0]))
     DenseVector([5.0])
+
+    .. versionadded:: 1.4.0
     """
     def __init__(self, numTopFeatures):
         self.numTopFeatures = int(numTopFeatures)
 
+    @since('1.4.0')
     def fit(self, data):
         """
         Returns a ChiSquared feature selector.
@@ -265,6 +284,8 @@ def fit(self, data):
 class PCAModel(JavaVectorTransformer):
     """
     Model fitted by [[PCA]] that can project vectors to a low-dimensional space using PCA.
+
+    .. versionadded:: 1.5.0
     """
 
 
@@ -281,6 +302,8 @@ class PCA(object):
     1.648...
     >>> pcArray[1]
     -4.013...
+
+    .. versionadded:: 1.5.0
     """
     def __init__(self, k):
         """
@@ -288,6 +311,7 @@ def __init__(self, k):
         """
         self.k = int(k)
 
+    @since('1.5.0')
     def fit(self, data):
         """
         Computes a [[PCAModel]] that contains the principal components of the input vectors.
@@ -312,14 +336,18 @@ class HashingTF(object):
     >>> doc = "a a b b c d".split(" ")
     >>> htf.transform(doc)
     SparseVector(100, {...})
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, numFeatures=1 << 20):
         self.numFeatures = numFeatures
 
+    @since('1.2.0')
     def indexOf(self, term):
         """ Returns the index of the input term. """
         return hash(term) % self.numFeatures
 
+    @since('1.2.0')
     def transform(self, document):
         """
         Transforms the input document (list of terms) to term frequency
@@ -339,7 +367,10 @@ def transform(self, document):
 class IDFModel(JavaVectorTransformer):
     """
     Represents an IDF model that can transform term frequency vectors.
+
+    .. versionadded:: 1.2.0
     """
+    @since('1.2.0')
     def transform(self, x):
         """
         Transforms term frequency (TF) vectors to TF-IDF vectors.
@@ -358,6 +389,7 @@ def transform(self, x):
         """
         return JavaVectorTransformer.transform(self, x)
 
+    @since('1.4.0')
     def idf(self):
         """
         Returns the current IDF vector.
@@ -401,10 +433,13 @@ class IDF(object):
     DenseVector([0.0, 0.0, 1.3863, 0.863])
     >>> model.transform(Vectors.sparse(n, (1, 3), (1.0, 2.0)))
     SparseVector(4, {1: 0.0, 3: 0.5754})
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, minDocFreq=0):
         self.minDocFreq = minDocFreq
 
+    @since('1.2.0')
     def fit(self, dataset):
         """
         Computes the inverse document frequency.
@@ -420,7 +455,10 @@ def fit(self, dataset):
 class Word2VecModel(JavaVectorTransformer, JavaSaveable, JavaLoader):
     """
     class for Word2Vec model
+
+    .. versionadded:: 1.2.0
     """
+    @since('1.2.0')
     def transform(self, word):
         """
         Transforms a word to its vector representation
@@ -435,6 +473,7 @@ def transform(self, word):
         except Py4JJavaError:
             raise ValueError("%s not found" % word)
 
+    @since('1.2.0')
     def findSynonyms(self, word, num):
         """
         Find synonyms of a word
@@ -450,6 +489,7 @@ def findSynonyms(self, word, num):
         words, similarity = self.call("findSynonyms", word, num)
         return zip(words, similarity)
 
+    @since('1.4.0')
     def getVectors(self):
         """
         Returns a map of words to their vector representations.
@@ -457,7 +497,11 @@ def getVectors(self):
         return self.call("getVectors")
 
     @classmethod
+    @since('1.5.0')
     def load(cls, sc, path):
+        """
+        Load a model from the given path.
+        """
         jmodel = sc._jvm.org.apache.spark.mllib.feature \
             .Word2VecModel.load(sc._jsc.sc(), path)
         return Word2VecModel(jmodel)
@@ -507,6 +551,8 @@ class Word2Vec(object):
     ...     rmtree(path)
     ... except OSError:
     ...     pass
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self):
         """
@@ -519,6 +565,7 @@ def __init__(self):
         self.seed = random.randint(0, sys.maxsize)
         self.minCount = 5
 
+    @since('1.2.0')
     def setVectorSize(self, vectorSize):
         """
         Sets vector size (default: 100).
@@ -526,6 +573,7 @@ def setVectorSize(self, vectorSize):
         self.vectorSize = vectorSize
         return self
 
+    @since('1.2.0')
     def setLearningRate(self, learningRate):
         """
         Sets initial learning rate (default: 0.025).
@@ -533,6 +581,7 @@ def setLearningRate(self, learningRate):
         self.learningRate = learningRate
         return self
 
+    @since('1.2.0')
     def setNumPartitions(self, numPartitions):
         """
         Sets number of partitions (default: 1). Use a small number for
@@ -541,6 +590,7 @@ def setNumPartitions(self, numPartitions):
         self.numPartitions = numPartitions
         return self
 
+    @since('1.2.0')
     def setNumIterations(self, numIterations):
         """
         Sets number of iterations (default: 1), which should be smaller
@@ -549,6 +599,7 @@ def setNumIterations(self, numIterations):
         self.numIterations = numIterations
         return self
 
+    @since('1.2.0')
     def setSeed(self, seed):
         """
         Sets random seed.
@@ -556,6 +607,7 @@ def setSeed(self, seed):
         self.seed = seed
         return self
 
+    @since('1.4.0')
     def setMinCount(self, minCount):
         """
         Sets minCount, the minimum number of times a token must appear
@@ -564,6 +616,7 @@ def setMinCount(self, minCount):
         self.minCount = minCount
         return self
 
+    @since('1.2.0')
     def fit(self, data):
         """
         Computes the vector representation of each word in vocabulary.
@@ -596,10 +649,13 @@ class ElementwiseProduct(VectorTransformer):
     >>> rdd = sc.parallelize([a, b])
     >>> eprod.transform(rdd).collect()
     [DenseVector([2.0, 2.0, 9.0]), DenseVector([9.0, 6.0, 12.0])]
+
+    .. versionadded:: 1.5.0
     """
     def __init__(self, scalingVector):
         self.scalingVector = _convert_to_vector(scalingVector)
 
+    @since('1.5.0')
     def transform(self, vector):
         """
         Computes the Hadamard product of the vector.

From a2249359d5b0368318a714b292bb1d0dc70c0e27 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 14 Sep 2015 21:59:40 -0700
Subject: [PATCH 1428/1454] [SPARK-10275] [MLLIB] Add @since annotation to
 pyspark.mllib.random

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8666 from yu-iskw/SPARK-10275.
---
 python/pyspark/mllib/random.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 06fbc0eb6aef0..9c733b1332bc0 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -21,6 +21,7 @@
 
 from functools import wraps
 
+from pyspark import since
 from pyspark.mllib.common import callMLlibFunc
 
 
@@ -39,9 +40,12 @@ class RandomRDDs(object):
     """
     Generator methods for creating RDDs comprised of i.i.d samples from
     some distribution.
+
+    .. addedversion:: 1.1.0
     """
 
     @staticmethod
+    @since("1.1.0")
     def uniformRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the
@@ -72,6 +76,7 @@ def uniformRDD(sc, size, numPartitions=None, seed=None):
         return callMLlibFunc("uniformRDD", sc._jsc, size, numPartitions, seed)
 
     @staticmethod
+    @since("1.1.0")
     def normalRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the standard normal
@@ -100,6 +105,7 @@ def normalRDD(sc, size, numPartitions=None, seed=None):
         return callMLlibFunc("normalRDD", sc._jsc, size, numPartitions, seed)
 
     @staticmethod
+    @since("1.3.0")
     def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the log normal
@@ -132,6 +138,7 @@ def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
                              size, numPartitions, seed)
 
     @staticmethod
+    @since("1.1.0")
     def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the Poisson
@@ -158,6 +165,7 @@ def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         return callMLlibFunc("poissonRDD", sc._jsc, float(mean), size, numPartitions, seed)
 
     @staticmethod
+    @since("1.3.0")
     def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the Exponential
@@ -184,6 +192,7 @@ def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
         return callMLlibFunc("exponentialRDD", sc._jsc, float(mean), size, numPartitions, seed)
 
     @staticmethod
+    @since("1.3.0")
     def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the Gamma
@@ -216,6 +225,7 @@ def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.1.0")
     def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -241,6 +251,7 @@ def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.1.0")
     def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -266,6 +277,7 @@ def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.3.0")
     def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -300,6 +312,7 @@ def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed
 
     @staticmethod
     @toArray
+    @since("1.1.0")
     def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -330,6 +343,7 @@ def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.3.0")
     def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -360,6 +374,7 @@ def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=No
 
     @staticmethod
     @toArray
+    @since("1.3.0")
     def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn

From 833be73314b85b390a9007ed6ed63dc47bbd9e4f Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek.laskowski@deepsense.io>
Date: Mon, 14 Sep 2015 23:40:29 -0700
Subject: [PATCH 1429/1454] Small fixes to docs

Links work now properly + consistent use of *Spark standalone cluster* (Spark uppercase + lowercase the rest -- seems agreed in the other places in the docs).

Author: Jacek Laskowski <jacek.laskowski@deepsense.io>

Closes #8759 from jaceklaskowski/docs-submitting-apps.
---
 docs/submitting-applications.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md
index e58645274e525..7ea4d6f1a3f8f 100644
--- a/docs/submitting-applications.md
+++ b/docs/submitting-applications.md
@@ -65,8 +65,8 @@ For Python applications, simply pass a `.py` file in the place of `<application-
 and add Python `.zip`, `.egg` or `.py` files to the search path with `--py-files`.
 
 There are a few options available that are specific to the
-[cluster manager](#cluster-overview.html#cluster-manager-types) that is being used.
-For example, with a [Spark Standalone](#spark-standalone) cluster with `cluster` deploy mode,
+[cluster manager](cluster-overview.html#cluster-manager-types) that is being used.
+For example, with a [Spark standalone cluster](spark-standalone.html) with `cluster` deploy mode,
 you can also specify `--supervise` to make sure that the driver is automatically restarted if it
 fails with non-zero exit code. To enumerate all such options available to `spark-submit`,
 run it with `--help`. Here are a few examples of common options:
@@ -79,7 +79,7 @@ run it with `--help`. Here are a few examples of common options:
   /path/to/examples.jar \
   100
 
-# Run on a Spark Standalone cluster in client deploy mode
+# Run on a Spark standalone cluster in client deploy mode
 ./bin/spark-submit \
   --class org.apache.spark.examples.SparkPi \
   --master spark://207.184.161.138:7077 \
@@ -88,7 +88,7 @@ run it with `--help`. Here are a few examples of common options:
   /path/to/examples.jar \
   1000
 
-# Run on a Spark Standalone cluster in cluster deploy mode with supervise
+# Run on a Spark standalone cluster in cluster deploy mode with supervise
 ./bin/spark-submit \
   --class org.apache.spark.examples.SparkPi \
   --master spark://207.184.161.138:7077 \
@@ -109,7 +109,7 @@ export HADOOP_CONF_DIR=XXX
   /path/to/examples.jar \
   1000
 
-# Run a Python application on a Spark Standalone cluster
+# Run a Python application on a Spark standalone cluster
 ./bin/spark-submit \
   --master spark://207.184.161.138:7077 \
   examples/src/main/python/pi.py \

From 6503c4b5f3cf3ba9aefc850e7874a62a218b9b0a Mon Sep 17 00:00:00 2001
From: Robin East <robin.east@xense.co.uk>
Date: Mon, 14 Sep 2015 23:41:06 -0700
Subject: [PATCH 1430/1454] [SPARK-10598] [DOCS]

Comments preceding toMessage method state: "The edge partition is encoded in the lower
   * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.". References to bytes should be changed to bits.

This contribution is my original work and I license the work to the Spark project under it's open source license.

Author: Robin East <robin.east@xense.co.uk>

Closes #8756 from insidedctm/master.
---
 .../org/apache/spark/graphx/impl/RoutingTablePartition.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index eb3c997e0f3c0..4f1260a5a67b2 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -34,7 +34,7 @@ object RoutingTablePartition {
   /**
    * A message from an edge partition to a vertex specifying the position in which the edge
    * partition references the vertex (src, dst, or both). The edge partition is encoded in the lower
-   * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.
+   * 30 bits of the Int, and the position is encoded in the upper 2 bits of the Int.
    */
   type RoutingTableMessage = (VertexId, Int)
 

From 09b7e7c19897549a8622aec095f27b8b38a1a4d3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 15 Sep 2015 00:54:20 -0700
Subject: [PATCH 1431/1454] Update version to 1.6.0-SNAPSHOT.

Author: Reynold Xin <rxin@databricks.com>

Closes #8350 from rxin/1.6.
---
 R/pkg/DESCRIPTION                                  |  2 +-
 assembly/pom.xml                                   |  2 +-
 bagel/pom.xml                                      |  2 +-
 core/pom.xml                                       |  2 +-
 core/src/main/scala/org/apache/spark/package.scala |  2 +-
 docs/_config.yml                                   |  4 ++--
 examples/pom.xml                                   |  2 +-
 external/flume-assembly/pom.xml                    |  2 +-
 external/flume-sink/pom.xml                        |  2 +-
 external/flume/pom.xml                             |  2 +-
 external/kafka-assembly/pom.xml                    |  2 +-
 external/kafka/pom.xml                             |  2 +-
 external/mqtt-assembly/pom.xml                     |  2 +-
 external/mqtt/pom.xml                              |  2 +-
 external/twitter/pom.xml                           |  2 +-
 external/zeromq/pom.xml                            |  2 +-
 extras/java8-tests/pom.xml                         |  2 +-
 extras/kinesis-asl-assembly/pom.xml                |  2 +-
 extras/kinesis-asl/pom.xml                         |  2 +-
 extras/spark-ganglia-lgpl/pom.xml                  |  2 +-
 graphx/pom.xml                                     |  2 +-
 launcher/pom.xml                                   |  2 +-
 mllib/pom.xml                                      |  2 +-
 network/common/pom.xml                             |  2 +-
 network/shuffle/pom.xml                            |  2 +-
 network/yarn/pom.xml                               |  2 +-
 pom.xml                                            |  2 +-
 project/MimaBuild.scala                            |  2 +-
 project/MimaExcludes.scala                         | 13 +++++++++++--
 repl/pom.xml                                       |  2 +-
 sql/catalyst/pom.xml                               |  2 +-
 sql/core/pom.xml                                   |  2 +-
 sql/hive-thriftserver/pom.xml                      |  2 +-
 sql/hive/pom.xml                                   |  2 +-
 streaming/pom.xml                                  |  2 +-
 tools/pom.xml                                      |  2 +-
 unsafe/pom.xml                                     |  2 +-
 yarn/pom.xml                                       |  2 +-
 38 files changed, 49 insertions(+), 40 deletions(-)

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index d0d7201f004a2..a3a16c42a6214 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SparkR
 Type: Package
 Title: R frontend for Spark
-Version: 1.5.0
+Version: 1.6.0
 Date: 2013-09-09
 Author: The Apache Software Foundation
 Maintainer: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
diff --git a/assembly/pom.xml b/assembly/pom.xml
index e9c6d26ccddc7..4b60ee00ffbe5 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/bagel/pom.xml b/bagel/pom.xml
index ed5c37e595a96..3baf8d47b4dc7 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/core/pom.xml b/core/pom.xml
index a46292c13bcc0..e31d90f608892 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/core/src/main/scala/org/apache/spark/package.scala b/core/src/main/scala/org/apache/spark/package.scala
index 8ae76c5f72f2e..7515aad09db73 100644
--- a/core/src/main/scala/org/apache/spark/package.scala
+++ b/core/src/main/scala/org/apache/spark/package.scala
@@ -43,5 +43,5 @@ package org.apache
 
 package object spark {
   // For package docs only
-  val SPARK_VERSION = "1.5.0-SNAPSHOT"
+  val SPARK_VERSION = "1.6.0-SNAPSHOT"
 }
diff --git a/docs/_config.yml b/docs/_config.yml
index c0e031a83ba9c..c59cc465ef89d 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -14,8 +14,8 @@ include:
 
 # These allow the documentation to be updated with newer releases
 # of Spark, Scala, and Mesos.
-SPARK_VERSION: 1.5.0-SNAPSHOT
-SPARK_VERSION_SHORT: 1.5.0
+SPARK_VERSION: 1.6.0-SNAPSHOT
+SPARK_VERSION_SHORT: 1.6.0
 SCALA_BINARY_VERSION: "2.10"
 SCALA_VERSION: "2.10.4"
 MESOS_VERSION: 0.21.0
diff --git a/examples/pom.xml b/examples/pom.xml
index e6884b09dca94..f5ab2a7fdc098 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
index 561ed4babe5d0..dceedcf23ed5b 100644
--- a/external/flume-assembly/pom.xml
+++ b/external/flume-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index 0664cfb2021e1..d7c2ac474a18d 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 14f7daaf417e0..132062f94fb45 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml
index 6f4e2a89e9af7..a9ed39ef8c9a0 100644
--- a/external/kafka-assembly/pom.xml
+++ b/external/kafka-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index ded863bd985e8..05abd9e2e6810 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
index 8412600633734..89713a28ca6a8 100644
--- a/external/mqtt-assembly/pom.xml
+++ b/external/mqtt-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 69b309876a0db..05e6338a08b0a 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 178ae8de13b57..244ad58ae9593 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 37bfd10d43663..171df8682c848 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 3636a9037d43f..81794a8536318 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/kinesis-asl-assembly/pom.xml b/extras/kinesis-asl-assembly/pom.xml
index 51af3e6f2225f..61ba4787fbf90 100644
--- a/extras/kinesis-asl-assembly/pom.xml
+++ b/extras/kinesis-asl-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index 521b53e230c4a..6dd8ff69c2943 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/spark-ganglia-lgpl/pom.xml b/extras/spark-ganglia-lgpl/pom.xml
index 478d0019a25f0..87a4f05a05961 100644
--- a/extras/spark-ganglia-lgpl/pom.xml
+++ b/extras/spark-ganglia-lgpl/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 853dea9a7795e..202fc19002d12 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/launcher/pom.xml b/launcher/pom.xml
index 2fd768d8119c4..ed38e66aa2467 100644
--- a/launcher/pom.xml
+++ b/launcher/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/mllib/pom.xml b/mllib/pom.xml
index a5db14407b4fc..22c0c6008ba37 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/network/common/pom.xml b/network/common/pom.xml
index 4141fcb8267a5..1cc054a8936c5 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index 3d2edf9d94515..7a66c968041ce 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/network/yarn/pom.xml b/network/yarn/pom.xml
index a99f7c4392d3d..e745180eace78 100644
--- a/network/yarn/pom.xml
+++ b/network/yarn/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/pom.xml b/pom.xml
index 421357e141572..6535994641145 100644
--- a/pom.xml
+++ b/pom.xml
@@ -26,7 +26,7 @@
   </parent>
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-parent_2.10</artifactId>
-  <version>1.5.0-SNAPSHOT</version>
+  <version>1.6.0-SNAPSHOT</version>
   <packaging>pom</packaging>
   <name>Spark Project Parent POM</name>
   <url>http://spark.apache.org/</url>
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index f16bf989f200b..519052620246f 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -91,7 +91,7 @@ object MimaBuild {
 
   def mimaSettings(sparkHome: File, projectRef: ProjectRef) = {
     val organization = "org.apache.spark"
-    val previousSparkVersion = "1.4.0"
+    val previousSparkVersion = "1.5.0"
     val fullId = "spark-" + projectRef.project + "_2.10"
     mimaDefaultSettings ++
     Seq(previousArtifact := Some(organization % fullId % previousSparkVersion),
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 3b8b6c8ffa375..87b141cd3b058 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -35,8 +35,17 @@ object MimaExcludes {
   def excludes(version: String) = version match {
     case v if v.startsWith("1.6") =>
       Seq(
-        MimaBuild.excludeSparkPackage("network")
-        )
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("network"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in 1.3.
+        excludePackage("org.spark-project.jetty"),
+        MimaBuild.excludeSparkPackage("unused"),
+        // SQL execution is considered private.
+        excludePackage("org.apache.spark.sql.execution")
+      ) ++
+      MimaBuild.excludeSparkClass("streaming.flume.FlumeTestUtils") ++
+      MimaBuild.excludeSparkClass("streaming.flume.PollingFlumeTestUtils")
     case v if v.startsWith("1.5") =>
       Seq(
         MimaBuild.excludeSparkPackage("network"),
diff --git a/repl/pom.xml b/repl/pom.xml
index a5a0f1fc2c857..5cf416a4a5448 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 75ab575dfde83..6cfd53e868f83 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 349007789f634..465aa3a3888c2 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index 3566c87dd248c..f7fe085f34d84 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index be1607476e254..ac67fe5f47be9 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 697895e72fe5b..5cc9001b0e9ab 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/tools/pom.xml b/tools/pom.xml
index 298ee2348b58e..1e64f280e5bed 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index 89475ee3cf5a1..066abe92e51c0 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/yarn/pom.xml b/yarn/pom.xml
index f6737695307a2..d8e4a4bbead81 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 

From c35fdcb7e9c01271ce560dba4e0bd37569c8f5d1 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 15 Sep 2015 09:58:49 -0700
Subject: [PATCH 1432/1454] [SPARK-10491] [MLLIB] move RowMatrix.dspr to BLAS

jira: https://issues.apache.org/jira/browse/SPARK-10491

We implemented dspr with sparse vector support in `RowMatrix`. This method is also used in WeightedLeastSquares and other places. It would be useful to move it to `linalg.BLAS`.

Let me know if new UT needed.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8663 from hhbyyh/movedspr.
---
 .../spark/ml/optim/WeightedLeastSquares.scala |  4 +-
 .../org/apache/spark/mllib/linalg/BLAS.scala  | 44 +++++++++++++++++++
 .../mllib/linalg/distributed/RowMatrix.scala  | 40 +----------------
 .../apache/spark/mllib/linalg/BLASSuite.scala | 25 +++++++++++
 4 files changed, 72 insertions(+), 41 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
index a99e2ac4c6913..0ff8931b0bab4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
@@ -88,7 +88,7 @@ private[ml] class WeightedLeastSquares(
     if (fitIntercept) {
       // shift centers
       // A^T A - aBar aBar^T
-      RowMatrix.dspr(-1.0, aBar, aaValues)
+      BLAS.spr(-1.0, aBar, aaValues)
       // A^T b - bBar aBar
       BLAS.axpy(-bBar, aBar, abBar)
     }
@@ -203,7 +203,7 @@ private[ml] object WeightedLeastSquares {
       bbSum += w * b * b
       BLAS.axpy(w, a, aSum)
       BLAS.axpy(w * b, a, abSum)
-      RowMatrix.dspr(w, a, aaSum.values)
+      BLAS.spr(w, a, aaSum)
       this
     }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index 9ee81eda8a8c0..df9f4ae145b88 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -236,6 +236,50 @@ private[spark] object BLAS extends Serializable with Logging {
     _nativeBLAS
   }
 
+  /**
+   * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's ?SPR.
+   *
+   * @param U the upper triangular part of the matrix in a [[DenseVector]](column major)
+   */
+  def spr(alpha: Double, v: Vector, U: DenseVector): Unit = {
+    spr(alpha, v, U.values)
+  }
+
+  /**
+   * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's ?SPR.
+   *
+   * @param U the upper triangular part of the matrix packed in an array (column major)
+   */
+  def spr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
+    val n = v.size
+    v match {
+      case DenseVector(values) =>
+        NativeBLAS.dspr("U", n, alpha, values, 1, U)
+      case SparseVector(size, indices, values) =>
+        val nnz = indices.length
+        var colStartIdx = 0
+        var prevCol = 0
+        var col = 0
+        var j = 0
+        var i = 0
+        var av = 0.0
+        while (j < nnz) {
+          col = indices(j)
+          // Skip empty columns.
+          colStartIdx += (col - prevCol) * (col + prevCol + 1) / 2
+          col = indices(j)
+          av = alpha * values(j)
+          i = 0
+          while (i <= j) {
+            U(colStartIdx + indices(i)) += av * values(i)
+            i += 1
+          }
+          j += 1
+          prevCol = col
+        }
+    }
+  }
+
   /**
    * A := alpha * x * x^T^ + A
    * @param alpha a real scalar that will be multiplied to x * x^T^.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 83779ac88989b..e55ef26858adb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -24,7 +24,6 @@ import scala.collection.mutable.ListBuffer
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, SparseVector => BSV, axpy => brzAxpy,
   svd => brzSvd, MatrixSingularException, inv}
 import breeze.numerics.{sqrt => brzSqrt}
-import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
@@ -123,7 +122,7 @@ class RowMatrix @Since("1.0.0") (
     // Compute the upper triangular part of the gram matrix.
     val GU = rows.treeAggregate(new BDV[Double](new Array[Double](nt)))(
       seqOp = (U, v) => {
-        RowMatrix.dspr(1.0, v, U.data)
+        BLAS.spr(1.0, v, U.data)
         U
       }, combOp = (U1, U2) => U1 += U2)
 
@@ -673,43 +672,6 @@ class RowMatrix @Since("1.0.0") (
 @Experimental
 object RowMatrix {
 
-  /**
-   * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's DSPR.
-   *
-   * @param U the upper triangular part of the matrix packed in an array (column major)
-   */
-  // TODO: SPARK-10491 - move this method to linalg.BLAS
-  private[spark] def dspr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
-    // TODO: Find a better home (breeze?) for this method.
-    val n = v.size
-    v match {
-      case DenseVector(values) =>
-        blas.dspr("U", n, alpha, values, 1, U)
-      case SparseVector(size, indices, values) =>
-        val nnz = indices.length
-        var colStartIdx = 0
-        var prevCol = 0
-        var col = 0
-        var j = 0
-        var i = 0
-        var av = 0.0
-        while (j < nnz) {
-          col = indices(j)
-          // Skip empty columns.
-          colStartIdx += (col - prevCol) * (col + prevCol + 1) / 2
-          col = indices(j)
-          av = alpha * values(j)
-          i = 0
-          while (i <= j) {
-            U(colStartIdx + indices(i)) += av * values(i)
-            i += 1
-          }
-          j += 1
-          prevCol = col
-        }
-    }
-  }
-
   /**
    * Fills a full square matrix from its upper triangular part.
    */
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
index 8db5c8424abe9..96e5ffef7a131 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
@@ -126,6 +126,31 @@ class BLASSuite extends SparkFunSuite {
     }
   }
 
+  test("spr") {
+    // test dense vector
+    val alpha = 0.1
+    val x = new DenseVector(Array(1.0, 2, 2.1, 4))
+    val U = new DenseVector(Array(1.0, 2, 2, 3, 3, 3, 4, 4, 4, 4))
+    val expected = new DenseVector(Array(1.1, 2.2, 2.4, 3.21, 3.42, 3.441, 4.4, 4.8, 4.84, 5.6))
+
+    spr(alpha, x, U)
+    assert(U ~== expected absTol 1e-9)
+
+    val matrix33 = new DenseVector(Array(1.0, 2, 3, 4, 5))
+    withClue("Size of vector must match the rank of matrix") {
+      intercept[Exception] {
+        spr(alpha, x, matrix33)
+      }
+    }
+
+    // test sparse vector
+    val sv = new SparseVector(4, Array(0, 3), Array(1.0, 2))
+    val U2 = new DenseVector(Array(1.0, 2, 2, 3, 3, 3, 4, 4, 4, 4))
+    spr(0.1, sv, U2)
+    val expectedSparse = new DenseVector(Array(1.1, 2.0, 2.0, 3.0, 3.0, 3.0, 4.2, 4.0, 4.0, 4.4))
+    assert(U2 ~== expectedSparse absTol 1e-15)
+  }
+
   test("syr") {
     val dA = new DenseMatrix(4, 4,
       Array(0.0, 1.2, 2.2, 3.1, 1.2, 3.2, 5.3, 4.6, 2.2, 5.3, 1.8, 3.0, 3.1, 4.6, 3.0, 0.8))

From 8abef21dac1a6538c4e4e0140323b83d804d602b Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 15 Sep 2015 10:45:02 -0700
Subject: [PATCH 1433/1454] [SPARK-10300] [BUILD] [TESTS] Add support for test
 tags in run-tests.py.

This change does two things:

- tag a few tests and adds the mechanism in the build to be able to disable those tags,
  both in maven and sbt, for both junit and scalatest suites.
- add some logic to run-tests.py to disable some tags depending on what files have
  changed; that's used to disable expensive tests when a module hasn't explicitly
  been changed, to speed up testing for changes that don't directly affect those
  modules.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8437 from vanzin/test-tags.
---
 core/pom.xml                                  | 10 -------
 dev/run-tests.py                              | 19 ++++++++++++--
 dev/sparktestsupport/modules.py               | 24 ++++++++++++++++-
 external/flume/pom.xml                        | 10 -------
 external/kafka/pom.xml                        | 10 -------
 external/mqtt/pom.xml                         | 10 -------
 external/twitter/pom.xml                      | 10 -------
 external/zeromq/pom.xml                       | 10 -------
 extras/java8-tests/pom.xml                    | 10 -------
 extras/kinesis-asl/pom.xml                    |  5 ----
 launcher/pom.xml                              |  5 ----
 mllib/pom.xml                                 | 10 -------
 network/common/pom.xml                        | 10 -------
 network/shuffle/pom.xml                       | 10 -------
 pom.xml                                       | 17 ++++++++++--
 project/SparkBuild.scala                      | 13 ++++++++--
 sql/core/pom.xml                              |  5 ----
 .../execution/HiveCompatibilitySuite.scala    |  2 ++
 sql/hive/pom.xml                              |  5 ----
 .../spark/sql/hive/ExtendedHiveTest.java      | 26 +++++++++++++++++++
 .../spark/sql/hive/client/VersionsSuite.scala |  2 ++
 streaming/pom.xml                             | 10 -------
 unsafe/pom.xml                                | 10 -------
 .../spark/deploy/yarn/ExtendedYarnTest.java   | 26 +++++++++++++++++++
 .../spark/deploy/yarn/YarnClusterSuite.scala  |  1 +
 .../yarn/YarnShuffleIntegrationSuite.scala    |  1 +
 26 files changed, 124 insertions(+), 147 deletions(-)
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
 create mode 100644 yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java

diff --git a/core/pom.xml b/core/pom.xml
index e31d90f608892..8a20181096223 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -331,16 +331,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.apache.curator</groupId>
       <artifactId>curator-test</artifactId>
diff --git a/dev/run-tests.py b/dev/run-tests.py
index d8b22e1665e7b..1a816585187d9 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -118,6 +118,14 @@ def determine_modules_to_test(changed_modules):
     return modules_to_test.union(set(changed_modules))
 
 
+def determine_tags_to_exclude(changed_modules):
+    tags = []
+    for m in modules.all_modules:
+        if m not in changed_modules:
+            tags += m.test_tags
+    return tags
+
+
 # -------------------------------------------------------------------------------------------------
 # Functions for working with subprocesses and shell tools
 # -------------------------------------------------------------------------------------------------
@@ -369,6 +377,7 @@ def detect_binary_inop_with_mima():
 
 def run_scala_tests_maven(test_profiles):
     mvn_test_goals = ["test", "--fail-at-end"]
+
     profiles_and_goals = test_profiles + mvn_test_goals
 
     print("[info] Running Spark tests using Maven with these arguments: ",
@@ -392,7 +401,7 @@ def run_scala_tests_sbt(test_modules, test_profiles):
     exec_sbt(profiles_and_goals)
 
 
-def run_scala_tests(build_tool, hadoop_version, test_modules):
+def run_scala_tests(build_tool, hadoop_version, test_modules, excluded_tags):
     """Function to properly execute all tests passed in as a set from the
     `determine_test_suites` function"""
     set_title_and_block("Running Spark unit tests", "BLOCK_SPARK_UNIT_TESTS")
@@ -401,6 +410,10 @@ def run_scala_tests(build_tool, hadoop_version, test_modules):
 
     test_profiles = get_hadoop_profiles(hadoop_version) + \
         list(set(itertools.chain.from_iterable(m.build_profile_flags for m in test_modules)))
+
+    if excluded_tags:
+        test_profiles += ['-Dtest.exclude.tags=' + ",".join(excluded_tags)]
+
     if build_tool == "maven":
         run_scala_tests_maven(test_profiles)
     else:
@@ -500,8 +513,10 @@ def main():
         target_branch = os.environ["ghprbTargetBranch"]
         changed_files = identify_changed_files_from_git_commits("HEAD", target_branch=target_branch)
         changed_modules = determine_modules_for_files(changed_files)
+        excluded_tags = determine_tags_to_exclude(changed_modules)
     if not changed_modules:
         changed_modules = [modules.root]
+        excluded_tags = []
     print("[info] Found the following changed modules:",
           ", ".join(x.name for x in changed_modules))
 
@@ -541,7 +556,7 @@ def main():
         detect_binary_inop_with_mima()
 
     # run the test suites
-    run_scala_tests(build_tool, hadoop_version, test_modules)
+    run_scala_tests(build_tool, hadoop_version, test_modules, excluded_tags)
 
     modules_with_python_tests = [m for m in test_modules if m.python_test_goals]
     if modules_with_python_tests:
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 346452f3174e4..65397f1f3e0bc 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -31,7 +31,7 @@ class Module(object):
 
     def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(), environ={},
                  sbt_test_goals=(), python_test_goals=(), blacklisted_python_implementations=(),
-                 should_run_r_tests=False):
+                 test_tags=(), should_run_r_tests=False):
         """
         Define a new module.
 
@@ -50,6 +50,8 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         :param blacklisted_python_implementations: A set of Python implementations that are not
             supported by this module's Python components. The values in this set should match
             strings returned by Python's `platform.python_implementation()`.
+        :param test_tags A set of tags that will be excluded when running unit tests if the module
+            is not explicitly changed.
         :param should_run_r_tests: If true, changes in this module will trigger all R tests.
         """
         self.name = name
@@ -60,6 +62,7 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         self.environ = environ
         self.python_test_goals = python_test_goals
         self.blacklisted_python_implementations = blacklisted_python_implementations
+        self.test_tags = test_tags
         self.should_run_r_tests = should_run_r_tests
 
         self.dependent_modules = set()
@@ -85,6 +88,9 @@ def contains_file(self, filename):
         "catalyst/test",
         "sql/test",
         "hive/test",
+    ],
+    test_tags=[
+        "org.apache.spark.sql.hive.ExtendedHiveTest"
     ]
 )
 
@@ -398,6 +404,22 @@ def contains_file(self, filename):
 )
 
 
+yarn = Module(
+    name="yarn",
+    dependencies=[],
+    source_file_regexes=[
+        "yarn/",
+        "network/yarn/",
+    ],
+    sbt_test_goals=[
+        "yarn/test",
+        "network-yarn/test",
+    ],
+    test_tags=[
+        "org.apache.spark.deploy.yarn.ExtendedYarnTest"
+    ]
+)
+
 # The root module is a dummy module which is used to run all of the tests.
 # No other modules should directly depend on this module.
 root = Module(
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 132062f94fb45..3154e36c21ef5 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -66,16 +66,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 05abd9e2e6810..7d0d46dadc727 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -86,16 +86,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 05e6338a08b0a..913c47d33f488 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -58,16 +58,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.apache.activemq</groupId>
       <artifactId>activemq-core</artifactId>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 244ad58ae9593..9137bf25ee8ae 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -58,16 +58,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 171df8682c848..6fec4f0e8a0f9 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -57,16 +57,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 81794a8536318..dba3dda8a9562 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -58,16 +58,6 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
 
   <profiles>
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index 6dd8ff69c2943..760f183a2ef37 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -74,11 +74,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/launcher/pom.xml b/launcher/pom.xml
index ed38e66aa2467..80696280a1d18 100644
--- a/launcher/pom.xml
+++ b/launcher/pom.xml
@@ -42,11 +42,6 @@
       <artifactId>log4j</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index 22c0c6008ba37..5dedacb38874e 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -94,16 +94,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/network/common/pom.xml b/network/common/pom.xml
index 1cc054a8936c5..9c12cca0df609 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -64,16 +64,6 @@
     </dependency>
 
     <!-- Test dependencies -->
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>log4j</groupId>
       <artifactId>log4j</artifactId>
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index 7a66c968041ce..e4f4c57b683c8 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -78,16 +78,6 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>log4j</groupId>
       <artifactId>log4j</artifactId>
diff --git a/pom.xml b/pom.xml
index 6535994641145..2927d3e107563 100644
--- a/pom.xml
+++ b/pom.xml
@@ -181,6 +181,7 @@
     <libthrift.version>0.9.2</libthrift.version>
 
     <test.java.home>${java.home}</test.java.home>
+    <test.exclude.tags></test.exclude.tags>
 
     <!--
       Dependency scopes that can be overridden by enabling certain profiles. These profiles are
@@ -339,6 +340,16 @@
       <artifactId>scalatest_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <dependencyManagement>
     <dependencies>
@@ -742,7 +753,7 @@
       <dependency>
         <groupId>junit</groupId>
         <artifactId>junit</artifactId>
-        <version>4.10</version>
+        <version>4.11</version>
         <scope>test</scope>
       </dependency>
       <dependency>
@@ -760,7 +771,7 @@
       <dependency>
         <groupId>com.novocode</groupId>
         <artifactId>junit-interface</artifactId>
-        <version>0.10</version>
+        <version>0.11</version>
         <scope>test</scope>
       </dependency>
       <dependency>
@@ -1915,6 +1926,7 @@
               <test.src.tables>src</test.src.tables>
             </systemProperties>
             <failIfNoTests>false</failIfNoTests>
+            <excludedGroups>${test.exclude.tags}</excludedGroups>
           </configuration>
         </plugin>
         <!-- Scalatest runs all Scala tests -->
@@ -1952,6 +1964,7 @@
               <!-- Needed by sql/hive tests. -->
               <test.src.tables>__not_used__</test.src.tables>
             </systemProperties>
+            <tagsToExclude>${test.exclude.tags}</tagsToExclude>
           </configuration>
           <executions>
             <execution>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 901cfa538d23e..d80d300f1c3b2 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -567,11 +567,20 @@ object TestSettings {
     javaOptions in Test ++= "-Xmx3g -Xss4096k -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g"
       .split(" ").toSeq,
     javaOptions += "-Xmx3g",
+    // Exclude tags defined in a system property
+    testOptions in Test += Tests.Argument(TestFrameworks.ScalaTest,
+      sys.props.get("test.exclude.tags").map { tags =>
+        tags.split(",").flatMap { tag => Seq("-l", tag) }.toSeq
+      }.getOrElse(Nil): _*),
+    testOptions in Test += Tests.Argument(TestFrameworks.JUnit,
+      sys.props.get("test.exclude.tags").map { tags =>
+        Seq("--exclude-categories=" + tags)
+      }.getOrElse(Nil): _*),
     // Show full stack trace and duration in test cases.
     testOptions in Test += Tests.Argument("-oDF"),
-    testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
+    testOptions in Test += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
     // Enable Junit testing.
-    libraryDependencies += "com.novocode" % "junit-interface" % "0.9" % "test",
+    libraryDependencies += "com.novocode" % "junit-interface" % "0.11" % "test",
     // Only allow one test at a time, even across projects, since they run in the same JVM
     parallelExecution in Test := false,
     // Make sure the test temp directory exists.
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 465aa3a3888c2..fa6732db183d8 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -73,11 +73,6 @@
       <artifactId>jackson-databind</artifactId>
       <version>${fasterxml.jackson.version}</version>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.scalacheck</groupId>
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index ab309e0a1d36b..ffc4c32794ca4 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -24,11 +24,13 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.hive.ExtendedHiveTest
 import org.apache.spark.sql.hive.test.TestHive
 
 /**
  * Runs the test cases that are included in the hive distribution.
  */
+@ExtendedHiveTest
 class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
   // TODO: bundle in jar files... get from classpath
   private lazy val hiveQueryDir = TestHive.getHiveFile(
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index ac67fe5f47be9..82cfeb2bb95d3 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -160,11 +160,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-sql_${scala.binary.version}</artifactId>
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
new file mode 100644
index 0000000000000..e2183183fb559
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
@@ -0,0 +1,26 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive;
+
+import java.lang.annotation.*;
+import org.scalatest.TagAnnotation;
+
+@TagAnnotation
+@Retention(RetentionPolicy.RUNTIME)
+@Target({ElementType.METHOD, ElementType.TYPE})
+public @interface ExtendedHiveTest { }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index f0bb77092c0cf..888d1b7b45532 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
+import org.apache.spark.sql.hive.ExtendedHiveTest
 import org.apache.spark.sql.types.IntegerType
 import org.apache.spark.util.Utils
 
@@ -32,6 +33,7 @@ import org.apache.spark.util.Utils
  * sure that reflective calls are not throwing NoSuchMethod error, but the actually functionality
  * is not fully tested.
  */
+@ExtendedHiveTest
 class VersionsSuite extends SparkFunSuite with Logging {
 
   // Do not use a temp path here to speed up subsequent executions of the unit test during
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 5cc9001b0e9ab..1e6ee009ca6d5 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -84,21 +84,11 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.seleniumhq.selenium</groupId>
       <artifactId>selenium-java</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index 066abe92e51c0..4e8b9a84bb67f 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -55,16 +55,6 @@
     </dependency>
 
     <!-- Test dependencies -->
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java b/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java
new file mode 100644
index 0000000000000..7a8f2fe979c1f
--- /dev/null
+++ b/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java
@@ -0,0 +1,26 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn;
+
+import java.lang.annotation.*;
+import org.scalatest.TagAnnotation;
+
+@TagAnnotation
+@Retention(RetentionPolicy.RUNTIME)
+@Target({ElementType.METHOD, ElementType.TYPE})
+public @interface ExtendedYarnTest { }
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index b5a42fd6afd98..105c3090d489d 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -39,6 +39,7 @@ import org.apache.spark.util.Utils
  * applications, and require the Spark assembly to be built before they can be successfully
  * run.
  */
+@ExtendedYarnTest
 class YarnClusterSuite extends BaseYarnClusterSuite {
 
   override def newYarnConfig(): YarnConfiguration = new YarnConfiguration()
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
index 8d9c9b3004eda..4700e2428df08 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
@@ -32,6 +32,7 @@ import org.apache.spark.network.yarn.{YarnShuffleService, YarnTestAccessor}
 /**
  * Integration test for the external shuffle service with a yarn mini-cluster
  */
+@ExtendedYarnTest
 class YarnShuffleIntegrationSuite extends BaseYarnClusterSuite {
 
   override def newYarnConfig(): YarnConfiguration = {

From 7ca30b505c3561dc2832b463be4c6301a90380e4 Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Tue, 15 Sep 2015 12:23:20 -0700
Subject: [PATCH 1434/1454] [PYSPARK] [MLLIB] [DOCS] Replaced addversion with
 versionadded in mllib.random

Missed this when reviewing `pyspark.mllib.random` for SPARK-10275.

Author: noelsmith <mail@noelsmith.com>

Closes #8773 from noel-smith/mllib-random-versionadded-fix.
---
 python/pyspark/mllib/random.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 9c733b1332bc0..6a3c643b66417 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -41,7 +41,7 @@ class RandomRDDs(object):
     Generator methods for creating RDDs comprised of i.i.d samples from
     some distribution.
 
-    .. addedversion:: 1.1.0
+    .. versionadded:: 1.1.0
     """
 
     @staticmethod

From 0d9ab016755d5b56ce4043f229602169fd752e88 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 15 Sep 2015 12:25:31 -0700
Subject: [PATCH 1435/1454] Closes #8738 Closes #8767 Closes #2491 Closes #6795
 Closes #2096 Closes #7722


From 416003b26401894ec712e1a5291a92adfbc5af01 Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek.laskowski@deepsense.io>
Date: Tue, 15 Sep 2015 20:42:33 +0100
Subject: [PATCH 1436/1454] [DOCS] Small fixes to Spark on Yarn doc

* a follow-up to 16b6d18613e150c7038c613992d80a7828413e66 as `--num-executors` flag is not suppported.
* links + formatting

Author: Jacek Laskowski <jacek.laskowski@deepsense.io>

Closes #8762 from jaceklaskowski/docs-spark-on-yarn.
---
 docs/running-on-yarn.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 5159ef9e3394e..d1244323edfff 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -18,16 +18,16 @@ Spark application's configuration (driver, executors, and the AM when running in
 
 There are two deploy modes that can be used to launch Spark applications on YARN. In `yarn-cluster` mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In `yarn-client` mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
 
-Unlike in Spark standalone and Mesos mode, in which the master's address is specified in the `--master` parameter, in YARN mode the ResourceManager's address is picked up from the Hadoop configuration. Thus, the `--master` parameter is `yarn-client` or `yarn-cluster`. 
+Unlike [Spark standalone](spark-standalone.html) and [Mesos](running-on-mesos.html) modes, in which the master's address is specified in the `--master` parameter, in YARN mode the ResourceManager's address is picked up from the Hadoop configuration. Thus, the `--master` parameter is `yarn-client` or `yarn-cluster`.
+
 To launch a Spark application in `yarn-cluster` mode:
 
-   `$ ./bin/spark-submit --class path.to.your.Class --master yarn-cluster [options] <app jar> [app options]`
+    $ ./bin/spark-submit --class path.to.your.Class --master yarn-cluster [options] <app jar> [app options]
     
 For example:
 
     $ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
         --master yarn-cluster \
-        --num-executors 3 \
         --driver-memory 4g \
         --executor-memory 2g \
         --executor-cores 1 \
@@ -37,7 +37,7 @@ For example:
 
 The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Debugging your Application" section below for how to see driver and executor logs.
 
-To launch a Spark application in `yarn-client` mode, do the same, but replace `yarn-cluster` with `yarn-client`.  To run spark-shell:
+To launch a Spark application in `yarn-client` mode, do the same, but replace `yarn-cluster` with `yarn-client`. The following shows how you can run `spark-shell` in `yarn-client` mode:
 
     $ ./bin/spark-shell --master yarn-client
 
@@ -54,8 +54,8 @@ In `yarn-cluster` mode, the driver runs on a different machine than the client,
 
 # Preparations
 
-Running Spark-on-YARN requires a binary distribution of Spark which is built with YARN support.
-Binary distributions can be downloaded from the Spark project website. 
+Running Spark on YARN requires a binary distribution of Spark which is built with YARN support.
+Binary distributions can be downloaded from the [downloads page](http://spark.apache.org/downloads.html) of the project website.
 To build Spark yourself, refer to [Building Spark](building-spark.html).
 
 # Configuration

From b42059d2efdf3322334694205a6d951bcc291644 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 15 Sep 2015 13:03:38 -0700
Subject: [PATCH 1437/1454] Revert "[SPARK-10300] [BUILD] [TESTS] Add support
 for test tags in run-tests.py."

This reverts commit 8abef21dac1a6538c4e4e0140323b83d804d602b.
---
 core/pom.xml                                  | 10 +++++++
 dev/run-tests.py                              | 19 ++------------
 dev/sparktestsupport/modules.py               | 24 +----------------
 external/flume/pom.xml                        | 10 +++++++
 external/kafka/pom.xml                        | 10 +++++++
 external/mqtt/pom.xml                         | 10 +++++++
 external/twitter/pom.xml                      | 10 +++++++
 external/zeromq/pom.xml                       | 10 +++++++
 extras/java8-tests/pom.xml                    | 10 +++++++
 extras/kinesis-asl/pom.xml                    |  5 ++++
 launcher/pom.xml                              |  5 ++++
 mllib/pom.xml                                 | 10 +++++++
 network/common/pom.xml                        | 10 +++++++
 network/shuffle/pom.xml                       | 10 +++++++
 pom.xml                                       | 17 ++----------
 project/SparkBuild.scala                      | 13 ++--------
 sql/core/pom.xml                              |  5 ++++
 .../execution/HiveCompatibilitySuite.scala    |  2 --
 sql/hive/pom.xml                              |  5 ++++
 .../spark/sql/hive/ExtendedHiveTest.java      | 26 -------------------
 .../spark/sql/hive/client/VersionsSuite.scala |  2 --
 streaming/pom.xml                             | 10 +++++++
 unsafe/pom.xml                                | 10 +++++++
 .../spark/deploy/yarn/ExtendedYarnTest.java   | 26 -------------------
 .../spark/deploy/yarn/YarnClusterSuite.scala  |  1 -
 .../yarn/YarnShuffleIntegrationSuite.scala    |  1 -
 26 files changed, 147 insertions(+), 124 deletions(-)
 delete mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
 delete mode 100644 yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java

diff --git a/core/pom.xml b/core/pom.xml
index 8a20181096223..e31d90f608892 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -331,6 +331,16 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.curator</groupId>
       <artifactId>curator-test</artifactId>
diff --git a/dev/run-tests.py b/dev/run-tests.py
index 1a816585187d9..d8b22e1665e7b 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -118,14 +118,6 @@ def determine_modules_to_test(changed_modules):
     return modules_to_test.union(set(changed_modules))
 
 
-def determine_tags_to_exclude(changed_modules):
-    tags = []
-    for m in modules.all_modules:
-        if m not in changed_modules:
-            tags += m.test_tags
-    return tags
-
-
 # -------------------------------------------------------------------------------------------------
 # Functions for working with subprocesses and shell tools
 # -------------------------------------------------------------------------------------------------
@@ -377,7 +369,6 @@ def detect_binary_inop_with_mima():
 
 def run_scala_tests_maven(test_profiles):
     mvn_test_goals = ["test", "--fail-at-end"]
-
     profiles_and_goals = test_profiles + mvn_test_goals
 
     print("[info] Running Spark tests using Maven with these arguments: ",
@@ -401,7 +392,7 @@ def run_scala_tests_sbt(test_modules, test_profiles):
     exec_sbt(profiles_and_goals)
 
 
-def run_scala_tests(build_tool, hadoop_version, test_modules, excluded_tags):
+def run_scala_tests(build_tool, hadoop_version, test_modules):
     """Function to properly execute all tests passed in as a set from the
     `determine_test_suites` function"""
     set_title_and_block("Running Spark unit tests", "BLOCK_SPARK_UNIT_TESTS")
@@ -410,10 +401,6 @@ def run_scala_tests(build_tool, hadoop_version, test_modules, excluded_tags):
 
     test_profiles = get_hadoop_profiles(hadoop_version) + \
         list(set(itertools.chain.from_iterable(m.build_profile_flags for m in test_modules)))
-
-    if excluded_tags:
-        test_profiles += ['-Dtest.exclude.tags=' + ",".join(excluded_tags)]
-
     if build_tool == "maven":
         run_scala_tests_maven(test_profiles)
     else:
@@ -513,10 +500,8 @@ def main():
         target_branch = os.environ["ghprbTargetBranch"]
         changed_files = identify_changed_files_from_git_commits("HEAD", target_branch=target_branch)
         changed_modules = determine_modules_for_files(changed_files)
-        excluded_tags = determine_tags_to_exclude(changed_modules)
     if not changed_modules:
         changed_modules = [modules.root]
-        excluded_tags = []
     print("[info] Found the following changed modules:",
           ", ".join(x.name for x in changed_modules))
 
@@ -556,7 +541,7 @@ def main():
         detect_binary_inop_with_mima()
 
     # run the test suites
-    run_scala_tests(build_tool, hadoop_version, test_modules, excluded_tags)
+    run_scala_tests(build_tool, hadoop_version, test_modules)
 
     modules_with_python_tests = [m for m in test_modules if m.python_test_goals]
     if modules_with_python_tests:
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 65397f1f3e0bc..346452f3174e4 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -31,7 +31,7 @@ class Module(object):
 
     def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(), environ={},
                  sbt_test_goals=(), python_test_goals=(), blacklisted_python_implementations=(),
-                 test_tags=(), should_run_r_tests=False):
+                 should_run_r_tests=False):
         """
         Define a new module.
 
@@ -50,8 +50,6 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         :param blacklisted_python_implementations: A set of Python implementations that are not
             supported by this module's Python components. The values in this set should match
             strings returned by Python's `platform.python_implementation()`.
-        :param test_tags A set of tags that will be excluded when running unit tests if the module
-            is not explicitly changed.
         :param should_run_r_tests: If true, changes in this module will trigger all R tests.
         """
         self.name = name
@@ -62,7 +60,6 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         self.environ = environ
         self.python_test_goals = python_test_goals
         self.blacklisted_python_implementations = blacklisted_python_implementations
-        self.test_tags = test_tags
         self.should_run_r_tests = should_run_r_tests
 
         self.dependent_modules = set()
@@ -88,9 +85,6 @@ def contains_file(self, filename):
         "catalyst/test",
         "sql/test",
         "hive/test",
-    ],
-    test_tags=[
-        "org.apache.spark.sql.hive.ExtendedHiveTest"
     ]
 )
 
@@ -404,22 +398,6 @@ def contains_file(self, filename):
 )
 
 
-yarn = Module(
-    name="yarn",
-    dependencies=[],
-    source_file_regexes=[
-        "yarn/",
-        "network/yarn/",
-    ],
-    sbt_test_goals=[
-        "yarn/test",
-        "network-yarn/test",
-    ],
-    test_tags=[
-        "org.apache.spark.deploy.yarn.ExtendedYarnTest"
-    ]
-)
-
 # The root module is a dummy module which is used to run all of the tests.
 # No other modules should directly depend on this module.
 root = Module(
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 3154e36c21ef5..132062f94fb45 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -66,6 +66,16 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 7d0d46dadc727..05abd9e2e6810 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -86,6 +86,16 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 913c47d33f488..05e6338a08b0a 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -58,6 +58,16 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.activemq</groupId>
       <artifactId>activemq-core</artifactId>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 9137bf25ee8ae..244ad58ae9593 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -58,6 +58,16 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 6fec4f0e8a0f9..171df8682c848 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -57,6 +57,16 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index dba3dda8a9562..81794a8536318 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -58,6 +58,16 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
 
   <profiles>
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index 760f183a2ef37..6dd8ff69c2943 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -74,6 +74,11 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/launcher/pom.xml b/launcher/pom.xml
index 80696280a1d18..ed38e66aa2467 100644
--- a/launcher/pom.xml
+++ b/launcher/pom.xml
@@ -42,6 +42,11 @@
       <artifactId>log4j</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index 5dedacb38874e..22c0c6008ba37 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -94,6 +94,16 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/network/common/pom.xml b/network/common/pom.xml
index 9c12cca0df609..1cc054a8936c5 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -64,6 +64,16 @@
     </dependency>
 
     <!-- Test dependencies -->
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>log4j</groupId>
       <artifactId>log4j</artifactId>
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index e4f4c57b683c8..7a66c968041ce 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -78,6 +78,16 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>log4j</groupId>
       <artifactId>log4j</artifactId>
diff --git a/pom.xml b/pom.xml
index 2927d3e107563..6535994641145 100644
--- a/pom.xml
+++ b/pom.xml
@@ -181,7 +181,6 @@
     <libthrift.version>0.9.2</libthrift.version>
 
     <test.java.home>${java.home}</test.java.home>
-    <test.exclude.tags></test.exclude.tags>
 
     <!--
       Dependency scopes that can be overridden by enabling certain profiles. These profiles are
@@ -340,16 +339,6 @@
       <artifactId>scalatest_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <dependencyManagement>
     <dependencies>
@@ -753,7 +742,7 @@
       <dependency>
         <groupId>junit</groupId>
         <artifactId>junit</artifactId>
-        <version>4.11</version>
+        <version>4.10</version>
         <scope>test</scope>
       </dependency>
       <dependency>
@@ -771,7 +760,7 @@
       <dependency>
         <groupId>com.novocode</groupId>
         <artifactId>junit-interface</artifactId>
-        <version>0.11</version>
+        <version>0.10</version>
         <scope>test</scope>
       </dependency>
       <dependency>
@@ -1926,7 +1915,6 @@
               <test.src.tables>src</test.src.tables>
             </systemProperties>
             <failIfNoTests>false</failIfNoTests>
-            <excludedGroups>${test.exclude.tags}</excludedGroups>
           </configuration>
         </plugin>
         <!-- Scalatest runs all Scala tests -->
@@ -1964,7 +1952,6 @@
               <!-- Needed by sql/hive tests. -->
               <test.src.tables>__not_used__</test.src.tables>
             </systemProperties>
-            <tagsToExclude>${test.exclude.tags}</tagsToExclude>
           </configuration>
           <executions>
             <execution>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index d80d300f1c3b2..901cfa538d23e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -567,20 +567,11 @@ object TestSettings {
     javaOptions in Test ++= "-Xmx3g -Xss4096k -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g"
       .split(" ").toSeq,
     javaOptions += "-Xmx3g",
-    // Exclude tags defined in a system property
-    testOptions in Test += Tests.Argument(TestFrameworks.ScalaTest,
-      sys.props.get("test.exclude.tags").map { tags =>
-        tags.split(",").flatMap { tag => Seq("-l", tag) }.toSeq
-      }.getOrElse(Nil): _*),
-    testOptions in Test += Tests.Argument(TestFrameworks.JUnit,
-      sys.props.get("test.exclude.tags").map { tags =>
-        Seq("--exclude-categories=" + tags)
-      }.getOrElse(Nil): _*),
     // Show full stack trace and duration in test cases.
     testOptions in Test += Tests.Argument("-oDF"),
-    testOptions in Test += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
+    testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
     // Enable Junit testing.
-    libraryDependencies += "com.novocode" % "junit-interface" % "0.11" % "test",
+    libraryDependencies += "com.novocode" % "junit-interface" % "0.9" % "test",
     // Only allow one test at a time, even across projects, since they run in the same JVM
     parallelExecution in Test := false,
     // Make sure the test temp directory exists.
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index fa6732db183d8..465aa3a3888c2 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -73,6 +73,11 @@
       <artifactId>jackson-databind</artifactId>
       <version>${fasterxml.jackson.version}</version>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.scalacheck</groupId>
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index ffc4c32794ca4..ab309e0a1d36b 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -24,13 +24,11 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.hive.ExtendedHiveTest
 import org.apache.spark.sql.hive.test.TestHive
 
 /**
  * Runs the test cases that are included in the hive distribution.
  */
-@ExtendedHiveTest
 class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
   // TODO: bundle in jar files... get from classpath
   private lazy val hiveQueryDir = TestHive.getHiveFile(
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 82cfeb2bb95d3..ac67fe5f47be9 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -160,6 +160,11 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-sql_${scala.binary.version}</artifactId>
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
deleted file mode 100644
index e2183183fb559..0000000000000
--- a/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
+++ /dev/null
@@ -1,26 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive;
-
-import java.lang.annotation.*;
-import org.scalatest.TagAnnotation;
-
-@TagAnnotation
-@Retention(RetentionPolicy.RUNTIME)
-@Target({ElementType.METHOD, ElementType.TYPE})
-public @interface ExtendedHiveTest { }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index 888d1b7b45532..f0bb77092c0cf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -23,7 +23,6 @@ import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
-import org.apache.spark.sql.hive.ExtendedHiveTest
 import org.apache.spark.sql.types.IntegerType
 import org.apache.spark.util.Utils
 
@@ -33,7 +32,6 @@ import org.apache.spark.util.Utils
  * sure that reflective calls are not throwing NoSuchMethod error, but the actually functionality
  * is not fully tested.
  */
-@ExtendedHiveTest
 class VersionsSuite extends SparkFunSuite with Logging {
 
   // Do not use a temp path here to speed up subsequent executions of the unit test during
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 1e6ee009ca6d5..5cc9001b0e9ab 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -84,11 +84,21 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.seleniumhq.selenium</groupId>
       <artifactId>selenium-java</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index 4e8b9a84bb67f..066abe92e51c0 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -55,6 +55,16 @@
     </dependency>
 
     <!-- Test dependencies -->
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java b/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java
deleted file mode 100644
index 7a8f2fe979c1f..0000000000000
--- a/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java
+++ /dev/null
@@ -1,26 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.deploy.yarn;
-
-import java.lang.annotation.*;
-import org.scalatest.TagAnnotation;
-
-@TagAnnotation
-@Retention(RetentionPolicy.RUNTIME)
-@Target({ElementType.METHOD, ElementType.TYPE})
-public @interface ExtendedYarnTest { }
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 105c3090d489d..b5a42fd6afd98 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -39,7 +39,6 @@ import org.apache.spark.util.Utils
  * applications, and require the Spark assembly to be built before they can be successfully
  * run.
  */
-@ExtendedYarnTest
 class YarnClusterSuite extends BaseYarnClusterSuite {
 
   override def newYarnConfig(): YarnConfiguration = new YarnConfiguration()
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
index 4700e2428df08..8d9c9b3004eda 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
@@ -32,7 +32,6 @@ import org.apache.spark.network.yarn.{YarnShuffleService, YarnTestAccessor}
 /**
  * Integration test for the external shuffle service with a yarn mini-cluster
  */
-@ExtendedYarnTest
 class YarnShuffleIntegrationSuite extends BaseYarnClusterSuite {
 
   override def newYarnConfig(): YarnConfiguration = {

From 841972e22c653ba58e9a65433fed203ff288f13a Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Tue, 15 Sep 2015 13:33:32 -0700
Subject: [PATCH 1438/1454] [SPARK-10437] [SQL] Support aggregation expressions
 in Order By

JIRA: https://issues.apache.org/jira/browse/SPARK-10437

If an expression in `SortOrder` is a resolved one, such as `count(1)`, the corresponding rule in `Analyzer` to make it work in order by will not be applied.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #8599 from viirya/orderby-agg.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 14 +++++++++----
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 20 +++++++++++++++++++
 2 files changed, 30 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 591747b45c376..02f34cbf58ad0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -561,7 +561,7 @@ class Analyzer(
         }
 
       case sort @ Sort(sortOrder, global, aggregate: Aggregate)
-        if aggregate.resolved && !sort.resolved =>
+        if aggregate.resolved =>
 
         // Try resolving the ordering as though it is in the aggregate clause.
         try {
@@ -598,9 +598,15 @@ class Analyzer(
               }
           }
 
-          Project(aggregate.output,
-            Sort(evaluatedOrderings, global,
-              aggregate.copy(aggregateExpressions = originalAggExprs ++ needsPushDown)))
+          // Since we don't rely on sort.resolved as the stop condition for this rule,
+          // we need to check this and prevent applying this rule multiple times
+          if (sortOrder == evaluatedOrderings) {
+            sort
+          } else {
+            Project(aggregate.output,
+              Sort(evaluatedOrderings, global,
+                aggregate.copy(aggregateExpressions = originalAggExprs ++ needsPushDown)))
+          }
         } catch {
           // Attempting to resolve in the aggregate can result in ambiguity.  When this happens,
           // just return the original plan.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 962b100b532c9..f9981356f364f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1562,6 +1562,26 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
             |ORDER BY sum(b) + 1
           """.stripMargin),
       Row("4", 3) :: Row("1", 7) :: Row("3", 11) :: Row("2", 15) :: Nil)
+
+    checkAnswer(
+      sql(
+        """
+            |SELECT count(*)
+            |FROM orderByData
+            |GROUP BY a
+            |ORDER BY count(*)
+          """.stripMargin),
+      Row(2) :: Row(2) :: Row(2) :: Row(2) :: Nil)
+
+    checkAnswer(
+      sql(
+        """
+            |SELECT a
+            |FROM orderByData
+            |GROUP BY a
+            |ORDER BY a, count(*), sum(b)
+          """.stripMargin),
+      Row("1") :: Row("2") :: Row("3") :: Row("4") :: Nil)
   }
 
   test("SPARK-7952: fix the equality check between boolean and numeric types") {

From 31a229aa739b6d05ec6d91b820fcca79b6b7d6fe Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 15 Sep 2015 13:36:52 -0700
Subject: [PATCH 1439/1454] [SPARK-10475] [SQL] improve column prunning for
 Project on Sort

Sometimes we can't push down the whole `Project` though `Sort`, but we still have a chance to push down part of it.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8644 from cloud-fan/column-prune.
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 19 +++++++++++++++----
 .../optimizer/ColumnPruningSuite.scala        | 11 +++++++++++
 2 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 0f4caec7451a2..648a65e7c0eb3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -228,10 +228,21 @@ object ColumnPruning extends Rule[LogicalPlan] {
     case Project(projectList, Limit(exp, child)) =>
       Limit(exp, Project(projectList, child))
 
-    // Push down project if possible when the child is sort
-    case p @ Project(projectList, s @ Sort(_, _, grandChild))
-      if s.references.subsetOf(p.outputSet) =>
-      s.copy(child = Project(projectList, grandChild))
+    // Push down project if possible when the child is sort.
+    case p @ Project(projectList, s @ Sort(_, _, grandChild)) =>
+      if (s.references.subsetOf(p.outputSet)) {
+        s.copy(child = Project(projectList, grandChild))
+      } else {
+        val neededReferences = s.references ++ p.references
+        if (neededReferences == grandChild.outputSet) {
+          // No column we can prune, return the original plan.
+          p
+        } else {
+          // Do not use neededReferences.toSeq directly, should respect grandChild's output order.
+          val newProjectList = grandChild.output.filter(neededReferences.contains)
+          p.copy(child = s.copy(child = Project(newProjectList, grandChild)))
+        }
+      }
 
     // Eliminate no-op Projects
     case Project(projectList, child) if child.output == projectList => child
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala
index dbebcb86809de..4a1e7ceaf394b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala
@@ -80,5 +80,16 @@ class ColumnPruningSuite extends PlanTest {
     comparePlans(optimized, correctAnswer)
   }
 
+  test("Column pruning for Project on Sort") {
+    val input = LocalRelation('a.int, 'b.string, 'c.double)
+
+    val query = input.orderBy('b.asc).select('a).analyze
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer = input.select('a, 'b).orderBy('b.asc).select('a).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
   // todo: add more tests for column pruning
 }

From be52faa7c72fb4b95829f09a7dc5eb5dccd03524 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbt@netflix.com>
Date: Tue, 15 Sep 2015 15:46:47 -0700
Subject: [PATCH 1440/1454] [SPARK-7685] [ML] Apply weights to different
 samples in Logistic Regression

In fraud detection dataset, almost all the samples are negative while only couple of them are positive. This type of high imbalanced data will bias the models toward negative resulting poor performance. In python-scikit, they provide a correction allowing users to Over-/undersample the samples of each class according to the given weights. In auto mode, selects weights inversely proportional to class frequencies in the training set. This can be done in a more efficient way by multiplying the weights into loss and gradient instead of doing actual over/undersampling in the training dataset which is very expensive.
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
On the other hand, some of the training data maybe more important like the training samples from tenure users while the training samples from new users maybe less important. We should be able to provide another "weight: Double" information in the LabeledPoint to weight them differently in the learning algorithm.

Author: DB Tsai <dbt@netflix.com>
Author: DB Tsai <dbt@dbs-mac-pro.corp.netflix.com>

Closes #7884 from dbtsai/SPARK-7685.
---
 .../classification/LogisticRegression.scala   | 199 +++++++++++-------
 .../ml/param/shared/SharedParamsCodeGen.scala |   6 +-
 .../spark/ml/param/shared/sharedParams.scala  |  12 +-
 .../stat/MultivariateOnlineSummarizer.scala   |  75 ++++---
 .../LogisticRegressionSuite.scala             | 102 ++++++++-
 .../MultivariateOnlineSummarizerSuite.scala   |  27 +++
 project/MimaExcludes.scala                    |  10 +-
 7 files changed, 303 insertions(+), 128 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index a460262b87e43..bd96e8d000ff2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -29,12 +29,12 @@ import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.BLAS._
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.{col, lit}
 import org.apache.spark.storage.StorageLevel
 
 /**
@@ -42,7 +42,7 @@ import org.apache.spark.storage.StorageLevel
  */
 private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol
-  with HasStandardization with HasThreshold {
+  with HasStandardization with HasWeightCol with HasThreshold {
 
   /**
    * Set threshold in binary classification, in range [0, 1].
@@ -146,6 +146,17 @@ private[classification] trait LogisticRegressionParams extends ProbabilisticClas
   }
 }
 
+/**
+ * Class that represents an instance of weighted data point with label and features.
+ *
+ * TODO: Refactor this class to proper place.
+ *
+ * @param label Label for this data point.
+ * @param weight The weight of this instance.
+ * @param features The vector of features for this data point.
+ */
+private[classification] case class Instance(label: Double, weight: Double, features: Vector)
+
 /**
  * :: Experimental ::
  * Logistic regression.
@@ -218,31 +229,42 @@ class LogisticRegression(override val uid: String)
 
   override def getThreshold: Double = super.getThreshold
 
+  /**
+   * Whether to over-/under-sample training instances according to the given weights in weightCol.
+   * If empty, all instances are treated equally (weight 1.0).
+   * Default is empty, so all instances have weight one.
+   * @group setParam
+   */
+  def setWeightCol(value: String): this.type = set(weightCol, value)
+  setDefault(weightCol -> "")
+
   override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
 
   override def getThresholds: Array[Double] = super.getThresholds
 
   override protected def train(dataset: DataFrame): LogisticRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
-    val instances = extractLabeledPoints(dataset).map {
-      case LabeledPoint(label: Double, features: Vector) => (label, features)
+    val w = if ($(weightCol).isEmpty) lit(1.0) else col($(weightCol))
+    val instances: RDD[Instance] = dataset.select(col($(labelCol)), w, col($(featuresCol))).map {
+      case Row(label: Double, weight: Double, features: Vector) =>
+        Instance(label, weight, features)
     }
+
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
-    val (summarizer, labelSummarizer) = instances.treeAggregate(
-      (new MultivariateOnlineSummarizer, new MultiClassSummarizer))(
-        seqOp = (c, v) => (c, v) match {
-          case ((summarizer: MultivariateOnlineSummarizer, labelSummarizer: MultiClassSummarizer),
-          (label: Double, features: Vector)) =>
-            (summarizer.add(features), labelSummarizer.add(label))
-        },
-        combOp = (c1, c2) => (c1, c2) match {
-          case ((summarizer1: MultivariateOnlineSummarizer,
-          classSummarizer1: MultiClassSummarizer), (summarizer2: MultivariateOnlineSummarizer,
-          classSummarizer2: MultiClassSummarizer)) =>
-            (summarizer1.merge(summarizer2), classSummarizer1.merge(classSummarizer2))
-      })
+    val (summarizer, labelSummarizer) = {
+      val seqOp = (c: (MultivariateOnlineSummarizer, MultiClassSummarizer),
+        instance: Instance) =>
+          (c._1.add(instance.features, instance.weight), c._2.add(instance.label, instance.weight))
+
+      val combOp = (c1: (MultivariateOnlineSummarizer, MultiClassSummarizer),
+        c2: (MultivariateOnlineSummarizer, MultiClassSummarizer)) =>
+          (c1._1.merge(c2._1), c1._2.merge(c2._2))
+
+      instances.treeAggregate(
+        new MultivariateOnlineSummarizer, new MultiClassSummarizer)(seqOp, combOp)
+    }
 
     val histogram = labelSummarizer.histogram
     val numInvalid = labelSummarizer.countInvalid
@@ -295,7 +317,7 @@ class LogisticRegression(override val uid: String)
       new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun, $(tol))
     }
 
-    val initialWeightsWithIntercept =
+    val initialCoefficientsWithIntercept =
       Vectors.zeros(if ($(fitIntercept)) numFeatures + 1 else numFeatures)
 
     if ($(fitIntercept)) {
@@ -312,14 +334,14 @@ class LogisticRegression(override val uid: String)
          b = \log{P(1) / P(0)} = \log{count_1 / count_0}
          }}}
        */
-      initialWeightsWithIntercept.toArray(numFeatures)
-        = math.log(histogram(1).toDouble / histogram(0).toDouble)
+      initialCoefficientsWithIntercept.toArray(numFeatures)
+        = math.log(histogram(1) / histogram(0))
     }
 
     val states = optimizer.iterations(new CachedDiffFunction(costFun),
-      initialWeightsWithIntercept.toBreeze.toDenseVector)
+      initialCoefficientsWithIntercept.toBreeze.toDenseVector)
 
-    val (weights, intercept, objectiveHistory) = {
+    val (coefficients, intercept, objectiveHistory) = {
       /*
          Note that in Logistic Regression, the objective history (loss + regularization)
          is log-likelihood which is invariance under feature standardization. As a result,
@@ -339,28 +361,29 @@ class LogisticRegression(override val uid: String)
       }
 
       /*
-         The weights are trained in the scaled space; we're converting them back to
+         The coefficients are trained in the scaled space; we're converting them back to
          the original space.
          Note that the intercept in scaled space and original space is the same;
          as a result, no scaling is needed.
        */
-      val rawWeights = state.x.toArray.clone()
+      val rawCoefficients = state.x.toArray.clone()
       var i = 0
       while (i < numFeatures) {
-        rawWeights(i) *= { if (featuresStd(i) != 0.0) 1.0 / featuresStd(i) else 0.0 }
+        rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 / featuresStd(i) else 0.0 }
         i += 1
       }
 
       if ($(fitIntercept)) {
-        (Vectors.dense(rawWeights.dropRight(1)).compressed, rawWeights.last, arrayBuilder.result())
+        (Vectors.dense(rawCoefficients.dropRight(1)).compressed, rawCoefficients.last,
+          arrayBuilder.result())
       } else {
-        (Vectors.dense(rawWeights).compressed, 0.0, arrayBuilder.result())
+        (Vectors.dense(rawCoefficients).compressed, 0.0, arrayBuilder.result())
       }
     }
 
     if (handlePersistence) instances.unpersist()
 
-    val model = copyValues(new LogisticRegressionModel(uid, weights, intercept))
+    val model = copyValues(new LogisticRegressionModel(uid, coefficients, intercept))
     val logRegSummary = new BinaryLogisticRegressionTrainingSummary(
       model.transform(dataset),
       $(probabilityCol),
@@ -501,22 +524,29 @@ class LogisticRegressionModel private[ml] (
  * corresponding joint dataset.
  */
 private[classification] class MultiClassSummarizer extends Serializable {
-  private val distinctMap = new mutable.HashMap[Int, Long]
+  // The first element of value in distinctMap is the actually number of instances,
+  // and the second element of value is sum of the weights.
+  private val distinctMap = new mutable.HashMap[Int, (Long, Double)]
   private var totalInvalidCnt: Long = 0L
 
   /**
    * Add a new label into this MultilabelSummarizer, and update the distinct map.
    * @param label The label for this data point.
+   * @param weight The weight of this instances.
    * @return This MultilabelSummarizer
    */
-  def add(label: Double): this.type = {
+  def add(label: Double, weight: Double = 1.0): this.type = {
+    require(weight >= 0.0, s"instance weight, ${weight} has to be >= 0.0")
+
+    if (weight == 0.0) return this
+
     if (label - label.toInt != 0.0 || label < 0) {
       totalInvalidCnt += 1
       this
     }
     else {
-      val counts: Long = distinctMap.getOrElse(label.toInt, 0L)
-      distinctMap.put(label.toInt, counts + 1)
+      val (counts: Long, weightSum: Double) = distinctMap.getOrElse(label.toInt, (0L, 0.0))
+      distinctMap.put(label.toInt, (counts + 1L, weightSum + weight))
       this
     }
   }
@@ -537,8 +567,8 @@ private[classification] class MultiClassSummarizer extends Serializable {
     }
     smallMap.distinctMap.foreach {
       case (key, value) =>
-        val counts = largeMap.distinctMap.getOrElse(key, 0L)
-        largeMap.distinctMap.put(key, counts + value)
+        val (counts: Long, weightSum: Double) = largeMap.distinctMap.getOrElse(key, (0L, 0.0))
+        largeMap.distinctMap.put(key, (counts + value._1, weightSum + value._2))
     }
     largeMap.totalInvalidCnt += smallMap.totalInvalidCnt
     largeMap
@@ -550,13 +580,13 @@ private[classification] class MultiClassSummarizer extends Serializable {
   /** @return The number of distinct labels in the input dataset. */
   def numClasses: Int = distinctMap.keySet.max + 1
 
-  /** @return The counts of each label in the input dataset. */
-  def histogram: Array[Long] = {
-    val result = Array.ofDim[Long](numClasses)
+  /** @return The weightSum of each label in the input dataset. */
+  def histogram: Array[Double] = {
+    val result = Array.ofDim[Double](numClasses)
     var i = 0
     val len = result.length
     while (i < len) {
-      result(i) = distinctMap.getOrElse(i, 0L)
+      result(i) = distinctMap.getOrElse(i, (0L, 0.0))._2
       i += 1
     }
     result
@@ -565,6 +595,8 @@ private[classification] class MultiClassSummarizer extends Serializable {
 
 /**
  * Abstraction for multinomial Logistic Regression Training results.
+ * Currently, the training summary ignores the training weights except
+ * for the objective trace.
  */
 sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary {
 
@@ -584,10 +616,10 @@ sealed trait LogisticRegressionSummary extends Serializable {
   /** Dataframe outputted by the model's `transform` method. */
   def predictions: DataFrame
 
-  /** Field in "predictions" which gives the calibrated probability of each sample as a vector. */
+  /** Field in "predictions" which gives the calibrated probability of each instance as a vector. */
   def probabilityCol: String
 
-  /** Field in "predictions" which gives the the true label of each sample. */
+  /** Field in "predictions" which gives the the true label of each instance. */
   def labelCol: String
 
 }
@@ -597,8 +629,8 @@ sealed trait LogisticRegressionSummary extends Serializable {
  * Logistic regression training results.
  * @param predictions dataframe outputted by the model's `transform` method.
  * @param probabilityCol field in "predictions" which gives the calibrated probability of
- *                       each sample as a vector.
- * @param labelCol field in "predictions" which gives the true label of each sample.
+ *                       each instance as a vector.
+ * @param labelCol field in "predictions" which gives the true label of each instance.
  * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
  */
 @Experimental
@@ -617,8 +649,8 @@ class BinaryLogisticRegressionTrainingSummary private[classification] (
  * Binary Logistic regression results for a given model.
  * @param predictions dataframe outputted by the model's `transform` method.
  * @param probabilityCol field in "predictions" which gives the calibrated probability of
- *                       each sample.
- * @param labelCol field in "predictions" which gives the true label of each sample.
+ *                       each instance.
+ * @param labelCol field in "predictions" which gives the true label of each instance.
  */
 @Experimental
 class BinaryLogisticRegressionSummary private[classification] (
@@ -687,14 +719,14 @@ class BinaryLogisticRegressionSummary private[classification] (
 
 /**
  * LogisticAggregator computes the gradient and loss for binary logistic loss function, as used
- * in binary classification for samples in sparse or dense vector in a online fashion.
+ * in binary classification for instances in sparse or dense vector in a online fashion.
  *
  * Note that multinomial logistic loss is not supported yet!
  *
  * Two LogisticAggregator can be merged together to have a summary of loss and gradient of
  * the corresponding joint dataset.
  *
- * @param weights The weights/coefficients corresponding to the features.
+ * @param coefficients The coefficients corresponding to the features.
  * @param numClasses the number of possible outcomes for k classes classification problem in
  *                   Multinomial Logistic Regression.
  * @param fitIntercept Whether to fit an intercept term.
@@ -702,25 +734,25 @@ class BinaryLogisticRegressionSummary private[classification] (
  * @param featuresMean The mean values of the features.
  */
 private class LogisticAggregator(
-    weights: Vector,
+    coefficients: Vector,
     numClasses: Int,
     fitIntercept: Boolean,
     featuresStd: Array[Double],
     featuresMean: Array[Double]) extends Serializable {
 
-  private var totalCnt: Long = 0L
+  private var weightSum = 0.0
   private var lossSum = 0.0
 
-  private val weightsArray = weights match {
+  private val coefficientsArray = coefficients match {
     case dv: DenseVector => dv.values
     case _ =>
       throw new IllegalArgumentException(
-        s"weights only supports dense vector but got type ${weights.getClass}.")
+        s"coefficients only supports dense vector but got type ${coefficients.getClass}.")
   }
 
-  private val dim = if (fitIntercept) weightsArray.length - 1 else weightsArray.length
+  private val dim = if (fitIntercept) coefficientsArray.length - 1 else coefficientsArray.length
 
-  private val gradientSumArray = Array.ofDim[Double](weightsArray.length)
+  private val gradientSumArray = Array.ofDim[Double](coefficientsArray.length)
 
   /**
    * Add a new training data to this LogisticAggregator, and update the loss and gradient
@@ -729,13 +761,17 @@ private class LogisticAggregator(
    * @param label The label for this data point.
    * @param data The features for one data point in dense/sparse vector format to be added
    *             into this aggregator.
+   * @param weight The weight for over-/undersamples each of training instance. Default is one.
    * @return This LogisticAggregator object.
    */
-  def add(label: Double, data: Vector): this.type = {
-    require(dim == data.size, s"Dimensions mismatch when adding new sample." +
+  def add(label: Double, data: Vector, weight: Double = 1.0): this.type = {
+    require(dim == data.size, s"Dimensions mismatch when adding new instance." +
       s" Expecting $dim but got ${data.size}.")
+    require(weight >= 0.0, s"instance weight, ${weight} has to be >= 0.0")
 
-    val localWeightsArray = weightsArray
+    if (weight == 0.0) return this
+
+    val localCoefficientsArray = coefficientsArray
     val localGradientSumArray = gradientSumArray
 
     numClasses match {
@@ -745,13 +781,13 @@ private class LogisticAggregator(
           var sum = 0.0
           data.foreachActive { (index, value) =>
             if (featuresStd(index) != 0.0 && value != 0.0) {
-              sum += localWeightsArray(index) * (value / featuresStd(index))
+              sum += localCoefficientsArray(index) * (value / featuresStd(index))
             }
           }
-          sum + { if (fitIntercept) localWeightsArray(dim) else 0.0 }
+          sum + { if (fitIntercept) localCoefficientsArray(dim) else 0.0 }
         }
 
-        val multiplier = (1.0 / (1.0 + math.exp(margin))) - label
+        val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - label)
 
         data.foreachActive { (index, value) =>
           if (featuresStd(index) != 0.0 && value != 0.0) {
@@ -765,15 +801,15 @@ private class LogisticAggregator(
 
         if (label > 0) {
           // The following is equivalent to log(1 + exp(margin)) but more numerically stable.
-          lossSum += MLUtils.log1pExp(margin)
+          lossSum += weight * MLUtils.log1pExp(margin)
         } else {
-          lossSum += MLUtils.log1pExp(margin) - margin
+          lossSum += weight * (MLUtils.log1pExp(margin) - margin)
         }
       case _ =>
         new NotImplementedError("LogisticRegression with ElasticNet in ML package only supports " +
           "binary classification for now.")
     }
-    totalCnt += 1
+    weightSum += weight
     this
   }
 
@@ -789,8 +825,8 @@ private class LogisticAggregator(
     require(dim == other.dim, s"Dimensions mismatch when merging with another " +
       s"LeastSquaresAggregator. Expecting $dim but got ${other.dim}.")
 
-    if (other.totalCnt != 0) {
-      totalCnt += other.totalCnt
+    if (other.weightSum != 0.0) {
+      weightSum += other.weightSum
       lossSum += other.lossSum
 
       var i = 0
@@ -805,13 +841,17 @@ private class LogisticAggregator(
     this
   }
 
-  def count: Long = totalCnt
-
-  def loss: Double = lossSum / totalCnt
+  def loss: Double = {
+    require(weightSum > 0.0, s"The effective number of instances should be " +
+      s"greater than 0.0, but $weightSum.")
+    lossSum / weightSum
+  }
 
   def gradient: Vector = {
+    require(weightSum > 0.0, s"The effective number of instances should be " +
+      s"greater than 0.0, but $weightSum.")
     val result = Vectors.dense(gradientSumArray.clone())
-    scal(1.0 / totalCnt, result)
+    scal(1.0 / weightSum, result)
     result
   }
 }
@@ -823,7 +863,7 @@ private class LogisticAggregator(
  * It's used in Breeze's convex optimization routines.
  */
 private class LogisticCostFun(
-    data: RDD[(Double, Vector)],
+    data: RDD[Instance],
     numClasses: Int,
     fitIntercept: Boolean,
     standardization: Boolean,
@@ -831,22 +871,23 @@ private class LogisticCostFun(
     featuresMean: Array[Double],
     regParamL2: Double) extends DiffFunction[BDV[Double]] {
 
-  override def calculate(weights: BDV[Double]): (Double, BDV[Double]) = {
+  override def calculate(coefficients: BDV[Double]): (Double, BDV[Double]) = {
     val numFeatures = featuresStd.length
-    val w = Vectors.fromBreeze(weights)
+    val w = Vectors.fromBreeze(coefficients)
 
-    val logisticAggregator = data.treeAggregate(new LogisticAggregator(w, numClasses, fitIntercept,
-      featuresStd, featuresMean))(
-        seqOp = (c, v) => (c, v) match {
-          case (aggregator, (label, features)) => aggregator.add(label, features)
-        },
-        combOp = (c1, c2) => (c1, c2) match {
-          case (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
-        })
+    val logisticAggregator = {
+      val seqOp = (c: LogisticAggregator, instance: Instance) =>
+        c.add(instance.label, instance.features, instance.weight)
+      val combOp = (c1: LogisticAggregator, c2: LogisticAggregator) => c1.merge(c2)
+
+      data.treeAggregate(
+        new LogisticAggregator(w, numClasses, fitIntercept, featuresStd, featuresMean)
+      )(seqOp, combOp)
+    }
 
     val totalGradientArray = logisticAggregator.gradient.toArray
 
-    // regVal is the sum of weight squares excluding intercept for L2 regularization.
+    // regVal is the sum of coefficients squares excluding intercept for L2 regularization.
     val regVal = if (regParamL2 == 0.0) {
       0.0
     } else {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index e9e99ed1db40e..8049d51fee5ea 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -42,7 +42,7 @@ private[shared] object SharedParamsCodeGen {
         Some("\"rawPrediction\"")),
       ParamDesc[String]("probabilityCol", "Column name for predicted class conditional" +
         " probabilities. Note: Not all models output well-calibrated probability estimates!" +
-        " These probabilities should be treated as confidences, not precise probabilities.",
+        " These probabilities should be treated as confidences, not precise probabilities",
         Some("\"probability\"")),
       ParamDesc[Double]("threshold",
         "threshold in binary classification prediction, in range [0, 1]", Some("0.5"),
@@ -65,10 +65,10 @@ private[shared] object SharedParamsCodeGen {
         "options may be added later.",
         isValid = "ParamValidators.inArray(Array(\"skip\", \"error\"))"),
       ParamDesc[Boolean]("standardization", "whether to standardize the training features" +
-        " before fitting the model.", Some("true")),
+        " before fitting the model", Some("true")),
       ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")),
       ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." +
-        " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.",
+        " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty",
         isValid = "ParamValidators.inRange(0, 1)"),
       ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms"),
       ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization."),
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index 30092170863ad..aff47fc326c4a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -127,10 +127,10 @@ private[ml] trait HasRawPredictionCol extends Params {
 private[ml] trait HasProbabilityCol extends Params {
 
   /**
-   * Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities..
+   * Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.
    * @group param
    */
-  final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.")
+  final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities")
 
   setDefault(probabilityCol, "probability")
 
@@ -270,10 +270,10 @@ private[ml] trait HasHandleInvalid extends Params {
 private[ml] trait HasStandardization extends Params {
 
   /**
-   * Param for whether to standardize the training features before fitting the model..
+   * Param for whether to standardize the training features before fitting the model.
    * @group param
    */
-  final val standardization: BooleanParam = new BooleanParam(this, "standardization", "whether to standardize the training features before fitting the model.")
+  final val standardization: BooleanParam = new BooleanParam(this, "standardization", "whether to standardize the training features before fitting the model")
 
   setDefault(standardization, true)
 
@@ -304,10 +304,10 @@ private[ml] trait HasSeed extends Params {
 private[ml] trait HasElasticNetParam extends Params {
 
   /**
-   * Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty..
+   * Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
    * @group param
    */
-  final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", ParamValidators.inRange(0, 1))
+  final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty", ParamValidators.inRange(0, 1))
 
   /** @group getParam */
   final def getElasticNetParam: Double = $(elasticNetParam)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index 51b713e263e0c..201333c3690df 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -23,16 +23,19 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
 /**
  * :: DeveloperApi ::
  * MultivariateOnlineSummarizer implements [[MultivariateStatisticalSummary]] to compute the mean,
- * variance, minimum, maximum, counts, and nonzero counts for samples in sparse or dense vector
+ * variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector
  * format in a online fashion.
  *
  * Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of
  * the corresponding joint dataset.
  *
- * A numerically stable algorithm is implemented to compute sample mean and variance:
+ * A numerically stable algorithm is implemented to compute the mean and variance of instances:
  * Reference: [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]]
  * Zero elements (including explicit zero values) are skipped when calling add(),
  * to have time complexity O(nnz) instead of O(n) for each column.
+ *
+ * For weighted instances, the unbiased estimation of variance is defined by the reliability
+ * weights: [[https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Reliability_weights]].
  */
 @Since("1.1.0")
 @DeveloperApi
@@ -44,6 +47,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   private var currM2: Array[Double] = _
   private var currL1: Array[Double] = _
   private var totalCnt: Long = 0
+  private var weightSum: Double = 0.0
+  private var weightSquareSum: Double = 0.0
   private var nnz: Array[Double] = _
   private var currMax: Array[Double] = _
   private var currMin: Array[Double] = _
@@ -55,10 +60,15 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    * @return This MultivariateOnlineSummarizer object.
    */
   @Since("1.1.0")
-  def add(sample: Vector): this.type = {
+  def add(sample: Vector): this.type = add(sample, 1.0)
+
+  private[spark] def add(instance: Vector, weight: Double): this.type = {
+    require(weight >= 0.0, s"sample weight, ${weight} has to be >= 0.0")
+    if (weight == 0.0) return this
+
     if (n == 0) {
-      require(sample.size > 0, s"Vector should have dimension larger than zero.")
-      n = sample.size
+      require(instance.size > 0, s"Vector should have dimension larger than zero.")
+      n = instance.size
 
       currMean = Array.ofDim[Double](n)
       currM2n = Array.ofDim[Double](n)
@@ -69,8 +79,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
       currMin = Array.fill[Double](n)(Double.MaxValue)
     }
 
-    require(n == sample.size, s"Dimensions mismatch when adding new sample." +
-      s" Expecting $n but got ${sample.size}.")
+    require(n == instance.size, s"Dimensions mismatch when adding new sample." +
+      s" Expecting $n but got ${instance.size}.")
 
     val localCurrMean = currMean
     val localCurrM2n = currM2n
@@ -79,7 +89,7 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     val localNnz = nnz
     val localCurrMax = currMax
     val localCurrMin = currMin
-    sample.foreachActive { (index, value) =>
+    instance.foreachActive { (index, value) =>
       if (value != 0.0) {
         if (localCurrMax(index) < value) {
           localCurrMax(index) = value
@@ -90,15 +100,17 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
 
         val prevMean = localCurrMean(index)
         val diff = value - prevMean
-        localCurrMean(index) = prevMean + diff / (localNnz(index) + 1.0)
-        localCurrM2n(index) += (value - localCurrMean(index)) * diff
-        localCurrM2(index) += value * value
-        localCurrL1(index) += math.abs(value)
+        localCurrMean(index) = prevMean + weight * diff / (localNnz(index) + weight)
+        localCurrM2n(index) += weight * (value - localCurrMean(index)) * diff
+        localCurrM2(index) += weight * value * value
+        localCurrL1(index) += weight * math.abs(value)
 
-        localNnz(index) += 1.0
+        localNnz(index) += weight
       }
     }
 
+    weightSum += weight
+    weightSquareSum += weight * weight
     totalCnt += 1
     this
   }
@@ -112,10 +124,12 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   @Since("1.1.0")
   def merge(other: MultivariateOnlineSummarizer): this.type = {
-    if (this.totalCnt != 0 && other.totalCnt != 0) {
+    if (this.weightSum != 0.0 && other.weightSum != 0.0) {
       require(n == other.n, s"Dimensions mismatch when merging with another summarizer. " +
         s"Expecting $n but got ${other.n}.")
       totalCnt += other.totalCnt
+      weightSum += other.weightSum
+      weightSquareSum += other.weightSquareSum
       var i = 0
       while (i < n) {
         val thisNnz = nnz(i)
@@ -138,13 +152,15 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
         nnz(i) = totalNnz
         i += 1
       }
-    } else if (totalCnt == 0 && other.totalCnt != 0) {
+    } else if (weightSum == 0.0 && other.weightSum != 0.0) {
       this.n = other.n
       this.currMean = other.currMean.clone()
       this.currM2n = other.currM2n.clone()
       this.currM2 = other.currM2.clone()
       this.currL1 = other.currL1.clone()
       this.totalCnt = other.totalCnt
+      this.weightSum = other.weightSum
+      this.weightSquareSum = other.weightSquareSum
       this.nnz = other.nnz.clone()
       this.currMax = other.currMax.clone()
       this.currMin = other.currMin.clone()
@@ -158,28 +174,28 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   @Since("1.1.0")
   override def mean: Vector = {
-    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+    require(weightSum > 0, s"Nothing has been added to this summarizer.")
 
     val realMean = Array.ofDim[Double](n)
     var i = 0
     while (i < n) {
-      realMean(i) = currMean(i) * (nnz(i) / totalCnt)
+      realMean(i) = currMean(i) * (nnz(i) / weightSum)
       i += 1
     }
     Vectors.dense(realMean)
   }
 
   /**
-   * Sample variance of each dimension.
+   * Unbiased estimate of sample variance of each dimension.
    *
    */
   @Since("1.1.0")
   override def variance: Vector = {
-    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+    require(weightSum > 0, s"Nothing has been added to this summarizer.")
 
     val realVariance = Array.ofDim[Double](n)
 
-    val denominator = totalCnt - 1.0
+    val denominator = weightSum - (weightSquareSum / weightSum)
 
     // Sample variance is computed, if the denominator is less than 0, the variance is just 0.
     if (denominator > 0.0) {
@@ -187,9 +203,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
       var i = 0
       val len = currM2n.length
       while (i < len) {
-        realVariance(i) =
-          currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt
-        realVariance(i) /= denominator
+        realVariance(i) = (currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) *
+          (weightSum - nnz(i)) / weightSum) / denominator
         i += 1
       }
     }
@@ -209,7 +224,7 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   @Since("1.1.0")
   override def numNonzeros: Vector = {
-    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+    require(weightSum > 0, s"Nothing has been added to this summarizer.")
 
     Vectors.dense(nnz)
   }
@@ -220,11 +235,11 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   @Since("1.1.0")
   override def max: Vector = {
-    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+    require(weightSum > 0, s"Nothing has been added to this summarizer.")
 
     var i = 0
     while (i < n) {
-      if ((nnz(i) < totalCnt) && (currMax(i) < 0.0)) currMax(i) = 0.0
+      if ((nnz(i) < weightSum) && (currMax(i) < 0.0)) currMax(i) = 0.0
       i += 1
     }
     Vectors.dense(currMax)
@@ -236,11 +251,11 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   @Since("1.1.0")
   override def min: Vector = {
-    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+    require(weightSum > 0, s"Nothing has been added to this summarizer.")
 
     var i = 0
     while (i < n) {
-      if ((nnz(i) < totalCnt) && (currMin(i) > 0.0)) currMin(i) = 0.0
+      if ((nnz(i) < weightSum) && (currMin(i) > 0.0)) currMin(i) = 0.0
       i += 1
     }
     Vectors.dense(currMin)
@@ -252,7 +267,7 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   @Since("1.2.0")
   override def normL2: Vector = {
-    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+    require(weightSum > 0, s"Nothing has been added to this summarizer.")
 
     val realMagnitude = Array.ofDim[Double](n)
 
@@ -271,7 +286,7 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   @Since("1.2.0")
   override def normL1: Vector = {
-    require(totalCnt > 0, s"Nothing has been added to this summarizer.")
+    require(weightSum > 0, s"Nothing has been added to this summarizer.")
 
     Vectors.dense(currL1)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index cce39f382f738..f5219f9f574be 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -17,11 +17,14 @@
 
 package org.apache.spark.ml.classification
 
+import scala.util.Random
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
@@ -59,8 +62,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
 
       val testData = generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
 
-      sqlContext.createDataFrame(
-        generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42))
+      sqlContext.createDataFrame(sc.parallelize(testData, 4))
     }
   }
 
@@ -77,6 +79,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lr.getPredictionCol === "prediction")
     assert(lr.getRawPredictionCol === "rawPrediction")
     assert(lr.getProbabilityCol === "probability")
+    assert(lr.getWeightCol === "")
     assert(lr.getFitIntercept)
     assert(lr.getStandardization)
     val model = lr.fit(dataset)
@@ -216,43 +219,65 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   test("MultiClassSummarizer") {
     val summarizer1 = (new MultiClassSummarizer)
       .add(0.0).add(3.0).add(4.0).add(3.0).add(6.0)
-    assert(summarizer1.histogram.zip(Array[Long](1, 0, 0, 2, 1, 0, 1)).forall(x => x._1 === x._2))
+    assert(summarizer1.histogram === Array[Double](1, 0, 0, 2, 1, 0, 1))
     assert(summarizer1.countInvalid === 0)
     assert(summarizer1.numClasses === 7)
 
     val summarizer2 = (new MultiClassSummarizer)
       .add(1.0).add(5.0).add(3.0).add(0.0).add(4.0).add(1.0)
-    assert(summarizer2.histogram.zip(Array[Long](1, 2, 0, 1, 1, 1)).forall(x => x._1 === x._2))
+    assert(summarizer2.histogram === Array[Double](1, 2, 0, 1, 1, 1))
     assert(summarizer2.countInvalid === 0)
     assert(summarizer2.numClasses === 6)
 
     val summarizer3 = (new MultiClassSummarizer)
       .add(0.0).add(1.3).add(5.2).add(2.5).add(2.0).add(4.0).add(4.0).add(4.0).add(1.0)
-    assert(summarizer3.histogram.zip(Array[Long](1, 1, 1, 0, 3)).forall(x => x._1 === x._2))
+    assert(summarizer3.histogram === Array[Double](1, 1, 1, 0, 3))
     assert(summarizer3.countInvalid === 3)
     assert(summarizer3.numClasses === 5)
 
     val summarizer4 = (new MultiClassSummarizer)
       .add(3.1).add(4.3).add(2.0).add(1.0).add(3.0)
-    assert(summarizer4.histogram.zip(Array[Long](0, 1, 1, 1)).forall(x => x._1 === x._2))
+    assert(summarizer4.histogram === Array[Double](0, 1, 1, 1))
     assert(summarizer4.countInvalid === 2)
     assert(summarizer4.numClasses === 4)
 
     // small map merges large one
     val summarizerA = summarizer1.merge(summarizer2)
     assert(summarizerA.hashCode() === summarizer2.hashCode())
-    assert(summarizerA.histogram.zip(Array[Long](2, 2, 0, 3, 2, 1, 1)).forall(x => x._1 === x._2))
+    assert(summarizerA.histogram === Array[Double](2, 2, 0, 3, 2, 1, 1))
     assert(summarizerA.countInvalid === 0)
     assert(summarizerA.numClasses === 7)
 
     // large map merges small one
     val summarizerB = summarizer3.merge(summarizer4)
     assert(summarizerB.hashCode() === summarizer3.hashCode())
-    assert(summarizerB.histogram.zip(Array[Long](1, 2, 2, 1, 3)).forall(x => x._1 === x._2))
+    assert(summarizerB.histogram === Array[Double](1, 2, 2, 1, 3))
     assert(summarizerB.countInvalid === 5)
     assert(summarizerB.numClasses === 5)
   }
 
+  test("MultiClassSummarizer with weighted samples") {
+    val summarizer1 = (new MultiClassSummarizer)
+      .add(label = 0.0, weight = 0.2).add(3.0, 0.8).add(4.0, 3.2).add(3.0, 1.3).add(6.0, 3.1)
+    assert(Vectors.dense(summarizer1.histogram) ~==
+      Vectors.dense(Array(0.2, 0, 0, 2.1, 3.2, 0, 3.1)) absTol 1E-10)
+    assert(summarizer1.countInvalid === 0)
+    assert(summarizer1.numClasses === 7)
+
+    val summarizer2 = (new MultiClassSummarizer)
+      .add(1.0, 1.1).add(5.0, 2.3).add(3.0).add(0.0).add(4.0).add(1.0).add(2, 0.0)
+    assert(Vectors.dense(summarizer2.histogram) ~==
+      Vectors.dense(Array[Double](1.0, 2.1, 0.0, 1, 1, 2.3)) absTol 1E-10)
+    assert(summarizer2.countInvalid === 0)
+    assert(summarizer2.numClasses === 6)
+
+    val summarizer = summarizer1.merge(summarizer2)
+    assert(Vectors.dense(summarizer.histogram) ~==
+      Vectors.dense(Array(1.2, 2.1, 0.0, 3.1, 4.2, 2.3, 3.1)) absTol 1E-10)
+    assert(summarizer.countInvalid === 0)
+    assert(summarizer.numClasses === 7)
+  }
+
   test("binary logistic regression with intercept without regularization") {
     val trainer1 = (new LogisticRegression).setFitIntercept(true).setStandardization(true)
     val trainer2 = (new LogisticRegression).setFitIntercept(true).setStandardization(false)
@@ -713,7 +738,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        b = \log{P(1) / P(0)} = \log{count_1 / count_0}
        }}}
      */
-    val interceptTheory = math.log(histogram(1).toDouble / histogram(0).toDouble)
+    val interceptTheory = math.log(histogram(1) / histogram(0))
     val weightsTheory = Vectors.dense(0.0, 0.0, 0.0, 0.0)
 
     assert(model1.intercept ~== interceptTheory relTol 1E-5)
@@ -781,4 +806,63 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
         .forall(x => x(0) >= x(1)))
 
   }
+
+  test("binary logistic regression with weighted samples") {
+    val (dataset, weightedDataset) = {
+      val nPoints = 1000
+      val weights = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191)
+      val xMean = Array(5.843, 3.057, 3.758, 1.199)
+      val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
+      val testData = generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
+
+      // Let's over-sample the positive samples twice.
+      val data1 = testData.flatMap { case labeledPoint: LabeledPoint =>
+        if (labeledPoint.label == 1.0) {
+          Iterator(labeledPoint, labeledPoint)
+        } else {
+          Iterator(labeledPoint)
+        }
+      }
+
+      val rnd = new Random(8392)
+      val data2 = testData.flatMap { case LabeledPoint(label: Double, features: Vector) =>
+        if (rnd.nextGaussian() > 0.0) {
+          if (label == 1.0) {
+            Iterator(
+              Instance(label, 1.2, features),
+              Instance(label, 0.8, features),
+              Instance(0.0, 0.0, features))
+          } else {
+            Iterator(
+              Instance(label, 0.3, features),
+              Instance(1.0, 0.0, features),
+              Instance(label, 0.1, features),
+              Instance(label, 0.6, features))
+          }
+        } else {
+          if (label == 1.0) {
+            Iterator(Instance(label, 2.0, features))
+          } else {
+            Iterator(Instance(label, 1.0, features))
+          }
+        }
+      }
+
+      (sqlContext.createDataFrame(sc.parallelize(data1, 4)),
+        sqlContext.createDataFrame(sc.parallelize(data2, 4)))
+    }
+
+    val trainer1a = (new LogisticRegression).setFitIntercept(true)
+      .setRegParam(0.0).setStandardization(true)
+    val trainer1b = (new LogisticRegression).setFitIntercept(true).setWeightCol("weight")
+      .setRegParam(0.0).setStandardization(true)
+    val model1a0 = trainer1a.fit(dataset)
+    val model1a1 = trainer1a.fit(weightedDataset)
+    val model1b = trainer1b.fit(weightedDataset)
+    assert(model1a0.weights !~= model1a1.weights absTol 1E-3)
+    assert(model1a0.intercept !~= model1a1.intercept absTol 1E-3)
+    assert(model1a0.weights ~== model1b.weights absTol 1E-3)
+    assert(model1a0.intercept ~== model1b.intercept absTol 1E-3)
+
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
index 07efde4f5e6dc..b6d41db69be0a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
@@ -218,4 +218,31 @@ class MultivariateOnlineSummarizerSuite extends SparkFunSuite {
     s0.merge(s1)
     assert(s0.mean(0) ~== 1.0 absTol 1e-14)
   }
+
+  test("merging summarizer with weighted samples") {
+    val summarizer = (new MultivariateOnlineSummarizer)
+      .add(instance = Vectors.sparse(3, Seq((0, -0.8), (1, 1.7))), weight = 0.1)
+      .add(Vectors.dense(0.0, -1.2, -1.7), 0.2).merge(
+        (new MultivariateOnlineSummarizer)
+          .add(Vectors.sparse(3, Seq((0, -0.7), (1, 0.01), (2, 1.3))), 0.15)
+          .add(Vectors.dense(-0.5, 0.3, -1.5), 0.05))
+
+    assert(summarizer.count === 4)
+
+    // The following values are hand calculated using the formula:
+    // [[https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Reliability_weights]]
+    // which defines the reliability weight used for computing the unbiased estimation of variance
+    // for weighted instances.
+    assert(summarizer.mean ~== Vectors.dense(Array(-0.42, -0.107, -0.44))
+      absTol 1E-10, "mean mismatch")
+    assert(summarizer.variance ~== Vectors.dense(Array(0.17657142857, 1.645115714, 2.42057142857))
+      absTol 1E-8, "variance mismatch")
+    assert(summarizer.numNonzeros ~== Vectors.dense(Array(0.3, 0.5, 0.4))
+      absTol 1E-10, "numNonzeros mismatch")
+    assert(summarizer.max ~== Vectors.dense(Array(0.0, 1.7, 1.3)) absTol 1E-10, "max mismatch")
+    assert(summarizer.min ~== Vectors.dense(Array(-0.8, -1.2, -1.7)) absTol 1E-10, "min mismatch")
+    assert(summarizer.normL2 ~== Vectors.dense(0.387298335, 0.762571308141, 0.9715966241192)
+      absTol 1E-8, "normL2 mismatch")
+    assert(summarizer.normL1 ~== Vectors.dense(0.21, 0.4265, 0.61) absTol 1E-10, "normL1 mismatch")
+  }
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 87b141cd3b058..46026c1e90ea0 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -45,7 +45,15 @@ object MimaExcludes {
         excludePackage("org.apache.spark.sql.execution")
       ) ++
       MimaBuild.excludeSparkClass("streaming.flume.FlumeTestUtils") ++
-      MimaBuild.excludeSparkClass("streaming.flume.PollingFlumeTestUtils")
+      MimaBuild.excludeSparkClass("streaming.flume.PollingFlumeTestUtils") ++ 
+      Seq(
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.classification.LogisticCostFun.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.classification.LogisticAggregator.add"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.classification.LogisticAggregator.count")
+      )
     case v if v.startsWith("1.5") =>
       Seq(
         MimaBuild.excludeSparkPackage("network"),

From b6e998634e05db0bb6267173e7b28f885c808c16 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 15 Sep 2015 16:45:47 -0700
Subject: [PATCH 1441/1454] [SPARK-10548] [SPARK-10563] [SQL] Fix concurrent
 SQL executions

*Note: this is for master branch only.* The fix for branch-1.5 is at #8721.

The query execution ID is currently passed from a thread to its children, which is not the intended behavior. This led to `IllegalArgumentException: spark.sql.execution.id is already set` when running queries in parallel, e.g.:
```
(1 to 100).par.foreach { _ =>
  sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count()
}
```
The cause is `SparkContext`'s local properties are inherited by default. This patch adds a way to exclude keys we don't want to be inherited, and makes SQL go through that code path.

Author: Andrew Or <andrew@databricks.com>

Closes #8710 from andrewor14/concurrent-sql-executions.
---
 .../scala/org/apache/spark/SparkContext.scala |   9 +-
 .../org/apache/spark/ThreadingSuite.scala     |  65 +++++------
 .../sql/execution/SQLExecutionSuite.scala     | 101 ++++++++++++++++++
 3 files changed, 132 insertions(+), 43 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/SQLExecutionSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index dee6091ce3caf..a2f34eafa2c38 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -33,6 +33,7 @@ import scala.collection.mutable.HashMap
 import scala.reflect.{ClassTag, classTag}
 import scala.util.control.NonFatal
 
+import org.apache.commons.lang.SerializationUtils
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable,
@@ -347,8 +348,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   private[spark] var checkpointDir: Option[String] = None
 
   // Thread Local variable that can be used by users to pass information down the stack
-  private val localProperties = new InheritableThreadLocal[Properties] {
-    override protected def childValue(parent: Properties): Properties = new Properties(parent)
+  protected[spark] val localProperties = new InheritableThreadLocal[Properties] {
+    override protected def childValue(parent: Properties): Properties = {
+      // Note: make a clone such that changes in the parent properties aren't reflected in
+      // the those of the children threads, which has confusing semantics (SPARK-10563).
+      SerializationUtils.clone(parent).asInstanceOf[Properties]
+    }
     override protected def initialValue(): Properties = new Properties()
   }
 
diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
index a96a4ce201c21..54c131cdae367 100644
--- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
@@ -147,7 +147,7 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
       }.start()
     }
     sem.acquire(2)
-    throwable.foreach { t => throw t }
+    throwable.foreach { t => throw improveStackTrace(t) }
     if (ThreadingSuiteState.failed.get()) {
       logError("Waited 1 second without seeing runningThreads = 4 (it was " +
                 ThreadingSuiteState.runningThreads.get() + "); failing test")
@@ -178,7 +178,7 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     threads.foreach(_.start())
 
     sem.acquire(5)
-    throwable.foreach { t => throw t }
+    throwable.foreach { t => throw improveStackTrace(t) }
     assert(sc.getLocalProperty("test") === null)
   }
 
@@ -207,58 +207,41 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     threads.foreach(_.start())
 
     sem.acquire(5)
-    throwable.foreach { t => throw t }
+    throwable.foreach { t => throw improveStackTrace(t) }
     assert(sc.getLocalProperty("test") === "parent")
     assert(sc.getLocalProperty("Foo") === null)
   }
 
-  test("mutations to local properties should not affect submitted jobs (SPARK-6629)") {
-    val jobStarted = new Semaphore(0)
-    val jobEnded = new Semaphore(0)
-    @volatile var jobResult: JobResult = null
-    var throwable: Option[Throwable] = None
-
+  test("mutation in parent local property does not affect child (SPARK-10563)") {
     sc = new SparkContext("local", "test")
-    sc.setJobGroup("originalJobGroupId", "description")
-    sc.addSparkListener(new SparkListener {
-      override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
-        jobStarted.release()
-      }
-      override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
-        jobResult = jobEnd.jobResult
-        jobEnded.release()
-      }
-    })
-
-    // Create a new thread which will inherit the current thread's properties
-    val thread = new Thread() {
+    val originalTestValue: String = "original-value"
+    var threadTestValue: String = null
+    sc.setLocalProperty("test", originalTestValue)
+    var throwable: Option[Throwable] = None
+    val thread = new Thread {
       override def run(): Unit = {
         try {
-          assert(sc.getLocalProperty(SparkContext.SPARK_JOB_GROUP_ID) === "originalJobGroupId")
-          // Sleeps for a total of 10 seconds, but allows cancellation to interrupt the task
-          try {
-            sc.parallelize(1 to 100).foreach { x =>
-              Thread.sleep(100)
-            }
-          } catch {
-            case s: SparkException => // ignored so that we don't print noise in test logs
-          }
+          threadTestValue = sc.getLocalProperty("test")
         } catch {
           case t: Throwable =>
             throwable = Some(t)
         }
       }
     }
+    sc.setLocalProperty("test", "this-should-not-be-inherited")
     thread.start()
-    // Wait for the job to start, then mutate the original properties, which should have been
-    // inherited by the running job but hopefully defensively copied or snapshotted:
-    jobStarted.tryAcquire(10, TimeUnit.SECONDS)
-    sc.setJobGroup("modifiedJobGroupId", "description")
-    // Canceling the original job group should cancel the running job. In other words, the
-    // modification of the properties object should not affect the properties of running jobs
-    sc.cancelJobGroup("originalJobGroupId")
-    jobEnded.tryAcquire(10, TimeUnit.SECONDS)
-    throwable.foreach { t => throw t }
-    assert(jobResult.isInstanceOf[JobFailed])
+    thread.join()
+    throwable.foreach { t => throw improveStackTrace(t) }
+    assert(threadTestValue === originalTestValue)
   }
+
+  /**
+   * Improve the stack trace of an error thrown from within a thread.
+   * Otherwise it's difficult to tell which line in the test the error came from.
+   */
+  private def improveStackTrace(t: Throwable): Throwable = {
+    t.setStackTrace(t.getStackTrace ++ Thread.currentThread.getStackTrace)
+    t
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SQLExecutionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SQLExecutionSuite.scala
new file mode 100644
index 0000000000000..63639681ef80a
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SQLExecutionSuite.scala
@@ -0,0 +1,101 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import java.util.Properties
+
+import scala.collection.parallel.CompositeThrowable
+
+import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark.sql.SQLContext
+
+class SQLExecutionSuite extends SparkFunSuite {
+
+  test("concurrent query execution (SPARK-10548)") {
+    // Try to reproduce the issue with the old SparkContext
+    val conf = new SparkConf()
+      .setMaster("local[*]")
+      .setAppName("test")
+    val badSparkContext = new BadSparkContext(conf)
+    try {
+      testConcurrentQueryExecution(badSparkContext)
+      fail("unable to reproduce SPARK-10548")
+    } catch {
+      case e: IllegalArgumentException =>
+        assert(e.getMessage.contains(SQLExecution.EXECUTION_ID_KEY))
+    } finally {
+      badSparkContext.stop()
+    }
+
+    // Verify that the issue is fixed with the latest SparkContext
+    val goodSparkContext = new SparkContext(conf)
+    try {
+      testConcurrentQueryExecution(goodSparkContext)
+    } finally {
+      goodSparkContext.stop()
+    }
+  }
+
+  /**
+   * Trigger SPARK-10548 by mocking a parent and its child thread executing queries concurrently.
+   */
+  private def testConcurrentQueryExecution(sc: SparkContext): Unit = {
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    // Initialize local properties. This is necessary for the test to pass.
+    sc.getLocalProperties
+
+    // Set up a thread that runs executes a simple SQL query.
+    // Before starting the thread, mutate the execution ID in the parent.
+    // The child thread should not see the effect of this change.
+    var throwable: Option[Throwable] = None
+    val child = new Thread {
+      override def run(): Unit = {
+        try {
+          sc.parallelize(1 to 100).map { i => (i, i) }.toDF("a", "b").collect()
+        } catch {
+          case t: Throwable =>
+            throwable = Some(t)
+        }
+
+      }
+    }
+    sc.setLocalProperty(SQLExecution.EXECUTION_ID_KEY, "anything")
+    child.start()
+    child.join()
+
+    // The throwable is thrown from the child thread so it doesn't have a helpful stack trace
+    throwable.foreach { t =>
+      t.setStackTrace(t.getStackTrace ++ Thread.currentThread.getStackTrace)
+      throw t
+    }
+  }
+
+}
+
+/**
+ * A bad [[SparkContext]] that does not clone the inheritable thread local properties
+ * when passing them to children threads.
+ */
+private class BadSparkContext(conf: SparkConf) extends SparkContext(conf) {
+  protected[spark] override val localProperties = new InheritableThreadLocal[Properties] {
+    override protected def childValue(parent: Properties): Properties = new Properties(parent)
+    override protected def initialValue(): Properties = new Properties()
+  }
+}

From a63cdc769f511e98b38c3318bcc732c9a6c76c22 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 15 Sep 2015 16:53:27 -0700
Subject: [PATCH 1442/1454] [SPARK-10612] [SQL] Add prepare to LocalNode.

The idea is that we should separate the function call that does memory reservation (i.e. prepare) from the function call that consumes the input (e.g. open()), so all operators can be a chance to reserve memory before they are all consumed.

Author: Reynold Xin <rxin@databricks.com>

Closes #8761 from rxin/SPARK-10612.
---
 .../org/apache/spark/sql/execution/local/LocalNode.scala  | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index 9840080e16953..569cff565c092 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -45,6 +45,14 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
 
   def output: Seq[Attribute]
 
+  /**
+   * Called before open(). Prepare can be used to reserve memory needed. It must NOT consume
+   * any input data.
+   *
+   * Implementations of this must also call the `prepare()` function of its children.
+   */
+  def prepare(): Unit = children.foreach(_.prepare())
+
   /**
    * Initializes the iterator state. Must be called before calling `next()`.
    *

From 99ecfa5945aedaa71765ecf5cce59964ae52eebe Mon Sep 17 00:00:00 2001
From: vinodkc <vinod.kc.in@gmail.com>
Date: Tue, 15 Sep 2015 17:01:10 -0700
Subject: [PATCH 1443/1454] [SPARK-10575] [SPARK CORE] Wrapped RDD.takeSample
 with Scope

Remove return statements in RDD.takeSample and wrap it withScope

Author: vinodkc <vinod.kc.in@gmail.com>
Author: vinodkc <vinodkc@users.noreply.github.com>
Author: Vinod K C <vinod.kc@huawei.com>

Closes #8730 from vinodkc/fix_takesample_return.
---
 .../main/scala/org/apache/spark/rdd/RDD.scala | 68 +++++++++----------
 1 file changed, 31 insertions(+), 37 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 7dd2bc5d7cd72..a56e542242d5f 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -469,50 +469,44 @@ abstract class RDD[T: ClassTag](
    * @param seed seed for the random number generator
    * @return sample of specified size in an array
    */
-  // TODO: rewrite this without return statements so we can wrap it in a scope
   def takeSample(
       withReplacement: Boolean,
       num: Int,
-      seed: Long = Utils.random.nextLong): Array[T] = {
+      seed: Long = Utils.random.nextLong): Array[T] = withScope {
     val numStDev = 10.0
 
-    if (num < 0) {
-      throw new IllegalArgumentException("Negative number of elements requested")
-    } else if (num == 0) {
-      return new Array[T](0)
-    }
-
-    val initialCount = this.count()
-    if (initialCount == 0) {
-      return new Array[T](0)
-    }
-
-    val maxSampleSize = Int.MaxValue - (numStDev * math.sqrt(Int.MaxValue)).toInt
-    if (num > maxSampleSize) {
-      throw new IllegalArgumentException("Cannot support a sample size > Int.MaxValue - " +
-        s"$numStDev * math.sqrt(Int.MaxValue)")
-    }
-
-    val rand = new Random(seed)
-    if (!withReplacement && num >= initialCount) {
-      return Utils.randomizeInPlace(this.collect(), rand)
-    }
-
-    val fraction = SamplingUtils.computeFractionForSampleSize(num, initialCount,
-      withReplacement)
-
-    var samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
+    require(num >= 0, "Negative number of elements requested")
+    require(num <= (Int.MaxValue - (numStDev * math.sqrt(Int.MaxValue)).toInt),
+      "Cannot support a sample size > Int.MaxValue - " +
+      s"$numStDev * math.sqrt(Int.MaxValue)")
 
-    // If the first sample didn't turn out large enough, keep trying to take samples;
-    // this shouldn't happen often because we use a big multiplier for the initial size
-    var numIters = 0
-    while (samples.length < num) {
-      logWarning(s"Needed to re-sample due to insufficient sample size. Repeat #$numIters")
-      samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
-      numIters += 1
+    if (num == 0) {
+      new Array[T](0)
+    } else {
+      val initialCount = this.count()
+      if (initialCount == 0) {
+        new Array[T](0)
+      } else {
+        val rand = new Random(seed)
+        if (!withReplacement && num >= initialCount) {
+          Utils.randomizeInPlace(this.collect(), rand)
+        } else {
+          val fraction = SamplingUtils.computeFractionForSampleSize(num, initialCount,
+            withReplacement)
+          var samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
+
+          // If the first sample didn't turn out large enough, keep trying to take samples;
+          // this shouldn't happen often because we use a big multiplier for the initial size
+          var numIters = 0
+          while (samples.length < num) {
+            logWarning(s"Needed to re-sample due to insufficient sample size. Repeat #$numIters")
+            samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
+            numIters += 1
+          }
+          Utils.randomizeInPlace(samples, rand).take(num)
+        }
+      }
     }
-
-    Utils.randomizeInPlace(samples, rand).take(num)
   }
 
   /**

From 38700ea40cb1dd0805cc926a9e629f93c99527ad Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 15 Sep 2015 17:11:21 -0700
Subject: [PATCH 1444/1454] [SPARK-10381] Fix mixup of taskAttemptNumber &
 attemptId in OutputCommitCoordinator

When speculative execution is enabled, consider a scenario where the authorized committer of a particular output partition fails during the OutputCommitter.commitTask() call. In this case, the OutputCommitCoordinator is supposed to release that committer's exclusive lock on committing once that task fails. However, due to a unit mismatch (we used task attempt number in one place and task attempt id in another) the lock will not be released, causing Spark to go into an infinite retry loop.

This bug was masked by the fact that the OutputCommitCoordinator does not have enough end-to-end tests (the current tests use many mocks). Other factors contributing to this bug are the fact that we have many similarly-named identifiers that have different semantics but the same data types (e.g. attemptNumber and taskAttemptId, with inconsistent variable naming which makes them difficult to distinguish).

This patch adds a regression test and fixes this bug by always using task attempt numbers throughout this code.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8544 from JoshRosen/SPARK-10381.
---
 .../org/apache/spark/SparkHadoopWriter.scala  |  3 +-
 .../org/apache/spark/TaskEndReason.scala      |  7 +-
 .../executor/CommitDeniedException.scala      |  4 +-
 .../spark/mapred/SparkHadoopMapRedUtil.scala  | 20 ++----
 .../apache/spark/scheduler/DAGScheduler.scala |  7 +-
 .../scheduler/OutputCommitCoordinator.scala   | 48 +++++++------
 .../org/apache/spark/scheduler/TaskInfo.scala |  7 +-
 .../status/api/v1/AllStagesResource.scala     |  2 +-
 .../org/apache/spark/ui/jobs/StagePage.scala  |  4 +-
 .../org/apache/spark/util/JsonProtocol.scala  |  2 +-
 ...putCommitCoordinatorIntegrationSuite.scala | 68 +++++++++++++++++++
 .../OutputCommitCoordinatorSuite.scala        | 24 ++++---
 .../apache/spark/util/JsonProtocolSuite.scala |  2 +-
 project/MimaExcludes.scala                    | 36 +++++++++-
 .../datasources/WriterContainer.scala         |  3 +-
 .../sql/execution/ui/SQLListenerSuite.scala   |  4 +-
 .../spark/sql/hive/hiveWriterContainers.scala |  2 +-
 17 files changed, 174 insertions(+), 69 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorIntegrationSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
index ae5926dd534a6..ac6eaab20d8d2 100644
--- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
+++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
@@ -104,8 +104,7 @@ class SparkHadoopWriter(jobConf: JobConf)
   }
 
   def commit() {
-    SparkHadoopMapRedUtil.commitTask(
-      getOutputCommitter(), getTaskContext(), jobID, splitID, attemptID)
+    SparkHadoopMapRedUtil.commitTask(getOutputCommitter(), getTaskContext(), jobID, splitID)
   }
 
   def commitJob() {
diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index 2ae878b3e6087..7137246bc34f2 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -193,9 +193,12 @@ case object TaskKilled extends TaskFailedReason {
  * Task requested the driver to commit, but was denied.
  */
 @DeveloperApi
-case class TaskCommitDenied(jobID: Int, partitionID: Int, attemptID: Int) extends TaskFailedReason {
+case class TaskCommitDenied(
+    jobID: Int,
+    partitionID: Int,
+    attemptNumber: Int) extends TaskFailedReason {
   override def toErrorString: String = s"TaskCommitDenied (Driver denied task commit)" +
-    s" for job: $jobID, partition: $partitionID, attempt: $attemptID"
+    s" for job: $jobID, partition: $partitionID, attemptNumber: $attemptNumber"
   /**
    * If a task failed because its attempt to commit was denied, do not count this failure
    * towards failing the stage. This is intended to prevent spurious stage failures in cases
diff --git a/core/src/main/scala/org/apache/spark/executor/CommitDeniedException.scala b/core/src/main/scala/org/apache/spark/executor/CommitDeniedException.scala
index f47d7ef511da1..7d84889a2def0 100644
--- a/core/src/main/scala/org/apache/spark/executor/CommitDeniedException.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CommitDeniedException.scala
@@ -26,8 +26,8 @@ private[spark] class CommitDeniedException(
     msg: String,
     jobID: Int,
     splitID: Int,
-    attemptID: Int)
+    attemptNumber: Int)
   extends Exception(msg) {
 
-  def toTaskEndReason: TaskEndReason = TaskCommitDenied(jobID, splitID, attemptID)
+  def toTaskEndReason: TaskEndReason = TaskCommitDenied(jobID, splitID, attemptNumber)
 }
diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
index f405b732e4725..f7298e8d5c62c 100644
--- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
+++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
@@ -91,8 +91,7 @@ object SparkHadoopMapRedUtil extends Logging {
       committer: MapReduceOutputCommitter,
       mrTaskContext: MapReduceTaskAttemptContext,
       jobId: Int,
-      splitId: Int,
-      attemptId: Int): Unit = {
+      splitId: Int): Unit = {
 
     val mrTaskAttemptID = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(mrTaskContext)
 
@@ -122,7 +121,8 @@ object SparkHadoopMapRedUtil extends Logging {
 
       if (shouldCoordinateWithDriver) {
         val outputCommitCoordinator = SparkEnv.get.outputCommitCoordinator
-        val canCommit = outputCommitCoordinator.canCommit(jobId, splitId, attemptId)
+        val taskAttemptNumber = TaskContext.get().attemptNumber()
+        val canCommit = outputCommitCoordinator.canCommit(jobId, splitId, taskAttemptNumber)
 
         if (canCommit) {
           performCommit()
@@ -132,7 +132,7 @@ object SparkHadoopMapRedUtil extends Logging {
           logInfo(message)
           // We need to abort the task so that the driver can reschedule new attempts, if necessary
           committer.abortTask(mrTaskContext)
-          throw new CommitDeniedException(message, jobId, splitId, attemptId)
+          throw new CommitDeniedException(message, jobId, splitId, taskAttemptNumber)
         }
       } else {
         // Speculation is disabled or a user has chosen to manually bypass the commit coordination
@@ -143,16 +143,4 @@ object SparkHadoopMapRedUtil extends Logging {
       logInfo(s"No need to commit output of task because needsTaskCommit=false: $mrTaskAttemptID")
     }
   }
-
-  def commitTask(
-      committer: MapReduceOutputCommitter,
-      mrTaskContext: MapReduceTaskAttemptContext,
-      sparkTaskContext: TaskContext): Unit = {
-    commitTask(
-      committer,
-      mrTaskContext,
-      sparkTaskContext.stageId(),
-      sparkTaskContext.partitionId(),
-      sparkTaskContext.attemptNumber())
-  }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index b4f90e8347894..3c9a66e504403 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1128,8 +1128,11 @@ class DAGScheduler(
     val stageId = task.stageId
     val taskType = Utils.getFormattedClassName(task)
 
-    outputCommitCoordinator.taskCompleted(stageId, task.partitionId,
-      event.taskInfo.attempt, event.reason)
+    outputCommitCoordinator.taskCompleted(
+      stageId,
+      task.partitionId,
+      event.taskInfo.attemptNumber, // this is a task attempt number
+      event.reason)
 
     // The success case is dealt with separately below, since we need to compute accumulator
     // updates before posting.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
index 5d926377ce86b..add0dedc03f44 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
@@ -25,7 +25,7 @@ import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, RpcEndpoint
 private sealed trait OutputCommitCoordinationMessage extends Serializable
 
 private case object StopCoordinator extends OutputCommitCoordinationMessage
-private case class AskPermissionToCommitOutput(stage: Int, task: Long, taskAttempt: Long)
+private case class AskPermissionToCommitOutput(stage: Int, partition: Int, attemptNumber: Int)
 
 /**
  * Authority that decides whether tasks can commit output to HDFS. Uses a "first committer wins"
@@ -44,8 +44,8 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
   var coordinatorRef: Option[RpcEndpointRef] = None
 
   private type StageId = Int
-  private type PartitionId = Long
-  private type TaskAttemptId = Long
+  private type PartitionId = Int
+  private type TaskAttemptNumber = Int
 
   /**
    * Map from active stages's id => partition id => task attempt with exclusive lock on committing
@@ -57,7 +57,8 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
    * Access to this map should be guarded by synchronizing on the OutputCommitCoordinator instance.
    */
   private val authorizedCommittersByStage: CommittersByStageMap = mutable.Map()
-  private type CommittersByStageMap = mutable.Map[StageId, mutable.Map[PartitionId, TaskAttemptId]]
+  private type CommittersByStageMap =
+    mutable.Map[StageId, mutable.Map[PartitionId, TaskAttemptNumber]]
 
   /**
    * Returns whether the OutputCommitCoordinator's internal data structures are all empty.
@@ -75,14 +76,15 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
    *
    * @param stage the stage number
    * @param partition the partition number
-   * @param attempt a unique identifier for this task attempt
+   * @param attemptNumber how many times this task has been attempted
+   *                      (see [[TaskContext.attemptNumber()]])
    * @return true if this task is authorized to commit, false otherwise
    */
   def canCommit(
       stage: StageId,
       partition: PartitionId,
-      attempt: TaskAttemptId): Boolean = {
-    val msg = AskPermissionToCommitOutput(stage, partition, attempt)
+      attemptNumber: TaskAttemptNumber): Boolean = {
+    val msg = AskPermissionToCommitOutput(stage, partition, attemptNumber)
     coordinatorRef match {
       case Some(endpointRef) =>
         endpointRef.askWithRetry[Boolean](msg)
@@ -95,7 +97,7 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
 
   // Called by DAGScheduler
   private[scheduler] def stageStart(stage: StageId): Unit = synchronized {
-    authorizedCommittersByStage(stage) = mutable.HashMap[PartitionId, TaskAttemptId]()
+    authorizedCommittersByStage(stage) = mutable.HashMap[PartitionId, TaskAttemptNumber]()
   }
 
   // Called by DAGScheduler
@@ -107,7 +109,7 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
   private[scheduler] def taskCompleted(
       stage: StageId,
       partition: PartitionId,
-      attempt: TaskAttemptId,
+      attemptNumber: TaskAttemptNumber,
       reason: TaskEndReason): Unit = synchronized {
     val authorizedCommitters = authorizedCommittersByStage.getOrElse(stage, {
       logDebug(s"Ignoring task completion for completed stage")
@@ -117,12 +119,12 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
       case Success =>
       // The task output has been committed successfully
       case denied: TaskCommitDenied =>
-        logInfo(
-          s"Task was denied committing, stage: $stage, partition: $partition, attempt: $attempt")
+        logInfo(s"Task was denied committing, stage: $stage, partition: $partition, " +
+          s"attempt: $attemptNumber")
       case otherReason =>
-        if (authorizedCommitters.get(partition).exists(_ == attempt)) {
-          logDebug(s"Authorized committer $attempt (stage=$stage, partition=$partition) failed;" +
-            s" clearing lock")
+        if (authorizedCommitters.get(partition).exists(_ == attemptNumber)) {
+          logDebug(s"Authorized committer (attemptNumber=$attemptNumber, stage=$stage, " +
+            s"partition=$partition) failed; clearing lock")
           authorizedCommitters.remove(partition)
         }
     }
@@ -140,21 +142,23 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
   private[scheduler] def handleAskPermissionToCommit(
       stage: StageId,
       partition: PartitionId,
-      attempt: TaskAttemptId): Boolean = synchronized {
+      attemptNumber: TaskAttemptNumber): Boolean = synchronized {
     authorizedCommittersByStage.get(stage) match {
       case Some(authorizedCommitters) =>
         authorizedCommitters.get(partition) match {
           case Some(existingCommitter) =>
-            logDebug(s"Denying $attempt to commit for stage=$stage, partition=$partition; " +
-              s"existingCommitter = $existingCommitter")
+            logDebug(s"Denying attemptNumber=$attemptNumber to commit for stage=$stage, " +
+              s"partition=$partition; existingCommitter = $existingCommitter")
             false
           case None =>
-            logDebug(s"Authorizing $attempt to commit for stage=$stage, partition=$partition")
-            authorizedCommitters(partition) = attempt
+            logDebug(s"Authorizing attemptNumber=$attemptNumber to commit for stage=$stage, " +
+              s"partition=$partition")
+            authorizedCommitters(partition) = attemptNumber
             true
         }
       case None =>
-        logDebug(s"Stage $stage has completed, so not allowing task attempt $attempt to commit")
+        logDebug(s"Stage $stage has completed, so not allowing attempt number $attemptNumber of" +
+          s"partition $partition to commit")
         false
     }
   }
@@ -174,9 +178,9 @@ private[spark] object OutputCommitCoordinator {
     }
 
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
-      case AskPermissionToCommitOutput(stage, partition, taskAttempt) =>
+      case AskPermissionToCommitOutput(stage, partition, attemptNumber) =>
         context.reply(
-          outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, taskAttempt))
+          outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, attemptNumber))
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
index 132a9ced77700..f113c2b1b8433 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala
@@ -29,7 +29,7 @@ import org.apache.spark.annotation.DeveloperApi
 class TaskInfo(
     val taskId: Long,
     val index: Int,
-    val attempt: Int,
+    val attemptNumber: Int,
     val launchTime: Long,
     val executorId: String,
     val host: String,
@@ -95,7 +95,10 @@ class TaskInfo(
     }
   }
 
-  def id: String = s"$index.$attempt"
+  @deprecated("Use attemptNumber", "1.6.0")
+  def attempt: Int = attemptNumber
+
+  def id: String = s"$index.$attemptNumber"
 
   def duration: Long = {
     if (!finished) {
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
index 390c136df79b3..24a0b5220695c 100644
--- a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
@@ -127,7 +127,7 @@ private[v1] object AllStagesResource {
     new TaskData(
       taskId = uiData.taskInfo.taskId,
       index = uiData.taskInfo.index,
-      attempt = uiData.taskInfo.attempt,
+      attempt = uiData.taskInfo.attemptNumber,
       launchTime = new Date(uiData.taskInfo.launchTime),
       executorId = uiData.taskInfo.executorId,
       host = uiData.taskInfo.host,
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 2b71f55b7bb4f..712782d27b3cf 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -621,7 +621,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
           serializationTimeProportionPos + serializationTimeProportion
 
         val index = taskInfo.index
-        val attempt = taskInfo.attempt
+        val attempt = taskInfo.attemptNumber
 
         val svgTag =
           if (totalExecutionTime == 0) {
@@ -967,7 +967,7 @@ private[ui] class TaskDataSource(
     new TaskTableRowData(
       info.index,
       info.taskId,
-      info.attempt,
+      info.attemptNumber,
       info.speculative,
       info.status,
       info.taskLocality.toString,
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 24f78744ad74c..99614a786bd93 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -266,7 +266,7 @@ private[spark] object JsonProtocol {
   def taskInfoToJson(taskInfo: TaskInfo): JValue = {
     ("Task ID" -> taskInfo.taskId) ~
     ("Index" -> taskInfo.index) ~
-    ("Attempt" -> taskInfo.attempt) ~
+    ("Attempt" -> taskInfo.attemptNumber) ~
     ("Launch Time" -> taskInfo.launchTime) ~
     ("Executor ID" -> taskInfo.executorId) ~
     ("Host" -> taskInfo.host) ~
diff --git a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorIntegrationSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorIntegrationSuite.scala
new file mode 100644
index 0000000000000..1ae5b030f0832
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorIntegrationSuite.scala
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import org.apache.hadoop.mapred.{FileOutputCommitter, TaskAttemptContext}
+import org.scalatest.concurrent.Timeouts
+import org.scalatest.time.{Span, Seconds}
+
+import org.apache.spark.{SparkConf, SparkContext, LocalSparkContext, SparkFunSuite, TaskContext}
+import org.apache.spark.util.Utils
+
+/**
+ * Integration tests for the OutputCommitCoordinator.
+ *
+ * See also: [[OutputCommitCoordinatorSuite]] for unit tests that use mocks.
+ */
+class OutputCommitCoordinatorIntegrationSuite
+  extends SparkFunSuite
+  with LocalSparkContext
+  with Timeouts {
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    val conf = new SparkConf()
+      .set("master", "local[2,4]")
+      .set("spark.speculation", "true")
+      .set("spark.hadoop.mapred.output.committer.class",
+        classOf[ThrowExceptionOnFirstAttemptOutputCommitter].getCanonicalName)
+    sc = new SparkContext("local[2, 4]", "test", conf)
+  }
+
+  test("exception thrown in OutputCommitter.commitTask()") {
+    // Regression test for SPARK-10381
+    failAfter(Span(60, Seconds)) {
+      val tempDir = Utils.createTempDir()
+      try {
+        sc.parallelize(1 to 4, 2).map(_.toString).saveAsTextFile(tempDir.getAbsolutePath + "/out")
+      } finally {
+        Utils.deleteRecursively(tempDir)
+      }
+    }
+  }
+}
+
+private class ThrowExceptionOnFirstAttemptOutputCommitter extends FileOutputCommitter {
+  override def commitTask(context: TaskAttemptContext): Unit = {
+    val ctx = TaskContext.get()
+    if (ctx.attemptNumber < 1) {
+      throw new java.io.FileNotFoundException("Intentional exception")
+    }
+    super.commitTask(context)
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala
index e5ecd4b7c2610..6d08d7c5b7d2a 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala
@@ -63,6 +63,9 @@ import scala.language.postfixOps
  * was not in SparkHadoopWriter, the tests would still pass because only one of the
  * increments would be captured even though the commit in both tasks was executed
  * erroneously.
+ *
+ * See also: [[OutputCommitCoordinatorIntegrationSuite]] for integration tests that do
+ * not use mocks.
  */
 class OutputCommitCoordinatorSuite extends SparkFunSuite with BeforeAndAfter {
 
@@ -164,27 +167,28 @@ class OutputCommitCoordinatorSuite extends SparkFunSuite with BeforeAndAfter {
 
   test("Only authorized committer failures can clear the authorized committer lock (SPARK-6614)") {
     val stage: Int = 1
-    val partition: Long = 2
-    val authorizedCommitter: Long = 3
-    val nonAuthorizedCommitter: Long = 100
+    val partition: Int = 2
+    val authorizedCommitter: Int = 3
+    val nonAuthorizedCommitter: Int = 100
     outputCommitCoordinator.stageStart(stage)
-    assert(outputCommitCoordinator.canCommit(stage, partition, attempt = authorizedCommitter))
-    assert(!outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter))
+
+    assert(outputCommitCoordinator.canCommit(stage, partition, authorizedCommitter))
+    assert(!outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter))
     // The non-authorized committer fails
     outputCommitCoordinator.taskCompleted(
-      stage, partition, attempt = nonAuthorizedCommitter, reason = TaskKilled)
+      stage, partition, attemptNumber = nonAuthorizedCommitter, reason = TaskKilled)
     // New tasks should still not be able to commit because the authorized committer has not failed
     assert(
-      !outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 1))
+      !outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter + 1))
     // The authorized committer now fails, clearing the lock
     outputCommitCoordinator.taskCompleted(
-      stage, partition, attempt = authorizedCommitter, reason = TaskKilled)
+      stage, partition, attemptNumber = authorizedCommitter, reason = TaskKilled)
     // A new task should now be allowed to become the authorized committer
     assert(
-      outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 2))
+      outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter + 2))
     // There can only be one authorized committer
     assert(
-      !outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 3))
+      !outputCommitCoordinator.canCommit(stage, partition, nonAuthorizedCommitter + 3))
   }
 }
 
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 47e548ef0d442..143c1b901df11 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -499,7 +499,7 @@ class JsonProtocolSuite extends SparkFunSuite {
   private def assertEquals(info1: TaskInfo, info2: TaskInfo) {
     assert(info1.taskId === info2.taskId)
     assert(info1.index === info2.index)
-    assert(info1.attempt === info2.attempt)
+    assert(info1.attemptNumber === info2.attemptNumber)
     assert(info1.launchTime === info2.launchTime)
     assert(info1.executorId === info2.executorId)
     assert(info1.host === info2.host)
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 46026c1e90ea0..1c96b0958586f 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -45,7 +45,7 @@ object MimaExcludes {
         excludePackage("org.apache.spark.sql.execution")
       ) ++
       MimaBuild.excludeSparkClass("streaming.flume.FlumeTestUtils") ++
-      MimaBuild.excludeSparkClass("streaming.flume.PollingFlumeTestUtils") ++ 
+      MimaBuild.excludeSparkClass("streaming.flume.PollingFlumeTestUtils") ++
       Seq(
         ProblemFilters.exclude[MissingMethodProblem](
           "org.apache.spark.ml.classification.LogisticCostFun.this"),
@@ -53,6 +53,23 @@ object MimaExcludes {
           "org.apache.spark.ml.classification.LogisticAggregator.add"),
         ProblemFilters.exclude[MissingMethodProblem](
           "org.apache.spark.ml.classification.LogisticAggregator.count")
+      ) ++ Seq(
+        // SPARK-10381 Fix types / units in private AskPermissionToCommitOutput RPC message.
+        // This class is marked as `private` but MiMa still seems to be confused by the change.
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.task"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.copy$default$2"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.copy"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.taskAttempt"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.copy$default$3"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.this"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.apply")
       )
     case v if v.startsWith("1.5") =>
       Seq(
@@ -213,6 +230,23 @@ object MimaExcludes {
         // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
         ProblemFilters.exclude[IncompatibleResultTypeProblem](
           "org.apache.spark.mllib.linalg.VectorUDT.serialize")
+      ) ++ Seq(
+        // SPARK-10381 Fix types / units in private AskPermissionToCommitOutput RPC message.
+        // This class is marked as `private` but MiMa still seems to be confused by the change.
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.task"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.copy$default$2"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.copy"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.taskAttempt"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.copy$default$3"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.this"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.scheduler.AskPermissionToCommitOutput.apply")
       )
 
     case v if v.startsWith("1.4") =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index f8ef674ed29c1..cfd64c1d9eb34 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -198,8 +198,7 @@ private[sql] abstract class BaseWriterContainer(
   }
 
   def commitTask(): Unit = {
-    SparkHadoopMapRedUtil.commitTask(
-      outputCommitter, taskAttemptContext, jobId.getId, taskId.getId, taskAttemptId.getId)
+    SparkHadoopMapRedUtil.commitTask(outputCommitter, taskAttemptContext, jobId.getId, taskId.getId)
   }
 
   def abortTask(): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
index 2bbb41ca777b7..7a46c69a056b1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
@@ -54,9 +54,9 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
     details = ""
   )
 
-  private def createTaskInfo(taskId: Int, attempt: Int): TaskInfo = new TaskInfo(
+  private def createTaskInfo(taskId: Int, attemptNumber: Int): TaskInfo = new TaskInfo(
     taskId = taskId,
-    attempt = attempt,
+    attemptNumber = attemptNumber,
     // The following fields are not used in tests
     index = 0,
     launchTime = 0,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 4ca8042d22367..c8d6b718045a5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -121,7 +121,7 @@ private[hive] class SparkHiveWriterContainer(
   }
 
   protected def commit() {
-    SparkHadoopMapRedUtil.commitTask(committer, taskContext, jobID, splitID, attemptID)
+    SparkHadoopMapRedUtil.commitTask(committer, taskContext, jobID, splitID)
   }
 
   private def setIDs(jobId: Int, splitId: Int, attemptId: Int) {

From 35a19f3357d2ec017cfefb90f1018403e9617de4 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 15 Sep 2015 17:24:32 -0700
Subject: [PATCH 1445/1454] [SPARK-10613] [SPARK-10624] [SQL] Reduce LocalNode
 tests dependency on SQLContext

Instead of relying on `DataFrames` to verify our answers, we can just use simple arrays. This significantly simplifies the test logic for `LocalNode`s and reduces a lot of code duplicated from `SparkPlanTest`.

This also fixes an additional issue [SPARK-10624](https://issues.apache.org/jira/browse/SPARK-10624) where the output of `TakeOrderedAndProjectNode` is not actually ordered.

Author: Andrew Or <andrew@databricks.com>

Closes #8764 from andrewor14/sql-local-tests-cleanup.
---
 .../spark/sql/execution/local/LocalNode.scala |   8 +-
 .../sql/execution/local/SampleNode.scala      |  16 +-
 .../local/TakeOrderedAndProjectNode.scala     |   2 +-
 .../spark/sql/execution/SparkPlanTest.scala   |   2 +-
 .../spark/sql/execution/local/DummyNode.scala |  68 ++++
 .../sql/execution/local/ExpandNodeSuite.scala |  54 ++-
 .../sql/execution/local/FilterNodeSuite.scala |  34 +-
 .../execution/local/HashJoinNodeSuite.scala   | 141 ++++----
 .../execution/local/IntersectNodeSuite.scala  |  24 +-
 .../sql/execution/local/LimitNodeSuite.scala  |  28 +-
 .../sql/execution/local/LocalNodeSuite.scala  |  73 +---
 .../sql/execution/local/LocalNodeTest.scala   | 165 ++-------
 .../local/NestedLoopJoinNodeSuite.scala       | 316 ++++++------------
 .../execution/local/ProjectNodeSuite.scala    |  39 ++-
 .../sql/execution/local/SampleNodeSuite.scala |  35 +-
 .../TakeOrderedAndProjectNodeSuite.scala      |  50 ++-
 .../sql/execution/local/UnionNodeSuite.scala  |  49 +--
 17 files changed, 468 insertions(+), 636 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/DummyNode.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index 569cff565c092..f96b62a67a254 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.{SQLConf, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.types.StructType
 
 /**
@@ -33,18 +33,14 @@ import org.apache.spark.sql.types.StructType
  * Before consuming the iterator, open function must be called.
  * After consuming the iterator, close function must be called.
  */
-abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging {
+abstract class LocalNode(conf: SQLConf) extends QueryPlan[LocalNode] with Logging {
 
   protected val codegenEnabled: Boolean = conf.codegenEnabled
 
   protected val unsafeEnabled: Boolean = conf.unsafeEnabled
 
-  lazy val schema: StructType = StructType.fromAttributes(output)
-
   private[this] lazy val isTesting: Boolean = sys.props.contains("spark.testing")
 
-  def output: Seq[Attribute]
-
   /**
    * Called before open(). Prepare can be used to reserve memory needed. It must NOT consume
    * any input data.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
index abf3df1c0c2af..793700803f216 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
@@ -17,13 +17,12 @@
 
 package org.apache.spark.sql.execution.local
 
-import java.util.Random
-
 import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.util.random.{BernoulliCellSampler, PoissonSampler}
 
+
 /**
  * Sample the dataset.
  *
@@ -51,18 +50,15 @@ case class SampleNode(
 
   override def open(): Unit = {
     child.open()
-    val (sampler, _seed) = if (withReplacement) {
-        val random = new Random(seed)
+    val sampler =
+      if (withReplacement) {
         // Disable gap sampling since the gap sampling method buffers two rows internally,
         // requiring us to copy the row, which is more expensive than the random number generator.
-        (new PoissonSampler[InternalRow](upperBound - lowerBound, useGapSamplingIfPossible = false),
-          // Use the seed for partition 0 like PartitionwiseSampledRDD to generate the same result
-          // of DataFrame
-          random.nextLong())
+        new PoissonSampler[InternalRow](upperBound - lowerBound, useGapSamplingIfPossible = false)
       } else {
-        (new BernoulliCellSampler[InternalRow](lowerBound, upperBound), seed)
+        new BernoulliCellSampler[InternalRow](lowerBound, upperBound)
       }
-    sampler.setSeed(_seed)
+    sampler.setSeed(seed)
     iterator = sampler.sample(child.asIterator)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
index 53f1dcc65d8cf..ae672fbca8d83 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
@@ -50,7 +50,7 @@ case class TakeOrderedAndProjectNode(
     }
     // Close it eagerly since we don't need it.
     child.close()
-    iterator = queue.iterator
+    iterator = queue.toArray.sorted(ord).iterator
   }
 
   override def next(): Boolean = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index de45ae4635fb7..3d218f01c9ead 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -238,7 +238,7 @@ object SparkPlanTest {
       outputPlan transform {
         case plan: SparkPlan =>
           val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
-          plan.transformExpressions {
+          plan transformExpressions {
             case UnresolvedAttribute(Seq(u)) =>
               inputMap.getOrElse(u,
                 sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap"))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/DummyNode.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/DummyNode.scala
new file mode 100644
index 0000000000000..efc3227dd60d8
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/DummyNode.scala
@@ -0,0 +1,68 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+
+/**
+ * A dummy [[LocalNode]] that just returns rows from a [[LocalRelation]].
+ */
+private[local] case class DummyNode(
+    output: Seq[Attribute],
+    relation: LocalRelation,
+    conf: SQLConf)
+  extends LocalNode(conf) {
+
+  import DummyNode._
+
+  private var index: Int = CLOSED
+  private val input: Seq[InternalRow] = relation.data
+
+  def this(output: Seq[Attribute], data: Seq[Product], conf: SQLConf = new SQLConf) {
+    this(output, LocalRelation.fromProduct(output, data), conf)
+  }
+
+  def isOpen: Boolean = index != CLOSED
+
+  override def children: Seq[LocalNode] = Seq.empty
+
+  override def open(): Unit = {
+    index = -1
+  }
+
+  override def next(): Boolean = {
+    index += 1
+    index < input.size
+  }
+
+  override def fetch(): InternalRow = {
+    assert(index >= 0 && index < input.size)
+    input(index)
+  }
+
+  override def close(): Unit = {
+    index = CLOSED
+  }
+}
+
+private object DummyNode {
+  val CLOSED: Int = Int.MinValue
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
index cfa7f3f6dcb97..bbd94d8da2d11 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
@@ -17,35 +17,33 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.catalyst.dsl.expressions._
+
+
 class ExpandNodeSuite extends LocalNodeTest {
 
-  import testImplicits._
-
-  test("expand") {
-    val input = Seq((1, 1), (2, 2), (3, 3), (4, 4), (5, 5)).toDF("key", "value")
-    checkAnswer(
-      input,
-      node =>
-        ExpandNode(conf, Seq(
-          Seq(
-            input.col("key") + input.col("value"), input.col("key") - input.col("value")
-          ).map(_.expr),
-          Seq(
-            input.col("key") * input.col("value"), input.col("key") / input.col("value")
-          ).map(_.expr)
-        ), node.output, node),
-      Seq(
-        (2, 0),
-        (1, 1),
-        (4, 0),
-        (4, 1),
-        (6, 0),
-        (9, 1),
-        (8, 0),
-        (16, 1),
-        (10, 0),
-        (25, 1)
-      ).toDF().collect()
-    )
+  private def testExpand(inputData: Array[(Int, Int)] = Array.empty): Unit = {
+    val inputNode = new DummyNode(kvIntAttributes, inputData)
+    val projections = Seq(Seq('k + 'v, 'k - 'v), Seq('k * 'v, 'k / 'v))
+    val expandNode = new ExpandNode(conf, projections, inputNode.output, inputNode)
+    val resolvedNode = resolveExpressions(expandNode)
+    val expectedOutput = {
+      val firstHalf = inputData.map { case (k, v) => (k + v, k - v) }
+      val secondHalf = inputData.map { case (k, v) => (k * v, k / v) }
+      firstHalf ++ secondHalf
+    }
+    val actualOutput = resolvedNode.collect().map { case row =>
+      (row.getInt(0), row.getInt(1))
+    }
+    assert(actualOutput.toSet === expectedOutput.toSet)
+  }
+
+  test("empty") {
+    testExpand()
   }
+
+  test("basic") {
+    testExpand((1 to 100).map { i => (i, i * 1000) }.toArray)
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
index a12670e347c25..4eadce646d379 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
@@ -17,25 +17,29 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.catalyst.dsl.expressions._
 
-class FilterNodeSuite extends LocalNodeTest with SharedSQLContext {
 
-  test("basic") {
-    val condition = (testData.col("key") % 2) === 0
-    checkAnswer(
-      testData,
-      node => FilterNode(conf, condition.expr, node),
-      testData.filter(condition).collect()
-    )
+class FilterNodeSuite extends LocalNodeTest {
+
+  private def testFilter(inputData: Array[(Int, Int)] = Array.empty): Unit = {
+    val cond = 'k % 2 === 0
+    val inputNode = new DummyNode(kvIntAttributes, inputData)
+    val filterNode = new FilterNode(conf, cond, inputNode)
+    val resolvedNode = resolveExpressions(filterNode)
+    val expectedOutput = inputData.filter { case (k, _) => k % 2 == 0 }
+    val actualOutput = resolvedNode.collect().map { case row =>
+      (row.getInt(0), row.getInt(1))
+    }
+    assert(actualOutput === expectedOutput)
   }
 
   test("empty") {
-    val condition = (emptyTestData.col("key") % 2) === 0
-    checkAnswer(
-      emptyTestData,
-      node => FilterNode(conf, condition.expr, node),
-      emptyTestData.filter(condition).collect()
-    )
+    testFilter()
+  }
+
+  test("basic") {
+    testFilter((1 to 100).map { i => (i, i) }.toArray)
   }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
index 78d891351f4a9..5c1bdb088eeed 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
@@ -18,99 +18,80 @@
 package org.apache.spark.sql.execution.local
 
 import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.execution.joins
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.execution.joins.{BuildLeft, BuildRight, BuildSide}
+
 
 class HashJoinNodeSuite extends LocalNodeTest {
 
-  import testImplicits._
+  // Test all combinations of the two dimensions: with/out unsafe and build sides
+  private val maybeUnsafeAndCodegen = Seq(false, true)
+  private val buildSides = Seq(BuildLeft, BuildRight)
+  maybeUnsafeAndCodegen.foreach { unsafeAndCodegen =>
+    buildSides.foreach { buildSide =>
+      testJoin(unsafeAndCodegen, buildSide)
+    }
+  }
 
-  def joinSuite(suiteName: String, confPairs: (String, String)*): Unit = {
-    test(s"$suiteName: inner join with one match per row") {
-      withSQLConf(confPairs: _*) {
-        checkAnswer2(
-          upperCaseData,
-          lowerCaseData,
-          wrapForUnsafe(
-            (node1, node2) => HashJoinNode(
-              conf,
-              Seq(upperCaseData.col("N").expr),
-              Seq(lowerCaseData.col("n").expr),
-              joins.BuildLeft,
-              node1,
-              node2)
-          ),
-          upperCaseData.join(lowerCaseData, $"n" === $"N").collect()
-        )
+  /**
+   * Test inner hash join with varying degrees of matches.
+   */
+  private def testJoin(
+      unsafeAndCodegen: Boolean,
+      buildSide: BuildSide): Unit = {
+    val simpleOrUnsafe = if (!unsafeAndCodegen) "simple" else "unsafe"
+    val testNamePrefix = s"$simpleOrUnsafe / $buildSide"
+    val someData = (1 to 100).map { i => (i, "burger" + i) }.toArray
+    val conf = new SQLConf
+    conf.setConf(SQLConf.UNSAFE_ENABLED, unsafeAndCodegen)
+    conf.setConf(SQLConf.CODEGEN_ENABLED, unsafeAndCodegen)
+
+    // Actual test body
+    def runTest(leftInput: Array[(Int, String)], rightInput: Array[(Int, String)]): Unit = {
+      val rightInputMap = rightInput.toMap
+      val leftNode = new DummyNode(joinNameAttributes, leftInput)
+      val rightNode = new DummyNode(joinNicknameAttributes, rightInput)
+      val makeNode = (node1: LocalNode, node2: LocalNode) => {
+        resolveExpressions(new HashJoinNode(
+          conf, Seq('id1), Seq('id2), buildSide, node1, node2))
+      }
+      val makeUnsafeNode = if (unsafeAndCodegen) wrapForUnsafe(makeNode) else makeNode
+      val hashJoinNode = makeUnsafeNode(leftNode, rightNode)
+      val expectedOutput = leftInput
+        .filter { case (k, _) => rightInputMap.contains(k) }
+        .map { case (k, v) => (k, v, k, rightInputMap(k)) }
+      val actualOutput = hashJoinNode.collect().map { row =>
+        // (id, name, id, nickname)
+        (row.getInt(0), row.getString(1), row.getInt(2), row.getString(3))
       }
+      assert(actualOutput === expectedOutput)
     }
 
-    test(s"$suiteName: inner join with multiple matches") {
-      withSQLConf(confPairs: _*) {
-        val x = testData2.where($"a" === 1).as("x")
-        val y = testData2.where($"a" === 1).as("y")
-        checkAnswer2(
-          x,
-          y,
-          wrapForUnsafe(
-            (node1, node2) => HashJoinNode(
-              conf,
-              Seq(x.col("a").expr),
-              Seq(y.col("a").expr),
-              joins.BuildLeft,
-              node1,
-              node2)
-          ),
-          x.join(y).where($"x.a" === $"y.a").collect()
-        )
-      }
+    test(s"$testNamePrefix: empty") {
+      runTest(Array.empty, Array.empty)
+      runTest(someData, Array.empty)
+      runTest(Array.empty, someData)
     }
 
-    test(s"$suiteName: inner join, no matches") {
-      withSQLConf(confPairs: _*) {
-        val x = testData2.where($"a" === 1).as("x")
-        val y = testData2.where($"a" === 2).as("y")
-        checkAnswer2(
-          x,
-          y,
-          wrapForUnsafe(
-            (node1, node2) => HashJoinNode(
-              conf,
-              Seq(x.col("a").expr),
-              Seq(y.col("a").expr),
-              joins.BuildLeft,
-              node1,
-              node2)
-          ),
-          Nil
-        )
-      }
+    test(s"$testNamePrefix: no matches") {
+      val someIrrelevantData = (10000 to 100100).map { i => (i, "piper" + i) }.toArray
+      runTest(someData, Array.empty)
+      runTest(Array.empty, someData)
+      runTest(someData, someIrrelevantData)
+      runTest(someIrrelevantData, someData)
     }
 
-    test(s"$suiteName: big inner join, 4 matches per row") {
-      withSQLConf(confPairs: _*) {
-        val bigData = testData.unionAll(testData).unionAll(testData).unionAll(testData)
-        val bigDataX = bigData.as("x")
-        val bigDataY = bigData.as("y")
+    test(s"$testNamePrefix: partial matches") {
+      val someOtherData = (50 to 150).map { i => (i, "finnegan" + i) }.toArray
+      runTest(someData, someOtherData)
+      runTest(someOtherData, someData)
+    }
 
-        checkAnswer2(
-          bigDataX,
-          bigDataY,
-          wrapForUnsafe(
-            (node1, node2) =>
-              HashJoinNode(
-                conf,
-                Seq(bigDataX.col("key").expr),
-                Seq(bigDataY.col("key").expr),
-                joins.BuildLeft,
-                node1,
-                node2)
-          ),
-          bigDataX.join(bigDataY).where($"x.key" === $"y.key").collect())
-      }
+    test(s"$testNamePrefix: full matches") {
+      val someSuperRelevantData = someData.map { case (k, v) => (k, "cooper" + v) }.toArray
+      runTest(someData, someSuperRelevantData)
+      runTest(someSuperRelevantData, someData)
     }
   }
 
-  joinSuite(
-    "general", SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
-  joinSuite("tungsten", SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
index 7deaa375fcfc2..c0ad2021b204a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
@@ -17,19 +17,21 @@
 
 package org.apache.spark.sql.execution.local
 
-class IntersectNodeSuite extends LocalNodeTest {
 
-  import testImplicits._
+class IntersectNodeSuite extends LocalNodeTest {
 
   test("basic") {
-    val input1 = (1 to 10).map(i => (i, i.toString)).toDF("key", "value")
-    val input2 = (1 to 10).filter(_ % 2 == 0).map(i => (i, i.toString)).toDF("key", "value")
-
-    checkAnswer2(
-      input1,
-      input2,
-      (node1, node2) => IntersectNode(conf, node1, node2),
-      input1.intersect(input2).collect()
-    )
+    val n = 100
+    val leftData = (1 to n).filter { i => i % 2 == 0 }.map { i => (i, i) }.toArray
+    val rightData = (1 to n).filter { i => i % 3 == 0 }.map { i => (i, i) }.toArray
+    val leftNode = new DummyNode(kvIntAttributes, leftData)
+    val rightNode = new DummyNode(kvIntAttributes, rightData)
+    val intersectNode = new IntersectNode(conf, leftNode, rightNode)
+    val expectedOutput = leftData.intersect(rightData)
+    val actualOutput = intersectNode.collect().map { case row =>
+      (row.getInt(0), row.getInt(1))
+    }
+    assert(actualOutput === expectedOutput)
   }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
index 3b183902007e4..fb790636a3689 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
@@ -17,23 +17,25 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.sql.test.SharedSQLContext
 
-class LimitNodeSuite extends LocalNodeTest with SharedSQLContext {
+class LimitNodeSuite extends LocalNodeTest {
 
-  test("basic") {
-    checkAnswer(
-      testData,
-      node => LimitNode(conf, 10, node),
-      testData.limit(10).collect()
-    )
+  private def testLimit(inputData: Array[(Int, Int)] = Array.empty, limit: Int = 10): Unit = {
+    val inputNode = new DummyNode(kvIntAttributes, inputData)
+    val limitNode = new LimitNode(conf, limit, inputNode)
+    val expectedOutput = inputData.take(limit)
+    val actualOutput = limitNode.collect().map { case row =>
+      (row.getInt(0), row.getInt(1))
+    }
+    assert(actualOutput === expectedOutput)
   }
 
   test("empty") {
-    checkAnswer(
-      emptyTestData,
-      node => LimitNode(conf, 10, node),
-      emptyTestData.limit(10).collect()
-    )
+    testLimit()
   }
+
+  test("basic") {
+    testLimit((1 to 100).map { i => (i, i) }.toArray, 20)
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala
index b89fa46f8b3b4..0d1ed99eec6cd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala
@@ -17,28 +17,24 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.IntegerType
 
-class LocalNodeSuite extends SparkFunSuite {
-  private val data = (1 to 100).toArray
+class LocalNodeSuite extends LocalNodeTest {
+  private val data = (1 to 100).map { i => (i, i) }.toArray
 
   test("basic open, next, fetch, close") {
-    val node = new DummyLocalNode(data)
+    val node = new DummyNode(kvIntAttributes, data)
     assert(!node.isOpen)
     node.open()
     assert(node.isOpen)
-    data.foreach { i =>
+    data.foreach { case (k, v) =>
       assert(node.next())
       // fetch should be idempotent
       val fetched = node.fetch()
       assert(node.fetch() === fetched)
       assert(node.fetch() === fetched)
-      assert(node.fetch().numFields === 1)
-      assert(node.fetch().getInt(0) === i)
+      assert(node.fetch().numFields === 2)
+      assert(node.fetch().getInt(0) === k)
+      assert(node.fetch().getInt(1) === v)
     }
     assert(!node.next())
     node.close()
@@ -46,16 +42,17 @@ class LocalNodeSuite extends SparkFunSuite {
   }
 
   test("asIterator") {
-    val node = new DummyLocalNode(data)
+    val node = new DummyNode(kvIntAttributes, data)
     val iter = node.asIterator
     node.open()
-    data.foreach { i =>
+    data.foreach { case (k, v) =>
       // hasNext should be idempotent
       assert(iter.hasNext)
       assert(iter.hasNext)
       val item = iter.next()
-      assert(item.numFields === 1)
-      assert(item.getInt(0) === i)
+      assert(item.numFields === 2)
+      assert(item.getInt(0) === k)
+      assert(item.getInt(1) === v)
     }
     intercept[NoSuchElementException] {
       iter.next()
@@ -64,53 +61,13 @@ class LocalNodeSuite extends SparkFunSuite {
   }
 
   test("collect") {
-    val node = new DummyLocalNode(data)
+    val node = new DummyNode(kvIntAttributes, data)
     node.open()
     val collected = node.collect()
     assert(collected.size === data.size)
-    assert(collected.forall(_.size === 1))
-    assert(collected.map(_.getInt(0)) === data)
+    assert(collected.forall(_.size === 2))
+    assert(collected.map { case row => (row.getInt(0), row.getInt(0)) } === data)
     node.close()
   }
 
 }
-
-/**
- * A dummy [[LocalNode]] that just returns one row per integer in the input.
- */
-private case class DummyLocalNode(conf: SQLConf, input: Array[Int]) extends LocalNode(conf) {
-  private var index = Int.MinValue
-
-  def this(input: Array[Int]) {
-    this(new SQLConf, input)
-  }
-
-  def isOpen: Boolean = {
-    index != Int.MinValue
-  }
-
-  override def output: Seq[Attribute] = {
-    Seq(AttributeReference("something", IntegerType)())
-  }
-
-  override def children: Seq[LocalNode] = Seq.empty
-
-  override def open(): Unit = {
-    index = -1
-  }
-
-  override def next(): Boolean = {
-    index += 1
-    index < input.size
-  }
-
-  override def fetch(): InternalRow = {
-    assert(index >= 0 && index < input.size)
-    val values = Array(input(index).asInstanceOf[Any])
-    new GenericInternalRow(values)
-  }
-
-  override def close(): Unit = {
-    index = Int.MinValue
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
index 86dd28064cc6a..098050bcd2236 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
@@ -17,147 +17,54 @@
 
 package org.apache.spark.sql.execution.local
 
-import scala.util.control.NonFatal
-
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{DataFrame, Row, SQLConf}
-import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils}
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.types.{IntegerType, StringType}
 
-class LocalNodeTest extends SparkFunSuite with SharedSQLContext {
 
-  def conf: SQLConf = sqlContext.conf
+class LocalNodeTest extends SparkFunSuite {
 
-  protected def wrapForUnsafe(
-      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
-    if (conf.unsafeEnabled) {
-      (left: LocalNode, right: LocalNode) => {
-        val _left = ConvertToUnsafeNode(conf, left)
-        val _right = ConvertToUnsafeNode(conf, right)
-        val r = f(_left, _right)
-        ConvertToSafeNode(conf, r)
-      }
-    } else {
-      f
-    }
-  }
-
-  /**
-   * Runs the LocalNode and makes sure the answer matches the expected result.
-   * @param input the input data to be used.
-   * @param nodeFunction a function which accepts the input LocalNode and uses it to instantiate
-   *                     the local physical operator that's being tested.
-   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
-   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
-   *                    to being compared.
-   */
-  protected def checkAnswer(
-      input: DataFrame,
-      nodeFunction: LocalNode => LocalNode,
-      expectedAnswer: Seq[Row],
-      sortAnswers: Boolean = true): Unit = {
-    doCheckAnswer(
-      input :: Nil,
-      nodes => nodeFunction(nodes.head),
-      expectedAnswer,
-      sortAnswers)
-  }
-
-  /**
-   * Runs the LocalNode and makes sure the answer matches the expected result.
-   * @param left the left input data to be used.
-   * @param right the right input data to be used.
-   * @param nodeFunction a function which accepts the input LocalNode and uses it to instantiate
-   *                     the local physical operator that's being tested.
-   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
-   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
-   *                    to being compared.
-   */
-  protected def checkAnswer2(
-      left: DataFrame,
-      right: DataFrame,
-      nodeFunction: (LocalNode, LocalNode) => LocalNode,
-      expectedAnswer: Seq[Row],
-      sortAnswers: Boolean = true): Unit = {
-    doCheckAnswer(
-      left :: right :: Nil,
-      nodes => nodeFunction(nodes(0), nodes(1)),
-      expectedAnswer,
-      sortAnswers)
-  }
+  protected val conf: SQLConf = new SQLConf
+  protected val kvIntAttributes = Seq(
+    AttributeReference("k", IntegerType)(),
+    AttributeReference("v", IntegerType)())
+  protected val joinNameAttributes = Seq(
+    AttributeReference("id1", IntegerType)(),
+    AttributeReference("name", StringType)())
+  protected val joinNicknameAttributes = Seq(
+    AttributeReference("id2", IntegerType)(),
+    AttributeReference("nickname", StringType)())
 
   /**
-   * Runs the `LocalNode`s and makes sure the answer matches the expected result.
-   * @param input the input data to be used.
-   * @param nodeFunction a function which accepts a sequence of input `LocalNode`s and uses them to
-   *                     instantiate the local physical operator that's being tested.
-   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
-   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
-   *                    to being compared.
+   * Wrap a function processing two [[LocalNode]]s such that:
+   *   (1) all input rows are automatically converted to unsafe rows
+   *   (2) all output rows are automatically converted back to safe rows
    */
-  protected def doCheckAnswer(
-    input: Seq[DataFrame],
-    nodeFunction: Seq[LocalNode] => LocalNode,
-    expectedAnswer: Seq[Row],
-    sortAnswers: Boolean = true): Unit = {
-    LocalNodeTest.checkAnswer(
-      input.map(dataFrameToSeqScanNode), nodeFunction, expectedAnswer, sortAnswers) match {
-      case Some(errorMessage) => fail(errorMessage)
-      case None =>
+  protected def wrapForUnsafe(
+      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
+    (left: LocalNode, right: LocalNode) => {
+      val _left = ConvertToUnsafeNode(conf, left)
+      val _right = ConvertToUnsafeNode(conf, right)
+      val r = f(_left, _right)
+      ConvertToSafeNode(conf, r)
     }
   }
 
-  protected def dataFrameToSeqScanNode(df: DataFrame): SeqScanNode = {
-    new SeqScanNode(
-      conf,
-      df.queryExecution.sparkPlan.output,
-      df.queryExecution.toRdd.map(_.copy()).collect())
-  }
-
-}
-
-/**
- * Helper methods for writing tests of individual local physical operators.
- */
-object LocalNodeTest {
-
   /**
-   * Runs the `LocalNode`s and makes sure the answer matches the expected result.
-   * @param input the input data to be used.
-   * @param nodeFunction a function which accepts the input `LocalNode`s and uses them to
-   *                     instantiate the local physical operator that's being tested.
-   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
-   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
-   *                    to being compared.
+   * Recursively resolve all expressions in a [[LocalNode]] using the node's attributes.
    */
-  def checkAnswer(
-    input: Seq[SeqScanNode],
-    nodeFunction: Seq[LocalNode] => LocalNode,
-    expectedAnswer: Seq[Row],
-    sortAnswers: Boolean): Option[String] = {
-
-    val outputNode = nodeFunction(input)
-
-    val outputResult: Seq[Row] = try {
-      outputNode.collect()
-    } catch {
-      case NonFatal(e) =>
-        val errorMessage =
-          s"""
-              | Exception thrown while executing local plan:
-              | $outputNode
-              | == Exception ==
-              | $e
-              | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
-          """.stripMargin
-        return Some(errorMessage)
-    }
-
-    SQLTestUtils.compareAnswers(outputResult, expectedAnswer, sortAnswers).map { errorMessage =>
-      s"""
-          | Results do not match for local plan:
-          | $outputNode
-          | $errorMessage
-       """.stripMargin
+  protected def resolveExpressions(outputNode: LocalNode): LocalNode = {
+    outputNode transform {
+      case node: LocalNode =>
+        val inputMap = node.output.map { a => (a.name, a) }.toMap
+        node transformExpressions {
+          case UnresolvedAttribute(Seq(u)) =>
+            inputMap.getOrElse(u,
+              sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap"))
+        }
     }
   }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala
index b1ef26ba82f16..40299d9d5ee37 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala
@@ -18,222 +18,128 @@
 package org.apache.spark.sql.execution.local
 
 import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, RightOuter}
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.joins.{BuildLeft, BuildRight, BuildSide}
 
+
 class NestedLoopJoinNodeSuite extends LocalNodeTest {
 
-  import testImplicits._
-
-  private def joinSuite(
-      suiteName: String, buildSide: BuildSide, confPairs: (String, String)*): Unit = {
-    test(s"$suiteName: left outer join") {
-      withSQLConf(confPairs: _*) {
-        checkAnswer2(
-          upperCaseData,
-          lowerCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              LeftOuter,
-              Some((upperCaseData.col("N") === lowerCaseData.col("n")).expr))
-          ),
-          upperCaseData.join(lowerCaseData, $"n" === $"N", "left").collect())
-
-        checkAnswer2(
-          upperCaseData,
-          lowerCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              LeftOuter,
-              Some(
-                (upperCaseData.col("N") === lowerCaseData.col("n") &&
-                  lowerCaseData.col("n") > 1).expr))
-          ),
-          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"n" > 1, "left").collect())
-
-        checkAnswer2(
-          upperCaseData,
-          lowerCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              LeftOuter,
-              Some(
-                (upperCaseData.col("N") === lowerCaseData.col("n") &&
-                  upperCaseData.col("N") > 1).expr))
-          ),
-          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"N" > 1, "left").collect())
-
-        checkAnswer2(
-          upperCaseData,
-          lowerCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              LeftOuter,
-              Some(
-                (upperCaseData.col("N") === lowerCaseData.col("n") &&
-                  lowerCaseData.col("l") > upperCaseData.col("L")).expr))
-          ),
-          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"l" > $"L", "left").collect())
+  // Test all combinations of the three dimensions: with/out unsafe, build sides, and join types
+  private val maybeUnsafeAndCodegen = Seq(false, true)
+  private val buildSides = Seq(BuildLeft, BuildRight)
+  private val joinTypes = Seq(LeftOuter, RightOuter, FullOuter)
+  maybeUnsafeAndCodegen.foreach { unsafeAndCodegen =>
+    buildSides.foreach { buildSide =>
+      joinTypes.foreach { joinType =>
+        testJoin(unsafeAndCodegen, buildSide, joinType)
       }
     }
+  }
 
-    test(s"$suiteName: right outer join") {
-      withSQLConf(confPairs: _*) {
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              RightOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N")).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N", "right").collect())
-
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              RightOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
-                lowerCaseData.col("n") > 1).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > 1, "right").collect())
-
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              RightOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
-                upperCaseData.col("N") > 1).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > 1, "right").collect())
-
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              RightOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
-                lowerCaseData.col("l") > upperCaseData.col("L")).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"l" > $"L", "right").collect())
+  /**
+   * Test outer nested loop joins with varying degrees of matches.
+   */
+  private def testJoin(
+      unsafeAndCodegen: Boolean,
+      buildSide: BuildSide,
+      joinType: JoinType): Unit = {
+    val simpleOrUnsafe = if (!unsafeAndCodegen) "simple" else "unsafe"
+    val testNamePrefix = s"$simpleOrUnsafe / $buildSide / $joinType"
+    val someData = (1 to 100).map { i => (i, "burger" + i) }.toArray
+    val conf = new SQLConf
+    conf.setConf(SQLConf.UNSAFE_ENABLED, unsafeAndCodegen)
+    conf.setConf(SQLConf.CODEGEN_ENABLED, unsafeAndCodegen)
+
+    // Actual test body
+    def runTest(
+        joinType: JoinType,
+        leftInput: Array[(Int, String)],
+        rightInput: Array[(Int, String)]): Unit = {
+      val leftNode = new DummyNode(joinNameAttributes, leftInput)
+      val rightNode = new DummyNode(joinNicknameAttributes, rightInput)
+      val cond = 'id1 === 'id2
+      val makeNode = (node1: LocalNode, node2: LocalNode) => {
+        resolveExpressions(
+          new NestedLoopJoinNode(conf, node1, node2, buildSide, joinType, Some(cond)))
       }
+      val makeUnsafeNode = if (unsafeAndCodegen) wrapForUnsafe(makeNode) else makeNode
+      val hashJoinNode = makeUnsafeNode(leftNode, rightNode)
+      val expectedOutput = generateExpectedOutput(leftInput, rightInput, joinType)
+      val actualOutput = hashJoinNode.collect().map { row =>
+        // (id, name, id, nickname)
+        (row.getInt(0), row.getString(1), row.getInt(2), row.getString(3))
+      }
+      assert(actualOutput.toSet === expectedOutput.toSet)
     }
 
-    test(s"$suiteName: full outer join") {
-      withSQLConf(confPairs: _*) {
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              FullOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N")).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N", "full").collect())
-
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              FullOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
-                lowerCaseData.col("n") > 1).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > 1, "full").collect())
-
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              FullOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
-                upperCaseData.col("N") > 1).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > 1, "full").collect())
-
-        checkAnswer2(
-          lowerCaseData,
-          upperCaseData,
-          wrapForUnsafe(
-            (node1, node2) => NestedLoopJoinNode(
-              conf,
-              node1,
-              node2,
-              buildSide,
-              FullOuter,
-              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
-                lowerCaseData.col("l") > upperCaseData.col("L")).expr))
-          ),
-          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"l" > $"L", "full").collect())
-      }
+    test(s"$testNamePrefix: empty") {
+      runTest(joinType, Array.empty, Array.empty)
+    }
+
+    test(s"$testNamePrefix: no matches") {
+      val someIrrelevantData = (10000 to 10100).map { i => (i, "piper" + i) }.toArray
+      runTest(joinType, someData, Array.empty)
+      runTest(joinType, Array.empty, someData)
+      runTest(joinType, someData, someIrrelevantData)
+      runTest(joinType, someIrrelevantData, someData)
+    }
+
+    test(s"$testNamePrefix: partial matches") {
+      val someOtherData = (50 to 150).map { i => (i, "finnegan" + i) }.toArray
+      runTest(joinType, someData, someOtherData)
+      runTest(joinType, someOtherData, someData)
+    }
+
+    test(s"$testNamePrefix: full matches") {
+      val someSuperRelevantData = someData.map { case (k, v) => (k, "cooper" + v) }
+      runTest(joinType, someData, someSuperRelevantData)
+      runTest(joinType, someSuperRelevantData, someData)
+    }
+  }
+
+  /**
+   * Helper method to generate the expected output of a test based on the join type.
+   */
+  private def generateExpectedOutput(
+      leftInput: Array[(Int, String)],
+      rightInput: Array[(Int, String)],
+      joinType: JoinType): Array[(Int, String, Int, String)] = {
+    joinType match {
+      case LeftOuter =>
+        val rightInputMap = rightInput.toMap
+        leftInput.map { case (k, v) =>
+          val rightKey = rightInputMap.get(k).map { _ => k }.getOrElse(0)
+          val rightValue = rightInputMap.getOrElse(k, null)
+          (k, v, rightKey, rightValue)
+        }
+
+      case RightOuter =>
+        val leftInputMap = leftInput.toMap
+        rightInput.map { case (k, v) =>
+          val leftKey = leftInputMap.get(k).map { _ => k }.getOrElse(0)
+          val leftValue = leftInputMap.getOrElse(k, null)
+          (leftKey, leftValue, k, v)
+        }
+
+      case FullOuter =>
+        val leftInputMap = leftInput.toMap
+        val rightInputMap = rightInput.toMap
+        val leftOutput = leftInput.map { case (k, v) =>
+          val rightKey = rightInputMap.get(k).map { _ => k }.getOrElse(0)
+          val rightValue = rightInputMap.getOrElse(k, null)
+          (k, v, rightKey, rightValue)
+        }
+        val rightOutput = rightInput.map { case (k, v) =>
+          val leftKey = leftInputMap.get(k).map { _ => k }.getOrElse(0)
+          val leftValue = leftInputMap.getOrElse(k, null)
+          (leftKey, leftValue, k, v)
+        }
+        (leftOutput ++ rightOutput).distinct
+
+      case other =>
+        throw new IllegalArgumentException(s"Join type $other is not applicable")
     }
   }
 
-  joinSuite(
-    "general-build-left",
-    BuildLeft,
-    SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
-  joinSuite(
-    "general-build-right",
-    BuildRight,
-    SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
-  joinSuite(
-    "tungsten-build-left",
-    BuildLeft,
-    SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
-  joinSuite(
-    "tungsten-build-right",
-    BuildRight,
-    SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
index 38e0a230c46d8..02ecb23d34b2f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
@@ -17,28 +17,33 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, NamedExpression}
+import org.apache.spark.sql.types.{IntegerType, StringType}
 
-class ProjectNodeSuite extends LocalNodeTest with SharedSQLContext {
 
-  test("basic") {
-    val output = testData.queryExecution.sparkPlan.output
-    val columns = Seq(output(1), output(0))
-    checkAnswer(
-      testData,
-      node => ProjectNode(conf, columns, node),
-      testData.select("value", "key").collect()
-    )
+class ProjectNodeSuite extends LocalNodeTest {
+  private val pieAttributes = Seq(
+    AttributeReference("id", IntegerType)(),
+    AttributeReference("age", IntegerType)(),
+    AttributeReference("name", StringType)())
+
+  private def testProject(inputData: Array[(Int, Int, String)] = Array.empty): Unit = {
+    val inputNode = new DummyNode(pieAttributes, inputData)
+    val columns = Seq[NamedExpression](inputNode.output(0), inputNode.output(2))
+    val projectNode = new ProjectNode(conf, columns, inputNode)
+    val expectedOutput = inputData.map { case (id, age, name) => (id, name) }
+    val actualOutput = projectNode.collect().map { case row =>
+      (row.getInt(0), row.getString(1))
+    }
+    assert(actualOutput === expectedOutput)
   }
 
   test("empty") {
-    val output = emptyTestData.queryExecution.sparkPlan.output
-    val columns = Seq(output(1), output(0))
-    checkAnswer(
-      emptyTestData,
-      node => ProjectNode(conf, columns, node),
-      emptyTestData.select("value", "key").collect()
-    )
+    testProject()
+  }
+
+  test("basic") {
+    testProject((1 to 100).map { i => (i, i + 1, "pie" + i) }.toArray)
   }
 
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
index 87a7da453999c..a3e83bbd51457 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
@@ -17,21 +17,32 @@
 
 package org.apache.spark.sql.execution.local
 
-class SampleNodeSuite extends LocalNodeTest {
+import org.apache.spark.util.random.{BernoulliCellSampler, PoissonSampler}
+
 
-  import testImplicits._
+class SampleNodeSuite extends LocalNodeTest {
 
   private def testSample(withReplacement: Boolean): Unit = {
-    test(s"withReplacement: $withReplacement") {
-      val seed = 0L
-      val input = sqlContext.sparkContext.
-        parallelize((1 to 10).map(i => (i, i.toString)), 1). // Should be only 1 partition
-        toDF("key", "value")
-      checkAnswer(
-        input,
-        node => SampleNode(conf, 0.0, 0.3, withReplacement, seed, node),
-        input.sample(withReplacement, 0.3, seed).collect()
-      )
+    val seed = 0L
+    val lowerb = 0.0
+    val upperb = 0.3
+    val maybeOut = if (withReplacement) "" else "out"
+    test(s"with$maybeOut replacement") {
+      val inputData = (1 to 1000).map { i => (i, i) }.toArray
+      val inputNode = new DummyNode(kvIntAttributes, inputData)
+      val sampleNode = new SampleNode(conf, lowerb, upperb, withReplacement, seed, inputNode)
+      val sampler =
+        if (withReplacement) {
+          new PoissonSampler[(Int, Int)](upperb - lowerb, useGapSamplingIfPossible = false)
+        } else {
+          new BernoulliCellSampler[(Int, Int)](lowerb, upperb)
+        }
+      sampler.setSeed(seed)
+      val expectedOutput = sampler.sample(inputData.iterator).toArray
+      val actualOutput = sampleNode.collect().map { case row =>
+        (row.getInt(0), row.getInt(1))
+      }
+      assert(actualOutput === expectedOutput)
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala
index ff28b24eeff14..42ebc7bfcaadc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala
@@ -17,38 +17,34 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.sql.Column
-import org.apache.spark.sql.catalyst.expressions.{Ascending, Expression, SortOrder}
+import scala.util.Random
 
-class TakeOrderedAndProjectNodeSuite extends LocalNodeTest {
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.SortOrder
 
-  import testImplicits._
 
-  private def columnToSortOrder(sortExprs: Column*): Seq[SortOrder] = {
-    val sortOrder: Seq[SortOrder] = sortExprs.map { col =>
-      col.expr match {
-        case expr: SortOrder =>
-          expr
-        case expr: Expression =>
-          SortOrder(expr, Ascending)
-      }
-    }
-    sortOrder
-  }
+class TakeOrderedAndProjectNodeSuite extends LocalNodeTest {
 
-  private def testTakeOrderedAndProjectNode(desc: Boolean): Unit = {
-    val testCaseName = if (desc) "desc" else "asc"
-    test(testCaseName) {
-      val input = (1 to 10).map(i => (i, i.toString)).toDF("key", "value")
-      val sortColumn = if (desc) input.col("key").desc else input.col("key")
-      checkAnswer(
-        input,
-        node => TakeOrderedAndProjectNode(conf, 5, columnToSortOrder(sortColumn), None, node),
-        input.sort(sortColumn).limit(5).collect()
-      )
+  private def testTakeOrderedAndProject(desc: Boolean): Unit = {
+    val limit = 10
+    val ascOrDesc = if (desc) "desc" else "asc"
+    test(ascOrDesc) {
+      val inputData = Random.shuffle((1 to 100).toList).map { i => (i, i) }.toArray
+      val inputNode = new DummyNode(kvIntAttributes, inputData)
+      val firstColumn = inputNode.output(0)
+      val sortDirection = if (desc) Descending else Ascending
+      val sortOrder = SortOrder(firstColumn, sortDirection)
+      val takeOrderAndProjectNode = new TakeOrderedAndProjectNode(
+        conf, limit, Seq(sortOrder), Some(Seq(firstColumn)), inputNode)
+      val expectedOutput = inputData
+        .map { case (k, _) => k }
+        .sortBy { k => k * (if (desc) -1 else 1) }
+        .take(limit)
+      val actualOutput = takeOrderAndProjectNode.collect().map { row => row.getInt(0) }
+      assert(actualOutput === expectedOutput)
     }
   }
 
-  testTakeOrderedAndProjectNode(desc = false)
-  testTakeOrderedAndProjectNode(desc = true)
+  testTakeOrderedAndProject(desc = false)
+  testTakeOrderedAndProject(desc = true)
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
index eedd7320900f9..666b0235c061d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
@@ -17,36 +17,39 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.sql.test.SharedSQLContext
 
-class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
+class UnionNodeSuite extends LocalNodeTest {
 
-  test("basic") {
-    checkAnswer2(
-      testData,
-      testData,
-      (node1, node2) => UnionNode(conf, Seq(node1, node2)),
-      testData.unionAll(testData).collect()
-    )
+  private def testUnion(inputData: Seq[Array[(Int, Int)]]): Unit = {
+    val inputNodes = inputData.map { data =>
+      new DummyNode(kvIntAttributes, data)
+    }
+    val unionNode = new UnionNode(conf, inputNodes)
+    val expectedOutput = inputData.flatten
+    val actualOutput = unionNode.collect().map { case row =>
+      (row.getInt(0), row.getInt(1))
+    }
+    assert(actualOutput === expectedOutput)
   }
 
   test("empty") {
-    checkAnswer2(
-      emptyTestData,
-      emptyTestData,
-      (node1, node2) => UnionNode(conf, Seq(node1, node2)),
-      emptyTestData.unionAll(emptyTestData).collect()
-    )
+    testUnion(Seq(Array.empty))
+    testUnion(Seq(Array.empty, Array.empty))
+  }
+
+  test("self") {
+    val data = (1 to 100).map { i => (i, i) }.toArray
+    testUnion(Seq(data))
+    testUnion(Seq(data, data))
+    testUnion(Seq(data, data, data))
   }
 
-  test("complicated union") {
-    val dfs = Seq(testData, emptyTestData, emptyTestData, testData, testData, emptyTestData,
-      emptyTestData, emptyTestData, testData, emptyTestData)
-    doCheckAnswer(
-      dfs,
-      nodes => UnionNode(conf, nodes),
-      dfs.reduce(_.unionAll(_)).collect()
-    )
+  test("basic") {
+    val zero = Array.empty[(Int, Int)]
+    val one = (1 to 100).map { i => (i, i) }.toArray
+    val two = (50 to 150).map { i => (i, i) }.toArray
+    val three = (800 to 900).map { i => (i, i) }.toArray
+    testUnion(Seq(zero, one, two, three))
   }
 
 }

From 64c29afcb787d9f176a197c25314295108ba0471 Mon Sep 17 00:00:00 2001
From: sureshthalamati <suresh.thalamati@gmail.com>
Date: Tue, 15 Sep 2015 19:41:38 -0700
Subject: [PATCH 1446/1454] [SPARK-9078] [SQL] Allow jdbc dialects to override
 the query used to check the table.

Current implementation uses query with a LIMIT clause to find if table already exists. This syntax works only in some database systems. This patch changes the default query to the one that is likely to work on most databases, and adds a new method to the  JdbcDialect abstract class to allow  dialects to override the default query.

I looked at using the JDBC meta data calls, it turns out there is no common way to find the current schema, catalog..etc.  There is a new method Connection.getSchema() , but that is available only starting jdk1.7 , and existing jdbc drivers may not have implemented it.  Other option was to use jdbc escape syntax clause for LIMIT, not sure on how well this supported in all the databases also. After looking at all the jdbc metadata options my conclusion was most common way is to use the simple select query with 'where 1 =0' , and allow dialects to customize as needed

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #8676 from sureshthalamati/table_exists_spark-9078.
---
 .../apache/spark/sql/DataFrameWriter.scala    |  2 +-
 .../datasources/jdbc/JdbcUtils.scala          |  9 ++++++---
 .../apache/spark/sql/jdbc/JdbcDialects.scala  | 20 +++++++++++++++++++
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 14 +++++++++++++
 4 files changed, 41 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index b2a66dd417b4c..745bb4ec9cf1c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -255,7 +255,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
     val conn = JdbcUtils.createConnection(url, props)
 
     try {
-      var tableExists = JdbcUtils.tableExists(conn, table)
+      var tableExists = JdbcUtils.tableExists(conn, url, table)
 
       if (mode == SaveMode.Ignore && tableExists) {
         return
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 26788b2a4fd69..f89d55b20e212 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -42,10 +42,13 @@ object JdbcUtils extends Logging {
   /**
    * Returns true if the table already exists in the JDBC database.
    */
-  def tableExists(conn: Connection, table: String): Boolean = {
+  def tableExists(conn: Connection, url: String, table: String): Boolean = {
+    val dialect = JdbcDialects.get(url)
+
     // Somewhat hacky, but there isn't a good way to identify whether a table exists for all
-    // SQL database systems, considering "table" could also include the database name.
-    Try(conn.prepareStatement(s"SELECT 1 FROM $table LIMIT 1").executeQuery().next()).isSuccess
+    // SQL database systems using JDBC meta data calls, considering "table" could also include
+    // the database name. Query used to find table exists can be overriden by the dialects.
+    Try(conn.prepareStatement(dialect.getTableExistsQuery(table)).executeQuery()).isSuccess
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
index c6d05c9b83b98..68ebaaca6c53d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
@@ -88,6 +88,17 @@ abstract class JdbcDialect {
   def quoteIdentifier(colName: String): String = {
     s""""$colName""""
   }
+
+  /**
+   * Get the SQL query that should be used to find if the given table exists. Dialects can
+   * override this method to return a query that works best in a particular database.
+   * @param table  The name of the table.
+   * @return The SQL query to use for checking the table.
+   */
+  def getTableExistsQuery(table: String): String = {
+    s"SELECT * FROM $table WHERE 1=0"
+  }
+
 }
 
 /**
@@ -198,6 +209,11 @@ case object PostgresDialect extends JdbcDialect {
     case BooleanType => Some(JdbcType("BOOLEAN", java.sql.Types.BOOLEAN))
     case _ => None
   }
+
+  override def getTableExistsQuery(table: String): String = {
+    s"SELECT 1 FROM $table LIMIT 1"
+  }
+
 }
 
 /**
@@ -222,6 +238,10 @@ case object MySQLDialect extends JdbcDialect {
   override def quoteIdentifier(colName: String): String = {
     s"`$colName`"
   }
+
+  override def getTableExistsQuery(table: String): String = {
+    s"SELECT 1 FROM $table LIMIT 1"
+  }
 }
 
 /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index ed710689cc670..5ab9381de4d66 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -450,4 +450,18 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
     assert(db2Dialect.getJDBCType(StringType).map(_.databaseTypeDefinition).get == "CLOB")
     assert(db2Dialect.getJDBCType(BooleanType).map(_.databaseTypeDefinition).get == "CHAR(1)")
   }
+
+  test("table exists query by jdbc dialect") {
+    val MySQL = JdbcDialects.get("jdbc:mysql://127.0.0.1/db")
+    val Postgres = JdbcDialects.get("jdbc:postgresql://127.0.0.1/db")
+    val db2 = JdbcDialects.get("jdbc:db2://127.0.0.1/db")
+    val h2 = JdbcDialects.get(url)
+    val table = "weblogs"
+    val defaultQuery = s"SELECT * FROM $table WHERE 1=0"
+    val limitQuery = s"SELECT 1 FROM $table LIMIT 1"
+    assert(MySQL.getTableExistsQuery(table) == limitQuery)
+    assert(Postgres.getTableExistsQuery(table) == limitQuery)
+    assert(db2.getTableExistsQuery(table) == defaultQuery)
+    assert(h2.getTableExistsQuery(table) == defaultQuery)
+  }
 }

From b921fe4dc0442aa133ab7d55fba24bc798d59aa2 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 15 Sep 2015 19:43:26 -0700
Subject: [PATCH 1447/1454] [SPARK-10595] [ML] [MLLIB] [DOCS] Various ML guide
 cleanups
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Various ML guide cleanups.

* ml-guide.md: Make it easier to access the algorithm-specific guides.
* LDA user guide: EM often begins with useless topics, but running longer generally improves them dramatically.  E.g., 10 iterations on a Wikipedia dataset produces useless topics, but 50 iterations produces very meaningful topics.
* mllib-feature-extraction.html#elementwiseproduct: “w” parameter should be “scalingVec”
* Clean up Binarizer user guide a little.
* Document in Pipeline that users should not put an instance into the Pipeline in more than 1 place.
* spark.ml Word2Vec user guide: clean up grammar/writing
* Chi Sq Feature Selector docs: Improve text in doc.

CC: mengxr feynmanliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8752 from jkbradley/mlguide-fixes-1.5.
---
 docs/ml-features.md              | 34 +++++++++++++++++---
 docs/ml-guide.md                 | 31 ++++++++++++-------
 docs/mllib-clustering.md         |  4 +++
 docs/mllib-feature-extraction.md | 53 +++++++++++++++++++++-----------
 docs/mllib-guide.md              |  4 +--
 5 files changed, 91 insertions(+), 35 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index a414c21b5c280..b70da4ac63845 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -123,12 +123,21 @@ for features_label in rescaledData.select("features", "label").take(3):
 
 ## Word2Vec
 
-`Word2Vec` is an `Estimator` which takes sequences of words that represents documents and trains a `Word2VecModel`. The model is a `Map(String, Vector)` essentially, which maps each word to an unique fix-sized vector. The `Word2VecModel` transforms each documents into a vector using the average of all words in the document, which aims to other computations of documents such as similarity calculation consequencely. Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#Word2Vec) for more details on Word2Vec.
+`Word2Vec` is an `Estimator` which takes sequences of words representing documents and trains a
+`Word2VecModel`. The model maps each word to a unique fixed-size vector. The `Word2VecModel`
+transforms each document into a vector using the average of all words in the document; this vector
+can then be used for as features for prediction, document similarity calculations, etc.
+Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#Word2Vec) for more
+details.
 
-Word2Vec is implemented in [Word2Vec](api/scala/index.html#org.apache.spark.ml.feature.Word2Vec). In the following code segment, we start with a set of documents, each of them is represented as a sequence of words. For each document, we transform it into a feature vector. This feature vector could then be passed to a learning algorithm.
+In the following code segment, we start with a set of documents, each of which is represented as a sequence of words. For each document, we transform it into a feature vector. This feature vector could then be passed to a learning algorithm.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
+
+Refer to the [Word2Vec Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Word2Vec)
+for more details on the API.
+
 {% highlight scala %}
 import org.apache.spark.ml.feature.Word2Vec
 
@@ -152,6 +161,10 @@ result.select("result").take(3).foreach(println)
 </div>
 
 <div data-lang="java" markdown="1">
+
+Refer to the [Word2Vec Java docs](api/java/org/apache/spark/ml/feature/Word2Vec.html)
+for more details on the API.
+
 {% highlight java %}
 import java.util.Arrays;
 
@@ -192,6 +205,10 @@ for (Row r: result.select("result").take(3)) {
 </div>
 
 <div data-lang="python" markdown="1">
+
+Refer to the [Word2Vec Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Word2Vec)
+for more details on the API.
+
 {% highlight python %}
 from pyspark.ml.feature import Word2Vec
 
@@ -621,12 +638,15 @@ for ngrams_label in ngramDataFrame.select("ngrams", "label").take(3):
 
 ## Binarizer
 
-Binarization is the process of thresholding numerical features to binary features. As some probabilistic estimators make assumption that the input data is distributed according to [Bernoulli distribution](http://en.wikipedia.org/wiki/Bernoulli_distribution), a binarizer is useful for pre-processing the input data with continuous numerical features.
+Binarization is the process of thresholding numerical features to binary (0/1) features.
 
-A simple [Binarizer](api/scala/index.html#org.apache.spark.ml.feature.Binarizer) class provides this functionality. Besides the common parameters of `inputCol` and `outputCol`, `Binarizer` has the parameter `threshold` used for binarizing continuous numerical features. The features greater than the threshold, will be binarized to 1.0. The features equal to or less than the threshold, will be binarized to 0.0. The example below shows how to binarize numerical features.
+`Binarizer` takes the common parameters `inputCol` and `outputCol`, as well as the `threshold` for binarization. Feature values greater than the threshold are binarized to 1.0; values equal to or less than the threshold are binarized to 0.0.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
+
+Refer to the [Binarizer API doc](api/scala/index.html#org.apache.spark.ml.feature.Binarizer) for more details.
+
 {% highlight scala %}
 import org.apache.spark.ml.feature.Binarizer
 import org.apache.spark.sql.DataFrame
@@ -650,6 +670,9 @@ binarizedFeatures.collect().foreach(println)
 </div>
 
 <div data-lang="java" markdown="1">
+
+Refer to the [Binarizer API doc](api/java/org/apache/spark/ml/feature/Binarizer.html) for more details.
+
 {% highlight java %}
 import java.util.Arrays;
 
@@ -687,6 +710,9 @@ for (Row r : binarizedFeatures.collect()) {
 </div>
 
 <div data-lang="python" markdown="1">
+
+Refer to the [Binarizer API doc](api/python/pyspark.ml.html#pyspark.ml.feature.Binarizer) for more details.
+
 {% highlight python %}
 from pyspark.ml.feature import Binarizer
 
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 78c93a95c7807..c5d7f990021f1 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -32,7 +32,21 @@ See the [algorithm guides](#algorithm-guides) section below for guides on sub-pa
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-# Main concepts
+# Algorithm guides
+
+We provide several algorithm guides specific to the Pipelines API.
+Several of these algorithms, such as certain feature transformers, are not in the `spark.mllib` API.
+Also, some algorithms have additional capabilities in the `spark.ml` API; e.g., random forests
+provide class probabilities, and linear models provide model summaries.
+
+* [Feature extraction, transformation, and selection](ml-features.html)
+* [Decision Trees for classification and regression](ml-decision-tree.html)
+* [Ensembles](ml-ensembles.html)
+* [Linear methods with elastic net regularization](ml-linear-methods.html)
+* [Multilayer perceptron classifier](ml-ann.html)
+
+
+# Main concepts in Pipelines
 
 Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
 algorithms into a single pipeline, or workflow.
@@ -166,6 +180,11 @@ compile-time type checking.
 `Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
 This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.
 
+*Unique Pipeline stages*: A `Pipeline`'s stages should be unique instances.  E.g., the same instance
+`myHashingTF` should not be inserted into the `Pipeline` twice since `Pipeline` stages must have
+unique IDs.  However, different instances `myHashingTF1` and `myHashingTF2` (both of type `HashingTF`)
+can be put into the same `Pipeline` since different instances will be created with different IDs.
+
 ## Parameters
 
 Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
@@ -184,16 +203,6 @@ Parameters belong to specific instances of `Estimator`s and `Transformer`s.
 For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
 This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
 
-# Algorithm guides
-
-There are now several algorithms in the Pipelines API which are not in the `spark.mllib` API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
-
-* [Feature extraction, transformation, and selection](ml-features.html)
-* [Decision Trees for classification and regression](ml-decision-tree.html)
-* [Ensembles](ml-ensembles.html)
-* [Linear methods with elastic net regularization](ml-linear-methods.html)
-* [Multilayer perceptron classifier](ml-ann.html)
-
 # Code examples
 
 This section gives code examples illustrating the functionality discussed above.
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 3fb35d3c50b06..c2711cf82deb4 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -507,6 +507,10 @@ must also be $> 1.0$. Providing `Vector(-1)` results in default behavior
 $> 1.0$. Providing `-1` results in defaulting to a value of $0.1 + 1$.
 * `maxIterations`: The maximum number of EM iterations.
 
+*Note*: It is important to do enough iterations.  In early iterations, EM often has useless topics,
+but those topics improve dramatically after more iterations.  Using at least 20 and possibly
+50-100 iterations is often reasonable, depending on your dataset.
+
 `EMLDAOptimizer` produces a `DistributedLDAModel`, which stores not only
 the inferred topics but also the full training corpus and topic
 distributions for each document in the training corpus. A
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index de86aba2ae627..7e417ed5f37a9 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -380,35 +380,43 @@ data2 = labels.zip(normalizer2.transform(features))
 </div>
 </div>
 
-## Feature selection
-[Feature selection](http://en.wikipedia.org/wiki/Feature_selection) allows selecting the most relevant features for use in model construction. Feature selection reduces the size of the vector space and, in turn, the complexity of any subsequent operation with vectors. The number of features to select can be tuned using a held-out validation set.
+## ChiSqSelector
 
-### ChiSqSelector
-[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) stands for Chi-Squared feature selection. It operates on labeled data with categorical features. `ChiSqSelector` orders features based on a Chi-Squared test of independence from the class, and then filters (selects) the top features which the class label depends on the most. This is akin to yielding the features with the most predictive power.
+[Feature selection](http://en.wikipedia.org/wiki/Feature_selection) tries to identify relevant
+features for use in model construction. It reduces the size of the feature space, which can improve
+both speed and statistical learning behavior.
 
-#### Model Fitting
+[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) implements
+Chi-Squared feature selection. It operates on labeled data with categorical features.
+`ChiSqSelector` orders features based on a Chi-Squared test of independence from the class,
+and then filters (selects) the top features which the class label depends on the most.
+This is akin to yielding the features with the most predictive power.
 
-[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) has the
-following parameters in the constructor:
+The number of features to select can be tuned using a held-out validation set.
 
-* `numTopFeatures` number of top features that the selector will select (filter).
+### Model Fitting
 
-We provide a [`fit`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) method in
-`ChiSqSelector` which can take an input of `RDD[LabeledPoint]` with categorical features, learn the summary statistics, and then
-return a `ChiSqSelectorModel` which can transform an input dataset into the reduced feature space.
+`ChiSqSelector` takes a `numTopFeatures` parameter specifying the number of top features that
+the selector will select.
 
-This model implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer)
-which can apply the Chi-Squared feature selection on a `Vector` to produce a reduced `Vector` or on
+The [`fit`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) method takes
+an input of `RDD[LabeledPoint]` with categorical features, learns the summary statistics, and then
+returns a `ChiSqSelectorModel` which can transform an input dataset into the reduced feature space.
+The `ChiSqSelectorModel` can be applied either to a `Vector` to produce a reduced `Vector`, or to
 an `RDD[Vector]` to produce a reduced `RDD[Vector]`.
 
 Note that the user can also construct a `ChiSqSelectorModel` by hand by providing an array of selected feature indices (which must be sorted in ascending order).
 
-#### Example
+### Example
 
 The following example shows the basic use of ChiSqSelector. The data set used has a feature matrix consisting of greyscale values that vary from 0 to 255 for each feature.
 
 <div class="codetabs">
-<div data-lang="scala">
+<div data-lang="scala" markdown="1">
+
+Refer to the [`ChiSqSelector` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector)
+for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vectors
@@ -434,7 +442,11 @@ val filteredData = discretizedData.map { lp =>
 {% endhighlight %}
 </div>
 
-<div data-lang="java">
+<div data-lang="java" markdown="1">
+
+Refer to the [`ChiSqSelector` Java docs](api/java/org/apache/spark/mllib/feature/ChiSqSelector.html)
+for details on the API.
+
 {% highlight java %}
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
@@ -486,7 +498,12 @@ sc.stop();
 
 ## ElementwiseProduct
 
-ElementwiseProduct multiplies each input vector by a provided "weight" vector, using element-wise multiplication. In other words, it scales each column of the dataset by a scalar multiplier.  This represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between the input vector, `v` and transforming vector, `w`, to yield a result vector.
+`ElementwiseProduct` multiplies each input vector by a provided "weight" vector, using element-wise
+multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This
+represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29)
+between the input vector, `v` and transforming vector, `scalingVec`, to yield a result vector.
+Qu8T948*1#
+Denoting the `scalingVec` as "`w`," this transformation may be written as:
 
 `\[ \begin{pmatrix}
 v_1 \\
@@ -506,7 +523,7 @@ v_N
 
 [`ElementwiseProduct`](api/scala/index.html#org.apache.spark.mllib.feature.ElementwiseProduct) has the following parameter in the constructor:
 
-* `w`: the transforming vector.
+* `scalingVec`: the transforming vector.
 
 `ElementwiseProduct` implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer) which can apply the weighting on a `Vector` to produce a transformed `Vector` or on an `RDD[Vector]` to produce a transformed `RDD[Vector]`.
 
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 257f7cc7603fa..91e50ccfecec4 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -13,9 +13,9 @@ primitives and higher-level pipeline APIs.
 
 It divides into two packages:
 
-* [`spark.mllib`](mllib-guide.html#mllib-types-algorithms-and-utilities) contains the original API
+* [`spark.mllib`](mllib-guide.html#data-types-algorithms-and-utilities) contains the original API
   built on top of [RDDs](programming-guide.html#resilient-distributed-datasets-rdds).
-* [`spark.ml`](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) provides higher-level API
+* [`spark.ml`](ml-guide.html) provides higher-level API
   built on top of [DataFrames](sql-programming-guide.html#dataframes) for constructing ML pipelines.
 
 Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.

From 95b6a8103fb527f501ca26b1d6e3a5859970a1e2 Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Tue, 15 Sep 2015 23:25:51 -0700
Subject: [PATCH 1448/1454] [SPARK-10516] [ MLLIB] Added values property in
 DenseVector

Author: Vinod K C <vinod.kc@huawei.com>

Closes #8682 from vinodkc/fix_SPARK-10516.
---
 python/pyspark/mllib/linalg/__init__.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/python/pyspark/mllib/linalg/__init__.py b/python/pyspark/mllib/linalg/__init__.py
index 380f86e9b44f8..4829acb16ed8a 100644
--- a/python/pyspark/mllib/linalg/__init__.py
+++ b/python/pyspark/mllib/linalg/__init__.py
@@ -399,6 +399,10 @@ def squared_distance(self, other):
     def toArray(self):
         return self.array
 
+    @property
+    def values(self):
+        return self.array
+
     def __getitem__(self, item):
         return self.array[item]
 

From 1894653edce718e874d1ddc9ba442bce43cbc082 Mon Sep 17 00:00:00 2001
From: Luciano Resende <lresende@apache.org>
Date: Wed, 16 Sep 2015 10:47:30 +0100
Subject: [PATCH 1449/1454] [SPARK-10511] [BUILD] Reset git repository before
 packaging source distro

The calculation of Spark version is downloading
Scala and Zinc in the build directory which is
inflating the size of the source distribution.

Reseting the repo before packaging the source
distribution fix this issue.

Author: Luciano Resende <lresende@apache.org>

Closes #8774 from lresende/spark-10511.
---
 dev/create-release/release-build.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/dev/create-release/release-build.sh b/dev/create-release/release-build.sh
index d0b3a54dde1dc..9dac43ce54425 100755
--- a/dev/create-release/release-build.sh
+++ b/dev/create-release/release-build.sh
@@ -99,6 +99,7 @@ fi
 DEST_DIR_NAME="spark-$SPARK_PACKAGE_VERSION"
 USER_HOST="$ASF_USERNAME@people.apache.org"
 
+git clean -d -f -x
 rm .gitignore
 rm -rf .git
 cd ..

From d9b7f3e4dbceb91ea4d1a1fed3ab847335f8588b Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 16 Sep 2015 04:34:14 -0700
Subject: [PATCH 1450/1454] [SPARK-10276] [MLLIB] [PYSPARK] Add @since
 annotation to pyspark.mllib.recommendation

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8677 from yu-iskw/SPARK-10276.
---
 python/pyspark/mllib/recommendation.py | 36 +++++++++++++++++++++++++-
 1 file changed, 35 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 506ca2151cce7..95047b5b7b4b7 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -18,7 +18,7 @@
 import array
 from collections import namedtuple
 
-from pyspark import SparkContext
+from pyspark import SparkContext, since
 from pyspark.rdd import RDD
 from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
 from pyspark.mllib.util import JavaLoader, JavaSaveable
@@ -36,6 +36,8 @@ class Rating(namedtuple("Rating", ["user", "product", "rating"])):
     (1, 2, 5.0)
     >>> (r[0], r[1], r[2])
     (1, 2, 5.0)
+
+    .. versionadded:: 1.2.0
     """
 
     def __reduce__(self):
@@ -111,13 +113,17 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
     ...     rmtree(path)
     ... except OSError:
     ...     pass
+
+    .. versionadded:: 0.9.0
     """
+    @since("0.9.0")
     def predict(self, user, product):
         """
         Predicts rating for the given user and product.
         """
         return self._java_model.predict(int(user), int(product))
 
+    @since("0.9.0")
     def predictAll(self, user_product):
         """
         Returns a list of predicted ratings for input user and product pairs.
@@ -128,6 +134,7 @@ def predictAll(self, user_product):
         user_product = user_product.map(lambda u_p: (int(u_p[0]), int(u_p[1])))
         return self.call("predict", user_product)
 
+    @since("1.2.0")
     def userFeatures(self):
         """
         Returns a paired RDD, where the first element is the user and the
@@ -135,6 +142,7 @@ def userFeatures(self):
         """
         return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v))
 
+    @since("1.2.0")
     def productFeatures(self):
         """
         Returns a paired RDD, where the first element is the product and the
@@ -142,6 +150,7 @@ def productFeatures(self):
         """
         return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v))
 
+    @since("1.4.0")
     def recommendUsers(self, product, num):
         """
         Recommends the top "num" number of users for a given product and returns a list
@@ -149,6 +158,7 @@ def recommendUsers(self, product, num):
         """
         return list(self.call("recommendUsers", product, num))
 
+    @since("1.4.0")
     def recommendProducts(self, user, num):
         """
         Recommends the top "num" number of products for a given user and returns a list
@@ -157,17 +167,25 @@ def recommendProducts(self, user, num):
         return list(self.call("recommendProducts", user, num))
 
     @property
+    @since("1.4.0")
     def rank(self):
+        """Rank for the features in this model"""
         return self.call("rank")
 
     @classmethod
+    @since("1.3.1")
     def load(cls, sc, path):
+        """Load a model from the given path"""
         model = cls._load_java(sc, path)
         wrapper = sc._jvm.MatrixFactorizationModelWrapper(model)
         return MatrixFactorizationModel(wrapper)
 
 
 class ALS(object):
+    """Alternating Least Squares matrix factorization
+
+    .. versionadded:: 0.9.0
+    """
 
     @classmethod
     def _prepare(cls, ratings):
@@ -188,15 +206,31 @@ def _prepare(cls, ratings):
         return ratings
 
     @classmethod
+    @since("0.9.0")
     def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False,
               seed=None):
+        """
+        Train a matrix factorization model given an RDD of ratings given by users to some products,
+        in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the
+        product of two lower-rank matrices of a given rank (number of features). To solve for these
+        features, we run a given number of iterations of ALS. This is done using a level of
+        parallelism given by `blocks`.
+        """
         model = callMLlibFunc("trainALSModel", cls._prepare(ratings), rank, iterations,
                               lambda_, blocks, nonnegative, seed)
         return MatrixFactorizationModel(model)
 
     @classmethod
+    @since("0.9.0")
     def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01,
                       nonnegative=False, seed=None):
+        """
+        Train a matrix factorization model given an RDD of 'implicit preferences' given by users
+        to some products, in the form of (userID, productID, preference) pairs. We approximate the
+        ratings matrix as the product of two lower-rank matrices of a given rank (number of
+        features).  To solve for these features, we run a given number of iterations of ALS.
+        This is done using a level of parallelism given by `blocks`.
+        """
         model = callMLlibFunc("trainImplicitALSModel", cls._prepare(ratings), rank,
                               iterations, lambda_, blocks, alpha, nonnegative, seed)
         return MatrixFactorizationModel(model)

From 5dbaf3d3911bbfa003bc75459aaad66b4f6e0c67 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Wed, 16 Sep 2015 19:19:23 +0100
Subject: [PATCH 1451/1454] [SPARK-10589] [WEBUI] Add defense against external
 site framing

Set `X-Frame-Options: SAMEORIGIN` to protect against frame-related vulnerability

Author: Sean Owen <sowen@cloudera.com>

Closes #8745 from srowen/SPARK-10589.
---
 .../spark/deploy/worker/ui/WorkerWebUI.scala     |  7 ++++---
 .../org/apache/spark/metrics/MetricsSystem.scala |  2 +-
 .../spark/metrics/sink/MetricsServlet.scala      |  6 +++---
 .../scala/org/apache/spark/ui/JettyUtils.scala   | 16 ++++++++++++++--
 .../main/scala/org/apache/spark/ui/WebUI.scala   |  4 ++--
 5 files changed, 24 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
index 709a27233598c..1a0598e50dcf1 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala
@@ -20,9 +20,8 @@ package org.apache.spark.deploy.worker.ui
 import java.io.File
 import javax.servlet.http.HttpServletRequest
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.Logging
 import org.apache.spark.deploy.worker.Worker
-import org.apache.spark.deploy.worker.ui.WorkerWebUI._
 import org.apache.spark.ui.{SparkUI, WebUI}
 import org.apache.spark.ui.JettyUtils._
 import org.apache.spark.util.RpcUtils
@@ -49,7 +48,9 @@ class WorkerWebUI(
     attachPage(new WorkerPage(this))
     attachHandler(createStaticHandler(WorkerWebUI.STATIC_RESOURCE_BASE, "/static"))
     attachHandler(createServletHandler("/log",
-      (request: HttpServletRequest) => logPage.renderLog(request), worker.securityMgr))
+      (request: HttpServletRequest) => logPage.renderLog(request),
+      worker.securityMgr,
+      worker.conf))
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
index 4517f465ebd3b..48afe3ae3511f 100644
--- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
+++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala
@@ -88,7 +88,7 @@ private[spark] class MetricsSystem private (
    */
   def getServletHandlers: Array[ServletContextHandler] = {
     require(running, "Can only call getServletHandlers on a running MetricsSystem")
-    metricsServlet.map(_.getHandlers).getOrElse(Array())
+    metricsServlet.map(_.getHandlers(conf)).getOrElse(Array())
   }
 
   metricsConfig.initialize()
diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala
index 0c2e212a33074..4193e1d21d3c1 100644
--- a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala
+++ b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala
@@ -27,7 +27,7 @@ import com.codahale.metrics.json.MetricsModule
 import com.fasterxml.jackson.databind.ObjectMapper
 import org.eclipse.jetty.servlet.ServletContextHandler
 
-import org.apache.spark.SecurityManager
+import org.apache.spark.{SparkConf, SecurityManager}
 import org.apache.spark.ui.JettyUtils._
 
 private[spark] class MetricsServlet(
@@ -49,10 +49,10 @@ private[spark] class MetricsServlet(
   val mapper = new ObjectMapper().registerModule(
     new MetricsModule(TimeUnit.SECONDS, TimeUnit.MILLISECONDS, servletShowSample))
 
-  def getHandlers: Array[ServletContextHandler] = {
+  def getHandlers(conf: SparkConf): Array[ServletContextHandler] = {
     Array[ServletContextHandler](
       createServletHandler(servletPath,
-        new ServletParams(request => getMetricsSnapshot(request), "text/json"), securityMgr)
+        new ServletParams(request => getMetricsSnapshot(request), "text/json"), securityMgr, conf)
     )
   }
 
diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
index 779c0ba083596..b796a44fe01ac 100644
--- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
@@ -59,7 +59,17 @@ private[spark] object JettyUtils extends Logging {
 
   def createServlet[T <% AnyRef](
       servletParams: ServletParams[T],
-      securityMgr: SecurityManager): HttpServlet = {
+      securityMgr: SecurityManager,
+      conf: SparkConf): HttpServlet = {
+
+    // SPARK-10589 avoid frame-related click-jacking vulnerability, using X-Frame-Options
+    // (see http://tools.ietf.org/html/rfc7034). By default allow framing only from the
+    // same origin, but allow framing for a specific named URI.
+    // Example: spark.ui.allowFramingFrom = https://example.com/
+    val allowFramingFrom = conf.getOption("spark.ui.allowFramingFrom")
+    val xFrameOptionsValue =
+      allowFramingFrom.map(uri => s"ALLOW-FROM $uri").getOrElse("SAMEORIGIN")
+
     new HttpServlet {
       override def doGet(request: HttpServletRequest, response: HttpServletResponse) {
         try {
@@ -68,6 +78,7 @@ private[spark] object JettyUtils extends Logging {
             response.setStatus(HttpServletResponse.SC_OK)
             val result = servletParams.responder(request)
             response.setHeader("Cache-Control", "no-cache, no-store, must-revalidate")
+            response.setHeader("X-Frame-Options", xFrameOptionsValue)
             // scalastyle:off println
             response.getWriter.println(servletParams.extractFn(result))
             // scalastyle:on println
@@ -97,8 +108,9 @@ private[spark] object JettyUtils extends Logging {
       path: String,
       servletParams: ServletParams[T],
       securityMgr: SecurityManager,
+      conf: SparkConf,
       basePath: String = ""): ServletContextHandler = {
-    createServletHandler(path, createServlet(servletParams, securityMgr), basePath)
+    createServletHandler(path, createServlet(servletParams, securityMgr, conf), basePath)
   }
 
   /** Create a context handler that responds to a request with the given path prefix */
diff --git a/core/src/main/scala/org/apache/spark/ui/WebUI.scala b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
index 61449847add3d..81a121fd441bd 100644
--- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
@@ -76,9 +76,9 @@ private[spark] abstract class WebUI(
   def attachPage(page: WebUIPage) {
     val pagePath = "/" + page.prefix
     val renderHandler = createServletHandler(pagePath,
-      (request: HttpServletRequest) => page.render(request), securityManager, basePath)
+      (request: HttpServletRequest) => page.render(request), securityManager, conf, basePath)
     val renderJsonHandler = createServletHandler(pagePath.stripSuffix("/") + "/json",
-      (request: HttpServletRequest) => page.renderJson(request), securityManager, basePath)
+      (request: HttpServletRequest) => page.renderJson(request), securityManager, conf, basePath)
     attachHandler(renderHandler)
     attachHandler(renderJsonHandler)
     pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]())

From 896edb51ab7a88bbb31259e565311a9be6f2ca6d Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Wed, 16 Sep 2015 13:20:39 -0700
Subject: [PATCH 1452/1454] [SPARK-10050] [SPARKR] Support collecting data of
 MapType in DataFrame.

1. Support collecting data of MapType from DataFrame.
2. Support data of MapType in createDataFrame.

Author: Sun Rui <rui.sun@intel.com>

Closes #8711 from sun-rui/SPARK-10050.
---
 R/pkg/R/SQLContext.R                          |  5 +-
 R/pkg/R/deserialize.R                         | 14 +++++
 R/pkg/R/schema.R                              | 34 ++++++++---
 R/pkg/inst/tests/test_sparkSQL.R              | 56 +++++++++++++++----
 .../scala/org/apache/spark/api/r/SerDe.scala  | 31 ++++++++++
 .../org/apache/spark/sql/api/r/SQLUtils.scala |  6 ++
 6 files changed, 123 insertions(+), 23 deletions(-)

diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 4ac057d0f2d83..1c58fd96d750a 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -41,10 +41,7 @@ infer_type <- function(x) {
   if (type == "map") {
     stopifnot(length(x) > 0)
     key <- ls(x)[[1]]
-    list(type = "map",
-         keyType = "string",
-         valueType = infer_type(get(key, x)),
-         valueContainsNull = TRUE)
+    paste0("map<string,", infer_type(get(key, x)), ">")
   } else if (type == "array") {
     stopifnot(length(x) > 0)
     names <- names(x)
diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index d1858ec227b56..ce88d0b071b72 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -50,6 +50,7 @@ readTypedObject <- function(con, type) {
     "t" = readTime(con),
     "a" = readArray(con),
     "l" = readList(con),
+    "e" = readEnv(con),
     "n" = NULL,
     "j" = getJobj(readString(con)),
     stop(paste("Unsupported type for deserialization", type)))
@@ -121,6 +122,19 @@ readList <- function(con) {
   }
 }
 
+readEnv <- function(con) {
+  env <- new.env()
+  len <- readInt(con)
+  if (len > 0) {
+    for (i in 1:len) {
+      key <- readString(con)
+      value <- readObject(con)
+      env[[key]] <- value
+    }
+  }
+  env
+}
+
 readRaw <- function(con) {
   dataLen <- readInt(con)
   readBin(con, raw(), as.integer(dataLen), endian = "big")
diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R
index 62d4f73878d29..8df1563f8ebc0 100644
--- a/R/pkg/R/schema.R
+++ b/R/pkg/R/schema.R
@@ -131,13 +131,33 @@ checkType <- function(type) {
   if (type %in% primtiveTypes) {
     return()
   } else {
-    m <- regexec("^array<(.*)>$", type)
-    matchedStrings <- regmatches(type, m)
-    if (length(matchedStrings[[1]]) >= 2) {
-      elemType <- matchedStrings[[1]][2]
-      checkType(elemType)
-      return()
-    }
+    # Check complex types
+    firstChar <- substr(type, 1, 1)
+    switch (firstChar,
+            a = {
+              # Array type
+              m <- regexec("^array<(.*)>$", type)
+              matchedStrings <- regmatches(type, m)
+              if (length(matchedStrings[[1]]) >= 2) {
+                elemType <- matchedStrings[[1]][2]
+                checkType(elemType)
+                return()
+              }
+            },
+            m = {
+              # Map type
+              m <- regexec("^map<(.*),(.*)>$", type)
+              matchedStrings <- regmatches(type, m)
+              if (length(matchedStrings[[1]]) >= 3) {
+                keyType <- matchedStrings[[1]][2]
+                if (keyType != "string" && keyType != "character") {
+                  stop("Key type in a map must be string or character")
+                }
+                valueType <- matchedStrings[[1]][3]
+                checkType(valueType)
+                return()
+              }
+            })
   }
 
   stop(paste("Unsupported type for Dataframe:", type))
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 98d4402d368e1..e159a69584274 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -57,7 +57,7 @@ mockLinesComplexType <-
 complexTypeJsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
 writeLines(mockLinesComplexType, complexTypeJsonPath)
 
-test_that("infer types", {
+test_that("infer types and check types", {
   expect_equal(infer_type(1L), "integer")
   expect_equal(infer_type(1.0), "double")
   expect_equal(infer_type("abc"), "string")
@@ -72,9 +72,9 @@ test_that("infer types", {
   checkStructField(testStruct$fields()[[2]], "b", "StringType", TRUE)
   e <- new.env()
   assign("a", 1L, envir = e)
-  expect_equal(infer_type(e),
-               list(type = "map", keyType = "string", valueType = "integer",
-                    valueContainsNull = TRUE))
+  expect_equal(infer_type(e), "map<string,integer>")
+
+  expect_error(checkType("map<integer,integer>"), "Key type in a map must be string or character")
 })
 
 test_that("structType and structField", {
@@ -242,7 +242,7 @@ test_that("create DataFrame with different data types", {
   expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE))
 })
 
-test_that("create DataFrame with nested array and struct", {
+test_that("create DataFrame with nested array and map", {
 #  e <- new.env()
 #  assign("n", 3L, envir = e)
 #  l <- list(1:10, list("a", "b"), e, list(a="aa", b=3L))
@@ -253,21 +253,35 @@ test_that("create DataFrame with nested array and struct", {
 #  ldf <- collect(df)
 #  expect_equal(ldf[1,], l[[1]])
 
+  #  ArrayType and MapType
+  e <- new.env()
+  assign("n", 3L, envir = e)
 
-  #  ArrayType only for now
-  l <- list(as.list(1:10), list("a", "b"))
-  df <- createDataFrame(sqlContext, list(l), c("a", "b"))
-  expect_equal(dtypes(df), list(c("a", "array<int>"), c("b", "array<string>")))
+  l <- list(as.list(1:10), list("a", "b"), e)
+  df <- createDataFrame(sqlContext, list(l), c("a", "b", "c"))
+  expect_equal(dtypes(df), list(c("a", "array<int>"),
+                                c("b", "array<string>"),
+                                c("c", "map<string,int>")))
   expect_equal(count(df), 1)
   ldf <- collect(df)
-  expect_equal(names(ldf), c("a", "b"))
+  expect_equal(names(ldf), c("a", "b", "c"))
   expect_equal(ldf[1, 1][[1]], l[[1]])
   expect_equal(ldf[1, 2][[1]], l[[2]])
+  e <- ldf$c[[1]]
+  expect_equal(class(e), "environment")
+  expect_equal(ls(e), "n")
+  expect_equal(e$n, 3L)
 })
 
+# For test map type in DataFrame
+mockLinesMapType <- c("{\"name\":\"Bob\",\"info\":{\"age\":16,\"height\":176.5}}",
+                      "{\"name\":\"Alice\",\"info\":{\"age\":20,\"height\":164.3}}",
+                      "{\"name\":\"David\",\"info\":{\"age\":60,\"height\":180}}")
+mapTypeJsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
+writeLines(mockLinesMapType, mapTypeJsonPath)
+
 test_that("Collect DataFrame with complex types", {
-  # only ArrayType now
-  # TODO: tests for StructType and MapType after they are supported
+  # ArrayType
   df <- jsonFile(sqlContext, complexTypeJsonPath)
 
   ldf <- collect(df)
@@ -277,6 +291,24 @@ test_that("Collect DataFrame with complex types", {
   expect_equal(ldf$c1, list(list(1, 2, 3), list(4, 5, 6), list (7, 8, 9)))
   expect_equal(ldf$c2, list(list("a", "b", "c"), list("d", "e", "f"), list ("g", "h", "i")))
   expect_equal(ldf$c3, list(list(1.0, 2.0, 3.0), list(4.0, 5.0, 6.0), list (7.0, 8.0, 9.0)))
+
+  # MapType
+  schema <- structType(structField("name", "string"),
+                       structField("info", "map<string,double>"))
+  df <- read.df(sqlContext, mapTypeJsonPath, "json", schema)
+  expect_equal(dtypes(df), list(c("name", "string"),
+                                c("info", "map<string,double>")))
+  ldf <- collect(df)
+  expect_equal(nrow(ldf), 3)
+  expect_equal(ncol(ldf), 2)
+  expect_equal(names(ldf), c("name", "info"))
+  expect_equal(ldf$name, c("Bob", "Alice", "David"))
+  bob <- ldf$info[[1]]
+  expect_equal(class(bob), "environment")
+  expect_equal(bob$age, 16)
+  expect_equal(bob$height, 176.5)
+
+  # TODO: tests for StructType after it is supported
 })
 
 test_that("jsonFile() on a local file returns a DataFrame", {
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 3c92bb7a1c73c..0c78613e406e1 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -209,11 +209,23 @@ private[spark] object SerDe {
       case "array" => dos.writeByte('a')
       // Array of objects
       case "list" => dos.writeByte('l')
+      case "map" => dos.writeByte('e')
       case "jobj" => dos.writeByte('j')
       case _ => throw new IllegalArgumentException(s"Invalid type $typeStr")
     }
   }
 
+  private def writeKeyValue(dos: DataOutputStream, key: Object, value: Object): Unit = {
+    if (key == null) {
+      throw new IllegalArgumentException("Key in map can't be null.")
+    } else if (!key.isInstanceOf[String]) {
+      throw new IllegalArgumentException(s"Invalid map key type: ${key.getClass.getName}")
+    }
+
+    writeString(dos, key.asInstanceOf[String])
+    writeObject(dos, value)
+  }
+
   def writeObject(dos: DataOutputStream, obj: Object): Unit = {
     if (obj == null) {
       writeType(dos, "void")
@@ -306,6 +318,25 @@ private[spark] object SerDe {
           writeInt(dos, v.length)
           v.foreach(elem => writeObject(dos, elem))
 
+        // Handle map
+        case v: java.util.Map[_, _] =>
+          writeType(dos, "map")
+          writeInt(dos, v.size)
+          val iter = v.entrySet.iterator
+          while(iter.hasNext) {
+            val entry = iter.next
+            val key = entry.getKey
+            val value = entry.getValue
+
+            writeKeyValue(dos, key.asInstanceOf[Object], value.asInstanceOf[Object])
+          }
+        case v: scala.collection.Map[_, _] =>
+          writeType(dos, "map")
+          writeInt(dos, v.size)
+          v.foreach { case (key, value) =>
+            writeKeyValue(dos, key.asInstanceOf[Object], value.asInstanceOf[Object])
+          }
+
         case _ =>
           writeType(dos, "jobj")
           writeJObj(dos, value)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
index d4b834adb6e39..f45d119c8cfdf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
@@ -64,6 +64,12 @@ private[r] object SQLUtils {
       case r"\Aarray<(.*)${elemType}>\Z" => {
         org.apache.spark.sql.types.ArrayType(getSQLDataType(elemType))
       }
+      case r"\Amap<(.*)${keyType},(.*)${valueType}>\Z" => {
+        if (keyType != "string" && keyType != "character") {
+          throw new IllegalArgumentException("Key type of a map must be string or character")
+        }
+        org.apache.spark.sql.types.MapType(getSQLDataType(keyType), getSQLDataType(valueType))
+      }
       case _ => throw new IllegalArgumentException(s"Invaid type $dataType")
     }
   }

From d39f15ea2b8bed5342d2f8e3c1936f915c470783 Mon Sep 17 00:00:00 2001
From: Kevin Cox <kevincox@kevincox.ca>
Date: Wed, 16 Sep 2015 15:30:17 -0700
Subject: [PATCH 1453/1454] [SPARK-9794] [SQL] Fix datetime parsing in
 SparkSQL.

This fixes https://issues.apache.org/jira/browse/SPARK-9794 by using a real ISO8601 parser. (courtesy of the xml component of the standard java library)

cc: angelini

Author: Kevin Cox <kevincox@kevincox.ca>

Closes #8396 from kevincox/kevincox-sql-time-parsing.
---
 .../sql/catalyst/util/DateTimeUtils.scala     | 27 ++++++----------
 .../catalyst/util/DateTimeUtilsSuite.scala    | 32 +++++++++++++++++++
 2 files changed, 42 insertions(+), 17 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 687ca000d12bb..400c4327be1c7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.util
 import java.sql.{Date, Timestamp}
 import java.text.{DateFormat, SimpleDateFormat}
 import java.util.{TimeZone, Calendar}
+import javax.xml.bind.DatatypeConverter;
 
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -109,30 +110,22 @@ object DateTimeUtils {
   }
 
   def stringToTime(s: String): java.util.Date = {
-    if (!s.contains('T')) {
+    var indexOfGMT = s.indexOf("GMT");
+    if (indexOfGMT != -1) {
+      // ISO8601 with a weird time zone specifier (2000-01-01T00:00GMT+01:00)
+      val s0 = s.substring(0, indexOfGMT)
+      val s1 = s.substring(indexOfGMT + 3)
+      // Mapped to 2000-01-01T00:00+01:00
+      stringToTime(s0 + s1)
+    } else if (!s.contains('T')) {
       // JDBC escape string
       if (s.contains(' ')) {
         Timestamp.valueOf(s)
       } else {
         Date.valueOf(s)
       }
-    } else if (s.endsWith("Z")) {
-      // this is zero timezone of ISO8601
-      stringToTime(s.substring(0, s.length - 1) + "GMT-00:00")
-    } else if (s.indexOf("GMT") == -1) {
-      // timezone with ISO8601
-      val inset = "+00.00".length
-      val s0 = s.substring(0, s.length - inset)
-      val s1 = s.substring(s.length - inset, s.length)
-      if (s0.substring(s0.lastIndexOf(':')).contains('.')) {
-        stringToTime(s0 + "GMT" + s1)
-      } else {
-        stringToTime(s0 + ".0GMT" + s1)
-      }
     } else {
-      // ISO8601 with GMT insert
-      val ISO8601GMT: SimpleDateFormat = new SimpleDateFormat( "yyyy-MM-dd'T'HH:mm:ss.SSSz" )
-      ISO8601GMT.parse(s)
+      DatatypeConverter.parseDateTime(s).getTime()
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 6b9a11f0ff743..46335941b62d6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -136,6 +136,38 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     assert(stringToDate(UTF8String.fromString("2015-031-8")).isEmpty)
   }
 
+  test("string to time") {
+    // Tests with UTC.
+    var c = Calendar.getInstance(TimeZone.getTimeZone("UTC"))
+    c.set(Calendar.MILLISECOND, 0)
+
+    c.set(1900, 0, 1, 0, 0, 0)
+    assert(stringToTime("1900-01-01T00:00:00GMT-00:00") === c.getTime())
+
+    c.set(2000, 11, 30, 10, 0, 0)
+    assert(stringToTime("2000-12-30T10:00:00Z") === c.getTime())
+
+    // Tests with set time zone.
+    c.setTimeZone(TimeZone.getTimeZone("GMT-04:00"))
+    c.set(Calendar.MILLISECOND, 0)
+
+    c.set(1900, 0, 1, 0, 0, 0)
+    assert(stringToTime("1900-01-01T00:00:00-04:00") === c.getTime())
+
+    c.set(1900, 0, 1, 0, 0, 0)
+    assert(stringToTime("1900-01-01T00:00:00GMT-04:00") === c.getTime())
+
+    // Tests with local time zone.
+    c.setTimeZone(TimeZone.getDefault())
+    c.set(Calendar.MILLISECOND, 0)
+
+    c.set(2000, 11, 30, 0, 0, 0)
+    assert(stringToTime("2000-12-30") === new Date(c.getTimeInMillis()))
+
+    c.set(2000, 11, 30, 10, 0, 0)
+    assert(stringToTime("2000-12-30 10:00:00") === new Timestamp(c.getTimeInMillis()))
+  }
+
   test("string to timestamp") {
     var c = Calendar.getInstance()
     c.set(1969, 11, 31, 16, 0, 0)

From 49c649fa0b6affed108dbae85373b4b7247b338c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 16 Sep 2015 15:32:01 -0700
Subject: [PATCH 1454/1454] Tiny style fix for
 d39f15ea2b8bed5342d2f8e3c1936f915c470783.

---
 .../org/apache/spark/sql/catalyst/util/DateTimeUtils.scala      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 400c4327be1c7..781ed1688a327 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.util
 import java.sql.{Date, Timestamp}
 import java.text.{DateFormat, SimpleDateFormat}
 import java.util.{TimeZone, Calendar}
-import javax.xml.bind.DatatypeConverter;
+import javax.xml.bind.DatatypeConverter
 
 import org.apache.spark.unsafe.types.UTF8String